diff --git a/public/index.html b/index.html
similarity index 81%
rename from public/index.html
rename to index.html
index a718a8d1c..89d375c52 100644
--- a/public/index.html
+++ b/index.html
@@ -2,27 +2,27 @@
 <html lang="en">
   <head>
     <meta charset="utf-8" />
-    <link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
+    <link rel="icon" href="/favicon.ico" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="theme-color" content="#000000" />
     <meta
       name="description"
       content="HID Diagnostic tool"
     />
-    <link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
+    <link rel="apple-touch-icon" href="/logo192.png" />
     <script src="//unpkg.com/brain.js"></script>
 
     <!--
       manifest.json provides metadata used when your web app is installed on a
       user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/
     -->
-    <link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
+    <link rel="manifest" href="/manifest.json" />
     <!--
-      Notice the use of %PUBLIC_URL% in the tags above.
+      Notice the use of  in the tags above.
       It will be replaced with the URL of the `public` folder during the build.
       Only files inside the `public` folder can be referenced from the HTML.
 
-      Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
+      Unlike "/favicon.ico" or "favicon.ico", "/favicon.ico" will
       work correctly both with client-side routing and a non-root public URL.
       Learn how to configure a non-root public URL by running `npm run build`.
     -->
@@ -40,6 +40,8 @@
 
       To begin the development, run `npm start` or `yarn start`.
       To create a production bundle, use `npm run build` or `yarn build`.
+
     -->
+    <script type="module" src="src/index.jsx"></script>
   </body>
 </html>
diff --git a/package-lock.json b/package-lock.json
deleted file mode 100644
index b0c77ddb0..000000000
--- a/package-lock.json
+++ /dev/null
@@ -1,22204 +0,0 @@
-{
-  "name": "hidiag",
-  "version": "0.1.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "hidiag",
-      "version": "0.1.0",
-      "dependencies": {
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-        "@mantine/carousel": "^6.0.11",
-        "@mantine/core": "^6.0.11",
-        "@mantine/dates": "^6.0.11",
-        "@mantine/dropzone": "^6.0.11",
-        "@mantine/form": "^6.0.11",
-        "@mantine/hooks": "^6.0.11",
-        "@mantine/modals": "^6.0.11",
-        "@mantine/notifications": "^6.0.11",
-        "@mantine/nprogress": "^6.0.11",
-        "@mantine/prism": "^6.0.11",
-        "@mantine/spotlight": "^6.0.11",
-        "@mantine/tiptap": "^6.0.11",
-        "@tabler/icons-react": "^2.20.0",
-        "@tensorflow/tfjs": "^4.13.0",
-        "@tensorflow/tfjs-node": "^1.7.4",
-        "@testing-library/jest-dom": "^5.16.5",
-        "@testing-library/react": "^13.4.0",
-        "@testing-library/user-event": "^13.5.0",
-        "@tiptap/extension-link": "^2.0.3",
-        "@tiptap/react": "^2.0.3",
-        "@tiptap/starter-kit": "^2.0.3",
-        "axios": "^1.4.0",
-        "brainjs": "^0.7.4",
-        "dayjs": "^1.11.7",
-        "embla-carousel-react": "^7.1.0",
-        "esdoc": "^1.1.0",
-        "esdoc-standard-plugin": "^1.0.0",
-        "express": "^4.18.2",
-        "https": "^1.0.0",
-        "https-browserify": "^1.0.0",
-        "localforage": "^1.10.0",
-        "match-sorter": "^6.3.1",
-        "path-browserify": "^1.0.1",
-        "pvtsutils": "^1.3.5",
-        "qrcode": "^1.5.3",
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-      "integrity": "sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==",
-      "engines": {
-        "node": ">=0.4"
-      }
-    },
-    "node_modules/y18n": {
-      "version": "5.0.8",
-      "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz",
-      "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==",
-      "engines": {
-        "node": ">=10"
-      }
-    },
-    "node_modules/yallist": {
-      "version": "3.1.1",
-      "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
-      "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g=="
-    },
-    "node_modules/yaml": {
-      "version": "1.10.2",
-      "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz",
-      "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==",
-      "engines": {
-        "node": ">= 6"
-      }
-    },
-    "node_modules/yargs": {
-      "version": "16.2.0",
-      "resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz",
-      "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==",
-      "dependencies": {
-        "cliui": "^7.0.2",
-        "escalade": "^3.1.1",
-        "get-caller-file": "^2.0.5",
-        "require-directory": "^2.1.1",
-        "string-width": "^4.2.0",
-        "y18n": "^5.0.5",
-        "yargs-parser": "^20.2.2"
-      },
-      "engines": {
-        "node": ">=10"
-      }
-    },
-    "node_modules/yargs-parser": {
-      "version": "20.2.9",
-      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz",
-      "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==",
-      "engines": {
-        "node": ">=10"
-      }
-    },
-    "node_modules/yocto-queue": {
-      "version": "0.1.0",
-      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
-      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
-      "engines": {
-        "node": ">=10"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    }
-  }
-}
diff --git a/package.json b/package.json
index c3d365d3c..ba9885641 100644
--- a/package.json
+++ b/package.json
@@ -2,6 +2,7 @@
   "name": "hidiag",
   "version": "0.1.0",
   "private": true,
+  "type": "module",
   "dependencies": {
     "@emotion/react": "^11.11.0",
     "@google/generative-ai": "^0.1.3",
@@ -18,20 +19,16 @@
     "@mantine/spotlight": "^6.0.11",
     "@mantine/tiptap": "^6.0.11",
     "@tabler/icons-react": "^2.20.0",
-    "@tensorflow/tfjs": "^4.13.0",
-    "@tensorflow/tfjs-node": "^1.7.4",
     "@testing-library/jest-dom": "^5.16.5",
     "@testing-library/react": "^13.4.0",
     "@testing-library/user-event": "^13.5.0",
     "@tiptap/extension-link": "^2.0.3",
     "@tiptap/react": "^2.0.3",
     "@tiptap/starter-kit": "^2.0.3",
+    "@vitejs/plugin-react": "^4.2.1",
     "axios": "^1.4.0",
-    "brainjs": "^0.7.4",
     "dayjs": "^1.11.7",
     "embla-carousel-react": "^7.1.0",
-    "esdoc": "^1.1.0",
-    "esdoc-standard-plugin": "^1.0.0",
     "express": "^4.18.2",
     "https": "^1.0.0",
     "https-browserify": "^1.0.0",
@@ -43,7 +40,6 @@
     "react": "^18.2.0",
     "react-dom": "^18.2.0",
     "react-router-dom": "^6.11.2",
-    "react-scripts": "^5.0.1",
     "rfc4648": "^1.5.3",
     "socket.io": "^4.7.3",
     "socket.io-client": "^4.7.3",
@@ -54,15 +50,15 @@
     "url": "^0.11.0",
     "use-effect-x": "^0.1.5",
     "util": "^0.12.5",
+    "vite": "^5.0.11",
+    "vite-tsconfig-paths": "^4.3.1",
     "web-vitals": "^2.1.4",
     "webpack": "^5.89.0"
   },
   "scripts": {
-    "start": "react-scripts start",
-    "build": "react-scripts build",
-    "test": "react-scripts test",
-    "eject": "react-scripts eject",
-    "dev": "export NODE_TLS_REJECT_UNAUTHORIZED=0"
+    "start": "vite",
+    "build": "tsc && vite build",
+    "preview": "vite preview"
   },
   "eslintConfig": {
     "extends": [
diff --git a/src/App.js b/src/App.jsx
similarity index 100%
rename from src/App.js
rename to src/App.jsx
diff --git a/src/components/AuthService.jsx b/src/components/AuthService.jsx
index cb6977014..7c35f385e 100644
--- a/src/components/AuthService.jsx
+++ b/src/components/AuthService.jsx
@@ -124,28 +124,31 @@ function AuthService() {
            let hostname = form.values.Host;
            let tenant = form.values.Tenant;
            let access_token = form.values.access_token;
-           axios.post('https://api.bz9.net/userinfo',
-           {
-            access_token:access_token,
-            hostname: hostname,
-            tenant: tenant
-           },{
-            headers:{
-              'Content-Type': 'application/x-www-form-urlencoded'
-            }
-
-
-           }).then(function(response){
-            notifications.show({
-              id: 'load-data',
-              color: 'teal',
-              title: 'Connected!',
-              message: "Successfully recieved User Info",
-              icon: <IconCheck size="1rem" />,
-              autoClose: 2000
-            });
-            document.getElementById('resBody').innerHTML = JSON.stringify(response.data);
-           })
+           if(hostname!=null && tenant!=null && access_token!=null){
+            axios.post('https://api.bz9.net/userinfo',
+            {
+             access_token:access_token,
+             hostname: hostname,
+             tenant: tenant
+            },{
+             headers:{
+               'Content-Type': 'application/x-www-form-urlencoded'
+             }
+ 
+ 
+            }).then(function(response){
+             notifications.show({
+               id: 'load-data',
+               color: 'teal',
+               title: 'Connected!',
+               message: "Successfully recieved User Info",
+               icon: <IconCheck size="1rem" />,
+               autoClose: 2000
+             });
+             document.getElementById('resBody').innerHTML = JSON.stringify(response.data);
+            })
+           }
+ 
            
            
         }}><IconUserDown/> Get User Info</Button>
@@ -197,7 +200,6 @@ function AuthService() {
             let password = form.values.password;
             let client_id = form.values.client_id;
             let client_secret = form.values.client_secret;
-            
               sessionStorage.setItem("hostname", hostname);
               sessionStorage.setItem("tenant", tenant);
               sessionStorage.setItem("grant_type", grant_type);
diff --git a/src/components/provisiondevice.jsx b/src/components/provisiondevice.jsx
index 324c943da..7d0b32925 100644
--- a/src/components/provisiondevice.jsx
+++ b/src/components/provisiondevice.jsx
@@ -1,5 +1,5 @@
 
-import { Text, TextInput, Button, JsonInput, Card, Grid, Center } from '@mantine/core';
+import {TextInput, Button, JsonInput, Card, Grid, Center } from '@mantine/core';
 import axios from 'axios';
 import { notifications } from '@mantine/notifications';
 import { IconFaceIdError, IconFaceId, IconAlertCircle } from '@tabler/icons-react';
diff --git a/src/index.js b/src/index.jsx
similarity index 93%
rename from src/index.js
rename to src/index.jsx
index 2d3afffdc..df18f1579 100644
--- a/src/index.js
+++ b/src/index.jsx
@@ -1,10 +1,10 @@
 import React from "react";
-import App from "./App";
+import App from "./App.jsx";
 import AuthService from "./components/AuthService.jsx";
 import RegisterUser from "./components/RegisterUser.jsx";
 import * as ReactDOM from "react-dom/client";
-import ViewUsers from "./components/ViewUsers";
-import CreatePasswordAuth from "./components/CreatePasswordAuth";
+import ViewUsers from "./components/ViewUsers.jsx";
+import CreatePasswordAuth from "./components/CreatePasswordAuth.jsx";
 import PasswordAuth from "./components/passwordauth.jsx";
 import CreateOTPAuth from "./components/CreateOTPAuth.jsx";
 import OTPAuth from "./components/otpAuth.jsx";
@@ -20,7 +20,6 @@ import ProvisionDevice from "./components/provisiondevice.jsx";
 import ApproveOTPAuth from "./components/approveotp.jsx";
 import ApprovePushAuth from "./components/approveciba.jsx";
 import Dashboard from "./components/dashboad.jsx";
-
 import UpdateCiba from "./components/updateciba.jsx";
   const router = createBrowserRouter([
     {
@@ -100,7 +99,7 @@ import UpdateCiba from "./components/updateciba.jsx";
 
   ]);
   ReactDOM.createRoot(document.getElementById("root")).render(
-    <React.StrictMode>
+
       <RouterProvider router={router} />
-    </React.StrictMode>
+  
   );
\ No newline at end of file
diff --git a/tfjs-master/.bazelignore b/tfjs-master/.bazelignore
deleted file mode 100644
index b0c1b5eb6..000000000
--- a/tfjs-master/.bazelignore
+++ /dev/null
@@ -1,20 +0,0 @@
-node_modules
-tfjs/node_modules
-tfjs-automl/node_modules
-tfjs-backend-cpu/node_modules
-tfjs-backend-nodegl/node_modules
-tfjs-backend-wasm/node_modules
-tfjs-backend-webgl/node_modules
-tfjs-backend-webgpu/node_modules
-tfjs-converter/node_modules
-tfjs-core/node_modules
-tfjs-data/node_modules
-tfjs-inference/node_modules
-tfjs-layers/node_modules
-tfjs-node/node_modules
-tfjs-node-gpu/node_modules
-tfjs-react-native/node_modules
-tfjs-tfdf/node_modules
-tfjs-tflite/node_modules
-tfjs-vis/node_modules
-e2e/node_modules
diff --git a/tfjs-master/.bazelrc b/tfjs-master/.bazelrc
deleted file mode 100644
index 8da25af63..000000000
--- a/tfjs-master/.bazelrc
+++ /dev/null
@@ -1,98 +0,0 @@
-# Bazel will create symlinks from the workspace directory to output artifacts.
-# Build results will be placed in a directory called "dist/bin"
-# Other directories will be created like "dist/testlogs"
-# Be aware that this will still create a bazel-out symlink in
-# your project directory, which you must exclude from version control and your
-# editor's search path.
-build --symlink_prefix=dist/
-
-# These compile flags are active no matter which build mode we are in
-# (dbg vs opt). For flags specific to build mode, see cc_toolchain_config.bzl.
-build --cxxopt="-std=c++17"
-build --cxxopt="-fno-rtti"
-build --cxxopt="-fno-exceptions"
-build --cxxopt="-fomit-frame-pointer"
-
-# The following --define=EXECUTOR=remote will be able to be removed
-# once https://github.com/bazelbuild/bazel/issues/7254 is fixed
-build:rbe --define=EXECUTOR=remote
-
-build:rbe --jobs=100
-
-# Remote cache config. Users can add credentials in their .bazelrc.user files.
-build:remote --remote_cache=remotebuildexecution.googleapis.com
-build:rbe --remote_executor=remotebuildexecution.googleapis.com
-
-# Force remote exeuctions to consider the entire run as linux
-build:rbe --cpu=k8
-build:rbe --host_cpu=k8
-
-# Toolchain and platform related flags
-build:rbe --crosstool_top=@//remote-execution/cpp:cc_toolchain_suite
-build:rbe --extra_toolchains=@//remote-execution/cpp:cc_toolchain
-build:rbe --extra_execution_platforms=@//remote-execution:platform
-build:rbe --host_platform=@//remote-execution:platform
-build:rbe --platforms=@//remote-execution:platform
-
-build:remote --remote_instance_name=projects/learnjs-174218/instances/default_instance
-build:remote --bes_instance_name=learnjs-174218
-build:remote --google_default_credentials
-build:remote --remote_timeout=180s
-
-# Stream build results to the results UI
-build:bes --config=remote --bes_backend="buildeventservice.googleapis.com" --bes_timeout=60s --bes_results_url="https://source.cloud.google.com/results/invocations/"
-build:rbe --config=remote
-
-# Config for Google Cloud continuous integration that uses default credentials.
-build:ci --config=bes
-
-# This flag is needed to prevent the bazel cache from being invalidated when
-# running bazel via `yarn bazel`.
-# See https://github.com/angular/angular/issues/27514.
-build --incompatible_strict_action_env
-run --incompatible_strict_action_env
-test --incompatible_strict_action_env
-
-# Use a sandboxed build where available to avoid a possible issue with the rules_nodejs linker for Linux and MacOS. b/250727292
-# Remote builds are sandboxed.
-# TODO: Regain Windows sandboxed build when https://github.com/bazelbuild/bazel/issues/5136 fixed.
-common --enable_platform_specific_config
-build:linux --spawn_strategy=dynamic,remote,sandboxed,worker,local --internal_spawn_scheduler
-build:macos --spawn_strategy=remote,sandboxed,worker,local
-build:windows --spawn_strategy=remote,worker,local
-
-# Use dynamic execution to improve remote build performance when the autoscaling
-# build pool has few machines available. https://bazel.build/remote/dynamic
-build:rbe --dynamic_local_strategy=sandboxed
-
-# Pass BrowserStack credentials
-build --action_env=BROWSERSTACK_USERNAME --action_env=BROWSERSTACK_KEY
-run --action_env=BROWSERSTACK_USERNAME --action_env=BROWSERSTACK_KEY
-test --action_env=BROWSERSTACK_USERNAME --action_env=BROWSERSTACK_KEY
-
-# Make python debugging refer to the real files instead of symlinks
-build --action_env=PYDEVD_RESOLVE_SYMLINKS=true
-run --action_env=PYDEVD_RESOLVE_SYMLINKS=true
-test --action_env=PYDEVD_RESOLVE_SYMLINKS=true
-
-# Platform specific DISPLAY environment variable for webgl and headless setting
-# for browser tests.
-test:linux --test_env=DISPLAY --test_env=XAUTHORITY=/run/user/1001/.mutter-Xwaylandauth.ONEU31
-test:macos --define DISPLAY=true
-test:windows --define DISPLAY=true --//:headless=false
-
-# Enable debugging tests with --config=debug
-run:debug --test_arg=--node_options=--inspect-brk --test_output=streamed --test_strategy=exclusive --test_timeout=36000 --nocache_test_results
-test:debug --test_arg=--node_options=--inspect-brk --test_output=streamed --test_strategy=exclusive --test_timeout=36000 --nocache_test_results
-
-test:debugpy --test_output=streamed --test_strategy=exclusive --test_timeout=999999 --nocache_test_results
-run:debugpy --test_output=streamed --test_strategy=exclusive --test_timeout=999999 --nocache_test_results
-
-# Load any settings specific to the current user.
-# .bazelrc.user should appear in .gitignore so that settings are not shared with
-# team members. This needs to be last statement in this config, as the user
-# configuration should be able to overwrite flags from this file.
-# See https://docs.bazel.build/versions/master/best-practices.html#bazelrc
-# (Note that we use .bazelrc.user so the file appears next to .bazelrc in
-# directory listing, rather than user.bazelrc as suggested in the Bazel docs).
-try-import %workspace%/.bazelrc.user
diff --git a/tfjs-master/.bazelversion b/tfjs-master/.bazelversion
deleted file mode 100644
index e230c8396..000000000
--- a/tfjs-master/.bazelversion
+++ /dev/null
@@ -1 +0,0 @@
-5.3.0
\ No newline at end of file
diff --git a/tfjs-master/.github/ISSUE_TEMPLATE/00-bug-issue.md b/tfjs-master/.github/ISSUE_TEMPLATE/00-bug-issue.md
deleted file mode 100644
index 1d1e2c3ef..000000000
--- a/tfjs-master/.github/ISSUE_TEMPLATE/00-bug-issue.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-name: Bug Issue
-about: Use this template for reporting a bug
-labels: 'type:bug'
-
----
-
-<em>Please make sure that this is a bug. As per our
-[GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md),
-we only address code/doc bugs, performance issues, feature requests and
-build/installation issues on GitHub. tag:bug_template</em>
-
-**System information**
-- Have I written custom code (as opposed to using a stock example script provided in TensorFlow.js):
-- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
-- Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
-- TensorFlow.js installed from (npm or script link):
-- TensorFlow.js version (use command below):
-- Browser version:
-- Tensorflow.js Converter Version:
-
-
-
-**Describe the current behavior**
-
-**Describe the expected behavior**
-
-**Standalone code to reproduce the issue**
-Provide a reproducible test case that is the bare minimum necessary to generate
-the problem. If possible, please share a link to Colab/CodePen/any notebook.
-
-**Other info / logs** Include any logs or source code that would be helpful to
-diagnose the problem. If including tracebacks, please include the full
-traceback. Large logs and files should be attached.
diff --git a/tfjs-master/.github/ISSUE_TEMPLATE/10-build-installation-issue.md b/tfjs-master/.github/ISSUE_TEMPLATE/10-build-installation-issue.md
deleted file mode 100644
index e3bc77604..000000000
--- a/tfjs-master/.github/ISSUE_TEMPLATE/10-build-installation-issue.md
+++ /dev/null
@@ -1,24 +0,0 @@
----
-name: Build/Installation Issue
-about: Use this template for build/installation issues
-labels: 'type:build/install'
-
----
-
-<em>Please make sure that this is a build/installation issue. As per our [GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md), we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:build_template</em>
-
-**System information**
-- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
-- Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
-- TensorFlow.js installed from (npm or script link):
-- TensorFlow.js version:
-- CUDA/cuDNN version:
-
-
-**Describe the problem**
-
-**Provide the exact sequence of commands / steps that you executed before running into the problem**
-
-
-**Any other info / logs**
-Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
diff --git a/tfjs-master/.github/ISSUE_TEMPLATE/20-feature-request.md b/tfjs-master/.github/ISSUE_TEMPLATE/20-feature-request.md
deleted file mode 100644
index a77610ee9..000000000
--- a/tfjs-master/.github/ISSUE_TEMPLATE/20-feature-request.md
+++ /dev/null
@@ -1,23 +0,0 @@
----
-name: Feature Request
-about: Use this template for raising a feature request
-labels: 'type:feature'
-
----
-
-<em>Please make sure that this is a feature request. As per our [GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md), we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:feature_template</em>
-
-
-**System information**
-- TensorFlow.js version (you are using):
-- Are you willing to contribute it (Yes/No):
-
-
-
-**Describe the feature and the current behavior/state.**
-
-**Will this change the current api? How?**
-
-**Who will benefit with this feature?**
-
-**Any Other info.**
diff --git a/tfjs-master/.github/ISSUE_TEMPLATE/30-other-issues.md b/tfjs-master/.github/ISSUE_TEMPLATE/30-other-issues.md
deleted file mode 100644
index b517d953e..000000000
--- a/tfjs-master/.github/ISSUE_TEMPLATE/30-other-issues.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-name: Other Issues
-about: Use this template for any other non-support related issues
-labels: 'type:others'
-
----
-
-This template is for miscellaneous issues not covered by the other issue categories.
-
-For questions on how to work with TensorFlow.js, or support for problems that are not verified bugs in TensorFlow.js, please go to [StackOverflow](https://stackoverflow.com/tags/tensorflow.js).
diff --git a/tfjs-master/.github/workflows/deploy-benchmark-preview.yml b/tfjs-master/.github/workflows/deploy-benchmark-preview.yml
deleted file mode 100644
index 8ebd70fba..000000000
--- a/tfjs-master/.github/workflows/deploy-benchmark-preview.yml
+++ /dev/null
@@ -1,25 +0,0 @@
-name: Deploy Benchmark Site to Preview Channel
-
-on:
-  pull_request:
-    paths:
-    - "e2e/benchmarks/**"
-
-permissions:
-  contents: read
-
-jobs:
-  build_and_preview:
-    permissions:
-      pull-requests: write # for FirebaseExtended/action-hosting-deploy to comment on PRs
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v2
-      - uses: FirebaseExtended/action-hosting-deploy@v0
-        with:
-          repoToken: "${{ secrets.GITHUB_TOKEN }}"
-          firebaseServiceAccount: "${{ secrets.FIREBASE_SERVICE_ACCOUNT_JSTENSORFLOW }}"
-          expires: 30d
-          projectId: jstensorflow
-          target: tfjs-benchmarks
-          entryPoint: e2e/benchmarks/
diff --git a/tfjs-master/.github/workflows/deploy-benchmark-prod.yml b/tfjs-master/.github/workflows/deploy-benchmark-prod.yml
deleted file mode 100644
index e9bf26627..000000000
--- a/tfjs-master/.github/workflows/deploy-benchmark-prod.yml
+++ /dev/null
@@ -1,27 +0,0 @@
-name: Deploy Benchmark Site to Live
-
-on:
-  push:
-    branches:
-      - master
-    paths:
-    - "e2e/benchmarks/**"
-
-permissions:
-  contents: read
-
-jobs:
-  deploy_live_website:
-    permissions:
-      pull-requests: write # for FirebaseExtended/action-hosting-deploy to comment on PRs
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v2
-      - uses: FirebaseExtended/action-hosting-deploy@v0
-        with:
-          repoToken: "${{ secrets.GITHUB_TOKEN }}"
-          firebaseServiceAccount: "${{ secrets.FIREBASE_SERVICE_ACCOUNT_JSTENSORFLOW }}"
-          projectId: jstensorflow
-          target: tfjs-benchmarks
-          channelId: live
-          entryPoint: e2e/benchmarks/
diff --git a/tfjs-master/.github/workflows/stale.yaml b/tfjs-master/.github/workflows/stale.yaml
deleted file mode 100644
index c79824a42..000000000
--- a/tfjs-master/.github/workflows/stale.yaml
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# This workflow alerts and then closes the stale issues/PRs after specific time
-# You can adjust the behavior by modifying this file.
-# For more information, see:
-# https://github.com/actions/stale
-
-name: 'Close stale issues and PRs'
-"on":
-  schedule:
-  - cron: "30 1 * * *"
-permissions:
-  contents: read
-  issues: write
-  pull-requests: write
-jobs:
-  stale:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: 'actions/stale@v7'
-      with:
-        # Comma separated list of labels that can be assigned to issues to exclude them from being marked as stale.
-        exempt-issue-labels: 'override-stale'
-        # Comma separated list of labels that can be assigned to PRs to exclude them from being marked as stale.
-        exempt-pr-labels: "override-stale"
-        # Limit the No. of API calls in one run default value is 30.
-        operations-per-run: 500
-        # Prevent to remove stale label when PRs or issues are updated.
-        remove-stale-when-updated: false
-        # comment on issue if not active for more then 7 days.
-        stale-issue-message: 'This issue has been marked stale because it has no recent activity since 7 days. It will be closed if no further activity occurs. Thank you.'
-        # comment on PR if not active for more then 14 days.
-        stale-pr-message: 'This PR has been marked stale because it has no recent activity since 14 days. It will be closed if no further activity occurs. Thank you.'
-        # comment on issue if stale for more then 7 days.
-        close-issue-message: This issue was closed due to lack of activity after being marked stale for past 7 days.
-        # comment on PR if stale for more then 14 days.
-        close-pr-message: This PR was closed due to lack of activity after being marked stale for past 14 days.
-        # Number of days of inactivity before an Issue Request becomes stale
-        days-before-issue-stale: 7
-        # Number of days of inactivity before a stale Issue is closed
-        days-before-issue-close: 7
-        # reason for closed the issue default value is not_planned
-        close-issue-reason: completed
-        # Number of days of inactivity before a stale PR is closed
-        days-before-pr-close: 14
-        # Number of days of inactivity before an PR Request becomes stale
-        days-before-pr-stale: 14
-        # Check for label to stale or close the issue/PR
-        any-of-labels: 'stat:awaiting response'
-        # override stale to stalled for PR
-        stale-pr-label: 'stale'
-        # override stale to stalled for Issue
-        stale-issue-label: "stale"
\ No newline at end of file
diff --git a/tfjs-master/.gitignore b/tfjs-master/.gitignore
deleted file mode 100644
index b3a2ac1c2..000000000
--- a/tfjs-master/.gitignore
+++ /dev/null
@@ -1,78 +0,0 @@
-*.log
-*.tar.gz
-*.tgz
-*.zip
-*/diff
-*/run-ci
-**/*.pyc
-**/bundle.js
-**/.parcel-cache
-**/*.js.map
-.DS_Store
-.idea/
-.nyc_output/
-.rpt2_cache/
-.cache/
-.yalc/
-.vscode/ipch/
-/compiled_api.js
-bazel-out/
-bazel-*
-build-tmp-napi-v*/
-build/
-clone/
-coverage/
-demo/train-*
-deps/
-dist/
-tfjs-layers/integration_tests/tfjs2keras/test-data/
-tfjs-layers/integration/typescript/yarn.lock
-e2e/integration_tests/create_save_predict_data
-e2e/integration_tests/convert_predict_data
-e2e/integration_tests/metadata
-e2e/integration_tests/graph_model_golden_data/*.golden.json
-e2e/scripts/storage
-e2e/scripts/htpasswd
-e2e/benchmarks/browserstack-benchmark/browsers.json
-e2e/benchmarks/browserstack-benchmark/benchmark_parameters.json
-e2e/benchmarks/browserstack-benchmark/benchmark_results.json
-e2e/benchmarks/browserstack-benchmark/benchmark_results.js
-e2e/benchmarks/browserstack-benchmark/benchmark_test_results.json
-tfjs-converter/python/tensorflowjs.egg-info
-tfjs-inference/binaries
-
-# Ignore the src, binding, scripts of tfjs-node-gpu since it is copied over when
-# building.
-/tfjs-node-gpu/src
-/tfjs-node-gpu/binding
-/tfjs-node-gpu/scripts
-/tfjs-node-gpu/binding.gyp
-
-# This gets autogenerated during publishing of the union package.
-tfjs/README.md
-
-lib/
-local.log
-node_modules/
-npm-debug.log
-package
-package-lock.json
-release-notes.md
-tensorflow-tfjs-*.tgz
-tfjs-backend-wasm/dist
-tfjs-backend-wasm/wasm-out/*.js
-tfjs-backend-wasm/wasm-out/*.wasm
-yalc.lock
-yarn-error.log
-cloudbuild_generated.yml
-wasm-dist/
-.firebase/
-
-# e2e custom modules
-e2e/custom_module/blazeface/custom_tfjs_blazeface
-e2e/custom_module/dense_model/custom_tfjs
-e2e/custom_module/universal_sentence_encoder/custom_tfjs
-
-# User-specific .bazelrc
-.bazelrc.user
-**/.parcel-cache
diff --git a/tfjs-master/.tslint/noImportsFromDistRule.js b/tfjs-master/.tslint/noImportsFromDistRule.js
deleted file mode 100644
index 11c537583..000000000
--- a/tfjs-master/.tslint/noImportsFromDistRule.js
+++ /dev/null
@@ -1,44 +0,0 @@
-"use strict";
-var __extends = (this && this.__extends) || (function () {
-    var extendStatics = function (d, b) {
-        extendStatics = Object.setPrototypeOf ||
-            ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
-            function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
-        return extendStatics(d, b);
-    };
-    return function (d, b) {
-        extendStatics(d, b);
-        function __() { this.constructor = d; }
-        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
-    };
-})();
-exports.__esModule = true;
-var Lint = require("tslint");
-var Rule = /** @class */ (function (_super) {
-    __extends(Rule, _super);
-    function Rule() {
-        return _super !== null && _super.apply(this, arguments) || this;
-    }
-    Rule.prototype.apply = function (sourceFile) {
-        return this.applyWithWalker(new NoImportsFromDistWalker(sourceFile, this.getOptions()));
-    };
-    Rule.FAILURE_STRING = "importing from dist/ is prohibited. Please use public API";
-    return Rule;
-}(Lint.Rules.AbstractRule));
-exports.Rule = Rule;
-var NoImportsFromDistWalker = /** @class */ (function (_super) {
-    __extends(NoImportsFromDistWalker, _super);
-    function NoImportsFromDistWalker() {
-        return _super !== null && _super.apply(this, arguments) || this;
-    }
-    NoImportsFromDistWalker.prototype.visitImportDeclaration = function (node) {
-        var importFrom = node.moduleSpecifier.getText();
-        var reg = /@tensorflow\/tfjs[-a-z]*\/dist/;
-        if (importFrom.match(reg)) {
-            var fix = new Lint.Replacement(node.moduleSpecifier.getStart(), node.moduleSpecifier.getWidth(), importFrom.replace(/\/dist[\/]*/, ''));
-            this.addFailure(this.createFailure(node.moduleSpecifier.getStart(), node.moduleSpecifier.getWidth(), Rule.FAILURE_STRING, fix));
-        }
-        _super.prototype.visitImportDeclaration.call(this, node);
-    };
-    return NoImportsFromDistWalker;
-}(Lint.RuleWalker));
diff --git a/tfjs-master/.tslint/noImportsFromDistRule.ts b/tfjs-master/.tslint/noImportsFromDistRule.ts
deleted file mode 100644
index 26317e5db..000000000
--- a/tfjs-master/.tslint/noImportsFromDistRule.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-import * as Lint from "tslint";
-import * as ts from "typescript";
-
-export class Rule extends Lint.Rules.AbstractRule {
-  public static FAILURE_STRING = "importing from dist/ is prohibited. Please use public API";
-
-  public apply(sourceFile: ts.SourceFile): Lint.RuleFailure[] {
-    return this.applyWithWalker(
-        new NoImportsFromDistWalker(sourceFile, this.getOptions()));
-  }
-}
-
-class NoImportsFromDistWalker extends Lint.RuleWalker {
-  public visitImportDeclaration(node: ts.ImportDeclaration) {
-    const importFrom = node.moduleSpecifier.getText();
-    const reg = /@tensorflow\/tfjs[-a-z]*\/dist/;
-    if (importFrom.match(reg)) {
-      const fix = new Lint.Replacement(node.moduleSpecifier.getStart(),
-          node.moduleSpecifier.getWidth(),
-          importFrom.replace(/\/dist[\/]*/, ''));
-
-      this.addFailure(this.createFailure(node.moduleSpecifier.getStart(),
-          node.moduleSpecifier.getWidth(),
-          Rule.FAILURE_STRING, fix));
-    }
-
-    super.visitImportDeclaration(node);
-  }
-
-}
diff --git a/tfjs-master/.tslint/noImportsWithRegexRule.js b/tfjs-master/.tslint/noImportsWithRegexRule.js
deleted file mode 100644
index afd3734c5..000000000
--- a/tfjs-master/.tslint/noImportsWithRegexRule.js
+++ /dev/null
@@ -1,46 +0,0 @@
-"use strict";
-var __extends = (this && this.__extends) || (function () {
-    var extendStatics = function (d, b) {
-        extendStatics = Object.setPrototypeOf ||
-            ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
-            function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
-        return extendStatics(d, b);
-    };
-    return function (d, b) {
-        extendStatics(d, b);
-        function __() { this.constructor = d; }
-        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
-    };
-})();
-exports.__esModule = true;
-var Lint = require("tslint");
-var Rule = /** @class */ (function (_super) {
-    __extends(Rule, _super);
-    function Rule() {
-        return _super !== null && _super.apply(this, arguments) || this;
-    }
-    Rule.prototype.apply = function (sourceFile) {
-        return this.applyWithWalker(new NoImportsWithRegexWalker(sourceFile, this.getOptions()));
-    };
-    return Rule;
-}(Lint.Rules.AbstractRule));
-exports.Rule = Rule;
-var NoImportsWithRegexWalker = /** @class */ (function (_super) {
-    __extends(NoImportsWithRegexWalker, _super);
-    function NoImportsWithRegexWalker() {
-        return _super !== null && _super.apply(this, arguments) || this;
-    }
-    NoImportsWithRegexWalker.prototype.visitImportDeclaration = function (node) {
-        var options = this.getOptions();
-        for (var _i = 0, options_1 = options; _i < options_1.length; _i++) {
-            var regStr = options_1[_i];
-            var importFrom = node.moduleSpecifier.getText();
-            var reg = new RegExp(regStr);
-            if (importFrom.match(reg)) {
-                this.addFailure(this.createFailure(node.moduleSpecifier.getStart(), node.moduleSpecifier.getWidth(), "importing from " + regStr + " is prohibited."));
-            }
-        }
-        _super.prototype.visitImportDeclaration.call(this, node);
-    };
-    return NoImportsWithRegexWalker;
-}(Lint.RuleWalker));
diff --git a/tfjs-master/.tslint/noImportsWithRegexRule.ts b/tfjs-master/.tslint/noImportsWithRegexRule.ts
deleted file mode 100644
index 3377a61ed..000000000
--- a/tfjs-master/.tslint/noImportsWithRegexRule.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-import * as Lint from 'tslint';
-import * as ts from 'typescript';
-
-export class Rule extends Lint.Rules.AbstractRule {
-  public apply(sourceFile: ts.SourceFile): Lint.RuleFailure[] {
-    return this.applyWithWalker(
-        new NoImportsWithRegexWalker(sourceFile, this.getOptions()));
-  }
-}
-
-class NoImportsWithRegexWalker extends Lint.RuleWalker {
-  public visitImportDeclaration(node: ts.ImportDeclaration) {
-    const options = this.getOptions();
-    for (const regStr of options) {
-      const importFrom = node.moduleSpecifier.getText();
-      const reg = new RegExp(regStr);
-      if (importFrom.match(reg)) {
-        this.addFailure(this.createFailure(
-            node.moduleSpecifier.getStart(), node.moduleSpecifier.getWidth(),
-            `importing from ${regStr} is prohibited.`));
-      }
-    }
-
-    super.visitImportDeclaration(node);
-  }
-}
diff --git a/tfjs-master/.vscode/settings.json b/tfjs-master/.vscode/settings.json
deleted file mode 100644
index 7b4c1a38d..000000000
--- a/tfjs-master/.vscode/settings.json
+++ /dev/null
@@ -1,149 +0,0 @@
-// Place your settings in this file to overwrite default and user settings.
-{
-  "search.exclude": {
-    "**/node_modules": true,
-    "**/coverage/": true,
-    "**/dist/": true,
-    "**/yarn.lock": true,
-    "**/.rpt2_cache/": true,
-    "**/.yalc/**/*": true,
-    "**/.cache/**/*": true,
-    "**/bazel-bin/**/*": true,
-    "**/bazel-out/**/*": true,
-    "**/bazel-genfiles/**/*": true,
-    "**/bazel-testlogs/**/*": true,
-    "**/bazel-tfjs*/**/*": true
-  },
-  "files.trimTrailingWhitespace": true,
-  "editor.tabSize": 2,
-  "editor.insertSpaces": true,
-  "[typescript]": {
-    "editor.formatOnSave": true,
-    "editor.defaultFormatter": "xaver.clang-format"
-  },
-  "[javascript]": {
-    "editor.formatOnSave": true
-  },
-  "[cpp]": {
-    "editor.formatOnSave": true
-  },
-  "emeraldwalk.runonsave": {
-    "commands": [
-      {
-        "match": "(BUILD|.*bzl)",
-        "cmd": "yarn bazel:format && yarn bazel:lint"
-      }
-    ]
-  },
-  "editor.defaultFormatter": "xaver.clang-format",
-  "editor.rulers": [80],
-  "clang-format.style": "Google",
-  "files.insertFinalNewline": true,
-  "editor.detectIndentation": false,
-  "editor.wrappingIndent": "none",
-  "typescript.tsdk": "node_modules/typescript/lib",
-  "clang-format.executable": "${workspaceRoot}/node_modules/.bin/clang-format",
-  "files.associations": {
-    "memory": "cpp",
-    "any": "cpp",
-    "array": "cpp",
-    "atomic": "cpp",
-    "strstream": "cpp",
-    "*.tcc": "cpp",
-    "bitset": "cpp",
-    "cctype": "cpp",
-    "chrono": "cpp",
-    "cinttypes": "cpp",
-    "clocale": "cpp",
-    "cmath": "cpp",
-    "codecvt": "cpp",
-    "complex": "cpp",
-    "condition_variable": "cpp",
-    "cstdarg": "cpp",
-    "cstddef": "cpp",
-    "cstdint": "cpp",
-    "cstdio": "cpp",
-    "cstdlib": "cpp",
-    "cstring": "cpp",
-    "ctime": "cpp",
-    "cwchar": "cpp",
-    "cwctype": "cpp",
-    "deque": "cpp",
-    "list": "cpp",
-    "unordered_map": "cpp",
-    "unordered_set": "cpp",
-    "vector": "cpp",
-    "exception": "cpp",
-    "algorithm": "cpp",
-    "filesystem": "cpp",
-    "functional": "cpp",
-    "iterator": "cpp",
-    "map": "cpp",
-    "memory_resource": "cpp",
-    "numeric": "cpp",
-    "optional": "cpp",
-    "random": "cpp",
-    "ratio": "cpp",
-    "regex": "cpp",
-    "set": "cpp",
-    "string": "cpp",
-    "string_view": "cpp",
-    "system_error": "cpp",
-    "tuple": "cpp",
-    "type_traits": "cpp",
-    "utility": "cpp",
-    "fstream": "cpp",
-    "future": "cpp",
-    "initializer_list": "cpp",
-    "iomanip": "cpp",
-    "iosfwd": "cpp",
-    "iostream": "cpp",
-    "istream": "cpp",
-    "limits": "cpp",
-    "mutex": "cpp",
-    "new": "cpp",
-    "ostream": "cpp",
-    "shared_mutex": "cpp",
-    "sstream": "cpp",
-    "stdexcept": "cpp",
-    "streambuf": "cpp",
-    "thread": "cpp",
-    "cfenv": "cpp",
-    "typeinfo": "cpp",
-    "valarray": "cpp",
-    "variant": "cpp",
-    "charconv": "cpp",
-    "__bit_reference": "cpp",
-    "__config": "cpp",
-    "__debug": "cpp",
-    "__errc": "cpp",
-    "__functional_base": "cpp",
-    "__hash_table": "cpp",
-    "__locale": "cpp",
-    "__mutex_base": "cpp",
-    "__node_handle": "cpp",
-    "__nullptr": "cpp",
-    "__split_buffer": "cpp",
-    "__sso_allocator": "cpp",
-    "__std_stream": "cpp",
-    "__string": "cpp",
-    "__threading_support": "cpp",
-    "__tree": "cpp",
-    "__tuple": "cpp",
-    "bit": "cpp",
-    "ios": "cpp",
-    "locale": "cpp",
-    "queue": "cpp",
-    "stack": "cpp",
-    "*.ipp": "cpp",
-    "forward_list": "cpp",
-    "typeindex": "cpp",
-    "*.inc": "cpp",
-    "hash_map": "cpp",
-    "__refstring": "cpp"
-  },
-  "editor.formatOnSave": true,
-  "[json]": {
-    "editor.defaultFormatter": "vscode.json-language-features"
-  }
-}
diff --git a/tfjs-master/.vscode/tasks.json b/tfjs-master/.vscode/tasks.json
deleted file mode 100644
index 594b0eea8..000000000
--- a/tfjs-master/.vscode/tasks.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
-    "version": "2.0.0",
-    "tasks": [
-      {
-        "command": "yarn",
-        "label": "lint",
-        "type": "shell",
-        "args": [
-          "lint"
-        ],
-        "problemMatcher": {
-          "base": "$tslint5",
-          "owner": "tslint-type-checked",
-          "fileLocation": "absolute"
-        }
-      },
-      {
-        "command": "yarn",
-        "label": "build",
-        "type": "shell",
-        "args": ["build", "--pretty", "false", "--noEmit"],
-        "problemMatcher": [
-          "$tsc"
-        ]
-      }
-    ]
-  }
diff --git a/tfjs-master/BAZEL_MIGRATION.md b/tfjs-master/BAZEL_MIGRATION.md
deleted file mode 100644
index e4fea3219..000000000
--- a/tfjs-master/BAZEL_MIGRATION.md
+++ /dev/null
@@ -1,442 +0,0 @@
-# Bazel Migration
-
-This document details the steps to migrate a package to build with Bazel. These steps are easiest to understand with a working example, so this doc references `tfjs-core`'s setup as much as possible. Since this migration is still in progress, the steps and processes listed here may change as we improve on the process, add features to each package's build, and create tfjs-specific build functions.
-
-## Scope
-Migrating a package to Bazel involves adding Bazel targets that build the package, running its tests, and packing the package for publishing to npm. To ease the transition to Bazel, we're incrementally transitioning packages to build with Bazel, starting with root packages (tfjs-core, tfjs-backend-cpu) and gradually expanding to leaf packages. This is different from our original approach of maintaining our current build and a new Bazel build in parallel, which ended up not working due to some changes that Bazel required to the ts sources.
-
-## Caveats
-- Bazel will only make a dependency or file available to the build if you explicitly declare it as a dependency / input to the rule you're using.
-- All Bazel builds use the root package.json, so you may have to add packages to it. As long as you use `@npm//dependency-name` in BUILD files to add dependencies, you won't need to worry about the build accidentally seeing the package's `node_modules` directory instead of the root `node_modules`. Bazel will only make the root `node_modules` directory visible to the build.
-  - Even though the build doesn't use the package's `node_modules`, you may have to run `yarn` within the package to get code completion to work correctly. We're looking into why this is the case.
-- There may be issues with depending on explicitly pinned versions of `@tensorflow` scoped packages, which might affect some demos if they're migrated to use Bazel. We might just want to leave demos out of Bazel so they're easier to understand.
-
-## Steps
-These steps are general guidelines for how to build a package with Bazel. They should work for most packages, but there may be some exceptions (e.g. wasm, react native).
-
-### Make sure all dependencies build with Bazel
-A package's dependencies must be migrated before it can be migrated. Take a look at the package's issue, which can be found by checking [#5287](https://github.com/tensorflow/tfjs/issues/5287), to find its dependencies.
-
-### Add dependencies to the root `package.json`
-Bazel (through `rules_nodejs`) uses a single root `package.json` for its npm dependencies. When converting a package to build with Bazel, dependencies in the package's `package.json` will need to be added to the root `package.json` as well.
-
-### Create a `BUILD.bazel` file in the package's root
-Bazel looks for targets to run in `BUILD` and `BUILD.bazel` files. Use the `.bazel` extension since blaze uses `BUILD`. You may want to install an extension for your editor to get syntax highlighting. [Here's the vscode extension](https://marketplace.visualstudio.com/items?itemName=BazelBuild.vscode-bazel).
-
-This BUILD file will handle package-wide rules like bundling for npm.
-
-### Create another `BUILD.bazel` file in `src`
-This BUILD file will compile the source files of the package using `ts_library` and may also define test bundles.
-
-### Compile the package with `ts_library`
-In the `src` BUILD.bazel file, we use `ts_library` to compile the package's typescript files.  [ts_library](https://bazelbuild.github.io/rules_nodejs/TypeScript.html#ts_library) is a rule provided by rules_nodejs. We wrap `ts_library` in a macro that sets some project-specific settings.
-
-Here's an example of how `tfjs-core` uses `ts_library` to build.
-
-`tfjs-core/src/BUILD.bazel`
-```starlark
-load("//tools:defaults.bzl", "ts_library")
-
-TEST_SRCS = [
-    "**/*_test.ts",
-    "image_test_util.ts",
-]
-
-# Compiles the majority of tfjs-core using the `@tensorflow/tfjs-core/dist`
-# module name.
-ts_library(
-    name = "tfjs-core_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-core/dist",
-    deps = [
-        "@npm//@types",
-        "@npm//jasmine-core",
-        "@npm//seedrandom",
-    ],
-)
-
-# Compiles the `index.ts` entrypoint of tfjs-core separately from the rest of
-# the sources in order to use the `@tensorflow/tfjs-core` module name instead
-# of `@tensorflow/tfjs-core/dist`,
-ts_library(
-    name = "tfjs-core_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-core",
-    deps = [
-        ":tfjs-core_src_lib",
-    ],
-)
-```
-`ts_library` is used twice in order to have the correct `module_name` for the output files. Most files are imported relative to `@tensorflow/tfjs-core/src/`, but `index.ts`, the entrypoint of `tfjs-core`, should be importable as `@tensorflow/tfjs-core`.
-
-If your package imports from `dist` (e.g. `import {} from @tensorflow/tfjs-core/dist/ops/ops_for_converter`), that import likely corresponds to a rule in that packages `src/BUILD.bazel` file. Look for a rule that includes the file you're importing and has `module_name` set correctly for that import.
-
-### Bundle the package
-This step involves bundling the compiled files from the compilation step into a single file, and the rules are added to the package's root BUILD file (instead of `src/BUILD.bazel`). In order to support different execution environments, TFJS generates several bundles for each package. We provide a `tfjs_bundle` macro to generate these bundles.
-
-`tfjs-core/BUILD.bazel`
-```starlark
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-
-tfjs_bundle(
-    name = "tf-core",
-    entry_point = "//tfjs-core/src:index.ts",
-    external = [
-        "node-fetch",
-        "util",
-    ],
-    umd_name = "tf",
-    deps = [
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-    ],
-)
-```
-The `tfjs_bundle` macro generates several different bundles which are published in the package publishing step.
-
-
-### Compile the tests with `ts_library`
-In the `src/BUILD.bazel` file, we compile the tests with `ts_library`. In the case of `tfjs-core`, we actually publish the test files, since other packages use them in their tests. Therefore, it's important that we set the `module_name` to `@tensorflow/tfjs-core/dist`. If a package's tests are not published, the `module_name` can probably be omitted. In a future major version of tfjs, we may stop publishing the tests to npm.
-
-`tfjs-core/src/BUILD.bazel`
-```starlark
-load("//tools:defaults.bzl", "ts_library")
-
-ts_library(
-    name = "tfjs-core_test_lib",
-    srcs = glob(TEST_SRCS),
-    # TODO(msoulanille): Mark this as testonly once it's no longer needed in the
-    # npm package (for other downstream packages' tests).
-    module_name = "@tensorflow/tfjs-core/dist",
-    deps = [
-        ":tfjs-core_lib",
-        ":tfjs-core_src_lib",
-    ],
-)
-```
-
-### Update the tests entrypoint
-Many packages have a `src/run_tests.ts` file (or similar) that they use for selecting which tests to run. That file defines the paths to the test files that Jasmine uses. Since Bazel outputs appearin a different location, the paths to the test files must be updated. As an example, the following paths
-```ts
-const coreTests = 'node_modules/@tensorflow/tfjs-core/src/tests.ts';
-const unitTests = 'src/**/*_test.ts';
-```
-would need to be updated to
-```ts
-const coreTests = 'tfjs-core/src/tests.js';
-const unitTests = 'the-package-name/src/**/*_test.js';
-```
-Note that `.ts` has been changed to `.js`. This is because we're no longer running node tests with `ts-node`, so the input test files are now `.js` outputs created by the `ts_library` rule that compiled the tests.
-
-It's also important to make sure the `nodejs_test` rule that runs the test has [`link_workspace_root = True`](https://bazelbuild.github.io/rules_nodejs/Built-ins.html#nodejs_binary-link_workspace_root). Otherwise, the test files will not be accessable at runtime.
-
-### Run node tests with nodejs_test
-Our test setup allows fine-tuning of exactly what tests are run via `setTestEnvs` and `setupTestFilters` in `jasmine_util.ts`, which are used in a custom Jasmine entrypoint file `setup_test.ts`. This setup does not work well with [jasmine_node_test](https://bazelbuild.github.io/rules_nodejs/Jasmine.html#jasmine_node_test), which provides its own entrypoint for starting Jasmine. Instead, we use the [nodejs_test](https://bazelbuild.github.io/rules_nodejs/Built-ins.html#nodejs_test) rule.
-
-`tfjs-core/BUILD.bazel`
-```starlark
-load("@build_bazel_rules_nodejs//:index.bzl", "js_library", "nodejs_test")
-
-# This is necessary for tests to have acess to
-# the package.json so src/version_test.ts can 'require()' it.
-js_library(
-    name = "package_json",
-    srcs = [
-        ":package.json",
-    ],
-)
-
-nodejs_test(
-    name = "tfjs-core_node_test",
-    data = [
-        ":package_json",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-    ],
-    entry_point = "//tfjs-core/src:test_node.ts",
-    link_workspace_root = True,
-    tags = ["ci"],
-)
-```
-It's important to tag tests with `ci` if you would like them to run in continuous integration.
-
-
-### Run browser tests with karma_web_test
-We use `esbuild` to bundle the tests into a single file.
-
-`tfjs-core/src/BUILD.bazel`
-```starlark
-load("//tools:defaults.bzl", "esbuild")
-
-esbuild(
-    name = "tfjs-core_test_bundle",
-    testonly = True,
-    entry_point = "setup_test.ts",
-    external = [
-        # webworker tests call 'require('@tensorflow/tfjs')', which
-        # is external to the test bundle.
-        # Note: This is not a bazel target. It's just a string.
-        "@tensorflow/tfjs",
-        "worker_threads",
-        "util",
-    ],
-    sources_content = True,
-    deps = [
-        ":tfjs-core_lib",
-        ":tfjs-core_test_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core:package_json",
-    ],
-)
-```
-
-The esbuild bundle is then used in the tfjs_web_test macro, which uses [karma_web_test](https://bazelbuild.github.io/rules_nodejs/Concatjs.html#karma_web_test) to serve it to a browser to be run. Different browserstack browsers can be enabled or disabled in the `browsers` argument, and the full list of browsers is located in `tools/karma_template.conf.js`. Browserstack browser tests are automatically tagged with `ci`.
-
-`tfjs-core/BUILD.bazel`
-```starlark
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-tfjs_web_test(
-    name = "tfjs-core_test",
-    srcs = [
-        "//tfjs-core/src:tfjs-core_test_bundle",
-    ],
-    browsers = [
-        "bs_chrome_mac",
-        "bs_firefox_mac",
-        "bs_safari_mac",
-        "bs_ios_12",
-        "bs_android_10",
-        "win_10_chrome",
-    ],
-    static_files = [
-        # Listed here so sourcemaps are served
-        "//tfjs-core/src:tfjs-core_test_bundle",
-        # For the webworker
-        ":tf-core.min.js",
-        ":tf-core.min.js.map",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js.map",
-    ],
-)
-```
-
-Whereas before, tests were included based on the [`karma.conf.js`](https://github.com/tensorflow/tfjs/blob/2603aac08c5e41a9c6e5b79d294c6a61800acb67/tfjs-backend-webgl/karma.conf.js#L54-L58) file, now, tests must be included in the test bundle to be run. Make sure to `import` each test file in the test bundle's entrypoint. To help with this, we provide an `enumerate_tests` Bazel rule to generate a `tests.ts` file with the required imports.
-
-[`tfjs-core/src/BUILD.bazel`](https://github.com/tensorflow/tfjs/blob/a32cc50dbd0dce2e6a53bb962eedc0d87a064b1e/tfjs-core/src/BUILD.bazel#L32-L48)
-```starlark
-load("//tools:enumerate_tests.bzl", "enumerate_tests")
-
-# Generates the 'tests.ts' file that imports all test entrypoints.
-enumerate_tests(
-    name = "tests",
-    srcs = [":all_test_entrypoints"], # all_test_entrypoints is a filegroup
-    root_path = "tfjs-core/src",
-)
-```
-
-### Update the package.json
-1. Verify the entrypoints of the package.json to match the outputs generated by `tfjs_bundle` and `ts_library`. [tfjs-core/package.json](https://github.com/tensorflow/tfjs/blob/a32cc50dbd0dce2e6a53bb962eedc0d87a064b1e/tfjs-core/package.json#L6-L12) is an example.
-    1. The main entrypoint should point to the node bundle, `dist/tf-package-name.node.js`.
-    2. `jsnext:main` and `module` should point to the ESModule output `dist/index.js` created by `copy_ts_library_to_dist`.
-2. If the package has browser tests, update the `sideEffects` field to include `.mjs` files generated by the `ts_library` under `./src` (e.g. `src/foo.mjs`). Bazel outputs directly to `src`, and although we copy those outputs to `dist` with another Bazel rule, the browser test bundles still import from `src`, so we need to mark them as sideEffects.
-
-### Package for npm
-We use the [pkg_npm](https://bazelbuild.github.io/rules_nodejs/Built-ins.html#pkg_npm) rule to create and publish the package to npm. However, there are a few steps needed before we can declare the package. For most packages, we distribute all our compiled outputs in the `dist` directory. However, due to how `ts_library` works, it creates outputs in the same directory as the source files were compiled from (except they show up in Bazel's `dist/bin` output dir). We need to copy these from `src` to `dist` while making sure Bazel is aware of this copy (so we can still use `pkg_npm`).
-
-We also need to copy several other files to `dist`, such as the bundles created by `tfjs_bundle`, and we need to create [miniprogram](https://walkthechat.com/wechat-mini-programs-simple-introduction/) files for WeChat.
-
-To copy files, we usually use the `copy_to_dist` rule. This rule creates symlinks to all the files in `srcs` and places them in a filetree with the same structure in `dest_dir` (which defaults to `dist`).
-
-However, we can't just copy the output of a `ts_library`, since its default output is the `.d.ts` declaration files. We need to extract the desired ES Module `.mjs` outputs of the rule and rename them to have the `.js` extension. The `copy_ts_library_to_dist` does this rename, and it also copies the files to `dist` (including the `.d.ts` declaration files).
-
-```starlark
-load("//tools:copy_to_dist.bzl", "copy_ts_library_to_dist")
-
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-    ],
-    root = "src", # Consider 'src' to be the root directory of the copy
-                  # (i.e. create 'dist/index.js' instead of 'dist/src/index.js')
-    dest_dir = "dist", # Where to copy the files to. Defaults to 'dist', so it can
-                       # actually be omitted in this case.
-)
-```
-
-We can also copy the bundles output from `tfjs_bundle`
-
-```starlark
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-core",
-        ":tf-core.node",
-        ":tf-core.es2017",
-        ":tf-core.es2017.min",
-        ":tf-core.fesm",
-        ":tf-core.fesm.min",
-        ":tf-core.min",
-    ],
-)
-```
-
-We copy the miniprogram files as well, this time using the `copy_file` rule, which copies a single file to a destination.
-
-```starlark
-load("@bazel_skylib//rules:copy_file.bzl", "copy_file")
-
-copy_file(
-    name = "copy_miniprogram",
-    src = ":tf-core.min.js",
-    out = "dist/miniprogram/index.js",
-)
-
-copy_file(
-    name = "copy_miniprogram_map",
-    src = ":tf-core.min.js.map",
-    out = "dist/miniprogram/index.js.map",
-)
-```
-
-Now that all the files are copied, we can declare a `pkg_npm`
-
-```starlark
-load("@build_bazel_rules_nodejs//:index.bzl", "pkg_npm")
-
-pkg_npm(
-    name = "tfjs-core_pkg",
-    package_name = "@tensorflow/tfjs-core",
-    srcs = [
-        # Add any static files the package should include here
-        "package.json",
-        "README.md",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_miniprogram",
-        ":copy_miniprogram_map",
-        ":copy_src_to_dist",
-        ":copy_test_snippets", # <- This is only in core, so I've omitted its
-                               # definition in these docs.
-    ],
-)
-```
-
-Now the package can be published to npm with `bazel run //tfjs-core:tfjs-core_pkg.publish`.
-
-
-### Configure Publishing to npm
-With a `pkg_npm` rule defined, we add a script to `package.json` to run it. This script will be used by the main script that publishes the monorepo. 
-
-```json
-"scripts" {
-    "publish-npm": "bazel run :tfjs-core_pkg.publish"
-}
-```
-
-Since we now use the `publish-npm` script to publish this package instead of `npm publish`, we need to make sure the release tests and release script know how to publish it.
-
-1. In `scripts/publish-npm.ts`, add your package's name to the `BAZEL_PACKAGES` set.
-2. In `e2e/scripts/publish-tfjs-ci.sh`, add your package's name to the `BAZEL_PACKAGES` list.
-
-You should also add a script to build the package itself without publishing (used for the `link-package`).
-
-```json
-"build": "bazel build :tfjs-core_pkg",
-```
-
-### Update Downstream `package.json` Paths
-
-If no packages depend on your package (i.e. no `package.json` file includes your package via a `link` dependency), then you can skip this section.
-
-As a core featue of its design, Bazel places outputs in a different directory than sources. Outputs are symlinked to `dist/bin/[package-name]/.....` instead of appearing in `[package-name]/dist`. Due to the different location, all downstream packages' `package.json` files need to be updated to point to the new outputs. However, due to some details of how Bazel and the Node module resolution algorithm work, we can't directly `link:` to Bazel's output.
-
-Instead, we maintain a `link-package` pseudopackage where we copy the Bazel outputs. This package allows for correct Node module resolution between Bazel outputs because it has its own `node_modules` folder. This package will never be published and will be removed once the migration is complete.
-
-#### Add the Package to `link-package`
-Add your package to the `PACKAGES` list in the `build_deps.ts` script in `link-package`. For a package with npm name `@tensorflow/tfjs-foo`, the package's directory in the monorepo and the value to add to `PACKAGES` should both be `tfjs-foo`. The name of the package's `pkg_npm` target should be `tfjs-foo_pkg`.
-
-```typescript
-const PACKAGES: ReadonlySet<string> = new Set([
-  ..., 'tfjs-foo',
-]);  
-```
-
-#### Change Downstream Dependency `package.json` Paths
-Update all downstream dependencies that depend on the package to point to its location in the `link-package`.
-
-```json
-"devDependencies": {
-  "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core",
-  "@tensorflow/tfjs-foo": "link:../link-package/node_modules/@tensorflow/tfjs-foo",
-},
-```
-
-To find downstream packages, run `grep -r --exclude=yarn.lock --exclude-dir=node_modules "link:.*tfjs-foo" .` in the root of the repository.
-
-#### Remove the 'build-tfjs-foo' Script from Downstream Packages
-Remove the `build-tfjs-foo` script from downstream packages' package.json files.
-```json
-"scripts": {
-  "build-deps": "....... && yarn build-tfjs-foo" // <-- Remove 'yarn build-deps-foo'.
-  "build-tfjs-foo": "remove this script", // <-- Also remove it here.
-}
-```
-### Move linting to the repo-wide lint script
-#### Add the new Bazel package to the [repo-wide tslint tsconfig](tsconfig_tslint.json):
-
-Add the path mapping:
-```json
-"paths": {
-  ...,
-  "@tensorflow/the-new-package": ["the-new-package/src/index.ts"],
-  "@tensorflow/the-new-package/dist/*": ["the-new-package/src/*"]
-```
-
-Also, remove the package from the `exclude` list.
-
-It's a good idea to test that linting is working on the package. Create a lint error in one if its files, e.g. `const x = "Hello, world!"` (note the double quotes), and then run `yarn lint` in the root of the repository.
-
-#### Remove the package's own TypeScript linting scripts:
-Remove the `package.json` `lint` script, the `tslint.json` file, and the cloudbuild `lint` step from the package's `cloudbuild.yml` file. Remove `tslint`-related dependencies from the package's `package.json` and run `yarn` to regenerate the `yarn.lock` file.
-
-### Update or Remove `cloudbuild.yml`
-Update the `cloudbuild.yml` to remove any steps that are now built with Bazel. These will be run by the `bazel-tests` step, which runs before other packages' steps. Any Bazel rule tagged as `ci` will be tested / build in CI.
-
-Note that the output paths of Bazel-created outputs will be different, so any remaining steps that now rely on Bazel outputs may need to be updated. Bazel outputs are located in `tfjs/dist/bin/...`.
-
-If all steps of the `cloudbuild.yml` file are handled by Bazel, it can be deleted. Do not remove the package from `tfjs/scripts/package_dependencies.json`.
-
-Rebuild the cloudbuild golden files by running `yarn update-cloudbuild-tests` in the root of the repository.
-
-### Push to Git
-Before pushing to Git, run the Bazel linter by running `yarn bazel:format` and `yarn bazel:lint-fix` in the root of the repo. We run the linter in CI, so if your build is failing in CI only, incorrectly formatted files may be the reason.
-
-### Done!
-🎉🎉🎉
-
-## Notes for Reviewing PRs
-* Make sure the package is added to `BAZEL_PACKAGES` in [e2e/scripts/publish-tfjs-ci.sh](https://github.com/tensorflow/tfjs/blob/master/e2e/scripts/publish-tfjs-ci.sh#L61)
-* Make sure the package is added to `BAZEL_PACKAGES` in [scripts/publish-npm.ts](https://github.com/tensorflow/tfjs/blob/master/scripts/publish-npm.ts#L31)
-* Make sure the package generated by `pkg_npm` has all the files it needs, e.g. the README.
-* Make sure the package is added to the link-package's package.json and that downstream pakcages are updated to point to the link package's copy instead of the package's directory.
-* For browser tests, it may be worth checking that all desired browser configurations will run in nightly CI.
-* Make sure browser tests include all required tests. The `enumerate_tests` rule is usually necessary to make the browser actually run tests.
-* Make sure as many cloudbuild steps as possible are converted to Bazel, and that those steps are removed from the cloudbuild file.
-* If the build and tests are fully handled by Bazel and don't need any other cloudbuild steps, make sure the package's `cloudbuild.yml` file is removed. Do not remove the package from [scripts/package_dependencies.json](https://github.com/tensorflow/tfjs/blob/master/scripts/package_dependencies.json).
-* Make sure tests are tagged with `nightly` or `ci` (`tfjs_web_test` automatically tags tests with `nightly` and `ci`).
-* Make sure the main `pkg_npm` rule is tagged with `ci` or `nightly` so all parts of the build are tested.
-* Make sure the `package.json` scripts are updated and that the package.json includes `@bazel/bazelisk` as a dev dependency.
-* Make sure the package has a `build-npm` script and a `publish-npm` script. These are used by the release script.
-* Check the generated bundle sizes and make sure they don't include any unexpected files. Check the `_stats` files for info on this.
-* Make sure the package is added to the [repo-wide tslint tsconfig](tsconfig_tslint.json) and that its original lint scripts are removed.
diff --git a/tfjs-master/BUILD.bazel b/tfjs-master/BUILD.bazel
deleted file mode 100644
index d1c21570a..000000000
--- a/tfjs-master/BUILD.bazel
+++ /dev/null
@@ -1,64 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@npm//@bazel/concatjs:index.bzl", "ts_config")
-load("//tools:tfjs_web_test.bzl", "grep_flag", "headless_flag")
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files([
-    "babel.config.json",
-    "tsconfig.json",
-    "tsconfig_es5.json",
-    "tsconfig.test.json",
-    "tsconfig_base.json",
-    "tsconfig_ts_library.json",
-])
-
-ts_config(
-    name = "tsconfig_ts_library",
-    src = "tsconfig_ts_library.json",
-    deps = [
-        ":tsconfig.json",
-        ":tsconfig_base.json",
-    ],
-)
-
-grep_flag(
-    name = "grep",
-    build_setting_default = "",
-    visibility = ["//visibility:public"],
-)
-
-headless_flag(
-    name = "headless",
-    build_setting_default = True,
-    visibility = ["//visibility:public"],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        "//tfjs-backend-cpu:tests",
-        "//tfjs-backend-wasm:tests",
-        "//tfjs-backend-webgl:tests",
-        "//tfjs-converter:tests",
-        "//tfjs-core:tests",
-        "//tfjs-data:tests",
-        "//tfjs-layers:tests",
-        "//tfjs-tfdf:tests",
-        "//tfjs-tflite:tests",
-    ],
-)
diff --git a/tfjs-master/CONTRIBUTING.md b/tfjs-master/CONTRIBUTING.md
deleted file mode 100644
index 3473e796c..000000000
--- a/tfjs-master/CONTRIBUTING.md
+++ /dev/null
@@ -1,86 +0,0 @@
-# How to Contribute
-
-We'd love to accept your patches and contributions to this project. There are
-just a few small guidelines you need to follow.
-
-## Contributor License Agreement
-
-Contributions to this project must be accompanied by a Contributor License
-Agreement. You (or your employer) retain the copyright to your contribution,
-this simply gives us permission to use and redistribute your contributions as
-part of the project. Head over to <https://cla.developers.google.com/> to see
-your current agreements on file or to sign a new one.
-
-You generally only need to submit a CLA once, so if you've already submitted one
-(even if it was for a different project), you probably don't need to do it
-again.
-
-## Code reviews
-
-All submissions, including submissions by project members, require review. We
-use GitHub pull requests for this purpose. Consult
-[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
-information on using pull requests.
-
-## Continuous Integration
-Continuous Integration tests run for every PR that is sent against TensorFlow.js repositories. Any time you push to the branch, they will re-run. Before asking for a review, make sure that the continuous integration tests are passing.
-
-To see the logs from the Cloud Build CI, please join either
-our [discussion](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfjs)
-or [announcement](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfjs-announce) mailing list.
-
-#### Failing tests (click details for information):
-
-Click "Details", and then "View more details on Google Cloud Build" for information about the failure.
-
-<img src="https://user-images.githubusercontent.com/1100749/59696200-8fdb4500-91b9-11e9-9351-949a23fd7c75.png" data-canonical-src="https://user-images.githubusercontent.com/1100749/59696200-8fdb4500-91b9-11e9-9351-949a23fd7c75.png" width=500/>
-
-#### Passing tests:
-
-<img src="https://user-images.githubusercontent.com/1100749/59696439-fa8c8080-91b9-11e9-933f-a775779970f3.png" data-canonical-src="https://user-images.githubusercontent.com/1100749/59696439-fa8c8080-91b9-11e9-933f-a775779970f3.png" width=500/>
-
-## Filing GitHub issues
-
-Please follow the guidelines specified in the
-[ISSUE_TEMPLATE](https://github.com/tensorflow/tfjs/tree/master/.github/ISSUE_TEMPLATE)
-file.
-
-## Good places to start
-
-Consider joining the [tfjs mailing list](https://groups.google.com/a/tensorflow.org/d/forum/tfjs)
-to discuss the development of TensorFlow.js. If you're working on anything
-substantial it's better to discuss your design before submitting a PR.
-If you're looking for tasks to work on, we also mark issues as ["good first issue"](https://github.com/tensorflow/tfjs/labels/good%20first%20issue) and ["stat:contributions welcome"](https://github.com/tensorflow/tfjs/labels/stat%3Acontributions%20welcome)
-
-## Adding functionality
-
-One way to ensure that your PR will be accepted is to add functionality that
-has been requested in Github issues. If there is something you think is
-important and we're missing it but does not show up in Github issues, it would
-be good to file an issue there first so we can have the discussion before
-sending us a PR.
-
-In general, we're trying to add functionality when driven by use-cases instead of
-adding functionality for the sake of parity with TensorFlow python. This will
-help us keep the bundle size smaller and have less to maintain especially as we
-add new backends.
-
-### Adding an op
-
-When adding ops to the library and deciding whether to write a kernel
-implementation in [backend.ts](/tfjs-core/src/backends/backend.ts),
-be sure to check out the TensorFlow ops list [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/ops.pbtxt).
-This list shows the kernels available for the TensorFlow C API. To ensure that
-we can bind to this with node.js, we should ensure that our backend.ts
-interface matches ops in the TensorFlow C API. For detailed instructions on
-how to add a new op, see [CONTRIBUTING_MISSING_OP.md](/CONTRIBUTING_MISSING_OP.md).
-
-## Code reviews
-
-All submissions, including submissions by project members, require review. We
-use GitHub pull requests for this purpose. Consult
-[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
-information on using pull requests.
-
-We require unit tests for most code, instructions for running our unit test
-suites are in the documentation.
diff --git a/tfjs-master/CONTRIBUTING_MISSING_OP.md b/tfjs-master/CONTRIBUTING_MISSING_OP.md
deleted file mode 100644
index 84bf3d1d7..000000000
--- a/tfjs-master/CONTRIBUTING_MISSING_OP.md
+++ /dev/null
@@ -1,84 +0,0 @@
-## How to Contribute a New op to TF.js Repository
-
-### Step 1. Add the new op to tfjs-core ops directory.
-**Prerequisite**
-1. The op should already be supported in [TF](https://www.tensorflow.org/api_docs/python/tf/all_symbols) api.
-2. The op should not have been supported in TF.js, see [op support list](https://github.com/tensorflow/tfjs/blob/master/tfjs-converter/docs/supported_ops.md).
-3. Check [here](https://docs.google.com/spreadsheets/d/1D25XtWaBrmUEErbGQB0QmNhH-xtwHo9LDl59w0TbxrI/edit#gid=0) for various ops supported in different backends. 
-
-**Implementation Details**
-1. Create a new op in `tfjs-core/ops` directory.
-
-    The op file should have the following elements:
-        1. License information.
-        2. JSDoc comment.
-        3. Input validations, if any.
-        4. Delegate the execution to the right kernel through `ENGINE.runKernel()`.
-
-    In addition, for the kernel delegation to work properly, in `kernel_names.ts`
-  file, define:
-        1. kernel name.
-        2. input type.
-        3. attribute type.
-
-2. Export the op file in ops.ts in the same directory.
-3. Add tests for the op in the same directory. Test file name should be the same as the op file’s name with _test suffix.
-4. Exclude the test in all the backends, and add annotation “Not implemented yet.”. See below for where to exclude the test in each backend:
-
-**cpu backend**
-
-In `run_tests.ts`, `customInclude` method, add:
-```
-    // Not implemented yet.
-    if (testName.includes(test_name)) {
-      return false;
-    }
-```
-
-**webgl backend**
-
-In `setup_test.ts`, `customInclude` method, add:
-```
-    // Not implemented yet.
-    if (testName.includes(test_name)) {
-      return false;
-    }
-```
-
-**node backend**
-
-In `run_tests.ts`, `IGNORE_LIST`, add test_name to the list.
-
-**Example PRs**
-* [CPU and WebGL backend SparseReshape](https://github.com/tensorflow/tfjs/pull/4956)
-
-### Step 2. Add a new kernel in a backend.
-**Implementation Details**
-1. Create a new kernel in the `kernels` directory of a backend.
-
-    The kernel file should have the following elements:
-    1. License information.
-    2. The kernel implementation, and export it.
-    3. Export a `kernelConfig`.
-
-2. Register the kernel in `register_all_kernels.ts` in the corresponding backend.
-
-3. Remove the op from test exclusion list in the corresponding backend. For wasm
-   backend, add the test to the inclusion list.
-   
-**Example PRs**
-* [CPU and WebGL backend SparseReshape](https://github.com/tensorflow/tfjs/pull/4956)
-* [WASM backend Round](https://github.com/tensorflow/tfjs/pull/4486)
-
-### Step 3. Add the op to Converter’s executor.
-1. Add op mapping in the op list ts file, use your best judgement to assign an op category: `tfjs-converter/python/tensorflowjs/op_list/{corresponding_op_category}.json`. The corresponding `.ts` file will be automatically generated when converter is built. Use
-the [TF C++ op api](https://www.tensorflow.org/api_docs/cc/) as reference for tfOpName, inputs, and attrs.
-
-2. Find the corresponding executor for the op and add the op to the switch, the [executors](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter/src/operations/executors) are in `tfjs-converter/src/operations/executors`.
-
-3. Add a test to the corresponding executor test file.
-
-4. Update the supported op doc in `tfjs-converter/docs/supported_ops.md`.
-
-**Example PRs**
-* [SparseReshape Op](https://github.com/tensorflow/tfjs/pull/4963)
diff --git a/tfjs-master/DEVELOPMENT.md b/tfjs-master/DEVELOPMENT.md
deleted file mode 100644
index 6e99f27d7..000000000
--- a/tfjs-master/DEVELOPMENT.md
+++ /dev/null
@@ -1,207 +0,0 @@
-## Development
-
-This repository is a monorepo that contains the following NPM packages:
-
-APIs:
-- [TensorFlow.js Core](/tfjs-core),
-  a flexible low-level API for neural networks and numerical computation.
-- [TensorFlow.js Layers](/tfjs-layers),
-  a high-level API which implements functionality similar to
-  [Keras](https://keras.io/).
-- [TensorFlow.js Data](/tfjs-data),
-  a simple API to load and prepare data analogous to
-  [tf.data](https://www.tensorflow.org/guide/datasets).
-- [TensorFlow.js Converter](/tfjs-converter),
-  tools to import a TensorFlow SavedModel to TensorFlow.js
-- [TensorFlow.js Vis](/tfjs-vis),
-  in-browser visualization for TensorFlow.js models
-- [TensorFlow.js AutoML](/tfjs-automl),
-  Set of APIs to load and run models produced by
-  [AutoML Edge](https://cloud.google.com/vision/automl/docs/edge-quickstart).
-
-
-Backends/Platforms:
-- [TensorFlow.js CPU Backend](/tfjs-backend-cpu), pure-JS backend for Node.js and the browser.
-- [TensorFlow.js WebGL Backend](/tfjs-backend-webgl), WebGL backend for the browser.
-- [TensorFlow.js WASM Backend](/tfjs-backend-wasm), WebAssembly backend for the browser.
-- [TensorFlow.js WebGPU](/tfjs-backend-webgpu), WebGPU backend for the browser.
-- [TensorFlow.js Node](/tfjs-node), Node.js platform via TensorFlow C++ adapter.
-- [TensorFlow.js React Native](/tfjs-react-native), React Native platform via expo-gl adapter.
-
-#### Yarn
-We use yarn, and if you are adding or removing dependencies you should use yarn
-to keep the `yarn.lock` file up to date.
-
-#### Code editor
-We recommend using [Visual Studio Code](https://code.visualstudio.com/) for
-development. Make sure to install
-[TSLint VSCode extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode.vscode-typescript-tslint-plugin)
-and the npm [clang-format](https://github.com/angular/clang-format) `1.2.2` or later
-with the
-[Clang-Format VSCode extension](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)
-for auto-formatting.
-
-#### Testing
-Before submitting a pull request, make sure the code passes all the tests and is clean of lint errors:
-
-```bash
-# Run this at the root of the repository
-$ yarn lint
-# cd into the package directory you want to test
-$ yarn test
-# You may also need to run 'yarn lint' in the directory you want to test
-$ yarn lint
-```
-This will install yarn dependencies, build the other TensorFlow.js packages that the package being tested depeds on,
-and run the tests for the package. During development, you may want to run `yarn test-dev` instead to avoid
-unnecessarily rebuilding dependencies.
-
-Many TensorFlow.js packages use Karma to run tests in a browser. These tests can be configured by command-line options.
-
-To run a subset of tests:
-
-```bash
-$ yarn test --//:grep=multinomial
- 
-> ...
-> Chrome 62.0.3202 (Mac OS X 10.12.6): Executed 28 of 1891 (skipped 1863) SUCCESS (6.914 secs / 0.634 secs)
-```
-
-By default, the tests run once and exit. To keep the karma server active, run the following:
-
-```bash
-# For packages with only browser tests
-$ yarn test-debug
-# For packages with browser and node tests
-$ yarn test-browser-debug
-```
-
-You can now connect to the server with your own browser.
-
-On most platforms, Karma tests will run with a headless Chrome browser. This can be overridden by setting the
-`--//:headless` flag.
-
-```bash
-# Run tests with a visible browser window. Note this does not work well with
-# 'watch' modes such as 'test-dev' or 'ibazel'.
-$ yarn test --//:headless=false
-```
-
-#### Packaging (browser and npm)
-
-In any of the directories the following commands build the NPM tarball:
-
-```bash
-# Example for tfjs-core
-$ yarn build-npm
-# The output is located at ../dist/bin/tfjs-core/tfjs-core_pkg/
-# You can also package as a tar with
-$ yarn bazel run :tfjs-core_pkg.pack
-```
-
-To install it locally, run `yarn add ./tensorflow-tf-core-VERSION.tgz`.
-
-> On Windows, use bash (available through git or WSL2) to use the scripts above. For the best experience, we recommend using [WSL2](https://docs.microsoft.com/en-us/windows/wsl/install).
-
-Looking to contribute, and don't know where to start? Check out our "stat:contributions welcome" [issues](https://github.com/tensorflow/tfjs/labels/stat%3Acontributions%20welcome).
-
-#### Developing on Windows
-Developing on Windows is supported through the [Windows Subsystem for Linux (WSL)](https://docs.microsoft.com/en-us/windows/wsl/about) running Debian.
-
-1. Install WSL2 (if necessary) by following [Microsoft's instructions](https://docs.microsoft.com/en-us/windows/wsl/install). WSL1 has not been tested, but it may work.
-2. Install Debian in WSL2. [Debian is available from the Microsoft store](https://www.microsoft.com/en-us/p/debian/9msvkqc78pk6?activetab=pivot:overviewtab).
-3. Open Debian and install node and yarn with 
-`sudo apt update && sudo apt install nodejs && npm i -g yarn`. 
-If you need to reset the root debian password, you can get a root shell from command prompt with `wsl -u root`.
-4. Make sure Chrome is installed on Windows. Then, find the path to `chrome.exe`. It's probably `C:\Program Files\Google\Chrome\Application\chrome.exe`.
-5. Run the following to set up the `CHROME_BIN` variable, clone the `tfjs` repo, and create a custom `.bazelrc.user` config for WSL. If your `chrome.exe` is not located at the above path, you will need to change it to the correct path in the command below.
-```bash
-# Add yarn bin to the path
-echo "export PATH=$PATH:~/.yarn/bin/" >> ~/.bashrc &&
-# Set CHROME_BIN. Change this if your CHROME_BIN has a different path.
-echo "export CHROME_BIN=/mnt/c/Program\ Files/Google/Chrome/Application/chrome.exe" >> ~/.bashrc &&
-source ~/.bashrc &&
-# Clone tfjs.
-git clone https://github.com/tensorflow/tfjs.git &&
-cd tfjs &&
-# Create the .bazelrc.user file for WSL.
-echo "# Pass necessary WSL variables for running in Windows Subsystem for Linux.
-# WSLENV and WSL_DISTRO_NAME are build-in variables that are needed for running
-# the 'wslpath' command, which Karma uses to resolve file paths.
-# DISPLAY=:0 is passed to the Chrome process to make it launch in a window
-# since running Chrome headlessly from WSL does not seem to work. If you get
-# this working, please send a PR updating these docs (or open an issue :).
-run --test_env=CHROME_BIN --test_env=WSLENV --test_env=WSL_DISTRO_NAME --define DISPLAY=:0
-test --test_env=CHROME_BIN --test_env=WSLENV --test_env=WSL_DISTRO_NAME --define DISPLAY=:0" > .bazelrc.user &&
-printf "\n\nDone! Try running a browser test to verify the installation worked, e.g. 'cd tfjs-core && yarn && yarn test-browser'\n"
-```
-6. To access this repo from VScode, follow [Microsoft's WSL VSCode Tutorial](https://docs.microsoft.com/en-us/windows/wsl/tutorials/wsl-vscode).
-
-## For repository owners: commit style guide
-
-When merging commits into master, it is important to follow a few conventions
-so that we can automatically generate release notes and have a uniform commit
-history.
-
-1. When you squash and merge, the default commit body will be all of the
-commits on your development branch (not the PR description). These are usually
-not very useful, so you should remove them, or replace them with the PR
-description.
-
-2. Release notes are automatically generated from commits. We have introduced a
-few tags which help sort commits into categories for release notes:
-
-- FEATURE (when new functionality / API is added)
-- BREAKING (when there is API breakage)
-- BUG (bug fixes)
-- PERF (performance improvements)
-- DEV (development flow changes)
-- DOC (documentation changes)
-- SECURITY (security changes)
-
-These tags correspond to GitHub labels which are automatically prepended to your PR description.
-Please add the appropriate labels to your PR.
-
-A typical commit may look something like:
-
-```
-Subject: Add tf.toPixels. (#900)
-Body:
-FEATURE
-
-tf.toPixels is the inverse of tf.fromPixels, writing a tensor to a canvas.
-
-```
-
-This will show up under "Features" as:
-- Add tf.toPixels. (#900). Thanks, @externalcontributor.
-
-
-You can also use multiple tags for the same commit if you want it to show up in
-two sections. You can add clarifying text on the line of the tags.
-
-You can add clarifying messages on the line of the tag as well.
-
-For example:
-
-```
-Subject: Improvements to matMul. (#900)
-Body:
-
-FEATURE Add transpose bits to matmul.
-PERFORMANCE Improve matMul CPU speed by 100%.
-```
-
-This will show up under "Features" as:
-- Add transpose bits to matmul (Improvements to matMul.) (#900). Thanks, @externalcontributor.
-
-This will also show up under "Performance" as:
-- Improve matMul CPU speed by 100%. (Improvements to matMul.) (#900). Thanks, @externalcontributor.
-To build **TensorFlow.js Core API** from source, we need to clone the project and prepare
-the dev environment:
-
-```bash
-$ git clone https://github.com/tensorflow/tfjs-core.git
-$ cd tfjs-core
-$ yarn # Installs dependencies.
-```
diff --git a/tfjs-master/GALLERY.md b/tfjs-master/GALLERY.md
deleted file mode 100644
index 17d104bd9..000000000
--- a/tfjs-master/GALLERY.md
+++ /dev/null
@@ -1,144 +0,0 @@
-
-# Gallery
-
-Welcome to the TensorFlow.js gallery! This is a collection of TensorFlow.js projects, tutorials, videos, and more.
-
-Please reach out to us if you'd like to put your project on the list.
-
-## Demos & Applications
-
-- [HumanGPS: Dense Human Correspondences](https://feitongt.github.io/HumanGPS/#Live_demo) -  A deep learning framework that maps each pixel to a feature space, where the feature distances reflect the geodesic distances among pixels as if they were projected onto the surface of a 3D human scan.
-- [Time Series Forecasting](https://jinglescode.github.io/time-series-forecasting-tensorflowjs/) - Pull stock prices from online API, train and perform predictions using Long Short Term Memory, and predict the next value. [[demo](https://jinglescode.github.io/time-series-forecasting-tensorflowjs/)][[repo](https://github.com/jinglescode/time-series-forecasting-tensorflowjs)]
-- [Textual Similarity Analysis](https://jinglescode.github.io/textual-similarity-universal-sentence-encoder/) - Group similar sentences with universal sentence encoder word embeddings [[demo](https://jinglescode.github.io/textual-similarity-universal-sentence-encoder/)][[repo](https://github.com/jinglescode/textual-similarity-universal-sentence-encoder)]
-- [PoseAnimator](https://github.com/yemount/pose-animator/) - Animate any SVG character using your own body live via your webcam. Uses posenet and facemesh to create some really unique animations.
-- [3D Face Doodles](https://github.com/cyrildiagne/ar-facedoodle) - What if your face was a canvas and you could paint on it in real time? Check out this experiment that does just that in real time in the browser.
-- [Shaderbooth](https://shaderbooth.com/?85daa) - Experiment with WebGL Shaders + TensorFlow.js to create stunning visual effects such as shooting lasers from your eyes and more!
-- [Veremin](https://github.com/vabarbosa/veremin) - Play and create music in thin air with this digital take of a Theramin instrument. [Even better when hooked up to Tesla Coils](https://www.youtube.com/watch?v=ywbNLHydgzA)
-- [Man of many faces](https://twitter.com/thespite/status/1263576045577555969) - Create 3D masks of any face which you can then wear yourself.
-- [Confront yourself](https://yiwenl.github.io/Sketches/exps/66/) - An artistic peice that allows you to confront yourself in this beautiful WebGL experience.
-- [Virtual Tailor](https://www.youtube.com/watch?v=kFtIddNLcuM) - Get an estimate of your clothing size measurements in less than 15 seconds.
-- [Paper Doll](https://github.com/bengfarrell/paperdoll) - What if you could colour in a drawing of a character and then bring the drawing to life using your own body as the controller? Well now you can. Bring drawings to life with Paper Doll.
-- [Invisibility Cloak](https://github.com/jasonmayes/Real-Time-Person-Removal) - Bring your scifi movie dreams to life with this real time person removal system that runs in the browser.
-- [Bring magazines to life](https://twitter.com/AlexandreDevaux/status/1254719491755433985) - Scan a magazine of a person in it and bring that person to life right infront of you using WebGL and WebXR.
-- [Music Generation using Hands](https://twitter.com/prenalys/status/1250509352458268673) - turn your hands into a musical instrument using handpose.
-- [Posenet2Scratch](https://github.com/champierre/posenet2scratch) Use the popular posenet model within the Scratch environment - a great way to get younger folk or those interested in using visual based editors to take their first steps. Also see [Handpose2Scratch](https://github.com/champierre/handpose2scratch) and [FaceMesh2Scratch](https://github.com/champierre/facemesh2scratch).
-- [MLBlock](http://mlblock.org) A very advanced visual based edtior to explore many TensorFlow.js models and more. Combine sensors with models and see results live in your browser as you chain items together creating complex apps in minutes.
-- [Touch The Dot](http://touch-the-dot.herokuapp.com/) - A game created using PoseNet which the player must hit the colored dots with the correct hand and gain as many points as possible (A webcam is a must). [library](https://github.com/OmriGM/body-pose-beat-game) by [Omri Grossman](http://github.com/OmriGM)
-- [Object Tracking JS](https://cp4ms.csb.app/) - Track any object as it moves through a video with no training required. [library](https://github.com/cloud-annotations/object-tracking-js) by [Nick Bourdakos](http://github.com/bourdakos1)
-- [NSFW JS](https://nsfwjs.com/) - Client-side indecent content checker. [library](https://github.com/infinitered/nsfwjs) and [blogpost](https://shift.infinite.red/avoid-nightmares-nsfw-js-ab7b176978b1) by [Gant Laborde](http://gantlaborde.com/)
-- [Honkling](https://castorini.github.io/honkling/) - An in-browser keyword spotting system by Jaejun Lee, Raphael Tang, Jimmy Lin
-- [TensorSpace.js](https://github.com/tensorspace-team/tensorspace) - A 3D visualization framework for neural networks, featuring interactive and intuitive display of models in the browser. Supports pre-trained models from TensorFlow, Keras, and TensorFlow.js.
-- [Magenta Studio](https://magenta.tensorflow.org/studio) - A collection of music plugins built on Magenta’s open source tools and models using cutting-edge machine learning techniques for music generation.
-- [Move Mirror](https://experiments.withgoogle.com/collection/ai/move-mirror/view) - An AI Experiment with Pose Estimation in the Browser using TensorFlow.js
-- [Emoji Scavenger Hunt](https://emojiscavengerhunt.withgoogle.com/) - Locate the emoji we show you in the real world with your phone’s camera.
-- [Semi-Conductor](https://semiconductor.withgoogle.com/) - Conduct your own orchestra in the browser by moving your arms.
-- [Metacar](https://www.metacar-project.com/) - A reinforcement learning environment for self-driving cars in the browser.
-- [Evolution Simulator](https://github.com/adityathebe/evolutionSimulator) - Evolution Simulator using NeuroEvolution
-- [Play pong with webcam](https://ml4a.github.io/demos/tfjs/regression-pong.html) by Gene Kogan
-- [Make music with PoseNet](https://ml4a.github.io/demos/tfjs/posenet-music.html) by Gene Kogan
-- [Neural drum machine](https://codepen.io/teropa/pen/JLjXGK) by Tero Parviainen
-- [Pose Music](https://codepen.io/teropa/pen/QxLrMp) by Tero Parviainen
-- [Neural Arpeggiator](https://codepen.io/teropa/pen/ddqEwj) by Tero Parviainen
-- [Neural Melody Autocompletion](https://codepen.io/teropa/pen/gvwwZL) by Tero Parviainen
-- [Deep Roll](https://codepen.io/teropa/pen/zpbLOj) by Tero Parviainen
-- [Latent Cycles](https://codepen.io/teropa/pen/rdoPbG) by Tero Parviainen
-- [PoseNet + Pts.js](https://github.com/williamngan/pts/tree/master/demo/more/tfjs_posenet) - Visualizing pose with pts.js
-- [Falling balls + DQN](http://web.sfc.keio.ac.jp/~t15704yn/falling/index.html) - A demo of a DQN agent that learns to dodge falling balls by seann999
-- [Tenori Off](https://tenori-off.glitch.me/) - An ML-powered music sequencer by Monica Dinculescu
-- [Hello TensorFlow.js](https://hello-tensorflow.glitch.me/) - Polynomial Regression by Monica Dinculescu
-- [Simple MNIST GAN](https://mwdchang.github.io/tfjs-gan/) by Daniel Chang
-- [Complementary Color Prediction](http://stelling.cc/complementary-color-prediction/) by Roberto Stelling
-- [Mars at Home](https://github.com/MarsAtHome/marsjs) - Mars@Home client for Firefox & Chrome - Labels image from Unsplash in browser
-- [Nxt Word](https://github.com/rajveermalviya/language-modeling) - Next Word Predictor - by Rajveer Malviya
-- [Interactive Classification](https://github.com/poloclub/interactive-classification) - Modify images and see how deep learning classifiers respond
-- [Hidden Markov Model with Gaussian emissions](https://github.com/nearform/node-hidden-markov-model-tf) - A trainable Hidden Markov Model with Gaussian emissions using TensorFlow.js. Used in [Node Clinic](https://clinicjs.org/blog/clinic-doctor-just-got-more-advanced-with-tensorflow-js/)
-- [Emotion Extractor](https://brendansudol.com/faces/) by Brendan Sudol
-- [Aida](https://aida.dor.ai/) Named entity recognition and text classification pipeline for creating chatbots by Rodrigo Pimentel
-- [GAN Lab](https://poloclub.github.io/ganlab/) - An Interactive Visualization Tool for Playing with Generative Adversarial Networks (GANs)!
-- [Canvas Friends](https://www.y8.com/games/canvas_friends) - Half game, half experiment to see if software can improve the drawing and artistic skills of people.
-- [High School Level Tensorflowjs](http://rocksetta.com/tensorflowjs/) - Greater than 40 demos that can be online edited made as simply as possible using single web pages. All examples use the [<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs">](https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.js) specific version tag so the pages always work and no installation is needed - By Jeremy Ellis.
-- [Gboard Physical Handwriting Version](https://landing.google.co.jp/tegaki/) - You can input a character by drawing it on your keyboard.
-- [AICAMCAM](https://aicamcam.github.io) - A browser-based webcam video recording service with people/dog/cat detection functionality.
-- [trynottolaugh](http://trynottolaugh.net) - Try not to laugh game with face landmark detection if you laugh during video you lose!
-- [Neuroglancer](https://github.com/google/neuroglancer) - A browser-based app for examining 3D volumetric neuroimaging data.
-- [Chester Radiology Assistant](https://mlmed.org/tools/xray) - A web-based tool for diagnosing chest x-ray images.
-- [Membrane](https://membrane.today) - An interactive audiovisual tone mirror that responds to nose movement, by Ashlin Aronin.
-- [MedSeg](https://www.medseg.ai) - Online interactive segmentation tool for radiological images.
-- [Arabic Font Classification](https://mhmoodlan.github.io/blog/arabic-font-classification) - Tackling the visual font recognition problem for Arabic fonts by synthesizing a [dataset](https://github.com/mhmoodlan/arabic-font-classification/releases/tag/v0.1.0) and addressing domain mismatch challenges, by [Mahmoud Aslan](https://mhmoodlan.github.io)
-- [NSFW Filter](https://github.com/nsfw-filter/nsfw-filter) - A browser extension to block NSFW images.
-- [MIRNet-TFJS](https://github.com/Rishit-dagli/MIRNet-TFJS) - Converts and runs the MIRNet Model on the web which is capable of enhancing low-light images upto a really great extent - By Rishit Dagli. [[demo](https://mirnet-tfjs.rishit.tech/)][[repo](https://github.com/Rishit-dagli/MIRNet-TFJS)]
-- [Custom Object Detection on browser using TensorFlow.js](https://github.com/NSTiwari/TensorFlow.js-Custom-Object-Detection) - An E2E app for custom object detection on the browser using TensorFlow.js; by Nitin Tiwari & Rony Benny.
-- [audiate](https://cjbayron.github.io/audiate/) - Ear training game using Pitch Transcription model in the browser - By CJ Bayron [[demo](https://www.youtube.com/watch?v=VZ3i4V7i7Iw&t=2s)][[repo](https://github.com/cjbayron/audiate)]
-- [Plant AI](https://github.com/Rishit-dagli/Greenathon-Plant-AI) - This web app identifies diseases in plants across 38 healthy and unhealthy plant categories by species and diseases all in the browser - By Rishit Dagli, Rishabh Singh and Rucha Yagnik. [[demo](https://www.plant-ai.tech/)][[repo](https://github.com/Rishit-dagli/Greenathon-Plant-AI)]
-- [Brainchop.org](https://neuroneural.github.io/brainchop/) - Brings automatic 3D MRI segmentation capability to neuroimaging by running a deep learning model in the web-browser on the user side - By Mohamed Masoud and Sergey Plis. [[demo](https://neuroneural.github.io/brainchop/)][[repo](https://github.com/neuroneural/brainchop)][[wiki](https://github.com/neuroneural/brainchop/wiki)]
-- [chestxray.ai](https://github.com/jordimassaguerpla/chestxray.ai) - X-Ray image of you Chest, and predicts the probability of having any of these 4 diseases:Cardiomegaly,Mass,Pneumotorax,Edema - By Jordi Massaguer Pla. [[repo](https://github.com/jordimassaguerpla/chestxray.ai)]
-- [asanAI](https://asanai.scads.ai/) - A tool to experiment with different layer types, optimizers and so on with default and custom data, fully in the browser, see and visualize results and export to a python-model that can be run locally.
-  
-## Tutorials / Codelabs
-
-These tutorials augment the [official tutorials](https://js.tensorflow.org/tutorials/).
-
-- [Use Firebase Hosting to deploy and host a machine learning model at scale](https://codelabs.developers.google.com/codelabs/tensorflowjs-firebase-hosting-model/index.html?index=..%2F..index#0) by Jason Mayes
-- [Using TensorFlow.js to train a "Rock-Paper-Scissors" model](https://heartbeat.fritz.ai/using-tensorflow-js-to-train-a-rock-paper-scissors-model-b5f393b548eb) by [Gant Laborde](http://gantlaborde.com/)
-- [A gentle introduction to TensorFlow.js](https://medium.com/tensorflow/a-gentle-introduction-to-tensorflow-js-dba2e5257702) by Zaid Alyafeai
-- [Train a model in tf.keras with Colab, and run it in the browser with TensorFlow.js](https://medium.com/tensorflow/train-on-google-colab-and-run-on-the-browser-a-case-study-8a45f9b1474e) by Zaid Alyafeai
-- [Python Programming - Deep learning in the browser with TensorFlow.js](https://pythonprogramming.net/deep-learning-browser-introduction-tensorflowjs/) by Harrison Kinsley
-- [Basic Tutorial with TensorFlow.js: Linear Regression](https://medium.com/@tristansokol/basic-tutorial-with-tensorflow-js-linear-regression-aa68b16e5b8e) by Tristan Sokol
-- [Make a smart webcam in JavaScript with a pre-trained Machine Learning model](https://codelabs.developers.google.com/codelabs/tensorflowjs-object-detection/index.html?index=..%2F..index#8) by Jason Mayes
-- [Time Series Forecasting with TensorFlow.js](https://jinglescode.github.io/2019/05/17/time-series-forecasting-with-tensorflow-js/) by [Jingles](https://github.com/jinglescode/)
-- [Build Textual Similarity Analysis Web App](https://jinglescode.github.io/2020/02/10/build-textual-similarity-analysis-web-app/) by [Jingles](https://github.com/jinglescode/)
-- [Train a Text Classifier on top of BERT within the Browser](https://alexfi.dev/blog/tensorflowjs-bert-train) by [Alexander Fischer](https://github.com/alexander-fischer/)
-
-## Video tutorials
-
-- [Beginning Machine Learning with TensorFlow.js](https://academy.infinite.red/p/beginning-machine-learning-with-tensorflow-js)
-- [The Coding Train - TensorFlow.js Introduction](https://www.youtube.com/playlist?list=PLRqwX-V7Uu6YIeVA3dNxbR9PYj4wV31oQ)
-- [The Coding Train - TensorFlow.js Color Classifier](https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bmMRCIoTi72aNWHo7epX4L)
-- [Siraj Raval - TensorFlow.js Explained](https://www.youtube.com/watch?v=Nc8kZABv-KE)
-- [Siraj Raval - Financial Forecasting using TensorFlow.js](https://www.youtube.com/watch?v=5Uw1iSwvHH8)
-- [Siraj Raval - Webcam tracking with TensorFlow.js](https://www.youtube.com/watch?v=9KqNk5keyCc)
-- [Angular Firebase - TensorFlow.js Quickstart](https://www.youtube.com/watch?v=Y_XM3Bu-4yc)
-
-## Talks
-
-- [TensorFlow dev summit](https://www.youtube.com/watch?v=YB-kfeNIPCE) - Official TensorFlow.js launch
-- [Google I/O, Node.js binding launch](https://www.youtube.com/watch?v=OmofOvMApTU) - Official TensorFlow.js + Node.js launch
-- [Deep Learning in JS - Ashi Krishnan - JSConf EU 2018](https://www.youtube.com/watch?v=SV-cgdobtTA)
-
-## Blog posts
-
-- [Custom object detection in the browser using TensorFlow.js](https://blog.tensorflow.org/2021/01/custom-object-detection-in-browser.html) by Hugo Zanini
-- [Move Mirror](https://medium.com/tensorflow/move-mirror-an-ai-experiment-with-pose-estimation-in-the-browser-using-tensorflow-js-2f7b769f9b23?linkId=54484629) - An AI Experiment with Pose Estimation in the Browser using TensorFlow.js
-- [Abstract art with ML](https://janhuenermann.com/blog/abstract-art-with-ml) by Jan Huenermann
-- [Deep Overwatch with TensorFlow.js](https://medium.com/@farzatv/deepoverwatch-combining-tensorflow-js-overwatch-computer-vision-and-music-1a84d4598bc0) -  combining TensorFlow.js, Overwatch, Computer Vision, and Music by Farza
-- [Real-time Human Pose Estimation in the Browser with TensorFlow.js](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5) by Dan Oved
-- [Getting Started with TensorFlow.js](https://medium.com/tensorflow/getting-started-with-tensorflow-js-50f6783489b2) by Laurence Moroney
-- [Introducing TensorFlow.js](https://medium.com/tensorflow/introducing-tensorflow-js-machine-learning-in-javascript-bf3eab376db) by Josh Gordon and Sara Robinson
-- [Solving the Chrome T-Rex Game with Neural Networks, Genetic Algorithms and TensorFlow.js (Chinese: 前端人工智能？TensorFlow.js 学会游戏通关)](https://zhuanlan.zhihu.com/p/35451395) by [@MagicCube](https://github.com/MagicCube)
-- [A look at how we built the Emoji Scavenger Hunt using TensorFlow.js](https://medium.com/tensorflow/a-look-at-how-we-built-the-emoji-scavenger-hunt-using-tensorflow-js-3d760a7ebfe6) by Jacques Bruwer, JK Kafalas, and Shuhei Iitsuka from Google Brand Studio
-- [Read a tensorflow demo to get started with machine learning](https://mp.weixin.qq.com/s/sffUvod_KlmvtVI-W3PLEQ) by [EuryChen](https://github.com/netpi) (Chinese: 读懂一个Tensorflow Demo 入门机器学习)
-
-## Books
-
-- [Learning Tensorflow.js: Powerful Machine Learning in JavaScript](https://amzn.to/3dR3vpY) - In this guide, author [Gant Laborde](https://github.com/gantman) provides a hands-on, end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers.
-
-## Notebooks
-
-- [Introduction to TensorFlow.js](https://beta.observablehq.com/@nsthorat/introduction-to-deeplearn-js) by Nikhil Thorat
-- [How to build a Teachable Machine](https://beta.observablehq.com/@nsthorat/how-to-build-a-teachable-machine-with-tensorflow-js) by Nikhil Thorat
-- [Smoothgrad in TensorFlow.js](https://beta.observablehq.com/@aman-tiwari/smoothgrad-in-tensorflow-js?linkId=53020429) by Aman Tiwari
-- [Visualizing training using TensorFlow.js + MLB data](https://beta.observablehq.com/@nkreeger/visualizing-ml-training-using-tensorflow-js-and-baseball-d) by Nick Kreeger
-- [Animation CPPNs](https://beta.observablehq.com/@emilyreif/animation-with-cppns) -  by Emily Reif
-
-## Libraries using TensorFlow.js
-
-- [ML5](https://ml5js.org/) - Friendly machine learning for the web
-- [magenta.js](https://magenta.tensorflow.org/js) - Music and Art Generation with Machine Intelligence in the Browser.
-- [Handsfree.js](https://github.com/handsfreejs/handsfree) - A library for adding face-controlled pointers to your site.
-- [Handtrack.js](https://github.com/victordibia/handtrack.js) - A library for real-time hand detection directly in the browser.
-- [face-api.js](https://github.com/justadudewhohacks/face-api.js) - A JavaScript API for face detection and face recognition in the browser.
-- [machinelearn.js](https://github.com/machinelearnjs/machinelearnjs) - A general machine learning library like ScikitLearn directly in the Browser and Node.
-- [TensorFlow.js GPU-accelerated t-SNE](https://github.com/tensorflow/tfjs-tsne) - A linear t-SNE for the web.
-- [Pipcook](https://github.com/alibaba/pipcook) - A front-end algorithm framework to create a ML pipeline based on tfjs-node, which is similar to TFX but in JS.
-- [danfo.js](https://github.com/opensource9ja/danfojs) - A powerful JavaScript data analysis toolkit inspired by the Python PANDAS library.
-- [NSFW JS](https://github.com/infinitered/nsfwjs) - Client-side indecent content checker.
diff --git a/tfjs-master/LICENSE b/tfjs-master/LICENSE
deleted file mode 100644
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+++ /dev/null
@@ -1,202 +0,0 @@
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diff --git a/tfjs-master/README.md b/tfjs-master/README.md
deleted file mode 100644
index 7c0c9016a..000000000
--- a/tfjs-master/README.md
+++ /dev/null
@@ -1,184 +0,0 @@
-# TensorFlow.js
-
-TensorFlow.js is an open-source hardware-accelerated JavaScript library for
-training and deploying machine learning models.
-
-
-**Develop ML in the Browser** <br/>
-Use flexible and intuitive APIs to build models from scratch using the low-level
-JavaScript linear algebra library or the high-level layers API.
-
-**Develop ML in Node.js** <br/>
-Execute native TensorFlow with the same TensorFlow.js API under the Node.js
-runtime.
-
-**Run Existing models** <br/>
-Use TensorFlow.js model converters to run pre-existing TensorFlow models right
-in the browser.
-
-**Retrain Existing models** <br/>
-Retrain pre-existing ML models using sensor data connected to the browser or
-other client-side data.
-
-## About this repo
-
-This repository contains the logic and scripts that combine
-several packages.
-
-APIs:
-- [TensorFlow.js Core](/tfjs-core),
-  a flexible low-level API for neural networks and numerical computation.
-- [TensorFlow.js Layers](/tfjs-layers),
-  a high-level API which implements functionality similar to
-  [Keras](https://keras.io/).
-- [TensorFlow.js Data](/tfjs-data),
-  a simple API to load and prepare data analogous to
-  [tf.data](https://www.tensorflow.org/guide/datasets).
-- [TensorFlow.js Converter](/tfjs-converter),
-  tools to import a TensorFlow SavedModel to TensorFlow.js
-- [TensorFlow.js Vis](/tfjs-vis),
-  in-browser visualization for TensorFlow.js models
-- [TensorFlow.js AutoML](/tfjs-automl),
-  Set of APIs to load and run models produced by
-  [AutoML Edge](https://cloud.google.com/vision/automl/docs/edge-quickstart).
-
-
-Backends/Platforms:
-- [TensorFlow.js CPU Backend](/tfjs-backend-cpu), pure-JS backend for Node.js and the browser.
-- [TensorFlow.js WebGL Backend](/tfjs-backend-webgl), WebGL backend for the browser.
-- [TensorFlow.js WASM Backend](/tfjs-backend-wasm), WebAssembly backend for the browser.
-- [TensorFlow.js WebGPU](/tfjs-backend-webgpu), WebGPU backend for the browser.
-- [TensorFlow.js Node](/tfjs-node), Node.js platform via TensorFlow C++ adapter.
-- [TensorFlow.js React Native](/tfjs-react-native), React Native platform via expo-gl adapter.
-
-If you care about bundle size, you can import those packages individually.
-
-If you are looking for Node.js support, check out the [TensorFlow.js Node directory](/tfjs-node).
-
-## Examples
-
-Check out our
-[examples repository](https://github.com/tensorflow/tfjs-examples)
-and our [tutorials](https://js.tensorflow.org/tutorials/).
-
-## Gallery
-
-Be sure to check out [the gallery](GALLERY.md) of all projects related to TensorFlow.js.
-
-## Pre-trained models
-
-Be sure to also check out our [models repository](https://github.com/tensorflow/tfjs-models) where we host pre-trained models
-on NPM.
-
-## Benchmarks
-
-* [Local benchmark tool](https://tfjs-benchmarks.web.app/). Use this webpage tool to collect the performance related metrics (speed, memory, etc) of TensorFlow.js models and kernels **on your local device** with CPU, WebGL or WASM backends. You can benchmark custom models by following this [guide](https://github.com/tensorflow/tfjs/blob/master/e2e/benchmarks/local-benchmark/README.md).
-* [Multi-device benchmark tool](https://github.com/tensorflow/tfjs/tree/master/e2e/benchmarks/browserstack-benchmark/README.md). Use this tool to collect the same performance related metrics **on a collection of remote devices**.
-
-## Getting started
-
-There are two main ways to get TensorFlow.js in your JavaScript project:
-via <a href="https://developer.mozilla.org/en-US/docs/Learn/HTML/Howto/Use_JavaScript_within_a_webpage" target="_blank">script tags</a> <strong>or</strong> by installing it from <a href="https://www.npmjs.com/" target="_blank">NPM</a>
-and using a build tool like <a href="https://parceljs.org/" target="_blank">Parcel</a>,
-<a href="https://webpack.js.org/" target="_blank">WebPack</a>, or <a href="https://rollupjs.org/guide/en" target="_blank">Rollup</a>.
-
-### via Script Tag
-
-Add the following code to an HTML file:
-
-```html
-<html>
-  <head>
-    <!-- Load TensorFlow.js -->
-    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
-
-
-    <!-- Place your code in the script tag below. You can also use an external .js file -->
-    <script>
-      // Notice there is no 'import' statement. 'tf' is available on the index-page
-      // because of the script tag above.
-
-      // Define a model for linear regression.
-      const model = tf.sequential();
-      model.add(tf.layers.dense({units: 1, inputShape: [1]}));
-
-      // Prepare the model for training: Specify the loss and the optimizer.
-      model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-      // Generate some synthetic data for training.
-      const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
-      const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
-
-      // Train the model using the data.
-      model.fit(xs, ys).then(() => {
-        // Use the model to do inference on a data point the model hasn't seen before:
-        // Open the browser devtools to see the output
-        model.predict(tf.tensor2d([5], [1, 1])).print();
-      });
-    </script>
-  </head>
-
-  <body>
-  </body>
-</html>
-```
-
-Open up that HTML file in your browser, and the code should run!
-
-### via NPM
-
-Add TensorFlow.js to your project using <a href="https://yarnpkg.com/en/" target="_blank">yarn</a> <em>or</em> <a href="https://docs.npmjs.com/cli/npm" target="_blank">npm</a>. <b>Note:</b> Because
-we use ES2017 syntax (such as `import`), this workflow assumes you are using a modern browser or a bundler/transpiler
-to convert your code to something older browsers understand. See our
-<a href='https://github.com/tensorflow/tfjs-examples' target="_blank">examples</a>
-to see how we use <a href="https://parceljs.org/" target="_blank">Parcel</a> to build
-our code. However, you are free to use any build tool that you prefer.
-
-
-
-```js
-import * as tf from '@tensorflow/tfjs';
-
-// Define a model for linear regression.
-const model = tf.sequential();
-model.add(tf.layers.dense({units: 1, inputShape: [1]}));
-
-// Prepare the model for training: Specify the loss and the optimizer.
-model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-// Generate some synthetic data for training.
-const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
-const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
-
-// Train the model using the data.
-model.fit(xs, ys).then(() => {
-  // Use the model to do inference on a data point the model hasn't seen before:
-  model.predict(tf.tensor2d([5], [1, 1])).print();
-});
-```
-
-See our <a href="https://js.tensorflow.org/tutorials/" target="_blank">tutorials</a>, <a href="https://github.com/tensorflow/tfjs-examples" target="_blank">examples</a>
-and <a href="https://js.tensorflow.org/api/latest/">documentation</a> for more details.
-
-## Importing pre-trained models
-
-We support porting pre-trained models from:
-- [TensorFlow SavedModel](https://www.tensorflow.org/js/tutorials/conversion/import_saved_model)
-- [Keras](https://js.tensorflow.org/tutorials/import-keras.html)
-
-## Various ops supported in different backends
-
-Please refer below :
-- [TFJS Ops Matrix](https://docs.google.com/spreadsheets/d/1D25XtWaBrmUEErbGQB0QmNhH-xtwHo9LDl59w0TbxrI/edit#gid=0)
-
-## Find out more
-
-[TensorFlow.js](https://js.tensorflow.org) is a part of the
-[TensorFlow](https://www.tensorflow.org) ecosystem. For more info:
-- For help from the community, use the `tfjs` tag on the [TensorFlow Forum](https://discuss.tensorflow.org/tag/tfjs).
-- [TensorFlow.js Website](https://js.tensorflow.org)
-- [Tutorials](https://js.tensorflow.org/tutorials)
-- [API reference](https://js.tensorflow.org/api/latest/)
-- [TensorFlow.js Blog](https://blog.tensorflow.org/search?label=TensorFlow.js)
-
-Thanks, <a href="https://www.browserstack.com/">BrowserStack</a>, for providing testing support.
diff --git a/tfjs-master/WORKSPACE b/tfjs-master/WORKSPACE
deleted file mode 100644
index 02d1d98a8..000000000
--- a/tfjs-master/WORKSPACE
+++ /dev/null
@@ -1,264 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-workspace(
-    name = "tfjs",
-    managed_directories = {
-        "@npm": ["node_modules"],
-    },
-)
-
-load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
-
-http_archive(
-    name = "bazel_skylib",
-    sha256 = "1c531376ac7e5a180e0237938a2536de0c54d93f5c278634818e0efc952dd56c",
-    urls = [
-        "https://github.com/bazelbuild/bazel-skylib/releases/download/1.0.3/bazel-skylib-1.0.3.tar.gz",
-        "https://mirror.bazel.build/github.com/bazelbuild/bazel-skylib/releases/download/1.0.3/bazel-skylib-1.0.3.tar.gz",
-    ],
-)
-
-load("@bazel_skylib//:workspace.bzl", "bazel_skylib_workspace")
-
-bazel_skylib_workspace()
-
-http_archive(
-    name = "build_bazel_rules_nodejs",
-    sha256 = "94070eff79305be05b7699207fbac5d2608054dd53e6109f7d00d923919ff45a",
-    urls = ["https://github.com/bazelbuild/rules_nodejs/releases/download/5.8.2/rules_nodejs-5.8.2.tar.gz"],
-)
-
-# Install rules_nodejs dependencies.
-load("@build_bazel_rules_nodejs//:repositories.bzl", "build_bazel_rules_nodejs_dependencies")
-
-build_bazel_rules_nodejs_dependencies()
-
-load("@rules_nodejs//nodejs:repositories.bzl", "nodejs_register_toolchains")
-
-nodejs_register_toolchains(
-    name = "nodejs",
-    node_version = "18.13.0",
-)
-
-# Install the yarn tool
-load("@rules_nodejs//nodejs:yarn_repositories.bzl", "yarn_repositories")
-
-yarn_repositories(
-    name = "yarn",
-    node_repository = "nodejs",
-)
-
-# Install yarn packages
-load("@build_bazel_rules_nodejs//:index.bzl", "yarn_install")
-
-yarn_install(
-    name = "npm",
-    exports_directories_only = False,  # Required for ts_library
-    package_json = "//:package.json",
-    package_path = "/",
-    symlink_node_modules = True,
-    yarn = "@yarn//:bin/yarn",
-    yarn_lock = "//:yarn.lock",
-)
-
-# Fetch transitive Bazel dependencies of karma_web_test
-http_archive(
-    name = "io_bazel_rules_webtesting",
-    sha256 = "9bb461d5ef08e850025480bab185fd269242d4e533bca75bfb748001ceb343c3",
-    urls = ["https://github.com/bazelbuild/rules_webtesting/releases/download/0.3.3/rules_webtesting.tar.gz"],
-)
-
-# Set up web testing, choose browsers we can test on
-load("@io_bazel_rules_webtesting//web:repositories.bzl", "web_test_repositories")
-
-web_test_repositories()
-
-load("@io_bazel_rules_webtesting//web/versioned:browsers-0.3.2.bzl", "browser_repositories")
-
-browser_repositories(
-    chromium = True,
-)
-
-# Esbuild toolchain
-load("@build_bazel_rules_nodejs//toolchains/esbuild:esbuild_repositories.bzl", "esbuild_repositories")
-
-esbuild_repositories(npm_repository = "npm")
-
-# Emscripten toolchain
-http_archive(
-    name = "emsdk",
-    # TODO: Remove repo_mapping when emsdk updates to rules_nodejs 5
-    repo_mapping = {"@nodejs": "@nodejs_host"},
-    sha256 = "b8270749b99d8d14922d1831b93781a5560fba6f7bce65cd477fc1b6aa262535",
-    strip_prefix = "emsdk-3.1.28/bazel",
-    urls = ["https://github.com/emscripten-core/emsdk/archive/refs/tags/3.1.28.tar.gz"],
-)
-
-load("@emsdk//:deps.bzl", emsdk_deps = "deps")
-
-emsdk_deps()
-
-load("@emsdk//:emscripten_deps.bzl", emsdk_emscripten_deps = "emscripten_deps")
-
-emsdk_emscripten_deps()
-
-load("@emsdk//:toolchains.bzl", "register_emscripten_toolchains")
-
-register_emscripten_toolchains()
-
-load("@bazel_tools//tools/build_defs/repo:git.bzl", "git_repository")
-
-# xnnpack used for fast vectorized wasm operations
-git_repository(
-    name = "xnnpack",
-    commit = "5e8033a72a8d0f1c2b1f06e29137cc697c6b661d",
-    remote = "https://github.com/google/XNNPACK.git",
-    shallow_since = "1643627844 -0800",
-)
-
-# The libraries below are transitive dependencies of XNNPACK that we need to
-# explicitly enumerate here. See https://docs.bazel.build/versions/master/external.html#transitive-dependencies
-
-# FP16 library, used for half-precision conversions
-http_archive(
-    name = "FP16",
-    build_file = "@xnnpack//third_party:FP16.BUILD",
-    sha256 = "0d56bb92f649ec294dbccb13e04865e3c82933b6f6735d1d7145de45da700156",
-    strip_prefix = "FP16-3c54eacb74f6f5e39077300c5564156c424d77ba",
-    urls = [
-        "https://github.com/Maratyszcza/FP16/archive/3c54eacb74f6f5e39077300c5564156c424d77ba.zip",
-    ],
-)
-
-# FXdiv library, used for repeated integer division by the same factor
-http_archive(
-    name = "FXdiv",
-    sha256 = "ab7dfb08829bee33dca38405d647868fb214ac685e379ec7ef2bebcd234cd44d",
-    strip_prefix = "FXdiv-b408327ac2a15ec3e43352421954f5b1967701d1",
-    urls = [
-        "https://github.com/Maratyszcza/FXdiv/archive/b408327ac2a15ec3e43352421954f5b1967701d1.zip",
-    ],
-)
-
-# pthreadpool library, used for parallelization
-http_archive(
-    name = "pthreadpool",
-    sha256 = "8461f6540ae9f777ce20d1c0d1d249e5e61c438744fb390c0c6f91940aa69ea3",
-    strip_prefix = "pthreadpool-545ebe9f225aec6dca49109516fac02e973a3de2",
-    urls = [
-        "https://github.com/Maratyszcza/pthreadpool/archive/545ebe9f225aec6dca49109516fac02e973a3de2.zip",
-    ],
-)
-
-# clog library, used for logging
-http_archive(
-    name = "clog",
-    build_file = "@xnnpack//third_party:clog.BUILD",
-    sha256 = "3f2dc1970f397a0e59db72f9fca6ff144b216895c1d606f6c94a507c1e53a025",
-    strip_prefix = "cpuinfo-d5e37adf1406cf899d7d9ec1d317c47506ccb970",
-    urls = [
-        "https://github.com/pytorch/cpuinfo/archive/d5e37adf1406cf899d7d9ec1d317c47506ccb970.tar.gz",
-    ],
-)
-
-# cpuinfo library, used for detecting processor characteristics
-http_archive(
-    name = "cpuinfo",
-    build_file = "@xnnpack//third_party:cpuinfo.BUILD",
-    patches = ["@xnnpack//third_party:cpuinfo.patch"],
-    sha256 = "a7f9a188148a1660149878f737f42783e72f33a4f842f3e362fee2c981613e53",
-    strip_prefix = "cpuinfo-ed8b86a253800bafdb7b25c5c399f91bff9cb1f3",
-    urls = [
-        "https://github.com/pytorch/cpuinfo/archive/ed8b86a253800bafdb7b25c5c399f91bff9cb1f3.zip",
-    ],
-)
-
-# psimd library, used for fallback 128-bit SIMD micro-kernels
-http_archive(
-    name = "psimd",
-    build_file = "@xnnpack//third_party:psimd.BUILD",
-    sha256 = "dc615342bcbe51ca885323e51b68b90ed9bb9fa7df0f4419dbfa0297d5e837b7",
-    strip_prefix = "psimd-072586a71b55b7f8c584153d223e95687148a900",
-    urls = [
-        "https://github.com/Maratyszcza/psimd/archive/072586a71b55b7f8c584153d223e95687148a900.zip",
-    ],
-)
-
-git_repository(
-    name = "com_google_googletest",
-    commit = "cd17fa2abda2a2e4111cdabd62a87aea16835014",
-    remote = "https://github.com/google/googletest.git",
-    shallow_since = "1570558426 -0400",
-)
-
-http_archive(
-    name = "rules_cc",
-    sha256 = "90d5a66950b492cbf86201cdc49c4b59796a85a4eb9fd63c07afe5f7132ea623",
-    strip_prefix = "rules_cc-8346df34b6593b051403b8e429db15c7f4ead937",
-    urls = [
-        "https://github.com/bazelbuild/rules_cc/archive/8346df34b6593b051403b8e429db15c7f4ead937.zip",
-    ],
-)
-
-http_archive(
-    name = "rules_python",
-    sha256 = "29a801171f7ca190c543406f9894abf2d483c206e14d6acbd695623662320097",
-    strip_prefix = "rules_python-0.18.1",
-    url = "https://github.com/bazelbuild/rules_python/releases/download/0.18.1/rules_python-0.18.1.tar.gz",
-)
-
-load("@rules_python//python:repositories.bzl", "python_register_toolchains")
-
-# TODO(mattSoulanille): Change the docker so it doesn't run as root?
-# https://github.com/bazelbuild/rules_python/pull/713
-# https://github.com/GoogleCloudPlatform/cloud-builders/issues/641
-python_register_toolchains(
-    name = "python3_8",
-    ignore_root_user_error = True,
-    # Available versions are listed in @rules_python//python:versions.bzl.
-    python_version = "3.8",
-)
-
-load("@python3_8//:defs.bzl", "interpreter")
-load("@rules_python//python:pip.bzl", "pip_parse")
-
-pip_parse(
-    name = "tensorflowjs_deps",
-    python_interpreter_target = interpreter,
-    requirements_lock = "@//tfjs-converter/python:requirements_lock.txt",
-)
-
-load("@tensorflowjs_deps//:requirements.bzl", install_tfjs_deps = "install_deps")
-
-install_tfjs_deps()
-
-pip_parse(
-    name = "tensorflowjs_dev_deps",
-    python_interpreter_target = interpreter,
-    requirements_lock = "@//tfjs-converter/python:requirements-dev_lock.txt",
-)
-
-load("@tensorflowjs_dev_deps//:requirements.bzl", install_tfjs_dev_deps = "install_deps")
-
-install_tfjs_dev_deps()
-
-load("//tfjs-tflite:tflite_repositories.bzl", "tflite_repositories")
-
-tflite_repositories()
-
-load("//tfjs-tfdf:tfdf_repositories.bzl", "tfdf_repositories")
-
-tfdf_repositories()
diff --git a/tfjs-master/cloudbuild-release.yml b/tfjs-master/cloudbuild-release.yml
deleted file mode 100644
index 89b4e24b9..000000000
--- a/tfjs-master/cloudbuild-release.yml
+++ /dev/null
@@ -1,29 +0,0 @@
-steps:
-
-# Install top-level deps.
-- name: 'gcr.io/learnjs-174218/release'
-  entrypoint: 'yarn'
-  id: 'yarn-common'
-  args: ['install']
-
-# Release e2e flow.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'e2e'
-  entrypoint: 'bash'
-  id: 'verdaccio'
-  args: ['./scripts/release-e2e.sh']
-  env: ['BROWSERSTACK_USERNAME=deeplearnjs1', 'RELEASE=$_RELEASE']
-  secretEnv: ['BROWSERSTACK_KEY']
-
-secrets:
-- kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-  secretEnv:
-    BROWSERSTACK_KEY: CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-timeout: 7200s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _RELEASE: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'N1_HIGHCPU_32'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/cloudbuild-verdaccio.yml b/tfjs-master/cloudbuild-verdaccio.yml
deleted file mode 100644
index b95c7123c..000000000
--- a/tfjs-master/cloudbuild-verdaccio.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-steps:
-# Install top-level deps.
-- name: 'gcr.io/learnjs-174218/release'
-  entrypoint: 'yarn'
-  id: 'yarn-common'
-  args: ['install']
-
-# Run verdaccio nightly publishing tests.
-- name: 'gcr.io/learnjs-174218/release'
-  entrypoint: 'bash'
-  id: 'nightly-verdaccio-test'
-  env: ['BROWSERSTACK_USERNAME=deeplearnjs1', 'RELEASE=true']
-  secretEnv: ['BROWSERSTACK_KEY']
-  waitFor: ['yarn-common']
-  args:
-    - '-eEuo'
-    - 'pipefail'
-    - '-c'
-    - |-
-      yarn release-tfjs --dry --guess-version release --use-local-changes --force
-      cd /tmp/tfjs-release/tfjs/e2e/
-      bash scripts/release-e2e.sh
-
-secrets:
-- kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-  secretEnv:
-    BROWSERSTACK_KEY: CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-timeout: 7200s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'N1_HIGHCPU_32'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/cloudbuild.yml b/tfjs-master/cloudbuild.yml
deleted file mode 100644
index a366c3834..000000000
--- a/tfjs-master/cloudbuild.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-steps:
-# Install top-level deps.
-- name: 'gcr.io/learnjs-174218/release'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-
-# Generate cloudbuild_generated.yml,
-# which builds and tests all affected packages.
-- name: 'gcr.io/learnjs-174218/release'
-  entrypoint: 'yarn'
-  id: 'generate-cloudbuild-for-packages'
-  args: ['generate-cloudbuild-for-packages']
-  waitFor: ['yarn']
-  env:
-  - 'COMMIT_SHA=$COMMIT_SHA'
-  - 'BRANCH_NAME=$BRANCH_NAME'
-  - 'BASE_BRANCH=$_BASE_BRANCH'
-  - 'NIGHTLY=$_NIGHTLY'
-
-# Run the generated cloudbuild file
-- name: 'gcr.io/cloud-builders/gcloud'
-  entrypoint: 'bash'
-  id: 'run-cloudbuild'
-  args: ['./scripts/run-build.sh']
-  waitFor: ['generate-cloudbuild-for-packages']
-  env: ['NIGHTLY=$_NIGHTLY']
-
-# General settings.
-timeout: 7200s
-logsBucket: 'gs://tfjs-build-logs'
-options:
-  logStreamingOption: 'STREAM_ON'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/dockers/release-docker/Dockerfile b/tfjs-master/dockers/release-docker/Dockerfile
deleted file mode 100644
index 3f3efbdff..000000000
--- a/tfjs-master/dockers/release-docker/Dockerfile
+++ /dev/null
@@ -1,49 +0,0 @@
-FROM node:18.13.0-bullseye-slim
-
-# Install cloud-sdk
-ARG CLOUD_SDK_VERSION=355.0.0
-ENV CLOUD_SDK_VERSION=$CLOUD_SDK_VERSION
-ENV CLOUDSDK_PYTHON=python3
-ENV PATH "$PATH:/opt/google-cloud-sdk/bin/"
-RUN apt-get update -qqy && apt-get install -qqy \
-        curl \
-        gcc \
-        parallel \
-        python \
-        python-dev \
-        python3 \
-        python3-setuptools \
-        python3-dev \
-        python3-pip \
-        apt-transport-https \
-        lsb-release \
-        openssh-client \
-        git \
-        gnupg \
-        file \
-        wget \
-        unzip \
-        libssl-dev \
-        libffi-dev \
-        zlib1g-dev \
-        procps && \
-    pip3 install -U crcmod && \
-    export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" && \
-    echo "deb https://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" > /etc/apt/sources.list.d/google-cloud-sdk.list && \
-    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - && \
-    apt-get update && apt-get install -y google-cloud-sdk=${CLOUD_SDK_VERSION}-0 && \
-    gcloud config set core/disable_usage_reporting true && \
-    gcloud config set component_manager/disable_update_check true && \
-    gcloud config set metrics/environment github_docker_image && \
-    gcloud --version
-
-RUN git config --system credential.'https://source.developers.google.com'.helper gcloud.sh
-
-VOLUME ["/root/.config"]
-
-# Install virtualenv
-RUN pip3 install virtualenv
-
-# Install absl
-RUN pip3 install absl-py
-
diff --git a/tfjs-master/dockers/wasm-docker/Dockerfile b/tfjs-master/dockers/wasm-docker/Dockerfile
deleted file mode 100644
index 3881adae3..000000000
--- a/tfjs-master/dockers/wasm-docker/Dockerfile
+++ /dev/null
@@ -1,16 +0,0 @@
- # Official emsdk docker: https://hub.docker.com/r/emscripten/emsdk
-FROM emscripten/emsdk:2.0.14
-
-# Install yarn
-RUN npm install -g yarn
-
-RUN apt-get update -qqy && apt-get install -qqy \
-        gcc \
-        python3 \
-        python3-pip \
-        python \
-        python-pip \
-        file
-
-# Install absl
-RUN pip3 install -U absl-py
diff --git a/tfjs-master/docs/OPTIMIZATION_PURE_GPU_PIPELINE.md b/tfjs-master/docs/OPTIMIZATION_PURE_GPU_PIPELINE.md
deleted file mode 100644
index a17e6e91a..000000000
--- a/tfjs-master/docs/OPTIMIZATION_PURE_GPU_PIPELINE.md
+++ /dev/null
@@ -1,97 +0,0 @@
-# Optimization Topic - How to write a high performance GPU pipeline
-This document shows how to write a high performance GPU pipeline with TF.js. A typical ML pipeline is composed of some ML steps (e.g. taking some inputs, running a model and getting outputs) and non-ML steps (e.g. image processing, rendering, etc.). General rule of thumb is that if the whole pipeline can run on GPU without downloading any data to CPU, it is usually much faster than a fragmented pipeline that requires data transfer between CPU and GPU. This additional time for GPU to CPU and CPU to GPU sync adds to the pipeline latency.
-
-In the latest TF.js release (version 3.13.0), we provide a new tensor
-API that allows data to stay in GPU. Our users are pretty faimilar with
-`tensor.dataSync()` and `tensor.data()`, however both will download data from GPU to CPU. In the latest release, we have added `tensor.dataToGPU()`,
-which returns a `GPUData` type.
-
-# For webgl backend
-The example below shows how to get the tensor texture by calling
-`dataToGPU`. Also see this [example code](https://github.com/tensorflow/tfjs-examples/tree/master/gpu-pipeline/webgl) for details.
-
-```javascript
-// Getting the texture that holds the data on GPU.
-// data has below fields:
-// {texture, texShape, tensorRef}
-// texture: A WebGLTexture.
-// texShape: the [height, width] of the texture.
-// tensorRef: the tensor associated with the texture.
-//
-// Here the tensor has a shape of [1, videoHeight, videoWidth, 4],
-// which represents an image. In this case, we can specify the texture
-// to use the image's shape as its shape.
-const data =
-      tensor.dataToGPU({customTexShape: [videoHeight, videoWidth]});
-
-// Once we have the texture, we can bind it and pass it to the downstream
-// webgl processing steps to use the texture.
-gl.bindTexture(gl.TEXTURE_2D, data.texture);
-
-// Some webgl processing steps.
-
-// Once all the processing is done, we can render the result on the canvas.
-gl.blitFramebuffer(0, 0, videoWidth, videoHeight, 0, videoHeight, videoWidth, 0, gl.COLOR_BUFFER_BIT, gl.LINEAR);
-
-// Remember to dispose the texture after use, otherwise, there will be
-// memory leak.
-data.tensorRef.dispose();
-```
-
-The texture from `dataToGPU()` has a specific format. The data is densely
-packed, meaning all four channels (r, g, b, a) in a texel is used to store
-data. This is how the texture with tensor shape = [2, 3, 4] looks like:
-
-```
-    000|001|002|003   010|011|012|013   020|021|022|023
-
-    100|101|102|103   110|111|112|113   120|121|122|123
-```
-
-So if a tensor's shape is `[1, height, width, 4]` or `[height, width, 4]`, the way we store the data aligns with how image is stored, therefore if the
-downstream processing assumes the texture is an image, it doesn't need to
-do any additional coordination conversion. Each texel is mapped to a [CSS pixel](https://developer.mozilla.org/en-US/docs/Glossary/CSS_pixel) in an image. However, if the tensor has other shapes, downstream
-processing steps may need to reformat the data onto another texture. The `texShape` field has the `[height, width]` information of the texture, which can be used for transformation.
-
-# For webgpu backend
-The example below shows how to get the tensor buffer by calling
-`dataToGPU`. Also see this [example code](https://github.com/tensorflow/tfjs-examples/tree/master/gpu-pipeline/webgpu) for details.
-
-```javascript
-// Getting the buffer that holds the data on GPU.
-// data has below fields:
-// {buffer, bufSize, tensorRef}
-// buffer: A GPUBuffer.
-// bufSize: the size of the buffer.
-// tensorRef: the tensor associated with the buffer.
-//
-// Unlike webgl backend, There is no parameter for dataToGPU API on the webgpu
-// backend, and the size of returned buffer is the same as the tensor size.
-const data = tensor.dataToGPU();
-
-// Once we have the buffer, we can bind it and pass it to the downstream
-// webgpu processing steps to use the buffer.
-const uniformBindGroup = device.createBindGroup({
-  layout: pipeline.getBindGroupLayout(0),
-  entries: [
-    {
-      binding: 1,
-      resource: {
-        buffer: data.buffer,
-      },
-    },
-    ...
-  ],
-});
-
-// Some webgpu processing steps.
-
-// Defines the mapping between resources of all GPUBindGroup objects
-passEncoder.setBindGroup(0, uniformBindGroup);
-
-// Some webgpu processing steps.
-
-// Remember to dispose the buffer after use, otherwise, there will be
-// memory leak.
-data.tensorRef.dispose();
-```
diff --git a/tfjs-master/e2e/.npmignore b/tfjs-master/e2e/.npmignore
deleted file mode 100644
index 6292b5fdb..000000000
--- a/tfjs-master/e2e/.npmignore
+++ /dev/null
@@ -1,27 +0,0 @@
-.babelrc
-.DS_Store
-.idea/
-.rpt2_cache
-.travis.yml
-.vscode
-*.tgz
-*.txt
-**.yalc
-**yalc.lock
-cloudbuild.yml
-coverage/
-demo/
-dist/**/*_test.d.ts
-dist/**/*_test.js
-karma.conf.js
-node_modules/
-npm-debug.log
-package-lock.json
-package/
-rollup.config.js
-scripts/
-src/**/*_test.ts
-tsconfig.json
-tslint.json
-yarn-error.log
-yarn.lock
diff --git a/tfjs-master/e2e/.npmrc b/tfjs-master/e2e/.npmrc
deleted file mode 100644
index 43c97e719..000000000
--- a/tfjs-master/e2e/.npmrc
+++ /dev/null
@@ -1 +0,0 @@
-package-lock=false
diff --git a/tfjs-master/e2e/README.md b/tfjs-master/e2e/README.md
deleted file mode 100644
index ac838c205..000000000
--- a/tfjs-master/e2e/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Usage
-
-This package contains integration and e2e tests for TensorFlow.js.
-
-## To run the tests in local:
-
-### Filter tests by tag:
-
-```js
-export TAGS=#SMOKE,#REGRESSION,#GOLDEN
-yarn test
-```
-
-### Filter tests by grep:
-
-```js
-yarn test --grep cpu
-```
-
-## Add new tests:
-
-When creating new test, add at least one tag to the test description.
-
-Supported tags:
-
-- **SMOKE**:
-  Smoke tests should be light weight. Run in every PR and nightly
-  builds. Criteria for smoke test is that the test should run fast and only
-  test critical CUJ.
-- **REGRESSION**:
-  Regression tests compare results across backends, previous
-  builds, with other platform, etc. Run in nightly builds. Need additional
-  steps to run in local.
-- **GOLDEN**:
-  Golden tests validate pretrained model outputs against the prebuilt golden
-  outputs. Need additional steps to run in local.
-
-To add a new tag: Extend the TAGS list in integration_tests/util
diff --git a/tfjs-master/e2e/benchmarks/SpecRunner.html b/tfjs-master/e2e/benchmarks/SpecRunner.html
deleted file mode 100644
index 8e1dc4fbf..000000000
--- a/tfjs-master/e2e/benchmarks/SpecRunner.html
+++ /dev/null
@@ -1,45 +0,0 @@
-<!-- Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset="utf-8">
-  <title>Jasmine Test</title>
-
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/jasmine.css">
-
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/jasmine.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/jasmine-html.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/boot.js"></script>
-
-  <script src="https://unpkg.com/@tensorflow/tfjs-core@latest/dist/tf-core.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-backend-cpu@latest/dist/tf-backend-cpu.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-backend-webgl@latest/dist/tf-backend-webgl.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-layers@latest/dist/tf-layers.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-converter@latest/dist/tf-converter.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-backend-wasm@latest/dist/tf-backend-wasm.js"></script>
-
-  <!-- include source files here... -->
-  <script src="./benchmark_util.js"></script>
-
-  <!-- include spec files here... -->
-  <script src="./benchmark_util_test.js"></script>
-
-</head>
-
-<body>
-</body>
-</html>
diff --git a/tfjs-master/e2e/benchmarks/benchmark_util.js b/tfjs-master/e2e/benchmarks/benchmark_util.js
deleted file mode 100644
index d6a9d249d..000000000
--- a/tfjs-master/e2e/benchmarks/benchmark_util.js
+++ /dev/null
@@ -1,708 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This tool depends on tf-core, tf-layers, tf-converter and the backends
- * (tf-backend-cpu, tf-backend-webgl or tf-backend-wasm) that you would use.
- */
-
-/**
- * Generates a random input for `model`, based on `model.inputs`. For
- * tf.GraphModel, `NamedTensorMap` input will be returned; otherwise,
- * `Tensor[]` will be returned.
- *
- * ```js
- * const model = tf.sequential(
- *    {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
- * const input = generateInput(model);
- * const prediction = await model.predict(input);
- *
- * console.log(`Generated input: ${Object.values(input)}`);
- * console.log(`Prediction for the generated input: ${prediction}`);
- * ```
- *
- * @param model The model object that is used to generated the input.
- */
-function generateInput(model) {
-  if (model == null) {
-    throw new Error('The model does not exist.');
-  } else if (model.inputs == null) {
-    throw new Error('The model.inputs cannot be found.');
-  }
-
-  const inputDefs = model.inputs.map((inputNode, inputNodeIndex) => {
-    // Replace -1 or null in input tensor shape.
-    const inputShape = inputNode.shape.map(shapeValue => {
-      if (shapeValue == null || shapeValue < 0) {
-        return 1;
-      } else {
-        return shapeValue;
-      }
-    });
-    return {
-      shape: inputShape,
-      name: inputNode.name,
-      dtype: inputNode.dtype,
-      range: [0, 1000]
-    };
-  });
-
-  return generateInputFromDef(inputDefs, model instanceof tf.GraphModel);
-}
-
-/**
- * Generates a random input for input definition.
- *
- * ```js
- * const input = generateInput(inputDefs);
- *
- * console.log(`Generated input: ${Object.values(input)}`);
- * console.log(`Prediction for the generated input: ${prediction}`);
- * ```
- *
- * @param inputDefs The input definition that is used to generate the input.
- * @param isForGraphModel flag for whether to generate inputs for GraphModel
- */
-function generateInputFromDef(inputDefs, isForGraphModel = false) {
-  if (inputDefs == null) {
-    throw new Error('The inputDef cannot be found.');
-  }
-
-  const tensorArray = [];
-  try {
-    inputDefs.forEach((inputDef, inputDefIndex) => {
-      const inputShape = inputDef.shape;
-
-      // Construct the input tensor.
-      let inputTensor;
-      if (inputDef.dtype === 'float32' || inputDef.dtype === 'int32') {
-        // We assume a bell curve normal distribution. In this case,
-        // we use below approximation:
-        // mean ~= (min + max) / 2
-        // std ~= (max - min) / 4
-        // Note: for std, our approximation is based on the fact that
-        // 95% of the data is within the range of 2 stds above and
-        // below the mean. So 95% of the data falls in the range of
-        // 4 stds.
-        const min = inputDef.range[0];
-        const max = inputDef.range[1];
-        const mean = (min + max) / 2;
-        const std = (max - min) / 4;
-        generatedRaw = tf.randomNormal(inputShape, mean, std, inputDef.dtype);
-        // We clip the value to be within [min, max], because 5% of
-        // the data generated maybe outside of [min, max].
-        inputTensor = tf.clipByValue(generatedRaw, min, max);
-        generatedRaw.dispose();
-      } else if (inputDef.dtype === 'string') {
-        size = tf.util.sizeFromShape(inputDef.shape);
-        data = [...Array(size)].map(
-            () => Math.random().toString(36).substring(2, 7));
-        inputTensor = tf.tensor(data, inputShape, inputDef.dtype);
-      } else {
-        throw new Error(
-            `The ${inputDef.dtype} dtype of '${inputDef.name}' input ` +
-            `at model.inputs[${inputDefIndex}] is not supported.`);
-      }
-      tensorArray.push(inputTensor);
-    });
-
-    // Return tensor map for tf.GraphModel.
-    if (isForGraphModel) {
-      const tensorMap = inputDefs.reduce((map, inputDef, i) => {
-        map[inputDef.name] = tensorArray[i];
-        return map;
-      }, {});
-      return tensorMap;
-    }
-
-    return tensorArray;
-  } catch (e) {
-    // Dispose all input tensors when the input construction is failed.
-    tensorArray.forEach(tensor => {
-      if (tensor instanceof tf.Tensor) {
-        tensor.dispose();
-      }
-    });
-    throw e;
-  }
-}
-
-/**
- * Wrap the model's predict function (`model.predict` for tf.LayersModel
- * and `model.executeAsync` for tf.GraphModel) with the input.
- *
- * @param model An instance of tf.GraphModel or tf.LayersModel for finding and
- *     wrapping the predict function.
- * @param input The input tensor container for model inference.
- */
-function getPredictFnForModel(model, input) {
-  let predict;
-  if (model instanceof tf.GraphModel) {
-    // Because there's no straightforward way to analyze whether a graph has
-    // dynamic op, so we try to use `execute` and, if it fails, we will fall
-    // back to `executeAsync`.
-    try {
-      tf.tidy(() => {
-        model.execute(input);
-      });
-      predict = () => model.execute(input);
-    } catch (e) {
-      predict = async () => await model.executeAsync(input);
-    }
-  } else if (model instanceof tf.LayersModel) {
-    predict = () => model.predict(input);
-  } else {
-    throw new Error(
-        'Predict function was not found. Please provide a tf.GraphModel or ' +
-        'tf.LayersModel');
-  }
-  return predict;
-}
-
-/**
- * Executes the predict function for `model` (`model.predict` for tf.LayersModel
- * and `model.executeAsync` for tf.GraphModel) and times the inference process
- * for `numRuns` rounds. Then returns a promise that resolves with information
- * about the model's inference time:
- * - `times`: an array of inference time for each inference
- * - `averageTime`: the average time of all inferences
- * - `averageTimeExclFirst`: the average time of all inferences except the
- *    first.
- * - `minTime`: the minimum time of all inferences
- * - `maxTime`: the maximum time of all inferences
- *
- * The inference time contains the time spent by both `predict()` and `data()`
- * called by tensors in the prediction.
- *
- * ```js
- * const modelUrl =
- *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
- * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
- * const zeros = tf.zeros([1, 224, 224, 3]);
- * const timeInfo =
- *    await timeModelInference(model, zeros, 2);
- *
- * console.log(`Elapsed time array: ${timeInfo.times}`);
- * console.log(`Average time: ${timeInfo.averageTime}`);
- * console.log(`Minimum time: ${timeInfo.minTime}`);
- * console.log(`Maximum time: ${timeInfo.maxTime}`);
- * ```
- *
- * @param model An instance of tf.GraphModel or tf.LayersModel for timing the
- *     inference process.
- * @param input The input tensor container for model inference.
- * @param numRuns The number of rounds for timing the inference process.
- */
-async function timeModelInference(model, input, numRuns = 1) {
-  const predict = getPredictFnForModel(model, input);
-  return timeInference(predict, numRuns);
-}
-
-/**
- * Executes `predict()` and times the inference process for `numRuns` rounds.
- * Then returns a promise that resolves with information about the inference
- * time:
- * - `times`: an array of inference time for each inference
- * - `averageTime`: the average time of all inferences
- * - `averageTimeExclFirst`: the average time of all inferences except the
- *    first.
- * - `minTime`: the minimum time of all inferences
- * - `maxTime`: the maximum time of all inferences
- *
- * The inference time contains the time spent by both `predict()` and `data()`
- * called by tensors in the prediction.
- *
- * ```js
- * const modelUrl =
- *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
- * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
- * const zeros = tf.zeros([1, 224, 224, 3]);
- * const timeInfo =
- *    await timeInference(() => model.predict(zeros), 2);
- *
- * console.log(`Elapsed time array: ${timeInfo.times}`);
- * console.log(`Average time: ${timeInfo.averageTime}`);
- * console.log(`Minimum time: ${timeInfo.minTime}`);
- * console.log(`Maximum time: ${timeInfo.maxTime}`);
- * ```
- *
- * @param predict The predict function to execute and time.
- * @param numRuns The number of rounds for `predict` to execute and time.
- */
-async function timeInference(predict, numRuns = 1) {
-  if (typeof predict !== 'function') {
-    throw new Error(
-        'The first parameter should be a function, while ' +
-        `a(n) ${typeof predict} is found.`);
-  }
-
-  const times = [];
-  for (let i = 0; i < numRuns; i++) {
-    const start = performance.now();
-    const res = await predict();
-    // Prediction from tflite backend generates in the worker thread,
-    // we don't post the result back to main thread to avoid unnecessary
-    // overhead in transferring between worker and main thread.
-    if (!isTflite()) {
-      // The prediction can be tf.Tensor|tf.Tensor[]|{[name: string]:
-      // tf.Tensor}.
-      const value = await downloadValuesFromTensorContainer(res);
-    }
-    const elapsedTime = performance.now() - start;
-
-    tf.dispose(res);
-    times.push(elapsedTime);
-  }
-
-  const averageTime = times.reduce((acc, curr) => acc + curr, 0) / times.length;
-  const averageTimeExclFirst = times.length > 1 ?
-      times.slice(1).reduce((acc, curr) => acc + curr, 0) / (times.length - 1) :
-      'NA';
-  const minTime = Math.min(...times);
-  const maxTime = Math.max(...times);
-  const timeInfo = {
-    times,
-    averageTime,
-    averageTimeExclFirst,
-    minTime,
-    maxTime
-
-  };
-  return timeInfo;
-}
-
-/**
- * Time one model inference with parallel compilation feature, based on the
- * current backend.
- *
- * The inference time contains the time spent by both `predict()` and `data()`
- * called by tensors in the prediction.
- *
- * ```js
- * // Benchmark the first infernece time with parallel compilation.
- * const modelUrl =
- *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
- * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
- * const zeros = tf.zeros([1, 224, 224, 3]);
- * const firstInferenceTime =
- *    await timeFirstInference(() => model.predict(zeros), true);
- * ```
- *
- * @param predict The predict function to execute and time for.
- * @param parallelCompile The boolean value to indicate whether to use parallel
- *     compilation. This currently only has effect for WebGL backend and WebGPU
- * backend.
- */
-async function timeFirstInference(predict, parallelCompile = false) {
-  const start = performance.now();
-
-  // Parallel Compile
-  if (parallelCompile && tf.getBackend() === 'webgl') {
-    tf.env().set('ENGINE_COMPILE_ONLY', true);
-    const compileRes = predict();
-    tf.env().set('ENGINE_COMPILE_ONLY', false);
-    await tf.backend().checkCompileCompletionAsync();
-    tf.backend().getUniformLocations();
-    tf.dispose(compileRes);
-  } else if (parallelCompile && tf.getBackend() === 'webgpu') {
-    tf.env().set('WEBGPU_ENGINE_COMPILE_ONLY', true);
-    const compileRes = predict();
-    tf.env().set('WEBGPU_ENGINE_COMPILE_ONLY', false);
-    await tf.backend().checkCompileCompletionAsync();
-    tf.dispose(compileRes);
-  } else if (parallelCompile && isTflite()) {
-    throw new Error('Parallel Compilation for TFlite is not supported.');
-  }
-
-  // First inference
-  let res = predict();
-  if (parallelCompile && res instanceof Promise) {
-    throw new Error(
-        'Parallel Compilation for async function is not supported.');
-  }
-  res = await res;
-  await downloadValuesFromTensorContainer(res);
-  const elapsedTime = performance.now() - start;
-
-  tf.dispose(res);
-  return elapsedTime;
-}
-
-/**
- * Downloads the values from the `tensorContainer` from any `tf.Tensor`s found
- * within the `tensorContainer`. Returns a promise of `TypedArray` or
- * `TypedArray[]` that resolves when the computation has finished.
- *
- * The values are asynchronously downloaded in parallel.
- *
- * @param tensorContainer The container of tensors to be downloaded.
- */
-async function downloadValuesFromTensorContainer(tensorContainer) {
-  let valueContainer;
-  const readSync = tf.getBackend() === 'webgl';
-  if (tensorContainer instanceof tf.Tensor) {
-    if (readSync) {
-      valueContainer = tensorContainer.dataSync();
-    } else {
-      valueContainer = await tensorContainer.data();
-    }
-  } else if (Array.isArray(tensorContainer)) {
-    // Start value downloads from all tensors.
-    const valuePromiseContainer = tensorContainer.map(async item => {
-      if (item instanceof tf.Tensor) {
-        if (readSync) {
-          return item.dataSync();
-        } else {
-          return item.data();
-        }
-      }
-      return item;
-    });
-    // Wait until all values are downloaded.
-    valueContainer = await Promise.all(valuePromiseContainer);
-  } else if (tensorContainer != null && typeof tensorContainer === 'object') {
-    const valuePromiseContainer = [];
-    // Start value downloads from all tensors.
-    for (const property in tensorContainer) {
-      if (tensorContainer[property] instanceof tf.Tensor) {
-        if (readSync) {
-          valuePromiseContainer.push(tensorContainer[property].dataSync());
-        } else {
-          valuePromiseContainer.push(tensorContainer[property].data());
-        }
-      } else {
-        valuePromiseContainer.push(tensorContainer[property]);
-      }
-    }
-    // Wait until all values are downloaded.
-    valueContainer = await Promise.all(valuePromiseContainer);
-  }
-  return valueContainer;
-}
-
-/**
- * Executes the predict function for `model` (`model.predict` for
- * tf.LayersModel and `model.executeAsync` for tf.GraphModel) and returns a
- * promise that resolves with information about the memory usage:
- * - `newBytes`: the number of new bytes allocated.
- * - `newTensors`: the number of new tensors created.
- * - `peakBytes`: the peak number of bytes allocated.
- * - `kernels`: an array of kernel information objects about their input and
- * output shapes, number of bytes used, number of new tensors created and kernel
- * time (ms). The array is sorted by `kernelTimeMs` field in non-ascending
- * order.
- * - `aggregatedKernels`: an array of aggregated kernel information objects with
- * `name` and `timeMs` fields. The array is sorted by `timeMs` field in
- * non-ascending order.
- *
- * ```js
- * const modelUrl =
- *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
- * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
- * const zeros = tf.zeros([1, 224, 224, 3]);
- * const profileInfo = await profileModelInference(model, zeros);
- *
- * console.log(`newBytes: ${profileInfo.newBytes}`);
- * console.log(`newTensors: ${profileInfo.newTensors}`);
- * console.log(`peakBytes: ${profileInfo.peakBytes}`);
- * ```
- *
- * @param model An instance of tf.GraphModel or tf.LayersModel for profiling
- *     memory usage in the inference process.
- * @param input The input tensor container for model inference.
- * @param numProfiles The number of rounds for profiling the inference process.
- */
-async function profileModelInference(model, input, numProfiles = 1) {
-  const predict = getPredictFnForModel(model, input);
-  return profileInference(predict, false, numProfiles);
-}
-
-/**
- * Executes `predict()` and returns a promise that resolves with information
- * about the memory usage:
- * - `newBytes`: the number of new bytes allocated.
- * - `newTensors`: the number of new tensors created.
- * - `peakBytes`: the peak number of bytes allocated.
- * - `kernels`: an array of kernel information objects about their input and
- * output shapes, number of bytes used, number of new tensors created and kernel
- * time (ms). The array is sorted by `kernelTimeMs` field in non-ascending
- * order.
- * - `aggregatedKernels`: an array of aggregated kernel information objects with
- * `name` and `timeMs` fields. The array is sorted by `timeMs` field in
- * non-ascending order.
- *
- * ```js
- * const modelUrl =
- *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
- * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
- * const zeros = tf.zeros([1, 224, 224, 3]);
- * const profileInfo = await profileInference(() =>
- * model.predict(zeros));
- *
- * console.log(`newBytes: ${profileInfo.newBytes}`);
- * console.log(`newTensors: ${profileInfo.newTensors}`);
- * console.log(`peakBytes: ${profileInfo.peakBytes}`);
- * ```
- *
- * @param predict The predict function to execute for profiling memory usage.
- * @param isTflite Whether a TFLite model is being profiled or not.
- * @param numProfiles The number of rounds for `predict` to execute and profile.
- */
-async function profileInference(predict, isTflite = false, numProfiles = 1) {
-  if (typeof predict !== 'function') {
-    throw new Error(
-        'The first parameter should be a function, while ' +
-        `a(n) ${typeof predict} is found.`);
-  }
-
-  let kernelInfo = {};
-  let kernelInfos = [];
-  if (isTflite) {
-    for (let i = 0; i < numProfiles; i++) {
-      await predict();
-      const profileItems = await tfliteModel.getProfilingResults();
-      kernelInfo.kernels = profileItems.map(item => {
-        return {
-          name: item.nodeType,
-          kernelTimeMs: item.nodeExecMs,
-          // TODO: Shapes are not supported yet.
-          inputShapes: [],
-          outputShapes: [],
-        };
-      });
-      kernelInfos.push(kernelInfo);
-    }
-  } else {
-    for (let i = 0; i < numProfiles; i++) {
-      kernelInfo = await tf.profile(async () => {
-        const res = await predict();
-        await downloadValuesFromTensorContainer(res);
-        tf.dispose(res);
-      });
-      kernelInfos.push(kernelInfo);
-    }
-  }
-  for (let i = 0; i < kernelInfos[0].kernels.length; i++) {
-    let totalTimeMs = 0;
-    for (let j = 0; j < kernelInfos.length; j++) {
-      totalTimeMs += kernelInfos[j].kernels[i].kernelTimeMs;
-    }
-    kernelInfo.kernels[i].kernelTimeMs = totalTimeMs / kernelInfos.length;
-  }
-  kernelInfo.kernels =
-      kernelInfo.kernels.sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);
-  kernelInfo.aggregatedKernels = aggregateKernelTime(kernelInfo.kernels);
-  return kernelInfo;
-}
-
-/**
- * Aggregate kernels by name and sort the array in non-ascending order of time.
- * Return an array of objects with `name` and `timeMs` fields.
- *
- * @param {Array<Object>} kernels An array of kernel information objects. Each
- *     object must include `name` (string) and `kernelTimeMs` (number) fields.
- */
-function aggregateKernelTime(kernels) {
-  const aggregatedKernelTime = {};
-  kernels.forEach(kernel => {
-    const oldAggregatedKernelTime = aggregatedKernelTime[kernel.name];
-    if (oldAggregatedKernelTime == null) {
-      aggregatedKernelTime[kernel.name] = kernel.kernelTimeMs;
-    } else {
-      aggregatedKernelTime[kernel.name] =
-          oldAggregatedKernelTime + kernel.kernelTimeMs;
-    }
-  });
-
-  return Object.entries(aggregatedKernelTime)
-      .map(([name, timeMs]) => ({name, timeMs}))
-      .sort((a, b) => b.timeMs - a.timeMs);
-}
-
-/**
- * This map descripes tunable flags and theior corresponding types.
- *
- * The flags (keys) in the map satisfy the following two conditions:
- * - Is tunable. For example, `IS_BROWSER` and `IS_CHROME` is not tunable,
- * because they are fixed when running the scripts.
- * - Does not depend on other flags when registering in `ENV.registerFlag()`.
- * This rule aims to make the list streamlined, and, since there are
- * dependencies between flags, only modifying an independent flag without
- * modifying its dependents may cause inconsistency.
- * (`WEBGL_RENDER_FLOAT32_CAPABLE` is an exception, because only exposing
- * `WEBGL_FORCE_F16_TEXTURES` may confuse users.)
- */
-const TUNABLE_FLAG_VALUE_RANGE_MAP = {
-  WEBGL_VERSION: [1, 2],
-  WASM_HAS_SIMD_SUPPORT: [true, false],
-  WASM_HAS_MULTITHREAD_SUPPORT: [true, false],
-  WEBGL_CPU_FORWARD: [true, false],
-  WEBGL_PACK: [true, false],
-  WEBGL_FORCE_F16_TEXTURES: [true, false],
-  WEBGL_RENDER_FLOAT32_CAPABLE: [true, false],
-  WEBGL_FLUSH_THRESHOLD: [-1, 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2],
-  WEBGL_PACK_DEPTHWISECONV: [true, false],
-  CHECK_COMPUTATION_FOR_ERRORS: [true, false],
-  KEEP_INTERMEDIATE_TENSORS: [true, false],
-  WEBGL_USE_SHAPES_UNIFORMS: [true, false],
-  WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE: [1, 5, 10, 15, 20, 25, 30, 35, 40]
-};
-
-/**
- * Set environment flags for testing.
- *
- * This will first set tunable flags (the keys of `TUNABLE_FLAG_TYPE_MAP`). Then
- * set URL parameter flags. If there are overlap, URL parameter flags will
- * override tunable flags.
- *
- * ```js
- * const flagConfig = {
- *        WEBGL_PACK: false,
- *      };
- * await setEnvFlags(flagConfig);
- *
- * console.log(tf.env().getBool('WEBGL_PACK')); // false
- * console.log(tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS')); // false
- * ```
- *
- * @param flagConfig An object to store flag-value pairs.
- */
-async function setEnvFlags(flagConfig) {
-  if (flagConfig == null) {
-    return true;
-  } else if (typeof flagConfig !== 'object') {
-    throw new Error(
-        `An object is expected, while a(n) ${typeof flagConfig} is found.`);
-  }
-
-  // Check the validation of flags and values.
-  for (const flag in flagConfig) {
-    // TODO: check whether flag can be set as flagConfig[flag].
-    if (!(flag in TUNABLE_FLAG_VALUE_RANGE_MAP)) {
-      throw new Error(`${flag} is not a tunable or valid environment flag.`);
-    }
-    if (TUNABLE_FLAG_VALUE_RANGE_MAP[flag].indexOf(flagConfig[flag]) === -1) {
-      throw new Error(
-          `${flag} value is expected to be in the range [${
-              TUNABLE_FLAG_VALUE_RANGE_MAP[flag]}], while ${flagConfig[flag]}` +
-          ' is found.');
-    }
-  }
-
-  tf.env().setFlags(flagConfig);
-  setEnvFlagsFromUrlParameters();
-
-  // `WASM_HAS_SIMD_SUPPORT` and `WEBGL_VERSION` are also evaluated when
-  // initializing backends, not only inferring.
-  // TODO: The following backend rebuild logics can be implemented in `setHook`
-  // when registering these flags.
-  if ('WASM_HAS_SIMD_SUPPORT' in flagConfig) {
-    return await resetBackend('wasm');
-  }
-
-  if ('WEBGL_VERSION' in flagConfig) {
-    return await resetBackend('webgl');
-  }
-}
-
-/**
- * Set flags from URL. URL should be in the format:
- * ?tfjsflags=FLAG1:1,FLAG2:true.
- */
-function setEnvFlagsFromUrlParameters() {
-  const TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';
-  const urlParams = tf.env().getQueryParams(location.search);
-  if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
-    const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');
-    keyValues.forEach(keyValue => {
-      const [key, value] = keyValue.split(':');
-      try {
-        tf.env().set(key, parseValue(value));
-      } catch (err) {
-        console.error(err);
-      }
-    });
-  }
-}
-
-/**
- * Converted a URL parameter to a typed value, such a boolean, number, string.
- */
-function parseValue(value) {
-  const lowerCaseValue = value.toLowerCase();
-  if (lowerCaseValue === 'true' || lowerCaseValue === 'false') {
-    return lowerCaseValue === 'true';
-  } else if (`${+ lowerCaseValue}` === lowerCaseValue) {
-    return +lowerCaseValue;
-  } else {
-    return value;
-  }
-}
-
-/**
- * Reset the target backend.
- *
- * @param backendName The name of the backend to be reset.
- */
-async function resetBackend(backendName) {
-  const ENGINE = tf.engine();
-  if (!(backendName in ENGINE.registryFactory)) {
-    throw new Error(`${backendName} backend is not registed.`);
-  }
-
-  const currentBackend = tf.getBackend();
-
-  if (backendName in ENGINE.registry) {
-    const backendFactory = tf.findBackendFactory(backendName);
-    tf.removeBackend(backendName);
-    tf.registerBackend(backendName, backendFactory);
-  }
-
-  if (currentBackend === backendName) {
-    const isSuccessful = await tf.setBackend(backendName);
-    if (!isSuccessful) {
-      showMsg(`Failed to set backend ${backendName}.`);
-      return false;
-    }
-  }
-
-  return true;
-}
-
-/**
- * Get the renderer info from the WebGL backend.
- */
-async function getRendererInfo() {
-  const curBackendName = tf.getBackend();
-  let webglRenderer;
-  try {
-    let webglBackend = tf.findBackend('webgl');
-    if (webglBackend == null) {
-      if (!(await tf.setBackend('webgl'))) {
-        throw new Error('Failed to initialize WebGL backend.');
-      }
-      webglBackend = tf.backend();
-    }
-    const gl = webglBackend.gpgpu.gl;
-    const dbgRenderInfo = gl.getExtension('WEBGL_debug_renderer_info');
-    webglRenderer = gl.getParameter(dbgRenderInfo.UNMASKED_RENDERER_WEBGL);
-  } catch (e) {
-    webglRenderer = 'NA';
-  }
-  await tf.setBackend(curBackendName);
-  return webglRenderer;
-}
diff --git a/tfjs-master/e2e/benchmarks/benchmark_util_test.js b/tfjs-master/e2e/benchmarks/benchmark_util_test.js
deleted file mode 100644
index 9074807e1..000000000
--- a/tfjs-master/e2e/benchmarks/benchmark_util_test.js
+++ /dev/null
@@ -1,424 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * The unit tests in this file can be run by opening `./SpecRunner.html` in
- * browser.
- */
-
-function sleep(timeMs) {
-  return new Promise(resolve => setTimeout(resolve, timeMs));
-}
-
-describe('benchmark_util', () => {
-  beforeEach(() => tf.setBackend('cpu'));
-
-  describe('generateInput', () => {
-    it('LayersModel', () => {
-      const model = tf.sequential(
-          {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
-      const input = generateInput(model);
-      expect(input.length).toEqual(1);
-      expect(input[0]).toBeInstanceOf(tf.Tensor);
-      expect(input[0].shape).toEqual([1, 3]);
-    });
-  });
-
-  describe('generateInputFromDef', () => {
-    it('should respect int32 data range', async () => {
-      const inputDef =
-          [{shape: [1, 1, 10], dtype: 'int32', range: [0, 255], name: 'test'}];
-      const input = generateInputFromDef(inputDef);
-      expect(input.length).toEqual(1);
-      expect(input[0]).toBeInstanceOf(tf.Tensor);
-      expect(input[0].shape).toEqual([1, 1, 10]);
-      expect(input[0].dtype).toEqual('int32');
-      const data = await input[0].dataSync();
-      expect(data.every(value => value >= 0 && value <= 255));
-    });
-    it('should respect flaot32 data range', async () => {
-      const inputDef =
-          [{shape: [1, 1, 10], dtype: 'float32', range: [0, 1], name: 'test'}];
-      const input = generateInputFromDef(inputDef);
-      expect(input.length).toEqual(1);
-      expect(input[0]).toBeInstanceOf(tf.Tensor);
-      expect(input[0].shape).toEqual([1, 1, 10]);
-      expect(input[0].dtype).toEqual('float32');
-      const data = await input[0].dataSync();
-      expect(data.every(value => value >= 0 && value <= 1));
-    });
-  });
-
-  describe('profile inference time', () => {
-    describe('timeInference', () => {
-      it('throws when passing in invalid predict', async () => {
-        const predict = {};
-        await expectAsync(timeInference(predict)).toBeRejected();
-      });
-
-      it('does not add new tensors', async () => {
-        const model = tf.sequential(
-            {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
-        const input = tf.zeros([1, 3]);
-
-        const tensorsBefore = tf.memory().numTensors;
-        await timeInference(() => model.predict(input));
-        expect(tf.memory().numTensors).toEqual(tensorsBefore);
-
-        model.dispose();
-        input.dispose();
-      });
-    });
-  });
-
-  describe('Profile Inference', () => {
-    describe('profileInference', () => {
-      it('pass in invalid predict', async () => {
-        const predict = {};
-        await expectAsync(profileInference(predict)).toBeRejected();
-      });
-
-      it('check tensor leak', async () => {
-        const model = tf.sequential(
-            {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
-        const input = tf.zeros([1, 3]);
-
-        const tensorsBefore = tf.memory().numTensors;
-        await profileInference(() => model.predict(input));
-        expect(tf.memory().numTensors).toEqual(tensorsBefore);
-
-        model.dispose();
-        input.dispose();
-      });
-
-      it('profile all statements in async predict function', async () => {
-        const predict = async () => {
-          await sleep(1);
-          const x = tf.tensor1d([1, 2, 3]);
-          const x2 = x.square();
-          x2.dispose();
-          return x;
-        };
-
-        const oldTensorCount = tf.memory().numTensors;
-        let profileInfo = await profileInference(predict);
-        expect(tf.memory().numTensors).toEqual(oldTensorCount);
-
-        // If `profileInference` cannot profile async function, it would
-        // fail to profile all the statements after `await sleep(1);` and the
-        // peak memory would be `-Infinity`.
-        expect(profileInfo.peakBytes).toBeGreaterThan(0);
-      });
-    });
-  });
-
-  describe('aggregateKernelTime', () => {
-    it('aggregates the kernels according to names', () => {
-      const kernels = [
-        {name: 'testKernel1', kernelTimeMs: 1},
-        {name: 'testKernel1', kernelTimeMs: 1},
-        {name: 'testKernel1', kernelTimeMs: 1},
-        {name: 'testKernel2', kernelTimeMs: 1},
-        {name: 'testKernel2', kernelTimeMs: 1},
-      ];
-
-      const aggregatedKernels = aggregateKernelTime(kernels);
-      expect(aggregatedKernels.length).toBe(2);
-      expect(aggregatedKernels[0].name).toBe('testKernel1');
-      expect(aggregatedKernels[0].timeMs).toBe(3);
-      expect(aggregatedKernels[1].name).toBe('testKernel2');
-      expect(aggregatedKernels[1].timeMs).toBe(2);
-    });
-  });
-
-  describe('getPredictFnForModel', () => {
-    it('graph model with async ops uses executeAsync to run', () => {
-      const model = new tf.GraphModel();
-      const input = tf.tensor([1]);
-      const oldTensorNum = tf.memory().numTensors;
-      spyOn(model, 'execute').and.callFake(() => {
-        const leakedTensor = tf.tensor([1]);
-        throw new Error(
-            'This model has dynamic ops, ' +
-            'please use model.executeAsync() instead');
-        return leakedTensor;
-      });
-      spyOn(model, 'executeAsync');
-
-      const wrappedPredict = getPredictFnForModel(model, input);
-      expect(tf.memory().numTensors).toBe(oldTensorNum);
-      expect(model.execute.calls.count()).toBe(1);
-      expect(model.execute.calls.first().args).toEqual([input]);
-
-      wrappedPredict();
-      expect(model.execute.calls.count()).toBe(1);
-      expect(model.executeAsync.calls.count()).toBe(1);
-      expect(model.executeAsync.calls.first().args).toEqual([input]);
-
-      tf.dispose(input);
-    });
-
-    it('graph model without async ops uses execute to run', () => {
-      const model = new tf.GraphModel();
-      const input = tf.tensor([1]);
-      const oldTensorNum = tf.memory().numTensors;
-      spyOn(model, 'execute').and.callFake(() => {
-        const leakedTensor = tf.tensor([1]);
-      });
-      spyOn(model, 'executeAsync');
-
-      const wrappedPredict = getPredictFnForModel(model, input);
-      expect(tf.memory().numTensors).toBe(oldTensorNum);
-      expect(model.execute.calls.count()).toBe(1);
-      expect(model.execute.calls.first().args).toEqual([input]);
-
-      wrappedPredict();
-      expect(model.execute.calls.count()).toBe(2);
-      expect(model.execute.calls.argsFor(1)).toEqual([input]);
-      expect(model.executeAsync.calls.count()).toBe(0);
-
-      tf.dispose(input);
-    });
-
-    it('layers model uses predict to run', () => {
-      const model = tf.sequential(
-          {layers: [tf.layers.dense({units: 1, inputShape: [1]})]});
-      const input = tf.ones([1, 1]);
-      spyOn(model, 'predict');
-
-      const wrappedPredict = getPredictFnForModel(model, input);
-      wrappedPredict();
-
-      expect(model.predict.calls.count()).toBe(1);
-      expect(model.predict.calls.first().args).toEqual([input]);
-
-      tf.dispose(input);
-      model.dispose();
-    });
-
-    it('throws when passed in a model that is not layers or graph model',
-       () => {
-         const model = {};
-         const input = [];
-         expect(() => getPredictFnForModel(model, input)).toThrowError(Error);
-       });
-  });
-
-  describe('setEnvFlags', () => {
-    describe('changes nothing when setting empty config or rejecting', () => {
-      let originalFlags = {};
-
-      beforeEach(() => {
-        originalFlags = {...tf.env().flags};
-      });
-      afterAll(() => tf.env().reset());
-
-      it('empty config', async () => {
-        await setEnvFlags();
-        expect(tf.env().flags).toEqual(originalFlags);
-      });
-
-      it('rejects when setting untunable flags', async () => {
-        const flagConfig = {
-          IS_BROWSER: false,
-        };
-        expectAsync(setEnvFlags(flagConfig))
-            .toBeRejectedWithError(
-                Error, /is not a tunable or valid environment flag./);
-        expect(tf.env().flags).toEqual(originalFlags);
-      });
-
-      it('rejects when setting a number flag by a boolean value', async () => {
-        const flagConfig = {
-          WEBGL_VERSION: false,
-        };
-        expectAsync(setEnvFlags(flagConfig)).toBeRejectedWithError(Error);
-        expect(tf.env().flags).toEqual(originalFlags);
-      });
-
-      it('rejects when setting boolean flag by a number', async () => {
-        const flagConfig = {
-          WEBGL_PACK: 1,
-        };
-        expectAsync(setEnvFlags(flagConfig)).toBeRejectedWithError(Error);
-        expect(tf.env().flags).toEqual(originalFlags);
-      });
-
-      it('rejects when setting flag value out of the range', async () => {
-        const outOfRangeValue =
-            Math.max(...TUNABLE_FLAG_VALUE_RANGE_MAP.WEBGL_VERSION) + 1;
-        const flagConfig = {
-          WEBGL_VERSION: outOfRangeValue,
-        };
-        expectAsync(setEnvFlags(flagConfig)).toBeRejectedWithError(Error);
-        expect(tf.env().flags).toEqual(originalFlags);
-      });
-    });
-
-    describe('reset simple flags', () => {
-      beforeEach(() => tf.env().reset());
-      afterEach(() => tf.env().reset());
-
-      it('reset number type flags', async () => {
-        const flagConfig = {
-          WEBGL_VERSION: 1,
-        };
-        await setEnvFlags(flagConfig);
-        expect(tf.env().getNumber('WEBGL_VERSION')).toBe(1);
-      });
-
-      it('reset boolean flags', async () => {
-        const flagConfig = {
-          WASM_HAS_SIMD_SUPPORT: false,
-          WEBGL_CPU_FORWARD: false,
-          WEBGL_PACK: false,
-          WEBGL_FORCE_F16_TEXTURES: false,
-          WEBGL_RENDER_FLOAT32_CAPABLE: false,
-        };
-        await setEnvFlags(flagConfig);
-        expect(tf.env().getBool('WASM_HAS_SIMD_SUPPORT')).toBe(false);
-        expect(tf.env().getBool('WEBGL_CPU_FORWARD')).toBe(false);
-        expect(tf.env().getBool('WEBGL_PACK')).toBe(false);
-        expect(tf.env().getBool('WEBGL_FORCE_F16_TEXTURES')).toBe(false);
-        expect(tf.env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')).toBe(false);
-      });
-    });
-
-    describe('reset flags related to environment initialization', () => {
-      beforeEach(() => tf.engine().reset());
-      afterAll(() => {
-        tf.engine().reset();
-        tf.setBackend('cpu');
-      });
-
-      it(`set 'WEBGL_VERSION' to 2`, async () => {
-        if (!tf.webgl_util.isWebGLVersionEnabled(2)) {
-          pending(
-              'Please use a browser supporting WebGL 2.0 to run this test.');
-        }
-        const flagConfig = {
-          WEBGL_VERSION: 2,
-        };
-        await setEnvFlags(flagConfig);
-        expect(tf.env().getBool('WEBGL_BUFFER_SUPPORTED')).toBe(true);
-      });
-
-      it(`set 'WEBGL_VERSION' to 1`, async () => {
-        if (!tf.webgl_util.isWebGLVersionEnabled(1)) {
-          pending(
-              'Please use a browser supporting WebGL 1.0 to run this test.');
-        }
-        const flagConfig = {
-          WEBGL_VERSION: 1,
-        };
-        await setEnvFlags(flagConfig);
-        expect(tf.env().getBool('WEBGL_BUFFER_SUPPORTED')).toBe(false);
-      });
-
-      it(`reset flags when the related backend is active`, async () => {
-        if (!tf.webgl_util.isWebGLVersionEnabled(1)) {
-          pending(
-              'Please use a browser supporting WebGL 1.0 to run this test.');
-        }
-        await tf.setBackend('webgl');
-        const flagConfig = {
-          WEBGL_VERSION: 1,
-        };
-        await setEnvFlags(flagConfig);
-        expect(tf.getBackend()).toBe('webgl');
-      });
-
-      it(`reset 'WASM_HAS_SIMD_SUPPORT' as true`,
-         async () => {
-             // TODO: add test for SIMD after SIMD implementation.
-             // const simdSupported = await
-             // env().getAsync('WASM_HAS_SIMD_SUPPORT');
-         });
-
-      it(`reset 'WASM_HAS_SIMD_SUPPORT' as false`, async () => {
-        const flagConfig = {
-          WASM_HAS_SIMD_SUPPORT: false,
-        };
-        await setEnvFlags(flagConfig);
-        expect(tf.env().getBool('WASM_HAS_SIMD_SUPPORT')).toBe(false);
-      });
-    });
-  });
-
-  describe('resetBackend', () => {
-    beforeEach(() => tf.setBackend('cpu'));
-    afterAll(() => tf.engine().reset());
-
-    it('rejects when resetting a backend that is not registed', async () => {
-      expectAsync(resetBackend('invalidBackendName'))
-          .toBeRejectedWithError(
-              Error, 'invalidBackendName backend is not registed.');
-    });
-
-    it('do nothing when resetting a backend that is not created', async () => {
-      const testCpuBackend = 'testCpuBackend';
-      tf.registerBackend(testCpuBackend, tf.findBackendFactory('cpu'));
-      expect(tf.engine().registry[testCpuBackend]).toBeUndefined();
-      spyOn(tf, 'findBackendFactory');
-      spyOn(tf, 'removeBackend');
-      spyOn(tf, 'registerBackend');
-
-      await resetBackend(testCpuBackend);
-
-      expect(tf.findBackendFactory.calls.count()).toBe(0);
-      expect(tf.removeBackend.calls.count()).toBe(0);
-      expect(tf.registerBackend.calls.count()).toBe(0);
-      tf.removeBackend(testCpuBackend);
-    });
-
-    it('reset the backend when resetting an existed backend', async () => {
-      await tf.ready();
-      const currentBackend = tf.getBackend();
-      expect(tf.engine().registry[currentBackend]).toBeDefined();
-      spyOn(tf, 'findBackendFactory');
-      spyOn(tf, 'removeBackend');
-      spyOn(tf, 'registerBackend');
-
-      await resetBackend(currentBackend);
-
-      expect(tf.findBackendFactory.calls.count()).toBe(1);
-      expect(tf.removeBackend.calls.count()).toBe(1);
-      expect(tf.registerBackend.calls.count()).toBe(1);
-    });
-
-    it('tf.setBackend is called when resetting the active backend',
-       async () => {
-         const currentBackend = tf.getBackend();
-         spyOn(tf, 'setBackend');
-         await resetBackend(currentBackend);
-         expect(tf.setBackend.calls.count()).toBe(1);
-       });
-
-    it('tf.setBackend is not called when resetting an inactive backend',
-       async () => {
-         const testCpuBackend = 'testCpuBackend';
-         tf.registerBackend(testCpuBackend, tf.findBackendFactory('cpu'));
-         expect(tf.getBackend()).not.toBe(testCpuBackend);
-         spyOn(tf, 'setBackend');
-
-         await resetBackend(testCpuBackend);
-
-         expect(tf.setBackend.calls.count()).toBe(0);
-         tf.removeBackend(testCpuBackend);
-       });
-  });
-});
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/README.md b/tfjs-master/e2e/benchmarks/browserstack-benchmark/README.md
deleted file mode 100644
index d928c375b..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/README.md
+++ /dev/null
@@ -1,167 +0,0 @@
-# Benchmark on multiple devices
-
-> :warning: To use this tool, you need to sign up for BrowserStack's [Automate](https://automate.browserstack.com/dashboard) service.
-
-The Multi-device benchmark tool can benchmark the performance (time, memory) of model inference on a collection of remote devices. Using this tool you will be able to:
-  * Select a collection of BrowserStack devices, based on the following fields:
-    - OS
-    - OS version
-    - Browser
-    - Browser version
-    - Device
-  * Select a backend:
-    - WASM
-    - WebGL
-    - CPU
-  * Set the number of rounds for model inference.
-  * Select a model to benchmark.
-
-## Usage
-1. Export your access key of BrowserStack's Automate service:
-  ``` shell
-  export BROWSERSTACK_USERNAME=YOUR_USERNAME
-  export BROWSERSTACK_ACCESS_KEY=YOUR_ACCESS_KEY
-  ```
-2. Download and run the tool:
-  ``` shell
-  git clone https://github.com/tensorflow/tfjs.git
-  cd tfjs/e2e/benchmarks/browserstack-benchmark
-  yarn install
-
-  node app.js
-  ```
-  Then you can see `> Running socket on port: 8001` on your Command-line interface.
-
-3. Open http://localhost:8001/ and start to benchmark.
-  3.1 If you want to benchmark code snippet. Please update [`benchmarkCodeSnippet` ](https://github.com/tensorflow/tfjs/pull/6704/files#diff-a7c2ef12f0f2bc1a6cabb45bc9850aa68d10644cd2786e6505456e5537dccadbR92)with your code snippet before running `node app.js` and select `codeSnippet` in `model name`:
-<div style="text-align:center">
-  <img src="https://user-images.githubusercontent.com/40653845/182249695-021db9a7-e0ef-47e6-8110-ddf777b598a5.png" alt="drawing" height="300px"/>
-</div>
-4. When the benchmark is complete, you can see the benchmark results in the webpage, like:
-<div style="text-align:center">
-  <img src="https://user-images.githubusercontent.com/40653845/90341914-a432f180-dfb8-11ea-841e-0d9078c6d50d.png" alt="drawing" height="300px"/>
-</div>
-
-### Command Line Arguments
-The following are supported options arguments which trigger options features:
-  * --benchmarks
-    - Runs benchmarks from a user-specified, pre-configured JSON file.
-    ``` shell
-    node app.js --benchmarks=relative_file_path.json
-    ```
-    A pre-configuration file consists of a JSON object with the following format:
-    ```
-    {
-      "benchmark": {
-        "model": ["model_name"], //List of one or more custom or official models to be benchmarked
-        "numRuns": positive_integer,
-        "backend": ["backend_name"] //List of one or more backends to be benchmarked
-      },
-      "browsers": {
-        "local": {},  // Benchmark on your local device
-        "unique_identifier_laptop_or_desktop": {
-          "base": "BrowserStack",
-          "browser": "browser_name",
-          "browser_version": "browser_version",
-          "os": "os_name",
-          "os_version": "os_version",
-          "device": null
-        },
-        "unique_identifier_mobile_device": {
-          "base": "BrowserStack",
-          "browser": "iphone_or_android",
-          "browser_version": null,
-          "os": "os_name",
-          "os_version": "os_version",
-          "device": "device_name"
-        }
-      }
-    }
-    ```
-    Each model in the model list will be run on each backend in the backend list. Each model-backend combination will run on every browser. If you would like to test specific backends on specific models, the recommended method is to create multiple configuration files.
-
-    For more examples of documentation, refer to the links below:
-    [List of officially supported TFJS browsers](https://github.com/tensorflow/tfjs/blob/master/e2e/benchmarks/browserstack-benchmark/browser_list.json)
-    [Example benchmark pre-configuration](https://github.com/tensorflow/tfjs/blob/master/e2e/benchmarks/browserstack-benchmark/preconfigured_browser.json)
-  * --cloud
-    - Runs GCP compatible version of benchmarking by blocking the local server.
-    ``` shell
-    node app.js --cloud
-    ```
-  * --firestore
-    - Pushes successful benchmark results to a Firestore database.
-    ``` shell
-    node app.js --firestore
-    ```
-  * --h, --help
-    - Shows help menu and all optional arguments in the shell window.
-    ``` shell
-    node app.js --h
-    ```
-    or
-    ``` shell
-    node app.js --help
-    ```
-  * --period
-    - Runs a part of models specified in `--benchmarks`'s file in a cycle and the part of models to run is determined by the date of a month. The value could be or 1~31 (representing Sunday to Saturday). This argument takes effect only if `--benchmarks` is set.
-    ``` shell
-    node app.js --period=15
-    ```
-  * --date
-    - Set the date for selecting models and this works only if `--period` is set. The value could be 1~31. If it is not declared, the date will the real date at runtime.
-    ``` shell
-    node app.js --period=15 --date=1
-    ```
-  * --maxBenchmarks
-    - Sets maximum for number of benchmarks run in parallel. Expects a positive integer.
-    ``` shell
-    node app.js --maxBenchmarks=positive_integer
-    ```
-  * --maxTries
-    - Sets maximum for number of tries a given benchmark has to succeed. Expects a positive integer.
-    ``` shell
-    node app.js --maxTries=positive_integer
-    ```
-  * --outfile
-    - Writes results to an accessible external file, benchmark_results.js or benchmark_results.json. Expects 'html' or 'json'. If you set it as \'html\', benchmark_results.js will be generated and you could review the benchmark results by openning benchmark_result.html file.
-    ``` shell
-    node app.js --outfile=js
-    ```
-  * --v, --version
-    - Shows node version in use.
-    ``` shell
-    node app.js --v
-    ```
-    or
-    ``` shell
-    node app.js --version
-    ```
-  * --localBuild
-    - Uses local build dependencies, instead of public CDNs. (**When using localBuild targets, please make sure you have built the targets (eg. run `yarn build-individual-link-package tfjs-backend-webgl`) you need.**)
-    ``` shell
-    node app.js --localBuild=core,webgl,wasm,cpu,layers,converter,automl
-    ```
-  * --npmVersion
-    - Specify the npm version of TFJS library to benchmark. By default the latest version of TFJS will be benchmarked. 
-   ``` shell
-    node app.js --npmVersion=4.4.0 
-   ```
-
-## Custom model
-The custom model is supported, but is constrained by:
-  * A URL path to the model is required, while the model in local file system is not supported. The following URLs are examples:
-    - TF Hub: https://tfhub.dev/google/tfjs-model/imagenet/resnet_v2_50/feature_vector/1/default/1
-    - Storage: https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json
-  * Currently only `tf.GraphModel` and `tf.LayersModel` are supported.
-
-If you want to benchmark more complex models with customized input preprocessing logic, you need to add your model with `load` and `predictFunc` methods into [`tfjs/e2e/benchmarks/model_config.js`](https://github.com/tensorflow/tfjs/blob/master/e2e/benchmarks/model_config.js), following this [example PR](https://github.com/tensorflow/tfjs/pull/3168/files).
-
-## About this tool
-The tool contains:
-  * A test runner - Karma:
-    - [benchmark_models.js](https://github.com/tensorflow/tfjs/blob/master/e2e/benchmarks/browserstack-benchmark/benchmark_models.js) warps the all benchmark logics into a Jasmine spec.
-    - [browser_list.json](https://github.com/tensorflow/tfjs/blob/master/e2e/benchmarks/browserstack-benchmark/browser_list.json) lists the supported BrowserStack combinations. If you want to add more combinations or refactor this list, you can follow this [conversation](https://github.com/tensorflow/tfjs/pull/3737#issue-463759838).
-  * A node server. [app.js](https://github.com/tensorflow/tfjs/blob/master/e2e/benchmarks/browserstack-benchmark/app.js) runs the test runner and send the benchmark results back to the webpage.
-  * A webpage.
-
-Thanks, <a href="https://www.browserstack.com/">BrowserStack</a>, for providing supports.
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/SpecRunner.html b/tfjs-master/e2e/benchmarks/browserstack-benchmark/SpecRunner.html
deleted file mode 100644
index dad6b23c3..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/SpecRunner.html
+++ /dev/null
@@ -1,42 +0,0 @@
-<!-- Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset="utf-8">
-  <title>Jasmine Test</title>
-
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/jasmine.css">
-
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/jasmine.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/jasmine-html.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/boot.js"></script>
-
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/dat-gui/0.7.7/dat.gui.min.js"></script>
-  <script src='socket.io/socket.io.js'></script>
-
-  <!-- include source files here... -->
-  <script src="./index.js"></script>
-
-  <!-- include spec files here... -->
-  <script src="./index_test.js"></script>
-  <script src="./app_test.js"></script>
-
-</head>
-
-<body>
-</body>
-</html>
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/app.js b/tfjs-master/e2e/benchmarks/browserstack-benchmark/app.js
deleted file mode 100644
index c1db439cd..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/app.js
+++ /dev/null
@@ -1,563 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const http = require('http');
-const socketio = require('socket.io');
-const fs = require('fs');
-const path = require('path');
-const { execFile } = require('child_process');
-const { ArgumentParser } = require('argparse');
-const { version } = require('./package.json');
-const { resolve } = require('path');
-const {
-  addResultToFirestore,
-  runFirestore,
-  firebaseConfig,
-  endFirebaseInstance
-} = require('./firestore.js');
-const { PromiseQueue } = require('./promise_queue');
-const JSONStream = require('JSONStream');
-
-const jsonwriter = JSONStream.stringify();
-const port = process.env.PORT || 8001;
-let io;
-let parser;
-let cliArgs;
-let db;
-
-function checkBrowserStackAccount() {
-  if (process.env.BROWSERSTACK_USERNAME == null ||
-    process.env.BROWSERSTACK_ACCESS_KEY == null) {
-    throw new Error(
-      `Please export your BrowserStack username and access key by running` +
-      `the following commands in the terminal:
-          export BROWSERSTACK_USERNAME=YOUR_USERNAME
-          export BROWSERSTACK_ACCESS_KEY=YOUR_ACCESS_KEY`);
-  }
-}
-
-function runServer() {
-  const app = http.createServer((request, response) => {
-    const url = request.url === '/' ? '/index.html' : request.url;
-    let filePath = path.join(__dirname, url);
-    if (!fs.existsSync(filePath)) {
-      filePath = path.join(__dirname, '../', url);
-    }
-    fs.readFile(filePath, (err, data) => {
-      if (err) {
-        response.writeHead(404);
-        response.end(JSON.stringify(err));
-        return;
-      }
-      response.writeHead(200);
-      response.end(data);
-    });
-  });
-  app.listen(port, () => {
-    console.log(`  > Running socket on: 127.0.0.1:${port}`);
-  });
-
-  io = socketio(app);
-  io.on('connection', socket => {
-    const availableBrowsers = require('./browser_list.json');
-    socket.emit('availableBrowsers', availableBrowsers);
-    socket.on('run', benchmark);
-  });
-}
-
-/**
- * Supplement the browser configurations and create `browsers.json` and
- * `benchmark_parameters.json` configuration files for karma.
- *
- * @param {{browsers, benchmark}} config
- */
-function setupBenchmarkEnv(config) {
-  // Write the map (tabId - browser setting) to `./browsers.json`.
-  for (const tabId in config.browsers) {
-    if (tabId === 'local') {
-      continue;
-    }
-    const browser = config.browsers[tabId];
-    browser.base = 'BrowserStack';
-    // For mobile devices, we would use real devices instead of emulators.
-    if (browser.os === 'ios' || browser.os === 'android') {
-      browser.real_mobile = true;
-    }
-  }
-  fs.writeFileSync('./browsers.json', JSON.stringify(config.browsers, null, 2));
-
-  // Write benchmark parameters to './benchmark_parameters.json'.
-  fs.writeFileSync(
-    './benchmark_parameters.json', JSON.stringify(config.benchmark, null, 2));
-}
-
-/**
- * Creates and runs benchmark configurations for each model-backend pairing.
- *
- * @param browsers The target browsers to run benchmark.
- * @param {{backend, model, numRuns, codeSnippets}} benchmarkInfo
- */
-async function benchmarkAll(benchmarkInfo, browsers) {
-  const allResults = [];
-  for (backend of benchmarkInfo.backend) {
-    for (model of benchmarkInfo.model) {
-      if (model === 'codeSnippet') {
-        for (codeSnippetPair of benchmarkInfo.codeSnippets) {
-          console.log(
-            `\nRunning codeSnippet benchmarks over ${backend} backend...`);
-          const result = await benchmark({
-            'benchmark': {
-              'model': model,
-              'numRuns': benchmarkInfo.numRuns,
-              'backend': backend,
-              'codeSnippet': codeSnippetPair.codeSnippet || '',
-              'setupCodeSnippetEnv': codeSnippetPair.setupCodeSnippetEnv || ''
-            },
-            'browsers': browsers
-          });
-          allResults.push(result);
-        }
-      } else {
-        console.log(
-          `\nRunning ${model} model benchmarks over ${backend} backend...`);
-        const result = await benchmark({
-          'benchmark': {
-            'model': model,
-            'numRuns': benchmarkInfo.numRuns,
-            'backend': backend
-          },
-          'browsers': browsers
-        });
-        allResults.push(result);
-      }
-    }
-  }
-  console.log('\nAll benchmarks complete!');
-  return allResults;
-}
-
-/**
- * Run model benchmark on BrowserStack.
- *
- * Each browser-device pairing is benchmarked in parallel. Results are sent to
- * the webpage staggered as they are returned to the server.
- *
- * The benchmark configuration object contains two objects:
- * - `browsers`: Each key-value pair represents a browser instance to be
- * benchmarked. The key is a unique string id/tabId (assigned by the webpage)
- * for the browser instance, while the value is the browser configuration.
- *
- * - `benchmark`: An object with the following properties:
- *  - `model`: The name of model (registed at
- * 'tfjs/e2e/benchmarks/model_config.js') or `custom`.
- *  - modelUrl: The URL to the model description file. Only applicable when
- * the `model` is `custom`.
- *  - `numRuns`: The number of rounds for model inference.
- *  - `backend`: The backend to be benchmarked on.
- *
- *
- * @param {{browsers, benchmark}} config Benchmark configuration
- * @param runOneBenchmark Function that benchmarks one browser-device pair
- */
-async function benchmark(config, runOneBenchmark = getOneBenchmarkResult) {
-  console.log('Preparing configuration files for the test runner.\n');
-  setupBenchmarkEnv(config);
-  if (require.main === module) {
-    console.log(
-      `Starting benchmarks using ${cliArgs.localBuild || 'cdn'} ` +
-      `dependencies...`);
-  }
-
-  const promiseQueue = new PromiseQueue(cliArgs?.maxBenchmarks ?? 9);
-  const results = [];
-
-  // Runs and gets result of each queued benchmark
-  let tabIndex = 1;
-  for (const tabId in config.browsers) {
-    results.push(promiseQueue.add(() => {
-      return runOneBenchmark(tabId, cliArgs?.maxTries, tabIndex++).then((value) => {
-        value.deviceInfo = config.browsers[tabId];
-        value.modelInfo = config.benchmark;
-        return value;
-      }).catch(error => {
-        console.log(
-          `${tabId} ${config.benchmark.model} ${config.benchmark.backend}`,
-          error);
-        return {
-          error, deviceInfo: config.browsers[tabId], modelInfo: config.benchmark
-        }
-      });
-    }));
-  }
-
-  // Optionally written to an outfile or pushed to a database once all
-  // benchmarks return results
-  const fulfilled = await Promise.allSettled(results);
-  if (cliArgs?.outfile === 'html' || cliArgs?.outfile === 'json') {
-    for (const benchmarkResult of fulfilled) {
-      jsonwriter.write(benchmarkResult);
-    }
-  }
-  if (cliArgs?.firestore) {
-    await pushToFirestore(fulfilled);
-  }
-  console.log(
-    `\n${config.benchmark?.model} model benchmark over ${config.benchmark?.backend} backend complete.\n`);
-  return fulfilled;
-}
-
-function sleep(timeMs) {
-  return new Promise(resolve => setTimeout(resolve, timeMs));
-}
-
-/**
- * Gets the benchmark result of a singular browser-device pairing.
- *
- * If benchmarking produces an error, the given browser-device pairing is
- * retried up to the specific max number of tries. Default is 3.
- *
- * @param tabId Indicates browser-device pairing for benchmark
- * @param triesLeft Number of tries left for a benchmark to succeed
- * @param tabIndex Indicates the sequential position for the browser-device
- *     pairing, which is used to delay initiating runner.
- * @param runOneBenchmark Function that runs a singular BrowserStack
- *     performance test
- * @param retyOneBenchmark Function that retries a singular BrowserStack
- *     performance test
- */
-async function getOneBenchmarkResult(
-  tabId, triesLeft, tabIndex = 0,
-  runOneBenchmark = runBrowserStackBenchmark) {
-  // Since karma will throw out `spawn ETXTBSY` error if initiating multiple
-  // benchmark runners at the same time, adds delays between initiating runners
-  // to resolve this race condition.
-  const numFailed = cliArgs.maxTries - triesLeft;
-  // The delay increase exponentially when benchmark fails.
-  const delayInitiatingRunnerTimeMs = tabIndex * (3 ** numFailed) * 1000;
-  await sleep(delayInitiatingRunnerTimeMs);
-
-  triesLeft--;
-  try {
-    const result = await runOneBenchmark(tabId);
-    console.log(`${tabId} benchmark succeeded.`);
-    return result;
-  } catch (err) {
-    // Retries benchmark until resolved or until no retries left
-    if (triesLeft > 0) {
-      console.log(`Retrying ${tabId} benchmark. ${triesLeft} tries left...`);
-      return await getOneBenchmarkResult(tabId, triesLeft, runOneBenchmark);
-    } else {
-      console.log(`${tabId} benchmark failed.`);
-      throw err;
-    }
-  }
-}
-
-/**
- * Run benchmark for singular browser-device combination.
- *
- * This function utilizes a promise that is fulfilled once the corresponding
- * result is returned from BrowserStack.
- *
- * @param tabId Indicates browser-device pairing for benchmark
- */
-function runBrowserStackBenchmark(tabId) {
-  return new Promise((resolve, reject) => {
-    const args = ['test'];
-    if (tabId !== 'local') {
-      args.push('--browserstack', `--browsers=${tabId}`);
-    }
-    if (cliArgs.localBuild) {
-      args.push(`--localBuild=${cliArgs.localBuild}`)
-    };
-    if (cliArgs.npmVersion) {
-      args.push(`--npmVersion=${cliArgs.npmVersion}`)
-    };
-    
-    const command = `yarn ${args.join(' ')}`;
-    console.log(`Running: ${command}`);
-    execFile('yarn', args, { timeout: 3e5 }, (error, stdout, stderr) => {
-      if (error) {
-        console.log(`\n${error}`);
-        console.log(`stdout: ${stdout}`);
-        if (!cliArgs.cloud) {
-          io.emit(
-            'benchmarkComplete',
-            { tabId, error: `Failed to run ${command}:\n${error}` });
-        }
-        return reject(`Failed to run ${command}:\n${error}`);
-      }
-
-      const errorReg = /.*\<tfjs_error\>(.*)\<\/tfjs_error\>/;
-      const matchedError = stdout.match(errorReg);
-      if (matchedError != null) {
-        if (!cliArgs.cloud) {
-          io.emit('benchmarkComplete', { tabId, error: matchedError[1] });
-        }
-        return reject(matchedError[1]);
-      }
-
-      const resultReg = /.*\<tfjs_benchmark\>(.*)\<\/tfjs_benchmark\>/;
-      const matchedResult = stdout.match(resultReg);
-      if (matchedResult != null) {
-        const benchmarkResult = JSON.parse(matchedResult[1]);
-        benchmarkResult.tabId = tabId;
-        if (!cliArgs.cloud) {
-          io.emit('benchmarkComplete', benchmarkResult)
-        };
-        return resolve(benchmarkResult);
-      }
-
-      const errorMessage = 'Did not find benchmark results from the logs ' +
-        'of the benchmark test (benchmark_models.js).';
-      if (!cliArgs.cloud) {
-        io.emit('benchmarkComplete', { error: errorMessage })
-      };
-      return reject(errorMessage);
-    });
-  });
-}
-
-/**
- * Pushes all benchmark results to Firestore.
- *
- * @param benchmarkResults List of all benchmark results
- */
-async function pushToFirestore(benchmarkResults) {
-  let firestoreResults = [];
-  let numRejectedPromises = 0;
-  console.log('\Pushing results to Firestore...');
-  for (result of benchmarkResults) {
-    if (result.status == 'fulfilled') {
-      firestoreResults.push(
-        addResultToFirestore(db, result.value.tabId, result.value));
-    } else if (result.status == 'rejected') {
-      numRejectedPromises++;
-    }
-  }
-  return await Promise.allSettled(firestoreResults).then(() => {
-    console.log(
-      `Encountered ${numRejectedPromises} rejected promises that were not ` +
-      `added to the database.`);
-  });
-}
-
-/** Set up --help menu for file description and available optional commands */
-function setupHelpMessage() {
-  parser = new ArgumentParser({
-    description: 'This file launches a server to connect to BrowserStack ' +
-      'so that the performance of a TensorFlow model on one or more ' +
-      'browsers can be benchmarked.'
-  });
-  parser.add_argument('--benchmarks', {
-    help: 'run a preconfigured benchmark from a user-specified JSON',
-    action: 'store'
-  });
-  parser.add_argument('--cloud', {
-    help: 'runs GCP compatible version of benchmarking system',
-    action: 'store_true'
-  });
-  parser.add_argument('--period', {
-    help: 'runs a part of models specified in --benchmarks\'s file in a ' +
-      'cycle and the part of models to run is determined by the date ' +
-      'of a month. The value could be 1~31.',
-    type: 'int',
-    action: 'store'
-  });
-  parser.add_argument('--date', {
-    help: 'set the date for selecting models and this works only if period ' +
-      'is set. The value could be 1~31. If it is not set, the date would be ' +
-      'the date at runtime).',
-    type: 'int',
-    action: 'store'
-  });
-  parser.add_argument('--maxBenchmarks', {
-    help: 'the maximum number of benchmarks run in parallel',
-    type: 'int',
-    default: 5,
-    action: 'store'
-  });
-  parser.add_argument('--maxTries', {
-    help: 'the maximum number of times a given benchmark is tried befor it ' +
-      'officially fails',
-    type: 'int',
-    default: 3,
-    action: 'store'
-  });
-  parser.add_argument('--firestore', {
-    help: 'Store benchmark results in Firestore database',
-    action: 'store_true'
-  });
-  parser.add_argument('--outfile', {
-    help: 'write results to outfile. Expects \'html\' or \'json\'. ' +
-      'If you set it as \'html\', benchmark_results.js will be generated ' +
-      'and you could review the benchmark results by openning ' +
-      'benchmark_result.html file.',
-    type: 'string',
-    action: 'store'
-  });
-  parser.add_argument('-v', '--version', { action: 'version', version });
-  parser.add_argument('--localBuild', {
-    help: 'local build name list, separated by comma. The name is in short ' +
-      'form (in general the name without the tfjs- and backend- prefixes, ' +
-      'for example webgl for tfjs-backend-webgl, core for tfjs-core). ' +
-      'Example: --localBuild=webgl,core.',
-    type: 'string',
-    default: '',
-    action: 'store'
-  });
-  parser.add_argument('--npmVersion', {
-    help: 'specify the npm version of TFJS library to benchmark.' +
-      'By default the latest version of TFJS will be benchmarked' +
-      'Example: --npmVersion=4.4.0.',
-    type: 'string',
-    action: 'store'
-  });
-  cliArgs = parser.parse_args();
-  console.dir(cliArgs);
-}
-
-/**
- * Get the models to benchmark for the day running the script. (All models are
- * spilted to n buckets and n === period, associated with the date of the month,
- * and the function returns a certain bucket.)
- *
- * @param models The models to schedule.
- * @param period The period to run all models.
- * @param date The value could be 1~31, and it determines the models to
- *    benchmark. By default, the date would be the date at runtime.
- */
-function scheduleModels(models, period, date = new Date().getDate()) {
-  if (period < 1 || period > 31) {
-    throw new Error('--period must be an integer of 1~31.');
-  }
-  if (date <= 0 || date > 31) {
-    throw new Error('--date must be an integer of 1~31.');
-  }
-  date = (date - 1) % period;
-  const bucketSize = Math.ceil(models.length / period);
-  return models.slice(date * bucketSize, (date + 1) * bucketSize);
-}
-
-/**
- * Runs a benchmark with a preconfigured file
- *
- * @param file Relative filepath to preset benchmark configuration
- * @param runBenchmark Function to run a benchmark configuration
- */
-async function runBenchmarkFromFile(file, runBenchmark = benchmarkAll) {
-  console.log('Running a preconfigured benchmark...');
-  const { benchmark, browsers } = file;
-  if (cliArgs?.period != null) {
-    benchmark.model = scheduleModels(benchmark.model, cliArgs.period, cliArgs.date);
-    console.log(
-      `\nWill benchmark the following models: \n\t` +
-      `${benchmark.model.join('\n\t')} \n`);
-  } else {
-    console.log(
-      `\nWill benchmark all models in '${cliArgs.benchmarks}'.\n`);
-  }
-  await runBenchmark(benchmark, browsers);
-}
-
-async function initializeWriting() {
-  if (cliArgs.firestore) {
-    db = await runFirestore(firebaseConfig)
-  };
-
-  let file;
-  if (cliArgs?.outfile === 'html') {
-    await fs.writeFile(
-      './benchmark_results.js', 'const benchmarkResults = ', 'utf8', err => {
-        if (err) {
-          console.log(`Error: ${err}.`);
-          return reject(err);
-        } else {
-          return resolve();
-        }
-      });
-    file = fs.createWriteStream('benchmark_results.js', { 'flags': 'a' });
-  } else if (cliArgs?.outfile === 'json') {
-    file = fs.createWriteStream('./benchmark_results.json');
-  } else {
-    return;
-  }
-
-  // Pipe the JSON data to the file.
-  jsonwriter.pipe(file);
-  console.log(`\nStart writing.`);
-
-  // If having outfile, add a listener to Ctrl+C to finalize writing.
-  process.on('SIGINT', async () => {
-    await finalizeWriting();
-    process.exit();
-  });
-}
-
-
-async function finalizeWriting() {
-  if (cliArgs.firestore) {
-    await endFirebaseInstance();
-  }
-
-  if (cliArgs?.outfile === 'html') {
-    jsonwriter.end();
-    console.log('\nOutput written to ./benchmark_results.js.');
-  } else if (cliArgs?.outfile === 'json') {
-    jsonwriter.end();
-    console.log('\nOutput written to ./benchmark_results.json.');
-  }
-}
-
-/** Sets up the local or remote environment for benchmarking. */
-async function prebenchmarkSetup() {
-  checkBrowserStackAccount();
-  await initializeWriting();
-
-  if (!cliArgs.cloud) {
-    runServer()
-  };
-
-  try {
-    if (cliArgs.benchmarks) {
-      const filePath = resolve(cliArgs.benchmarks);
-      if (fs.existsSync(filePath)) {
-        console.log(`\nFound file at ${filePath}`);
-        const config = require(filePath);
-        await runBenchmarkFromFile(config);
-        console.log('finish')
-      } else {
-        throw new Error(
-          `File could not be found at ${filePath}. ` +
-          `Please provide a valid path.`);
-      }
-    }
-  } finally {
-    finalizeWriting();
-  }
-}
-
-/* Only run this code if app.js is called from the command line */
-if (require.main === module) {
-  setupHelpMessage();
-  prebenchmarkSetup();
-}
-
-exports.runBenchmarkFromFile = runBenchmarkFromFile;
-exports.getOneBenchmarkResult = getOneBenchmarkResult;
-exports.benchmark = benchmark;
-exports.scheduleModels = scheduleModels;
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/app_node_test.js b/tfjs-master/e2e/benchmarks/browserstack-benchmark/app_node_test.js
deleted file mode 100644
index a8abc7d4d..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/app_node_test.js
+++ /dev/null
@@ -1,397 +0,0 @@
-const fs = require('fs');
-const { benchmark, write, getOneBenchmarkResult, runBenchmarkFromFile, scheduleModels } =
-  require('./app.js');
-const {
-  addResultToFirestore,
-  makeCompatableWithFirestore,
-  addGpuInfo,
-  getReadableDate,
-  formatForFirestore,
-  runFirestore,
-  firebaseConfig
-} = require('./firestore.js');
-const { PromiseQueue } = require('./promise_queue.js');
-
-describe('test app.js cli', () => {
-  const filePath = './benchmark_test_results.json';
-  let config;
-  let mockRunOneBenchmark;
-  let failMockRunOneBenchmark;
-  let mockResults;
-  let mockBenchmark;
-
-  beforeAll(() => {
-    // Set a longer jasmine timeout than 5 seconds
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-  });
-
-  beforeEach(() => {
-    // Preset mock results and corresponding config
-    mockResults = {
-      'iPhone_XS_1': {
-        timeInfo: {
-          times: [218.00000000000045, 216.00000000000045],
-          averageTime: 217.00000000000045,
-          averageTimeExclFirst: 216.00000000000045,
-          minTime: 216.00000000000045,
-          maxTime: 218.00000000000045
-        },
-        tabId: 'iPhone_XS_1'
-      },
-      'Samsung_Galaxy_S20_1': {
-        timeInfo: {
-          times: [428.89999999897555, 430.89999999897555],
-          averageTime: 429.89999999897555,
-          averageTimeExclFirst: 430.89999999897555,
-          minTime: 428.89999999897555,
-          maxTime: 430.89999999897555
-        },
-        tabId: 'Samsung_Galaxy_S20_1'
-      },
-      'Windows_10_1': {
-        timeInfo: {
-          times: [395.8500000001095, 397.8500000001095],
-          averageTime: 396.8500000001095,
-          averageTimeExclFirst: 397.8500000001095,
-          minTime: 395.8500000001095,
-          maxTime: 397.8500000001095
-        },
-        tabId: 'Windows_10_1'
-      },
-      'OS_X_Catalina_1': {
-        timeInfo: {
-          times: [178.19500000728294, 176.19500000728294],
-          averageTime: 177.19500000728294,
-          averageTimeExclFirst: 176.19500000728294,
-          minTime: 176.19500000728294,
-          maxTime: 178.19500000728294
-        },
-        tabId: 'OS_X_Catalina_1'
-      }
-    };
-    config = {
-      benchmark: { model: 'mobilenet_v2', numRuns: 1, backend: 'wasm' },
-      browsers: {
-        iPhone_XS_1: {
-          base: 'BrowserStack',
-          browser: 'iphone',
-          browser_version: 'null',
-          os: 'ios',
-          os_version: '12',
-          device: 'iPhone XS',
-          real_mobile: true
-        },
-        Samsung_Galaxy_S20_1: {
-          base: 'BrowserStack',
-          browser: 'android',
-          browser_version: 'null',
-          os: 'android',
-          os_version: '10.0',
-          device: 'Samsung Galaxy S20',
-          real_mobile: true
-        },
-        Windows_10_1: {
-          base: 'BrowserStack',
-          browser: 'chrome',
-          browser_version: '84.0',
-          os: 'Windows',
-          os_version: '10',
-          device: null
-        },
-        OS_X_Catalina_1: {
-          base: 'BrowserStack',
-          browser: 'chrome',
-          browser_version: '84.0',
-          os: 'OS X',
-          os_version: 'Catalina',
-          device: null
-        }
-      }
-    };
-
-    // Bypasses BrowserStack with preset successful mock results
-    mockRunOneBenchmark =
-      jasmine.createSpy('mockRunOneBenchmark').and.callFake((tabId) => {
-        return Promise.resolve(mockResults[tabId]);
-      });
-
-    // Bypasses Browserstack with preset failed mock results
-    failMockRunOneBenchmark =
-      jasmine.createSpy('mockRunOneBenchmark').and.callFake((tabId) => {
-        return Promise.reject(`Error: ${tabId} failed.`);
-      });
-
-    // Before each spec, create a mock benchmark and set testing browser
-    // configuration this helps ensure that everything is set to the expected
-    // contents before the spec is run
-    mockBenchmark = jasmine.createSpy('mockBenchmark');
-    testingConfig = require('./test_config.json');
-  })
-
-  it('checks for outfile accuracy', async () => {
-    // Writes to mock results file
-    await write(filePath, mockResults);
-
-    const contents = fs.readFileSync(filePath, 'utf8');
-    expect(contents).toEqual(JSON.stringify(mockResults, null, 2));
-  });
-
-  it('benchmark function benchmarks each browser-device pairing ', async () => {
-    // Receives list of promises from benchmark function call
-    const testResults = await benchmark(config, mockRunOneBenchmark);
-
-    // Extracts value results from promises, effectively formatting
-    const formattedResults = {};
-    for (let i = 0; i < Object.keys(config.browsers).length; i++) {
-      await new Promise(resolve => {
-        const result = testResults[i].value;
-        formattedResults[result.tabId] = result;
-        return resolve();
-      });
-    }
-
-    // Expected mockRunOneBenchmark stats
-    expect(mockRunOneBenchmark.calls.count())
-      .toEqual(Object.keys(config.browsers).length);
-    expect(mockRunOneBenchmark).toHaveBeenCalledWith('iPhone_XS_1', undefined);
-    expect(mockRunOneBenchmark)
-      .toHaveBeenCalledWith('Samsung_Galaxy_S20_1', undefined);
-    expect(mockRunOneBenchmark).toHaveBeenCalledWith('Windows_10_1', undefined);
-    expect(mockRunOneBenchmark)
-      .toHaveBeenCalledWith('OS_X_Catalina_1', undefined);
-
-    // Expected value from promise all
-    expect(formattedResults).toEqual(mockResults);
-  });
-
-  it('getOneBenchmark rejects if a benchmark consistently fails', async () => {
-    // Expected failed mock benchmark results
-    await expectAsync(
-      getOneBenchmarkResult('iPhone_XS_1', 3, failMockRunOneBenchmark))
-      .toBeRejectedWith(`Error: iPhone_XS_1 failed.`);
-
-    // Expected mock function call stats
-    expect(failMockRunOneBenchmark.calls.count()).toEqual(3);
-  });
-
-  it('getOneBenchmark fulfills if a benchmark fails and then succeeds',
-    async () => {
-      /* Bypasses Browserstack with preset results. Benchmark will fail on the
-       * call, but succeed on the second call */
-      let called = false;
-      const failThenSucceedMockRunOneBenchmark =
-        jasmine.createSpy('mockRunOneBenchmark').and.callFake((tabId) => {
-          if (called) {
-            return mockRunOneBenchmark(tabId);
-          }
-          called = true;
-          return failMockRunOneBenchmark(tabId);
-        });
-
-      // Gets a successful benchmark result
-      const succeedBenchmarkResult = await getOneBenchmarkResult(
-        'iPhone_XS_1', 3, failThenSucceedMockRunOneBenchmark);
-
-      // Expected mock function call stats
-      expect(failMockRunOneBenchmark.calls.count()).toEqual(1);
-      expect(mockRunOneBenchmark.calls.count()).toEqual(1);
-
-      // Expected successful mock benchmark results
-      expect(succeedBenchmarkResult).toEqual(mockResults.iPhone_XS_1);
-    });
-
-  it('getOneBenchmark fulfills if a benchmark succeeds immediately',
-    async () => {
-      // Gets a successful benchmark result
-      const succeedBenchmarkResult =
-        await getOneBenchmarkResult('iPhone_XS_1', 3, mockRunOneBenchmark);
-
-      // Expected mock funciton call stats
-      expect(mockRunOneBenchmark.calls.count()).toEqual(1);
-
-      // Expected successful mock benchmark results
-      expect(succeedBenchmarkResult).toEqual(mockResults.iPhone_XS_1);
-    });
-
-  it('checks that the benchmark is being run with the correct JSON', () => {
-    runBenchmarkFromFile(testingConfig, mockBenchmark);
-    expect(mockBenchmark).toHaveBeenCalledWith(testingConfig);
-  });
-});
-
-describe('test adding to firestore', () => {
-  let db;
-  let mockResultValue;
-  let mockSerialization;
-  let mockDate;
-
-  beforeAll(async () => {
-    // Set a longer jasmine timeout than 5 seconds
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1_000_000;
-  });
-
-  beforeEach(() => {
-    // mockResultValue is the result of a successful benchmark
-    mockResultValue = require('./firestore_test_value.json');
-    db = jasmine.createSpyObj('firestore', ['add']);
-    mockSerialization = jasmine.createSpy('mockSerialization');
-    mockDate = jasmine.createSpy('mockDate').and.returnValue('7/21/2021');
-  });
-
-  it('Expects db.add to be called with formatted results', () => {
-    db.add.and.returnValue(Promise.resolve({ id: 123 }));
-    let expectedAdd = {
-      result:
-        formatForFirestore(mockResultValue, makeCompatableWithFirestore,
-          getReadableDate)
-    };
-    addResultToFirestore(db, mockResultValue.tabId, mockResultValue);
-    expect(db.add).toHaveBeenCalledWith(expectedAdd);
-  });
-
-  it('Expects gpu info is appended to device info', () => {
-    addGpuInfo(mockResultValue);
-    expect(mockResultValue.deviceInfo.device).toEqual(
-      '(GPU: ANGLE (ATI Technologies Inc., AMD Radeon Pro 5300M OpenGL ' +
-      'Engine, OpenGL 4.1))');
-  });
-
-  it('Expects a date key to exist and have the correct value', () => {
-    let testFormat =
-      formatForFirestore(mockResultValue, mockSerialization, mockDate);
-    expect(testFormat.date).toEqual('7/21/2021');
-  });
-
-  it('Expects serialization to cover all nested arrays', () => {
-    const mockSerializedResults =
-      formatForFirestore(mockResultValue, makeCompatableWithFirestore,
-        mockDate);
-    for (kernel of mockSerializedResults.benchmarkInfo.memoryInfo.kernels) {
-      expect(typeof (kernel.inputShapes)).toEqual('string');
-      expect(typeof (kernel.outputShapes)).toEqual('string');
-    }
-  });
-});
-
-function sleep(n) {
-  return new Promise((resolve) => {
-    setTimeout(() => { resolve(); }, n);
-  });
-}
-
-describe('promise queue', () => {
-  let queue;
-  beforeEach(() => {
-    queue = new PromiseQueue(3);
-    jasmine.clock().install();
-  });
-
-  afterEach(() => {
-    jasmine.clock().uninstall();
-  });
-
-  it('runs a given number of functions at once', async () => {
-    let promises = [];
-    let started = [false, false, false, false, false];
-    let resolved = [false, false, false, false, false];
-    for (let i = 0; i < 5; i++) {
-      resolved[i] = false;
-      promises.push(queue.add(async () => {
-        started[i] = true;
-        await sleep((i + 1) * 10);
-        resolved[i] = true;
-      }));
-    }
-
-    // Queue should immediately start 3 promises.
-    expect(started).toEqual(
-      [true, true, true, false, false]
-    );
-    expect(resolved).toEqual(
-      [false, false, false, false, false]
-    );
-
-    // After the first promise is done, queue should start the fourth one.
-    jasmine.clock().tick(15);
-    await promises[0];
-    expect(started).toEqual(
-      [true, true, true, true, false]
-    );
-    expect(resolved).toEqual(
-      [true, false, false, false, false]
-    );
-
-    // All running promises should finish, and the last should start.
-    jasmine.clock().tick(1000);
-    await promises[1];
-    await promises[2];
-    await promises[3];
-    expect(started).toEqual(
-      [true, true, true, true, true]
-    );
-    expect(resolved).toEqual(
-      [true, true, true, true, false]
-    );
-
-    // The last promise should finish
-    jasmine.clock().tick(1000);
-    await promises[4];
-    expect(started).toEqual(
-      [true, true, true, true, true]
-    );
-    expect(resolved).toEqual(
-      [true, true, true, true, true]
-    );
-  });
-});
-
-
-describe('schedule models', () => {
-  it('scheduling models works for the first day of a period', () => {
-    models = Array.from(Array(25).keys());
-    const res = scheduleModels(models, 7, 1);
-    expect(res).toEqual(
-      [0, 1, 2, 3]
-    );
-  });
-
-  it('scheduling models works for weekly period', () => {
-    models = Array.from(Array(25).keys());
-    const res = scheduleModels(models, 7, 4);
-    expect(res).toEqual(
-      [12, 13, 14, 15]
-    );
-  });
-
-  it('scheduling models works for the last day of a period', () => {
-    models = Array.from(Array(25).keys());
-    const res = scheduleModels(models, 7, 7);
-    expect(res).toEqual(
-      [24]
-    );
-  });
-
-  it('scheduling models works for half-month', () => {
-    models = Array.from(Array(25).keys());
-    const res = scheduleModels(models, 15, 6);
-    expect(res).toEqual(
-      [10, 11]
-    );
-  });
-
-  it('scheduling models works for default date', () => {
-    jasmine.clock().install();
-
-    const baseTime = new Date(2022, 12, 6);
-    jasmine.clock().mockDate(baseTime);
-    expect(new Date().getDate()).toEqual(6);
-
-    models = Array.from(Array(25).keys());
-    const res = scheduleModels(models, 15);
-    expect(res).toEqual(
-      [10, 11]
-    );
-
-    jasmine.clock().uninstall();
-  });
-});
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/app_test.js b/tfjs-master/e2e/benchmarks/browserstack-benchmark/app_test.js
deleted file mode 100644
index 3a730e029..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/app_test.js
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * The unit tests in this file can be run by opening `./SpecRunner.html` in
- * browser.
- */
-
-describe('benchmark multiple browsers', () => {
-  const browsersList = [
-    {
-      'os': 'OS X',
-      'os_version': 'Catalina',
-      'browser': 'chrome',
-      'device': null,
-      'browser_version': '84.0'
-    },
-    {
-      'os': 'android',
-      'os_version': '9.0',
-      'browser': 'android',
-      'device': 'Samsung Galaxy Note 10 Plus',
-      'browser_version': null,
-      'real_mobile': true
-    },
-    {
-      'os': 'Windows',
-      'os_version': '10',
-      'browser': 'chrome',
-      'device': null,
-      'browser_version': '84.0',
-      'real_mobile': null
-    }
-  ];
-  const benchmark = {model: 'mobilenet_v2', numRuns: 1, backend: 'cpu'};
-  const browsers = {};
-
-  beforeAll(() => {
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-
-    // Populate `browsers`.
-    for (const browserConfig of browsersList) {
-      const tabId = getTabId(browserConfig);
-      browsers[tabId] = browserConfig;
-    }
-  });
-
-  it('the number of received results is equal to the number of browsers',
-     done => {
-       socket.emit('run', {benchmark, browsers});
-
-       benchmarkResults = [];
-       socket.on('benchmarkComplete', benchmarkResult => {
-         expect(benchmarkResult.error).toBeUndefined();
-         benchmarkResults.push(benchmarkResult);
-         if (benchmarkResults.length === browsersList.length) {
-           done();
-         }
-       });
-     });
-});
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/benchmark_models.js b/tfjs-master/e2e/benchmarks/browserstack-benchmark/benchmark_models.js
deleted file mode 100644
index 8c4216435..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/benchmark_models.js
+++ /dev/null
@@ -1,147 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * The purpose of this test file is to benchmark models by a test runner, such
- * as karma. To invoke this test, inlude this file to the `files` field of
- * `karma.conf.js`.
- *
- * This file wraps the model benchmarking into a Jasmine test and the benchmark
- * results will be logged to the console.
- */
-
-async function getEnvSummary() {
-  let envSummary = `${tf.getBackend()} backend`;
-  if (tf.getBackend() === 'webgl') {
-    envSummary += `, version ${tf.env().get('WEBGL_VERSION')}`;
-  } else if (tf.getBackend() === 'wasm') {
-    const hasSIMD = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');
-    envSummary += hasSIMD ? ' with SIMD' : ' without SIMD';
-  }
-  return envSummary;
-}
-
-async function getBenchmarkSummary(timeInfo, memoryInfo, modelName = 'model') {
-  if (timeInfo == null) {
-    throw new Error('Missing the timeInfo parameter.');
-  } else if (timeInfo.times.length === 0) {
-    throw new Error('Missing the memoryInfo parameter.');
-  } else if (memoryInfo == null) {
-    throw new Error('The length of timeInfo.times is at least 1.');
-  }
-
-  const numRuns = timeInfo.times.length;
-  const envSummary = await getEnvSummary();
-  const benchmarkSummary = `
-  benchmark the ${modelName} on ${envSummary}
-  1st inference time: ${printTime(timeInfo.times[0])}
-  Subsequent average inference time (${numRuns} runs): ${printTime(timeInfo.averageTimeExclFirst)}
-  Best inference time: ${printTime(timeInfo.minTime)}
-  Peak memory: ${printMemory(memoryInfo.peakBytes)}
-  `;
-  return benchmarkSummary;
-}
-
-const KARMA_SERVER = './base';
-
-async function benchmarkModel(benchmarkParameters) {
-  // Load the model.
-  const benchmark = benchmarks[benchmarkParameters.model];
-  const numRuns = benchmarkParameters.numRuns;
-  let model;
-  if (benchmarkParameters.model === 'custom') {
-    if (benchmarkParameters.modelUrl == null) {
-      throw new Error('Please provide model url for the custom model.');
-    }
-    model = await loadModelByUrl(benchmarkParameters.modelUrl);
-  } else {
-    model = await benchmark.load();
-  }
-
-  // Benchmark.
-  let timeInfo;
-  let memoryInfo;
-  if (benchmark.predictFunc != null) {
-    const predict = benchmark.predictFunc();
-    timeInfo = await timeInference(() => predict(model), numRuns);
-    memoryInfo = await profileInference(() => predict(model));
-  } else {
-    const input = generateInput(model);
-    timeInfo = await timeModelInference(model, input, numRuns);
-    memoryInfo = await profileModelInference(model, input);
-  }
-
-  return { timeInfo, memoryInfo };
-}
-
-async function benchmarkCodeSnippet(benchmarkParameters) {
-  let predict = null;
-
-  const setupCodeSnippetEnv = benchmarkParameters.setupCodeSnippetEnv || '';
-  const codeSnippet = benchmarkParameters.codeSnippet || ''
-  eval(setupCodeSnippetEnv.concat(codeSnippet));
-
-  if (predict == null) {
-    throw new Error(
-      'predict function is suppoed to be defined in codeSnippet.');
-  }
-
-  // Warm up.
-  await timeInference(predict, 1);
-
-  // Benchmark code snippet.
-  timeInfo = await timeInference(predict, benchmarkParameters.numRuns);
-  memoryInfo = await profileInference(predict);
-
-  return { timeInfo, memoryInfo };
-}
-
-describe('BrowserStack benchmark', () => {
-  let benchmarkParameters;
-  beforeAll(async () => {
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-    const response = await fetch(`${KARMA_SERVER}/benchmark_parameters.json`);
-    benchmarkParameters = await response.json();
-    tf.env().set('SOFTWARE_WEBGL_ENABLED', true);
-  });
-
-  it(`benchmark`, async () => {
-    try {
-      // Setup benchmark environments.
-      const targetBackend = benchmarkParameters.backend;
-      await tf.setBackend(targetBackend);
-
-      // Run benchmark and stringify results.
-      let resultObj;
-      if (benchmarkParameters.model === 'codeSnippet') {
-        resultObj = await benchmarkCodeSnippet(benchmarkParameters);
-      } else {
-        resultObj = await benchmarkModel(benchmarkParameters);
-      }
-
-      // Get GPU hardware info.
-      resultObj.gpuInfo =
-        targetBackend === 'webgl' ? (await getRendererInfo()) : 'MISS';
-
-      // Report results.
-      console.log(
-        `<tfjs_benchmark>${JSON.stringify(resultObj)}</tfjs_benchmark>`);
-    } catch (error) {
-      console.log(`<tfjs_error>${error}</tfjs_error>`);
-    }
-  });
-});
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/benchmark_results.html b/tfjs-master/e2e/benchmarks/browserstack-benchmark/benchmark_results.html
deleted file mode 100644
index de8c342e8..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/benchmark_results.html
+++ /dev/null
@@ -1,162 +0,0 @@
-<!-- Copyright 2022 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-
-<html>
-<body style="margin: 5%"></body>
-<script src="./benchmark_results.js"></script>
-<script>
-
-  /**
-   * Returns the table name for this benchmark record, which is a combination
-   * of benchmark category (model or codeSnippet) and backend.
-   *
-   * Example: 'Code Snippet (webgl)'.
-   */
-  function getTableName(benchmarkReocrd) {
-    let benchmarkCategory =
-      benchmarkReocrd?.value?.modelInfo?.model !== 'codeSnippet'?
-          'Model' : 'Code Snippet';
-    return `${benchmarkCategory} (${benchmarkReocrd?.value?.modelInfo?.backend})`;
-  }
-
-  /**
-   * Returns the combination between device os and device name.
-   *
-   * Example: 'OS X(Monterey) safari15.3', 'android(12.0) Google Pixel 5'.
-   */
-  function getDeviceName(benchmarkReocrd) {
-    let deviceInfo = benchmarkReocrd?.value?.deviceInfo || {};
-    return `${deviceInfo.os}(${deviceInfo.os_version})  ${deviceInfo.device || deviceInfo.browser + deviceInfo.browser_version} `;
-  }
-
-  /**
-   * Returns the codeSnippet or model name.
-   *
-   * Example: 'MobileNetV2'.
-   */
-  function getBenchmarkTargetName(benchmarkReocrd) {
-    let benchmarkTargetName =
-      benchmarkReocrd?.value?.modelInfo?.model;
-      if (benchmarkTargetName === 'codeSnippet') {
-        benchmarkTargetName = benchmarkReocrd?.value?.codeSnippet || 'NA'
-      }
-    return `${benchmarkTargetName.replaceAll('\n', '').replaceAll('\t', '')}`;
-  }
-
-  /**
-   * Reorganize the data in benchmarkResults to  dict (object) that is aligned
-   * with tables. The generated dict is a mapping between table name and table
-   * dict. Each tale dict is also a mapping between device name and device
-   * benchmark info dict. The device benchmark info dict is a mapping between the
-   * model name and the average inference time for it.
-   *
-   * @param {object} benchmarkResults This is supposed to be loaded from
-   *     `benchmark_results.js` file and it is an array of benchmarkRecords. Each
-   * benchmarkRecord contains `status` and `value` fileds and `value` also
-   * contains an object with `timeInfo`, `memoryInfo`, `deviceInfo` and
-   * `modelInfo` fileds.
-   */
-  function parseBenchmarkResults(benchmarkResults) {
-    const structuredBenchmarkResults = {};
-    for (let benchmarkReocrd of benchmarkResults) {
-      if (benchmarkReocrd.status !== 'fulfilled') {
-        return;
-      }
-      let tableName = getTableName(benchmarkReocrd);
-      let deviceName = getDeviceName(benchmarkReocrd);
-      let benchmarkTargetName = getBenchmarkTargetName(benchmarkReocrd);
-
-      if (structuredBenchmarkResults[tableName] == null) {
-        structuredBenchmarkResults[tableName] = {};
-      }
-      if (structuredBenchmarkResults[tableName][deviceName] == null) {
-        structuredBenchmarkResults[tableName][deviceName] = {};
-      }
-
-      structuredBenchmarkResults[tableName][deviceName][benchmarkTargetName]
-        = benchmarkReocrd?.value?.timeInfo?.averageTimeExclFirst;
-    }
-    return structuredBenchmarkResults;
-  }
-
-  /**
-   * Create a table element, with table head and caption.
-   */
-  function initializeTable(tableName, tableHeaderfileds) {
-    const tableElem = document.createElement("TABLE");
-    let header = tableElem.createTHead();
-    let row = header.insertRow(0);
-    row.insertCell(0);
-    for (let filedIndex in tableHeaderfileds) {
-      let cell = row.insertCell(row.cells.length);
-      cell.innerHTML = `<b>${tableHeaderfileds[filedIndex]}</b>`;
-    }
-    tableElem.createCaption().innerHTML = tableName;
-    tableElem.setAttribute('border', '1');
-    return tableElem;
-  }
-
-
-  /**
-   * Add a row to the table element with a certain device benchmark record.
-   */
-  function addDevice(tableElem, tableHeaderfileds, tableData, deviceName) {
-    const deviceData = tableData[deviceName];
-    const row = tableElem.insertRow(tableElem.rows.length);
-    let deviceNameCell = row.insertCell(0);
-    deviceNameCell.innerHTML = deviceName;
-
-    for (const filedName of tableHeaderfileds) {
-      const cell = row.insertCell(row.cells.length);
-      cell.innerHTML = deviceData[filedName] || 'NA';
-    }
-  }
-
-  function buildTableElem(tableName, structuredBenchmarkResults) {
-    const tableData = structuredBenchmarkResults[tableName];
-
-    if (tableData == null || Object.keys(tableData).length === 0) {
-      return;
-    }
-
-    // Pick up one random device to know column names for the table.
-    const firstDeviceName = Object.keys(tableData)[0];
-    const tableHeaderfileds = Object.keys(tableData[firstDeviceName]);
-    const tableElem = initializeTable(tableName, tableHeaderfileds);
-
-    for (const deviceName in tableData) {
-      addDevice(tableElem, tableHeaderfileds, tableData, deviceName);
-    }
-    return tableElem;
-  }
-
-  function renderStructuredBenchmarkResults(structuredBenchmarkResults) {
-    for (let tableName in structuredBenchmarkResults) {
-      const tableElem = buildTableElem(tableName, structuredBenchmarkResults);
-      document.body.appendChild(tableElem);
-    }
-  }
-
-  function onPageLoad() {
-    // benchmarkResults is supposed to be loaded from benchmark_results.js.
-    if (benchmarkResults != null) {
-      const structuredBenchmarkResults = parseBenchmarkResults(benchmarkResults);
-      renderStructuredBenchmarkResults(structuredBenchmarkResults);
-    }
-  }
-
-  onPageLoad();
-</script>
-</html>
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/browser_list.json b/tfjs-master/e2e/benchmarks/browserstack-benchmark/browser_list.json
deleted file mode 100644
index 2034de186..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/browser_list.json
+++ /dev/null
@@ -1,631 +0,0 @@
-[
-  {
-    "os": "Windows",
-    "os_version": "11",
-    "browser": "chrome",
-    "device": null,
-    "browser_version": "103.0",
-    "real_mobile": null
-  },
-  {
-    "os": "Windows",
-    "os_version": "10",
-    "browser": "ie",
-    "device": null,
-    "browser_version": "11.0",
-    "real_mobile": null
-  },
-  {
-    "os": "Windows",
-    "os_version": "11",
-    "browser": "edge",
-    "device": null,
-    "browser_version": "103.0",
-    "real_mobile": null
-  },
-  {
-    "os": "Windows",
-    "os_version": "11",
-    "browser": "firefox",
-    "device": null,
-    "browser_version": "103.0",
-    "real_mobile": null
-  },
-  {
-    "os": "OS X",
-    "os_version": "Monterey",
-    "browser": "chrome",
-    "device": null,
-    "browser_version": "103.0",
-    "real_mobile": null
-  },
-  {
-    "os": "OS X",
-    "os_version": "Monterey",
-    "browser": "safari",
-    "device": null,
-    "browser_version": "15.3",
-    "real_mobile": null
-  },
-  {
-    "os": "OS X",
-    "os_version": "Monterey",
-    "browser": "firefox",
-    "device": null,
-    "browser_version": "103.0",
-    "real_mobile": null
-  },
-  {
-    "os": "ios",
-    "os_version": "15",
-    "browser": "iphone",
-    "device": "iPhone 13 Pro Max",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "15",
-    "browser": "iphone",
-    "device": "iPhone 13",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "14",
-    "browser": "iphone",
-    "device": "iPhone 12 Pro Max",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "14",
-    "browser": "iphone",
-    "device": "iPhone 12",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "15",
-    "browser": "iphone",
-    "device": "iPhone XS",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "14",
-    "browser": "iphone",
-    "device": "iPhone XS",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "13",
-    "browser": "iphone",
-    "device": "iPhone 11 Pro Max",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "14",
-    "browser": "iphone",
-    "device": "iPhone 11 Pro",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "14",
-    "browser": "iphone",
-    "device": "iPhone 11",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "13",
-    "browser": "iphone",
-    "device": "iPhone 8",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "13",
-    "browser": "iphone",
-    "device": "iPhone SE 2020",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "15",
-    "browser": "iphone",
-    "device": "iPhone 13",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "ios",
-    "os_version": "15",
-    "browser": "ipad",
-    "device": "iPad 9th",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "ios",
-    "os_version": "15",
-    "browser": "ipad",
-    "device": "iPad Air 5",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "ios",
-    "os_version": "14",
-    "browser": "ipad",
-    "device": "iPad Air 4",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "ios",
-    "os_version": "14",
-    "browser": "ipad",
-    "device": "iPad 8th",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "13",
-    "browser": "ipad",
-    "device": "iPad Pro 12.9 2020",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "13",
-    "browser": "ipad",
-    "device": "iPad Pro 11 2020",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "13",
-    "browser": "ipad",
-    "device": "iPad Mini 2019",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "13",
-    "browser": "ipad",
-    "device": "iPad Air 2019",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "13",
-    "browser": "ipad",
-    "device": "iPad 7th",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "iphone",
-    "device": "iPhone XS",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "iphone",
-    "device": "iPhone XS Max",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "iphone",
-    "device": "iPhone XR",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "iphone",
-    "device": "iPhone 8",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "iphone",
-    "device": "iPhone 8 Plus",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "iphone",
-    "device": "iPhone 7",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "iphone",
-    "device": "iPhone 6S",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "ipad",
-    "device": "iPad Pro 11 2018",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "ipad",
-    "device": "iPad Mini 2019",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "ios",
-    "os_version": "12",
-    "browser": "ipad",
-    "device": "iPad Air 2019",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "12.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S22 Ultra",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "android",
-    "os_version": "12.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S22 Plus",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "android",
-    "os_version": "12.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S22",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "android",
-    "os_version": "12.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S21",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "android",
-    "os_version": "12.0",
-    "browser": "android",
-    "device": "Google Pixel 6 Pro",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "android",
-    "os_version": "12.0",
-    "browser": "android",
-    "device": "Google Pixel 6",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "android",
-    "os_version": "12.0",
-    "browser": "android",
-    "device": "Google Pixel 5",
-    "browser_version": null,
-    "real_mobile": true
-  },{
-    "os": "android",
-    "os_version": "12.0",
-    "browser": "android",
-    "device": "Samsung Galaxy Tab S8",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "11.0",
-    "browser": "android",
-    "device": "Google Pixel 5",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S20",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S20 Plus",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S20 Ultra",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "Samsung Galaxy A51",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "Samsung Galaxy A11",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "Google Pixel 4 XL",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "Google Pixel 4",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "Google Pixel 3",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "OnePlus 8",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "10.0",
-    "browser": "android",
-    "device": "OnePlus 7T",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S9 Plus",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S8 Plus",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S10e",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S10 Plus",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy S10",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy Note 10 Plus",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy Note 10",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy A10",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Google Pixel 3a XL",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Google Pixel 3a",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Google Pixel 3 XL",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Google Pixel 3",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Google Pixel 2",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Motorola Moto G7 Play",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "OnePlus 7",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "OnePlus 6T",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Xiaomi Redmi Note 8",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Xiaomi Redmi Note 7",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy Tab S6",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "9.0",
-    "browser": "android",
-    "device": "Samsung Galaxy Tab S5e",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "8.1",
-    "browser": "android",
-    "device": "Samsung Galaxy Note 9",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "8.1",
-    "browser": "android",
-    "device": "Samsung Galaxy J7 Prime",
-    "browser_version": null,
-    "real_mobile": true
-  },
-  {
-    "os": "android",
-    "os_version": "8.1",
-    "browser": "android",
-    "device": "Samsung Galaxy Tab S4",
-    "browser_version": null,
-    "real_mobile": true
-  }
-]
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/cloudbuild.yml b/tfjs-master/e2e/benchmarks/browserstack-benchmark/cloudbuild.yml
deleted file mode 100644
index 6be4ca333..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/cloudbuild.yml
+++ /dev/null
@@ -1,33 +0,0 @@
-steps:
-
-# Install packages.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'e2e/benchmarks/browserstack-benchmark'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-
-# Run benchmarks.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'e2e/benchmarks/browserstack-benchmark'
-  entrypoint: 'yarn'
-  id: 'test'
-  args: ['run-cloud-benchmarks-half-month-cycle']
-  env: ['BROWSERSTACK_USERNAME=deeplearnjs1', 'NIGHTLY=$_NIGHTLY']
-  secretEnv: ['BROWSERSTACK_ACCESS_KEY', 'FIREBASE_KEY']
-  waitFor: ['yarn']
-
-availableSecrets:
-  secretManager:
-    - versionName: projects/834911136599/secrets/BROWSERSTACK_ACCESS_KEY/versions/latest
-      env: 'BROWSERSTACK_ACCESS_KEY'
-    - versionName: projects/834911136599/secrets/FIREBASE_KEY/versions/latest
-      env: 'FIREBASE_KEY'
-timeout: 3600s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'N1_HIGHCPU_8'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/firestore.js b/tfjs-master/e2e/benchmarks/browserstack-benchmark/firestore.js
deleted file mode 100644
index a002e6216..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/firestore.js
+++ /dev/null
@@ -1,156 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const { initializeApp, deleteApp, applicationDefault, cert } = require('firebase-admin/app');
-const { getFirestore, Timestamp, FieldValue } = require('firebase-admin/firestore');
-
-let app;
-/**
- * Initializes Firebase, signs in with secret credentials, and accesses the
- * Firestore collection of results.
- *
- * @param firebaseConfig A configuration with Firebase credentials
- */
-async function runFirestore() {
-  try {
-    app = initializeApp({
-      credential: applicationDefault()
-    });
-    const db = getFirestore();
-
-    console.log('\nSuccesfuly signed into Firebase.');
-    return db.collection('BenchmarkResults');
-  } catch (err) {
-    console.warn(`Failed to connect to firebase database: ${err.message}`);
-    throw err;
-  }
-}
-
-/**
- * Deletes the Firebase instance, which allows the Node.js process to finish.
- */
-async function endFirebaseInstance() {
-  await deleteApp(app);
-  console.log('Exited Firebase instance.');
-}
-
-/**
- * After being returned from Browserstack, benchmark results are stored as
- * a list of fulfilled promises.
- *
- * As results are being iterated through, this function handles taking a result,
- * serializing it, and pushing it to Firestore.
- *
- * @param db Reference to Firestore collection
- * @param resultId ID of value added to Firestore
- * @param result Individual result in a list of fulfilled promises
- */
-async function addResultToFirestore(db, resultId, result) {
-  try {
-    const firestoreMap =
-      formatForFirestore(result, makeCompatableWithFirestore, getReadableDate);
-    await db.add({ result: firestoreMap }).then((ref) => {
-      console.log(`Added ${resultId} to Firestore with ID: ${ref.id}`);
-    });
-  } catch (err) {
-    throw err;
-  }
-}
-
-/**
- * This functions calls other formatting functions on a benchmark result so that
- * every Firestore entry is compatable and contains desired information
- *
- * @param result Individual result in a list of fulfilled promises
- */
-function formatForFirestore(
-  result, makeCompatable = makeCompatableWithFirestore,
-  getDate = getReadableDate) {
-  let firestoreMap = {};
-  firestoreMap.benchmarkInfo = makeCompatable(result);
-  firestoreMap.date = getDate();
-
-  return firestoreMap;
-}
-
-/**
- * This function makes the result object returned from benchmark app aligned
- * with target firestore collection's schema.
- *
- * @param result Individual result in a list of fulfilled promises
- */
-function makeCompatableWithFirestore(result) {
-  addGpuInfo(result);
-  serializeTensors(result);
-  return result;
-}
-
-/**
- * Append GPU info to device name.
- *
- * @param result Individual result in a list of fulfilled promises
- */
-function addGpuInfo(result) {
-  const gpuInfo = result.gpuInfo;
-  delete result.gpuInfo;
-  if (gpuInfo == null || gpuInfo === 'MISS') {
-    return;
-  }
-
-  if (result.deviceInfo.device == null) {
-    result.deviceInfo.device = `(GPU: ${gpuInfo})`;
-  } else {
-    result.deviceInfo.device = `${result.deviceInfo.device} (GPU: ${gpuInfo})`;
-  }
-  return result;
-}
-
-/**
- * Benchmark results contain tensors that are represented as nested arrays.
- * Nested arrays are not supported on Firestore, so they are serialized
- * before they are stored.
- *
- * @param result Individual result in a list of fulfilled promises
- */
-function serializeTensors(result) {
-  let kernels = result.memoryInfo.kernels;
-  for (kernel of kernels) {
-    kernel.inputShapes = JSON.stringify(kernel.inputShapes);
-    kernel.outputShapes = JSON.stringify(kernel.outputShapes);
-  }
-  return result;
-}
-
-/**
- * Returns a human readable date so each benchmark has an associated date.
- * Gets date in ISO format so that it is compatible with internal visualisation
- * tool.
- */
-function getReadableDate() {
-  const fullISODateString = new Date().toISOString();
-  const dateOnly = fullISODateString.split('T')[0];
-  return dateOnly;
-}
-
-exports.addResultToFirestore = addResultToFirestore;
-exports.makeCompatableWithFirestore = makeCompatableWithFirestore;
-exports.addGpuInfo = addGpuInfo;
-exports.serializeTensors = serializeTensors;
-exports.getReadableDate = getReadableDate;
-exports.formatForFirestore = formatForFirestore;
-exports.runFirestore = runFirestore;
-exports.endFirebaseInstance = endFirebaseInstance;
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/firestore_test_value.json b/tfjs-master/e2e/benchmarks/browserstack-benchmark/firestore_test_value.json
deleted file mode 100644
index 32b7a4f71..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/firestore_test_value.json
+++ /dev/null
@@ -1,4199 +0,0 @@
-{
-  "timeInfo": {
-    "times": [
-      240.42999999801395
-    ],
-    "averageTime": 240.42999999801395,
-    "minTime": 240.42999999801395,
-    "maxTime": 240.42999999801395
-  },
-  "memoryInfo": {
-    "newBytes": 0,
-    "newTensors": 0,
-    "peakBytes": 20579628,
-    "kernels": [
-      {
-        "name": "fusedConv2d__op",
-        "bytesAdded": 4816896,
-        "totalBytesSnapshot": 20178220,
-        "tensorsAdded": 1,
-        "totalTensorsSnapshot": 111,
-        "inputShapes": [
-          [
-            1,
-            112,
-            112,
-            16
-          ],
-          [
-            1,
-            1,
-            16,
-            96
-          ],
-          [
-            96
-          ]
-        ],
-        "outputShapes": [
-          [
-            1,
-            112,
-            112,
-            96
-          ]
-        ],
-        "kernelTimeMs": 23.860000001150183,
-        "extraInfo": ""
-      },
-      {
-        "name": "FusedConv2D",
-        "bytesAdded": 4816896,
-        "totalBytesSnapshot": 20178220,
-        "tensorsAdded": 1,
-        "totalTensorsSnapshot": 111,
-        "inputShapes": [
-          [
-            1,
-            112,
-            112,
-            16
-          ],
-          [
-            1,
-            1,
-            16,
-            96
-          ],
-          [
-            96
-          ],
-          null
-        ],
-        "outputShapes": [
-          [
-            1,
-            112,
-            112,
-            96
-          ]
-        ],
-        "kernelTimeMs": 17.025000001012813,
-        "extraInfo": ""
-      },
-      {
-        "name": "fusedConv2d__op",
-        "bytesAdded": 1605632,
-        "totalBytesSnapshot": 16766252,
-        "tensorsAdded": 1,
-        "totalTensorsSnapshot": 111,
-        "inputShapes": [
-          [
-            1,
-            224,
-            224,
-            3
-          ],
-          [
-            3,
-            3,
-            3,
-            32
-          ],
-          [
-            32
-          ]
-        ],
-        "outputShapes": [
-          [
-            1,
-            112,
-            112,
-            32
-          ]
-        ],
-        "kernelTimeMs": 8.620000000519212,
-        "extraInfo": ""
-      },
-      {
-        "name": "fusedDepthwiseConv2d__op",
-        "bytesAdded": 1605632,
-        "totalBytesSnapshot": 17769772,
-        "tensorsAdded": 1,
-        "totalTensorsSnapshot": 111,
-        "inputShapes": [
-          [
-            1,
-            112,
-            112,
-            32
-          ],
-          [
-            3,
-            3,
-            32,
-            1
-          ],
-          [
-            32
-          ]
-        ],
-        "outputShapes": [
-          [
-            1,
-            112,
-            112,
-            32
-          ]
-        ],
-        "kernelTimeMs": 8.200000003853347,
-        "extraInfo": ""
-      },
-      {
-        "name": "fusedDepthwiseConv2d__op",
-        "bytesAdded": 1806336,
-        "totalBytesSnapshot": 18472236,
-        "tensorsAdded": 1,
-        "totalTensorsSnapshot": 112,
-        "inputShapes": [
-          [
-            1,
-            56,
-            56,
-            144
-          ],
-          [
-            3,
-            3,
-            144,
-            1
-          ],
-          [
-            144
-          ]
-        ],
-        "outputShapes": [
-          [
-            1,
-            56,
-            56,
-            144
-          ]
-        ],
-        "kernelTimeMs": 7.769999996526167,
-        "extraInfo": ""
-      },
-      {
-        "name": "FusedConv2D",
-        "bytesAdded": 1605632,
-        "totalBytesSnapshot": 16766252,
-        "tensorsAdded": 1,
-        "totalTensorsSnapshot": 111,
-        "inputShapes": [
-          [
-            1,
-            224,
-            224,
-            3
-          ],
-          [
-            3,
-            3,
-            3,
-            32
-          ],
-          [
-            32
-          ],
-          null
-        ],
-        "outputShapes": [
-          [
-            1,
-            112,
-            112,
-            32
-          ]
-        ],
-        "kernelTimeMs": 7.579999997687992,
-        "extraInfo": ""
-      },
-      {
-        "name": "fusedConv2d__op",
-        "bytesAdded": 1806336,
-        "totalBytesSnapshot": 16665900,
-        "tensorsAdded": 1,
-        "totalTensorsSnapshot": 111,
-        "inputShapes": [
-          [
-            1,
-            56,
-            56,
-            24
-          ],
-          [
-            1,
-            1,
-            24,
-            144
-          ],
-          [
-            144
-          ]
-        ],
-        "outputShapes": [
-          [
-            1,
-            56,
-            56,
-            144
-          ]
-        ],
-        "kernelTimeMs": 7.515000004786998,
-        "extraInfo": ""
-      },
-      {
-        "name": "fusedConv2d__op",
-        "bytesAdded": 250880,
-        "totalBytesSnapshot": 14872108,
-        "tensorsAdded": 1,
-        "totalTensorsSnapshot": 111,
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-          [
-            1,
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-            320,
-            1280
-          ],
-          [
-            1280
-          ]
-        ],
-        "outputShapes": [
-          [
-            1,
-            7,
-            7,
-            1280
-          ]
-        ],
-        "kernelTimeMs": 7.265000000188593,
-        "extraInfo": ""
-      },
-      {
-        "name": "FusedDepthwiseConv2D",
-        "bytesAdded": 1605632,
-        "totalBytesSnapshot": 17769772,
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-        "totalTensorsSnapshot": 111,
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-          [
-            3,
-            3,
-            32,
-            1
-          ],
-          [
-            32
-          ],
-          null
-        ],
-        "outputShapes": [
-          [
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-            112,
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-        ],
-        "kernelTimeMs": 7.149999997636769,
-        "extraInfo": ""
-      },
-      {
-        "name": "FusedConv2D",
-        "bytesAdded": 250880,
-        "totalBytesSnapshot": 14872108,
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-            320,
-            1280
-          ],
-          [
-            1280
-          ],
-          null
-        ],
-        "outputShapes": [
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-            1,
-            7,
-            7,
-            1280
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-        ],
-        "kernelTimeMs": 6.990000001678709,
-        "extraInfo": ""
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-        "bytesAdded": 1204224,
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-        "kernelTimeMs": 6.560000001627486,
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-        "kernelTimeMs": 0.029999995604157448,
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-        ],
-        "kernelTimeMs": 0.019999999494757503,
-        "extraInfo": ""
-      }
-    ],
-    "kernelNames": [
-      "fusedConv2d__op",
-      "FusedConv2D",
-      "fusedDepthwiseConv2d__op",
-      "FusedDepthwiseConv2D",
-      "Sub",
-      "Multiply",
-      "Add",
-      "AvgPool",
-      "Cast",
-      "Identity",
-      "Reshape"
-    ],
-    "aggregatedKernels": [
-      {
-        "name": "fusedConv2d__op",
-        "timeMs": 139.04500002536224
-      },
-      {
-        "name": "FusedConv2D",
-        "timeMs": 120.01000002055662
-      },
-      {
-        "name": "fusedDepthwiseConv2d__op",
-        "timeMs": 41.514999997161794
-      },
-      {
-        "name": "FusedDepthwiseConv2D",
-        "timeMs": 33.85000002162997
-      },
-      {
-        "name": "Sub",
-        "timeMs": 0.9400000053574331
-      },
-      {
-        "name": "Multiply",
-        "timeMs": 0.9399999980814755
-      },
-      {
-        "name": "Add",
-        "timeMs": 0.8899999957066029
-      },
-      {
-        "name": "AvgPool",
-        "timeMs": 0.1049999991664663
-      },
-      {
-        "name": "Cast",
-        "timeMs": 0.030000002880115062
-      },
-      {
-        "name": "Identity",
-        "timeMs": 0.029999995604157448
-      },
-      {
-        "name": "Reshape",
-        "timeMs": 0.019999999494757503
-      }
-    ]
-  },
-  "deviceInfo": {
-    "base": "BrowserStack",
-    "browser": "chrome",
-    "browser_version": "103.0",
-    "device": null,
-    "os": "OS X",
-    "os_version": "Monterey"
-  },
-  "tabId": "OS_X_Catalina_1",
-  "gpuInfo": "ANGLE (ATI Technologies Inc., AMD Radeon Pro 5300M OpenGL Engine, OpenGL 4.1)"
-}
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/index.html b/tfjs-master/e2e/benchmarks/browserstack-benchmark/index.html
deleted file mode 100644
index 4f6010e41..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/index.html
+++ /dev/null
@@ -1,30 +0,0 @@
-<!-- Copyright 2019 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-
-<!doctype html>
-<html>
-    <head>
-        <link href="./main.css" rel="stylesheet">
-        <script src="https://cdnjs.cloudflare.com/ajax/libs/dat-gui/0.7.7/dat.gui.min.js"></script>
-        <script src='/socket.io/socket.io.js'></script>
-        <script src="./node_modules/@tensorflow/tfjs/dist/tf.min.js"> </script>
-        <script>window.tf || document.write('<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"><\/script>')</script>
-        <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis"></script>
-    </head>
-    <body onload="onPageLoad()">
-        <script src='/../model_config.js'></script>
-        <script src='./index.js'></script>
-    </body>
-</html>
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/index.js b/tfjs-master/e2e/benchmarks/browserstack-benchmark/index.js
deleted file mode 100644
index bf5ee9574..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/index.js
+++ /dev/null
@@ -1,735 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const TUNABLE_BROWSER_FIELDS =
-  ['os', 'os_version', 'browser', 'browser_version', 'device'];
-const WAITING_STATUS_COLOR = '#AAAAAA';
-const COMPLETE_STATUS_COLOR = '#357edd';
-const ERROR_STATUS_COLOR = '#e8564b';
-const DISABLED_BUTTON_OPACITY = 0.8;
-const ENABLED_BUTTON_OPACITY = 1;
-
-/**
- * Helps assign unique id for visor tabs.
- * @type {Object<string, number>}
- */
-const visorTabNameCounter = {};
-
-const state = {
-  isVisorInitiated: false,
-  isDatGuiHidden: false,
-
-  // The following `browser` and `benchmark` fields are kept updated by dat.gui.
-  browser: {
-    base: 'BrowserStack',
-    browser: 'chrome',
-    browser_version: '103.0',
-    os: 'OS X',
-    os_version: 'Monterey',
-    device: 'null'
-  },
-  benchmark: {
-    model: 'mobilenet_v2',
-    modelUrl: '',
-    numRuns: 10,
-    backend: 'webgl',
-    setupCodeSnippetEnv:
-      'const img = tf.randomUniform([1, 240, 240, 3], 0, 1000); const filter = tf.randomUniform([3, 3, 3, 3], 0, 1000);',
-    codeSnippet:
-      'predict = () => { return tf.conv2d(img, filter, 2, \'same\');};'
-  },
-
-  /**
-   * An array of browser configurations, used to record the browsers to
-   * benchmark in this round.
-   * @type {!Array<!Object<string, string>>}
-   */
-  browsers: [],
-
-  /**
-   * The id for the visor tab that is used to show the summary for the incoming
-   * round of benchmark (has not started).
-   * @type {string}
-   */
-  summaryTabId: getTabId(),
-
-  addBrowser: () => {
-    // Add browser config to `state.browsers` array.
-    state.browsers.push({ ...state.browser });
-
-    // Enable the benchmark button.
-    benchmarkButton.__li.style.pointerEvents = '';
-    benchmarkButton.__li.style.opacity = ENABLED_BUTTON_OPACITY;
-
-    // Initialize tfvis, if it is the first call.
-    initVisor();
-    // (Re-)draw the browser list table, based on the current browsers.
-    drawTunableBrowserSummaryTable(state.summaryTabId, state.browsers);
-  },
-
-  removeBrowser: index => {
-    if (index >= state.browsers.length) {
-      throw new Error(
-        `Invalid index ${index}, while the state.browsers only ` +
-        `has ${state.browsers.length} items.`);
-    }
-
-    // Remove the browser from the `state.browsers` array.
-    state.browsers.splice(index, 1);
-
-    if (state.browsers.length === 0) {
-      // Disable the benchmark button.
-      benchmarkButton.__li.style.pointerEvents = 'none';
-      benchmarkButton.__li.style.opacity = DISABLED_BUTTON_OPACITY;
-    }
-
-    // Re-draw the browser list table, based on the current browsers.
-    drawTunableBrowserSummaryTable(state.summaryTabId, state.browsers);
-  },
-
-  clearBrowsers: () => {
-    state.browsers.splice(0, state.browsers.length);
-
-    // Disable the benchmark button.
-    benchmarkButton.__li.style.pointerEvents = 'none';
-    benchmarkButton.__li.style.opacity = DISABLED_BUTTON_OPACITY;
-  },
-
-  run: () => {
-    // Disable the 'Add browser' button.
-    addingBrowserButton.__li.style.pointerEvents = 'none';
-    addingBrowserButton.__li.style.opacity = DISABLED_BUTTON_OPACITY;
-
-    // Initialize tfvis, if it is the first call.
-    initVisor();
-
-    // Build 'tabId - browser config' map (one of benchmark config arguments).
-    const browserTabIdConfigMap = {};
-    state.browsers.forEach(browser => {
-      const tabId = createTab(browser);
-      if (browser.device === 'null') {
-        browser.device = null;
-      }
-      browserTabIdConfigMap[tabId] = browser;
-    });
-
-    const benchmark = { ...state.benchmark };
-    if (state.benchmark.model !== 'custom') {
-      delete benchmark['modelUrl'];
-    }
-
-    // Send the configuration object to the server to start the benchmark.
-    socket.emit('run', {
-      summaryTabId: state.summaryTabId,
-      benchmark,
-      browsers: browserTabIdConfigMap,
-    });
-
-    // Re-draw the browser list table (untunable) with tabId.
-    drawUntunableBrowserSummaryTable(state.summaryTabId, browserTabIdConfigMap);
-
-    // Prepare for the next round benchmark.
-    state.summaryTabId = getTabId();
-    state.clearBrowsers();
-  }
-};
-
-let socket;
-
-// UI controllers.
-let gui;
-let browserFolder;
-let benchmarkButton;
-let addingBrowserButton;
-let browserSettingControllers = [];
-
-// Available BrowserStack's browsers will be collected in a tree when the array
-// of available browsers is recieved in runtime.
-let browserTreeRoot;
-
-/**
- * Collect all given browsers to a tree. The field of each level is defined by
- * `TUNABLE_BROWSER_FIELDS`.
- *
- * The tree node is implemented by Map/Object:
- * - For non-leaf nodes, each node stores a map: each key is the index to a
- * child node and the correspoding value is the child node.
- * - For leaf nodes, each leaf node stores the full configuration for a certain
- * browser.
- *
- * @param {Array<object>} browsersArray An array of browser configurations.
- */
-function constructBrowserTree(browsersArray) {
-  const browserTreeRoot = {};
-  browsersArray.forEach(browser => {
-    let currentNode = browserTreeRoot;
-
-    // Route through non-leaf nodes.
-    for (let fieldIndex = 0; fieldIndex <= TUNABLE_BROWSER_FIELDS.length - 2;
-      fieldIndex++) {
-      const fieldName = TUNABLE_BROWSER_FIELDS[fieldIndex];
-      if (currentNode[browser[fieldName]] == null) {
-        currentNode[browser[fieldName]] = {};
-      }
-      currentNode = currentNode[browser[fieldName]];
-    }
-
-    // Set the full configuration as the leaf node.
-    const leafFieldName =
-      TUNABLE_BROWSER_FIELDS[TUNABLE_BROWSER_FIELDS.length - 1];
-    const leafFieldValue = browser[leafFieldName];
-    if (currentNode[leafFieldValue] == null) {
-      currentNode[leafFieldValue] = browser;
-    } else {
-      console.warn(
-        `The browser ${browser} shares the same ` +
-        'configuration with another browser.');
-    }
-  });
-  return browserTreeRoot;
-}
-
-/**
- * Once the value of a certain browser field is changed, the values and options
- * of the following fields will be invalid. This function updates the following
- * fields recursively and does nothing for the leaf nodes.
- *
- * @param {number} currentFieldIndex
- * @param {string} currentFieldValue
- * @param {object} currentNode
- */
-function updateFollowingFields(
-  currentFieldIndex, currentFieldValue, currentNode) {
-  const nextFieldIndex = currentFieldIndex + 1;
-  if (nextFieldIndex === TUNABLE_BROWSER_FIELDS.length) {
-    return;
-  }
-
-  const nextFieldName = TUNABLE_BROWSER_FIELDS[nextFieldIndex];
-  const nextNode = currentNode[currentFieldValue];
-  const nextFieldAvailableValues = Object.keys(nextNode);
-  let nextFieldValue = state.browser[nextFieldName];
-
-  // Update the value of the next field, if the old value is not applicable.
-  if (nextNode[nextFieldValue] == null) {
-    nextFieldValue = nextFieldAvailableValues[0];
-  }
-
-  // Update the options for the next field.
-  const nextFieldController = browserSettingControllers[nextFieldIndex].options(
-    nextFieldAvailableValues);
-
-  // When updating options for a dat.gui controller, a new controller instacne
-  // will be created, so we need to bind the event again and record the new
-  // controller.
-  nextFieldController.onFinishChange(() => {
-    const newValue = state.browser[nextFieldName];
-    updateFollowingFields(nextFieldIndex, newValue, nextNode);
-  });
-  browserSettingControllers[nextFieldIndex] = nextFieldController;
-
-  nextFieldController.setValue(nextFieldValue);
-
-  if (nextFieldValue === 'null') {
-    nextFieldController.__li.hidden = true;
-  } else {
-    nextFieldController.__li.hidden = false;
-  }
-
-  updateFollowingFields(nextFieldIndex, nextFieldValue, nextNode);
-}
-
-/**
- * This is a wrapper function of `dat.gui.GUI.add()` with:
- * - Binds the `finishChange` event to update the value and options for the
- * controller of its child field.
- * - Hides the dropdown menu, if the field is not applicable for this browser.
- *
- * @param {number} fieldIndex The index of the browser field to be shown.
- * @param {object} currentNode The keys of this map are available values
- *     for this field.
- */
-function showBrowserField(fieldIndex, currentNode) {
-  const fieldName = TUNABLE_BROWSER_FIELDS[fieldIndex];
-  const fieldController =
-    browserFolder.add(state.browser, fieldName, Object.keys(currentNode));
-
-  fieldController.onFinishChange(() => {
-    const newValue = state.browser[fieldName];
-    updateFollowingFields(fieldIndex, newValue, currentNode);
-  });
-
-  // Null represents the field is not applicable for this browser. For example,
-  // `browser_version` is normally not applicable for mobile devices.
-  if (state.browser[fieldName] === 'null') {
-    fieldController.__li.hidden = true;
-  }
-
-  // The controller will be used to reset options when the value of its parent
-  // field is changed.
-  browserSettingControllers.push(fieldController);
-}
-
-/**
- * Create a tunable browser list table that can be used to remove browsers.
- *
- * This table is shown before benchmarking.
- *
- * @param {string} summaryTabId The summary tab id.
- * @param {Array<!Object<string, string>>} browsers A list of browsers to
- *     benchmark. The browser object should include fields in
- *     `TUNABLE_BROWSER_FIELDS`.
- */
-function drawTunableBrowserSummaryTable(summaryTabId, browsers) {
-  const headers = [...TUNABLE_BROWSER_FIELDS, ''];
-  const values = [];
-
-  for (let index = 0; index < browsers.length; index++) {
-    const browser = browsers[index];
-    const row = [];
-    for (const fieldName of TUNABLE_BROWSER_FIELDS) {
-      if (browser[fieldName] == null || browser[fieldName] === 'null') {
-        row.push('-');
-      } else {
-        row.push(browser[fieldName]);
-      }
-    }
-
-    // Whenever a browser configuration is removed, this table will be re-drawn,
-    // so the index (the argument for state.removeBrowser) will be re-assigned.
-    const removeBrowserButtonElement =
-      `<button onclick="state.removeBrowser(${index})">Remove</button>`;
-    row.push(removeBrowserButtonElement);
-
-    values.push(row);
-  }
-
-  const surface = {
-    name: 'Browsers to benchmark',
-    tab: summaryTabId,
-    styles: { width: '100%' }
-  };
-  tfvis.render.table(surface, { headers, values });
-}
-
-/**
- * Create a untunable browser list table, including tab ids. The tab id will be
- * used in the following charts under this summary tab.
- *
- * This table is shown when it is benchmarking or the benchmark is complete.
- *
- * @param {string} summaryTabId The summary tab id.
- * @param {!Object<string, !Object<string, string>>} browserTabIdConfigMap A map
- *     of ' tabId - browser' pairs.
- */
-function drawUntunableBrowserSummaryTable(summaryTabId, browserTabIdConfigMap) {
-  const headers = ['tabId', ...TUNABLE_BROWSER_FIELDS];
-  const values = [];
-  for (const browserTabId in browserTabIdConfigMap) {
-    const browser = browserTabIdConfigMap[browserTabId];
-    const row = [browserTabId];
-    for (const fieldName of TUNABLE_BROWSER_FIELDS) {
-      row.push(browser[fieldName] || '-');
-    }
-    values.push(row);
-  }
-
-  const surface = {
-    name: 'Browsers to benchmark',
-    tab: summaryTabId,
-    styles: { width: '100%' }
-  };
-  tfvis.render.table(surface, { headers, values });
-}
-
-function initVisor() {
-  if (state.isVisorInitiated) {
-    return;
-  }
-  state.isVisorInitiated = true;
-
-  // Bind an event to visor's 'Maximize/Minimize' button.
-  const visorFullScreenButton =
-    tfvis.visor().el.getElementsByTagName('button')[0];
-  const guiCloseButton = document.getElementsByClassName('close-button')[0];
-  const originalGuiWidth = gui.domElement.style.width;
-
-  // The following two bound events are to implemet:
-  // - When the visor is minimized, the controlled panel is hidden;
-  // - When the visor is maximized, the controlled panel appears;
-  gui.domElement.style.width = originalGuiWidth;
-  visorFullScreenButton.onclick = () => {
-    if (state.isDatGuiHidden) {
-      // When opening the controll panel, recover the size.
-      gui.open();
-      gui.domElement.style.width = originalGuiWidth;
-    } else {
-      // When closing the controll panel, narrow the size.
-      gui.close();
-      gui.domElement.style.width = '10%';
-    }
-    state.isDatGuiHidden = !state.isDatGuiHidden;
-  };
-  guiCloseButton.onclick = () => {
-    if (state.isDatGuiHidden) {
-      // When opening the controll panel, recover the size.
-      gui.domElement.style.width = originalGuiWidth;
-    } else {
-      // When closing the controll panel, narrow the size.
-      gui.domElement.style.width = '10%';
-    }
-    tfvis.visor().toggleFullScreen();
-    state.isDatGuiHidden = !state.isDatGuiHidden;
-  };
-
-  // If this button (hide visor) is exposed, then too much extra logics will be
-  // needed to tell the full story.
-  const visorHideButton = tfvis.visor().el.getElementsByTagName('button')[1];
-  visorHideButton.style.display = 'none';
-}
-
-/**
- *  Generate a unique id/name for the given setting. tfvis uses tab name as the
- * index for the tab.
- *
- * @param {?Object<string, string>=} browserConf An object including fields in
- *     `TUNABLE_BROWSER_FIELDS`.
- */
-function getTabId(browserConf) {
-  let baseName;
-  if (browserConf == null) {
-    // The tab is a summary tab.
-    baseName = 'Summary';
-  } else if (browserConf.os === 'android' || browserConf.os === 'ios') {
-    // For mobile devices.
-    baseName = browserConf.device;
-  } else {
-    baseName = `${browserConf.os}_${browserConf.os_version}`;
-  }
-  baseName = baseName.split(' ').join('_');
-  if (visorTabNameCounter[baseName] == null) {
-    visorTabNameCounter[baseName] = 0;
-  }
-  visorTabNameCounter[baseName] += 1;
-  return `${baseName}_${visorTabNameCounter[baseName]}`;
-}
-
-function createTab(browserConf) {
-  const tabId = getTabId(browserConf);
-
-  // For tfvis, the tab name is not only a name but also the index to the tab.
-  drawBrowserSettingTable(tabId, browserConf);
-  drawBenchmarkParameterTable(tabId);
-
-  // Add a status indicator into the tab button.
-  const visorTabList = document.getElementsByClassName('tf-tab');
-  const curTabElement = visorTabList[visorTabList.length - 1];
-  const indicatorElement = document.createElement('span');
-  indicatorElement.innerHTML = '.';
-  indicatorElement.style.fontSize = '20px';
-  indicatorElement.id = `${tabId}-indicator`;
-  curTabElement.appendChild(indicatorElement);
-
-  setTabStatus(tabId, 'WAITING');
-  addLoaderElement(tabId);
-  return tabId;
-}
-
-function reportBenchmarkResult(benchmarkResult) {
-  const tabId = benchmarkResult.tabId;
-  removeLoaderElement(tabId);
-
-  if (benchmarkResult.error != null) {
-    setTabStatus(tabId, 'ERROR');
-    // TODO: show error message under the tab.
-    console.log(benchmarkResult.error);
-  } else {
-    setTabStatus(tabId, 'COMPLETE');
-    drawInferenceTimeLineChart(benchmarkResult);
-    drawBenchmarkResultSummaryTable(benchmarkResult);
-    // TODO: draw a table for inference kernel information.
-    // This will be done, when we can get kernel timing info from
-    // `tf.profile()`.
-  }
-}
-
-/**
- * Set the status for the given tab. The status can be 'WAITING', 'COMPLETE' or
- * 'ERROR'.
- *
- * @param {string} tabId  The index element id of the tab.
- * @param {string} status The status to be set for the tab.
- */
-function setTabStatus(tabId, status) {
-  const indicatorElementId = `${tabId}-indicator`;
-  const indicatorElement = document.getElementById(indicatorElementId);
-  switch (status) {
-    case 'WAITING':
-      indicatorElement.style.color = WAITING_STATUS_COLOR;
-      break;
-    case 'COMPLETE':
-      indicatorElement.style.color = COMPLETE_STATUS_COLOR;
-      break;
-    case 'ERROR':
-      indicatorElement.style.color = ERROR_STATUS_COLOR;
-      break;
-    default:
-      throw new Error(`Undefined status: ${status}.`);
-  }
-}
-
-/**
- * Add a loader element under the tab page.
- *
- * @param {string} tabId
- */
-function addLoaderElement(tabId) {
-  const surface = tfvis.visor().surface(
-    { name: 'Benchmark Summary', tab: tabId, styles: { width: '100%' } });
-  const loaderElement = document.createElement('div');
-  loaderElement.className = 'loader';
-  loaderElement.id = `${tabId}-loader`;
-  surface.drawArea.appendChild(loaderElement);
-}
-
-/**
- * Remove the loader element under the tab page.
- *
- * @param {string} tabId
- */
-function removeLoaderElement(tabId) {
-  const loaderElementId = `${tabId}-loader`;
-  const loaderElement = document.getElementById(loaderElementId);
-  if (loaderElement != null) {
-    loaderElement.remove();
-  }
-}
-
-function drawBenchmarkResultSummaryTable(benchmarkResult) {
-  const headers = ['Field', 'Value'];
-  const values = [];
-
-  const { timeInfo, memoryInfo, tabId } = benchmarkResult;
-  const timeArray = benchmarkResult.timeInfo.times;
-  const numRuns = timeArray.length;
-
-  if (numRuns >= 1) {
-    values.push(['1st inference time', printTime(timeArray[0])]);
-    if (numRuns >= 2) {
-      values.push(['2nd inference time', printTime(timeArray[1])]);
-    }
-    values.push([
-      `Average inference time (${numRuns} runs) except the first`,
-      printTime(timeInfo.averageTimeExclFirst)
-    ]);
-    values.push(['Best time', printTime(timeInfo.minTime)]);
-    values.push(['Worst time', printTime(timeInfo.maxTime)]);
-  }
-
-  values.push(['Peak memory', printMemory(memoryInfo.peakBytes)]);
-  values.push(['Leaked tensors', memoryInfo.newTensors]);
-
-  values.push(['Number of kernels', memoryInfo.kernels.length]);
-
-  if ('codeSnippet' in benchmarkResult) {
-    values.push(['Code snippet', benchmarkResult.codeSnippet]);
-  }
-
-  const surface = {
-    name: 'Benchmark Summary',
-    tab: tabId,
-    styles: { width: '100%' }
-  };
-  tfvis.render.table(surface, { headers, values });
-}
-
-async function drawInferenceTimeLineChart(benchmarkResult) {
-  const inferenceTimeArray = benchmarkResult.timeInfo.times;
-  if (inferenceTimeArray.length <= 2) {
-    return;
-  }
-
-  const tabId = benchmarkResult.tabId;
-  const values = [];
-  inferenceTimeArray.forEach((time, index) => {
-    // The first inference time is much larger than other times for webgl,
-    // skewing the scaling, so it is removed from the line chart.
-    if (index === 0) {
-      return;
-    }
-    values.push({ x: index + 1, y: time });
-  });
-
-  const surface = {
-    name: `2nd - ${inferenceTimeArray.length}st Inference Time`,
-    tab: tabId,
-    styles: { width: '100%' }
-  };
-  const data = { values };
-  const drawOptions =
-    { zoomToFit: true, xLabel: '', yLabel: 'time (ms)', xType: 'ordinal' };
-
-  await tfvis.render.linechart(surface, data, drawOptions);
-
-  // Whenever resize the parent div element, re-draw the chart canvas.
-  try {
-    const originalCanvasHeight = tfvis.visor()
-      .surface(surface)
-      .drawArea.getElementsByTagName('canvas')[0]
-      .height;
-    const labelElement = tfvis.visor().surface(surface).label;
-
-    new ResizeObserver(() => {
-      // Keep the height of chart/canvas unchanged.
-      tfvis.visor()
-        .surface(surface)
-        .drawArea.getElementsByTagName('canvas')[0]
-        .height = originalCanvasHeight;
-      tfvis.render.linechart(surface, data, drawOptions);
-    }).observe(labelElement);
-  } catch (e) {
-    console.warn(`The browser does not support the ResizeObserver API: ${e}`);
-  }
-}
-
-function drawBrowserSettingTable(tabId, browserConf) {
-  const headers = ['Field', 'Value'];
-  const values = [];
-  for (const fieldName of TUNABLE_BROWSER_FIELDS) {
-    if (browserConf[fieldName] != null && browserConf[fieldName] !== 'null') {
-      const row = [fieldName, browserConf[fieldName]];
-      values.push(row);
-    }
-  }
-  const surface = {
-    name: 'Browser Setting',
-    tab: tabId,
-    styles: { width: '100%' }
-  };
-  tfvis.render.table(surface, { headers, values });
-}
-
-function drawBenchmarkParameterTable(tabId) {
-  const headers = ['Field', 'Value'];
-  const values = [];
-
-  for (const entry of Object.entries(state.benchmark)) {
-    if (entry[1] != null && entry[1] !== '') {
-      values.push(entry);
-    }
-  }
-
-  const surface = {
-    name: 'Benchmark Parameter',
-    tab: tabId,
-    styles: { width: '100%' }
-  };
-  tfvis.render.table(surface, { headers, values });
-}
-
-function showModelSelection() {
-  const modelFolder = gui.addFolder('Model');
-  let modelUrlController = null;
-
-  modelFolder
-    .add(
-      state.benchmark, 'model', [...Object.keys(benchmarks), 'codeSnippet'])
-    .name('model name')
-    .onChange(async model => {
-      if (model === 'custom') {
-        if (modelUrlController === null) {
-          modelUrlController = modelFolder.add(state.benchmark, 'modelUrl');
-          modelUrlController.domElement.querySelector('input').placeholder =
-            'https://your-domain.com/model-path/model.json';
-        }
-      } else if (modelUrlController != null) {
-        modelFolder.remove(modelUrlController);
-        modelUrlController = null;
-      }
-    });
-  modelFolder.open();
-  return modelFolder;
-}
-
-function showParameterSettings() {
-  const parameterFolder = gui.addFolder('Parameters');
-  parameterFolder.add(state.benchmark, 'numRuns');
-  parameterFolder.add(state.benchmark, 'backend', ['wasm', 'webgl', 'cpu']);
-  parameterFolder.open();
-  return parameterFolder;
-}
-
-function printTime(elapsed) {
-  return `${elapsed.toFixed(1)} ms`;
-}
-
-function printMemory(bytes) {
-  if (bytes < 1024) {
-    return `${bytes} B`;
-  } else if (bytes < 1024 * 1024) {
-    return `${(bytes / 1024).toFixed(2)} KB`;
-  } else {
-    return `${(bytes / (1024 * 1024)).toFixed(2)} MB`;
-  }
-}
-
-function onPageLoad() {
-  gui = new dat.gui.GUI({ width: 400 });
-  gui.domElement.id = 'gui';
-  socket = io();
-
-  // Once the server is connected, the server will send back all
-  // BrowserStack's available browsers in an array.
-  socket.on('availableBrowsers', availableBrowsersArray => {
-    if (browserTreeRoot == null) {
-      // Build UI folders.
-      showModelSelection();
-      showParameterSettings();
-      browserFolder = gui.addFolder('Browser');
-
-      // Initialize the browser tree.
-      browserTreeRoot = constructBrowserTree(availableBrowsersArray);
-
-      // Show browser settings.
-      let currentNode = browserTreeRoot;
-      TUNABLE_BROWSER_FIELDS.forEach((field, index) => {
-        showBrowserField(index, currentNode);
-        currentNode = currentNode[state.browser[field]];
-      });
-      browserFolder.open();
-
-      // Enable users to benchmark.
-      addingBrowserButton =
-        browserFolder.add(state, 'addBrowser').name('Add browser');
-      benchmarkButton = gui.add(state, 'run').name('Run benchmark');
-
-      // Disable the 'Run benchmark' button until a browser is added.
-      benchmarkButton.__li.style.pointerEvents = 'none';
-      benchmarkButton.__li.style.opacity = DISABLED_BUTTON_OPACITY;
-    }
-  });
-
-  socket.on('benchmarkComplete', benchmarkResult => {
-    // Enable the 'Add browser' button.
-    addingBrowserButton.__li.style.pointerEvents = '';
-    addingBrowserButton.__li.style.opacity = ENABLED_BUTTON_OPACITY;
-
-    reportBenchmarkResult(benchmarkResult);
-
-    // TODO: We can add a summary for the benchmark results in this round.
-  });
-}
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/index_test.js b/tfjs-master/e2e/benchmarks/browserstack-benchmark/index_test.js
deleted file mode 100644
index 0d1bf91ca..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/index_test.js
+++ /dev/null
@@ -1,142 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * The unit tests in this file can be run by opening `./SpecRunner.html` in
- * browser.
- */
-
-describe('constructBrowserTree', () => {
-  const mac = {
-    base: 'BrowserStack',
-    browser: 'firefox',
-    browser_version: '70.0',
-    os: 'OS X',
-    os_version: 'High Sierra',
-    device: 'null'
-  };
-  const iphoneX = {
-    base: 'BrowserStack',
-    browser: 'ios',
-    browser_version: 'null',
-    os: 'ios',
-    os_version: '11.0',
-    device: 'iPhone X'
-  };
-
-  const expectedTree = {
-    'OS X': {'High Sierra': {'firefox': {'70.0': {'null': mac}}}},
-    'ios': {'11.0': {'ios': {'null': {'iPhone X': iphoneX}}}}
-  };
-
-  it('constructs a tree', () => {
-    const browsersArray = [mac, iphoneX];
-    expect(constructBrowserTree(browsersArray)).toEqual(expectedTree);
-  });
-
-  it('warns when finding duplicate nodes', () => {
-    const browsersArray = [mac, iphoneX, iphoneX];
-    spyOn(console, 'warn');
-    expect(constructBrowserTree(browsersArray)).toEqual(expectedTree);
-    expect(console.warn).toHaveBeenCalled();
-  });
-});
-
-describe('getTabId', () => {
-  const mac = {
-    base: 'BrowserStack',
-    browser: 'firefox',
-    browser_version: '70.0',
-    os: 'OS X',
-    os_version: 'High Sierra',
-    device: 'null'
-  };
-  const iphoneX = {
-    base: 'BrowserStack',
-    browser: 'ios',
-    browser_version: 'null',
-    os: 'ios',
-    os_version: '11.0',
-    device: 'iPhone X'
-  };
-
-  it('gives different names for the same browser configs', () => {
-    const name1 = getTabId(mac);
-    const name2 = getTabId(mac);
-    expect(name1).not.toBe(name2);
-  });
-
-  it('for mobile devices, uses device name as part of the tab name', () => {
-    const mobileName = getTabId(iphoneX);
-    expect(mobileName).toContain('iPhone_X');
-  });
-
-  it('for desktop devices, uses OS name as part of the tab name', () => {
-    const desktopName = getTabId(mac);
-    expect(desktopName).toContain('OS_X');
-    expect(desktopName).toContain('High_Sierra');
-  });
-
-  it('assigns unique summary names for undefined config', () => {
-    expect(state.summaryTabId).toBe('Summary_1');
-    expect(getTabId()).toBe('Summary_2');
-    expect(getTabId()).toBe('Summary_3');
-  });
-});
-
-describe('state methods', () => {
-  beforeAll(() => {
-    this.originalInitVisor = initVisor;
-    this.originalDrawTunableBrowserSummaryTable =
-        drawTunableBrowserSummaryTable;
-    initVisor = jasmine.createSpy();
-    drawTunableBrowserSummaryTable = jasmine.createSpy();
-  });
-
-  beforeEach(() => {
-    gui = new dat.gui.GUI();
-    benchmarkButton = gui.add(state, 'run').name('Run benchmark');
-  });
-
-  afterEach(() => {
-    gui.destroy();
-  });
-
-  afterAll(() => {
-    initVisor = this.originalInitVisor;
-    drawTunableBrowserSummaryTable =
-        this.originalDrawTunableBrowserSummaryTable;
-  });
-
-  it(`enables 'Run benchmark' button, when adding the first browser'`, () => {
-    state.addBrowser();
-
-    expect(benchmarkButton.__li.style.pointerEvents).toBe('');
-    expect(benchmarkButton.__li.style.opacity)
-        .toBe(ENABLED_BUTTON_OPACITY.toString());
-  });
-
-  it(`disables 'Run benchmark' button, when 'state.browsers' is empty'`, () => {
-    state.addBrowser();
-    state.addBrowser();
-    state.clearBrowsers();
-
-    expect(benchmarkButton.__li.style.pointerEvents).toBe('none');
-    expect(benchmarkButton.__li.style.opacity)
-        .toBe(DISABLED_BUTTON_OPACITY.toString());
-  });
-});
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/karma.conf.js b/tfjs-master/e2e/benchmarks/browserstack-benchmark/karma.conf.js
deleted file mode 100644
index cdff8cd25..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/karma.conf.js
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const localRunConfig = {
-  reporters: ['progress'],
-  plugins: ['karma-jasmine', 'karma-chrome-launcher'],
-  browsers: ['Chrome']
-};
-
-function getBrowserStackConfig() {
-  const browserstackConfig = {
-    hostname: 'bs-local.com',
-    plugins: ['karma-jasmine', 'karma-browserstack-launcher'],
-    reporters: ['progress', 'BrowserStack'],
-    port: 9200,
-    browserStack: {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_ACCESS_KEY,
-      apiClientEndpoint: 'https://api.browserstack.com',
-      tunnelIdentifier: `e2e_browserstack_benchmark_${Date.now()}_${
-          Math.floor(Math.random() * 1000)}`
-    },
-    customLaunchers: {},
-    browsers: []
-  };
-
-  // The JSON file `./browsers.json` stores the `customLaunchers`. The key is
-  // tabId, while the value is specific browser setting.
-  const browsers = require('./browsers.json');
-  browserstackConfig.customLaunchers = browsers;
-  return browserstackConfig;
-}
-
-module.exports = function(config) {
-  let extraConfig = null;
-  if (config.browserstack) {
-    extraConfig = getBrowserStackConfig();
-  } else {
-    extraConfig = localRunConfig;
-  }
-
-  let localBuildableFiles = [
-    'tfjs-core/dist/tf-core.js',
-    'tfjs-backend-cpu/dist/tf-backend-cpu.js',
-    'tfjs-backend-webgl/dist/tf-backend-webgl.js',
-    'tfjs-layers/dist/tf-layers.js',
-    'tfjs-converter/dist/tf-converter.js',
-    'tfjs-backend-wasm/dist/tf-backend-wasm.js',
-    'tfjs-automl/dist/tf-automl.js',
-  ].map(url => {
-    let name = url.split('/')[0].replace('tfjs-', '').replace('backend-', '');
-    if (config.localBuild?.includes(name)) {
-      return `../../../dist/bin/${url}`;
-    } else {
-      if (config.npmVersion){
-        let version = config.npmVersion;
-        return `https://unpkg.com/@tensorflow/${url.replace('/', '@' + version + '/')}`;
-      } else {
-        return `https://unpkg.com/@tensorflow/${url.replace('/', '@latest/')}`;
-      }
-    }
-  });
-
-  let files = localBuildableFiles.concat([
-    'https://cdn.jsdelivr.net/npm/@tensorflow-models/universal-sentence-encoder@latest/dist/universal-sentence-encoder.min.js',
-    'https://cdn.jsdelivr.net/npm/@tensorflow-models/speech-commands@latest/dist/speech-commands.min.js',
-    'https://cdn.jsdelivr.net/npm/@tensorflow-models/posenet@latest/dist/posenet.min.js',
-    'https://cdn.jsdelivr.net/npm/@tensorflow-models/body-pix@latest/dist/body-pix.min.js',
-    {pattern: './benchmark_parameters.json', included: false, served: true},
-    '../util.js', '../benchmark_util.js', '../model_config.js',
-    'benchmark_models.js'
-  ]);
-
-  config.set({
-    ...extraConfig,
-    frameworks: ['jasmine'],
-    files: files,
-    preprocessors: {},
-    singleRun: true,
-    captureTimeout: 3e5,
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 1.2e5,
-    browserDisconnectTimeout: 1.2e5,
-    browserDisconnectTolerance: 0,
-    browserSocketTimeout: 1.2e5,
-    client: {jasmine: {random: false}},
-
-    // The following configurations are generated by karma
-    colors: true,
-    logLevel: config.LOG_INFO,
-    autoWatch: false,
-    concurrency: Infinity
-  })
-}
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/main.css b/tfjs-master/e2e/benchmarks/browserstack-benchmark/main.css
deleted file mode 100644
index 586054173..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/main.css
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
-* @license
-* Copyright 2020 Google LLC. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 (the "License");
-* you may not use this file except in compliance with the License.
-* You may obtain a copy of the License at
-*
-* http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing, software
-* distributed under the License is distributed on an "AS IS" BASIS,
-* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-* See the License for the specific language governing permissions and
-* limitations under the License.
-* =============================================================================
-*/
-
-.loader {
-  animation: spin 2s linear infinite;
-  -webkit-animation: spin 2s linear infinite; /* Safari */
-  border: 5px solid #f3f3f3;
-  border-radius: 50%;
-  border-top: 5px solid #357edd;
-  height: 20px;
-  margin: auto;
-  width: 20px;
-}
-
-/* Safari */
-@-webkit-keyframes spin {
-  0% { -webkit-transform: rotate(0deg); }
-  100% { -webkit-transform: rotate(360deg); }
-}
-
-@keyframes spin {
-  0% { transform: rotate(0deg); }
-  100% { transform: rotate(360deg); }
-}
-
-#gui {
-  left: 0px;
-  position: absolute;
-  top: 2px;
-}
-
-th {
-  width: 50% !important;
-  text-align: center !important;
-}
-
-td {
-  width: 50% !important;
-  text-align: center !important;
-}
-
-.close-button{
-  width: 100% !important;
-}
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/package.json b/tfjs-master/e2e/benchmarks/browserstack-benchmark/package.json
deleted file mode 100644
index 5f208b47b..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/package.json
+++ /dev/null
@@ -1,50 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-benchmark",
-  "version": "0.0.1",
-  "description": "Benchmark models' and ops' performance",
-  "private": true,
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs"
-  },
-  "devDependencies": {
-    "@tensorflow/tfjs": "link:../../../tfjs",
-    "@tensorflow/tfjs-backend-wasm": "link:../../../link-package/node_modules/@tensorflow/tfjs-backend-wasm",
-    "argparse": "^2.0.1",
-    "firebase-admin": "^11.0.1",
-    "jasmine": "^3.7.0",
-    "karma": "^6.3.16",
-    "karma-browserstack-launcher": "^1.6.0",
-    "karma-chrome-launcher": "^3.1.0",
-    "karma-jasmine": "^3.3.1",
-    "socket.io": "~4.4.1"
-  },
-  "staticFiles": {
-    "staticPath": "./node_modules/@tensorflow/tfjs-backend-wasm/dist",
-    "excludeGlob": [
-      "**/!(*.wasm)"
-    ]
-  },
-  "scripts": {
-    "test": "karma start",
-    "test-node": "jasmine app_node_test.js",
-    "build-all-deps": "yarn build-all-link-packages && yarn build-tfjs",
-    "build-tfjs": "cd ../../../tfjs && yarn && yarn build-npm",
-    "build-all-link-packages": "cd ../../../ && yarn && cd ./link-package && yarn build",
-    "build-individual-link-package": "cd ../../../ && yarn && cd ./link-package && yarn build-deps-for",
-    "run-cloud-benchmarks": "node app.js --benchmark='./preconfigured_browser.json' --cloud --maxBenchmarks=12 --firestore",
-    "run-cloud-benchmarks-half-month-cycle": "yarn run-cloud-benchmarks --period=15"
-  },
-  "license": "Apache-2.0",
-  "engines": {
-    "yarn": ">= 1.0.0"
-  },
-  "resolutions": {
-    "node-fetch": "2.6.7",
-    "minimist": "1.2.6"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs-vis": "^1.5.1",
-    "JSONStream": "^1.3.5"
-  }
-}
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/preconfigured_browser.json b/tfjs-master/e2e/benchmarks/browserstack-benchmark/preconfigured_browser.json
deleted file mode 100644
index b216c0c55..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/preconfigured_browser.json
+++ /dev/null
@@ -1,151 +0,0 @@
-{
-  "benchmark": {
-    "model": ["MobileNetV3", "MobileNetV2", "HandPoseDetector",
-    "HandPoseLandmark", "MoveNet-SinglePose", "MoveNet-MultiPose",
-    "BlazePoseDetector", "BlazePoseLandmark", "Coco-SSD", "DeepLabV3",
-    "FaceDetection", "FaceLandmarkDetection", "ArPortraitDepth",
-    "SelfieSegmentation-General", "SelfieSegmentation-Landscape",
-    "AutoML Image", "AutoML Object", "USE - batchsize 30", "USE - batchsize 1",
-    "TextToxicity", "MobileBert", "posenet", "bodypix", "speech-commands"],
-    "numRuns": 10,
-    "backend": ["webgl"]
-  },
-  "browsers": {
-    "iPhone_13_Pro_Max_1": {
-      "base": "BrowserStack",
-      "os": "ios",
-      "os_version": "15",
-      "browser": "iphone",
-      "device": "iPhone 13 Pro Max",
-      "browser_version": null
-    },
-    "iPhone_13_1": {
-      "base": "BrowserStack",
-      "os": "ios",
-      "os_version": "15",
-      "browser": "iphone",
-      "device": "iPhone 13",
-      "browser_version": null
-    },
-    "iPhone_12_Pro_Max_1": {
-      "base": "BrowserStack",
-      "browser": "iphone",
-      "browser_version": null,
-      "os": "ios",
-      "os_version": "14",
-      "device": "iPhone 12 Pro Max"
-    },
-    "iPhone_12_1": {
-      "base": "BrowserStack",
-      "browser": "iphone",
-      "browser_version": null,
-      "os": "ios",
-      "os_version": "14",
-      "device": "iPhone 12"
-    },
-    "iPhone_11_Pro_Max_1": {
-      "base": "BrowserStack",
-      "browser": "iphone",
-      "browser_version": null,
-      "os": "ios",
-      "os_version": "14",
-      "device": "iPhone 11 Pro Max"
-    },
-    "iPhone_11_1": {
-      "base": "BrowserStack",
-      "browser": "iphone",
-      "browser_version": null,
-      "os": "ios",
-      "os_version": "14",
-      "device": "iPhone 11"
-    },
-    "iPhone_XS_1": {
-      "base": "BrowserStack",
-      "browser": "iphone",
-      "browser_version": null,
-      "os": "ios",
-      "os_version": "15",
-      "device": "iPhone XS"
-    },
-    "Google_Pixel_6_Pro_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "12.0",
-      "device": "Google Pixel 6 Pro"
-    },
-    "Google_Pixel_5_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "12.0",
-      "device": "Google Pixel 5"
-    },
-    "Samsung_Galaxy_S22_Ultra_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "12.0",
-      "device": "Samsung Galaxy S22 Ultra"
-    },
-    "Samsung_Galaxy_M52_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "11.0",
-      "device": "Samsung Galaxy M52"
-    },
-    "Samsung_Galaxy_A52_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "11.0",
-      "device": "Samsung Galaxy A52"
-    },
-    "Xiaomi_Redmi_Note_11_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "11.0",
-      "device": "Xiaomi Redmi Note 11"
-    },
-    "Samsung_Galaxy_Note_20_Ultra_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "10.0",
-      "device": "Samsung Galaxy Note 20 Ultra"
-    },
-    "Samsung_Galaxy_A11_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "10.0",
-      "device": "Samsung Galaxy A11"
-    },
-    "Google_Pixel_4_XL_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "10.0",
-      "device": "Google Pixel 4 XL"
-    },
-    "Samsung_Galaxy_S10_Plus_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": null,
-      "os": "android",
-      "os_version": "9.0",
-      "device": "Samsung Galaxy S10 Plus"
-    }
-  }
-}
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/promise_queue.js b/tfjs-master/e2e/benchmarks/browserstack-benchmark/promise_queue.js
deleted file mode 100644
index d8484fe52..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/promise_queue.js
+++ /dev/null
@@ -1,31 +0,0 @@
-class PromiseQueue {
-  queue = [];
-  running = 0;
-
-  constructor(concurrency = Infinity) {
-    this.concurrency = concurrency;
-  }
-
-  add(func) {
-    return new Promise((resolve, reject) => {
-      this.queue.push(async () => {
-	this.running++;
-	try {
-	  resolve(await func());
-	} finally {
-	  this.running--;
-	  this.run();
-	}
-      });
-      this.run();
-    });
-  }
-
-  run() {
-    while (this.running < this.concurrency && this.queue.length > 0) {
-      this.queue.shift()();
-    }
-  }
-}
-
-module.exports = {PromiseQueue};
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/test_config.json b/tfjs-master/e2e/benchmarks/browserstack-benchmark/test_config.json
deleted file mode 100644
index 7a040afad..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/test_config.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "summaryTabId": "Summary_1",
-  "benchmark": { "model": "face_detector", "numRuns": 10, "backend": "wasm" },
-  "browsers": {
-    "iPhone_12_1": {
-      "base": "BrowserStack",
-      "browser": "iphone",
-      "browser_version": "null",
-      "os": "ios",
-      "os_version": "14",
-      "device": "iPhone 12"
-    },
-    "Google_Pixel_5_1": {
-      "base": "BrowserStack",
-      "browser": "android",
-      "browser_version": "null",
-      "os": "android",
-      "os_version": "11.0",
-      "device": "Google Pixel 5"
-    }
-  }
-}
diff --git a/tfjs-master/e2e/benchmarks/browserstack-benchmark/yarn.lock b/tfjs-master/e2e/benchmarks/browserstack-benchmark/yarn.lock
deleted file mode 100644
index 43d9360ff..000000000
--- a/tfjs-master/e2e/benchmarks/browserstack-benchmark/yarn.lock
+++ /dev/null
@@ -1,3428 +0,0 @@
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-# yarn lockfile v1
-
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-  version "7.20.3"
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-  version "1.5.0"
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-  integrity sha1-XQKkaFCt8bSjF5RqOSj8y1v9BCU=
-
-"d3-array@1 - 2", d3-array@2, d3-array@^2.3.0, d3-array@^2.5.0, d3-array@^2.7.1:
-  version "2.12.1"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-2.12.1.tgz#e20b41aafcdffdf5d50928004ececf815a465e81"
-  integrity sha512-B0ErZK/66mHtEsR1TkPEEkwdy+WDesimkM5gpZr5Dsg54BiTA5RXtYW5qTLIAcekaS9xfZrzBLF/OAkB3Qn1YQ==
-  dependencies:
-    internmap "^1.0.0"
-
-"d3-array@1 - 3", "d3-array@2 - 3", "d3-array@2.10.0 - 3", "d3-array@2.5.0 - 3", d3-array@3.1.1, d3-array@^3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-3.1.1.tgz#7797eb53ead6b9083c75a45a681e93fc41bc468c"
-  integrity sha512-33qQ+ZoZlli19IFiQx4QEpf2CBEayMRzhlisJHSCsSUbDXv6ZishqS1x7uFVClKG4Wr7rZVHvaAttoLow6GqdQ==
-  dependencies:
-    internmap "1 - 2"
-
-"d3-color@1 - 2", d3-color@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-2.0.0.tgz#8d625cab42ed9b8f601a1760a389f7ea9189d62e"
-  integrity sha512-SPXi0TSKPD4g9tw0NMZFnR95XVgUZiBH+uUTqQuDu1OsE2zomHU7ho0FISciaPvosimixwHFl3WHLGabv6dDgQ==
-
-"d3-color@1 - 3", d3-color@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-3.0.1.tgz#03316e595955d1fcd39d9f3610ad41bb90194d0a"
-  integrity sha512-6/SlHkDOBLyQSJ1j1Ghs82OIUXpKWlR0hCsw0XrLSQhuUPuCSmLQ1QPH98vpnQxMUQM2/gfAkUEWsupVpd9JGw==
-
-d3-delaunay@^5.3.0:
-  version "5.3.0"
-  resolved "https://registry.yarnpkg.com/d3-delaunay/-/d3-delaunay-5.3.0.tgz#b47f05c38f854a4e7b3cea80e0bb12e57398772d"
-  integrity sha512-amALSrOllWVLaHTnDLHwMIiz0d1bBu9gZXd1FiLfXf8sHcX9jrcj81TVZOqD4UX7MgBZZ07c8GxzEgBpJqc74w==
-  dependencies:
-    delaunator "4"
-
-"d3-dispatch@1 - 2":
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-dispatch/-/d3-dispatch-2.0.0.tgz#8a18e16f76dd3fcaef42163c97b926aa9b55e7cf"
-  integrity sha512-S/m2VsXI7gAti2pBoLClFFTMOO1HTtT0j99AuXLoGFKO6deHDdnv6ZGTxSTTUTgO1zVcv82fCOtDjYK4EECmWA==
-
-d3-dsv@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-2.0.0.tgz#b37b194b6df42da513a120d913ad1be22b5fe7c5"
-  integrity sha512-E+Pn8UJYx9mViuIUkoc93gJGGYut6mSDKy2+XaPwccwkRGlR+LO97L2VCCRjQivTwLHkSnAJG7yo00BWY6QM+w==
-  dependencies:
-    commander "2"
-    iconv-lite "0.4"
-    rw "1"
-
-d3-dsv@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-3.0.1.tgz#c63af978f4d6a0d084a52a673922be2160789b73"
-  integrity sha512-UG6OvdI5afDIFP9w4G0mNq50dSOsXHJaRE8arAS5o9ApWnIElp8GZw1Dun8vP8OyHOZ/QJUKUJwxiiCCnUwm+Q==
-  dependencies:
-    commander "7"
-    iconv-lite "0.6"
-    rw "1"
-
-d3-force@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/d3-force/-/d3-force-2.1.1.tgz#f20ccbf1e6c9e80add1926f09b51f686a8bc0937"
-  integrity sha512-nAuHEzBqMvpFVMf9OX75d00OxvOXdxY+xECIXjW6Gv8BRrXu6gAWbv/9XKrvfJ5i5DCokDW7RYE50LRoK092ew==
-  dependencies:
-    d3-dispatch "1 - 2"
-    d3-quadtree "1 - 2"
-    d3-timer "1 - 2"
-
-"d3-format@1 - 2", d3-format@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-2.0.0.tgz#a10bcc0f986c372b729ba447382413aabf5b0767"
-  integrity sha512-Ab3S6XuE/Q+flY96HXT0jOXcM4EAClYFnRGY5zsjRGNy6qCYrQsMffs7cV5Q9xejb35zxW5hf/guKw34kvIKsA==
-
-"d3-format@1 - 3", d3-format@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-3.1.0.tgz#9260e23a28ea5cb109e93b21a06e24e2ebd55641"
-  integrity sha512-YyUI6AEuY/Wpt8KWLgZHsIU86atmikuoOmCfommt0LYHiQSPjvX2AcFc38PX0CBpr2RCyZhjex+NS/LPOv6YqA==
-
-d3-format@~1.3.0:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-1.3.2.tgz#6a96b5e31bcb98122a30863f7d92365c00603562"
-  integrity sha512-Z18Dprj96ExragQ0DeGi+SYPQ7pPfRMtUXtsg/ChVIKNBCzjO8XYJvRTC1usblx52lqge56V5ect+frYTQc8WQ==
-
-d3-geo-projection@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-3.0.0.tgz#45ad8ce756cdbfa8340b11b2988644d8e1fa42e4"
-  integrity sha512-1JE+filVbkEX2bT25dJdQ05iA4QHvUwev6o0nIQHOSrNlHCAKfVss/U10vEM3pA4j5v7uQoFdQ4KLbx9BlEbWA==
-  dependencies:
-    commander "2"
-    d3-array "1 - 2"
-    d3-geo "1.12.0 - 2"
-    resolve "^1.1.10"
-
-d3-geo-projection@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-4.0.0.tgz#dc229e5ead78d31869a4e87cf1f45bd2716c48ca"
-  integrity sha512-p0bK60CEzph1iqmnxut7d/1kyTmm3UWtPlwdkM31AU+LW+BXazd5zJdoCn7VFxNCHXRngPHRnsNn5uGjLRGndg==
-  dependencies:
-    commander "7"
-    d3-array "1 - 3"
-    d3-geo "1.12.0 - 3"
-
-"d3-geo@1.12.0 - 2", d3-geo@^2.0.1:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-2.0.2.tgz#c065c1b71fe8c5f1be657e5f43d9bdd010383c40"
-  integrity sha512-8pM1WGMLGFuhq9S+FpPURxic+gKzjluCD/CHTuUF3mXMeiCo0i6R0tO1s4+GArRFde96SLcW/kOFRjoAosPsFA==
-  dependencies:
-    d3-array "^2.5.0"
-
-"d3-geo@1.12.0 - 3", d3-geo@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-3.0.1.tgz#4f92362fd8685d93e3b1fae0fd97dc8980b1ed7e"
-  integrity sha512-Wt23xBych5tSy9IYAM1FR2rWIBFWa52B/oF/GYe5zbdHrg08FU8+BuI6X4PvTwPDdqdAdq04fuWJpELtsaEjeA==
-  dependencies:
-    d3-array "2.5.0 - 3"
-
-d3-hierarchy@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-hierarchy/-/d3-hierarchy-2.0.0.tgz#dab88a58ca3e7a1bc6cab390e89667fcc6d20218"
-  integrity sha512-SwIdqM3HxQX2214EG9GTjgmCc/mbSx4mQBn+DuEETubhOw6/U3fmnji4uCVrmzOydMHSO1nZle5gh6HB/wdOzw==
-
-"d3-interpolate@1.2.0 - 2", d3-interpolate@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-2.0.1.tgz#98be499cfb8a3b94d4ff616900501a64abc91163"
-  integrity sha512-c5UhwwTs/yybcmTpAVqwSFl6vrQ8JZJoT5F7xNFK9pymv5C0Ymcc9/LIJHtYIggg/yS9YHw8i8O8tgb9pupjeQ==
-  dependencies:
-    d3-color "1 - 2"
-
-"d3-interpolate@1.2.0 - 3", d3-interpolate@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-3.0.1.tgz#3c47aa5b32c5b3dfb56ef3fd4342078a632b400d"
-  integrity sha512-3bYs1rOD33uo8aqJfKP3JWPAibgw8Zm2+L9vBKEHJ2Rg+viTR7o5Mmv5mZcieN+FRYaAOWX5SJATX6k1PWz72g==
-  dependencies:
-    d3-color "1 - 3"
-
-"d3-path@1 - 2", d3-path@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-2.0.0.tgz#55d86ac131a0548adae241eebfb56b4582dd09d8"
-  integrity sha512-ZwZQxKhBnv9yHaiWd6ZU4x5BtCQ7pXszEV9CU6kRgwIQVQGLMv1oiL4M+MK/n79sYzsj+gcgpPQSctJUsLN7fA==
-
-"d3-path@1 - 3", d3-path@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-3.0.1.tgz#f09dec0aaffd770b7995f1a399152bf93052321e"
-  integrity sha512-gq6gZom9AFZby0YLduxT1qmrp4xpBA1YZr19OI717WIdKE2OM5ETq5qrHLb301IgxhLwcuxvGZVLeeWc/k1I6w==
-
-"d3-quadtree@1 - 2":
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-quadtree/-/d3-quadtree-2.0.0.tgz#edbad045cef88701f6fee3aee8e93fb332d30f9d"
-  integrity sha512-b0Ed2t1UUalJpc3qXzKi+cPGxeXRr4KU9YSlocN74aTzp6R/Ud43t79yLLqxHRWZfsvWXmbDWPpoENK1K539xw==
-
-d3-scale@^3.2.2:
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-3.3.0.tgz#28c600b29f47e5b9cd2df9749c206727966203f3"
-  integrity sha512-1JGp44NQCt5d1g+Yy+GeOnZP7xHo0ii8zsQp6PGzd+C1/dl0KGsp9A7Mxwp+1D1o4unbTTxVdU/ZOIEBoeZPbQ==
-  dependencies:
-    d3-array "^2.3.0"
-    d3-format "1 - 2"
-    d3-interpolate "1.2.0 - 2"
-    d3-time "^2.1.1"
-    d3-time-format "2 - 3"
-
-d3-scale@^4.0.2:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-4.0.2.tgz#82b38e8e8ff7080764f8dcec77bd4be393689396"
-  integrity sha512-GZW464g1SH7ag3Y7hXjf8RoUuAFIqklOAq3MRl4OaWabTFJY9PN/E1YklhXLh+OQ3fM9yS2nOkCoS+WLZ6kvxQ==
-  dependencies:
-    d3-array "2.10.0 - 3"
-    d3-format "1 - 3"
-    d3-interpolate "1.2.0 - 3"
-    d3-time "2.1.1 - 3"
-    d3-time-format "2 - 4"
-
-d3-selection@~1.3.0:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/d3-selection/-/d3-selection-1.3.2.tgz#6e70a9df60801c8af28ac24d10072d82cbfdf652"
-  integrity sha512-OoXdv1nZ7h2aKMVg3kaUFbLLK5jXUFAMLD/Tu5JA96mjf8f2a9ZUESGY+C36t8R1WFeWk/e55hy54Ml2I62CRQ==
-
-d3-shape@^2.0.0:
-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-2.1.0.tgz#3b6a82ccafbc45de55b57fcf956c584ded3b666f"
-  integrity sha512-PnjUqfM2PpskbSLTJvAzp2Wv4CZsnAgTfcVRTwW03QR3MkXF8Uo7B1y/lWkAsmbKwuecto++4NlsYcvYpXpTHA==
-  dependencies:
-    d3-path "1 - 2"
-
-d3-shape@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-3.1.0.tgz#c8a495652d83ea6f524e482fca57aa3f8bc32556"
-  integrity sha512-tGDh1Muf8kWjEDT/LswZJ8WF85yDZLvVJpYU9Nq+8+yW1Z5enxrmXOhTArlkaElU+CTn0OTVNli+/i+HP45QEQ==
-  dependencies:
-    d3-path "1 - 3"
-
-"d3-time-format@2 - 3", d3-time-format@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-time-format/-/d3-time-format-3.0.0.tgz#df8056c83659e01f20ac5da5fdeae7c08d5f1bb6"
-  integrity sha512-UXJh6EKsHBTjopVqZBhFysQcoXSv/5yLONZvkQ5Kk3qbwiUYkdX17Xa1PT6U1ZWXGGfB1ey5L8dKMlFq2DO0Ag==
-  dependencies:
-    d3-time "1 - 2"
-
-"d3-time-format@2 - 4", d3-time-format@^4.1.0:
-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/d3-time-format/-/d3-time-format-4.1.0.tgz#7ab5257a5041d11ecb4fe70a5c7d16a195bb408a"
-  integrity sha512-dJxPBlzC7NugB2PDLwo9Q8JiTR3M3e4/XANkreKSUxF8vvXKqm1Yfq4Q5dl8budlunRVlUUaDUgFt7eA8D6NLg==
-  dependencies:
-    d3-time "1 - 3"
-
-"d3-time@1 - 2", d3-time@^2.0.0, d3-time@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-2.1.1.tgz#e9d8a8a88691f4548e68ca085e5ff956724a6682"
-  integrity sha512-/eIQe/eR4kCQwq7yxi7z4c6qEXf2IYGcjoWB5OOQy4Tq9Uv39/947qlDcN2TLkiTzQWzvnsuYPB9TrWaNfipKQ==
-  dependencies:
-    d3-array "2"
-
-"d3-time@1 - 3", "d3-time@2.1.1 - 3", d3-time@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-3.0.0.tgz#65972cb98ae2d4954ef5c932e8704061335d4975"
-  integrity sha512-zmV3lRnlaLI08y9IMRXSDshQb5Nj77smnfpnd2LrBa/2K281Jijactokeak14QacHs/kKq0AQ121nidNYlarbQ==
-  dependencies:
-    d3-array "2 - 3"
-
-"d3-timer@1 - 2", d3-timer@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-timer/-/d3-timer-2.0.0.tgz#055edb1d170cfe31ab2da8968deee940b56623e6"
-  integrity sha512-TO4VLh0/420Y/9dO3+f9abDEFYeCUr2WZRlxJvbp4HPTQcSylXNiL6yZa9FIUvV1yRiFufl1bszTCLDqv9PWNA==
-
-date-format@^4.0.5:
-  version "4.0.5"
-  resolved "https://registry.yarnpkg.com/date-format/-/date-format-4.0.5.tgz#ba385f89782c6cb114cf45dfa4704c6bb29fca51"
-  integrity sha512-zBhRiN/M0gDxUoM2xRtzTjJzSg0XEi1ofYpF84PfXeS3hN2PsGxmc7jw3DNQtFlimRbMmob5FC3G0cJq6jQQpw==
-
-debug@2.6.9:
-  version "2.6.9"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-2.6.9.tgz#5d128515df134ff327e90a4c93f4e077a536341f"
-  integrity sha512-bC7ElrdJaJnPbAP+1EotYvqZsb3ecl5wi6Bfi6BJTUcNowp6cvspg0jXznRTKDjm/E7AdgFBVeAPVMNcKGsHMA==
-  dependencies:
-    ms "2.0.0"
-
-debug@4, debug@^4.1.1, debug@^4.3.3, debug@^4.3.4, debug@~4.3.1, debug@~4.3.2:
-  version "4.3.4"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.4.tgz#1319f6579357f2338d3337d2cdd4914bb5dcc865"
-  integrity sha512-PRWFHuSU3eDtQJPvnNY7Jcket1j0t5OuOsFzPPzsekD52Zl8qUfFIPEiswXqIvHWGVHOgX+7G/vCNNhehwxfkQ==
-  dependencies:
-    ms "2.1.2"
-
-debug@^3.1.0:
-  version "3.2.7"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-3.2.7.tgz#72580b7e9145fb39b6676f9c5e5fb100b934179a"
-  integrity sha512-CFjzYYAi4ThfiQvizrFQevTTXHtnCqWfe7x1AhgEscTz6ZbLbfoLRLPugTQyBth6f8ZERVUSyWHFD/7Wu4t1XQ==
-  dependencies:
-    ms "^2.1.1"
-
-decamelize@^1.2.0:
-  version "1.2.0"
-  resolved "https://registry.yarnpkg.com/decamelize/-/decamelize-1.2.0.tgz#f6534d15148269b20352e7bee26f501f9a191290"
-  integrity sha1-9lNNFRSCabIDUue+4m9QH5oZEpA=
-
-deep-is@~0.1.3:
-  version "0.1.4"
-  resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.4.tgz#a6f2dce612fadd2ef1f519b73551f17e85199831"
-  integrity sha512-oIPzksmTg4/MriiaYGO+okXDT7ztn/w3Eptv/+gSIdMdKsJo0u4CfYNFJPy+4SKMuCqGw2wxnA+URMg3t8a/bQ==
-
-delaunator@4:
-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/delaunator/-/delaunator-4.0.1.tgz#3d779687f57919a7a418f8ab947d3bddb6846957"
-  integrity sha512-WNPWi1IRKZfCt/qIDMfERkDp93+iZEmOxN2yy4Jg+Xhv8SLk2UTqqbe1sfiipn0and9QrE914/ihdx82Y/Giag==
-
-delayed-stream@~1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
-  integrity sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ==
-
-depd@~1.1.2:
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/depd/-/depd-1.1.2.tgz#9bcd52e14c097763e749b274c4346ed2e560b5a9"
-  integrity sha1-m81S4UwJd2PnSbJ0xDRu0uVgtak=
-
-di@^0.0.1:
-  version "0.0.1"
-  resolved "https://registry.yarnpkg.com/di/-/di-0.0.1.tgz#806649326ceaa7caa3306d75d985ea2748ba913c"
-  integrity sha1-gGZJMmzqp8qjMG112YXqJ0i6kTw=
-
-dom-serialize@^2.2.1:
-  version "2.2.1"
-  resolved "https://registry.yarnpkg.com/dom-serialize/-/dom-serialize-2.2.1.tgz#562ae8999f44be5ea3076f5419dcd59eb43ac95b"
-  integrity sha1-ViromZ9Evl6jB29UGdzVnrQ6yVs=
-  dependencies:
-    custom-event "~1.0.0"
-    ent "~2.2.0"
-    extend "^3.0.0"
-    void-elements "^2.0.0"
-
-duplexer@~0.1.1:
-  version "0.1.2"
-  resolved "https://registry.yarnpkg.com/duplexer/-/duplexer-0.1.2.tgz#3abe43aef3835f8ae077d136ddce0f276b0400e6"
-  integrity sha512-jtD6YG370ZCIi/9GTaJKQxWTZD045+4R4hTk/x1UyoqadyJ9x9CgSi1RlVDQF8U2sxLLSnFkCaMihqljHIWgMg==
-
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-  version "4.1.2"
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-  version "2.0.0"
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-  version "5.0.1"
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-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/fast-json-patch/-/fast-json-patch-3.1.1.tgz#85064ea1b1ebf97a3f7ad01e23f9337e72c66947"
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-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz#874bf69c6f404c2b5d99c481341399fd55892633"
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-  version "2.0.6"
-  resolved "https://registry.yarnpkg.com/fast-levenshtein/-/fast-levenshtein-2.0.6.tgz#3d8a5c66883a16a30ca8643e851f19baa7797917"
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-
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-  integrity sha1-lmRU6HZUYuN2RNNib2dCzotwll0=
-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
-which-module@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/which-module/-/which-module-2.0.0.tgz#d9ef07dce77b9902b8a3a8fa4b31c3e3f7e6e87a"
-  integrity sha1-2e8H3Od7mQK4o6j6SzHD4/fm6Ho=
-
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-  version "1.3.1"
-  resolved "https://registry.yarnpkg.com/which/-/which-1.3.1.tgz#a45043d54f5805316da8d62f9f50918d3da70b0a"
-  integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ==
-  dependencies:
-    isexe "^2.0.0"
-
-word-wrap@~1.2.3:
-  version "1.2.3"
-  resolved "https://registry.yarnpkg.com/word-wrap/-/word-wrap-1.2.3.tgz#610636f6b1f703891bd34771ccb17fb93b47079c"
-  integrity sha512-Hz/mrNwitNRh/HUAtM/VT/5VH+ygD6DV7mYKZAtHOrbs8U7lvPS6xf7EJKMF0uW1KJCl0H701g3ZGus+muE5vQ==
-
-wrap-ansi@^6.2.0:
-  version "6.2.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-6.2.0.tgz#e9393ba07102e6c91a3b221478f0257cd2856e53"
-  integrity sha512-r6lPcBGxZXlIcymEu7InxDMhdW0KDxpLgoFLcguasxCaJ/SOIZwINatK9KY/tf+ZrlywOKU0UDj3ATXUBfxJXA==
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-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-ws@~8.2.3:
-  version "8.2.3"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-8.2.3.tgz#63a56456db1b04367d0b721a0b80cae6d8becbba"
-  integrity sha512-wBuoj1BDpC6ZQ1B7DWQBYVLphPWkm8i9Y0/3YdHjHKHiohOJ1ws+3OccDWtH+PoC9DZD5WOTrJvNbWvjS6JWaA==
-
-xmlcreate@^2.0.4:
-  version "2.0.4"
-  resolved "https://registry.yarnpkg.com/xmlcreate/-/xmlcreate-2.0.4.tgz#0c5ab0f99cdd02a81065fa9cd8f8ae87624889be"
-  integrity sha512-nquOebG4sngPmGPICTS5EnxqhKbCmz5Ox5hsszI2T6U5qdrJizBc+0ilYSEjTSzU0yZcmvppztXe/5Al5fUwdg==
-
-y18n@^4.0.0:
-  version "4.0.3"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-4.0.3.tgz#b5f259c82cd6e336921efd7bfd8bf560de9eeedf"
-  integrity sha512-JKhqTOwSrqNA1NY5lSztJ1GrBiUodLMmIZuLiDaMRJ+itFd+ABVE8XBjOvIWL+rSqNDC74LCSFmlb/U4UZ4hJQ==
-
-y18n@^5.0.5:
-  version "5.0.8"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.8.tgz#7f4934d0f7ca8c56f95314939ddcd2dd91ce1d55"
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-
-yallist@^2.0.0:
-  version "2.1.2"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-2.1.2.tgz#1c11f9218f076089a47dd512f93c6699a6a81d52"
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-
-yallist@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
-  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
-
-yargs-parser@^18.1.1:
-  version "18.1.3"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-18.1.3.tgz#be68c4975c6b2abf469236b0c870362fab09a7b0"
-  integrity sha512-o50j0JeToy/4K6OZcaQmW6lyXXKhq7csREXcDwk2omFPJEwUNOVtJKvmDr9EI1fAJZUyZcRF7kxGBWmRXudrCQ==
-  dependencies:
-    camelcase "^5.0.0"
-    decamelize "^1.2.0"
-
-yargs-parser@^20.2.2:
-  version "20.2.9"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
-  integrity sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==
-
-yargs@^16.0.3, yargs@^16.1.1, yargs@^16.2.0:
-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yargs@~15.3.1:
-  version "15.3.1"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-15.3.1.tgz#9505b472763963e54afe60148ad27a330818e98b"
-  integrity sha512-92O1HWEjw27sBfgmXiixJWT5hRBp2eobqXicLtPBIDBhYB+1HpwZlXmbW2luivBJHBzki+7VyCLRtAkScbTBQA==
-  dependencies:
-    cliui "^6.0.0"
-    decamelize "^1.2.0"
-    find-up "^4.1.0"
-    get-caller-file "^2.0.1"
-    require-directory "^2.1.1"
-    require-main-filename "^2.0.0"
-    set-blocking "^2.0.0"
-    string-width "^4.2.0"
-    which-module "^2.0.0"
-    y18n "^4.0.0"
-    yargs-parser "^18.1.1"
-
-yocto-queue@^0.1.0:
-  version "0.1.0"
-  resolved "https://registry.yarnpkg.com/yocto-queue/-/yocto-queue-0.1.0.tgz#0294eb3dee05028d31ee1a5fa2c556a6aaf10a1b"
-  integrity sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==
diff --git a/tfjs-master/e2e/benchmarks/firebase.json b/tfjs-master/e2e/benchmarks/firebase.json
deleted file mode 100644
index 99c5af415..000000000
--- a/tfjs-master/e2e/benchmarks/firebase.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
-  "hosting": {
-    "site": "tfjs-benchmarks",
-    "public": ".",
-    "ignore": [
-      "firebase_prod.json",
-      "firebase_staging.json"
-    ],
-    "redirects": [{
-      "source": "/",
-      "destination": "local-benchmark/",
-      "type": 301
-    }],
-    "headers": [{
-      "source": "local-benchmark/",
-      "headers": [
-        {
-          "key": "Cross-Origin-Embedder-Policy",
-          "value": "require-corp"
-        },
-        {
-          "key": "Cross-Origin-Opener-Policy",
-          "value": "same-origin"
-        }
-      ]
-    }]
-  }
-}
diff --git a/tfjs-master/e2e/benchmarks/firebase_staging.json b/tfjs-master/e2e/benchmarks/firebase_staging.json
deleted file mode 100644
index ffa2eff6b..000000000
--- a/tfjs-master/e2e/benchmarks/firebase_staging.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
-  "hosting": {
-    "site": "tfjs-benchmarks-staging",
-    "public": ".",
-    "ignore": [
-      "firebase_prod.json",
-      "firebase_staging.json"
-    ],
-    "redirects": [{
-      "source": "/",
-      "destination": "local-benchmark/",
-      "type": 301
-    }],
-    "headers": [{
-      "source": "local-benchmark/",
-      "headers": [
-        {
-          "key": "Cross-Origin-Embedder-Policy",
-          "value": "require-corp"
-        },
-        {
-          "key": "Cross-Origin-Opener-Policy",
-          "value": "same-origin"
-        }
-      ]
-    }]
-  }
-}
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/README.md b/tfjs-master/e2e/benchmarks/local-benchmark/README.md
deleted file mode 100644
index d594593f8..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/README.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Benchmark custom models
-
-The `custom model` in the [local benchmark tool](https://tensorflow.github.io/tfjs/e2e/benchmarks/local-benchmark/index.html) currently only supports `tf.GraphModel` or `tf.LayersModel`.
-
-If you want to benchmark more complex TensorFlow.js models with customized input preprocessing logic, you need to implement `load` and `predictFunc` methods, following this [example PR](https://github.com/tensorflow/tfjs/pull/3168/files).
-
-## Models in local file system
-If you have a TensorFlow.js model in local file system, you can benchmark it by: locally host the [local benchmark tool](https://tensorflow.github.io/tfjs/e2e/benchmarks/local-benchmark/index.html) and the model on a http server. In addition, if the online [local benchmark tool](https://tensorflow.github.io/tfjs/e2e/benchmarks/local-benchmark/index.html) is blocked by `CORS` problems when fetching custom models, this solution also works.
-
-### Example
-You can benchmark the [MobileNet model](https://tfhub.dev/google/tfjs-model/imagenet/mobilenet_v2_130_224/classification/3/default/1) in local file system through the following steps:
-1. Download the tool.
-```shell
-git clone https://github.com/tensorflow/tfjs.git
-cd tfjs/e2e/benchmarks/
-```
-2. Download the model.
-```shell
-wget -O model.tar.gz "https://tfhub.dev/google/tfjs-model/imagenet/mobilenet_v2_130_224/classification/3/default/1?tfjs-format=compressed"
-mkdir model
-tar -xf model.tar.gz -C model/
-```
-3. Run a http server to host the model and the local benchmark tool.
-```
-npx http-server
-```
-4. Open http://127.0.0.1:8080/local-benchmark/ through the browser.
-5. Select `custom` in the `models` field.
-6. Fill `http://127.0.0.1:8080/model/model.json` into the `modelUrl` field.
-7. Run benchmark.
-
-## Paths to custom models
-The benchmark tool suopports three kinds of paths to the custom models.
-
-### URL
-Examples:
-- TF Hub: https://tfhub.dev/google/tfjs-model/imagenet/resnet_v2_50/feature_vector/1/default/1
-- Storage: https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json
-
-
-### LocalStorage
-Store the model in LocalStorage at first. Run the following codes in the browser console:
-```javascript
-const localStorageModel = tf.sequential(
-     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
-const saveResults = await localStorageModel.save('localstorage://my-model-1');
-```
-Then use "localstorage://my-model-1" as the custom model URL.
-
-### IndexDB
-Store the model in IndexDB at first. Run the following codes in the browser console:
-```javascript
-const indexDBModel = tf.sequential(
-     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
-const saveResults = await indexDBModel.save('indexeddb://my-model-1');
-```
-Then use "indexeddb://my-model-1" as the custom model URL.
-
-### Variable input shapes
-If input shapes for you model contain dynamic dimension (i.e. for mobilenet
-shape = `[-1, 224, 224, 3]`), you are requires to set it to a valid shape
-`[1, 224, 224, 3]` before you can perform the benchmark.
-In the Inputs section you will see an input box for you to update the shape.
-Once the shape is set, you can click the 'Run benchmark' button again to run
-the benchmark.
-
-# Benchmark test
-It's easy to set up a web server to host benchmarks and run against them via e2e/benchmarks/local-benchmark/index.html. You can manually specify the optional url parameters as needed. Here are the list of supported url parameters:
-
-* Model related parameters:
-
-  <b>architecture</b>: same as architecture (only certain models has it, such as MobileNetV3 and posenet)<br>
-  <b>benchmark</b>: same as models<br>
-  <b>inputSize</b>: same as inputSizes<br>
-  <b>inputType</b>: same as inputTypes<br>
-  <b>modelUrl</b>: same as modelUrl, for custom models only<br>
-  <b>${InputeName}Shape</b>: the input shape array, separated by comma, for custom models only. For example, bodypix's [graph model](https://storage.googleapis.com/tfjs-models/savedmodel/bodypix/mobilenet/float/075/model-stride16.json) has an input named sub_2, then users could add '`sub_2Shape=1,1,1,3`' in the URL to populate its shape.<br>
-
-* Environment related parameters:
-
-  <b>backend</b>: same as backend<br>
-  <b>localBuild</b>: local build name list, separated by comma. The name is in short form (in general the name without the tfjs- and backend- prefixes, for example webgl for tfjs-backend-webgl, core for tfjs-core). Example: 'webgl,core'.<br>
-  <b>run</b>: same as numRuns<br>
-  <b>task</b>: correctness to "Test correctness" or performance to "Run benchmark"<br>
-  <b>warmup</b>: same as numWarmups<br>
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/SpecRunner.html b/tfjs-master/e2e/benchmarks/local-benchmark/SpecRunner.html
deleted file mode 100644
index 4a57886f9..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/SpecRunner.html
+++ /dev/null
@@ -1,48 +0,0 @@
-<!-- Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset="utf-8">
-  <title>Jasmine Test</title>
-
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/jasmine.css">
-
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/jasmine.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/jasmine-html.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jasmine/3.5.0/boot.js"></script>
-
-  <script src="https://unpkg.com/@tensorflow/tfjs-core@latest/dist/tf-core.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-backend-cpu@latest/dist/tf-backend-cpu.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-backend-webgl@latest/dist/tf-backend-webgl.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-layers@latest/dist/tf-layers.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-converter@latest/dist/tf-converter.js"></script>
-  <script src="https://unpkg.com/@tensorflow/tfjs-backend-wasm@latest/dist/tf-backend-wasm.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/dat-gui/0.7.7/dat.gui.min.js"></script>
-
-  <script src="../benchmark_util.js"></script>
-
-  <!-- include source files here... -->
-  <script src="./index.js"></script>
-
-  <!-- include spec files here... -->
-  <script src="./index_test.js"></script>
-
-</head>
-
-<body>
-</body>
-</html>
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/dump.js b/tfjs-master/e2e/benchmarks/local-benchmark/dump.js
deleted file mode 100644
index eb6dfb17c..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/dump.js
+++ /dev/null
@@ -1,228 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * DUMP_LEVEL.BIGDIFF: dumping when difference is greater than the default
- * epsilon. DUMP_LEVEL.ANYDIFF: dumping when difference is greater than 0.
- */
-const DUMP_LEVEL = {
-  BIGDIFF: 0,
-  ANYDIFF: 1,
-};
-
-function compareData(data1, data2, level = DUMP_LEVEL.BIGDIFF) {
-  let epsilon = level == DUMP_LEVEL.ANYDIFF ? 0 : -1;
-  let match = true;
-  try {
-    expectObjectsClose(data1, data2, epsilon);
-  } catch (e) {
-    match = false;
-  }
-  return match;
-}
-
-function getGraphModel(model) {
-  if (model instanceof tf.GraphModel) {
-    return model;
-  } else if (model.model instanceof tf.GraphModel) {
-    return model.model;
-  } else if (
-      model.baseModel && model.baseModel.model instanceof tf.GraphModel) {
-    return model.baseModel.model;
-  } else {
-    console.warn(`Model doesn't support dump!`);
-    return null;
-  }
-}
-
-async function getIntermediateTensorInfo(tensorsMap) {
-  if (!tensorsMap) {
-    return;
-  }
-  const jsonObject = {};
-  const keysOfTensors = Object.keys(tensorsMap);
-  for (let i = 0; i < keysOfTensors.length; i++) {
-    const key = keysOfTensors[i];
-    jsonObject[key] = [];
-    for (let j = 0; j < tensorsMap[key].length; j++) {
-      if (tensorsMap[key][j] == null) {
-        continue;
-      }
-      // For universal-sentence-encoder, its inputs are disposed by model.
-      try {
-        const data = await (tensorsMap[key][j]).data();
-        jsonObject[key].push({
-          value: data,
-          shape: tensorsMap[key][j].shape,
-          dtype: tensorsMap[key][j].dtype
-        });
-      } catch (e) {
-        console.error(`${keysOfTensors[i]} ` + e.message);
-      }
-    }
-  }
-  return jsonObject;
-}
-
-async function saveObjectsToFile(jsonObjects, prefix) {
-  let newPrefix = '';
-  if (prefix !== '') {
-    newPrefix = `${prefix.replace(/\//g, '-')}_`;
-  }
-  const backends = Object.keys(jsonObjects);
-  if (Object.keys(jsonObjects[backends[0]]).length == 0) {
-    return;
-  }
-  for (let i = 0; i < backends.length; i++) {
-    const object = jsonObjects[backends[i]];
-    const fileName = `${newPrefix}${backends[i]}.json`;
-    const a = document.createElement('a');
-    const file = new Blob([JSON.stringify(object)], {type: 'application/json'});
-    a.href = URL.createObjectURL(file);
-    a.download = fileName;
-    a.click();
-    // This log informs tools file has been saved.
-    console.log(fileName);
-  }
-}
-
-/**
- * Create a NamedTensorMap from an output node name.
- * @param outputNodeName Output node name.
- * @param modelJson The parsed model.json.
- * @param dumpedJson The dumped tensor infomation (including shape, dtype,
- *     value).
- *
- * @returns A NamedTensorMap.
- */
-async function createNamedTensorMap(outputNodeName, modelJson, dumpedJson) {
-  const modelNodes = modelJson['modelTopology']['node'];
-  let inputs = [];
-  for (let i = 0; i < modelNodes.length; i++) {
-    if (outputNodeName === modelNodes[i].name && modelNodes[i].input) {
-      inputs = modelNodes[i].input;
-      break;
-    }
-  }
-  // In
-  // https://storage.googleapis.com/tfhub-tfjs-modules/mediapipe/tfjs-model/face_landmarks_detection/attention_mesh/1/model.json,
-  // some inputs are prefixed with '^'.
-  if (!inputs || inputs.length == 0 || inputs[0].startsWith('^')) {
-    return null;
-  }
-
-  let tensorMap = {};
-  for (let i = 0; i < inputs.length; i++) {
-    const key = inputs[i].split(':')[0];
-    if (dumpedJson[key] == null || dumpedJson[key][0] == null) {
-      console.warn('Tensor ' + key + ' is null!');
-      return null;
-    }
-    const tensorInfo = dumpedJson[key][0];
-    const tensor = tf.tensor(
-        Object.values(tensorInfo.value), tensorInfo.shape, tensorInfo.dtype);
-    tensorMap[key] = tensor;
-  }
-
-  return tensorMap;
-}
-
-async function predictOp(
-    model, modelJson, dumpedJson, outputNodeName, backend) {
-  await tf.setBackend(backend);
-  const tensorMap =
-      await createNamedTensorMap(outputNodeName, modelJson, dumpedJson);
-  if (tensorMap == null) {
-    return null;
-  }
-  let prediction;
-  let savedKeepIntermediateTensors;
-  try {
-    savedKeepIntermediateTensors =
-        tf.env().getBool('KEEP_INTERMEDIATE_TENSORS');
-    tf.env().set('KEEP_INTERMEDIATE_TENSORS', false);
-  } catch (e) {
-    console.warn(e.message);
-  }
-  try {
-    // TODO(#6861): Support tensor with type conversion.
-    prediction = await model.executeAsync(tensorMap, outputNodeName);
-  } catch (e) {
-    tf.env().set('KEEP_INTERMEDIATE_TENSORS', savedKeepIntermediateTensors);
-    console.warn(e.message);
-    return null;
-  }
-
-  const predictOpObject = await getPredictionData(prediction, true);
-  tf.env().set('KEEP_INTERMEDIATE_TENSORS', savedKeepIntermediateTensors);
-  return predictOpObject;
-}
-
-/**
- * Dump the predict results of two backends and save diffs to files.
- * @param model The loaded model.
- * @param input The actual and expected results from different backends.
- * @param prefix Used for generating dump file name.
- * @param level 0, dump big diffs. 1, dump any diffs.
- * @param length Used for controlling how many tensors will be dumped. -1 dump
- *     all.
- */
-async function dump(
-    model, input, prefix = '', level = DUMP_LEVEL.BIGDIFF, length = 1) {
-  const graphModel = getGraphModel(model);
-  if (graphModel == null || length == 0) {
-    return;
-  }
-  const backends = Object.keys(input);
-  const actualObject = input[backends[0]];
-  const expectedObject = input[backends[1]];
-  const dumpActualObject = {};
-  const dumpExpectedObject = {};
-  const keys = Object.keys(actualObject);
-  prefix = `dump_${prefix}_${level}`;
-  let dumpCount = 0;
-  const modelJson = graphModel.artifacts;
-  for (let i = 0; i < keys.length; i++) {
-    const key = keys[i];
-    if (compareData(actualObject[key], expectedObject[key], level)) {
-      continue;
-    }
-    const predictOpObject = await predictOp(
-        graphModel, modelJson, expectedObject, key, backends[0]);
-    const [actualOpObject, expectedOpObject] = predictOpObject ?
-        [{...predictOpObject, i}, {...expectedObject[key], i}] :
-        [null, null];
-    if (compareData(actualOpObject, expectedOpObject, level)) {
-      continue;
-    }
-    if (actualOpObject && expectedOpObject) {
-      dumpActualObject[key] = actualOpObject;
-      dumpExpectedObject[key] = expectedOpObject;
-      dumpCount++;
-    }
-    // Break when diff count equals dumpLength to avoid downloading large file.
-    if (length != -1 && dumpCount == length) {
-      break;
-    }
-  }
-  const dumpData =
-      {[backends[0]]: dumpActualObject, [backends[1]]: dumpExpectedObject};
-  await saveObjectsToFile(dumpData, prefix);
-  if (dumpCount) {
-    console.log(`Total dumped ${dumpCount} item(s).`);
-  }
-}
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/index.html b/tfjs-master/e2e/benchmarks/local-benchmark/index.html
deleted file mode 100644
index b5f3be09d..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/index.html
+++ /dev/null
@@ -1,1056 +0,0 @@
-<!-- Copyright 2019 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-
-<html>
-
-<head>
-  <title>TensorFlow.js Model Benchmark</title>
-  <link href="https://fonts.googleapis.com/css?family=Roboto" rel="stylesheet">
-  <link href="./main.css" rel="stylesheet">
-  <script src="https://cdn.jsdelivr.net/npm/lil-gui@0.18"></script>
-  <script src="loader.js"></script>
-  <link rel="shortcut icon"
-        href="https://www.gstatic.com/devrel-devsite/prod/vf4ca28c48392b1412e7b030290622a0dd55b62dec1202c59f119b1e23227c988/tensorflow/images/favicon.png">
-</head>
-
-<body>
-  <h2>TensorFlow.js Model Benchmark</h2>
-  <div id="modal-msg"></div>
-  <div id="container">
-    <div id="stats">
-      <div class="box">
-        <pre id="env"></pre>
-      </div>
-      <table class="table" id="parameters">
-        <caption>Parameters</caption>
-        <thead>
-          <tr>
-            <th>Type</th>
-            <th>Value</th>
-          </tr>
-        </thead>
-        <tbody>
-        </tbody>
-      </table>
-      <table class="table" id="modelInputs" style="display: none;">
-        <caption>Inputs</caption>
-        <thead>
-          <tr>
-            <th>Type</th>
-            <th>Value</th>
-          </tr>
-        </thead>
-        <tbody></tbody>
-      </table>
-      <table class="table" id="timings">
-        <caption>Model</caption>
-        <thead>
-          <tr>
-            <th>Type</th>
-            <th>Value</th>
-          </tr>
-        </thead>
-        <tbody>
-        </tbody>
-      </table>
-      <div class="box" id="perf-trendline-container">
-        <div class="label">Inference times</div>
-        <div class="trendline">
-          <div class="yMax"></div>
-          <div class="yMin"></div>
-          <svg>
-            <path></path>
-          </svg>
-        </div>
-      </div>
-    </div>
-    <table class="table" id="kernels">
-      <caption>Kernel</caption>
-      <thead id="kernels-thead">
-        <tr>
-          <th>Type</th>
-          <th>Time(ms)</th>
-        </tr>
-      </thead>
-      <tbody></tbody>
-    </table>
-  </div>
-  <script>
-    'use strict';
-
-    let urlState = null;
-    let runTimes = 50;
-    let profileTimes = 1;
-    let warmupTimes = 1;
-    let paralllelCompile = false;
-    // Default do not run any task.
-    let task = '';
-    function getURLState(url) {
-      let params = new URLSearchParams(url);
-      const keys = [...params.keys()];
-      if (keys.length === 0)
-        return null;
-      if (params.has('run')) {
-        runTimes = Number(params.get('run'));
-      }
-      if (params.has('profile')) {
-        profileTimes = Number(params.get('profile'));
-      }
-      if (params.has('warmup')) {
-        warmupTimes = Number(params.get('warmup'));
-      }
-      if (params.has('task')) {
-        task = params.get('task');
-      }
-      if (params.has('parallelCompile')) {
-        paralllelCompile = Boolean(params.get('parallelCompile'));
-      }
-      return params;
-    }
-    // Controllers can be updated by URI or clicked by user.
-    let modelController = null;
-    let numWarmupsController = null;
-    let numRunsController = null;
-    let numProfilesController = null;
-    let parallelCompileController = null;
-    let modelParameterFolder = null;
-    let runButton = null;
-    let correctnessButton = null;
-    let inputSizeController = null;
-    let inputTypeController = null;
-    let modelArchitectureController = null;
-    let tunableFlagsControllers = null;
-
-    function updateGUIFromURLState() {
-      if (urlState == null)
-        return;
-      if (tunableFlagsControllers != null) {
-        const urlFlagStrings = ['WEBGL_USE_SHAPES_UNIFORMS',
-                                'KEEP_INTERMEDIATE_TENSORS',
-                                'CHECK_COMPUTATION_FOR_ERRORS'];
-        for (let i = 0; i < urlFlagStrings.length; i++) {
-          if (tunableFlagsControllers[urlFlagStrings[i]] != null &&
-              urlState.has(urlFlagStrings[i])) {
-            const value = urlState.get(urlFlagStrings[i]);
-            if (value === 'true') {
-              tunableFlagsControllers[urlFlagStrings[i]].setValue(true);
-            } else {
-              tunableFlagsControllers[urlFlagStrings[i]].setValue(false);
-            }
-          }
-        }
-
-        if (tunableFlagsControllers['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE'] != null &&
-          urlState.has('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE')) {
-          const value = Number(urlState.get('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE'));
-          tunableFlagsControllers['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE'].setValue(value);
-        }
-      }
-      // The model parameter will be update automically by onChange.
-      if (modelController != null && urlState.has('benchmark'))
-        modelController.setValue(urlState.get('benchmark'));
-      if (numWarmupsController != null && urlState.has('warmup'))
-        numWarmupsController.setValue(urlState.get('warmup'));
-      if (numRunsController != null && urlState.has('run'))
-        numRunsController.setValue(urlState.get('run'));
-      if (numProfilesController != null && urlState.has('profile'))
-        numProfilesController.setValue(urlState.get('profile'));
-      // Only auto run when URI component exists.
-      if (correctnessButton != null && task == 'correctness') {
-        correctnessButton.$button.click();
-      }
-      if (runButton != null && task == 'performance') {
-        runButton.$button.click();
-      }
-    }
-
-    async function predictAndGetData(predict, model, inferenceInput, enableDump) {
-      const prediction = await predict(model, inferenceInput);
-      let intermediateData = {};
-      if (enableDump) {
-        const graphModel = getGraphModel(model);
-        if (graphModel) {
-          intermediateData = await getIntermediateTensorInfo(graphModel.getIntermediateTensors());
-          graphModel.disposeIntermediateTensors();
-        }
-      }
-      const predictionData = await getPredictionData(prediction);
-      return {data: predictionData, intermediateData};
-    }
-
-    const state = {
-      numWarmups: warmupTimes,
-      numRuns: runTimes,
-      numProfiles: profileTimes,
-      parallelCompile: paralllelCompile,
-      benchmark: 'MobileNetV3',
-      run: (v) => {
-        runBenchmark().catch(e => {
-          showMsg('Error: ' + e.message);
-          throw e;
-        });
-      },
-      testCorrectness: async () => {
-        if (!tf.engine().backendNames().includes(state.backend)) {
-          return;
-        }
-        await tf.ready();
-        if (state.isFlagChanged) {
-          await setEnvFlags(state.flags);
-          envDiv.innerHTML = '';
-          showVersions();
-          await showEnvironment();
-          await showGpuInfo();
-        }
-
-        let match, actualData, expectedData;
-        await cleanUpTable();
-
-        // load model and run inference
-        try {
-          const expectedBackend = 'cpu';
-          tf.setBackend(expectedBackend);
-          await loadModelAndRecordTime();
-          await showMsg('Testing correctness');
-          await showInputs();
-          await showCorrectnessTestParameters();
-
-          let inferenceInput;
-          await showMsg(`Runing on ${expectedBackend}`);
-          if (state.benchmark === 'custom') {
-            inferenceInput = generateInputFromDef(
-              state.inputs, model instanceof tf.GraphModel);
-          }
-
-          let keepIntermediateTensors = false;
-          try {
-            keepIntermediateTensors = tf.env().getBool('KEEP_INTERMEDIATE_TENSORS');
-          } catch (e) {
-            console.warn(e.message);
-          }
-
-          const enableDump = keepIntermediateTensors & (benchmarks[state.benchmark].supportDump !== false);
-          const expectedResult = await predictAndGetData(predict, model, inferenceInput, enableDump);
-          expectedData = expectedResult['data'];
-
-          await tf.setBackend(state.backend);
-          await showMsg(`Runing on ${state.backend}`);
-
-          // willReadFrequently is set to true for CPU backend. So to compare
-          // the result, we also set it to true for GPU backends.
-          let savedWillReadFrequently;
-          if (state.backend === 'webgl' || state.backend === 'webgpu') {
-            savedWillReadFrequently = tf.env().getBool('CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU');
-            tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU', true);
-          }
-
-          const actualResult = await predictAndGetData(predict, model, inferenceInput, enableDump);
-          actualData = actualResult['data'];
-          if (enableDump) {
-            const actualIntermediateObject = actualResult['intermediateData'];
-            const expectedIntermediateObject = expectedResult['intermediateData'];
-
-            const dumpLevel = urlState.has('dumpLevel') ? Number(urlState.get('dumpLevel')) : 0;
-            const dumpLength = urlState.has('dumpLength') ? Number(urlState.get('dumpLength')) : 1;
-            const dumpPrefix = state.benchmark + '_'+ state.architecture + '_' + state.inputType + '_' + state.inputSize;
-            const dumpInput = {[state.backend] : actualIntermediateObject, [expectedBackend] : expectedIntermediateObject};
-            await dump(model, dumpInput, dumpPrefix, dumpLevel, dumpLength);
-          }
-          if (state.backend === 'webgl' || state.backend === 'webgpu') {
-            tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU', savedWillReadFrequently);
-          }
-        } catch (e) {
-          showMsg('Error: ' + e.message);
-          throw e;
-        }
-
-        // compare results
-        try {
-          await showMsg(null);
-          expectObjectsClose(actualData, expectedData);
-          match = true;
-        } catch (e) {
-          match = false;
-          throw e;
-        } finally {
-          appendRow(timeTable, `backend`, state.backend);
-          appendRow(timeTable, `Prediction matches CPU`, match);
-          appendRow(timeTable, '', '');
-        }
-      },
-      backend: 'webgl',
-      kernelTiming: 'aggregate',
-      inputSize: 0,
-      inputType: '',
-      architecture: 'small_075',
-      modelType: '',
-      modelUrl: '',
-      isModelChanged: false,
-      flags: {},
-      isFlagChanged: false,
-      isModelLoaded: false,
-      inputs: {},
-      shouldReloadTfliteModel: false
-    };
-
-    const modalDiv = document.getElementById('modal-msg');
-    const timeTable = document.querySelector('#timings tbody');
-    const envDiv = document.getElementById('env');
-    const kernelsTableHead = document.getElementById('kernels-thead');
-    const kernelTable = document.querySelector('#kernels tbody');
-    const parameterTable = document.querySelector('#parameters tbody');
-    const modelInputsTable = document.querySelector('#modelInputs tbody');
-
-    let model, tfliteModel, worker, tfliteWorkerAPI, predict, chartWidth;
-
-    async function showMsg(message) {
-      if (message != null) {
-        modalDiv.innerHTML = message + '...';
-        modalDiv.style.display = 'block';
-      } else {
-        modalDiv.style.display = 'none';
-      }
-      await tf.nextFrame();
-      await tf.nextFrame();
-    }
-
-    function showVersions() {
-      envDiv.innerHTML = JSON.stringify({
-        core: tf.version_core,
-        layers: tf.version_layers,
-        converter: tf.version_converter
-      }, null, 2);
-    }
-
-    async function showGpuInfo() {
-      const gpuHardwareInfo = await getRendererInfo();
-      envDiv.innerHTML += `<br>{<br>  GPU info: \n  ${gpuHardwareInfo}<br>}<br>`;
-    }
-
-    async function showEnvironment() {
-      await tf.time(() => tf.add(tf.tensor1d([1]), tf.tensor1d([1])).data());
-      const sortedFeatures = Object.keys(tf.env().features).sort().map(
-        name => `  ${name}: ${tf.env().features[name]}`);
-      envDiv.innerHTML += `<br/>{<br>${sortedFeatures.join('<br>')}<br>}`;
-    }
-
-    function showBenchmarkingParameters() {
-      appendRow(parameterTable, 'task', 'Performance Benchmark');
-      appendRow(parameterTable, 'benchmark', state.benchmark);
-      appendRow(parameterTable, 'modelType', state.modelType || 'Unknown');
-      appendRow(parameterTable, 'convertedBy', model?.artifacts?.convertedBy || 'Unknown');
-      if (state.benchmark === 'custom') {
-        appendRow(parameterTable, 'modelUrl', state.modelUrl);
-      }
-      appendRow(parameterTable, 'numRuns', state.numRuns);
-      appendRow(parameterTable, 'numProfiles', state.numProfiles);
-      appendRow(parameterTable, 'backend', state.backend);
-      appendRow(parameterTable, 'kernelTiming', state.kernelTiming);
-      if (isParameterDefined('inputSizes')) {
-        appendRow(parameterTable, 'inputSize', state.inputSize);
-      }
-      if (isParameterDefined('inputTypes')) {
-        appendRow(parameterTable, 'inputType', state.inputType);
-      }
-      if (isParameterDefined('architectures')) {
-        appendRow(parameterTable, 'architecture', state.architecture);
-      }
-    }
-
-    async function setupKernelTable() {
-      kernelsTableHead.innerText = '';
-      kernelTable.innerHTML = '';
-      const rows = ['<b>Kernel</b>', '<b>Time(ms)</b>'];
-      if (state.kernelTiming === 'individual') {
-        rows.push('<b>Inputs</b>', '<b>Output</b>');
-        if (state.backend === 'webgl') {
-          rows.push('<b>GPUPrograms</b>');
-        }
-      }
-      appendRow(kernelsTableHead, ...rows);
-
-      await tf.nextFrame();
-    }
-
-    async function showCorrectnessTestParameters() {
-      appendRow(parameterTable, 'task', 'Correctness Test');
-      appendRow(parameterTable, 'benchmark', state.benchmark);
-      appendRow(parameterTable, 'modelType', state.modelType || 'Unknown');
-      if (state.benchmark === 'custom') {
-        appendRow(parameterTable, 'modelUrl', state.modelUrl);
-      }
-      appendRow(parameterTable, 'numRuns', 1);
-      appendRow(parameterTable, 'numProfiles', 1);
-      appendRow(parameterTable, 'backend', state.backend);
-
-      await tf.nextFrame();
-    }
-
-    function appendRow(tbody, ...cells) {
-      const tr = document.createElement('tr');
-      cells.forEach(c => {
-        const td = document.createElement('td');
-        if (c instanceof HTMLElement) {
-          td.appendChild(c);
-        } else {
-          td.innerHTML = c;
-        }
-        tr.appendChild(td);
-      });
-      tbody.appendChild(tr);
-    }
-
-    async function measureFirstPredictTime() {
-      let predictFn;
-      if (state.benchmark === 'custom') {
-        const input = generateInputFromDef(
-            state.inputs, model instanceof tf.GraphModel);
-        predictFn = isTflite() ?
-            () => predict(undefined, input) :
-            getPredictFnForModel(model, input);
-      } else {
-        predictFn = () => predict(model);
-      }
-
-      const firstInferenceTime = await timeFirstInference(
-          predictFn, state.parallelCompile);
-      const tag = '1st inference time' +
-          (state.parallelCompile ? ' (parallel compile)' : '');
-      appendRow(timeTable, 'backend', state.backend);
-      appendRow(timeTable, tag, printTime(firstInferenceTime));
-    }
-
-    async function warmUp() {
-      const numWarmups = state.numWarmups;
-      if (numWarmups == 0) {
-        await showMsg('Skip warming up');
-        appendRow(timeTable, 'backend', state.backend);
-        appendRow(timeTable, 'Warmup time', '0 ms');
-        return;
-      }
-      await showMsg(`Warming up for ${numWarmups} time(s)`);
-
-      if (state.benchmark === 'custom') {
-        const input = generateInputFromDef(state.inputs, model instanceof tf.GraphModel);
-        try {
-          isTflite() ?
-              await timeInference(() => predict(undefined, input), numWarmups) :
-              await timeModelInference(model, input, numWarmups);
-        } finally {
-          tf.dispose(input);
-        }
-      } else {
-        await timeInference(() => predict(model), numWarmups);
-      }
-      await showMsg(null);
-    }
-
-    async function showInputs() {
-      if (modelInputsTable.innerHTML !== '') {
-        // the input table for the model have been constructed
-        return;
-      }
-      let needInputShape = false;
-      const modelInputsElement = document.getElementById('modelInputs');
-      const inputs = isTflite() ? await tfliteModel.inputs : model.inputs;
-      if (inputs == null) {
-        modelInputsElement.style.display = 'none';
-      } else {
-        modelInputsElement.style.display = '';
-        // construct the input table for the new model
-        state.inputs.forEach((modelInput, inputIndex) => {
-          if (modelInput.shape != null) {
-            let shape = modelInput.shape;
-            let shapeName = modelInput.name.concat('Shape');
-            let variableShape = modelInput.shape.some(e => e == -1);
-            if (variableShape && state.benchmark === 'custom') {
-              const shapeInput = document.createElement("INPUT");
-              shapeInput.setAttribute('type', 'text');
-              if (urlState != null && urlState.has(shapeName)) {
-                shapeInput.setAttribute('value', urlState.get(shapeName));
-                state.inputs[inputIndex].shape =
-                  shapeInput.value.split(',').map(Number);
-              } else {
-                shapeInput.setAttribute('value', shape);
-                needInputShape = true;
-              }
-              shapeInput.addEventListener('focusout', () => {
-                state.inputs[inputIndex].shape =
-                  shapeInput.value.split(',').map(Number);
-              });
-              shape = shapeInput;
-            }
-            appendRow(modelInputsTable, 'name', modelInput.name);
-            appendRow(modelInputsTable, 'shape', shape);
-            appendRow(modelInputsTable, 'data type', modelInput.dtype);
-            // add data range for input generator
-            if (state.benchmark === 'custom') {
-              const minInput = createRangeInput(state.inputs[inputIndex].range,
-                0);
-              const maxInput = createRangeInput(state.inputs[inputIndex].range,
-                1);
-              const seperator = document.createElement('span');
-              seperator.textContent = ' - ';
-              const div = document.createElement('div');
-              div.appendChild(minInput);
-              div.appendChild(seperator);
-              div.appendChild(maxInput);
-              appendRow(modelInputsTable, 'data range', div);
-            }
-            appendRow(modelInputsTable, '', '');
-          }
-        });
-      }
-
-      await tf.nextFrame();
-      return needInputShape;
-    }
-
-    function createRangeInput(range, index) {
-      const input = document.createElement("INPUT");
-      input.setAttribute('type', 'text');
-      input.setAttribute('value', range[index]);
-      input.addEventListener('focusout', () => {
-        range[index] = +input.value;
-      });
-      return input;
-    }
-
-    function updateStateFromURLState() {
-      if (urlState == null)
-        return;
-      if (urlState.has('run'))
-        state.numRuns = Number(urlState.get('run'));
-      if (urlState.has('profile'))
-        state.numProfiles = Number(urlState.get('profile'));
-      if (urlState.has('benchmark'))
-        state.benchmark = urlState.get('benchmark');
-      if (urlState.has('modelUrl'))
-        state.modelUrl = urlState.get('modelUrl');
-      if (urlState.has('backend'))
-        state.backend = urlState.get('backend');
-      if (urlState.has('inputSize'))
-        state.inputSize = Number(urlState.get('inputSize'));
-      if (urlState.has('inputType'))
-        state.inputType = urlState.get('inputType');
-      if (urlState.has('architecture'))
-        state.architecture = urlState.get('architecture');
-    }
-
-    async function loadModelAndRecordTime() {
-      updateStateFromURLState();
-      const benchmark = benchmarks[state.benchmark];
-      state.modelType = benchmark['type'];
-      state.isModelChanged = false; // used to clean the performance history
-
-      if (benchmark['load'] == null) {
-        throw new Error(`Please provide a load method for '${state.benchmark}' model.`);
-      }
-      if ((benchmark['loadTflite'] == null) && isTflite()) {
-        throw new Error(`Please provide a loadTflite method for '${state.benchmark}' model.`);
-      }
-
-      await showMsg('Loading the model');
-      let start = performance.now();
-      const inputSize = parseFloat(state.inputSize);
-      if (isTflite()) {
-        await loadTfliteModel();
-      } else {
-        model = await benchmark.load(inputSize, state.architecture, state.inputType);
-      }
-      state.inputs = [];
-      const inputs = isTflite() ? await tfliteModel.inputs : model.inputs;
-      if (inputs) {
-        // construct the input state for the model
-        for (let modelInputIndex = 0; modelInputIndex < inputs.length; modelInputIndex++) {
-          let modelInput = inputs[modelInputIndex];
-          if (modelInput.shape == null) continue;
-          let shape = modelInput.shape.map(e => e == null ? -1 : e);
-          state.inputs.push({
-            name: modelInput.name,
-            shape: [...shape],
-            dtype: modelInput.dtype,
-            range: [0, 1000]
-          });
-        }
-      }
-      predict = benchmark.predictFunc(inputSize);
-      const elapsed = performance.now() - start;
-
-      await showMsg(null);
-      appendRow(timeTable, 'Model load', printTime(elapsed));
-    }
-
-    const chartHeight = 150;
-    function populateTrendline(node, data, forceYMinToZero = false, yFormatter = d => d) {
-      node.querySelector('svg').setAttribute('width', chartWidth);
-      node.querySelector('svg').setAttribute('height', chartHeight);
-
-      const yMax = Math.max(...data);
-      let yMin = forceYMinToZero ? 0 : Math.min(...data);
-      if (yMin === yMax) {
-        yMin = 0;
-      }
-
-      node.querySelector('.yMin').textContent = yFormatter(yMin);
-      node.querySelector('.yMax').textContent = yFormatter(yMax);
-      let xIncrement = 0;
-      if (data.length != 1) {
-        xIncrement = chartWidth / (data.length - 1);
-      }
-
-      node.querySelector('path')
-        .setAttribute('d', `M${data.map((d, i) => `${i * xIncrement},${chartHeight - ((d - yMin) / (yMax - yMin)) * chartHeight}`).join('L')} `);
-    }
-
-    async function measureAveragePredictTime() {
-      if (state.numRuns == 0) {
-        return;
-      }
-      await showMsg(`Running predict ${state.numRuns} times`);
-      chartWidth = document.querySelector('#perf-trendline-container').getBoundingClientRect().width;
-
-      let timeInfo;
-      const numRuns = state.numRuns;
-      if (state.benchmark === 'custom') {
-        const input = generateInputFromDef(state.inputs, model instanceof tf.GraphModel);
-        try {
-          timeInfo = isTflite() ?
-              await timeInference(()=>predict(undefined, input), numRuns) :
-              await timeModelInference(model, input, numRuns);
-        } finally {
-          tf.dispose(input);
-        }
-      } else {
-        timeInfo = await timeInference(() => predict(model), numRuns);
-      }
-
-      const forceInferenceTrendYMinToZero = true;
-      populateTrendline(document.querySelector('#perf-trendline-container'), timeInfo.times, forceInferenceTrendYMinToZero, printTime);
-
-      await showMsg(null);
-      appendRow(timeTable, `Subsequent average(${state.numRuns} runs)`, printTime(timeInfo.averageTime));
-      appendRow(timeTable, 'Best time', printTime(timeInfo.minTime));
-      // Add an empty row at the end of a round of benchmark.
-      appendRow(timeTable, '', '');
-    }
-
-    async function profileMemoryAndKernelTime() {
-      if (state.numProfiles == 0) {
-        return;
-      }
-      await showMsg('Profile memory and kernels');
-
-      // Reload tflite model with profiling enabled.
-      if (isTflite()) {
-        await loadTfliteModel(true);
-        // This will make sure the model will be reloaded (with profiling disabled) next time
-        // when "Run benchmark" is clicked.
-        //
-        // Without this, the model with profiling *enabled* will continued to be used to measure
-        // the total run time when "Run benchmark" is clicked, which is not accurate.
-        state.shouldReloadTfliteModel = true;
-      }
-
-      const start = performance.now();
-
-      let profileInfo;
-      const numProfiles = state.numProfiles;
-      if (state.benchmark === 'custom') {
-        const input = generateInputFromDef(state.inputs, model instanceof tf.GraphModel);
-        try {
-          if (isTflite()) {
-            profileInfo = await profileInference(() => predict(undefined, input), true, numProfiles);
-          } else {
-            profileInfo = await profileModelInference(model, input, numProfiles);
-          }
-        } finally {
-          tf.dispose(input);
-        }
-      } else {
-        profileInfo = await profileInference(() => predict(model), isTflite(), numProfiles);
-      }
-
-      const elapsed = performance.now() - start;
-      await showMsg(null);
-      // TODO: These are not supported by tflite models yet.
-      if (!isTflite()) {
-        appendRow(timeTable, 'Peak memory', printMemory(profileInfo.peakBytes));
-        appendRow(timeTable, 'Leaked tensors', profileInfo.newTensors);
-      }
-      appendRow(timeTable, 'Profile time', printTime(elapsed / numProfiles));
-
-      if (state.backend === 'webgl' && !queryTimerIsEnabled()) {
-        await showMsg('Query timer extension is not available. <br/> ' +
-          'The kernel result is not accurate since it is ' +
-          'not a direct measure of GPU execution time.');
-      }
-
-      await showMsg('Profiling kernels');
-      appendRow(timeTable, 'Number of kernels', profileInfo.kernels.length);
-      showKernelTime(profileInfo);
-      await showMsg(null);
-    }
-
-    function showKernelTime(profileInfo) {
-      const tbody = document.querySelector('#kernels tbody');
-      if (state.kernelTiming === 'individual') {
-        profileInfo.kernels.forEach(kernel => {
-          const nameSpan = document.createElement('span');
-          nameSpan.setAttribute('title', kernel.name);
-          nameSpan.textContent = kernel.name;
-          let inputInfo;
-          kernel.inputShapes.forEach((inputShape, index) => {
-            if (inputInfo == null) {
-              inputInfo = '';
-            } else {
-              inputInfo += '<br>';
-            }
-            // TODO: Add the name for each input node in the returned value of
-            // `tf.profile` at https://github.com/tensorflow/tfjs/blob/master/tfjs-core/src/engine.ts#L657.
-            if (inputShape == null) {
-              inputInfo += `input${index}: null`;
-            } else {
-              inputInfo += `input${index}: ${inputShape.length}D[${inputShape}]`;
-            }
-          });
-          appendRow(tbody, nameSpan, kernel.kernelTimeMs.toFixed(2),
-                    inputInfo || '-',
-                    kernel.outputShapes.length !== 0 ? kernel.outputShapes : '-',
-                    kernel.extraInfo || '-');
-        });
-      } else {
-        profileInfo.aggregatedKernels.forEach(r => {
-          const nameSpan = document.createElement('span');
-          nameSpan.setAttribute('title', r.name);
-          nameSpan.textContent = r.name;
-          appendRow(tbody, nameSpan, r.timeMs.toFixed(2));
-        });
-      }
-    }
-
-    async function cleanUpTable() {
-      parameterTable.innerHTML = '';
-      if (state.isModelChanged) {
-        timeTable.innerHTML = '';
-        kernelTable.innerHTML = '';
-        modelInputsTable.innerHTML = '';
-      }
-      await tf.nextFrame();
-    }
-
-    async function loadModel() {
-      if (state.isModelChanged || !state.isModelLoaded || state.isFlagChanged ||
-          (isTflite() && state.shouldReloadTfliteModel)) {
-        await loadModelAndRecordTime();
-        state.isModelLoaded = true;
-        const needInputShape = await showInputs();
-        if (needInputShape) {
-          showMsg('Please set the variable input shapes, update -1 to the desired value.');
-          return false;
-        }
-      }
-      return true;
-    }
-
-    async function runBenchmark() {
-      if (!tf.engine().backendNames().includes(state.backend) && !isTflite()) {
-        return;
-      }
-      await tf.ready();
-      if (state.isFlagChanged) {
-        const canProceed = await setEnvFlags(state.flags);
-        envDiv.innerHTML = '';
-        showVersions();
-        await showEnvironment();
-        await showGpuInfo();
-        if (!canProceed) {
-          return;
-        }
-      }
-      await cleanUpTable();
-      await setupKernelTable();
-
-      const canProceed = await loadModel();
-      if (state.isFlagChanged) {
-        state.isFlagChanged = false;
-      }
-      // return immediately if load model failed or need user input.
-      if (!canProceed) {
-        return;
-      }
-      await showBenchmarkingParameters();
-
-      await measureFirstPredictTime();
-      await warmUp();
-      await measureAveragePredictTime();
-      await profileMemoryAndKernelTime();
-      urlState = null;
-    }
-
-    function isParameterDefined(parameter) {
-      const benchmark = benchmarks[state.benchmark];
-      if (benchmark[parameter] != null) {
-        return true;
-      }
-      // Model doesn't support this parameter.
-      return false;
-    }
-
-    function isURLParameterDefined(parameter) {
-      if (urlState != null && urlState.has(parameter)) {
-        return true;
-      }
-      // URL doesn't support this parameter.
-      return false;
-    }
-
-    async function onPageLoad() {
-      const gui = new lil.GUI({ width: 350 });
-      gui.domElement.id = 'gui';
-
-      urlState = getURLState(location.search);
-      let localBuild = [];
-      if (urlState && urlState.has('localBuild')) {
-        localBuild = urlState.get('localBuild').split(',');
-      }
-
-      await loadTFJS(localBuild);
-
-      if (urlState && urlState.has('backend'))
-        state.backend = urlState.get('backend');
-      await tf.setBackend(state.backend);
-
-      const modelFolder = gui.addFolder('Benchmark');
-      let modelUrlController = null;
-      modelController = modelFolder.add(state, 'benchmark', Object.keys(benchmarks));
-      modelController.name('models').onChange(async model => {
-        await showMsg(null);
-        const benchmark = benchmarks[state.benchmark];
-        state.isModelChanged = true;
-        state.isModelLoaded = false;
-        if (modelArchitectureController !== null) {
-          modelArchitectureController.destroy();
-          modelArchitectureController = null;
-        }
-        if (inputSizeController !== null) {
-          inputSizeController.destroy();
-          inputSizeController = null;
-        }
-        if (inputTypeController !== null) {
-          inputTypeController.destroy();
-          inputTypeController = null;
-        }
-
-        if (isParameterDefined('inputSizes')) {
-          inputSizeController = modelParameterFolder.add(state, 'inputSize', benchmark['inputSizes']).name('inputSizes').onChange(async inputSize => {
-            state.inputSize = inputSize;
-            state.isModelChanged = true;
-          });
-          // Current use first value as default.
-          let defaultInputSize = 0;
-          if (isURLParameterDefined('inputSize')) {
-            defaultInputSize = urlState.get('inputSize');
-          } else {
-            defaultInputSize = benchmark['inputSizes'][0];
-          }
-          inputSizeController.setValue(defaultInputSize);
-          state.inputSize = defaultInputSize;
-        } else {
-          // Model doesn't support input size.
-          state.inputSize = 0;
-        }
-
-        if (isParameterDefined('architectures')) {
-          modelArchitectureController = modelParameterFolder.add(state, 'architecture', benchmark['architectures']).name('architectures').onChange(async architecture => {
-            state.architecture = architecture;
-            state.isModelChanged = true;
-          });
-          // Current use first value as default.
-          let defaultModelArchitecture = null;
-          if (isURLParameterDefined('architecture'))
-            defaultModelArchitecture = urlState.get('architecture');
-          else
-            defaultModelArchitecture = benchmark['architectures'][0];
-
-          modelArchitectureController.setValue(defaultModelArchitecture);
-          state.architecture = defaultModelArchitecture;
-        } else {
-          // Model doesn't support architecture.
-          state.architecture = '';
-        }
-
-        if (isParameterDefined('inputTypes')) {
-          inputTypeController = modelParameterFolder.add(state, 'inputType', benchmark['inputTypes']).name('inputTypes').onChange(async inputType => {
-            state.inputType = inputType;
-            state.isModelChanged = true;
-          });
-          // Current use first value as default.
-          let defaultInputType = null;
-          if (isURLParameterDefined('inputType')) {
-            defaultInputType = urlState.get('inputType');
-          } else {
-            defaultInputType = benchmark['inputTypes'][0];
-          }
-          inputTypeController.setValue(defaultInputType);
-          state.inputType = defaultInputType;
-        } else {
-          // Model doesn't support input type.
-          state.inputType = '';
-        }
-
-        // Unfolding the model parameter UI if any model parameters are deinfed
-        // in the current model.
-        if (isParameterDefined('inputSizes') || isParameterDefined('inputTypes') || isParameterDefined('architectures')) {
-          modelParameterFolder.open();
-        }
-
-        if (model === 'custom') {
-          if (modelUrlController === null) {
-            modelUrlController = modelFolder.add(state, 'modelUrl').onChange(async modelUrl => {
-              state.isModelChanged = true;
-              state.isModelLoaded = false;
-              await showMsg(null);
-            });
-            modelUrlController.domElement.querySelector('input').placeholder =
-              'https://your-domain.com/model-path/model.json';
-            if (modelUrlController != null && urlState && urlState.has('modelUrl')) {
-              modelUrlController.setValue(urlState.get('modelUrl'));
-            }
-          }
-        } else if (modelUrlController != null) {
-          modelUrlController.destroy();
-          modelUrlController = null;
-        }
-      });
-      modelFolder.open();
-
-      const parameterFolder = gui.addFolder('Parameters');
-      numWarmupsController = parameterFolder.add(state, 'numWarmups');
-      numRunsController = parameterFolder.add(state, 'numRuns');
-      numProfilesController = parameterFolder.add(state, 'numProfiles');
-      parameterFolder.add(state, 'kernelTiming', ['aggregate', 'individual']);
-      parallelCompileController = parameterFolder.add(state, 'parallelCompile');
-      parameterFolder.open();
-
-      // Show model parameter UI when loading the page.
-      modelParameterFolder = gui.addFolder('Model Parameters');
-      // For each model parameter, show it only if it is defined in the
-      // pre-selected model.
-      if (isParameterDefined('architectures')) {
-        modelArchitectureController = modelParameterFolder.add(state, 'architecture', benchmarks[state.benchmark]['architectures']);
-        modelArchitectureController.setValue(state.architecture);
-      }
-      if (isParameterDefined('inputSizes')) {
-        inputSizeController = modelParameterFolder.add(state, 'inputSize', benchmarks[state.benchmark]['inputSizes']);
-        inputSizeController.setValue(state.inputSize);
-      }
-      if (isParameterDefined('inputTypes')) {
-        inputTypeController = modelParameterFolder.add(state, 'inputType', benchmarks[state.benchmark]['inputTypes']);
-        inputTypeController.setValue(state.inputType);
-      }
-
-      // Unfolding the model parameter UI if any model parameters are deinfed
-      // in the pre-selected model.
-      if (isParameterDefined('inputSizes') || isParameterDefined('inputTypes') || isParameterDefined('architectures')) {
-          modelParameterFolder.open();
-      }
-
-      const envFolder = gui.addFolder('Environment');
-      const backendsController = envFolder.add(
-        state, 'backend', ['wasm', 'webgl', 'cpu', 'webgpu', 'tflite']);
-      backendsController.onChange(async backend => {
-        // TODO: Failed to set backend
-        if (state.backend === 'webgpu' && !tf.engine().backendNames().includes(state.backend)) {
-          showMsg(
-            `WebGPU is not supported. Please use Chrome Canary browser with flag "--enable-unsafe-webgpu" enabled.`);
-          return;
-        } else {
-          showMsg(null);
-        }
-        if (isTflite()) {
-          // Remove the "Test correctness" button which doesn't apply to tfjs-tflite.
-          correctnessButton.destroy();
-          correctnessButton = null;
-          parallelCompileController.destroy();
-          parallelCompileController = null;
-
-          // Update models drop down to only show models that support tflite.
-          const tfliteBenchmarks = Object.keys(benchmarks)
-            .filter(name => benchmarks[name].loadTflite);
-          updateModelsDropdown(tfliteBenchmarks);
-          modelController.setValue(tfliteBenchmarks[0]);
-          state.isModelLoaded = false;
-          state.isModelChanged = true;
-        } else {
-          // Show the "Test correctness" button and re-populate models dropdown when changed from
-          // tflite to non-tflite backend.
-          if (correctnessButton == null) {
-            correctnessButton = gui.add(state, 'testCorrectness');
-            parallelCompileController = parameterFolder.add(state, 'parallelCompile');
-            const allBenchmarks = Object.keys(benchmarks);
-            updateModelsDropdown(allBenchmarks);
-            modelController.setValue(allBenchmarks[0]);
-            state.isModelLoaded = false;
-            state.isModelChanged = true;
-          }
-          await tf.setBackend(backend);
-        }
-        await showFlagSettingsAndReturnTunableFlagControllers(envFolder, state.backend);
-      });
-      envFolder.open();
-
-      runButton = gui.add(state, 'run');
-      runButton.name('Run benchmark');
-      correctnessButton = gui.add(state, 'testCorrectness');
-      correctnessButton.name('Test correctness');
-
-      tunableFlagsControllers = await showFlagSettingsAndReturnTunableFlagControllers(envFolder, state.backend);
-      showVersions();
-      await showEnvironment();
-      await showGpuInfo();
-      updateGUIFromURLState();
-    }
-
-    function isTflite() {
-      return state.backend === 'tflite';
-    }
-
-    async function loadTfliteModel(enableProfiling = false) {
-      if (!worker) {
-        worker = new Worker('./tflite_worker.js');
-        tfliteWorkerAPI = Comlink.wrap(worker);
-      }
-      if (tfliteModel) {
-        await tfliteModel.cleanUp();
-      }
-      state.shouldReloadTfliteModel = false;
-      const benchmark = benchmarks[state.benchmark];
-
-      tfliteModel = await benchmark.loadTflite(enableProfiling, state.architecture);
-    }
-
-    function updateModelsDropdown(newValues) {
-      modelController._names = newValues;
-      modelController._values = newValues;
-      const tfliteBenchmarksHtml = newValues.map(name => `<option value="${name}">${name}</option>`);
-      modelController.domElement.children[1].children[0].innerHTML = tfliteBenchmarksHtml;
-    }
-
-    onPageLoad();
-  </script>
-</body>
-
-</html>
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/index.js b/tfjs-master/e2e/benchmarks/local-benchmark/index.js
deleted file mode 100644
index 31b4be9d2..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/index.js
+++ /dev/null
@@ -1,239 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const BACKEND_FLAGS_MAP = {
-  general: [],
-  cpu: [],
-  wasm: [
-    'WASM_HAS_SIMD_SUPPORT',
-    'WASM_HAS_MULTITHREAD_SUPPORT',
-    'CHECK_COMPUTATION_FOR_ERRORS',
-    'KEEP_INTERMEDIATE_TENSORS',
-  ],
-  webgl: [
-    'WEBGL_VERSION', 'WEBGL_CPU_FORWARD', 'WEBGL_PACK',
-    'WEBGL_FORCE_F16_TEXTURES', 'WEBGL_RENDER_FLOAT32_CAPABLE',
-    'WEBGL_FLUSH_THRESHOLD', 'WEBGL_PACK_DEPTHWISECONV',
-    'CHECK_COMPUTATION_FOR_ERRORS', 'WEBGL_USE_SHAPES_UNIFORMS',
-    'KEEP_INTERMEDIATE_TENSORS'
-  ],
-  tflite: [],
-};
-if (tf.engine().backendNames().includes('webgpu')) {
-  BACKEND_FLAGS_MAP['webgpu'] =
-    ['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 'KEEP_INTERMEDIATE_TENSORS'];
-}
-
-const TUNABLE_FLAG_NAME_MAP = {
-  PROD: 'production mode',
-  WEBGL_VERSION: 'webgl version',
-  WASM_HAS_SIMD_SUPPORT: 'wasm SIMD',
-  WASM_HAS_MULTITHREAD_SUPPORT: 'wasm multithread',
-  WEBGL_CPU_FORWARD: 'cpu forward',
-  WEBGL_PACK: 'webgl pack',
-  WEBGL_FORCE_F16_TEXTURES: 'enforce float16',
-  WEBGL_RENDER_FLOAT32_CAPABLE: 'enable float32',
-  WEBGL_FLUSH_THRESHOLD: 'GL flush wait time(ms)',
-  WEBGL_PACK_DEPTHWISECONV: 'Packed depthwise Conv2d',
-  WEBGL_USE_SHAPES_UNIFORMS: 'Use shapes uniforms',
-  CHECK_COMPUTATION_FOR_ERRORS: 'Check each op result',
-  KEEP_INTERMEDIATE_TENSORS: 'Print intermediate tensors',
-};
-if (tf.engine().backendNames().includes('webgpu')) {
-  TUNABLE_FLAG_NAME_MAP['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE'] =
-    'deferred submit batch size';
-}
-
-/**
- * Records each flag's default value under the runtime environment and is a
- * constant in runtime.
- */
-let TUNABLE_FLAG_DEFAULT_VALUE_MAP;
-
-/**
- * Set up flag settings under the UI element of `folderController`:
- * - If it is the first call, initialize the flags' default value and show flag
- * settings for both the general and the given backend.
- * - Else, clean up flag settings for the previous backend and show flag
- * settings for the new backend.
- *
- * @param {dat.gui.GUI} folderController
- * @param {string} backendName
- */
-async function showFlagSettingsAndReturnTunableFlagControllers(
-  folderController, backendName) {
-  // Determine wether it is the first call.
-  if (TUNABLE_FLAG_DEFAULT_VALUE_MAP == null) {
-    await initDefaultValueMap();
-    showBackendFlagSettingsAndReturnTunableFlagControllers(
-      folderController, 'general');
-  } else {
-    // Clean up flag settings for the previous backend.
-    // The first constroller under the `folderController` is the backend
-    // setting.
-    const fixedSelectionCount = BACKEND_FLAGS_MAP.general.length + 1;
-    while (folderController.controllers.length > fixedSelectionCount) {
-      folderController
-        .controllers[folderController.controllers.length - 1].destroy();
-    }
-  }
-
-  // Show flag settings for the new backend and return the tunable flags
-  // controllers.
-  return showBackendFlagSettingsAndReturnTunableFlagControllers(
-    folderController, backendName);
-}
-
-const stringValueMap = {};
-/**
- * Show flag settings for the given backend under the UI element of
- * `folderController`.
- *
- * @param {dat.gui.GUI} folderController
- * @param {string} backendName
- */
-function showBackendFlagSettingsAndReturnTunableFlagControllers(
-  folderController, backendName) {
-  const tunableFlags = BACKEND_FLAGS_MAP[backendName];
-  const tunableFlagControllers = {};
-
-  // Remove it once we figure out how to correctly read the tensor data
-  // before the tensor is disposed in profiling mode.
-  if (backendName === 'webgpu' &&
-    state.flags['CHECK_COMPUTATION_FOR_ERRORS'] === true) {
-    state.flags['CHECK_COMPUTATION_FOR_ERRORS'] = false;
-    state.isFlagChanged = true;
-  }
-
-  for (let index = 0; index < tunableFlags.length; index++) {
-    const flag = tunableFlags[index];
-    const flagName = TUNABLE_FLAG_NAME_MAP[flag] || flag;
-
-    // When tunable (bool) and range (array) attributes of `flagRegistry` is
-    // implemented, we can apply them to here.
-    const flagValueRange = getTunableRange(flag);
-    // Heuristically consider a flag with at least two options as tunable.
-    if (flagValueRange.length < 2) {
-      console.warn(
-        `The ${flag} is considered as untunable, ` +
-        `because its value range is [${flagValueRange}].`);
-      continue;
-    }
-    let flagController;
-    if (typeof flagValueRange[0] === 'boolean') {
-      // Show checkbox for boolean flags.
-      try {
-        flagController = folderController.add(state.flags, flag);
-      } catch (ex) {
-        console.warn(ex.message);
-        continue;
-      }
-    } else {
-      // Show dropdown for other types of flags.
-      try {
-        flagController =
-          folderController.add(state.flags, flag, flagValueRange);
-      } catch (ex) {
-        console.warn(ex.message);
-        continue;
-      }
-      // Because dat.gui always casts dropdown option values to string, we need
-      // `stringValueMap` and `onFinishChange()` to recover the value type.
-      if (stringValueMap[flag] == null) {
-        stringValueMap[flag] = {};
-        for (let index = 0; index < flagValueRange.length; index++) {
-          const realValue = flagValueRange[index];
-          const stringValue = String(flagValueRange[index]);
-          stringValueMap[flag][stringValue] = realValue;
-        }
-      }
-      flagController.onFinishChange(stringValue => {
-        state.flags[flag] = stringValueMap[flag][stringValue];
-      });
-    }
-    flagController.name(flagName).onChange(() => {
-      state.isFlagChanged = true;
-    });
-    tunableFlagControllers[flag] = flagController;
-  }
-  return tunableFlagControllers;
-}
-
-/**
- * Query all tunable flags' default value and populate `state.flags` with them.
- */
-async function initDefaultValueMap() {
-  // Clean up the cache to query tunable flags' default values.
-  setEnvFlags({});
-  TUNABLE_FLAG_DEFAULT_VALUE_MAP = {};
-  for (const backend in BACKEND_FLAGS_MAP) {
-    for (let index = 0; index < BACKEND_FLAGS_MAP[backend].length; index++) {
-      const flag = BACKEND_FLAGS_MAP[backend][index];
-      try {
-        TUNABLE_FLAG_DEFAULT_VALUE_MAP[flag] = await tf.env().getAsync(flag);
-      } catch (ex) {
-        console.warn(ex.message);
-      }
-    }
-  }
-
-  // Initialize state.flags with tunable flags' default values.
-  for (const flag in TUNABLE_FLAG_DEFAULT_VALUE_MAP) {
-    state.flags[flag] = TUNABLE_FLAG_DEFAULT_VALUE_MAP[flag];
-  }
-  state.isFlagChanged = false;
-}
-
-/**
- * Heuristically determine flag's value range based on flag's default value.
- *
- * Assume that the flag's default value has already chosen the best option for
- * the runtime environment, so users can only tune the flag value downwards.
- *
- * For example, if the default value of `WEBGL_RENDER_FLOAT32_CAPABLE` is false,
- * the tunable range is [false]; otherwise, the tunable range is [true. false].
- *
- * @param {string} flag
- */
-function getTunableRange(flag) {
-  const defaultValue = TUNABLE_FLAG_DEFAULT_VALUE_MAP[flag];
-  if (flag === 'WEBGL_FORCE_F16_TEXTURES' ||
-    flag === 'WEBGL_PACK_DEPTHWISECONV' ||
-    flag === 'KEEP_INTERMEDIATE_TENSORS') {
-    return [false, true];
-  } else if (flag === 'WEBGL_VERSION') {
-    const tunableRange = [];
-    for (let value = 1; value <= defaultValue; value++) {
-      tunableRange.push(value);
-    }
-    return tunableRange;
-  } else if (flag === 'WEBGL_FLUSH_THRESHOLD') {
-    const tunableRange = [-1];
-    for (let value = 0; value <= 2; value += 0.25) {
-      tunableRange.push(value);
-    }
-    return tunableRange;
-  } else if (typeof defaultValue === 'boolean') {
-    return defaultValue || flag === 'WEBGL_USE_SHAPES_UNIFORMS' ?
-      [false, true] :
-      [false];
-  } else if (TUNABLE_FLAG_VALUE_RANGE_MAP[flag] != null) {
-    return TUNABLE_FLAG_VALUE_RANGE_MAP[flag];
-  } else {
-    return [defaultValue];
-  }
-}
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/index_test.js b/tfjs-master/e2e/benchmarks/local-benchmark/index_test.js
deleted file mode 100644
index 2ad24c77b..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/index_test.js
+++ /dev/null
@@ -1,260 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * The unit tests in this file can be run by opening `./SpecRunner.html` in
- * browser.
- */
-
-const state = {
-  backend: 'wasm',
-  flags: {}
-};
-
-describe('index', () => {
-  describe('showFlagSettings', () => {
-    beforeAll(() => {
-      this.originalInitDefaultValueMap = initDefaultValueMap;
-      this.originalShowBackendFlagSettings = showBackendFlagSettings;
-      this.originalTUNABLE_FLAG_DEFAULT_VALUE_MAP =
-          TUNABLE_FLAG_DEFAULT_VALUE_MAP;
-    });
-
-    afterAll(() => {
-      initDefaultValueMap = this.originalInitDefaultValueMap;
-      showBackendFlagSettings = this.originalShowBackendFlagSettings;
-      TUNABLE_FLAG_DEFAULT_VALUE_MAP =
-          this.originalTUNABLE_FLAG_DEFAULT_VALUE_MAP;
-    });
-
-    describe('at the first call', () => {
-      beforeAll(() => {
-        // Only the backend setting is shown and the general flag settings have
-        // not been shown.
-        this.folderController = new dat.gui.GUI();
-        this.folderController.add(state, 'backend', ['wasm', 'webgl', 'cpu']);
-
-        // The flag default value map has not been initialized.
-        TUNABLE_FLAG_DEFAULT_VALUE_MAP = null;
-
-        BACKEND_FLAGS_MAP.general.push('testGeneralFlag');
-      });
-
-      afterAll(() => {
-        this.folderController.destroy();
-        BACKEND_FLAGS_MAP.general.pop();  // Pop 'testGeneralFlag'.
-      });
-
-      it('shows general flag settings', async () => {
-        initDefaultValueMap = jasmine.createSpy();
-        showBackendFlagSettings = jasmine.createSpy();
-
-        await showFlagSettings(this.folderController, 'webgl');
-
-        expect(showBackendFlagSettings.calls.count()).toBe(2);
-        expect(showBackendFlagSettings.calls.first().args).toEqual([
-          this.folderController, 'general'
-        ]);
-      });
-
-      it('initializes default value map', async () => {
-        initDefaultValueMap = jasmine.createSpy();
-        showBackendFlagSettings = jasmine.createSpy();
-        await showFlagSettings(this.folderController, 'webgl');
-        expect(initDefaultValueMap.calls.count()).toBe(1);
-      });
-
-      it('shows flag settings for the given backend', async () => {
-        initDefaultValueMap = jasmine.createSpy();
-        showBackendFlagSettings = jasmine.createSpy();
-
-        await showFlagSettings(this.folderController, 'webgl');
-
-        expect(showBackendFlagSettings.calls.count()).toBe(2);
-        expect(showBackendFlagSettings.calls.argsFor(0)).toEqual([
-          this.folderController, 'general'
-        ]);
-        expect(showBackendFlagSettings.calls.argsFor(1)).toEqual([
-          this.folderController, 'webgl'
-        ]);
-      });
-    });
-
-    describe('When switching the backend', () => {
-      beforeAll(() => {
-        // The flag default value map has been initialized and the state.flags
-        // is populated with all tunable flags.
-        this.originalInitDefaultValueMap();
-        BACKEND_FLAGS_MAP.general.push('testGeneralFlag');
-        TUNABLE_FLAG_DEFAULT_VALUE_MAP.testGeneralFlag = true;
-        state.flags.testGeneralFlag = true;
-      });
-
-      afterAll(() => {
-        BACKEND_FLAGS_MAP.general.pop();  // Pop 'testGeneralFlag'.
-        delete TUNABLE_FLAG_DEFAULT_VALUE_MAP['testGeneralFlag'];
-        delete state.flags['testGeneralFlag'];
-      });
-
-      beforeEach(() => {
-        // The backend setting and the general flag settings have been shown.
-        this.folderController = new dat.gui.GUI();
-        this.folderController.add(state, 'backend', ['wasm', 'webgl', 'cpu']);
-        this.originalShowBackendFlagSettings(this.folderController, 'general');
-
-        // Flag settings for a certain backend have been shown.
-        this.originalShowBackendFlagSettings(this.folderController, 'webgl');
-      });
-
-      afterEach(() => {
-        this.folderController.destroy();
-      });
-
-      it('removes flag settings of the previous backend', async () => {
-        initDefaultValueMap = jasmine.createSpy();
-        showBackendFlagSettings = jasmine.createSpy();
-        spyOn(this.folderController, 'remove').and.callThrough();
-
-        await showFlagSettings(this.folderController, 'wasm');
-
-        expect(initDefaultValueMap.calls.count()).toBe(0);
-        expect(this.folderController.remove.calls.count())
-            .toBe(BACKEND_FLAGS_MAP.webgl.length);
-      });
-
-      it('only add flag settings for the new backend', async () => {
-        initDefaultValueMap = jasmine.createSpy();
-        showBackendFlagSettings = jasmine.createSpy();
-
-        await showFlagSettings(folderController, 'webgl');
-
-        expect(initDefaultValueMap.calls.count()).toBe(0);
-        expect(showBackendFlagSettings.calls.count()).toBe(1);
-        expect(showBackendFlagSettings.calls.first().args).toEqual([
-          this.folderController, 'webgl'
-        ]);
-      });
-    });
-  });
-
-  describe('showBackendFlagSettings', () => {
-    beforeAll(() => {
-      // Assume testBackend has only one flag, testFlag.
-      // A DOM element is shown based on this flag's tunable range.
-      BACKEND_FLAGS_MAP['testBackend'] = ['testFlag'];
-      state.flags['testFlag'] = null;
-      this.originalGetTunableRange = getTunableRange;
-
-      this.folderController = new dat.gui.GUI();
-    });
-
-    afterAll(() => {
-      delete BACKEND_FLAGS_MAP['testBackend'];
-      delete state.flags['testFlag'];
-      getTunableRange = this.originalGetTunableRange;
-
-      this.folderController.destroy();
-    });
-
-    it('does not show DOM element for untunable flags', () => {
-      // The flag with only one value option is considered as untunable.
-      const flagValueRange = [false];
-      getTunableRange = jasmine.createSpy().and.returnValue(flagValueRange);
-      spyOn(this.folderController, 'add');
-      spyOn(console, 'warn');
-
-      showBackendFlagSettings(this.folderController, 'testBackend');
-
-      expect(this.folderController.add.calls.count()).toBe(0);
-      expect(console.warn.calls.count()).toBe(1);
-    });
-
-    it('shows a checkbox for a tunable boolean flag', () => {
-      state.flags.testFlag = true;
-      const flagValueRange = [true, false];
-      getTunableRange = jasmine.createSpy().and.returnValue(flagValueRange);
-      spyOn(this.folderController, 'add').and.callThrough();
-
-      showBackendFlagSettings(this.folderController, 'testBackend');
-
-      expect(this.folderController.add.calls.count()).toBe(1);
-      expect(this.folderController.add.calls.first().args).toEqual([
-        state.flags, 'testFlag'
-      ]);
-      expect(this.folderController.add.calls.first().returnValue.__checkbox)
-          .toBeDefined();
-    });
-
-    it('shows a dropdown menu for a tunable number type flag', () => {
-      state.flags.testFlag = 1;
-      const flagValueRange = [1, 2];
-      getTunableRange = jasmine.createSpy().and.returnValue(flagValueRange);
-      spyOn(this.folderController, 'add').and.callThrough();
-
-      showBackendFlagSettings(this.folderController, 'testBackend');
-
-      expect(this.folderController.add.calls.count()).toBe(1);
-      expect(this.folderController.add.calls.first().args).toEqual([
-        state.flags, 'testFlag', flagValueRange
-      ]);
-      expect(this.folderController.add.calls.first().returnValue.__select)
-          .toBeDefined();
-    });
-  });
-
-  describe('getTunableRange', () => {
-    beforeAll(() => {
-      this.originalTUNABLE_FLAG_DEFAULT_VALUE_MAP =
-          TUNABLE_FLAG_DEFAULT_VALUE_MAP;
-      TUNABLE_FLAG_DEFAULT_VALUE_MAP = {};
-    });
-
-    afterAll(() => {
-      TUNABLE_FLAG_DEFAULT_VALUE_MAP =
-          this.originalTUNABLE_FLAG_DEFAULT_VALUE_MAP;
-    });
-
-    it(`returns [false, true] for 'WEBGL_FORCE_F16_TEXTURES'`, () => {
-      const flagValueRange = getTunableRange('WEBGL_FORCE_F16_TEXTURES');
-      expect(flagValueRange).toEqual([false, true]);
-    });
-
-    it(`returns [1, 2] for 'WEBGL_VERSION' if its default value is 2`, () => {
-      TUNABLE_FLAG_DEFAULT_VALUE_MAP.WEBGL_VERSION = 2;
-      const flagValueRange = getTunableRange('WEBGL_VERSION');
-      expect(flagValueRange).toEqual([1, 2]);
-    });
-
-    it(`returns [1] for 'WEBGL_VERSION' if its default value is 1`, () => {
-      TUNABLE_FLAG_DEFAULT_VALUE_MAP.WEBGL_VERSION = 1;
-      const flagValueRange = getTunableRange('WEBGL_VERSION');
-      expect(flagValueRange).toEqual([1]);
-    });
-
-    it('returns [false] for the flag with false as default value', () => {
-      TUNABLE_FLAG_DEFAULT_VALUE_MAP.testFlag = false;
-      const flagValueRange = getTunableRange('testFlag');
-      expect(flagValueRange).toEqual([false]);
-    });
-
-    it('returns [false, true] for the flag with true as default value', () => {
-      TUNABLE_FLAG_DEFAULT_VALUE_MAP.testFlag = true;
-      const flagValueRange = getTunableRange('testFlag');
-      expect(flagValueRange).toEqual([false, true]);
-    });
-  });
-});
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/loader.js b/tfjs-master/e2e/benchmarks/local-benchmark/loader.js
deleted file mode 100644
index 90f71a5b1..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/loader.js
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Promise is used to guarantee scripts are loaded in order.
-function loadScript(url) {
-  return new Promise((resolve, reject) => {
-    const script = document.createElement('script');
-    script.onload = resolve;
-    script.onerror = reject;
-    script.src = url;
-    if (url.startsWith('http')) {
-      script.crossOrigin = 'anonymous';
-    }
-    document.body.append(script);
-  })
-}
-
-function processUrls(urls, localBuild) {
-  for (let i = 0; i < urls.length; i++) {
-    let name =
-        urls[i].split('/')[0].replace('tfjs-', '').replace('backend-', '');
-    if (localBuild.includes(name)) {
-      urls[i] = `../../../dist/bin/${urls[i]}`;
-    } else {
-      urls[i] =
-          `https://unpkg.com/@tensorflow/${urls[i].replace('/', '@latest/')}`;
-    }
-  }
-}
-
-async function loadTFJS(localBuild) {
-  let urls = [
-    'tfjs-core/dist/tf-core.js',
-    'tfjs-backend-cpu/dist/tf-backend-cpu.js',
-    'tfjs-backend-webgl/dist/tf-backend-webgl.js',
-    'tfjs-backend-webgpu/dist/tf-backend-webgpu.js',
-    'tfjs-layers/dist/tf-layers.js',
-    'tfjs-converter/dist/tf-converter.js',
-    'tfjs-backend-wasm/dist/tf-backend-wasm.js',
-    'tfjs-automl/dist/tf-automl.js',
-  ];
-
-  processUrls(urls, localBuild);
-  urls = urls.concat([
-    'https://cdn.jsdelivr.net/npm/@tensorflow-models/universal-sentence-encoder',
-    'https://cdn.jsdelivr.net/npm/@tensorflow-models/speech-commands',
-    'https://cdn.jsdelivr.net/npm/@tensorflow-models/posenet@2',
-    'https://cdn.jsdelivr.net/npm/@tensorflow-models/body-pix@2',
-    'https://cdn.jsdelivr.net/npm/comlink@latest/dist/umd/comlink.js',
-    '../model_config.js',
-    '../benchmark_util.js',
-    './util.js',
-    './index.js',
-    './dump.js',
-  ]);
-
-  for (let url of urls) {
-    await loadScript(url);
-  }
-}
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/main.css b/tfjs-master/e2e/benchmarks/local-benchmark/main.css
deleted file mode 100644
index dd719ceb7..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/main.css
+++ /dev/null
@@ -1,168 +0,0 @@
-/**
-* @license
-* Copyright 2019 Google LLC. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 (the "License");
-* you may not use this file except in compliance with the License.
-* You may obtain a copy of the License at
-*
-* http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing, software
-* distributed under the License is distributed on an "AS IS" BASIS,
-* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-* See the License for the specific language governing permissions and
-* limitations under the License.
-* =============================================================================
-*/
-
-html,
-body {
-  box-sizing: border-box;
-  display: flex;
-  font-family: 'Roboto', sans-serif;
-  font-size: 13px;
-  flex-direction: column;
-  position: relative;
-}
-
-body {
-  margin: 20px 100px;
-}
-
-h2 {
-  margin-bottom: 30px;
-}
-
-#kernels {
-  max-width: 750px;
-}
-
-#container {
-  display: flex;
-  flex-direction: row;
-  flex-wrap: wrap;
-}
-
-.box {
-  margin-bottom: 30px;
-  margin-right: 30px;
-}
-
-.box pre {
-  border: 1px solid #ccc;
-  font-size: 10px;
-  margin: 0;
-  padding: 8px;
-}
-
-div[id*='trendline-container'] svg {
-  border-bottom: 1px solid #ccc;
-  border-left: 1px solid #ccc;
-  overflow: visible;
-}
-
-div[id*='trendline-container'] .label {
-  font-size: 14px;
-  font-weight: bold;
-}
-
-div[id*='trendline-container'] path {
-  fill: none;
-  stroke: #222;
-}
-
-.trendline {
-  margin-top: 20px;
-  position: relative;
-}
-
-.trendline .yMax,
-.trendline .yMin {
-  font-size: 11px;
-  position: absolute;
-  right: calc(100% + 6px);
-  white-space: nowrap;
-}
-
-.trendline .yMin {
-  bottom: 0;
-}
-
-.trendline .yMax {
-  top: 0;
-}
-
-#modal-msg {
-  border-radius: 5px;
-  background-color: black;
-  color: white;
-  display: none;
-  left: 45%;
-  padding: 7px;
-  position: absolute;
-  top: 15px;
-  z-index: 100;
-}
-
-.table {
-  border: 1px solid #ccc;
-  border-collapse: collapse;
-  border-spacing: 0;
-  margin-right: 30px;
-  margin-bottom: 30px;
-}
-
-.table caption {
-  font-size: 14px;
-  font-weight: bold;
-}
-
-.table tr {
-  border-bottom: 1px solid #ddd;
-}
-
-.table tr:nth-child(even) {
-  background-color: #f1f1f1;
-}
-
-.table th {
-  font-weight: bold;
-}
-
-.table td,
-th {
-  font-size: 13px;
-  padding: 8px 8px;
-  text-align: left;
-  vertical-align: top;
-}
-
-.table td:first-child,
-th:first-child {
-  padding-left: 16px;
-}
-
-#gui input{
-  text-align:center;
-}
-
-#gui select {
-  width: 100%;
-}
-
-#gui .display {
-  width: 100%;
-  text-align:center;
-}
-
-#gui input[type=checkbox] {
-  width: 100%;
-}
-
-#gui .dg .property-name {
-  width: 25%;
-}
-
-#gui .dg .c {
-  width: 75%;
-}
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/tflite_worker.js b/tfjs-master/e2e/benchmarks/local-benchmark/tflite_worker.js
deleted file mode 100644
index 0508f0acc..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/tflite_worker.js
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Load scripts from jsdelivr because it correctly sets the
-// "cross-origin-resource-policy" header.
-importScripts('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core/dist/tf-core.js');
-importScripts('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.js');
-importScripts('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-tflite/dist/tf-tflite.js');
-importScripts('https://cdn.jsdelivr.net/npm/comlink@latest/dist/umd/comlink.js');
-
-tflite.setWasmPath('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-tflite/dist/');
-
-const tfliteWorkerAPI = {
-  async loadTFLiteModel(modelPath, options) {
-    const model = await tflite.loadTFLiteModel(modelPath, options);
-
-    const wrapped = {
-      inputs: model.inputs,
-      getProfilingResults() {
-        return model.getProfilingResults();
-      },
-      cleanUp() {
-        model.modelRunner.cleanUp();
-      },
-      async predict(inputData) {
-        const inputTensorArray = [];
-        let outputTensor;
-        if (!inputData[0].length) {
-          // Single input, move it into an arrary
-          inputData = [inputData];
-        }
-        for (let i = 0; i< this.inputs.length; i++) {
-          const inputTensor = tf.tensor(
-              inputData[i], this.inputs[i].shape, this.inputs[i].dtype);
-          inputTensorArray.push(inputTensor);
-        }
-        outputTensor = model.predict(inputTensorArray);
-        if (model.outputs.length > 1) {
-          // Multiple outputs
-          let outputData = [];
-          for (let tensorName in outputTensor) {
-            outputData.push(outputTensor[tensorName].dataSync());
-          }
-        } else {
-          // Single output
-          const outputData = outputTensor.dataSync();
-        }
-        // dispose input and output tensors
-        tf.dispose(inputTensorArray);
-        tf.dispose(outputTensor);
-        // We encourage the user to process output data in the worker thread
-        // rather than posting output data to main thread directly, as
-        // this would bring much overhead if the output size is huge.
-        // From this perspective, we don't post output data to main thread
-        // in this benchmark.
-        return 'OK';
-      }
-    };
-
-    return Comlink.proxy(wrapped);
-  },
-};
-
-Comlink.expose(tfliteWorkerAPI);
diff --git a/tfjs-master/e2e/benchmarks/local-benchmark/util.js b/tfjs-master/e2e/benchmarks/local-benchmark/util.js
deleted file mode 100644
index b6eae0e67..000000000
--- a/tfjs-master/e2e/benchmarks/local-benchmark/util.js
+++ /dev/null
@@ -1,183 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-
-async function convertTensorToData(tensor, needInfo = false) {
-  const data = await tensor.data();
-
-  tensor.dispose();
-  if (needInfo) {
-    return {value: data, shape: tensor.shape, dtype: tensor.dtype};
-  }
-  return data;
-}
-
-async function getPredictionData(output, needInfo = false) {
-  if (output instanceof Promise) {
-    output = await output;
-  }
-
-  if (output instanceof tf.Tensor) {
-    output = [await convertTensorToData(output, needInfo)];
-  } else if (Array.isArray(output)) {
-    for (let i = 0; i < output.length; i++) {
-      if (output[i] instanceof tf.Tensor) {
-        output[i] = await convertTensorToData(output[i], needInfo);
-      }
-    }
-  } else if (output != null && typeof output === 'object') {
-    for (const property in output) {
-      if (output[property] instanceof tf.Tensor) {
-        output[property] =
-            await convertTensorToData(output[property], needInfo);
-      }
-    }
-  }
-  return output;
-}
-
-function printTime(elapsed) {
-  return elapsed.toFixed(1) + ' ms';
-}
-
-function printMemory(bytes) {
-  if (bytes < 1024) {
-    return bytes + ' B';
-  } else if (bytes < 1024 * 1024) {
-    return (bytes / 1024).toFixed(2) + ' KB';
-  } else {
-    return (bytes / (1024 * 1024)).toFixed(2) + ' MB';
-  }
-}
-
-function sleep(timeMs) {
-  return new Promise(resolve => setTimeout(resolve, timeMs));
-}
-
-function queryTimerIsEnabled() {
-  return _tfengine.ENV.getNumber(
-             'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0;
-}
-
-function areClose(
-    a, e, epsilon, epsilonOfBigNumber = 0.1, relativeEpsilon = 0.01) {
-  if (!isFinite(a) && !isFinite(e)) {
-    return true;
-  } else if (isNaN(a) || isNaN(e)) {
-    return false;
-  }
-
-  const absoluteError = Math.abs(a - e);
-  if (Math.abs(a) >= 1) {
-    if ((absoluteError > epsilonOfBigNumber) ||
-        absoluteError / Math.min(Math.abs(a), Math.abs(e)) > relativeEpsilon) {
-      return false;
-    }
-  } else {
-    if (absoluteError > epsilon) {
-      return false;
-    }
-  }
-  return true;
-}
-
-function expectObjectsPredicate(actual, expected, epsilon, predicate) {
-  let actualKeys = Object.getOwnPropertyNames(actual);
-  let expectedKeys = Object.getOwnPropertyNames(expected);
-  if (actualKeys.length != expectedKeys.length) {
-    throw new Error(`Actual length ${
-        actualKeys.length} not equal Expected length ${expectedKeys.length}`);
-  }
-  for (let i = 0; i < actualKeys.length; i++) {
-    let key = actualKeys[i];
-    let isObject = typeof (actual[key]) === 'object' &&
-        typeof (expected[key]) === 'object';
-    let isArray = tf.util.isTypedArray(actual[key]) &&
-        tf.util.isTypedArray(expected[key]);
-    if (isArray) {
-      expectArraysClose(actual[key], expected[key], epsilon, key);
-    } else if (isObject) {
-      expectObjectsPredicate(actual[key], expected[key], epsilon, predicate);
-    } else {
-      if (!predicate(actual[key], expected[key])) {
-        throw new Error(`Objects differ: actual[${key}] = ${
-            JSON.stringify(actual[key])}, expected[${key}] = ${
-            JSON.stringify(expected[key])}!`);
-      }
-    }
-  }
-  return true;
-}
-
-function expectObjectsClose(actual, expected, epsilon = -1) {
-  if (epsilon === -1) {
-    epsilon = tf.test_util.testEpsilon();
-  }
-  expectObjectsPredicate(
-      actual, expected, epsilon, (a, b) => areClose(a, b, epsilon));
-}
-
-function expectArraysPredicateFuzzy(actual, expected, predicate, errorRate) {
-  if (tf.util.isTypedArray(actual) == false ||
-      tf.util.isTypedArray(expected) == false) {
-    throw new Error(`Actual and Expected are not arrays.`);
-  }
-
-  if (actual.length !== expected.length) {
-    throw new Error(
-        `Arrays have different lengths actual: ${actual.length} vs ` +
-        `expected: ${expected.length}.\n` +
-        `Actual:   ${actual}.\n` +
-        `Expected: ${expected}.`);
-  }
-  let mismatchCount = 0;
-  for (let i = 0; i < expected.length; ++i) {
-    const a = actual[i];
-    const e = expected[i];
-    if (!predicate(a, e)) {
-      mismatchCount++;
-      const maxMismatch = Math.floor(errorRate * expected.length);
-      if (mismatchCount > maxMismatch) {
-        throw new Error(
-            `Arrays data has more than ${maxMismatch} differs from ${
-                expected.length}: actual[${i}] = ${a}, expected[${i}] = ${
-                e}.\n` +
-            `Actual:   ${actual}.\n` +
-            `Expected: ${expected}.`);
-      }
-    }
-  }
-}
-
-// TODO: support relative comparison for array.
-function expectArraysClose(actual, expected, epsilon, key) {
-  if (epsilon === -1) {
-    epsilon = tf.test_util.testEpsilon();
-  }
-
-  if (key == 'data') {
-    // For bodypix, the value in data memeber means "1 for the pixels that are
-    // part of the person, and 0 otherwise".
-    // So for these models, we don't expect all data is exactly match. Default
-    // use error rate 0.001 (1/1000).
-    const ERROR_RATE = 0.001;
-    return expectArraysPredicateFuzzy(
-        actual, expected, (a, b) => areClose(a, b, epsilon), ERROR_RATE);
-  } else {
-    return tf.test_util.expectArraysClose(actual, expected, epsilon);
-  }
-}
diff --git a/tfjs-master/e2e/benchmarks/model_config.js b/tfjs-master/e2e/benchmarks/model_config.js
deleted file mode 100644
index 995c7302a..000000000
--- a/tfjs-master/e2e/benchmarks/model_config.js
+++ /dev/null
@@ -1,663 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const DEFAULT_MODEL_NAME = 'model.json';
-const TFHUB_SEARCH_PARAM = '?tfjs-format=file';
-
-const sentences = [
-  'add clem burke in my playlist Pre-Party R&B Jams',
-  'Add Live from Aragon Ballroom to Trapeo',
-  'add Unite and Win to my night out',
-  'Add track to my Digster Future Hits',
-  'add the piano bar to my Cindy Wilson',
-  'Add Spanish Harlem Incident to cleaning the house',
-  'add The Greyest of Blue Skies in Indie Español my playlist',
-  'Add the name kids in the street to the plylist New Indie Mix',
-  'add album radar latino',
-  'Add Tranquility to the Latin Pop Rising playlist.',
-  'play something from the twenties',
-  'Play The View From The Afternoon by Malese Jow on Last Fm',
-  'play songs by Sammy Fain',
-  'Play music from the year 1964',
-  'Play the heinz strobl ep from 2016 on Groove Shark',
-  'Play me Leonid Soybelman on Vimeo.',
-  'Play a song from my workout playlist on Groove Shark',
-  'play some Alte Kameraden music',
-  'Will it be warm 1 week from now in DC',
-  'what is the forecast for temperate conditions in Thailand in Lopeno',
-  'Is the weather colder in Costa Rica',
-  'Will it be colder in Delaware?',
-  '"I need to know the weather for Hamorton, TN"',
-  'What will the weather be in Albania at 11:56.',
-  'Is it going to hail in Mount San Jacinto State Park',
-  'What\'s the forecast for Walker Bay Nature Reserve for next year ? ',
-  'is it supposed to be sunny here?',
-  'in California will it be cold in East Trenton Heights',
-  'What is the weather like in Wallis and Futuna? What will the weather be in Romania at 4?',
-  'What is the weather going to be like in Reidland New Mexico next Jun.?',
-  'How cold is it in Cargray, Argentina?',
-  'Is the forecast chillier in 1 hour in Mali',
-  'Tell me if there will be wind in NE Will it be cloudy not far  from Allenton Will there be a blizzard in AR what is the New Caledonia forecast for Bagnell on sep. the 5th Weather for apr. the thirteenth in Djibouti',
-  'Can you give me the weather forecast in Tajikistan? How cold is it going to be in San Marcial, AK in one second? What will the weather be in a month from now at my current location?',
-  'What is the weather like in IA in april How windy is it in Anderson Lake State Fish and Wildlife Area? Is it going to be stormy in Austin Creek State Recreation Area at 09:42?',
-  'When will the weather be temperate like it is now in Stansbury Park in Tuvalu, What is the weather in neighboring OH, What\'s the weather forecast for Spain ? ',
-  'Play the music Hands Up',
-  'Play some twenties theme music on Google Music.',
-  'How will the weather be in New Mexico around 00:09:07 am?',
-  'What will the humidity be in AR in 49 weeks and a half from now',
-  'Is it humid in Parc national de Killarney',
-  'is it supposed to get colder here on 12/28/2019',
-  'How is the forecast for OK?',
-  'what is the Posey Island State Park forecast for colder temps at meal time',
-  'Is it supposed to be chilly in Kuwait?',
-  'Tell me if it\'ll be chilly here at 0 pm',
-  'what is the forecast for colder conditions within the same area of this current place',
-  'Will it hail today in West Point at 11:36:48',
-  'Is it overcast in South Carolina',
-  'Will the sun be out close-by Admiralty Island National Monument?',
-  'What will the weather be in Wakarusa',
-  'How temperate will it be here this week?',
-  'what is the forecast for here at tea time',
-];
-
-function predictFunction(input) {
-  return model => model.predict(input);
-}
-
-// Common predict function for MobileNetV3 and MobileNetV2Lite
-function commonMobileNetPredictFunc() {
-  const input = tf.randomNormal([1, 224, 224, 3]);
-  const inputData = input.dataSync();
-  if (typeof isTflite === 'function' && isTflite()) {
-    return async () => {
-      // Do copy from 'inputData' in each predict as its buffer
-      // will be detached after transferring to worker.
-      const input = inputData.slice(0);
-      return await tfliteModel.predict(
-          Comlink.transfer(input, [input.buffer]));
-    };
-  } else {
-    return predictFunction(input);
-  }
-}
-
-const benchmarks = {
-  'MobileNetV3': {
-    type: 'GraphModel',
-    architectures: ['small_075', 'small_100', 'large_075', 'large_100'],
-    load: async (inputResolution = 224, modelArchitecture = 'small_075') => {
-      const url = `https://tfhub.dev/google/tfjs-model/imagenet/mobilenet_v3_${
-          modelArchitecture}_224/classification/5/default/1`;
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    loadTflite: async (
-        enableProfiling = false, modelArchitecture = 'small_075') => {
-      const url = `https://tfhub.dev/google/lite-model/imagenet/mobilenet_v3_${
-          modelArchitecture}_224/classification/5/metadata/1`;
-      return await tfliteWorkerAPI.loadTFLiteModel(url, {enableProfiling});
-    },
-    predictFunc: commonMobileNetPredictFunc,
-  },
-  'MobileNetV2': {
-    type: 'GraphModel',
-    architectures: ['050', '075', '100'],
-    load: async (inputResolution = 224, modelArchitecture = '050') => {
-      const url = `https://tfhub.dev/google/tfjs-model/imagenet/mobilenet_v2_${
-          modelArchitecture}_224/classification/3/default/1`;
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.randomNormal([1, 224, 224, 3]);
-      return predictFunction(input);
-    },
-  },
-  // Currently, for mobilenet_v2, only the architectures with alpha=100 has
-  // tflite model. Since users could tune the alpha for 'mobilenet_v2' tfjs
-  // models, while we could only provides mobilnet_v2_lite with alpha=100 on the
-  // tflite backend, so mibilnet_v2_lite is separated from mibilnet_v2 and fixes
-  // alpha=100; othwise it would confuse users.
-  'MobileNetV2Lite': {
-    type: 'GraphModel',
-    load: async () => {
-      throw new Error(`Please set tflite as the backend to run this model.`);
-    },
-    loadTflite: async (enableProfiling = false) => {
-      const url =
-          'https://tfhub.dev/tensorflow/lite-model/mobilenet_v2_1.0_224/1/metadata/1';
-      return await tfliteWorkerAPI.loadTFLiteModel(url, {enableProfiling});
-    },
-    predictFunc: commonMobileNetPredictFunc,
-  },
-  'HandPoseDetector': {
-    type: 'GraphModel',
-    architectures: ['lite', 'full'],
-    load: async (inputResolution = 192, modelArchitecture = 'lite') => {
-      const url =
-          `https://tfhub.dev/mediapipe/tfjs-model/handpose_3d/detector/${
-              modelArchitecture}/1`;
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 192, 192, 3]);
-      return predictFunction(input);
-    },
-  },
-  'HandPoseLandmark': {
-    type: 'GraphModel',
-    architectures: ['lite', 'full'],
-    load: async (inputResolution = 224, modelArchitecture = 'lite') => {
-      const url =
-          `https://tfhub.dev/mediapipe/tfjs-model/handpose_3d/landmark/${
-              modelArchitecture}/1`;
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 224, 224, 3]);
-      return predictFunction(input);
-    },
-  },
-  'MoveNet-SinglePose': {
-    type: 'GraphModel',
-    inputSizes: [192, 256],
-    load: async (inputResolution = 192) => {
-      const modelType = inputResolution === 192 ? 'lightning' : 'thunder';
-      const url = `https://tfhub.dev/google/tfjs-model/movenet/singlepose/${
-          modelType}/4`;
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: (inputResolution = 192) => {
-      const input = tf.zeros([1, inputResolution, inputResolution, 3], 'int32');
-      return predictFunction(input);
-    },
-  },
-  'MoveNet-MultiPose': {
-    type: 'GraphModel',
-    load: async () => {
-      const url =
-          'https://tfhub.dev/google/tfjs-model/movenet/multipose/lightning/1';
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 256, 256, 3], 'int32');
-      return predictFunction(input);
-    },
-  },
-  'BlazePoseDetector': {
-    type: 'GraphModel',
-    load: async () => {
-      const url =
-          'https://tfhub.dev/mediapipe/tfjs-model/blazeposedetector/1/default/1';
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 224, 224, 3]);
-      return predictFunction(input);
-    },
-  },
-  'BlazePoseLandmark': {
-    type: 'GraphModel',
-    architectures: ['lite', 'full', 'heavy'],
-    load: async (inputResolution = 256, modelArchitecture = 'lite') => {
-      const url = `https://tfhub.dev/mediapipe/tfjs-model/blazeposelandmark_${
-          modelArchitecture}/2/default/2`;
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 256, 256, 3]);
-      return predictFunction(input);
-    },
-  },
-  'Coco-SSD': {
-    type: 'GraphModel',
-    // The model has has the dynamic ops, so it is supposed to use executeAsync.
-    architectures: ['MobileNetV2', 'MobileNetV1', 'liteMobileNetV2'],
-    load: async (inputResolution = 227, modelArchitecture = 'MobileNetV2') => {
-      const tfliteBased = modelArchitecture.split('MobileNetV')[0];
-      const mobileNetVersion = modelArchitecture.split('MobileNetV')[1];
-      const url = `https://storage.googleapis.com/tfjs-models/savedmodel/ssd${
-          tfliteBased}_mobilenet_v${mobileNetVersion}/model.json`;
-      return tf.loadGraphModel(url);
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 227, 227, 3], 'int32');
-      return model => model.executeAsync(input);
-    },
-  },
-  'DeepLabV3': {
-    type: 'GraphModel',
-    // TODO: Add cityscapes architecture.
-    // https://github.com/tensorflow/tfjs/issues/6733
-    architectures: ['pascal', 'ade20k'],
-    load: async (inputResolution = 227, modelArchitecture = 'pascal') => {
-      const url =
-          `https://storage.googleapis.com/tfhub-tfjs-modules/tensorflow/tfjs-model/deeplab/${
-              modelArchitecture}/1/quantized/2/1/model.json`;
-      return tf.loadGraphModel(url);
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 227, 500, 3], 'int32');
-      return predictFunction(input);
-    },
-  },
-  'FaceDetection': {
-    type: 'GraphModel',
-    inputSizes: [128, 192],
-    load: async (inputResolution = 128) => {
-      const modelSize = inputResolution === 128 ? 'short' : 'full';
-      const url = `https://tfhub.dev/mediapipe/tfjs-model/face_detection/${
-          modelSize}/1`;
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: (inputResolution = 128) => {
-      const input = tf.zeros([1, inputResolution, inputResolution, 3]);
-      return predictFunction(input);
-    },
-  },
-  'FaceLandmarkDetection': {
-    type: 'GraphModel',
-    architectures: ['attention_mesh', 'face_mesh'],
-    load: async (
-        inputResolution = 192, modelArchitecture = 'attention_mesh') => {
-      const url =
-          `https://tfhub.dev/mediapipe/tfjs-model/face_landmarks_detection/${
-              modelArchitecture}/1`;
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 192, 192, 3]);
-      return predictFunction(input);
-    },
-  },
-  'ArPortraitDepth': {
-    type: 'GraphModel',
-    load: async () => {
-      const url = 'https://tfhub.dev/tensorflow/tfjs-model/ar_portrait_depth/1';
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 256, 192, 3]);
-      return predictFunction(input);
-    },
-  },
-  'SelfieSegmentation-General': {
-    type: 'GraphModel',
-    load: async () => {
-      const url =
-          'https://tfhub.dev/mediapipe/tfjs-model/selfie_segmentation/general/1';
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 256, 256, 3]);
-      return predictFunction(input);
-    },
-  },
-  'SelfieSegmentation-Landscape': {
-    type: 'GraphModel',
-    load: async () => {
-      const url =
-          'https://tfhub.dev/mediapipe/tfjs-model/selfie_segmentation/landscape/1';
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const input = tf.zeros([1, 144, 256, 3]);
-      return predictFunction(input);
-    },
-  },
-  'AutoML Image': {
-    type: 'GraphModel',
-    load: async () => {
-      const url =
-          'https://storage.googleapis.com/tfjs-testing/tfjs-automl/img_classification/model.json';
-      return tf.loadGraphModel(url);
-    },
-    predictFunc: () => {
-      const zeros = tf.zeros([1, 224, 224, 3]);
-      return predictFunction(zeros);
-    }
-  },
-  'AutoML Object': {
-    type: 'GraphModel',
-    load: async () => {
-      const url =
-          'https://storage.googleapis.com/tfjs-testing/tfjs-automl/object_detection/model.json';
-      return tf.loadGraphModel(url);
-    },
-    predictFunc: () => {
-      const OUTPUT_NODE_NAMES =
-          ['Postprocessor/convert_scores', 'Postprocessor/Decode/transpose_1'];
-      const feedDict = {ToFloat: tf.zeros([1, 224, 224, 3])};
-      return model => model.executeAsync(feedDict, OUTPUT_NODE_NAMES);
-    }
-  },
-  'USE - batchsize 30': {
-    type: 'GraphModel',
-    load: async () => {
-      return use.load();
-    },
-    predictFunc: () => {
-      const sentences30 = sentences.slice(0, 30);
-      return async model => {
-        const res = await model.embed(sentences30);
-        return res;
-      };
-    }
-  },
-  'USE - batchsize 1': {
-    type: 'GraphModel',
-    load: async () => {
-      return use.load();
-    },
-    predictFunc: () => {
-      return async model => {
-        const next = [sentences[0]];
-        const res = await model.embed(next);
-        return res;
-      };
-    }
-  },
-  'TextToxicity': {
-    type: 'GraphModel',
-    // The model has has the dynamic ops, so it is supposed to use executeAsync.
-    load: async () => {
-      const url =
-          'https://storage.googleapis.com/tfhub-tfjs-modules/tensorflow/tfjs-model/toxicity/1/default/1/model.json';
-      return tf.loadGraphModel(url);
-    },
-    predictFunc: () => {
-      const indices = tf.tensor2d(
-          [0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, 9], [10, 2],
-          'int32');
-      const values = tf.randomUniform([10], 0, 1000, 'int32');
-      const input = {Placeholder_1: indices, Placeholder: values};
-      return model => model.executeAsync(input);
-    }
-  },
-  'MobileBert': {
-    type: 'GraphModel',
-    load: async () => {
-      const url = 'https://tfhub.dev/tensorflow/tfjs-model/mobilebert/1';
-      return tf.loadGraphModel(url, {fromTFHub: true});
-    },
-    predictFunc: () => {
-      const batchSize = 1;
-      const INPUT_SIZE = 384;
-      const inputIds = tf.ones([batchSize, INPUT_SIZE], 'int32');
-      const segmentIds = tf.ones([1, INPUT_SIZE], 'int32');
-      const inputMask = tf.ones([1, INPUT_SIZE], 'int32');
-      const input = {
-        input_ids: inputIds,
-        segment_ids: segmentIds,
-        input_mask: inputMask,
-        global_step: tf.scalar(1, 'int32')
-      };
-      return predictFunction(input);
-    }
-  },
-  'posenet': {
-    type: 'GraphModel',
-    inputSizes: [128, 256, 512, 1024],
-    architectures: ['MobileNetV1', 'ResNet50'],
-    inputTypes: ['image', 'tensor'],
-    load: async (
-        inputResolution = 128, modelArchitecture = 'MobileNetV1',
-        inputType = 'image') => {
-      let config = null;
-      if (modelArchitecture === 'MobileNetV1') {
-        config = {
-          architecture: modelArchitecture,
-          outputStride: 16,
-          multiplier: 0.75,
-          inputResolution: inputResolution,
-        };
-      } else if (modelArchitecture === 'ResNet50') {
-        config = {
-          architecture: modelArchitecture,
-          outputStride: 32,
-          quantBytes: 2,
-          inputResolution: inputResolution,
-        };
-      }
-      const model = await posenet.load(config);
-      if (inputType === 'tensor') {
-        model.input = tf.zeros([inputResolution, inputResolution, 3]);
-      } else {
-        model.input = await loadImage('tennis_standing.jpg');
-      }
-      return model;
-    },
-    predictFunc: () => {
-      return async model => {
-        return model.estimateSinglePose(model.input);
-      };
-    }
-  },
-  'bodypix': {
-    type: 'GraphModel',
-    // The ratio to the default camera size [480, 640].
-    inputSizes: [0.25, 0.5, 0.75, 1.0],
-    architectures: ['ResNet50'],
-    inputTypes: ['image', 'tensor'],
-    load: async (
-        internalResolution, modelArchitecture = 'MobileNetV1',
-        inputType = 'image') => {
-      let config = {
-        architecture: 'ResNet50',
-        outputStride: 32,
-        quantBytes: 4,
-      };
-      const model = await bodyPix.load(config);
-      if (inputType === 'tensor') {
-        model.input =
-            tf.zeros([480 * internalResolution, 640 * internalResolution, 3]);
-      } else {
-        model.input = await loadImage('tennis_standing.jpg');
-      }
-      return model;
-    },
-    predictFunc: (internalResolution = 0.5) => {
-      return async model => {
-        const PERSON_INFERENCE_CONFIG = {
-          internalResolution: internalResolution,
-        };
-        return model.segmentPerson(model.input, PERSON_INFERENCE_CONFIG);
-      };
-    }
-  },
-  'speech-commands': {
-    // Layer model doesn't support dump. TODO(xing.xu@intel.com):
-    // https://github.com/tensorflow/tfjs/issues/7010
-    supportDump: false,
-    load: async () => {
-      const recognizer = speechCommands.create('BROWSER_FFT');
-      await recognizer.ensureModelLoaded();
-      return recognizer;
-    },
-    predictFunc: () => {
-      return async (model) => {
-        const shape = model.modelInputShape();
-        // Cannot use tf.util.sizeFromShape because shape includes null.
-        const mySpectrogramData = new Float32Array(shape.reduce((acc, curr) => {
-          if (curr == null) {
-            return acc;
-          }
-          return acc * curr;
-        }, 1));
-        const x = tf.tensor4d(mySpectrogramData, [1].concat(shape.slice(1)));
-        return await model.recognize(x);
-      }
-    }
-  },
-  'custom': {
-    type: '',
-    load: async () => {
-      return loadModelByUrlWithState(state.modelUrl, {}, state);
-    },
-    loadTflite: async (enableProfiling = false) => {
-      return await tfliteWorkerAPI.loadTFLiteModel(state.modelUrl, {enableProfiling});
-    },
-    predictFunc: () => {
-      return async (model, customInput) => {
-        let inferenceInput;
-        try {
-          inferenceInput = customInput ||
-              generateInputFromDef(
-                               state.inputs, model instanceof tf.GraphModel);
-          if (typeof isTflite === 'function' && isTflite()) {
-            const inputDataArray = [];
-            inferenceInput = inferenceInput instanceof
-                Array ? inferenceInput : [inferenceInput];
-            for (let tensor of inferenceInput) {
-              const inputData = tensor.dataSync();
-              // Do copy from 'inputData' in each predict as its buffer
-              // will be detached after transferring to worker.
-              inputDataArray.push(inputData.slice(0));
-            }
-
-            const buffer = inputDataArray.map(data => data.buffer);
-            return await tfliteModel.predict(
-                Comlink.transfer(inputDataArray, buffer));
-          } else {
-            const predict = getPredictFnForModel(model, inferenceInput);
-            return await predict();
-          }
-        } finally {
-          // dispose input tensors
-          if (!customInput && inferenceInput) {
-            tf.dispose(inferenceInput);
-          }
-        }
-      };
-    },
-  },
-};
-
-
-const imageBucket =
-    'https://storage.googleapis.com/tfjs-models/assets/posenet/';
-async function loadImage(imagePath) {
-  const image = new Image();
-  const promise = new Promise((resolve, reject) => {
-    image.crossOrigin = '';
-    image.onload = () => {
-      resolve(image);
-    };
-  });
-
-  image.src = `${imageBucket}${imagePath}`;
-  return promise;
-}
-
-function findIOHandler(path, loadOptions = {}) {
-  let handler;
-  if (path.load != null) {
-    handler = path;
-  } else if (loadOptions.requestInit != null) {
-    handler = tf.io.browserHTTPRequest(path, loadOptions);
-  } else {
-    const handlers = tf.io.getLoadHandlers(path, loadOptions);
-    if (handlers.length === 0) {
-      handlers.push(tf.io.browserHTTPRequest(path, loadOptions));
-    } else if (handlers.length > 1) {
-      throw new Error(
-          `Found more than one (${handlers.length}) load handlers for ` +
-          `URL '${[path]}'.`);
-    }
-    handler = handlers[0];
-  }
-  return handler;
-}
-
-async function tryAllLoadingMethods(
-    modelHandler, loadOptions = {}, state = {}) {
-  let model;
-  // TODO: download weights once
-  try {
-    model = await tf.loadGraphModel(modelHandler, loadOptions);
-    state.modelType = 'GraphModel';
-    return model;
-  } catch (e) {
-  }
-
-  try {
-    model = await tf.loadLayersModel(modelHandler, loadOptions);
-    state.modelType = 'LayersModel';
-    return model;
-  } catch (e) {
-  }
-
-  throw new Error(`Didn't find a fit loading method for this model.`);
-}
-
-/**
- * Load a graph model or a a model composed of Layer objects and record the
- * model type (GraphModel or LayersModel) at `state.modelType`, given a URL to
- * the model definition.
- *
- * @param {string} modelUrl
- * @param {io.LoadOptions} loadOptions
- * @param {object} state  The object that is used to record the model type.
- *     This can be extended with more model information if needed.
- */
-async function loadModelByUrlWithState(modelUrl, loadOptions = {}, state = {}) {
-  let model, ioHandler, modelType;
-
-  const supportedSchemes = /^(https?|localstorage|indexeddb):\/\/.+$/;
-  if (!supportedSchemes.test(modelUrl)) {
-    throw new Error(`Please use a valid URL, such as 'https://'.`);
-  }
-
-  const tfHubUrl = /^https:\/\/tfhub.dev\/.+$/;
-  if (loadOptions.fromTFHub || tfHubUrl.test(modelUrl)) {
-    if (!modelUrl.endsWith('/')) {
-      modelUrl = modelUrl + '/';
-    }
-    modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;
-  }
-
-  // Convert URL to IOHandler and parse the model type
-  try {
-    ioHandler = findIOHandler(modelUrl, loadOptions);
-    const artifacts = await ioHandler.load();
-    modelType = artifacts.format;
-  } catch (e) {
-    console.error(`Failed to fetch or parse 'model.json' file.`);
-    throw e;
-  }
-
-  // load models
-  try {
-    if (modelType === 'graph-model') {
-      model = await tf.loadGraphModel(ioHandler, loadOptions);
-      state.modelType = 'GraphModel';
-    } else if (modelType === 'layers-model') {
-      model = await tf.loadLayersModel(ioHandler, loadOptions);
-      state.modelType = 'LayersModel';
-    } else {
-      model = await tryAllLoadingMethods(ioHandler, loadOptions, state);
-    }
-  } catch (e) {
-    console.error('Failed to load the model.');
-    throw e;
-  }
-
-  return model;
-}
-
-async function loadModelByUrl(modelUrl, loadOptions = {}) {
-  const state = {};
-  return loadModelByUrlWithState(modelUrl, loadOptions, state);
-}
diff --git a/tfjs-master/e2e/benchmarks/movenet/group1-shard1of3.bin b/tfjs-master/e2e/benchmarks/movenet/group1-shard1of3.bin
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diff --git a/tfjs-master/e2e/benchmarks/movenet/group1-shard2of3.bin b/tfjs-master/e2e/benchmarks/movenet/group1-shard2of3.bin
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diff --git a/tfjs-master/e2e/benchmarks/movenet/group1-shard3of3.bin b/tfjs-master/e2e/benchmarks/movenet/group1-shard3of3.bin
deleted file mode 100644
index 0f0cd3927..000000000
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diff --git a/tfjs-master/e2e/benchmarks/movenet/model.json b/tfjs-master/e2e/benchmarks/movenet/model.json
deleted file mode 100644
index 1f67a4e06..000000000
--- a/tfjs-master/e2e/benchmarks/movenet/model.json
+++ /dev/null
@@ -1 +0,0 @@
-{"format": "graph-model", "generatedBy": "2.7.0", "convertedBy": "TensorFlow.js Converter v3.7.0", "signature": {"inputs": {"input": {"name": "input:0", "dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}, {"size": "320"}, {"size": "320"}, {"size": "3"}]}}}, "outputs": {"output_0": {"name": "Identity:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "10"}, {"size": "56"}]}}}}, "modelTopology": {"node": [{"name": "StatefulPartitionedCall/map/while/loop_counter", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/map/while/maximum_iterations", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/map/Const", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/map/TensorArrayV2_2/element_shape", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/map/TensorArrayV2_2/num_elements", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/map/TensorArrayUnstack/TensorListFromTensor/element_shape", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}}}, {"name": "StatefulPartitionedCall/Tile_16", "op": "Const", "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "4"}]}}}}}, {"name": "StatefulPartitionedCall/map/TensorArrayUnstack_1/TensorListFromTensor/element_shape", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/map/TensorArrayV2Stack/TensorListStack/element_shape", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}}}, {"name": "StatefulPartitionedCall/ExpandDims_17/dim", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/concat_2/axis", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/Reshape_37/shape", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}}}, {"name": "StatefulPartitionedCall/mul_5", "op": "Const", "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "4"}]}}}}}, {"name": "StatefulPartitionedCall/clip_by_value_4/Minimum/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/clip_by_value_4/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/x_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/Sum_4/reduction_indices", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/Maximum_2/y", "op": "Const", "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}}}}}, {"name": "StatefulPartitionedCall/strided_slice_22/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_22/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_22/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_20/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_20/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_20/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}}}, {"name": "StatefulPartitionedCall/Tile_17/multiples", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "3"}]}}}}}, {"name": "StatefulPartitionedCall/x", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/Greater_5/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/strided_slice_16/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_16/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_16/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/GreaterEqual_2/y", "op": "Const", "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}}}}}, {"name": "StatefulPartitionedCall/strided_slice_17/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_17/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_17/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/LessEqual/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/strided_slice_18/stack", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}}}, {"name": "StatefulPartitionedCall/strided_slice_18/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_18/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}}}, {"name": "StatefulPartitionedCall/GreaterEqual_3/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/ExpandDims_16/dim", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/Tile_15/multiples", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/Reshape_36", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "1"}, {"size": "2"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/strided_slice_19/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_19/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_19/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/LessEqual_1/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/strided_slice_12/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_12/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "StatefulPartitionedCall/strided_slice_12/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}}}, "dtype": {"type": 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\ No newline at end of file
diff --git a/tfjs-master/e2e/cloudbuild.yml b/tfjs-master/e2e/cloudbuild.yml
deleted file mode 100644
index d5c7ad388..000000000
--- a/tfjs-master/e2e/cloudbuild.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-steps:
-
-# Install packages.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'e2e'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-
-# Build deps.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'e2e'
-  entrypoint: 'yarn'
-  id: 'build-deps'
-  args: ['build-deps-ci']
-  env: ['NIGHTLY=$_NIGHTLY']
-
-# Fetch graph model golden data from public TFJS gcp bucket.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'e2e'
-  entrypoint: 'yarn'
-  id: 'fetch-graph-model-golden-data'
-  args: ['fetch-graph-model-golden-data-ci']
-
-# Test.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'e2e'
-  entrypoint: 'yarn'
-  id: 'test'
-  args: ['test-ci']
-  env: ['BROWSERSTACK_USERNAME=deeplearnjs1', 'NIGHTLY=$_NIGHTLY']
-  secretEnv: ['BROWSERSTACK_KEY']
-  waitFor: ['yarn', 'build-deps', 'fetch-graph-model-golden-data']
-
-secrets:
-- kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-  secretEnv:
-    BROWSERSTACK_KEY: CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-timeout: 7200s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'N1_HIGHCPU_8'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/e2e/custom_module/blazeface/app.js b/tfjs-master/e2e/custom_module/blazeface/app.js
deleted file mode 100644
index ab283433d..000000000
--- a/tfjs-master/e2e/custom_module/blazeface/app.js
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-core/dist/public/chained_ops/to_float';
-import '@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims';
-import '@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear';
-import '@tensorflow/tfjs-core/dist/public/chained_ops/squeeze';
-import '@tensorflow/tfjs-core/dist/public/chained_ops/reshape';
-import '@tensorflow/tfjs-core/dist/public/chained_ops/div';
-
-import * as blazeface from '@tensorflow-models/blazeface';
-import * as tf from '@tensorflow/tfjs';
-import {setWasmPaths} from '@tensorflow/tfjs-backend-wasm/dist/backend_wasm';
-
-import wasmSimdPath from './node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm-simd.wasm';
-import wasmSimdThreadedPath from './node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm-threaded-simd.wasm';
-import wasmPath from './node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm.wasm';
-
-setWasmPaths({
-  'tfjs-backend-wasm.wasm': wasmPath,
-  'tfjs-backend-wasm-simd.wasm': wasmSimdPath,
-  'tfjs-backend-wasm-threaded-simd.wasm': wasmSimdThreadedPath
-});
-
-import {STUBBED_IMAGE_VALS} from './test_data';
-
-async function main() {
-  tf.setBackend('wasm');
-  tf.env().set('WASM_HAS_SIMD_SUPPORT', false);
-  tf.env().set('WASM_HAS_MULTITHREAD_SUPPORT', false);
-  self.postMessage({msg: true, payload: 'in worker main function'});
-  await tf.ready();
-
-  const backend = tf.getBackend();
-  self.postMessage({msg: true, payload: `'${backend}' backend ready`});
-
-  const registeredKernels = tf.getKernelsForBackend(backend)
-  // Debug messsage with info about the registered kernels.
-  self.postMessage({
-    msg: true,
-    payload: {
-      numKernels: registeredKernels.length,
-      kernelNames: registeredKernels.map(k => k.kernelName),
-      backend,
-    }
-  });
-
-  const model = await blazeface.load();
-
-  self.postMessage({msg: true, payload: `model loaded`});
-
-  let predictions;
-  try {
-    const input = tf.tensor3d(STUBBED_IMAGE_VALS, [128, 128, 3]);
-    predictions = await model.estimateFaces(input, false /*returnTensors*/);
-    input.dispose();
-  } catch (e) {
-    self.postMessage({error: true, payload: {e}});
-  }
-
-  // send the final result of the test.
-  self.postMessage({
-    result: true,
-    payload: {
-      numKernels: registeredKernels.length,
-      kernelNames: registeredKernels.map(k => k.kernelName),
-      backend,
-      predictions: predictions
-    }
-  });
-}
-
-self.addEventListener('message', function(e) {
-  try {
-    main();
-  } catch (e) {
-    self.postMessage({error: true, payload: e});
-  }
-}, false);
diff --git a/tfjs-master/e2e/custom_module/blazeface/app_tfjs_config.json b/tfjs-master/e2e/custom_module/blazeface/app_tfjs_config.json
deleted file mode 100644
index 8df1edb77..000000000
--- a/tfjs-master/e2e/custom_module/blazeface/app_tfjs_config.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-    "kernels": [
-        "Cast",
-        "ExpandDims",
-        "Reshape",
-        "ResizeBilinear",
-        "RealDiv",
-        "Sub",
-        "Multiply",
-        "FusedConv2D",
-        "fusedConv2d__op",
-        "DepthwiseConv2dNative",
-        "Add",
-        "Relu",
-        "PadV2",
-        "Pack",
-        "MaxPool",
-        "Slice",
-        "StridedSlice",
-        "Concat",
-        "Identity",
-        "Sigmoid",
-        "NonMaxSuppressionV3"
-    ],
-    "backends": [
-        "wasm"
-    ],
-    "models": [
-        "./model.json"
-    ],
-    "outputPath": "./custom_tfjs_blazeface",
-    "forwardModeOnly": true
-}
diff --git a/tfjs-master/e2e/custom_module/blazeface/build.sh b/tfjs-master/e2e/custom_module/blazeface/build.sh
deleted file mode 100644
index bd12790c0..000000000
--- a/tfjs-master/e2e/custom_module/blazeface/build.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-yarn --mutex network
-# Ensure that we test against freshly generated custom modules.
-rm -f ./custom_tfjs_blazeface/*.js
-echo "npm version $(npm --version)"
-yarn make-custom-tfjs-modules
-# TODO(yassogba) once blazeface kernels are modularized in cpu
-# switch the config to cpu and also run and test rollup bundle.
-parallel ::: "yarn webpack:full" "yarn webpack:custom"
diff --git a/tfjs-master/e2e/custom_module/blazeface/model.json b/tfjs-master/e2e/custom_module/blazeface/model.json
deleted file mode 100644
index 1f1aa5625..000000000
--- a/tfjs-master/e2e/custom_module/blazeface/model.json
+++ /dev/null
@@ -1,5474 +0,0 @@
-{
-    "format": "graph-model",
-    "generatedBy": "1.15.0",
-    "convertedBy": "TensorFlow.js Converter v1.3.2",
-    "signature": {
-      "inputs": {
-          "input:0": {
-              "name": "input:0",
-              "dtype": "DT_FLOAT",
-              "tensorShape": {
-                  "dim": [
-                      {
-                          "size": "-1"
-                      },
-                      {
-                          "size": "128"
-                      },
-                      {
-                          "size": "128"
-                      },
-                      {
-                          "size": "3"
-                      }
-                  ]
-              }
-          }
-      },
-      "outputs": {
-          "Identity:0": {
-              "name": "Identity:0",
-              "dtype": "DT_FLOAT",
-              "tensorShape": {
-                  "dim": [
-                      {
-                          "size": "-1"
-                      },
-                      {
-                          "size": "-1"
-                      },
-                      {
-                          "size": "17"
-                      }
-                  ]
-              }
-          }
-      }
-    },
-    "modelTopology": {
-        "node": [
-            {
-                "name": "input",
-                "op": "Placeholder",
-                "attr": {
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    },
-                    "shape": {
-                        "shape": {
-                            "dim": [
-                                {
-                                    "size": "-1"
-                                },
-                                {
-                                    "size": "128"
-                                },
-                                {
-                                    "size": "128"
-                                },
-                                {
-                                    "size": "3"
-                                }
-                            ]
-                        }
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "5"
-                                    },
-                                    {
-                                        "size": "5"
-                                    },
-                                    {
-                                        "size": "3"
-                                    },
-                                    {
-                                        "size": "24"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_16/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    },
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "96"
-                                    },
-                                    {
-                                        "size": "96"
-                                    }
-                                ]
-                            }
-                        }
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "24"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_1/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "24"
-                                    },
-                                    {
-                                        "size": "24"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_16/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "96"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_1/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "24"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_2/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "24"
-                                    },
-                                    {
-                                        "size": "28"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_2/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "28"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_3/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "28"
-                                    },
-                                    {
-                                        "size": "32"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_3/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "32"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_4/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    },
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "32"
-                                    },
-                                    {
-                                        "size": "36"
-                                    }
-                                ]
-                            }
-                        }
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_4/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "36"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_5/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "36"
-                                    },
-                                    {
-                                        "size": "42"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_5/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "42"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_6/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "42"
-                                    },
-                                    {
-                                        "size": "48"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_6/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "48"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_7/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "48"
-                                    },
-                                    {
-                                        "size": "56"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_7/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "56"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_8/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    },
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "56"
-                                    },
-                                    {
-                                        "size": "64"
-                                    }
-                                ]
-                            }
-                        }
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_8/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    },
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "64"
-                                    }
-                                ]
-                            }
-                        }
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_9/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "64"
-                                    },
-                                    {
-                                        "size": "72"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_9/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "72"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_10/Conv2D_weights",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "72"
-                                    },
-                                    {
-                                        "size": "80"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
-                "name": "StatefulPartitionedCall/model/conv2d_10/Conv2D_bn_offset",
-                "op": "Const",
-                "attr": {
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "80"
-                                    }
-                                ]
-                            }
-                        }
-                    },
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    }
-                }
-            },
-            {
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-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_10/Conv2D_bn_offset",
-                    "shape": [
-                        80
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_11/Conv2D_weights",
-                    "shape": [
-                        1,
-                        1,
-                        80,
-                        88
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_11/Conv2D_bn_offset",
-                    "shape": [
-                        88
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_12/Conv2D_weights",
-                    "shape": [
-                        1,
-                        1,
-                        88,
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_12/Conv2D_bn_offset",
-                    "shape": [
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_13/Conv2D_weights",
-                    "shape": [
-                        1,
-                        1,
-                        96,
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_13/Conv2D_bn_offset",
-                    "shape": [
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_14/Conv2D_weights",
-                    "shape": [
-                        1,
-                        1,
-                        96,
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_14/Conv2D_bn_offset",
-                    "shape": [
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_15/Conv2D_weights",
-                    "shape": [
-                        1,
-                        1,
-                        96,
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/conv2d_15/Conv2D_bn_offset",
-                    "shape": [
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        24,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/channel_padding/Pad/paddings",
-                    "shape": [
-                        4,
-                        2
-                    ],
-                    "dtype": "int32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_1/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        24,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_2/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        28,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_3/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        32,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/channel_padding_3/Pad/paddings",
-                    "shape": [
-                        4,
-                        2
-                    ],
-                    "dtype": "int32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_4/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        36,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_5/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        42,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/channel_padding_5/Pad/paddings",
-                    "shape": [
-                        4,
-                        2
-                    ],
-                    "dtype": "int32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_6/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        48,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_7/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        56,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_8/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        64,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_9/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        72,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_10/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        80,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/classificator_8/Conv2D/ReadVariableOp",
-                    "shape": [
-                        1,
-                        1,
-                        88,
-                        2
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/classificator_8/BiasAdd/ReadVariableOp",
-                    "shape": [
-                        2
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/reshape/strided_slice/stack",
-                    "shape": [
-                        1
-                    ],
-                    "dtype": "int32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/reshape/strided_slice/stack_1",
-                    "shape": [
-                        1
-                    ],
-                    "dtype": "int32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/reshape/Reshape/shape/1",
-                    "shape": [],
-                    "dtype": "int32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/reshape/Reshape/shape/2",
-                    "shape": [],
-                    "dtype": "int32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_11/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        88,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_12/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        96,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_13/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        96,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_14/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        96,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/depthwise_conv2d_15/depthwise/ReadVariableOp",
-                    "shape": [
-                        3,
-                        3,
-                        96,
-                        1
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/classificator_16/Conv2D/ReadVariableOp",
-                    "shape": [
-                        1,
-                        1,
-                        96,
-                        6
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/classificator_16/BiasAdd/ReadVariableOp",
-                    "shape": [
-                        6
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/regressor_8/Conv2D/ReadVariableOp",
-                    "shape": [
-                        1,
-                        1,
-                        88,
-                        32
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/regressor_8/BiasAdd/ReadVariableOp",
-                    "shape": [
-                        32
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/reshape_1/Reshape/shape/2",
-                    "shape": [],
-                    "dtype": "int32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/regressor_16/Conv2D/ReadVariableOp",
-                    "shape": [
-                        1,
-                        1,
-                        96,
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/regressor_16/BiasAdd/ReadVariableOp",
-                    "shape": [
-                        96
-                    ],
-                    "dtype": "float32"
-                },
-                {
-                    "name": "StatefulPartitionedCall/model/objects/concat/axis",
-                    "shape": [],
-                    "dtype": "int32"
-                }
-            ]
-        }
-    ]
-}
diff --git a/tfjs-master/e2e/custom_module/blazeface/package.json b/tfjs-master/e2e/custom_module/blazeface/package.json
deleted file mode 100644
index 29b94249f..000000000
--- a/tfjs-master/e2e/custom_module/blazeface/package.json
+++ /dev/null
@@ -1,37 +0,0 @@
-{
-  "name": "blazeface-treeshake",
-  "version": "1.0.0",
-  "description": "",
-  "main": "app.js",
-  "scripts": {
-    "test": "echo \"Error: no test specified\" && exit 1",
-    "make-custom-tfjs-modules": "node ./node_modules/@tensorflow/tfjs/dist/tools/custom_module/cli.js --config app_tfjs_config.json",
-    "rollup:full": "rollup -c",
-    "rollup:custom": "rollup -c --useCustomTfjs",
-    "webpack:full": "webpack",
-    "webpack:custom": "webpack --env useCustomTfjs"
-  },
-  "dependencies": {
-    "@tensorflow-models/blazeface": "^0.0.5",
-    "@tensorflow/tfjs": "link:../../../tfjs",
-    "@tensorflow/tfjs-backend-wasm": "link:../../../link-package/node_modules/@tensorflow/tfjs-backend-wasm"
-  },
-  "devDependencies": {
-    "@rollup/plugin-alias": "^3.1.1",
-    "@rollup/plugin-commonjs": "^14.0.0",
-    "@rollup/plugin-node-resolve": "^8.4.0",
-    "file-loader": "^6.1.0",
-    "rollup": "^2.23.0",
-    "rollup-plugin-commonjs": "^10.1.0",
-    "rollup-plugin-node-resolve": "^5.2.0",
-    "rollup-plugin-terser": "^6.1.0",
-    "rollup-plugin-visualizer": "^4.0.4",
-    "terser-webpack-plugin": "^4.2.1",
-    "webpack": "^5.76.0",
-    "webpack-cli": "^4.2.0"
-  },
-  "resolutions": {
-    "node-fetch": "2.6.7",
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/e2e/custom_module/blazeface/rollup.config.js b/tfjs-master/e2e/custom_module/blazeface/rollup.config.js
deleted file mode 100644
index 9d6dde593..000000000
--- a/tfjs-master/e2e/custom_module/blazeface/rollup.config.js
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import alias from '@rollup/plugin-alias';
-import commonjs from '@rollup/plugin-commonjs';
-import resolve from '@rollup/plugin-node-resolve';
-import * as path from 'path';
-import {terser} from 'rollup-plugin-terser';
-import visualizer from 'rollup-plugin-visualizer';
-
-
-const sourcemap = false;
-
-function getPlugins(options) {
-  let plugins = [];
-
-  if (options.useCustomTfjs) {
-    plugins.push(
-        // replace top level imports to tfjs-core with custom import.
-        // after v3 is out we still need to do this in converter.
-        alias({
-          entries: [
-            {
-              find: /@tensorflow\/tfjs$/,
-              replacement: path.resolve(__dirname, options.customTfjsPath),
-            },
-            {
-              find: /@tensorflow\/tfjs-core$/,
-              replacement: path.resolve(__dirname, options.customTfjsCorePath),
-            },
-            {
-              find: '@tensorflow/tfjs-core/dist/ops/ops_for_converter',
-              replacement: path.resolve(__dirname, options.customOpsPath),
-            },
-          ],
-        }));
-  }
-
-  plugins = [
-    ...plugins,
-    resolve({browser: true, dedupe: ['seedrandom']}),
-    commonjs({include: ['node_modules/**']}),
-    terser({output: {comments: false}}),
-  ];
-
-  if (options.visualize) {
-    plugins.push(visualizer({sourcemap, filename: options.visPath}));
-  }
-
-  return plugins;
-}
-
-
-module.exports = (cmdOptions) => {
-  const {useCustomTfjs, visualize} = cmdOptions;
-  // remove custom command line options from being passed onto rollup.
-  delete cmdOptions.useCustomTfjs;
-  delete cmdOptions.visualize;
-
-  const bundles = [];
-  const outputPath = useCustomTfjs ? 'dist/custom' : 'dist/full';
-
-  bundles.push(
-      {
-        input: 'app.js',
-        output: {
-          file: `${outputPath}/app_rollup.js`,
-          sourcemap,
-          format: 'umd',
-        },
-        plugins: [
-          ...getPlugins({
-            useCustomTfjs: useCustomTfjs,
-            customTfjsPath: './custom_tfjs_blazeface/custom_tfjs.js',
-            customTfjsCorePath: './custom_tfjs_blazeface/custom_tfjs_core.js',
-            customOpsPath:
-                './custom_tfjs_blazeface/custom_ops_for_converter.js',
-            visualize: visualize,
-            visPath: `${outputPath}/app_rollup.js.html`,
-          }),
-        ],
-      },
-  );
-
-  return bundles;
-};
diff --git a/tfjs-master/e2e/custom_module/blazeface/test_data.js b/tfjs-master/e2e/custom_module/blazeface/test_data.js
deleted file mode 100644
index 11be44fc1..000000000
--- a/tfjs-master/e2e/custom_module/blazeface/test_data.js
+++ /dev/null
@@ -1,3296 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export const STUBBED_IMAGE_VALS = [
-  178, 179, 183, 151, 152, 156, 121, 122, 126, 106, 107, 111, 63,  62,  67,
-  5,   4,   9,   17,  17,  19,  13,  13,  15,  19,  19,  21,  13,  13,  15,
-  18,  16,  17,  23,  21,  22,  20,  18,  19,  15,  13,  14,  16,  15,  13,
-  19,  15,  12,  33,  23,  21,  54,  43,  39,  56,  43,  37,  63,  50,  44,
-  83,  68,  61,  55,  40,  33,  58,  41,  33,  77,  60,  52,  56,  39,  31,
-  52,  35,  27,  61,  44,  36,  60,  43,  35,  53,  36,  28,  69,  52,  44,
-  78,  63,  56,  62,  48,  39,  30,  11,  4,   48,  31,  23,  57,  42,  37,
-  46,  35,  31,  38,  26,  26,  39,  29,  27,  46,  32,  29,  47,  32,  25,
-  40,  20,  11,  57,  35,  22,  50,  26,  14,  60,  38,  25,  85,  67,  57,
-  56,  41,  34,  30,  19,  17,  63,  55,  53,  40,  31,  32,  12,  4,   2,
-  16,  6,   4,   20,  7,   1,   21,  6,   0,   18,  1,   0,   26,  9,   1,
-  24,  10,  1,   28,  13,  8,   32,  19,  13,  37,  26,  22,  27,  16,  12,
-  18,  7,   1,   28,  17,  11,  36,  21,  14,  47,  32,  25,  34,  20,  11,
-  39,  25,  16,  48,  31,  21,  20,  3,   0,   62,  42,  31,  58,  38,  27,
-  68,  44,  32,  73,  49,  37,  73,  47,  34,  80,  54,  41,  65,  37,  25,
-  51,  23,  11,  56,  26,  16,  70,  40,  30,  84,  54,  44,  78,  51,  40,
-  70,  47,  39,  44,  26,  16,  55,  38,  30,  63,  46,  38,  60,  45,  38,
-  22,  7,   0,   26,  13,  7,   31,  18,  12,  22,  9,   3,   23,  10,  4,
-  38,  23,  16,  29,  15,  6,   58,  39,  32,  70,  52,  42,  68,  48,  37,
-  84,  70,  61,  65,  60,  57,  82,  82,  82,  77,  77,  79,  72,  72,  74,
-  63,  64,  69,  59,  60,  65,  47,  50,  57,  58,  61,  68,  56,  56,  64,
-  56,  56,  64,  55,  56,  61,  55,  56,  61,  56,  55,  60,  57,  57,  59,
-  59,  57,  60,  59,  57,  58,  58,  57,  62,  57,  56,  61,  56,  55,  60,
-  55,  54,  59,  55,  54,  59,  55,  54,  59,  55,  54,  59,  55,  54,  59,
-  54,  53,  58,  53,  52,  57,  53,  52,  57,  54,  53,  58,  55,  54,  59,
-  55,  54,  59,  54,  53,  58,  54,  53,  58,  193, 194, 198, 162, 163, 167,
-  127, 128, 132, 114, 115, 119, 81,  80,  85,  25,  24,  29,  15,  15,  17,
-  12,  12,  14,  11,  11,  13,  23,  23,  23,  16,  14,  15,  10,  8,   9,
-  22,  20,  21,  19,  18,  16,  14,  13,  11,  30,  25,  22,  29,  19,  17,
-  29,  18,  12,  65,  52,  46,  84,  71,  63,  74,  59,  52,  27,  13,  4,
-  56,  39,  31,  54,  37,  29,  45,  28,  20,  60,  43,  35,  73,  56,  48,
-  68,  51,  43,  67,  50,  42,  82,  65,  57,  61,  46,  39,  11,  0,   0,
-  64,  47,  39,  57,  40,  32,  46,  31,  26,  38,  27,  25,  39,  29,  28,
-  46,  36,  34,  51,  40,  36,  56,  41,  34,  61,  41,  32,  65,  43,  32,
-  59,  35,  23,  71,  49,  36,  71,  53,  43,  50,  35,  28,  45,  34,  32,
-  44,  34,  33,  32,  23,  24,  15,  7,   5,   14,  3,   1,   15,  2,   0,
-  15,  0,   0,   18,  1,   0,   28,  11,  3,   32,  18,  9,   36,  21,  16,
-  30,  17,  11,  33,  22,  18,  42,  31,  27,  33,  22,  16,  16,  5,   0,
-  53,  38,  31,  34,  19,  12,  25,  11,  2,   47,  30,  20,  42,  25,  15,
-  61,  43,  31,  67,  47,  36,  64,  42,  29,  77,  53,  41,  76,  52,  40,
-  82,  56,  43,  59,  33,  20,  50,  22,  10,  67,  39,  27,  76,  46,  36,
-  75,  45,  35,  80,  50,  40,  72,  45,  34,  54,  32,  21,  36,  18,  8,
-  59,  40,  33,  47,  30,  22,  56,  41,  34,  38,  23,  16,  18,  5,   0,
-  29,  16,  10,  12,  0,   0,   36,  23,  15,  32,  17,  10,  43,  26,  18,
-  55,  37,  27,  68,  50,  38,  70,  50,  39,  85,  68,  60,  86,  81,  77,
-  70,  70,  70,  75,  75,  75,  86,  86,  88,  70,  69,  74,  57,  58,  63,
-  55,  56,  61,  56,  56,  64,  55,  55,  63,  56,  56,  64,  57,  58,  63,
-  58,  59,  64,  59,  58,  63,  58,  57,  62,  58,  56,  59,  57,  55,  58,
-  57,  56,  61,  57,  56,  61,  56,  55,  60,  55,  54,  59,  55,  54,  59,
-  54,  53,  58,  55,  54,  59,  55,  54,  59,  54,  53,  58,  54,  53,  58,
-  53,  52,  57,  53,  52,  57,  54,  53,  58,  54,  53,  58,  53,  52,  57,
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-  223, 163, 152, 218, 158, 147, 209, 151, 137, 203, 145, 131, 199, 143, 126,
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-  225, 231, 231, 228, 232, 231, 233, 237, 236, 232, 234, 231, 251, 251, 249,
-  253, 252, 250, 249, 249, 247, 242, 242, 242, 153, 155, 154, 13,  13,  11,
-  13,  12,  8,   20,  15,  11,  21,  12,  5,   43,  26,  18,  39,  17,  6,
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-  217, 161, 146, 213, 157, 142, 212, 154, 140, 205, 147, 133, 211, 152, 136,
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diff --git a/tfjs-master/e2e/custom_module/blazeface/webpack.config.js b/tfjs-master/e2e/custom_module/blazeface/webpack.config.js
deleted file mode 100644
index d2d62ed93..000000000
--- a/tfjs-master/e2e/custom_module/blazeface/webpack.config.js
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const path = require('path');
-const TerserPlugin = require('terser-webpack-plugin');
-
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-    entry: './app.js',
-    target: 'web',
-    output: {
-      path: path.resolve(__dirname, outputPath),
-      filename: 'app_webpack.js',
-    },
-    optimization: {
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-          cache: true,
-          parallel: true,
-          sourceMap: false,
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-    module: {
-      rules: [
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-          test: /\.wasm$/i,
-          type: 'javascript/auto',
-          use: [
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-        '@tensorflow/tfjs-core/dist/ops/ops_for_converter': path.resolve(
-            __dirname, './custom_tfjs_blazeface/custom_ops_for_converter.js'),
-      }
-    }
-  }
-  return config;
-}
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deleted file mode 100644
index 1173fe11b..000000000
--- a/tfjs-master/e2e/custom_module/blazeface/yarn.lock
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-  integrity "sha1-mqk5QVWZNHboMbqOWftXleveaAA= sha512-Svqw723a3R34KvsMgpjFBYCgNOSdcW3mQFK4wIfhGQhtaFVOJmdYoXgi63ne3dTlWgatVcUc7t4HtQ/+bUVIzQ=="
-  dependencies:
-    reduce-flatten "^2.0.0"
-    typical "^5.0.0"
-
-wrap-ansi@^6.2.0:
-  version "6.2.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-6.2.0.tgz#e9393ba07102e6c91a3b221478f0257cd2856e53"
-  integrity sha512-r6lPcBGxZXlIcymEu7InxDMhdW0KDxpLgoFLcguasxCaJ/SOIZwINatK9KY/tf+ZrlywOKU0UDj3ATXUBfxJXA==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-y18n@^4.0.0:
-  version "4.0.3"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-4.0.3.tgz#b5f259c82cd6e336921efd7bfd8bf560de9eeedf"
-  integrity sha512-JKhqTOwSrqNA1NY5lSztJ1GrBiUodLMmIZuLiDaMRJ+itFd+ABVE8XBjOvIWL+rSqNDC74LCSFmlb/U4UZ4hJQ==
-
-y18n@^5.0.1:
-  version "5.0.2"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.2.tgz#48218df5da2731b4403115c39a1af709c873f829"
-  integrity sha512-CkwaeZw6dQgqgPGeTWKMXCRmMcBgETFlTml1+ZOO+q7kGst8NREJ+eWwFNPVUQ4QGdAaklbqCZHH6Zuep1RjiA==
-
-yallist@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
-  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
-
-yargs-parser@^18.1.2:
-  version "18.1.3"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-18.1.3.tgz#be68c4975c6b2abf469236b0c870362fab09a7b0"
-  integrity sha512-o50j0JeToy/4K6OZcaQmW6lyXXKhq7csREXcDwk2omFPJEwUNOVtJKvmDr9EI1fAJZUyZcRF7kxGBWmRXudrCQ==
-  dependencies:
-    camelcase "^5.0.0"
-    decamelize "^1.2.0"
-
-yargs-parser@^20.0.0:
-  version "20.2.1"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.1.tgz#28f3773c546cdd8a69ddae68116b48a5da328e77"
-  integrity sha512-yYsjuSkjbLMBp16eaOt7/siKTjNVjMm3SoJnIg3sEh/JsvqVVDyjRKmaJV4cl+lNIgq6QEco2i3gDebJl7/vLA==
-
-yargs@^15.0.0:
-  version "15.4.1"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-15.4.1.tgz#0d87a16de01aee9d8bec2bfbf74f67851730f4f8"
-  integrity sha512-aePbxDmcYW++PaqBsJ+HYUFwCdv4LVvdnhBy78E57PIor8/OVvhMrADFFEDh8DHDFRv/O9i3lPhsENjO7QX0+A==
-  dependencies:
-    cliui "^6.0.0"
-    decamelize "^1.2.0"
-    find-up "^4.1.0"
-    get-caller-file "^2.0.1"
-    require-directory "^2.1.1"
-    require-main-filename "^2.0.0"
-    set-blocking "^2.0.0"
-    string-width "^4.2.0"
-    which-module "^2.0.0"
-    y18n "^4.0.0"
-    yargs-parser "^18.1.2"
-
-yargs@^16.0.3:
-  version "16.0.3"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.0.3.tgz#7a919b9e43c90f80d4a142a89795e85399a7e54c"
-  integrity sha512-6+nLw8xa9uK1BOEOykaiYAJVh6/CjxWXK/q9b5FpRgNslt8s22F2xMBqVIKgCRjNgGvGPBy8Vog7WN7yh4amtA==
-  dependencies:
-    cliui "^7.0.0"
-    escalade "^3.0.2"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.1"
-    yargs-parser "^20.0.0"
diff --git a/tfjs-master/e2e/custom_module/dense_model/app.js b/tfjs-master/e2e/custom_module/dense_model/app.js
deleted file mode 100644
index 9d94ca4ed..000000000
--- a/tfjs-master/e2e/custom_module/dense_model/app.js
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-
-async function main(modelUrl) {
-  await tf.ready();
-  const backend = tf.getBackend();
-  self.postMessage({
-    msg: true,
-    payload: `Backend ready: ${backend}. Got model url: ${modelUrl}`
-  });
-
-  const registeredKernels = tf.getKernelsForBackend(backend);
-  let model;
-  try {
-    model = await tf.loadGraphModel(modelUrl)
-  } catch (e) {
-    self.postMessage({error: true, payload: e});
-  }
-
-  const predictions = model.predict(tf.tensor2d([20], [1, 1])).dataSync();
-  // result is 38.17822265625
-
-  // send the final result of the test.
-  self.postMessage({
-    result: true,
-    payload: {
-      numKernels: registeredKernels.length,
-      kernelNames: registeredKernels.map(k => k.kernelName),
-      backend,
-      predictions: predictions
-    }
-  });
-}
-
-self.addEventListener('message', function(e) {
-  try {
-    main(e.data.modelUrl);
-  } catch (e) {
-    self.postMessage({error: true, payload: e});
-  }
-}, false);
diff --git a/tfjs-master/e2e/custom_module/dense_model/app_tfjs_config.json b/tfjs-master/e2e/custom_module/dense_model/app_tfjs_config.json
deleted file mode 100644
index 6bb4cedf3..000000000
--- a/tfjs-master/e2e/custom_module/dense_model/app_tfjs_config.json
+++ /dev/null
@@ -1,11 +0,0 @@
-{
-  "kernels": ["Reshape", "_FusedMatMul", "fusedMatMul__op", "Identity"],
-  "backends": [
-      "cpu"
-  ],
-  "models": [
-      "./model/model.json"
-  ],
-  "outputPath": "./custom_tfjs",
-  "forwardModeOnly": true
-}
diff --git a/tfjs-master/e2e/custom_module/dense_model/build.sh b/tfjs-master/e2e/custom_module/dense_model/build.sh
deleted file mode 100644
index 705b93531..000000000
--- a/tfjs-master/e2e/custom_module/dense_model/build.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-yarn --mutex network
-# Ensure that we test against freshly generated custom modules.
-rm -f ./custom_tfjs/*.js
-yarn make-custom-tfjs-modules
-
-parallel ::: "yarn webpack:full" \
-  "yarn webpack:custom" \
-  "yarn rollup:full" \
-  "yarn rollup:custom"
diff --git a/tfjs-master/e2e/custom_module/dense_model/model/group1-shard1of1.bin b/tfjs-master/e2e/custom_module/dense_model/model/group1-shard1of1.bin
deleted file mode 100644
index b23eb545d..000000000
--- a/tfjs-master/e2e/custom_module/dense_model/model/group1-shard1of1.bin
+++ /dev/null
@@ -1 +0,0 @@
-���?�gY�
\ No newline at end of file
diff --git a/tfjs-master/e2e/custom_module/dense_model/model/model.json b/tfjs-master/e2e/custom_module/dense_model/model/model.json
deleted file mode 100644
index 9f51cc112..000000000
--- a/tfjs-master/e2e/custom_module/dense_model/model/model.json
+++ /dev/null
@@ -1,189 +0,0 @@
-{
-    "format": "graph-model",
-    "signature": {
-      "inputs": {
-          "dense_dense1_input:0": {
-              "dtype": "DT_FLOAT",
-              "name": "dense_dense1_input:0",
-              "tensorShape": {
-                  "dim": [
-                      {
-                          "size": "-1"
-                      },
-                      {
-                          "size": "1"
-                      }
-                  ]
-              }
-          }
-      },
-      "outputs": {
-          "Identity:0": {
-              "dtype": "DT_FLOAT",
-              "name": "Identity:0",
-              "tensorShape": {
-                  "dim": [
-                      {
-                          "size": "-1"
-                      },
-                      {
-                          "size": "1"
-                      }
-                  ]
-              }
-          }
-      }
-    },
-    "modelTopology": {
-        "node": [
-            {
-                "attr": {
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    },
-                    "shape": {
-                        "shape": {
-                            "dim": [
-                                {
-                                    "size": "-1"
-                                },
-                                {
-                                    "size": "1"
-                                }
-                            ]
-                        }
-                    }
-                },
-                "name": "dense_dense1_input",
-                "op": "Placeholder"
-            },
-            {
-                "input": [
-                    "^dense_dense1_input"
-                ],
-                "attr": {
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    },
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    },
-                                    {
-                                        "size": "1"
-                                    }
-                                ]
-                            }
-                        }
-                    }
-                },
-                "name": "StatefulPartitionedCall/sequential_1/dense_Dense1/MatMul/ReadVariableOp",
-                "op": "Const"
-            },
-            {
-                "input": [
-                    "^dense_dense1_input"
-                ],
-                "attr": {
-                    "dtype": {
-                        "type": "DT_FLOAT"
-                    },
-                    "value": {
-                        "tensor": {
-                            "dtype": "DT_FLOAT",
-                            "tensorShape": {
-                                "dim": [
-                                    {
-                                        "size": "1"
-                                    }
-                                ]
-                            }
-                        }
-                    }
-                },
-                "name": "StatefulPartitionedCall/sequential_1/dense_Dense1/BiasAdd/ReadVariableOp",
-                "op": "Const"
-            },
-            {
-                "device": "/device:CPU:0",
-                "input": [
-                    "dense_dense1_input",
-                    "StatefulPartitionedCall/sequential_1/dense_Dense1/MatMul/ReadVariableOp",
-                    "StatefulPartitionedCall/sequential_1/dense_Dense1/BiasAdd/ReadVariableOp"
-                ],
-                "attr": {
-                    "epsilon": {
-                        "f": 0
-                    },
-                    "num_args": {
-                        "i": "1"
-                    },
-                    "T": {
-                        "type": "DT_FLOAT"
-                    },
-                    "transpose_b": {
-                        "b": false
-                    },
-                    "fused_ops": {
-                        "list": {
-                            "s": [
-                                "Qmlhc0FkZA=="
-                            ]
-                        }
-                    },
-                    "transpose_a": {
-                        "b": false
-                    }
-                },
-                "name": "StatefulPartitionedCall/sequential_1/dense_Dense1/BiasAdd",
-                "op": "_FusedMatMul"
-            },
-            {
-                "input": [
-                    "StatefulPartitionedCall/sequential_1/dense_Dense1/BiasAdd"
-                ],
-                "attr": {
-                    "T": {
-                        "type": "DT_FLOAT"
-                    }
-                },
-                "name": "Identity",
-                "op": "Identity"
-            }
-        ],
-        "library": {},
-        "versions": {
-            "producer": 175
-        }
-    },
-    "generatedBy": "2.1.0",
-    "weightsManifest": [
-        {
-            "paths": [
-                "group1-shard1of1.bin"
-            ],
-            "weights": [
-                {
-                    "dtype": "float32",
-                    "shape": [
-                        1,
-                        1
-                    ],
-                    "name": "StatefulPartitionedCall/sequential_1/dense_Dense1/MatMul/ReadVariableOp"
-                },
-                {
-                    "dtype": "float32",
-                    "shape": [
-                        1
-                    ],
-                    "name": "StatefulPartitionedCall/sequential_1/dense_Dense1/BiasAdd/ReadVariableOp"
-                }
-            ]
-        }
-    ],
-    "convertedBy": "TensorFlow.js Converter v2.4.0"
-}
diff --git a/tfjs-master/e2e/custom_module/dense_model/package.json b/tfjs-master/e2e/custom_module/dense_model/package.json
deleted file mode 100644
index e9fa76394..000000000
--- a/tfjs-master/e2e/custom_module/dense_model/package.json
+++ /dev/null
@@ -1,35 +0,0 @@
-{
-  "name": "dense-model-treeshake",
-  "version": "1.0.0",
-  "description": "",
-  "main": "app.js",
-  "scripts": {
-    "test": "echo \"Error: no test specified\" && exit 1",
-    "make-custom-tfjs-modules": "node ./node_modules/@tensorflow/tfjs/dist/tools/custom_module/cli.js --config app_tfjs_config.json",
-    "rollup:full": "rollup -c",
-    "rollup:custom": "rollup -c --useCustomTfjs",
-    "webpack:full": "webpack",
-    "webpack:custom": "webpack --env useCustomTfjs"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs": "link:../../../tfjs"
-  },
-  "devDependencies": {
-    "@rollup/plugin-alias": "^3.1.1",
-    "@rollup/plugin-commonjs": "^14.0.0",
-    "@rollup/plugin-node-resolve": "^8.4.0",
-    "file-loader": "^6.1.0",
-    "rollup": "^2.23.0",
-    "rollup-plugin-commonjs": "^10.1.0",
-    "rollup-plugin-node-resolve": "^5.2.0",
-    "rollup-plugin-terser": "^6.1.0",
-    "rollup-plugin-visualizer": "^4.0.4",
-    "terser-webpack-plugin": "^5.3.1",
-    "webpack": "^5.76.0",
-    "webpack-cli": "^4.9.2"
-  },
-  "resolutions": {
-    "node-fetch": "2.6.7",
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/e2e/custom_module/dense_model/rollup.config.js b/tfjs-master/e2e/custom_module/dense_model/rollup.config.js
deleted file mode 100644
index b5a9a68b2..000000000
--- a/tfjs-master/e2e/custom_module/dense_model/rollup.config.js
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import alias from '@rollup/plugin-alias';
-import commonjs from '@rollup/plugin-commonjs';
-import resolve from '@rollup/plugin-node-resolve';
-import * as path from 'path';
-import {terser} from 'rollup-plugin-terser';
-import visualizer from 'rollup-plugin-visualizer';
-
-
-const sourcemap = false;
-
-function getPlugins(options) {
-  let plugins = [];
-
-  if (options.useCustomTfjs) {
-    plugins.push(
-        // replace top level imports to tfjs-core with custom import.
-        // after v3 is out we still need to do this in converter.
-        alias({
-          entries: [
-            {
-              find: /@tensorflow\/tfjs$/,
-              replacement: path.resolve(__dirname, options.customTfjsPath),
-            },
-            {
-              find: /@tensorflow\/tfjs-core$/,
-              replacement: path.resolve(__dirname, options.customTfjsCorePath),
-            },
-            {
-              find: '@tensorflow/tfjs-core/dist/ops/ops_for_converter',
-              replacement: path.resolve(__dirname, options.customOpsPath),
-            },
-          ],
-        }));
-  }
-
-  plugins = [
-    ...plugins,
-    resolve({browser: true, dedupe: ['seedrandom']}),
-    commonjs({include: ['node_modules/**']}),
-    terser({output: {comments: false}}),
-  ];
-
-  if (options.visualize) {
-    plugins.push(visualizer({sourcemap, filename: options.visPath}));
-  }
-
-  return plugins;
-}
-
-
-module.exports = (cmdOptions) => {
-  const {useCustomTfjs, visualize} = cmdOptions;
-  // remove custom command line options from being passed onto rollup.
-  delete cmdOptions.useCustomTfjs;
-  delete cmdOptions.visualize;
-
-  const bundles = [];
-  const outputPath = useCustomTfjs ? 'dist/custom' : 'dist/full';
-
-  bundles.push(
-      {
-        input: 'app.js',
-        output: {
-          file: `${outputPath}/app_rollup.js`,
-          sourcemap,
-          format: 'umd',
-        },
-        plugins: [
-          ...getPlugins({
-            useCustomTfjs: useCustomTfjs,
-            customTfjsPath: './custom_tfjs/custom_tfjs.js',
-            customTfjsCorePath: './custom_tfjs/custom_tfjs_core.js',
-            customOpsPath: './custom_tfjs/custom_ops_for_converter.js',
-            visualize: visualize,
-            visPath: `${outputPath}/app_rollup.js.html`,
-          }),
-        ],
-      },
-  );
-
-  return bundles;
-};
diff --git a/tfjs-master/e2e/custom_module/dense_model/webpack.config.js b/tfjs-master/e2e/custom_module/dense_model/webpack.config.js
deleted file mode 100644
index 14bfdd538..000000000
--- a/tfjs-master/e2e/custom_module/dense_model/webpack.config.js
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const path = require('path');
-const TerserPlugin = require('terser-webpack-plugin');
-
-module.exports = function(env) {
-  const outputPath = (env && env.useCustomTfjs) ? 'dist/custom' : 'dist/full'
-
-  const config = {
-    mode: 'production',
-    entry: './app.js',
-    target: 'web',
-    output: {
-      path: path.resolve(__dirname, outputPath),
-      filename: 'app_webpack.js',
-    },
-    optimization: {
-      minimizer: [
-        new TerserPlugin({parallel: true, terserOptions: {}}),
-      ]
-    },
-    module: {
-      rules: [
-        {
-          test: /\.wasm$/i,
-          type: 'javascript/auto',
-          use: [
-            {
-              loader: 'file-loader',
-            },
-          ],
-        },
-      ],
-    }
-  };
-
-  if (env && env.useCustomTfjs) {
-    config.resolve = {
-      alias: {
-        '@tensorflow/tfjs$':
-            path.resolve(__dirname, './custom_tfjs/custom_tfjs.js'),
-        '@tensorflow/tfjs-core$':
-            path.resolve(__dirname, './custom_tfjs/custom_tfjs_core.js'),
-        '@tensorflow/tfjs-core/dist/ops/ops_for_converter': path.resolve(
-            __dirname, './custom_tfjs/custom_ops_for_converter.js'),
-      }
-    }
-  }
-  return config;
-}
diff --git a/tfjs-master/e2e/custom_module/dense_model/yarn.lock b/tfjs-master/e2e/custom_module/dense_model/yarn.lock
deleted file mode 100644
index ca6d72ed3..000000000
--- a/tfjs-master/e2e/custom_module/dense_model/yarn.lock
+++ /dev/null
@@ -1,1613 +0,0 @@
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-    "@webassemblyjs/wasm-edit" "1.11.1"
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-    acorn "^8.7.1"
-    acorn-import-assertions "^1.7.6"
-    browserslist "^4.14.5"
-    chrome-trace-event "^1.0.2"
-    enhanced-resolve "^5.10.0"
-    es-module-lexer "^0.9.0"
-    eslint-scope "5.1.1"
-    events "^3.2.0"
-    glob-to-regexp "^0.4.1"
-    graceful-fs "^4.2.9"
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-    loader-runner "^4.2.0"
-    mime-types "^2.1.27"
-    neo-async "^2.6.2"
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-    tapable "^2.1.1"
-    terser-webpack-plugin "^5.1.3"
-    watchpack "^2.4.0"
-    webpack-sources "^3.2.3"
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-  version "5.0.0"
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-  resolved "https://registry.yarnpkg.com/which-module/-/which-module-2.0.0.tgz#d9ef07dce77b9902b8a3a8fa4b31c3e3f7e6e87a"
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-  version "7.0.0"
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diff --git a/tfjs-master/e2e/custom_module/development.md b/tfjs-master/e2e/custom_module/development.md
deleted file mode 100644
index 04e1be669..000000000
--- a/tfjs-master/e2e/custom_module/development.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# A few notes on the custom_module subfolders
-
-<!-- because json does not support comments -->
-
-In each folders package.json there is an entry like:
-
-`"make-custom-tfjs-modules": "node ./node_modules/@tensorflow/tfjs/dist/tools/custom_module/cli.js --config app_tfjs_config.json",`
-
-This would normally look like:
-
-`"make-custom-tfjs-modules": "npx tfjs-custom-module --config app_tfjs_config.json",`
-
-However when yarn is installing a dependency specified with `link://` it does
-not properly symlink `bin` scripts in the dependencies.
-
-This can be fixed by changing `link://` to (the potentially more standard)
-`file:..` (it also works when installing from npm). However that would
-potentially introduce skew in the lock files as the paths generated after
-running yarn are have parts local to the specific file system. We also have
-other scripts that look to replace `link` dependencies with npm ones.
-
-This file is just to document that quirk.
-
diff --git a/tfjs-master/e2e/custom_module/universal_sentence_encoder/app.js b/tfjs-master/e2e/custom_module/universal_sentence_encoder/app.js
deleted file mode 100644
index 379ef6436..000000000
--- a/tfjs-master/e2e/custom_module/universal_sentence_encoder/app.js
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as use from '@tensorflow-models/universal-sentence-encoder';
-import * as tf from '@tensorflow/tfjs';
-
-async function main() {
-  await tf.ready();
-  const backend = tf.getBackend();
-  self.postMessage({msg: true, payload: `Backend ready: ${backend}.`});
-
-  const registeredKernels = tf.getKernelsForBackend(backend);
-  let model;
-  try {
-    model = await use.load();
-  } catch (e) {
-    self.postMessage({error: true, payload: e});
-  }
-
-  const sentences = ['Hello.', 'How are you?'];
-  const predictions = await model.embed(sentences);
-  // shape of predictions should be [2, 512]
-
-  // send the final result of the test.
-  self.postMessage({
-    result: true,
-    payload: {
-      numKernels: registeredKernels.length,
-      kernelNames: registeredKernels.map(k => k.kernelName),
-      backend,
-      predictions: predictions
-    }
-  });
-}
-
-self.addEventListener('message', function(e) {
-  try {
-    if (e.data.profile) {
-      tf.profile(() => main()).then((profile) => {
-        const usedKernels = profile.kernelNames;
-        self.postMessage({msg: true, payload: {usedKernels}});
-      });
-    } else {
-      main();
-    }
-  } catch (e) {
-    self.postMessage({error: true, payload: e});
-  }
-}, false);
diff --git a/tfjs-master/e2e/custom_module/universal_sentence_encoder/app_tfjs_config.json b/tfjs-master/e2e/custom_module/universal_sentence_encoder/app_tfjs_config.json
deleted file mode 100644
index d64b91171..000000000
--- a/tfjs-master/e2e/custom_module/universal_sentence_encoder/app_tfjs_config.json
+++ /dev/null
@@ -1,47 +0,0 @@
-{
-  "kernels": [
-    "StridedSlice",
-    "Less",
-    "Cast",
-    "Reshape",
-    "GatherV2",
-    "Max",
-    "Add",
-    "Maximum",
-    "SparseToDense",
-    "Greater",
-    "Sum",
-    "ExpandDims",
-    "Concat",
-    "LogicalNot",
-    "Multiply",
-    "ScatterNd",
-    "GatherNd",
-    "Cos",
-    "Sin",
-    "BatchMatMul",
-    "Mean",
-    "Sub",
-    "Square",
-    "Rsqrt",
-    "Conv2D",
-    "SplitV",
-    "Pack",
-    "Transpose",
-    "Slice",
-    "Softmax",
-    "Prod",
-    "Relu",
-    "Range",
-    "RealDiv",
-    "Tanh"
-  ],
-  "backends": [
-      "webgl"
-  ],
-  "models": [
-      "./model.json"
-  ],
-  "outputPath": "./custom_tfjs",
-  "forwardModeOnly": true
-}
diff --git a/tfjs-master/e2e/custom_module/universal_sentence_encoder/build.sh b/tfjs-master/e2e/custom_module/universal_sentence_encoder/build.sh
deleted file mode 100644
index 81fa5f87c..000000000
--- a/tfjs-master/e2e/custom_module/universal_sentence_encoder/build.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-yarn --mutex network
-# Ensure that we test against freshly generated custom modules.
-rm -f ./custom_tfjs/*.js
-yarn make-custom-tfjs-modules
-
-parallel ::: "yarn webpack:full" \
-  "yarn webpack:custom" \
-  "yarn rollup:full" \
-  "yarn rollup:custom"
-
diff --git a/tfjs-master/e2e/custom_module/universal_sentence_encoder/model.json b/tfjs-master/e2e/custom_module/universal_sentence_encoder/model.json
deleted file mode 100644
index 175278573..000000000
--- a/tfjs-master/e2e/custom_module/universal_sentence_encoder/model.json
+++ /dev/null
@@ -1,7182 +0,0 @@
-{
-  "modelTopology": {
-    "node": [
-      {
-        "input": [],
-        "attr": {
-          "value": {
-            "tensor": {
-              "floatVal": [],
-              "doubleVal": [],
-              "intVal": [],
-              "stringVal": [],
-              "scomplexVal": [],
-              "int64Val": [],
-              "boolVal": [],
-              "uint32Val": [],
-              "uint64Val": [],
-              "dtype": 1,
-              "tensorShape": {
-                "dim": [
-                  {
-                    "size": "1536"
-                  },
-                  {
-                    "size": "512"
-                  }
-                ]
-              }
-            }
-          },
-          "dtype": {
-            "type": 1
-          }
-        },
-        "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/TransformerStack/Layer_1/TransformerLayer/FFN/conv2/Tensordot/Reshape_1",
-        "op": "Const"
-      },
-      {
-        "input": [],
-        "attr": {
-          "value": {
-            "tensor": {
-              "floatVal": [],
-              "doubleVal": [],
-              "intVal": [],
-              "stringVal": [],
-              "scomplexVal": [],
-              "int64Val": [],
-              "boolVal": [],
-              "uint32Val": [],
-              "uint64Val": [],
-              "dtype": 1,
-              "tensorShape": {
-                "dim": [
-                  {
-                    "size": "512"
-                  },
-                  {
-                    "size": "1536"
-                  }
-                ]
-              }
-            }
-          },
-          "dtype": {
-            "type": 1
-          }
-        },
-        "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/TransformerStack/Layer_1/TransformerLayer/FFN/conv1/Tensordot/Reshape_1",
-        "op": "Const"
-      },
-      {
-        "input": [],
-        "attr": {
-          "dtype": {
-            "type": 1
-          },
-          "value": {
-            "tensor": {
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-                "dim": [
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-                  }
-                ]
-              }
-            }
-          }
-        },
-        "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/Layer_1/TransformerLayer/FFN/layer_prepostprocess/layer_norm/layer_norm_scale/ConcatPartitions/concat",
-        "op": "Const"
-      },
-      {
-        "input": [],
-        "attr": {
-          "value": {
-            "tensor": {
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-        "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/Layer_1/TransformerLayer/FFN/layer_prepostprocess/layer_norm/layer_norm_bias/ConcatPartitions/concat",
-        "op": "Const"
-      },
-      {
-        "input": [],
-        "attr": {
-          "value": {
-            "tensor": {
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-        },
-        "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/Layer_1/TransformerLayer/FFN/conv1/bias/ConcatPartitions/concat",
-        "op": "Const"
-      },
-      {
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-        "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/Layer_1/TransformerLayer/FFN/conv2/bias/ConcatPartitions/concat",
-        "op": "Const"
-      },
-      {
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-        "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/TransformerStack/Layer_1/TransformerLayer/MultiheadAttention/mul/y",
-        "op": "Const"
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-        "name": "module/Encoder_en/KonaTransformer/Encode/Layer_1/TransformerLayer/MultiheadAttention/qkv_transform_single/kernel/part_0",
-        "op": "Const"
-      },
-      {
-        "input": [],
-        "attr": {
-          "value": {
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-          "dtype": "float32"
-        },
-        {
-          "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/TransformerStack/Layer_1/TransformerLayer/MultiheadAttention/DotProductAttention/attention_bias_ignore_padding/mul/y",
-          "shape": [],
-          "dtype": "float32"
-        },
-        {
-          "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/Layer_0/TransformerLayer/layer_prepostprocess/layer_norm/layer_norm_scale/ConcatPartitions/concat",
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-          "dtype": "float32"
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-        {
-          "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/TransformerStack/Layer_1/TransformerLayer/FFN/layer_prepostprocess/layer_norm/Cast/x",
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-          "name": "module/Encoder_en/KonaTransformer/Encode/Layer_0/TransformerLayer/MultiheadAttention/qkv_transform_single/kernel/part_0",
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-            256,
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-          "dtype": "int32"
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-          "shape": [],
-          "dtype": "int32"
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-          "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/TransformerStack/Layer_0/TransformerLayer/MultiheadAttention/split_heads/split_last_dimension/Reshape/shape/3",
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-          "dtype": "int32"
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-          "shape": [],
-          "dtype": "int32"
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-        {
-          "name": "module/Encoder_en/KonaTransformer/Encode/Layer_0/TransformerLayer/MultiheadAttention/output_transform_single/kernel/part_0",
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-          "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/Layer_0/TransformerLayer/MultiheadAttention/output_transform_single/bias/ConcatPartitions/concat",
-          "shape": [
-            512
-          ],
-          "dtype": "float32"
-        },
-        {
-          "name": "module/Embeddings_en",
-          "shape": [
-            8002,
-            256
-          ],
-          "dtype": "float32"
-        },
-        {
-          "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/TransformerStack/Layer_0/AddTimingSignal/TimingSignal/ExpandDims_1",
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-          "dtype": "int32"
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-          "dtype": "int32"
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-          "dtype": "int32"
-        },
-        {
-          "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/SparseToDense/default_value",
-          "shape": [],
-          "dtype": "int32"
-        },
-        {
-          "name": "module/Encoder_en/hidden_layers/tanh_layer_0/weights",
-          "shape": [
-            512,
-            512
-          ],
-          "dtype": "float32"
-        },
-        {
-          "name": "module/Encoder_en/hidden_layers/tanh_layer_0/bias",
-          "shape": [
-            512
-          ],
-          "dtype": "float32"
-        },
-        {
-          "name": "module_apply_default/Encoder_en/KonaTransformer/Encode/TransformerStack/Layer_1/TransformerLayer/MultiheadAttention/DotProductAttention/attention_bias_ignore_padding/ExpandDims/dim",
-          "shape": [],
-          "dtype": "int32"
-        },
-        {
-          "name": "module_apply_default/Encoder_en/hidden_layers/l2_normalize/Maximum/y",
-          "shape": [],
-          "dtype": "float32"
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/tfjs-master/e2e/custom_module/universal_sentence_encoder/package.json b/tfjs-master/e2e/custom_module/universal_sentence_encoder/package.json
deleted file mode 100644
index 6faf624cd..000000000
--- a/tfjs-master/e2e/custom_module/universal_sentence_encoder/package.json
+++ /dev/null
@@ -1,35 +0,0 @@
-{
-  "name": "use-treeshake",
-  "version": "1.0.0",
-  "description": "",
-  "main": "app.js",
-  "scripts": {
-    "test": "echo \"Error: no test specified\" && exit 1",
-    "make-custom-tfjs-modules": "node ./node_modules/@tensorflow/tfjs/dist/tools/custom_module/cli.js --config app_tfjs_config.json",
-    "rollup:full": "rollup -c",
-    "rollup:custom": "rollup -c --useCustomTfjs",
-    "webpack:full": "webpack",
-    "webpack:custom": "webpack --env useCustomTfjs"
-  },
-  "dependencies": {
-    "@tensorflow-models/universal-sentence-encoder": "^1.3.2",
-    "@tensorflow/tfjs": "link:../../../tfjs"
-  },
-  "devDependencies": {
-    "@rollup/plugin-alias": "^3.1.1",
-    "@rollup/plugin-commonjs": "^14.0.0",
-    "@rollup/plugin-node-resolve": "^8.4.0",
-    "file-loader": "^6.1.0",
-    "rollup": "^2.23.0",
-    "rollup-plugin-commonjs": "^10.1.0",
-    "rollup-plugin-node-resolve": "^5.2.0",
-    "rollup-plugin-terser": "^6.1.0",
-    "rollup-plugin-visualizer": "^4.0.4",
-    "terser-webpack-plugin": "^5.3.1",
-    "webpack": "^5.76.0",
-    "webpack-cli": "^4.9.2"
-  },
-  "resolutions": {
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/e2e/custom_module/universal_sentence_encoder/rollup.config.js b/tfjs-master/e2e/custom_module/universal_sentence_encoder/rollup.config.js
deleted file mode 100644
index b5a9a68b2..000000000
--- a/tfjs-master/e2e/custom_module/universal_sentence_encoder/rollup.config.js
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import alias from '@rollup/plugin-alias';
-import commonjs from '@rollup/plugin-commonjs';
-import resolve from '@rollup/plugin-node-resolve';
-import * as path from 'path';
-import {terser} from 'rollup-plugin-terser';
-import visualizer from 'rollup-plugin-visualizer';
-
-
-const sourcemap = false;
-
-function getPlugins(options) {
-  let plugins = [];
-
-  if (options.useCustomTfjs) {
-    plugins.push(
-        // replace top level imports to tfjs-core with custom import.
-        // after v3 is out we still need to do this in converter.
-        alias({
-          entries: [
-            {
-              find: /@tensorflow\/tfjs$/,
-              replacement: path.resolve(__dirname, options.customTfjsPath),
-            },
-            {
-              find: /@tensorflow\/tfjs-core$/,
-              replacement: path.resolve(__dirname, options.customTfjsCorePath),
-            },
-            {
-              find: '@tensorflow/tfjs-core/dist/ops/ops_for_converter',
-              replacement: path.resolve(__dirname, options.customOpsPath),
-            },
-          ],
-        }));
-  }
-
-  plugins = [
-    ...plugins,
-    resolve({browser: true, dedupe: ['seedrandom']}),
-    commonjs({include: ['node_modules/**']}),
-    terser({output: {comments: false}}),
-  ];
-
-  if (options.visualize) {
-    plugins.push(visualizer({sourcemap, filename: options.visPath}));
-  }
-
-  return plugins;
-}
-
-
-module.exports = (cmdOptions) => {
-  const {useCustomTfjs, visualize} = cmdOptions;
-  // remove custom command line options from being passed onto rollup.
-  delete cmdOptions.useCustomTfjs;
-  delete cmdOptions.visualize;
-
-  const bundles = [];
-  const outputPath = useCustomTfjs ? 'dist/custom' : 'dist/full';
-
-  bundles.push(
-      {
-        input: 'app.js',
-        output: {
-          file: `${outputPath}/app_rollup.js`,
-          sourcemap,
-          format: 'umd',
-        },
-        plugins: [
-          ...getPlugins({
-            useCustomTfjs: useCustomTfjs,
-            customTfjsPath: './custom_tfjs/custom_tfjs.js',
-            customTfjsCorePath: './custom_tfjs/custom_tfjs_core.js',
-            customOpsPath: './custom_tfjs/custom_ops_for_converter.js',
-            visualize: visualize,
-            visPath: `${outputPath}/app_rollup.js.html`,
-          }),
-        ],
-      },
-  );
-
-  return bundles;
-};
diff --git a/tfjs-master/e2e/custom_module/universal_sentence_encoder/webpack.config.js b/tfjs-master/e2e/custom_module/universal_sentence_encoder/webpack.config.js
deleted file mode 100644
index 14bfdd538..000000000
--- a/tfjs-master/e2e/custom_module/universal_sentence_encoder/webpack.config.js
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const path = require('path');
-const TerserPlugin = require('terser-webpack-plugin');
-
-module.exports = function(env) {
-  const outputPath = (env && env.useCustomTfjs) ? 'dist/custom' : 'dist/full'
-
-  const config = {
-    mode: 'production',
-    entry: './app.js',
-    target: 'web',
-    output: {
-      path: path.resolve(__dirname, outputPath),
-      filename: 'app_webpack.js',
-    },
-    optimization: {
-      minimizer: [
-        new TerserPlugin({parallel: true, terserOptions: {}}),
-      ]
-    },
-    module: {
-      rules: [
-        {
-          test: /\.wasm$/i,
-          type: 'javascript/auto',
-          use: [
-            {
-              loader: 'file-loader',
-            },
-          ],
-        },
-      ],
-    }
-  };
-
-  if (env && env.useCustomTfjs) {
-    config.resolve = {
-      alias: {
-        '@tensorflow/tfjs$':
-            path.resolve(__dirname, './custom_tfjs/custom_tfjs.js'),
-        '@tensorflow/tfjs-core$':
-            path.resolve(__dirname, './custom_tfjs/custom_tfjs_core.js'),
-        '@tensorflow/tfjs-core/dist/ops/ops_for_converter': path.resolve(
-            __dirname, './custom_tfjs/custom_ops_for_converter.js'),
-      }
-    }
-  }
-  return config;
-}
diff --git a/tfjs-master/e2e/custom_module/universal_sentence_encoder/yarn.lock b/tfjs-master/e2e/custom_module/universal_sentence_encoder/yarn.lock
deleted file mode 100644
index e94b3433e..000000000
--- a/tfjs-master/e2e/custom_module/universal_sentence_encoder/yarn.lock
+++ /dev/null
@@ -1,1499 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@babel/code-frame@^7.8.3":
-  version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.16.7.tgz#44416b6bd7624b998f5b1af5d470856c40138789"
-  integrity sha512-iAXqUn8IIeBTNd72xsFlgaXHkMBMt6y4HJp1tIaK465CWLT/fG1aqB7ykr95gHHmlBdGbFeWWfyB4NJJ0nmeIg==
-  dependencies:
-    "@babel/highlight" "^7.16.7"
-
-"@babel/helper-validator-identifier@^7.16.7":
-  version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-validator-identifier/-/helper-validator-identifier-7.16.7.tgz#e8c602438c4a8195751243da9031d1607d247cad"
-  integrity sha512-hsEnFemeiW4D08A5gUAZxLBTXpZ39P+a+DGDsHw1yxqyQ/jzFEnxf5uTEGp+3bzAbNOxU1paTgYS4ECU/IgfDw==
-
-"@babel/highlight@^7.16.7":
-  version "7.16.10"
-  resolved "https://registry.yarnpkg.com/@babel/highlight/-/highlight-7.16.10.tgz#744f2eb81579d6eea753c227b0f570ad785aba88"
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-  dependencies:
-    "@babel/helper-validator-identifier" "^7.16.7"
-    chalk "^2.0.0"
-    js-tokens "^4.0.0"
-
-"@discoveryjs/json-ext@^0.5.0":
-  version "0.5.7"
-  resolved "https://registry.yarnpkg.com/@discoveryjs/json-ext/-/json-ext-0.5.7.tgz#1d572bfbbe14b7704e0ba0f39b74815b84870d70"
-  integrity sha512-dBVuXR082gk3jsFp7Rd/JI4kytwGHecnCoTtXFb7DB6CNHp4rg5k1bhg0nWdLGLnOV71lmDzGQaLMy8iPLY0pw==
-
-"@rollup/plugin-alias@^3.1.1":
-  version "3.1.9"
-  resolved "https://registry.yarnpkg.com/@rollup/plugin-alias/-/plugin-alias-3.1.9.tgz#a5d267548fe48441f34be8323fb64d1d4a1b3fdf"
-  integrity sha512-QI5fsEvm9bDzt32k39wpOwZhVzRcL5ydcffUHMyLVaVaLeC70I8TJZ17F1z1eMoLu4E/UOcH9BWVkKpIKdrfiw==
-  dependencies:
-    slash "^3.0.0"
-
-"@rollup/plugin-commonjs@^14.0.0":
-  version "14.0.0"
-  resolved "https://registry.yarnpkg.com/@rollup/plugin-commonjs/-/plugin-commonjs-14.0.0.tgz#4285f9ec2db686a31129e5a2b415c94aa1f836f0"
-  integrity sha512-+PSmD9ePwTAeU106i9FRdc+Zb3XUWyW26mo5Atr2mk82hor8+nPwkztEjFo8/B1fJKfaQDg9aM2bzQkjhi7zOw==
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-    source-map "~0.6.1"
-    source-map-support "~0.5.12"
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-  version "5.12.1"
-  resolved "https://registry.yarnpkg.com/terser/-/terser-5.12.1.tgz#4cf2ebed1f5bceef5c83b9f60104ac4a78b49e9c"
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-  dependencies:
-    acorn "^8.5.0"
-    commander "^2.20.0"
-    source-map "~0.7.2"
-    source-map-support "~0.5.20"
-
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-  version "0.0.3"
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-  version "4.4.1"
-  resolved "https://registry.yarnpkg.com/uri-js/-/uri-js-4.4.1.tgz#9b1a52595225859e55f669d928f88c6c57f2a77e"
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-    punycode "^2.1.0"
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-  version "2.4.0"
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-    glob-to-regexp "^0.4.1"
-    graceful-fs "^4.1.2"
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-  version "3.0.1"
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-
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-  version "4.9.2"
-  resolved "https://registry.yarnpkg.com/webpack-cli/-/webpack-cli-4.9.2.tgz#77c1adaea020c3f9e2db8aad8ea78d235c83659d"
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-    "@discoveryjs/json-ext" "^0.5.0"
-    "@webpack-cli/configtest" "^1.1.1"
-    "@webpack-cli/info" "^1.4.1"
-    "@webpack-cli/serve" "^1.6.1"
-    colorette "^2.0.14"
-    commander "^7.0.0"
-    execa "^5.0.0"
-    fastest-levenshtein "^1.0.12"
-    import-local "^3.0.2"
-    interpret "^2.2.0"
-    rechoir "^0.7.0"
-    webpack-merge "^5.7.3"
-
-webpack-merge@^5.7.3:
-  version "5.8.0"
-  resolved "https://registry.yarnpkg.com/webpack-merge/-/webpack-merge-5.8.0.tgz#2b39dbf22af87776ad744c390223731d30a68f61"
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-    clone-deep "^4.0.1"
-    wildcard "^2.0.0"
-
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-  version "3.2.3"
-  resolved "https://registry.yarnpkg.com/webpack-sources/-/webpack-sources-3.2.3.tgz#2d4daab8451fd4b240cc27055ff6a0c2ccea0cde"
-  integrity sha512-/DyMEOrDgLKKIG0fmvtz+4dUX/3Ghozwgm6iPp8KRhvn+eQf9+Q7GWxVNMk3+uCPWfdXYC4ExGBckIXdFEfH1w==
-
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-  version "5.76.0"
-  resolved "https://registry.yarnpkg.com/webpack/-/webpack-5.76.0.tgz#f9fb9fb8c4a7dbdcd0d56a98e56b8a942ee2692c"
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-    "@types/eslint-scope" "^3.7.3"
-    "@types/estree" "^0.0.51"
-    "@webassemblyjs/ast" "1.11.1"
-    "@webassemblyjs/wasm-edit" "1.11.1"
-    "@webassemblyjs/wasm-parser" "1.11.1"
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-    chrome-trace-event "^1.0.2"
-    enhanced-resolve "^5.10.0"
-    es-module-lexer "^0.9.0"
-    eslint-scope "5.1.1"
-    events "^3.2.0"
-    glob-to-regexp "^0.4.1"
-    graceful-fs "^4.2.9"
-    json-parse-even-better-errors "^2.3.1"
-    loader-runner "^4.2.0"
-    mime-types "^2.1.27"
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-    tapable "^2.1.1"
-    terser-webpack-plugin "^5.1.3"
-    watchpack "^2.4.0"
-    webpack-sources "^3.2.3"
-
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-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
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-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
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-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/which/-/which-2.0.2.tgz#7c6a8dd0a636a0327e10b59c9286eee93f3f51b1"
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-  dependencies:
-    isexe "^2.0.0"
-
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/wildcard/-/wildcard-2.0.0.tgz#a77d20e5200c6faaac979e4b3aadc7b3dd7f8fec"
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-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
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-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
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-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
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-  version "20.2.9"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
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-
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-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
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-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
diff --git a/tfjs-master/e2e/integration_tests/backends_test.ts b/tfjs-master/e2e/integration_tests/backends_test.ts
deleted file mode 100644
index b3cc7630a..000000000
--- a/tfjs-master/e2e/integration_tests/backends_test.ts
+++ /dev/null
@@ -1,204 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-import '@tensorflow/tfjs-backend-webgpu';
-
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {SMOKE} from './constants';
-
-/**
- *  This file tests backend switching scenario.
- */
-// TODO: Support backend switching between wasm and cpu.
-// https://github.com/tensorflow/tfjs/issues/7623
-describeWithFlags(
-    `${SMOKE} backend switching`, {
-      predicate: testEnv =>
-          testEnv.backendName !== 'cpu' && testEnv.backendName !== 'wasm'
-    },
-
-    (env) => {
-      it(`from ${env.name} to cpu.`, async () => {
-        await tfc.setBackend(env.name);
-
-        const backendBefore = tfc.engine().backend.numDataIds();
-
-        const input = tfc.tensor2d([1, 1, 1, 1], [2, 2], 'float32');
-        // input is stored in backend.
-
-        const inputReshaped = tfc.reshape(input, [2, 2]);
-
-        const backendAfter = tfc.engine().backend.numDataIds();
-
-        expect(backendAfter).toEqual(backendBefore + 1);
-
-        await tfc.setBackend('cpu');
-
-        const cpuBefore = tfc.engine().backend.numDataIds();
-
-        const inputReshaped2 = tfc.reshape(inputReshaped, [2, 2]);
-        // input moved to cpu.
-
-        // Because input is moved to cpu, data should be deleted from backend.
-        expect(tfc.findBackend(env.name).numDataIds())
-            .toEqual(backendAfter - 1);
-
-        const cpuAfter = tfc.engine().backend.numDataIds();
-
-        expect(cpuAfter).toEqual(cpuBefore + 1);
-
-        input.dispose();
-        expect(tfc.engine().backend.numDataIds()).toEqual(cpuAfter);
-
-        inputReshaped.dispose();
-
-        expect(tfc.engine().backend.numDataIds()).toEqual(cpuAfter);
-
-        inputReshaped2.dispose();
-
-        const after = tfc.engine().backend.numDataIds();
-
-        expect(after).toBe(cpuBefore);
-      });
-
-      it(`from cpu to ${env.name}.`, async () => {
-        await tfc.setBackend('cpu');
-
-        const cpuBefore = tfc.engine().backend.numDataIds();
-
-        const input = tfc.tensor2d([1, 1, 1, 1], [2, 2], 'float32');
-        // input is stored in cpu backend.
-
-        const inputReshaped = tfc.reshape(input, [2, 2]);
-
-        const cpuAfter = tfc.engine().backend.numDataIds();
-
-        expect(cpuAfter).toEqual(cpuBefore + 1);
-
-        await tfc.setBackend(env.name);
-
-        const backendBefore = tfc.engine().backend.numDataIds();
-
-        const inputReshaped2 = tfc.reshape(inputReshaped, [2, 2]);
-        // input moved to webgl or webgpu.
-
-        // Because input is moved to backend, data should be deleted
-        // from cpu.
-        expect(tfc.findBackend('cpu').numDataIds()).toEqual(cpuAfter - 1);
-
-        const backendAfter = tfc.engine().backend.numDataIds();
-
-        expect(backendAfter).toEqual(backendBefore + 1);
-
-        input.dispose();
-
-        expect(tfc.engine().backend.numDataIds()).toEqual(backendAfter);
-
-        inputReshaped.dispose();
-
-        expect(tfc.engine().backend.numDataIds()).toEqual(backendAfter);
-
-        inputReshaped2.dispose();
-
-        const after = tfc.engine().backend.numDataIds();
-
-        expect(after).toBe(backendBefore);
-      });
-
-      it('can execute op with data from mixed backends', async () => {
-        const numTensors = tfc.memory().numTensors;
-        const backendNumDataIds = tfc.findBackend(env.name).numDataIds();
-        const cpuNumDataIds = tfc.findBackend('cpu').numDataIds();
-
-        await tfc.setBackend('cpu');
-        // This scalar lives in cpu.
-        const a = tfc.scalar(5);
-
-        await tfc.setBackend(env.name);
-        // This scalar lives in webgl or webgpu.
-        const b = tfc.scalar(3);
-
-        // Verify that ops can execute with mixed backend data.
-        tfc.engine().startScope();
-
-        await tfc.setBackend('cpu');
-        const result = tfc.add(a, b);
-        tfc.test_util.expectArraysClose(await result.data(), [8]);
-        expect(tfc.findBackend('cpu').numDataIds()).toBe(cpuNumDataIds + 3);
-
-        await tfc.setBackend(env.name);
-        tfc.test_util.expectArraysClose(await tfc.add(a, b).data(), [8]);
-        expect(tfc.findBackend(env.name).numDataIds())
-            .toBe(backendNumDataIds + 3);
-
-        tfc.engine().endScope();
-
-        expect(tfc.memory().numTensors).toBe(numTensors + 2);
-        expect(tfc.findBackend(env.name).numDataIds())
-            .toBe(backendNumDataIds + 2);
-        expect(tfc.findBackend('cpu').numDataIds()).toBe(cpuNumDataIds);
-
-        tfc.dispose([a, b]);
-
-        expect(tfc.memory().numTensors).toBe(numTensors);
-        expect(tfc.findBackend(env.name).numDataIds()).toBe(backendNumDataIds);
-        expect(tfc.findBackend('cpu').numDataIds()).toBe(cpuNumDataIds);
-      });
-
-      // tslint:disable-next-line: ban
-      xit(`can move complex tensor from cpu to ${env.name}.`, async () => {
-        await tfc.setBackend('cpu');
-
-        const real1 = tfc.tensor1d([1]);
-        const imag1 = tfc.tensor1d([2]);
-        const complex1 = tfc.complex(real1, imag1);
-
-        await tfc.setBackend(env.name);
-
-        const real2 = tfc.tensor1d([3]);
-        const imag2 = tfc.tensor1d([4]);
-        const complex2 = tfc.complex(real2, imag2);
-
-        const result = complex1.add(complex2);
-
-        tfc.test_util.expectArraysClose(await result.data(), [4, 6]);
-      });
-
-      // tslint:disable-next-line: ban
-      xit(`can move complex tensor from ${env.name} to cpu.`, async () => {
-        await tfc.setBackend(env.name);
-
-        const real1 = tfc.tensor1d([1]);
-        const imag1 = tfc.tensor1d([2]);
-        const complex1 = tfc.complex(real1, imag1);
-
-        await tfc.setBackend('cpu');
-
-        const real2 = tfc.tensor1d([3]);
-        const imag2 = tfc.tensor1d([4]);
-        const complex2 = tfc.complex(real2, imag2);
-
-        const result = complex1.add(complex2);
-
-        tfc.test_util.expectArraysClose(await result.data(), [4, 6]);
-      });
-    });
diff --git a/tfjs-master/e2e/integration_tests/constants.ts b/tfjs-master/e2e/integration_tests/constants.ts
deleted file mode 100644
index e6cba15c0..000000000
--- a/tfjs-master/e2e/integration_tests/constants.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/** Smoke tests run in PR and nightly builds. */
-export const SMOKE = '#SMOKE';
-/** Regression tests run in nightly builds. */
-export const REGRESSION = '#REGRESSION';
-// TODO: Make golden tests part of regression tests.
-/** Golden regression tests */
-export const GOLDEN = '#GOLDEN';
-
-/** Testing tags. */
-export const TAGS = [SMOKE, REGRESSION, GOLDEN];
-
-/** Testing backends. */
-export const BACKENDS = ['cpu', 'webgl', 'webgpu'];
-
-/** Testing models for CUJ: create -> save -> predict. */
-export const LAYERS_MODELS = [
-  'mlp', 'cnn', 'depthwise_cnn', 'simple_rnn', 'gru', 'bidirectional_lstm',
-  'time_distributed_lstm', 'one_dimensional', 'functional_merge'
-];
-
-export const CONVERT_PREDICT_MODELS = {
-  graph_model: [
-    'saved_model_v1', 'saved_model_v2', 'saved_model_v2_with_control_flow',
-    'saved_model_with_conv2d', 'saved_model_with_prelu',
-    'saved_model_v2_complex64', 'saved_model_v2_with_control_flow_v2',
-    'saved_model_v2_with_tensorlist_ops', 'saved_model_v1_with_hashtable',
-    'saved_model_v2_with_hashtable'
-  ],
-  layers_model: ['mobilenet']
-};
-
-/** Karma server directory serving local files. */
-export const KARMA_SERVER = './base/integration_tests';
diff --git a/tfjs-master/e2e/integration_tests/convert_predict.py b/tfjs-master/e2e/integration_tests/convert_predict.py
deleted file mode 100644
index a44b96aaa..000000000
--- a/tfjs-master/e2e/integration_tests/convert_predict.py
+++ /dev/null
@@ -1,520 +0,0 @@
-# @license
-# Copyright 2019 Google LLC. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# This file is 1/2 of the test suites for CUJ: convert->predict.
-#
-# This file does below things:
-# - Create saved models with TensorFlow.
-# - Convert the saved models to tfjs format and store in files.
-# - Store inputs in files.
-# - Make inference and store outputs in files.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import functools
-import json
-import os
-import subprocess
-import shutil
-import sys
-import tempfile
-import time
-
-import numpy as np
-import tensorflow as tf
-from tensorflow.python.eager import def_function
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import tensor_spec
-from tensorflow.python.ops import variables
-from tensorflow.python.trackable import autotrackable
-from tensorflow.python.saved_model.save import save
-import tensorflow_hub as hub
-import tensorflowjs as tfjs
-
-curr_dir = os.path.dirname(os.path.realpath(__file__))
-_tmp_dir = os.path.join(curr_dir, 'convert_predict_data')
-
-def _save_and_convert_model(model_fn, model_path, control_flow_v2=False):
-  """Benchmark a model's fit() and predict() calls; serialize the model.
-
-  Args:
-    model_fn: A function that creates the saved model.
-    model_path: Path to construct files related to the model.
-
-  Returns:
-    predict task_log hash that specifies the inputs and outputs for
-    validation test.
-  """
-  # Generate model, inputs, and outputs using Tensorflow.
-  tmp_saved_model_dir = tempfile.mkdtemp()
-  model_info = model_fn(tmp_saved_model_dir)
-
-  # Write inputs to file.
-  xs_data = []
-  xs_shape = []
-  xs_dtype = []
-  xs_names = []
-  keys = model_info['inputs'].keys()
-  for key in keys:
-    xs_names.append(key)
-    xs_data.append(model_info['inputs'][key]['value'])
-    xs_shape.append(model_info['inputs'][key]['shape'])
-    xs_dtype.append(model_info['inputs'][key]['dtype'])
-
-  xs_name_path = os.path.join(_tmp_dir, model_path + '.xs-name.json')
-  xs_shape_path = os.path.join(_tmp_dir, model_path + '.xs-shapes.json')
-  xs_data_path = os.path.join(_tmp_dir, model_path + '.xs-data.json')
-  xs_dtype_path = os.path.join(_tmp_dir, model_path + '.xs-dtype.json')
-  with open(xs_name_path, 'w') as f:
-    f.write(json.dumps(xs_names))
-  with open(xs_data_path, 'w') as f:
-    f.write(json.dumps(xs_data))
-  with open(xs_shape_path, 'w') as f:
-    f.write(json.dumps(xs_shape))
-  with open(xs_dtype_path, 'w') as f:
-    f.write(json.dumps(xs_dtype))
-  # Write outputs to file.
-  ys_data = []
-  ys_shape = []
-  ys_dtype = []
-  ys_names = []
-  keys = model_info['outputs'].keys()
-  for key in keys:
-    ys_names.append(key)
-    ys_data.append(model_info['outputs'][key]['value'])
-    ys_shape.append(model_info['outputs'][key]['shape'])
-    ys_dtype.append(model_info['outputs'][key]['dtype'])
-
-  ys_name_path = os.path.join(_tmp_dir, model_path + '.ys-name.json')
-  ys_data_path = os.path.join(_tmp_dir, model_path + '.ys-data.json')
-  ys_shape_path = os.path.join(_tmp_dir, model_path + '.ys-shapes.json')
-  ys_dtype_path = os.path.join(_tmp_dir, model_path + '.ys-dtype.json')
-  with open(ys_name_path, 'w') as f:
-    f.write(json.dumps(ys_names))
-  with open(ys_data_path, 'w') as f:
-    f.write(json.dumps(ys_data))
-  with open(ys_shape_path, 'w') as f:
-    f.write(json.dumps(ys_shape))
-  with open(ys_dtype_path, 'w') as f:
-    f.write(json.dumps(ys_dtype))
-  artifacts_dir = os.path.join(_tmp_dir, model_path)
-
-  # Convert and store model to file.
-  args = [
-      'tensorflowjs_converter',
-      '--input_format', 'tf_saved_model',
-      '--output_format', 'tfjs_graph_model',
-      '--signature_name', 'serving_default',
-      '--saved_model_tags', 'serve'];
-  if control_flow_v2:
-    args = args + ['--control_flow_v2', 'True']
-
-  print(args, tmp_saved_model_dir, artifacts_dir)
-  subprocess.check_output(args +[tmp_saved_model_dir, artifacts_dir])
-
-def _create_saved_model_v1(save_dir):
-  """Create a TensorFlow V1 SavedModel for testing.
-
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-
-  graph = tf.Graph()
-  with graph.as_default():
-    input = tf.compat.v1.placeholder(tf.float32, shape=[2, 2])
-    w = tf.compat.v1.get_variable('w', shape=[2, 2])
-    output = tf.compat.v1.matmul(input, w)
-    init_op = w.initializer
-
-    # Create a builder.
-    builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(save_dir)
-
-    with tf.compat.v1.Session() as sess:
-      # Run the initializer on `w`.
-      sess.run(init_op)
-      output_val = sess.run(output, {input: [[1, 1], [1, 1]]})
-      builder.add_meta_graph_and_variables(
-          sess, [tf.compat.v1.saved_model.tag_constants.SERVING],
-          signature_def_map={
-              "serving_default":
-                  tf.compat.v1.saved_model \
-                      .signature_def_utils.predict_signature_def(
-                          inputs={"input": input},
-                          outputs={"output": output})
-          },
-          assets_collection=None)
-
-    builder.save()
-    return {
-        "async": False,
-        "inputs": {
-            "Placeholder": {
-                "value": [[1, 1], [1, 1]], "shape": [2, 2], "dtype": 'float32'
-            }
-        },
-        "outputs": {
-            "MatMul": {
-                "value": output_val.tolist(), "shape": [2, 2], "dtype": "float32"
-            }
-        }
-    }
-
-def _create_saved_model_v2(save_dir):
-  """Test a basic TF V2 model with functions to make sure functions are inlined.
-
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-  input_data = constant_op.constant(1., shape=[1])
-  root = autotrackable.AutoTrackable()
-  root.v1 = variables.Variable(3.)
-  root.v2 = variables.Variable(2.)
-  root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
-  to_save = root.f.get_concrete_function(input_data)
-
-  save(root, save_dir, to_save)
-  return {
-      "async": False,
-      "inputs": {
-          "x": {"value": [1], "shape": [1], "dtype": 'float32'}},
-      "outputs": {
-          "Identity:0": {"value": [6], "shape": [1], "dtype": "float32"}}}
-
-
-def _create_saved_model_v2_with_control_flow(save_dir):
-  """Test a basic TF v2 model with control flow to inlined.
-
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-  @tf.function
-  def square_if_positive(v):
-    if v > 0:
-      v = v * v
-    else:
-      v = v + 1
-    return v
-
-  root = autotrackable.AutoTrackable()
-  root.f = square_if_positive
-  to_save = root.f.get_concrete_function(
-      tensor_spec.TensorSpec([], dtypes.float32))
-
-  save(root, save_dir, to_save)
-  print(square_if_positive(tf.constant(-2)))
-  print(square_if_positive(tf.constant(3)))
-  print(to_save.structured_input_signature)
-  print(to_save.structured_outputs)
-  return {
-      "async": True,
-      "inputs": {"v": {"value": 3, "shape": [], "dtype": 'float32'}},
-      "outputs": {"Identity:0": {"value": [9], "shape": [], "dtype": "float32"}}}
-
-def _create_saved_model_with_conv2d(save_dir):
-  """Test a basic model with fusable conv2d.
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-  layers = [
-      tf.keras.layers.Conv2D(
-          16, [3, 3], padding='same', use_bias=False),
-      tf.keras.layers.BatchNormalization(),
-      tf.keras.layers.ReLU()
-  ]
-  model = tf.keras.Sequential(layers)
-  result = model.predict(tf.ones((1, 24, 24, 3)))
-  # set the learning phase to avoid keara learning placeholder, which
-  # will cause error when saving.
-  tf.keras.backend.set_learning_phase(0)
-  tf.saved_model.save(model, save_dir)
-  return {
-      "async": False,
-      "inputs": {
-          "conv2d_input:0": {"value": np.ones((1, 24, 24, 3)).tolist(),
-                "shape": [1, 24, 24, 3],
-                "dtype": 'float32'}},
-      "outputs": {
-          "Identity:0": {"value": result.tolist(),
-                         "shape": result.shape,
-                         "dtype": "float32"}}}
-
-
-def _create_saved_model_with_prelu(save_dir):
-  """Test a basic model with prelu activation.
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-  # set the bias and alpha intitialize to make them constant and ensure grappler
-  # be able to fuse the op.
-  layers = [
-      tf.keras.layers.Conv2D(
-          16, [3, 3], padding='same', use_bias=True,
-          bias_initializer=tf.initializers.constant(0.25)),
-      tf.keras.layers.PReLU(alpha_initializer=tf.initializers.constant(0.25))
-  ]
-  model = tf.keras.Sequential(layers)
-  result = model.predict(tf.ones((1, 24, 24, 3)))
-  tf.keras.backend.set_learning_phase(0)
-  tf.saved_model.save(model, save_dir)
-  return {
-      "async": False,
-      "inputs": {
-          "conv2d_1_input": {"value": np.ones((1, 24, 24, 3)).tolist(),
-                "shape": [1, 24, 24, 3],
-                "dtype": 'float32'}},
-      "outputs": {
-          "Identity:0": {"value": result.tolist(),
-                         "shape": result.shape,
-                         "dtype": "float32"}}}
-
-def _create_saved_model_v2_complex64(save_dir):
-  """Test a TF V2 model with complex dtype.
-
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-  input_data = constant_op.constant(1., shape=[1])
-  root = autotrackable.AutoTrackable()
-  root.v1 = variables.Variable(3 + 1j, dtype=tf.complex64)
-  root.f = def_function.function(lambda x: tf.complex(x, x) + root.v1)
-  to_save = root.f.get_concrete_function(input_data)
-
-  save(root, save_dir, to_save)
-  return {
-      "async": False,
-      "inputs": {
-          "x": {"value": [1], "shape": [1], "dtype": 'float32'}},
-      "outputs": {
-          "Identity:0": {"value": [4, 2], "shape": [1], "dtype": "complex64"}}}
-
-def _create_saved_model_v2_with_control_flow_v2(save_dir):
-  """Test a TF V2 model with control flow v2.
-
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-  class CustomModule(tf.Module):
-
-    def __init__(self):
-        super(CustomModule, self).__init__()
-
-    @tf.function(input_signature=[tf.TensorSpec([], tf.int32),
-                                  tf.TensorSpec([], tf.int32),
-                                  tf.TensorSpec([], tf.int32)])
-    def control_flow(self, x, y, z):
-        i = 0
-        while i < z:
-            i += 1
-            j = 0
-            while j < y:
-                j += 1
-                if z > 0:
-                    x += 1
-                else:
-                    x += 2
-        return x
-
-
-  module = CustomModule()
-  print(module.control_flow(0, 2, 10))
-  tf.saved_model.save(module, save_dir,
-                      signatures=module.control_flow)
-
-  return {
-      "async": False,
-      "inputs": {
-          "x": {"value": [0], "shape": [], "dtype": 'int32'},
-          "y": {"value": [2], "shape": [], "dtype": 'int32'},
-          "z": {"value": [10], "shape": [], "dtype": 'int32'}},
-      "outputs": {
-          "Identity:0": {"value": [20], "shape": [], "dtype": "int32"}}}
-
-def _create_saved_model_v2_with_tensorlist_ops(save_dir):
-  """Test a TF V2 model with TensorList Ops.
-
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-  model = tf.keras.Sequential()
-  model.add(tf.keras.layers.Embedding(100, 20, input_shape=[10]))
-  model.add(tf.keras.layers.GRU(4))
-
-  result = model.predict(tf.ones([1, 10]))
-
-  tf.keras.backend.set_learning_phase(0)
-  tf.saved_model.save(model, save_dir)
-
-  return {
-      "async": False,
-      "inputs": {
-          "embedding_input": {
-            "value": np.ones((1, 10)).tolist(),
-            "shape": [1, 10], "dtype": 'float32'}},
-      "outputs": {
-          "Identity:0": {
-              "value": result.tolist(),
-              "shape": result.shape,
-              "dtype": "float32"}}}
-
-def _create_saved_model_v1_with_hashtable(save_dir):
-  """Test a TF V1 model with HashTable Ops.
-
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-  graph = tf.Graph()
-
-  with graph.as_default():
-    # Create a builder.
-    builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(save_dir)
-
-    with tf.compat.v1.Session() as sess:
-      keys_tensor = tf.constant(["a", "b"])
-      vals_tensor = tf.constant([3, 4])
-
-      table = tf.lookup.StaticHashTable(
-        tf.lookup.KeyValueTensorInitializer(keys=keys_tensor, values=vals_tensor
-        ),
-        default_value=-1
-      )
-      input = tf.compat.v1.placeholder(tf.string, shape=[2])
-      output = table.lookup(input)
-
-      sess.run(tf.compat.v1.tables_initializer())
-
-      # output_val = [3, -1]
-      output_val = sess.run(output, {input: ["a", "c"]})
-
-      builder.add_meta_graph_and_variables(
-          sess, [tf.compat.v1.saved_model.tag_constants.SERVING],
-          signature_def_map={
-              "serving_default":
-                  tf.compat.v1.saved_model \
-                      .signature_def_utils.predict_signature_def(
-                          inputs={"input": input},
-                          outputs={"output": output})
-          },
-          assets_collection=None)
-
-    builder.save()
-    return {
-        "async": False,
-        "inputs": {
-            "Placeholder:0": {
-                "value": ["a", "c"], "shape": [2], "dtype": "string"
-            }
-        },
-        "outputs": {
-            "hash_table_Lookup/LookupTableFindV2:0": {
-                "value": output_val.tolist(), "shape": [2], "dtype": "int32"
-            }
-        }
-    }
-
-def _create_saved_model_v2_with_hashtable(save_dir):
-  """Test a TF V2 model with HashTable Ops.
-
-  Args:
-    save_dir: directory name of where the saved model will be stored.
-  """
-  class Table(tf.Module):
-    def __init__(self):
-        super(Table, self).__init__()
-        keys = tf.constant(['a', 'b'])
-        vals= tf.constant([0, 1])
-        init = tf.lookup.KeyValueTensorInitializer(keys, vals)
-        self.table = tf.lookup.StaticHashTable(init, -1)
-
-    def initializeTable(self):
-        @tf.function
-        def lookup(input):
-            return self.table.lookup(input)
-
-        return lookup
-
-  model = Table()
-  concrete_fn = model.initializeTable().get_concrete_function(
-    input=tf.TensorSpec([None], tf.string))
-
-  tf.saved_model.save(model, save_dir, signatures={"serving_default": concrete_fn})
-
-  return {
-      "async": False,
-      "inputs": {
-          "input:0": {
-              "value": ["a", "b", "c"], "shape": [3], "dtype": "string"
-          }
-      },
-      "outputs": {
-          "StatefulPartitionedCall/None_Lookup/LookupTableFindV2:0": {
-              "value": [0, 1, -1], "shape": [3], "dtype": "int32"
-          }
-      }
-  }
-
-def _layers_mobilenet():
-  model = tf.keras.applications.MobileNetV2()
-  model_path = 'mobilenet'
-  tfjs.converters.save_keras_model(model, os.path.join(
-      _tmp_dir, model_path))
-  xs_data_path = os.path.join(_tmp_dir, model_path + '.xs-data.json')
-  xs_shape_path = os.path.join(_tmp_dir, model_path + '.xs-shapes.json')
-  ys_data_path = os.path.join(_tmp_dir, model_path + '.ys-data.json')
-  ys_shape_path = os.path.join(_tmp_dir, model_path + '.ys-shapes.json')
-
-  input = tf.ones([1, 224, 224, 3])
-  output = model.predict(input)
-
-  with open(xs_data_path, 'w') as f:
-    f.write(json.dumps([input.numpy().tolist()]))
-  with open(xs_shape_path, 'w') as f:
-    f.write(json.dumps([input.shape.as_list()]))
-  with open(ys_data_path, 'w') as f:
-    f.write(json.dumps([output.tolist()]))
-  with open(ys_shape_path, 'w') as f:
-    f.write(json.dumps([output.shape]))
-
-def main():
-  # Create the directory to store model and data.
-  if os.path.exists(_tmp_dir) and os.path.isdir(_tmp_dir):
-    shutil.rmtree(_tmp_dir)
-  os.mkdir(_tmp_dir)
-
-  _save_and_convert_model(_create_saved_model_v1, 'saved_model_v1')
-  _save_and_convert_model(_create_saved_model_v2, 'saved_model_v2')
-  _save_and_convert_model(_create_saved_model_v2_complex64,
-                          'saved_model_v2_complex64')
-  _save_and_convert_model(_create_saved_model_v2_with_control_flow,
-      'saved_model_v2_with_control_flow')
-  _save_and_convert_model(_create_saved_model_v2_with_control_flow_v2,
-      'saved_model_v2_with_control_flow_v2', control_flow_v2=True)
-  _save_and_convert_model(_create_saved_model_with_conv2d,
-      'saved_model_with_conv2d')
-  _save_and_convert_model(_create_saved_model_with_prelu,
-      'saved_model_with_prelu')
-  _save_and_convert_model(_create_saved_model_v2_with_tensorlist_ops,
-      'saved_model_v2_with_tensorlist_ops', control_flow_v2=True)
-  _save_and_convert_model(_create_saved_model_v1_with_hashtable,
-      'saved_model_v1_with_hashtable')
-  _save_and_convert_model(_create_saved_model_v2_with_hashtable,
-      'saved_model_v2_with_hashtable')
-
-  _layers_mobilenet()
-if __name__ == '__main__':
-  main()
diff --git a/tfjs-master/e2e/integration_tests/convert_predict.ts b/tfjs-master/e2e/integration_tests/convert_predict.ts
deleted file mode 100644
index 97f67b23f..000000000
--- a/tfjs-master/e2e/integration_tests/convert_predict.ts
+++ /dev/null
@@ -1,157 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This file is 2/2 of the test suites for CUJ: convert->predict.
- *
- * This file does below things:
- *  - Load graph models using Converter api.
- *  - Load inputs.
- *  - Make inference using each backends, and validate the results against TF
- *    results.
- */
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-import '@tensorflow/tfjs-backend-webgpu';
-
-import * as tfconverter from '@tensorflow/tfjs-converter';
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import * as tfl from '@tensorflow/tfjs-layers';
-
-import {CONVERT_PREDICT_MODELS, KARMA_SERVER, REGRESSION} from './constants';
-import {createInputTensors} from './test_util';
-
-const DATA_URL = 'convert_predict_data';
-
-describeWithFlags(`${REGRESSION} convert_predict`, ALL_ENVS, (env) => {
-  let originalTimeout: number;
-
-  beforeAll(() => {
-    // This test needs more time to finish the async fetch, adjusting
-    // jasmine timeout for this test to avoid flakiness. See jasmine
-    // documentation for detail:
-    // https://jasmine.github.io/2.0/introduction.html#section-42
-    originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-  });
-
-  afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-  for (const modelType in CONVERT_PREDICT_MODELS) {
-    const models =
-        (CONVERT_PREDICT_MODELS as {[key: string]: string[]})[modelType];
-    for (const model of models) {
-      if (env.backendName === 'wasm' && model.includes('complex')) {
-        // WASM does not support complex
-        continue;
-      }
-      it(`${model}.`, async () => {
-        await tfc.setBackend(env.name);
-        let inputsNames: string[];
-        let inputsData: tfc.TypedArray[];
-        let inputsShapes: number[][];
-        let inputsDtypes: tfc.DataType[];
-        let tfOutputNames: string[];
-        let tfOutputData: tfc.TypedArray[];
-        let tfOutputShapes: number[][];
-        let tfOutputDtypes: tfc.DataType[];
-
-        const fetches = [
-          fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.xs-data.json`)
-              .then(response => response.json()),
-          fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.xs-shapes.json`)
-              .then(response => response.json()),
-          fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.ys-data.json`)
-              .then(response => response.json()),
-          fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.ys-shapes.json`)
-              .then(response => response.json())
-        ];
-        if (modelType === 'graph_model') {
-          fetches.push(
-              ...[fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.xs-name.json`)
-                      .then(response => response.json()),
-                  fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.xs-dtype.json`)
-                      .then(response => response.json()),
-                  fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.ys-name.json`)
-                      .then(response => response.json()),
-                  fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.ys-dtype.json`)
-                      .then(response => response.json())]);
-        }
-        [inputsData, inputsShapes, tfOutputData, tfOutputShapes, inputsNames,
-         inputsDtypes, tfOutputNames, tfOutputDtypes] =
-            await Promise.all(fetches);
-
-        if (modelType === 'graph_model') {
-          const numTensors = tfc.memory().numTensors;
-
-          const $model = await tfconverter.loadGraphModel(
-              `${KARMA_SERVER}/${DATA_URL}/${model}/model.json`);
-
-          const namedInputs = createInputTensors(
-                                  inputsData, inputsShapes, inputsDtypes,
-                                  inputsNames) as tfc.NamedTensorMap;
-
-          const result = await $model.executeAsync(namedInputs, tfOutputNames);
-
-          const ys =
-              ($model.outputs.length === 1 ? [result] : result) as tfc.Tensor[];
-
-          // Validate outputs with tf results.
-          for (let i = 0; i < ys.length; i++) {
-            const y = ys[i];
-            expect(y.shape).toEqual(tfOutputShapes[i]);
-            expect(y.dtype).toEqual(tfOutputDtypes[i]);
-            tfc.test_util.expectArraysClose(await y.data(), tfOutputData[i]);
-          }
-
-          // Dispose all tensors;
-          Object.keys(namedInputs).forEach(key => namedInputs[key].dispose());
-          ys.forEach(tensor => tensor.dispose());
-          $model.dispose();
-
-          expect(tfc.memory().numTensors).toEqual(numTensors);
-        }
-        if (modelType === 'layers_model') {
-          const $model = await tfl.loadLayersModel(
-              `${KARMA_SERVER}/${DATA_URL}/${model}/model.json`);
-
-          const xs =
-              createInputTensors(inputsData, inputsShapes) as tfc.Tensor[];
-
-          const result = $model.predict(xs);
-
-          const ys =
-              ($model.outputs.length === 1 ? [result] : result) as tfc.Tensor[];
-
-          // Validate outputs with keras results.
-          for (let i = 0; i < ys.length; i++) {
-            const y = ys[i];
-            expect(y.shape).toEqual(tfOutputShapes[i]);
-            tfc.test_util.expectArraysClose(
-                await y.data(), tfOutputData[i], 0.005);
-          }
-
-          // Dispose all tensors;
-          xs.forEach(tensor => tensor.dispose());
-          ys.forEach(tensor => tensor.dispose());
-        }
-      });
-    }
-  }
-});
diff --git a/tfjs-master/e2e/integration_tests/cpu_forwarding_test.ts b/tfjs-master/e2e/integration_tests/cpu_forwarding_test.ts
deleted file mode 100644
index 02a671b5e..000000000
--- a/tfjs-master/e2e/integration_tests/cpu_forwarding_test.ts
+++ /dev/null
@@ -1,246 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-import '@tensorflow/tfjs-backend-webgpu';
-
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {SMOKE} from './constants';
-
-/**
- *  This file tests cpu forwarding from webgl backend.
- */
-
-describeWithFlags(
-    `${SMOKE} cpu forwarding)`,
-    {predicate: testEnv => testEnv.backendName !== 'cpu'},
-
-    (env) => {
-      let webglCpuForwardFlagSaved: boolean;
-      let webgpuCpuForwardFlagSaved: boolean;
-
-      beforeAll(async () => {
-        webglCpuForwardFlagSaved = tfc.env().getBool('WEBGL_CPU_FORWARD');
-        tfc.env().set('WEBGL_CPU_FORWARD', true);
-        webgpuCpuForwardFlagSaved = tfc.env().getBool('WEBGPU_CPU_FORWARD');
-        tfc.env().set('WEBGPU_CPU_FORWARD', true);
-        await tfc.setBackend(env.name);
-      });
-
-      afterAll(() => {
-        tfc.env().set('WEBGL_CPU_FORWARD', webglCpuForwardFlagSaved);
-        tfc.env().set('WEBGPU_CPU_FORWARD', webgpuCpuForwardFlagSaved);
-      });
-
-      it('should work for slice.', async () => {
-        const a = tfc.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-        const result = a.slice([0, 1, 1]);
-        expect(result.shape).toEqual([2, 1, 1]);
-        tfc.test_util.expectArraysClose(await result.data(), [4, 8]);
-      });
-
-      it('should work for stridedSlice.', async () => {
-        const t = tfc.tensor2d([
-          [1, 2, 3, 4, 5],
-          [2, 3, 4, 5, 6],
-          [3, 4, 5, 6, 7],
-          [4, 5, 6, 7, 8],
-          [5, 6, 7, 8, 9],
-          [6, 7, 8, 9, 10],
-          [7, 8, 9, 10, 11],
-          [8, 8, 9, 10, 11],
-          [9, 8, 9, 10, 11],
-          [10, 8, 9, 10, 11],
-        ]);
-        const begin = [0, 4];
-        const end = [0, 5];
-        const strides = [1, 1];
-        const beginMask = 0;
-        const endMask = 0;
-        const ellipsisMask = 1;
-        const output = t.stridedSlice(
-            begin, end, strides, beginMask, endMask, ellipsisMask);
-        expect(output.shape).toEqual([10, 1]);
-        tfc.test_util.expectArraysClose(
-            await output.data(), [5, 6, 7, 8, 9, 10, 11, 11, 11, 11]);
-      });
-
-      it('should work for concat.', async () => {
-        const a = tfc.tensor1d([3]);
-        const b = tfc.tensor1d([5]);
-
-        const result = tfc.concat1d([a, b]);
-        const expected = [3, 5];
-        tfc.test_util.expectArraysClose(await result.data(), expected);
-      });
-
-      it('should work for neg.', async () => {
-        const a = tfc.tensor1d([1, -3, 2, 7, -4]);
-        const result = tfc.neg(a);
-        tfc.test_util.expectArraysClose(
-            await result.data(), [-1, 3, -2, -7, 4]);
-      });
-
-      it('should work for multiply.', async () => {
-        const a = tfc.tensor2d([1, 2, -3, -4], [2, 2]);
-        const b = tfc.tensor2d([5, 3, 4, -7], [2, 2]);
-        const expected = [5, 6, -12, 28];
-        const result = tfc.mul(a, b);
-
-        expect(result.shape).toEqual([2, 2]);
-        tfc.test_util.expectArraysClose(await result.data(), expected);
-      });
-
-      it('should work for gather.', async () => {
-        const t = tfc.tensor1d([1, 2, 3]);
-
-        const t2 = tfc.gather(t, tfc.scalar(1, 'int32'), 0);
-
-        expect(t2.shape).toEqual([]);
-        tfc.test_util.expectArraysClose(await t2.data(), [2]);
-      });
-
-      it('should work for prod.', async () => {
-        const a = tfc.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-        const result = tfc.prod(a);
-        tfc.test_util.expectArraysClose(await result.data(), 0);
-      });
-
-      it('should work for less.', async () => {
-        const a = tfc.tensor1d([1, 4, 5], 'int32');
-        const b = tfc.tensor1d([2, 3, 5], 'int32');
-        const res = tfc.less(a, b);
-
-        expect(res.dtype).toBe('bool');
-        tfc.test_util.expectArraysClose(await res.data(), [1, 0, 0]);
-      });
-
-      it('should work for greater.', async () => {
-        const a = tfc.tensor1d([1, 4, 5], 'int32');
-        const b = tfc.tensor1d([2, 3, 5], 'int32');
-        const res = tfc.greater(a, b);
-
-        expect(res.dtype).toBe('bool');
-        tfc.test_util.expectArraysClose(await res.data(), [0, 1, 0]);
-      });
-
-      it('should work for minimum.', async () => {
-        const a = tfc.tensor1d([0.5, 3, -0.1, -4]);
-        const b = tfc.tensor1d([0.2, 0.4, 0.25, 0.15]);
-        const result = tfc.minimum(a, b);
-
-        expect(result.shape).toEqual(a.shape);
-        tfc.test_util.expectArraysClose(
-            await result.data(), [0.2, 0.4, -0.1, -4]);
-      });
-
-      it('should work for maximum.', async () => {
-        const a = tfc.tensor1d([0.5, 3, -0.1, -4]);
-        const b = tfc.tensor1d([0.2, 0.4, 0.25, 0.15]);
-        const result = tfc.maximum(a, b);
-
-        expect(result.shape).toEqual(a.shape);
-        tfc.test_util.expectArraysClose(
-            await result.data(), [0.5, 3, 0.25, 0.15]);
-      });
-
-      it('should work for max.', async () => {
-        const a = tfc.tensor1d([3, -1, 0, 100, -7, 2]);
-        const r = tfc.max(a);
-        tfc.test_util.expectArraysClose(await r.data(), 100);
-      });
-
-      it('should work for add.', async () => {
-        const c = tfc.scalar(5);
-        const a = tfc.tensor1d([1, 2, 3]);
-
-        const result = tfc.add(c, a);
-
-        tfc.test_util.expectArraysClose(await result.data(), [6, 7, 8]);
-      });
-
-      it('should work for sub.', async () => {
-        const c = tfc.scalar(5);
-        const a = tfc.tensor1d([7, 2, 3]);
-
-        const result = tfc.sub(c, a);
-
-        tfc.test_util.expectArraysClose(await result.data(), [-2, 3, 2]);
-      });
-
-      it('should work for ceil.', async () => {
-        const a = tfc.tensor1d([1.5, 2.1, -1.4]);
-        const r = tfc.ceil(a);
-        tfc.test_util.expectArraysClose(await r.data(), [2, 3, -1]);
-      });
-
-      it('should work for floor.', async () => {
-        const a = tfc.tensor1d([1.5, 2.1, -1.4]);
-        const r = tfc.floor(a);
-
-        tfc.test_util.expectArraysClose(await r.data(), [1, 2, -2]);
-      });
-
-      it('should work for exp.', async () => {
-        const a = tfc.tensor1d([1, 2, 0]);
-        const r = tfc.exp(a);
-
-        tfc.test_util.expectArraysClose(
-            await r.data(), [Math.exp(1), Math.exp(2), 1]);
-      });
-
-      it('should work for expm1.', async () => {
-        const a = tfc.tensor1d([1, 2, 0]);
-        const r = tfc.expm1(a);
-
-        tfc.test_util.expectArraysClose(
-            await r.data(), [Math.expm1(1), Math.expm1(2), Math.expm1(0)]);
-      });
-
-      it('should work for log.', async () => {
-        const a = tfc.tensor1d([1, 2]);
-        const r = tfc.log(a);
-        tfc.test_util.expectArraysClose(
-            await r.data(), [Math.log(1), Math.log(2)]);
-      });
-
-      it('should work for rsqrt.', async () => {
-        const a = tfc.tensor1d([2, 4]);
-        const r = tfc.rsqrt(a);
-        tfc.test_util.expectArraysClose(
-            await r.data(), [1 / Math.sqrt(2), 1 / Math.sqrt(4)]);
-      });
-
-      it('should work for abs.', async () => {
-        const a = tfc.tensor1d([1, -2, 0, 3, -0.1]);
-        const result = tfc.abs(a);
-        tfc.test_util.expectArraysClose(await result.data(), [1, 2, 0, 3, 0.1]);
-      });
-
-      it('should work for transpose.', async () => {
-        const t = tfc.tensor2d([1, 11, 2, 22, 3, 33, 4, 44], [2, 4]);
-        const t2 = tfc.transpose(t, [1, 0]);
-
-        expect(t2.shape).toEqual([4, 2]);
-        tfc.test_util.expectArraysClose(
-            await t2.data(), [1, 3, 11, 33, 2, 4, 22, 44]);
-      });
-    });
diff --git a/tfjs-master/e2e/integration_tests/create_save_predict.js b/tfjs-master/e2e/integration_tests/create_save_predict.js
deleted file mode 100644
index 396ee5ffe..000000000
--- a/tfjs-master/e2e/integration_tests/create_save_predict.js
+++ /dev/null
@@ -1,240 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This file is 1/3 of the test suites for CUJ: create->save->predict.
- *
- * This file does below things:
- * - Create and save models using Layers' API.
- * - Generate random inputs and stored in local file.
- */
-const tfc = require('@tensorflow/tfjs-core');
-const tfl = require('@tensorflow/tfjs-layers');
-const tfjsNode = require('@tensorflow/tfjs-node');
-const fs = require('fs');
-const join = require('path').join;
-
-process.on('unhandledRejection', ex => {
-  throw ex;
-});
-
-/**
- * Generate random input(s), get predict() output(s), and save them along with
- * the model.
- *
- * @param model The `tf.LayersModel` instance in question. It may have one or
- * more inputs and one or more outputs. It is assumed that for each input, only
- *   the first dimension (i.e., the batch dimension) is undetermined.
- * @param exportPathprefix The path prefix to which the model, the input and
- *   output tensors will be saved
- * @param inputIntegerMax (Optional) Maximum integer value for the input
- *   tensors. Used for models that take integer tensors as inputs.
- */
-async function saveModelAndRandomInputs(
-    model, exportPathprefix, inputIntegerMax) {
-  await model.save(tfjsNode.io.fileSystem(exportPathprefix));
-
-  const xs = [];
-  const xsData = [];
-  const xsShapes = [];
-  for (const inputTensor of model.inputs) {
-    const inputShape = inputTensor.shape;
-    inputShape[0] = 1;
-    if (inputShape.indexOf(null) !== -1) {
-      throw new Error(
-          `It is assumed that the only the first dimension of the tensor ` +
-          `is undetermined, but the assumption is not satisfied for ` +
-          `input shape ${JSON.stringify(inputTensor.shape)}`);
-    }
-    const xTensor = inputIntegerMax == null ?
-        tfc.randomNormal(inputShape) :
-        tfc.floor(tfc.randomUniform(inputShape, 0, inputIntegerMax));
-    xs.push(xTensor);
-    xsData.push(Array.from(xTensor.dataSync()));
-    xsShapes.push(xTensor.shape);
-  }
-  fs.writeFileSync(exportPathprefix + '.xs-data.json', JSON.stringify(xsData));
-  fs.writeFileSync(
-      exportPathprefix + '.xs-shapes.json', JSON.stringify(xsShapes));
-}
-
-// Multi-layer perceptron (MLP).
-async function exportMLPModel(exportPath) {
-  const model = tfl.sequential();
-  // Test both activations encapsulated in other layers and as standalone
-  // layers.
-  model.add(
-      tfl.layers.dense({units: 100, inputShape: [200], activation: 'relu'}));
-  model.add(tfl.layers.dense({units: 50, activation: 'elu'}));
-  model.add(tfl.layers.dense({units: 24}));
-  model.add(tfl.layers.activation({activation: 'elu'}));
-  model.add(tfl.layers.dense({units: 8, activation: 'softmax'}));
-
-  await saveModelAndRandomInputs(model, exportPath);
-}
-
-// Convolutional neural network (CNN).
-async function exportCNNModel(exportPath) {
-  const model = tfl.sequential();
-
-  // Cover separable and non-separable convoluational layers.
-  const inputShape = [40, 40, 3];
-  model.add(tfl.layers.conv2d({
-    filters: 32,
-    kernelSize: [3, 3],
-    strides: [2, 2],
-    inputShape,
-    padding: 'valid',
-  }));
-  model.add(tfl.layers.batchNormalization({}));
-  model.add(tfl.layers.activation({activation: 'relu'}));
-  model.add(tfl.layers.dropout({rate: 0.5}));
-  model.add(tfl.layers.maxPooling2d({poolSize: 2}));
-  model.add(tfl.layers.separableConv2d({
-    filters: 32,
-    kernelSize: [4, 4],
-    strides: [3, 3],
-  }));
-  model.add(tfl.layers.batchNormalization({}));
-  model.add(tfl.layers.activation({activation: 'relu'}));
-  model.add(tfl.layers.dropout({rate: 0.5}));
-  model.add(tfl.layers.avgPooling2d({poolSize: [2, 2]}));
-  model.add(tfl.layers.flatten({}));
-  model.add(tfl.layers.dense({units: 100, activation: 'softmax'}));
-
-  await saveModelAndRandomInputs(model, exportPath);
-}
-
-async function exportDepthwiseCNNModel(exportPath) {
-  const model = tfl.sequential();
-
-  // Cover depthwise 2D convoluational layer.
-  model.add(tfl.layers.depthwiseConv2d({
-    depthMultiplier: 2,
-    kernelSize: [3, 3],
-    strides: [2, 2],
-    inputShape: [40, 40, 3],
-    padding: 'valid',
-  }));
-  model.add(tfl.layers.batchNormalization({}));
-  model.add(tfl.layers.activation({activation: 'relu'}));
-  model.add(tfl.layers.dropout({rate: 0.5}));
-  model.add(tfl.layers.maxPooling2d({poolSize: 2}));
-  model.add(tfl.layers.flatten({}));
-  model.add(tfl.layers.dense({units: 100, activation: 'softmax'}));
-
-  await saveModelAndRandomInputs(model, exportPath);
-}
-
-// SimpleRNN with embedding.
-async function exportSimpleRNNModel(exportPath) {
-  const model = tfl.sequential();
-  const inputDim = 100;
-  model.add(tfl.layers.embedding({inputDim, outputDim: 20, inputShape: [10]}));
-  model.add(tfl.layers.simpleRNN({units: 4}));
-
-  await saveModelAndRandomInputs(model, exportPath, inputDim);
-}
-
-// GRU with embedding.
-async function exportGRUModel(exportPath) {
-  const model = tfl.sequential();
-  const inputDim = 100;
-  model.add(tfl.layers.embedding({inputDim, outputDim: 20, inputShape: [10]}));
-  model.add(tfl.layers.gru({units: 4, goBackwards: true}));
-
-  await saveModelAndRandomInputs(model, exportPath, inputDim);
-}
-
-// Bidirecitonal LSTM with embedding.
-async function exportBidirectionalLSTMModel(exportPath) {
-  const model = tfl.sequential();
-  const inputDim = 100;
-  model.add(tfl.layers.embedding({inputDim, outputDim: 20, inputShape: [10]}));
-  // TODO(cais): Investigate why the `tfl.layers.RNN` typing doesn't work.
-  const lstm = tfl.layers.lstm({units: 4, goBackwards: true});
-  model.add(tfl.layers.bidirectional({layer: lstm, mergeMode: 'concat'}));
-
-  await saveModelAndRandomInputs(model, exportPath, inputDim);
-}
-
-// LSTM + time-distributed layer with embedding.
-async function exportTimeDistributedLSTMModel(exportPath) {
-  const model = tfl.sequential();
-  const inputDim = 100;
-  model.add(tfl.layers.embedding({inputDim, outputDim: 20, inputShape: [10]}));
-  model.add(tfl.layers.lstm({units: 4, returnSequences: true}));
-  model.add(tfl.layers.timeDistributed({
-    layer: tfl.layers.dense({units: 2, useBias: false, activation: 'softmax'})
-  }));
-
-  await saveModelAndRandomInputs(model, exportPath, inputDim);
-}
-
-// Model with Conv1D and Pooling1D layers.
-async function exportOneDimensionalModel(exportPath) {
-  const model = tfl.sequential();
-  model.add(tfl.layers.conv1d(
-      {filters: 16, kernelSize: [4], inputShape: [80, 1], activation: 'relu'}));
-  model.add(tfl.layers.maxPooling1d({poolSize: 3}));
-  model.add(
-      tfl.layers.conv1d({filters: 8, kernelSize: [3], activation: 'relu'}));
-  model.add(tfl.layers.avgPooling1d({poolSize: 5}));
-  model.add(tfl.layers.flatten());
-
-  await saveModelAndRandomInputs(model, exportPath);
-}
-
-// Functional model with two Merge layers.
-async function exportFunctionalMergeModel(exportPath) {
-  const input1 = tfl.input({shape: [2, 5]});
-  const input2 = tfl.input({shape: [4, 5]});
-  const input3 = tfl.input({shape: [30]});
-  const reshaped1 = tfl.layers.reshape({targetShape: [10]}).apply(input1);
-  const reshaped2 = tfl.layers.reshape({targetShape: [20]}).apply(input2);
-  const dense1 = tfl.layers.dense({units: 5}).apply(reshaped1);
-  const dense2 = tfl.layers.dense({units: 5}).apply(reshaped2);
-  const dense3 = tfl.layers.dense({units: 5}).apply(input3);
-  const avg = tfl.layers.average().apply([dense1, dense2]);
-  const concat = tfl.layers.concatenate({axis: -1}).apply([avg, dense3]);
-  const output = tfl.layers.dense({units: 1}).apply(concat);
-  const model = tfl.model({inputs: [input1, input2, input3], outputs: output});
-
-  await saveModelAndRandomInputs(model, exportPath);
-}
-
-console.log(`Using tfjs-core version: ${tfc.version_core}`);
-console.log(`Using tfjs-layers version: ${tfl.version_layers}`);
-console.log(`Using tfjs-node version: ${JSON.stringify(tfjsNode.version)}`);
-
-if (process.argv.length !== 3) {
-  throw new Error('Usage: node tfjs_save.ts <test_data_dir>');
-}
-const testDataDir = process.argv[2];
-
-(async function() {
-  await exportMLPModel(join(testDataDir, 'mlp'));
-  await exportCNNModel(join(testDataDir, 'cnn'));
-  await exportDepthwiseCNNModel(join(testDataDir, 'depthwise_cnn'));
-  await exportSimpleRNNModel(join(testDataDir, 'simple_rnn'));
-  await exportGRUModel(join(testDataDir, 'gru'));
-  await exportBidirectionalLSTMModel(join(testDataDir, 'bidirectional_lstm'));
-  await exportTimeDistributedLSTMModel(
-      join(testDataDir, 'time_distributed_lstm'));
-  await exportOneDimensionalModel(join(testDataDir, 'one_dimensional'));
-  await exportFunctionalMergeModel(join(testDataDir, 'functional_merge'));
-})();
diff --git a/tfjs-master/e2e/integration_tests/create_save_predict.py b/tfjs-master/e2e/integration_tests/create_save_predict.py
deleted file mode 100644
index d55caf6da..000000000
--- a/tfjs-master/e2e/integration_tests/create_save_predict.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Use of this source code is governed by an MIT-style
-# license that can be found in the LICENSE file or at
-# https://opensource.org/licenses/MIT.
-# =============================================================================
-
-# This file is 2/3 of the test suites for CUJ: create->save->predict.
-#
-# This file does below things:
-# - Load Keras models equivalent with models generated by Layers.
-# - Load inputs.
-# - Make inference and store in local files.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import os
-import shutil
-import tempfile
-
-import numpy as np
-import tensorflow as tf
-import tensorflowjs as tfjs
-
-from tensorflow import keras
-
-curr_dir = os.path.dirname(os.path.realpath(__file__))
-_tmp_dir = os.path.join(curr_dir, 'create_save_predict_data')
-
-def _load_predict_save(model_path):
-  """Load a Keras Model from artifacts generated by tensorflow.js and inputs.
-     Make inference with the model and inputs.
-     Write outputs to file.
-
-  Args:
-    model_path: Path to the model JSON file.
-  """
-  xs_shape_path = os.path.join(
-      _tmp_dir, model_path + '.xs-shapes.json')
-  xs_data_path = os.path.join(
-      _tmp_dir, model_path + '.xs-data.json')
-  with open(xs_shape_path, 'rt') as f:
-    xs_shapes = json.load(f)
-  with open(xs_data_path, 'rt') as f:
-    xs_values = json.load(f)
-  xs = [np.array(value, dtype=np.float32).reshape(shape)
-        for value, shape in zip(xs_values, xs_shapes)]
-  if len(xs) == 1:
-    xs = xs[0]
-
-  session = tf.Session() if hasattr(tf, 'Session') else tf.compat.v1.Session()
-  with tf.Graph().as_default(), session:
-    model_json_path = os.path.join(_tmp_dir, model_path, 'model.json')
-    print('Loading model from path %s' % model_json_path)
-    model = tfjs.converters.load_keras_model(model_json_path)
-    ys = model.predict(xs)
-
-    ys_data = None
-    ys_shape = None
-
-    if isinstance(ys, list):
-      ys_data = [y.tolist() for y in ys]
-      ys_shape = [list(y.shape) for y in ys]
-    else:
-      ys_data = ys.tolist()
-      ys_shape = [list(ys.shape)]
-
-    ys_data_path = os.path.join(
-      _tmp_dir, model_path + '.ys-data.json')
-    ys_shape_path = os.path.join(
-      _tmp_dir, model_path + '.ys-shapes.json')
-    with open(ys_data_path, 'w') as f:
-      f.write(json.dumps(ys_data))
-    with open(ys_shape_path, 'w') as f:
-      f.write(json.dumps(ys_shape))
-
-def main():
-  _load_predict_save('mlp')
-  _load_predict_save('cnn')
-  _load_predict_save('depthwise_cnn')
-  _load_predict_save('simple_rnn')
-  _load_predict_save('gru')
-  _load_predict_save('bidirectional_lstm')
-  _load_predict_save('time_distributed_lstm')
-  _load_predict_save('one_dimensional')
-  _load_predict_save('functional_merge')
-
-if __name__ == '__main__':
-  main()
diff --git a/tfjs-master/e2e/integration_tests/create_save_predict.ts b/tfjs-master/e2e/integration_tests/create_save_predict.ts
deleted file mode 100644
index baea45d0e..000000000
--- a/tfjs-master/e2e/integration_tests/create_save_predict.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-import '@tensorflow/tfjs-backend-webgpu';
-
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import * as tfl from '@tensorflow/tfjs-layers';
-
-import {KARMA_SERVER, LAYERS_MODELS, REGRESSION} from './constants';
-import {createInputTensors} from './test_util';
-
-/** Directory that stores the model. */
-const DATA_URL = 'create_save_predict_data';
-
-/**
- *  This file is 3/3 of the test suites for CUJ: create->save->predict.
- *
- *  This file test below things:
- *  - Load layers models using Layers api.
- *  - Load inputs.
- *  - Make inference using each backends, and validate the results against
- *    Keras results.
- */
-describeWithFlags(`${REGRESSION} create_save_predict`, ALL_ENVS, (env) => {
-  let originalTimeout: number;
-
-  beforeAll(() => {
-    // This test needs more time to finish the async fetch, adjusting
-    // jasmine timeout for this test to avoid flakiness. See jasmine
-    // documentation for detail:
-    // https://jasmine.github.io/2.0/introduction.html#section-42
-    originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-  });
-
-  afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-  LAYERS_MODELS.forEach(model => {
-    it(`${model}.`, async () => {
-      await tfc.setBackend(env.name);
-      let inputsData: tfc.TypedArray[];
-      let inputsShapes: number[][];
-      let kerasOutputData: tfc.TypedArray[];
-      let kerasOutputShapes: number[][];
-
-      [inputsData, inputsShapes, kerasOutputData, kerasOutputShapes] =
-          await Promise.all([
-            fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.xs-data.json`)
-                .then(response => response.json()),
-            fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.xs-shapes.json`)
-                .then(response => response.json()),
-            fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.ys-data.json`)
-                .then(response => response.json()),
-            fetch(`${KARMA_SERVER}/${DATA_URL}/${model}.ys-shapes.json`)
-                .then(response => response.json())
-          ]);
-
-      const $model = await tfl.loadLayersModel(
-          `${KARMA_SERVER}/${DATA_URL}/${model}/model.json`);
-
-      const xs = createInputTensors(inputsData, inputsShapes) as tfc.Tensor[];
-      const result = $model.predict(xs);
-
-      const ys =
-          ($model.outputs.length === 1 ? [result] : result) as tfc.Tensor[];
-
-      // Validate outputs with keras results.
-      for (let i = 0; i < ys.length; i++) {
-        const y = ys[i];
-        expect(y.shape).toEqual(kerasOutputShapes[i]);
-        tfc.test_util.expectArraysClose(
-            await y.data(), kerasOutputData[i], 0.005);
-      }
-
-      // Dispose all tensors;
-      xs.forEach(tensor => tensor.dispose());
-      ys.forEach(tensor => tensor.dispose());
-    });
-  });
-});
diff --git a/tfjs-master/e2e/integration_tests/custom_bundle_test.ts b/tfjs-master/e2e/integration_tests/custom_bundle_test.ts
deleted file mode 100644
index 4bd24a815..000000000
--- a/tfjs-master/e2e/integration_tests/custom_bundle_test.ts
+++ /dev/null
@@ -1,340 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import {CHROME_ENVS, Constraints, describeWithFlags, HAS_WORKER} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {REGRESSION} from './constants';
-
-const CHROME_ENVS_WITH_WORKER: Constraints =
-    Object.assign({}, CHROME_ENVS, HAS_WORKER);
-/**
- *  This file is the test suite for CUJ: custom_module->custom_module->predict.
- */
-
-function getBundleUrl(folder: string, custom: boolean, bundler: string) {
-  const distFolder = custom ? 'custom' : 'full';
-  return `./base/custom_module/${folder}/dist/${distFolder}/app_${bundler}.js`;
-}
-
-const DEBUG_WORKER_SCRIPT = true;
-
-describe(`${REGRESSION} blazeface`, () => {
-  describeWithFlags('webpack', CHROME_ENVS_WITH_WORKER, () => {
-    let webpackBundle: {full: string, custom: string};
-    let originalTimeout: number;
-    beforeAll(async () => {
-      originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-      jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-
-      const [webpackFull, webpackCustom] = await Promise.all([
-        fetch(getBundleUrl('blazeface', false /* custom */, 'webpack'))
-            .then(r => r.text())
-            .catch(e => {
-              console.error(
-                  'Failed to fetch blazeface full bundle at ',
-                  getBundleUrl('blazeface', false /* custom */, 'webpack'));
-              throw e;
-            }),
-        fetch(getBundleUrl('blazeface', true /* custom */, 'webpack'))
-            .then(r => r.text())
-            .catch(e => {
-              console.error(
-                  'Failed to fetch blazeface custom bundle at ',
-                  getBundleUrl('blazeface', false /* custom */, 'webpack'));
-              throw e;
-            }),
-      ]);
-
-      webpackBundle = {full: webpackFull, custom: webpackCustom};
-    });
-
-    afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-    it('custom webpack should be smaller', async () => {
-      expect(webpackBundle.custom.length)
-          .toBeLessThan(
-              webpackBundle.full.length,
-              'Custom bundle should be smaller than full bundle');
-    });
-
-    it('custom bundle should execute with exact kernels', async () => {
-      const programUrl =
-          getBundleUrl('blazeface', true /* custom */, 'webpack');
-      // tslint:disable-next-line: no-any
-      const result: any =
-          await executeInWorker(programUrl, {debug: DEBUG_WORKER_SCRIPT});
-      const kernelNames = result.kernelNames;
-      expect(kernelNames).toEqual(jasmine.arrayWithExactContents([
-        'Cast',
-        'ExpandDims',
-        'Reshape',
-        'ResizeBilinear',
-        'RealDiv',
-        'Sub',
-        'Multiply',
-        'FusedConv2D',
-        'DepthwiseConv2dNative',
-        'Add',
-        'Relu',
-        'Pack',
-        'PadV2',
-        'MaxPool',
-        'Slice',
-        'StridedSlice',
-        'Concat',
-        'Identity',
-        'Sigmoid',
-        'NonMaxSuppressionV3'
-      ]));
-
-      expect(result.predictions.length).toEqual(1);
-    });
-  });
-});
-
-describe(`${REGRESSION} dense model`, () => {
-  describeWithFlags('webpack', CHROME_ENVS_WITH_WORKER, () => {
-    let webpackBundle: {full: string, custom: string};
-    let originalTimeout: number;
-
-    let modelUrl: string;
-    beforeAll(async () => {
-      originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-      jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-
-      modelUrl = `/base/custom_module/dense_model/model/model.json`;
-      const [webpackFull, webpackCustom] = await Promise.all([
-        fetch(getBundleUrl('dense_model', false /* custom */, 'webpack'))
-            .then(r => r.text()),
-        fetch(getBundleUrl('dense_model', true /* custom */, 'webpack'))
-            .then(r => r.text()),
-      ]);
-
-      webpackBundle = {full: webpackFull, custom: webpackCustom};
-    });
-
-    afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-    it('custom webpack should be smaller', async () => {
-      expect(webpackBundle.custom.length)
-          .toBeLessThan(
-              webpackBundle.full.length / 2,
-              'Custom bundle should be smaller than full bundle');
-    });
-
-    it('custom bundle should execute with exact kernels', async () => {
-      const programUrl =
-          getBundleUrl('dense_model', true /* custom */, 'webpack');
-
-      // tslint:disable-next-line: no-any
-      const result: any = await executeInWorker(
-          programUrl, {debug: DEBUG_WORKER_SCRIPT, workerParams: {modelUrl}});
-      const kernelNames = result.kernelNames;
-      expect(kernelNames).toEqual(jasmine.arrayWithExactContents([
-        'Reshape', '_FusedMatMul', 'Identity'
-      ]));
-
-      expect(Math.floor(result.predictions[0])).toEqual(38);
-    });
-  });
-
-  describeWithFlags('rollup', CHROME_ENVS_WITH_WORKER, () => {
-    let rollupBundle: {full: string, custom: string};
-    let originalTimeout: number;
-
-    let modelUrl: string;
-    beforeAll(async () => {
-      originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-      jasmine.DEFAULT_TIMEOUT_INTERVAL = 500000;
-
-      modelUrl = `/base/custom_module/dense_model/model/model.json`;
-      const [rollupFull, rollupCustom] = await Promise.all([
-        fetch(getBundleUrl('dense_model', false /* custom */, 'rollup'))
-            .then(r => r.text()),
-        fetch(getBundleUrl('dense_model', true /* custom */, 'rollup'))
-            .then(r => r.text()),
-      ]);
-
-      rollupBundle = {full: rollupFull, custom: rollupCustom};
-    });
-
-    afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-    it('custom rollup should be smaller', async () => {
-      expect(rollupBundle.custom.length)
-          .toBeLessThan(
-              rollupBundle.full.length / 2,
-              'Custom bundle should be smaller than full bundle');
-    });
-
-    it('custom bundle should execute with exact kernels', async () => {
-      const programUrl =
-          getBundleUrl('dense_model', true /* custom */, 'webpack');
-
-      // tslint:disable-next-line: no-any
-      const result: any = await executeInWorker(
-          programUrl, {debug: DEBUG_WORKER_SCRIPT, workerParams: {modelUrl}});
-      const kernelNames = result.kernelNames;
-      expect(kernelNames).toEqual(jasmine.arrayWithExactContents([
-        'Reshape', '_FusedMatMul', 'Identity'
-      ]));
-
-      expect(Math.floor(result.predictions[0])).toEqual(38);
-    });
-  });
-});
-
-describe(`${REGRESSION} universal sentence encoder model`, () => {
-  const expectedKernels = [
-    'StridedSlice', 'Less',       'Cast',      'Reshape',       'GatherV2',
-    'Max',          'Add',        'Maximum',   'SparseToDense', 'Greater',
-    'Sum',          'ExpandDims', 'Concat',    'LogicalNot',    'Multiply',
-    'ScatterNd',    'GatherNd',   'Cos',       'Sin',           'BatchMatMul',
-    'Mean',         'Sub',        'Square',    'Rsqrt',         'Conv2D',
-    'SplitV',       'Pack',       'Transpose', 'Slice',         'Softmax',
-    'Prod',         'Relu',       'Range',     'RealDiv',       'Tanh'
-  ];
-
-  describeWithFlags('webpack', CHROME_ENVS_WITH_WORKER, () => {
-    let webpackBundle: {full: string, custom: string};
-    let originalTimeout: number;
-
-    beforeAll(async () => {
-      originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-      jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-
-      const [webpackFull, webpackCustom] = await Promise.all([
-        fetch(getBundleUrl(
-                  'universal_sentence_encoder', false /* custom */, 'webpack'))
-            .then(r => r.text()),
-        fetch(getBundleUrl(
-                  'universal_sentence_encoder', true /* custom */, 'webpack'))
-            .then(r => r.text()),
-      ]);
-
-      webpackBundle = {full: webpackFull, custom: webpackCustom};
-    });
-
-    afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-    it('custom webpack should be smaller', async () => {
-      expect(webpackBundle.custom.length)
-          .toBeLessThan(
-              webpackBundle.full.length / 2,
-              'Custom bundle should be smaller than full bundle');
-    });
-
-    it('custom bundle should execute with exact kernels', async () => {
-      const programUrl = getBundleUrl(
-          'universal_sentence_encoder', true /* custom */, 'webpack');
-
-      // tslint:disable-next-line: no-any
-      const result: any = await executeInWorker(
-          programUrl,
-          {debug: DEBUG_WORKER_SCRIPT, workerParams: {profile: false}});
-      const kernelNames = result.kernelNames;
-      expect(kernelNames)
-          .toEqual(jasmine.arrayWithExactContents(expectedKernels));
-
-      expect(result.predictions.shape[0]).toEqual(2);
-      expect(result.predictions.shape[1]).toEqual(512);
-      expect(result.predictions.shape.length).toEqual(2);
-    });
-  });
-
-  describeWithFlags('rollup', CHROME_ENVS_WITH_WORKER, () => {
-    let rollupBundle: {full: string, custom: string};
-    let originalTimeout: number;
-
-    beforeAll(async () => {
-      originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-      jasmine.DEFAULT_TIMEOUT_INTERVAL = 500000;
-
-      const [rollupFull, rollupCustom] = await Promise.all([
-        fetch(getBundleUrl(
-                  'universal_sentence_encoder', false /* custom */, 'rollup'))
-            .then(r => r.text()),
-        fetch(getBundleUrl(
-                  'universal_sentence_encoder', true /* custom */, 'rollup'))
-            .then(r => r.text()),
-      ]);
-
-      rollupBundle = {full: rollupFull, custom: rollupCustom};
-    });
-
-    afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-    it('custom rollup should be smaller', async () => {
-      expect(rollupBundle.custom.length)
-          .toBeLessThan(
-              rollupBundle.full.length / 2,
-              'Custom bundle should be smaller than full bundle');
-    });
-
-    it('custom bundle should execute with exact kernels', async () => {
-      const programUrl = getBundleUrl(
-          'universal_sentence_encoder', true /* custom */, 'webpack');
-
-      // tslint:disable-next-line: no-any
-      const result: any = await executeInWorker(
-          programUrl,
-          {debug: DEBUG_WORKER_SCRIPT, workerParams: {profile: false}});
-      const kernelNames = result.kernelNames;
-      expect(kernelNames)
-          .toEqual(jasmine.arrayWithExactContents(expectedKernels));
-
-      expect(result.predictions.shape[0]).toEqual(2);
-      expect(result.predictions.shape[1]).toEqual(512);
-      expect(result.predictions.shape.length).toEqual(2);
-    });
-  });
-});
-
-/**
- * Helper function for executing scripts in a webworker. We use
- * webworkers to get isolated contexts for tests for custom bundles.
- *
- * @param programUrl url to script to run in worker
- * @param debug debug mode
- */
-async function executeInWorker(
-    programUrl: string, opts: {debug?: boolean, workerParams?: {}}) {
-  return new Promise((resolve, reject) => {
-    const debug = opts.debug || false;
-    const workerParams = opts.workerParams || {};
-    const worker = new Worker(programUrl);
-
-    worker.addEventListener('message', (evt) => {
-      if (evt.data.error) {
-        reject(evt.data.payload);
-      }
-
-      if (debug && evt.data.msg) {
-        console.log('msg from worker: ', evt.data);
-      }
-
-      if (evt.data.result) {
-        if (debug) {
-          console.log('result from worker: ', evt.data.result);
-        }
-        resolve(evt.data.payload);
-      }
-    }, false);
-
-    worker.postMessage(workerParams);  // Send data to our worker.
-  });
-}
diff --git a/tfjs-master/e2e/integration_tests/grad_layers_test.ts b/tfjs-master/e2e/integration_tests/grad_layers_test.ts
deleted file mode 100644
index 3f20efc9a..000000000
--- a/tfjs-master/e2e/integration_tests/grad_layers_test.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-import '@tensorflow/tfjs-backend-webgpu';
-
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {Constraints, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import * as tfl from '@tensorflow/tfjs-layers';
-
-import {SMOKE} from './constants';
-
-// TODO(#6518): Test against wasm as well.
-const NOT_WASM: Constraints = {
-  predicate: testEnv => testEnv.backendName !== 'wasm',
-};
-/**
- *  Tests that tf.grad works for layers models.
- *  Regression test for https://github.com/tensorflow/tfjs/issues/4130
- */
-describe(`${SMOKE} tf.grad for layers models`, () => {
-  describeWithFlags(`layers_model`, NOT_WASM, (env) => {
-    it(`can compute grad of prediction`, async () => {
-      await tfc.setBackend(env.name);
-      const model = tfl.sequential();
-      model.add(tfl.layers.dense({inputShape: [1], units: 1}));
-      const forward = (x: tfc.Tensor) => model.predict(x) as tfc.Tensor;
-      const grad = tfc.grad(forward);
-
-      const input = tfc.tensor([1], [1, 1]);
-      const dy = tfc.onesLike(input);
-      expect(() => {
-        grad(input, dy);
-      }).not.toThrow();
-    });
-  });
-});
diff --git a/tfjs-master/e2e/integration_tests/graph_model_golden_data/filenames.json b/tfjs-master/e2e/integration_tests/graph_model_golden_data/filenames.json
deleted file mode 100644
index 64e8a872d..000000000
--- a/tfjs-master/e2e/integration_tests/graph_model_golden_data/filenames.json
+++ /dev/null
@@ -1 +0,0 @@
-["MobileNetV3_small_075.golden.json"]
\ No newline at end of file
diff --git a/tfjs-master/e2e/integration_tests/graph_model_golden_tests.ts b/tfjs-master/e2e/integration_tests/graph_model_golden_tests.ts
deleted file mode 100644
index 70022122b..000000000
--- a/tfjs-master/e2e/integration_tests/graph_model_golden_tests.ts
+++ /dev/null
@@ -1,190 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-
-import * as tfconverter from '@tensorflow/tfjs-converter';
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {GOLDEN, KARMA_SERVER} from './constants';
-import * as GOLDEN_MODEL_DATA_FILENAMES from './graph_model_golden_data/filenames.json';
-import {GraphModeGoldenData, TensorDetail} from './types';
-
-/** Directory that stores the model golden data. */
-const DATA_URL = 'graph_model_golden_data';
-const INTERMEDIATE_NODE_TESTS_NUM = 5;
-
-describeWithFlags(`${GOLDEN} graph_model_golden`, ALL_ENVS, (env) => {
-  let originalTimeout: number;
-
-  beforeAll(async () => {
-    // This test needs more time to finish the async fetch, adjusting
-    // jasmine timeout for this test to avoid flakiness. See jasmine
-    // documentation for detail:
-    // https://jasmine.github.io/2.0/introduction.html#section-42
-    originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-    await tfc.setBackend(env.backendName);
-  });
-
-  afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-  for (const goldenFilename of GOLDEN_MODEL_DATA_FILENAMES) {
-    describe(goldenFilename, () => {
-      it('model.predict(...)', async () => {
-        const [modelGolden, model] = await loadModelGolden(goldenFilename);
-        const outputs = model.predict(createGoldenInputTensors(modelGolden));
-        await expectTensorsToEqualGoldens(outputs, modelGolden.outputDetails);
-        tfc.dispose(outputs);
-      });
-
-      it('model.execute(...) with default outputs', async () => {
-        const [modelGolden, model] = await loadModelGolden(goldenFilename);
-        const outputs = model.execute(createGoldenInputTensors(modelGolden));
-        await expectTensorsToEqualGoldens(outputs, modelGolden.outputDetails);
-        tfc.dispose(outputs);
-      });
-
-      for (let batchId = 1; batchId <= INTERMEDIATE_NODE_TESTS_NUM; ++batchId) {
-        it(`model.execute(...) with intermediate node names #${batchId}`,
-           async () => {
-             const [modelGolden, model] = await loadModelGolden(goldenFilename);
-             const intermediateNodeNames =
-                 Object.keys(modelGolden.intermediateDetails);
-
-             // Validates the intermediate node tensor values and output values.
-             // Every `INTERMEDIATE_NODE_TESTS_NUM` nodes in
-             // `intermediateDetails` are chosen to be validated.
-             const targetNodeNames = [
-               ...intermediateNodeNames.filter(
-                   (unused, i) =>
-                       (i % INTERMEDIATE_NODE_TESTS_NUM) + 1 === batchId),
-               ...model.outputs.map(output => output.name),
-             ];
-
-             const goldens = targetNodeNames.map((name) => {
-               const details = modelGolden.intermediateDetails[name];
-               if (details == null) {
-                 throw new Error(
-                     `Golden file is missing tensor details for ` +
-                     `${name}`);
-               }
-               return details;
-             });
-
-             const outputs = model.execute(
-                                 createGoldenInputTensors(modelGolden),
-                                 targetNodeNames) as tfc.Tensor[];
-
-             expect(outputs.length).toEqual(goldens.length);
-             await expectTensorsToEqualGoldens(outputs, goldens);
-             tfc.dispose(outputs);
-           });
-      }
-    });
-  }
-});
-
-async function loadModelGolden(goldenFilename: string) {
-  const modelGoldenPromise: Promise<GraphModeGoldenData> =
-      fetch(`${KARMA_SERVER}/${DATA_URL}/${goldenFilename}`)
-          .then(response => response.json());
-  const modelPromise = modelGoldenPromise.then((modelGolden) => {
-    return tfconverter.loadGraphModel(modelGolden.url, {
-      fromTFHub: modelGolden.fromTFHub,
-    });
-  });
-
-  return Promise.all([modelGoldenPromise, modelPromise]);
-}
-
-async function expectTensorToEqualGolden(
-    tensor: tfc.Tensor, golden: TensorDetail) {
-  expect(tensor).toEqual(jasmine.anything());
-  expect(golden).toEqual(jasmine.anything());
-
-  expect(isTensorDetail(golden));
-  expect(tensor.isDisposed).toEqual(false);
-  expect(tensor.dtype).toEqual(golden.dtype);
-  expect(tensor.shape).toEqual(golden.shape);
-  tfc.test_util.expectArraysClose(Array.from(await tensor.data()), golden.data);
-}
-
-async function expectTensorsToEqualGoldens(
-    tensors: tfc.Tensor|tfc.Tensor[]|tfc.NamedTensorMap,
-    goldens: TensorDetail|TensorDetail[]|Record<string, TensorDetail>) {
-  expect(tensors).toEqual(jasmine.anything());
-  expect(goldens).toEqual(jasmine.anything());
-  if (tensors instanceof tfc.Tensor) {
-    await expectTensorToEqualGolden(tensors, goldens as TensorDetail);
-  } else if (Array.isArray(tensors)) {
-    expect(Array.isArray(goldens)).toEqual(true);
-    const details = goldens as TensorDetail[];
-    expect(tensors.length).toEqual(details.length);
-    for (let i = 0; i < tensors.length; ++i) {
-      await expectTensorToEqualGolden(tensors[i], details[i]);
-    }
-  } else {
-    const detailMap = goldens as Record<string, TensorDetail>;
-    expect(new Set(Object.keys(detailMap)))
-        .toEqual(new Set(Object.keys(tensors)));
-    for (const [name, detail] of Object.entries(detailMap)) {
-      await expectTensorToEqualGolden(tensors[name], detail);
-    }
-  }
-}
-
-function isTensorDetail(x: any): x is TensorDetail {
-  return x != null && typeof x === 'object' && 'dtype' in x &&
-      typeof x.dtype === 'string' && 'shape' in x && Array.isArray(x.shape) &&
-      'data' in x && Array.isArray(x.data);
-}
-
-function createGoldenInputTensors({inputs}: GraphModeGoldenData) {
-  function toTensor({data, dtype, shape}: TensorDetail) {
-    let typedArray: tfc.TypedArray;
-    switch (dtype) {
-      case 'bool':
-        typedArray = Uint8Array.from(data);
-        break;
-      case 'float32':
-        typedArray = Float32Array.from(data);
-        break;
-      case 'int32':
-        typedArray = Int32Array.from(data);
-        break;
-      default:
-        throw new Error(`Unsupported input tensor type ${dtype}`);
-    }
-    return tfc.tensor(typedArray, shape, dtype);
-  }
-
-  if (Array.isArray(inputs)) {
-    return inputs.map(toTensor);
-  }
-  if (isTensorDetail(inputs)) {
-    return toTensor(inputs as TensorDetail);
-  }
-  return Object.entries(inputs).reduce(
-      (map: tfc.NamedTensorMap, [name, detail]) => {
-        map[name] = toTensor(detail);
-        return map;
-      },
-      {});
-}
diff --git a/tfjs-master/e2e/integration_tests/load_predict_data/graph_model/model.json b/tfjs-master/e2e/integration_tests/load_predict_data/graph_model/model.json
deleted file mode 100644
index 5ad2cdf88..000000000
--- a/tfjs-master/e2e/integration_tests/load_predict_data/graph_model/model.json
+++ /dev/null
@@ -1 +0,0 @@
-{"format": "graph-model", "generatedBy": "2.1.0", "convertedBy": "TensorFlow.js Converter v1.7.4r1", "userDefinedMetadata": {"signature": {"inputs": {"Placeholder:0": {"name": "Placeholder:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}, "outputs": {"MatMul:0": {"name": "MatMul:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}}}, "modelTopology": {"node": [{"name": "w", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "Placeholder", "op": "Placeholder", "attr": {"dtype": {"type": "DT_FLOAT"}, "shape": {"shape": {"dim": [{"size": "2"}, {"size": "2"}]}}}}, {"name": "MatMul", "op": "MatMul", "input": ["Placeholder", "w"], "device": "/device:CPU:0", "attr": {"transpose_a": {"b": false}, "transpose_b": {"b": false}, "T": {"type": "DT_FLOAT"}}}], "versions": {"producer": 175}}, "weightsManifest": [{"paths": ["weights.bin"], "weights": [{"name": "w", "shape": [2, 2], "dtype": "float32"}]}]}
diff --git a/tfjs-master/e2e/integration_tests/load_predict_data/graph_model/model_new.json b/tfjs-master/e2e/integration_tests/load_predict_data/graph_model/model_new.json
deleted file mode 100644
index 88a620729..000000000
--- a/tfjs-master/e2e/integration_tests/load_predict_data/graph_model/model_new.json
+++ /dev/null
@@ -1 +0,0 @@
-{"format": "graph-model", "generatedBy": "2.8.0", "convertedBy": "TensorFlow.js Converter v2.8.0", "signature": {"inputs": {"Placeholder:0": {"name": "Placeholder:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}, "outputs": {"MatMul:0": {"name": "MatMul:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}}, "modelTopology": {"node": [{"name": "w", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "Placeholder", "op": "Placeholder", "attr": {"dtype": {"type": "DT_FLOAT"}, "shape": {"shape": {"dim": [{"size": "2"}, {"size": "2"}]}}}}, {"name": "MatMul", "op": "MatMul", "input": ["Placeholder", "w"], "device": "/device:CPU:0", "attr": {"transpose_a": {"b": false}, "transpose_b": {"b": false}, "T": {"type": "DT_FLOAT"}}}], "versions": {"producer": 175}}, "weightsManifest": [{"paths": ["weights.bin"], "weights": [{"name": "w", "shape": [2, 2], "dtype": "float32"}]}]}
diff --git a/tfjs-master/e2e/integration_tests/load_predict_data/graph_model/weights.bin b/tfjs-master/e2e/integration_tests/load_predict_data/graph_model/weights.bin
deleted file mode 100644
index 527027940..000000000
Binary files a/tfjs-master/e2e/integration_tests/load_predict_data/graph_model/weights.bin and /dev/null differ
diff --git a/tfjs-master/e2e/integration_tests/load_predict_data/layers_model/model.json b/tfjs-master/e2e/integration_tests/load_predict_data/layers_model/model.json
deleted file mode 100644
index 2e67d8163..000000000
--- a/tfjs-master/e2e/integration_tests/load_predict_data/layers_model/model.json
+++ /dev/null
@@ -1 +0,0 @@
-{"modelTopology":{"class_name":"Sequential","config":{"name":"sequential_5","layers":[{"class_name":"Embedding","config":{"input_dim":100,"output_dim":20,"embeddings_initializer":{"class_name":"RandomUniform","config":{"minval":-0.05,"maxval":0.05,"seed":null}},"embeddings_regularizer":null,"activity_regularizer":null,"embeddings_constraint":null,"mask_zero":null,"input_length":null,"name":"embedding_Embedding2","trainable":true,"batch_input_shape":[null,10],"dtype":"float32"}},{"class_name":"GRU","config":{"units":4,"activation":"tanh","recurrent_activation":"hard_sigmoid","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"recurrent_initializer":{"class_name":"Orthogonal","config":{"gain":1,"seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"recurrent_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"recurrent_constraint":null,"bias_constraint":null,"dropout":0,"recurrent_dropout":0,"implementation":null,"return_sequences":false,"return_state":false,"go_backwards":true,"stateful":false,"unroll":false,"name":"gru_GRU1","trainable":true}}]},"keras_version":"tfjs-layers 0.0.0","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["weights.bin"],"weights":[{"name":"embedding_Embedding2/embeddings","shape":[100,20],"dtype":"float32"},{"name":"gru_GRU1/kernel","shape":[20,12],"dtype":"float32"},{"name":"gru_GRU1/recurrent_kernel","shape":[4,12],"dtype":"float32"},{"name":"gru_GRU1/bias","shape":[12],"dtype":"float32"}]}],"format":"layers-model","generatedBy":"TensorFlow.js tfjs-layers v0.0.0","convertedBy":null}
\ No newline at end of file
diff --git a/tfjs-master/e2e/integration_tests/load_predict_data/layers_model/weights.bin b/tfjs-master/e2e/integration_tests/load_predict_data/layers_model/weights.bin
deleted file mode 100644
index 37107d181..000000000
Binary files a/tfjs-master/e2e/integration_tests/load_predict_data/layers_model/weights.bin and /dev/null differ
diff --git a/tfjs-master/e2e/integration_tests/load_predict_test.ts b/tfjs-master/e2e/integration_tests/load_predict_test.ts
deleted file mode 100644
index a2de61c76..000000000
--- a/tfjs-master/e2e/integration_tests/load_predict_test.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-import '@tensorflow/tfjs-backend-webgpu';
-
-import * as tfconverter from '@tensorflow/tfjs-converter';
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import * as tfl from '@tensorflow/tfjs-layers';
-
-import {KARMA_SERVER, SMOKE} from './constants';
-
-/**
- *  This file is the test suites for CUJ: load->predict.
- *
- *  This file test below things:
- *  - Load layers or graph model.
- *  - Make inference using each backends.
- */
-describe(`${SMOKE} load_predict`, () => {
-  describeWithFlags(`layers_model`, ALL_ENVS, (env) => {
-    it(`predict`, async () => {
-      await tfc.setBackend(env.name);
-      const model = await tfl.loadLayersModel(
-          `${KARMA_SERVER}/load_predict_data/layers_model/model.json`);
-      const inputs =
-          tfc.tensor([86, 11, 62, 40, 36, 75, 82, 94, 67, 75], [1, 10]);
-      const expected = [
-        -0.003578941337764263, 0.0028922036290168762, -0.002957976423203945,
-        0.00955402385443449
-      ];
-      const result = model.predict(inputs) as tfc.Tensor;
-      tfc.test_util.expectArraysClose(await result.data(), expected);
-      inputs.dispose();
-    });
-  });
-
-  describeWithFlags(`graph_model`, ALL_ENVS, async (env) => {
-    let a: tfc.Tensor;
-
-    const expected = [
-      0.7567615509033203, -0.18349379301071167, 0.7567615509033203,
-      -0.18349379301071167
-    ];
-
-    beforeEach(async () => {
-      await tfc.setBackend(env.name);
-      a = tfc.tensor2d([1, 1, 1, 1], [2, 2], 'float32');
-    });
-
-    afterEach(() => {
-      a.dispose();
-    });
-
-    it(`predict for old model.`, async () => {
-      const model = await tfconverter.loadGraphModel(
-          `${KARMA_SERVER}/load_predict_data/graph_model/model.json`);
-      const result = await model.executeAsync({'Placeholder': a}) as tfc.Tensor;
-      tfc.test_util.expectArraysClose(await result.data(), expected);
-    });
-
-    it(`predict for new model.`, async () => {
-      const model = await tfconverter.loadGraphModel(
-          `${KARMA_SERVER}/load_predict_data/graph_model/model_new.json`);
-      const result = await model.executeAsync({'Placeholder': a}) as tfc.Tensor;
-      tfc.test_util.expectArraysClose(await result.data(), expected);
-    });
-  });
-});
diff --git a/tfjs-master/e2e/integration_tests/memory_leak_test.ts b/tfjs-master/e2e/integration_tests/memory_leak_test.ts
deleted file mode 100644
index bc849475e..000000000
--- a/tfjs-master/e2e/integration_tests/memory_leak_test.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-import '@tensorflow/tfjs-backend-webgpu';
-
-import * as tfconverter from '@tensorflow/tfjs-converter';
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {SMOKE} from './constants';
-
-const HOST = 'http://example.org';
-const MODEL_URL = `${HOST}/model.json`;
-
-const CUSTOM_OP_MODEL = {
-  node: [
-    {
-      name: 'Input',
-      op: 'Placeholder',
-      attr: {
-        dtype: {
-          type: 1,  // DT_FLOAT
-        },
-        shape: {shape: {dim: [{size: 4}]}}
-      }
-    },
-    {name: 'CustomOp', op: 'CustomOp', input: ['Input'], attr: {}}
-  ],
-  versions: {producer: 1.0, minConsumer: 3}
-};
-
-const weightsManifest: tfc.io.WeightsManifestEntry[] =
-    [{'name': 'Const', 'dtype': 'float32', 'shape': [1]}];
-
-const CUSTOM_HTTP_MODEL_LOADER = {
-  load: async () => {
-    const bias = tfc.tensor1d([0], 'float32');
-    return {
-      modelTopology: CUSTOM_OP_MODEL,
-      weightSpecs: weightsManifest,
-      weightData: bias.dataSync(),
-      format: 'tfjs-graph-model',
-      generatedBy: '1.15',
-      convertedBy: '1.3.1'
-    };
-  }
-};
-
-describeWithFlags(
-    `${SMOKE} A custom op that calls unmodularized kernels and modularized ` +
-        `kernels`,
-    ALL_ENVS, () => {
-      it('should have no memory leak in a model run.', async () => {
-        const model = new tfconverter.GraphModel(MODEL_URL);
-
-        spyOn(tfc.io, 'getLoadHandlers').and.returnValue([
-          CUSTOM_HTTP_MODEL_LOADER
-        ]);
-
-        // A custom op that calls unmodularized kernels and modularized kernels.
-        tfconverter.registerOp('CustomOp', (nodeValue) => {
-          const x = nodeValue.inputs[0];
-          const softMax = tfc.softmax(x);
-          const clone = tfc.clone(softMax);
-          return [tfc.reshape(clone, [2, 2])];
-        });
-
-        await model.load();
-
-        const before = tfc.memory().numTensors;
-
-        const input = tfc.tensor1d([1, 2, 3, 4]);
-        const output = model.predict(input) as tfc.Tensor;
-
-        tfc.test_util.expectArraysClose(await output.data(), [
-          0.032058604061603546, 0.08714432269334793, 0.23688283562660217,
-          0.6439142823219299
-        ]);
-
-        input.dispose();
-        output.dispose();
-
-        const after = tfc.memory().numTensors;
-
-        expect(after).toEqual(before);
-      });
-    });
diff --git a/tfjs-master/e2e/integration_tests/metadata.py b/tfjs-master/e2e/integration_tests/metadata.py
deleted file mode 100644
index 31a577024..000000000
--- a/tfjs-master/e2e/integration_tests/metadata.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# @license
-# Copyright 2020 Google LLC. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# This file is 1/2 of the test suites for CUJ: convert->predict.
-#
-# This file does below things:
-# - Create saved models with TensorFlow.
-# - Convert the saved models to tfjs format and store in files.
-# - Store inputs in files.
-# - Make inference and store outputs in files.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import functools
-import json
-import os
-import subprocess
-import shutil
-import sys
-import tempfile
-import time
-
-import numpy as np
-import tensorflow as tf
-from tensorflow.python.eager import def_function
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import tensor_spec
-from tensorflow.python.ops import variables
-from tensorflow.python.trackable import autotrackable
-from tensorflow.python.saved_model.save import save
-import tensorflow_hub as hub
-import tensorflowjs as tfjs
-
-curr_dir = os.path.dirname(os.path.realpath(__file__))
-_tmp_dir = os.path.join(curr_dir, 'metadata')
-
-def _create_model_with_metadata():
-  # Generate model, inputs, and outputs using Tensorflow.
-  tmp_saved_model_dir = tempfile.mkdtemp()
-  model_info = _create_saved_model(tmp_saved_model_dir)
-
-  metadata1 = {'a': 1}
-  metadata2 = {'label1': 0, 'label2': 1}
-  metadata1_path = os.path.join(_tmp_dir, 'metadata1.json')
-  metadata2_path = os.path.join(_tmp_dir, 'metadata2.json')
-  with open(metadata1_path, 'w') as f:
-    f.write(json.dumps(metadata1))
-  with open(metadata2_path, 'w') as f:
-    f.write(json.dumps(metadata2))
-  metadata_option = 'metadata1:'+metadata1_path+','+'metadata2:'+metadata2_path
-
-  # Convert and store model to file.
-  args = [
-      'tensorflowjs_converter',
-      '--input_format', 'tf_saved_model',
-      '--output_format', 'tfjs_graph_model',
-      '--signature_name', 'serving_default',
-      '--saved_model_tags', 'serve',
-      '--metadata', metadata_option];
-
-  print(args, tmp_saved_model_dir, _tmp_dir)
-  subprocess.check_output(args +[tmp_saved_model_dir, _tmp_dir])
-
-def _create_saved_model(save_dir):
-  input_data = constant_op.constant(1., shape=[1])
-  root = autotrackable.AutoTrackable()
-  root.v1 = variables.Variable(3.)
-  root.v2 = variables.Variable(2.)
-  root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
-  to_save = root.f.get_concrete_function(input_data)
-
-  save(root, save_dir, to_save)
-  return {
-      "async": False,
-      "inputs": {
-          "x": {"value": [1], "shape": [1], "dtype": 'float32'}},
-      "outputs": {
-          "Identity:0": {"value": [6], "shape": [1], "dtype": "float32"}}}
-
-def main():
-  # Create the directory to store model and data.
-  if os.path.exists(_tmp_dir) and os.path.isdir(_tmp_dir):
-    shutil.rmtree(_tmp_dir)
-  os.mkdir(_tmp_dir)
-
-  _create_model_with_metadata()
-
-if __name__ == '__main__':
-  main()
diff --git a/tfjs-master/e2e/integration_tests/metadata.ts b/tfjs-master/e2e/integration_tests/metadata.ts
deleted file mode 100644
index a17997991..000000000
--- a/tfjs-master/e2e/integration_tests/metadata.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tfconverter from '@tensorflow/tfjs-converter';
-import {KARMA_SERVER, REGRESSION} from './constants';
-import * as tfc from '@tensorflow/tfjs-core';
-
-const DATA_URL = 'metadata';
-
-describe(`${REGRESSION} Metadata`, () => {
-  it('can load metadata.', async () => {
-    await tfc.ready();
-    const model = await tfconverter.loadGraphModel(
-        `${KARMA_SERVER}/${DATA_URL}/model.json`);
-    const metadata = {metadata1: {a: 1}, metadata2: {label1: 0, label2: 1}};
-    expect(model.metadata).toEqual(metadata);
-  });
-});
diff --git a/tfjs-master/e2e/integration_tests/requirements-dev.txt b/tfjs-master/e2e/integration_tests/requirements-dev.txt
deleted file mode 100644
index 577fbe57a..000000000
--- a/tfjs-master/e2e/integration_tests/requirements-dev.txt
+++ /dev/null
@@ -1 +0,0 @@
--r ../../tfjs-converter/python/requirements.txt
diff --git a/tfjs-master/e2e/integration_tests/requirements-stable.txt b/tfjs-master/e2e/integration_tests/requirements-stable.txt
deleted file mode 100644
index 577fbe57a..000000000
--- a/tfjs-master/e2e/integration_tests/requirements-stable.txt
+++ /dev/null
@@ -1 +0,0 @@
--r ../../tfjs-converter/python/requirements.txt
diff --git a/tfjs-master/e2e/integration_tests/setup_test.ts b/tfjs-master/e2e/integration_tests/setup_test.ts
deleted file mode 100644
index 8e319b1a6..000000000
--- a/tfjs-master/e2e/integration_tests/setup_test.ts
+++ /dev/null
@@ -1,136 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Import core for side effects (e.g. flag registration)
-import '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-import '@tensorflow/tfjs-backend-wasm';
-
-// tslint:disable-next-line: no-imports-from-dist
-import {parseTestEnvFromKarmaFlags, registerTestEnv, setTestEnvs, TEST_ENVS} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {TAGS} from './constants';
-
-registerTestEnv({
-  name: 'webgl',
-  backendName: 'webgl',
-  flags: {
-    'WEBGL_VERSION': 2,
-    'WEBGL_CPU_FORWARD': false,
-    'WEBGL_SIZE_UPLOAD_UNIFORM': 0
-  },
-  isDataSync: true
-});
-
-registerTestEnv({
-  name: 'wasm',
-  backendName: 'wasm',
-  isDataSync: true,
-});
-
-registerTestEnv({name: 'cpu', backendName: 'cpu', isDataSync: true});
-
-// TODO: Support test windows on WebGPU. Bug:
-// https://github.com/tensorflow/tfjs/issues/7616.
-if (navigator.platform.toUpperCase().indexOf('MAC') >= 0 &&
-    (window as any).chrome != null) {
-  registerTestEnv({
-    name: 'webgpu',
-    backendName: 'webgpu',
-    flags: {'WEBGPU_CPU_FORWARD': false},
-    isDataSync: true
-  });
-}
-
-// tslint:disable-next-line:no-any
-declare let __karma__: any;
-if (typeof __karma__ !== 'undefined') {
-  const args = __karma__.config.args || [];
-
-  const testEnv = parseTestEnvFromKarmaFlags(__karma__.config.args, TEST_ENVS);
-  if (testEnv != null) {
-    setTestEnvs([testEnv]);
-  }
-
-  let tags;
-
-  args.forEach((arg: string, i: number) => {
-    if (arg === '--tags') {
-      tags = parseTags(args[i + 1]);
-    }
-  });
-
-  setupTestFilters(tags);
-}
-
-/**
- * Given a string separated with comma, validate and return tags as an array.
- */
-function parseTags(tagsInput: string): string[] {
-  if (!tagsInput || tagsInput === '') {
-    throw new Error(
-        '--tags did not have any value. Please specify tags separated ' +
-        'by comma.');
-  }
-
-  const tags = tagsInput.split(',');
-
-  const $tags = [];
-
-  for (let i = 0; i < tags.length; i++) {
-    const tag = tags[i].trim();
-
-    if (!TAGS.includes(tag)) {
-      throw new Error(`Tag ${tag} is not supported. Supported tags: ${TAGS}`);
-    }
-    $tags.push(tag);
-  }
-
-  return $tags;
-}
-
-/**
- * Run Jasmine tests only for allowlisted tags.
- */
-function setupTestFilters(tags: string[] = []) {
-  const env = jasmine.getEnv();
-
-  // Account for --grep flag passed to karma by saving the existing specFilter.
-  const grepFilter = env.specFilter;
-
-  // tslint:disable-next-line: no-any
-  env.specFilter = (spec: any) => {
-    // Filter out tests if the --grep flag is passed.
-    if (!grepFilter(spec)) {
-      return false;
-    }
-
-    const name = spec.getFullName();
-
-    // Only include a test if it belongs to one of the specified tags.
-    for (let i = 0; i < tags.length; i++) {
-      const tag = tags[i];
-      if (name.includes(tag)) {
-        return true;
-      }
-    }
-
-    // Otherwise ignore the test.
-    return false;
-  };
-}
diff --git a/tfjs-master/e2e/integration_tests/test_util.ts b/tfjs-master/e2e/integration_tests/test_util.ts
deleted file mode 100644
index faeca3caa..000000000
--- a/tfjs-master/e2e/integration_tests/test_util.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-
-/**
- * Create a list of input tensors.
- * @param inputsData An array with each element being the value to create a
- *    tensor.
- * @param inputsShapes An array with each element being the shape to create a
- *    tensor.
- */
-export function createInputTensors(
-    inputsData: tfc.TypedArray[], inputsShapes: number[][],
-    inputDtypes?: tfc.DataType[], inputNames?: string[]): tfc.Tensor[]|
-    tfc.NamedTensorMap {
-  const xs: tfc.Tensor[] = [];
-  for (let i = 0; i < inputsData.length; i++) {
-    const input = tfc.tensor(
-        inputsData[i], inputsShapes[i],
-        inputDtypes ? inputDtypes[i] : 'float32');
-    xs.push(input);
-  }
-  if (inputNames) {
-    return inputNames.reduce((map: tfc.NamedTensorMap, name, index) => {
-      map[name] = xs[index];
-      return map;
-    }, {});
-  }
-  return xs;
-}
diff --git a/tfjs-master/e2e/integration_tests/types.ts b/tfjs-master/e2e/integration_tests/types.ts
deleted file mode 100644
index 274447313..000000000
--- a/tfjs-master/e2e/integration_tests/types.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tfc from '@tensorflow/tfjs-core';
-
-export interface TensorDetail {
-  data: number[];
-  shape: number[];
-  dtype: tfc.DataType;
-}
-
-export interface GraphModeGoldenData {
-  /** The model name. */
-  readonly name: string;
-  /** Url for loading the model with `tf.loadGraphModel`. */
-  readonly url: string;
-  /** Whether model is to be loaded from TF Hub. */
-  readonly fromTFHub?: boolean;
-  /**
-   * Golden tensor values for `model.predict`.
-   */
-  readonly inputs: TensorDetail|TensorDetail[]|Record<string, TensorDetail>;
-  /**
-   * The returned tensor values of `model.predict(this.inputs)`.
-   */
-  readonly outputDetails: TensorDetail|TensorDetail[]|
-      Record<string, TensorDetail>;
-  /**
-   * All intermediate node tensor values after calling
-   * `model.predict(this.inputs)`. The object keys are the node names.
-   */
-  readonly intermediateDetails: Record<string, TensorDetail>;
-}
diff --git a/tfjs-master/e2e/karma.conf.js b/tfjs-master/e2e/karma.conf.js
deleted file mode 100644
index e478c88bf..000000000
--- a/tfjs-master/e2e/karma.conf.js
+++ /dev/null
@@ -1,213 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const karmaTypescriptConfig = {
-  tsconfig: 'tsconfig.json',
-  coverageOptions: {instrumentation: false},
-  bundlerOptions: {
-    sourceMap: true,
-    transforms: [
-      require('karma-typescript-es6-transform')({
-        presets: [
-          // ensure we get es5 by adding IE 11 as a target
-          ['@babel/env', {targets: {browsers: ['defaults', 'IE 11']}}]
-        ]
-      }),
-    ]
-  }
-};
-
-// Enable coverage reports and instrumentation under KARMA_COVERAGE=1 env
-const coverageEnabled = !!process.env.KARMA_COVERAGE;
-if (coverageEnabled) {
-  karmaTypescriptConfig.coverageOptions.instrumentation = true;
-  karmaTypescriptConfig.coverageOptions.exclude = /_test\.ts$/;
-  karmaTypescriptConfig.reports = {html: 'coverage', 'text-summary': ''};
-}
-
-const devConfig = {
-  frameworks: ['jasmine', 'karma-typescript'],
-  files: [
-    {pattern: './node_modules/@babel/polyfill/dist/polyfill.js'},
-    'integration_tests/setup_test.ts',
-    {pattern: 'integration_tests/**/*.ts'},
-    {
-      pattern: 'integration_tests/*_data/**/*',
-      watched: true,
-      included: false,
-      served: true,
-      nocache: true
-    },
-    {
-      pattern: 'integration_tests/metadata/**/*',
-      watched: true,
-      included: false,
-      served: true,
-      nocache: true
-    },
-    // Serve program bundles as files
-    {
-      pattern: 'custom_module/*/dist/**/*',
-      watched: true,
-      included: false,
-      served: true,
-      nocache: true
-    },
-    // Serve model assets as files
-    {
-      pattern: 'custom_module/*/model/**/*',
-      watched: true,
-      included: false,
-      served: true,
-      nocache: true
-    },
-    // Serve wasm files
-    {
-      pattern: 'node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/*.wasm',
-      watched: true,
-      included: false,
-      served: true,
-    },
-  ],
-  basePath: '',
-  proxies: {
-    '/base/node_modules/karma-typescript/dist/client/tfjs-backend-wasm.wasm':
-        '/base/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.wasm',
-    '/base/node_modules/karma-typescript/dist/client/tfjs-backend-wasm-simd.wasm':
-        '/base/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-simd.wasm',
-    '/base/node_modules/karma-typescript/dist/client/tfjs-backend-wasm-threaded-simd.wasm':
-        '/base/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.wasm',
-  },
-  exclude: ['integration_tests/custom_bundle_test.ts'],
-  include: ['integration_tests/**/*.ts'],
-  preprocessors: {
-    '**/*.ts': ['karma-typescript'],  // *.tsx for React Jsx
-  },
-  karmaTypescriptConfig,
-  reporters: ['spec', 'karma-typescript']
-};
-
-const browserstackConfig = {
-  ...devConfig,
-  // TODONT: do not use `hostname: 'bs-local.com'. This is automatically changed
-  // by BrowserStack when necessary (i.e. on ios safari). Setting it manually
-  // breaks WASM file serving.
-  // See https://www.browserstack.com/question/39574
-  singleRun: true,
-  port: 9200
-};
-
-const chromeWebgpuFlags = [
-  '--enable-unsafe-webgpu',  // Can be removed after WebGPU release
-  '--use-webgpu-adapter=swiftshader',
-  // https://github.com/tensorflow/tfjs/issues/7631
-  '--disable-vulkan-fallback-to-gl-for-testing',
-];
-
-module.exports = function(config) {
-  const args = [];
-
-  if (config.testEnv) {
-    args.push('--testEnv', config.testEnv);
-  }
-  if (config.flags) {
-    args.push('--flags', config.flags);
-  }
-  if (config.grep) {
-    args.push('--grep', config.grep);
-  }
-  if (config.tags) {
-    args.push('--tags', config.tags);
-  }
-
-  let extraConfig = null;
-
-  if (config.browserstack) {
-    extraConfig = browserstackConfig;
-  } else {
-    extraConfig = devConfig;
-  }
-
-  config.set({
-    ...extraConfig,
-    reporters: [
-      'spec',
-      'jasmine-order',
-    ],
-    browsers: ['Chrome'],
-    browserStack: {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_KEY,
-      timeout: 1800,
-      tunnelIdentifier: `e2e_${Date.now()}_${Math.floor(Math.random() * 1000)}`
-    },
-    captureTimeout: 3e5,
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 3e5,
-    browserDisconnectTimeout: 3e5,
-    browserDisconnectTolerance: 0,
-    browserSocketTimeout: 1.2e5,
-    customLaunchers: {
-      bs_chrome_mac: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra',
-        flags: chromeWebgpuFlags,
-      },
-      bs_firefox_mac: {
-        base: 'BrowserStack',
-        browser: 'firefox',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_safari_mac: {
-        base: 'BrowserStack',
-        browser: 'safari',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'Mojave'
-      },
-      bs_ios_12: {
-        base: 'BrowserStack',
-        device: 'iPhone XS',
-        os: 'ios',
-        os_version: '12.3',
-        real_mobile: true
-      },
-      bs_android_10: {
-        base: 'BrowserStack',
-        device: 'Google Pixel 4 XL',
-        os: 'android',
-        os_version: '10.0',
-        real_mobile: true
-      },
-      win_10_chrome: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: '101.0',
-        os: 'Windows',
-        os_version: '10',
-        flags: chromeWebgpuFlags,
-      }
-    },
-    client: {jasmine: {random: false}, args: args, captureConsole: true},
-    logLevel: 'info'
-  });
-};
diff --git a/tfjs-master/e2e/package.json b/tfjs-master/e2e/package.json
deleted file mode 100644
index eab1b88f7..000000000
--- a/tfjs-master/e2e/package.json
+++ /dev/null
@@ -1,69 +0,0 @@
-{
-  "name": "benchmarks",
-  "version": "0.0.1",
-  "description": "Benchmark for TensorFlow.js",
-  "private": true,
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs"
-  },
-  "devDependencies": {
-    "@babel/polyfill": "^7.10.4",
-    "@tensorflow/tfjs": "link:../tfjs",
-    "@tensorflow/tfjs-backend-cpu": "link:../link-package/node_modules/@tensorflow/tfjs-backend-cpu",
-    "@tensorflow/tfjs-backend-wasm": "link:../link-package/node_modules/@tensorflow/tfjs-backend-wasm",
-    "@tensorflow/tfjs-backend-webgl": "link:../link-package/node_modules/@tensorflow/tfjs-backend-webgl",
-    "@tensorflow/tfjs-backend-webgpu": "link:../link-package/node_modules/@tensorflow/tfjs-backend-webgpu",
-    "@tensorflow/tfjs-converter": "link:../link-package/node_modules/@tensorflow/tfjs-converter",
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core",
-    "@tensorflow/tfjs-data": "link:../link-package/node_modules/@tensorflow/tfjs-data",
-    "@tensorflow/tfjs-layers": "link:../link-package/node_modules/@tensorflow/tfjs-layers",
-    "@tensorflow/tfjs-node": "link:../tfjs-node",
-    "@types/detect-browser": "^4.0.0",
-    "@types/jasmine": "~3.0.0",
-    "@types/mathjs": "^5.0.1",
-    "clang-format": "~1.2.4",
-    "detect-browser": "~4.2.0",
-    "jasmine": "~3.1.0",
-    "jasmine-core": "~3.1.0",
-    "karma": "~6.3.20",
-    "karma-browserstack-launcher": "~1.6.0",
-    "karma-chrome-launcher": "~3.1.1",
-    "karma-jasmine": "~1.1.0",
-    "karma-jasmine-order-reporter": "^1.1.0",
-    "karma-spec-reporter": "~0.0.32",
-    "karma-typescript": "~5.5.1",
-    "karma-typescript-es6-transform": "^5.1.0",
-    "mathjs": "^7.5.1",
-    "request": "^2.88.0",
-    "ts-node": "^10.9.1",
-    "tsify": "^3.0.4",
-    "tslint": "^6.1.3",
-    "tslint-no-circular-imports": "~0.7.0",
-    "typescript": "5.0.4"
-  },
-  "scripts": {
-    "build": "tsc",
-    "build-link-package": "cd ../link-package && yarn build",
-    "build-union": "cd ../tfjs && yarn && yarn build",
-    "build-union-ci": "cd ../tfjs && yarn && yarn build-ci",
-    "build-node": "cd ../tfjs-node && yarn && yarn build",
-    "build-node-ci": "cd ../tfjs-node && yarn && yarn build-ci",
-    "build-deps": "yarn build-link-package && yarn build-union && yarn build-node",
-    "build-deps-ci": "./scripts/build-deps-ci.sh",
-    "build-graph-model-golden-data": "ts-node ./scripts/build-graph-model-golden-data.ts",
-    "fetch-graph-model-golden-data-ci": "./scripts/fetch-graph-model-golden-data-ci.sh",
-    "lint": "tslint -p . -t verbose",
-    "run-browserstack": "karma start --browserstack",
-    "test": "./scripts/test.sh",
-    "coverage": "KARMA_COVERAGE=1 ./scripts/test.sh",
-    "test-ci": "./scripts/test-ci.sh",
-    "deploy-benchmarks": "./scripts/deploy-benchmarks.sh",
-    "deploy-benchmarks-dev": "./scripts/deploy-benchmarks.sh --dev",
-    "deploy-benchmarks-staging": "./scripts/deploy-benchmarks.sh --staging"
-  },
-  "license": "Apache-2.0",
-  "engines": {
-    "yarn": ">= 1.0.0"
-  }
-}
diff --git a/tfjs-master/e2e/script_tag_tests/tfjs-core-cpu/core_test.js b/tfjs-master/e2e/script_tag_tests/tfjs-core-cpu/core_test.js
deleted file mode 100644
index 03178844d..000000000
--- a/tfjs-master/e2e/script_tag_tests/tfjs-core-cpu/core_test.js
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-describe('loaded packages', () => {
-  it('should have core', () => {
-    expect(tf.version_core).toBeTruthy();
-  });
-
-  it('should have cpu backend', () => {
-    expect(tf.version_cpu).toBeTruthy();
-  });
-});
-
-describe('ops', () => {
-  beforeAll(async () => {
-    await tf.setBackend('cpu');
-  });
-
-  it('should support basic math', () => {
-    tf.tidy(() => {
-      const a = tf.scalar(3);
-      const b = tf.scalar(4);
-      expect(tf.add(a, b).dataSync()[0]).toBe(7);
-    });
-  });
-
-  it('should clone', () => {
-    tf.tidy(() => {
-      const a = tf.tensor([1, 2, 3, 4]);
-      const b = tf.tensor([1, 2, 3, 4]).clone();
-      expect(a.dataSync()).toEqual(b.dataSync());
-    });
-  });
-
-  it('should reshape', () => {
-    tf.tidy(() => {
-      const a = tf.tensor([1, 2, 3, 4], [1, 4]);
-      const b = tf.reshape(a, [2, 2]);
-      expect(a.dataSync()).toEqual(b.dataSync());
-    });
-  });
-
-  it('should cast', () => {
-    tf.tidy(() => {
-      const a = tf.tensor([1, 2, 3, 4], [1, 4], 'float32');
-      const b = a.cast('int32');
-      expect(Array.from(a.dataSync())).toEqual(Array.from(b.dataSync()));
-    });
-  });
-});
-
-describe('backends are registered', () => {
-  it('should find cpu backend', () => {
-    expect(tf.findBackend('cpu')).toBeTruthy();
-  });
-
-  it('should not find fake backend', () => {
-    expect(tf.findBackend('fake')).toBeFalsy();
-  });
-});
diff --git a/tfjs-master/e2e/script_tag_tests/tfjs-core-cpu/karma.conf.js b/tfjs-master/e2e/script_tag_tests/tfjs-core-cpu/karma.conf.js
deleted file mode 100644
index 55bc70140..000000000
--- a/tfjs-master/e2e/script_tag_tests/tfjs-core-cpu/karma.conf.js
+++ /dev/null
@@ -1,124 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-module.exports = function(config) {
-  const args = [];
-
-  const coreBundle = config.coreBundle ? config.coreBundle : 'tf-core.min.js';
-  const coreBundlePath =
-      `../../node_modules/@tensorflow/tfjs-core/dist/${coreBundle}`;
-  const cpuBundle =
-      config.cpuBundle ? config.cpuBundle : 'tf-backend-cpu.min.js';
-  const cpuBundlePath =
-      `../../node_modules/@tensorflow/tfjs-backend-cpu/dist/${cpuBundle}`;
-
-  const devConfig = {
-    frameworks: ['jasmine'],
-    files: [
-      {
-        pattern: coreBundlePath,
-        nocache: true,
-      },
-      {
-        pattern: cpuBundlePath,
-        nocache: true,
-      },
-      {pattern: './**/*_test.js'},
-    ],
-    reporters: ['progress']
-  };
-
-  const browserstackConfig =
-      {...devConfig, hostname: 'bs-local.com', singleRun: true, port: 9200};
-
-  if (config.grep) {
-    args.push('--grep', config.grep);
-  }
-  if (config.tags) {
-    args.push('--tags', config.tags);
-  }
-
-  let extraConfig = null;
-
-  if (config.browserstack) {
-    extraConfig = browserstackConfig;
-  } else {
-    extraConfig = devConfig;
-  }
-
-  config.set({
-    ...extraConfig,
-    browsers: ['Chrome'],
-    browserStack: {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_KEY,
-      tunnelIdentifier: `e2e_script_tag_tests_${Date.now()}_${
-          Math.floor(Math.random() * 1000)}`
-    },
-    captureTimeout: 3e5,
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 3e5,
-    browserDisconnectTimeout: 3e5,
-    browserDisconnectTolerance: 0,
-    browserSocketTimeout: 1.2e5,
-    customLaunchers: {
-      bs_chrome_mac: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_firefox_mac: {
-        base: 'BrowserStack',
-        browser: 'firefox',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_safari_mac: {
-        base: 'BrowserStack',
-        browser: 'safari',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'Mojave'
-      },
-      bs_ios_12: {
-        base: 'BrowserStack',
-        device: 'iPhone X',
-        os: 'iOS',
-        os_version: '12.3',
-        real_mobile: true
-      },
-      bs_android_10: {
-        base: 'BrowserStack',
-        device: 'Google Pixel 4 XL',
-        os: 'android',
-        os_version: '10.0',
-        real_mobile: true
-      },
-      win_10_chrome: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: '101.0',
-        os: 'Windows',
-        os_version: '10'
-      }
-    },
-    client: {jasmine: {random: false}, args: args}
-  });
-};
diff --git a/tfjs-master/e2e/script_tag_tests/tfjs/karma.conf.js b/tfjs-master/e2e/script_tag_tests/tfjs/karma.conf.js
deleted file mode 100644
index 46146a61e..000000000
--- a/tfjs-master/e2e/script_tag_tests/tfjs/karma.conf.js
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-module.exports = function(config) {
-  const args = [];
-
-  const tfjsBundle = config.testBundle ? config.testBundle : 'tf.min.js';
-  const tfjsBundlePath =
-      `../../node_modules/@tensorflow/tfjs/dist/${tfjsBundle}`;
-
-  const devConfig = {
-    frameworks: ['jasmine'],
-    files: [
-      {
-        pattern: tfjsBundlePath,
-        nocache: true,
-      },
-      {pattern: './**/*_test.js'},
-    ],
-    reporters: ['progress']
-  };
-
-  const browserstackConfig =
-      {...devConfig, hostname: 'bs-local.com', singleRun: true, port: 9200};
-
-  if (config.grep) {
-    args.push('--grep', config.grep);
-  }
-  if (config.tags) {
-    args.push('--tags', config.tags);
-  }
-
-  let extraConfig = null;
-
-  if (config.browserstack) {
-    extraConfig = browserstackConfig;
-  } else {
-    extraConfig = devConfig;
-  }
-
-  config.set({
-    ...extraConfig,
-    browsers: ['Chrome'],
-    browserStack: {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_KEY,
-      tunnelIdentifier: `e2e_script_tag_tests_${Date.now()}_${
-          Math.floor(Math.random() * 1000)}`
-    },
-    captureTimeout: 3e5,
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 3e5,
-    browserDisconnectTimeout: 3e5,
-    browserDisconnectTolerance: 0,
-    browserSocketTimeout: 1.2e5,
-    customLaunchers: {
-      bs_chrome_mac: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_firefox_mac: {
-        base: 'BrowserStack',
-        browser: 'firefox',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_safari_mac: {
-        base: 'BrowserStack',
-        browser: 'safari',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'Mojave'
-      },
-      bs_ios_12: {
-        base: 'BrowserStack',
-        device: 'iPhone X',
-        os: 'iOS',
-        os_version: '12.3',
-        real_mobile: true
-      },
-      bs_android_10: {
-        base: 'BrowserStack',
-        device: 'Google Pixel 4 XL',
-        os: 'android',
-        os_version: '10.0',
-        real_mobile: true
-      },
-      win_10_chrome: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: 'latest',
-        os: 'Windows',
-        os_version: '10'
-      }
-    },
-    client: {jasmine: {random: false}, args: args}
-  });
-};
diff --git a/tfjs-master/e2e/script_tag_tests/tfjs/symbols_and_basic_api_test.js b/tfjs-master/e2e/script_tag_tests/tfjs/symbols_and_basic_api_test.js
deleted file mode 100644
index 5adcdd696..000000000
--- a/tfjs-master/e2e/script_tag_tests/tfjs/symbols_and_basic_api_test.js
+++ /dev/null
@@ -1,127 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-describe('tfjs union sub-packages', () => {
-  it('should have core', () => {
-    expect(tf.version['tfjs-core']).toBeTruthy();
-  });
-
-  it('should have cpu backend', () => {
-    expect(tf.version['tfjs-backend-cpu']).toBeTruthy();
-  });
-
-  it('should have webgl backend', () => {
-    expect(tf.version['tfjs-backend-webgl']).toBeTruthy();
-  });
-
-  it('should have converter', () => {
-    expect(tf.version['tfjs-converter']).toBeTruthy();
-  });
-
-  it('should have layers', () => {
-    expect(tf.version['tfjs-layers']).toBeTruthy();
-  });
-
-  it('should have data', () => {
-    expect(tf.version['tfjs-data']).toBeTruthy();
-  });
-
-  it('should have union version', () => {
-    expect(tf.version['tfjs']).toBeTruthy();
-  });
-});
-
-describe('ops', () => {
-  beforeAll(async () => {
-    await tf.setBackend('cpu');
-  });
-
-  it('should support basic math', () => {
-    tf.tidy(() => {
-      const a = tf.scalar(3);
-      const b = tf.scalar(4);
-      expect(tf.add(a, b).dataSync()[0]).toBe(7);
-    });
-  });
-
-  it('should clone', () => {
-    tf.tidy(() => {
-      const a = tf.tensor([1, 2, 3, 4]);
-      const b = tf.tensor([1, 2, 3, 4]).clone();
-      expect(a.dataSync()).toEqual(b.dataSync());
-    });
-  });
-
-  it('should reshape', () => {
-    tf.tidy(() => {
-      const a = tf.tensor([1, 2, 3, 4], [1, 4]);
-      const b = a.reshape([2, 2]);
-      expect(a.dataSync()).toEqual(b.dataSync());
-    });
-  });
-
-  it('should cast', () => {
-    tf.tidy(() => {
-      const a = tf.tensor([1, 2, 3, 4], [1, 4], 'float32');
-      const b = a.cast('int32');
-      expect(Array.from(a.dataSync())).toEqual(Array.from(b.dataSync()));
-    });
-  });
-});
-
-describe('backends are registered', () => {
-  it('should find cpu backend', () => {
-    expect(tf.findBackend('cpu')).toBeTruthy();
-  });
-
-  it('should find webgl backend', () => {
-    expect(tf.findBackend('webgl')).toBeTruthy();
-  });
-
-  it('should not find fake backend', () => {
-    expect(tf.findBackend('fake')).toBeFalsy();
-  });
-});
-
-describe('chaining api is present', () => {
-  it('tensor should have max method', () => {
-    tf.tidy(() => {
-      const a = tf.scalar(3);
-      expect(a.max).toBeDefined();
-    });
-  });
-
-  it('tensor should have resizeBilinear method', () => {
-    tf.tidy(() => {
-      const a = tf.scalar(3);
-      expect(a.resizeBilinear).toBeDefined();
-    });
-  });
-});
-
-
-describe('gradients are registered', () => {
-  it('SplitV gradient should be registered', () => {
-    const gradient = tf.getGradient(tf.SplitV);
-    expect(gradient).toBeDefined();
-  });
-
-  it('fake gradient does not exist', () => {
-    const gradient = tf.getGradient('NotARealKernel');
-    expect(gradient).toBeUndefined();
-  });
-});
diff --git a/tfjs-master/e2e/scripts/build-deps-ci.sh b/tfjs-master/e2e/scripts/build-deps-ci.sh
deleted file mode 100644
index 50069ff04..000000000
--- a/tfjs-master/e2e/scripts/build-deps-ci.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-yarn build-link-package
-yarn build-union-ci
-yarn build-node-ci
-
-if [[ "$NIGHTLY" = true || "$RELEASE" = true ]]; then
-  # Build the wasm backend
-  yarn build-backend-wasm-ci
-fi
diff --git a/tfjs-master/e2e/scripts/build-graph-model-golden-data.ts b/tfjs-master/e2e/scripts/build-graph-model-golden-data.ts
deleted file mode 100644
index b100e47db..000000000
--- a/tfjs-master/e2e/scripts/build-graph-model-golden-data.ts
+++ /dev/null
@@ -1,135 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '@tensorflow/tfjs';
-import * as fs from 'fs';
-import * as path from 'path';
-
-import {GraphModeGoldenData, TensorDetail} from '../integration_tests/types';
-
-const GRAPH_MODEL_GOLDEN_DATA_DIR =
-    './integration_tests/graph_model_golden_data/';
-
-// Defines models to be used in the golden tests here.
-// TODO: Support async models (models with control flow ops).
-const MODEL_CONFIGS: GraphModelConfig[] = [
-  {
-    name: 'MobileNetV3_small_075',
-    url:
-        'https://tfhub.dev/google/tfjs-model/imagenet/mobilenet_v3_small_075_224/classification/5/default/1',
-    fromTFHub: true,
-    inputs: tf.randomNormal([1, 224, 224, 3]),
-  },
-];
-
-type GraphModelInputs = tf.Tensor|tf.Tensor[]|tf.NamedTensorMap;
-
-interface GraphModelConfig {
-  readonly name: string;
-  readonly url: string;
-  readonly fromTFHub?: boolean;
-  readonly inputs: GraphModelInputs;
-}
-
-async function getTensorDetail(tensor: tf.Tensor): Promise<TensorDetail> {
-  const data = await tensor.data();
-  return {
-    data: Array.from(data),
-    shape: tensor.shape,
-    dtype: tensor.dtype,
-  };
-}
-
-async function getTensorDetails(tensors: tf.Tensor|tf.Tensor[]|
-                                tf.NamedTensorMap) {
-  if (tensors instanceof tf.Tensor) {
-    return await getTensorDetail(tensors);
-  }
-
-  if (Array.isArray(tensors)) {
-    return await Promise.all(tensors.map(getTensorDetail));
-  }
-
-  const details: Record<string, TensorDetail> = {};
-  for (const [name, tensor] of Object.entries(tensors)) {
-    details[name] = await getTensorDetail(tensor);
-  }
-  return details;
-}
-
-function getGraphModelGoldenDataFilename(modelName: string) {
-  return `${modelName}.golden.json`;
-}
-
-function writeGraphModelGoldenData(data: GraphModeGoldenData) {
-  if (!fs.existsSync(GRAPH_MODEL_GOLDEN_DATA_DIR)) {
-    fs.mkdirSync(GRAPH_MODEL_GOLDEN_DATA_DIR, {recursive: true});
-  }
-  fs.writeFileSync(
-      path.join(
-          GRAPH_MODEL_GOLDEN_DATA_DIR,
-          getGraphModelGoldenDataFilename(data.name)),
-      JSON.stringify(data));
-}
-
-(async function main() {
-  if (MODEL_CONFIGS.length === 0) {
-    return;
-  }
-
-  const models = await Promise.all(MODEL_CONFIGS.map(
-      ({url, fromTFHub}) => tf.loadGraphModel(url, {fromTFHub})));
-
-  tf.env().set('KEEP_INTERMEDIATE_TENSORS', true);
-  for (let i = 0; i < MODEL_CONFIGS.length; ++i) {
-    const model = models[i];
-    const {inputs} = MODEL_CONFIGS[i];
-
-    const outputTensors = model.predict(inputs);
-    const outputDetails = await getTensorDetails(outputTensors);
-
-    const intermediateTensors = model.getIntermediateTensors();
-    const intermediateDetails: Record<string, TensorDetail> = {};
-
-    for (const [name, tensors] of Object.entries(intermediateTensors)) {
-      const details = await Promise.all(tensors.map(getTensorDetail));
-      intermediateDetails[name] = details[0];
-    }
-
-    writeGraphModelGoldenData({
-      ...MODEL_CONFIGS[i],
-      inputs: await getTensorDetails(inputs),
-      outputDetails,
-      intermediateDetails,
-    });
-
-    // Dispose tensors
-    if (outputTensors instanceof tf.Tensor) {
-      outputTensors.dispose();
-    } else if (Array.isArray(outputTensors)) {
-      for (const tensor of outputTensors) tensor.dispose();
-    } else {
-      for (const tensor of Object.values(outputTensors)) tensor.dispose();
-    }
-    model.disposeIntermediateTensors();
-  }
-
-  // Write all golden filenames to GRAPH_MODEL_GOLDEN_DATA_DIR/filenames.json.
-  fs.writeFileSync(
-      path.join(GRAPH_MODEL_GOLDEN_DATA_DIR, 'filenames.json'),
-      JSON.stringify(MODEL_CONFIGS.map(
-          ({name}) => getGraphModelGoldenDataFilename(name))));
-}());
diff --git a/tfjs-master/e2e/scripts/cleanup-py-env.sh b/tfjs-master/e2e/scripts/cleanup-py-env.sh
deleted file mode 100644
index fbb5829d3..000000000
--- a/tfjs-master/e2e/scripts/cleanup-py-env.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# Clean up virtualenv directory.
-rm -rf "${VENV_DIR}"
diff --git a/tfjs-master/e2e/scripts/create-python-models.sh b/tfjs-master/e2e/scripts/create-python-models.sh
deleted file mode 100644
index 80170bfa4..000000000
--- a/tfjs-master/e2e/scripts/create-python-models.sh
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-# Generate canonical layers models and inputs.
-./scripts/create_save_predict.sh
-
-cd integration_tests
-
-source ../scripts/setup-py-env.sh --dev
-
-parallel ::: 'echo "Load equivalent keras models and generate outputs." && python create_save_predict.py' \
-  'echo "Create saved models and convert." && python convert_predict.py' \
-  'echo "Convert model with user defined metadata." && python metadata.py'
-
-# Cleanup python env.
-source ../scripts/cleanup-py-env.sh
-
-cd ..
diff --git a/tfjs-master/e2e/scripts/create_save_predict.sh b/tfjs-master/e2e/scripts/create_save_predict.sh
deleted file mode 100644
index f4910f965..000000000
--- a/tfjs-master/e2e/scripts/create_save_predict.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-TEST_DATA="integration_tests/create_save_predict_data/"
-
-rm -rf "$TEST_DATA"
-mkdir "$TEST_DATA"
-
-node integration_tests/create_save_predict.js "$TEST_DATA"
diff --git a/tfjs-master/e2e/scripts/deploy-benchmarks.sh b/tfjs-master/e2e/scripts/deploy-benchmarks.sh
deleted file mode 100644
index 1a2a8b03c..000000000
--- a/tfjs-master/e2e/scripts/deploy-benchmarks.sh
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2021 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# Deploy content in e2e/benchmarks to firebase hosting.
-
-# Halt if a single command errors
-set -e
-
-PROJECT="jstensorflow"
-
-if [[ "$1" == "--dev" ]]; then
-  # Deploy to the preview channel.
-  #
-  # Use the second command line parameter as the feature name which will be
-  # part of the preview URL. Use the current date+time as fallback.
-  feature_name="${2:-$(date '+%Y%m%d-%H%M%S')}"
-  firebase hosting:channel:deploy \
-    "${feature_name}" \
-    --config benchmarks/firebase.json \
-    --only tfjs-benchmarks \
-    --project ${PROJECT} \
-    --expires 14d
-elif [[ "$1" == "--staging"  ]]; then
-  firebase deploy \
-    --config benchmarks/firebase_staging.json \
-    --only hosting:tfjs-benchmarks-staging \
-    --project ${PROJECT}
-else
-  firebase deploy \
-    --config benchmarks/firebase.json \
-    --only hosting:tfjs-benchmarks \
-    --project ${PROJECT}
-fi
diff --git a/tfjs-master/e2e/scripts/fetch-graph-model-golden-data-ci.sh b/tfjs-master/e2e/scripts/fetch-graph-model-golden-data-ci.sh
deleted file mode 100644
index 1af969e26..000000000
--- a/tfjs-master/e2e/scripts/fetch-graph-model-golden-data-ci.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-STORAGE_URL="https://storage.googleapis.com/tfjs-e2e-graph-model-golden-data"
-
-cd ./integration_tests/graph_model_golden_data
-
-golden_files=$(python -c "import json; print('\n'.join(json.load(open('./filenames.json'))))")
-
-while read golden_file; do
-  url="$STORAGE_URL/$golden_file"
-  echo "Downloading $url"
-  curl -O "$url"
-done <<<"$(echo "$golden_files")"
diff --git a/tfjs-master/e2e/scripts/local-registry.sh b/tfjs-master/e2e/scripts/local-registry.sh
deleted file mode 100644
index 67b6161dd..000000000
--- a/tfjs-master/e2e/scripts/local-registry.sh
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-custom_registry_url=http://127.0.0.1:4873
-original_npm_registry_url=`npm get registry`
-original_yarn_registry_url=`yarn config get registry`
-default_verdaccio_package=verdaccio@5.9.0
-
-function startLocalRegistry {
-  # Start local registry
-  tmp_registry_log=`mktemp`
-  echo "Registry output file: $tmp_registry_log"
-  (cd && nohup npx ${VERDACCIO_PACKAGE:-$default_verdaccio_package} -c $1 &>$tmp_registry_log &)
-  # Wait for Verdaccio to boot
-  grep -q 'http address' <(tail -f $tmp_registry_log)
-
-  # Login so we can publish packages
-  npx npm-cli-login -u user -p password -e user@example.com -r $custom_registry_url
-
-  # Set registry to local registry
-  npm set registry "$custom_registry_url"
-  yarn config set registry "$custom_registry_url"
-}
-
-function stopLocalRegistry {
-  # Restore the original NPM and Yarn registry URLs and stop Verdaccio
-  npm set registry "$original_npm_registry_url"
-  yarn config set registry "$original_yarn_registry_url"
-}
diff --git a/tfjs-master/e2e/scripts/release-e2e.sh b/tfjs-master/e2e/scripts/release-e2e.sh
deleted file mode 100644
index b87ac814b..000000000
--- a/tfjs-master/e2e/scripts/release-e2e.sh
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# This script is used for starting Verdaccio, a private npm registry.
-
-# Start in scripts/ even if run from root directory
-cd "$(dirname "$0")"
-
-function cleanup {
-  echo 'Cleaning up.'
-  # Restore the original NPM and Yarn registry URLs and stop Verdaccio
-  stopLocalRegistry
-}
-
-# Error messages are redirected to stderr
-function handle_error {
-  echo "$(basename $0): ERROR! An error was encountered executing line $1." 1>&2;
-  cleanup
-  echo 'Exiting with error.' 1>&2;
-  exit 1
-}
-
-function handle_exit {
-  cleanup
-  echo 'Exiting without error.' 1>&2;
-  exit
-}
-
-# Exit the script with a helpful error message when any error is encountered
-trap 'set +x; handle_error $LINENO $BASH_COMMAND' ERR
-
-# Cleanup before exit on any termination signal
-trap 'set +x; handle_exit' SIGQUIT SIGTERM SIGINT SIGKILL SIGHUP
-
-# Echo every command being executed
-set -x
-
-# Go to e2e root
-cd ..
-e2e_root_path=$PWD
-
-# ****************************************************************************
-# First, publish the monorepo.
-# ****************************************************************************
-# Load functions for working with local NPM registry (Verdaccio)
-source "$e2e_root_path"/scripts/local-registry.sh
-
-# Publish the monorepo to the local NPM registry. Note this publish script will
-# automatically start and stop the registry while it publishes.
-cd "$e2e_root_path"/../
-yarn publish-npm --release-this-branch --dry --ci 'tfjs-core' \
-  'tfjs-backend-cpu' 'tfjs-backend-webgl' 'tfjs-backend-webgpu' \
-  'tfjs-backend-wasm' 'tfjs-layers' 'tfjs-converter' 'tfjs-data' 'tfjs' \
-  'tfjs-node' 'tfjs-node-gpu'
-cd "$e2e_root_path"
-
-# ****************************************************************************
-# Second, install the packages from local registry and fetch golden data for testing.
-# ****************************************************************************
-# Start the local NPM registry
-startLocalRegistry "$e2e_root_path"/scripts/verdaccio.yaml
-# First make sure npm install succeeds.
-npm install
-rm -rf node_modules
-# Then install dependencies via yarn.
-yarn
-# Fetch golden data for testing
-yarn fetch-graph-model-golden-data-ci
-
-# ****************************************************************************
-# Third, run integration tests against locally published version.
-# ****************************************************************************
-yarn test-ci
-
-# Cleanup
-cleanup
diff --git a/tfjs-master/e2e/scripts/run-browserstack-tests.sh b/tfjs-master/e2e/scripts/run-browserstack-tests.sh
deleted file mode 100644
index 8822421b5..000000000
--- a/tfjs-master/e2e/scripts/run-browserstack-tests.sh
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# This script runs browserstack tests on all configured browsers. It requires
-# the TAGS variable to be set in the environment.
-
-set -e
-
-# Smoke and regression tests run in PR and nightly builds.
-TAGS="#SMOKE,#REGRESSION"
-TAGS_WITH_GOLDEN="$TAGS,#GOLDEN"
-
-# Test macOS with smoke/regression tests.
-# Skip golden tests because they time out on browserstack (they work locally).
-# TODO(mattSoulanille): Make golden tests work on BrowserStack Mac.
-COMMANDS+=("yarn run-browserstack --browsers=bs_chrome_mac --tags '$TAGS'")
-
-# Test windows 10 with smoke/regression/golden tests.
-COMMANDS+=("yarn run-browserstack --browsers=win_10_chrome --tags '$TAGS_WITH_GOLDEN'")
-
-# Test script tag bundles
-COMMANDS+=("karma start ./script_tag_tests/tfjs/karma.conf.js --browserstack --browsers=bs_chrome_mac --testBundle tf.min.js")
-
-# Additional tests to run in nightly only.
-if [[ "$NIGHTLY" = true || "$RELEASE" = true ]]; then
-  COMMANDS+=(
-    "yarn run-browserstack --browsers=bs_ios_12 --tags '$TAGS' --testEnv webgl --flags '{\"\\"\"WEBGL_VERSION\"\\"\": 1, \"\\"\"WEBGL_CPU_FORWARD\"\\"\": false, \"\\"\"WEBGL_SIZE_UPLOAD_UNIFORM\"\\"\": 0}'"
-    "yarn run-browserstack --browsers=bs_safari_mac --tags '$TAGS' --testEnv webgl --flags '{\"\\"\"WEBGL_VERSION\"\\"\": 1, \"\\"\"WEBGL_CPU_FORWARD\"\\"\": false, \"\\"\"WEBGL_SIZE_UPLOAD_UNIFORM\"\\"\": 0}'"
-    "yarn run-browserstack --browsers=bs_firefox_mac --tags '$TAGS'"
-    "yarn run-browserstack --browsers=bs_android_10 --tags '$TAGS'"
-    # Test script tag bundles
-    "karma start ./script_tag_tests/tfjs-core-cpu/karma.conf.js --browserstack --browsers=bs_chrome_mac"
-  )
-fi
-
-for command in "${COMMANDS[@]}"; do
-  TO_RUN+=("node ../scripts/run_flaky.js \"$command\"")
-done
-
-parallel ::: "${TO_RUN[@]}"
diff --git a/tfjs-master/e2e/scripts/run-custom-builds.sh b/tfjs-master/e2e/scripts/run-custom-builds.sh
deleted file mode 100644
index 033340dec..000000000
--- a/tfjs-master/e2e/scripts/run-custom-builds.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# Start in scripts/ even if run from root directory
-cd "$(dirname "$0")"
-
-# Go to e2e root
-cd ..
-
-parallel ::: 'cd custom_module/blazeface && ./build.sh' \
-  'cd custom_module/dense_model && ./build.sh' \
-  'cd custom_module/universal_sentence_encoder && ./build.sh'
diff --git a/tfjs-master/e2e/scripts/setup-py-env.sh b/tfjs-master/e2e/scripts/setup-py-env.sh
deleted file mode 100644
index 1897f678b..000000000
--- a/tfjs-master/e2e/scripts/setup-py-env.sh
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# Decide which environment to use.
-DEV_VERSION=""
-while [[ ! -z "$1" ]]; do
-  if [[ "$1" == "--stable" ]]; then
-    DEV_VERSION="stable"
-  elif [[ "$1" == "--dev" ]]; then
-    DEV_VERSION="dev"
-  else
-    echo "ERROR: Unrecognized command-line flag $1"
-    exit 1
-  fi
-  shift
-done
-
-echo "DEV_VERSION: ${DEV_VERSION}"
-
-if [[ -z "${DEV_VERSION}" ]]; then
-  echo "Must specify one of --stable and --dev."
-  exit 1
-fi
-
-VENV_DIR="$(mktemp -d)_venv"
-echo "Creating virtualenv at ${VENV_DIR} ..."
-PLATFORM="$(python -m platform)"
-if [[ $PLATFORM =~ "Windows" ]]
-then
-  python -m virtualenv -p python3 "${VENV_DIR}"
-  source "${VENV_DIR}/Scripts/activate"
-else
-  virtualenv -p python3 "${VENV_DIR}"
-  source "${VENV_DIR}/bin/activate"
-fi
-
-# Install python packages.
-if [[ "${DEV_VERSION}" == "stable" ]]; then
-  pip3 install -r requirements-stable.txt
-else
-  pip3 install -r requirements-dev.txt
-fi
-
-echo "Loading tensorflowjs pip from source ...."
-pip3 install -e ../../tfjs-converter/python
diff --git a/tfjs-master/e2e/scripts/test-ci.sh b/tfjs-master/e2e/scripts/test-ci.sh
deleted file mode 100644
index f622ef357..000000000
--- a/tfjs-master/e2e/scripts/test-ci.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-# Generate custom bundle files and model files for tests
-parallel ::: ./scripts/run-custom-builds.sh \
-  ./scripts/create-python-models.sh
-
-# Run browserstack tests (and webpack test)
-parallel ::: ./scripts/run-browserstack-tests.sh \
-  "cd webpack_test && yarn --mutex network && yarn build"
diff --git a/tfjs-master/e2e/scripts/test.sh b/tfjs-master/e2e/scripts/test.sh
deleted file mode 100644
index fa2b561db..000000000
--- a/tfjs-master/e2e/scripts/test.sh
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# This script is used for local testing. Set environment variable TAGS to
-# filter tests. If no TAGS is specified, the script will the TAGS to #SMOKE.
-# Multiple tags are allowed, separate by comma.
-
-set -e
-
-if [[ -z "$TAGS" ]]; then
-  echo "Env variable TAGS is not found, set TAGS='#SMOKE'"
-  TAGS="#SMOKE"
-fi
-
-if [[ "$NIGHTLY" = true ]]; then
-  TAGS="${TAGS},#GOLDEN,#REGRESSION"
-fi
-
-# Additional setup for regression tests.
-if [[ "$TAGS" == *"#REGRESSION"* ]]; then
-  # Generate canonical layers models and inputs.
-  ./scripts/create-python-models.sh
-
-  # Test webpack
-  cd webpack_test
-  yarn
-  yarn build
-  cd ..
-
-  # Generate custom bundle files for tests
-  ./scripts/run-custom-builds.sh
-fi
-
-echo "Karma tests."
-yarn karma start --tags $TAGS
diff --git a/tfjs-master/e2e/scripts/verdaccio.yaml b/tfjs-master/e2e/scripts/verdaccio.yaml
deleted file mode 100644
index ff7ed6064..000000000
--- a/tfjs-master/e2e/scripts/verdaccio.yaml
+++ /dev/null
@@ -1,42 +0,0 @@
-# Path to a directory with all packages.
-storage: ./storage
-
-# Maximum body size for a JSON document.
-max_body_size: 1000mb
-
-auth:
-  htpasswd:
-    file: ./htpasswd
-
-# a list of other known repositories we can talk to
-uplinks:
-  npmjs:
-    url: https://registry.npmjs.org/
-
-# Enable messaging so node can tell when verdaccio starts.
-# https://verdaccio.org/docs/verdaccio-programmatically/#using-fork-from-child_process-module
-_debug: true
-
-# Fine-grained control of package access, we set it
-# to allow all users to read and publish all packages.
-packages:
-  '@tensorflow/**':
-    access: $all
-    publish: $all
-    proxy: npmjs
-  '@*/*':
-    access: $all
-    publish: $all
-    proxy: npmjs
-  '**':
-    access: $all
-    publish: $all
-    # If package is not available locally, proxy requests to 'npmjs' registry
-    proxy: npmjs
-
-# Log settings.
-logs:
-  - {type: stdout, format: pretty, level: http}
-
-listen:
-  - 127.0.0.1:4873
diff --git a/tfjs-master/e2e/tsconfig.json b/tfjs-master/e2e/tsconfig.json
deleted file mode 100644
index fa5643201..000000000
--- a/tfjs-master/e2e/tsconfig.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "commonjs",
-    "moduleResolution": "node",
-    "allowJs": true,
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "removeComments": true,
-    "preserveConstEnums": true,
-    "declaration": false,
-    "target": "es5",
-    "lib": ["es2017", "dom"],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false,
-    "resolveJsonModule":true
-  },
-  "include": [
-    "*.ts",
-    "integration_tests/**/*.ts"
-  ],
-  "exclude": [
-    "node_modules/"
-  ]
-}
diff --git a/tfjs-master/e2e/tslint.json b/tfjs-master/e2e/tslint.json
deleted file mode 100644
index ec365f164..000000000
--- a/tfjs-master/e2e/tslint.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "../tslint.json"
-}
diff --git a/tfjs-master/e2e/webpack_test/app.js b/tfjs-master/e2e/webpack_test/app.js
deleted file mode 100644
index 2d2a962f7..000000000
--- a/tfjs-master/e2e/webpack_test/app.js
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import '@tensorflow/tfjs-backend-cpu';
-
-const t = tf.tensor1d([1,2,3,4,5]);
-t.print();
diff --git a/tfjs-master/e2e/webpack_test/package.json b/tfjs-master/e2e/webpack_test/package.json
deleted file mode 100644
index f2e4895e5..000000000
--- a/tfjs-master/e2e/webpack_test/package.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "name": "webpack_test",
-  "version": "1.0.0",
-  "main": "index.js",
-  "license": "Apache-2.0",
-  "scripts": {
-    "build-deps": "cd ../../link-package && yarn build",
-    "build": "webpack"
-  },
-  "devDependencies": {
-    "@tensorflow/tfjs-core": "link:../../link-package/node_modules/@tensorflow/tfjs-core",
-    "@tensorflow/tfjs-backend-cpu": "link:../../link-package/node_modules/@tensorflow/tfjs-backend-cpu",
-    "webpack": "^5.76.0",
-    "webpack-cli": "^4.7.2"
-  }
-}
diff --git a/tfjs-master/e2e/webpack_test/webpack.config.js b/tfjs-master/e2e/webpack_test/webpack.config.js
deleted file mode 100644
index f1a9ed8ba..000000000
--- a/tfjs-master/e2e/webpack_test/webpack.config.js
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const path = require('path');
-
-module.exports = {
-  mode: 'production',
-  entry: './app.js',
-  target: 'web',
-  output: {
-    path: path.resolve(__dirname, 'dist'),
-    filename: 'app_webpack.js',
-  },
-};
diff --git a/tfjs-master/e2e/webpack_test/yarn.lock b/tfjs-master/e2e/webpack_test/yarn.lock
deleted file mode 100644
index b3b39b2d9..000000000
--- a/tfjs-master/e2e/webpack_test/yarn.lock
+++ /dev/null
@@ -1,914 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@discoveryjs/json-ext@^0.5.0":
-  version "0.5.3"
-  resolved "https://registry.yarnpkg.com/@discoveryjs/json-ext/-/json-ext-0.5.3.tgz#90420f9f9c6d3987f176a19a7d8e764271a2f55d"
-  integrity sha512-Fxt+AfXgjMoin2maPIYzFZnQjAXjAL0PHscM5pRTtatFqB+vZxAM9tLp2Optnuw3QOQC40jTNeGYFOMvyf7v9g==
-
-"@jridgewell/gen-mapping@^0.3.0":
-  version "0.3.2"
-  resolved "https://registry.yarnpkg.com/@jridgewell/gen-mapping/-/gen-mapping-0.3.2.tgz#c1aedc61e853f2bb9f5dfe6d4442d3b565b253b9"
-  integrity sha512-mh65xKQAzI6iBcFzwv28KVWSmCkdRBWoOh+bYQGW3+6OZvbbN3TqMGo5hqYxQniRcH9F2VZIoJCm4pa3BPDK/A==
-  dependencies:
-    "@jridgewell/set-array" "^1.0.1"
-    "@jridgewell/sourcemap-codec" "^1.4.10"
-    "@jridgewell/trace-mapping" "^0.3.9"
-
-"@jridgewell/resolve-uri@^3.0.3":
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/@jridgewell/resolve-uri/-/resolve-uri-3.1.0.tgz#2203b118c157721addfe69d47b70465463066d78"
-  integrity sha512-F2msla3tad+Mfht5cJq7LSXcdudKTWCVYUgw6pLFOOHSTtZlj6SWNYAp+AhuqLmWdBO2X5hPrLcu8cVP8fy28w==
-
-"@jridgewell/set-array@^1.0.1":
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/@jridgewell/set-array/-/set-array-1.1.2.tgz#7c6cf998d6d20b914c0a55a91ae928ff25965e72"
-  integrity sha512-xnkseuNADM0gt2bs+BvhO0p78Mk762YnZdsuzFV018NoG1Sj1SCQvpSqa7XUaTam5vAGasABV9qXASMKnFMwMw==
-
-"@jridgewell/source-map@^0.3.2":
-  version "0.3.2"
-  resolved "https://registry.yarnpkg.com/@jridgewell/source-map/-/source-map-0.3.2.tgz#f45351aaed4527a298512ec72f81040c998580fb"
-  integrity sha512-m7O9o2uR8k2ObDysZYzdfhb08VuEml5oWGiosa1VdaPZ/A6QyPkAJuwN0Q1lhULOf6B7MtQmHENS743hWtCrgw==
-  dependencies:
-    "@jridgewell/gen-mapping" "^0.3.0"
-    "@jridgewell/trace-mapping" "^0.3.9"
-
-"@jridgewell/sourcemap-codec@^1.4.10":
-  version "1.4.14"
-  resolved "https://registry.yarnpkg.com/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.14.tgz#add4c98d341472a289190b424efbdb096991bb24"
-  integrity sha512-XPSJHWmi394fuUuzDnGz1wiKqWfo1yXecHQMRf2l6hztTO+nPru658AyDngaBe7isIxEkRsPR3FZh+s7iVa4Uw==
-
-"@jridgewell/trace-mapping@^0.3.9":
-  version "0.3.14"
-  resolved "https://registry.yarnpkg.com/@jridgewell/trace-mapping/-/trace-mapping-0.3.14.tgz#b231a081d8f66796e475ad588a1ef473112701ed"
-  integrity sha512-bJWEfQ9lPTvm3SneWwRFVLzrh6nhjwqw7TUFFBEMzwvg7t7PCDenf2lDwqo4NQXzdpgBXyFgDWnQA+2vkruksQ==
-  dependencies:
-    "@jridgewell/resolve-uri" "^3.0.3"
-    "@jridgewell/sourcemap-codec" "^1.4.10"
-
-"@tensorflow/tfjs-backend-cpu@link:../../link-package/node_modules/@tensorflow/tfjs-backend-cpu":
-  version "0.0.0"
-  uid ""
-
-"@tensorflow/tfjs-core@link:../../link-package/node_modules/@tensorflow/tfjs-core":
-  version "0.0.0"
-  uid ""
-
-"@types/eslint-scope@^3.7.3":
-  version "3.7.4"
-  resolved "https://registry.yarnpkg.com/@types/eslint-scope/-/eslint-scope-3.7.4.tgz#37fc1223f0786c39627068a12e94d6e6fc61de16"
-  integrity sha512-9K4zoImiZc3HlIp6AVUDE4CWYx22a+lhSZMYNpbjW04+YF0KWj4pJXnEMjdnFTiQibFFmElcsasJXDbdI/EPhA==
-  dependencies:
-    "@types/eslint" "*"
-    "@types/estree" "*"
-
-"@types/eslint@*":
-  version "7.28.0"
-  resolved "https://registry.yarnpkg.com/@types/eslint/-/eslint-7.28.0.tgz#7e41f2481d301c68e14f483fe10b017753ce8d5a"
-  integrity sha512-07XlgzX0YJUn4iG1ocY4IX9DzKSmMGUs6ESKlxWhZRaa0fatIWaHWUVapcuGa8r5HFnTqzj+4OCjd5f7EZ/i/A==
-  dependencies:
-    "@types/estree" "*"
-    "@types/json-schema" "*"
-
-"@types/estree@*", "@types/estree@^0.0.51":
-  version "0.0.51"
-  resolved "https://registry.yarnpkg.com/@types/estree/-/estree-0.0.51.tgz#cfd70924a25a3fd32b218e5e420e6897e1ac4f40"
-  integrity sha512-CuPgU6f3eT/XgKKPqKd/gLZV1Xmvf1a2R5POBOGQa6uv82xpls89HU5zKeVoyR8XzHd1RGNOlQlvUe3CFkjWNQ==
-
-"@types/json-schema@*", "@types/json-schema@^7.0.8":
-  version "7.0.8"
-  resolved "https://registry.yarnpkg.com/@types/json-schema/-/json-schema-7.0.8.tgz#edf1bf1dbf4e04413ca8e5b17b3b7d7d54b59818"
-  integrity sha512-YSBPTLTVm2e2OoQIDYx8HaeWJ5tTToLH67kXR7zYNGupXMEHa2++G8k+DczX2cFVgalypqtyZIcU19AFcmOpmg==
-
-"@types/long@^4.0.1":
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/@types/long/-/long-4.0.2.tgz#b74129719fc8d11c01868010082d483b7545591a"
-  integrity sha512-MqTGEo5bj5t157U6fA/BiDynNkn0YknVdh48CMPkTSpFTVmvao5UQmm7uEF6xBEo7qIMAlY/JSleYaE6VOdpaA==
-
-"@types/node@*":
-  version "16.4.3"
-  resolved "https://registry.yarnpkg.com/@types/node/-/node-16.4.3.tgz#c01c1a215721f6dec71b47d88b4687463601ba48"
-  integrity sha512-GKM4FLMkWDc0sfx7tXqPWkM6NBow1kge0fgQh0bOnlqo4iT1kvTvMEKE0c1RtUGnbLlGRXiAA8SumE//90uKAg==
-
-"@types/offscreencanvas@~2019.7.0":
-  version "2019.7.0"
-  resolved "https://registry.yarnpkg.com/@types/offscreencanvas/-/offscreencanvas-2019.7.0.tgz#e4a932069db47bb3eabeb0b305502d01586fa90d"
-  integrity sha512-PGcyveRIpL1XIqK8eBsmRBt76eFgtzuPiSTyKHZxnGemp2yzGzWpjYKAfK3wIMiU7eH+851yEpiuP8JZerTmWg==
-
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-  version "2.4.30"
-  resolved "https://registry.yarnpkg.com/@types/seedrandom/-/seedrandom-2.4.30.tgz#d2efe425869b84163c2d56e779dddadb9372cbfa"
-  integrity sha512-AnxLHewubLVzoF/A4qdxBGHCKifw8cY32iro3DQX9TPcetE95zBeVt3jnsvtvAUf1vwzMfwzp4t/L2yqPlnjkQ==
-
-"@webassemblyjs/ast@1.11.1":
-  version "1.11.1"
-  resolved "https://registry.yarnpkg.com/@webassemblyjs/ast/-/ast-1.11.1.tgz#2bfd767eae1a6996f432ff7e8d7fc75679c0b6a7"
-  integrity sha512-ukBh14qFLjxTQNTXocdyksN5QdM28S1CxHt2rdskFyL+xFV7VremuBLVbmCePj+URalXBENx/9Lm7lnhihtCSw==
-  dependencies:
-    "@webassemblyjs/helper-numbers" "1.11.1"
-    "@webassemblyjs/helper-wasm-bytecode" "1.11.1"
-
-"@webassemblyjs/floating-point-hex-parser@1.11.1":
-  version "1.11.1"
-  resolved "https://registry.yarnpkg.com/@webassemblyjs/floating-point-hex-parser/-/floating-point-hex-parser-1.11.1.tgz#f6c61a705f0fd7a6aecaa4e8198f23d9dc179e4f"
-  integrity sha512-iGRfyc5Bq+NnNuX8b5hwBrRjzf0ocrJPI6GWFodBFzmFnyvrQ83SHKhmilCU/8Jv67i4GJZBMhEzltxzcNagtQ==
-
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-  version "1.11.1"
-  resolved "https://registry.yarnpkg.com/@webassemblyjs/helper-api-error/-/helper-api-error-1.11.1.tgz#1a63192d8788e5c012800ba6a7a46c705288fd16"
-  integrity sha512-RlhS8CBCXfRUR/cwo2ho9bkheSXG0+NwooXcc3PAILALf2QLdFyj7KGsKRbVc95hZnhnERon4kW/D3SZpp6Tcg==
-
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-  version "1.11.1"
-  resolved "https://registry.yarnpkg.com/@webassemblyjs/helper-buffer/-/helper-buffer-1.11.1.tgz#832a900eb444884cde9a7cad467f81500f5e5ab5"
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-
-"@webassemblyjs/helper-numbers@1.11.1":
-  version "1.11.1"
-  resolved "https://registry.yarnpkg.com/@webassemblyjs/helper-numbers/-/helper-numbers-1.11.1.tgz#64d81da219fbbba1e3bd1bfc74f6e8c4e10a62ae"
-  integrity sha512-vDkbxiB8zfnPdNK9Rajcey5C0w+QJugEglN0of+kmO8l7lDb77AnlKYQF7aarZuCrv+l0UvqL+68gSDr3k9LPQ==
-  dependencies:
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-    "@webassemblyjs/helper-api-error" "1.11.1"
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-  resolved "https://registry.yarnpkg.com/@webassemblyjs/helper-wasm-bytecode/-/helper-wasm-bytecode-1.11.1.tgz#f328241e41e7b199d0b20c18e88429c4433295e1"
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-  dependencies:
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-    "@webassemblyjs/helper-buffer" "1.11.1"
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-
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-  version "1.11.1"
-  resolved "https://registry.yarnpkg.com/@webassemblyjs/ieee754/-/ieee754-1.11.1.tgz#963929e9bbd05709e7e12243a099180812992614"
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-  dependencies:
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-
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-  resolved "https://registry.yarnpkg.com/@webassemblyjs/leb128/-/leb128-1.11.1.tgz#ce814b45574e93d76bae1fb2644ab9cdd9527aa5"
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-  resolved "https://registry.yarnpkg.com/@webassemblyjs/utf8/-/utf8-1.11.1.tgz#d1f8b764369e7c6e6bae350e854dec9a59f0a3ff"
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index d7f3dd60e..000000000
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-  version "4.0.3"
-  resolved "https://registry.yarnpkg.com/public-encrypt/-/public-encrypt-4.0.3.tgz#4fcc9d77a07e48ba7527e7cbe0de33d0701331e0"
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-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
-
-whatwg-url@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
-  integrity sha1-lmRU6HZUYuN2RNNib2dCzotwll0=
-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
-which-boxed-primitive@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/which-boxed-primitive/-/which-boxed-primitive-1.0.2.tgz#13757bc89b209b049fe5d86430e21cf40a89a8e6"
-  integrity sha512-bwZdv0AKLpplFY2KZRX6TvyuN7ojjr7lwkg6ml0roIy9YeuSr7JS372qlNW18UQYzgYK9ziGcerWqZOmEn9VNg==
-  dependencies:
-    is-bigint "^1.0.1"
-    is-boolean-object "^1.1.0"
-    is-number-object "^1.0.4"
-    is-string "^1.0.5"
-    is-symbol "^1.0.3"
-
-which-typed-array@^1.1.2:
-  version "1.1.7"
-  resolved "https://registry.yarnpkg.com/which-typed-array/-/which-typed-array-1.1.7.tgz#2761799b9a22d4b8660b3c1b40abaa7739691793"
-  integrity sha512-vjxaB4nfDqwKI0ws7wZpxIlde1XrLX5uB0ZjpfshgmapJMD7jJWhZI+yToJTqaFByF0eNBcYxbjmCzoRP7CfEw==
-  dependencies:
-    available-typed-arrays "^1.0.5"
-    call-bind "^1.0.2"
-    es-abstract "^1.18.5"
-    foreach "^2.0.5"
-    has-tostringtag "^1.0.0"
-    is-typed-array "^1.1.7"
-
-which@^1.2.1:
-  version "1.3.1"
-  resolved "https://registry.yarnpkg.com/which/-/which-1.3.1.tgz#a45043d54f5805316da8d62f9f50918d3da70b0a"
-  integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ==
-  dependencies:
-    isexe "^2.0.0"
-
-wide-align@^1.1.2:
-  version "1.1.5"
-  resolved "https://registry.yarnpkg.com/wide-align/-/wide-align-1.1.5.tgz#df1d4c206854369ecf3c9a4898f1b23fbd9d15d3"
-  integrity sha512-eDMORYaPNZ4sQIuuYPDHdQvf4gyCF9rEEV/yPxGfwPkRodwEgiMUUXTx/dex+Me0wxx53S+NgUHaP7y3MGlDmg==
-  dependencies:
-    string-width "^1.0.2 || 2 || 3 || 4"
-
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-ws@~8.2.3:
-  version "8.2.3"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-8.2.3.tgz#63a56456db1b04367d0b721a0b80cae6d8becbba"
-  integrity sha512-wBuoj1BDpC6ZQ1B7DWQBYVLphPWkm8i9Y0/3YdHjHKHiohOJ1ws+3OccDWtH+PoC9DZD5WOTrJvNbWvjS6JWaA==
-
-xtend@^4.0.2, xtend@~4.0.1:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/xtend/-/xtend-4.0.2.tgz#bb72779f5fa465186b1f438f674fa347fdb5db54"
-  integrity sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==
-
-y18n@^5.0.5:
-  version "5.0.8"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.8.tgz#7f4934d0f7ca8c56f95314939ddcd2dd91ce1d55"
-  integrity sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==
-
-yallist@^3.0.0, yallist@^3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.1.1.tgz#dbb7daf9bfd8bac9ab45ebf602b8cbad0d5d08fd"
-  integrity sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==
-
-yallist@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
-  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
-
-yargs-parser@^20.2.2:
-  version "20.2.9"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
-  integrity sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==
-
-yargs@^16.0.3, yargs@^16.1.1:
-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yn@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
-  integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==
diff --git a/tfjs-master/link-package/build_deps.ts b/tfjs-master/link-package/build_deps.ts
deleted file mode 100644
index ba41e7a64..000000000
--- a/tfjs-master/link-package/build_deps.ts
+++ /dev/null
@@ -1,247 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as argparse from 'argparse';
-import {spawnSync, exec} from 'child_process';
-import * as fs from 'fs';
-import * as path from 'path';
-import * as rimraf from 'rimraf';
-import {BAZEL_PACKAGES} from '../scripts/bazel_packages';
-
-const parser = new argparse.ArgumentParser();
-
-parser.add_argument('tfjs_package', {
-  type: String,
-  help: 'tfjs package to build dependencies for',
-  nargs: '*',
-});
-
-parser.add_argument('--all', {
-  action: 'store_true',
-  help: 'Build all packages',
-});
-
-parser.add_argument('--bazel_options', {
-  type: String,
-  default: '',
-  help: 'Options to pass to Bazel',
-});
-
-/**
- * Build bazel dependencies for the packages specified by the repeated argument
- * tfjs_package. If the package is a Bazel package, also build the package
- * itself.
- *
- * @example 'yarn build-deps-for tfjs-react-native' builds all bazel
- * dependencies for @tensorflow/tfjs-react-native.
- *
- * @example 'yarn build-deps-for tfjs-backend-cpu' builds all bazel
- * dependencies for @tensorflow/tfjs-backend-cpu and builds tfjs-backend-cpu
- * (since it's a Bazel package).
- *
- * @example 'yarn build-deps-for --all' builds all bazel packages.
- */
-async function main() {
-  const args = parser.parse_args();
-  let packageNames: string[] = args.tfjs_package;
-
-  let targets: string[];
-  if (args.all) {
-    targets = [...BAZEL_PACKAGES].map(dirToTarget);
-  } else {
-    const bazelDeps = findTransitiveBazelDeps(packageNames);
-    targets = [...bazelDeps].map(dirToTarget);
-  }
-
-  if (targets.length > 0) {
-    if (process.platform === 'win32') {
-      const child = exec('yarn bazel build --color=yes '
-        + `${args.bazel_options} ${targets.join(' ')}`);
-      await new Promise((resolve, reject) => {
-        child.stdout.pipe(process.stdout);
-        child.stderr.pipe(process.stderr);
-        child.on('exit', code => {
-          if (code !== 0) {
-            reject(code);
-          }
-          resolve(code);
-        });
-      });
-    } else {
-      // Use spawnSync intead of exec for prettier printing.
-      const bazelArgs = ['bazel', 'build'];
-      if (args.bazel_options) {
-        bazelArgs.push(args.bazel_options);
-      }
-      bazelArgs.push(...targets);
-      spawnSync('yarn', bazelArgs, {stdio:'inherit'});
-    }
-  }
-
-  const tfjsDir = `${__dirname}/node_modules/@tensorflow`;
-  rimraf.sync(tfjsDir);
-  fs.mkdirSync(tfjsDir, {recursive: true});
-
-  // Copy all built packages to node_modules. Note that this does not install
-  // their dependencies, but that's okay since the node resolution algorithm
-  // will find dependencies in the root node_modules folder of the repository.
-  for (const pkg of BAZEL_PACKAGES) {
-    const pkgPath = path.normalize(
-      `${__dirname}/../dist/bin/${pkg}/${pkg}_pkg`);
-
-    if (fs.existsSync(pkgPath)) {
-      const newPath = path.join(tfjsDir, pkg);
-      copyRecursive(pkgPath, newPath);
-    }
-  }
-  chmodRecursive(tfjsDir);
-}
-
-/**
- * Get all dependencies and devDependencies of a tfjs package.
- *
- * @param packageName The package name (without @tensorflow/).
- */
-function getDeps(packageName: string): Set<string> {
-  const packageJsonPath = path.normalize(
-    `${__dirname}/../${packageName}/package.json`);
-
-  const pkg = fs.readFileSync(packageJsonPath).toString('utf8');
-  const parsedPkg = JSON.parse(pkg) as {
-    dependencies?: Record<string, string>,
-    devDependencies?: Record<string, string>,
-  };
-
-  const allDeps = new Set<string>();
-  function addDepsFrom(record?: Record<string, string>) {
-    if (record) {
-      for (const key of Object.keys(record)) {
-        allDeps.add(key);
-      }
-    }
-  }
-  addDepsFrom(parsedPkg.dependencies);
-  addDepsFrom(parsedPkg.devDependencies);
-  return allDeps;
-}
-
-/**
- * Get just the @tensorflow/ scoped deps of a tfjs package.
- *
- * @param packageName The package name (without @tensorflow/).
- * @returns The tfjs package names (not paths) of immediate dependencies.
- */
-function getTfjsDeps(packageName: string): Set<string> {
-  const deps = getDeps(packageName);
-  const tfjsDeps = [...deps]
-                     .map(dep => dep.match(/@tensorflow\/(.*)/))
-                     .filter(Boolean)
-                     .map(dep => dep[1]);
-  return new Set(tfjsDeps);
-}
-
-/**
- * Find the set of tfjs Bazel packages that the given packages depend on,
- * including transitively.
- *
- * @param packages An iterable of tfjs package names to check for Bazel
- * dependencies
- * @returns The set of TFJS packages that constitute the edge between the Bazel
- * build graph and the npm build graph, i.e. the minimum set of Bazel packages
- * that need to be built in order to build the packages listed in `packages`.
- */
-function findTransitiveBazelDeps(packages: Iterable<string>): Set<string> {
-  let toVisit = new Set([...packages]);
-  const visited = new Set<string>();
-  const bazelPackages = new Set<string>();
-
-  while (toVisit.size > 0) {
-    for (const pkg of toVisit) {
-      if (BAZEL_PACKAGES.has(pkg)) {
-        bazelPackages.add(pkg);
-      } else {
-        const deps = getTfjsDeps(pkg);
-        // Only add deps that haven't been visited
-        const newDeps = [...deps].filter(dep => !visited.has(dep));
-        toVisit = new Set([...toVisit, ...newDeps]);
-      }
-      visited.add(pkg);
-      toVisit.delete(pkg);
-    }
-  }
-
-  return bazelPackages;
-}
-
-/**
- * Get the bazel target from a tfjs package's directory.
- *
- * @param dir The tfjs package's directory.
- * @returns The bazel target to build the tfjs package.
- */
-function dirToTarget(dir: string) {
-  return `//${dir}:${dir}_pkg`;
-}
-
-/**
- * Recursively copy a file or directory.
- *
- * @param src The source directory.
- * @param dest The destination to copy src to.
- */
-function copyRecursive(src: string, dest: string) {
-  // Avoid 'cp -r', which Windows does not suppport
-  const stat = fs.lstatSync(src);
-  if (stat.isFile()) {
-    fs.copyFileSync(src, dest);
-  } else if (stat.isDirectory()) {
-    const contents = fs.readdirSync(src);
-    fs.mkdirSync(dest);
-    for (let name of contents) {
-      copyRecursive(path.join(src, name),
-                    path.join(dest, name));
-    }
-  }
-}
-
-/**
- * Map a function on all files and directories under a path.
- *
- * @param rootPath The path where the function will be mapped.
- * @param mapFn The function to map on all subpaths of the rootPath.
- */
-function mapFiles(rootPath: string, mapFn: (path: string) => void) {
-  mapFn(rootPath);
-  const stat = fs.lstatSync(rootPath);
-  if (stat.isDirectory()) {
-    const contents = fs.readdirSync(rootPath);
-    for (let subPath of contents) {
-      mapFiles(path.join(rootPath, subPath), mapFn);
-    }
-  }
-}
-
-/**
- * Recursively change permissions of files to 775.
- *
- * @param rootPath The path where permissions are changed.
- */
-function chmodRecursive(rootPath: string) {
-  mapFiles(rootPath, path => fs.chmodSync(path, 0o775));
-}
-
-main();
diff --git a/tfjs-master/link-package/package.json b/tfjs-master/link-package/package.json
deleted file mode 100644
index 763a5eda1..000000000
--- a/tfjs-master/link-package/package.json
+++ /dev/null
@@ -1,21 +0,0 @@
-{
-  "name": "link-package",
-  "version": "1.0.0",
-  "description": "A package to help resolve npm link dependencies during the Bazel transition. Other packages link to the packages installed in its node_modules directory. Should never be published.",
-  "license": "Apache 2.0",
-  "private": true,
-  "scripts": {
-    "build": "yarn build-deps-for --all",
-    "build-deps-for": "yarn && ts-node build_deps.ts"
-  },
-  "devDependencies": {
-    "@types/rimraf": "^3.0.2",
-    "rimraf": "^3.0.2",
-    "ts-node": "^10.7.0"
-  },
-  "dependencies": {
-    "@bazel/bazelisk": "^1.12.0",
-    "@types/argparse": "^2.0.10",
-    "argparse": "^2.0.1"
-  }
-}
diff --git a/tfjs-master/link-package/tsconfig.json b/tfjs-master/link-package/tsconfig.json
deleted file mode 100644
index 8f446823b..000000000
--- a/tfjs-master/link-package/tsconfig.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "compilerOptions": {
-    "module": "commonjs",
-    "target": "es5",
-    "lib": [
-      "esnext"
-    ],
-    "downlevelIteration": true,
-    "esModuleInterop": true
-  },
-  "include": [
-    "**/*.ts"
-  ]
-}
diff --git a/tfjs-master/link-package/yarn.lock b/tfjs-master/link-package/yarn.lock
deleted file mode 100644
index e28b20a46..000000000
--- a/tfjs-master/link-package/yarn.lock
+++ /dev/null
@@ -1,214 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@bazel/bazelisk@^1.12.0":
-  version "1.12.0"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.12.0.tgz#f08aebbf4afcb12684422450b0845dd6ef5cfe50"
-  integrity sha512-7oQusq1e4AIyFgotxVV7Pc40Et0QyvoVjujL+7/qV5Vrbfh0Nj3CfqSgl63weEyI4r0+K6RlGVsjfRuBi05p5w==
-
-"@cspotcode/source-map-consumer@0.8.0":
-  version "0.8.0"
-  resolved "https://registry.yarnpkg.com/@cspotcode/source-map-consumer/-/source-map-consumer-0.8.0.tgz#33bf4b7b39c178821606f669bbc447a6a629786b"
-  integrity sha512-41qniHzTU8yAGbCp04ohlmSrZf8bkf/iJsl3V0dRGsQN/5GFfx+LbCSsCpp2gqrqjTVg/K6O8ycoV35JIwAzAg==
-
-"@cspotcode/source-map-support@0.7.0":
-  version "0.7.0"
-  resolved "https://registry.yarnpkg.com/@cspotcode/source-map-support/-/source-map-support-0.7.0.tgz#4789840aa859e46d2f3173727ab707c66bf344f5"
-  integrity sha512-X4xqRHqN8ACt2aHVe51OxeA2HjbcL4MqFqXkrmQszJ1NOUuUu5u6Vqx/0lZSVNku7velL5FC/s5uEAj1lsBMhA==
-  dependencies:
-    "@cspotcode/source-map-consumer" "0.8.0"
-
-"@tsconfig/node10@^1.0.7":
-  version "1.0.8"
-  resolved "https://registry.yarnpkg.com/@tsconfig/node10/-/node10-1.0.8.tgz#c1e4e80d6f964fbecb3359c43bd48b40f7cadad9"
-  integrity sha512-6XFfSQmMgq0CFLY1MslA/CPUfhIL919M1rMsa5lP2P097N2Wd1sSX0tx1u4olM16fLNhtHZpRhedZJphNJqmZg==
-
-"@tsconfig/node12@^1.0.7":
-  version "1.0.9"
-  resolved "https://registry.yarnpkg.com/@tsconfig/node12/-/node12-1.0.9.tgz#62c1f6dee2ebd9aead80dc3afa56810e58e1a04c"
-  integrity sha512-/yBMcem+fbvhSREH+s14YJi18sp7J9jpuhYByADT2rypfajMZZN4WQ6zBGgBKp53NKmqI36wFYDb3yaMPurITw==
-
-"@tsconfig/node14@^1.0.0":
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/@tsconfig/node14/-/node14-1.0.1.tgz#95f2d167ffb9b8d2068b0b235302fafd4df711f2"
-  integrity sha512-509r2+yARFfHHE7T6Puu2jjkoycftovhXRqW328PDXTVGKihlb1P8Z9mMZH04ebyajfRY7dedfGynlrFHJUQCg==
-
-"@tsconfig/node16@^1.0.2":
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/@tsconfig/node16/-/node16-1.0.2.tgz#423c77877d0569db20e1fc80885ac4118314010e"
-  integrity sha512-eZxlbI8GZscaGS7kkc/trHTT5xgrjH3/1n2JDwusC9iahPKWMRvRjJSAN5mCXviuTGQ/lHnhvv8Q1YTpnfz9gA==
-
-"@types/argparse@^2.0.10":
-  version "2.0.10"
-  resolved "https://registry.yarnpkg.com/@types/argparse/-/argparse-2.0.10.tgz#664e84808accd1987548d888b9d21b3e9c996a6c"
-  integrity sha512-C4wahC3gz3vQtvPazrJ5ONwmK1zSDllQboiWvpMM/iOswCYfBREFnjFbq/iWKIVOCl8+m5Pk6eva6/ZSsDuIGA==
-
-"@types/glob@*":
-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/@types/glob/-/glob-7.2.0.tgz#bc1b5bf3aa92f25bd5dd39f35c57361bdce5b2eb"
-  integrity sha512-ZUxbzKl0IfJILTS6t7ip5fQQM/J3TJYubDm3nMbgubNNYS62eXeUpoLUC8/7fJNiFYHTrGPQn7hspDUzIHX3UA==
-  dependencies:
-    "@types/minimatch" "*"
-    "@types/node" "*"
-
-"@types/minimatch@*":
-  version "3.0.5"
-  resolved "https://registry.yarnpkg.com/@types/minimatch/-/minimatch-3.0.5.tgz#1001cc5e6a3704b83c236027e77f2f58ea010f40"
-  integrity sha512-Klz949h02Gz2uZCMGwDUSDS1YBlTdDDgbWHi+81l29tQALUtvz4rAYi5uoVhE5Lagoq6DeqAUlbrHvW/mXDgdQ==
-
-"@types/node@*":
-  version "17.0.31"
-  resolved "https://registry.yarnpkg.com/@types/node/-/node-17.0.31.tgz#a5bb84ecfa27eec5e1c802c6bbf8139bdb163a5d"
-  integrity sha512-AR0x5HbXGqkEx9CadRH3EBYx/VkiUgZIhP4wvPn/+5KIsgpNoyFaRlVe0Zlx9gRtg8fA06a9tskE2MSN7TcG4Q==
-
-"@types/rimraf@^3.0.2":
-  version "3.0.2"
-  resolved "https://registry.yarnpkg.com/@types/rimraf/-/rimraf-3.0.2.tgz#a63d175b331748e5220ad48c901d7bbf1f44eef8"
-  integrity sha512-F3OznnSLAUxFrCEu/L5PY8+ny8DtcFRjx7fZZ9bycvXRi3KPTRS9HOitGZwvPg0juRhXFWIeKX58cnX5YqLohQ==
-  dependencies:
-    "@types/glob" "*"
-    "@types/node" "*"
-
-acorn-walk@^8.1.1:
-  version "8.2.0"
-  resolved "https://registry.yarnpkg.com/acorn-walk/-/acorn-walk-8.2.0.tgz#741210f2e2426454508853a2f44d0ab83b7f69c1"
-  integrity sha512-k+iyHEuPgSw6SbuDpGQM+06HQUa04DZ3o+F6CSzXMvvI5KMvnaEqXe+YVe555R9nn6GPt404fos4wcgpw12SDA==
-
-acorn@^8.4.1:
-  version "8.7.1"
-  resolved "https://registry.yarnpkg.com/acorn/-/acorn-8.7.1.tgz#0197122c843d1bf6d0a5e83220a788f278f63c30"
-  integrity sha512-Xx54uLJQZ19lKygFXOWsscKUbsBZW0CPykPhVQdhIeIwrbPmJzqeASDInc8nKBnp/JT6igTs82qPXz069H8I/A==
-
-arg@^4.1.0:
-  version "4.1.3"
-  resolved "https://registry.yarnpkg.com/arg/-/arg-4.1.3.tgz#269fc7ad5b8e42cb63c896d5666017261c144089"
-  integrity sha512-58S9QDqG0Xx27YwPSt9fJxivjYl432YCwfDMfZ+71RAqUrZef7LrKQZ3LHLOwCS4FLNBplP533Zx895SeOCHvA==
-
-argparse@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/argparse/-/argparse-2.0.1.tgz#246f50f3ca78a3240f6c997e8a9bd1eac49e4b38"
-  integrity sha512-8+9WqebbFzpX9OR+Wa6O29asIogeRMzcGtAINdpMHHyAg10f05aSFVBbcEqGf/PXw1EjAZ+q2/bEBg3DvurK3Q==
-
-balanced-match@^1.0.0:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
-  integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
-
-brace-expansion@^1.1.7:
-  version "1.1.11"
-  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd"
-  integrity sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==
-  dependencies:
-    balanced-match "^1.0.0"
-    concat-map "0.0.1"
-
-concat-map@0.0.1:
-  version "0.0.1"
-  resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
-  integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
-
-create-require@^1.1.0:
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/create-require/-/create-require-1.1.1.tgz#c1d7e8f1e5f6cfc9ff65f9cd352d37348756c333"
-  integrity sha512-dcKFX3jn0MpIaXjisoRvexIJVEKzaq7z2rZKxf+MSr9TkdmHmsU4m2lcLojrj/FHl8mk5VxMmYA+ftRkP/3oKQ==
-
-diff@^4.0.1:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/diff/-/diff-4.0.2.tgz#60f3aecb89d5fae520c11aa19efc2bb982aade7d"
-  integrity sha512-58lmxKSA4BNyLz+HHMUzlOEpg09FV+ev6ZMe3vJihgdxzgcwZ8VoEEPmALCZG9LmqfVoNMMKpttIYTVG6uDY7A==
-
-fs.realpath@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
-  integrity sha1-FQStJSMVjKpA20onh8sBQRmU6k8=
-
-glob@^7.1.3:
-  version "7.1.7"
-  resolved "https://registry.yarnpkg.com/glob/-/glob-7.1.7.tgz#3b193e9233f01d42d0b3f78294bbeeb418f94a90"
-  integrity sha512-OvD9ENzPLbegENnYP5UUfJIirTg4+XwMWGaQfQTY0JenxNvvIKP3U3/tAQSPIu/lHxXYSZmpXlUHeqAIdKzBLQ==
-  dependencies:
-    fs.realpath "^1.0.0"
-    inflight "^1.0.4"
-    inherits "2"
-    minimatch "^3.0.4"
-    once "^1.3.0"
-    path-is-absolute "^1.0.0"
-
-inflight@^1.0.4:
-  version "1.0.6"
-  resolved "https://registry.yarnpkg.com/inflight/-/inflight-1.0.6.tgz#49bd6331d7d02d0c09bc910a1075ba8165b56df9"
-  integrity sha1-Sb1jMdfQLQwJvJEKEHW6gWW1bfk=
-  dependencies:
-    once "^1.3.0"
-    wrappy "1"
-
-inherits@2:
-  version "2.0.4"
-  resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
-  integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
-
-make-error@^1.1.1:
-  version "1.3.6"
-  resolved "https://registry.yarnpkg.com/make-error/-/make-error-1.3.6.tgz#2eb2e37ea9b67c4891f684a1394799af484cf7a2"
-  integrity sha512-s8UhlNe7vPKomQhC1qFelMokr/Sc3AgNbso3n74mVPA5LTZwkB9NlXf4XPamLxJE8h0gh73rM94xvwRT2CVInw==
-
-minimatch@^3.0.4:
-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.1.2.tgz#19cd194bfd3e428f049a70817c038d89ab4be35b"
-  integrity sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==
-  dependencies:
-    brace-expansion "^1.1.7"
-
-once@^1.3.0:
-  version "1.4.0"
-  resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
-  integrity sha1-WDsap3WWHUsROsF9nFC6753Xa9E=
-  dependencies:
-    wrappy "1"
-
-path-is-absolute@^1.0.0:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
-  integrity sha1-F0uSaHNVNP+8es5r9TpanhtcX18=
-
-rimraf@^3.0.2:
-  version "3.0.2"
-  resolved "https://registry.yarnpkg.com/rimraf/-/rimraf-3.0.2.tgz#f1a5402ba6220ad52cc1282bac1ae3aa49fd061a"
-  integrity sha512-JZkJMZkAGFFPP2YqXZXPbMlMBgsxzE8ILs4lMIX/2o0L9UBw9O/Y3o6wFw/i9YLapcUJWwqbi3kdxIPdC62TIA==
-  dependencies:
-    glob "^7.1.3"
-
-ts-node@^10.7.0:
-  version "10.7.0"
-  resolved "https://registry.yarnpkg.com/ts-node/-/ts-node-10.7.0.tgz#35d503d0fab3e2baa672a0e94f4b40653c2463f5"
-  integrity sha512-TbIGS4xgJoX2i3do417KSaep1uRAW/Lu+WAL2doDHC0D6ummjirVOXU5/7aiZotbQ5p1Zp9tP7U6cYhA0O7M8A==
-  dependencies:
-    "@cspotcode/source-map-support" "0.7.0"
-    "@tsconfig/node10" "^1.0.7"
-    "@tsconfig/node12" "^1.0.7"
-    "@tsconfig/node14" "^1.0.0"
-    "@tsconfig/node16" "^1.0.2"
-    acorn "^8.4.1"
-    acorn-walk "^8.1.1"
-    arg "^4.1.0"
-    create-require "^1.1.0"
-    diff "^4.0.1"
-    make-error "^1.1.1"
-    v8-compile-cache-lib "^3.0.0"
-    yn "3.1.1"
-
-v8-compile-cache-lib@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/v8-compile-cache-lib/-/v8-compile-cache-lib-3.0.1.tgz#6336e8d71965cb3d35a1bbb7868445a7c05264bf"
-  integrity sha512-wa7YjyUGfNZngI/vtK0UHAN+lgDCxBPCylVXGp0zu59Fz5aiGtNXaq3DhIov063MorB+VfufLh3JlF2KdTK3xg==
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-yn@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
-  integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==
diff --git a/tfjs-master/package.json b/tfjs-master/package.json
deleted file mode 100644
index 16b119a9c..000000000
--- a/tfjs-master/package.json
+++ /dev/null
@@ -1,111 +0,0 @@
-{
-  "devDependencies": {
-    "@bazel/bazelisk": "^1.12.0",
-    "@bazel/buildifier": "6.1.0",
-    "@bazel/concatjs": "5.8.1",
-    "@bazel/esbuild": "5.8.1",
-    "@bazel/ibazel": "^0.16.2",
-    "@bazel/jasmine": "5.8.1",
-    "@bazel/rollup": "5.8.1",
-    "@bazel/terser": "5.8.1",
-    "@bazel/typescript": "5.8.1",
-    "@octokit/rest": "15.17.0",
-    "@rollup/plugin-commonjs": "^24.1.0",
-    "@rollup/plugin-node-resolve": "^15.0.2",
-    "@types/argparse": "^1.0.38",
-    "@types/emscripten": "~0.0.34",
-    "@types/estree": "^0.0.51",
-    "@types/inquirer": "^8.0.0",
-    "@types/jasmine": "~4.0.3",
-    "@types/js-yaml": "^4.0.5",
-    "@types/long": "4.0.1",
-    "@types/mkdirp": "^0.5.2",
-    "@types/node": "^18.11.15",
-    "@types/node-fetch": "~2.1.2",
-    "@types/offscreencanvas": "^2019.7.0",
-    "@types/rimraf": "^3.0.2",
-    "@types/rollup-plugin-visualizer": "^4.2.1",
-    "@types/seedrandom": "^2.4.28",
-    "@types/semver": "^7.3.9",
-    "@types/shelljs": "^0.8.7",
-    "@types/dom-webcodecs": "0.1.4",
-    "@webgpu/types": "0.1.38",
-    "ajv": "~6.12.3",
-    "argparse": "^1.0.10",
-    "chalk": "~2.4.2",
-    "clang-format": "~1.2.4",
-    "console-table-printer": "^2.4.32",
-    "copyfiles": "~1.2.0",
-    "core-js": "3",
-    "deep-equal": "^1.0.1",
-    "estree-walker": "~1.0.1",
-    "express": "4.18.2",
-    "inquirer": "^8.0.0",
-    "jasmine": "~4.2.1",
-    "jasmine-core": "~4.2.0",
-    "js-yaml": "^3.14.0",
-    "jszip": "^3.10.0",
-    "karma": "^6.4.0",
-    "karma-browserstack-launcher": "^1.6.0",
-    "karma-chrome-launcher": "^3.1.1",
-    "karma-firefox-launcher": "^2.1.2",
-    "karma-jasmine": "~5.1.0",
-    "karma-jasmine-html-reporter": "^2.0.0",
-    "karma-jasmine-order-reporter": "^1.1.0",
-    "karma-requirejs": "^1.1.0",
-    "karma-sourcemap-loader": "^0.3.8",
-    "long": "4.0.0",
-    "mkdirp": "~0.5.1",
-    "opn": "~5.1.0",
-    "protobufjs": "~7.2.4",
-    "requirejs": "^2.3.6",
-    "rimraf": "~2.6.2",
-    "rollup": "^3.20.2",
-    "rollup-plugin-sourcemaps": "^0.6.3",
-    "rollup-plugin-terser": "^7.0.2",
-    "rollup-plugin-visualizer": "~5.9.0",
-    "seedrandom": "^3.0.5",
-    "semver": "^7.5.2",
-    "shelljs": "~0.8.5",
-    "string_decoder": "^1.3.0",
-    "terser": "^5.14.2",
-    "ts-morph": "^11.0.3",
-    "ts-node": "~8.8.2",
-    "tslib": "^2.4.0",
-    "tslint": "^6.1.3",
-    "tslint-no-circular-imports": "~0.7.0",
-    "typescript": "5.0.4",
-    "verdaccio": "^5.9.0"
-  },
-  "scripts": {
-    "lint": "tslint -p tsconfig_tslint.json",
-    "test": "bazel test //:tests",
-    "test-packages-ci": "yarn generate-cloudbuild-for-packages && ./scripts/run-build.sh",
-    "nightly-cloudbuild": "NIGHTLY=true yarn generate-cloudbuild-for-packages && gcloud builds submit . --config=cloudbuild_generated.yml --substitutions=_NIGHTLY=true",
-    "generate-cloudbuild-for-packages": "ts-node -s ./scripts/generate_cloudbuild_for_packages.ts",
-    "test-generate-cloudbuild": "cd scripts && node --require ts-node/register ../node_modules/jasmine/bin/jasmine.js run generate_cloudbuild_test.ts",
-    "test-run-flaky": "jasmine run scripts/run_flaky_test.js",
-    "release": "ts-node -s ./scripts/release.ts",
-    "release-tfjs": "ts-node -s ./scripts/release-tfjs.ts",
-    "publish-npm": "ts-node -s ./scripts/publish-npm.ts",
-    "publish-pypi": "ts-node -s ./scripts/publish-pypi.ts",
-    "release-notes": "ts-node -s ./scripts/release_notes/release_notes.ts",
-    "test-release-notes": "ts-node -s ./scripts/release_notes/run_tests.ts",
-    "update-tfjs-lockfiles": "ts-node -s ./scripts/update-tfjs-lockfiles",
-    "tag-tfjs-release": "ts-node -s ./scripts/tag-tfjs-release",
-    "update-cloudbuild-tests": "yarn generate-cloudbuild-for-packages tfjs-node -o scripts/cloudbuild_tfjs_node_expected.yml && yarn generate-cloudbuild-for-packages e2e -o scripts/cloudbuild_e2e_expected.yml",
-    "bazel:format": "find . -type f \\( -name \"*.bzl\" -or -name WORKSPACE -or -name BUILD -or -name BUILD.bazel \\) ! -path \"*/node_modules/*\" | xargs buildifier -v --warnings=attr-cfg,attr-license,attr-non-empty,attr-output-default,attr-single-file,constant-glob,ctx-actions,ctx-args,depset-iteration,depset-union,dict-concatenation,duplicated-name,filetype,git-repository,http-archive,integer-division,load,load-on-top,native-build,native-package,out-of-order-load,output-group,package-name,package-on-top,positional-args,redefined-variable,repository-name,same-origin-load,string-iteration,unsorted-dict-items,unused-variable",
-    "bazel:format-check": "yarn bazel:format --mode=check",
-    "bazel:lint": "yarn bazel:format --lint=warn",
-    "bazel:lint-fix": "yarn bazel:format --lint=fix",
-    "bazel:lint-check": "yarn bazel:format --lint=warn --mode=check",
-    "buildifier-ci": "./scripts/buildifier-ci.sh",
-    "start-local-debugger-server": "node ./scripts/start_local_debugger_server.js"
-  },
-  "dependencies": {
-    "node-fetch": "^2.6.7"
-  },
-  "resolutions": {
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/pull_request_template.md b/tfjs-master/pull_request_template.md
deleted file mode 100644
index f1a5010ff..000000000
--- a/tfjs-master/pull_request_template.md
+++ /dev/null
@@ -1 +0,0 @@
-To see the logs from the Cloud Build CI, please join either our [discussion](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfjs) or [announcement](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfjs-announce) mailing list.
\ No newline at end of file
diff --git a/tfjs-master/remote-execution/BUILD.bazel b/tfjs-master/remote-execution/BUILD.bazel
deleted file mode 100644
index 21703436a..000000000
--- a/tfjs-master/remote-execution/BUILD.bazel
+++ /dev/null
@@ -1,41 +0,0 @@
-package(default_visibility = ["//visibility:public"])
-
-platform(
-    name = "platform",
-    constraint_values = [
-        "@bazel_tools//platforms:linux",
-        "@bazel_tools//platforms:x86_64",
-        "@bazel_tools//tools/cpp:clang",
-    ],
-    exec_properties = {
-        # We use the same docker image for remote builds as we use for CI testing.
-        "container-image": "docker://gcr.io/learnjs-174218/release@sha256:d85abab6146eaf1e01312bdb9e353a5efa0508b913dccf30fc5e505d009026ff",
-        # By default in Google Cloud Remote build execution, network access is disabled. We explicitly set the
-        # property in the platform again in case the default ever changes. Network access is not desirable in
-        # Bazel builds as it is potential source of flaky tests and therefore also breaks hermeticity.
-        "dockerNetwork": "off",
-    },
-)
-
-platform(
-    name = "platform_with_network",
-    exec_properties = {
-        # By default we have network access disabled with the `:platform` target. This is an
-        # additional platform that extends from the default one but enables network access.
-        # Network is generally not recommended, but for some exceptions, like integration tests
-        # running a Yarn install, network access is reasonable. In such special cases, Bazel can
-        # be invoked to run with this platform. It is recommended that exec platforms with network
-        # access are used in combination with `--sandbox_default_allow_network=false` as this allows
-        # specific targets to be granted network access, while others will not have access.
-        "dockerNetwork": "standard",
-    },
-    parents = [":platform"],
-)
-
-filegroup(
-    name = "files",
-    srcs = [
-        "BUILD.bazel",
-        "@npm//@angular/dev-infra-private/bazel/remote-execution/cpp:files",
-    ],
-)
diff --git a/tfjs-master/remote-execution/cpp/BUILD.bazel b/tfjs-master/remote-execution/cpp/BUILD.bazel
deleted file mode 100644
index a94de2526..000000000
--- a/tfjs-master/remote-execution/cpp/BUILD.bazel
+++ /dev/null
@@ -1,58 +0,0 @@
-load("@bazel_tools//tools/cpp:cc_toolchain_config.bzl", "cc_toolchain_config")
-
-package(default_visibility = ["//visibility:public"])
-
-filegroup(
-    name = "files",
-    srcs = ["BUILD.bazel"],
-)
-
-cc_toolchain_suite(
-    name = "cc_toolchain_suite",
-    tags = ["manual"],
-    toolchains = {
-        "k8": ":cc_compiler_k8",
-    },
-)
-
-toolchain(
-    name = "cc_toolchain",
-    exec_compatible_with = [
-        "@bazel_tools//platforms:linux",
-        "@bazel_tools//platforms:x86_64",
-        "@bazel_tools//tools/cpp:clang",
-    ],
-    target_compatible_with = [
-        "@bazel_tools//platforms:linux",
-        "@bazel_tools//platforms:x86_64",
-    ],
-    toolchain = ":cc_compiler_k8",
-    toolchain_type = "@bazel_tools//tools/cpp:toolchain_type",
-)
-
-# Basic CC toolchain for k8 remote containers. Based on the default k8
-# toolchain provided in Bazel (but unfortunately internal).
-# https://github.com/bazelbuild/bazel/blob/c951753097b45cfb9be512c02199aa891b9646b8/tools/cpp/BUILD.tools#L298-L311
-cc_toolchain(
-    name = "cc_compiler_k8",
-    all_files = ":empty",
-    ar_files = ":empty",
-    as_files = ":empty",
-    compiler_files = ":empty",
-    dwp_files = ":empty",
-    linker_files = ":empty",
-    objcopy_files = ":empty",
-    strip_files = ":empty",
-    supports_param_files = 1,
-    toolchain_config = ":k8_toolchain_config",
-    toolchain_identifier = "cc-k8-compiler",
-)
-
-cc_toolchain_config(
-    name = "k8_toolchain_config",
-    compiler = "compiler",
-    cpu = "local",
-)
-
-# Empty filegroup used for defining the CC toolchain.
-filegroup(name = "empty")
diff --git a/tfjs-master/rollup.config.helpers.js b/tfjs-master/rollup.config.helpers.js
deleted file mode 100644
index 7ad2be4f0..000000000
--- a/tfjs-master/rollup.config.helpers.js
+++ /dev/null
@@ -1,114 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {terser} from 'rollup-plugin-terser';
-
-/**
- * Returns a standardized list of browser package configuration options
- * that we want to use in all our rollup files and ship to NPM.
- *
- * @param {string} fileName
- * @param {string} preamble
- * @param {boolean} visualize - produce bundle visualizations for certain
- *     bundles
- * @param {boolean} ci is this a CI build
- * @param {object} terserExtraOptions is any extra options passed to terser
- */
-export function getBrowserBundleConfigOptions(
-    config, name, fileName, preamble, visualize, ci, terserExtraOptions = {}) {
-  const bundles = [];
-
-  const terserPlugin =
-      terser({output: {preamble, comments: false}, ...terserExtraOptions});
-  const extend = true;
-  const umdFormat = 'umd';
-  const fesmFormat = 'es';
-
-  // UMD ES5 minified
-  bundles.push(config({
-    plugins: [terserPlugin],
-    output: {
-      format: umdFormat,
-      name,
-      extend,
-      file: `dist/${fileName}.min.js`,
-      freeze: false
-    },
-    tsCompilerOptions: {target: 'es5'},
-    visualize
-  }));
-
-  if (ci) {
-    // In CI we do not build all the possible bundles.
-    return bundles;
-  }
-
-  // UMD ES5 unminified
-  bundles.push(config({
-    output: {
-      format: umdFormat,
-      name,
-      extend,
-      file: `dist/${fileName}.js`,
-      freeze: false
-    },
-    tsCompilerOptions: {target: 'es5'}
-  }));
-
-  // UMD ES2017
-  bundles.push(config({
-    output:
-        {format: umdFormat, name, extend, file: `dist/${fileName}.es2017.js`},
-    tsCompilerOptions: {target: 'es2017'}
-  }));
-
-  // UMD ES2017 minified
-  bundles.push(config({
-    plugins: [terserPlugin],
-    output: {
-      format: umdFormat,
-      name,
-      extend,
-      file: `dist/${fileName}.es2017.min.js`
-    },
-    tsCompilerOptions: {target: 'es2017'},
-    visualize
-  }));
-
-  // FESM ES2017
-  bundles.push(config({
-    output:
-        {format: fesmFormat, name, extend, file: `dist/${fileName}.fesm.js`},
-    tsCompilerOptions: {target: 'es2017'}
-  }));
-
-  // FESM ES2017 minified
-  bundles.push(config({
-    plugins: [terserPlugin],
-    output: {
-      format: fesmFormat,
-      name,
-      extend,
-      file: `dist/${fileName}.fesm.min.js`
-    },
-    tsCompilerOptions: {target: 'es2017'},
-    visualize
-  }));
-
-
-  return bundles;
-}
diff --git a/tfjs-master/scripts/bazel_packages.ts b/tfjs-master/scripts/bazel_packages.ts
deleted file mode 100644
index dafb6ee79..000000000
--- a/tfjs-master/scripts/bazel_packages.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-export const BAZEL_PACKAGES = new Set([
-  'tfjs-core',
-  'tfjs-backend-cpu',
-  'tfjs-tfdf',
-  'tfjs-tflite',
-  'tfjs-converter',
-  'tfjs-backend-webgl',
-  'tfjs-backend-webgpu',
-  'tfjs-layers',
-  'tfjs-data',
-  'tfjs-backend-wasm',
-]);
diff --git a/tfjs-master/scripts/buildifier-ci.sh b/tfjs-master/scripts/buildifier-ci.sh
deleted file mode 100644
index e114452ea..000000000
--- a/tfjs-master/scripts/buildifier-ci.sh
+++ /dev/null
@@ -1,45 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-yarn bazel:format-check
-
-if [ $? -eq 0 ]
-then
-  echo
-else
-  echo
-  echo "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
-  echo "'yarn bazel:format-check' failed!"
-  echo "Please run 'yarn bazel:format'."
-  echo "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
-  echo
-  exit 1
-fi
-
-yarn bazel:lint-check
-
-if [ $? -eq 0 ]
-then
-  echo
-else
-  echo
-  echo "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
-  echo "'yarn bazel:lint-check' failed!"
-  echo "Please run 'yarn bazel:lint'."
-  echo "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
-  echo
-  exit 1
-fi
diff --git a/tfjs-master/scripts/bundle-size-util.js b/tfjs-master/scripts/bundle-size-util.js
deleted file mode 100644
index 40fd6ba5a..000000000
--- a/tfjs-master/scripts/bundle-size-util.js
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-const {exec} = require('./test-util');
-
-function getFileSizeBytes(filename) {
-  const fileSizeBytes =
-      +(exec(`cat ${filename} | wc -c`, {silent: true}));
-  const gzipFileSizeBytes =
-      +(exec(`gzip -c ${filename} | wc -c`, {silent: true}));
-  return {fileSizeBytes, gzipFileSizeBytes};
-}
-
-function showDiff(newSize, masterSize) {
-  const diffBytes = newSize - masterSize;
-  const diffPercent = (100 * diffBytes / masterSize).toFixed(2);
-  const sign = diffBytes > 0 ? '+' : '';
-
-  const charWidth = 7;
-  const diffKiloBytes =
-      (sign + (diffBytes / 1024).toFixed(2)).padStart(charWidth, ' ');
-  const masterKiloBytes =
-      ((masterSize / 1024).toFixed(2)).padStart(charWidth, ' ');
-  const newKiloBytes = ((newSize / 1024).toFixed(2)).padStart(charWidth, ' ');
-
-  console.log(`  diff:   ${diffKiloBytes} K  (${sign}${diffPercent}%)`);
-  console.log(`  master: ${masterKiloBytes} K`);
-  console.log(`  change: ${newKiloBytes} K`);
-}
-
-exports.getFileSizeBytes = getFileSizeBytes;
-exports.showDiff = showDiff;
diff --git a/tfjs-master/scripts/cloud_funcs/README.md b/tfjs-master/scripts/cloud_funcs/README.md
deleted file mode 100644
index 359f3cc48..000000000
--- a/tfjs-master/scripts/cloud_funcs/README.md
+++ /dev/null
@@ -1,50 +0,0 @@
-This directory contains the following Google Cloud Functions.
-
-### `trigger_nightly`
-Programatically triggers a Cloud Build on master. This function is called by the Cloud Scheduler around 4am "America/New York" time every day (configurable via the Cloud Scheduler UI).
-You can also trigger the function manually via the Cloud UI.
-
-Command to re-deploy:
-```sh
-gcloud functions deploy nightly_tfjs \
-  --runtime nodejs14 \
-  --trigger-topic nightly_tfjs
-```
-
-If a build was triggered by nightly, there is a substitution variable `_NIGHTLY=true`.
-You can forward the substitution as the `NIGHTLY` environment variable so the scripts can use it, by specifying `env: ['NIGHTLY=$_NIGHTLY']` in `cloudbuild.yml`. E.g. `integration_tests/benchmarks/benchmark_cloud.sh` uses the `NIGHTLY` bit to always run on nightly.
-
-### `send_email`
-Sends an email and a chat message with the nightly build status. Every build sends a message to the `cloud-builds` topic with its build information. The `send_email` function is subscribed to that topic and ignores all builds (e.g. builds triggered by pull requests) **except** for the nightly build and sends an email to an internal mailing list with its build status around 4:40am.
-
-Command to re-deploy:
-
-```sh
-gcloud functions deploy send_email \
-  --runtime nodejs14 \
-  --stage-bucket learnjs-174218_cloudbuild \
-  --trigger-topic cloud-builds \
-  --set-env-vars MAILGUN_API_KEY="[API_KEY_HERE]",HANGOUTS_URL="[URL_HERE]"
-```
-
-### `sync_reactnative`
-Makes a request to browserStack to sync the current build of the tfjs-react-native integration app to browserstack. The app itself is stored in a GCP bucket. This needs to be done at least once every 30 days and is triggered via cloud scheduler via the `sync_reactnative` topic.
-Currently set to run weekly on Thursdays at 3AM.
-
-Command to re-deploy:
-
-```sh
-gcloud functions deploy sync_reactnative \
-  --runtime nodejs14 \
-  --trigger-topic sync_reactnative \
-  --set-env-vars HANGOUTS_URL="[URL_HERE]",BOTS_HANGOUTS_URL="[URL_HERE]"
-```
-
-### The pipeline
-
-The pipeline looks like this:
-
-1) At 4am, Cloud Scheduler writes to `nightly_tfjs` topic
-2) That triggers the `nightly_tfjs` function, which starts a build programatically
-3) That build runs and writes its status to `cloud-builds` topic
-4) That triggers the `send_email` function, which sends email and chat with the build status.
diff --git a/tfjs-master/scripts/cloud_funcs/send_email/.gcloudignore b/tfjs-master/scripts/cloud_funcs/send_email/.gcloudignore
deleted file mode 100644
index ccc4eb240..000000000
--- a/tfjs-master/scripts/cloud_funcs/send_email/.gcloudignore
+++ /dev/null
@@ -1,16 +0,0 @@
-# This file specifies files that are *not* uploaded to Google Cloud Platform
-# using gcloud. It follows the same syntax as .gitignore, with the addition of
-# "#!include" directives (which insert the entries of the given .gitignore-style
-# file at that point).
-#
-# For more information, run:
-#   $ gcloud topic gcloudignore
-#
-.gcloudignore
-# If you would like to upload your .git directory, .gitignore file or files
-# from your .gitignore file, remove the corresponding line
-# below:
-.git
-.gitignore
-
-node_modules
diff --git a/tfjs-master/scripts/cloud_funcs/send_email/index.js b/tfjs-master/scripts/cloud_funcs/send_email/index.js
deleted file mode 100644
index 83fdfdcbd..000000000
--- a/tfjs-master/scripts/cloud_funcs/send_email/index.js
+++ /dev/null
@@ -1,71 +0,0 @@
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const humanizeDuration = require('humanize-duration');
-const request = require('request-promise-native');
-const fetch = require('node-fetch');
-
-
-const TRIGGER_ID = '43c56710-ccb3-4db9-b746-603cffbf0c02';
-
-// The main function called by Cloud Functions.
-module.exports.send_email = async event => {
-  // Parse the build information.
-  const build = JSON.parse(Buffer.from(event.data, 'base64').toString());
-  // Also added 'SUCCESS' to monitor successful builds.
-  const status = [
-    'SUCCESS', 'FAILURE', 'INTERNAL_ERROR', 'TIMEOUT', 'CANCELLED', 'FAILED'
-  ];
-  // Email only known status.
-  if (status.indexOf(build.status) === -1) {
-    return;
-  }
-  // Email only on nightly builds.
-  if (build.buildTriggerId !== TRIGGER_ID) {
-    return;
-  }
-
-  let duration =
-      humanizeDuration(new Date(build.finishTime) - new Date(build.startTime));
-  const msg = `${build.substitutions.REPO_NAME} nightly finished with status ` +
-      `${build.status}, in ${duration}.`;
-
-  await sendChatMsg(build, msg);
-};
-
-async function sendChatMsg(build, msg) {
-  let chatMsg = `${msg} <${build.logUrl}|See logs>.`;
-
-  const success = build.status === 'SUCCESS';
-
-  if (!success) {
-    const joke = (await (await fetch('https://icanhazdadjoke.com/', {
-                    headers: {'Accept': 'application/json'}
-                  })).json())
-                     .joke;
-    const jokeMsg = `Oh no! Failed builds are not fun... So here's a joke ` +
-        `to brighten your day :) -- ${joke}`;
-    chatMsg = `${chatMsg} ${jokeMsg}`;
-  }
-
-  const res = await request(process.env.HANGOUTS_URL, {
-    resolveWithFullResponse: true,
-    method: 'POST',
-    json: true,
-    body: {text: chatMsg},
-  });
-  console.log(`statusCode: ${res.statusCode}`);
-  console.log(res.body);
-}
diff --git a/tfjs-master/scripts/cloud_funcs/send_email/package.json b/tfjs-master/scripts/cloud_funcs/send_email/package.json
deleted file mode 100644
index 6f4e6e7e1..000000000
--- a/tfjs-master/scripts/cloud_funcs/send_email/package.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "name": "cloudbuild-email",
-  "version": "0.0.2",
-  "description": "Email integration for Google Cloud Build, using Google Cloud Functions",
-  "main": "index.js",
-  "dependencies": {
-    "humanize-duration": "3.10.0",
-    "node-fetch": "^2.6.7",
-    "request": "^2.88.0",
-    "request-promise-native": "^1.0.7"
-  }
-}
diff --git a/tfjs-master/scripts/cloud_funcs/send_email/yarn.lock b/tfjs-master/scripts/cloud_funcs/send_email/yarn.lock
deleted file mode 100644
index 31aa9d5aa..000000000
--- a/tfjs-master/scripts/cloud_funcs/send_email/yarn.lock
+++ /dev/null
@@ -1,377 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-ajv@^6.12.3:
-  version "6.12.6"
-  resolved "https://registry.npmjs.org/ajv/-/ajv-6.12.6.tgz"
-  integrity sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g==
-  dependencies:
-    fast-deep-equal "^3.1.1"
-    fast-json-stable-stringify "^2.0.0"
-    json-schema-traverse "^0.4.1"
-    uri-js "^4.2.2"
-
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-  version "0.2.4"
-  resolved "https://registry.npmjs.org/asn1/-/asn1-0.2.4.tgz"
-  integrity sha512-jxwzQpLQjSmWXgwaCZE9Nz+glAG01yF1QnWgbhGwHI5A6FRIEY6IVqtHhIepHqI7/kyEyQEagBC5mBEFlIYvdg==
-  dependencies:
-    safer-buffer "~2.1.0"
-
-assert-plus@1.0.0, assert-plus@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.npmjs.org/assert-plus/-/assert-plus-1.0.0.tgz"
-  integrity sha1-8S4PPF13sLHN2RRpQuTpbB5N1SU=
-
-asynckit@^0.4.0:
-  version "0.4.0"
-  resolved "https://registry.npmjs.org/asynckit/-/asynckit-0.4.0.tgz"
-  integrity sha1-x57Zf380y48robyXkLzDZkdLS3k=
-
-aws-sign2@~0.7.0:
-  version "0.7.0"
-  resolved "https://registry.npmjs.org/aws-sign2/-/aws-sign2-0.7.0.tgz"
-  integrity sha1-tG6JCTSpWR8tL2+G1+ap8bP+dqg=
-
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-  version "1.11.0"
-  resolved "https://registry.npmjs.org/aws4/-/aws4-1.11.0.tgz"
-  integrity sha512-xh1Rl34h6Fi1DC2WWKfxUTVqRsNnr6LsKz2+hfwDxQJWmrx8+c7ylaqBMcHfl1U1r2dsifOvKX3LQuLNZ+XSvA==
-
-bcrypt-pbkdf@^1.0.0:
-  version "1.0.2"
-  resolved "https://registry.npmjs.org/bcrypt-pbkdf/-/bcrypt-pbkdf-1.0.2.tgz"
-  integrity sha1-pDAdOJtqQ/m2f/PKEaP2Y342Dp4=
-  dependencies:
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-
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-  version "0.12.0"
-  resolved "https://registry.npmjs.org/caseless/-/caseless-0.12.0.tgz"
-  integrity sha1-G2gcIf+EAzyCZUMJBolCDRhxUdw=
-
-combined-stream@^1.0.6, combined-stream@~1.0.6:
-  version "1.0.8"
-  resolved "https://registry.npmjs.org/combined-stream/-/combined-stream-1.0.8.tgz"
-  integrity sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==
-  dependencies:
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-
-core-util-is@1.0.2:
-  version "1.0.2"
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-  integrity sha1-tf1UIgqivFq1eqtxQMlAdUUDwac=
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-  version "1.14.1"
-  resolved "https://registry.npmjs.org/dashdash/-/dashdash-1.14.1.tgz"
-  integrity sha1-hTz6D3y+L+1d4gMmuN1YEDX24vA=
-  dependencies:
-    assert-plus "^1.0.0"
-
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-  version "1.0.0"
-  resolved "https://registry.npmjs.org/delayed-stream/-/delayed-stream-1.0.0.tgz"
-  integrity sha1-3zrhmayt+31ECqrgsp4icrJOxhk=
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-  integrity sha1-4mifjzVvrWLMplo6kcXfX5VRaS8=
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-fast-json-stable-stringify@^2.0.0:
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-  integrity sha512-lhd/wF+Lk98HZoTCtlVraHtfh5XYijIjalXck7saUtuanSDyLMxnHhSXEDJqHxD7msR8D0uCmqlkwjCV8xvwHw==
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-  resolved "https://registry.npmjs.org/forever-agent/-/forever-agent-0.6.1.tgz"
-  integrity sha1-+8cfDEGt6zf5bFd60e1C2P2sypE=
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-  integrity sha512-1lLKB2Mu3aGP1Q/2eCOx0fNbRMe7XdwktwOruhfqqd0rIJWwN4Dh+E3hrPSlDCXnSR7UtZ1N38rVXm+6+MEhJQ==
-  dependencies:
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-    combined-stream "^1.0.6"
-    mime-types "^2.1.12"
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-  version "0.1.7"
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-  integrity sha1-Xv+OPmhNVprkyysSgmBOi6YhSfo=
-  dependencies:
-    assert-plus "^1.0.0"
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-  integrity sha1-qUwiJOvKwEeCoNkDVSHyRzW37JI=
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-  dependencies:
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-    har-schema "^2.0.0"
-
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-  integrity sha1-muzZJRFHcvPZW2WmCruPfBj7rOE=
-  dependencies:
-    assert-plus "^1.0.0"
-    jsprim "^1.2.2"
-    sshpk "^1.7.0"
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-    tough-cookie "^2.3.3"
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-    aws4 "^1.8.0"
-    caseless "~0.12.0"
-    combined-stream "~1.0.6"
-    extend "~3.0.2"
-    forever-agent "~0.6.1"
-    form-data "~2.3.2"
-    har-validator "~5.1.3"
-    http-signature "~1.2.0"
-    is-typedarray "~1.0.0"
-    isstream "~0.1.2"
-    json-stringify-safe "~5.0.1"
-    mime-types "~2.1.19"
-    oauth-sign "~0.9.0"
-    performance-now "^2.1.0"
-    qs "~6.5.2"
-    safe-buffer "^5.1.2"
-    tough-cookie "~2.5.0"
-    tunnel-agent "^0.6.0"
-    uuid "^3.3.2"
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-    assert-plus "^1.0.0"
-    bcrypt-pbkdf "^1.0.0"
-    dashdash "^1.12.0"
-    ecc-jsbn "~0.1.1"
-    getpass "^0.1.1"
-    jsbn "~0.1.0"
-    safer-buffer "^2.0.2"
-    tweetnacl "~0.14.0"
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-  version "1.1.1"
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diff --git a/tfjs-master/scripts/cloud_funcs/sync_reactnative/.gcloudignore b/tfjs-master/scripts/cloud_funcs/sync_reactnative/.gcloudignore
deleted file mode 100644
index ccc4eb240..000000000
--- a/tfjs-master/scripts/cloud_funcs/sync_reactnative/.gcloudignore
+++ /dev/null
@@ -1,16 +0,0 @@
-# This file specifies files that are *not* uploaded to Google Cloud Platform
-# using gcloud. It follows the same syntax as .gitignore, with the addition of
-# "#!include" directives (which insert the entries of the given .gitignore-style
-# file at that point).
-#
-# For more information, run:
-#   $ gcloud topic gcloudignore
-#
-.gcloudignore
-# If you would like to upload your .git directory, .gitignore file or files
-# from your .gitignore file, remove the corresponding line
-# below:
-.git
-.gitignore
-
-node_modules
diff --git a/tfjs-master/scripts/cloud_funcs/sync_reactnative/index.js b/tfjs-master/scripts/cloud_funcs/sync_reactnative/index.js
deleted file mode 100644
index 7d20ac418..000000000
--- a/tfjs-master/scripts/cloud_funcs/sync_reactnative/index.js
+++ /dev/null
@@ -1,70 +0,0 @@
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const kms = require('@google-cloud/kms');
-const request = require('request-promise-native');
-
-const projectId = 'learnjs-174218';
-const locationId = 'global';
-const keyRingId = 'tfjs';
-const cryptoKeyId = 'enc'
-const ciphertext =
-    'CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=';
-const browserStackUploadUrl =
-    'https://api-cloud.browserstack.com/app-automate/upload';
-const browserStackUser = 'deeplearnjs1';
-const testAppUrl =
-    'https://storage.googleapis.com/tfjs-rn/integration-tests/app-debug.apk';
-const appUploadId = 'tfjs-rn-integration-android';
-
-async function sync_reactnative(event, context, callback) {
-  const client = new kms.KeyManagementServiceClient();
-  const name =
-      client.cryptoKeyPath(projectId, locationId, keyRingId, cryptoKeyId);
-
-  const [result] = await client.decrypt({name, ciphertext});
-  const browserStackKey = result.plaintext.toString();
-
-  try {
-    const syncRes = await request.post(browserStackUploadUrl, {
-      auth: {
-        user: browserStackUser,
-        pass: browserStackKey,
-      },
-      form: {
-        data: JSON.stringify({'url': testAppUrl, 'custom_id': appUploadId}),
-      }
-    });
-    sendChatMsg(
-        process.env.BOTS_HANGOUTS_URL,
-        'Success syncing tfjs-react-native integration test app to BrowserStack');
-  } catch (e) {
-    console.log('Error syncing app to browserstack', e);
-    sendChatMsg(
-        process.env.HANGOUTS_URL,
-        'Error syncing tfjs-react-native integration test app to BrowserStack');
-  }
-};
-
-async function sendChatMsg(url, msg) {
-  const res = await request(url, {
-    resolveWithFullResponse: true,
-    method: 'POST',
-    json: true,
-    body: {text: msg},
-  });
-}
-
-module.exports.sync_reactnative = sync_reactnative;
diff --git a/tfjs-master/scripts/cloud_funcs/sync_reactnative/package.json b/tfjs-master/scripts/cloud_funcs/sync_reactnative/package.json
deleted file mode 100644
index b125bad44..000000000
--- a/tfjs-master/scripts/cloud_funcs/sync_reactnative/package.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "name": "cloudfunc-tfjs-rn",
-  "version": "1.0.0",
-  "description": "",
-  "main": "index.js",
-  "scripts": {
-    "start": "functions-framework --target=sync_reactnative"
-  },
-  "dependencies": {
-    "@google-cloud/functions-framework": "^1.3.2",
-    "@google-cloud/kms": "^1.5.3",
-    "request": "^2.88.0",
-    "request-promise-native": "^1.0.8"
-  }
-}
diff --git a/tfjs-master/scripts/cloud_funcs/trigger_nightly/.gcloudignore b/tfjs-master/scripts/cloud_funcs/trigger_nightly/.gcloudignore
deleted file mode 100644
index ccc4eb240..000000000
--- a/tfjs-master/scripts/cloud_funcs/trigger_nightly/.gcloudignore
+++ /dev/null
@@ -1,16 +0,0 @@
-# This file specifies files that are *not* uploaded to Google Cloud Platform
-# using gcloud. It follows the same syntax as .gitignore, with the addition of
-# "#!include" directives (which insert the entries of the given .gitignore-style
-# file at that point).
-#
-# For more information, run:
-#   $ gcloud topic gcloudignore
-#
-.gcloudignore
-# If you would like to upload your .git directory, .gitignore file or files
-# from your .gitignore file, remove the corresponding line
-# below:
-.git
-.gitignore
-
-node_modules
diff --git a/tfjs-master/scripts/cloud_funcs/trigger_nightly/index.js b/tfjs-master/scripts/cloud_funcs/trigger_nightly/index.js
deleted file mode 100644
index 928a5e4a7..000000000
--- a/tfjs-master/scripts/cloud_funcs/trigger_nightly/index.js
+++ /dev/null
@@ -1,29 +0,0 @@
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const {google} = require('googleapis');
-
-module.exports.nightly_tfjs = async data => {
-  const cloudbuild = google.cloudbuild('v1');
-  const auth = await google.auth.getClient(
-      {scopes: ['https://www.googleapis.com/auth/cloud-platform']});
-  google.options({auth});
-  const resp = await cloudbuild.projects.triggers.run({
-    'projectId': 'learnjs-174218',
-    'triggerId': '43c56710-ccb3-4db9-b746-603cffbf0c02',
-    'resource': {'branchName': 'master'}
-  });
-  console.log(resp);
-};
diff --git a/tfjs-master/scripts/cloud_funcs/trigger_nightly/package.json b/tfjs-master/scripts/cloud_funcs/trigger_nightly/package.json
deleted file mode 100644
index b31eb43eb..000000000
--- a/tfjs-master/scripts/cloud_funcs/trigger_nightly/package.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-  "name": "cloudbuild",
-  "version": "1.0.0",
-  "description": "",
-  "main": "index.js",
-  "dependencies": {
-    "googleapis": "^68.0.0"
-  }
-}
diff --git a/tfjs-master/scripts/cloudbuild_e2e_expected.yml b/tfjs-master/scripts/cloudbuild_e2e_expected.yml
deleted file mode 100644
index 917d66e64..000000000
--- a/tfjs-master/scripts/cloudbuild_e2e_expected.yml
+++ /dev/null
@@ -1,227 +0,0 @@
-steps:
-  - name: gcr.io/learnjs-174218/release
-    entrypoint: yarn
-    id: yarn-common
-    args:
-      - install
-  - name: gcr.io/learnjs-174218/release
-    dir: scripts
-    id: test-generate-cloudbuild
-    entrypoint: yarn
-    args:
-      - test-generate-cloudbuild
-    waitFor:
-      - yarn-common
-  - name: gcr.io/learnjs-174218/release
-    id: test-run-flaky
-    entrypoint: yarn
-    args:
-      - test-run-flaky
-    waitFor:
-      - yarn-common
-  - name: gcr.io/learnjs-174218/release
-    id: buildifier
-    entrypoint: yarn
-    args:
-      - buildifier-ci
-    waitFor:
-      - yarn-common
-  - name: gcr.io/learnjs-174218/release
-    id: tslint
-    entrypoint: yarn
-    args:
-      - lint
-    waitFor:
-      - yarn-common
-  - name: gcr.io/learnjs-174218/release
-    id: bazel-tests
-    waitFor:
-      - yarn-common
-    entrypoint: bash
-    args:
-      - ./scripts/run_bazel_ci_tests.sh
-    env:
-      - BROWSERSTACK_USERNAME=deeplearnjs1
-      - NIGHTLY=$_NIGHTLY
-    secretEnv:
-      - BROWSERSTACK_KEY
-  - name: gcr.io/learnjs-174218/release
-    dir: link-package
-    entrypoint: yarn
-    id: yarn-link-package
-    args:
-      - install
-    waitFor:
-      - bazel-tests
-  - name: gcr.io/learnjs-174218/release
-    dir: link-package
-    entrypoint: yarn
-    id: yarn-link-package-build
-    args:
-      - build
-      - '--bazel_options=--config=ci'
-    waitFor:
-      - yarn-link-package
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs
-    entrypoint: yarn
-    id: yarn-tfjs
-    args:
-      - install
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs
-    entrypoint: yarn
-    id: build-tfjs
-    args:
-      - build-ci
-    waitFor:
-      - yarn-tfjs
-      - yarn-common
-      - yarn-link-package-build
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs
-    entrypoint: yarn
-    id: lint-tfjs
-    args:
-      - lint
-    waitFor:
-      - yarn-tfjs
-      - yarn-common
-      - yarn-link-package-build
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: yarn-tfjs-node
-    args:
-      - install
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: build-addon-tfjs-node
-    args:
-      - build-addon-from-source
-    waitFor:
-      - yarn-tfjs-node
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: build-tfjs-node
-    args:
-      - build-ci
-    waitFor:
-      - yarn-tfjs-node
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: lint-tfjs-node
-    args:
-      - lint
-    waitFor:
-      - yarn-tfjs-node
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: ensure-cpu-gpu-packages-align-tfjs-node
-    args:
-      - ensure-cpu-gpu-packages-align
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: e2e
-    entrypoint: yarn
-    id: yarn-e2e
-    args:
-      - install
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-      - yarn-tfjs-node
-      - build-addon-tfjs-node
-      - build-tfjs-node
-      - lint-tfjs-node
-      - ensure-cpu-gpu-packages-align-tfjs-node
-  - name: gcr.io/learnjs-174218/release
-    dir: e2e
-    entrypoint: yarn
-    id: fetch-graph-model-golden-data-e2e
-    args:
-      - fetch-graph-model-golden-data-ci
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-      - yarn-tfjs-node
-      - build-addon-tfjs-node
-      - build-tfjs-node
-      - lint-tfjs-node
-      - ensure-cpu-gpu-packages-align-tfjs-node
-  - name: gcr.io/learnjs-174218/release
-    dir: e2e
-    entrypoint: yarn
-    id: test-e2e
-    args:
-      - test-ci
-    env:
-      - BROWSERSTACK_USERNAME=deeplearnjs1
-      - NIGHTLY=$_NIGHTLY
-    secretEnv:
-      - BROWSERSTACK_KEY
-    waitFor:
-      - yarn-e2e
-      - fetch-graph-model-golden-data-e2e
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-      - yarn-tfjs-node
-      - build-addon-tfjs-node
-      - build-tfjs-node
-      - lint-tfjs-node
-      - ensure-cpu-gpu-packages-align-tfjs-node
-secrets:
-  - kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-    secretEnv:
-      BROWSERSTACK_KEY: >-
-        CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-timeout: 7200s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: STREAM_ON
-  machineType: E2_HIGHCPU_32
-  substitution_option: ALLOW_LOOSE
diff --git a/tfjs-master/scripts/cloudbuild_general_config.yml b/tfjs-master/scripts/cloudbuild_general_config.yml
deleted file mode 100644
index a7d7b2491..000000000
--- a/tfjs-master/scripts/cloudbuild_general_config.yml
+++ /dev/null
@@ -1,86 +0,0 @@
-steps:
-# Install top-level deps.
-- name: 'gcr.io/learnjs-174218/release'
-  entrypoint: 'yarn'
-  id: 'yarn-common'
-  args: ['install']
-  waitedForByPackages: true
-
-# Test generate_cloudbuild.js
-# See #4523 for why this is special-cased into every cloudbuild.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'scripts'
-  id: 'test-generate-cloudbuild'
-  entrypoint: 'yarn'
-  args: ['test-generate-cloudbuild']
-  waitFor: ['yarn-common']
-
-# Test run_flaky.js
-# The flaky test runner is important enough to test every time and takes less
-# than 5 seconds.
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'test-run-flaky'
-  entrypoint: 'yarn'
-  args: ['test-run-flaky']
-  waitFor: ['yarn-common']
-
-# Lint bazel files.
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'buildifier'
-  entrypoint: 'yarn'
-  args: ['buildifier-ci']
-  waitFor: ['yarn-common']
-
-# Lint Bazel package typescript files.
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'tslint'
-  entrypoint: 'yarn'
-  args: ['lint']
-  waitFor: ['yarn-common']
-
-# Bazel tests
-# These use a remote cache and only re-run if changes occurred, so we run them
-# in every build.
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'bazel-tests'
-  waitFor: ['yarn-common']
-  entrypoint: 'bash'
-  args:
-    - './scripts/run_bazel_ci_tests.sh'
-  env:
-    - 'BROWSERSTACK_USERNAME=deeplearnjs1'
-    - 'NIGHTLY=$_NIGHTLY'
-  secretEnv: ['BROWSERSTACK_KEY']
-
-# The following step builds the link package, which is a temporary package
-# that helps packages that don't build with Bazel load outputs from packages
-# that build with Bazel.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'link-package'
-  entrypoint: 'yarn'
-  id: 'yarn-link-package'
-  args: ['install']
-  waitFor: ['bazel-tests']
-
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'link-package'
-  entrypoint: 'yarn'
-  id: 'yarn-link-package-build'
-  args: ['build', '--bazel_options=--config=ci']
-  waitFor: ['yarn-link-package']
-  waitedForByPackages: true
-
-# General configuration
-secrets:
-- kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-  secretEnv:
-    BROWSERSTACK_KEY: CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-    FIREBASE_KEY: CiQAkwyoIXmET39jOD3ywloCIa6+WUpu3w49twpMmkMqy0vS+YsSUAAD8BdZQGOL8FKEBxr/1jl0G78OigwlNVHjD3usZobNtlOp8tV/9iacb8zPFqy0SwIO1gvz3HRr+VU7c7LS2qqaTCdacZF+dx3VJNewvdZu
-timeout: 7200s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'E2_HIGHCPU_32'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/scripts/cloudbuild_tfjs_node_expected.yml b/tfjs-master/scripts/cloudbuild_tfjs_node_expected.yml
deleted file mode 100644
index 72cb08b64..000000000
--- a/tfjs-master/scripts/cloudbuild_tfjs_node_expected.yml
+++ /dev/null
@@ -1,242 +0,0 @@
-steps:
-  - name: gcr.io/learnjs-174218/release
-    entrypoint: yarn
-    id: yarn-common
-    args:
-      - install
-  - name: gcr.io/learnjs-174218/release
-    dir: scripts
-    id: test-generate-cloudbuild
-    entrypoint: yarn
-    args:
-      - test-generate-cloudbuild
-    waitFor:
-      - yarn-common
-  - name: gcr.io/learnjs-174218/release
-    id: test-run-flaky
-    entrypoint: yarn
-    args:
-      - test-run-flaky
-    waitFor:
-      - yarn-common
-  - name: gcr.io/learnjs-174218/release
-    id: buildifier
-    entrypoint: yarn
-    args:
-      - buildifier-ci
-    waitFor:
-      - yarn-common
-  - name: gcr.io/learnjs-174218/release
-    id: tslint
-    entrypoint: yarn
-    args:
-      - lint
-    waitFor:
-      - yarn-common
-  - name: gcr.io/learnjs-174218/release
-    id: bazel-tests
-    waitFor:
-      - yarn-common
-    entrypoint: bash
-    args:
-      - ./scripts/run_bazel_ci_tests.sh
-    env:
-      - BROWSERSTACK_USERNAME=deeplearnjs1
-      - NIGHTLY=$_NIGHTLY
-    secretEnv:
-      - BROWSERSTACK_KEY
-  - name: gcr.io/learnjs-174218/release
-    dir: link-package
-    entrypoint: yarn
-    id: yarn-link-package
-    args:
-      - install
-    waitFor:
-      - bazel-tests
-  - name: gcr.io/learnjs-174218/release
-    dir: link-package
-    entrypoint: yarn
-    id: yarn-link-package-build
-    args:
-      - build
-      - '--bazel_options=--config=ci'
-    waitFor:
-      - yarn-link-package
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs
-    entrypoint: yarn
-    id: yarn-tfjs
-    args:
-      - install
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs
-    entrypoint: yarn
-    id: build-tfjs
-    args:
-      - build-ci
-    waitFor:
-      - yarn-tfjs
-      - yarn-common
-      - yarn-link-package-build
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs
-    entrypoint: yarn
-    id: lint-tfjs
-    args:
-      - lint
-    waitFor:
-      - yarn-tfjs
-      - yarn-common
-      - yarn-link-package-build
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: yarn-tfjs-node
-    args:
-      - install
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: build-addon-tfjs-node
-    args:
-      - build-addon-from-source
-    waitFor:
-      - yarn-tfjs-node
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: build-tfjs-node
-    args:
-      - build-ci
-    waitFor:
-      - yarn-tfjs-node
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: lint-tfjs-node
-    args:
-      - lint
-    waitFor:
-      - yarn-tfjs-node
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: test-tfjs-node
-    args:
-      - test-ci
-    waitFor:
-      - yarn-tfjs-node
-      - build-addon-tfjs-node
-      - lint-tfjs-node
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: tfjs-node
-    entrypoint: yarn
-    id: ensure-cpu-gpu-packages-align-tfjs-node
-    args:
-      - ensure-cpu-gpu-packages-align
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-  - name: gcr.io/learnjs-174218/release
-    dir: e2e
-    entrypoint: yarn
-    id: yarn-e2e
-    args:
-      - install
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-      - yarn-tfjs-node
-      - build-addon-tfjs-node
-      - build-tfjs-node
-      - lint-tfjs-node
-      - ensure-cpu-gpu-packages-align-tfjs-node
-  - name: gcr.io/learnjs-174218/release
-    dir: e2e
-    entrypoint: yarn
-    id: fetch-graph-model-golden-data-e2e
-    args:
-      - fetch-graph-model-golden-data-ci
-    waitFor:
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-      - yarn-tfjs-node
-      - build-addon-tfjs-node
-      - build-tfjs-node
-      - lint-tfjs-node
-      - ensure-cpu-gpu-packages-align-tfjs-node
-  - name: gcr.io/learnjs-174218/release
-    dir: e2e
-    entrypoint: yarn
-    id: test-e2e
-    args:
-      - test-ci
-    env:
-      - BROWSERSTACK_USERNAME=deeplearnjs1
-      - NIGHTLY=$_NIGHTLY
-    secretEnv:
-      - BROWSERSTACK_KEY
-    waitFor:
-      - yarn-e2e
-      - fetch-graph-model-golden-data-e2e
-      - yarn-common
-      - yarn-link-package-build
-      - yarn-tfjs
-      - build-tfjs
-      - lint-tfjs
-      - yarn-tfjs-node
-      - build-addon-tfjs-node
-      - build-tfjs-node
-      - lint-tfjs-node
-      - ensure-cpu-gpu-packages-align-tfjs-node
-secrets:
-  - kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-    secretEnv:
-      BROWSERSTACK_KEY: >-
-        CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-timeout: 7200s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: STREAM_ON
-  machineType: E2_HIGHCPU_32
-  substitution_option: ALLOW_LOOSE
diff --git a/tfjs-master/scripts/find_packages_with_diff.js b/tfjs-master/scripts/find_packages_with_diff.js
deleted file mode 100644
index 28810ca14..000000000
--- a/tfjs-master/scripts/find_packages_with_diff.js
+++ /dev/null
@@ -1,124 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const {exec} = require('./test-util');
-const shell = require('shelljs');
-const {readFileSync} = require('fs');
-const {join} = require('path');
-
-
-const filesAllowlistToTriggerBuild = [
-  'cloudbuild.yml', 'package.json', 'tsconfig.json', 'tslint.json',
-  'scripts/find_packages_with_diff.js', 'scripts/run-build.sh',
-  'scripts/generate_cloudbuild.js'
-];
-
-const CLONE_PATH = '/tmp/tfjs-diff-clone';
-let commitSha = process.env['COMMIT_SHA'];
-let branchName = process.env['BRANCH_NAME'];
-let baseBranch = process.env['BASE_BRANCH'];
-
-
-const packageDependencies = JSON.parse(readFileSync(
-    join(__dirname, 'package_dependencies.json'), 'utf8'));
-const allPackages = Object.keys(packageDependencies);
-
-
-function findPackagesWithDiff() {
-  // For Nightly build, baseBranch is one of the falsey values. We use master
-  // for Nightly build.
-  if (!baseBranch) {
-    baseBranch = 'master';
-  }
-  console.log('commitSha: ', commitSha);
-  console.log('branchName: ', branchName);
-  console.log('baseBranch: ', baseBranch);
-
-  // We cannot do --depth=1 here because we need to check out an old merge base.
-  // We cannot do --single-branch here because we need multiple branches.
-  console.log(`Clone branch ${baseBranch}`);
-  shell.rm('-rf', CLONE_PATH);
-  exec(`git clone -b ${baseBranch} https://github.com/tensorflow/tfjs ${
-      CLONE_PATH}`);
-
-  console.log();  // Break up the console for readability.
-
-  const originalPath = process.cwd();
-  shell.cd(CLONE_PATH);
-
-  // If we cannot check out the commit then this PR is coming from a fork.
-  const res = shell.exec(`git checkout ${commitSha}`, {silent: true});
-  const isPullRequestFromFork = res.code !== 0;
-
-  // Only checkout the merge base if the pull requests comes from a
-  // tensorflow/tfjs branch. Otherwise clone master and diff against master.
-  if (!isPullRequestFromFork) {
-    console.log('PR is coming from tensorflow/tfjs. Finding the merge base...');
-    exec(`git checkout ${branchName}`);
-    const mergeBase =
-        exec(`git merge-base ${baseBranch} ${branchName}`).stdout.trim();
-    exec(`git fetch origin ${mergeBase}`);
-    exec(`git checkout ${mergeBase}`);
-    console.log('mergeBase: ', mergeBase);
-  } else {
-    console.log(`PR is going to diff against branch ${baseBranch}.`);
-  }
-  shell.cd(originalPath);
-  console.log();  // Break up the console for readability.
-
-  let triggerAllBuilds = false;
-  let allowlistDiffOutput = [];
-  filesAllowlistToTriggerBuild.forEach(fileToTriggerBuild => {
-    const diffOutput = diff(fileToTriggerBuild);
-    if (diffOutput !== '') {
-      console.log(fileToTriggerBuild, 'has changed. Triggering all builds.');
-      triggerAllBuilds = true;
-      allowlistDiffOutput.push(diffOutput);
-    }
-  });
-
-  console.log();  // Break up the console for readability.
-
-  let packagesWithDiff = [];
-  allPackages.forEach(dir => {
-    const diffOutput = diff(`${dir}/`);
-    if (diffOutput !== '') {
-      console.log(`${dir} has modified files.`);
-    } else {
-      console.log(`No modified files found in ${dir}`);
-    }
-
-    const shouldDiff = diffOutput !== '' || triggerAllBuilds;
-    if (shouldDiff) {
-      packagesWithDiff.push(dir);
-    }
-  });
-
-  console.log();  // Break up the console for readability.
-
-  console.log(`Packages directly affected: ${packagesWithDiff.join(', ')}`);
-  return packagesWithDiff;
-}
-
-function diff(fileOrDirName) {
-  const diffCmd = `diff -rq --exclude='settings.json' ` +
-      `${CLONE_PATH}/${fileOrDirName} ` +
-      `${join(__dirname, '../', fileOrDirName)}`;
-  return exec(diffCmd, {silent: true}, true).stdout.trim();
-}
-
-exports.findPackagesWithDiff = findPackagesWithDiff;
-exports.allPackages = allPackages;
diff --git a/tfjs-master/scripts/generate_cloudbuild.ts b/tfjs-master/scripts/generate_cloudbuild.ts
deleted file mode 100644
index d9450cf8b..000000000
--- a/tfjs-master/scripts/generate_cloudbuild.ts
+++ /dev/null
@@ -1,223 +0,0 @@
-// Copyright 2020 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-import {printTable} from 'console-table-printer';
-import * as fs from 'fs';
-import * as yaml from 'js-yaml';
-import * as path from 'path';
-import {BAZEL_PACKAGES} from './bazel_packages';
-import {DEPENDENCY_GRAPH, DEPENDENCY_ORDER, findDeps, findReverseDeps} from './graph_utils';
-
-// Steps to exclude from cloudbuild files.
-const EXCLUDE_STEPS = new Set(['build-deps', 'yarn-common']);
-
-interface CloudbuildStep {
-  name: string,
-  id: string,
-  waitFor?: string[],
-  secretEnv?: string[];
-}
-
-const CUSTOM_PROPS = new Set(['nightlyOnly', 'waitedForByPackages']);
-interface CustomCloudbuildStep extends CloudbuildStep {
-  nightlyOnly?: boolean; // Only run during nightly tests
-  waitedForByPackages?: boolean; // Other non-bazel pacakges `waitFor` this step
-}
-
-function removeCustomProps(step: CustomCloudbuildStep): CloudbuildStep {
-  return Object.fromEntries(
-    Object.entries(step).filter(([k, ]) => !CUSTOM_PROPS.has(k))
-  ) as CloudbuildStep;
-}
-
-interface CloudbuildSecret {
-  kmsKeyName: string,
-  secretEnv: {
-    [index: string]: string,
-  }
-}
-
-export interface CloudbuildYaml {
-  steps: CustomCloudbuildStep[],
-  secrets: CloudbuildSecret[],
-}
-
-
-/**
- * Construct a cloudbuild.yml file that does the following:
- * 1. Builds all the dependencies of `packages`
- * 2. Builds and tests all the packages in `packages`
- * 3. Builds and tests all the reverse dependnecies of `packages`
- */
-export function generateCloudbuild(packages: Iterable<string>, nightly = false, print = true) {
-  // Make sure all packages are declared in package_dependencies.json.
-  const allPackages = new Set(Object.keys(DEPENDENCY_GRAPH));
-  for (const packageName of packages) {
-    if (!allPackages.has(packageName) &&
-        // TODO: remove this check once tfjs-react-native nightly test is fixed.
-        packageName !== 'tfjs-react-native') {
-      throw new Error(
-          `Package ${packageName} was not declared in ` +
-          'package_dependencies.json');
-    }
-  }
-
-  const deps = findDeps(packages);
-  const reverseDeps = findReverseDeps(packages);
-  const depsOfReverseDeps = findDeps(reverseDeps);
-
-  const toBuild =
-      new Set([...deps, ...packages, ...reverseDeps, ...depsOfReverseDeps]);
-  const toTest = new Set([...packages, ...reverseDeps]);
-
-  if (print) {
-    // Log what will be built and tested
-    const buildTestTable = [];
-    for (const packageName of allPackages) {
-      const bazel = BAZEL_PACKAGES.has(packageName);
-      const bazelStr = 'bazel      '; // Spaces for left alignment
-      buildTestTable.push({
-        'Package': packageName,
-        'Will Build': bazel ? bazelStr : toBuild.has(packageName) ? '✔' : '',
-        'Will Test': bazel ? bazelStr : toTest.has(packageName) ? '✔' : '',
-      });
-    }
-    printTable(buildTestTable);
-  }
-
-  // Load the general cloudbuild config
-  const baseCloudbuild =
-    yaml.load(fs.readFileSync(path.join(
-      __dirname, 'cloudbuild_general_config.yml'), 'utf8')) as CloudbuildYaml;
-
-  // Filter steps that only run in nightly tests.
-  const nightlyFilter = (step: CustomCloudbuildStep) => nightly || !step.nightlyOnly;
-  const customSteps = baseCloudbuild.steps.filter(nightlyFilter);
-
-  // Steps that are waited for by non-bazel packages.
-  const waitedForByPackages = customSteps
-    .filter(step => step.waitedForByPackages)
-    .map(step => step.id);
-
-  const steps = customSteps.map(removeCustomProps);
-
-  // Load all the cloudbuild files for the packages
-  // that need to be built or tested.
-  const packageCloudbuildSteps = new Map<string, Set<CloudbuildStep>>();
-  for (const packageName of new Set([...toBuild, ...toTest])) {
-    if (BAZEL_PACKAGES.has(packageName)) {
-      // Do not build or test Bazel packages. The bazel-tests step does this.
-      continue;
-    }
-    const doc = yaml.load(
-      fs.readFileSync(path.join(__dirname, '../', packageName,
-                                'cloudbuild.yml'), 'utf8')) as CloudbuildYaml;
-    packageCloudbuildSteps.set(packageName, new Set(doc.steps));
-  }
-
-  // Filter out excluded steps. Also remove test steps if the package is
-  // not going to be tested. Change step ids to avoid name conflicts.
-  for (const [packageName, steps] of packageCloudbuildSteps.entries()) {
-    // TODO(msoulanille): Steps that depend on excluded steps might still
-    // need to wait for the steps that the excluded steps wait for.
-    for (const step of steps) {
-      if (!step.id) {
-        throw new Error(`Step from ${packageName} missing id`);
-      }
-
-      // Exclude a specific set of steps defined in `excludeSteps`.
-      // Only include test steps if the package
-      // is to be tested.
-      if (EXCLUDE_STEPS.has(step.id) ||
-          (!toTest.has(packageName) && isTestStep(step.id))) {
-        steps.delete(step);
-        continue;
-      }
-
-      // Append package name to each step's id.
-      if (step.id) {
-        // Test steps are not required to have ids.
-        step.id = makeStepId(step.id, packageName);
-      }
-
-      // Append package names to step ids in the 'waitFor' field.
-      if (step.waitFor) {
-        step.waitFor = step.waitFor.filter(id => id && !EXCLUDE_STEPS.has(id))
-                           .map(id => makeStepId(id, packageName));
-      }
-    }
-  }
-
-  // Set 'waitFor' fields based on dependencies.
-  for (const [packageName, steps] of packageCloudbuildSteps.entries()) {
-    // Construct the set of step ids that rules in this package must wait for.
-    // All packages depend on 'yarn-common' and 'yarn-link-package-build', so
-    // we special-case them here.
-    const waitForSteps = new Set(waitedForByPackages);
-    for (const dependencyName of (DEPENDENCY_GRAPH[packageName] || new Set())) {
-      const cloudbuildSteps =
-          packageCloudbuildSteps.get(dependencyName) || new Set();
-
-      for (const step of cloudbuildSteps) {
-        if (!isTestStep(step.id)) {
-          waitForSteps.add(step.id);
-        }
-      }
-    }
-
-    // Add the above step ids to the `waitFor` field in each step.
-    for (const step of steps) {
-      step.waitFor = [...new Set([...(step.waitFor || []), ...waitForSteps])]
-    }
-  }
-
-  // Arrange steps in dependency order
-  for (const packageName of DEPENDENCY_ORDER) {
-    const packageSteps = packageCloudbuildSteps.get(packageName);
-    if (packageSteps) {
-      for (const step of packageSteps) {
-        steps.push(step);
-      }
-    }
-  }
-
-  // Remove unused secrets. Cloudbuild fails if extra secrets are included.
-  const usedSecrets = new Set();
-  for (const step of steps) {
-    for (const secret of step.secretEnv || []) {
-      usedSecrets.add(secret);
-    }
-  }
-  const secretEnv = baseCloudbuild.secrets[0].secretEnv;
-  for (const secret of Object.keys(secretEnv)) {
-    if (!usedSecrets.has(secret)) {
-      delete secretEnv[secret];
-    }
-  }
-  if (Object.keys(secretEnv).length === 0) {
-    delete baseCloudbuild.secrets;
-  }
-
-  baseCloudbuild.steps = steps;
-  return baseCloudbuild;
-}
-
-function isTestStep(id: string) {
-  return id.includes('test');
-}
-
-function makeStepId(id: string, packageName: string) {
-  return `${id}-${packageName}`;
-}
diff --git a/tfjs-master/scripts/generate_cloudbuild_for_packages.ts b/tfjs-master/scripts/generate_cloudbuild_for_packages.ts
deleted file mode 100644
index b63c0377b..000000000
--- a/tfjs-master/scripts/generate_cloudbuild_for_packages.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const {findPackagesWithDiff, allPackages} =
-    require('./find_packages_with_diff.js');
-const {generateCloudbuild} = require('./generate_cloudbuild');
-const {ArgumentParser} = require('argparse');
-const yaml = require('js-yaml');
-const fs = require('fs');
-
-const parser = new ArgumentParser({
-  description: 'Generate a cloudbuild file to test packages. When run' +
-      ' with no arguments, tests packages affected by the current' +
-      ' changes.'
-});
-
-parser.addArgument('packages', {
-  type: String,
-  nargs: '*',
-  help: 'packages to consider as having changed',
-});
-
-parser.addArgument(['-o', '--output'], {
-  type: String,
-  nargs: '?',
-  defaultValue: 'cloudbuild_generated.yml',
-});
-
-const args = parser.parseArgs(process.argv.slice(2));
-
-let packages;
-const nightly = process.env['NIGHTLY']
-if (args.packages.length > 0) {
-  // Test packages specified in command line args.
-  packages = args.packages;
-} else if (nightly) {
-  // Test all packages during the nightly build.
-  packages = allPackages;
-} else {
-  // Test packages that have changed.
-  packages = findPackagesWithDiff();
-}
-
-const cloudbuild = generateCloudbuild(packages, nightly);
-fs.writeFileSync(args.output, yaml.safeDump(cloudbuild));
diff --git a/tfjs-master/scripts/generate_cloudbuild_test.ts b/tfjs-master/scripts/generate_cloudbuild_test.ts
deleted file mode 100644
index 5cf578dbf..000000000
--- a/tfjs-master/scripts/generate_cloudbuild_test.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-// Copyright 2020 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-import { generateCloudbuild, CloudbuildYaml} from './generate_cloudbuild';
-//const generateCloudbuild = require('./generate_cloudbuild').generateCloudbuild;
-import * as yaml from 'js-yaml';
-import * as fs from 'fs';
-import * as path from 'path';
-
-// These tests only detect changes in the generated file, and may need to be
-// updated if the project structure changes. To update them, run
-// 'yarn update-cloudbuild-tests`.
-// TODO(mattsoulanille): When Jasmine is updated to >=3.3.0, Use
-// jasmine.withContext to show the above message if the tests fail.
-describe('generateCloudbuild', () => {
-  it('generates the correct cloudbuild file for e2e', () => {
-    const expectedCloudbuild = yaml.load(fs.readFileSync(
-      path.join(__dirname, 'cloudbuild_e2e_expected.yml'), 'utf8'));
-    const cloudbuild = generateCloudbuild(['e2e'], /* nightly */ false,
-                                          /* print */ false);
-    expect(cloudbuild).toEqual(expectedCloudbuild as CloudbuildYaml);
-  });
-
-  it('generates the correct cloudbuild file for tfjs-node', () => {
-    const expectedCloudbuild = yaml.load(fs.readFileSync(
-      path.join(__dirname, 'cloudbuild_tfjs_node_expected.yml'), 'utf8'));
-    const cloudbuild = generateCloudbuild(['tfjs-node'], /* nightly */ false,
-                                          /* print */ false);
-    expect(cloudbuild).toEqual(expectedCloudbuild as CloudbuildYaml);
-  });
-});
diff --git a/tfjs-master/scripts/graph_utils.ts b/tfjs-master/scripts/graph_utils.ts
deleted file mode 100644
index f19b02633..000000000
--- a/tfjs-master/scripts/graph_utils.ts
+++ /dev/null
@@ -1,150 +0,0 @@
-// Copyright 2023 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-import * as fs from 'fs';
-import * as path from 'path';
-
-export const DEPENDENCY_GRAPH = JSON.parse(
-  fs.readFileSync(path.join(__dirname, 'package_dependencies.json'), 'utf8'));
-
-// This is a reverse dependencies graph. Each entry in the graph lists the
-// packages that depend on it.
-export const REVERSE_DEPENDENCY_GRAPH = transposeGraph(DEPENDENCY_GRAPH);
-
-// Topologically sort the dependency tree and arrange
-// steps in dependency order.
-export const DEPENDENCY_ORDER = topologicalSort(DEPENDENCY_GRAPH);
-
-export type Graph<V extends Iterable<string> = Iterable<string>> = {
-  [node: string]: V
-}
-
-/**
- * Verify that an object is a valid graph.
- */
-export function verifyGraph(graph: Graph) {
-  const nodes = new Set(Object.keys(graph));
-  for (const [node, edges] of Object.entries(graph)) {
-    for (const edge of edges) {
-      if (!nodes.has(edge)) {
-        throw new Error(
-            `Graph edge ${edge} of node ${node} not found in the graph`);
-      }
-    }
-  }
-}
-
-/**
- * Transpose a directed graph i.e. reverse the direction of the edges.
- */
-export function transposeGraph(graph: Graph) {
-  verifyGraph(graph);
-  const transposed: Graph<Set<string>> = {};
-  for (const [nodeName, connectedNodes] of Object.entries(graph)) {
-    for (const connectedNode of connectedNodes) {
-      if (!transposed[connectedNode]) {
-        transposed[connectedNode] = new Set();
-      }
-      if (!transposed[nodeName]) {
-        // Make sure the node itself ends up in the transposed graph.
-        transposed[nodeName] = new Set();
-      }
-      transposed[connectedNode].add(nodeName);
-    }
-  }
-  return transposed;
-}
-
-/**
- * Topologically sort a directed acyclic graph.
- *
- * Returns a list of graph nodes such that, by following edges,
- * you can only move forward in the list, not backward.
- */
-export function topologicalSort(graph: Graph) {
-  // We can't use a standard sorting algorithm because
-  // often, two packages won't have any dependency relationship
-  // between each other, meaning they are incomparable.
-  verifyGraph(graph);
-  const sorted: string[] = [];
-
-  while (sorted.length < Object.keys(graph).length) {
-    // Find nodes not yet in 'sorted' that have edges
-    // only to nodes already in 'sorted'
-    const emptyNodes = Object.entries(graph)
-                           .filter(([node, edges]) => {
-                             if (sorted.includes(node)) {
-                               return false;
-                             }
-                             for (const edge of edges) {
-                               if (!sorted.includes(edge)) {
-                                 return false;
-                               }
-                             }
-                             return true;
-                           })
-                           .map(([node, edges]) => node);
-
-    // If there are no such nodes, then the graph has a cycle.
-    if (emptyNodes.length === 0) {
-      throw new Error('Dependency graph has a cycle.');
-    }
-
-    for (let node of emptyNodes) {
-      sorted.push(node);
-    }
-  }
-  return sorted;
-}
-
-/**
- * Find all subnodes in the subgraph generated by taking the transitive
- * closure at `node`.
- */
-export function findSubgraph(node: string, graph: Graph, subnodes = new Set()) {
-  const directSubnodes = graph[node];
-  if (directSubnodes) {
-    for (const directSubnode of directSubnodes) {
-      if (!subnodes.has(directSubnode)) {
-        subnodes.add(directSubnode);
-        findSubgraph(directSubnode, graph, subnodes);
-      }
-    }
-  }
-
-  return subnodes;
-}
-
-/**
- * Find the transitive closure of dependencies of the given packages.
- */
-export function findDeps(packages: Iterable<string>): Set<string> {
-  return new Set(
-      [...packages]
-          .map(packageName => findSubgraph(packageName, DEPENDENCY_GRAPH))
-          .reduce((a, b) => [...a, ...b], []));
-}
-
-/**
- * Find the reverse dependencies of the given packages, i.e. find the
- * set of packages that include at least one of the given packages in
- * their transitive closure of dependencies.
- */
-export function findReverseDeps(packages: Iterable<string>): Set<string> {
-  return new Set(
-    [...packages]
-      .map(packageName => findSubgraph(packageName, REVERSE_DEPENDENCY_GRAPH))
-      .reduce((a, b) => [...a, ...b], []));
-}
diff --git a/tfjs-master/scripts/make-version.js b/tfjs-master/scripts/make-version.js
deleted file mode 100644
index 77ac3458e..000000000
--- a/tfjs-master/scripts/make-version.js
+++ /dev/null
@@ -1,79 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2018 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-// Run this script from the base directory (not the package directory):
-// ./scripts/make-version.js DIR_NAME
-// Where DIR_NAME is the directory name for the package you want to make a
-// version for.
-const fs = require('fs');
-const path = require('path');
-
-const dirName = process.argv[2];
-const packageJsonFile = path.join(dirName, 'package.json');
-if (!fs.existsSync(packageJsonFile)) {
-  console.log(
-      packageJsonFile, 'does not exist. Please call this script as follows:');
-  console.log('./scripts/make-version.js DIR_NAME');
-  process.exit(1);
-}
-
-const version = JSON.parse(fs.readFileSync(packageJsonFile, 'utf8')).version;
-
-const versionCode = `/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '${version}';
-export {version};
-`
-
-fs.writeFile(path.join(dirName, 'src/version.ts'), versionCode, err => {
-  if (err) {
-    throw new Error(`Could not save version file ${version}: ${err}`);
-  }
-  console.log(`Version file for version ${version} saved sucessfully.`);
-});
-
-if (dirName === 'tfjs-converter') {
-  const pipVersionCode = `# @license See the LICENSE file.
-
-# This code is auto-generated, do not modify this file!
-version = '${version}'
-`;
-
-  fs.writeFile(
-      path.join(dirName, '/python/tensorflowjs/version.py'),
-      pipVersionCode, err => {
-        if (err != null) {
-          throw new Error(`Could not save pip version file ${version}: ${err}`);
-        }
-        console.log(
-            `Version file for pip version ${version} saved sucessfully.`);
-      });
-
-  const buildFilename = path.join(dirName, '/python/BUILD.bazel');
-  fs.readFile(buildFilename, 'utf-8', function(err, data) {
-    if (err != null) {
-      throw new Error(`Could not update the BUILD.bazel file: ${err}`);
-    }
-
-    const newValue = data.replace(
-        /version\ =\ \"[0-9]+\.[0-9]+\.[0-9]+\"/g, `version = "${version}"`);
-    fs.writeFileSync(buildFilename, newValue, 'utf-8');
-
-    console.log(
-        `pip version ${version} for BUILD.bazel file is updated sucessfully.`);
-  });
-}
diff --git a/tfjs-master/scripts/package_dependencies.json b/tfjs-master/scripts/package_dependencies.json
deleted file mode 100644
index 5cf8b6d5c..000000000
--- a/tfjs-master/scripts/package_dependencies.json
+++ /dev/null
@@ -1,19 +0,0 @@
-{
-  "e2e": ["tfjs", "tfjs-converter", "tfjs-node", "tfjs-backend-wasm", "tfjs-backend-webgpu"],
-  "tfjs": ["tfjs-backend-cpu", "tfjs-backend-webgl", "tfjs-converter", "tfjs-core", "tfjs-data", "tfjs-layers"],
-  "tfjs-automl": [],
-  "tfjs-backend-cpu": ["tfjs-core"],
-  "tfjs-backend-wasm": ["tfjs-core", "tfjs-backend-cpu"],
-  "tfjs-backend-webgl": ["tfjs-core", "tfjs-backend-cpu"],
-  "tfjs-backend-webgpu": ["tfjs-core", "tfjs-backend-cpu"],
-  "tfjs-converter": ["tfjs-core", "tfjs-backend-cpu"],
-  "tfjs-core": [],
-  "tfjs-data": ["tfjs-core", "tfjs-backend-cpu", "tfjs-layers"],
-  "tfjs-inference": [],
-  "tfjs-layers": ["tfjs-core", "tfjs-backend-cpu", "tfjs-backend-webgl"],
-  "tfjs-node": ["tfjs-core", "tfjs"],
-  "tfjs-node-gpu": ["tfjs-core", "tfjs"],
-  "tfjs-tfdf": ["tfjs-core", "tfjs-backend-cpu", "tfjs-converter"],
-  "tfjs-tflite": ["tfjs-core", "tfjs-backend-cpu"],
-  "tfjs-vis": []
-}
diff --git a/tfjs-master/scripts/publish-npm.sh b/tfjs-master/scripts/publish-npm.sh
deleted file mode 100644
index e9888d1ea..000000000
--- a/tfjs-master/scripts/publish-npm.sh
+++ /dev/null
@@ -1,72 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2017 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Before you run this script, run `yarn release` and commit the PRs.
-
-# Then:
-# 1) Checkout the master branch of this repo.
-# 2) Run this script as `./scripts/publish-npm.sh DIR_NAME`
-#      from the project base dir where DIR_NAME is the directory name of the
-#      package you want to publish, e.g. "tfjs-core".
-
-set -e
-
-BRANCH=`git rev-parse --abbrev-ref HEAD`
-ORIGIN=`git config --get remote.origin.url`
-CHANGES=`git status --porcelain`
-
-# Yarn in the top-level and in the directory,
-yarn
-cd $1
-# Yarn above the other checks to make sure yarn doesn't change the lock file.
-yarn
-cd ..
-
-PACKAGE_JSON_FILE="$1/package.json"
-if ! test -f "$PACKAGE_JSON_FILE"; then
-  echo "$PACKAGE_JSON_FILE does not exist."
-  echo "Please pass the package name as the first argument."
-  exit 1
-fi
-
-if [ "$BRANCH" != "master" ]; then
-  echo "Error: Switch to the master branch before publishing."
-  exit
-fi
-
-if ! [[ "$ORIGIN" =~ tensorflow/tfjs ]]; then
-  echo "Error: Switch to the main repo (tensorflow/tfjs) before publishing."
-  exit
-fi
-
-if [ ! -z "$CHANGES" ];
-then
-    echo "Make sure the master branch is clean. Found changes:"
-    echo $CHANGES
-    exit 1
-fi
-
-./scripts/make-version.js $1
-
-cd $1
-yarn build-npm for-publish
-cd ..
-
-./scripts/tag-version.js $1
-
-cd $1
-npm publish
-echo 'Yay! Published a new package to npm.'
diff --git a/tfjs-master/scripts/publish-npm.ts b/tfjs-master/scripts/publish-npm.ts
deleted file mode 100644
index d62f78b00..000000000
--- a/tfjs-master/scripts/publish-npm.ts
+++ /dev/null
@@ -1,424 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/*
- * This script publish to npm for all the TensorFlow.js packages. Before you run
- * this script, run `yarn release` and commit the PRs.
- * Then run this script as `yarn publish-npm`.
- */
-
-import * as argparse from 'argparse';
-import chalk from 'chalk';
-import * as shell from 'shelljs';
-import { RELEASE_UNITS, question, $, getReleaseBranch, checkoutReleaseBranch, ALPHA_RELEASE_UNIT, TFJS_RELEASE_UNIT, selectPackages, getLocalVersion, getNpmVersion, memoize, printReleaseUnit, checkPublishable, runVerdaccio, ReleaseUnit, getVersion, getTagFromVersion, filterPackages, ALL_PACKAGES, WEBSITE_RELEASE_UNIT, getPackages } from './release-util';
-import semverCompare from 'semver/functions/compare';
-import * as child_process from 'child_process';
-
-import {BAZEL_PACKAGES} from './bazel_packages';
-
-const TMP_DIR = '/tmp/tfjs-publish';
-const VERDACCIO_REGISTRY = 'http://127.0.0.1:4873';
-const NPM_REGISTRY = 'https://registry.npmjs.org/';
-
-// This script can not publish the tfjs website
-const PUBLISHABLE_RELEASE_UNITS = RELEASE_UNITS.filter(r => r !== WEBSITE_RELEASE_UNIT);
-
-async function retry<T>(f: () => T, tries = 3, sleep=5_000): Promise<T> {
-  let lastError;
-  for (let i = 0; i < tries; i++) {
-    try {
-      return f();
-    } catch (e) {
-      lastError = e;
-      console.warn(e);
-      if (i + 1 < tries) {
-        // Only delay if the loop will run again.
-        await delay(sleep);
-      }
-    }
-  }
-  throw lastError;
-}
-
-/**
- * For sets `a` and `b`, compute the set difference `a \ b`
- *
- * The set difference of `a` and `b`, denoted `a \ b`, is the set containing all
- * elements of `a` that are not in `b`
- *
- * @param a The set to subtract from
- * @param b The set to remove from `a` when creating the output set
- */
-function setDifference<T>(a: Set<T>, b: Set<T>): Set<T> {
-  const difference = new Set<T>();
-  for (const val of a) {
-    if (!b.has(val)) {
-      difference.add(val);
-    }
-  }
-  return difference;
-}
-
-const parser = new argparse.ArgumentParser();
-parser.addArgument('--git-protocol', {
-  action: 'storeTrue',
-  help: 'Use the git protocal rather than the http protocol when cloning repos.'
-});
-
-parser.addArgument('--registry', {
-  type: 'string',
-  defaultValue: NPM_REGISTRY,
-  help: 'Which registry to install packages from and publish to.',
-});
-
-parser.addArgument('--no-otp', {
-  action: 'storeTrue',
-  help: 'Do not use an OTP when publishing to the registry.',
-});
-
-parser.addArgument(['--release-this-branch', '--release-current-branch'], {
-  action: 'storeTrue',
-  help: 'Release the current branch instead of checking out a new one.',
-});
-
-parser.addArgument(['--dry'], {
-  action: 'storeTrue',
-  help: 'Dry run. Stage all packages in verdaccio but do not publish them to '
-      + 'the registry.',
-});
-
-parser.addArgument(['--auto-publish-local-newer'], {
-  action: 'storeTrue',
-  help: 'Automatically publish local packages that have newer versions than'
-      + ' the packages in the registry',
-});
-
-parser.addArgument(['--ci'], {
-  action: 'storeTrue',
-  help: 'Enable CI bazel flags for faster compilation and don\'t ask for user '
-      + 'input before closing the verdaccio server once tests are done. '
-      + 'Has no effect on results.',
-});
-
-parser.addArgument(['packages'], {
-  type: 'string',
-  nargs: '*',
-  help: 'Packages to publish. Leave empty to select interactively',
-});
-
-function delay(ms: number): Promise<void> {
-  return new Promise((resolve) => {
-    setTimeout(resolve, ms);
-  });
-}
-
-async function publish(pkg: string, registry: string, otp?: string,
-                       build = true) {
-  const registryEnv = {
-    YARN_REGISTRY: registry,
-    NPM_CONFIG_REGISTRY: registry,
-    NPM_REGISTRY: registry, // For npx npm-cli-login
-  };
-
-  function run(command: string) {
-    return $(command, registryEnv);
-  }
-
-  function yarn(args: string) {
-    return run(`yarn --registry '${registry}' ${args}`);
-  }
-
-  const startDir = process.cwd();
-  try {
-    // Use a try block so cwd can be restored in 'finally' if an error occurs.
-    shell.cd(pkg);
-
-    checkPublishable('./package.json');
-
-    if (build && !BAZEL_PACKAGES.has(pkg)) {
-      console.log(chalk.magenta.bold(`~~~ Preparing package ${pkg}~~~`));
-      console.log(chalk.magenta('~~~ Installing packages ~~~'));
-      // Without a delay, this sometimes has issues downloading dependencies.
-      await delay(5_000);
-
-      // tfjs-node-gpu needs to get some files from tfjs-node.
-      if (pkg === 'tfjs-node-gpu') {
-        yarn('prep-gpu');
-      }
-
-      // Yarn above the other checks to make sure yarn doesn't change the lock
-      // file.
-      await retry(() =>
-          console.log(run(`yarn --registry '${registry}'`)));
-      console.log(chalk.magenta('~~~ Build npm ~~~'));
-
-      if (pkg === 'tfjs-react-native') {
-        yarn('build-npm');
-      } else {
-        yarn('build-npm for-publish');
-      }
-    }
-
-    // Used for nightly dev releases.
-    const version = getVersion('package.json');
-    const tag = getTagFromVersion(version);
-
-    let otpFlag = '';
-    if (otp) {
-      otpFlag = `--otp=${otp} `;
-    }
-
-    console.log(
-      chalk.magenta.bold(`~~~ Publishing ${pkg} to ${registry} with tag `
-        + `${tag} ~~~`));
-
-    let login = '';
-    if (registry === VERDACCIO_REGISTRY) {
-      // If publishing to verdaccio, we must log in before every command.
-      login = 'npx npm-cli-login -u user -p password -e user@example.com && ';
-    }
-
-    if (BAZEL_PACKAGES.has(pkg)) {
-      let dashes = '-- --';
-      if (pkg === 'tfjs-backend-webgpu') {
-        // Special case for webgpu, which has an additional call to `yarn`
-        // in publish-npm.
-        dashes = '-- -- --';
-      }
-      await retry(() =>
-          run(`${login}yarn --registry '${registry}' publish-npm ${dashes} ${otpFlag} --tag=${tag} --force`));
-    } else {
-      // Special case for tfjs-node(-gpu), which must upload the node addon
-      // to GCP as well. Only do this when publishing to NPM.
-      if (registry === NPM_REGISTRY && pkg.startsWith('tfjs-node')) {
-        $('yarn build-and-upload-addon publish');
-      }
-
-      // Publish the package to the registry.
-      await retry(() =>
-          run(`${login}npm --registry '${registry}' publish --tag=${tag} ${otpFlag}`));
-    }
-    console.log(`Published ${pkg} to ${registry}.`);
-
-  } finally {
-    shell.cd(startDir);
-  }
-}
-
-async function main() {
-  const args = parser.parseArgs();
-
-  const killVerdaccio = await runVerdaccio();
-
-  let releaseUnits: ReleaseUnit[];
-  if (args.release_this_branch) {
-    console.log('Releasing current branch');
-    releaseUnits = PUBLISHABLE_RELEASE_UNITS;
-  } else {
-    PUBLISHABLE_RELEASE_UNITS.forEach(printReleaseUnit);
-    console.log();
-
-    const releaseUnitStr =
-      await question('Which release unit (leave empty for 0): ');
-    const releaseUnitInt = Number(releaseUnitStr);
-    if (releaseUnitInt < 0 || releaseUnitInt >= PUBLISHABLE_RELEASE_UNITS.length) {
-      console.log(chalk.red(`Invalid release unit: ${releaseUnitStr}`));
-      process.exit(1);
-    }
-    console.log(chalk.blue(`Using release unit ${releaseUnitInt}`));
-    console.log();
-
-    const releaseUnit = PUBLISHABLE_RELEASE_UNITS[releaseUnitInt];
-    const {name, } = releaseUnit;
-
-    let releaseBranch: string;
-    if (releaseUnit === ALPHA_RELEASE_UNIT) {
-      // Alpha release unit is published with the tfjs release unit.
-      releaseBranch = await getReleaseBranch(TFJS_RELEASE_UNIT.name);
-    } else {
-      releaseBranch = await getReleaseBranch(name);
-    }
-    console.log();
-
-    releaseUnits = [releaseUnit];
-    checkoutReleaseBranch(releaseBranch, args.git_protocol, TMP_DIR);
-    shell.cd(TMP_DIR);
-  }
-
-  const getNpmVersionMemoized = memoize((pkg: string) => {
-    const version = getLocalVersion(pkg);
-    const tag = getTagFromVersion(version);
-    return getNpmVersion(pkg, args.registry, tag);
-  });
-
-  async function getVersions(pkg: string) {
-    const localVersion = getLocalVersion(pkg);
-    const npmVersion = await getNpmVersionMemoized(pkg);
-    let localIsNewer = true;
-    if (npmVersion !== '') {
-      // Unpublished tags return '' for their version.
-      localIsNewer = semverCompare(localVersion, npmVersion) > 0;
-    }
-    return {localVersion, npmVersion, localIsNewer};
-  }
-
-  async function packageSelected(pkg: string) {
-      // Automatically select local packages with version numbers greater than
-      // npm.
-      try {
-        const {localVersion, localIsNewer} = await getVersions(pkg);
-        return localVersion !== '0.0.0' && localIsNewer;
-      } catch (e) {
-        console.warn(e);
-        return false;
-      }
-  }
-
-  // Get the list of packages to build and publish.
-  // There are three ways packages can be selected.
-  // 1. By passing them as CLI arguments in `packages`.
-  // 2. Automatically based on the versions on npm.
-  // 3. Interactively on the command line.
-  let packages: string[];
-  if (args.packages.length > 0) {
-    // Get packages to publish from the 'packages' arg
-    // Filter from the set of all packages to make sure they end up
-    // in topological order.
-    const allPackages = getPackages(PUBLISHABLE_RELEASE_UNITS);
-    const requestedPackages = new Set(args.packages);
-    packages = allPackages.filter(pkg => requestedPackages.has(pkg));
-
-    // Check if there are any unsupported packages requested by the user
-    const unsupportedPackages = setDifference(requestedPackages,
-                                              new Set(packages));
-    if (unsupportedPackages.size > 0) {
-      throw new Error(`Can not publish ${[...unsupportedPackages]}. `
-              + `Supported packages are:\n${[...ALL_PACKAGES].join('\n')}`);
-    }
-  } else if (args.auto_publish_local_newer) {
-    // Automatically select packages based on npm versions
-    packages = await filterPackages(packageSelected, PUBLISHABLE_RELEASE_UNITS);
-    console.log(`Publishing ${packages}`);
-  } else {
-    // Select packages interactively
-    packages = await selectPackages({
-      message: 'Select packages to publish',
-      releaseUnits,
-      selected: packageSelected,
-      async modifyName(pkg) {
-        // Add the local and remote versions to the printed name.
-        try {
-          const {localVersion, npmVersion, localIsNewer} = await getVersions(pkg);
-          const pkgWithVersion =
-            `${pkg.padEnd(20)} (${npmVersion ?? 'unpublished'} → ${localVersion})`;
-          if (localIsNewer) {
-            return chalk.bold(pkgWithVersion);
-          } else {
-            return pkgWithVersion;
-          }
-        } catch (e) {
-          console.warn(e);
-          return pkg;
-        }
-      }
-    });
-  }
-
-  // Yarn in the top-level to download Bazel
-  $('yarn');
-  console.log();
-
-  // Pre-build all the bazel packages in a single bazel command for better
-  // efficiency.
-  const bazelTargets = packages.filter(pkg => BAZEL_PACKAGES.has(pkg))
-    .map(name => `//${name}:${name}_pkg`);
-
-  const bazelArgs = ['bazel', 'build']
-  if (args.ci) {
-    bazelArgs.push('--config=ci');
-  }
-  // Use child_process.spawnSync to show bazel build progress.
-  const result = child_process.spawnSync('yarn',
-                                         [...bazelArgs, ...bazelTargets],
-                                         {stdio:'inherit'});
-  if (result.status !== 0) {
-    throw new Error(`Bazel process failed with exit code ${result.status}`);
-  }
-
-  // Build and publish all packages to a local Verdaccio repo for staging.
-  console.log(
-    chalk.magenta.bold('~~~ Staging packages locally in Verdaccio ~~~'));
-
-  try {
-    for (const pkg of packages) {
-      await publish(pkg, VERDACCIO_REGISTRY);
-    }
-  } catch (e) {
-    // Make sure to kill the verdaccio server before exiting even if publish
-    // throws an error. Otherwise, it blocks the port for the next run.
-    killVerdaccio();
-    throw e;
-  }
-
-  if (args.dry) {
-    console.log('Not publishing packages due to \'--dry\'');
-    if (!args.ci) {
-      await question('Press enter to quit verdaccio.');
-    }
-    killVerdaccio();
-  } else {
-    // Publish all built packages to the selected registry
-    let otp = '';
-    if (!args.no_otp) {
-      otp = await question(`Enter one-time password from your authenticator: `);
-    }
-    console.log(`Publishing packages to ${args.registry}`);
-
-    killVerdaccio();
-
-    const toPublish = [...packages];
-    while (toPublish.length > 0) {
-      // Using a while loop instead of .map since a stale OTP will require
-      // a retry.
-      let pkg = toPublish[0];
-      if (args.no_otp) {
-        await publish(pkg, args.registry, '', false);
-        toPublish.shift(); // Remove the published package from 'toPublish'.
-        continue;
-      }
-
-      try {
-        await publish(pkg, args.registry, otp, false)
-        toPublish.shift(); // Remove the published package from 'toPublish'.
-      } catch (err) {
-        if ((err as Error).message.includes('code EOTP')) {
-          // Try again with a new otp
-          otp = await question(`OTP ${otp} failed. Enter a new one-time `
-                               + `password from your authenticator: `);
-          continue; // Don't shift the package since it failed to publish.
-        }
-        throw err;
-      }
-    }
-
-    console.log(`Published packages to ${args.registry}`);
-  }
-  process.exit(0);
-}
-
-main();
diff --git a/tfjs-master/scripts/publish-pypi.ts b/tfjs-master/scripts/publish-pypi.ts
deleted file mode 100644
index d0498a2f6..000000000
--- a/tfjs-master/scripts/publish-pypi.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-// publish-pypi.ts
-import * as argparse from 'argparse';
-import { checkoutReleaseBranch, question, $ } from './release-util';
-import * as shell from 'shelljs';
-import * as fs from 'fs';
-import chalk from 'chalk';
-
-const TMP_DIR = '/tmp/tfjs-pypi';
-
-const parser = new argparse.ArgumentParser();
-parser.addArgument('--git-protocol', {
-  action: 'storeTrue',
-  help: 'Use the git protocol rather than the http protocol when cloning repos.',
-});
-
-async function getNewlyCreatedBranches(): Promise<string[]> {
-  const branchesStr = $(`git branch -r --sort=-authordate --format='%(HEAD) %(refname:lstrip=-1)'`);
-
-  const branches = branchesStr.split('\n').map((line: string) => line.trim());
-
-  const pattern = /^tfjs_\d+\.\d+\.\d+.*$/;
-  const tfjsBranches = branches.filter((branch: string) => pattern.test(branch));
-
-  return tfjsBranches;
-}
-
-async function main() {
-  const args = parser.parseArgs();
-
-  try {
-    const branches = await getNewlyCreatedBranches();
-    console.log('Branches:', branches);
-
-    const latestBranch = branches[0];
-    console.log('Latest Branch:', latestBranch);
-
-    const answer = await question(chalk.cyan.bold(`Is this the right branch '${latestBranch}' you are looking for? (y/N): `));
-
-    if (answer.toLowerCase() === 'y') {
-      checkoutReleaseBranch(latestBranch, args.git_protocol, TMP_DIR);
-      const targetDir = `${TMP_DIR}/tfjs-converter/python`;
-      shell.cd(targetDir);
-      console.log(chalk.blue.bold('Current directory:', shell.pwd().toString()));
-      
-      $('bazel clean');
-      $('bazel build python3_wheel');
-
-      // Remove dist folder if it exists
-      if (fs.existsSync('./dist')) {
-        fs.rmdirSync('./dist', { recursive: true });
-      }
-
-      $('./build-pip-package.sh --upload ./dist');
-
-      // Command executed successfully
-      console.log('Command executed successfully');
-    } else {
-      console.log('Aborted.');
-    }
-  } catch (error) {
-    console.error('Error:', error);
-  }
-}
-
-// Invoke the main function
-main();
diff --git a/tfjs-master/scripts/release-tfjs.ts b/tfjs-master/scripts/release-tfjs.ts
deleted file mode 100644
index c4c37251f..000000000
--- a/tfjs-master/scripts/release-tfjs.ts
+++ /dev/null
@@ -1,301 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This script creates pull requests to make releases for all the TensorFlow.js
- * packages.
- *
- * This script requires hub to be installed: https://hub.github.com/
- */
-
-import * as argparse from 'argparse';
-import chalk from 'chalk';
-import semver from 'semver';
-import * as fs from 'fs';
-import * as shell from 'shelljs';
-import {TMP_DIR, $, question, makeReleaseDir, createPR, TFJS_RELEASE_UNIT, updateTFJSDependencyVersions, ALPHA_RELEASE_UNIT, getMinorUpdateVersion, getPatchUpdateVersion, E2E_PHASE, getReleaseBlockers, getNightlyVersion} from './release-util';
-import * as path from 'path';
-import {findDeps} from './graph_utils';
-
-const parser = new argparse.ArgumentParser({
-  description: 'Create a release PR for the tfjs monorepo.',
-});
-
-parser.addArgument('--git-protocol', {
-  action: 'storeTrue',
-  help: 'Use the git protocol rather than the http protocol when cloning repos.'
-});
-
-parser.addArgument(['--dry'], {
-  action: 'storeTrue',
-  help: 'Only create the release branch locally. Do not push or create a PR.',
-});
-
-parser.addArgument('--guess-version', {
-  type: 'string',
-  choices: ['release', 'nightly'],
-  help: 'Use the guessed version without asking for confirmation.',
-});
-
-parser.addArgument(['--commit-hash', '--hash'], {
-  type: 'string',
-  help: 'Commit hash to publish. Usually the latest successful nightly run.',
-});
-
-parser.addArgument(['--use-local-changes'], {
-  action: 'storeTrue',
-  help: 'Use local changes to the repo instead of a remote branch. Only for' +
-      ' testing and debugging.',
-});
-
-parser.addArgument('--force', {
-  action: 'storeTrue',
-  help: 'Force a release even if there are release blockers.',
-});
-
-async function getNewVersion(
-    packageName: string, incrementVersion: (version: string) => string,
-    ask = true) {
-  let newVersion: string|undefined;
-  try {
-    const versions: string[] =
-        JSON.parse($(`npm view @tensorflow/${packageName} versions --json`));
-    if (Array.isArray(versions) && versions.length !== 0) {
-      const latestVersion = semver.rsort(versions)[0];
-      newVersion = incrementVersion(latestVersion);
-    }
-  } catch (e) {
-    // Suppress errors when guessing the version.
-  }
-
-  if (newVersion != null) {
-    if (!ask) {
-      return newVersion;
-    }
-    newVersion = await question(`New version for ${
-                     packageName} (leave empty for ${newVersion}): `) ||
-        newVersion;
-    return newVersion;
-  }
-
-  if (!ask) {
-    console.warn(
-        'Guessing version 0.0.1 for unpublished package ' +
-        `${packageName}`);
-    return '0.0.1';
-  }
-
-  // Repeat until the user answers.
-  while (true) {
-    newVersion = await question(
-        `New Version for ${packageName} (no current version found on npm): `);
-    if (newVersion !== '') {
-      return newVersion;
-    }
-    console.log(
-        `${packageName} has no version on npm. ` +
-        'Please provide an initial version.');
-  }
-}
-
-async function main() {
-  const args = parser.parseArgs();
-
-  let incrementVersion: ((version: string) => string)|undefined;
-  if (args.guess_version === 'nightly') {
-    incrementVersion = v => getNightlyVersion(getMinorUpdateVersion(v));
-  }
-
-  if (args.use_local_changes) {
-    // Force dry run when using local files instead of a release branch.
-    // This is for debugging.
-    args.dry = true;
-  }
-  const urlBase = args.git_protocol ? 'git@github.com:' : 'https://github.com/';
-  const dir = `${TMP_DIR}/tfjs`;
-  makeReleaseDir(dir);
-
-  if (args.force) {
-    console.warn('Ignoring any potential release blockerse due to \'--force\'');
-  } else {
-    const blockers = getReleaseBlockers();
-    if (blockers) {
-      throw new Error(`Can not release due to release blockers:\n ${blockers}`);
-    }
-  }
-
-  // Guess release version from tfjs-core's latest version, with a minor update.
-  const newVersion = await getNewVersion(
-      'tfjs-core', incrementVersion ?? getMinorUpdateVersion,
-      !args.guess_version);
-
-  // Populate the versions map with new versions for monorepo packages.
-  const versions = new Map<string /* package name */, string /* version */>();
-  for (const phase of TFJS_RELEASE_UNIT.phases) {
-    for (const packageName of phase.packages) {
-      versions.set(packageName, newVersion);
-    }
-  }
-
-  // Add versions for alpha monorepo packages, which do not have the same
-  // version as the other monorepo packages.
-  for (const phase of ALPHA_RELEASE_UNIT.phases) {
-    for (const packageName of phase.packages) {
-      const newVersion = await getNewVersion(
-          packageName, incrementVersion ?? getPatchUpdateVersion,
-          !args.guess_version);
-      versions.set(packageName, newVersion);
-    }
-  }
-
-  // Get release candidate commit.
-  let commit = args.commit_hash;
-  if (!args.use_local_changes) {
-    if (!commit) {
-      commit = await question(
-          'Commit of release candidate (the last ' +
-          'successful nightly build): ');
-    }
-    if (commit === '') {
-      console.log(chalk.red('Commit cannot be empty.'));
-      process.exit(1);
-    }
-  }
-
-  // Create a release branch in remote.
-  $(`git clone ${urlBase}tensorflow/tfjs ${dir}`);
-
-  const releaseBranch = `tfjs_${newVersion}`;
-
-  if (args.use_local_changes) {
-    shell.cd(path.join(__dirname, '../'));
-    console.log(chalk.magenta.bold(
-        '~~~ Copying current changes to a new release branch' +
-        ` ${releaseBranch} ~~~`));
-    // Avoid copying `.git/` because this script will `git push`
-    // to origin, which it expects to be the tfjs repo as was set
-    // up when the script ran 'git clone' above.
-    // This makes sure other hidden files like .bazelrc are copied.
-    $(`cp -r \`ls -A | grep -v ".git"\` ${dir}`);
-    shell.cd(dir);
-  } else {
-    shell.cd(dir);
-    console.log(chalk.magenta.bold(
-        `~~~ Creating new release branch ${releaseBranch} ~~~`));
-    $(`git checkout -b ${releaseBranch} ${commit}`);
-  }
-  if (!args.dry) {
-    $(`git push origin ${releaseBranch}`);
-  }
-
-  // Update versions in package.json files.
-  const phases =
-      [...TFJS_RELEASE_UNIT.phases, ...ALPHA_RELEASE_UNIT.phases, E2E_PHASE];
-  const errors: Error[] = [];
-  for (const phase of phases) {
-    for (const packageName of phase.packages) {
-      shell.cd(packageName);
-
-      // Update the version number of the package.json
-      const packagePath = path.join(dir, packageName);
-      const packageJsonPath = path.join(packagePath, 'package.json');
-      let pkg = fs.readFileSync(packageJsonPath, 'utf8');
-      const parsedPkg = JSON.parse(`${pkg}`);
-
-      console.log(chalk.magenta.bold(`~~~ Processing ${packageName} ~~~`));
-      const newVersion = versions.get(packageName);
-      pkg = `${pkg}`.replace(
-          `"version": "${parsedPkg.version}"`, `"version": "${newVersion}"`);
-
-      fs.writeFileSync(packageJsonPath, pkg);
-
-      // Update dependency versions of all package.json files found in the
-      // package to use the new verison numbers (except ones in node_modules).
-      const subpackages =
-          $(`find ${
-                packagePath} -name package.json -not -path \'*/node_modules/*\'`)
-              .split('\n');
-      for (const packageJsonPath of subpackages) {
-        const pkg = fs.readFileSync(packageJsonPath, 'utf8');
-        console.log(chalk.magenta.bold(
-            `~~~ Update dependency versions for ${packageJsonPath} ~~~`));
-
-        // Only update versions that are a (possibly transitive) dependency of
-        // the package and are listed in the phase deps (we throw an error
-        // if we find a dependency that doesn't satisfy these conditions).
-        const transitiveDeps = [...findDeps([packageName])].filter(
-            dep => phase.deps.includes(dep));
-
-        // Also add the package itself so subpackages can use it.
-        // Some packages, like e2e, are never published to npm, so check first.
-        if (versions.has(packageName)) {
-          transitiveDeps.push(packageName);
-        }
-
-        const packageDependencyVersions =
-            new Map(transitiveDeps.map(dep => [dep, versions.get(dep)!]));
-
-        try {
-          const updated =
-              updateTFJSDependencyVersions(pkg, packageDependencyVersions);
-
-          fs.writeFileSync(packageJsonPath, updated);
-        } catch (e) {
-          e.message = `For ${packageJsonPath}, ${packageName} ${e.message}`;
-          console.error(e.stack);
-          errors.push(e);
-        }
-      }
-
-      shell.cd('..');
-
-      // Make version for all packages other than tfjs-node-gpu and e2e.
-      if (packageName !== 'tfjs-node-gpu' && packageName !== 'e2e') {
-        $(`./scripts/make-version.js ${packageName}`);
-      }
-    }
-  }
-  if (errors.length > 0) {
-    throw new Error('Some package version updates had errors' + errors);
-  }
-
-
-  // Use dev prefix to avoid branch being locked.
-  const devBranchName = `dev_${releaseBranch}`;
-
-  const message = `Update monorepo to ${newVersion}.`;
-  if (!args.dry) {
-    createPR(devBranchName, releaseBranch, message);
-  }
-
-  console.log(
-      'Done. FYI, this script does not publish to NPM. ' +
-      'Please publish by running  ' +
-      'YARN_REGISTRY="https://registry.npmjs.org/" yarn publish-npm ' +
-      'after you merge the PR.' +
-      'Remember to delete the dev branch once PR is merged.' +
-      'Please remeber to update the website once you have released ' +
-      'a new package version.');
-
-  if (args.dry) {
-    console.log(`No PR was created. Local output is located in ${dir}.`);
-  }
-  process.exit(0);
-}
-
-main();
diff --git a/tfjs-master/scripts/release-util.ts b/tfjs-master/scripts/release-util.ts
deleted file mode 100644
index 5e6107fb2..000000000
--- a/tfjs-master/scripts/release-util.ts
+++ /dev/null
@@ -1,701 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import chalk from 'chalk';
-import * as fs from 'fs';
-import * as inquirer from 'inquirer';
-import {Separator} from 'inquirer';
-import mkdirp from 'mkdirp';
-import * as readline from 'readline';
-import * as shell from 'shelljs';
-import rimraf from 'rimraf';
-import * as path from 'path';
-import {fork} from 'child_process';
-
-export interface Phase {
-  // The list of packages that will be updated with this change.
-  packages: string[];
-  // The list of dependencies that all of the packages will update to.
-  // TODO(mattSoulanille): Parse this from package_dependencies.json or from the
-  // package.json file of each package.
-  deps?: string[];
-  // An ordered map of scripts, key is package name, value is an object with two
-  // optional fields: `before-yarn` with scripts to run before `yarn`, and
-  // `after-yarn` with scripts to run after yarn is called and before the pull
-  // request is sent out.
-  scripts?: {[key: string]: {[key: string]: string[]}};
-  // Whether to leave the version of the package alone. Defaults to false
-  // (change the version).
-  leaveVersion?: boolean;
-  title?: string;
-}
-
-export interface ReleaseUnit {
-  // A human-readable name. Used for generating release branch.
-  name: string;
-  // The phases in this release unit.
-  phases: Phase[];
-  // The repository *only if it is not the same as tfjs*.
-  repo?: string;
-}
-
-export const CORE_PHASE: Phase = {
-  packages: ['tfjs-core'],
-  // Do not mark tfjs-backend-cpu as a dependency during releases. As a
-  // devDependency it should keep the link:// path. Once tests have passed in CI
-  // building and releasing core should not depend on the cpu backend
-};
-
-export const CPU_PHASE: Phase = {
-  packages: ['tfjs-backend-cpu'],
-  deps: ['tfjs-core']
-};
-
-export const WEBGL_PHASE: Phase = {
-  packages: ['tfjs-backend-webgl'],
-  deps: ['tfjs-core', 'tfjs-backend-cpu']
-};
-
-export const LAYERS_CONVERTER_PHASE: Phase = {
-  packages: ['tfjs-layers', 'tfjs-converter'],
-  deps: ['tfjs-core', 'tfjs-backend-cpu', 'tfjs-backend-webgl']
-};
-
-export const DATA_PHASE: Phase = {
-  packages: ['tfjs-data'],
-  deps: ['tfjs-core', 'tfjs-layers', 'tfjs-backend-cpu']
-}
-
-export const UNION_PHASE: Phase = {
-  packages: ['tfjs'],
-  deps: [
-    'tfjs-core', 'tfjs-layers', 'tfjs-converter', 'tfjs-data',
-    'tfjs-backend-cpu', 'tfjs-backend-webgl'
-  ]
-};
-
-// We added tfjs-core and tfjs-layers because Node has unit tests that directly
-// use tf.core and tf.layers to test serialization of models. Consider moving
-// the test to tf.layers.
-export const NODE_PHASE: Phase = {
-  packages: ['tfjs-node', 'tfjs-node-gpu'],
-  deps: ['tfjs', 'tfjs-core'],
-  scripts: {'tfjs-node-gpu': {'before-yarn': ['yarn prep-gpu']}}
-};
-
-export const WASM_PHASE: Phase = {
-  packages: ['tfjs-backend-wasm'],
-  deps: ['tfjs-core', 'tfjs-backend-cpu']
-};
-
-export const WEBGPU_PHASE: Phase = {
-  packages: ['tfjs-backend-webgpu'],
-  deps: ['tfjs-core', 'tfjs-backend-cpu'],
-};
-
-export const VIS_PHASE: Phase = {
-  packages: ['tfjs-vis']
-};
-
-export const REACT_NATIVE_PHASE: Phase = {
-  packages: ['tfjs-react-native'],
-  deps: ['tfjs-core', 'tfjs-backend-cpu', 'tfjs-backend-webgl']
-};
-
-export const TFDF_PHASE: Phase = {
-  packages: ['tfjs-tfdf'],
-  deps: ['tfjs-core', 'tfjs-backend-cpu', 'tfjs-converter']
-};
-
-export const TFLITE_PHASE: Phase = {
-  packages: ['tfjs-tflite'],
-  deps: ['tfjs-core', 'tfjs-backend-cpu']
-};
-
-export const AUTOML_PHASE: Phase = {
-  packages: ['tfjs-automl'],
-  deps: ['tfjs-core', 'tfjs-backend-webgl', 'tfjs-converter']
-};
-
-export const WEBSITE_PHASE: Phase = {
-  packages: ['tfjs-website'],
-  deps: [
-    'tfjs', 'tfjs-node', 'tfjs-vis', 'tfjs-react-native', 'tfjs-tfdf',
-    'tfjs-tflite', '@tensorflow-models/tasks'
-  ],
-  scripts: {'tfjs-website': {'after-yarn': ['yarn prep && yarn build-prod']}},
-  leaveVersion: true,
-  title: 'Update website to latest dependencies.'
-};
-
-// Note that e2e is not actually published. As a result, this phase is not
-// included in any release unit, however, it is used for updating dependencies.
-export const E2E_PHASE: Phase = {
-  packages: ['e2e'],
-  deps: [
-    'tfjs', 'tfjs-backend-cpu', 'tfjs-backend-wasm', 'tfjs-backend-webgl',
-    'tfjs-backend-webgpu', 'tfjs-converter', 'tfjs-core', 'tfjs-data',
-    'tfjs-layers', 'tfjs-node'
-  ],
-}
-
-export const TFJS_RELEASE_UNIT: ReleaseUnit = {
-  name: 'tfjs',
-  phases: [
-    CORE_PHASE, CPU_PHASE, WEBGL_PHASE, WEBGPU_PHASE, LAYERS_CONVERTER_PHASE,
-    DATA_PHASE, UNION_PHASE, NODE_PHASE, WASM_PHASE
-  ]
-};
-
-// TODO(mattsoulanille): Move WEBGPU_PHASE to TFJS_RELEASE_UNIT when webgpu
-// is out of alpha.
-// Alpha packages use monorepo dependencies at the latest version but are
-// not yet released at the same version number as the monorepo packages.
-// Use this for packages that will be a part of the monorepo in the future.
-// The release script will ask for a new version for each phase, and it will
-// replace 'link' dependencies with the new monorepo version.
-export const ALPHA_RELEASE_UNIT: ReleaseUnit = {
-  name: 'alpha-monorepo-packages',
-  phases: [TFDF_PHASE],
-};
-
-export const VIS_RELEASE_UNIT: ReleaseUnit = {
-  name: 'vis',
-  phases: [VIS_PHASE]
-};
-
-export const REACT_NATIVE_RELEASE_UNIT: ReleaseUnit = {
-  name: 'react-native',
-  phases: [REACT_NATIVE_PHASE]
-};
-
-export const TFLITE_RELEASE_UNIT: ReleaseUnit = {
-  name: 'tflite',
-  phases: [TFLITE_PHASE]
-};
-
-export const AUTOML_RELEASE_UNIT: ReleaseUnit = {
-  name: 'automl',
-  phases: [AUTOML_PHASE]
-};
-
-export const WEBSITE_RELEASE_UNIT: ReleaseUnit = {
-  name: 'website',
-  phases: [WEBSITE_PHASE],
-  repo: 'tfjs-website'
-};
-
-export const RELEASE_UNITS: ReleaseUnit[] = [
-  TFJS_RELEASE_UNIT,
-  ALPHA_RELEASE_UNIT,
-  VIS_RELEASE_UNIT,
-  REACT_NATIVE_RELEASE_UNIT,
-  TFLITE_RELEASE_UNIT,
-  AUTOML_RELEASE_UNIT,
-  WEBSITE_RELEASE_UNIT,
-];
-
-export const ALL_PACKAGES: Set<string> = new Set(getPackages(RELEASE_UNITS));
-
-export const TMP_DIR = '/tmp/tfjs-release';
-
-export async function question(questionStr: string): Promise<string> {
-  const rl =
-    readline.createInterface({ input: process.stdin, output: process.stdout });
-
-  console.log(chalk.bold(questionStr));
-  return new Promise<string>(
-    resolve => {
-      rl.question('> ', response => {
-        resolve(response);
-        rl.close();
-      });
-    });
-}
-
-/**
- * A wrapper around shell.exec for readability.
- * @param cmd The bash command to execute.
- * @returns stdout returned by the executed bash script.
- */
-export function $(cmd: string, env: Record<string, string> = {}) {
-  env = {...process.env, ...env};
-
-  const result = shell.exec(cmd, {silent: true, env});
-  if (result.code > 0) {
-    throw new Error(`$ ${cmd}\n ${result.stderr}`);
-  }
-  return result.stdout.trim();
-}
-
-/**
- * An async wrapper around shell.exec for readability.
- * @param cmd The bash command to execute.
- * @returns stdout returned by the executed bash script.
- */
-export function $async(cmd: string,
-                       env: Record<string, string> = {}): Promise<string> {
-  env = {...shell.env, ...env};
-  return new Promise((resolve, reject) => {
-    shell.exec(cmd, {silent: true, env}, (code, stdout, stderr) => {
-      if (code > 0) {
-        console.log('$', cmd);
-        console.log(stdout);
-        console.log(stderr);
-        reject(stderr);
-      }
-      resolve(stdout.trim());
-    })
-  });
-}
-
-export function printReleaseUnit(releaseUnit: ReleaseUnit, id: number) {
-  console.log(chalk.green(`Release unit ${id}:`));
-  console.log(` packages: ${
-      chalk.blue(releaseUnit.phases.map(phase => phase.packages.join(', '))
-                     .join(', '))}`);
-}
-
-export function printPhase(phases: Phase[], phaseId: number) {
-  const phase = phases[phaseId];
-  console.log(chalk.green(`Phase ${phaseId}:`));
-  console.log(`  packages: ${chalk.blue(phase.packages.join(', '))}`);
-  if (phase.deps != null) {
-    console.log(`   deps: ${phase.deps.join(', ')}`);
-  }
-}
-
-export function makeReleaseDir(dir: string) {
-  mkdirp(TMP_DIR, err => {
-    if (err) {
-      console.log('Error creating temp dir', TMP_DIR);
-      process.exit(1);
-    }
-  });
-  $(`rm -f -r ${dir}/*`);
-  $(`rm -f -r ${dir}`);
-  $(`mkdir ${dir}`);
-}
-
-export async function updateDependency(
-    deps: string[], pkg: string, parsedPkg: any): Promise<string> {
-  console.log(chalk.magenta.bold(`~~~ Update dependency versions ~~~`));
-
-  if (deps != null) {
-    const depsLatestVersion: string[] = deps.map(
-        dep => $(`npm view ${
-            dep.includes('@') ? dep : '@tensorflow/' + dep} dist-tags.latest`));
-
-    for (let j = 0; j < deps.length; j++) {
-      const dep = deps[j];
-
-      let version = '';
-      const depNpmName = dep.includes('@') ? dep : `@tensorflow/${dep}`;
-      if (parsedPkg['dependencies'] != null &&
-          parsedPkg['dependencies'][depNpmName] != null) {
-        version = parsedPkg['dependencies'][depNpmName];
-      } else if (
-          parsedPkg['peerDependencies'] != null &&
-          parsedPkg['peerDependencies'][depNpmName] != null) {
-        version = parsedPkg['peerDependencies'][depNpmName];
-      } else if (
-          parsedPkg['devDependencies'] != null &&
-          parsedPkg['devDependencies'][depNpmName] != null) {
-        version = parsedPkg['devDependencies'][depNpmName];
-      }
-      if (version == null) {
-        throw new Error(`No dependency found for ${dep}.`);
-      }
-
-      let relaxedVersionPrefix = '';
-      if (version.startsWith('~') || version.startsWith('^')) {
-        relaxedVersionPrefix = version.slice(0, 1);
-      }
-      const depVersionLatest = relaxedVersionPrefix + depsLatestVersion[j];
-
-      let depVersion = await question(
-          `Updated version for ` +
-          `${dep} (current is ${version}, leave empty for latest ${
-              depVersionLatest}): `);
-      if (depVersion === '') {
-        depVersion = depVersionLatest;
-      }
-      console.log(chalk.blue(`Using version ${depVersion}`));
-
-      pkg = `${pkg}`.replace(
-          new RegExp(`"${depNpmName}": "${version}"`, 'g'),
-          `"${depNpmName}": "${depVersion}"`);
-    }
-  }
-
-  return pkg;
-}
-
-// Update package.json dependencies of tfjs packages. This method is different
-// than `updateDependency`, it does not rely on published versions, instead it
-// uses a map from packageName to newVersion to update the versions.
-export function updateTFJSDependencyVersions(
-    pkg: string, versions: Map<string, string>,
-    depsToReplace = [...versions.keys()]): string {
-
-  const parsedPkg = JSON.parse(pkg);
-
-  const dependencyMaps: Array<{[index: string]: string}> = [
-    parsedPkg['dependencies'],
-    parsedPkg['peerDependencies'],
-    parsedPkg['devDependencies'],
-  ].filter(v => v != null);
-
-  for (const dependencyMap of dependencyMaps) {
-    for (const [name, version] of Object.entries(dependencyMap)) {
-      const prefix = '@tensorflow/';
-      if (name.startsWith(prefix) && version.startsWith('link:')) {
-        const tfjsName = name.slice(prefix.length);
-        const newVersion = versions.get(tfjsName);
-        if (newVersion == null) {
-          throw new Error(`Versions map does not include ${tfjsName}`);
-        }
-
-        let relaxedVersionPrefix = '';
-        if (version.startsWith('~') || version.startsWith('^')) {
-          relaxedVersionPrefix = version.slice(0, 1);
-        }
-        const versionLatest = relaxedVersionPrefix + newVersion;
-        pkg = `${pkg}`.replace(
-          new RegExp(`"${name}": "${version}"`, 'g'),
-          `"${name}": "${versionLatest}"`);
-      }
-    }
-  }
-
-  return pkg;
-}
-
-export function prepareReleaseBuild(phase: Phase, packageName: string) {
-  console.log(chalk.magenta.bold(`~~~ Prepare release build ~~~`));
-  console.log(chalk.bold('Prepare before-yarn'));
-  if (phase.scripts != null && phase.scripts[packageName] != null &&
-      phase.scripts[packageName]['before-yarn'] != null) {
-    phase.scripts[packageName]['before-yarn'].forEach(script => $(script));
-  }
-
-  console.log(chalk.bold('yarn'));
-  $(`yarn`);
-
-  console.log(chalk.bold('Prepare after-yarn'));
-  if (phase.scripts != null && phase.scripts[packageName] != null &&
-      phase.scripts[packageName]['after-yarn'] != null) {
-    phase.scripts[packageName]['after-yarn'].forEach(script => $(script));
-  }
-}
-
-export async function getReleaseBranch(name: string): Promise<string> {
-  // Infer release branch name.
-  let releaseBranch = '';
-
-  // Get a list of branches sorted by timestamp in descending order.
-  const branchesStr = $(
-      `git branch -r --sort=-authordate --format='%(HEAD) %(refname:lstrip=-1)'`);
-  const branches =
-      Array.from(branchesStr.split(/\n/)).map(line => line.toString().trim());
-
-  // Find the latest matching branch, e.g. tfjs_1.7.1
-  // It will not match temprary generated branches such as tfjs_1.7.1_phase0.
-  const exp = '^' + name + '_([^_]+)$';
-  const regObj = new RegExp(exp);
-  const maybeBranch = branches.find(branch => branch.match(regObj));
-  releaseBranch = await question(
-      `Which release branch (leave empty for ` +
-      `${maybeBranch}):`);
-  if (releaseBranch === '') {
-    releaseBranch = maybeBranch;
-  }
-
-  return releaseBranch;
-}
-
-export function checkoutReleaseBranch(
-    releaseBranch: string, git_protocol: string, dir: string) {
-  console.log(chalk.magenta.bold(
-      `~~~ Checking out release branch ${releaseBranch} ~~~`));
-  $(`rm -f -r ${dir}`);
-  mkdirp(dir, err => {
-    if (err) {
-      console.log('Error creating temp dir', dir);
-      process.exit(1);
-    }
-  });
-
-  const urlBase = git_protocol ? 'git@github.com:' : 'https://github.com/';
-  $(`git clone -b ${releaseBranch} ${urlBase}tensorflow/tfjs ${dir} --depth=1`);
-}
-
-export function createPR(
-    devBranchName: string, releaseBranch: string, message: string) {
-  console.log(
-      chalk.magenta.bold('~~~ Creating PR to update release branch ~~~'));
-  $(`git checkout -b ${devBranchName}`);
-  $(`git push -u origin ${devBranchName}`);
-  $(`git add .`);
-  $(`git commit -a -m "${message}"`);
-  $(`git push`);
-
-  $(`hub pull-request -b ${releaseBranch} -m "${message}" -l INTERNAL -o`);
-  console.log();
-}
-
-/**
- * Get all GitHub issues tagged as release blockers.
- *
- * @return A string of all the issues. Empty if there are none.
- */
-export function getReleaseBlockers() {
-  return $('hub issue -l "RELEASE BLOCKER"');
-}
-
-// Computes the default updated version (does a patch version update).
-export function getPatchUpdateVersion(version: string): string {
-  const versionSplit = version.split('.');
-
-  // For alpha or beta version string (e.g. "0.0.1-alpha.5"), increase the
-  // number after alpha/beta.
-  if (versionSplit[2].includes('alpha') || versionSplit[2].includes('beta')) {
-    return [
-      versionSplit[0], versionSplit[1], versionSplit[2], +versionSplit[3] + 1
-    ].join('.');
-  }
-
-  return [versionSplit[0], versionSplit[1], +versionSplit[2] + 1].join('.');
-}
-
-
-/**
- * Get the next minor update version for the given version.
- *
- * e.g. given 1.2.3, return 1.3.0
- */
-export function getMinorUpdateVersion(version: string): string {
-  const versionSplit = version.split('.');
-
-  return [versionSplit[0], + versionSplit[1] + 1, '0'].join('.');
-}
-
-/**
- * Create the nightly version string by appending `dev-{current date}` to the
- * given version.
- *
- * Versioning format is from semver: https://semver.org/spec/v2.0.0.html
- * This version should be published with the 'next' tag and should increment the
- * current 'latest' tfjs version.
- * We approximate TypeScript's versioning practice as seen on their npm page
- * https://www.npmjs.com/package/typescript?activeTab=versions
- */
-export function getNightlyVersion(version: string): string {
-  // Format date to YYYYMMDD.
-  const date =
-      new Date().toISOString().split('T')[0].replace(new RegExp('-', 'g'), '');
-  return `${version}-dev.${date}`;
-}
-
-/**
- * Filter a list with an async filter function
- */
-async function filterAsync<T>(
-  array: T[],
-  condition: (t: T) => Promise<boolean>): Promise<T[]> {
-
-  const results = await Promise.all(array.map(condition));
-  return array.filter((_val, index) => results[index]);
-}
-
-/**
- * Get the packages contained in the given release units.
- */
-export function getPackages(releaseUnits: ReleaseUnit[]): string[] {
-  return releaseUnits.map(releaseUnit => releaseUnit.phases)
-    .flat().map(phase => phase.packages)
-    .flat();
-}
-
-/**
- * Filter packages in release units according to an async filter.
- */
-export async function filterPackages(filter: (pkg: string) => Promise<boolean>,
-                                     releaseUnits = RELEASE_UNITS) {
-  return filterAsync(getPackages(releaseUnits), filter);
-}
-
-export async function selectPackages({
-  message = "Select packages",
-  selected = async (_pkg: string) => false,
-  modifyName = async (name: string) => name,
-  releaseUnits = RELEASE_UNITS}) {
-
-  type SeparatorInstance = InstanceType<typeof Separator>;
-  type Choice = {name: string, checked: boolean};
-
-  // Using Array.map instead of for loops for better performance from
-  // Promise.all. Otherwise, it can take ~10 seconds to show the packages
-  // if modifyName or selected take a long time.
-  const choices = await Promise.all<SeparatorInstance | Promise<Choice>>(
-    releaseUnits
-      .map(releaseUnit => [
-        new inquirer.Separator( // Separate release units with a line
-          chalk.underline(releaseUnit.name)),
-        ...releaseUnit.phases // Display the packages of a release unit.
-          .map(phase => phase.packages
-               .map(async pkg => {
-                 const [name, checked] = await Promise.all([
-                   modifyName(pkg), selected(pkg)]);
-                 return {name, value: pkg, checked};
-               }) // Promise<Choice>[] from one phase's packages
-              ).flat() // Promise<Choice>[] from one release unit
-      ]).flat() // (Separator | Promise<Choice>)[] for all release units
-  );
-
-  const choice = await inquirer.prompt({
-    name: 'packages',
-    type: 'checkbox',
-    message,
-    pageSize: 30,
-    choices,
-    loop: false,
-  } as {name: 'packages'});
-
-  return choice['packages'] as string[];
-}
-
-export function getVersion(packageJsonPath: string) {
-  return JSON.parse(fs.readFileSync(packageJsonPath)
-                    .toString('utf8')).version as string;
-}
-
-export function getLocalVersion(pkg: string) {
-  return getVersion(path.join(pkg, 'package.json'));
-}
-
-export async function getNpmVersion(pkg: string, registry?: string,
-                                    tag = 'latest') {
-  const env: Record<string, string> = {};
-  if (registry) {
-    env['NPM_CONFIG_REGISTRY'] = registry;
-  }
-  return $async(`npm view @tensorflow/${pkg} dist-tags.${tag}`, env);
-}
-
-export function getTagFromVersion(version: string): string {
-  if (version.includes('dev')) {
-    return 'nightly';
-  }else if (version.includes('rc')) {
-    return 'next';
-  }
-  return 'latest';
-}
-
-export function memoize<I, O>(f: (arg: I) => Promise<O>): (arg: I) => Promise<O> {
-  const map = new Map<I, Promise<O>>();
-  return async (i: I) => {
-    if (!map.has(i)) {
-      map.set(i, f(i));
-    }
-    return map.get(i)!;
-  }
-}
-
-export async function runVerdaccio(): Promise<() => void> {
-  // Remove the verdaccio package store.
-  // TODO(mattsoulanille): Move the verdaccio storage and config file here
-  // once the nightly verdaccio tests are handled by this script.
-  rimraf.sync(path.join(__dirname, '../e2e/scripts/storage'));
-
-  // Start verdaccio. It must be started directly from its binary so that IPC
-  // messaging works and verdaccio can tell node that it has started.
-  // https://verdaccio.org/docs/verdaccio-programmatically/#using-fork-from-child_process-module
-  const verdaccioBin = require.resolve('verdaccio/bin/verdaccio');
-  const config = path.join(__dirname, '../e2e/scripts/verdaccio.yaml');
-  const serverProcess = fork(verdaccioBin, [`--config=${config}`]);
-  const ready = new Promise<void>((resolve, reject) => {
-    const timeLimitMilliseconds = 30_000;
-    console.log(`Waiting ${timeLimitMilliseconds / 1000} seconds for ` +
-                'verdaccio to start....');
-    const timeout = setTimeout(() => {
-      serverProcess.kill();
-      reject(`Verdaccio did not start in ${timeLimitMilliseconds} seconds.`);
-    }, timeLimitMilliseconds);
-
-    serverProcess.on('message', (msg: {verdaccio_started: boolean}) => {
-      if (msg.verdaccio_started) {
-        console.log(chalk.magenta.bold(
-            `Verdaccio Started. Visit http://localhost:4873 to see packages.`));
-        clearTimeout(timeout);
-        resolve();
-      }
-    });
-  });
-
-  serverProcess.on('error', (err: unknown) => {
-    throw new Error(`Verdaccio error: ${err}`);
-  });
-
-  const onUnexpectedDisconnect = (err: unknown) => {
-    throw new Error(`Verdaccio process unexpectedly disconnected: ${err}`);
-  };
-  serverProcess.on('disconnect', onUnexpectedDisconnect);
-
-  const killVerdaccio = () => {
-    serverProcess.off('disconnect', onUnexpectedDisconnect);
-    serverProcess.kill();
-  };
-
-  // Kill verdaccio when node exits.
-  process.on('exit', killVerdaccio);
-
-  await ready;
-  return killVerdaccio;
-}
-
-/**
- * Check a package.json path for `link://` and `file://` dependencies.
- */
-export function checkPublishable(packageJsonPath: string): void {
-  const packageJson = JSON.parse(
-    fs.readFileSync(packageJsonPath)
-      .toString('utf8')) as {
-        name?: string,
-        private?: boolean,
-        dependencies?: Record<string, string>,
-      };
-
-  if (!packageJson.name) {
-    throw new Error(`${packageJsonPath} has no name.`);
-  }
-  const pkg = packageJson.name;
-  if (packageJson.private) {
-    throw new Error(`${pkg} is private.`);
-  }
-
-  if (packageJson.dependencies) {
-    for (let [dep, depVersion] of Object.entries(packageJson.dependencies)) {
-      const start = depVersion.slice(0,5);
-      if (start === 'link:' || start === 'file:') {
-        throw new Error(`${pkg} has a '${start}' dependency on ${dep}. `
-                        + 'Refusing to publish.');
-      }
-    }
-  }
-}
diff --git a/tfjs-master/scripts/release-website.ts b/tfjs-master/scripts/release-website.ts
deleted file mode 100644
index c55ae4094..000000000
--- a/tfjs-master/scripts/release-website.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This script creates pull requests to make release for tfjs website. Once the
- * pull request is merged, you must deploy the website.
- *
- * This script requires hub to be installed: https://hub.github.com/
- */
-import chalk from 'chalk';
-import * as fs from 'fs';
-import * as shell from 'shelljs';
-
-import {$, makeReleaseDir, TMP_DIR, WEBSITE_RELEASE_UNIT, updateDependency, prepareReleaseBuild, createPR} from './release-util';
-
-export async function releaseWebsite(args: any) {
-  const {phases, repo} = WEBSITE_RELEASE_UNIT;
-  // Website release only has one phase.
-  const phase = phases[0];
-  const packages = phases[0].packages;
-  const deps = phases[0].deps || [];
-
-  const dir = `${TMP_DIR}/${repo}`;
-  makeReleaseDir(dir);
-
-  const urlBase = args.git_protocol ? 'git@github.com:' : 'https://github.com/';
-
-  // Publishing website, another repo.
-  $(`git clone ${urlBase}tensorflow/${repo} ${dir} --depth=1`);
-  shell.cd(dir);
-
-  for (let i = 0; i < packages.length; i++) {
-    const packageName = packages[i];
-
-    // Update the version.
-    const packageJsonPath = `${dir}/package.json`;
-    let pkg = `${fs.readFileSync(packageJsonPath)}`;
-    const parsedPkg = JSON.parse(`${pkg}`);
-    const latestVersion = parsedPkg.version;
-
-    console.log(chalk.magenta.bold(
-        `~~~ Processing ${packageName} (${latestVersion}) ~~~`));
-
-    pkg = await updateDependency(deps, pkg, parsedPkg);
-
-    fs.writeFileSync(packageJsonPath, pkg);
-
-    prepareReleaseBuild(phase, packageName);
-  }
-
-  const timestamp = Date.now();
-  const branchName = `master_${timestamp}`;
-
-  createPR(branchName, 'master', phase.title);
-}
diff --git a/tfjs-master/scripts/release.ts b/tfjs-master/scripts/release.ts
deleted file mode 100644
index c0aa9e8fb..000000000
--- a/tfjs-master/scripts/release.ts
+++ /dev/null
@@ -1,183 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This script creates pull requests to make releases for all the TensorFlow.js
- * packages. The release process is split up into multiple phases. Each
- * phase will update the version of a package and the dependency versions and
- * send a pull request. Once the pull request is merged, you must publish the
- * packages manually from the individual packages.
- *
- * This script requires hub to be installed: https://hub.github.com/
- */
-
-import * as argparse from 'argparse';
-import chalk from 'chalk';
-import semver from 'semver';
-import * as fs from 'fs';
-import * as shell from 'shelljs';
-import {RELEASE_UNITS, WEBSITE_RELEASE_UNIT, TMP_DIR, $, question, printReleaseUnit, printPhase, makeReleaseDir, updateDependency, prepareReleaseBuild, createPR, getPatchUpdateVersion, ALPHA_RELEASE_UNIT} from './release-util';
-import {releaseWebsite} from './release-website';
-
-const parser = new argparse.ArgumentParser();
-
-parser.addArgument('--git-protocol', {
-  action: 'storeTrue',
-  help: 'Use the git protocal rather than the http protocol when cloning repos.'
-});
-
-async function main() {
-  const args = parser.parseArgs();
-
-  // The alpha release unit is released with the monorepo and should not be
-  // released by this script. Packages in the alpha release unit need their
-  // package.json dependencies rewritten.
-  const releaseUnits = RELEASE_UNITS.filter(r => r !== ALPHA_RELEASE_UNIT);
-  releaseUnits.forEach(printReleaseUnit);
-  console.log();
-
-  const releaseUnitStr =
-      await question('Which release unit (leave empty for 0): ');
-  const releaseUnitInt = +releaseUnitStr;
-  if (releaseUnitInt < 0 || releaseUnitInt >= releaseUnits.length) {
-    console.log(chalk.red(`Invalid release unit: ${releaseUnitStr}`));
-    process.exit(1);
-  }
-  console.log(chalk.blue(`Using release unit ${releaseUnitInt}`));
-  console.log();
-
-  const releaseUnit = releaseUnits[releaseUnitInt];
-  const {name, phases} = releaseUnit;
-
-  phases.forEach((_, i) => printPhase(phases, i));
-  console.log();
-
-  const phaseStr = await question('Which phase (leave empty for 0): ');
-  const phaseInt = +phaseStr;
-  if (phaseInt < 0 || phaseInt >= phases.length) {
-    console.log(chalk.red(`Invalid phase: ${phaseStr}`));
-    process.exit(1);
-  }
-  console.log(chalk.blue(`Using phase ${phaseInt}`));
-  console.log();
-
-  const phase = phases[phaseInt];
-  const packages = phases[phaseInt].packages;
-  const deps = phases[phaseInt].deps || [];
-
-  if (releaseUnit === WEBSITE_RELEASE_UNIT) {
-    await releaseWebsite(args);
-
-    console.log(
-        'Done. Please remember to deploy the website once you merged the PR.');
-
-    process.exit(0);
-  }
-
-  // Release packages in tfjs repo.
-  const dir = `${TMP_DIR}/tfjs`;
-  makeReleaseDir(dir);
-
-  const urlBase = args.git_protocol ? 'git@github.com:' : 'https://github.com/';
-  let releaseBranch = '';
-
-  if (phaseInt !== 0) {
-    // Phase0 should be published and release branch should have been created.
-    const firstPackageVersions: string[] = JSON.parse(
-        $(`npm view @tensorflow/${phases[0].packages[0]} versions --json`));
-    const firstPackageLatestVersion = semver.rsort(firstPackageVersions)[0];
-
-    releaseBranch = `${name}_${firstPackageLatestVersion}`;
-
-    $(`git clone -b ${releaseBranch} ${urlBase}tensorflow/tfjs ${
-        dir} --depth=1`);
-    shell.cd(dir);
-  } else {
-    // Phase0 needs user input of the release version to create release
-    // branch.
-    $(`git clone ${urlBase}tensorflow/tfjs ${dir} --depth=1`);
-    shell.cd(dir);
-  }
-
-  const newVersions = [];
-  for (let i = 0; i < packages.length; i++) {
-    const packageName = packages[i];
-    shell.cd(packageName);
-
-    // Update the version.
-    const packageJsonPath = `${dir}/${packageName}/package.json`;
-    let pkg = `${fs.readFileSync(packageJsonPath)}`;
-    const parsedPkg = JSON.parse(`${pkg}`);
-    const packageVersions: string[] =
-        JSON.parse($(`npm view @tensorflow/${packageName} versions --json`));
-    const latestVersion = semver.rsort(packageVersions)[0];
-
-    console.log(chalk.magenta.bold(
-        `~~~ Processing ${packageName} (${latestVersion}) ~~~`));
-
-    const patchUpdateVersion = getPatchUpdateVersion(latestVersion);
-    let newVersion = latestVersion;
-    newVersion =
-        await question(`New version (leave empty for ${patchUpdateVersion}): `);
-    if (newVersion === '') {
-      newVersion = patchUpdateVersion;
-    }
-
-    if (releaseBranch === '') {
-      releaseBranch = `${name}_${newVersion}`;
-      console.log(chalk.magenta.bold(
-          `~~~ Creating new release branch ${releaseBranch} ~~~`));
-      $(`git checkout -b ${releaseBranch}`);
-      $(`git push origin ${releaseBranch}`);
-    }
-
-    pkg = `${pkg}`.replace(
-        `"version": "${parsedPkg.version}"`, `"version": "${newVersion}"`);
-
-    pkg = await updateDependency(deps, pkg, parsedPkg);
-
-    fs.writeFileSync(packageJsonPath, pkg);
-
-    prepareReleaseBuild(phase, packageName);
-
-    shell.cd('..');
-
-    $(`./scripts/make-version.js ${packageName}`);
-
-    newVersions.push(newVersion);
-  }
-
-  const packageNames = packages.join(', ');
-  const versionNames = newVersions.join(', ');
-  const devBranchName = `dev_${releaseBranch}_phase${phaseInt}`;
-
-  const message = `Update ${packageNames} to ${versionNames}.`;
-  createPR(devBranchName, releaseBranch, message);
-
-  console.log(
-      `Done. FYI, this script does not publish to NPM. ` +
-      `Please publish by running  ` +
-      `YARN_REGISTRY="https://registry.npmjs.org/" yarn publish-npm ` +
-      `after you merge the PR.` +
-      `Please remeber to update the website once you have released ` +
-      'a new package version');
-
-  process.exit(0);
-}
-
-main();
diff --git a/tfjs-master/scripts/release_notes/release_notes.ts b/tfjs-master/scripts/release_notes/release_notes.ts
deleted file mode 100644
index 107f03fb3..000000000
--- a/tfjs-master/scripts/release_notes/release_notes.ts
+++ /dev/null
@@ -1,356 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Generates a draft release notes markdown file for a release. This script
- * takes a start version of the union package, and optionally an end version.
- * It then finds the matching versions for all the dependency packages, and
- * finds all commit messages between those versions.
- *
- * The release notes are grouped by repository, and then bucketed by a set of
- * tags which committers can use to organize commits into sections. See
- * DEVELOPMENT.md for more details on the available tags.
- *
- * This script will ask for your github token which is used to make requests
- * to the github API for usernames for commits as this is not stored in git
- * logs. You can generate a token for your account here;
- * https://github.com/settings/tokens
- *
- * Usage:
- *   yarn release-notes
- */
-
-import * as argparse from 'argparse';
-import * as fs from 'fs';
-import * as util from './util';
-import * as path from 'path';
-import {$, Commit, Repo, RepoCommits} from './util';
-
-// tslint:disable-next-line:no-require-imports
-const octokit = require('@octokit/rest')();
-
-const OUT_FILE = path.resolve('release-notes.md');
-
-const TFJS_REPOS: Repo[] = [
-  {name: 'Core', identifier: 'tfjs', path: 'tfjs-core'},
-  {name: 'Data', identifier: 'tfjs', path: 'tfjs-data'},
-  {name: 'Layers', identifier: 'tfjs', path: 'tfjs-layers'},
-  {name: 'Converter', identifier: 'tfjs', path: 'tfjs-converter'},
-  {name: 'Node', identifier: 'tfjs', path: 'tfjs-node'},
-  {name: 'Wasm', identifier: 'tfjs', path: 'tfjs-backend-wasm'},
-  {name: 'Cpu', identifier: 'tfjs', path: 'tfjs-backend-cpu'},
-  {name: 'Webgl', identifier: 'tfjs', path: 'tfjs-backend-webgl'},
-  {name: 'WebGPU', identifier: 'tfjs', path: 'tfjs-backend-webgpu'},
-];
-
-const VIS_REPO: Repo = {
-  name: 'tfjs-vis',
-  identifier: 'tfjs-vis',
-  path: 'tfjs-vis',
-};
-
-const RN_REPO: Repo = {
-  name: 'tfjs-react-native',
-  identifier: 'tfjs-react-native',
-  path: 'tfjs-react-native',
-};
-
-const TFDF_REPO: Repo = {
-  name: 'tfjs-tfdf',
-  identifier: 'tfjs-tfdf',
-  path: 'tfjs-tfdf',
-};
-
-const TFLITE_REPO: Repo = {
-  name: 'tfjs-tflite',
-  identifier: 'tfjs-tflite',
-  path: 'tfjs-tflite',
-};
-
-const WEBGPU_REPO: Repo = {
-  name: 'tfjs-backend-webgpu',
-  identifier: 'tfjs-backend-webgpu',
-  path: 'tfjs-backend-webgpu',
-};
-
-const AUTOML_REPO: Repo = {
-  name: 'tfjs-automl',
-  identifier: 'tfjs-automl',
-  path: 'tfjs-automl',
-};
-
-async function askUserForVersions(validVersions: string[], packageName: string):
-    Promise<{startVersion: string, endVersion: string}> {
-  const YELLOW_TERMINAL_COLOR = '\x1b[33m%s\x1b[0m';
-  const RED_TERMINAL_COLOR = '\x1b[31m%s\x1b[0m';
-
-  console.log(YELLOW_TERMINAL_COLOR, packageName + ' versions');
-  console.log(validVersions.join(', '));
-  const startVersion = await util.question(`Enter the start version: `);
-  if (validVersions.indexOf(startVersion) === -1) {
-    console.log(RED_TERMINAL_COLOR, `Unknown start version: ${startVersion}`);
-    process.exit(1);
-  }
-  const defaultVersion = validVersions[validVersions.length - 1];
-  let endVersion = await util.question(
-      `Enter the end version (leave empty for ${defaultVersion}): `);
-  if (endVersion === '') {
-    endVersion = defaultVersion;
-  }
-  if (validVersions.indexOf(endVersion) === -1) {
-    console.log(RED_TERMINAL_COLOR, `Unknown end version: ${endVersion}`);
-    process.exit(1);
-  }
-  return {startVersion, endVersion};
-}
-
-function getTaggedVersions(packageName: string) {
-  const versions =
-      $(`git tag`)
-          .split('\n')
-          .filter(x => new RegExp('^' + packageName + '-v([0-9])').test(x))
-          .map(x => x.substring((packageName + '-v').length));
-  return versions;
-}
-
-function getTagName(packageName: string, version: string) {
-  return packageName + '-v' + version;
-}
-
-async function generateTfjsPackageNotes() {
-  // Get union start version and end version.
-  const identifier = 'tfjs';
-  const versions = getTaggedVersions(identifier);
-  const {startVersion, endVersion} =
-      await askUserForVersions(versions, identifier);
-  const startCommit =
-      `git rev-list -n 1 ` + getTagName(identifier, startVersion);
-
-  // Populate start and end for each of the tfjs packages.
-  TFJS_REPOS.forEach(repo => {
-    // Find the version of the dependency from the package.json from the
-    // earliest tfjs tag.
-    repo.startCommit = startCommit;
-    repo.startVersion = startVersion;
-    repo.endVersion = endVersion;
-  });
-
-  await generateNotes(TFJS_REPOS);
-}
-
-async function generateVisNotes() {
-  // Get union start version and end version.
-  const versions = getTaggedVersions('tfjs-vis');
-  const {startVersion, endVersion} =
-      await askUserForVersions(versions, 'tfjs-vis');
-
-  // Get tfjs-vis start version and end version.
-  VIS_REPO.startVersion = startVersion;
-  VIS_REPO.endVersion = endVersion;
-  VIS_REPO.startCommit = $(`git rev-list -n 1 ${
-      getTagName(VIS_REPO.identifier, VIS_REPO.startVersion)}`);
-
-  await generateNotes([VIS_REPO]);
-}
-
-
-async function generateReactNativeNotes() {
-  // Get start version and end version.
-  const versions = getTaggedVersions('tfjs-react-native');
-  const {startVersion, endVersion} =
-      await askUserForVersions(versions, 'tfjs-react-native');
-
-  // Get tfjs-react-native start version and end version.
-  RN_REPO.startVersion = startVersion;
-  RN_REPO.endVersion = endVersion;
-  RN_REPO.startCommit = $(`git rev-list -n 1 ${
-      getTagName(RN_REPO.identifier, RN_REPO.startVersion)}`);
-
-  await generateNotes([RN_REPO]);
-}
-
-async function generateTfdfNotes() {
-  // Get start version and end version.
-  const versions = getTaggedVersions('tfjs-tfdf');
-  const {startVersion, endVersion} =
-      await askUserForVersions(versions, 'tfjs-tfdf');
-
-  // Get tfjs-tfdf start version and end version.
-  TFDF_REPO.startVersion = startVersion;
-  TFDF_REPO.endVersion = endVersion;
-  TFDF_REPO.startCommit = $(`git rev-list -n 1 ${
-      getTagName(TFDF_REPO.identifier, TFDF_REPO.startVersion)}`);
-
-  await generateNotes([TFDF_REPO]);
-}
-
-async function generateTfliteNotes() {
-  // Get start version and end version.
-  const versions = getTaggedVersions('tfjs-tflite');
-  const {startVersion, endVersion} =
-      await askUserForVersions(versions, 'tfjs-tflite');
-
-  // Get tfjs-tflite start version and end version.
-  TFLITE_REPO.startVersion = startVersion;
-  TFLITE_REPO.endVersion = endVersion;
-  TFLITE_REPO.startCommit = $(`git rev-list -n 1 ${
-      getTagName(TFLITE_REPO.identifier, TFLITE_REPO.startVersion)}`);
-
-  await generateNotes([TFLITE_REPO]);
-}
-
-async function generateWebgpuNotes() {
-  // Get start version and end version.
-  const versions = getTaggedVersions('tfjs-backend-webgpu');
-  const {startVersion, endVersion} =
-      await askUserForVersions(versions, 'tfjs-backend-webgpu');
-
-  // Get tfjs-webgpu start version and end version.
-  WEBGPU_REPO.startVersion = startVersion;
-  WEBGPU_REPO.endVersion = endVersion;
-  WEBGPU_REPO.startCommit = $(`git rev-list -n 1 ${
-      getTagName(WEBGPU_REPO.identifier, WEBGPU_REPO.startVersion)}`);
-
-  await generateNotes([WEBGPU_REPO]);
-}
-
-async function generateAutomlNotes() {
-  // Get start version and end version.
-  const versions = getTaggedVersions('tfjs-automl');
-  const {startVersion, endVersion} =
-      await askUserForVersions(versions, 'tfjs-automl');
-
-  // Get tfjs-automl start version and end version.
-  AUTOML_REPO.startVersion = startVersion;
-  AUTOML_REPO.endVersion = endVersion;
-  AUTOML_REPO.startCommit = $(`git rev-list -n 1 ${
-      getTagName(AUTOML_REPO.identifier, AUTOML_REPO.startVersion)}`);
-
-  await generateNotes([AUTOML_REPO]);
-}
-
-async function generateNotes(repositories: util.Repo[]) {
-  const repoCommits: RepoCommits[] = [];
-  // Clone all of the dependencies into the tmp directory.
-  repositories.forEach(repo => {
-    console.log(
-        `${repo.name}: ${repo.startVersion}` +
-        ` =====> ${repo.endVersion}`);
-
-    console.log('Querying commits...');
-    // Get subjects, bodies, emails, etc from commit metadata.
-    const commitFieldQueries = ['%s', '%b', '%aE', '%H'];
-    const commitFields = commitFieldQueries.map(query => {
-      // Use a unique delimiter so we can split the log.
-      const uniqueDelimiter = '--^^&&';
-      const versionQuery = repo.startVersion != null ?
-          `${getTagName(repo.identifier, repo.startVersion)}..` +
-              `${getTagName(repo.identifier, repo.endVersion)}` :
-          `#${repo.startCommit}..${
-              getTagName(repo.identifier, repo.endVersion)}`;
-      return $(`git log --pretty=format:"${query}${uniqueDelimiter}" ` +
-               `${versionQuery}`)
-          .trim()
-          .split(uniqueDelimiter)
-          .slice(0, -1)
-          .map(str => str.trim());
-    });
-
-    const commits: Commit[] = [];
-    for (let i = 0; i < commitFields[0].length; i++) {
-      // Make sure the files touched contain the repo directory.
-      const filesTouched =
-          $(`git show --pretty="format:" --name-only ${commitFields[3][i]}`)
-              .split('\n');
-      let touchedDir = false;
-      for (let j = 0; j < filesTouched.length; j++) {
-        if (filesTouched[j].startsWith(repo.path)) {
-          touchedDir = true;
-          break;
-        }
-      }
-      if (!touchedDir) {
-        continue;
-      }
-
-      commits.push({
-        subject: commitFields[0][i],
-        body: commitFields[1][i],
-        authorEmail: commitFields[2][i],
-        sha: commitFields[3][i]
-      });
-    }
-
-    repoCommits.push({
-      repo,
-      startVersion: repo.startVersion,
-      endVersion: repo.endVersion,
-      startCommit: repo.startCommit,
-      commits
-    });
-  });
-
-  // Ask for github token.
-
-  // Getting the github token.
-  let token = process.env.GITHUB_TOKEN;
-  if (token == null) {
-    token = await util.question(
-        'Enter GitHub token (https://github.com/settings/tokens): ');
-
-  } else {
-    console.log('The GITHUB_TOKEN is present as environment variable');
-  }
-  octokit.authenticate({type: 'token', token});
-
-  const notes = await util.getReleaseNotesDraft(octokit, repoCommits);
-
-  fs.writeFileSync(OUT_FILE, notes);
-
-  console.log('Done writing notes to', OUT_FILE);
-
-  // So the script doesn't just hang.
-  process.exit(0);
-}
-
-const parser = new argparse.ArgumentParser();
-
-parser.addArgument('--project', {
-  help:
-      'Which project to generate release notes for. One of union|vis|rn|tfdf|tflite|webgpu|automl. Defaults to union.',
-  defaultValue: 'union',
-  choices: ['union', 'vis', 'rn', 'tfdf', 'tflite', 'webgpu', 'automl']
-});
-
-const args = parser.parseArgs();
-
-if (args.project === 'union') {
-  generateTfjsPackageNotes();
-} else if (args.project === 'vis') {
-  generateVisNotes();
-} else if (args.project === 'rn') {
-  generateReactNativeNotes();
-} else if (args.project === 'tfdf') {
-  generateTfdfNotes();
-} else if (args.project === 'tflite') {
-  generateTfliteNotes();
-} else if (args.project === 'webgpu') {
-  generateWebgpuNotes();
-} else if (args.project === 'automl') {
-  generateAutomlNotes();
-}
diff --git a/tfjs-master/scripts/release_notes/release_notes_test.ts b/tfjs-master/scripts/release_notes/release_notes_test.ts
deleted file mode 100644
index df369377f..000000000
--- a/tfjs-master/scripts/release_notes/release_notes_test.ts
+++ /dev/null
@@ -1,218 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as util from './util';
-import {OctokitGetCommit, RepoCommits} from './util';
-
-const fakeCommitContributors: {[key: string]: string;} = {
-  'sha1': 'fakecontributor1',
-  'sha2': 'fakecontributor2',
-  'sha3': 'fakecontributor3'
-};
-
-const missingAuthorLoginFake = {
-  name: 'example',
-  email: 'example@example.com'
-};
-
-
-const fakeOctokit: OctokitGetCommit = {
-  repos: {
-    getCommit: (config: {owner: string, repo: string, sha: string}) => {
-      return {
-        data: {
-          author: {
-            login: fakeCommitContributors[config.sha],
-          },
-          commit: {
-            author: {
-              name: missingAuthorLoginFake.name,
-              email: missingAuthorLoginFake.email
-            }
-          }
-        }
-      };
-    }
-  }
-};
-
-describe('getReleaseNotesDraft', () => {
-  it('Basic draft written', done => {
-    const repoCommits: RepoCommits[] = [{
-      repo: {name: 'Core', identifier: 'tfjs', path: 'tfjs-core'},
-      startVersion: '0.9.0',
-      endVersion: '0.10.0',
-      startCommit: 'fakecommit',
-      commits: [{
-        subject: 'Add tf.toPixels. (#900)',
-        body: `
-          tf.toPixels is the inverse of tf.fromPixels.
-          FEATURE
-        `,
-        authorEmail: 'test@google.com',
-        sha: 'sha1'
-      }]
-    }];
-
-    util.getReleaseNotesDraft(fakeOctokit, repoCommits).then(notes => {
-      expect(notes).toEqual([
-        '## Core (0.9.0 ==> 0.10.0)', '', '### Features',
-        '- Add tf.toPixels. ([#900]' +
-            '(https://github.com/tensorflow/tfjs/pull/900)).'
-      ].join('\n'));
-      done();
-    });
-  });
-
-  it('Basic draft external contributor thanks them', done => {
-    const repoCommits: RepoCommits[] = [{
-      repo: {name: 'Core', identifier: 'tfjs', path: 'tfjs-core'},
-      startVersion: '0.9.0',
-      endVersion: '0.10.0',
-      startCommit: 'fakecommit',
-      commits: [{
-        subject: 'Add tf.toPixels. (#900)',
-        body: `
-          tf.toPixels is the inverse of tf.fromPixels.
-          FEATURE
-        `,
-        authorEmail: 'test@gmail.com',
-        sha: 'sha1'
-      }]
-    }];
-
-    util.getReleaseNotesDraft(fakeOctokit, repoCommits).then(notes => {
-      expect(notes).toEqual([
-        '## Core (0.9.0 ==> 0.10.0)', '', '### Features',
-        '- Add tf.toPixels. ([#900]' +
-            '(https://github.com/tensorflow/tfjs/pull/900)).' +
-            ' Thanks, @fakecontributor1.'
-      ].join('\n'));
-      done();
-    });
-  });
-
-  it('Complex draft', done => {
-    const repoCommits: RepoCommits[] = [
-      {
-        repo: {name: 'Core', identifier: 'tfjs', path: 'tfjs-core'},
-        startVersion: '0.9.0',
-        endVersion: '0.10.0',
-        startCommit: 'fakecommit',
-        commits: [
-          {
-            subject: 'Add tf.toPixels. (#900)',
-            body: `
-              tf.toPixels is the inverse of tf.fromPixels.
-            `,
-            authorEmail: 'test@gmail.com',
-            sha: 'sha1'
-          },
-          {
-            subject: 'Improvements to matMul. (#901)',
-            body: `
-              Makes matmul better overall.
-              FEATURE Adds transpose bit.
-              PERF Improves speed of matMul by 100%.
-            `,
-            authorEmail: 'test@gmail.com',
-            sha: 'sha2'
-          }
-        ]
-      },
-      {
-        repo: {name: 'Layers', identifier: 'tfjs', path: 'tfjs-layers'},
-        startVersion: '0.4.0',
-        endVersion: '0.5.1',
-        startCommit: 'fakecommit2',
-        commits: [{
-          subject: 'Change API of layers. (#100)',
-          body: `
-            tf.toPixels is the inverse of tf.fromPixels.
-            BREAKING
-            DOC Update docstrings of tf.layers.dense.
-            FEATURE Add automatic argument parsing.
-          `,
-          authorEmail: 'test@gmail.com',
-          sha: 'sha1'
-        }]
-      }
-    ];
-
-    util.getReleaseNotesDraft(fakeOctokit, repoCommits).then(notes => {
-      expect(notes).toEqual([
-        '## Core (0.9.0 ==> 0.10.0)',
-        '',
-        '### Features',
-        '- Adds transpose bit. [Improvements to matMul.] ([#901]' +
-            '(https://github.com/tensorflow/tfjs/pull/901)).' +
-            ' Thanks, @fakecontributor2.',
-        '### Performance',
-        '- Improves speed of matMul by 100%. [Improvements to matMul.]' +
-            ' ([#901](https://github.com/tensorflow/tfjs/pull/901)).' +
-            ' Thanks, @fakecontributor2.',
-        '### Misc',
-        '- Add tf.toPixels. ([#900]' +
-            '(https://github.com/tensorflow/tfjs/pull/900)).' +
-            ' Thanks, @fakecontributor1.',
-        '',
-        '## Layers (0.4.0 ==> 0.5.1)',
-        '',
-        '### Features',
-        '- Add automatic argument parsing. [Change API of layers.] ' +
-            '([#100](https://github.com/tensorflow/tfjs/pull/100)).' +
-            ' Thanks, @fakecontributor1.',
-
-        '### Breaking changes',
-        '- Change API of layers. ' +
-            '([#100](https://github.com/tensorflow/tfjs/pull/100)).' +
-            ' Thanks, @fakecontributor1.',
-        '### Documentation',
-        '- Update docstrings of tf.layers.dense. [Change API of layers.] ' +
-            '([#100](https://github.com/tensorflow/tfjs/pull/100)).' +
-            ' Thanks, @fakecontributor1.',
-      ].join('\n'));
-      done();
-    });
-  });
-
-  it('Subject has no pull request number', done => {
-    const repoCommits: RepoCommits[] = [{
-      repo: {name: 'Core', identifier: 'tfjs', path: 'tfjs-core'},
-      startVersion: '0.9.0',
-      endVersion: '0.10.0',
-      startCommit: 'fakecommit',
-      commits: [{
-        subject: 'Add tf.toPixels.',
-        body: `
-          tf.toPixels is the inverse of tf.fromPixels.
-          FEATURE
-        `,
-        authorEmail: 'test@google.com',
-        sha: 'sha1'
-      }]
-    }];
-
-    util.getReleaseNotesDraft(fakeOctokit, repoCommits).then(notes => {
-      expect(notes).toEqual([
-        '## Core (0.9.0 ==> 0.10.0)', '', '### Features', '- Add tf.toPixels.'
-      ].join('\n'));
-      done();
-    });
-  });
-});
diff --git a/tfjs-master/scripts/release_notes/run_tests.ts b/tfjs-master/scripts/release_notes/run_tests.ts
deleted file mode 100644
index 413f972f0..000000000
--- a/tfjs-master/scripts/release_notes/run_tests.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line:no-require-imports
-const jasmineCtor = require('jasmine');
-const runner = new jasmineCtor();
-
-runner.loadConfig({spec_files: ['scripts/release_notes/**/*_test.ts']});
-runner.execute();
diff --git a/tfjs-master/scripts/release_notes/tsconfig.json b/tfjs-master/scripts/release_notes/tsconfig.json
deleted file mode 100644
index 3f840fa91..000000000
--- a/tfjs-master/scripts/release_notes/tsconfig.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "extends": "../../tsconfig",
-  "compilerOptions": {
-    "module": "commonjs",
-    "target": "es5",
-  }
-}
diff --git a/tfjs-master/scripts/release_notes/util.ts b/tfjs-master/scripts/release_notes/util.ts
deleted file mode 100644
index 3d40c9105..000000000
--- a/tfjs-master/scripts/release_notes/util.ts
+++ /dev/null
@@ -1,242 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as shell from 'shelljs';
-import * as readline from 'readline';
-
-const GOOGLERS_WITH_GMAIL = new Set([
-  'dsmilkov',
-  'kainino0x',
-  'davidsoergel',
-  'pyu10055',
-  'nkreeger',
-  'tafsiri',
-  'annxingyuan',
-  'Kangyi Zhang',
-  'lina128',
-  'mattsoulanille',
-  'jinjingforever',
-  'chunnienc',
-  'Linchenn',
-  'fengwuyao',
-].map((s) => s.trim().toLowerCase()));
-
-function isGooglerUsername(username: string): boolean {
-  return GOOGLERS_WITH_GMAIL.has(username.trim().toLocaleLowerCase());
-}
-
-const rl =
-    readline.createInterface({input: process.stdin, output: process.stdout});
-
-/**
- * A wrapper around shell.exec for readability.
- * @param cmd The bash command to execute.
- * @returns stdout returned by the executed bash script.
- */
-export function $(cmd: string) {
-  const result = shell.exec(cmd, {silent: true});
-  if (result.code > 0) {
-    console.log('$', cmd);
-    console.log(result.stderr);
-    process.exit(1);
-  }
-  return result.stdout.trim();
-}
-
-export async function question(questionStr: string): Promise<string> {
-  return new Promise<string>(
-      resolve => rl.question(questionStr, response => resolve(response)));
-}
-
-export interface Repo {
-  name: string;
-  identifier: string;
-  path: string;
-  startVersion?: string;
-  startCommit?: string;
-  endVersion?: string;
-}
-
-export interface RepoCommits {
-  repo: Repo;
-  startVersion: string;
-  endVersion: string;
-  startCommit: string;
-  commits: Commit[];
-}
-
-export interface Commit {
-  subject: string;
-  body: string;
-  authorEmail: string;
-  sha: string;
-}
-
-export interface OctokitGetCommit {
-  repos: {
-    getCommit:
-        (config: {owner: string, repo: string, sha: string}) => {
-          data: {
-            commit: {
-              author: {
-                name: string,
-                email: string,
-              }
-            },
-            author: {login: string},
-          }
-        }
-  };
-}
-
-interface SectionTag {
-  section: string;
-  tag: string;
-}
-
-const SECTION_TAGS: SectionTag[] = [
-  {section: 'Features', tag: 'FEATURE'},
-  {section: 'Breaking changes', tag: 'BREAKING'},
-  {section: 'Bug fixes', tag: 'BUG'}, {section: 'Performance', tag: 'PERF'},
-  {section: 'Development', tag: 'DEV'}, {section: 'Documentation', tag: 'DOC'},
-  {section: 'Security', tag: 'SECURITY'}, {section: 'Misc', tag: 'MISC'},
-  {section: 'Internal', tag: 'INTERNAL'}
-];
-
-/**
- * Assembles the release note drafts from a set of commits.
- *
- * @param octokit An authenticated octokit object (to make github API
- *     requests).
- * It only needs to satisfy the OctokitGetCommit interface which gets commit
- * metadata.
- * @param repoCommits An object representing the metadata for commits to
- * assmemble into release notes.
- * @returns The release notes markdown draft as a string.
- */
-export async function getReleaseNotesDraft(
-    octokit: OctokitGetCommit, repoCommits: RepoCommits[]): Promise<string> {
-  const repoNotes: string[] = [];
-  for (let i = 0; i < repoCommits.length; i++) {
-    const repoCommit = repoCommits[i];
-
-    const getUsernameForCommit = async (sha: string) => {
-      let result;
-      try {
-        result = await octokit.repos.getCommit(
-            {owner: 'tensorflow', repo: 'tfjs', sha});
-        return result.data.author.login;
-      } catch (e) {
-        console.log(`Error fetching username for commit ${sha}`);
-        console.log(`Using ${result.data.commit.author.name}`);
-        return result.data.commit.author.name;
-      }
-    };
-
-    const tagEntries: {[tag: string]: string[]} = {};
-    SECTION_TAGS.forEach(({tag}) => tagEntries[tag] = []);
-
-    for (let j = 0; j < repoCommit.commits.length; j++) {
-      const commit = repoCommit.commits[j];
-
-      const tagsFound: Array<{tag: string, tagMessage: string}> = [];
-      const bodyLines = commit.body.split('\n').map(line => line.trim());
-      // Get tags for the body by finding lines that start with tags. Do
-      // this without a regex for readability.
-      SECTION_TAGS.forEach(({tag}) => {
-        if (tag === 'INTERNAL') {
-          return;
-        }
-
-        bodyLines.forEach(line => {
-          // Split by word boundaries, and make sure the first word is the
-          // tag.
-          const split = line.split(/\b/);
-          if (split[0] === tag) {
-            const tagMessage = line.substring(tag.length).trim();
-            tagsFound.push({tag, tagMessage});
-          }
-        });
-      });
-      // If no explicit tags, put this under misc.
-      if (tagsFound.length === 0) {
-        tagsFound.push({tag: 'MISC', tagMessage: ''});
-      }
-
-      const username = await getUsernameForCommit(commit.sha);
-      const isExternalContributor =
-          !commit.authorEmail.endsWith('@google.com') &&
-          !isGooglerUsername(username);
-
-      const pullRequestRegexp = /\(#([0-9]+)\)/;
-      const pullRequestMatch = commit.subject.match(pullRequestRegexp);
-
-      let subject = commit.subject;
-      let pullRequestNumber: string|null = null;
-      if (pullRequestMatch != null) {
-        subject = subject.replace(pullRequestRegexp, '').trim();
-        pullRequestNumber = pullRequestMatch[1];
-      }
-
-      for (let k = 0; k < tagsFound.length; k++) {
-        const {tag, tagMessage} = tagsFound[k];
-
-        // When the tag has no message, use the subject.
-        let entry;
-        if (tagMessage === '') {
-          entry = '- ' + subject;
-        } else {
-          entry = '- ' + tagMessage + ' [' + subject + ']';
-        }
-
-        // Attach the link to the pull request.
-        const pullRequestSuffix = pullRequestNumber != null ?
-            ` ([#${pullRequestNumber}]` +
-                `(https://github.com/tensorflow/tfjs/pull/${
-                    pullRequestNumber})).` :
-            '';
-
-        entry = entry.trim() + pullRequestSuffix;
-
-        // For external contributors, we need to query github because git
-        // does not contain github username metadatea.
-        if (isExternalContributor) {
-          entry += (!entry.endsWith('.') ? '.' : '') + ` Thanks, @${username}.`;
-        }
-
-        tagEntries[tag].push(entry);
-      }
-    }
-
-    const repoLines: string[] = [];
-    SECTION_TAGS.forEach(({tag, section}) => {
-      if (tagEntries[tag].length !== 0) {
-        const sectionNotes = tagEntries[tag].join('\n');
-        repoLines.push(`### ${section}`);
-        repoLines.push(sectionNotes);
-      }
-    });
-
-    const repoSection = `## ${repoCommit.repo.name} ` +
-        `(${repoCommit.startVersion} ==> ` +
-        `${repoCommit.endVersion})\n\n` + repoLines.join('\n');
-    repoNotes.push(repoSection);
-  }
-
-  return repoNotes.join('\n\n');
-}
diff --git a/tfjs-master/scripts/run-build.sh b/tfjs-master/scripts/run-build.sh
deleted file mode 100644
index ea5ae07e8..000000000
--- a/tfjs-master/scripts/run-build.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Echo every command being executed
-set -x
-
-# Exit the script on any command with non 0 return code
-set -e
-
-gcloud builds submit . --config=cloudbuild_generated.yml \
-  --substitutions _NIGHTLY=$NIGHTLY
diff --git a/tfjs-master/scripts/run_bazel_ci_tests.sh b/tfjs-master/scripts/run_bazel_ci_tests.sh
deleted file mode 100644
index a083a588e..000000000
--- a/tfjs-master/scripts/run_bazel_ci_tests.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Exit the script on any command with non 0 return code
-set -e
-
-if [ "$NIGHTLY" = true ]; then
-  # Run `bazel test` on all targets that have the "ci" or "nightly" tag.
-  TARGETS=`./node_modules/.bin/bazel query --output label 'attr("tags", "ci", ...) union attr("tags", "nightly", ...)'`
-else
-  # Run `bazel test` on all targets that have the "ci" tag.
-  TARGETS=`./node_modules/.bin/bazel query --output label 'attr("tags", "ci", ...)'`
-fi
-
-yarn bazel test --config=ci --flaky_test_attempts=3 --local_test_jobs=25 $TARGETS
diff --git a/tfjs-master/scripts/run_flaky.js b/tfjs-master/scripts/run_flaky.js
deleted file mode 100644
index acd2dddc5..000000000
--- a/tfjs-master/scripts/run_flaky.js
+++ /dev/null
@@ -1,69 +0,0 @@
-// Copyright 2021 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const {ArgumentParser} = require('argparse');
-const {exec} = require('shelljs');
-
-
-function main(args) {
-  const command = args['command'];
-  const times = parseInt(args['times']);
-
-  if (times === 0) {
-    throw new Error(`Flaky test asked to run zero times: '${command}'`);
-  }
-
-  console.log(`Running flaky test at most ${times} times`);
-  console.log(`Command: '${command}'`);
-  const exitCodes = [];
-  for (let i = 0; i < times; i++) {
-    console.log(`Flaky run ${i + 1} of a potential ${times} for '${command}'`);
-    const exitCode = exec(command).code;
-    exitCodes.push(exitCode);
-    if (exitCode === 0) {
-      break;
-    }
-  }
-
-  const success = exitCodes[exitCodes.length - 1] === 0;
-  if (!success) {
-    console.error(`Flaky test failed ${times} times`);
-  } else if (exitCodes.length > 1) {
-    console.warn(
-        `Flaky test failed ${exitCodes.length - 1} times before passing.`);
-  } else {
-    // Test passed with no fails
-    console.log('Flaky test passed on the first run');
-  }
-  console.error(`Command: '${command}'`);
-  console.error(`Exit codes: ${exitCodes}`);
-
-  if (!success) {
-    process.exit(1);
-  }
-}
-
-const parser = new ArgumentParser(
-    {description: 'Run a flaky test a number of times or until it passes'});
-
-parser.addArgument('command', {help: 'Flaky command to run'})
-parser.addArgument('--times', {
-  help: 'Maximum number of times to run the command',
-  defaultValue: 3,
-  nargs: '?',
-  type: 'int',
-});
-
-main(parser.parseArgs());
diff --git a/tfjs-master/scripts/run_flaky_test.js b/tfjs-master/scripts/run_flaky_test.js
deleted file mode 100644
index 03c34a181..000000000
--- a/tfjs-master/scripts/run_flaky_test.js
+++ /dev/null
@@ -1,46 +0,0 @@
-// Copyright 2021 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const {exec} = require('shelljs');
-const fs = require('fs');
-
-const {FILE_NAME} = require('./run_flaky_test_helper.js');
-
-describe('run_flaky', () => {
-  it('exits with zero if the command succeeds', () => {
-    expect(exec('node ./scripts/run_flaky.js "echo success"').code).toEqual(0);
-  });
-
-  it('exits with one if the command fails', () => {
-    expect(exec('node ./scripts/run_flaky.js "exit 1"').code).toEqual(1);
-  });
-
-  it('exits with zero if the command eventually succeeds', () => {
-    // Remove test file that was not cleaned up from a prior run.
-    if (fs.existsSync(FILE_NAME)) {
-      fs.unlinkSync(FILE_NAME);
-    }
-
-    // This command should fail once and then succeed the next run.
-    expect(exec(
-               'node ./scripts/run_flaky.js --times 2' +
-               ' \'node ./scripts/run_flaky_test_helper.js\'')
-               .code)
-        .toEqual(0);
-
-    // Clean up test file
-    fs.unlinkSync(FILE_NAME);
-  });
-});
diff --git a/tfjs-master/scripts/run_flaky_test_helper.js b/tfjs-master/scripts/run_flaky_test_helper.js
deleted file mode 100644
index 0c146818f..000000000
--- a/tfjs-master/scripts/run_flaky_test_helper.js
+++ /dev/null
@@ -1,32 +0,0 @@
-// Copyright 2021 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const fs = require('fs');
-const FILE_NAME = './flaky_test_has_run';
-module.exports.FILE_NAME = FILE_NAME;
-
-function main() {
-  if (!fs.existsSync(FILE_NAME)) {
-    fs.writeFileSync(
-        FILE_NAME,
-        'This temp file is used for testing' +
-            ' scripts/flaky_test.js and can be safely removed.');
-    process.exit(1);
-  }
-}
-
-if (require.main === module) {
-  main();
-}
diff --git a/tfjs-master/scripts/start_local_debugger_server.js b/tfjs-master/scripts/start_local_debugger_server.js
deleted file mode 100644
index 17ad7c9ab..000000000
--- a/tfjs-master/scripts/start_local_debugger_server.js
+++ /dev/null
@@ -1,126 +0,0 @@
-// Copyright 2022 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const express = require('express');
-const {ArgumentParser, ArgumentDefaultsHelpFormatter} = require('argparse');
-const JSZip = require('jszip');
-const fetch = require('node-fetch');
-const path = require('node:path');
-
-const DEFAULT_VERSION = '20221122-100434';
-const TFJS_DEBUGGER_BUNDLE_URL_BASE =
-    'https://storage.googleapis.com/tfweb/tfjs-debugger-bundle';
-const EXTS_WITH_ARRAY_BUFFER_CONTENT = ['.ttf'];
-
-const app = express();
-const debuggerStaticFile = {};
-
-function main(args) {
-  const port = parseInt(args['port']);
-  const version = args['version'];
-
-  app.get('/*', (req, res) => {
-    // Remove leading '/'.
-    let filePath = req.path.substring(1);
-    if (filePath === '') {
-      filePath = 'index.html';
-    }
-
-    // Send file from the unzipped tfjs-debugger bundle from memory.
-    if (debuggerStaticFile[filePath]) {
-      res.contentType(path.basename(filePath));
-      res.send(debuggerStaticFile[filePath]);
-    }
-    // Send other files from the local file system.
-    else {
-      const tfjsRoot = __dirname.replace('/scripts', '/');
-      res.sendFile(tfjsRoot + filePath);
-    }
-  });
-
-  app.listen(port, async () => {
-    // On server start-up, fetch the zipped debugger bundle and unzip in memory.
-    console.log('Fetching tfjs debugger static files...');
-    await fetchAndUnzipTfjsDebuggerBundle(version);
-    console.log('Done');
-    console.log(`Local debugger server started at http://localhost:${
-        port}/?bv__0=Local%20build&bv__1=`);
-  });
-}
-
-async function fetchAndUnzipTfjsDebuggerBundle(version) {
-  let resp;
-  try {
-    resp = await new Promise(resolve => {
-      const fileUrl =
-          `${TFJS_DEBUGGER_BUNDLE_URL_BASE}/tfjs-debugger_${version}.zip`;
-      fetch(fileUrl).then(response => {
-        if (!response.ok) {
-          throw new Error(`Failed to load bundle: ${fileUrl}`);
-        }
-        resolve(response);
-      })
-    });
-  } catch (e) {
-    console.error(e.message);
-  }
-  const buffer = await resp.buffer();
-
-  // Read zip objects.
-  const zipObjects = await new Promise(resolve => {
-    const zipObjects = [];
-    JSZip.loadAsync(buffer).then((zip) => {
-      zip.folder('dist').forEach((relativePath, zipObject) => {
-        zipObjects.push(zipObject);
-      });
-      resolve(zipObjects);
-    })
-  });
-
-  // Read and index files content.
-  for (const zipObject of zipObjects) {
-    await new Promise(resolve => {
-      const name = zipObject.name;
-      zipObject
-          .async(
-              EXTS_WITH_ARRAY_BUFFER_CONTENT.some(ext => name.endsWith(ext)) ?
-                  'nodebuffer' :
-                  'text')
-          .then(content => {
-            const fileName = name.replace('dist/', '');
-            debuggerStaticFile[fileName] = content;
-            resolve();
-          });
-    });
-  }
-}
-
-const parser = new ArgumentParser({
-  description: 'Run tfjs-debugger locally that supports loading local packages',
-});
-
-parser.addArgument('--port', {
-  help: 'Server port',
-  defaultValue: 9876,
-  type: 'int',
-});
-
-parser.addArgument('--version', {
-  help: `The verison of the bundle. Default: ${DEFAULT_VERSION}`,
-  defaultValue: DEFAULT_VERSION,
-  type: 'string',
-});
-
-main(parser.parseArgs());
diff --git a/tfjs-master/scripts/tag-tfjs-release.ts b/tfjs-master/scripts/tag-tfjs-release.ts
deleted file mode 100644
index a23536767..000000000
--- a/tfjs-master/scripts/tag-tfjs-release.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This script tags a release after lockfiles are updated
- */
-
-import * as argparse from 'argparse';
-import * as shell from 'shelljs';
-import {$, checkoutReleaseBranch, getReleaseBranch} from './release-util';
-
-const parser = new argparse.ArgumentParser();
-
-parser.addArgument('--git-protocol', {
-  action: 'storeTrue',
-  help: 'Use the git protocol rather than the http protocol when cloning repos.'
-});
-
-async function main() {
-  const args = parser.parseArgs();
-  // ========== Get release branch. ============================================
-  const releaseBranch = await getReleaseBranch('tfjs');
-
-  const TMP_DIR = '/tmp/tfjs-tag';
-
-  // ========== Checkout release branch. =======================================
-  checkoutReleaseBranch(releaseBranch, args.git_protocol, TMP_DIR);
-
-  shell.cd(TMP_DIR);
-
-  // ========== Tag the release. ===============================================
-  const version = releaseBranch.split('_')[1];
-  const tag = `tfjs-v${version}`;
-  console.log(`Tagging with ${tag}`);
-  $(`git tag ${tag} && git push --tags`);
-  console.log('Done.');
-
-  // So the script doesn't just hang.
-  process.exit(0);
-}
-
-main();
diff --git a/tfjs-master/scripts/tag-version.js b/tfjs-master/scripts/tag-version.js
deleted file mode 100644
index 511c340e4..000000000
--- a/tfjs-master/scripts/tag-version.js
+++ /dev/null
@@ -1,45 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2018 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-// Run this script from the base directory (not the package directory):
-// ./scripts/tag-version.js DIR_NAME
-// Where DIR_NAME is the directory name for the package you want to make a
-// version for.
-const fs = require('fs');
-
-let dirName = process.argv[2];
-if (dirName.endsWith('/')) {
-  dirName = dirName.slice(0, -1);
-}
-const packageJsonFile = dirName + '/package.json';
-if (!fs.existsSync(packageJsonFile)) {
-  console.log(
-    packageJsonFile, 'does not exist. Please call this script as follows:');
-  console.log('./scripts/tag-version.js DIR_NAME');
-  process.exit(1);
-}
-
-var exec = require('child_process').exec;
-
-var version = JSON.parse(fs.readFileSync(packageJsonFile, 'utf8')).version;
-var tag = `${dirName}-v${version}`;
-
-exec(`git tag ${tag} && git push --tags`, (err, stdout, stderr) => {
-  if (err) {
-    throw new Error(`Could not git tag with ${tag}: ${err.message}.`);
-  }
-  console.log(`Successfully tagged with ${tag}.`);
-});
diff --git a/tfjs-master/scripts/test-util.js b/tfjs-master/scripts/test-util.js
deleted file mode 100644
index d2d3b085a..000000000
--- a/tfjs-master/scripts/test-util.js
+++ /dev/null
@@ -1,81 +0,0 @@
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const shell = require('shelljs');
-const fs = require('fs');
-
-function exec(command, opt, ignoreCode) {
-  const res = shell.exec(command, opt);
-  if (!ignoreCode && res.code !== 0) {
-    shell.echo('command', command, 'returned code', res.code);
-    process.exit(1);
-  }
-  return res;
-}
-
-// Construct a dependency graph keyed by dependency package.
-// Example:
-//   dependencyGraph = {
-//     "tfjs-core": ["tfjs-converter", "tfjs", ...],
-//     "tfjs": ["tfjs-node"],
-//     ...
-//   }
-function constructDependencyGraph(dependencyFilePath) {
-  const str = fs.readFileSync(dependencyFilePath, 'utf8');
-  const dependencyInfo = JSON.parse(str);
-
-  const dependencyGraph = {};
-
-  Object.keys(dependencyInfo)
-      .forEach(package => dependencyInfo[package].forEach(dependency => {
-        if (!dependencyGraph[dependency]) {
-          dependencyGraph[dependency] = [];
-        }
-        dependencyGraph[dependency].push(package);
-      }));
-
-  return dependencyGraph;
-}
-
-function computeAffectedPackages(dependencyGraph, package) {
-  const affectedPackages = new Set();
-  traverseDependencyGraph(dependencyGraph, package, affectedPackages);
-
-  return Array.from(affectedPackages);
-}
-
-// This function performs a depth-first-search to add affected packages that
-// transitively depend on the given package.
-function traverseDependencyGraph(graph, package, affectedPackages) {
-  // Terminate early if the package has been visited.
-  if (affectedPackages.has(package)) {
-    return;
-  }
-
-  const consumingPackages = graph[package];
-
-  if (!consumingPackages) {
-    return;
-  }
-
-  consumingPackages.forEach(consumingPackage => {
-    traverseDependencyGraph(graph, consumingPackage, affectedPackages);
-    affectedPackages.add(consumingPackage);
-  });
-}
-
-exports.exec = exec;
-exports.constructDependencyGraph = constructDependencyGraph;
-exports.computeAffectedPackages = computeAffectedPackages;
diff --git a/tfjs-master/scripts/tsconfig.json b/tfjs-master/scripts/tsconfig.json
deleted file mode 100644
index 0a66f17bc..000000000
--- a/tfjs-master/scripts/tsconfig.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "compilerOptions": {
-    "module": "commonjs",
-    "target": "es6",
-    "lib": [
-      "esnext"
-    ],
-    "downlevelIteration": true,
-    "esModuleInterop": true
-  },
-  "include": [
-    "**/*.ts"
-  ]
-}
diff --git a/tfjs-master/scripts/update-tfjs-lockfiles.ts b/tfjs-master/scripts/update-tfjs-lockfiles.ts
deleted file mode 100644
index da036aca1..000000000
--- a/tfjs-master/scripts/update-tfjs-lockfiles.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This script update yarn.lock after tfjs packages are published.
- *
- * This script requires hub to be installed: https://hub.github.com/
- */
-
-import chalk from 'chalk';
-import * as argparse from 'argparse';
-import * as shell from 'shelljs';
-
-import {$, TFJS_RELEASE_UNIT, prepareReleaseBuild, getReleaseBranch, checkoutReleaseBranch, createPR} from './release-util';
-
-const parser = new argparse.ArgumentParser();
-
-parser.addArgument('--git-protocol', {
-  action: 'storeTrue',
-  help: 'Use the git protocol rather than the http protocol when cloning repos.'
-});
-
-const TMP_DIR = '/tmp/tfjs-publish-after';
-
-async function main() {
-  const args = parser.parseArgs();
-
-  // ========== Get release branch. ============================================
-  // Infer release branch name.
-  let releaseBranch = await getReleaseBranch('tfjs');
-  console.log();
-
-  // ========== Checkout release branch. =======================================
-  checkoutReleaseBranch(releaseBranch, args.git_protocol, TMP_DIR);
-
-  shell.cd(TMP_DIR);
-
-  // ========== Delete a possible prior lockfiles branch from a failed run =====
-  const lockfilesBranch = `${releaseBranch}_lockfiles`;
-  console.log(chalk.magenta.bold(
-      `~~~ Creating new lockfiles branch ${lockfilesBranch} ~~~`));
-
-  // Delete possible branch from a prior execution of this script
-  const branchCmd = `git branch -D ${lockfilesBranch}`;
-  const result = shell.exec(branchCmd, {silent: true});
-  const okErrCode = `branch '${lockfilesBranch}' not found`;
-  if (result.code > 0 && !result.stderr.trim().match(okErrCode)) {
-    console.log('$', branchCmd);
-    console.log(result.stderr);
-    process.exit(1);
-  }
-
-  // ========== Run yarn to update yarn.lock file for each package. ============
-  // Yarn in the top-level.
-  $('yarn');
-
-  // run yarn for every tfjs package
-  const phases = TFJS_RELEASE_UNIT.phases;
-
-  for (let i = 0; i < phases.length; i++) {
-    const phase = phases[i];
-    const packages = phase.packages;
-
-    for (let i = 0; i < packages.length; i++) {
-      const packageName = packages[i];
-      shell.cd(packageName);
-
-      prepareReleaseBuild(phase, packageName);
-
-      shell.cd('..');
-    }
-  }
-
-  // ========== Send a PR to the release branch =====================
-  const message = `Update lockfiles branch ${lockfilesBranch} lock files.`;
-  createPR(lockfilesBranch, releaseBranch, message);
-
-  console.log('Done.');
-
-  process.exit(0);
-}
-
-main();
diff --git a/tfjs-master/tfjs-automl/.npmignore b/tfjs-master/tfjs-automl/.npmignore
deleted file mode 100644
index 72daebb83..000000000
--- a/tfjs-master/tfjs-automl/.npmignore
+++ /dev/null
@@ -1,21 +0,0 @@
-.vscode/
-.rpt2_cache/
-.cache/
-demo/
-src/**/*_test.ts
-dist/**/*_test.*
-coverage/
-package/
-scripts/
-**/node_modules/
-karma.conf.js
-*.tgz
-*.log
-.travis.yml
-CONTRIBUTING.md
-tslint.json
-yarn.lock
-rollup.config.js
-tsconfig.json
-.yalc/
-yalc.lock
diff --git a/tfjs-master/tfjs-automl/.vscode/settings.json b/tfjs-master/tfjs-automl/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-automl/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-automl/.vscode/tasks.json b/tfjs-master/tfjs-automl/.vscode/tasks.json
deleted file mode 100644
index 5edb3db96..000000000
--- a/tfjs-master/tfjs-automl/.vscode/tasks.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/tasks.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-automl/README.md b/tfjs-master/tfjs-automl/README.md
deleted file mode 100644
index 6dda15844..000000000
--- a/tfjs-master/tfjs-automl/README.md
+++ /dev/null
@@ -1,206 +0,0 @@
-# AutoML Edge API
-
-This packages provides a set of APIs to load and run models produced by AutoML
-Edge.
-
-## Installation
-
-If you are using npm/yarn
-```sh
-npm i @tensorflow/tfjs-automl
-```
-
-If you are using CDN:
-```html
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-automl"></script>
-```
-
-We support the following types of AutoML Edge models:
-1) [Image classification](#image-classification)
-2) [Object detection](#object-detection)
-
-## Image classification
-
-AutoML Image classification model will output the following set of files:
-- `model.json`, the model topology
-- `dict.txt`, a newline-separated list of labels
-- One or more of `*.bin` files which hold the weights
-
-Make sure you can access those files as static assets from your web app by serving them locally or on Google Cloud Storage.
-
-### Demo
-
-The image classification demo lives in
-[demo/img_classification](./demo/img_classification). To run it:
-
-```sh
-cd demo/img_classification
-yarn
-yarn watch
-```
-
-This will start a local HTTP server on port 1234 that serves the demo.
-
-### Loading the model
-```js
-import * as automl from '@tensorflow/tfjs-automl';
-const modelUrl = 'model.json'; // URL to the model.json file.
-const model = await automl.loadImageClassification(modelUrl);
-```
-
-If you do not want (or cannot) load the model over HTTP you can also load the model separately and directly use the constuctor. 
-This is particularly relevant for __non-browser__ platforms.
-
-The following psuedocode demonstrates this approach:
-
-```js
-import * as automl from '@tensorflow/tfjs-automl';
-import * as tf from '@tensorflow/tfjs';
-// You can load the graph model using any IO handler
-const graphModel = await tf.loadGraphModel(string|io.IOHandler); // a url or ioHandler instance
-// You can load the dictionary using any api available to the platform
-const dict = loadDictionary("path/to/dict.txt");
-const model = new automl.ImageClassificationModel(graphModel, dict);
-```
-
-### Making a prediction
-
-The AutoML library takes care of any image preprocessing
-(normalize, resize, crop). The input `img` you provide can be
-[`HTMLImageElement`](https://developer.mozilla.org/en-US/docs/Web/API/HTMLImageElement),
-[`HTMLCanvasElement`](https://developer.mozilla.org/en-US/docs/Web/API/HTMLCanvasElement),
-[`HTMLVideoElement`](https://developer.mozilla.org/en-US/docs/Web/API/HTMLVideoElement),
-[`ImageData`](https://developer.mozilla.org/en-US/docs/Web/API/ImageData) or
-a 3D [`Tensor`](https://js.tensorflow.org/api/latest/#class:Tensor):
-
-```html
-<img id="img" src="PATH_TO_IMAGE" />
-```
-
-```js
-const img = document.getElementById('img');
-const options = {centerCrop: true};
-const predictions = await model.classify(img, options);
-```
-
-`options` is optional and has the following properties:
-- `centerCrop` - Defaults to true. Since the ML model expects a square image,
-we need to resize. If true, the image will be cropped first to the center before
-resizing.
-
-The result `predictions` is a sorted list of predicted labels and their
-probabilities:
-
-```js
-[
-  {label: "daisy", prob: 0.931},
-  {label: "dandelion", prob: 0.027},
-  {label: "roses", prob: 0.013},
-  ...
-]
-```
-
-### Advanced usage
-
-Advanced users can access the underlying
-[`GraphModel`](https://js.tensorflow.org/api/latest/#class:GraphModel) via
-`model.graphModel`. The `GraphModel` allows users to call lower level methods
-such as `predict()`, `execute()` and `executeAsync()` which return tensors.
-
-`model.dictionary` gives you access to the ordered list of labels.
-
-## Object detection
-
-AutoML Object detection model will output the following set of files:
-- `model.json`, the model topology
-- `dict.txt`, a newline-separated list of labels
-- One or more of `*.bin` files which hold the weights
-
-Make sure you can access those files as static assets from your web app by serving them locally or on Google Cloud Storage.
-
-### Demo
-
-The object detection demo lives in
-[demo/object_detection](./demo/object_detection). To run it:
-
-```sh
-cd demo/object_detection
-yarn
-yarn watch
-```
-
-This will start a local HTTP server on port 1234 that serves the demo.
-
-### Loading the model
-```js
-import * as automl from '@tensorflow/tfjs-automl';
-const modelUrl = 'model.json'; // URL to the model.json file.
-const model = await automl.loadObjectDetection(modelUrl);
-```
-
-If you do not want (or cannot) load the model over HTTP you can also load the model separately and directly use the constuctor. 
-This is particularly relevant for __non-browser__ platforms.
-
-The following psuedocode demonstrates this approach:
-
-```js
-import * as automl from '@tensorflow/tfjs-automl';
-import * as tf from '@tensorflow/tfjs';
-// You can load the graph model using any IO handler
-const graphModel = await tf.loadGraphModel(string|io.IOHandler); // a url or ioHandler instance
-// You can load the dictionary using any api available to the platform
-const dict = readDictionary("path/to/dict.txt");
-const model = new automl.ObjectDetectionModel(graphModel, dict);
-```
-
-### Making a prediction
-
-The AutoML library takes care of any image preprocessing
-(normalize, resize, crop). The input `img` you provide can be
-[`HTMLImageElement`](https://developer.mozilla.org/en-US/docs/Web/API/HTMLImageElement),
-[`HTMLCanvasElement`](https://developer.mozilla.org/en-US/docs/Web/API/HTMLCanvasElement),
-[`HTMLVideoElement`](https://developer.mozilla.org/en-US/docs/Web/API/HTMLVideoElement),
-[`ImageData`](https://developer.mozilla.org/en-US/docs/Web/API/ImageData) or
-a 3D [`Tensor`](https://js.tensorflow.org/api/latest/#class:Tensor):
-
-```html
-<img id="img" src="PATH_TO_IMAGE" />
-```
-
-```js
-const img = document.getElementById('img');
-const options = {score: 0.5, iou: 0.5, topk: 20};
-const predictions = await model.detect(img, options);
-```
-
-`options` is optional and has the following properties:
-- `score` - Probability score between 0 and 1. Defaults to 0.5. Boxes with score lower than this threshold will be ignored.
-- `topk` - Only the `topk` most likely objects are returned. The actual number of objects might be less than this number.
-- `iou` - Intersection over union threshold. IoU is a metric between 0 and 1 used to measure the overlap of two boxes. The predicted boxes will not overlap more than the specified threshold.
-
-The result `predictions` is a sorted list of predicted objects:
-
-```js
-[
-  {
-    box: {
-      left: 105.1,
-      top: 22.2,
-      width: 70.6,
-      height: 55.7
-    },
-    label: "Tomato",
-    score: 0.972
-  },
-  ...
-]
-```
-
-### Advanced usage
-
-Advanced users can access the underlying
-[`GraphModel`](https://js.tensorflow.org/api/latest/#class:GraphModel) via
-`model.graphModel`. The `GraphModel` allows users to call lower level methods
-such as `predict()`, `execute()` and `executeAsync()` which return tensors.
-
-`model.dictionary` gives you access to the ordered list of labels.
diff --git a/tfjs-master/tfjs-automl/cloudbuild.yml b/tfjs-master/tfjs-automl/cloudbuild.yml
deleted file mode 100644
index 3d927092b..000000000
--- a/tfjs-master/tfjs-automl/cloudbuild.yml
+++ /dev/null
@@ -1,62 +0,0 @@
-steps:
-# Install common dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  entrypoint: 'yarn'
-  id: 'yarn-common'
-  args: ['install']
-
-# Install tfjs-automl dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-automl'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-  waitFor: ['yarn-common']
-
-# Build.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-automl'
-  entrypoint: 'yarn'
-  id: 'build'
-  args: ['build']
-  waitFor: ['yarn']
-
-# Lint.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-automl'
-  entrypoint: 'yarn'
-  id: 'lint'
-  args: ['lint']
-  waitFor: ['yarn']
-
-# Run node tests.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-automl'
-  entrypoint: 'yarn'
-  id: 'test-js'
-  args: ['test-node']
-  waitFor: ['yarn', 'build', 'lint']
-
-  # Run browser tests.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-automl'
-  entrypoint: 'yarn'
-  id: 'test-browser'
-  args: ['test-ci']
-  env: ['BROWSERSTACK_USERNAME=deeplearnjs1']
-  secretEnv: ['BROWSERSTACK_KEY']
-  waitFor: ['yarn', 'build', 'lint']
-
-# General settings.
-secrets:
-- kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-  secretEnv:
-    BROWSERSTACK_KEY: CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-timeout: 1800s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  substitution_option: 'ALLOW_LOOSE'
-  machineType: 'N1_HIGHCPU_8'
diff --git a/tfjs-master/tfjs-automl/code_snippets/img_classification.html b/tfjs-master/tfjs-automl/code_snippets/img_classification.html
deleted file mode 100644
index d52041da7..000000000
--- a/tfjs-master/tfjs-automl/code_snippets/img_classification.html
+++ /dev/null
@@ -1,24 +0,0 @@
-<!-- [START entire_app]  -->
-<!-- [START deps]  -->
-<script src="https://unpkg.com/@tensorflow/tfjs"></script>
-<script src="https://unpkg.com/@tensorflow/tfjs-automl"></script>
-<!-- [END deps]  -->
-<!-- [START img_tag]  -->
-<img id="daisy" crossorigin="anonymous" src="https://storage.googleapis.com/tfjs-testing/tfjs-automl/img_classification/daisy.jpg">
-<!-- [END img_tag]  -->
-<script>
-async function run() {
-  // [START load_and_run_model]
-  const model = await tf.automl.loadImageClassification('model.json');
-  const image = document.getElementById('daisy');
-  const predictions = await model.classify(image);
-  // [END load_and_run_model]
-  console.log(predictions);
-  // Show the resulting object on the page.
-  const pre = document.createElement('pre');
-  pre.textContent = JSON.stringify(predictions, null, 2);
-  document.body.append(pre);
-}
-run();
-</script>
-<!-- [END entire_app]  -->
diff --git a/tfjs-master/tfjs-automl/code_snippets/object_detection.html b/tfjs-master/tfjs-automl/code_snippets/object_detection.html
deleted file mode 100644
index e6a8e7723..000000000
--- a/tfjs-master/tfjs-automl/code_snippets/object_detection.html
+++ /dev/null
@@ -1,25 +0,0 @@
-<!-- [START entire_app]  -->
-<!-- [START deps]  -->
-<script src="https://unpkg.com/@tensorflow/tfjs"></script>
-<script src="https://unpkg.com/@tensorflow/tfjs-automl"></script>
-<!-- [END deps]  -->
-<!-- [START img_tag]  -->
-<img id="salad" crossorigin="anonymous" src="https://storage.googleapis.com/tfjs-testing/tfjs-automl/object_detection/test_image.jpg">
-<!-- [END img_tag]  -->
-<script>
-async function run() {
-  // [START load_and_run_model]
-  const model = await tf.automl.loadObjectDetection('model.json');
-  const img = document.getElementById('salad');
-  const options = {score: 0.5, iou: 0.5, topk: 20};
-  const predictions = await model.detect(img, options);
-  // [END load_and_run_model]
-  console.log(predictions);
-  // Show the resulting object on the page.
-  const pre = document.createElement('pre');
-  pre.textContent = JSON.stringify(predictions, null, 2);
-  document.body.append(pre);
-}
-run();
-</script>
-<!-- [END entire_app]  -->
diff --git a/tfjs-master/tfjs-automl/demo/img_classification/index.html b/tfjs-master/tfjs-automl/demo/img_classification/index.html
deleted file mode 100644
index 3ddb8864e..000000000
--- a/tfjs-master/tfjs-automl/demo/img_classification/index.html
+++ /dev/null
@@ -1,12 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-  <title>AutoML Image classification demo</title>
-  <style>
-  </style>
-</head>
-<body>
-  <img id="daisy" crossorigin="anonymous" src="https://storage.googleapis.com/tfjs-testing/tfjs-automl/img_classification/daisy.jpg">
-  <script type="module" src="index.js"></script>
-</body>
-</html>
diff --git a/tfjs-master/tfjs-automl/demo/img_classification/index.js b/tfjs-master/tfjs-automl/demo/img_classification/index.js
deleted file mode 100644
index bea8f1b65..000000000
--- a/tfjs-master/tfjs-automl/demo/img_classification/index.js
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-webgl';
-import * as automl from '@tensorflow/tfjs-automl';
-
-const MODEL_URL =
-    'https://storage.googleapis.com/tfjs-testing/tfjs-automl/img_classification/model.json';
-
-async function run() {
-  const model = await automl.loadImageClassification(MODEL_URL);
-  const image = document.getElementById('daisy');
-  const predictions = await model.classify(image);
-
-  // Show the resulting object on the page.
-  const pre = document.createElement('pre');
-  pre.textContent = JSON.stringify(predictions, null, 2);
-  document.body.append(pre);
-}
-
-run();
diff --git a/tfjs-master/tfjs-automl/demo/img_classification/package.json b/tfjs-master/tfjs-automl/demo/img_classification/package.json
deleted file mode 100644
index 9f74d534c..000000000
--- a/tfjs-master/tfjs-automl/demo/img_classification/package.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "name": "automl-img-classification-demo",
-  "version": "0.0.1",
-  "private": true,
-  "description": "Image classification demo using the AutoML NPM library",
-  "main": "index.js",
-  "browserslist": "> 0.25%, not dead",
-  "scripts": {
-    "watch": "cross-env NODE_ENV=development parcel index.html --no-hmr --open ",
-    "build": "cross-env NODE_ENV=production parcel build index.html --public-url ./"
-  },
-  "devDependencies": {
-    "babel-core": "^6.26.3",
-    "babel-plugin-transform-runtime": "~6.23.0",
-    "babel-polyfill": "~6.26.0",
-    "babel-preset-env": "~1.7.0",
-    "clang-format": "~1.8.0",
-    "cross-env": "^7.0.3",
-    "parcel": "~2.8.3",
-    "yalc": "~1.0.0-pre.27"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs-automl": "link:../../",
-    "@tensorflow/tfjs-backend-webgl": "^4.2.0",
-    "@tensorflow/tfjs-converter": "^4.2.0",
-    "@tensorflow/tfjs-core": "^4.2.0"
-  },
-  "resolutions": {
-    "minimist": "1.2.6"
-  },
-  "license": "Apache-2.0"
-}
diff --git a/tfjs-master/tfjs-automl/demo/img_classification/yarn.lock b/tfjs-master/tfjs-automl/demo/img_classification/yarn.lock
deleted file mode 100644
index f74bb130c..000000000
--- a/tfjs-master/tfjs-automl/demo/img_classification/yarn.lock
+++ /dev/null
@@ -1,2626 +0,0 @@
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-# yarn lockfile v1
-
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diff --git a/tfjs-master/tfjs-automl/demo/object_detection/index.html b/tfjs-master/tfjs-automl/demo/object_detection/index.html
deleted file mode 100644
index 481059045..000000000
--- a/tfjs-master/tfjs-automl/demo/object_detection/index.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-  <title>AutoML Object detection demo</title>
-  <style>
-  </style>
-</head>
-<body>
-  <div id="imgDiv" style="position:relative;">
-    <img id="salad" width="500" crossorigin="anonymous" src="https://storage.googleapis.com/tfjs-testing/tfjs-automl/object_detection/test_image.jpg" />
-    <svg width="500" height="375" style="position: absolute;top:0;left:0;">
-      <style>
-        .box {
-          stroke-width: 2;
-          fill: none;
-          stroke: red;
-        }
-        .label {
-          font-size: 12px;
-          fill: white;
-          text-anchor: middle;
-        }
-        .label-rect {
-          fill: black;
-        }
-      </style>
-    </svg>
-  </div>
-  <script type="module" src="index.js"></script>
-</body>
-</html>
diff --git a/tfjs-master/tfjs-automl/demo/object_detection/index.js b/tfjs-master/tfjs-automl/demo/object_detection/index.js
deleted file mode 100644
index f8fe56605..000000000
--- a/tfjs-master/tfjs-automl/demo/object_detection/index.js
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-webgl';
-import * as automl from '@tensorflow/tfjs-automl';
-
-const MODEL_URL =
-    'https://storage.googleapis.com/tfjs-testing/tfjs-automl/object_detection/model.json';
-
-async function run() {
-  const model = await automl.loadObjectDetection(MODEL_URL);
-  const image = document.getElementById('salad');
-  // These are the default options.
-  const options = {score: 0.5, iou: 0.5, topk: 20};
-  const predictions = await model.detect(image, options);
-
-  // Show the resulting object on the page.
-  const pre = document.createElement('pre');
-  pre.textContent = JSON.stringify(predictions, null, 2);
-  document.body.append(pre);
-
-  drawBoxes(predictions);
-}
-
-// Overlays boxes with labels onto the image using `rect` and `text` svg
-// elements.
-function drawBoxes(predictions) {
-  const svg = document.querySelector('svg');
-  predictions.forEach(prediction => {
-    const {box, label, score} = prediction;
-    const {left, top, width, height} = box;
-    const rect = document.createElementNS('http://www.w3.org/2000/svg', 'rect');
-    rect.setAttribute('width', width);
-    rect.setAttribute('height', height);
-    rect.setAttribute('x', left);
-    rect.setAttribute('y', top);
-    rect.setAttribute('class', 'box');
-    const text = document.createElementNS('http://www.w3.org/2000/svg', 'text');
-    text.setAttribute('x', left + width / 2);
-    text.setAttribute('y', top);
-    text.setAttribute('dy', 12);
-    text.setAttribute('class', 'label');
-    text.textContent = `${label}: ${score.toFixed(3)}`;
-    svg.appendChild(rect);
-    svg.appendChild(text);
-    const textBBox = text.getBBox();
-    const textRect =
-        document.createElementNS('http://www.w3.org/2000/svg', 'rect');
-    textRect.setAttribute('x', textBBox.x);
-    textRect.setAttribute('y', textBBox.y);
-    textRect.setAttribute('width', textBBox.width);
-    textRect.setAttribute('height', textBBox.height);
-    textRect.setAttribute('class', 'label-rect');
-    svg.insertBefore(textRect, text);
-  });
-}
-
-run();
diff --git a/tfjs-master/tfjs-automl/demo/object_detection/package.json b/tfjs-master/tfjs-automl/demo/object_detection/package.json
deleted file mode 100644
index f713c139c..000000000
--- a/tfjs-master/tfjs-automl/demo/object_detection/package.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
-  "name": "automl-object-detection-demo",
-  "version": "0.0.1",
-  "private": true,
-  "description": "Object detection demo using the AutoML NPM library",
-  "main": "index.js",
-  "browserslist": "> 0.25%, not dead",
-  "scripts": {
-    "watch": "cross-env NODE_ENV=development parcel index.html --no-hmr --open ",
-    "build": "cross-env NODE_ENV=production parcel build index.html --public-url ./"
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diff --git a/tfjs-master/tfjs-automl/demo/object_detection/yarn.lock b/tfjs-master/tfjs-automl/demo/object_detection/yarn.lock
deleted file mode 100644
index 254791ad8..000000000
--- a/tfjs-master/tfjs-automl/demo/object_detection/yarn.lock
+++ /dev/null
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-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.3.tgz#a7d0558bd89c42f795dd42328f740831ca53bc25"
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-
-y18n@^5.0.5:
-  version "5.0.8"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.8.tgz#7f4934d0f7ca8c56f95314939ddcd2dd91ce1d55"
-  integrity sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==
-
-yalc@~1.0.0-pre.53:
-  version "1.0.0-pre.53"
-  resolved "https://registry.yarnpkg.com/yalc/-/yalc-1.0.0-pre.53.tgz#c51db2bb924a6908f4cb7e82af78f7e5606810bc"
-  integrity sha512-tpNqBCpTXplnduzw5XC+FF8zNJ9L/UXmvQyyQj7NKrDNavbJtHvzmZplL5ES/RCnjX7JR7W9wz5GVDXVP3dHUQ==
-  dependencies:
-    chalk "^4.1.0"
-    detect-indent "^6.0.0"
-    fs-extra "^8.0.1"
-    glob "^7.1.4"
-    ignore "^5.0.4"
-    ini "^2.0.0"
-    npm-packlist "^2.1.5"
-    yargs "^16.1.1"
-
-yaml@^1.10.0:
-  version "1.10.2"
-  resolved "https://registry.yarnpkg.com/yaml/-/yaml-1.10.2.tgz#2301c5ffbf12b467de8da2333a459e29e7920e4b"
-  integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==
-
-yargs-parser@^20.2.2:
-  version "20.2.9"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
-  integrity sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==
-
-yargs@^16.1.1:
-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
diff --git a/tfjs-master/tfjs-automl/karma.conf.js b/tfjs-master/tfjs-automl/karma.conf.js
deleted file mode 100644
index 67a8c219b..000000000
--- a/tfjs-master/tfjs-automl/karma.conf.js
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const karmaTypescriptConfig = {
-  tsconfig: 'tsconfig.test.json',
-  // Disable coverage reports and instrumentation by default for tests
-  coverageOptions: {instrumentation: false},
-  reports: {},
-  bundlerOptions: {
-    sourceMap: true,  // Process any non es5 code through
-                      // karma-typescript-es6-transform (babel)
-    acornOptions: {ecmaVersion: 11},
-    transforms: [
-      require('karma-typescript-es6-transform')({
-        presets: [
-          // ensure we get es5 by adding IE 11 as a target
-          ['@babel/env', {'targets': {'ie': '11'}, 'loose': true}]
-        ]
-      }),
-    ]
-  }
-};
-
-// Enable coverage reports and instrumentation under KARMA_COVERAGE=1 env
-const coverageEnabled = !!process.env.KARMA_COVERAGE;
-if (coverageEnabled) {
-  karmaTypescriptConfig.coverageOptions.instrumentation = true;
-  karmaTypescriptConfig.coverageOptions.exclude = [
-    /test_browser\.ts/,
-    /test_util\.ts/,
-    /_test\.ts/
-  ];
-  karmaTypescriptConfig.reports = {html: 'coverage', 'text-summary': ''};
-}
-
-module.exports = function(config) {
-  const args = [];
-  if (config.testEnv) {
-    args.push('--testEnv', config.testEnv);
-  }
-  if (config.grep) {
-    args.push('--grep', config.grep);
-  }
-  if (config.flags) {
-    args.push('--flags', config.flags);
-  }
-
-  config.set({
-    basePath: '',
-    frameworks: ['jasmine', 'karma-typescript'],
-    files: [
-      {pattern: './node_modules/@babel/polyfill/dist/polyfill.js'},
-      'src/test_browser.ts',
-      {pattern: 'src/**/*.ts'},
-    ],
-    exclude: ['src/test_node.ts'],
-    preprocessors: {'**/*.ts': ['karma-typescript']},
-    karmaTypescriptConfig,
-    reporters: ['progress', 'karma-typescript'],
-    port: 9200,
-    colors: true,
-    browsers: ['Chrome'],
-    client: {jasmine: {random: false}, args: args},
-    browserStack: {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_KEY,
-      timeout: 900,  // Seconds
-      tunnelIdentifier:
-          `tfjs_automl_${Date.now()}_${Math.floor(Math.random() * 1000)}`
-    },
-    captureTimeout: 10 * 1000,
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 3e5,
-    browserDisconnectTimeout: 3e5,
-    browserDisconnectTolerance: 0,
-    browserSocketTimeout: 1.2e5,
-    customLaunchers: {
-      bs_chrome_mac: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'Ventura'
-      },
-      bs_firefox_mac: {
-        base: 'BrowserStack',
-        browser: 'firefox',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'Ventura'
-      },
-      bs_safari_mac: {
-        base: 'BrowserStack',
-        browser: 'safari',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'Ventura'
-      },
-      bs_ios_12: {
-        base: 'BrowserStack',
-        device: 'iPhone X',
-        os: 'iOS',
-        os_version: '11.0',
-        real_mobile: true
-      },
-    }
-  })
-}
diff --git a/tfjs-master/tfjs-automl/package.json b/tfjs-master/tfjs-automl/package.json
deleted file mode 100644
index dca63ac78..000000000
--- a/tfjs-master/tfjs-automl/package.json
+++ /dev/null
@@ -1,65 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-automl",
-  "version": "1.2.0",
-  "main": "dist/tf-automl.node.js",
-  "types": "dist/index.d.ts",
-  "module": "dist/tf-automl.esm.js",
-  "unpkg": "dist/tf-automl.min.js",
-  "jsdelivr": "dist/tf-automl.min.js",
-  "scripts": {
-    "build": "tsc && yarn bundle",
-    "bundle": "rollup -c",
-    "lint": "tslint -p . -t verbose",
-    "test": "yarn && yarn build && karma start",
-    "test-dev": "karma start",
-    "test-node": "tsc && ts-node --transpile-only --skip-ignore --project tsconfig.test.json src/test_node.ts",
-    "coverage": "KARMA_COVERAGE=1 karma start --singleRun",
-    "run-flaky": "node ../scripts/run_flaky.js",
-    "run-browserstack": "karma start --singleRun --reporters='dots,karma-typescript,BrowserStack'",
-    "test-ci": "./scripts/test-ci.sh",
-    "build-npm": "./scripts/build-npm.sh"
-  },
-  "license": "Apache-2.0",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-automl"
-  },
-  "devDependencies": {
-    "@babel/polyfill": "^7.8.7",
-    "@tensorflow/tfjs-backend-webgl": "^4.9.0",
-    "@tensorflow/tfjs-converter": "^4.9.0",
-    "@tensorflow/tfjs-core": "^4.9.0",
-    "@types/jasmine": "~4.3.1",
-    "clang-format": "~1.8.0",
-    "jasmine": "4.5.0",
-    "jasmine-core": "~4.5.0",
-    "karma": "~6.4.1",
-    "karma-browserstack-launcher": "~1.6.0",
-    "karma-chrome-launcher": "~3.1.1",
-    "karma-commonjs": "^1.0.0",
-    "karma-firefox-launcher": "~2.1.2",
-    "karma-jasmine": "~5.1.0",
-    "karma-safari-launcher": "~1.0.0",
-    "karma-typescript": "~5.5.3",
-    "karma-typescript-es6-transform": "^5.5.3",
-    "npm-run-all": "~4.1.5",
-    "rimraf": "~4.1.2",
-    "rollup": "^3.17.2",
-    "rollup-plugin-commonjs": "10.1.0",
-    "rollup-plugin-node-resolve": "5.2.0",
-    "rollup-plugin-terser": "^7.0.2",
-    "rollup-plugin-typescript2": "0.34.1",
-    "rollup-plugin-uglify": "~6.0.4",
-    "ts-node": "^10.9.1",
-    "tslint": "~6.1.3",
-    "tslint-no-circular-imports": "^0.7.0",
-    "typescript": "5.0.4",
-    "yalc": "~1.0.0-pre.21"
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-backend-webgl": "^3.9.0",
-    "@tensorflow/tfjs-converter": "^3.9.0",
-    "@tensorflow/tfjs-core": "^3.9.0"
-  }
-}
diff --git a/tfjs-master/tfjs-automl/rollup.config.mjs b/tfjs-master/tfjs-automl/rollup.config.mjs
deleted file mode 100644
index 92dac69ab..000000000
--- a/tfjs-master/tfjs-automl/rollup.config.mjs
+++ /dev/null
@@ -1,125 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import commonjs from 'rollup-plugin-commonjs';
-import node from 'rollup-plugin-node-resolve';
-import {terser} from 'rollup-plugin-terser';
-import typescript from 'rollup-plugin-typescript2';
-
-const PREAMBLE = `/**
- * @license
- * Copyright ${(new Date).getFullYear()} Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */`;
-
-function config({plugins = [], output = {}}) {
-  return {
-    input: 'src/index.ts',
-    plugins: [
-      typescript({tsconfigOverride: {compilerOptions: {module: 'ES2015'}}}),
-      node(),
-      // Polyfill require() from dependencies.
-      commonjs({
-        ignore: ['crypto', 'node-fetch', 'util'],
-        include: 'node_modules/**',
-      }),
-      ...plugins
-    ],
-    output: {
-      banner: PREAMBLE,
-      sourcemap: true,
-      globals: {
-        'node-fetch': 'nodeFetch',
-        '@tensorflow/tfjs-core': 'tf',
-        '@tensorflow/tfjs-converter': 'tf',
-      },
-      ...output,
-    },
-    external: [
-      'crypto',
-      'node-fetch',
-      '@tensorflow/tfjs-core',
-      '@tensorflow/tfjs-converter',
-    ],
-    onwarn: warning => {
-      let {code} = warning;
-      if (code === 'CIRCULAR_DEPENDENCY' || code === 'CIRCULAR' ||
-          code === 'THIS_IS_UNDEFINED') {
-        return;
-      }
-      console.warn('WARNING: ', warning.toString());
-    }
-  };
-}
-
-export default cmdOptions => {
-  const bundles = [];
-
-  // Node
-  bundles.push(config({
-    output: {
-      format: 'cjs',
-      name: 'tf.automl',
-      extend: true,
-      file: `dist/tf-automl.node.js`,
-      freeze: false
-    },
-    tsCompilerOptions: {target: 'es5'}
-  }));
-
-  // tf-automl.js
-  bundles.push(config({
-    output: {
-      format: 'umd',
-      name: 'tf.automl',
-      extend: true,
-      file: 'dist/tf-automl.js',
-    }
-  }));
-
-  // tf-automl.min.js
-  bundles.push(config({
-    plugins: [terser({output: {preamble: PREAMBLE}})],
-    output: {
-      format: 'umd',
-      name: 'tf.automl',
-      extend: true,
-      file: 'dist/tf-automl.min.js',
-    }
-  }));
-
-  // tf-automl.esm.js
-  bundles.push(config({
-    plugins: [terser({output: {preamble: PREAMBLE}})],
-    output: {
-      format: 'es',
-      file: 'dist/tf-automl.esm.js',
-    }
-  }));
-  return bundles;
-};
diff --git a/tfjs-master/tfjs-automl/scripts/build-npm.sh b/tfjs-master/tfjs-automl/scripts/build-npm.sh
deleted file mode 100644
index 6f48cfb8a..000000000
--- a/tfjs-master/tfjs-automl/scripts/build-npm.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn rimraf dist/
-yarn
-yarn build
-yarn rollup -c
-
-echo "Stored standalone library at dist/tf-automl(.min).js"
diff --git a/tfjs-master/tfjs-automl/scripts/test-ci.sh b/tfjs-master/tfjs-automl/scripts/test-ci.sh
deleted file mode 100644
index 6c4dee491..000000000
--- a/tfjs-master/tfjs-automl/scripts/test-ci.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2018 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn
-yarn lint
-yarn build
-
-# Run the first karma separately so it can download the BrowserStack binary
-# without conflicting with others.
-yarn run-flaky "yarn run-browserstack --browsers=bs_chrome_mac"
-
-# Run the rest of the karma tests in parallel. These runs will reuse the
-# already downloaded binary.
-npm-run-all -p -c --aggregate-output \
-  "run-flaky \"yarn run-browserstack --browsers=bs_firefox_mac\"" \
-  "run-flaky \"yarn run-browserstack --browsers=bs_safari_mac --testEnv webgl1\""
diff --git a/tfjs-master/tfjs-automl/src/img_classification.ts b/tfjs-master/tfjs-automl/src/img_classification.ts
deleted file mode 100644
index 5626dc6c7..000000000
--- a/tfjs-master/tfjs-automl/src/img_classification.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GraphModel, loadGraphModel} from '@tensorflow/tfjs-converter';
-import {cast, div, expandDims, image, sub, Tensor, Tensor3D, Tensor4D, tidy} from '@tensorflow/tfjs-core';
-
-import {ImageInput} from './types';
-import {imageToTensor, loadDictionary} from './util';
-
-export interface ImagePrediction {
-  prob: number;
-  label: string;
-}
-
-export interface ImageClassificationOptions {
-  centerCrop: boolean;
-}
-
-/** Input size as expected by the model. */
-const IMG_SIZE: [number, number] = [224, 224];
-// Constants used to normalize the image between -1 and 1.
-const DIV_FACTOR = 127.5;
-const SUB_FACTOR = 1;
-
-export class ImageClassificationModel {
-  constructor(public graphModel: GraphModel, public dictionary: string[]) {}
-
-  async classify(input: ImageInput, options?: ImageClassificationOptions):
-      Promise<ImagePrediction[]> {
-    options = sanitizeOptions(options);
-
-    const scores = tidy(() => {
-      const preprocessedImg = this.preprocess(input, options);
-      return this.graphModel.predict(preprocessedImg) as Tensor;
-    });
-    const probabilities = await scores.data() as Float32Array;
-    scores.dispose();
-    const result = Array.from(probabilities)
-                       .map((prob, i) => ({label: this.dictionary[i], prob}));
-    return result;
-  }
-
-  private preprocess(input: ImageInput, options: ImageClassificationOptions) {
-    // Preprocessing involves center crop and normalizing between [-1, 1].
-    const img = imageToTensor(input);
-    const croppedImg = options.centerCrop ?
-        centerCropAndResize(img) :
-        expandDims(image.resizeBilinear(img, IMG_SIZE));
-    return sub(div(croppedImg, DIV_FACTOR), SUB_FACTOR);
-  }
-}
-
-export async function loadImageClassification(modelUrl: string):
-    Promise<ImageClassificationModel> {
-  const [model, dict] =
-      await Promise.all([loadGraphModel(modelUrl), loadDictionary(modelUrl)]);
-  return new ImageClassificationModel(model, dict);
-}
-
-function sanitizeOptions(options: ImageClassificationOptions) {
-  options = options || {} as ImageClassificationOptions;
-  if (options.centerCrop == null) {
-    options.centerCrop = true;
-  }
-  return options;
-}
-
-/** Center crops an image */
-function centerCropAndResize(img: Tensor3D) {
-  return tidy(() => {
-    const [height, width] = img.shape.slice(0, 2);
-    let top = 0;
-    let left = 0;
-    if (height > width) {
-      top = (height - width) / 2;
-    } else {
-      left = (width - height) / 2;
-    }
-    const size = Math.min(width, height);
-    const boxes = [
-      [top / height, left / width, (top + size) / height, (left + size) / width]
-    ];
-    const boxIndices = [0];
-    return image.cropAndResize(
-        // tslint:disable-next-line
-        expandDims(cast(img, 'float32')) as Tensor4D, boxes, boxIndices,
-        IMG_SIZE);
-  });
-}
diff --git a/tfjs-master/tfjs-automl/src/img_classification_test.ts b/tfjs-master/tfjs-automl/src/img_classification_test.ts
deleted file mode 100644
index 1616f5d95..000000000
--- a/tfjs-master/tfjs-automl/src/img_classification_test.ts
+++ /dev/null
@@ -1,131 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GraphModel} from '@tensorflow/tfjs-converter';
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {BROWSER_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import * as automl from './index';
-import {fetchImage} from './test_util';
-
-const MODEL_URL =
-    'https://storage.googleapis.com/tfjs-testing/tfjs-automl/img_classification/model.json';
-
-const DAISY_URL =
-    'https://storage.googleapis.com/tfjs-testing/tfjs-automl/img_classification/daisy.jpg';
-
-describeWithFlags('image classification', {}, () => {
-  let model: automl.ImageClassificationModel = null;
-
-  beforeAll(async () => {
-    model = await automl.loadImageClassification(MODEL_URL);
-  });
-
-  it('make prediction from a tensor', async () => {
-    const img: tf.Tensor3D = tf.zeros([100, 80, 3]);
-    const predictions = await model.classify(img);
-    expect(predictions[0].label).toBe('daisy');
-    expect(predictions[1].label).toBe('dandelion');
-    expect(predictions[2].label).toBe('roses');
-
-    tf.test_util.expectNumbersClose(predictions[0].prob, 0.5806022);
-    tf.test_util.expectNumbersClose(predictions[1].prob, 0.32249659);
-    tf.test_util.expectNumbersClose(predictions[2].prob, 0.0283515);
-  });
-
-  it('make prediction from a tensor without cropping', async () => {
-    const img: tf.Tensor3D = tf.zeros([100, 80, 3]);
-    const predictions = await model.classify(img, {centerCrop: false});
-    expect(predictions[0].label).toBe('daisy');
-    expect(predictions[1].label).toBe('dandelion');
-    expect(predictions[2].label).toBe('roses');
-
-    tf.test_util.expectNumbersClose(predictions[0].prob, 0.5806022);
-    tf.test_util.expectNumbersClose(predictions[1].prob, 0.32249659);
-    tf.test_util.expectNumbersClose(predictions[2].prob, 0.0283515);
-  });
-
-  it('no memory leak when making a prediction', async () => {
-    const img: tf.Tensor3D = tf.zeros([100, 80, 3]);
-    const numTensorsBefore = tf.memory().numTensors;
-    await model.classify(img);
-    const numTensorsAfter = tf.memory().numTensors;
-    expect(numTensorsAfter).toEqual(numTensorsBefore);
-  });
-
-  it('has access to dictionary', () => {
-    expect(model.dictionary).toEqual([
-      'daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'
-    ]);
-  });
-
-  it('can access the underlying graph model', () => {
-    expect(model.graphModel instanceof GraphModel).toBe(true);
-    expect(model.graphModel.inputNodes).toEqual(['image']);
-    expect(model.graphModel.outputNodes).toEqual(['scores']);
-    const img: tf.Tensor = tf.zeros([1, 224, 224, 3]);
-    const scores = model.graphModel.predict(img) as tf.Tensor;
-    expect(scores.shape).toEqual([1, 5]);
-  });
-});
-
-describeWithFlags('image classification browser', BROWSER_ENVS, () => {
-  let model: automl.ImageClassificationModel = null;
-  let daisyImg: HTMLImageElement;
-
-  beforeAll(async () => {
-    model = await automl.loadImageClassification(MODEL_URL);
-    daisyImg = await fetchImage(DAISY_URL);
-  });
-
-  function assertTop3PredsForDaisy(
-      predictions: automl.ImagePrediction[], centerCrop: boolean) {
-    const probs = centerCrop ? [0.9310929, 0.0273733, 0.0130559] :
-                               [0.8411523, 0.0729438, 0.03020708];
-    expect(predictions[0].label).toBe('daisy');
-    tf.test_util.expectNumbersClose(predictions[0].prob, probs[0]);
-
-    expect(predictions[1].label).toBe('dandelion');
-    tf.test_util.expectNumbersClose(predictions[1].prob, probs[1]);
-
-    expect(predictions[2].label).toBe('roses');
-    tf.test_util.expectNumbersClose(predictions[2].prob, probs[2]);
-  }
-
-  it('make prediction from an image element', async () => {
-    const predictions = await model.classify(daisyImg);
-    assertTop3PredsForDaisy(predictions, true /* centerCrop */);
-  });
-
-  it('make prediction from a canvas element', async () => {
-    // Copy the pixels from the image to a canvas.
-    const canvas = document.createElement('canvas');
-    const ctx = canvas.getContext('2d');
-    canvas.width = daisyImg.width;
-    canvas.height = daisyImg.height;
-    ctx.drawImage(daisyImg, 0, 0, daisyImg.width, daisyImg.height);
-
-    const predictions = await model.classify(canvas);
-    assertTop3PredsForDaisy(predictions, true /* centerCrop */);
-  });
-
-  it('make prediction without center cropping', async () => {
-    const predictions = await model.classify(daisyImg, {centerCrop: false});
-    assertTop3PredsForDaisy(predictions, false /* centerCrop */);
-  });
-});
diff --git a/tfjs-master/tfjs-automl/src/index.ts b/tfjs-master/tfjs-automl/src/index.ts
deleted file mode 100644
index a93c3b7d8..000000000
--- a/tfjs-master/tfjs-automl/src/index.ts
+++ /dev/null
@@ -1,25 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Image classification API.
-export {ImageClassificationModel, ImageClassificationOptions, ImagePrediction, loadImageClassification} from './img_classification';
-// Object detection API.
-export {Box, loadObjectDetection, ObjectDetectionModel, ObjectDetectionOptions, PredictedObject} from './object_detection';
-
-// Shared API.
-export {ImageInput} from './types';
-export {version} from './version';
diff --git a/tfjs-master/tfjs-automl/src/index_test.ts b/tfjs-master/tfjs-automl/src/index_test.ts
deleted file mode 100644
index 4f0a04ebd..000000000
--- a/tfjs-master/tfjs-automl/src/index_test.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as automl from './index';
-
-describe('public API', () => {
-  it('exported version matches package version', () => {
-    // tslint:disable-next-line:no-require-imports
-    const expected = require('../package.json').version;
-    expect(automl.version).toBe(expected);
-  });
-});
diff --git a/tfjs-master/tfjs-automl/src/object_detection.ts b/tfjs-master/tfjs-automl/src/object_detection.ts
deleted file mode 100644
index cc0dbcdba..000000000
--- a/tfjs-master/tfjs-automl/src/object_detection.ts
+++ /dev/null
@@ -1,173 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GraphModel, loadGraphModel} from '@tensorflow/tfjs-converter';
-import {cast, dispose, expandDims, image, Tensor, Tensor2D, tidy} from '@tensorflow/tfjs-core';
-
-import {ImageInput} from './types';
-import {imageToTensor, loadDictionary} from './util';
-
-const DEFAULT_TOPK = 20;
-const DEFAULT_IOU_THRESHOLD = 0.5;
-const DEFAULT_SCORE_THRESHOLD = 0.5;
-
-const INPUT_NODE_NAME = 'ToFloat';
-const OUTPUT_NODE_NAMES =
-    ['Postprocessor/convert_scores', 'Postprocessor/Decode/transpose_1'];
-
-export interface ObjectDetectionOptions {
-  /**
-   * Only the `topk` most likely objects are returned. The actual number of
-   * objects might be less than this number.
-   */
-  topk?: number;
-  /**
-   * Intersection over union threshold. IoU is a metric between 0 and 1 used to
-   * measure the overlap of two boxes. The predicted boxes will not overlap more
-   * than the specified threshold.
-   */
-  iou?: number;
-  /** Boxes with score lower than this threshold will be ignored. */
-  score?: number;
-}
-
-/** Contains the coordinates of a bounding box. */
-export interface Box {
-  /** Number of pixels from the top of the image (top padding). */
-  top: number;
-  /** Number of pixels from the left of the image (left padding). */
-  left: number;
-  /** The width of the box. */
-  width: number;
-  /** The height of the box. */
-  height: number;
-}
-
-/** The predicted object, which holds the score, label and bounding box. */
-export interface PredictedObject {
-  box: Box;
-  score: number;
-  label: string;
-}
-
-export class ObjectDetectionModel {
-  constructor(public graphModel: GraphModel, public dictionary: string[]) {}
-
-  async detect(input: ImageInput, options?: ObjectDetectionOptions):
-      Promise<PredictedObject[]> {
-    options = sanitizeOptions(options);
-    const img = tidy(() => this.preprocess(input, options));
-    const [height, width] = [img.shape[1], img.shape[2]];
-    const feedDict: {[name: string]: Tensor} = {};
-    feedDict[INPUT_NODE_NAME] = img;
-    const [scoresTensor, boxesTensor] =
-        await this.graphModel.executeAsync(feedDict, OUTPUT_NODE_NAMES) as
-        Tensor[];
-
-    const [, numBoxes, numClasses] = scoresTensor.shape;
-    const [scores, boxes] =
-        await Promise.all([scoresTensor.data(), boxesTensor.data()]);
-    const {boxScores, boxLabels} =
-        calculateMostLikelyLabels(scores as Float32Array, numBoxes, numClasses);
-
-    // Sort the boxes by score, ignoring overlapping boxes.
-    const selectedBoxesTensor = await image.nonMaxSuppressionAsync(
-        boxesTensor as Tensor2D, boxScores, options.topk, options.iou,
-        options.score);
-    const selectedBoxes = await selectedBoxesTensor.data() as Int32Array;
-    dispose([img, scoresTensor, boxesTensor, selectedBoxesTensor]);
-
-    const result = buildDetectedObjects(
-        width, height, boxes as Float32Array, boxScores, boxLabels,
-        selectedBoxes, this.dictionary);
-    return result;
-  }
-
-  private preprocess(input: ImageInput, options: ObjectDetectionOptions) {
-    return cast(expandDims(imageToTensor(input)), 'float32');
-  }
-}
-
-export async function loadObjectDetection(modelUrl: string):
-    Promise<ObjectDetectionModel> {
-  const [model, dict] =
-      await Promise.all([loadGraphModel(modelUrl), loadDictionary(modelUrl)]);
-  return new ObjectDetectionModel(model, dict);
-}
-
-function sanitizeOptions(options: ObjectDetectionOptions) {
-  options = options || {} as ObjectDetectionOptions;
-  if (options.topk == null) {
-    options.topk = DEFAULT_TOPK;
-  }
-  if (options.iou == null) {
-    options.iou = DEFAULT_IOU_THRESHOLD;
-  }
-  if (options.score == null) {
-    options.score = DEFAULT_SCORE_THRESHOLD;
-  }
-  return options;
-}
-
-function calculateMostLikelyLabels(
-    scores: Float32Array, numBoxes: number,
-    numClasses: number): {boxScores: number[], boxLabels: number[]} {
-  // Holds a score for each box.
-  const boxScores: number[] = [];
-  // Holds the label id for each box.
-  const boxLabels: number[] = [];
-  for (let i = 0; i < numBoxes; i++) {
-    let maxScore = Number.MIN_VALUE;
-    let mostLikelyLabel = -1;
-    for (let j = 0; j < numClasses; j++) {
-      const flatIndex = i * numClasses + j;
-      const score = scores[flatIndex];
-      if (score > maxScore) {
-        maxScore = scores[flatIndex];
-        mostLikelyLabel = j;
-      }
-    }
-    boxScores[i] = maxScore;
-    boxLabels[i] = mostLikelyLabel;
-  }
-  return {boxScores, boxLabels};
-}
-
-function buildDetectedObjects(
-    width: number, height: number, boxes: Float32Array, boxScores: number[],
-    boxLabels: number[], selectedBoxes: Int32Array,
-    dictionary: string[]): PredictedObject[] {
-  const objects: PredictedObject[] = [];
-  // Each 2d rectangle is fully described with 4 coordinates.
-  const numBoxCoords = 4;
-  for (let i = 0; i < selectedBoxes.length; i++) {
-    const boxIndex = selectedBoxes[i];
-    const [top, left, bottom, right] = Array.from(boxes.slice(
-        boxIndex * numBoxCoords, boxIndex * numBoxCoords + numBoxCoords));
-    objects.push({
-      box: {
-        left: left * width,
-        top: top * height,
-        width: (right - left) * width,
-        height: (bottom - top) * height,
-      },
-      label: dictionary[boxLabels[boxIndex]],
-      score: boxScores[boxIndex],
-    });
-  }
-  return objects;
-}
diff --git a/tfjs-master/tfjs-automl/src/object_detection_test.ts b/tfjs-master/tfjs-automl/src/object_detection_test.ts
deleted file mode 100644
index ca96ba379..000000000
--- a/tfjs-master/tfjs-automl/src/object_detection_test.ts
+++ /dev/null
@@ -1,148 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GraphModel} from '@tensorflow/tfjs-converter';
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {BROWSER_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import * as automl from './index';
-import {fetchImage} from './test_util';
-
-const MODEL_URL =
-    'https://storage.googleapis.com/tfjs-testing/tfjs-automl/object_detection/model.json';
-
-const SALAD_IMAGE =
-    'https://storage.googleapis.com/tfjs-testing/tfjs-automl/object_detection/test_image.jpg';
-
-describeWithFlags('object detection', {}, () => {
-  let model: automl.ObjectDetectionModel = null;
-  const originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-  jasmine.DEFAULT_TIMEOUT_INTERVAL = 40000;
-
-  beforeAll(async () => {
-    model = await automl.loadObjectDetection(MODEL_URL);
-  });
-
-  afterAll(() => {
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout;
-  });
-
-  it('prediction from a zero tensor', async () => {
-    const img: tf.Tensor3D = tf.zeros([100, 80, 3]);
-    const predictions = await model.detect(img);
-    expect(predictions.length).toBe(0);
-  });
-
-  it('prediction from a zero tensor with score threshold', async () => {
-    const img: tf.Tensor3D = tf.zeros([100, 80, 3]);
-    const options = {score: 0.11};
-    const predictions = await model.detect(img, options);
-    expect(predictions.length).toBe(1);
-    const {box: {left, top, width, height}, label, score} = predictions[0];
-
-    expect(label).toBe('Salad');
-    tf.test_util.expectNumbersClose(score, 0.116391);
-    tf.test_util.expectNumbersClose(left, 26.6741156);
-    tf.test_util.expectNumbersClose(top, 94.003981);
-    tf.test_util.expectNumbersClose(width, 22.6277661);
-    tf.test_util.expectNumbersClose(height, 6.30063414);
-  });
-
-  it('prediction with iou threshold', async () => {
-    const img: tf.Tensor3D = tf.zeros([100, 80, 3]);
-    const options: automl.ObjectDetectionOptions = {score: 0.10};
-    options.iou = 0.9;
-    let predictions = await model.detect(img, options);
-    expect(predictions.length).toBe(9);
-
-    options.iou = 0.1;
-    predictions = await model.detect(img, options);
-    expect(predictions.length).toBe(1);
-  });
-
-  it('no memory leak when making a prediction', async () => {
-    const img: tf.Tensor3D = tf.zeros([100, 80, 3]);
-    const numTensorsBefore = tf.memory().numTensors;
-    await model.detect(img);
-    const numTensorsAfter = tf.memory().numTensors;
-    // The increased tensor is for the hashtable id handle
-    expect(numTensorsAfter).toBeLessThanOrEqual(numTensorsBefore + 1);
-  });
-
-  it('has access to dictionary', () => {
-    expect(model.dictionary).toEqual([
-      'background', 'Tomato', 'Seafood', 'Salad', 'Baked Goods', 'Cheese'
-    ]);
-  });
-
-  it('can access the underlying graph model', () => {
-    expect(model.graphModel instanceof GraphModel).toBe(true);
-  });
-});
-
-describeWithFlags('object detection browser', BROWSER_ENVS, () => {
-  let model: automl.ObjectDetectionModel = null;
-  let saladImg: HTMLImageElement;
-
-  beforeAll(async () => {
-    model = await automl.loadObjectDetection(MODEL_URL);
-    saladImg = await fetchImage(SALAD_IMAGE);
-  });
-
-  function assertTop3PredsForSalad(predictions: automl.PredictedObject[]) {
-    expect(predictions.length).toBe(3);
-    const [top1, top2, top3] = predictions;
-    expect(top1.label).toBe('Tomato');
-    tf.test_util.expectNumbersClose(top1.score, 0.97170084);
-
-    expect(top2.label).toBe('Tomato');
-    tf.test_util.expectNumbersClose(top2.score, 0.93456619);
-
-    expect(top3.label).toBe('Salad');
-    tf.test_util.expectNumbersClose(top3.score, 0.9074271);
-  }
-
-  it('make prediction from an image element', async () => {
-    const predictions = await model.detect(saladImg);
-    assertTop3PredsForSalad(predictions);
-  });
-
-  it('make prediction from a canvas element', async () => {
-    // Copy the pixels from the image to a canvas.
-    const canvas = document.createElement('canvas');
-    const ctx = canvas.getContext('2d');
-    canvas.width = saladImg.width;
-    canvas.height = saladImg.height;
-    ctx.drawImage(saladImg, 0, 0, saladImg.width, saladImg.height);
-
-    const predictions = await model.detect(canvas);
-    assertTop3PredsForSalad(predictions);
-  });
-
-  it('make prediction from image data', async () => {
-    const canvas = document.createElement('canvas');
-    const ctx = canvas.getContext('2d');
-    canvas.width = saladImg.width;
-    canvas.height = saladImg.height;
-    ctx.drawImage(saladImg, 0, 0, saladImg.width, saladImg.height);
-    const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
-
-    const predictions = await model.detect(imageData);
-    assertTop3PredsForSalad(predictions);
-  });
-});
diff --git a/tfjs-master/tfjs-automl/src/test_browser.ts b/tfjs-master/tfjs-automl/src/test_browser.ts
deleted file mode 100644
index 989bab447..000000000
--- a/tfjs-master/tfjs-automl/src/test_browser.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import '@tensorflow/tfjs-backend-webgl';
-
-// tslint:disable-next-line: no-imports-from-dist
-import {parseTestEnvFromKarmaFlags, registerTestEnv, setTestEnvs, TEST_ENVS} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-// Increase test timeout since we are fetching the model files from GCS.
-jasmine.DEFAULT_TIMEOUT_INTERVAL = 20000;
-
-registerTestEnv({
-  name: 'webgl1',
-  backendName: 'webgl',
-  flags: {
-    'WEBGL_VERSION': 1,
-    'WEBGL_CPU_FORWARD': false,
-    'WEBGL_SIZE_UPLOAD_UNIFORM': 0
-  },
-  isDataSync: true
-});
-
-// tslint:disable-next-line:no-any
-declare let __karma__: any;
-const testEnv = parseTestEnvFromKarmaFlags(__karma__.config.args, TEST_ENVS);
-if (testEnv != null) {
-  setTestEnvs([testEnv]);
-} else {
-  // Run browser tests againts both the webgl backends.
-  setTestEnvs([
-    // WebGL.
-    {
-      name: 'test-webgl',
-      backendName: 'webgl',
-      flags: {
-        'WEBGL_VERSION': 2,
-        'WEBGL_CPU_FORWARD': false,
-        'WEBGL_SIZE_UPLOAD_UNIFORM': 0
-      },
-      isDataSync: true
-    }
-  ]);
-}
diff --git a/tfjs-master/tfjs-automl/src/test_node.ts b/tfjs-master/tfjs-automl/src/test_node.ts
deleted file mode 100644
index ee60399ea..000000000
--- a/tfjs-master/tfjs-automl/src/test_node.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import '@tensorflow/tfjs-backend-cpu';
-// tslint:disable-next-line: no-imports-from-dist
-import {setTestEnvs} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-// tslint:disable-next-line:no-require-imports
-const jasmine = require('jasmine');
-
-// Increase test timeout since we are fetching the model files from GCS.
-jasmine.DEFAULT_TIMEOUT_INTERVAL = 20000;
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-// Run node tests againts the cpu backend.
-setTestEnvs([{name: 'node', backendName: 'cpu'}]);
-
-const runner = new jasmine();
-runner.loadConfig({spec_files: ['src/**/*_test.ts'], jsLoader: 'require',
-                   random: false});
-runner.execute();
diff --git a/tfjs-master/tfjs-automl/src/test_util.ts b/tfjs-master/tfjs-automl/src/test_util.ts
deleted file mode 100644
index 0a11a0acf..000000000
--- a/tfjs-master/tfjs-automl/src/test_util.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export async function fetchImage(url: string): Promise<HTMLImageElement> {
-  const response = await fetch(url);
-  const blob = await response.blob();
-  const img = new Image();
-  const blobUrl = URL.createObjectURL(blob);
-  return new Promise((resolve, reject) => {
-    img.onload = () => {
-      URL.revokeObjectURL(blobUrl);
-      resolve(img);
-    };
-    img.onerror = (evt /* Arg is an event, not error. Can't rethrow it */) => {
-      reject(new Error('Failed to load blob as image.'));
-    };
-    img.src = blobUrl;
-  });
-}
diff --git a/tfjs-master/tfjs-automl/src/types.ts b/tfjs-master/tfjs-automl/src/types.ts
deleted file mode 100644
index e969b37c0..000000000
--- a/tfjs-master/tfjs-automl/src/types.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor3D} from '@tensorflow/tfjs-core';
-
-export type ImageInput =
-    ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement|Tensor3D;
diff --git a/tfjs-master/tfjs-automl/src/util.ts b/tfjs-master/tfjs-automl/src/util.ts
deleted file mode 100644
index 0457cf19d..000000000
--- a/tfjs-master/tfjs-automl/src/util.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {browser, Tensor, Tensor3D, util} from '@tensorflow/tfjs-core';
-import {ImageInput} from './types';
-
-export function imageToTensor(img: ImageInput): Tensor3D {
-  return img instanceof Tensor ? img : browser.fromPixels(img);
-}
-
-/** Loads and parses the dictionary. */
-export async function loadDictionary(modelUrl: string): Promise<string[]> {
-  const lastIndexOfSlash = modelUrl.lastIndexOf('/');
-  const prefixUrl =
-      lastIndexOfSlash >= 0 ? modelUrl.slice(0, lastIndexOfSlash + 1) : '';
-  const dictUrl = `${prefixUrl}dict.txt`;
-  const response = await util.fetch(dictUrl);
-  const text = await response.text();
-  return text.trim().split('\n');
-}
diff --git a/tfjs-master/tfjs-automl/src/util_test.ts b/tfjs-master/tfjs-automl/src/util_test.ts
deleted file mode 100644
index 4316b3d30..000000000
--- a/tfjs-master/tfjs-automl/src/util_test.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-import {util} from '@tensorflow/tfjs-core';
-import {loadDictionary} from './util';
-
-describe('load dictionary', () => {
-  it('relative url to model.json', async () => {
-    spyOn(util, 'fetch').and.callFake(async (dictUrl: string) => {
-      expect(dictUrl).toBe('dict.txt');
-      return {text: async () => 'first\nsecond\nthird'} as Response;
-    });
-    const res = await loadDictionary('model.json');
-    expect(res).toEqual(['first', 'second', 'third']);
-  });
-
-  it('relative url to model.json with a base path', async () => {
-    spyOn(util, 'fetch').and.callFake(async (dictUrl: string) => {
-      expect(dictUrl).toBe('base/path/dict.txt');
-      return {text: async () => 'first\nsecond\nthird'} as Response;
-    });
-    const res = await loadDictionary('base/path/model.json');
-    expect(res).toEqual(['first', 'second', 'third']);
-  });
-
-  it('absolute url to model.json', async () => {
-    spyOn(util, 'fetch').and.callFake(async (dictUrl: string) => {
-      expect(dictUrl).toBe('/dict.txt');
-      return {text: async () => 'first\nsecond\nthird\n'} as Response;
-    });
-    const res = await loadDictionary('/model.json');
-    expect(res).toEqual(['first', 'second', 'third']);
-  });
-
-  it('absolute url to model.json with a base path', async () => {
-    spyOn(util, 'fetch').and.callFake(async (dictUrl: string) => {
-      expect(dictUrl).toBe('/base/path/dict.txt');
-      return {text: async () => 'first\nsecond\nthird\n'} as Response;
-    });
-    const res = await loadDictionary('/base/path/model.json');
-    expect(res).toEqual(['first', 'second', 'third']);
-  });
-});
diff --git a/tfjs-master/tfjs-automl/src/version.ts b/tfjs-master/tfjs-automl/src/version.ts
deleted file mode 100644
index 4bdc4cd85..000000000
--- a/tfjs-master/tfjs-automl/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '1.2.0';
-export {version};
diff --git a/tfjs-master/tfjs-automl/tsconfig.json b/tfjs-master/tfjs-automl/tsconfig.json
deleted file mode 100644
index 7941039d6..000000000
--- a/tfjs-master/tfjs-automl/tsconfig.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "extends": "../tsconfig_base.json",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist",
-    "skipLibCheck": true
-  }
-}
diff --git a/tfjs-master/tfjs-automl/tsconfig.test.json b/tfjs-master/tfjs-automl/tsconfig.test.json
deleted file mode 100644
index 537324152..000000000
--- a/tfjs-master/tfjs-automl/tsconfig.test.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig.test",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-automl/tslint.json b/tfjs-master/tfjs-automl/tslint.json
deleted file mode 100644
index ec365f164..000000000
--- a/tfjs-master/tfjs-automl/tslint.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "../tslint.json"
-}
diff --git a/tfjs-master/tfjs-automl/yarn.lock b/tfjs-master/tfjs-automl/yarn.lock
deleted file mode 100644
index e1a7f56b7..000000000
--- a/tfjs-master/tfjs-automl/yarn.lock
+++ /dev/null
@@ -1,4052 +0,0 @@
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-# yarn lockfile v1
-
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-  resolved "https://registry.yarnpkg.com/which-typed-array/-/which-typed-array-1.1.4.tgz#8fcb7d3ee5adf2d771066fba7cf37e32fe8711ff"
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-    foreach "^2.0.5"
-    function-bind "^1.1.1"
-    has-symbols "^1.0.1"
-    is-typed-array "^1.1.3"
-
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-    isexe "^2.0.0"
-
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-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/which/-/which-2.0.2.tgz#7c6a8dd0a636a0327e10b59c9286eee93f3f51b1"
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-  dependencies:
-    isexe "^2.0.0"
-
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-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
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-
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-  version "1.0.2"
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-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
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-ws@~8.2.3:
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-
-xtend@^4.0.2:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/xtend/-/xtend-4.0.2.tgz#bb72779f5fa465186b1f438f674fa347fdb5db54"
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-
-y18n@^5.0.5:
-  version "5.0.5"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.5.tgz#8769ec08d03b1ea2df2500acef561743bbb9ab18"
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-
-yalc@~1.0.0-pre.21:
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-  resolved "https://registry.yarnpkg.com/yalc/-/yalc-1.0.0-pre.53.tgz#c51db2bb924a6908f4cb7e82af78f7e5606810bc"
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-  dependencies:
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-    detect-indent "^6.0.0"
-    fs-extra "^8.0.1"
-    glob "^7.1.4"
-    ignore "^5.0.4"
-    ini "^2.0.0"
-    npm-packlist "^2.1.5"
-    yargs "^16.1.1"
-
-yallist@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
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-
-yargs-parser@^20.2.2:
-  version "20.2.7"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.7.tgz#61df85c113edfb5a7a4e36eb8aa60ef423cbc90a"
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-
-yargs@^16.1.1:
-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yn@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
-  integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==
diff --git a/tfjs-master/tfjs-backend-cpu/.npmignore b/tfjs-master/tfjs-backend-cpu/.npmignore
deleted file mode 100644
index 6292b5fdb..000000000
--- a/tfjs-master/tfjs-backend-cpu/.npmignore
+++ /dev/null
@@ -1,27 +0,0 @@
-.babelrc
-.DS_Store
-.idea/
-.rpt2_cache
-.travis.yml
-.vscode
-*.tgz
-*.txt
-**.yalc
-**yalc.lock
-cloudbuild.yml
-coverage/
-demo/
-dist/**/*_test.d.ts
-dist/**/*_test.js
-karma.conf.js
-node_modules/
-npm-debug.log
-package-lock.json
-package/
-rollup.config.js
-scripts/
-src/**/*_test.ts
-tsconfig.json
-tslint.json
-yarn-error.log
-yarn.lock
diff --git a/tfjs-master/tfjs-backend-cpu/.npmrc b/tfjs-master/tfjs-backend-cpu/.npmrc
deleted file mode 100644
index 43c97e719..000000000
--- a/tfjs-master/tfjs-backend-cpu/.npmrc
+++ /dev/null
@@ -1 +0,0 @@
-package-lock=false
diff --git a/tfjs-master/tfjs-backend-cpu/.nycrc b/tfjs-master/tfjs-backend-cpu/.nycrc
deleted file mode 100644
index c13fc92d2..000000000
--- a/tfjs-master/tfjs-backend-cpu/.nycrc
+++ /dev/null
@@ -1,19 +0,0 @@
-{
-    "extension": [
-      ".ts"
-    ],
-    "reporter": [
-      "html",
-      "text-summary"
-    ],
-    "include": [
-      "src/**/*"
-    ],
-    "exclude": [
-      "src/run_tests.ts",
-      "src/**/*_test.ts"
-    ],
-    "cache": false,
-    "sourceMap": true,
-    "instrument": true
-}
diff --git a/tfjs-master/tfjs-backend-cpu/.vscode/launch.json b/tfjs-master/tfjs-backend-cpu/.vscode/launch.json
deleted file mode 100644
index 0aa0bb52b..000000000
--- a/tfjs-master/tfjs-backend-cpu/.vscode/launch.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-  // Use IntelliSense to learn about possible attributes.
-  // Hover to view descriptions of existing attributes.
-  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-  "version": "0.2.0",
-  "configurations": [
-    {
-      "name": "Run tests",
-      "type": "node",
-      "request": "launch",
-      "runtimeExecutable": "yarn",
-      "runtimeArgs": ["test"],
-      "sourceMaps": true,
-      "cwd": "${workspaceRoot}",
-      "protocol": "inspector",
-    }
-  ]
-}
diff --git a/tfjs-master/tfjs-backend-cpu/BUILD.bazel b/tfjs-master/tfjs-backend-cpu/BUILD.bazel
deleted file mode 100644
index ece7e27f2..000000000
--- a/tfjs-master/tfjs-backend-cpu/BUILD.bazel
+++ /dev/null
@@ -1,110 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@build_bazel_rules_nodejs//:index.bzl", "nodejs_test", "pkg_npm")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-nodejs_test(
-    name = "tfjs-backend-cpu_test",
-    data = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_test_lib",
-    ],
-    entry_point = "//tfjs-backend-cpu/src:run_tests.ts",
-    link_workspace_root = True,
-    tags = ["ci"],
-)
-
-tfjs_web_test(
-    name = "tfjs-backend-cpu_browser_test",
-    srcs = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_test_bundle",
-    ],
-    args = [],
-    browsers = [
-        "bs_chrome_mac",
-    ],
-    headless = False,
-    presubmit_browsers = [
-        "bs_chrome_mac",
-    ],
-)
-
-tfjs_bundle(
-    name = "tf-backend-cpu",
-    entry_point = "//tfjs-backend-cpu/src:index.ts",
-    external = [
-        "@tensorflow/tfjs-core",
-        "crypto",
-        "node-fetch",
-        "seedrandom",
-        "util",
-    ],
-    globals = {
-        "@tensorflow/tfjs-core": "tf",
-        "seedrandom": "seedrandom",
-    },
-    umd_name = "tf",
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_src_lib",
-    ],
-)
-
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_src_lib",
-    ],
-    root = "src",
-)
-
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-backend-cpu",
-        ":tf-backend-cpu.es2017",
-        ":tf-backend-cpu.es2017.min",
-        ":tf-backend-cpu.fesm",
-        ":tf-backend-cpu.fesm.min",
-        ":tf-backend-cpu.min",
-        ":tf-backend-cpu.node",
-    ],
-)
-
-pkg_npm(
-    name = "tfjs-backend-cpu_pkg",
-    package_name = "@tensorflow/tfjs-backend-cpu",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_src_to_dist",
-    ],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        ":tfjs-backend-cpu_test",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-cpu/README.md b/tfjs-master/tfjs-backend-cpu/README.md
deleted file mode 100644
index 5e1e55361..000000000
--- a/tfjs-master/tfjs-backend-cpu/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Usage
-
-This package implements a JavaScript based CPU backend to TensorFlow.js.
-
-## Importing the backend
-
-Note: this backend is included by default in `@tensorflow/tfjs`.
-
-### Via NPM
-
-```js
-// Import @tensorflow/tfjs-core
-import * as tf from '@tensorflow/tfjs-core';
-// Adds the CPU backend to the global backend registry.
-import '@tensorflow/tfjs-backend-cpu';
-```
-
-### Via a script tag
-
-```html
-<!-- Import @tensorflow/tfjs-core -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
-
-<!-- Adds the CPU backend to the global backend registry -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-cpu"></script>
-```
-
-You can also get ES2017 code using the following links
-
-```html
-<!-- Import @tensorflow/tfjs-core -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core@2.0.0-rc.4/dist/tf-core.es2017.js"></script>
-
-<!-- Adds the CPU backend to the global backend registry -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-cpu@2.0.0-rc.4/dist/tf-backend-cpu.es2017.js"></script>
-```
-
diff --git a/tfjs-master/tfjs-backend-cpu/package.json b/tfjs-master/tfjs-backend-cpu/package.json
deleted file mode 100644
index a93ea02f4..000000000
--- a/tfjs-master/tfjs-backend-cpu/package.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-backend-cpu",
-  "version": "0.0.0",
-  "description": "Vanilla JavaScript backend for TensorFlow.js",
-  "private": false,
-  "main": "dist/tf-backend-cpu.node.js",
-  "jsdelivr": "dist/tf-backend-cpu.min.js",
-  "unpkg": "dist/tf-backend-cpu.min.js",
-  "types": "dist/index.d.ts",
-  "jsnext:main": "dist/index.js",
-  "module": "dist/index.js",
-  "miniprogram": "dist/miniprogram",
-  "engines": {
-    "yarn": ">= 1.3.2"
-  },
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-backend-cpu"
-  },
-  "license": "Apache-2.0",
-  "devDependencies": {
-    "@bazel/bazelisk": "^1.12.0",
-    "@bazel/ibazel": "^0.16.2"
-  },
-  "scripts": {
-    "build": "bazel build :tfjs-backend-cpu_pkg",
-    "publish-npm": "bazel run :tfjs-backend-cpu_pkg.publish",
-    "coverage": "bazel coverage :tfjs-backend-cpu_test",
-    "test": "bazel test :tests --test_output=streamed",
-    "test-debug": "bazel run :tfjs-backend-cpu_test --config=debug",
-    "test-dev": "ibazel run :tests --test-output=streamed"
-  },
-  "dependencies": {
-    "@types/seedrandom": "^2.4.28",
-    "seedrandom": "^3.0.5"
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core"
-  },
-  "browser": {
-    "util": false,
-    "crypto": false
-  },
-  "sideEffects": [
-    "./dist/register_all_kernels.js",
-    "./dist/base.js",
-    "./dist/index.js",
-    "./src/register_all_kernels.mjs",
-    "./src/base.mjs",
-    "./src/index.mjs"
-  ],
-  "resolutions": {
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-backend-cpu/scripts/build-npm.sh b/tfjs-master/tfjs-backend-cpu/scripts/build-npm.sh
deleted file mode 100644
index d1e157e89..000000000
--- a/tfjs-master/tfjs-backend-cpu/scripts/build-npm.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2017 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn rimraf dist/
-yarn
-
-yarn build
-yarn rollup -c --visualize --npm
-
-# Use minified files for miniprogram
-mkdir dist/miniprogram
-cp dist/tf-backend-cpu.min.js dist/miniprogram/index.js
-cp dist/tf-backend-cpu.min.js.map dist/miniprogram/index.js.map
-
-echo "Stored standalone library at dist/tf-backend-cpu(.min).js"
diff --git a/tfjs-master/tfjs-backend-cpu/src/BUILD.bazel b/tfjs-master/tfjs-backend-cpu/src/BUILD.bazel
deleted file mode 100644
index 64d9d36b1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/BUILD.bazel
+++ /dev/null
@@ -1,101 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("//tools:defaults.bzl", "esbuild", "ts_library")
-load("//tools:enumerate_tests.bzl", "enumerate_tests")
-
-package(default_visibility = ["//visibility:public"])
-
-TEST_SRCS = [
-    "**/*_test.ts",
-    "run_tests.ts",
-]
-
-filegroup(
-    name = "all_test_entrypoints",
-    srcs = glob(
-        ["**/*_test.ts"],
-        exclude = [
-            "setup_test.ts",
-        ],
-    ),
-)
-
-# Generates the 'tests.ts' file that imports all test entrypoints.
-enumerate_tests(
-    name = "tests",
-    srcs = [":all_test_entrypoints"],
-    root_path = "tfjs-backend-cpu/src",
-)
-
-ts_library(
-    name = "tfjs-backend-cpu_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-backend-cpu/dist",
-    deps = [
-        "//tfjs-core/src:tfjs-core_lib",
-        "@npm//@types/seedrandom",
-        "@npm//seedrandom",
-    ],
-)
-
-ts_library(
-    name = "tfjs-backend-cpu_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-backend-cpu",
-    deps = [
-        ":tfjs-backend-cpu_src_lib",
-    ],
-)
-
-ts_library(
-    name = "tfjs-backend-cpu_test_lib",
-    testonly = True,
-    srcs = glob(TEST_SRCS) + [
-        ":tests",
-    ],
-    module_name = "@tensorflow/tfjs-backend-cpu/dist",
-    deps = [
-        ":tfjs-backend-cpu_lib",
-        ":tfjs-backend-cpu_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-        "@npm//@types/jasmine",
-        "@npm//jasmine",
-    ],
-)
-
-esbuild(
-    name = "tfjs-backend-cpu_test_bundle",
-    testonly = True,
-    entry_point = "setup_test.ts",
-    external = [
-        # webworker tests call 'require('@tensorflow/tfjs')', which
-        # is external to the test bundle.
-        # Note: This is not a bazel target. It's just a string.
-        "@tensorflow/tfjs",
-        "worker_threads",
-        "util",
-    ],
-    sources_content = True,
-    deps = [
-        ":tfjs-backend-cpu_lib",
-        ":tfjs-backend-cpu_test_lib",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-cpu/src/backend_cpu.ts b/tfjs-master/tfjs-backend-cpu/src/backend_cpu.ts
deleted file mode 100644
index 854cb366c..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/backend_cpu.ts
+++ /dev/null
@@ -1,233 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BackendTimingInfo, buffer, DataStorage, DataType, engine, env, kernel_impls, KernelBackend, Rank, ShapeMap, Tensor, Tensor2D, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-const whereImpl = kernel_impls.whereImpl;
-import {assertNotComplex} from './cpu_util';
-
-interface DataId {}
-
-export interface TensorData<D extends DataType> {
-  values?: backend_util.BackendValues;
-  dtype: D;
-  // For complex numbers, the real and imaginary parts are stored as their own
-  // individual tensors, with a parent joining the two with the
-  // complexTensorInfos field.
-  complexTensorInfos?: {real: TensorInfo, imag: TensorInfo};
-  // refCount keeps track of how many tensors reference it. Used for memory
-  // management.
-  refCount: number;
-}
-
-export class MathBackendCPU extends KernelBackend {
-  public blockSize = 48;
-
-  data: DataStorage<TensorData<DataType>>;
-  private firstUse = true;
-  private static nextDataId = 0;
-  private nextDataId(): number {
-    return MathBackendCPU.nextDataId++;
-  }
-
-  constructor() {
-    super();
-    this.data = new DataStorage(this, engine());
-  }
-
-  override write(
-      values: backend_util.BackendValues, shape: number[],
-      dtype: DataType): DataId {
-    if (this.firstUse) {
-      this.firstUse = false;
-      if (env().get('IS_NODE')) {
-        backend_util.warn(
-            '\n============================\n' +
-            'Hi, looks like you are running TensorFlow.js in ' +
-            'Node.js. To speed things up dramatically, install our node ' +
-            'backend, visit https://github.com/tensorflow/tfjs-node for more details. ' +
-            '\n============================');
-      }
-    }
-    const dataId = {id: this.nextDataId()};
-
-    this.data.set(dataId, {values, dtype, refCount: 1});
-
-    return dataId;
-  }
-
-  /**
-   * Create a data bucket in cpu backend.
-   * @param shape Shape of the `TensorInfo`.
-   * @param dtype DType of the `TensorInfo`.
-   * @param values The value of the `TensorInfo` stored as a flattened array.
-   */
-  makeTensorInfo(
-      shape: number[], dtype: DataType,
-      values?: backend_util.BackendValues|string[]): TensorInfo {
-    let outId;
-    if (dtype === 'string' && values != null && values.length > 0 &&
-        util.isString(values[0])) {
-      const encodedValues =
-          (values as unknown as string[]).map(d => util.encodeString(d));
-
-      outId = this.write(encodedValues, shape, dtype);
-    } else {
-      outId = this.write(values as TypedArray, shape, dtype);
-    }
-
-    return {dataId: outId, shape, dtype};
-  }
-
-  /** Return refCount of a `TensorData`. */
-  override refCount(dataId: DataId): number {
-    if (this.data.has(dataId)) {
-      const tensorData = this.data.get(dataId);
-      return tensorData.refCount;
-    }
-    return 0;
-  }
-
-  /** Increase refCount of a `TensorData`. */
-  override incRef(dataId: DataId): void {
-    const tensorData = this.data.get(dataId);
-    tensorData.refCount++;
-  }
-
-  /** Decrease refCount of a `TensorData`. */
-  decRef(dataId: DataId): void {
-    if (this.data.has(dataId)) {
-      const tensorData = this.data.get(dataId);
-      tensorData.refCount--;
-    }
-  }
-
-  override move(
-      dataId: DataId, values: backend_util.BackendValues, shape: number[],
-      dtype: DataType, refCount: number): void {
-    this.data.set(dataId, {values, dtype, refCount});
-  }
-
-  override numDataIds(): number {
-    return this.data.numDataIds();
-  }
-
-  override async read(dataId: DataId): Promise<backend_util.BackendValues> {
-    return this.readSync(dataId);
-  }
-  override readSync(dataId: DataId): backend_util.BackendValues {
-    const {dtype, complexTensorInfos} = this.data.get(dataId);
-
-    if (dtype === 'complex64') {
-      const realValues =
-          this.readSync(complexTensorInfos.real.dataId) as Float32Array;
-      const imagValues =
-          this.readSync(complexTensorInfos.imag.dataId) as Float32Array;
-      return backend_util.mergeRealAndImagArrays(realValues, imagValues);
-    }
-    return util.convertBackendValuesAndArrayBuffer(
-        this.data.get(dataId).values, dtype);
-  }
-
-  bufferSync<R extends Rank, D extends DataType>(t: TensorInfo):
-      TensorBuffer<R, D> {
-    const data = this.readSync(t.dataId);
-    if (t.dtype === 'string') {
-      try {
-        // Decode the bytes into string.
-        const strings = (data as Uint8Array[]).map(d => util.decodeString(d));
-        return buffer(t.shape as ShapeMap[R], t.dtype, strings) as
-            TensorBuffer<R, D>;
-      } catch {
-        throw new Error('Failed to decode encoded string bytes into utf-8');
-      }
-    }
-    return buffer(t.shape as ShapeMap[R], t.dtype, data as TypedArray) as
-        TensorBuffer<R, D>;
-  }
-
-  makeOutput<T extends Tensor>(
-      values: backend_util.BackendValues, shape: number[], dtype: DataType): T {
-    return engine().makeTensorFromTensorInfo(
-               this.makeTensorInfo(shape, dtype, values), this) as T;
-  }
-
-  /**
-   * Dispose the memory if the dataId has 0 refCount. Return true if the memory
-   * is released or memory is not managed in this backend, false if memory is
-   * not cleared.
-   * @param dataId
-   * @oaram force Optional, remove the data regardless of refCount
-   */
-  override disposeData(dataId: DataId, force = false): boolean {
-    if (this.data.has(dataId)) {
-      this.data.get(dataId).refCount--;
-      if (!force && this.data.get(dataId).refCount > 0) {
-        return false;
-      }
-
-      const {complexTensorInfos} = this.data.get(dataId);
-
-      if (complexTensorInfos != null) {
-        this.disposeData(complexTensorInfos.real.dataId, true);
-        this.disposeData(complexTensorInfos.imag.dataId, true);
-      }
-
-      this.data.delete(dataId);
-    }
-    return true;
-  }
-
-  disposeIntermediateTensorInfo(tensorInfo: TensorInfo): void {
-    this.disposeData(tensorInfo.dataId);
-  }
-
-  override async time(f: () => void): Promise<BackendTimingInfo> {
-    const start = util.now();
-    f();
-    const kernelMs = util.now() - start;
-    return {kernelMs};
-  }
-
-  override memory() {
-    return {
-      // Unreliable due to automatic gc. The numbers above are cumulative.
-      unreliable: true,
-      reasons:
-          ['The reported memory is an upper bound. Due to automatic garbage ' +
-           'collection, the true allocated memory may be less.']
-    };
-  }
-
-  where(condition: Tensor): Tensor2D {
-    assertNotComplex([condition], 'where');
-
-    const condVals = this.readSync(condition.dataId) as TypedArray;
-    return whereImpl(condition.shape, condVals);
-  }
-
-  override dispose() {}
-
-  override floatPrecision(): 16|32 {
-    return 32;
-  }
-
-  /** Returns the smallest representable number.  */
-  override epsilon(): number {
-    return super.epsilon();
-  }
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/backend_cpu_test.ts b/tfjs-master/tfjs-backend-cpu/src/backend_cpu_test.ts
deleted file mode 100644
index b4aa455df..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/backend_cpu_test.ts
+++ /dev/null
@@ -1,135 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {engine, test_util, util} from '@tensorflow/tfjs-core';
-
-const {expectArraysEqual} = test_util;
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags, ALL_ENVS} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {MathBackendCPU} from './backend_cpu';
-
-/** Private test util for decoding array of strings in utf-8. */
-function decodeStrings(bytes: Uint8Array[]): string[] {
-  return bytes.map(b => util.decodeString(b));
-}
-
-describeWithFlags('backendCPU', ALL_ENVS, () => {
-  let backend: MathBackendCPU;
-  beforeEach(() => {
-    backend = tf.backend() as MathBackendCPU;
-  });
-
-  it('register string tensor with values', () => {
-    const t = engine().makeTensor(['a', 'b', 'c'], [3], 'string');
-    expectArraysEqual(
-        decodeStrings(backend.readSync(t.dataId) as Uint8Array[]),
-        ['a', 'b', 'c']);
-  });
-
-  it('register string tensor with values and mismatched shape', () => {
-    expect(() => tf.tensor(['a', 'b', 'c'], [4], 'string')).toThrowError();
-  });
-});
-
-describeWithFlags('depthToSpace', ALL_ENVS, () => {
-  it('throws when CPU backend used with data format NCHW', () => {
-    const t = tf.tensor4d([1, 2, 3, 4], [1, 4, 1, 1]);
-    const blockSize = 2;
-    const dataFormat = 'NCHW';
-
-    expect(() => tf.depthToSpace(t, blockSize, dataFormat))
-        .toThrowError(
-            `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${
-                dataFormat}`);
-  });
-});
-
-describeWithFlags('gatherND CPU', ALL_ENVS, () => {
-  it('should throw error when index out of range', () => {
-    const indices = tf.tensor2d([0, 2, 99], [3, 1], 'int32');
-    const input = tf.tensor2d(
-        [100, 101, 102, 777, 778, 779, 10000, 10001, 10002], [3, 3], 'float32');
-    expect(() => tf.gatherND(input, indices)).toThrow();
-  });
-});
-
-describeWithFlags('scatterND CPU', ALL_ENVS, () => {
-  it('should throw error when index out of range', () => {
-    const indices = tf.tensor2d([0, 4, 99], [3, 1], 'int32');
-    const updates = tf.tensor2d(
-        [100, 101, 102, 777, 778, 779, 10000, 10001, 10002], [3, 3], 'float32');
-    const shape = [5, 3];
-    expect(() => tf.scatterND(indices, updates, shape)).toThrow();
-  });
-
-  it('should throw error when indices has wrong dimension', () => {
-    const indices = tf.tensor2d([0, 4, 99], [3, 1], 'int32');
-    const updates = tf.tensor2d(
-        [100, 101, 102, 777, 778, 779, 10000, 10001, 10002], [3, 3], 'float32');
-    const shape = [2, 3];
-    expect(() => tf.scatterND(indices, updates, shape)).toThrow();
-  });
-});
-
-describeWithFlags('sparseToDense CPU', ALL_ENVS, () => {
-  it('should throw error when index out of range', () => {
-    const defaultValue = 2;
-    const indices = tf.tensor1d([0, 2, 6], 'int32');
-    const values = tf.tensor1d([100, 101, 102], 'int32');
-    const shape = [6];
-    expect(() => tf.sparseToDense(indices, values, shape, defaultValue))
-        .toThrow();
-  });
-});
-
-describeWithFlags('memory cpu', ALL_ENVS, () => {
-  it('unreliable is true due to auto gc', () => {
-    tf.tensor(1);
-    const mem = tf.memory();
-    expect(mem.numTensors).toBe(1);
-    expect(mem.numDataBuffers).toBe(1);
-    expect(mem.numBytes).toBe(4);
-    expect(mem.unreliable).toBe(true);
-
-    const expectedReason =
-        'The reported memory is an upper bound. Due to automatic garbage ' +
-        'collection, the true allocated memory may be less.';
-    expect(mem.reasons.indexOf(expectedReason) >= 0).toBe(true);
-  });
-
-  it('unreliable is true due to both auto gc and string tensors', () => {
-    tf.tensor(1);
-    tf.tensor('a');
-
-    const mem = tf.memory();
-    expect(mem.numTensors).toBe(2);
-    expect(mem.numDataBuffers).toBe(2);
-    expect(mem.numBytes).toBe(5);
-    expect(mem.unreliable).toBe(true);
-
-    const expectedReasonGC =
-        'The reported memory is an upper bound. Due to automatic garbage ' +
-        'collection, the true allocated memory may be less.';
-    expect(mem.reasons.indexOf(expectedReasonGC) >= 0).toBe(true);
-    const expectedReasonString =
-        'Memory usage by string tensors is approximate ' +
-        '(2 bytes per character)';
-    expect(mem.reasons.indexOf(expectedReasonString) >= 0).toBe(true);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-cpu/src/base.ts b/tfjs-master/tfjs-backend-cpu/src/base.ts
deleted file mode 100644
index 60d137700..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/base.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/*
- * base.ts contains all the exports from tfjs-backend-cpu
- * without auto-kernel registration
- */
-import {registerBackend} from '@tensorflow/tfjs-core';
-import {MathBackendCPU} from './backend_cpu';
-import * as shared from './shared';
-
-export {MathBackendCPU} from './backend_cpu';
-export {version as version_cpu} from './version';
-export {shared};
-
-// Side effects for default initialization of MathBackendCPU
-registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);
diff --git a/tfjs-master/tfjs-backend-cpu/src/cpu_util.ts b/tfjs-master/tfjs-backend-cpu/src/cpu_util.ts
deleted file mode 100644
index 28ed23e0f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/cpu_util.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorInfo, util} from '@tensorflow/tfjs-core';
-
-export function assertNotComplex(
-    tensor: TensorInfo|TensorInfo[], opName: string): void {
-  if (!Array.isArray(tensor)) {
-    tensor = [tensor];
-  }
-  tensor.forEach(t => {
-    if (t != null) {
-      util.assert(
-          t.dtype !== 'complex64',
-          () => `${
-              opName} does not support complex64 tensors in the CPU backend.`);
-    }
-  });
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/index.ts b/tfjs-master/tfjs-backend-cpu/src/index.ts
deleted file mode 100644
index abf89246e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/index.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// All exports from this package should be in base.
-export * from './base';
-import './register_all_kernels';
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Abs.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Abs.ts
deleted file mode 100644
index 970838ad4..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Abs.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Abs, AbsInputs, KernelConfig, KernelFunc, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function simpleAbsImpl(vals: TypedArray): Float32Array {
-  const resultValues = new Float32Array(vals.length);
-  for (let i = 0; i < vals.length; ++i) {
-    resultValues[i] = Math.abs(vals[i]);
-  }
-  return resultValues;
-}
-
-export const abs = (args: {inputs: AbsInputs, backend: MathBackendCPU}) => {
-  const {x} = args.inputs;
-  const cpuBackend = args.backend;
-
-  assertNotComplex(x, 'abs');
-
-  let resultValues = new Float32Array(util.sizeFromShape(x.shape));
-  const values = cpuBackend.data.get(x.dataId).values as TypedArray;
-  resultValues = simpleAbsImpl(values);
-
-  return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);
-};
-
-export const absConfig: KernelConfig = {
-  kernelName: Abs,
-  backendName: 'cpu',
-  kernelFunc: abs as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Acos.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Acos.ts
deleted file mode 100644
index 1311ac7a4..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Acos.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acos, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const acos = unaryKernelFunc(Acos, (xi) => Math.acos(xi));
-
-export const acosConfig: KernelConfig = {
-  kernelName: Acos,
-  backendName: 'cpu',
-  kernelFunc: acos,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Acosh.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Acosh.ts
deleted file mode 100644
index 332eb6f35..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Acosh.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acosh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const acosh = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));
-
-export const acoshConfig: KernelConfig = {
-  kernelName: Acosh,
-  backendName: 'cpu',
-  kernelFunc: acosh,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Add.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Add.ts
deleted file mode 100644
index 2cd873730..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Add.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Add, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc, createComplexBinaryKernelImpl} from '../utils/binary_utils';
-
-export const addImpl =
-    createSimpleBinaryKernelImpl(((a: number, b: number) => a + b));
-export const addComplexImpl =
-    createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {
-      return {real: aReal + bReal, imag: aImag + bImag};
-    }));
-
-export const add = binaryKernelFunc(Add, addImpl, addComplexImpl);
-
-export const addConfig: KernelConfig = {
-  kernelName: Add,
-  backendName: 'cpu',
-  kernelFunc: add
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/AddN.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/AddN.ts
deleted file mode 100644
index c43b4d2b6..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/AddN.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AddN, AddNInputs, buffer, KernelConfig, KernelFunc, Tensor, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function addN(args: {inputs: AddNInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const tensors = inputs as Tensor[];
-
-  assertNotComplex(inputs, 'addN');
-
-  const vals =
-      tensors.map(t => backend.data.get(t.dataId).values as TypedArray);
-  const outBuf = buffer(tensors[0].shape, tensors[0].dtype as 'float32');
-  const outVals = outBuf.values;
-  for (let i = 0; i < tensors.length; i++) {
-    const currVals = vals[i];
-    for (let j = 0; j < outVals.length; j++) {
-      outVals[j] += currVals[j];
-    }
-  }
-
-  return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
-}
-
-export const addNConfig: KernelConfig = {
-  kernelName: AddN,
-  backendName: 'cpu',
-  kernelFunc: addN as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/All.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/All.ts
deleted file mode 100644
index bcf821cf8..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/All.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {All, AllAttrs, AllInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function all(
-    args: {inputs: AllInputs, backend: MathBackendCPU, attrs: AllAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  assertNotComplex(x, 'all');
-
-  const origAxes = util.parseAxisParam(axis, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
-  let $x = x;
-  if (permutedAxes != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    axes = backend_util.getInnerMostAxes(axes.length, x.shape.length);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('all', axes, $x.shape.length);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes($x.shape, axes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-  const vals = util.makeZerosTypedArray(util.sizeFromShape(outShape), $x.dtype);
-
-  const aVals = backend.data.get($x.dataId).values as TypedArray;
-  for (let i = 0; i < vals.length; ++i) {
-    const offset = i * reduceSize;
-    let all = aVals[offset];
-    for (let j = 0; j < reduceSize; ++j) {
-      const value = aVals[offset + j];
-      all = all && value;
-    }
-    vals[i] = all;
-  }
-
-  if (permutedAxes != null) {
-    backend.disposeIntermediateTensorInfo($x);
-  }
-
-  const result = backend.makeTensorInfo(outShape, $x.dtype, vals);
-
-  if (keepDims) {
-    const expandedShape = backend_util.expandShapeToKeepDim(outShape, origAxes);
-    const reshapedResult =
-        reshape({inputs: {x: result}, backend, attrs: {shape: expandedShape}});
-
-    backend.disposeIntermediateTensorInfo(result);
-
-    return reshapedResult;
-  }
-
-  return result;
-}
-
-export const allConfig: KernelConfig = {
-  kernelName: All,
-  backendName: 'cpu',
-  kernelFunc: all as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Any.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Any.ts
deleted file mode 100644
index 5f9888dc1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Any.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Any, AnyAttrs, AnyInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function any(
-    args: {inputs: AnyInputs, backend: MathBackendCPU, attrs: AnyAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  assertNotComplex(x, 'any');
-
-  const origAxes = util.parseAxisParam(axis, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
-  let $x = x;
-  if (permutedAxes != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    axes = backend_util.getInnerMostAxes(axes.length, x.shape.length);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('any', axes, $x.shape.length);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes($x.shape, axes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-  const vals = util.makeZerosTypedArray(util.sizeFromShape(outShape), $x.dtype);
-
-  const aVals = backend.data.get($x.dataId).values as TypedArray;
-  for (let i = 0; i < vals.length; ++i) {
-    const offset = i * reduceSize;
-    let anyVal = aVals[offset];
-    for (let j = 0; j < reduceSize; ++j) {
-      const value = aVals[offset + j];
-      anyVal = anyVal || value;
-    }
-    vals[i] = anyVal;
-  }
-
-  if (permutedAxes != null) {
-    backend.disposeIntermediateTensorInfo($x);
-  }
-
-  const result = backend.makeTensorInfo(outShape, $x.dtype, vals);
-
-  if (keepDims) {
-    const expandedShape = backend_util.expandShapeToKeepDim(outShape, origAxes);
-    const reshapedResult =
-        reshape({inputs: {x: result}, backend, attrs: {shape: expandedShape}});
-
-    backend.disposeIntermediateTensorInfo(result);
-
-    return reshapedResult;
-  }
-
-  return result;
-}
-
-export const anyConfig: KernelConfig = {
-  kernelName: Any,
-  backendName: 'cpu',
-  kernelFunc: any as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ArgMax.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ArgMax.ts
deleted file mode 100644
index f5d202d23..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ArgMax.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMax, ArgMaxAttrs, ArgMaxInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {transpose} from './Transpose';
-
-export function argMax(
-    args: {inputs: ArgMaxInputs, backend: MathBackendCPU, attrs: ArgMaxAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis} = attrs;
-
-  assertNotComplex(x, 'argMax');
-
-  let axes = util.parseAxisParam(axis, x.shape);
-  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
-  let $x = x;
-  const intermediateTensorInfos = [];
-  if (permutedAxes != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    intermediateTensorInfos.push($x);
-    axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);
-  }
-
-  axes = [axes[0]];
-  backend_util.assertAxesAreInnerMostDims('argMax', axes, $x.shape.length);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes($x.shape, axes);
-
-  const outSize = util.sizeFromShape(outShape);
-  const vals = util.makeZerosTypedArray(outSize, 'int32');
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  const aVals = backend.data.get($x.dataId).values as TypedArray;
-  for (let i = 0; i < vals.length; ++i) {
-    const offset = i * reduceSize;
-    let max = aVals[offset];
-    let maxIndex = 0;
-    for (let j = 0; j < reduceSize; ++j) {
-      const value = aVals[offset + j];
-      if (value > max) {
-        max = value;
-        maxIndex = j;
-      }
-    }
-    vals[i] = maxIndex;
-  }
-
-  intermediateTensorInfos.forEach(
-      t => backend.disposeIntermediateTensorInfo(t));
-
-  return backend.makeTensorInfo(outShape, 'int32', vals);
-}
-
-export const argMaxConfig: KernelConfig = {
-  kernelName: ArgMax,
-  backendName: 'cpu',
-  kernelFunc: argMax as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ArgMin.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ArgMin.ts
deleted file mode 100644
index e0c300105..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ArgMin.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMin, ArgMinAttrs, ArgMinInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {transpose} from './Transpose';
-
-export function argMin(
-    args: {inputs: ArgMinInputs, backend: MathBackendCPU, attrs: ArgMinAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis} = attrs;
-
-  assertNotComplex(x, 'argMin');
-
-  let axes = util.parseAxisParam(axis, x.shape);
-  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
-  let $x = x;
-  const intermediateTensorInfos = [];
-  if (permutedAxes != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    intermediateTensorInfos.push($x);
-    axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);
-  }
-
-  axes = [axes[0]];
-  backend_util.assertAxesAreInnerMostDims('argMin', axes, $x.shape.length);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes($x.shape, axes);
-
-  const outSize = util.sizeFromShape(outShape);
-  const vals = util.makeZerosTypedArray(outSize, 'int32');
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  const aVals = backend.data.get($x.dataId).values as TypedArray;
-  for (let i = 0; i < vals.length; ++i) {
-    const offset = i * reduceSize;
-    let min = aVals[offset];
-    let minIndex = 0;
-    for (let j = 0; j < reduceSize; ++j) {
-      const value = aVals[offset + j];
-      if (value < min) {
-        min = value;
-        minIndex = j;
-      }
-    }
-    vals[i] = minIndex;
-  }
-
-  intermediateTensorInfos.forEach(
-      t => backend.disposeIntermediateTensorInfo(t));
-
-  return backend.makeTensorInfo(outShape, 'int32', vals);
-}
-
-export const argMinConfig: KernelConfig = {
-  kernelName: ArgMin,
-  backendName: 'cpu',
-  kernelFunc: argMin as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Asin.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Asin.ts
deleted file mode 100644
index e2cd8f9bb..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Asin.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asin, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const asin = unaryKernelFunc(Asin, (xi) => Math.asin(xi));
-
-export const asinConfig: KernelConfig = {
-  kernelName: Asin,
-  backendName: 'cpu',
-  kernelFunc: asin,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Asinh.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Asinh.ts
deleted file mode 100644
index 292de1968..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Asinh.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asinh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const asinh = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));
-
-export const asinhConfig: KernelConfig = {
-  kernelName: Asinh,
-  backendName: 'cpu',
-  kernelFunc: asinh,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Atan.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Atan.ts
deleted file mode 100644
index 1a51b3d23..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Atan.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const atan = unaryKernelFunc(Atan, (xi) => Math.atan(xi));
-
-export const atanConfig: KernelConfig = {
-  kernelName: Atan,
-  backendName: 'cpu',
-  kernelFunc: atan,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Atan2.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Atan2.ts
deleted file mode 100644
index b434a55db..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Atan2.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan2, KernelConfig} from '@tensorflow/tfjs-core';
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const atan2Impl = createSimpleBinaryKernelImpl(
-    (aValue, bValue) => Math.atan2(aValue as number, bValue as number));
-
-export const atan2 = binaryKernelFunc(Atan2, atan2Impl);
-
-export const atan2Config: KernelConfig = {
-  kernelName: Atan2,
-  backendName: 'cpu',
-  kernelFunc: atan2,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Atanh.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Atanh.ts
deleted file mode 100644
index 7306d1321..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Atanh.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atanh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const atanh = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));
-
-export const atanhConfig: KernelConfig = {
-  kernelName: Atanh,
-  backendName: 'cpu',
-  kernelFunc: atanh,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPool.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPool.ts
deleted file mode 100644
index 0b5210f1c..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPool.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {AvgPool, AvgPoolAttrs, AvgPoolInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {pool} from '../utils/pool_utils';
-import {identity} from './Identity';
-
-export function avgPool(
-    args:
-        {inputs: AvgPoolInputs, backend: MathBackendCPU, attrs: AvgPoolAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  assertNotComplex(x, 'avgPool');
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const dilations = 1;
-
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in avgPool: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode);
-  let res: TensorInfo;
-
-  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
-      util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
-    res = identity({inputs: {x}, backend});
-  } else {
-    const xValues = backend.data.get(x.dataId).values as TypedArray;
-    const strides = util.computeStrides(x.shape);
-    const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');
-    res = backend.makeTensorInfo(
-        convInfo.outShape, x.dtype, buffer.values as TypedArray);
-  }
-  return res;
-}
-
-export const avgPoolConfig: KernelConfig = {
-  kernelName: AvgPool,
-  backendName: 'cpu',
-  kernelFunc: avgPool as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPool3D.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPool3D.ts
deleted file mode 100644
index 9cf67f4df..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPool3D.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {pool3d} from '../utils/pool_utils';
-
-export function avgPool3D(args: {
-  inputs: AvgPool3DInputs,
-  backend: MathBackendCPU,
-  attrs: AvgPool3DAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode, dataFormat} = attrs;
-
-  assertNotComplex(x, 'avgPool3d');
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      1 /* dilations */, pad, dimRoundingMode, dataFormat);
-
-  const xValues = backend.data.get(x.dataId).values as TypedArray;
-  const outBuf = pool3d(
-      xValues, x.shape, x.dtype, util.computeStrides(x.shape), convInfo, 'avg');
-
-  return backend.makeTensorInfo(outBuf.shape, 'float32', outBuf.values);
-}
-
-export const avgPool3DConfig: KernelConfig = {
-  kernelName: AvgPool3D,
-  backendName: 'cpu',
-  kernelFunc: avgPool3D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPool3DGrad.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPool3DGrad.ts
deleted file mode 100644
index f1586a8f0..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPool3DGrad.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool3DGrad, AvgPool3DGradAttrs, AvgPool3DGradInputs, backend_util, buffer, KernelConfig, KernelFunc, Rank, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function avgPool3DGrad(args: {
-  inputs: AvgPool3DGradInputs,
-  backend: MathBackendCPU,
-  attrs: AvgPool3DGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-
-  assertNotComplex([dy, input], 'avgPool3DGrad');
-
-  const convInfo = backend_util.computePool3DInfo(
-      input.shape as [number, number, number, number, number], filterSize,
-      strides, 1 /* dilations */, pad, dimRoundingMode);
-
-  const strideDepth = convInfo.strideDepth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const filterDepth = convInfo.filterDepth;
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-  const dilationDepth = convInfo.dilationDepth;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
-  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
-  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
-  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
-  const dx = buffer(input.shape, 'float32');
-
-  const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
-
-  const dyBuf = backend.bufferSync<Rank, 'float32'>(dy);
-
-  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
-    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
-      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
-        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
-          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
-            // Shader code begins.
-            const dyDepthCorner = dxDepth - padFront;
-            const dyRowCorner = dxRow - padTop;
-            const dyColCorner = dxCol - padLeft;
-            let dotProd = 0;
-            for (let wDepth = 0; wDepth < effectiveFilterDepth;
-                 wDepth += dilationDepth) {
-              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
-              if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||
-                  Math.floor(dyDepth) !== dyDepth) {
-                continue;
-              }
-              for (let wRow = 0; wRow < effectiveFilterHeight;
-                   wRow += dilationHeight) {
-                const dyRow = (dyRowCorner + wRow) / strideHeight;
-                if (dyRow < 0 || dyRow >= convInfo.outHeight ||
-                    Math.floor(dyRow) !== dyRow) {
-                  continue;
-                }
-                for (let wCol = 0; wCol < effectiveFilterWidth;
-                     wCol += dilationWidth) {
-                  const dyCol = (dyColCorner + wCol) / strideWidth;
-                  if (dyCol < 0 || dyCol >= convInfo.outWidth ||
-                      Math.floor(dyCol) !== dyCol) {
-                    continue;
-                  }
-
-                  const pixel =
-                      dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
-                  dotProd += pixel;
-                }
-              }
-            }
-            dx.set(
-                dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);
-          }
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
-}
-
-export const avgPool3DGradConfig: KernelConfig = {
-  kernelName: AvgPool3DGrad,
-  backendName: 'cpu',
-  kernelFunc: avgPool3DGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPoolGrad.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPoolGrad.ts
deleted file mode 100644
index 4e629672e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/AvgPoolGrad.ts
+++ /dev/null
@@ -1,92 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {AvgPoolGrad, AvgPoolGradAttrs, AvgPoolGradInputs, backend_util, buffer, KernelConfig, KernelFunc, Rank, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function avgPoolGrad(args: {
-  inputs: AvgPoolGradInputs,
-  backend: MathBackendCPU,
-  attrs: AvgPoolGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const x = input;
-  assertNotComplex([dy, input], 'avgPoolGrad');
-  const {filterSize, strides, pad} = attrs;
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      1 /* dilations */, pad);
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
-  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
-  const dx =
-      buffer<Rank.R4>(x.shape as [number, number, number, number], 'float32');
-
-  const avgMultiplier = 1 / (filterHeight * filterWidth);
-
-  const dyData = backend.data.get(dy.dataId).values as Float32Array;
-  const dyBuf = buffer<Rank.R4>(
-      dy.shape as [number, number, number, number], 'float32', dyData);
-
-  for (let b = 0; b < convInfo.batchSize; ++b) {
-    for (let d = 0; d < convInfo.inChannels; ++d) {
-      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
-        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
-          // Shader code begins.
-          const dyRCorner = dxR - padTop;
-          const dyCCorner = dxC - padLeft;
-          let dotProd = 0;
-          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
-            const dyR = (dyRCorner + wR) / strideHeight;
-            if (dyR < 0 || dyR >= convInfo.outHeight ||
-                Math.floor(dyR) !== dyR) {
-              continue;
-            }
-            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
-              const dyC = (dyCCorner + wC) / strideWidth;
-              if (dyC < 0 || dyC >= convInfo.outWidth ||
-                  Math.floor(dyC) !== dyC) {
-                continue;
-              }
-
-              const pixel = dyBuf.get(b, dyR, dyC, d);
-              dotProd += pixel;
-            }
-          }
-          dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);
-        }
-      }
-    }
-  }
-  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
-}
-
-export const avgPoolGradConfig: KernelConfig = {
-  kernelName: AvgPoolGrad,
-  backendName: 'cpu',
-  kernelFunc: avgPoolGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/BatchMatMul.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/BatchMatMul.ts
deleted file mode 100644
index 22a6a01ac..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/BatchMatMul.ts
+++ /dev/null
@@ -1,139 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs, broadcast_util, buffer, KernelConfig, KernelFunc, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-import {reshape} from './Reshape';
-
-export function batchMatMul(args: {
-  inputs: BatchMatMulInputs,
-  attrs: BatchMatMulAttrs,
-  backend: MathBackendCPU
-}) {
-  const {inputs, backend, attrs} = args;
-  const {a, b} = inputs;
-  const {transposeA, transposeB} = attrs;
-
-  assertNotComplex([a, b], 'matMul');
-
-  const aRank = a.shape.length;
-  const bRank = b.shape.length;
-
-  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
-  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
-
-  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
-  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
-
-  const outerDimsA = a.shape.slice(0, -2);
-  const outerDimsB = b.shape.slice(0, -2);
-
-  const batchDimA = util.sizeFromShape(outerDimsA);
-  const batchDimB = util.sizeFromShape(outerDimsB);
-
-  const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape(
-      a.shape.slice(0, -2), b.shape.slice(0, -2));
-  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
-
-  util.assert(
-      innerShapeA === innerShapeB,
-      () => `Error in matMul: inner shapes (${innerShapeA}) and (` +
-          `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +
-          `${b.shape} and transposeA=${transposeA}` +
-          ` and transposeB=${transposeB} must match.`);
-
-  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] :
-                                [batchDimA, outerShapeA, innerShapeA];
-  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] :
-                                [batchDimB, innerShapeB, outerShapeB];
-
-  // The rest of the implementation is designed to operate on rank-3 tensors
-  const a3d = reshape({inputs: {x: a}, backend, attrs: {shape: a3dShape}});
-  const b3d = reshape({inputs: {x: b}, backend, attrs: {shape: b3dShape}});
-
-  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
-  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
-  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
-  const batchDim = Math.max(batchDimA, batchDimB);
-
-  const a3dValues = backend.data.get(a3d.dataId).values as TypedArray;
-  const b3dValues = backend.data.get(b3d.dataId).values as TypedArray;
-
-  const a3dStrides = util.computeStrides(a3d.shape);
-  const b3dStrides = util.computeStrides(b3d.shape);
-
-  const [aBatch, aOuterStep, aInnerStep] = transposeA ?
-      [a3dStrides[0], 1, a3dStrides[1]] :
-      [a3dStrides[0], a3dStrides[1], 1];
-  const [bInnerStep, bOuterStep, bBatch] = transposeB ?
-      [1, b3dStrides[1], b3dStrides[0]] :
-      [b3dStrides[1], 1, b3dStrides[0]];
-
-  const size = leftDim * rightDim;
-  const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);
-
-  const resVals = result.values as TypedArray;
-  const blockSize = backend.blockSize;
-
-  for (let bi = 0; bi < batchDim; bi++) {
-    const batchIndexA = bi % batchDimA;
-    const batchIndexB = bi % batchDimB;
-    for (let i0 = 0; i0 < leftDim; i0 += blockSize) {
-      // for when blockSize doesn't evenly divide the input
-      const iBlock = Math.min(i0 + blockSize, leftDim);
-      for (let j0 = 0; j0 < rightDim; j0 += blockSize) {
-        const jBlock = Math.min(j0 + blockSize, rightDim);
-        for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {
-          const kBlock = Math.min(k0 + blockSize, sharedDim);
-
-          for (let i = i0; i < iBlock; i++) {
-            for (let j = j0; j < jBlock; j++) {
-              let sum = 0.0;
-
-              for (let k = k0; k < kBlock; k++) {
-                const aVal =
-                    // tslint:disable-next-line: max-line-length
-                    a3dValues[batchIndexA * aBatch + i * aOuterStep + k * aInnerStep];
-                const bVal =
-                    // tslint:disable-next-line: max-line-length
-                    b3dValues[k * bInnerStep + j * bOuterStep + batchIndexB * bBatch];
-                sum += aVal * bVal;
-              }
-              resVals[bi * size + (i * rightDim + j)] += sum;
-            }
-          }
-        }
-      }
-    }
-  }
-
-  backend.disposeIntermediateTensorInfo(a3d);
-  backend.disposeIntermediateTensorInfo(b3d);
-
-  // set correct shape on output.
-  return backend.makeTensorInfo(
-      outShape, result.dtype, result.values as TypedArray);
-}
-
-export const batchMatMulConfig: KernelConfig = {
-  kernelName: BatchMatMul,
-  backendName: 'cpu',
-  kernelFunc: batchMatMul as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/BatchNorm.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/BatchNorm.ts
deleted file mode 100644
index a7af6f5a1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/BatchNorm.ts
+++ /dev/null
@@ -1,94 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function batchNorm(args: {
-  inputs: FusedBatchNormInputs,
-  backend: MathBackendCPU,
-  attrs: FusedBatchNormAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, scale, offset, mean, variance} = inputs;
-
-  util.assert(
-      mean.shape.length === variance.shape.length,
-      () => 'Batch normalization gradient requires mean and variance to have ' +
-          'equal ranks.');
-  util.assert(
-      offset == null || mean.shape.length === offset.shape.length,
-      () => 'Batch normalization gradient requires mean and offset to have ' +
-          'equal ranks.');
-  util.assert(
-      scale == null || mean.shape.length === scale.shape.length,
-      () => 'Batch normalization gradient requires mean and scale to have ' +
-          'equal ranks.');
-
-  assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');
-
-  let {varianceEpsilon} = attrs;
-  if (varianceEpsilon == null) {
-    varianceEpsilon = 0.001;
-  }
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const mVals = backend.data.get(mean.dataId).values as TypedArray;
-  const varVals = backend.data.get(variance.dataId).values as TypedArray;
-  const sVals = scale ? backend.data.get(scale.dataId).values as TypedArray :
-                        new Float32Array([1]);
-  const offVals = offset ?
-      backend.data.get(offset.dataId).values as TypedArray :
-      new Float32Array([0]);
-  const outVals = new Float32Array(xVals.length);
-
-  const offValsLength = offVals.length;
-  const sValsLength = sVals.length;
-  const varValsLength = varVals.length;
-  const mValsLength = mVals.length;
-
-  let offi = 0;
-  let mi = 0;
-  let si = 0;
-  let vi = 0;
-  for (let i = 0; i < xVals.length; ++i) {
-    outVals[i] = offVals[offi++] +
-        (xVals[i] - mVals[mi++]) * sVals[si++] /
-            Math.sqrt(varVals[vi++] + varianceEpsilon);
-    if (offi >= offValsLength) {
-      offi = 0;
-    }
-    if (mi >= mValsLength) {
-      mi = 0;
-    }
-    if (si >= sValsLength) {
-      si = 0;
-    }
-    if (vi >= varValsLength) {
-      vi = 0;
-    }
-  }
-  return backend.makeTensorInfo(x.shape, x.dtype, outVals);
-}
-
-export const batchNormConfig: KernelConfig = {
-  kernelName: FusedBatchNorm,
-  backendName: 'cpu',
-  kernelFunc: batchNorm as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/BatchToSpaceND.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/BatchToSpaceND.ts
deleted file mode 100644
index 30106a948..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/BatchToSpaceND.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-import {transpose} from './Transpose';
-
-export function batchToSpaceND(args: {
-  inputs: BatchToSpaceNDInputs,
-  backend: MathBackendCPU,
-  attrs: BatchToSpaceNDAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockShape, crops} = attrs;
-
-  assertNotComplex([x], 'batchToSpaceND');
-
-  const prod = blockShape.reduce((a, b) => a * b);
-
-  const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);
-  const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);
-  const reshapedPermuted =
-      backend_util.getReshapedPermuted(x.shape, blockShape, prod);
-  const sliceBeginCoords =
-      backend_util.getSliceBeginCoords(crops, blockShape.length);
-  const sliceSize =
-      backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);
-
-  const xReshaped = reshape({inputs: {x}, backend, attrs: {shape: reshaped}});
-  const xTransposed =
-      transpose({inputs: {x: xReshaped}, backend, attrs: {perm: permuted}});
-  const xTransposedReshaped = reshape(
-      {inputs: {x: xTransposed}, backend, attrs: {shape: reshapedPermuted}});
-  const result = slice({
-    inputs: {x: xTransposedReshaped},
-    backend,
-    attrs: {begin: sliceBeginCoords, size: sliceSize}
-  });
-
-  backend.disposeIntermediateTensorInfo(xReshaped);
-  backend.disposeIntermediateTensorInfo(xTransposed);
-  backend.disposeIntermediateTensorInfo(xTransposedReshaped);
-
-  return result;
-}
-
-export const batchToSpaceNDConfig: KernelConfig = {
-  kernelName: BatchToSpaceND,
-  backendName: 'cpu',
-  kernelFunc: batchToSpaceND as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Bincount.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Bincount.ts
deleted file mode 100644
index 718b59df8..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Bincount.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Bincount, BincountAttrs, BincountInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {bincountImpl} from './Bincount_impl';
-
-export function bincount(args: {
-  inputs: BincountInputs,
-  backend: MathBackendCPU,
-  attrs: BincountAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, weights} = inputs;
-  const {size} = attrs;
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const weightsVals = backend.data.get(weights.dataId).values as TypedArray;
-
-  const outVals =
-      bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);
-
-  return backend.makeTensorInfo([size], weights.dtype, outVals);
-}
-
-export const bincountConfig: KernelConfig = {
-  kernelName: Bincount,
-  backendName: 'cpu',
-  kernelFunc: bincount as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Bincount_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Bincount_impl.ts
deleted file mode 100644
index efed2861e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Bincount_impl.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, DataType, Rank, TensorBuffer, TypedArray, util} from '@tensorflow/tfjs-core';
-
-export function bincountImpl(
-    xVals: TypedArray, weightsVals: TypedArray, weightsDtype: DataType,
-    weightsShape: number[], size: number): TypedArray {
-  const weightsSize = util.sizeFromShape(weightsShape);
-  const outVals = util.makeZerosTypedArray(size, weightsDtype) as TypedArray;
-
-  for (let i = 0; i < xVals.length; i++) {
-    const value = xVals[i];
-    if (value < 0) {
-      throw new Error('Input x must be non-negative!');
-    }
-
-    if (value >= size) {
-      continue;
-    }
-
-    if (weightsSize > 0) {
-      outVals[value] += weightsVals[i];
-    } else {
-      outVals[value] += 1;
-    }
-  }
-
-  return outVals;
-}
-
-export function bincountReduceImpl<R extends Rank>(
-    xBuf: TensorBuffer<R>, weightsBuf: TensorBuffer<R>, size: number,
-    binaryOutput = false): TensorBuffer<R> {
-  const numRows = xBuf.shape[0];
-  const numCols = xBuf.shape[1];
-
-  const outBuf = buffer([numRows, size], weightsBuf.dtype);
-
-  for (let i = 0; i < numRows; i++) {
-    for (let j = 0; j < numCols; j++) {
-      const value = xBuf.get(i, j);
-      if (value < 0) {
-        throw new Error('Input x must be non-negative!');
-      }
-
-      if (value >= size) {
-        continue;
-      }
-
-      if (binaryOutput) {
-        outBuf.set(1, i, value);
-      } else {
-        if (weightsBuf.size > 0) {
-          outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);
-        } else {
-          outBuf.set(outBuf.get(i, value) + 1, i, value);
-        }
-      }
-    }
-  }
-
-  return outBuf as TensorBuffer<R>;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/BitwiseAnd.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/BitwiseAnd.ts
deleted file mode 100644
index 899f5c401..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/BitwiseAnd.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BitwiseAnd, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const bitwiseAndImpl =
-    createSimpleBinaryKernelImpl(((a: number, b: number) => a & b));
-
-export const bitwiseAnd = binaryKernelFunc(BitwiseAnd, bitwiseAndImpl);
-
-export const bitwiseAndConfig: KernelConfig = {
-  kernelName: BitwiseAnd,
-  backendName: 'cpu',
-  kernelFunc: bitwiseAnd
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/BroadcastArgs.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/BroadcastArgs.ts
deleted file mode 100644
index 7082b4a58..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/BroadcastArgs.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BroadcastArgs, BroadcastArgsInputs, KernelConfig, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function broadcastArgs(args: {
-  inputs: BroadcastArgsInputs,
-  backend: MathBackendCPU,
-}): TensorInfo {
-  const {inputs, backend} = args;
-  const {s0, s1} = inputs;
-
-  const s0Vals = backend.data.get(s0.dataId).values as TypedArray;
-  const s1Vals = backend.data.get(s1.dataId).values as TypedArray;
-
-  const broadcastShape = backend_util.assertAndGetBroadcastShape(
-      Array.from(s0Vals), Array.from(s1Vals));
-
-  return backend.makeTensorInfo(
-      [broadcastShape.length], 'int32', Int32Array.from(broadcastShape));
-}
-
-export const broadcastArgsConfig: KernelConfig = {
-  kernelName: BroadcastArgs,
-  backendName: 'cpu',
-  kernelFunc: broadcastArgs
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Cast.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Cast.ts
deleted file mode 100644
index 6f0e916f0..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Cast.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Cast, CastAttrs, CastInputs, DataType, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {zeros} from '../utils/zeros_impl';
-
-import {complex} from './Complex';
-import {identity} from './Identity';
-import {real} from './Real';
-
-export function castImpl(
-    values: TypedArray, shape: number[], inputType: DataType,
-    dtype: DataType): [number[], DataType, TypedArray] {
-  if (dtype === 'int32') {
-    const resultValues = Int32Array.from(values);
-    return [shape, 'int32', resultValues];
-  }
-
-  if (dtype === 'bool') {
-    // This is essentially the result of notEqual(x, 0). We avoid using
-    // kernel notEqual to avoid circular dependency, i.e. binary_utils ->
-    // cast -> notEqual -> binary_utils.
-    const zero = util.toTypedArray([0], inputType);
-
-    const [resultData, resultShape] = createSimpleBinaryKernelImpl(
-        (a, b) => (a !== b) ? 1 : 0)(shape, [], values, zero, 'bool');
-
-    return [resultShape, 'bool', resultData];
-  }
-  throw new Error(`Error in Cast: failed to cast ${inputType} to ${dtype}`);
-}
-
-export function cast(
-    args: {inputs: CastInputs, backend: MathBackendCPU, attrs: CastAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {dtype} = attrs;
-
-  // Casting to complex64.
-  if (dtype === 'complex64') {
-    if (x.dtype === 'complex64') {
-      return identity({inputs: {x}, backend});
-    }
-
-    const zerosTensorInfo = zeros(backend, x.shape, x.dtype);
-    const floatX = cast({inputs: {x}, backend, attrs: {dtype: 'float32'}});
-
-    const result =
-        complex({inputs: {real: floatX, imag: zerosTensorInfo}, backend});
-
-    backend.disposeIntermediateTensorInfo(zerosTensorInfo);
-    backend.disposeIntermediateTensorInfo(floatX);
-
-    return result;
-  }
-
-  // Casting from complex64
-  if (x.dtype === 'complex64') {
-    const realPart = real({inputs: {input: x}, backend});
-    const result = cast({inputs: {x: realPart}, backend, attrs: {dtype}});
-
-    backend.disposeIntermediateTensorInfo(realPart);
-
-    return result;
-  }
-
-  if (!util.hasEncodingLoss(x.dtype, dtype)) {
-    // We don't change the underlying data, since we cast to higher
-    // precision.
-    const result = identity({inputs: {x}, backend});
-    return {dataId: result.dataId, shape: result.shape, dtype};
-  }
-
-  const values = backend.data.get(x.dataId).values as TypedArray;
-  const [resultShape, resultType, resultData] =
-      castImpl(values, x.shape, x.dtype, dtype);
-  return backend.makeTensorInfo(resultShape, resultType, resultData);
-}
-
-export const castConfig: KernelConfig = {
-  kernelName: Cast,
-  backendName: 'cpu',
-  kernelFunc: cast as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Ceil.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Ceil.ts
deleted file mode 100644
index 527049139..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Ceil.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Ceil, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleUnaryImpl} from '../utils/unary_impl';
-import {unaryKernelFuncFromImpl} from '../utils/unary_utils';
-
-export const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));
-export const ceil = unaryKernelFuncFromImpl(Ceil, ceilImpl);
-
-export const ceilConfig: KernelConfig = {
-  kernelName: Ceil,
-  backendName: 'cpu',
-  kernelFunc: ceil,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ClipByValue.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ClipByValue.ts
deleted file mode 100644
index 3c26ef486..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ClipByValue.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ClipByValue, ClipByValueAttrs, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const clipByValue = unaryKernelFunc(ClipByValue, (xi, attrs) => {
-  const clipAttrs = attrs as unknown as ClipByValueAttrs;
-  if (xi > clipAttrs.clipValueMax) {
-    return clipAttrs.clipValueMax;
-  }
-  return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;
-});
-
-export const clipByValueConfig: KernelConfig = {
-  kernelName: ClipByValue,
-  backendName: 'cpu',
-  kernelFunc: clipByValue,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Complex.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Complex.ts
deleted file mode 100644
index ed3b73355..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Complex.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Complex, ComplexInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function complex(args: {inputs: ComplexInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {real, imag} = inputs;
-
-  const realVals = backend.data.get(real.dataId).values as TypedArray;
-  const imagVals = backend.data.get(imag.dataId).values as TypedArray;
-
-  const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');
-
-  const complex = backend.data.get(complexInfo.dataId);
-
-  // The complex tensor owns the underlying real and imag tensorInfos, only the
-  // complex tensor tracks refCount, when complexData is disposed the
-  // underlying tensorData will be disposed.
-  complex.complexTensorInfos = {
-    real: backend.makeTensorInfo(real.shape, 'float32', realVals),
-    imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)
-  };
-
-  return complexInfo;
-}
-
-export const complexConfig: KernelConfig = {
-  kernelName: Complex,
-  backendName: 'cpu',
-  kernelFunc: complex as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ComplexAbs.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ComplexAbs.ts
deleted file mode 100644
index 23133dd6c..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ComplexAbs.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ComplexAbs, ComplexAbsInputs, KernelConfig, KernelFunc, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export const complexAbs =
-    (args: {inputs: ComplexAbsInputs, backend: MathBackendCPU}) => {
-      const {x} = args.inputs;
-      const cpuBackend = args.backend;
-      const resultValues = new Float32Array(util.sizeFromShape(x.shape));
-      const complexVals = cpuBackend.data.get(x.dataId);
-      const real = complexVals.complexTensorInfos.real;
-      const imag = complexVals.complexTensorInfos.imag;
-      const realVals = cpuBackend.data.get(real.dataId).values as Float32Array;
-      const imagVals = cpuBackend.data.get(imag.dataId).values as Float32Array;
-      for (let i = 0; i < realVals.length; i++) {
-        const real = realVals[i];
-        const imag = imagVals[i];
-        resultValues[i] = Math.hypot(real, imag);
-      }
-
-      return cpuBackend.makeOutput(resultValues, x.shape, 'float32');
-    };
-
-export const complexAbsConfig: KernelConfig = {
-  kernelName: ComplexAbs,
-  backendName: 'cpu',
-  kernelFunc: complexAbs as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Complex_test.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Complex_test.ts
deleted file mode 100644
index 865610946..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Complex_test.ts
+++ /dev/null
@@ -1,283 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-
-const {expectArraysClose} = test_util;
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags, ALL_ENVS} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-describeWithFlags('Complex.', ALL_ENVS, () => {
-  it('memory usage.', async () => {
-    let numTensors = tf.memory().numTensors;
-    let numDataIds = tf.engine().backend.numDataIds();
-    const startTensors = numTensors;
-    const startDataIds = numDataIds;
-
-    const real1 = tf.tensor1d([1]);
-    const imag1 = tf.tensor1d([2]);
-
-    // 2 new Tensors: real1, imag1.
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 2);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const complex1 = tf.complex(real1, imag1);
-
-    // 1 new Tensor and 3 new TensorData for complex, real and imag.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 3);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const real2 = tf.tensor1d([3]);
-    const imag2 = tf.tensor1d([4]);
-
-    // 2 new Tensors: real2, imag2.
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 2);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const complex2 = tf.complex(real2, imag2);
-
-    // 1 new Tensor and 3 new TensorData for complex, real and imag.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 3);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const result = complex1.add(complex2);
-
-    // A complex tensor is created, which is composed of real and imag parts.
-    // They should not increase tensor count, only complex tensor does.
-    // 3 new tensorData is created for complex, real and imag.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 3);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    expect(result.dtype).toBe('complex64');
-    expect(result.shape).toEqual([1]);
-    expectArraysClose(await result.data(), [4, 6]);
-
-    const real = tf.real(result);
-
-    // A new tensor is created. A new tensorData is created.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 1);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    expectArraysClose(await real.data(), [4]);
-
-    const imag = tf.imag(result);
-
-    // A new tensor is created. A new tensorData is created.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 1);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    expectArraysClose(await imag.data(), [6]);
-
-    // After disposing, there should be no tensors.
-    real1.dispose();
-    imag1.dispose();
-    real2.dispose();
-    imag2.dispose();
-    complex1.dispose();
-    complex2.dispose();
-    result.dispose();
-    real.dispose();
-    imag.dispose();
-    expect(tf.memory().numTensors).toBe(startTensors);
-    expect(tf.engine().backend.numDataIds()).toBe(startDataIds);
-  });
-
-  it('Creating tf.real, tf.imag from complex.', async () => {
-    let numTensors = tf.memory().numTensors;
-    let numDataIds = tf.engine().backend.numDataIds();
-
-    const startTensors = numTensors;
-    const startDataIds = numDataIds;
-
-    const complex = tf.complex([3, 30], [4, 40]);
-
-    // 1 new tensor, 3 new data buckets.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 3);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const real = tf.real(complex);
-    const imag = tf.imag(complex);
-
-    // 2 new tensors, 2 new data buckets.
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 2);
-
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    complex.dispose();
-
-    // 1 fewer tensor, 3 fewer data buckets.
-    expect(tf.memory().numTensors).toBe(numTensors - 1);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds - 3);
-
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    real.dispose();
-    imag.dispose();
-
-    // Zero net tensors / data buckets.
-    expect(tf.memory().numTensors).toBe(startTensors);
-    expect(tf.engine().backend.numDataIds()).toBe(startDataIds);
-  });
-
-  it('tf.complex disposing underlying tensors', async () => {
-    const numTensors = tf.memory().numTensors;
-    const numDataIds = tf.engine().backend.numDataIds();
-
-    const real = tf.tensor1d([3, 30]);
-    const imag = tf.tensor1d([4, 40]);
-    expect(tf.memory().numTensors).toEqual(numTensors + 2);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 2);
-
-    const complex = tf.complex(real, imag);
-
-    // 1 new tensor is created for complex. real and imag tensorData is created.
-    expect(tf.memory().numTensors).toEqual(numTensors + 3);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 5);
-
-    real.dispose();
-    imag.dispose();
-
-    expect(tf.memory().numTensors).toEqual(numTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 3);
-
-    expect(complex.dtype).toBe('complex64');
-    expect(complex.shape).toEqual(real.shape);
-    expectArraysClose(await complex.data(), [3, 4, 30, 40]);
-
-    complex.dispose();
-
-    expect(tf.memory().numTensors).toEqual(numTensors);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds);
-  });
-
-  it('reshape', async () => {
-    const memoryBefore = tf.memory();
-    const numDataIdsBefore = tf.engine().backend.numDataIds();
-
-    const a = tf.complex([[1, 3, 5], [7, 9, 11]], [[2, 4, 6], [8, 10, 12]]);
-
-    // 1 new tensor, the complex64 tensor
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 1);
-    // 1 new tensor and 2 underlying tensors for real and imag.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-
-    const b = a.reshape([6]);
-
-    // 1 new tensor from the reshape.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 2);
-    // No new tensor data.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([6]);
-    expectArraysClose(await a.data(), await b.data());
-
-    b.dispose();
-    // 1 complex tensor should be disposed.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 1);
-    // Tensor data not deleted yet.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-
-    a.dispose();
-
-    // All the tensors should now be disposed.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore);
-  });
-
-  it('clone', async () => {
-    const memoryBefore = tf.memory();
-    const numDataIdsBefore = tf.engine().backend.numDataIds();
-
-    const a = tf.complex([[1, 3, 5], [7, 9, 11]], [[2, 4, 6], [8, 10, 12]]);
-
-    // 1 new tensor, the complex64 tensor
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 1);
-    // 1 new tensor and 2 underlying tensors for real and imag.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-
-    const b = a.clone();
-
-    // 1 new tensor from the clone.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 2);
-    // No new tensor data.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-
-    expect(b.dtype).toBe('complex64');
-    expectArraysClose(await a.data(), await b.data());
-
-    b.dispose();
-
-    // 1 complex tensor should be disposed.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 1);
-    // Tensor data not deleted yet.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-
-    a.dispose();
-
-    // All the tensors should now be disposed.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore);
-  });
-
-  it('tidy should not have mem leak', async () => {
-    const numTensors = tf.memory().numTensors;
-    const numDataIds = tf.engine().backend.numDataIds();
-    const complex = tf.tidy(() => {
-      const real = tf.tensor1d([3, 30]);
-      const realReshape = tf.reshape(real, [2]);
-      const imag = tf.tensor1d([4, 40]);
-      const imagReshape = tf.reshape(imag, [2]);
-      expect(tf.memory().numTensors).toEqual(numTensors + 4);
-      expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 2);
-
-      const complex = tf.complex(realReshape, imagReshape);
-
-      // 1 new tensor is created for complex. real and imag data buckets
-      // created.
-      expect(tf.memory().numTensors).toEqual(numTensors + 5);
-      expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 5);
-
-      return complex;
-    });
-
-    complex.dispose();
-
-    expect(tf.memory().numTensors).toEqual(numTensors);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Concat.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Concat.ts
deleted file mode 100644
index 42936694a..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Concat.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Concat, ConcatAttrs, ConcatInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {complex} from './Complex';
-import {concatImpl} from './Concat_impl';
-import {identity} from './Identity';
-import {imag} from './Imag';
-import {real} from './Real';
-import {reshape} from './Reshape';
-
-export function concat(
-    args: {inputs: ConcatInputs, backend: MathBackendCPU, attrs: ConcatAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {axis} = attrs;
-
-  const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];
-
-  const shapes = inputs.map(t => t.shape);
-  backend_util.assertParamsConsistent(shapes, $axis);
-
-  let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);
-
-  if (util.sizeFromShape(outShape) === 0) {
-    return backend.makeTensorInfo(outShape, inputs[0].dtype, []);
-  }
-
-  // Keep only non-empty tensors (ignore tensors with 0 in their shape).
-  const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);
-  if ($inputs.length === 1) {
-    return identity({inputs: {x: $inputs[0]}, backend});
-  }
-
-  if ($inputs[0].dtype === 'complex64') {
-    const reals = $inputs.map((t) => real({inputs: {input: t}, backend}));
-    const imags = $inputs.map((t) => imag({inputs: {input: t}, backend}));
-
-    const realConcated = concat({inputs: reals, backend, attrs: {axis: $axis}});
-    const imagConcated = concat({inputs: imags, backend, attrs: {axis: $axis}});
-
-    const result =
-        complex({inputs: {real: realConcated, imag: imagConcated}, backend});
-
-    reals.forEach(r => backend.disposeIntermediateTensorInfo(r));
-    imags.forEach(i => backend.disposeIntermediateTensorInfo(i));
-    backend.disposeIntermediateTensorInfo(realConcated);
-    backend.disposeIntermediateTensorInfo(imagConcated);
-
-    return result;
-  }
-
-  // Any concat of n-dimensional tensors across any axis can be reduced to
-  // a concatenation of two-dimensional tensors across the axis 1 by first
-  // partitioning the axes of the original tensors into those less than the
-  // axis to be concatenated and the rest. Then reshape the tensors
-  // into a two-dimensional tensor by collapsing these two sets of axes and
-  // concatenate the resulting matrices across the axis 1, finally reshaping
-  // the result to have the proper shape.
-  const inputs2D = $inputs.map(t => {
-    const innerSize = util.sizeFromShape(t.shape.slice($axis));
-    const shape = [-1, innerSize];
-    return reshape({inputs: {x: t}, backend, attrs: {shape}});
-  });
-
-  const inputsValShapes = inputs2D.map(t => {
-    return {vals: backend.data.get(t.dataId).values, shape: t.shape};
-  });
-
-  // Concats 2d tensors along axis=1.
-  outShape =
-      backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);
-  const simplyConcat = inputs2D[0].shape[0] === 1;
-  const outVals =
-      concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
-
-  const finalOutShape =
-      backend_util.computeOutShape($inputs.map(t => t.shape), $axis);
-
-  const outInfo =
-      backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);
-
-  inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-  return outInfo;
-}
-
-export const concatConfig: KernelConfig = {
-  kernelName: Concat,
-  backendName: 'cpu',
-  kernelFunc: concat as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Concat_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Concat_impl.ts
deleted file mode 100644
index 33913515a..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Concat_impl.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BackendValues, DataType, TypedArray, util} from '@tensorflow/tfjs-core';
-
-export function concatImpl(
-    inputs: Array<{vals: BackendValues, shape: number[]}>, outShape: number[],
-    dtype: DataType, simplyConcat: boolean): TypedArray|string[] {
-  const outVals = util.getArrayFromDType(dtype, util.sizeFromShape(outShape));
-
-  if (simplyConcat && dtype !== 'string') {
-    // Use built-in TypedArray.set() method for speed.
-    let offset = 0;
-    inputs.forEach(input => {
-      const size = util.sizeFromShape(input.shape);
-
-      (outVals as TypedArray).set(input.vals as TypedArray, offset);
-      offset += size;
-    });
-  } else {
-    let colOffset = 0;
-
-    inputs.forEach(input => {
-      const decodedData = dtype === 'string' ?
-          backend_util.fromUint8ToStringArray(input.vals as Uint8Array[]) :
-          input.vals as TypedArray;
-
-      let tIdx = 0;
-
-      for (let row = 0; row < input.shape[0]; ++row) {
-        const resIdx = row * outShape[1] + colOffset;
-        for (let col = 0; col < input.shape[1]; ++col) {
-          outVals[resIdx + col] = decodedData[tIdx++];
-        }
-      }
-
-      colOffset += input.shape[1];
-    });
-  }
-
-  return outVals;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv2D.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Conv2D.ts
deleted file mode 100644
index e67e8d4ed..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv2D.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2D, Conv2DAttrs, Conv2DInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function conv2D(
-    args: {inputs: Conv2DInputs, backend: MathBackendCPU, attrs: Conv2DAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dataFormat, dilations, dimRoundingMode} = attrs;
-
-  assertNotComplex([x, filter], 'conv2d');
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, dilations, pad,
-      dimRoundingMode, false /* depthwise */, $dataFormat);
-
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const padLeft = convInfo.padInfo.left;
-  const padTop = convInfo.padInfo.top;
-  const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-
-  const y = new TensorBuffer(convInfo.outShape, x.dtype as 'float32');
-
-  const xStrides = util.computeStrides(x.shape);
-  const filterStrides = util.computeStrides(filter.shape);
-
-  const xBatchStride = xStrides[0];
-  const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];
-  const xColStride = isChannelsLast ? xStrides[2] : 1;
-  const xChannelStride = isChannelsLast ? 1 : xStrides[1];
-  const yBatchStride = y.strides[0];
-  const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];
-  const yColStride = isChannelsLast ? y.strides[2] : 1;
-  const yChannelStride = isChannelsLast ? 1 : y.strides[1];
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const wVals = backend.data.get(filter.dataId).values as TypedArray;
-  const yVals = y.values;
-
-  for (let b = 0; b < convInfo.batchSize; ++b) {
-    const xOffset1 = b * xBatchStride;
-    const yOffset1 = b * yBatchStride;
-    for (let yR = 0; yR < convInfo.outHeight; ++yR) {
-      const yOffset2 = yOffset1 + yR * yRowStride;
-      const xRCorner = yR * convInfo.strideHeight - padTop;
-      for (let wR = 0; wR < filterHeight; ++wR) {
-        const xR = xRCorner + wR * dilationHeight;
-        if (xR < 0 || xR >= convInfo.inHeight) {
-          continue;
-        }
-        const wOffset1 = wR * filterStrides[0];
-        const xOffset2 = xOffset1 + xR * xRowStride;
-        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
-          const yOffset3 = yOffset2 + yC * yColStride;
-          const xCCorner = yC * convInfo.strideWidth - padLeft;
-          for (let wC = 0; wC < filterWidth; ++wC) {
-            const xC = xCCorner + wC * dilationWidth;
-            if (xC < 0 || xC >= convInfo.inWidth) {
-              continue;
-            }
-            const wOffset2 = wOffset1 + wC * filterStrides[1];
-            const xOffset3 = xOffset2 + xC * xColStride;
-            let wOffset3 = wOffset2;
-            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
-              const xVal = xVals[xOffset3 + d1 * xChannelStride];
-              for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
-                yVals[yOffset3 + d2 * yChannelStride] +=
-                    xVal * wVals[wOffset3 + d2];
-              }
-              wOffset3 += convInfo.outChannels;
-            }
-          }
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(y.shape, y.dtype, yVals);
-}
-
-export const conv2DConfig: KernelConfig = {
-  kernelName: Conv2D,
-  backendName: 'cpu',
-  kernelFunc: conv2D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv2DBackpropFilter.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Conv2DBackpropFilter.ts
deleted file mode 100644
index 3947a1b93..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv2DBackpropFilter.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function conv2DBackpropFilter(args: {
-  inputs: Conv2DBackpropFilterInputs,
-  backend: MathBackendCPU,
-  attrs: Conv2DBackpropFilterAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, pad, dataFormat, dimRoundingMode, filterShape} = attrs;
-
-  assertNotComplex([x, dy], 'conv2dBackpropFilter');
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number], filterShape, strides,
-      1 /* dilations */, pad, dimRoundingMode, false /* depthwise */,
-      $dataFormat);
-
-  const {strideHeight, strideWidth, filterHeight, filterWidth} = convInfo;
-  const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-  const dW = new TensorBuffer(convInfo.filterShape, 'float32');
-
-  const leftPad = convInfo.padInfo.left;
-  const topPad = convInfo.padInfo.top;
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const dyVals = backend.data.get(dy.dataId).values as TypedArray;
-
-  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
-  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
-
-  for (let wR = 0; wR < filterHeight; ++wR) {
-    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
-    const yRMax = Math.min(
-        convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
-
-    for (let wC = 0; wC < filterWidth; ++wC) {
-      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
-      const yCMax = Math.min(
-          convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
-
-      for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
-        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
-          let dotProd = 0;
-          for (let b = 0; b < convInfo.batchSize; ++b) {
-            for (let yR = yRMin; yR < yRMax; ++yR) {
-              const xR = wR + yR * strideHeight - topPad;
-              for (let yC = yCMin; yC < yCMax; ++yC) {
-                const xC = wC + yC * strideWidth - leftPad;
-                if (isChannelsLast) {
-                  dotProd += (xBuf.get(b, xR, xC, d1) as number) *
-                      (dyBuf.get(b, yR, yC, d2) as number);
-                } else {
-                  dotProd += (xBuf.get(b, d1, xR, xC) as number) *
-                      (dyBuf.get(b, d2, yR, yC) as number);
-                }
-              }
-            }
-          }
-          dW.set(dotProd, wR, wC, d1, d2);
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);
-}
-
-export const conv2DBackpropFilterConfig: KernelConfig = {
-  kernelName: Conv2DBackpropFilter,
-  backendName: 'cpu',
-  kernelFunc: conv2DBackpropFilter as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv2DBackpropInput.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Conv2DBackpropInput.ts
deleted file mode 100644
index 851809921..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv2DBackpropInput.ts
+++ /dev/null
@@ -1,121 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function conv2DBackpropInput(args: {
-  inputs: Conv2DBackpropInputInputs,
-  backend: MathBackendCPU,
-  attrs: Conv2DBackpropInputAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {inputShape, strides, pad, dataFormat, dimRoundingMode} = attrs;
-
-  assertNotComplex([dy, filter], 'conv2dBackpropInput');
-
-  const filterStrides = util.computeStrides(filter.shape);
-  const dyStrides = util.computeStrides(dy.shape);
-
-  let $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      inputShape, filter.shape as [number, number, number, number], strides,
-      1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);
-
-  const dx = new TensorBuffer(convInfo.inShape, 'float32');
-  const dxValues = dx.values;
-  const dyValues = backend.data.get(dy.dataId).values as TypedArray;
-  const fltValues = backend.data.get(filter.dataId).values as TypedArray;
-  const [fltS0, fltS1, fltS2] = filterStrides;
-  const {
-    batchSize,
-    filterHeight,
-    filterWidth,
-    inChannels,
-    inHeight,
-    inWidth,
-    outChannels,
-    outHeight,
-    outWidth,
-    strideHeight,
-    strideWidth
-  } = convInfo;
-  $dataFormat = convInfo.dataFormat;
-  const topPad = filterHeight - 1 - convInfo.padInfo.top;
-  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
-
-  const isChannelsLast = $dataFormat === 'channelsLast';
-  const xBatchStride = dx.strides[0];
-  const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];
-  const xColStride = isChannelsLast ? dx.strides[2] : 1;
-  const xChannelStride = isChannelsLast ? 1 : dx.strides[1];
-  const yBatchStride = dyStrides[0];
-  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
-  const yColStride = isChannelsLast ? dyStrides[2] : 1;
-  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
-
-  for (let b = 0; b < batchSize; ++b) {
-    for (let d1 = 0; d1 < inChannels; ++d1) {
-      for (let xR = 0; xR < inHeight; ++xR) {
-        const xRCorner = xR - topPad;
-        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
-        const yRMax =
-            Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
-
-        for (let xC = 0; xC < inWidth; ++xC) {
-          const xCCorner = xC - leftPad;
-          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
-          const yCMax =
-              Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
-
-          let dotProd = 0;
-          for (let yR = xRMin; yR < yRMax; ++yR) {
-            const wR = yR * strideHeight - xRCorner;
-
-            for (let yC = xCMin; yC < yCMax; ++yC) {
-              const wC = yC * strideWidth - xCCorner;
-              const dyOffset =
-                  yBatchStride * b + yRowStride * yR + yColStride * yC;
-              const fltOffset = fltS0 * (filterHeight - 1 - wR) +
-                  fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
-
-              for (let d2 = 0; d2 < outChannels; ++d2) {
-                const pixel = dyValues[dyOffset + yChannelStride * d2];
-                const weight = fltValues[fltOffset + d2];
-                dotProd += pixel * weight;
-              }
-            }
-          }
-          const dxOffset = xBatchStride * b + xRowStride * xR +
-              xColStride * xC + xChannelStride * d1;
-          dxValues[dxOffset] = dotProd;
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
-}
-
-export const conv2DBackpropInputConfig: KernelConfig = {
-  kernelName: Conv2DBackpropInput,
-  backendName: 'cpu',
-  kernelFunc: conv2DBackpropInput as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv3D.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Conv3D.ts
deleted file mode 100644
index fc006b621..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv3D.ts
+++ /dev/null
@@ -1,115 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3D, Conv3DAttrs, Conv3DInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function conv3D(
-    args: {inputs: Conv3DInputs, backend: MathBackendCPU, attrs: Conv3DAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  assertNotComplex([x, filter], 'conv3d');
-
-  const convInfo = backend_util.computeConv3DInfo(
-      x.shape as [number, number, number, number, number],
-      filter.shape as [number, number, number, number, number], strides,
-      dilations, pad);
-
-  const {
-    filterDepth,
-    filterHeight,
-    filterWidth,
-    dilationDepth,
-    dilationHeight,
-    dilationWidth,
-    padInfo
-  } = convInfo;
-  const padFront = padInfo.front;
-  const padLeft = padInfo.left;
-  const padTop = padInfo.top;
-  const y = new TensorBuffer(convInfo.outShape, x.dtype as 'float32');
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const wVals = backend.data.get(filter.dataId).values as TypedArray;
-  const yVals = y.values;
-
-  const xStrides = util.computeStrides(x.shape);
-  const filterStrides = util.computeStrides(filter.shape);
-
-  for (let b = 0; b < convInfo.batchSize; ++b) {
-    const xOffset1 = b * xStrides[0];
-    const yOffset1 = b * y.strides[0];
-    for (let yF = 0; yF < convInfo.outDepth; ++yF) {
-      const yOffset2 = yOffset1 + yF * y.strides[1];
-      const xFCorner = yF * convInfo.strideDepth - padFront;
-      for (let wF = 0; wF < filterDepth; ++wF) {
-        const xF = xFCorner + wF * dilationDepth;
-        if (xF < 0 || xF >= convInfo.inDepth) {
-          continue;
-        }
-        const wOffset1 = wF * filterStrides[0];
-        const xOffset2 = xOffset1 + xF * xStrides[1];
-
-        for (let yR = 0; yR < convInfo.outHeight; ++yR) {
-          const yOffset3 = yOffset2 + yR * y.strides[2];
-          const xRCorner = yR * convInfo.strideHeight - padTop;
-          for (let wR = 0; wR < filterHeight; ++wR) {
-            const xR = xRCorner + wR * dilationHeight;
-            if (xR < 0 || xR >= convInfo.inHeight) {
-              continue;
-            }
-            const wOffset2 = wOffset1 + wR * filterStrides[1];
-            const xOffset3 = xOffset2 + xR * xStrides[2];
-            for (let yC = 0; yC < convInfo.outWidth; ++yC) {
-              const yOffset4 = yOffset3 + yC * convInfo.outChannels;
-              const xCCorner = yC * convInfo.strideWidth - padLeft;
-              for (let wC = 0; wC < filterWidth; ++wC) {
-                const xC = xCCorner + wC * dilationWidth;
-                if (xC < 0 || xC >= convInfo.inWidth) {
-                  continue;
-                }
-                const wOffset3 = wOffset2 + wC * filterStrides[2];
-                const xOffset4 = xOffset3 + xC * convInfo.inChannels;
-                let wOffset4 = wOffset3;
-                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
-                  const xVal = xVals[xOffset4 + d1];
-                  for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
-                    yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
-                  }
-                  wOffset4 += convInfo.outChannels;
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(y.shape, y.dtype, y.values);
-}
-
-export const conv3DConfig: KernelConfig = {
-  kernelName: Conv3D,
-  backendName: 'cpu',
-  kernelFunc: conv3D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv3DBackpropFilterV2.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Conv3DBackpropFilterV2.ts
deleted file mode 100644
index 71b80c030..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv3DBackpropFilterV2.ts
+++ /dev/null
@@ -1,121 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropFilterV2, Conv3DBackpropFilterV2Attrs, Conv3DBackpropFilterV2Inputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function conv3DBackpropFilterV2(args: {
-  inputs: Conv3DBackpropFilterV2Inputs,
-  backend: MathBackendCPU,
-  attrs: Conv3DBackpropFilterV2Attrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, pad, filterShape} = attrs;
-
-  assertNotComplex([x, dy], 'conv3dBackpropFilterV2');
-
-  const xStrides = util.computeStrides(x.shape);
-  const dyStrides = util.computeStrides(dy.shape);
-
-  const convInfo = backend_util.computeConv3DInfo(
-      x.shape as [number, number, number, number, number], filterShape, strides,
-      1 /* dilations */, pad);
-
-  const strideDepth = convInfo.strideDepth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const filterDepth = convInfo.filterDepth;
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-
-  const dw = new TensorBuffer(convInfo.filterShape, 'float32');
-  const dwValues = dw.values;
-  const [dwS0, dwS1, dwS2, dwS3] = dw.strides;
-  const dyValues = backend.data.get(dy.dataId).values as TypedArray;
-  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
-  const xValues = backend.data.get(x.dataId).values as TypedArray;
-  const [xS0, xS1, xS2, xS3] = xStrides;
-
-  const frontPad = convInfo.padInfo.front;
-  const leftPad = convInfo.padInfo.left;
-  const topPad = convInfo.padInfo.top;
-
-  for (let wF = 0; wF < filterDepth; ++wF) {
-    const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
-    const yFMax = Math.min(
-        convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
-    const wOffset1 = wF * dwS0;
-
-    for (let wR = 0; wR < filterHeight; ++wR) {
-      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
-      const yRMax = Math.min(
-          convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
-      const wOffset2 = wR * dwS1 + wOffset1;
-
-      for (let wC = 0; wC < filterWidth; ++wC) {
-        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
-        const yCMax = Math.min(
-            convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
-        const wOffset3 = wC * dwS2 + wOffset2;
-
-        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
-          const wOffset4 = d1 * dwS3 + wOffset3;
-
-          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
-            let dotProd = 0;
-            for (let b = 0; b < convInfo.batchSize; ++b) {
-              const xOffset1 = b * xS0;
-              const yOffset1 = b * dyS0;
-
-              for (let yF = yFMin; yF < yFMax; ++yF) {
-                const xF = wF + yF * strideDepth - frontPad;
-                const xOffset2 = xF * xS1 + xOffset1;
-                const yOffset2 = yF * dyS1 + yOffset1;
-
-                for (let yR = yRMin; yR < yRMax; ++yR) {
-                  const xR = wR + yR * strideHeight - topPad;
-                  const xOffset3 = xR * xS2 + xOffset2;
-                  const yOffset3 = yR * dyS2 + yOffset2;
-
-                  for (let yC = yCMin; yC < yCMax; ++yC) {
-                    const xC = wC + yC * strideWidth - leftPad;
-                    const xOffset4 = xC * xS3 + xOffset3;
-                    const yOffset4 = yC * dyS3 + yOffset3;
-
-                    dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
-                  }
-                }
-              }
-            }
-            dwValues[wOffset4 + d2] = dotProd;
-          }
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values);
-}
-
-export const conv3DBackpropFilterV2Config: KernelConfig = {
-  kernelName: Conv3DBackpropFilterV2,
-  backendName: 'cpu',
-  kernelFunc: conv3DBackpropFilterV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv3DBackpropInputV2.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Conv3DBackpropInputV2.ts
deleted file mode 100644
index 0b0d015d4..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Conv3DBackpropInputV2.ts
+++ /dev/null
@@ -1,128 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function conv3DBackpropInputV2(args: {
-  inputs: Conv3DBackpropInputV2Inputs,
-  backend: MathBackendCPU,
-  attrs: Conv3DBackpropInputV2Attrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {pad, strides, inputShape} = attrs;
-
-  assertNotComplex([dy], 'conv3dBackpropInputV2');
-
-  const dyStrides = util.computeStrides(dy.shape);
-  const filterStrides = util.computeStrides(filter.shape);
-
-  const convInfo = backend_util.computeConv3DInfo(
-      inputShape, filter.shape as [number, number, number, number, number],
-      strides, 1 /* dilations */, pad);
-
-  const dx = new TensorBuffer(convInfo.inShape, 'float32');
-  const dxValues = dx.values;
-  const [dxS0, dxS1, dxS2, dxS3] = dx.strides;
-  const dyValues = backend.data.get(dy.dataId).values as TypedArray;
-  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
-  const fltValues = backend.data.get(filter.dataId).values as TypedArray;
-  const [fltS0, fltS1, fltS2, fltS3] = filterStrides;
-  const {
-    batchSize,
-    filterDepth,
-    filterHeight,
-    filterWidth,
-    inChannels,
-    inDepth,
-    inHeight,
-    inWidth,
-    outChannels,
-    outDepth,
-    outHeight,
-    outWidth,
-    strideDepth,
-    strideHeight,
-    strideWidth
-  } = convInfo;
-  const frontPad = filterDepth - 1 - convInfo.padInfo.front;
-  const topPad = filterHeight - 1 - convInfo.padInfo.top;
-  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
-
-  for (let b = 0; b < batchSize; ++b) {
-    for (let d1 = 0; d1 < inChannels; ++d1) {
-      // Frames of depth
-      for (let xF = 0; xF < inDepth; ++xF) {
-        const xFCorner = xF - frontPad;
-        const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
-        const yFMax =
-            Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
-
-        // Rows as per standard 2d matrix notation
-        for (let xR = 0; xR < inHeight; ++xR) {
-          const xRCorner = xR - topPad;
-          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
-          const yRMax =
-              Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
-          // Columns as per standard 2d matrix notation
-          for (let xC = 0; xC < inWidth; ++xC) {
-            const xCCorner = xC - leftPad;
-            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
-            const yCMax =
-                Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
-
-            let dotProd = 0;
-            for (let yF = xFMin; yF < yFMax; ++yF) {
-              const wF = yF * strideDepth - xFCorner;
-
-              for (let yR = xRMin; yR < yRMax; ++yR) {
-                const wR = yR * strideHeight - xRCorner;
-
-                for (let yC = xCMin; yC < yCMax; ++yC) {
-                  const wC = yC * strideWidth - xCCorner;
-                  const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
-                  const fltOffset = fltS0 * (filterDepth - 1 - wF) +
-                      fltS1 * (filterHeight - 1 - wR) +
-                      fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
-
-                  for (let d2 = 0; d2 < outChannels; ++d2) {
-                    const pixel = dyValues[dyOffset + d2];
-                    const weight = fltValues[fltOffset + d2];
-                    dotProd += pixel * weight;
-                  }
-                }
-              }
-            }
-            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =
-                dotProd;
-          }
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
-}
-
-export const conv3DBackpropInputV2Config: KernelConfig = {
-  kernelName: Conv3DBackpropInputV2,
-  backendName: 'cpu',
-  kernelFunc: conv3DBackpropInputV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Cos.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Cos.ts
deleted file mode 100644
index 7e7e6fcfa..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Cos.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cos, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const cos = unaryKernelFunc(Cos, (xi) => Math.cos(xi));
-
-export const cosConfig: KernelConfig = {
-  kernelName: Cos,
-  backendName: 'cpu',
-  kernelFunc: cos,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Cosh.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Cosh.ts
deleted file mode 100644
index d12dd44ec..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Cosh.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cosh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const cosh = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));
-
-export const coshConfig: KernelConfig = {
-  kernelName: Cosh,
-  backendName: 'cpu',
-  kernelFunc: cosh,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/CropAndResize.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/CropAndResize.ts
deleted file mode 100644
index 9ab4f29a9..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/CropAndResize.ts
+++ /dev/null
@@ -1,166 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function cropAndResize(args: {
-  inputs: CropAndResizeInputs,
-  backend: MathBackendCPU,
-  attrs: CropAndResizeAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {image, boxes, boxInd} = inputs;
-  const {cropSize, method, extrapolationValue} = attrs;
-
-  const [batch, imageHeight, imageWidth, numChannels] = image.shape;
-  const numBoxes = boxes.shape[0];
-
-  const [cropHeight, cropWidth] = cropSize;
-  const output =
-      buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');
-
-  const boxVals = backend.data.get(boxes.dataId).values as TypedArray;
-  const boxIndVals = backend.data.get(boxInd.dataId).values as TypedArray;
-  const imageVals = backend.data.get(image.dataId).values as TypedArray;
-
-  const inStride =
-      util.computeStrides(image.shape);  // to calculate flat indexes into image
-  const outStride = util.computeStrides(
-      output.shape);  // to calculate flat indexes into output
-
-  // Reference implementation
-  // tslint:disable-next-line:max-line-length
-  // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc
-  for (let b = 0; b < numBoxes; b++) {
-    const startInd = b * 4;
-    const y1 = boxVals[startInd];
-    const x1 = boxVals[startInd + 1];
-    const y2 = boxVals[startInd + 2];
-    const x2 = boxVals[startInd + 3];
-
-    const bInd: number = boxIndVals[b];
-    if (bInd >= batch) {
-      continue;
-    }
-
-    const heightScale =
-        (cropHeight > 1) ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;
-    const widthScale =
-        (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
-
-    for (let y = 0; y < cropHeight; y++) {
-      const yInd: number = (cropHeight > 1) ?
-          y1 * (imageHeight - 1) + y * (heightScale) :
-          0.5 * (y1 + y2) * (imageHeight - 1);
-
-      if (yInd < 0 || yInd > imageHeight - 1) {
-        for (let x = 0; x < cropWidth; x++) {
-          for (let c = 0; c < numChannels; c++) {
-            const ind =
-                c + x * outStride[2] + y * outStride[1] + b * outStride[0];
-            output.values[ind] = extrapolationValue;
-          }
-        }
-        continue;
-      }
-
-      if (method === 'bilinear') {
-        const topInd = Math.floor(yInd);
-        const bottomInd = Math.ceil(yInd);
-        const yLerp = yInd - topInd;
-
-        for (let x = 0; x < cropWidth; x++) {
-          const xInd = (cropWidth > 1) ?
-              x1 * (imageWidth - 1) + x * widthScale :
-              0.5 * (x1 + x2) * (imageWidth - 1);
-
-          if (xInd < 0 || xInd > imageWidth - 1) {
-            for (let c = 0; c < numChannels; c++) {
-              const ind =
-                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];
-              output.values[ind] = extrapolationValue;
-            }
-            continue;
-          }
-
-          const leftInd = Math.floor(xInd);
-          const rightInd = Math.ceil(xInd);
-          const xLerp = xInd - leftInd;
-
-          for (let c = 0; c < numChannels; c++) {
-            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +
-                bInd * inStride[0];
-            const topLeft = imageVals[ind];
-
-            ind = c + rightInd * inStride[2] + topInd * inStride[1] +
-                bInd * inStride[0];
-            const topRight = imageVals[ind];
-
-            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +
-                bInd * inStride[0];
-            const bottomLeft = imageVals[ind];
-
-            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +
-                bInd * inStride[0];
-            const bottomRight = imageVals[ind];
-
-            const top = topLeft + (topRight - topLeft) * xLerp;
-            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
-
-            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
-            output.values[ind] = top + ((bottom - top) * yLerp);
-          }
-        }
-      } else {  // method == "nearest"
-        for (let x = 0; x < cropWidth; ++x) {
-          const xInd = (cropWidth > 1) ?
-              x1 * (imageWidth - 1) + x * widthScale :
-              0.5 * (x1 + x2) * (imageWidth - 1);
-
-          if (xInd < 0 || xInd > imageWidth - 1) {
-            for (let c = 0; c < numChannels; c++) {
-              const ind =
-                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];
-              output.values[ind] = extrapolationValue;
-            }
-            continue;
-          }
-
-          const closestX = Math.round(xInd);
-          const closestY = Math.round(yInd);
-          for (let c = 0; c < numChannels; c++) {
-            const inInd = c + closestX * inStride[2] + closestY * inStride[1] +
-                bInd * inStride[0];
-            const outInd =
-                c + x * outStride[2] + y * outStride[1] + b * outStride[0];
-            output.values[outInd] = imageVals[inInd];
-          }
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(output.shape, output.dtype, output.values);
-}
-
-export const cropAndResizeConfig: KernelConfig = {
-  kernelName: CropAndResize,
-  backendName: 'cpu',
-  kernelFunc: cropAndResize as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Cumprod.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Cumprod.ts
deleted file mode 100644
index 40f01d883..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Cumprod.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Cumprod, CumprodAttrs, CumprodInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, upcastType, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {transpose} from './Transpose';
-
-export function cumprod(
-    args: {inputs: CumprodInputs, backend: MathBackendCPU,
-           attrs: CumprodAttrs}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, exclusive, reverse} = attrs;
-
-  assertNotComplex(x, 'cumprod');
-
-  const permutation = backend_util.getAxesPermutation([axis], x.shape.length);
-  let $x = x;
-  if (permutation != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutation}});
-  }
-  const permutedAxis = backend_util.getInnerMostAxes(1, x.shape.length)[0];
-
-  if (permutedAxis !== $x.shape.length - 1) {
-    throw new Error(
-        `backend.cumprod in CPU expects an inner-most ` +
-        `axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
-  }
-
-  const resultDtype = upcastType($x.dtype, 'int32');
-  const vals = util.makeOnesTypedArray(
-                   util.sizeFromShape($x.shape), resultDtype) as TypedArray;
-
-  const aVals = backend.data.get($x.dataId).values as TypedArray;
-  const finalDim = $x.shape[$x.shape.length - 1];
-  const indexAdjuster = reverse ?
-      (i: number, j: number) => i + finalDim - j - 1 :
-      (i: number, j: number) => i + j;
-  for (let i = 0; i < aVals.length; i += finalDim) {
-    for (let j = 0; j < finalDim; j++) {
-      const idx = indexAdjuster(i, j);
-      if (j === 0) {
-        vals[idx] = exclusive ? 1 : aVals[idx];
-      } else {
-        const prevIdx = indexAdjuster(i, j - 1);
-        vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] :
-                                aVals[idx] * vals[prevIdx];
-      }
-    }
-  }
-
-  const result = backend.makeTensorInfo($x.shape, resultDtype, vals);
-
-  if (permutation != null) {
-    const reversePermutation = backend_util.getUndoAxesPermutation(permutation);
-    const reverseTransposedResult = transpose(
-        {inputs: {x: result}, backend, attrs: {perm: reversePermutation}});
-
-    backend.disposeIntermediateTensorInfo(result);
-    backend.disposeIntermediateTensorInfo($x);
-
-    return reverseTransposedResult;
-  }
-
-  return result;
-}
-
-export const cumprodConfig: KernelConfig = {
-  kernelName: Cumprod,
-  backendName: 'cpu',
-  kernelFunc: cumprod as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Cumsum.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Cumsum.ts
deleted file mode 100644
index 9a29f9229..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Cumsum.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Cumsum, CumsumAttrs, CumsumInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, upcastType, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {transpose} from './Transpose';
-
-export function cumsum(
-    args: {inputs: CumsumInputs, backend: MathBackendCPU, attrs: CumsumAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, exclusive, reverse} = attrs;
-
-  assertNotComplex(x, 'cumsum');
-
-  const permutation = backend_util.getAxesPermutation([axis], x.shape.length);
-  let $x = x;
-  if (permutation != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutation}});
-  }
-  const permutedAxis = backend_util.getInnerMostAxes(1, x.shape.length)[0];
-
-  if (permutedAxis !== $x.shape.length - 1) {
-    throw new Error(
-        `backend.cumsum in CPU expects an inner-most ` +
-        `axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
-  }
-
-  const resultDtype = upcastType($x.dtype, 'int32');
-  const vals = util.makeZerosTypedArray(
-                   util.sizeFromShape($x.shape), resultDtype) as TypedArray;
-
-  const aVals = backend.data.get($x.dataId).values as TypedArray;
-  const finalDim = $x.shape[$x.shape.length - 1];
-  const indexAdjuster = reverse ?
-      (i: number, j: number) => i + finalDim - j - 1 :
-      (i: number, j: number) => i + j;
-  for (let i = 0; i < aVals.length; i += finalDim) {
-    for (let j = 0; j < finalDim; j++) {
-      const idx = indexAdjuster(i, j);
-      if (j === 0) {
-        vals[idx] = exclusive ? 0 : aVals[idx];
-      } else {
-        const prevIdx = indexAdjuster(i, j - 1);
-        vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :
-                                aVals[idx] + vals[prevIdx];
-      }
-    }
-  }
-
-  const result = backend.makeTensorInfo($x.shape, resultDtype, vals);
-
-  if (permutation != null) {
-    const reversePermutation = backend_util.getUndoAxesPermutation(permutation);
-    const reverseTransposedResult = transpose(
-        {inputs: {x: result}, backend, attrs: {perm: reversePermutation}});
-
-    backend.disposeIntermediateTensorInfo(result);
-    backend.disposeIntermediateTensorInfo($x);
-
-    return reverseTransposedResult;
-  }
-
-  return result;
-}
-
-export const cumsumConfig: KernelConfig = {
-  kernelName: Cumsum,
-  backendName: 'cpu',
-  kernelFunc: cumsum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/DenseBincount.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/DenseBincount.ts
deleted file mode 100644
index bff662685..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/DenseBincount.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DenseBincount, DenseBincountAttrs, DenseBincountInputs, KernelConfig, KernelFunc, Rank, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {bincountImpl, bincountReduceImpl} from './Bincount_impl';
-
-export function denseBincount(args: {
-  inputs: DenseBincountInputs,
-  backend: MathBackendCPU,
-  attrs: DenseBincountAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, weights} = inputs;
-  const {size, binaryOutput} = attrs;
-
-  if (x.shape.length === 1) {
-    const xVals = backend.data.get(x.dataId).values as TypedArray;
-    const weightsVals = backend.data.get(weights.dataId).values as TypedArray;
-
-    const outVals =
-        bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);
-
-    return backend.makeTensorInfo([size], weights.dtype, outVals);
-  } else if (x.shape.length === 2) {
-    const xBuf = backend.bufferSync<Rank, 'float32'>(x);
-    const weightsBuf = backend.bufferSync<Rank, 'float32'>(weights);
-
-    const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput);
-
-    return backend.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
-  }
-
-  throw new Error(
-      `Error in denseBincount: input must be at most rank 2, but got rank` +
-      `${x.shape.length}.`);
-}
-
-export const denseBincountConfig: KernelConfig = {
-  kernelName: DenseBincount,
-  backendName: 'cpu',
-  kernelFunc: denseBincount as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/DepthToSpace.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/DepthToSpace.ts
deleted file mode 100644
index 67a742655..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/DepthToSpace.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function depthToSpace(args: {
-  inputs: DepthToSpaceInputs,
-  backend: MathBackendCPU,
-  attrs: DepthToSpaceAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockSize, dataFormat} = attrs;
-
-  util.assert(
-      dataFormat === 'NHWC',
-      () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${
-          dataFormat}`);
-
-  const batchSize = x.shape[0];
-  const inputHeight = x.shape[1];
-  const inputWidth = x.shape[2];
-  const inputDepth = x.shape[3];
-
-  const outputHeight = inputHeight * blockSize;
-  const outputWidth = inputWidth * blockSize;
-  const outputDepth = inputDepth / (blockSize * blockSize);
-
-  const xValues = backend.data.get(x.dataId).values as TypedArray;
-  const result =
-      new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
-
-  let outputIdx = 0;
-  for (let b = 0; b < batchSize; ++b) {
-    for (let h = 0; h < outputHeight; ++h) {
-      const inH = Math.floor(h / blockSize);
-      const offsetH = (h % blockSize);
-      for (let w = 0; w < outputWidth; ++w) {
-        const inW = Math.floor(w / blockSize);
-        const offsetW = (w % blockSize);
-        const offsetD = (offsetH * blockSize + offsetW) * outputDepth;
-        for (let d = 0; d < outputDepth; ++d) {
-          const inD = d + offsetD;
-          const inputIdx =
-              inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
-          result[outputIdx++] = xValues[inputIdx];
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(
-      [batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);
-}
-
-export const depthToSpaceConfig: KernelConfig = {
-  kernelName: DepthToSpace,
-  backendName: 'cpu',
-  kernelFunc: depthToSpace as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/DepthwiseConv2dNative.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/DepthwiseConv2dNative.ts
deleted file mode 100644
index 9356d79a9..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/DepthwiseConv2dNative.ts
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function depthwiseConv2dNative(args: {
-  inputs: DepthwiseConv2dNativeInputs,
-  backend: MathBackendCPU,
-  attrs: DepthwiseConv2dNativeAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dilations, dimRoundingMode} = attrs;
-
-  assertNotComplex([x, filter], 'depthwiseConv2DNative');
-
-  const xStrides = util.computeStrides(x.shape);
-  const filterStrides = util.computeStrides(filter.shape);
-
-  let $dilations = dilations;
-  if ($dilations == null) {
-    $dilations = [1, 1];
-  }
-
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, $dilations),
-      () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +
-          `1. Got strides ${strides} and dilations '${$dilations}'`);
-
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, $dilations,
-      pad, dimRoundingMode, true /* depthwise */);
-
-  const {filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo} =
-      convInfo;
-  const padLeft = padInfo.left;
-  const padTop = padInfo.top;
-  const chMul = convInfo.outChannels / convInfo.inChannels;
-  const y = new TensorBuffer(convInfo.outShape, x.dtype as 'float32');
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const wVals = backend.data.get(filter.dataId).values as TypedArray;
-  const yVals = y.values;
-
-  for (let b = 0; b < convInfo.batchSize; ++b) {
-    const xOffset1 = b * xStrides[0];
-    const yOffset1 = b * y.strides[0];
-    for (let yR = 0; yR < convInfo.outHeight; ++yR) {
-      const yOffset2 = yOffset1 + yR * y.strides[1];
-      const xRCorner = yR * convInfo.strideHeight - padTop;
-      for (let wR = 0; wR < filterHeight; ++wR) {
-        const xR = xRCorner + wR * dilationHeight;
-        if (xR < 0 || xR >= convInfo.inHeight) {
-          continue;
-        }
-        const wOffset1 = wR * filterStrides[0];
-        const xOffset2 = xOffset1 + xR * xStrides[1];
-        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
-          const yOffset3 = yOffset2 + yC * y.strides[2];
-          const xCCorner = yC * convInfo.strideWidth - padLeft;
-          for (let wC = 0; wC < filterWidth; ++wC) {
-            const xC = xCCorner + wC * dilationWidth;
-            if (xC < 0 || xC >= convInfo.inWidth) {
-              continue;
-            }
-            const wOffset2 = wOffset1 + wC * filterStrides[1];
-            const xOffset3 = xOffset2 + xC * convInfo.inChannels;
-            let yOffset4 = yOffset3;
-            let wOffset3 = wOffset2;
-            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
-              const xVal = xVals[xOffset3 + d1];
-              for (let q = 0; q < chMul; ++q) {
-                yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];
-              }
-              yOffset4 += chMul;
-              wOffset3 += chMul;
-            }
-          }
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(y.shape, y.dtype, y.values);
-}
-
-export const depthwiseConv2dNativeConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNative,
-  backendName: 'cpu',
-  kernelFunc: depthwiseConv2dNative as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts
deleted file mode 100644
index edd361ff8..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropFilterAttrs, DepthwiseConv2dNativeBackpropFilterInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function depthwiseConv2dNativeBackpropFilter(args: {
-  inputs: DepthwiseConv2dNativeBackpropFilterInputs,
-  backend: MathBackendCPU,
-  attrs: DepthwiseConv2dNativeBackpropFilterAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, dilations, pad, dimRoundingMode, filterShape} = attrs;
-
-  assertNotComplex([x, dy], 'depthwiseConv2dNativeBackpropFilter');
-
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number], filterShape, strides,
-      dilations, pad, dimRoundingMode, true /* depthwise */);
-
-  const {strideHeight, strideWidth, filterHeight, filterWidth} = convInfo;
-
-  const dW = new TensorBuffer(convInfo.filterShape, 'float32');
-
-  const leftPad = convInfo.padInfo.left;
-  const topPad = convInfo.padInfo.top;
-  const chMul = convInfo.outChannels / convInfo.inChannels;
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
-  const dyVals = backend.data.get(dy.dataId).values as TypedArray;
-  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
-  for (let wR = 0; wR < filterHeight; ++wR) {
-    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
-    const yRMax = Math.min(
-        convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
-
-    for (let wC = 0; wC < filterWidth; ++wC) {
-      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
-      const yCMax = Math.min(
-          convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
-
-      for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
-        const d1 = Math.trunc(d2 / chMul);
-        const dm = d2 % chMul;
-
-        let dotProd = 0;
-        for (let b = 0; b < convInfo.batchSize; ++b) {
-          for (let yR = yRMin; yR < yRMax; ++yR) {
-            const xR = wR + yR * strideHeight - topPad;
-            for (let yC = yCMin; yC < yCMax; ++yC) {
-              const xC = wC + yC * strideWidth - leftPad;
-              dotProd += (xBuf.get(b, xR, xC, d1) as number) *
-                  (dyBuf.get(b, yR, yC, d2) as number);
-            }
-          }
-        }
-        dW.set(dotProd, wR, wC, d1, dm);
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);
-}
-
-export const depthwiseConv2dNativeBackpropFilterConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNativeBackpropFilter,
-  backendName: 'cpu',
-  kernelFunc: depthwiseConv2dNativeBackpropFilter as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/DepthwiseConv2dNativeBackpropInput.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/DepthwiseConv2dNativeBackpropInput.ts
deleted file mode 100644
index ab6771e31..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/DepthwiseConv2dNativeBackpropInput.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNativeBackpropInput, DepthwiseConv2dNativeBackpropInputAttrs, DepthwiseConv2dNativeBackpropInputInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function depthwiseConv2dNativeBackpropInput(args: {
-  inputs: DepthwiseConv2dNativeBackpropInputInputs,
-  backend: MathBackendCPU,
-  attrs: DepthwiseConv2dNativeBackpropInputAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {strides, dilations, pad, dimRoundingMode, inputShape} = attrs;
-
-  assertNotComplex([dy, filter], 'depthwiseConv2DNativeBackpropInput');
-
-  const dyStrides = util.computeStrides(dy.shape);
-  const filterStrides = util.computeStrides(filter.shape);
-
-  const convInfo = backend_util.computeConv2DInfo(
-      inputShape, filter.shape as [number, number, number, number], strides,
-      dilations, pad, dimRoundingMode, true /* depthwise */);
-
-  const dx = new TensorBuffer(convInfo.inShape, 'float32');
-  const dxValues = dx.values;
-  const [dxS0, dxS1, dxS2] = dx.strides;
-  const dyValues = backend.data.get(dy.dataId).values as TypedArray;
-  const [dyS0, dyS1, dyS2] = dyStrides;
-  const fltValues = backend.data.get(filter.dataId).values as TypedArray;
-  const [fltS0, fltS1, fltS2] = filterStrides;
-  const {
-    batchSize,
-    filterHeight,
-    filterWidth,
-    inChannels,
-    inHeight,
-    inWidth,
-    outChannels,
-    outHeight,
-    outWidth,
-    strideHeight,
-    strideWidth
-  } = convInfo;
-  const topPad = filterHeight - 1 - convInfo.padInfo.top;
-  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
-  const chMul = outChannels / inChannels;
-
-  for (let b = 0; b < batchSize; ++b) {
-    for (let d1 = 0; d1 < inChannels; ++d1) {
-      for (let xR = 0; xR < inHeight; ++xR) {
-        const xRCorner = xR - topPad;
-        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
-        const yRMax =
-            Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
-
-        for (let xC = 0; xC < inWidth; ++xC) {
-          const xCCorner = xC - leftPad;
-          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
-          const yCMax =
-              Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
-
-          let dotProd = 0;
-          for (let yR = xRMin; yR < yRMax; ++yR) {
-            const wR = yR * strideHeight - xRCorner;
-
-            for (let yC = xCMin; yC < yCMax; ++yC) {
-              const wC = yC * strideWidth - xCCorner;
-              const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
-              const fltOffset = fltS0 * (filterHeight - 1 - wR) +
-                  fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
-
-              for (let dm = 0; dm < chMul; ++dm) {
-                const d2 = d1 * chMul + dm;
-                const pixel = dyValues[dyOffset + d2];
-                const weight = fltValues[fltOffset + dm];
-                dotProd += pixel * weight;
-              }
-            }
-          }
-          dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
-}
-
-export const depthwiseConv2dNativeBackpropInputConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNativeBackpropInput,
-  backendName: 'cpu',
-  kernelFunc: depthwiseConv2dNativeBackpropInput as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Diag.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Diag.ts
deleted file mode 100644
index 6dd1788bd..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Diag.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, Diag, DiagInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function diag(args: {inputs: DiagInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  const xSize = util.sizeFromShape(x.shape);
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const outBuf = buffer([xSize, xSize], x.dtype);
-  const vals = outBuf.values;
-  for (let i = 0; i < xVals.length; i++) {
-    vals[i * xSize + i] = xVals[i];
-  }
-
-  const outShape = [...x.shape, ...x.shape];
-
-  return backend.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);
-}
-
-export const diagConfig: KernelConfig = {
-  kernelName: Diag,
-  backendName: 'cpu',
-  kernelFunc: diag as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Dilation2D.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Dilation2D.ts
deleted file mode 100644
index 4abbe3644..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Dilation2D.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2D, Dilation2DAttrs, Dilation2DInputs, KernelConfig, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export const dilation2DConfig: KernelConfig = {
-  kernelName: Dilation2D,
-  backendName: 'cpu',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {x, filter} = inputs as Dilation2DInputs;
-    const {strides, pad, dilations} = attrs as unknown as Dilation2DAttrs;
-    const cpuBackend = backend as MathBackendCPU;
-
-    const xVals = cpuBackend.data.get(x.dataId).values as TypedArray;
-    const xRank = x.shape.length;
-
-    const filterVals = cpuBackend.data.get(filter.dataId).values as TypedArray;
-    const filterRank = filter.shape.length;
-
-    const {
-      batchSize,
-      inHeight,
-      inWidth,
-      inChannels,
-      outHeight,
-      outWidth,
-      padInfo,
-      strideHeight,
-      strideWidth,
-      filterHeight,
-      filterWidth,
-      dilationHeight,
-      dilationWidth,
-      outShape
-    } =
-        backend_util.computeDilation2DInfo(
-            x.shape as [number, number, number, number],
-            filter.shape as [number, number, number], strides, pad,
-            'NHWC' /* dataFormat */, dilations);
-
-    const outSize = util.sizeFromShape(outShape);
-    const outRank = outShape.length;
-    const outputVals = util.getArrayFromDType(x.dtype, outSize);
-
-    // Upsampling the input by fill in `dilation size - 1` values between each
-    // input value.
-    // This implementation follows the TF c++ implementation:
-    // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
-    for (let b = 0; b < batchSize; ++b) {
-      for (let hOut = 0; hOut < outHeight; ++hOut) {
-        const hBeg = hOut * strideHeight - padInfo.top;
-        for (let wOut = 0; wOut < outWidth; ++wOut) {
-          const wBeg = wOut * strideWidth - padInfo.left;
-          for (let d = 0; d < inChannels; ++d) {
-            let curVal = Number.MIN_SAFE_INTEGER;
-            for (let h = 0; h < filterHeight; ++h) {
-              const hIn = hBeg + h * dilationHeight;
-              if (hIn >= 0 && hIn < inHeight) {
-                for (let w = 0; w < filterWidth; ++w) {
-                  const wIn = wBeg + w * dilationWidth;
-                  if (wIn >= 0 && wIn < inWidth) {
-                    const xIndex = util.locToIndex(
-                        [b, hIn, wIn, d], xRank, util.computeStrides(x.shape));
-                    const filterIndex = util.locToIndex(
-                        [h, w, d], filterRank,
-                        util.computeStrides(filter.shape));
-                    const val = xVals[xIndex] + filterVals[filterIndex];
-                    if (val > curVal) {
-                      curVal = val;
-                    }
-                  }
-                }
-              }
-            }
-            const outputIndex = util.locToIndex(
-                [b, hOut, wOut, d], outRank, util.computeStrides(outShape));
-            outputVals[outputIndex] = curVal;
-          }
-        }
-      }
-    }
-
-    const dataId = cpuBackend.write(
-        util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);
-
-    return {dataId, shape: outShape, dtype: x.dtype};
-  }
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Dilation2DBackpropFilter.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Dilation2DBackpropFilter.ts
deleted file mode 100644
index 9924c903e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Dilation2DBackpropFilter.ts
+++ /dev/null
@@ -1,120 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2DAttrs, Dilation2DBackpropFilter, Tensor3D, Tensor4D, TypedArray, util} from '@tensorflow/tfjs-core';
-import {KernelConfig} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export const dilation2DBackpropFilterConfig: KernelConfig = {
-  kernelName: Dilation2DBackpropFilter,
-  backendName: 'cpu',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {x, filter, dy} =
-        inputs as {x: Tensor4D, filter: Tensor3D, dy: Tensor4D};
-    const {strides, pad, dilations} = attrs as unknown as Dilation2DAttrs;
-    const cpuBackend = backend as MathBackendCPU;
-
-    const $x =
-        util.toNestedArray(
-            x.shape, cpuBackend.data.get(x.dataId).values as TypedArray) as
-        number[][][][];
-
-    const $filter = util.toNestedArray(
-                        filter.shape,
-                        cpuBackend.data.get(filter.dataId).values as
-                            TypedArray) as number[][][];
-
-    const {
-      batchSize,
-      inHeight,
-      inWidth,
-      inChannels,
-      outHeight,
-      outWidth,
-      padInfo,
-      strideHeight,
-      strideWidth,
-      filterHeight,
-      filterWidth,
-      dilationHeight,
-      dilationWidth,
-      outShape
-    } =
-        backend_util.computeDilation2DInfo(
-            x.shape as [number, number, number, number],
-            filter.shape as [number, number, number], strides, pad,
-            'NHWC' /* dataFormat */, dilations);
-
-    util.assert(
-        dy.rank === outShape.length,
-        () => `Error in ${Dilation2DBackpropFilter}, dy ` +
-            `must have the same rank as output ${outShape.length}, but got ` +
-            `${dy.rank}`);
-
-    const $dy =
-        util.toNestedArray(
-            outShape, cpuBackend.data.get(dy.dataId).values as TypedArray) as
-        number[][][][];
-
-    // The computed filter gradients has the same dimensions as the filter:
-    // [filterHeight, filterWidth, depth]
-    const gradients = util.makeZerosNestedTypedArray(
-                          filter.shape, filter.dtype) as number[][][];
-
-    // In the case of multiple argmax branches, we only back-propagate along the
-    // last branch, i.e., the one with largest value of `h * filter_cols + w`,
-    // similarly to the max-pooling backward routines.
-    // This implementation follows the TF c++ implementation:
-    // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
-    for (let b = 0; b < batchSize; ++b) {
-      for (let hOut = 0; hOut < outHeight; ++hOut) {
-        const hBeg = hOut * strideHeight - padInfo.top;
-        for (let wOut = 0; wOut < outWidth; ++wOut) {
-          const wBeg = wOut * strideWidth - padInfo.left;
-          for (let d = 0; d < inChannels; ++d) {
-            let curVal = Number.MIN_SAFE_INTEGER;
-            let hMax = 0;
-            let wMax = 0;
-            for (let h = 0; h < filterHeight; ++h) {
-              const hIn = hBeg + h * dilationHeight;
-              if (hIn >= 0 && hIn < inHeight) {
-                for (let w = 0; w < filterWidth; ++w) {
-                  const wIn = wBeg + w * dilationWidth;
-                  if (wIn >= 0 && wIn < inWidth) {
-                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
-                    if (val > curVal) {
-                      curVal = val;
-                      hMax = h;
-                      wMax = w;
-                    }
-                  }
-                }
-              }
-            }
-            gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];
-          }
-        }
-      }
-    }
-
-    const dataId = cpuBackend.write(
-        util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);
-
-    return {dataId, shape: filter.shape, dtype: filter.dtype};
-  }
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Dilation2DBackpropInput.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Dilation2DBackpropInput.ts
deleted file mode 100644
index b4592a454..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Dilation2DBackpropInput.ts
+++ /dev/null
@@ -1,120 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2DAttrs, Dilation2DBackpropInput, Tensor3D, Tensor4D, TypedArray, util} from '@tensorflow/tfjs-core';
-import {KernelConfig} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export const dilation2DBackpropInputConfig: KernelConfig = {
-  kernelName: Dilation2DBackpropInput,
-  backendName: 'cpu',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {x, filter, dy} =
-        inputs as {x: Tensor4D, filter: Tensor3D, dy: Tensor4D};
-    const {strides, pad, dilations} = attrs as unknown as Dilation2DAttrs;
-    const cpuBackend = backend as MathBackendCPU;
-
-    const $x =
-        util.toNestedArray(
-            x.shape, cpuBackend.data.get(x.dataId).values as TypedArray) as
-        number[][][][];
-
-    const $filter = util.toNestedArray(
-                        filter.shape,
-                        cpuBackend.data.get(filter.dataId).values as
-                            TypedArray) as number[][][];
-
-    const {
-      batchSize,
-      inHeight,
-      inWidth,
-      inChannels,
-      outHeight,
-      outWidth,
-      padInfo,
-      strideHeight,
-      strideWidth,
-      filterHeight,
-      filterWidth,
-      dilationHeight,
-      dilationWidth,
-      outShape
-    } =
-        backend_util.computeDilation2DInfo(
-            x.shape as [number, number, number, number],
-            filter.shape as [number, number, number], strides, pad,
-            'NHWC' /* dataFormat */, dilations);
-
-    util.assert(
-        dy.rank === outShape.length,
-        () => `Error in ${Dilation2DBackpropInput}, dy ` +
-            `must have the same rank as output ${outShape.length}, but got ` +
-            `${dy.rank}`);
-
-    const $dy =
-        util.toNestedArray(
-            outShape, cpuBackend.data.get(dy.dataId).values as TypedArray) as
-        number[][][][];
-
-    // The computed gradients has the same dimensions as the input:
-    // [batch, inputHeight, inputCols, inChannel]
-    const gradients =
-        util.makeZerosNestedTypedArray(x.shape, x.dtype) as number[][][][];
-
-    // In the case of multiple argmax branches, we only back-propagate along the
-    // last branch, i.e., the one with largest value of `h * filter_cols + w`,
-    // similarly to the max-pooling backward routines.
-    // This implementation follows the TF c++ implementation:
-    // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
-    for (let b = 0; b < batchSize; ++b) {
-      for (let hOut = 0; hOut < outHeight; ++hOut) {
-        const hBeg = hOut * strideHeight - padInfo.top;
-        for (let wOut = 0; wOut < outWidth; ++wOut) {
-          const wBeg = wOut * strideWidth - padInfo.left;
-          for (let d = 0; d < inChannels; ++d) {
-            let curVal = Number.MIN_SAFE_INTEGER;
-            let hInMax = (hBeg < 0) ? 0 : hBeg;
-            let wInMax = (wBeg < 0) ? 0 : wBeg;
-            for (let h = 0; h < filterHeight; ++h) {
-              const hIn = hBeg + h * dilationHeight;
-              if (hIn >= 0 && hIn < inHeight) {
-                for (let w = 0; w < filterWidth; ++w) {
-                  const wIn = wBeg + w * dilationWidth;
-                  if (wIn >= 0 && wIn < inWidth) {
-                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
-                    if (val > curVal) {
-                      curVal = val;
-                      hInMax = hIn;
-                      wInMax = wIn;
-                    }
-                  }
-                }
-              }
-            }
-            gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];
-          }
-        }
-      }
-    }
-
-    const dataId = cpuBackend.write(
-        util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);
-
-    return {dataId, shape: x.shape, dtype: x.dtype};
-  }
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Draw.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Draw.ts
deleted file mode 100644
index b71d7c2c3..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Draw.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Draw, DrawAttrs, DrawInputs, KernelConfig, KernelFunc, TypedArray} from '@tensorflow/tfjs-core';
-import {TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function draw(
-    args: {inputs: DrawInputs, backend: MathBackendCPU, attrs: DrawAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {image} = inputs;
-  const {canvas, options} = attrs;
-  const {contextOptions, imageOptions} = options || {};
-  const alpha = imageOptions ?.alpha || 1;
-
-  const contextType = contextOptions ?.contextType || '2d';
-  if (contextType !== '2d') {
-    throw new Error(`Context type ${
-        contextOptions.contextType} is not supported by the CPU backend.`);
-  }
-  const ctx = canvas.getContext(contextType,
-    contextOptions?.contextAttributes || {}) as CanvasRenderingContext2D ;
-  if (ctx == null) {
-    throw new Error(`Could not get the context with ${contextType} type.`);
-  }
-
-  const [height, width] = image.shape.slice(0, 2);
-  const depth = image.shape.length === 2 ? 1 : image.shape[2];
-  const data = backend.data.get(image.dataId).values as TypedArray;
-  const multiplier = image.dtype === 'float32' ? 255 : 1;
-  const bytes = new Uint8ClampedArray(width * height * 4);
-
-  for (let i = 0; i < height * width; ++i) {
-    const rgba = [0, 0, 0, 255 * alpha];
-
-    for (let d = 0; d < depth; d++) {
-      const value = data[i * depth + d];
-
-      if (image.dtype === 'float32') {
-        if (value < 0 || value > 1) {
-          throw new Error(
-              `Tensor values for a float32 Tensor must be in the ` +
-              `range [0 - 1] but encountered ${value}.`);
-        }
-      } else if (image.dtype === 'int32') {
-        if (value < 0 || value > 255) {
-          throw new Error(
-              `Tensor values for a int32 Tensor must be in the ` +
-              `range [0 - 255] but encountered ${value}.`);
-        }
-      }
-
-      if (depth === 1) {
-        rgba[0] = value * multiplier;
-        rgba[1] = value * multiplier;
-        rgba[2] = value * multiplier;
-      } else {
-        rgba[d] = value * multiplier;
-      }
-    }
-
-    const j = i * 4;
-    bytes[j + 0] = Math.round(rgba[0]);
-    bytes[j + 1] = Math.round(rgba[1]);
-    bytes[j + 2] = Math.round(rgba[2]);
-    bytes[j + 3] = Math.round(rgba[3]);
-  }
-
-  canvas.width = width;
-  canvas.height = height;
-  const imageData = new ImageData(bytes, width, height);
-  ctx.putImageData(imageData, 0, 0);
-  return image;
-}
-
-export const drawConfig: KernelConfig = {
-  kernelName: Draw,
-  backendName: 'cpu',
-  kernelFunc: draw as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Einsum.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Einsum.ts
deleted file mode 100644
index 40c2c24ae..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Einsum.ts
+++ /dev/null
@@ -1,102 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Einsum, EinsumAttrs, EinsumInputs, KernelConfig, KernelFunc, Tensor, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {multiply} from './Multiply';
-import {reshape} from './Reshape';
-import {sum} from './Sum';
-import {transpose} from './Transpose';
-
-export function einsum(
-    args: {inputs: EinsumInputs, backend: MathBackendCPU, attrs: EinsumAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {equation} = attrs;
-  const tensors = inputs as Tensor[];
-
-  const {allDims, summedDims, idDims} =
-      backend_util.decodeEinsumEquation(equation, tensors.length);
-  backend_util.checkEinsumDimSizes(allDims.length, idDims, tensors);
-  const {path, steps} = backend_util.getEinsumComputePath(summedDims, idDims);
-
-  const nSteps = steps.length;
-  let out: TensorInfo|null = null;
-  let numDimsRemaining = allDims.length;
-  const tensorsToDispose: TensorInfo[] = [];
-  for (let i = 0; i < nSteps; ++i) {
-    for (const idTerm of steps[i]) {
-      const {permutationIndices: perm, expandDims: dimsToExpand} =
-          backend_util.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
-      let x: TensorInfo;
-      if (backend_util.isIdentityPermutation(perm)) {
-        x = tensors[idTerm];
-      } else {
-        x = transpose({inputs: {x: tensors[idTerm]}, backend, attrs: {perm}});
-        tensorsToDispose.push(x);
-      }
-      const targetShape: number[] = x.shape.slice();
-      for (let k = 0; k < dimsToExpand.length; ++k) {
-        targetShape.splice(dimsToExpand[k], 0, 1);
-      }
-
-      if (!util.arraysEqual(x.shape, targetShape)) {
-        x = reshape({inputs: {x}, backend, attrs: {shape: targetShape}});
-        tensorsToDispose.push(x);
-      }
-      if (out === null) {
-        out = x;
-      } else {
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        out = multiply({inputs: {a: x, b: out}, backend}) as TensorInfo;
-        tensorsToDispose.push(out);
-      }
-    }
-    if (i < nSteps - 1) {
-      if (path[i] >= 0) {
-        out = sum({
-          inputs: {x: out},
-          backend,
-          attrs: {
-            axis: path[i] - (allDims.length - numDimsRemaining),
-            keepDims: false
-          }
-        });
-        tensorsToDispose.push(out);
-      }
-      numDimsRemaining--;
-    }
-  }
-
-  // Clean up intermediate tensors.
-  for (const tensorInfo of tensorsToDispose) {
-    if (tensorInfo === out) {
-      continue;
-    }
-    backend.disposeIntermediateTensorInfo(tensorInfo);
-  }
-
-  return out;
-}
-
-export const einsumConfig: KernelConfig = {
-  kernelName: Einsum,
-  backendName: 'cpu',
-  kernelFunc: einsum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Elu.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Elu.ts
deleted file mode 100644
index 555d402df..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Elu.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Elu, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const elu =
-    unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));
-
-export const eluConfig: KernelConfig = {
-  kernelName: Elu,
-  backendName: 'cpu',
-  kernelFunc: elu,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/EluGrad.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/EluGrad.ts
deleted file mode 100644
index f7495bc97..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/EluGrad.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {EluGrad, EluGradInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function eluGrad(args: {inputs: EluGradInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {dy, y} = inputs;
-
-  assertNotComplex([dy, y], 'eluGrad');
-
-  const resultValues = new Float32Array(util.sizeFromShape(y.shape));
-  const values = backend.data.get(y.dataId).values as TypedArray;
-  const dyValues = backend.data.get(dy.dataId).values as TypedArray;
-  for (let i = 0; i < values.length; ++i) {
-    const v = values[i];
-    if (v >= 0) {
-      resultValues[i] = dyValues[i];
-    } else {
-      resultValues[i] = dyValues[i] * (v + 1);
-    }
-  }
-
-  return backend.makeTensorInfo(y.shape, 'float32', resultValues);
-}
-
-export const eluGradConfig: KernelConfig = {
-  kernelName: EluGrad,
-  backendName: 'cpu',
-  kernelFunc: eluGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Equal.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Equal.ts
deleted file mode 100644
index bd21f00c4..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Equal.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Equal, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const equalImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => (a === b) ? 1 : 0);
-export const equal =
-    binaryKernelFunc(Equal, equalImpl, null /* complexImpl */, 'bool');
-
-export const equalConfig: KernelConfig = {
-  kernelName: Equal,
-  backendName: 'cpu',
-  kernelFunc: equal
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Erf.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Erf.ts
deleted file mode 100644
index 6c41d9cff..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Erf.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Erf, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-const p = backend_util.ERF_P;
-const a1 = backend_util.ERF_A1;
-const a2 = backend_util.ERF_A2;
-const a3 = backend_util.ERF_A3;
-const a4 = backend_util.ERF_A4;
-const a5 = backend_util.ERF_A5;
-
-export const erf = unaryKernelFunc(
-    Erf,
-    (xi) => {
-      const sign = Math.sign(xi);
-      const v = Math.abs(xi);
-      const t = 1.0 / (1.0 + p * v);
-      return sign *
-          (1.0 -
-           (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *
-               Math.exp(-v * v));
-    },
-);
-
-export const erfConfig: KernelConfig = {
-  kernelName: Erf,
-  backendName: 'cpu',
-  kernelFunc: erf,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Exp.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Exp.ts
deleted file mode 100644
index 612cbb79b..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Exp.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Exp, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleUnaryImpl} from '../utils/unary_impl';
-import {unaryKernelFuncFromImpl} from '../utils/unary_utils';
-
-export const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));
-export const exp = unaryKernelFuncFromImpl(Exp, expImpl, 'float32');
-
-export const expConfig: KernelConfig = {
-  kernelName: Exp,
-  backendName: 'cpu',
-  kernelFunc: exp,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ExpandDims.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ExpandDims.ts
deleted file mode 100644
index de606e7bf..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ExpandDims.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ExpandDims, ExpandDimsAttrs, ExpandDimsInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {reshape} from './Reshape';
-
-export function expandDims(args: {
-  inputs: ExpandDimsInputs,
-  backend: MathBackendCPU,
-  attrs: ExpandDimsAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {input} = inputs;
-  const {dim} = attrs;
-
-  const inputRank = input.shape.length;
-  const newShape = input.shape.slice();
-  let $dim = dim;
-  if (dim < 0) {
-    // Negative value is counted from the tail of rank.
-    util.assert(
-        -(inputRank + 1) <= dim,
-        () => `Axis must be in the interval [${- (inputRank + 1)}, ${
-            inputRank}]`);
-    $dim = inputRank + dim + 1;
-  }
-  newShape.splice($dim, 0, 1);
-
-  return reshape({inputs: {x: input}, backend, attrs: {shape: newShape}});
-}
-
-export const expandDimsConfig: KernelConfig = {
-  kernelName: ExpandDims,
-  backendName: 'cpu',
-  kernelFunc: expandDims as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Expm1.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Expm1.ts
deleted file mode 100644
index 3b52a7b06..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Expm1.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Expm1, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleUnaryImpl} from '../utils/unary_impl';
-import {unaryKernelFuncFromImpl} from '../utils/unary_utils';
-
-export const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));
-export const expm1 = unaryKernelFuncFromImpl(Expm1, expm1Impl);
-
-export const expm1Config: KernelConfig = {
-  kernelName: Expm1,
-  backendName: 'cpu',
-  kernelFunc: expm1,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/FFT.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/FFT.ts
deleted file mode 100644
index a8c3dd272..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/FFT.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FFT, FFTInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {fftBatch} from '../utils/fft_utils';
-import {reshape} from './Reshape';
-
-export function fft(args: {inputs: FFTInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-
-  const inputSize = util.sizeFromShape(input.shape);
-
-  // Collapse all outer dimensions to a single batch dimension.
-  const innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = inputSize / innerDimensionSize;
-
-  const input2D = reshape({
-    inputs: {x: input},
-    backend,
-    attrs: {shape: [batch, innerDimensionSize]}
-  });
-
-  const result = fftBatch(input2D, false, backend);
-
-  const resultReshaped =
-      reshape({inputs: {x: result}, backend, attrs: {shape: input.shape}});
-
-  backend.disposeIntermediateTensorInfo(input2D);
-  backend.disposeIntermediateTensorInfo(result);
-
-  return resultReshaped;
-}
-
-export const fftConfig: KernelConfig = {
-  kernelName: FFT,
-  backendName: 'cpu',
-  kernelFunc: fft as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Fill.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Fill.ts
deleted file mode 100644
index d4c64a2e1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Fill.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, DataValues, Fill, FillAttrs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function fill(args: {backend: MathBackendCPU, attrs: FillAttrs}):
-    TensorInfo {
-  const {backend, attrs} = args;
-  const {shape, value, dtype} = attrs;
-
-  const $dtype = dtype || util.inferDtype(value);
-  const values = util.getArrayFromDType($dtype, util.sizeFromShape(shape));
-  fillValues(values, value, $dtype);
-
-  return backend.makeTensorInfo(shape, $dtype, values);
-}
-
-export const fillConfig: KernelConfig = {
-  kernelName: Fill,
-  backendName: 'cpu',
-  kernelFunc: fill as unknown as KernelFunc
-};
-
-function fillValues(
-    values: DataValues, value: string|number, dtype: DataType): void {
-  if (dtype === 'string') {
-    (values as string[]).fill(value as string);
-  } else {
-    (values as TypedArray).fill(value as number);
-  }
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/FlipLeftRight.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/FlipLeftRight.ts
deleted file mode 100644
index b3acba7e2..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/FlipLeftRight.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, NumericDataType, TypedArray} from '@tensorflow/tfjs-core';
-import {FlipLeftRight, FlipLeftRightInputs, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export const flipLeftRightConfig: KernelConfig = {
-  kernelName: FlipLeftRight,
-  backendName: 'cpu',
-  kernelFunc: ({inputs, attrs, backend}) => {
-    const {image} = inputs as FlipLeftRightInputs;
-    const cpuBackend = backend as MathBackendCPU;
-
-    const output = util.getTypedArrayFromDType(
-        image.dtype as NumericDataType, util.sizeFromShape(image.shape));
-    const [batch, imageHeight, imageWidth, numChannels] = image.shape;
-
-    const imageVals = cpuBackend.data.get(image.dataId).values as TypedArray;
-
-    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
-      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
-
-      for (let row = 0; row < imageHeight; row++) {
-        const rowOffset = row * (imageWidth * numChannels);
-
-        for (let col = 0; col < imageWidth; col++) {
-          const colOffset = col * numChannels;
-
-          for (let channel = 0; channel < numChannels; channel++) {
-            const coordX = Math.round(imageWidth - col - 1);
-            const outIdx = batchOffset + rowOffset + colOffset + channel;
-
-            let outputValue = imageVals[outIdx];
-            // If the coordinate position falls within the image boundaries...
-            if (coordX >= 0 && coordX < imageWidth) {
-              // set the output to the image value at the coordinate position.
-              const rotatedColOffset = coordX * numChannels;
-              const imageIdx =
-                  batchOffset + rowOffset + rotatedColOffset + channel;
-              outputValue = imageVals[imageIdx];
-            }
-            output[outIdx] = outputValue;
-          }
-        }
-      }
-    }
-
-    const dataId = cpuBackend.write(output, image.shape, image.dtype);
-    return {dataId, shape: image.shape, dtype: image.dtype};
-  }
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Floor.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Floor.ts
deleted file mode 100644
index 5b6636471..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Floor.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Floor, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleUnaryImpl} from '../utils/unary_impl';
-import {unaryKernelFuncFromImpl} from '../utils/unary_utils';
-
-export const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));
-export const floor = unaryKernelFuncFromImpl(Floor, floorImpl);
-
-export const floorConfig: KernelConfig = {
-  kernelName: Floor,
-  backendName: 'cpu',
-  kernelFunc: floor,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/FloorDiv.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/FloorDiv.ts
deleted file mode 100644
index 71346b866..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/FloorDiv.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FloorDiv, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const floorDivImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => Math.floor(a / b));
-export const floorDiv =
-    binaryKernelFunc(FloorDiv, floorDivImpl, null /* complexImpl */, 'int32');
-
-export const floorDivConfig: KernelConfig = {
-  kernelName: FloorDiv,
-  backendName: 'cpu',
-  kernelFunc: floorDiv
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/FusedConv2D.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/FusedConv2D.ts
deleted file mode 100644
index 0af847dfa..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/FusedConv2D.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {applyActivation} from '../utils/fused_utils';
-import {add} from './Add';
-import {conv2D} from './Conv2D';
-import {reshape} from './Reshape';
-
-export function fusedConv2D(args: {
-  inputs: FusedConv2DInputs,
-  backend: MathBackendCPU,
-  attrs: FusedConv2DAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, bias, preluActivationWeights} = inputs;
-  const {
-    strides,
-    pad,
-    dataFormat,
-    dilations,
-    dimRoundingMode,
-    activation,
-    leakyreluAlpha
-  } = attrs;
-
-  let result = conv2D({
-    inputs: {x, filter},
-    backend,
-    attrs: {strides, pad, dataFormat, dilations, dimRoundingMode}
-  });
-
-  if (bias) {
-    const resultOld = result;
-    // For NCHW format, if bias is a 1-D tensor, it is supposed to be aligned
-    // to the channel of the conv2d's result; if the bias is a scalar, the
-    // bias_add is computed as if the bias was broadcasted to the shape of the
-    // conv2d's result.
-    if (dataFormat === 'NCHW' && bias.shape.length === 1 &&
-        bias.shape[0] !== 1) {
-      const reshapedBias = reshape(
-          {inputs: {x: bias}, backend, attrs: {shape: [bias.shape[0], 1, 1]}});
-      result =
-          add({inputs: {a: result, b: reshapedBias}, backend}) as TensorInfo;
-      backend.disposeIntermediateTensorInfo(reshapedBias);
-    } else {
-      // This condition handles NHWC and NCHW (scalar case). The only other case
-      // for NCHW (1D case) is handled above.
-      result = add({inputs: {a: result, b: bias}, backend}) as TensorInfo;
-    }
-    backend.disposeIntermediateTensorInfo(resultOld);
-  }
-
-  if (activation) {
-    const resultOld = result;
-    // For NCHW format, if PReLu activation weights is a 1-D tensor, it is
-    // supposed to be aligned with the channel of the conv2d's result. For other
-    // cases, whether NCHW or NHWC data format, the conv2d result is
-    // already aligned with the activation weights.
-    if (dataFormat === 'NCHW' && activation === 'prelu' &&
-        preluActivationWeights.shape.length === 1 &&
-        preluActivationWeights.shape[0] !== 1) {
-      const reshapedAlpha = reshape({
-        inputs: {x: preluActivationWeights},
-        backend,
-        attrs: {shape: [preluActivationWeights.shape[0], 1, 1]}
-      });
-      result = applyActivation(
-          backend, result, activation, reshapedAlpha, leakyreluAlpha);
-      backend.disposeIntermediateTensorInfo(reshapedAlpha);
-    } else {
-      result = applyActivation(
-          backend, result, activation, preluActivationWeights, leakyreluAlpha);
-    }
-    backend.disposeIntermediateTensorInfo(resultOld);
-  }
-
-  return result;
-}
-
-export const fusedConv2DConfig: KernelConfig = {
-  kernelName: FusedConv2D,
-  backendName: 'cpu',
-  kernelFunc: fusedConv2D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/FusedDepthwiseConv2D.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/FusedDepthwiseConv2D.ts
deleted file mode 100644
index c37ae7787..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/FusedDepthwiseConv2D.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {applyActivation} from '../utils/fused_utils';
-import {add} from './Add';
-import {depthwiseConv2dNative} from './DepthwiseConv2dNative';
-
-export function fusedDepthwiseConv2D(args: {
-  inputs: FusedDepthwiseConv2DInputs,
-  backend: MathBackendCPU,
-  attrs: FusedDepthwiseConv2DAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, bias, preluActivationWeights} = inputs;
-  const {
-    strides,
-    pad,
-    dataFormat,
-    dilations,
-    dimRoundingMode,
-    activation,
-    leakyreluAlpha
-  } = attrs;
-
-  let result = depthwiseConv2dNative({
-    inputs: {x, filter},
-    backend,
-    attrs: {strides, pad, dataFormat, dilations, dimRoundingMode}
-  });
-
-  if (bias) {
-    const oldResult = result;
-    result = add({inputs: {a: result, b: bias}, backend}) as TensorInfo;
-    backend.disposeIntermediateTensorInfo(oldResult);
-  }
-  if (activation) {
-    const oldResult = result;
-    result = applyActivation(
-        backend, result, activation, preluActivationWeights, leakyreluAlpha);
-    backend.disposeIntermediateTensorInfo(oldResult);
-  }
-
-  return result;
-}
-
-export const fusedDepthwiseConv2DConfig: KernelConfig = {
-  kernelName: FusedDepthwiseConv2D,
-  backendName: 'cpu',
-  kernelFunc: fusedDepthwiseConv2D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/GatherNd.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/GatherNd.ts
deleted file mode 100644
index 80ade55cc..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/GatherNd.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, GatherNd, GatherNdInputs, KernelConfig, KernelFunc, Rank, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {gatherNdImpl} from './GatherNd_Impl';
-
-export function gatherNd(
-    args: {inputs: GatherNdInputs, backend: MathBackendCPU}): TensorInfo {
-  const {inputs, backend} = args;
-  const {params, indices} = inputs;
-
-  const paramsSize = util.sizeFromShape(params.shape);
-
-  const indicesShape = indices.shape;
-  const sliceRank = indicesShape[indicesShape.length - 1];
-
-  const [resultShape, numSlices, sliceSize, strides] =
-      backend_util.prepareAndValidate(params, indices);
-  if (numSlices === 0) {
-    return backend.makeTensorInfo(resultShape, params.dtype, []);
-  }
-
-  const indicesData = backend.data.get(indices.dataId).values as TypedArray;
-  const paramsBuf = backend.bufferSync<Rank, 'float32'>(params);
-  const outBuf = gatherNdImpl(
-      indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize,
-      strides, params.shape, paramsSize);
-
-  return backend.makeTensorInfo(resultShape, params.dtype, outBuf.values);
-}
-
-export const gatherNdConfig: KernelConfig = {
-  kernelName: GatherNd,
-  backendName: 'cpu',
-  kernelFunc: gatherNd as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/GatherNd_Impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/GatherNd_Impl.ts
deleted file mode 100644
index 36e6f2b5f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/GatherNd_Impl.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, DataType, Rank, TensorBuffer, TypedArray} from '@tensorflow/tfjs-core';
-
-export function gatherNdImpl<R extends Rank>(
-    indicesData: TypedArray, paramsBuf: TensorBuffer<R>, dtype: DataType,
-    numSlices: number, sliceRank: number, sliceSize: number, strides: number[],
-    paramsShape: number[], paramsSize: number): TensorBuffer<R> {
-  const outBuf = buffer([numSlices, sliceSize], dtype);
-
-  for (let i = 0; i < numSlices; i++) {
-    const index = [];
-    let flattenIndex = 0;
-    for (let j = 0; j < sliceRank; j++) {
-      const dim = indicesData[i * sliceRank + j];
-      flattenIndex += dim * strides[j];
-      index.push(dim);
-    }
-    if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {
-      throw new Error(
-          `Invalid indices: ${index} does not index into ${paramsShape}`);
-    }
-
-    for (let k = 0; k < sliceSize; k++) {
-      outBuf.values[i * sliceSize + k] =
-          paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
-    }
-  }
-
-  return outBuf as TensorBuffer<R>;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/GatherV2.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/GatherV2.ts
deleted file mode 100644
index 57d0eecc5..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/GatherV2.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, GatherV2, GatherV2Attrs, GatherV2Inputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {gatherV2Impl} from './GatherV2_impl';
-import {reshape} from './Reshape';
-
-export function gatherV2(args: {
-  inputs: GatherV2Inputs,
-  backend: MathBackendCPU,
-  attrs: GatherV2Attrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, indices} = inputs;
-  const {axis, batchDims} = attrs;
-
-  assertNotComplex([x, indices], 'gatherV2');
-
-  // Throw error when any index is out of bound.
-  const parsedAxis = util.parseAxisParam(axis, x.shape)[0];
-  const indicesVals = backend.data.get(indices.dataId).values as TypedArray;
-  const axisDim = x.shape[parsedAxis];
-  for (let i = 0; i < indicesVals.length; ++i) {
-    const index = indicesVals[i];
-    util.assert(
-        index <= axisDim - 1 && index >= 0,
-        () =>
-            `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
-  }
-
-  let $batchDims = batchDims;
-
-  if (batchDims == null) {
-    $batchDims = 0;
-  }
-
-  const indicesSize = util.sizeFromShape(indices.shape);
-
-  const shapeInfo = backend_util.segment_util.collectGatherOpShapeInfo(
-      x, indices, parsedAxis, $batchDims);
-
-  const flattenX = reshape({
-    inputs: {x},
-    backend,
-    attrs: {
-      shape: [
-        shapeInfo.batchSize, shapeInfo.outerSize, shapeInfo.dimSize,
-        shapeInfo.sliceSize
-      ]
-    }
-  });
-
-  const flattenIndex = reshape({
-    inputs: {x: indices},
-    backend,
-    attrs: {shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize]}
-  });
-
-  const flattenOutputShape = [
-    shapeInfo.batchSize, shapeInfo.outerSize, indicesSize / shapeInfo.batchSize,
-    shapeInfo.sliceSize
-  ];
-
-  const indicesBuf = backend.bufferSync(flattenIndex);
-  const xBuf = backend.bufferSync(flattenX);
-  const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);
-
-  backend.disposeIntermediateTensorInfo(flattenX);
-  backend.disposeIntermediateTensorInfo(flattenIndex);
-
-  return backend.makeTensorInfo(
-      shapeInfo.outputShape, outBuf.dtype, outBuf.values);
-}
-
-export const gatherV2Config: KernelConfig = {
-  kernelName: GatherV2,
-  backendName: 'cpu',
-  kernelFunc: gatherV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/GatherV2_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/GatherV2_impl.ts
deleted file mode 100644
index 5e8414338..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/GatherV2_impl.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, DataType, Rank, TensorBuffer} from '@tensorflow/tfjs-core';
-
-export function gatherV2Impl<R extends Rank, D extends DataType>(
-    xBuf: TensorBuffer<R, D>, indicesBuf: TensorBuffer<R, D>,
-    flattenOutputShape: number[]): TensorBuffer<R, D> {
-  const outBuf = buffer(flattenOutputShape, xBuf.dtype);
-  for (let i = 0; i < outBuf.size; ++i) {
-    const newLoc = outBuf.indexToLoc(i);
-
-    const originalLoc: number[] = newLoc.slice();
-    const batchIdx = originalLoc[0];
-    const indicesIdx = originalLoc[2];
-    const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);
-    originalLoc[2] = indicesBuf.values[indicesIndex] as number;
-
-    const originalIndex = xBuf.locToIndex(originalLoc);
-
-    if (0 <= originalIndex && originalIndex < xBuf.values.length) {
-      outBuf.values[i] = xBuf.values[originalIndex];
-    } // Else, index is out of bounds, so leave the default zero val in outBuf.
-  }
-
-  return outBuf as TensorBuffer<R, D>;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Greater.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Greater.ts
deleted file mode 100644
index fe8094057..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Greater.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Greater, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const greaterImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => (a > b) ? 1 : 0);
-export const greater =
-    binaryKernelFunc(Greater, greaterImpl, null /* complexImpl */, 'bool');
-
-export const greaterConfig: KernelConfig = {
-  kernelName: Greater,
-  backendName: 'cpu',
-  kernelFunc: greater
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/GreaterEqual.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/GreaterEqual.ts
deleted file mode 100644
index 827c54233..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/GreaterEqual.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GreaterEqual, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const greaterEqualImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => (a >= b) ? 1 : 0);
-export const greaterEqual = binaryKernelFunc(
-    GreaterEqual, greaterEqualImpl, null /* complexImpl */, 'bool');
-
-export const greaterEqualConfig: KernelConfig = {
-  kernelName: GreaterEqual,
-  backendName: 'cpu',
-  kernelFunc: greaterEqual
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/IFFT.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/IFFT.ts
deleted file mode 100644
index a733fab6b..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/IFFT.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IFFT, IFFTInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {fftBatch} from '../utils/fft_utils';
-import {reshape} from './Reshape';
-
-export function ifft(args: {inputs: IFFTInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-
-  const inputSize = util.sizeFromShape(input.shape);
-
-  // Collapse all outer dimensions to a single batch dimension.
-  const innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = inputSize / innerDimensionSize;
-
-  const input2D = reshape({
-    inputs: {x: input},
-    backend,
-    attrs: {shape: [batch, innerDimensionSize]}
-  });
-
-  const result = fftBatch(input2D, true, backend);
-
-  const resultReshaped =
-      reshape({inputs: {x: result}, backend, attrs: {shape: input.shape}});
-
-  backend.disposeIntermediateTensorInfo(input2D);
-  backend.disposeIntermediateTensorInfo(result);
-
-  return resultReshaped;
-}
-
-export const ifftConfig: KernelConfig = {
-  kernelName: IFFT,
-  backendName: 'cpu',
-  kernelFunc: ifft as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Identity.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Identity.ts
deleted file mode 100644
index 8f0cf0389..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Identity.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Identity, IdentityInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function identity(
-    args: {inputs: IdentityInputs, backend: MathBackendCPU}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  backend.incRef(x.dataId);
-
-  return {dataId: x.dataId, shape: x.shape, dtype: x.dtype};
-}
-
-export const identityConfig: KernelConfig = {
-  kernelName: Identity,
-  backendName: 'cpu',
-  kernelFunc: identity as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Imag.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Imag.ts
deleted file mode 100644
index 758ad683a..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Imag.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Imag, ImagInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function imag(args: {inputs: ImagInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-
-  const imag = backend.data.get(input.dataId).complexTensorInfos.imag;
-  const imagVal = backend.data.get(imag.dataId).values;
-
-  // When complex tensor is disposed, its underlying parts will be disposed too.
-  // Make new tensor out of the imag value of the complex. This makes sure the
-  // value is still accessible even if complex tensor is disposed.
-  return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);
-}
-
-export const imagConfig: KernelConfig = {
-  kernelName: Imag,
-  backendName: 'cpu',
-  kernelFunc: imag as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/IsFinite.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/IsFinite.ts
deleted file mode 100644
index 36ce04241..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/IsFinite.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsFinite, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const isFinite =
-    unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');
-
-export const isFiniteConfig: KernelConfig = {
-  kernelName: IsFinite,
-  backendName: 'cpu',
-  kernelFunc: isFinite,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/IsInf.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/IsInf.ts
deleted file mode 100644
index ce511d601..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/IsInf.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsInf, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const isInf =
-    unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');
-
-export const isInfConfig: KernelConfig = {
-  kernelName: IsInf,
-  backendName: 'cpu',
-  kernelFunc: isInf,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/IsNaN.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/IsNaN.ts
deleted file mode 100644
index 0c3607fcc..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/IsNaN.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsNan, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const isNaN =
-    unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');
-
-export const isNaNConfig: KernelConfig = {
-  kernelName: IsNan,
-  backendName: 'cpu',
-  kernelFunc: isNaN,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/LRN.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/LRN.ts
deleted file mode 100644
index 0e0516e1e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/LRN.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LRN, LRNAttrs, LRNInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function lRN(
-    args: {inputs: LRNInputs, backend: MathBackendCPU, attrs: LRNAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {depthRadius, bias, alpha, beta} = attrs;
-
-  assertNotComplex(x, 'LRN');
-
-  const channels = x.shape[3];
-  const maxD = channels - 1;
-  const xValues = backend.data.get(x.dataId).values as TypedArray;
-  const size = util.sizeFromShape(x.shape);
-  const result = new Float32Array(size);
-
-  function sumAcrossChannels(offset: number) {
-    const currentChannel = offset % channels;
-    let beginSumOffset =
-        offset - currentChannel + Math.max(0, currentChannel - depthRadius);
-    const endSumOffset =
-        offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);
-
-    let sum = 0.0;
-    for (; beginSumOffset <= endSumOffset; beginSumOffset++) {
-      const z = xValues[beginSumOffset];
-      sum += z * z;
-    }
-    return sum;
-  }
-
-  for (let offset = 0; offset < size; offset++) {
-    const sum = sumAcrossChannels(offset);
-    const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);
-    result[offset] = val;
-  }
-
-  return backend.makeTensorInfo(x.shape, x.dtype, result);
-}
-
-// tslint:disable-next-line: variable-name
-export const LRNConfig: KernelConfig = {
-  kernelName: LRN,
-  backendName: 'cpu',
-  kernelFunc: lRN as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/LRNGrad.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/LRNGrad.ts
deleted file mode 100644
index 45528d46b..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/LRNGrad.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LRNGrad, LRNGradAttrs, LRNGradInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function lRNGrad(
-    args:
-        {inputs: LRNGradInputs, backend: MathBackendCPU, attrs: LRNGradAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, y, dy} = inputs;
-  const {depthRadius, bias, alpha, beta} = attrs;
-
-  assertNotComplex(dy, 'LRNGrad');
-
-  const dySize = util.sizeFromShape(dy.shape);
-
-  const channels = dy.shape[3];
-  const dyValues = backend.data.get(dy.dataId).values as TypedArray;
-  const xValues = backend.data.get(x.dataId).values as TypedArray;
-  const yValues = backend.data.get(y.dataId).values as TypedArray;
-  const result = new Float32Array(dySize);
-  const size = dySize;
-
-  for (let offset = 0; offset < size; offset++) {
-    const currentChannel = offset % channels;
-    const depthBegin =
-        (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);
-    const depthEnd = (offset - currentChannel) +
-        Math.min(channels, currentChannel + depthRadius + 1);
-
-    let norm = 0;
-    for (let k = depthBegin; k < depthEnd; k++) {
-      norm += Math.pow(xValues[k], 2);
-    }
-    norm = alpha * norm + bias;
-
-    for (let k = depthBegin; k < depthEnd; k++) {
-      let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm;
-      if (offset === k) {
-        dyi += Math.pow(norm, -beta);
-      }
-      dyi *= dyValues[offset];
-      result[k] += dyi;
-    }
-  }
-
-  return backend.makeTensorInfo(dy.shape, x.dtype, result);
-}
-
-// tslint:disable-next-line: variable-name
-export const LRNGradConfig: KernelConfig = {
-  kernelName: LRNGrad,
-  backendName: 'cpu',
-  kernelFunc: lRNGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/LeakyRelu.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/LeakyRelu.ts
deleted file mode 100644
index 3de5b91e1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/LeakyRelu.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LeakyRelu, LeakyReluAttrs, LeakyReluInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function leakyRelu(args: {
-  inputs: LeakyReluInputs,
-  backend: MathBackendCPU,
-  attrs: LeakyReluAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {alpha} = attrs;
-
-  assertNotComplex([x], 'leakyRelu');
-
-  const xSize = util.sizeFromShape(x.shape);
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const outVals = util.getTypedArrayFromDType('float32', xSize);
-
-  for (let i = 0; i < xVals.length; i++) {
-    outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];
-  }
-
-  return backend.makeTensorInfo(x.shape, 'float32', outVals);
-}
-
-export const leakyReluConfig: KernelConfig = {
-  kernelName: LeakyRelu,
-  backendName: 'cpu',
-  kernelFunc: leakyRelu as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Less.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Less.ts
deleted file mode 100644
index 567d60b67..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Less.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Less} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const lessImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => (a < b) ? 1 : 0);
-export const less =
-    binaryKernelFunc(Less, lessImpl, null /* complexImpl */, 'bool');
-
-export const lessConfig: KernelConfig = {
-  kernelName: Less,
-  backendName: 'cpu',
-  kernelFunc: less
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/LessEqual.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/LessEqual.ts
deleted file mode 100644
index c0ec04f00..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/LessEqual.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LessEqual} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const lessEqualImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => (a <= b) ? 1 : 0);
-export const lessEqual =
-    binaryKernelFunc(LessEqual, lessEqualImpl, null /* complexImpl */, 'bool');
-
-export const lessEqualConfig: KernelConfig = {
-  kernelName: LessEqual,
-  backendName: 'cpu',
-  kernelFunc: lessEqual
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/LinSpace.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/LinSpace.ts
deleted file mode 100644
index 75675c63e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/LinSpace.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LinSpace, LinSpaceAttrs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {linSpaceImpl} from './LinSpace_impl';
-
-export function linSpace(args: {backend: MathBackendCPU, attrs: LinSpaceAttrs}):
-    TensorInfo {
-  const {backend, attrs} = args;
-  const {start, stop, num} = attrs;
-
-  const outVals = linSpaceImpl(start, stop, num);
-
-  return backend.makeTensorInfo([outVals.length], 'float32', outVals);
-}
-
-export const linSpaceConfig: KernelConfig = {
-  kernelName: LinSpace,
-  backendName: 'cpu',
-  kernelFunc: linSpace as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/LinSpace_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/LinSpace_impl.ts
deleted file mode 100644
index dd92521dc..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/LinSpace_impl.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TypedArray, util} from '@tensorflow/tfjs-core';
-
-export function linSpaceImpl(
-    start: number, stop: number, num: number): TypedArray {
-  const step = (stop - start) / (num - 1);
-
-  const values = util.makeZerosTypedArray(num, 'float32');
-  values[0] = start;
-  for (let i = 1; i < values.length; i++) {
-    values[i] = values[i - 1] + step;
-  }
-
-  return values;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Log.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Log.ts
deleted file mode 100644
index 06a91e8b4..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Log.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Log} from '@tensorflow/tfjs-core';
-
-import {createSimpleUnaryImpl} from '../utils/unary_impl';
-import {unaryKernelFuncFromImpl} from '../utils/unary_utils';
-
-export const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));
-export const log = unaryKernelFuncFromImpl(Log, logImpl);
-
-export const logConfig: KernelConfig = {
-  kernelName: Log,
-  backendName: 'cpu',
-  kernelFunc: log,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Log1p.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Log1p.ts
deleted file mode 100644
index f173f5c7c..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Log1p.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Log1p} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const log1p = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));
-
-export const log1pConfig: KernelConfig = {
-  kernelName: Log1p,
-  backendName: 'cpu',
-  kernelFunc: log1p,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/LogicalAnd.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/LogicalAnd.ts
deleted file mode 100644
index 6b2178b0f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/LogicalAnd.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalAnd} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const logicalAndImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => a && b);
-export const logicalAnd = binaryKernelFunc(
-    LogicalAnd, logicalAndImpl, null /* complexImpl */, 'bool');
-
-export const logicalAndConfig: KernelConfig = {
-  kernelName: LogicalAnd,
-  backendName: 'cpu',
-  kernelFunc: logicalAnd
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/LogicalNot.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/LogicalNot.ts
deleted file mode 100644
index e56316ba3..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/LogicalNot.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalNot} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const logicalNot =
-    unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');
-
-export const logicalNotConfig: KernelConfig = {
-  kernelName: LogicalNot,
-  backendName: 'cpu',
-  kernelFunc: logicalNot,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/LogicalOr.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/LogicalOr.ts
deleted file mode 100644
index 7867fdfc9..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/LogicalOr.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalOr} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const logicalOrImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => a || b);
-export const logicalOr =
-    binaryKernelFunc(LogicalOr, logicalOrImpl, null /* complexImpl */, 'bool');
-
-export const logicalOrConfig: KernelConfig = {
-  kernelName: LogicalOr,
-  backendName: 'cpu',
-  kernelFunc: logicalOr
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Max.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Max.ts
deleted file mode 100644
index 56f3de1e0..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Max.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelFunc, Max, MaxAttrs, MaxInputs, TensorInfo} from '@tensorflow/tfjs-core';
-import {backend_util, KernelConfig} from '@tensorflow/tfjs-core';
-import {TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-import {maxImpl} from './Max_impl';
-import {transposeImpl} from './Transpose_impl';
-
-export function max(
-    args: {inputs: MaxInputs, backend: MathBackendCPU, attrs: MaxAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {reductionIndices, keepDims} = attrs;
-  const cpuBackend = backend;
-  let xShape = x.shape;
-  const xRank = xShape.length;
-
-  const origAxes = util.parseAxisParam(reductionIndices, xShape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-  let xVals = cpuBackend.data.get(x.dataId).values as TypedArray;
-  if (permutedAxes != null) {
-    const newShape: number[] = new Array(xRank);
-    for (let i = 0; i < newShape.length; i++) {
-      newShape[i] = xShape[permutedAxes[i]];
-    }
-
-    xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);
-    axes = backend_util.getInnerMostAxes(axes.length, xRank);
-
-    xShape = newShape;
-  }
-
-  assertNotComplex(x, 'max');
-  backend_util.assertAxesAreInnerMostDims('max', axes, xRank);
-  const [maxOutShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(xShape, axes);
-
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);
-  const dataId = cpuBackend.write(result, maxOutShape, x.dtype);
-
-  let outShape = maxOutShape;
-  if (keepDims) {
-    // reshape
-    const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);
-    outShape = newShape;
-  }
-
-  return {dataId, shape: outShape, dtype: x.dtype};
-}
-
-export const maxConfig: KernelConfig = {
-  kernelName: Max,
-  backendName: 'cpu',
-  kernelFunc: max as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPool.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPool.ts
deleted file mode 100644
index fa3a6a193..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPool.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, KernelConfig, KernelFunc, MaxPool, MaxPoolAttrs, MaxPoolInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {pool} from '../utils/pool_utils';
-import {identity} from './Identity';
-
-export function maxPool(
-    args:
-        {inputs: MaxPoolInputs, backend: MathBackendCPU, attrs: MaxPoolAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  assertNotComplex(x, 'maxPool');
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const dilations = 1;
-
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in maxPool: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode);
-  let res: TensorInfo;
-
-  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
-      util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
-    res = identity({inputs: {x}, backend});
-  } else {
-    const xValues = backend.data.get(x.dataId).values as TypedArray;
-    const strides = util.computeStrides(x.shape);
-    const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');
-    res = backend.makeTensorInfo(
-        convInfo.outShape, x.dtype, buffer.values as TypedArray);
-  }
-  return res;
-}
-
-export const maxPoolConfig: KernelConfig = {
-  kernelName: MaxPool,
-  backendName: 'cpu',
-  kernelFunc: maxPool as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPool3D.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPool3D.ts
deleted file mode 100644
index 3036007f4..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPool3D.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {pool3d} from '../utils/pool_utils';
-
-export function maxPool3D(args: {
-  inputs: MaxPool3DInputs,
-  backend: MathBackendCPU,
-  attrs: MaxPool3DAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode, dataFormat} = attrs;
-
-  assertNotComplex(x, 'maxPool3d');
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      1 /* dilations */, pad, dimRoundingMode, dataFormat);
-
-  const xValues = backend.data.get(x.dataId).values as TypedArray;
-  const outBuf = pool3d(
-      xValues, x.shape, x.dtype, util.computeStrides(x.shape), convInfo, 'max');
-
-  return backend.makeTensorInfo(outBuf.shape, 'float32', outBuf.values);
-}
-
-export const maxPool3DConfig: KernelConfig = {
-  kernelName: MaxPool3D,
-  backendName: 'cpu',
-  kernelFunc: maxPool3D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPool3DGrad.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPool3DGrad.ts
deleted file mode 100644
index 280328f4d..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPool3DGrad.ts
+++ /dev/null
@@ -1,123 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, buffer, KernelConfig, KernelFunc, MaxPool3DGrad, MaxPool3DGradAttrs, MaxPool3DGradInputs, Rank, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {maxPool3dPositions} from '../utils/pool_utils';
-
-export function maxPool3DGrad(args: {
-  inputs: MaxPool3DGradInputs,
-  backend: MathBackendCPU,
-  attrs: MaxPool3DGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-
-  assertNotComplex([dy, input], 'maxPool3DGrad');
-
-  const convInfo = backend_util.computePool3DInfo(
-      input.shape as [number, number, number, number, number], filterSize,
-      strides, 1 /* dilations */, pad, dimRoundingMode);
-
-  const inputBuf = backend.bufferSync(input);
-  const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);
-  const strideDepth = convInfo.strideDepth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const dilationDepth = convInfo.dilationDepth;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
-  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
-  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
-  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
-  const dx = buffer(input.shape, 'float32');
-
-  const dyBuf = backend.bufferSync<Rank, 'float32'>(dy);
-
-  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
-    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
-      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
-        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
-          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
-            // Shader code begins
-            const dyDepthCorner = dxDepth - padFront;
-            const dyRowCorner = dxRow - padTop;
-            const dyColCorner = dxCol - padLeft;
-            let dotProd = 0;
-            for (let wDepth = 0; wDepth < effectiveFilterDepth;
-                 wDepth += dilationDepth) {
-              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
-              if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||
-                  Math.floor(dyDepth) !== dyDepth) {
-                continue;
-              }
-              for (let wRow = 0; wRow < effectiveFilterHeight;
-                   wRow += dilationHeight) {
-                const dyRow = (dyRowCorner + wRow) / strideHeight;
-                if (dyRow < 0 || dyRow >= convInfo.outHeight ||
-                    Math.floor(dyRow) !== dyRow) {
-                  continue;
-                }
-                for (let wCol = 0; wCol < effectiveFilterWidth;
-                     wCol += dilationWidth) {
-                  const dyCol = (dyColCorner + wCol) / strideWidth;
-                  if (dyCol < 0 || dyCol >= convInfo.outWidth ||
-                      Math.floor(dyCol) !== dyCol) {
-                    continue;
-                  }
-
-                  const maxPos = effectiveFilterDepth * effectiveFilterHeight *
-                          effectiveFilterWidth -
-                      1 -
-                      (maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel) as
-                       number);
-                  const curPos =
-                      wDepth * effectiveFilterHeight * effectiveFilterWidth +
-                      wRow * effectiveFilterWidth + wCol;
-
-                  const mask = maxPos === curPos ? 1 : 0;
-                  if (mask === 0) {
-                    continue;
-                  }
-
-                  const pixel =
-                      dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
-                  dotProd += pixel * mask;
-                }
-              }
-            }
-            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);
-          }
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
-}
-
-export const maxPool3DGradConfig: KernelConfig = {
-  kernelName: MaxPool3DGrad,
-  backendName: 'cpu',
-  kernelFunc: maxPool3DGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPoolGrad.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPoolGrad.ts
deleted file mode 100644
index aeb1fc7e1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPoolGrad.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, buffer, KernelConfig, KernelFunc, MaxPoolGrad, MaxPoolGradAttrs, MaxPoolGradInputs, Rank, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {maxPoolPositions} from '../utils/pool_utils';
-
-export function maxPoolGrad(args: {
-  inputs: MaxPoolGradInputs,
-  backend: MathBackendCPU,
-  attrs: MaxPoolGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input, output} = inputs;
-  const x = input;
-  assertNotComplex([input, output], 'maxPoolGrad');
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      1 /* dilations */, pad, dimRoundingMode);
-  const xValues = backend.data.get(x.dataId).values as TypedArray;
-  const maxPosBuf = buffer(
-      convInfo.outShape, x.dtype,
-      maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
-  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
-  const dx =
-      buffer<Rank.R4>(x.shape as [number, number, number, number], 'float32');
-
-  const dyData = backend.data.get(dy.dataId).values as Float32Array;
-  const dyBuf = buffer<Rank.R4>(
-      dy.shape as [number, number, number, number], 'float32', dyData);
-
-  for (let b = 0; b < convInfo.batchSize; ++b) {
-    for (let d = 0; d < convInfo.inChannels; ++d) {
-      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
-        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
-          // Shader code begins.
-          const dyRCorner = dxR - padTop;
-          const dyCCorner = dxC - padLeft;
-          let dotProd = 0;
-          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
-            const dyR = (dyRCorner + wR) / strideHeight;
-            if (dyR < 0 || dyR >= convInfo.outHeight ||
-                Math.floor(dyR) !== dyR) {
-              continue;
-            }
-            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
-              const dyC = (dyCCorner + wC) / strideWidth;
-              if (dyC < 0 || dyC >= convInfo.outWidth ||
-                  Math.floor(dyC) !== dyC) {
-                continue;
-              }
-              const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -
-                  (maxPosBuf.get(b, dyR, dyC, d) as number);
-              const curPos = wR * effectiveFilterWidth + wC;
-
-              const mask = maxPos === curPos ? 1 : 0;
-              if (mask === 0) {
-                continue;
-              }
-
-              const pixel = dyBuf.get(b, dyR, dyC, d);
-              dotProd += pixel * mask;
-            }
-          }
-          dx.set(dotProd, b, dxR, dxC, d);
-        }
-      }
-    }
-  }
-  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
-}
-
-export const maxPoolGradConfig: KernelConfig = {
-  kernelName: MaxPoolGrad,
-  backendName: 'cpu',
-  kernelFunc: maxPoolGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPoolWithArgmax.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPoolWithArgmax.ts
deleted file mode 100644
index 18d147f27..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPoolWithArgmax.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs} from '@tensorflow/tfjs-core';
-import {backend_util, KernelConfig, TypedArray} from '@tensorflow/tfjs-core';
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-import {maxPoolWithArgmaxImpl} from './MaxPoolWithArgmax_impl';
-
-export const maxPoolWithArgmaxConfig: KernelConfig = {
-  kernelName: MaxPoolWithArgmax,
-  backendName: 'cpu',
-  kernelFunc: ({inputs, attrs, backend}) => {
-    const {x} = inputs as MaxPoolWithArgmaxInputs;
-    const {filterSize, strides, pad, includeBatchInIndex} =
-        attrs as unknown as MaxPoolWithArgmaxAttrs;
-    const cpuBackend = backend as MathBackendCPU;
-    assertNotComplex(x, 'MaxPoolWithArgmax');
-
-    const values = cpuBackend.data.get(x.dataId).values as TypedArray;
-    const convInfo = backend_util.computePool2DInfo(
-        x.shape as [number, number, number, number], filterSize, strides,
-        [1, 1], pad);
-    const [pooled, indexes] = maxPoolWithArgmaxImpl(
-        values, x.shape, x.dtype, includeBatchInIndex, convInfo);
-
-    const pooledDataId =
-        cpuBackend.write(pooled as Float32Array, convInfo.outShape, x.dtype);
-    const indexesDataId =
-        cpuBackend.write(indexes as Int32Array, convInfo.outShape, x.dtype);
-    return [
-      {dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype},
-      {dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32'}
-    ];
-  }
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPoolWithArgmax_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPoolWithArgmax_impl.ts
deleted file mode 100644
index 5a40153c3..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/MaxPoolWithArgmax_impl.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, DataType, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {maxPoolPositions, pool} from '../utils/pool_utils';
-export function maxPoolWithArgmaxImpl(
-    xValues: TypedArray, xShape: number[], dtype: DataType,
-    includeBatchInIndex: boolean, convInfo: backend_util.Conv2DInfo) {
-  const strides = util.computeStrides(xShape);
-  const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');
-  const maxPositions = maxPoolPositions(
-      xValues, xShape, dtype, convInfo, true, includeBatchInIndex);
-
-  return [maxPools.values, maxPositions.values];
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Max_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Max_impl.ts
deleted file mode 100644
index 2983784e0..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Max_impl.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, NumericDataType, TypedArray, util} from '@tensorflow/tfjs-core';
-
-export function maxImpl(
-    aVals: TypedArray, reduceSize: number, outShape: number[],
-    dtype: DataType): TypedArray {
-  const vals = util.getTypedArrayFromDType(
-      dtype as NumericDataType, util.sizeFromShape(outShape));
-
-  for (let i = 0; i < vals.length; ++i) {
-    const offset = i * reduceSize;
-    let max = aVals[offset];
-    for (let j = 0; j < reduceSize; ++j) {
-      const value = aVals[offset + j];
-      if (Number.isNaN(value) ||
-          value > max) {  // comparison with NaN always return false
-        max = value;
-      }
-    }
-    vals[i] = max;
-  }
-  return vals;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Maximum.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Maximum.ts
deleted file mode 100644
index 3e6d9308d..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Maximum.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Maximum} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const maximumImpl = createSimpleBinaryKernelImpl(
-    ((aValue, bValue) => Math.max(aValue as number, bValue as number)));
-export const maximum = binaryKernelFunc(Maximum, maximumImpl);
-
-export const maximumConfig: KernelConfig = {
-  kernelName: Maximum,
-  backendName: 'cpu',
-  kernelFunc: maximum
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Mean.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Mean.ts
deleted file mode 100644
index c2e1d508f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Mean.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Mean, MeanAttrs, MeanInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {cast} from './Cast';
-import {div} from './RealDiv';
-import {sum} from './Sum';
-
-export function mean(
-    args: {inputs: MeanInputs, backend: MathBackendCPU, attrs: MeanAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  const axes = util.parseAxisParam(axis, x.shape);
-  const shapes = backend_util.computeOutAndReduceShapes(x.shape, axes);
-  const reduceShape = shapes[1];
-  const reduceSize = util.sizeFromShape(reduceShape);
-  const toDispose = [];
-  const reduceSizeScalar =
-      backend.makeTensorInfo([], 'float32', new Float32Array([reduceSize]));
-  toDispose.push(reduceSizeScalar);
-
-  const $x = cast({inputs: {x}, backend, attrs: {dtype: 'float32'}});
-  toDispose.push($x);
-
-  const res =
-      div({inputs: {a: $x, b: reduceSizeScalar}, backend}) as TensorInfo;
-  toDispose.push(res);
-
-  const result = sum({inputs: {x: res}, backend, attrs: {axis, keepDims}});
-
-  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-  return result;
-}
-
-export const meanConfig: KernelConfig = {
-  kernelName: Mean,
-  backendName: 'cpu',
-  kernelFunc: mean as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Min.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Min.ts
deleted file mode 100644
index 346692d8b..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Min.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Min, MinAttrs, MinInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function min(
-    args: {inputs: MinInputs, backend: MathBackendCPU, attrs: MinAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  assertNotComplex(x, 'min');
-
-  const origAxes = util.parseAxisParam(axis, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
-  let $x = x;
-  if (permutedAxes != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    axes = backend_util.getInnerMostAxes(axes.length, x.shape.length);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('min', axes, $x.shape.length);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes($x.shape, axes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-  const vals = util.makeZerosTypedArray(util.sizeFromShape(outShape), $x.dtype);
-
-  const aVals = backend.data.get($x.dataId).values as TypedArray;
-  for (let i = 0; i < vals.length; ++i) {
-    const offset = i * reduceSize;
-    let min = aVals[offset];
-    for (let j = 0; j < reduceSize; ++j) {
-      const value = aVals[offset + j];
-      if (Number.isNaN(value) ||
-          value < min) {  // comparison with NaN always return false
-        min = value;
-      }
-    }
-    vals[i] = min;
-  }
-
-  if (permutedAxes != null) {
-    backend.disposeIntermediateTensorInfo($x);
-  }
-
-  const result = backend.makeTensorInfo(outShape, $x.dtype, vals);
-
-  if (keepDims) {
-    const expandedShape = backend_util.expandShapeToKeepDim(outShape, origAxes);
-    const reshapedResult =
-        reshape({inputs: {x: result}, backend, attrs: {shape: expandedShape}});
-
-    backend.disposeIntermediateTensorInfo(result);
-
-    return reshapedResult;
-  }
-
-  return result;
-}
-
-export const minConfig: KernelConfig = {
-  kernelName: Min,
-  backendName: 'cpu',
-  kernelFunc: min as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Minimum.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Minimum.ts
deleted file mode 100644
index a8b64277a..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Minimum.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Minimum} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const minimumImpl = createSimpleBinaryKernelImpl(
-    ((aValue, bValue) => Math.min(aValue as number, bValue as number)));
-export const minimum = binaryKernelFunc(Minimum, minimumImpl);
-
-export const minimumConfig: KernelConfig = {
-  kernelName: Minimum,
-  backendName: 'cpu',
-  kernelFunc: minimum
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/MirrorPad.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/MirrorPad.ts
deleted file mode 100644
index f05e1e73e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/MirrorPad.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, MirrorPad, MirrorPadAttrs, MirrorPadInputs, NumericDataType, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function mirrorPad(args: {
-  inputs: MirrorPadInputs,
-  backend: MathBackendCPU,
-  attrs: MirrorPadAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {paddings, mode} = attrs;
-
-  assertNotComplex(x, 'mirrorPad');
-
-  const outShape = paddings.map(
-      (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);
-
-  const start = paddings.map(p => p[0]);
-  const end = paddings.map((p, i) => p[0] + x.shape[i]);
-  const offset = mode === 'reflect' ? 0 : 1;
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const xRank = x.shape.length;
-  const xStrides = util.computeStrides(x.shape);
-
-  const resultSize = util.sizeFromShape(outShape);
-  const resultRank = outShape.length;
-  const resultStrides = util.computeStrides(outShape);
-  const resVals =
-      util.getTypedArrayFromDType(x.dtype as NumericDataType, resultSize);
-
-  for (let i = 0; i < resultSize; i++) {
-    let coords = util.indexToLoc(i, resultRank, resultStrides);
-    for (let i = 0; i < resultRank; i++) {
-      if (coords[i] < start[i]) {
-        coords[i] = start[i] * 2 - coords[i] - offset;
-      } else if (coords[i] >= end[i]) {
-        coords[i] = (end[i] - 1) * 2 - coords[i] + offset;
-      }
-    }
-    coords = coords.map((c, i) => c - start[i]);
-
-    const inIndex = util.locToIndex(coords, xRank, xStrides);
-
-    resVals[i] = xVals[inIndex];
-  }
-
-  const outId = backend.write(resVals, outShape, x.dtype);
-
-  return {dataId: outId, shape: outShape, dtype: x.dtype};
-}
-
-export const mirrorPadConfig: KernelConfig = {
-  kernelName: MirrorPad,
-  backendName: 'cpu',
-  kernelFunc: mirrorPad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Mod.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Mod.ts
deleted file mode 100644
index 860a6d2d7..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Mod.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Mod} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const modImpl =
-    createSimpleBinaryKernelImpl(((aValue: number, bValue: number) => {
-      const rem = aValue % bValue;
-      if ((aValue < 0 && bValue < 0) || (aValue >= 0 && bValue >= 0)) {
-        return rem;
-      } else {
-        return (rem + bValue) % bValue;
-      }
-    }));
-
-export const mod = binaryKernelFunc(Mod, modImpl);
-
-export const modConfig: KernelConfig = {
-  kernelName: Mod,
-  backendName: 'cpu',
-  kernelFunc: mod
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Multinomial.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Multinomial.ts
deleted file mode 100644
index beab6a747..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Multinomial.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Multinomial, MultinomialAttrs, MultinomialInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-import * as seedrandom from 'seedrandom';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-import {softmax} from './Softmax';
-
-export function multinomial(args: {
-  inputs: MultinomialInputs,
-  backend: MathBackendCPU,
-  attrs: MultinomialAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {logits} = inputs;
-  const {numSamples, seed, normalized} = attrs;
-
-  assertNotComplex(logits, 'multinomial');
-
-  const probabilities = normalized ?
-      logits :
-      softmax({inputs: {logits}, backend, attrs: {dim: -1}});
-
-  const batchSize = probabilities.shape[0];
-  const numEvents = probabilities.shape[1];
-  const probVals = backend.data.get(probabilities.dataId).values as TypedArray;
-  const resShape = [batchSize, numSamples];
-  const resVals =
-      util.makeZerosTypedArray(util.sizeFromShape(resShape), 'int32');
-
-  for (let b = 0; b < batchSize; ++b) {
-    const offset = b * numEvents;
-    // The cdf won't include the last event. It will be implicit if no other
-    // event happened.
-    const cdf = new Float32Array(numEvents - 1);
-    cdf[0] = probVals[offset];
-    for (let event = 1; event < cdf.length; ++event) {
-      cdf[event] = cdf[event - 1] + probVals[offset + event];
-    }
-
-    const random = seedrandom.alea(seed.toString());
-    const outOffset = b * numSamples;
-    for (let sampleId = 0; sampleId < numSamples; ++sampleId) {
-      const r = random();
-
-      // Assume last event happened by default.
-      resVals[outOffset + sampleId] = cdf.length;
-
-      for (let event = 0; event < cdf.length; event++) {
-        if (r < cdf[event]) {
-          resVals[outOffset + sampleId] = event;
-          break;
-        }
-      }
-    }
-  }
-
-  if (!normalized) {
-    backend.disposeIntermediateTensorInfo(probabilities);
-  }
-
-  return backend.makeTensorInfo(resShape, 'int32', resVals);
-}
-
-export const multinomialConfig: KernelConfig = {
-  kernelName: Multinomial,
-  backendName: 'cpu',
-  kernelFunc: multinomial as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Multiply.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Multiply.ts
deleted file mode 100644
index cb6223e17..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Multiply.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Multiply} from '@tensorflow/tfjs-core';
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc, createComplexBinaryKernelImpl} from '../utils/binary_utils';
-
-export const multiplyImpl = createSimpleBinaryKernelImpl(
-    ((aValue: number, bValue: number) => aValue * bValue));
-export const multiplyComplexImpl =
-    createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {
-      return {
-        real: aReal * bReal - aImag * bImag,
-        imag: aReal * bImag + aImag * bReal
-      };
-    }));
-
-export const multiply =
-    binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);
-
-export const multiplyConfig: KernelConfig = {
-  kernelName: Multiply,
-  backendName: 'cpu',
-  kernelFunc: multiply
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Neg.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Neg.ts
deleted file mode 100644
index 815cb5e47..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Neg.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, KernelConfig, KernelFunc, Neg, TensorInfo, TypedArray, UnaryInputs, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {multiplyImpl} from './Multiply';
-
-export function negImpl(xVals: TypedArray, xShape: number[], xDtype: DataType):
-    [TypedArray, number[]] {
-  const minusOne =
-      util.createScalarValue(-1 as unknown as 'float32', xDtype) as TypedArray;
-  return multiplyImpl([], xShape, minusOne, xVals, xDtype);
-}
-
-export function neg(args: {inputs: UnaryInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  assertNotComplex(x, 'neg');
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const [res, newShape] = negImpl(xVals, x.shape, x.dtype);
-
-  return backend.makeTensorInfo(newShape, x.dtype, res);
-}
-
-export const negConfig: KernelConfig = {
-  kernelName: Neg,
-  backendName: 'cpu',
-  kernelFunc: neg as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/NonMaxSuppressionV3.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/NonMaxSuppressionV3.ts
deleted file mode 100644
index adbfd4dca..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/NonMaxSuppressionV3.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV3, NonMaxSuppressionV3Attrs, NonMaxSuppressionV3Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-const nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function nonMaxSuppressionV3(args: {
-  inputs: NonMaxSuppressionV3Inputs,
-  backend: MathBackendCPU,
-  attrs: NonMaxSuppressionV3Attrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {boxes, scores} = inputs;
-  const {maxOutputSize, iouThreshold, scoreThreshold} = attrs;
-
-  assertNotComplex(boxes, 'NonMaxSuppression');
-
-  const boxesVals = backend.data.get(boxes.dataId).values as TypedArray;
-  const scoresVals = backend.data.get(scores.dataId).values as TypedArray;
-
-  const {selectedIndices} = nonMaxSuppressionV3Impl(
-      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
-
-  return backend.makeTensorInfo(
-      [selectedIndices.length], 'int32', new Int32Array(selectedIndices));
-}
-
-export const nonMaxSuppressionV3Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV3,
-  backendName: 'cpu',
-  kernelFunc: nonMaxSuppressionV3 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/NonMaxSuppressionV4.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/NonMaxSuppressionV4.ts
deleted file mode 100644
index 51765b0b1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/NonMaxSuppressionV4.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-const nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function nonMaxSuppressionV4(args: {
-  inputs: NonMaxSuppressionV4Inputs,
-  backend: MathBackendCPU,
-  attrs: NonMaxSuppressionV4Attrs
-}): [TensorInfo, TensorInfo] {
-  const {inputs, backend, attrs} = args;
-  const {boxes, scores} = inputs;
-  const {maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize} =
-      attrs;
-
-  assertNotComplex(boxes, 'NonMaxSuppressionPadded');
-
-  const boxesVals = backend.data.get(boxes.dataId).values as TypedArray;
-  const scoresVals = backend.data.get(scores.dataId).values as TypedArray;
-
-  const {selectedIndices, validOutputs} = nonMaxSuppressionV4Impl(
-      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold,
-      padToMaxOutputSize);
-
-  return [
-    backend.makeTensorInfo(
-        [selectedIndices.length], 'int32', new Int32Array(selectedIndices)),
-    backend.makeTensorInfo([], 'int32', new Int32Array([validOutputs]))
-  ];
-}
-export const nonMaxSuppressionV4Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV4,
-  backendName: 'cpu',
-  kernelFunc: nonMaxSuppressionV4 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/NonMaxSuppressionV5.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/NonMaxSuppressionV5.ts
deleted file mode 100644
index bef6bcaa7..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/NonMaxSuppressionV5.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV5, NonMaxSuppressionV5Attrs, NonMaxSuppressionV5Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-const nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function nonMaxSuppressionV5(args: {
-  inputs: NonMaxSuppressionV5Inputs,
-  backend: MathBackendCPU,
-  attrs: NonMaxSuppressionV5Attrs
-}): [TensorInfo, TensorInfo] {
-  const {inputs, backend, attrs} = args;
-  const {boxes, scores} = inputs;
-  const {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} = attrs;
-
-  assertNotComplex(boxes, 'NonMaxSuppressionWithScore');
-
-  const boxesVals = backend.data.get(boxes.dataId).values as TypedArray;
-  const scoresVals = backend.data.get(scores.dataId).values as TypedArray;
-
-  const maxOutputSizeVal = maxOutputSize;
-  const iouThresholdVal = iouThreshold;
-  const scoreThresholdVal = scoreThreshold;
-  const softNmsSigmaVal = softNmsSigma;
-
-  const {selectedIndices, selectedScores} = nonMaxSuppressionV5Impl(
-      boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal,
-      scoreThresholdVal, softNmsSigmaVal);
-
-  return [
-    backend.makeTensorInfo(
-        [selectedIndices.length], 'int32', new Int32Array(selectedIndices)),
-    backend.makeTensorInfo(
-        [selectedScores.length], 'float32', new Float32Array(selectedScores))
-  ];
-}
-
-export const nonMaxSuppressionV5Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV5,
-  backendName: 'cpu',
-  kernelFunc: nonMaxSuppressionV5 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/NotEqual.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/NotEqual.ts
deleted file mode 100644
index 31276dbea..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/NotEqual.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, NotEqual} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const notEqualImpl =
-    createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));
-export const notEqual =
-    binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');
-
-export const notEqualConfig: KernelConfig = {
-  kernelName: NotEqual,
-  backendName: 'cpu',
-  kernelFunc: notEqual
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/OneHot.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/OneHot.ts
deleted file mode 100644
index 955aaf607..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/OneHot.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, OneHot, OneHotAttrs, OneHotInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function oneHot(
-    args: {inputs: OneHotInputs, backend: MathBackendCPU, attrs: OneHotAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {indices} = inputs;
-  const {dtype, depth, onValue, offValue} = attrs;
-
-  assertNotComplex(indices, 'oneHot');
-
-  const indicesSize = util.sizeFromShape(indices.shape);
-
-  const res = new Float32Array(indicesSize * depth);
-  res.fill(offValue);
-  const indicesVal = backend.data.get(indices.dataId).values as TypedArray;
-
-  for (let event = 0; event < indicesSize; ++event) {
-    if (indicesVal[event] >= 0 && indicesVal[event] < depth) {
-      res[event * depth + indicesVal[event]] = onValue;
-    }
-  }
-
-  return backend.makeTensorInfo([...indices.shape, depth], dtype, res);
-}
-
-export const oneHotConfig: KernelConfig = {
-  kernelName: OneHot,
-  backendName: 'cpu',
-  kernelFunc: oneHot as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/OnesLike.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/OnesLike.ts
deleted file mode 100644
index 21cc30bdf..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/OnesLike.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, OnesLike, OnesLikeInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {complex} from './Complex';
-import {fill} from './Fill';
-import {imag} from './Imag';
-import {real} from './Real';
-import {zerosLike} from './ZerosLike';
-
-export function onesLike(
-    args: {inputs: OnesLikeInputs, backend: MathBackendCPU}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  if (x.dtype === 'string') {
-    throw new Error('onesLike is not supported for string tensors');
-  } else if (x.dtype === 'complex64') {
-    const realPart = real({inputs: {input: x}, backend});
-    const r = onesLike({inputs: {x: realPart}, backend});
-    const imagPart = imag({inputs: {input: x}, backend});
-    const i = zerosLike({inputs: {x: imagPart}, backend});
-
-    const result = complex({inputs: {real: r, imag: i}, backend});
-
-    backend.disposeIntermediateTensorInfo(realPart);
-    backend.disposeIntermediateTensorInfo(r);
-    backend.disposeIntermediateTensorInfo(imagPart);
-    backend.disposeIntermediateTensorInfo(i);
-
-    return result;
-  } else {
-    return fill({backend, attrs: {shape: x.shape, value: 1, dtype: x.dtype}});
-  }
-}
-
-export const onesLikeConfig: KernelConfig = {
-  kernelName: OnesLike,
-  backendName: 'cpu',
-  kernelFunc: onesLike as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Pack.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Pack.ts
deleted file mode 100644
index da7b80f2a..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Pack.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Pack, PackAttrs, PackInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {concat} from './Concat';
-import {expandDims} from './ExpandDims';
-
-export function pack(
-    args: {inputs: PackInputs, backend: MathBackendCPU, attrs: PackAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {axis} = attrs;
-
-  if (inputs.length === 1) {
-    return expandDims(
-        {inputs: {input: inputs[0]}, backend, attrs: {dim: axis}});
-  }
-
-  const shape = inputs[0].shape;
-  const dtype = inputs[0].dtype;
-
-  inputs.forEach(t => {
-    util.assertShapesMatch(
-        shape, t.shape,
-        'All tensors passed to stack must have matching shapes');
-    util.assert(
-        dtype === t.dtype,
-        () => 'All tensors passed to stack must have matching dtypes');
-  });
-
-  const intermediateTensorInfos: TensorInfo[] = [];
-  const expandedTensors = inputs.map(t => {
-    const expandedT =
-        expandDims({inputs: {input: t}, backend, attrs: {dim: axis}});
-    intermediateTensorInfos.push(expandedT);
-    return expandedT;
-  });
-
-  const result = concat({inputs: expandedTensors, backend, attrs: {axis}});
-
-  intermediateTensorInfos.forEach(
-      t => backend.disposeIntermediateTensorInfo(t));
-
-  return result;
-}
-
-export const packConfig: KernelConfig = {
-  kernelName: Pack,
-  backendName: 'cpu',
-  kernelFunc: pack as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/PadV2.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/PadV2.ts
deleted file mode 100644
index 5e1b55aba..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/PadV2.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, NumericDataType, PadV2, PadV2Attrs, PadV2Inputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function padV2(
-    args: {inputs: PadV2Inputs, backend: MathBackendCPU, attrs: PadV2Attrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {paddings, constantValue} = attrs;
-
-  assertNotComplex(x, 'pad');
-
-  const outShape = paddings.map(
-      (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);
-
-  const start = paddings.map(p => p[0]);
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const xSize = util.sizeFromShape(x.shape);
-  const xRank = x.shape.length;
-  const xStrides = util.computeStrides(x.shape);
-
-  const resultSize = util.sizeFromShape(outShape);
-  const resultRank = outShape.length;
-  const resultStrides = util.computeStrides(outShape);
-  const resVals =
-      util.getTypedArrayFromDType(x.dtype as NumericDataType, resultSize);
-
-  if (constantValue !== 0) {
-    resVals.fill(constantValue);
-  }
-
-  for (let i = 0; i < xSize; i++) {
-    const coords = util.indexToLoc(i, xRank, xStrides);
-    const outCoords = coords.map((c, i) => c + start[i]);
-    const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);
-
-    resVals[outIndex] = xVals[i];
-  }
-
-  const outId = backend.write(resVals, outShape, x.dtype);
-
-  return {dataId: outId, shape: outShape, dtype: x.dtype};
-}
-
-export const padV2Config: KernelConfig = {
-  kernelName: PadV2,
-  backendName: 'cpu',
-  kernelFunc: padV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Pow.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Pow.ts
deleted file mode 100644
index 39398e078..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Pow.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Pow} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const powImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => Math.pow(a, b));
-export const pow = binaryKernelFunc(Pow, powImpl);
-
-export const powConfig: KernelConfig = {
-  kernelName: Pow,
-  backendName: 'cpu',
-  kernelFunc: pow
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Prelu.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Prelu.ts
deleted file mode 100644
index 4bfa36751..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Prelu.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Prelu, PreluInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-
-const preluImpl = createSimpleBinaryKernelImpl(
-    (xValue: number, aValue: number) => xValue < 0 ? aValue * xValue : xValue);
-
-export function prelu(args: {inputs: PreluInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x, alpha} = inputs;
-
-  assertNotComplex([x, alpha], 'prelu');
-
-  const aVals = backend.data.get(x.dataId).values as TypedArray;
-  const bVals = backend.data.get(alpha.dataId).values as TypedArray;
-
-  const [resultData, resultShape] =
-      preluImpl(x.shape, alpha.shape, aVals, bVals, 'float32');
-
-  return backend.makeTensorInfo(resultShape, 'float32', resultData);
-}
-
-export const preluConfig: KernelConfig = {
-  kernelName: Prelu,
-  backendName: 'cpu',
-  kernelFunc: prelu,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Prod.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Prod.ts
deleted file mode 100644
index 3472c3da5..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Prod.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType, KernelConfig, KernelFunc, Prod, ProdAttrs, ProdInputs, TensorInfo, TypedArray, upcastType, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {transpose} from './Transpose';
-
-export function prodImpl(
-    xShape: number[], xDtype: DataType, xVals: TypedArray,
-    reductionAxes: number[]):
-    {outVals: TypedArray, outShape: number[], outDtype: DataType} {
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(xShape, reductionAxes);
-  const outDtype = upcastType(xDtype, 'int32');
-  const outVals = util.makeZerosTypedArray(
-                      util.sizeFromShape(outShape), outDtype) as TypedArray;
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  for (let i = 0; i < outVals.length; ++i) {
-    const offset = i * reduceSize;
-    let prod = 1;
-    for (let j = 0; j < reduceSize; ++j) {
-      prod *= xVals[offset + j];
-    }
-    outVals[i] = prod;
-  }
-
-  return {outVals, outShape, outDtype};
-}
-
-export function prod(
-    args: {inputs: ProdInputs, backend: MathBackendCPU, attrs: ProdAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  assertNotComplex(x, 'prod');
-
-  const xRank = x.shape.length;
-  const axes = util.parseAxisParam(axis, x.shape);
-
-  const permutation = backend_util.getAxesPermutation(axes, xRank);
-  let reductionAxes = axes;
-  let permutedX = x;
-  const intermediateTensorInfos = [];
-  if (permutation != null) {
-    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutation}});
-    intermediateTensorInfos.push(permutedX);
-    reductionAxes = backend_util.getInnerMostAxes(reductionAxes.length, xRank);
-  }
-
-  const xVals = backend.data.get(permutedX.dataId).values as TypedArray;
-  const {outVals, outShape, outDtype} =
-      prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);
-
-  let resultShape = outShape;
-  if (keepDims) {
-    resultShape = backend_util.expandShapeToKeepDim(outShape, axes);
-  }
-
-  intermediateTensorInfos.forEach(
-      t => backend.disposeIntermediateTensorInfo(t));
-
-  return backend.makeTensorInfo(resultShape, outDtype, outVals);
-}
-
-export const prodConfig: KernelConfig = {
-  kernelName: Prod,
-  backendName: 'cpu',
-  kernelFunc: prod as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedGather.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedGather.ts
deleted file mode 100644
index 436dd0915..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedGather.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, RaggedGather, RaggedGatherAttrs, RaggedGatherInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {raggedGatherImpl} from './RaggedGather_impl';
-
-export function raggedGather(args: {
-  inputs: RaggedGatherInputs,
-  backend: MathBackendCPU,
-  attrs: RaggedGatherAttrs
-}): TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {paramsNestedSplits, paramsDenseValues, indices} = inputs;
-  const {outputRaggedRank} = attrs;
-
-  const $paramsNestedSplits = paramsNestedSplits.map(
-      t => backend.data.get(t.dataId).values as TypedArray);
-  const $paramsNestedSplitsShapes = paramsNestedSplits.map(t => t.shape);
-  const $paramsDenseValues =
-      backend.data.get(paramsDenseValues.dataId).values as TypedArray;
-  const $indices = backend.data.get(indices.dataId).values as TypedArray;
-
-  const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] =
-      raggedGatherImpl(
-          $paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues,
-          paramsDenseValues.shape, paramsDenseValues.dtype, $indices,
-          indices.shape, outputRaggedRank);
-
-  const outputNestedSplitsTensors = outputNestedSplits.map(
-      (splits) => backend.makeTensorInfo([splits.length], 'int32', splits));
-
-  const outputDenseValuesTensor = backend.makeTensorInfo(
-      outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues);
-
-  return outputNestedSplitsTensors.concat([outputDenseValuesTensor]);
-}
-
-export const raggedGatherConfig: KernelConfig = {
-  kernelName: RaggedGather,
-  backendName: 'cpu',
-  kernelFunc: raggedGather as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedGather_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedGather_impl.ts
deleted file mode 100644
index e7580b497..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedGather_impl.ts
+++ /dev/null
@@ -1,226 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, TypedArray, util} from '@tensorflow/tfjs-core';
-
-function validateIndices(
-    indices: TypedArray, indicesShape: number[], numParams: number) {
-  indices.forEach((index: number, i: number) => {
-    if (index < 0 || index >= numParams) {
-      const locString =
-          util.indexToLoc(
-                  i, indicesShape.length, util.computeStrides(indicesShape))
-              .join(',');
-      throw new Error(
-          `indices[${locString}] = ${index} is not in [0, ${numParams})`);
-    }
-  });
-}
-
-function validateSplits(
-    paramsNestedSplits: TypedArray[], numParamsDenseValues: number) {
-  // Validate
-  for (let dim = 0; dim < paramsNestedSplits.length; ++dim) {
-    const splits = paramsNestedSplits[dim];
-    const lastSplit = (dim === paramsNestedSplits.length - 1) ?
-        numParamsDenseValues :
-        paramsNestedSplits[dim + 1].length;
-    if (splits.length === 0) {
-      throw new Error('Ragged splits may not be empty');
-    }
-    if (splits[0] < 0) {
-      throw new Error('Ragged splits must be non-negative');
-    }
-    if (splits[splits.length - 1] > lastSplit) {
-      throw new Error('Ragged splits must not point past values');
-    }
-    for (let i = 1; i < splits.length; ++i) {
-      if (splits[i - 1] > splits[i]) {
-        throw new Error('Ragged splits must be sorted in ascending order');
-      }
-    }
-  }
-}
-
-// Construct the `splits` output tensors, encoded using a nested vector.
-// Also find the slices of values that need to be copied, and store them
-// in `valueSlices`.  The total number of values that will be copied (which
-// we need for allocating the output values tensor) is stored in `numValues`.
-function makeSplits(
-    indices: TypedArray, indicesShape: number[],
-    paramsNestedSplits: TypedArray[], numParamsDenseValues: number) {
-  const valueSlices: Array<[number, number]> = [];
-  let numValues = 0;
-
-  const numSplits = indicesShape.length - 1 + paramsNestedSplits.length;
-  const outSplits = new Array(numSplits).fill(null).map(() => [0]);
-
-  validateSplits(paramsNestedSplits, numParamsDenseValues);
-
-  // Add `splits` that come from all but the last dimension of the dense
-  // Tensor `indices`.  In particular, for each dimension D, we add a
-  // splits tensor whose values are:
-  //   range(reduceProd(splits.shape[:D]) + 1) * splits.shape[D+1]
-  // E.g., if indices.shape=[2, 3, 4] then we will add splits tensors:
-  //   [0, 3, 6]                    # length=2+1, stride=3
-  //   [0, 4, 8, 12, 16, 20, 24]    # length=2*3+1, stride=4
-  let nrows = 1;
-  for (let dim = 0; dim < indicesShape.length - 1; ++dim) {
-    nrows *= indicesShape[dim];
-    const rowLength = indicesShape[dim + 1];
-    for (let i = 1; i < nrows + 1; ++i) {
-      outSplits[dim].push(i * rowLength);
-    }
-  }
-
-  // Add `splits` that come from `paramsNestedSplits`.  Starting with the
-  // outermost ragged dimension (i.e., the first `splits` tensor), we work
-  // our way in, finding the range of values that should be copied.  As we
-  // go, we update the output `splits` for each dimension with the appropriate
-  // values.  In particular, the *lengths* of the slices from `param_splits`
-  // should be copied to generate corresponding slice lengths in the output
-  // splits.  E.g., if we are copying a ragged row with length 4, then we
-  // should add a new split point to outSplits that is 4 greater than the
-  // previous split point in outSplits.
-  for (let i = 0; i < indices.length; ++i) {
-    let start = indices[i];
-    let limit = indices[i] + 1;
-
-    // Copy splits.
-    for (let dim = 0; dim < paramsNestedSplits.length; ++dim) {
-      const splits = paramsNestedSplits[dim];
-      const outDim = dim + indicesShape.length - 1;
-      if (outDim >= 0) {
-        const outSplitsOutDim = outSplits[outDim];
-        const delta =
-            outSplitsOutDim[outSplitsOutDim.length - 1] - splits[start];
-        for (let j = start; j < limit; ++j) {
-          outSplits[outDim].push(splits[j + 1] + delta);
-        }
-      }
-      start = splits[start];
-      limit = splits[limit];
-    }
-    if (limit !== start) {
-      valueSlices.push([start, limit]);
-      numValues += limit - start;
-    }
-  }
-
-  return {outSplits, valueSlices, numValues};
-}
-
-function getSplits(outSplits: number[][]) {
-  const splitsOut: TypedArray[] = [];
-  for (let i = 0; i < outSplits.length; ++i) {
-    const numSplits = outSplits[i].length;
-    const splits = util.getArrayFromDType('int32', numSplits) as TypedArray;
-    splitsOut.push(splits);
-
-    outSplits[i].forEach((value, j: number) => splits[j] = value);
-  }
-
-  return splitsOut;
-}
-
-function computeFlatOuterDims(orig: number[], numOutDims: number) {
-  const outDims = orig.slice(0, numOutDims);
-  while (outDims.length < numOutDims) {
-    outDims.push(1);
-  }
-
-  for (let inDim = numOutDims; inDim < orig.length; inDim++) {
-    outDims[numOutDims - 1] *= orig[inDim];
-  }
-
-  return outDims;
-}
-// For each slice in `(start, limit)` in `valueSlices`, append
-// `paramsDenseValues[start,...,limit] to `values`.  `valueSize` indicates
-// the number of scalars contained in each value paramsDenseValues[i].
-function writeValueSlices(
-    paramsDenseValues: TypedArray, paramsDenseValuesShape: number[],
-    valueSlices: Array<[number, number]>, valueSize: number, values: TypedArray,
-    valuesShape: number[]) {
-  const denseM = computeFlatOuterDims(paramsDenseValuesShape, 2)[1];
-  const valuesM = computeFlatOuterDims(valuesShape, 2)[1];
-
-  let outPos = 0;
-  for (const slice of valueSlices) {
-    for (let i = slice[0]; i < slice[1]; ++i) {
-      for (let j = 0; j < valueSize; ++j) {
-        values[outPos * valuesM + j] = paramsDenseValues[i * denseM + j];
-      }
-      ++outPos;
-    }
-  }
-}
-
-function getValues(
-    paramsDenseValues: TypedArray, paramsDenseValuesShape: number[],
-    paramsDenseValuesDType: DataType, valueSlices: Array<[number, number]>,
-    numValues: number): [TypedArray, number[]] {
-  const valuesShape = paramsDenseValuesShape.slice();
-  valuesShape[0] = numValues;
-
-  const valuesOut = util.getArrayFromDType(
-                        paramsDenseValuesDType,
-                        util.sizeFromShape(valuesShape)) as TypedArray;
-
-  const numElements = paramsDenseValues.length;
-  const valueSize =
-      numElements === 0 ? 0 : (numElements / paramsDenseValuesShape[0]);
-  writeValueSlices(
-      paramsDenseValues, paramsDenseValuesShape, valueSlices, valueSize,
-      valuesOut, valuesShape);
-
-  return [valuesOut, valuesShape];
-}
-export function raggedGatherImpl(
-    paramsNestedSplits: TypedArray[], paramsNestedSplitsShapes: number[][],
-    paramsDenseValues: TypedArray, paramsDenseValuesShape: number[],
-    paramsDenseValuesDType: DataType, indices: TypedArray,
-    indicesShape: number[],
-    outputRaggedRank: number): [TypedArray[], TypedArray, number[]] {
-  if (paramsNestedSplits.length === 0) {
-    throw new Error('paramsNestedSplits must be non empty');
-  }
-
-  if (paramsNestedSplitsShapes[0].length === 0) {
-    throw new Error('Split tensors must not be scalars');
-  }
-  const numParams = paramsNestedSplitsShapes[0][0] - 1;
-  validateIndices(indices, indicesShape, numParams);
-
-  if (paramsDenseValuesShape.length === 0) {
-    throw new Error('params.rank must be nonzero');
-  }
-  const numParamsDenseValues = paramsDenseValuesShape[0];
-
-  // Calculate the `splits`, and store the value slices that we need to
-  // copy in `valueSlices`.
-  const {outSplits, valueSlices, numValues} = makeSplits(
-      indices, indicesShape, paramsNestedSplits, numParamsDenseValues);
-
-  // Write the output tensors.
-  const outputNestedSplits = getSplits(outSplits);
-  const outputDenseValues = getValues(
-      paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType,
-      valueSlices, numValues);
-
-  return [outputNestedSplits, outputDenseValues[0], outputDenseValues[1]];
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedRange.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedRange.ts
deleted file mode 100644
index 41efb6d8c..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedRange.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, RaggedRange, RaggedRangeInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {raggedRangeImpl} from './RaggedRange_impl';
-
-export function raggedRange(
-    args: {inputs: RaggedRangeInputs, backend: MathBackendCPU}):
-    [TensorInfo, TensorInfo] {
-  const {inputs, backend} = args;
-  const {starts, limits, deltas} = inputs;
-
-  const $starts = backend.data.get(starts.dataId).values as TypedArray;
-  const $limits = backend.data.get(limits.dataId).values as TypedArray;
-  const $deltas = backend.data.get(deltas.dataId).values as TypedArray;
-
-  const [rtNestedSplitsData, rtDenseValuesData] = raggedRangeImpl(
-      $starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas,
-      deltas.shape);
-
-  const rtNestedSplits = backend.makeTensorInfo(
-      [rtNestedSplitsData.length], 'int32', rtNestedSplitsData);
-  const rtDenseValues = backend.makeTensorInfo(
-      [rtDenseValuesData.length], starts.dtype, rtDenseValuesData);
-
-  return [rtNestedSplits, rtDenseValues];
-}
-
-export const raggedRangeConfig: KernelConfig = {
-  kernelName: RaggedRange,
-  backendName: 'cpu',
-  kernelFunc: raggedRange as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedRange_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedRange_impl.ts
deleted file mode 100644
index 256ca4b0a..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedRange_impl.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, TypedArray, util} from '@tensorflow/tfjs-core';
-
-const INT32_MAX = 2147483647;
-
-export function raggedRangeImpl(
-    starts: TypedArray, startsShape: number[], startsDType: DataType,
-    limits: TypedArray, limitsShape: number[], deltas: TypedArray,
-    deltasShape: number[]): [TypedArray, TypedArray] {
-  // Check input tensor shapes.
-  if (startsShape.length > 1) {
-    throw new Error('starts must be a scalar or vector');
-  }
-  if (limitsShape.length > 1) {
-    throw new Error('limits must be a scalar or vector');
-  }
-  if (deltasShape.length > 1) {
-    throw new Error('deltas must be a scalar or vector');
-  }
-
-  // Determine which tensors we need to broadcast.
-  const broadcastStarts = startsShape.length === 0;
-  const broadcastLimits = limitsShape.length === 0;
-  const broadcastDeltas = deltasShape.length === 0;
-
-  // nRows (number of output rows) is the size of the non-broadcast inputs,
-  // or 1 if all inputs are scalars.
-  const inSizes: number[] = [];
-  if (!broadcastStarts) {
-    inSizes.push(startsShape[0]);
-  }
-  if (!broadcastLimits) {
-    inSizes.push(limitsShape[0]);
-  }
-  if (!broadcastDeltas) {
-    inSizes.push(deltasShape[0]);
-  }
-
-  for (let i = 1; i < inSizes.length; ++i) {
-    if (inSizes[i] !== inSizes[i - 1]) {
-      throw new Error('starts, limits, and deltas must have the same shape');
-    }
-  }
-  const nRows = inSizes.length === 0 ? 1 : inSizes[0];
-
-  // Construct the rtNestedSplits tensor.
-  const rtNestedSplits =
-      util.getArrayFromDType('int32', nRows + 1) as TypedArray;
-  rtNestedSplits[0] = 0;
-  for (let row = 0; row < nRows; ++row) {
-    const start = broadcastStarts ? starts[0] : starts[row];
-    const limit = broadcastLimits ? limits[0] : limits[row];
-    const delta = broadcastDeltas ? deltas[0] : deltas[row];
-    if (delta === 0) {
-      throw new Error('Requires delta != 0');
-    }
-    let size: number;  // The number of elements in the specified range.
-    if (((delta > 0) && (limit < start)) || ((delta < 0) && (limit > start))) {
-      size = 0;
-    } else {
-      size = Math.ceil(Math.abs((limit - start) / delta));
-
-      if (size > INT32_MAX) {
-        throw new Error(`Requires ((limit - start) / delta) <= ${INT32_MAX}`);
-      }
-    }
-    rtNestedSplits[row + 1] = rtNestedSplits[row] + size;
-  }
-
-  const nVals = rtNestedSplits[nRows];
-
-  // Construct the rtDenseValues tensor.
-  const rtDenseValues =
-      util.getArrayFromDType(startsDType, nVals) as TypedArray;
-
-  let valueIndex = 0;
-  for (let row = 0; row < nRows; ++row) {
-    const rowSize = rtNestedSplits[row + 1] - rtNestedSplits[row];
-    let value = broadcastStarts ? starts[0] : starts[row];
-    const delta = broadcastDeltas ? deltas[0] : deltas[row];
-    for (let i = 0; i < rowSize; ++i) {
-      rtDenseValues[valueIndex++] = value;
-      value += delta;
-    }
-  }
-
-  return [rtNestedSplits, rtDenseValues];
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedTensorToTensor.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedTensorToTensor.ts
deleted file mode 100644
index 3849cba0b..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedTensorToTensor.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, RaggedTensorToTensor, RaggedTensorToTensorAttrs, RaggedTensorToTensorInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {raggedTensorToTensorImpl} from './RaggedTensorToTensor_impl';
-
-export function raggedTensorToTensor(args: {
-  inputs: RaggedTensorToTensorInputs,
-  backend: MathBackendCPU,
-  attrs: RaggedTensorToTensorAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {shape, values, defaultValue, rowPartitionTensors} = inputs;
-  const {rowPartitionTypes} = attrs;
-
-  const $shape = backend.data.get(shape.dataId).values as TypedArray;
-  const $values = backend.data.get(values.dataId).values as TypedArray;
-  const $defaultValue =
-      backend.data.get(defaultValue.dataId).values as TypedArray;
-  const $rowPartitionValues = rowPartitionTensors.map(
-      t => backend.data.get(t.dataId).values as TypedArray);
-  const rowPartitionValuesShapes = rowPartitionTensors.map(t => t.shape);
-
-  const [outputShape, output] = raggedTensorToTensorImpl(
-      $shape, shape.shape, $values, values.shape, values.dtype, $defaultValue,
-      defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes,
-      rowPartitionTypes);
-  return backend.makeTensorInfo(outputShape, values.dtype, output);
-}
-
-export const raggedTensorToTensorConfig: KernelConfig = {
-  kernelName: RaggedTensorToTensor,
-  backendName: 'cpu',
-  kernelFunc: raggedTensorToTensor as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedTensorToTensor_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedTensorToTensor_impl.ts
deleted file mode 100644
index 06ae621cc..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/RaggedTensorToTensor_impl.ts
+++ /dev/null
@@ -1,479 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, broadcastTo, DataType, reshape, tidy, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import RowPartitionType = backend_util.RowPartitionType;
-// Based on
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc
-class RaggedTensorToTensorOp {
-  private readonly rowPartitionTypes: RowPartitionType[];
-  private readonly raggedRank: number;
-  constructor(
-      private shape: TypedArray, private shapeShape: number[],
-      private values: TypedArray, private valuesShape: number[],
-      private valuesDType: DataType, private defaultValue: TypedArray,
-      private defaultValueShape: number[],
-      private readonly rowPartitionValues: TypedArray[],
-      private readonly rowPartitionValuesShapes: number[][],
-      rowPartitionTypeStrings: string[]) {
-    this.rowPartitionTypes =
-        backend_util.getRowPartitionTypesHelper(rowPartitionTypeStrings);
-    this.raggedRank = backend_util.getRaggedRank(this.rowPartitionTypes);
-  }
-
-  private getRowPartitionTypeByDimension(dimension: number) {
-    if (this.rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) {
-      return this.rowPartitionTypes[dimension + 1];
-    } else {
-      return this.rowPartitionTypes[dimension];
-    }
-  }
-
-  // Returns the relationship between dimension and dimension + 1.
-  private getRowPartitionTensor(dimension: number) {
-    if (this.rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) {
-      return this.rowPartitionValues[dimension + 1];
-    } else {
-      return this.rowPartitionValues[dimension];
-    }
-  }
-
-  private getMaxWidth(dimension: number) {
-    const rowPartitionTensor = this.getRowPartitionTensor(dimension - 1);
-    switch (this.getRowPartitionTypeByDimension(dimension - 1)) {
-      case RowPartitionType.VALUE_ROWIDS:
-        return RaggedTensorToTensorOp.getMaxWidthValueRowID(rowPartitionTensor);
-      case RowPartitionType.ROW_SPLITS:
-        return RaggedTensorToTensorOp.getMaxWidthRowSplit(rowPartitionTensor);
-      default:
-        throw new Error(`Cannot handle partition type ${
-            RowPartitionType[this.getRowPartitionTypeByDimension(
-                dimension - 1)]}`);
-    }
-  }
-
-  static getMaxWidthRowSplit(rowSplit: TypedArray) {
-    const tensorLength = rowSplit.length;
-    if (tensorLength === 0 || tensorLength === 1) {
-      return 0;
-    }
-    let maxWidth = 0;
-    for (let i = 0; i < tensorLength - 1; ++i) {
-      const currentWidth = rowSplit[i + 1] - rowSplit[i];
-      if (currentWidth > maxWidth) {
-        maxWidth = currentWidth;
-      }
-    }
-    return maxWidth;
-  }
-
-  static getMaxWidthValueRowID(valueRowIds: TypedArray) {
-    const indexLength = valueRowIds.length;
-    if (indexLength === 0) {
-      return 0;
-    }
-    let firstEqualIndex = 0;
-    let firstEqualIndexValue = valueRowIds[0];
-    let maxWidth = 0;
-    for (let i = 1; i < indexLength; ++i) {
-      const value = valueRowIds[i];
-      if (value !== firstEqualIndexValue) {
-        firstEqualIndexValue = value;
-        maxWidth = Math.max(i - firstEqualIndex, maxWidth);
-        firstEqualIndex = i;
-      }
-    }
-    return Math.max(indexLength - firstEqualIndex, maxWidth);
-  }
-
-  private tensorShapeFromTensor(
-      t: TypedArray, tShape: number[], isPartial = true) {
-    if (tShape.length === 0) {
-      if (t[0] === -1) {
-        return [];
-      }
-      throw new Error(
-          `The only valid scalar shape tensor is the fully unknown shape specified as -1.`);
-    }
-    // MakePartialShape/MakeShapeHelper.
-    return makeShape(t, isPartial);
-  }
-
-  private calculateOutputSize(firstDim: number) {
-    const valueShape = this.valuesShape;
-    const defaultValueShape = this.defaultValueShape;
-
-    backend_util.validateDefaultValueShape(defaultValueShape, valueShape);
-
-    const shape = this.tensorShapeFromTensor(this.shape, this.shapeShape);
-    const outputShape = backend_util.combineRaggedTensorToTensorShapes(
-        this.raggedRank, shape, valueShape);
-
-    const result = outputShape;
-
-    if (result[0] < 0) {
-      result[0] = firstDim;
-    }
-    for (let i = 1; i <= this.raggedRank; ++i) {
-      if (result[i] < 0) {
-        result[i] = this.getMaxWidth(i);
-      }
-    }
-
-    return result;
-  }
-
-  /**
-   * The outputIndex represents the index in the output tensor
-   * where the first element of a particular dimension would be written.
-   * If it is -1, it indicates that the index is out of scope.
-   * Example, given firstDimension = 10, firstDimensionOutput = 6,
-   * and outputIndexMultiplier = 100:
-   * result = [0 100 200 300 400 500 -1 -1 -1 -1]
-   * If firstDimensionOutput = 11 instead, then:
-   * result = [0 100 200 300 400 500 600 700 800 900]
-   */
-  private calculateFirstParentOutputIndex(
-      firstDimension: number, outputIndexMultiplier: number,
-      firstDimensionOutput: number) {
-    const minDimension = Math.min(firstDimension, firstDimensionOutput);
-    const result: number[] = [];
-    let currentOutputIndex = 0;
-    for (let i = 0; i < minDimension;
-         ++i, currentOutputIndex += outputIndexMultiplier) {
-      result.push(currentOutputIndex);
-    }
-    for (let i = minDimension; i < firstDimension; ++i) {
-      result.push(-1);
-    }
-    util.assert(
-        result.length === firstDimension,
-        () => 'Final length of result must be equal to firstDimension.');
-
-    return result;
-  }
-
-  private calculateOutputIndexRowSplit(
-      rowSplit: TypedArray, parentOutputIndex: number[],
-      outputIndexMultiplier: number, outputSize: number) {
-    const rowSplitSize = rowSplit.length;
-    const result: number[] = [];
-    for (let i = 0; i < rowSplitSize - 1; ++i) {
-      const rowLength = rowSplit[i + 1] - rowSplit[i];
-      let realLength = Math.min(outputSize, rowLength);
-      let parentOutputIndexCurrent = parentOutputIndex[i];
-
-      if (parentOutputIndexCurrent === -1) {
-        realLength = 0;
-      }
-      for (let j = 0; j < realLength; ++j) {
-        result.push(parentOutputIndexCurrent);
-        parentOutputIndexCurrent += outputIndexMultiplier;
-      }
-      for (let j = 0; j < rowLength - realLength; ++j) {
-        result.push(-1);
-      }
-    }
-    if (rowSplitSize > 0 && result.length !== rowSplit[rowSplitSize - 1]) {
-      throw new Error('Invalid row split size.');
-    }
-
-    return result;
-  }
-
-  // Calculate the output index of the first element of a list.
-  // The parentOutputIndex is the same computation for the previous list.
-  // -1 indicates an element or list that is out of range.
-  // The outputIndexMultiplier is the number of output indices one moves
-  // forward for each column.
-  // E.g., given:
-  // valueRowIds:[0 1 2 2 2 3 5 5 6]
-  // parentOutputIndex:[1000 1100 2000 2100 -1 3000 4000]
-  // outputIndexMultiplier: 10
-  // outputSize: 2
-  // You get:
-  // result = [1000 1100 2000 2010 -1 2100 -1 -1 3000]
-  // result[0] = parentOutputIndex[valueRowIds[0]]
-  // result[1] = parentOutputIndex[valueRowIds[1]]
-  // result[2] = parentOutputIndex[valueRowIds[2]]
-  // result[3] = parentOutputIndex[valueRowIds[2] + 10]
-  // result[4] = -1 because it is the third element the size is 2.
-  // result[5] = parentOutputIndex[valueRowIds[3]]
-  // result[6] = -1 because parentOutputIndex[valueRowIds[6]] == -1
-  // result[7] = -1 because parentOutputIndex[valueRowIds[6]] == -1
-  // result[8] = parentOutputIndex[valueRowIds[7]]
-  private calculateOutputIndexValueRowID(
-      valueRowIds: TypedArray, parentOutputIndex: number[],
-      outputIndexMultiplier: number, outputSize: number) {
-    const indexSize = valueRowIds.length;
-    const result: number[] = [];
-    if (indexSize === 0) {
-      return [];
-    }
-
-    let currentOutputColumn = 0;
-    let currentValueRowId = valueRowIds[0];
-
-    if (currentValueRowId >= parentOutputIndex.length) {
-      throw new Error(
-          `Got currentValueRowId=${currentValueRowId}, which is not less than ${
-              parentOutputIndex.length}`);
-    }
-
-    let currentOutputIndex = parentOutputIndex[currentValueRowId];
-    result.push(currentOutputIndex);
-    for (let i = 1; i < indexSize; ++i) {
-      const nextValueRowId = valueRowIds[i];
-      if (nextValueRowId === currentValueRowId) {
-        if (currentOutputIndex >= 0) {
-          ++currentOutputColumn;
-          if (currentOutputColumn < outputSize) {
-            currentOutputIndex += outputIndexMultiplier;
-          } else {
-            currentOutputIndex = -1;
-          }
-        }
-      } else {
-        currentOutputColumn = 0;
-        currentValueRowId = nextValueRowId;
-
-        if (nextValueRowId >= parentOutputIndex.length) {
-          throw new Error(
-              `Got nextValueRowId=${nextValueRowId} which is not less than ${
-                  parentOutputIndex.length}`);
-        }
-
-        currentOutputIndex = parentOutputIndex[nextValueRowId];
-      }
-      result.push(currentOutputIndex);
-    }
-
-    if (result.length !== valueRowIds.length) {
-      throw new Error('Invalid row ids.');
-    }
-
-    return result;
-  }
-
-  private calculateOutputIndex(
-      dimension: number, parentOutputIndex: number[],
-      outputIndexMultiplier: number, outputSize: number) {
-    const rowPartitionTensor = this.getRowPartitionTensor(dimension);
-    const partitionType = this.getRowPartitionTypeByDimension(dimension);
-    switch (partitionType) {
-      case RowPartitionType.VALUE_ROWIDS:
-        return this.calculateOutputIndexValueRowID(
-            rowPartitionTensor, parentOutputIndex, outputIndexMultiplier,
-            outputSize);
-      case RowPartitionType.ROW_SPLITS:
-        if (rowPartitionTensor.length - 1 > parentOutputIndex.length) {
-          throw new Error(`Row partition size is greater than output size: ${
-              rowPartitionTensor.length - 1} > ${parentOutputIndex.length}`);
-        }
-        return this.calculateOutputIndexRowSplit(
-            rowPartitionTensor, parentOutputIndex, outputIndexMultiplier,
-            outputSize);
-      default:
-        throw new Error(
-            `Unsupported partition type: ${RowPartitionType[partitionType]}`);
-    }
-  }
-
-  private getFirstDimensionSize() {
-    const firstPartitionTensor = this.rowPartitionValues[0];
-    if (this.rowPartitionTypes.length === 0) {
-      throw new Error('No row_partition_types given.');
-    }
-    const firstPartitionType = this.rowPartitionTypes[0];
-    switch (firstPartitionType) {
-      case RowPartitionType.FIRST_DIM_SIZE:
-        return firstPartitionTensor[0];
-      case RowPartitionType.VALUE_ROWIDS:
-        throw new Error('Cannot handle VALUE_ROWIDS in first dimension.');
-      case RowPartitionType.ROW_SPLITS:
-        return this.rowPartitionValuesShapes[0][0] - 1;
-      default:
-        throw new Error(
-            `Cannot handle type ${RowPartitionType[firstPartitionType]}`);
-    }
-  }
-
-  compute(): [number[], TypedArray] {
-    const firstPartitionTensor = this.rowPartitionValues[0];
-    if (firstPartitionTensor.length <= 0) {
-      throw new Error(
-          'Invalid first partition input. ' +
-          'Tensor requires at least one element.');
-    }
-    const firstDimension = this.getFirstDimensionSize();
-    const outputSize = this.calculateOutputSize(firstDimension);
-    const multiplier: number[] = new Array(this.raggedRank + 1);
-
-    multiplier[multiplier.length - 1] = 1;
-    for (let i = multiplier.length - 2; i >= 0; --i) {
-      multiplier[i] = multiplier[i + 1] * outputSize[i + 1];
-    }
-    // Full size of the tensor.
-    const outputShape: number[] = makeShape(outputSize, false);
-    const outputTensor =
-        util.getArrayFromDType(
-            this.valuesDType, util.sizeFromShape(outputShape)) as TypedArray;
-
-    const fullSize = multiplier[0] * outputSize[0];
-    if (fullSize > 0) {
-      let outputIndex = this.calculateFirstParentOutputIndex(
-          firstDimension, multiplier[0], outputSize[0]);
-      for (let i = 1; i <= this.raggedRank; ++i) {
-        const newOutputIndex = this.calculateOutputIndex(
-            i - 1, outputIndex, multiplier[i], outputSize[i]);
-        outputIndex = newOutputIndex;
-      }
-
-      this.setOutput(this.raggedRank, outputIndex, outputTensor, outputShape);
-    }
-
-    return [outputShape, outputTensor];
-  }
-  setOutput(
-      raggedRank: number, outputIndex: number[], outputTensor: TypedArray,
-      outputShape: number[]) {
-    if (outputTensor.length === 0) {
-      return;
-    }
-
-    const valuesBase = this.values;
-    const outputBase = outputTensor;
-
-    let elementShape = outputShape.slice();
-    elementShape = elementShape.slice(raggedRank + 1);
-    const valueElementSize = util.sizeFromShape(elementShape);
-    const outputIndexSize = outputIndex.length;
-
-    // Broadcast the default value to value_element_size.  (We can skip this
-    // if defaultValueTensor.size == 1, since we use fill when that's true.)
-    let defaultValue = this.defaultValue;
-    if (defaultValue.length !== valueElementSize && defaultValue.length !== 1) {
-      const srcShape = this.defaultValueShape;
-      tidy(() => {
-        const defaultValueTensor = reshape(defaultValue, srcShape);
-        const bCastDefault = broadcastTo(defaultValueTensor, elementShape);
-        defaultValue = bCastDefault.dataSync();
-      });
-    }
-
-    // Loop through the outputIndex array, finding contiguous regions that
-    // should be copied.  Once we find the end of a contiguous region, copy it
-    // and add any necessary padding (with defaultValue).
-    let srcStart = 0;  // Start of contiguous region (in values)
-    let dstStart = 0;  // Destination for contiguous region (in output)
-    let dstEnd = 0;    // Destination for contiguous region (in output)
-    for (let srcI = 0; srcI <= outputIndexSize; ++srcI) {
-      // dstI is the destination where the value at srcI should be copied.
-      let dstI = srcI < outputIndexSize ? outputIndex[srcI] : -1;
-
-      // If we're still in a contiguous region, then update dstEnd go to the
-      // next srcI.
-      if (dstI === dstEnd) {
-        ++dstEnd;
-        continue;
-      }
-
-      // We found the end of contiguous region.  This can be because we found
-      // a gap (dstI > dstEnd), or a source value that shouldn't be copied
-      // because it's out-of-bounds (dstI == -1), or the end of the tensor
-      // (dstI === -1).
-      if (dstStart < dstEnd) {
-        // Copy the contiguous region.
-        const src = valuesBase.subarray(srcStart * valueElementSize);
-        const dst = outputBase.subarray(dstStart * valueElementSize);
-        const nVals = (dstEnd - dstStart) * valueElementSize;
-        copyArray(dst, src, nVals);
-      }
-
-      // Add any necessary padding (w/ defaultValue).
-      if (srcI >= outputIndexSize) {
-        // We reached the end of values: pad to the end of output.
-        const outputSize = outputTensor.length;
-        dstI = Math.floor(outputSize / valueElementSize);
-      }
-      if (dstI > dstEnd) {
-        if (this.defaultValue.length === 1) {
-          outputBase
-              .subarray(dstEnd * valueElementSize, dstI * valueElementSize)
-              .fill(this.defaultValue[0]);
-          dstEnd = dstI;
-        } else {
-          while (dstI > dstEnd) {
-            const dst = outputBase.slice(dstEnd * valueElementSize);
-            copyArray(dst, defaultValue, valueElementSize);
-            ++dstEnd;
-          }
-        }
-      }
-
-      // Update indices.
-      if (dstI < 0) {
-        // srcI should be skipped -- leave it out of the contiguous region.
-        srcStart = srcI + 1;
-        dstStart = dstEnd;
-      } else {
-        // srcI should be copied -- include it in the contiguous region.
-        srcStart = srcI;
-        dstStart = dstEnd;
-        dstEnd = dstStart + 1;
-      }
-    }
-  }
-}
-
-function copyArray(dst: TypedArray, src: TypedArray, size: number) {
-  for (let i = 0; i < size; i++) {
-    dst[i] = src[i];
-  }
-}
-
-function makeShape(shape: number[]|TypedArray, isPartial: boolean) {
-  const out: number[] = [];
-  for (let dim of shape) {
-    if (dim < 0) {
-      if (!isPartial) {
-        throw new Error(`Dimension ${dim} must be >= 0`);
-      }
-      if (dim < -1) {
-        throw new Error(`Dimension ${dim} must be >= -1`);
-      }
-      dim = -1;
-    }
-    out.push(dim);
-  }
-
-  return out;
-}
-
-export function raggedTensorToTensorImpl(
-    shape: TypedArray, shapesShape: number[], values: TypedArray,
-    valuesShape: number[], valuesDType: DataType, defaultValue: TypedArray,
-    defaultValueShape: number[], rowPartitionValues: TypedArray[],
-    rowPartitionValuesShapes: number[][],
-    rowPartitionTypes: string[]): [number[], TypedArray] {
-  return new RaggedTensorToTensorOp(
-             shape, shapesShape, values, valuesShape, valuesDType, defaultValue,
-             defaultValueShape, rowPartitionValues, rowPartitionValuesShapes,
-             rowPartitionTypes)
-      .compute();
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Range.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Range.ts
deleted file mode 100644
index 713a4735e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Range.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Range, RangeAttrs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {rangeImpl} from './Range_impl';
-
-export function range(args: {backend: MathBackendCPU, attrs: RangeAttrs}):
-    TensorInfo {
-  const {backend, attrs} = args;
-  const {start, stop, dtype, step} = attrs;
-
-  const values = rangeImpl(start, stop, step, dtype);
-  return backend.makeTensorInfo([values.length], dtype, values);
-}
-
-export const rangeConfig: KernelConfig = {
-  kernelName: Range,
-  backendName: 'cpu',
-  kernelFunc: range as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Range_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Range_impl.ts
deleted file mode 100644
index 8b0b34caf..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Range_impl.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataTypeMap, util} from '@tensorflow/tfjs-core';
-
-export function rangeImpl(
-    start: number, stop: number, step: number,
-    dtype: 'float32'|'int32'): DataTypeMap['float32' | 'int32'] {
-  const sameStartStop = start === stop;
-  const increasingRangeNegativeStep = start < stop && step < 0;
-  const decreasingRangePositiveStep = stop < start && step > 1;
-
-  if (sameStartStop || increasingRangeNegativeStep ||
-      decreasingRangePositiveStep) {
-    return util.makeZerosTypedArray(0, dtype);
-  }
-
-  const numElements = Math.abs(Math.ceil((stop - start) / step));
-  const values = util.makeZerosTypedArray(numElements, dtype);
-
-  if (stop < start && step === 1) {
-    // Auto adjust the step's sign if it hasn't been set
-    // (or was set to 1)
-    step = -1;
-  }
-
-  values[0] = start;
-  for (let i = 1; i < values.length; i++) {
-    values[i] = values[i - 1] + step;
-  }
-  return values;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Real.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Real.ts
deleted file mode 100644
index 2c25f254a..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Real.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Real, RealInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function real(args: {inputs: RealInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-
-  const real = backend.data.get(input.dataId).complexTensorInfos.real;
-  const realVal = backend.data.get(real.dataId).values;
-
-  // When complex tensor is disposed, its underlying parts will be disposed too.
-  // Make new tensor out of the real value of the complex. This makes sure the
-  // value is still accessible even if complex tensor is disposed.
-  return backend.makeTensorInfo(real.shape, real.dtype, realVal);
-}
-
-export const realConfig: KernelConfig = {
-  kernelName: Real,
-  backendName: 'cpu',
-  kernelFunc: real as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/RealDiv.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/RealDiv.ts
deleted file mode 100644
index 92714ed34..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/RealDiv.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, RealDiv} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const realDivImpl =
-    createSimpleBinaryKernelImpl((a: number, b: number) => a / b);
-export const div = binaryKernelFunc(RealDiv, realDivImpl);
-
-export const realDivConfig: KernelConfig = {
-  kernelName: RealDiv,
-  backendName: 'cpu',
-  kernelFunc: div
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Reciprocal.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Reciprocal.ts
deleted file mode 100644
index 2b63199ae..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Reciprocal.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Reciprocal} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const reciprocal = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);
-
-export const reciprocalConfig: KernelConfig = {
-  kernelName: Reciprocal,
-  backendName: 'cpu',
-  kernelFunc: reciprocal,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Relu.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Relu.ts
deleted file mode 100644
index 19c2f80a4..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Relu.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Relu} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const relu = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));
-
-export const reluConfig: KernelConfig = {
-  kernelName: Relu,
-  backendName: 'cpu',
-  kernelFunc: relu,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Relu6.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Relu6.ts
deleted file mode 100644
index 891d80903..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Relu6.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Relu6} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const relu6 =
-    unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));
-
-export const relu6Config: KernelConfig = {
-  kernelName: Relu6,
-  backendName: 'cpu',
-  kernelFunc: relu6,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Reshape.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Reshape.ts
deleted file mode 100644
index 02c455d98..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Reshape.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Reshape, ReshapeAttrs, ReshapeInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function reshape(
-    args:
-        {inputs: ReshapeInputs, backend: MathBackendCPU, attrs: ReshapeAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {shape} = attrs;
-
-  const xSize = util.sizeFromShape(x.shape);
-  const $shape = util.inferFromImplicitShape(shape, xSize);
-  const $xSize = util.sizeFromShape($shape);
-
-  util.assert(
-      xSize === $xSize,
-      () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +
-          `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +
-          `shape must have the same number of elements.`);
-
-  backend.incRef(x.dataId);
-
-  const xData = backend.data.get(x.dataId);
-
-  if (xData.complexTensorInfos != null) {
-    const real = xData.complexTensorInfos.real;
-    const imag = xData.complexTensorInfos.imag;
-
-    real.shape = $shape;
-    imag.shape = $shape;
-  }
-
-  return {dataId: x.dataId, shape: $shape, dtype: x.dtype};
-}
-
-export const reshapeConfig: KernelConfig = {
-  kernelName: Reshape,
-  backendName: 'cpu',
-  kernelFunc: reshape as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Reshape_test.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Reshape_test.ts
deleted file mode 100644
index 7796fded4..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Reshape_test.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {Tensor, test_util} from '@tensorflow/tfjs-core';
-
-const {expectArraysClose, expectArraysEqual} = test_util;
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags, ALL_ENVS} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-describeWithFlags('Reshape.', ALL_ENVS, () => {
-  it('does not have memory leak.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const x = tf.tensor1d([1, 1, 1, 1]);
-    const res =
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        tf.engine().runKernel('Reshape', {x}, {shape: [2, 2]}) as Tensor;
-
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-    expectArraysEqual(res.shape, [2, 2]);
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 1);
-
-    x.dispose();
-    res.dispose();
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-
-  it('does not have memory leak calling reshape twice.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    // Adding 1 new dataId.
-    const x = tf.tensor1d([1, 1, 1, 1]);
-
-    // Does not add new dataId;
-    const res =
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        tf.engine().runKernel('Reshape', {x}, {shape: [2, 2]}) as Tensor;
-
-    expectArraysEqual(res.shape, [2, 2]);
-
-    // Does not add new dataId.
-    const res2 =
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        tf.engine().runKernel('Reshape', {x: res}, {shape: [1, 4]}) as Tensor;
-    expectArraysEqual(res2.shape, [1, 4]);
-
-    const afterRes2DataIds = tf.engine().backend.numDataIds();
-    expect(afterRes2DataIds).toEqual(beforeDataIds + 1);
-
-    res.dispose();
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 1);
-
-    x.dispose();
-    res2.dispose();
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    // Should be able to dispose the dataId.
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeBilinear.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeBilinear.ts
deleted file mode 100644
index 024dd4f04..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeBilinear.ts
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function resizeBilinear(args: {
-  inputs: ResizeBilinearInputs,
-  backend: MathBackendCPU,
-  attrs: ResizeBilinearAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images} = inputs;
-  const {alignCorners, halfPixelCenters, size} = attrs;
-
-  assertNotComplex(images, 'resizeBilinear');
-
-  const imagesStrides = util.computeStrides(images.shape);
-  const [newHeight, newWidth] = size;
-
-  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
-  const xValues = backend.data.get(images.dataId).values as TypedArray;
-  const result = new Float32Array(
-      util.sizeFromShape([batch, newHeight, newWidth, numChannels]));
-
-  const effectiveInputSize: [number, number] = [
-    (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
-    (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
-  ];
-
-  const effectiveOutputSize: [number, number] = [
-    (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
-    (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
-  ];
-  let outputIdx = 0;
-  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
-  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
-  for (let b = 0; b < batch; b++) {
-    for (let r = 0; r < newHeight; r++) {
-      let sourceFracRow: number;
-      if (halfPixelCenters) {
-        sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;
-      } else {
-        sourceFracRow = effectiveRowSizeRatio * r;
-      }
-
-      const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));
-      const rowFrac = sourceFracRow - sourceRowFloor;
-      const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
-      const topRowOffset =
-          b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];
-      const botRowOffset =
-          b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];
-      for (let c = 0; c < newWidth; c++) {
-        let sourceFracCol: number;
-        if (halfPixelCenters) {
-          sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;
-        } else {
-          sourceFracCol = effectiveColSizeRatio * c;
-        }
-        const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));
-        const colFrac = sourceFracCol - sourceColFloor;
-        const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
-        const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];
-        const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];
-        const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];
-        const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];
-        for (let d = 0; d < numChannels; d++) {
-          // Begin shader.
-
-          // Compute the fractional index of the source.
-          const topLeft = xValues[topLeftOffest + d];
-          const bottomLeft = xValues[botLeftOffset + d];
-          const topRight = xValues[topRightOffset + d];
-          const bottomRight = xValues[botRightOffest + d];
-
-          const top = topLeft + (topRight - topLeft) * colFrac;
-          const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
-          const newValue = top + (bottom - top) * rowFrac;
-
-          result[outputIdx++] = newValue;
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(
-      [batch, newHeight, newWidth, numChannels], 'float32', result);
-}
-
-export const resizeBilinearConfig: KernelConfig = {
-  kernelName: ResizeBilinear,
-  backendName: 'cpu',
-  kernelFunc: resizeBilinear as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeBilinearGrad.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeBilinearGrad.ts
deleted file mode 100644
index fda7c4c5c..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeBilinearGrad.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeBilinearGrad, ResizeBilinearGradAttrs, ResizeBilinearGradInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function resizeBilinearGrad(args: {
-  inputs: ResizeBilinearGradInputs,
-  backend: MathBackendCPU,
-  attrs: ResizeBilinearGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images, dy} = inputs;
-  const {alignCorners} = attrs;
-
-  assertNotComplex([dy, images], 'resizeBilinearGrad');
-
-  const imagesStrides = util.computeStrides(images.shape);
-
-  const [batch, xHeight, xWidth, depth] = images.shape;
-  const [, yHeight, yWidth] = dy.shape;
-
-  const output = new Float32Array(batch * xHeight * xWidth * depth);
-
-  // In the backwards pass, we want to find the pixels that were generated
-  // for each pixel in the input image the forward pass and add the
-  // corresponding coefficient from dy to the gradient (with some
-  // interpolation).
-
-  const effectiveXSize: [number, number] = [
-    (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
-    (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
-  ];
-
-  const effectiveYSize: [number, number] = [
-    (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
-    (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
-  ];
-
-  const heightScale = effectiveXSize[0] / effectiveYSize[0];
-  const widthScale = effectiveXSize[1] / effectiveYSize[1];
-
-  // Reference implementation
-  // tslint:disable-next-line:max-line-length
-  // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275
-  const dyValues = backend.data.get(dy.dataId).values as TypedArray;
-  let offset = 0;
-  for (let b = 0; b < batch; b++) {
-    const bOffset = b * imagesStrides[0];
-    for (let r = 0; r < yHeight; r++) {
-      const dxR = r * heightScale;
-      const topDxRIndex = Math.floor(dxR);
-      const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
-
-      const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];
-      const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];
-
-      const dxRLerp = dxR - topDxRIndex;
-      const inverseDxRLerp = 1.0 - dxRLerp;
-      for (let c = 0; c < yWidth; c++) {
-        const dxC = c * widthScale;
-        const leftDxCIndex = Math.floor(dxC);
-        const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);
-        const dxCLerp = dxC - leftDxCIndex;
-        const inverseDxCLerp = 1.0 - dxCLerp;
-
-        const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];
-        const topRightRCOffset =
-            topDxROffset + rightDxCIndex * imagesStrides[2];
-        const bottomLeftRCOffset =
-            bottomDxROffset + leftDxCIndex * imagesStrides[2];
-        const bottomRightRCOffset =
-            bottomDxROffset + rightDxCIndex * imagesStrides[2];
-
-        const inverseDxRLerpTimesInverseDxCLerp =
-            inverseDxRLerp * inverseDxCLerp;
-        const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;
-        const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;
-        const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;
-        for (let d = 0; d < depth; d++) {
-          const dyVal = dyValues[offset++];
-          output[topLeftRCOffset + d] +=
-              dyVal * inverseDxRLerpTimesInverseDxCLerp;
-          output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;
-          output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;
-          output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(
-      [batch, xWidth, xHeight, depth], 'float32', output);
-}
-
-export const resizeBilinearGradConfig: KernelConfig = {
-  kernelName: ResizeBilinearGrad,
-  backendName: 'cpu',
-  kernelFunc: resizeBilinearGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeNearestNeighbor.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeNearestNeighbor.ts
deleted file mode 100644
index b9545dea2..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeNearestNeighbor.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function resizeNearestNeighbor(args: {
-  inputs: ResizeNearestNeighborInputs,
-  backend: MathBackendCPU,
-  attrs: ResizeNearestNeighborAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images} = inputs;
-  const {alignCorners, halfPixelCenters, size} = attrs;
-
-  assertNotComplex(images, 'resizeNearestNeighbor');
-
-  const imagesStrides = util.computeStrides(images.shape);
-  const [newHeight, newWidth] = size;
-
-  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
-  const xValues = backend.data.get(images.dataId).values as TypedArray;
-  const output = new Float32Array(batch * newHeight * newWidth * numChannels);
-
-  const effectiveInputSize: [number, number] = [
-    (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
-    (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
-  ];
-
-  const effectiveOutputSize: [number, number] = [
-    (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
-    (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
-  ];
-
-  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
-  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
-
-  let outputOffset = 0;
-  for (let b = 0; b < batch; b++) {
-    const batchOffset = b * imagesStrides[0];
-    for (let r = 0; r < newHeight; r++) {
-      const sourceFracRow = halfPixelCenters ?
-          effectiveRowSizeRatio * (r + 0.5) :
-          effectiveRowSizeRatio * r;
-      let sourceNearestRow = Math.min(
-          oldHeight - 1,
-          alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
-      if (halfPixelCenters) {
-        sourceNearestRow = Math.max(0, sourceNearestRow);
-      }
-      const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];
-      for (let c = 0; c < newWidth; c++) {
-        const sourceFracCol = halfPixelCenters ?
-            effectiveColSizeRatio * (c + 0.5) :
-            effectiveColSizeRatio * c;
-        let sourceNearestCol = Math.min(
-            oldWidth - 1,
-            alignCorners ? Math.round(sourceFracCol) :
-                           Math.floor(sourceFracCol));
-        if (halfPixelCenters) {
-          sourceNearestCol = Math.max(0, sourceNearestCol);
-        }
-        const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];
-        for (let d = 0; d < numChannels; d++) {
-          // Begin shader.
-          // Compute the fractional index of the source.
-          const newVal = xValues[colOffset + d];
-          output[outputOffset++] = newVal;
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(
-      [batch, newHeight, newWidth, numChannels], images.dtype, output);
-}
-
-export const resizeNearestNeighborConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighbor,
-  backendName: 'cpu',
-  kernelFunc: resizeNearestNeighbor as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeNearestNeighborGrad.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeNearestNeighborGrad.ts
deleted file mode 100644
index bd524ac39..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ResizeNearestNeighborGrad.ts
+++ /dev/null
@@ -1,134 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeNearestNeighborGrad, ResizeNearestNeighborGradAttrs, ResizeNearestNeighborGradInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function resizeNearestNeighborGrad(args: {
-  inputs: ResizeNearestNeighborGradInputs,
-  backend: MathBackendCPU,
-  attrs: ResizeNearestNeighborGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images, dy} = inputs;
-  const {alignCorners} = attrs;
-
-  assertNotComplex([dy, images], 'resizeNearestNeighborGrad');
-
-  const imagesStrides = util.computeStrides(images.shape);
-  const dyStrides = util.computeStrides(dy.shape);
-  const [batch, xHeight, xWidth, depth] = images.shape;
-  const [, yHeight, yWidth] = dy.shape;
-
-  const output = new Float32Array(batch * xHeight * xWidth * depth);
-  const dyValues = backend.data.get(dy.dataId).values as TypedArray;
-
-  // In the backwards pass, we want to find the pixels that were generated
-  // for each pixel in the input image the forward pass
-
-  const effectiveXSize: [number, number] = [
-    (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
-    (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
-  ];
-
-  const effectiveYSize: [number, number] = [
-    (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
-    (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
-  ];
-
-  const heightScale = effectiveXSize[0] / effectiveYSize[0];
-  const widthScale = effectiveXSize[1] / effectiveYSize[1];
-
-  const invHeightScale = 1 / heightScale;
-  const invWidthScale = 1 / widthScale;
-
-  // This defines the size of the window of values around a particular
-  // index in dy that we want to search for contributions to dx.
-  const winHeight = (Math.ceil(invHeightScale) * 2) + 2;
-  const winWidth = (Math.ceil(invWidthScale) * 2) + 2;
-
-  // Loop over the output space.
-  for (let b = 0; b < batch; b++) {
-    const batchOffset = b * imagesStrides[0];
-    for (let r = 0; r < xHeight; r++) {
-      const rowOffset = batchOffset + r * imagesStrides[1];
-
-      // Compute bounds for where in dy we will look
-      const startRLerp = Math.floor(r * invHeightScale);
-      const startDyR = Math.floor(startRLerp - (winHeight / 2));
-      for (let c = 0; c < xWidth; c++) {
-        const colOffset = rowOffset + c * imagesStrides[2];
-
-        // Compute bounds for where in dy we will look
-        const startCLerp = Math.floor(c * invWidthScale);
-        const startDyC = Math.floor(startCLerp - (winWidth / 2));
-
-        for (let d = 0; d < depth; d++) {
-          let accum = 0;
-          // loop over dy
-
-          for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {
-            const dyR = dyRIndex + startDyR;
-            // Guard against the window exceeding the bounds of dy
-            if (dyR < 0 || dyR >= yHeight) {
-              continue;
-            }
-
-            const dyROffset = batchOffset + dyR * dyStrides[1];
-            const sourceFracRow = dyR * heightScale;
-            const sourceNearestRow = Math.min(
-                xHeight - 1,
-                alignCorners ? Math.round(sourceFracRow) :
-                               Math.floor(sourceFracRow));
-            if (r !== sourceNearestRow) {
-              continue;
-            }
-            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
-              const dyC = dyCIndex + startDyC;
-              // Guard against the window exceeding the bounds of dy
-              if (dyC < 0 || dyC >= yWidth) {
-                continue;
-              }
-
-              const dyCOffset = dyROffset + dyC * dyStrides[2];
-              const sourceFracCol = dyC * widthScale;
-              const sourceNearestCol = Math.min(
-                  xWidth - 1,
-                  alignCorners ? Math.round(sourceFracCol) :
-                                 Math.floor(sourceFracCol));
-
-              if (c === sourceNearestCol) {
-                accum += dyValues[dyCOffset + d];
-              }
-            }
-          }
-          output[colOffset + d] = accum;
-        }
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(images.shape, images.dtype, output);
-}
-
-export const resizeNearestNeighborGradConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighborGrad,
-  backendName: 'cpu',
-  kernelFunc: resizeNearestNeighborGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Reverse.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Reverse.ts
deleted file mode 100644
index 5b3b2ea81..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Reverse.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Reverse, ReverseAttrs, ReverseInputs, TensorBuffer, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {identity} from './Identity';
-
-export function reverse(
-    args:
-        {inputs: ReverseInputs, backend: MathBackendCPU, attrs: ReverseAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {dims} = attrs;
-
-  assertNotComplex(x, 'reverse');
-
-  const xRank = x.shape.length;
-
-  const $dims = util.parseAxisParam(dims, x.shape);
-  if (xRank === 0) {
-    return identity({inputs: {x}, backend});
-  }
-
-  const outBuf = new TensorBuffer(x.shape, x.dtype);
-  const xBuf = backend.bufferSync(x);
-
-  for (let i = 0; i < outBuf.size; i++) {
-    const outLoc = outBuf.indexToLoc(i);
-    const inLoc = outLoc.slice();
-    $dims.forEach(d => inLoc[d] = x.shape[d] - 1 - inLoc[d]);
-    outBuf.set(xBuf.get(...inLoc), ...outLoc);
-  }
-
-  return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
-}
-
-export const reverseConfig: KernelConfig = {
-  kernelName: Reverse,
-  backendName: 'cpu',
-  kernelFunc: reverse as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/RotateWithOffset.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/RotateWithOffset.ts
deleted file mode 100644
index 2beffa455..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/RotateWithOffset.ts
+++ /dev/null
@@ -1,95 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, NumericDataType, TypedArray} from '@tensorflow/tfjs-core';
-import {backend_util, RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export const rotateWithOffsetConfig: KernelConfig = {
-  kernelName: RotateWithOffset,
-  backendName: 'cpu',
-  kernelFunc: ({inputs, attrs, backend}) => {
-    const {image} = inputs as RotateWithOffsetInputs;
-    const {radians, fillValue, center} =
-      attrs as unknown as RotateWithOffsetAttrs;
-    const cpuBackend = backend as MathBackendCPU;
-
-    const output = util.getTypedArrayFromDType(
-        image.dtype as NumericDataType, util.sizeFromShape(image.shape));
-    const [batch, imageHeight, imageWidth, numChannels] = image.shape;
-
-    const [centerX, centerY] =
-        backend_util.getImageCenter(center, imageHeight, imageWidth);
-    const fullOpacityValue = 255;
-
-    const sinFactor = Math.sin(radians);
-    const cosFactor = Math.cos(radians);
-    const imageVals = cpuBackend.data.get(image.dataId).values as TypedArray;
-
-    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
-      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
-
-      for (let row = 0; row < imageHeight; row++) {
-        const rowOffset = row * (imageWidth * numChannels);
-
-        for (let col = 0; col < imageWidth; col++) {
-          const colOffset = col * numChannels;
-
-          for (let channel = 0; channel < numChannels; channel++) {
-            const coords = [batch, row, col, channel];
-
-            const x = coords[2];
-            const y = coords[1];
-
-            // coordX/coordY are the result of rotating and translating x/y.
-            let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;
-            let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;
-            coordX = Math.round(coordX + centerX);
-            coordY = Math.round(coordY + centerY);
-
-            let outputValue = fillValue;
-            if (typeof fillValue !== 'number') {
-              if (channel === 3) {
-                outputValue = fullOpacityValue;
-              } else {
-                outputValue = fillValue[channel];
-              }
-            }
-
-            // If the coordinate position falls within the image boundaries...
-            if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&
-                coordY < imageHeight) {
-              // set the output to the image value at the coordinate position.
-              const rotatedRowOffset = coordY * (imageWidth * numChannels);
-              const rotatedColOffset = coordX * numChannels;
-              const imageIdx =
-                  batchOffset + rotatedRowOffset + rotatedColOffset + channel;
-              outputValue = imageVals[imageIdx];
-            }
-
-            const outIdx = batchOffset + rowOffset + colOffset + channel;
-            output[outIdx] = outputValue as number;
-          }
-        }
-      }
-    }
-
-    const dataId = cpuBackend.write(output, image.shape, image.dtype);
-    return {dataId, shape: image.shape, dtype: image.dtype};
-  }
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Round.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Round.ts
deleted file mode 100644
index 7fc9d3a02..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Round.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Round} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const round = unaryKernelFunc(Round, (xi) => {
-  // The algorithm is based on banker's rounding.
-  const base = Math.floor(xi);
-  if (xi - base < 0.5) {
-    return Math.floor(xi);
-  } else if (xi - base > 0.5) {
-    return Math.ceil(xi);
-  } else {
-    if (base % 2.0 === 0.0) {
-      return base;
-    } else {
-      return base + 1.0;
-    }
-  }
-});
-
-export const roundConfig: KernelConfig = {
-  kernelName: Round,
-  backendName: 'cpu',
-  kernelFunc: round,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Rsqrt.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Rsqrt.ts
deleted file mode 100644
index 5aa0ba6f9..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Rsqrt.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Rsqrt} from '@tensorflow/tfjs-core';
-
-import {createSimpleUnaryImpl} from '../utils/unary_impl';
-import {unaryKernelFuncFromImpl} from '../utils/unary_utils';
-
-export const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));
-export const rsqrt = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);
-
-export const rsqrtConfig: KernelConfig = {
-  kernelName: Rsqrt,
-  backendName: 'cpu',
-  kernelFunc: rsqrt,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/STFT_test.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/STFT_test.ts
deleted file mode 100644
index dc79027b6..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/STFT_test.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-describeWithFlags('stft memory test', ALL_ENVS, () => {
-  it('should have no mem leak', async () => {
-    const win = 320;
-    const fft = 320;
-    const hop = 160;
-    const input = tf.zeros<tf.Rank.R1>([1760]);
-
-    const startTensors = tf.memory().numTensors;
-    const startDataIds = tf.engine().backend.numDataIds();
-    const result = await tf.signal.stft(input, win, hop, fft);
-
-    // 1 new tensor, 3 new data buckets.
-    expect(tf.memory().numTensors).toBe(startTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toBe(startTensors + 3);
-
-    result.dispose();
-
-    // Zero net tensors / data buckets.
-    expect(tf.memory().numTensors).toBe(startTensors);
-    expect(tf.engine().backend.numDataIds()).toBe(startDataIds);
-    input.dispose();
-  });
-});
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ScatterNd.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ScatterNd.ts
deleted file mode 100644
index 43d049833..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ScatterNd.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Rank, ScatterNd, ScatterNdAttrs, ScatterNdInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {scatterImpl} from './Scatter_impl';
-
-export function scatterNd(args: {
-  inputs: ScatterNdInputs,
-  backend: MathBackendCPU,
-  attrs: ScatterNdAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {indices, updates} = inputs;
-  const {shape} = attrs;
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(updates, indices, shape);
-  const sumDupeIndices = true;
-
-  const indicesBuf = backend.bufferSync<Rank, 'int32'>(indices);
-  const updatesBuf = backend.bufferSync<Rank, 'int32'|'float32'>(updates);
-
-  const outBuf = scatterImpl(
-      indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates,
-      sliceRank, strides, 0 /* defaultValue */, sumDupeIndices);
-
-  return backend.makeTensorInfo(shape, outBuf.dtype, outBuf.values);
-}
-
-export const scatterNdConfig: KernelConfig = {
-  kernelName: ScatterNd,
-  backendName: 'cpu',
-  kernelFunc: scatterNd as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Scatter_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Scatter_impl.ts
deleted file mode 100644
index 77a4d444f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Scatter_impl.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {buffer, Rank, ShapeMap, TensorBuffer, TypedArray} from '@tensorflow/tfjs-core';
-
-interface DefaultValueTypeMap {
-  bool: boolean;
-  int32: number;
-  float32: number;
-  string: string;
-}
-
-export function
-scatterImpl<R extends Rank, D extends 'float32'|'int32'|'bool'|'string'>(
-    indices: TensorBuffer<R, 'int32'>, updates: TensorBuffer<R, D>,
-    shape: number[], outputSize: number, sliceSize: number, numUpdates: number,
-    sliceRank: number, strides: number[],
-    defaultValue: TensorBuffer<R, D>|DefaultValueTypeMap[D],
-    sumDupeIndices: boolean): TensorBuffer<R, D> {
-  const flattenShape = [outputSize / sliceSize, sliceSize];
-
-  const indicesData = indices.values as TypedArray;
-  const updatesData = updates.values;
-
-  if (outputSize === 0) {
-    return buffer(shape as ShapeMap[R], updates.dtype);
-  }
-
-  const outBuf = (defaultValue instanceof TensorBuffer) ?
-      defaultValue :
-      buffer(flattenShape, updates.dtype);
-  if (typeof defaultValue === 'string') {
-    (outBuf.values as string[]).fill(defaultValue);
-  } else if (typeof defaultValue === 'number') {
-    (outBuf.values as TypedArray).fill(defaultValue);
-  } else if (typeof defaultValue === 'boolean') {
-    (outBuf.values as TypedArray).fill(+defaultValue);
-  }
-
-  for (let i = 0; i < numUpdates; i++) {
-    const index = [];
-    let flattenIndex = 0;
-    for (let j = 0; j < sliceRank; j++) {
-      const dim = indicesData[i * sliceRank + j];
-      index.push(dim);
-      flattenIndex += dim * strides[j];
-    }
-
-    if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {
-      throw new Error(`Invalid indices: ${index} does not index into ${shape}`);
-    }
-
-    for (let k = 0; k < sliceSize; k++) {
-      if (sumDupeIndices) {
-        (outBuf.values as TypedArray)[flattenIndex * sliceSize + k] +=
-            (updatesData as TypedArray)[i * sliceSize + k];
-      } else {
-        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?
-            updatesData[0] :
-            updatesData[i * sliceSize + k];
-      }
-    }
-  }
-
-  return outBuf as TensorBuffer<R, D>;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SearchSorted.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SearchSorted.ts
deleted file mode 100644
index 34b466176..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SearchSorted.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SearchSorted, SearchSortedAttrs, SearchSortedInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {searchSortedImpl} from './SearchSorted_impl';
-
-export function searchSorted(args: {
-  inputs: SearchSortedInputs,
-  backend: MathBackendCPU,
-  attrs: SearchSortedAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {sortedSequence, values} = inputs;
-  const {side} = attrs;
-
-  const $sortedSequence =
-      backend.data.get(sortedSequence.dataId).values as TypedArray;
-  const $values = backend.data.get(values.dataId).values as TypedArray;
-
-  const output = searchSortedImpl(
-      $sortedSequence, $values, sortedSequence.shape[0],
-      sortedSequence.shape[1], values.shape[1], side);
-  return backend.makeTensorInfo(values.shape, 'int32', output);
-}
-
-export const searchSortedConfig: KernelConfig = {
-  kernelName: SearchSorted,
-  backendName: 'cpu',
-  kernelFunc: searchSorted as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SearchSorted_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SearchSorted_impl.ts
deleted file mode 100644
index 09a5d105d..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SearchSorted_impl.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TypedArray, util} from '@tensorflow/tfjs-core';
-
-function lowerBound(array: TypedArray, value: number) {
-  let left = 0;
-  let right = array.length;
-  let mid = 0;
-  while (left < right) {
-    mid = Math.floor((left + right) / 2);
-    if (array[mid] < value) {
-      left = mid + 1;
-    } else {
-      right = mid;
-    }
-  }
-  return right;
-}
-
-function upperBound(array: TypedArray, value: number) {
-  let left = 0;
-  let right = array.length;
-  let mid = 0;
-  while (left < right) {
-    mid = Math.floor((left + right) / 2);
-    if (array[mid] <= value) {
-      left = mid + 1;
-    } else {
-      right = mid;
-    }
-  }
-  return right;
-}
-
-export function searchSortedImpl(
-    sortedInputs: TypedArray, values: TypedArray, batchSize: number,
-    numInputs: number, numValues: number, side: 'left'|'right'): TypedArray {
-  const output =
-      util.getArrayFromDType('int32', batchSize * numValues) as TypedArray;
-  for (let b = 0; b < batchSize; ++b) {
-    const sortedInputsSlice =
-        sortedInputs.slice(b * numInputs, (b + 1) * numInputs);
-    const outputOffset = b * numValues;
-    for (let i = 0; i < numValues; ++i) {
-      output[outputOffset + i] = side === 'left' ?
-          lowerBound(sortedInputsSlice, values[i + outputOffset]) :
-          upperBound(sortedInputsSlice, values[i + outputOffset]);
-    }
-  }
-  return output;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Select.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Select.ts
deleted file mode 100644
index e11ae9627..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Select.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Select, SelectInputs, TensorInfo, TypedArray, upcastType, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function select(args: {inputs: SelectInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {condition, t, e} = inputs;
-
-  assertNotComplex([condition, t, e], 'select');
-  const conditionRank = condition.shape.length;
-
-  const values = backend.data.get(condition.dataId).values as TypedArray;
-  const tValues = backend.data.get(t.dataId).values as TypedArray;
-  const eValues = backend.data.get(e.dataId).values as TypedArray;
-  const resultDtype = upcastType(t.dtype, e.dtype);
-  const newValues =
-      util.makeZerosTypedArray(util.sizeFromShape(t.shape), resultDtype);
-
-  let index = 0;
-  const offset =
-      conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ?
-      1 :
-      util.sizeFromShape(t.shape.slice(1));
-
-  for (let i = 0; i < values.length; i++) {
-    for (let j = 0; j < offset; j++) {
-      if (values[i] === 1) {
-        newValues[index++] = tValues[i];
-      } else {
-        newValues[index++] = eValues[i];
-      }
-    }
-  }
-
-  return backend.makeTensorInfo(t.shape, resultDtype, newValues);
-}
-
-export const selectConfig: KernelConfig = {
-  kernelName: Select,
-  backendName: 'cpu',
-  kernelFunc: select as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Selu.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Selu.ts
deleted file mode 100644
index e2d6bb28e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Selu.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Selu} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-const scaleAlpha = backend_util.SELU_SCALEALPHA;
-const scale = backend_util.SELU_SCALE;
-
-export const selu = unaryKernelFunc(Selu, (xi) => {
-  if (xi >= 0) {
-    return scale * xi;
-  } else {
-    return scaleAlpha * (Math.exp(xi) - 1);
-  }
-});
-
-export const seluConfig: KernelConfig = {
-  kernelName: Selu,
-  backendName: 'cpu',
-  kernelFunc: selu,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Sigmoid.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Sigmoid.ts
deleted file mode 100644
index 86aac6e3f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Sigmoid.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sigmoid} from '@tensorflow/tfjs-core';
-
-import {createSimpleUnaryImpl} from '../utils/unary_impl';
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const sigmoidImpl =
-    createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi)));
-export const sigmoid =
-    unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));
-
-export const sigmoidConfig: KernelConfig = {
-  kernelName: Sigmoid,
-  backendName: 'cpu',
-  kernelFunc: sigmoid,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Sign.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Sign.ts
deleted file mode 100644
index 6baa70cb3..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Sign.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sign} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const sign = unaryKernelFunc(Sign, (xi) => {
-  if (xi < 0) {
-    return -1;
-  } else if (xi > 0) {
-    return 1;
-  } else {
-    return 0;
-  }
-});
-
-export const signConfig: KernelConfig = {
-  kernelName: Sign,
-  backendName: 'cpu',
-  kernelFunc: sign,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Sin.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Sin.ts
deleted file mode 100644
index bd900bb9f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Sin.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sin} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const sin = unaryKernelFunc(Sin, (xi) => Math.sin(xi));
-
-export const sinConfig: KernelConfig = {
-  kernelName: Sin,
-  backendName: 'cpu',
-  kernelFunc: sin,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Sinh.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Sinh.ts
deleted file mode 100644
index 60518d8df..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Sinh.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sinh} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const sinh = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));
-
-export const sinhConfig: KernelConfig = {
-  kernelName: Sinh,
-  backendName: 'cpu',
-  kernelFunc: sinh,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Slice.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Slice.ts
deleted file mode 100644
index ccfff5e3f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Slice.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BackendValues, buffer, DataType, KernelConfig, KernelFunc, Slice, slice_util, SliceAttrs, SliceInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export function sliceImpl(
-    vals: BackendValues, begin: number[], size: number[], shape: number[],
-    dtype: DataType): BackendValues {
-  const isContinous = slice_util.isSliceContinous(shape, begin, size);
-  const length = util.sizeFromShape(size);
-  const xStrides = util.computeStrides(shape);
-
-  if (isContinous) {
-    const flatOffset = slice_util.computeFlatOffset(begin, xStrides);
-
-    if (dtype === 'string') {
-      return (vals as Uint8Array[]).slice(flatOffset, flatOffset + length);
-    }
-
-    return (vals as TypedArray).subarray(flatOffset, flatOffset + length);
-  }
-
-  const decodedData = dtype === 'string' ?
-      backend_util.fromUint8ToStringArray(vals as Uint8Array[]) :
-      vals as TypedArray;
-
-  const inBuf = buffer(shape, dtype, decodedData);
-  const outBuf = buffer(size, dtype);
-  for (let i = 0; i < outBuf.size; ++i) {
-    const outLoc = outBuf.indexToLoc(i);
-    const inLoc = outLoc.map((idx: number, j) => idx + begin[j]);
-    outBuf.set(inBuf.get(...inLoc), ...outLoc);
-  }
-
-  if (dtype === 'string') {
-    return backend_util.fromStringArrayToUint8(outBuf.values as string[]);
-  }
-  return outBuf.values as TypedArray;
-}
-
-export function slice(
-    args: {inputs: SliceInputs, backend: MathBackendCPU, attrs: SliceAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {begin, size} = attrs;
-
-  assertNotComplex(x, 'slice');
-
-  const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);
-  slice_util.assertParamsValid(x, $begin, $size);
-
-  const vals = backend.data.get(x.dataId).values;
-  const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);
-  return backend.makeTensorInfo($size, x.dtype, outVals);
-}
-
-export const sliceConfig: KernelConfig = {
-  kernelName: Slice,
-  backendName: 'cpu',
-  kernelFunc: slice as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Softmax.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Softmax.ts
deleted file mode 100644
index 2c77c2431..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Softmax.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Softmax, SoftmaxAttrs, SoftmaxInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {exp} from './Exp';
-import {max} from './Max';
-import {div} from './RealDiv';
-import {reshape} from './Reshape';
-import {sub} from './Sub';
-import {sum} from './Sum';
-
-export function softmax(
-    args:
-        {inputs: SoftmaxInputs, backend: MathBackendCPU, attrs: SoftmaxAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {logits} = inputs;
-  const {dim} = attrs;
-
-  const logitsRank = logits.shape.length;
-
-  let $dim = dim;
-  if ($dim === -1) {
-    $dim = logitsRank - 1;
-  }
-  if ($dim !== logitsRank - 1) {
-    throw Error(
-        'Softmax along a non-last dimension is not yet supported. ' +
-        `Logits was rank ${logitsRank} and dim was ${$dim}`);
-  }
-
-  const axes = util.parseAxisParam([$dim], logits.shape);
-  const maxLogit = max({
-    inputs: {x: logits},
-    backend,
-    attrs: {reductionIndices: axes, keepDims: false}
-  });
-  const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);
-
-  const maxLogitReshaped =
-      reshape({inputs: {x: maxLogit}, backend, attrs: {shape: expandedShape}});
-  const a =
-      sub({inputs: {a: logits, b: maxLogitReshaped}, backend}) as TensorInfo;
-  const b = exp({inputs: {x: a}, backend}) as TensorInfo;
-  const sumExp =
-      sum({inputs: {x: b}, backend, attrs: {axis: axes, keepDims: false}});
-  const sumReshaped =
-      reshape({inputs: {x: sumExp}, backend, attrs: {shape: expandedShape}});
-
-  const result = div({inputs: {a: b, b: sumReshaped}, backend}) as TensorInfo;
-
-  backend.disposeIntermediateTensorInfo(maxLogit);
-  backend.disposeIntermediateTensorInfo(maxLogitReshaped);
-  backend.disposeIntermediateTensorInfo(a);
-  backend.disposeIntermediateTensorInfo(b);
-  backend.disposeIntermediateTensorInfo(sumExp);
-  backend.disposeIntermediateTensorInfo(sumReshaped);
-
-  return result;
-}
-
-export const softmaxConfig: KernelConfig = {
-  kernelName: Softmax,
-  backendName: 'cpu',
-  kernelFunc: softmax as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Softplus.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Softplus.ts
deleted file mode 100644
index 53466048b..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Softplus.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Softplus} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX
-
-// epsilon is the difference between 1.0 and the next representable float.
-// For a single precision 32 bit float this should be 2^-23, see:
-// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm
-const epsilon = 1.1920928955078125e-7;
-const threshold = Math.log(epsilon) + 2.0;
-
-export const softplus = unaryKernelFunc(Softplus, (xi) => {
-  // Value above which exp(x) may overflow, but softplus(x) == x
-  // is within machine epsilon.
-  const tooLarge = xi > -threshold;
-
-  // Value below which exp(x) may underflow, but softplus(x) == exp(x)
-  // is within machine epsilon.
-  const tooSmall = xi < threshold;
-
-  const expX = Math.exp(xi);
-  let result;
-
-  if (tooSmall) {
-    result = expX;
-  } else if (tooLarge) {
-    result = xi;
-  } else {
-    result = Math.log(1.0 + expX);
-  }
-  return result;
-});
-
-export const softplusConfig: KernelConfig = {
-  kernelName: Softplus,
-  backendName: 'cpu',
-  kernelFunc: softplus,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SpaceToBatchND.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SpaceToBatchND.ts
deleted file mode 100644
index 20120956e..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SpaceToBatchND.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, ReshapeAttrs, ReshapeInputs, SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs, TensorInfo, TransposeAttrs, TransposeInputs, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-import {padV2Config} from './PadV2';
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function spaceToBatchND(args: {
-  inputs: SpaceToBatchNDInputs,
-  backend: MathBackendCPU,
-  attrs: SpaceToBatchNDAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockShape, paddings} = attrs;
-
-  assertNotComplex([x], 'spaceToBatchND');
-
-  const prod = util.sizeFromShape(blockShape);
-
-  const completePaddings: Array<[number, number]> = [[0, 0]];
-  completePaddings.push(...(paddings as Array<[number, number]>));
-
-  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
-    completePaddings.push([0, 0]);
-  }
-
-  const paddedX = padV2Config.kernelFunc({
-    inputs: {x},
-    backend,
-    attrs: {paddings: completePaddings, constantValue: 0}
-  }) as TensorInfo;
-
-  const reshapedPaddedShape =
-      backend_util.getReshaped(paddedX.shape, blockShape, prod, false);
-
-  const permutedReshapedPaddedPermutation = backend_util.getPermuted(
-      reshapedPaddedShape.length, blockShape.length, false);
-
-  const flattenShape =
-      backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);
-
-  const reshapeInputs: ReshapeInputs = {x: paddedX};
-  const reshapeAttrs: ReshapeAttrs = {shape: reshapedPaddedShape};
-  const paddedXReshaped =
-      reshape({inputs: reshapeInputs, backend, attrs: reshapeAttrs});
-
-  const transposeInputs: TransposeInputs = {x: paddedXReshaped};
-  const transposeAttrs:
-      TransposeAttrs = {perm: permutedReshapedPaddedPermutation};
-  const paddedXT =
-      transpose({inputs: transposeInputs, backend, attrs: transposeAttrs});
-
-  const resultReshapeInputs: ReshapeInputs = {x: paddedXT};
-  const resultReshapeAttrs: ReshapeAttrs = {shape: flattenShape};
-  const result = reshape(
-      {inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs});
-
-  backend.disposeIntermediateTensorInfo(paddedX);
-  backend.disposeIntermediateTensorInfo(paddedXReshaped);
-  backend.disposeIntermediateTensorInfo(paddedXT);
-
-  return result;
-}
-
-export const spaceToBatchNDConfig: KernelConfig = {
-  kernelName: SpaceToBatchND,
-  backendName: 'cpu',
-  kernelFunc: spaceToBatchND as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseFillEmptyRows.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SparseFillEmptyRows.ts
deleted file mode 100644
index e1683752f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseFillEmptyRows.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SparseFillEmptyRows, SparseFillEmptyRowsInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {sparseFillEmptyRowsImpl} from './SparseFillEmptyRows_impl';
-
-export function sparseFillEmptyRows(args: {
-  inputs: SparseFillEmptyRowsInputs,
-  backend: MathBackendCPU
-}): [TensorInfo, TensorInfo, TensorInfo, TensorInfo] {
-  const {inputs, backend} = args;
-  const {indices, values, denseShape, defaultValue} = inputs;
-  if (denseShape.shape.length !== 1) {
-    throw new Error(`Dense shape must be a vector, saw:
-        ${denseShape.shape}`);
-  }
-  if (indices.shape.length !== 2) {
-    throw new Error(`Indices must be a matrix, saw:
-        ${indices.shape}`);
-  }
-  if (values.shape.length !== 1) {
-    throw new Error(`Values must be a vector, saw:
-        ${values.shape}`);
-  }
-  if (defaultValue.shape.length !== 0) {
-    throw new Error(`Default value must be a scalar, saw:
-        ${defaultValue.shape}`);
-  }
-
-  const $indices = backend.data.get(indices.dataId).values as TypedArray;
-  const $values = backend.data.get(values.dataId).values as TypedArray;
-  const $denseShape = backend.data.get(denseShape.dataId).values as TypedArray;
-  const $defaultValue =
-      backend.data.get(defaultValue.dataId).values[0] as number;
-
-  const [outputIndices, outputIndicesShape, outputValues,
-         emptyRowIndicator, reverseIndexMap] =
-      sparseFillEmptyRowsImpl(
-          $indices, indices.shape, indices.dtype, $values, values.dtype,
-          $denseShape, $defaultValue);
-  return [
-    backend.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
-    backend.makeTensorInfo(
-        [outputIndicesShape[0]], values.dtype, outputValues),
-    backend.makeTensorInfo(
-        [emptyRowIndicator.length], 'bool',
-        new Uint8Array(
-            emptyRowIndicator.map((value: boolean) => Number(value)))),
-    backend.makeTensorInfo(
-        [reverseIndexMap.length], indices.dtype,
-        new Int32Array(reverseIndexMap)),
-  ];
-}
-
-export const sparseFillEmptyRowsConfig: KernelConfig = {
-  kernelName: SparseFillEmptyRows,
-  backendName: 'cpu',
-  kernelFunc: sparseFillEmptyRows as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseFillEmptyRows_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SparseFillEmptyRows_impl.ts
deleted file mode 100644
index 4ffb0584f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseFillEmptyRows_impl.ts
+++ /dev/null
@@ -1,141 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType, TypedArray, util} from '@tensorflow/tfjs-core';
-
-export function sparseFillEmptyRowsImpl(
-    indices: TypedArray, indicesShape: number[], indicesDType: DataType,
-    values: TypedArray, valuesDType: DataType, denseShape: TypedArray,
-    defaultValue: number):
-    [TypedArray, number[], TypedArray, boolean[], number[]] {
-  const indicesCount = indicesShape[0];
-  const denseRows = denseShape[0];
-
-  const emptyRowIndicator: boolean[] = new Array(denseRows);
-  const reverseIndexMap: number[] = new Array(indicesCount);
-
-  const rank = indicesShape[1];
-
-  if (denseRows === 0) {
-    if (indicesCount !== 0) {
-      throw new Error(
-          backend_util.getSparseFillEmptyRowsIndicesDenseShapeMismatch(
-              indicesCount));
-    }
-    const outputIndices = util.getArrayFromDType(indicesDType, 0) as TypedArray;
-    const outputValues = util.getArrayFromDType(valuesDType, 0) as TypedArray;
-    return [
-      outputIndices, [0, rank], outputValues, emptyRowIndicator, reverseIndexMap
-    ];
-  }
-
-  let rowsAreOrdered = true;
-  let lastIndicesRow = 0;
-  const csrOffset: number[] = new Array(denseRows).fill(0);
-
-  for (let i = 0; i < indicesCount; ++i) {
-    // indices is a 2d tensor with shape of [N, rank]
-    const row = indices[i * rank];
-    if (row < 0) {
-      throw new Error(
-          backend_util.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));
-    }
-    if (row >= denseRows) {
-      throw new Error(
-          backend_util.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(
-              i, row, denseRows));
-    }
-    ++csrOffset[row];
-    rowsAreOrdered = rowsAreOrdered && (row >= lastIndicesRow);
-    lastIndicesRow = row;
-  }
-
-  let allRowsFull = true;
-  for (let row = 0; row < denseRows; ++row) {
-    // csrOffset here describes the number of elements in this dense row
-    const rowEmpty = (csrOffset[row] === 0);
-    emptyRowIndicator[row] = rowEmpty;
-    allRowsFull = allRowsFull && !rowEmpty;
-    // In filled version, each row has at least one element.
-    csrOffset[row] = Math.max(csrOffset[row], 1);
-    // Update csrOffset to represent the number of elements up to and
-    // including denseRows + 1:
-    //  csrOffset[0] == #{elements of row 0}
-    //  csrOffset[1] == #{elements of row 1} + #{elements of row 0}
-    //  ..
-    //  csrOffset[i] == starting index for elements in row i + 1.
-    if (row > 0) {
-      csrOffset[row] += csrOffset[row - 1];
-    }
-  }
-
-  if (allRowsFull && rowsAreOrdered) {
-    const outputIndices: TypedArray = indices;
-    const outputValues: TypedArray = values;
-    for (let i = 0; i < indicesCount; ++i) {
-      reverseIndexMap[i] = i;
-    }
-    return [
-      outputIndices, [indicesCount, rank], outputValues, emptyRowIndicator,
-      reverseIndexMap
-    ];
-  } else {
-    const fullIndicesCount = csrOffset[denseRows - 1];
-    const outputIndices =
-        util.getArrayFromDType(indicesDType, fullIndicesCount * rank) as
-        TypedArray;
-    const outputValues =
-        util.getArrayFromDType(valuesDType, fullIndicesCount) as TypedArray;
-    const filledCount: number[] = new Array(denseRows).fill(0);
-
-    // Fill in values for rows that are not missing
-    for (let i = 0; i < indicesCount; ++i) {
-      // indices is a 2d tensor with shape of [N, rank]
-      const row = indices[i * rank];
-      const offset = filledCount[row];
-      const outputI = ((row === 0) ? 0 : csrOffset[row - 1]) + offset;
-      filledCount[row]++;  // Increment the filled count for this row.
-      for (let j = 0; j < rank; ++j) {
-        // indices and outputIndices are 2d tensors with shape of [N, rank]
-        outputIndices[outputI * rank + j] = indices[i * rank + j];
-      }
-      outputValues[outputI] = values[i];
-      // We'll need this reverse index map to backprop correctly.
-      reverseIndexMap[i] = outputI;
-    }
-
-    // Fill in values for rows that are missing
-    for (let row = 0; row < denseRows; ++row) {
-      const rowCount = filledCount[row];
-      if (rowCount === 0) {  // We haven't filled this row
-        const startingIndex = (row === 0) ? 0 : csrOffset[row - 1];
-        // Remaining index values were set to zero already.
-        // Just need to set the row index in the right location.
-        // outputIndices is a 2d tensor with shape of [N, rank]
-        outputIndices[startingIndex * rank + 0] = row;
-        for (let col = 1; col < rank; ++col) {
-          outputIndices[startingIndex * rank + col] = 0;
-        }
-        outputValues[startingIndex] = defaultValue;
-      }
-    }
-    return [
-      outputIndices, [fullIndicesCount, rank], outputValues, emptyRowIndicator,
-      reverseIndexMap
-    ];
-  }
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseReshape.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SparseReshape.ts
deleted file mode 100644
index 107710196..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseReshape.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SparseReshape, SparseReshapeInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {sparseReshapeImpl} from './SparseReshape_impl';
-
-export function sparseReshape(
-    args: {inputs: SparseReshapeInputs, backend: MathBackendCPU}):
-    [TensorInfo, TensorInfo] {
-  const {inputs, backend} = args;
-  const {inputIndices, inputShape, newShape} = inputs;
-  if (inputIndices.shape.length !== 2) {
-    throw new Error(`Input indices should be a matrix but received shape
-        ${inputIndices.shape}`);
-  }
-  if (inputShape.shape.length !== 1) {
-    throw new Error(`Input shape should be a vector but received shape
-        ${inputShape.shape}`);
-  }
-
-  if (newShape.shape.length !== 1) {
-    throw new Error(
-        `Target shape should be a vector but received shape ${newShape.shape}`);
-  }
-
-  const $inputShape =
-      Array.from(backend.data.get(inputShape.dataId).values as TypedArray);
-  const $inputIndices =
-      backend.data.get(inputIndices.dataId).values as TypedArray;
-  const targetShape =
-      Array.from(backend.data.get(newShape.dataId).values as TypedArray);
-
-  const [newIndices, indicesShape, outputShape] = sparseReshapeImpl(
-      $inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape,
-      targetShape);
-  return [
-    backend.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
-    backend.makeTensorInfo(
-        [outputShape.length], newShape.dtype, new Int32Array(outputShape)),
-  ];
-}
-
-export const sparseReshapeConfig: KernelConfig = {
-  kernelName: SparseReshape,
-  backendName: 'cpu',
-  kernelFunc: sparseReshape,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseReshape_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SparseReshape_impl.ts
deleted file mode 100644
index c8d3d8e9b..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseReshape_impl.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType, TypedArray, util} from '@tensorflow/tfjs-core';
-
-export function sparseReshapeImpl(
-    inputIndices: TypedArray, inputIndicesShape: number[], inputDType: DataType,
-    inputShape: number[],
-    targetShape: number[]): [TypedArray, number[], number[]] {
-  const denseSize = util.sizeFromShape(inputShape);
-  const nnz = inputIndicesShape[0];
-  const outputRank = targetShape.length;
-
-  // Compute the output shape. Determine product of specified dimensions, and
-  // find the index of the unspecified one.
-  const outputShape: number[] = [];
-  let product = 1;
-  let unknownIndex = -1;
-  for (let d = 0; d < outputRank; ++d) {
-    const size = targetShape[d];
-    if (size === -1) {
-      if (unknownIndex !== -1) {
-        throw new Error(
-            backend_util
-                .getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(
-                    unknownIndex, d));
-      }
-      unknownIndex = d;
-      outputShape.push(1);
-    } else {
-      if (size < 0) {
-        throw new Error(
-            backend_util.getSparseReshapeNegativeOutputDimErrorMessage(
-                d, size));
-      }
-      product *= size;
-      outputShape.push(size);
-    }
-  }
-  if (unknownIndex !== -1) {
-    if (product <= 0) {
-      throw new Error(
-          backend_util.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
-    }
-    const missing = Math.trunc(denseSize / product);
-    if (product * missing !== denseSize) {
-      throw new Error(
-          backend_util.getSparseReshapeInputOutputMultipleErrorMessage(
-              inputShape, outputShape));
-    }
-
-    outputShape[unknownIndex] = missing;
-  }
-  const outputSize = util.sizeFromShape(outputShape);
-  if (outputSize !== denseSize) {
-    throw new Error(
-        backend_util.getSparseReshapeInputOutputMismatchErrorMessage(
-            inputShape, outputShape));
-  }
-
-  const inputRank = inputShape.length;
-  const inputStrides: number[] = [];
-  if (inputRank > 0) {
-    inputStrides[inputRank - 1] = 1;
-    for (let d = inputRank - 2; d >= 0; --d) {
-      inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];
-    }
-  }
-
-  const outputStrides: number[] = [];
-  if (outputRank > 0) {
-    outputStrides[outputRank - 1] = 1;
-    for (let d = outputRank - 2; d >= 0; --d) {
-      outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];
-    }
-  }
-
-  const newIndices =
-      util.getArrayFromDType(inputDType, nnz * outputRank) as TypedArray;
-  for (let i = 0; i < nnz; ++i) {
-    let id = 0;
-    for (let j = 0; j < inputRank; ++j) {
-      // inputIndices is a 2d tensor with shape of [nnz, inputRank]
-      id += inputIndices[i * inputRank + j] * inputStrides[j];
-    }
-    for (let j = 0; j < outputRank; ++j) {
-      // newIndices is a 2d tensor with shape of [nnz, outputRank]
-      newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);
-      id %= outputStrides[j];
-    }
-  }
-  return [newIndices, [nnz, outputRank], outputShape];
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseSegmentMean.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SparseSegmentMean.ts
deleted file mode 100644
index 0c87e0929..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseSegmentMean.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SparseSegmentMean, SparseSegmentMeanInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {sparseSegmentReductionImpl} from './SparseSegmentReduction_impl';
-
-export function sparseSegmentMean(
-    args: {inputs: SparseSegmentMeanInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {data, indices, segmentIds} = inputs;
-  if (data.shape.length < 1) {
-    throw new Error(
-        `Data should be at least 1 dimensional but received scalar`);
-  }
-  if (indices.shape.length !== 1) {
-    throw new Error(`Indices should be a vector but received shape
-          ${indices.shape}`);
-  }
-  if (segmentIds.shape.length !== 1) {
-    throw new Error(`Segment ids should be a vector but received shape
-          ${segmentIds.shape}`);
-  }
-  if (indices.shape[0] !== segmentIds.shape[0]) {
-    throw new Error(`segmentIds and indices should have same size.`);
-  }
-
-  const $data = backend.data.get(data.dataId).values as TypedArray;
-  const $indices = backend.data.get(indices.dataId).values as TypedArray;
-  const $segmentIds = backend.data.get(segmentIds.dataId).values as TypedArray;
-
-  const [outputData, outputDataShape] = sparseSegmentReductionImpl(
-      $data, data.shape, data.dtype, $indices, $segmentIds, true);
-  return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
-}
-
-export const sparseSegmentMeanConfig: KernelConfig = {
-  kernelName: SparseSegmentMean,
-  backendName: 'cpu',
-  kernelFunc: sparseSegmentMean,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseSegmentReduction_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SparseSegmentReduction_impl.ts
deleted file mode 100644
index 089689d72..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseSegmentReduction_impl.ts
+++ /dev/null
@@ -1,128 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType, TypedArray, util} from '@tensorflow/tfjs-core';
-
-export function sparseSegmentReductionImpl(
-    input: TypedArray, inputShape: number[], inputDType: DataType,
-    indices: TypedArray, segmentIds: TypedArray, isMean = false,
-    defaultValue = 0): [TypedArray, number[]] {
-  const numIndices = indices.length;
-
-  // Flatten the array to two dimensions
-  const inputFlat: number[] = [inputShape[0], input.length / inputShape[0]];
-  const numCol = inputFlat[1];
-  // Note that the current implementation assumes that segmentIds values are
-  // sorted.
-  const lastSegmentIdPlusOne =
-      numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;
-  const outputRows = lastSegmentIdPlusOne;
-
-  if (outputRows < 0) {
-    throw new Error(
-        backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
-  }
-
-  const outputShape = inputShape.slice();
-  outputShape[0] = outputRows;
-
-  const outputLength =
-      outputShape.reduce((product, value) => product * value, 1);
-  // Output array is initialized with the value 0 by default.
-  const output = util.getArrayFromDType(inputDType, outputLength) as TypedArray;
-
-  // Note that we do not initialize the output buffer with a default value, so
-  // we need to explicitly set missing indices to the default value.
-  if (numIndices === 0) {
-    if (outputRows > 0) {
-      output.fill(defaultValue);
-    }
-    return [output, outputShape];
-  }
-
-  if (outputRows <= 0) {
-    throw new Error(
-        backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
-  }
-
-  let start = 0, end = 1;
-  // Index from which the output is not initialized.
-  let uninitializedIndex = 0;
-  let outIndex = segmentIds[start];
-
-  while (true) {
-    // We initialize nextIndex to 0 to avoid may be uninitialized warning
-    let nextIndex = 0;
-    if (end < numIndices) {
-      nextIndex = segmentIds[end];
-      if (outIndex === nextIndex) {
-        ++end;
-        continue;
-      }
-      // We have a new segment here.  Verify that the segment ids are growing.
-      if (outIndex >= nextIndex) {
-        throw new Error(backend_util
-            .getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
-      }
-    }
-
-    if (outIndex < 0 || outIndex >= outputRows) {
-      throw new Error(
-          backend_util.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(
-              outIndex, outputRows));
-    }
-
-    // If there is a gap between two indices, we need to set that gap to the
-    // default value.
-    if (outIndex > uninitializedIndex) {
-      output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);
-    }
-
-    for (let i = start; i < end; ++i) {
-      const index = indices[i];
-      if (index < 0 || index >= inputFlat[0]) {
-        throw new Error(
-            backend_util.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(
-                i, indices[i], inputFlat[0]));
-      }
-      for (let j = 0; j < numCol; j++) {
-        output[outIndex * numCol + j] += input[index * numCol + j];
-      }
-    }
-
-    if (isMean) {
-      for (let j = 0; j < numCol; j++) {
-        output[outIndex * numCol + j] /= end - start;
-      }
-    }
-
-    start = end;
-    ++end;
-    uninitializedIndex = outIndex + 1;
-    outIndex = nextIndex;
-    if (end > numIndices) {
-      break;
-    }
-  }
-
-  // Fill the gap at the end with the default value.
-  if (uninitializedIndex < outputRows) {
-    output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);
-  }
-
-  return [output, outputShape];
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseSegmentSum.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SparseSegmentSum.ts
deleted file mode 100644
index 4afed5abd..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseSegmentSum.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SparseSegmentSum, SparseSegmentSumInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {sparseSegmentReductionImpl} from './SparseSegmentReduction_impl';
-
-export function sparseSegmentSum(
-    args: {inputs: SparseSegmentSumInputs, backend: MathBackendCPU}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {data, indices, segmentIds} = inputs;
-  if (data.shape.length < 1) {
-    throw new Error(
-        `Data should be at least 1 dimensional but received scalar`);
-  }
-  if (indices.shape.length !== 1) {
-    throw new Error(`Indices should be a vector but received shape
-         ${indices.shape}`);
-  }
-  if (segmentIds.shape.length !== 1) {
-    throw new Error(`Segment ids should be a vector but received shape
-         ${segmentIds.shape}`);
-  }
-  if (indices.shape[0] !== segmentIds.shape[0]) {
-    throw new Error(`segmentIds and indices should have same size.`);
-  }
-
-  const $data = backend.data.get(data.dataId).values as TypedArray;
-  const $indices = backend.data.get(indices.dataId).values as TypedArray;
-  const $segmentIds = backend.data.get(segmentIds.dataId).values as TypedArray;
-
-  const [outputData, outputDataShape] = sparseSegmentReductionImpl(
-      $data, data.shape, data.dtype, $indices, $segmentIds);
-  return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
-}
-
-export const sparseSegmentSumConfig: KernelConfig = {
-  kernelName: SparseSegmentSum,
-  backendName: 'cpu',
-  kernelFunc: sparseSegmentSum,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseToDense.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SparseToDense.ts
deleted file mode 100644
index 5c82f0478..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SparseToDense.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Rank, SparseToDense, SparseToDenseAttrs, SparseToDenseInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {scatterImpl} from './Scatter_impl';
-
-export function sparseToDense(args: {
-  inputs: SparseToDenseInputs,
-  backend: MathBackendCPU,
-  attrs: SparseToDenseAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {sparseIndices, sparseValues, defaultValue} = inputs;
-  const {outputShape} = attrs;
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);
-  const sumDupeIndices = false;
-
-  const indicesBuf = backend.bufferSync<Rank, 'int32'>(sparseIndices);
-
-  let outBuf;
-  switch (sparseValues.dtype) {
-    case 'bool': {
-      const updatesBuf = backend.bufferSync<Rank, 'bool'>(sparseValues);
-      const $defaultValue =
-          Boolean(backend.data.get(defaultValue.dataId).values[0]);
-      outBuf = scatterImpl(
-          indicesBuf, updatesBuf, outputShape, outputSize, sliceSize,
-          numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
-      break;
-    }
-    case 'float32': {
-      const updatesBuf = backend.bufferSync<Rank, 'float32'>(sparseValues);
-      const $defaultValue =
-          backend.data.get(defaultValue.dataId).values[0] as number;
-      outBuf = scatterImpl(
-          indicesBuf, updatesBuf, outputShape, outputSize, sliceSize,
-          numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
-      break;
-    }
-    case 'int32': {
-      const updatesBuf = backend.bufferSync<Rank, 'int32'>(sparseValues);
-      const $defaultValue =
-          backend.data.get(defaultValue.dataId).values[0] as number;
-      outBuf = scatterImpl(
-          indicesBuf, updatesBuf, outputShape, outputSize, sliceSize,
-          numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
-      break;
-    }
-    case 'string': {
-      const updatesBuf = backend.bufferSync<Rank, 'string'>(sparseValues);
-      const $defaultValue = util.decodeString(
-          backend.data.get(defaultValue.dataId).values[0] as Uint8Array);
-      outBuf = scatterImpl(
-          indicesBuf, updatesBuf, outputShape, outputSize, sliceSize,
-          numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
-      break;
-    }
-    default:
-      throw new Error(`Unsupported type ${sparseValues.dtype}`);
-  }
-  return backend.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
-}
-
-export const sparseToDenseConfig: KernelConfig = {
-  kernelName: SparseToDense,
-  backendName: 'cpu',
-  kernelFunc: sparseToDense as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SplitV.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SplitV.ts
deleted file mode 100644
index 7d2899ed3..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SplitV.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, SplitVAttrs, SplitVInputs} from '@tensorflow/tfjs-core';
-import {KernelConfig, KernelFunc, SplitV, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {slice} from './Slice';
-
-export function splitV(
-    args: {inputs: SplitVInputs, backend: MathBackendCPU, attrs: SplitVAttrs}):
-    TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {numOrSizeSplits, axis} = attrs;
-
-  const $axis = util.parseAxisParam(axis, x.shape)[0];
-  const splitSizes = backend_util.prepareSplitSize(x, numOrSizeSplits, $axis);
-
-  const begin = new Array(x.shape.length).fill(0);
-  const size = x.shape.slice();
-  return splitSizes.map(s => {
-    const sliceSize = [...size];
-    sliceSize[$axis] = s;
-    const sliceT =
-        slice({inputs: {x}, backend, attrs: {begin, size: sliceSize}});
-    begin[$axis] += s;
-    return sliceT;
-  });
-}
-
-export const splitVConfig: KernelConfig = {
-  kernelName: SplitV,
-  backendName: 'cpu',
-  kernelFunc: splitV as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Sqrt.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Sqrt.ts
deleted file mode 100644
index f8d3ec017..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Sqrt.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sqrt} from '@tensorflow/tfjs-core';
-
-import {createSimpleUnaryImpl} from '../utils/unary_impl';
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi));
-export const sqrt = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));
-
-export const sqrtConfig: KernelConfig = {
-  kernelName: Sqrt,
-  backendName: 'cpu',
-  kernelFunc: sqrt,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Square.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Square.ts
deleted file mode 100644
index 849e36a47..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Square.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Square, SquareInputs} from '@tensorflow/tfjs-core';
-import {KernelConfig} from '@tensorflow/tfjs-core';
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-export const squareConfig: KernelConfig = {
-  kernelName: Square,
-  backendName: 'cpu',
-  kernelFunc: ({inputs, backend}) => {
-    const {x} = inputs as SquareInputs;
-    const cpuBackend = backend as MathBackendCPU;
-    assertNotComplex(x, 'square');
-
-    const values = cpuBackend.data.get(x.dataId).values as Float32Array;
-    const newValues = new Float32Array(values.length);
-    for (let i = 0; i < values.length; ++i) {
-      const value = values[i];
-      newValues[i] = value * value;
-    }
-    const dataId = cpuBackend.write(newValues, x.shape, x.dtype);
-    return {dataId, shape: x.shape, dtype: x.dtype};
-  }
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/SquaredDifference.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/SquaredDifference.ts
deleted file mode 100644
index 5fafa2136..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/SquaredDifference.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SquaredDifference} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc} from '../utils/binary_utils';
-
-export const squaredDifferenceImpl =
-    createSimpleBinaryKernelImpl(((a: number, b: number) => {
-      const diff = a - b;
-      return diff * diff;
-    }));
-export const squaredDifference =
-    binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);
-
-export const squaredDifferenceConfig: KernelConfig = {
-  kernelName: SquaredDifference,
-  backendName: 'cpu',
-  kernelFunc: squaredDifference
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/StaticRegexReplace.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/StaticRegexReplace.ts
deleted file mode 100644
index 5e8c51d5a..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/StaticRegexReplace.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, StaticRegexReplace, StaticRegexReplaceAttrs} from '@tensorflow/tfjs-core';
-import {createSimpleUnaryImpl} from '../utils/unary_impl';
-import {unaryKernelFuncFromImpl} from '../utils/unary_utils';
-
-export const staticRegexReplaceImpl = createSimpleUnaryImpl<string,
-  string>((x: string, attrs) => {
-    const {pattern, replaceGlobal, rewrite} =
-      attrs as unknown as StaticRegexReplaceAttrs;
-    // TODO(mattSoulanille): Don't create a regex each time.
-    return x.replace(new RegExp(pattern, replaceGlobal ? 'g' : ''), rewrite);
-});
-
-const staticRegexReplace =
-  unaryKernelFuncFromImpl(StaticRegexReplace, staticRegexReplaceImpl);
-
-export const staticRegexReplaceConfig: KernelConfig = {
-  kernelName: StaticRegexReplace,
-  backendName: 'cpu',
-  kernelFunc: staticRegexReplace,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Step.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Step.ts
deleted file mode 100644
index d7b2e4426..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Step.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Step, StepAttrs} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const step = unaryKernelFunc(Step, (xi, attrs) => {
-  const stepAttrs = attrs as unknown as StepAttrs;
-  if (isNaN(xi)) {
-    return NaN;
-  } else {
-    return xi > 0 ? 1 : stepAttrs.alpha;
-  }
-});
-
-export const stepConfig: KernelConfig = {
-  kernelName: Step,
-  backendName: 'cpu',
-  kernelFunc: step,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/StridedSlice.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/StridedSlice.ts
deleted file mode 100644
index cfd93cd37..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/StridedSlice.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Rank, slice_util, StridedSlice, StridedSliceAttrs, StridedSliceInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-import {stridedSliceImpl} from './StridedSlice_impl';
-
-export function stridedSlice(args: {
-  inputs: StridedSliceInputs,
-  backend: MathBackendCPU,
-  attrs: StridedSliceAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {
-    begin,
-    end,
-    strides,
-    beginMask,
-    endMask,
-    ellipsisMask,
-    newAxisMask,
-    shrinkAxisMask
-  } = attrs;
-
-  assertNotComplex(x, 'stridedSlice');
-
-  const {
-    finalShapeSparse,
-    finalShape,
-    isIdentity,
-    sliceDim0,
-    isSimpleSlice,
-    begin: $begin,
-    end: $end,
-    strides: $strides
-  } =
-      slice_util.sliceInfo(
-          x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,
-          newAxisMask, shrinkAxisMask);
-
-  let result;
-
-  // ref:
-  // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/strided_slice_op.cc
-  if (isIdentity) {
-    // Optimization #1, slice is a no-op plus reshape
-    result = reshape({inputs: {x}, backend, attrs: {shape: finalShape}});
-  } else if (sliceDim0 || isSimpleSlice) {
-    // Optimization #2, slice is memory contiguous (only occurs in dim 0)
-    util.assert(
-        x.shape.length >= 1,
-        () => `Input must have rank at least 1, got: ${x.shape.length}`);
-
-    const size = slice_util.computeOutShape($begin, $end, $strides);
-    // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).
-    const sliced = slice({inputs: {x}, backend, attrs: {begin: $begin, size}});
-    result =
-        reshape({inputs: {x: sliced}, backend, attrs: {shape: finalShape}});
-    backend.disposeIntermediateTensorInfo(sliced);
-  } else {
-    const xBuf = backend.bufferSync<Rank, 'float32'>(x);
-    const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);
-
-    result = backend.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);
-  }
-
-  return result;
-}
-
-export const stridedSliceConfig: KernelConfig = {
-  kernelName: StridedSlice,
-  backendName: 'cpu',
-  kernelFunc: stridedSlice as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/StridedSlice_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/StridedSlice_impl.ts
deleted file mode 100644
index c8107bad8..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/StridedSlice_impl.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, Rank, TensorBuffer} from '@tensorflow/tfjs-core';
-
-export function stridedSliceImpl<R extends Rank>(
-    outShape: number[], xBuf: TensorBuffer<R>, strides: number[],
-    begin: number[]): TensorBuffer<R> {
-  const outBuf = buffer(outShape, xBuf.dtype);
-
-  for (let i = 0; i < outBuf.size; i++) {
-    const loc = outBuf.indexToLoc(i);
-
-    const newLoc: number[] = new Array(loc.length);
-    for (let j = 0; j < newLoc.length; j++) {
-      newLoc[j] = loc[j] * strides[j] + begin[j];
-    }
-    outBuf.set(xBuf.get(...newLoc), ...loc);
-  }
-
-  return outBuf as TensorBuffer<R>;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/StringNGrams.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/StringNGrams.ts
deleted file mode 100644
index 34405373c..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/StringNGrams.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringNGrams, StringNGramsAttrs, StringNGramsInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {stringNGramsImpl} from './StringNGrams_impl';
-
-export function stringNGrams(args: {
-  inputs: StringNGramsInputs,
-  backend: MathBackendCPU,
-  attrs: StringNGramsAttrs
-}): [TensorInfo, TensorInfo] {
-  const {inputs, backend, attrs} = args;
-  const {
-    separator,
-    nGramWidths,
-    leftPad,
-    rightPad,
-    padWidth,
-    preserveShortSequences
-  } = attrs;
-  const {data, dataSplits} = inputs;
-  const $data = backend.data.get(data.dataId).values as Uint8Array[];
-  const $dataSplits = backend.data.get(dataSplits.dataId).values as Int32Array;
-
-  const [nGrams, nGramsSplits] = stringNGramsImpl(
-      $data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth,
-      preserveShortSequences);
-  return [
-    backend.makeTensorInfo([nGrams.length], 'string', nGrams),
-    backend.makeTensorInfo(dataSplits.shape, 'int32', nGramsSplits),
-  ];
-}
-
-export const stringNGramsConfig: KernelConfig = {
-  kernelName: StringNGrams,
-  backendName: 'cpu',
-  kernelFunc: stringNGrams as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/StringNGrams_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/StringNGrams_impl.ts
deleted file mode 100644
index 4ff068f4c..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/StringNGrams_impl.ts
+++ /dev/null
@@ -1,225 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {util} from '@tensorflow/tfjs-core';
-
-/**
- * The StringNGramsOp class creates ngrams from ragged string data.
- * The constructor contains all attributes related to the operation such as
- * padding widths and strings, and the compute function can be used to
- * compute the ngrams for different ragged tensor inputs.
- */
-class StringNGramsOp {
-  private separator: Uint8Array;
-  private nGramWidths: number[];
-  private padWidth: number;
-  private leftPad: Uint8Array;
-  private rightPad: Uint8Array;
-  private preserveShort: boolean;
-
-  constructor(
-      separator: string, nGramWidths: number[], leftPad: string,
-      rightPad: string, padWidth: number, preserveShortSequences: boolean) {
-    this.separator = util.encodeString(separator);
-    this.nGramWidths = nGramWidths;
-    this.leftPad = util.encodeString(leftPad);
-    this.rightPad = util.encodeString(rightPad);
-    this.padWidth = padWidth;
-    this.preserveShort = preserveShortSequences;
-  }
-
-  private getPadWidth(nGramWidth: number) {
-    // Ngrams can be padded with either a fixed pad width or a dynamic pad
-    // width depending on the 'padWidth' arg, but in no case should the padding
-    // ever be wider than 'nGramWidth' - 1.
-    return Math.min(
-        this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);
-  }
-
-  private getNumNGrams(length: number, nGramWidth: number) {
-    const padWidth = this.getPadWidth(nGramWidth);
-    return Math.max(0, ((length + 2 * padWidth) - nGramWidth) + 1);
-  }
-
-  private createNGrams(
-      data: Uint8Array[], splitIndex: number, output: Uint8Array[],
-      outputStartIndex: number, numNGrams: number, nGramWidth: number) {
-    for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {
-      const padWidth = this.getPadWidth(nGramWidth);
-      const leftPadding = Math.max(0, padWidth - nGramIndex);
-      const rightPadding =
-          Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));
-      const numTokens = nGramWidth - (leftPadding + rightPadding);
-      const dataStartIndex =
-          splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);
-
-      // Calculate the total expected size of the nGram so we can reserve the
-      // correct amount of space in the string.
-      let nGramSize = 0;
-      // Size of the left padding.
-      nGramSize += leftPadding * this.leftPad.length;
-      // Size of the tokens.
-      for (let n = 0; n < numTokens; ++n) {
-        nGramSize += data[dataStartIndex + n].length;
-      }
-      // Size of the right padding.
-      nGramSize += rightPadding * this.rightPad.length;
-      // Size of the separators.
-      const numSeparators = leftPadding + rightPadding + numTokens - 1;
-      nGramSize += numSeparators * this.separator.length;
-
-      // Build the nGram.
-      output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);
-      const nGram = output[outputStartIndex + nGramIndex];
-
-      let nextNGramIndex = 0;
-      const appendToNGram = (str: Uint8Array) =>
-          str.forEach((value) => nGram[nextNGramIndex++] = value);
-
-      for (let n = 0; n < leftPadding; ++n) {
-        appendToNGram(this.leftPad);
-        appendToNGram(this.separator);
-      }
-      // Only output first numTokens - 1 pairs of data and separator
-      for (let n = 0; n < numTokens - 1; ++n) {
-        appendToNGram(data[dataStartIndex + n]);
-        appendToNGram(this.separator);
-      }
-      // Handle case when there are no tokens or no right padding as these
-      // can result in consecutive separators.
-      if (numTokens > 0) {
-        // If we have tokens, then output last and then pair each separator
-        // with the right padding that follows, to ensure nGram ends either with
-        // the token or with the right pad.
-        appendToNGram(data[dataStartIndex + numTokens - 1]);
-        for (let n = 0; n < rightPadding; ++n) {
-          appendToNGram(this.separator);
-          appendToNGram(this.rightPad);
-        }
-      } else {
-        // If we don't have tokens, then the last item inserted into the nGram
-        // has been the separator from the left padding loop above. Hence,
-        // output right pad and separator and make sure to finish with a
-        // padding, not a separator.
-        for (let n = 0; n < rightPadding - 1; ++n) {
-          appendToNGram(this.rightPad);
-          appendToNGram(this.separator);
-        }
-        appendToNGram(this.rightPad);
-      }
-    }
-  }
-
-  // Data and splits together form the definition of the ragged tensor,
-  // where data is 1 dimensional and contains the values of the tensor
-  // and splits denotes the indices at which each row starts.
-  public compute(data: Uint8Array[], splits: Int32Array):
-      [Uint8Array[], Int32Array] {
-    // Validate that the splits are valid indices into data, only if there are
-    // splits specified.
-    const inputDataSize = data.length;
-    const splitsSize = splits.length;
-    if (splitsSize > 0) {
-      let prevSplit = splits[0];
-      if (prevSplit !== 0) {
-        throw new Error(`First split value must be 0, got ${prevSplit}`);
-      }
-      for (let i = 1; i < splitsSize; ++i) {
-        let validSplits = splits[i] >= prevSplit;
-        validSplits = validSplits && (splits[i] <= inputDataSize);
-        if (!validSplits) {
-          throw new Error(`Invalid split value ${splits[i]}, must be in [${
-              prevSplit}, ${inputDataSize}]`);
-        }
-        prevSplit = splits[i];
-      }
-      if (prevSplit !== inputDataSize) {
-        throw new Error(`Last split value must be data size. Expected ${
-            inputDataSize}, got ${prevSplit}`);
-      }
-    }
-
-    const numBatchItems = splitsSize - 1;
-    const nGramsSplits = util.getArrayFromDType('int32', splitsSize);
-    // If there is no data or size, return an empty ragged tensor.
-    if (inputDataSize === 0 || splitsSize === 0) {
-      const empty: Uint8Array[] = new Array(inputDataSize);
-      for (let i = 0; i <= numBatchItems; ++i) {
-        nGramsSplits[i] = 0;
-      }
-      return [empty, nGramsSplits];
-    }
-
-    nGramsSplits[0] = 0;
-    for (let i = 1; i <= numBatchItems; ++i) {
-      const length = splits[i] - splits[i - 1];
-      let numNGrams = 0;
-      this.nGramWidths.forEach((nGramWidth) => {
-        numNGrams += this.getNumNGrams(length, nGramWidth);
-      });
-      if (this.preserveShort && length > 0 && numNGrams === 0) {
-        numNGrams = 1;
-      }
-      nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;
-    }
-
-    const nGrams: Uint8Array[] = new Array(nGramsSplits[numBatchItems]);
-
-    for (let i = 0; i < numBatchItems; ++i) {
-      const splitIndex = splits[i];
-      let outputStartIdx = nGramsSplits[i];
-      this.nGramWidths.forEach((nGramWidth) => {
-        const length = splits[i + 1] - splits[i];
-        const numNGrams = this.getNumNGrams(length, nGramWidth);
-        this.createNGrams(
-            data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
-        outputStartIdx += numNGrams;
-      });
-      // If we're preserving short sequences, check to see if no sequence was
-      // generated by comparing the current output start idx to the original
-      // one (nGramSplitsdata). If no ngrams were generated, then they will
-      // be equal (since we increment outputStartIdx by numNGrams every
-      // time we create a set of ngrams.)
-      if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {
-        const dataLength = splits[i + 1] - splits[i];
-        // One legitimate reason to not have any ngrams when this.preserveShort
-        // is true is if the sequence itself is empty. In that case, move on.
-        if (dataLength === 0) {
-          continue;
-        }
-        // We don't have to worry about dynamic padding sizes here: if padding
-        // was dynamic, every sequence would have had sufficient padding to
-        // generate at least one nGram.
-        const nGramWidth = dataLength + 2 * this.padWidth;
-        const numNGrams = 1;
-        this.createNGrams(
-            data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
-      }
-    }
-    return [nGrams, nGramsSplits];
-  }
-}
-
-export function stringNGramsImpl(
-    data: Uint8Array[], dataSplits: Int32Array, separator: string,
-    nGramWidths: number[], leftPad: string, rightPad: string, padWidth: number,
-    preserveShortSequences: boolean): [Uint8Array[], Int32Array] {
-  return new StringNGramsOp(
-             separator, nGramWidths, leftPad, rightPad, padWidth,
-             preserveShortSequences)
-      .compute(data, dataSplits);
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/StringSplit.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/StringSplit.ts
deleted file mode 100644
index 8bf74a5ff..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/StringSplit.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringSplit, StringSplitAttrs, StringSplitInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {stringSplitImpl} from './StringSplit_impl';
-
-export function stringSplit(args: {
-  inputs: StringSplitInputs,
-  backend: MathBackendCPU,
-  attrs: StringSplitAttrs
-}): [TensorInfo, TensorInfo, TensorInfo] {
-  const {inputs, backend, attrs} = args;
-  const {skipEmpty} = attrs;
-  const {input, delimiter} = inputs;
-
-  if (input.dtype !== 'string') {
-    throw new Error('Input must be of datatype string');
-  }
-  if (input.shape.length !== 1) {
-    throw new Error(`Input must be a vector, got shape: ${input.shape}`);
-  }
-  if (delimiter.shape.length !== 0) {
-    throw new Error(
-        `Delimiter must be a scalar, got shape: ${delimiter.shape}`);
-  }
-
-  const $input = backend.data.get(input.dataId).values as Uint8Array[];
-  const $delimiter = backend.data.get(delimiter.dataId).values[0] as Uint8Array;
-
-  const [indices, values, shape] =
-      stringSplitImpl($input, $delimiter, skipEmpty);
-  const outputSize = values.length;
-  return [
-    backend.makeTensorInfo([outputSize, 2], 'int32', indices),
-    backend.makeTensorInfo([outputSize], 'string', values),
-    backend.makeTensorInfo([2], 'int32', new Int32Array(shape))
-  ];
-}
-
-export const stringSplitConfig: KernelConfig = {
-  kernelName: StringSplit,
-  backendName: 'cpu',
-  kernelFunc: stringSplit as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/StringSplit_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/StringSplit_impl.ts
deleted file mode 100644
index 352add3b6..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/StringSplit_impl.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TypedArray, util} from '@tensorflow/tfjs-core';
-
-function split(
-    str: Uint8Array, delimiters: Uint8Array, skipEmpty: boolean,
-    result: Uint8Array[]): void {
-  if (!str.length) {
-    return;
-  }
-  // When the delimiter is empty, the input is split into individual characters.
-  if (delimiters.length === 0) {
-    for (let i = 0; i < str.length; ++i) {
-      result.push(str.subarray(i, i + 1));
-    }
-    return;
-  }
-  // When there is one delimiter, the input is split only at that delimiter.
-  if (delimiters.length === 1) {
-    const delimiter = delimiters[0];
-    let f = str.indexOf(delimiter);
-    while (f !== -1) {
-      const token = str.subarray(0, f);
-      if (!skipEmpty || token.length !== 0) {
-        result.push(token);
-      }
-      str = str.subarray(f + 1);
-      f = str.indexOf(delimiter);
-    }
-    if (!skipEmpty || str.length !== 0) {
-      result.push(str);
-    }
-    return;
-  }
-  // When there are multiple delimiters, the input is split at every instance
-  // one of the delimiters appears.
-  let tokenStart = 0;
-  for (let i = 0; i < str.length + 1; i++) {
-    if ((i === str.length) || (delimiters.indexOf(str[i]) !== -1)) {
-      const token = str.subarray(tokenStart, i);
-      if (!skipEmpty || token.length !== 0) {
-        result.push(token);
-      }
-      tokenStart = i + 1;
-    }
-  }
-}
-
-export function stringSplitImpl(
-    input: Uint8Array[], delimiter: Uint8Array,
-    skipEmpty: boolean): [TypedArray, Uint8Array[], [number, number]] {
-  const batchSize = input.length;
-
-  // Empty delimiter means split the input character by character.
-  const tokens: Uint8Array[] = [];
-
-  let outputSize = 0;
-  let maxNumEntries = 0;
-  const numIndices: number[] = new Array(batchSize);
-  for (let i = 0; i < batchSize; ++i) {
-    const prevTokensLength = tokens.length;
-    split(input[i], delimiter, skipEmpty, tokens);
-    const nEntries = tokens.length - prevTokensLength;
-    numIndices[i] = nEntries;
-    outputSize += nEntries;
-    maxNumEntries = Math.max(maxNumEntries, nEntries);
-  }
-
-  const indices = util.getArrayFromDType('int32', outputSize * 2) as TypedArray;
-  const values: Uint8Array[] = new Array(outputSize);
-  const shape: [number, number] = [batchSize, maxNumEntries];
-
-  let c = 0;
-  for (let i = 0; i < batchSize; ++i) {
-    for (let j = 0; j < numIndices[i]; ++j) {
-      // indices is a 2d tensor with shape of [outputSize, 2]
-      indices[c * 2] = i;
-      indices[c * 2 + 1] = j;
-      values[c] = tokens[c];
-      ++c;
-    }
-  }
-
-  return [indices, values, shape];
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/StringToHashBucketFast.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/StringToHashBucketFast.ts
deleted file mode 100644
index 98bb6de1f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/StringToHashBucketFast.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringToHashBucketFast, StringToHashBucketFastAttrs, StringToHashBucketFastInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {stringToHashBucketFastImpl} from './StringToHashBucketFast_impl';
-
-export function stringToHashBucketFast(args: {
-  inputs: StringToHashBucketFastInputs,
-  backend: MathBackendCPU,
-  attrs: StringToHashBucketFastAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {numBuckets} = attrs;
-  const {input} = inputs;
-
-  if (input.dtype !== 'string') {
-    throw new Error('Input must be of datatype string');
-  }
-  if (numBuckets <= 0) {
-    throw new Error(`Number of buckets must be at least 1`);
-  }
-
-  const $input = backend.data.get(input.dataId).values as Uint8Array[];
-
-  const output = stringToHashBucketFastImpl($input, numBuckets);
-  return backend.makeTensorInfo(input.shape, 'int32', output);
-}
-
-export const stringToHashBucketFastConfig: KernelConfig = {
-  kernelName: StringToHashBucketFast,
-  backendName: 'cpu',
-  kernelFunc: stringToHashBucketFast as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/StringToHashBucketFast_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/StringToHashBucketFast_impl.ts
deleted file mode 100644
index b2ad6bde3..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/StringToHashBucketFast_impl.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TypedArray, util} from '@tensorflow/tfjs-core';
-
-export function stringToHashBucketFastImpl(
-    input: Uint8Array[], numBuckets: number): TypedArray {
-  const output = util.getArrayFromDType('int32', input.length) as TypedArray;
-
-  for (let i = 0; i < input.length; ++i) {
-    output[i] =
-        util.fingerPrint64(input[i]).modulo(numBuckets).getLowBitsUnsigned();
-  }
-
-  return output;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Sub.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Sub.ts
deleted file mode 100644
index 1456d3658..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Sub.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sub} from '@tensorflow/tfjs-core';
-
-import {createSimpleBinaryKernelImpl} from '../utils/binary_impl';
-import {binaryKernelFunc, createComplexBinaryKernelImpl} from '../utils/binary_utils';
-
-export const subImpl = createSimpleBinaryKernelImpl(
-    ((aValue: number, bValue: number) => aValue - bValue));
-export const subComplexImpl =
-    createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {
-      return {real: aReal - bReal, imag: aImag - bImag};
-    }));
-export const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);
-
-export const subConfig: KernelConfig = {
-  kernelName: Sub,
-  backendName: 'cpu',
-  kernelFunc: sub
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Sum.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Sum.ts
deleted file mode 100644
index 1ff128333..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Sum.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Sum, SumAttrs, SumInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {zeros} from '../utils/zeros_impl';
-import {cast} from './Cast';
-import {identity} from './Identity';
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function sum(
-    args: {inputs: SumInputs, backend: MathBackendCPU, attrs: SumAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  assertNotComplex(x, 'sum');
-
-  let $x;
-  if (x.dtype === 'bool') {
-    $x = cast({inputs: {x}, backend, attrs: {dtype: 'int32'}});
-  } else {
-    $x = identity({inputs: {x}, backend});
-  }
-
-  const xRank = $x.shape.length;
-  const axes = util.parseAxisParam(axis, $x.shape);
-  const permutation = backend_util.getAxesPermutation(axes, xRank);
-
-  let reductionAxes = axes;
-  let permutedX = $x;
-  if (permutation != null) {
-    permutedX =
-        transpose({inputs: {x: $x}, backend, attrs: {perm: permutation}});
-    reductionAxes = backend_util.getInnerMostAxes(reductionAxes.length, xRank);
-  }
-
-  backend_util.assertAxesAreInnerMostDims(
-      'sum', reductionAxes, permutedX.shape.length);
-
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(permutedX.shape, reductionAxes);
-  const resultDtype = backend_util.upcastType(permutedX.dtype, 'int32');
-  let result = zeros(backend, outShape, resultDtype);
-  const reduceSize = util.sizeFromShape(reduceShape);
-  const vals = backend.data.get(result.dataId).values as TypedArray;
-
-  const aVals = backend.data.get(permutedX.dataId).values as TypedArray;
-  for (let i = 0; i < vals.length; ++i) {
-    const offset = i * reduceSize;
-    let sum = 0;
-    for (let j = 0; j < reduceSize; ++j) {
-      sum += aVals[offset + j];
-    }
-    vals[i] = sum;
-  }
-
-  if (keepDims) {
-    const newShape = backend_util.expandShapeToKeepDim(result.shape, axes);
-    const oldResult = result;
-    result = reshape({inputs: {x: result}, backend, attrs: {shape: newShape}});
-    backend.disposeIntermediateTensorInfo(oldResult);
-  }
-
-  backend.disposeIntermediateTensorInfo($x);
-
-  if (permutation != null) {
-    backend.disposeIntermediateTensorInfo(permutedX);
-  }
-
-  return result;
-}
-
-export const sumConfig: KernelConfig = {
-  kernelName: Sum,
-  backendName: 'cpu',
-  kernelFunc: sum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Tan.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Tan.ts
deleted file mode 100644
index 4a828cabd..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Tan.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tan} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const tan = unaryKernelFunc(Tan, (xi) => Math.tan(xi));
-
-export const tanConfig: KernelConfig = {
-  kernelName: Tan,
-  backendName: 'cpu',
-  kernelFunc: tan,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Tanh.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Tanh.ts
deleted file mode 100644
index 89a324d79..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Tanh.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tanh} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../utils/unary_utils';
-
-export const tanh = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));
-
-export const tanhConfig: KernelConfig = {
-  kernelName: Tanh,
-  backendName: 'cpu',
-  kernelFunc: tanh,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/TensorScatterUpdate.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/TensorScatterUpdate.ts
deleted file mode 100644
index f1306a5cb..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/TensorScatterUpdate.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Rank, TensorInfo, TensorScatterUpdate, TensorScatterUpdateAttrs, TensorScatterUpdateInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {scatterImpl} from './Scatter_impl';
-
-export function tensorScatterUpdate(args: {
-  inputs: TensorScatterUpdateInputs,
-  backend: MathBackendCPU,
-  attrs: TensorScatterUpdateAttrs
-}): TensorInfo {
-  const {inputs, backend} = args;
-  const {tensor, indices, updates} = inputs;
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(updates, indices, tensor.shape);
-  const sumDupeIndices = false;
-
-  const indicesBuf = backend.bufferSync<Rank, 'int32'>(indices);
-  const updatesBuf = backend.bufferSync<Rank, 'int32'|'float32'>(updates);
-  const tensorBuf = backend.bufferSync<Rank, 'int32'|'float32'>(tensor);
-  const outBuf = scatterImpl(
-      indicesBuf, updatesBuf, tensor.shape, outputSize, sliceSize, numUpdates,
-      sliceRank, strides, tensorBuf, sumDupeIndices);
-  return backend.makeTensorInfo(tensor.shape, outBuf.dtype, outBuf.values);
-}
-
-export const tensorScatterUpdateConfig: KernelConfig = {
-  kernelName: TensorScatterUpdate,
-  backendName: 'cpu',
-  kernelFunc: tensorScatterUpdate as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Tile.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Tile.ts
deleted file mode 100644
index 0bd900d14..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Tile.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Tile, TileAttrs, TileInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {tileImpl} from './Tile_impl';
-
-export function tile(
-    args: {inputs: TileInputs, backend: MathBackendCPU, attrs: TileAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {reps} = attrs;
-
-  assertNotComplex(x, 'tile');
-  const outBuf = tileImpl(backend.bufferSync(x), reps);
-
-  return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
-}
-
-export const tileConfig: KernelConfig = {
-  kernelName: Tile,
-  backendName: 'cpu',
-  kernelFunc: tile as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Tile_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Tile_impl.ts
deleted file mode 100644
index 8953f9c79..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Tile_impl.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, DataType, Rank, TensorBuffer} from '@tensorflow/tfjs-core';
-
-/**
- * An implementation of the tile kernel shared between webgl and cpu for string
- * tensors only.
- */
-
-export function tileImpl<R extends Rank>(
-    xBuf: TensorBuffer<R, DataType>,
-    reps: number[]): TensorBuffer<R, DataType> {
-  const newShape: number[] = new Array(xBuf.rank);
-  for (let i = 0; i < newShape.length; i++) {
-    newShape[i] = xBuf.shape[i] * reps[i];
-  }
-  const result = buffer(newShape, xBuf.dtype);
-  for (let i = 0; i < result.values.length; ++i) {
-    const newLoc = result.indexToLoc(i);
-
-    const originalLoc: number[] = new Array(xBuf.rank);
-    for (let j = 0; j < originalLoc.length; j++) {
-      originalLoc[j] = newLoc[j] % xBuf.shape[j];
-    }
-
-    const originalIndex = xBuf.locToIndex(originalLoc);
-
-    result.values[i] = xBuf.values[originalIndex];
-  }
-  return result as TensorBuffer<R, DataType>;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/TopK.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/TopK.ts
deleted file mode 100644
index 74b170575..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/TopK.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, NumericDataType, TensorInfo, TopK, TopKAttrs, TopKInputs, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {topKImpl} from './TopK_impl';
-
-export function topK(
-    args: {inputs: TopKInputs, backend: MathBackendCPU, attrs: TopKAttrs}):
-    [TensorInfo, TensorInfo] {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {k, sorted} = attrs;
-
-  assertNotComplex(x, 'topk');
-
-  const xVals = backend.data.get(x.dataId).values as TypedArray;
-  const [allTopKVals, allTopKIndices] =
-      topKImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);
-
-  return [
-    backend.makeTensorInfo(
-        allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
-    backend.makeTensorInfo(
-        allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
-  ];
-}
-
-export const topKConfig: KernelConfig = {
-  kernelName: TopK,
-  backendName: 'cpu',
-  kernelFunc: topK as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/TopK_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/TopK_impl.ts
deleted file mode 100644
index 88b6d0bf1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/TopK_impl.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/** An implementation of the TopK kernel shared between webgl and cpu. */
-
-import {buffer, NumericDataType, Rank, ShapeMap, Tensor, TensorBuffer, TypedArray, util} from '@tensorflow/tfjs-core';
-
-type Pair = {
-  value: number,
-  index: number
-};
-
-const comparePair = (a: Pair, b: Pair) => {
-  const valueDiff = b.value - a.value;
-  return valueDiff === 0 ? a.index - b.index : valueDiff;
-};
-
-/**
- * Partitions array where all elements smaller than the (k+1) smallest element
- * are found to the left of it, and all larger to the right of it.
- * Based on the Floyd-Rivest Algorithm, ref:
- * https://en.wikipedia.org/wiki/Floyd%E2%80%93Rivest_algorithm
- * @param array: Array to partition
- * @param left: Left index for the interval
- * @param right: Right index for the interval
- * @param k: Desired index value, where array[k] is the (k+1)th smallest element
- *           when left = 0
- */
-function select(array: Pair[], k: number, left = 0, right = array.length - 1) {
-  while (right > left) {
-    // Use select recursively to sample a smaller set of size s
-    // the arbitrary constants 600 and 0.5 are used in the original
-    // version to minimize execution time.
-    if (right - left > 600) {
-      const n = right - left + 1;
-      const i = k - left + 1;
-      const z = Math.log(n);
-      const s = 0.5 * Math.exp(2 * z / 3);
-      const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i - n / 2);
-      const newLeft = Math.max(left, Math.floor(k - i * s / n + sd));
-      const newRight = Math.min(right, Math.floor(k + (n - i) * s / n + sd));
-      select(array, k, newLeft, newRight);
-    }
-    // partition the elements between left and right around t
-    const t = array[k];
-    let i = left;
-    let j = right;
-
-    util.swap(array, left, k);
-
-    if (comparePair(array[right], t) > 0) {
-      util.swap(array, left, right);
-    }
-    while (i < j) {
-      util.swap(array, i, j);
-      i++;
-      j--;
-      while (comparePair(array[i], t) < 0) {
-        i = i + 1;
-      }
-      while (comparePair(array[j], t) > 0) {
-        j = j - 1;
-      }
-    }
-    if (comparePair(array[left], t) === 0) {
-      util.swap(array, left, j);
-    } else {
-      j = j + 1;
-      util.swap(array, j, right);
-    }
-    // Adjust left and right towards the boundaries of the subset
-    // containing the (k - left + 1)th smallest element.
-    if (j <= k) {
-      left = j + 1;
-    }
-    if (k <= j) {
-      right = j - 1;
-    }
-  }
-}
-
-export function topKImpl<T extends Tensor, R extends Rank>(
-    x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number,
-    sorted: boolean):
-    [TensorBuffer<R, NumericDataType>, TensorBuffer<R, 'int32'>] {
-  // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.
-  const lastDim = xShape[xShape.length - 1];
-  const [batch, size] = [x.length / lastDim, lastDim];
-  const allTopKVals = util.getTypedArrayFromDType(xDtype, batch * k);
-  const allTopKIndices = util.getTypedArrayFromDType('int32', batch * k);
-
-  for (let b = 0; b < batch; b++) {
-    const offset = b * size;
-    const vals = x.subarray(offset, offset + size);
-
-    let valAndInd: Pair[] = new Array(vals.length);
-    vals.forEach(
-        (value: number, index: number) => valAndInd[index] = {value, index});
-
-    if (k < valAndInd.length) {
-      select(valAndInd, k);
-      valAndInd = valAndInd.slice(0, k);
-    }
-
-    if (sorted) {
-      valAndInd.sort(comparePair);
-    }
-    
-    const outOffset = b * k;
-    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
-    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
-    for (let i = 0; i < k; i++) {
-      topKVals[i] = valAndInd[i].value;
-      topKIndices[i] = valAndInd[i].index;
-    }
-  }
-  // Reshape back to the original input shape, except that the last
-  // dimension is k.
-  const outputShape = xShape.slice();
-  outputShape[outputShape.length - 1] = k;
-
-  return [
-    buffer(outputShape as ShapeMap[R], xDtype, allTopKVals),
-    buffer(outputShape as ShapeMap[R], 'int32', allTopKIndices)
-  ];
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Transform.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Transform.ts
deleted file mode 100644
index f15138a02..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Transform.ts
+++ /dev/null
@@ -1,257 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, NumericDataType, TensorInfo, Transform, TransformAttrs, TransformInputs, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-export function transform(args: {
-  inputs: TransformInputs,
-  attrs: TransformAttrs,
-  backend: MathBackendCPU
-}): TensorInfo {
-  const {inputs, attrs, backend} = args;
-  const {image, transforms} = inputs;
-  const {interpolation, fillMode, fillValue, outputShape} = attrs;
-
-  const [batch, imageHeight, imageWidth, numChannels] = image.shape;
-  const [outHeight, outWidth] =
-      outputShape != null ? outputShape : [imageHeight, imageWidth];
-  const outShape = [batch, outHeight, outWidth, numChannels];
-
-  const inStrides = util.computeStrides(image.shape);
-  const batchInStride = inStrides[0];
-  const rowInStride = inStrides[1];
-  const colInStride = inStrides[2];
-
-  const outStrides = util.computeStrides(outShape);
-  const batchOutStride = outStrides[0];
-  const rowOutStride = outStrides[1];
-  const colOutStride = outStrides[2];
-
-  const outVals = util.getTypedArrayFromDType(
-      image.dtype as NumericDataType, util.sizeFromShape(outShape));
-
-  outVals.fill(fillValue);
-
-  const imageVals = backend.data.get(image.dataId).values as TypedArray;
-  const transformVals =
-      backend.data.get(transforms.dataId).values as TypedArray;
-
-  // Ref TF implementation:
-  // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/image_ops.h
-  for (let b = 0; b < batch; ++b) {
-    const transform = transforms.shape[0] === 1 ?
-        transformVals :
-        transformVals.subarray(b * 8, b * 8 + 8);
-
-    for (let outY = 0; outY < outHeight; ++outY) {
-      for (let outX = 0; outX < outWidth; ++outX) {
-        for (let channel = 0; channel < numChannels; ++channel) {
-          let val;
-
-          const projection = transform[6] * outX + transform[7] * outY + 1;
-
-          if (projection === 0) {
-            // Return the fill value for infinite coordinates,
-            // which are outside the input image
-            continue;
-          }
-
-          const inX =
-              (transform[0] * outX + transform[1] * outY + transform[2]) /
-              projection;
-          const inY =
-              (transform[3] * outX + transform[4] * outY + transform[5]) /
-              projection;
-
-          const x = mapCoord(inX, imageWidth, fillMode);
-          const y = mapCoord(inY, imageHeight, fillMode);
-
-          switch (interpolation) {
-            case 'nearest':
-              val = nearestInterpolation(
-                  imageVals, imageHeight, imageWidth, batchInStride,
-                  rowInStride, colInStride, b, y, x, channel, fillValue);
-              break;
-            case 'bilinear':
-              val = bilinearInterpolation(
-                  imageVals, imageHeight, imageWidth, batchInStride,
-                  rowInStride, colInStride, b, y, x, channel, fillValue);
-              break;
-            default:
-              throw new Error(
-                  `Error in Transform: Expect 'nearest' or ` +
-                  `'bilinear', but got ${interpolation}`);
-          }
-
-          const ind =
-              b * batchOutStride + outY * rowOutStride +
-              outX * colOutStride + channel;
-
-          outVals[ind] = val;
-        }
-      }
-    }
-
-    return backend.makeTensorInfo(outShape, image.dtype, outVals);
-  }
-
-  const dataId = backend.write(outVals, outShape, image.dtype);
-  return {dataId, shape: image.shape, dtype: image.dtype};
-}
-
-export const transformConfig: KernelConfig = {
-  kernelName: Transform,
-  backendName: 'cpu',
-  kernelFunc: transform as unknown as KernelFunc
-};
-
-function mapCoord(
-    outCoord: number, len: number,
-    mode: 'constant'|'reflect'|'wrap'|'nearest') {
-  switch (mode) {
-    case 'reflect':
-      return mapCoordReflect(outCoord, len);
-    case 'wrap':
-      return mapCoordWrap(outCoord, len);
-    case 'nearest':
-      return mapCoordNearest(outCoord, len);
-    case 'constant':
-    default:
-      return mapCoordConstant(outCoord, len);
-  }
-}
-
-function mapCoordReflect(outCoord: number, len: number): number {
-  // Reflect [abcd] to [dcba|abcd|dcba].
-  let inCoord = outCoord;
-  if (inCoord < 0) {
-    if (len <= 1) {
-      inCoord = 0;
-    } else {
-      const sz2 = 2 * len;
-      if (inCoord < sz2) {
-        inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;
-      }
-      inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;
-    }
-  } else if (inCoord > len - 1) {
-    if (len <= 1) {
-      inCoord = 0;
-    } else {
-      const sz2 = 2 * len;
-      inCoord -= sz2 * Math.trunc(inCoord / sz2);
-      if (inCoord >= len) {
-        inCoord = sz2 - inCoord - 1;
-      }
-    }
-  }
-  // clamp is necessary because when outCoord = 3.5 and len = 4,
-  // inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
-  return util.clamp(0, inCoord, len - 1);
-}
-
-function mapCoordWrap(outCoord: number, len: number): number {
-  // Wrap [abcd] to [abcd|abcd|abcd].
-  let inCoord = outCoord;
-  if (inCoord < 0) {
-    if (len <= 1) {
-      inCoord = 0;
-    } else {
-      const sz = len - 1;
-      inCoord += len * (Math.trunc(-inCoord / sz) + 1);
-    }
-  } else if (inCoord > len - 1) {
-    if (len <= 1) {
-      inCoord = 0;
-    } else {
-      const sz = len - 1;
-      inCoord -= len * Math.trunc(inCoord / sz);
-    }
-  }
-  // clamp is necessary because when outCoord = -0.5 and len = 4,
-  // inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
-  return util.clamp(0, inCoord, len - 1);
-}
-
-function mapCoordConstant(outCoord: number, len: number): number {
-  return outCoord;
-}
-
-function mapCoordNearest(outCoord: number, len: number): number {
-  return util.clamp(0, outCoord, len - 1);
-}
-
-function readWithFillValue(
-    imageVals: TypedArray, imageHeight: number, imageWidth: number,
-    batchStride: number, rowStride: number, colStride: number, batch: number,
-    y: number, x: number, channel: number, fillValue: number): number {
-  const ind = batch * batchStride + y * rowStride + x * colStride + channel;
-  if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {
-    return imageVals[ind];
-  } else {
-    return fillValue;
-  }
-}
-
-function nearestInterpolation(
-    imageVals: TypedArray, imageHeight: number, imageWidth: number,
-    batchStride: number, rowStride: number, colStride: number, batch: number,
-    y: number, x: number, channel: number, fillValue: number): number {
-  const $y = Math.round(y);
-  const $x = Math.round(x);
-
-  return readWithFillValue(
-      imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride,
-      batch, $y, $x, channel, fillValue);
-}
-
-function bilinearInterpolation(
-    imageVals: TypedArray, imageHeight: number, imageWidth: number,
-    batchStride: number, rowStride: number, colStride: number, batch: number,
-    y: number, x: number, channel: number, fillValue: number) {
-  const yFloor = Math.floor(y);
-  const xFloor = Math.floor(x);
-  const yCeil = yFloor + 1;
-  const xCeil = xFloor + 1;
-  // f(x, yFloor) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yFloor)
-  //               + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yFloor)
-  const valueYFloor =
-      (xCeil - x) *
-          readWithFillValue(
-              imageVals, imageHeight, imageWidth, batchStride, rowStride,
-              colStride, batch, yFloor, xFloor, channel, fillValue) +
-      (x - xFloor) *
-          readWithFillValue(
-              imageVals, imageHeight, imageWidth, batchStride, rowStride,
-              colStride, batch, yFloor, xCeil, channel, fillValue);
-  // f(x, yCeil) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yCeil)
-  //             + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yCeil)
-  const valueYCeil =
-      (xCeil - x) *
-          readWithFillValue(
-              imageVals, imageHeight, imageWidth, batchStride, rowStride,
-              colStride, batch, yCeil, xFloor, channel, fillValue) +
-      (x - xFloor) *
-          readWithFillValue(
-              imageVals, imageHeight, imageWidth, batchStride, rowStride,
-              colStride, batch, yCeil, xCeil, channel, fillValue);
-  // f(x, y) = (yCeil - y) / (yCeil - yFloor) * f(x, yFloor)
-  //         + (y - yFloor) / (yCeil - yFloor) * f(x, yCeil)
-  return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Transpose.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Transpose.ts
deleted file mode 100644
index 06a1fbf5a..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Transpose.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Transpose, TransposeAttrs, TransposeInputs, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-import {transposeImpl} from './Transpose_impl';
-
-export function transpose(args: {
-  inputs: TransposeInputs,
-  attrs: TransposeAttrs,
-  backend: MathBackendCPU
-}): TensorInfo {
-  const {inputs, attrs, backend} = args;
-  const {x} = inputs;
-  const {perm} = attrs;
-
-  assertNotComplex(x, 'transpose');
-
-  const xRank = x.shape.length;
-
-  const newShape: number[] = new Array(xRank);
-  for (let i = 0; i < newShape.length; i++) {
-    newShape[i] = x.shape[perm[i]];
-  }
-
-  const values = backend.data.get(x.dataId).values as TypedArray;
-  const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);
-
-  const dataId = backend.write(result, newShape, x.dtype);
-  return {dataId, shape: newShape, dtype: x.dtype};
-}
-
-export const transposeConfig: KernelConfig = {
-  kernelName: Transpose,
-  backendName: 'cpu',
-  kernelFunc: transpose as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Transpose_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Transpose_impl.ts
deleted file mode 100644
index ffa2afa88..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Transpose_impl.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, NumericDataType, TypedArray} from '@tensorflow/tfjs-core';
-import {util} from '@tensorflow/tfjs-core';
-
-export function transposeImpl(
-    xVals: TypedArray, xShape: number[], dtype: DataType, perm: number[],
-    newShape: number[]): TypedArray {
-  const xRank = xShape.length;
-  const xSize = util.sizeFromShape(xShape);
-  const xStrides = util.computeStrides(xShape);
-  const newStrides = util.computeStrides(newShape);
-
-  const result = util.getTypedArrayFromDType(
-      dtype as NumericDataType, util.sizeFromShape(newShape));
-
-  for (let i = 0; i < xSize; ++i) {
-    const loc = util.indexToLoc(i, xRank, xStrides);
-
-    // Permute location.
-    const newLoc: number[] = new Array(loc.length);
-    for (let i = 0; i < newLoc.length; i++) {
-      newLoc[i] = loc[perm[i]];
-    }
-
-    const newIndex = util.locToIndex(newLoc, xRank, newStrides);
-    result[newIndex] = xVals[i];
-  }
-  return result;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Unique.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Unique.ts
deleted file mode 100644
index f0dd50fcb..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Unique.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Unique, UniqueAttrs, UniqueInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-
-import {uniqueImpl} from './Unique_impl';
-
-export function unique(
-    args: {inputs: UniqueInputs, attrs: UniqueAttrs, backend: MathBackendCPU}):
-    TensorInfo[] {
-  const {inputs, attrs, backend} = args;
-  const {axis} = attrs;
-  const {x} = inputs;
-  assertNotComplex(x, 'unique');
-
-  const values = backend.data.get(x.dataId).values;
-  const {outputValues, outputShape, indices} =
-      uniqueImpl(values, axis, x.shape, x.dtype);
-  return [
-    backend.makeTensorInfo(outputShape, x.dtype, outputValues),
-    backend.makeTensorInfo([indices.length], 'int32', indices),
-  ];
-}
-
-export const uniqueConfig: KernelConfig = {
-  kernelName: Unique,
-  backendName: 'cpu',
-  kernelFunc: unique as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Unique_impl.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Unique_impl.ts
deleted file mode 100644
index ab9bfd171..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Unique_impl.ts
+++ /dev/null
@@ -1,157 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BackendValues, DataType, TensorBuffer, TypedArray, util} from '@tensorflow/tfjs-core';
-
-export function uniqueImpl(
-    values: BackendValues, axis: number, shape: number[], dtype: DataType): {
-  outputValues: BackendValues,
-  outputShape: number[],
-  indices: BackendValues
-} {
-  // Normalize and validate axis.
-  const $axis = util.parseAxisParam(axis, shape)[0];
-
-  // Calculate the new shape that is suitable for extracting data along the
-  // given axis.
-  //
-  // The rank is 3.
-  // The size of the 1st dimension is the size of all the axes < the given axis.
-  // The size of the 2nd dimension is the same as the size of the given axis.
-  // The size of the 3rd dimension is the size of all the axes > the given axis.
-  //
-  // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the
-  // newShape would be: [2*3, 5, 4].
-  //
-  // Note that this is not the final output shape. This will be the shape for an
-  // intermediate TensorBuffer (see inputBuffer below) to allow us to extract
-  // values along the given axis. To demonstrate how it works, consider the
-  // following example:
-  //
-  // Input: a 3D tensor, with shape [1, 2, 3]
-  // [
-  //   [
-  //      [1,2,3],
-  //      [4,5,6]
-  //   ]
-  // ]
-  // Axis: 2 (the last axis).
-  // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].
-  //
-  // For this example, newShape would be: [2, 3, 1], where 2 is calculated from
-  // 1*2. The re-shaped data would look like:
-  //
-  // [
-  //   [
-  //     [1], [2], [3]
-  //   ],
-  //   [
-  //     [4], [5], [6]
-  //   ]
-  // ]
-  //
-  // Then, we can construct a 3-level nested loop by the following dimension
-  // order to extract the values along the axis (dimension1):
-  // i: dimension1       // 0,1,2 (newShape[1])
-  //   m: dimension0     // 0,1   (newShape[0])
-  //     n: dimension2   // 0     (newShape[2])
-  //
-  //                       m, i, n
-  //                      ---------
-  // Iteration 0: data at [0, 0, 0] => "1"
-  // Iteration 1: data at [1, 0, 0] => "4"
-  // We got [1,4].
-  // Iteration 2: data at [0, 1, 0] => "2"
-  // Iteration 3: data at [1, 1, 0] => "5"
-  // We got [2,5].
-  // Iteration 4: data at [0, 2, 0] => "3"
-  // Iteration 5: data at [1, 2, 0] => "6"
-  // We got [3,6].
-  const newShape = [1, shape[0], 1];
-  for (let i = 0; i < $axis; i++) {
-    newShape[0] *= shape[i];
-  }
-  newShape[1] = shape[$axis];
-  for (let i = $axis + 1; i < shape.length; i++) {
-    newShape[2] *= shape[i];
-  }
-
-  // A map from unique elements (their string representations) to their values
-  // in "indices" (below).
-  const uniqueElements = new Map<string, number>();
-  // The indices of each unique element in the original tensor along the given
-  // axis. It is 1D and has the same size as the given axis.
-  const indices = new Int32Array(shape[$axis]);
-  // Create a buffer so we can easily extract value at a given location.
-  const inputBuffer = new TensorBuffer(newShape, dtype, values as TypedArray);
-  // The indices along the given axis that have unique elements. This is a
-  // de-duped version of "indices" above.
-  const uniqueIndices: number[] = [];
-  const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
-  for (let i = 0; i < shape[$axis]; i++) {
-    // Extract values along the axis.
-    let element: string;
-    if (is1DTensor) {
-      // Fast path for 1D tensor input.
-      element = values[i].toString();
-    } else {
-      const axisValues = [];
-      for (let m = 0; m < newShape[0]; m++) {
-        for (let n = 0; n < newShape[2]; n++) {
-          axisValues.push(inputBuffer.get(m, i, n));
-        }
-      }
-      element = axisValues.join(',');
-    }
-
-    // Dedup and update various indices.
-    const existingIndex = uniqueElements.get(element);
-    if (existingIndex != null) {
-      indices[i] = existingIndex;
-    } else {
-      const uniqueIndex = uniqueElements.size;
-      uniqueElements.set(element, uniqueIndex);
-      indices[i] = uniqueIndex;
-      uniqueIndices.push(i);
-    }
-  }
-
-  // Now we know where each of the unique elements are located along the axis
-  // (uniqueIndices). Extract them from input buffer and store them in the
-  // output buffer.
-  const outputTmpShape = newShape.slice();
-  outputTmpShape[1] = uniqueElements.size;
-  const outputBuffer = new TensorBuffer(outputTmpShape, dtype);
-  uniqueIndices.forEach((uniqueElementIndex, i) => {
-    for (let m = 0; m < newShape[0]; m++) {
-      for (let n = 0; n < newShape[2]; n++) {
-        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);
-      }
-    }
-  });
-
-  // The output shape can be calculated from the input shape with the size of
-  // the given axis replaced by the number of unique elements along that axis.
-  const outputShape = shape.slice();
-  outputShape[$axis] = outputTmpShape[1];
-
-  return {
-    outputValues: outputBuffer.values as BackendValues,
-    outputShape,
-    indices,
-  };
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/Unpack.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/Unpack.ts
deleted file mode 100644
index 3f0637bf1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/Unpack.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Unpack, UnpackAttrs, UnpackInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-
-export function unpack(
-    args: {inputs: UnpackInputs, backend: MathBackendCPU, attrs: UnpackAttrs}):
-    TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {value} = inputs;
-  let {axis} = attrs;
-
-  if (axis < 0) {
-    axis += value.shape.length;
-  }
-
-  const valueRank = value.shape.length;
-
-  const num = value.shape[axis];
-  const outShape: number[] = new Array(valueRank - 1);
-  let outIndex = 0;
-  for (let i = 0; i < valueRank; i++) {
-    if (i !== axis) {
-      outShape[outIndex++] = value.shape[i];
-    }
-  }
-
-  const begin = new Array(valueRank).fill(0);
-  const size = value.shape.slice();
-  size[axis] = 1;
-  const res = new Array(num);
-  for (let i = 0; i < res.length; i++) {
-    begin[axis] = i;
-    const tempRes = slice({inputs: {x: value}, backend, attrs: {begin, size}});
-    res[i] = reshape({inputs: {x: tempRes}, backend, attrs: {shape: outShape}});
-    backend.disposeIntermediateTensorInfo(tempRes);
-  }
-
-  return res;
-}
-
-export const unpackConfig: KernelConfig = {
-  kernelName: Unpack,
-  backendName: 'cpu',
-  kernelFunc: unpack as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/UnsortedSegmentSum.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/UnsortedSegmentSum.ts
deleted file mode 100644
index aa0a80021..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/UnsortedSegmentSum.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, UnsortedSegmentSum, UnsortedSegmentSumAttrs, UnsortedSegmentSumInputs, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {cast} from './Cast';
-import {equal} from './Equal';
-import {expandDims} from './ExpandDims';
-import {multiply} from './Multiply';
-import {pack} from './Pack';
-import {sum} from './Sum';
-
-export function unsortedSegmentSum(args: {
-  inputs: UnsortedSegmentSumInputs,
-  backend: MathBackendCPU,
-  attrs: UnsortedSegmentSumAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, segmentIds} = inputs;
-  const {numSegments} = attrs;
-
-  assertNotComplex(x, 'unsortedSegmentSum');
-
-  const xRank = x.shape.length;
-  const segmentIdsRank = segmentIds.shape.length;
-  const res = [];
-  const intermediates: TensorInfo[] = [];
-
-  // Reshape the segment id's so that they can be broadcast with
-  // x. The new shape should be [segmentIds.shape, 1, ..., 1]
-  const numIters = xRank - segmentIdsRank;
-  let $segmentIds = segmentIds;
-
-  for (let i = 0; i < numIters; ++i) {
-    const expanded = expandDims(
-        {inputs: {input: $segmentIds}, backend, attrs: {dim: i + 1}});
-    $segmentIds = expanded;
-    intermediates.push(expanded);
-  }
-
-  for (let i = 0; i < numSegments; ++i) {
-    const scalarValue = util.createScalarValue(
-      i as unknown as 'int32', 'int32');
-    const segmentId = backend.makeTensorInfo([], 'int32', scalarValue);
-    const mask =
-        equal({inputs: {a: segmentId, b: $segmentIds}, backend}) as TensorInfo;
-    const maskCasted =
-        cast({inputs: {x: mask}, backend, attrs: {dtype: 'float32'}});
-    const mul =
-        multiply({inputs: {a: maskCasted, b: x}, backend}) as TensorInfo;
-    const sumTensorInfo =
-        sum({inputs: {x: mul}, backend, attrs: {axis: 0, keepDims: false}});
-    res.push(sumTensorInfo);
-    intermediates.push(segmentId);
-    intermediates.push(mask);
-    intermediates.push(maskCasted);
-    intermediates.push(mul);
-    intermediates.push(sumTensorInfo);
-  }
-
-  const result = pack({inputs: res, backend, attrs: {axis: 0}});
-
-  intermediates.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-  return result;
-}
-
-export const unsortedSegmentSumConfig: KernelConfig = {
-  kernelName: UnsortedSegmentSum,
-  backendName: 'cpu',
-  kernelFunc: unsortedSegmentSum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/ZerosLike.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/ZerosLike.ts
deleted file mode 100644
index 2a13ff9e6..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/ZerosLike.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, ZerosLike, ZerosLikeInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-
-import {complex} from './Complex';
-import {fill} from './Fill';
-import {imag} from './Imag';
-import {real} from './Real';
-
-export function zerosLike(
-    args: {inputs: ZerosLikeInputs, backend: MathBackendCPU}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  if (x.dtype === 'string') {
-    throw new Error('zerosLike is not supported for string tensors');
-  } else if (x.dtype === 'complex64') {
-    const realPart = real({inputs: {input: x}, backend});
-    const r = zerosLike({inputs: {x: realPart}, backend});
-    const imagPart = imag({inputs: {input: x}, backend});
-    const i = zerosLike({inputs: {x: imagPart}, backend});
-
-    const result = complex({inputs: {real: r, imag: i}, backend});
-
-    backend.disposeIntermediateTensorInfo(realPart);
-    backend.disposeIntermediateTensorInfo(r);
-    backend.disposeIntermediateTensorInfo(imagPart);
-    backend.disposeIntermediateTensorInfo(i);
-
-    return result;
-  } else {
-    return fill({backend, attrs: {shape: x.shape, value: 0, dtype: x.dtype}});
-  }
-}
-
-export const zerosLikeConfig: KernelConfig = {
-  kernelName: ZerosLike,
-  backendName: 'cpu',
-  kernelFunc: zerosLike as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/kernels/_FusedMatMul.ts b/tfjs-master/tfjs-backend-cpu/src/kernels/_FusedMatMul.ts
deleted file mode 100644
index d493d97a9..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/kernels/_FusedMatMul.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {applyActivation} from '../utils/fused_utils';
-
-import {add} from './Add';
-import {batchMatMul} from './BatchMatMul';
-
-export function _fusedMatMul(args: {
-  inputs: _FusedMatMulInputs,
-  attrs: _FusedMatMulAttrs,
-  backend: MathBackendCPU
-}) {
-  const {inputs, backend, attrs} = args;
-  const {a, b, bias, preluActivationWeights} = inputs;
-  const {transposeA, transposeB, activation, leakyreluAlpha} = attrs;
-
-  let current;
-  let addRes;
-  let activationRes;
-
-  const intermediates: TensorInfo[] = [];
-
-  const matMulRes =
-      batchMatMul({inputs: {a, b}, attrs: {transposeA, transposeB}, backend});
-  current = matMulRes;
-
-  if (bias) {
-    addRes = add({inputs: {a: current, b: bias}, backend}) as TensorInfo;
-    intermediates.push(current);
-    current = addRes;
-  }
-  if (activation) {
-    activationRes = applyActivation(
-        backend, current, activation, preluActivationWeights, leakyreluAlpha);
-    intermediates.push(current);
-    current = activationRes;
-  }
-
-  for (const i of intermediates) {
-    backend.disposeIntermediateTensorInfo(i);
-  }
-
-  return current;
-}
-
-export const _fusedMatMulConfig: KernelConfig = {
-  kernelName: _FusedMatMul,
-  backendName: 'cpu',
-  kernelFunc: _fusedMatMul as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-cpu/src/register_all_kernels.ts b/tfjs-master/tfjs-backend-cpu/src/register_all_kernels.ts
deleted file mode 100644
index 60f61bfd1..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/register_all_kernels.ts
+++ /dev/null
@@ -1,373 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// We explicitly import the modular kernels so they get registered in the
-// global registry when we compile the library. A modular build would replace
-// the contents of this file and import only the kernels that are needed.
-import {KernelConfig, registerKernel} from '@tensorflow/tfjs-core';
-
-import {_fusedMatMulConfig} from './kernels/_FusedMatMul';
-import {absConfig} from './kernels/Abs';
-import {acosConfig} from './kernels/Acos';
-import {acoshConfig} from './kernels/Acosh';
-import {addConfig} from './kernels/Add';
-import {addNConfig} from './kernels/AddN';
-import {allConfig} from './kernels/All';
-import {anyConfig} from './kernels/Any';
-import {argMaxConfig} from './kernels/ArgMax';
-import {argMinConfig} from './kernels/ArgMin';
-import {asinConfig} from './kernels/Asin';
-import {asinhConfig} from './kernels/Asinh';
-import {atanConfig} from './kernels/Atan';
-import {atan2Config} from './kernels/Atan2';
-import {atanhConfig} from './kernels/Atanh';
-import {avgPoolConfig} from './kernels/AvgPool';
-import {avgPool3DConfig} from './kernels/AvgPool3D';
-import {avgPool3DGradConfig} from './kernels/AvgPool3DGrad';
-import {avgPoolGradConfig} from './kernels/AvgPoolGrad';
-import {batchMatMulConfig} from './kernels/BatchMatMul';
-import {batchNormConfig} from './kernels/BatchNorm';
-import {batchToSpaceNDConfig} from './kernels/BatchToSpaceND';
-import {bincountConfig} from './kernels/Bincount';
-import {bitwiseAndConfig} from './kernels/BitwiseAnd';
-import {broadcastArgsConfig} from './kernels/BroadcastArgs';
-import {castConfig} from './kernels/Cast';
-import {ceilConfig} from './kernels/Ceil';
-import {clipByValueConfig} from './kernels/ClipByValue';
-import {complexConfig} from './kernels/Complex';
-import {complexAbsConfig} from './kernels/ComplexAbs';
-import {concatConfig} from './kernels/Concat';
-import {conv2DConfig} from './kernels/Conv2D';
-import {conv2DBackpropFilterConfig} from './kernels/Conv2DBackpropFilter';
-import {conv2DBackpropInputConfig} from './kernels/Conv2DBackpropInput';
-import {conv3DConfig} from './kernels/Conv3D';
-import {conv3DBackpropFilterV2Config} from './kernels/Conv3DBackpropFilterV2';
-import {conv3DBackpropInputV2Config} from './kernels/Conv3DBackpropInputV2';
-import {cosConfig} from './kernels/Cos';
-import {coshConfig} from './kernels/Cosh';
-import {cropAndResizeConfig} from './kernels/CropAndResize';
-import {cumprodConfig} from './kernels/Cumprod';
-import {cumsumConfig} from './kernels/Cumsum';
-import {denseBincountConfig} from './kernels/DenseBincount';
-import {depthToSpaceConfig} from './kernels/DepthToSpace';
-import {depthwiseConv2dNativeConfig} from './kernels/DepthwiseConv2dNative';
-import {depthwiseConv2dNativeBackpropFilterConfig} from './kernels/DepthwiseConv2dNativeBackpropFilter';
-import {depthwiseConv2dNativeBackpropInputConfig} from './kernels/DepthwiseConv2dNativeBackpropInput';
-import {diagConfig} from './kernels/Diag';
-import {dilation2DConfig} from './kernels/Dilation2D';
-import {dilation2DBackpropFilterConfig} from './kernels/Dilation2DBackpropFilter';
-import {dilation2DBackpropInputConfig} from './kernels/Dilation2DBackpropInput';
-import {drawConfig} from './kernels/Draw';
-import {einsumConfig} from './kernels/Einsum';
-import {eluConfig} from './kernels/Elu';
-import {eluGradConfig} from './kernels/EluGrad';
-import {equalConfig} from './kernels/Equal';
-import {erfConfig} from './kernels/Erf';
-import {expConfig} from './kernels/Exp';
-import {expandDimsConfig} from './kernels/ExpandDims';
-import {expm1Config} from './kernels/Expm1';
-import {fftConfig} from './kernels/FFT';
-import {fillConfig} from './kernels/Fill';
-import {flipLeftRightConfig} from './kernels/FlipLeftRight';
-import {floorConfig} from './kernels/Floor';
-import {floorDivConfig} from './kernels/FloorDiv';
-import {fusedConv2DConfig} from './kernels/FusedConv2D';
-import {fusedDepthwiseConv2DConfig} from './kernels/FusedDepthwiseConv2D';
-import {gatherNdConfig} from './kernels/GatherNd';
-import {gatherV2Config} from './kernels/GatherV2';
-import {greaterConfig} from './kernels/Greater';
-import {greaterEqualConfig} from './kernels/GreaterEqual';
-import {identityConfig} from './kernels/Identity';
-import {ifftConfig} from './kernels/IFFT';
-import {imagConfig} from './kernels/Imag';
-import {isFiniteConfig} from './kernels/IsFinite';
-import {isInfConfig} from './kernels/IsInf';
-import {isNaNConfig} from './kernels/IsNaN';
-import {leakyReluConfig} from './kernels/LeakyRelu';
-import {lessConfig} from './kernels/Less';
-import {lessEqualConfig} from './kernels/LessEqual';
-import {linSpaceConfig} from './kernels/LinSpace';
-import {logConfig} from './kernels/Log';
-import {log1pConfig} from './kernels/Log1p';
-import {logicalAndConfig} from './kernels/LogicalAnd';
-import {logicalNotConfig} from './kernels/LogicalNot';
-import {logicalOrConfig} from './kernels/LogicalOr';
-import {LRNConfig} from './kernels/LRN';
-import {LRNGradConfig} from './kernels/LRNGrad';
-import {maxConfig} from './kernels/Max';
-import {maximumConfig} from './kernels/Maximum';
-import {maxPoolConfig} from './kernels/MaxPool';
-import {maxPool3DConfig} from './kernels/MaxPool3D';
-import {maxPool3DGradConfig} from './kernels/MaxPool3DGrad';
-import {maxPoolGradConfig} from './kernels/MaxPoolGrad';
-import {maxPoolWithArgmaxConfig} from './kernels/MaxPoolWithArgmax';
-import {meanConfig} from './kernels/Mean';
-import {minConfig} from './kernels/Min';
-import {minimumConfig} from './kernels/Minimum';
-import {mirrorPadConfig} from './kernels/MirrorPad';
-import {modConfig} from './kernels/Mod';
-import {multinomialConfig} from './kernels/Multinomial';
-import {multiplyConfig} from './kernels/Multiply';
-import {negConfig} from './kernels/Neg';
-import {nonMaxSuppressionV3Config} from './kernels/NonMaxSuppressionV3';
-import {nonMaxSuppressionV4Config} from './kernels/NonMaxSuppressionV4';
-import {nonMaxSuppressionV5Config} from './kernels/NonMaxSuppressionV5';
-import {notEqualConfig} from './kernels/NotEqual';
-import {oneHotConfig} from './kernels/OneHot';
-import {onesLikeConfig} from './kernels/OnesLike';
-import {packConfig} from './kernels/Pack';
-import {padV2Config} from './kernels/PadV2';
-import {powConfig} from './kernels/Pow';
-import {preluConfig} from './kernels/Prelu';
-import {prodConfig} from './kernels/Prod';
-import {raggedGatherConfig} from './kernels/RaggedGather';
-import {raggedRangeConfig} from './kernels/RaggedRange';
-import {raggedTensorToTensorConfig} from './kernels/RaggedTensorToTensor';
-import {rangeConfig} from './kernels/Range';
-import {realConfig} from './kernels/Real';
-import {realDivConfig} from './kernels/RealDiv';
-import {reciprocalConfig} from './kernels/Reciprocal';
-import {reluConfig} from './kernels/Relu';
-import {relu6Config} from './kernels/Relu6';
-import {reshapeConfig} from './kernels/Reshape';
-import {resizeBilinearConfig} from './kernels/ResizeBilinear';
-import {resizeBilinearGradConfig} from './kernels/ResizeBilinearGrad';
-import {resizeNearestNeighborConfig} from './kernels/ResizeNearestNeighbor';
-import {resizeNearestNeighborGradConfig} from './kernels/ResizeNearestNeighborGrad';
-import {reverseConfig} from './kernels/Reverse';
-import {rotateWithOffsetConfig} from './kernels/RotateWithOffset';
-import {roundConfig} from './kernels/Round';
-import {rsqrtConfig} from './kernels/Rsqrt';
-import {scatterNdConfig} from './kernels/ScatterNd';
-import {searchSortedConfig} from './kernels/SearchSorted';
-import {selectConfig} from './kernels/Select';
-import {seluConfig} from './kernels/Selu';
-import {sigmoidConfig} from './kernels/Sigmoid';
-import {signConfig} from './kernels/Sign';
-import {sinConfig} from './kernels/Sin';
-import {sinhConfig} from './kernels/Sinh';
-import {sliceConfig} from './kernels/Slice';
-import {softmaxConfig} from './kernels/Softmax';
-import {softplusConfig} from './kernels/Softplus';
-import {spaceToBatchNDConfig} from './kernels/SpaceToBatchND';
-import {sparseFillEmptyRowsConfig} from './kernels/SparseFillEmptyRows';
-import {sparseReshapeConfig} from './kernels/SparseReshape';
-import {sparseSegmentMeanConfig} from './kernels/SparseSegmentMean';
-import {sparseSegmentSumConfig} from './kernels/SparseSegmentSum';
-import {sparseToDenseConfig} from './kernels/SparseToDense';
-import {splitVConfig} from './kernels/SplitV';
-import {sqrtConfig} from './kernels/Sqrt';
-import {squareConfig} from './kernels/Square';
-import {squaredDifferenceConfig} from './kernels/SquaredDifference';
-import {staticRegexReplaceConfig} from './kernels/StaticRegexReplace';
-import {stepConfig} from './kernels/Step';
-import {stridedSliceConfig} from './kernels/StridedSlice';
-import {stringNGramsConfig} from './kernels/StringNGrams';
-import {stringSplitConfig} from './kernels/StringSplit';
-import {stringToHashBucketFastConfig} from './kernels/StringToHashBucketFast';
-import {subConfig} from './kernels/Sub';
-import {sumConfig} from './kernels/Sum';
-import {tanConfig} from './kernels/Tan';
-import {tanhConfig} from './kernels/Tanh';
-import {tensorScatterUpdateConfig} from './kernels/TensorScatterUpdate';
-import {tileConfig} from './kernels/Tile';
-import {topKConfig} from './kernels/TopK';
-import {transformConfig} from './kernels/Transform';
-import {transposeConfig} from './kernels/Transpose';
-import {uniqueConfig} from './kernels/Unique';
-import {unpackConfig} from './kernels/Unpack';
-import {unsortedSegmentSumConfig} from './kernels/UnsortedSegmentSum';
-import {zerosLikeConfig} from './kernels/ZerosLike';
-
-// List all kernel configs here
-const kernelConfigs: KernelConfig[] = [
-  _fusedMatMulConfig,
-  absConfig,
-  acosConfig,
-  acoshConfig,
-  addConfig,
-  addNConfig,
-  allConfig,
-  anyConfig,
-  argMaxConfig,
-  argMinConfig,
-  asinConfig,
-  asinhConfig,
-  atanConfig,
-  atan2Config,
-  atanhConfig,
-  avgPoolConfig,
-  avgPool3DConfig,
-  avgPool3DGradConfig,
-  avgPoolGradConfig,
-  batchMatMulConfig,
-  batchNormConfig,
-  batchToSpaceNDConfig,
-  bincountConfig,
-  bitwiseAndConfig,
-  broadcastArgsConfig,
-  castConfig,
-  ceilConfig,
-  clipByValueConfig,
-  complexConfig,
-  complexAbsConfig,
-  concatConfig,
-  conv2DConfig,
-  conv2DBackpropFilterConfig,
-  conv2DBackpropInputConfig,
-  conv3DConfig,
-  conv3DBackpropFilterV2Config,
-  conv3DBackpropInputV2Config,
-  cosConfig,
-  coshConfig,
-  cropAndResizeConfig,
-  cumprodConfig,
-  cumsumConfig,
-  denseBincountConfig,
-  depthToSpaceConfig,
-  depthwiseConv2dNativeConfig,
-  depthwiseConv2dNativeBackpropFilterConfig,
-  depthwiseConv2dNativeBackpropInputConfig,
-  diagConfig,
-  dilation2DConfig,
-  dilation2DBackpropFilterConfig,
-  dilation2DBackpropInputConfig,
-  drawConfig,
-  einsumConfig,
-  eluConfig,
-  eluGradConfig,
-  equalConfig,
-  erfConfig,
-  expConfig,
-  expandDimsConfig,
-  expm1Config,
-  fftConfig,
-  fillConfig,
-  flipLeftRightConfig,
-  floorConfig,
-  floorDivConfig,
-  fusedConv2DConfig,
-  fusedDepthwiseConv2DConfig,
-  gatherNdConfig,
-  gatherV2Config,
-  greaterConfig,
-  greaterEqualConfig,
-  identityConfig,
-  ifftConfig,
-  imagConfig,
-  isFiniteConfig,
-  isInfConfig,
-  isNaNConfig,
-  leakyReluConfig,
-  lessConfig,
-  lessEqualConfig,
-  linSpaceConfig,
-  logConfig,
-  log1pConfig,
-  logicalAndConfig,
-  logicalNotConfig,
-  logicalOrConfig,
-  LRNConfig,
-  LRNGradConfig,
-  maxConfig,
-  maximumConfig,
-  maxPoolConfig,
-  maxPool3DConfig,
-  maxPool3DGradConfig,
-  maxPoolGradConfig,
-  maxPoolWithArgmaxConfig,
-  meanConfig,
-  minConfig,
-  minimumConfig,
-  mirrorPadConfig,
-  modConfig,
-  multinomialConfig,
-  multiplyConfig,
-  negConfig,
-  nonMaxSuppressionV3Config,
-  nonMaxSuppressionV4Config,
-  nonMaxSuppressionV5Config,
-  notEqualConfig,
-  oneHotConfig,
-  onesLikeConfig,
-  packConfig,
-  padV2Config,
-  powConfig,
-  preluConfig,
-  prodConfig,
-  raggedGatherConfig,
-  raggedRangeConfig,
-  raggedTensorToTensorConfig,
-  rangeConfig,
-  realConfig,
-  realDivConfig,
-  reciprocalConfig,
-  reluConfig,
-  relu6Config,
-  reshapeConfig,
-  resizeBilinearConfig,
-  resizeBilinearGradConfig,
-  resizeNearestNeighborConfig,
-  resizeNearestNeighborGradConfig,
-  reverseConfig,
-  rotateWithOffsetConfig,
-  roundConfig,
-  rsqrtConfig,
-  scatterNdConfig,
-  searchSortedConfig,
-  selectConfig,
-  seluConfig,
-  sigmoidConfig,
-  signConfig,
-  sinConfig,
-  sinhConfig,
-  sliceConfig,
-  softmaxConfig,
-  softplusConfig,
-  spaceToBatchNDConfig,
-  sparseFillEmptyRowsConfig,
-  sparseReshapeConfig,
-  sparseSegmentMeanConfig,
-  sparseSegmentSumConfig,
-  sparseToDenseConfig,
-  splitVConfig,
-  sqrtConfig,
-  squareConfig,
-  squaredDifferenceConfig,
-  staticRegexReplaceConfig,
-  stepConfig,
-  stridedSliceConfig,
-  stringNGramsConfig,
-  stringSplitConfig,
-  stringToHashBucketFastConfig,
-  subConfig,
-  sumConfig,
-  tanConfig,
-  tanhConfig,
-  tensorScatterUpdateConfig,
-  tileConfig,
-  topKConfig,
-  transformConfig,
-  transposeConfig,
-  uniqueConfig,
-  unpackConfig,
-  unsortedSegmentSumConfig,
-  zerosLikeConfig
-];
-
-for (const kernelConfig of kernelConfigs) {
-  registerKernel(kernelConfig);
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/run_tests.ts b/tfjs-master/tfjs-backend-cpu/src/run_tests.ts
deleted file mode 100644
index ff56ee4da..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/run_tests.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Register the backend.
-import './index';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-// tslint:disable-next-line: no-imports-from-dist
-import {setTestEnvs, setupTestFilters, TestFilter} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-// tslint:disable-next-line:no-require-imports
-const jasmineCtor = require('jasmine');
-// tslint:disable-next-line:no-require-imports
-
-Error.stackTraceLimit = Infinity;
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-setTestEnvs([{name: 'cpu', backendName: 'cpu', isDataSync: true}]);
-
-const coreTests = 'tfjs-core/src/tests.js';
-const cpuTests = 'tfjs-backend-cpu/src/**/*_test.js';
-
-const runner = new jasmineCtor();
-runner.loadConfig({spec_files: [cpuTests, coreTests], random: false});
-
-const TEST_FILTERS: TestFilter[] = [];
-const customInclude = (testName: string) => {
-  // Exclude webworker test
-  if (testName.includes('computation in worker')) {
-    return false;
-  }
-
-  // Include all other tests.
-  return true;
-};
-setupTestFilters(TEST_FILTERS, customInclude);
-
-runner.execute();
diff --git a/tfjs-master/tfjs-backend-cpu/src/setup_test.ts b/tfjs-master/tfjs-backend-cpu/src/setup_test.ts
deleted file mode 100644
index 13d720a55..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/setup_test.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-// Register the backend.
-import './index';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-
-// tslint:disable-next-line: no-imports-from-dist
-import {parseTestEnvFromKarmaFlags, setTestEnvs, setupTestFilters, TEST_ENVS, TestFilter} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-setTestEnvs([{name: 'cpu', backendName: 'cpu', isDataSync: true}]);
-
-const TEST_FILTERS: TestFilter[] = [];
-const customInclude = (testName: string) => {
-  return true;
-};
-setupTestFilters(TEST_FILTERS, customInclude);
-
-// Allow flags to override test envs
-// tslint:disable-next-line:no-any
-declare let __karma__: any;
-if (typeof __karma__ !== 'undefined') {
-  const testEnv = parseTestEnvFromKarmaFlags(__karma__.config.args, TEST_ENVS);
-  if (testEnv != null) {
-    setTestEnvs([testEnv]);
-  }
-}
-
-// Import and run tests from cpu.
-// tslint:disable-next-line:no-imports-from-dist
-// tslint:disable-next-line:no-require-imports
-require('./tests');
diff --git a/tfjs-master/tfjs-backend-cpu/src/shared.ts b/tfjs-master/tfjs-backend-cpu/src/shared.ts
deleted file mode 100644
index 23b6cfdb3..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/shared.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Shared functionality among backends.
-export {simpleAbsImpl} from './kernels/Abs';
-export {addImpl} from './kernels/Add';
-export {bincountImpl, bincountReduceImpl} from './kernels/Bincount_impl';
-export {bitwiseAndImpl} from './kernels/BitwiseAnd';
-export {castImpl} from './kernels/Cast';
-export {ceilImpl} from './kernels/Ceil';
-export {concatImpl} from './kernels/Concat_impl';
-export {equalImpl} from './kernels/Equal';
-export {expImpl} from './kernels/Exp';
-export {expm1Impl} from './kernels/Expm1';
-export {floorImpl} from './kernels/Floor';
-export {floorDivImpl} from './kernels/FloorDiv';
-export {gatherNdImpl} from './kernels/GatherNd_Impl';
-export {gatherV2Impl} from './kernels/GatherV2_impl';
-export {greaterImpl} from './kernels/Greater';
-export {greaterEqualImpl} from './kernels/GreaterEqual';
-export {lessImpl} from './kernels/Less';
-export {lessEqualImpl} from './kernels/LessEqual';
-export {linSpaceImpl} from './kernels/LinSpace_impl';
-export {logImpl} from './kernels/Log';
-export {maxImpl} from './kernels/Max_impl';
-export {maximumImpl} from './kernels/Maximum';
-export {minimumImpl} from './kernels/Minimum';
-export {multiplyImpl} from './kernels/Multiply';
-export {negImpl} from './kernels/Neg';
-export {notEqualImpl} from './kernels/NotEqual';
-export {prodImpl} from './kernels/Prod';
-export {raggedGatherImpl} from './kernels/RaggedGather_impl';
-export {raggedRangeImpl} from './kernels/RaggedRange_impl';
-export {raggedTensorToTensorImpl} from './kernels/RaggedTensorToTensor_impl';
-export {rangeImpl} from './kernels/Range_impl';
-export {rsqrtImpl} from './kernels/Rsqrt';
-export {scatterImpl} from './kernels/Scatter_impl';
-export {sigmoidImpl} from './kernels/Sigmoid';
-export {sliceImpl} from './kernels/Slice';
-export {sparseFillEmptyRowsImpl} from './kernels/SparseFillEmptyRows_impl';
-export {sparseReshapeImpl} from './kernels/SparseReshape_impl';
-export {sparseSegmentReductionImpl} from './kernels/SparseSegmentReduction_impl';
-export {sqrtImpl} from './kernels/Sqrt';
-export {squaredDifferenceImpl} from './kernels/SquaredDifference';
-export {staticRegexReplaceImpl} from './kernels/StaticRegexReplace';
-export {stridedSliceImpl} from './kernels/StridedSlice_impl';
-export {stringNGramsImpl} from './kernels/StringNGrams_impl';
-export {stringSplitImpl} from './kernels/StringSplit_impl';
-export {stringToHashBucketFastImpl} from './kernels/StringToHashBucketFast_impl';
-export {subImpl} from './kernels/Sub';
-export {tileImpl} from './kernels/Tile_impl';
-export {topKImpl} from './kernels/TopK_impl';
-export {transposeImpl} from './kernels/Transpose_impl';
-export {uniqueImpl} from './kernels/Unique_impl';
-export {ComplexBinaryKernelImpl, SimpleBinaryKernelImpl} from './utils/binary_types';
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/binary_impl.ts b/tfjs-master/tfjs-backend-cpu/src/utils/binary_impl.ts
deleted file mode 100644
index 79366bfe4..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/binary_impl.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType, DataValues, NumericDataType, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {SimpleBinaryKernelImpl, SimpleBinaryOperation} from './binary_types';
-
-/**
- * Template that creates implementation for binary ops. Supports broadcast.
- */
-export function createSimpleBinaryKernelImpl(op: SimpleBinaryOperation):
-    SimpleBinaryKernelImpl {
-  return (aShape: number[], bShape: number[], aVals: DataValues,
-          bVals: DataValues, dtype: DataType): [TypedArray, number[]] => {
-    const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);
-
-    const resultRank = newShape.length;
-    const resultStrides = util.computeStrides(newShape);
-    const resultSize = util.sizeFromShape(newShape);
-
-    const result =
-        util.getTypedArrayFromDType(dtype as NumericDataType, resultSize);
-
-    const aRank = aShape.length;
-    const bRank = bShape.length;
-
-    const aStrides = util.computeStrides(aShape);
-    const bStrides = util.computeStrides(bShape);
-
-    const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);
-    const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);
-
-    if (aBroadcastDims.length + bBroadcastDims.length === 0) {
-      for (let i = 0; i < result.length; ++i) {
-        result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);
-      }
-    } else {
-      for (let i = 0; i < result.length; ++i) {
-        const loc = util.indexToLoc(i, resultRank, resultStrides);
-
-        const aLoc = loc.slice(-aRank);
-        aBroadcastDims.forEach(d => aLoc[d] = 0);
-        const aIndex = util.locToIndex(aLoc, aRank, aStrides);
-
-        const bLoc = loc.slice(-bRank);
-        bBroadcastDims.forEach(d => bLoc[d] = 0);
-        const bIndex = util.locToIndex(bLoc, bRank, bStrides);
-
-        result[i] = op(aVals[aIndex], bVals[bIndex]);
-      }
-    }
-
-    return [result, newShape];
-  };
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/binary_types.ts b/tfjs-master/tfjs-backend-cpu/src/utils/binary_types.ts
deleted file mode 100644
index b0f74b2fc..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/binary_types.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, TypedArray} from '@tensorflow/tfjs-core';
-
-export type SimpleBinaryOperation = (a: number|string, b: number|string) =>
-    number;
-export type SimpleBinaryKernelImpl =
-    (aShape: number[], bShape: number[], aVals: TypedArray|string[],
-     bVals: TypedArray|string[], dtype: DataType) => [TypedArray, number[]];
-export type ComplexBinaryOperation =
-    (aReal: number, aImag: number, bReal: number, bImag: number) => {
-      real: number, imag: number
-    };
-export type ComplexBinaryKernelImpl =
-    (aShape: number[], bShape: number[], aRealVals: Float32Array,
-     aImagVals: Float32Array, bRealVals: Float32Array,
-     bImagVals: Float32Array) => [TypedArray, TypedArray, number[]];
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/binary_utils.ts b/tfjs-master/tfjs-backend-cpu/src/utils/binary_utils.ts
deleted file mode 100644
index e9f116f1d..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/binary_utils.ts
+++ /dev/null
@@ -1,194 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BinaryInputs, DataType, KernelFunc, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {cast} from '../kernels/Cast';
-import {complex} from '../kernels/Complex';
-
-import {ComplexBinaryKernelImpl, ComplexBinaryOperation, SimpleBinaryKernelImpl} from './binary_types';
-
-/**
- * Template that creates a `KernelFunc` for binary ops.
- * @param name Kernel name.
- * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.
- * @param binaryKernelComplexImpl Optional. If exists, represents a
- *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype
- *     is `complex64`.
- * @param dtype Optional. If set, the result has this dtype. Otherwise, the
- *     result has the same dtype as the first input. This is mainly used in
- *     comparison kernels, such as Equal, Less, Greater, etc.
- */
-export function binaryKernelFunc(
-    name: string, simpleImpl: SimpleBinaryKernelImpl,
-    complexImpl?: ComplexBinaryKernelImpl, dtype?: DataType): KernelFunc {
-  if (complexImpl == null) {
-    return ({inputs, backend}) => {
-      const {a, b} = inputs as BinaryInputs;
-      const cpuBackend = backend as MathBackendCPU;
-
-      assertNotComplex([a, b], name);
-
-      const aVals = cpuBackend.data.get(a.dataId).values as TypedArray;
-      const bVals = cpuBackend.data.get(b.dataId).values as TypedArray;
-
-      const decodedAVals = a.dtype === 'string' ?
-          // tslint:disable-next-line: no-any
-          backend_util.fromUint8ToStringArray(aVals as any as Uint8Array[]) :
-          aVals;
-      const decodedBVals = a.dtype === 'string' ?
-          // tslint:disable-next-line: no-any
-          backend_util.fromUint8ToStringArray(bVals as any as Uint8Array[]) :
-          bVals;
-      const $dtype = dtype || a.dtype;
-
-      const [resultData, resultShape] =
-          simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);
-
-      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
-    };
-  }
-
-  return ({inputs, backend}) => {
-    const {a, b} = inputs as BinaryInputs;
-    const cpuBackend = backend as MathBackendCPU;
-
-    if (a.dtype === 'complex64' || b.dtype === 'complex64') {
-      const $aComplex = cast(
-          {inputs: {x: a}, backend: cpuBackend, attrs: {dtype: 'complex64'}});
-
-      const $aComplexVals = cpuBackend.data.get($aComplex.dataId);
-
-      const aReal = $aComplexVals.complexTensorInfos.real;
-      const aImag = $aComplexVals.complexTensorInfos.imag;
-
-      const aRealVals =
-          cpuBackend.data.get(aReal.dataId).values as Float32Array;
-      const aImagVals =
-          cpuBackend.data.get(aImag.dataId).values as Float32Array;
-
-      const $bComplex = cast(
-          {inputs: {x: b}, backend: cpuBackend, attrs: {dtype: 'complex64'}});
-
-      const $bComplexVals = cpuBackend.data.get($bComplex.dataId);
-
-      const bReal = $bComplexVals.complexTensorInfos.real;
-      const bImag = $bComplexVals.complexTensorInfos.imag;
-
-      const bRealVals =
-          cpuBackend.data.get(bReal.dataId).values as Float32Array;
-      const bImagVals =
-          cpuBackend.data.get(bImag.dataId).values as Float32Array;
-
-      const [resultRealData, resultImagData, resultShape] = complexImpl(
-          a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);
-
-      const resultReal =
-          cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);
-
-      const resultImag =
-          cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);
-
-      const result = complex(
-          {inputs: {real: resultReal, imag: resultImag}, backend: cpuBackend});
-
-      cpuBackend.disposeIntermediateTensorInfo($aComplex);
-      cpuBackend.disposeIntermediateTensorInfo($bComplex);
-      cpuBackend.disposeIntermediateTensorInfo(resultReal);
-      cpuBackend.disposeIntermediateTensorInfo(resultImag);
-
-      return result;
-    } else {
-      const aVals = cpuBackend.data.get(a.dataId).values as TypedArray;
-      const bVals = cpuBackend.data.get(b.dataId).values as TypedArray;
-
-      const $dtype = dtype || a.dtype;
-
-      const [resultData, resultShape] =
-          simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);
-
-      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
-    }
-  };
-}
-
-/**
- * Template that creates the complex type implementation for binary ops.
- * Supports broadcast.
- */
-export function createComplexBinaryKernelImpl(op: ComplexBinaryOperation):
-    ComplexBinaryKernelImpl {
-  return (aShape: number[], bShape: number[], aRealVals: Float32Array,
-          aImagVals: Float32Array, bRealVals: Float32Array,
-          bImagVals: Float32Array): [TypedArray, TypedArray, number[]] => {
-    const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);
-    const resultSize = util.sizeFromShape(resultShape);
-    const resultRank = resultShape.length;
-    const resultStrides = util.computeStrides(resultShape);
-
-    const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);
-    const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);
-
-    const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);
-    const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);
-
-    const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);
-    const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);
-
-    const aRank = aShape.length;
-    const aStrides = util.computeStrides(aShape);
-
-    const bRank = bShape.length;
-    const bStrides = util.computeStrides(bShape);
-
-    if (aBroadcastDims.length + bBroadcastDims.length === 0) {
-      for (let i = 0; i < resultRealVals.length; i++) {
-        const aIdx = i % aVals.length;
-        const bIdx = i % bVals.length;
-
-        const result =
-            op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2],
-               bVals[bIdx * 2 + 1]);
-
-        resultRealVals[i] = result.real;
-        resultImagVals[i] = result.imag;
-      }
-    } else {
-      for (let i = 0; i < resultRealVals.length; i++) {
-        const loc = util.indexToLoc(i, resultRank, resultStrides);
-
-        const aLoc = loc.slice(-aRank);
-        aBroadcastDims.forEach(d => aLoc[d] = 0);
-        const aIndex = util.locToIndex(aLoc, aRank, aStrides);
-
-        const bLoc = loc.slice(-bRank);
-        bBroadcastDims.forEach(d => bLoc[d] = 0);
-        const bIndex = util.locToIndex(bLoc, bRank, bStrides);
-
-        const opResult =
-            op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2],
-               bVals[bIndex * 2 + 1]);
-
-        resultRealVals[i] = opResult.real;
-        resultImagVals[i] = opResult.imag;
-      }
-    }
-    return [resultRealVals, resultImagVals, resultShape];
-  };
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/fft_utils.ts b/tfjs-master/tfjs-backend-cpu/src/utils/fft_utils.ts
deleted file mode 100644
index bf5250ad3..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/fft_utils.ts
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Tensor, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {add} from '../kernels/Add';
-import {complex} from '../kernels/Complex';
-import {concat} from '../kernels/Concat';
-import {identity} from '../kernels/Identity';
-import {imag} from '../kernels/Imag';
-import {multiply} from '../kernels/Multiply';
-import {real} from '../kernels/Real';
-import {realDivConfig} from '../kernels/RealDiv';
-import {slice} from '../kernels/Slice';
-import {sub} from '../kernels/Sub';
-
-/**
- * Calculate FFT of inner most elements of batch tensor.
- */
-export function fftBatch(
-    input: TensorInfo, inverse: boolean,
-    cpuBackend: MathBackendCPU): TensorInfo {
-  const inputShape = input.shape;
-  const batch = inputShape[0];
-  const innerDim = inputShape[1];
-
-  const inputVals = cpuBackend.data.get(input.dataId);
-
-  const real2D = inputVals.complexTensorInfos.real;
-  const imag2D = inputVals.complexTensorInfos.imag;
-
-  // Collects real and imaginary values separately.
-  const resultShape = [batch, innerDim];
-  const resultSize = util.sizeFromShape(resultShape);
-  const resultReal = util.getTypedArrayFromDType('float32', resultSize);
-  const resultImag = util.getTypedArrayFromDType('float32', resultSize);
-
-  for (let b = 0; b < batch; b++) {
-    // TODO: Support slice ops for complex type.
-    const r = slice({
-      inputs: {x: real2D},
-      backend: cpuBackend,
-      attrs: {begin: [b, 0], size: [1, innerDim]}
-    });
-    const i = slice({
-      inputs: {x: imag2D},
-      backend: cpuBackend,
-      attrs: {begin: [b, 0], size: [1, innerDim]}
-    });
-
-    const input = complex({inputs: {real: r, imag: i}, backend: cpuBackend});
-
-    // Run FFT by batch element.
-    const {real, imag} = fftImpl(input, inverse, cpuBackend);
-    const res = backend_util.mergeRealAndImagArrays(real, imag);
-
-    for (let d = 0; d < innerDim; d++) {
-      const c = backend_util.getComplexWithIndex(res, d);
-      resultReal[b * innerDim + d] = c.real;
-      resultImag[b * innerDim + d] = c.imag;
-    }
-
-    cpuBackend.disposeIntermediateTensorInfo(r);
-    cpuBackend.disposeIntermediateTensorInfo(i);
-    cpuBackend.disposeIntermediateTensorInfo(input);
-  }
-
-  const $realInfo: TensorInfo =
-      cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);
-  const $imagInfo: TensorInfo =
-      cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);
-
-  const result = complex(
-      {inputs: {real: $realInfo, imag: $imagInfo}, backend: cpuBackend});
-
-  cpuBackend.disposeIntermediateTensorInfo($realInfo);
-  cpuBackend.disposeIntermediateTensorInfo($imagInfo);
-
-  return result;
-}
-
-export function fftImpl(
-    input: TensorInfo, inverse: boolean,
-    cpuBackend: MathBackendCPU): {real: Float32Array, imag: Float32Array} {
-  const inputSize = util.sizeFromShape(input.shape);
-
-  const inputVals = cpuBackend.data.get(input.dataId);
-
-  const realVals =
-      cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values as
-      Float32Array;
-
-  const imagVals =
-      cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values as
-      Float32Array;
-
-  if (isExponentOf2(inputSize)) {
-    const result =
-        fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);
-
-    const resultShape = [input.shape[0], input.shape[1]];
-
-    if (inverse) {
-      const realInfo: TensorInfo =
-          cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);
-      const imagInfo: TensorInfo =
-          cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);
-
-      const sizeInfo: TensorInfo = cpuBackend.makeTensorInfo(
-          [], 'float32',
-          util.createScalarValue(inputSize as unknown as 'float32', 'float32'));
-      const sizeInfoCopy =
-          identity({inputs: {x: sizeInfo}, backend: cpuBackend});
-
-      const divRealInfo =
-          realDivConfig.kernelFunc(
-              {inputs: {a: realInfo, b: sizeInfo}, backend: cpuBackend}) as
-          TensorInfo;
-      const divImagInfo =
-          realDivConfig.kernelFunc(
-              {inputs: {a: imagInfo, b: sizeInfoCopy}, backend: cpuBackend}) as
-          TensorInfo;
-
-      const divRealVals =
-          cpuBackend.data.get(divRealInfo.dataId).values as Float32Array;
-      const divImagVals =
-          cpuBackend.data.get(divImagInfo.dataId).values as Float32Array;
-
-      cpuBackend.disposeIntermediateTensorInfo(realInfo);
-      cpuBackend.disposeIntermediateTensorInfo(imagInfo);
-      cpuBackend.disposeIntermediateTensorInfo(sizeInfo);
-      cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);
-      cpuBackend.disposeIntermediateTensorInfo(divRealInfo);
-      cpuBackend.disposeIntermediateTensorInfo(divImagInfo);
-
-      return {real: divRealVals, imag: divImagVals};
-    }
-
-    return result;
-  } else {
-    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);
-
-    const rawOutput =
-        fourierTransformByMatmul(data, inputSize, inverse) as Float32Array;
-
-    return backend_util.splitRealAndImagArrays(rawOutput);
-  }
-}
-
-function isExponentOf2(size: number): boolean {
-  return (size & size - 1) === 0;
-}
-
-// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.
-function fftRadix2(
-    realVals: Float32Array, imagVals: Float32Array, size: number,
-    inverse: boolean,
-    cpuBackend: MathBackendCPU): {real: Float32Array, imag: Float32Array} {
-  if (size === 1) {
-    return {real: realVals, imag: imagVals};
-  }
-
-  const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);
-
-  const half = size / 2;
-
-  const evenComplex = backend_util.complexWithEvenIndex(data);
-
-  const evenRealVals = evenComplex.real;
-  const evenImagVals = evenComplex.imag;
-
-  const evenShape = [evenRealVals.length];
-
-  const evenRealInfo =
-      cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);
-  const evenImagInfo =
-      cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);
-
-  const evenTensorInfo = complex(
-      {inputs: {real: evenRealInfo, imag: evenImagInfo}, backend: cpuBackend});
-
-  const oddComplex = backend_util.complexWithOddIndex(data);
-
-  const oddRealVals = oddComplex.real;
-  const oddImagVals = oddComplex.imag;
-
-  const oddShape = [oddRealVals.length];
-
-  const oddRealInfo =
-      cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);
-  const oddImagInfo =
-      cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);
-
-  const oddTensorInfo = complex(
-      {inputs: {real: oddRealInfo, imag: oddImagInfo}, backend: cpuBackend});
-
-  // Recursive call for half part of original input.
-  const $evenComplex =
-      fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);
-
-  const $evenRealVals = $evenComplex.real;
-  const $evenImagVals = $evenComplex.imag;
-
-  const $evenShape = [$evenRealVals.length];
-
-  const $evenRealInfo =
-      cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);
-  const $evenImagInfo =
-      cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);
-
-  const $evenTensorInfo = complex({
-    inputs: {real: $evenRealInfo, imag: $evenImagInfo},
-    backend: cpuBackend
-  });
-
-  const $oddComplex =
-      fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);
-
-  const $oddRealVals = $oddComplex.real;
-  const $oddImagVals = $oddComplex.imag;
-
-  const $oddShape = [$oddRealVals.length];
-
-  const $oddRealInfo =
-      cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);
-  const $oddImagInfo =
-      cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);
-
-  const $oddTensorInfo = complex(
-      {inputs: {real: $oddRealInfo, imag: $oddImagInfo}, backend: cpuBackend});
-
-  const e = backend_util.exponents(size, inverse);
-  const eShape = [e.real.length];
-
-  const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);
-  const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);
-
-  const complexInfo = complex(
-      {inputs: {real: eRealInfo, imag: eImagInfo}, backend: cpuBackend});
-
-  const exponentInfo =
-      multiply(
-          {inputs: {a: complexInfo, b: $oddTensorInfo}, backend: cpuBackend}) as
-      TensorInfo;
-
-  const addPart = add({
-                    inputs: {a: $evenTensorInfo, b: exponentInfo},
-                    backend: cpuBackend
-                  }) as TensorInfo;
-  const subPart = sub({
-                    inputs: {a: $evenTensorInfo, b: exponentInfo},
-                    backend: cpuBackend
-                  }) as TensorInfo;
-
-  const addPartReal = real({inputs: {input: addPart}, backend: cpuBackend});
-  const subPartReal = real({inputs: {input: subPart}, backend: cpuBackend});
-
-  const addPartImag = imag({inputs: {input: addPart}, backend: cpuBackend});
-  const subPartImag = imag({inputs: {input: subPart}, backend: cpuBackend});
-
-  const $real = concat({
-    inputs: [addPartReal as Tensor, subPartReal as Tensor],
-    backend: cpuBackend,
-    attrs: {axis: 0}
-  });
-  const $imag = concat({
-    inputs: [addPartImag as Tensor, subPartImag as Tensor],
-    backend: cpuBackend,
-    attrs: {axis: 0}
-  });
-
-  const $realVals = cpuBackend.data.get($real.dataId).values as Float32Array;
-  const $imagVals = cpuBackend.data.get($imag.dataId).values as Float32Array;
-
-  cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);
-  cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);
-  cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);
-  cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);
-  cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);
-  cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);
-  cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);
-  cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);
-  cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);
-  cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);
-  cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);
-  cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);
-  cpuBackend.disposeIntermediateTensorInfo(eRealInfo);
-  cpuBackend.disposeIntermediateTensorInfo(eImagInfo);
-  cpuBackend.disposeIntermediateTensorInfo(complexInfo);
-  cpuBackend.disposeIntermediateTensorInfo(exponentInfo);
-  cpuBackend.disposeIntermediateTensorInfo(addPart);
-  cpuBackend.disposeIntermediateTensorInfo(subPart);
-  cpuBackend.disposeIntermediateTensorInfo(addPartReal);
-  cpuBackend.disposeIntermediateTensorInfo(addPartImag);
-  cpuBackend.disposeIntermediateTensorInfo(subPartReal);
-  cpuBackend.disposeIntermediateTensorInfo(subPartImag);
-  cpuBackend.disposeIntermediateTensorInfo($real);
-  cpuBackend.disposeIntermediateTensorInfo($imag);
-
-  return {real: $realVals, imag: $imagVals};
-}
-
-// Calculate fourier transform by multplying sinusoid matrix.
-function fourierTransformByMatmul(
-    data: TypedArray, size: number, inverse: boolean): TypedArray {
-  const ret = new Float32Array(size * 2);
-  // TODO: Use matmul instead once it supports complex64 type.
-  for (let r = 0; r < size; r++) {
-    let real = 0.0;
-    let imag = 0.0;
-    for (let c = 0; c < size; c++) {
-      const e = backend_util.exponent(r * c, size, inverse);
-      const term = backend_util.getComplexWithIndex(data as Float32Array, c);
-      real += term.real * e.real - term.imag * e.imag;
-      imag += term.real * e.imag + term.imag * e.real;
-    }
-    if (inverse) {
-      real /= size;
-      imag /= size;
-    }
-    backend_util.assignToTypedArray(ret, real, imag, r);
-  }
-  return ret;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/fused_utils.ts b/tfjs-master/tfjs-backend-cpu/src/utils/fused_utils.ts
deleted file mode 100644
index b2edefbe6..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/fused_utils.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs, backend_util, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {elu} from '../kernels/Elu';
-import {identity} from '../kernels/Identity';
-import {leakyRelu} from '../kernels/LeakyRelu';
-import {prelu} from '../kernels/Prelu';
-import {relu} from '../kernels/Relu';
-import {relu6} from '../kernels/Relu6';
-import {sigmoid} from '../kernels/Sigmoid';
-
-export function applyActivation(
-    backend: MathBackendCPU, x: TensorInfo, activation: backend_util.Activation,
-    preluActivationWeights?: TensorInfo, leakyreluAlpha?: number): TensorInfo {
-  if (activation === 'linear') {
-    return identity({inputs: {x}, backend});
-  } else if (activation === 'relu') {
-    return relu({inputs: {x}, backend}) as TensorInfo;
-  } else if (activation === 'elu') {
-    return elu({inputs: {x}, backend}) as TensorInfo;
-  } else if (activation === 'relu6') {
-    return relu6({inputs: {x}, backend}) as TensorInfo;
-  } else if (activation === 'prelu') {
-    return prelu({inputs: {x, alpha: preluActivationWeights}, backend});
-  } else if (activation === 'leakyrelu') {
-    return leakyRelu({inputs: {x}, backend, attrs: {alpha: leakyreluAlpha}});
-  } else if (activation === 'sigmoid') {
-    return sigmoid({inputs: {x}, backend}) as TensorInfo;
-  }
-  throw new Error(
-      `Activation ${activation} has not been implemented for the CPU backend.`);
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/pool_utils.ts b/tfjs-master/tfjs-backend-cpu/src/utils/pool_utils.ts
deleted file mode 100644
index fa6fa2522..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/pool_utils.ts
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, buffer, DataType, Rank, TensorBuffer, TypedArray} from '@tensorflow/tfjs-core';
-
-export function pool(
-    xValues: TypedArray, xShape: number[], dtype: DataType, strides: number[],
-    convInfo: backend_util.Conv2DInfo,
-    poolType: 'max'|'avg'): TensorBuffer<Rank, DataType> {
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-  const padTop = convInfo.padInfo.top;
-  const padLeft = convInfo.padInfo.left;
-
-  const initialValue =
-      (poolType === 'max' ? Number.NEGATIVE_INFINITY :
-                            Number.POSITIVE_INFINITY);
-
-  const output = buffer(convInfo.outShape, dtype);
-  const outputVals = output.values;
-
-  const outputBatchStrides =
-      convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];
-  const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];
-  const outputColStrides = convInfo.outShape[3];
-
-  for (let b = 0; b < convInfo.batchSize; ++b) {
-    const outputBatchOffset = b * outputBatchStrides;
-    const inputBatchOffset = b * strides[0];
-    for (let d = 0; d < convInfo.inChannels; ++d) {
-      for (let yR = 0; yR < convInfo.outHeight; ++yR) {
-        const xRCorner = yR * strideHeight - padTop;
-        const xRMin = Math.max(0, xRCorner);
-        const xRMax =
-            Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
-        const outputRowOffset = outputBatchOffset + yR * outputRowStrides;
-        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
-          const xCCorner = yC * strideWidth - padLeft;
-          const xCMin = Math.max(0, xCCorner);
-          const xCMax =
-              Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
-          let minMaxValue = initialValue;
-          let avgValue = 0;
-          let count = 0;
-          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
-            const xROffset = inputBatchOffset + xR * strides[1];
-            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
-              const xCOffset = xROffset + xC * strides[2];
-              const pixel = xValues[xCOffset + d];
-              if ((poolType === 'max' && pixel > minMaxValue)) {
-                minMaxValue = pixel;
-              } else if (poolType === 'avg') {
-                avgValue += pixel;
-                count++;
-              }
-            }
-            if (isNaN(minMaxValue)) {
-              break;
-            }
-          }
-          const outputOffset = outputRowOffset + yC * outputColStrides + d;
-          outputVals[outputOffset] =
-              poolType === 'avg' ? avgValue / count : minMaxValue;
-        }
-      }
-    }
-  }
-  return output;
-}
-
-export function maxPoolPositions(
-    xValues: TypedArray, xShape: number[], dtype: DataType,
-    convInfo: backend_util.Conv2DInfo, flattenPositions = false,
-    includeBatchInIndex = false): TensorBuffer<Rank, 'int32'> {
-  const maxPositions = buffer(convInfo.outShape, 'int32');
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-  const padTop = convInfo.padInfo.top;
-  const padLeft = convInfo.padInfo.left;
-
-  const xBuf = buffer(xShape, dtype, xValues);
-  for (let b = 0; b < convInfo.batchSize; ++b) {
-    for (let d = 0; d < convInfo.inChannels; ++d) {
-      for (let yR = 0; yR < convInfo.outHeight; ++yR) {
-        const xRCorner = yR * strideHeight - padTop;
-        let xRMin = xRCorner;
-        while (xRMin < 0) {
-          xRMin += dilationHeight;
-        }
-        // const xRMin = Math.max(0, xRCorner);
-        const xRMax =
-            Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
-        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
-          const xCCorner = yC * strideWidth - padLeft;
-          let xCMin = xCCorner;
-          while (xCMin < 0) {
-            xCMin += dilationWidth;
-          }
-          const xCMax =
-              Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
-          let maxValue = Number.NEGATIVE_INFINITY;
-          let maxPosition = -1;
-
-          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
-            const wR = xR - xRCorner;
-            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
-              const wC = xC - xCCorner;
-              // For some reason, disable-next-line is not working
-              // TODO(mattsoulanille): Remove this when switching to TS5.
-              /* tslint:disable: no-unnecessary-type-assertion */
-              const pixel = xBuf.get(b, xR, xC, d) as number;
-              if (pixel > maxValue) {
-                maxValue = pixel as number;
-                if (flattenPositions) {
-                  maxPosition = includeBatchInIndex ?
-                      ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *
-                              convInfo.inChannels +
-                          d :
-                      (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;
-                } else {
-                  maxPosition = wR * effectiveFilterWidth + wC;
-                }
-              }
-            }
-          }
-          maxPositions.set(maxPosition, b, yR, yC, d);
-        }
-      }
-    }
-  }
-  return maxPositions;
-}
-
-export function pool3d(
-    xValues: TypedArray, xShape: number[], dtype: DataType, strides: number[],
-    convInfo: backend_util.Conv3DInfo,
-    poolType: 'max'|'avg'): TensorBuffer<Rank, DataType> {
-  const strideDepth = convInfo.strideDepth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const dilationDepth = convInfo.dilationDepth;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
-  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-  const padFront = convInfo.padInfo.front;
-  const padTop = convInfo.padInfo.top;
-  const padLeft = convInfo.padInfo.left;
-
-  const initialValue =
-      (poolType === 'max' ? Number.NEGATIVE_INFINITY :
-                            Number.POSITIVE_INFINITY);
-
-  const output = buffer(convInfo.outShape, dtype);
-  const outputVals = output.values;
-
-  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *
-      convInfo.outShape[3] * convInfo.outShape[4];
-  const outputDepthStrides =
-      convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
-  const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];
-  const outputColStrides = convInfo.outShape[4];
-
-  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
-    const outputBatchOffset = batch * outputBatchStrides;
-    const inputBatchOffset = batch * strides[0];
-    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
-      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
-        const xDepthCorner = yDepth * strideDepth - padFront;
-        let xDepthMin = xDepthCorner;
-        while (xDepthMin < 0) {
-          xDepthMin += dilationDepth;
-        }
-        const xDepthMax =
-            Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
-        const outputDepthOffset =
-            outputBatchOffset + yDepth * outputDepthStrides;
-        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
-          const xRowCorner = yRow * strideHeight - padTop;
-          let xRowMin = xRowCorner;
-          while (xRowMin < 0) {
-            xRowMin += dilationHeight;
-          }
-          const xRowMax =
-              Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
-          const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;
-          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
-            const xColCorner = yCol * strideWidth - padLeft;
-            let xColMin = xColCorner;
-            while (xColMin < 0) {
-              xColMin += dilationWidth;
-            }
-            const xColMax =
-                Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
-            // Shader code begins
-            const outputColOffset = outputRowOffset + yCol * outputColStrides;
-            let minMaxValue = initialValue;
-            let avgValue = 0;
-            let count = 0;
-            for (let xDepth = xDepthMin; xDepth < xDepthMax;
-                 xDepth += dilationDepth) {
-              const xDepthOffset = inputBatchOffset + xDepth * strides[1];
-              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
-                const xRowOffset = xDepthOffset + xRow * strides[2];
-                for (let xCol = xColMin; xCol < xColMax;
-                     xCol += dilationWidth) {
-                  const xColOffset = xRowOffset + xCol * strides[3];
-                  const pixel = xValues[xColOffset + channel];
-                  if ((poolType === 'max' && pixel > minMaxValue)) {
-                    minMaxValue = pixel;
-                  } else if (poolType === 'avg') {
-                    avgValue += pixel;
-                    count++;
-                  }
-                  if (isNaN(minMaxValue)) {
-                    break;
-                  }
-                }
-                if (isNaN(minMaxValue)) {
-                  break;
-                }
-              }
-              if (isNaN(minMaxValue)) {
-                break;
-              }
-            }
-            const outputOffset = outputColOffset + channel;
-            outputVals[outputOffset] = poolType === 'avg' ?
-                avgValue / Math.max(count, 1) :
-                minMaxValue;
-          }
-        }
-      }
-    }
-  }
-
-  return output;
-}
-
-export function maxPool3dPositions(
-    xBuf: TensorBuffer<Rank, DataType>,
-    convInfo: backend_util.Conv3DInfo): TensorBuffer<Rank, DataType> {
-  const maxPositions = buffer(convInfo.outShape, 'int32');
-  const strideDepth = convInfo.strideDepth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const dilationDepth = convInfo.dilationDepth;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
-  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-  const padFront = convInfo.padInfo.front;
-  const padTop = convInfo.padInfo.top;
-  const padLeft = convInfo.padInfo.left;
-
-  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
-    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
-      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
-        const xDepthCorner = yDepth * strideDepth - padFront;
-        let xDepthMin = xDepthCorner;
-        while (xDepthMin < 0) {
-          xDepthMin += dilationDepth;
-        }
-        const xDepthMax =
-            Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
-        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
-          const xRowCorner = yRow * strideHeight - padTop;
-          let xRowMin = xRowCorner;
-          while (xRowMin < 0) {
-            xRowMin += dilationHeight;
-          }
-          const xRowMax =
-              Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
-          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
-            const xColCorner = yCol * strideWidth - padLeft;
-            let xColMin = xColCorner;
-            while (xColMin < 0) {
-              xColMin += dilationWidth;
-            }
-            const xColMax =
-                Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
-
-            // Shader code begins
-            let maxValue = Number.NEGATIVE_INFINITY;
-            let maxPosition = -1;
-
-            for (let xDepth = xDepthMin; xDepth < xDepthMax;
-                 xDepth += dilationDepth) {
-              const wDepth = xDepth - xDepthCorner;
-              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
-                const wRow = xRow - xRowCorner;
-                for (let xCol = xColMin; xCol < xColMax;
-                     xCol += dilationWidth) {
-                  const wCol = xCol - xColCorner;
-                  const pixel = xBuf.get(batch, xDepth, xRow, xCol,
-                                         channel) as number;
-                  if (pixel >= maxValue) {
-                    maxValue = pixel as number;
-                    maxPosition =
-                        wDepth * effectiveFilterHeight * effectiveFilterWidth +
-                        wRow * effectiveFilterHeight + wCol;
-                  }
-                }
-              }
-            }
-
-            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);
-          }
-        }
-      }
-    }
-  }
-
-  return maxPositions;
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/unary_impl.ts b/tfjs-master/tfjs-backend-cpu/src/utils/unary_impl.ts
deleted file mode 100644
index fcf27ad2f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/unary_impl.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {util} from '@tensorflow/tfjs-core';
-
-import {SimpleUnaryImpl, SimpleUnaryOperation} from './unary_types';
-
-/**
- * Template that creates implementation for unary op.
- */
-export function createSimpleUnaryImpl<I extends number | string = number,
-  O extends number | string = number>(op: SimpleUnaryOperation<I, O>):
-    SimpleUnaryImpl<I, O> {
-  return (values, dtype, attrs) => {
-    const newValues =
-        util.getArrayFromDType(dtype, values.length);
-    for (let i = 0; i < values.length; ++i) {
-      newValues[i] = op(values[i], attrs);
-    }
-    return newValues;
-  };
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/unary_types.ts b/tfjs-master/tfjs-backend-cpu/src/utils/unary_types.ts
deleted file mode 100644
index 50af2116c..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/unary_types.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataTypeFor, DataTypeMap, NamedAttrMap} from '@tensorflow/tfjs-core';
-
-export type SimpleUnaryOperation<I extends number | string = number,
-    O extends number | string = number> = (x: I, attrs?: NamedAttrMap) => O;
-
-export type SimpleUnaryImpl<I extends number | string = number | string,
-  O extends number | string = number | string> =
-  (values: ArrayLike<I>, dtype: DataTypeFor<O>,
-   attrs?: NamedAttrMap) => DataTypeMap[DataTypeFor<O>];
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/unary_utils.ts b/tfjs-master/tfjs-backend-cpu/src/utils/unary_utils.ts
deleted file mode 100644
index 1f913804f..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/unary_utils.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataTypeFor, KernelFunc, UnaryInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendCPU} from '../backend_cpu';
-import {assertNotComplex} from '../cpu_util';
-import {createSimpleUnaryImpl} from './unary_impl';
-
-import {SimpleUnaryImpl, SimpleUnaryOperation} from './unary_types';
-
-/**
- * Template that creates a `KernelFunc` for unary ops.
- * @param name Kernel name.
- * @param op A `SimpleUnaryOperation` for the kernel.
- * @param dtype Optional. If set, the result has this dtype. Otherwise, the
- *     result has the same dtype as the input. This is mainly used in certain
- *     kernels that return bool type, such as isFinite, isInf, etc.
- */
-export function unaryKernelFunc<I extends number | string = number,
-  O extends number | string = number>(
-  name: string, op: SimpleUnaryOperation<I, O>,
-  dtype?: DataTypeFor<O>): KernelFunc {
-
-  const impl = createSimpleUnaryImpl<I, O>(op);
-
-  return unaryKernelFuncFromImpl<I, O>(name, impl, dtype);
-}
-
-/**
- * Template that creates a `KernelFunc` for unary ops from the given
- * `SimpleUnaryImpl`..
- * @param name Kernel name.
- * @param unaryImpl A `SimpleUnaryImpl` that implements the op.
- * @param dtype Optional. If set, the result has this dtype. Otherwise, the
- *     result has the same dtype as the input. This is mainly used in certain
- *     kernels that return bool type, such as isFinite, isInf, etc.
- */
-export function unaryKernelFuncFromImpl<I extends number | string = number,
-  O extends number | string = number>(
-  name: string, unaryImpl: SimpleUnaryImpl<I, O>,
-  dtype?: DataTypeFor<O>): KernelFunc {
-
-  return ({inputs, attrs, backend}) => {
-    const {x} = inputs as UnaryInputs;
-    assertNotComplex(x, name);
-
-    const cpuBackend = backend as MathBackendCPU;
-    const values = cpuBackend.data.get(x.dataId).values;
-    let decoded: ArrayLike<I>;
-    if (x.dtype === 'string') {
-      if (!Array.isArray(values)) {
-        throw new Error('String tensor\'s value was not an instance of Array');
-      }
-      decoded = backend_util.fromUint8ToStringArray(values) as unknown as
-        ArrayLike<I>;
-    } else {
-      decoded = values as unknown as ArrayLike<I>;
-    }
-
-    const $dtype = dtype || x.dtype as DataTypeFor<O>;
-    const newValues = unaryImpl(decoded, $dtype, attrs);
-    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
-  };
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/utils/zeros_impl.ts b/tfjs-master/tfjs-backend-cpu/src/utils/zeros_impl.ts
deleted file mode 100644
index b6699faf9..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/utils/zeros_impl.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, TensorInfo, util} from '@tensorflow/tfjs-core';
-import {MathBackendCPU} from '../backend_cpu';
-import {complex} from '../kernels/Complex';
-
-/**
- * Generates a tensorInfo with all zeros value.
- * @param backend cpu backend.
- * @param shape Shape for the zeros tensor.
- * @param dtype Optional. If set, the result has this dtype.
- */
-export function zeros(
-    backend: MathBackendCPU, shape: number[],
-    dtype: DataType = 'float32'): TensorInfo {
-  if (dtype === 'complex64') {
-    const real = zeros(backend, shape, 'float32');
-    const imag = zeros(backend, shape, 'float32');
-
-    return complex({inputs: {real, imag}, backend});
-  }
-
-  const values = util.makeZerosTypedArray(util.sizeFromShape(shape), dtype);
-
-  return backend.makeTensorInfo(shape, dtype, values);
-}
diff --git a/tfjs-master/tfjs-backend-cpu/src/version.ts b/tfjs-master/tfjs-backend-cpu/src/version.ts
deleted file mode 100644
index 5fa574e7d..000000000
--- a/tfjs-master/tfjs-backend-cpu/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.0';
-export {version};
diff --git a/tfjs-master/tfjs-backend-cpu/tsconfig.json b/tfjs-master/tfjs-backend-cpu/tsconfig.json
deleted file mode 100644
index ecb732e21..000000000
--- a/tfjs-master/tfjs-backend-cpu/tsconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-backend-cpu/yarn.lock b/tfjs-master/tfjs-backend-cpu/yarn.lock
deleted file mode 100644
index db79cb196..000000000
--- a/tfjs-master/tfjs-backend-cpu/yarn.lock
+++ /dev/null
@@ -1,28 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@bazel/bazelisk@^1.12.0":
-  version "1.12.0"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.12.0.tgz#f08aebbf4afcb12684422450b0845dd6ef5cfe50"
-  integrity sha512-7oQusq1e4AIyFgotxVV7Pc40Et0QyvoVjujL+7/qV5Vrbfh0Nj3CfqSgl63weEyI4r0+K6RlGVsjfRuBi05p5w==
-
-"@bazel/ibazel@^0.16.2":
-  version "0.16.2"
-  resolved "https://registry.yarnpkg.com/@bazel/ibazel/-/ibazel-0.16.2.tgz#05dd7f06659759fda30f87b15534f1e42f1201bb"
-  integrity sha512-KgqAWMH0emL6f3xH6nqyTryoBMqlJ627LBIe9PT1PRRQPz2FtHib3FIHJPukp1slzF3hJYZvdiVwgPnHbaSOOA==
-
-"@types/seedrandom@^2.4.28":
-  version "2.4.30"
-  resolved "https://registry.yarnpkg.com/@types/seedrandom/-/seedrandom-2.4.30.tgz#d2efe425869b84163c2d56e779dddadb9372cbfa"
-  integrity sha512-AnxLHewubLVzoF/A4qdxBGHCKifw8cY32iro3DQX9TPcetE95zBeVt3jnsvtvAUf1vwzMfwzp4t/L2yqPlnjkQ==
-
-minimist@1.2.6:
-  version "1.2.6"
-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.6.tgz#8637a5b759ea0d6e98702cfb3a9283323c93af44"
-  integrity sha512-Jsjnk4bw3YJqYzbdyBiNsPWHPfO++UGG749Cxs6peCu5Xg4nrena6OVxOYxrQTqww0Jmwt+Ref8rggumkTLz9Q==
-
-seedrandom@^3.0.5:
-  version "3.0.5"
-  resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-3.0.5.tgz#54edc85c95222525b0c7a6f6b3543d8e0b3aa0a7"
-  integrity sha512-8OwmbklUNzwezjGInmZ+2clQmExQPvomqjL7LFqOYqtmuxRgQYqOD3mHaU+MvZn5FLUeVxVfQjwLZW/n/JFuqg==
\ No newline at end of file
diff --git a/tfjs-master/tfjs-backend-nodegl/.vscode/settings.json b/tfjs-master/tfjs-backend-nodegl/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-backend-nodegl/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-backend-nodegl/README.md b/tfjs-master/tfjs-backend-nodegl/README.md
deleted file mode 100644
index f423d58ab..000000000
--- a/tfjs-master/tfjs-backend-nodegl/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Headless WebGL backend for TensorFlow.js via Node.js
-
-** This project is under heavy development **
-
-This new backend will provide a light-weight headless WebGL runtime for TensorFlow.js running under Node.js. This new backend is powered by the [node-gles](https://github.com/google/node-gles) module which uses [ANGLE](https://github.com/google/angle) to provide an integration layer to system GL runtime. This package aims to provide a think acceleration engine for IoT, desktop, and Node.js applications where CUDA (size/OS compatibility) is not an option.
diff --git a/tfjs-master/tfjs-backend-nodegl/demo/README.md b/tfjs-master/tfjs-backend-nodegl/demo/README.md
deleted file mode 100644
index 761f5dd87..000000000
--- a/tfjs-master/tfjs-backend-nodegl/demo/README.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# MobileNet tfjs-backend-nodegl Demo
-
-*This is a very early demo to show how tfjs-backend-nodegl can be used for headless WebGL acceleration.*
-
-To run this demo, perform the following:
-
-1. Move into `tfjs-backend-nodegl` (parent directory of this demo folder):
-```sh
-$ cd tfjs-backend-nodegl
-```
-
-2. Build package and compile TypeScript:
-```sh
-$ yarn && yarn tsc
-```
-
-3. Move into the demo directory:
-```sh
-$ cd demo
-```
-
-4. Prep and build demo:
-```sh
-$ yarn
-```
-
-5. Run demo:
-```sh
-$ node run_mobilenet_inference.js dog.jpg
-```
-
-Expected output:
-```sh
-$ node run_mobilenet_inference.js dog.jpg
-Platform node has already been set. Overwriting the platform with [object Object].
-  - gl.VERSION: OpenGL ES 3.0 (ANGLE 2.1.0.9512a0ef062a)
-  - gl.RENDERER: ANGLE (Intel Inc., Intel(R) Iris(TM) Plus Graphics 640, OpenGL 4.1 core)
-  - Loading model...
-  - Mobilenet load: 6450.763924002647ms
-  - Coldstarting model...
-  - Mobilenet cold start: 297.92842200398445ms
-  - Running inference (100x) ...
-  - Mobilenet inference: (100x) : 35.75772546708584ms
-```
diff --git a/tfjs-master/tfjs-backend-nodegl/demo/dog.jpg b/tfjs-master/tfjs-backend-nodegl/demo/dog.jpg
deleted file mode 100644
index 815e2cae8..000000000
Binary files a/tfjs-master/tfjs-backend-nodegl/demo/dog.jpg and /dev/null differ
diff --git a/tfjs-master/tfjs-backend-nodegl/demo/package.json b/tfjs-master/tfjs-backend-nodegl/demo/package.json
deleted file mode 100644
index d9830beb7..000000000
--- a/tfjs-master/tfjs-backend-nodegl/demo/package.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "name": "tfjs-backend-nodegl-demo",
-  "version": "1.0.0",
-  "description": "",
-  "main": "index.js",
-  "scripts": {
-    "test": "echo \"Error: no test specified\" && exit 1"
-  },
-  "dependencies": {
-    "@tensorflow-models/mobilenet": "^2.1.0",
-    "@tensorflow/tfjs": "^3.18.0",
-    "jpeg-js": "^0.4.4"
-  },
-  "author": "",
-  "license": "ISC"
-}
diff --git a/tfjs-master/tfjs-backend-nodegl/demo/run_mobilenet_inference.js b/tfjs-master/tfjs-backend-nodegl/demo/run_mobilenet_inference.js
deleted file mode 100644
index 7d2d52309..000000000
--- a/tfjs-master/tfjs-backend-nodegl/demo/run_mobilenet_inference.js
+++ /dev/null
@@ -1,90 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const mobilenet = require('@tensorflow-models/mobilenet');
-const tf = require('@tensorflow/tfjs');
-const fs = require('fs');
-const jpeg = require('jpeg-js');
-
-const backendNodeGL = require('./../dist/index');
-
-const gl = tf.backend().getGPGPUContext().gl;
-console.log(`  - gl.VERSION: ${gl.getParameter(gl.VERSION)}`);
-console.log(`  - gl.RENDERER: ${gl.getParameter(gl.RENDERER)}`);
-
-const NUMBER_OF_CHANNELS = 3;
-const PREPROCESS_DIVISOR = 255 / 2;
-
-function readImageAsJpeg(path) {
-  return jpeg.decode(fs.readFileSync(path), true);
-}
-
-function imageByteArray(image, numChannels) {
-  const pixels = image.data;
-  const numPixels = image.width * image.height;
-  const values = new Int32Array(numPixels * numChannels);
-  for (let i = 0; i < numPixels; i++) {
-    for (let j = 0; j < numChannels; j++) {
-      values[i * numChannels + j] = pixels[i * 4 + j];
-    }
-  }
-  return values;
-}
-
-function imageToInput(image, numChannels) {
-  const values = imageByteArray(image, numChannels);
-  const outShape = [1, image.height, image.width, numChannels];
-  const input = tf.tensor4d(values, outShape, 'float32');
-  return tf.div(tf.sub(input, PREPROCESS_DIVISOR), PREPROCESS_DIVISOR);
-}
-
-async function run(path) {
-  const image = readImageAsJpeg(path);
-  const input = imageToInput(image, NUMBER_OF_CHANNELS);
-
-  console.log('  - Loading model...');
-  let start = tf.util.now();
-  const model = await mobilenet.load();
-  let end = tf.util.now();
-  console.log(`  - Mobilenet load: ${end - start}ms`);
-
-  start = tf.util.now();
-  console.log('  - Coldstarting model...');
-  await model.classify(input);
-  end = tf.util.now();
-  console.log(`  - Mobilenet cold start: ${end - start}ms`);
-
-  const times = 100;
-  let totalMs = 0;
-  console.log(`  - Running inference (${times}x) ...`);
-  for (let i = 0; i < times; i++) {
-    start = tf.util.now();
-    await model.classify(input);
-    end = tf.util.now();
-
-    totalMs += end - start;
-  }
-
-  console.log(`  - Mobilenet inference: (${times}x) : ${(totalMs / times)}ms`);
-}
-
-if (process.argv.length !== 3) {
-  throw new Error(
-      'incorrect arguments: node packaged-mobilenet-test.js <IMAGE_FILE>');
-}
-
-run(process.argv[2]);
diff --git a/tfjs-master/tfjs-backend-nodegl/demo/yarn.lock b/tfjs-master/tfjs-backend-nodegl/demo/yarn.lock
deleted file mode 100644
index fd5e05191..000000000
--- a/tfjs-master/tfjs-backend-nodegl/demo/yarn.lock
+++ /dev/null
@@ -1,307 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
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-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow-models/mobilenet/-/mobilenet-2.1.0.tgz#58583f0793a7091eda370aa441d09d94b808aeb1"
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-
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-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-3.18.0.tgz#f0596911c14adf3dfa428e7d04305ef37c6f65e1"
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-  dependencies:
-    "@types/seedrandom" "2.4.27"
-    seedrandom "2.4.3"
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-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-3.18.0.tgz#56d806a43b4695b1af4ee04c01d6381b1973c2c7"
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-  dependencies:
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-    "@types/offscreencanvas" "~2019.3.0"
-    "@types/seedrandom" "2.4.27"
-    "@types/webgl-ext" "0.0.30"
-    "@types/webgl2" "0.0.6"
-    seedrandom "2.4.3"
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-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-converter/-/tfjs-converter-3.18.0.tgz#f4b6d8812d133aa0b6eaa06f75abbde1dbb3550f"
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-    "@types/offscreencanvas" "~2019.3.0"
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-    "@types/webgl-ext" "0.0.30"
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-    long "4.0.0"
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-    seedrandom "2.4.3"
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-    node-fetch "~2.6.1"
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-  version "3.18.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-layers/-/tfjs-layers-3.18.0.tgz#8b7d32030e797456e49fd5c26d6f169e0c59b9ca"
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-
-"@tensorflow/tfjs@^3.18.0":
-  version "3.18.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs/-/tfjs-3.18.0.tgz#be59951d1981c887d2b786100e43abc4c2332164"
-  integrity "sha1-vlmVHRmByIfSt4YQDkOrxMIzIWQ= sha512-mOzz4jJdgIpqFS7EHndVuxrQnLUDVIKGyTqOPTYps89fZwcOFfTVxi4BHemDNQpqlVE8IaGh9UUxVXpjgPY5+Q=="
-  dependencies:
-    "@tensorflow/tfjs-backend-cpu" "3.18.0"
-    "@tensorflow/tfjs-backend-webgl" "3.18.0"
-    "@tensorflow/tfjs-converter" "3.18.0"
-    "@tensorflow/tfjs-core" "3.18.0"
-    "@tensorflow/tfjs-data" "3.18.0"
-    "@tensorflow/tfjs-layers" "3.18.0"
-    argparse "^1.0.10"
-    chalk "^4.1.0"
-    core-js "3"
-    regenerator-runtime "^0.13.5"
-    yargs "^16.0.3"
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-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/@types/long/-/long-4.0.2.tgz#b74129719fc8d11c01868010082d483b7545591a"
-  integrity sha512-MqTGEo5bj5t157U6fA/BiDynNkn0YknVdh48CMPkTSpFTVmvao5UQmm7uEF6xBEo7qIMAlY/JSleYaE6VOdpaA==
-
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-  version "2.3.7"
-  resolved "https://registry.yarnpkg.com/@types/node-fetch/-/node-fetch-2.3.7.tgz#b7212e895100f8642dbdab698472bab5f3c1d2f1"
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-  dependencies:
-    "@types/node" "*"
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-  version "12.6.1"
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-
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-  version "2019.3.0"
-  resolved "https://registry.yarnpkg.com/@types/offscreencanvas/-/offscreencanvas-2019.3.0.tgz#3336428ec7e9180cf4566dfea5da04eb586a6553"
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-  version "2.4.27"
-  resolved "https://registry.yarnpkg.com/@types/seedrandom/-/seedrandom-2.4.27.tgz#9db563937dd86915f69092bc43259d2f48578e41"
-  integrity sha1-nbVjk33YaRX2kJK8QyWdL0hXjkE=
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-  version "0.0.30"
-  resolved "https://registry.yarnpkg.com/@types/webgl-ext/-/webgl-ext-0.0.30.tgz#0ce498c16a41a23d15289e0b844d945b25f0fb9d"
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-  resolved "https://registry.yarnpkg.com/@types/webgl2/-/webgl2-0.0.6.tgz#1ea2db791362bd8521548d664dbd3c5311cdf4b6"
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-  dependencies:
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-  resolved "https://registry.yarnpkg.com/require-directory/-/require-directory-2.1.1.tgz#8c64ad5fd30dab1c976e2344ffe7f792a6a6df42"
-  integrity sha1-jGStX9MNqxyXbiNE/+f3kqam30I=
-
-seedrandom@2.4.3:
-  version "2.4.3"
-  resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-2.4.3.tgz#2438504dad33917314bff18ac4d794f16d6aaecc"
-  integrity sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw=
-
-sprintf-js@~1.0.2:
-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/sprintf-js/-/sprintf-js-1.0.3.tgz#04e6926f662895354f3dd015203633b857297e2c"
-  integrity sha1-BOaSb2YolTVPPdAVIDYzuFcpfiw=
-
-string-width@^4.1.0, string-width@^4.2.0:
-  version "4.2.3"
-  resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.3.tgz#269c7117d27b05ad2e536830a8ec895ef9c6d010"
-  integrity sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==
-  dependencies:
-    emoji-regex "^8.0.0"
-    is-fullwidth-code-point "^3.0.0"
-    strip-ansi "^6.0.1"
-
-strip-ansi@^6.0.0, strip-ansi@^6.0.1:
-  version "6.0.1"
-  resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-6.0.1.tgz#9e26c63d30f53443e9489495b2105d37b67a85d9"
-  integrity sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==
-  dependencies:
-    ansi-regex "^5.0.1"
-
-supports-color@^7.1.0:
-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.2.0.tgz#1b7dcdcb32b8138801b3e478ba6a51caa89648da"
-  integrity sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==
-  dependencies:
-    has-flag "^4.0.0"
-
-tr46@~0.0.3:
-  version "0.0.3"
-  resolved "https://registry.yarnpkg.com/tr46/-/tr46-0.0.3.tgz#8184fd347dac9cdc185992f3a6622e14b9d9ab6a"
-  integrity sha1-gYT9NH2snNwYWZLzpmIuFLnZq2o=
-
-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
-
-whatwg-url@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
-  integrity sha1-lmRU6HZUYuN2RNNib2dCzotwll0=
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-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
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-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
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-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
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-  version "5.0.8"
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-
-yargs-parser@^20.2.2:
-  version "20.2.9"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
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-
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-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
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-  dependencies:
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-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
diff --git a/tfjs-master/tfjs-backend-nodegl/package.json b/tfjs-master/tfjs-backend-nodegl/package.json
deleted file mode 100644
index 9d9a43a4f..000000000
--- a/tfjs-master/tfjs-backend-nodegl/package.json
+++ /dev/null
@@ -1,39 +0,0 @@
-{
-  "name": "tfjs-backend-nodegl",
-  "version": "0.0.1",
-  "description": "",
-  "main": "dist/index.js",
-  "types": "dist/index.d.ts",
-  "scripts": {
-    "build": "tsc",
-    "lint": "tslint -p . -t verbose",
-    "test": "yarn && yarn build && ts-node --transpile-only src/run_tests.ts",
-    "test-dev": "tsc && ts-node --transpile-only src/run_tests.ts"
-  },
-  "author": "",
-  "license": "Apache-2.0",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-backend-nodegl"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs-core": "^3.3.0",
-    "node-gles": "^0.0.13"
-  },
-  "devDependencies": {
-    "@types/jasmine": "~2.8.6",
-    "@types/node": "^10.5.1",
-    "@types/rimraf": "~2.0.2",
-    "clang-format": "^1.2.4",
-    "jasmine": "~3.1.0",
-    "ts-node": "^8.1.0",
-    "tslint": "~5.9.1",
-    "tslint-no-circular-imports": "^0.5.0",
-    "typescript": "3.5.3"
-  },
-  "resolutions": {
-    "node-fetch": "2.6.7",
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-backend-nodegl/src/index.ts b/tfjs-master/tfjs-backend-nodegl/src/index.ts
deleted file mode 100644
index 1175c58bb..000000000
--- a/tfjs-master/tfjs-backend-nodegl/src/index.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// TODO(kreeger): Fix binding definition in node-gles before switching to a
-// non-require import style.
-// tslint:disable-next-line:no-require-imports
-const nodeGles = require('node-gles');
-
-const nodeGl = nodeGles.binding.createWebGLRenderingContext();
-
-// TODO(kreeger): These are hard-coded GL integration flags. These need to be
-// updated to ensure they work on all systems with proper exception reporting.
-tf.env().set('WEBGL_VERSION', 2);
-tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);
-tf.env().set('WEBGL_DOWNLOAD_FLOAT_ENABLED', true);
-tf.env().set('WEBGL_FENCE_API_ENABLED', true);  // OpenGL ES 3.0 and higher..
-tf.env().set(
-    'WEBGL_MAX_TEXTURE_SIZE', nodeGl.getParameter(nodeGl.MAX_TEXTURE_SIZE));
-tf.webgl.setWebGLContext(2, nodeGl);
-
-tf.registerBackend('headless-nodegl', () => {
-  // TODO(kreeger): Consider moving all GL creation here. However, weak-ref to
-  // GL context tends to cause an issue when running unit tests:
-  // https://github.com/tensorflow/tfjs/issues/1732
-  return new tf.webgl.MathBackendWebGL(new tf.webgl.GPGPUContext(nodeGl));
-}, 3 /* priority */);
diff --git a/tfjs-master/tfjs-backend-nodegl/src/run_tests.ts b/tfjs-master/tfjs-backend-nodegl/src/run_tests.ts
deleted file mode 100644
index 9d96865cf..000000000
--- a/tfjs-master/tfjs-backend-nodegl/src/run_tests.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// We import index.ts so that the Node backend gets registered.
-import './index';
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line:no-imports-from-dist
-import * as jasmine_util from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-Error.stackTraceLimit = Infinity;
-
-// tslint:disable-next-line:no-require-imports
-const jasmineCtor = require('jasmine');
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-jasmine_util.setTestEnvs(
-    [{name: 'test-tensorflow', backendName: 'headless-nodegl', flags: {}}]);
-
-const IGNORE_LIST: string[] = [
-  // https://github.com/tensorflow/tfjs/issues/1711
-  'time cpu test-tensorflow {} simple upload',
-  // TODO(kreeger): File issue: bad uniform in input.uniformValues.
-  'sparseToDense test-tensorflow {} should work with 0-sized tensors',
-  // TODO(kreeger): File issue: fromPixels doesn't have data field.
-  // tslint:disable:max-line-length
-  'fromPixels, mock canvas test-tensorflow {} accepts a canvas-like element, numChannels=4',
-  'fromPixels, mock canvas test-tensorflow {} accepts a canvas-like element'
-];
-
-const runner = new jasmineCtor();
-runner.loadConfig({
-  spec_files: [
-    'src/**/*_test.ts', 'node_modules/@tensorflow/tfjs-core/dist/**/*_test.js'
-  ],
-  random: false
-});
-
-if (process.env.JASMINE_SEED) {
-  runner.seed(process.env.JASMINE_SEED);
-}
-
-const env = jasmine.getEnv();
-
-// Filter method that returns boolean, if a given test should return.
-env.specFilter = spec => {
-  // Return false (skip the test) if the test is in the ignore list.
-  for (let i = 0; i < IGNORE_LIST.length; ++i) {
-    if (spec.getFullName().indexOf(IGNORE_LIST[i]) > -1) {
-      return false;
-    }
-  }
-  // Otherwise run the test.
-  return true;
-};
-
-console.log(`Running tests with the following GL info`);
-const gl = (tf.backend() as tf.webgl.MathBackendWebGL).getGPGPUContext().gl;
-console.log(`  GL_VERSION: ${gl.getParameter(gl.VERSION)}`);
-console.log(`  GL_RENDERER: ${gl.getParameter(gl.RENDERER)}`);
-
-runner.execute();
diff --git a/tfjs-master/tfjs-backend-nodegl/src/version.ts b/tfjs-master/tfjs-backend-nodegl/src/version.ts
deleted file mode 100644
index 2dc61bb92..000000000
--- a/tfjs-master/tfjs-backend-nodegl/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.1';
-export {version};
diff --git a/tfjs-master/tfjs-backend-nodegl/tasks.json b/tfjs-master/tfjs-backend-nodegl/tasks.json
deleted file mode 100644
index 594b0eea8..000000000
--- a/tfjs-master/tfjs-backend-nodegl/tasks.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
-    "version": "2.0.0",
-    "tasks": [
-      {
-        "command": "yarn",
-        "label": "lint",
-        "type": "shell",
-        "args": [
-          "lint"
-        ],
-        "problemMatcher": {
-          "base": "$tslint5",
-          "owner": "tslint-type-checked",
-          "fileLocation": "absolute"
-        }
-      },
-      {
-        "command": "yarn",
-        "label": "build",
-        "type": "shell",
-        "args": ["build", "--pretty", "false", "--noEmit"],
-        "problemMatcher": [
-          "$tsc"
-        ]
-      }
-    ]
-  }
diff --git a/tfjs-master/tfjs-backend-nodegl/tsconfig.json b/tfjs-master/tfjs-backend-nodegl/tsconfig.json
deleted file mode 100644
index 1ad387d16..000000000
--- a/tfjs-master/tfjs-backend-nodegl/tsconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig_base.json",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-backend-nodegl/tslint.json b/tfjs-master/tfjs-backend-nodegl/tslint.json
deleted file mode 100644
index ec365f164..000000000
--- a/tfjs-master/tfjs-backend-nodegl/tslint.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "../tslint.json"
-}
diff --git a/tfjs-master/tfjs-backend-nodegl/yarn.lock b/tfjs-master/tfjs-backend-nodegl/yarn.lock
deleted file mode 100644
index 1b6ed2561..000000000
--- a/tfjs-master/tfjs-backend-nodegl/yarn.lock
+++ /dev/null
@@ -1,606 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@tensorflow/tfjs-core@^3.3.0":
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-3.3.0.tgz#3d26bd03cb58e0ecf46c96d118c39c4a90b7f5ed"
-  integrity "sha1-PSa9A8tY4Oz0bJbRGMOcSpC39e0= sha512-6G+LcCiQBl4Kza5mDbWbf8QSWBTW3l7SDjGhQzMO1ITtQatHzxkuHGHcJ4CTUJvNA0JmKf4QJWOvlFqEmxwyLQ=="
-  dependencies:
-    "@types/offscreencanvas" "~2019.3.0"
-    "@types/seedrandom" "2.4.27"
-    "@types/webgl-ext" "0.0.30"
-    node-fetch "~2.6.1"
-    seedrandom "2.4.3"
-
-"@types/glob@*":
-  version "7.1.3"
-  resolved "https://registry.yarnpkg.com/@types/glob/-/glob-7.1.3.tgz#e6ba80f36b7daad2c685acd9266382e68985c183"
-  integrity sha512-SEYeGAIQIQX8NN6LDKprLjbrd5dARM5EXsd8GI/A5l0apYI1fGMWgPHSe4ZKL4eozlAyI+doUE9XbYS4xCkQ1w==
-  dependencies:
-    "@types/minimatch" "*"
-    "@types/node" "*"
-
-"@types/jasmine@~2.8.6":
-  version "2.8.17"
-  resolved "https://registry.yarnpkg.com/@types/jasmine/-/jasmine-2.8.17.tgz#65fa3be377126253f6c7988b365dfc78d62d536e"
-  integrity sha512-lXmY2lBjE38ASvP7ah38yZwXCdc7DTCKhHqx4J3WGNiVzp134U0BD9VKdL5x9q9AAfhnpJeQr4owL6ZOXhOpfA==
-
-"@types/minimatch@*":
-  version "3.0.4"
-  resolved "https://registry.yarnpkg.com/@types/minimatch/-/minimatch-3.0.4.tgz#f0ec25dbf2f0e4b18647313ac031134ca5b24b21"
-  integrity sha512-1z8k4wzFnNjVK/tlxvrWuK5WMt6mydWWP7+zvH5eFep4oj+UkrfiJTRtjCeBXNpwaA/FYqqtb4/QS4ianFpIRA==
-
-"@types/node@*":
-  version "14.14.37"
-  resolved "https://registry.yarnpkg.com/@types/node/-/node-14.14.37.tgz#a3dd8da4eb84a996c36e331df98d82abd76b516e"
-  integrity sha512-XYmBiy+ohOR4Lh5jE379fV2IU+6Jn4g5qASinhitfyO71b/sCo6MKsMLF5tc7Zf2CE8hViVQyYSobJNke8OvUw==
-
-"@types/node@^10.5.1":
-  version "10.17.56"
-  resolved "https://registry.yarnpkg.com/@types/node/-/node-10.17.56.tgz#010c9e047c3ff09ddcd11cbb6cf5912725cdc2b3"
-  integrity sha512-LuAa6t1t0Bfw4CuSR0UITsm1hP17YL+u82kfHGrHUWdhlBtH7sa7jGY5z7glGaIj/WDYDkRtgGd+KCjCzxBW1w==
-
-"@types/offscreencanvas@~2019.3.0":
-  version "2019.3.0"
-  resolved "https://registry.yarnpkg.com/@types/offscreencanvas/-/offscreencanvas-2019.3.0.tgz#3336428ec7e9180cf4566dfea5da04eb586a6553"
-  integrity sha512-esIJx9bQg+QYF0ra8GnvfianIY8qWB0GBx54PK5Eps6m+xTj86KLavHv6qDhzKcu5UUOgNfJ2pWaIIV7TRUd9Q==
-
-"@types/rimraf@~2.0.2":
-  version "2.0.4"
-  resolved "https://registry.yarnpkg.com/@types/rimraf/-/rimraf-2.0.4.tgz#403887b0b53c6100a6c35d2ab24f6ccc042fec46"
-  integrity sha512-8gBudvllD2A/c0CcEX/BivIDorHFt5UI5m46TsNj8DjWCCTTZT74kEe4g+QsY7P/B9WdO98d82zZgXO/RQzu2Q==
-  dependencies:
-    "@types/glob" "*"
-    "@types/node" "*"
-
-"@types/seedrandom@2.4.27":
-  version "2.4.27"
-  resolved "https://registry.yarnpkg.com/@types/seedrandom/-/seedrandom-2.4.27.tgz#9db563937dd86915f69092bc43259d2f48578e41"
-  integrity sha1-nbVjk33YaRX2kJK8QyWdL0hXjkE=
-
-"@types/webgl-ext@0.0.30":
-  version "0.0.30"
-  resolved "https://registry.yarnpkg.com/@types/webgl-ext/-/webgl-ext-0.0.30.tgz#0ce498c16a41a23d15289e0b844d945b25f0fb9d"
-  integrity sha512-LKVgNmBxN0BbljJrVUwkxwRYqzsAEPcZOe6S2T6ZaBDIrFp0qu4FNlpc5sM1tGbXUYFgdVQIoeLk1Y1UoblyEg==
-
-adm-zip@^0.4.13:
-  version "0.4.16"
-  resolved "https://registry.yarnpkg.com/adm-zip/-/adm-zip-0.4.16.tgz#cf4c508fdffab02c269cbc7f471a875f05570365"
-  integrity sha512-TFi4HBKSGfIKsK5YCkKaaFG2m4PEDyViZmEwof3MTIgzimHLto6muaHVpbrljdIvIrFZzEq/p4nafOeLcYegrg==
-
-agent-base@^4.3.0:
-  version "4.3.0"
-  resolved "https://registry.yarnpkg.com/agent-base/-/agent-base-4.3.0.tgz#8165f01c436009bccad0b1d122f05ed770efc6ee"
-  integrity sha512-salcGninV0nPrwpGNn4VTXBb1SOuXQBiqbrNXoeizJsHrsL6ERFM2Ne3JUSBWRE6aeNJI2ROP/WEEIDUiDe3cg==
-  dependencies:
-    es6-promisify "^5.0.0"
-
-ansi-regex@^2.0.0:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-2.1.1.tgz#c3b33ab5ee360d86e0e628f0468ae7ef27d654df"
-  integrity sha1-w7M6te42DYbg5ijwRorn7yfWVN8=
-
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-  resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-2.2.1.tgz#b432dd3358b634cf75e1e4664368240533c1ddbe"
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-  resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-3.2.1.tgz#41fbb20243e50b12be0f04b8dedbf07520ce841d"
-  integrity sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA==
-  dependencies:
-    color-convert "^1.9.0"
-
-arg@^4.1.0:
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-  version "0.5.19"
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-
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-  version "0.6.1"
-  resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
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-
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-  version "1.0.3"
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-  integrity sha1-BOaSb2YolTVPPdAVIDYzuFcpfiw=
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-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-3.0.1.tgz#6a385fb8853d952d5ff05d0e8aaf94278dc63dcf"
-  integrity sha1-ajhfuIU9lS1f8F0Oiq+UJ43GPc8=
-  dependencies:
-    ansi-regex "^2.0.0"
-
-supports-color@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-2.0.0.tgz#535d045ce6b6363fa40117084629995e9df324c7"
-  integrity sha1-U10EXOa2Nj+kARcIRimZXp3zJMc=
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-supports-color@^5.3.0:
-  version "5.5.0"
-  resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.5.0.tgz#e2e69a44ac8772f78a1ec0b35b689df6530efc8f"
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-  dependencies:
-    has-flag "^3.0.0"
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-tar@^4.4.8:
-  version "4.4.19"
-  resolved "https://registry.yarnpkg.com/tar/-/tar-4.4.19.tgz#2e4d7263df26f2b914dee10c825ab132123742f3"
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-    chownr "^1.1.4"
-    fs-minipass "^1.2.7"
-    minipass "^2.9.0"
-    minizlib "^1.3.3"
-    mkdirp "^0.5.5"
-    safe-buffer "^5.2.1"
-    yallist "^3.1.1"
-
-tr46@~0.0.3:
-  version "0.0.3"
-  resolved "https://registry.yarnpkg.com/tr46/-/tr46-0.0.3.tgz#8184fd347dac9cdc185992f3a6622e14b9d9ab6a"
-  integrity sha1-gYT9NH2snNwYWZLzpmIuFLnZq2o=
-
-ts-node@^8.1.0:
-  version "8.10.2"
-  resolved "https://registry.yarnpkg.com/ts-node/-/ts-node-8.10.2.tgz#eee03764633b1234ddd37f8db9ec10b75ec7fb8d"
-  integrity sha512-ISJJGgkIpDdBhWVu3jufsWpK3Rzo7bdiIXJjQc0ynKxVOVcg2oIrf2H2cejminGrptVc6q6/uynAHNCuWGbpVA==
-  dependencies:
-    arg "^4.1.0"
-    diff "^4.0.1"
-    make-error "^1.1.1"
-    source-map-support "^0.5.17"
-    yn "3.1.1"
-
-tslib@^1.8.0, tslib@^1.8.1:
-  version "1.14.1"
-  resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.14.1.tgz#cf2d38bdc34a134bcaf1091c41f6619e2f672d00"
-  integrity sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==
-
-tslint-no-circular-imports@^0.5.0:
-  version "0.5.2"
-  resolved "https://registry.yarnpkg.com/tslint-no-circular-imports/-/tslint-no-circular-imports-0.5.2.tgz#47ac9194ec531101ac19198d5144f7ac9f57d980"
-  integrity sha512-Uzu2NiVX7b/kAk9vPrLqUmoCkth0dXUUNOaqwfxpvYgsHfoH67cmj0IHOUMZ1bC0swksJjaTyq7acHe/gU8H4A==
-
-tslint@~5.9.1:
-  version "5.9.1"
-  resolved "https://registry.yarnpkg.com/tslint/-/tslint-5.9.1.tgz#1255f87a3ff57eb0b0e1f0e610a8b4748046c9ae"
-  integrity "sha1-ElX4ej/1frCw4fDmEKi0dIBGya4= sha512-EDEyKYflb79XSj/rX9abiYrpBJtdHvhYGnLaHNf3fW0KPlByePKwhlAmBtH4Y0PYQVkwPsrYSE6Fg1s8gDqucA=="
-  dependencies:
-    babel-code-frame "^6.22.0"
-    builtin-modules "^1.1.1"
-    chalk "^2.3.0"
-    commander "^2.12.1"
-    diff "^3.2.0"
-    glob "^7.1.1"
-    js-yaml "^3.7.0"
-    minimatch "^3.0.4"
-    resolve "^1.3.2"
-    semver "^5.3.0"
-    tslib "^1.8.0"
-    tsutils "^2.12.1"
-
-tsutils@^2.12.1:
-  version "2.29.0"
-  resolved "https://registry.yarnpkg.com/tsutils/-/tsutils-2.29.0.tgz#32b488501467acbedd4b85498673a0812aca0b99"
-  integrity sha512-g5JVHCIJwzfISaXpXE1qvNalca5Jwob6FjI4AoPlqMusJ6ftFE7IkkFoMhVLRgK+4Kx3gkzb8UZK5t5yTTvEmA==
-  dependencies:
-    tslib "^1.8.1"
-
-typescript@3.5.3:
-  version "3.5.3"
-  resolved "https://registry.yarnpkg.com/typescript/-/typescript-3.5.3.tgz#c830f657f93f1ea846819e929092f5fe5983e977"
-  integrity sha512-ACzBtm/PhXBDId6a6sDJfroT2pOWt/oOnk4/dElG5G33ZL776N3Y6/6bKZJBFpd+b05F3Ct9qDjMeJmRWtE2/g==
-
-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
-
-whatwg-url@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
-  integrity sha1-lmRU6HZUYuN2RNNib2dCzotwll0=
-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-yallist@^3.0.0, yallist@^3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.1.1.tgz#dbb7daf9bfd8bac9ab45ebf602b8cbad0d5d08fd"
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-
-yn@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
-  integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==
diff --git a/tfjs-master/tfjs-backend-wasm/.npmignore b/tfjs-master/tfjs-backend-wasm/.npmignore
deleted file mode 100644
index 7f15f07af..000000000
--- a/tfjs-master/tfjs-backend-wasm/.npmignore
+++ /dev/null
@@ -1,23 +0,0 @@
-.vscode/
-.rpt2_cache/
-src/**/*_test.ts
-
-cloudbuild.yml
-scripts/
-tools/
-toolchain/
-**/node_modules/
-karma.conf.js
-*.tgz
-*.log
-WORKSPACE.md
-tslint.json
-yarn.lock
-DEVELOPMENT.md
-ISSUE_TEMPLATE.md
-PULL_REQUEST_TEMPLATE.md
-rollup.config.js
-tsconfig.json
-.yalc/
-yalc.lock
-*.png
diff --git a/tfjs-master/tfjs-backend-wasm/.npmrc b/tfjs-master/tfjs-backend-wasm/.npmrc
deleted file mode 100644
index 43c97e719..000000000
--- a/tfjs-master/tfjs-backend-wasm/.npmrc
+++ /dev/null
@@ -1 +0,0 @@
-package-lock=false
diff --git a/tfjs-master/tfjs-backend-wasm/.vscode/c_cpp_properties.json b/tfjs-master/tfjs-backend-wasm/.vscode/c_cpp_properties.json
deleted file mode 100644
index 5aa2575ac..000000000
--- a/tfjs-master/tfjs-backend-wasm/.vscode/c_cpp_properties.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
-    "configurations": [
-        {
-            "name": "Linux",
-            "includePath": [
-                "${workspaceFolder}/..",
-                "${workspaceFolder}/../dist/tfjs/external/emsdk/emsdk/upstream/emscripten/system/include",
-                "${workspaceFolder}/../dist/tfjs/external/com_google_googletest/googletest/include",
-                "${workspaceFolder}/../dist/tfjs/external/xnnpack/include",
-                "${workspaceFolder}/../dist/tfjs/external/pthreadpool/include"
-            ],
-            "defines": [],
-            "compilerPath": "/usr/bin/clang",
-            "cStandard": "c11",
-            "cppStandard": "c++11",
-            "intelliSenseMode": "clang-x64"
-        }
-    ],
-    "version": 4
-}
diff --git a/tfjs-master/tfjs-backend-wasm/.vscode/settings.json b/tfjs-master/tfjs-backend-wasm/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-backend-wasm/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-backend-wasm/BUILD.bazel b/tfjs-master/tfjs-backend-wasm/BUILD.bazel
deleted file mode 100644
index e7a3051a7..000000000
--- a/tfjs-master/tfjs-backend-wasm/BUILD.bazel
+++ /dev/null
@@ -1,185 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//rules:copy_file.bzl", "copy_file")
-load("@build_bazel_rules_nodejs//:index.bzl", "nodejs_test", "pkg_npm")
-load("@npm//@bazel/rollup:index.bzl", "rollup_bundle")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:get_extension.bzl", "get_extension")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-tfjs_bundle(
-    name = "tf-backend-wasm",
-    entry_point = "//tfjs-backend-wasm/src:index.ts",
-    # When updating external, also update rollup_wechat.config.mjs
-    external = [
-        "crypto",
-        "@tensorflow/tfjs-core",
-        "fs",
-        "path",
-        "worker_threads",
-        "perf_hooks",
-    ],
-    # When updating globals, also update rollup_wechat.config.mjs
-    globals = {
-        "@tensorflow/tfjs-core": "tf",
-        "fs": "fs",
-        "path": "path",
-        "perf_hooks": "perf_hooks",
-        "worker_threads": "worker_threads",
-    },
-    leave_as_require = [
-        "crypto",
-        "node-fetch",
-        "util",
-    ],
-    umd_name = "tf.wasm",
-    deps = [
-        "//tfjs-backend-wasm/src:tfjs-backend-wasm_lib",
-        "//tfjs-backend-wasm/src:tfjs-backend-wasm_src_lib",
-    ],
-)
-
-rollup_bundle(
-    name = "tf-backend-wasm-miniprogram.min",
-    args = ["--output.name=tf"],
-    config_file = "rollup_wechat.config.mjs",
-    entry_point = "//tfjs-backend-wasm/src:index.ts",
-    format = "umd",
-    sourcemap = "true",
-    deps = [
-        "//tfjs-backend-wasm/scripts:patch_wechat_webassembly",
-        "//tfjs-backend-wasm/src:tfjs-backend-wasm_lib",
-        "//tools:make_rollup_config",
-        "@npm//tslib",
-    ],
-)
-
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-backend-wasm/src:tfjs-backend-wasm_lib",
-        "//tfjs-backend-wasm/src:tfjs-backend-wasm_src_lib",
-    ],
-    root = "src",
-)
-
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-backend-wasm",
-        ":tf-backend-wasm.es2017",
-        ":tf-backend-wasm.es2017.min",
-        ":tf-backend-wasm.fesm",
-        ":tf-backend-wasm.fesm.min",
-        ":tf-backend-wasm.min",
-        ":tf-backend-wasm.node",
-    ],
-)
-
-get_extension(
-    name = "wasm_files",
-    srcs = [
-        "//tfjs-backend-wasm/wasm-out",
-    ],
-    include = [
-        ".wasm",
-    ],
-)
-
-copy_to_dist(
-    name = "copy_wasm_files",
-    srcs = [
-        ":wasm_files",
-    ],
-    root = "wasm-out",
-)
-
-copy_file(
-    name = "copy_miniprogram",
-    src = ":tf-backend-wasm-miniprogram.min.js",
-    out = "dist/miniprogram/index.js",
-)
-
-copy_file(
-    name = "copy_miniprogram_map",
-    src = ":tf-backend-wasm-miniprogram.min.js.map",
-    out = "dist/miniprogram/index.js.map",
-)
-
-pkg_npm(
-    name = "tfjs-backend-wasm_pkg",
-    package_name = "@tensorflow/tfjs-backend-wasm",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_miniprogram",
-        ":copy_miniprogram_map",
-        ":copy_src_to_dist",
-        ":copy_wasm_files",
-        "//tfjs-backend-wasm/wasm-out",
-    ],
-)
-
-# TODO(mattsoulanille): This test uses the default chrome flags and tests
-# the simd implementation. We should also test the non-simd implementation and
-# the threaded implementation. When writing those tests, also try to depend on
-# only the required wasm outputs, since it takes a long time to compile them.
-tfjs_web_test(
-    name = "tfjs-backend-wasm_test",
-    srcs = [
-        "//tfjs-backend-wasm/src:tfjs-backend-wasm_test_bundle",
-    ],
-    browsers = [
-        "bs_chrome_mac",
-        "bs_firefox_mac",
-        "bs_safari_mac",
-        "bs_ios_12",
-        # TODO(mattsoulanille): Fix clipByValue on Android.
-        # "bs_android_10",
-        "win_10_chrome",
-    ],
-    static_files = [
-        "//tfjs-backend-wasm/src:tfjs-backend-wasm_test_bundle",
-        "//tfjs-backend-wasm/wasm-out",
-    ],
-)
-
-nodejs_test(
-    name = "tfjs-backend-wasm_node_test",
-    data = [
-        "//tfjs-backend-wasm/src:tfjs-backend-wasm_src_lib",
-        "//tfjs-backend-wasm/src:tfjs-backend-wasm_test_lib",
-        "//tfjs-backend-wasm/wasm-out",
-    ],
-    entry_point = "//tfjs-backend-wasm/src:test_node.ts",
-    tags = ["ci"],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        ":tfjs-backend-wasm_node_test",
-        ":tfjs-backend-wasm_test",
-        "//tfjs-backend-wasm/src/cc:cc_tests",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-wasm/README.md b/tfjs-master/tfjs-backend-wasm/README.md
deleted file mode 100644
index 59397cfb8..000000000
--- a/tfjs-master/tfjs-backend-wasm/README.md
+++ /dev/null
@@ -1,348 +0,0 @@
-# Usage
-
-This package adds a WebAssembly backend to TensorFlow.js. It currently supports
-the following models from our
-[models](https://github.com/tensorflow/tfjs-models) repo:
-- BlazeFace
-- BodyPix
-- CocoSSD
-- Face landmarks detection
-- HandPose
-- KNN classifier
-- MobileNet
-- PoseDetection
-- Q&A
-- Universal sentence encoder
-- AutoML Image classification
-- AutoML Object detection
-
-## Importing the backend
-
-### Via NPM
-
-```js
-// Import @tensorflow/tfjs or @tensorflow/tfjs-core
-import * as tf from '@tensorflow/tfjs';
-// Adds the WASM backend to the global backend registry.
-import '@tensorflow/tfjs-backend-wasm';
-// Set the backend to WASM and wait for the module to be ready.
-tf.setBackend('wasm').then(() => main());
-```
-
-### Via a script tag
-
-```html
-<!-- Import @tensorflow/tfjs or @tensorflow/tfjs-core -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
-
-<!-- Adds the WASM backend to the global backend registry -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm/dist/tf-backend-wasm.js"></script>
-<script>
-tf.setBackend('wasm').then(() => main());
-</script>
-```
-
-## Setting up cross-origin isolation
-
-Starting from Chrome 92 (to be released around July 2021), **cross-origin
-isolation** needs to be set up in your site in order to take advantage of
-the multi-threading support in WASM backend. Without this, the backend
-will fallback to the WASM binary with SIMD-only support (or the vanila version
-if SIMD is not enabled). Without multi-threading support, certain models might
-not achieve the best performance.
-
-Here are the high-level steps to set up the cross-origin isolation. You can
-learn more about this topic [here](https://web.dev/coop-coep/).
-
-1. Send the following two HTTP headers when your main document (e.g.index.html)
-   that uses the WASM backend is served. You may need to configure or ask your
-   web host provider to enable these headers.
-
-   - `Cross-Origin-Opener-Policy: same-origin`
-   - `Cross-Origin-Embedder-Policy: require-corp`
-
-1. If you are loading the WASM backend from `jsdelivr` through the script tag,
-   you are good to go. No more steps are needed.
-
-   If you are loading the WASM backend from your own or other third-party
-   servers, you need to make sure the script is served with either CORS or CORP
-   header.
-
-   - CORS header: `Access-Control-Allow-Origin: *`. In addition, you will also
-     need to add the "crossorigin" attribute to your script tags.
-
-   - CORP header:
-
-     - If the resource is loaded from the *same origin* as your main site
-       (e.g. main site: mysite.com/, script: mysite.com/script.js), set:
-
-       `Cross-Origin-Resource-Policy: same-origin`
-     - If the resource is loaded from the *same site but cross origin*
-       (e.g. main site: mysite.com/, script: static.mysite.com:8080/script.js),
-       set:
-
-
-       `Cross-Origin-Resource-Policy: same-site`
-     - If the resource is loaded from the *cross origin(s)*
-       (e.g. main site: mysite.com/, script: mystatic.com/script.js), set:
-
-       `Cross-Origin-Resource-Policy: cross-origin`
-
-If the steps above are correctly done, you can check the Network tab from the
-console and make sure the
-<code>tfjs-backend-wasm-<b>threaded-simd</b>.wasm</code> WASM binary is loaded.
-
-## Threads count
-
-By default, the backend will use the number of logical CPU cores as the
-threads count when creating the threadpool used by XNNPACK. You can use the
-`setThreadsCount` API to manually set it (must be called before calling
-`tf.setBackend('wasm')`). `getThreadsCount` API can be used to get the actual
-number of threads being used (must be called after the WASM backend is
-initialized).
-
-### Via NPM
-
-```js
-import * as tf from '@tensorflow/tfjs';
-import {getThreadsCount, setThreadsCount} from '@tensorflow/tfjs-backend-wasm';
-
-setThreadsCount(2);
-tf.setBackend('wasm').then(() => {
-  console.log(getThreadsCount());
-});
-```
-
-### Via script tag
-
-```js
-tf.wasm.setThreadsCount(2);
-tf.setBackend('wasm').then(() => {
-  console.log(tf.wasm.getThreadsCount());
-});
-```
-
-## Running MobileNet
-
-```js
-async function main() {
-  let img = tf.browser.fromPixels(document.getElementById('img'))
-      .resizeBilinear([224, 224])
-      .expandDims(0)
-      .toFloat();
-
-  let model = await tf.loadGraphModel(
-    'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/2',
-    {fromTFHub: true});
-  const y = model.predict(img);
-
-  y.print();
-}
-main();
-```
-
-Our WASM backend builds on top of the
-[XNNPACK library](https://github.com/google/XNNPACK) which provides
-high-efficiency floating-point neural network inference operators.
-
-## Using bundlers
-
-The shipped library on NPM consists of 2 files:
-- the main js file (bundled js for browsers)
-- the WebAssembly binary in `dist/tfjs-backend-wasm.wasm`
-
-There is a [proposal](https://github.com/WebAssembly/esm-integration) to add
-WASM support for ES6 modules. In the meantime, we have to manually read the wasm
-file. When the WASM backend is initialized, we make a `fetch`/`readFile`
-for `tfjs-backend-wasm.wasm` relative from the main js file. This means that
-bundlers such as Parcel and WebPack need to be able to serve the `.wasm` file in
-production. See [starter/parcel](./starter/parcel/) and
-[starter/webpack](./starter/webpack/) for how to setup your favorite bundler.
-
-If you are serving the `.wasm` files from a different directory, call
-`setWasmPaths` with the location of that directory before you initialize the
-backend:
-
-```ts
-import {setWasmPaths} from '@tensorflow/tfjs-backend-wasm';
-// setWasmPaths accepts a `prefixOrFileMap` argument which can be either a
-// string or an object. If passing in a string, this indicates the path to
-// the directory where your WASM binaries are located.
-setWasmPaths('www.yourdomain.com/');
-tf.setBackend('wasm').then(() => {...});
-```
-
-If the WASM backend is imported through `<script>` tag, `setWasmPaths` needs to
-be called on the `tf.wasm` object:
-
-```ts
-tf.wasm.setWasmPaths('www.yourdomain.com/');
-```
-
-Note that if you call `setWasmPaths` with a string, it will be used to load
-each binary (SIMD-enabled, threading-enabled, etc.) Alternatively you can specify
-overrides for individual WASM binaries via a file map object. This is also helpful
-in case your binaries have been renamed.
-
-For example:
-
-```ts
-import {setWasmPaths} from '@tensorflow/tfjs-backend-wasm';
-setWasmPaths({
-  'tfjs-backend-wasm.wasm': 'www.yourdomain.com/renamed.wasm',
-  'tfjs-backend-wasm-simd.wasm': 'www.yourdomain.com/renamed-simd.wasm',
-  'tfjs-backend-wasm-threaded-simd.wasm': 'www.yourdomain.com/renamed-threaded-simd.wasm'
-  });
-tf.setBackend('wasm').then(() => {...});
-```
-
-If you are using a platform that does not support fetch directly, please set the
-optional `usePlatformFetch` argument to `true`:
-
-```ts
-import {setWasmPath} from '@tensorflow/tfjs-backend-wasm';
-const usePlatformFetch = true;
-setWasmPaths(yourCustomPathPrefix, usePlatformFetch);
-tf.setBackend('wasm').then(() => {...});
-```
-
-## JS Minification
-
-If your bundler is capable of minifying JS code, please turn off the option
-that transforms ```typeof foo == "undefined"``` into ```foo === void 0```. For
-example, in [terser](https://github.com/terser/terser), the option is called
-"typeofs" (located under the
-[Compress options](https://github.com/terser/terser#compress-options) section).
-Without this feature turned off, the minified code will throw "_scriptDir is not
-defined" error from web workers when running in browsers with
-SIMD+multi-threading support.
-
-## Use with Angular
-
-If you see the `Cannot find name 'EmscriptenModule'` error when building your
-Angular app, make sure to add `"@types/emscripten"` to the
-`compilerOptions.types` field in your `tsconfig.app.json` (or `tsconfig.json`):
-
-```
-{
-  ...
-  "compilerOptions": {
-    "types": [
-      "@types/emscripten"
-    ]
-  },
-  ...
-}
-```
-
-By default, the generated Angular app sets this field to an empty array
-which will prevent the Angular compiler from automatically adding
-"global types" (such as `EmscriptenModule`) defined in `d.ts` files to your app.
-
-
-## Benchmarks
-
-The benchmarks below show inference times (ms) for two different edge-friendly
-models: MobileNet V2 (a medium-sized model) and Face Detector (a lite model).
-All the benchmarks were run in Chrome 79.0 using
-[this benchmark page](https://tfjs-benchmarks.web.app/local-benchmark/) across our three
-backends: Plain JS (CPU), WebGL and WASM. Inference times are averaged
-across 200 runs.
-
-### MobileNet V2
-
-MobileNet is a medium-sized model with 3.48M params and ~300M multiply-adds.
-For this model, the WASM backend is between ~3X-11.5X faster than the plain
-JS backend, and ~5.3-7.7X slower than the WebGL backend.
-
-<img src="./mobilenet-v2-bench.png" width="750">
-
-| MobileNet inference (ms) | WASM  | WebGL | Plain JS | WASM + SIMD | WASM + SIMD + threads
-|--------------------------|-------|-------|----------|-------------|----------------------
-| iPhone X                 | 147.1 | 20.3  | 941.3    | N/A         | N/A                 |
-| iPhone XS                | 140   | 18.1  | 426.4    | N/A         | N/A                 |
-| Pixel 4                  | 182   | 76.4  | 1628     | 82          | N/A                 |
-| ThinkPad X1 Gen6 w/Linux | 122.7 | 44.8  | 1489.4   | 34.6        | 12.4                |
-| Desktop Windows          | 123.1 | 41.6  | 1117     | 37.2        | N/A                 |
-| Macbook Pro 15 2019      | 98.4  | 19.6  | 893.5    | 30.2        | 10.3                |
-| Node v.14 on Macbook Pro | 290   | N/A   | 1404.3   | 64.2        | N/A                 |
-
-
-### Face Detector
-
-Face detector is a lite model with 0.1M params and ~20M multiply-adds. For this model,
-the WASM backend is between ~8.2-19.8X faster than the plain JS backend and
-comparable to the WebGL backend (up to ~1.7X faster, or 2X slower, depending on
-the device).
-
-<img src="./face-detector-bench.png" width="750">
-
-| Face Detector inference (ms) | WASM | WebGL | Plain JS | WASM + SIMD | WASM + SIMD + threads
-|------------------------------|------|-------|----------|-------------|----------------------
-| iPhone X                     | 22.4 | 13.5  | 318      | N/A         | N/A                 |
-| iPhone XS                    | 21.4 | 10.5  | 176.9    | N/A         | N/A                 |
-| Pixel 4                      | 28   | 28    | 368      | 15.9        | N/A                 |
-| Desktop Linux                | 12.6 | 12.7  | 249.5    | 8.0         | 6.2                 |
-| Desktop Windows              | 16.2 | 7.1   | 270.9    | 7.5         | N/A                 |
-| Macbook Pro 15 2019          | 13.6 | 22.7  | 209.1    | 7.9         | 4.0                 |
-
-# FAQ
-
-### When should I use the WASM backend?
-You should always try to use the WASM backend over the plain JS backend since
-it is strictly faster on all devices, across all model sizes.
-Compared to the WebGL backend, the WASM backend has better numerical stability,
-and wider device support. Performance-wise, our benchmarks show that:
-- For medium-sized models (~100-500M multiply-adds), the WASM backend is several
-times slower than the WebGL backend.
-- For lite models (~20-60M multiply-adds), the WASM backend has comparable
-performance to the WebGL backend
-(see the [Face Detector model](#face-detector) above).
-
-We are committed to supporting the WASM backend and will continue to improve
-performance. We plan to follow the WebAssembly standard closely and benefit from
-its upcoming features such as multi-threading.
-
-### How many ops have you implemented?
-See [`register_all_kernels.ts`](https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/src/register_all_kernels.ts)
-for an up-to-date list of supported ops. We love contributions. See the
-[contributing](https://github.com/tensorflow/tfjs/blob/master/CONTRIBUTING.md#adding-functionality)
-document for more info.
-
-### Do you support training?
-Maybe. There are still a decent number of ops that we are missing in WASM that
-are needed for gradient computation. At this point we are focused on making
-inference as fast as possible.
-
-### Do you work in node?
-Yes. If you run into issues, please let us know.
-
-### Do you support SIMD and multi-threading?
-Yes. We take advantage of SIMD and multi-threading wherever they are supported by testing the capabilities of your runtime and loading the appropriate WASM binary. If you intend to serve the WASM binaries from a custom location (via `setWasmPaths`), please note that the SIMD-enabled and threading-enabled binaries are separate from the regular binary.
-
-### How do I give feedback?
-We'd love your feedback as we develop this backend! Please file an issue
-[here](https://github.com/tensorflow/tfjs/issues/new).
-
-# Development
-
-## Emscripten installation
-The Emscripten installation necessary to build the WASM backend is managed automatically by the [Bazel Emscripten Toolchain](https://github.com/emscripten-core/emsdk/tree/master/bazel).
-
-## Building
-
-```sh
-yarn build
-```
-
-## Testing
-
-```sh
-yarn test
-```
-
-## Deployment
-```sh
-./scripts/build-npm.sh
-npm publish
-```
diff --git a/tfjs-master/tfjs-backend-wasm/face-detector-bench.png b/tfjs-master/tfjs-backend-wasm/face-detector-bench.png
deleted file mode 100644
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diff --git a/tfjs-master/tfjs-backend-wasm/mobilenet-v2-bench.png b/tfjs-master/tfjs-backend-wasm/mobilenet-v2-bench.png
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diff --git a/tfjs-master/tfjs-backend-wasm/package.json b/tfjs-master/tfjs-backend-wasm/package.json
deleted file mode 100644
index 20e530475..000000000
--- a/tfjs-master/tfjs-backend-wasm/package.json
+++ /dev/null
@@ -1,50 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-backend-wasm",
-  "version": "0.0.0",
-  "main": "dist/tf-backend-wasm.node.js",
-  "types": "dist/index.d.ts",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-backend-wasm"
-  },
-  "jsnext:main": "dist/index.js",
-  "module": "dist/index.js",
-  "unpkg": "dist/tf-backend-wasm.min.js",
-  "jsdelivr": "dist/tf-backend-wasm.min.js",
-  "miniprogram": "dist/miniprogram",
-  "scripts": {
-    "build": "bazel build -c opt :tfjs-backend-wasm_pkg",
-    "build-dbg": "bazel build -c dbg :tfjs-backend-wasm_pkg",
-    "publish-npm": "bazel run -c opt :tfjs-backend-wasm_pkg.publish",
-    "clean": "bazel clean --expunge",
-    "cpplint": "./scripts/cpplint.js",
-    "test": "bazel test -c dbg :tests --test_output=all",
-    "test-browser": "bazel test -c dbg :tfjs-backend-wasm_test --test_output=all",
-    "test-browser-debug": "bazel run -c dbg :tfjs-backend-wasm_test --test_output=all",
-    "test-node": "bazel test -c dbg :tfjs-backend-wasm_node_test --test_output=all",
-    "test-node-debug": "bazel run --config=debug -c dbg :tfjs-backend-wasm_node_test --test_output=all",
-    "test-bundle-size": "./scripts/test-bundle-size.js",
-    "test-cc": "bazel test -c dbg //tfjs-backend-wasm/src/cc:cc_tests --test_output=all"
-  },
-  "browser": {
-    "fs": false,
-    "path": false,
-    "os": false,
-    "worker_threads": false,
-    "perf_hooks": false
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs-backend-cpu": "link:../link-package/node_modules/@tensorflow/tfjs-backend-cpu",
-    "@types/emscripten": "~0.0.34"
-  },
-  "devDependencies": {
-    "@babel/polyfill": "^7.8.7",
-    "@bazel/bazelisk": "^1.12.0",
-    "clang-format": "~1.2.4"
-  },
-  "license": "Apache-2.0"
-}
diff --git a/tfjs-master/tfjs-backend-wasm/rollup_wechat.config.mjs b/tfjs-master/tfjs-backend-wasm/rollup_wechat.config.mjs
deleted file mode 100644
index e09fb4444..000000000
--- a/tfjs-master/tfjs-backend-wasm/rollup_wechat.config.mjs
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {patchWechatWebAssembly} from './scripts/patch_wechat_webassembly.js';
-import {makeRollupConfig} from 'make_rollup_config/make_rollup_config.js';
-
-export default makeRollupConfig({
-  globals: {
-    '@tensorflow/tfjs-core': 'tf',
-    'fs': 'fs',
-    'path': 'path',
-    'perf_hooks': 'perf_hooks',
-    'worker_threads': 'worker_threads',
-  },
-  external: [
-    'crypto',
-    '@tensorflow/tfjs-core',
-    'fs',
-    'path',
-    'worker_threads',
-    'perf_hooks',
-  ],
-  leave_as_require: [
-    'crypto',
-    'node-fetch',
-    'util',
-    'fs',
-    'path',
-    'worker_threads',
-    'perf_hooks',
-    'os',
-  ],
-  es5: true,
-  plugins: [patchWechatWebAssembly()],
-});
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/BUILD.bazel b/tfjs-master/tfjs-backend-wasm/scripts/BUILD.bazel
deleted file mode 100644
index a0d2298d3..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/BUILD.bazel
+++ /dev/null
@@ -1,50 +0,0 @@
-load("@//tools:defaults.bzl", "ts_library")
-
-# Copyright 2022 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-load("@build_bazel_rules_nodejs//:index.bzl", "nodejs_binary")
-
-package(default_visibility = ["//visibility:public"])
-
-nodejs_binary(
-    name = "create_worker_module_bin",
-    data = [
-        "create-worker-module.js",
-        "@npm//argparse",
-    ],
-    entry_point = "create-worker-module.js",
-)
-
-nodejs_binary(
-    name = "patch_threaded_simd_module_bin",
-    data = [
-        "patch-threaded-simd-module.js",
-        "@npm//argparse",
-    ],
-    entry_point = "patch-threaded-simd-module.js",
-)
-
-ts_library(
-    name = "patch_wechat_webassembly",
-    srcs = [
-        "patch_wechat_webassembly.ts",
-    ],
-    module_name = "patch_wechat_webassembly",
-    deps = [
-        "@npm//@types/estree",
-        "@npm//estree-walker",
-        "@npm//rollup",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/build-benchmark.sh b/tfjs-master/tfjs-backend-wasm/scripts/build-benchmark.sh
deleted file mode 100644
index a276be439..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/build-benchmark.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-yarn build
-yarn rollup -c
-
-cp dist/tf-backend-wasm.js ../e2e/benchmarks/local-benchmark/
-cp wasm-out/tfjs-backend-wasm.wasm ../e2e/benchmarks/local-benchmark/
-cp wasm-out/tfjs-backend-wasm-simd.wasm ../e2e/benchmarks/local-benchmark/
-cp wasm-out/tfjs-backend-wasm-threaded-simd.wasm ../e2e/benchmarks/local-benchmark/
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/build-ci.sh b/tfjs-master/tfjs-backend-wasm/scripts/build-ci.sh
deleted file mode 100644
index a503705f4..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/build-ci.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn tsc
-BAZEL_REMOTE="--config=ci" ./scripts/build-wasm.sh
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/build-npm.sh b/tfjs-master/tfjs-backend-wasm/scripts/build-npm.sh
deleted file mode 100644
index dca48ab7f..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/build-npm.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn
-yarn rimraf dist/
-yarn tsc
-./scripts/build-wasm.sh
-yarn rollup -c --npm
-
-echo "Stored standalone library at dist/tf-backend-wasm(.min).js"
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/build-wasm.sh b/tfjs-master/tfjs-backend-wasm/scripts/build-wasm.sh
deleted file mode 100644
index f39b938cc..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/build-wasm.sh
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Start in scripts/ even if run from root directory
-cd "$(dirname "$0")"
-
-# Halt if a single command errors
-set -e
-
-# Echo every command being executed
-set -x
-
-# Default build.
-# We pass '--symlink_prefix="wasm-dist/"' because otherwise wasm will change
-# 'dist/bin' to point to k8-opt instead of k8-fastbuild (because of '-c opt')
-# https://docs.bazel.build/versions/main/user-manual.html#configuration-specific-data
-yarn bazel build $BAZEL_REMOTE --symlink_prefix="wasm-dist/" -c opt //tfjs-backend-wasm/src/cc:tfjs-backend-wasm
-# The typescript code and karma config expect the output of emscripten to be in
-# wasm-out/ so we copy the bazel output there.
-cp -f ../../wasm-dist/bin/tfjs-backend-wasm/src/cc/tfjs-backend-wasm/tfjs-backend-wasm.js \
-      ../../wasm-dist/bin/tfjs-backend-wasm/src/cc/tfjs-backend-wasm/tfjs-backend-wasm.wasm \
-      ../wasm-out/
-
-if [[ "$1" != "--dev" ]]; then
-  # SIMD and threaded + SIMD builds.
-  yarn bazel build $BAZEL_REMOTE --symlink_prefix="wasm-dist/" -c opt --copt="-msimd128" //tfjs-backend-wasm/src/cc:tfjs-backend-wasm-simd
-  yarn bazel build $BAZEL_REMOTE --symlink_prefix="wasm-dist/" -c opt --copt="-pthread" --copt="-msimd128" //tfjs-backend-wasm/src/cc:tfjs-backend-wasm-threaded-simd
-
-  # Copy SIMD
-  cp -f ../../wasm-dist/bin/tfjs-backend-wasm/src/cc/tfjs-backend-wasm-simd/tfjs-backend-wasm-simd.wasm \
-    ../wasm-out/tfjs-backend-wasm-simd.wasm
-
-  # Copy threaded
-  cp -f ../../wasm-dist/bin/tfjs-backend-wasm/src/cc/tfjs-backend-wasm-threaded-simd/tfjs-backend-wasm-threaded-simd.js \
-    ../wasm-out/tfjs-backend-wasm-threaded-simd.js
-  cp -f ../../wasm-dist/bin/tfjs-backend-wasm/src/cc/tfjs-backend-wasm-threaded-simd/tfjs-backend-wasm-threaded-simd.worker.js \
-    ../wasm-out/tfjs-backend-wasm-threaded-simd.worker.js
-  cp -f ../../wasm-dist/bin/tfjs-backend-wasm/src/cc/tfjs-backend-wasm-threaded-simd/tfjs-backend-wasm-threaded-simd.wasm \
-    ../wasm-out/tfjs-backend-wasm-threaded-simd.wasm
-
-  node ./create-worker-module.js
-  node ./patch-threaded-simd-module.js
-fi
-
-mkdir -p ../dist
-# Only copying binaries into dist because the js modules get bundled.
-cp ../wasm-out/*.wasm ../dist/
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/cpplint.js b/tfjs-master/tfjs-backend-wasm/scripts/cpplint.js
deleted file mode 100644
index f7d7d5939..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/cpplint.js
+++ /dev/null
@@ -1,50 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const shell = require('shelljs');
-const {exec} = require('../../scripts/test-util');
-const fs = require('fs');
-
-const CC_FILEPATH = 'src/cc';
-
-let python2Cmd;
-
-const ignoreCode = true;
-const commandOpts = null;
-
-let pythonVersion = exec('python --version', commandOpts, ignoreCode);
-if (pythonVersion['stderr'].includes('Python 2')) {
-  python2Cmd = 'python';
-} else {
-  pythonVersion = exec('python2 --version', commandOpts, ignoreCode);
-  if (pythonVersion.code === 0) {
-    python2Cmd = 'python2';
-  }
-}
-
-if (python2Cmd != null) {
-  const result = shell.find('src/cc').filter(
-      fileName => fileName.endsWith('.cc') || fileName.endsWith('.h'));
-
-  const cwd = process.cwd() + '/' + CC_FILEPATH;
-  const filenameArgument = result.join(' ');
-
-  exec(`${python2Cmd} tools/cpplint.py --root ${cwd} ${filenameArgument}`);
-} else {
-  console.warn(
-      'No python2.x version found - please install python2. ' +
-      'cpplint.py only works correctly with python 2.x.');
-}
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/create-worker-module.js b/tfjs-master/tfjs-backend-wasm/scripts/create-worker-module.js
deleted file mode 100644
index 3e0b0ace6..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/create-worker-module.js
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This file modifies the Emscripten-generated WASM worker script so that it can
- * be inlined by the tf-backend-wasm bundle.
- */
-
-const fs = require('fs');
-const {ArgumentParser} = require('argparse');
-
-const parser = new ArgumentParser();
-
-parser.addArgument('workerFile', {
-  type: String,
-  help: 'The input worker file to transform.',
-});
-
-parser.addArgument('outFile', {
-  type: String,
-  help: 'The output file path.',
-});
-
-parser.addArgument('--cjs', {
-  action: 'storeTrue',
-  default: false,
-  optional: true,
-  help: 'Whether to output commonjs instead of esm.',
-});
-
-const args = parser.parseArgs();
-const workerContents = fs.readFileSync(args.workerFile, "utf8");
-const escaped = workerContents.replace(/`/g, '\\`');
-
-if (args.cjs) {
-  fs.writeFileSync(`${args.outFile}`,
-    `module.exports.wasmWorkerContents = \`${escaped.trim()}\`;`);
-} else {
-  fs.writeFileSync(`${args.outFile}`,
-    `export const wasmWorkerContents = \`${escaped.trim()}\`;`);
-}
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/create_worker_module.bzl b/tfjs-master/tfjs-backend-wasm/scripts/create_worker_module.bzl
deleted file mode 100644
index 159bb4e18..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/create_worker_module.bzl
+++ /dev/null
@@ -1,48 +0,0 @@
-load("@build_bazel_rules_nodejs//:providers.bzl", "run_node")
-
-def _create_worker_module_impl(ctx):
-    output_file = ctx.actions.declare_file(ctx.attr.out_file)
-    if (len(ctx.files.worker_file) != 1):
-        fail("Expected a single file but got " + str(ctx.files.worker_file))
-
-    cjs = ["--cjs"] if ctx.attr.cjs else []
-    run_node(
-        ctx,
-        executable = "create_worker_module_bin",
-        inputs = ctx.files.worker_file,
-        outputs = [output_file],
-        arguments = cjs + [
-            ctx.files.worker_file[0].path,
-            output_file.path,
-        ],
-    )
-
-    return [DefaultInfo(files = depset([output_file]))]
-
-create_worker_module = rule(
-    implementation = _create_worker_module_impl,
-    attrs = {
-        "cjs": attr.bool(
-            default = False,
-            doc = "Whether to output commonjs",
-        ),
-        "create_worker_module_bin": attr.label(
-            executable = True,
-            cfg = "exec",
-            default = Label("@//tfjs-backend-wasm/scripts:create_worker_module_bin"),
-            doc = "The script that creates the worker module",
-        ),
-        "out_file": attr.string(
-            mandatory = True,
-            doc = "The name for the output file",
-        ),
-        "worker_file": attr.label(
-            doc = "The worker file to transform",
-            allow_files = [".js"],
-        ),
-    },
-    doc = """Modify the Emscripten WASM worker script so it can be inlined
-
-    ...by the tf-backend-wasm bundle.
-    """,
-)
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/patch-threaded-simd-module.js b/tfjs-master/tfjs-backend-wasm/scripts/patch-threaded-simd-module.js
deleted file mode 100644
index 5b8c8b2a0..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/patch-threaded-simd-module.js
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This file patches the Emscripten-generated WASM JS script so that it can be
- * properly loaded in web worker.
- *
- * We need to pass the content of this script to WASM module's
- * mainScriptUrlOrBlob field so that the web worker can correctly load the
- * script "inline". The returned content of the script (after it self-executes)
- * is a anonymous function object in which we have the following if block:
- *
- * if (_scriptDir) {
- *   scriptDirectory = _scriptDir;
- * }
- *
- * It works great if the script runs in the main page, where _scriptDir is
- * initialized to the path of the tf-backend-wasm.js file, outside of the
- * function object. However, when the script runs in a web worker, the
- * code that initializes _scriptDir won't be present since it is outside
- * of the scope of the function object. As a result, a "Uncaught
- * ReferenceError: _scriptDir is not defined" error will be thrown fron
- * the web worker.
- *
- * To fix this, we will replace all the occurences of "if(_scriptDir)"
- * with a better version that first checks whether _scriptDir is defined
- * or not
- *
- * For more context, see:
- * https://github.com/emscripten-core/emscripten/pull/12832
- */
-const fs = require('fs');
-const {ArgumentParser} = require('argparse');
-
-const parser = new ArgumentParser();
-
-parser.addArgument('jsFile', {
-  type: String,
-  help: 'The input js file to transform.',
-});
-
-parser.addArgument('outFile', {
-  type: String,
-  help: 'The output file path.',
-});
-
-const args = parser.parseArgs();
-
-let content = fs.readFileSync(args.jsFile, 'utf8');
-content = content.replace(
-    /if\s*\(\s*_scriptDir\s*\)/g,
-    'if(typeof _scriptDir !== "undefined" && _scriptDir)');
-fs.writeFileSync(args.outFile, content);
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/patch_threaded_simd_module.bzl b/tfjs-master/tfjs-backend-wasm/scripts/patch_threaded_simd_module.bzl
deleted file mode 100644
index 6e6210e94..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/patch_threaded_simd_module.bzl
+++ /dev/null
@@ -1,43 +0,0 @@
-load("@build_bazel_rules_nodejs//:providers.bzl", "run_node")
-
-def _patch_threaded_simd_module_impl(ctx):
-    output_file = ctx.actions.declare_file(ctx.attr.out_file)
-    if (len(ctx.files.threaded_simd_file) != 1):
-        fail("Expected a single file but got " + str(ctx.files.threaded_simd_file))
-
-    run_node(
-        ctx,
-        executable = "patch_threaded_simd_module_bin",
-        inputs = ctx.files.threaded_simd_file,
-        outputs = [output_file],
-        arguments = [
-            ctx.files.threaded_simd_file[0].path,
-            output_file.path,
-        ],
-    )
-
-    return [DefaultInfo(files = depset([output_file]))]
-
-patch_threaded_simd_module = rule(
-    implementation = _patch_threaded_simd_module_impl,
-    attrs = {
-        "out_file": attr.string(
-            mandatory = True,
-            doc = "The name for the output file",
-        ),
-        "patch_threaded_simd_module_bin": attr.label(
-            executable = True,
-            cfg = "exec",
-            default = Label("@//tfjs-backend-wasm/scripts:patch_threaded_simd_module_bin"),
-            doc = "The script that creates the worker module",
-        ),
-        "threaded_simd_file": attr.label(
-            doc = "The threaded simd file to transform",
-            allow_files = [".js"],
-        ),
-    },
-    doc = """Fix the check for _scriptDir. See patch-threaded-simd-module.js
-    
-    ...for more details.
-    """,
-)
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/patch_wechat_webassembly.ts b/tfjs-master/tfjs-backend-wasm/scripts/patch_wechat_webassembly.ts
deleted file mode 100644
index 2dd2c7ff9..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/patch_wechat_webassembly.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-import {walk} from 'estree-walker';
-import {FunctionDeclaration, BaseNode} from 'estree';
-import {Plugin} from 'rollup';
-
-const createInstantiateWasmFuncString = `
-function createInstantiateWasmFunc(path) {
-  return function (imports, callback) {
-    WebAssembly.instantiate(path, imports).then(function (output) {
-      callback(output.instance, output.module);
-    });
-    return {};
-  };
-}
-`;
-
-type NodeType = BaseNode | FunctionDeclaration & {start: number, end: number};
-/**
- * WebAssembly has changed to WXWebAssembly after WeChat 8.0
- * 0. not simd or thread support.
- * 1. only load local wasm file is allowed.
- * 2. WebAssembly.validate not working so env registerFlag doesn't work
- * @see https://developers.weixin.qq.com/community/develop/doc/000e2c019f8a003d5dfbb54c251c00?jumpto=comment&commentid=000eac66934960576d0cb1a7256c
- */
-export function patchWechatWebAssembly(): Plugin {
-  return {
-    name: 'patchWechatWebAssembly',
-    transform(code, file) {
-      // remove node imports
-      if (
-        file.endsWith('tfjs-backend-wasm-threaded-simd.worker.js') ||
-        file.endsWith('tfjs-backend-wasm-threaded-simd.js')
-      ) {
-        code = code.replace(`require("worker_threads")`, 'null');
-        code = code.replace(`require("perf_hooks")`, 'null');
-      }
-
-      // it is not a nice way, but WebAssembly.validate not working and SIMD is not support in WXWebAssembly
-      // tf.env().set('WASM_HAS_SIMD_SUPPORT', false) will be done in tfjs-wechat or application code
-      // so does the WASM_HAS_MULTITHREAD_SUPPORT
-      if (file.endsWith('backend_wasm.ts')) {
-        const ast = (this as {parse: (code: string) => BaseNode}).parse(code);
-        walk(ast, {
-          enter(node: NodeType) {
-            if (
-              node.type === 'FunctionDeclaration' &&
-              'id' in node &&
-              node.id.name === 'createInstantiateWasmFunc'
-            ) {
-              code = code.replace(
-                code.slice(node.start, node.end),
-                createInstantiateWasmFuncString,
-              );
-            }
-          },
-        });
-      }
-
-      code = code.replace(/WebAssembly\./g, `WXWebAssembly.`);
-      code = code.replace(/typeof WebAssembly/g, `typeof WXWebAssembly`);
-      return { code, map: null };
-    },
-  };
-}
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/test-bundle-size.js b/tfjs-master/tfjs-backend-wasm/scripts/test-bundle-size.js
deleted file mode 100644
index f460dda94..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/test-bundle-size.js
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-// This script has not been bazelized yet because it uses several imports from
-// the root `scripts/` directory.
-const shell = require('shelljs');
-const {exec} = require('../../scripts/test-util');
-const {showDiff, getFileSizeBytes} = require('../../scripts/bundle-size-util');
-const path = require('path');
-
-// Get the bundle sizes from this change.
-exec(`yarn build-npm`, {silent: false});
-
-const basePath = '../dist/bin/tfjs-backend-wasm/tfjs-backend-wasm_pkg/dist/';
-const bundleFilename = path.join(basePath, 'tf-backend-wasm.min.js');
-const minBundleSize = getFileSizeBytes(bundleFilename);
-const wasmFileName = path.join(basePath, 'tfjs-backend-wasm.wasm');
-const wasmSize = getFileSizeBytes(wasmFileName);
-
-console.log(`~~~~ WASM file ~~~~`);
-console.log(`==> post-gzip`)
-console.log(`size: ${wasmSize.gzipFileSizeBytes}`);
-console.log();
-console.log(`==> pre-gzip`)
-console.log(`size: ${wasmSize.fileSizeBytes}`);
-console.log();
-console.log();
-
-console.log(`~~~~ minified bundle (JavaScript) ~~~~`);
-console.log(`==> post-gzip`)
-console.log(`size: ${minBundleSize.gzipFileSizeBytes}`);
-console.log();
-console.log(`==> pre-gzip`)
-console.log(`size: ${minBundleSize.fileSizeBytes}`);
-console.log();
-console.log();
diff --git a/tfjs-master/tfjs-backend-wasm/scripts/test-ci.sh b/tfjs-master/tfjs-backend-wasm/scripts/test-ci.sh
deleted file mode 100644
index 43de95662..000000000
--- a/tfjs-master/tfjs-backend-wasm/scripts/test-ci.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2020 Google LLC
-#
-# Use of this source code is governed by an MIT-style
-# license that can be found in the LICENSE file or at
-# https://opensource.org/licenses/MIT.
-# =============================================================================
-
-set -e
-
-# Regular testing.
-yarn test-node
-
-if [ "$NIGHTLY" = true ]; then
-  node ../scripts/run_flaky.js "yarn run-browserstack --browsers=bs_safari_mac"
-  node ../scripts/run_flaky.js "yarn run-browserstack --browsers=bs_firefox_mac"
-  node ../scripts/run_flaky.js "yarn run-browserstack --browsers=bs_chrome_mac"
-  node ../scripts/run_flaky.js "yarn run-browserstack --browsers=win_10_chrome"
-  node ../scripts/run_flaky.js "yarn run-browserstack --browsers=bs_ios_12"
-else
-  node ../scripts/run_flaky.js "yarn run-browserstack --browsers=bs_chrome_mac"
-fi
diff --git a/tfjs-master/tfjs-backend-wasm/src/BUILD.bazel b/tfjs-master/tfjs-backend-wasm/src/BUILD.bazel
deleted file mode 100644
index ae61a0a6d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/BUILD.bazel
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("//tools:defaults.bzl", "esbuild", "ts_library")
-load("//tools:enumerate_tests.bzl", "enumerate_tests")
-load("//tools:make_version_test_file.bzl", "make_version_test_file")
-
-package(default_visibility = ["//visibility:public"])
-
-TEST_SRCS = [
-    "**/*_test.ts",
-    "test_node.ts",
-    "test_util.ts",
-]
-
-TEST_ENTRYPOINTS = [
-    "test_node.ts",
-    "setup_test.ts",
-]
-
-make_version_test_file(
-    name = "version_test",
-    package_json = "//tfjs-core:package.json",
-    version_name = "version_wasm",
-)
-
-filegroup(
-    name = "test_srcs",
-    srcs = glob(
-        ["**/*_test.ts"],
-        exclude = TEST_ENTRYPOINTS,
-    ) + [
-        ":version_test",
-    ],
-)
-
-# Generates the 'tests.ts' file that imports all test entrypoints.
-enumerate_tests(
-    name = "tests",
-    srcs = [":test_srcs"],
-    root_path = "tfjs-backend-wasm/src",
-)
-
-ts_library(
-    name = "tfjs-backend-wasm_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-backend-wasm/dist",
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_src_lib",
-        "//tfjs-backend-wasm/wasm-out",
-        "//tfjs-core/src:tfjs-core_lib",
-        "@npm//@types/seedrandom",
-        "@npm//seedrandom",
-    ],
-)
-
-ts_library(
-    name = "tfjs-backend-wasm_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-backend-wasm",
-    deps = [
-        ":tfjs-backend-wasm_src_lib",
-    ],
-)
-
-ts_library(
-    name = "tfjs-backend-wasm_test_lib",
-    testonly = True,
-    srcs = glob(
-        TEST_SRCS,
-    ) + [
-        ":tests",
-        ":version_test",
-    ],
-    module_name = "@tensorflow/tfjs-backend-wasm/dist",
-    deps = [
-        ":tfjs-backend-wasm_lib",
-        ":tfjs-backend-wasm_src_lib",
-        "//tfjs-backend-wasm/wasm-out",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-        "@npm//@types/jasmine",
-        "@npm//core-js",
-        "@npm//jasmine",
-    ],
-)
-
-esbuild(
-    name = "tfjs-backend-wasm_test_bundle",
-    testonly = True,
-    entry_point = "setup_test.ts",
-    external = [
-        "@tensorflow/tfjs",
-        "worker_threads",
-        "util",
-        "fs",
-        "path",
-        "perf_hooks",
-        "os",
-    ],
-    sources_content = True,
-    target = "es6",
-    deps = [
-        ":tfjs-backend-wasm_lib",
-        ":tfjs-backend-wasm_test_lib",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-wasm/src/backend_wasm.ts b/tfjs-master/tfjs-backend-wasm/src/backend_wasm.ts
deleted file mode 100644
index 8fc4ee7d5..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/backend_wasm.ts
+++ /dev/null
@@ -1,539 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import './flags_wasm';
-
-import {backend_util, BackendTimingInfo, DataStorage, DataType, deprecationWarn, engine, env, KernelBackend, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasmModule, WasmFactoryConfig} from '../wasm-out/tfjs-backend-wasm';
-import {BackendWasmThreadedSimdModule} from '../wasm-out/tfjs-backend-wasm-threaded-simd';
-import * as wasmFactoryThreadedSimd_import from '../wasm-out/tfjs-backend-wasm-threaded-simd.js';
-// @ts-ignore
-import {wasmWorkerContents} from '../wasm-out/tfjs-backend-wasm-threaded-simd.worker.js';
-import * as wasmFactory_import from '../wasm-out/tfjs-backend-wasm.js';
-
-// This workaround is required for importing in Node.js without using
-// the node bundle (for testing). This would not be necessary if we
-// flipped esModuleInterop to true, but we likely can't do that since
-// google3 does not use it.
-const wasmFactoryThreadedSimd = (wasmFactoryThreadedSimd_import.default ||
-                                 wasmFactoryThreadedSimd_import) as
-    typeof wasmFactoryThreadedSimd_import.default;
-const wasmFactory = (wasmFactory_import.default || wasmFactory_import) as
-    typeof wasmFactory_import.default;
-
-interface TensorData {
-  id: number;
-  memoryOffset: number;
-  shape: number[];
-  dtype: DataType;
-  refCount: number;
-  /** Only used for string tensors, storing encoded bytes. */
-  stringBytes?: Uint8Array[];
-}
-
-export type DataId = object;  // object instead of {} to force non-primitive.
-
-export class BackendWasm extends KernelBackend {
-  // 0 is reserved for null data ids.
-  private dataIdNextNumber = 1;
-  dataIdMap: DataStorage<TensorData>;
-
-  constructor(public wasm: BackendWasmModule|BackendWasmThreadedSimdModule) {
-    super();
-    this.wasm.tfjs.initWithThreadsCount(threadsCount);
-    actualThreadsCount = this.wasm.tfjs.getThreadsCount();
-    this.dataIdMap = new DataStorage(this, engine());
-  }
-
-  override write(
-      values: backend_util.BackendValues|null, shape: number[],
-      dtype: DataType): DataId {
-    const dataId = {id: this.dataIdNextNumber++};
-    this.move(dataId, values, shape, dtype, 1);
-    return dataId;
-  }
-
-  override numDataIds(): number {
-    return this.dataIdMap.numDataIds();
-  }
-
-  override async time(f: () => void): Promise<BackendTimingInfo> {
-    const start = util.now();
-    f();
-    const kernelMs = util.now() - start;
-    return {kernelMs};
-  }
-
-  override move(
-      dataId: DataId, values: backend_util.BackendValues|null, shape: number[],
-      dtype: DataType, refCount: number): void {
-    const id = this.dataIdNextNumber++;
-    if (dtype === 'string') {
-      const stringBytes = values as Uint8Array[];
-      this.dataIdMap.set(
-          dataId,
-          {id, stringBytes, shape, dtype, memoryOffset: null, refCount});
-      return;
-    }
-
-    const size = util.sizeFromShape(shape);
-    const numBytes = size * util.bytesPerElement(dtype);
-
-    // `>>> 0` is needed for above 2GB allocations because wasm._malloc returns
-    // a signed int32 instead of an unsigned int32.
-    // https://v8.dev/blog/4gb-wasm-memory
-    const memoryOffset = this.wasm._malloc(numBytes) >>> 0;
-
-    this.dataIdMap.set(dataId, {id, memoryOffset, shape, dtype, refCount});
-
-    this.wasm.tfjs.registerTensor(id, size, memoryOffset);
-
-    if (values != null) {
-      this.wasm.HEAPU8.set(
-          new Uint8Array(
-              (values as backend_util.TypedArray).buffer,
-              (values as backend_util.TypedArray).byteOffset, numBytes),
-          memoryOffset);
-    }
-  }
-
-  override async read(dataId: DataId): Promise<backend_util.BackendValues> {
-    return this.readSync(dataId);
-  }
-
-  override readSync(dataId: DataId, start?: number, end?: number):
-      backend_util.BackendValues {
-    const {memoryOffset, dtype, shape, stringBytes} =
-        this.dataIdMap.get(dataId);
-    if (dtype === 'string') {
-      // Slice all elements.
-      if ((start == null || start === 0) &&
-          (end == null || end >= stringBytes.length)) {
-        return stringBytes;
-      }
-      return stringBytes.slice(start, end);
-    }
-    start = start || 0;
-    end = end || util.sizeFromShape(shape);
-    const bytesPerElement = util.bytesPerElement(dtype);
-    const bytes = this.wasm.HEAPU8.slice(
-        memoryOffset + start * bytesPerElement,
-        memoryOffset + end * bytesPerElement);
-    return typedArrayFromBuffer(bytes.buffer, dtype);
-  }
-
-  /**
-   * Dispose the memory if the dataId has 0 refCount. Return true if the memory
-   * is released, false otherwise.
-   * @param dataId
-   * @oaram force Optional, remove the data regardless of refCount
-   */
-  override disposeData(dataId: DataId, force = false): boolean {
-    if (this.dataIdMap.has(dataId)) {
-      const data = this.dataIdMap.get(dataId);
-      data.refCount--;
-      if (!force && data.refCount > 0) {
-        return false;
-      }
-
-      this.wasm._free(data.memoryOffset);
-      this.wasm.tfjs.disposeData(data.id);
-      this.dataIdMap.delete(dataId);
-    }
-    return true;
-  }
-
-  /** Return refCount of a `TensorData`. */
-  override refCount(dataId: DataId): number {
-    if (this.dataIdMap.has(dataId)) {
-      const tensorData = this.dataIdMap.get(dataId);
-      return tensorData.refCount;
-    }
-    return 0;
-  }
-
-  override incRef(dataId: DataId) {
-    const data = this.dataIdMap.get(dataId);
-    if (data != null) {
-      data.refCount++;
-    }
-  }
-
-  override floatPrecision(): 32 {
-    return 32;
-  }
-
-  // Returns the memory offset of a tensor. Useful for debugging and unit
-  // testing.
-  getMemoryOffset(dataId: DataId): number {
-    return this.dataIdMap.get(dataId).memoryOffset;
-  }
-
-  override dispose() {
-    this.wasm.tfjs.dispose();
-    if ('PThread' in this.wasm) {
-      this.wasm.PThread.terminateAllThreads();
-    }
-    this.wasm = null;
-  }
-
-  override memory() {
-    return {unreliable: false};
-  }
-
-  /**
-   * Make a tensor info for the output of an op. If `memoryOffset` is not
-   * present, this method allocates memory on the WASM heap. If `memoryOffset`
-   * is present, the memory was allocated elsewhere (in c++) and we just record
-   * the pointer where that memory lives.
-   */
-  makeOutput(
-      shape: number[], dtype: DataType, memoryOffset?: number,
-      values?: backend_util.BackendValues): TensorInfo {
-    let dataId: {};
-    if (memoryOffset == null) {
-      dataId = this.write(values ?? null, shape, dtype);
-    } else {
-      const id = this.dataIdNextNumber++;
-      dataId = {id};
-      this.dataIdMap.set(dataId, {id, memoryOffset, shape, dtype, refCount: 1});
-      const size = util.sizeFromShape(shape);
-      this.wasm.tfjs.registerTensor(id, size, memoryOffset);
-    }
-    return {dataId, shape, dtype};
-  }
-
-  typedArrayFromHeap({shape, dtype, dataId}: TensorInfo):
-      backend_util.TypedArray {
-    const buffer = this.wasm.HEAPU8.buffer;
-    const {memoryOffset} = this.dataIdMap.get(dataId);
-    const size = util.sizeFromShape(shape);
-    switch (dtype) {
-      case 'float32':
-        return new Float32Array(buffer, memoryOffset, size);
-      case 'int32':
-        return new Int32Array(buffer, memoryOffset, size);
-      case 'bool':
-        return new Uint8Array(buffer, memoryOffset, size);
-      default:
-        throw new Error(`Unknown dtype ${dtype}`);
-    }
-  }
-}
-
-function createInstantiateWasmFunc(path: string) {
-  // this will be replace by rollup plugin patchWechatWebAssembly in
-  // minprogram's output.
-  // tslint:disable-next-line:no-any
-  return (imports: any, callback: any) => {
-    util.fetch(path, {credentials: 'same-origin'}).then((response) => {
-      if (!response['ok']) {
-        imports.env.a(`failed to load wasm binary file at '${path}'`);
-      }
-      response.arrayBuffer().then(binary => {
-        WebAssembly.instantiate(binary, imports).then(output => {
-          callback(output.instance, output.module);
-        });
-      });
-    });
-    return {};
-  };
-}
-
-/**
- * Returns the path of the WASM binary.
- * @param simdSupported whether SIMD is supported
- * @param threadsSupported whether multithreading is supported
- * @param wasmModuleFolder the directory containing the WASM binaries.
- */
-function getPathToWasmBinary(
-    simdSupported: boolean, threadsSupported: boolean,
-    wasmModuleFolder: string) {
-  if (wasmPath != null) {
-    // If wasmPath is defined, the user has supplied a full path to
-    // the vanilla .wasm binary.
-    return wasmPath;
-  }
-
-  let path: WasmBinaryName = 'tfjs-backend-wasm.wasm';
-  if (simdSupported && threadsSupported) {
-    path = 'tfjs-backend-wasm-threaded-simd.wasm';
-  } else if (simdSupported) {
-    path = 'tfjs-backend-wasm-simd.wasm';
-  }
-
-  if (wasmFileMap != null) {
-    if (wasmFileMap[path] != null) {
-      return wasmFileMap[path];
-    }
-  }
-
-  return wasmModuleFolder + path;
-}
-
-/**
- * Initializes the wasm module and creates the js <--> wasm bridge.
- *
- * NOTE: We wrap the wasm module in a object with property 'wasm' instead of
- * returning Promise<BackendWasmModule> to avoid freezing Chrome (last tested
- * in Chrome 76).
- */
-export async function init(): Promise<{wasm: BackendWasmModule}> {
-  const [simdSupported, threadsSupported] = await Promise.all([
-    env().getAsync('WASM_HAS_SIMD_SUPPORT'),
-    env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT')
-  ]);
-
-  return new Promise((resolve, reject) => {
-    const factoryConfig: WasmFactoryConfig = {};
-
-    /**
-     * This function overrides the Emscripten module locateFile utility.
-     * @param path The relative path to the file that needs to be loaded.
-     * @param prefix The path to the main JavaScript file's directory.
-     */
-    factoryConfig.locateFile = (path, prefix) => {
-      if (path.endsWith('.worker.js')) {
-        // Escape '\n' because Blob will turn it into a newline.
-        // There should be a setting for this, but 'endings: "native"' does
-        // not seem to work.
-        const response = (wasmWorkerContents as string).replace(/\n/g, '\\n');
-        const blob = new Blob([response], {type: 'application/javascript'});
-        return URL.createObjectURL(blob);
-      }
-
-      if (path.endsWith('.wasm')) {
-        return getPathToWasmBinary(
-            simdSupported as boolean, threadsSupported as boolean,
-            wasmPathPrefix != null ? wasmPathPrefix : prefix);
-      }
-      return prefix + path;
-    };
-
-    // Use the instantiateWasm override when system fetch is not available.
-    // Reference:
-    // https://github.com/emscripten-core/emscripten/blob/2bca083cbbd5a4133db61fbd74d04f7feecfa907/tests/manual_wasm_instantiate.html#L170
-    if (customFetch) {
-      factoryConfig.instantiateWasm =
-          createInstantiateWasmFunc(getPathToWasmBinary(
-              simdSupported as boolean, threadsSupported as boolean,
-              wasmPathPrefix != null ? wasmPathPrefix : ''));
-    }
-
-    let initialized = false;
-    factoryConfig.onAbort = () => {
-      if (initialized) {
-        // Emscripten already called console.warn so no need to double log.
-        return;
-      }
-      if (initAborted) {
-        // Emscripten calls `onAbort` twice, resulting in double error
-        // messages.
-        return;
-      }
-      initAborted = true;
-      const rejectMsg =
-          'Make sure the server can serve the `.wasm` file relative to the ' +
-          'bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers';
-      reject({message: rejectMsg});
-    };
-
-    let wasm: Promise<BackendWasmModule>;
-    // If `wasmPath` has been defined we must initialize the vanilla module.
-    if (threadsSupported && simdSupported && wasmPath == null) {
-      factoryConfig.mainScriptUrlOrBlob = new Blob(
-          [`var WasmBackendModuleThreadedSimd = ` +
-           wasmFactoryThreadedSimd.toString()],
-          {type: 'text/javascript'});
-      wasm = wasmFactoryThreadedSimd(factoryConfig);
-    } else {
-      // The wasmFactory works for both vanilla and SIMD binaries.
-      wasm = wasmFactory(factoryConfig);
-    }
-
-    // The `wasm` promise will resolve to the WASM module created by
-    // the factory, but it might have had errors during creation. Most
-    // errors are caught by the onAbort callback defined above.
-    // However, some errors, such as those occurring from a
-    // failed fetch, result in this promise being rejected. These are
-    // caught and re-rejected below.
-    wasm.then((module) => {
-          initialized = true;
-          initAborted = false;
-
-          const voidReturnType: string = null;
-          // Using the tfjs namespace to avoid conflict with emscripten's API.
-          module.tfjs = {
-            init: module.cwrap('init', null, []),
-            initWithThreadsCount:
-                module.cwrap('init_with_threads_count', null, ['number']),
-            getThreadsCount: module.cwrap('get_threads_count', 'number', []),
-            registerTensor: module.cwrap(
-                'register_tensor', null,
-                [
-                  'number',  // id
-                  'number',  // size
-                  'number',  // memoryOffset
-                ]),
-            disposeData:
-                module.cwrap('dispose_data', voidReturnType, ['number']),
-            dispose: module.cwrap('dispose', voidReturnType, []),
-          };
-
-          resolve({wasm: module});
-        })
-        .catch(reject);
-  });
-}
-
-function typedArrayFromBuffer(
-    buffer: ArrayBuffer, dtype: DataType): backend_util.TypedArray {
-  switch (dtype) {
-    case 'float32':
-      return new Float32Array(buffer);
-    case 'int32':
-      return new Int32Array(buffer);
-    case 'bool':
-      return new Uint8Array(buffer);
-    default:
-      throw new Error(`Unknown dtype ${dtype}`);
-  }
-}
-
-const wasmBinaryNames = [
-  'tfjs-backend-wasm.wasm', 'tfjs-backend-wasm-simd.wasm',
-  'tfjs-backend-wasm-threaded-simd.wasm'
-] as const ;
-type WasmBinaryName = typeof wasmBinaryNames[number];
-
-let wasmPath: string = null;
-let wasmPathPrefix: string = null;
-let wasmFileMap: {[key in WasmBinaryName]?: string} = {};
-let initAborted = false;
-let customFetch = false;
-
-/**
- * @deprecated Use `setWasmPaths` instead.
- * Sets the path to the `.wasm` file which will be fetched when the wasm
- * backend is initialized. See
- * https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers
- * for more details.
- * @param path wasm file path or url
- * @param usePlatformFetch optional boolean to use platform fetch to download
- *     the wasm file, default to false.
- *
- * @doc {heading: 'Environment', namespace: 'wasm'}
- */
-export function setWasmPath(path: string, usePlatformFetch = false): void {
-  deprecationWarn(
-      'setWasmPath has been deprecated in favor of setWasmPaths and' +
-      ' will be removed in a future release.');
-  if (initAborted) {
-    throw new Error(
-        'The WASM backend was already initialized. Make sure you call ' +
-        '`setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`');
-  }
-  wasmPath = path;
-  customFetch = usePlatformFetch;
-}
-
-/**
- * Configures the locations of the WASM binaries.
- *
- * ```js
- * setWasmPaths({
- *  'tfjs-backend-wasm.wasm': 'renamed.wasm',
- *  'tfjs-backend-wasm-simd.wasm': 'renamed-simd.wasm',
- *  'tfjs-backend-wasm-threaded-simd.wasm': 'renamed-threaded-simd.wasm'
- * });
- * tf.setBackend('wasm');
- * ```
- *
- * @param prefixOrFileMap This can be either a string or object:
- *  - (string) The path to the directory where the WASM binaries are located.
- *     Note that this prefix will be used to load each binary (vanilla,
- *     SIMD-enabled, threading-enabled, etc.).
- *  - (object) Mapping from names of WASM binaries to custom
- *     full paths specifying the locations of those binaries. This is useful if
- *     your WASM binaries are not all located in the same directory, or if your
- *     WASM binaries have been renamed.
- * @param usePlatformFetch optional boolean to use platform fetch to download
- *     the wasm file, default to false.
- *
- * @doc {heading: 'Environment', namespace: 'wasm'}
- */
-export function setWasmPaths(
-    prefixOrFileMap: string|{[key in WasmBinaryName]?: string},
-    usePlatformFetch = false): void {
-  if (initAborted) {
-    throw new Error(
-        'The WASM backend was already initialized. Make sure you call ' +
-        '`setWasmPaths()` before you call `tf.setBackend()` or ' +
-        '`tf.ready()`');
-  }
-
-  if (typeof prefixOrFileMap === 'string') {
-    wasmPathPrefix = prefixOrFileMap;
-  } else {
-    wasmFileMap = prefixOrFileMap;
-    const missingPaths =
-        wasmBinaryNames.filter(name => wasmFileMap[name] == null);
-    if (missingPaths.length > 0) {
-      throw new Error(
-          `There were no entries found for the following binaries: ` +
-          `${missingPaths.join(',')}. Please either call setWasmPaths with a ` +
-          `map providing a path for each binary, or with a string indicating ` +
-          `the directory where all the binaries can be found.`);
-    }
-  }
-
-  customFetch = usePlatformFetch;
-}
-
-/** Used in unit tests. */
-export function resetWasmPath(): void {
-  wasmPath = null;
-  wasmPathPrefix = null;
-  wasmFileMap = {};
-  customFetch = false;
-  initAborted = false;
-}
-
-let threadsCount = -1;
-let actualThreadsCount = -1;
-
-/**
- * Sets the number of threads that will be used by XNNPACK to create
- * threadpool (default to the number of logical CPU cores).
- *
- * This must be called before calling `tf.setBackend('wasm')`.
- */
-export function setThreadsCount(numThreads: number) {
-  threadsCount = numThreads;
-}
-
-/**
- * Gets the actual threads count that is used by XNNPACK.
- *
- * It is set after the backend is intialized.
- */
-export function getThreadsCount(): number {
-  if (actualThreadsCount === -1) {
-    throw new Error(`WASM backend not initialized.`);
-  }
-  return actualThreadsCount;
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/base.ts b/tfjs-master/tfjs-backend-wasm/src/base.ts
deleted file mode 100644
index 25c050c20..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/base.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import './flags_wasm';
-
-import {registerBackend} from '@tensorflow/tfjs-core';
-
-import {BackendWasm, init} from './backend_wasm';
-
-export {BackendWasm, getThreadsCount, setThreadsCount, setWasmPath, setWasmPaths} from './backend_wasm';
-export {version as version_wasm} from './version';
-
-const WASM_PRIORITY = 2;
-registerBackend('wasm', async () => {
-  const {wasm} = await init();
-  return new BackendWasm(wasm);
-}, WASM_PRIORITY);
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/BUILD.bazel b/tfjs-master/tfjs-backend-wasm/src/cc/BUILD.bazel
deleted file mode 100644
index bba48d8d1..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/BUILD.bazel
+++ /dev/null
@@ -1,1799 +0,0 @@
-load("@emsdk//emscripten_toolchain:wasm_rules.bzl", "wasm_cc_binary")
-load(":build_defs.bzl", "tfjs_cc_library", "tfjs_unit_test")
-
-package(default_visibility = ["//visibility:public"])
-
-# Emcripten produces a much larger wasm bundle unless the cc_binary has srcs
-# explicitly pointing to files with exported methods (EMSCRIPTEN_KEEPALIVE).
-KERNELS_WITH_KEEPALIVE = glob(
-    ["kernels/*.cc"],
-    exclude = ["**/*_test.cc"],
-)
-
-BASE_LINKOPTS = [
-    "-s ALLOW_MEMORY_GROWTH=1",
-    "-s MAXIMUM_MEMORY=4GB",
-    "-s DEFAULT_LIBRARY_FUNCS_TO_INCLUDE=[]",
-    "-s DISABLE_EXCEPTION_CATCHING=1",
-    "-s FILESYSTEM=0",
-    "-s EXIT_RUNTIME=0",
-    "-s EXPORTED_FUNCTIONS='[\"_malloc\", \"_free\"]'",
-    "-s EXPORTED_RUNTIME_METHODS='[\"cwrap\"]'",
-    "-s MODULARIZE=1",
-    "-s MALLOC=emmalloc",
-    "--pre-js $(location :pre.js)",
-    "--post-js $(location :post.js)",
-]
-
-# This build rule generates tfjs-backend-wasm.{js,wasm}.
-#
-# The ".js" at the end of the build name is significant because it determines
-# the output file types. See the "-o <target>" flag in
-# https://emscripten.org/docs/tools_reference/emcc.html
-#
-# Note that tfjs-backend-wasm.js will be used to load tfjs-backend-wasm.wasm
-# as well as tfjs-backend-wasm-simd.wasm (the output of the next build rule).
-cc_binary(
-    name = "tfjs-backend-wasm.js",
-    srcs = ["backend.cc"] + KERNELS_WITH_KEEPALIVE,
-    additional_linker_inputs = [
-        "pre.js",
-        "post.js",
-    ],
-    linkopts = BASE_LINKOPTS + [
-        "-s EXPORT_NAME=WasmBackendModule",
-    ],
-    deps = [
-        ":all_kernels",
-        ":backend",
-    ],
-)
-
-cc_binary(
-    name = "tfjs-backend-wasm-simd.js",
-    srcs = ["backend.cc"] + KERNELS_WITH_KEEPALIVE,
-    additional_linker_inputs = [
-        "pre.js",
-        "post.js",
-    ],
-    copts = [
-        "-msimd128",
-    ],
-    linkopts = BASE_LINKOPTS,
-    deps = [
-        ":all_kernels",
-        ":backend",
-    ],
-)
-
-cc_binary(
-    name = "tfjs-backend-wasm-threaded-simd.js",
-    srcs = ["backend.cc"] + KERNELS_WITH_KEEPALIVE,
-    additional_linker_inputs = [
-        "pre.js",
-        "post.js",
-    ],
-    copts = [
-        "-msimd128",
-        "-pthread",
-    ],
-    linkopts = BASE_LINKOPTS + [
-        "-s EXPORT_NAME=WasmBackendModuleThreadedSimd",
-        "-s MALLOC=emmalloc",
-        "-s USE_PTHREADS=1",
-        # Pre-create 8 webworkers (threads). The actual number of threads that
-        # will be used by XNNPACK for creating the threadpool might be fewer. It
-        # is by default set to the number of logical cores which in most cases
-        # should be fewer than 8. It can also be set manually by calling the
-        # `setThreadsCount` API.
-        "-s PTHREAD_POOL_SIZE=8",
-    ],
-    deps = [
-        ":all_kernels",
-        ":backend",
-    ],
-)
-
-wasm_cc_binary(
-    name = "tfjs-backend-wasm",
-    cc_target = ":tfjs-backend-wasm.js",
-    tags = ["local"],
-)
-
-wasm_cc_binary(
-    name = "tfjs-backend-wasm-simd",
-    cc_target = ":tfjs-backend-wasm-simd.js",
-    simd = True,
-    tags = ["local"],
-)
-
-wasm_cc_binary(
-    name = "tfjs-backend-wasm-threaded-simd",
-    cc_target = ":tfjs-backend-wasm-threaded-simd.js",
-    simd = True,
-    tags = ["local"],
-    threads = "emscripten",
-)
-
-test_suite(
-    name = "cc_tests",
-)
-
-## Library
-
-tfjs_cc_library(
-    name = "backend",
-    srcs = ["backend.cc"],
-    hdrs = ["backend.h"],
-    deps = [
-        ":check_macros",
-        ":util",
-        "@xnnpack//:xnnpack_for_tfjs",
-    ],
-)
-
-tfjs_unit_test(
-    name = "backend_tests",
-    srcs = glob(["*_test.cc"]),
-    tags = ["ci"],
-    deps = [
-        ":Prelu",
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "binary",
-    srcs = ["binary.cc"],
-    hdrs = ["binary.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "argminmax_impl",
-    hdrs = ["argminmax_impl.h"],
-    deps = [
-        ":backend",
-    ],
-)
-
-tfjs_cc_library(
-    name = "bincount_impl",
-    hdrs = ["bincount_impl.h"],
-    deps = [":util"],
-)
-
-tfjs_cc_library(
-    name = "check_macros",
-    srcs = ["check_macros.h"],
-    deps = [
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "sin_cos_workaround",
-    srcs = ["sin_cos_workaround.cc"],
-    hdrs = ["sin_cos_workaround.h"],
-)
-
-tfjs_cc_library(
-    name = "clamp_impl",
-    srcs = ["clamp_impl.cc"],
-    hdrs = ["clamp_impl.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "conv2d_impl",
-    srcs = ["conv2d_impl.cc"],
-    hdrs = ["conv2d_impl.h"],
-    deps = [
-        ":backend",
-        ":elu_impl",
-        ":leakyrelu_impl",
-        ":prelu_impl",
-        ":sigmoid_impl",
-        ":transpose_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "batch_mat_mul_impl",
-    srcs = ["batch_mat_mul_impl.cc"],
-    hdrs = ["batch_mat_mul_impl.h"],
-    deps = [
-        ":backend",
-        ":elu_impl",
-        ":leakyrelu_impl",
-        ":prelu_impl",
-        ":sigmoid_impl",
-        ":transpose_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "dilation2d_impl",
-    hdrs = ["dilation2d_impl.h"],
-)
-
-tfjs_cc_library(
-    name = "elu_impl",
-    srcs = ["elu_impl.cc"],
-    hdrs = ["elu_impl.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "interpolate_bilinear_impl",
-    srcs = ["interpolate_bilinear_impl.cc"],
-    hdrs = ["interpolate_bilinear_impl.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "leakyrelu_impl",
-    srcs = ["leakyrelu_impl.cc"],
-    hdrs = ["leakyrelu_impl.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "non_max_suppression_impl",
-    srcs = ["non_max_suppression_impl.cc"],
-    hdrs = ["non_max_suppression_impl.h"],
-    deps = [
-        ":backend",
-    ],
-)
-
-tfjs_cc_library(
-    name = "pool3d_impl",
-    hdrs = ["pool3d_impl.h"],
-    deps = [
-        ":shape",
-    ],
-)
-
-tfjs_cc_library(
-    name = "conv3d_impl",
-    hdrs = ["conv3d_impl.h"],
-    deps = [
-        ":shape",
-    ],
-)
-
-tfjs_cc_library(
-    name = "prelu_impl",
-    srcs = ["prelu_impl.cc"],
-    hdrs = ["prelu_impl.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "scatter_impl",
-    srcs = ["scatter_impl.cc"],
-    hdrs = ["scatter_impl.h"],
-)
-
-tfjs_cc_library(
-    name = "sigmoid_impl",
-    srcs = ["sigmoid_impl.cc"],
-    hdrs = ["sigmoid_impl.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "transpose_impl",
-    srcs = ["transpose_impl.cc"],
-    hdrs = ["transpose_impl.h"],
-    deps = [":util"],
-)
-
-tfjs_cc_library(
-    name = "shape",
-    hdrs = ["shape.h"],
-)
-
-tfjs_cc_library(
-    name = "util",
-    srcs = ["util.cc"],
-    hdrs = ["util.h"],
-    deps = [
-        ":shape",
-    ],
-)
-
-tfjs_cc_library(
-    name = "unary",
-    srcs = ["unary.cc"],
-    hdrs = ["unary.h"],
-    deps = [
-        ":backend",
-    ],
-)
-
-# Kernels
-
-tfjs_cc_library(
-    name = "all_kernels",
-    deps = [
-        ":Abs",
-        ":Acos",
-        ":Acosh",
-        ":Add",
-        ":AddN",
-        ":All",
-        ":Any",
-        ":ArgMax",
-        ":ArgMin",
-        ":Atan",
-        ":Atan2",
-        ":Atanh",
-        ":AvgPool",
-        ":AvgPool3D",
-        ":AvgPool3DGrad",
-        ":AvgPoolGrad",
-        ":BatchMatMul",
-        ":Bincount",
-        ":BitwiseAnd",
-        ":Ceil",
-        ":ClipByValue",
-        ":Conv2D",
-        ":Conv2DBackpropInput",
-        ":Conv3D",
-        ":Conv3DBackpropFilterV2",
-        ":Conv3DBackpropInputV2",
-        ":Cos",
-        ":Cosh",
-        ":CropAndResize",
-        ":Cumprod",
-        ":Cumsum",
-        ":DenseBincount",
-        ":DepthToSpace",
-        ":DepthwiseConv2dNative",
-        ":Dilation2D",
-        ":Dilation2DBackpropFilter",
-        ":Dilation2DBackpropInput",
-        ":Elu",
-        ":Equal",
-        ":Erf",
-        ":Exp",
-        ":FlipLeftRight",
-        ":FloorDiv",
-        ":FusedBatchNorm",
-        ":FusedConv2D",
-        ":FusedDepthwiseConv2D",
-        ":Gather",
-        ":GatherNd",
-        ":Greater",
-        ":GreaterEqual",
-        ":IsFinite",
-        ":IsInf",
-        ":IsNan",
-        ":LRN",
-        ":LRNGrad",
-        ":LeakyRelu",
-        ":Less",
-        ":LessEqual",
-        ":LinSpace",
-        ":Log1p",
-        ":Max",
-        ":MaxPool",
-        ":MaxPool3D",
-        ":MaxPool3DGrad",
-        ":MaxPoolGrad",
-        ":MaxPoolWithArgmax",
-        ":Maximum",
-        ":Min",
-        ":Minimum",
-        ":MirrorPad",
-        ":Mod",
-        ":Multinomial",
-        ":Multiply",
-        ":Neg",
-        ":NonMaxSuppressionV3",
-        ":NonMaxSuppressionV4",
-        ":NonMaxSuppressionV5",
-        ":NotEqual",
-        ":OneHot",
-        ":PadV2",
-        ":Pow",
-        ":Prelu",
-        ":RealDiv",
-        ":Reciprocal",
-        ":Relu",
-        ":Relu6",
-        ":ResizeBilinear",
-        ":ResizeNearestNeighbor",
-        ":ResizeNearestNeighborGrad",
-        ":Reverse",
-        ":RotateWithOffset",
-        ":Round",
-        ":ScatterNd",
-        ":SearchSorted",
-        ":SelectV2",
-        ":Selu",
-        ":Sigmoid",
-        ":Sign",
-        ":Sin",
-        ":Sinh",
-        ":Softmax",
-        ":Softplus",
-        ":SparseFillEmptyRows",
-        ":SparseReshape",
-        ":SparseSegmentReduction",
-        ":SparseToDense",
-        ":Square",
-        ":SquaredDifference",
-        ":StridedSlice",
-        ":Sub",
-        ":Tan",
-        ":Tile",
-        ":Transform",
-        ":Transpose",
-        ":_FusedMatMul",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Abs",
-    srcs = ["kernels/Abs.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Acos",
-    srcs = ["kernels/Acos.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Acosh",
-    srcs = ["kernels/Acosh.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Add",
-    srcs = ["kernels/Add.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "AddN",
-    srcs = ["kernels/AddN.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "All",
-    srcs = ["kernels/All.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Any",
-    srcs = ["kernels/Any.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "ArgMax",
-    srcs = ["kernels/ArgMax.cc"],
-    deps = [
-        ":argminmax_impl",
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "ArgMin",
-    srcs = ["kernels/ArgMin.cc"],
-    deps = [
-        ":argminmax_impl",
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Atan",
-    srcs = ["kernels/Atan.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Atan2",
-    srcs = ["kernels/Atan2.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Atanh",
-    srcs = ["kernels/Atanh.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Asin",
-    srcs = ["kernels/Asin.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Asinh",
-    srcs = ["kernels/Asinh.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "AvgPool",
-    srcs = ["kernels/AvgPool.cc"],
-    hdrs = ["kernels/AvgPool.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "AvgPoolGrad",
-    srcs = ["kernels/AvgPoolGrad.cc"],
-    deps = [
-        ":backend",
-        ":pool3d_impl",
-    ],
-)
-
-tfjs_cc_library(
-    name = "AvgPool3D",
-    srcs = ["kernels/AvgPool3D.cc"],
-    deps = [
-        ":backend",
-        ":pool3d_impl",
-    ],
-)
-
-tfjs_cc_library(
-    name = "AvgPool3DGrad",
-    srcs = ["kernels/AvgPool3DGrad.cc"],
-    deps = [
-        ":backend",
-        ":pool3d_impl",
-    ],
-)
-
-tfjs_unit_test(
-    name = "AvgPool_test",
-    srcs = ["kernels/AvgPool_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":AvgPool",
-    ],
-)
-
-tfjs_cc_library(
-    name = "BatchMatMul",
-    srcs = ["kernels/BatchMatMul.cc"],
-    hdrs = ["kernels/BatchMatMul.h"],
-    deps = [
-        ":backend",
-        ":batch_mat_mul_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Bincount",
-    srcs = ["kernels/Bincount.cc"],
-    deps = [
-        ":backend",
-        ":bincount_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "BitwiseAnd",
-    srcs = ["kernels/BitwiseAnd.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "_FusedMatMul",
-    srcs = ["kernels/_FusedMatMul.cc"],
-    hdrs = ["kernels/_FusedMatMul.h"],
-    deps = [
-        ":backend",
-        ":batch_mat_mul_impl",
-    ],
-)
-
-tfjs_unit_test(
-    name = "BatchMatMul_test",
-    srcs = ["kernels/BatchMatMul_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":BatchMatMul",
-    ],
-)
-
-tfjs_unit_test(
-    name = "_FusedMatMul_test",
-    srcs = ["kernels/_FusedMatMul_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":_FusedMatMul",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Ceil",
-    srcs = ["kernels/Ceil.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "ClipByValue",
-    srcs = ["kernels/ClipByValue.cc"],
-    hdrs = ["kernels/ClipByValue.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_unit_test(
-    name = "ClipByValue_test",
-    srcs = ["kernels/ClipByValue_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":ClipByValue",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Conv2D",
-    srcs = ["kernels/Conv2D.cc"],
-    hdrs = ["kernels/Conv2D.h"],
-    deps = [
-        ":backend",
-        ":conv2d_impl",
-    ],
-)
-
-tfjs_unit_test(
-    name = "Conv2D_test",
-    srcs = ["kernels/Conv2D_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":Conv2D",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Conv2DBackpropInput",
-    srcs = ["kernels/Conv2DBackpropInput.cc"],
-    deps = [
-        ":backend",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Conv3D",
-    srcs = ["kernels/Conv3D.cc"],
-    deps = [
-        ":backend",
-        ":conv3d_impl",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Conv3DBackpropFilterV2",
-    srcs = ["kernels/Conv3DBackpropFilterV2.cc"],
-    deps = [
-        ":backend",
-        ":conv3d_impl",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Conv3DBackpropInputV2",
-    srcs = ["kernels/Conv3DBackpropInputV2.cc"],
-    deps = [
-        ":backend",
-        ":conv3d_impl",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Cos",
-    srcs = ["kernels/Cos.cc"],
-    deps = [
-        ":backend",
-        ":sin_cos_workaround",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "CropAndResize",
-    srcs = ["kernels/CropAndResize.cc"],
-    deps = [
-        ":backend",
-        ":interpolate_bilinear_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Cumprod",
-    srcs = ["kernels/Cumprod.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Cumsum",
-    srcs = ["kernels/Cumsum.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "DenseBincount",
-    srcs = ["kernels/DenseBincount.cc"],
-    deps = [
-        ":backend",
-        ":bincount_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "DepthToSpace",
-    srcs = ["kernels/DepthToSpace.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "DepthwiseConv2dNative",
-    srcs = ["kernels/DepthwiseConv2dNative.cc"],
-    hdrs = ["kernels/DepthwiseConv2dNative.h"],
-    deps = [
-        ":backend",
-        ":conv2d_impl",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Dilation2D",
-    srcs = ["kernels/Dilation2D.cc"],
-    deps = [
-        ":backend",
-        ":dilation2d_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Dilation2DBackpropFilter",
-    srcs = ["kernels/Dilation2DBackpropFilter.cc"],
-    deps = [
-        ":backend",
-        ":dilation2d_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Dilation2DBackpropInput",
-    srcs = ["kernels/Dilation2DBackpropInput.cc"],
-    deps = [
-        ":backend",
-        ":dilation2d_impl",
-        ":util",
-    ],
-)
-
-tfjs_unit_test(
-    name = "DepthwiseConv2dNative_test",
-    srcs = ["kernels/DepthwiseConv2dNative_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":DepthwiseConv2dNative",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Diag",
-    srcs = ["kernels/Diag.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "RealDiv",
-    srcs = ["kernels/RealDiv.cc"],
-    deps = [
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Elu",
-    srcs = ["kernels/Elu.cc"],
-    deps = [
-        ":backend",
-        ":elu_impl",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Equal",
-    srcs = ["kernels/Equal.cc"],
-    deps = [
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Erf",
-    srcs = ["kernels/Erf.cc"],
-    deps = [
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Exp",
-    srcs = ["kernels/Exp.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Expm1",
-    srcs = ["kernels/Expm1.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "FlipLeftRight",
-    srcs = ["kernels/FlipLeftRight.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "FloorDiv",
-    srcs = ["kernels/FloorDiv.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "FusedBatchNorm",
-    srcs = ["kernels/FusedBatchNorm.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "FusedConv2D",
-    srcs = ["kernels/FusedConv2D.cc"],
-    hdrs = ["kernels/FusedConv2D.h"],
-    deps = [
-        ":conv2d_impl",
-    ],
-)
-
-tfjs_unit_test(
-    name = "FusedConv2D_test",
-    srcs = ["kernels/FusedConv2D_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":FusedConv2D",
-    ],
-)
-
-tfjs_cc_library(
-    name = "FusedDepthwiseConv2D",
-    srcs = ["kernels/FusedDepthwiseConv2D.cc"],
-    hdrs = ["kernels/FusedDepthwiseConv2D.h"],
-    deps = [
-        ":backend",
-        ":conv2d_impl",
-    ],
-)
-
-tfjs_unit_test(
-    name = "FusedDepthwiseConv2D_test",
-    srcs = ["kernels/FusedDepthwiseConv2D_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":FusedDepthwiseConv2D",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Gather",
-    srcs = ["kernels/Gather.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "GatherNd",
-    srcs = ["kernels/GatherNd.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Greater",
-    srcs = ["kernels/Greater.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "GreaterEqual",
-    srcs = ["kernels/GreaterEqual.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "LRN",
-    srcs = ["kernels/LRN.cc"],
-    deps = [
-        ":backend",
-    ],
-)
-
-tfjs_cc_library(
-    name = "LRNGrad",
-    srcs = ["kernels/LRNGrad.cc"],
-    deps = [
-        ":backend",
-    ],
-)
-
-tfjs_cc_library(
-    name = "LeakyRelu",
-    srcs = ["kernels/LeakyRelu.cc"],
-    hdrs = ["kernels/LeakyRelu.h"],
-    deps = [
-        ":backend",
-        ":leakyrelu_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Less",
-    srcs = ["kernels/Less.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "LessEqual",
-    srcs = ["kernels/LessEqual.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "LinSpace",
-    srcs = ["kernels/LinSpace.cc"],
-    deps = [
-        ":backend",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Log1p",
-    srcs = ["kernels/Log1p.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "NotEqual",
-    srcs = ["kernels/NotEqual.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "LogicalAnd",
-    srcs = ["kernels/LogicalAnd.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "LogicalNot",
-    srcs = ["kernels/LogicalNot.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "LogicalOr",
-    srcs = ["kernels/LogicalOr.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "LogicalXor",
-    srcs = ["kernels/LogicalXor.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Log",
-    srcs = ["kernels/Log.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Max",
-    srcs = ["kernels/Max.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Maximum",
-    srcs = ["kernels/Maximum.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "MaxPool",
-    srcs = ["kernels/MaxPool.cc"],
-    hdrs = ["kernels/MaxPool.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "MaxPoolGrad",
-    srcs = ["kernels/MaxPoolGrad.cc"],
-    deps = [
-        ":backend",
-        ":pool3d_impl",
-    ],
-)
-
-tfjs_unit_test(
-    name = "MaxPool_test",
-    srcs = ["kernels/MaxPool_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":MaxPool",
-    ],
-)
-
-tfjs_cc_library(
-    name = "MaxPool3D",
-    srcs = ["kernels/MaxPool3D.cc"],
-    deps = [
-        ":backend",
-        ":pool3d_impl",
-    ],
-)
-
-tfjs_cc_library(
-    name = "MaxPool3DGrad",
-    srcs = ["kernels/MaxPool3DGrad.cc"],
-    deps = [
-        ":backend",
-        ":pool3d_impl",
-    ],
-)
-
-tfjs_cc_library(
-    name = "MaxPoolWithArgmax",
-    srcs = ["kernels/MaxPoolWithArgmax.cc"],
-    deps = [
-        ":backend",
-        ":pool3d_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Min",
-    srcs = ["kernels/Min.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Minimum",
-    srcs = ["kernels/Minimum.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "MirrorPad",
-    srcs = ["kernels/MirrorPad.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Mod",
-    srcs = ["kernels/Mod.cc"],
-    deps = [
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Multiply",
-    srcs = ["kernels/Multiply.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Multinomial",
-    srcs = ["kernels/Multinomial.cc"],
-    deps = [
-        ":backend",
-        ":shape",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Neg",
-    srcs = ["kernels/Neg.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "NonMaxSuppressionV3",
-    srcs = ["kernels/NonMaxSuppressionV3.cc"],
-    deps = [
-        ":backend",
-        ":non_max_suppression_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "NonMaxSuppressionV4",
-    srcs = ["kernels/NonMaxSuppressionV4.cc"],
-    deps = [
-        ":backend",
-        ":non_max_suppression_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "NonMaxSuppressionV5",
-    srcs = ["kernels/NonMaxSuppressionV5.cc"],
-    deps = [
-        ":backend",
-        ":non_max_suppression_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "OneHot",
-    srcs = ["kernels/OneHot.cc"],
-    deps = [
-        ":backend",
-    ],
-)
-
-tfjs_cc_library(
-    name = "PadV2",
-    srcs = ["kernels/PadV2.cc"],
-    hdrs = ["kernels/PadV2.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_unit_test(
-    name = "PadV2_test",
-    srcs = ["kernels/PadV2_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":PadV2",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Pow",
-    srcs = ["kernels/Pow.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Prelu",
-    srcs = ["kernels/Prelu.cc"],
-    hdrs = ["kernels/Prelu.h"],
-    deps = [
-        ":backend",
-        ":prelu_impl",
-        ":util",
-    ],
-)
-
-tfjs_unit_test(
-    name = "Prelu_test",
-    srcs = ["kernels/Prelu_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":Prelu",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Relu",
-    srcs = ["kernels/Relu.cc"],
-    deps = [
-        ":backend",
-        ":clamp_impl",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Relu6",
-    srcs = ["kernels/Relu6.cc"],
-    deps = [
-        ":backend",
-        ":clamp_impl",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "ResizeBilinear",
-    srcs = ["kernels/ResizeBilinear.cc"],
-    hdrs = ["kernels/ResizeBilinear.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "ResizeBilinearGrad",
-    srcs = ["kernels/ResizeBilinearGrad.cc"],
-    deps = [
-        ":backend",
-        ":shape",
-    ],
-)
-
-tfjs_cc_library(
-    name = "ResizeNearestNeighbor",
-    srcs = ["kernels/ResizeNearestNeighbor.cc"],
-    hdrs = ["kernels/ResizeNearestNeighbor.h"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "ResizeNearestNeighborGrad",
-    srcs = ["kernels/ResizeNearestNeighborGrad.cc"],
-    deps = [
-        ":backend",
-        ":shape",
-    ],
-)
-
-tfjs_unit_test(
-    name = "ResizeBilinear_test",
-    srcs = ["kernels/ResizeBilinear_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":ResizeBilinear",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Reverse",
-    srcs = ["kernels/Reverse.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "RotateWithOffset",
-    srcs = ["kernels/RotateWithOffset.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Round",
-    srcs = ["kernels/Round.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "ScatterNd",
-    srcs = ["kernels/ScatterNd.cc"],
-    deps = [
-        ":backend",
-        ":scatter_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "SearchSorted",
-    srcs = ["kernels/SearchSorted.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "SelectV2",
-    srcs = ["kernels/SelectV2.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Selu",
-    srcs = ["kernels/Selu.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Sigmoid",
-    srcs = ["kernels/Sigmoid.cc"],
-    hdrs = ["kernels/Sigmoid.h"],
-    deps = [
-        ":backend",
-        ":sigmoid_impl",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Sign",
-    srcs = ["kernels/Sign.cc"],
-    deps = [
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Sin",
-    srcs = ["kernels/Sin.cc"],
-    deps = [
-        ":backend",
-        ":sin_cos_workaround",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Sinh",
-    srcs = ["kernels/Sinh.cc"],
-    deps = [
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Softmax",
-    srcs = ["kernels/Softmax.cc"],
-    hdrs = ["kernels/Softmax.h"],
-    deps = [
-        ":backend",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Softplus",
-    srcs = ["kernels/Softplus.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-    ],
-)
-
-tfjs_unit_test(
-    name = "Sigmoid_test",
-    srcs = ["kernels/Sigmoid_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":Sigmoid",
-    ],
-)
-
-tfjs_unit_test(
-    name = "Softmax_test",
-    srcs = ["kernels/Softmax_test.cc"],
-    tags = ["ci"],
-    deps = [
-        ":Softmax",
-    ],
-)
-
-tfjs_cc_library(
-    name = "SparseFillEmptyRows",
-    srcs = ["kernels/SparseFillEmptyRows.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "SparseReshape",
-    srcs = ["kernels/SparseReshape.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "SparseSegmentReduction",
-    srcs = ["kernels/SparseSegmentReduction.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "SparseToDense",
-    srcs = ["kernels/SparseToDense.cc"],
-    deps = [
-        ":backend",
-        ":scatter_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Square",
-    srcs = ["kernels/Square.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "SquaredDifference",
-    srcs = ["kernels/SquaredDifference.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":unary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "StridedSlice",
-    srcs = ["kernels/StridedSlice.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Sub",
-    srcs = ["kernels/Sub.cc"],
-    deps = [
-        ":backend",
-        ":binary",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Tile",
-    srcs = ["kernels/Tile.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "TopK",
-    srcs = ["kernels/TopK.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Transform",
-    srcs = ["kernels/Transform.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Transpose",
-    srcs = ["kernels/Transpose.cc"],
-    deps = [
-        ":backend",
-        ":transpose_impl",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Step",
-    srcs = ["kernels/Step.cc"],
-    deps = [
-        ":backend",
-        ":util",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Tan",
-    srcs = ["kernels/Tan.cc"],
-    deps = [
-        ":backend",
-        ":sin_cos_workaround",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Cosh",
-    srcs = ["kernels/Cosh.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "Reciprocal",
-    srcs = ["kernels/Reciprocal.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "IsNan",
-    srcs = ["kernels/IsNan.cc"],
-    deps = [
-        ":backend",
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "IsFinite",
-    srcs = ["kernels/IsFinite.cc"],
-    deps = [
-        ":unary",
-    ],
-)
-
-tfjs_cc_library(
-    name = "IsInf",
-    srcs = ["kernels/IsInf.cc"],
-    deps = [
-        ":unary",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/argminmax_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/argminmax_impl.h
deleted file mode 100644
index 4b7ed2619..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/argminmax_impl.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef ARGMINMAX_IMPL_H_
-#define ARGMINMAX_IMPL_H_
-
-#include <cstddef>
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-template <typename T, typename F>
-inline void ArgMinMaxInner(const T* x, const size_t outer_size,
-                           const size_t inner_size, int32_t* out_buf,
-                           const F& update_cond) {
-  for (int i = 0; i < outer_size; ++i) {
-    const int offset = i * inner_size;
-    T target_value = x[offset];
-    int target_index = 0;
-    for (int j = 1; j < inner_size; ++j) {
-      T value = x[offset + j];
-      if (update_cond(target_value, value)) {
-        target_value = value;
-        target_index = j;
-      }
-    }
-    out_buf[i] = target_index;
-  }
-}
-
-}  // namespace
-
-template <typename T>
-inline void ArgMaxImpl(const T* x, const size_t outer_size,
-                       const size_t inner_size, int32_t* out_buf) {
-  ArgMinMaxInner(
-      x, outer_size, inner_size, out_buf,
-      [](const T& target, const T& current) { return target < current; });
-}
-
-template <typename T>
-inline void ArgMinImpl(const T* x, const size_t outer_size,
-                       const size_t inner_size, int32_t* out_buf) {
-  ArgMinMaxInner(
-      x, outer_size, inner_size, out_buf,
-      [](const T& target, const T& current) { return target > current; });
-}
-
-}  // namespace tfjs::wasm
-
-#endif
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/backend.cc b/tfjs-master/tfjs-backend-wasm/src/cc/backend.cc
deleted file mode 100644
index 5a5fee863..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/backend.cc
+++ /dev/null
@@ -1,177 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#include <emscripten/threading.h>
-#endif
-
-#include <xnnpack.h>
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <unordered_map>
-#include <utility>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/check_macros.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// Maps a unique tensor id to info about that tensor. The map owns all of its
-// entries.
-std::unordered_map<size_t, TensorInfo> data;
-
-// Maps a tensor id to a vector of disposal functions registered on that tensor
-// id.
-std::unordered_map<size_t, std::vector<tfjs::backend::DisposeFunction>>
-    disposal_callbacks;
-}  // namespace
-
-namespace tfjs {
-namespace backend {
-const TensorInfo &get_tensor_info(const size_t tensor_id) {
-  return data.at(tensor_id);
-}
-
-TensorInfo &get_tensor_info_out(const size_t tensor_id) {
-  return data.at(tensor_id);
-}
-
-size_t xnn_operator_count = 0;
-
-pthreadpool *threadpool = NULL;
-
-// Registers a disposal callback for a tensor id with a given callback function.
-void register_disposal_callback(const size_t tensor_id,
-                                const DisposeFunction dispose_fn) {
-  if (disposal_callbacks.count(tensor_id) == 0) {
-    // We move callbacks to avoid a copy.
-    disposal_callbacks.insert({tensor_id, {dispose_fn}});
-  } else {
-    auto &callbacks = disposal_callbacks[tensor_id];
-    callbacks.push_back(dispose_fn);
-  }
-}
-
-const size_t num_tensors() { return data.size(); }
-
-}  // namespace backend
-
-namespace wasm {
-
-// emscripten_num_logical_cores corresponds to navigator.hardwareConcurrency.
-// Many x86-64 processors have 2 threads per core, so we are dividing by 2.
-#ifdef __EMSCRIPTEN_PTHREADS__
-int num_cores = emscripten_num_logical_cores() / 2;
-#else
-int num_cores = 1;
-#endif
-
-int min_num_threads = 1;
-// In cc/BUILD, we pre-created 8 webworker threads which is the maximum number
-// of threads that the threadpool could use here.
-int max_num_threads = 8;
-
-// We use C-style API to interface with Javascript.
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void init() { init_with_threads_count(num_cores); }
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void init_with_threads_count(const int threads_count) {
-  int count = threads_count;
-  if (threads_count < 0) {
-    count = num_cores;
-  }
-  int capped_num_threads = std::min(
-      std::min(std::max(count, min_num_threads), max_num_threads), num_cores);
-  tfjs::backend::threadpool = pthreadpool_create(capped_num_threads);
-  xnn_initialize(nullptr);
-}
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-int get_threads_count() {
-  if (tfjs::backend::threadpool == NULL) {
-    return -1;
-  }
-  return pthreadpool_get_threads_count(tfjs::backend::threadpool);
-}
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void register_tensor(const size_t tensor_id, const size_t size,
-                     void *memory_offset) {
-  DCHECK(tensor_id > 0,
-         "register_tensor: tensor_id must a positive number but got %d.",
-         tensor_id);
-  DCHECK(data.find(tensor_id) == data.end(),
-         "register_tensor: tensor_id %d has already been registered.",
-         tensor_id);
-
-  data.emplace(tensor_id, TensorInfo{memory_offset, size});
-}
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void dispose_data(const size_t tensor_id) {
-  data.erase(tensor_id);
-
-  // Call all disposal callbacks for this tensor id.
-  auto disposal_callback_idx = disposal_callbacks.find(tensor_id);
-  if (disposal_callback_idx != disposal_callbacks.end()) {
-    auto &callbacks = disposal_callback_idx->second;
-    for (auto &dispose_function : callbacks) {
-      dispose_function(tensor_id);
-    }
-
-    disposal_callbacks.erase(tensor_id);
-  }
-}
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void dispose() {
-  // We have to create a separate vector of tensor ids because we erase from the
-  // map while we're iterating it.
-  std::vector<size_t> tensor_ids_to_dispose;
-  for (const auto &element : data) {
-    tensor_ids_to_dispose.push_back(element.first);
-  }
-  for (const auto tensor_id : tensor_ids_to_dispose) {
-    dispose_data(tensor_id);
-  }
-
-  data.clear();
-  disposal_callbacks.clear();
-
-  pthreadpool_destroy(tfjs::backend::threadpool);
-  tfjs::backend::threadpool = NULL;
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/backend.h b/tfjs-master/tfjs-backend-wasm/src/cc/backend.h
deleted file mode 100644
index 89bfc8161..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/backend.h
+++ /dev/null
@@ -1,123 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef BACKEND_H_
-#define BACKEND_H_
-
-#include <xnnpack.h>
-#include <cstddef>
-#include <cstdint>
-
-// This enum should align with the DType defined in kernels/types.ts.
-enum DType {
-  float32 = 0,
-  int32 = 1,
-  boolean = 2,
-  str = 3,
-  complex64 = 4,
-};
-
-// Must match enum in kernels/types.ts.
-enum FusableActivation {
-  LINEAR = 0,
-  RELU = 1,
-  RELU6 = 2,
-  PRELU = 3,
-  LEAKYRELU = 4,
-  SIGMOID = 5,
-  ELU = 6
-};
-
-// Holds the memory offset and the size of a tensor.
-struct TensorInfo {
-  // Pointer to the bytes where the data is allocated.
-  void *memory_offset;
-  // Total number of elements.
-  const size_t size;
-
-  const float *f32() const {
-    return reinterpret_cast<const float *>(memory_offset);
-  }
-
-  float *f32_write() { return reinterpret_cast<float *>(memory_offset); }
-
-  const int32_t *i32() const {
-    return reinterpret_cast<const int *>(memory_offset);
-  }
-
-  int32_t *i32_write() { return reinterpret_cast<int32_t *>(memory_offset); }
-
-  const bool *b() const {
-    return reinterpret_cast<const bool *>(memory_offset);
-  }
-
-  bool *b_write() { return reinterpret_cast<bool *>(memory_offset); }
-
-  // Delete the copy constructor to avoid copying.
-  TensorInfo(const TensorInfo &) = delete;
-  void operator=(const TensorInfo &) = delete;
-
-  // Bring back the move constructor.
-  TensorInfo(TensorInfo &&) = default;
-};
-
-namespace tfjs {
-namespace backend {
-// Returns the tensor information object associated with a given tensor_id
-// bucket.
-const TensorInfo &get_tensor_info(size_t tensor_id);
-// Same as above, but gives write access to the tensor info.
-TensorInfo &get_tensor_info_out(size_t tensor_id);
-
-// Registers a function callback to be called when a tensor with a given ID is
-// disposed.
-typedef void (*DisposeFunction)(size_t);
-void register_disposal_callback(size_t tensor_id, DisposeFunction dispose_fn);
-
-// Returns the number of tensors registered and owned by the backend.
-const size_t num_tensors();
-
-// The number of instantiated XNN operators.
-extern size_t xnn_operator_count;
-
-extern pthreadpool *threadpool;
-}  // namespace backend
-
-namespace wasm {
-extern "C" {
-// Initializes the WASM backend.
-void init();
-
-// Initializes the WASM backend with the given threads count.
-void init_with_threads_count(const int threads_count);
-
-// Get the actual number of threads used in the XNNPACK threadpool.
-int get_threads_count();
-
-// Registers a tensor with a tensor ID, size, and the pointer to where the
-// tensor data lives.
-void register_tensor(const size_t tensor_id, const size_t size,
-                     void *memory_offset);
-
-// Disposes the internal bookeeping for a given tensor ID.
-void dispose_data(const size_t tensor_id);
-
-// Disposes all internal state.
-void dispose();
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // BACKEND_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/backend_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/backend_test.cc
deleted file mode 100644
index 4b2202222..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/backend_test.cc
+++ /dev/null
@@ -1,122 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/Prelu.h"
-
-TEST(BACKEND, register_tensor) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t tensor_id = 1;
-  const size_t size = 2;
-  float values[size] = {1, 2};
-
-  tfjs::wasm::register_tensor(tensor_id, size, values);
-
-  ASSERT_EQ(1, tfjs::backend::num_tensors());
-
-  auto& tensor_info = tfjs::backend::get_tensor_info(tensor_id);
-
-  ASSERT_EQ(size, tensor_info.size);
-
-  ASSERT_EQ(values, tensor_info.memory_offset);
-
-  tfjs::wasm::dispose_data(tensor_id);
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  tfjs::wasm::dispose();
-}
-
-// C++ doesn't allow lambda functions with captures so we define the callback
-// outside the function. In the future we can consider changing the signature
-// of register_disposal_callback to take a std::function.
-size_t tensor_0_callback_count = 0;
-size_t tensor_1_callback_count = 0;
-void fake_dispose_tensor_callback(size_t tensor_id) {
-  if (tensor_id == 1) {
-    tensor_0_callback_count++;
-  } else if (tensor_id == 2) {
-    tensor_1_callback_count++;
-  }
-}
-TEST(BACKEND, disposal_callback) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t tensor_id_0 = 1;
-  const size_t tensor_id_1 = 2;
-  const size_t size = 2;
-  float values_0[size] = {1, 2};
-  float values_1[size] = {3, 4};
-
-  tfjs::wasm::register_tensor(tensor_id_0, size, values_0);
-  tfjs::wasm::register_tensor(tensor_id_1, size, values_1);
-
-  // Register two disposal callbacks on 0 but not 1.
-  tfjs::backend::register_disposal_callback(tensor_id_0,
-                                            *fake_dispose_tensor_callback);
-  tfjs::backend::register_disposal_callback(tensor_id_0,
-                                            *fake_dispose_tensor_callback);
-
-  tfjs::wasm::dispose_data(tensor_id_0);
-
-  ASSERT_EQ(2, tensor_0_callback_count);
-  ASSERT_EQ(0, tensor_1_callback_count);
-
-  tfjs::wasm::dispose_data(tensor_id_1);
-
-  ASSERT_EQ(2, tensor_0_callback_count);
-  ASSERT_EQ(0, tensor_1_callback_count);
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  tfjs::wasm::dispose();
-
-  tensor_0_callback_count = 0;
-  tensor_1_callback_count = 0;
-}
-
-TEST(BACKEND, dispose_backend) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t tensor_id_0 = 1;
-  const size_t tensor_id_1 = 2;
-  const size_t size = 2;
-  float values_0[size] = {1, 2};
-  float values_1[size] = {3, 4};
-
-  tfjs::wasm::register_tensor(tensor_id_0, size, values_0);
-  tfjs::wasm::register_tensor(tensor_id_1, size, values_1);
-  ASSERT_EQ(2, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the first call to prelu.
-  tfjs::wasm::Prelu(tensor_id_0, tensor_id_0, tensor_id_1);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // Dispose removes all tensors and xnn operators.
-  tfjs::wasm::dispose();
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/batch_mat_mul_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/batch_mat_mul_impl.cc
deleted file mode 100644
index 13f2aa92a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/batch_mat_mul_impl.cc
+++ /dev/null
@@ -1,316 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <xnnpack.h>
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <limits>
-#include <map>
-#include <memory>
-#include <tuple>
-#include <unordered_map>
-#include <utility>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/elu_impl.h"
-#include "tfjs-backend-wasm/src/cc/leakyrelu_impl.h"
-#include "tfjs-backend-wasm/src/cc/prelu_impl.h"
-#include "tfjs-backend-wasm/src/cc/sigmoid_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-#include "tfjs-backend-wasm/src/cc/batch_mat_mul_impl.h"
-
-const size_t kBlockSize = 48;
-
-namespace {
-// We use std::tuple as the cache key as it implements the compare operator
-// needed for std::map.
-typedef std::tuple<size_t, size_t, size_t> OperatorCacheKey;
-
-// The operator cache maps the weights id to the xnn_operator_t instantiated for
-// this set of weights.
-std::map<OperatorCacheKey, xnn_operator_t> operator_cache;
-
-std::unordered_map<size_t, std::vector<OperatorCacheKey>>
-    b_operator_cache_key_map;
-
-std::unordered_map<size_t, std::vector<OperatorCacheKey>>
-    bias_operator_cache_key_map;
-
-void erase_from_cache(const size_t tensor_id,
-                      std::unordered_map<size_t, std::vector<OperatorCacheKey>>&
-                          operator_cache_key_map) {
-  auto operator_cache_keys_idx = operator_cache_key_map.find(tensor_id);
-  if (operator_cache_keys_idx != operator_cache_key_map.end()) {
-    std::vector<OperatorCacheKey>& operator_cache_keys =
-        operator_cache_keys_idx->second;
-    for (auto& operator_cache_key : operator_cache_keys) {
-      auto operator_cache_key_idx = operator_cache.find(operator_cache_key);
-      if (operator_cache_key_idx != operator_cache.end()) {
-        auto& cached_op = operator_cache_key_idx->second;
-        xnn_delete_operator(cached_op);
-        tfjs::backend::xnn_operator_count--;
-
-        operator_cache.erase(operator_cache_key);
-      }
-    }
-    operator_cache_key_map.erase(tensor_id);
-  }
-}
-
-void delete_xnn_operators(const size_t tensor_id) {
-  erase_from_cache(tensor_id, b_operator_cache_key_map);
-  erase_from_cache(tensor_id, bias_operator_cache_key_map);
-}
-
-void associate_tensor_with_key(
-    const size_t tensor_id, const OperatorCacheKey& cache_key,
-    std::unordered_map<size_t, std::vector<OperatorCacheKey>>&
-        operator_cache_key_map) {
-  auto cache_keys_idx = operator_cache_key_map.find(tensor_id);
-  if (cache_keys_idx == operator_cache_key_map.end()) {
-    std::vector<OperatorCacheKey> cache_keys = {cache_key};
-    operator_cache_key_map.emplace(tensor_id, std::move(cache_keys));
-    tfjs::backend::register_disposal_callback(tensor_id, *delete_xnn_operators);
-
-  } else {
-    auto& cache_keys = operator_cache_key_map.at(tensor_id);
-    cache_keys.emplace_back(cache_key);
-  }
-}
-
-void xnn_matmul(const size_t a_id, const size_t* a_shape_ptr,
-                const size_t a_shape_len, const size_t b_id,
-                const size_t* b_shape_ptr, const size_t b_shape_len,
-                const size_t out_id, const size_t bias_id,
-                const float output_min, const float output_max,
-                const size_t clamp_method) {
-  auto& a_info = tfjs::backend::get_tensor_info(a_id);
-  auto& b_info = tfjs::backend::get_tensor_info(b_id);
-  auto& out_info = tfjs::backend::get_tensor_info_out(out_id);
-
-  const float* a_buf = a_info.f32();
-  const float* b_buf = b_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  const float* bias_buf = nullptr;
-  if (bias_id != 0) {
-    bias_buf = tfjs::backend::get_tensor_info_out(bias_id).f32();
-  }
-
-  xnn_operator_t fully_connected_op = nullptr;
-
-  OperatorCacheKey cache_key = {b_id, bias_id, clamp_method};
-
-  // We assume b is the weights and cache the xnn operator on it.
-  auto operator_cache_idx = operator_cache.find(cache_key);
-  if (operator_cache_idx == operator_cache.end()) {
-    const size_t input_channels = b_shape_ptr[1];
-    const size_t output_channels = b_shape_ptr[2];
-    const size_t input_stride = input_channels;
-    const size_t output_stride = output_channels;
-
-    // XNNPack expects b to already be transposed. TensorFlow.js doesn't do this
-    // automatically so we have to tell XNNPack to do the transposing.
-    const uint32_t flags = XNN_FLAG_TRANSPOSE_WEIGHTS;
-    xnn_status status = xnn_create_fully_connected_nc_f32(
-        input_channels, output_channels, input_stride, output_stride, b_buf,
-        bias_buf, output_min, output_max, flags, &fully_connected_op);
-    if (status != xnn_status_success) {
-      tfjs::util::warn(
-          "XNN status for xnn_create_fully_connected_nc_f32 is not successful. "
-          "Got status %d. Use -c dbg to see XNN logs.",
-          status);
-      return;
-    }
-
-    operator_cache.insert({cache_key, fully_connected_op});
-
-    associate_tensor_with_key(b_id, cache_key, b_operator_cache_key_map);
-    if (bias_id != 0) {
-      associate_tensor_with_key(bias_id, cache_key,
-                                bias_operator_cache_key_map);
-    }
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    fully_connected_op = operator_cache_idx->second;
-  }
-
-  const size_t batch_size = a_shape_ptr[1];
-  xnn_status status =
-      xnn_setup_fully_connected_nc_f32(fully_connected_op, batch_size, a_buf,
-                                       out_buf, tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    tfjs::util::warn(
-        "XNN status for xnn_setup_fully_connected_nc_f32 is not successful. "
-        "Got status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(fully_connected_op, tfjs::backend::threadpool);
-}
-
-void slow_batch_matmul(const size_t a_id, const size_t* a_shape_ptr,
-                       const size_t a_shape_len, const size_t b_id,
-                       const size_t* b_shape_ptr, const size_t b_shape_len,
-                       const bool transpose_a, const bool transpose_b,
-                       const size_t out_id, const size_t bias_id,
-                       const float output_min, const float output_max) {
-  const size_t shared_dim = transpose_a ? a_shape_ptr[1] : a_shape_ptr[2];
-  const size_t left_dim = transpose_a ? a_shape_ptr[2] : a_shape_ptr[1];
-  const size_t right_dim = transpose_b ? b_shape_ptr[1] : b_shape_ptr[2];
-  const size_t batch_dim = std::max(a_shape_ptr[0], b_shape_ptr[0]);
-
-  std::vector<size_t> a_shape(a_shape_ptr, a_shape_ptr + a_shape_len);
-  std::vector<size_t> b_shape(b_shape_ptr, b_shape_ptr + b_shape_len);
-  const std::vector<size_t> a_strides = tfjs::util::compute_strides(a_shape);
-  const std::vector<size_t> b_strides = tfjs::util::compute_strides(b_shape);
-
-  size_t a_batch = a_strides[0];
-  size_t a_outer_step, a_inner_step;
-  if (transpose_a) {
-    a_outer_step = 1;
-    a_inner_step = a_strides[1];
-  } else {
-    a_outer_step = a_strides[1];
-    a_inner_step = 1;
-  }
-  size_t b_batch = b_strides[0];
-  size_t b_outer_step, b_inner_step;
-  if (transpose_b) {
-    b_outer_step = b_strides[1];
-    b_inner_step = 1;
-  } else {
-    b_outer_step = 1;
-    b_inner_step = b_strides[1];
-  }
-
-  auto& a_info = tfjs::backend::get_tensor_info(a_id);
-  auto& b_info = tfjs::backend::get_tensor_info(b_id);
-  auto& out_info = tfjs::backend::get_tensor_info_out(out_id);
-
-  const float* a_buf = a_info.f32();
-  const float* b_buf = b_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  const float* bias_buf = nullptr;
-  size_t bias_buf_size = 0;
-  if (bias_id != 0) {
-    auto& bias_info = tfjs::backend::get_tensor_info_out(bias_id);
-    bias_buf = bias_info.f32();
-    bias_buf_size = bias_info.size;
-  }
-
-  const size_t size = left_dim * right_dim;
-
-  // Zero out the output buffer because it might have been used before.
-  std::fill(out_buf, out_buf + batch_dim * size, 0);
-
-  for (size_t b = 0; b < batch_dim; ++b) {
-    const size_t batch_index_a = b % a_shape[0];
-    const size_t batch_index_b = b % b_shape[0];
-    for (size_t i0 = 0; i0 < left_dim; i0 += kBlockSize) {
-      // for when kBlockSize doesn't evenly divide the input
-      const size_t i_block = std::min(i0 + kBlockSize, left_dim);
-      for (size_t j0 = 0; j0 < right_dim; j0 += kBlockSize) {
-        const size_t j_block = std::min(j0 + kBlockSize, right_dim);
-        for (size_t k0 = 0; k0 < shared_dim; k0 += kBlockSize) {
-          const size_t k_block = std::min(k0 + kBlockSize, shared_dim);
-
-          for (size_t i = i0; i < i_block; ++i) {
-            for (size_t j = j0; j < j_block; ++j) {
-              float sum = 0.0;
-
-              for (size_t k = k0; k < k_block; ++k) {
-                sum += a_buf[batch_index_a * a_batch + i * a_outer_step +
-                             k * a_inner_step] *
-                       b_buf[k * b_inner_step + j * b_outer_step +
-                             batch_index_b * b_batch];
-              }
-              size_t innermost_dim = i * right_dim + j;
-              size_t out_buf_index = b * size + innermost_dim;
-              float current = out_buf[out_buf_index];
-
-              float bias_val = 0;
-              if (bias_id != 0) {
-                // Handles 1D broadcasting.
-                size_t bias_index = std::min(innermost_dim, bias_buf_size - 1);
-
-                bias_val = bias_buf[bias_index];
-              }
-
-              out_buf[out_buf_index] = std::max(
-                  std::min(current + sum + bias_val, output_max), output_min);
-            }
-          }
-        }
-      }
-    }
-  }
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-void fused_batch_mat_mul(const size_t a_id, const size_t* a_shape_ptr,
-                         const size_t a_shape_len, const size_t b_id,
-                         const size_t* b_shape_ptr, const size_t b_shape_len,
-                         const bool transpose_a, const bool transpose_b,
-                         const FusableActivation activation,
-                         const size_t bias_id, const size_t prelu_weights_id,
-                         const float leakyrelu_alpha, const size_t out_id) {
-  FusableActivation clamp_method = activation;
-  if (activation == FusableActivation::PRELU ||
-      activation == FusableActivation::LEAKYRELU) {
-    clamp_method = FusableActivation::LINEAR;
-  }
-
-  float output_min = -std::numeric_limits<float>::infinity();
-  float output_max = std::numeric_limits<float>::infinity();
-
-  if (activation == FusableActivation::RELU) {
-    output_min = 0;
-  } else if (activation == FusableActivation::RELU6) {
-    output_min = 0;
-    output_max = 6;
-  }
-
-  if (!transpose_a && !transpose_b && a_shape_ptr[0] == 1 &&
-      b_shape_ptr[0] == 1) {
-    xnn_matmul(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-               out_id, bias_id, output_min, output_max, clamp_method);
-  } else {
-    slow_batch_matmul(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                      b_shape_len, transpose_a, transpose_b, out_id, bias_id,
-                      output_min, output_max);
-  }
-
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  float* out_buf = out_info.f32_write();
-  if (activation == FusableActivation::PRELU) {
-    prelu(out_buf, out_info.size, prelu_weights_id, out_id);
-  } else if (activation == FusableActivation::LEAKYRELU) {
-    leakyrelu(out_buf, out_info.size, leakyrelu_alpha, out_id);
-  } else if (activation == FusableActivation::SIGMOID) {
-    sigmoid(out_buf, out_info.size, out_id);
-  } else if (activation == FusableActivation::ELU) {
-    EluImpl(out_buf, out_info.size, out_buf);
-  }
-}
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/batch_mat_mul_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/batch_mat_mul_impl.h
deleted file mode 100644
index 0b82ea5ee..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/batch_mat_mul_impl.h
+++ /dev/null
@@ -1,34 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef BATCH_MAT_MUL_IMPL_H_
-#define BATCH_MAT_MUL_IMPL_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-
-void fused_batch_mat_mul(const size_t a_id, const size_t* a_shape_ptr,
-                         const size_t a_shape_len, const size_t b_id,
-                         const size_t* b_shape_ptr, const size_t b_shape_len,
-                         const bool transpose_a, const bool transpose_b,
-                         const FusableActivation activation,
-                         const size_t bias_id, const size_t prelu_weights_id,
-                         const float leakyrelu_alpha, const size_t out_id);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // BATCH_MAT_MUL_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/binary.cc b/tfjs-master/tfjs-backend-wasm/src/cc/binary.cc
deleted file mode 100644
index 18edc1768..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/binary.cc
+++ /dev/null
@@ -1,213 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-
-#include <xnnpack.h>
-#include <cstddef>
-#include <limits>
-#include <unordered_map>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// Maps an `xnn_create_*_nd_f32` function pointer to an instantiated operator.
-std::unordered_map<tfjs::wasm::xnn_create_binary_op, xnn_operator_t> op_cache;
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-template <class I, class O>
-void binary_impl(const I* a_buf, const size_t a_size, const I* b_buf,
-                 const size_t b_size, O* out_buf, O operation(I, I),
-                 const size_t* a_shape_ptr, const size_t a_rank,
-                 const size_t* b_shape_ptr, const size_t b_rank) {
-  std::vector<size_t> a_shape(a_shape_ptr, a_shape_ptr + a_rank);
-  std::vector<size_t> b_shape(b_shape_ptr, b_shape_ptr + b_rank);
-  const std::vector<size_t> new_shape =
-      tfjs::util::assert_and_get_broadcast_shape(a_shape, b_shape);
-  const std::vector<size_t> result_strides =
-      tfjs::util::compute_strides(new_shape);
-  const size_t result_size = tfjs::util::size_from_shape(new_shape);
-  const std::vector<size_t> a_strides = tfjs::util::compute_strides(a_shape);
-  const std::vector<size_t> b_strides = tfjs::util::compute_strides(b_shape);
-  const std::vector<size_t> a_broadcast_dims =
-      tfjs::util::get_broadcast_dims(a_shape, new_shape);
-  const std::vector<size_t> b_broadcast_dims =
-      tfjs::util::get_broadcast_dims(b_shape, new_shape);
-
-  if (a_broadcast_dims.size() == 0 && b_broadcast_dims.size() == 0) {
-    for (size_t i = 0; i < result_size; ++i) {
-      out_buf[i] = operation(a_buf[i % a_size], b_buf[i % b_size]);
-    }
-  } else {
-    for (size_t i = 0; i < result_size; ++i) {
-      const std::vector<size_t> loc =
-          tfjs::util::offset_to_loc(i, result_strides);
-
-      std::vector<size_t> a_loc =
-          std::vector<size_t>(loc.end() - a_rank, loc.end());
-      for (size_t j = 0; j < a_broadcast_dims.size(); ++j) {
-        const size_t d = a_broadcast_dims[j];
-        a_loc[d] = 0;
-      }
-      const size_t a_idx = tfjs::util::loc_to_offset(a_loc, a_strides);
-
-      std::vector<size_t> b_loc =
-          std::vector<size_t>(loc.end() - b_rank, loc.end());
-      for (size_t k = 0; k < b_broadcast_dims.size(); ++k) {
-        const size_t d = b_broadcast_dims[k];
-        b_loc[d] = 0;
-      }
-      const size_t b_idx = tfjs::util::loc_to_offset(b_loc, b_strides);
-      out_buf[i] = operation(a_buf[a_idx], b_buf[b_idx]);
-    }
-  }
-}
-
-void binary_f32(const int a_id, const size_t* a_shape_ptr,
-                const size_t a_shape_len, const int b_id,
-                const size_t* b_shape_ptr, const size_t b_shape_len,
-                const size_t out_id, float operation(float, float)) {
-  auto& a_info = backend::get_tensor_info(a_id);
-  auto& b_info = backend::get_tensor_info(b_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  binary_impl<float, float>(a_info.f32(), a_info.size, b_info.f32(),
-                            b_info.size, out_info.f32_write(), operation,
-                            a_shape_ptr, a_shape_len, b_shape_ptr, b_shape_len);
-}
-
-void binary_i32(const int a_id, const size_t* a_shape_ptr,
-                const size_t a_shape_len, const int b_id,
-                const size_t* b_shape_ptr, const size_t b_shape_len,
-                const size_t out_id, int operation(int, int)) {
-  auto& a_info = backend::get_tensor_info(a_id);
-  auto& b_info = backend::get_tensor_info(b_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  binary_impl<int32_t, int32_t>(a_info.i32(), a_info.size, b_info.i32(),
-                                b_info.size, out_info.i32_write(), operation,
-                                a_shape_ptr, a_shape_len, b_shape_ptr,
-                                b_shape_len);
-}
-
-void binary_bool(const int a_id, const size_t* a_shape_ptr,
-                 const size_t a_shape_len, const int b_id,
-                 const size_t* b_shape_ptr, const size_t b_shape_len,
-                 const size_t out_id, bool operation(bool, bool)) {
-  auto& a_info = backend::get_tensor_info(a_id);
-  auto& b_info = backend::get_tensor_info(b_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  binary_impl<bool, bool>(a_info.b(), a_info.size, b_info.b(), b_info.size,
-                          out_info.b_write(), operation, a_shape_ptr,
-                          a_shape_len, b_shape_ptr, b_shape_len);
-}
-
-void compare_f32(const int a_id, const size_t* a_shape_ptr,
-                 const size_t a_shape_len, const int b_id,
-                 const size_t* b_shape_ptr, const size_t b_shape_len,
-                 const int out_id, bool operation(float, float)) {
-  auto& a_info = backend::get_tensor_info(a_id);
-  auto& b_info = backend::get_tensor_info(b_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  binary_impl<float, bool>(a_info.f32(), a_info.size, b_info.f32(), b_info.size,
-                           out_info.b_write(), operation, a_shape_ptr,
-                           a_shape_len, b_shape_ptr, b_shape_len);
-}
-
-void compare_i32(const int a_id, const size_t* a_shape_ptr,
-                 const size_t a_shape_len, const int b_id,
-                 const size_t* b_shape_ptr, const size_t b_shape_len,
-                 const int out_id, bool operation(int, int)) {
-  auto& a_info = backend::get_tensor_info(a_id);
-  auto& b_info = backend::get_tensor_info(b_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  binary_impl<int, bool>(a_info.i32(), a_info.size, b_info.i32(), b_info.size,
-                         out_info.b_write(), operation, a_shape_ptr,
-                         a_shape_len, b_shape_ptr, b_shape_len);
-}
-
-void compare_bool(const int a_id, const size_t* a_shape_ptr,
-                  const size_t a_shape_len, const int b_id,
-                  const size_t* b_shape_ptr, const size_t b_shape_len,
-                  const int out_id, bool operation(bool, bool)) {
-  auto& a_info = backend::get_tensor_info(a_id);
-  auto& b_info = backend::get_tensor_info(b_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  binary_impl<bool, bool>(a_info.b(), a_info.size, b_info.b(), b_info.size,
-                          out_info.b_write(), operation, a_shape_ptr,
-                          a_shape_len, b_shape_ptr, b_shape_len);
-}
-
-void logical(const int a_id, const size_t* a_shape_ptr,
-             const size_t a_shape_len, const int b_id,
-             const size_t* b_shape_ptr, const size_t b_shape_len,
-             const int out_id, bool operation(bool, bool)) {
-  auto& a_info = backend::get_tensor_info(a_id);
-  auto& b_info = backend::get_tensor_info(b_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  binary_impl<bool, bool>(a_info.b(), a_info.size, b_info.b(), b_info.size,
-                          out_info.b_write(), operation, a_shape_ptr,
-                          a_shape_len, b_shape_ptr, b_shape_len);
-}
-
-void binary_xnn_f32(const size_t a_id, const size_t* a_shape_ptr,
-                    const size_t a_shape_len, const size_t b_id,
-                    const size_t* b_shape_ptr, const size_t b_shape_len,
-                    const size_t out_id, xnn_create_binary_op create_op,
-                    xnn_setup_binary_op setup_op) {
-  auto& a_info = backend::get_tensor_info(a_id);
-  auto& b_info = backend::get_tensor_info(b_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  const float* a_buf = a_info.f32();
-  const float* b_buf = b_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  xnn_operator_t binary_op = nullptr;
-
-  auto cache_result = op_cache.find(create_op);
-  if (cache_result == op_cache.end()) {
-    const float sum_min = -std::numeric_limits<float>::infinity(),
-                sum_max = std::numeric_limits<float>::infinity();
-    const uint32_t flags = 0;
-    xnn_status status = create_op(sum_min, sum_max, flags, &binary_op);
-    if (status != xnn_status_success) {
-      util::warn(
-          "XNN status for xnn_create_*_nd_f32 is not successful. Got "
-          "status %d. Use -c dbg to see XNN logs.");
-      return;
-    }
-    op_cache.insert({create_op, binary_op});
-    backend::xnn_operator_count++;
-  } else {
-    binary_op = cache_result->second;
-  }
-  xnn_status status =
-      setup_op(binary_op, a_shape_len, a_shape_ptr, b_shape_len, b_shape_ptr,
-               a_buf, b_buf, out_buf, tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    util::warn(
-        "XNN status for xnn_setup_*_nd_f32 is not successful. Got "
-        "status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(binary_op, tfjs::backend::threadpool);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/binary.h b/tfjs-master/tfjs-backend-wasm/src/cc/binary.h
deleted file mode 100644
index 8c227d33c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/binary.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef BINARY_H_
-#define BINARY_H_
-
-#include <xnnpack.h>
-#include <algorithm>
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-
-template <class I, class O>
-void binary_impl(const I* a_buf, const size_t a_size, const I* b_buf,
-                 const size_t b_size, O* out_buf, O operation(I, I),
-                 const size_t* a_shape_ptr, const size_t a_rank,
-                 const size_t* b_shape_ptr, const size_t b_rank);
-
-void binary_f32(const int a_id, const size_t* a_shape_ptr,
-                const size_t a_shape_len, const int b_id,
-                const size_t* b_shape_ptr, const size_t b_shape_len,
-                const size_t out_id, float operation(float, float));
-
-void binary_i32(const int a_id, const size_t* a_shape_ptr,
-                const size_t a_shape_len, const int b_id,
-                const size_t* b_shape_ptr, const size_t b_shape_len,
-                const size_t out_id, int operation(int, int));
-
-void binary_bool(const int a_id, const size_t* a_shape_ptr,
-                 const size_t a_shape_len, const int b_id,
-                 const size_t* b_shape_ptr, const size_t b_shape_len,
-                 const size_t out_id, bool operation(bool, bool));
-
-void compare_f32(const int a_id, const size_t* a_shape_ptr,
-                 const size_t a_shape_len, const int b_id,
-                 const size_t* b_shape_ptr, const size_t b_shape_len,
-                 const int out_id, bool operation(float, float));
-
-void compare_i32(const int a_id, const size_t* a_shape_ptr,
-                 const size_t a_shape_len, const int b_id,
-                 const size_t* b_shape_ptr, const size_t b_shape_len,
-                 const int out_id, bool operation(int, int));
-
-void compare_bool(const int a_id, const size_t* a_shape_ptr,
-                  const size_t a_shape_len, const int b_id,
-                  const size_t* b_shape_ptr, const size_t b_shape_len,
-                  const int out_id, bool operation(bool, bool));
-
-void logical(const int a_id, const size_t* a_shape_ptr,
-             const size_t a_shape_len, const int b_id,
-             const size_t* b_shape_ptr, const size_t b_shape_len,
-             const int out_id, bool operation(bool, bool));
-
-typedef xnn_status (*xnn_create_binary_op)(float, float, uint32_t,
-                                           xnn_operator_t*);
-typedef xnn_status (*xnn_setup_binary_op)(xnn_operator_t, size_t, const size_t*,
-                                          size_t, const size_t*, const float*,
-                                          const float*, float*, pthreadpool_t);
-
-void binary_xnn_f32(const size_t a_id, const size_t* a_shape_ptr,
-                    const size_t a_shape_len, const size_t b_id,
-                    const size_t* b_shape_ptr, const size_t b_shape_len,
-                    const size_t out_id, xnn_create_binary_op create_op,
-                    xnn_setup_binary_op setup_op);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // BINARY_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/bincount_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/bincount_impl.h
deleted file mode 100644
index 49a6451bc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/bincount_impl.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef BINCOUNT_IMPL_H_
-#define BINCOUNT_IMPL_H_
-
-#include <cstdint>
-
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-// REQUIRES:
-// weight_buf must have the same size as x_buf.
-template <bool reset_out_buf = true, typename T>
-inline void BincountImpl(const int32_t* x_buf, int32_t x_len, int32_t size,
-                         const T* weight_buf, bool binary_output, T* out_buf) {
-  if (reset_out_buf) {
-    std::fill(out_buf, out_buf + size, 0);
-  }
-  for (int32_t i = 0; i < x_len; ++i) {
-    int32_t value = x_buf[i];
-    if (value < 0) {
-      util::warn("DenseBincount error: input x must be non-negative.");
-      continue;
-    }
-    if (value >= size) {
-      continue;
-    }
-
-    if (binary_output) {
-      out_buf[value] = 1;
-    } else if (weight_buf == nullptr) {
-      out_buf[value] += 1;
-    } else {
-      out_buf[value] += weight_buf[i];
-    }
-  }
-}
-}  // namespace tfjs::wasm
-#endif
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/build_defs.bzl b/tfjs-master/tfjs-backend-wasm/src/cc/build_defs.bzl
deleted file mode 100644
index 32d695fbc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/build_defs.bzl
+++ /dev/null
@@ -1,35 +0,0 @@
-def tfjs_unit_test(name, srcs, deps = [], **kwargs):
-    """Unit test binary based on Google Test.
-    Args:
-      name: The name of the test target to define.
-      srcs: The list of source and header files.
-      deps: The list of additional libraries to be linked. Google Test library
-            (with main() function) is always added as a dependency and does not
-            need to be explicitly specified.
-    """
-
-    linkopts = kwargs.pop("linkopts", [])
-    linkopts += ["-lm"]  # Link math.h.
-
-    native.cc_test(
-        name = name,
-        srcs = srcs,
-        linkstatic = True,
-        deps = [
-            "@com_google_googletest//:gtest_main",
-        ] + deps,
-        linkopts = linkopts,
-        **kwargs
-    )
-
-def tfjs_cc_library(name, **kwargs):
-    """CC library for TensorFlow.js
-    Args:
-      name: The name of the TensorFlow.js cc library to define.
-    """
-
-    native.cc_library(
-        name = name,
-        linkstatic = True,
-        **kwargs
-    )
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/check_macros.h b/tfjs-master/tfjs-backend-wasm/src/cc/check_macros.h
deleted file mode 100644
index b1e57edf0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/check_macros.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef CHECK_MACROS_H_
-#define CHECK_MACROS_H_
-
-#ifndef NDEBUG
-
-#include "tfjs-backend-wasm/src/cc/util.h"
-#define DCHECK(condition, message, ...) \
-  if (!(condition)) {                   \
-    util::warn(message, __VA_ARGS__);   \
-    abort();                            \
-  }
-
-#else
-
-#define DCHECK(condition, message, ...) \
-  if (false) {                          \
-    abort();                            \
-  }
-
-#endif  // NDEBUG
-
-#endif  // CHECK_MACROS_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/clamp_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/clamp_impl.cc
deleted file mode 100644
index 81bd51fbb..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/clamp_impl.cc
+++ /dev/null
@@ -1,78 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include "tfjs-backend-wasm/src/cc/clamp_impl.h"
-
-#include <xnnpack.h>
-#include <cstddef>
-#include <map>
-#include <tuple>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// These values are keys to creating the xnn clamp operator. We use
-// std::tuple since it implements the compare operator needed for std::map.
-typedef std::tuple<float, float> CacheKey;
-// The operator cache maps the params of xnn_create_clamp_nc_f32 to an operator.
-std::map<CacheKey, xnn_operator_t> op_cache;
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-void xnn_clamp(const size_t x_id, const size_t out_id, const float min,
-               const float max) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  const float* x_buf = x_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  xnn_operator_t op = nullptr;
-  CacheKey cache_key = {min, max};
-  const auto& cache_result = op_cache.find(cache_key);
-  if (cache_result == op_cache.end()) {
-    const size_t channels = 1, input_stride = 1, output_stride = 1, flags = 1;
-    xnn_status status = xnn_create_clamp_nc_f32(
-        channels, input_stride, output_stride, min, max, flags, &op);
-    if (status != xnn_status_success) {
-      util::warn(
-          "XNN status for xnn_create_clamp_nc_f32 is not successful. "
-          "Got status %d. Use -c dbg to see XNN logs.",
-          status);
-      return;
-    }
-    op_cache.emplace(cache_key, op);
-    backend::xnn_operator_count++;
-  } else {
-    op = cache_result->second;
-  }
-
-  const size_t batch_size = out_info.size;
-  xnn_status status = xnn_setup_clamp_nc_f32(op, batch_size, x_buf, out_buf,
-                                             tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    util::warn(
-        "XNN status for xnn_setup_clamp_nc_f32 is not successful. "
-        "Got status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(op, tfjs::backend::threadpool);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/clamp_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/clamp_impl.h
deleted file mode 100644
index 8e3c9c25e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/clamp_impl.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef CLAMP_IMPL_H_
-#define CLAMP_IMPL_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-
-void xnn_clamp(const size_t x_id, const size_t out_id, const float min,
-               const float max);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // CLAMP_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/conv2d_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/conv2d_impl.cc
deleted file mode 100644
index aae4091a4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/conv2d_impl.cc
+++ /dev/null
@@ -1,291 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/conv2d_impl.h"
-
-#include <xnnpack.h>
-#include <cmath>
-#include <cstddef>
-#include <limits>
-#include <map>
-#include <memory>
-#include <tuple>
-#include <unordered_map>
-#include <utility>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/elu_impl.h"
-#include "tfjs-backend-wasm/src/cc/leakyrelu_impl.h"
-#include "tfjs-backend-wasm/src/cc/prelu_impl.h"
-#include "tfjs-backend-wasm/src/cc/sigmoid_impl.h"
-#include "tfjs-backend-wasm/src/cc/transpose_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// We use std::tuple as the cache key as it implements the compare operator
-// needed for std::map.
-typedef std::tuple<size_t, size_t, size_t, size_t, size_t, size_t, size_t,
-                   size_t, size_t, size_t, size_t, size_t, size_t, size_t,
-                   size_t, size_t, size_t, size_t, size_t, float, float>
-    OperatorCacheKey;
-
-struct CachedInfo {
-  xnn_operator_t op;
-  std::vector<float> transposed_filter;
-};
-
-// The operator cache maps the cache key to the xnn_operator_t instantiated for
-// this set of arguments to the xnn_operator.
-std::map<OperatorCacheKey, CachedInfo> operator_cache;
-
-// Maps a filter id to a list of operator cache keys that this filter belongs
-// to.
-std::unordered_map<size_t, std::vector<OperatorCacheKey>>
-    filter_operator_cache_key_map;
-
-// Maps a bias id to a list of operator cache keys that this filter belongs
-// to.
-std::unordered_map<size_t, std::vector<OperatorCacheKey>>
-    bias_operator_cache_key_map;
-
-void erase_from_cache(const size_t tensor_id,
-                      std::unordered_map<size_t, std::vector<OperatorCacheKey>>&
-                          operator_cache_key_map) {
-  auto operator_cache_keys_idx = operator_cache_key_map.find(tensor_id);
-  if (operator_cache_keys_idx != operator_cache_key_map.end()) {
-    std::vector<OperatorCacheKey>& operator_cache_keys =
-        operator_cache_keys_idx->second;
-    for (auto& operator_cache_key : operator_cache_keys) {
-      auto operator_cache_key_idx = operator_cache.find(operator_cache_key);
-      if (operator_cache_key_idx != operator_cache.end()) {
-        auto& cached_info = operator_cache_key_idx->second;
-        xnn_delete_operator(cached_info.op);
-        tfjs::backend::xnn_operator_count--;
-
-        operator_cache.erase(operator_cache_key);
-      }
-    }
-    operator_cache_key_map.erase(tensor_id);
-  }
-}
-
-void delete_xnn_operators(size_t tensor_id) {
-  erase_from_cache(tensor_id, filter_operator_cache_key_map);
-  erase_from_cache(tensor_id, bias_operator_cache_key_map);
-}
-
-void associate_tensor_with_key(
-    const size_t tensor_id, const OperatorCacheKey& cache_key,
-    std::unordered_map<size_t, std::vector<OperatorCacheKey>>&
-        operator_cache_key_map) {
-  auto cache_keys_idx = operator_cache_key_map.find(tensor_id);
-  if (cache_keys_idx == operator_cache_key_map.end()) {
-    std::vector<OperatorCacheKey> cache_keys = {cache_key};
-    operator_cache_key_map.emplace(tensor_id, std::move(cache_keys));
-    tfjs::backend::register_disposal_callback(tensor_id, *delete_xnn_operators);
-
-  } else {
-    auto& cache_keys = operator_cache_key_map.at(tensor_id);
-    cache_keys.emplace_back(cache_key);
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-void conv2d(const size_t x_id, const size_t batch_size,
-            const size_t input_height, const size_t input_width,
-            const size_t filter_id, const size_t filter_height,
-            const size_t filter_width, const size_t bias_id, size_t pad_top,
-            size_t pad_right, size_t pad_bottom, size_t pad_left,
-            const bool is_same_pad, const size_t dilation_height,
-            const size_t dilation_width, const size_t stride_height,
-            const size_t stride_width, const size_t input_channels,
-            const size_t output_channels, const bool is_depthwise,
-            const FusableActivation activation, const size_t prelu_weights_id,
-            const float leakyrelu_alpha, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& filter_info = backend::get_tensor_info(filter_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* x_buf = x_info.f32();
-  const float* filter_buf = filter_info.f32();
-  const float* bias_buf = nullptr;
-  if (bias_id != 0) {
-    bias_buf = backend::get_tensor_info_out(bias_id).f32();
-  }
-
-  float* out_buf = out_info.f32_write();
-  std::vector<float> intermediate_output;
-
-  if (prelu_weights_id != 0 || activation == FusableActivation::LEAKYRELU) {
-    intermediate_output.resize(out_info.size);
-    out_buf = intermediate_output.data();
-  }
-
-  xnn_operator_t conv2d_op = nullptr;
-
-  size_t flags = 0;
-  if (is_same_pad) {
-    pad_top = 0, pad_right = 0, pad_bottom = 0, pad_left = 0;
-    flags |= XNN_FLAG_TENSORFLOW_SAME_PADDING;
-  }
-
-  size_t groups;
-  size_t group_input_channels;
-  size_t group_output_channels;
-  const size_t input_pixel_stride = input_channels;
-  const size_t output_pixel_stride = output_channels;
-  if (is_depthwise) {
-    groups = input_channels;
-    group_input_channels = 1;
-    group_output_channels = output_channels / input_channels;
-    flags |= XNN_FLAG_DEPTHWISE_CONVOLUTION;
-  } else {
-    groups = 1;
-    group_input_channels = input_channels;
-    group_output_channels = output_channels;
-  }
-
-  FusableActivation clamp_method = activation;
-  if (activation == FusableActivation::PRELU ||
-      activation == FusableActivation::LEAKYRELU) {
-    clamp_method = FusableActivation::LINEAR;
-  }
-
-  float output_min = -std::numeric_limits<float>::infinity();
-  float output_max = std::numeric_limits<float>::infinity();
-
-  if (activation == FusableActivation::RELU) {
-    output_min = 0;
-  } else if (activation == FusableActivation::RELU6) {
-    output_min = 0;
-    output_max = 6;
-  }
-
-  OperatorCacheKey cache_key = {pad_top,
-                                pad_right,
-                                pad_bottom,
-                                pad_left,
-                                filter_height,
-                                filter_width,
-                                stride_height,
-                                stride_width,
-                                dilation_height,
-                                dilation_width,
-                                groups,
-                                group_input_channels,
-                                group_output_channels,
-                                input_pixel_stride,
-                                output_pixel_stride,
-                                clamp_method,
-                                filter_id,
-                                bias_id,
-                                flags,
-                                output_min,
-                                output_max};
-
-  auto operator_cache_idx = operator_cache.find(cache_key);
-  if (operator_cache_idx == operator_cache.end()) {
-    // This lives outside the if statement so the data survives the scope.
-    std::vector<float> transposed_filter;
-
-    const float* filter_xnn;
-    if (is_depthwise) {
-      // For depthwiseConv2d, xnn pack and TensorFlow expect the same weights
-      // layout:
-      //   [filter_height, filter_width, input_channels, channel_multiplier]
-      filter_xnn = filter_buf;
-    } else {
-      // For regular conv2d, xnn pack expects weights layed out like:
-      //   [output_channels, filter_height, filter_width, input_channels]
-      // TensorFlow has weights layed out like:
-      //   [filter_height, filter_width, input_channels, output_channels]
-      // This can be transposed with a 2d transpose to move output_channels to
-      // the outer most dimension.
-      transposed_filter.resize(filter_info.size);
-      std::vector<size_t> filter_shape = {
-          filter_height * filter_width * input_channels, output_channels};
-      std::vector<size_t> perm = {1, 0};
-
-      transpose(filter_buf, filter_shape, perm, transposed_filter.data());
-
-      filter_xnn = transposed_filter.data();
-    }
-
-    xnn_status status = xnn_create_convolution2d_nhwc_f32(
-        pad_top, pad_right, pad_bottom, pad_left, filter_height, filter_width,
-        stride_height, stride_width, dilation_height, dilation_width, groups,
-        group_input_channels, group_output_channels, input_pixel_stride,
-        output_pixel_stride, filter_xnn, bias_buf, output_min, output_max,
-        flags, &conv2d_op);
-    if (status != xnn_status_success) {
-      util::warn(
-          "XNN status for xnn_create_convolution2d_nhwc_f32 is not successful. "
-          "Got status %d. Use -c dbg to see XNN logs.",
-          status);
-    }
-
-    operator_cache.emplace(
-        cache_key,
-        // Move ownership of the transposed filter to the cache map.
-        CachedInfo{conv2d_op, std::move(transposed_filter)});
-
-    associate_tensor_with_key(filter_id, cache_key,
-                              filter_operator_cache_key_map);
-    if (bias_id != 0) {
-      associate_tensor_with_key(bias_id, cache_key,
-                                bias_operator_cache_key_map);
-    }
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    conv2d_op = operator_cache_idx->second.op;
-  }
-
-  xnn_status status = xnn_setup_convolution2d_nhwc_f32(
-      conv2d_op, batch_size, input_height, input_width, x_buf, out_buf,
-      tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    util::warn(
-        "XNN status for xnn_setup_convolution2d_nhwc_f32 is not successful. "
-        "Got status %d. Use -c dbg to see XNN logs.",
-        status);
-  }
-
-  xnn_run_operator(conv2d_op, tfjs::backend::threadpool);
-
-  if (activation == FusableActivation::PRELU) {
-    prelu(out_buf, out_info.size, prelu_weights_id, out_id);
-  }
-  if (activation == FusableActivation::LEAKYRELU) {
-    leakyrelu(out_buf, out_info.size, leakyrelu_alpha, out_id);
-  }
-  if (activation == FusableActivation::SIGMOID) {
-    sigmoid(out_buf, out_info.size, out_id);
-  }
-  if (activation == FusableActivation::ELU) {
-    EluImpl(out_buf, out_info.size, out_buf);
-  }
-}
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/conv2d_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/conv2d_impl.h
deleted file mode 100644
index cf4c96664..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/conv2d_impl.h
+++ /dev/null
@@ -1,39 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef CONV2D_IMPL_H_
-#define CONV2D_IMPL_H_
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-
-void conv2d(const size_t x_id, const size_t batch_size,
-            const size_t input_height, const size_t input_width,
-            const size_t filter_id, const size_t filter_height,
-            const size_t filter_width, const size_t bias_id, size_t pad_top,
-            size_t pad_right, size_t pad_bottom, size_t pad_left,
-            const bool is_same_pad, const size_t dilation_height,
-            const size_t dilation_width, const size_t stride_height,
-            const size_t stride_width, const size_t input_channels,
-            const size_t output_channels, const bool is_depthwise,
-            const FusableActivation activation, const size_t prelu_weighs_id,
-            const float leakyrelu_alpha, const size_t out_id);
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // CONV2D_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/conv3d_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/conv3d_impl.h
deleted file mode 100644
index b3204e620..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/conv3d_impl.h
+++ /dev/null
@@ -1,241 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef CONV3D_IMPL_H_
-#define CONV3D_IMPL_H_
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include "tfjs-backend-wasm/src/cc/shape.h"
-
-namespace tfjs::wasm {
-
-struct NDHWCConv3DInfo {
-  int batch_size;
-  int in_depth;
-  int in_height;
-  int in_width;
-  int in_channels;
-  int out_depth;
-  int out_height;
-  int out_width;
-  int out_channels;
-
-  int stride_depth;
-  int stride_height;
-  int stride_width;
-  int dilation_depth;
-  int dilation_height;
-  int dilation_width;
-  int filter_depth;
-  int filter_height;
-  int filter_width;
-  int pad_front;
-  int pad_top;
-  int pad_left;
-
-  inline Shape<int, 5> in_shape() const {
-    return Shape<int, 5>(
-        {batch_size, in_depth, in_height, in_width, in_channels});
-  }
-  inline Shape<int, 5> out_shape() const {
-    return Shape<int, 5>(
-        {batch_size, out_depth, out_height, out_width, out_channels});
-  }
-  inline Shape<int, 5> filter_shape() const {
-    return Shape<int, 5>(
-        {filter_depth, filter_height, filter_width, in_channels, out_channels});
-  }
-
-  inline int in_offset(int b, int d, int h, int w, int c) const {
-    return in_shape().offset({b, d, h, w, c});
-  }
-  inline int out_offset(int b, int d, int h, int w, int c) const {
-    return out_shape().offset({b, d, h, w, c});
-  }
-  inline int filter_offset(int d, int h, int w, int c1, int c2) const {
-    return filter_shape().offset({d, h, w, c1, c2});
-  }
-
-  inline int int_size() const { return in_shape().size(); }
-  inline int out_size() const { return out_shape().size(); }
-};
-
-template <typename IN, typename FILTER, typename OUT>
-inline void NDHWCConv3DImpl(const IN* x_buf, const FILTER* filter_buf,
-                            OUT* out_buf, const NDHWCConv3DInfo& info) {
-  memset(out_buf, 0, sizeof(OUT) * info.out_size());
-  for (int b = 0; b < info.batch_size; ++b) {
-    for (int yf = 0; yf < info.out_depth; ++yf) {
-      int xf_corner = yf * info.stride_depth - info.pad_front;
-      for (int wf = 0; wf < info.filter_depth; ++wf) {
-        int xf = xf_corner + wf * info.dilation_depth;
-        if (xf < 0 || xf >= info.in_depth) {
-          continue;
-        }
-        for (int yr = 0; yr < info.out_height; ++yr) {
-          int xr_corner = yr * info.stride_height - info.pad_top;
-          for (int wr = 0; wr < info.filter_height; ++wr) {
-            int xr = xr_corner + wr * info.dilation_height;
-            if (xr < 0 || xr >= info.in_height) {
-              continue;
-            }
-
-            for (int yc = 0; yc < info.out_width; ++yc) {
-              int xc_corner = yc * info.stride_width - info.pad_left;
-              for (int wc = 0; wc < info.filter_width; ++wc) {
-                int xc = xc_corner + wc * info.dilation_width;
-                if (xc < 0 || xc >= info.in_width) {
-                  continue;
-                }
-
-                for (int d1 = 0; d1 < info.in_channels; ++d1) {
-                  for (int d2 = 0; d2 < info.out_channels; ++d2) {
-                    out_buf[info.out_offset(b, yf, yr, yc, d2)] +=
-                        x_buf[info.in_offset(b, xf, xr, xc, d1)] *
-                        filter_buf[info.filter_offset(wf, wr, wc, d1, d2)];
-                  }
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-namespace {
-
-inline int DivCeil(int a, int b) {
-  return (a / b) + static_cast<int>(a % b != 0);
-}
-inline int DivFloor(int a, int b) { return a / b; }
-
-}  // namespace
-
-template <typename IN, typename FILTER, typename OUT>
-inline void NDHWCConv3DBackpropFilterV2Impl(
-    const IN* x_buf, const OUT* dy_buf, FILTER* dw_buf,
-    const NDHWCConv3DInfo& forward_info) {
-  const NDHWCConv3DInfo& info = forward_info;
-  for (int wf = 0; wf < info.filter_depth; ++wf) {
-    int yf_min = std::max(0, DivCeil(info.pad_front - wf, info.stride_depth));
-    int yf_max = std::min(
-        info.out_depth,
-        DivFloor(info.in_depth + info.pad_front - wf, info.stride_depth));
-
-    for (int wr = 0; wr < info.filter_height; ++wr) {
-      int yr_min = std::max(0, DivCeil(info.pad_top - wr, info.stride_height));
-      int yr_max = std::min(
-          info.out_height,
-          DivFloor(info.in_height + info.pad_top - wr, info.stride_height));
-
-      for (int wc = 0; wc < info.filter_width; ++wc) {
-        int yc_min =
-            std::max(0, DivCeil(info.pad_left - wc, info.stride_width));
-        int yc_max = std::min(
-            info.out_width,
-            DivFloor(info.in_width + info.pad_left - wc, info.stride_width));
-
-        for (int d1 = 0; d1 < info.in_channels; ++d1) {
-          for (int d2 = 0; d2 < info.out_channels; ++d2) {
-            FILTER dot_prod = 0;
-            for (int b = 0; b < info.batch_size; ++b) {
-              for (int yf = yf_min; yf < yf_max; ++yf) {
-                for (int yr = yr_min; yr < yr_max; ++yr) {
-                  for (int yc = yc_min; yc < yc_max; ++yc) {
-                    int xf = wf + yf * info.stride_depth - info.pad_front;
-                    int xr = wr + yr * info.stride_height - info.pad_top;
-                    int xc = wc + yc * info.stride_width - info.pad_left;
-                    dot_prod += x_buf[info.in_offset(b, xf, xr, xc, d1)] *
-                                dy_buf[info.out_offset(b, yf, yr, yc, d2)];
-                  }
-                }
-              }
-            }
-            dw_buf[info.filter_offset(wf, wr, wc, d1, d2)] = dot_prod;
-          }
-        }
-      }
-    }
-  }
-}
-
-template <typename IN, typename FILTER, typename OUT>
-inline void NDHWCConv3DBackpropInputV2Impl(
-    const FILTER* filter_buf, const OUT* dy_buf, IN* dx_buf,
-    const NDHWCConv3DInfo& forward_info) {
-  NDHWCConv3DInfo info = forward_info;
-  info.pad_front = info.filter_depth - 1 - info.pad_front;
-  info.pad_top = info.filter_height - 1 - info.pad_top;
-  info.pad_left = info.filter_width - 1 - info.pad_left;
-
-  for (int b = 0; b < info.batch_size; ++b) {
-    for (int d1 = 0; d1 < info.in_channels; ++d1) {
-      // Frames of depth
-      for (int xf = 0; xf < info.in_depth; ++xf) {
-        int xf_corner = xf - info.pad_front;
-        int xf_min = std::max(0, DivCeil(xf_corner, info.stride_depth));
-        int yf_max = std::min(info.out_depth,
-                              DivFloor(info.filter_depth, info.stride_depth));
-
-        // Rows as per standard 2d matrix notation
-        for (int xr = 0; xr < info.in_height; ++xr) {
-          int xr_corner = xr - info.pad_top;
-          int xr_min = std::max(0, DivCeil(xr_corner, info.stride_height));
-          int yr_max = std::min(
-              info.out_height,
-              DivFloor(info.filter_height + xr_corner, info.stride_height));
-
-          // Columns as per standard 2d matrix notation
-          for (int xc = 0; xc < info.in_width; ++xc) {
-            int xc_corner = xc - info.pad_left;
-            int xc_min = std::max(0, DivCeil(xc_corner, info.stride_width));
-            int yc_max = std::min(
-                info.out_width,
-                DivFloor(info.filter_width + xc_corner, info.stride_width));
-
-            IN dot_prod = 0;
-            for (int yf = xf_min; yf < yf_max; ++yf) {
-              for (int yr = xr_min; yr < yr_max; ++yr) {
-                for (int yc = xc_min; yc < yc_max; ++yc) {
-                  int wf = yf * info.stride_depth - xf_corner;
-                  int wr = yr * info.stride_height - xr_corner;
-                  int wc = yc * info.stride_width - xc_corner;
-
-                  for (int d2 = 0; d2 < info.out_channels; ++d2) {
-                    dot_prod += dy_buf[info.out_offset(b, yf, yr, yc, d2)] *
-                                filter_buf[info.filter_offset(
-                                    info.filter_depth - 1 - wf,
-                                    info.filter_height - 1 - wr,
-                                    info.filter_width - 1 - wc, d1, d2)];
-                  }
-                }
-              }
-            }
-            dx_buf[info.in_offset(b, xf, xr, xc, d1)] = dot_prod;
-          }
-        }
-      }
-    }
-  }
-}
-
-}  // namespace tfjs::wasm
-
-#endif
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/dilation2d_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/dilation2d_impl.h
deleted file mode 100644
index 70db22963..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/dilation2d_impl.h
+++ /dev/null
@@ -1,169 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cstddef>
-#include <limits>
-#include <tuple>
-
-namespace tfjs::wasm {
-
-struct Dilation2DInfo {
-  int batch;
-  int depth;
-  int in_height;
-  int in_width;
-  int out_height;
-  int out_width;
-  int stride_height;
-  int stride_width;
-  int dilation_height;
-  int dilation_width;
-  int filter_height;
-  int filter_width;
-  int pad_top;
-  int pad_left;
-
-  inline int in_offset(int b, int h, int w, int d) const {
-    return d + (w + (h + b * in_height) * in_width) * depth;
-  }
-  inline int filter_offset(int h, int w, int d) const { return d + (w + h * filter_width) * depth; }
-  inline int out_offset(int b, int h, int w, int d) const {
-    return d + (w + (h + b * out_height) * out_width) * depth;
-  }
-
-  inline int in_size() const { return batch * in_height * in_width * depth; }
-  inline int filter_size() const { return filter_height * filter_width * depth; }
-  inline int out_size() const { return batch * out_height * out_width * depth; }
-};
-
-namespace {
-
-// Internal implementation for iterating through loops in Dilation2D ops.
-template <typename FI, typename FAP, typename FAG>
-inline void IterateDilation2DLoops(const Dilation2DInfo& info, const FI& filter_init,
-                                   const FAP& filter_apply, const FAG& filter_aggregate) {
-  for (int b = 0; b < info.batch; ++b) {
-    for (int h_out = 0; h_out < info.out_height; ++h_out) {
-      int h_beg = h_out * info.stride_height - info.pad_top;
-      for (int w_out = 0; w_out < info.out_width; ++w_out) {
-        int w_beg = w_out * info.stride_width - info.pad_left;
-        for (int d = 0; d < info.depth; ++d) {
-          auto data = filter_init(h_beg, w_beg);
-
-          for (int h = 0; h < info.filter_height; ++h) {
-            int h_in = h_beg + h * info.dilation_height;
-            if (h_in < 0 || h_in >= info.in_height) {
-              continue;
-            }
-
-            for (int w = 0; w < info.filter_width; ++w) {
-              int w_in = w_beg + w * info.dilation_width;
-              if (w_in < 0 || w_in >= info.in_width) {
-                continue;
-              }
-              filter_apply(data, b, d, h_in, w_in, h, w);
-            }
-          }
-
-          filter_aggregate(data, b, d, h_out, w_out);
-        }
-      }
-    }
-  }
-}
-
-}  // namespace
-
-template <typename T>
-inline void Dilation2DImpl(const T* x_buf, const T* filter_buf, T* out_buf,
-                           const Dilation2DInfo& info) {
-  IterateDilation2DLoops(
-      info,
-      /*filter_init=*/
-      [](int /*h_beg*/, int /*w_beg*/) -> T { return std::numeric_limits<T>::min(); },
-      /*filter_apply=*/
-      [&](T& cur_val, int b, int d, int h_in, int w_in, int h, int w) {
-        int x_offset = info.in_offset(b, h_in, w_in, d);
-        int filter_offset = info.filter_offset(h, w, d);
-        cur_val = std::max(x_buf[x_offset] + filter_buf[filter_offset], cur_val);
-      },
-      /*filter_aggregate=*/
-      [&](const T& cur_val, int b, int d, int h_out, int w_out) {
-        int out_offset = info.out_offset(b, h_out, w_out, d);
-        out_buf[out_offset] = cur_val;
-      });
-}
-
-template <typename T>
-inline void Dilation2DBackpropFilterImpl(const T* x_buf, const T* filter_buf, const T* dy_buf,
-                                         T* grad_buf, const Dilation2DInfo& info) {
-  std::fill(grad_buf, grad_buf + info.filter_size(), 0);
-  IterateDilation2DLoops(
-      info,
-      /*filter_init=*/
-      [](int /*h_beg*/, int /*w_beg*/) -> std::tuple<T, int, int> {
-        return {std::numeric_limits<T>::min(), 0, 0};
-      },
-      /*filter_apply=*/
-      [&](std::tuple<T, int, int>& data, int b, int d, int h_in, int w_in, int h, int w) {
-        int x_offset = info.in_offset(b, h_in, w_in, d);
-        int filter_offset = info.filter_offset(h, w, d);
-        T val = x_buf[x_offset] + filter_buf[filter_offset];
-        if (val > std::get<0>(data)) {
-          data = {val, h, w};
-        }
-      },
-      /*filter_aggregate=*/
-      [&](const std::tuple<T, int, int>& data, int b, int d, int h_out, int w_out) {
-        const auto& [unused, h_max, w_max] = data;
-        const int dy_offset = info.out_offset(b, h_out, w_out, d);
-        int grad_offset = info.filter_offset(h_max, w_max, d);
-        grad_buf[grad_offset] += dy_buf[dy_offset];
-      });
-}
-
-template <typename T>
-inline void Dilation2DBackpropInputImpl(const T* x_buf, const T* filter_buf, const T* dy_buf,
-                                        T* grad_buf, const Dilation2DInfo& info) {
-  std::fill(grad_buf, grad_buf + info.in_size(), 0);
-  IterateDilation2DLoops(
-      info,
-      /*filter_init=*/
-      [](int h_beg, int w_beg) -> std::tuple<T, int, int> {
-        return {std::numeric_limits<T>::min(), std::max(h_beg, 0), std::max(w_beg, 0)};
-      },
-      /*filter_apply=*/
-      [&](std::tuple<T, int, int>& data, int b, int d, int h_in, int w_in, int h, int w) {
-        int x_offset = info.in_offset(b, h_in, w_in, d);
-        int filter_offset = info.filter_offset(h, w, d);
-        T val = x_buf[x_offset] + filter_buf[filter_offset];
-        if (val > std::get<0>(data)) {
-          data = {val, h_in, w_in};
-        }
-      },
-      /*filter_aggregate=*/
-      [&](const std::tuple<T, int, int>& data, int b, int d, int h_out, int w_out) {
-        const auto& [unused, h_in_max, w_in_max] = data;
-        const int dy_offset = info.out_offset(b, h_out, w_out, d);
-        int grad_offset = info.in_offset(b, h_in_max, w_in_max, d);
-        grad_buf[grad_offset] += dy_buf[dy_offset];
-      });
-}
-
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/elu_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/elu_impl.cc
deleted file mode 100644
index 30adb2793..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/elu_impl.cc
+++ /dev/null
@@ -1,88 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/elu_impl.h"
-
-#include <xnnpack.h>
-#include <cmath>
-#include <cstddef>
-#include <map>
-#include <tuple>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-// We use std::tuple as the cache key as it implements the compare operator
-// needed for std::map.
-typedef std::tuple<size_t> EluOperatorCacheKey;
-
-// The operator cache maps the weights id to the xnn_operator_t instantiated for
-// this set of weights.
-std::map<EluOperatorCacheKey, xnn_operator_t> operator_cache;
-
-}  // namespace
-
-void EluImpl(const float* x_buf, size_t x_size, float* out_buf) {
-  xnn_operator_t elu_op = nullptr;
-
-  const size_t channels = 1;
-  EluOperatorCacheKey cache_key = {channels};
-
-  auto operator_cache_idx = operator_cache.find(cache_key);
-  if (operator_cache_idx == operator_cache.end()) {
-    const size_t input_stride = channels;
-    const size_t output_stride = channels;
-    const float alpha = 1.0f;
-    const uint32_t flags = 0;
-
-    xnn_status status = xnn_create_elu_nc_f32(
-        channels, input_stride, output_stride, alpha, flags, &elu_op);
-    if (status != xnn_status_success) {
-      tfjs::util::warn(
-          "XNN status for xnn_create_elu_nc_f32 is not "
-          "successful. Got status %d. Use -c dbg to see XNN logs.",
-          status);
-      return;
-    }
-
-    operator_cache.insert({cache_key, elu_op});
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    elu_op = operator_cache_idx->second;
-  }
-
-  const size_t batch = x_size;
-  xnn_status status = xnn_setup_elu_nc_f32(elu_op, batch, x_buf, out_buf,
-                                           tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    tfjs::util::warn(
-        "XNN status for xnn_setup_elu_nc_f32 is not "
-        "successful. Got status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(elu_op, tfjs::backend::threadpool);
-}
-
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/elu_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/elu_impl.h
deleted file mode 100644
index a739dff30..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/elu_impl.h
+++ /dev/null
@@ -1,26 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef ELU_IMPL_H_
-#define ELU_IMPL_H_
-
-#include <cstddef>
-
-namespace tfjs::wasm {
-
-void EluImpl(const float* x_buf, size_t x_size, float* out_buf);
-
-}  // namespace tfjs::wasm
-
-#endif  // ELU_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/interpolate_bilinear_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/interpolate_bilinear_impl.cc
deleted file mode 100644
index b45419564..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/interpolate_bilinear_impl.cc
+++ /dev/null
@@ -1,92 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/interpolate_bilinear_impl.h"
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-void interpolate_bilinear(float* out_buf_ptr, const float* images_buf,
-                          const std::vector<size_t>& images_strides,
-                          size_t crop_width, size_t image_width,
-                          size_t image_width_m1, size_t image_height_m1,
-                          size_t num_channels, bool should_extrapolate,
-                          float extrapolation_value, size_t batch_offset,
-                          float y_ind, float width_scale, float x1, float x2) {
-  float top_ind = floor(y_ind);
-  float image_height_m1_f = image_height_m1;
-  float bottom_ind = std::min(image_height_m1_f,
-                              static_cast<float>(ceil(y_ind)));
-  float y_lerp = y_ind - top_ind;
-
-  for (size_t x = 0; x < crop_width; ++x) {
-    float x_ind = (crop_width > 1) ? x1 * image_width_m1 + x * width_scale
-                                   : 0.5 * (x1 + x2) * image_width_m1;
-
-    if (should_extrapolate && (x_ind < 0 || x_ind > image_width - 1)) {
-      for (size_t c = 0; c < num_channels; ++c) {
-        *out_buf_ptr = extrapolation_value;
-        out_buf_ptr++;
-      }
-      continue;
-    }
-
-    float left_ind = floor(x_ind);
-    float image_width_m1_f = image_width_m1;
-    float right_ind = std::min(image_width_m1_f,
-                               static_cast<float>(ceil(x_ind)));
-    float x_lerp = x_ind - left_ind;
-
-    for (size_t c = 0; c < num_channels; ++c) {
-      size_t ind = c + left_ind * images_strides[2] +
-                   top_ind * images_strides[1] + batch_offset;
-      const float top_left = images_buf[ind];
-
-      ind = c + right_ind * images_strides[2] + top_ind * images_strides[1] +
-            batch_offset;
-
-      const float top_right = images_buf[ind];
-
-      ind = c + left_ind * images_strides[2] + bottom_ind * images_strides[1] +
-            batch_offset;
-
-      const float bottom_left = images_buf[ind];
-
-      ind = c + right_ind * images_strides[2] + bottom_ind * images_strides[1] +
-            batch_offset;
-
-      const float bottom_right = images_buf[ind];
-
-      const float top = top_left + (top_right - top_left) * x_lerp;
-      const float bottom = bottom_left + (bottom_right - bottom_left) * x_lerp;
-
-      *out_buf_ptr = top + ((bottom - top) * y_lerp);
-      out_buf_ptr++;
-    }
-  }
-}
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/interpolate_bilinear_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/interpolate_bilinear_impl.h
deleted file mode 100644
index 4ba6e8c3b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/interpolate_bilinear_impl.h
+++ /dev/null
@@ -1,35 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef INTERPOLATE_BILINEAR_IMPL_H_
-#define INTERPOLATE_BILINEAR_IMPL_H_
-
-#include <cstddef>
-#include <vector>
-
-namespace tfjs {
-namespace wasm {
-
-void interpolate_bilinear(float* out_buf_ptr, const float* images_buf,
-                          const std::vector<size_t>& images_strides,
-                          size_t crop_width, size_t image_width,
-                          size_t image_width_m1, size_t image_height_m1,
-                          size_t num_channels, bool should_extrapolate,
-                          float extrapolation_value, size_t batch_offset,
-                          float y_ind, float width_scale, float x1, float x2);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // INTERPOLATE_BILINEAR_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Abs.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Abs.cc
deleted file mode 100644
index 6513c15c2..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Abs.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-#include <stdlib.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Abs(const size_t x_id, const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_xnn_f32(x_id, out_id, xnn_create_abs_nc_f32, xnn_setup_abs_nc_f32);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, [](int a) {
-        return static_cast<int32_t>(abs(static_cast<float>(a)));
-      });
-      break;
-    default:
-      util::warn(
-          "Abs for tensor ids %d failed. "
-          "Unknown dtype %d",
-          x_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Acos.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Acos.cc
deleted file mode 100644
index 0dc95a7c7..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Acos.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline T acos_impl(T n) {
-  return static_cast<T>(std::acosf(static_cast<float>(n)));
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Acos(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, acos_impl<float>);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, acos_impl<int>);
-      break;
-    default:
-      util::warn("Acos for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Acosh.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Acosh.cc
deleted file mode 100644
index 4d60f2b5c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Acosh.cc
+++ /dev/null
@@ -1,55 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline T acosh_impl(T n) {
-  return static_cast<T>(std::acoshf(static_cast<float>(n)));
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Acosh(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, acosh_impl<float>);
-      break;
-    default:
-      util::warn("Acosh for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Add.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Add.cc
deleted file mode 100644
index e7f51a925..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Add.cc
+++ /dev/null
@@ -1,65 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline T add(T a, T b) {
-  return a + b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Add(const size_t a_id, const size_t* a_shape_ptr, const size_t a_shape_len,
-         const size_t b_id, const size_t* b_shape_ptr, const size_t b_shape_len,
-         const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_xnn_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                     b_shape_len, out_id, xnn_create_add_nd_f32,
-                     xnn_setup_add_nd_f32);
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, add<int32_t>);
-      break;
-    case DType::boolean:
-      binary_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, add<bool>);
-      break;
-    default:
-      util::warn("Add for tensor ids %d and %d failed. Unknown dtype %d", a_id,
-                 b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AddN.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AddN.cc
deleted file mode 100644
index e2f6e19d8..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AddN.cc
+++ /dev/null
@@ -1,82 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cstddef>
-#include <cstring>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-void addn(const std::vector<const T*>& inputs_buf, const size_t size,
-          T* out_buf) {
-  // Initialize the output to 0.
-  memset(out_buf, 0, size * sizeof(T));
-
-  for (size_t in_idx = 0; in_idx < inputs_buf.size(); ++in_idx) {
-    const T* input = inputs_buf[in_idx];
-    for (size_t i = 0; i < size; ++i) {
-      out_buf[i] += input[i];
-    }
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void AddN(const size_t* input_ids_ptr, const size_t input_ids_len,
-          const DType dtype, const size_t out_id) {
-  std::vector<size_t> inputs(input_ids_ptr, input_ids_ptr + input_ids_len);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  std::vector<void*> inputs_buf;
-  std::transform(
-      inputs.begin(), inputs.end(), std::back_inserter(inputs_buf),
-      [](size_t id) { return backend::get_tensor_info(id).memory_offset; });
-
-  switch (dtype) {
-    case DType::float32:
-      addn<float>(reinterpret_cast<std::vector<const float*>&>(inputs_buf),
-                  out_info.size, out_info.f32_write());
-      break;
-    case DType::int32:
-      addn<int32_t>(reinterpret_cast<std::vector<const int*>&>(inputs_buf),
-                    out_info.size, out_info.i32_write());
-      break;
-    case DType::boolean:
-      addn<bool>(reinterpret_cast<std::vector<const bool*>&>(inputs_buf),
-                 out_info.size, out_info.b_write());
-      break;
-    default:
-      util::warn("AddN failed. Unknown dtype %d", dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/All.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/All.cc
deleted file mode 100644
index 5bd1c7f69..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/All.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void All(const size_t x_id, const size_t reduce_size, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const bool* x_buf = x_info.b();
-
-  bool* out_buf = out_info.b_write();
-  const size_t out_size = out_info.size;
-
-  const bool* x_offset = x_buf;
-
-  for (size_t i = 0; i < out_size; ++i) {
-    const size_t offset = i * reduce_size;
-    bool all = x_buf[offset];
-
-    const bool* x_iter_end = x_offset + reduce_size;
-
-    for (const bool* x = x_offset; x < x_iter_end; ++x) {
-      bool value = *x;
-      all = all && value;
-    }
-
-    x_offset += reduce_size;
-
-    out_buf[i] = all;
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Any.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Any.cc
deleted file mode 100644
index 6e27d22a9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Any.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Any(const size_t x_id, const size_t reduce_size, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const bool* x_buf = x_info.b();
-
-  bool* out_buf = out_info.b_write();
-  const size_t out_size = out_info.size;
-
-  const bool* x_offset = x_buf;
-
-  for (size_t i = 0; i < out_size; ++i) {
-    const size_t offset = i * reduce_size;
-    bool any = x_buf[offset];
-
-    const bool* x_iter_end = x_offset + reduce_size;
-
-    for (const bool* x = x_offset; x < x_iter_end; ++x) {
-      bool value = *x;
-      any = any || value;
-    }
-
-    x_offset += reduce_size;
-
-    out_buf[i] = any;
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ArgMax.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ArgMax.cc
deleted file mode 100644
index c4d1b2397..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ArgMax.cc
+++ /dev/null
@@ -1,55 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/argminmax_impl.h"
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void ArgMax(const int x_id, const DType dtype, const int outer_size,
-            const int inner_size, const int out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  int32_t* out_buf = out_info.i32_write();
-
-  switch (dtype) {
-    case DType::float32:
-      ArgMaxImpl<float>(x_info.f32(), outer_size, inner_size, out_buf);
-      break;
-    case DType::int32:
-      ArgMaxImpl<int32_t>(x_info.i32(), outer_size, inner_size, out_buf);
-      break;
-    case DType::boolean:
-      ArgMaxImpl<bool>(x_info.b(), outer_size, inner_size, out_buf);
-      break;
-    default:
-      util::warn("ArgMax failed. Unknown dtype %d", dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ArgMin.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ArgMin.cc
deleted file mode 100644
index 6328177c1..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ArgMin.cc
+++ /dev/null
@@ -1,55 +0,0 @@
-/* Copyright 2023 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/argminmax_impl.h"
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void ArgMin(const int x_id, const DType dtype, const int outer_size,
-            const int inner_size, const int out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  int32_t* out_buf = out_info.i32_write();
-
-  switch (dtype) {
-    case DType::float32:
-      ArgMinImpl<float>(x_info.f32(), outer_size, inner_size, out_buf);
-      break;
-    case DType::int32:
-      ArgMinImpl<int32_t>(x_info.i32(), outer_size, inner_size, out_buf);
-      break;
-    case DType::boolean:
-      ArgMinImpl<bool>(x_info.b(), outer_size, inner_size, out_buf);
-      break;
-    default:
-      util::warn("ArgMin failed. Unknown dtype %d", dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Asin.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Asin.cc
deleted file mode 100644
index 59af49b96..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Asin.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline T asin_impl(T n) {
-  return static_cast<T>(std::asinf(static_cast<float>(n)));
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Asin(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, asin_impl<float>);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, asin_impl<int>);
-      break;
-    default:
-      util::warn("Asin for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Asinh.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Asinh.cc
deleted file mode 100644
index e1ddfc903..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Asinh.cc
+++ /dev/null
@@ -1,55 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline T asinh_impl(T n) {
-  return static_cast<T>(std::asinhf(static_cast<float>(n)));
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Asinh(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, asinh_impl<float>);
-      break;
-    default:
-      util::warn("Asinh for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Atan.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Atan.cc
deleted file mode 100644
index e2bed3f6b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Atan.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline T atan_impl(T n) {
-  return static_cast<T>(std::atanf(static_cast<float>(n)));
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Atan(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, atan_impl<float>);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, atan_impl<int>);
-      break;
-    default:
-      util::warn("Atan for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Atan2.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Atan2.cc
deleted file mode 100644
index e46f5f086..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Atan2.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-template <typename T>
-inline T Atan2Impl(T a, T b) {
-  return static_cast<T>(
-      std::atan2f(static_cast<float>(a), static_cast<float>(b)));
-}
-
-}  // namespace
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Atan2(const int a_id, const size_t* a_shape_ptr, const int a_shape_len,
-           const int b_id, const size_t* b_shape_ptr, const int b_shape_len,
-           const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, Atan2Impl<float>);
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, Atan2Impl<int32_t>);
-      break;
-    default:
-      util::warn("Atan2 for tensor id %d failed. Unsupported dtype %d", a_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Atanh.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Atanh.cc
deleted file mode 100644
index 1a47108b7..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Atanh.cc
+++ /dev/null
@@ -1,57 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline T AtanhImpl(T n) {
-  return static_cast<T>(std::atanhf(static_cast<float>(n)));
-}
-
-}  // namespace
-
-namespace tfjs::wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Atanh(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, AtanhImpl<float>);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, AtanhImpl<int32_t>);
-      break;
-    default:
-      util::warn("Atanh for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool.cc
deleted file mode 100644
index 477f55d98..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool.cc
+++ /dev/null
@@ -1,120 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <cstring>
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <stdio.h>
-
-#include <xnnpack.h>
-#include <array>
-#include <cmath>
-#include <cstddef>
-#include <limits>
-#include <map>
-#include <unordered_map>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/AvgPool.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-typedef std::array<size_t, 14> OperatorCacheKey;
-
-std::map<OperatorCacheKey, xnn_operator_t> operator_cache;
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void AvgPool(const size_t x_id, const size_t batch_size,
-             const size_t input_height, const size_t input_width,
-             const size_t filter_height, const size_t filter_width,
-             size_t pad_top, size_t pad_right, size_t pad_bottom,
-             size_t pad_left, const size_t stride_height,
-             const size_t stride_width, const size_t channels,
-             const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info(out_id);
-
-  const float* x_buf = reinterpret_cast<float*>(x_info.memory_offset);
-  float* out_buf = reinterpret_cast<float*>(out_info.memory_offset);
-
-  // XNNPack does not support 1x1 filters for AvgPool
-  if (filter_width == 1 && filter_height == 1) {
-    tfjs::util::identity_pool(x_id, x_buf, out_buf, out_info.size, batch_size,
-                              input_height, input_width, stride_height,
-                              stride_width, channels);
-    return;
-  }
-
-  xnn_operator_t avg_pool_op = nullptr;
-
-  const uint32_t flags = 0;
-  const size_t input_pixel_stride = channels;
-  const size_t output_pixel_stride = channels;
-
-  OperatorCacheKey cache_key = {
-      pad_top,       pad_right,          pad_bottom,          pad_left,
-      filter_height, filter_width,       stride_height,       stride_width,
-      channels,      input_pixel_stride, output_pixel_stride, flags};
-
-  auto operator_cache_idx = operator_cache.find(cache_key);
-
-  if (operator_cache_idx == operator_cache.end()) {
-    const float output_min = -std::numeric_limits<float>::infinity();
-    const float output_max = std::numeric_limits<float>::infinity();
-
-    xnn_status status = xnn_create_average_pooling2d_nhwc_f32(
-        pad_top, pad_right, pad_bottom, pad_left, filter_height, filter_width,
-        stride_height, stride_width, channels, input_pixel_stride,
-        output_pixel_stride, output_min, output_max, flags, &avg_pool_op);
-
-    if (status != xnn_status_success) {
-      util::warn(
-          "XNN status for xnn_create_average_pooling2d_nhwc_f32 is not "
-          "successful. ",
-          "Got status %d. Use -c dbg to see XNN logs.", status);
-      return;
-    }
-
-    operator_cache.emplace(cache_key, avg_pool_op);
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    avg_pool_op = operator_cache_idx->second;
-  }
-
-  xnn_status status = xnn_setup_average_pooling2d_nhwc_f32(
-      avg_pool_op, batch_size, input_height, input_width, x_buf, out_buf,
-      tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    util::warn(
-        "XNN status for xnn_setup_average_pooling2d_nhwc_f32 is not "
-        "successful. "
-        "Got status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(avg_pool_op, tfjs::backend::threadpool);
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool.h
deleted file mode 100644
index c19b85b3a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_AVGPOOL_H_
-#define KERNELS_AVGPOOL_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void AvgPool(const size_t x_id, const size_t batch_size,
-             const size_t input_height, const size_t input_width,
-             const size_t filter_height, const size_t filter_width,
-             size_t pad_top, size_t pad_right, size_t pad_bottom,
-             size_t pad_left, const size_t stride_height,
-             const size_t stride_width, const size_t channels,
-             const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_AVGPOOL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool3D.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool3D.cc
deleted file mode 100644
index 07188471a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool3D.cc
+++ /dev/null
@@ -1,89 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <utility>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/pool3d_impl.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x` and `out` must have dtype float32 (checked in tfjs-core)
-// - Tensor `x` and `out` must have data format 'NDHWC' (checked in tfjs-core)
-void AvgPool3D(int x_id, int out_id, int batch_size, int channel_size,
-               int in_depth, int in_height, int in_width, int out_depth,
-               int out_height, int out_width, int stride_depth,
-               int stride_height, int stride_width, int dilation_depth,
-               int dilation_height, int dilation_width,
-               int effective_filter_depth, int effective_filter_height,
-               int effective_filter_width, int pad_front, int pad_top,
-               int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-
-  NDHWCPool3DImpl(x_info.f32(),
-                  NDHWCPool3DInfo{
-                      .batch_size = batch_size,
-                      .channel_size = channel_size,
-                      .in_depth = in_depth,
-                      .in_height = in_height,
-                      .in_width = in_width,
-                      .out_depth = out_depth,
-                      .out_height = out_height,
-                      .out_width = out_width,
-                      .stride_depth = stride_depth,
-                      .stride_height = stride_height,
-                      .stride_width = stride_width,
-                      .dilation_depth = dilation_depth,
-                      .dilation_height = dilation_height,
-                      .dilation_width = dilation_width,
-                      .effective_filter_depth = effective_filter_depth,
-                      .effective_filter_height = effective_filter_height,
-                      .effective_filter_width = effective_filter_width,
-                      .pad_front = pad_front,
-                      .pad_top = pad_top,
-                      .pad_left = pad_left,
-                  },
-                  /*filter_init=*/
-                  []() -> std::pair<float, int> {
-                    return {0.0, 0};
-                  },
-                  /*filter_apply=*/
-                  [](std::pair<float, int>& data, int, const float& val) {
-                    data.first += val;
-                    ++data.second;
-                  },
-                  /*filter_assign=*/
-                  [buf = out_info.f32_write()](
-                      int offset, const std::pair<float, int>& data) {
-                    buf[offset] = data.first /
-                                  static_cast<float>(std::max(data.second, 1));
-                  });
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool3DGrad.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool3DGrad.cc
deleted file mode 100644
index 71e2380cc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool3DGrad.cc
+++ /dev/null
@@ -1,81 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/pool3d_impl.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `dx` and `dy` must have dtype float32 (checked in tfjs-core)
-// - Tensor `dx` and `dy` must have data format 'NDHWC' (checked in tfjs-core)
-void AvgPool3DGrad(int dy_id, int dx_id, int batch_size, int channel_size,
-                   int in_depth, int in_height, int in_width, int out_depth,
-                   int out_height, int out_width, int stride_depth,
-                   int stride_height, int stride_width, int dilation_depth,
-                   int dilation_height, int dilation_width,
-                   int effective_filter_depth, int effective_filter_height,
-                   int effective_filter_width, int pad_front, int pad_top,
-                   int pad_left, int filter_depth, int filter_height,
-                   int filter_width) {
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& dx_info = backend::get_tensor_info_out(dx_id);
-
-  NDHWCPool3DGradImpl(
-      dy_info.f32(), dx_info.f32_write(),
-      NDHWCPool3DInfo{
-          .batch_size = batch_size,
-          .channel_size = channel_size,
-          .in_depth = in_depth,
-          .in_height = in_height,
-          .in_width = in_width,
-          .out_depth = out_depth,
-          .out_height = out_height,
-          .out_width = out_width,
-          .stride_depth = stride_depth,
-          .stride_height = stride_height,
-          .stride_width = stride_width,
-          .dilation_depth = dilation_depth,
-          .dilation_height = dilation_height,
-          .dilation_width = dilation_width,
-          .effective_filter_depth = effective_filter_depth,
-          .effective_filter_height = effective_filter_height,
-          .effective_filter_width = effective_filter_width,
-          .pad_front = pad_front,
-          .pad_top = pad_top,
-          .pad_left = pad_left,
-      },
-      /*pixel_mask=*/
-      [avg_multiplier = 1.0f / (static_cast<float>(filter_depth) *
-                                static_cast<float>(filter_height) *
-                                static_cast<float>(filter_width))](int, int) {
-        return avg_multiplier;
-      });
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPoolGrad.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPoolGrad.cc
deleted file mode 100644
index 5f50361d8..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPoolGrad.cc
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/pool3d_impl.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `dx` and `dy` must have dtype float32 (checked in tfjs-core)
-void AvgPoolGrad(int dy_id, int dx_id, int batch_size, int channel_size,
-                 int in_height, int in_width, int out_height, int out_width,
-                 int stride_height, int stride_width, int dilation_height,
-                 int dilation_width, int effective_filter_height,
-                 int effective_filter_width, int pad_top, int pad_left,
-                 int filter_height, int filter_width) {
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& dx_info = backend::get_tensor_info_out(dx_id);
-
-  NDHWCPool3DGradImpl(
-      dy_info.f32(), dx_info.f32_write(),
-      NDHWCPool3DInfo{
-          .batch_size = batch_size,
-          .channel_size = channel_size,
-          .in_depth = 1,
-          .in_height = in_height,
-          .in_width = in_width,
-          .out_depth = 1,
-          .out_height = out_height,
-          .out_width = out_width,
-          .stride_depth = 1,
-          .stride_height = stride_height,
-          .stride_width = stride_width,
-          .dilation_depth = 1,
-          .dilation_height = dilation_height,
-          .dilation_width = dilation_width,
-          .effective_filter_depth = 1,
-          .effective_filter_height = effective_filter_height,
-          .effective_filter_width = effective_filter_width,
-          .pad_front = 0,
-          .pad_top = pad_top,
-          .pad_left = pad_left,
-      },
-      /*pixel_mask=*/
-      [avg_multiplier = 1.0f / (static_cast<float>(filter_height) *
-                                static_cast<float>(filter_width))](int, int) {
-        return avg_multiplier;
-      });
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool_test.cc
deleted file mode 100644
index 78b0d8349..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/AvgPool_test.cc
+++ /dev/null
@@ -1,80 +0,0 @@
-
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/AvgPool.h"
-
-TEST(MAXPOOL, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t size = 9;
-  float x_values[size] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
-
-  const size_t out_id = 3;
-  const size_t out_size = 9;
-  float out_values[out_size] = {};
-
-  tfjs::wasm::register_tensor(x0_id, size, x_values);
-  tfjs::wasm::register_tensor(x1_id, size, x_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(3, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One xnn_operator should be created for first call to avgPool.
-  const size_t batch_size = 1;
-  const size_t input_height = 3;
-  const size_t input_width = 3;
-  const size_t filter_height = 2;
-  const size_t filter_width = 2;
-  const size_t pad_top = 0;
-  const size_t pad_right = 1;
-  const size_t pad_bottom = 1;
-  const size_t pad_left = 0;
-  const size_t stride_height = 1;
-  const size_t stride_width = 1;
-  const size_t channels = 1;
-  tfjs::wasm::AvgPool(x0_id, batch_size, input_height, input_width,
-                      filter_height, filter_width, pad_top, pad_right,
-                      pad_bottom, pad_left, stride_height, stride_width,
-                      channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to avgPool with
-  // the same arguments.
-  tfjs::wasm::AvgPool(x0_id, batch_size, input_height, input_width,
-                      filter_height, filter_width, pad_top, pad_right,
-                      pad_bottom, pad_left, stride_height, stride_width,
-                      channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the next call to avgPool with
-  // 'valid' padding.
-  tfjs::wasm::AvgPool(x0_id, batch_size, input_height, input_width,
-                      filter_height, filter_width, pad_top, 0 /* pad_right */,
-                      0 /* pad_bottom */, pad_left, stride_height, stride_width,
-                      channels, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BatchMatMul.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BatchMatMul.cc
deleted file mode 100644
index 81d5fb31a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BatchMatMul.cc
+++ /dev/null
@@ -1,50 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/batch_mat_mul_impl.h"
-#include "tfjs-backend-wasm/src/cc/kernels/BatchMatMul.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void BatchMatMul(const size_t a_id, const size_t* a_shape_ptr,
-                 const size_t a_shape_len, const size_t b_id,
-                 const size_t* b_shape_ptr, const size_t b_shape_len,
-                 const bool transpose_a, const bool transpose_b,
-                 const size_t out_id) {
-  const size_t bias_id = 0;
-  const size_t prelu_weights_id = 0;
-  const float leakyrelu_alpha = 0;
-  const FusableActivation activation = FusableActivation::LINEAR;
-  tfjs::wasm::fused_batch_mat_mul(a_id, a_shape_ptr, a_shape_len, b_id,
-                                  b_shape_ptr, b_shape_len, transpose_a,
-                                  transpose_b, activation, bias_id,
-                                  prelu_weights_id, leakyrelu_alpha, out_id);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BatchMatMul.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BatchMatMul.h
deleted file mode 100644
index bc5b5728c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BatchMatMul.h
+++ /dev/null
@@ -1,34 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_BATCHMATMUL_H_
-#define KERNELS_BATCHMATMUL_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void BatchMatMul(const size_t a_id, const size_t* a_shape_ptr,
-                 const size_t a_shape_len, const size_t b_id,
-                 const size_t* b_shape_ptr, const size_t b_shape_len,
-                 const bool transpose_a, const bool transpose_b,
-                 const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_BATCHMATMUL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BatchMatMul_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BatchMatMul_test.cc
deleted file mode 100644
index 3ff2ff2fa..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BatchMatMul_test.cc
+++ /dev/null
@@ -1,93 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/BatchMatMul.h"
-
-TEST(BATCH_MATMUL, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  size_t a0_id = 1;
-  size_t a1_id = 2;
-  size_t size = 2;
-  float a_values[2] = {1, 2};
-  std::vector<size_t> a_shape = {1, 2, 1};
-  size_t* a_shape_ptr = a_shape.data();
-
-  size_t b0_id = 3;
-  size_t b1_id = 4;
-  float b_values[2] = {1, 2};
-  std::vector<size_t> b_shape = {1, 1, 2};
-  size_t* b_shape_ptr = b_shape.data();
-
-  size_t out_id = 5;
-  float out_values[4] = {};
-
-  tfjs::wasm::register_tensor(a0_id, size, a_values);
-  tfjs::wasm::register_tensor(a1_id, size, a_values);
-  tfjs::wasm::register_tensor(b0_id, size, b_values);
-  tfjs::wasm::register_tensor(b1_id, size, b_values);
-  tfjs::wasm::register_tensor(out_id, size, out_values);
-
-  ASSERT_EQ(5, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the first call to BatchMatMul.
-  tfjs::wasm::BatchMatMul(a0_id, a_shape_ptr, a_shape.size(), b0_id,
-                          b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                          false /* transpose_b */, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to BatchMatMul
-  // with the same b's.
-  tfjs::wasm::BatchMatMul(a0_id, a_shape_ptr, a_shape.size(), b0_id,
-                          b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                          false /* transpose_b */, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for another call to BatchMatMul with
-  // new b's.
-  tfjs::wasm::BatchMatMul(a0_id, a_shape_ptr, a_shape.size(), b1_id,
-                          b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                          false /* transpose_b */, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the next call to BatchMatMul
-  // with the same b's.
-  tfjs::wasm::BatchMatMul(a0_id, a_shape_ptr, a_shape.size(), b1_id,
-                          b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                          false /* transpose_b */, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing a's should not remove xnn operators.
-  tfjs::wasm::dispose_data(a0_id);
-  tfjs::wasm::dispose_data(a1_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing b's should remove xnn operators.
-  tfjs::wasm::dispose_data(b0_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose_data(b1_id);
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Bincount.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Bincount.cc
deleted file mode 100644
index e7afde6d3..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Bincount.cc
+++ /dev/null
@@ -1,69 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/bincount_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x` must have dtype int32 (checked in tfjs-core)
-// - Tensor `x` must be 1D (checked in tfjs-core)
-// - If has_weights is true, tensor `weights` must be 1D and have the same size
-// as `x` (checked in tfjs-core)
-// - Tensor `out` must have shape [size]
-// - Tensor `out` must have the same dtype as weights
-void Bincount(const int32_t x_id, const int32_t size, const bool has_weights,
-              const int32_t weights_id, const DType weights_dtype,
-              const int32_t out_id) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo* weights_info =
-      has_weights ? &backend::get_tensor_info(weights_id) : nullptr;
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-
-  const int32_t* x_buf = x_info.i32();
-  switch (weights_dtype) {
-    case DType::float32:
-      BincountImpl(x_buf, x_info.size, size,
-                   weights_info ? weights_info->f32() : nullptr,
-                   /*binary_output=*/false, out_info.f32_write());
-      break;
-    case DType::int32:
-      BincountImpl(x_buf, x_info.size, size,
-                   weights_info ? weights_info->i32() : nullptr,
-                   /*binary_output=*/false, out_info.i32_write());
-      break;
-    default:
-      util::warn(
-          "DenseBincount with weights tensor id %d failed. Unsupported "
-          "weights "
-          "dtype %d",
-          weights_id, weights_dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BitwiseAnd.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BitwiseAnd.cc
deleted file mode 100644
index d73ccaee2..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/BitwiseAnd.cc
+++ /dev/null
@@ -1,50 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <typename T>
-inline T BitwiseAndImp(T a, T b) {
-  return a & b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-// REQUIRES:
-// - Tensor `a` and `b` must have dtype int32 (checked in tfjs-core)
-// - Tensor `a` and `b` must have the same shape (checked in tfjs-core)
-void BitwiseAnd(const size_t a_id, const size_t* a_shape_ptr,
-                const size_t a_shape_len, const size_t b_id,
-                const size_t* b_shape_ptr, const size_t b_shape_len,
-                const DType dtype, const size_t out_id) {
-  binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-             out_id,
-             BitwiseAndImp<int32_t>);  // input numbers are ensured to be int32
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Ceil.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Ceil.cc
deleted file mode 100644
index 3747e95af..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Ceil.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Ceil(const size_t x_id, const DType dtype, const size_t out_id) {
-  unary_xnn_f32(x_id, out_id, xnn_create_ceiling_nc_f32,
-                xnn_setup_ceiling_nc_f32);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ClipByValue.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ClipByValue.cc
deleted file mode 100644
index d69412e4d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ClipByValue.cc
+++ /dev/null
@@ -1,95 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/kernels/ClipByValue.h"
-
-#include <xnnpack.h>
-#include <array>
-#include <cmath>
-#include <cstddef>
-#include <map>
-#include <unordered_map>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// These float values are keys to creating the clip operator. We use
-// std::array instead of a vanilla array as it implements the compare operator
-// needed for std::map.
-typedef std::array<float, 2> OperatorCacheKey;
-
-// The operator cache maps the cache key to the xnn_operator_t instantiated for
-// this set of arguments to the xnn_operator.
-std::map<OperatorCacheKey, xnn_operator_t> operator_cache;
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void ClipByValue(const size_t x_id, const float min, const float max,
-                 const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* x_buf = x_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  xnn_operator_t clamp_op = nullptr;
-  OperatorCacheKey cache_key = {min, max};
-  auto operator_cache_idx = operator_cache.find(cache_key);
-  if (operator_cache_idx == operator_cache.end()) {
-    const size_t channels = 1;
-    const size_t strides = channels;
-    const uint32_t flags = 0;
-    xnn_status status = xnn_create_clamp_nc_f32(channels, strides, strides, min,
-                                                max, flags, &clamp_op);
-    if (status != xnn_status_success) {
-      util::warn(
-          "XNN status for xnn_create_clamp_nc_f32 is not successful. Got "
-          "status %d. Use -c dbg to see XNN logs.",
-          status);
-    }
-    operator_cache.emplace(cache_key, clamp_op);
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    clamp_op = operator_cache_idx->second;
-  }
-
-  const size_t batch_size = x_info.size;
-  xnn_status status = xnn_setup_clamp_nc_f32(
-      clamp_op, batch_size, x_buf, out_buf, tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    util::warn(
-        "XNN status for xnn_setup_clamp_nc_f32 is not successful. Got "
-        "status %d. Use -c dbg to see XNN logs.",
-        status);
-  }
-
-  xnn_run_operator(clamp_op, tfjs::backend::threadpool);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ClipByValue.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ClipByValue.h
deleted file mode 100644
index e5295dd4c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ClipByValue.h
+++ /dev/null
@@ -1,30 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_CLIPBYVALUE_H_
-#define KERNELS_CLIPBYVALUE_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void ClipByValue(const size_t x_id, const float min, const float max,
-                 const size_t out_id);
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_CLIPBYVALUE_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ClipByValue_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ClipByValue_test.cc
deleted file mode 100644
index 55a05274d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ClipByValue_test.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/ClipByValue.h"
-
-TEST(ClipByValue, xnn_operator_count) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  size_t x0_id = 1;
-  size_t x1_id = 2;
-  size_t size = 2;
-  size_t min = 0;
-  size_t max = 1;
-  float x_values[2] = {1, 2};
-  size_t out_id = 3;
-  float out_values[2] = {0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, size, x_values);
-  tfjs::wasm::register_tensor(x1_id, size, x_values);
-  tfjs::wasm::register_tensor(out_id, size, out_values);
-
-  ASSERT_EQ(3, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the first call to clip.
-  tfjs::wasm::ClipByValue(x0_id, min, max, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to clip with
-  // the same min/max.
-  tfjs::wasm::ClipByValue(x1_id, min, max, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for another call to clip with new
-  // min/max.
-  tfjs::wasm::ClipByValue(x0_id, min, max + 1, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the next call to prelu with
-  // the same min/max.
-  tfjs::wasm::ClipByValue(x1_id, min, max + 1, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing x's should not remove xnn operators.
-  tfjs::wasm::dispose_data(x0_id);
-  tfjs::wasm::dispose_data(x1_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2D.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2D.cc
deleted file mode 100644
index f48297b8e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2D.cc
+++ /dev/null
@@ -1,57 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/kernels/Conv2D.h"
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Conv2D(const size_t x_id, const size_t batch_size,
-            const size_t input_height, const size_t input_width,
-            const size_t filter_id, const size_t filter_height,
-            const size_t filter_width, size_t pad_top, size_t pad_right,
-            size_t pad_bottom, size_t pad_left, const size_t is_same_pad,
-            const size_t dilation_height, const size_t dilation_width,
-            const size_t stride_height, const size_t stride_width,
-            const size_t input_channels, const size_t output_channels,
-            const size_t out_id) {
-  const size_t bias_id = 0;
-  const size_t prelu_weights_id = 0;
-  const float leakyrelu_alpha = 0;
-  const bool is_depthwise = false;
-  const FusableActivation activation = FusableActivation::LINEAR;
-  tfjs::wasm::conv2d(x_id, batch_size, input_height, input_width, filter_id,
-                     filter_height, filter_width, bias_id, pad_top, pad_right,
-                     pad_bottom, pad_left, is_same_pad, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, is_depthwise, activation,
-                     prelu_weights_id, leakyrelu_alpha, out_id);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2D.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2D.h
deleted file mode 100644
index 34d21509f..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2D.h
+++ /dev/null
@@ -1,40 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_CONV2D_H_
-#define KERNELS_CONV2D_H_
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/conv2d_impl.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void Conv2D(const size_t x_id, const size_t batch_size,
-            const size_t input_height, const size_t input_width,
-            const size_t filter_id, const size_t filter_height,
-            const size_t filter_width, size_t pad_top, size_t pad_right,
-            size_t pad_bottom, size_t pad_left, const size_t is_same_pad,
-            const size_t dilation_height, const size_t dilation_width,
-            const size_t stride_height, const size_t stride_width,
-            const size_t input_channels, const size_t output_channels,
-            const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_CONV2D_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2DBackpropInput.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2DBackpropInput.cc
deleted file mode 100644
index 50e5fe7d4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2DBackpropInput.cc
+++ /dev/null
@@ -1,99 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-#include <vector>
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// Follows the implementation in the TFJS CPU backend.
-void Conv2DBackpropInput(
-    const size_t dy_id, const size_t filter_id, const size_t batch_size,
-    const size_t filter_height, const size_t filter_width,
-    const size_t in_height, const size_t in_width, const size_t in_channels,
-    const size_t out_height, const size_t out_width, const size_t out_channels,
-    const size_t stride_height, const size_t stride_width, const size_t top_pad,
-    const size_t left_pad, const size_t flt_s0, const size_t flt_s1,
-    const size_t flt_s2, const size_t x_batch_stride, const size_t x_row_stride,
-    const size_t x_col_stride, const size_t x_channel_stride,
-    const size_t y_batch_stride, const size_t y_row_stride,
-    const size_t y_col_stride, const size_t y_channel_stride,
-    const size_t out_id) {
-  auto& dy_info = backend::get_tensor_info(dy_id);
-  auto& filter_info = backend::get_tensor_info(filter_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* dy_buf = dy_info.f32();
-  const float* filter_buf = filter_info.f32();
-  float* out_buf_ptr = out_info.f32_write();
-
-  for (size_t b = 0; b < batch_size; ++b) {
-    for (size_t d1 = 0; d1 < in_channels; ++d1) {
-      for (size_t xr = 0; xr < in_height; ++xr) {
-        int32_t xr_corner = xr - top_pad;
-        float stride_height_multiples = ceil(static_cast<float>(xr_corner) /
-                                             static_cast<float>(stride_height));
-        float xr_min = std::max(0.0f, stride_height_multiples);
-        float yr_max = std::min(static_cast<float>(out_height),
-                                static_cast<float>(filter_height + xr_corner) /
-                                    static_cast<float>(stride_height));
-        for (size_t xc = 0; xc < in_width; ++xc) {
-          int32_t xc_corner = xc - left_pad;
-          float stride_width_multiples = ceil(static_cast<float>(xc_corner) /
-                                              static_cast<float>(stride_width));
-          float xc_min = std::max(0.0f, stride_width_multiples);
-          float yc_max = std::min(static_cast<float>(out_width),
-                                  static_cast<float>(filter_width + xc_corner) /
-                                      static_cast<float>(stride_width));
-
-          float dot_prod = 0.0;
-          for (size_t yr = xr_min; yr < yr_max; ++yr) {
-            int32_t wr = yr * stride_height - xr_corner;
-
-            for (size_t yc = xc_min; yc < yc_max; ++yc) {
-              int32_t wc = yc * stride_width - xc_corner;
-              size_t dy_offset =
-                  y_batch_stride * b + y_row_stride * yr + y_col_stride * yc;
-              size_t flt_offset = flt_s0 * (filter_height - 1 - wr) +
-                                  flt_s1 * (filter_width - 1 - wc) +
-                                  flt_s2 * d1;
-              for (size_t d2 = 0; d2 < out_channels; ++d2) {
-                float pixel = dy_buf[dy_offset + y_channel_stride * d2];
-                float weight = filter_buf[flt_offset + d2];
-                dot_prod += pixel * weight;
-              }
-            }
-          }
-          size_t dx_offset = x_batch_stride * b + x_row_stride * xr +
-                             x_col_stride * xc + x_channel_stride * d1;
-          *(out_buf_ptr + dx_offset) = dot_prod;
-        }
-      }
-    }
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2D_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2D_test.cc
deleted file mode 100644
index 63d30825d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv2D_test.cc
+++ /dev/null
@@ -1,217 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <xnnpack.h>
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/Conv2D.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-TEST(CONV2D, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t size = 8;
-  float x_values[size] = {1, 2, 3, 4, 5, 6, 7, 8};
-
-  const size_t weights0_id = 3;
-  const size_t weights1_id = 4;
-  const size_t weights_size = 8;
-  float weights_values[weights_size] = {1, 2, 3, 4, 5, 6, 7, 8};
-
-  const size_t out_id = 5;
-  const size_t out_size = 12;
-  float out_values[out_size] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, size, x_values);
-  tfjs::wasm::register_tensor(x1_id, size, x_values);
-  tfjs::wasm::register_tensor(weights0_id, weights_size, weights_values);
-  tfjs::wasm::register_tensor(weights1_id, weights_size, weights_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(5, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One xnn_operator should be created for the first call to conv2d.
-  const size_t batch_size = 1;
-  const size_t input_height = 4;
-  const size_t input_width = 2;
-  const size_t filter_height = 4;
-  const size_t filter_width = 2;
-  const size_t pad_top0 = 1;
-  const size_t pad_right = 0;
-  const size_t pad_bottom0 = 0;
-  const size_t pad_left = 0;
-  const bool is_same_pad0 = false;
-  const size_t dilation_height = 1;
-  const size_t dilation_width = 1;
-  const size_t stride_height = 1;
-  const size_t stride_width = 1;
-  const size_t input_channels = 1;
-  const size_t output_channels = 1;
-  tfjs::wasm::Conv2D(x0_id, batch_size, input_height, input_width, weights0_id,
-                     filter_height, filter_width, pad_top0, pad_right,
-                     pad_bottom0, pad_left, is_same_pad0, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to conv2d with
-  // the same arguments.
-  tfjs::wasm::Conv2D(x0_id, batch_size, input_height, input_width, weights0_id,
-                     filter_height, filter_width, pad_top0, pad_right,
-                     pad_bottom0, pad_left, is_same_pad0, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to conv2d with
-  // the same arguments but different input.
-  tfjs::wasm::Conv2D(x1_id, batch_size, input_height, input_width, weights0_id,
-                     filter_height, filter_width, pad_top0, pad_right,
-                     pad_bottom0, pad_left, is_same_pad0, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the next call to conv2d with the
-  // same weights but different arguments.
-  const size_t pad_top1 = 0;
-  const size_t pad_bottom1 = 1;
-  tfjs::wasm::Conv2D(x0_id, batch_size, input_height, input_width, weights0_id,
-                     filter_height, filter_width, pad_top1, pad_right,
-                     pad_bottom1, pad_left, is_same_pad0, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // new weights and same input.
-  tfjs::wasm::Conv2D(x0_id, batch_size, input_height, input_width, weights1_id,
-                     filter_height, filter_width, pad_top0, pad_right,
-                     pad_bottom0, pad_left, is_same_pad0, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, out_id);
-  ASSERT_EQ(3, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // 'SAME' padding.
-  const bool is_same_pad1 = true;
-  tfjs::wasm::Conv2D(x0_id, batch_size, input_height, input_width, weights1_id,
-                     filter_height, filter_width, pad_top0, pad_right,
-                     pad_bottom0, pad_left, is_same_pad1, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, out_id);
-  ASSERT_EQ(4, tfjs::backend::xnn_operator_count);
-
-  // No new XNN operators should be created for the next call to conv2d with
-  // 'SAME' padding and different input and raw padding values.
-  tfjs::wasm::Conv2D(x1_id, batch_size, input_height, input_width, weights1_id,
-                     filter_height, filter_width, pad_top1, pad_right,
-                     pad_bottom1, pad_left, is_same_pad1, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, out_id);
-  ASSERT_EQ(4, tfjs::backend::xnn_operator_count);
-
-  // Disposing the first weights should remove 2 operators.
-  tfjs::wasm::dispose_data(weights0_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing the second weights should remove the last 2 operator.
-  tfjs::wasm::dispose_data(weights1_id);
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
-
-TEST(CONV2D, transposed_filter_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t weights1_id = 2;
-
-  const size_t x_size = 8;
-  const size_t weights_size = 8;
-  std::vector<float> x{1, 2, 3, 4, 5, 6, 7, 8};
-  std::vector<float> weights{1, 2, 3, 4, 5, 6, 7, 8};
-
-  const size_t out_id = 3;
-  const size_t out_size = 12;
-  std::vector<float> out{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, x_size, x.data());
-  tfjs::wasm::register_tensor(weights1_id, weights_size, weights.data());
-  tfjs::wasm::register_tensor(out_id, out_size, out.data());
-
-  ASSERT_EQ(3, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One xnn_operator should be created for the first call to conv2d.
-  const size_t batch_size = 1;
-  const size_t input_height = 4;
-  const size_t input_width = 2;
-  const size_t filter_height = 4;
-  const size_t filter_width = 2;
-  const size_t pad_top0 = 1;
-  const size_t pad_right = 0;
-  const size_t pad_bottom0 = 0;
-  const size_t pad_left = 0;
-  const bool is_same_pad0 = false;
-  const size_t dilation_height = 1;
-  const size_t dilation_width = 1;
-  const size_t stride_height = 1;
-  const size_t stride_width = 1;
-  const size_t input_channels = 1;
-  const size_t output_channels = 1;
-  tfjs::wasm::Conv2D(x0_id, batch_size, input_height, input_width, weights1_id,
-                     filter_height, filter_width, pad_top0, pad_right,
-                     pad_bottom0, pad_left, is_same_pad0, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-  EXPECT_EQ(out, (std::vector<float>{133, 204, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}));
-
-  // Make a new filter in case the memory of the first filter was released and
-  // this filter reuses it.
-  std::vector<float> weights2 = {8, 7, 6, 5, 4, 3, 2, 1};
-  const size_t weights2_id = 4;
-  tfjs::wasm::register_tensor(weights2_id, weights_size, weights2.data());
-
-  // No new xnn_operators should be created for the second call to conv2d with
-  // the same filter.
-  tfjs::wasm::Conv2D(x0_id, batch_size, input_height, input_width, weights1_id,
-                     filter_height, filter_width, pad_top0, pad_right,
-                     pad_bottom0, pad_left, is_same_pad0, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // The output should be the same as the first time since we are still using
-  // the first filter.
-  EXPECT_EQ(out, (std::vector<float>{133, 204, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}));
-
-  tfjs::wasm::dispose_data(weights1_id);
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv3D.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv3D.cc
deleted file mode 100644
index 7fd1e050b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv3D.cc
+++ /dev/null
@@ -1,76 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/conv3d_impl.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x`, `filter`, and `out` must have dtype float32.
-// - Tensor `x` and `out` must be in data format 'NDHWC' (checked in tfjs-core).
-void Conv3D(int x_id, int filter_id, int out_id, int batch_size, int in_depth,
-            int in_height, int in_width, int in_channels, int out_depth,
-            int out_height, int out_width, int out_channels, int stride_depth,
-            int stride_height, int stride_width, int dilation_depth,
-            int dilation_height, int dilation_width, int filter_depth,
-            int filter_height, int filter_width, int pad_front, int pad_top,
-            int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo& filter_info = backend::get_tensor_info(filter_id);
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-
-  NDHWCConv3DImpl</*IN=*/float, /*FILTER=*/float, /*OUT=*/float>(
-      x_info.f32(), filter_info.f32(), out_info.f32_write(),
-      NDHWCConv3DInfo{
-          .batch_size = batch_size,
-          .in_depth = in_depth,
-          .in_height = in_height,
-          .in_width = in_width,
-          .in_channels = in_channels,
-          .out_depth = out_depth,
-          .out_height = out_height,
-          .out_width = out_width,
-          .out_channels = out_channels,
-          .stride_depth = stride_depth,
-          .stride_height = stride_height,
-          .stride_width = stride_width,
-          .dilation_depth = dilation_depth,
-          .dilation_height = dilation_height,
-          .dilation_width = dilation_width,
-          .filter_depth = filter_depth,
-          .filter_height = filter_height,
-          .filter_width = filter_width,
-          .pad_front = pad_front,
-          .pad_top = pad_top,
-          .pad_left = pad_left,
-      });
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv3DBackpropFilterV2.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv3DBackpropFilterV2.cc
deleted file mode 100644
index 11dfe5ede..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv3DBackpropFilterV2.cc
+++ /dev/null
@@ -1,79 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/conv3d_impl.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x`, `dy`, and `dw` must have dtype float32.
-// - Tensor `dy` and `x` must be in data format 'NDHWC' (checked in tfjs-core).
-void Conv3DBackpropFilterV2(int x_id, int dy_id, int dw_id, int batch_size,
-                            int in_depth, int in_height, int in_width,
-                            int in_channels, int out_depth, int out_height,
-                            int out_width, int out_channels, int stride_depth,
-                            int stride_height, int stride_width,
-                            int dilation_depth, int dilation_height,
-                            int dilation_width, int filter_depth,
-                            int filter_height, int filter_width, int pad_front,
-                            int pad_top, int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& dw_info = backend::get_tensor_info_out(dw_id);
-
-  NDHWCConv3DBackpropFilterV2Impl</*IN=*/float, /*FILTER=*/float,
-                                  /*OUT=*/float>(
-      x_info.f32(), dy_info.f32(), dw_info.f32_write(),
-      NDHWCConv3DInfo{
-          .batch_size = batch_size,
-          .in_depth = in_depth,
-          .in_height = in_height,
-          .in_width = in_width,
-          .in_channels = in_channels,
-          .out_depth = out_depth,
-          .out_height = out_height,
-          .out_width = out_width,
-          .out_channels = out_channels,
-          .stride_depth = stride_depth,
-          .stride_height = stride_height,
-          .stride_width = stride_width,
-          .dilation_depth = dilation_depth,
-          .dilation_height = dilation_height,
-          .dilation_width = dilation_width,
-          .filter_depth = filter_depth,
-          .filter_height = filter_height,
-          .filter_width = filter_width,
-          .pad_front = pad_front,
-          .pad_top = pad_top,
-          .pad_left = pad_left,
-      });
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv3DBackpropInputV2.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv3DBackpropInputV2.cc
deleted file mode 100644
index 293c905d9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Conv3DBackpropInputV2.cc
+++ /dev/null
@@ -1,78 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/conv3d_impl.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `filter`, `dy`, and `dx` must have dtype float32.
-// - Tensor `dy` and `dx` must be in data format 'NDHWC' (checked in tfjs-core).
-void Conv3DBackpropInputV2(int filter_id, int dy_id, int dx_id, int batch_size,
-                           int in_depth, int in_height, int in_width,
-                           int in_channels, int out_depth, int out_height,
-                           int out_width, int out_channels, int stride_depth,
-                           int stride_height, int stride_width,
-                           int dilation_depth, int dilation_height,
-                           int dilation_width, int filter_depth,
-                           int filter_height, int filter_width, int pad_front,
-                           int pad_top, int pad_left) {
-  const TensorInfo& filter_info = backend::get_tensor_info(filter_id);
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& dx_info = backend::get_tensor_info_out(dx_id);
-
-  NDHWCConv3DBackpropInputV2Impl</*IN=*/float, /*FILTER=*/float, /*OUT=*/float>(
-      filter_info.f32(), dy_info.f32(), dx_info.f32_write(),
-      NDHWCConv3DInfo{
-          .batch_size = batch_size,
-          .in_depth = in_depth,
-          .in_height = in_height,
-          .in_width = in_width,
-          .in_channels = in_channels,
-          .out_depth = out_depth,
-          .out_height = out_height,
-          .out_width = out_width,
-          .out_channels = out_channels,
-          .stride_depth = stride_depth,
-          .stride_height = stride_height,
-          .stride_width = stride_width,
-          .dilation_depth = dilation_depth,
-          .dilation_height = dilation_height,
-          .dilation_width = dilation_width,
-          .filter_depth = filter_depth,
-          .filter_height = filter_height,
-          .filter_width = filter_width,
-          .pad_front = pad_front,
-          .pad_top = pad_top,
-          .pad_left = pad_left,
-      });
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cos.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cos.cc
deleted file mode 100644
index 28749083f..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cos.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/sin_cos_workaround.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Cos(const int x_id, const DType dtype, const int out_id) {
-  unary_f32(x_id, out_id, tfjs::sin_cos_workaround::CosFixed);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cosh.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cosh.cc
deleted file mode 100644
index d10298ee6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cosh.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Cosh(const int x_id, const DType dtype, const int out_id) {
-  unary_f32(x_id, out_id, cosh);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/CropAndResize.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/CropAndResize.cc
deleted file mode 100644
index 2b184e204..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/CropAndResize.cc
+++ /dev/null
@@ -1,204 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-#include <cstring>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/interpolate_bilinear_impl.h"
-
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-// Must match enum in CropAndResize.ts
-enum InterpolationMethod {
-  BILINEAR = 0,
-  NEAREST = 1,
-};
-
-namespace {
-void interpolate_nearest(float* out_buf_ptr, const float* images_buf,
-                         std::vector<size_t> images_strides, size_t crop_width,
-                         size_t image_width, size_t image_width_m1,
-                         size_t num_channels, float extrapolation_value,
-                         size_t box_offset, float y_ind, float width_scale,
-                         float x1, float x2) {
-  for (size_t x = 0; x < crop_width; ++x) {
-    const float x_ind = (crop_width > 1) ? x1 * image_width_m1 + x * width_scale
-                                         : 0.5 * (x1 + x2) * image_width_m1;
-
-    if (x_ind < 0 || x_ind > image_width - 1) {
-      for (size_t c = 0; c < num_channels; ++c) {
-        *out_buf_ptr = extrapolation_value;
-        out_buf_ptr++;
-      }
-      continue;
-    }
-
-    float closest_x = round(x_ind);
-    float closest_y = round(y_ind);
-    for (size_t c = 0; c < num_channels; ++c) {
-      const size_t in_ind = c + closest_x * images_strides[2] +
-                            closest_y * images_strides[1] + box_offset;
-      *out_buf_ptr = images_buf[in_ind];
-      out_buf_ptr++;
-    }
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void CropAndResize(size_t images_id, size_t boxes_id, size_t box_ind_id,
-                   size_t num_boxes, size_t* images_shape_ptr,
-                   size_t crop_height, size_t crop_width,
-                   InterpolationMethod method, float extrapolation_value,
-                   size_t out_id) {
-  const size_t images_shape_length = 4;
-  const std::vector<size_t>& images_shape = std::vector<size_t>(
-      images_shape_ptr, images_shape_ptr + images_shape_length);
-  const auto images_strides = util::compute_strides(images_shape);
-
-  const std::vector<size_t>& output_shape = {num_boxes, crop_height, crop_width,
-                                             images_shape[3]};
-  const auto output_strides = util::compute_strides(output_shape);
-
-  auto& images_info = backend::get_tensor_info(images_id);
-  auto& boxes_info = backend::get_tensor_info(boxes_id);
-  auto& box_ind_info = backend::get_tensor_info(box_ind_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* images_buf = images_info.f32();
-
-  const float* boxes_buf = boxes_info.f32();
-
-  const int* box_ind_buf = box_ind_info.i32();
-
-  float* out_buf = out_info.f32_write();
-
-  const size_t batch = images_shape[0];
-  const size_t image_height = images_shape[1];
-  const size_t image_width = images_shape[2];
-  const size_t num_channels = images_shape[3];
-
-  const size_t image_height_m1 = image_height - 1;
-  const size_t image_width_m1 = image_width - 1;
-
-  const bool should_extrapolate = true;
-
-  for (size_t b = 0; b < num_boxes; ++b) {
-    const float y1 = *boxes_buf;
-    boxes_buf++;
-    const float x1 = *boxes_buf;
-    boxes_buf++;
-    const float y2 = *boxes_buf;
-    boxes_buf++;
-    const float x2 = *boxes_buf;
-    boxes_buf++;
-
-    if (*box_ind_buf >= batch) {
-      continue;
-    }
-
-    const size_t box_offset = *box_ind_buf * images_strides[0];
-
-    const float height_scale =
-        (crop_height > 1) ? (y2 - y1) * image_height_m1 / (crop_height - 1) : 0;
-    const float width_scale =
-        (crop_width > 1) ? (x2 - x1) * image_width_m1 / (crop_width - 1) : 0;
-
-    const bool crop_size_eq_box_size =
-        crop_width == 1 + (x2 - x1) * image_width_m1;
-    bool requires_interpolation = false;
-    if (method == InterpolationMethod::BILINEAR) {
-      const float y_lerp_factor = crop_height > 1
-                                      ? y1 * image_height + height_scale
-                                      : 0.5 * (y1 + y2) * image_height_m1;
-
-      if (y_lerp_factor - floor(y_lerp_factor) != 0.0) {
-        requires_interpolation = true;
-      } else {
-        const float x_lerp_factor = crop_width > 1
-                                        ? x1 * image_width_m1 + width_scale
-                                        : 0.5 * (x1 + x2) * image_width_m1;
-
-        if (x_lerp_factor - floor(x_lerp_factor) != 0.0) {
-          requires_interpolation = true;
-        }
-      }
-    }
-
-    const bool should_memcpy = x2 > x1 && x1 >= 0 &&
-                               crop_size_eq_box_size == true &&
-                               requires_interpolation == false;
-
-    for (size_t y = 0; y < crop_height; ++y) {
-      const float y_ind = (crop_height > 1)
-                              ? y1 * image_height_m1 + y * height_scale
-                              : 0.5 * (y1 + y2) * image_height_m1;
-
-      float* out_buf_ptr =
-          out_buf + y * output_strides[1] + b * output_strides[0];
-
-      if (y_ind < 0 || y_ind > image_height - 1) {
-        for (size_t x = 0; x < crop_width; ++x) {
-          for (size_t c = 0; c < num_channels; ++c) {
-            *out_buf_ptr = extrapolation_value;
-            out_buf_ptr++;
-          }
-        }
-        continue;
-      }
-
-      if (should_memcpy) {
-        size_t y_ind_int = y_ind;
-        size_t offset = box_offset + y_ind_int * images_strides[1];
-        memcpy(out_buf_ptr, images_buf + offset,
-               sizeof(float) * crop_width * num_channels);
-        continue;
-      }
-
-      if (method == InterpolationMethod::BILINEAR) {
-        tfjs::wasm::interpolate_bilinear(
-            out_buf_ptr, images_buf, images_strides, crop_width, image_width,
-            image_width_m1, image_height_m1, num_channels, should_extrapolate,
-            extrapolation_value, box_offset, y_ind, width_scale, x1, x2);
-
-      } else {
-        interpolate_nearest(out_buf_ptr, images_buf, images_strides, crop_width,
-                            image_width, image_width_m1, num_channels,
-                            extrapolation_value, box_offset, y_ind, width_scale,
-                            x1, x2);
-      }
-    }
-
-    box_ind_buf++;
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cumprod.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cumprod.cc
deleted file mode 100644
index 13780a17b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cumprod.cc
+++ /dev/null
@@ -1,74 +0,0 @@
-/* Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include <cstdint>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-
-template <typename T>
-void cumprod(const size_t x_id, const size_t exclusive, const size_t reverse,
-            const size_t final_dim, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const T* x_buf = reinterpret_cast<const T*>(x_info.memory_offset);
-  T* out_buf = reinterpret_cast<T*>(out_info.memory_offset);
-
-  for (size_t i = 0; i < x_info.size; i += final_dim) {
-    for (size_t j = 0; j < final_dim; ++j) {
-      const size_t idx = reverse ? i + final_dim - j - 1 : i + j;
-      if (j == 0) {
-        out_buf[idx] = exclusive ? 1 : x_buf[idx];
-      } else {
-        const size_t prev_idx = reverse ? idx + 1 : idx - 1;
-        out_buf[idx] = exclusive ? x_buf[prev_idx] * out_buf[prev_idx] :
-                                   x_buf[idx] * out_buf[prev_idx];
-      }
-    }
-  }
-}
-
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Cumprod(const size_t x_id, const size_t exclusive, const size_t reverse,
-            const size_t final_dim, const size_t out_id, const DType dtype) {
-  switch (dtype) {
-    case DType::float32:
-      cumprod<float>(x_id, exclusive, reverse, final_dim, out_id);
-      break;
-    case DType::int32:
-      cumprod<int32_t>(x_id, exclusive, reverse, final_dim, out_id);
-      break;
-    default:
-      util::warn("Cumprod for tensor id %d failed. Unsupported dtype %d",
-                 x_id, dtype);
-  }
-}
-
-}   // extern "C"
-}   // namespace wasm
-}   // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cumsum.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cumsum.cc
deleted file mode 100644
index 7581a79cb..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Cumsum.cc
+++ /dev/null
@@ -1,74 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include <cstdint>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-
-template <typename T>
-void cumsum(const size_t x_id, const size_t exclusive, const size_t reverse,
-            const size_t final_dim, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const T* x_buf = reinterpret_cast<const T*>(x_info.memory_offset);
-  T* out_buf = reinterpret_cast<T*>(out_info.memory_offset);
-
-  for (size_t i = 0; i < x_info.size; i += final_dim) {
-    for (size_t j = 0; j < final_dim; ++j) {
-      const size_t idx = reverse ? i + final_dim - j - 1 : i + j;
-      if (j == 0) {
-        out_buf[idx] = exclusive ? 0 : x_buf[idx];
-      } else {
-        const size_t prev_idx = reverse ? idx + 1 : idx - 1;
-        out_buf[idx] = exclusive ? x_buf[prev_idx] + out_buf[prev_idx] :
-                                   x_buf[idx] + out_buf[prev_idx];
-      }
-    }
-  }
-}
-
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Cumsum(const size_t x_id, const size_t exclusive, const size_t reverse,
-            const size_t final_dim, const size_t out_id, const DType dtype) {
-  switch (dtype) {
-    case DType::float32:
-      cumsum<float>(x_id, exclusive, reverse, final_dim, out_id);
-      break;
-    case DType::int32:
-      cumsum<int32_t>(x_id, exclusive, reverse, final_dim, out_id);
-      break;
-    default:
-      util::warn("Cumsum for tensor id %d failed. Unsupported dtype %d",
-                 x_id, dtype);
-  }
-}
-
-}   // extern "C"
-}   // namespace wasm
-}   // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DenseBincount.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DenseBincount.cc
deleted file mode 100644
index e47d465f9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DenseBincount.cc
+++ /dev/null
@@ -1,111 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/bincount_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-template <typename T>
-inline void Bincount2D(const int32_t* x_buf, int32_t x_shape_0,
-                       int32_t x_shape_1, int32_t size, const T* weight_buf,
-                       bool binary_output, T* out_buf) {
-  std::fill(out_buf, out_buf + (x_shape_0 * size), 0);
-  for (int32_t i = 0; i < x_shape_0; ++i) {
-    BincountImpl</*reset_out_buf=*/false>(
-        x_buf + i * x_shape_1, x_shape_1, size,
-        weight_buf != nullptr ? weight_buf + i * x_shape_1 : nullptr,
-        binary_output, out_buf + i * size);
-  }
-}
-
-}  // namespace
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x` must have dtype int32 (checked in tfjs-core)
-// - Tensor `x` must be 1D or 2D (checked in tfjs-core)
-// - If has_weights is true, tensor `weights` must have the same shape as `x`
-// (checked in tfjs-core)
-// - Tensor `out` must have shape [x.shape[0], size] or [size]
-// - Tensor `out` must have the same dtype as weights
-void DenseBincount(const int32_t x_id, const int32_t* x_shape_ptr,
-                   const int32_t x_shape_len, const int32_t size,
-                   const bool has_weights, const int32_t weights_id,
-                   const DType weights_dtype, const bool binary_output,
-                   const int32_t out_id) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo* weights_info =
-      has_weights ? &backend::get_tensor_info(weights_id) : nullptr;
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-
-  const int32_t* x_buf = x_info.i32();
-  if (x_shape_len == 1) {
-    switch (weights_dtype) {
-      case DType::float32:
-        BincountImpl(x_buf, x_shape_ptr[0], size,
-                     weights_info ? weights_info->f32() : nullptr,
-                     binary_output, out_info.f32_write());
-        break;
-      case DType::int32:
-        BincountImpl(x_buf, x_shape_ptr[0], size,
-                     weights_info ? weights_info->i32() : nullptr,
-                     binary_output, out_info.i32_write());
-        break;
-      default:
-        util::warn(
-            "DenseBincount with weights tensor id %d failed. Unsupported "
-            "weights "
-            "dtype %d",
-            weights_id, weights_dtype);
-    }
-    return;
-  }
-
-  // x_shape_len == 2
-  switch (weights_dtype) {
-    case DType::float32:
-      Bincount2D(x_buf, x_shape_ptr[0], x_shape_ptr[1], size,
-                 weights_info ? weights_info->f32() : nullptr, binary_output,
-                 out_info.f32_write());
-      break;
-    case DType::int32:
-      Bincount2D(x_buf, x_shape_ptr[0], x_shape_ptr[1], size,
-                 weights_info ? weights_info->i32() : nullptr, binary_output,
-                 out_info.i32_write());
-      break;
-    default:
-      util::warn(
-          "DenseBincount with weights tensor id %d failed. Unsupported "
-          "weights "
-          "dtype %d",
-          weights_id, weights_dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthToSpace.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthToSpace.cc
deleted file mode 100644
index aa649b395..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthToSpace.cc
+++ /dev/null
@@ -1,75 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void DepthToSpace(const size_t x_id, const size_t block_size,
-                  const bool channels_last, const int32_t* x_strides_ptr,
-                  const size_t x_strides_size, const int32_t* out_shape_ptr,
-                  const int32_t* out_strides_ptr, const size_t out_shape_size,
-                  const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  const size_t out_size = out_info.size;
-
-  const float* x_ptr = x_info.f32();
-  float* out_buf_ptr = out_info.f32_write();
-
-  const auto x_strides =
-      std::vector<size_t>(x_strides_ptr, x_strides_ptr + x_strides_size);
-  const auto out_shape =
-      std::vector<size_t>(out_shape_ptr, out_shape_ptr + out_shape_size);
-  const auto out_strides = std::vector<size_t>(
-      out_strides_ptr, out_strides_ptr + out_shape_size - 1);
-
-  for (size_t i = 0; i < out_size; ++i) {
-    auto coords = tfjs::util::offset_to_loc(i, out_strides);
-    const size_t b = coords[0];
-    const size_t h = channels_last ? coords[1] : coords[2];
-    const size_t w = channels_last ? coords[2] : coords[3];
-    const size_t d = channels_last ? coords[3] : coords[1];
-    const size_t out_depth_size = channels_last ? out_shape[3] : out_shape[1];
-
-    const size_t in_h = h / block_size;
-    const size_t offset_h = h % block_size;
-    const size_t in_w = w / block_size;
-    const size_t offset_w = w % block_size;
-    const size_t offset_d = (offset_h * block_size + offset_w) * out_depth_size;
-
-    const size_t in_d = d + offset_d;
-
-    size_t x_index =
-        channels_last
-            ? tfjs::util::loc_to_offset({b, in_h, in_w, in_d}, x_strides)
-            : tfjs::util::loc_to_offset({b, in_d, in_h, in_w}, x_strides);
-    *out_buf_ptr = x_ptr[x_index];
-
-    out_buf_ptr++;
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative.cc
deleted file mode 100644
index 93fc017d9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative.cc
+++ /dev/null
@@ -1,57 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/conv2d_impl.h"
-#include "tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void DepthwiseConv2dNative(
-    const size_t x_id, const size_t batch_size, const size_t input_height,
-    const size_t input_width, const size_t filter_id,
-    const size_t filter_height, const size_t filter_width, size_t pad_top,
-    size_t pad_right, size_t pad_bottom, size_t pad_left,
-    const size_t is_same_pad, const size_t dilation_height,
-    const size_t dilation_width, const size_t stride_height,
-    const size_t stride_width, const size_t input_channels,
-    const size_t output_channels, const size_t out_id) {
-  const size_t bias_id = 0;
-  const size_t prelu_weights_id = 0;
-  const float leakyrelu_alpha = 0;
-  const bool is_depthwise = true;
-  const FusableActivation activation = FusableActivation::LINEAR;
-  tfjs::wasm::conv2d(x_id, batch_size, input_height, input_width, filter_id,
-                     filter_height, filter_width, bias_id, pad_top, pad_right,
-                     pad_bottom, pad_left, is_same_pad, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, is_depthwise, activation,
-                     prelu_weights_id, leakyrelu_alpha, out_id);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative.h
deleted file mode 100644
index f9e854633..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative.h
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_DEPTHWISECONV2DNATIVE_H_
-#define KERNELS_DEPTHWISECONV2DNATIVE_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void DepthwiseConv2dNative(
-    const size_t x_id, const size_t batch_size, const size_t input_height,
-    const size_t input_width, const size_t filter_id,
-    const size_t filter_height, const size_t filter_width, size_t pad_top,
-    size_t pad_right, size_t pad_bottom, size_t pad_left,
-    const size_t is_same_pad, const size_t dilation_height,
-    const size_t dilation_width, const size_t stride_height,
-    const size_t stride_width, const size_t input_channels,
-    const size_t output_channels, const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_DEPTHWISECONV2DNATIVE_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative_test.cc
deleted file mode 100644
index 241f1930d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative_test.cc
+++ /dev/null
@@ -1,142 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-#include <xnnpack.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/DepthwiseConv2dNative.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-TEST(CONV2D, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t size = 9;
-  float x_values[size] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
-
-  const size_t weights0_id = 3;
-  const size_t weights1_id = 4;
-  const size_t weights_size = 8;
-  float weights_values[weights_size] = {1, 2, 3, 4, 5, 6, 7, 8};
-
-  const size_t out_id = 5;
-  const size_t out_size = 36;
-  float out_values[out_size] = {};
-
-  tfjs::wasm::register_tensor(x0_id, size, x_values);
-  tfjs::wasm::register_tensor(x1_id, size, x_values);
-  tfjs::wasm::register_tensor(weights0_id, weights_size, weights_values);
-  tfjs::wasm::register_tensor(weights1_id, weights_size, weights_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(5, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One xnn_operator should be created for the first call to depthwiseConv2d.
-  const size_t batch_size = 1;
-  const size_t input_height = 4;
-  const size_t input_width = 2;
-  const size_t filter_height = 4;
-  const size_t filter_width = 2;
-  const size_t pad_top0 = 1;
-  const size_t pad_right = 0;
-  const size_t pad_bottom0 = 0;
-  const size_t pad_left = 0;
-  const bool is_same_pad0 = false;
-  const size_t dilation_height = 1;
-  const size_t dilation_width = 1;
-  const size_t stride_height = 1;
-  const size_t stride_width = 1;
-  const size_t input_channels = 1;
-  const size_t output_channels = 1;
-  tfjs::wasm::DepthwiseConv2dNative(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, pad_top0, pad_right, pad_bottom0, pad_left, is_same_pad0,
-      dilation_height, dilation_width, stride_height, stride_width,
-      input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to
-  // depthwiseConv2d with the same arguments.
-  tfjs::wasm::DepthwiseConv2dNative(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, pad_top0, pad_right, pad_bottom0, pad_left, is_same_pad0,
-      dilation_height, dilation_width, stride_height, stride_width,
-      input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to
-  // depthwiseConv2d with the same arguments but different input.
-  tfjs::wasm::DepthwiseConv2dNative(
-      x1_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, pad_top0, pad_right, pad_bottom0, pad_left, is_same_pad0,
-      dilation_height, dilation_width, stride_height, stride_width,
-      input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the next call to depthwiseConv2d
-  // with the same weights but different arguments.
-  const size_t pad_top1 = 0;
-  const size_t pad_bottom1 = 1;
-  tfjs::wasm::DepthwiseConv2dNative(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, pad_top1, pad_right, pad_bottom1, pad_left, is_same_pad0,
-      dilation_height, dilation_width, stride_height, stride_width,
-      input_channels, output_channels, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to
-  // depthwiseConv2d with new weights and same input.
-  tfjs::wasm::DepthwiseConv2dNative(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, pad_top0, pad_right, pad_bottom0, pad_left, is_same_pad0,
-      dilation_height, dilation_width, stride_height, stride_width,
-      input_channels, output_channels, out_id);
-  ASSERT_EQ(3, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to
-  // depthwiseConv2d with 'SAME' padding.
-  const bool is_same_pad1 = true;
-  tfjs::wasm::DepthwiseConv2dNative(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, pad_top0, pad_right, pad_bottom0, pad_left, is_same_pad1,
-      dilation_height, dilation_width, stride_height, stride_width,
-      input_channels, output_channels, out_id);
-  ASSERT_EQ(4, tfjs::backend::xnn_operator_count);
-
-  // No new XNN operators should be created for the next call to depthwiseConv2d
-  // with 'SAME' padding and different input and raw padding values.
-  tfjs::wasm::DepthwiseConv2dNative(
-      x1_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, pad_top1, pad_right, pad_bottom1, pad_left, is_same_pad1,
-      dilation_height, dilation_width, stride_height, stride_width,
-      input_channels, output_channels, out_id);
-  ASSERT_EQ(4, tfjs::backend::xnn_operator_count);
-
-  // Disposing the first weights should remove 2 operators.
-  tfjs::wasm::dispose_data(weights0_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing the second weights should remove the last 2 operator.
-  tfjs::wasm::dispose_data(weights1_id);
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Diag.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Diag.cc
deleted file mode 100644
index 15ae48a6b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Diag.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-
-namespace {
-
-template <typename T>
-inline void DiagImpl(const T* x_buf, int32_t x_size, T* out_buf) {
-  std::fill(out_buf, out_buf + x_size * x_size, 0);
-  for (int32_t i = 0; i < x_size; ++i) {
-    out_buf[x_size * i + i] = x_buf[i];
-  }
-}
-
-}  // namespace
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Diag(const int32_t x_id, const DType dtype, const int32_t x_size,
-          const int32_t out_id) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-  switch (dtype) {
-    case DType::float32:
-      DiagImpl(x_info.f32(), x_size, out_info.f32_write());
-      break;
-    case DType::int32:
-      DiagImpl(x_info.i32(), x_size, out_info.i32_write());
-      break;
-    case DType::boolean:
-      DiagImpl(x_info.b(), x_size, out_info.b_write());
-      break;
-    default:
-      util::warn("Diag for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Dilation2D.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Dilation2D.cc
deleted file mode 100644
index 411c801f0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Dilation2D.cc
+++ /dev/null
@@ -1,80 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <utility>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/dilation2d_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x_id`, `filter_id`, and `out` must have the same dtype.
-void Dilation2D(int x_id, int filter_id, int out_id, DType dtype, int batch,
-                int depth, int in_height, int in_width, int out_height,
-                int out_width, int stride_height, int stride_width,
-                int dilation_height, int dilation_width, int filter_height,
-                int filter_width, int pad_top, int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo& filter_info = backend::get_tensor_info(filter_id);
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-
-  Dilation2DInfo info{
-      .batch = batch,
-      .depth = depth,
-      .in_height = in_height,
-      .in_width = in_width,
-      .out_height = out_height,
-      .out_width = out_width,
-      .stride_height = stride_height,
-      .stride_width = stride_width,
-      .dilation_height = dilation_height,
-      .dilation_width = dilation_width,
-      .filter_height = filter_height,
-      .filter_width = filter_width,
-      .pad_top = pad_top,
-      .pad_left = pad_left,
-  };
-
-  switch (dtype) {
-    case DType::float32:
-      Dilation2DImpl(x_info.f32(), filter_info.f32(), out_info.f32_write(),
-                     info);
-      break;
-    case DType::int32:
-      Dilation2DImpl(x_info.i32(), filter_info.i32(), out_info.i32_write(),
-                     info);
-      break;
-    default:
-      util::warn("Dilation2D for tensor id %d failed. Unsupported dtype %d",
-                 x_id, dtype);
-      break;
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Dilation2DBackpropFilter.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Dilation2DBackpropFilter.cc
deleted file mode 100644
index cd2524dcd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Dilation2DBackpropFilter.cc
+++ /dev/null
@@ -1,85 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <utility>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/dilation2d_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x_id`, `filter_id`, `dy_id`, and `grad` must have the same dtype.
-void Dilation2DBackpropFilter(int x_id, int filter_id, int dy_id, int grad_id,
-                              DType dtype, int batch, int depth, int in_height,
-                              int in_width, int out_height, int out_width,
-                              int stride_height, int stride_width,
-                              int dilation_height, int dilation_width,
-                              int filter_height, int filter_width, int pad_top,
-                              int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo& filter_info = backend::get_tensor_info(filter_id);
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& grad_info = backend::get_tensor_info_out(grad_id);
-
-  Dilation2DInfo info{
-      .batch = batch,
-      .depth = depth,
-      .in_height = in_height,
-      .in_width = in_width,
-      .out_height = out_height,
-      .out_width = out_width,
-      .stride_height = stride_height,
-      .stride_width = stride_width,
-      .dilation_height = dilation_height,
-      .dilation_width = dilation_width,
-      .filter_height = filter_height,
-      .filter_width = filter_width,
-      .pad_top = pad_top,
-      .pad_left = pad_left,
-  };
-
-  switch (dtype) {
-    case DType::float32:
-      Dilation2DBackpropFilterImpl(x_info.f32(), filter_info.f32(),
-                                   dy_info.f32(), grad_info.f32_write(), info);
-      break;
-    case DType::int32:
-      Dilation2DBackpropFilterImpl(x_info.i32(), filter_info.i32(),
-                                   dy_info.i32(), grad_info.i32_write(), info);
-      break;
-    default:
-      util::warn(
-          "Dilation2DBackpropFilter for tensor id %d failed. Unsupported dtype "
-          "%d",
-          x_id, dtype);
-      break;
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Dilation2DBackpropInput.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Dilation2DBackpropInput.cc
deleted file mode 100644
index e576ba7fd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Dilation2DBackpropInput.cc
+++ /dev/null
@@ -1,85 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <utility>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/dilation2d_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x_id`, `filter_id`, `dy_id`, and `grad` must have the same dtype.
-void Dilation2DBackpropInput(int x_id, int filter_id, int dy_id, int grad_id,
-                             DType dtype, int batch, int depth, int in_height,
-                             int in_width, int out_height, int out_width,
-                             int stride_height, int stride_width,
-                             int dilation_height, int dilation_width,
-                             int filter_height, int filter_width, int pad_top,
-                             int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo& filter_info = backend::get_tensor_info(filter_id);
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& grad_info = backend::get_tensor_info_out(grad_id);
-
-  Dilation2DInfo info{
-      .batch = batch,
-      .depth = depth,
-      .in_height = in_height,
-      .in_width = in_width,
-      .out_height = out_height,
-      .out_width = out_width,
-      .stride_height = stride_height,
-      .stride_width = stride_width,
-      .dilation_height = dilation_height,
-      .dilation_width = dilation_width,
-      .filter_height = filter_height,
-      .filter_width = filter_width,
-      .pad_top = pad_top,
-      .pad_left = pad_left,
-  };
-
-  switch (dtype) {
-    case DType::float32:
-      Dilation2DBackpropInputImpl(x_info.f32(), filter_info.f32(),
-                                  dy_info.f32(), grad_info.f32_write(), info);
-      break;
-    case DType::int32:
-      Dilation2DBackpropInputImpl(x_info.i32(), filter_info.i32(),
-                                  dy_info.i32(), grad_info.i32_write(), info);
-      break;
-    default:
-      util::warn(
-          "Dilation2DBackpropInput for tensor id %d failed. Unsupported dtype "
-          "%d",
-          x_id, dtype);
-      break;
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Elu.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Elu.cc
deleted file mode 100644
index 94398ab4b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Elu.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/elu_impl.h"
-
-namespace tfjs::wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES
-// - Tensor `x` and `out` must have dtype float32 (checked in tfjs-core)
-void Elu(const int x_id, DType, const int out_id) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-
-  tfjs::wasm::EluImpl(x_info.f32(), x_info.size, out_info.f32_write());
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/EluGrad.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/EluGrad.cc
deleted file mode 100644
index fd1f896ef..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/EluGrad.cc
+++ /dev/null
@@ -1,49 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/elu_impl.h"
-
-namespace tfjs::wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES
-// - Tensor `y`, `dy`, and `out` must have dtype float32 (checked in tfjs-core)
-// - Tensor `y` and `dy` must have the same shape (checked in tfjs-core)
-void EluGrad(const int y_id, const int dy_id, const int out_id) {
-  const size_t y_size = backend::get_tensor_info(y_id).size;
-  const float* y_buf = backend::get_tensor_info(y_id).f32();
-  const float* dy_buf = backend::get_tensor_info(dy_id).f32();
-  float* out_buf = backend::get_tensor_info_out(out_id).f32_write();
-
-  for (size_t i = 0; i < y_size; ++i) {
-    if (y_buf[i] >= 0) {
-      out_buf[i] = dy_buf[i];
-    } else {
-      out_buf[i] = dy_buf[i] * (y_buf[i] + 1.0);
-    }
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Equal.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Equal.cc
deleted file mode 100644
index 335caf4e8..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Equal.cc
+++ /dev/null
@@ -1,64 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline bool equal(T a, T b) {
-  return a == b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Equal(const size_t a_id, const size_t* a_shape_ptr,
-           const size_t a_shape_len, const size_t b_id,
-           const size_t* b_shape_ptr, const size_t b_shape_len,
-           const DType input_type, const size_t out_id) {
-  switch (input_type) {
-    case DType::float32:
-      compare_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, equal<float>);
-      break;
-    case DType::int32:
-      compare_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, equal<int>);
-      break;
-    case DType::boolean:
-      compare_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                   b_shape_len, out_id, equal<bool>);
-      break;
-    default:
-      util::warn(
-          "Equal for tensor ids %d and %d failed. Unsupported input_type %d",
-          a_id, b_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Erf.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Erf.cc
deleted file mode 100644
index 70fe654b0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Erf.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-
-namespace {
-template <typename T>
-inline T ErfImpl(T n) {
-  return static_cast<T>(std::erff(static_cast<float>(n)));
-}
-}  // namespace
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Erf(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, ErfImpl<float>);
-      break;
-    default:
-      util::warn("Erf for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Exp.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Exp.cc
deleted file mode 100644
index ec3e028cd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Exp.cc
+++ /dev/null
@@ -1,49 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Exp(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, std::exp);
-      break;
-    case DType::int32:
-      unary_i32_with_f32_out(
-          x_id, out_id, [](int a) { return std::exp(static_cast<float>(a)); });
-      break;
-    default:
-      util::warn("Exp failed. Unknown dtype %d", dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Expm1.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Expm1.cc
deleted file mode 100644
index 72ac17629..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Expm1.cc
+++ /dev/null
@@ -1,57 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline float expm1_impl(T n) {
-  return std::expm1f(static_cast<float>(n));
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Expm1(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, expm1_impl<float>);
-      break;
-    case DType::int32:
-      unary_i32_with_f32_out(x_id, out_id, expm1_impl<int>);
-      break;
-    default:
-      util::warn("Expm1 for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FlipLeftRight.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FlipLeftRight.cc
deleted file mode 100644
index 3133c212e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FlipLeftRight.cc
+++ /dev/null
@@ -1,77 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-#include <cmath>
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void FlipLeftRight(const size_t image_id, const size_t batch,
-                   const size_t image_height, const size_t image_width,
-                   const size_t num_channels, const size_t out_id) {
-  auto& image_info = backend::get_tensor_info(image_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* image_buf = image_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  for (size_t batch_idx = 0; batch_idx < batch; ++batch_idx) {
-    const size_t batch_offset =
-        batch_idx * image_width * image_height * num_channels;
-    for (size_t row = 0; row < image_height; ++row) {
-      const size_t row_offset = row * (image_width * num_channels);
-      for (size_t col = 0; col < image_width; ++col) {
-        const size_t col_offset = col * num_channels;
-
-        for (size_t channel = 0; channel < num_channels; ++channel) {
-          const size_t coord_x = image_width - col - 1;
-          const size_t image_idx =
-              batch_offset + row_offset + col_offset + channel;
-
-          float output_value = image_buf[image_idx];
-          // If the coordinate position falls within the image boundaries...
-          if (coord_x >= 0 && coord_x < image_width) {
-            const size_t flipped_col_offset = coord_x * num_channels;
-            const size_t rotated_image_idx =
-                batch_offset + row_offset + flipped_col_offset + channel;
-            output_value = image_buf[rotated_image_idx];
-          }
-
-          *out_buf = output_value;
-          out_buf++;
-        }
-      }
-    }
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Floor.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Floor.cc
deleted file mode 100644
index 00e9a1010..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Floor.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Floor(const size_t x_id, const DType dtype, const size_t out_id) {
-  unary_xnn_f32(x_id, out_id, xnn_create_floor_nc_f32, xnn_setup_floor_nc_f32);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FloorDiv.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FloorDiv.cc
deleted file mode 100644
index 620afa91c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FloorDiv.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void FloorDiv(const size_t a_id, const size_t* a_shape_ptr,
-              const size_t a_shape_len, const size_t b_id,
-              const size_t* b_shape_ptr, const size_t b_shape_len,
-              const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, [](float a, float b) {
-                   return static_cast<float>(floor(a / b));
-                 });
-      break;
-    case DType::int32:
-      binary_i32(
-          a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-          out_id, [](int a, int b) {
-            return static_cast<int32_t>(floor(static_cast<float>(a) / b));
-          });
-      break;
-    default:
-      util::warn(
-          "FloorDiv for tensor ids %d and %d failed. Unsupported dtype %d",
-          a_id, b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedBatchNorm.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedBatchNorm.cc
deleted file mode 100644
index 9da8bf414..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedBatchNorm.cc
+++ /dev/null
@@ -1,112 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void FusedBatchNorm(const size_t x_id, const size_t mean_id,
-                    const size_t variance_id, const size_t offset_id,
-                    const size_t scale_id, const float variance_epsilon,
-                    const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& mean_info = backend::get_tensor_info(mean_id);
-  auto& variance_info = backend::get_tensor_info(variance_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* x_buf = x_info.f32();
-  const size_t x_size = x_info.size;
-  const float* mean_buf = mean_info.f32();
-  const size_t mean_size = mean_info.size;
-  const float* variance_buf = variance_info.f32();
-  const size_t variance_size = variance_info.size;
-
-  float* out_buf = out_info.f32_write();
-
-  size_t offset_i = 0;
-  size_t mean_i = 0;
-  size_t scale_i = 0;
-  size_t variance_i = 0;
-
-  const float scale_buf_default[1] = {1};
-  const float* scale_buf;
-  size_t scale_size;
-  if (scale_id == 0) {
-    scale_buf = scale_buf_default;
-    scale_size = 1;
-  } else {
-    auto& scale_info = backend::get_tensor_info(scale_id);
-    scale_buf = scale_info.f32();
-    scale_size = scale_info.size;
-  }
-
-  const float offset_buf_default[1] = {0};
-  const float* offset_buf;
-  size_t offset_size;
-  if (offset_id == 0) {
-    offset_buf = offset_buf_default;
-    offset_size = 1;
-  } else {
-    auto& offset_info = backend::get_tensor_info(offset_id);
-    offset_buf = offset_info.f32();
-    offset_size = offset_info.size;
-  }
-
-  std::vector<double> normalization_factor(variance_size);
-  for (size_t i = 0; i < variance_size; ++i) {
-    normalization_factor[i] = std::sqrt(variance_buf[i] + variance_epsilon);
-  }
-
-  for (size_t i = 0; i < x_size; ++i) {
-    out_buf[i] = offset_buf[offset_i] + (x_buf[i] - mean_buf[mean_i]) *
-                                            scale_buf[scale_i] /
-                                            normalization_factor[variance_i];
-
-    ++offset_i;
-    ++mean_i;
-    ++scale_i;
-    ++variance_i;
-
-    if (offset_i >= offset_size) {
-      offset_i = 0;
-    }
-    if (mean_i >= mean_size) {
-      mean_i = 0;
-    }
-    if (scale_i >= scale_size) {
-      scale_i = 0;
-    }
-    if (variance_i >= variance_size) {
-      variance_i = 0;
-    }
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedConv2D.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedConv2D.cc
deleted file mode 100644
index a5b876665..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedConv2D.cc
+++ /dev/null
@@ -1,56 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/kernels/FusedConv2D.h"
-
-#include "tfjs-backend-wasm/src/cc/conv2d_impl.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void FusedConv2D(const size_t x_id, const size_t batch_size,
-                 const size_t input_height, const size_t input_width,
-                 const size_t filter_id, const size_t filter_height,
-                 const size_t filter_width, const size_t bias_id,
-                 size_t pad_top, size_t pad_right, size_t pad_bottom,
-                 size_t pad_left, const size_t is_same_pad,
-                 const size_t dilation_height, const size_t dilation_width,
-                 const size_t stride_height, const size_t stride_width,
-                 const size_t input_channels, const size_t output_channels,
-                 const FusableActivation activation,
-                 const size_t prelu_weights_id, const float leakyrelu_alpha,
-                 const size_t out_id) {
-  const bool is_depthwise = false;
-  tfjs::wasm::conv2d(x_id, batch_size, input_height, input_width, filter_id,
-                     filter_height, filter_width, bias_id, pad_top, pad_right,
-                     pad_bottom, pad_left, is_same_pad, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, is_depthwise, activation,
-                     prelu_weights_id, leakyrelu_alpha, out_id);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedConv2D.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedConv2D.h
deleted file mode 100644
index efcf1a856..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedConv2D.h
+++ /dev/null
@@ -1,43 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_FUSEDCONV2D_H_
-#define KERNELS_FUSEDCONV2D_H_
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/conv2d_impl.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void FusedConv2D(const size_t x_id, const size_t batch_size,
-                 const size_t input_height, const size_t input_width,
-                 const size_t filter_id, const size_t filter_height,
-                 const size_t filter_width, const size_t bias_id,
-                 size_t pad_top, size_t pad_right, size_t pad_bottom,
-                 size_t pad_left, const size_t is_same_pad,
-                 const size_t dilation_height, const size_t dilation_width,
-                 const size_t stride_height, const size_t stride_width,
-                 const size_t input_channels, const size_t output_channels,
-                 const FusableActivation activation,
-                 const size_t prelu_weights_id, const float leakyrelu_alpha,
-                 const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_FUSEDCONV2D_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedConv2D_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedConv2D_test.cc
deleted file mode 100644
index f7d96a609..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedConv2D_test.cc
+++ /dev/null
@@ -1,200 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-#include <xnnpack.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/FusedConv2D.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-TEST(FUSEDCONV2D, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t size = 8;
-  float x_values[size] = {1, 2, 3, 4, 5, 6, 7, 8};
-
-  const size_t weights0_id = 3;
-  const size_t weights1_id = 4;
-  const size_t weights_size = 8;
-  float weights_values[weights_size] = {1, 2, 3, 4, 5, 6, 7, 8};
-
-  const size_t bias0_id = 5;
-  const size_t bias1_id = 6;
-  const size_t bias_size = 1;
-  float bias_values[bias_size] = {1};
-
-  const size_t out_id = 7;
-  const size_t out_size = 12;
-  float out_values[out_size] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, size, x_values);
-  tfjs::wasm::register_tensor(x1_id, size, x_values);
-  tfjs::wasm::register_tensor(weights0_id, weights_size, weights_values);
-  tfjs::wasm::register_tensor(weights1_id, weights_size, weights_values);
-  tfjs::wasm::register_tensor(bias0_id, bias_size, bias_values);
-  tfjs::wasm::register_tensor(bias1_id, bias_size, bias_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(7, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One xnn_operator should be created for the first call to conv2d with no
-  // bias.
-  const size_t batch_size = 1;
-  const size_t input_height = 4;
-  const size_t input_width = 2;
-  const size_t filter_height = 4;
-  const size_t filter_width = 2;
-  const size_t pad_top0 = 1;
-  const size_t pad_right = 0;
-  const size_t pad_bottom0 = 0;
-  const size_t pad_left = 0;
-  const bool is_same_pad0 = false;
-  const size_t dilation_height = 1;
-  const size_t dilation_width = 1;
-  const size_t stride_height = 1;
-  const size_t stride_width = 1;
-  const size_t input_channels = 1;
-  const size_t output_channels = 1;
-
-  const FusableActivation activation = FusableActivation::LINEAR;
-  const size_t prelu_weights_id = 0;
-
-  tfjs::wasm::FusedConv2D(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, 0 /* bias */, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to conv2d with
-  // the same arguments.
-  tfjs::wasm::FusedConv2D(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, 0 /* bias */, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to conv2d with
-  // the same arguments but different input.
-  tfjs::wasm::FusedConv2D(
-      x1_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, 0 /* bias */, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the next call to conv2d with the
-  // same weights and bias but different arguments.
-  const size_t pad_top1 = 0;
-  const size_t pad_bottom1 = 1;
-  tfjs::wasm::FusedConv2D(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, 0 /* bias */, pad_top1, pad_right, pad_bottom1, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // new weights and same input.
-  tfjs::wasm::FusedConv2D(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, 0 /* bias */, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(3, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // bias, same input, same weights.
-  tfjs::wasm::FusedConv2D(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias0_id, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(4, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // new bias, same input, same weights.
-  tfjs::wasm::FusedConv2D(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias1_id, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(5, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // 'SAME' padding.
-  const bool is_same_pad1 = true;
-  tfjs::wasm::FusedConv2D(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias1_id, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad1, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(6, tfjs::backend::xnn_operator_count);
-
-  // No new XNN operators should be created for the next call to conv2d with
-  // 'SAME' padding and different input and raw padding values.
-  tfjs::wasm::FusedConv2D(
-      x1_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias1_id, pad_top1, pad_right, pad_bottom1, pad_left,
-      is_same_pad1, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(6, tfjs::backend::xnn_operator_count);
-
-  // One new XNN operator should be created for the next call to conv2d with a
-  // different activation.
-  const FusableActivation activation2 = FusableActivation::RELU6;
-  tfjs::wasm::FusedConv2D(
-      x1_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias1_id, pad_top1, pad_right, pad_bottom1, pad_left,
-      is_same_pad1, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation2,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(7, tfjs::backend::xnn_operator_count);
-
-  // Disposing the first weights should remove 2 operators.
-  tfjs::wasm::dispose_data(weights0_id);
-  ASSERT_EQ(5, tfjs::backend::xnn_operator_count);
-
-  // Disposing the second bias should remove 2 operators it's associated with.
-  tfjs::wasm::dispose_data(bias1_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing the first bias should remove one operator.
-  tfjs::wasm::dispose_data(bias0_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // Disposing the second weights should remove the last operator (no bias).
-  tfjs::wasm::dispose_data(weights1_id);
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D.cc
deleted file mode 100644
index 90dd6543a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D.cc
+++ /dev/null
@@ -1,56 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D.h"
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/conv2d_impl.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void FusedDepthwiseConv2D(
-    const size_t x_id, const size_t batch_size, const size_t input_height,
-    const size_t input_width, const size_t filter_id,
-    const size_t filter_height, const size_t filter_width, const size_t bias_id,
-    size_t pad_top, size_t pad_right, size_t pad_bottom, size_t pad_left,
-    const size_t is_same_pad, const size_t dilation_height,
-    const size_t dilation_width, const size_t stride_height,
-    const size_t stride_width, const size_t input_channels,
-    const size_t output_channels, const FusableActivation activation,
-    const size_t prelu_weights_id, const float leakyrelu_alpha,
-    const size_t out_id) {
-  const bool is_depthwise = true;
-  tfjs::wasm::conv2d(x_id, batch_size, input_height, input_width, filter_id,
-                     filter_height, filter_width, bias_id, pad_top, pad_right,
-                     pad_bottom, pad_left, is_same_pad, dilation_height,
-                     dilation_width, stride_height, stride_width,
-                     input_channels, output_channels, is_depthwise, activation,
-                     prelu_weights_id, leakyrelu_alpha, out_id);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D.h
deleted file mode 100644
index 2cc0feabd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D.h
+++ /dev/null
@@ -1,42 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_FUSEDDEPTHWISECONV2D_H_
-#define KERNELS_FUSEDDEPTHWISECONV2D_H_
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-
-namespace wasm {
-extern "C" {
-void FusedDepthwiseConv2D(
-    const size_t x_id, const size_t batch_size, const size_t input_height,
-    const size_t input_width, const size_t filter_id,
-    const size_t filter_height, const size_t filter_width, const size_t bias_id,
-    size_t pad_top, size_t pad_right, size_t pad_bottom, size_t pad_left,
-    const size_t is_same_pad, const size_t dilation_height,
-    const size_t dilation_width, const size_t stride_height,
-    const size_t stride_width, const size_t input_channels,
-    const size_t output_channels, const FusableActivation activation,
-    const size_t prelu_weights_id, const float leakyrelu_alpha,
-    const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_FUSEDDEPTHWISECONV2D_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D_test.cc
deleted file mode 100644
index 43aad4210..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D_test.cc
+++ /dev/null
@@ -1,221 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-#include <xnnpack.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/conv2d_impl.h"
-#include "tfjs-backend-wasm/src/cc/kernels/FusedDepthwiseConv2D.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-TEST(FUSEDDEPTHWISECONV2D, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t size = 8;
-  float x_values[size] = {1, 2, 3, 4, 5, 6, 7, 8};
-
-  const size_t weights0_id = 3;
-  const size_t weights1_id = 4;
-  const size_t weights_size = 8;
-  float weights_values[weights_size] = {1, 2, 3, 4, 5, 6, 7, 8};
-
-  const size_t bias0_id = 5;
-  const size_t bias1_id = 6;
-  const size_t bias_size = 1;
-  float bias_values[bias_size] = {1};
-
-  const size_t out_id = 7;
-  const size_t out_size = 12;
-  float out_values[out_size] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, size, x_values);
-  tfjs::wasm::register_tensor(x1_id, size, x_values);
-  tfjs::wasm::register_tensor(weights0_id, weights_size, weights_values);
-  tfjs::wasm::register_tensor(weights1_id, weights_size, weights_values);
-  tfjs::wasm::register_tensor(bias0_id, bias_size, bias_values);
-  tfjs::wasm::register_tensor(bias1_id, bias_size, bias_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(7, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One xnn_operator should be created for the first call to conv2d with no
-  // bias.
-  const size_t batch_size = 1;
-  const size_t input_height = 4;
-  const size_t input_width = 2;
-  const size_t filter_height = 4;
-  const size_t filter_width = 2;
-  const size_t pad_top0 = 1;
-  const size_t pad_right = 0;
-  const size_t pad_bottom0 = 0;
-  const size_t pad_left = 0;
-  const bool is_same_pad0 = false;
-  const size_t dilation_height = 1;
-  const size_t dilation_width = 1;
-  const size_t stride_height = 1;
-  const size_t stride_width = 1;
-  const size_t input_channels = 1;
-  const size_t output_channels = 1;
-
-  const FusableActivation activation = FusableActivation::LINEAR;
-
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, 0 /* bias */, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      0 /* prelu weights */, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn operator should be created for second call to conv2d with no
-  // bias and prelu activation.
-  const FusableActivation prelu_activation = FusableActivation::PRELU;
-
-  const size_t prelu_weights_id = 8;
-  const size_t prelu_size = 8;
-  float prelu_values[prelu_size] = {1, 2, 3, 4, 5, 6, 7, 8};
-  tfjs::wasm::register_tensor(prelu_weights_id, prelu_size, prelu_values);
-
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, 0 /* bias */, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, prelu_activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to conv2d with
-  // the same arguments.
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, 0 /* bias */, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to conv2d with
-  // the same arguments but different input.
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x1_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, 0 /* bias */, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the next call to conv2d with the
-  // same weights and bias but different arguments.
-  const size_t pad_top1 = 0;
-  const size_t pad_bottom1 = 1;
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x0_id, batch_size, input_height, input_width, weights0_id, filter_height,
-      filter_width, 0 /* bias */, pad_top1, pad_right, pad_bottom1, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(3, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // new weights and same input.
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, 0 /* bias */, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(4, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // bias, same input, same weights.
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias0_id, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(5, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // new bias, same input, same weights.
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias1_id, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad0, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(6, tfjs::backend::xnn_operator_count);
-
-  // One more xnn operator should be created for the next call to conv2d with
-  // 'SAME' padding.
-  const bool is_same_pad1 = true;
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x0_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias1_id, pad_top0, pad_right, pad_bottom0, pad_left,
-      is_same_pad1, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(7, tfjs::backend::xnn_operator_count);
-
-  // No new XNN operators should be created for the next call to conv2d with
-  // 'SAME' padding and different input and raw padding values.
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x1_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias1_id, pad_top1, pad_right, pad_bottom1, pad_left,
-      is_same_pad1, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(7, tfjs::backend::xnn_operator_count);
-
-  // One new XNN operator should be created for the next call to conv2d with a
-  // different activation.
-  const FusableActivation activation2 = FusableActivation::RELU6;
-  tfjs::wasm::FusedDepthwiseConv2D(
-      x1_id, batch_size, input_height, input_width, weights1_id, filter_height,
-      filter_width, bias1_id, pad_top1, pad_right, pad_bottom1, pad_left,
-      is_same_pad1, dilation_height, dilation_width, stride_height,
-      stride_width, input_channels, output_channels, activation2,
-      prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(8, tfjs::backend::xnn_operator_count);
-
-  // Disposing the first weights should remove 2 operators.
-  tfjs::wasm::dispose_data(weights0_id);
-  ASSERT_EQ(6, tfjs::backend::xnn_operator_count);
-
-  // Disposing the second bias should remove 2 operators it's associated with.
-  tfjs::wasm::dispose_data(bias1_id);
-  ASSERT_EQ(3, tfjs::backend::xnn_operator_count);
-
-  // Disposing the first bias should remove one operator.
-  tfjs::wasm::dispose_data(bias0_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing the second weights should remove one operator.
-  tfjs::wasm::dispose_data(weights1_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // Disposing prelu weights should remove the last operator.
-  tfjs::wasm::dispose_data(prelu_weights_id);
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Gather.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Gather.cc
deleted file mode 100644
index 5ce0a5f3c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Gather.cc
+++ /dev/null
@@ -1,87 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-void gather_impl(const T* x_ptr, const std::vector<size_t>& x_strides,
-                 const int32_t* indices_ptr, const size_t out_size,
-                 const size_t batch_size,
-                 const std::vector<size_t>& out_strides, T* out_buf_ptr) {
-  for (size_t i = 0; i < out_size; ++i) {
-    auto loc = tfjs::util::offset_to_loc(i, out_strides);
-    const size_t batch_loc = loc[0];
-    const size_t indices_loc = loc[2];
-    loc[2] = indices_ptr[batch_loc * batch_size + indices_loc];
-
-    const size_t original_index = tfjs::util::loc_to_offset(loc, x_strides);
-
-    *out_buf_ptr = x_ptr[original_index];
-
-    out_buf_ptr++;
-  }
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Gather(const size_t x_id, const DType dtype, const int32_t* x_strides_ptr,
-            const size_t strides_size, const size_t indices_id,
-            const size_t batch_size, const int32_t* out_strides_ptr,
-            const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& indices_info = backend::get_tensor_info(indices_id);
-
-  const int* indices_buf = indices_info.i32();
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  const size_t out_size = out_info.size;
-
-  const auto x_strides =
-      std::vector<size_t>(x_strides_ptr, x_strides_ptr + strides_size);
-  const auto out_strides =
-      std::vector<size_t>(out_strides_ptr, out_strides_ptr + strides_size);
-
-  switch (dtype) {
-    case DType::float32:
-      gather_impl<float>(x_info.f32(), x_strides, indices_buf, out_size,
-                         batch_size, out_strides, out_info.f32_write());
-      break;
-    case DType::int32:
-      gather_impl<int32_t>(x_info.i32(), x_strides, indices_buf, out_size,
-                           batch_size, out_strides, out_info.i32_write());
-      break;
-    case DType::boolean:
-      gather_impl<bool>(x_info.b(), x_strides, indices_buf, out_size,
-                        batch_size, out_strides, out_info.b_write());
-      break;
-    default:
-      util::warn("Gather for tensor id %d failed. Unknown dtype %d", x_id,
-                 dtype);
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/GatherNd.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/GatherNd.cc
deleted file mode 100644
index 5ce41fcda..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/GatherNd.cc
+++ /dev/null
@@ -1,90 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-void gathernd_impl(const T* x_ptr, const int32_t* indices_ptr,
-                   const size_t num_slices, const size_t slice_rank,
-                   const size_t slice_size,
-                   const std::vector<int32_t>& strides_ptr, T* out_buf_ptr) {
-  for (size_t i = 0; i < num_slices; ++i) {
-    size_t flattened_index = 0;
-    for (size_t j = 0; j < slice_rank; ++j) {
-      flattened_index += (*indices_ptr * strides_ptr[j]);
-
-      indices_ptr++;
-    }
-
-    x_ptr += flattened_index * slice_size;
-
-    for (size_t k = 0; k < slice_size; ++k) {
-      *out_buf_ptr = *x_ptr;
-
-      out_buf_ptr++;
-      x_ptr++;
-    }
-
-    x_ptr -= ((flattened_index + 1) * slice_size);
-  }
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void GatherNd(const size_t x_id, const DType dtype, const size_t indices_id,
-              const size_t num_slices, const size_t slice_rank,
-              const size_t slice_size, const int32_t* strides_ptr,
-              const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& indices_info = backend::get_tensor_info(indices_id);
-  const std::vector<int32_t>& strides =
-      std::vector<int32_t>(strides_ptr, strides_ptr + slice_rank);
-
-  const int* indices_buf = indices_info.i32();
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  switch (dtype) {
-    case DType::float32:
-      gathernd_impl<float>(x_info.f32(), indices_buf, num_slices, slice_rank,
-                           slice_size, strides, out_info.f32_write());
-      break;
-    case DType::int32:
-      gathernd_impl<int32_t>(x_info.i32(), indices_buf, num_slices, slice_rank,
-                             slice_size, strides, out_info.i32_write());
-      break;
-    case DType::boolean:
-      gathernd_impl<bool>(x_info.b(), indices_buf, num_slices, slice_rank,
-                          slice_size, strides, out_info.b_write());
-      break;
-    default:
-      util::warn("GatherNd for tensor id %d failed. Unknown dtype %d",
-                 indices_id, dtype);
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Greater.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Greater.cc
deleted file mode 100644
index fe6e86ff9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Greater.cc
+++ /dev/null
@@ -1,62 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline bool greater(T a, T b) {
-  return a > b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Greater(const int a_id, const size_t* a_shape_ptr, const int a_shape_len,
-             const int b_id, const size_t* b_shape_ptr, const int b_shape_len,
-             const DType input_type, const int out_id) {
-  switch (input_type) {
-    case DType::float32:
-      compare_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, greater<float>);
-      break;
-    case DType::int32:
-      compare_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, greater<int>);
-      break;
-    case DType::boolean:
-      compare_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                   b_shape_len, out_id, greater<bool>);
-      break;
-    default:
-      util::warn(
-          "Greater for tensor ids %d and %d failed. Unsupported input_type %d",
-          a_id, b_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/GreaterEqual.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/GreaterEqual.cc
deleted file mode 100644
index 718c33574..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/GreaterEqual.cc
+++ /dev/null
@@ -1,64 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline bool greaterEqual(T a, T b) {
-  return a >= b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void GreaterEqual(const int a_id, const size_t* a_shape_ptr,
-                  const int a_shape_len, const int b_id,
-                  const size_t* b_shape_ptr, const int b_shape_len,
-                  const DType input_type, const int out_id) {
-  switch (input_type) {
-    case DType::float32:
-      compare_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, greaterEqual<float>);
-      break;
-    case DType::int32:
-      compare_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, greaterEqual<int>);
-      break;
-    case DType::boolean:
-      compare_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                   b_shape_len, out_id, greaterEqual<bool>);
-      break;
-    default:
-      util::warn(
-          "GreaterEqual for tensor ids %d and %d failed."
-          "Unsupported input_type %d",
-          a_id, b_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/IsFinite.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/IsFinite.cc
deleted file mode 100644
index 97068ba41..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/IsFinite.cc
+++ /dev/null
@@ -1,36 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs::wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void IsFinite(const int x_id, const DType dtype, const int out_id) {
-  unary_f32_with_bool_out(x_id, out_id, std::isfinite);
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/IsInf.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/IsInf.cc
deleted file mode 100644
index 85142353b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/IsInf.cc
+++ /dev/null
@@ -1,36 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs::wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void IsInf(const int x_id, const DType dtype, const int out_id) {
-  unary_f32_with_bool_out(x_id, out_id, std::isinf);
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/IsNan.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/IsNan.cc
deleted file mode 100644
index 9961fd267..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/IsNan.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void IsNan(const int x_id, const DType dtype, const int out_id) {
-  unary_f32_with_bool_out(x_id, out_id, isnan);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LRN.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LRN.cc
deleted file mode 100644
index 5cca861e3..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LRN.cc
+++ /dev/null
@@ -1,70 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-template <typename T>
-inline void LRNImpl(const T* x_buf, T* out_buf, int size, int channels,
-                    int depth_radius, float bias, float alpha, float beta) {
-  for (int offset = 0; offset < size; ++offset) {
-    int current_channel = offset % channels;
-    int begin_sum_offset =
-        offset - current_channel + std::max(0, current_channel - depth_radius);
-    int end_sum_offset = offset - current_channel +
-                         std::min(current_channel + depth_radius, channels - 1);
-
-    T sum = 0;
-    for (int i = begin_sum_offset; i <= end_sum_offset; ++i) {
-      sum += x_buf[i] * x_buf[i];
-    }
-    T val = x_buf[offset] * static_cast<T>(std::pow(bias + alpha * sum, -beta));
-    out_buf[offset] = val;
-  }
-}
-
-}  // namespace
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES
-// - Tensor `x` and `out` must have dtype float32.
-// - Tensor `x` and `out` must have the same size.
-void LRN(const int x_id, const int out_id, const int channels,
-         const int depth_radius, const float bias, const float alpha,
-         const float beta) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-
-  LRNImpl<float>(x_info.f32(), out_info.f32_write(), x_info.size, channels,
-                 depth_radius, bias, alpha, beta);
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LRNGrad.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LRNGrad.cc
deleted file mode 100644
index 3bb588b72..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LRNGrad.cc
+++ /dev/null
@@ -1,83 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-template <typename T>
-inline void LRNGradImpl(const T* x_buf, const T* y_buf, const T* dy_buf,
-                        T* dx_buf, int size, int channels, int depth_radius,
-                        float bias, float alpha, float beta) {
-  std::fill(dx_buf, dx_buf + size, 0);
-  for (int offset = 0; offset < size; ++offset) {
-    int current_channel = offset % channels;
-    int depth_begin =
-        offset - current_channel + std::max(0, current_channel - depth_radius);
-    int depth_end = offset - current_channel +
-                    std::min(channels, current_channel + depth_radius + 1);
-
-    T norm = 0;
-    for (int i = depth_begin; i < depth_end; ++i) {
-      norm += static_cast<T>(std::pow(x_buf[i], 2));
-    }
-    norm = alpha * norm + bias;
-
-    for (int i = depth_begin; i < depth_end; ++i) {
-      T dyi = -2 * alpha * beta * x_buf[i] * y_buf[offset] / norm;
-      if (offset == i) {
-        dyi += static_cast<T>(std::pow(norm, -beta));
-      }
-      dyi *= dy_buf[offset];
-      dx_buf[i] += dyi;
-    }
-  }
-}
-
-}  // namespace
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES
-// - Tensor `x`, `y`, 'dx', and 'dy' must have dtype float32.
-// - Tensor `x`, `y`, 'dx', and 'dy' must have the same size.
-void LRNGrad(const int x_id, const int y_id, const int dy_id, const int dx_id,
-             const int channels, const int depth_radius, const float bias,
-             const float alpha, const float beta) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo& y_info = backend::get_tensor_info(y_id);
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& dx_info = backend::get_tensor_info_out(dx_id);
-
-  LRNGradImpl<float>(x_info.f32(), y_info.f32(), dy_info.f32(),
-                     dx_info.f32_write(), x_info.size, channels, depth_radius,
-                     bias, alpha, beta);
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LeakyRelu.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LeakyRelu.cc
deleted file mode 100644
index 3af5887fc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LeakyRelu.cc
+++ /dev/null
@@ -1,57 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/LeakyRelu.h"
-#include "tfjs-backend-wasm/src/cc/leakyrelu_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void LeakyRelu(const size_t x_id, const DType dtype,
-               const float leakyrelu_alpha, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-
-  switch (dtype) {
-    case DType::float32:
-      tfjs::wasm::leakyrelu<float>(x_info.f32(), x_info.size, leakyrelu_alpha,
-                                   out_id);
-      break;
-    case DType::int32:
-      tfjs::wasm::leakyrelu<int32_t>(x_info.i32(), x_info.size, leakyrelu_alpha,
-                                     out_id);
-      break;
-    default:
-      util::warn(
-          "LeakyRelu for tensor ids %d failed. "
-          "Unknown dtype %d",
-          x_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LeakyRelu.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LeakyRelu.h
deleted file mode 100644
index fa9cf02a1..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LeakyRelu.h
+++ /dev/null
@@ -1,33 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_LEAKYRELU_H_
-#define KERNELS_LEAKYRELU_H_
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void LeakyRelu(const size_t x_id, const DType dtype,
-               const float leakyrelu_alpha, const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_LEAKYRELU_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Less.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Less.cc
deleted file mode 100644
index 3f9dedd10..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Less.cc
+++ /dev/null
@@ -1,62 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline bool less(T a, T b) {
-  return a < b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Less(const int a_id, const size_t* a_shape_ptr, const int a_shape_len,
-          const int b_id, const size_t* b_shape_ptr, const int b_shape_len,
-          const DType input_type, const int out_id) {
-  switch (input_type) {
-    case DType::float32:
-      compare_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, less<float>);
-      break;
-    case DType::int32:
-      compare_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, less<int>);
-      break;
-    case DType::boolean:
-      compare_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                   b_shape_len, out_id, less<bool>);
-      break;
-    default:
-      util::warn(
-          "Less for tensor ids %d and %d failed. Unsupported input_type %d",
-          a_id, b_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LessEqual.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LessEqual.cc
deleted file mode 100644
index 259b95a38..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LessEqual.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline bool lessEqual(T a, T b) {
-  return a <= b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void LessEqual(const int a_id, const size_t* a_shape_ptr, const int a_shape_len,
-               const int b_id, const size_t* b_shape_ptr, const int b_shape_len,
-               const DType input_type, const int out_id) {
-  switch (input_type) {
-    case DType::float32:
-      compare_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, lessEqual<float>);
-      break;
-    case DType::int32:
-      compare_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, lessEqual<int>);
-      break;
-    case DType::boolean:
-      compare_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                   b_shape_len, out_id, lessEqual<bool>);
-      break;
-    default:
-      util::warn(
-          "LessEqual for tensor ids %d and %d failed."
-          "Unsupported input_type %d",
-          a_id, b_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LinSpace.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LinSpace.cc
deleted file mode 100644
index c80d82333..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LinSpace.cc
+++ /dev/null
@@ -1,49 +0,0 @@
-/* Copyright 2023 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs::wasm {
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `out` must have dtype float32.
-// - Param `num` must be positive (checked in tfjs-core).
-void LinSpace(int32_t out_id, float start, float stop, int32_t num) {
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-  float* out_buf = out_info.f32_write();
-
-  out_buf[0] = start;
-  if (num == 1) {
-    return;
-  }
-
-  float step = (stop - start) / static_cast<float>(num - 1);
-  for (int i = 1; i < num; ++i) {
-    out_buf[i] = out_buf[i - 1] + step;
-  }
-}
-
-}  // extern "C"
-
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Log.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Log.cc
deleted file mode 100644
index 35ecd8178..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Log.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Log(const int x_id, const DType dtype, const int out_id) {
-  unary_f32(x_id, out_id, std::log);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Log1p.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Log1p.cc
deleted file mode 100644
index 5447ae648..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Log1p.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline T Log1pImpl(T n) {
-  return static_cast<T>(std::logf(static_cast<float>(n) + 1.0));
-}
-
-}  // namespace
-
-namespace tfjs::wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Log1p(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, Log1pImpl<float>);
-      break;
-    default:
-      util::warn("Log1p for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalAnd.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalAnd.cc
deleted file mode 100644
index 49de5727c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalAnd.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-inline bool logical_and(bool a, bool b) { return a && b; }
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void LogicalAnd(const int a_id, const size_t* a_shape_ptr,
-                const int a_shape_len, const int b_id,
-                const size_t* b_shape_ptr, const int b_shape_len,
-                const DType input_type, const int out_id) {
-  switch (input_type) {
-    case DType::boolean:
-      compare_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                   b_shape_len, out_id, logical_and);
-      break;
-    default:
-      util::warn(
-          "LogicalAnd for tensor ids %d and %d failed. Unsupported input_type "
-          "%d",
-          a_id, b_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalNot.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalNot.cc
deleted file mode 100644
index b7f6fea29..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalNot.cc
+++ /dev/null
@@ -1,50 +0,0 @@
-/* Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-inline bool logical_not(bool a) { return !a; }
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void LogicalNot(const size_t x_id, const DType input_type,
-                const size_t out_id) {
-  switch (input_type) {
-    case DType::boolean:
-      unary_bool(x_id, out_id, logical_not);
-      break;
-    default:
-      util::warn("LogicalNot for tensor ids %d failed. Unsupported input_type "
-                 "%d",
-                 x_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalOr.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalOr.cc
deleted file mode 100644
index c639cc4b4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalOr.cc
+++ /dev/null
@@ -1,52 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-inline bool logical_or(bool a, bool b) { return a || b; }
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void LogicalOr(const int a_id, const size_t *a_shape_ptr, const int a_shape_len,
-               const int b_id, const size_t *b_shape_ptr, const int b_shape_len,
-               const DType input_type, const int out_id) {
-  switch (input_type) {
-    case DType::boolean:
-      compare_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                   b_shape_len, out_id, logical_or);
-      break;
-    default:
-      util::warn(
-          "LogicalOr for tensor ids %d and %d failed. Unsupported input_type "
-          "%d",
-          a_id, b_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalXor.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalXor.cc
deleted file mode 100644
index ff5d6fb5f..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/LogicalXor.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-inline bool logical_xor(bool a, bool b) { return a != b; }
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void LogicalXor(const int a_id, const size_t *a_shape_ptr,
-                const int a_shape_len, const int b_id,
-                const size_t *b_shape_ptr, const int b_shape_len,
-                const DType input_type, const int out_id) {
-  switch (input_type) {
-    case DType::boolean:
-      compare_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                   b_shape_len, out_id, logical_xor);
-      break;
-    default:
-      util::warn(
-          "LogicalXor for tensor ids %d and %d failed. Unsupported input_type "
-          "%d",
-          a_id, b_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Max.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Max.cc
deleted file mode 100644
index 5adba2def..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Max.cc
+++ /dev/null
@@ -1,85 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <math.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-void max(const T* x_buf, const size_t out_size, const size_t reduce_size,
-         T* out_buf) {
-  const T* x_offset = x_buf;
-
-  for (size_t i = 0; i < out_size; ++i) {
-    const size_t offset = i * reduce_size;
-    T max = x_buf[offset];
-
-    const T* x_iter_end = x_offset + reduce_size;
-
-    for (const T* x = x_offset; x < x_iter_end; ++x) {
-      T value = *x;
-      if (isnan(value) || value > max) {
-        max = value;
-      }
-    }
-
-    x_offset += reduce_size;
-
-    out_buf[i] = max;
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Max(const size_t x_id, const DType dtype, const size_t reduce_size,
-         const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const size_t out_size = out_info.size;
-
-  switch (dtype) {
-    case DType::float32:
-      max<float>(x_info.f32(), out_size, reduce_size, out_info.f32_write());
-      break;
-    case DType::int32:
-      max<int32_t>(x_info.i32(), out_size, reduce_size, out_info.i32_write());
-      break;
-    case DType::boolean:
-      max<bool>(x_info.b(), out_size, reduce_size, out_info.b_write());
-      break;
-    default:
-      util::warn("Max failed. Unknown dtype %d", dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool.cc
deleted file mode 100644
index 841f273a0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool.cc
+++ /dev/null
@@ -1,119 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <xnnpack.h>
-#include <array>
-#include <cmath>
-#include <cstddef>
-#include <limits>
-#include <map>
-#include <unordered_map>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/MaxPool.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-typedef std::array<size_t, 14> OperatorCacheKey;
-
-std::map<OperatorCacheKey, xnn_operator_t> operator_cache;
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void MaxPool(const size_t x_id, const size_t batch_size,
-             const size_t input_height, const size_t input_width,
-             const size_t filter_height, const size_t filter_width,
-             size_t pad_top, size_t pad_right, size_t pad_bottom,
-             size_t pad_left, const size_t dilation_height,
-             const size_t dilation_width, const size_t stride_height,
-             const size_t stride_width, const size_t input_channels,
-             const size_t output_channels, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info(out_id);
-
-  const float* x_buf = reinterpret_cast<float*>(x_info.memory_offset);
-  float* out_buf = reinterpret_cast<float*>(out_info.memory_offset);
-
-  // XNNPack does not support 1x1 filters for MaxPool
-  if (filter_width == 1 && filter_height == 1) {
-    tfjs::util::identity_pool(x_id, x_buf, out_buf, out_info.size, batch_size,
-                              input_height, input_width, stride_height,
-                              stride_width, input_channels);
-    return;
-  }
-
-  xnn_operator_t max_pool_op = nullptr;
-
-  const uint32_t flags = 0;
-  const size_t channels = input_channels;
-
-  OperatorCacheKey cache_key = {pad_top,         pad_right,     pad_bottom,
-                                pad_left,        filter_height, filter_width,
-                                stride_height,   stride_width,  dilation_height,
-                                dilation_width,  channels,      input_channels,
-                                output_channels, flags};
-
-  auto operator_cache_idx = operator_cache.find(cache_key);
-
-  if (operator_cache_idx == operator_cache.end()) {
-    const float output_min = -std::numeric_limits<float>::infinity();
-    const float output_max = std::numeric_limits<float>::infinity();
-
-    xnn_status status = xnn_create_max_pooling2d_nhwc_f32(
-        pad_top, pad_right, pad_bottom, pad_left, filter_height, filter_width,
-        stride_height, stride_width, dilation_height, dilation_width, channels,
-        input_channels /* input_pixel_stride */,
-        output_channels /* output_pixel_stride */, output_min, output_max,
-        flags, &max_pool_op);
-
-    if (status != xnn_status_success) {
-      util::warn(
-          "XNN status for xnn_create_max_pooling2d_nhwc_f32 is not "
-          "successful. ",
-          "Got status %d. Use -c dbg to see XNN logs.", status);
-    }
-
-    operator_cache.emplace(cache_key, max_pool_op);
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    max_pool_op = operator_cache_idx->second;
-  }
-
-  xnn_status status = xnn_setup_max_pooling2d_nhwc_f32(
-      max_pool_op, batch_size, input_height, input_width, x_buf, out_buf,
-      tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    util::warn(
-        "XNN status for xnn_setup_max_pooling2d_nhwc_f32 is not successful. "
-        "Got status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(max_pool_op, tfjs::backend::threadpool);
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool.h
deleted file mode 100644
index 04df01d70..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool.h
+++ /dev/null
@@ -1,37 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_MAXPOOL_H_
-#define KERNELS_MAXPOOL_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void MaxPool(const size_t x_id, const size_t batch_size,
-             const size_t input_height, const size_t input_width,
-             const size_t filter_height, const size_t filter_width,
-             size_t pad_top, size_t pad_right, size_t pad_bottom,
-             size_t pad_left, const size_t dilation_height,
-             const size_t dilation_width, const size_t stride_height,
-             const size_t stride_width, const size_t input_channels,
-             const size_t output_channels, const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_MAXPOOL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool3D.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool3D.cc
deleted file mode 100644
index 0d0506418..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool3D.cc
+++ /dev/null
@@ -1,83 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/pool3d_impl.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x` and `out` must have dtype float32 (checked in tfjs-core)
-// - Tensor `x` and `out` must have data format 'NDHWC' (checked in tfjs-core)
-void MaxPool3D(int x_id, int out_id, int batch_size, int channel_size,
-               int in_depth, int in_height, int in_width, int out_depth,
-               int out_height, int out_width, int stride_depth,
-               int stride_height, int stride_width, int dilation_depth,
-               int dilation_height, int dilation_width,
-               int effective_filter_depth, int effective_filter_height,
-               int effective_filter_width, int pad_front, int pad_top,
-               int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-
-  NDHWCPool3DImpl(
-      x_info.f32(),
-      NDHWCPool3DInfo{
-          .batch_size = batch_size,
-          .channel_size = channel_size,
-          .in_depth = in_depth,
-          .in_height = in_height,
-          .in_width = in_width,
-          .out_depth = out_depth,
-          .out_height = out_height,
-          .out_width = out_width,
-          .stride_depth = stride_depth,
-          .stride_height = stride_height,
-          .stride_width = stride_width,
-          .dilation_depth = dilation_depth,
-          .dilation_height = dilation_height,
-          .dilation_width = dilation_width,
-          .effective_filter_depth = effective_filter_depth,
-          .effective_filter_height = effective_filter_height,
-          .effective_filter_width = effective_filter_width,
-          .pad_front = pad_front,
-          .pad_top = pad_top,
-          .pad_left = pad_left,
-      },
-      /*filter_init=*/
-      []() -> float { return std::numeric_limits<float>::min(); },
-      /*filter_apply=*/
-      [](float& data, int, const float& val) { data = std::max(data, val); },
-      /*filter_assign=*/
-      [buf = out_info.f32_write()](int offset, const float& data) {
-        buf[offset] = data;
-      });
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool3DGrad.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool3DGrad.cc
deleted file mode 100644
index e30578957..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool3DGrad.cc
+++ /dev/null
@@ -1,101 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/pool3d_impl.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x`, `dx` and `dy` must have dtype float32 (checked in tfjs-core)
-// - Tensor `x`, `dx` and `dy` must have data format 'NDHWC' (checked in
-// tfjs-core)
-void MaxPool3DGrad(int x_id, int dy_id, int dx_id, int batch_size,
-                   int channel_size, int in_depth, int in_height, int in_width,
-                   int out_depth, int out_height, int out_width,
-                   int stride_depth, int stride_height, int stride_width,
-                   int dilation_depth, int dilation_height, int dilation_width,
-                   int effective_filter_depth, int effective_filter_height,
-                   int effective_filter_width, int pad_front, int pad_top,
-                   int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& dx_info = backend::get_tensor_info_out(dx_id);
-  NDHWCPool3DInfo pool3d_info{
-      .batch_size = batch_size,
-      .channel_size = channel_size,
-      .in_depth = in_depth,
-      .in_height = in_height,
-      .in_width = in_width,
-      .out_depth = out_depth,
-      .out_height = out_height,
-      .out_width = out_width,
-      .stride_depth = stride_depth,
-      .stride_height = stride_height,
-      .stride_width = stride_width,
-      .dilation_depth = dilation_depth,
-      .dilation_height = dilation_height,
-      .dilation_width = dilation_width,
-      .effective_filter_depth = effective_filter_depth,
-      .effective_filter_height = effective_filter_height,
-      .effective_filter_width = effective_filter_width,
-      .pad_front = pad_front,
-      .pad_top = pad_top,
-      .pad_left = pad_left,
-  };
-
-  int* max_positions = new int[pool3d_info.out_size()];
-  NDHWCPool3DImpl</*IN=*/float>(
-      x_info.f32(), pool3d_info,
-      /*filter_init=*/
-      []() -> std::pair<float, int> {
-        return {std::numeric_limits<float>::min(), 0};
-      },
-      /*filter_apply=*/
-      [](std::pair<float, int>& data, int x_offset, const float& x_val) {
-        if (x_val >= data.first) {
-          data = {x_val, x_offset};
-        }
-      },
-      /*filter_assign=*/
-      [max_positions](int offset, const std::pair<float, int>& data) {
-        max_positions[offset] = data.second;
-      });
-
-  NDHWCPool3DGradImpl(
-      dy_info.f32(), dx_info.f32_write(), pool3d_info,
-      /*pixel_mask=*/
-      [&max_positions](int dy_offset, int dx_offset) {
-        return static_cast<float>(dx_offset == max_positions[dy_offset]);
-      });
-
-  delete[] max_positions;
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPoolGrad.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPoolGrad.cc
deleted file mode 100644
index 992fc3eda..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPoolGrad.cc
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/pool3d_impl.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x`, `dx` and `dy` must have dtype float32 (checked in tfjs-core)
-void MaxPoolGrad(int x_id, int dy_id, int dx_id, int batch_size,
-                 int channel_size, int in_height, int in_width, int out_height,
-                 int out_width, int stride_height, int stride_width,
-                 int dilation_height, int dilation_width,
-                 int effective_filter_height, int effective_filter_width,
-                 int pad_top, int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& dx_info = backend::get_tensor_info_out(dx_id);
-  NDHWCPool3DInfo pool_info{
-      .batch_size = batch_size,
-      .channel_size = channel_size,
-      .in_depth = 1,
-      .in_height = in_height,
-      .in_width = in_width,
-      .out_depth = 1,
-      .out_height = out_height,
-      .out_width = out_width,
-      .stride_depth = 1,
-      .stride_height = stride_height,
-      .stride_width = stride_width,
-      .dilation_depth = 1,
-      .dilation_height = dilation_height,
-      .dilation_width = dilation_width,
-      .effective_filter_depth = 1,
-      .effective_filter_height = effective_filter_height,
-      .effective_filter_width = effective_filter_width,
-      .pad_front = 0,
-      .pad_top = pad_top,
-      .pad_left = pad_left,
-  };
-
-  int* max_positions = new int[pool_info.out_size()];
-  NDHWCPool3DImpl</*IN=*/float>(
-      x_info.f32(), pool_info,
-      /*filter_init=*/
-      []() -> std::pair<float, int> {
-        return {std::numeric_limits<float>::min(), 0};
-      },
-      /*filter_apply=*/
-      [](std::pair<float, int>& data, int x_offset, const float& x_val) {
-        if (x_val >= data.first) {
-          data = {x_val, x_offset};
-        }
-      },
-      /*filter_assign=*/
-      [max_positions](int offset, const std::pair<float, int>& data) {
-        max_positions[offset] = data.second;
-      });
-
-  NDHWCPool3DGradImpl(
-      dy_info.f32(), dx_info.f32_write(), pool_info,
-      /*pixel_mask=*/
-      [&max_positions](int dy_offset, int dx_offset) {
-        return static_cast<float>(dx_offset == max_positions[dy_offset]);
-      });
-
-  delete[] max_positions;
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPoolWithArgmax.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPoolWithArgmax.cc
deleted file mode 100644
index 8ff43092b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPoolWithArgmax.cc
+++ /dev/null
@@ -1,127 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/pool3d_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-template <typename T>
-inline void MaxPoolWithArgmaxImpl(const T* x_buf, T* pooled_buf,
-                                  int32_t* indexes_buf,
-                                  bool include_batch_index,
-                                  const NDHWCPool3DInfo& pool_info) {
-  NDHWCPool3DImpl(x_buf, pool_info,
-                  /*filter_init=*/
-                  []() -> std::pair<T, int> {
-                    return {std::numeric_limits<T>::min(), 0};
-                  },
-                  /*filter_apply=*/
-                  [](std::pair<T, int>& data, int x_offset, const T& x_val) {
-                    if (x_val >= data.first) {
-                      data = {x_val, x_offset};
-                    }
-                  },
-                  /*filter_assign=*/
-                  [pooled_buf, indexes_buf, include_batch_index,
-                   index_mod = pool_info.in_height * pool_info.in_width *
-                               pool_info.channel_size](
-                      int offset, const std::pair<T, int>& data) {
-                    pooled_buf[offset] = data.first;
-                    indexes_buf[offset] = include_batch_index
-                                              ? data.second
-                                              : data.second % index_mod;
-                  });
-}
-
-}  // namespace
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `x` and `out` must have the same dtype.
-// - Tensor `indexes` must have dtype `int32`.
-void MaxPoolWithArgmax(int x_id, int pooled_id, int indexes_id, DType dtype,
-                       bool include_batch_index, int batch_size,
-                       int channel_size, int in_height, int in_width,
-                       int out_height, int out_width, int stride_height,
-                       int stride_width, int dilation_height,
-                       int dilation_width, int effective_filter_height,
-                       int effective_filter_width, int pad_top, int pad_left) {
-  const TensorInfo& x_info = backend::get_tensor_info(x_id);
-  TensorInfo& pooled_info = backend::get_tensor_info_out(pooled_id);
-  TensorInfo& indexes_info = backend::get_tensor_info_out(indexes_id);
-
-  NDHWCPool3DInfo pool_info{
-      .batch_size = batch_size,
-      .channel_size = channel_size,
-      .in_depth = 1,
-      .in_height = in_height,
-      .in_width = in_width,
-      .out_depth = 1,
-      .out_height = out_height,
-      .out_width = out_width,
-      .stride_depth = 1,
-      .stride_height = stride_height,
-      .stride_width = stride_width,
-      .dilation_depth = 1,
-      .dilation_height = dilation_height,
-      .dilation_width = dilation_width,
-      .effective_filter_depth = 1,
-      .effective_filter_height = effective_filter_height,
-      .effective_filter_width = effective_filter_width,
-      .pad_front = 0,
-      .pad_top = pad_top,
-      .pad_left = pad_left,
-  };
-
-  switch (dtype) {
-    case DType::float32:
-      MaxPoolWithArgmaxImpl(x_info.f32(), pooled_info.f32_write(),
-                            indexes_info.i32_write(), include_batch_index,
-                            pool_info);
-      break;
-    case DType::int32:
-      MaxPoolWithArgmaxImpl(x_info.i32(), pooled_info.i32_write(),
-                            indexes_info.i32_write(), include_batch_index,
-                            pool_info);
-      break;
-    case DType::boolean:
-      MaxPoolWithArgmaxImpl(x_info.b(), pooled_info.b_write(),
-                            indexes_info.i32_write(), include_batch_index,
-                            pool_info);
-      break;
-    default:
-      util::warn("MaxPoolWithArgmax for tensor id failed. Unknown dtype %d",
-                 x_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool_test.cc
deleted file mode 100644
index 8dd7653d2..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MaxPool_test.cc
+++ /dev/null
@@ -1,84 +0,0 @@
-
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/MaxPool.h"
-
-TEST(MAXPOOL, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t size = 9;
-  float x_values[size] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
-
-  const size_t out_id = 3;
-  const size_t out_size = 9;
-  float out_values[out_size] = {};
-
-  tfjs::wasm::register_tensor(x0_id, size, x_values);
-  tfjs::wasm::register_tensor(x1_id, size, x_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(3, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One xnn_operator should be created for first call to maxPool.
-  const size_t batch_size = 1;
-  const size_t input_height = 3;
-  const size_t input_width = 3;
-  const size_t filter_height = 2;
-  const size_t filter_width = 2;
-  const size_t pad_top = 0;
-  const size_t pad_right = 1;
-  const size_t pad_bottom = 1;
-  const size_t pad_left = 0;
-  const size_t dilation_height = 1;
-  const size_t dilation_width = 1;
-  const size_t stride_height = 1;
-  const size_t stride_width = 1;
-  const size_t input_channels = 1;
-  const size_t output_channels = 1;
-  tfjs::wasm::MaxPool(
-      x0_id, batch_size, input_height, input_width, filter_height, filter_width,
-      pad_top, pad_right, pad_bottom, pad_left, dilation_height, dilation_width,
-      stride_height, stride_width, input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to maxPool with
-  // the same arguments.
-  tfjs::wasm::MaxPool(
-      x0_id, batch_size, input_height, input_width, filter_height, filter_width,
-      pad_top, pad_right, pad_bottom, pad_left, dilation_height, dilation_width,
-      stride_height, stride_width, input_channels, output_channels, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the next call to maxPool with
-  // 'valid' padding.
-  tfjs::wasm::MaxPool(x0_id, batch_size, input_height, input_width,
-                      filter_height, filter_width, pad_top, 0 /* pad_right */,
-                      0 /* pad_bottom */, pad_left, dilation_height,
-                      dilation_width, stride_height, stride_width,
-                      input_channels, output_channels, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Maximum.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Maximum.cc
deleted file mode 100644
index f27da55fb..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Maximum.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Maximum(const int a_id, const size_t* a_shape_ptr, const int a_shape_len,
-             const int b_id, const size_t* b_shape_ptr, const int b_shape_len,
-             const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, [](float a, float b) { return std::max(a, b); });
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, [](int a, int b) { return std::max(a, b); });
-      break;
-    default:
-      util::warn(
-          "Maximum for tensor ids %d and %d failed. Unsupported dtype %d", a_id,
-          b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Mean.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Mean.cc
deleted file mode 100644
index f1328c410..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Mean.cc
+++ /dev/null
@@ -1,60 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Mean(const size_t x_id, const size_t reduce_size, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* x_buf = x_info.f32();
-
-  float* out_buf = out_info.f32_write();
-  const size_t out_size = out_info.size;
-
-  const float* x_offset = x_buf;
-
-  for (size_t i = 0; i < out_size; ++i) {
-    float sum = 0;
-
-    const float* x_iter_end = x_offset + reduce_size;
-
-    for (const float* x = x_offset; x < x_iter_end; ++x) {
-      sum += *x;
-    }
-
-    x_offset += reduce_size;
-
-    out_buf[i] = sum / reduce_size;
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Min.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Min.cc
deleted file mode 100644
index 642888b5c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Min.cc
+++ /dev/null
@@ -1,86 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <math.h>
-
-#include <cstddef>
-#include <iostream>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-void min(const T* x_buf, const size_t out_size, const size_t reduce_size,
-         T* out_buf) {
-  const T* x_offset = x_buf;
-
-  for (size_t i = 0; i < out_size; ++i) {
-    const size_t offset = i * reduce_size;
-    T min = x_buf[offset];
-
-    const T* x_iter_end = x_offset + reduce_size;
-
-    for (const T* x = x_offset; x < x_iter_end; ++x) {
-      T value = *x;
-      if (isnan(value) || value < min) {
-        min = value;
-      }
-    }
-
-    x_offset += reduce_size;
-
-    out_buf[i] = min;
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Min(const size_t x_id, const DType dtype, const size_t reduce_size,
-         const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const size_t out_size = out_info.size;
-
-  switch (dtype) {
-    case DType::float32:
-      min<float>(x_info.f32(), out_size, reduce_size, out_info.f32_write());
-      break;
-    case DType::int32:
-      min<int32_t>(x_info.i32(), out_size, reduce_size, out_info.i32_write());
-      break;
-    case DType::boolean:
-      min<bool>(x_info.b(), out_size, reduce_size, out_info.b_write());
-      break;
-    default:
-      util::warn("Min failed. Unknown dtype %d", dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Minimum.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Minimum.cc
deleted file mode 100644
index 8bb59649c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Minimum.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Minimum(const int a_id, const size_t* a_shape_ptr, const int a_shape_len,
-             const int b_id, const size_t* b_shape_ptr, const int b_shape_len,
-             const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, [](float a, float b) { return std::min(a, b); });
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, [](int a, int b) { return std::min(a, b); });
-      break;
-    default:
-      util::warn(
-          "Minimum for tensor ids %d and %d failed. Unsupported dtype %d", a_id,
-          b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MirrorPad.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MirrorPad.cc
deleted file mode 100644
index 3c899647b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/MirrorPad.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include <cstring>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-// Must match enum in MirrorPad.ts
-enum MirrorPaddingMode {
-  REFLECT = 0,
-  SYMMETRIC = 1,
-};
-
-namespace {
-
-using tfjs::util::compute_strides;
-using tfjs::util::size_from_shape;
-
-template <typename T>
-void mirror_pad_recursion(const T* x_data, const std::vector<size_t>& x_shape,
-                          const std::vector<size_t>& pre_paddings,
-                          const std::vector<size_t>& post_paddings,
-                          const int32_t offset,
-                          const std::vector<size_t>& in_strides,
-                          const std::vector<size_t>& out_strides, T* out_data,
-                          const size_t dim, size_t in_offset,
-                          size_t out_offset) {
-  const size_t depth = x_shape[dim];
-  const size_t rank = x_shape.size();
-  const size_t in_stride = dim == rank - 1 ? 1 : in_strides[dim];
-  const size_t out_stride = dim == rank - 1 ? 1 : out_strides[dim];
-
-  out_offset += pre_paddings[dim] * out_stride;
-
-  if (dim == rank - 1) {
-    memcpy(out_data + out_offset, x_data + in_offset, sizeof(T) * depth);
-  } else {
-    for (size_t i = 0; i < depth; ++i) {
-      mirror_pad_recursion(x_data, x_shape, pre_paddings, post_paddings, offset,
-                           in_strides, out_strides, out_data, dim + 1,
-                           in_offset + in_stride * i,
-                           out_offset + out_stride * i);
-    }
-  }
-
-  const T* src = out_data + out_offset + offset * out_stride;
-  T* dist = out_data + out_offset - out_stride;
-  for (size_t i = 0; i < pre_paddings[dim]; ++i) {
-    memcpy(dist, src, out_stride * sizeof(T));
-    src += out_stride;
-    dist -= out_stride;
-  }
-
-  out_offset += (depth - 1) * out_stride;
-
-  src = out_data + out_offset - offset * out_stride;
-  dist = out_data + out_offset + out_stride;
-  for (size_t i = 0; i < post_paddings[dim]; ++i) {
-    memcpy(dist, src, out_stride * sizeof(T));
-    src -= out_stride;
-    dist += out_stride;
-  }
-}
-
-template <typename T>
-void mirror_pad(const T* x_data, const std::vector<size_t>& x_shape,
-                const std::vector<size_t>& pre_paddings,
-                const std::vector<size_t>& post_paddings,
-                const MirrorPaddingMode mode, T* out_data) {
-  const size_t rank = x_shape.size();
-  std::vector<size_t> out_shape(rank);
-  for (size_t i = 0; i < rank; ++i) {
-    const size_t pad_left = pre_paddings[i];
-    const size_t pad_right = post_paddings[i];
-    out_shape[i] = x_shape[i] + pad_left + pad_right;
-  }
-  const int32_t offset = mode == MirrorPaddingMode::REFLECT ? 1 : 0;
-  std::vector<size_t> in_strides = compute_strides(x_shape);
-  std::vector<size_t> out_strides = compute_strides(out_shape);
-
-  mirror_pad_recursion(x_data, x_shape, pre_paddings, post_paddings, offset,
-                       in_strides, out_strides, out_data, 0, 0, 0);
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void MirrorPad(const size_t x_id, const size_t* x_shape_ptr,
-               const size_t x_shape_length, const DType dtype,
-               const size_t* pre_paddings_ptr, const size_t* post_paddings_ptr,
-               const MirrorPaddingMode mode, const size_t out_id) {
-  auto x_shape = std::vector<size_t>(x_shape_ptr, x_shape_ptr + x_shape_length);
-  auto pre_paddings =
-      std::vector<size_t>(pre_paddings_ptr, pre_paddings_ptr + x_shape_length);
-  auto post_paddings = std::vector<size_t>(post_paddings_ptr,
-                                           post_paddings_ptr + x_shape_length);
-
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  switch (dtype) {
-    case DType::float32:
-      mirror_pad<float>(x_info.f32(), x_shape, pre_paddings, post_paddings,
-                        mode, out_info.f32_write());
-      break;
-    case DType::int32:
-      mirror_pad<int32_t>(x_info.i32(), x_shape, pre_paddings, post_paddings,
-                          mode, out_info.i32_write());
-      break;
-    case DType::boolean:
-      mirror_pad<bool>(x_info.b(), x_shape, pre_paddings, post_paddings, mode,
-                       out_info.b_write());
-      break;
-    default:
-      util::warn("MirrorPad for tensor id %d failed. Unknown dtype % d ", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Mod.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Mod.cc
deleted file mode 100644
index a73dd2288..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Mod.cc
+++ /dev/null
@@ -1,76 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-
-namespace {
-
-template <typename T>
-inline T ModInt(T a, T b) {
-  T rem = a % b;
-  if ((a < 0 && b < 0) || (a >= 0 && b >= 0)) {
-    return rem;
-  }
-  return (rem + b) % b;
-}
-
-template <typename T>
-inline T ModFloat(T a, T b) {
-  T rem = std::fmod(a, b);
-  if ((a < 0 && b < 0) || (a >= 0 && b >= 0)) {
-    return rem;
-  }
-  return std::fmod(rem + b, b);
-}
-
-}  // namespace
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Mod(const size_t a_id, const size_t* a_shape_ptr, const size_t a_shape_len,
-         const size_t b_id, const size_t* b_shape_ptr, const size_t b_shape_len,
-         const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, ModFloat<float>);
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, ModInt<int32_t>);
-      break;
-    default:
-      util::warn("Mod for tensor ids %d and %d failed. Unsupported dtype %d",
-                 a_id, b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Multinomial.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Multinomial.cc
deleted file mode 100644
index 931512363..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Multinomial.cc
+++ /dev/null
@@ -1,62 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <random>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/shape.h"
-
-namespace tfjs::wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES
-// - Tensor `probabilities` is produced from normalized `logits`.
-// - Tensor `probabilities` must have dtype float32.
-// - Tensor `out` must have dtype int32.
-// - Tensor `probabilities` must have shape [batch_size, num_events].
-// - Tensor `out` must have shape [batch_size, num_samples].
-void Multinomial(const int probabilities_id, const int batch_size,
-                 const int num_events, const int num_samples, const float seed,
-                 const int out_id) {
-  const TensorInfo& prob_info = backend::get_tensor_info(probabilities_id);
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-  Shape<int, 2> probs_shape({batch_size, num_events});
-  Shape<int, 2> out_shape({batch_size, num_samples});
-  const float* probs_buf = prob_info.f32();
-  int* out_buf = out_info.i32_write();
-
-  std::mt19937 gen(*reinterpret_cast<const int32_t*>(&seed));
-  for (int b = 0; b < batch_size; ++b) {
-    const float* weights_begin = probs_buf + probs_shape.offset({b, 0});
-    const float* weights_end = weights_begin + num_events;
-    std::discrete_distribution<int32_t> distribution(weights_begin,
-                                                     weights_end);
-    for (int i = 0; i < num_samples; ++i) {
-      out_buf[out_shape.offset({b, i})] = distribution(gen);
-    }
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Multiply.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Multiply.cc
deleted file mode 100644
index 7baaaaad3..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Multiply.cc
+++ /dev/null
@@ -1,66 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline T mul(T a, T b) {
-  return a * b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Multiply(const size_t a_id, const size_t* a_shape_ptr,
-              const size_t a_shape_len, const size_t b_id,
-              const size_t* b_shape_ptr, const size_t b_shape_len,
-              const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_xnn_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                     b_shape_len, out_id, xnn_create_multiply_nd_f32,
-                     xnn_setup_multiply_nd_f32);
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, mul<int32_t>);
-      break;
-    case DType::boolean:
-      binary_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, mul<bool>);
-      break;
-    default:
-      util::warn("Mul for tensor ids %d and %d failed. Unknown dtype %d", a_id,
-                 b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Neg.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Neg.cc
deleted file mode 100644
index b21e9e4b6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Neg.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Neg(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_xnn_f32(x_id, out_id, xnn_create_negate_nc_f32,
-                    xnn_setup_negate_nc_f32);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, [](int a) {
-        return static_cast<int32_t>(static_cast<float>(-a));
-      });
-      break;
-    default:
-      util::warn(
-          "Neg for tensor ids %d failed. "
-          "Unknown dtype %d",
-          x_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NonMaxSuppressionV3.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NonMaxSuppressionV3.cc
deleted file mode 100644
index 018fc793e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NonMaxSuppressionV3.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cstddef>
-#include <cstring>
-#include <memory>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/non_max_suppression_impl.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-const NonMaxSuppressionResult* NonMaxSuppressionV3(
-    const size_t boxes_id, const size_t scores_id, const size_t max_out_size,
-    const float iou_threshold, const float score_threshold) {
-  return tfjs::wasm::non_max_suppression_impl(
-      boxes_id, scores_id, max_out_size, iou_threshold, score_threshold,
-      0.0 /* soft_nms_sigma */, false /* pad_to_max_output_size */);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NonMaxSuppressionV4.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NonMaxSuppressionV4.cc
deleted file mode 100644
index 4aeac0291..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NonMaxSuppressionV4.cc
+++ /dev/null
@@ -1,48 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <cstring>
-#include <memory>
-#include <queue>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/non_max_suppression_impl.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-const NonMaxSuppressionResult* NonMaxSuppressionV4(
-    const size_t boxes_id, const size_t scores_id, const size_t max_out_size,
-    const float iou_threshold, const float score_threshold,
-    const bool pad_to_max_output_size) {
-  return tfjs::wasm::non_max_suppression_impl(
-      boxes_id, scores_id, max_out_size, iou_threshold, score_threshold,
-      0.0 /* soft_nms_sigma */, pad_to_max_output_size);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NonMaxSuppressionV5.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NonMaxSuppressionV5.cc
deleted file mode 100644
index 84645a244..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NonMaxSuppressionV5.cc
+++ /dev/null
@@ -1,50 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <cstring>
-#include <memory>
-#include <queue>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/non_max_suppression_impl.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-const NonMaxSuppressionResult* NonMaxSuppressionV5(const size_t boxes_id,
-                                                   const size_t scores_id,
-                                                   const size_t max_out_size,
-                                                   const float iou_threshold,
-                                                   const float score_threshold,
-                                                   const float soft_nms_sigma) {
-  return tfjs::wasm::non_max_suppression_impl(
-      boxes_id, scores_id, max_out_size, iou_threshold, score_threshold,
-      soft_nms_sigma, false /* pad_to_max_output_size */);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NotEqual.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NotEqual.cc
deleted file mode 100644
index 0d67041ff..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/NotEqual.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline bool notEqual(T a, T b) {
-  return a != b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void NotEqual(const int a_id, const size_t* a_shape_ptr, const int a_shape_len,
-              const int b_id, const size_t* b_shape_ptr, const int b_shape_len,
-              const DType input_type, const int out_id) {
-  switch (input_type) {
-    case DType::float32:
-      compare_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, notEqual<float>);
-      break;
-    case DType::int32:
-      compare_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, notEqual<int>);
-      break;
-    case DType::boolean:
-      compare_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                   b_shape_len, out_id, notEqual<bool>);
-      break;
-    default:
-      util::warn(
-          "NotEqual for tensor ids %d and %d failed."
-          "Unsupported input_type %d",
-          a_id, b_id, input_type);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/OneHot.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/OneHot.cc
deleted file mode 100644
index c040f26f4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/OneHot.cc
+++ /dev/null
@@ -1,55 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void OneHot(const size_t indices_id, const size_t depth, const int32_t on_value,
-            const int32_t off_value, const size_t out_id) {
-  auto& indices_info = backend::get_tensor_info(indices_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const int* indices_buf = indices_info.i32();
-  const size_t indices_size = indices_info.size;
-
-  int* out_buf = out_info.i32_write();
-  const size_t out_size = out_info.size;
-
-  // Initialize output with off_value.
-  std::fill(out_buf, out_buf + out_size, off_value);
-
-  for (size_t i = 0; i < indices_size; ++i) {
-    if (indices_buf[i] >= 0 && indices_buf[i] < depth) {
-      out_buf[i * depth + indices_buf[i]] = on_value;
-    }
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/PadV2.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/PadV2.cc
deleted file mode 100644
index 03cf569fd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/PadV2.cc
+++ /dev/null
@@ -1,282 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <xnnpack.h>
-#include <cstddef>
-#include <cstring>
-#include <map>
-#include <memory>
-#include <tuple>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/PadV2.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// We use std::tuple as the cache key as it implements the compare operator
-// needed for std::map.
-typedef std::tuple<float, uint32_t> OperatorCacheKey;
-
-// The operator cache maps the weights id to the xnn_operator_t instantiated for
-// this set of weights.
-std::map<OperatorCacheKey, xnn_operator_t> operator_cache;
-
-}  // namespace
-
-namespace {
-
-using tfjs::util::compute_strides;
-using tfjs::util::loc_to_offset;
-using tfjs::util::offset_to_loc;
-using tfjs::util::size_from_shape;
-
-// Reference:
-// https://github.com/tensorflow/tensorflow/blob/a4190274142b70f3d5b7a27f93fc14abc7448240/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L4873
-template <typename T>
-void typed_memset(void* ptr, T value, size_t num) {
-  // Optimization for common cases where memset() will suffice.
-  if (value == 0 || std::is_same<T, uint8_t>::value) {
-    memset(ptr, value, num * sizeof(T));
-  } else {
-    // Default implementation for cases where memset() will not preserve the
-    // bytes, e.g., typically when sizeof(T) > sizeof(uint8_t).
-    char* pos = static_cast<char*>(ptr);
-    for (size_t i = 0; i < num; ++i) {
-      memcpy(pos, &value, sizeof(T));
-      pos += sizeof(T);
-    }
-  }
-}
-
-template <typename T>
-void pad_4d(const T* x_data, size_t x_shape[4], size_t paddings[8],
-            const T pad_value, size_t out_shape[4], T* out_data) {
-  const size_t left_b_pad = paddings[0];
-  const size_t right_b_pad = paddings[1];
-  const size_t left_h_pad = paddings[2];
-  const size_t right_h_pad = paddings[3];
-  const size_t left_w_pad = paddings[4];
-  const size_t right_w_pad = paddings[5];
-  const size_t left_d_pad = paddings[6];
-  const size_t right_d_pad = paddings[7];
-
-  const size_t batch = x_shape[0];
-  const size_t height = x_shape[1];
-  const size_t width = x_shape[2];
-  const size_t depth = x_shape[3];
-
-  const size_t out_height = out_shape[1];
-  const size_t out_width = out_shape[2];
-  const size_t out_depth = out_shape[3];
-
-  T* out_offset = out_data;
-  const T* x_offset = x_data;
-
-  if (left_b_pad != 0) {
-    typed_memset<T>(out_offset, pad_value,
-                    left_b_pad * out_height * out_width * out_depth);
-    out_offset += left_b_pad * out_height * out_width * out_depth;
-  }
-  for (size_t b = 0; b < batch; ++b) {
-    if (left_h_pad != 0) {
-      typed_memset<T>(out_offset, pad_value,
-                      left_h_pad * out_width * out_depth);
-      out_offset += left_h_pad * out_width * out_depth;
-    }
-    for (size_t h = 0; h < height; ++h) {
-      if (left_w_pad != 0) {
-        typed_memset<T>(out_offset, pad_value, left_w_pad * out_depth);
-        out_offset += left_w_pad * out_depth;
-      }
-      for (size_t w = 0; w < width; ++w) {
-        if (left_d_pad != 0) {
-          typed_memset<T>(out_offset, pad_value, left_d_pad);
-          out_offset += left_d_pad;
-        }
-        memcpy(out_offset, x_offset, depth * sizeof(T));
-        x_offset += depth;
-        out_offset += depth;
-
-        if (right_d_pad != 0) {
-          typed_memset<T>(out_offset, pad_value, right_d_pad);
-          out_offset += right_d_pad;
-        }
-      }
-      if (right_w_pad != 0) {
-        typed_memset<T>(out_offset, pad_value, right_w_pad * out_depth);
-        out_offset += right_w_pad * out_depth;
-      }
-    }
-    if (right_h_pad != 0) {
-      typed_memset<T>(out_offset, pad_value,
-                      right_h_pad * out_width * out_depth);
-      out_offset += right_h_pad * out_width * out_depth;
-    }
-  }
-  if (right_b_pad != 0) {
-    typed_memset<T>(out_offset, pad_value,
-                    right_b_pad * out_height * out_width * out_depth);
-  }
-}
-
-// Generic pad implementation for n-dim tensors.
-template <typename T>
-void slow_pad_nd(const T* x_data, const std::vector<size_t>& x_shape,
-                 const std::vector<size_t>& pre_paddings,
-                 const std::vector<size_t>& post_paddings, const T pad_value,
-                 T* out_data) {
-  const size_t rank = x_shape.size();
-  std::vector<size_t> out_shape(rank);
-  for (size_t i = 0; i < rank; ++i) {
-    const size_t pad_left = pre_paddings[i];
-    const size_t pad_right = post_paddings[i];
-    out_shape[i] = x_shape[i] + pad_left + pad_right;
-  }
-  const auto& in_strides = compute_strides(x_shape);
-  const auto& out_strides = compute_strides(out_shape);
-  const size_t in_size = size_from_shape(x_shape);
-  const size_t out_size = size_from_shape(out_shape);
-
-  typed_memset<T>(out_data, pad_value, out_size);
-
-  for (size_t i = 0; i < in_size; ++i) {
-    auto out_loc = offset_to_loc(i, in_strides);
-    for (size_t j = 0; j < rank; ++j) {
-      out_loc[j] += pre_paddings[j];
-    }
-    const size_t out_offset = loc_to_offset(out_loc, out_strides);
-    out_data[out_offset] = x_data[i];
-  }
-}
-
-template <typename T>
-void pad(const T* x_data, const std::vector<size_t>& x_shape,
-         const std::vector<size_t>& pre_paddings,
-         const std::vector<size_t>& post_paddings, const T pad_value,
-         T* out_data) {
-  const size_t rank = x_shape.size();
-  if (rank <= 4) {
-    // Expand the shape to be 4d.
-    size_t x_shape_4d[4];
-    size_t paddings_4d[8];
-    size_t out_shape_4d[4];
-    const size_t rank_shift = 4 - rank;
-    for (size_t i = 0; i < rank_shift; ++i) {
-      x_shape_4d[i] = 1;
-      out_shape_4d[i] = 1;
-      paddings_4d[i * 2] = 0;
-      paddings_4d[i * 2 + 1] = 0;
-    }
-
-    for (size_t i = 0; i < rank; ++i) {
-      size_t j = i + rank_shift;
-      const size_t pad_left = pre_paddings[i];
-      const size_t pad_right = post_paddings[i];
-      x_shape_4d[j] = x_shape[i];
-      out_shape_4d[j] = x_shape[i] + pad_left + pad_right;
-      paddings_4d[j * 2] = pad_left;
-      paddings_4d[j * 2 + 1] = pad_right;
-    }
-    pad_4d(x_data, x_shape_4d, paddings_4d, pad_value, out_shape_4d, out_data);
-  } else {
-    slow_pad_nd(x_data, x_shape, pre_paddings, post_paddings, pad_value,
-                out_data);
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void PadV2(const size_t x_id, const size_t* x_shape_ptr,
-           const size_t x_shape_length, const DType dtype,
-           const size_t* pre_paddings_ptr, const size_t* post_paddings_ptr,
-           const float pad_value, const size_t out_id) {
-  auto x_shape = std::vector<size_t>(x_shape_ptr, x_shape_ptr + x_shape_length);
-  auto pre_paddings =
-      std::vector<size_t>(pre_paddings_ptr, pre_paddings_ptr + x_shape_length);
-  auto post_paddings = std::vector<size_t>(post_paddings_ptr,
-                                           post_paddings_ptr + x_shape_length);
-
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  switch (dtype) {
-    case DType::float32: {
-      xnn_operator_t pad_op = nullptr;
-      const uint32_t flags = 0;
-
-      OperatorCacheKey cache_key = {pad_value, flags};
-
-      auto operator_cache_idx = operator_cache.find(cache_key);
-      if (operator_cache_idx == operator_cache.end()) {
-        xnn_status status =
-            xnn_create_constant_pad_nd_x32(&pad_value, flags, &pad_op);
-        if (status != xnn_status_success) {
-          tfjs::util::warn(
-              "XNN status for xnn_create_constant_pad_nd_x32 is not "
-              "successful. Got status %d. Use -c dbg to see XNN logs.",
-              status);
-          return;
-        }
-
-        operator_cache.insert({cache_key, pad_op});
-
-        tfjs::backend::xnn_operator_count++;
-      } else {
-        pad_op = operator_cache_idx->second;
-      }
-
-      xnn_status status = xnn_setup_constant_pad_nd_x32(
-          pad_op, x_shape_length, x_shape_ptr, pre_paddings_ptr,
-          post_paddings_ptr, x_info.f32(), out_info.f32_write(),
-          tfjs::backend::threadpool);
-      if (status != xnn_status_success) {
-        tfjs::util::warn(
-            "XNN status for xnn_setup_constant_pad_nd_x32 is not "
-            "successful. Got status %d. Use -c dbg to see XNN logs.",
-            status);
-        return;
-      }
-
-      xnn_run_operator(pad_op, tfjs::backend::threadpool);
-      break;
-    }
-    case DType::int32:
-      pad<int32_t>(x_info.i32(), x_shape, pre_paddings, post_paddings,
-                   static_cast<int32_t>(pad_value), out_info.i32_write());
-      break;
-    case DType::boolean:
-      pad<bool>(x_info.b(), x_shape, pre_paddings, post_paddings,
-                static_cast<bool>(pad_value), out_info.b_write());
-      break;
-    default:
-      util::warn("Pad for tensor id %d failed. Unknown dtype % d ", x_id,
-                 dtype);
-  }
-}
-
-}  // namespace wasm
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/PadV2.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/PadV2.h
deleted file mode 100644
index c7f600af5..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/PadV2.h
+++ /dev/null
@@ -1,35 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_PADV2_H_
-#define KERNELS_PADV2_H_
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void PadV2(const size_t x_id, const size_t* x_shape_ptr,
-           const size_t x_shape_length, const DType dtype,
-           const size_t* pre_paddings_ptr, const size_t* post_paddings_ptr,
-           const float pad_value, const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_PADV2_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/PadV2_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/PadV2_test.cc
deleted file mode 100644
index 1ce2b1a6a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/PadV2_test.cc
+++ /dev/null
@@ -1,84 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/PadV2.h"
-
-TEST(PADV2, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t x_size = 4;
-  float x_values[x_size] = {1, 2, 2, 2};
-
-  const size_t out_id = 3;
-  const size_t out_size = 8;
-  float out_values[out_size] = {0, 0, 0, 0, 0, 0, 0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, x_size, x_values);
-  tfjs::wasm::register_tensor(x1_id, x_size, x_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(3, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  const size_t x_rank = 4;
-
-  std::vector<size_t> x_shape = {1, 1, 1, 4};
-  std::vector<size_t> pre_paddings = {0, 0, 0, 0};
-  std::vector<size_t> post_paddings = {0, 0, 0, 0};
-
-  const float pad_value = 0.0;
-
-  size_t* x_shape_ptr = x_shape.data();
-  size_t* pre_paddings_ptr = pre_paddings.data();
-  size_t* post_paddings_ptr = post_paddings.data();
-
-  const DType dtype = float32;
-
-  // One new xnn_operator should be created for the first call to
-  // PadV2.
-  tfjs::wasm::PadV2(x0_id, x_shape_ptr, x_rank, dtype, pre_paddings_ptr,
-                    post_paddings_ptr, pad_value, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // // No new xnn_operators should be created for the second call to
-  // // PadV2 with the same arguments.
-  tfjs::wasm::PadV2(x0_id, x_shape_ptr, x_rank, dtype, pre_paddings_ptr,
-                    post_paddings_ptr, pad_value, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // // No new xnn_operators should be created for the second call to
-  // // PadV2 with a new x id but same arguments.
-  tfjs::wasm::PadV2(x1_id, x_shape_ptr, x_rank, dtype, pre_paddings_ptr,
-                    post_paddings_ptr, pad_value, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // // One new xnn_operator should be created for another call to PadV2
-  // // with a different pad_value.
-  const float new_pad_value = 0.5;
-  tfjs::wasm::PadV2(x0_id, x_shape_ptr, x_rank, dtype, pre_paddings_ptr,
-                    post_paddings_ptr, new_pad_value, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Pow.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Pow.cc
deleted file mode 100644
index fcb68f71a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Pow.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline T power(T a, T b) {
-  return pow(a, b);
-}
-
-inline bool power_bool(bool a, bool b) {
-  return static_cast<bool>(pow(static_cast<float>(a), static_cast<float>(b)));
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Pow(const size_t a_id, const size_t* a_shape_ptr, const size_t a_shape_len,
-         const size_t b_id, const size_t* b_shape_ptr, const size_t b_shape_len,
-         const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, power<float>);
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, power<int32_t>);
-      break;
-    case DType::boolean:
-      binary_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, power_bool);
-      break;
-    default:
-      util::warn("Pow for tensor ids %d and %d failed. Unknown dtype %d", a_id,
-                 b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prelu.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prelu.cc
deleted file mode 100644
index 118dea5a6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prelu.cc
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/kernels/Prelu.h"
-
-#include <xnnpack.h>
-#include <cmath>
-#include <cstddef>
-#include <unordered_map>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/prelu_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Prelu(const size_t x_id, const size_t weights_id, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  const float* x_buf = x_info.f32();
-
-  tfjs::wasm::prelu(x_buf, x_info.size, weights_id, out_id);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prelu.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prelu.h
deleted file mode 100644
index 72afeed46..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prelu.h
+++ /dev/null
@@ -1,30 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_PRELU_H_
-#define KERNELS_PRELU_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void Prelu(const size_t x_id, const size_t weights_id, const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_PRELU_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prelu_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prelu_test.cc
deleted file mode 100644
index 02942b093..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prelu_test.cc
+++ /dev/null
@@ -1,80 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/Prelu.h"
-
-TEST(PRELU, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  size_t x0_id = 1;
-  size_t x1_id = 2;
-  size_t size = 2;
-  float x_values[2] = {1, 2};
-
-  size_t weights0_id = 3;
-  size_t weights1_id = 4;
-  float weights_values[2] = {1, 2};
-
-  size_t out_id = 5;
-  float out_values[2] = {0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, size, x_values);
-  tfjs::wasm::register_tensor(x1_id, size, x_values);
-  tfjs::wasm::register_tensor(weights0_id, size, weights_values);
-  tfjs::wasm::register_tensor(weights1_id, size, weights_values);
-  tfjs::wasm::register_tensor(out_id, size, out_values);
-
-  ASSERT_EQ(5, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the first call to prelu.
-  tfjs::wasm::Prelu(x0_id, weights0_id, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to prelu with
-  // the same weights.
-  tfjs::wasm::Prelu(x1_id, weights0_id, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for another call to prelu with new
-  // weights.
-  tfjs::wasm::Prelu(x0_id, weights1_id, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the next call to prelu with
-  // the same weights.
-  tfjs::wasm::Prelu(x1_id, weights1_id, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing x's should not remove xnn operators.
-  tfjs::wasm::dispose_data(x0_id);
-  tfjs::wasm::dispose_data(x1_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing weights should remove xnn operators.
-  tfjs::wasm::dispose_data(weights0_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose_data(weights1_id);
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prod.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prod.cc
deleted file mode 100644
index e4effe531..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Prod.cc
+++ /dev/null
@@ -1,83 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-void prod(const size_t x_id, const size_t reduce_size, const size_t out_id) {
-  auto& x_info = tfjs::backend::get_tensor_info(x_id);
-  auto& out_info = tfjs::backend::get_tensor_info_out(out_id);
-
-  const T* x_buf = reinterpret_cast<const T *>(x_info.memory_offset);
-
-  T* out_buf = reinterpret_cast<T *>(out_info.memory_offset);
-  const size_t out_size = out_info.size;
-
-  const T* x_offset = x_buf;
-
-  for (size_t i = 0; i < out_size; ++i) {
-    T product = 1;
-
-    const T* x_iter_end = x_offset + reduce_size;
-
-    for (const T* x = x_offset; x < x_iter_end; ++x) {
-      product *= (*x);
-    }
-
-    x_offset += reduce_size;
-    out_buf[i] = product;
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Prod(const size_t x_id, const size_t reduce_size,
-          const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      prod<float>(x_id, reduce_size, out_id);
-      break;
-    case DType::int32:
-      prod<int32_t>(x_id, reduce_size, out_id);
-      break;
-    case DType::boolean:
-      prod<bool>(x_id, reduce_size, out_id);
-      break;
-    default:
-      util::warn("Prod failed. Unknown dtype %d", dtype);
-  }
-}
-
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/RealDiv.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/RealDiv.cc
deleted file mode 100644
index 702eb3d8a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/RealDiv.cc
+++ /dev/null
@@ -1,66 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <xnnpack.h>
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline T div(T a, T b) {
-  return a / b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void RealDiv(const size_t a_id, const size_t* a_shape_ptr,
-             const size_t a_shape_len, const size_t b_id,
-             const size_t* b_shape_ptr, const size_t b_shape_len,
-             const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_xnn_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                     b_shape_len, out_id, xnn_create_divide_nd_f32,
-                     xnn_setup_divide_nd_f32);
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, div<int32_t>);
-      break;
-    case DType::boolean:
-      binary_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, div<bool>);
-      break;
-    default:
-      util::warn("Div for tensor ids %d and %d failed. Unknown dtype %d", a_id,
-                 b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Reciprocal.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Reciprocal.cc
deleted file mode 100644
index 8ad46dc6c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Reciprocal.cc
+++ /dev/null
@@ -1,42 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace {
-float reciprocal(const float a) { return 1 / a; }
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Reciprocal(const int x_id, const DType dtype, const int out_id) {
-  unary_f32(x_id, out_id, reciprocal);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Relu.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Relu.cc
deleted file mode 100644
index 5b0360494..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Relu.cc
+++ /dev/null
@@ -1,56 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/clamp_impl.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Relu(const size_t x_id, const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32: {
-      const float min = 0;
-      const float max = std::numeric_limits<float>::infinity();
-      xnn_clamp(x_id, out_id, min, max);
-      break;
-    }
-    case DType::int32:
-      unary_i32(x_id, out_id, [](int a) { return (a > 0) ? a : 0; });
-      break;
-    default:
-      util::warn(
-          "Relu for tensor ids %d failed. "
-          "Unknown dtype %d",
-          x_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Relu6.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Relu6.cc
deleted file mode 100644
index ec046dcf2..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Relu6.cc
+++ /dev/null
@@ -1,56 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/clamp_impl.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Relu6(const size_t x_id, const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32: {
-      const float min = 0;
-      const float max = 6;
-      xnn_clamp(x_id, out_id, min, max);
-      break;
-    }
-    case DType::int32:
-      unary_i32(x_id, out_id,
-                [](int a) { return (a > 0) ? ((a > 6) ? 6 : a) : 0; });
-      break;
-    default:
-      util::warn(
-          "Relu6 for tensor ids %d failed. "
-          "Unknown dtype %d",
-          x_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinear.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinear.cc
deleted file mode 100644
index 567e54961..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinear.cc
+++ /dev/null
@@ -1,112 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <xnnpack.h>
-#include <cmath>
-#include <cstddef>
-#include <map>
-#include <tuple>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/kernels/ResizeBilinear.h"
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// We use std::tuple as the cache key as it implements the compare operator
-// needed for std::map.
-typedef std::tuple<size_t, uint32_t> OperatorCacheKey;
-
-// The operator cache maps the weights id to the xnn_operator_t instantiated for
-// this set of weights.
-std::map<OperatorCacheKey, xnn_operator_t> operator_cache;
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void ResizeBilinear(size_t x_id, size_t batch, size_t old_height,
-                    size_t old_width, size_t num_channels, size_t new_height,
-                    size_t new_width, bool align_corners,
-                    bool half_pixel_centers, size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* x_buf = x_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  xnn_operator_t resize_bilinear_op = nullptr;
-
-  uint32_t flags = 0;
-  if (align_corners) {
-    flags |= XNN_FLAG_ALIGN_CORNERS;
-  } else if (!half_pixel_centers) {
-    flags |= XNN_FLAG_TENSORFLOW_LEGACY_MODE;
-  }
-
-  OperatorCacheKey cache_key = {num_channels, flags};
-
-  auto operator_cache_idx = operator_cache.find(cache_key);
-  if (operator_cache_idx == operator_cache.end()) {
-    const size_t channels = num_channels;
-    const size_t input_pixel_stride = num_channels;
-    const size_t output_pixel_stride = num_channels;
-
-    xnn_status status = xnn_create_resize_bilinear2d_nhwc_f32(
-        channels, input_pixel_stride, output_pixel_stride, flags,
-        &resize_bilinear_op);
-    if (status != xnn_status_success) {
-      tfjs::util::warn(
-          "XNN status for xnn_create_resize_bilinear2d_nhwc_f32 is not "
-          "successful. Got status %d. Use -c dbg to see XNN logs.",
-          status);
-      return;
-    }
-
-    operator_cache.insert({cache_key, resize_bilinear_op});
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    resize_bilinear_op = operator_cache_idx->second;
-  }
-
-  xnn_status status = xnn_setup_resize_bilinear2d_nhwc_f32(
-      resize_bilinear_op, batch, old_height, old_width, new_height, new_width,
-      x_buf, out_buf, tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    tfjs::util::warn(
-        "XNN status for xnn_setup_resize_bilinear2d_nhwc_f32 is not "
-        "successful. Got status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(resize_bilinear_op, tfjs::backend::threadpool);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinear.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinear.h
deleted file mode 100644
index 64f4bde4d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinear.h
+++ /dev/null
@@ -1,33 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_RESIZEBILINEAR_H_
-#define KERNELS_RESIZEBILINEAR_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void ResizeBilinear(size_t x_id, size_t batch, size_t old_height,
-                    size_t old_width, size_t num_channels, size_t new_height,
-                    size_t new_width, bool align_corners,
-                    bool half_pixel_centers, size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_RESIZEBILINEAR_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinearGrad.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinearGrad.cc
deleted file mode 100644
index 6354db860..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinearGrad.cc
+++ /dev/null
@@ -1,114 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/shape.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-template <typename T, bool align_corners>
-inline void ResizeBilinearGradImpl(const T* x_buf, const T* dy_buf, T* dx_buf,
-                                   const Shape<int32_t, 4>& x_shape,
-                                   const Shape<int32_t, 4>& dy_shape) {
-  const auto [batch, x_height, x_width, depth] = x_shape.array();
-  const auto [y_batch, y_height, y_width, y_depth] = dy_shape.array();
-
-  // In the backwards pass, we want to find the pixels that were generated
-  // for each pixel in the input image the forward pass and add the
-  // corresponding coefficient from dy to the gradient (with some
-  // interpolation).
-  float height_scale =
-      static_cast<float>(x_height - (align_corners && y_height > 1)) /
-      static_cast<float>(y_height - (align_corners && y_height > 1));
-  float width_scale =
-      static_cast<float>(x_width - (align_corners && y_width > 1)) /
-      static_cast<float>(y_width - (align_corners && y_width > 1));
-
-  std::fill(dx_buf, dx_buf + x_shape.size(), 0);
-  for (int b = 0; b < batch; ++b) {
-    for (int r = 0; r < y_height; ++r) {
-      float dx_r = r * height_scale;
-      int top_dx_r = static_cast<int>(dx_r);
-      int bottom_dx_r =
-          std::min(static_cast<int>(std::ceil(dx_r)), x_height - 1);
-      float dx_r_lerp = dx_r - std::floor(dx_r);
-
-      for (int c = 0; c < y_width; ++c) {
-        float dx_c = c * width_scale;
-        int left_dx_c = static_cast<int>(dx_c);
-        int right_dx_c =
-            std::min(static_cast<int>(std::ceil(dx_c)), x_width - 1);
-        float dx_c_lerp = dx_c - std::floor(dx_c);
-
-        for (int d = 0; d < depth; ++d) {
-          float val = static_cast<float>(dy_buf[dy_shape.offset({b, r, c, d})]);
-          dx_buf[x_shape.offset({b, top_dx_r, left_dx_c, d})] +=
-              val * (1.0 - dx_r_lerp) * (1.0 - dx_c_lerp);
-          dx_buf[x_shape.offset({b, top_dx_r, right_dx_c, d})] +=
-              val * (1.0 - dx_r_lerp) * dx_c_lerp;
-
-          dx_buf[x_shape.offset({b, bottom_dx_r, left_dx_c, d})] +=
-              val * dx_r_lerp * (1.0 - dx_c_lerp);
-          dx_buf[x_shape.offset({b, bottom_dx_r, right_dx_c, d})] +=
-              val * dx_r_lerp * dx_c_lerp;
-        }
-      }
-    }
-  }
-}
-
-}  // namespace
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `images`, `dy`, and `dx` must be 4-dimensional (checked in
-// tfjs-core).
-// - Tensor `dy` must have dtype float32 (guaranteed in forward kernel).
-// - Tensor `images` and `dx` must have dtype float32 (casted in kernel).
-void ResizeBilinearGrad(int32_t images_id, int32_t dy_id, int32_t dx_id,
-                        const int32_t* images_shape_ptr,
-                        const int32_t* dy_shape_ptr, bool align_corners) {
-  // `x` here refers to `images` in the input.
-  const TensorInfo& x_info = backend::get_tensor_info(images_id);
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& dx_info = backend::get_tensor_info_out(dx_id);
-
-  Shape<int32_t, 4> x_shape(images_shape_ptr);
-  Shape<int32_t, 4> dy_shape(dy_shape_ptr);
-
-  if (align_corners) {
-    ResizeBilinearGradImpl<float, /*align_corners=*/true>(
-        x_info.f32(), dy_info.f32(), dx_info.f32_write(), x_shape, dy_shape);
-  } else {
-    ResizeBilinearGradImpl<float, /*align_corners=*/false>(
-        x_info.f32(), dy_info.f32(), dx_info.f32_write(), x_shape, dy_shape);
-  }
-}
-
-}  // extern "C"
-
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinear_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinear_test.cc
deleted file mode 100644
index f88ef9580..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeBilinear_test.cc
+++ /dev/null
@@ -1,94 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/ResizeBilinear.h"
-
-TEST(BATCH_MATMUL, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t x_size = 4;
-  float x_values[x_size] = {1, 2, 2, 2};
-
-  const size_t out_id = 3;
-  const size_t out_size = 8;
-  float out_values[out_size] = {0, 0, 0, 0, 0, 0, 0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, x_size, x_values);
-  tfjs::wasm::register_tensor(x1_id, x_size, x_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(3, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  const size_t batch_size = 1;
-  // One new xnn_operator should be created for the first call to
-  // ResizeBilinear.
-  const size_t old_height0 = 2;
-  const size_t old_width0 = 1;
-  const size_t num_channels0 = 2;
-  const size_t new_height0 = 2;
-  const size_t new_width0 = 2;
-  bool align_corners0 = false;
-  bool half_pixel_centers0 = false;
-  tfjs::wasm::ResizeBilinear(x0_id, batch_size, old_height0, old_width0,
-                             num_channels0, new_height0, new_width0,
-                             align_corners0, half_pixel_centers0, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to
-  // ResizeBilinear with the same arguments.
-  tfjs::wasm::ResizeBilinear(x0_id, batch_size, old_height0, old_width0,
-                             num_channels0, new_height0, new_width0,
-                             align_corners0, half_pixel_centers0, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to
-  // ResizeBilinear with a new x id but same arguments.
-  tfjs::wasm::ResizeBilinear(x1_id, batch_size, old_height0, old_width0,
-                             num_channels0, new_height0, new_width0,
-                             align_corners0, half_pixel_centers0, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for another call to ResizeBilinear
-  // with a different num_channels argument.
-  const size_t old_height1 = 2;
-  const size_t old_width1 = 2;
-  const size_t num_channels1 = 1;
-  const size_t new_height1 = 2;
-  const size_t new_width1 = 4;
-  tfjs::wasm::ResizeBilinear(x0_id, batch_size, old_height1, old_width1,
-                             num_channels1, new_height1, new_width1,
-                             align_corners0, half_pixel_centers0, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for another call to ResizeBilinear
-  // with a different align_corners argument
-  bool align_corners1 = true;
-  tfjs::wasm::ResizeBilinear(x0_id, batch_size, old_height1, old_width1,
-                             num_channels1, new_height1, new_width1,
-                             align_corners1, half_pixel_centers0, out_id);
-  ASSERT_EQ(3, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighbor.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighbor.cc
deleted file mode 100644
index e59d7aedc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighbor.cc
+++ /dev/null
@@ -1,125 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <algorithm>
-#include <vector>
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighbor.h"
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void ResizeNearestNeighbor(size_t x_id, size_t batch, size_t old_height,
-                    size_t old_width, size_t num_channels, size_t new_height,
-                    size_t new_width, bool align_corners,
-                    bool half_pixel_centers, size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* x_buf = x_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  const std::vector<size_t> x_shape = {
-    batch, old_height, old_width, num_channels
-  };
-  const auto image_strides = util::compute_strides(x_shape);
-
-  const float effective_input_height =
-      (align_corners && new_height > 1) ? old_height - 1 : old_height;
-
-  const float effective_input_width =
-      (align_corners && new_width > 1) ? old_width - 1 : old_width;
-
-
-  const float effective_output_height =
-      (align_corners && new_height > 1) ? new_height - 1 : new_height;
-
-  const float effective_output_width =
-      (align_corners && new_width > 1) ? new_width - 1 : new_width;
-
-
-  const float height_scale = effective_input_height / effective_output_height;
-  const float width_scale = effective_input_width / effective_output_width;
-
-  const float old_height_m1 = old_height - 1;
-  const float old_width_m1 = old_width - 1;
-
-  for (int b = 0; b < batch; ++b) {
-    const int batch_offset = b * image_strides[0];
-    for (int r = 0; r < new_height; ++r) {
-      float* out_buf_ptr = out_buf +
-        b * (new_height * new_width * num_channels) +
-        r * (new_width * num_channels);
-
-      const float y_ind = half_pixel_centers ?
-
-        height_scale * (r + 0.5) :
-        height_scale * r;
-
-      int top_ind = std::min(
-        old_height_m1,
-        align_corners ? std::roundf(y_ind) : std::floor(y_ind));
-
-      if (half_pixel_centers) {
-        top_ind = std::max(0, top_ind);
-      }
-
-      const float y_lerp = y_ind - top_ind;
-
-      if (width_scale == 1 && y_lerp == 0) {
-        memcpy(out_buf_ptr, x_buf + batch_offset + top_ind * image_strides[1],
-               sizeof(float) * new_width * num_channels);
-      } else {
-        for (size_t x = 0; x < new_width; ++x) {
-          const float x_ind = half_pixel_centers ?
-                            width_scale * (x + 0.5) :
-                            width_scale * x;
-
-          int closest_x = std::min(old_width_m1, align_corners ?
-                                  std::round(x_ind) :
-                                  std::floor(x_ind));
-
-          if (half_pixel_centers) {
-            closest_x = std::max(0, closest_x);
-          }
-
-          for (size_t c = 0; c < num_channels; ++c) {
-            const size_t in_ind = c + closest_x * image_strides[2] +
-                                  top_ind * image_strides[1] + batch_offset;
-            *out_buf_ptr = x_buf[in_ind];
-            out_buf_ptr++;
-          }
-        }
-      }
-    }
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighbor.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighbor.h
deleted file mode 100644
index 1a3b18906..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighbor.h
+++ /dev/null
@@ -1,33 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_RESIZENEARESTNEIGHBOR_H_
-#define KERNELS_RESIZENEARESTNEIGHBOR_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void ResizeNearestNeighbor(size_t x_id, size_t batch, size_t old_height,
-                    size_t old_width, size_t num_channels, size_t new_height,
-                    size_t new_width, bool align_corners,
-                    bool half_pixel_centers, size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_RESIZENEARESTNEIGHBOR_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighborGrad.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighborGrad.cc
deleted file mode 100644
index dfc66b1fc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ResizeNearestNeighborGrad.cc
+++ /dev/null
@@ -1,132 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/shape.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-template <typename T, bool align_corners>
-inline void ResizeNearestNeighborGradImpl(const T* x_buf, const T* dy_buf,
-                                          T* dx_buf,
-                                          const Shape<int32_t, 4>& x_shape,
-                                          const Shape<int32_t, 4>& dy_shape) {
-  const auto [batch, x_height, x_width, depth] = x_shape.array();
-  const auto [y_batch, y_height, y_width, y_depth] = dy_shape.array();
-
-  // In the backwards pass, we want to find the pixels that were generated
-  // for each pixel in the input image the forward pass and add the
-  // corresponding coefficient from dy to the gradient (with some
-  // interpolation).
-  float height_scale =
-      static_cast<float>(x_height - (align_corners && y_height > 1)) /
-      static_cast<float>(y_height - (align_corners && y_height > 1));
-  float width_scale =
-      static_cast<float>(x_width - (align_corners && y_width > 1)) /
-      static_cast<float>(y_width - (align_corners && y_width > 1));
-
-  // This defines the size of the window of values around a particular
-  // index in dy that we want to search for contributions to dx.
-  int window_height = static_cast<int>(std::ceil(1.0 / height_scale)) * 2 + 2;
-  int window_width = static_cast<int>(std::ceil(1.0 / width_scale)) * 2 + 2;
-
-  // Loop over the output space.
-  for (int b = 0; b < batch; ++b) {
-    for (int r = 0; r < x_height; ++r) {
-      int start_dy_r = static_cast<int>(r / height_scale) - (window_height / 2);
-
-      for (int c = 0; c < x_width; ++c) {
-        int start_dy_c = static_cast<int>(c / width_scale) - (window_width / 2);
-
-        for (int d = 0; d < depth; ++d) {
-          T accum = 0;
-          for (int dy_r = std::max(start_dy_r, 0);
-               dy_r < std::min(start_dy_r + window_height, y_height); ++dy_r) {
-            float source_frac_r = dy_r * height_scale;
-
-            int source_nearest_r = std::min(
-                x_height - 1,
-                static_cast<int>(align_corners ? std::round(source_frac_r)
-                                               : source_frac_r));
-            if (r != source_nearest_r) {
-              continue;
-            }
-
-            for (int dy_c = std::max(start_dy_c, 0);
-                 dy_c < std::min(start_dy_c + window_width, y_width); ++dy_c) {
-              float source_frac_c = dy_c * width_scale;
-              int source_nearest_c = std::min(
-                  x_width - 1,
-                  static_cast<int>(align_corners ? std::round(source_frac_c)
-                                                 : source_frac_c));
-
-              if (c != source_nearest_c) {
-                continue;
-              }
-
-              accum += dy_buf[dy_shape.offset({b, dy_r, dy_c, d})];
-            }
-          }
-          dx_buf[x_shape.offset({b, r, c, d})] = accum;
-        }
-      }
-    }
-  }
-}
-
-}  // namespace
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `images`, `dy`, and `dx` must be 4-dimensional (checked in
-// tfjs-core).
-// - Tensor `dy` must have dtype float32 (guaranteed in forward kernel).
-// - Tensor `images` and `dx` must have dtype float32 (casted in kernel).
-void ResizeNearestNeighborGrad(int32_t images_id, int32_t dy_id, int32_t dx_id,
-                               const int32_t* images_shape_ptr,
-                               const int32_t* dy_shape_ptr,
-                               bool align_corners) {
-  // `x` here refers to `images` in the input.
-  const TensorInfo& x_info = backend::get_tensor_info(images_id);
-  const TensorInfo& dy_info = backend::get_tensor_info(dy_id);
-  TensorInfo& dx_info = backend::get_tensor_info_out(dx_id);
-
-  Shape<int32_t, 4> x_shape(images_shape_ptr);
-  Shape<int32_t, 4> dy_shape(dy_shape_ptr);
-
-  if (align_corners) {
-    ResizeNearestNeighborGradImpl<float, /*align_corners=*/true>(
-        x_info.f32(), dy_info.f32(), dx_info.f32_write(), x_shape, dy_shape);
-  } else {
-    ResizeNearestNeighborGradImpl<float, /*align_corners=*/false>(
-        x_info.f32(), dy_info.f32(), dx_info.f32_write(), x_shape, dy_shape);
-  }
-}
-
-}  // extern "C"
-
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Reverse.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Reverse.cc
deleted file mode 100644
index 690bf8b60..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Reverse.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-// Note that the following implementation also works with int32 since it just
-// moves values around.
-void Reverse(const size_t x_id, const size_t* axes_ptr,
-             const size_t axes_length, const size_t* out_shape_ptr,
-             const size_t out_shape_length, const size_t out_id) {
-  auto out_shape =
-      std::vector<size_t>(out_shape_ptr, out_shape_ptr + out_shape_length);
-  auto axes = std::vector<size_t>(axes_ptr, axes_ptr + axes_length);
-
-  auto& x_info = backend::get_tensor_info(x_id);
-  const float* x_buf = x_info.f32();
-
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  float* out_buf = out_info.f32_write();
-
-  size_t x_size = x_info.size;
-
-  const std::vector<size_t> out_strides =
-      tfjs::util::compute_strides(out_shape);
-
-  for (size_t i = 0; i < x_size; ++i) {
-    std::vector<size_t> in_loc = tfjs::util::offset_to_loc(i, out_strides);
-
-    for (size_t ax_i = 0; ax_i < axes_length; ++ax_i) {
-      size_t ax = axes[ax_i];
-      in_loc[ax] = out_shape[ax] - 1 - in_loc[ax];
-    }
-
-    const size_t x_position = tfjs::util::loc_to_offset(in_loc, out_strides);
-    out_buf[i] = x_buf[x_position];
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/RotateWithOffset.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/RotateWithOffset.cc
deleted file mode 100644
index 3b7be5ee4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/RotateWithOffset.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-#include <cmath>
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void RotateWithOffset(const size_t image_id, const size_t batch,
-                      const size_t image_height, const size_t image_width,
-                      const size_t num_channels, const float radians,
-                      const float center_x, const float center_y,
-                      size_t* fill_ptr, const size_t fill_length,
-                      const size_t out_id) {
-  auto fill = std::vector<size_t>(fill_ptr, fill_ptr + fill_length);
-  auto& image_info = backend::get_tensor_info(image_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* image_buf = image_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  const float sin_factor = sin(radians);
-  const float cos_factor = cos(radians);
-
-  for (size_t batch_idx = 0; batch_idx < batch; ++batch_idx) {
-    const size_t batch_offset =
-        batch_idx * image_width * image_height * num_channels;
-    for (size_t row = 0; row < image_height; ++row) {
-      for (size_t col = 0; col < image_width; ++col) {
-        for (size_t channel = 0; channel < num_channels; ++channel) {
-          const size_t x = col;
-          const size_t y = row;
-
-          float coord_x_f =
-              (x - center_x) * cos_factor - (y - center_y) * sin_factor;
-          float coord_y_f =
-              (x - center_x) * sin_factor + (y - center_y) * cos_factor;
-          const int coord_x = round(coord_x_f + center_x);
-          const int coord_y = round(coord_y_f + center_y);
-
-          float output_value = fill[channel];
-          // If the coordinate position falls within the image boundaries...
-          if (coord_x >= 0 && coord_x < image_width && coord_y >= 0 &&
-              coord_y < image_height) {
-            // set the output to the image value at the coordinate position.
-            const size_t rotated_row_offset =
-                coord_y * (image_width * num_channels);
-            const size_t rotated_col_offset = coord_x * num_channels;
-            const size_t image_idx = batch_offset + rotated_row_offset +
-                                     rotated_col_offset + channel;
-            output_value = image_buf[image_idx];
-          }
-
-          *out_buf = output_value;
-          out_buf++;
-        }
-      }
-    }
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Round.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Round.cc
deleted file mode 100644
index 469a2eb19..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Round.cc
+++ /dev/null
@@ -1,52 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Round(const size_t x_id, const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32: {
-      unary_xnn_f32(x_id, out_id, xnn_create_bankers_rounding_nc_f32,
-                    xnn_setup_bankers_rounding_nc_f32);
-      break;
-    }
-    case DType::int32:
-      unary_i32(x_id, out_id, [](int a) { return a; });
-      break;
-    default:
-      util::warn(
-          "Round for tensor ids %d failed. "
-          "Unknown dtype %d",
-          x_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Rsqrt.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Rsqrt.cc
deleted file mode 100644
index e1892d25d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Rsqrt.cc
+++ /dev/null
@@ -1,41 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace {
-float rsqrt(const float a) { return 1 / sqrt(a); }
-}  // namespace
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Rsqrt(const int x_id, const DType dtype, const int out_id) {
-  unary_f32(x_id, out_id, rsqrt);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ScatterNd.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ScatterNd.cc
deleted file mode 100644
index 05f23836d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/ScatterNd.cc
+++ /dev/null
@@ -1,83 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstring>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/scatter_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <typename T>
-void scatter_nd(const int* indices_ptr, const T* updates_ptr,
-                const size_t slice_rank, const size_t num_updates,
-                const size_t slice_size, const std::vector<size_t>& strides_ptr,
-                const size_t output_size, T* out_buf_ptr) {
-  T default_value{};
-  bool sum_dupe_indices = true;
-  bool update_as_scalar = false;
-  tfjs::wasm::scatter(indices_ptr, updates_ptr, slice_rank, num_updates,
-                      slice_size, strides_ptr, output_size, default_value,
-                      sum_dupe_indices, update_as_scalar, out_buf_ptr);
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void ScatterNd(const size_t indices_id, const size_t updates_id,
-               const DType dtype, const size_t slice_rank,
-               const size_t num_updates, const size_t slice_size,
-               const size_t* strides_ptr, const size_t output_size,
-               const size_t out_id) {
-  auto& indices_info = backend::get_tensor_info(indices_id);
-  auto& updates_info = backend::get_tensor_info(updates_id);
-  const std::vector<size_t>& strides =
-      std::vector<size_t>(strides_ptr, strides_ptr + slice_rank);
-  const int* indices_buf = indices_info.i32();
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  switch (dtype) {
-    case DType::float32:
-      scatter_nd<float>(indices_buf, updates_info.f32(), slice_rank,
-                        num_updates, slice_size, strides, output_size,
-                        out_info.f32_write());
-      break;
-    case DType::int32:
-      scatter_nd<int32_t>(indices_buf, updates_info.i32(), slice_rank,
-                          num_updates, slice_size, strides, output_size,
-                          out_info.i32_write());
-      break;
-    case DType::boolean:
-      scatter_nd<bool>(indices_buf, updates_info.b(), slice_rank, num_updates,
-                       slice_size, strides, output_size, out_info.b_write());
-      break;
-    default:
-      util::warn("Scatter for tensor id %d failed. Unknown dtype %d",
-                 indices_id, dtype);
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SearchSorted.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SearchSorted.cc
deleted file mode 100644
index 449366e45..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SearchSorted.cc
+++ /dev/null
@@ -1,100 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-template <typename T>
-inline void SearchSortedImpl(const T* sorted_sequence_buf, const T* values_buf,
-                             int32_t* out_buf, int batch_size,
-                             int sequence_size, int values_size,
-                             const bool is_lower_bound) {
-  for (int b = 0; b < batch_size; ++b) {
-    const T* seq_it = sorted_sequence_buf + b * sequence_size;
-    const T* val_it = values_buf + b * values_size;
-    int32_t* out_it = out_buf + b * values_size;
-    for (int i = 0; i < values_size; ++i) {
-      if (is_lower_bound) {
-        out_it[i] =
-            std::lower_bound(seq_it, seq_it + sequence_size, val_it[i]) -
-            seq_it;
-      } else {
-        out_it[i] =
-            std::upper_bound(seq_it, seq_it + sequence_size, val_it[i]) -
-            seq_it;
-      }
-    }
-  }
-}
-
-}  // namespace
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// REQUIRES:
-// - Tensor `sorted_sequence` must have shape [batch_size, sequence_size]
-// (checked in tfjs-core)
-// - Tensor `values` must have shape [batch_size, values_size], with batch_size
-// equals to sorted_sequence's batch_size (checked in tfjs-core)
-// - Tensor `sorted_sequence` and `values` must have the same dtype, as param
-// `dtype`.
-void SearchSorted(const int sorted_sequence_id, int values_id,
-                  const int batch_size, const int sequence_size,
-                  const int values_size, const DType dtype,
-                  const bool is_side_left, const int out_id) {
-  const TensorInfo& sorted_sequence_info =
-      backend::get_tensor_info(sorted_sequence_id);
-  const TensorInfo& values_info = backend::get_tensor_info(values_id);
-  TensorInfo& out_info = backend::get_tensor_info_out(out_id);
-
-  switch (dtype) {
-    case DType::float32:
-      SearchSortedImpl<float>(sorted_sequence_info.f32(), values_info.f32(),
-                              out_info.i32_write(),
-                              /*batch_size=*/batch_size,
-                              /*sequence_size*/ sequence_size,
-                              /*values_size=*/values_size,
-                              /*is_lower_bound=*/is_side_left);
-      break;
-    case DType::int32:
-      SearchSortedImpl<int32_t>(sorted_sequence_info.i32(), values_info.i32(),
-                                out_info.i32_write(),
-                                /*batch_size=*/batch_size,
-                                /*sequence_size*/ sequence_size,
-                                /*values_size=*/values_size,
-                                /*is_lower_bound=*/is_side_left);
-      break;
-    default:
-      util::warn("SearchSorted for tensor id %d failed. Unsupported dtype %d",
-                 sorted_sequence_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SelectV2.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SelectV2.cc
deleted file mode 100644
index 9b96ce793..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SelectV2.cc
+++ /dev/null
@@ -1,60 +0,0 @@
-
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-// Note that the following implementation also works with int32 since it just
-// moves values from either t_buf or e_buf to out_buf.
-void SelectV2(const int condition_id, const int t_id, const int e_id,
-              const int offset, const int out_id) {
-  auto& condition_info = backend::get_tensor_info(condition_id);
-  auto& t_info = backend::get_tensor_info(t_id);
-  auto& e_info = backend::get_tensor_info(e_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const bool* condition_buf = condition_info.b();
-  const float* t_buf = t_info.f32();
-  const float* e_buf = e_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  const size_t values_size = condition_info.size;
-
-  for (size_t i = 0; i < values_size; ++i) {
-    for (size_t j = 0; j < offset; ++j) {
-      if (condition_buf[i]) {
-        *out_buf = t_buf[i];
-      } else {
-        *out_buf = e_buf[i];
-      }
-      out_buf++;
-    }
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Selu.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Selu.cc
deleted file mode 100644
index 9147136ba..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Selu.cc
+++ /dev/null
@@ -1,60 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline T SeluImpl(T n) {
-  static constexpr float kScaleAlpha = 1.7580993408473768599402175208123;
-  static constexpr float kScale = 1.0507009873554804934193349852946;
-
-  if (n > 0) return kScale * n;
-  return kScaleAlpha * (std::expf(static_cast<float>(n)) - 1.0);
-}
-
-}  // namespace
-
-namespace tfjs::wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Selu(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, SeluImpl<float>);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, SeluImpl<int32_t>);
-      break;
-    default:
-      util::warn("Selu for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sigmoid.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sigmoid.cc
deleted file mode 100644
index b88a6caa0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sigmoid.cc
+++ /dev/null
@@ -1,47 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <xnnpack.h>
-#include <cmath>
-#include <cstddef>
-#include <map>
-#include <tuple>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/Sigmoid.h"
-#include "tfjs-backend-wasm/src/cc/sigmoid_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Sigmoid(const size_t x_id, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  const float* x_buf = x_info.f32();
-
-  tfjs::wasm::sigmoid(x_buf, x_info.size, out_id);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sigmoid.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sigmoid.h
deleted file mode 100644
index 57966325a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sigmoid.h
+++ /dev/null
@@ -1,30 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_SIGMOID_H_
-#define KERNELS_SIGMOID_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void Sigmoid(const size_t x_id, const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_SIGMOID_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sigmoid_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sigmoid_test.cc
deleted file mode 100644
index e90c04019..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sigmoid_test.cc
+++ /dev/null
@@ -1,59 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/Sigmoid.h"
-
-TEST(BATCH_MATMUL, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t x_size = 4;
-  float x_values[x_size] = {1, 2, 2, 2};
-
-  const size_t out_id = 3;
-  const size_t out_size = 8;
-  float out_values[out_size] = {0, 0, 0, 0, 0, 0, 0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, x_size, x_values);
-  tfjs::wasm::register_tensor(x1_id, x_size, x_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(3, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the first call to Sigmoid.
-  tfjs::wasm::Sigmoid(x0_id, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to
-  // Sigmoid with the same arguments.
-  tfjs::wasm::Sigmoid(x0_id, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to
-  // Sigmoid with different arguments.
-  tfjs::wasm::Sigmoid(x1_id, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sign.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sign.cc
deleted file mode 100644
index fda60cc68..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sign.cc
+++ /dev/null
@@ -1,56 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs::wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Sign(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, [](float n) -> float {
-        static constexpr float kEps = std::numeric_limits<float>::epsilon();
-        if (std::isnan(n)) return 0;
-        if (n < kEps && n > -kEps) return 0;
-        return n > 0 ? 1 : -1;
-      });
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, [](int32_t n) {
-        if (n == 0) return 0;
-        return n > 0 ? 1 : -1;
-      });
-      break;
-    default:
-      util::warn("Sign for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sin.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sin.cc
deleted file mode 100644
index 223a2de9d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sin.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/sin_cos_workaround.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Sin(const int x_id, const DType dtype, const int out_id) {
-  unary_f32(x_id, out_id, tfjs::sin_cos_workaround::SinFixed);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sinh.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sinh.cc
deleted file mode 100644
index dff6d07fa..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sinh.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-
-namespace {
-
-template <typename T>
-inline T SinhImpl(T x) {
-  return static_cast<T>(std::sinhf(static_cast<float>(x)));
-}
-
-}  // namespace
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Sinh(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, SinhImpl<float>);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, SinhImpl<int32_t>);
-      break;
-    default:
-      util::warn("Sinh for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softmax.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softmax.cc
deleted file mode 100644
index 8195a5782..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softmax.cc
+++ /dev/null
@@ -1,99 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <xnnpack.h>
-#include <cmath>
-#include <cstddef>
-#include <map>
-#include <tuple>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/Softmax.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// We use std::tuple as the cache key as it implements the compare operator
-// needed for std::map.
-typedef std::tuple<size_t> OperatorCacheKey;
-
-// The operator cache maps the weights id to the xnn_operator_t instantiated for
-// this set of weights.
-std::map<OperatorCacheKey, xnn_operator_t> operator_cache;
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Softmax(const size_t x_id, const size_t out_id, const size_t channels,
-             const size_t batch) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* x_buf = x_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  xnn_operator_t softmax_op = nullptr;
-
-  OperatorCacheKey cache_key = {channels};
-
-  auto operator_cache_idx = operator_cache.find(cache_key);
-  if (operator_cache_idx == operator_cache.end()) {
-    const size_t input_stride = channels;
-    const size_t output_stride = channels;
-    const uint32_t flags = 0;
-
-    xnn_status status = xnn_create_softmax_nc_f32(
-        channels, input_stride, output_stride, flags, &softmax_op);
-    if (status != xnn_status_success) {
-      tfjs::util::warn(
-          "XNN status for xnn_create_softmax_nc_f32 is not "
-          "successful. Got status %d. Use -c dbg to see XNN logs.",
-          status);
-      return;
-    }
-
-    operator_cache.insert({cache_key, softmax_op});
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    softmax_op = operator_cache_idx->second;
-  }
-
-  xnn_status status = xnn_setup_softmax_nc_f32(
-      softmax_op, batch, x_buf, out_buf, tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    tfjs::util::warn(
-        "XNN status for xnn_setup_softmax_nc_f32 is not "
-        "successful. Got status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(softmax_op, tfjs::backend::threadpool);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softmax.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softmax.h
deleted file mode 100644
index f2cd8ebc0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softmax.h
+++ /dev/null
@@ -1,30 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS_SOFTMAX_H_
-#define KERNELS_SOFTMAX_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-void Softmax(const size_t x_id, const size_t out_id, const size_t channels,
-             const size_t batch);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS_SOFTMAX_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softmax_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softmax_test.cc
deleted file mode 100644
index 0632874e9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softmax_test.cc
+++ /dev/null
@@ -1,59 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/Softmax.h"
-
-TEST(SOFTMAX, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  const size_t x0_id = 1;
-  const size_t x1_id = 2;
-  const size_t x_size = 4;
-  float x_values[x_size] = {1, 2, 2, 2};
-
-  const size_t out_id = 3;
-  const size_t out_size = 4;
-  float out_values[out_size] = {0, 0, 0, 0};
-
-  tfjs::wasm::register_tensor(x0_id, x_size, x_values);
-  tfjs::wasm::register_tensor(x1_id, x_size, x_values);
-  tfjs::wasm::register_tensor(out_id, out_size, out_values);
-
-  ASSERT_EQ(3, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for the first call to Softmax.
-  tfjs::wasm::Softmax(x0_id, out_id, 4, 1);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to
-  // Softmax with the same arguments.
-  tfjs::wasm::Softmax(x0_id, out_id, 4, 1);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to
-  // Softmax with different arguments.
-  tfjs::wasm::Softmax(x1_id, out_id, 4, 1);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softplus.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softplus.cc
deleted file mode 100644
index a59a21a5d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Softplus.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <limits>
-
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-inline T SoftplusImpl(T n) {
-  static const float kThreshold =
-      std::logf(std::numeric_limits<float>::epsilon()) + 2.0;
-
-  float f = static_cast<float>(n);
-  if (f < kThreshold) return std::expf(f);
-  if (f > -kThreshold) return n;
-  return std::log(1.0 + std::expf(f));
-}
-
-}  // namespace
-
-namespace tfjs::wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Softplus(const int x_id, const DType dtype, const int out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_f32(x_id, out_id, SoftplusImpl<float>);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, SoftplusImpl<int32_t>);
-      break;
-    default:
-      util::warn("Softplus for tensor id %d failed. Unsupported dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseFillEmptyRows.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseFillEmptyRows.cc
deleted file mode 100644
index ef158916c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseFillEmptyRows.cc
+++ /dev/null
@@ -1,188 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-#include <numeric>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-size_t sparse_fill_empty_rows(const int32_t* indices,
-                              const size_t indices_count, const size_t rank,
-                              const T* values, const size_t dense_rows,
-                              const T default_value, int32_t* output_indices,
-                              T* output_values, bool* empty_row_indicator,
-                              int32_t* reverse_index_map,
-                              int32_t* exception_values) {
-  // Initialize to no exceptions.
-  exception_values[0] = 0;
-
-  if (dense_rows == 0) {
-    if (indices_count != 0) {
-      exception_values[0] = 1;
-      exception_values[1] = indices_count;
-    }
-    return 0;
-  }
-
-  bool rows_are_ordered = true;
-  size_t last_indices_row = 0;
-  std::vector<size_t> csr_offset(dense_rows, 0);
-
-  for (size_t i = 0; i < indices_count; ++i) {
-    // indices is a 2d tensor with shape of [N, rank]
-    const int32_t row = indices[i * rank];
-    if (row < 0) {
-      exception_values[0] = 2;
-      exception_values[1] = i;
-      exception_values[2] = row;
-      return 0;
-    }
-    if (row >= dense_rows) {
-      exception_values[0] = 3;
-      exception_values[1] = i;
-      exception_values[2] = row;
-      exception_values[3] = dense_rows;
-      return 0;
-    }
-    ++csr_offset[row];
-    rows_are_ordered = rows_are_ordered && (row >= last_indices_row);
-    last_indices_row = row;
-  }
-
-  bool all_rows_full = true;
-  for (size_t row = 0; row < dense_rows; ++row) {
-    // csr_offset here describes the number of elements in this dense row
-    const bool row_empty = (csr_offset[row] == 0);
-    empty_row_indicator[row] = row_empty;
-    all_rows_full = all_rows_full && !row_empty;
-    // In filled version, each row has at least one element.
-    csr_offset[row] = std::max<size_t>(csr_offset[row], 1);
-    // Update csr_offset to represent the number of elements up to and
-    // including dense_rows + 1:
-    //  csr_offset[0] == #{elements of row 0}
-    //  csr_offset[1] == #{elements of row 1} + #{elements of row 0}
-    //  ..
-    //  csr_offset[i] == starting index for elements in row i + 1.
-    if (row > 0) {
-      csr_offset[row] += csr_offset[row - 1];
-    }
-  }
-
-  if (all_rows_full && rows_are_ordered) {
-    std::copy(indices, indices + indices_count * rank, output_indices);
-    std::copy(values, values + indices_count, output_values);
-    std::iota(reverse_index_map, reverse_index_map + indices_count, 0);
-    return indices_count;
-  } else {
-    const size_t full_indices_count = csr_offset[dense_rows - 1];
-    std::vector<size_t> filled_count(dense_rows, 0 * full_indices_count);
-
-    // Fill in values for rows that are not missing
-    for (size_t i = 0; i < indices_count; ++i) {
-      // indices is a 2d tensor with shape of [N, rank]
-      const int32_t row = indices[i * rank];
-      const size_t offset = filled_count[row];
-      const size_t output_i = ((row == 0) ? 0 : csr_offset[row - 1]) + offset;
-      filled_count[row]++;  // Increment the filled count for this row.
-      for (size_t j = 0; j < rank; ++j) {
-        // indices and output_indices are 2d tensors with shape of [_, rank]
-        output_indices[output_i * rank + j] = indices[i * rank + j];
-      }
-      output_values[output_i] = values[i];
-      // We'll need this reverse index map to backprop correctly.
-      reverse_index_map[i] = output_i;
-    }
-
-    // Fill in values for rows that are missing
-    for (size_t row = 0; row < dense_rows; ++row) {
-      const size_t row_count = filled_count[row];
-      if (row_count == 0) {  // We haven't filled this row
-        const size_t starting_index = (row == 0) ? 0 : csr_offset[row - 1];
-        // Remaining index values were set to zero already.
-        // Just need to set the row index in the right location.
-        // output_indices is a 2d tensor with shape of [_, rank]
-        output_indices[starting_index * rank + 0] = row;
-        for (size_t col = 1; col < rank; ++col) {
-          output_indices[starting_index * rank + col] = 0;
-        }
-        output_values[starting_index] = default_value;
-      }
-    }
-    return full_indices_count;
-  }
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-size_t SparseFillEmptyRows(
-    const size_t indices_id, const size_t values_id, const DType values_dtype,
-    const size_t indices_count, const size_t dense_rows, const size_t rank,
-    const size_t default_value_id, const size_t output_indices_id,
-    const size_t output_values_id, const size_t empty_row_indicator_id,
-    const size_t reverse_index_map_id, const size_t exception_values_id) {
-  auto& indices_info = backend::get_tensor_info(indices_id);
-  auto& values_info = backend::get_tensor_info(values_id);
-  auto& default_value_info = backend::get_tensor_info(default_value_id);
-  auto& output_indices_info = backend::get_tensor_info_out(output_indices_id);
-  auto& output_values_info = backend::get_tensor_info_out(output_values_id);
-  auto& empty_row_indicator_info =
-      backend::get_tensor_info_out(empty_row_indicator_id);
-  auto& reverse_index_map_info =
-      backend::get_tensor_info_out(reverse_index_map_id);
-  auto& exception_values_info =
-      backend::get_tensor_info_out(exception_values_id);
-
-  const int32_t* indices = indices_info.i32();
-  int32_t* output_indices = output_indices_info.i32_write();
-  bool* empty_row_indicator = empty_row_indicator_info.b_write();
-  int32_t* reverse_index_map = reverse_index_map_info.i32_write();
-  int32_t* exception_values = exception_values_info.i32_write();
-
-  switch (values_dtype) {
-    case DType::float32:
-      return sparse_fill_empty_rows<float>(
-          indices, indices_count, rank, values_info.f32(), dense_rows,
-          default_value_info.f32()[0], output_indices,
-          output_values_info.f32_write(), empty_row_indicator,
-          reverse_index_map, exception_values);
-    case DType::int32:
-      return sparse_fill_empty_rows<int32_t>(
-          indices, indices_count, rank, values_info.i32(), dense_rows,
-          default_value_info.i32()[0], output_indices,
-          output_values_info.i32_write(), empty_row_indicator,
-          reverse_index_map, exception_values);
-    default:
-      util::warn("SparseFillEmptyRows failed. Unknown dtype %d", values_dtype);
-      return 0;
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseReshape.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseReshape.cc
deleted file mode 100644
index 9123d531e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseReshape.cc
+++ /dev/null
@@ -1,143 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void SparseReshape(const size_t input_indices_id, const size_t input_shape_id,
-                   const size_t target_shape_id, const size_t nnz,
-                   const size_t new_indices_id, const size_t output_shape_id,
-                   const size_t exception_values_id) {
-  auto& input_indices_info = backend::get_tensor_info(input_indices_id);
-  auto& input_shape_info = backend::get_tensor_info(input_shape_id);
-  auto& target_shape_info = backend::get_tensor_info_out(target_shape_id);
-  auto& new_indices_info = backend::get_tensor_info_out(new_indices_id);
-  auto& output_shape_info = backend::get_tensor_info_out(output_shape_id);
-  auto& exception_values_info =
-      backend::get_tensor_info_out(exception_values_id);
-
-  const int32_t* input_indices = input_indices_info.i32();
-  const int32_t* input_shape = input_shape_info.i32();
-  const int32_t* target_shape = target_shape_info.i32();
-  int32_t* new_indices = new_indices_info.i32_write();
-  int32_t* output_shape = output_shape_info.i32_write();
-  int32_t* exception_values = exception_values_info.i32_write();
-
-  // Initialize to no exceptions.
-  exception_values[0] = -1;
-
-  const int32_t dense_size =
-      util::size_from_shape({input_shape, input_shape + input_shape_info.size});
-  const int32_t output_rank = target_shape_info.size;
-
-  // Compute the output shape. Determine product of specified dimensions, and
-  // find the index of the unspecified one.
-  int32_t product = 1;
-  int32_t unknown_index = -1;
-  for (int32_t d = 0; d < output_rank; ++d) {
-    const int32_t size = target_shape[d];
-    if (size == -1) {
-      if (unknown_index != -1) {
-        exception_values[0] = 0;
-        exception_values[1] = unknown_index;
-        exception_values[2] = d;
-        return;
-      }
-      unknown_index = d;
-      output_shape[d] = 1;
-    } else {
-      if (size < 0) {
-        exception_values[0] = 1;
-        exception_values[1] = d;
-        exception_values[2] = size;
-        return;
-      }
-      product *= size;
-      output_shape[d] = size;
-    }
-  }
-  if (unknown_index != -1) {
-    if (product <= 0) {
-      exception_values[0] = 2;
-      return;
-    }
-    const int32_t missing =
-        std::trunc(static_cast<double>(dense_size) / product);
-    if (product * missing != dense_size) {
-      exception_values[0] = 3;
-      exception_values[1] = dense_size;
-      exception_values[2] = product;
-      return;
-    }
-
-    output_shape[unknown_index] = missing;
-  }
-  const int32_t output_size =
-      util::size_from_shape({output_shape, output_shape + output_rank});
-  if (output_size != dense_size) {
-    exception_values[0] = 4;
-    exception_values[1] = dense_size;
-    exception_values[2] = output_size;
-    return;
-  }
-
-  const int32_t input_rank = input_shape_info.size;
-  std::vector<int32_t> input_strides(input_rank);
-  if (input_rank > 0) {
-    input_strides[input_rank - 1] = 1;
-    for (int32_t d = input_rank - 2; d >= 0; --d) {
-      input_strides[d] = input_strides[d + 1] * input_shape[d + 1];
-    }
-  }
-
-  std::vector<int32_t> output_strides(output_rank);
-  if (output_rank > 0) {
-    output_strides[output_rank - 1] = 1;
-    for (int32_t d = output_rank - 2; d >= 0; --d) {
-      output_strides[d] = output_strides[d + 1] * output_shape[d + 1];
-    }
-  }
-
-  for (size_t i = 0; i < nnz; ++i) {
-    size_t id = 0;
-    for (size_t j = 0; j < input_rank; ++j) {
-      // input_indices is a 2d tensor with shape of [nnz, input_rank]
-      id += input_indices[i * input_rank + j] * input_strides[j];
-    }
-    for (size_t j = 0; j < output_rank; ++j) {
-      // new_indices is a 2d tensor with shape of [nnz, output_rank]
-      new_indices[i * output_rank + j] =
-          std::trunc(static_cast<double>(id) / output_strides[j]);
-      id %= output_strides[j];
-    }
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseSegmentReduction.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseSegmentReduction.cc
deleted file mode 100644
index dcdf19909..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseSegmentReduction.cc
+++ /dev/null
@@ -1,181 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-void sparse_segment_reduction(const T* input, const size_t input_length,
-                              const size_t num_row, const int32_t* indices,
-                              const size_t num_indices,
-                              const int32_t* segment_ids, T* output,
-                              size_t output_length, int32_t* exception_values,
-                              const bool is_mean, const int32_t default_value) {
-  // Initialize to no exceptions.
-  exception_values[0] = -1;
-
-  // Flatten the array to two dimensions
-  const size_t input_flat[] = {num_row, input_length / num_row};
-  const size_t num_col = input_flat[1];
-  // Note that the current implementation assumes that segment_ids values are
-  // sorted.
-  const int32_t last_segment_id_plus_one =
-      num_indices > 0 ? segment_ids[num_indices - 1] + 1 : 0;
-  const int32_t output_rows = last_segment_id_plus_one;
-
-  if (output_rows < 0) {
-    exception_values[0] = 0;
-    return;
-  }
-
-  // Note that we do not initialize the output buffer with a default value, so
-  // we need to explicitly set missing indices to the default value.
-  if (num_indices == 0) {
-    if (output_rows > 0) {
-      std::fill(output, output + output_length, default_value);
-    }
-    return;
-  }
-
-  if (output_rows <= 0) {
-    exception_values[0] = 0;
-    return;
-  }
-
-  std::fill(output, output + output_length, 0);
-
-  int32_t start = 0, end = 1;
-  // Index from which the output is not initialized.
-  int32_t uninitialized_index = 0;
-  int32_t out_index = segment_ids[start];
-
-  while (true) {
-    // We initialize next_index to 0 to avoid may be uninitialized warning
-    int32_t next_index = 0;
-    if (end < num_indices) {
-      next_index = segment_ids[end];
-      if (out_index == next_index) {
-        ++end;
-        continue;
-      }
-      // We have a new segment here.  Verify that the segment ids are growing.
-      if (out_index >= next_index) {
-        exception_values[0] = 1;
-        return;
-      }
-    }
-
-    if (out_index < 0 || out_index >= output_rows) {
-      exception_values[0] = 2;
-      exception_values[1] = out_index;
-      exception_values[2] = output_rows;
-      return;
-    }
-
-    // If there is a gap between two indices, we need to set that gap to the
-    // default value.
-    if (out_index > uninitialized_index) {
-      std::fill(output + uninitialized_index * num_col,
-                output + out_index * num_col, default_value);
-    }
-
-    for (int32_t i = start; i < end; ++i) {
-      const int32_t index = indices[i];
-      if (index < 0 || index >= input_flat[0]) {
-        exception_values[0] = 3;
-        exception_values[1] = i;
-        exception_values[2] = indices[i];
-        exception_values[3] = input_flat[0];
-      }
-      for (int32_t j = 0; j < num_col; j++) {
-        output[out_index * num_col + j] += input[index * num_col + j];
-      }
-    }
-
-    if (is_mean) {
-      for (size_t j = 0; j < num_col; j++) {
-        output[out_index * num_col + j] /= end - start;
-      }
-    }
-
-    start = end;
-    ++end;
-    uninitialized_index = out_index + 1;
-    out_index = next_index;
-    if (end > num_indices) {
-      break;
-    }
-  }
-
-  // Fill the gap at the end with the default value.
-  if (uninitialized_index < output_rows) {
-    std::fill(output + uninitialized_index * num_col,
-              output + output_rows * num_col, default_value);
-  }
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void SparseSegmentReduction(const size_t input_id, const DType dtype,
-                            const size_t num_row, const size_t indices_id,
-                            const size_t segment_ids_id, const size_t output_id,
-                            const size_t exception_values_id,
-                            const bool is_mean, const int32_t default_value) {
-  auto& input_info = backend::get_tensor_info(input_id);
-  auto& indices_info = backend::get_tensor_info(indices_id);
-  auto& segment_ids_info = backend::get_tensor_info_out(segment_ids_id);
-  auto& output_info = backend::get_tensor_info_out(output_id);
-  auto& exception_values_info =
-      backend::get_tensor_info_out(exception_values_id);
-
-  const int32_t* indices = indices_info.i32();
-  const int32_t* segment_ids = segment_ids_info.i32();
-  int32_t* exception_values = exception_values_info.i32_write();
-
-  switch (dtype) {
-    case DType::float32:
-      sparse_segment_reduction<float>(
-          input_info.f32(), input_info.size, num_row, indices,
-          indices_info.size, segment_ids, output_info.f32_write(),
-          output_info.size, exception_values, is_mean, default_value);
-      break;
-    case DType::int32:
-      sparse_segment_reduction<int32_t>(
-          input_info.i32(), input_info.size, num_row, indices,
-          indices_info.size, segment_ids, output_info.i32_write(),
-          output_info.size, exception_values, is_mean, default_value);
-      break;
-    default:
-      util::warn("SparseSegmentReduction failed. Unknown dtype %d", dtype);
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseToDense.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseToDense.cc
deleted file mode 100644
index e120c1c63..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SparseToDense.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/scatter_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <typename T>
-void sparse_to_dense(const int* sparse_indices_ptr, const T* sparse_values_ptr,
-                     const size_t sparse_values_rank,
-                     const T* default_value_ptr, const size_t slice_rank,
-                     const size_t num_updates, const size_t slice_size,
-                     const std::vector<size_t>& strides_ptr,
-                     const size_t output_size, T* out_buf_ptr) {
-  T default_value = *default_value_ptr;
-  bool sum_dupe_indices = false;
-  bool update_as_scalar = sparse_values_rank == 0;
-  tfjs::wasm::scatter(sparse_indices_ptr, sparse_values_ptr, slice_rank,
-                      num_updates, slice_size, strides_ptr, output_size,
-                      default_value, sum_dupe_indices, update_as_scalar,
-                      out_buf_ptr);
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void SparseToDense(const size_t sparse_indices_id,
-                   const size_t sparse_values_id,
-                   const size_t sparse_values_rank,
-                   const size_t default_value_id, const DType dtype,
-                   const size_t slice_rank, const size_t num_updates,
-                   const size_t slice_size, const size_t* strides_ptr,
-                   const size_t output_size, const size_t out_id) {
-  auto& sparse_indices_info = backend::get_tensor_info(sparse_indices_id);
-  auto& sparse_values_info = backend::get_tensor_info(sparse_values_id);
-  auto& default_value_info = backend::get_tensor_info(default_value_id);
-  const std::vector<size_t> strides(strides_ptr, strides_ptr + slice_rank);
-  const int* sparse_indices_buf = sparse_indices_info.i32();
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  switch (dtype) {
-    case DType::float32:
-      sparse_to_dense<float>(sparse_indices_buf, sparse_values_info.f32(),
-                             sparse_values_rank, default_value_info.f32(),
-                             slice_rank, num_updates, slice_size, strides,
-                             output_size, out_info.f32_write());
-      break;
-    case DType::int32:
-      sparse_to_dense<int32_t>(sparse_indices_buf, sparse_values_info.i32(),
-                               sparse_values_rank, default_value_info.i32(),
-                               slice_rank, num_updates, slice_size, strides,
-                               output_size, out_info.i32_write());
-      break;
-    case DType::boolean:
-      sparse_to_dense<bool>(sparse_indices_buf, sparse_values_info.b(),
-                            sparse_values_rank, default_value_info.b(),
-                            slice_rank, num_updates, slice_size, strides,
-                            output_size, out_info.b_write());
-      break;
-    default:
-      util::warn("SparseToDense for tensor id %d failed. Unknown dtype %d",
-                 sparse_indices_id, dtype);
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sqrt.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sqrt.cc
deleted file mode 100644
index 06561a191..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sqrt.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Sqrt(const int x_id, const DType dtype, const int out_id) {
-  unary_f32(x_id, out_id, sqrt);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Square.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Square.cc
deleted file mode 100644
index 260d0a648..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Square.cc
+++ /dev/null
@@ -1,51 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Square(const size_t x_id, const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      unary_xnn_f32(x_id, out_id, xnn_create_square_nc_f32,
-                    xnn_setup_square_nc_f32);
-      break;
-    case DType::int32:
-      unary_i32(x_id, out_id, [](int a) { return a * a; });
-      break;
-    default:
-      util::warn(
-          "Square for tensor ids %d failed. "
-          "Unknown dtype %d",
-          x_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SquaredDifference.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SquaredDifference.cc
deleted file mode 100644
index 3f7766073..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/SquaredDifference.cc
+++ /dev/null
@@ -1,71 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline T squared_diff(T a, T b) {
-  return (a - b) * (a - b);
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void SquaredDifference(const size_t a_id, const size_t* a_shape_ptr,
-                       const size_t a_shape_len, const size_t b_id,
-                       const size_t* b_shape_ptr, const size_t b_shape_len,
-                       const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_xnn_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                     b_shape_len, out_id, xnn_create_subtract_nd_f32,
-                     xnn_setup_subtract_nd_f32);
-      unary_xnn_f32(out_id, out_id, xnn_create_square_nc_f32,
-                    xnn_setup_square_nc_f32);
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, squared_diff<int32_t>);
-      break;
-    case DType::boolean:
-      binary_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, squared_diff<bool>);
-      break;
-    default:
-      util::warn(
-          "SquaredDifference for tensor ids %d and %d failed. "
-          "Unknown dtype %d",
-          a_id, b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Step.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Step.cc
deleted file mode 100644
index 8602c0fdb..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Step.cc
+++ /dev/null
@@ -1,67 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-void step(const T* x_buf, const size_t x_size, const T alpha, T* out_buf) {
-  for (size_t i = 0; i < x_size; ++i) {
-    if (isnan(static_cast<float>(x_buf[i]))) {
-      out_buf[i] = x_buf[i];
-    } else {
-      out_buf[i] = x_buf[i] > 0 ? 1 : alpha;
-    }
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Step(const int x_id, const float alpha, const DType dtype,
-          const int out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  switch (dtype) {
-    case DType::float32:
-      step<float>(x_info.f32(), x_info.size, alpha, out_info.f32_write());
-      break;
-    case DType::int32:
-      step<int32_t>(x_info.i32(), x_info.size, static_cast<int32_t>(alpha),
-                    out_info.i32_write());
-      break;
-    default:
-      util::warn("Step failed. Unknown dtype %d", dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/StridedSlice.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/StridedSlice.cc
deleted file mode 100644
index 938817fb1..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/StridedSlice.cc
+++ /dev/null
@@ -1,69 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-// Note that the following implementation also works with int32 since it just
-// moves values around.
-void StridedSlice(const size_t x_id, const int32_t* x_strides_ptr,
-                  const size_t x_rank, const int32_t* begin_ptr,
-                  const int32_t* end_ptr, const int32_t* strides_ptr,
-                  const int32_t* out_shape_ptr, const int32_t* out_strides_ptr,
-                  const size_t out_shape_size, const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  const size_t out_size = out_info.size;
-
-  const float* x_ptr = x_info.f32();
-  float* out_buf_ptr = out_info.f32_write();
-
-  const auto x_strides =
-      std::vector<size_t>(x_strides_ptr, x_strides_ptr + x_rank - 1);
-  const auto begin = std::vector<size_t>(begin_ptr, begin_ptr + x_rank);
-  const auto end = std::vector<size_t>(end_ptr, end_ptr + x_rank);
-  const auto strides = std::vector<size_t>(strides_ptr, strides_ptr + x_rank);
-  const auto out_shape =
-      std::vector<size_t>(out_shape_ptr, out_shape_ptr + out_shape_size);
-  const auto out_strides = std::vector<size_t>(
-      out_strides_ptr, out_strides_ptr + out_shape_size - 1);
-
-  for (size_t i = 0; i < out_size; ++i) {
-    auto coords = tfjs::util::offset_to_loc(i, out_strides);
-
-    std::vector<size_t> new_loc = {};
-    for (size_t j = 0; j < out_shape_size; ++j) {
-      new_loc.push_back(coords[j] * strides[j] + begin[j]);
-    }
-
-    const size_t x_index = tfjs::util::loc_to_offset(new_loc, x_strides);
-    *out_buf_ptr = x_ptr[x_index];
-
-    out_buf_ptr++;
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sub.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sub.cc
deleted file mode 100644
index 39f1d26e6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sub.cc
+++ /dev/null
@@ -1,65 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-#include <xnnpack.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/binary.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline T sub(T a, T b) {
-  return a - b;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Sub(const size_t a_id, const size_t* a_shape_ptr, const size_t a_shape_len,
-         const size_t b_id, const size_t* b_shape_ptr, const size_t b_shape_len,
-         const DType dtype, const size_t out_id) {
-  switch (dtype) {
-    case DType::float32:
-      binary_xnn_f32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                     b_shape_len, out_id, xnn_create_subtract_nd_f32,
-                     xnn_setup_subtract_nd_f32);
-      break;
-    case DType::int32:
-      binary_i32(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr, b_shape_len,
-                 out_id, sub<int32_t>);
-      break;
-    case DType::boolean:
-      binary_bool(a_id, a_shape_ptr, a_shape_len, b_id, b_shape_ptr,
-                  b_shape_len, out_id, sub<bool>);
-      break;
-    default:
-      util::warn("Sub for tensor ids %d and %d failed. Unknown dtype %d", a_id,
-                 b_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sum.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sum.cc
deleted file mode 100644
index 88b1cb975..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Sum.cc
+++ /dev/null
@@ -1,79 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include <cstdio>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-template <typename T>
-void sum(const T* x_buf, const size_t out_size, const size_t reduce_size,
-         T* out_buf) {
-  const T* x_offset = x_buf;
-
-  for (size_t i = 0; i < out_size; ++i) {
-    T sum = 0;
-
-    const T* x_iter_end = x_offset + reduce_size;
-
-    for (const T* x = x_offset; x < x_iter_end; ++x) {
-      sum += *x;
-    }
-
-    x_offset += reduce_size;
-
-    out_buf[i] = sum;
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Sum(const size_t x_id, const size_t reduce_size, const DType dtype,
-         const size_t out_id) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const size_t out_size = out_info.size;
-
-  switch (dtype) {
-    case DType::float32:
-      sum<float>(x_info.f32(), out_size, reduce_size, out_info.f32_write());
-      break;
-    case DType::int32:
-      sum<int32_t>(x_info.i32(), out_size, reduce_size, out_info.i32_write());
-      break;
-    default:
-      util::warn("Sum failed. Unknown dtype %d", dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Tan.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Tan.cc
deleted file mode 100644
index ae8fc0410..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Tan.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/sin_cos_workaround.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Tan(const int x_id, const DType dtype, const int out_id) {
-  unary_f32(x_id, out_id, tfjs::sin_cos_workaround::TanFixed);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Tanh.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Tanh.cc
deleted file mode 100644
index 775d9fb7a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Tanh.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <math.h>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Tanh(const int x_id, const DType dtype, const int out_id) {
-  unary_f32(x_id, out_id, tanh);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/TensorScatterUpdate.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/TensorScatterUpdate.cc
deleted file mode 100644
index 15ba0b273..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/TensorScatterUpdate.cc
+++ /dev/null
@@ -1,98 +0,0 @@
-/* Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstring>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <typename T>
-void scatter(const int* indices_ptr, const T* updates_ptr,
-             const size_t slice_rank, const size_t num_updates,
-             const size_t slice_size, const std::vector<size_t>& strides_ptr,
-             const size_t output_size, const size_t dtype_size, T* out_buf_ptr,
-             const T* tensor_ptr) {
-  // Initialize output to tensor.
-  memcpy(out_buf_ptr, tensor_ptr, output_size * dtype_size);
-
-  for (size_t i = 0; i < num_updates; ++i) {
-    size_t flattened_index = 0;
-    for (size_t j = 0; j < slice_rank; ++j) {
-      flattened_index += *indices_ptr * strides_ptr[j];
-
-      indices_ptr++;
-    }
-
-    out_buf_ptr += flattened_index * slice_size;
-
-    memcpy(out_buf_ptr, updates_ptr, slice_size * dtype_size);
-    out_buf_ptr += slice_size;
-    updates_ptr += slice_size;
-
-    out_buf_ptr -= (flattened_index * slice_size + slice_size);
-  }
-}
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void TensorScatterUpdate(const size_t indices_id, const size_t updates_id,
-                         const DType dtype, const size_t slice_rank,
-                         const size_t num_updates, const size_t slice_size,
-                         const size_t* strides_ptr, const size_t output_size,
-                         const size_t out_id, const size_t tensor_id) {
-  auto& indices_info = backend::get_tensor_info(indices_id);
-  auto& updates_info = backend::get_tensor_info(updates_id);
-  auto& tensor_info = backend::get_tensor_info(tensor_id);
-  const std::vector<size_t>& strides =
-      std::vector<size_t>(strides_ptr, strides_ptr + slice_rank);
-  const int* indices_buf = indices_info.i32();
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  switch (dtype) {
-    case DType::float32:
-      scatter<float>(indices_buf, updates_info.f32(), slice_rank, num_updates,
-                     slice_size, strides, output_size, sizeof(float),
-                     out_info.f32_write(), tensor_info.f32());
-      break;
-    case DType::int32:
-      scatter<int32_t>(indices_buf, updates_info.i32(), slice_rank, num_updates,
-                       slice_size, strides, output_size, sizeof(int32_t),
-                       out_info.i32_write(), tensor_info.i32());
-      break;
-    case DType::boolean:
-      scatter<bool>(indices_buf, updates_info.b(), slice_rank, num_updates,
-                    slice_size, strides, output_size, sizeof(bool),
-                    out_info.b_write(), tensor_info.b());
-      break;
-    default:
-      util::warn("Scatter for tensor id %d failed. Unknown dtype %d",
-                 indices_id, dtype);
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Tile.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Tile.cc
deleted file mode 100644
index 2f1ff228f..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Tile.cc
+++ /dev/null
@@ -1,90 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <typename T>
-void tile_slow(const T* x_data, const std::vector<size_t>& x_shape,
-               const std::vector<size_t>& new_shape, T* out_data) {
-  const size_t x_rank = x_shape.size();
-  const std::vector<size_t> x_strides = tfjs::util::compute_strides(x_shape);
-
-  const size_t out_size = tfjs::util::size_from_shape(new_shape);
-  const std::vector<size_t> out_strides =
-      tfjs::util::compute_strides(new_shape);
-
-  for (size_t i = 0; i < out_size; ++i) {
-    const std::vector<size_t> new_loc =
-        tfjs::util::offset_to_loc(i, out_strides);
-
-    std::vector<size_t> original_loc(x_rank);
-
-    for (size_t j = 0; j < original_loc.size(); ++j) {
-      original_loc[j] = new_loc[j] % x_shape[j];
-    }
-
-    const size_t original_index =
-        tfjs::util::loc_to_offset(original_loc, x_strides);
-
-    out_data[i] = x_data[original_index];
-  }
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Tile(const size_t x_id, const size_t* x_shape_ptr,
-          const size_t x_shape_length, const size_t* new_shape_ptr,
-          const size_t new_shape_length, const DType dtype,
-          const size_t out_id) {
-  auto x_shape = std::vector<size_t>(x_shape_ptr, x_shape_ptr + x_shape_length);
-  auto new_shape =
-      std::vector<size_t>(new_shape_ptr, new_shape_ptr + new_shape_length);
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  switch (dtype) {
-    case DType::float32:
-      tile_slow<float>(x_info.f32(), x_shape, new_shape, out_info.f32_write());
-      break;
-    case DType::int32:
-      tile_slow<int32_t>(x_info.i32(), x_shape, new_shape,
-                         out_info.i32_write());
-      break;
-    case DType::boolean:
-      tile_slow<bool>(x_info.b(), x_shape, new_shape, out_info.b_write());
-      break;
-    default:
-      util::warn("Tile for tensor id %d failed. Unknown dtype %d", x_id, dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/TopK.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/TopK.cc
deleted file mode 100644
index 0122f46da..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/TopK.cc
+++ /dev/null
@@ -1,173 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-using std::swap;
-using std::vector;
-
-namespace tfjs {
-
-template <typename T>
-struct ValAndInd {
-  T value;
-  size_t index;
-  bool operator<(const ValAndInd& other) const {
-    return value == other.value ? index < other.index : value > other.value;
-  }
-  bool operator==(const ValAndInd& other) const {
-    return value == other.value && index == other.index;
-  }
-};
-
-template <typename T>
-T sign(T value) {
-  if (value == 0) return 0;
-  return value < 0 ? -1 : 1;
-}
-
-template <typename T>
-void select(ValAndInd<T>* array, int k, int left, int right) {
-  while (right > left) {
-    // Use select recursively to sample a smaller set of size s
-    // the arbitrary constants 600 and 0.5 are used in the original
-    // version to minimize execution time.
-    if (right - left > 600) {
-      const int n = right - left + 1;
-      const int i = k - left + 1;
-      const auto z = log(n);
-      const auto s = 0.5 * exp(2 * z / 3);
-      const auto sd = 0.5 * sqrt(z * s * (n - s) / n) * sign(i - n / 2);
-      const int newLeft = std::max(left, static_cast<int>(k - i * s / n + sd));
-      const int newRight =
-          std::min(right, static_cast<int>(k + (n - i) * s / n + sd));
-      select(array, k, newLeft, newRight);
-    }
-    // partition the elements between left and right around t
-    auto t = array[k];
-    int i = left;
-    int j = right;
-
-    swap(array[left], array[k]);
-
-    if (t < array[right]) {
-      swap(array[left], array[right]);
-    }
-    while (i < j) {
-      swap(array[i], array[j]);
-      i++;
-      j--;
-      while (array[i] < t) {
-        i = i + 1;
-      }
-      while (t < array[j]) {
-        j = j - 1;
-      }
-    }
-    if (array[left] == t) {
-      swap(array[left], array[j]);
-    } else {
-      j = j + 1;
-      swap(array[j], array[right]);
-    }
-    // Adjust left and right towards the boundaries of the subset
-    // containing the (k - left + 1)th smallest element.
-    if (j <= k) {
-      left = j + 1;
-    }
-    if (k <= j) {
-      right = j - 1;
-    }
-  }
-}
-
-// Based on tfjs-core/src/backends/topk_impl.ts
-template <typename T>
-void topk(const T* x_data, const size_t x_len, const vector<size_t>& x_shape,
-          const int k, const bool sorted, T* out_values_data,
-          int32_t* out_indices_data) {
-  size_t last_dim = x_shape.back();
-  size_t batch = x_len / last_dim;
-  size_t size = last_dim;
-
-  for (size_t b = 0; b < batch; b++) {
-    size_t offset = b * size;
-    vector<ValAndInd<T>> val_and_ind;
-    val_and_ind.reserve(size);
-    for (size_t i = offset; i < offset + size; i++) {
-      val_and_ind.push_back({.value = x_data[i], .index = i - offset});
-    }
-
-    if (k < size) {
-      select(val_and_ind.data(), k, 0, size - 1);
-      val_and_ind.resize(k);
-    }
-
-    if (sorted) {
-      std::sort(val_and_ind.begin(), val_and_ind.end());
-    }
-
-    size_t out_offset = b * k;
-    for (size_t i = 0; i < k; i++) {
-      size_t index = out_offset + i;
-      out_values_data[index] = val_and_ind[i].value;
-      out_indices_data[index] = static_cast<int32_t>(val_and_ind[i].index);
-    }
-  }
-}
-
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void TopK(const size_t x_id, const size_t* x_shape_ptr,
-          const size_t x_shape_length, const DType x_dtype, const int k,
-          const bool sorted, const size_t out_values_id,
-          const size_t out_indices_id) {
-  auto x_shape = vector<size_t>(x_shape_ptr, x_shape_ptr + x_shape_length);
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_values_info = backend::get_tensor_info_out(out_values_id);
-  auto& out_indices_info = backend::get_tensor_info_out(out_indices_id);
-  switch (x_dtype) {
-    case DType::float32:
-      topk<float>(x_info.f32(), x_info.size, x_shape, k, sorted,
-                  out_values_info.f32_write(), out_indices_info.i32_write());
-      break;
-    case DType::int32:
-      topk<int32_t>(x_info.i32(), x_info.size, x_shape, k, sorted,
-                    out_values_info.i32_write(), out_indices_info.i32_write());
-      break;
-    default:
-      util::warn("TopK for tensor id %d failed. Unsupported dtype %d", x_id,
-                 x_dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Transform.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Transform.cc
deleted file mode 100644
index f29b2dc30..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Transform.cc
+++ /dev/null
@@ -1,270 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-float clamp(const float min, const float x, const float max) {
-  return std::max(min, std::min(x, max));
-}
-
-float map_coord_constant(const float out_coord, const size_t len) {
-  return out_coord;
-}
-
-float map_coord_reflect(const float out_coord, const size_t len) {
-  // Reflect [abcd] to [dcba|abcd|dcba].
-  float in_coord = out_coord;
-  if (in_coord < 0) {
-    if (len <= 1) {
-      in_coord = 0;
-    } else {
-      size_t sz2 = 2 * len;
-      if (in_coord < sz2) {
-        in_coord = sz2 * static_cast<int32_t>(-in_coord / sz2) + in_coord;
-      }
-      in_coord = (in_coord < -len) ? in_coord + sz2 : -in_coord - 1;
-    }
-  } else if (in_coord > len - 1) {
-    if (len <= 1) {
-      in_coord = 0;
-    } else {
-      size_t sz2 = 2 * len;
-      in_coord -= sz2 * static_cast<int32_t>(in_coord / sz2);
-      if (in_coord >= len) {
-        in_coord = sz2 - in_coord - 1;
-      }
-    }
-  }
-  // clamp is necessary because when out_coord = 3.5 and len = 4,
-  // in_coord = 3.5 and will be rounded to 4 in nearest interpolation.
-  return clamp(0, in_coord, len - 1);
-}
-
-float map_coord_wrap(const float out_coord, const size_t len) {
-  // Wrap [abcd] to [abcd|abcd|abcd].
-  float in_coord = out_coord;
-  if (in_coord < 0) {
-    if (len <= 1) {
-      in_coord = 0;
-    } else {
-      size_t sz = len - 1;
-      in_coord += len * (static_cast<int32_t>(-in_coord / sz) + 1);
-    }
-  } else if (in_coord > len - 1) {
-    if (len <= 1) {
-      in_coord = 0;
-    } else {
-      size_t sz = len - 1;
-      in_coord -= len * static_cast<int32_t>(in_coord / sz);
-    }
-  }
-  // clamp is necessary because when out_coord = -0.5 and len = 4,
-  // in_coord = 3.5 and will be rounded to 4 in nearest interpolation.
-  return clamp(0, in_coord, len - 1);
-}
-
-float map_coord_nearest(const float out_coord, const size_t len) {
-  return clamp(0, out_coord, len - 1);
-}
-
-float map_coord(const float out_coord, const size_t len, const size_t mode) {
-  switch (mode) {
-    case 1:
-      return map_coord_constant(out_coord, len);
-    case 2:
-      return map_coord_reflect(out_coord, len);
-    case 3:
-      return map_coord_wrap(out_coord, len);
-    case 4:
-      return map_coord_nearest(out_coord, len);
-    default:
-      return map_coord_constant(out_coord, len);
-  }
-}
-
-float read_with_fill_value(const float* image, const size_t image_height,
-                           const size_t image_width, const size_t batch_stride,
-                           const size_t row_stride, const size_t col_stride,
-                           const size_t batch, const int32_t y, const int32_t x,
-                           const size_t channel, const float fill_value) {
-  // batch and channel must be correct, because they are passed unchanged from
-  // the input.
-  if (0 <= y && y < image_height && 0 <= x && x < image_width) {
-    size_t offset =
-        batch * batch_stride + y * row_stride + x * col_stride + channel;
-    return image[offset];
-  } else {
-    return fill_value;
-  }
-}
-
-float nearest_interpolation(const float* image, const size_t image_height,
-                            const size_t image_width, const size_t batch_stride,
-                            const size_t row_stride, const size_t col_stride,
-                            const size_t batch, const float y, const float x,
-                            const size_t channel, const float fill_value) {
-  return read_with_fill_value(
-      image, image_height, image_width, batch_stride, row_stride, col_stride,
-      batch, static_cast<int32_t>(round(y)), static_cast<int32_t>(round(x)),
-      channel, fill_value);
-}
-
-float bilinear_interpolation(const float* image, const size_t image_height,
-                             const size_t image_width,
-                             const size_t batch_stride, const size_t row_stride,
-                             const size_t col_stride, const size_t batch,
-                             const float y, const float x, const size_t channel,
-                             const float fill_value) {
-  float y_floor = floor(y);
-  float x_floor = floor(x);
-  float y_ceil = y_floor + 1;
-  float x_ceil = x_floor + 1;
-
-  float value_yfloor =
-      (x_ceil - x) * read_with_fill_value(image, image_height, image_width,
-                                          batch_stride, row_stride, col_stride,
-                                          batch, static_cast<int32_t>(y_floor),
-                                          static_cast<int32_t>(x_floor),
-                                          channel, fill_value) +
-      (x - x_floor) * read_with_fill_value(image, image_height, image_width,
-                                           batch_stride, row_stride, col_stride,
-                                           batch, static_cast<int32_t>(y_floor),
-                                           static_cast<int32_t>(x_ceil),
-                                           channel, fill_value);
-
-  float value_yceil =
-      (x_ceil - x) * read_with_fill_value(image, image_height, image_width,
-                                          batch_stride, row_stride, col_stride,
-                                          batch, static_cast<int32_t>(y_ceil),
-                                          static_cast<int32_t>(x_floor),
-                                          channel, fill_value) +
-      (x - x_floor) * read_with_fill_value(image, image_height, image_width,
-                                           batch_stride, row_stride, col_stride,
-                                           batch, static_cast<int32_t>(y_ceil),
-                                           static_cast<int32_t>(x_ceil),
-                                           channel, fill_value);
-
-  return (y_ceil - y) * value_yfloor + (y - y_floor) * value_yceil;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-
-void Transform(const size_t image_id, const size_t transforms_id,
-               const bool is_batch_transform, const size_t batch,
-               const size_t out_height, const size_t out_width,
-               const size_t num_channels, const size_t image_width,
-               const size_t image_height, const int32_t* in_strides_ptr,
-               const size_t in_strides_size, const int32_t* out_strides_ptr,
-               const size_t out_strides_size, const size_t interpolation_mode_id,
-               const size_t fill_mode_id, const float fill_value,
-               const size_t out_id) {
-  auto& image_info = backend::get_tensor_info(image_id);
-  auto& transforms_info = backend::get_tensor_info(transforms_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* image = image_info.f32();
-  const float* transforms = transforms_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  const auto image_strides =
-      std::vector<size_t>(in_strides_ptr, in_strides_ptr + in_strides_size);
-  const size_t batch_img_stride = image_strides[0];
-  const size_t row_img_stride = image_strides[1];
-  const size_t col_img_stride = image_strides[2];
-
-  const auto output_strides =
-      std::vector<size_t>(out_strides_ptr, out_strides_ptr + out_strides_size);
-  const size_t batch_out_stride = output_strides[0];
-  const size_t row_out_stride = output_strides[1];
-  const size_t col_out_stride = output_strides[2];
-
-  // Ref TF implementation:
-  // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/image_ops.h
-  for (size_t b = 0; b < batch; ++b) {
-    size_t transforms_offset = 0;
-    if (is_batch_transform) {
-      transforms_offset = b * 8;
-    }
-
-    for (size_t out_y = 0; out_y < out_height; ++out_y) {
-      for (size_t out_x = 0; out_x < out_width; ++out_x) {
-        for (size_t channel = 0; channel < num_channels; ++channel) {
-          float val = 0;
-          float projection = transforms[transforms_offset + 6] * out_x +
-                             transforms[transforms_offset + 7] * out_y + 1.0;
-
-          if (projection == 0) {
-            // Set the fill value for infinite coordinates,
-            // which are outside the input image.
-            val = fill_value;
-          } else {
-            float in_x = (transforms[transforms_offset] * out_x +
-                          transforms[transforms_offset + 1] * out_y +
-                          transforms[transforms_offset + 2]) /
-                         projection;
-            float in_y = (transforms[transforms_offset + 3] * out_x +
-                          transforms[transforms_offset + 4] * out_y +
-                          transforms[transforms_offset + 5]) /
-                         projection;
-
-            // Map out-of-boundary input coordinates to in-boundary based on
-            // fill_mode_id.
-            float x = map_coord(in_x, image_width, fill_mode_id);
-            float y = map_coord(in_y, image_height, fill_mode_id);
-
-            switch (interpolation_mode_id) {
-              case 1:
-                val = nearest_interpolation(
-                    image, image_height, image_width, batch_img_stride, row_img_stride,
-                    col_img_stride, b, y, x, channel, fill_value);
-                break;
-              case 2:
-                val = bilinear_interpolation(
-                    image, image_height, image_width, batch_img_stride, row_img_stride,
-                    col_img_stride, b, y, x, channel, fill_value);
-                break;
-              default:
-                val = fill_value;
-                break;
-            }
-          }
-
-          size_t offset = b * batch_out_stride + out_y * row_out_stride +
-                          out_x * col_out_stride + channel;
-
-          out_buf[offset] = val;
-        }
-      }
-    }
-  }
-}
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Transpose.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Transpose.cc
deleted file mode 100644
index 104e0a4de..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/Transpose.cc
+++ /dev/null
@@ -1,64 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/transpose_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void Transpose(const size_t x_id, const size_t* x_shape_ptr,
-               const size_t x_shape_length, const DType dtype,
-               const size_t out_id, size_t* perm_ptr,
-               const size_t perm_length) {
-  auto x_shape = std::vector<size_t>(x_shape_ptr, x_shape_ptr + x_shape_length);
-  auto perm = std::vector<size_t>(perm_ptr, perm_ptr + perm_length);
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  switch (dtype) {
-    case DType::float32:
-      tfjs::wasm::transpose<float>(x_info.f32(), x_shape, perm,
-                                   out_info.f32_write());
-      break;
-    case DType::int32:
-      tfjs::wasm::transpose<int32_t>(x_info.i32(), x_shape, perm,
-                                     out_info.i32_write());
-      break;
-    case DType::boolean:
-      tfjs::wasm::transpose<bool>(x_info.b(), x_shape, perm,
-                                  out_info.b_write());
-      break;
-    default:
-      util::warn("Transpose for tensor id %d failed. Unknown dtype %d", x_id,
-                 dtype);
-  }
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/_FusedMatMul.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/_FusedMatMul.cc
deleted file mode 100644
index 20cf21691..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/_FusedMatMul.cc
+++ /dev/null
@@ -1,48 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/batch_mat_mul_impl.h"
-#include "tfjs-backend-wasm/src/cc/kernels/_FusedMatMul.h"
-
-namespace tfjs {
-namespace wasm {
-// We use C-style API to interface with Javascript.
-extern "C" {
-
-#ifdef __EMSCRIPTEN__
-EMSCRIPTEN_KEEPALIVE
-#endif
-void _FusedMatMul(const size_t a_id, const size_t* a_shape_ptr,
-                  const size_t a_shape_len, const size_t b_id,
-                  const size_t* b_shape_ptr, const size_t b_shape_len,
-                  const bool transpose_a, const bool transpose_b,
-                  const FusableActivation activation, const size_t bias_id,
-                  const size_t prelu_weights_id, const float leakyrelu_alpha,
-                  const size_t out_id) {
-  tfjs::wasm::fused_batch_mat_mul(a_id, a_shape_ptr, a_shape_len, b_id,
-                                  b_shape_ptr, b_shape_len, transpose_a,
-                                  transpose_b, activation, bias_id,
-                                  prelu_weights_id, leakyrelu_alpha, out_id);
-}
-
-}  // extern "C"
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/_FusedMatMul.h b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/_FusedMatMul.h
deleted file mode 100644
index 253836e98..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/_FusedMatMul.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef KERNELS__FUSEDMATMUL_H_
-#define KERNELS__FUSEDMATMUL_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-extern "C" {
-
-void _FusedMatMul(const size_t a_id, const size_t* a_shape_ptr,
-                  const size_t a_shape_len, const size_t b_id,
-                  const size_t* b_shape_ptr, const size_t b_shape_len,
-                  const bool transpose_a, const bool transpose_b,
-                  const FusableActivation activation, const size_t bias_id,
-                  const size_t prelu_weights_id, const float leakyrelu_alpha,
-                  const size_t out_id);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // KERNELS__FUSEDMATMUL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/_FusedMatMul_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/kernels/_FusedMatMul_test.cc
deleted file mode 100644
index b1fcf94f0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/kernels/_FusedMatMul_test.cc
+++ /dev/null
@@ -1,136 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <gtest/gtest.h>
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/kernels/_FusedMatMul.h"
-
-TEST(_FUSED_MATMUL, xnn_operator_lifetime) {
-  tfjs::wasm::init();
-
-  ASSERT_EQ(0, tfjs::backend::num_tensors());
-
-  size_t a0_id = 1;
-  size_t a1_id = 2;
-  size_t size = 2;
-  float a_values[2] = {1, 2};
-  std::vector<size_t> a_shape = {1, 2, 1};
-  size_t* a_shape_ptr = a_shape.data();
-
-  size_t b0_id = 3;
-  size_t b1_id = 4;
-  float b_values[2] = {1, 2};
-  std::vector<size_t> b_shape = {1, 1, 2};
-  size_t* b_shape_ptr = b_shape.data();
-
-  size_t out_id = 5;
-  float out_values[4] = {};
-
-  tfjs::wasm::register_tensor(a0_id, size, a_values);
-  tfjs::wasm::register_tensor(a1_id, size, a_values);
-  tfjs::wasm::register_tensor(b0_id, size, b_values);
-  tfjs::wasm::register_tensor(b1_id, size, b_values);
-  tfjs::wasm::register_tensor(out_id, size, out_values);
-
-  ASSERT_EQ(5, tfjs::backend::num_tensors());
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  const FusableActivation activation = FusableActivation::LINEAR;
-  size_t bias_id = 0;
-  size_t prelu_weights_id = 0;
-
-  // One new xnn_operator should be created for the first call to
-  // _FusedMatMul with no bias.
-  tfjs::wasm::_FusedMatMul(a0_id, a_shape_ptr, a_shape.size(), b0_id,
-                           b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                           false /* transpose_b */, activation, bias_id,
-                           prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the second call to
-  // _FusedMatMul with the same arguments.
-  tfjs::wasm::_FusedMatMul(a0_id, a_shape_ptr, a_shape.size(), b0_id,
-                           b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                           false /* transpose_b */, activation, bias_id,
-                           prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for calling _FusedMatMul
-  // with a new a.
-  tfjs::wasm::_FusedMatMul(a1_id, a_shape_ptr, a_shape.size(), b0_id,
-                           b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                           false /* transpose_b */, activation, bias_id,
-                           prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for calling _FusedMatMul
-  // with a new b.
-  tfjs::wasm::_FusedMatMul(a0_id, a_shape_ptr, a_shape.size(), b1_id,
-                           b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                           false /* transpose_b */, activation, bias_id,
-                           prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // No new xnn_operators should be created for the next call to
-  // _FusedMatMul with the same b.
-  tfjs::wasm::_FusedMatMul(a0_id, a_shape_ptr, a_shape.size(), b1_id,
-                           b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                           false /* transpose_b */, activation, bias_id,
-                           prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  const size_t bias1_id = 6;
-  const size_t bias_size = 1;
-  float bias_values[bias_size] = {1};
-  tfjs::wasm::register_tensor(bias1_id, bias_size, bias_values);
-  // One new xnn_operator should be created for calling _FusedMatMul with a
-  // new bias.
-  tfjs::wasm::_FusedMatMul(a0_id, a_shape_ptr, a_shape.size(), b1_id,
-                           b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                           false /* transpose_b */, activation, bias1_id,
-                           prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(3, tfjs::backend::xnn_operator_count);
-
-  // One new xnn_operator should be created for calling _FusedMatMul with a
-  // different activation.
-  const FusableActivation activation2 = FusableActivation::RELU;
-  tfjs::wasm::_FusedMatMul(a0_id, a_shape_ptr, a_shape.size(), b1_id,
-                           b_shape_ptr, b_shape.size(), false /* transpose_a */,
-                           false /* transpose_b */, activation2, bias1_id,
-                           prelu_weights_id, 0 /* leakyrelu alpha */, out_id);
-  ASSERT_EQ(4, tfjs::backend::xnn_operator_count);
-
-  // Disposing a's should not remove xnn operators.
-  tfjs::wasm::dispose_data(a0_id);
-  tfjs::wasm::dispose_data(a1_id);
-  ASSERT_EQ(4, tfjs::backend::xnn_operator_count);
-
-  // Disposing the second bias should remove the xnn_operators it's associated
-  // with.
-  tfjs::wasm::dispose_data(bias1_id);
-  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);
-
-  // Disposing b's should remove xnn operators.
-  tfjs::wasm::dispose_data(b0_id);
-  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose_data(b1_id);
-  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);
-
-  tfjs::wasm::dispose();
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/leakyrelu_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/leakyrelu_impl.cc
deleted file mode 100644
index 28f155590..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/leakyrelu_impl.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cmath>
-#include <cstddef>
-#include <iostream>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/leakyrelu_impl.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-template <class T>
-inline T leakyrelu_op(T a, T b) {
-  return a < 0 ? b * a : a;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-template <typename T>
-void leakyrelu(const T* x_buf, const size_t x_size, const float leakyrelu_alpha,
-               const size_t out_id) {
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  float* out_buf = out_info.f32_write();
-
-  for (size_t i = 0; i < out_info.size; i++) {
-    float float_x = static_cast<float>(x_buf[i]);
-    out_buf[i] = float_x < 0 ? leakyrelu_alpha * float_x : float_x;
-  }
-}
-
-// Following is required to avoid linker error.
-//
-// See https://isocpp.org/wiki/faq/templates#separate-template-fn-defn-from-decl
-
-template void leakyrelu<float>(const float* x_buf, const size_t x_size,
-                               const float leakyrelu_alpha,
-                               const size_t out_id);
-
-template void leakyrelu<int32_t>(const int32_t* x_buf, const size_t x_size,
-                                 const float leakyrelu_alpha,
-                                 const size_t out_id);
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/leakyrelu_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/leakyrelu_impl.h
deleted file mode 100644
index 06f5b3292..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/leakyrelu_impl.h
+++ /dev/null
@@ -1,30 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef LEAKYRELU_IMPL_H_
-#define LEAKYRELU_IMPL_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-
-template <typename T>
-void leakyrelu(const T* x_buf, const size_t x_size, const float leakyrelu_alpha,
-               const size_t out_id);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // LEAKYRELU_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/non_max_suppression_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/non_max_suppression_impl.cc
deleted file mode 100644
index 2aeda8d27..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/non_max_suppression_impl.cc
+++ /dev/null
@@ -1,212 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <cstring>
-#include <deque>
-#include <memory>
-#include <queue>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/non_max_suppression_impl.h"
-
-namespace {
-
-struct Candidate {
-  int32_t box_index;
-  float score;
-  int32_t suppress_begin_index;
-};
-
-float compute_iou(const float* boxes, const size_t i, const size_t j) {
-  const float* i_coord = boxes + i * 4;
-  const float* j_coord = boxes + j * 4;
-
-  const float y_min_i = std::min(i_coord[0], i_coord[2]);
-  const float x_min_i = std::min(i_coord[1], i_coord[3]);
-
-  const float y_max_i = std::max(i_coord[0], i_coord[2]);
-  const float x_max_i = std::max(i_coord[1], i_coord[3]);
-
-  const float y_min_j = std::min(j_coord[0], j_coord[2]);
-  const float x_min_j = std::min(j_coord[1], j_coord[3]);
-
-  const float y_max_j = std::max(j_coord[0], j_coord[2]);
-  const float x_max_j = std::max(j_coord[1], j_coord[3]);
-
-  const float area_i = (y_max_i - y_min_i) * (x_max_i - x_min_i);
-  const float area_j = (y_max_j - y_min_j) * (x_max_j - x_min_j);
-
-  if (area_i <= 0 || area_j <= 0) {
-    return 0.0;
-  }
-
-  const float intersect_y_min = std::max(y_min_i, y_min_j);
-  const float intersect_x_min = std::max(x_min_i, x_min_j);
-  const float intersect_y_max = std::min(y_max_i, y_max_j);
-  const float intersect_x_max = std::min(x_max_i, x_max_j);
-  const float intersect_area =
-      std::max(intersect_y_max - intersect_y_min, .0f) *
-      std::max(intersect_x_max - intersect_x_min, .0f);
-  return intersect_area / (area_i + area_j - intersect_area);
-}
-
-float suppress_weight(const float iou_threshold, const float scale,
-                      const float iou) {
-  const float weight = std::exp(scale * iou * iou);
-  return iou <= iou_threshold ? weight : 0.0;
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-const NonMaxSuppressionResult* non_max_suppression_impl(
-    const size_t boxes_id, const size_t scores_id, const size_t max_out_size,
-    const float iou_threshold, const float score_threshold,
-    const float soft_nms_sigma, const bool pad_to_max_output_size) {
-  auto& boxes_info = backend::get_tensor_info(boxes_id);
-  auto& scores_info = backend::get_tensor_info_out(scores_id);
-  const float* boxes = boxes_info.f32();
-  const float* scores = scores_info.f32();
-  const size_t num_boxes = boxes_info.size / 4;
-
-  auto score_comparator = [](const Candidate i, const Candidate j) {
-    return i.score < j.score ||
-           ((i.score == j.score) && (i.box_index > j.box_index));
-  };
-  // Construct a max heap by candidate scores.
-  std::priority_queue<Candidate, std::deque<Candidate>,
-                      decltype(score_comparator)>
-      candidate_priority_queue(score_comparator);
-
-  const int32_t suppress_at_start = 0;
-  // Filter out boxes that are below the score threshold and also maintain
-  // the order of boxes by scores.
-  for (int32_t i = 0; i < num_boxes; i++) {
-    if (scores[i] > score_threshold) {
-      candidate_priority_queue.emplace(
-          Candidate({i, scores[i], suppress_at_start}));
-    }
-  }
-
-  // If soft_nms_sigma is 0, the outcome of this algorithm is exactly same as
-  // before.
-  const float scale = soft_nms_sigma > 0.0 ? (-0.5 / soft_nms_sigma) : 0.0;
-
-  // Select a box only if it doesn't overlap beyond the threshold with the
-  // already selected boxes.
-  std::vector<int32_t> selected_indices;
-  std::vector<float> selected_scores;
-  Candidate candidate;
-  float original_score;
-
-  while (selected_indices.size() < max_out_size &&
-         !candidate_priority_queue.empty()) {
-    candidate = candidate_priority_queue.top();
-    original_score = candidate.score;
-    candidate_priority_queue.pop();
-
-    if (original_score < score_threshold) {
-      break;
-    }
-
-    // Overlapping boxes are likely to have similar scores, therefore we
-    // iterate through the previously selected boxes backwards in order to
-    // see if candidate's score should be suppressed. We use
-    // suppress_begin_index to track and ensure a candidate can be suppressed
-    // by a selected box no more than once. Also, if the overlap exceeds
-    // iou_threshold, we simply ignore the candidate.
-    bool ignore_candidate = false;
-    for (int32_t j = selected_indices.size() - 1;
-         j >= candidate.suppress_begin_index; --j) {
-      const float iou =
-          compute_iou(boxes, candidate.box_index, selected_indices[j]);
-
-      if (iou >= iou_threshold) {
-        ignore_candidate = true;
-        break;
-      }
-
-      candidate.score *= suppress_weight(iou_threshold, scale, iou);
-
-      if (candidate.score <= score_threshold) {
-        break;
-      }
-    }
-
-    // At this point, if `candidate.score` has not dropped below
-    // `score_threshold`, then we know that we went through all of the
-    // previous selections and can safely update `suppress_begin_index` to the
-    // end of the selected array. Then we can re-insert the candidate with
-    // the updated score and suppress_begin_index back in the candidate queue.
-    // If on the other hand, `candidate.score` has dropped below the score
-    // threshold, we will not add it back to the candidates queue.
-    candidate.suppress_begin_index = selected_indices.size();
-
-    if (!ignore_candidate) {
-      // Candidate has passed all the tests, and is not suppressed, so
-      // select the candidate.
-      if (candidate.score == original_score) {
-        selected_indices.push_back(candidate.box_index);
-        selected_scores.push_back(candidate.score);
-      } else if (candidate.score > score_threshold) {
-        // Candidate's score is suppressed but is still high enough to be
-        // considered, so add back to the candidates queue.
-        candidate_priority_queue.push(candidate);
-      }
-    }
-  }
-
-  size_t num_valid_outputs = selected_indices.size();
-  if (pad_to_max_output_size) {
-    selected_indices.resize(max_out_size, 0);
-    selected_scores.resize(max_out_size, 0.0);
-  }
-
-  // Allocate memory on the heap for the results and copy the data from the
-  // `selected_indices` and `selected_scores` vector since we can't "steal" the
-  // data from the vector.
-  size_t selected_indices_data_size = selected_indices.size() * sizeof(int32_t);
-  int32_t* selected_indices_data =
-      static_cast<int32_t*>(malloc(selected_indices_data_size));
-  std::memcpy(selected_indices_data, selected_indices.data(),
-              selected_indices_data_size);
-
-  size_t selected_scores_data_size = selected_scores.size() * sizeof(float);
-  float* selected_scores_data =
-      static_cast<float*>(malloc(selected_scores_data_size));
-  std::memcpy(selected_scores_data, selected_scores.data(),
-              selected_scores_data_size);
-
-  size_t valid_outputs_data_size = sizeof(size_t);
-  size_t* valid_outputs_data =
-      static_cast<size_t*>(malloc(valid_outputs_data_size));
-  *valid_outputs_data = num_valid_outputs;
-
-  // Allocate the result of the method on the heap so it survives past this
-  // function and we can read it in js.
-  return new NonMaxSuppressionResult{selected_indices_data,
-                                     selected_indices.size(),
-                                     selected_scores_data, valid_outputs_data};
-}
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/non_max_suppression_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/non_max_suppression_impl.h
deleted file mode 100644
index 71cc2190e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/non_max_suppression_impl.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef NON_MAX_SUPPRESSION_IMPL_H_
-#define NON_MAX_SUPPRESSION_IMPL_H_
-
-#include <cstddef>
-#include <vector>
-
-namespace tfjs {
-namespace wasm {
-
-// Structure to store the result of the kernel. In this case we give js a
-// a pointer in memory where the result is stored and how big it is.
-struct NonMaxSuppressionResult {
-  int32_t* selected_indices;
-  size_t selected_size;
-  float* selected_scores;
-  size_t* valid_outputs;
-};
-
-const NonMaxSuppressionResult* non_max_suppression_impl(
-    const size_t boxes_id, const size_t scores_id, const size_t max_out_size,
-    const float iou_threshold, const float score_threshold,
-    const float soft_nms_sigma, const bool pad_to_max_output_size);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // NON_MAX_SUPPRESSION_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/pool3d_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/pool3d_impl.h
deleted file mode 100644
index 6faa75961..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/pool3d_impl.h
+++ /dev/null
@@ -1,187 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include <cstddef>
-#include "tfjs-backend-wasm/src/cc/shape.h"
-
-namespace tfjs::wasm {
-
-namespace {
-
-inline int AddUntilNonNegative(int v, int d) {
-  if (v >= 0) {
-    return v;
-  }
-  return (v % d + d) % d;
-}
-
-}  // namespace
-
-struct NDHWCPool3DInfo {
-  int batch_size;
-  // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in
-  // channels should always equal to out channels.
-  int channel_size;
-  int in_depth;
-  int in_height;
-  int in_width;
-  int out_depth;
-  int out_height;
-  int out_width;
-
-  int stride_depth;
-  int stride_height;
-  int stride_width;
-  int dilation_depth;
-  int dilation_height;
-  int dilation_width;
-  int effective_filter_depth;
-  int effective_filter_height;
-  int effective_filter_width;
-  int pad_front;
-  int pad_top;
-  int pad_left;
-
-  inline Shape<int, 5> in_shape() const {
-    return Shape<int, 5>(
-        {batch_size, in_depth, in_height, in_width, channel_size});
-  }
-  inline Shape<int, 5> out_shape() const {
-    return Shape<int, 5>(
-        {batch_size, out_depth, out_height, out_width, channel_size});
-  }
-
-  inline int in_offset(int b, int d, int h, int w, int c) const {
-    return in_shape().offset({b, d, h, w, c});
-  }
-  inline int out_offset(int b, int d, int h, int w, int c) const {
-    return out_shape().offset({b, d, h, w, c});
-  }
-  inline int int_size() const { return in_shape().size(); }
-  inline int out_size() const { return out_shape().size(); }
-};
-template <typename IN, typename FI, typename FAP, typename FAG>
-inline void NDHWCPool3DImpl(const IN* x_buf, const NDHWCPool3DInfo& info,
-                            const FI& filter_init, const FAP& filter_apply,
-                            const FAG& filter_assign) {
-  for (int batch = 0; batch < info.batch_size; ++batch) {
-    for (int channel = 0; channel < info.channel_size; ++channel) {
-      for (int y_depth = 0; y_depth < info.out_depth; ++y_depth) {
-        int x_depth_corner = y_depth * info.stride_depth - info.pad_front;
-        int x_depth_min =
-            AddUntilNonNegative(x_depth_corner, info.dilation_depth);
-        int x_depth_max = std::min(
-            info.in_depth, info.effective_filter_depth + x_depth_corner);
-
-        for (int y_row = 0; y_row < info.out_height; ++y_row) {
-          int x_row_corner = y_row * info.stride_height - info.pad_top;
-          int x_row_min =
-              AddUntilNonNegative(x_row_corner, info.dilation_height);
-          int x_row_max = std::min(info.in_height,
-                                   info.effective_filter_height + x_row_corner);
-          for (int y_col = 0; y_col < info.out_width; ++y_col) {
-            int x_col_corner = y_col * info.stride_width - info.pad_left;
-            int x_col_min =
-                AddUntilNonNegative(x_col_corner, info.dilation_width);
-            int x_col_max = std::min(
-                info.in_width, info.effective_filter_width + x_col_corner);
-
-            // Apply the filter
-            auto filter_data = filter_init();
-            for (int x_depth = x_depth_min; x_depth < x_depth_max;
-                 x_depth += info.dilation_depth) {
-              for (int x_row = x_row_min; x_row < x_row_max;
-                   x_row += info.dilation_height) {
-                for (int x_col = x_col_min; x_col < x_col_max;
-                     x_col += info.dilation_width) {
-                  int x_offset =
-                      info.in_offset(batch, x_depth, x_row, x_col, channel);
-                  filter_apply(filter_data, x_offset, x_buf[x_offset]);
-                }
-              }
-            }
-            int out_offset =
-                info.out_offset(batch, y_depth, y_row, y_col, channel);
-            filter_assign(out_offset, filter_data);
-          }
-        }
-      }
-    }
-  }
-}
-
-template <typename DY, typename DX, typename FM>
-inline void NDHWCPool3DGradImpl(const DY* dy_buf, DX* dx_buf,
-                                const NDHWCPool3DInfo& forward_info,
-                                const FM& pixel_mask) {
-  auto info = forward_info;
-  info.pad_front = info.effective_filter_depth - 1 - info.pad_front;
-  info.pad_top = info.effective_filter_height - 1 - info.pad_top;
-  info.pad_left = info.effective_filter_width - 1 - info.pad_left;
-
-  for (int batch = 0; batch < info.batch_size; ++batch) {
-    for (int channel = 0; channel < info.channel_size; ++channel) {
-      for (int dx_depth = 0; dx_depth < info.in_depth; ++dx_depth) {
-        for (int dx_row = 0; dx_row < info.in_height; ++dx_row) {
-          for (int dx_col = 0; dx_col < info.in_width; ++dx_col) {
-            // Sharder code begins
-            int dy_depth_corner = dx_depth - info.pad_front;
-            int dy_row_corner = dx_row - info.pad_top;
-            int dy_col_corner = dx_col - info.pad_left;
-
-            int dx_offset =
-                info.in_offset(batch, dx_depth, dx_row, dx_col, channel);
-            DX dot_prod = 0;
-            for (int w_depth = 0; w_depth < info.effective_filter_depth;
-                 w_depth += info.dilation_depth) {
-              int dy_depth = (dy_depth_corner + w_depth) / info.stride_depth;
-              if (int rem = (dy_depth_corner + w_depth) % info.stride_depth;
-                  dy_depth < 0 || dy_depth >= info.out_depth || rem != 0) {
-                continue;
-              }
-              for (int w_row = 0; w_row < info.effective_filter_height;
-                   w_row += info.dilation_height) {
-                int dy_row = (dy_row_corner + w_row) / info.stride_height;
-                if (int rem = (dy_row_corner + w_row) % info.stride_height;
-                    dy_row < 0 || dy_row >= info.out_height || rem != 0) {
-                  continue;
-                }
-                for (int w_col = 0; w_col < info.effective_filter_width;
-                     w_col += info.dilation_width) {
-                  int dy_col = (dy_col_corner + w_col) / info.stride_width;
-                  if (int rem = (dy_col_corner + w_col) % info.stride_width;
-                      dy_col < 0 || dy_col >= info.out_width || rem != 0) {
-                    continue;
-                  }
-
-                  int dy_offset =
-                      info.out_offset(batch, dy_depth, dy_row, dy_col, channel);
-                  DY pixel = dy_buf[dy_offset];
-                  dot_prod += pixel * pixel_mask(dy_offset, dx_offset);
-                }
-              }
-            }
-            dx_buf[dx_offset] = dot_prod;
-          }
-        }
-      }
-    }
-  }
-}
-
-}  // namespace tfjs::wasm
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/post.js b/tfjs-master/tfjs-backend-wasm/src/cc/post.js
deleted file mode 100644
index 9b4d8ef8b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/post.js
+++ /dev/null
@@ -1,44 +0,0 @@
-// This file is not pre-processed or downleveled to es5, so it must be es5
-// compatible.
-
-// Track and remove listeners previously added by emscripten. See pre.js for the
-// other half of the logic.
-
-var listenersAdded;
-if (beforeListeners) {
-  listenersAdded = {
-    uncaughtException: process.listeners('uncaughtException').filter(
-        function(listener) {
-          return !beforeListeners.uncaughtException.indexOf(listener) > -1;
-        }
-    ),
-    unhandledRejection: process.listeners('unhandledRejection').filter(
-        function(listener) {
-          return !beforeListeners.unhandledRejection.indexOf(listener) > -1;
-        }
-    ),
-  };
-}
-
-var actualModule;
-if (typeof WasmBackendModule !== 'undefined') {
-  actualModule = WasmBackendModule;
-} else if (typeof WasmBackendModuleThreadedSimd !== 'undefined') {
-  actualModule = WasmBackendModuleThreadedSimd;
-} else {
-  throw new Error('Could not find wasm module in post.js');
-}
-
-if (listenersAdded) {
-  // Patch the wasm module's dispose method to also unregister listeners.
-  var tmpDispose = actualModule['_dispose'];
-  actualModule['_dispose'] = function() {
-    tmpDispose();
-    listenersAdded.uncaughtException.forEach(function(listener) {
-      process.removeListener('uncaughtException', listener);
-    });
-    listenersAdded.unhandledRejection.forEach(function(listener) {
-      process.removeListener('unhandledRejection', listener);
-    });
-  }
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/pre.js b/tfjs-master/tfjs-backend-wasm/src/cc/pre.js
deleted file mode 100644
index edc300727..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/pre.js
+++ /dev/null
@@ -1,13 +0,0 @@
-// This file is not pre-processed or downleveled to es5, so it must be es5
-// compatible.
-
-// Keep track of listeners that are added by emscripten so they can later be
-// removed. See post.js for the other half of the logic.
-
-var beforeListeners;
-if (typeof process !== 'undefined' && process.listeners) {
-  beforeListeners = {
-    uncaughtException: process.listeners('uncaughtException'),
-    unhandledRejection: process.listeners('unhandledRejection'),
-  }
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/prelu_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/prelu_impl.cc
deleted file mode 100644
index e795eef40..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/prelu_impl.cc
+++ /dev/null
@@ -1,95 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/prelu_impl.h"
-
-#include <xnnpack.h>
-#include <cmath>
-#include <cstddef>
-#include <limits>
-#include <unordered_map>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// The operator cache maps the weights id to the xnn_operator_t instantiated for
-// this set of weights.
-std::unordered_map<size_t, xnn_operator_t> operator_cache;
-
-void delete_xnn_operator(const size_t weights_id) {
-  xnn_operator_t prelu_op = operator_cache.at(weights_id);
-  xnn_delete_operator(prelu_op);
-  tfjs::backend::xnn_operator_count--;
-
-  operator_cache.erase(weights_id);
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-void prelu(const float* x_buf, const size_t x_size, const size_t weights_id,
-           const size_t out_id) {
-  auto& weights_info = backend::get_tensor_info(weights_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* weights_buf = weights_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  xnn_operator_t prelu_op = nullptr;
-
-  auto operator_cache_idx = operator_cache.find(weights_id);
-  if (operator_cache_idx == operator_cache.end()) {
-    const size_t channels = weights_info.size;
-    const size_t strides = channels;
-
-    const uint32_t flags = 0;
-    xnn_status status = xnn_create_prelu_nc_f32(channels, strides, strides,
-                                                weights_buf, flags, &prelu_op);
-    if (status != xnn_status_success) {
-      util::warn(
-          "XNN status for xnn_create_prelu_nc_f32 is not successful. Got "
-          "status %d. Use -c dbg to see XNN logs.",
-          status);
-    }
-
-    operator_cache.insert({weights_id, prelu_op});
-
-    backend::register_disposal_callback(weights_id, *delete_xnn_operator);
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    prelu_op = operator_cache_idx->second;
-  }
-
-  const size_t batch_size = x_size / weights_info.size;
-  xnn_status status = xnn_setup_prelu_nc_f32(
-      prelu_op, batch_size, x_buf, out_buf, tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    util::warn(
-        "XNN status for xnn_setup_prelu_nc_f32 is not successful. Got "
-        "status %d. Use -c dbg to see XNN logs.",
-        status);
-  }
-
-  xnn_run_operator(prelu_op, tfjs::backend::threadpool);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/prelu_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/prelu_impl.h
deleted file mode 100644
index c156c7522..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/prelu_impl.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef PRELU_IMPL_H_
-#define PRELU_IMPL_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-
-void prelu(const float* x_buf, const size_t x_size, const size_t weights_id,
-           const size_t out_id);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // PRELU_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/scatter_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/scatter_impl.cc
deleted file mode 100644
index e1b638266..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/scatter_impl.cc
+++ /dev/null
@@ -1,86 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef SCATTER_IMPL_H_
-#define SCATTER_IMPL_H_
-
-#include <cstddef>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/scatter_impl.h"
-
-namespace tfjs {
-namespace wasm {
-
-template <typename T>
-void scatter(const int* indices_ptr, const T* updates_ptr,
-             const size_t slice_rank, const size_t num_updates,
-             const size_t slice_size, const std::vector<size_t>& strides_ptr,
-             const size_t output_size, const T default_value,
-             const bool sum_dupe_indices, const bool update_as_scalar,
-             T* out_buf_ptr) {
-  std::fill(out_buf_ptr, out_buf_ptr + output_size, default_value);
-
-  for (size_t i = 0; i < num_updates; ++i) {
-    size_t flattened_index = 0;
-    for (size_t j = 0; j < slice_rank; ++j) {
-      flattened_index += *indices_ptr * strides_ptr[j];
-
-      indices_ptr++;
-    }
-
-    T* out = out_buf_ptr + flattened_index * slice_size;
-
-    for (size_t k = 0; k < slice_size; ++k) {
-      if (sum_dupe_indices) {
-        *out += *updates_ptr;
-      } else {
-        *out = *updates_ptr;
-      }
-
-      out++;
-      if (!update_as_scalar) {
-        updates_ptr++;
-      }
-    }
-  }
-}
-
-// Templates need explicit instantiation when implemented in a .cc file.
-template void scatter<float>(const int* indices_ptr, const float* updates_ptr,
-                             const size_t slice_rank, const size_t num_updates,
-                             const size_t slice_size,
-                             const std::vector<size_t>& strides_ptr,
-                             const size_t output_size,
-                             const float default_value,
-                             const bool sum_dupe_indices,
-                             const bool update_as_scalar, float* out_buf_ptr);
-template void scatter<int32_t>(
-    const int* indices_ptr, const int32_t* updates_ptr, const size_t slice_rank,
-    const size_t num_updates, const size_t slice_size,
-    const std::vector<size_t>& strides_ptr, const size_t output_size,
-    const int32_t default_value, const bool sum_dupe_indices,
-    const bool update_as_scalar, int32_t* out_buf_ptr);
-template void scatter<bool>(const int* indices_ptr, const bool* updates_ptr,
-                            const size_t slice_rank, const size_t num_updates,
-                            const size_t slice_size,
-                            const std::vector<size_t>& strides_ptr,
-                            const size_t output_size, const bool default_value,
-                            const bool sum_dupe_indices,
-                            const bool update_as_scalar, bool* out_buf_ptr);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // SCATTER_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/scatter_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/scatter_impl.h
deleted file mode 100644
index d4f8c64a6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/scatter_impl.h
+++ /dev/null
@@ -1,35 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef SCATTER_IMPL_H_
-#define SCATTER_IMPL_H_
-
-#include <cstddef>
-#include <vector>
-
-namespace tfjs {
-namespace wasm {
-
-template <typename T>
-void scatter(const int* indices_ptr, const T* updates_ptr,
-             const size_t slice_rank, const size_t num_updates,
-             const size_t slice_size, const std::vector<size_t>& strides_ptr,
-             const size_t output_size, const T default_value,
-             const bool sum_dupe_indices, const bool update_as_scalar,
-             T* out_buf_ptr);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // SCATTER_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/shape.h b/tfjs-master/tfjs-backend-wasm/src/cc/shape.h
deleted file mode 100644
index b1fc92226..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/shape.h
+++ /dev/null
@@ -1,78 +0,0 @@
-/* Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef SHAPE_H_
-#define SHAPE_H_
-
-#include <array>
-#include <limits>
-#include <utility>
-#include <vector>
-
-namespace tfjs {
-
-// The shape of a tensor.
-template <typename T, int N>
-class Shape {
- public:
-  explicit Shape(std::array<T, N> data) : data_(std::move(data)) {
-    static_assert(std::numeric_limits<T>::is_integer,
-                  "Tensor shape must be a list of integers.");
-    static_assert(N > 0, "Tensor shape must not have 0 length");
-  }
-  explicit Shape(const T* data_ptr)
-      : Shape(BuildShapeFromData(data_ptr, std::make_index_sequence<N>{})) {}
-
-  // Returns the size of the tensor with this shape.
-  inline T size() const {
-    T size = 1;
-    for (size_t i = 0; i < N; ++i) {
-      size *= data_[i];
-    }
-    return size;
-  }
-
-  // Returns the flat offset of a N-D tensor given the indices.
-  inline T offset(const std::array<T, N>& indices) const {
-    T offset = 0;
-    for (size_t i = 0; i < N - 1; ++i) {
-      offset += indices[i];
-      offset *= data_[i + 1];
-    }
-    offset += indices[N - 1];
-    return offset;
-  }
-
-  inline T& operator[](size_t i) { return data_[i]; }
-  inline const T& operator[](size_t i) const { return data_[i]; }
-
-  inline T& at(size_t i) { return data_.at(i); }
-  inline const T& at(size_t i) const { return data_.at(i); }
-
-  const std::array<T, N>& array() const { return data_; }
-
- private:
-  // Internal constructor helpers for converting data into std::array.
-  template <typename Array, size_t... I>
-  inline static Shape BuildShapeFromData(const Array& a,
-                                         std::index_sequence<I...>) {
-    return Shape({a[I]...});
-  }
-
-  std::array<T, N> data_;
-};
-
-}  // namespace tfjs
-
-#endif
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/sigmoid_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/sigmoid_impl.cc
deleted file mode 100644
index 1e7ac046f..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/sigmoid_impl.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifdef __EMSCRIPTEN__
-#include <emscripten.h>
-#endif
-
-#include "tfjs-backend-wasm/src/cc/sigmoid_impl.h"
-
-#include <xnnpack.h>
-#include <cmath>
-#include <cstddef>
-#include <map>
-#include <tuple>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// We use std::tuple as the cache key as it implements the compare operator
-// needed for std::map.
-typedef std::tuple<size_t> OperatorCacheKey;
-
-// The operator cache maps the weights id to the xnn_operator_t instantiated for
-// this set of weights.
-std::map<OperatorCacheKey, xnn_operator_t> operator_cache;
-
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-void sigmoid(const float* x_buf, const size_t x_size, const size_t out_id) {
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  float* out_buf = out_info.f32_write();
-
-  xnn_operator_t sigmoid_op = nullptr;
-
-  const size_t channels = 1;
-  OperatorCacheKey cache_key = {channels};
-
-  auto operator_cache_idx = operator_cache.find(cache_key);
-  if (operator_cache_idx == operator_cache.end()) {
-    const size_t input_stride = channels;
-    const size_t output_stride = channels;
-    const uint32_t flags = 0;
-
-    xnn_status status = xnn_create_sigmoid_nc_f32(
-        channels, input_stride, output_stride, flags, &sigmoid_op);
-    if (status != xnn_status_success) {
-      tfjs::util::warn(
-          "XNN status for xnn_create_sigmoid_nc_f32 is not "
-          "successful. Got status %d. Use -c dbg to see XNN logs.",
-          status);
-      return;
-    }
-
-    operator_cache.insert({cache_key, sigmoid_op});
-
-    tfjs::backend::xnn_operator_count++;
-  } else {
-    sigmoid_op = operator_cache_idx->second;
-  }
-
-  const size_t batch = x_size;
-  xnn_status status = xnn_setup_sigmoid_nc_f32(
-      sigmoid_op, batch, x_buf, out_buf, tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    tfjs::util::warn(
-        "XNN status for xnn_setup_sigmoid_nc_f32 is not "
-        "successful. Got status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(sigmoid_op, tfjs::backend::threadpool);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/sigmoid_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/sigmoid_impl.h
deleted file mode 100644
index 00eac36dc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/sigmoid_impl.h
+++ /dev/null
@@ -1,28 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef SIGMOID_IMPL_H_
-#define SIGMOID_IMPL_H_
-
-#include <cstddef>
-
-namespace tfjs {
-namespace wasm {
-
-void sigmoid(const float* x_buf, const size_t x_size, const size_t out_id);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // SIGMOID_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/sin_cos_workaround.cc b/tfjs-master/tfjs-backend-wasm/src/cc/sin_cos_workaround.cc
deleted file mode 100644
index d654bef7f..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/sin_cos_workaround.cc
+++ /dev/null
@@ -1,75 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <cmath>
-
-#include "tfjs-backend-wasm/src/cc/sin_cos_workaround.h"
-
-namespace tfjs {
-namespace sin_cos_workaround {
-
-namespace {
-
-template <typename T>
-inline T ShiftRadianToZeroTo2PI(const T& x) {
-  if (std::isnan(x)) {
-    return x;
-  }
-  return std::fmod(std::fmod(x, 2 * M_PI) + 2 * M_PI, 2 * M_PI);
-}
-
-template <typename T>
-inline T SinZeroTo2PI(const T& x) {
-  if (std::isnan(x)) {
-    return x;
-  }
-
-  if (x < M_PI_4) {
-    return std::sin(x);
-  } else if (x < M_PI_2) {
-    return std::cos(M_PI_2 - x);
-  } else if (x < M_PI) {
-    return SinZeroTo2PI<T>(M_PI - x);
-  } else {
-    return -SinZeroTo2PI<T>(2 * M_PI - x);
-  }
-}
-
-template <typename T>
-inline T CosZeroTo2PI(const T& x) {
-  if (std::isnan(x)) {
-    return x;
-  }
-
-  if (x < M_PI_4) {
-    return std::cos(x);
-  } else if (x < M_PI_2) {
-    return std::sin(M_PI_2 - x);
-  } else if (x < M_PI) {
-    return -CosZeroTo2PI<T>(M_PI - x);
-  } else {
-    return CosZeroTo2PI<T>(2 * M_PI - x);
-  }
-}
-
-}  // namespace
-
-float SinFixed(float x) { return SinZeroTo2PI(ShiftRadianToZeroTo2PI(x)); }
-
-float CosFixed(float x) { return CosZeroTo2PI(ShiftRadianToZeroTo2PI(x)); }
-
-float TanFixed(float x) { return std::tan(x); }
-
-}  // namespace sin_cos_workaround
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/sin_cos_workaround.h b/tfjs-master/tfjs-backend-wasm/src/cc/sin_cos_workaround.h
deleted file mode 100644
index b8bc0cd0c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/sin_cos_workaround.h
+++ /dev/null
@@ -1,32 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef SIN_COS_WORKAROUND_H_
-#define SIN_COS_WORKAROUND_H_
-
-// Workaround a bug related to sin/cos with emscripten/webkit on iOS 11/12:
-// https://github.com/emscripten-core/emscripten/issues/13130
-namespace tfjs {
-namespace sin_cos_workaround {
-
-float SinFixed(float x);
-
-float CosFixed(float x);
-
-float TanFixed(float x);
-
-}  // namespace sin_cos_workaround
-}  // namespace tfjs
-
-#endif  // SIN_COS_WORKAROUND_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/transpose_impl.cc b/tfjs-master/tfjs-backend-wasm/src/cc/transpose_impl.cc
deleted file mode 100644
index ddc988e8a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/transpose_impl.cc
+++ /dev/null
@@ -1,293 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include "tfjs-backend-wasm/src/cc/transpose_impl.h"
-
-#include <cstddef>
-#include <cstdint>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-
-// Optimized transpose 2D that uses direct pointer arithmetic instead of bracket
-// indexing.
-template <typename T>
-void transpose_2d(const T* x_data, const std::vector<size_t>& x_shape,
-                  T* out_data) {
-  const size_t d0 = x_shape[0];
-  const size_t d1 = x_shape[1];
-  const T* input = x_data;
-  for (size_t i = 0; i < d0; ++i) {
-    T* output = out_data + i;
-    for (size_t j = 0; j < d1; ++j) {
-      *output = *input;
-      output += d0;
-      ++input;
-    }
-  }
-}
-
-// Optimized transpose 3D. Reference:
-// https://github.com/tensorflow/tensorflow/blob/87388b7b6040bbf0baa67e4ef1ddc3e930ff6edd/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L7248
-template <typename T>
-void transpose_3d(const T* x_data, const std::vector<size_t>& x_shape,
-                  const std::vector<size_t>& perm, T* out_data) {
-  size_t s2, s3;
-  s2 = x_shape[1];
-  s3 = x_shape[2];
-
-  size_t p1, p2, p3;
-  if (perm[0] == 2) {
-    p1 = 1;
-  } else if (perm[1] == 2) {
-    p2 = 1;
-  } else {
-    p3 = 1;
-  }
-
-  if (perm[0] == 1) {
-    p1 = s3;
-  } else if (perm[1] == 1) {
-    p2 = s3;
-  } else {
-    p3 = s3;
-  }
-
-  if (perm[0] == 0) {
-    p1 = s2 * s3;
-  } else if (perm[1] == 0) {
-    p2 = s2 * s3;
-  } else {
-    p3 = s2 * s3;
-  }
-
-  size_t out_shape[3];
-  out_shape[0] = x_shape[perm[0]];
-  out_shape[1] = x_shape[perm[1]];
-  out_shape[2] = x_shape[perm[2]];
-  const size_t out_stride1 = out_shape[1] * out_shape[2];
-  const size_t out_stride2 = out_shape[2];
-
-  for (size_t i1 = 0; i1 < out_shape[0]; ++i1) {
-    for (size_t i2 = 0; i2 < out_shape[1]; ++i2) {
-      for (size_t i3 = 0; i3 < out_shape[2]; ++i3) {
-        const size_t i = tfjs::util::offset(i1, i2, i3, 0, p1, p2, p3);
-        const size_t o =
-            tfjs::util::offset(i1, i2, i3, out_stride1, out_stride2);
-        out_data[o] = x_data[i];
-      }
-    }
-  }
-}
-
-// Optimized transpose 4D. For reference see `tranpose_3d`.
-template <typename T>
-void transpose_4d(const T* x_data, const std::vector<size_t>& x_shape,
-                  const std::vector<size_t>& perm, T* out_data) {
-  size_t s2, s3, s4;
-  s2 = x_shape[1];
-  s3 = x_shape[2];
-  s4 = x_shape[3];
-
-  size_t p1, p2, p3, p4;
-  if (perm[0] == 3) {
-    p1 = 1;
-  } else if (perm[1] == 3) {
-    p2 = 1;
-  } else if (perm[2] == 3) {
-    p3 = 1;
-  } else {
-    p4 = 1;
-  }
-
-  if (perm[0] == 2) {
-    p1 = s4;
-  } else if (perm[1] == 2) {
-    p2 = s4;
-  } else if (perm[2] == 2) {
-    p3 = s4;
-  } else {
-    p4 = s4;
-  }
-
-  if (perm[0] == 1) {
-    p1 = s3 * s4;
-  } else if (perm[1] == 1) {
-    p2 = s3 * s4;
-  } else if (perm[2] == 1) {
-    p3 = s3 * s4;
-  } else {
-    p4 = s3 * s4;
-  }
-
-  if (perm[0] == 0) {
-    p1 = s2 * s3 * s4;
-  } else if (perm[1] == 0) {
-    p2 = s2 * s3 * s4;
-  } else if (perm[2] == 0) {
-    p3 = s2 * s3 * s4;
-  } else {
-    p4 = s2 * s3 * s4;
-  }
-
-  size_t out_shape[4];
-  out_shape[0] = x_shape[perm[0]];
-  out_shape[1] = x_shape[perm[1]];
-  out_shape[2] = x_shape[perm[2]];
-  out_shape[3] = x_shape[perm[3]];
-  const size_t out_stride1 = out_shape[1] * out_shape[2] * out_shape[3];
-  const size_t out_stride2 = out_shape[2] * out_shape[3];
-  const size_t out_stride3 = out_shape[3];
-
-  for (size_t i1 = 0; i1 < out_shape[0]; ++i1) {
-    for (size_t i2 = 0; i2 < out_shape[1]; ++i2) {
-      for (size_t i3 = 0; i3 < out_shape[2]; ++i3) {
-        for (size_t i4 = 0; i4 < out_shape[3]; ++i4) {
-          const size_t i =
-              tfjs::util::offset(i1, i2, i3, i4, 0, p1, p2, p3, p4);
-          const size_t o = tfjs::util::offset(i1, i2, i3, i4, out_stride1,
-                                              out_stride2, out_stride3);
-          out_data[o] = x_data[i];
-        }
-      }
-    }
-  }
-}
-
-// Generic transpose implementation for n-dim tensors.
-template <typename T>
-void slow_transpose_nd(const T* x_data, const std::vector<size_t>& x_shape,
-                       const std::vector<size_t>& perm, T* out_data) {
-  const size_t size = tfjs::util::size_from_shape(x_shape);
-  const auto x_strides = tfjs::util::compute_strides(x_shape);
-
-  std::vector<size_t> out_shape(x_shape.size());
-  for (size_t i = 0; i < x_shape.size(); ++i) {
-    out_shape[i] = x_shape[perm[i]];
-  }
-  const auto out_strides = tfjs::util::compute_strides(out_shape);
-
-  for (size_t i = 0; i < size; ++i) {
-    const auto loc = tfjs::util::offset_to_loc(i, x_strides);
-
-    // Permute location.
-    std::vector<size_t> new_loc(loc.size());
-    for (size_t i = 0; i < loc.size(); ++i) {
-      new_loc[i] = loc[perm[i]];
-    }
-
-    const size_t new_i = tfjs::util::loc_to_offset(new_loc, out_strides);
-    out_data[new_i] = x_data[i];
-  }
-}
-
-// Flatten finds the dimensions that can be flatten, shrinks the given shapes
-// and the given perm parameter to reflect the non-flatten dimensions, and
-// returns the total size of the non-flatten dimensions.
-//
-// E.g, Given shape [2, 3, 4, 5] and perm [0,1,3,2] case,
-// this method flattens the first two dimensions and returns a new shape [5,4],
-// new perm [1,0] and 4*5=20 as the total size of the non-flat dims. Reference:
-// https://github.com/tensorflow/tensorflow/blob/1f404fcaad58bf61a107d4fa7c4f6004168a50fa/tensorflow/lite/kernels/internal/transpose_utils.h#L42
-size_t flatten(const std::vector<size_t>& x_shape,
-               const std::vector<size_t>& perm,
-               std::vector<size_t>* new_x_shape_ptr,
-               std::vector<size_t>* new_perm_ptr) {
-  auto& new_input_shape = *new_x_shape_ptr;
-  auto& new_perm = *new_perm_ptr;
-
-  // Calculate the total size of non-flatten dimensions.
-  size_t num_dims_to_skip = 0;
-  size_t rank = perm.size();
-  size_t flat_size = tfjs::util::size_from_shape(x_shape);
-  for (size_t i = 0; i < rank; ++i) {
-    if (perm[i] == i) {
-      flat_size /= x_shape[i];
-      ++num_dims_to_skip;
-    } else {
-      break;
-    }
-  }
-  // Shrink the shapes and re-calculate the perm parameter.
-  const size_t new_rank = rank - num_dims_to_skip;
-  new_perm.resize(new_rank);
-  new_input_shape.resize(new_rank);
-
-  for (size_t i = num_dims_to_skip; i < rank; ++i) {
-    new_input_shape[i - num_dims_to_skip] = x_shape[i];
-    new_perm[i - num_dims_to_skip] = perm[i];
-  }
-  for (size_t i = 0; i < new_rank; ++i) {
-    int min_val_idx = -1;
-    for (size_t j = 0; j < new_rank; ++j) {
-      if (new_perm[j] >= i &&
-          (min_val_idx == -1 || new_perm[min_val_idx] > new_perm[j])) {
-        min_val_idx = j;
-      }
-    }
-    new_perm[min_val_idx] = i;
-  }
-  return flat_size;
-}
-
-template <typename T>
-void transpose_impl(const T* x_data, const std::vector<size_t>& x_shape,
-                    const std::vector<size_t>& perm, T* out_data) {
-  if (x_shape.size() == 2) {
-    transpose_2d(x_data, x_shape, out_data);
-  } else if (x_shape.size() == 3) {
-    transpose_3d(x_data, x_shape, perm, out_data);
-  } else if (x_shape.size() == 4) {
-    transpose_4d(x_data, x_shape, perm, out_data);
-  } else {
-    slow_transpose_nd(x_data, x_shape, perm, out_data);
-  }
-}
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-template <typename T>
-void transpose(const T* x_data, const std::vector<size_t>& x_shape,
-               const std::vector<size_t>& perm, T* out_data) {
-  std::vector<size_t> new_x_shape;
-  std::vector<size_t> new_perm;
-  // Try to reduce the rank of the transpose by flattening any outer-most
-  // dimensions.
-  const size_t non_flatten_size =
-      flatten(x_shape, perm, &new_x_shape, &new_perm);
-  const size_t total_size = tfjs::util::size_from_shape(x_shape);
-  for (size_t offset = 0; offset < total_size; offset += non_flatten_size) {
-    transpose_impl(x_data + offset, new_x_shape, new_perm, out_data + offset);
-  }
-}
-
-// Templates need explicit instantiation when implemented in a .cc file.
-template void transpose<float>(const float* x_data,
-                               const std::vector<size_t>& x_shape,
-                               const std::vector<size_t>& perm,
-                               float* out_data);
-template void transpose<int32_t>(const int32_t* x_data,
-                                 const std::vector<size_t>& x_shape,
-                                 const std::vector<size_t>& perm,
-                                 int32_t* out_data);
-template void transpose<bool>(const bool* x_data,
-                              const std::vector<size_t>& x_shape,
-                              const std::vector<size_t>& perm, bool* out_data);
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/transpose_impl.h b/tfjs-master/tfjs-backend-wasm/src/cc/transpose_impl.h
deleted file mode 100644
index ba84c0096..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/transpose_impl.h
+++ /dev/null
@@ -1,30 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef TRANSPOSE_IMPL_H_
-#define TRANSPOSE_IMPL_H_
-
-#include <cstddef>
-#include <vector>
-
-namespace tfjs {
-namespace wasm {
-
-template <typename T>
-void transpose(const T* x_data, const std::vector<size_t>& x_shape,
-               const std::vector<size_t>& perm, T* out_data);
-
-}  // namespace wasm
-}  // namespace tfjs
-#endif  // TRANSPOSE_IMPL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/unary.cc b/tfjs-master/tfjs-backend-wasm/src/cc/unary.cc
deleted file mode 100644
index 65a1f28d3..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/unary.cc
+++ /dev/null
@@ -1,74 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include "tfjs-backend-wasm/src/cc/unary.h"
-
-#include <xnnpack.h>
-#include <cstddef>
-#include <limits>
-#include <unordered_map>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace {
-// Maps an `xnn_create_*_nc_f32` function pointer to an instantiated operator.
-std::unordered_map<tfjs::wasm::xnn_create_unary_op, xnn_operator_t> op_cache;
-}  // namespace
-
-namespace tfjs {
-namespace wasm {
-
-void unary_xnn_f32(const size_t x_id, const size_t out_id,
-                   xnn_create_unary_op create_op, xnn_setup_unary_op setup_op) {
-  auto& x_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-  const float* x_buf = x_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  xnn_operator_t unary_op = nullptr;
-
-  auto cache_result = op_cache.find(create_op);
-  if (cache_result == op_cache.end()) {
-    const size_t channels = 1, input_stride = 1, output_stride = 1;
-    const uint32_t flags = 1;
-    xnn_status status =
-        create_op(channels, input_stride, output_stride, flags, &unary_op);
-    if (status != xnn_status_success) {
-      util::warn(
-          "XNN status for xnn_create_*_nd_f32 is not successful. Got "
-          "status %d. Use -c dbg to see XNN logs.");
-      return;
-    }
-    op_cache.insert({create_op, unary_op});
-    backend::xnn_operator_count++;
-  } else {
-    unary_op = cache_result->second;
-  }
-  const size_t batch_size = out_info.size;
-  xnn_status status =
-      setup_op(unary_op, batch_size, x_buf, out_buf, tfjs::backend::threadpool);
-  if (status != xnn_status_success) {
-    util::warn(
-        "XNN status for xnn_setup_*_nd_f32 is not successful. Got "
-        "status %d. Use -c dbg to see XNN logs.",
-        status);
-    return;
-  }
-
-  xnn_run_operator(unary_op, tfjs::backend::threadpool);
-}
-
-}  // namespace wasm
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/unary.h b/tfjs-master/tfjs-backend-wasm/src/cc/unary.h
deleted file mode 100644
index 72d6f6bba..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/unary.h
+++ /dev/null
@@ -1,102 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef UNARY_H_
-#define UNARY_H_
-
-#include <xnnpack.h>
-
-#include <cstddef>
-
-#include "tfjs-backend-wasm/src/cc/backend.h"
-
-namespace tfjs {
-namespace wasm {
-
-inline void unary_bool(const int x_id, const int out_id, bool operation(bool)) {
-  auto& a_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const bool* a_buf = a_info.b();
-  bool* out_buf = out_info.b_write();
-
-  for (size_t i = 0; i < a_info.size; ++i) {
-    out_buf[i] = operation(a_buf[i]);
-  }
-}
-
-inline void unary_f32(const size_t x_id, const size_t out_id,
-                      float operation(float)) {
-  auto& a_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* a_buf = a_info.f32();
-  float* out_buf = out_info.f32_write();
-
-  for (size_t i = 0; i < a_info.size; ++i) {
-    out_buf[i] = operation(a_buf[i]);
-  }
-}
-
-inline void unary_i32(const size_t x_id, const size_t out_id,
-                      int operation(int)) {
-  auto& a_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const int32_t* a_buf = a_info.i32();
-  int32_t* out_buf = out_info.i32_write();
-
-  for (size_t i = 0; i < a_info.size; ++i) {
-    out_buf[i] = operation(a_buf[i]);
-  }
-}
-
-inline void unary_i32_with_f32_out(const size_t x_id, const size_t out_id,
-                                   float operation(int)) {
-  auto& a_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const int32_t* a_buf = a_info.i32();
-  float* out_buf = out_info.f32_write();
-
-  for (size_t i = 0; i < a_info.size; ++i) {
-    out_buf[i] = operation(a_buf[i]);
-  }
-}
-
-inline void unary_f32_with_bool_out(const size_t x_id, const size_t out_id,
-                                    bool operation(float)) {
-  auto& a_info = backend::get_tensor_info(x_id);
-  auto& out_info = backend::get_tensor_info_out(out_id);
-
-  const float* a_buf = a_info.f32();
-  bool* out_buf = out_info.b_write();
-
-  for (size_t i = 0; i < a_info.size; ++i) {
-    out_buf[i] = operation(a_buf[i]);
-  }
-}
-
-typedef xnn_status (*xnn_create_unary_op)(size_t, size_t, size_t, uint32_t,
-                                          xnn_operator_t*);
-typedef xnn_status (*xnn_setup_unary_op)(xnn_operator_t, size_t, const float*,
-                                         float*, pthreadpool_t);
-
-void unary_xnn_f32(const size_t x_id, const size_t out_id,
-                   xnn_create_unary_op create_op, xnn_setup_unary_op setup_op);
-
-}  // namespace wasm
-}  // namespace tfjs
-
-#endif  // UNARY_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/util.cc b/tfjs-master/tfjs-backend-wasm/src/cc/util.cc
deleted file mode 100644
index 4eec35e58..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/util.cc
+++ /dev/null
@@ -1,140 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include <algorithm>
-#include <cstddef>
-#include <cstring>
-#include <vector>
-
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-namespace tfjs {
-namespace util {
-
-const std::vector<size_t> compute_strides(const std::vector<size_t> shape) {
-  const size_t rank = shape.size();
-
-  if (rank < 2) {
-    return {};
-  }
-
-  // Last dimension has implicit stride of 1, thus having D-1 (instead of D)
-  // strides.
-  std::vector<size_t> strides(rank - 1);
-  strides[rank - 2] = shape[rank - 1];
-
-  if (rank < 3) {
-    return strides;
-  }
-
-  // We do i < rank here because i <= 0 is always true for unsigned integers and
-  // decrementing will wrap to the max int.
-  for (size_t i = rank - 3; i < rank; i--) {
-    strides[i] = strides[i + 1] * shape[i + 1];
-  }
-
-  return strides;
-}
-
-const std::vector<size_t> assert_and_get_broadcast_shape(
-    const std::vector<size_t> shape_a, const std::vector<size_t> shape_b) {
-  std::vector<size_t> result = {};
-  const size_t l = std::max(shape_a.size(), shape_b.size());
-  for (size_t i = 0; i < l; ++i) {
-    const int a_idx = static_cast<int32_t>(shape_a.size()) - i - 1;
-    const size_t a = (a_idx < 0) ? 1 : shape_a[a_idx];
-    const int b_idx = static_cast<int32_t>(shape_b.size()) - i - 1;
-    const size_t b = b_idx < 0 ? 1 : shape_b[b_idx];
-    if (a == 1) {
-      result.push_back(b);
-    } else if (b == 1) {
-      result.push_back(a);
-    } else if (a != b) {
-      warn("Operands could not be broadcast together, shape mismatch.");
-    } else {
-      result.push_back(a);
-    }
-  }
-  std::reverse(result.begin(), result.end());
-  return result;
-}
-
-const std::vector<size_t> get_broadcast_dims(
-    const std::vector<size_t> in_shape, const std::vector<size_t> out_shape) {
-  const size_t in_rank = in_shape.size();
-  const size_t out_rank = out_shape.size();
-  std::vector<size_t> dims = {};
-  for (int i = 0; i < in_rank; ++i) {
-    const int in_dim = in_rank - 1 - i;
-    const int out_dim = out_rank - 1 - i;
-    const size_t a = in_shape[in_dim];
-    const size_t b = out_dim < 0 ? 1 : out_shape[out_dim];
-    if (b > 1 && a == 1) {
-      dims.push_back(in_dim);
-    }
-  }
-  std::reverse(dims.begin(), dims.end());
-  return dims;
-}
-
-const void identity_pool(const size_t x_id, const float* x_buf, float* out_buf,
-                         const size_t out_size, const size_t batch_size,
-                         const size_t input_height, const size_t input_width,
-                         const size_t stride_height, const size_t stride_width,
-                         const size_t channels) {
-  // Early bailout for the identity case to use memcpy for efficiency.
-  if (stride_width == 1 && stride_height == 1) {
-    std::memcpy(out_buf, x_buf, out_size * sizeof(*out_buf));
-    return;
-  }
-
-  // Values per row and column are determined by the stride size.
-  // ceil(input_height / stride_height) instead of floor because strides do
-  // not guarantee that more than one value is available.
-  // e.g. a stride of 3 would 'partition' range(1, 10) into
-  // [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10]]
-  // and would include 10 in the output: [1, 4, 7, 10]
-  size_t vals_per_col = (input_height + stride_height - 1) / stride_height;
-  size_t vals_per_row = (input_width + stride_width - 1) / stride_width;
-
-  size_t x_batch_vals_count = input_width * input_height;
-  size_t out_batch_vals_count = vals_per_row * vals_per_col;
-
-  // Copy values specified by the strides.
-  // Only NHWC is currently supported.
-  for (size_t n = 0; n < batch_size; n++) {
-    for (size_t h = 0; h < vals_per_col; h++) {
-      for (size_t w = 0; w < vals_per_row; w++) {
-        for (size_t c = 0; c < channels; c++) {
-          size_t x_n_index = n * x_batch_vals_count;
-          size_t x_hw_index = h * stride_height * input_width
-                              + w * stride_width;
-          size_t x_nhw_index = x_n_index + x_hw_index;
-          size_t x_nhwc_index = c + channels * x_nhw_index;
-
-          size_t out_n_index = n * out_batch_vals_count;
-          size_t out_hw_index = h * vals_per_row + w;
-          size_t out_nhw_index = out_n_index + out_hw_index;
-          size_t out_nhwc_index = c + channels * out_nhw_index;
-
-          out_buf[out_nhwc_index] = x_buf[x_nhwc_index];
-        }
-      }
-    }
-  }
-  return;
-}
-
-}  // namespace util
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/util.h b/tfjs-master/tfjs-backend-wasm/src/cc/util.h
deleted file mode 100644
index 34c6bc4b8..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/util.h
+++ /dev/null
@@ -1,169 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#ifndef UTIL_H_
-#define UTIL_H_
-
-#include <cstdarg>
-#include <cstddef>
-#include <cstdio>
-#include <vector>
-
-namespace tfjs {
-namespace util {
-
-inline void print_log(const char* format, va_list args) {
-  // TODO(smilkov): Bind directly to `console.log` instead of depending on
-  // stdio, to reduce wasm binary size.
-  vfprintf(stdout, format, args);
-}
-
-inline void print_warn(const char* format, va_list args) {
-  // TODO(smilkov): Bind directly to `console.warn` instead of depending on
-  // stdio, to reduce wasm binary size.
-  vfprintf(stderr, format, args);
-}
-
-// Logs the message to the console without flushing.
-inline void print_log(const char* format, ...) {
-  va_list args;
-  va_start(args, format);
-  print_log(format, args);
-}
-
-inline void print_warn(const char* format, ...) {
-  va_list args;
-  va_start(args, format);
-  print_warn(format, args);
-}
-
-// Logs and flushes the message to the js console (console.log).
-inline void log(const char* format, ...) {
-  va_list args;
-  va_start(args, format);
-  print_log(format, args);
-  print_log("\n");
-}
-
-// Logs and flushes the message to the js console (console.err).
-inline void warn(const char* format, ...) {
-  va_list args;
-  va_start(args, format);
-  print_warn(format, args);
-  print_warn("\n");
-}
-
-// Helper method to log values in a vector. Used for debugging.
-template <class T>
-inline void log_vector(const std::vector<T>& v) {
-  print_log("[", 0);
-  for (const auto& value : v) {
-    print_log("%d,", value);
-  }
-  print_log("]\n", 0);
-}
-
-// Returns the size of the vector, given its shape.
-inline size_t size_from_shape(const std::vector<size_t>& shape) {
-  size_t prod = 1;
-  for (const auto& v : shape) {
-    prod *= v;
-  }
-  return prod;
-}
-
-// Returns the indices of an n-dim tensor given the flat offset and its strides.
-inline std::vector<size_t> offset_to_loc(size_t index,
-                                         const std::vector<size_t>& strides) {
-  size_t rank = strides.size() + 1;
-  std::vector<size_t> loc(rank);
-  if (rank == 0) {
-    return loc;
-  } else if (rank == 1) {
-    loc[0] = index;
-    return loc;
-  }
-  for (size_t i = 0; i < rank - 1; ++i) {
-    size_t stride = strides[i];
-    loc[i] = index / stride;
-    index -= loc[i] * stride;
-  }
-  loc[rank - 1] = index;
-  return loc;
-}
-
-// Returns the flat offset of an n-dim tensor given the indices and strides.
-inline size_t loc_to_offset(const std::vector<size_t>& loc,
-                            const std::vector<size_t>& strides) {
-  size_t rank = loc.size();
-  if (rank == 0) {
-    return 0;
-  } else if (rank == 1) {
-    return loc[0];
-  }
-  size_t index = loc[loc.size() - 1];
-  for (size_t i = 0; i < loc.size() - 1; ++i) {
-    index += strides[i] * loc[i];
-  }
-  return index;
-}
-
-// Returns the flat offset of a 2D tensor given the indices and the stride.
-inline size_t offset(size_t i1, size_t i2, size_t s1) { return i1 * s1 + i2; }
-
-// Returns the flat offset of a 3D tensor given the indices and the strides.
-inline size_t offset(size_t i1, size_t i2, size_t i3, size_t s1, size_t s2) {
-  return i1 * s1 + i2 * s2 + i3;
-}
-
-// Returns the flat offset of a 4D tensor given the indices and the strides.
-inline size_t offset(size_t i1, size_t i2, size_t i3, size_t i4, size_t s1,
-                     size_t s2, size_t s3) {
-  return i1 * s1 + i2 * s2 + i3 * s3 + i4;
-}
-
-// Returns the flat offset of a 5D tensor given the indices and the strides.
-inline size_t offset(size_t i1, size_t i2, size_t i3, size_t i4, size_t i5,
-                     size_t s1, size_t s2, size_t s3, size_t s4) {
-  return i1 * s1 + i2 * s2 + i3 * s3 + i4 * s4 + i5;
-}
-
-// Returns the strides of a tensor given its shape. Note that the strides
-// are of length R-1 where R is the rank of the tensor.
-const std::vector<size_t> compute_strides(const std::vector<size_t> shape);
-
-// Returns the broadcasted new shape given two shapes.
-const std::vector<size_t> assert_and_get_broadcast_shape(
-    const std::vector<size_t> shape_a, const std::vector<size_t> shape_b);
-
-// Returns the dimensions in the input shape that are broadcasted to produce the
-// provided output shape.
-const std::vector<size_t> get_broadcast_dims(
-    const std::vector<size_t> in_shape, const std::vector<size_t> out_shape);
-
-// Generates the output for AvgPool, MaxPool, etc where xnnpack does not support
-// a 1x1 filter. Applies batching, channels, and strides.
-// TODONT(mattsoulanille): Padding support is not necessary because it is
-// meaningless for a 1x1 kernel. It would be undefined for regions where the
-// kernel does not overlap the input tensor.
-// https://www.tensorflow.org/api_docs/python/tf/nn#difference_between_convolution_and_pooling_layers_2
-const void identity_pool(const size_t x_id, const float* x_buf, float* out_buf,
-                         const size_t out_size, const size_t batch_size,
-                         const size_t input_height, const size_t input_width,
-                         const size_t stride_height, const size_t stride_width,
-                         const size_t channels);
-
-}  // namespace util
-}  // namespace tfjs
-#endif  // UTIL_H_
diff --git a/tfjs-master/tfjs-backend-wasm/src/cc/util_test.cc b/tfjs-master/tfjs-backend-wasm/src/cc/util_test.cc
deleted file mode 100644
index e23080e36..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/cc/util_test.cc
+++ /dev/null
@@ -1,150 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ===========================================================================*/
-
-#include "tfjs-backend-wasm/src/cc/util.h"
-
-#include <gtest/gtest.h>
-
-#include <array>
-#include <cstddef>
-#include <vector>
-
-namespace tfjs {
-namespace util {
-namespace {
-
-TEST(util, offset_2d) {
-  std::array<size_t, 2> coord = {0, 0};
-  std::array<size_t, 1> stride = {0};
-  EXPECT_EQ(0, offset(coord[0], coord[1], stride[0]));
-
-  coord = {2, 3};
-  stride = {5};
-  EXPECT_EQ(13, offset(coord[0], coord[1], stride[0]));
-}
-
-TEST(util, offset_3d) {
-  std::array<size_t, 3> coord = {0, 0, 0};
-  std::array<size_t, 2> stride = {0, 0};
-  EXPECT_EQ(0, offset(coord[0], coord[1], coord[2], stride[0], stride[1]));
-
-  coord = {3, 5, 7};
-  stride = {4, 3};
-  EXPECT_EQ(34, offset(coord[0], coord[1], coord[2], stride[0], stride[1]));
-}
-
-TEST(util, offset_4d) {
-  std::array<size_t, 4> coord = {0, 0, 0, 0};
-  std::array<size_t, 3> stride = {0, 0, 0};
-  EXPECT_EQ(0, offset(coord[0], coord[1], coord[2], coord[3], stride[0],
-                      stride[1], stride[2]));
-
-  coord = {1, 2, 3, 4};
-  stride = {5, 7, 9};
-  EXPECT_EQ(50, offset(coord[0], coord[1], coord[2], coord[3], stride[0],
-                       stride[1], stride[2]));
-}
-
-TEST(util, offset_5d) {
-  std::array<size_t, 5> coord = {0, 0, 0, 0, 0};
-  std::array<size_t, 4> stride = {0, 0, 0, 0};
-  EXPECT_EQ(0, offset(coord[0], coord[1], coord[2], coord[3], coord[4],
-                      stride[0], stride[1], stride[2], stride[3]));
-
-  coord = {1, 2, 3, 4, 5};
-  stride = {5, 7, 9, 11};
-  EXPECT_EQ(95, offset(coord[0], coord[1], coord[2], coord[3], coord[4],
-                       stride[0], stride[1], stride[2], stride[3]));
-}
-
-TEST(util, size_from_shape) {
-  std::vector<size_t> shape = {};
-  EXPECT_EQ(1, size_from_shape(shape));
-
-  shape = {3};
-  EXPECT_EQ(3, size_from_shape(shape));
-
-  shape = {3, 4};
-  EXPECT_EQ(12, size_from_shape(shape));
-
-  shape = {1, 3, 5};
-  EXPECT_EQ(15, size_from_shape(shape));
-
-  shape = {2, 3, 4};
-  EXPECT_EQ(24, size_from_shape(shape));
-
-  shape = {2, 3, 4, 5};
-  EXPECT_EQ(120, size_from_shape(shape));
-}
-
-TEST(util, loc_to_offset) {
-  std::vector<size_t> loc = {};
-  std::vector<size_t> strides = {};
-  EXPECT_EQ(0, loc_to_offset(loc, strides));
-
-  loc = {5};
-  strides = {};
-  EXPECT_EQ(5, loc_to_offset(loc, strides));
-
-  loc = {3, 5};
-  strides = {7};
-  EXPECT_EQ(26, loc_to_offset(loc, strides));
-
-  loc = {6, 0, 3};
-  strides = {8, 4};
-  EXPECT_EQ(51, loc_to_offset(loc, strides));
-
-  loc = {8, 0, 1, 1};
-  strides = {8, 4, 2};
-  EXPECT_EQ(67, loc_to_offset(loc, strides));
-}
-
-TEST(util, offset_to_loc) {
-  size_t offset = 5;
-  std::vector<size_t> strides = {};
-  EXPECT_EQ(std::vector<size_t>({5}), offset_to_loc(offset, strides));
-
-  offset = 26;
-  strides = {7};
-  EXPECT_EQ(std::vector<size_t>({3, 5}), offset_to_loc(offset, strides));
-
-  offset = 51;
-  strides = {8, 4};  // shape is [7, 2, 4]
-  EXPECT_EQ(std::vector<size_t>({6, 0, 3}), offset_to_loc(offset, strides));
-
-  offset = 67;
-  strides = {8, 4, 2};  // shape is [9, 2, 2, 2]
-  EXPECT_EQ(std::vector<size_t>({8, 0, 1, 1}), offset_to_loc(offset, strides));
-}
-
-TEST(util, compute_strides) {
-  std::vector<size_t> shape = {5, 7};
-  EXPECT_EQ(std::vector<size_t>({7}), compute_strides(shape));
-
-  shape = {3, 5, 7};
-  EXPECT_EQ(std::vector<size_t>({35, 7}), compute_strides(shape));
-
-  shape = {3, 5, 7, 9};
-  EXPECT_EQ(std::vector<size_t>({315, 63, 9}), compute_strides(shape));
-
-  shape = {2, 3, 5, 7, 9};
-  EXPECT_EQ(std::vector<size_t>({945, 315, 63, 9}), compute_strides(shape));
-
-  shape = {2, 2, 2, 2, 2, 2};
-  EXPECT_EQ(std::vector<size_t>({32, 16, 8, 4, 2}), compute_strides(shape));
-}
-
-}  // namespace
-}  // namespace util
-}  // namespace tfjs
diff --git a/tfjs-master/tfjs-backend-wasm/src/flags_wasm.ts b/tfjs-master/tfjs-backend-wasm/src/flags_wasm.ts
deleted file mode 100644
index fff6c28c2..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/flags_wasm.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-
-const ENV = env();
-
-/**
- * True if SIMD is supported.
- */
-// From: https://github.com/GoogleChromeLabs/wasm-feature-detect
-ENV.registerFlag('WASM_HAS_SIMD_SUPPORT', async () => {
-  try {
-    // This typed array passed in to WebAssembly.validate is WebAssembly binary
-    // code. In this case it is a small program that contains SIMD
-    // instructions.
-    return WebAssembly.validate(new Uint8Array([
-      0, 97, 115, 109, 1, 0, 0, 0, 1,  4, 1,   96, 0,  0, 3,
-      2, 1,  0,   10,  9, 1, 7, 0, 65, 0, 253, 15, 26, 11
-    ]));
-  } catch (e) {
-    return false;
-  }
-});
-
-/**
- * True if threads are supported.
- */
-// From: https://github.com/GoogleChromeLabs/wasm-feature-detect
-ENV.registerFlag('WASM_HAS_MULTITHREAD_SUPPORT', async () => {
-  // TODO(annxingyuan): Enable node support once this is resolved:
-  // https://github.com/tensorflow/tfjs/issues/3830
-  if (ENV.get('IS_NODE')) {
-    return false;
-  }
-
-  try {
-    // Test for transferability of SABs (needed for Firefox)
-    // https://groups.google.com/forum/#!msg/mozilla.dev.platform/IHkBZlHETpA/dwsMNchWEQAJ
-    new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));
-    // This typed array is a WebAssembly program containing threaded
-    // instructions.
-    return WebAssembly.validate(new Uint8Array([
-      0, 97, 115, 109, 1, 0,  0,  0, 1, 4, 1,  96, 0,   0,  3, 2, 1,  0, 5,
-      4, 1,  3,   1,   1, 10, 11, 1, 9, 0, 65, 0,  254, 16, 2, 0, 26, 11
-    ]));
-  } catch (e) {
-    return false;
-  }
-});
diff --git a/tfjs-master/tfjs-backend-wasm/src/index.ts b/tfjs-master/tfjs-backend-wasm/src/index.ts
deleted file mode 100644
index b33b35c17..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/index.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import './register_all_kernels';
-export * from './base';
diff --git a/tfjs-master/tfjs-backend-wasm/src/index_test.ts b/tfjs-master/tfjs-backend-wasm/src/index_test.ts
deleted file mode 100644
index d9428cb99..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/index_test.ts
+++ /dev/null
@@ -1,268 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {registerBackend, removeBackend, test_util, env} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line:no-imports-from-dist
-import {ALL_ENVS, BROWSER_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {init, resetWasmPath} from './backend_wasm';
-import {BackendWasm, setWasmPath, setWasmPaths} from './index';
-import {VALID_PREFIX, setupCachedWasmPaths} from './test_util';
-
-/**
- * Tests specific to the wasm backend. The name of these tests must start with
- * 'wasm' so that they are always included in the test runner. See
- * `env.specFilter` in `setup_test.ts` for details.
- */
-describeWithFlags('wasm read/write', ALL_ENVS, () => {
-  it('write and read values', async () => {
-    const x = tf.tensor1d([1, 2, 3]);
-    test_util.expectArraysClose([1, 2, 3], await x.data());
-  });
-
-  it('allocate repetitively and confirm reuse of heap space', () => {
-    const backend = tf.backend() as BackendWasm;
-    const size = 100;
-    // Allocate for the first time, record the memory offset and dispose.
-    const t1 = tf.zeros([size]);
-    const memOffset1 = backend.getMemoryOffset(t1.dataId);
-    t1.dispose();
-
-    // Allocate again and make sure the offset is the same (memory was reused).
-    const t2 = tf.zeros([size]);
-    const memOffset2 = backend.getMemoryOffset(t2.dataId);
-    // This should fail in case of a memory leak.
-    expect(memOffset1).toBe(memOffset2);
-  });
-
-  it('allocates buffers with byteOffsets', async () => {
-    const data = [-0.5, 0.5, 3.14];
-    const buffer = new ArrayBuffer(32);
-    const view = new Float32Array(buffer, 8, data.length);
-
-    // Write values to buffer.
-    for (let i = 0; i < data.length; ++i) {
-      view[i] = data[i];
-    }
-
-    const t = tf.tensor(view);
-    // Tensor values should match.
-    test_util.expectArraysClose(await t.data(), view);
-  });
-
-  // TODO(mattSoulanille): Re-enable this once it's working on iOS.
-  // tslint:disable-next-line: ban
-  xit('allocates more than two gigabytes', async () => {
-    const size = 2**30 / 4; // 2**30 bytes (4 bytes per number) = 1GB
-
-    // Allocate 3 gigabytes.
-    const t0 = tf.zeros([size], 'float32');
-    const t1 = tf.ones([size], 'float32');
-    const t2 = t1.mul(2);
-
-    // Helper function to check if all the values in a tensor equal an expected
-    // value.
-    async function check(tensor: tf.Tensor, name: string, val: number) {
-      const arr = await tensor.data();
-      for (let i = 0; i < size; i++) {
-        if (arr[i] !== val) {
-          throw new Error(`${name}[${i}] == ${arr[i]} but should be ${val}`);
-        }
-      }
-    }
-
-    await check(t0, 't0', 0);
-    await check(t1, 't1', 1);
-    await check(t2, 't2', 2);
-  });
-});
-
-describeWithFlags('wasm init', BROWSER_ENVS, () => {
-  beforeEach(async () => {
-    await setupCachedWasmPaths();
-    registerBackend('wasm-test', async () => {
-      const {wasm} = await init();
-      return new BackendWasm(wasm);
-    }, 100);
-
-    // Silences backend registration warnings.
-    spyOn(console, 'warn');
-    spyOn(console, 'log');
-  });
-
-  afterEach(async () => {
-    removeBackend('wasm-test');
-  });
-
-  afterAll(setupCachedWasmPaths);
-
-  it('backend init fails when the path is invalid', async () => {
-    resetWasmPath();
-    setWasmPath('invalid/path');
-    let wasmPath: string;
-    spyOn(self, 'fetch').and.callFake((path: string) => {
-      wasmPath = path;
-      return Promise.reject(
-        new TypeError('Failed to fetch because invalid path'));
-    });
-    expect(await tf.setBackend('wasm-test')).toBe(false);
-    expect(wasmPath).toBe('invalid/path');
-  });
-
-  it('backend init fails when setWasmPaths is called with ' +
-         'an invalid prefix',
-     async () => {
-       resetWasmPath();
-       setWasmPaths('invalid/prefix/');
-       let wasmPath: string;
-       spyOn(self, 'fetch').and.callFake((path: string) => {
-         wasmPath = path;
-         return Promise.reject(
-           new TypeError('Failed to fetch because invalid prefix'));
-       });
-       expect(await tf.setBackend('wasm-test')).toBe(false);
-       expect(wasmPath).toContain('invalid/prefix');
-     });
-
-  it('backend init fails when setWasmPaths is called with ' +
-         'an invalid fileMap',
-     async () => {
-       resetWasmPath();
-       setWasmPaths({
-         'tfjs-backend-wasm.wasm': 'invalid/path',
-         'tfjs-backend-wasm-simd.wasm': 'invalid/path',
-         'tfjs-backend-wasm-threaded-simd.wasm': 'invalid/path'
-       });
-       let wasmPathPrefix: string;
-       spyOn(self, 'fetch').and.callFake((path: string) => {
-         wasmPathPrefix = path;
-         return Promise.reject(
-           new TypeError('Failed to fetch because invalid paths'));
-       });
-       expect(await tf.setBackend('wasm-test')).toBe(false);
-       expect(wasmPathPrefix).toBe('invalid/path');
-     });
-
-  it('setWasmPaths throws error when called without specifying a path for ' +
-         'each binary',
-     async () => {
-       expect(() => {
-         setWasmPaths({
-           'tfjs-backend-wasm.wasm': '/base/wasm-out/tfjs-backend-wasm.wasm'
-         });
-       }).toThrow();
-     });
-
-  describe('platform fetch', () => {
-    let fetchSpy: jasmine.Spy;
-    let realFetch: typeof fetch;
-
-    beforeEach(() => {
-      realFetch = env().platform.fetch;
-      fetchSpy = spyOn(env().platform, 'fetch');
-    });
-
-    afterEach(() => {
-      env().platform.fetch = realFetch;
-    });
-
-    it('backend init works when the path is valid', async () => {
-      const usePlatformFetch = true;
-      resetWasmPath();
-      setWasmPaths(VALID_PREFIX, usePlatformFetch);
-      let wasmPath: string;
-      fetchSpy.and.callFake((path: string) => {
-        wasmPath = path;
-        return realFetch(path);
-      });
-      expect(await tf.setBackend('wasm-test')).toBe(true);
-      const validPaths = new Set([
-        VALID_PREFIX + 'tfjs-backend-wasm.wasm',
-        VALID_PREFIX + 'tfjs-backend-wasm-simd.wasm',
-        VALID_PREFIX + 'tfjs-backend-wasm-threaded-simd.wasm',
-      ]);
-      expect(validPaths).toContain(wasmPath);
-    });
-
-    it('backend init works when the wasm paths overrides map is valid',
-        async () => {
-          const usePlatformFetch = true;
-          setWasmPaths({
-            'tfjs-backend-wasm.wasm': `${VALID_PREFIX}tfjs-backend-wasm.wasm`,
-            'tfjs-backend-wasm-simd.wasm':
-            `${VALID_PREFIX}tfjs-backend-wasm-simd.wasm`,
-            'tfjs-backend-wasm-threaded-simd.wasm':
-            `${VALID_PREFIX}tfjs-backend-wasm-threaded-simd.wasm`,
-          }, usePlatformFetch);
-          let wasmPath: string;
-          fetchSpy.and.callFake((path: string) => {
-            wasmPath = path;
-            return realFetch(path);
-          });
-          expect(await tf.setBackend('wasm-test')).toBe(true);
-          const validPaths = new Set([
-            VALID_PREFIX + 'tfjs-backend-wasm.wasm',
-            VALID_PREFIX + 'tfjs-backend-wasm-simd.wasm',
-            VALID_PREFIX + 'tfjs-backend-wasm-threaded-simd.wasm',
-          ]);
-          expect(validPaths).toContain(wasmPath);
-        });
-
-    it('backend init works when the path is valid', async () => {
-      const usePlatformFetch = true;
-      const validPath = VALID_PREFIX + 'tfjs-backend-wasm.wasm';
-      setWasmPath(validPath, usePlatformFetch);
-      let wasmPath: string;
-      fetchSpy.and.callFake((path: string) => {
-        wasmPath = path;
-        return realFetch(path);
-      });
-      expect(await tf.setBackend('wasm-test')).toBe(true);
-      expect(wasmPath).toBe(validPath);
-    });
-
-    // Disabling this test because it intermittently times out on CI.
-    // tslint:disable-next-line: ban
-    xit('backend init fails when the path is invalid', async () => {
-      const usePlatformFetch = true;
-      setWasmPath('invalid/path', usePlatformFetch);
-      let wasmPath: string;
-      fetchSpy.and.callFake((path: string) => {
-        wasmPath = path;
-        return Promise.reject(
-          new TypeError('Failed to fetch because invalid path'));
-      });
-      expect(await tf.setBackend('wasm-test')).toBe(false);
-      expect(wasmPath).toBe('invalid/path');
-    });
-  });
-
-  it('backend init succeeds with default path', async () => {
-    expect(await tf.setBackend('wasm-test')).toBe(true);
-  });
-
-  it('setWasmPath called too late', async () => {
-    // Set an invalid path.
-    setWasmPath('invalid/path');
-    await tf.setBackend('wasm-test');
-
-    // Setting the path too late.
-    expect(() => setWasmPath('too/late'))
-        .toThrowError(/The WASM backend was already initialized. Make sure/);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernel_utils/shared.ts b/tfjs-master/tfjs-backend-wasm/src/kernel_utils/shared.ts
deleted file mode 100644
index 3b6f42022..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernel_utils/shared.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Import shared functionality from tfjs-backend-cpu without triggering
-// side effects.
-// tslint:disable-next-line: no-imports-from-dist
-import {concatImpl as concatImplCPU} from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {sliceImpl as sliceImplCPU} from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {rangeImpl as rangeImplCPU} from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {stringNGramsImpl as stringNGramsImplCPU} from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {stringSplitImpl as stringSplitImplCPU} from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {stringToHashBucketFastImpl as stringToHashBucketFastImplCPU} from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {uniqueImpl as uniqueImplCPU} from '@tensorflow/tfjs-backend-cpu/dist/shared';
-
-export {
-  concatImplCPU,
-  rangeImplCPU,
-  sliceImplCPU,
-  stringNGramsImplCPU,
-  stringSplitImplCPU,
-  stringToHashBucketFastImplCPU,
-  uniqueImplCPU,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Abs.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Abs.ts
deleted file mode 100644
index 236401fb8..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Abs.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Abs, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const absConfig: KernelConfig = createUnaryKernelConfig(Abs);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Acos.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Acos.ts
deleted file mode 100644
index cc9beb827..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Acos.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acos, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const acosConfig: KernelConfig = createUnaryKernelConfig(Acos);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Acosh.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Acosh.ts
deleted file mode 100644
index 9fca53298..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Acosh.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acosh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const acoshConfig: KernelConfig = createUnaryKernelConfig(Acosh);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Add.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Add.ts
deleted file mode 100644
index c8f3d0049..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Add.ts
+++ /dev/null
@@ -1,25 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Add, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-const supportsFullBroadcast = true;
-
-export const addConfig: KernelConfig =
-    createBinaryKernelConfig(Add, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/AddN.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/AddN.ts
deleted file mode 100644
index ecb95030b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/AddN.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AddN, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmFunc:
-    (inputIds: Uint8Array, inputIdsLen: number, dtype: number, outId: number) =>
-        void;
-
-function setupFunc(backend: BackendWasm): void {
-  wasmFunc = backend.wasm.cwrap(AddN, null /* void */, [
-    'array',   // input_ids
-    'number',  // input_ids.length
-    'number',  // dtype
-    'number',  // out_id
-  ]);
-}
-
-function addn(args: {inputs: TensorInfo[], backend: BackendWasm}) {
-  const {inputs, backend} = args;
-  const out = backend.makeOutput(inputs[0].shape, inputs[0].dtype);
-
-  // Short-circuit zero-sized tensors.
-  if (util.sizeFromShape(out.shape) === 0) {
-    return out;
-  }
-
-  const inputIds = inputs.map(x => backend.dataIdMap.get(x.dataId).id);
-  const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);
-
-  return out;
-}
-
-export const addNConfig: KernelConfig = {
-  kernelName: AddN,
-  backendName: 'wasm',
-  setupFunc,
-  kernelFunc: addn as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/All.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/All.ts
deleted file mode 100644
index 418e1d3c4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/All.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {All, AllAttrs, AllInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {permuteAxesAndTranspose} from './kernel_utils';
-
-let wasmAll: (xId: number, reduceSize: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmAll = backend.wasm.cwrap(All, null /*void*/, ['number, number, number']);
-}
-
-function all(args: {backend: BackendWasm, inputs: AllInputs, attrs: AllAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {axis, keepDims} = attrs;
-  const {x} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  let inputId = xId;
-  let input = x;
-
-  const {transposed, axes, originalAxes, inputWasTransposed} =
-      permuteAxesAndTranspose(x, axis, backend);
-
-  if (inputWasTransposed) {
-    const transposedId = backend.dataIdMap.get(transposed.dataId).id;
-    input = transposed;
-    inputId = transposedId;
-  }
-
-  const inputRank = input.shape.length;
-  backend_util.assertAxesAreInnerMostDims('all', axes, inputRank);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(input.shape, axes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  const out = backend.makeOutput(outShape, x.dtype);
-  if (util.sizeFromShape(input.shape) !== 0) {
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    wasmAll(inputId, reduceSize, outId);
-  }
-
-  if (inputWasTransposed) {
-    // dispose of the transposed tensor.
-    backend.disposeData(transposed.dataId);
-  }
-
-  if (keepDims) {
-    // reshape
-    const newShape = backend_util.expandShapeToKeepDim(out.shape, originalAxes);
-    out.shape = newShape;
-  }
-
-  return out;
-}
-
-export const allConfig: KernelConfig = {
-  kernelName: All,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: all as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Any.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Any.ts
deleted file mode 100644
index 8dae3fcfd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Any.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Any, AnyAttrs, AnyInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {permuteAxesAndTranspose} from './kernel_utils';
-
-let wasmAny: (xId: number, reduceSize: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmAny = backend.wasm.cwrap(Any, null /*void*/, ['number, number, number']);
-}
-
-function any(args: {backend: BackendWasm, inputs: AnyInputs, attrs: AnyAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {axis, keepDims} = attrs;
-  const {x} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  let inputId = xId;
-  let input = x;
-
-  const {transposed, axes, originalAxes, inputWasTransposed} =
-      permuteAxesAndTranspose(x, axis, backend);
-
-  if (inputWasTransposed) {
-    const transposedId = backend.dataIdMap.get(transposed.dataId).id;
-    input = transposed;
-    inputId = transposedId;
-  }
-
-  const inputRank = input.shape.length;
-  backend_util.assertAxesAreInnerMostDims('any', axes, inputRank);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(input.shape, axes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  const out = backend.makeOutput(outShape, x.dtype);
-  if (util.sizeFromShape(input.shape) !== 0) {
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    wasmAny(inputId, reduceSize, outId);
-  }
-
-  if (inputWasTransposed) {
-    // dispose of the transposed tensor.
-    backend.disposeData(transposed.dataId);
-  }
-
-  if (keepDims) {
-    // reshape
-    const newShape = backend_util.expandShapeToKeepDim(out.shape, originalAxes);
-    out.shape = newShape;
-  }
-
-  return out;
-}
-
-export const anyConfig: KernelConfig = {
-  kernelName: Any,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: any as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ArgMax.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ArgMax.ts
deleted file mode 100644
index 47174f9e9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ArgMax.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMax, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createArgMinMaxKernelConfig} from './argminmax_kernel';
-
-export const argMaxConfig: KernelConfig = createArgMinMaxKernelConfig(ArgMax);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ArgMin.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ArgMin.ts
deleted file mode 100644
index cbbb181e6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ArgMin.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMin, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createArgMinMaxKernelConfig} from './argminmax_kernel';
-
-export const argMinConfig: KernelConfig = createArgMinMaxKernelConfig(ArgMin);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Asin.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Asin.ts
deleted file mode 100644
index 4bc384cca..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Asin.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asin, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const asinConfig: KernelConfig = createUnaryKernelConfig(Asin);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Asinh.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Asinh.ts
deleted file mode 100644
index 448526102..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Asinh.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asinh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const asinhConfig: KernelConfig = createUnaryKernelConfig(Asinh);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Atan.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Atan.ts
deleted file mode 100644
index 03a01ece7..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Atan.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const atanConfig: KernelConfig = createUnaryKernelConfig(Atan);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Atan2.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Atan2.ts
deleted file mode 100644
index f826fe5d4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Atan2.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan2, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-export const atan2Config: KernelConfig =
-    createBinaryKernelConfig(Atan2, /*supportsFullBroadcast=*/false);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Atanh.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Atanh.ts
deleted file mode 100644
index 9dee5d59e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Atanh.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atanh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const atanhConfig: KernelConfig = createUnaryKernelConfig(Atanh);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPool.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPool.ts
deleted file mode 100644
index 0753f4e49..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPool.ts
+++ /dev/null
@@ -1,95 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool, AvgPoolAttrs, AvgPoolInputs, backend_util, KernelConfig, KernelFunc, Tensor4D} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmAvgPool: (
-    xId: number, batchSize: number, inputHeight: number, inputWidth: number,
-    filterHeight: number, filterWidth: number, padTop: number, padRight: number,
-    padBottom: number, padLeft: number, strideHeight: number,
-    strideWidth: number, channels: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmAvgPool = backend.wasm.cwrap(AvgPool, null /* void */, [
-    'number',  // xId
-    'number',  // batchSize
-    'number',  // inputHeight
-    'number',  // inputWidth
-    'number',  // filterHeight
-    'number',  // filterWidth
-    'number',  // padTop
-    'number',  // padRight
-    'number',  // padBottom
-    'number',  // padLeft
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // channels
-    'number',  // outId
-  ]);
-}
-
-function avgPool(
-    args: {inputs: AvgPoolInputs, backend: BackendWasm, attrs: AvgPoolAttrs}) {
-  const {inputs, attrs, backend} = args;
-
-  const x = inputs.x as Tensor4D;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
-
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-  const padTop = convInfo.padInfo.top;
-  const padRight = convInfo.padInfo.right;
-  const padBottom = convInfo.padInfo.bottom;
-  const padLeft = convInfo.padInfo.left;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const channels = convInfo.inChannels;
-
-  if (convInfo.dataFormat !== 'channelsLast') {
-    throw new Error(
-        `wasm backend does not support dataFormat:'` +
-        `${convInfo.dataFormat}'. Please use 'channelsLast'.`);
-  }
-
-  if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {
-    throw new Error(
-        `was backend only supports average pooling with dilation = [1, 1], ` +
-        `got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);
-  }
-
-  const out = backend.makeOutput(convInfo.outShape, 'float32');
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  wasmAvgPool(
-      xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth,
-      padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels,
-      outId);
-  return out;
-}
-
-export const avgPoolConfig: KernelConfig = {
-  kernelName: AvgPool,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: avgPool as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPool3D.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPool3D.ts
deleted file mode 100644
index 34d54ff60..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPool3D.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmAvgPool3D: (
-    xId: number, outId: number, batchSize: number, channelSize: number,
-    inDepth: number, inHeight: number, inWidth: number, outDepth: number,
-    outHeight: number, outWidth: number, strideDepth: number,
-    strideHeight: number, strideWidth: number, dilationDepth: number,
-    dilationHeight: number, dilationWidth: number, effectiveFilterDepth: number,
-    effectiveFilterHeight: number, effectiveFilterWidth: number,
-    padFront: number, padTop: number, padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmAvgPool3D = backend.wasm.cwrap('AvgPool3D', null, [
-    'number',  // xId
-    'number',  // outId
-    'number',  // batchSize
-    'number',  // channelSize
-    'number',  // inDepth
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // outDepth
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // strideDepth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // dilationDepth
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // effectiveFilterDepth
-    'number',  // effectiveFilterHeight
-    'number',  // effectiveFilterWidth
-    'number',  // padFront
-    'number',  // padTop
-    'number',  // padLeft
-  ]);
-}
-
-export function avgPool3D(args: {
-  inputs: AvgPool3DInputs,
-  attrs: AvgPool3DAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode, dataFormat} = attrs;
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      /*dilations=*/1, pad, dimRoundingMode, dataFormat);
-  const out = backend.makeOutput(convInfo.outShape, x.dtype);
-
-  wasmAvgPool3D(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(out.dataId).id,
-      convInfo.batchSize,
-      // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in
-      // channels should always equal to out channels.
-      /*channelSize=*/convInfo.inChannels,
-      convInfo.inDepth,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.outDepth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.strideDepth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationDepth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.effectiveFilterDepth,
-      convInfo.effectiveFilterHeight,
-      convInfo.effectiveFilterWidth,
-      convInfo.padInfo.front,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-  );
-  return out;
-}
-
-export const avgPool3DConfig: KernelConfig = {
-  kernelName: AvgPool3D,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: avgPool3D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPool3DGrad.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPool3DGrad.ts
deleted file mode 100644
index 6528cee2f..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPool3DGrad.ts
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool3DGrad, AvgPool3DGradAttrs, AvgPool3DGradInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmAvgPool3DGrad: (
-    dyId: number, dxId: number, batchSize: number, channelSize: number,
-    inDepth: number, inHeight: number, inWidth: number, outDepth: number,
-    outHeight: number, outWidth: number, strideDepth: number,
-    strideHeight: number, strideWidth: number, dilationDepth: number,
-    dilationHeight: number, dilationWidth: number, effectiveFilterDepth: number,
-    effectiveFilterHeight: number, effectiveFilterWidth: number,
-    padFront: number, padTop: number, padLeft: number, filterDepth: number,
-    filterHeight: number, filterWidth: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmAvgPool3DGrad = backend.wasm.cwrap('AvgPool3DGrad', null, [
-    'number',  // dyId
-    'number',  // dxId
-    'number',  // batchSize
-    'number',  // channelSize
-    'number',  // inDepth
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // outDepth
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // strideDepth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // dilationDepth
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // effectiveFilterDepth
-    'number',  // effectiveFilterHeight
-    'number',  // effectiveFilterWidth
-    'number',  // padFront
-    'number',  // padTop
-    'number',  // padLeft
-    'number',  // filterDepth
-    'number',  // filterHeight
-    'number',  // filterWidth
-  ]);
-}
-
-export function avgPool3DGrad(args: {
-  inputs: AvgPool3DGradInputs,
-  attrs: AvgPool3DGradAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-
-  const convInfo = backend_util.computePool3DInfo(
-      input.shape as [number, number, number, number, number], filterSize,
-      strides, /*dilations=*/1, pad, dimRoundingMode);
-  const dx = backend.makeOutput(input.shape, input.dtype);
-
-  wasmAvgPool3DGrad(
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(dx.dataId).id,
-      convInfo.batchSize,
-      // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in
-      // channels should always equal to out channels.
-      /*channelSize=*/convInfo.inChannels,
-      convInfo.inDepth,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.outDepth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.strideDepth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationDepth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.effectiveFilterDepth,
-      convInfo.effectiveFilterHeight,
-      convInfo.effectiveFilterWidth,
-      convInfo.padInfo.front,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-      convInfo.filterDepth,
-      convInfo.filterHeight,
-      convInfo.filterWidth,
-  );
-  return dx;
-}
-
-export const avgPool3DGradConfig: KernelConfig = {
-  kernelName: AvgPool3DGrad,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: avgPool3DGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPoolGrad.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPoolGrad.ts
deleted file mode 100644
index e74d094c7..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/AvgPoolGrad.ts
+++ /dev/null
@@ -1,97 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPoolGrad, AvgPoolGradAttrs, AvgPoolGradInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmAvgPoolGrad: (
-    dyId: number, dxId: number, batchSize: number, channelSize: number,
-    inHeight: number, inWidth: number, outHeight: number, outWidth: number,
-    strideHeight: number, strideWidth: number, dilationHeight: number,
-    dilationWidth: number, effectiveFilterHeight: number,
-    effectiveFilterWidth: number, padTop: number, padLeft: number,
-    filterHeight: number, filterWidth: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmAvgPoolGrad = backend.wasm.cwrap('AvgPoolGrad', null, [
-    'number',  // dyId
-    'number',  // dxId
-    'number',  // batchSize
-    'number',  // channelSize
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // effectiveFilterHeight
-    'number',  // effectiveFilterWidth
-    'number',  // padTop
-    'number',  // padLeft
-    'number',  // filterHeight
-    'number',  // filterWidth
-  ]);
-}
-
-export function avgPoolGrad(args: {
-  inputs: AvgPoolGradInputs,
-  attrs: AvgPoolGradAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const {filterSize, strides, pad} = attrs;
-
-  const convInfo = backend_util.computePool2DInfo(
-      input.shape as [number, number, number, number], filterSize, strides,
-      /*dilations=*/1, pad);
-  const dx = backend.makeOutput(input.shape, input.dtype);
-
-  wasmAvgPoolGrad(
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(dx.dataId).id,
-      convInfo.batchSize,
-      // Since Pool ops (AvgPool and MaxPool) support 2D filter only, in
-      // channels should always equal to out channels.
-      /*channelSize=*/convInfo.inChannels,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.effectiveFilterHeight,
-      convInfo.effectiveFilterWidth,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-      convInfo.filterHeight,
-      convInfo.filterWidth,
-  );
-  return dx;
-}
-
-export const avgPoolGradConfig: KernelConfig = {
-  kernelName: AvgPoolGrad,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: avgPoolGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/BatchMatMul.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/BatchMatMul.ts
deleted file mode 100644
index 9813491dc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/BatchMatMul.ts
+++ /dev/null
@@ -1,121 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs, broadcast_util, KernelConfig, KernelFunc, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {reshape} from './Reshape';
-
-let wasmBatchMatMul: (
-    aId: number, aShape: Uint8Array, aShapeSize: number, bId: number,
-    bShape: Uint8Array, bShapeSize: number, transposeA: boolean,
-    transposeB: boolean, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmBatchMatMul = backend.wasm.cwrap(BatchMatMul, null /* void */, [
-    'number',  // a_id
-    'array',   // a_shape
-    'number',  // a_shape.length
-    'number',  // b_id
-    'array',   // b_shape
-    'number',  // b_shape.length
-    'number',  // transpose_a
-    'number',  // transpose_b
-    'number'   // out_id
-  ]);
-}
-
-function batchMatMul(args: {
-  inputs: BatchMatMulInputs,
-  backend: BackendWasm,
-  attrs: BatchMatMulAttrs
-}) {
-  const {inputs, backend, attrs} = args;
-  const {a, b} = inputs;
-  const {transposeA, transposeB} = attrs;
-
-  if (a.dtype !== 'float32' || b.dtype !== 'float32') {
-    throw new Error(
-        `BatchMatMul for non non-float32 tensors not yet supported.`);
-  }
-
-  const aRank = a.shape.length;
-  const bRank = b.shape.length;
-
-  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
-  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
-
-  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
-  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
-
-  const outerDimsA = a.shape.slice(0, -2);
-  const outerDimsB = b.shape.slice(0, -2);
-
-  const batchDimA = util.sizeFromShape(outerDimsA);
-  const batchDimB = util.sizeFromShape(outerDimsB);
-
-  const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape(
-      a.shape.slice(0, -2), b.shape.slice(0, -2));
-  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
-
-  util.assert(
-      innerShapeA === innerShapeB,
-      () => `Error in matMul: inner shapes (${innerShapeA}) and (` +
-          `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +
-          `${b.shape} and transposeA=${transposeA}` +
-          ` and transposeB=${transposeB} must match.`);
-
-  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] :
-                                [batchDimA, outerShapeA, innerShapeA];
-  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] :
-                                [batchDimB, innerShapeB, outerShapeB];
-
-  // The rest of the implementation is designed to operate on rank-3 tensors
-  const a3d = reshape({inputs: {x: a}, backend, attrs: {shape: a3dShape}});
-  const b3d = reshape({inputs: {x: b}, backend, attrs: {shape: b3dShape}});
-
-  const a3dId = backend.dataIdMap.get(a3d.dataId).id;
-  const b3dId = backend.dataIdMap.get(b3d.dataId).id;
-
-  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
-  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
-  const batchDim = Math.max(batchDimA, batchDimB);
-
-  const out = backend.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer);
-  const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer);
-
-  wasmBatchMatMul(
-      a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes,
-      b3d.shape.length, transposeA, transposeB, outId);
-
-  backend.disposeData(a3d.dataId);
-  backend.disposeData(b3d.dataId);
-
-  out.shape = outShape;
-  return out;
-}
-
-export const batchMatMulConfig: KernelConfig = {
-  kernelName: BatchMatMul,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: batchMatMul as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/BatchToSpaceND.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/BatchToSpaceND.ts
deleted file mode 100644
index 037e18f8e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/BatchToSpaceND.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-import {transpose} from './Transpose';
-
-function batchToSpaceND(args: {
-  inputs: BatchToSpaceNDInputs,
-  backend: BackendWasm,
-  attrs: BatchToSpaceNDAttrs
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockShape, crops} = attrs;
-
-  const prod = blockShape.reduce((a, b) => a * b);
-
-  const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);
-  const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);
-  const reshapedPermuted =
-      backend_util.getReshapedPermuted(x.shape, blockShape, prod);
-  const sliceBeginCoords =
-      backend_util.getSliceBeginCoords(crops, blockShape.length);
-  const sliceSize =
-      backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);
-
-  const xReshaped = reshape({inputs: {x}, backend, attrs: {shape: reshaped}});
-  const xTransposed =
-      transpose({inputs: {x: xReshaped}, backend, attrs: {perm: permuted}});
-  const xTransposedReshaped = reshape(
-      {inputs: {x: xTransposed}, backend, attrs: {shape: reshapedPermuted}});
-  const result = slice({
-    inputs: {x: xTransposedReshaped},
-    backend,
-    attrs: {begin: sliceBeginCoords, size: sliceSize}
-  });
-
-  backend.disposeData(xReshaped.dataId);
-  backend.disposeData(xTransposed.dataId);
-  backend.disposeData(xTransposedReshaped.dataId);
-
-  return result;
-}
-
-export const batchToSpaceNDConfig: KernelConfig = {
-  kernelName: BatchToSpaceND,
-  backendName: 'wasm',
-  kernelFunc: batchToSpaceND as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Bincount.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Bincount.ts
deleted file mode 100644
index d126ee878..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Bincount.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Bincount, BincountAttrs, BincountInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmBincount: (
-    xId: number, size: number, hasWeights: boolean, weightsId: number,
-    weightsDType: CppDType, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmBincount = backend.wasm.cwrap(Bincount, null /*void*/, [
-    'number',   // xId
-    'number',   // size
-    'boolean',  // hasWeights
-    'number',   // weightsId
-    'number',   // weightsDType
-    'number',   // outId
-  ]);
-}
-
-function bincount(
-    args: {backend: BackendWasm, inputs: BincountInputs, attrs: BincountAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {x, weights} = inputs;
-  const {size} = attrs;
-
-  const hasWeights = weights.shape.reduce((p, v) => p * v, 1) !== 0;
-  const outShape = x.shape.length === 1 ? [size] : [x.shape[0], size];
-  const out = backend.makeOutput(outShape, weights.dtype);
-
-  function tensorId(x: TensorInfo) {
-    return backend.dataIdMap.get(x.dataId).id;
-  }
-  wasmBincount(
-      tensorId(x), size, hasWeights, tensorId(weights), CppDType[weights.dtype],
-      tensorId(out));
-
-  return out;
-}
-
-export const bincountConfig: KernelConfig = {
-  kernelName: Bincount,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: bincount as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/BitwiseAnd.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/BitwiseAnd.ts
deleted file mode 100644
index a761c435c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/BitwiseAnd.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BitwiseAnd, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-const supportsFullBroadcast = true;
-
-export const bitwiseAndConfig: KernelConfig =
-    createBinaryKernelConfig(BitwiseAnd, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/BroadcastArgs.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/BroadcastArgs.ts
deleted file mode 100644
index d98bb8713..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/BroadcastArgs.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, BroadcastArgs, BroadcastArgsInputs, KernelConfig, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-export function broadcastArgs(args: {
-  inputs: BroadcastArgsInputs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend} = args;
-  const {s0, s1} = inputs;
-
-  const s0Vals = backend.typedArrayFromHeap(s0);
-  const s1Vals = backend.typedArrayFromHeap(s1);
-
-  const broadcastShape = backend_util.assertAndGetBroadcastShape(
-      Array.from(s0Vals), Array.from(s1Vals));
-
-  return backend.makeOutput(
-      [broadcastShape.length], 'int32', /*memoryOffset=*/undefined,
-      /*values=*/new Int32Array(broadcastShape));
-}
-
-export const broadcastArgsConfig: KernelConfig = {
-  kernelName: BroadcastArgs,
-  backendName: 'wasm',
-  kernelFunc: broadcastArgs
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Cast.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Cast.ts
deleted file mode 100644
index 3d6720387..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Cast.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cast, CastAttrs, CastInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-import {TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-export function cast(
-    args: {inputs: CastInputs, attrs: CastAttrs, backend: BackendWasm}):
-    TensorInfo {
-  const {inputs: {x}, attrs: {dtype}, backend} = args;
-  const out = backend.makeOutput(x.shape, dtype);
-  const inVals = backend.typedArrayFromHeap(x);
-  const outVals = backend.typedArrayFromHeap(out);
-  outVals.set(inVals);
-  return out;
-}
-
-export const castConfig: KernelConfig = {
-  kernelName: Cast,
-  backendName: 'wasm',
-  kernelFunc: cast as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Ceil.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Ceil.ts
deleted file mode 100644
index f58355c18..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Ceil.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Ceil} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const ceilConfig: KernelConfig = createUnaryKernelConfig(Ceil);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ClipByValue.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ClipByValue.ts
deleted file mode 100644
index 16dbec662..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ClipByValue.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ClipByValue, ClipByValueAttrs, ClipByValueInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmClip: (xId: number, min: number, max: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmClip = backend.wasm.cwrap(ClipByValue, null /* void */, [
-    'number',  // x_id
-    'number',  // min
-    'number',  // max
-    'number'   // out_id
-  ]);
-}
-
-function clip(args: {
-  inputs: ClipByValueInputs,
-  backend: BackendWasm,
-  attrs: ClipByValueAttrs
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {clipValueMin, clipValueMax} = attrs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const out = backend.makeOutput(x.shape, x.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmClip(xId, clipValueMin, clipValueMax, outId);
-  return out;
-}
-
-export const clipByValueConfig: KernelConfig = {
-  kernelName: ClipByValue,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: clip as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Concat.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Concat.ts
deleted file mode 100644
index 99b820a13..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Concat.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Concat, ConcatAttrs, ConcatInputs, KernelConfig, KernelFunc, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {concatImplCPU} from '../kernel_utils/shared';
-import {identity} from './Identity';
-import {reshape} from './Reshape';
-
-export function concat(
-    args: {inputs: ConcatInputs, backend: BackendWasm, attrs: ConcatAttrs}) {
-  const {inputs, backend} = args;
-
-  const axis = util.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];
-
-  const shapes = inputs.map(t => t.shape);
-  backend_util.assertParamsConsistent(shapes, axis);
-
-  let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), axis);
-
-  // Keep only non-empty tensors (ignore tensors with 0 in their shape).
-  const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);
-  if ($inputs.length === 1) {
-    return identity({inputs: {x: $inputs[0]}, backend});
-  }
-
-  const out = backend.makeOutput(outShape, inputs[0].dtype);
-
-  if (util.sizeFromShape(outShape) === 0) {
-    return out;
-  }
-
-  if ($inputs[0].dtype === 'string') {
-    // Any concat of n-dimensional tensors across any axis can be reduced to
-    // a concatenation of two-dimensional tensors across the axis 1 by first
-    // partitioning the axes of the original tensors into those less than the
-    // axis to be concatenated and the rest. Then reshape the tensors
-    // into a two-dimensional tensor by collapsing these two sets of axes and
-    // concatenate the resulting matrices across the axis 1, finally reshaping
-    // the result to have the proper shape.
-    const inputs2D = $inputs.map(t => {
-      const innerSize = util.sizeFromShape(t.shape.slice(axis));
-      const shape = [-1, innerSize];
-      return reshape({inputs: {x: t}, backend, attrs: {shape}});
-    });
-
-    const inputsValShapes = inputs2D.map(t => {
-      return {vals: backend.readSync(t.dataId), shape: t.shape};
-    });
-
-    // Concats 2d tensors along axis=1.
-    outShape =
-        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);
-    const simplyConcat = inputs2D[0].shape[0] === 1;
-    const outVals = concatImplCPU(
-                        inputsValShapes, outShape, inputs[0].dtype,
-                        simplyConcat) as string[];
-
-    const finalOutShape =
-        backend_util.computeOutShape($inputs.map(t => t.shape), axis);
-
-    out.shape = finalOutShape;
-    const outData = backend.dataIdMap.get(out.dataId);
-    outData.stringBytes = backend_util.fromStringArrayToUint8(outVals);
-
-    inputs2D.forEach(t => backend.disposeData(t.dataId));
-
-    return out;
-  }
-
-  const batchDim = util.sizeFromShape($inputs[0].shape.slice(0, axis));
-  let sumInnerDims = 0;
-  const innerDims = $inputs.map(input => {
-    const innerDim = util.sizeFromShape(input.shape.slice(axis));
-    sumInnerDims += innerDim;
-    return innerDim;
-  });
-  const inVals = $inputs.map(input => backend.typedArrayFromHeap(input));
-  const outVals = backend.typedArrayFromHeap(out);
-  for (let b = 0; b < batchDim; b++) {
-    let outOffset = b * sumInnerDims;
-    for (let i = 0; i < inVals.length; i++) {
-      const innerDim = innerDims[i];
-      const inOffset = b * innerDim;
-      const vals = inVals[i].subarray(inOffset, inOffset + innerDim);
-      outVals.set(vals, outOffset);
-      outOffset += innerDim;
-    }
-  }
-  return out;
-}
-
-export const concatConfig: KernelConfig = {
-  kernelName: Concat,
-  backendName: 'wasm',
-  kernelFunc: concat as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv2D.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Conv2D.ts
deleted file mode 100644
index acf912780..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv2D.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2D, Conv2DAttrs, Conv2DInputs, KernelConfig, KernelFunc, Tensor4D} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmConv2d: (
-    xId: number, batchSize: number, inputHeight: number, inputWidth: number,
-    filterId: number, filterHeight: number, filterWidth: number, padTop: number,
-    padRight: number, padBottom: number, padLeft: number, isSamePad: number,
-    dilationHeight: number, dilationWidth: number, strideHeight: number,
-    strideWidth: number, inputChannels: number, outputChannels: number,
-    outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmConv2d = backend.wasm.cwrap(Conv2D, null /* void */, [
-    'number',  // xId
-    'number',  // batchSize
-    'number',  // inputHeight
-    'number',  // inputWidth
-    'number',  // filterId
-    'number',  // filterHeight
-    'number',  // filterWidth
-    'number',  // padTop
-    'number',  // padRight
-    'number',  // padBottom
-    'number',  // padLeft
-    'number',  // isSamePad
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // inputChannels
-    'number',  // outputChannels
-    'number',  // outId
-  ]);
-}
-
-function conv2d(
-    args: {inputs: Conv2DInputs, backend: BackendWasm, attrs: Conv2DAttrs}) {
-  const {inputs, attrs, backend} = args;
-
-  const {x, filter} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const filterId = backend.dataIdMap.get(filter.dataId).id;
-
-  const {strides, dilations, pad, dimRoundingMode, dataFormat} = attrs;
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      (x as Tensor4D).shape, (filter as Tensor4D).shape, strides, dilations,
-      pad, dimRoundingMode, false, $dataFormat);
-
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-  const padTop = convInfo.padInfo.top;
-  const padRight = convInfo.padInfo.right;
-  const padBottom = convInfo.padInfo.bottom;
-  const padLeft = convInfo.padInfo.left;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const inputChannels = convInfo.inChannels;
-  const outputChannels = convInfo.outChannels;
-  const isSamePad = convInfo.padInfo.type === 'SAME' ? 1 : 0;
-
-  if (convInfo.dataFormat !== 'channelsLast') {
-    throw new Error(
-        `wasm backend Conv2D does not support dataFormat:'` +
-        `${convInfo.dataFormat}'. Please use 'channelsLast'.`);
-  }
-
-  const out = backend.makeOutput(convInfo.outShape, 'float32');
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmConv2d(
-      xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight,
-      filterWidth, padTop, padRight, padBottom, padLeft, isSamePad,
-      dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels,
-      outputChannels, outId);
-  return out;
-}
-
-export const conv2DConfig: KernelConfig = {
-  kernelName: Conv2D,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: conv2d as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv2DBackpropInput.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Conv2DBackpropInput.ts
deleted file mode 100644
index 6bdf0af28..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv2DBackpropInput.ts
+++ /dev/null
@@ -1,128 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmConv2DBackpropInput: (
-    dyId: number, filterId: number, batchSize: number, filterHeight: number,
-    filterWidth: number, inHeight: number, inWidth: number, inChannels: number,
-    outHeight: number, outWidth: number, outChannels: number,
-    strideHeight: number, strideWidth: number, topPad: number, leftPad: number,
-    fltS0: number, fltS1: number, fltS2: number, xBatchStride: number,
-    xRowStride: number, xColStride: number, xChannelStride: number,
-    yBatchStride: number, yRowStride: number, yColStride: number,
-    yChannelStride: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmConv2DBackpropInput = backend.wasm.cwrap(Conv2DBackpropInput, null, [
-    'number',  // dyId
-    'number',  // filterId
-    'number',  // batchSize
-    'number',  // filterHeight
-    'number',  // filterWidth
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // inChannels
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // outChannels
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // topPad
-    'number',  // leftPad
-    'number',  // fltS0
-    'number',  // fltS1
-    'number',  // fltS2
-    'number',  // xBatchStride
-    'number',  // xRowStride
-    'number',  // xColStride
-    'number',  // xChannelStride
-    'number',  // yBatchStride
-    'number',  // yRowStride
-    'number',  // yColStride
-    'number',  // yChannelStride
-    'number',  // outId
-  ]);
-}
-
-function conv2DBackpropInput(args: {
-  backend: BackendWasm,
-  inputs: Conv2DBackpropInputInputs,
-  attrs: Conv2DBackpropInputAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {dy, filter} = inputs;
-  const {strides, pad, dataFormat, dimRoundingMode, inputShape} = attrs;
-
-  const dilations = 1;
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      inputShape, filter.shape as [number, number, number, number], strides,
-      dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat);
-  const {
-    batchSize,
-    filterHeight,
-    filterWidth,
-    inChannels,
-    inHeight,
-    inWidth,
-    outChannels,
-    outHeight,
-    outWidth,
-    strideHeight,
-    strideWidth
-  } = convInfo;
-
-  const topPad = filterHeight - 1 - convInfo.padInfo.top;
-  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
-
-  const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-  const dxStrides = util.computeStrides(convInfo.inShape);
-  const dyStrides = util.computeStrides(dy.shape);
-  const [fltS0, fltS1, fltS2] = util.computeStrides(filter.shape);
-  const xBatchStride = dxStrides[0];
-  const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];
-  const xColStride = isChannelsLast ? dxStrides[2] : 1;
-  const xChannelStride = isChannelsLast ? 1 : dxStrides[1];
-  const yBatchStride = dyStrides[0];
-  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
-  const yColStride = isChannelsLast ? dyStrides[2] : 1;
-  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
-
-  const out = backend.makeOutput(convInfo.inShape, 'float32');
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  const dyId = backend.dataIdMap.get(dy.dataId).id;
-  const filterId = backend.dataIdMap.get(filter.dataId).id;
-
-  wasmConv2DBackpropInput(
-      dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth,
-      inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth,
-      topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride,
-      xColStride, xChannelStride, yBatchStride, yRowStride, yColStride,
-      yChannelStride, outId);
-  return out;
-}
-
-export const conv2DBackpropInputConfig: KernelConfig = {
-  kernelName: Conv2DBackpropInput,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: conv2DBackpropInput as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv3D.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Conv3D.ts
deleted file mode 100644
index 69e526099..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv3D.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3D, Conv3DAttrs, Conv3DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmConv3D: (
-    xId: number, filterId: number, outId: number, batchSize: number,
-    inDepth: number, inHeight: number, inWidth: number, inChannels: number,
-    outDepth: number, outHeight: number, outWidth: number, outChannels: number,
-    strideDepth: number, strideHeight: number, strideWidth: number,
-    dilationDepth: number, dilationHeight: number, dilationWidth: number,
-    filterDepth: number, filterHeight: number, filterWidth: number,
-    padFront: number, padTop: number, padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmConv3D = backend.wasm.cwrap(Conv3D, null, [
-    'number',  // xId
-    'number',  // filterId
-    'number',  // outId
-    'number',  // batchSize
-    'number',  // inDepth
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // inChannels
-    'number',  // outDepth
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // outChannels
-    'number',  // strideDepth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // dilationDepth
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // filterDepth
-    'number',  // filterHeight
-    'number',  // filterWidth
-    'number',  // padFront
-    'number',  // padTop
-    'number',  // padLeft
-  ]);
-}
-
-export function conv3D(args: {
-  inputs: Conv3DInputs,
-  attrs: Conv3DAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dilations} = attrs;
-  if (x.dtype !== 'float32') {
-    throw new Error(`Tensor x must have dtype float32, got ${x.dtype}`);
-  }
-  if (filter.dtype !== 'float32') {
-    throw new Error(
-        `Tensor filter must have dtype float32, got ${filter.dtype}`);
-  }
-
-  const convInfo = backend_util.computeConv3DInfo(
-      x.shape as [number, number, number, number, number],
-      filter.shape as [number, number, number, number, number], strides,
-      dilations, pad);
-
-  const out = backend.makeOutput(convInfo.outShape, x.dtype);
-  wasmConv3D(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(filter.dataId).id,
-      backend.dataIdMap.get(out.dataId).id,
-      convInfo.batchSize,
-      convInfo.inDepth,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.inChannels,
-      convInfo.outDepth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.outChannels,
-      convInfo.strideDepth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationDepth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.filterDepth,
-      convInfo.filterHeight,
-      convInfo.filterWidth,
-      convInfo.padInfo.front,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-  );
-  return out;
-}
-
-export const conv3DConfig: KernelConfig = {
-  kernelName: Conv3D,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: conv3D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv3DBackpropFilterV2.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Conv3DBackpropFilterV2.ts
deleted file mode 100644
index b800e83be..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv3DBackpropFilterV2.ts
+++ /dev/null
@@ -1,117 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropFilterV2, Conv3DBackpropFilterV2Attrs, Conv3DBackpropFilterV2Inputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmConv3DBackpropFilterV2: (
-    xId: number, dyId: number, dwId: number, batchSize: number, inDepth: number,
-    inHeight: number, inWidth: number, inChannels: number, outDepth: number,
-    outHeight: number, outWidth: number, outChannels: number,
-    strideDepth: number, strideHeight: number, strideWidth: number,
-    dilationDepth: number, dilationHeight: number, dilationWidth: number,
-    filterDepth: number, filterHeight: number, filterWidth: number,
-    padFront: number, padTop: number, padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmConv3DBackpropFilterV2 =
-      backend.wasm.cwrap(Conv3DBackpropFilterV2, null, [
-        'number',  // xId
-        'number',  // dyId
-        'number',  // dwId
-        'number',  // batchSize
-        'number',  // inDepth
-        'number',  // inHeight
-        'number',  // inWidth
-        'number',  // inChannels
-        'number',  // outDepth
-        'number',  // outHeight
-        'number',  // outWidth
-        'number',  // outChannels
-        'number',  // strideDepth
-        'number',  // strideHeight
-        'number',  // strideWidth
-        'number',  // dilationDepth
-        'number',  // dilationHeight
-        'number',  // dilationWidth
-        'number',  // filterDepth
-        'number',  // filterHeight
-        'number',  // filterWidth
-        'number',  // padFront
-        'number',  // padTop
-        'number',  // padLeft
-      ]);
-}
-
-export function conv3DBackpropFilterV2(args: {
-  inputs: Conv3DBackpropFilterV2Inputs,
-  attrs: Conv3DBackpropFilterV2Attrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, pad, filterShape} = attrs;
-
-  if (x.dtype !== 'float32') {
-    throw new Error(`Tensor dy must have dtype float32, got ${x.dtype}`);
-  }
-  if (dy.dtype !== 'float32') {
-    throw new Error(`Tensor filter must have dtype float32, got ${dy.dtype}`);
-  }
-
-  const convInfo = backend_util.computeConv3DInfo(
-      x.shape as [number, number, number, number, number], filterShape, strides,
-      /*dilations=*/1, pad);
-
-  const dw = backend.makeOutput(convInfo.filterShape, dy.dtype);
-
-  wasmConv3DBackpropFilterV2(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(dw.dataId).id,
-      convInfo.batchSize,
-      convInfo.inDepth,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.inChannels,
-      convInfo.outDepth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.outChannels,
-      convInfo.strideDepth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationDepth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.filterDepth,
-      convInfo.filterHeight,
-      convInfo.filterWidth,
-      convInfo.padInfo.front,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-  );
-  return dw;
-}
-
-export const conv3DBackpropFilterV2Config: KernelConfig = {
-  kernelName: Conv3DBackpropFilterV2,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: conv3DBackpropFilterV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv3DBackpropInputV2.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Conv3DBackpropInputV2.ts
deleted file mode 100644
index 8fd16890e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Conv3DBackpropInputV2.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmConv3DBackpropInputV2: (
-    filterId: number, dyId: number, dxId: number, batchSize: number,
-    inDepth: number, inHeight: number, inWidth: number, inChannels: number,
-    outDepth: number, outHeight: number, outWidth: number, outChannels: number,
-    strideDepth: number, strideHeight: number, strideWidth: number,
-    dilationDepth: number, dilationHeight: number, dilationWidth: number,
-    filterDepth: number, filterHeight: number, filterWidth: number,
-    padFront: number, padTop: number, padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmConv3DBackpropInputV2 = backend.wasm.cwrap(Conv3DBackpropInputV2, null, [
-    'number',  // filterId
-    'number',  // dyId
-    'number',  // dxId
-    'number',  // batchSize
-    'number',  // inDepth
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // inChannels
-    'number',  // outDepth
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // outChannels
-    'number',  // strideDepth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // dilationDepth
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // filterDepth
-    'number',  // filterHeight
-    'number',  // filterWidth
-    'number',  // padFront
-    'number',  // padTop
-    'number',  // padLeft
-  ]);
-}
-
-export function conv3DBackpropInputV2(args: {
-  inputs: Conv3DBackpropInputV2Inputs,
-  attrs: Conv3DBackpropInputV2Attrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {pad, strides, inputShape} = attrs;
-  if (dy.dtype !== 'float32') {
-    throw new Error(`Tensor dy must have dtype float32, got ${dy.dtype}`);
-  }
-  if (filter.dtype !== 'float32') {
-    throw new Error(
-        `Tensor filter must have dtype float32, got ${filter.dtype}`);
-  }
-
-  const convInfo = backend_util.computeConv3DInfo(
-      inputShape, filter.shape as [number, number, number, number, number],
-      strides, /*dilations=*/1, pad);
-
-  const dx = backend.makeOutput(convInfo.inShape, dy.dtype);
-
-  wasmConv3DBackpropInputV2(
-      backend.dataIdMap.get(filter.dataId).id,
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(dx.dataId).id,
-      convInfo.batchSize,
-      convInfo.inDepth,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.inChannels,
-      convInfo.outDepth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.outChannels,
-      convInfo.strideDepth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationDepth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.filterDepth,
-      convInfo.filterHeight,
-      convInfo.filterWidth,
-      convInfo.padInfo.front,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-  );
-  return dx;
-}
-
-export const conv3DBackpropInputV2Config: KernelConfig = {
-  kernelName: Conv3DBackpropInputV2,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: conv3DBackpropInputV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Cos.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Cos.ts
deleted file mode 100644
index 58c9ba461..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Cos.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cos, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const cosConfig: KernelConfig = createUnaryKernelConfig(Cos);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Cosh.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Cosh.ts
deleted file mode 100644
index df4534cc5..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Cosh.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Cosh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const coshConfig: KernelConfig = createUnaryKernelConfig(Cosh);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/CropAndResize.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/CropAndResize.ts
deleted file mode 100644
index 7d00653d1..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/CropAndResize.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {cast} from './Cast';
-
-// Must match enum in CropAndResize.cc
-enum InterpolationMethod {
-  bilinear = 0,
-  nearest = 1
-}
-
-let wasmCropAndResize: (
-    imagesId: number, boxesId: number, boxIndId: number, numBoxes: number,
-    imagesShape: Uint8Array, cropHeight: number, cropWidth: number,
-    method: number, extrapolationValue: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmCropAndResize = backend.wasm.cwrap(CropAndResize, null /*void*/, [
-    'number',  // imagesId
-    'number',  // boxesId
-    'number',  // boxIndId
-    'number',  // numBoxes
-    'array',   // images shape
-    'number',  // cropHeight
-    'number',  // cropWidth
-    'number',  // method
-    'number',  // extrapolation value
-    'number'   // out id
-  ]);
-}
-
-function cropAndResize(args: {
-  backend: BackendWasm,
-  inputs: CropAndResizeInputs,
-  attrs: CropAndResizeAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {method, extrapolationValue, cropSize} = attrs;
-  const {image, boxes, boxInd} = inputs;
-
-  const numBoxes = boxes.shape[0];
-
-  const [cropHeight, cropWidth] = cropSize as [number, number];
-  const outShape = [numBoxes, cropHeight, cropWidth, image.shape[3]];
-
-  let imagesData = backend.dataIdMap.get(image.dataId);
-  let castedData;
-  if (image.dtype !== 'float32') {
-    castedData = cast({backend, inputs: {x: image}, attrs: {dtype: 'float32'}});
-    imagesData = backend.dataIdMap.get(castedData.dataId);
-  }
-
-  const imagesId = imagesData.id;
-  const boxesId = backend.dataIdMap.get(boxes.dataId).id;
-  const boxIndId = backend.dataIdMap.get(boxInd.dataId).id;
-
-  const out = backend.makeOutput(outShape, 'float32');
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const imagesShapeBytes = new Uint8Array(new Int32Array(image.shape).buffer);
-
-  wasmCropAndResize(
-      imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight,
-      cropWidth,
-      InterpolationMethod[method as unknown as
-                          keyof typeof InterpolationMethod],
-      extrapolationValue, outId);
-
-  if (castedData != null) {
-    backend.disposeData(castedData.dataId);
-  }
-
-  return out;
-}
-
-export const cropAndResizeConfig: KernelConfig = {
-  kernelName: CropAndResize,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: cropAndResize as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Cumprod.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Cumprod.ts
deleted file mode 100644
index 5911da633..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Cumprod.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Cumprod, CumprodAttrs, CumprodInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-import {transpose} from './Transpose';
-
-let wasmCumprod: (xId: number, exclusive: number, reverse: number,
-                 finalDim: number, outId: number, dtype: CppDType) => void;
-
-function setup(backend: BackendWasm) {
-  wasmCumprod = backend.wasm.cwrap(Cumprod, null /* void */, [
-    'number', // x_id
-    'number', // exclusive
-    'number', // reverse
-    'number', // final_dim
-    'number', // out_id
-    'number'  // dtype
-  ]);
-}
-
-export function cumprod(
-  args: {inputs: CumprodInputs, backend: BackendWasm, attrs: CumprodAttrs}):
-TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, exclusive, reverse} = attrs;
-  const xRank = x.shape.length;
-
-  util.assert(x.dtype === 'float32' || x.dtype === 'int32',
-    () => `cumprod does not support ${x.dtype} tensors in the WASM backend`);
-  // permute required axis to inner most axis
-  const permutation = backend_util.getAxesPermutation([axis], xRank);
-  let permutedX = x;
-  if (permutation !== null) {
-    permutedX = transpose({inputs: {x}, attrs: {perm: permutation}, backend});
-  }
-  const permutedAxis = backend_util.getInnerMostAxes(1, xRank)[0];
-  backend_util.assertAxesAreInnerMostDims('cumprod', [permutedAxis], xRank);
-
-  const permutedOut = backend.makeOutput(permutedX.shape, permutedX.dtype);
-  const finalDim = permutedX.shape[permutedAxis];
-  const permutedXId = backend.dataIdMap.get(permutedX.dataId).id;
-  const permutedOutId = backend.dataIdMap.get(permutedOut.dataId).id;
-  wasmCumprod(permutedXId, exclusive ? 1 : 0, reverse ? 1 : 0, finalDim,
-              permutedOutId, CppDType[x.dtype]);
-
-  // transpose data back if permuted
-  let out = permutedOut;
-  if (permutation !== null) {
-    const undoPermutation = backend_util.getUndoAxesPermutation(permutation);
-    out = transpose(
-      {inputs: {x: permutedOut}, attrs: {perm: undoPermutation}, backend});
-    backend.disposeData(permutedX.dataId);
-    backend.disposeData(permutedOut.dataId);
-  }
-  return out;
-}
-
-export const cumprodConfig: KernelConfig = {
-  kernelName: Cumprod,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: cumprod as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Cumsum.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Cumsum.ts
deleted file mode 100644
index 67eaa66c5..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Cumsum.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Cumsum, CumsumAttrs, CumsumInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-import {transpose} from './Transpose';
-
-let wasmCumsum: (xId: number, exclusive: number, reverse: number,
-                 finalDim: number, outId: number, dtype: CppDType) => void;
-
-function setup(backend: BackendWasm) {
-  wasmCumsum = backend.wasm.cwrap(Cumsum, null /* void */, [
-    'number', // x_id
-    'number', // exclusive
-    'number', // reverse
-    'number', // final_dim
-    'number', // out_id
-    'number'  // dtype
-  ]);
-}
-
-export function cumsum(
-  args: {inputs: CumsumInputs, backend: BackendWasm, attrs: CumsumAttrs}):
-TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, exclusive, reverse} = attrs;
-  const xRank = x.shape.length;
-
-  util.assert(x.dtype === 'float32' || x.dtype === 'int32',
-    () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);
-  // permute required axis to inner most axis
-  const permutation = backend_util.getAxesPermutation([axis], xRank);
-  let permutedX = x;
-  if (permutation !== null) {
-    permutedX = transpose({inputs: {x}, attrs: {perm: permutation}, backend});
-  }
-  const permutedAxis = backend_util.getInnerMostAxes(1, xRank)[0];
-  backend_util.assertAxesAreInnerMostDims('cumsum', [permutedAxis], xRank);
-
-  const permutedOut = backend.makeOutput(permutedX.shape, permutedX.dtype);
-  const finalDim = permutedX.shape[permutedAxis];
-  const permutedXId = backend.dataIdMap.get(permutedX.dataId).id;
-  const permutedOutId = backend.dataIdMap.get(permutedOut.dataId).id;
-  wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse ? 1 : 0, finalDim,
-             permutedOutId, CppDType[x.dtype]);
-
-  // transpose data back if permuted
-  let out = permutedOut;
-  if (permutation !== null) {
-    const undoPermutation = backend_util.getUndoAxesPermutation(permutation);
-    out = transpose(
-      {inputs: {x: permutedOut}, attrs: {perm: undoPermutation}, backend});
-    backend.disposeData(permutedX.dataId);
-    backend.disposeData(permutedOut.dataId);
-  }
-  return out;
-}
-
-export const cumsumConfig: KernelConfig = {
-  kernelName: Cumsum,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: cumsum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/DenseBincount.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/DenseBincount.ts
deleted file mode 100644
index f38e8bcb4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/DenseBincount.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {DenseBincount, DenseBincountAttrs, DenseBincountInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmDenseBincount: (
-    xId: number, xShape: Uint8Array, xShapeLen: number, size: number,
-    hasWeights: boolean, weightsId: number, weightsDType: CppDType,
-    binaryOutput: boolean, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmDenseBincount = backend.wasm.cwrap('DenseBincount', null /*void*/, [
-    'number',   // xId
-    'array',    // xShape
-    'number',   // xShapeLen
-    'number',   // size
-    'boolean',  // hasWeights
-    'number',   // weightsId
-    'number',   // weightsDType
-    'boolean',  // binaryOutput
-    'number',   // outId
-  ]);
-}
-
-function denseBincount(args: {
-  backend: BackendWasm,
-  inputs: DenseBincountInputs,
-  attrs: DenseBincountAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {x, weights} = inputs;
-  const {size, binaryOutput} = attrs;
-
-  const hasWeights = weights.shape.reduce((p, v) => p * v, 1) !== 0;
-  const outShape = x.shape.length === 1 ? [size] : [x.shape[0], size];
-  const out = backend.makeOutput(outShape, weights.dtype);
-
-  function tensorId(x: TensorInfo) {
-    return backend.dataIdMap.get(x.dataId).id;
-  }
-  wasmDenseBincount(
-      tensorId(x), new Uint8Array(new Int32Array(x.shape).buffer),
-      x.shape.length, size, hasWeights, tensorId(weights),
-      CppDType[weights.dtype], binaryOutput, tensorId(out));
-
-  return out;
-}
-
-export const denseBincountConfig: KernelConfig = {
-  kernelName: DenseBincount,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: denseBincount as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/DepthToSpace.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/DepthToSpace.ts
deleted file mode 100644
index b4995348d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/DepthToSpace.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmDepthToSpace: (
-    xId: number, blockSize: number, channelsLast: number, xStrides: Uint8Array,
-    xStridesLength: number, outputShape: Uint8Array, outputStrides: Uint8Array,
-    outSize: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmDepthToSpace = backend.wasm.cwrap(DepthToSpace, null /*void*/, [
-    'number',  // xId
-    'number',  // blockSize
-    'number',  // channelsLast
-    'array',   // xStrides
-    'number',  // xStridesLength
-    'array',   // outputShape
-    'array',   // outputStrides
-    'number',  // outSize
-    'number',  // outId
-  ]);
-}
-
-export function depthToSpace(args: {
-  backend: BackendWasm,
-  inputs: DepthToSpaceInputs,
-  attrs: DepthToSpaceAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {x} = inputs;
-  const {blockSize, dataFormat} = attrs;
-
-  const batchSize = x.shape[0];
-  const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];
-  const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];
-  const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];
-
-  const outputHeight = inputHeight * blockSize;
-  const outputWidth = inputWidth * blockSize;
-  const outputDepth = inputDepth / (blockSize * blockSize);
-
-  const outputShape = (dataFormat === 'NHWC') ?
-      [batchSize, outputHeight, outputWidth, outputDepth] :
-      [batchSize, outputDepth, outputHeight, outputWidth];
-
-  const out = backend.makeOutput(outputShape, 'float32');
-
-  const xData = backend.dataIdMap.get(x.dataId);
-  const xId = xData.id;
-  const xStridesBytes =
-      new Uint8Array(new Int32Array(util.computeStrides(x.shape)).buffer);
-
-  const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);
-  const outStridesBytes =
-      new Uint8Array(new Int32Array(util.computeStrides(outputShape)).buffer);
-
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  const channelsLast = dataFormat === 'NHWC' ? 1 : 0;
-  wasmDepthToSpace(
-      xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1,
-      outputShapeBytes, outStridesBytes, outputShape.length, outId);
-
-  return out;
-}
-
-export const depthToSpaceConfig: KernelConfig = {
-  kernelName: DepthToSpace,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: depthToSpace as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/DepthwiseConv2dNative.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/DepthwiseConv2dNative.ts
deleted file mode 100644
index 7753c9128..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/DepthwiseConv2dNative.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs, KernelConfig, KernelFunc, Tensor4D} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmDepthwiseConv2d: (
-    xId: number, batchSize: number, inputHeight: number, inputWidth: number,
-    filterId: number, filterHeight: number, filterWidth: number, padTop: number,
-    padRight: number, padBottom: number, padLeft: number, isSamePad: number,
-    dilationHeight: number, dilationWidth: number, strideHeight: number,
-    strideWidth: number, inputChannels: number, outputChannels: number,
-    outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmDepthwiseConv2d =
-      backend.wasm.cwrap(DepthwiseConv2dNative, null /* void */, [
-        'number',  // xId
-        'number',  // batchSize
-        'number',  // inputHeight
-        'number',  // inputWidth
-        'number',  // filterId
-        'number',  // filterHeight
-        'number',  // filterWidth
-        'number',  // padTop
-        'number',  // padRight
-        'number',  // padBottom
-        'number',  // padLeft
-        'number',  // isSamePad
-        'number',  // dilationHeight
-        'number',  // dilationWidth
-        'number',  // strideHeight
-        'number',  // strideWidth
-        'number',  // inputChannels
-        'number',  // outputChannels
-        'number',  // outId
-      ]);
-}
-
-function depthwiseConv2d(args: {
-  inputs: DepthwiseConv2dNativeInputs,
-  backend: BackendWasm,
-  attrs: DepthwiseConv2dNativeAttrs
-}) {
-  const {inputs, attrs, backend} = args;
-
-  const {x, filter} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const filterId = backend.dataIdMap.get(filter.dataId).id;
-
-  const {strides, dilations, pad, dimRoundingMode} = attrs;
-
-  const $dilations = dilations == null ? [1, 1] : dilations;
-
-  const convInfo = backend_util.computeConv2DInfo(
-      (x as Tensor4D).shape, (filter as Tensor4D).shape, strides,
-      ($dilations as number | [number, number]), pad, dimRoundingMode,
-      true /* depthwise */);
-
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-  const padTop = convInfo.padInfo.top;
-  const padRight = convInfo.padInfo.right;
-  const padBottom = convInfo.padInfo.bottom;
-  const padLeft = convInfo.padInfo.left;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const inputChannels = convInfo.inChannels;
-  const outputChannels = convInfo.outChannels;
-  const isSamePad = convInfo.padInfo.type === 'SAME' ? 1 : 0;
-
-  if (convInfo.dataFormat !== 'channelsLast') {
-    throw new Error(
-        `wasm backend DepthwiseConv2dNative does not support dataFormat:'` +
-        `${convInfo.dataFormat}'. Please use 'channelsLast'.`);
-  }
-
-  const out = backend.makeOutput(convInfo.outShape, 'float32');
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmDepthwiseConv2d(
-      xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight,
-      filterWidth, padTop, padRight, padBottom, padLeft, isSamePad,
-      dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels,
-      outputChannels, outId);
-  return out;
-}
-
-export const depthwiseConv2dNativeConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNative,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: depthwiseConv2d as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Diag.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Diag.ts
deleted file mode 100644
index c1266d138..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Diag.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Diag, DiagInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmDiag: (xId: number, xDType: CppDType, xSize: number, outId: number) =>
-    void;
-
-function setup(backend: BackendWasm) {
-  wasmDiag = backend.wasm.cwrap('Diag', null, [
-    'number',  // xId
-    'number',  // xDType,
-    'number',  // xSize,
-    'number',  // outId
-  ]);
-}
-
-export function diag(args: {inputs: DiagInputs, backend: BackendWasm}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  const xSize = util.sizeFromShape(x.shape);
-  const out = backend.makeOutput([...x.shape, ...x.shape], x.dtype);
-
-  wasmDiag(
-      backend.dataIdMap.get(x.dataId).id, CppDType[x.dtype], xSize,
-      backend.dataIdMap.get(out.dataId).id);
-  return out;
-}
-
-export const diagConfig: KernelConfig = {
-  kernelName: Diag,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: diag as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Dilation2D.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Dilation2D.ts
deleted file mode 100644
index a3e7b2f77..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Dilation2D.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2D, Dilation2DAttrs, Dilation2DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {CppDType} from './types';
-
-let wasmDilation2D: (
-    xId: number, filterId: number, outId: number, dtype: number, batch: number,
-    depth: number, inHeight: number, inWidth: number, outHeight: number,
-    outWidth: number, strideHeight: number, strideWidth: number,
-    dilationHeight: number, dilationWidth: number, filterHeight: number,
-    filterWidth: number, padTop: number, padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmDilation2D = backend.wasm.cwrap(Dilation2D, null, [
-    'number',  // xId
-    'number',  // filterId
-    'number',  // outId
-    'number',  // dtype
-    'number',  // batch
-    'number',  // depth
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // filterHeight
-    'number',  // filterWidth
-    'number',  // padTop
-    'number',  // padLeft
-  ]);
-}
-
-export function dilation2D(args: {
-  inputs: Dilation2DInputs,
-  attrs: Dilation2DAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  if (x.dtype !== filter.dtype) {
-    throw new Error(
-        `Dilation2D error: x must have the same dtype as filter. Got ${
-            x.dtype} and ${filter.dtype}`);
-  }
-
-  const dilationInfo = backend_util.computeDilation2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number], strides, pad,
-      /*dataFormat=*/'NHWC', dilations);
-
-  const out = backend.makeOutput(dilationInfo.outShape, x.dtype);
-
-  wasmDilation2D(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(filter.dataId).id,
-      backend.dataIdMap.get(out.dataId).id,
-      CppDType[x.dtype],
-      dilationInfo.batchSize,
-      /*depth=*/dilationInfo.inChannels,
-      dilationInfo.inHeight,
-      dilationInfo.inWidth,
-      dilationInfo.outHeight,
-      dilationInfo.outWidth,
-      dilationInfo.strideHeight,
-      dilationInfo.strideWidth,
-      dilationInfo.dilationHeight,
-      dilationInfo.dilationWidth,
-      dilationInfo.filterHeight,
-      dilationInfo.filterWidth,
-      dilationInfo.padInfo.top,
-      dilationInfo.padInfo.left,
-  );
-  return out;
-}
-
-export const dilation2DConfig: KernelConfig = {
-  kernelName: Dilation2D,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: dilation2D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Dilation2DBackpropFilter.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Dilation2DBackpropFilter.ts
deleted file mode 100644
index f5d91cb04..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Dilation2DBackpropFilter.ts
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2DAttrs, Dilation2DBackpropFilter, KernelConfig, KernelFunc, Tensor3D, Tensor4D, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmDilation2DBackpropFilter: (
-    xId: number, filterId: number, dyId: number, gradId: number, dtype: number,
-    batch: number, depth: number, inHeight: number, inWidth: number,
-    outHeight: number, outWidth: number, strideHeight: number,
-    strideWidth: number, dilationHeight: number, dilationWidth: number,
-    filterHeight: number, filterWidth: number, padTop: number,
-    padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmDilation2DBackpropFilter =
-      backend.wasm.cwrap(Dilation2DBackpropFilter, null, [
-        'number',  // xId
-        'number',  // filterId
-        'number',  // dyId
-        'number',  // gradId
-        'number',  // dtype
-        'number',  // batch
-        'number',  // depth
-        'number',  // inHeight
-        'number',  // inWidth
-        'number',  // outHeight
-        'number',  // outWidth
-        'number',  // strideHeight
-        'number',  // strideWidth
-        'number',  // dilationHeight
-        'number',  // dilationWidth
-        'number',  // filterHeight
-        'number',  // filterWidth
-        'number',  // padTop
-        'number',  // padLeft
-      ]);
-}
-
-export function dilation2DBackpropFilter(args: {
-  inputs: {x: Tensor4D, filter: Tensor3D, dy: Tensor4D},
-  attrs: Dilation2DAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, dy} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  if (x.dtype !== filter.dtype || x.dtype !== dy.dtype) {
-    throw new Error(
-        `Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${
-            x.dtype}, ${filter.dtype}, and ${dy.dtype}`);
-  }
-
-  const dilationInfo = backend_util.computeDilation2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number], strides, pad,
-      /*dataFormat=*/'NHWC', dilations);
-
-  const gradients = backend.makeOutput(filter.shape, filter.dtype);
-
-  wasmDilation2DBackpropFilter(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(filter.dataId).id,
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(gradients.dataId).id,
-      CppDType[x.dtype],
-      dilationInfo.batchSize,
-      /*depth=*/dilationInfo.inChannels,
-      dilationInfo.inHeight,
-      dilationInfo.inWidth,
-      dilationInfo.outHeight,
-      dilationInfo.outWidth,
-      dilationInfo.strideHeight,
-      dilationInfo.strideWidth,
-      dilationInfo.dilationHeight,
-      dilationInfo.dilationWidth,
-      dilationInfo.filterHeight,
-      dilationInfo.filterWidth,
-      dilationInfo.padInfo.top,
-      dilationInfo.padInfo.left,
-  );
-  return gradients;
-}
-
-export const dilation2DBackpropFilterConfig: KernelConfig = {
-  kernelName: Dilation2DBackpropFilter,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: dilation2DBackpropFilter as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Dilation2DBackpropInput.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Dilation2DBackpropInput.ts
deleted file mode 100644
index a65347287..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Dilation2DBackpropInput.ts
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2DAttrs, Dilation2DBackpropInput, KernelConfig, KernelFunc, Tensor3D, Tensor4D, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmDilation2DBackpropInput: (
-    xId: number, filterId: number, dyId: number, gradId: number, dtype: number,
-    batch: number, depth: number, inHeight: number, inWidth: number,
-    outHeight: number, outWidth: number, strideHeight: number,
-    strideWidth: number, dilationHeight: number, dilationWidth: number,
-    filterHeight: number, filterWidth: number, padTop: number,
-    padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmDilation2DBackpropInput =
-      backend.wasm.cwrap(Dilation2DBackpropInput, null, [
-        'number',  // xId
-        'number',  // filterId
-        'number',  // dyId
-        'number',  // gradId
-        'number',  // dtype
-        'number',  // batch
-        'number',  // depth
-        'number',  // inHeight
-        'number',  // inWidth
-        'number',  // outHeight
-        'number',  // outWidth
-        'number',  // strideHeight
-        'number',  // strideWidth
-        'number',  // dilationHeight
-        'number',  // dilationWidth
-        'number',  // filterHeight
-        'number',  // filterWidth
-        'number',  // padTop
-        'number',  // padLeft
-      ]);
-}
-
-export function dilation2DBackpropInput(args: {
-  inputs: {x: Tensor4D, filter: Tensor3D, dy: Tensor4D},
-  attrs: Dilation2DAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, dy} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  if (x.dtype !== filter.dtype || x.dtype !== dy.dtype) {
-    throw new Error(
-        `Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${
-            x.dtype}, ${filter.dtype}, and ${dy.dtype}`);
-  }
-
-  const dilationInfo = backend_util.computeDilation2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number], strides, pad,
-      /*dataFormat=*/'NHWC', dilations);
-
-  const gradients = backend.makeOutput(x.shape, x.dtype);
-
-  wasmDilation2DBackpropInput(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(filter.dataId).id,
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(gradients.dataId).id,
-      CppDType[x.dtype],
-      dilationInfo.batchSize,
-      /*depth=*/dilationInfo.inChannels,
-      dilationInfo.inHeight,
-      dilationInfo.inWidth,
-      dilationInfo.outHeight,
-      dilationInfo.outWidth,
-      dilationInfo.strideHeight,
-      dilationInfo.strideWidth,
-      dilationInfo.dilationHeight,
-      dilationInfo.dilationWidth,
-      dilationInfo.filterHeight,
-      dilationInfo.filterWidth,
-      dilationInfo.padInfo.top,
-      dilationInfo.padInfo.left,
-  );
-  return gradients;
-}
-
-export const dilation2DBackpropInputConfig: KernelConfig = {
-  kernelName: Dilation2DBackpropInput,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: dilation2DBackpropInput as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Elu.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Elu.ts
deleted file mode 100644
index 72619af2a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Elu.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Elu, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const eluConfig: KernelConfig = createUnaryKernelConfig(Elu);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/EluGrad.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/EluGrad.ts
deleted file mode 100644
index 840d401b7..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/EluGrad.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {EluGrad, EluGradInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmEluGrad: (yId: number, dyId: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmEluGrad = backend.wasm.cwrap(EluGrad, null, [
-    'number',  // yId
-    'number',  // dyId,
-    'number',  // outId
-  ]);
-}
-
-export function eluGrad(args: {inputs: EluGradInputs, backend: BackendWasm}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {dy, y} = inputs;
-
-  const out = backend.makeOutput(y.shape, 'float32');
-  const tensorId = (x: TensorInfo) => {
-    return backend.dataIdMap.get(x.dataId).id!;
-  };
-  wasmEluGrad(tensorId(y), tensorId(dy), tensorId(out));
-  return out;
-}
-
-export const eluGradConfig: KernelConfig = {
-  kernelName: EluGrad,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: eluGrad as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Equal.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Equal.ts
deleted file mode 100644
index 182e2c205..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Equal.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Equal, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-const supportsFullBroadcast = false;
-export const equalConfig: KernelConfig =
-    createBinaryKernelConfig(Equal, supportsFullBroadcast, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Erf.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Erf.ts
deleted file mode 100644
index af39a05a4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Erf.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Erf, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const erfConfig: KernelConfig = createUnaryKernelConfig(Erf);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Exp.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Exp.ts
deleted file mode 100644
index 2c2338e83..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Exp.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Exp, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const expConfig: KernelConfig = createUnaryKernelConfig(Exp, 'float32');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ExpandDims.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ExpandDims.ts
deleted file mode 100644
index a95fbeee6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ExpandDims.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ExpandDims, ExpandDimsAttrs, ExpandDimsInputs, KernelConfig, KernelFunc, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {reshape} from './Reshape';
-
-export function expandDims(args: {
-  inputs: ExpandDimsInputs,
-  attrs: ExpandDimsAttrs,
-  backend: BackendWasm
-}) {
-  const {inputs, attrs, backend} = args;
-  const {input} = inputs;
-  const {dim} = attrs;
-
-  const inputRank = input.shape.length;
-  const newShape = input.shape.slice();
-  let $dim = dim;
-  if (dim < 0) {
-    // Negative value is counted from the tail of rank.
-    util.assert(
-        -(inputRank + 1) <= dim,
-        () => `Axis must be in the interval [${- (inputRank + 1)}, ${
-            inputRank}]`);
-    $dim = inputRank + dim + 1;
-  }
-  newShape.splice($dim, 0, 1);
-
-  return reshape({inputs: {x: input}, backend, attrs: {shape: newShape}});
-}
-
-export const expandDimsConfig: KernelConfig = {
-  kernelName: ExpandDims,
-  backendName: 'wasm',
-  kernelFunc: expandDims as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Expm1.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Expm1.ts
deleted file mode 100644
index c22f3dbfd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Expm1.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Expm1, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const expm1Config: KernelConfig =
-    createUnaryKernelConfig(Expm1, 'float32');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Fill.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Fill.ts
deleted file mode 100644
index 09b72a438..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Fill.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, util} from '@tensorflow/tfjs-core';
-import {Fill, FillAttrs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-export function fill(args: {attrs: FillAttrs, backend: BackendWasm}) {
-  const {attrs: {shape, value}, backend} = args;
-  let {attrs: {dtype}} = args;
-
-  dtype = dtype || util.inferDtype(value);
-
-  const out = backend.makeOutput(shape, dtype);
-  const outVals = backend.typedArrayFromHeap(out);
-  outVals.fill(value as number);
-  return out;
-}
-
-export const fillConfig: KernelConfig = {
-  kernelName: Fill,
-  backendName: 'wasm',
-  kernelFunc: fill as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/FlipLeftRight.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/FlipLeftRight.ts
deleted file mode 100644
index 027b1f5dc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/FlipLeftRight.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FlipLeftRight, FlipLeftRightInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmFlipLeftRight: (
-    xId: number, batch: number, imageHeight: number, imageWidth: number,
-    numChannels: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmFlipLeftRight = backend.wasm.cwrap(FlipLeftRight, null /* void */, [
-    'number',  // xId
-    'number',  // batch
-    'number',  // imageHeight
-    'number',  // imageWidth
-    'number',  // numChannels
-    'number',  // outId
-  ]);
-}
-
-export function flipLeftRight(
-    args: {inputs: FlipLeftRightInputs, backend: BackendWasm}): TensorInfo {
-  const {inputs, backend} = args;
-  const {image} = inputs;
-
-  const out = backend.makeOutput(image.shape, image.dtype);
-  const imageId = backend.dataIdMap.get(image.dataId).id;
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const [batch, imageHeight, imageWidth, numChannels] = image.shape;
-
-  wasmFlipLeftRight(
-      imageId, batch, imageHeight, imageWidth, numChannels, outId);
-  return out;
-}
-
-export const flipLeftRightConfig: KernelConfig = {
-  kernelName: FlipLeftRight,
-  backendName: 'wasm',
-  kernelFunc: flipLeftRight as unknown as KernelFunc,
-  setupFunc: setup
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Floor.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Floor.ts
deleted file mode 100644
index ad3cf2b32..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Floor.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Floor, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const floorConfig: KernelConfig = createUnaryKernelConfig(Floor);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/FloorDiv.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/FloorDiv.ts
deleted file mode 100644
index d2a3ff096..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/FloorDiv.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FloorDiv, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-const supportsFullBroadcast = false;
-export const floorDivConfig: KernelConfig =
-    createBinaryKernelConfig(FloorDiv, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/FusedBatchNorm.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/FusedBatchNorm.ts
deleted file mode 100644
index aed5ac8f2..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/FusedBatchNorm.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmBatchNorm: (
-    xId: number, meanId: number, varianceId: number, offsetId: number,
-    scaleId: number, varianceEpsilon: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmBatchNorm = backend.wasm.cwrap(
-      FusedBatchNorm, null /* void */,
-      ['number', 'number', 'number', 'number', 'number', 'number', 'number']);
-}
-
-function fusedBatchNorm(args: {
-  backend: BackendWasm,
-  inputs: FusedBatchNormInputs,
-  attrs: FusedBatchNormAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {varianceEpsilon} = attrs;
-  const {x, mean, variance, offset, scale} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const meanId = backend.dataIdMap.get(mean.dataId).id;
-  const varianceId = backend.dataIdMap.get(variance.dataId).id;
-  const offsetId = offset != null ? backend.dataIdMap.get(offset.dataId).id : 0;
-  const scaleId = scale != null ? backend.dataIdMap.get(scale.dataId).id : 0;
-
-  const out = backend.makeOutput(x.shape, x.dtype);
-  // Short-circuit zero-sized tensors.
-  if (util.sizeFromShape(x.shape) === 0) {
-    return out;
-  }
-
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  wasmBatchNorm(
-      xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);
-  return out;
-}
-
-export const fusedBatchNormConfig: KernelConfig = {
-  kernelName: FusedBatchNorm,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: fusedBatchNorm as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/FusedConv2D.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/FusedConv2D.ts
deleted file mode 100644
index 39ff3e011..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/FusedConv2D.ts
+++ /dev/null
@@ -1,155 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs, KernelConfig, KernelFunc, Tensor4D} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {FusableActivation} from './types';
-
-let wasmFusedConv2d:
-    (xId: number, batchSize: number, inputHeight: number, inputWidth: number,
-     filterId: number, filterHeight: number, filterWidth: number,
-     biasId: number, padTop: number, padRight: number, padBottom: number,
-     padLeft: number, isSamePad: number, dilationHeight: number,
-     dilationWidth: number, strideHeight: number, strideWidth: number,
-     inputChannels: number, outputChannels: number, activation: number,
-     preluActivationWeightsId: number, leakyreluAlpha: number, outId: number) =>
-        void;
-
-function setup(backend: BackendWasm) {
-  wasmFusedConv2d = backend.wasm.cwrap(FusedConv2D, null /* void */, [
-    'number',  // xId
-    'number',  // batchSize
-    'number',  // inputHeight
-    'number',  // inputWidth
-    'number',  // filterId
-    'number',  // filterHeight
-    'number',  // filterWidth
-    'number',  // biasId
-    'number',  // padTop
-    'number',  // padRight
-    'number',  // padBottom
-    'number',  // padLeft
-    'number',  // isSamePad
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // inputChannels
-    'number',  // outputChannels
-    'number',  // activation
-    'number',  // preluActivationWeightsId
-    'number',  // leakyreluAlpha
-    'number',  // outId
-  ]);
-}
-
-function fusedConv2d(args: {
-  inputs: FusedConv2DInputs,
-  backend: BackendWasm,
-  attrs: FusedConv2DAttrs
-}) {
-  const {inputs, attrs, backend} = args;
-  const {x, filter, bias, preluActivationWeights} = inputs;
-  const {
-    strides,
-    pad,
-    dilations,
-    dataFormat,
-    dimRoundingMode,
-    activation,
-    leakyreluAlpha
-  } = attrs;
-
-  const convInfo = backend_util.computeConv2DInfo(
-      (x as Tensor4D).shape, (filter as Tensor4D).shape, strides, dilations,
-      pad, dimRoundingMode);
-
-  const fusedActivation =
-      FusableActivation[activation as unknown as
-                        keyof typeof FusableActivation];
-  if (fusedActivation == null) {
-    throw new Error(
-        `${activation} activation not yet supported for FusedConv2D ` +
-        `in the wasm backend.`);
-  }
-
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const filterId = backend.dataIdMap.get(filter.dataId).id;
-
-  const outputChannels = convInfo.outChannels;
-
-  let biasId = 0;
-  if (bias != null) {
-    const biasData = backend.dataIdMap.get(bias.dataId);
-    if (biasData.shape.length !== 1) {
-      throw new Error(
-          `FusedConv2D only supports rank-1 bias but got ` +
-          `rank ${biasData.shape.length}.`);
-    }
-    if (biasData.shape[0] !== outputChannels) {
-      throw new Error(
-          `FusedConv2D bias shape (${biasData.shape}) does not ` +
-          `match the number of output channels (${outputChannels})`);
-    }
-    biasId = biasData.id;
-  }
-
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-  const padTop = convInfo.padInfo.top;
-  const padRight = convInfo.padInfo.right;
-  const padBottom = convInfo.padInfo.bottom;
-  const padLeft = convInfo.padInfo.left;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const inputChannels = convInfo.inChannels;
-  const isSamePad = convInfo.padInfo.type === 'SAME' ? 1 : 0;
-  const batchSize = convInfo.batchSize;
-  const inHeight = convInfo.inHeight;
-  const inWidth = convInfo.inWidth;
-
-  if (dataFormat !== 'NHWC') {
-    throw new Error(
-        `wasm backend FusedConv2D does not support dataFormat:'` +
-        `${dataFormat}'. Please use 'NHWC'.`);
-  }
-
-  const out = backend.makeOutput(convInfo.outShape, 'float32');
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  const preluActivationWeightsId = preluActivationWeights == null ?
-      0 :
-      backend.dataIdMap.get(preluActivationWeights.dataId).id;
-
-  wasmFusedConv2d(
-      xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth,
-      biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight,
-      dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels,
-      fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);
-
-  return out;
-}
-
-export const fusedConv2DConfig: KernelConfig = {
-  kernelName: FusedConv2D,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: fusedConv2d as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/FusedDepthwiseConv2D.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/FusedDepthwiseConv2D.ts
deleted file mode 100644
index 5d0cb62d6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/FusedDepthwiseConv2D.ts
+++ /dev/null
@@ -1,156 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs, KernelConfig, KernelFunc, Tensor4D} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {FusableActivation} from './types';
-
-let wasmFusedDepthwiseConv2d:
-    (xId: number, batchSize: number, inputHeight: number, inputWidth: number,
-     filterId: number, filterHeight: number, filterWidth: number,
-     biasId: number, padTop: number, padRight: number, padBottom: number,
-     padLeft: number, isSamePad: number, dilationHeight: number,
-     dilationWidth: number, strideHeight: number, strideWidth: number,
-     inputChannels: number, outputChannels: number, activation: number,
-     preluActivationWeightsId: number, leakyreluAlpha: number, outId: number) =>
-        void;
-
-function setup(backend: BackendWasm) {
-  wasmFusedDepthwiseConv2d =
-      backend.wasm.cwrap(FusedDepthwiseConv2D, null /* void */, [
-        'number',  // xId
-        'number',  // batchSize
-        'number',  // inputHeight
-        'number',  // inputWidth
-        'number',  // filterId
-        'number',  // filterHeight
-        'number',  // filterWidth
-        'number',  // biasId
-        'number',  // padTop
-        'number',  // padRight
-        'number',  // padBottom
-        'number',  // padLeft
-        'number',  // isSamePad
-        'number',  // dilationHeight
-        'number',  // dilationWidth
-        'number',  // strideHeight
-        'number',  // strideWidth
-        'number',  // inputChannels
-        'number',  // outputChannels
-        'number',  // activation
-        'number',  // preluActivationWeightsId
-        'number',  // leakyreluAlpha
-        'number',  // outId
-      ]);
-}
-
-function fusedDepthwiseConv2d(args: {
-  inputs: FusedDepthwiseConv2DInputs,
-  backend: BackendWasm,
-  attrs: FusedDepthwiseConv2DAttrs
-}) {
-  const {inputs, attrs, backend} = args;
-  const {x, filter, bias, preluActivationWeights} = inputs;
-  const {
-    strides,
-    pad,
-    dilations,
-    dataFormat,
-    dimRoundingMode,
-    activation,
-    leakyreluAlpha
-  } = attrs;
-
-  const convInfo = backend_util.computeConv2DInfo(
-      (x as Tensor4D).shape, (filter as Tensor4D).shape, strides, dilations,
-      pad, dimRoundingMode, true /* depthwise */);
-
-  const fusedActivation =
-      FusableActivation[activation as unknown as
-                        keyof typeof FusableActivation];
-  if (fusedActivation == null) {
-    throw new Error(
-        `${activation} activation not yet supported for FusedDepthwiseConv2D ` +
-        `in the wasm backend.`);
-  }
-
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const filterId = backend.dataIdMap.get(filter.dataId).id;
-
-  const outputChannels = convInfo.outChannels;
-
-  let biasId = 0;
-  if (bias != null) {
-    const biasData = backend.dataIdMap.get(bias.dataId);
-    if (biasData.shape.length !== 1) {
-      throw new Error(
-          `FusedDepthwiseConv2D only supports rank-1 bias but got ` +
-          `rank ${biasData.shape.length}.`);
-    }
-    if (biasData.shape[0] !== outputChannels) {
-      throw new Error(
-          `FusedDepthwiseConv2D bias shape (${biasData.shape}) does not ` +
-          `match the number of output channels (${outputChannels})`);
-    }
-    biasId = biasData.id;
-  }
-
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-  const padTop = convInfo.padInfo.top;
-  const padRight = convInfo.padInfo.right;
-  const padBottom = convInfo.padInfo.bottom;
-  const padLeft = convInfo.padInfo.left;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const inputChannels = convInfo.inChannels;
-  const isSamePad = convInfo.padInfo.type === 'SAME' ? 1 : 0;
-  const batchSize = convInfo.batchSize;
-  const inHeight = convInfo.inHeight;
-  const inWidth = convInfo.inWidth;
-
-  if (dataFormat !== 'NHWC') {
-    throw new Error(
-        `wasm backend FusedDepthwiseConv2D does not support dataFormat:'` +
-        `${dataFormat}'. Please use 'NHWC'.`);
-  }
-
-  const out = backend.makeOutput(convInfo.outShape, 'float32');
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  const preluActivationWeightsId = preluActivationWeights == null ?
-      0 :
-      backend.dataIdMap.get(preluActivationWeights.dataId).id;
-
-  wasmFusedDepthwiseConv2d(
-      xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth,
-      biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight,
-      dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels,
-      fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);
-
-  return out;
-}
-
-export const fusedDepthwiseConv2DConfig: KernelConfig = {
-  kernelName: FusedDepthwiseConv2D,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: fusedDepthwiseConv2d as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/GatherNd.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/GatherNd.ts
deleted file mode 100644
index c8b007ab9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/GatherNd.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {gather_util, GatherNd, GatherNdInputs, KernelConfig, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmGatherNd: (
-    xId: number, dtype: CppDType, indicesId: number, numSlices: number,
-    sliceRank: number, sliceSize: number, strides: Uint8Array, outId: number) =>
-    void;
-
-function setup(backend: BackendWasm): void {
-  wasmGatherNd = backend.wasm.cwrap(GatherNd, null /*void*/, [
-    'number',  // xId
-    'number',  // dtype
-    'number',  // indicesId
-    'number',  // numSlices
-    'number',  // sliceRank
-    'number',  // sliceSize
-    'array',   // strides
-    'number'   // outId
-  ]);
-}
-
-function gatherNd(args: {backend: BackendWasm, inputs: GatherNdInputs}):
-    TensorInfo {
-  const {backend, inputs} = args;
-  const {params, indices} = inputs;
-
-  const [resultShape, numSlices, sliceSize, strides] =
-      gather_util.prepareAndValidate(params, indices);
-
-  const out = backend.makeOutput(resultShape, params.dtype);
-  if (numSlices === 0) {
-    return out;
-  }
-
-  const indicesShape = indices.shape;
-  const sliceRank = indicesShape[indicesShape.length - 1];
-
-  const xData = backend.dataIdMap.get(params.dataId);
-  const xId = xData.id;
-  const indicesData = backend.dataIdMap.get(indices.dataId);
-  const indicesId = indicesData.id;
-
-  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
-
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmGatherNd(
-      xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize,
-      stridesBytes, outId);
-
-  return out;
-}
-
-export const gatherNdConfig: KernelConfig = {
-  kernelName: GatherNd,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: gatherNd
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/GatherV2.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/GatherV2.ts
deleted file mode 100644
index b686b8013..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/GatherV2.ts
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, GatherV2, GatherV2Attrs, GatherV2Inputs, KernelConfig, KernelFunc, Tensor, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {reshape} from './Reshape';
-import {CppDType} from './types';
-
-let wasmGather: (
-    xId: number, dtype: CppDType, xStrides: Uint8Array, stridesSize: number,
-    indicesId: number, batchSize: number, outStrides: Uint8Array,
-    outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmGather = backend.wasm.cwrap('Gather', null /*void*/, [
-    'number',  // xId
-    'number',  // dtype
-    'array',   // xStrides
-    'number',  // stridesSize
-    'number',  // indicesId
-    'number',  // batchSize
-    'array',   // outStrides
-    'number'   // outId
-  ]);
-}
-
-function gatherV2(
-    args: {backend: BackendWasm, inputs: GatherV2Inputs, attrs: GatherV2Attrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {x, indices} = inputs;
-  const {axis, batchDims} = attrs;
-
-  // Throw error when any index is out of bound.
-  const parsedAxis = util.parseAxisParam(axis, x.shape)[0];
-  const indicesVals = backend.readSync(indices.dataId) as TypedArray;
-  const axisDim = x.shape[parsedAxis];
-  for (let i = 0; i < indicesVals.length; ++i) {
-    const index = indicesVals[i];
-    util.assert(
-        index <= axisDim - 1 && index >= 0,
-        () =>
-            `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
-  }
-
-  const shapeInfo = backend_util.segment_util.collectGatherOpShapeInfo(
-      x as Tensor, indices as Tensor, parsedAxis, batchDims);
-
-  const flattenX = reshape({
-    inputs: {x},
-    attrs: {
-      shape: [
-        shapeInfo.batchSize, shapeInfo.outerSize, shapeInfo.dimSize,
-        shapeInfo.sliceSize
-      ]
-    },
-    backend
-  });
-  const indicesSize = util.sizeFromShape(indices.shape);
-  const flattenIndex = reshape({
-    inputs: {x: indices},
-    attrs: {shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize]},
-    backend
-  });
-  const flattenOutputShape = [
-    shapeInfo.batchSize, shapeInfo.outerSize, indicesSize / shapeInfo.batchSize,
-    shapeInfo.sliceSize
-  ];
-
-  const out = backend.makeOutput(flattenOutputShape, x.dtype);
-  if (util.sizeFromShape(x.shape) === 0) {
-    return out;
-  }
-  const stridesSize = flattenX.shape.length - 1;
-
-  const xData = backend.dataIdMap.get(flattenX.dataId);
-  const xId = xData.id;
-
-  const indicesData = backend.dataIdMap.get(flattenIndex.dataId);
-  const indicesId = indicesData.id;
-
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const xStridesBytes = new Uint8Array(
-      new Int32Array(util.computeStrides(flattenX.shape)).buffer);
-  const outStridesBytes = new Uint8Array(
-      new Int32Array(util.computeStrides(flattenOutputShape)).buffer);
-
-  wasmGather(
-      xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId,
-      shapeInfo.batchSize, outStridesBytes, outId);
-
-  backend.disposeData(flattenX.dataId);
-  backend.disposeData(flattenIndex.dataId);
-
-  // reshape
-  out.shape = shapeInfo.outputShape;
-  return out;
-}
-
-export const gatherV2Config: KernelConfig = {
-  kernelName: GatherV2,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: gatherV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Greater.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Greater.ts
deleted file mode 100644
index fa24434e5..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Greater.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Greater, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-const supportsFullBroadcast = false;
-export const greaterConfig: KernelConfig =
-    createBinaryKernelConfig(Greater, supportsFullBroadcast, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/GreaterEqual.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/GreaterEqual.ts
deleted file mode 100644
index f0ecc3ca1..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/GreaterEqual.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {GreaterEqual, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-const supportsFullBroadcast = false;
-export const greaterEqualConfig: KernelConfig =
-    createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Identity.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Identity.ts
deleted file mode 100644
index 03bb5bbd2..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Identity.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Identity, IdentityInputs, KernelConfig, KernelFunc, tensor} from '@tensorflow/tfjs-core';
-import {TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-export function identity(args: {inputs: IdentityInputs, backend: BackendWasm}):
-    TensorInfo {
-  const {inputs: {x}, backend} = args;
-
-  if (x.dtype === 'string') {
-    return tensor(backend.readSync(x.dataId), x.shape, x.dtype);
-  }
-
-  const out = backend.makeOutput(x.shape, x.dtype);
-  const inVals = backend.typedArrayFromHeap(x);
-  const outVals = backend.typedArrayFromHeap(out);
-  outVals.set(inVals);
-  return out;
-}
-
-export const identityConfig: KernelConfig = {
-  kernelName: Identity,
-  backendName: 'wasm',
-  kernelFunc: identity as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/IsFinite.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/IsFinite.ts
deleted file mode 100644
index 3a329f170..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/IsFinite.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsFinite, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const isFiniteConfig: KernelConfig =
-    createUnaryKernelConfig(IsFinite, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/IsInf.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/IsInf.ts
deleted file mode 100644
index 985e0e3a4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/IsInf.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsInf, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const isInfConfig: KernelConfig = createUnaryKernelConfig(IsInf, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/IsNan.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/IsNan.ts
deleted file mode 100644
index 6ce0c6ec4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/IsNan.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2022 The TensorFlow Authors. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsNan, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const isNaNConfig: KernelConfig = createUnaryKernelConfig(IsNan, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/LRN.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/LRN.ts
deleted file mode 100644
index b10a88a08..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/LRN.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LRN, LRNAttrs, LRNInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmLRN: (
-    xId: number, outId: number, channels: number, depthRadius: number,
-    bias: number, alpha: number, beta: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmLRN = backend.wasm.cwrap(LRN, null, [
-    'number',  // xId
-    'number',  // outId
-    'number',  // channels
-    'number',  // depthRadius
-    'number',  // bias
-    'number',  // alpha
-    'number',  // beta
-  ]);
-}
-
-export function lrn(args: {
-  inputs: LRNInputs,
-  attrs: LRNAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {depthRadius, bias, alpha, beta} = attrs;
-
-  if (x.dtype !== 'float32') {
-    throw new Error('LRN error: x must have dtype float32');
-  }
-
-  const out = backend.makeOutput(x.shape, x.dtype);
-
-  wasmLRN(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(out.dataId).id,
-      /*channels=*/x.shape[3],
-      depthRadius,
-      bias,
-      alpha,
-      beta,
-  );
-  return out;
-}
-
-export const lrnConfig: KernelConfig = {
-  kernelName: LRN,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: lrn as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/LRNGrad.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/LRNGrad.ts
deleted file mode 100644
index daf5c4f4d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/LRNGrad.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LRNGrad, LRNGradAttrs, LRNGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmLRNGrad: (
-    xId: number, yId: number, dyId: number, dxId: number, channels: number,
-    depthRadius: number, bias: number, alpha: number, beta: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmLRNGrad = backend.wasm.cwrap(LRNGrad, null, [
-    'number',  // xId
-    'number',  // yId
-    'number',  // dyId
-    'number',  // dxId
-    'number',  // channels
-    'number',  // depthRadius
-    'number',  // bias
-    'number',  // alpha
-    'number',  // beta
-  ]);
-}
-
-export function lrnGrad(args: {
-  inputs: LRNGradInputs,
-  attrs: LRNGradAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, y, dy} = inputs;
-  const {depthRadius, bias, alpha, beta} = attrs;
-
-  if (x.dtype !== 'float32' || y.dtype !== 'float32' ||
-      dy.dtype !== 'float32') {
-    throw new Error('LRNGrad error: x, y, and dy must have dtype float32');
-  }
-
-  const dx = backend.makeOutput(x.shape, x.dtype);
-
-  wasmLRNGrad(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(y.dataId).id,
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(dx.dataId).id,
-      /*channels=*/dy.shape[3],
-      depthRadius,
-      bias,
-      alpha,
-      beta,
-  );
-  return dx;
-}
-
-export const lrnGradConfig: KernelConfig = {
-  kernelName: LRNGrad,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: lrnGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/LeakyRelu.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/LeakyRelu.ts
deleted file mode 100644
index 16a315367..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/LeakyRelu.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LeakyRelu, LeakyReluAttrs, LeakyReluInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmFunc: (
-    xId: number, dtype: number, leakyreluAlpha: number, outId: number) => void;
-
-function setupFunc(backend: BackendWasm): void {
-  wasmFunc = backend.wasm.cwrap(LeakyRelu, null /* void */, [
-    'number',  // x_id
-    'number',  // dtype
-    'number',  // leakyrelu_alpha
-    'number',  // out_id
-  ]);
-}
-
-export function leakyRelu(
-    args:
-        {inputs: LeakyReluInputs, attrs: LeakyReluAttrs, backend: BackendWasm}):
-    TensorInfo {
-  const {inputs: {x}, attrs: {alpha}, backend} = args;
-
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  // According to TF API, LeakyRelu returns float32 when input is either float32
-  // or int32.
-  const out = backend.makeOutput(x.shape, 'float32');
-
-  if (util.sizeFromShape(x.shape) !== 0) {
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    wasmFunc(xId, CppDType[x.dtype], alpha, outId);
-  }
-
-  return out;
-}
-
-export const leakyReluConfig: KernelConfig = {
-  kernelName: LeakyRelu,
-  backendName: 'wasm',
-  setupFunc,
-  kernelFunc: leakyRelu as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Less.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Less.ts
deleted file mode 100644
index ee7562a65..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Less.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Less} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = false;
-export const lessConfig: KernelConfig =
-    createBinaryKernelConfig(Less, supportsFullBroadcast, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/LessEqual.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/LessEqual.ts
deleted file mode 100644
index d3b62da2d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/LessEqual.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LessEqual} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = false;
-export const lessEqualConfig: KernelConfig =
-    createBinaryKernelConfig(LessEqual, supportsFullBroadcast, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/LinSpace.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/LinSpace.ts
deleted file mode 100644
index e2aaab923..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/LinSpace.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LinSpace, LinSpaceAttrs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmLinSpace: (outId: number, start: number, stop: number, num: number) =>
-    void;
-
-function setup(backend: BackendWasm) {
-  wasmLinSpace = backend.wasm.cwrap(LinSpace, null, [
-    'number',  // outId
-    'number',  // start
-    'number',  // stop
-    'number',  // num
-  ]);
-}
-
-export function linSpace(args: {attrs: LinSpaceAttrs, backend: BackendWasm}):
-    TensorInfo {
-  const {attrs, backend} = args;
-  const {start, stop, num} = attrs;
-
-  // TFJS Cpu backend supports num as a float and returns undetermined tensor in
-  // that case. However, according to TensorFlow spec, num should be a integer.
-  const numInt = Math.floor(num);
-
-  const out = backend.makeOutput([numInt], 'float32');
-  wasmLinSpace(backend.dataIdMap.get(out.dataId).id, start, stop, numInt);
-  return out;
-}
-
-export const linSpaceConfig: KernelConfig = {
-  kernelName: LinSpace,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: linSpace as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Log.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Log.ts
deleted file mode 100644
index 9301a6a52..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Log.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Log} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const logConfig: KernelConfig = createUnaryKernelConfig(Log);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Log1p.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Log1p.ts
deleted file mode 100644
index 2c5a4d75f..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Log1p.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Log1p} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const log1pConfig: KernelConfig = createUnaryKernelConfig(Log1p);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalAnd.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalAnd.ts
deleted file mode 100644
index a6747cea9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalAnd.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, LogicalAnd} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = false;
-export const logicalAndConfig: KernelConfig =
-    createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalNot.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalNot.ts
deleted file mode 100644
index ebdcf557a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalNot.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalNot} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const logicalNotConfig: KernelConfig =
-  createUnaryKernelConfig(LogicalNot);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalOr.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalOr.ts
deleted file mode 100644
index cfc2df7bf..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalOr.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, LogicalOr} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = false;
-export const logicalOrConfig: KernelConfig =
-    createBinaryKernelConfig(LogicalOr, supportsFullBroadcast, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalXor.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalXor.ts
deleted file mode 100644
index e10ea1fb9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/LogicalXor.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, LogicalXor} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = false;
-export const logicalXorConfig: KernelConfig =
-    createBinaryKernelConfig(LogicalXor, supportsFullBroadcast, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Max.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Max.ts
deleted file mode 100644
index aacfb02ae..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Max.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Max, MaxAttrs, MaxInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {permuteAxesAndTranspose} from './kernel_utils';
-import {CppDType} from './types';
-
-let wasmMax: (xId: number, dtype: number, reduceSize: number, outId: number) =>
-    void;
-
-function setup(backend: BackendWasm): void {
-  wasmMax = backend.wasm.cwrap(Max, null /*void*/, [
-    'number',  // x_id
-    'number',  // dtype
-    'number',  // reduce_size
-    'number',  // out_id
-  ]);
-}
-
-function max(args: {backend: BackendWasm, inputs: MaxInputs, attrs: MaxAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {reductionIndices: axis, keepDims} = attrs;
-  const {x} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  let inputId = xId;
-  let input = x;
-
-  const {transposed, axes, originalAxes, inputWasTransposed} =
-      permuteAxesAndTranspose(x, axis, backend);
-
-  if (inputWasTransposed) {
-    const transposedId = backend.dataIdMap.get(transposed.dataId).id;
-    input = transposed;
-    inputId = transposedId;
-  }
-
-  const inputRank = input.shape.length;
-  backend_util.assertAxesAreInnerMostDims('max', axes, inputRank);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(input.shape, axes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  const out = backend.makeOutput(outShape, x.dtype);
-  if (util.sizeFromShape(input.shape) !== 0) {
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    wasmMax(inputId, CppDType[x.dtype], reduceSize, outId);
-  }
-
-  if (inputWasTransposed) {
-    // dispose of the transposed tensor.
-    backend.disposeData(transposed.dataId);
-  }
-
-  if (keepDims) {
-    // reshape
-    const newShape = backend_util.expandShapeToKeepDim(out.shape, originalAxes);
-    out.shape = newShape;
-  }
-
-  return out;
-}
-
-export const maxConfig: KernelConfig = {
-  kernelName: Max,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: max as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPool.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPool.ts
deleted file mode 100644
index c4d11e03c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPool.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, MaxPool, MaxPoolAttrs, MaxPoolInputs, Tensor4D, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmMaxPool: (
-    xId: number, batchSize: number, inputHeight: number, inputWidth: number,
-    filterHeight: number, filterWidth: number, padTop: number, padRight: number,
-    padBottom: number, padLeft: number, dilationHeight: number,
-    dilationWidth: number, strideHeight: number, strideWidth: number,
-    inputChannels: number, outputChannels: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmMaxPool = backend.wasm.cwrap(MaxPool, null /* void */, [
-    'number',  // xId
-    'number',  // batchSize
-    'number',  // inputHeight
-    'number',  // inputWidth
-    'number',  // filterHeight
-    'number',  // filterWidth
-    'number',  // padTop
-    'number',  // padRight
-    'number',  // padBottom
-    'number',  // padLeft
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // inputChannels
-    'number',  // outputChannels
-    'number',  // outId
-  ]);
-}
-
-function maxPool(
-    args: {inputs: MaxPoolInputs, backend: BackendWasm, attrs: MaxPoolAttrs}) {
-  const {inputs, attrs, backend} = args;
-
-  const x = inputs.x as Tensor4D;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-
-  // TF API supports int32 input. CPU and WebGL backend also support int32
-  // input. WASM backend doesn't support it because it uses xnnpack which only
-  // supports float32.
-  //
-  // Add the following assert only for the WASM backend instead of at core op
-  // level.
-  //
-  // TODO: add support for int32 input.
-  util.assert(
-      x.dtype === 'float32',
-      () =>
-          `Error in MaxPool: only float32 input is supported. Got ${x.dtype}.`);
-
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
-
-  const filterHeight = convInfo.filterHeight;
-  const filterWidth = convInfo.filterWidth;
-  const padTop = convInfo.padInfo.top;
-  const padRight = convInfo.padInfo.right;
-  const padBottom = convInfo.padInfo.bottom;
-  const padLeft = convInfo.padInfo.left;
-  const dilationHeight = convInfo.dilationHeight;
-  const dilationWidth = convInfo.dilationWidth;
-  const strideHeight = convInfo.strideHeight;
-  const strideWidth = convInfo.strideWidth;
-  const inputChannels = convInfo.inChannels;
-  const outputChannels = convInfo.outChannels;
-
-  if (convInfo.dataFormat !== 'channelsLast') {
-    throw new Error(
-        `wasm backend does not support dataFormat:'` +
-        `${convInfo.dataFormat}'. Please use 'channelsLast'.`);
-  }
-
-  const out = backend.makeOutput(convInfo.outShape, 'float32');
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  wasmMaxPool(
-      xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth,
-      padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth,
-      strideHeight, strideWidth, inputChannels, outputChannels, outId);
-  return out;
-}
-
-export const maxPoolConfig: KernelConfig = {
-  kernelName: MaxPool,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: maxPool as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPool3D.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPool3D.ts
deleted file mode 100644
index 5ab51b9b7..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPool3D.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmMaxPool3D: (
-    xId: number, outId: number, batchSize: number, channelSize: number,
-    inDepth: number, inHeight: number, inWidth: number, outDepth: number,
-    outHeight: number, outWidth: number, strideDepth: number,
-    strideHeight: number, strideWidth: number, dilationDepth: number,
-    dilationHeight: number, dilationWidth: number, effectiveFilterDepth: number,
-    effectiveFilterHeight: number, effectiveFilterWidth: number,
-    padFront: number, padTop: number, padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmMaxPool3D = backend.wasm.cwrap('MaxPool3D', null, [
-    'number',  // xId
-    'number',  // outId
-    'number',  // batchSize
-    'number',  // channelSize
-    'number',  // inDepth
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // outDepth
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // strideDepth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // dilationDepth
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // effectiveFilterDepth
-    'number',  // effectiveFilterHeight
-    'number',  // effectiveFilterWidth
-    'number',  // padFront
-    'number',  // padTop
-    'number',  // padLeft
-  ]);
-}
-
-export function maxPool3D(args: {
-  inputs: MaxPool3DInputs,
-  attrs: MaxPool3DAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode, dataFormat} = attrs;
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      /*dilations=*/1, pad, dimRoundingMode, dataFormat);
-  const out = backend.makeOutput(convInfo.outShape, x.dtype);
-
-  wasmMaxPool3D(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(out.dataId).id,
-      convInfo.batchSize,
-      // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in
-      // channels should always equal to out channels.
-      /*channelSize=*/convInfo.inChannels,
-      convInfo.inDepth,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.outDepth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.strideDepth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationDepth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.effectiveFilterDepth,
-      convInfo.effectiveFilterHeight,
-      convInfo.effectiveFilterWidth,
-      convInfo.padInfo.front,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-  );
-  return out;
-}
-
-export const maxPool3DConfig: KernelConfig = {
-  kernelName: MaxPool3D,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: maxPool3D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPool3DGrad.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPool3DGrad.ts
deleted file mode 100644
index e820b467c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPool3DGrad.ts
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, MaxPool3DGrad, MaxPool3DGradAttrs, MaxPool3DGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmMaxPool3DGrad: (
-    xId: number, dyId: number, dxId: number, batchSize: number,
-    channelSize: number, inDepth: number, inHeight: number, inWidth: number,
-    outDepth: number, outHeight: number, outWidth: number, strideDepth: number,
-    strideHeight: number, strideWidth: number, dilationDepth: number,
-    dilationHeight: number, dilationWidth: number, effectiveFilterDepth: number,
-    effectiveFilterHeight: number, effectiveFilterWidth: number,
-    padFront: number, padTop: number, padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmMaxPool3DGrad = backend.wasm.cwrap('MaxPool3DGrad', null, [
-    'number',  // xId
-    'number',  // dyId
-    'number',  // dxId
-    'number',  // batchSize
-    'number',  // channelSize
-    'number',  // inDepth
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // outDepth
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // strideDepth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // dilationDepth
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // effectiveFilterDepth
-    'number',  // effectiveFilterHeight
-    'number',  // effectiveFilterWidth
-    'number',  // padFront
-    'number',  // padTop
-    'number',  // padLeft
-  ]);
-}
-
-export function maxPool3DGrad(args: {
-  inputs: MaxPool3DGradInputs,
-  attrs: MaxPool3DGradAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-
-  const convInfo = backend_util.computePool3DInfo(
-      input.shape as [number, number, number, number, number], filterSize,
-      strides, /*dilations=*/1, pad, dimRoundingMode);
-  const dx = backend.makeOutput(input.shape, input.dtype);
-
-  wasmMaxPool3DGrad(
-      backend.dataIdMap.get(input.dataId).id,
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(dx.dataId).id,
-      convInfo.batchSize,
-      // Since Pool3D ops (MaxPool3D and MaxPool3D) support 3D filter only, in
-      // channels should always equal to out channels.
-      /*channelSize=*/convInfo.inChannels,
-      convInfo.inDepth,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.outDepth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.strideDepth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationDepth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.effectiveFilterDepth,
-      convInfo.effectiveFilterHeight,
-      convInfo.effectiveFilterWidth,
-      convInfo.padInfo.front,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-  );
-  return dx;
-}
-
-export const maxPool3DGradConfig: KernelConfig = {
-  kernelName: MaxPool3DGrad,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: maxPool3DGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPoolGrad.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPoolGrad.ts
deleted file mode 100644
index 792202c40..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPoolGrad.ts
+++ /dev/null
@@ -1,95 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, MaxPoolGrad, MaxPoolGradAttrs, MaxPoolGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmMaxPoolGrad: (
-    xId: number, dyId: number, dxId: number, batchSize: number,
-    channelSize: number, inHeight: number, inWidth: number, outHeight: number,
-    outWidth: number, strideHeight: number, strideWidth: number,
-    dilationHeight: number, dilationWidth: number,
-    effectiveFilterHeight: number, effectiveFilterWidth: number, padTop: number,
-    padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmMaxPoolGrad = backend.wasm.cwrap('MaxPoolGrad', null, [
-    'number',  // xId
-    'number',  // dyId
-    'number',  // dxId
-    'number',  // batchSize
-    'number',  // channelSize
-    'number',  // inHeight
-    'number',  // inWidth
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // strideHeight
-    'number',  // strideWidth
-    'number',  // dilationHeight
-    'number',  // dilationWidth
-    'number',  // effectiveFilterHeight
-    'number',  // effectiveFilterWidth
-    'number',  // padTop
-    'number',  // padLeft
-  ]);
-}
-
-export function maxPoolGrad(args: {
-  inputs: MaxPoolGradInputs,
-  attrs: MaxPoolGradAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-
-  const convInfo = backend_util.computePool2DInfo(
-      input.shape as [number, number, number, number], filterSize, strides,
-      /*dilations=*/1, pad, dimRoundingMode);
-  const dx = backend.makeOutput(input.shape, input.dtype);
-
-  wasmMaxPoolGrad(
-      backend.dataIdMap.get(input.dataId).id,
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(dx.dataId).id,
-      convInfo.batchSize,
-      // Since Pool ops (MaxPool and MaxPool) support 2D filter only, in
-      // channels should always equal to out channels.
-      /*channelSize=*/convInfo.inChannels,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.effectiveFilterHeight,
-      convInfo.effectiveFilterWidth,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-  );
-  return dx;
-}
-
-export const maxPoolGradConfig: KernelConfig = {
-  kernelName: MaxPoolGrad,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: maxPoolGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPoolWithArgmax.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPoolWithArgmax.ts
deleted file mode 100644
index f2d5a33ca..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/MaxPoolWithArgmax.ts
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmMaxPoolWithArgmax: (
-    xId: number, pooledId: number, indexesId: number, dtype: number,
-    includeBatchIndex: boolean, batchSize: number, channelSize: number,
-    inHeight: number, inWidth: number, outHeight: number, outWidth: number,
-    strideHeight: number, strideWidth: number, dilationHeight: number,
-    dilationWidth: number, effectiveFilterHeight: number,
-    effectiveFilterWidth: number, padTop: number, padLeft: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmMaxPoolWithArgmax = backend.wasm.cwrap('MaxPoolWithArgmax', null, [
-    'number',   // xId
-    'number',   // pooledId
-    'number',   // indexesId
-    'number',   // dtype
-    'boolean',  // includeBatchIndex
-    'number',   // batchSize
-    'number',   // channelSize
-    'number',   // inHeight
-    'number',   // inWidth
-    'number',   // outHeight
-    'number',   // outWidth
-    'number',   // strideHeight
-    'number',   // strideWidth
-    'number',   // dilationHeight
-    'number',   // dilationWidth
-    'number',   // effectiveFilterHeight
-    'number',   // effectiveFilterWidth
-    'number',   // padTop
-    'number',   // padLeft
-  ]);
-}
-
-export function maxPoolWithArgmax(args: {
-  inputs: MaxPoolWithArgmaxInputs,
-  attrs: MaxPoolWithArgmaxAttrs,
-  backend: BackendWasm,
-}): TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, includeBatchInIndex} = attrs;
-
-  util.assert(
-      x.shape.length === 4,
-      () => `Error in maxPool: input must be rank 4 but got rank ${
-          x.shape.length}.`);
-  const dilations: [number, number] = [1, 1];
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in maxPool: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides, [1, 1],
-      pad);
-
-  const pooled = backend.makeOutput(convInfo.outShape, x.dtype);
-  const indexes = backend.makeOutput(convInfo.outShape, 'int32');
-
-  wasmMaxPoolWithArgmax(
-      backend.dataIdMap.get(x.dataId).id,
-      backend.dataIdMap.get(pooled.dataId).id,
-      backend.dataIdMap.get(indexes.dataId).id,
-      CppDType[x.dtype],
-      includeBatchInIndex,
-      convInfo.batchSize,
-      convInfo.inChannels,
-      convInfo.inHeight,
-      convInfo.inWidth,
-      convInfo.outHeight,
-      convInfo.outWidth,
-      convInfo.strideHeight,
-      convInfo.strideWidth,
-      convInfo.dilationHeight,
-      convInfo.dilationWidth,
-      convInfo.effectiveFilterHeight,
-      convInfo.effectiveFilterWidth,
-      convInfo.padInfo.top,
-      convInfo.padInfo.left,
-  );
-  return [pooled, indexes];
-}
-
-export const maxPoolWithArgmaxConfig: KernelConfig = {
-  kernelName: MaxPoolWithArgmax,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: maxPoolWithArgmax as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Maximum.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Maximum.ts
deleted file mode 100644
index d5761f773..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Maximum.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Maximum} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = false;
-export const maximumConfig: KernelConfig =
-    createBinaryKernelConfig(Maximum, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Mean.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Mean.ts
deleted file mode 100644
index 8a8d4a2af..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Mean.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Mean, MeanAttrs, MeanInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {cast} from './Cast';
-
-import {permuteAxesAndTranspose} from './kernel_utils';
-
-let wasmMean: (xId: number, reduceSize: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmMean =
-      backend.wasm.cwrap(Mean, null /*void*/, ['number, number, number']);
-}
-
-export function mean(
-    args: {backend: BackendWasm, inputs: MeanInputs, attrs: MeanAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {axis, keepDims} = attrs;
-  const {x} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  let inputId = xId;
-  let input = x;
-
-  const {transposed, axes, originalAxes, inputWasTransposed} =
-      permuteAxesAndTranspose(x, axis, backend);
-
-  let reductionAxes = axes;
-  if (inputWasTransposed) {
-    const transposedId = backend.dataIdMap.get(transposed.dataId).id;
-    if (transposedId !== xId) {
-      // transpose was not a no-op. We will need to dispose of this
-      // once we are done.
-      input = transposed;
-      inputId = transposedId;
-      reductionAxes = backend_util.getInnerMostAxes(
-          reductionAxes.length, input.shape.length);
-    }
-  }
-
-  backend_util.assertAxesAreInnerMostDims(
-      'mean', reductionAxes, input.shape.length);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(input.shape, reductionAxes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-  let castedInput = input;
-  if (input.dtype !== 'float32') {
-    castedInput =
-        cast({backend, inputs: {x: input}, attrs: {dtype: 'float32'}});
-    inputId = backend.dataIdMap.get(castedInput.dataId).id;
-  }
-
-  const out = backend.makeOutput(outShape, 'float32');
-  if (util.sizeFromShape(input.shape) !== 0) {
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    wasmMean(inputId, reduceSize, outId);
-  }
-
-  if (inputWasTransposed) {
-    // dispose of the transposed tensor.
-    backend.disposeData(transposed.dataId);
-  }
-
-  if (keepDims) {
-    // reshape
-    const newShape = backend_util.expandShapeToKeepDim(out.shape, originalAxes);
-    out.shape = newShape;
-  }
-
-  if (input.dtype !== 'float32') {
-    backend.disposeData(castedInput.dataId);
-  }
-
-  return out;
-}
-
-export const meanConfig: KernelConfig = {
-  kernelName: Mean,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: mean as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Min.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Min.ts
deleted file mode 100644
index 6596be931..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Min.ts
+++ /dev/null
@@ -1,91 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Min, MinAttrs, MinInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {permuteAxesAndTranspose} from './kernel_utils';
-import {CppDType} from './types';
-
-let wasmMin: (xId: number, dtype: number, reduceSize: number, outId: number) =>
-    void;
-
-function setup(backend: BackendWasm): void {
-  wasmMin = backend.wasm.cwrap(Min, null /*void*/, [
-    'number',  // x_id
-    'number',  // dtype
-    'number',  // reduce_size
-    'number',  // out_id
-  ]);
-}
-
-function min(args: {backend: BackendWasm, inputs: MinInputs, attrs: MinAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {axis, keepDims} = attrs;
-  const {x} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  let inputId = xId;
-  let input = x;
-
-  const {transposed, axes, originalAxes, inputWasTransposed} =
-      permuteAxesAndTranspose(x, axis, backend);
-
-  if (inputWasTransposed) {
-    const transposedId = backend.dataIdMap.get(transposed.dataId).id;
-    if (transposedId !== xId) {
-      // transpose was not a no-op. We will need to dispose of this
-      // once we are done.
-      input = transposed;
-      inputId = transposedId;
-    }
-  }
-
-  const inputRank = input.shape.length;
-
-  backend_util.assertAxesAreInnerMostDims('min', axes, inputRank);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(input.shape, axes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  const out = backend.makeOutput(outShape, input.dtype);
-  if (util.sizeFromShape(input.shape) !== 0) {
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    wasmMin(inputId, CppDType[x.dtype], reduceSize, outId);
-  }
-
-  if (inputWasTransposed) {
-    // dispose of the transposed tensor.
-    backend.disposeData(transposed.dataId);
-  }
-
-  if (keepDims) {
-    // reshape
-    const newShape = backend_util.expandShapeToKeepDim(out.shape, originalAxes);
-    out.shape = newShape;
-  }
-
-  return out;
-}
-
-export const minConfig: KernelConfig = {
-  kernelName: Min,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: min as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Minimum.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Minimum.ts
deleted file mode 100644
index d1dc752bd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Minimum.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Minimum} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = false;
-export const minimumConfig: KernelConfig =
-    createBinaryKernelConfig(Minimum, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/MirrorPad.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/MirrorPad.ts
deleted file mode 100644
index 6f586c888..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/MirrorPad.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, MirrorPad, MirrorPadAttrs, MirrorPadInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-// Must match enum in MirrorPad.cc
-enum MirrorPaddingMode {
-  reflect = 0,
-  symmetric = 1
-}
-
-let wasmMirrorPad: (
-    xId: number, xShapeBytes: Uint8Array, xShapeLength: number, xDtype: number,
-    prePaddingsBytes: Uint8Array, postPaddingsBytes: Uint8Array, mode: number,
-    outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmMirrorPad = backend.wasm.cwrap(MirrorPad, null /* void */, [
-    'number',  // xId
-    'array',   // x.shape
-    'number',  // x.shape.length
-    'number',  // x.dtype
-    'array',   // pre-paddings
-    'array',   // post-paddings
-    'number',  // mode
-    'number',  // outId
-  ]);
-}
-
-function mirrorPad(args: {
-  inputs: MirrorPadInputs,
-  backend: BackendWasm,
-  attrs: MirrorPadAttrs
-}) {
-  const {inputs: {x}, backend, attrs: {paddings, mode}} = args;
-
-  const outShape = paddings.map(
-      (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const out = backend.makeOutput(outShape, x.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
-
-  const prePaddingsFlat = paddings.map(padTuple => padTuple[0]);
-  const postPaddingsFlat = paddings.map(padTuple => padTuple[1]);
-  const prePaddingsBytes =
-      new Uint8Array(new Int32Array(prePaddingsFlat).buffer);
-  const postPaddingsBytes =
-      new Uint8Array(new Int32Array(postPaddingsFlat).buffer);
-
-  wasmMirrorPad(
-      xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes,
-      postPaddingsBytes, MirrorPaddingMode[mode], outId);
-  return out;
-}
-
-export const mirrorPadConfig: KernelConfig = {
-  kernelName: MirrorPad,
-  backendName: 'wasm',
-  kernelFunc: mirrorPad as unknown as KernelFunc,
-  setupFunc: setup
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Mod.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Mod.ts
deleted file mode 100644
index 9ac377a4a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Mod.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Mod} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-export const modConfig: KernelConfig =
-    createBinaryKernelConfig(Mod, /*supportsFullBroadcast=*/true);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Multinomial.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Multinomial.ts
deleted file mode 100644
index bd842131c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Multinomial.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Multinomial, MultinomialAttrs, MultinomialInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {softmax} from './Softmax';
-
-let wasmMultinomial: (
-    probabilitiesId: number, batchSize: number, numEvents: number,
-    numSamples: number, seed: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmMultinomial = backend.wasm.cwrap(Multinomial, null, [
-    'number',  // probabilitiesId
-    'number',  // batchSize
-    'number',  // numEvents
-    'number',  // numSamples
-    'number',  // seed
-    'number',  // outId
-  ]);
-}
-
-export function multinomial(args: {
-  inputs: MultinomialInputs,
-  attrs: MultinomialAttrs,
-  backend: BackendWasm,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {logits} = inputs;
-  const {numSamples, seed, normalized} = attrs;
-
-  if (logits.dtype !== 'float32') {
-    throw new Error(
-        `Tensor logits must have dtype float32, got ${logits.dtype}`);
-  }
-
-  const probabilities = normalized ? logits : softmax({
-    inputs: {logits},
-    backend,
-    attrs: {dim: logits.shape.length - 1},
-  });
-
-  const [batchSize, numEvents] = probabilities.shape;
-  const out = backend.makeOutput([batchSize, numSamples], 'int32');
-
-  wasmMultinomial(
-      backend.dataIdMap.get(probabilities.dataId).id,
-      batchSize,
-      numEvents,
-      numSamples,
-      seed,
-      backend.dataIdMap.get(out.dataId).id,
-  );
-  if (!normalized) {
-    backend.disposeData(probabilities.dataId);
-  }
-  return out;
-}
-
-export const multinomialConfig: KernelConfig = {
-  kernelName: Multinomial,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: multinomial as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Multiply.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Multiply.ts
deleted file mode 100644
index b6f882ce0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Multiply.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Multiply} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-const supportsFullBroadcast = true;
-export const multiplyConfig: KernelConfig =
-    createBinaryKernelConfig(Multiply, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Neg.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Neg.ts
deleted file mode 100644
index 4b9b1db04..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Neg.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Neg} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const negConfig: KernelConfig = createUnaryKernelConfig(Neg);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV3.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV3.ts
deleted file mode 100644
index f604c196c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV3.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, NonMaxSuppressionV3, NonMaxSuppressionV3Attrs, NonMaxSuppressionV3Inputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {parseResultStruct} from './NonMaxSuppression_util';
-
-let wasmFunc: (
-    boxesId: number, scoresId: number, maxOutputSize: number,
-    iouThreshold: number, scoreThreshold: number) => number;
-
-function setup(backend: BackendWasm): void {
-  wasmFunc = backend.wasm.cwrap(
-      NonMaxSuppressionV3,
-      'number',  // Result*
-      [
-        'number',  // boxesId
-        'number',  // scoresId
-        'number',  // maxOutputSize
-        'number',  // iouThreshold
-        'number',  // scoreThreshold
-      ]);
-}
-
-function kernelFunc(args: {
-  backend: BackendWasm,
-  inputs: NonMaxSuppressionV3Inputs,
-  attrs: NonMaxSuppressionV3Attrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {iouThreshold, maxOutputSize, scoreThreshold} = attrs;
-  const {boxes, scores} = inputs;
-
-  const boxesId = backend.dataIdMap.get(boxes.dataId).id;
-  const scoresId = backend.dataIdMap.get(scores.dataId).id;
-
-  const resOffset =
-      wasmFunc(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);
-
-  const {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs} =
-      parseResultStruct(backend, resOffset);
-
-  // Since we are not using scores for V3, we have to delete it from the heap.
-  backend.wasm._free(pSelectedScores);
-  backend.wasm._free(pValidOutputs);
-
-  const selectedIndicesTensor =
-      backend.makeOutput([selectedSize], 'int32', pSelectedIndices);
-
-  return selectedIndicesTensor;
-}
-
-export const nonMaxSuppressionV3Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV3,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: kernelFunc as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV4.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV4.ts
deleted file mode 100644
index 9d348211a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV4.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {parseResultStruct} from './NonMaxSuppression_util';
-
-let wasmFunc: (
-    boxesId: number, scoresId: number, maxOutputSize: number,
-    iouThreshold: number, scoreThreshold: number,
-    padToMaxOutputSize: boolean) => number;
-
-function setup(backend: BackendWasm): void {
-  wasmFunc = backend.wasm.cwrap(
-      NonMaxSuppressionV4,
-      'number',  // Result*
-      [
-        'number',  // boxesId
-        'number',  // scoresId
-        'number',  // maxOutputSize
-        'number',  // iouThreshold
-        'number',  // scoreThreshold
-        'bool',    // padToMaxOutputSize
-      ]);
-}
-
-function nonMaxSuppressionV4(args: {
-  backend: BackendWasm,
-  inputs: NonMaxSuppressionV4Inputs,
-  attrs: NonMaxSuppressionV4Attrs
-}): TensorInfo[] {
-  const {backend, inputs, attrs} = args;
-  const {iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize} =
-      attrs;
-  const {boxes, scores} = inputs;
-
-  const boxesId = backend.dataIdMap.get(boxes.dataId).id;
-  const scoresId = backend.dataIdMap.get(scores.dataId).id;
-
-  const resOffset = wasmFunc(
-      boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold,
-      padToMaxOutputSize);
-
-  const {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs} =
-      parseResultStruct(backend, resOffset);
-
-  // Since we are not using scores for V4, we have to delete it from the heap.
-  backend.wasm._free(pSelectedScores);
-
-  const selectedIndicesTensor =
-      backend.makeOutput([selectedSize], 'int32', pSelectedIndices);
-
-  const validOutputsTensor = backend.makeOutput([], 'int32', pValidOutputs);
-
-  return [selectedIndicesTensor, validOutputsTensor];
-}
-
-export const nonMaxSuppressionV4Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV4,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: nonMaxSuppressionV4 as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV5.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV5.ts
deleted file mode 100644
index 3f4a9df91..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV5.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, NonMaxSuppressionV5, NonMaxSuppressionV5Attrs, NonMaxSuppressionV5Inputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {parseResultStruct} from './NonMaxSuppression_util';
-
-let wasmFunc:
-    (boxesId: number, scoresId: number, maxOutputSize: number,
-     iouThreshold: number, scoreThreshold: number, softNmsSigma: number) =>
-        number;
-
-function setup(backend: BackendWasm): void {
-  wasmFunc = backend.wasm.cwrap(
-      NonMaxSuppressionV5,
-      'number',  // Result*
-      [
-        'number',  // boxesId
-        'number',  // scoresId
-        'number',  // maxOutputSize
-        'number',  // iouThreshold
-        'number',  // scoreThreshold
-        'number',  // softNmsSigma
-      ]);
-}
-
-function kernelFunc(args: {
-  backend: BackendWasm,
-  inputs: NonMaxSuppressionV5Inputs,
-  attrs: NonMaxSuppressionV5Attrs
-}): TensorInfo[] {
-  const {backend, inputs, attrs} = args;
-  const {iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma} = attrs;
-  const {boxes, scores} = inputs;
-
-  const boxesId = backend.dataIdMap.get(boxes.dataId).id;
-  const scoresId = backend.dataIdMap.get(scores.dataId).id;
-
-  const resOffset = wasmFunc(
-      boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold,
-      softNmsSigma);
-
-  const {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs} =
-      parseResultStruct(backend, resOffset);
-
-  // Since we are not using validOutputs for V5, we have to delete it from the
-  // heap.
-  backend.wasm._free(pValidOutputs);
-
-  const selectedIndicesTensor =
-      backend.makeOutput([selectedSize], 'int32', pSelectedIndices);
-  const selectedScoresTensor =
-      backend.makeOutput([selectedSize], 'float32', pSelectedScores);
-
-  return [selectedIndicesTensor, selectedScoresTensor];
-}
-
-export const nonMaxSuppressionV5Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV5,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: kernelFunc as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppression_util.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppression_util.ts
deleted file mode 100644
index 2b2fc6deb..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/NonMaxSuppression_util.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BackendWasm} from '../backend_wasm';
-
-// Analogous to `struct Result` in `non_max_suppression_impl.h`.
-interface Result {
-  pSelectedIndices: number;
-  selectedSize: number;
-  pSelectedScores: number;
-  pValidOutputs: number;
-}
-/**
- * Parse the result of the c++ method, which has the shape equivalent to
- * `Result`.
- */
-export function parseResultStruct(
-    backend: BackendWasm, resOffset: number): Result {
-  const result = new Int32Array(backend.wasm.HEAPU8.buffer, resOffset, 4);
-  const pSelectedIndices = result[0];
-  const selectedSize = result[1];
-  const pSelectedScores = result[2];
-  const pValidOutputs = result[3];
-  // Since the result was allocated on the heap, we have to delete it.
-  backend.wasm._free(resOffset);
-  return {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs};
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/NotEqual.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/NotEqual.ts
deleted file mode 100644
index 9ca0446b9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/NotEqual.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, NotEqual} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = false;
-export const notEqualConfig: KernelConfig =
-    createBinaryKernelConfig(NotEqual, supportsFullBroadcast, 'bool');
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/OneHot.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/OneHot.ts
deleted file mode 100644
index 190658de0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/OneHot.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, OneHot, OneHotAttrs, OneHotInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmOneHot: (
-    indicesId: number, depth: number, onValue: number, offValue: number,
-    outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmOneHot = backend.wasm.cwrap(OneHot, null /* void */, [
-    'number',  // indices_id
-    'number',  // depth,
-    'number',  // onValue
-    'number',  // offValue
-    'number'   // out_id
-  ]);
-}
-
-function oneHot(
-    args: {inputs: OneHotInputs, attrs: OneHotAttrs, backend: BackendWasm}) {
-  const {inputs, backend, attrs} = args;
-  const {indices} = inputs;
-  const {dtype, depth, onValue, offValue} = attrs;
-
-  const out = backend.makeOutput([...indices.shape, depth], dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const indicesData = backend.dataIdMap.get(indices.dataId);
-  const indicesId = indicesData.id;
-
-  wasmOneHot(indicesId, depth, onValue, offValue, outId);
-
-  return out;
-}
-
-export const oneHotConfig: KernelConfig = {
-  kernelName: OneHot,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: oneHot as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/OnesLike.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/OnesLike.ts
deleted file mode 100644
index 2f52d19ba..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/OnesLike.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, OnesLike, OnesLikeInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-function onesLike(args: {inputs: OnesLikeInputs, backend: BackendWasm}) {
-  const {inputs: {x}, backend} = args;
-  const out = backend.makeOutput(x.shape, x.dtype);
-  const outVals = backend.typedArrayFromHeap(out);
-  outVals.fill(1);
-  return out;
-}
-
-export const onesLikeConfig: KernelConfig = {
-  kernelName: OnesLike,
-  backendName: 'wasm',
-  kernelFunc: onesLike as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Pack.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Pack.ts
deleted file mode 100644
index a752877fb..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Pack.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Pack, PackAttrs, PackInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-import {BackendWasm} from '../backend_wasm';
-
-import {concat} from './Concat';
-import {expandDims} from './ExpandDims';
-
-export function pack(
-    args: {inputs: PackInputs, backend: BackendWasm, attrs: PackAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {axis} = attrs;
-
-  if (inputs.length === 1) {
-    return expandDims(
-        {inputs: {input: inputs[0]}, backend, attrs: {dim: axis}});
-  }
-
-  const shape = inputs[0].shape;
-  const dtype = inputs[0].dtype;
-
-  inputs.forEach(t => {
-    util.assertShapesMatch(
-        shape, t.shape,
-        'All tensors passed to stack must have matching shapes');
-    util.assert(
-        dtype === t.dtype,
-        () => 'All tensors passed to stack must have matching dtypes');
-  });
-
-  const intermediateTensorInfos: TensorInfo[] = [];
-  const expandedTensors = inputs.map(t => {
-    const expandedT =
-        expandDims({inputs: {input: t}, backend, attrs: {dim: axis}});
-    intermediateTensorInfos.push(expandedT);
-    return expandedT;
-  });
-
-  const result = concat({inputs: expandedTensors, backend, attrs: {axis}});
-
-  intermediateTensorInfos.forEach(t => backend.disposeData(t.dataId));
-
-  return result;
-}
-
-export const packConfig: KernelConfig = {
-  kernelName: Pack,
-  backendName: 'wasm',
-  kernelFunc: pack as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/PadV2.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/PadV2.ts
deleted file mode 100644
index 281abc696..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/PadV2.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, PadV2, PadV2Attrs, PadV2Inputs, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {fill} from './Fill';
-
-import {CppDType} from './types';
-
-let wasmPadV2: (
-    xId: number, xShapeBytes: Uint8Array, xShapeLength: number, xDtype: number,
-    prePaddingsBytes: Uint8Array, postPaddingsBytes: Uint8Array,
-    constantValue: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmPadV2 = backend.wasm.cwrap(PadV2, null /* void */, [
-    'number',  // xId
-    'array',   // x.shape
-    'number',  // x.shape.length
-    'number',  // x.dtype
-    'array',   // pre-paddings
-    'array',   // post-paddings
-    'number',  // constantValue
-    'number',  // outId
-  ]);
-}
-
-function pad(
-    args: {inputs: PadV2Inputs, backend: BackendWasm, attrs: PadV2Attrs}) {
-  const {inputs: {x}, backend, attrs: {paddings, constantValue}} = args;
-
-  const outShape = paddings.map(
-      (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);
-
-  if (util.sizeFromShape(x.shape) === 0) {
-    // Short-circuit the computation, since x doesn't have value, only
-    // the shape is used to compute output shape to pad.
-    return fill({
-      backend,
-      attrs: {shape: outShape, value: constantValue, dtype: x.dtype}
-    });
-  }
-
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const out = backend.makeOutput(outShape, x.dtype);
-  const outTensorData = backend.dataIdMap.get(out.dataId);
-  const outId = outTensorData.id;
-
-  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
-
-  const prePaddingsFlat = paddings.map(padTuple => padTuple[0]);
-  const postPaddingsFlat = paddings.map(padTuple => padTuple[1]);
-  const prePaddingsBytes =
-      new Uint8Array(new Int32Array(prePaddingsFlat).buffer);
-  const postPaddingsBytes =
-      new Uint8Array(new Int32Array(postPaddingsFlat).buffer);
-
-  wasmPadV2(
-      xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes,
-      postPaddingsBytes, constantValue, outId);
-  return out;
-}
-
-export const padV2Config: KernelConfig = {
-  kernelName: PadV2,
-  backendName: 'wasm',
-  kernelFunc: pad as unknown as KernelFunc,
-  setupFunc: setup
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Pow.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Pow.ts
deleted file mode 100644
index b766898ab..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Pow.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Pow} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = false;
-export const powConfig: KernelConfig =
-    createBinaryKernelConfig(Pow, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Prelu.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Prelu.ts
deleted file mode 100644
index 98b2dab32..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Prelu.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Prelu, PreluInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {cast} from './Cast';
-
-let wasmPrelu: (xId: number, weightsId: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmPrelu = backend.wasm.cwrap(Prelu, null /* void */, [
-    'number',  // x_id
-    'number',  // weights_id
-    'number'   // out_id
-  ]);
-}
-
-function prelu(args: {inputs: PreluInputs, backend: BackendWasm}) {
-  const {inputs, backend} = args;
-  const {x, alpha} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const weightsId = backend.dataIdMap.get(alpha.dataId).id;
-
-  let inputId = xId;
-  const input = x;
-  let castedInput = input;
-  if (input.dtype !== 'float32') {
-    castedInput = cast({backend, inputs: {x}, attrs: {dtype: 'float32'}});
-    inputId = backend.dataIdMap.get(castedInput.dataId).id;
-  }
-
-  const out = backend.makeOutput(x.shape, 'float32');
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmPrelu(inputId, weightsId, outId);
-
-  if (input.dtype !== 'float32') {
-    backend.disposeData(castedInput.dataId);
-  }
-  return out;
-}
-
-export const preluConfig: KernelConfig = {
-  kernelName: Prelu,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: prelu as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Prod.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Prod.ts
deleted file mode 100644
index f64dc089e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Prod.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Prod, ProdAttrs, ProdInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {permuteAxesAndTranspose} from './kernel_utils';
-
-import {CppDType} from './types';
-
-let wasmProd: (
-    xId: number, reduceSize: number,
-    dtype: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmProd = backend.wasm.cwrap(Prod, null /*void*/, [
-    'number',
-    'number',
-    'number',
-    'number'
-  ]);
-}
-
-function prod(args: {
-  backend: BackendWasm,
-  inputs: ProdInputs,
-  attrs: ProdAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {axis, keepDims} = attrs;
-  const {x} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  let inputId = xId;
-  let input = x;
-
-  const {transposed, axes, originalAxes, inputWasTransposed} =
-      permuteAxesAndTranspose(x, axis, backend);
-
-  let reductionAxes = axes;
-  if (inputWasTransposed) {
-    const transposedId = backend.dataIdMap.get(transposed.dataId).id;
-    if (transposedId !== xId) {
-      // transpose was not a no-op. We will need to dispose of this
-      // once we are done.
-      input = transposed;
-      inputId = transposedId;
-      reductionAxes = backend_util.getInnerMostAxes(
-          reductionAxes.length, input.shape.length);
-    }
-  }
-
-  backend_util.assertAxesAreInnerMostDims(
-      'prod', reductionAxes, input.shape.length);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(input.shape, reductionAxes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  const out = backend.makeOutput(outShape, input.dtype);
-  if (util.sizeFromShape(input.shape) !== 0) {
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    wasmProd(inputId, reduceSize, CppDType[out.dtype], outId);
-  }
-
-  if (inputWasTransposed) {
-    // dispose of the transposed tensor.
-    backend.disposeData(transposed.dataId);
-  }
-
-  if (keepDims) {
-    // reshape
-    const newShape = backend_util.expandShapeToKeepDim(out.shape, originalAxes);
-    out.shape = newShape;
-  }
-
-  return out;
-}
-
-export const prodConfig: KernelConfig = {
-  kernelName: Prod,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: prod as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Range.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Range.ts
deleted file mode 100644
index 090800f1e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Range.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Range, RangeAttrs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {rangeImplCPU} from '../kernel_utils/shared';
-
-export const range =
-    (args: {backend: BackendWasm, attrs: RangeAttrs}): TensorInfo => {
-      const {backend, attrs} = args;
-      const {start, stop, step, dtype} = attrs;
-      const values = rangeImplCPU(start, stop, step, dtype);
-
-      const out = backend.makeOutput([values.length], dtype);
-      const outVals = backend.typedArrayFromHeap(out);
-      outVals.set(values);
-      return out;
-    };
-
-export const rangeConfig: KernelConfig = {
-  kernelName: Range,
-  backendName: 'wasm',
-  kernelFunc: range as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/RealDiv.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/RealDiv.ts
deleted file mode 100644
index 2a0c46cc3..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/RealDiv.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, RealDiv} from '@tensorflow/tfjs-core';
-
-import {createBinaryKernelConfig} from './binary_kernel';
-
-const supportsFullBroadcast = true;
-export const realDivConfig: KernelConfig =
-    createBinaryKernelConfig(RealDiv, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Reciprocal.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Reciprocal.ts
deleted file mode 100644
index 197f4dde9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Reciprocal.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2022 The TensorFlow Authors. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Reciprocal} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const reciprocalConfig: KernelConfig =
-    createUnaryKernelConfig(Reciprocal);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Relu.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Relu.ts
deleted file mode 100644
index 111caa820..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Relu.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Relu} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const reluConfig: KernelConfig = createUnaryKernelConfig(Relu);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Relu6.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Relu6.ts
deleted file mode 100644
index f2cc91c9d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Relu6.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Relu6} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const relu6Config: KernelConfig = createUnaryKernelConfig(Relu6);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Reshape.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Reshape.ts
deleted file mode 100644
index cd33d324d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Reshape.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Reshape, ReshapeAttrs, ReshapeInputs, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-export function reshape(
-    args: {inputs: ReshapeInputs, attrs: ReshapeAttrs, backend: BackendWasm}) {
-  const {inputs, attrs} = args;
-  const {x} = inputs;
-  const {shape} = attrs;
-
-  const xSize = util.sizeFromShape(x.shape);
-  const $shape = util.inferFromImplicitShape(shape, xSize);
-
-  util.assert(
-      xSize === util.sizeFromShape($shape),
-      () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old ` +
-          `shape must have the same number of elements.`);
-
-  // Backend needs to track refCount for the dataId for reshape op
-  args.backend.incRef(x.dataId);
-  return {dataId: x.dataId, shape: $shape, dtype: x.dtype};
-}
-
-export const reshapeConfig: KernelConfig = {
-  kernelName: Reshape,
-  backendName: 'wasm',
-  kernelFunc: reshape as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeBilinear.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeBilinear.ts
deleted file mode 100644
index 593c4f6b6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeBilinear.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {cast} from './Cast';
-
-let wasmResizeBilinear: (
-    xId: number, batch: number, oldHeight: number, oldWidth: number,
-    numChannels: number, newHeight: number, newWidth: number,
-    alignCorners: number, halfPixelCenters: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmResizeBilinear = backend.wasm.cwrap(ResizeBilinear, null /*void*/, [
-    'number',  // xId
-    'number',  // batch
-    'number',  // oldHeight
-    'number',  // oldWidth
-    'number',  // numChannels
-    'number',  // newHeight
-    'number',  // newWidth
-    'number',  // alignCorners
-    'number',  // halfPixelCenters
-    'number'   // outId
-  ]);
-}
-
-function resizeBilinear(args: {
-  backend: BackendWasm,
-  inputs: ResizeBilinearInputs,
-  attrs: ResizeBilinearAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-
-  const {images} = inputs;
-  const {alignCorners, halfPixelCenters, size} = attrs;
-  const [newHeight, newWidth] = size;
-
-  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
-  const outShape = [batch, newHeight, newWidth, numChannels];
-
-  let xData = backend.dataIdMap.get(images.dataId);
-  let castedData;
-  if (xData.dtype !== 'float32') {
-    castedData =
-        cast({backend, inputs: {x: images}, attrs: {dtype: 'float32'}});
-    xData = backend.dataIdMap.get(castedData.dataId);
-  }
-  const xId = xData.id;
-
-  const out = backend.makeOutput(outShape, 'float32');
-  if (util.sizeFromShape(images.shape) === 0) {
-    return out;
-  }
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  wasmResizeBilinear(
-      xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth,
-      alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);
-
-  if (castedData != null) {
-    backend.disposeData(castedData.dataId);
-  }
-
-  return out;
-}
-
-export const resizeBilinearConfig: KernelConfig = {
-  kernelName: ResizeBilinear,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: resizeBilinear as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeBilinearGrad.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeBilinearGrad.ts
deleted file mode 100644
index 67c0db16c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeBilinearGrad.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeBilinearGrad, ResizeBilinearGradAttrs, ResizeBilinearGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {cast} from './Cast';
-
-let wasmResizeBilinearGrad: (
-    imagesId: number, dyId: number, dxId: number, imagesShape: Uint8Array,
-    dyShape: Uint8Array, alignCorners: boolean) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmResizeBilinearGrad = backend.wasm.cwrap(
-      ResizeBilinearGrad, null /*void*/,
-      [
-        'number',   // imagesId
-        'number',   // dyId
-        'number',   // dxId
-        'array',    // imagesShape
-        'array',    // dyShape
-        'boolean',  // alignCorners
-      ]);
-}
-
-function resizeBilinearGrad(args: {
-  backend: BackendWasm; inputs: ResizeBilinearGradInputs;
-  attrs: ResizeBilinearGradAttrs;
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images, dy} = inputs;
-  const {alignCorners} = attrs;
-
-  const dx = backend.makeOutput(images.shape, 'float32');
-
-  let xData = backend.dataIdMap.get(images.dataId);
-  let castedData;
-  if (xData.dtype !== 'float32') {
-    castedData = cast({
-      backend,
-      inputs: {x: images},
-      attrs: {dtype: 'float32'},
-    });
-    xData = backend.dataIdMap.get(castedData.dataId);
-  }
-
-  wasmResizeBilinearGrad(
-      backend.dataIdMap.get(images.dataId).id,
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(dx.dataId).id,
-      new Uint8Array(new Int32Array(images.shape).buffer),
-      new Uint8Array(new Int32Array(dy.shape).buffer),
-      alignCorners,
-  );
-
-  if (castedData != null) {
-    backend.disposeData(castedData.dataId);
-  }
-
-  return dx;
-}
-
-export const resizeBilinearGradConfig: KernelConfig = {
-  kernelName: ResizeBilinearGrad,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: resizeBilinearGrad as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeNearestNeighbor.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeNearestNeighbor.ts
deleted file mode 100644
index 58bfca580..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeNearestNeighbor.ts
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {
-  KernelConfig,
-  KernelFunc,
-  ResizeNearestNeighbor,
-  ResizeNearestNeighborAttrs,
-  ResizeNearestNeighborInputs,
-  TensorInfo,
-  util,
-} from '@tensorflow/tfjs-core';
-
-import { BackendWasm } from '../backend_wasm';
-
-import { cast } from './Cast';
-
-let wasmResizeNearestNeighbor: (
-  xId: number,
-  batch: number,
-  oldHeight: number,
-  oldWidth: number,
-  numChannels: number,
-  newHeight: number,
-  newWidth: number,
-  alignCorners: number,
-  halfPixelCenters: number,
-  outId: number
-) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmResizeNearestNeighbor = backend.wasm.cwrap(
-    ResizeNearestNeighbor,
-    null /*void*/,
-    [
-      'number', // xId
-      'number', // batch
-      'number', // oldHeight
-      'number', // oldWidth
-      'number', // numChannels
-      'number', // newHeight
-      'number', // newWidth
-      'number', // alignCorners
-      'number', // halfPixelCenters
-      'number', // outId
-    ]
-  );
-}
-
-function resizeNearestNeighbor(args: {
-  backend: BackendWasm;
-  inputs: ResizeNearestNeighborInputs;
-  attrs: ResizeNearestNeighborAttrs;
-}): TensorInfo {
-  const { backend, inputs, attrs } = args;
-  const { images } = inputs;
-  const { alignCorners, halfPixelCenters, size } = attrs;
-
-  const [newHeight, newWidth] = size;
-
-  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
-  const outShape = [batch, newHeight, newWidth, numChannels];
-
-  const out = backend.makeOutput(outShape, 'float32');
-  if (util.sizeFromShape(images.shape) === 0) {
-    return out;
-  }
-
-  let xData = backend.dataIdMap.get(images.dataId);
-  let castedData;
-  if (xData.dtype !== 'float32') {
-    castedData = cast({
-      backend,
-      inputs: { x: images },
-      attrs: { dtype: 'float32' },
-    });
-    xData = backend.dataIdMap.get(castedData.dataId);
-  }
-
-  const xId = xData.id;
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  wasmResizeNearestNeighbor(
-    xId,
-    batch,
-    oldHeight,
-    oldWidth,
-    numChannels,
-    newHeight,
-    newWidth,
-    alignCorners ? 1 : 0,
-    halfPixelCenters ? 1 : 0,
-    outId
-  );
-
-  if (castedData != null) {
-    backend.disposeData(castedData.dataId);
-  }
-
-  return out;
-}
-
-export const resizeNearestNeighborConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighbor,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: resizeNearestNeighbor as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeNearestNeighborGrad.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeNearestNeighborGrad.ts
deleted file mode 100644
index 05930b608..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ResizeNearestNeighborGrad.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeNearestNeighborGrad, ResizeNearestNeighborGradAttrs, ResizeNearestNeighborGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {cast} from './Cast';
-
-let wasmResizeNearestNeighborGrad: (
-    imagesId: number, dyId: number, dxId: number, imagesShape: Uint8Array,
-    dyShape: Uint8Array, alignCorners: boolean) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmResizeNearestNeighborGrad = backend.wasm.cwrap(
-      ResizeNearestNeighborGrad, null /*void*/,
-      [
-        'number',   // imagesId
-        'number',   // dyId
-        'number',   // dxId
-        'array',    // imagesShape
-        'array',    // dyShape
-        'boolean',  // alignCorners
-      ]);
-}
-
-function resizeNearestNeighborGrad(args: {
-  backend: BackendWasm; inputs: ResizeNearestNeighborGradInputs;
-  attrs: ResizeNearestNeighborGradAttrs;
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images, dy} = inputs;
-  const {alignCorners} = attrs;
-
-  const dx = backend.makeOutput(images.shape, 'float32');
-
-  let xData = backend.dataIdMap.get(images.dataId);
-  let castedData;
-  if (xData.dtype !== 'float32') {
-    castedData = cast({
-      backend,
-      inputs: {x: images},
-      attrs: {dtype: 'float32'},
-    });
-    xData = backend.dataIdMap.get(castedData.dataId);
-  }
-
-  wasmResizeNearestNeighborGrad(
-      backend.dataIdMap.get(images.dataId).id,
-      backend.dataIdMap.get(dy.dataId).id,
-      backend.dataIdMap.get(dx.dataId).id,
-      new Uint8Array(new Int32Array(images.shape).buffer),
-      new Uint8Array(new Int32Array(dy.shape).buffer),
-      alignCorners,
-  );
-
-  if (castedData != null) {
-    backend.disposeData(castedData.dataId);
-  }
-
-  return dx;
-}
-
-export const resizeNearestNeighborGradConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighborGrad,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: resizeNearestNeighborGrad as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Reverse.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Reverse.ts
deleted file mode 100644
index ca40c2bd6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Reverse.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Reverse, ReverseAttrs, ReverseInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {identity} from './Identity';
-import {reshape} from './Reshape';
-
-let wasmReverse: (
-    xId: number, axes: Uint8Array, axesLength: number, outShape: Uint8Array,
-    outShapeLength: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmReverse = backend.wasm.cwrap(Reverse, null, [
-    'number',  // x_id
-    'array',   // axes
-    'number',  // axes_length
-    'array',   // out_shape
-    'number',  // out_shape_length
-    'number'   // out_id
-  ]);
-}
-
-export function reverse(
-    args: {inputs: ReverseInputs, backend: BackendWasm, attrs: ReverseAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {dims} = attrs;
-
-  const axes = util.parseAxisParam(dims, x.shape);
-
-  if (x.shape.length === 0) {
-    return identity({inputs: {x}, backend});
-  }
-
-  const out = backend.makeOutput(x.shape, x.dtype);
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const axesBytes = new Uint8Array(new Int32Array(axes).buffer);
-  const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
-
-  wasmReverse(
-      xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);
-
-  const reshaped =
-      reshape({inputs: {x: out}, attrs: {shape: x.shape}, backend});
-
-  backend.disposeData(out.dataId);
-  return reshaped;
-}
-
-export const reverseConfig: KernelConfig = {
-  kernelName: Reverse,
-  backendName: 'wasm',
-  kernelFunc: reverse as unknown as KernelFunc,
-  setupFunc: setup
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/RotateWithOffset.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/RotateWithOffset.ts
deleted file mode 100644
index 9572a275b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/RotateWithOffset.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs, TensorInfo} from '@tensorflow/tfjs-core';
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmRotate: (
-    xId: number, batch: number, imageHeight: number, imageWidth: number,
-    numChannels: number, radians: number, centerX: number, centerY: number,
-    fillBytes: Uint8Array, fillLength: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmRotate = backend.wasm.cwrap(RotateWithOffset, null /* void */, [
-    'number',  // xId
-    'number',  // batch
-    'number',  // imageHeight
-    'number',  // imageWidth
-    'number',  // numChannels
-    'number',  // radians
-    'number',  // centerX
-    'number',  // centerY
-    'array',   // fillBytes
-    'number',  // fillLength
-    'number',  // outId
-  ]);
-}
-
-export function rotateWithOffset(args: {
-  inputs: RotateWithOffsetInputs,
-  backend: BackendWasm,
-  attrs: RotateWithOffsetAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {image} = inputs;
-  const {radians, fillValue, center} = attrs;
-
-  const out = backend.makeOutput(image.shape, image.dtype);
-  const imageId = backend.dataIdMap.get(image.dataId).id;
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const [batch, imageHeight, imageWidth, numChannels] = image.shape;
-
-  const [centerX, centerY] =
-      backend_util.getImageCenter(center, imageHeight, imageWidth);
-
-  const fillIsBlack = fillValue === 0;
-  const fullOpacityValue = 255;
-
-  const fillValues = typeof fillValue === 'number' ?
-      [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] :
-      [...fillValue, fullOpacityValue];
-  const fillBytes = new Uint8Array(new Int32Array(fillValues).buffer);
-
-  wasmRotate(
-      imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX,
-      centerY, fillBytes, fillValues.length, outId);
-  return out;
-}
-
-export const rotateWithOffsetConfig: KernelConfig = {
-  kernelName: RotateWithOffset,
-  backendName: 'wasm',
-  kernelFunc: rotateWithOffset as unknown as KernelFunc,
-  setupFunc: setup
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Round.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Round.ts
deleted file mode 100644
index de7360bed..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Round.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Round} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const roundConfig: KernelConfig = createUnaryKernelConfig(Round);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Rsqrt.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Rsqrt.ts
deleted file mode 100644
index f45d8c507..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Rsqrt.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Rsqrt} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const rsqrtConfig: KernelConfig = createUnaryKernelConfig(Rsqrt);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ScatterNd.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ScatterNd.ts
deleted file mode 100644
index 264476f2e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ScatterNd.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, scatter_util, ScatterNd, ScatterNdAttrs, ScatterNdInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmScatterNd: (
-    indicesId: number, updatesId: number, dtype: CppDType, sliceRank: number,
-    numUpdates: number, sliceSize: number, strides: Uint8Array,
-    outputSize: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmScatterNd = backend.wasm.cwrap(ScatterNd, null /*void*/, [
-    'number',  // indicesId
-    'number',  // updatesId
-    'number',  // dtype
-    'number',  // sliceRank
-    'number',  // numUpdates
-    'number',  // sliceSize
-    'array',   // strides
-    'number',  // outputSize
-    'number'   // outId
-  ]);
-}
-
-function scatterNd(
-    args:
-        {backend: BackendWasm, inputs: ScatterNdInputs, attrs: ScatterNdAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {indices, updates} = inputs;
-  const {shape} = attrs;
-
-  const out = backend.makeOutput(shape, updates.dtype);
-  if (util.sizeFromShape(shape) === 0) {
-    return out;
-  }
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      scatter_util.calculateShapes(updates, indices, shape);
-
-  const indicesData = backend.dataIdMap.get(indices.dataId);
-  const indicesId = indicesData.id;
-
-  const updatesData = backend.dataIdMap.get(updates.dataId);
-  const updatesId = updatesData.id;
-
-  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
-
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmScatterNd(
-      indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates,
-      sliceSize, stridesBytes, outputSize, outId);
-
-  return out;
-}
-
-export const scatterNdConfig: KernelConfig = {
-  kernelName: ScatterNd,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: scatterNd as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SearchSorted.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SearchSorted.ts
deleted file mode 100644
index 4afc6d4a4..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SearchSorted.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SearchSorted, SearchSortedAttrs, SearchSortedInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {CppDType} from './types';
-
-let wasmSearchSorted: (
-    sortedSequenceId: number, valuesId: number, batchSize: number,
-    sequenceSize: number, valuesSize: number, dtype: number,
-    isSideLeft: boolean, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmSearchSorted = backend.wasm.cwrap(SearchSorted, null /* void */, [
-    'number',  // sortedSequenceId
-    'number',  // valuesId
-    'number',  // batchSize
-    'number',  // sequenceSize
-    'number',  // valuesSize
-    'number',  // dtype
-    'bool',    // isSideLeft
-    'number',  // outId
-  ]);
-}
-
-function searchSorted(args: {
-  inputs: SearchSortedInputs,
-  backend: BackendWasm,
-  attrs: SearchSortedAttrs,
-}) {
-  const {inputs, backend, attrs} = args;
-  const {sortedSequence, values} = inputs;
-  const {side} = attrs;
-
-  if (sortedSequence.dtype !== values.dtype) {
-    throw new Error(
-        `SearchSorted error: sorted_sequence must have the same dtype as values. Got ${
-            sortedSequence.dtype} and ${values.dtype}`);
-  }
-
-  const out = backend.makeOutput(values.shape, 'int32');
-
-  function tensorId(x: TensorInfo) {
-    return backend.dataIdMap.get(x.dataId).id!;
-  }
-  wasmSearchSorted(
-      tensorId(sortedSequence),
-      tensorId(values),
-      /*batchSize=*/sortedSequence.shape[0],
-      /*sequenceSize=*/sortedSequence.shape[1],
-      /*valuesSize=*/values.shape[1],
-      /*dtype=*/CppDType[sortedSequence.dtype],
-      /*isSideLeft=*/side === 'left',
-      tensorId(out),
-  );
-
-  return out;
-}
-
-export const searchSortedConfig: KernelConfig = {
-  kernelName: SearchSorted,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: searchSorted as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Select.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Select.ts
deleted file mode 100644
index 639a25005..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Select.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Select, SelectInputs, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmSelect: (
-    conditionId: number, tId: number, eId: number, offset: number,
-    outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmSelect = backend.wasm.cwrap('SelectV2', null, [
-    'number',  // conditionId
-    'number',  // tId
-    'number',  // eId
-    'number',  // offset
-    'number',  // outId
-  ]);
-}
-
-function select(args: {inputs: SelectInputs, backend: BackendWasm}) {
-  const {inputs, backend} = args;
-  const {condition, t, e} = inputs;
-
-  const conditionId = backend.dataIdMap.get(condition.dataId).id;
-  const tId = backend.dataIdMap.get(t.dataId).id;
-  const eId = backend.dataIdMap.get(e.dataId).id;
-  const out = backend.makeOutput(t.shape, t.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const cRank = condition.shape.length;
-  const tRank = t.shape.length;
-
-  const offset = cRank === 0 || cRank > 1 || tRank === 1 ?
-      1 :
-      util.sizeFromShape(t.shape.slice(1));
-
-  wasmSelect(conditionId, tId, eId, offset, outId);
-  return out;
-}
-
-export const selectConfig: KernelConfig = {
-  kernelName: Select,
-  backendName: 'wasm',
-  kernelFunc: select as unknown as KernelFunc,
-  setupFunc: setup
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Selu.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Selu.ts
deleted file mode 100644
index 6ba02c5ea..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Selu.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Selu} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const seluConfig: KernelConfig = createUnaryKernelConfig(Selu);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Sigmoid.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Sigmoid.ts
deleted file mode 100644
index bb747d70b..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Sigmoid.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Sigmoid, SigmoidInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmFunc: (xId: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmFunc = backend.wasm.cwrap(Sigmoid, null /* void */, ['number', 'number']);
-}
-
-function sigmoid(args: {backend: BackendWasm, inputs: SigmoidInputs}):
-    TensorInfo {
-  const {backend, inputs: {x}} = args;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const out = backend.makeOutput(x.shape, x.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  // Short-circuit zero-sized tensors.
-  if (util.sizeFromShape(out.shape) === 0) {
-    return out;
-  }
-
-  wasmFunc(xId, outId);
-  return out;
-}
-
-export const sigmoidConfig: KernelConfig = {
-  kernelName: 'Sigmoid',
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: sigmoid as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Sign.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Sign.ts
deleted file mode 100644
index e803ecaf0..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Sign.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sign} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const signConfig: KernelConfig = createUnaryKernelConfig(Sign);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Sin.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Sin.ts
deleted file mode 100644
index 4e61b4339..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Sin.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Sin} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const sinConfig: KernelConfig = createUnaryKernelConfig(Sin);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Sinh.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Sinh.ts
deleted file mode 100644
index 1506965e3..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Sinh.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Sinh} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const sinhConfig: KernelConfig = createUnaryKernelConfig(Sinh);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Slice.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Slice.ts
deleted file mode 100644
index 5a86f492e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Slice.ts
+++ /dev/null
@@ -1,145 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Slice, slice_util, SliceAttrs, SliceInputs, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {sliceImplCPU} from '../kernel_utils/shared';
-
-export function slice(
-    args: {inputs: SliceInputs, attrs: SliceAttrs, backend: BackendWasm}) {
-  const {inputs: {x}, attrs: {begin, size}, backend} = args;
-
-  const [begin_, size_] = slice_util.parseSliceParams(x, begin, size);
-
-  const isContinous = slice_util.isSliceContinous(x.shape, begin_, size_);
-  const xVals = backend.readSync(x.dataId);
-  const out = backend.makeOutput(size_, x.dtype);
-  const xStrides = util.computeStrides(x.shape);
-  const outData = backend.dataIdMap.get(out.dataId);
-
-  if (isContinous) {
-    const flatOffset = slice_util.computeFlatOffset(begin_, xStrides);
-
-    if (x.dtype === 'string') {
-      outData.stringBytes =
-          (xVals as Uint8Array[])
-              .slice(flatOffset, flatOffset + util.sizeFromShape(size_));
-    } else {
-      const outVals = backend.typedArrayFromHeap(out);
-      outVals.set(
-          (xVals as TypedArray)
-              .subarray(flatOffset, flatOffset + util.sizeFromShape(size_)));
-    }
-
-    return out;
-  }
-
-  if (x.dtype === 'string') {
-    const res = sliceImplCPU(xVals, begin_, size_, x.shape, x.dtype);
-    outData.stringBytes = res as Uint8Array[];
-    return out;
-  }
-
-  const outVals = backend.typedArrayFromHeap(out);
-  const rank = x.shape.length;
-  if (rank === 2) {
-    slice2d(
-        xVals as TypedArray, xStrides[0], outVals, begin_ as [number, number],
-        size_ as [number, number]);
-  } else if (rank === 3) {
-    slice3d(
-        xVals as TypedArray, xStrides[0], xStrides[1], outVals,
-        begin_ as [number, number, number], size_ as [number, number, number]);
-  } else if (rank === 4) {
-    slice4d(
-        xVals as TypedArray, xStrides[0], xStrides[1], xStrides[2], outVals,
-        begin_ as [number, number, number, number],
-        size_ as [number, number, number, number]);
-  } else {
-    const res =
-        sliceImplCPU(xVals, begin_, size_, x.shape, x.dtype) as TypedArray;
-    outVals.set(res);
-  }
-
-  return out;
-}
-
-function slice2d(
-    xVals: backend_util.TypedArray, xStride: number,
-    outVals: backend_util.TypedArray, begin: [number, number],
-    size: [number, number]): void {
-  let outOffset = 0;
-  const beginI = begin[0];
-  const beginJ = begin[1];
-  const endI = beginI + size[0];
-  for (let i = beginI; i < endI; i++) {
-    const xOffset = i * xStride + beginJ;
-    outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset);
-    outOffset += size[1];
-  }
-}
-
-function slice3d(
-    xVals: backend_util.TypedArray, xStride1: number, xStride2: number,
-    outVals: backend_util.TypedArray, begin: [number, number, number],
-    size: [number, number, number]): void {
-  let outOffset = 0;
-  const beginI = begin[0];
-  const beginJ = begin[1];
-  const beginK = begin[2];
-  const endI = beginI + size[0];
-  const endJ = beginJ + size[1];
-  for (let i = beginI; i < endI; i++) {
-    for (let j = beginJ; j < endJ; j++) {
-      const xOffset = i * xStride1 + j * xStride2 + beginK;
-      outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset);
-      outOffset += size[2];
-    }
-  }
-}
-
-function slice4d(
-    xVals: backend_util.TypedArray, xStride1: number, xStride2: number,
-    xStride3: number, outVals: backend_util.TypedArray,
-    begin: [number, number, number, number],
-    size: [number, number, number, number]): void {
-  let outOffset = 0;
-  const beginI = begin[0];
-  const beginJ = begin[1];
-  const beginK = begin[2];
-  const endI = beginI + size[0];
-  const endJ = beginJ + size[1];
-  const endK = beginK + size[2];
-  const beginL = begin[3];
-
-  for (let i = beginI; i < endI; i++) {
-    for (let j = beginJ; j < endJ; j++) {
-      for (let k = beginK; k < endK; k++) {
-        const xOffset = i * xStride1 + j * xStride2 + k * xStride3 + beginL;
-        outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset);
-        outOffset += size[3];
-      }
-    }
-  }
-}
-
-export const sliceConfig: KernelConfig = {
-  kernelName: Slice,
-  backendName: 'wasm',
-  kernelFunc: slice as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Softmax.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Softmax.ts
deleted file mode 100644
index 104750613..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Softmax.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Softmax, SoftmaxAttrs, SoftmaxInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmFunc: (xId: number, outId: number, channels: number, batch: number) =>
-    void;
-
-function setup(backend: BackendWasm): void {
-  wasmFunc = backend.wasm.cwrap(Softmax, null /* void */, [
-    'number',  // xId
-    'number',  // outId
-    'number',  // channels
-    'number'   // batch
-  ]);
-}
-
-export function softmax(
-    args: {backend: BackendWasm, inputs: SoftmaxInputs, attrs: SoftmaxAttrs}):
-    TensorInfo {
-  const {backend, inputs: {logits}, attrs: {dim}} = args;
-  const xId = backend.dataIdMap.get(logits.dataId).id;
-  const out = backend.makeOutput(logits.shape, logits.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const channels = logits.shape[dim];
-  const batch = util.sizeFromShape(logits.shape) / channels;
-
-  // Short-circuit zero-sized tensors.
-  if (util.sizeFromShape(out.shape) === 0) {
-    return out;
-  }
-
-  wasmFunc(xId, outId, channels, batch);
-  return out;
-}
-
-export const softmaxConfig: KernelConfig = {
-  kernelName: Softmax,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: softmax as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Softplus.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Softplus.ts
deleted file mode 100644
index 97adb2cf3..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Softplus.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Softplus} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-
-export const softplusConfig: KernelConfig = createUnaryKernelConfig(Softplus);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SpaceToBatchND.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SpaceToBatchND.ts
deleted file mode 100644
index 2f50d282e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SpaceToBatchND.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, ReshapeAttrs, ReshapeInputs, SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs, TensorInfo, TransposeAttrs, TransposeInputs, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {padV2Config} from './PadV2';
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-function spaceToBatchND(args: {
-  inputs: SpaceToBatchNDInputs,
-  backend: BackendWasm,
-  attrs: SpaceToBatchNDAttrs
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockShape, paddings} = attrs;
-
-  const prod = util.sizeFromShape(blockShape);
-
-  const completePaddings: Array<[number, number]> = [[0, 0]];
-  completePaddings.push(...(paddings as Array<[number, number]>));
-
-  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
-    completePaddings.push([0, 0]);
-  }
-
-  const paddedX = padV2Config.kernelFunc({
-    inputs: {x},
-    backend,
-    attrs: {paddings: completePaddings, constantValue: 0}
-  }) as TensorInfo;
-
-  const reshapedPaddedShape =
-      backend_util.getReshaped(paddedX.shape, blockShape, prod, false);
-
-  const permutedReshapedPaddedPermutation = backend_util.getPermuted(
-      reshapedPaddedShape.length, blockShape.length, false);
-
-  const flattenShape =
-      backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);
-
-  const reshapeInputs: ReshapeInputs = {x: paddedX};
-  const reshapeAttrs: ReshapeAttrs = {shape: reshapedPaddedShape};
-  const paddedXReshaped =
-      reshape({inputs: reshapeInputs, backend, attrs: reshapeAttrs});
-
-  const transposeInputs: TransposeInputs = {x: paddedXReshaped};
-  const transposeAttrs:
-      TransposeAttrs = {perm: permutedReshapedPaddedPermutation};
-  const paddedXT =
-      transpose({inputs: transposeInputs, backend, attrs: transposeAttrs});
-
-  const resultReshapeInputs: ReshapeInputs = {x: paddedXT};
-  const resultReshapeAttrs: ReshapeAttrs = {shape: flattenShape};
-  const result = reshape(
-      {inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs});
-
-  backend.disposeData(paddedX.dataId);
-  backend.disposeData(paddedXReshaped.dataId);
-  backend.disposeData(paddedXT.dataId);
-
-  return result;
-}
-
-export const spaceToBatchNDConfig: KernelConfig = {
-  kernelName: SpaceToBatchND,
-  backendName: 'wasm',
-  kernelFunc: spaceToBatchND as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseFillEmptyRows.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SparseFillEmptyRows.ts
deleted file mode 100644
index e87d70e38..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseFillEmptyRows.ts
+++ /dev/null
@@ -1,154 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, SparseFillEmptyRows, SparseFillEmptyRowsInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {slice} from './Slice';
-
-import {CppDType} from './types';
-
-let wasmSparseFillEmptyRows: (
-    indicesId: number, valuesId: number, valuesDType: number,
-    indicesCount: number, denseRows: number, rank: number,
-    defaultValueId: number, outputIndicesId: number, outputValuesId: number,
-    emptyRowIndicatorId: number, reverseIndexMapId: number,
-    exceptionValuesId: number) => number;
-
-export function setup(backend: BackendWasm): void {
-  wasmSparseFillEmptyRows =
-      backend.wasm.cwrap('SparseFillEmptyRows', 'number', [
-        'number',  // indicesId
-        'number',  // valuesId
-        'number',  // valuesDType
-        'number',  // indicesCount
-        'number',  // denseRows
-        'number',  // rank
-        'number',  // defaultValueId
-        'number',  // outputIndicesId
-        'number',  // outputValuesId
-        'number',  // emptyRowIndicatorId
-        'number',  // reverseIndexMapId
-        'number',  // exceptionValuesId
-      ]);
-}
-
-export function sparseFillEmptyRows(args: {
-  backend: BackendWasm,
-  inputs: SparseFillEmptyRowsInputs,
-}): [TensorInfo, TensorInfo, TensorInfo, TensorInfo] {
-  const {backend, inputs} = args;
-  const {indices, values, denseShape, defaultValue} = inputs;
-
-  const indicesCount = indices.shape[0];
-  const rank = indices.shape[1];
-  const denseRows = backend.readSync(denseShape.dataId)[0] as number;
-
-  // Set output size to maximum possible and resize later (actual result
-  // might be smaller).
-  const maxOutputIndicesShape = [indicesCount + denseRows, rank];
-
-  const indicesId = backend.dataIdMap.get(indices.dataId).id;
-  const valuesId = backend.dataIdMap.get(values.dataId).id;
-  const defaultValueId = backend.dataIdMap.get(defaultValue.dataId).id;
-
-  const outputIndices =
-      backend.makeOutput(maxOutputIndicesShape, indices.dtype);
-  const outputIndicesId = backend.dataIdMap.get(outputIndices.dataId).id;
-
-  const outputValues =
-      backend.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype);
-  const outputValuesId = backend.dataIdMap.get(outputValues.dataId).id;
-
-  const emptyRowIndicator = backend.makeOutput([denseRows], 'bool');
-  const emptyRowIndicatorId =
-      backend.dataIdMap.get(emptyRowIndicator.dataId).id;
-
-  const reverseIndexMap = backend.makeOutput([indicesCount], indices.dtype);
-  const reverseIndexMapId = backend.dataIdMap.get(reverseIndexMap.dataId).id;
-
-  const exceptionValues = backend.makeOutput([4], 'int32');
-  const exceptionValuesId = backend.dataIdMap.get(exceptionValues.dataId).id;
-
-  const outputRows = wasmSparseFillEmptyRows(
-      indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows,
-      rank, defaultValueId, outputIndicesId, outputValuesId,
-      emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId);
-
-  const exceptionValuesArray =
-      backend.readSync(exceptionValues.dataId) as Int32Array;
-
-  let exceptionMessage: string;
-  switch (exceptionValuesArray[0]) {
-    case 1: {
-      exceptionMessage =
-          backend_util.getSparseFillEmptyRowsIndicesDenseShapeMismatch(
-              exceptionValuesArray[1]);
-      break;
-    }
-    case 2: {
-      exceptionMessage =
-          backend_util.getSparseFillEmptyRowsNegativeIndexErrorMessage(
-              exceptionValuesArray[1], exceptionValuesArray[2]);
-      break;
-    }
-    case 3:
-      exceptionMessage =
-          backend_util.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(
-              exceptionValuesArray[1], exceptionValuesArray[2],
-              exceptionValuesArray[3]);
-      break;
-    default:
-      exceptionMessage = '';
-  }
-
-  backend.disposeData(exceptionValues.dataId);
-  if (exceptionMessage) {
-    backend.disposeData(outputIndices.dataId);
-    backend.disposeData(outputValues.dataId);
-    backend.disposeData(emptyRowIndicator.dataId);
-    backend.disposeData(reverseIndexMap.dataId);
-    throw new Error(exceptionMessage);
-  }
-
-  let resizedIndices = outputIndices;
-  let resizedValues = outputValues;
-  // Overestimated output size.
-  if (outputRows !== maxOutputIndicesShape[0]) {
-    resizedIndices = slice({
-      inputs: {x: outputIndices},
-      attrs: {begin: 0, size: [outputRows, rank]},
-      backend
-    });
-    resizedValues = slice({
-      inputs: {x: outputValues},
-      attrs: {begin: 0, size: outputRows},
-      backend
-    });
-    backend.disposeData(outputIndices.dataId);
-    backend.disposeData(outputValues.dataId);
-  }
-
-  return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap];
-}
-
-export const sparseFillEmptyRowsConfig: KernelConfig = {
-  kernelName: SparseFillEmptyRows,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: sparseFillEmptyRows as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseReshape.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SparseReshape.ts
deleted file mode 100644
index bdc3e957d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseReshape.ts
+++ /dev/null
@@ -1,139 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, SparseReshape, SparseReshapeInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmSparseReshape: (
-    inputIndicesId: number, inputShapeId: number, newShapeId: number,
-    nnz: number, newIndicesId: number, outputShapeId: number,
-    exceptionValuesId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmSparseReshape = backend.wasm.cwrap(SparseReshape, null /*void*/, [
-    'number',  // inputIndicesId
-    'number',  // inputShapeId
-    'number',  // newShapeId
-    'number',  // nnz
-    'number',  // newIndicesId
-    'number',  // outputShapeId
-    'number',  // exceptionValuesId
-  ]);
-}
-
-function sparseReshape(args: {
-  backend: BackendWasm,
-  inputs: SparseReshapeInputs,
-}): [TensorInfo, TensorInfo] {
-  const {backend, inputs} = args;
-  const {inputIndices, inputShape, newShape} = inputs;
-
-  if (inputIndices.shape.length !== 2) {
-    throw new Error(`Input indices should be a matrix but received shape
-        ${inputIndices.shape}`);
-  }
-  if (inputShape.shape.length !== 1) {
-    throw new Error(`Input shape should be a vector but received shape
-        ${inputShape.shape}`);
-  }
-  if (newShape.shape.length !== 1) {
-    throw new Error(
-        `Target shape should be a vector but received shape ${newShape.shape}`);
-  }
-
-  const inputIndicesId = backend.dataIdMap.get(inputIndices.dataId).id;
-  const inputShapeId = backend.dataIdMap.get(inputShape.dataId).id;
-  const newShapeId = backend.dataIdMap.get(newShape.dataId).id;
-
-  const nnz = inputIndices.shape[0];
-  const outputRank = util.sizeFromShape(newShape.shape);
-
-  const newIndices = backend.makeOutput([nnz, outputRank], inputIndices.dtype);
-  const newIndicesId = backend.dataIdMap.get(newIndices.dataId).id;
-
-  const outputShape = backend.makeOutput([outputRank], newShape.dtype);
-  const outputShapeId = backend.dataIdMap.get(outputShape.dataId).id;
-
-  const exceptionValues = backend.makeOutput([3], 'int32');
-  const exceptionValuesId = backend.dataIdMap.get(exceptionValues.dataId).id;
-
-  wasmSparseReshape(
-      inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId,
-      outputShapeId, exceptionValuesId);
-
-  const exceptionValuesArray =
-      backend.readSync(exceptionValues.dataId) as Int32Array;
-
-  let exceptionMessage: string;
-  switch (exceptionValuesArray[0]) {
-    case 0: {
-      exceptionMessage =
-          backend_util.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(
-              exceptionValuesArray[1], exceptionValuesArray[2]);
-      break;
-    }
-    case 1: {
-      exceptionMessage =
-          backend_util.getSparseReshapeNegativeOutputDimErrorMessage(
-              exceptionValuesArray[1], exceptionValuesArray[2]);
-      break;
-    }
-    case 2:
-      exceptionMessage =
-          backend_util.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();
-      break;
-    case 3: {
-      const inputShapeValues =
-          Array.from(backend.readSync(inputShape.dataId) as Int32Array),
-            outputShapeValues =
-                Array.from(backend.readSync(outputShape.dataId) as Int32Array);
-      exceptionMessage =
-          backend_util.getSparseReshapeInputOutputMultipleErrorMessage(
-              inputShapeValues, outputShapeValues);
-      break;
-    }
-    case 4: {
-      const inputShapeValues =
-          Array.from(backend.readSync(inputShape.dataId) as Int32Array),
-            outputShapeValues =
-                Array.from(backend.readSync(outputShape.dataId) as Int32Array);
-      exceptionMessage =
-          backend_util.getSparseReshapeInputOutputMismatchErrorMessage(
-              inputShapeValues, outputShapeValues);
-      break;
-    }
-    default:
-      exceptionMessage = '';
-  }
-
-  backend.disposeData(exceptionValues.dataId);
-  if (exceptionMessage) {
-    backend.disposeData(newIndices.dataId);
-    backend.disposeData(outputShape.dataId);
-    throw new Error(exceptionMessage);
-  }
-
-  return [newIndices, outputShape];
-}
-
-export const sparseReshapeConfig: KernelConfig = {
-  kernelName: SparseReshape,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: sparseReshape as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseSegmentMean.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SparseSegmentMean.ts
deleted file mode 100644
index bd25530c8..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseSegmentMean.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SparseSegmentMean, SparseSegmentMeanInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {setup, sparseSegmentReduction} from './SparseSegmentReduction';
-
-function sparseSegmentMean(args: {
-  backend: BackendWasm,
-  inputs: SparseSegmentMeanInputs,
-}): TensorInfo {
-  return sparseSegmentReduction(args, true);
-}
-
-export const sparseSegmentMeanConfig: KernelConfig = {
-  kernelName: SparseSegmentMean,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: sparseSegmentMean as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseSegmentReduction.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SparseSegmentReduction.ts
deleted file mode 100644
index c8b2d5139..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseSegmentReduction.ts
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, SparseSegmentMeanInputs, SparseSegmentSumInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmSparseSegmentReduction: (
-    dataId: number, dtype: number, numRow: number, indicesId: number,
-    segmentIdsId: number, outputId: number, exceptionValuesId: number,
-    isMean: boolean, defaultValue: number) => void;
-
-export function setup(backend: BackendWasm): void {
-  wasmSparseSegmentReduction =
-      backend.wasm.cwrap('SparseSegmentReduction', null /*void*/, [
-        'number',  // dataId
-        'number',  // dtype
-        'number',  // numRow
-        'number',  // indicesId
-        'number',  // segmentIdsId
-        'number',  // outputId
-        'number',  // exceptionValuesId,
-        'number',  // isMean
-        'number',  // defaultValue
-      ]);
-}
-
-export function sparseSegmentReduction(
-    args: {
-      backend: BackendWasm,
-      inputs: SparseSegmentSumInputs|SparseSegmentMeanInputs,
-    },
-    isMean: boolean): TensorInfo {
-  const {backend, inputs} = args;
-  const {data, indices, segmentIds} = inputs;
-
-  const numIndices = indices.shape[0];
-  const segmentIdsBack =
-      (backend.readSync(segmentIds.dataId, numIndices - 1, numIndices) as
-       Int32Array)[0];
-  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0;
-  const outputRows = lastSegmentIdPlusOne;
-
-  if (outputRows < 0) {
-    throw (new Error(
-        backend_util
-            .getSparseSegmentReductionNegativeSegmentIdsErrorMessage()));
-  }
-
-  const outputShape = data.shape.slice();
-  outputShape[0] = outputRows;
-
-  const dataId = backend.dataIdMap.get(data.dataId).id;
-  const indicesId = backend.dataIdMap.get(indices.dataId).id;
-  const segmentIdsId = backend.dataIdMap.get(segmentIds.dataId).id;
-
-  const output = backend.makeOutput(outputShape, data.dtype);
-  const outputId = backend.dataIdMap.get(output.dataId).id;
-
-  const exceptionValues = backend.makeOutput([4], 'int32');
-  const exceptionValuesId = backend.dataIdMap.get(exceptionValues.dataId).id;
-
-  wasmSparseSegmentReduction(
-      dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId,
-      outputId, exceptionValuesId, isMean, 0);
-
-  const exceptionValuesArray =
-      backend.readSync(exceptionValues.dataId) as Int32Array;
-
-  let exceptionMessage: string;
-  switch (exceptionValuesArray[0]) {
-    case 0: {
-      exceptionMessage =
-          backend_util
-              .getSparseSegmentReductionNegativeSegmentIdsErrorMessage();
-      break;
-    }
-    case 1: {
-      exceptionMessage =
-          backend_util
-              .getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();
-      break;
-    }
-    case 2:
-      exceptionMessage =
-          backend_util.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(
-              exceptionValuesArray[1], exceptionValuesArray[2]);
-      break;
-    case 3:
-      exceptionMessage =
-          backend_util.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(
-              exceptionValuesArray[1], exceptionValuesArray[2],
-              exceptionValuesArray[3]);
-      break;
-    default:
-      exceptionMessage = '';
-  }
-
-  backend.disposeData(exceptionValues.dataId);
-  if (exceptionMessage) {
-    backend.disposeData(output.dataId);
-    throw new Error(exceptionMessage);
-  }
-
-  return output;
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseSegmentSum.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SparseSegmentSum.ts
deleted file mode 100644
index 8fc71c096..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseSegmentSum.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SparseSegmentSum, SparseSegmentSumInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {setup, sparseSegmentReduction} from './SparseSegmentReduction';
-
-function sparseSegmentSum(args: {
-  backend: BackendWasm,
-  inputs: SparseSegmentSumInputs,
-}): TensorInfo {
-  return sparseSegmentReduction(args, false);
-}
-
-export const sparseSegmentSumConfig: KernelConfig = {
-  kernelName: SparseSegmentSum,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: sparseSegmentSum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseToDense.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SparseToDense.ts
deleted file mode 100644
index 75f53e6ec..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SparseToDense.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, SparseToDense, SparseToDenseAttrs, SparseToDenseInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmSparseToDense: (
-    sparseIndicesId: number, sparseValuesId: number, sparseValuesRank: number,
-    defaultValueId: number, dtype: CppDType, sliceRank: number,
-    numUpdates: number, sliceSize: number, strides: Uint8Array,
-    outputSize: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmSparseToDense = backend.wasm.cwrap(SparseToDense, null /*void*/, [
-    'number',  // sparseIndicesId
-    'number',  // sparseValuesId
-    'number',  // sparseValuesRank
-    'number',  // defaultValueId
-    'number',  // dtype
-    'number',  // sliceRank
-    'number',  // numUpdates
-    'number',  // sliceSize
-    'array',   // strides
-    'number',  // outputSize
-    'number',  // outId
-  ]);
-}
-
-function sparseToDense(args: {
-  backend: BackendWasm,
-  inputs: SparseToDenseInputs,
-  attrs: SparseToDenseAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {sparseIndices, sparseValues, defaultValue} = inputs;
-  const {outputShape} = attrs;
-
-  const out = backend.makeOutput(outputShape, defaultValue.dtype);
-  if (util.sizeFromShape(outputShape) === 0) {
-    return out;
-  }
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);
-
-  const sparseIndicesId = backend.dataIdMap.get(sparseIndices.dataId).id;
-  const sparseValuesId = backend.dataIdMap.get(sparseValues.dataId).id;
-  const defaultValueId = backend.dataIdMap.get(defaultValue.dataId).id;
-
-  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
-
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  wasmSparseToDense(
-      sparseIndicesId, sparseValuesId, sparseValues.shape.length,
-      defaultValueId, CppDType[defaultValue.dtype], sliceRank, numUpdates,
-      sliceSize, stridesBytes, outputSize, outId);
-
-  return out;
-}
-
-export const sparseToDenseConfig: KernelConfig = {
-  kernelName: SparseToDense,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: sparseToDense as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SplitV.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SplitV.ts
deleted file mode 100644
index cafbe3690..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SplitV.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SplitV, SplitVAttrs, SplitVInputs, util} from '@tensorflow/tfjs-core';
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {slice} from './Slice';
-
-export function splitV(
-    args: {inputs: SplitVInputs, attrs: SplitVAttrs, backend: BackendWasm}) {
-  const {inputs, attrs, backend} = args;
-  const {x} = inputs;
-  const {numOrSizeSplits, axis} = attrs;
-
-  const $axis = util.parseAxisParam(axis, x.shape)[0];
-
-  const splitSizes = backend_util.prepareSplitSize(x, numOrSizeSplits, $axis);
-  const begin = new Array(x.shape.length).fill(0);
-  const size = x.shape.slice();
-  return splitSizes.map(s => {
-    const xSliceSize = [...size];
-    xSliceSize[$axis] = s;
-    const xSlice =
-        slice({inputs: {x}, attrs: {begin, size: xSliceSize}, backend});
-    begin[$axis] += s;
-    return xSlice;
-  });
-}
-
-export const splitVConfig: KernelConfig = {
-  kernelName: SplitV,
-  backendName: 'wasm',
-  kernelFunc: splitV as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Sqrt.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Sqrt.ts
deleted file mode 100644
index 1a0c17483..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Sqrt.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sqrt} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const sqrtConfig: KernelConfig = createUnaryKernelConfig(Sqrt);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Square.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Square.ts
deleted file mode 100644
index f1cf7c69e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Square.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Square} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const squareConfig: KernelConfig = createUnaryKernelConfig(Square);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/SquaredDifference.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/SquaredDifference.ts
deleted file mode 100644
index e0127f812..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/SquaredDifference.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, SquaredDifference} from '@tensorflow/tfjs-core';
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = true;
-export const squaredDifferenceConfig: KernelConfig =
-    createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Step.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Step.ts
deleted file mode 100644
index 30d5cd9ca..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Step.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Step, StepAttrs, StepInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmStep: (xId: number, alpha: number, dtype: number, outId: number) =>
-    void;
-
-function setup(backend: BackendWasm): void {
-  wasmStep = backend.wasm.cwrap(Step, null /*void*/, [
-    'number',  // x_id
-    'number',  // alpha
-    'number',  // dtype
-    'number',  // out_id
-  ]);
-}
-
-function step(
-    args: {backend: BackendWasm, inputs: StepInputs, attrs: StepAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {alpha} = attrs;
-  const {x} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-
-  const out = backend.makeOutput(x.shape, x.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmStep(xId, alpha, CppDType[x.dtype], outId);
-  return out;
-}
-
-export const stepConfig: KernelConfig = {
-  kernelName: Step,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: step as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/StridedSlice.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/StridedSlice.ts
deleted file mode 100644
index 17f2163dd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/StridedSlice.ts
+++ /dev/null
@@ -1,128 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, slice_util, StridedSlice, StridedSliceAttrs, StridedSliceInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-
-let wasmStridedSlice: (
-    xId: number, xStridesBytes: Uint8Array, xRank: number,
-    beginBytes: Uint8Array, endBytes: Uint8Array, stridesBytes: Uint8Array,
-    outShapeBytes: Uint8Array, outStridesBytes: Uint8Array,
-    outShapeLength: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmStridedSlice = backend.wasm.cwrap(StridedSlice, null /*void*/, [
-    'number',  // xId
-    'array',   // xStrides
-    'number',  // xRank
-    'array',   // beginBytes
-    'array',   // endBytes
-    'array',   // stridesBytes
-    'array',   // outShapeBytes
-    'array',   // outStridesBytes
-    'number',  // outShapeLength
-    'number',  // outId
-  ]);
-}
-
-export function stridedSlice(args: {
-  backend: BackendWasm,
-  inputs: StridedSliceInputs,
-  attrs: StridedSliceAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {x} = inputs;
-
-  const {
-    begin,
-    end,
-    strides,
-    beginMask,
-    endMask,
-    ellipsisMask,
-    newAxisMask,
-    shrinkAxisMask
-  } = attrs;
-
-  const {
-    finalShapeSparse,
-    finalShape,
-    isIdentity,
-    sliceDim0,
-    isSimpleSlice,
-    begin: $begin,
-    end: $end,
-    strides: $strides
-  } =
-      slice_util.sliceInfo(
-          x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,
-          newAxisMask, shrinkAxisMask);
-
-  let result;
-
-  if (isIdentity) {
-    // Optimization #1, slice is a no-op plus reshape
-    result = reshape({inputs: {x}, backend, attrs: {shape: finalShape}});
-  } else if (sliceDim0 || isSimpleSlice) {
-    // Optimization #2, slice is memory contiguous (only occurs in dim 0)
-    util.assert(
-        x.shape.length >= 1,
-        () => `Input must have rank at least 1, got: ${x.shape.length}`);
-
-    const size = slice_util.computeOutShape($begin, $end, $strides);
-    // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).
-    const sliced = slice({inputs: {x}, backend, attrs: {begin: $begin, size}});
-    result =
-        reshape({inputs: {x: sliced}, backend, attrs: {shape: finalShape}});
-    backend.disposeData(sliced.dataId);
-  } else {
-    const out = backend.makeOutput(finalShapeSparse, 'float32');
-
-    const xId = backend.dataIdMap.get(x.dataId).id;
-    const xStridesBytes =
-        new Uint8Array(new Int32Array(util.computeStrides(x.shape)).buffer);
-    const beginBytes = new Uint8Array(new Int32Array($begin).buffer);
-    const endBytes = new Uint8Array(new Int32Array($end).buffer);
-    const stridesBytes = new Uint8Array(new Int32Array($strides).buffer);
-
-    const outputShapeBytes =
-        new Uint8Array(new Int32Array(finalShapeSparse).buffer);
-    const outStridesBytes = new Uint8Array(
-        new Int32Array(util.computeStrides(finalShapeSparse)).buffer);
-    const outId = backend.dataIdMap.get(out.dataId).id;
-
-    wasmStridedSlice(
-        xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes,
-        outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId);
-
-    result = reshape({inputs: {x: out}, backend, attrs: {shape: finalShape}});
-
-    backend.disposeData(out.dataId);
-  }
-
-  return result;
-}
-
-export const stridedSliceConfig: KernelConfig = {
-  kernelName: StridedSlice,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: stridedSlice as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/StringNGrams.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/StringNGrams.ts
deleted file mode 100644
index 6b95694a6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/StringNGrams.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringNGrams, StringNGramsAttrs, StringNGramsInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {stringNGramsImplCPU} from '../kernel_utils/shared';
-
-function stringNGrams(args: {
-  backend: BackendWasm,
-  inputs: StringNGramsInputs,
-  attrs: StringNGramsAttrs
-}): [TensorInfo, TensorInfo] {
-  const {backend, inputs, attrs} = args;
-  const {data, dataSplits} = inputs;
-  const {
-    separator,
-    nGramWidths,
-    leftPad,
-    rightPad,
-    padWidth,
-    preserveShortSequences,
-  } = attrs;
-
-  const $data = backend.readSync(data.dataId) as Uint8Array[];
-  const $dataSplits = backend.readSync(dataSplits.dataId) as Int32Array;
-
-  const [nGrams, nGramsSplits] = stringNGramsImplCPU(
-      $data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth,
-      preserveShortSequences);
-
-  const nGramsOut = backend.makeOutput([nGrams.length], 'string');
-  const nGramsOutData = backend.dataIdMap.get(nGramsOut.dataId);
-  nGramsOutData.stringBytes = nGrams;
-
-  const nGramsSplitsOut = backend.makeOutput(dataSplits.shape, 'int32');
-  const nGramsSplitsOutVals = backend.typedArrayFromHeap(nGramsSplitsOut);
-  nGramsSplitsOutVals.set(nGramsSplits);
-
-  return [nGramsOut, nGramsSplitsOut];
-}
-
-export const stringNGramsConfig: KernelConfig = {
-  kernelName: StringNGrams,
-  backendName: 'wasm',
-  kernelFunc: stringNGrams as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/StringSplit.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/StringSplit.ts
deleted file mode 100644
index cc228b1eb..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/StringSplit.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringSplit, StringSplitAttrs, StringSplitInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {stringSplitImplCPU} from '../kernel_utils/shared';
-
-function stringSplit(args: {
-  backend: BackendWasm,
-  inputs: StringSplitInputs,
-  attrs: StringSplitAttrs
-}): [TensorInfo, TensorInfo, TensorInfo] {
-  const {backend, inputs, attrs} = args;
-  const {input, delimiter} = inputs;
-  const {skipEmpty} = attrs;
-
-  const inputVals = backend.readSync(input.dataId) as Uint8Array[];
-  const delimiterVals = backend.readSync(delimiter.dataId) as Uint8Array[];
-
-  const [indices, values, shape] =
-      stringSplitImplCPU(inputVals, delimiterVals[0], skipEmpty);
-  const outputSize = values.length;
-
-  const indicesOut = backend.makeOutput([outputSize, 2], 'int32');
-  const indicesOutVals = backend.typedArrayFromHeap(indicesOut);
-  indicesOutVals.set(indices);
-
-  const valuesOut = backend.makeOutput([outputSize], 'string');
-  const valuesOutData = backend.dataIdMap.get(valuesOut.dataId);
-  valuesOutData.stringBytes = values;
-
-  const shapeOut = backend.makeOutput([2], 'int32');
-  const shapeOutVals = backend.typedArrayFromHeap(shapeOut);
-  shapeOutVals.set(shape);
-
-  return [indicesOut, valuesOut, shapeOut];
-}
-
-export const stringSplitConfig: KernelConfig = {
-  kernelName: StringSplit,
-  backendName: 'wasm',
-  kernelFunc: stringSplit as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/StringToHashBucketFast.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/StringToHashBucketFast.ts
deleted file mode 100644
index 6cf3a3ad8..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/StringToHashBucketFast.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringToHashBucketFast, StringToHashBucketFastAttrs, StringToHashBucketFastInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {stringToHashBucketFastImplCPU} from '../kernel_utils/shared';
-
-function stringToHashBucketFast(args: {
-  backend: BackendWasm,
-  inputs: StringToHashBucketFastInputs,
-  attrs: StringToHashBucketFastAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {input} = inputs;
-  const {numBuckets} = attrs;
-
-  const inputVals = backend.readSync(input.dataId) as Uint8Array[];
-
-  const values = stringToHashBucketFastImplCPU(inputVals, numBuckets);
-
-  const out = backend.makeOutput(input.shape, 'int32');
-  const outVals = backend.typedArrayFromHeap(out);
-  outVals.set(values);
-  return out;
-}
-
-export const stringToHashBucketFastConfig: KernelConfig = {
-  kernelName: StringToHashBucketFast,
-  backendName: 'wasm',
-  kernelFunc: stringToHashBucketFast as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Sub.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Sub.ts
deleted file mode 100644
index 6716ccda5..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Sub.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Sub} from '@tensorflow/tfjs-core';
-import {createBinaryKernelConfig} from './binary_kernel';
-const supportsFullBroadcast = true;
-export const subConfig: KernelConfig =
-    createBinaryKernelConfig(Sub, supportsFullBroadcast);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Sum.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Sum.ts
deleted file mode 100644
index d13e77efb..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Sum.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Sum, SumAttrs, SumInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {permuteAxesAndTranspose} from './kernel_utils';
-import {CppDType} from './types';
-
-let wasmSum: (xId: number, reduceSize: number, dtype: number, outId: number) =>
-    void;
-
-function setup(backend: BackendWasm): void {
-  wasmSum = backend.wasm.cwrap(Sum, null /*void*/, [
-    'number',  // input_id
-    'number',  // reduce_size
-    'number',  // dtype
-    'number',  // out_id
-  ]);
-}
-
-function sum(args: {backend: BackendWasm, inputs: SumInputs, attrs: SumAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {axis, keepDims} = attrs;
-  const {x} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  let inputId = xId;
-  let input = x;
-
-  const {transposed, axes, originalAxes, inputWasTransposed} =
-      permuteAxesAndTranspose(x, axis, backend);
-
-  let reductionAxes = axes;
-  if (inputWasTransposed) {
-    const transposedId = backend.dataIdMap.get(transposed.dataId).id;
-    if (transposedId !== xId) {
-      // transpose was not a no-op. We will need to dispose of this
-      // once we are done.
-      input = transposed;
-      inputId = transposedId;
-      reductionAxes = backend_util.getInnerMostAxes(
-          reductionAxes.length, input.shape.length);
-    }
-  }
-
-  backend_util.assertAxesAreInnerMostDims(
-      'sum', reductionAxes, input.shape.length);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(input.shape, reductionAxes);
-  const reduceSize = util.sizeFromShape(reduceShape);
-
-  const out = backend.makeOutput(outShape, input.dtype);
-  if (util.sizeFromShape(input.shape) !== 0) {
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    wasmSum(inputId, reduceSize, CppDType[out.dtype], outId);
-  }
-
-  if (inputWasTransposed) {
-    // dispose of the transposed tensor.
-    backend.disposeData(transposed.dataId);
-  }
-
-  if (keepDims) {
-    // reshape
-    const newShape = backend_util.expandShapeToKeepDim(out.shape, originalAxes);
-    out.shape = newShape;
-  }
-
-  return out;
-}
-
-export const sumConfig: KernelConfig = {
-  kernelName: Sum,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: sum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Tan.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Tan.ts
deleted file mode 100644
index 82af93ba6..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Tan.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Tan} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const tanConfig: KernelConfig = createUnaryKernelConfig(Tan);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Tanh.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Tanh.ts
deleted file mode 100644
index a842a63aa..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Tanh.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, Tanh} from '@tensorflow/tfjs-core';
-
-import {createUnaryKernelConfig} from './unary_kernel';
-export const tanhConfig: KernelConfig = createUnaryKernelConfig(Tanh);
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/TensorScatterUpdate.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/TensorScatterUpdate.ts
deleted file mode 100644
index 700985ba5..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/TensorScatterUpdate.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, scatter_util, TensorInfo, TensorScatterUpdate, TensorScatterUpdateAttrs, TensorScatterUpdateInputs, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmTensorScatterUpdate: (
-    indicesId: number, updatesId: number, dtype: CppDType, sliceRank: number,
-    numUpdates: number, sliceSize: number, strides: Uint8Array,
-    outputSize: number, outId: number, tensorId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmTensorScatterUpdate =
-      backend.wasm.cwrap(TensorScatterUpdate, null /*void*/, [
-        'number',  // indicesId
-        'number',  // updatesId
-        'number',  // dtype
-        'number',  // sliceRank
-        'number',  // numUpdates
-        'number',  // sliceSize
-        'array',   // strides
-        'number',  // outputSize
-        'number',  // outId
-        'number',  // tensorId
-      ]);
-}
-
-function tensorScatterUpdate(args: {
-  backend: BackendWasm,
-  inputs: TensorScatterUpdateInputs,
-  attrs: TensorScatterUpdateAttrs
-}): TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {tensor, indices, updates} = inputs;
-  const {} = attrs;
-
-  const out = backend.makeOutput(tensor.shape, tensor.dtype);
-  if (util.sizeFromShape(tensor.shape) === 0) {
-    return out;
-  }
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      scatter_util.calculateShapes(updates, indices, tensor.shape);
-
-  const indicesData = backend.dataIdMap.get(indices.dataId);
-  const indicesId = indicesData.id;
-
-  const updatesData = backend.dataIdMap.get(updates.dataId);
-  const updatesId = updatesData.id;
-
-  const tensorData = backend.dataIdMap.get(tensor.dataId);
-  const tensorId = tensorData.id;
-
-  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
-
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmTensorScatterUpdate(
-      indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates,
-      sliceSize, stridesBytes, outputSize, outId, tensorId);
-
-  return out;
-}
-
-export const tensorScatterUpdateConfig: KernelConfig = {
-  kernelName: TensorScatterUpdate,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: tensorScatterUpdate as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Tile.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Tile.ts
deleted file mode 100644
index 61fff6793..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Tile.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Tile, TileAttrs, TileInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-let wasmTile: (
-    xId: number, xShape: Uint8Array, xShapeSize: number, newShape: Uint8Array,
-    newShapeSize: number, dtype: number, outId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmTile = backend.wasm.cwrap(Tile, null /* void */, [
-    'number',  // x_id
-    'array',   // x_shape
-    'number',  // x_shape.length
-    'array',   // new_shape
-    'number',  // new_shape.length
-    'number'   // out_id
-  ]);
-}
-
-function tile(
-    args: {inputs: TileInputs, backend: BackendWasm, attrs: TileAttrs}) {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const {reps} = attrs;
-
-  const newShape: number[] = new Array(x.shape.length);
-  for (let i = 0; i < newShape.length; i++) {
-    newShape[i] = x.shape[i] * reps[i];
-  }
-  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
-  const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);
-
-  const out = backend.makeOutput(newShape, x.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  wasmTile(
-      xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length,
-      CppDType[out.dtype], outId);
-  return out;
-}
-
-export const tileConfig: KernelConfig = {
-  kernelName: Tile,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: tile as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/TopK.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/TopK.ts
deleted file mode 100644
index e3b6390ed..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/TopK.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, TopK, TopKAttrs, TopKInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {CppDType} from './types';
-
-let wasmTopK: (
-    xId: number, xShapeBytes: Uint8Array, xShapeLength: number,
-    xDtype: CppDType, k: number, sorted: boolean, outValuesId: number,
-    outIndicesId: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmTopK = backend.wasm.cwrap(TopK, null /* void */, [
-    'number',  // xId
-    'array',   // x.shape
-    'number',  // x.shape.length
-    'number',  // x.dtype
-    'number',  // k
-    'bool',    // sorted
-    'number',  // outValuesId
-    'number',  // outIndicesId
-  ]);
-}
-
-export const topk:
-    (args: {inputs: TopKInputs, backend: BackendWasm, attrs: TopKAttrs}) =>
-        TensorInfo[] | TensorInfo = ({inputs, backend, attrs}) => {
-          const {x} = inputs;
-          const {k, sorted} = attrs;
-
-          const xId = backend.dataIdMap.get(x.dataId).id;
-          const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
-          const outputShape = x.shape.slice();
-          outputShape[outputShape.length - 1] = k;
-          const outValues = backend.makeOutput(outputShape, x.dtype);
-          const outValuesId = backend.dataIdMap.get(outValues.dataId).id;
-          const outIndices = backend.makeOutput(outputShape, 'int32');
-          const outIndicesId = backend.dataIdMap.get(outIndices.dataId).id;
-
-          wasmTopK(
-              xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted,
-              outValuesId, outIndicesId);
-
-          return [outValues, outIndices];
-        };
-
-export const topKConfig: KernelConfig = {
-  kernelName: TopK,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: topk as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Transform.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Transform.ts
deleted file mode 100644
index b296dce96..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Transform.ts
+++ /dev/null
@@ -1,115 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Transform, TransformAttrs, TransformInputs, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-let wasmTransform: (
-    imageId: number, transformsId: number, isBatchTransform: boolean,
-    batch: number, outHeight: number, outWidth: number, numChannels: number,
-    imageWidth: number, imageHeight: number, inputStrides: Uint8Array,
-    inputStridesLength: number, outputStrides: Uint8Array,
-    outputStridesLength: number, interpolationModeId: number,
-    fillModeId: number, fillValue: number, outId: number) => void;
-
-function setup(backend: BackendWasm): void {
-  wasmTransform = backend.wasm.cwrap(Transform, null /*void*/, [
-    'number',  // imageId
-    'number',  // transformsId
-    'bool',    // isBatchTransform
-    'number',  // batch
-    'number',  // outHeight
-    'number',  // outWidth
-    'number',  // numChannels
-    'number',  // imageWidth
-    'number',  // imageHeight
-    'array',   // inputStrides
-    'number',  // inputStridesLength
-    'array',   // outputStrides
-    'number',  // outputStridesLength
-    'number',  // interpolationModeId
-    'number',  // fillModeId
-    'number',  // fillValue
-    'number'   // outId
-  ]);
-}
-
-function transform(
-    args:
-        {backend: BackendWasm, inputs: TransformInputs, attrs: TransformAttrs}):
-    TensorInfo {
-  const {backend, inputs, attrs} = args;
-  const {image, transforms} = inputs;
-  const {interpolation, fillMode, fillValue, outputShape} = attrs;
-
-  const [batch, imageHeight, imageWidth, numChannels] = image.shape;
-  const [outHeight, outWidth] =
-      outputShape != null ? outputShape : [imageHeight, imageWidth];
-  const outShape =
-      [batch, outHeight, outWidth,
-       numChannels] as [number, number, number, number];
-  const inputStrides =
-      new Uint8Array(new Int32Array(util.computeStrides(image.shape)).buffer);
-
-  const outputStrides =
-      new Uint8Array(new Int32Array(util.computeStrides(outShape)).buffer);
-
-  const out = backend.makeOutput(outShape, image.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const imageData = backend.dataIdMap.get(image.dataId);
-  const imageId = imageData.id;
-
-  const transformsData = backend.dataIdMap.get(transforms.dataId);
-  const transformsId = transformsData.id;
-
-  const interpolationModeId = interpolation === 'nearest' ? 1 : 2;
-  let fillModeId;
-  switch (fillMode) {
-    case 'constant':
-      fillModeId = 1;
-      break;
-    case 'reflect':
-      fillModeId = 2;
-      break;
-    case 'wrap':
-      fillModeId = 3;
-      break;
-    case 'nearest':
-      fillModeId = 4;
-      break;
-    default:
-      fillModeId = 1;
-      break;
-  }
-
-  wasmTransform(
-      imageId, transformsId, (transforms.shape[0] > 1), batch, outHeight,
-      outWidth, numChannels, imageWidth, imageHeight, inputStrides,
-      image.shape.length - 1, outputStrides, outShape.length - 1,
-      interpolationModeId, fillModeId, fillValue, outId);
-
-  return out;
-}
-
-export const transformConfig: KernelConfig = {
-  kernelName: Transform,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: transform as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Transpose.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Transpose.ts
deleted file mode 100644
index e14d10bbc..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Transpose.ts
+++ /dev/null
@@ -1,119 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Transpose, TransposeAttrs, TransposeInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {identity} from './Identity';
-import {CppDType} from './types';
-
-let wasmTranspose: (
-    xId: number, xShape: Uint8Array, xShapeLength: number, dtype: CppDType,
-    outId: number, perm: Uint8Array, permLength: number) => void;
-
-function setup(backend: BackendWasm) {
-  wasmTranspose = backend.wasm.cwrap(Transpose, null /* void */, [
-    'number',  // xId
-    'array',   // x.shape
-    'number',  // x.shape.length
-    'number',  // dtype
-    'number',  // outId
-    'array',   // perm
-    'number',  // perm.length
-  ]);
-}
-
-export function transpose(
-    args:
-        {inputs: TransposeInputs, backend: BackendWasm, attrs: TransposeAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  // Reduce any dimensions with size one. Lower-rank transpose kernel performs
-  // better due to simpler memory access pattern.
-  const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);
-
-  let permIsNoOp = true;
-  for (let i = 0; i < perm.length; i++) {
-    if (perm[i] !== i) {
-      permIsNoOp = false;
-    }
-  }
-  const outShape = computeOutShape(inputs.x.shape, attrs.perm);
-  const x = {
-    dataId: inputs.x.dataId,
-    shape: reducedShape,
-    dtype: inputs.x.dtype
-  };
-
-  if (permIsNoOp) {
-    const cloned = identity({inputs, backend});
-    cloned.shape = outShape;
-    return cloned;
-  }
-
-  const out = backend.makeOutput(outShape, x.dtype);
-  const xId = backend.dataIdMap.get(x.dataId).id;
-  const outId = backend.dataIdMap.get(out.dataId).id;
-  const permBytes = new Uint8Array(new Int32Array(perm).buffer);
-  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
-
-  wasmTranspose(
-      xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes,
-      perm.length);
-  return out;
-}
-
-function computeOutShape(inShape: number[], perm: number[]): number[] {
-  const outShape = new Array(inShape.length);
-  for (let i = 0; i < outShape.length; i++) {
-    outShape[i] = inShape[perm[i]];
-  }
-  return outShape;
-}
-
-function removeOneSizeDims(
-    shape: number[], perm: number[]): [number[], number[]] {
-  const newShape: number[] = [];
-  const newPerm: number[] = [];
-  for (let i = 0; i < shape.length; ++i) {
-    if (shape[i] !== 1) {
-      newShape.push(shape[i]);
-    }
-    if (shape[perm[i]] !== 1) {
-      newPerm.push(perm[i]);
-    }
-  }
-  for (let i = 0; i < newPerm.length; ++i) {
-    let minValIdx = -1;
-    for (let j = 0; j < newPerm.length; ++j) {
-      if (newPerm[j] >= i &&
-          (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
-        minValIdx = j;
-      }
-    }
-    newPerm[minValIdx] = i;
-  }
-  return [newShape, newPerm];
-}
-
-export const transposeConfig: KernelConfig = {
-  kernelName: Transpose,
-  backendName: 'wasm',
-  kernelFunc: transpose as unknown as KernelFunc,
-  setupFunc: setup,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Unique.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Unique.ts
deleted file mode 100644
index 4ea90069c..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Unique.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, KernelFunc, TensorInfo, Unique, UniqueAttrs, UniqueInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-import {uniqueImplCPU} from '../kernel_utils/shared';
-
-function unique(
-    args: {inputs: UniqueInputs, attrs: UniqueAttrs, backend: BackendWasm}):
-    TensorInfo[] {
-  const {inputs, attrs, backend} = args;
-  const {axis} = attrs;
-  const {x} = inputs;
-
-  const {outputValues, outputShape, indices} =
-      uniqueImplCPU(backend.readSync(x.dataId), axis, x.shape, x.dtype);
-
-  return [
-    backend.makeOutput(
-        outputShape, x.dtype, /*memoryOffset=*/undefined, outputValues),
-    backend.makeOutput(
-        [indices.length], 'int32', /*memoryOffset=*/undefined, indices),
-  ];
-}
-
-export const uniqueConfig: KernelConfig = {
-  kernelName: Unique,
-  backendName: 'wasm',
-  kernelFunc: unique as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/Unpack.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/Unpack.ts
deleted file mode 100644
index 43738fba5..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/Unpack.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Unpack, UnpackAttrs, UnpackInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {slice} from './Slice';
-
-function unpack(
-    args: {inputs: UnpackInputs, backend: BackendWasm, attrs: UnpackAttrs}):
-    TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {value} = inputs;
-  let {axis} = attrs;
-
-  if (axis < 0) {
-    axis += value.shape.length;
-  }
-
-  const numOutputs = value.shape[axis];
-  const rank = value.shape.length;
-  const outShape: number[] = new Array(rank - 1);
-  let outIndex = 0;
-  for (let i = 0; i < rank; i++) {
-    if (i !== axis) {
-      outShape[outIndex++] = value.shape[i];
-    }
-  }
-  const outs: TensorInfo[] = new Array(numOutputs);
-  const begin = new Array(rank).fill(0);
-  const size = value.shape.slice();
-  size[axis] = 1;
-  for (let i = 0; i < outs.length; i++) {
-    begin[axis] = i;
-    outs[i] = slice({inputs: {x: value}, attrs: {begin, size}, backend});
-  }
-  return outs.map(({dataId, dtype}) => ({dataId, dtype, shape: outShape}));
-}
-
-export const unpackConfig: KernelConfig = {
-  kernelName: Unpack,
-  backendName: 'wasm',
-  kernelFunc: unpack as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/ZerosLike.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/ZerosLike.ts
deleted file mode 100644
index 675764ef9..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/ZerosLike.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ZerosLike, ZerosLikeInputs} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-function zerosLike(args: {inputs: ZerosLikeInputs, backend: BackendWasm}) {
-  const {inputs: {x}, backend} = args;
-  const out = backend.makeOutput(x.shape, x.dtype);
-  const outVals = backend.typedArrayFromHeap(out);
-  outVals.fill(0);
-  return out;
-}
-
-export const zerosLikeConfig: KernelConfig = {
-  kernelName: ZerosLike,
-  backendName: 'wasm',
-  kernelFunc: zerosLike as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/_FusedMatMul.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/_FusedMatMul.ts
deleted file mode 100644
index 57423306d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/_FusedMatMul.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs, broadcast_util, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {FusableActivation} from './types';
-
-let wasmFusedMatMul:
-    (aId: number, aShape: Uint8Array, aShapeSize: number, bId: number,
-     bShape: Uint8Array, bShapeSize: number, transposeA: boolean,
-     transposeB: boolean, activation: number, biasId: number,
-     preluActivationWeightsId: number, leakyreluAlpha: number, outId: number) =>
-        void;
-
-function setup(backend: BackendWasm) {
-  wasmFusedMatMul = backend.wasm.cwrap(_FusedMatMul, null /* void */, [
-    'number',  // a_id
-    'array',   // a_shape
-    'number',  // a_shape.length
-    'number',  // b_id
-    'array',   // b_shape
-    'number',  // b_shape.length
-    'number',  // transpose_a
-    'number',  // transpose_b
-    'number',  // activation
-    'number',  // biasId
-    'number',  // preluActivationWeightsId
-    'number',  // leakyreluAlpha
-    'number'   // out_id
-  ]);
-}
-
-function fusedBatchMatMul(args: {
-  inputs: _FusedMatMulInputs,
-  backend: BackendWasm,
-  attrs: _FusedMatMulAttrs
-}) {
-  const {inputs, backend, attrs} = args;
-  const {a, b, bias, preluActivationWeights} = inputs;
-
-  if (a.dtype !== 'float32' || b.dtype !== 'float32') {
-    throw new Error(
-        `_FusedMatMul for non non-float32 tensors not yet supported.`);
-  }
-
-  const {transposeA, transposeB, activation, leakyreluAlpha} = attrs;
-  const aId = backend.dataIdMap.get(a.dataId).id;
-  const bId = backend.dataIdMap.get(b.dataId).id;
-
-  let biasId = 0;
-  if (bias != null) {
-    const biasData = backend.dataIdMap.get(bias.dataId);
-    if (biasData.shape.length !== 1) {
-      throw new Error(
-          `_FusedMatMul only supports rank-1 bias but got ` +
-          `rank ${biasData.shape.length}.`);
-    }
-    biasId = biasData.id;
-  }
-  const preluActivationWeightsId = preluActivationWeights == null ?
-      0 :
-      backend.dataIdMap.get(preluActivationWeights.dataId).id;
-  const fusedActivation =
-      FusableActivation[activation as unknown as
-                        keyof typeof FusableActivation];
-  if (fusedActivation == null) {
-    throw new Error(
-        `${activation} activation not yet supported for FusedConv2D ` +
-        `in the wasm backend.`);
-  }
-
-  const leftDim = transposeA ? a.shape[2] : a.shape[1];
-  const rightDim = transposeB ? b.shape[1] : b.shape[2];
-  const batchDims = broadcast_util.assertAndGetBroadcastShape(
-      a.shape.slice(0, -2), b.shape.slice(0, -2));
-
-  const out = backend.makeOutput([...batchDims, leftDim, rightDim], a.dtype);
-  const outId = backend.dataIdMap.get(out.dataId).id;
-
-  const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);
-  const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
-
-  wasmFusedMatMul(
-      aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length,
-      transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId,
-      leakyreluAlpha || 0, outId);
-
-  return out;
-}
-
-export const _fusedMatMulConfig: KernelConfig = {
-  kernelName: _FusedMatMul,
-  backendName: 'wasm',
-  setupFunc: setup,
-  kernelFunc: fusedBatchMatMul as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/argminmax_kernel.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/argminmax_kernel.ts
deleted file mode 100644
index 4f5e7fc73..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/argminmax_kernel.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ArgMaxAttrs, ArgMaxInputs, ArgMinAttrs, ArgMinInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {permuteAxesAndTranspose} from './kernel_utils';
-import {CppDType} from './types';
-
-export function createArgMinMaxKernelConfig(kernelName: 'ArgMin'|
-                                            'ArgMax'): KernelConfig {
-  let wasmFunc: (
-      xId: number, dtype: number, outerSize: number, innerSize: number,
-      outId: number) => void;
-
-  function setupFunc(backend: BackendWasm) {
-    wasmFunc = backend.wasm.cwrap(kernelName, null /* void */, [
-      'number',  // x_id
-      'number',  // dtype
-      'number',  // outer_size
-      'number',  // inner_size
-      'number'   // out_id
-    ]);
-  }
-
-  function kernelFunc(args: {
-    backend: BackendWasm,
-    inputs: ArgMinInputs&ArgMaxInputs,
-    attrs: ArgMinAttrs&ArgMaxAttrs,
-  }): TensorInfo {
-    const {backend, inputs, attrs} = args;
-    const {axis} = attrs;
-    const {x} = inputs;
-    const xId = backend.dataIdMap.get(x.dataId).id;
-    let inputId = xId;
-    let input = x;
-
-    const {transposed, axes, inputWasTransposed} =
-        permuteAxesAndTranspose(x, axis, backend);
-
-    if (inputWasTransposed) {
-      const transposedId = backend.dataIdMap.get(transposed.dataId).id;
-      if (transposedId !== xId) {
-        // transpose was not a no-op. We will need to dispose of this
-        // once we are done.
-        input = transposed;
-        inputId = transposedId;
-      }
-    }
-
-    const outShape = input.shape.slice(0, -1);
-    const out = backend.makeOutput(outShape, 'int32');
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    const outerSize = util.sizeFromShape(out.shape);
-    const innerSize = input.shape[axes[0]];
-    wasmFunc(inputId, CppDType[input.dtype], outerSize, innerSize, outId);
-
-    if (inputWasTransposed) {
-      // dispose of the transposed tensor.
-      backend.disposeData(transposed.dataId);
-    }
-
-    return out;
-  }
-
-  return {
-    kernelName,
-    backendName: 'wasm',
-    setupFunc,
-    kernelFunc: kernelFunc as unknown as KernelFunc,
-  };
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/binary_kernel.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/binary_kernel.ts
deleted file mode 100644
index 8c69e5571..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/binary_kernel.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BinaryInputs, DataType, KernelConfig, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-export function createBinaryKernelConfig(
-    kernelName: string, supportsFullBroadcast: boolean,
-    dtype?: DataType): KernelConfig {
-  let wasmFunc:
-      (aId: number, aShape: Uint8Array, aShapeLen: number, bId: number,
-       bShape: Uint8Array, bShapeLen: number, dtype: number, outId: number) =>
-          void;
-
-  function setupFunc(backend: BackendWasm): void {
-    wasmFunc = backend.wasm.cwrap(kernelName, null /* void */, [
-      'number',  // a_id,
-      'array',   // a_shape
-      'number',  // a_shape.length
-      'number',  // b_id
-      'array',   // b_shape
-      'number',  // b_shape.length
-      'number',  // dtype
-      'number'   // out_id
-    ]);
-  }
-
-  function kernelFunc(args: {backend: BackendWasm, inputs: BinaryInputs}):
-      TensorInfo {
-    const {backend, inputs} = args;
-    const {a, b} = inputs;
-    const aId = backend.dataIdMap.get(a.dataId).id;
-    const bId = backend.dataIdMap.get(b.dataId).id;
-
-    const outputType = dtype != null ? dtype : a.dtype;
-    const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);
-    const out = backend.makeOutput(newShape, outputType);
-
-    // Short-circuit zero-sized tensors.
-    if (util.sizeFromShape(newShape) === 0) {
-      return out;
-    }
-
-    const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);
-    const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
-    const outId = backend.dataIdMap.get(out.dataId).id;
-    const kernelFunc = () => wasmFunc(
-        aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length,
-        CppDType[a.dtype], outId);
-
-    kernelFunc();
-    return out;
-  }
-
-  return {kernelName, backendName: 'wasm', setupFunc, kernelFunc};
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/kernel_utils.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/kernel_utils.ts
deleted file mode 100644
index 87b54d21e..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/kernel_utils.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, TensorInfo, util} from '@tensorflow/tfjs-core';
-import {BackendWasm} from '../backend_wasm';
-import {transpose} from './Transpose';
-
-/**
- * Compute permutation axes and do a transpose if necessary.
- *
- * Used by reduction ops.
- * @param x input TensorInfo
- * @param axis reduction axes
- * @param backend wasm backend instance
- */
-export function permuteAxesAndTranspose(
-    x: TensorInfo, axis: number|number[], backend: BackendWasm): {
-  transposed: TensorInfo|null,
-  axes: number[],
-  originalAxes: number[],
-  inputWasTransposed: boolean
-} {
-  const xShape = x.shape;
-  const xRank = x.shape.length;
-
-  const originalAxes = util.parseAxisParam(axis, xShape);
-  let axes = originalAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-  let xTransposed = null;
-  let inputWasTransposed = false;
-  if (permutedAxes != null) {
-    const newShape: number[] = new Array(xRank);
-    for (let i = 0; i < newShape.length; i++) {
-      newShape[i] = xShape[permutedAxes[i]];
-    }
-
-    axes = backend_util.getInnerMostAxes(axes.length, xRank);
-    xTransposed =
-        transpose({inputs: {x}, attrs: {perm: permutedAxes}, backend});
-
-    const xId = backend.dataIdMap.get(x.dataId).id;
-    const transposedId = backend.dataIdMap.get(xTransposed.dataId).id;
-    if (transposedId !== xId) {
-      inputWasTransposed = true;
-    }
-  }
-
-  return {transposed: xTransposed, originalAxes, axes, inputWasTransposed};
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/types.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/types.ts
deleted file mode 100644
index 51d4faf4a..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/types.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// This enum must align with the enum defined in cc/backend.h.
-export enum CppDType {
-  float32 = 0,
-  int32 = 1,
-  bool = 2,
-  string = 3,
-  complex64 = 4
-}
-
-// Must match enum in cc/fusable_activations.h.
-export enum FusableActivation {
-  linear = 0,
-  relu = 1,
-  relu6 = 2,
-  prelu = 3,
-  leakyrelu = 4,
-  sigmoid = 5,
-  elu = 6
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/kernels/unary_kernel.ts b/tfjs-master/tfjs-backend-wasm/src/kernels/unary_kernel.ts
deleted file mode 100644
index cc1eff4ea..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/kernels/unary_kernel.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, KernelConfig, TensorInfo, UnaryInputs, util} from '@tensorflow/tfjs-core';
-
-import {BackendWasm} from '../backend_wasm';
-
-import {CppDType} from './types';
-
-export function createUnaryKernelConfig(
-    kernelName: string, outType?: DataType): KernelConfig {
-  let wasmFunc: (xId: number, dtype: number, outId: number) => void;
-
-  function setupFunc(backend: BackendWasm): void {
-    wasmFunc = backend.wasm.cwrap(kernelName, null /* void */, [
-      'number',  // x_id
-      'number',  // dtype
-      'number',  // out_id
-    ]);
-  }
-
-  function kernelFunc(args: {backend: BackendWasm, inputs: UnaryInputs}):
-      TensorInfo {
-    const {backend, inputs: {x}} = args;
-    const xId = backend.dataIdMap.get(x.dataId).id;
-    const out = backend.makeOutput(x.shape, outType || x.dtype);
-    const outId = backend.dataIdMap.get(out.dataId).id;
-
-    // Short-circuit zero-sized tensors.
-    if (util.sizeFromShape(out.shape) === 0) {
-      return out;
-    }
-
-    wasmFunc(xId, CppDType[x.dtype], outId);
-    return out;
-  }
-
-  return {kernelName, backendName: 'wasm', setupFunc, kernelFunc};
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/register_all_kernels.ts b/tfjs-master/tfjs-backend-wasm/src/register_all_kernels.ts
deleted file mode 100644
index 9b9f758c2..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/register_all_kernels.ts
+++ /dev/null
@@ -1,343 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// We explicitly import the modular kernels so they get registered in the
-// global registry when we compile the library. A modular build would replace
-// the contents of this file and import only the kernels that are needed.
-import {KernelConfig, registerKernel} from '@tensorflow/tfjs-core';
-
-import {_fusedMatMulConfig} from './kernels/_FusedMatMul';
-import {absConfig} from './kernels/Abs';
-import {acosConfig} from './kernels/Acos';
-import {acoshConfig} from './kernels/Acosh';
-import {addConfig} from './kernels/Add';
-import {addNConfig} from './kernels/AddN';
-import {allConfig} from './kernels/All';
-import {anyConfig} from './kernels/Any';
-import {argMaxConfig} from './kernels/ArgMax';
-import {argMinConfig} from './kernels/ArgMin';
-import {asinConfig} from './kernels/Asin';
-import {asinhConfig} from './kernels/Asinh';
-import {atanConfig} from './kernels/Atan';
-import {atan2Config} from './kernels/Atan2';
-import {atanhConfig} from './kernels/Atanh';
-import {avgPoolConfig} from './kernels/AvgPool';
-import {avgPool3DConfig} from './kernels/AvgPool3D';
-import {avgPool3DGradConfig} from './kernels/AvgPool3DGrad';
-import {avgPoolGradConfig} from './kernels/AvgPoolGrad';
-import {batchMatMulConfig} from './kernels/BatchMatMul';
-import {batchToSpaceNDConfig} from './kernels/BatchToSpaceND';
-import {bincountConfig} from './kernels/Bincount';
-import {bitwiseAndConfig} from './kernels/BitwiseAnd';
-import {broadcastArgsConfig} from './kernels/BroadcastArgs';
-import {castConfig} from './kernels/Cast';
-import {ceilConfig} from './kernels/Ceil';
-import {clipByValueConfig} from './kernels/ClipByValue';
-import {concatConfig} from './kernels/Concat';
-import {conv2DConfig} from './kernels/Conv2D';
-import {conv2DBackpropInputConfig} from './kernels/Conv2DBackpropInput';
-import {conv3DConfig} from './kernels/Conv3D';
-import {conv3DBackpropFilterV2Config} from './kernels/Conv3DBackpropFilterV2';
-import {conv3DBackpropInputV2Config} from './kernels/Conv3DBackpropInputV2';
-import {cosConfig} from './kernels/Cos';
-import {coshConfig} from './kernels/Cosh';
-import {cropAndResizeConfig} from './kernels/CropAndResize';
-import {cumprodConfig} from './kernels/Cumprod';
-import {cumsumConfig} from './kernels/Cumsum';
-import {denseBincountConfig} from './kernels/DenseBincount';
-import {depthToSpaceConfig} from './kernels/DepthToSpace';
-import {depthwiseConv2dNativeConfig} from './kernels/DepthwiseConv2dNative';
-import {diagConfig} from './kernels/Diag';
-import {dilation2DConfig} from './kernels/Dilation2D';
-import {dilation2DBackpropFilterConfig} from './kernels/Dilation2DBackpropFilter';
-import {dilation2DBackpropInputConfig} from './kernels/Dilation2DBackpropInput';
-import {eluConfig} from './kernels/Elu';
-import {eluGradConfig} from './kernels/EluGrad';
-import {equalConfig} from './kernels/Equal';
-import {erfConfig} from './kernels/Erf';
-import {expConfig} from './kernels/Exp';
-import {expandDimsConfig} from './kernels/ExpandDims';
-import {expm1Config} from './kernels/Expm1';
-import {fillConfig} from './kernels/Fill';
-import {flipLeftRightConfig} from './kernels/FlipLeftRight';
-import {floorConfig} from './kernels/Floor';
-import {floorDivConfig} from './kernels/FloorDiv';
-import {fusedBatchNormConfig} from './kernels/FusedBatchNorm';
-import {fusedConv2DConfig} from './kernels/FusedConv2D';
-import {fusedDepthwiseConv2DConfig} from './kernels/FusedDepthwiseConv2D';
-import {gatherNdConfig} from './kernels/GatherNd';
-import {gatherV2Config} from './kernels/GatherV2';
-import {greaterConfig} from './kernels/Greater';
-import {greaterEqualConfig} from './kernels/GreaterEqual';
-import {identityConfig} from './kernels/Identity';
-import {isFiniteConfig} from './kernels/IsFinite';
-import {isInfConfig} from './kernels/IsInf';
-import {isNaNConfig} from './kernels/IsNan';
-import {leakyReluConfig} from './kernels/LeakyRelu';
-import {lessConfig} from './kernels/Less';
-import {lessEqualConfig} from './kernels/LessEqual';
-import {linSpaceConfig} from './kernels/LinSpace';
-import {logConfig} from './kernels/Log';
-import {log1pConfig} from './kernels/Log1p';
-import {logicalAndConfig} from './kernels/LogicalAnd';
-import {logicalNotConfig} from './kernels/LogicalNot';
-import {logicalOrConfig} from './kernels/LogicalOr';
-import {logicalXorConfig} from './kernels/LogicalXor';
-import {lrnConfig} from './kernels/LRN';
-import {lrnGradConfig} from './kernels/LRNGrad';
-import {maxConfig} from './kernels/Max';
-import {maximumConfig} from './kernels/Maximum';
-import {maxPoolConfig} from './kernels/MaxPool';
-import {maxPool3DConfig} from './kernels/MaxPool3D';
-import {maxPool3DGradConfig} from './kernels/MaxPool3DGrad';
-import {maxPoolGradConfig} from './kernels/MaxPoolGrad';
-import {maxPoolWithArgmaxConfig} from './kernels/MaxPoolWithArgmax';
-import {meanConfig} from './kernels/Mean';
-import {minConfig} from './kernels/Min';
-import {minimumConfig} from './kernels/Minimum';
-import {mirrorPadConfig} from './kernels/MirrorPad';
-import {multinomialConfig} from './kernels/Multinomial';
-import {modConfig} from './kernels/Mod';
-import {multiplyConfig} from './kernels/Multiply';
-import {negConfig} from './kernels/Neg';
-import {nonMaxSuppressionV3Config} from './kernels/NonMaxSuppressionV3';
-import {nonMaxSuppressionV4Config} from './kernels/NonMaxSuppressionV4';
-import {nonMaxSuppressionV5Config} from './kernels/NonMaxSuppressionV5';
-import {notEqualConfig} from './kernels/NotEqual';
-import {oneHotConfig} from './kernels/OneHot';
-import {onesLikeConfig} from './kernels/OnesLike';
-import {packConfig} from './kernels/Pack';
-import {padV2Config} from './kernels/PadV2';
-import {powConfig} from './kernels/Pow';
-import {preluConfig} from './kernels/Prelu';
-import {prodConfig} from './kernels/Prod';
-import {rangeConfig} from './kernels/Range';
-import {realDivConfig} from './kernels/RealDiv';
-import {reciprocalConfig} from './kernels/Reciprocal';
-import {reluConfig} from './kernels/Relu';
-import {relu6Config} from './kernels/Relu6';
-import {reshapeConfig} from './kernels/Reshape';
-import {resizeBilinearConfig} from './kernels/ResizeBilinear';
-import {resizeBilinearGradConfig} from './kernels/ResizeBilinearGrad';
-import {resizeNearestNeighborConfig} from './kernels/ResizeNearestNeighbor';
-import {resizeNearestNeighborGradConfig} from './kernels/ResizeNearestNeighborGrad';
-import {reverseConfig} from './kernels/Reverse';
-import {rotateWithOffsetConfig} from './kernels/RotateWithOffset';
-import {roundConfig} from './kernels/Round';
-import {rsqrtConfig} from './kernels/Rsqrt';
-import {scatterNdConfig} from './kernels/ScatterNd';
-import {searchSortedConfig} from './kernels/SearchSorted';
-import {selectConfig} from './kernels/Select';
-import {seluConfig} from './kernels/Selu';
-import {sigmoidConfig} from './kernels/Sigmoid';
-import {signConfig} from './kernels/Sign';
-import {sinConfig} from './kernels/Sin';
-import {sinhConfig} from './kernels/Sinh';
-import {sliceConfig} from './kernels/Slice';
-import {softmaxConfig} from './kernels/Softmax';
-import {softplusConfig} from './kernels/Softplus';
-import {spaceToBatchNDConfig} from './kernels/SpaceToBatchND';
-import {sparseFillEmptyRowsConfig} from './kernels/SparseFillEmptyRows';
-import {sparseReshapeConfig} from './kernels/SparseReshape';
-import {sparseSegmentMeanConfig} from './kernels/SparseSegmentMean';
-import {sparseSegmentSumConfig} from './kernels/SparseSegmentSum';
-import {sparseToDenseConfig} from './kernels/SparseToDense';
-import {splitVConfig} from './kernels/SplitV';
-import {sqrtConfig} from './kernels/Sqrt';
-import {squareConfig} from './kernels/Square';
-import {squaredDifferenceConfig} from './kernels/SquaredDifference';
-import {stepConfig} from './kernels/Step';
-import {stridedSliceConfig} from './kernels/StridedSlice';
-import {stringNGramsConfig} from './kernels/StringNGrams';
-import {stringSplitConfig} from './kernels/StringSplit';
-import {stringToHashBucketFastConfig} from './kernels/StringToHashBucketFast';
-import {subConfig} from './kernels/Sub';
-import {sumConfig} from './kernels/Sum';
-import {tanConfig} from './kernels/Tan';
-import {tanhConfig} from './kernels/Tanh';
-import {tensorScatterUpdateConfig} from './kernels/TensorScatterUpdate';
-import {tileConfig} from './kernels/Tile';
-import {topKConfig} from './kernels/TopK';
-import {transformConfig} from './kernels/Transform';
-import {transposeConfig} from './kernels/Transpose';
-import {uniqueConfig} from './kernels/Unique';
-import {unpackConfig} from './kernels/Unpack';
-import {zerosLikeConfig} from './kernels/ZerosLike';
-
-// List all kernel configs here
-const kernelConfigs: KernelConfig[] = [
-  _fusedMatMulConfig,
-  absConfig,
-  acosConfig,
-  acoshConfig,
-  addConfig,
-  addNConfig,
-  allConfig,
-  anyConfig,
-  argMaxConfig,
-  argMinConfig,
-  asinConfig,
-  asinhConfig,
-  atanConfig,
-  atan2Config,
-  atanhConfig,
-  avgPoolConfig,
-  avgPoolGradConfig,
-  avgPool3DConfig,
-  avgPool3DGradConfig,
-  batchMatMulConfig,
-  batchToSpaceNDConfig,
-  bincountConfig,
-  bitwiseAndConfig,
-  broadcastArgsConfig,
-  castConfig,
-  ceilConfig,
-  clipByValueConfig,
-  concatConfig,
-  conv2DConfig,
-  conv2DBackpropInputConfig,
-  conv3DConfig,
-  conv3DBackpropFilterV2Config,
-  conv3DBackpropInputV2Config,
-  cosConfig,
-  coshConfig,
-  cropAndResizeConfig,
-  cumprodConfig,
-  cumsumConfig,
-  denseBincountConfig,
-  depthToSpaceConfig,
-  depthwiseConv2dNativeConfig,
-  diagConfig,
-  dilation2DConfig,
-  dilation2DBackpropFilterConfig,
-  dilation2DBackpropInputConfig,
-  eluConfig,
-  eluGradConfig,
-  equalConfig,
-  erfConfig,
-  expConfig,
-  expandDimsConfig,
-  expm1Config,
-  fillConfig,
-  flipLeftRightConfig,
-  floorConfig,
-  floorDivConfig,
-  fusedBatchNormConfig,
-  fusedConv2DConfig,
-  fusedDepthwiseConv2DConfig,
-  gatherNdConfig,
-  gatherV2Config,
-  greaterConfig,
-  greaterEqualConfig,
-  identityConfig,
-  isFiniteConfig,
-  isInfConfig,
-  isNaNConfig,
-  leakyReluConfig,
-  lessConfig,
-  lessEqualConfig,
-  linSpaceConfig,
-  log1pConfig,
-  logConfig,
-  logicalAndConfig,
-  logicalNotConfig,
-  logicalOrConfig,
-  logicalXorConfig,
-  lrnConfig,
-  lrnGradConfig,
-  maxConfig,
-  maximumConfig,
-  maxPoolConfig,
-  maxPool3DConfig,
-  maxPool3DGradConfig,
-  maxPoolGradConfig,
-  maxPoolWithArgmaxConfig,
-  meanConfig,
-  minConfig,
-  minimumConfig,
-  mirrorPadConfig,
-  multinomialConfig,
-  modConfig,
-  multiplyConfig,
-  negConfig,
-  nonMaxSuppressionV3Config,
-  nonMaxSuppressionV4Config,
-  nonMaxSuppressionV5Config,
-  notEqualConfig,
-  oneHotConfig,
-  onesLikeConfig,
-  packConfig,
-  padV2Config,
-  powConfig,
-  preluConfig,
-  prodConfig,
-  rangeConfig,
-  realDivConfig,
-  reciprocalConfig,
-  reluConfig,
-  relu6Config,
-  reshapeConfig,
-  resizeBilinearConfig,
-  resizeBilinearGradConfig,
-  resizeNearestNeighborConfig,
-  resizeNearestNeighborGradConfig,
-  reverseConfig,
-  rotateWithOffsetConfig,
-  roundConfig,
-  rsqrtConfig,
-  scatterNdConfig,
-  searchSortedConfig,
-  selectConfig,
-  seluConfig,
-  sigmoidConfig,
-  signConfig,
-  sinConfig,
-  sinhConfig,
-  sliceConfig,
-  softmaxConfig,
-  softplusConfig,
-  spaceToBatchNDConfig,
-  sparseFillEmptyRowsConfig,
-  sparseReshapeConfig,
-  sparseSegmentMeanConfig,
-  sparseSegmentSumConfig,
-  sparseToDenseConfig,
-  splitVConfig,
-  sqrtConfig,
-  squareConfig,
-  squaredDifferenceConfig,
-  stepConfig,
-  stridedSliceConfig,
-  stringNGramsConfig,
-  stringSplitConfig,
-  stringToHashBucketFastConfig,
-  subConfig,
-  sumConfig,
-  tanConfig,
-  tanhConfig,
-  tensorScatterUpdateConfig,
-  tileConfig,
-  topKConfig,
-  transformConfig,
-  transposeConfig,
-  uniqueConfig,
-  unpackConfig,
-  zerosLikeConfig
-];
-
-for (const kernelConfig of kernelConfigs) {
-  registerKernel(kernelConfig);
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/setup_test.ts b/tfjs-master/tfjs-backend-wasm/src/setup_test.ts
deleted file mode 100644
index 0600637f8..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/setup_test.ts
+++ /dev/null
@@ -1,437 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Import Object.fromEntries polyfill for Safari 11
-import 'core-js/es/object/from-entries';
-// Import core for side effects (e.g. flag registration)
-import '@tensorflow/tfjs-core';
-// tslint:disable-next-line:no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-// Register the wasm backend.
-import './index';
-
-// tslint:disable-next-line: no-imports-from-dist
-import {setTestEnvs, setupTestFilters, TestFilter} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {setupCachedWasmPaths} from './test_util';
-
-setTestEnvs([{name: 'test-wasm', backendName: 'wasm', isDataSync: true}]);
-
-/**
- * Tests that have these substrings in their name will be included unless one
- * of the strings in excludes appears in the name.
- */
-const TEST_FILTERS: TestFilter[] = [
-  {
-    startsWith: 'tensor ',
-    excludes: [
-      'complex',     // Complex numbers not supported yet
-      'derivative',  // Gradients not yet supported.
-      // Downcasting broken, see: https://github.com/tensorflow/tfjs/issues/2590
-      'Tensor2D float32 -> bool', 'Tensor2D int32 -> bool'
-    ]
-  },
-  {include: 'softmax'},
-  {
-    include: 'pow',
-    excludes: [
-      'gradient'  // zerosLike not defined yet.
-    ]
-  },
-  {
-    include: 'add ',
-    excludes: [
-      'gradient',                        // Gradient is missing.
-      'upcasts when dtypes dont match',  // Uses the 'complex' dtype.
-      'complex',                         // Complex numbers not supported yet
-    ]
-  },
-  {include: 'depthToSpace'},
-  {include: 'avgPool '},
-  {
-    include: 'relu',
-    excludes: [
-      'derivative',               // Not yet implemented.
-      'gradient',                 // Not yet implemented.
-      'valueAndGradients',        // Not yet implemented.
-      'broadcasted bias',         // Not yet implemented.
-      'fused A x B with 2d bias'  // Fused matMul with 2D bias not yet
-                                  // supported.
-    ]
-  },
-  {
-    include: 'maxPool',
-    excludes: [
-      'ignores NaNs',      // Actual != expected.
-    ]
-  },
-  {include: 'cropAndResize'},
-  {include: 'resizeBilinear'},
-  {include: 'resizeNearestNeighbor'},
-  {
-    include: 'matmul ',
-    excludes: [
-      'valueAndGradients',         // Gradients not defined yet
-      'gradient',                  // Gradients not defined yet
-      'zero in its shape',         // Zero in shapes aren't supported yet
-      'matmul followed by mul',    // mul not supported yet
-      'upcasts',                   // Upcasting not supported yet.
-      'fused A x B with 2d bias',  // Fused matMul with 2D bias not yet
-                                   // supported.
-    ]
-  },
-  {
-    include: 'depthwiseConv2D ',
-    excludes: [
-      'broadcasted bias',  // Broadcasted bias not yet supported.
-      'gradient',          // Gradients not defined yet.
-      'NCHW',              // xnn pack does not support channels first.
-    ]
-  },
-  {
-    include: 'conv2d ',
-    excludes: [
-      'broadcasted bias',  // Broadcasted bias not yet supported.
-      'gradient',          // Gradients not defined yet.
-      'backProp input x=[2,3,3,1] f=[2,2,1,1] s=1 p=0',  // Gradients not
-                                                         // defined.
-      'NCHW',  // xnn pack does not support channels first.
-      // Issue: https://github.com/tensorflow/tfjs/issues/3104.
-      // Actual != expected.
-      'relu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same',
-      'prelu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same',
-    ]
-  },
-  {include: 'conv2dTranspose ', excludes: ['gradient']},
-  {
-    include: 'prelu ',
-    excludes: [
-      'gradient',   // Gradient is missing.
-      'derivative'  // Missing gradient.
-    ]
-  },
-  {
-    include: ' cast ',
-    excludes: [
-      'complex',  // Complex numbers not yet implemented.
-      'shallow slice an input that was cast'  // Slice is not implemented.
-    ]
-  },
-  {
-    include: 'gather',
-    excludes: [
-      'gradient'  // Not yet implemented.
-    ]
-  },
-  {
-    include: 'sigmoid ',
-    excludes: [
-      'sigmoidCrossEntropy',  // Not yet implemented.
-      'gradient'              // Not yet implemented.
-    ]
-  },
-  {include: 'scatterND '},
-  {include: 'tensorScatterUpdate '},
-  {
-    include: 'abs ',
-    excludes: [
-      'gradient',  // Not yet implemented.
-      'complex'    // Complex numbers not supported yet.
-    ]
-  },
-  {
-    include: 'sub ',
-    excludes: [
-      'complex',   // Complex numbers not yet implemented.
-      'gradient',  // Not yet implemented.
-      'upcasts',   // Upcasting not supported yet.
-    ]
-  },
-  {
-    include: 'mul ',
-    excludes: [
-      'complex',   // Complex numbers not yet supported.
-      'gradient',  // Gradient not defined yet.
-    ]
-  },
-  {
-    include: 'div ',
-    excludes: [
-      'gradient',  // Gradient not defined yet.
-      'upcasts',   // Cast not supported yet.
-      'divNoNan'   // divNoNan not yet implemented.
-    ]
-  },
-  {
-    include: 'batchNorm',
-    excludes: [
-      'gradient'  // Gradient is missing.
-    ]
-  },
-  {include: 'slice '},
-  {include: 'stridedSlice '},
-  {include: 'rotate '},
-  {include: 'rotateWithOffset'},
-  {include: 'flipLeftRight '},
-  {include: 'square '},
-  {include: 'squaredDifference'},
-  {
-    startsWith: 'min ',
-    excludes: [
-      '2D, axis=0',  // Permuted axes requires transpose, which is not yet
-                     // implemented.
-      'index corresponds to start of a non-initial window',  // argMin not yet
-                                                             // implemented.,
-      'gradient'  // Gradients not yet implemented
-    ]
-  },
-  {
-    startsWith: 'max ',
-    excludes: [
-      'gradient'  // Gradients not yet implemented
-    ]
-  },
-  {
-    include: 'concat',
-    excludes: [
-      'complex',  // Complex numbers not supported yet
-      'gradient'  // Split is not yet implemented
-    ]
-  },
-  {include: 'transpose', excludes: ['accepts complex64 input']},
-  {include: 'oneHot'},
-  {include: 'split'},
-  {include: 'pad ', excludes: ['complex', 'zerosLike']},
-  {
-    include: 'clip',
-    excludes: [
-      'gradient',
-      'propagates NaNs',  // clip delegates to XNNPACK which does not make
-                          // guarantees about behavior of nans.
-      'basic vec4'        // basic vec4 also includes nans.
-    ]
-  },
-  {include: 'addN'},
-  {include: 'nonMaxSuppression'},
-  {include: 'argmax '},
-  {include: 'argmin '},
-  {include: 'exp '},
-  {include: 'elu '},
-  {include: 'unstack'},
-  {
-    include: 'minimum',
-    excludes: [
-      'gradient',                                 // Not yet implemented.
-      'broadcasts 2x1 Tensor2D and 2x2 Tensor2D'  // Broadcasting along inner
-                                                  // dims not supported yet.
-    ]
-  },
-  {
-    include: 'maximum',
-    excludes: [
-      'gradient'  // Not yet implemented.
-    ]
-  },
-  {
-    include: 'log ',
-  },
-  {startsWith: 'equal ', excludes: ['string']},
-  {include: 'greater ', excludes: ['string']},
-  {
-    include: 'greaterEqual',
-    excludes: [
-      'gradient',  // Not yet implemented.
-      'string'
-    ]
-  },
-  {include: 'less ', excludes: ['string']},
-  {
-    include: 'lessEqual',
-    excludes: [
-      'gradient',  // Not yet implemented.
-      'string'
-    ]
-  },
-  {include: 'notEqual', excludes: ['string']},
-  {
-    include: 'mean ',
-    excludes: [
-      'axis=0',  // Reduction not supported along inner dimensions.
-    ]
-  },
-  {startsWith: 'reverse'},
-  {startsWith: 'sum '},
-  {startsWith: 'cumprod'},
-  {startsWith: 'cumsum'},
-  {startsWith: 'logicalAnd '},
-  {startsWith: 'logicalNot '},
-  {startsWith: 'logicalOr '},
-  {startsWith: 'logicalXor '},
-  {
-    startsWith: 'tile ',
-    excludes: [
-      'gradient',      // Gradient not yet implemented.
-      'string tensor'  // String tensors not yet implemented.
-    ]
-  },
-  {startsWith: 'erf'},
-  {startsWith: 'sin '},
-  {startsWith: 'sinh '},
-  {
-    startsWith: 'cos ',
-    excludes: [
-      'gradient',  // Gradient not yet implemented.
-    ]
-  },
-  {
-    startsWith: 'cosh',
-    excludes: [
-      'gradient'  // Gradient not yet implemented.
-    ]
-  },
-  {
-    startsWith: 'tan',
-    excludes: ['gradient']  // Gradient not yet implemented.
-
-  },
-  {
-    startsWith: 'tanh ',
-    excludes: ['gradient']  // Gradient not yet implemented.
-  },
-  {
-    startsWith: 'rsqrt ',
-    excludes: ['gradient']  // Gradient not yet implemented.
-  },
-  {
-    startsWith: 'sqrt ',
-    excludes: ['gradient']  // Gradient not yet implemented.
-  },
-  {
-    startsWith: 'where ',
-    excludes: [
-      '1D condition with higher rank a and b',  // Fill not yet implemented.
-      'gradient'                                // Gradient not yet implemented.
-    ]
-  },
-  {
-    startsWith: 'zerosLike',
-    // Complex numbers not supported yet.
-    excludes: ['complex'],
-  },
-  {
-    startsWith: 'onesLike',
-    // Complex numbers not supported yet.
-    excludes: ['complex'],
-  },
-  {include: 'prod'},
-  {include: 'floor'},
-  {include: 'floorDiv'},
-  {include: 'topk'},
-  {include: 'expandDims'},
-  {include: 'stack'},
-  {
-    include: 'round',
-    // Pool is not supported yet.
-    excludes: ['pool'],
-  },
-  {include: 'step kernel'},
-  {include: 'ceil'},
-  {include: 'mirrorPad'},
-  {
-    startsWith: 'all',
-    excludes: [
-      'ignores NaNs'  // Doesn't yet ignore NaN
-    ]
-  },
-  {
-    startsWith: 'any',
-    excludes: [
-      'ignores NaNs'  // Doesn't yet ignore NaN
-    ]
-  },
-  {include: 'image.transform'},
-  {include: 'batchToSpaceND'},
-  {include: 'spaceToBatchND'},
-  {include: 'sparseFillEmptyRows'},
-  {include: 'sparseReshape'},
-  {include: 'sparseSegmentMean'},
-  {include: 'sparseSegmentSum'},
-  {startsWith: 'sparseToDense', excludes: ['string']},
-  {include: 'stringNGrams'},
-  {include: 'stringSplit'},
-  {include: 'stringToHashBucketFast'},
-  {include: 'reciprocal'},
-  {include: 'isNaN'},
-  {include: 'atan '},
-  {include: 'acos '},
-  {include: 'acosh '},
-  {include: 'asin '},
-  {include: 'asinh '},
-  {include: 'diag '},
-  {include: 'denseBincount '},
-  {include: 'bitwiseAnd'},
-  {include: 'broadcastArgs '},
-  {include: 'searchSorted '},
-  {include: 'avgPool3d '},
-  {include: 'avgPool3dBackprop '},
-  {include: 'upperBound '},
-  {include: 'lowerBound '},
-  {include: 'dilation2d '},
-  {include: 'localResponseNormalization '},
-  {include: 'log1p '},
-  {include: 'atan2 '},
-  {include: 'atanh '},
-  {include: 'isInf '},
-  {include: 'isFinite '},
-  {include: 'sign '},
-  {include: 'selu '},
-  {include: 'softplus '},
-  {include: 'linspace'},
-  {include: 'bincount'},
-  {include: 'expm1 '},
-  {include: 'multinomial'},
-  {include: 'unique'},
-  {include: 'conv3d'},
-  {include: 'mod '},
-];
-
-const customInclude = (testName: string) => {
-  // Include all regular describe() tests.
-  if (testName.indexOf('test-wasm') < 0) {
-    return true;
-  }
-
-  // Include all of the wasm specific tests.
-  if (testName.startsWith('wasm')) {
-    return true;
-  }
-
-  return false;
-};
-setupTestFilters(TEST_FILTERS, customInclude);
-
-beforeAll(setupCachedWasmPaths, 30_000);
-
-// Import and run all the tests from core.
-// tslint:disable-next-line:no-imports-from-dist
-// tslint:disable-next-line:no-require-imports
-require('@tensorflow/tfjs-core/dist/tests');
-// Import and run wasm tests
-// tslint:disable-next-line:no-require-imports
-require('./tests');
diff --git a/tfjs-master/tfjs-backend-wasm/src/test_node.ts b/tfjs-master/tfjs-backend-wasm/src/test_node.ts
deleted file mode 100644
index c8ea4cffd..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/test_node.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Register the wasm backend.
-import '@tensorflow/tfjs-backend-wasm';
-import * as tf from '@tensorflow/tfjs-core';
-
-// tslint:disable-next-line: no-imports-from-dist
-import {setTestEnvs} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-// tslint:disable-next-line: no-require-imports
-const jasmine = require('jasmine');
-
-tf.setBackend('wasm');
-
-Error.stackTraceLimit = Infinity;
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-setTestEnvs([{name: 'wasm-node', backendName: 'wasm', isDataSync: true}]);
-
-const runner = new jasmine({});
-runner.loadConfig({
-  spec_files: ['tfjs-backend-wasm/src/setup_test.js'],
-  random: false,
-});
-
-if (process.env.JASMINE_SEED) {
-  runner.seed(process.env.JASMINE_SEED);
-}
-
-runner.execute();
diff --git a/tfjs-master/tfjs-backend-wasm/src/test_util.ts b/tfjs-master/tfjs-backend-wasm/src/test_util.ts
deleted file mode 100644
index 95b96ed58..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/test_util.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {resetWasmPath} from './backend_wasm';
-import {setWasmPaths} from './index';
-
-export const VALID_PREFIX = '/base/tfjs/tfjs-backend-wasm/wasm-out/';
-
-async function cacheUrl(url: string): Promise<string> {
-  const blob = await (await fetch(url)).blob();
-  return URL.createObjectURL(blob);
-}
-
-let cachedUrlsPromises: Array<Promise<readonly [
-  'tfjs-backend-wasm.wasm'
-    | 'tfjs-backend-wasm-simd.wasm'
-    | 'tfjs-backend-wasm-threaded-simd.wasm', string]>>;
-
-if (tf.device_util.isBrowser()) {
-  cachedUrlsPromises = ([
-    'tfjs-backend-wasm.wasm',
-    'tfjs-backend-wasm-simd.wasm',
-    'tfjs-backend-wasm-threaded-simd.wasm',
-  ] as const).map(async name => {
-    return [name, await cacheUrl(VALID_PREFIX + name)] as const;
-  });
-}
-
-/**
- * Set the wasm paths to the blob URLs that were loaded above. This is useful
- * for Karma tests because it prevents Karma from loading the same WASM files
- * every time the backend is reset (each `describe` block). This saves ~200MB
- * of network and should reduce flakiness due to network slowness / instability.
- */
-export async function setupCachedWasmPaths() {
-  resetWasmPath();
-  if (tf.device_util.isBrowser()) {
-    const cachedUrlsList = await Promise.all(cachedUrlsPromises);
-    const cachedUrls = Object.fromEntries(cachedUrlsList);
-    setWasmPaths(cachedUrls);
-  }
-}
diff --git a/tfjs-master/tfjs-backend-wasm/src/version.ts b/tfjs-master/tfjs-backend-wasm/src/version.ts
deleted file mode 100644
index 5fa574e7d..000000000
--- a/tfjs-master/tfjs-backend-wasm/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.0';
-export {version};
diff --git a/tfjs-master/tfjs-backend-wasm/starter/parcel/.parcelrc b/tfjs-master/tfjs-backend-wasm/starter/parcel/.parcelrc
deleted file mode 100644
index 1f3df894b..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/parcel/.parcelrc
+++ /dev/null
@@ -1,4 +0,0 @@
-{
-  "extends": ["@parcel/config-default"],
-  "reporters":  ["...", "parcel-reporter-static-files-copy"]
-}
diff --git a/tfjs-master/tfjs-backend-wasm/starter/parcel/README.md b/tfjs-master/tfjs-backend-wasm/starter/parcel/README.md
deleted file mode 100644
index 4e600bca2..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/parcel/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# Starter project for Parcel
-
-A minimal starter project using Parcel and the WASM backend.
-See [Using Bundlers](../../README.md#using-bundlers) section in the main readme
-for details on how we serve the WASM backend on NPM.
-
-To serve the `.wasm` file, we use the `parcel-plugin-static-files-copy` plugin
-and added the following to the `package.json`, which copies any `.wasm` file to
-the Parcel output directory:
-
-```json
-...,
-"staticFiles": {
-    "staticPath": "./node_modules/@tensorflow/tfjs-backend-wasm/dist",
-    "excludeGlob": ["**/!(*.wasm)"]
-  },
-...
-```
diff --git a/tfjs-master/tfjs-backend-wasm/starter/parcel/index.html b/tfjs-master/tfjs-backend-wasm/starter/parcel/index.html
deleted file mode 100644
index 8deab1573..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/parcel/index.html
+++ /dev/null
@@ -1,21 +0,0 @@
-<!-- Copyright 2019 Google LLC. All Rights Reserved.
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-    http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-============================================================================-->
-<!DOCTYPE html>
-<html>
-  <head>
-    <title>Parcel starter app</title>
-    <meta charset="UTF-8" />
-  </head>
-  <body>
-    <script type="module" src="index.js"></script>
-  </body>
-</html>
diff --git a/tfjs-master/tfjs-backend-wasm/starter/parcel/index.js b/tfjs-master/tfjs-backend-wasm/starter/parcel/index.js
deleted file mode 100644
index 6a4983988..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/parcel/index.js
+++ /dev/null
@@ -1,22 +0,0 @@
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-import '@tensorflow/tfjs-backend-wasm';
-import * as tf from '@tensorflow/tfjs-core';
-
-async function run() {
-  await tf.setBackend('wasm');
-  tf.add(5, 3).print();
-}
-run();
diff --git a/tfjs-master/tfjs-backend-wasm/starter/parcel/package.json b/tfjs-master/tfjs-backend-wasm/starter/parcel/package.json
deleted file mode 100644
index 584177216..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/parcel/package.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "name": "wasm-parcel",
-  "version": "1.0.0",
-  "description": "Sample parcel app that uses the WASM backend",
-  "scripts": {
-    "watch": "parcel index.html --open",
-    "build": "parcel build index.html"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs-backend-wasm": "3.14.0",
-    "@tensorflow/tfjs-core": "3.14.0"
-  },
-  "browserslist": [
-    "defaults"
-  ],
-  "staticFiles": {
-    "staticPath": "./node_modules/@tensorflow/tfjs-backend-wasm/dist",
-    "excludeGlob": [
-      "**/!(*.wasm)"
-    ]
-  },
-  "devDependencies": {
-    "parcel": "^2.3.2",
-    "parcel-reporter-static-files-copy": "^1.3.4"
-  },
-  "resolutions": {
-    "minimist": "1.2.6"
-  },
-  "keywords": []
-}
diff --git a/tfjs-master/tfjs-backend-wasm/starter/parcel/yarn.lock b/tfjs-master/tfjs-backend-wasm/starter/parcel/yarn.lock
deleted file mode 100644
index 9fa740278..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/parcel/yarn.lock
+++ /dev/null
@@ -1,1654 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@babel/code-frame@^7.0.0":
-  version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.16.7.tgz#44416b6bd7624b998f5b1af5d470856c40138789"
-  integrity sha512-iAXqUn8IIeBTNd72xsFlgaXHkMBMt6y4HJp1tIaK465CWLT/fG1aqB7ykr95gHHmlBdGbFeWWfyB4NJJ0nmeIg==
-  dependencies:
-    "@babel/highlight" "^7.16.7"
-
-"@babel/helper-validator-identifier@^7.16.7":
-  version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-validator-identifier/-/helper-validator-identifier-7.16.7.tgz#e8c602438c4a8195751243da9031d1607d247cad"
-  integrity sha512-hsEnFemeiW4D08A5gUAZxLBTXpZ39P+a+DGDsHw1yxqyQ/jzFEnxf5uTEGp+3bzAbNOxU1paTgYS4ECU/IgfDw==
-
-"@babel/highlight@^7.16.7":
-  version "7.16.10"
-  resolved "https://registry.yarnpkg.com/@babel/highlight/-/highlight-7.16.10.tgz#744f2eb81579d6eea753c227b0f570ad785aba88"
-  integrity sha512-5FnTQLSLswEj6IkgVw5KusNUUFY9ZGqe/TRFnP/BKYHYgfh7tc+C7mwiy95/yNP7Dh9x580Vv8r7u7ZfTBFxdw==
-  dependencies:
-    "@babel/helper-validator-identifier" "^7.16.7"
-    chalk "^2.0.0"
-    js-tokens "^4.0.0"
-
-"@jridgewell/gen-mapping@^0.3.0":
-  version "0.3.2"
-  resolved "https://registry.yarnpkg.com/@jridgewell/gen-mapping/-/gen-mapping-0.3.2.tgz#c1aedc61e853f2bb9f5dfe6d4442d3b565b253b9"
-  integrity sha512-mh65xKQAzI6iBcFzwv28KVWSmCkdRBWoOh+bYQGW3+6OZvbbN3TqMGo5hqYxQniRcH9F2VZIoJCm4pa3BPDK/A==
-  dependencies:
-    "@jridgewell/set-array" "^1.0.1"
-    "@jridgewell/sourcemap-codec" "^1.4.10"
-    "@jridgewell/trace-mapping" "^0.3.9"
-
-"@jridgewell/resolve-uri@^3.0.3":
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/@jridgewell/resolve-uri/-/resolve-uri-3.1.0.tgz#2203b118c157721addfe69d47b70465463066d78"
-  integrity sha512-F2msla3tad+Mfht5cJq7LSXcdudKTWCVYUgw6pLFOOHSTtZlj6SWNYAp+AhuqLmWdBO2X5hPrLcu8cVP8fy28w==
-
-"@jridgewell/set-array@^1.0.1":
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/@jridgewell/set-array/-/set-array-1.1.2.tgz#7c6cf998d6d20b914c0a55a91ae928ff25965e72"
-  integrity sha512-xnkseuNADM0gt2bs+BvhO0p78Mk762YnZdsuzFV018NoG1Sj1SCQvpSqa7XUaTam5vAGasABV9qXASMKnFMwMw==
-
-"@jridgewell/source-map@^0.3.2":
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-  resolved "https://registry.yarnpkg.com/css-declaration-sorter/-/css-declaration-sorter-6.1.4.tgz#b9bfb4ed9a41f8dcca9bf7184d849ea94a8294b4"
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-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/cssesc/-/cssesc-3.0.0.tgz#37741919903b868565e1c09ea747445cd18983ee"
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-  resolved "https://registry.yarnpkg.com/cssnano-preset-default/-/cssnano-preset-default-5.2.4.tgz#eced79bbc1ab7270337c4038a21891daac2329bc"
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-  resolved "https://registry.yarnpkg.com/cssnano/-/cssnano-5.1.4.tgz#c648192e8e2f1aacb7d839e6aa3706b50cc7f8e4"
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-  resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
-  integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=
-
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-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/utility-types/-/utility-types-3.10.0.tgz#ea4148f9a741015f05ed74fd615e1d20e6bed82b"
-  integrity sha512-O11mqxmi7wMKCo6HKFt5AhO4BwY3VV68YU07tgxfz8zJTIxr4BpsezN49Ffwy9j3ZpwwJp4fkRwjRzq3uWE6Rg==
-
-v8-compile-cache@^2.0.0:
-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/v8-compile-cache/-/v8-compile-cache-2.3.0.tgz#2de19618c66dc247dcfb6f99338035d8245a2cee"
-  integrity sha512-l8lCEmLcLYZh4nbunNZvQCJc5pv7+RCwa8q/LdUx8u7lsWvPDKmpodJAJNwkAhJC//dFY48KuIEmjtd4RViDrA==
-
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-  version "1.2.2"
-  resolved "https://registry.yarnpkg.com/weak-lru-cache/-/weak-lru-cache-1.2.2.tgz#fdbb6741f36bae9540d12f480ce8254060dccd19"
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-
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-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
-
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-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
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-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
-xxhash-wasm@^0.4.2:
-  version "0.4.2"
-  resolved "https://registry.yarnpkg.com/xxhash-wasm/-/xxhash-wasm-0.4.2.tgz#752398c131a4dd407b5132ba62ad372029be6f79"
-  integrity sha512-/eyHVRJQCirEkSZ1agRSCwriMhwlyUcFkXD5TPVSLP+IPzjsqMVzZwdoczLp1SoQU0R3dxz1RpIK+4YNQbCVOA==
-
-yaml@^1.10.0, yaml@^1.10.2:
-  version "1.10.2"
-  resolved "https://registry.yarnpkg.com/yaml/-/yaml-1.10.2.tgz#2301c5ffbf12b467de8da2333a459e29e7920e4b"
-  integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==
diff --git a/tfjs-master/tfjs-backend-wasm/starter/webpack/README.md b/tfjs-master/tfjs-backend-wasm/starter/webpack/README.md
deleted file mode 100644
index 586ee9bb4..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/webpack/README.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# Starter project for WebPack
-
-A minimal starter project using WebPack and the WASM backend.
-See [Using Bundlers](../../README.md#using-bundlers) section in the main readme
-for details on how we serve the WASM backend on NPM.
-
-To serve the `.wasm` file, we use the `file-loader` plugin and added the
-following to `webpack.config.js`, which tells WebPack to provide the serving url
-when importing `.wasm` files:
-
-```js
-module: {
-  rules: [
-    {
-      test: /\.wasm$/i,
-      type: 'javascript/auto',
-      use: [
-        {
-          loader: 'file-loader',
-        },
-      ],
-    },
-  ],
-}
-```
-
-Then we obtain the final serving path of the WASM binaries that were shipped on
-NPM, and use `setWasmPaths` to let the library know the serving locations:
-
-```ts
-import {setWasmPaths} from '@tensorflow/tfjs-backend-wasm';
-
-import wasmSimdPath from './node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm-simd.wasm';
-import wasmSimdThreadedPath from './node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm-threaded-simd.wasm';
-import wasmPath from './node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm.wasm';
-
-setWasmPaths({
-  'tfjs-backend-wasm.wasm': wasmPath,
-  'tfjs-backend-wasm-simd.wasm': wasmSimdPath,
-  'tfjs-backend-wasm-threaded-simd.wasm': wasmSimdThreadedPath
-});
-```
diff --git a/tfjs-master/tfjs-backend-wasm/starter/webpack/package.json b/tfjs-master/tfjs-backend-wasm/starter/webpack/package.json
deleted file mode 100644
index 5217a0395..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/webpack/package.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
-  "name": "wasm-webpack",
-  "version": "1.0.0",
-  "description": "Sample webpack app that uses the WASM backend",
-  "private": true,
-  "scripts": {
-    "watch": "webpack-dev-server --open",
-    "build": "webpack"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs-backend-wasm": "2.6.0",
-    "@tensorflow/tfjs-core": "2.6.0"
-  },
-  "browserslist": [
-    "defaults"
-  ],
-  "devDependencies": {
-    "file-loader": "^5.0.2",
-    "html-webpack-plugin": "^5.5.0",
-    "webpack": "^5.76.0",
-    "webpack-cli": "^4.9.2",
-    "webpack-dev-server": "^4.7.4"
-  },
-  "resolutions": {
-    "minimist": "1.2.6"
-  },
-  "keywords": []
-}
diff --git a/tfjs-master/tfjs-backend-wasm/starter/webpack/src/index.js b/tfjs-master/tfjs-backend-wasm/starter/webpack/src/index.js
deleted file mode 100644
index fde4332d8..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/webpack/src/index.js
+++ /dev/null
@@ -1,34 +0,0 @@
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-import {setWasmPaths} from '@tensorflow/tfjs-backend-wasm';
-import * as tf from '@tensorflow/tfjs-core';
-
-import wasmSimdPath from '../node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm-simd.wasm';
-import wasmSimdThreadedPath from '../node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm-threaded-simd.wasm';
-import wasmPath from '../node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm.wasm';
-
-
-async function run() {
-  setWasmPaths({
-    'tfjs-backend-wasm.wasm': wasmPath,
-    'tfjs-backend-wasm-simd.wasm': wasmSimdPath,
-    'tfjs-backend-wasm-threaded-simd.wasm': wasmSimdThreadedPath
-  });
-  await tf.setBackend('wasm');
-  tf.add(5, 3).print();
-}
-
-run();
diff --git a/tfjs-master/tfjs-backend-wasm/starter/webpack/webpack.config.js b/tfjs-master/tfjs-backend-wasm/starter/webpack/webpack.config.js
deleted file mode 100644
index 3f042cdf1..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/webpack/webpack.config.js
+++ /dev/null
@@ -1,29 +0,0 @@
-const HtmlWebpackPlugin = require('html-webpack-plugin');
-
-module.exports = {
-  mode: 'development',
-  entry: {
-    app: './src/index.js',
-  },
-  devServer: {
-    static: './dist',
-  },
-  plugins: [
-    new HtmlWebpackPlugin({
-      title: 'Webpack sample app',
-    }),
-  ],
-  module: {
-    rules: [
-      {
-        test: /\.wasm$/i,
-        type: 'javascript/auto',
-        use: [
-          {
-            loader: 'file-loader',
-          },
-        ],
-      },
-    ],
-  },
-};
diff --git a/tfjs-master/tfjs-backend-wasm/starter/webpack/yarn.lock b/tfjs-master/tfjs-backend-wasm/starter/webpack/yarn.lock
deleted file mode 100644
index 5b3f73bfb..000000000
--- a/tfjs-master/tfjs-backend-wasm/starter/webpack/yarn.lock
+++ /dev/null
@@ -1,2635 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@discoveryjs/json-ext@^0.5.0":
-  version "0.5.7"
-  resolved "https://registry.yarnpkg.com/@discoveryjs/json-ext/-/json-ext-0.5.7.tgz#1d572bfbbe14b7704e0ba0f39b74815b84870d70"
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-
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-  version "0.3.2"
-  resolved "https://registry.yarnpkg.com/@jridgewell/gen-mapping/-/gen-mapping-0.3.2.tgz#c1aedc61e853f2bb9f5dfe6d4442d3b565b253b9"
-  integrity sha512-mh65xKQAzI6iBcFzwv28KVWSmCkdRBWoOh+bYQGW3+6OZvbbN3TqMGo5hqYxQniRcH9F2VZIoJCm4pa3BPDK/A==
-  dependencies:
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-    "@jridgewell/sourcemap-codec" "^1.4.10"
-    "@jridgewell/trace-mapping" "^0.3.9"
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-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/@jridgewell/resolve-uri/-/resolve-uri-3.1.0.tgz#2203b118c157721addfe69d47b70465463066d78"
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-
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-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/@jridgewell/set-array/-/set-array-1.1.2.tgz#7c6cf998d6d20b914c0a55a91ae928ff25965e72"
-  integrity sha512-xnkseuNADM0gt2bs+BvhO0p78Mk762YnZdsuzFV018NoG1Sj1SCQvpSqa7XUaTam5vAGasABV9qXASMKnFMwMw==
-
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-  version "0.3.2"
-  resolved "https://registry.yarnpkg.com/@jridgewell/source-map/-/source-map-0.3.2.tgz#f45351aaed4527a298512ec72f81040c998580fb"
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-  dependencies:
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-  resolved "https://registry.yarnpkg.com/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.14.tgz#add4c98d341472a289190b424efbdb096991bb24"
-  integrity sha512-XPSJHWmi394fuUuzDnGz1wiKqWfo1yXecHQMRf2l6hztTO+nPru658AyDngaBe7isIxEkRsPR3FZh+s7iVa4Uw==
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-  version "0.3.14"
-  resolved "https://registry.yarnpkg.com/@jridgewell/trace-mapping/-/trace-mapping-0.3.14.tgz#b231a081d8f66796e475ad588a1ef473112701ed"
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-  version "2.1.5"
-  resolved "https://registry.yarnpkg.com/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz#7619c2eb21b25483f6d167548b4cfd5a7488c3d5"
-  integrity sha512-vq24Bq3ym5HEQm2NKCr3yXDwjc7vTsEThRDnkp2DK9p1uqLR+DHurm/NOTo0KG7HYHU7eppKZj3MyqYuMBf62g==
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-
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-  version "1.2.8"
-  resolved "https://registry.yarnpkg.com/@nodelib/fs.walk/-/fs.walk-1.2.8.tgz#e95737e8bb6746ddedf69c556953494f196fe69a"
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-  dependencies:
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-    fastq "^1.6.0"
-
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-  version "2.6.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-wasm/-/tfjs-backend-wasm-2.6.0.tgz#e6d43d09a52aa92050f29f779e1ae4f4afe03991"
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-
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-  version "2.6.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-2.6.0.tgz#ab2f2c9e8f46990643076d7d5bdb912885282bd2"
-  integrity "sha1-qy8sno9GmQZDB219W9uRKIUoK9I= sha512-akUB1iz663UCUdOfEUu91XeHzGpdYtdtMPxjsGEdF0CwENzSAcvHzQrEVoPBRD+RKpxrVXvQBoOd7GYBxMIIKQ=="
-  dependencies:
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-    "@types/seedrandom" "2.4.27"
-    "@types/webgl-ext" "0.0.30"
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-    node-fetch "~2.6.1"
-    seedrandom "2.4.3"
-
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-  version "1.19.2"
-  resolved "https://registry.yarnpkg.com/@types/body-parser/-/body-parser-1.19.2.tgz#aea2059e28b7658639081347ac4fab3de166e6f0"
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-  dependencies:
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-    "@types/node" "*"
-
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-  resolved "https://registry.yarnpkg.com/@types/connect-history-api-fallback/-/connect-history-api-fallback-1.3.5.tgz#d1f7a8a09d0ed5a57aee5ae9c18ab9b803205dae"
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-  dependencies:
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-    "@types/node" "*"
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-  dependencies:
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-    uri-js "^4.2.2"
-
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-  version "0.0.8"
-  resolved "https://registry.yarnpkg.com/ansi-html-community/-/ansi-html-community-0.0.8.tgz#69fbc4d6ccbe383f9736934ae34c3f8290f1bf41"
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-  version "5.0.1"
-  resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-5.0.1.tgz#082cb2c89c9fe8659a311a53bd6a4dc5301db304"
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-  version "6.0.1"
-  resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-6.0.1.tgz#3183e38fae9a65d7cb5e53945cd5897d0260a06a"
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-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/anymatch/-/anymatch-3.1.2.tgz#c0557c096af32f106198f4f4e2a383537e378716"
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-    picomatch "^2.0.4"
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-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/array-flatten/-/array-flatten-1.1.1.tgz#9a5f699051b1e7073328f2a008968b64ea2955d2"
-  integrity sha1-ml9pkFGx5wczKPKgCJaLZOopVdI=
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-  resolved "https://registry.yarnpkg.com/array-flatten/-/array-flatten-2.1.2.tgz#24ef80a28c1a893617e2149b0c6d0d788293b099"
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-  resolved "https://registry.yarnpkg.com/array-union/-/array-union-2.1.0.tgz#b798420adbeb1de828d84acd8a2e23d3efe85e8d"
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-  resolved "https://registry.yarnpkg.com/async/-/async-2.6.4.tgz#706b7ff6084664cd7eae713f6f965433b5504221"
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-  resolved "https://registry.yarnpkg.com/batch/-/batch-0.6.1.tgz#dc34314f4e679318093fc760272525f94bf25c16"
-  integrity sha1-3DQxT05nkxgJP8dgJyUl+UvyXBY=
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-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/binary-extensions/-/binary-extensions-2.2.0.tgz#75f502eeaf9ffde42fc98829645be4ea76bd9e2d"
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-  resolved "https://registry.yarnpkg.com/body-parser/-/body-parser-1.19.2.tgz#4714ccd9c157d44797b8b5607d72c0b89952f26e"
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-  version "3.5.0"
-  resolved "https://registry.yarnpkg.com/bonjour/-/bonjour-3.5.0.tgz#8e890a183d8ee9a2393b3844c691a42bcf7bc9f5"
-  integrity sha1-jokKGD2O6aI5OzhExpGkK897yfU=
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-    dns-equal "^1.0.0"
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-    multicast-dns "^6.0.1"
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/boolbase/-/boolbase-1.0.0.tgz#68dff5fbe60c51eb37725ea9e3ed310dcc1e776e"
-  integrity sha1-aN/1++YMUes3cl6p4+0xDcwed24=
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-  version "1.1.11"
-  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd"
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-  version "3.0.2"
-  resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.2.tgz#3454e1a462ee8d599e236df336cd9ea4f8afe107"
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-  resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.20.2.tgz#567b41508757ecd904dab4d1c646c612cd3d4f88"
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-    picocolors "^1.0.0"
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-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.2.tgz#2b146a6fd72e80b4f55d255f35ed59a3a9a41bd5"
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-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/buffer-indexof/-/buffer-indexof-1.1.1.tgz#52fabcc6a606d1a00302802648ef68f639da268c"
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-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/bytes/-/bytes-3.0.0.tgz#d32815404d689699f85a4ea4fa8755dd13a96048"
-  integrity sha1-0ygVQE1olpn4Wk6k+odV3ROpYEg=
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-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/bytes/-/bytes-3.1.2.tgz#8b0beeb98605adf1b128fa4386403c009e0221a5"
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-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/call-bind/-/call-bind-1.0.2.tgz#b1d4e89e688119c3c9a903ad30abb2f6a919be3c"
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-  version "4.1.2"
-  resolved "https://registry.yarnpkg.com/camel-case/-/camel-case-4.1.2.tgz#9728072a954f805228225a6deea6b38461e1bd5a"
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-    tslib "^2.0.3"
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-  resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001317.tgz#0548fb28fd5bc259a70b8c1ffdbe598037666a1b"
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-  version "3.5.3"
-  resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.5.3.tgz#1cf37c8707b932bd1af1ae22c0432e2acd1903bd"
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-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/chrome-trace-event/-/chrome-trace-event-1.0.3.tgz#1015eced4741e15d06664a957dbbf50d041e26ac"
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-  version "5.2.4"
-  resolved "https://registry.yarnpkg.com/clean-css/-/clean-css-5.2.4.tgz#982b058f8581adb2ae062520808fb2429bd487a4"
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-  dependencies:
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-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/clean-stack/-/clean-stack-2.2.0.tgz#ee8472dbb129e727b31e8a10a427dee9dfe4008b"
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-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/clone-deep/-/clone-deep-4.0.1.tgz#c19fd9bdbbf85942b4fd979c84dcf7d5f07c2387"
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-  version "2.20.3"
-  resolved "https://registry.yarnpkg.com/commander/-/commander-2.20.3.tgz#fd485e84c03eb4881c20722ba48035e8531aeb33"
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-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/commander/-/commander-7.2.0.tgz#a36cb57d0b501ce108e4d20559a150a391d97ab7"
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-  version "8.3.0"
-  resolved "https://registry.yarnpkg.com/commander/-/commander-8.3.0.tgz#4837ea1b2da67b9c616a67afbb0fafee567bca66"
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-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/compressible/-/compressible-2.0.18.tgz#af53cca6b070d4c3c0750fbd77286a6d7cc46fba"
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-  version "1.7.4"
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diff --git a/tfjs-master/tfjs-backend-wasm/tools/cpplint.py b/tfjs-master/tfjs-backend-wasm/tools/cpplint.py
deleted file mode 100644
index aea5da40c..000000000
--- a/tfjs-master/tfjs-backend-wasm/tools/cpplint.py
+++ /dev/null
@@ -1,6244 +0,0 @@
-#!/usr/bin/env python
-#
-# Copyright (c) 2009 Google LLC. All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are
-# met:
-#
-#    * Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-#    * Redistributions in binary form must reproduce the above
-# copyright notice, this list of conditions and the following disclaimer
-# in the documentation and/or other materials provided with the
-# distribution.
-#    * Neither the name of Google LLC nor the names of its
-# contributors may be used to endorse or promote products derived from
-# this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-"""Does google-lint on c++ files.
-
-The goal of this script is to identify places in the code that *may*
-be in non-compliance with google style.  It does not attempt to fix
-up these problems -- the point is to educate.  It does also not
-attempt to find all problems, or to ensure that everything it does
-find is legitimately a problem.
-
-In particular, we can get very confused by /* and // inside strings!
-We do a small hack, which is to ignore //'s with "'s after them on the
-same line, but it is far from perfect (in either direction).
-"""
-
-import codecs
-import copy
-import getopt
-import math  # for log
-import os
-import re
-import sre_compile
-import string
-import sys
-import unicodedata
-import sysconfig
-
-try:
-  xrange          # Python 2
-except NameError:
-  xrange = range  # Python 3
-
-
-_USAGE = """
-Syntax: cpplint.py [--verbose=#] [--output=vs7] [--filter=-x,+y,...]
-                   [--counting=total|toplevel|detailed] [--root=subdir]
-                   [--linelength=digits] [--headers=x,y,...]
-                   [--quiet]
-        <file> [file] ...
-
-  The style guidelines this tries to follow are those in
-    https://google-styleguide.googlecode.com/svn/trunk/cppguide.xml
-
-  Every problem is given a confidence score from 1-5, with 5 meaning we are
-  certain of the problem, and 1 meaning it could be a legitimate construct.
-  This will miss some errors, and is not a substitute for a code review.
-
-  To suppress false-positive errors of a certain category, add a
-  'NOLINT(category)' comment to the line.  NOLINT or NOLINT(*)
-  suppresses errors of all categories on that line.
-
-  The files passed in will be linted; at least one file must be provided.
-  Default linted extensions are .cc, .cpp, .cu, .cuh and .h.  Change the
-  extensions with the --extensions flag.
-
-  Flags:
-
-    output=vs7
-      By default, the output is formatted to ease emacs parsing.  Visual Studio
-      compatible output (vs7) may also be used.  Other formats are unsupported.
-
-    verbose=#
-      Specify a number 0-5 to restrict errors to certain verbosity levels.
-
-    quiet
-      Don't print anything if no errors are found.
-
-    filter=-x,+y,...
-      Specify a comma-separated list of category-filters to apply: only
-      error messages whose category names pass the filters will be printed.
-      (Category names are printed with the message and look like
-      "[whitespace/indent]".)  Filters are evaluated left to right.
-      "-FOO" and "FOO" means "do not print categories that start with FOO".
-      "+FOO" means "do print categories that start with FOO".
-
-      Examples: --filter=-whitespace,+whitespace/braces
-                --filter=whitespace,runtime/printf,+runtime/printf_format
-                --filter=-,+build/include_what_you_use
-
-      To see a list of all the categories used in cpplint, pass no arg:
-         --filter=
-
-    counting=total|toplevel|detailed
-      The total number of errors found is always printed. If
-      'toplevel' is provided, then the count of errors in each of
-      the top-level categories like 'build' and 'whitespace' will
-      also be printed. If 'detailed' is provided, then a count
-      is provided for each category like 'build/class'.
-
-    root=subdir
-      The root directory used for deriving header guard CPP variable.
-      By default, the header guard CPP variable is calculated as the relative
-      path to the directory that contains .git, .hg, or .svn.  When this flag
-      is specified, the relative path is calculated from the specified
-      directory. If the specified directory does not exist, this flag is
-      ignored.
-
-      Examples:
-        Assuming that top/src/.git exists (and cwd=top/src), the header guard
-        CPP variables for top/src/chrome/browser/ui/browser.h are:
-
-        No flag => CHROME_BROWSER_UI_BROWSER_H_
-        --root=chrome => BROWSER_UI_BROWSER_H_
-        --root=chrome/browser => UI_BROWSER_H_
-        --root=.. => SRC_CHROME_BROWSER_UI_BROWSER_H_
-
-    linelength=digits
-      This is the allowed line length for the project. The default value is
-      80 characters.
-
-      Examples:
-        --linelength=120
-
-    extensions=extension,extension,...
-      The allowed file extensions that cpplint will check
-
-      Examples:
-        --extensions=hpp,cpp
-
-    headers=x,y,...
-      The header extensions that cpplint will treat as .h in checks. Values are
-      automatically added to --extensions list.
-
-      Examples:
-        --headers=hpp,hxx
-        --headers=hpp
-
-    cpplint.py supports per-directory configurations specified in CPPLINT.cfg
-    files. CPPLINT.cfg file can contain a number of key=value pairs.
-    Currently the following options are supported:
-
-      set noparent
-      filter=+filter1,-filter2,...
-      exclude_files=regex
-      linelength=80
-      root=subdir
-      headers=x,y,...
-
-    "set noparent" option prevents cpplint from traversing directory tree
-    upwards looking for more .cfg files in parent directories. This option
-    is usually placed in the top-level project directory.
-
-    The "filter" option is similar in function to --filter flag. It specifies
-    message filters in addition to the |_DEFAULT_FILTERS| and those specified
-    through --filter command-line flag.
-
-    "exclude_files" allows to specify a regular expression to be matched against
-    a file name. If the expression matches, the file is skipped and not run
-    through liner.
-
-    "linelength" allows to specify the allowed line length for the project.
-
-    The "root" option is similar in function to the --root flag (see example
-    above). Paths are relative to the directory of the CPPLINT.cfg.
-
-    The "headers" option is similar in function to the --headers flag
-    (see example above).
-
-    CPPLINT.cfg has an effect on files in the same directory and all
-    sub-directories, unless overridden by a nested configuration file.
-
-      Example file:
-        filter=-build/include_order,+build/include_alpha
-        exclude_files=.*\.cc
-
-    The above example disables build/include_order warning and enables
-    build/include_alpha as well as excludes all .cc from being
-    processed by linter, in the current directory (where the .cfg
-    file is located) and all sub-directories.
-"""
-
-# We categorize each error message we print.  Here are the categories.
-# We want an explicit list so we can list them all in cpplint --filter=.
-# If you add a new error message with a new category, add it to the list
-# here!  cpplint_unittest.py should tell you if you forget to do this.
-_ERROR_CATEGORIES = [
-    'build/class',
-    'build/c++11',
-    'build/c++14',
-    'build/c++tr1',
-    'build/deprecated',
-    'build/endif_comment',
-    'build/explicit_make_pair',
-    'build/forward_decl',
-    'build/header_guard',
-    'build/include',
-    'build/include_alpha',
-    'build/include_order',
-    'build/include_what_you_use',
-    'build/namespaces',
-    'build/printf_format',
-    'build/storage_class',
-    'legal/copyright',
-    'readability/alt_tokens',
-    'readability/braces',
-    'readability/casting',
-    'readability/check',
-    'readability/constructors',
-    'readability/fn_size',
-    'readability/inheritance',
-    'readability/multiline_comment',
-    'readability/multiline_string',
-    'readability/namespace',
-    'readability/nolint',
-    'readability/nul',
-    'readability/strings',
-    'readability/todo',
-    'readability/utf8',
-    'runtime/arrays',
-    'runtime/casting',
-    'runtime/explicit',
-    'runtime/int',
-    'runtime/init',
-    'runtime/invalid_increment',
-    'runtime/member_string_references',
-    'runtime/memset',
-    'runtime/indentation_namespace',
-    'runtime/operator',
-    'runtime/printf',
-    'runtime/printf_format',
-    'runtime/references',
-    'runtime/string',
-    'runtime/threadsafe_fn',
-    'runtime/vlog',
-    'whitespace/blank_line',
-    'whitespace/braces',
-    'whitespace/comma',
-    'whitespace/comments',
-    'whitespace/empty_conditional_body',
-    'whitespace/empty_if_body',
-    'whitespace/empty_loop_body',
-    'whitespace/end_of_line',
-    'whitespace/ending_newline',
-    'whitespace/forcolon',
-    'whitespace/indent',
-    'whitespace/line_length',
-    'whitespace/newline',
-    'whitespace/operators',
-    'whitespace/parens',
-    'whitespace/semicolon',
-    'whitespace/tab',
-    'whitespace/todo',
-    ]
-
-# These error categories are no longer enforced by cpplint, but for backwards-
-# compatibility they may still appear in NOLINT comments.
-_LEGACY_ERROR_CATEGORIES = [
-    'readability/streams',
-    'readability/function',
-    ]
-
-# The default state of the category filter. This is overridden by the --filter=
-# flag. By default all errors are on, so only add here categories that should be
-# off by default (i.e., categories that must be enabled by the --filter= flags).
-# All entries here should start with a '-' or '+', as in the --filter= flag.
-_DEFAULT_FILTERS = ['-build/include_alpha']
-
-# The default list of categories suppressed for C (not C++) files.
-_DEFAULT_C_SUPPRESSED_CATEGORIES = [
-    'readability/casting',
-    ]
-
-# The default list of categories suppressed for Linux Kernel files.
-_DEFAULT_KERNEL_SUPPRESSED_CATEGORIES = [
-    'whitespace/tab',
-    ]
-
-# We used to check for high-bit characters, but after much discussion we
-# decided those were OK, as long as they were in UTF-8 and didn't represent
-# hard-coded international strings, which belong in a separate i18n file.
-
-# C++ headers
-_CPP_HEADERS = frozenset([
-    # Legacy
-    'algobase.h',
-    'algo.h',
-    'alloc.h',
-    'builtinbuf.h',
-    'bvector.h',
-    'complex.h',
-    'defalloc.h',
-    'deque.h',
-    'editbuf.h',
-    'fstream.h',
-    'function.h',
-    'hash_map',
-    'hash_map.h',
-    'hash_set',
-    'hash_set.h',
-    'hashtable.h',
-    'heap.h',
-    'indstream.h',
-    'iomanip.h',
-    'iostream.h',
-    'istream.h',
-    'iterator.h',
-    'list.h',
-    'map.h',
-    'multimap.h',
-    'multiset.h',
-    'ostream.h',
-    'pair.h',
-    'parsestream.h',
-    'pfstream.h',
-    'procbuf.h',
-    'pthread_alloc',
-    'pthread_alloc.h',
-    'rope',
-    'rope.h',
-    'ropeimpl.h',
-    'set.h',
-    'slist',
-    'slist.h',
-    'stack.h',
-    'stdiostream.h',
-    'stl_alloc.h',
-    'stl_relops.h',
-    'streambuf.h',
-    'stream.h',
-    'strfile.h',
-    'strstream.h',
-    'tempbuf.h',
-    'tree.h',
-    'type_traits.h',
-    'vector.h',
-    # 17.6.1.2 C++ library headers
-    'algorithm',
-    'array',
-    'atomic',
-    'bitset',
-    'chrono',
-    'codecvt',
-    'complex',
-    'condition_variable',
-    'deque',
-    'exception',
-    'forward_list',
-    'fstream',
-    'functional',
-    'future',
-    'initializer_list',
-    'iomanip',
-    'ios',
-    'iosfwd',
-    'iostream',
-    'istream',
-    'iterator',
-    'limits',
-    'list',
-    'locale',
-    'map',
-    'memory',
-    'mutex',
-    'new',
-    'numeric',
-    'ostream',
-    'queue',
-    'random',
-    'ratio',
-    'regex',
-    'scoped_allocator',
-    'set',
-    'sstream',
-    'stack',
-    'stdexcept',
-    'streambuf',
-    'string',
-    'strstream',
-    'system_error',
-    'thread',
-    'tuple',
-    'typeindex',
-    'typeinfo',
-    'type_traits',
-    'unordered_map',
-    'unordered_set',
-    'utility',
-    'valarray',
-    'vector',
-    # 17.6.1.2 C++ headers for C library facilities
-    'cassert',
-    'ccomplex',
-    'cctype',
-    'cerrno',
-    'cfenv',
-    'cfloat',
-    'cinttypes',
-    'ciso646',
-    'climits',
-    'clocale',
-    'cmath',
-    'csetjmp',
-    'csignal',
-    'cstdalign',
-    'cstdarg',
-    'cstdbool',
-    'cstddef',
-    'cstdint',
-    'cstdio',
-    'cstdlib',
-    'cstring',
-    'ctgmath',
-    'ctime',
-    'cuchar',
-    'cwchar',
-    'cwctype',
-    ])
-
-# Type names
-_TYPES = re.compile(
-    r'^(?:'
-    # [dcl.type.simple]
-    r'(char(16_t|32_t)?)|wchar_t|'
-    r'bool|short|int|long|signed|unsigned|float|double|'
-    # [support.types]
-    r'(ptrdiff_t|size_t|max_align_t|nullptr_t)|'
-    # [cstdint.syn]
-    r'(u?int(_fast|_least)?(8|16|32|64)_t)|'
-    r'(u?int(max|ptr)_t)|'
-    r')$')
-
-
-# These headers are excluded from [build/include] and [build/include_order]
-# checks:
-# - Anything not following google file name conventions (containing an
-#   uppercase character, such as Python.h or nsStringAPI.h, for example).
-# - Lua headers.
-_THIRD_PARTY_HEADERS_PATTERN = re.compile(
-    r'^(?:[^/]*[A-Z][^/]*\.h|lua\.h|lauxlib\.h|lualib\.h)$')
-
-# Pattern for matching FileInfo.BaseName() against test file name
-_TEST_FILE_SUFFIX = r'(_test|_unittest|_regtest)$'
-
-# Pattern that matches only complete whitespace, possibly across multiple lines.
-_EMPTY_CONDITIONAL_BODY_PATTERN = re.compile(r'^\s*$', re.DOTALL)
-
-# Assertion macros.  These are defined in base/logging.h and
-# testing/base/public/gunit.h.
-_CHECK_MACROS = [
-    'DCHECK', 'CHECK',
-    'EXPECT_TRUE', 'ASSERT_TRUE',
-    'EXPECT_FALSE', 'ASSERT_FALSE',
-    ]
-
-# Replacement macros for CHECK/DCHECK/EXPECT_TRUE/EXPECT_FALSE
-_CHECK_REPLACEMENT = dict([(m, {}) for m in _CHECK_MACROS])
-
-for op, replacement in [('==', 'EQ'), ('!=', 'NE'),
-                        ('>=', 'GE'), ('>', 'GT'),
-                        ('<=', 'LE'), ('<', 'LT')]:
-  _CHECK_REPLACEMENT['DCHECK'][op] = 'DCHECK_%s' % replacement
-  _CHECK_REPLACEMENT['CHECK'][op] = 'CHECK_%s' % replacement
-  _CHECK_REPLACEMENT['EXPECT_TRUE'][op] = 'EXPECT_%s' % replacement
-  _CHECK_REPLACEMENT['ASSERT_TRUE'][op] = 'ASSERT_%s' % replacement
-
-for op, inv_replacement in [('==', 'NE'), ('!=', 'EQ'),
-                            ('>=', 'LT'), ('>', 'LE'),
-                            ('<=', 'GT'), ('<', 'GE')]:
-  _CHECK_REPLACEMENT['EXPECT_FALSE'][op] = 'EXPECT_%s' % inv_replacement
-  _CHECK_REPLACEMENT['ASSERT_FALSE'][op] = 'ASSERT_%s' % inv_replacement
-
-# Alternative tokens and their replacements.  For full list, see section 2.5
-# Alternative tokens [lex.digraph] in the C++ standard.
-#
-# Digraphs (such as '%:') are not included here since it's a mess to
-# match those on a word boundary.
-_ALT_TOKEN_REPLACEMENT = {
-    'and': '&&',
-    'bitor': '|',
-    'or': '||',
-    'xor': '^',
-    'compl': '~',
-    'bitand': '&',
-    'and_eq': '&=',
-    'or_eq': '|=',
-    'xor_eq': '^=',
-    'not': '!',
-    'not_eq': '!='
-    }
-
-# Compile regular expression that matches all the above keywords.  The "[ =()]"
-# bit is meant to avoid matching these keywords outside of boolean expressions.
-#
-# False positives include C-style multi-line comments and multi-line strings
-# but those have always been troublesome for cpplint.
-_ALT_TOKEN_REPLACEMENT_PATTERN = re.compile(
-    r'[ =()](' + ('|'.join(_ALT_TOKEN_REPLACEMENT.keys())) + r')(?=[ (]|$)')
-
-
-# These constants define types of headers for use with
-# _IncludeState.CheckNextIncludeOrder().
-_C_SYS_HEADER = 1
-_CPP_SYS_HEADER = 2
-_LIKELY_MY_HEADER = 3
-_POSSIBLE_MY_HEADER = 4
-_OTHER_HEADER = 5
-
-# These constants define the current inline assembly state
-_NO_ASM = 0       # Outside of inline assembly block
-_INSIDE_ASM = 1   # Inside inline assembly block
-_END_ASM = 2      # Last line of inline assembly block
-_BLOCK_ASM = 3    # The whole block is an inline assembly block
-
-# Match start of assembly blocks
-_MATCH_ASM = re.compile(r'^\s*(?:asm|_asm|__asm|__asm__)'
-                        r'(?:\s+(volatile|__volatile__))?'
-                        r'\s*[{(]')
-
-# Match strings that indicate we're working on a C (not C++) file.
-_SEARCH_C_FILE = re.compile(r'\b(?:LINT_C_FILE|'
-                            r'vim?:\s*.*(\s*|:)filetype=c(\s*|:|$))')
-
-# Match string that indicates we're working on a Linux Kernel file.
-_SEARCH_KERNEL_FILE = re.compile(r'\b(?:LINT_KERNEL_FILE)')
-
-_regexp_compile_cache = {}
-
-# {str, set(int)}: a map from error categories to sets of linenumbers
-# on which those errors are expected and should be suppressed.
-_error_suppressions = {}
-
-# The root directory used for deriving header guard CPP variable.
-# This is set by --root flag.
-_root = None
-_root_debug = False
-
-# The allowed line length of files.
-# This is set by --linelength flag.
-_line_length = 80
-
-# The allowed extensions for file names
-# This is set by --extensions flag.
-_valid_extensions = set(['cc', 'h', 'cpp', 'cu', 'cuh'])
-
-# Treat all headers starting with 'h' equally: .h, .hpp, .hxx etc.
-# This is set by --headers flag.
-_hpp_headers = set(['h'])
-
-# {str, bool}: a map from error categories to booleans which indicate if the
-# category should be suppressed for every line.
-_global_error_suppressions = {}
-
-def ProcessHppHeadersOption(val):
-  global _hpp_headers
-  try:
-    _hpp_headers = set(val.split(','))
-    # Automatically append to extensions list so it does not have to be set 2 times
-    _valid_extensions.update(_hpp_headers)
-  except ValueError:
-    PrintUsage('Header extensions must be comma separated list.')
-
-def IsHeaderExtension(file_extension):
-  return file_extension in _hpp_headers
-
-def ParseNolintSuppressions(filename, raw_line, linenum, error):
-  """Updates the global list of line error-suppressions.
-
-  Parses any NOLINT comments on the current line, updating the global
-  error_suppressions store.  Reports an error if the NOLINT comment
-  was malformed.
-
-  Args:
-    filename: str, the name of the input file.
-    raw_line: str, the line of input text, with comments.
-    linenum: int, the number of the current line.
-    error: function, an error handler.
-  """
-  matched = Search(r'\bNOLINT(NEXTLINE)?\b(\([^)]+\))?', raw_line)
-  if matched:
-    if matched.group(1):
-      suppressed_line = linenum + 1
-    else:
-      suppressed_line = linenum
-    category = matched.group(2)
-    if category in (None, '(*)'):  # => "suppress all"
-      _error_suppressions.setdefault(None, set()).add(suppressed_line)
-    else:
-      if category.startswith('(') and category.endswith(')'):
-        category = category[1:-1]
-        if category in _ERROR_CATEGORIES:
-          _error_suppressions.setdefault(category, set()).add(suppressed_line)
-        elif category not in _LEGACY_ERROR_CATEGORIES:
-          error(filename, linenum, 'readability/nolint', 5,
-                'Unknown NOLINT error category: %s' % category)
-
-
-def ProcessGlobalSuppresions(lines):
-  """Updates the list of global error suppressions.
-
-  Parses any lint directives in the file that have global effect.
-
-  Args:
-    lines: An array of strings, each representing a line of the file, with the
-           last element being empty if the file is terminated with a newline.
-  """
-  for line in lines:
-    if _SEARCH_C_FILE.search(line):
-      for category in _DEFAULT_C_SUPPRESSED_CATEGORIES:
-        _global_error_suppressions[category] = True
-    if _SEARCH_KERNEL_FILE.search(line):
-      for category in _DEFAULT_KERNEL_SUPPRESSED_CATEGORIES:
-        _global_error_suppressions[category] = True
-
-
-def ResetNolintSuppressions():
-  """Resets the set of NOLINT suppressions to empty."""
-  _error_suppressions.clear()
-  _global_error_suppressions.clear()
-
-
-def IsErrorSuppressedByNolint(category, linenum):
-  """Returns true if the specified error category is suppressed on this line.
-
-  Consults the global error_suppressions map populated by
-  ParseNolintSuppressions/ProcessGlobalSuppresions/ResetNolintSuppressions.
-
-  Args:
-    category: str, the category of the error.
-    linenum: int, the current line number.
-  Returns:
-    bool, True iff the error should be suppressed due to a NOLINT comment or
-    global suppression.
-  """
-  return (_global_error_suppressions.get(category, False) or
-          linenum in _error_suppressions.get(category, set()) or
-          linenum in _error_suppressions.get(None, set()))
-
-
-def Match(pattern, s):
-  """Matches the string with the pattern, caching the compiled regexp."""
-  # The regexp compilation caching is inlined in both Match and Search for
-  # performance reasons; factoring it out into a separate function turns out
-  # to be noticeably expensive.
-  if pattern not in _regexp_compile_cache:
-    _regexp_compile_cache[pattern] = sre_compile.compile(pattern)
-  return _regexp_compile_cache[pattern].match(s)
-
-
-def ReplaceAll(pattern, rep, s):
-  """Replaces instances of pattern in a string with a replacement.
-
-  The compiled regex is kept in a cache shared by Match and Search.
-
-  Args:
-    pattern: regex pattern
-    rep: replacement text
-    s: search string
-
-  Returns:
-    string with replacements made (or original string if no replacements)
-  """
-  if pattern not in _regexp_compile_cache:
-    _regexp_compile_cache[pattern] = sre_compile.compile(pattern)
-  return _regexp_compile_cache[pattern].sub(rep, s)
-
-
-def Search(pattern, s):
-  """Searches the string for the pattern, caching the compiled regexp."""
-  if pattern not in _regexp_compile_cache:
-    _regexp_compile_cache[pattern] = sre_compile.compile(pattern)
-  return _regexp_compile_cache[pattern].search(s)
-
-
-def _IsSourceExtension(s):
-  """File extension (excluding dot) matches a source file extension."""
-  return s in ('c', 'cc', 'cpp', 'cxx')
-
-
-class _IncludeState(object):
-  """Tracks line numbers for includes, and the order in which includes appear.
-
-  include_list contains list of lists of (header, line number) pairs.
-  It's a lists of lists rather than just one flat list to make it
-  easier to update across preprocessor boundaries.
-
-  Call CheckNextIncludeOrder() once for each header in the file, passing
-  in the type constants defined above. Calls in an illegal order will
-  raise an _IncludeError with an appropriate error message.
-
-  """
-  # self._section will move monotonically through this set. If it ever
-  # needs to move backwards, CheckNextIncludeOrder will raise an error.
-  _INITIAL_SECTION = 0
-  _MY_H_SECTION = 1
-  _C_SECTION = 2
-  _CPP_SECTION = 3
-  _OTHER_H_SECTION = 4
-
-  _TYPE_NAMES = {
-      _C_SYS_HEADER: 'C system header',
-      _CPP_SYS_HEADER: 'C++ system header',
-      _LIKELY_MY_HEADER: 'header this file implements',
-      _POSSIBLE_MY_HEADER: 'header this file may implement',
-      _OTHER_HEADER: 'other header',
-      }
-  _SECTION_NAMES = {
-      _INITIAL_SECTION: "... nothing. (This can't be an error.)",
-      _MY_H_SECTION: 'a header this file implements',
-      _C_SECTION: 'C system header',
-      _CPP_SECTION: 'C++ system header',
-      _OTHER_H_SECTION: 'other header',
-      }
-
-  def __init__(self):
-    self.include_list = [[]]
-    self.ResetSection('')
-
-  def FindHeader(self, header):
-    """Check if a header has already been included.
-
-    Args:
-      header: header to check.
-    Returns:
-      Line number of previous occurrence, or -1 if the header has not
-      been seen before.
-    """
-    for section_list in self.include_list:
-      for f in section_list:
-        if f[0] == header:
-          return f[1]
-    return -1
-
-  def ResetSection(self, directive):
-    """Reset section checking for preprocessor directive.
-
-    Args:
-      directive: preprocessor directive (e.g. "if", "else").
-    """
-    # The name of the current section.
-    self._section = self._INITIAL_SECTION
-    # The path of last found header.
-    self._last_header = ''
-
-    # Update list of includes.  Note that we never pop from the
-    # include list.
-    if directive in ('if', 'ifdef', 'ifndef'):
-      self.include_list.append([])
-    elif directive in ('else', 'elif'):
-      self.include_list[-1] = []
-
-  def SetLastHeader(self, header_path):
-    self._last_header = header_path
-
-  def CanonicalizeAlphabeticalOrder(self, header_path):
-    """Returns a path canonicalized for alphabetical comparison.
-
-    - replaces "-" with "_" so they both cmp the same.
-    - removes '-inl' since we don't require them to be after the main header.
-    - lowercase everything, just in case.
-
-    Args:
-      header_path: Path to be canonicalized.
-
-    Returns:
-      Canonicalized path.
-    """
-    return header_path.replace('-inl.h', '.h').replace('-', '_').lower()
-
-  def IsInAlphabeticalOrder(self, clean_lines, linenum, header_path):
-    """Check if a header is in alphabetical order with the previous header.
-
-    Args:
-      clean_lines: A CleansedLines instance containing the file.
-      linenum: The number of the line to check.
-      header_path: Canonicalized header to be checked.
-
-    Returns:
-      Returns true if the header is in alphabetical order.
-    """
-    # If previous section is different from current section, _last_header will
-    # be reset to empty string, so it's always less than current header.
-    #
-    # If previous line was a blank line, assume that the headers are
-    # intentionally sorted the way they are.
-    if (self._last_header > header_path and
-        Match(r'^\s*#\s*include\b', clean_lines.elided[linenum - 1])):
-      return False
-    return True
-
-  def CheckNextIncludeOrder(self, header_type):
-    """Returns a non-empty error message if the next header is out of order.
-
-    This function also updates the internal state to be ready to check
-    the next include.
-
-    Args:
-      header_type: One of the _XXX_HEADER constants defined above.
-
-    Returns:
-      The empty string if the header is in the right order, or an
-      error message describing what's wrong.
-
-    """
-    error_message = ('Found %s after %s' %
-                     (self._TYPE_NAMES[header_type],
-                      self._SECTION_NAMES[self._section]))
-
-    last_section = self._section
-
-    if header_type == _C_SYS_HEADER:
-      if self._section <= self._C_SECTION:
-        self._section = self._C_SECTION
-      else:
-        self._last_header = ''
-        return error_message
-    elif header_type == _CPP_SYS_HEADER:
-      if self._section <= self._CPP_SECTION:
-        self._section = self._CPP_SECTION
-      else:
-        self._last_header = ''
-        return error_message
-    elif header_type == _LIKELY_MY_HEADER:
-      if self._section <= self._MY_H_SECTION:
-        self._section = self._MY_H_SECTION
-      else:
-        self._section = self._OTHER_H_SECTION
-    elif header_type == _POSSIBLE_MY_HEADER:
-      if self._section <= self._MY_H_SECTION:
-        self._section = self._MY_H_SECTION
-      else:
-        # This will always be the fallback because we're not sure
-        # enough that the header is associated with this file.
-        self._section = self._OTHER_H_SECTION
-    else:
-      assert header_type == _OTHER_HEADER
-      self._section = self._OTHER_H_SECTION
-
-    if last_section != self._section:
-      self._last_header = ''
-
-    return ''
-
-
-class _CppLintState(object):
-  """Maintains module-wide state.."""
-
-  def __init__(self):
-    self.verbose_level = 1  # global setting.
-    self.error_count = 0    # global count of reported errors
-    # filters to apply when emitting error messages
-    self.filters = _DEFAULT_FILTERS[:]
-    # backup of filter list. Used to restore the state after each file.
-    self._filters_backup = self.filters[:]
-    self.counting = 'total'  # In what way are we counting errors?
-    self.errors_by_category = {}  # string to int dict storing error counts
-    self.quiet = False  # Suppress non-error messagess?
-
-    # output format:
-    # "emacs" - format that emacs can parse (default)
-    # "vs7" - format that Microsoft Visual Studio 7 can parse
-    self.output_format = 'emacs'
-
-  def SetOutputFormat(self, output_format):
-    """Sets the output format for errors."""
-    self.output_format = output_format
-
-  def SetQuiet(self, quiet):
-    """Sets the module's quiet settings, and returns the previous setting."""
-    last_quiet = self.quiet
-    self.quiet = quiet
-    return last_quiet
-
-  def SetVerboseLevel(self, level):
-    """Sets the module's verbosity, and returns the previous setting."""
-    last_verbose_level = self.verbose_level
-    self.verbose_level = level
-    return last_verbose_level
-
-  def SetCountingStyle(self, counting_style):
-    """Sets the module's counting options."""
-    self.counting = counting_style
-
-  def SetFilters(self, filters):
-    """Sets the error-message filters.
-
-    These filters are applied when deciding whether to emit a given
-    error message.
-
-    Args:
-      filters: A string of comma-separated filters (eg "+whitespace/indent").
-               Each filter should start with + or -; else we die.
-
-    Raises:
-      ValueError: The comma-separated filters did not all start with '+' or '-'.
-                  E.g. "-,+whitespace,-whitespace/indent,whitespace/badfilter"
-    """
-    # Default filters always have less priority than the flag ones.
-    self.filters = _DEFAULT_FILTERS[:]
-    self.AddFilters(filters)
-
-  def AddFilters(self, filters):
-    """ Adds more filters to the existing list of error-message filters. """
-    for filt in filters.split(','):
-      clean_filt = filt.strip()
-      if clean_filt:
-        self.filters.append(clean_filt)
-    for filt in self.filters:
-      if not (filt.startswith('+') or filt.startswith('-')):
-        raise ValueError('Every filter in --filters must start with + or -'
-                         ' (%s does not)' % filt)
-
-  def BackupFilters(self):
-    """ Saves the current filter list to backup storage."""
-    self._filters_backup = self.filters[:]
-
-  def RestoreFilters(self):
-    """ Restores filters previously backed up."""
-    self.filters = self._filters_backup[:]
-
-  def ResetErrorCounts(self):
-    """Sets the module's error statistic back to zero."""
-    self.error_count = 0
-    self.errors_by_category = {}
-
-  def IncrementErrorCount(self, category):
-    """Bumps the module's error statistic."""
-    self.error_count += 1
-    if self.counting in ('toplevel', 'detailed'):
-      if self.counting != 'detailed':
-        category = category.split('/')[0]
-      if category not in self.errors_by_category:
-        self.errors_by_category[category] = 0
-      self.errors_by_category[category] += 1
-
-  def PrintErrorCounts(self):
-    """Print a summary of errors by category, and the total."""
-    for category, count in self.errors_by_category.iteritems():
-      sys.stderr.write('Category \'%s\' errors found: %d\n' %
-                       (category, count))
-    sys.stdout.write('Total errors found: %d\n' % self.error_count)
-
-_cpplint_state = _CppLintState()
-
-
-def _OutputFormat():
-  """Gets the module's output format."""
-  return _cpplint_state.output_format
-
-
-def _SetOutputFormat(output_format):
-  """Sets the module's output format."""
-  _cpplint_state.SetOutputFormat(output_format)
-
-def _Quiet():
-  """Return's the module's quiet setting."""
-  return _cpplint_state.quiet
-
-def _SetQuiet(quiet):
-  """Set the module's quiet status, and return previous setting."""
-  return _cpplint_state.SetQuiet(quiet)
-
-
-def _VerboseLevel():
-  """Returns the module's verbosity setting."""
-  return _cpplint_state.verbose_level
-
-
-def _SetVerboseLevel(level):
-  """Sets the module's verbosity, and returns the previous setting."""
-  return _cpplint_state.SetVerboseLevel(level)
-
-
-def _SetCountingStyle(level):
-  """Sets the module's counting options."""
-  _cpplint_state.SetCountingStyle(level)
-
-
-def _Filters():
-  """Returns the module's list of output filters, as a list."""
-  return _cpplint_state.filters
-
-
-def _SetFilters(filters):
-  """Sets the module's error-message filters.
-
-  These filters are applied when deciding whether to emit a given
-  error message.
-
-  Args:
-    filters: A string of comma-separated filters (eg "whitespace/indent").
-             Each filter should start with + or -; else we die.
-  """
-  _cpplint_state.SetFilters(filters)
-
-def _AddFilters(filters):
-  """Adds more filter overrides.
-
-  Unlike _SetFilters, this function does not reset the current list of filters
-  available.
-
-  Args:
-    filters: A string of comma-separated filters (eg "whitespace/indent").
-             Each filter should start with + or -; else we die.
-  """
-  _cpplint_state.AddFilters(filters)
-
-def _BackupFilters():
-  """ Saves the current filter list to backup storage."""
-  _cpplint_state.BackupFilters()
-
-def _RestoreFilters():
-  """ Restores filters previously backed up."""
-  _cpplint_state.RestoreFilters()
-
-class _FunctionState(object):
-  """Tracks current function name and the number of lines in its body."""
-
-  _NORMAL_TRIGGER = 250  # for --v=0, 500 for --v=1, etc.
-  _TEST_TRIGGER = 400    # about 50% more than _NORMAL_TRIGGER.
-
-  def __init__(self):
-    self.in_a_function = False
-    self.lines_in_function = 0
-    self.current_function = ''
-
-  def Begin(self, function_name):
-    """Start analyzing function body.
-
-    Args:
-      function_name: The name of the function being tracked.
-    """
-    self.in_a_function = True
-    self.lines_in_function = 0
-    self.current_function = function_name
-
-  def Count(self):
-    """Count line in current function body."""
-    if self.in_a_function:
-      self.lines_in_function += 1
-
-  def Check(self, error, filename, linenum):
-    """Report if too many lines in function body.
-
-    Args:
-      error: The function to call with any errors found.
-      filename: The name of the current file.
-      linenum: The number of the line to check.
-    """
-    if not self.in_a_function:
-      return
-
-    if Match(r'T(EST|est)', self.current_function):
-      base_trigger = self._TEST_TRIGGER
-    else:
-      base_trigger = self._NORMAL_TRIGGER
-    trigger = base_trigger * 2**_VerboseLevel()
-
-    if self.lines_in_function > trigger:
-      error_level = int(math.log(self.lines_in_function / base_trigger, 2))
-      # 50 => 0, 100 => 1, 200 => 2, 400 => 3, 800 => 4, 1600 => 5, ...
-      if error_level > 5:
-        error_level = 5
-      error(filename, linenum, 'readability/fn_size', error_level,
-            'Small and focused functions are preferred:'
-            ' %s has %d non-comment lines'
-            ' (error triggered by exceeding %d lines).'  % (
-                self.current_function, self.lines_in_function, trigger))
-
-  def End(self):
-    """Stop analyzing function body."""
-    self.in_a_function = False
-
-
-class _IncludeError(Exception):
-  """Indicates a problem with the include order in a file."""
-  pass
-
-
-class FileInfo(object):
-  """Provides utility functions for filenames.
-
-  FileInfo provides easy access to the components of a file's path
-  relative to the project root.
-  """
-
-  def __init__(self, filename):
-    self._filename = filename
-
-  def FullName(self):
-    """Make Windows paths like Unix."""
-    return os.path.abspath(self._filename).replace('\\', '/')
-
-  def RepositoryName(self):
-    """FullName after removing the local path to the repository.
-
-    If we have a real absolute path name here we can try to do something smart:
-    detecting the root of the checkout and truncating /path/to/checkout from
-    the name so that we get header guards that don't include things like
-    "C:\Documents and Settings\..." or "/home/username/..." in them and thus
-    people on different computers who have checked the source out to different
-    locations won't see bogus errors.
-    """
-    fullname = self.FullName()
-
-    if os.path.exists(fullname):
-      project_dir = os.path.dirname(fullname)
-
-      if os.path.exists(os.path.join(project_dir, ".svn")):
-        # If there's a .svn file in the current directory, we recursively look
-        # up the directory tree for the top of the SVN checkout
-        root_dir = project_dir
-        one_up_dir = os.path.dirname(root_dir)
-        while os.path.exists(os.path.join(one_up_dir, ".svn")):
-          root_dir = os.path.dirname(root_dir)
-          one_up_dir = os.path.dirname(one_up_dir)
-
-        prefix = os.path.commonprefix([root_dir, project_dir])
-        return fullname[len(prefix) + 1:]
-
-      # Not SVN <= 1.6? Try to find a git, hg, or svn top level directory by
-      # searching up from the current path.
-      root_dir = current_dir = os.path.dirname(fullname)
-      while current_dir != os.path.dirname(current_dir):
-        if (os.path.exists(os.path.join(current_dir, ".git")) or
-            os.path.exists(os.path.join(current_dir, ".hg")) or
-            os.path.exists(os.path.join(current_dir, ".svn"))):
-          root_dir = current_dir
-        current_dir = os.path.dirname(current_dir)
-
-      if (os.path.exists(os.path.join(root_dir, ".git")) or
-          os.path.exists(os.path.join(root_dir, ".hg")) or
-          os.path.exists(os.path.join(root_dir, ".svn"))):
-        prefix = os.path.commonprefix([root_dir, project_dir])
-        return fullname[len(prefix) + 1:]
-
-    # Don't know what to do; header guard warnings may be wrong...
-    return fullname
-
-  def Split(self):
-    """Splits the file into the directory, basename, and extension.
-
-    For 'chrome/browser/browser.cc', Split() would
-    return ('chrome/browser', 'browser', '.cc')
-
-    Returns:
-      A tuple of (directory, basename, extension).
-    """
-
-    googlename = self.RepositoryName()
-    project, rest = os.path.split(googlename)
-    return (project,) + os.path.splitext(rest)
-
-  def BaseName(self):
-    """File base name - text after the final slash, before the final period."""
-    return self.Split()[1]
-
-  def Extension(self):
-    """File extension - text following the final period."""
-    return self.Split()[2]
-
-  def NoExtension(self):
-    """File has no source file extension."""
-    return '/'.join(self.Split()[0:2])
-
-  def IsSource(self):
-    """File has a source file extension."""
-    return _IsSourceExtension(self.Extension()[1:])
-
-
-def _ShouldPrintError(category, confidence, linenum):
-  """If confidence >= verbose, category passes filter and is not suppressed."""
-
-  # There are three ways we might decide not to print an error message:
-  # a "NOLINT(category)" comment appears in the source,
-  # the verbosity level isn't high enough, or the filters filter it out.
-  if IsErrorSuppressedByNolint(category, linenum):
-    return False
-
-  if confidence < _cpplint_state.verbose_level:
-    return False
-
-  is_filtered = False
-  for one_filter in _Filters():
-    if one_filter.startswith('-'):
-      if category.startswith(one_filter[1:]):
-        is_filtered = True
-    elif one_filter.startswith('+'):
-      if category.startswith(one_filter[1:]):
-        is_filtered = False
-    else:
-      assert False  # should have been checked for in SetFilter.
-  if is_filtered:
-    return False
-
-  return True
-
-
-def Error(filename, linenum, category, confidence, message):
-  """Logs the fact we've found a lint error.
-
-  We log where the error was found, and also our confidence in the error,
-  that is, how certain we are this is a legitimate style regression, and
-  not a misidentification or a use that's sometimes justified.
-
-  False positives can be suppressed by the use of
-  "cpplint(category)"  comments on the offending line.  These are
-  parsed into _error_suppressions.
-
-  Args:
-    filename: The name of the file containing the error.
-    linenum: The number of the line containing the error.
-    category: A string used to describe the "category" this bug
-      falls under: "whitespace", say, or "runtime".  Categories
-      may have a hierarchy separated by slashes: "whitespace/indent".
-    confidence: A number from 1-5 representing a confidence score for
-      the error, with 5 meaning that we are certain of the problem,
-      and 1 meaning that it could be a legitimate construct.
-    message: The error message.
-  """
-  if _ShouldPrintError(category, confidence, linenum):
-    _cpplint_state.IncrementErrorCount(category)
-    if _cpplint_state.output_format == 'vs7':
-      sys.stderr.write('%s(%s): error cpplint: [%s] %s [%d]\n' % (
-          filename, linenum, category, message, confidence))
-    elif _cpplint_state.output_format == 'eclipse':
-      sys.stderr.write('%s:%s: warning: %s  [%s] [%d]\n' % (
-          filename, linenum, message, category, confidence))
-    else:
-      sys.stderr.write('%s:%s:  %s  [%s] [%d]\n' % (
-          filename, linenum, message, category, confidence))
-
-
-# Matches standard C++ escape sequences per 2.13.2.3 of the C++ standard.
-_RE_PATTERN_CLEANSE_LINE_ESCAPES = re.compile(
-    r'\\([abfnrtv?"\\\']|\d+|x[0-9a-fA-F]+)')
-# Match a single C style comment on the same line.
-_RE_PATTERN_C_COMMENTS = r'/\*(?:[^*]|\*(?!/))*\*/'
-# Matches multi-line C style comments.
-# This RE is a little bit more complicated than one might expect, because we
-# have to take care of space removals tools so we can handle comments inside
-# statements better.
-# The current rule is: We only clear spaces from both sides when we're at the
-# end of the line. Otherwise, we try to remove spaces from the right side,
-# if this doesn't work we try on left side but only if there's a non-character
-# on the right.
-_RE_PATTERN_CLEANSE_LINE_C_COMMENTS = re.compile(
-    r'(\s*' + _RE_PATTERN_C_COMMENTS + r'\s*$|' +
-    _RE_PATTERN_C_COMMENTS + r'\s+|' +
-    r'\s+' + _RE_PATTERN_C_COMMENTS + r'(?=\W)|' +
-    _RE_PATTERN_C_COMMENTS + r')')
-
-
-def IsCppString(line):
-  """Does line terminate so, that the next symbol is in string constant.
-
-  This function does not consider single-line nor multi-line comments.
-
-  Args:
-    line: is a partial line of code starting from the 0..n.
-
-  Returns:
-    True, if next character appended to 'line' is inside a
-    string constant.
-  """
-
-  line = line.replace(r'\\', 'XX')  # after this, \\" does not match to \"
-  return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1
-
-
-def CleanseRawStrings(raw_lines):
-  """Removes C++11 raw strings from lines.
-
-    Before:
-      static const char kData[] = R"(
-          multi-line string
-          )";
-
-    After:
-      static const char kData[] = ""
-          (replaced by blank line)
-          "";
-
-  Args:
-    raw_lines: list of raw lines.
-
-  Returns:
-    list of lines with C++11 raw strings replaced by empty strings.
-  """
-
-  delimiter = None
-  lines_without_raw_strings = []
-  for line in raw_lines:
-    if delimiter:
-      # Inside a raw string, look for the end
-      end = line.find(delimiter)
-      if end >= 0:
-        # Found the end of the string, match leading space for this
-        # line and resume copying the original lines, and also insert
-        # a "" on the last line.
-        leading_space = Match(r'^(\s*)\S', line)
-        line = leading_space.group(1) + '""' + line[end + len(delimiter):]
-        delimiter = None
-      else:
-        # Haven't found the end yet, append a blank line.
-        line = '""'
-
-    # Look for beginning of a raw string, and replace them with
-    # empty strings.  This is done in a loop to handle multiple raw
-    # strings on the same line.
-    while delimiter is None:
-      # Look for beginning of a raw string.
-      # See 2.14.15 [lex.string] for syntax.
-      #
-      # Once we have matched a raw string, we check the prefix of the
-      # line to make sure that the line is not part of a single line
-      # comment.  It's done this way because we remove raw strings
-      # before removing comments as opposed to removing comments
-      # before removing raw strings.  This is because there are some
-      # cpplint checks that requires the comments to be preserved, but
-      # we don't want to check comments that are inside raw strings.
-      matched = Match(r'^(.*?)\b(?:R|u8R|uR|UR|LR)"([^\s\\()]*)\((.*)$', line)
-      if (matched and
-          not Match(r'^([^\'"]|\'(\\.|[^\'])*\'|"(\\.|[^"])*")*//',
-                    matched.group(1))):
-        delimiter = ')' + matched.group(2) + '"'
-
-        end = matched.group(3).find(delimiter)
-        if end >= 0:
-          # Raw string ended on same line
-          line = (matched.group(1) + '""' +
-                  matched.group(3)[end + len(delimiter):])
-          delimiter = None
-        else:
-          # Start of a multi-line raw string
-          line = matched.group(1) + '""'
-      else:
-        break
-
-    lines_without_raw_strings.append(line)
-
-  # TODO(unknown): if delimiter is not None here, we might want to
-  # emit a warning for unterminated string.
-  return lines_without_raw_strings
-
-
-def FindNextMultiLineCommentStart(lines, lineix):
-  """Find the beginning marker for a multiline comment."""
-  while lineix < len(lines):
-    if lines[lineix].strip().startswith('/*'):
-      # Only return this marker if the comment goes beyond this line
-      if lines[lineix].strip().find('*/', 2) < 0:
-        return lineix
-    lineix += 1
-  return len(lines)
-
-
-def FindNextMultiLineCommentEnd(lines, lineix):
-  """We are inside a comment, find the end marker."""
-  while lineix < len(lines):
-    if lines[lineix].strip().endswith('*/'):
-      return lineix
-    lineix += 1
-  return len(lines)
-
-
-def RemoveMultiLineCommentsFromRange(lines, begin, end):
-  """Clears a range of lines for multi-line comments."""
-  # Having // dummy comments makes the lines non-empty, so we will not get
-  # unnecessary blank line warnings later in the code.
-  for i in range(begin, end):
-    lines[i] = '/**/'
-
-
-def RemoveMultiLineComments(filename, lines, error):
-  """Removes multiline (c-style) comments from lines."""
-  lineix = 0
-  while lineix < len(lines):
-    lineix_begin = FindNextMultiLineCommentStart(lines, lineix)
-    if lineix_begin >= len(lines):
-      return
-    lineix_end = FindNextMultiLineCommentEnd(lines, lineix_begin)
-    if lineix_end >= len(lines):
-      error(filename, lineix_begin + 1, 'readability/multiline_comment', 5,
-            'Could not find end of multi-line comment')
-      return
-    RemoveMultiLineCommentsFromRange(lines, lineix_begin, lineix_end + 1)
-    lineix = lineix_end + 1
-
-
-def CleanseComments(line):
-  """Removes //-comments and single-line C-style /* */ comments.
-
-  Args:
-    line: A line of C++ source.
-
-  Returns:
-    The line with single-line comments removed.
-  """
-  commentpos = line.find('//')
-  if commentpos != -1 and not IsCppString(line[:commentpos]):
-    line = line[:commentpos].rstrip()
-  # get rid of /* ... */
-  return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line)
-
-
-class CleansedLines(object):
-  """Holds 4 copies of all lines with different preprocessing applied to them.
-
-  1) elided member contains lines without strings and comments.
-  2) lines member contains lines without comments.
-  3) raw_lines member contains all the lines without processing.
-  4) lines_without_raw_strings member is same as raw_lines, but with C++11 raw
-     strings removed.
-  All these members are of <type 'list'>, and of the same length.
-  """
-
-  def __init__(self, lines):
-    self.elided = []
-    self.lines = []
-    self.raw_lines = lines
-    self.num_lines = len(lines)
-    self.lines_without_raw_strings = CleanseRawStrings(lines)
-    for linenum in range(len(self.lines_without_raw_strings)):
-      self.lines.append(CleanseComments(
-          self.lines_without_raw_strings[linenum]))
-      elided = self._CollapseStrings(self.lines_without_raw_strings[linenum])
-      self.elided.append(CleanseComments(elided))
-
-  def NumLines(self):
-    """Returns the number of lines represented."""
-    return self.num_lines
-
-  @staticmethod
-  def _CollapseStrings(elided):
-    """Collapses strings and chars on a line to simple "" or '' blocks.
-
-    We nix strings first so we're not fooled by text like '"http://"'
-
-    Args:
-      elided: The line being processed.
-
-    Returns:
-      The line with collapsed strings.
-    """
-    if _RE_PATTERN_INCLUDE.match(elided):
-      return elided
-
-    # Remove escaped characters first to make quote/single quote collapsing
-    # basic.  Things that look like escaped characters shouldn't occur
-    # outside of strings and chars.
-    elided = _RE_PATTERN_CLEANSE_LINE_ESCAPES.sub('', elided)
-
-    # Replace quoted strings and digit separators.  Both single quotes
-    # and double quotes are processed in the same loop, otherwise
-    # nested quotes wouldn't work.
-    collapsed = ''
-    while True:
-      # Find the first quote character
-      match = Match(r'^([^\'"]*)([\'"])(.*)$', elided)
-      if not match:
-        collapsed += elided
-        break
-      head, quote, tail = match.groups()
-
-      if quote == '"':
-        # Collapse double quoted strings
-        second_quote = tail.find('"')
-        if second_quote >= 0:
-          collapsed += head + '""'
-          elided = tail[second_quote + 1:]
-        else:
-          # Unmatched double quote, don't bother processing the rest
-          # of the line since this is probably a multiline string.
-          collapsed += elided
-          break
-      else:
-        # Found single quote, check nearby text to eliminate digit separators.
-        #
-        # There is no special handling for floating point here, because
-        # the integer/fractional/exponent parts would all be parsed
-        # correctly as long as there are digits on both sides of the
-        # separator.  So we are fine as long as we don't see something
-        # like "0.'3" (gcc 4.9.0 will not allow this literal).
-        if Search(r'\b(?:0[bBxX]?|[1-9])[0-9a-fA-F]*$', head):
-          match_literal = Match(r'^((?:\'?[0-9a-zA-Z_])*)(.*)$', "'" + tail)
-          collapsed += head + match_literal.group(1).replace("'", '')
-          elided = match_literal.group(2)
-        else:
-          second_quote = tail.find('\'')
-          if second_quote >= 0:
-            collapsed += head + "''"
-            elided = tail[second_quote + 1:]
-          else:
-            # Unmatched single quote
-            collapsed += elided
-            break
-
-    return collapsed
-
-
-def FindEndOfExpressionInLine(line, startpos, stack):
-  """Find the position just after the end of current parenthesized expression.
-
-  Args:
-    line: a CleansedLines line.
-    startpos: start searching at this position.
-    stack: nesting stack at startpos.
-
-  Returns:
-    On finding matching end: (index just after matching end, None)
-    On finding an unclosed expression: (-1, None)
-    Otherwise: (-1, new stack at end of this line)
-  """
-  for i in xrange(startpos, len(line)):
-    char = line[i]
-    if char in '([{':
-      # Found start of parenthesized expression, push to expression stack
-      stack.append(char)
-    elif char == '<':
-      # Found potential start of template argument list
-      if i > 0 and line[i - 1] == '<':
-        # Left shift operator
-        if stack and stack[-1] == '<':
-          stack.pop()
-          if not stack:
-            return (-1, None)
-      elif i > 0 and Search(r'\boperator\s*$', line[0:i]):
-        # operator<, don't add to stack
-        continue
-      else:
-        # Tentative start of template argument list
-        stack.append('<')
-    elif char in ')]}':
-      # Found end of parenthesized expression.
-      #
-      # If we are currently expecting a matching '>', the pending '<'
-      # must have been an operator.  Remove them from expression stack.
-      while stack and stack[-1] == '<':
-        stack.pop()
-      if not stack:
-        return (-1, None)
-      if ((stack[-1] == '(' and char == ')') or
-          (stack[-1] == '[' and char == ']') or
-          (stack[-1] == '{' and char == '}')):
-        stack.pop()
-        if not stack:
-          return (i + 1, None)
-      else:
-        # Mismatched parentheses
-        return (-1, None)
-    elif char == '>':
-      # Found potential end of template argument list.
-
-      # Ignore "->" and operator functions
-      if (i > 0 and
-          (line[i - 1] == '-' or Search(r'\boperator\s*$', line[0:i - 1]))):
-        continue
-
-      # Pop the stack if there is a matching '<'.  Otherwise, ignore
-      # this '>' since it must be an operator.
-      if stack:
-        if stack[-1] == '<':
-          stack.pop()
-          if not stack:
-            return (i + 1, None)
-    elif char == ';':
-      # Found something that look like end of statements.  If we are currently
-      # expecting a '>', the matching '<' must have been an operator, since
-      # template argument list should not contain statements.
-      while stack and stack[-1] == '<':
-        stack.pop()
-      if not stack:
-        return (-1, None)
-
-  # Did not find end of expression or unbalanced parentheses on this line
-  return (-1, stack)
-
-
-def CloseExpression(clean_lines, linenum, pos):
-  """If input points to ( or { or [ or <, finds the position that closes it.
-
-  If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
-  linenum/pos that correspond to the closing of the expression.
-
-  TODO(unknown): cpplint spends a fair bit of time matching parentheses.
-  Ideally we would want to index all opening and closing parentheses once
-  and have CloseExpression be just a simple lookup, but due to preprocessor
-  tricks, this is not so easy.
-
-  Args:
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    pos: A position on the line.
-
-  Returns:
-    A tuple (line, linenum, pos) pointer *past* the closing brace, or
-    (line, len(lines), -1) if we never find a close.  Note we ignore
-    strings and comments when matching; and the line we return is the
-    'cleansed' line at linenum.
-  """
-
-  line = clean_lines.elided[linenum]
-  if (line[pos] not in '({[<') or Match(r'<[<=]', line[pos:]):
-    return (line, clean_lines.NumLines(), -1)
-
-  # Check first line
-  (end_pos, stack) = FindEndOfExpressionInLine(line, pos, [])
-  if end_pos > -1:
-    return (line, linenum, end_pos)
-
-  # Continue scanning forward
-  while stack and linenum < clean_lines.NumLines() - 1:
-    linenum += 1
-    line = clean_lines.elided[linenum]
-    (end_pos, stack) = FindEndOfExpressionInLine(line, 0, stack)
-    if end_pos > -1:
-      return (line, linenum, end_pos)
-
-  # Did not find end of expression before end of file, give up
-  return (line, clean_lines.NumLines(), -1)
-
-
-def FindStartOfExpressionInLine(line, endpos, stack):
-  """Find position at the matching start of current expression.
-
-  This is almost the reverse of FindEndOfExpressionInLine, but note
-  that the input position and returned position differs by 1.
-
-  Args:
-    line: a CleansedLines line.
-    endpos: start searching at this position.
-    stack: nesting stack at endpos.
-
-  Returns:
-    On finding matching start: (index at matching start, None)
-    On finding an unclosed expression: (-1, None)
-    Otherwise: (-1, new stack at beginning of this line)
-  """
-  i = endpos
-  while i >= 0:
-    char = line[i]
-    if char in ')]}':
-      # Found end of expression, push to expression stack
-      stack.append(char)
-    elif char == '>':
-      # Found potential end of template argument list.
-      #
-      # Ignore it if it's a "->" or ">=" or "operator>"
-      if (i > 0 and
-          (line[i - 1] == '-' or
-           Match(r'\s>=\s', line[i - 1:]) or
-           Search(r'\boperator\s*$', line[0:i]))):
-        i -= 1
-      else:
-        stack.append('>')
-    elif char == '<':
-      # Found potential start of template argument list
-      if i > 0 and line[i - 1] == '<':
-        # Left shift operator
-        i -= 1
-      else:
-        # If there is a matching '>', we can pop the expression stack.
-        # Otherwise, ignore this '<' since it must be an operator.
-        if stack and stack[-1] == '>':
-          stack.pop()
-          if not stack:
-            return (i, None)
-    elif char in '([{':
-      # Found start of expression.
-      #
-      # If there are any unmatched '>' on the stack, they must be
-      # operators.  Remove those.
-      while stack and stack[-1] == '>':
-        stack.pop()
-      if not stack:
-        return (-1, None)
-      if ((char == '(' and stack[-1] == ')') or
-          (char == '[' and stack[-1] == ']') or
-          (char == '{' and stack[-1] == '}')):
-        stack.pop()
-        if not stack:
-          return (i, None)
-      else:
-        # Mismatched parentheses
-        return (-1, None)
-    elif char == ';':
-      # Found something that look like end of statements.  If we are currently
-      # expecting a '<', the matching '>' must have been an operator, since
-      # template argument list should not contain statements.
-      while stack and stack[-1] == '>':
-        stack.pop()
-      if not stack:
-        return (-1, None)
-
-    i -= 1
-
-  return (-1, stack)
-
-
-def ReverseCloseExpression(clean_lines, linenum, pos):
-  """If input points to ) or } or ] or >, finds the position that opens it.
-
-  If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
-  linenum/pos that correspond to the opening of the expression.
-
-  Args:
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    pos: A position on the line.
-
-  Returns:
-    A tuple (line, linenum, pos) pointer *at* the opening brace, or
-    (line, 0, -1) if we never find the matching opening brace.  Note
-    we ignore strings and comments when matching; and the line we
-    return is the 'cleansed' line at linenum.
-  """
-  line = clean_lines.elided[linenum]
-  if line[pos] not in ')}]>':
-    return (line, 0, -1)
-
-  # Check last line
-  (start_pos, stack) = FindStartOfExpressionInLine(line, pos, [])
-  if start_pos > -1:
-    return (line, linenum, start_pos)
-
-  # Continue scanning backward
-  while stack and linenum > 0:
-    linenum -= 1
-    line = clean_lines.elided[linenum]
-    (start_pos, stack) = FindStartOfExpressionInLine(line, len(line) - 1, stack)
-    if start_pos > -1:
-      return (line, linenum, start_pos)
-
-  # Did not find start of expression before beginning of file, give up
-  return (line, 0, -1)
-
-
-def CheckForCopyright(filename, lines, error):
-  """Logs an error if no Copyright message appears at the top of the file."""
-
-  # We'll say it should occur by line 10. Don't forget there's a
-  # dummy line at the front.
-  for line in xrange(1, min(len(lines), 11)):
-    if re.search(r'Copyright', lines[line], re.I): break
-  else:                       # means no copyright line was found
-    error(filename, 0, 'legal/copyright', 5,
-          'No copyright message found.  '
-          'You should have a line: "Copyright [year] <Copyright Owner>"')
-
-
-def GetIndentLevel(line):
-  """Return the number of leading spaces in line.
-
-  Args:
-    line: A string to check.
-
-  Returns:
-    An integer count of leading spaces, possibly zero.
-  """
-  indent = Match(r'^( *)\S', line)
-  if indent:
-    return len(indent.group(1))
-  else:
-    return 0
-
-def PathSplitToList(path):
-  """Returns the path split into a list by the separator.
-
-  Args:
-    path: An absolute or relative path (e.g. '/a/b/c/' or '../a')
-
-  Returns:
-    A list of path components (e.g. ['a', 'b', 'c]).
-  """
-  lst = []
-  while True:
-    (head, tail) = os.path.split(path)
-    if head == path: # absolute paths end
-      lst.append(head)
-      break
-    if tail == path: # relative paths end
-      lst.append(tail)
-      break
-
-    path = head
-    lst.append(tail)
-
-  lst.reverse()
-  return lst
-
-def GetHeaderGuardCPPVariable(filename):
-  """Returns the CPP variable that should be used as a header guard.
-
-  Args:
-    filename: The name of a C++ header file.
-
-  Returns:
-    The CPP variable that should be used as a header guard in the
-    named file.
-
-  """
-
-  # Restores original filename in case that cpplint is invoked from Emacs's
-  # flymake.
-  filename = re.sub(r'_flymake\.h$', '.h', filename)
-  filename = re.sub(r'/\.flymake/([^/]*)$', r'/\1', filename)
-  # Replace 'c++' with 'cpp'.
-  filename = filename.replace('C++', 'cpp').replace('c++', 'cpp')
-
-  fileinfo = FileInfo(filename)
-  file_path_from_root = fileinfo.RepositoryName()
-
-  def FixupPathFromRoot():
-    if _root_debug:
-      sys.stderr.write("\n_root fixup, _root = '%s', repository name = '%s'\n"
-          %(_root, fileinfo.RepositoryName()))
-
-    # Process the file path with the --root flag if it was set.
-    if not _root:
-      if _root_debug:
-        sys.stderr.write("_root unspecified\n")
-      return file_path_from_root
-
-    def StripListPrefix(lst, prefix):
-      # f(['x', 'y'], ['w, z']) -> None  (not a valid prefix)
-      if lst[:len(prefix)] != prefix:
-        return None
-      # f(['a, 'b', 'c', 'd'], ['a', 'b']) -> ['c', 'd']
-      return lst[(len(prefix)):]
-
-    # root behavior:
-    #   --root=subdir , lstrips subdir from the header guard
-    maybe_path = StripListPrefix(PathSplitToList(file_path_from_root),
-                                 PathSplitToList(_root))
-
-    if _root_debug:
-      sys.stderr.write(("_root lstrip (maybe_path=%s, file_path_from_root=%s," +
-          " _root=%s)\n") %(maybe_path, file_path_from_root, _root))
-
-    if maybe_path:
-      return os.path.join(*maybe_path)
-
-    #   --root=.. , will prepend the outer directory to the header guard
-    full_path = fileinfo.FullName()
-    root_abspath = os.path.abspath(_root)
-
-    maybe_path = StripListPrefix(PathSplitToList(full_path),
-                                 PathSplitToList(root_abspath))
-
-    if _root_debug:
-      sys.stderr.write(("_root prepend (maybe_path=%s, full_path=%s, " +
-          "root_abspath=%s)\n") %(maybe_path, full_path, root_abspath))
-
-    if maybe_path:
-      return os.path.join(*maybe_path)
-
-    if _root_debug:
-      sys.stderr.write("_root ignore, returning %s\n" %(file_path_from_root))
-
-    #   --root=FAKE_DIR is ignored
-    return file_path_from_root
-
-  file_path_from_root = FixupPathFromRoot()
-  return re.sub(r'[^a-zA-Z0-9]', '_', file_path_from_root).upper() + '_'
-
-
-def CheckForHeaderGuard(filename, clean_lines, error):
-  """Checks that the file contains a header guard.
-
-  Logs an error if no #ifndef header guard is present.  For other
-  headers, checks that the full pathname is used.
-
-  Args:
-    filename: The name of the C++ header file.
-    clean_lines: A CleansedLines instance containing the file.
-    error: The function to call with any errors found.
-  """
-
-  # Don't check for header guards if there are error suppression
-  # comments somewhere in this file.
-  #
-  # Because this is silencing a warning for a nonexistent line, we
-  # only support the very specific NOLINT(build/header_guard) syntax,
-  # and not the general NOLINT or NOLINT(*) syntax.
-  raw_lines = clean_lines.lines_without_raw_strings
-  for i in raw_lines:
-    if Search(r'//\s*NOLINT\(build/header_guard\)', i):
-      return
-
-  cppvar = GetHeaderGuardCPPVariable(filename)
-
-  ifndef = ''
-  ifndef_linenum = 0
-  define = ''
-  endif = ''
-  endif_linenum = 0
-  for linenum, line in enumerate(raw_lines):
-    linesplit = line.split()
-    if len(linesplit) >= 2:
-      # find the first occurrence of #ifndef and #define, save arg
-      if not ifndef and linesplit[0] == '#ifndef':
-        # set ifndef to the header guard presented on the #ifndef line.
-        ifndef = linesplit[1]
-        ifndef_linenum = linenum
-      if not define and linesplit[0] == '#define':
-        define = linesplit[1]
-    # find the last occurrence of #endif, save entire line
-    if line.startswith('#endif'):
-      endif = line
-      endif_linenum = linenum
-
-  if not ifndef or not define or ifndef != define:
-    error(filename, 0, 'build/header_guard', 5,
-          'No #ifndef header guard found, suggested CPP variable is: %s' %
-          cppvar)
-    return
-
-  # The guard should be PATH_FILE_H_, but we also allow PATH_FILE_H__
-  # for backward compatibility.
-  if ifndef != cppvar:
-    error_level = 0
-    if ifndef != cppvar + '_':
-      error_level = 5
-
-    ParseNolintSuppressions(filename, raw_lines[ifndef_linenum], ifndef_linenum,
-                            error)
-    error(filename, ifndef_linenum, 'build/header_guard', error_level,
-          '#ifndef header guard has wrong style, please use: %s' % cppvar)
-
-  # Check for "//" comments on endif line.
-  ParseNolintSuppressions(filename, raw_lines[endif_linenum], endif_linenum,
-                          error)
-  match = Match(r'#endif\s*//\s*' + cppvar + r'(_)?\b', endif)
-  if match:
-    if match.group(1) == '_':
-      # Issue low severity warning for deprecated double trailing underscore
-      error(filename, endif_linenum, 'build/header_guard', 0,
-            '#endif line should be "#endif  // %s"' % cppvar)
-    return
-
-  # Didn't find the corresponding "//" comment.  If this file does not
-  # contain any "//" comments at all, it could be that the compiler
-  # only wants "/**/" comments, look for those instead.
-  no_single_line_comments = True
-  for i in xrange(1, len(raw_lines) - 1):
-    line = raw_lines[i]
-    if Match(r'^(?:(?:\'(?:\.|[^\'])*\')|(?:"(?:\.|[^"])*")|[^\'"])*//', line):
-      no_single_line_comments = False
-      break
-
-  if no_single_line_comments:
-    match = Match(r'#endif\s*/\*\s*' + cppvar + r'(_)?\s*\*/', endif)
-    if match:
-      if match.group(1) == '_':
-        # Low severity warning for double trailing underscore
-        error(filename, endif_linenum, 'build/header_guard', 0,
-              '#endif line should be "#endif  /* %s */"' % cppvar)
-      return
-
-  # Didn't find anything
-  error(filename, endif_linenum, 'build/header_guard', 5,
-        '#endif line should be "#endif  // %s"' % cppvar)
-
-
-def CheckHeaderFileIncluded(filename, include_state, error):
-  """Logs an error if a .cc file does not include its header."""
-
-  # Do not check test files
-  fileinfo = FileInfo(filename)
-  if Search(_TEST_FILE_SUFFIX, fileinfo.BaseName()):
-    return
-
-  headerfile = filename[0:len(filename) - len(fileinfo.Extension())] + '.h'
-  if not os.path.exists(headerfile):
-    return
-  headername = FileInfo(headerfile).RepositoryName()
-  first_include = 0
-  for section_list in include_state.include_list:
-    for f in section_list:
-      if headername in f[0] or f[0] in headername:
-        return
-      if not first_include:
-        first_include = f[1]
-
-  error(filename, first_include, 'build/include', 5,
-        '%s should include its header file %s' % (fileinfo.RepositoryName(),
-                                                  headername))
-
-
-def CheckForBadCharacters(filename, lines, error):
-  """Logs an error for each line containing bad characters.
-
-  Two kinds of bad characters:
-
-  1. Unicode replacement characters: These indicate that either the file
-  contained invalid UTF-8 (likely) or Unicode replacement characters (which
-  it shouldn't).  Note that it's possible for this to throw off line
-  numbering if the invalid UTF-8 occurred adjacent to a newline.
-
-  2. NUL bytes.  These are problematic for some tools.
-
-  Args:
-    filename: The name of the current file.
-    lines: An array of strings, each representing a line of the file.
-    error: The function to call with any errors found.
-  """
-  for linenum, line in enumerate(lines):
-    if u'\ufffd' in line:
-      error(filename, linenum, 'readability/utf8', 5,
-            'Line contains invalid UTF-8 (or Unicode replacement character).')
-    if '\0' in line:
-      error(filename, linenum, 'readability/nul', 5, 'Line contains NUL byte.')
-
-
-def CheckForNewlineAtEOF(filename, lines, error):
-  """Logs an error if there is no newline char at the end of the file.
-
-  Args:
-    filename: The name of the current file.
-    lines: An array of strings, each representing a line of the file.
-    error: The function to call with any errors found.
-  """
-
-  # The array lines() was created by adding two newlines to the
-  # original file (go figure), then splitting on \n.
-  # To verify that the file ends in \n, we just have to make sure the
-  # last-but-two element of lines() exists and is empty.
-  if len(lines) < 3 or lines[-2]:
-    error(filename, len(lines) - 2, 'whitespace/ending_newline', 5,
-          'Could not find a newline character at the end of the file.')
-
-
-def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error):
-  """Logs an error if we see /* ... */ or "..." that extend past one line.
-
-  /* ... */ comments are legit inside macros, for one line.
-  Otherwise, we prefer // comments, so it's ok to warn about the
-  other.  Likewise, it's ok for strings to extend across multiple
-  lines, as long as a line continuation character (backslash)
-  terminates each line. Although not currently prohibited by the C++
-  style guide, it's ugly and unnecessary. We don't do well with either
-  in this lint program, so we warn about both.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  # Remove all \\ (escaped backslashes) from the line. They are OK, and the
-  # second (escaped) slash may trigger later \" detection erroneously.
-  line = line.replace('\\\\', '')
-
-  if line.count('/*') > line.count('*/'):
-    error(filename, linenum, 'readability/multiline_comment', 5,
-          'Complex multi-line /*...*/-style comment found. '
-          'Lint may give bogus warnings.  '
-          'Consider replacing these with //-style comments, '
-          'with #if 0...#endif, '
-          'or with more clearly structured multi-line comments.')
-
-  if (line.count('"') - line.count('\\"')) % 2:
-    error(filename, linenum, 'readability/multiline_string', 5,
-          'Multi-line string ("...") found.  This lint script doesn\'t '
-          'do well with such strings, and may give bogus warnings.  '
-          'Use C++11 raw strings or concatenation instead.')
-
-
-# (non-threadsafe name, thread-safe alternative, validation pattern)
-#
-# The validation pattern is used to eliminate false positives such as:
-#  _rand();               // false positive due to substring match.
-#  ->rand();              // some member function rand().
-#  ACMRandom rand(seed);  // some variable named rand.
-#  ISAACRandom rand();    // another variable named rand.
-#
-# Basically we require the return value of these functions to be used
-# in some expression context on the same line by matching on some
-# operator before the function name.  This eliminates constructors and
-# member function calls.
-_UNSAFE_FUNC_PREFIX = r'(?:[-+*/=%^&|(<]\s*|>\s+)'
-_THREADING_LIST = (
-    ('asctime(', 'asctime_r(', _UNSAFE_FUNC_PREFIX + r'asctime\([^)]+\)'),
-    ('ctime(', 'ctime_r(', _UNSAFE_FUNC_PREFIX + r'ctime\([^)]+\)'),
-    ('getgrgid(', 'getgrgid_r(', _UNSAFE_FUNC_PREFIX + r'getgrgid\([^)]+\)'),
-    ('getgrnam(', 'getgrnam_r(', _UNSAFE_FUNC_PREFIX + r'getgrnam\([^)]+\)'),
-    ('getlogin(', 'getlogin_r(', _UNSAFE_FUNC_PREFIX + r'getlogin\(\)'),
-    ('getpwnam(', 'getpwnam_r(', _UNSAFE_FUNC_PREFIX + r'getpwnam\([^)]+\)'),
-    ('getpwuid(', 'getpwuid_r(', _UNSAFE_FUNC_PREFIX + r'getpwuid\([^)]+\)'),
-    ('gmtime(', 'gmtime_r(', _UNSAFE_FUNC_PREFIX + r'gmtime\([^)]+\)'),
-    ('localtime(', 'localtime_r(', _UNSAFE_FUNC_PREFIX + r'localtime\([^)]+\)'),
-    ('rand(', 'rand_r(', _UNSAFE_FUNC_PREFIX + r'rand\(\)'),
-    ('strtok(', 'strtok_r(',
-     _UNSAFE_FUNC_PREFIX + r'strtok\([^)]+\)'),
-    ('ttyname(', 'ttyname_r(', _UNSAFE_FUNC_PREFIX + r'ttyname\([^)]+\)'),
-    )
-
-
-def CheckPosixThreading(filename, clean_lines, linenum, error):
-  """Checks for calls to thread-unsafe functions.
-
-  Much code has been originally written without consideration of
-  multi-threading. Also, engineers are relying on their old experience;
-  they have learned posix before threading extensions were added. These
-  tests guide the engineers to use thread-safe functions (when using
-  posix directly).
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-  for single_thread_func, multithread_safe_func, pattern in _THREADING_LIST:
-    # Additional pattern matching check to confirm that this is the
-    # function we are looking for
-    if Search(pattern, line):
-      error(filename, linenum, 'runtime/threadsafe_fn', 2,
-            'Consider using ' + multithread_safe_func +
-            '...) instead of ' + single_thread_func +
-            '...) for improved thread safety.')
-
-
-def CheckVlogArguments(filename, clean_lines, linenum, error):
-  """Checks that VLOG() is only used for defining a logging level.
-
-  For example, VLOG(2) is correct. VLOG(INFO), VLOG(WARNING), VLOG(ERROR), and
-  VLOG(FATAL) are not.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-  if Search(r'\bVLOG\((INFO|ERROR|WARNING|DFATAL|FATAL)\)', line):
-    error(filename, linenum, 'runtime/vlog', 5,
-          'VLOG() should be used with numeric verbosity level.  '
-          'Use LOG() if you want symbolic severity levels.')
-
-# Matches invalid increment: *count++, which moves pointer instead of
-# incrementing a value.
-_RE_PATTERN_INVALID_INCREMENT = re.compile(
-    r'^\s*\*\w+(\+\+|--);')
-
-
-def CheckInvalidIncrement(filename, clean_lines, linenum, error):
-  """Checks for invalid increment *count++.
-
-  For example following function:
-  void increment_counter(int* count) {
-    *count++;
-  }
-  is invalid, because it effectively does count++, moving pointer, and should
-  be replaced with ++*count, (*count)++ or *count += 1.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-  if _RE_PATTERN_INVALID_INCREMENT.match(line):
-    error(filename, linenum, 'runtime/invalid_increment', 5,
-          'Changing pointer instead of value (or unused value of operator*).')
-
-
-def IsMacroDefinition(clean_lines, linenum):
-  if Search(r'^#define', clean_lines[linenum]):
-    return True
-
-  if linenum > 0 and Search(r'\\$', clean_lines[linenum - 1]):
-    return True
-
-  return False
-
-
-def IsForwardClassDeclaration(clean_lines, linenum):
-  return Match(r'^\s*(\btemplate\b)*.*class\s+\w+;\s*$', clean_lines[linenum])
-
-
-class _BlockInfo(object):
-  """Stores information about a generic block of code."""
-
-  def __init__(self, linenum, seen_open_brace):
-    self.starting_linenum = linenum
-    self.seen_open_brace = seen_open_brace
-    self.open_parentheses = 0
-    self.inline_asm = _NO_ASM
-    self.check_namespace_indentation = False
-
-  def CheckBegin(self, filename, clean_lines, linenum, error):
-    """Run checks that applies to text up to the opening brace.
-
-    This is mostly for checking the text after the class identifier
-    and the "{", usually where the base class is specified.  For other
-    blocks, there isn't much to check, so we always pass.
-
-    Args:
-      filename: The name of the current file.
-      clean_lines: A CleansedLines instance containing the file.
-      linenum: The number of the line to check.
-      error: The function to call with any errors found.
-    """
-    pass
-
-  def CheckEnd(self, filename, clean_lines, linenum, error):
-    """Run checks that applies to text after the closing brace.
-
-    This is mostly used for checking end of namespace comments.
-
-    Args:
-      filename: The name of the current file.
-      clean_lines: A CleansedLines instance containing the file.
-      linenum: The number of the line to check.
-      error: The function to call with any errors found.
-    """
-    pass
-
-  def IsBlockInfo(self):
-    """Returns true if this block is a _BlockInfo.
-
-    This is convenient for verifying that an object is an instance of
-    a _BlockInfo, but not an instance of any of the derived classes.
-
-    Returns:
-      True for this class, False for derived classes.
-    """
-    return self.__class__ == _BlockInfo
-
-
-class _ExternCInfo(_BlockInfo):
-  """Stores information about an 'extern "C"' block."""
-
-  def __init__(self, linenum):
-    _BlockInfo.__init__(self, linenum, True)
-
-
-class _ClassInfo(_BlockInfo):
-  """Stores information about a class."""
-
-  def __init__(self, name, class_or_struct, clean_lines, linenum):
-    _BlockInfo.__init__(self, linenum, False)
-    self.name = name
-    self.is_derived = False
-    self.check_namespace_indentation = True
-    if class_or_struct == 'struct':
-      self.access = 'public'
-      self.is_struct = True
-    else:
-      self.access = 'private'
-      self.is_struct = False
-
-    # Remember initial indentation level for this class.  Using raw_lines here
-    # instead of elided to account for leading comments.
-    self.class_indent = GetIndentLevel(clean_lines.raw_lines[linenum])
-
-    # Try to find the end of the class.  This will be confused by things like:
-    #   class A {
-    #   } *x = { ...
-    #
-    # But it's still good enough for CheckSectionSpacing.
-    self.last_line = 0
-    depth = 0
-    for i in range(linenum, clean_lines.NumLines()):
-      line = clean_lines.elided[i]
-      depth += line.count('{') - line.count('}')
-      if not depth:
-        self.last_line = i
-        break
-
-  def CheckBegin(self, filename, clean_lines, linenum, error):
-    # Look for a bare ':'
-    if Search('(^|[^:]):($|[^:])', clean_lines.elided[linenum]):
-      self.is_derived = True
-
-  def CheckEnd(self, filename, clean_lines, linenum, error):
-    # If there is a DISALLOW macro, it should appear near the end of
-    # the class.
-    seen_last_thing_in_class = False
-    for i in xrange(linenum - 1, self.starting_linenum, -1):
-      match = Search(
-          r'\b(DISALLOW_COPY_AND_ASSIGN|DISALLOW_IMPLICIT_CONSTRUCTORS)\(' +
-          self.name + r'\)',
-          clean_lines.elided[i])
-      if match:
-        if seen_last_thing_in_class:
-          error(filename, i, 'readability/constructors', 3,
-                match.group(1) + ' should be the last thing in the class')
-        break
-
-      if not Match(r'^\s*$', clean_lines.elided[i]):
-        seen_last_thing_in_class = True
-
-    # Check that closing brace is aligned with beginning of the class.
-    # Only do this if the closing brace is indented by only whitespaces.
-    # This means we will not check single-line class definitions.
-    indent = Match(r'^( *)\}', clean_lines.elided[linenum])
-    if indent and len(indent.group(1)) != self.class_indent:
-      if self.is_struct:
-        parent = 'struct ' + self.name
-      else:
-        parent = 'class ' + self.name
-      error(filename, linenum, 'whitespace/indent', 3,
-            'Closing brace should be aligned with beginning of %s' % parent)
-
-
-class _NamespaceInfo(_BlockInfo):
-  """Stores information about a namespace."""
-
-  def __init__(self, name, linenum):
-    _BlockInfo.__init__(self, linenum, False)
-    self.name = name or ''
-    self.check_namespace_indentation = True
-
-  def CheckEnd(self, filename, clean_lines, linenum, error):
-    """Check end of namespace comments."""
-    line = clean_lines.raw_lines[linenum]
-
-    # Check how many lines is enclosed in this namespace.  Don't issue
-    # warning for missing namespace comments if there aren't enough
-    # lines.  However, do apply checks if there is already an end of
-    # namespace comment and it's incorrect.
-    #
-    # TODO(unknown): We always want to check end of namespace comments
-    # if a namespace is large, but sometimes we also want to apply the
-    # check if a short namespace contained nontrivial things (something
-    # other than forward declarations).  There is currently no logic on
-    # deciding what these nontrivial things are, so this check is
-    # triggered by namespace size only, which works most of the time.
-    if (linenum - self.starting_linenum < 10
-        and not Match(r'^\s*};*\s*(//|/\*).*\bnamespace\b', line)):
-      return
-
-    # Look for matching comment at end of namespace.
-    #
-    # Note that we accept C style "/* */" comments for terminating
-    # namespaces, so that code that terminate namespaces inside
-    # preprocessor macros can be cpplint clean.
-    #
-    # We also accept stuff like "// end of namespace <name>." with the
-    # period at the end.
-    #
-    # Besides these, we don't accept anything else, otherwise we might
-    # get false negatives when existing comment is a substring of the
-    # expected namespace.
-    if self.name:
-      # Named namespace
-      if not Match((r'^\s*};*\s*(//|/\*).*\bnamespace\s+' +
-                    re.escape(self.name) + r'[\*/\.\\\s]*$'),
-                   line):
-        error(filename, linenum, 'readability/namespace', 5,
-              'Namespace should be terminated with "// namespace %s"' %
-              self.name)
-    else:
-      # Anonymous namespace
-      if not Match(r'^\s*};*\s*(//|/\*).*\bnamespace[\*/\.\\\s]*$', line):
-        # If "// namespace anonymous" or "// anonymous namespace (more text)",
-        # mention "// anonymous namespace" as an acceptable form
-        if Match(r'^\s*}.*\b(namespace anonymous|anonymous namespace)\b', line):
-          error(filename, linenum, 'readability/namespace', 5,
-                'Anonymous namespace should be terminated with "// namespace"'
-                ' or "// anonymous namespace"')
-        else:
-          error(filename, linenum, 'readability/namespace', 5,
-                'Anonymous namespace should be terminated with "// namespace"')
-
-
-class _PreprocessorInfo(object):
-  """Stores checkpoints of nesting stacks when #if/#else is seen."""
-
-  def __init__(self, stack_before_if):
-    # The entire nesting stack before #if
-    self.stack_before_if = stack_before_if
-
-    # The entire nesting stack up to #else
-    self.stack_before_else = []
-
-    # Whether we have already seen #else or #elif
-    self.seen_else = False
-
-
-class NestingState(object):
-  """Holds states related to parsing braces."""
-
-  def __init__(self):
-    # Stack for tracking all braces.  An object is pushed whenever we
-    # see a "{", and popped when we see a "}".  Only 3 types of
-    # objects are possible:
-    # - _ClassInfo: a class or struct.
-    # - _NamespaceInfo: a namespace.
-    # - _BlockInfo: some other type of block.
-    self.stack = []
-
-    # Top of the previous stack before each Update().
-    #
-    # Because the nesting_stack is updated at the end of each line, we
-    # had to do some convoluted checks to find out what is the current
-    # scope at the beginning of the line.  This check is simplified by
-    # saving the previous top of nesting stack.
-    #
-    # We could save the full stack, but we only need the top.  Copying
-    # the full nesting stack would slow down cpplint by ~10%.
-    self.previous_stack_top = []
-
-    # Stack of _PreprocessorInfo objects.
-    self.pp_stack = []
-
-  def SeenOpenBrace(self):
-    """Check if we have seen the opening brace for the innermost block.
-
-    Returns:
-      True if we have seen the opening brace, False if the innermost
-      block is still expecting an opening brace.
-    """
-    return (not self.stack) or self.stack[-1].seen_open_brace
-
-  def InNamespaceBody(self):
-    """Check if we are currently one level inside a namespace body.
-
-    Returns:
-      True if top of the stack is a namespace block, False otherwise.
-    """
-    return self.stack and isinstance(self.stack[-1], _NamespaceInfo)
-
-  def InExternC(self):
-    """Check if we are currently one level inside an 'extern "C"' block.
-
-    Returns:
-      True if top of the stack is an extern block, False otherwise.
-    """
-    return self.stack and isinstance(self.stack[-1], _ExternCInfo)
-
-  def InClassDeclaration(self):
-    """Check if we are currently one level inside a class or struct declaration.
-
-    Returns:
-      True if top of the stack is a class/struct, False otherwise.
-    """
-    return self.stack and isinstance(self.stack[-1], _ClassInfo)
-
-  def InAsmBlock(self):
-    """Check if we are currently one level inside an inline ASM block.
-
-    Returns:
-      True if the top of the stack is a block containing inline ASM.
-    """
-    return self.stack and self.stack[-1].inline_asm != _NO_ASM
-
-  def InTemplateArgumentList(self, clean_lines, linenum, pos):
-    """Check if current position is inside template argument list.
-
-    Args:
-      clean_lines: A CleansedLines instance containing the file.
-      linenum: The number of the line to check.
-      pos: position just after the suspected template argument.
-    Returns:
-      True if (linenum, pos) is inside template arguments.
-    """
-    while linenum < clean_lines.NumLines():
-      # Find the earliest character that might indicate a template argument
-      line = clean_lines.elided[linenum]
-      match = Match(r'^[^{};=\[\]\.<>]*(.)', line[pos:])
-      if not match:
-        linenum += 1
-        pos = 0
-        continue
-      token = match.group(1)
-      pos += len(match.group(0))
-
-      # These things do not look like template argument list:
-      #   class Suspect {
-      #   class Suspect x; }
-      if token in ('{', '}', ';'): return False
-
-      # These things look like template argument list:
-      #   template <class Suspect>
-      #   template <class Suspect = default_value>
-      #   template <class Suspect[]>
-      #   template <class Suspect...>
-      if token in ('>', '=', '[', ']', '.'): return True
-
-      # Check if token is an unmatched '<'.
-      # If not, move on to the next character.
-      if token != '<':
-        pos += 1
-        if pos >= len(line):
-          linenum += 1
-          pos = 0
-        continue
-
-      # We can't be sure if we just find a single '<', and need to
-      # find the matching '>'.
-      (_, end_line, end_pos) = CloseExpression(clean_lines, linenum, pos - 1)
-      if end_pos < 0:
-        # Not sure if template argument list or syntax error in file
-        return False
-      linenum = end_line
-      pos = end_pos
-    return False
-
-  def UpdatePreprocessor(self, line):
-    """Update preprocessor stack.
-
-    We need to handle preprocessors due to classes like this:
-      #ifdef SWIG
-      struct ResultDetailsPageElementExtensionPoint {
-      #else
-      struct ResultDetailsPageElementExtensionPoint : public Extension {
-      #endif
-
-    We make the following assumptions (good enough for most files):
-    - Preprocessor condition evaluates to true from #if up to first
-      #else/#elif/#endif.
-
-    - Preprocessor condition evaluates to false from #else/#elif up
-      to #endif.  We still perform lint checks on these lines, but
-      these do not affect nesting stack.
-
-    Args:
-      line: current line to check.
-    """
-    if Match(r'^\s*#\s*(if|ifdef|ifndef)\b', line):
-      # Beginning of #if block, save the nesting stack here.  The saved
-      # stack will allow us to restore the parsing state in the #else case.
-      self.pp_stack.append(_PreprocessorInfo(copy.deepcopy(self.stack)))
-    elif Match(r'^\s*#\s*(else|elif)\b', line):
-      # Beginning of #else block
-      if self.pp_stack:
-        if not self.pp_stack[-1].seen_else:
-          # This is the first #else or #elif block.  Remember the
-          # whole nesting stack up to this point.  This is what we
-          # keep after the #endif.
-          self.pp_stack[-1].seen_else = True
-          self.pp_stack[-1].stack_before_else = copy.deepcopy(self.stack)
-
-        # Restore the stack to how it was before the #if
-        self.stack = copy.deepcopy(self.pp_stack[-1].stack_before_if)
-      else:
-        # TODO(unknown): unexpected #else, issue warning?
-        pass
-    elif Match(r'^\s*#\s*endif\b', line):
-      # End of #if or #else blocks.
-      if self.pp_stack:
-        # If we saw an #else, we will need to restore the nesting
-        # stack to its former state before the #else, otherwise we
-        # will just continue from where we left off.
-        if self.pp_stack[-1].seen_else:
-          # Here we can just use a shallow copy since we are the last
-          # reference to it.
-          self.stack = self.pp_stack[-1].stack_before_else
-        # Drop the corresponding #if
-        self.pp_stack.pop()
-      else:
-        # TODO(unknown): unexpected #endif, issue warning?
-        pass
-
-  # TODO(unknown): Update() is too long, but we will refactor later.
-  def Update(self, filename, clean_lines, linenum, error):
-    """Update nesting state with current line.
-
-    Args:
-      filename: The name of the current file.
-      clean_lines: A CleansedLines instance containing the file.
-      linenum: The number of the line to check.
-      error: The function to call with any errors found.
-    """
-    line = clean_lines.elided[linenum]
-
-    # Remember top of the previous nesting stack.
-    #
-    # The stack is always pushed/popped and not modified in place, so
-    # we can just do a shallow copy instead of copy.deepcopy.  Using
-    # deepcopy would slow down cpplint by ~28%.
-    if self.stack:
-      self.previous_stack_top = self.stack[-1]
-    else:
-      self.previous_stack_top = None
-
-    # Update pp_stack
-    self.UpdatePreprocessor(line)
-
-    # Count parentheses.  This is to avoid adding struct arguments to
-    # the nesting stack.
-    if self.stack:
-      inner_block = self.stack[-1]
-      depth_change = line.count('(') - line.count(')')
-      inner_block.open_parentheses += depth_change
-
-      # Also check if we are starting or ending an inline assembly block.
-      if inner_block.inline_asm in (_NO_ASM, _END_ASM):
-        if (depth_change != 0 and
-            inner_block.open_parentheses == 1 and
-            _MATCH_ASM.match(line)):
-          # Enter assembly block
-          inner_block.inline_asm = _INSIDE_ASM
-        else:
-          # Not entering assembly block.  If previous line was _END_ASM,
-          # we will now shift to _NO_ASM state.
-          inner_block.inline_asm = _NO_ASM
-      elif (inner_block.inline_asm == _INSIDE_ASM and
-            inner_block.open_parentheses == 0):
-        # Exit assembly block
-        inner_block.inline_asm = _END_ASM
-
-    # Consume namespace declaration at the beginning of the line.  Do
-    # this in a loop so that we catch same line declarations like this:
-    #   namespace proto2 { namespace bridge { class MessageSet; } }
-    while True:
-      # Match start of namespace.  The "\b\s*" below catches namespace
-      # declarations even if it weren't followed by a whitespace, this
-      # is so that we don't confuse our namespace checker.  The
-      # missing spaces will be flagged by CheckSpacing.
-      namespace_decl_match = Match(r'^\s*namespace\b\s*([:\w]+)?(.*)$', line)
-      if not namespace_decl_match:
-        break
-
-      new_namespace = _NamespaceInfo(namespace_decl_match.group(1), linenum)
-      self.stack.append(new_namespace)
-
-      line = namespace_decl_match.group(2)
-      if line.find('{') != -1:
-        new_namespace.seen_open_brace = True
-        line = line[line.find('{') + 1:]
-
-    # Look for a class declaration in whatever is left of the line
-    # after parsing namespaces.  The regexp accounts for decorated classes
-    # such as in:
-    #   class LOCKABLE API Object {
-    #   };
-    class_decl_match = Match(
-        r'^(\s*(?:template\s*<[\w\s<>,:]*>\s*)?'
-        r'(class|struct)\s+(?:[A-Z_]+\s+)*(\w+(?:::\w+)*))'
-        r'(.*)$', line)
-    if (class_decl_match and
-        (not self.stack or self.stack[-1].open_parentheses == 0)):
-      # We do not want to accept classes that are actually template arguments:
-      #   template <class Ignore1,
-      #             class Ignore2 = Default<Args>,
-      #             template <Args> class Ignore3>
-      #   void Function() {};
-      #
-      # To avoid template argument cases, we scan forward and look for
-      # an unmatched '>'.  If we see one, assume we are inside a
-      # template argument list.
-      end_declaration = len(class_decl_match.group(1))
-      if not self.InTemplateArgumentList(clean_lines, linenum, end_declaration):
-        self.stack.append(_ClassInfo(
-            class_decl_match.group(3), class_decl_match.group(2),
-            clean_lines, linenum))
-        line = class_decl_match.group(4)
-
-    # If we have not yet seen the opening brace for the innermost block,
-    # run checks here.
-    if not self.SeenOpenBrace():
-      self.stack[-1].CheckBegin(filename, clean_lines, linenum, error)
-
-    # Update access control if we are inside a class/struct
-    if self.stack and isinstance(self.stack[-1], _ClassInfo):
-      classinfo = self.stack[-1]
-      access_match = Match(
-          r'^(.*)\b(public|private|protected|signals)(\s+(?:slots\s*)?)?'
-          r':(?:[^:]|$)',
-          line)
-      if access_match:
-        classinfo.access = access_match.group(2)
-
-        # Check that access keywords are indented +1 space.  Skip this
-        # check if the keywords are not preceded by whitespaces.
-        indent = access_match.group(1)
-        if (len(indent) != classinfo.class_indent + 1 and
-            Match(r'^\s*$', indent)):
-          if classinfo.is_struct:
-            parent = 'struct ' + classinfo.name
-          else:
-            parent = 'class ' + classinfo.name
-          slots = ''
-          if access_match.group(3):
-            slots = access_match.group(3)
-          error(filename, linenum, 'whitespace/indent', 3,
-                '%s%s: should be indented +1 space inside %s' % (
-                    access_match.group(2), slots, parent))
-
-    # Consume braces or semicolons from what's left of the line
-    while True:
-      # Match first brace, semicolon, or closed parenthesis.
-      matched = Match(r'^[^{;)}]*([{;)}])(.*)$', line)
-      if not matched:
-        break
-
-      token = matched.group(1)
-      if token == '{':
-        # If namespace or class hasn't seen a opening brace yet, mark
-        # namespace/class head as complete.  Push a new block onto the
-        # stack otherwise.
-        if not self.SeenOpenBrace():
-          self.stack[-1].seen_open_brace = True
-        elif Match(r'^extern\s*"[^"]*"\s*\{', line):
-          self.stack.append(_ExternCInfo(linenum))
-        else:
-          self.stack.append(_BlockInfo(linenum, True))
-          if _MATCH_ASM.match(line):
-            self.stack[-1].inline_asm = _BLOCK_ASM
-
-      elif token == ';' or token == ')':
-        # If we haven't seen an opening brace yet, but we already saw
-        # a semicolon, this is probably a forward declaration.  Pop
-        # the stack for these.
-        #
-        # Similarly, if we haven't seen an opening brace yet, but we
-        # already saw a closing parenthesis, then these are probably
-        # function arguments with extra "class" or "struct" keywords.
-        # Also pop these stack for these.
-        if not self.SeenOpenBrace():
-          self.stack.pop()
-      else:  # token == '}'
-        # Perform end of block checks and pop the stack.
-        if self.stack:
-          self.stack[-1].CheckEnd(filename, clean_lines, linenum, error)
-          self.stack.pop()
-      line = matched.group(2)
-
-  def InnermostClass(self):
-    """Get class info on the top of the stack.
-
-    Returns:
-      A _ClassInfo object if we are inside a class, or None otherwise.
-    """
-    for i in range(len(self.stack), 0, -1):
-      classinfo = self.stack[i - 1]
-      if isinstance(classinfo, _ClassInfo):
-        return classinfo
-    return None
-
-  def CheckCompletedBlocks(self, filename, error):
-    """Checks that all classes and namespaces have been completely parsed.
-
-    Call this when all lines in a file have been processed.
-    Args:
-      filename: The name of the current file.
-      error: The function to call with any errors found.
-    """
-    # Note: This test can result in false positives if #ifdef constructs
-    # get in the way of brace matching. See the testBuildClass test in
-    # cpplint_unittest.py for an example of this.
-    for obj in self.stack:
-      if isinstance(obj, _ClassInfo):
-        error(filename, obj.starting_linenum, 'build/class', 5,
-              'Failed to find complete declaration of class %s' %
-              obj.name)
-      elif isinstance(obj, _NamespaceInfo):
-        error(filename, obj.starting_linenum, 'build/namespaces', 5,
-              'Failed to find complete declaration of namespace %s' %
-              obj.name)
-
-
-def CheckForNonStandardConstructs(filename, clean_lines, linenum,
-                                  nesting_state, error):
-  r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
-
-  Complain about several constructs which gcc-2 accepts, but which are
-  not standard C++.  Warning about these in lint is one way to ease the
-  transition to new compilers.
-  - put storage class first (e.g. "static const" instead of "const static").
-  - "%lld" instead of %qd" in printf-type functions.
-  - "%1$d" is non-standard in printf-type functions.
-  - "\%" is an undefined character escape sequence.
-  - text after #endif is not allowed.
-  - invalid inner-style forward declaration.
-  - >? and <? operators, and their >?= and <?= cousins.
-
-  Additionally, check for constructor/destructor style violations and reference
-  members, as it is very convenient to do so while checking for
-  gcc-2 compliance.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    nesting_state: A NestingState instance which maintains information about
-                   the current stack of nested blocks being parsed.
-    error: A callable to which errors are reported, which takes 4 arguments:
-           filename, line number, error level, and message
-  """
-
-  # Remove comments from the line, but leave in strings for now.
-  line = clean_lines.lines[linenum]
-
-  if Search(r'printf\s*\(.*".*%[-+ ]?\d*q', line):
-    error(filename, linenum, 'runtime/printf_format', 3,
-          '%q in format strings is deprecated.  Use %ll instead.')
-
-  if Search(r'printf\s*\(.*".*%\d+\$', line):
-    error(filename, linenum, 'runtime/printf_format', 2,
-          '%N$ formats are unconventional.  Try rewriting to avoid them.')
-
-  # Remove escaped backslashes before looking for undefined escapes.
-  line = line.replace('\\\\', '')
-
-  if Search(r'("|\').*\\(%|\[|\(|{)', line):
-    error(filename, linenum, 'build/printf_format', 3,
-          '%, [, (, and { are undefined character escapes.  Unescape them.')
-
-  # For the rest, work with both comments and strings removed.
-  line = clean_lines.elided[linenum]
-
-  if Search(r'\b(const|volatile|void|char|short|int|long'
-            r'|float|double|signed|unsigned'
-            r'|schar|u?int8|u?int16|u?int32|u?int64)'
-            r'\s+(register|static|extern|typedef)\b',
-            line):
-    error(filename, linenum, 'build/storage_class', 5,
-          'Storage-class specifier (static, extern, typedef, etc) should be '
-          'at the beginning of the declaration.')
-
-  if Match(r'\s*#\s*endif\s*[^/\s]+', line):
-    error(filename, linenum, 'build/endif_comment', 5,
-          'Uncommented text after #endif is non-standard.  Use a comment.')
-
-  if Match(r'\s*class\s+(\w+\s*::\s*)+\w+\s*;', line):
-    error(filename, linenum, 'build/forward_decl', 5,
-          'Inner-style forward declarations are invalid.  Remove this line.')
-
-  if Search(r'(\w+|[+-]?\d+(\.\d*)?)\s*(<|>)\?=?\s*(\w+|[+-]?\d+)(\.\d*)?',
-            line):
-    error(filename, linenum, 'build/deprecated', 3,
-          '>? and <? (max and min) operators are non-standard and deprecated.')
-
-  if Search(r'^\s*const\s*string\s*&\s*\w+\s*;', line):
-    # TODO(unknown): Could it be expanded safely to arbitrary references,
-    # without triggering too many false positives? The first
-    # attempt triggered 5 warnings for mostly benign code in the regtest, hence
-    # the restriction.
-    # Here's the original regexp, for the reference:
-    # type_name = r'\w+((\s*::\s*\w+)|(\s*<\s*\w+?\s*>))?'
-    # r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;'
-    error(filename, linenum, 'runtime/member_string_references', 2,
-          'const string& members are dangerous. It is much better to use '
-          'alternatives, such as pointers or simple constants.')
-
-  # Everything else in this function operates on class declarations.
-  # Return early if the top of the nesting stack is not a class, or if
-  # the class head is not completed yet.
-  classinfo = nesting_state.InnermostClass()
-  if not classinfo or not classinfo.seen_open_brace:
-    return
-
-  # The class may have been declared with namespace or classname qualifiers.
-  # The constructor and destructor will not have those qualifiers.
-  base_classname = classinfo.name.split('::')[-1]
-
-  # Look for single-argument constructors that aren't marked explicit.
-  # Technically a valid construct, but against style.
-  explicit_constructor_match = Match(
-      r'\s+(?:(?:inline|constexpr)\s+)*(explicit\s+)?'
-      r'(?:(?:inline|constexpr)\s+)*%s\s*'
-      r'\(((?:[^()]|\([^()]*\))*)\)'
-      % re.escape(base_classname),
-      line)
-
-  if explicit_constructor_match:
-    is_marked_explicit = explicit_constructor_match.group(1)
-
-    if not explicit_constructor_match.group(2):
-      constructor_args = []
-    else:
-      constructor_args = explicit_constructor_match.group(2).split(',')
-
-    # collapse arguments so that commas in template parameter lists and function
-    # argument parameter lists don't split arguments in two
-    i = 0
-    while i < len(constructor_args):
-      constructor_arg = constructor_args[i]
-      while (constructor_arg.count('<') > constructor_arg.count('>') or
-             constructor_arg.count('(') > constructor_arg.count(')')):
-        constructor_arg += ',' + constructor_args[i + 1]
-        del constructor_args[i + 1]
-      constructor_args[i] = constructor_arg
-      i += 1
-
-    defaulted_args = [arg for arg in constructor_args if '=' in arg]
-    noarg_constructor = (not constructor_args or  # empty arg list
-                         # 'void' arg specifier
-                         (len(constructor_args) == 1 and
-                          constructor_args[0].strip() == 'void'))
-    onearg_constructor = ((len(constructor_args) == 1 and  # exactly one arg
-                           not noarg_constructor) or
-                          # all but at most one arg defaulted
-                          (len(constructor_args) >= 1 and
-                           not noarg_constructor and
-                           len(defaulted_args) >= len(constructor_args) - 1))
-    initializer_list_constructor = bool(
-        onearg_constructor and
-        Search(r'\bstd\s*::\s*initializer_list\b', constructor_args[0]))
-    copy_constructor = bool(
-        onearg_constructor and
-        Match(r'(const\s+)?%s(\s*<[^>]*>)?(\s+const)?\s*(?:<\w+>\s*)?&'
-              % re.escape(base_classname), constructor_args[0].strip()))
-
-    if (not is_marked_explicit and
-        onearg_constructor and
-        not initializer_list_constructor and
-        not copy_constructor):
-      if defaulted_args:
-        error(filename, linenum, 'runtime/explicit', 5,
-              'Constructors callable with one argument '
-              'should be marked explicit.')
-      else:
-        error(filename, linenum, 'runtime/explicit', 5,
-              'Single-parameter constructors should be marked explicit.')
-    elif is_marked_explicit and not onearg_constructor:
-      if noarg_constructor:
-        error(filename, linenum, 'runtime/explicit', 5,
-              'Zero-parameter constructors should not be marked explicit.')
-
-
-def CheckSpacingForFunctionCall(filename, clean_lines, linenum, error):
-  """Checks for the correctness of various spacing around function calls.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  # Since function calls often occur inside if/for/while/switch
-  # expressions - which have their own, more liberal conventions - we
-  # first see if we should be looking inside such an expression for a
-  # function call, to which we can apply more strict standards.
-  fncall = line    # if there's no control flow construct, look at whole line
-  for pattern in (r'\bif\s*\((.*)\)\s*{',
-                  r'\bfor\s*\((.*)\)\s*{',
-                  r'\bwhile\s*\((.*)\)\s*[{;]',
-                  r'\bswitch\s*\((.*)\)\s*{'):
-    match = Search(pattern, line)
-    if match:
-      fncall = match.group(1)    # look inside the parens for function calls
-      break
-
-  # Except in if/for/while/switch, there should never be space
-  # immediately inside parens (eg "f( 3, 4 )").  We make an exception
-  # for nested parens ( (a+b) + c ).  Likewise, there should never be
-  # a space before a ( when it's a function argument.  I assume it's a
-  # function argument when the char before the whitespace is legal in
-  # a function name (alnum + _) and we're not starting a macro. Also ignore
-  # pointers and references to arrays and functions coz they're too tricky:
-  # we use a very simple way to recognize these:
-  # " (something)(maybe-something)" or
-  # " (something)(maybe-something," or
-  # " (something)[something]"
-  # Note that we assume the contents of [] to be short enough that
-  # they'll never need to wrap.
-  if (  # Ignore control structures.
-      not Search(r'\b(if|for|while|switch|return|new|delete|catch|sizeof)\b',
-                 fncall) and
-      # Ignore pointers/references to functions.
-      not Search(r' \([^)]+\)\([^)]*(\)|,$)', fncall) and
-      # Ignore pointers/references to arrays.
-      not Search(r' \([^)]+\)\[[^\]]+\]', fncall)):
-    if Search(r'\w\s*\(\s(?!\s*\\$)', fncall):      # a ( used for a fn call
-      error(filename, linenum, 'whitespace/parens', 4,
-            'Extra space after ( in function call')
-    elif Search(r'\(\s+(?!(\s*\\)|\()', fncall):
-      error(filename, linenum, 'whitespace/parens', 2,
-            'Extra space after (')
-    if (Search(r'\w\s+\(', fncall) and
-        not Search(r'_{0,2}asm_{0,2}\s+_{0,2}volatile_{0,2}\s+\(', fncall) and
-        not Search(r'#\s*define|typedef|using\s+\w+\s*=', fncall) and
-        not Search(r'\w\s+\((\w+::)*\*\w+\)\(', fncall) and
-        not Search(r'\bcase\s+\(', fncall)):
-      # TODO(unknown): Space after an operator function seem to be a common
-      # error, silence those for now by restricting them to highest verbosity.
-      if Search(r'\boperator_*\b', line):
-        error(filename, linenum, 'whitespace/parens', 0,
-              'Extra space before ( in function call')
-      else:
-        error(filename, linenum, 'whitespace/parens', 4,
-              'Extra space before ( in function call')
-    # If the ) is followed only by a newline or a { + newline, assume it's
-    # part of a control statement (if/while/etc), and don't complain
-    if Search(r'[^)]\s+\)\s*[^{\s]', fncall):
-      # If the closing parenthesis is preceded by only whitespaces,
-      # try to give a more descriptive error message.
-      if Search(r'^\s+\)', fncall):
-        error(filename, linenum, 'whitespace/parens', 2,
-              'Closing ) should be moved to the previous line')
-      else:
-        error(filename, linenum, 'whitespace/parens', 2,
-              'Extra space before )')
-
-
-def IsBlankLine(line):
-  """Returns true if the given line is blank.
-
-  We consider a line to be blank if the line is empty or consists of
-  only white spaces.
-
-  Args:
-    line: A line of a string.
-
-  Returns:
-    True, if the given line is blank.
-  """
-  return not line or line.isspace()
-
-
-def CheckForNamespaceIndentation(filename, nesting_state, clean_lines, line,
-                                 error):
-  is_namespace_indent_item = (
-      len(nesting_state.stack) > 1 and
-      nesting_state.stack[-1].check_namespace_indentation and
-      isinstance(nesting_state.previous_stack_top, _NamespaceInfo) and
-      nesting_state.previous_stack_top == nesting_state.stack[-2])
-
-  if ShouldCheckNamespaceIndentation(nesting_state, is_namespace_indent_item,
-                                     clean_lines.elided, line):
-    CheckItemIndentationInNamespace(filename, clean_lines.elided,
-                                    line, error)
-
-
-def CheckForFunctionLengths(filename, clean_lines, linenum,
-                            function_state, error):
-  """Reports for long function bodies.
-
-  For an overview why this is done, see:
-  https://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Write_Short_Functions
-
-  Uses a simplistic algorithm assuming other style guidelines
-  (especially spacing) are followed.
-  Only checks unindented functions, so class members are unchecked.
-  Trivial bodies are unchecked, so constructors with huge initializer lists
-  may be missed.
-  Blank/comment lines are not counted so as to avoid encouraging the removal
-  of vertical space and comments just to get through a lint check.
-  NOLINT *on the last line of a function* disables this check.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    function_state: Current function name and lines in body so far.
-    error: The function to call with any errors found.
-  """
-  lines = clean_lines.lines
-  line = lines[linenum]
-  joined_line = ''
-
-  starting_func = False
-  regexp = r'(\w(\w|::|\*|\&|\s)*)\('  # decls * & space::name( ...
-  match_result = Match(regexp, line)
-  if match_result:
-    # If the name is all caps and underscores, figure it's a macro and
-    # ignore it, unless it's TEST or TEST_F.
-    function_name = match_result.group(1).split()[-1]
-    if function_name == 'TEST' or function_name == 'TEST_F' or (
-        not Match(r'[A-Z_]+$', function_name)):
-      starting_func = True
-
-  if starting_func:
-    body_found = False
-    for start_linenum in xrange(linenum, clean_lines.NumLines()):
-      start_line = lines[start_linenum]
-      joined_line += ' ' + start_line.lstrip()
-      if Search(r'(;|})', start_line):  # Declarations and trivial functions
-        body_found = True
-        break                              # ... ignore
-      elif Search(r'{', start_line):
-        body_found = True
-        function = Search(r'((\w|:)*)\(', line).group(1)
-        if Match(r'TEST', function):    # Handle TEST... macros
-          parameter_regexp = Search(r'(\(.*\))', joined_line)
-          if parameter_regexp:             # Ignore bad syntax
-            function += parameter_regexp.group(1)
-        else:
-          function += '()'
-        function_state.Begin(function)
-        break
-    if not body_found:
-      # No body for the function (or evidence of a non-function) was found.
-      error(filename, linenum, 'readability/fn_size', 5,
-            'Lint failed to find start of function body.')
-  elif Match(r'^\}\s*$', line):  # function end
-    function_state.Check(error, filename, linenum)
-    function_state.End()
-  elif not Match(r'^\s*$', line):
-    function_state.Count()  # Count non-blank/non-comment lines.
-
-
-_RE_PATTERN_TODO = re.compile(r'^//(\s*)TODO(\(.+?\))?:?(\s|$)?')
-
-
-def CheckComment(line, filename, linenum, next_line_start, error):
-  """Checks for common mistakes in comments.
-
-  Args:
-    line: The line in question.
-    filename: The name of the current file.
-    linenum: The number of the line to check.
-    next_line_start: The first non-whitespace column of the next line.
-    error: The function to call with any errors found.
-  """
-  commentpos = line.find('//')
-  if commentpos != -1:
-    # Check if the // may be in quotes.  If so, ignore it
-    if re.sub(r'\\.', '', line[0:commentpos]).count('"') % 2 == 0:
-      # Allow one space for new scopes, two spaces otherwise:
-      if (not (Match(r'^.*{ *//', line) and next_line_start == commentpos) and
-          ((commentpos >= 1 and
-            line[commentpos-1] not in string.whitespace) or
-           (commentpos >= 2 and
-            line[commentpos-2] not in string.whitespace))):
-        error(filename, linenum, 'whitespace/comments', 2,
-              'At least two spaces is best between code and comments')
-
-      # Checks for common mistakes in TODO comments.
-      comment = line[commentpos:]
-      match = _RE_PATTERN_TODO.match(comment)
-      if match:
-        # One whitespace is correct; zero whitespace is handled elsewhere.
-        leading_whitespace = match.group(1)
-        if len(leading_whitespace) > 1:
-          error(filename, linenum, 'whitespace/todo', 2,
-                'Too many spaces before TODO')
-
-        username = match.group(2)
-        if not username:
-          error(filename, linenum, 'readability/todo', 2,
-                'Missing username in TODO; it should look like '
-                '"// TODO(my_username): Stuff."')
-
-        middle_whitespace = match.group(3)
-        # Comparisons made explicit for correctness -- pylint: disable=g-explicit-bool-comparison
-        if middle_whitespace != ' ' and middle_whitespace != '':
-          error(filename, linenum, 'whitespace/todo', 2,
-                'TODO(my_username) should be followed by a space')
-
-      # If the comment contains an alphanumeric character, there
-      # should be a space somewhere between it and the // unless
-      # it's a /// or //! Doxygen comment.
-      if (Match(r'//[^ ]*\w', comment) and
-          not Match(r'(///|//\!)(\s+|$)', comment)):
-        error(filename, linenum, 'whitespace/comments', 4,
-              'Should have a space between // and comment')
-
-
-def CheckSpacing(filename, clean_lines, linenum, nesting_state, error):
-  """Checks for the correctness of various spacing issues in the code.
-
-  Things we check for: spaces around operators, spaces after
-  if/for/while/switch, no spaces around parens in function calls, two
-  spaces between code and comment, don't start a block with a blank
-  line, don't end a function with a blank line, don't add a blank line
-  after public/protected/private, don't have too many blank lines in a row.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    nesting_state: A NestingState instance which maintains information about
-                   the current stack of nested blocks being parsed.
-    error: The function to call with any errors found.
-  """
-
-  # Don't use "elided" lines here, otherwise we can't check commented lines.
-  # Don't want to use "raw" either, because we don't want to check inside C++11
-  # raw strings,
-  raw = clean_lines.lines_without_raw_strings
-  line = raw[linenum]
-
-  # Before nixing comments, check if the line is blank for no good
-  # reason.  This includes the first line after a block is opened, and
-  # blank lines at the end of a function (ie, right before a line like '}'
-  #
-  # Skip all the blank line checks if we are immediately inside a
-  # namespace body.  In other words, don't issue blank line warnings
-  # for this block:
-  #   namespace {
-  #
-  #   }
-  #
-  # A warning about missing end of namespace comments will be issued instead.
-  #
-  # Also skip blank line checks for 'extern "C"' blocks, which are formatted
-  # like namespaces.
-  if (IsBlankLine(line) and
-      not nesting_state.InNamespaceBody() and
-      not nesting_state.InExternC()):
-    elided = clean_lines.elided
-    prev_line = elided[linenum - 1]
-    prevbrace = prev_line.rfind('{')
-    # TODO(unknown): Don't complain if line before blank line, and line after,
-    #                both start with alnums and are indented the same amount.
-    #                This ignores whitespace at the start of a namespace block
-    #                because those are not usually indented.
-    if prevbrace != -1 and prev_line[prevbrace:].find('}') == -1:
-      # OK, we have a blank line at the start of a code block.  Before we
-      # complain, we check if it is an exception to the rule: The previous
-      # non-empty line has the parameters of a function header that are indented
-      # 4 spaces (because they did not fit in a 80 column line when placed on
-      # the same line as the function name).  We also check for the case where
-      # the previous line is indented 6 spaces, which may happen when the
-      # initializers of a constructor do not fit into a 80 column line.
-      exception = False
-      if Match(r' {6}\w', prev_line):  # Initializer list?
-        # We are looking for the opening column of initializer list, which
-        # should be indented 4 spaces to cause 6 space indentation afterwards.
-        search_position = linenum-2
-        while (search_position >= 0
-               and Match(r' {6}\w', elided[search_position])):
-          search_position -= 1
-        exception = (search_position >= 0
-                     and elided[search_position][:5] == '    :')
-      else:
-        # Search for the function arguments or an initializer list.  We use a
-        # simple heuristic here: If the line is indented 4 spaces; and we have a
-        # closing paren, without the opening paren, followed by an opening brace
-        # or colon (for initializer lists) we assume that it is the last line of
-        # a function header.  If we have a colon indented 4 spaces, it is an
-        # initializer list.
-        exception = (Match(r' {4}\w[^\(]*\)\s*(const\s*)?(\{\s*$|:)',
-                           prev_line)
-                     or Match(r' {4}:', prev_line))
-
-      if not exception:
-        error(filename, linenum, 'whitespace/blank_line', 2,
-              'Redundant blank line at the start of a code block '
-              'should be deleted.')
-    # Ignore blank lines at the end of a block in a long if-else
-    # chain, like this:
-    #   if (condition1) {
-    #     // Something followed by a blank line
-    #
-    #   } else if (condition2) {
-    #     // Something else
-    #   }
-    if linenum + 1 < clean_lines.NumLines():
-      next_line = raw[linenum + 1]
-      if (next_line
-          and Match(r'\s*}', next_line)
-          and next_line.find('} else ') == -1):
-        error(filename, linenum, 'whitespace/blank_line', 3,
-              'Redundant blank line at the end of a code block '
-              'should be deleted.')
-
-    matched = Match(r'\s*(public|protected|private):', prev_line)
-    if matched:
-      error(filename, linenum, 'whitespace/blank_line', 3,
-            'Do not leave a blank line after "%s:"' % matched.group(1))
-
-  # Next, check comments
-  next_line_start = 0
-  if linenum + 1 < clean_lines.NumLines():
-    next_line = raw[linenum + 1]
-    next_line_start = len(next_line) - len(next_line.lstrip())
-  CheckComment(line, filename, linenum, next_line_start, error)
-
-  # get rid of comments and strings
-  line = clean_lines.elided[linenum]
-
-  # You shouldn't have spaces before your brackets, except maybe after
-  # 'delete []' or 'return []() {};'
-  if Search(r'\w\s+\[', line) and not Search(r'(?:delete|return)\s+\[', line):
-    error(filename, linenum, 'whitespace/braces', 5,
-          'Extra space before [')
-
-  # In range-based for, we wanted spaces before and after the colon, but
-  # not around "::" tokens that might appear.
-  if (Search(r'for *\(.*[^:]:[^: ]', line) or
-      Search(r'for *\(.*[^: ]:[^:]', line)):
-    error(filename, linenum, 'whitespace/forcolon', 2,
-          'Missing space around colon in range-based for loop')
-
-
-def CheckOperatorSpacing(filename, clean_lines, linenum, error):
-  """Checks for horizontal spacing around operators.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  # Don't try to do spacing checks for operator methods.  Do this by
-  # replacing the troublesome characters with something else,
-  # preserving column position for all other characters.
-  #
-  # The replacement is done repeatedly to avoid false positives from
-  # operators that call operators.
-  while True:
-    match = Match(r'^(.*\boperator\b)(\S+)(\s*\(.*)$', line)
-    if match:
-      line = match.group(1) + ('_' * len(match.group(2))) + match.group(3)
-    else:
-      break
-
-  # We allow no-spaces around = within an if: "if ( (a=Foo()) == 0 )".
-  # Otherwise not.  Note we only check for non-spaces on *both* sides;
-  # sometimes people put non-spaces on one side when aligning ='s among
-  # many lines (not that this is behavior that I approve of...)
-  if ((Search(r'[\w.]=', line) or
-       Search(r'=[\w.]', line))
-      and not Search(r'\b(if|while|for) ', line)
-      # Operators taken from [lex.operators] in C++11 standard.
-      and not Search(r'(>=|<=|==|!=|&=|\^=|\|=|\+=|\*=|\/=|\%=)', line)
-      and not Search(r'operator=', line)):
-    error(filename, linenum, 'whitespace/operators', 4,
-          'Missing spaces around =')
-
-  # It's ok not to have spaces around binary operators like + - * /, but if
-  # there's too little whitespace, we get concerned.  It's hard to tell,
-  # though, so we punt on this one for now.  TODO.
-
-  # You should always have whitespace around binary operators.
-  #
-  # Check <= and >= first to avoid false positives with < and >, then
-  # check non-include lines for spacing around < and >.
-  #
-  # If the operator is followed by a comma, assume it's be used in a
-  # macro context and don't do any checks.  This avoids false
-  # positives.
-  #
-  # Note that && is not included here.  This is because there are too
-  # many false positives due to RValue references.
-  match = Search(r'[^<>=!\s](==|!=|<=|>=|\|\|)[^<>=!\s,;\)]', line)
-  if match:
-    error(filename, linenum, 'whitespace/operators', 3,
-          'Missing spaces around %s' % match.group(1))
-  elif not Match(r'#.*include', line):
-    # Look for < that is not surrounded by spaces.  This is only
-    # triggered if both sides are missing spaces, even though
-    # technically should should flag if at least one side is missing a
-    # space.  This is done to avoid some false positives with shifts.
-    match = Match(r'^(.*[^\s<])<[^\s=<,]', line)
-    if match:
-      (_, _, end_pos) = CloseExpression(
-          clean_lines, linenum, len(match.group(1)))
-      if end_pos <= -1:
-        error(filename, linenum, 'whitespace/operators', 3,
-              'Missing spaces around <')
-
-    # Look for > that is not surrounded by spaces.  Similar to the
-    # above, we only trigger if both sides are missing spaces to avoid
-    # false positives with shifts.
-    match = Match(r'^(.*[^-\s>])>[^\s=>,]', line)
-    if match:
-      (_, _, start_pos) = ReverseCloseExpression(
-          clean_lines, linenum, len(match.group(1)))
-      if start_pos <= -1:
-        error(filename, linenum, 'whitespace/operators', 3,
-              'Missing spaces around >')
-
-  # We allow no-spaces around << when used like this: 10<<20, but
-  # not otherwise (particularly, not when used as streams)
-  #
-  # We also allow operators following an opening parenthesis, since
-  # those tend to be macros that deal with operators.
-  match = Search(r'(operator|[^\s(<])(?:L|UL|LL|ULL|l|ul|ll|ull)?<<([^\s,=<])', line)
-  if (match and not (match.group(1).isdigit() and match.group(2).isdigit()) and
-      not (match.group(1) == 'operator' and match.group(2) == ';')):
-    error(filename, linenum, 'whitespace/operators', 3,
-          'Missing spaces around <<')
-
-  # We allow no-spaces around >> for almost anything.  This is because
-  # C++11 allows ">>" to close nested templates, which accounts for
-  # most cases when ">>" is not followed by a space.
-  #
-  # We still warn on ">>" followed by alpha character, because that is
-  # likely due to ">>" being used for right shifts, e.g.:
-  #   value >> alpha
-  #
-  # When ">>" is used to close templates, the alphanumeric letter that
-  # follows would be part of an identifier, and there should still be
-  # a space separating the template type and the identifier.
-  #   type<type<type>> alpha
-  match = Search(r'>>[a-zA-Z_]', line)
-  if match:
-    error(filename, linenum, 'whitespace/operators', 3,
-          'Missing spaces around >>')
-
-  # There shouldn't be space around unary operators
-  match = Search(r'(!\s|~\s|[\s]--[\s;]|[\s]\+\+[\s;])', line)
-  if match:
-    error(filename, linenum, 'whitespace/operators', 4,
-          'Extra space for operator %s' % match.group(1))
-
-
-def CheckParenthesisSpacing(filename, clean_lines, linenum, error):
-  """Checks for horizontal spacing around parentheses.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  # No spaces after an if, while, switch, or for
-  match = Search(r' (if\(|for\(|while\(|switch\()', line)
-  if match:
-    error(filename, linenum, 'whitespace/parens', 5,
-          'Missing space before ( in %s' % match.group(1))
-
-  # For if/for/while/switch, the left and right parens should be
-  # consistent about how many spaces are inside the parens, and
-  # there should either be zero or one spaces inside the parens.
-  # We don't want: "if ( foo)" or "if ( foo   )".
-  # Exception: "for ( ; foo; bar)" and "for (foo; bar; )" are allowed.
-  match = Search(r'\b(if|for|while|switch)\s*'
-                 r'\(([ ]*)(.).*[^ ]+([ ]*)\)\s*{\s*$',
-                 line)
-  if match:
-    if len(match.group(2)) != len(match.group(4)):
-      if not (match.group(3) == ';' and
-              len(match.group(2)) == 1 + len(match.group(4)) or
-              not match.group(2) and Search(r'\bfor\s*\(.*; \)', line)):
-        error(filename, linenum, 'whitespace/parens', 5,
-              'Mismatching spaces inside () in %s' % match.group(1))
-    if len(match.group(2)) not in [0, 1]:
-      error(filename, linenum, 'whitespace/parens', 5,
-            'Should have zero or one spaces inside ( and ) in %s' %
-            match.group(1))
-
-
-def CheckCommaSpacing(filename, clean_lines, linenum, error):
-  """Checks for horizontal spacing near commas and semicolons.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  raw = clean_lines.lines_without_raw_strings
-  line = clean_lines.elided[linenum]
-
-  # You should always have a space after a comma (either as fn arg or operator)
-  #
-  # This does not apply when the non-space character following the
-  # comma is another comma, since the only time when that happens is
-  # for empty macro arguments.
-  #
-  # We run this check in two passes: first pass on elided lines to
-  # verify that lines contain missing whitespaces, second pass on raw
-  # lines to confirm that those missing whitespaces are not due to
-  # elided comments.
-  if (Search(r',[^,\s]', ReplaceAll(r'\boperator\s*,\s*\(', 'F(', line)) and
-      Search(r',[^,\s]', raw[linenum])):
-    error(filename, linenum, 'whitespace/comma', 3,
-          'Missing space after ,')
-
-  # You should always have a space after a semicolon
-  # except for few corner cases
-  # TODO(unknown): clarify if 'if (1) { return 1;}' is requires one more
-  # space after ;
-  if Search(r';[^\s};\\)/]', line):
-    error(filename, linenum, 'whitespace/semicolon', 3,
-          'Missing space after ;')
-
-
-def _IsType(clean_lines, nesting_state, expr):
-  """Check if expression looks like a type name, returns true if so.
-
-  Args:
-    clean_lines: A CleansedLines instance containing the file.
-    nesting_state: A NestingState instance which maintains information about
-                   the current stack of nested blocks being parsed.
-    expr: The expression to check.
-  Returns:
-    True, if token looks like a type.
-  """
-  # Keep only the last token in the expression
-  last_word = Match(r'^.*(\b\S+)$', expr)
-  if last_word:
-    token = last_word.group(1)
-  else:
-    token = expr
-
-  # Match native types and stdint types
-  if _TYPES.match(token):
-    return True
-
-  # Try a bit harder to match templated types.  Walk up the nesting
-  # stack until we find something that resembles a typename
-  # declaration for what we are looking for.
-  typename_pattern = (r'\b(?:typename|class|struct)\s+' + re.escape(token) +
-                      r'\b')
-  block_index = len(nesting_state.stack) - 1
-  while block_index >= 0:
-    if isinstance(nesting_state.stack[block_index], _NamespaceInfo):
-      return False
-
-    # Found where the opening brace is.  We want to scan from this
-    # line up to the beginning of the function, minus a few lines.
-    #   template <typename Type1,  // stop scanning here
-    #             ...>
-    #   class C
-    #     : public ... {  // start scanning here
-    last_line = nesting_state.stack[block_index].starting_linenum
-
-    next_block_start = 0
-    if block_index > 0:
-      next_block_start = nesting_state.stack[block_index - 1].starting_linenum
-    first_line = last_line
-    while first_line >= next_block_start:
-      if clean_lines.elided[first_line].find('template') >= 0:
-        break
-      first_line -= 1
-    if first_line < next_block_start:
-      # Didn't find any "template" keyword before reaching the next block,
-      # there are probably no template things to check for this block
-      block_index -= 1
-      continue
-
-    # Look for typename in the specified range
-    for i in xrange(first_line, last_line + 1, 1):
-      if Search(typename_pattern, clean_lines.elided[i]):
-        return True
-    block_index -= 1
-
-  return False
-
-
-def CheckBracesSpacing(filename, clean_lines, linenum, nesting_state, error):
-  """Checks for horizontal spacing near commas.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    nesting_state: A NestingState instance which maintains information about
-                   the current stack of nested blocks being parsed.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  # Except after an opening paren, or after another opening brace (in case of
-  # an initializer list, for instance), you should have spaces before your
-  # braces when they are delimiting blocks, classes, namespaces etc.
-  # And since you should never have braces at the beginning of a line,
-  # this is an easy test.  Except that braces used for initialization don't
-  # follow the same rule; we often don't want spaces before those.
-  match = Match(r'^(.*[^ ({>]){', line)
-
-  if match:
-    # Try a bit harder to check for brace initialization.  This
-    # happens in one of the following forms:
-    #   Constructor() : initializer_list_{} { ... }
-    #   Constructor{}.MemberFunction()
-    #   Type variable{};
-    #   FunctionCall(type{}, ...);
-    #   LastArgument(..., type{});
-    #   LOG(INFO) << type{} << " ...";
-    #   map_of_type[{...}] = ...;
-    #   ternary = expr ? new type{} : nullptr;
-    #   OuterTemplate<InnerTemplateConstructor<Type>{}>
-    #
-    # We check for the character following the closing brace, and
-    # silence the warning if it's one of those listed above, i.e.
-    # "{.;,)<>]:".
-    #
-    # To account for nested initializer list, we allow any number of
-    # closing braces up to "{;,)<".  We can't simply silence the
-    # warning on first sight of closing brace, because that would
-    # cause false negatives for things that are not initializer lists.
-    #   Silence this:         But not this:
-    #     Outer{                if (...) {
-    #       Inner{...}            if (...){  // Missing space before {
-    #     };                    }
-    #
-    # There is a false negative with this approach if people inserted
-    # spurious semicolons, e.g. "if (cond){};", but we will catch the
-    # spurious semicolon with a separate check.
-    leading_text = match.group(1)
-    (endline, endlinenum, endpos) = CloseExpression(
-        clean_lines, linenum, len(match.group(1)))
-    trailing_text = ''
-    if endpos > -1:
-      trailing_text = endline[endpos:]
-    for offset in xrange(endlinenum + 1,
-                         min(endlinenum + 3, clean_lines.NumLines() - 1)):
-      trailing_text += clean_lines.elided[offset]
-    # We also suppress warnings for `uint64_t{expression}` etc., as the style
-    # guide recommends brace initialization for integral types to avoid
-    # overflow/truncation.
-    if (not Match(r'^[\s}]*[{.;,)<>\]:]', trailing_text)
-        and not _IsType(clean_lines, nesting_state, leading_text)):
-      error(filename, linenum, 'whitespace/braces', 5,
-            'Missing space before {')
-
-  # Make sure '} else {' has spaces.
-  if Search(r'}else', line):
-    error(filename, linenum, 'whitespace/braces', 5,
-          'Missing space before else')
-
-  # You shouldn't have a space before a semicolon at the end of the line.
-  # There's a special case for "for" since the style guide allows space before
-  # the semicolon there.
-  if Search(r':\s*;\s*$', line):
-    error(filename, linenum, 'whitespace/semicolon', 5,
-          'Semicolon defining empty statement. Use {} instead.')
-  elif Search(r'^\s*;\s*$', line):
-    error(filename, linenum, 'whitespace/semicolon', 5,
-          'Line contains only semicolon. If this should be an empty statement, '
-          'use {} instead.')
-  elif (Search(r'\s+;\s*$', line) and
-        not Search(r'\bfor\b', line)):
-    error(filename, linenum, 'whitespace/semicolon', 5,
-          'Extra space before last semicolon. If this should be an empty '
-          'statement, use {} instead.')
-
-
-def IsDecltype(clean_lines, linenum, column):
-  """Check if the token ending on (linenum, column) is decltype().
-
-  Args:
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: the number of the line to check.
-    column: end column of the token to check.
-  Returns:
-    True if this token is decltype() expression, False otherwise.
-  """
-  (text, _, start_col) = ReverseCloseExpression(clean_lines, linenum, column)
-  if start_col < 0:
-    return False
-  if Search(r'\bdecltype\s*$', text[0:start_col]):
-    return True
-  return False
-
-
-def CheckSectionSpacing(filename, clean_lines, class_info, linenum, error):
-  """Checks for additional blank line issues related to sections.
-
-  Currently the only thing checked here is blank line before protected/private.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    class_info: A _ClassInfo objects.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  # Skip checks if the class is small, where small means 25 lines or less.
-  # 25 lines seems like a good cutoff since that's the usual height of
-  # terminals, and any class that can't fit in one screen can't really
-  # be considered "small".
-  #
-  # Also skip checks if we are on the first line.  This accounts for
-  # classes that look like
-  #   class Foo { public: ... };
-  #
-  # If we didn't find the end of the class, last_line would be zero,
-  # and the check will be skipped by the first condition.
-  if (class_info.last_line - class_info.starting_linenum <= 24 or
-      linenum <= class_info.starting_linenum):
-    return
-
-  matched = Match(r'\s*(public|protected|private):', clean_lines.lines[linenum])
-  if matched:
-    # Issue warning if the line before public/protected/private was
-    # not a blank line, but don't do this if the previous line contains
-    # "class" or "struct".  This can happen two ways:
-    #  - We are at the beginning of the class.
-    #  - We are forward-declaring an inner class that is semantically
-    #    private, but needed to be public for implementation reasons.
-    # Also ignores cases where the previous line ends with a backslash as can be
-    # common when defining classes in C macros.
-    prev_line = clean_lines.lines[linenum - 1]
-    if (not IsBlankLine(prev_line) and
-        not Search(r'\b(class|struct)\b', prev_line) and
-        not Search(r'\\$', prev_line)):
-      # Try a bit harder to find the beginning of the class.  This is to
-      # account for multi-line base-specifier lists, e.g.:
-      #   class Derived
-      #       : public Base {
-      end_class_head = class_info.starting_linenum
-      for i in range(class_info.starting_linenum, linenum):
-        if Search(r'\{\s*$', clean_lines.lines[i]):
-          end_class_head = i
-          break
-      if end_class_head < linenum - 1:
-        error(filename, linenum, 'whitespace/blank_line', 3,
-              '"%s:" should be preceded by a blank line' % matched.group(1))
-
-
-def GetPreviousNonBlankLine(clean_lines, linenum):
-  """Return the most recent non-blank line and its line number.
-
-  Args:
-    clean_lines: A CleansedLines instance containing the file contents.
-    linenum: The number of the line to check.
-
-  Returns:
-    A tuple with two elements.  The first element is the contents of the last
-    non-blank line before the current line, or the empty string if this is the
-    first non-blank line.  The second is the line number of that line, or -1
-    if this is the first non-blank line.
-  """
-
-  prevlinenum = linenum - 1
-  while prevlinenum >= 0:
-    prevline = clean_lines.elided[prevlinenum]
-    if not IsBlankLine(prevline):     # if not a blank line...
-      return (prevline, prevlinenum)
-    prevlinenum -= 1
-  return ('', -1)
-
-
-def CheckBraces(filename, clean_lines, linenum, error):
-  """Looks for misplaced braces (e.g. at the end of line).
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-
-  line = clean_lines.elided[linenum]        # get rid of comments and strings
-
-  if Match(r'\s*{\s*$', line):
-    # We allow an open brace to start a line in the case where someone is using
-    # braces in a block to explicitly create a new scope, which is commonly used
-    # to control the lifetime of stack-allocated variables.  Braces are also
-    # used for brace initializers inside function calls.  We don't detect this
-    # perfectly: we just don't complain if the last non-whitespace character on
-    # the previous non-blank line is ',', ';', ':', '(', '{', or '}', or if the
-    # previous line starts a preprocessor block. We also allow a brace on the
-    # following line if it is part of an array initialization and would not fit
-    # within the 80 character limit of the preceding line.
-    prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
-    if (not Search(r'[,;:}{(]\s*$', prevline) and
-        not Match(r'\s*#', prevline) and
-        not (GetLineWidth(prevline) > _line_length - 2 and '[]' in prevline)):
-      error(filename, linenum, 'whitespace/braces', 4,
-            '{ should almost always be at the end of the previous line')
-
-  # An else clause should be on the same line as the preceding closing brace.
-  if Match(r'\s*else\b\s*(?:if\b|\{|$)', line):
-    prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
-    if Match(r'\s*}\s*$', prevline):
-      error(filename, linenum, 'whitespace/newline', 4,
-            'An else should appear on the same line as the preceding }')
-
-  # If braces come on one side of an else, they should be on both.
-  # However, we have to worry about "else if" that spans multiple lines!
-  if Search(r'else if\s*\(', line):       # could be multi-line if
-    brace_on_left = bool(Search(r'}\s*else if\s*\(', line))
-    # find the ( after the if
-    pos = line.find('else if')
-    pos = line.find('(', pos)
-    if pos > 0:
-      (endline, _, endpos) = CloseExpression(clean_lines, linenum, pos)
-      brace_on_right = endline[endpos:].find('{') != -1
-      if brace_on_left != brace_on_right:    # must be brace after if
-        error(filename, linenum, 'readability/braces', 5,
-              'If an else has a brace on one side, it should have it on both')
-  elif Search(r'}\s*else[^{]*$', line) or Match(r'[^}]*else\s*{', line):
-    error(filename, linenum, 'readability/braces', 5,
-          'If an else has a brace on one side, it should have it on both')
-
-  # Likewise, an else should never have the else clause on the same line
-  if Search(r'\belse [^\s{]', line) and not Search(r'\belse if\b', line):
-    error(filename, linenum, 'whitespace/newline', 4,
-          'Else clause should never be on same line as else (use 2 lines)')
-
-  # In the same way, a do/while should never be on one line
-  if Match(r'\s*do [^\s{]', line):
-    error(filename, linenum, 'whitespace/newline', 4,
-          'do/while clauses should not be on a single line')
-
-  # Check single-line if/else bodies. The style guide says 'curly braces are not
-  # required for single-line statements'. We additionally allow multi-line,
-  # single statements, but we reject anything with more than one semicolon in
-  # it. This means that the first semicolon after the if should be at the end of
-  # its line, and the line after that should have an indent level equal to or
-  # lower than the if. We also check for ambiguous if/else nesting without
-  # braces.
-  if_else_match = Search(r'\b(if\s*\(|else\b)', line)
-  if if_else_match and not Match(r'\s*#', line):
-    if_indent = GetIndentLevel(line)
-    endline, endlinenum, endpos = line, linenum, if_else_match.end()
-    if_match = Search(r'\bif\s*\(', line)
-    if if_match:
-      # This could be a multiline if condition, so find the end first.
-      pos = if_match.end() - 1
-      (endline, endlinenum, endpos) = CloseExpression(clean_lines, linenum, pos)
-    # Check for an opening brace, either directly after the if or on the next
-    # line. If found, this isn't a single-statement conditional.
-    if (not Match(r'\s*{', endline[endpos:])
-        and not (Match(r'\s*$', endline[endpos:])
-                 and endlinenum < (len(clean_lines.elided) - 1)
-                 and Match(r'\s*{', clean_lines.elided[endlinenum + 1]))):
-      while (endlinenum < len(clean_lines.elided)
-             and ';' not in clean_lines.elided[endlinenum][endpos:]):
-        endlinenum += 1
-        endpos = 0
-      if endlinenum < len(clean_lines.elided):
-        endline = clean_lines.elided[endlinenum]
-        # We allow a mix of whitespace and closing braces (e.g. for one-liner
-        # methods) and a single \ after the semicolon (for macros)
-        endpos = endline.find(';')
-        if not Match(r';[\s}]*(\\?)$', endline[endpos:]):
-          # Semicolon isn't the last character, there's something trailing.
-          # Output a warning if the semicolon is not contained inside
-          # a lambda expression.
-          if not Match(r'^[^{};]*\[[^\[\]]*\][^{}]*\{[^{}]*\}\s*\)*[;,]\s*$',
-                       endline):
-            error(filename, linenum, 'readability/braces', 4,
-                  'If/else bodies with multiple statements require braces')
-        elif endlinenum < len(clean_lines.elided) - 1:
-          # Make sure the next line is dedented
-          next_line = clean_lines.elided[endlinenum + 1]
-          next_indent = GetIndentLevel(next_line)
-          # With ambiguous nested if statements, this will error out on the
-          # if that *doesn't* match the else, regardless of whether it's the
-          # inner one or outer one.
-          if (if_match and Match(r'\s*else\b', next_line)
-              and next_indent != if_indent):
-            error(filename, linenum, 'readability/braces', 4,
-                  'Else clause should be indented at the same level as if. '
-                  'Ambiguous nested if/else chains require braces.')
-          elif next_indent > if_indent:
-            error(filename, linenum, 'readability/braces', 4,
-                  'If/else bodies with multiple statements require braces')
-
-
-def CheckTrailingSemicolon(filename, clean_lines, linenum, error):
-  """Looks for redundant trailing semicolon.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-
-  line = clean_lines.elided[linenum]
-
-  # Block bodies should not be followed by a semicolon.  Due to C++11
-  # brace initialization, there are more places where semicolons are
-  # required than not, so we use an allowlist approach to check these
-  # rather than a denylist.  These are the places where "};" should
-  # be replaced by just "}":
-  # 1. Some flavor of block following closing parenthesis:
-  #    for (;;) {};
-  #    while (...) {};
-  #    switch (...) {};
-  #    Function(...) {};
-  #    if (...) {};
-  #    if (...) else if (...) {};
-  #
-  # 2. else block:
-  #    if (...) else {};
-  #
-  # 3. const member function:
-  #    Function(...) const {};
-  #
-  # 4. Block following some statement:
-  #    x = 42;
-  #    {};
-  #
-  # 5. Block at the beginning of a function:
-  #    Function(...) {
-  #      {};
-  #    }
-  #
-  #    Note that naively checking for the preceding "{" will also match
-  #    braces inside multi-dimensional arrays, but this is fine since
-  #    that expression will not contain semicolons.
-  #
-  # 6. Block following another block:
-  #    while (true) {}
-  #    {};
-  #
-  # 7. End of namespaces:
-  #    namespace {};
-  #
-  #    These semicolons seems far more common than other kinds of
-  #    redundant semicolons, possibly due to people converting classes
-  #    to namespaces.  For now we do not warn for this case.
-  #
-  # Try matching case 1 first.
-  match = Match(r'^(.*\)\s*)\{', line)
-  if match:
-    # Matched closing parenthesis (case 1).  Check the token before the
-    # matching opening parenthesis, and don't warn if it looks like a
-    # macro.  This avoids these false positives:
-    #  - macro that defines a base class
-    #  - multi-line macro that defines a base class
-    #  - macro that defines the whole class-head
-    #
-    # But we still issue warnings for macros that we know are safe to
-    # warn, specifically:
-    #  - TEST, TEST_F, TEST_P, MATCHER, MATCHER_P
-    #  - TYPED_TEST
-    #  - INTERFACE_DEF
-    #  - EXCLUSIVE_LOCKS_REQUIRED, SHARED_LOCKS_REQUIRED, LOCKS_EXCLUDED:
-    #
-    # We implement an allowlist of safe macros instead of a denylist of
-    # unsafe macros, even though the latter appears less frequently in
-    # google code and would have been easier to implement.  This is because
-    # the downside for getting the allowlist wrong means some extra
-    # semicolons, while the downside for getting the denylist wrong
-    # would result in compile errors.
-    #
-    # In addition to macros, we also don't want to warn on
-    #  - Compound literals
-    #  - Lambdas
-    #  - alignas specifier with anonymous structs
-    #  - decltype
-    closing_brace_pos = match.group(1).rfind(')')
-    opening_parenthesis = ReverseCloseExpression(
-        clean_lines, linenum, closing_brace_pos)
-    if opening_parenthesis[2] > -1:
-      line_prefix = opening_parenthesis[0][0:opening_parenthesis[2]]
-      macro = Search(r'\b([A-Z_][A-Z0-9_]*)\s*$', line_prefix)
-      func = Match(r'^(.*\])\s*$', line_prefix)
-      if ((macro and
-           macro.group(1) not in (
-               'TEST', 'TEST_F', 'MATCHER', 'MATCHER_P', 'TYPED_TEST',
-               'EXCLUSIVE_LOCKS_REQUIRED', 'SHARED_LOCKS_REQUIRED',
-               'LOCKS_EXCLUDED', 'INTERFACE_DEF')) or
-          (func and not Search(r'\boperator\s*\[\s*\]', func.group(1))) or
-          Search(r'\b(?:struct|union)\s+alignas\s*$', line_prefix) or
-          Search(r'\bdecltype$', line_prefix) or
-          Search(r'\s+=\s*$', line_prefix)):
-        match = None
-    if (match and
-        opening_parenthesis[1] > 1 and
-        Search(r'\]\s*$', clean_lines.elided[opening_parenthesis[1] - 1])):
-      # Multi-line lambda-expression
-      match = None
-
-  else:
-    # Try matching cases 2-3.
-    match = Match(r'^(.*(?:else|\)\s*const)\s*)\{', line)
-    if not match:
-      # Try matching cases 4-6.  These are always matched on separate lines.
-      #
-      # Note that we can't simply concatenate the previous line to the
-      # current line and do a single match, otherwise we may output
-      # duplicate warnings for the blank line case:
-      #   if (cond) {
-      #     // blank line
-      #   }
-      prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
-      if prevline and Search(r'[;{}]\s*$', prevline):
-        match = Match(r'^(\s*)\{', line)
-
-  # Check matching closing brace
-  if match:
-    (endline, endlinenum, endpos) = CloseExpression(
-        clean_lines, linenum, len(match.group(1)))
-    if endpos > -1 and Match(r'^\s*;', endline[endpos:]):
-      # Current {} pair is eligible for semicolon check, and we have found
-      # the redundant semicolon, output warning here.
-      #
-      # Note: because we are scanning forward for opening braces, and
-      # outputting warnings for the matching closing brace, if there are
-      # nested blocks with trailing semicolons, we will get the error
-      # messages in reversed order.
-
-      # We need to check the line forward for NOLINT
-      raw_lines = clean_lines.raw_lines
-      ParseNolintSuppressions(filename, raw_lines[endlinenum-1], endlinenum-1,
-                              error)
-      ParseNolintSuppressions(filename, raw_lines[endlinenum], endlinenum,
-                              error)
-
-      error(filename, endlinenum, 'readability/braces', 4,
-            "You don't need a ; after a }")
-
-
-def CheckEmptyBlockBody(filename, clean_lines, linenum, error):
-  """Look for empty loop/conditional body with only a single semicolon.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-
-  # Search for loop keywords at the beginning of the line.  Because only
-  # whitespaces are allowed before the keywords, this will also ignore most
-  # do-while-loops, since those lines should start with closing brace.
-  #
-  # We also check "if" blocks here, since an empty conditional block
-  # is likely an error.
-  line = clean_lines.elided[linenum]
-  matched = Match(r'\s*(for|while|if)\s*\(', line)
-  if matched:
-    # Find the end of the conditional expression.
-    (end_line, end_linenum, end_pos) = CloseExpression(
-        clean_lines, linenum, line.find('('))
-
-    # Output warning if what follows the condition expression is a semicolon.
-    # No warning for all other cases, including whitespace or newline, since we
-    # have a separate check for semicolons preceded by whitespace.
-    if end_pos >= 0 and Match(r';', end_line[end_pos:]):
-      if matched.group(1) == 'if':
-        error(filename, end_linenum, 'whitespace/empty_conditional_body', 5,
-              'Empty conditional bodies should use {}')
-      else:
-        error(filename, end_linenum, 'whitespace/empty_loop_body', 5,
-              'Empty loop bodies should use {} or continue')
-
-    # Check for if statements that have completely empty bodies (no comments)
-    # and no else clauses.
-    if end_pos >= 0 and matched.group(1) == 'if':
-      # Find the position of the opening { for the if statement.
-      # Return without logging an error if it has no brackets.
-      opening_linenum = end_linenum
-      opening_line_fragment = end_line[end_pos:]
-      # Loop until EOF or find anything that's not whitespace or opening {.
-      while not Search(r'^\s*\{', opening_line_fragment):
-        if Search(r'^(?!\s*$)', opening_line_fragment):
-          # Conditional has no brackets.
-          return
-        opening_linenum += 1
-        if opening_linenum == len(clean_lines.elided):
-          # Couldn't find conditional's opening { or any code before EOF.
-          return
-        opening_line_fragment = clean_lines.elided[opening_linenum]
-      # Set opening_line (opening_line_fragment may not be entire opening line).
-      opening_line = clean_lines.elided[opening_linenum]
-
-      # Find the position of the closing }.
-      opening_pos = opening_line_fragment.find('{')
-      if opening_linenum == end_linenum:
-        # We need to make opening_pos relative to the start of the entire line.
-        opening_pos += end_pos
-      (closing_line, closing_linenum, closing_pos) = CloseExpression(
-          clean_lines, opening_linenum, opening_pos)
-      if closing_pos < 0:
-        return
-
-      # Now construct the body of the conditional. This consists of the portion
-      # of the opening line after the {, all lines until the closing line,
-      # and the portion of the closing line before the }.
-      if (clean_lines.raw_lines[opening_linenum] !=
-          CleanseComments(clean_lines.raw_lines[opening_linenum])):
-        # Opening line ends with a comment, so conditional isn't empty.
-        return
-      if closing_linenum > opening_linenum:
-        # Opening line after the {. Ignore comments here since we checked above.
-        body = list(opening_line[opening_pos+1:])
-        # All lines until closing line, excluding closing line, with comments.
-        body.extend(clean_lines.raw_lines[opening_linenum+1:closing_linenum])
-        # Closing line before the }. Won't (and can't) have comments.
-        body.append(clean_lines.elided[closing_linenum][:closing_pos-1])
-        body = '\n'.join(body)
-      else:
-        # If statement has brackets and fits on a single line.
-        body = opening_line[opening_pos+1:closing_pos-1]
-
-      # Check if the body is empty
-      if not _EMPTY_CONDITIONAL_BODY_PATTERN.search(body):
-        return
-      # The body is empty. Now make sure there's not an else clause.
-      current_linenum = closing_linenum
-      current_line_fragment = closing_line[closing_pos:]
-      # Loop until EOF or find anything that's not whitespace or else clause.
-      while Search(r'^\s*$|^(?=\s*else)', current_line_fragment):
-        if Search(r'^(?=\s*else)', current_line_fragment):
-          # Found an else clause, so don't log an error.
-          return
-        current_linenum += 1
-        if current_linenum == len(clean_lines.elided):
-          break
-        current_line_fragment = clean_lines.elided[current_linenum]
-
-      # The body is empty and there's no else clause until EOF or other code.
-      error(filename, end_linenum, 'whitespace/empty_if_body', 4,
-            ('If statement had no body and no else clause'))
-
-
-def FindCheckMacro(line):
-  """Find a replaceable CHECK-like macro.
-
-  Args:
-    line: line to search on.
-  Returns:
-    (macro name, start position), or (None, -1) if no replaceable
-    macro is found.
-  """
-  for macro in _CHECK_MACROS:
-    i = line.find(macro)
-    if i >= 0:
-      # Find opening parenthesis.  Do a regular expression match here
-      # to make sure that we are matching the expected CHECK macro, as
-      # opposed to some other macro that happens to contain the CHECK
-      # substring.
-      matched = Match(r'^(.*\b' + macro + r'\s*)\(', line)
-      if not matched:
-        continue
-      return (macro, len(matched.group(1)))
-  return (None, -1)
-
-
-def CheckCheck(filename, clean_lines, linenum, error):
-  """Checks the use of CHECK and EXPECT macros.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-
-  # Decide the set of replacement macros that should be suggested
-  lines = clean_lines.elided
-  (check_macro, start_pos) = FindCheckMacro(lines[linenum])
-  if not check_macro:
-    return
-
-  # Find end of the boolean expression by matching parentheses
-  (last_line, end_line, end_pos) = CloseExpression(
-      clean_lines, linenum, start_pos)
-  if end_pos < 0:
-    return
-
-  # If the check macro is followed by something other than a
-  # semicolon, assume users will log their own custom error messages
-  # and don't suggest any replacements.
-  if not Match(r'\s*;', last_line[end_pos:]):
-    return
-
-  if linenum == end_line:
-    expression = lines[linenum][start_pos + 1:end_pos - 1]
-  else:
-    expression = lines[linenum][start_pos + 1:]
-    for i in xrange(linenum + 1, end_line):
-      expression += lines[i]
-    expression += last_line[0:end_pos - 1]
-
-  # Parse expression so that we can take parentheses into account.
-  # This avoids false positives for inputs like "CHECK((a < 4) == b)",
-  # which is not replaceable by CHECK_LE.
-  lhs = ''
-  rhs = ''
-  operator = None
-  while expression:
-    matched = Match(r'^\s*(<<|<<=|>>|>>=|->\*|->|&&|\|\||'
-                    r'==|!=|>=|>|<=|<|\()(.*)$', expression)
-    if matched:
-      token = matched.group(1)
-      if token == '(':
-        # Parenthesized operand
-        expression = matched.group(2)
-        (end, _) = FindEndOfExpressionInLine(expression, 0, ['('])
-        if end < 0:
-          return  # Unmatched parenthesis
-        lhs += '(' + expression[0:end]
-        expression = expression[end:]
-      elif token in ('&&', '||'):
-        # Logical and/or operators.  This means the expression
-        # contains more than one term, for example:
-        #   CHECK(42 < a && a < b);
-        #
-        # These are not replaceable with CHECK_LE, so bail out early.
-        return
-      elif token in ('<<', '<<=', '>>', '>>=', '->*', '->'):
-        # Non-relational operator
-        lhs += token
-        expression = matched.group(2)
-      else:
-        # Relational operator
-        operator = token
-        rhs = matched.group(2)
-        break
-    else:
-      # Unparenthesized operand.  Instead of appending to lhs one character
-      # at a time, we do another regular expression match to consume several
-      # characters at once if possible.  Trivial benchmark shows that this
-      # is more efficient when the operands are longer than a single
-      # character, which is generally the case.
-      matched = Match(r'^([^-=!<>()&|]+)(.*)$', expression)
-      if not matched:
-        matched = Match(r'^(\s*\S)(.*)$', expression)
-        if not matched:
-          break
-      lhs += matched.group(1)
-      expression = matched.group(2)
-
-  # Only apply checks if we got all parts of the boolean expression
-  if not (lhs and operator and rhs):
-    return
-
-  # Check that rhs do not contain logical operators.  We already know
-  # that lhs is fine since the loop above parses out && and ||.
-  if rhs.find('&&') > -1 or rhs.find('||') > -1:
-    return
-
-  # At least one of the operands must be a constant literal.  This is
-  # to avoid suggesting replacements for unprintable things like
-  # CHECK(variable != iterator)
-  #
-  # The following pattern matches decimal, hex integers, strings, and
-  # characters (in that order).
-  lhs = lhs.strip()
-  rhs = rhs.strip()
-  match_constant = r'^([-+]?(\d+|0[xX][0-9a-fA-F]+)[lLuU]{0,3}|".*"|\'.*\')$'
-  if Match(match_constant, lhs) or Match(match_constant, rhs):
-    # Note: since we know both lhs and rhs, we can provide a more
-    # descriptive error message like:
-    #   Consider using CHECK_EQ(x, 42) instead of CHECK(x == 42)
-    # Instead of:
-    #   Consider using CHECK_EQ instead of CHECK(a == b)
-    #
-    # We are still keeping the less descriptive message because if lhs
-    # or rhs gets long, the error message might become unreadable.
-    error(filename, linenum, 'readability/check', 2,
-          'Consider using %s instead of %s(a %s b)' % (
-              _CHECK_REPLACEMENT[check_macro][operator],
-              check_macro, operator))
-
-
-def CheckAltTokens(filename, clean_lines, linenum, error):
-  """Check alternative keywords being used in boolean expressions.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  # Avoid preprocessor lines
-  if Match(r'^\s*#', line):
-    return
-
-  # Last ditch effort to avoid multi-line comments.  This will not help
-  # if the comment started before the current line or ended after the
-  # current line, but it catches most of the false positives.  At least,
-  # it provides a way to workaround this warning for people who use
-  # multi-line comments in preprocessor macros.
-  #
-  # TODO(unknown): remove this once cpplint has better support for
-  # multi-line comments.
-  if line.find('/*') >= 0 or line.find('*/') >= 0:
-    return
-
-  for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(line):
-    error(filename, linenum, 'readability/alt_tokens', 2,
-          'Use operator %s instead of %s' % (
-              _ALT_TOKEN_REPLACEMENT[match.group(1)], match.group(1)))
-
-
-def GetLineWidth(line):
-  """Determines the width of the line in column positions.
-
-  Args:
-    line: A string, which may be a Unicode string.
-
-  Returns:
-    The width of the line in column positions, accounting for Unicode
-    combining characters and wide characters.
-  """
-  if isinstance(line, unicode):
-    width = 0
-    for uc in unicodedata.normalize('NFC', line):
-      if unicodedata.east_asian_width(uc) in ('W', 'F'):
-        width += 2
-      elif not unicodedata.combining(uc):
-        # Issue 337
-        # https://mail.python.org/pipermail/python-list/2012-August/628809.html
-        if (sys.version_info.major, sys.version_info.minor) <= (3, 2):
-          # https://github.com/python/cpython/blob/2.7/Include/unicodeobject.h#L81
-          is_wide_build = sysconfig.get_config_var("Py_UNICODE_SIZE") >= 4
-          # https://github.com/python/cpython/blob/2.7/Objects/unicodeobject.c#L564
-          is_low_surrogate = 0xDC00 <= ord(uc) <= 0xDFFF
-          if not is_wide_build and is_low_surrogate:
-            width -= 1
-
-        width += 1
-    return width
-  else:
-    return len(line)
-
-
-def CheckStyle(filename, clean_lines, linenum, file_extension, nesting_state,
-               error):
-  """Checks rules from the 'C++ style rules' section of cppguide.html.
-
-  Most of these rules are hard to test (naming, comment style), but we
-  do what we can.  In particular we check for 2-space indents, line lengths,
-  tab usage, spaces inside code, etc.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    file_extension: The extension (without the dot) of the filename.
-    nesting_state: A NestingState instance which maintains information about
-                   the current stack of nested blocks being parsed.
-    error: The function to call with any errors found.
-  """
-
-  # Don't use "elided" lines here, otherwise we can't check commented lines.
-  # Don't want to use "raw" either, because we don't want to check inside C++11
-  # raw strings,
-  raw_lines = clean_lines.lines_without_raw_strings
-  line = raw_lines[linenum]
-  prev = raw_lines[linenum - 1] if linenum > 0 else ''
-
-  if line.find('\t') != -1:
-    error(filename, linenum, 'whitespace/tab', 1,
-          'Tab found; better to use spaces')
-
-  # One or three blank spaces at the beginning of the line is weird; it's
-  # hard to reconcile that with 2-space indents.
-  # NOTE: here are the conditions rob pike used for his tests.  Mine aren't
-  # as sophisticated, but it may be worth becoming so:  RLENGTH==initial_spaces
-  # if(RLENGTH > 20) complain = 0;
-  # if(match($0, " +(error|private|public|protected):")) complain = 0;
-  # if(match(prev, "&& *$")) complain = 0;
-  # if(match(prev, "\\|\\| *$")) complain = 0;
-  # if(match(prev, "[\",=><] *$")) complain = 0;
-  # if(match($0, " <<")) complain = 0;
-  # if(match(prev, " +for \\(")) complain = 0;
-  # if(prevodd && match(prevprev, " +for \\(")) complain = 0;
-  scope_or_label_pattern = r'\s*\w+\s*:\s*\\?$'
-  classinfo = nesting_state.InnermostClass()
-  initial_spaces = 0
-  cleansed_line = clean_lines.elided[linenum]
-  while initial_spaces < len(line) and line[initial_spaces] == ' ':
-    initial_spaces += 1
-  # There are certain situations we allow one space, notably for
-  # section labels, and also lines containing multi-line raw strings.
-  # We also don't check for lines that look like continuation lines
-  # (of lines ending in double quotes, commas, equals, or angle brackets)
-  # because the rules for how to indent those are non-trivial.
-  if (not Search(r'[",=><] *$', prev) and
-      (initial_spaces == 1 or initial_spaces == 3) and
-      not Match(scope_or_label_pattern, cleansed_line) and
-      not (clean_lines.raw_lines[linenum] != line and
-           Match(r'^\s*""', line))):
-    error(filename, linenum, 'whitespace/indent', 3,
-          'Weird number of spaces at line-start.  '
-          'Are you using a 2-space indent?')
-
-  if line and line[-1].isspace():
-    error(filename, linenum, 'whitespace/end_of_line', 4,
-          'Line ends in whitespace.  Consider deleting these extra spaces.')
-
-  # Check if the line is a header guard.
-  is_header_guard = False
-  if IsHeaderExtension(file_extension):
-    cppvar = GetHeaderGuardCPPVariable(filename)
-    if (line.startswith('#ifndef %s' % cppvar) or
-        line.startswith('#define %s' % cppvar) or
-        line.startswith('#endif  // %s' % cppvar)):
-      is_header_guard = True
-  # #include lines and header guards can be long, since there's no clean way to
-  # split them.
-  #
-  # URLs can be long too.  It's possible to split these, but it makes them
-  # harder to cut&paste.
-  #
-  # The "$Id:...$" comment may also get very long without it being the
-  # developers fault.
-  if (not line.startswith('#include') and not is_header_guard and
-      not Match(r'^\s*//.*http(s?)://\S*$', line) and
-      not Match(r'^\s*//\s*[^\s]*$', line) and
-      not Match(r'^// \$Id:.*#[0-9]+ \$$', line)):
-    line_width = GetLineWidth(line)
-    if line_width > _line_length:
-      error(filename, linenum, 'whitespace/line_length', 2,
-            'Lines should be <= %i characters long' % _line_length)
-
-  if (cleansed_line.count(';') > 1 and
-      # for loops are allowed two ;'s (and may run over two lines).
-      cleansed_line.find('for') == -1 and
-      (GetPreviousNonBlankLine(clean_lines, linenum)[0].find('for') == -1 or
-       GetPreviousNonBlankLine(clean_lines, linenum)[0].find(';') != -1) and
-      # It's ok to have many commands in a switch case that fits in 1 line
-      not ((cleansed_line.find('case ') != -1 or
-            cleansed_line.find('default:') != -1) and
-           cleansed_line.find('break;') != -1)):
-    error(filename, linenum, 'whitespace/newline', 0,
-          'More than one command on the same line')
-
-  # Some more style checks
-  CheckBraces(filename, clean_lines, linenum, error)
-  CheckTrailingSemicolon(filename, clean_lines, linenum, error)
-  CheckEmptyBlockBody(filename, clean_lines, linenum, error)
-  CheckSpacing(filename, clean_lines, linenum, nesting_state, error)
-  CheckOperatorSpacing(filename, clean_lines, linenum, error)
-  CheckParenthesisSpacing(filename, clean_lines, linenum, error)
-  CheckCommaSpacing(filename, clean_lines, linenum, error)
-  CheckBracesSpacing(filename, clean_lines, linenum, nesting_state, error)
-  CheckSpacingForFunctionCall(filename, clean_lines, linenum, error)
-  CheckCheck(filename, clean_lines, linenum, error)
-  CheckAltTokens(filename, clean_lines, linenum, error)
-  classinfo = nesting_state.InnermostClass()
-  if classinfo:
-    CheckSectionSpacing(filename, clean_lines, classinfo, linenum, error)
-
-
-_RE_PATTERN_INCLUDE = re.compile(r'^\s*#\s*include\s*([<"])([^>"]*)[>"].*$')
-# Matches the first component of a filename delimited by -s and _s. That is:
-#  _RE_FIRST_COMPONENT.match('foo').group(0) == 'foo'
-#  _RE_FIRST_COMPONENT.match('foo.cc').group(0) == 'foo'
-#  _RE_FIRST_COMPONENT.match('foo-bar_baz.cc').group(0) == 'foo'
-#  _RE_FIRST_COMPONENT.match('foo_bar-baz.cc').group(0) == 'foo'
-_RE_FIRST_COMPONENT = re.compile(r'^[^-_.]+')
-
-
-def _DropCommonSuffixes(filename):
-  """Drops common suffixes like _test.cc or -inl.h from filename.
-
-  For example:
-    >>> _DropCommonSuffixes('foo/foo-inl.h')
-    'foo/foo'
-    >>> _DropCommonSuffixes('foo/bar/foo.cc')
-    'foo/bar/foo'
-    >>> _DropCommonSuffixes('foo/foo_internal.h')
-    'foo/foo'
-    >>> _DropCommonSuffixes('foo/foo_unusualinternal.h')
-    'foo/foo_unusualinternal'
-
-  Args:
-    filename: The input filename.
-
-  Returns:
-    The filename with the common suffix removed.
-  """
-  for suffix in ('test.cc', 'regtest.cc', 'unittest.cc',
-                 'inl.h', 'impl.h', 'internal.h'):
-    if (filename.endswith(suffix) and len(filename) > len(suffix) and
-        filename[-len(suffix) - 1] in ('-', '_')):
-      return filename[:-len(suffix) - 1]
-  return os.path.splitext(filename)[0]
-
-
-def _ClassifyInclude(fileinfo, include, is_system):
-  """Figures out what kind of header 'include' is.
-
-  Args:
-    fileinfo: The current file cpplint is running over. A FileInfo instance.
-    include: The path to a #included file.
-    is_system: True if the #include used <> rather than "".
-
-  Returns:
-    One of the _XXX_HEADER constants.
-
-  For example:
-    >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
-    _C_SYS_HEADER
-    >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
-    _CPP_SYS_HEADER
-    >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
-    _LIKELY_MY_HEADER
-    >>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
-    ...                  'bar/foo_other_ext.h', False)
-    _POSSIBLE_MY_HEADER
-    >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
-    _OTHER_HEADER
-  """
-  # This is a list of all standard c++ header files, except
-  # those already checked for above.
-  is_cpp_h = include in _CPP_HEADERS
-
-  if is_system:
-    if is_cpp_h:
-      return _CPP_SYS_HEADER
-    else:
-      return _C_SYS_HEADER
-
-  # If the target file and the include we're checking share a
-  # basename when we drop common extensions, and the include
-  # lives in . , then it's likely to be owned by the target file.
-  target_dir, target_base = (
-      os.path.split(_DropCommonSuffixes(fileinfo.RepositoryName())))
-  include_dir, include_base = os.path.split(_DropCommonSuffixes(include))
-  if target_base == include_base and (
-      include_dir == target_dir or
-      include_dir == os.path.normpath(target_dir + '/../public')):
-    return _LIKELY_MY_HEADER
-
-  # If the target and include share some initial basename
-  # component, it's possible the target is implementing the
-  # include, so it's allowed to be first, but we'll never
-  # complain if it's not there.
-  target_first_component = _RE_FIRST_COMPONENT.match(target_base)
-  include_first_component = _RE_FIRST_COMPONENT.match(include_base)
-  if (target_first_component and include_first_component and
-      target_first_component.group(0) ==
-      include_first_component.group(0)):
-    return _POSSIBLE_MY_HEADER
-
-  return _OTHER_HEADER
-
-
-
-def CheckIncludeLine(filename, clean_lines, linenum, include_state, error):
-  """Check rules that are applicable to #include lines.
-
-  Strings on #include lines are NOT removed from elided line, to make
-  certain tasks easier. However, to prevent false positives, checks
-  applicable to #include lines in CheckLanguage must be put here.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    include_state: An _IncludeState instance in which the headers are inserted.
-    error: The function to call with any errors found.
-  """
-  fileinfo = FileInfo(filename)
-  line = clean_lines.lines[linenum]
-
-  # "include" should use the new style "foo/bar.h" instead of just "bar.h"
-  # Only do this check if the included header follows google naming
-  # conventions.  If not, assume that it's a 3rd party API that
-  # requires special include conventions.
-  #
-  # We also make an exception for Lua headers, which follow google
-  # naming convention but not the include convention.
-  match = Match(r'#include\s*"([^/]+\.h)"', line)
-  if match and not _THIRD_PARTY_HEADERS_PATTERN.match(match.group(1)):
-    error(filename, linenum, 'build/include', 4,
-          'Include the directory when naming .h files')
-
-  # we shouldn't include a file more than once. actually, there are a
-  # handful of instances where doing so is okay, but in general it's
-  # not.
-  match = _RE_PATTERN_INCLUDE.search(line)
-  if match:
-    include = match.group(2)
-    is_system = (match.group(1) == '<')
-    duplicate_line = include_state.FindHeader(include)
-    if duplicate_line >= 0:
-      error(filename, linenum, 'build/include', 4,
-            '"%s" already included at %s:%s' %
-            (include, filename, duplicate_line))
-    elif (include.endswith('.cc') and
-          os.path.dirname(fileinfo.RepositoryName()) != os.path.dirname(include)):
-      error(filename, linenum, 'build/include', 4,
-            'Do not include .cc files from other packages')
-    elif not _THIRD_PARTY_HEADERS_PATTERN.match(include):
-      include_state.include_list[-1].append((include, linenum))
-
-      # We want to ensure that headers appear in the right order:
-      # 1) for foo.cc, foo.h  (preferred location)
-      # 2) c system files
-      # 3) cpp system files
-      # 4) for foo.cc, foo.h  (deprecated location)
-      # 5) other google headers
-      #
-      # We classify each include statement as one of those 5 types
-      # using a number of techniques. The include_state object keeps
-      # track of the highest type seen, and complains if we see a
-      # lower type after that.
-      error_message = include_state.CheckNextIncludeOrder(
-          _ClassifyInclude(fileinfo, include, is_system))
-      if error_message:
-        error(filename, linenum, 'build/include_order', 4,
-              '%s. Should be: %s.h, c system, c++ system, other.' %
-              (error_message, fileinfo.BaseName()))
-      canonical_include = include_state.CanonicalizeAlphabeticalOrder(include)
-      if not include_state.IsInAlphabeticalOrder(
-          clean_lines, linenum, canonical_include):
-        error(filename, linenum, 'build/include_alpha', 4,
-              'Include "%s" not in alphabetical order' % include)
-      include_state.SetLastHeader(canonical_include)
-
-
-
-def _GetTextInside(text, start_pattern):
-  r"""Retrieves all the text between matching open and close parentheses.
-
-  Given a string of lines and a regular expression string, retrieve all the text
-  following the expression and between opening punctuation symbols like
-  (, [, or {, and the matching close-punctuation symbol. This properly nested
-  occurrences of the punctuations, so for the text like
-    printf(a(), b(c()));
-  a call to _GetTextInside(text, r'printf\(') will return 'a(), b(c())'.
-  start_pattern must match string having an open punctuation symbol at the end.
-
-  Args:
-    text: The lines to extract text. Its comments and strings must be elided.
-           It can be single line and can span multiple lines.
-    start_pattern: The regexp string indicating where to start extracting
-                   the text.
-  Returns:
-    The extracted text.
-    None if either the opening string or ending punctuation could not be found.
-  """
-  # TODO(unknown): Audit cpplint.py to see what places could be profitably
-  # rewritten to use _GetTextInside (and use inferior regexp matching today).
-
-  # Give opening punctuations to get the matching close-punctuations.
-  matching_punctuation = {'(': ')', '{': '}', '[': ']'}
-  closing_punctuation = set(matching_punctuation.itervalues())
-
-  # Find the position to start extracting text.
-  match = re.search(start_pattern, text, re.M)
-  if not match:  # start_pattern not found in text.
-    return None
-  start_position = match.end(0)
-
-  assert start_position > 0, (
-      'start_pattern must ends with an opening punctuation.')
-  assert text[start_position - 1] in matching_punctuation, (
-      'start_pattern must ends with an opening punctuation.')
-  # Stack of closing punctuations we expect to have in text after position.
-  punctuation_stack = [matching_punctuation[text[start_position - 1]]]
-  position = start_position
-  while punctuation_stack and position < len(text):
-    if text[position] == punctuation_stack[-1]:
-      punctuation_stack.pop()
-    elif text[position] in closing_punctuation:
-      # A closing punctuation without matching opening punctuations.
-      return None
-    elif text[position] in matching_punctuation:
-      punctuation_stack.append(matching_punctuation[text[position]])
-    position += 1
-  if punctuation_stack:
-    # Opening punctuations left without matching close-punctuations.
-    return None
-  # punctuations match.
-  return text[start_position:position - 1]
-
-
-# Patterns for matching call-by-reference parameters.
-#
-# Supports nested templates up to 2 levels deep using this messy pattern:
-#   < (?: < (?: < [^<>]*
-#               >
-#           |   [^<>] )*
-#         >
-#     |   [^<>] )*
-#   >
-_RE_PATTERN_IDENT = r'[_a-zA-Z]\w*'  # =~ [[:alpha:]][[:alnum:]]*
-_RE_PATTERN_TYPE = (
-    r'(?:const\s+)?(?:typename\s+|class\s+|struct\s+|union\s+|enum\s+)?'
-    r'(?:\w|'
-    r'\s*<(?:<(?:<[^<>]*>|[^<>])*>|[^<>])*>|'
-    r'::)+')
-# A call-by-reference parameter ends with '& identifier'.
-_RE_PATTERN_REF_PARAM = re.compile(
-    r'(' + _RE_PATTERN_TYPE + r'(?:\s*(?:\bconst\b|[*]))*\s*'
-    r'&\s*' + _RE_PATTERN_IDENT + r')\s*(?:=[^,()]+)?[,)]')
-# A call-by-const-reference parameter either ends with 'const& identifier'
-# or looks like 'const type& identifier' when 'type' is atomic.
-_RE_PATTERN_CONST_REF_PARAM = (
-    r'(?:.*\s*\bconst\s*&\s*' + _RE_PATTERN_IDENT +
-    r'|const\s+' + _RE_PATTERN_TYPE + r'\s*&\s*' + _RE_PATTERN_IDENT + r')')
-# Stream types.
-_RE_PATTERN_REF_STREAM_PARAM = (
-    r'(?:.*stream\s*&\s*' + _RE_PATTERN_IDENT + r')')
-
-
-def CheckLanguage(filename, clean_lines, linenum, file_extension,
-                  include_state, nesting_state, error):
-  """Checks rules from the 'C++ language rules' section of cppguide.html.
-
-  Some of these rules are hard to test (function overloading, using
-  uint32 inappropriately), but we do the best we can.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    file_extension: The extension (without the dot) of the filename.
-    include_state: An _IncludeState instance in which the headers are inserted.
-    nesting_state: A NestingState instance which maintains information about
-                   the current stack of nested blocks being parsed.
-    error: The function to call with any errors found.
-  """
-  # If the line is empty or consists of entirely a comment, no need to
-  # check it.
-  line = clean_lines.elided[linenum]
-  if not line:
-    return
-
-  match = _RE_PATTERN_INCLUDE.search(line)
-  if match:
-    CheckIncludeLine(filename, clean_lines, linenum, include_state, error)
-    return
-
-  # Reset include state across preprocessor directives.  This is meant
-  # to silence warnings for conditional includes.
-  match = Match(r'^\s*#\s*(if|ifdef|ifndef|elif|else|endif)\b', line)
-  if match:
-    include_state.ResetSection(match.group(1))
-
-  # Make Windows paths like Unix.
-  fullname = os.path.abspath(filename).replace('\\', '/')
-
-  # Perform other checks now that we are sure that this is not an include line
-  CheckCasts(filename, clean_lines, linenum, error)
-  CheckGlobalStatic(filename, clean_lines, linenum, error)
-  CheckPrintf(filename, clean_lines, linenum, error)
-
-  if IsHeaderExtension(file_extension):
-    # TODO(unknown): check that 1-arg constructors are explicit.
-    #                How to tell it's a constructor?
-    #                (handled in CheckForNonStandardConstructs for now)
-    # TODO(unknown): check that classes declare or disable copy/assign
-    #                (level 1 error)
-    pass
-
-  # Check if people are using the verboten C basic types.  The only exception
-  # we regularly allow is "unsigned short port" for port.
-  if Search(r'\bshort port\b', line):
-    if not Search(r'\bunsigned short port\b', line):
-      error(filename, linenum, 'runtime/int', 4,
-            'Use "unsigned short" for ports, not "short"')
-  else:
-    match = Search(r'\b(short|long(?! +double)|long long)\b', line)
-    if match:
-      error(filename, linenum, 'runtime/int', 4,
-            'Use int16/int64/etc, rather than the C type %s' % match.group(1))
-
-  # Check if some verboten operator overloading is going on
-  # TODO(unknown): catch out-of-line unary operator&:
-  #   class X {};
-  #   int operator&(const X& x) { return 42; }  // unary operator&
-  # The trick is it's hard to tell apart from binary operator&:
-  #   class Y { int operator&(const Y& x) { return 23; } }; // binary operator&
-  if Search(r'\boperator\s*&\s*\(\s*\)', line):
-    error(filename, linenum, 'runtime/operator', 4,
-          'Unary operator& is dangerous.  Do not use it.')
-
-  # Check for suspicious usage of "if" like
-  # } if (a == b) {
-  if Search(r'\}\s*if\s*\(', line):
-    error(filename, linenum, 'readability/braces', 4,
-          'Did you mean "else if"? If not, start a new line for "if".')
-
-  # Check for potential format string bugs like printf(foo).
-  # We constrain the pattern not to pick things like DocidForPrintf(foo).
-  # Not perfect but it can catch printf(foo.c_str()) and printf(foo->c_str())
-  # TODO(unknown): Catch the following case. Need to change the calling
-  # convention of the whole function to process multiple line to handle it.
-  #   printf(
-  #       boy_this_is_a_really_long_variable_that_cannot_fit_on_the_prev_line);
-  printf_args = _GetTextInside(line, r'(?i)\b(string)?printf\s*\(')
-  if printf_args:
-    match = Match(r'([\w.\->()]+)$', printf_args)
-    if match and match.group(1) != '__VA_ARGS__':
-      function_name = re.search(r'\b((?:string)?printf)\s*\(',
-                                line, re.I).group(1)
-      error(filename, linenum, 'runtime/printf', 4,
-            'Potential format string bug. Do %s("%%s", %s) instead.'
-            % (function_name, match.group(1)))
-
-  # Check for potential memset bugs like memset(buf, sizeof(buf), 0).
-  match = Search(r'memset\s*\(([^,]*),\s*([^,]*),\s*0\s*\)', line)
-  if match and not Match(r"^''|-?[0-9]+|0x[0-9A-Fa-f]$", match.group(2)):
-    error(filename, linenum, 'runtime/memset', 4,
-          'Did you mean "memset(%s, 0, %s)"?'
-          % (match.group(1), match.group(2)))
-
-  if Search(r'\busing namespace\b', line):
-    error(filename, linenum, 'build/namespaces', 5,
-          'Do not use namespace using-directives.  '
-          'Use using-declarations instead.')
-
-  # Detect variable-length arrays.
-  match = Match(r'\s*(.+::)?(\w+) [a-z]\w*\[(.+)];', line)
-  if (match and match.group(2) != 'return' and match.group(2) != 'delete' and
-      match.group(3).find(']') == -1):
-    # Split the size using space and arithmetic operators as delimiters.
-    # If any of the resulting tokens are not compile time constants then
-    # report the error.
-    tokens = re.split(r'\s|\+|\-|\*|\/|<<|>>]', match.group(3))
-    is_const = True
-    skip_next = False
-    for tok in tokens:
-      if skip_next:
-        skip_next = False
-        continue
-
-      if Search(r'sizeof\(.+\)', tok): continue
-      if Search(r'arraysize\(\w+\)', tok): continue
-
-      tok = tok.lstrip('(')
-      tok = tok.rstrip(')')
-      if not tok: continue
-      if Match(r'\d+', tok): continue
-      if Match(r'0[xX][0-9a-fA-F]+', tok): continue
-      if Match(r'k[A-Z0-9]\w*', tok): continue
-      if Match(r'(.+::)?k[A-Z0-9]\w*', tok): continue
-      if Match(r'(.+::)?[A-Z][A-Z0-9_]*', tok): continue
-      # A catch all for tricky sizeof cases, including 'sizeof expression',
-      # 'sizeof(*type)', 'sizeof(const type)', 'sizeof(struct StructName)'
-      # requires skipping the next token because we split on ' ' and '*'.
-      if tok.startswith('sizeof'):
-        skip_next = True
-        continue
-      is_const = False
-      break
-    if not is_const:
-      error(filename, linenum, 'runtime/arrays', 1,
-            'Do not use variable-length arrays.  Use an appropriately named '
-            "('k' followed by CamelCase) compile-time constant for the size.")
-
-  # Check for use of unnamed namespaces in header files.  Registration
-  # macros are typically OK, so we allow use of "namespace {" on lines
-  # that end with backslashes.
-  if (IsHeaderExtension(file_extension)
-      and Search(r'\bnamespace\s*{', line)
-      and line[-1] != '\\'):
-    error(filename, linenum, 'build/namespaces', 4,
-          'Do not use unnamed namespaces in header files.  See '
-          'https://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Namespaces'
-          ' for more information.')
-
-
-def CheckGlobalStatic(filename, clean_lines, linenum, error):
-  """Check for unsafe global or static objects.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  # Match two lines at a time to support multiline declarations
-  if linenum + 1 < clean_lines.NumLines() and not Search(r'[;({]', line):
-    line += clean_lines.elided[linenum + 1].strip()
-
-  # Check for people declaring static/global STL strings at the top level.
-  # This is dangerous because the C++ language does not guarantee that
-  # globals with constructors are initialized before the first access, and
-  # also because globals can be destroyed when some threads are still running.
-  # TODO(unknown): Generalize this to also find static unique_ptr instances.
-  # TODO(unknown): File bugs for clang-tidy to find these.
-  match = Match(
-      r'((?:|static +)(?:|const +))(?::*std::)?string( +const)? +'
-      r'([a-zA-Z0-9_:]+)\b(.*)',
-      line)
-
-  # Remove false positives:
-  # - String pointers (as opposed to values).
-  #    string *pointer
-  #    const string *pointer
-  #    string const *pointer
-  #    string *const pointer
-  #
-  # - Functions and template specializations.
-  #    string Function<Type>(...
-  #    string Class<Type>::Method(...
-  #
-  # - Operators.  These are matched separately because operator names
-  #   cross non-word boundaries, and trying to match both operators
-  #   and functions at the same time would decrease accuracy of
-  #   matching identifiers.
-  #    string Class::operator*()
-  if (match and
-      not Search(r'\bstring\b(\s+const)?\s*[\*\&]\s*(const\s+)?\w', line) and
-      not Search(r'\boperator\W', line) and
-      not Match(r'\s*(<.*>)?(::[a-zA-Z0-9_]+)*\s*\(([^"]|$)', match.group(4))):
-    if Search(r'\bconst\b', line):
-      error(filename, linenum, 'runtime/string', 4,
-            'For a static/global string constant, use a C style string '
-            'instead: "%schar%s %s[]".' %
-            (match.group(1), match.group(2) or '', match.group(3)))
-    else:
-      error(filename, linenum, 'runtime/string', 4,
-            'Static/global string variables are not permitted.')
-
-  if (Search(r'\b([A-Za-z0-9_]*_)\(\1\)', line) or
-      Search(r'\b([A-Za-z0-9_]*_)\(CHECK_NOTNULL\(\1\)\)', line)):
-    error(filename, linenum, 'runtime/init', 4,
-          'You seem to be initializing a member variable with itself.')
-
-
-def CheckPrintf(filename, clean_lines, linenum, error):
-  """Check for printf related issues.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  # When snprintf is used, the second argument shouldn't be a literal.
-  match = Search(r'snprintf\s*\(([^,]*),\s*([0-9]*)\s*,', line)
-  if match and match.group(2) != '0':
-    # If 2nd arg is zero, snprintf is used to calculate size.
-    error(filename, linenum, 'runtime/printf', 3,
-          'If you can, use sizeof(%s) instead of %s as the 2nd arg '
-          'to snprintf.' % (match.group(1), match.group(2)))
-
-  # Check if some verboten C functions are being used.
-  if Search(r'\bsprintf\s*\(', line):
-    error(filename, linenum, 'runtime/printf', 5,
-          'Never use sprintf. Use snprintf instead.')
-  match = Search(r'\b(strcpy|strcat)\s*\(', line)
-  if match:
-    error(filename, linenum, 'runtime/printf', 4,
-          'Almost always, snprintf is better than %s' % match.group(1))
-
-
-def IsDerivedFunction(clean_lines, linenum):
-  """Check if current line contains an inherited function.
-
-  Args:
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-  Returns:
-    True if current line contains a function with "override"
-    virt-specifier.
-  """
-  # Scan back a few lines for start of current function
-  for i in xrange(linenum, max(-1, linenum - 10), -1):
-    match = Match(r'^([^()]*\w+)\(', clean_lines.elided[i])
-    if match:
-      # Look for "override" after the matching closing parenthesis
-      line, _, closing_paren = CloseExpression(
-          clean_lines, i, len(match.group(1)))
-      return (closing_paren >= 0 and
-              Search(r'\boverride\b', line[closing_paren:]))
-  return False
-
-
-def IsOutOfLineMethodDefinition(clean_lines, linenum):
-  """Check if current line contains an out-of-line method definition.
-
-  Args:
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-  Returns:
-    True if current line contains an out-of-line method definition.
-  """
-  # Scan back a few lines for start of current function
-  for i in xrange(linenum, max(-1, linenum - 10), -1):
-    if Match(r'^([^()]*\w+)\(', clean_lines.elided[i]):
-      return Match(r'^[^()]*\w+::\w+\(', clean_lines.elided[i]) is not None
-  return False
-
-
-def IsInitializerList(clean_lines, linenum):
-  """Check if current line is inside constructor initializer list.
-
-  Args:
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-  Returns:
-    True if current line appears to be inside constructor initializer
-    list, False otherwise.
-  """
-  for i in xrange(linenum, 1, -1):
-    line = clean_lines.elided[i]
-    if i == linenum:
-      remove_function_body = Match(r'^(.*)\{\s*$', line)
-      if remove_function_body:
-        line = remove_function_body.group(1)
-
-    if Search(r'\s:\s*\w+[({]', line):
-      # A lone colon tend to indicate the start of a constructor
-      # initializer list.  It could also be a ternary operator, which
-      # also tend to appear in constructor initializer lists as
-      # opposed to parameter lists.
-      return True
-    if Search(r'\}\s*,\s*$', line):
-      # A closing brace followed by a comma is probably the end of a
-      # brace-initialized member in constructor initializer list.
-      return True
-    if Search(r'[{};]\s*$', line):
-      # Found one of the following:
-      # - A closing brace or semicolon, probably the end of the previous
-      #   function.
-      # - An opening brace, probably the start of current class or namespace.
-      #
-      # Current line is probably not inside an initializer list since
-      # we saw one of those things without seeing the starting colon.
-      return False
-
-  # Got to the beginning of the file without seeing the start of
-  # constructor initializer list.
-  return False
-
-
-def CheckForNonConstReference(filename, clean_lines, linenum,
-                              nesting_state, error):
-  """Check for non-const references.
-
-  Separate from CheckLanguage since it scans backwards from current
-  line, instead of scanning forward.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    nesting_state: A NestingState instance which maintains information about
-                   the current stack of nested blocks being parsed.
-    error: The function to call with any errors found.
-  """
-  # Do nothing if there is no '&' on current line.
-  line = clean_lines.elided[linenum]
-  if '&' not in line:
-    return
-
-  # If a function is inherited, current function doesn't have much of
-  # a choice, so any non-const references should not be blamed on
-  # derived function.
-  if IsDerivedFunction(clean_lines, linenum):
-    return
-
-  # Don't warn on out-of-line method definitions, as we would warn on the
-  # in-line declaration, if it isn't marked with 'override'.
-  if IsOutOfLineMethodDefinition(clean_lines, linenum):
-    return
-
-  # Long type names may be broken across multiple lines, usually in one
-  # of these forms:
-  #   LongType
-  #       ::LongTypeContinued &identifier
-  #   LongType::
-  #       LongTypeContinued &identifier
-  #   LongType<
-  #       ...>::LongTypeContinued &identifier
-  #
-  # If we detected a type split across two lines, join the previous
-  # line to current line so that we can match const references
-  # accordingly.
-  #
-  # Note that this only scans back one line, since scanning back
-  # arbitrary number of lines would be expensive.  If you have a type
-  # that spans more than 2 lines, please use a typedef.
-  if linenum > 1:
-    previous = None
-    if Match(r'\s*::(?:[\w<>]|::)+\s*&\s*\S', line):
-      # previous_line\n + ::current_line
-      previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+[\w<>])\s*$',
-                        clean_lines.elided[linenum - 1])
-    elif Match(r'\s*[a-zA-Z_]([\w<>]|::)+\s*&\s*\S', line):
-      # previous_line::\n + current_line
-      previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+::)\s*$',
-                        clean_lines.elided[linenum - 1])
-    if previous:
-      line = previous.group(1) + line.lstrip()
-    else:
-      # Check for templated parameter that is split across multiple lines
-      endpos = line.rfind('>')
-      if endpos > -1:
-        (_, startline, startpos) = ReverseCloseExpression(
-            clean_lines, linenum, endpos)
-        if startpos > -1 and startline < linenum:
-          # Found the matching < on an earlier line, collect all
-          # pieces up to current line.
-          line = ''
-          for i in xrange(startline, linenum + 1):
-            line += clean_lines.elided[i].strip()
-
-  # Check for non-const references in function parameters.  A single '&' may
-  # found in the following places:
-  #   inside expression: binary & for bitwise AND
-  #   inside expression: unary & for taking the address of something
-  #   inside declarators: reference parameter
-  # We will exclude the first two cases by checking that we are not inside a
-  # function body, including one that was just introduced by a trailing '{'.
-  # TODO(unknown): Doesn't account for 'catch(Exception& e)' [rare].
-  if (nesting_state.previous_stack_top and
-      not (isinstance(nesting_state.previous_stack_top, _ClassInfo) or
-           isinstance(nesting_state.previous_stack_top, _NamespaceInfo))):
-    # Not at toplevel, not within a class, and not within a namespace
-    return
-
-  # Avoid initializer lists.  We only need to scan back from the
-  # current line for something that starts with ':'.
-  #
-  # We don't need to check the current line, since the '&' would
-  # appear inside the second set of parentheses on the current line as
-  # opposed to the first set.
-  if linenum > 0:
-    for i in xrange(linenum - 1, max(0, linenum - 10), -1):
-      previous_line = clean_lines.elided[i]
-      if not Search(r'[),]\s*$', previous_line):
-        break
-      if Match(r'^\s*:\s+\S', previous_line):
-        return
-
-  # Avoid preprocessors
-  if Search(r'\\\s*$', line):
-    return
-
-  # Avoid constructor initializer lists
-  if IsInitializerList(clean_lines, linenum):
-    return
-
-  # We allow non-const references in a few standard places, like functions
-  # called "swap()" or iostream operators like "<<" or ">>".  Do not check
-  # those function parameters.
-  #
-  # We also accept & in static_assert, which looks like a function but
-  # it's actually a declaration expression.
-  allowlisted_functions = (r'(?:[sS]wap(?:<\w:+>)?|'
-                           r'operator\s*[<>][<>]|'
-                           r'static_assert|COMPILE_ASSERT'
-                           r')\s*\(')
-  if Search(allowlisted_functions, line):
-    return
-  elif not Search(r'\S+\([^)]*$', line):
-    # Don't see an allowlisted function on this line.  Actually we
-    # didn't see any function name on this line, so this is likely a
-    # multi-line parameter list.  Try a bit harder to catch this case.
-    for i in xrange(2):
-      if (linenum > i and
-          Search(allowlisted_functions, clean_lines.elided[linenum - i - 1])):
-        return
-
-  decls = ReplaceAll(r'{[^}]*}', ' ', line)  # exclude function body
-  for parameter in re.findall(_RE_PATTERN_REF_PARAM, decls):
-    if (not Match(_RE_PATTERN_CONST_REF_PARAM, parameter) and
-        not Match(_RE_PATTERN_REF_STREAM_PARAM, parameter)):
-      error(filename, linenum, 'runtime/references', 2,
-            'Is this a non-const reference? '
-            'If so, make const or use a pointer: ' +
-            ReplaceAll(' *<', '<', parameter))
-
-
-def CheckCasts(filename, clean_lines, linenum, error):
-  """Various cast related checks.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  # Check to see if they're using an conversion function cast.
-  # I just try to capture the most common basic types, though there are more.
-  # Parameterless conversion functions, such as bool(), are allowed as they are
-  # probably a member operator declaration or default constructor.
-  match = Search(
-      r'(\bnew\s+(?:const\s+)?|\S<\s*(?:const\s+)?)?\b'
-      r'(int|float|double|bool|char|int32|uint32|int64|uint64)'
-      r'(\([^)].*)', line)
-  expecting_function = ExpectingFunctionArgs(clean_lines, linenum)
-  if match and not expecting_function:
-    matched_type = match.group(2)
-
-    # matched_new_or_template is used to silence two false positives:
-    # - New operators
-    # - Template arguments with function types
-    #
-    # For template arguments, we match on types immediately following
-    # an opening bracket without any spaces.  This is a fast way to
-    # silence the common case where the function type is the first
-    # template argument.  False negative with less-than comparison is
-    # avoided because those operators are usually followed by a space.
-    #
-    #   function<double(double)>   // bracket + no space = false positive
-    #   value < double(42)         // bracket + space = true positive
-    matched_new_or_template = match.group(1)
-
-    # Avoid arrays by looking for brackets that come after the closing
-    # parenthesis.
-    if Match(r'\([^()]+\)\s*\[', match.group(3)):
-      return
-
-    # Other things to ignore:
-    # - Function pointers
-    # - Casts to pointer types
-    # - Placement new
-    # - Alias declarations
-    matched_funcptr = match.group(3)
-    if (matched_new_or_template is None and
-        not (matched_funcptr and
-             (Match(r'\((?:[^() ]+::\s*\*\s*)?[^() ]+\)\s*\(',
-                    matched_funcptr) or
-              matched_funcptr.startswith('(*)'))) and
-        not Match(r'\s*using\s+\S+\s*=\s*' + matched_type, line) and
-        not Search(r'new\(\S+\)\s*' + matched_type, line)):
-      error(filename, linenum, 'readability/casting', 4,
-            'Using deprecated casting style.  '
-            'Use reinterpret_cast<%s>(...) instead' %
-            matched_type)
-
-  if not expecting_function:
-    CheckCStyleCast(filename, clean_lines, linenum, 'reinterpret_cast',
-                    r'\((int|float|double|bool|char|u?int(16|32|64))\)', error)
-
-  # This doesn't catch all cases. Consider (const char * const)"hello".
-  #
-  # (char *) "foo" should always be a const_cast (reinterpret_cast won't
-  # compile).
-  if CheckCStyleCast(filename, clean_lines, linenum, 'const_cast',
-                     r'\((char\s?\*+\s?)\)\s*"', error):
-    pass
-  else:
-    # Check pointer casts for other than string constants
-    CheckCStyleCast(filename, clean_lines, linenum, 'reinterpret_cast',
-                    r'\((\w+\s?\*+\s?)\)', error)
-
-  # In addition, we look for people taking the address of a cast.  This
-  # is dangerous -- casts can assign to temporaries, so the pointer doesn't
-  # point where you think.
-  #
-  # Some non-identifier character is required before the '&' for the
-  # expression to be recognized as a cast.  These are casts:
-  #   expression = &reinterpret_cast<int*>(temporary());
-  #   function(&(int*)(temporary()));
-  #
-  # This is not a cast:
-  #   reference_type&(int* function_param);
-  match = Search(
-      r'(?:[^\w]&\(([^)*][^)]*)\)[\w(])|'
-      r'(?:[^\w]&(static|dynamic|down|reinterpret)_cast\b)', line)
-  if match:
-    # Try a better error message when the & is bound to something
-    # dereferenced by the casted pointer, as opposed to the casted
-    # pointer itself.
-    parenthesis_error = False
-    match = Match(r'^(.*&(?:static|dynamic|down|reinterpret)_cast\b)<', line)
-    if match:
-      _, y1, x1 = CloseExpression(clean_lines, linenum, len(match.group(1)))
-      if x1 >= 0 and clean_lines.elided[y1][x1] == '(':
-        _, y2, x2 = CloseExpression(clean_lines, y1, x1)
-        if x2 >= 0:
-          extended_line = clean_lines.elided[y2][x2:]
-          if y2 < clean_lines.NumLines() - 1:
-            extended_line += clean_lines.elided[y2 + 1]
-          if Match(r'\s*(?:->|\[)', extended_line):
-            parenthesis_error = True
-
-    if parenthesis_error:
-      error(filename, linenum, 'readability/casting', 4,
-            ('Are you taking an address of something dereferenced '
-             'from a cast?  Wrapping the dereferenced expression in '
-             'parentheses will make the binding more obvious'))
-    else:
-      error(filename, linenum, 'runtime/casting', 4,
-            ('Are you taking an address of a cast?  '
-             'This is dangerous: could be a temp var.  '
-             'Take the address before doing the cast, rather than after'))
-
-
-def CheckCStyleCast(filename, clean_lines, linenum, cast_type, pattern, error):
-  """Checks for a C-style cast by looking for the pattern.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    cast_type: The string for the C++ cast to recommend.  This is either
-      reinterpret_cast, reinterpret_cast, or const_cast, depending.
-    pattern: The regular expression used to find C-style casts.
-    error: The function to call with any errors found.
-
-  Returns:
-    True if an error was emitted.
-    False otherwise.
-  """
-  line = clean_lines.elided[linenum]
-  match = Search(pattern, line)
-  if not match:
-    return False
-
-  # Exclude lines with keywords that tend to look like casts
-  context = line[0:match.start(1) - 1]
-  if Match(r'.*\b(?:sizeof|alignof|alignas|[_A-Z][_A-Z0-9]*)\s*$', context):
-    return False
-
-  # Try expanding current context to see if we one level of
-  # parentheses inside a macro.
-  if linenum > 0:
-    for i in xrange(linenum - 1, max(0, linenum - 5), -1):
-      context = clean_lines.elided[i] + context
-  if Match(r'.*\b[_A-Z][_A-Z0-9]*\s*\((?:\([^()]*\)|[^()])*$', context):
-    return False
-
-  # operator++(int) and operator--(int)
-  if context.endswith(' operator++') or context.endswith(' operator--'):
-    return False
-
-  # A single unnamed argument for a function tends to look like old style cast.
-  # If we see those, don't issue warnings for deprecated casts.
-  remainder = line[match.end(0):]
-  if Match(r'^\s*(?:;|const\b|throw\b|final\b|override\b|[=>{),]|->)',
-           remainder):
-    return False
-
-  # At this point, all that should be left is actual casts.
-  error(filename, linenum, 'readability/casting', 4,
-        'Using C-style cast.  Use %s<%s>(...) instead' %
-        (cast_type, match.group(1)))
-
-  return True
-
-
-def ExpectingFunctionArgs(clean_lines, linenum):
-  """Checks whether where function type arguments are expected.
-
-  Args:
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-
-  Returns:
-    True if the line at 'linenum' is inside something that expects arguments
-    of function types.
-  """
-  line = clean_lines.elided[linenum]
-  return (Match(r'^\s*MOCK_(CONST_)?METHOD\d+(_T)?\(', line) or
-          (linenum >= 2 and
-           (Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\((?:\S+,)?\s*$',
-                  clean_lines.elided[linenum - 1]) or
-            Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\(\s*$',
-                  clean_lines.elided[linenum - 2]) or
-            Search(r'\bstd::m?function\s*\<\s*$',
-                   clean_lines.elided[linenum - 1]))))
-
-
-_HEADERS_CONTAINING_TEMPLATES = (
-    ('<deque>', ('deque',)),
-    ('<functional>', ('unary_function', 'binary_function',
-                      'plus', 'minus', 'multiplies', 'divides', 'modulus',
-                      'negate',
-                      'equal_to', 'not_equal_to', 'greater', 'less',
-                      'greater_equal', 'less_equal',
-                      'logical_and', 'logical_or', 'logical_not',
-                      'unary_negate', 'not1', 'binary_negate', 'not2',
-                      'bind1st', 'bind2nd',
-                      'pointer_to_unary_function',
-                      'pointer_to_binary_function',
-                      'ptr_fun',
-                      'mem_fun_t', 'mem_fun', 'mem_fun1_t', 'mem_fun1_ref_t',
-                      'mem_fun_ref_t',
-                      'const_mem_fun_t', 'const_mem_fun1_t',
-                      'const_mem_fun_ref_t', 'const_mem_fun1_ref_t',
-                      'mem_fun_ref',
-                     )),
-    ('<limits>', ('numeric_limits',)),
-    ('<list>', ('list',)),
-    ('<map>', ('map', 'multimap',)),
-    ('<memory>', ('allocator', 'make_shared', 'make_unique', 'shared_ptr',
-                  'unique_ptr', 'weak_ptr')),
-    ('<queue>', ('queue', 'priority_queue',)),
-    ('<set>', ('set', 'multiset',)),
-    ('<stack>', ('stack',)),
-    ('<string>', ('char_traits', 'basic_string',)),
-    ('<tuple>', ('tuple',)),
-    ('<unordered_map>', ('unordered_map', 'unordered_multimap')),
-    ('<unordered_set>', ('unordered_set', 'unordered_multiset')),
-    ('<utility>', ('pair',)),
-    ('<vector>', ('vector',)),
-
-    # gcc extensions.
-    # Note: std::hash is their hash, ::hash is our hash
-    ('<hash_map>', ('hash_map', 'hash_multimap',)),
-    ('<hash_set>', ('hash_set', 'hash_multiset',)),
-    ('<slist>', ('slist',)),
-    )
-
-_HEADERS_MAYBE_TEMPLATES = (
-    ('<algorithm>', ('copy', 'max', 'min', 'min_element', 'sort',
-                     'transform',
-                    )),
-    ('<utility>', ('forward', 'make_pair', 'move', 'swap')),
-    )
-
-_RE_PATTERN_STRING = re.compile(r'\bstring\b')
-
-_re_pattern_headers_maybe_templates = []
-for _header, _templates in _HEADERS_MAYBE_TEMPLATES:
-  for _template in _templates:
-    # Match max<type>(..., ...), max(..., ...), but not foo->max, foo.max or
-    # type::max().
-    _re_pattern_headers_maybe_templates.append(
-        (re.compile(r'[^>.]\b' + _template + r'(<.*?>)?\([^\)]'),
-            _template,
-            _header))
-
-# Other scripts may reach in and modify this pattern.
-_re_pattern_templates = []
-for _header, _templates in _HEADERS_CONTAINING_TEMPLATES:
-  for _template in _templates:
-    _re_pattern_templates.append(
-        (re.compile(r'(\<|\b)' + _template + r'\s*\<'),
-         _template + '<>',
-         _header))
-
-
-def FilesBelongToSameModule(filename_cc, filename_h):
-  """Check if these two filenames belong to the same module.
-
-  The concept of a 'module' here is a as follows:
-  foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
-  same 'module' if they are in the same directory.
-  some/path/public/xyzzy and some/path/internal/xyzzy are also considered
-  to belong to the same module here.
-
-  If the filename_cc contains a longer path than the filename_h, for example,
-  '/absolute/path/to/base/sysinfo.cc', and this file would include
-  'base/sysinfo.h', this function also produces the prefix needed to open the
-  header. This is used by the caller of this function to more robustly open the
-  header file. We don't have access to the real include paths in this context,
-  so we need this guesswork here.
-
-  Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
-  according to this implementation. Because of this, this function gives
-  some false positives. This should be sufficiently rare in practice.
-
-  Args:
-    filename_cc: is the path for the .cc file
-    filename_h: is the path for the header path
-
-  Returns:
-    Tuple with a bool and a string:
-    bool: True if filename_cc and filename_h belong to the same module.
-    string: the additional prefix needed to open the header file.
-  """
-
-  fileinfo = FileInfo(filename_cc)
-  if not fileinfo.IsSource():
-    return (False, '')
-  filename_cc = filename_cc[:-len(fileinfo.Extension())]
-  matched_test_suffix = Search(_TEST_FILE_SUFFIX, fileinfo.BaseName())
-  if matched_test_suffix:
-    filename_cc = filename_cc[:-len(matched_test_suffix.group(1))]
-  filename_cc = filename_cc.replace('/public/', '/')
-  filename_cc = filename_cc.replace('/internal/', '/')
-
-  if not filename_h.endswith('.h'):
-    return (False, '')
-  filename_h = filename_h[:-len('.h')]
-  if filename_h.endswith('-inl'):
-    filename_h = filename_h[:-len('-inl')]
-  filename_h = filename_h.replace('/public/', '/')
-  filename_h = filename_h.replace('/internal/', '/')
-
-  files_belong_to_same_module = filename_cc.endswith(filename_h)
-  common_path = ''
-  if files_belong_to_same_module:
-    common_path = filename_cc[:-len(filename_h)]
-  return files_belong_to_same_module, common_path
-
-
-def UpdateIncludeState(filename, include_dict, io=codecs):
-  """Fill up the include_dict with new includes found from the file.
-
-  Args:
-    filename: the name of the header to read.
-    include_dict: a dictionary in which the headers are inserted.
-    io: The io factory to use to read the file. Provided for testability.
-
-  Returns:
-    True if a header was successfully added. False otherwise.
-  """
-  headerfile = None
-  try:
-    headerfile = io.open(filename, 'r', 'utf8', 'replace')
-  except IOError:
-    return False
-  linenum = 0
-  for line in headerfile:
-    linenum += 1
-    clean_line = CleanseComments(line)
-    match = _RE_PATTERN_INCLUDE.search(clean_line)
-    if match:
-      include = match.group(2)
-      include_dict.setdefault(include, linenum)
-  return True
-
-
-def CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error,
-                              io=codecs):
-  """Reports for missing stl includes.
-
-  This function will output warnings to make sure you are including the headers
-  necessary for the stl containers and functions that you use. We only give one
-  reason to include a header. For example, if you use both equal_to<> and
-  less<> in a .h file, only one (the latter in the file) of these will be
-  reported as a reason to include the <functional>.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    include_state: An _IncludeState instance.
-    error: The function to call with any errors found.
-    io: The IO factory to use to read the header file. Provided for unittest
-        injection.
-  """
-  required = {}  # A map of header name to linenumber and the template entity.
-                 # Example of required: { '<functional>': (1219, 'less<>') }
-
-  for linenum in xrange(clean_lines.NumLines()):
-    line = clean_lines.elided[linenum]
-    if not line or line[0] == '#':
-      continue
-
-    # String is special -- it is a non-templatized type in STL.
-    matched = _RE_PATTERN_STRING.search(line)
-    if matched:
-      # Don't warn about strings in non-STL namespaces:
-      # (We check only the first match per line; good enough.)
-      prefix = line[:matched.start()]
-      if prefix.endswith('std::') or not prefix.endswith('::'):
-        required['<string>'] = (linenum, 'string')
-
-    for pattern, template, header in _re_pattern_headers_maybe_templates:
-      if pattern.search(line):
-        required[header] = (linenum, template)
-
-    # The following function is just a speed up, no semantics are changed.
-    if not '<' in line:  # Reduces the cpu time usage by skipping lines.
-      continue
-
-    for pattern, template, header in _re_pattern_templates:
-      matched = pattern.search(line)
-      if matched:
-        # Don't warn about IWYU in non-STL namespaces:
-        # (We check only the first match per line; good enough.)
-        prefix = line[:matched.start()]
-        if prefix.endswith('std::') or not prefix.endswith('::'):
-          required[header] = (linenum, template)
-
-  # The policy is that if you #include something in foo.h you don't need to
-  # include it again in foo.cc. Here, we will look at possible includes.
-  # Let's flatten the include_state include_list and copy it into a dictionary.
-  include_dict = dict([item for sublist in include_state.include_list
-                       for item in sublist])
-
-  # Did we find the header for this file (if any) and successfully load it?
-  header_found = False
-
-  # Use the absolute path so that matching works properly.
-  abs_filename = FileInfo(filename).FullName()
-
-  # For Emacs's flymake.
-  # If cpplint is invoked from Emacs's flymake, a temporary file is generated
-  # by flymake and that file name might end with '_flymake.cc'. In that case,
-  # restore original file name here so that the corresponding header file can be
-  # found.
-  # e.g. If the file name is 'foo_flymake.cc', we should search for 'foo.h'
-  # instead of 'foo_flymake.h'
-  abs_filename = re.sub(r'_flymake\.cc$', '.cc', abs_filename)
-
-  # include_dict is modified during iteration, so we iterate over a copy of
-  # the keys.
-  header_keys = include_dict.keys()
-  for header in header_keys:
-    (same_module, common_path) = FilesBelongToSameModule(abs_filename, header)
-    fullpath = common_path + header
-    if same_module and UpdateIncludeState(fullpath, include_dict, io):
-      header_found = True
-
-  # If we can't find the header file for a .cc, assume it's because we don't
-  # know where to look. In that case we'll give up as we're not sure they
-  # didn't include it in the .h file.
-  # TODO(unknown): Do a better job of finding .h files so we are confident that
-  # not having the .h file means there isn't one.
-  if filename.endswith('.cc') and not header_found:
-    return
-
-  # All the lines have been processed, report the errors found.
-  for required_header_unstripped in required:
-    template = required[required_header_unstripped][1]
-    if required_header_unstripped.strip('<>"') not in include_dict:
-      error(filename, required[required_header_unstripped][0],
-            'build/include_what_you_use', 4,
-            'Add #include ' + required_header_unstripped + ' for ' + template)
-
-
-_RE_PATTERN_EXPLICIT_MAKEPAIR = re.compile(r'\bmake_pair\s*<')
-
-
-def CheckMakePairUsesDeduction(filename, clean_lines, linenum, error):
-  """Check that make_pair's template arguments are deduced.
-
-  G++ 4.6 in C++11 mode fails badly if make_pair's template arguments are
-  specified explicitly, and such use isn't intended in any case.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-  match = _RE_PATTERN_EXPLICIT_MAKEPAIR.search(line)
-  if match:
-    error(filename, linenum, 'build/explicit_make_pair',
-          4,  # 4 = high confidence
-          'For C++11-compatibility, omit template arguments from make_pair'
-          ' OR use pair directly OR if appropriate, construct a pair directly')
-
-
-def CheckRedundantVirtual(filename, clean_lines, linenum, error):
-  """Check if line contains a redundant "virtual" function-specifier.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  # Look for "virtual" on current line.
-  line = clean_lines.elided[linenum]
-  virtual = Match(r'^(.*)(\bvirtual\b)(.*)$', line)
-  if not virtual: return
-
-  # Ignore "virtual" keywords that are near access-specifiers.  These
-  # are only used in class base-specifier and do not apply to member
-  # functions.
-  if (Search(r'\b(public|protected|private)\s+$', virtual.group(1)) or
-      Match(r'^\s+(public|protected|private)\b', virtual.group(3))):
-    return
-
-  # Ignore the "virtual" keyword from virtual base classes.  Usually
-  # there is a column on the same line in these cases (virtual base
-  # classes are rare in google3 because multiple inheritance is rare).
-  if Match(r'^.*[^:]:[^:].*$', line): return
-
-  # Look for the next opening parenthesis.  This is the start of the
-  # parameter list (possibly on the next line shortly after virtual).
-  # TODO(unknown): doesn't work if there are virtual functions with
-  # decltype() or other things that use parentheses, but csearch suggests
-  # that this is rare.
-  end_col = -1
-  end_line = -1
-  start_col = len(virtual.group(2))
-  for start_line in xrange(linenum, min(linenum + 3, clean_lines.NumLines())):
-    line = clean_lines.elided[start_line][start_col:]
-    parameter_list = Match(r'^([^(]*)\(', line)
-    if parameter_list:
-      # Match parentheses to find the end of the parameter list
-      (_, end_line, end_col) = CloseExpression(
-          clean_lines, start_line, start_col + len(parameter_list.group(1)))
-      break
-    start_col = 0
-
-  if end_col < 0:
-    return  # Couldn't find end of parameter list, give up
-
-  # Look for "override" or "final" after the parameter list
-  # (possibly on the next few lines).
-  for i in xrange(end_line, min(end_line + 3, clean_lines.NumLines())):
-    line = clean_lines.elided[i][end_col:]
-    match = Search(r'\b(override|final)\b', line)
-    if match:
-      error(filename, linenum, 'readability/inheritance', 4,
-            ('"virtual" is redundant since function is '
-             'already declared as "%s"' % match.group(1)))
-
-    # Set end_col to check whole lines after we are done with the
-    # first line.
-    end_col = 0
-    if Search(r'[^\w]\s*$', line):
-      break
-
-
-def CheckRedundantOverrideOrFinal(filename, clean_lines, linenum, error):
-  """Check if line contains a redundant "override" or "final" virt-specifier.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  # Look for closing parenthesis nearby.  We need one to confirm where
-  # the declarator ends and where the virt-specifier starts to avoid
-  # false positives.
-  line = clean_lines.elided[linenum]
-  declarator_end = line.rfind(')')
-  if declarator_end >= 0:
-    fragment = line[declarator_end:]
-  else:
-    if linenum > 1 and clean_lines.elided[linenum - 1].rfind(')') >= 0:
-      fragment = line
-    else:
-      return
-
-  # Check that at most one of "override" or "final" is present, not both
-  if Search(r'\boverride\b', fragment) and Search(r'\bfinal\b', fragment):
-    error(filename, linenum, 'readability/inheritance', 4,
-          ('"override" is redundant since function is '
-           'already declared as "final"'))
-
-
-
-
-# Returns true if we are at a new block, and it is directly
-# inside of a namespace.
-def IsBlockInNameSpace(nesting_state, is_forward_declaration):
-  """Checks that the new block is directly in a namespace.
-
-  Args:
-    nesting_state: The _NestingState object that contains info about our state.
-    is_forward_declaration: If the class is a forward declared class.
-  Returns:
-    Whether or not the new block is directly in a namespace.
-  """
-  if is_forward_declaration:
-    if len(nesting_state.stack) >= 1 and (
-        isinstance(nesting_state.stack[-1], _NamespaceInfo)):
-      return True
-    else:
-      return False
-
-  return (len(nesting_state.stack) > 1 and
-          nesting_state.stack[-1].check_namespace_indentation and
-          isinstance(nesting_state.stack[-2], _NamespaceInfo))
-
-
-def ShouldCheckNamespaceIndentation(nesting_state, is_namespace_indent_item,
-                                    raw_lines_no_comments, linenum):
-  """This method determines if we should apply our namespace indentation check.
-
-  Args:
-    nesting_state: The current nesting state.
-    is_namespace_indent_item: If we just put a new class on the stack, True.
-      If the top of the stack is not a class, or we did not recently
-      add the class, False.
-    raw_lines_no_comments: The lines without the comments.
-    linenum: The current line number we are processing.
-
-  Returns:
-    True if we should apply our namespace indentation check. Currently, it
-    only works for classes and namespaces inside of a namespace.
-  """
-
-  is_forward_declaration = IsForwardClassDeclaration(raw_lines_no_comments,
-                                                     linenum)
-
-  if not (is_namespace_indent_item or is_forward_declaration):
-    return False
-
-  # If we are in a macro, we do not want to check the namespace indentation.
-  if IsMacroDefinition(raw_lines_no_comments, linenum):
-    return False
-
-  return IsBlockInNameSpace(nesting_state, is_forward_declaration)
-
-
-# Call this method if the line is directly inside of a namespace.
-# If the line above is blank (excluding comments) or the start of
-# an inner namespace, it cannot be indented.
-def CheckItemIndentationInNamespace(filename, raw_lines_no_comments, linenum,
-                                    error):
-  line = raw_lines_no_comments[linenum]
-  if Match(r'^\s+', line):
-    error(filename, linenum, 'runtime/indentation_namespace', 4,
-          'Do not indent within a namespace')
-
-
-def ProcessLine(filename, file_extension, clean_lines, line,
-                include_state, function_state, nesting_state, error,
-                extra_check_functions=[]):
-  """Processes a single line in the file.
-
-  Args:
-    filename: Filename of the file that is being processed.
-    file_extension: The extension (dot not included) of the file.
-    clean_lines: An array of strings, each representing a line of the file,
-                 with comments stripped.
-    line: Number of line being processed.
-    include_state: An _IncludeState instance in which the headers are inserted.
-    function_state: A _FunctionState instance which counts function lines, etc.
-    nesting_state: A NestingState instance which maintains information about
-                   the current stack of nested blocks being parsed.
-    error: A callable to which errors are reported, which takes 4 arguments:
-           filename, line number, error level, and message
-    extra_check_functions: An array of additional check functions that will be
-                           run on each source line. Each function takes 4
-                           arguments: filename, clean_lines, line, error
-  """
-  raw_lines = clean_lines.raw_lines
-  ParseNolintSuppressions(filename, raw_lines[line], line, error)
-  nesting_state.Update(filename, clean_lines, line, error)
-  CheckForNamespaceIndentation(filename, nesting_state, clean_lines, line,
-                               error)
-  if nesting_state.InAsmBlock(): return
-  CheckForFunctionLengths(filename, clean_lines, line, function_state, error)
-  CheckForMultilineCommentsAndStrings(filename, clean_lines, line, error)
-  CheckStyle(filename, clean_lines, line, file_extension, nesting_state, error)
-  CheckLanguage(filename, clean_lines, line, file_extension, include_state,
-                nesting_state, error)
-  CheckForNonConstReference(filename, clean_lines, line, nesting_state, error)
-  CheckForNonStandardConstructs(filename, clean_lines, line,
-                                nesting_state, error)
-  CheckVlogArguments(filename, clean_lines, line, error)
-  CheckPosixThreading(filename, clean_lines, line, error)
-  CheckInvalidIncrement(filename, clean_lines, line, error)
-  CheckMakePairUsesDeduction(filename, clean_lines, line, error)
-  CheckRedundantVirtual(filename, clean_lines, line, error)
-  CheckRedundantOverrideOrFinal(filename, clean_lines, line, error)
-  for check_fn in extra_check_functions:
-    check_fn(filename, clean_lines, line, error)
-
-def FlagCxx11Features(filename, clean_lines, linenum, error):
-  """Flag those c++11 features that we only allow in certain places.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  include = Match(r'\s*#\s*include\s+[<"]([^<"]+)[">]', line)
-
-  # Flag unapproved C++ TR1 headers.
-  if include and include.group(1).startswith('tr1/'):
-    error(filename, linenum, 'build/c++tr1', 5,
-          ('C++ TR1 headers such as <%s> are unapproved.') % include.group(1))
-
-  # Flag unapproved C++11 headers.
-  if include and include.group(1) in ('cfenv',
-                                      'condition_variable',
-                                      'fenv.h',
-                                      'future',
-                                      'mutex',
-                                      'thread',
-                                      'chrono',
-                                      'ratio',
-                                      'regex',
-                                      'system_error',
-                                     ):
-    error(filename, linenum, 'build/c++11', 5,
-          ('<%s> is an unapproved C++11 header.') % include.group(1))
-
-  # The only place where we need to worry about C++11 keywords and library
-  # features in preprocessor directives is in macro definitions.
-  if Match(r'\s*#', line) and not Match(r'\s*#\s*define\b', line): return
-
-  # These are classes and free functions.  The classes are always
-  # mentioned as std::*, but we only catch the free functions if
-  # they're not found by ADL.  They're alphabetical by header.
-  for top_name in (
-      # type_traits
-      'alignment_of',
-      'aligned_union',
-      ):
-    if Search(r'\bstd::%s\b' % top_name, line):
-      error(filename, linenum, 'build/c++11', 5,
-            ('std::%s is an unapproved C++11 class or function.  Send c-style '
-             'an example of where it would make your code more readable, and '
-             'they may let you use it.') % top_name)
-
-
-def FlagCxx14Features(filename, clean_lines, linenum, error):
-  """Flag those C++14 features that we restrict.
-
-  Args:
-    filename: The name of the current file.
-    clean_lines: A CleansedLines instance containing the file.
-    linenum: The number of the line to check.
-    error: The function to call with any errors found.
-  """
-  line = clean_lines.elided[linenum]
-
-  include = Match(r'\s*#\s*include\s+[<"]([^<"]+)[">]', line)
-
-  # Flag unapproved C++14 headers.
-  if include and include.group(1) in ('scoped_allocator', 'shared_mutex'):
-    error(filename, linenum, 'build/c++14', 5,
-          ('<%s> is an unapproved C++14 header.') % include.group(1))
-
-
-def ProcessFileData(filename, file_extension, lines, error,
-                    extra_check_functions=[]):
-  """Performs lint checks and reports any errors to the given error function.
-
-  Args:
-    filename: Filename of the file that is being processed.
-    file_extension: The extension (dot not included) of the file.
-    lines: An array of strings, each representing a line of the file, with the
-           last element being empty if the file is terminated with a newline.
-    error: A callable to which errors are reported, which takes 4 arguments:
-           filename, line number, error level, and message
-    extra_check_functions: An array of additional check functions that will be
-                           run on each source line. Each function takes 4
-                           arguments: filename, clean_lines, line, error
-  """
-  lines = (['// marker so line numbers and indices both start at 1'] + lines +
-           ['// marker so line numbers end in a known way'])
-
-  include_state = _IncludeState()
-  function_state = _FunctionState()
-  nesting_state = NestingState()
-
-  ResetNolintSuppressions()
-
-  CheckForCopyright(filename, lines, error)
-  ProcessGlobalSuppresions(lines)
-  RemoveMultiLineComments(filename, lines, error)
-  clean_lines = CleansedLines(lines)
-
-  if IsHeaderExtension(file_extension):
-    CheckForHeaderGuard(filename, clean_lines, error)
-
-  for line in xrange(clean_lines.NumLines()):
-    ProcessLine(filename, file_extension, clean_lines, line,
-                include_state, function_state, nesting_state, error,
-                extra_check_functions)
-    FlagCxx11Features(filename, clean_lines, line, error)
-  nesting_state.CheckCompletedBlocks(filename, error)
-
-  CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error)
-
-  # Check that the .cc file has included its header if it exists.
-  if _IsSourceExtension(file_extension):
-    CheckHeaderFileIncluded(filename, include_state, error)
-
-  # We check here rather than inside ProcessLine so that we see raw
-  # lines rather than "cleaned" lines.
-  CheckForBadCharacters(filename, lines, error)
-
-  CheckForNewlineAtEOF(filename, lines, error)
-
-def ProcessConfigOverrides(filename):
-  """ Loads the configuration files and processes the config overrides.
-
-  Args:
-    filename: The name of the file being processed by the linter.
-
-  Returns:
-    False if the current |filename| should not be processed further.
-  """
-
-  abs_filename = os.path.abspath(filename)
-  cfg_filters = []
-  keep_looking = True
-  while keep_looking:
-    abs_path, base_name = os.path.split(abs_filename)
-    if not base_name:
-      break  # Reached the root directory.
-
-    cfg_file = os.path.join(abs_path, "CPPLINT.cfg")
-    abs_filename = abs_path
-    if not os.path.isfile(cfg_file):
-      continue
-
-    try:
-      with open(cfg_file) as file_handle:
-        for line in file_handle:
-          line, _, _ = line.partition('#')  # Remove comments.
-          if not line.strip():
-            continue
-
-          name, _, val = line.partition('=')
-          name = name.strip()
-          val = val.strip()
-          if name == 'set noparent':
-            keep_looking = False
-          elif name == 'filter':
-            cfg_filters.append(val)
-          elif name == 'exclude_files':
-            # When matching exclude_files pattern, use the base_name of
-            # the current file name or the directory name we are processing.
-            # For example, if we are checking for lint errors in /foo/bar/baz.cc
-            # and we found the .cfg file at /foo/CPPLINT.cfg, then the config
-            # file's "exclude_files" filter is meant to be checked against "bar"
-            # and not "baz" nor "bar/baz.cc".
-            if base_name:
-              pattern = re.compile(val)
-              if pattern.match(base_name):
-                if _cpplint_state.quiet:
-                  # Suppress "Ignoring file" warning when using --quiet.
-                  return False
-                sys.stderr.write('Ignoring "%s": file excluded by "%s". '
-                                 'File path component "%s" matches '
-                                 'pattern "%s"\n' %
-                                 (filename, cfg_file, base_name, val))
-                return False
-          elif name == 'linelength':
-            global _line_length
-            try:
-                _line_length = int(val)
-            except ValueError:
-                sys.stderr.write('Line length must be numeric.')
-          elif name == 'root':
-            global _root
-            # root directories are specified relative to CPPLINT.cfg dir.
-            _root = os.path.join(os.path.dirname(cfg_file), val)
-          elif name == 'headers':
-            ProcessHppHeadersOption(val)
-          else:
-            sys.stderr.write(
-                'Invalid configuration option (%s) in file %s\n' %
-                (name, cfg_file))
-
-    except IOError:
-      sys.stderr.write(
-          "Skipping config file '%s': Can't open for reading\n" % cfg_file)
-      keep_looking = False
-
-  # Apply all the accumulated filters in reverse order (top-level directory
-  # config options having the least priority).
-  for filter in reversed(cfg_filters):
-     _AddFilters(filter)
-
-  return True
-
-
-def ProcessFile(filename, vlevel, extra_check_functions=[]):
-  """Does google-lint on a single file.
-
-  Args:
-    filename: The name of the file to parse.
-
-    vlevel: The level of errors to report.  Every error of confidence
-    >= verbose_level will be reported.  0 is a good default.
-
-    extra_check_functions: An array of additional check functions that will be
-                           run on each source line. Each function takes 4
-                           arguments: filename, clean_lines, line, error
-  """
-
-  _SetVerboseLevel(vlevel)
-  _BackupFilters()
-  old_errors = _cpplint_state.error_count
-
-  if not ProcessConfigOverrides(filename):
-    _RestoreFilters()
-    return
-
-  lf_lines = []
-  crlf_lines = []
-  try:
-    # Support the UNIX convention of using "-" for stdin.  Note that
-    # we are not opening the file with universal newline support
-    # (which codecs doesn't support anyway), so the resulting lines do
-    # contain trailing '\r' characters if we are reading a file that
-    # has CRLF endings.
-    # If after the split a trailing '\r' is present, it is removed
-    # below.
-    if filename == '-':
-      lines = codecs.StreamReaderWriter(sys.stdin,
-                                        codecs.getreader('utf8'),
-                                        codecs.getwriter('utf8'),
-                                        'replace').read().split('\n')
-    else:
-      lines = codecs.open(filename, 'r', 'utf8', 'replace').read().split('\n')
-
-    # Remove trailing '\r'.
-    # The -1 accounts for the extra trailing blank line we get from split()
-    for linenum in range(len(lines) - 1):
-      if lines[linenum].endswith('\r'):
-        lines[linenum] = lines[linenum].rstrip('\r')
-        crlf_lines.append(linenum + 1)
-      else:
-        lf_lines.append(linenum + 1)
-
-  except IOError:
-    sys.stderr.write(
-        "Skipping input '%s': Can't open for reading\n" % filename)
-    _RestoreFilters()
-    return
-
-  # Note, if no dot is found, this will give the entire filename as the ext.
-  file_extension = filename[filename.rfind('.') + 1:]
-
-  # When reading from stdin, the extension is unknown, so no cpplint tests
-  # should rely on the extension.
-  if filename != '-' and file_extension not in _valid_extensions:
-    sys.stderr.write('Ignoring %s; not a valid file name '
-                     '(%s)\n' % (filename, ', '.join(_valid_extensions)))
-  else:
-    ProcessFileData(filename, file_extension, lines, Error,
-                    extra_check_functions)
-
-    # If end-of-line sequences are a mix of LF and CR-LF, issue
-    # warnings on the lines with CR.
-    #
-    # Don't issue any warnings if all lines are uniformly LF or CR-LF,
-    # since critique can handle these just fine, and the style guide
-    # doesn't dictate a particular end of line sequence.
-    #
-    # We can't depend on os.linesep to determine what the desired
-    # end-of-line sequence should be, since that will return the
-    # server-side end-of-line sequence.
-    if lf_lines and crlf_lines:
-      # Warn on every line with CR.  An alternative approach might be to
-      # check whether the file is mostly CRLF or just LF, and warn on the
-      # minority, we bias toward LF here since most tools prefer LF.
-      for linenum in crlf_lines:
-        Error(filename, linenum, 'whitespace/newline', 1,
-              'Unexpected \\r (^M) found; better to use only \\n')
-
-  # Suppress printing anything if --quiet was passed unless the error
-  # count has increased after processing this file.
-  if not _cpplint_state.quiet or old_errors != _cpplint_state.error_count:
-    sys.stdout.write('Done processing %s\n' % filename)
-  _RestoreFilters()
-
-
-def PrintUsage(message):
-  """Prints a brief usage string and exits, optionally with an error message.
-
-  Args:
-    message: The optional error message.
-  """
-  sys.stderr.write(_USAGE)
-  if message:
-    sys.exit('\nFATAL ERROR: ' + message)
-  else:
-    sys.exit(1)
-
-
-def PrintCategories():
-  """Prints a list of all the error-categories used by error messages.
-
-  These are the categories used to filter messages via --filter.
-  """
-  sys.stderr.write(''.join('  %s\n' % cat for cat in _ERROR_CATEGORIES))
-  sys.exit(0)
-
-
-def ParseArguments(args):
-  """Parses the command line arguments.
-
-  This may set the output format and verbosity level as side-effects.
-
-  Args:
-    args: The command line arguments:
-
-  Returns:
-    The list of filenames to lint.
-  """
-  try:
-    (opts, filenames) = getopt.getopt(args, '', ['help', 'output=', 'verbose=',
-                                                 'counting=',
-                                                 'filter=',
-                                                 'root=',
-                                                 'linelength=',
-                                                 'extensions=',
-                                                 'headers=',
-                                                 'quiet'])
-  except getopt.GetoptError:
-    PrintUsage('Invalid arguments.')
-
-  verbosity = _VerboseLevel()
-  output_format = _OutputFormat()
-  filters = ''
-  quiet = _Quiet()
-  counting_style = ''
-
-  for (opt, val) in opts:
-    if opt == '--help':
-      PrintUsage(None)
-    elif opt == '--output':
-      if val not in ('emacs', 'vs7', 'eclipse'):
-        PrintUsage('The only allowed output formats are emacs, vs7 and eclipse.')
-      output_format = val
-    elif opt == '--quiet':
-      quiet = True
-    elif opt == '--verbose':
-      verbosity = int(val)
-    elif opt == '--filter':
-      filters = val
-      if not filters:
-        PrintCategories()
-    elif opt == '--counting':
-      if val not in ('total', 'toplevel', 'detailed'):
-        PrintUsage('Valid counting options are total, toplevel, and detailed')
-      counting_style = val
-    elif opt == '--root':
-      global _root
-      _root = val
-    elif opt == '--linelength':
-      global _line_length
-      try:
-          _line_length = int(val)
-      except ValueError:
-          PrintUsage('Line length must be digits.')
-    elif opt == '--extensions':
-      global _valid_extensions
-      try:
-          _valid_extensions = set(val.split(','))
-      except ValueError:
-          PrintUsage('Extensions must be comma separated list.')
-    elif opt == '--headers':
-      ProcessHppHeadersOption(val)
-
-  if not filenames:
-    PrintUsage('No files were specified.')
-
-  _SetOutputFormat(output_format)
-  _SetQuiet(quiet)
-  _SetVerboseLevel(verbosity)
-  _SetFilters(filters)
-  _SetCountingStyle(counting_style)
-
-  return filenames
-
-
-def main():
-  filenames = ParseArguments(sys.argv[1:])
-
-  # Change stderr to write with replacement characters so we don't die
-  # if we try to print something containing non-ASCII characters.
-  sys.stderr = codecs.StreamReaderWriter(sys.stderr,
-                                         codecs.getreader('utf8'),
-                                         codecs.getwriter('utf8'),
-                                         'replace')
-
-  _cpplint_state.ResetErrorCounts()
-  for filename in filenames:
-    ProcessFile(filename, _cpplint_state.verbose_level)
-  # If --quiet is passed, suppress printing error count unless there are errors.
-  if not _cpplint_state.quiet or _cpplint_state.error_count > 0:
-    _cpplint_state.PrintErrorCounts()
-
-  sys.exit(_cpplint_state.error_count > 0)
-
-
-if __name__ == '__main__':
-  main()
diff --git a/tfjs-master/tfjs-backend-wasm/tsconfig.json b/tfjs-master/tfjs-backend-wasm/tsconfig.json
deleted file mode 100644
index 14e5bcd71..000000000
--- a/tfjs-master/tfjs-backend-wasm/tsconfig.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "esModuleInterop": true,
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-backend-wasm/wasm-out/BUILD.bazel b/tfjs-master/tfjs-backend-wasm/wasm-out/BUILD.bazel
deleted file mode 100644
index 39a6a2bf0..000000000
--- a/tfjs-master/tfjs-backend-wasm/wasm-out/BUILD.bazel
+++ /dev/null
@@ -1,118 +0,0 @@
-load("@build_bazel_rules_nodejs//:index.bzl", "js_library")
-load("//tfjs-backend-wasm/scripts:create_worker_module.bzl", "create_worker_module")
-load("//tfjs-backend-wasm/scripts:patch_threaded_simd_module.bzl", "patch_threaded_simd_module")
-load("//tools:copy_to_dist.bzl", "copy_to_dist")
-load("//tools:get_extension.bzl", "get_extension")
-
-package(default_visibility = ["//visibility:public"])
-
-copy_to_dist(
-    name = "copy_wasm",
-    srcs = [
-        "//tfjs-backend-wasm/src/cc:tfjs-backend-wasm",
-    ],
-    dest_dir = ".",
-    root = "../src/cc/tfjs-backend-wasm",
-)
-
-copy_to_dist(
-    name = "copy_wasm_simd",
-    srcs = [
-        "//tfjs-backend-wasm/src/cc:tfjs-backend-wasm-simd",
-    ],
-    dest_dir = ".",
-    root = "../src/cc/tfjs-backend-wasm-simd",
-)
-
-# Copying the threaded wasm output is more complicated because postprocessing
-# of the output is required.
-get_extension(
-    name = "wasm_threaded_js",
-    srcs = ["//tfjs-backend-wasm/src/cc:tfjs-backend-wasm-threaded-simd"],
-    include = [
-        ".js",
-    ],
-    exclude = [
-        ".worker.js",
-        ".fetch.js",
-    ],
-)
-
-patch_threaded_simd_module(
-    name = "patch_threaded_simd_module",
-    out_file = "tfjs-backend-wasm-threaded-simd.js",
-    threaded_simd_file = ":wasm_threaded_js",
-)
-
-get_extension(
-    name = "wasm_threaded_worker",
-    srcs = ["//tfjs-backend-wasm/src/cc:tfjs-backend-wasm-threaded-simd"],
-    include = [
-        ".worker.js",
-    ],
-)
-
-create_worker_module(
-    name = "create_worker_module",
-    cjs = True,
-    out_file = "tfjs-backend-wasm-threaded-simd.worker.js",
-    worker_file = ":wasm_threaded_worker",
-)
-
-get_extension(
-    name = "wasm_threaded_unpatched",
-    srcs = ["//tfjs-backend-wasm/src/cc:tfjs-backend-wasm-threaded-simd"],
-    # Exclude all the files that were patched.
-    exclude = [
-        "tfjs-backend-wasm-threaded-simd.js",
-        "tfjs-backend-wasm-threaded-simd.worker.js",
-    ],
-)
-
-copy_to_dist(
-    name = "copy_wasm_threaded",
-    srcs = [
-        ":wasm_threaded_unpatched",
-    ],
-    dest_dir = ".",
-    root = "../src/cc/tfjs-backend-wasm-threaded-simd",
-)
-
-filegroup(
-    name = "wasm_types",
-    srcs = glob(["*.d.ts"]),
-)
-
-# This target selects the file extensions that should end up in the
-# wasm-out directory in the npm package.
-get_extension(
-    name = "wasm_for_dist",
-    srcs = [
-        ":copy_wasm",
-        ":copy_wasm_simd",
-        ":copy_wasm_threaded",
-        ":create_worker_module",
-        ":patch_threaded_simd_module",
-    ],
-    include = [
-        ".js",
-        ".cjs",
-        ".wasm",
-    ],
-    exclude = [
-        ".fetch.js",
-        ".debug.wasm",
-    ],
-)
-
-js_library(
-    name = "wasm-out",
-    package_name = "@tensorflow/tfjs-backend-wasm/wasm-out",
-    srcs = [
-        ":wasm_for_dist",
-        ":wasm_types",
-    ],
-    deps = [
-        "@npm//@types/emscripten",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.d.ts b/tfjs-master/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.d.ts
deleted file mode 100644
index 71ef9ef10..000000000
--- a/tfjs-master/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.d.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BackendWasmModule} from './tfjs-backend-wasm';
-
-export interface BackendWasmThreadedSimdModule extends BackendWasmModule {
-  PThread: {
-    // Terminates all webworkers
-    terminateAllThreads(): void,
-  };
-}
-
-export interface WasmFactoryConfig {
-  mainScriptUrlOrBlob?: string|Blob;
-  locateFile?(path: string, prefix: string): string;
-  instantiateWasm?: Function;
-  onRuntimeInitialized?: () => void;
-  onAbort?: (msg: string) => void;
-}
-
-declare var moduleFactory: (settings: WasmFactoryConfig) =>
-    Promise<BackendWasmModule>;
-export default moduleFactory;
diff --git a/tfjs-master/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.d.ts b/tfjs-master/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.d.ts
deleted file mode 100644
index f13a93692..000000000
--- a/tfjs-master/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.d.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export interface BackendWasmModule extends EmscriptenModule {
-  // Using the tfjs namespace to avoid conflict with emscripten's API.
-  tfjs: {
-    init(): void,
-    initWithThreadsCount(threadsCount: number): void,
-    getThreadsCount(): number,
-    registerTensor(id: number, size: number, memoryOffset: number): void,
-    // Disposes the data behind the data bucket.
-    disposeData(id: number): void,
-    // Disposes the backend and all of its associated data.
-    dispose(): void,
-  }
-}
-
-export interface WasmFactoryConfig {
-  mainScriptUrlOrBlob?: string|Blob;
-  locateFile?(path: string, prefix: string): string;
-  instantiateWasm?: Function;
-  onRuntimeInitialized?: () => void;
-  onAbort?: (msg: string) => void;
-}
-
-declare var moduleFactory: (settings: WasmFactoryConfig) =>
-    Promise<BackendWasmModule>;
-export default moduleFactory;
diff --git a/tfjs-master/tfjs-backend-wasm/yarn.lock b/tfjs-master/tfjs-backend-wasm/yarn.lock
deleted file mode 100644
index b430d866e..000000000
--- a/tfjs-master/tfjs-backend-wasm/yarn.lock
+++ /dev/null
@@ -1,152 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@babel/polyfill@^7.8.7":
-  version "7.12.1"
-  resolved "https://registry.yarnpkg.com/@babel/polyfill/-/polyfill-7.12.1.tgz#1f2d6371d1261bbd961f3c5d5909150e12d0bd96"
-  integrity sha512-X0pi0V6gxLi6lFZpGmeNa4zxtwEmCs42isWLNjZZDE0Y8yVfgu0T2OAHlzBbdYlqbW/YXVvoBHpATEM+goCj8g==
-  dependencies:
-    core-js "^2.6.5"
-    regenerator-runtime "^0.13.4"
-
-"@bazel/bazelisk@^1.12.0":
-  version "1.12.0"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.12.0.tgz#f08aebbf4afcb12684422450b0845dd6ef5cfe50"
-  integrity sha512-7oQusq1e4AIyFgotxVV7Pc40Et0QyvoVjujL+7/qV5Vrbfh0Nj3CfqSgl63weEyI4r0+K6RlGVsjfRuBi05p5w==
-
-"@tensorflow/tfjs-backend-cpu@link:../link-package/node_modules/@tensorflow/tfjs-backend-cpu":
-  version "0.0.0"
-
-"@types/emscripten@~0.0.34":
-  version "0.0.34"
-  resolved "https://registry.yarnpkg.com/@types/emscripten/-/emscripten-0.0.34.tgz#12b4a344274fb102ff2f6c877b37587bc3e46008"
-  integrity sha512-QSb9ojDincskc+uKMI0KXp8e1NALFINCrMlp8VGKGcTSxeEyRTTKyjWw75NYrCZHUsVEEEpr1tYHpbtaC++/sQ==
-
-async@^1.5.2:
-  version "1.5.2"
-  resolved "https://registry.yarnpkg.com/async/-/async-1.5.2.tgz#ec6a61ae56480c0c3cb241c95618e20892f9672a"
-  integrity sha512-nSVgobk4rv61R9PUSDtYt7mPVB2olxNR5RWJcAsH676/ef11bUZwvu7+RGYrYauVdDPcO519v68wRhXQtxsV9w==
-
-balanced-match@^1.0.0:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
-  integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
-
-brace-expansion@^1.1.7:
-  version "1.1.11"
-  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd"
-  integrity sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==
-  dependencies:
-    balanced-match "^1.0.0"
-    concat-map "0.0.1"
-
-clang-format@~1.2.4:
-  version "1.2.4"
-  resolved "https://registry.yarnpkg.com/clang-format/-/clang-format-1.2.4.tgz#4bb4b0a98180428deb093cf20982e9fc1af20b6c"
-  integrity sha512-sw+nrGUp3hvmANd1qF8vZPuezSYQAiXgGBiEtkXTtJnnu6b00fCqkkDIsnRKrNgg4nv6NYZE92ejvOMIXZoejw==
-  dependencies:
-    async "^1.5.2"
-    glob "^7.0.0"
-    resolve "^1.1.6"
-
-concat-map@0.0.1:
-  version "0.0.1"
-  resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
-  integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
-
-core-js@^2.6.5:
-  version "2.6.12"
-  resolved "https://registry.yarnpkg.com/core-js/-/core-js-2.6.12.tgz#d9333dfa7b065e347cc5682219d6f690859cc2ec"
-  integrity sha512-Kb2wC0fvsWfQrgk8HU5lW6U/Lcs8+9aaYcy4ZFc6DDlo4nZ7n70dEgE5rtR0oG6ufKDUnrwfWL1mXR5ljDatrQ==
-
-fs.realpath@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
-  integrity sha1-FQStJSMVjKpA20onh8sBQRmU6k8=
-
-function-bind@^1.1.1:
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.1.tgz#a56899d3ea3c9bab874bb9773b7c5ede92f4895d"
-  integrity sha512-yIovAzMX49sF8Yl58fSCWJ5svSLuaibPxXQJFLmBObTuCr0Mf1KiPopGM9NiFjiYBCbfaa2Fh6breQ6ANVTI0A==
-
-glob@^7.0.0:
-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/glob/-/glob-7.2.0.tgz#d15535af7732e02e948f4c41628bd910293f6023"
-  integrity sha512-lmLf6gtyrPq8tTjSmrO94wBeQbFR3HbLHbuyD69wuyQkImp2hWqMGB47OX65FBkPffO641IP9jWa1z4ivqG26Q==
-  dependencies:
-    fs.realpath "^1.0.0"
-    inflight "^1.0.4"
-    inherits "2"
-    minimatch "^3.0.4"
-    once "^1.3.0"
-    path-is-absolute "^1.0.0"
-
-has@^1.0.3:
-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/has/-/has-1.0.3.tgz#722d7cbfc1f6aa8241f16dd814e011e1f41e8796"
-  integrity sha512-f2dvO0VU6Oej7RkWJGrehjbzMAjFp5/VKPp5tTpWIV4JHHZK1/BxbFRtf/siA2SWTe09caDmVtYYzWEIbBS4zw==
-  dependencies:
-    function-bind "^1.1.1"
-
-inflight@^1.0.4:
-  version "1.0.6"
-  resolved "https://registry.yarnpkg.com/inflight/-/inflight-1.0.6.tgz#49bd6331d7d02d0c09bc910a1075ba8165b56df9"
-  integrity sha1-Sb1jMdfQLQwJvJEKEHW6gWW1bfk=
-  dependencies:
-    once "^1.3.0"
-    wrappy "1"
-
-inherits@2:
-  version "2.0.4"
-  resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
-  integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
-
-is-core-module@^2.2.0:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/is-core-module/-/is-core-module-2.2.0.tgz#97037ef3d52224d85163f5597b2b63d9afed981a"
-  integrity sha512-XRAfAdyyY5F5cOXn7hYQDqh2Xmii+DEfIcQGxK/uNwMHhIkPWO0g8msXcbzLe+MpGoR951MlqM/2iIlU4vKDdQ==
-  dependencies:
-    has "^1.0.3"
-
-minimatch@^3.0.4:
-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.1.2.tgz#19cd194bfd3e428f049a70817c038d89ab4be35b"
-  integrity sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==
-  dependencies:
-    brace-expansion "^1.1.7"
-
-once@^1.3.0:
-  version "1.4.0"
-  resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
-  integrity sha1-WDsap3WWHUsROsF9nFC6753Xa9E=
-  dependencies:
-    wrappy "1"
-
-path-is-absolute@^1.0.0:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
-  integrity sha1-F0uSaHNVNP+8es5r9TpanhtcX18=
-
-path-parse@^1.0.6:
-  version "1.0.7"
-  resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.7.tgz#fbc114b60ca42b30d9daf5858e4bd68bbedb6735"
-  integrity sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==
-
-regenerator-runtime@^0.13.4:
-  version "0.13.7"
-  resolved "https://registry.yarnpkg.com/regenerator-runtime/-/regenerator-runtime-0.13.7.tgz#cac2dacc8a1ea675feaabaeb8ae833898ae46f55"
-  integrity sha512-a54FxoJDIr27pgf7IgeQGxmqUNYrcV338lf/6gH456HZ/PhX+5BcwHXG9ajESmwe6WRO0tAzRUrRmNONWgkrew==
-
-resolve@^1.1.6:
-  version "1.20.0"
-  resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.20.0.tgz#629a013fb3f70755d6f0b7935cc1c2c5378b1975"
-  integrity sha512-wENBPt4ySzg4ybFQW2TT1zMQucPK95HSh/nq2CFTZVOGut2+pQvSsgtda4d26YrYcr067wjbmzOG8byDPBX63A==
-  dependencies:
-    is-core-module "^2.2.0"
-    path-parse "^1.0.6"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
diff --git a/tfjs-master/tfjs-backend-webgl/.npmignore b/tfjs-master/tfjs-backend-webgl/.npmignore
deleted file mode 100644
index 6292b5fdb..000000000
--- a/tfjs-master/tfjs-backend-webgl/.npmignore
+++ /dev/null
@@ -1,27 +0,0 @@
-.babelrc
-.DS_Store
-.idea/
-.rpt2_cache
-.travis.yml
-.vscode
-*.tgz
-*.txt
-**.yalc
-**yalc.lock
-cloudbuild.yml
-coverage/
-demo/
-dist/**/*_test.d.ts
-dist/**/*_test.js
-karma.conf.js
-node_modules/
-npm-debug.log
-package-lock.json
-package/
-rollup.config.js
-scripts/
-src/**/*_test.ts
-tsconfig.json
-tslint.json
-yarn-error.log
-yarn.lock
diff --git a/tfjs-master/tfjs-backend-webgl/.npmrc b/tfjs-master/tfjs-backend-webgl/.npmrc
deleted file mode 100644
index 43c97e719..000000000
--- a/tfjs-master/tfjs-backend-webgl/.npmrc
+++ /dev/null
@@ -1 +0,0 @@
-package-lock=false
diff --git a/tfjs-master/tfjs-backend-webgl/BUILD.bazel b/tfjs-master/tfjs-backend-webgl/BUILD.bazel
deleted file mode 100644
index cbe8e8c9d..000000000
--- a/tfjs-master/tfjs-backend-webgl/BUILD.bazel
+++ /dev/null
@@ -1,231 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//rules:copy_file.bzl", "copy_file")
-load("@build_bazel_rules_nodejs//:index.bzl", "js_library", "pkg_npm")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-tfjs_bundle(
-    name = "tf-backend-webgl",
-    entry_point = "//tfjs-backend-webgl/src:index.ts",
-    external = [
-        "crypto",
-        "@tensorflow/tfjs-core",
-        "seedrandom",
-        "@tensorflow/tfjs-backend-cpu",
-    ],
-    globals = {
-        "@tensorflow/tfjs-core": "tf",
-        "seedrandom": "seedrandom",
-    },
-    umd_name = "tf",
-    deps = [
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_lib",
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_src_lib",
-    ],
-)
-
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_lib",
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_src_lib",
-    ],
-    root = "src",
-)
-
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-backend-webgl",
-        ":tf-backend-webgl.es2017",
-        ":tf-backend-webgl.es2017.min",
-        ":tf-backend-webgl.fesm",
-        ":tf-backend-webgl.fesm.min",
-        ":tf-backend-webgl.min",
-        ":tf-backend-webgl.node",
-    ],
-)
-
-copy_file(
-    name = "copy_miniprogram",
-    src = ":tf-backend-webgl.min.js",
-    out = "dist/miniprogram/index.js",
-)
-
-copy_file(
-    name = "copy_miniprogram_map",
-    src = ":tf-backend-webgl.min.js.map",
-    out = "dist/miniprogram/index.js.map",
-)
-
-pkg_npm(
-    name = "tfjs-backend-webgl_pkg",
-    package_name = "@tensorflow/tfjs-backend-webgl",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_miniprogram",
-        ":copy_miniprogram_map",
-        ":copy_src_to_dist",
-    ],
-)
-
-STATIC_FILES = [
-    # Listed here so sourcemaps are served
-    "//tfjs-backend-webgl/src:tfjs-backend-webgl_test_bundle",
-]
-
-tfjs_web_test(
-    name = "tfjs-backend-webgl2_test",
-    srcs = [
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_test_bundle",
-    ],
-    args = [
-        "--testEnv",
-        "webgl2",
-    ],
-    browsers = [
-        "bs_android_10",
-        "bs_chrome_mac",
-        "bs_firefox_mac",
-        "win_10_chrome",
-    ],
-    headless = False,
-    presubmit_browsers = [
-        "bs_chrome_mac",
-        "bs_android_10",
-    ],
-    static_files = STATIC_FILES,
-)
-
-tfjs_web_test(
-    name = "tfjs-backend-webgl1_test",
-    srcs = [
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_test_bundle",
-    ],
-    args = [
-        "--testEnv",
-        "webgl1",
-    ],
-    browsers = [
-        "bs_ios_12",
-        "bs_safari_mac",
-    ],
-    headless = False,
-    presubmit_browsers = [
-        "bs_safari_mac",
-        "bs_ios_12",
-    ],
-    static_files = STATIC_FILES,
-)
-
-tfjs_web_test(
-    name = "tfjs-backend-webgl_unpacked_test",
-    srcs = [
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_test_bundle",
-    ],
-    args = [
-        "--testEnv",
-        "webgl2",
-        "--flags",
-        '{"WEBGL_PACK": false}',
-    ],
-    browsers = [
-        "bs_chrome_mac",
-    ],
-    headless = False,
-    presubmit_browsers = [],  # Only run in nightly
-    static_files = STATIC_FILES,
-)
-
-tfjs_web_test(
-    name = "tfjs-backend-webgl_forward_test",
-    srcs = [
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_test_bundle",
-    ],
-    args = [
-        "--testEnv",
-        "webgl2",
-        "--flags",
-        '{"WEBGL_CPU_FORWARD": true}',
-    ],
-    browsers = [
-        "bs_chrome_mac",
-    ],
-    headless = False,
-    presubmit_browsers = [],  # Only run in nightly
-    static_files = STATIC_FILES,
-)
-
-tfjs_web_test(
-    name = "tfjs-backend-webgl_uniforms_test",
-    srcs = [
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_test_bundle",
-    ],
-    args = [
-        "--testEnv",
-        "webgl2",
-        "--flags",
-        '{"WEBGL_USE_SHAPES_UNIFORMS": true}',
-    ],
-    browsers = [
-        "bs_chrome_mac",
-    ],
-    headless = False,
-    presubmit_browsers = [],  # Only run in nightly
-    static_files = STATIC_FILES,
-)
-
-tfjs_web_test(
-    name = "tfjs-backend-webgl_exp_conv_test",
-    srcs = [
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_test_bundle",
-    ],
-    args = [
-        "--testEnv",
-        "webgl2",
-        "--flags",
-        '{"WEBGL_EXP_CONV": true}',
-    ],
-    browsers = [
-        "bs_chrome_mac",
-    ],
-    headless = False,
-    presubmit_browsers = [],  # Only run in nightly
-    static_files = STATIC_FILES,
-)
-
-js_library(
-    name = "package_json",
-    srcs = [
-        ":package.json",
-    ],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        ":tfjs-backend-webgl2_test",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-webgl/README.md b/tfjs-master/tfjs-backend-webgl/README.md
deleted file mode 100644
index 489782a8d..000000000
--- a/tfjs-master/tfjs-backend-webgl/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Usage
-
-This package implements a GPU accelerated WebGL backend for TensorFlow.js.
-
-## Importing the backend
-
-Note: this backend is included by default in `@tensorflow/tfjs`.
-
-### Via NPM
-
-```js
-// Import @tensorflow/tfjs-core
-import * as tf from '@tensorflow/tfjs-core';
-// Adds the WebGL backend to the global backend registry.
-import '@tensorflow/tfjs-backend-webgl';
-```
-
-### Via a script tag
-
-```html
-<!-- Import @tensorflow/tfjs-core -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
-
-<!-- Adds the WebGL backend to the global backend registry -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl"></script>
-```
-
-You can also get ES2017 code using the following links
-
-```html
-<!-- Import @tensorflow/tfjs-core -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core@2.0.0-rc.4/dist/tf-core.es2017.js"></script>
-
-<!-- Adds the WebGL backend to the global backend registry -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl@2.0.0-rc.4/dist/tf-backend-webgl.es2017.js"></script>
-```
-
diff --git a/tfjs-master/tfjs-backend-webgl/package.json b/tfjs-master/tfjs-backend-webgl/package.json
deleted file mode 100644
index b7c9d98dd..000000000
--- a/tfjs-master/tfjs-backend-webgl/package.json
+++ /dev/null
@@ -1,59 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-backend-webgl",
-  "version": "0.0.0",
-  "description": "GPU accelerated WebGL backend for TensorFlow.js",
-  "private": false,
-  "main": "dist/tf-backend-webgl.node.js",
-  "jsdelivr": "dist/tf-backend-webgl.min.js",
-  "unpkg": "dist/tf-backend-webgl.min.js",
-  "types": "dist/index.d.ts",
-  "jsnext:main": "dist/index.js",
-  "module": "dist/index.js",
-  "miniprogram": "dist/miniprogram",
-  "engines": {
-    "yarn": ">= 1.3.2"
-  },
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git"
-  },
-  "license": "Apache-2.0",
-  "devDependencies": {
-    "@babel/polyfill": "^7.8.7",
-    "@bazel/bazelisk": "^1.12.0"
-  },
-  "scripts": {
-    "build": "bazel build :tfjs-backend-webgl_pkg",
-    "publish-npm": "bazel run :tfjs-backend-webgl_pkg.publish",
-    "test": "bazel test :tests --test_output=streamed",
-    "test-debug": "bazel run :tests --test_output=streamed",
-    "run-browserstack": "bazel test :browserstack_bs_chrome_mac_tfjs-backend-webgl2_test"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs-backend-cpu": "link:../link-package/node_modules/@tensorflow/tfjs-backend-cpu",
-    "@types/offscreencanvas": "~2019.3.0",
-    "@types/seedrandom": "^2.4.28",
-    "seedrandom": "^3.0.5"
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core"
-  },
-  "browser": {
-    "util": false,
-    "crypto": false
-  },
-  "sideEffects": [
-    "./dist/register_all_kernels.js",
-    "./dist/flags_webgl.js",
-    "./dist/base.js",
-    "./dist/index.js",
-    "./dist/register_all_kernels.mjs",
-    "./dist/flags_webgl.mjs",
-    "./dist/base.mjs",
-    "./dist/index.mjs",
-    "./src/register_all_kernels.mjs",
-    "./src/flags_webgl.mjs",
-    "./src/base.mjs",
-    "./src/index.mjs"
-  ]
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/BUILD.bazel b/tfjs-master/tfjs-backend-webgl/src/BUILD.bazel
deleted file mode 100644
index ea3326295..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/BUILD.bazel
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("//tools:defaults.bzl", "esbuild", "ts_library")
-load("//tools:enumerate_tests.bzl", "enumerate_tests")
-
-package(default_visibility = ["//visibility:public"])
-
-TEST_SRCS = [
-    "**/*_test.ts",
-]
-
-filegroup(
-    name = "all_test_entrypoints",
-    srcs = glob(
-        ["**/*_test.ts"],
-        exclude = [
-            "setup_test.ts",
-        ],
-    ),
-)
-
-# Generates the 'tests.ts' file that imports all test entrypoints.
-enumerate_tests(
-    name = "tests",
-    srcs = [":all_test_entrypoints"],
-    root_path = "tfjs-backend-webgl/src",
-)
-
-ts_library(
-    name = "tfjs-backend-webgl_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-backend-webgl/dist",
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "@npm//@types/offscreencanvas",
-        "@npm//@types/seedrandom",
-    ],
-)
-
-ts_library(
-    name = "tfjs-backend-webgl_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-backend-webgl",
-    deps = [
-        ":tfjs-backend-webgl_src_lib",
-    ],
-)
-
-ts_library(
-    name = "tfjs-backend-webgl_test_lib",
-    testonly = True,
-    srcs = glob(TEST_SRCS) + [":tests"],
-    module_name = "@tensorflow/tfjs-backend-webgl/dist",
-    deps = [
-        ":tfjs-backend-webgl_lib",
-        ":tfjs-backend-webgl_src_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-    ],
-)
-
-esbuild(
-    name = "tfjs-backend-webgl_test_bundle",
-    testonly = True,
-    entry_point = "setup_test.ts",
-    external = [
-        # webworker tests call 'require('@tensorflow/tfjs')', which
-        # is external to the test bundle.
-        # Note: This is not a bazel target. It's just a string.
-        "@tensorflow/tfjs",
-        "worker_threads",
-        "util",
-    ],
-    sources_content = True,
-    deps = [
-        ":tfjs-backend-webgl_lib",
-        ":tfjs-backend-webgl_test_lib",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-webgl/src/addn_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/addn_gpu.ts
deleted file mode 100644
index 190357b55..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/addn_gpu.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class AddNProgram implements GPGPUProgram {
-  variableNames: string[];
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(outputShape: number[], shapes: number[][]) {
-    this.outputShape = outputShape;
-    this.variableNames = shapes.map((_, i) => `T${i}`);
-
-    const snippets: string[] = [];
-    // Get target elements from every input tensor.
-    this.variableNames.forEach(variable => {
-      snippets.push(`float v${variable} = get${variable}AtOutCoords();`);
-    });
-
-    // Calculate the sum of all elements.
-    const operation = this.variableNames
-                          .map(variable => {
-                            return `v${variable}`;
-                          })
-                          .join(' + ');
-
-    this.userCode = `
-      void main() {
-        ${snippets.join('\n        ')}
-
-        float result = ${operation};
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/addn_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/addn_packed_gpu.ts
deleted file mode 100644
index d5d9f4d19..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/addn_packed_gpu.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class AddNPackedProgram implements GPGPUProgram {
-  variableNames: string[];
-  outputShape: number[] = [];
-  userCode: string;
-  packedInputs = true;
-  packedOutput = true;
-
-  constructor(outputShape: number[], shapes: number[][]) {
-    this.outputShape = outputShape;
-    this.variableNames = shapes.map((_, i) => `T${i}`);
-
-    const snippets: string[] = [];
-    // Get target elements from every input tensor.
-    this.variableNames.forEach(variable => {
-      snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);
-    });
-
-    // Calculate the sum of all elements.
-    const operation = this.variableNames
-                          .map(variable => {
-                            return `v${variable}`;
-                          })
-                          .join(' + ');
-
-    this.userCode = `
-      void main() {
-        ${snippets.join('\n        ')}
-
-        vec4 result = ${operation};
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/argminmax_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/argminmax_gpu.ts
deleted file mode 100644
index d3e63c0e6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/argminmax_gpu.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ArgMinMaxProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      reduceInfo: backend_util.ReduceInfo, op: 'max'|'min',
-      firstPass: boolean) {
-    const {windowSize, batchSize, outSize} = reduceInfo;
-    if (!firstPass) {
-      this.variableNames.push('bestIndicesA');
-    }
-    this.outputShape = [batchSize, outSize];
-    const compOp = (op === 'max') ? '>' : '<';
-    const indexSnippet = firstPass ?
-        'inOffset + i;' :
-        'round(getBestIndicesA(batch, inOffset + i));';
-
-    this.userCode = `
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int batch = coords[0];
-        int outIdx = coords[1];
-        int inOffset = outIdx * ${windowSize};
-
-        int bestIndex = inOffset;
-        float bestValue = getA(batch, bestIndex);
-
-        for (int i = 0; i < ${windowSize}; i++) {
-          int inIdx = ${indexSnippet};
-          float candidate = getA(batch, inIdx);
-          if (candidate ${compOp} bestValue) {
-            bestValue = candidate;
-            bestIndex = inIdx;
-          }
-        }
-        setOutput(float(bestIndex));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/argminmax_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/argminmax_packed_gpu.ts
deleted file mode 100644
index 99c66c83f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/argminmax_packed_gpu.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {util} from '@tensorflow/tfjs-core';
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getChannels} from './packing_util';
-import {getCoordsDataType} from './shader_compiler';
-
-export class ArgMinMaxPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[];
-  userCode: string;
-  packedInputs = true;
-  packedOutput = true;
-
-  constructor(
-      shape: number[], windowSize: number, op: 'max'|'min',
-      firstPass: boolean) {
-    util.assert(
-        shape.length > 2,
-        () => `Packed arg${
-            op.charAt(0).toUpperCase() +
-            op.slice(1)} supports only inputs with rank above 2.`);
-    const inSize = shape[shape.length - 1];
-    const outSize = Math.ceil(inSize / windowSize);
-    this.outputShape = shape.slice(0, -1);
-    if (outSize > 1) {
-      this.outputShape.push(outSize);
-    }
-    if (!firstPass) {
-      this.variableNames.push('bestIndicesA');
-    }
-    const outShape = this.outputShape;
-    const rank = outShape.length;
-    const dtype = getCoordsDataType(rank);
-    const coords = getChannels('coords', rank);
-
-    let sourceLocSetup;
-    let sourceRank;
-    if (outSize === 1) {
-      sourceRank = rank + 1;
-      const sourceLocDType = getCoordsDataType(sourceRank);
-      sourceLocSetup = `
-        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);
-        ++${coords[rank - 1]};
-        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);
-        ++${coords[rank - 2]};
-        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);
-        --${coords[rank - 1]};
-        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);
-        --${coords[rank - 2]};`;
-    } else {
-      sourceRank = rank;
-      sourceLocSetup = `
-        ${dtype} sourceLocR = coords;
-        ++${coords[rank - 1]};
-        ${dtype} sourceLocG = coords;
-        ++${coords[rank - 2]};
-        ${dtype} sourceLocA = coords;
-        --${coords[rank - 1]};
-        ${dtype} sourceLocB = coords;
-        --${coords[rank - 2]};`;
-    }
-    const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);
-    const inChannel = '.' + channels[sourceRank - 1];  // e.g. ".b" for rank 3.
-    const intChannels = channels.map(x => 'int ' + x);
-    const srcRCoords =
-        getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');
-    const srcGCoords =
-        getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');
-    const srcBCoords =
-        getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');
-    const srcACoords =
-        getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');
-
-    const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';
-    const fetchCandidateIdx = firstPass ? '' : `
-          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
-                             getBestIndicesAChannel(${srcGCoords.join()}),
-                             getBestIndicesAChannel(${srcBCoords.join()}),
-                             getBestIndicesAChannel(${srcACoords.join()})));`;
-
-    const fetchValue = `vec4(
-            getAChannel(${srcRCoords.join()}),
-            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
-            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
-            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;
-
-    const getBestIndicesAChannelSnippet = firstPass ? '' : `
-      float getBestIndicesAChannel(${intChannels.join()}) {
-        return getChannel(getBestIndicesA(${channels.join()}),
-                                          vec2(${channels.slice(-2).join()}));
-      }`;
-
-    this.userCode = `
-      float getAChannel(${intChannels.join()}) {
-        return getChannel(getA(${channels.join()}),
-                               vec2(${channels.slice(-2).join()}));
-      }
-      ${getBestIndicesAChannelSnippet}
-      void main() {
-        ${dtype} coords = getOutputCoords();
-        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};
-        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};
-        ${sourceLocSetup}
-        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
-          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
-        ivec4 inIdx = srcIdx;
-        vec4 bestIndex = vec4(inIdx);
-        vec4 bestValue = ${fetchValue};
-
-        for (int i = 0; i < ${windowSize}; i++) {
-          inIdx = srcIdx;
-          ${fetchCandidateIdx}
-          vec4 candidate = ${fetchValue};
-          bvec4 nan = isnan(candidate);
-          bvec4 replace = bvec4(
-            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
-
-          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
-                           replace.y  ? candidate.y : bestValue.y,
-                           replace.z  ? candidate.z : bestValue.z,
-                           replace.w  ? candidate.w : bestValue.w);
-          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
-          srcIdx++;
-        }
-        setOutput(bestIndex);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/avg_pool_backprop_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/avg_pool_backprop_gpu.ts
deleted file mode 100644
index dd542d1fb..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/avg_pool_backprop_gpu.ts
+++ /dev/null
@@ -1,172 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class AvgPool2DBackpropProgram implements GPGPUProgram {
-  variableNames = ['dy'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const dilationHeight = convInfo.dilationHeight;
-    const dilationWidth = convInfo.dilationWidth;
-    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-
-    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
-    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
-
-    const avgMultiplier = 1 / (filterHeight * filterWidth);
-
-    this.userCode = `
-      const ivec2 pads = ivec2(${padTop}, ${padLeft});
-      const float avgMultiplier = float(${avgMultiplier});
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int d = coords[3];
-
-        ivec2 dyRCCorner = coords.yz - pads;
-        int dyRCorner = dyRCCorner.x;
-        int dyCCorner = dyRCCorner.y;
-
-        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
-        // ? = to be determined. : = across all values in that axis.
-        float dotProd = 0.0;
-        for (int wR = 0; wR < ${effectiveFilterHeight};
-            wR += ${dilationHeight}) {
-          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
-
-          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
-            continue;
-          }
-          int idyR = int(dyR);
-
-          for (int wC = 0; wC < ${effectiveFilterWidth};
-            wC+= ${dilationWidth}) {
-            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
-
-            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
-                fract(dyC) > 0.0) {
-              continue;
-            }
-            int idyC = int(dyC);
-
-            float dyValue = getDy(b, idyR, idyC, d);
-
-            dotProd += dyValue * avgMultiplier;
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
-
-export class AvgPool3DBackpropProgram implements GPGPUProgram {
-  variableNames = ['dy'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.inShape;
-    const filterDepth = convInfo.filterDepth;
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-    const strideDepth = convInfo.strideDepth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const dilationDepth = convInfo.dilationDepth;
-    const dilationHeight = convInfo.dilationHeight;
-    const dilationWidth = convInfo.dilationWidth;
-    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
-    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-
-    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
-    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
-    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
-
-    const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
-
-    this.userCode = `
-      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
-      const float avgMultiplier = float(${avgMultiplier});
-
-      void main() {
-        ivec5 coords = getOutputCoords();
-        int batch = coords.x;
-        int ch = coords.u;
-
-        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
-        int dyDCorner = dyCorner.x;
-        int dyRCorner = dyCorner.y;
-        int dyCCorner = dyCorner.z;
-
-        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
-        // dx(xD, xR, xC, ch).
-        // ? = to be determined. : = across all values in that axis.
-        float dotProd = 0.0;
-
-        for (int wD = 0; wD < ${effectiveFilterDepth};
-            wD += ${dilationDepth}) {
-          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
-
-          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
-            continue;
-          }
-          int idyD = int(dyD);
-
-          for (int wR = 0; wR < ${effectiveFilterHeight};
-              wR += ${dilationHeight}) {
-            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
-
-            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
-                fract(dyR) > 0.0) {
-              continue;
-            }
-            int idyR = int(dyR);
-
-            for (int wC = 0; wC < ${effectiveFilterWidth};
-                wC += ${dilationWidth}) {
-              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
-
-              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
-                  fract(dyC) > 0.0) {
-                continue;
-              }
-              int idyC = int(dyC);
-
-              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
-
-              dotProd += dyValue * avgMultiplier;
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/backend_webgl.ts b/tfjs-master/tfjs-backend-webgl/src/backend_webgl.ts
deleted file mode 100644
index ecbf01354..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/backend_webgl.ts
+++ /dev/null
@@ -1,1346 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Import webgl flags.
-import './flags_webgl';
-
-import * as tf from '@tensorflow/tfjs-core';
-import {backend_util, BackendValues, buffer, DataId, DataStorage, DataToGPUWebGLOption, DataType, engine, env, GPUData, kernel_impls, KernelBackend, MemoryInfo, nextFrame, NumericDataType, Rank, RecursiveArray, scalar, ShapeMap, Tensor, Tensor2D, TensorBuffer, TensorInfo, tidy, TimingInfo, TypedArray, util, WebGLData} from '@tensorflow/tfjs-core';
-import {getWebGLContext} from './canvas_util';
-import {DecodeMatrixProgram} from './decode_matrix_gpu';
-import {DecodeMatrixPackedProgram} from './decode_matrix_packed_gpu';
-import {EncodeFloatProgram} from './encode_float_gpu';
-import {EncodeFloatPackedProgram} from './encode_float_packed_gpu';
-import {EncodeMatrixProgram} from './encode_matrix_gpu';
-import {EncodeMatrixPackedProgram} from './encode_matrix_packed_gpu';
-import {GPGPUContext} from './gpgpu_context';
-import * as gpgpu_math from './gpgpu_math';
-import {getUniformLocations, GPGPUBinary, GPGPUProgram, TensorData} from './gpgpu_math';
-import {simpleAbsImplCPU} from './kernel_utils/shared';
-import {PackProgram} from './pack_gpu';
-import {ReshapePackedProgram} from './reshape_packed_gpu';
-import * as tex_util from './tex_util';
-import {Texture, TextureData, TextureUsage} from './tex_util';
-import {TextureManager} from './texture_manager';
-import * as unary_op from './unaryop_gpu';
-import {UnaryOpProgram} from './unaryop_gpu';
-import {UnaryOpPackedProgram} from './unaryop_packed_gpu';
-import {UnpackProgram} from './unpack_gpu';
-import * as webgl_util from './webgl_util';
-
-const whereImpl = kernel_impls.whereImpl;
-
-export const EPSILON_FLOAT32 = 1e-7;
-export const EPSILON_FLOAT16 = 1e-4;
-
-type KernelInfo = {
-  name: string; query: Promise<number>;
-};
-
-export type TimerNode = RecursiveArray<KernelInfo>|KernelInfo;
-export interface CPUTimerQuery {
-  startMs: number;
-  endMs?: number;
-}
-
-export interface WebGLMemoryInfo extends MemoryInfo {
-  numBytesInGPU: number;
-  // Tracks the total number of bytes allocated on the GPU, accounting for the
-  // physical texture type.
-  numBytesInGPUAllocated: number;
-  // Tracks byte size of textures that were created and then made available for
-  // reuse (disposed).
-  numBytesInGPUFree: number;
-  unreliable: boolean;
-}
-
-export interface WebGLTimingInfo extends TimingInfo {
-  uploadWaitMs: number;
-  downloadWaitMs: number;
-}
-
-const binaryCaches: {[webGLVersion: string]: {[key: string]: GPGPUBinary}} = {};
-
-export function getBinaryCache(webGLVersion: number) {
-  if (webGLVersion in binaryCaches) {
-    return binaryCaches[webGLVersion];
-  }
-  binaryCaches[webGLVersion] = {};
-  return binaryCaches[webGLVersion];
-}
-
-// Empirically determined constant used to determine size threshold for handing
-// off execution to the CPU.
-const CPU_HANDOFF_SIZE_THRESHOLD =
-    env().getNumber('CPU_HANDOFF_SIZE_THRESHOLD');
-
-// Empirically determined constant used to decide the number of MB on GPU
-// before we warn about high memory use. The MB are this constant * screen area
-// * dpi / 1024 / 1024.
-const BEFORE_PAGING_CONSTANT = 600;
-function numMBBeforeWarning(): number {
-  if (env().global.screen == null) {
-    return 1024;  // 1 GB.
-  }
-  return (env().global.screen.height * env().global.screen.width *
-          window.devicePixelRatio) *
-      BEFORE_PAGING_CONSTANT / 1024 / 1024;
-}
-
-export class MathBackendWebGL extends KernelBackend {
-  texData: DataStorage<TextureData>;
-  gpgpu: GPGPUContext;
-
-  private static nextDataId = 0;
-  private nextDataId(): number {
-    return MathBackendWebGL.nextDataId++;
-  }
-  // Maps data ids that have a pending read operation, to list of subscribers.
-  private pendingRead = new WeakMap<DataId, Array<(arr: TypedArray) => void>>();
-  // List of data ids that are scheduled for disposal, but are waiting on a
-  // pending read operation.
-  private pendingDisposal = new WeakSet<DataId>();
-
-  // Used to count the number of 'shallow' sliced tensors that point to the
-  // same data id.
-  dataRefCount = new WeakMap<DataId, number>();
-  private numBytesInGPU = 0;
-
-  private canvas: HTMLCanvasElement|OffscreenCanvas;
-
-  private programTimersStack: TimerNode[];
-  private activeTimers: TimerNode[];
-  // Accumulated time spent (including blocking) in uploading data to webgl.
-  private uploadWaitMs = 0;
-  // Accumulated time spent (including blocking in downloading data from webgl.
-  private downloadWaitMs = 0;
-
-  // record the last manual GL Flush time.
-  private lastGlFlushTime = 0;
-
-  // Number of bits of precision of this backend.
-  private floatPrecisionValue: 32|16;
-
-  private textureManager: TextureManager;
-  private binaryCache: {[key: string]: GPGPUBinary};
-  private gpgpuCreatedLocally: boolean;
-  private numMBBeforeWarning: number;
-  private warnedAboutMemory = false;
-
-  constructor(gpuResource?: GPGPUContext|HTMLCanvasElement|OffscreenCanvas) {
-    super();
-    if (!env().getBool('HAS_WEBGL')) {
-      throw new Error('WebGL is not supported on this device');
-    }
-
-    let newGPGPU;
-    if (gpuResource != null) {
-      if (gpuResource instanceof GPGPUContext) {
-        newGPGPU = gpuResource;
-      } else {
-        const gl =
-            getWebGLContext(env().getNumber('WEBGL_VERSION'), gpuResource);
-        newGPGPU = new GPGPUContext(gl);
-      }
-      this.binaryCache = {};
-      this.gpgpuCreatedLocally = false;
-    } else {
-      const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));
-      newGPGPU = new GPGPUContext(gl);
-      this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));
-      this.gpgpuCreatedLocally = true;
-    }
-
-    this.gpgpu = newGPGPU;
-    this.canvas = this.gpgpu.gl.canvas;
-    this.textureManager = new TextureManager(this.gpgpu);
-    this.numMBBeforeWarning = numMBBeforeWarning();
-    this.texData = new DataStorage(this, engine());
-  }
-
-  override numDataIds() {
-    return this.texData.numDataIds() - this.pendingDeletes;
-  }
-
-  // Writes a new entry to the data store with a WebGL texture, and registers it
-  // to the texture manager.
-  writeTexture(
-      texture: WebGLTexture, shape: number[], dtype: DataType,
-      texHeight: number, texWidth: number, channels: string): DataId {
-    // Temporarily create an tensor info to make the texture compatible with
-    // the runWebGLProgram's input.
-    const input = this.makeTensorInfo(shape, dtype);
-    const inData = this.texData.get(input.dataId);
-    // Even though the input texture could be unpacked or dense packed, it is
-    // always considered as unpacked for EncodeMatrixProgram.
-    inData.isPacked = false;
-
-    // Bind texture to the input tensor.
-    inData.texture = {texture, texShape: [texHeight, texWidth]};
-    inData.texShape = [texHeight, texWidth];
-
-    const shapeAs3D = webgl_util.getShapeAs3D(shape);
-    const program =
-        new EncodeMatrixProgram(shapeAs3D, false /* isByteArray */, channels);
-    const output =
-        this.runWebGLProgram(program, [input], dtype, [[texHeight, texWidth]]);
-    output.shape = shape;
-
-    // Unbind the texture from the input tensor to avoid the texture being
-    // released.
-    inData.texture = null;
-    this.disposeIntermediateTensorInfo(input);
-
-    return output.dataId;
-  }
-
-  override write(values: BackendValues, shape: number[], dtype: DataType):
-      DataId {
-    if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||
-        env().getBool('DEBUG')) {
-      this.checkNumericalProblems(values);
-    }
-    if (dtype === 'complex64' && values != null) {
-      throw new Error(
-          `Cannot write to a complex64 dtype. ` +
-          `Please use tf.complex(real, imag).`);
-    }
-    const dataId = {id: this.nextDataId()};
-    this.texData.set(
-        dataId,
-        {shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1});
-    return dataId;
-  }
-
-  /** Return refCount of a `TensorData`. */
-  override refCount(dataId: DataId): number {
-    if (this.texData.has(dataId)) {
-      const tensorData = this.texData.get(dataId);
-      return tensorData.refCount;
-    }
-    return 0;
-  }
-
-  /** Increase refCount of a `TextureData`. */
-  override incRef(dataId: DataId): void {
-    const texData = this.texData.get(dataId);
-    texData.refCount++;
-  }
-
-  /** Decrease refCount of a `TextureData`. */
-  decRef(dataId: DataId): void {
-    if (this.texData.has(dataId)) {
-      const texData = this.texData.get(dataId);
-      texData.refCount--;
-    }
-  }
-
-  override move(
-      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,
-      refCount: number): void {
-    if (env().getBool('DEBUG')) {
-      this.checkNumericalProblems(values);
-    }
-    if (dtype === 'complex64') {
-      throw new Error(
-          `Cannot write to a complex64 dtype. ` +
-          `Please use tf.complex(real, imag).`);
-    }
-    this.texData.set(
-        dataId, {shape, dtype, values, usage: TextureUsage.UPLOAD, refCount});
-  }
-
-  disposeIntermediateTensorInfo(tensorInfo: TensorInfo): void {
-    this.disposeData(tensorInfo.dataId);
-  }
-
-  override readSync(dataId: DataId): BackendValues {
-    const texData = this.texData.get(dataId);
-    const {values, dtype, complexTensorInfos, slice, shape, isPacked} = texData;
-
-    // The presence of `slice` indicates this tensor is a shallow slice of a
-    // different tensor, and is using that original tensor's texture. Run
-    // `clone` in order to copy that texture and read from it.
-    if (slice != null) {
-      let program;
-      if (isPacked) {
-        program = new UnaryOpPackedProgram(shape, unary_op.CLONE);
-      } else {
-        program = new UnaryOpProgram(shape, unary_op.CLONE);
-      }
-      const res =
-          this.runWebGLProgram(program, [{dataId, shape, dtype}], dtype);
-      const data = this.readSync(res.dataId);
-      this.disposeIntermediateTensorInfo(res);
-      return data;
-    }
-    if (values != null) {
-      return this.convertAndCacheOnCPU(dataId);
-    }
-    if (dtype === 'string') {
-      return values;
-    }
-    const shouldTimeProgram = this.activeTimers != null;
-    let start: number;
-    if (shouldTimeProgram) {
-      start = util.now();
-    }
-
-    let result: Float32Array;
-    if (dtype === 'complex64') {
-      const realValues =
-          this.readSync(complexTensorInfos.real.dataId) as Float32Array;
-      const imagValues =
-          this.readSync(complexTensorInfos.imag.dataId) as Float32Array;
-      result = backend_util.mergeRealAndImagArrays(realValues, imagValues);
-    } else {
-      result = this.getValuesFromTexture(dataId);
-    }
-
-    if (shouldTimeProgram) {
-      this.downloadWaitMs += util.now() - start;
-    }
-    return this.convertAndCacheOnCPU(dataId, result);
-  }
-
-  override async read(dataId: DataId): Promise<BackendValues> {
-    if (this.pendingRead.has(dataId)) {
-      const subscribers = this.pendingRead.get(dataId);
-      return new Promise<TypedArray>(resolve => subscribers.push(resolve));
-    }
-    const texData = this.texData.get(dataId);
-    const {values, shape, slice, dtype, complexTensorInfos, isPacked} = texData;
-
-    // The presence of `slice` indicates this tensor is a shallow slice of a
-    // different tensor, and is using that original tensor's texture. Run
-    // `clone` in order to copy that texture and read from it.
-    if (slice != null) {
-      let program;
-      if (isPacked) {
-        program = new UnaryOpPackedProgram(shape, unary_op.CLONE);
-      } else {
-        program = new UnaryOpProgram(shape, unary_op.CLONE);
-      }
-      const res =
-          this.runWebGLProgram(program, [{dataId, shape, dtype}], dtype);
-      const data = this.read(res.dataId);
-      this.disposeIntermediateTensorInfo(res);
-      return data;
-    }
-
-    if (values != null) {
-      return this.convertAndCacheOnCPU(dataId);
-    }
-
-    if (env().getBool('DEBUG')) {
-      // getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') caused a blocking GPU call.
-      // For performance reason, only check it for debugging. In production,
-      // it doesn't handle this use case anyway, so behavior is not changed.
-      if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&
-          env().getNumber('WEBGL_VERSION') === 2) {
-        throw new Error(
-            `tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +
-            `WEBGL_VERSION=2 not yet supported.`);
-      }
-    }
-
-    let buffer: WebGLBuffer = null;
-    let tmpDownloadTarget: TensorInfo;
-
-    if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {
-      // Possibly copy the texture into a buffer before inserting a fence.
-      tmpDownloadTarget = this.decode(dataId);
-      const tmpData = this.texData.get(tmpDownloadTarget.dataId);
-
-      buffer = this.gpgpu.createBufferFromTexture(
-          tmpData.texture.texture, ...tex_util.getDenseTexShape(shape));
-    }
-
-    this.pendingRead.set(dataId, []);
-
-    if (dtype !== 'complex64') {
-      // Create a fence and wait for it to resolve.
-      await this.gpgpu.createAndWaitForFence();
-    }
-
-    // Download the values from the GPU.
-    let vals: Float32Array;
-    if (dtype === 'complex64') {
-      const ps = await Promise.all([
-        this.read(complexTensorInfos.real.dataId),
-        this.read(complexTensorInfos.imag.dataId)
-      ]);
-
-      const realValues = ps[0];
-      const imagValues = ps[1];
-      vals = backend_util.mergeRealAndImagArrays(
-          realValues as Float32Array, imagValues as Float32Array);
-    } else if (buffer == null) {
-      vals = this.getValuesFromTexture(dataId);
-    } else {
-      const size = util.sizeFromShape(shape);
-      vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);
-    }
-    if (tmpDownloadTarget != null) {
-      this.disposeIntermediateTensorInfo(tmpDownloadTarget);
-    }
-    if (buffer != null) {
-      const gl = this.gpgpu.gl;
-      webgl_util.callAndCheck(gl, () => gl.deleteBuffer(buffer));
-    }
-    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
-
-    const subscribers = this.pendingRead.get(dataId);
-    this.pendingRead.delete(dataId);
-
-    // Notify all pending reads.
-    subscribers.forEach(resolve => resolve(dTypeVals));
-    if (this.pendingDisposal.has(dataId)) {
-      this.pendingDisposal.delete(dataId);
-      if (this.disposeData(dataId)) {
-        engine().removeDataId(dataId, this);
-      }
-      this.pendingDeletes--;
-    }
-    return dTypeVals;
-  }
-
-  /**
-   * Read tensor to a new texture that is densely packed for ease of use.
-   * @param dataId The source tensor.
-   * @param options
-   *     customTexShape: Optional. If set, will use the user defined texture
-   *     shape to create the texture.
-   */
-  override readToGPU(dataId: DataId, options: DataToGPUWebGLOption = {}):
-      GPUData {
-    const texData = this.texData.get(dataId);
-    const {values, shape, slice, dtype, isPacked, texture} = texData;
-
-    if (dtype === 'complex64') {
-      throw new Error('Does not support reading texture for complex64 dtype.');
-    }
-
-    // The presence of `slice` indicates this tensor is a shallow slice of a
-    // different tensor, and is using that original tensor's texture. Run
-    // `clone` in order to copy that texture and read from it.
-    if (slice != null) {
-      let program;
-      if (isPacked) {
-        program = new UnaryOpPackedProgram(shape, unary_op.CLONE);
-      } else {
-        program = new UnaryOpProgram(shape, unary_op.CLONE);
-      }
-      const res =
-          this.runWebGLProgram(program, [{dataId, shape, dtype}], dtype);
-      const gpuResouorce = this.readToGPU(res, options);
-      this.disposeIntermediateTensorInfo(res);
-      return gpuResouorce;
-    }
-
-    if (texture == null) {
-      if (values != null) {
-        throw new Error('Data is not on GPU but on CPU.');
-      } else {
-        throw new Error('There is no data on GPU or CPU.');
-      }
-    }
-
-    // Decode the texture so that it is stored densely (using four channels).
-    const tmpTarget = this.decode(dataId, options.customTexShape);
-
-    // Make engine track this tensor, so that we can dispose it later.
-    const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget);
-
-    const tmpData = this.texData.get(tmpTarget.dataId);
-    return {tensorRef, ...tmpData.texture};
-  }
-
-  bufferSync<R extends Rank, D extends DataType>(t: TensorInfo):
-      TensorBuffer<R, D> {
-    const data = this.readSync(t.dataId);
-    if (t.dtype === 'string') {
-      try {
-        // Decode the bytes into string.
-        const strings = (data as Uint8Array[]).map(d => util.decodeString(d));
-        return buffer(t.shape as ShapeMap[R], t.dtype, strings) as
-            TensorBuffer<R, D>;
-      } catch {
-        throw new Error('Failed to decode encoded string bytes into utf-8');
-      }
-    }
-    return buffer(t.shape as ShapeMap[R], t.dtype, data as TypedArray) as
-        TensorBuffer<R, D>;
-  }
-
-  private checkNumericalProblems(values: BackendValues): void {
-    if (values == null) {
-      return;
-    }
-    for (let i = 0; i < values.length; i++) {
-      const num = values[i] as number;
-      if (!webgl_util.canBeRepresented(num)) {
-        if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {
-          throw Error(
-              `The value ${num} cannot be represented with your ` +
-              `current settings. Consider enabling float32 rendering: ` +
-              `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);
-        }
-        throw Error(`The value ${num} cannot be represented on this device.`);
-      }
-    }
-  }
-
-  private getValuesFromTexture(dataId: DataId): Float32Array {
-    const {shape, dtype, isPacked} = this.texData.get(dataId);
-    const size = util.sizeFromShape(shape);
-    if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {
-      const tmpTarget = this.decode(dataId);
-      const tmpData = this.texData.get(tmpTarget.dataId);
-      const vals =
-          this.gpgpu
-              .downloadMatrixFromPackedTexture(
-                  tmpData.texture.texture, ...tex_util.getDenseTexShape(shape))
-              .subarray(0, size);
-
-      this.disposeIntermediateTensorInfo(tmpTarget);
-
-      return vals;
-    }
-
-    const shouldUsePackedProgram =
-        env().getBool('WEBGL_PACK') && isPacked === true;
-    const outputShape =
-        shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;
-    const program = shouldUsePackedProgram ?
-        new EncodeFloatPackedProgram(outputShape as [number, number, number]) :
-        new EncodeFloatProgram(outputShape);
-    const output = this.runWebGLProgram(
-        program, [{shape: outputShape, dtype, dataId}], 'float32');
-    const tmpData = this.texData.get(output.dataId);
-    const vals = this.gpgpu
-                     .downloadByteEncodedFloatMatrixFromOutputTexture(
-                         tmpData.texture.texture, tmpData.texShape[0],
-                         tmpData.texShape[1])
-                     .subarray(0, size);
-    this.disposeIntermediateTensorInfo(output);
-
-    return vals;
-  }
-
-  override timerAvailable(): boolean {
-    return env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0;
-  }
-
-  override time(f: () => void): Promise<WebGLTimingInfo> {
-    const oldActiveTimers = this.activeTimers;
-    const newActiveTimers: TimerNode[] = [];
-
-    let outerMostTime = false;
-    if (this.programTimersStack == null) {
-      this.programTimersStack = newActiveTimers;
-      outerMostTime = true;
-    } else {
-      this.activeTimers.push(newActiveTimers);
-    }
-    this.activeTimers = newActiveTimers;
-
-    f();
-
-    // needing to split these up because util.flatten only accepts certain types
-    const flattenedActiveTimerQueries =
-        util.flatten(this.activeTimers.map((d: KernelInfo) => d.query))
-            .filter(d => d != null);
-    const flattenedActiveTimerNames =
-        util.flatten(this.activeTimers.map((d: KernelInfo) => d.name))
-            .filter(d => d != null);
-
-    this.activeTimers = oldActiveTimers;
-
-    if (outerMostTime) {
-      this.programTimersStack = null;
-    }
-
-    const res: WebGLTimingInfo = {
-      uploadWaitMs: this.uploadWaitMs,
-      downloadWaitMs: this.downloadWaitMs,
-      kernelMs: null,
-      wallMs: null  // will be filled by the engine
-    };
-
-    return (async () => {
-      if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') >
-          0) {
-        const kernelMs = await Promise.all(flattenedActiveTimerQueries);
-
-        res['kernelMs'] = util.sum(kernelMs);
-        res['getExtraProfileInfo'] = () =>
-            kernelMs
-                .map((d, i) => ({name: flattenedActiveTimerNames[i], ms: d}))
-                .map(d => `${d.name}: ${d.ms}`)
-                .join(', ');
-      } else {
-        res['kernelMs'] = {
-          error: 'WebGL query timers are not supported in this environment.'
-        };
-      }
-
-      this.uploadWaitMs = 0;
-      this.downloadWaitMs = 0;
-      return res;
-    })();
-  }
-  override memory(): WebGLMemoryInfo {
-    return {
-      unreliable: false,
-      numBytesInGPU: this.numBytesInGPU,
-      numBytesInGPUAllocated: this.textureManager.numBytesAllocated,
-      numBytesInGPUFree: this.textureManager.numBytesFree
-    } as WebGLMemoryInfo;
-  }
-
-  private startTimer(): WebGLQuery|CPUTimerQuery {
-    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {
-      return this.gpgpu.beginQuery();
-    }
-    return {startMs: util.now(), endMs: null};
-  }
-
-  private endTimer(query: WebGLQuery|CPUTimerQuery): WebGLQuery|CPUTimerQuery {
-    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {
-      this.gpgpu.endQuery();
-      return query;
-    }
-    (query as CPUTimerQuery).endMs = util.now();
-    return query;
-  }
-
-  private async getQueryTime(query: WebGLQuery|CPUTimerQuery): Promise<number> {
-    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {
-      return this.gpgpu.waitForQueryAndGetTime(query as WebGLQuery);
-    }
-    const timerQuery = query as CPUTimerQuery;
-    return timerQuery.endMs - timerQuery.startMs;
-  }
-
-  private pendingDeletes = 0;
-
-  /**
-   * Decrease the RefCount on the dataId and dispose the memory if the dataId
-   * has 0 refCount. If there are pending read on the data, the disposal would
-   * added to the pending delete queue. Return true if the dataId is removed
-   * from backend or the backend does not contain the dataId, false if the
-   * dataId is not removed. Memory may or may not be released even when dataId
-   * is removed, which also depends on dataRefCount, see `releaseGPU`.
-   * @param dataId
-   * @oaram force Optional, remove the data regardless of refCount
-   */
-  override disposeData(dataId: DataId, force = false): boolean {
-    if (this.pendingDisposal.has(dataId)) {
-      return false;
-    }
-
-    // No-op if already disposed.
-    if (!this.texData.has(dataId)) {
-      return true;
-    }
-
-    // if force flag is set, change refCount to 0, this would ensure disposal
-    // when added to the pendingDisposal queue. Memory may or may not be
-    // released, which also depends on dataRefCount, see `releaseGPU`.
-    if (force) {
-      this.texData.get(dataId).refCount = 0;
-    } else {
-      this.texData.get(dataId).refCount--;
-    }
-
-    if (!force && this.texData.get(dataId).refCount > 0) {
-      return false;
-    }
-
-    if (this.pendingRead.has(dataId)) {
-      this.pendingDisposal.add(dataId);
-      this.pendingDeletes++;
-      return false;
-    }
-
-    this.releaseGPUData(dataId);
-    const {complexTensorInfos} = this.texData.get(dataId);
-    if (complexTensorInfos != null) {
-      this.disposeData(complexTensorInfos.real.dataId, force);
-      this.disposeData(complexTensorInfos.imag.dataId, force);
-    }
-
-    this.texData.delete(dataId);
-
-    return true;
-  }
-
-  private releaseGPUData(dataId: DataId): void {
-    const {texture, dtype, texShape, usage, isPacked, slice} =
-        this.texData.get(dataId);
-    const key = slice && slice.origDataId || dataId;
-    const refCount = this.dataRefCount.get(key);
-
-    if (refCount > 1) {
-      this.dataRefCount.set(key, refCount - 1);
-    } else {
-      this.dataRefCount.delete(key);
-      if (texture != null) {
-        this.numBytesInGPU -= this.computeBytes(texShape, dtype);
-        this.textureManager.releaseTexture(texture, texShape, usage, isPacked);
-      }
-    }
-
-    const texData = this.texData.get(dataId);
-    texData.texture = null;
-    texData.texShape = null;
-    texData.isPacked = false;
-    texData.slice = null;
-  }
-
-  getTexture(dataId: DataId): WebGLTexture {
-    this.uploadToGPU(dataId);
-    return this.texData.get(dataId).texture.texture;
-  }
-
-  /**
-   * Returns internal information for the specific data bucket. Used in unit
-   * tests.
-   */
-  getDataInfo(dataId: DataId): TextureData {
-    return this.texData.get(dataId);
-  }
-
-  /*
-  Tests whether all the inputs to an op are small and on the CPU. This heuristic
-  determines when it would be faster to execute a kernel on the CPU. WebGL
-  kernels opt into running this check and forwarding when appropriate.
-  TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more
-  sustainable strategy for optimizing backend execution of ops.
-   */
-  shouldExecuteOnCPU(
-      inputs: TensorInfo[],
-      sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD): boolean {
-    return env().getBool('WEBGL_CPU_FORWARD') &&
-        inputs.every(
-            input => this.texData.get(input.dataId).texture == null &&
-                util.sizeFromShape(input.shape) < sizeThreshold);
-  }
-
-  getGPGPUContext(): GPGPUContext {
-    return this.gpgpu;
-  }
-
-  where(condition: Tensor): Tensor2D {
-    backend_util.warn(
-        'tf.where() in webgl locks the UI thread. ' +
-        'Call tf.whereAsync() instead');
-    const condVals = condition.dataSync();
-    return whereImpl(condition.shape, condVals);
-  }
-
-  private packedUnaryOp(x: TensorInfo, op: string, dtype: DataType) {
-    const program = new UnaryOpPackedProgram(x.shape, op);
-    const outInfo = this.compileAndRun(program, [x], dtype);
-    return engine().makeTensorFromTensorInfo(outInfo);
-  }
-
-  // TODO(msoulanille) remove this once the backend has been modularized
-  // a copy is needed here to break a circular dependency.
-  // Also remove the op from unary_op.
-  abs<T extends Tensor>(x: T): T {
-    // TODO: handle cases when x is complex.
-    if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {
-      const outValues =
-          simpleAbsImplCPU(this.texData.get(x.dataId).values as TypedArray);
-      return this.makeOutput(x.shape, x.dtype, outValues);
-    }
-
-    if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
-      return this.packedUnaryOp(x, unary_op.ABS, x.dtype) as T;
-    }
-
-    const program = new UnaryOpProgram(x.shape, unary_op.ABS);
-    const outInfo = this.compileAndRun(program, [x]);
-    return engine().makeTensorFromTensorInfo(outInfo) as T;
-  }
-
-  makeTensorInfo(
-      shape: number[], dtype: DataType,
-      values?: BackendValues|string[]): TensorInfo {
-    let dataId;
-    if (dtype === 'string' && values != null && values.length > 0 &&
-        util.isString(values[0])) {
-      const encodedValues =
-          (values as unknown as string[]).map(d => util.encodeString(d));
-
-      dataId = this.write(encodedValues, shape, dtype);
-    } else {
-      dataId = this.write(values as TypedArray, shape, dtype);
-    }
-
-    this.texData.get(dataId).usage = null;
-    return {dataId, shape, dtype};
-  }
-
-  private makeOutput<T extends Tensor>(
-      shape: number[], dtype: DataType, values?: BackendValues): T {
-    return engine().makeTensorFromTensorInfo(
-               this.makeTensorInfo(shape, dtype, values), this) as T;
-  }
-
-  unpackTensor(input: TensorInfo): TensorInfo {
-    const program = new UnpackProgram(input.shape);
-    return this.runWebGLProgram(program, [input], input.dtype);
-  }
-
-  packTensor(input: TensorInfo): TensorInfo {
-    const program = new PackProgram(input.shape);
-    const preventEagerUnpackingOutput = true;
-    return this.runWebGLProgram(
-        program, [input], input.dtype, null /* customUniformValues */,
-        preventEagerUnpackingOutput);
-  }
-
-  private packedReshape(input: TensorInfo, afterShape: number[]): TensorInfo {
-    const input3DShape = [
-      webgl_util.getBatchDim(input.shape),
-      ...webgl_util.getRowsCols(input.shape)
-    ] as [number, number, number];
-    const input3D: TensorInfo = {
-      dtype: input.dtype,
-      shape: input3DShape,
-      dataId: input.dataId
-    };
-    const afterShapeAs3D = [
-      webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)
-    ] as [number, number, number];
-
-    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
-    const preventEagerUnpackingOfOutput = true;
-    const customValues = [input3DShape];
-    const output = this.runWebGLProgram(
-        program, [input3D], input.dtype, customValues,
-        preventEagerUnpackingOfOutput);
-    return {dataId: output.dataId, shape: afterShape, dtype: output.dtype};
-  }
-
-  private decode(dataId: DataId, customTexShape?: [number, number]):
-      TensorInfo {
-    const texData = this.texData.get(dataId);
-    const {isPacked, shape, dtype} = texData;
-    if (customTexShape != null) {
-      const size = util.sizeFromShape(shape);
-      const texSize = customTexShape[0] * customTexShape[1] * 4;
-      util.assert(
-          size <= texSize,
-          () => 'customTexShape is too small. ' +
-              'Row * Column * 4 should be equal or larger than the ' +
-              'size of the tensor data.');
-    }
-    const shapeAs3D =
-        webgl_util.getShapeAs3D(shape) as [number, number, number];
-    let program;
-    if (isPacked) {
-      program = new DecodeMatrixPackedProgram(shapeAs3D);
-    } else {
-      program = new DecodeMatrixProgram(shapeAs3D);
-    }
-    const preventEagerUnpackingOfOutput = true;
-    const customValues =
-        [customTexShape != null ? customTexShape :
-                                  tex_util.getDenseTexShape(shapeAs3D)];
-    const out = this.runWebGLProgram(
-        program, [{shape: shapeAs3D, dtype, dataId}], dtype, customValues,
-        preventEagerUnpackingOfOutput, customTexShape);
-    return {dtype, shape, dataId: out.dataId};
-  }
-
-  runWebGLProgram(
-      program: GPGPUProgram, inputs: TensorInfo[], outputDtype: DataType,
-      customUniformValues?: number[][], preventEagerUnpackingOfOutput = false,
-      customTexShape?: [number, number]): TensorInfo {
-    const output = this.makeTensorInfo(program.outputShape, outputDtype);
-    const outData = this.texData.get(output.dataId);
-    if (program.packedOutput) {
-      outData.isPacked = true;
-    }
-    if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {
-      const texelShape = customTexShape != null ?
-          customTexShape :
-          tex_util.getDenseTexShape(program.outputShape);
-      // For a densely packed output, we explicitly set texShape
-      // so it doesn't get assigned later according to our typical packing
-      // scheme wherein a single texel can only contain values from adjacent
-      // rows/cols.
-      outData.texShape = texelShape.map(d => d * 2) as [number, number];
-    }
-    if (program.outTexUsage != null) {
-      outData.usage = program.outTexUsage;
-    }
-
-    if (util.sizeFromShape(output.shape) === 0) {
-      // Short-circuit the computation since the result is empty (has 0 in its
-      // shape).
-      outData.values =
-          util.getTypedArrayFromDType(output.dtype as 'float32', 0);
-      return output;
-    }
-
-    const dataToDispose: TensorInfo[] = [];
-    const inputsData: TensorData[] = inputs.map(input => {
-      if (input.dtype === 'complex64') {
-        throw new Error(
-            `GPGPUProgram does not support complex64 input. For complex64 ` +
-            `dtypes, please separate the program into real and imaginary ` +
-            `parts.`);
-      }
-
-      let texData = this.texData.get(input.dataId);
-
-      if (texData.texture == null) {
-        if (!program.packedInputs &&
-            util.sizeFromShape(input.shape) <=
-                env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {
-          // Upload small tensors that live on the CPU as uniforms, not as
-          // textures. Do this only when the environment supports 32bit floats
-          // due to problems when comparing 16bit floats with 32bit floats.
-          // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it
-          // possible for packed shaders to sample from uniforms.
-          return {
-            shape: input.shape,
-            texData: null,
-            isUniform: true,
-            uniformValues: texData.values as TypedArray
-          };
-        }
-
-        // This ensures that if a packed program's inputs have not yet been
-        // uploaded to the GPU, they get uploaded as packed right off the bat.
-        if (program.packedInputs) {
-          texData.isPacked = true;
-          texData.shape = input.shape;
-        }
-      }
-
-      this.uploadToGPU(input.dataId);
-      if (!!texData.isPacked !== !!program.packedInputs) {
-        input = texData.isPacked ? this.unpackTensor(input) :
-                                   this.packTensor(input);
-        dataToDispose.push(input);
-        texData = this.texData.get(input.dataId);
-      } else if (
-          texData.isPacked &&
-          !webgl_util.isReshapeFree(texData.shape, input.shape)) {
-        // This is a special case where a texture exists for a tensor
-        // but the shapes are incompatible (due to packing constraints) because
-        // the tensor did not have a chance to go through the packed reshape
-        // shader. This only happens when we reshape the *same* tensor to form
-        // *distinct* inputs to an op, e.g. dotting a vector with itself. This
-        // case will disappear once packed uploading is the default.
-
-        const savedInput = input;
-        const targetShape = input.shape;
-
-        input.shape = texData.shape;
-        input = this.packedReshape(input as Tensor, targetShape);
-        dataToDispose.push(input);
-        texData = this.texData.get(input.dataId);
-
-        savedInput.shape = targetShape;
-      }
-
-      return {shape: input.shape, texData, isUniform: false};
-    });
-
-    this.uploadToGPU(output.dataId);
-    const outputData:
-        TensorData = {shape: output.shape, texData: outData, isUniform: false};
-    const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);
-    const binary = this.getAndSaveBinary(key, () => {
-      return gpgpu_math.compileProgram(
-          this.gpgpu, program, inputsData, outputData);
-    });
-    const shouldTimeProgram = this.activeTimers != null;
-    let query: WebGLQuery|CPUTimerQuery;
-    if (shouldTimeProgram) {
-      query = this.startTimer();
-    }
-
-    if (!env().get('ENGINE_COMPILE_ONLY')) {
-      gpgpu_math.runProgram(
-          this.gpgpu, binary, inputsData, outputData, customUniformValues);
-    }
-
-    dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));
-
-    if (shouldTimeProgram) {
-      query = this.endTimer(query);
-      this.activeTimers.push(
-          {name: program.constructor.name, query: this.getQueryTime(query)});
-    }
-
-    const glFlushThreshold = env().getNumber('WEBGL_FLUSH_THRESHOLD');
-    // Manually GL flush requested
-    if (glFlushThreshold > 0) {
-      const time = util.now();
-      if ((time - this.lastGlFlushTime) > glFlushThreshold) {
-        this.gpgpu.gl.flush();
-        this.lastGlFlushTime = time;
-      }
-    }
-
-    if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&
-        preventEagerUnpackingOfOutput === false) {
-      const unpacked = this.unpackTensor(output);
-      this.disposeIntermediateTensorInfo(output);
-      return unpacked;
-    }
-    return output;
-  }
-
-  compileAndRun(
-      program: GPGPUProgram, inputs: TensorInfo[], outputDtype?: DataType,
-      customUniformValues?: number[][],
-      preventEagerUnpackingOfOutput = false): TensorInfo {
-    outputDtype = outputDtype || inputs[0].dtype;
-    const outInfo = this.runWebGLProgram(
-        program, inputs, outputDtype, customUniformValues,
-        preventEagerUnpackingOfOutput);
-    return outInfo;
-  }
-
-  private getAndSaveBinary(key: string, getBinary: () => GPGPUBinary):
-      GPGPUBinary {
-    if (!(key in this.binaryCache)) {
-      this.binaryCache[key] = getBinary();
-    }
-    return this.binaryCache[key];
-  }
-
-  getTextureManager(): TextureManager {
-    return this.textureManager;
-  }
-
-  private disposed = false;
-
-  override dispose() {
-    if (this.disposed) {
-      return;
-    }
-    // Avoid disposing the compiled webgl programs during unit testing because
-    // it slows down test execution.
-    if (!env().getBool('IS_TEST')) {
-      const allKeys = Object.keys(this.binaryCache);
-      allKeys.forEach(key => {
-        this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);
-        delete this.binaryCache[key];
-      });
-    }
-    this.textureManager.dispose();
-    if (this.canvas != null &&
-        (typeof (HTMLCanvasElement) !== 'undefined' &&
-         this.canvas instanceof HTMLCanvasElement)) {
-      this.canvas.remove();
-    } else {
-      this.canvas = null;
-    }
-    if (this.gpgpuCreatedLocally) {
-      this.gpgpu.program = null;
-      this.gpgpu.dispose();
-    }
-    this.disposed = true;
-  }
-
-  override floatPrecision(): 16|32 {
-    if (this.floatPrecisionValue == null) {
-      this.floatPrecisionValue = tidy(() => {
-        if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {
-          // Momentarily switching DEBUG flag to false so we don't throw an
-          // error trying to upload a small value.
-          const debugFlag = env().getBool('DEBUG');
-          env().set('DEBUG', false);
-          const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];
-          env().set('DEBUG', debugFlag);
-
-          if (underflowCheckValue > 0) {
-            return 32;
-          }
-        }
-        return 16;
-      });
-    }
-    return this.floatPrecisionValue;
-  }
-
-  /** Returns the smallest representable number.  */
-  override epsilon(): number {
-    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
-  }
-
-  uploadToGPU(dataId: DataId): void {
-    const texData = this.texData.get(dataId);
-    const {shape, dtype, values, texture, usage, isPacked} = texData;
-
-    if (texture != null) {
-      // Array is already on GPU. No-op.
-      return;
-    }
-    const shouldTimeProgram = this.activeTimers != null;
-    let start: number;
-    if (shouldTimeProgram) {
-      start = util.now();
-    }
-
-    let texShape = texData.texShape;
-    if (texShape == null) {
-      // This texShape may not be the final texture shape. For packed or dense
-      // textures, the texShape will be changed when textures are created.
-      texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);
-      texData.texShape = texShape;
-    }
-
-    if (values != null) {
-      const shapeAs3D = webgl_util.getShapeAs3D(shape);
-
-      let program;
-      let width = texShape[1], height = texShape[0];
-      const isByteArray =
-          values instanceof Uint8Array || values instanceof Uint8ClampedArray;
-
-      // texture for float array is PhysicalTextureType.PACKED_2X2_FLOAT32, we
-      // need to make sure the upload uses the same packed size
-      if (isPacked || !isByteArray) {
-        [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(
-            texShape[0], texShape[1]);
-      }
-
-      if (isPacked) {
-        program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray);
-      } else {
-        program = new EncodeMatrixProgram(shapeAs3D, isByteArray);
-      }
-
-      // TexShape for float array needs to be the original shape, which byte
-      // array needs to be packed size. This allow the data upload shape to be
-      // matched with texture creation logic.
-      const tempDenseInputTexShape: [number, number] =
-          isByteArray ? [height, width] : texShape;
-      const tempDenseInputHandle =
-          this.makeTensorInfo(tempDenseInputTexShape, dtype);
-      const tempDenseInputTexData =
-          this.texData.get(tempDenseInputHandle.dataId);
-      if (isByteArray) {
-        tempDenseInputTexData.usage = TextureUsage.PIXELS;
-      } else {
-        tempDenseInputTexData.usage = TextureUsage.UPLOAD;
-      }
-      tempDenseInputTexData.texShape = tempDenseInputTexShape;
-      this.gpgpu.uploadDenseMatrixToTexture(
-          this.getTexture(tempDenseInputHandle.dataId), width, height,
-          values as TypedArray);
-
-      const customValues = [[height, width]];
-      // We want the output to remain packed regardless of the value of
-      // WEBGL_PACK.
-      const preventEagerUnpacking = true;
-      const encodedOutputTarget = this.runWebGLProgram(
-          program, [tempDenseInputHandle], dtype, customValues,
-          preventEagerUnpacking);
-
-      // Have the original texture assume the identity of the encoded output.
-      const outputTexData = this.texData.get(encodedOutputTarget.dataId);
-      texData.texShape = outputTexData.texShape;
-      texData.isPacked = outputTexData.isPacked;
-      texData.usage = outputTexData.usage;
-
-      if (!env().get('ENGINE_COMPILE_ONLY')) {
-        texData.texture = outputTexData.texture;
-        // Once uploaded, don't store the values on cpu.
-        texData.values = null;
-        this.texData.delete(encodedOutputTarget.dataId);
-      } else {
-        this.disposeData(encodedOutputTarget.dataId);
-      }
-
-      this.disposeIntermediateTensorInfo(tempDenseInputHandle);
-
-      if (shouldTimeProgram) {
-        this.uploadWaitMs += util.now() - start;
-      }
-    } else {
-      const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);
-      texData.texture = newTexture;
-    }
-  }
-
-  private convertAndCacheOnCPU(dataId: DataId, float32Values?: Float32Array):
-      TypedArray {
-    const texData = this.texData.get(dataId);
-    const {dtype} = texData;
-
-    if (float32Values != null) {
-      texData.values = float32ToTypedArray(float32Values, dtype as 'float32');
-    }
-    return texData.values as TypedArray;
-  }
-
-  private acquireTexture(
-      texShape: [number, number], texType: TextureUsage, dtype: DataType,
-      isPacked: boolean): Texture {
-    this.numBytesInGPU += this.computeBytes(texShape, dtype);
-    if (!this.warnedAboutMemory &&
-        this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
-      const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
-      this.warnedAboutMemory = true;
-      console.warn(
-          `High memory usage in GPU: ${mb} MB, ` +
-          `most likely due to a memory leak`);
-    }
-    return this.textureManager.acquireTexture(texShape, texType, isPacked);
-  }
-
-  private computeBytes(shape: [number, number], dtype: DataType) {
-    return shape[0] * shape[1] * util.bytesPerElement(dtype);
-  }
-
-  checkCompileCompletion() {
-    for (const [, binary] of Object.entries(this.binaryCache)) {
-      this.checkCompletion_(binary);
-    }
-  }
-
-  async checkCompileCompletionAsync(): Promise<boolean[]> {
-    const ps = [];
-    if (this.gpgpu.parallelCompilationExtension) {
-      for (const [, binary] of Object.entries(this.binaryCache)) {
-        ps.push(this.checkCompletionAsync_(binary));
-      }
-      return Promise.all(ps);
-    } else {
-      for (const [, binary] of Object.entries(this.binaryCache)) {
-        const p: Promise<boolean> = new Promise((resolve) => {
-          try {
-            this.checkCompletion_(binary);
-            resolve(true);
-          } catch (error) {
-            throw error;
-          }
-        });
-        ps.push(p);
-      }
-      return Promise.all(ps);
-    }
-  }
-
-  private async checkCompletionAsync_(binary: GPGPUBinary): Promise<boolean> {
-    if (this.gpgpu.gl.getProgramParameter(
-            binary.webGLProgram,
-            this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) {
-      return this.checkCompletion_(binary);
-    } else {
-      await nextFrame();
-      return this.checkCompletionAsync_(binary);
-    }
-  }
-
-  private checkCompletion_(binary: GPGPUBinary): boolean {
-    if (this.gpgpu.gl.getProgramParameter(
-            binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) {
-      console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram));
-      if (this.gpgpu.gl.getShaderParameter(
-              binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) {
-        webgl_util.logShaderSourceAndInfoLog(
-            binary.source,
-            this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader));
-        throw new Error('Failed to compile fragment shader.');
-      }
-      throw new Error('Failed to link vertex and fragment shaders.');
-    }
-    return true;
-  }
-
-  getUniformLocations() {
-    for (const binary of Object.values(this.binaryCache)) {
-      // TODO: Iterating through all binaries to build VAOs is supposed to be in
-      // a seperate function, like 'setVaos'. However, to avoid breaking changes
-      // for the users using parallel compile feature now, buildVao is silently
-      // added here.
-      this.gpgpu.buildVao(binary.webGLProgram);
-
-      const {
-        variablesLocations,
-        customUniformLocations,
-        infLoc,
-        nanLoc,
-        outShapeLocation,
-        outShapeStridesLocation,
-        outTexShapeLocation
-      } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram);
-      binary.variablesLocations = variablesLocations;
-      binary.customUniformLocations = customUniformLocations;
-      binary.infLoc = infLoc;
-      binary.nanLoc = nanLoc;
-      binary.outShapeLocation = outShapeLocation;
-      binary.outShapeStridesLocation = outShapeStridesLocation;
-      binary.outTexShapeLocation = outTexShapeLocation;
-    }
-  }
-
-  /**
-   * Create a TF.js tensor out of an existing WebGL texture. A new texture will
-   * be created.
-   */
-  override createTensorFromGPUData(
-      values: WebGLData, shape: number[], dtype: DataType): Tensor {
-    values.channels = values.channels || 'RGBA';
-    const {texture, height, width, channels} = values;
-    const backend = engine().backend as MathBackendWebGL;
-
-    // Have to throw an error, otherwise WebGL just warns and returns wrong
-    // values.
-    if (!backend.gpgpu.gl.isTexture(texture)) {
-      throw new Error(
-          `The texture is invalid. Also, please make sure the texture and ` +
-          `the TFJS WebGL backend are using the same canvas. If you want to ` +
-          `use your own custom canvas, you have to create and use the custom ` +
-          `TFJS WebGL backend created from the canvas through ` +
-          `'new tf.MathBackendWebGL(customCanvas)'.`);
-    }
-
-    const dataId =
-        backend.writeTexture(texture, shape, dtype, height, width, channels);
-    return engine().makeTensorFromDataId(dataId, shape, dtype, backend);
-  }
-}
-
-function float32ToTypedArray<D extends NumericDataType>(
-    a: Float32Array, dtype: D): tf.DataTypeMap[D] {
-  if (dtype === 'float32' || dtype === 'complex64') {
-    return a as tf.DataTypeMap[D];
-  } else if (dtype === 'int32' || dtype === 'bool') {
-    const result = (dtype === 'int32') ? new Int32Array(a.length) :
-                                         new Uint8Array(a.length);
-    for (let i = 0; i < result.length; ++i) {
-      result[i] = Math.round(a[i]);
-    }
-    return result as tf.DataTypeMap[D];
-  } else {
-    throw new Error(`Unknown dtype ${dtype}`);
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/backend_webgl_test.ts b/tfjs-master/tfjs-backend-webgl/src/backend_webgl_test.ts
deleted file mode 100644
index 4587f40e5..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/backend_webgl_test.ts
+++ /dev/null
@@ -1,1529 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {engine, GPUData, test_util, util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-const {expectArraysClose, expectArraysEqual} = test_util;
-const {decodeString} = util;
-
-import {getBinaryCache, MathBackendWebGL, WebGLMemoryInfo, WebGLTimingInfo} from './backend_webgl';
-import {computeBytes} from './texture_manager';
-import {PhysicalTextureType} from './tex_util';
-import {WEBGL_ENVS} from './backend_webgl_test_registry';
-import {GPGPUContext} from './gpgpu_context';
-
-function decodeStrings(bytes: Uint8Array[]): string[] {
-  return bytes.map(b => decodeString(b));
-}
-
-const RENDER_FLOAT32_ENVS = {
-  flags: {'WEBGL_RENDER_FLOAT32_ENABLED': true},
-  predicate: WEBGL_ENVS.predicate
-};
-
-describeWithFlags('forced f16 render', RENDER_FLOAT32_ENVS, () => {
-  beforeEach(() => {
-    tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', false);
-  });
-  afterAll(() => tf.env().reset());
-
-  it('should overflow if larger than 66k', async () => {
-    const a = tf.tensor1d([Math.pow(2, 17)], 'float32');
-    const b = tf.relu(a);
-    const data = await b.data();
-    if (data && data.length === 1 && !isNaN(data[0])) {  // value could be NaN
-      expect(data).toBeLessThan(Math.pow(2, 17));
-    }
-  });
-
-  it('should error in debug mode', () => {
-    // Silence debug warnings.
-    spyOn(console, 'warn');
-
-    const debug = tf.env().get('DEBUG');
-    tf.env().set('DEBUG', true);
-    const a = () => tf.tensor1d([2, Math.pow(2, 17)], 'float32');
-    expect(a).toThrowError();
-    tf.env().set('DEBUG', debug);
-  });
-});
-
-describeWithFlags('lazy packing and unpacking', WEBGL_ENVS, () => {
-  let webglLazilyUnpackFlagSaved: boolean;
-  let webglCpuForwardFlagSaved: boolean;
-
-  beforeAll(() => {
-    webglLazilyUnpackFlagSaved = tf.env().getBool('WEBGL_LAZILY_UNPACK');
-    webglCpuForwardFlagSaved = tf.env().getBool('WEBGL_CPU_FORWARD');
-    tf.env().set('WEBGL_LAZILY_UNPACK', true);
-    tf.env().set('WEBGL_CPU_FORWARD', false);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGL_LAZILY_UNPACK', webglLazilyUnpackFlagSaved);
-    tf.env().set('WEBGL_CPU_FORWARD', webglCpuForwardFlagSaved);
-  });
-
-  it('should not leak memory when lazily unpacking', () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    // c is packed to 1x1 RGBA texture.
-    const c = tf.matMul(a, b);
-
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-    const startNumBytesInGPU = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-
-    const webglPackBinaryOperationsFlagSaved =
-        tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
-    tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', false);
-    // Add will unpack c before the operation to 2
-    tf.add(c, 1);
-    tf.env().set(
-        'WEBGL_PACK_BINARY_OPERATIONS', webglPackBinaryOperationsFlagSaved);
-
-    expect(tf.memory().numBytes - startNumBytes).toEqual(16);
-    expect(tf.memory().numTensors - startNumTensors).toEqual(1);
-    // result is unpacked 2x2 R texture.
-    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU - startNumBytesInGPU)
-        .toEqual(4 * tf.util.bytesPerElement(a.dtype));
-  });
-
-  it('should not leak memory when lazily packing', () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    const c = tf.add(a, 1);
-
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-    const startNumBytesInGPU = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-
-    tf.matMul(b, c);
-
-    expect(tf.memory().numBytes - startNumBytes).toEqual(36);
-    expect(tf.memory().numTensors - startNumTensors).toEqual(1);
-    // result [3, 3] is packed to four RGBA pixel texture b is packed to two
-    // RGBA texels texture: total 6 * 4 = 24 components.
-    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU - startNumBytesInGPU)
-        .toEqual(24 * tf.util.bytesPerElement(a.dtype));
-  });
-
-  it('should work when the same input must be represented by' +
-         'different textures',
-     async () => {
-       const a = tf.tensor1d([1, 2]);
-       const res = tf.dot(a, a);
-       expectArraysClose(await res.data(), [5]);
-     });
-});
-
-describeWithFlags('backendWebGL', WEBGL_ENVS, () => {
-  let prevBackend: string;
-
-  beforeAll(() => {
-    prevBackend = tf.getBackend();
-  });
-
-  afterEach(() => {
-    tf.setBackend(prevBackend);
-    tf.removeBackend('test-storage');
-  });
-
-  it('register string tensor with values', () => {
-    const backend = new MathBackendWebGL();
-    tf.registerBackend('test-storage', () => backend);
-    tf.setBackend('test-storage');
-
-    const t = engine().makeTensor(['a', 'b', 'c'], [3], 'string');
-    expectArraysEqual(
-        decodeStrings(backend.readSync(t.dataId) as Uint8Array[]),
-        ['a', 'b', 'c']);
-  });
-
-  it('register string tensor with values and wrong shape throws error', () => {
-    const backend = new MathBackendWebGL();
-    tf.registerBackend('test-storage', () => backend);
-    tf.setBackend('test-storage');
-    expect(() => tf.tensor(['a', 'b', 'c'], [4], 'string')).toThrowError();
-  });
-
-  it('reading', () => {
-    const backend = new MathBackendWebGL(null);
-    tf.registerBackend('test-storage', () => backend);
-    tf.setBackend('test-storage');
-
-    const texManager = backend.getTextureManager();
-    const t = engine().makeTensor(new Float32Array([1, 2, 3]), [3], 'float32');
-    expect(texManager.getNumUsedTextures()).toBe(0);
-    backend.getTexture(t.dataId);
-    expect(texManager.getNumUsedTextures()).toBe(1);
-    expectArraysClose(
-        backend.readSync(t.dataId) as Float32Array,
-        new Float32Array([1, 2, 3]));
-    expect(texManager.getNumUsedTextures()).toBe(1);
-    backend.getTexture(t.dataId);
-    expect(texManager.getNumUsedTextures()).toBe(1);
-    backend.disposeData(t.dataId);
-    expect(texManager.getNumUsedTextures()).toBe(0);
-  });
-
-  it('read packed and then use by an unpacked op', async () => {
-    const backend = new MathBackendWebGL(null);
-    tf.registerBackend('test-storage', () => backend);
-    tf.copyRegisteredKernels('webgl', 'test-storage');
-    tf.setBackend('test-storage');
-
-    const webglPackFlagSaved = tf.env().getBool('WEBGL_PACK');
-    tf.env().set('WEBGL_PACK', true);
-    const webglSizeUploadUniformSaved =
-        tf.env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM');
-    tf.env().set('WEBGL_SIZE_UPLOAD_UNIFORM', 0);
-    const a = tf.tensor2d([1, 2], [2, 1]);
-    const b = tf.tensor2d([1], [1, 1]);
-    const c = tf.matMul(a, b);
-    backend.readSync(c.dataId);
-    tf.env().set('WEBGL_PACK', false);
-    const d = tf.add(c, 1);
-    tf.env().set('WEBGL_PACK', webglPackFlagSaved);
-    tf.env().set('WEBGL_SIZE_UPLOAD_UNIFORM', webglSizeUploadUniformSaved);
-    expectArraysClose(await d.data(), [2, 3]);
-  });
-
-  it('delayed storage, overwriting', () => {
-    const backend = new MathBackendWebGL(null);
-    tf.registerBackend('test-storage', () => backend);
-    tf.setBackend('test-storage');
-
-    const texManager = backend.getTextureManager();
-    const t = engine().makeTensor(new Float32Array([1, 2, 3]), [3], 'float32');
-    backend.getTexture(t.dataId);
-    expect(texManager.getNumUsedTextures()).toBe(1);
-    expectArraysClose(
-        backend.readSync(t.dataId) as Float32Array,
-        new Float32Array([1, 2, 3]));
-    backend.getTexture(t.dataId);
-    expect(texManager.getNumUsedTextures()).toBe(1);
-    expectArraysClose(
-        backend.readSync(t.dataId) as Float32Array,
-        new Float32Array([1, 2, 3]));
-    expect(texManager.getNumUsedTextures()).toBe(1);
-  });
-});
-
-describeWithFlags('Webgl backend disposal', WEBGL_ENVS, () => {
-  it('register and dispose a backend outside unit test', () => {
-    // Simulate outside unit test environment.
-    tf.ENV.set('IS_TEST', false);
-
-    const backend = new MathBackendWebGL();
-    tf.registerBackend('test-disposal', () => backend);
-    tf.copyRegisteredKernels('webgl', 'test-disposal');
-    tf.setBackend('test-disposal');
-    // Compile and run a program.
-    tf.zeros([1000]).sqrt().dataSync();
-
-    // Dispose the backend.
-    tf.backend().dispose();
-
-    // Make sure the cache is empty.
-    const cache = getBinaryCache(tf.ENV.getNumber('WEBGL_VERSION'));
-    expect(Object.keys(cache).length).toBe(0);
-    tf.removeBackend('test-disposal');
-  });
-
-  it('register and dispose a backend inside unit test', () => {
-    // Simulate inside unit test environment.
-    tf.ENV.set('IS_TEST', true);
-
-    const backend = new MathBackendWebGL();
-    tf.registerBackend('test-disposal', () => backend);
-    tf.copyRegisteredKernels('webgl', 'test-disposal');
-    tf.setBackend('test-disposal');
-    // Compile and run a program.
-    tf.zeros([1000]).sqrt().dataSync();
-
-    // Dispose the backend.
-    tf.backend().dispose();
-
-    // Make sure the cache is NOT empty.
-    const cache = getBinaryCache(tf.ENV.getNumber('WEBGL_VERSION'));
-    expect(Object.keys(cache).length).toBeGreaterThan(0);
-    tf.removeBackend('test-disposal');
-  });
-
-  it('register, dispose and re-register a backend outside unit test', () => {
-    // Simulate outside unit test environment.
-    tf.ENV.set('IS_TEST', false);
-
-    tf.registerBackend('test-disposal', () => new MathBackendWebGL());
-    tf.copyRegisteredKernels('webgl', 'test-disposal');
-    tf.setBackend('test-disposal');
-    // Compile and run a program.
-    tf.zeros([1000]).sqrt().dataSync();
-
-    // Dispose the backend.
-    tf.backend().dispose();
-    tf.removeBackend('test-disposal');
-
-    // Re-register a backend.
-    tf.registerBackend('test-disposal', () => new MathBackendWebGL());
-    tf.copyRegisteredKernels('webgl', 'test-disposal');
-    tf.setBackend('test-disposal');
-    // Compile and run a program.
-    tf.zeros([1000]).sqrt().dataSync();
-
-    // Dispose the 2nd backend.
-    tf.backend().dispose();
-
-    // Make sure the cache is empty.
-    const cache = getBinaryCache(tf.ENV.getNumber('WEBGL_VERSION'));
-    expect(Object.keys(cache).length).toBe(0);
-    tf.removeBackend('test-disposal');
-  });
-});
-
-describeWithFlags('Custom window size', WEBGL_ENVS, () => {
-  const customBackendName = 'custom-webgl';
-
-  beforeAll(() => {
-    const kernelFunc = tf.getKernel('Square', 'webgl').kernelFunc;
-    tf.registerKernel(
-        {kernelName: 'Square', backendName: customBackendName, kernelFunc});
-  });
-
-  afterAll(() => {
-    tf.unregisterKernel('Square', customBackendName);
-  });
-
-  it('Set screen area to be 1x1', () => {
-    // This will set the screen size to 1x1 to make sure the page limit is
-    // very small.
-    spyOnProperty(window, 'screen', 'get')
-        .and.returnValue({height: 1, width: 1} as unknown as Screen);
-
-    tf.registerBackend(customBackendName, () => new MathBackendWebGL());
-    tf.setBackend(customBackendName);
-
-    // Allocate ~40KB.
-    const a = tf.ones([100, 100]);
-    // No gpu memory used yet because of delayed storage.
-    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU).toBe(0);
-
-    // Expect console.warn() to be called.
-    let numWarnCalls = 0;
-    spyOn(console, 'warn').and.callFake(() => {
-      numWarnCalls++;
-    });
-
-    a.square();
-    expect(numWarnCalls).toBe(1);
-    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU)
-        .toBe(100 * 100 * 4 * 2);
-
-    // Allocate another 40KB.
-    a.square();
-
-    // Expect console.warn() to NOT be called more than once.
-    expect(numWarnCalls).toBe(1);
-    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU)
-        .toBe(100 * 100 * 4 * 3);
-    tf.removeBackend(customBackendName);
-  });
-});
-
-const SIZE_UPLOAD_UNIFORM = 4;
-// Run only for environments that have 32bit floating point support.
-const FLOAT32_WEBGL_ENVS = {
-  flags: {'WEBGL_RENDER_FLOAT32_ENABLED': true},
-  predicate: WEBGL_ENVS.predicate
-};
-
-describeWithFlags('upload tensors as uniforms', FLOAT32_WEBGL_ENVS, () => {
-  let savedUploadUniformValue: number;
-
-  beforeAll(() => {
-    savedUploadUniformValue =
-        tf.env().get('WEBGL_SIZE_UPLOAD_UNIFORM') as number;
-    tf.env().set('WEBGL_SIZE_UPLOAD_UNIFORM', SIZE_UPLOAD_UNIFORM);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGL_SIZE_UPLOAD_UNIFORM', savedUploadUniformValue);
-  });
-
-  it('small tensor gets uploaded as scalar', () => {
-    let m = tf.memory() as WebGLMemoryInfo;
-    expect(m.numBytesInGPU).toBe(0);
-
-    const a = tf.zeros([SIZE_UPLOAD_UNIFORM - 1]);
-    a.square();
-
-    // Only the result lives on the gpu, the input is gone.
-    m = tf.memory() as WebGLMemoryInfo;
-    expect(m.numBytesInGPU).toBe(a.size * 4);
-  });
-
-  it('large tensor gets uploaded to gpu', () => {
-    let m = tf.memory() as WebGLMemoryInfo;
-    expect(m.numBytesInGPU).toBe(0);
-
-    const a = tf.zeros([SIZE_UPLOAD_UNIFORM + 1]);
-    a.square();
-
-    // Both the result and the input live on the gpu.
-    m = tf.memory() as WebGLMemoryInfo;
-    expect(m.numBytesInGPU).toBe(a.size * 4 * 2);
-  });
-
-  it('download and re-upload an output of a shader', async () => {
-    const vals = new Float32Array(SIZE_UPLOAD_UNIFORM + 1);
-    vals.fill(2);
-    const a = tf.square(vals);
-    a.dataSync();            // Download to CPU.
-    const res = a.square();  // Re-upload to GPU.
-
-    const expected = new Float32Array(SIZE_UPLOAD_UNIFORM + 1);
-    expected.fill(16);
-    expectArraysClose(await res.data(), expected);
-  });
-});
-
-describeWithFlags('indexing for large tensors', FLOAT32_WEBGL_ENVS, () => {
-  it('properly indexes large tensors', async () => {
-    const range = 3000 * 3000;
-    const aData = new Float32Array(range);
-    for (let i = 0; i < range; i++) {
-      aData[i] = i / range;
-    }
-
-    const a = tf.tensor1d(aData);
-    const aRelu = a.relu();
-
-    expectArraysClose(await a.data(), aData);
-    expectArraysClose(await aRelu.data(), aData);
-  });
-});
-
-describeWithFlags('debug on webgl', WEBGL_ENVS, () => {
-  beforeAll(() => {
-    // Silences debug warnings.
-    spyOn(console, 'warn');
-    tf.enableDebugMode();
-  });
-
-  it('debug mode errors when overflow in tensor construction', () => {
-    const savedRenderFloat32Flag =
-        tf.env().getBool('WEBGL_RENDER_FLOAT32_ENABLED');
-    tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', false);
-    const a = () => tf.tensor1d([2, Math.pow(2, 17)], 'float32');
-    expect(a).toThrowError();
-    tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', savedRenderFloat32Flag);
-  });
-
-  it('debug mode errors when underflow in tensor construction', () => {
-    const savedRenderFloat32Flag =
-        tf.env().getBool('WEBGL_RENDER_FLOAT32_ENABLED');
-    tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', false);
-    const a = () => tf.tensor1d([2, 1e-8], 'float32');
-    expect(a).toThrowError();
-    tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', savedRenderFloat32Flag);
-  });
-});
-
-const WEBGL1_ENVS = {
-  flags: {'WEBGL_VERSION': 1},
-  predicate: WEBGL_ENVS.predicate
-};
-
-const WEBGL2_ENVS = {
-  flags: {'WEBGL_VERSION': 2},
-  predicate: WEBGL_ENVS.predicate
-};
-
-describeWithFlags('computeBytes counts bytes correctly', WEBGL1_ENVS, () => {
-  it('for all physical texture types', () => {
-    const gpgpu = new GPGPUContext();
-
-    const shapeRC: [number, number] = [2, 3];
-
-    let bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.UNPACKED_FLOAT16, gpgpu.gl,
-        gpgpu.textureConfig, false /* isPacked */);
-    expect(bytesForTex).toBe(96);
-
-    bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.UNPACKED_FLOAT32, gpgpu.gl,
-        gpgpu.textureConfig, false /* isPacked */);
-    expect(bytesForTex).toBe(96);
-
-    bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE, gpgpu.gl,
-        gpgpu.textureConfig, true /* isPacked */);
-    expect(bytesForTex).toBe(32);
-
-    bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.PACKED_2X2_FLOAT32, gpgpu.gl,
-        gpgpu.textureConfig, true /* isPacked */);
-    expect(bytesForTex).toBe(32);
-
-    bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.PACKED_2X2_FLOAT16, gpgpu.gl,
-        gpgpu.textureConfig, true /* isPacked */);
-    expect(bytesForTex).toBe(32);
-
-    gpgpu.dispose();
-  });
-});
-
-describeWithFlags('computeBytes counts bytes correctly', WEBGL2_ENVS, () => {
-  it('test every physical tex type input to computeBytes', () => {
-    const gpgpu = new GPGPUContext();
-
-    const shapeRC: [number, number] = [2, 3];
-
-    let bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.UNPACKED_FLOAT16, gpgpu.gl,
-        gpgpu.textureConfig, false /* isPacked */);
-    expect(bytesForTex).toBe(12);
-
-    bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.UNPACKED_FLOAT32, gpgpu.gl,
-        gpgpu.textureConfig, false /* isPacked */);
-    expect(bytesForTex).toBe(24);
-
-    bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE, gpgpu.gl,
-        gpgpu.textureConfig, true /* isPacked */);
-    expect(bytesForTex).toBe(8);
-
-    bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.PACKED_2X2_FLOAT32, gpgpu.gl,
-        gpgpu.textureConfig, true /* isPacked */);
-    expect(bytesForTex).toBe(32);
-
-    bytesForTex = computeBytes(
-        shapeRC, PhysicalTextureType.PACKED_2X2_FLOAT16, gpgpu.gl,
-        gpgpu.textureConfig, true /* isPacked */);
-    expect(bytesForTex).toBe(16);
-
-    gpgpu.dispose();
-  });
-});
-
-describeWithFlags('aggressive texture deletion', WEBGL_ENVS, () => {
-  it('basic', () => {
-    const savedDeleteThreshold =
-        tf.env().get('WEBGL_DELETE_TEXTURE_THRESHOLD') as number;
-    tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);
-
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    tf.matMul(a, b);
-
-    const startNumBytesAllocated =
-        (tf.memory() as WebGLMemoryInfo).numBytesInGPUAllocated;
-
-    a.dispose();
-    b.dispose();
-
-    expect(
-        startNumBytesAllocated -
-        (tf.memory() as WebGLMemoryInfo).numBytesInGPUAllocated)
-        .toBeGreaterThan(0);
-
-    tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', savedDeleteThreshold);
-  });
-});
-
-describeWithFlags('memory webgl', WEBGL_ENVS, () => {
-  it('unreliable is falsy/not present when all tensors are numeric', () => {
-    tf.tensor(1);
-    const mem = tf.memory();
-    expect(mem.numTensors).toBe(1);
-    expect(mem.numDataBuffers).toBe(1);
-    expect(mem.numBytes).toBe(4);
-    expect(mem.unreliable).toBeFalsy();
-  });
-});
-
-describeWithFlags('manual gl flush', WEBGL_ENVS, () => {
-  it('works when manual gl flush is enabled', async () => {
-    const savedGlThreshold = tf.env().get('WEBGL_FLUSH_THRESHOLD') as number;
-    tf.env().set('WEBGL_FLUSH_THRESHOLD', 1);
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([1, 1, -3, 2, 2, 1], [2, 3]);
-
-    const result = tf.div(tf.div(tf.mul(a, b), a), b);
-    expectArraysClose(await result.data(), [1, 1, 1, 1, 1, 1]);
-    tf.env().set('WEBGL_FLUSH_THRESHOLD', savedGlThreshold);
-  });
-});
-// We do not yet fully support half float backends. These tests are a starting
-// point.
-describeWithFlags('backend without render float32 support', WEBGL_ENVS, () => {
-  const savedRenderFloat32Flag =
-      tf.env().getBool('WEBGL_RENDER_FLOAT32_ENABLED');
-  const customWebGLBackendName = 'half-float-webgl';
-
-  beforeAll(() => {
-    tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', false);
-  });
-
-  beforeEach(() => {
-    tf.copyRegisteredKernels('webgl', customWebGLBackendName);
-    tf.registerBackend(
-        customWebGLBackendName, () => new MathBackendWebGL(null));
-  });
-
-  afterEach(() => {
-    tf.removeBackend(customWebGLBackendName);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', savedRenderFloat32Flag);
-  });
-
-  it('basic usage', async () => {
-    tf.setBackend(customWebGLBackendName);
-
-    const a = tf.tensor2d([1, 2], [1, 2]);
-    const b = tf.tensor2d([1, 2], [1, 2]);
-    const c = tf.add(a, b);
-    expectArraysClose(await c.data(), [2, 4]);
-  });
-
-  it('disposing tensors should not cause errors', () => {
-    tf.setBackend(customWebGLBackendName);
-    expect(() => tf.tidy(() => {
-      const a = tf.tensor2d([1, 2], [1, 2]);
-      const b = tf.tensor2d([1, 2], [1, 2]);
-      const c = tf.add(a, b);
-      c.dataSync();
-      return c.add(tf.tensor2d([2, 4], [1, 2]));
-    })).not.toThrowError();
-  });
-});
-
-if (tf.env().getBool('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE')) {
-  describeWithFlags('time webgl', WEBGL2_ENVS, () => {
-    it('upload + compute', async () => {
-      const a = tf.zeros([10, 10]);
-      const time = await tf.time(() => a.square()) as WebGLTimingInfo;
-      expect(time.uploadWaitMs > 0);
-      expect(time.downloadWaitMs === 0);
-      expect(time.kernelMs).toBeGreaterThan(0);
-      expect(time.kernelMs).toBeLessThanOrEqual(time.wallMs);
-    });
-
-    it('upload + compute + dataSync', async () => {
-      const a = tf.zeros([10, 10]);
-      const time = await tf.time(() => a.square()
-        .dataSync()) as WebGLTimingInfo;
-      expect(time.uploadWaitMs > 0);
-      expect(time.downloadWaitMs > 0);
-      expect(time.kernelMs).toBeGreaterThan(0);
-      expect(time.kernelMs).toBeLessThanOrEqual(time.wallMs);
-    });
-
-    it('upload + compute + data', async () => {
-      const a = tf.zeros([10, 10]);
-      const time =
-        await tf.time(async () => a.square().data()) as WebGLTimingInfo;
-      expect(time.uploadWaitMs > 0);
-      expect(time.downloadWaitMs > 0);
-      expect(time.kernelMs).toBeGreaterThan(0);
-      expect(time.kernelMs).toBeLessThanOrEqual(time.wallMs);
-    });
-
-    it('preupload (not included) + compute + data', async () => {
-      const a = tf.zeros([10, 10]);
-      // Pre-upload a on gpu.
-      a.square();
-      const time = await tf.time(() => a.sqrt()) as WebGLTimingInfo;
-      // The tensor was already on gpu.
-      expect(time.uploadWaitMs === 0);
-      expect(time.downloadWaitMs === 0);
-      expect(time.kernelMs).toBeGreaterThan(0);
-      expect(time.kernelMs).toBeLessThanOrEqual(time.wallMs);
-    });
-
-    it('returns error for kernelMs if query timer extension is unavailable',
-      async () => {
-        const savedQueryReliableValue =
-          tf.env().get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE');
-        tf.env().set('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', false);
-
-        const a = tf.zeros([10, 10]);
-        const time = await tf.backend().time(() => a.sqrt()) as WebGLTimingInfo;
-        expect(time.kernelMs).toEqual({
-          error: 'WebGL query timers are not supported in this environment.'
-        });
-
-        tf.env().set(
-          'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE',
-          savedQueryReliableValue);
-      });
-  });
-}
-
-describeWithFlags('keeping data on gpu ', WEBGL2_ENVS, () => {
-  let flag: boolean;
-
-  beforeAll(() => {
-    flag = tf.env().getBool('WEBGL_CPU_FORWARD');
-    tf.env().set('WEBGL_CPU_FORWARD', false);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGL_CPU_FORWARD', flag);
-  });
-
-  it('has a valid texture for dtype=float32.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const texShape = [2, 2];
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-    const res = b.dataToGPU();
-    expectArraysEqual(res.texShape, texShape);
-
-    const webGLBackend = tf.backend() as MathBackendWebGL;
-    const buffer = webGLBackend.gpgpu.createBufferFromTexture(
-        res.texture, res.texShape[0], res.texShape[1]);
-    const vals = webGLBackend.gpgpu.downloadFloat32MatrixFromBuffer(buffer, 12);
-    expectArraysEqual(vals, data);
-  });
-
-  it('uses user defined texShape.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-    const texShape = [1, 3] as [number, number];
-    const res = b.dataToGPU({customTexShape: texShape});
-    expectArraysEqual(res.texShape, texShape);
-
-    const webGLBackend = tf.backend() as MathBackendWebGL;
-    const buffer = webGLBackend.gpgpu.createBufferFromTexture(
-        res.texture, res.texShape[0], res.texShape[1]);
-    const vals = webGLBackend.gpgpu.downloadFloat32MatrixFromBuffer(buffer, 12);
-    expectArraysEqual(vals, data);
-  });
-
-  it('has a valid texture for dtype=int32.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const texShape = [2, 2];
-    const a = tf.tensor(data, [1, 3, 4], 'int32');
-    const b = tf.add(a, 0);
-    const res = b.dataToGPU();
-    expectArraysEqual(res.texShape, texShape);
-
-    const webGLBackend = tf.backend() as MathBackendWebGL;
-
-    const buffer = webGLBackend.gpgpu.createBufferFromTexture(
-        res.texture, res.texShape[0], res.texShape[1]);
-    const vals = webGLBackend.gpgpu.downloadFloat32MatrixFromBuffer(buffer, 12);
-
-    expectArraysEqual(vals, data);
-  });
-
-  it('has no memory leak.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-
-    const webGLBackend = tf.backend() as MathBackendWebGL;
-    const startNumBytes = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-    const startTensor = tf.memory().numTensors;
-    const startDataBuckets = webGLBackend.numDataIds();
-
-    const res = b.dataToGPU();
-    res.tensorRef.dispose();
-
-    const endNumBytes = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-    const endTensor = tf.memory().numTensors;
-    const endDataBuckets = webGLBackend.numDataIds();
-
-    expect(endNumBytes).toEqual(startNumBytes);
-    expect(endTensor).toEqual(startTensor);
-    expect(endDataBuckets).toEqual(startDataBuckets);
-  });
-
-  it('can be used in tidy.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-
-    const webGLBackend = tf.backend() as MathBackendWebGL;
-    const startTensor = tf.memory().numTensors;
-    const startDataBuckets = webGLBackend.numDataIds();
-
-    const result = tf.tidy(() => {
-      const a = tf.tensor(data, [1, 3, 4]);
-      const b = tf.add(a, 0);
-      return b.dataToGPU() as unknown as tf.Tensor;
-    });
-
-    const endTensor = tf.memory().numTensors;
-    const endDataBuckets = webGLBackend.numDataIds();
-
-    expect(endTensor).toEqual(startTensor + 1);
-    expect(endDataBuckets).toEqual(startDataBuckets + 1);
-
-    const res = result as unknown as GPUData;
-    const buffer = webGLBackend.gpgpu.createBufferFromTexture(
-        res.texture, res.texShape[0], res.texShape[1]);
-    const vals = webGLBackend.gpgpu.downloadFloat32MatrixFromBuffer(buffer, 12);
-    expectArraysEqual(vals, data);
-  });
-
-  it('tidy has no memory leak.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-
-    const webGLBackend = tf.backend();
-    const startTensor = tf.memory().numTensors;
-    const startDataBuckets = webGLBackend.numDataIds();
-
-    tf.tidy(() => {
-      const a = tf.tensor(data, [1, 3, 4]);
-      const b = tf.add(a, 0);
-      b.dataToGPU();
-      return b;
-    });
-
-    const endTensor = tf.memory().numTensors;
-    const endDataBuckets = webGLBackend.numDataIds();
-
-    expect(endTensor).toEqual(startTensor + 1);
-    expect(endDataBuckets).toEqual(startDataBuckets + 1);
-  });
-
-  it('throws error when user defined texShape is too small.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-
-    expect(() => {
-      b.dataToGPU({customTexShape: [1, 1]});
-    }).toThrowError();
-  });
-});
-
-describeWithFlags('keeping data on gpu ', WEBGL1_ENVS, () => {
-  let flag: boolean;
-  const webGLBackend = (tf.backend() as MathBackendWebGL);
-
-  beforeAll(() => {
-    flag = tf.env().getBool('WEBGL_CPU_FORWARD');
-    tf.env().set('WEBGL_CPU_FORWARD', false);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGL_CPU_FORWARD', flag);
-  });
-
-  it('has a valid texture for dtype=float32.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const texShape = [2, 2];
-    const size = 12;
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-    const res = b.dataToGPU();
-    expectArraysEqual(res.texShape, texShape);
-
-    if (tf.env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {
-      const tmpData = webGLBackend.texData.get(res.tensorRef.dataId);
-      const vals = webGLBackend.gpgpu
-                       .downloadMatrixFromPackedTexture(
-                           tmpData.texture.texture, ...tmpData.texture.texShape)
-                       .subarray(0, size);
-      expectArraysEqual(vals, data);
-    }
-  });
-
-  it('uses user defined texShape.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-    const texShape = [1, 3] as [number, number];
-    const size = 12;
-    const res = b.dataToGPU({customTexShape: texShape});
-    expectArraysEqual(res.texShape, texShape);
-
-    if (tf.env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {
-      const tmpData = webGLBackend.texData.get(res.tensorRef.dataId);
-      const vals = webGLBackend.gpgpu
-                       .downloadMatrixFromPackedTexture(
-                           tmpData.texture.texture, ...tmpData.texture.texShape)
-                       .subarray(0, size);
-      expectArraysEqual(vals, data);
-    }
-  });
-
-  it('has a valid texture for dtype=int32.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const texShape = [2, 2];
-    const size = 12;
-    const a = tf.tensor(data, [1, 3, 4], 'int32');
-    const b = tf.add(a, 0);
-    const res = b.dataToGPU();
-    expectArraysEqual(res.texShape, texShape);
-
-    if (tf.env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {
-      const tmpData = webGLBackend.texData.get(res.tensorRef.dataId);
-      const vals = webGLBackend.gpgpu
-                       .downloadMatrixFromPackedTexture(
-                           tmpData.texture.texture, ...tmpData.texture.texShape)
-                       .subarray(0, size);
-      expectArraysEqual(vals, data);
-    }
-  });
-
-  it('has no memory leak.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-
-    const webGLBackend = tf.backend() as MathBackendWebGL;
-    const startNumBytes = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-    const startTensor = tf.memory().numTensors;
-    const startDataBuckets = webGLBackend.numDataIds();
-
-    const res = b.dataToGPU();
-    res.tensorRef.dispose();
-
-    const endNumBytes = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-    const endTensor = tf.memory().numTensors;
-    const endDataBuckets = webGLBackend.numDataIds();
-
-    expect(endNumBytes).toEqual(startNumBytes);
-    expect(endTensor).toEqual(startTensor);
-    expect(endDataBuckets).toEqual(startDataBuckets);
-  });
-
-  it('throws error when user defined texShape is too small.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-
-    expect(() => {
-      b.dataToGPU({customTexShape: [1, 1]});
-    }).toThrowError();
-  });
-});
-
-describeWithFlags('caching on cpu', WEBGL_ENVS, () => {
-  const customBackendName = 'cache-on-cpu';
-
-  beforeAll(() => {
-    tf.env().set('WEBGL_CPU_FORWARD', false);
-    const kernelFunc = tf.getKernel('Square', 'webgl').kernelFunc;
-    tf.registerKernel(
-        {kernelName: 'Square', backendName: customBackendName, kernelFunc});
-  });
-
-  afterAll(() => {
-    tf.unregisterKernel('Square', customBackendName);
-  });
-
-  it('caches on cpu after async read', async () => {
-    const backend = new MathBackendWebGL();
-    tf.registerBackend(customBackendName, () => backend);
-    tf.setBackend(customBackendName);
-
-    const t = tf.square(2);
-    const info = backend.getDataInfo(t.dataId);
-
-    // Make sure the tensor is on the GPU.
-    expect(info.values == null).toBe(true);
-
-    await t.data();
-
-    // Make sure the tensor is cached on CPU.
-    expect(info.values).not.toBe(null);
-
-    tf.removeBackend(customBackendName);
-  });
-
-  it('caches on cpu after sync read', () => {
-    const backend = new MathBackendWebGL();
-    tf.registerBackend(customBackendName, () => backend);
-    tf.setBackend(customBackendName);
-
-    const t = tf.square(2);
-    const info = backend.getDataInfo(t.dataId);
-
-    // Make sure the tensor is on the GPU.
-    expect(info.values == null).toBe(true);
-
-    t.dataSync();
-
-    // Make sure the tensor is cached on CPU.
-    expect(info.values).not.toBe(null);
-
-    tf.removeBackend(customBackendName);
-  });
-});
-
-describeWithFlags('WebGL backend has sync init', WEBGL_ENVS, () => {
-  it('can do matmul without waiting for ready', async () => {
-    const customWebGLBackendName = 'my-webgl';
-
-    tf.copyRegisteredKernels('webgl', customWebGLBackendName);
-
-    tf.registerBackend(customWebGLBackendName, () => {
-      return new MathBackendWebGL();
-    });
-    tf.setBackend(customWebGLBackendName);
-    const a = tf.tensor1d([5]);
-    const b = tf.tensor1d([3]);
-    const res = tf.dot(a, b);
-    expectArraysClose(await res.data(), 15);
-    tf.dispose([a, b, res]);
-    tf.removeBackend(customWebGLBackendName);
-  });
-});
-
-describeWithFlags('custom canvas ', WEBGL_ENVS, () => {
-  const customBackendName = 'custom-webgl';
-  let flag: boolean;
-
-  beforeAll(() => {
-    flag = tf.env().getBool('WEBGL_CPU_FORWARD');
-    tf.env().set('WEBGL_CPU_FORWARD', false);
-    const kernelFunc = tf.getKernel('Square', 'webgl').kernelFunc;
-    tf.registerKernel(
-        {kernelName: 'Square', backendName: customBackendName, kernelFunc});
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGL_CPU_FORWARD', flag);
-    tf.unregisterKernel('Square', customBackendName);
-  });
-
-  it('works.', () => {
-    const customCanvas = document.createElement('canvas');
-    customCanvas.width = 300;
-    customCanvas.height = 200;
-
-    const backend = new MathBackendWebGL(customCanvas);
-    tf.registerBackend(customBackendName, () => backend);
-    tf.setBackend(customBackendName);
-
-    const t = tf.square(2);
-    const data = t.dataSync();
-
-    expectArraysEqual(data, [4]);
-
-    tf.removeBackend(customBackendName);
-  });
-});
-describeWithFlags('Parallel compilation', WEBGL_ENVS, () => {
-  // TODO(lina128): Also test async after parallel compilation flag is
-  // implemented in context object. We have to keep the test sync for now,
-  // because it's a global flag, the async test will affect other tests.
-  it('does not have memory leak.', () => {
-    const savedWebGLCPUForward = tf.env().get('WEBGL_CPU_FORWARD');
-    tf.env().set('WEBGL_CPU_FORWARD', false);
-
-    const customWebGLBackendName = 'my-webgl';
-    tf.copyRegisteredKernels('webgl', customWebGLBackendName);
-    tf.registerBackend(customWebGLBackendName, () => {
-      return new MathBackendWebGL();
-    });
-    tf.setBackend(customWebGLBackendName);
-
-    const a0 = tf.tensor1d([1, 1, 1]);
-    const b0 = tf.tensor1d([1, 1, 1]);
-    const c0 = tf.add(a0, b0);
-    const data = c0.dataSync();
-
-    expectArraysClose(data, [2, 2, 2]);
-    tf.dispose([a0, b0, c0]);
-    tf.removeBackend(customWebGLBackendName);
-
-    // TODO(lina128): Also test use an existing backend after parallel
-    // compilation flag is implemented in context object. The current approach
-    // assumes there's no binary cache, and it doesn't check existing cache.
-    const customWebGLBackendName1 = 'my-webgl1';
-    tf.copyRegisteredKernels('webgl', customWebGLBackendName1);
-    tf.registerBackend(customWebGLBackendName1, () => {
-      return new MathBackendWebGL();
-    });
-    tf.setBackend(customWebGLBackendName1);
-    const webGLBackend = tf.backend() as MathBackendWebGL;
-
-    const startNumBytes = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-    const startTensor = tf.memory().numTensors;
-    const startDataBuckets = webGLBackend.numDataIds();
-
-    const a1 = tf.tensor1d([1, 1, 1]);
-    const b1 = tf.tensor1d([1, 1, 1]);
-
-    // Pre-compile round.
-    tf.env().set('ENGINE_COMPILE_ONLY', true);
-    const c1 = tf.add(a1, b1);
-    webGLBackend.checkCompileCompletion();
-    webGLBackend.getUniformLocations();
-
-    // Warm-up upload and download round.
-    tf.env().set('ENGINE_COMPILE_ONLY', false);
-    const c2 = tf.add(a1, b1);
-    c2.dataSync();
-
-    // Actual inference.
-    const c3 = tf.add(a1, b1);
-    expectArraysEqual(c3.dataSync(), [2, 2, 2]);
-
-    tf.dispose([a1, b1, c1, c2, c3]);
-    const endNumBytes = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-    const endTensor = tf.memory().numTensors;
-    const endDataBuckets = webGLBackend.numDataIds();
-
-    // We only check numBytesInGPU. For parallel compilation,
-    // numBytesInGPUAllocated will be more because of the two pass uploadToGPU,
-    // but they will all be freed, resulting in endNumbytes equal to
-    // startNumBytes.
-    expect(startNumBytes).toEqual(endNumBytes);
-    expect(startTensor).toEqual(endTensor);
-    expect(endDataBuckets).toEqual(startDataBuckets);
-
-    tf.removeBackend(customWebGLBackendName1);
-
-    tf.env().set('WEBGL_CPU_FORWARD', savedWebGLCPUForward);
-  });
-});
-
-describeWithFlags('create tensor from texture', WEBGL_ENVS, () => {
-  it('basic usage', async () => {
-    // In this test we create a WebGL texture using the GL context from the
-    // WebGL backend. Then we create a tensor from that texture, and ensure that
-    // we can get the expected result.
-
-    const width = 2;
-    const height = 2;
-
-    const webGlBackend = tf.backend() as MathBackendWebGL;
-    const gl = webGlBackend.gpgpu.gl;
-    const texture = gl.createTexture();
-    const tex2d = gl.TEXTURE_2D;
-    // tslint:disable-next-line:no-any
-    const glany = gl as any;
-    const internalFormat =
-        tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-    const textureFormat = glany.RGBA;
-    const textureType = glany.FLOAT;
-    const dataForUpload = new Float32Array(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-    gl.bindTexture(tex2d, texture);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-    gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-    gl.texImage2D(
-        tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType,
-        dataForUpload);
-
-    const logicalShape: [number, number] = [4, 4];
-    const a = tf.tensor(
-        {texture, height, width, channels: 'RGBA'}, logicalShape, 'float32');
-
-    expect(a.shape).toEqual(logicalShape);
-    expect(a.dtype).toEqual('float32');
-    expectArraysClose(
-        await a.data(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-    gl.deleteTexture(texture);
-  });
-
-  it('works for custom canvas', async () => {
-    const customBackendName = 'custom-webgl';
-    const customCanvas = document.createElement('canvas');
-    const customBackend = new MathBackendWebGL(customCanvas);
-    tf.registerBackend(customBackendName, () => customBackend);
-    const originalBackend = tf.getBackend();
-    await tf.setBackend(customBackendName);
-
-    const gl = customBackend.gpgpu.gl;
-    const width = 2;
-    const height = 2;
-
-    const texture = gl.createTexture();
-    const tex2d = gl.TEXTURE_2D;
-    // tslint:disable-next-line:no-any
-    const glany = gl as any;
-    const internalFormat =
-        tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-    const textureFormat = glany.RGBA;
-    const textureType = glany.FLOAT;
-    const dataForUpload = new Float32Array(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-    gl.bindTexture(tex2d, texture);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-    gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-    gl.texImage2D(
-        tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType,
-        dataForUpload);
-
-    const logicalShape: [number, number] = [4, 4];
-    const a = tf.tensor(
-        {texture, height, width, channels: 'RGBA'}, logicalShape, 'float32');
-
-    expect(a.shape).toEqual(logicalShape);
-    expect(a.dtype).toEqual('float32');
-    expectArraysClose(
-        await a.data(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-    gl.deleteTexture(texture);
-    tf.removeBackend(customBackendName);
-    await tf.setBackend(originalBackend);
-    customBackend.dispose();
-  });
-
-  it('default shape is aligned with texture shape and default dtype is float32',
-     async () => {
-       // In this test we create a WebGL texture using the GL context from the
-       // WebGL backend. Then we create a tensor from that texture, and ensure
-       // that we can get the expected result.
-
-       const width = 2;
-       const height = 2;
-
-       const webGlBackend = tf.backend() as MathBackendWebGL;
-       const gl = webGlBackend.gpgpu.gl;
-       const texture = gl.createTexture();
-       const tex2d = gl.TEXTURE_2D;
-       // tslint:disable-next-line:no-any
-       const glany = gl as any;
-       const internalFormat =
-           tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-       const textureFormat = glany.RGBA;
-       const textureType = glany.FLOAT;
-       const dataForUpload = new Float32Array(
-           [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-       gl.bindTexture(tex2d, texture);
-       gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-       gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-       gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-       gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-       gl.texImage2D(
-           tex2d, 0, internalFormat, width, height, 0, textureFormat,
-           textureType, dataForUpload);
-
-       const physicalShape: [number, number] = [width, height * 4];
-       const a = tf.tensor({texture, height, width, channels: 'RGBA'});
-
-       expect(a.shape).toEqual(physicalShape);
-       expect(a.dtype).toEqual('float32');
-       expectArraysClose(
-           await a.data(),
-           [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-       gl.deleteTexture(texture);
-     });
-
-  it('default channels is RGBA', async () => {
-    // In this test we create a WebGL texture using the GL context from the
-    // WebGL backend. Then we create a tensor from that texture, and ensure
-    // that we can get the expected result.
-
-    const width = 2;
-    const height = 2;
-
-    const webGlBackend = tf.backend() as MathBackendWebGL;
-    const gl = webGlBackend.gpgpu.gl;
-    const texture = gl.createTexture();
-    const tex2d = gl.TEXTURE_2D;
-    // tslint:disable-next-line:no-any
-    const glany = gl as any;
-    const internalFormat =
-        tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-    const textureFormat = glany.RGBA;
-    const textureType = glany.FLOAT;
-    const dataForUpload = new Float32Array(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-    gl.bindTexture(tex2d, texture);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-    gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-    gl.texImage2D(
-        tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType,
-        dataForUpload);
-
-    const logicalShape: [number, number] = [4, 4];
-    const a = tf.tensor({texture, height, width}, logicalShape, 'float32');
-
-    expect(a.shape).toEqual(logicalShape);
-    expect(a.dtype).toEqual('float32');
-    expectArraysClose(
-        await a.data(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-    gl.deleteTexture(texture);
-  });
-
-  it('works for channels GA', async () => {
-    // In this test we create a WebGL texture using the GL context from the
-    // WebGL backend. Then we create a tensor from that texture, and ensure
-    // that we can get the expected result.
-
-    const width = 2;
-    const height = 2;
-
-    const webGlBackend = tf.backend() as MathBackendWebGL;
-    const gl = webGlBackend.gpgpu.gl;
-    const texture = gl.createTexture();
-    const tex2d = gl.TEXTURE_2D;
-    // tslint:disable-next-line:no-any
-    const glany = gl as any;
-    const internalFormat =
-        tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-    const textureFormat = glany.RGBA;
-    const textureType = glany.FLOAT;
-    const dataForUpload = new Float32Array(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-    gl.bindTexture(tex2d, texture);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-    gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-    gl.texImage2D(
-        tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType,
-        dataForUpload);
-
-    const logicalShape: [number, number] = [2, 4];
-    const a = tf.tensor(
-        {texture, height, width, channels: 'GA'}, logicalShape, 'float32');
-
-    expect(a.shape).toEqual(logicalShape);
-    expect(a.dtype).toEqual('float32');
-    expectArraysClose(await a.data(), [1, 3, 5, 7, 9, 11, 13, 15]);
-
-    gl.deleteTexture(texture);
-  });
-
-  it('works for channels ABGR', async () => {
-    // In this test we create a WebGL texture using the GL context from the
-    // WebGL backend. Then we create a tensor from that texture, and ensure
-    // that we can get the expected result.
-
-    const width = 2;
-    const height = 2;
-
-    const webGlBackend = tf.backend() as MathBackendWebGL;
-    const gl = webGlBackend.gpgpu.gl;
-    const texture = gl.createTexture();
-    const tex2d = gl.TEXTURE_2D;
-    // tslint:disable-next-line:no-any
-    const glany = gl as any;
-    const internalFormat =
-        tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-    const textureFormat = glany.RGBA;
-    const textureType = glany.FLOAT;
-    const dataForUpload = new Float32Array(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-    gl.bindTexture(tex2d, texture);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-    gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-    gl.texImage2D(
-        tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType,
-        dataForUpload);
-
-    const logicalShape: [number, number] = [4, 4];
-    const a = tf.tensor(
-        {texture, height, width, channels: 'ABGR'}, logicalShape, 'float32');
-
-    expect(a.shape).toEqual(logicalShape);
-    expect(a.dtype).toEqual('float32');
-    expectArraysClose(
-        await a.data(), [3, 2, 1, 0, 7, 6, 5, 4, 11, 10, 9, 8, 15, 14, 13, 12]);
-
-    gl.deleteTexture(texture);
-  });
-
-  it('works for int32 dtype', async () => {
-    // In this test we create a WebGL texture using the GL context from the
-    // WebGL backend. Then we create a tensor from that texture, and ensure
-    // that we can get the expected result.
-
-    const width = 1;
-    const height = 1;
-
-    const webGlBackend = tf.backend() as MathBackendWebGL;
-    const gl = webGlBackend.gpgpu.gl;
-    const texture = gl.createTexture();
-    const tex2d = gl.TEXTURE_2D;
-    // tslint:disable-next-line:no-any
-    const glany = gl as any;
-    const internalFormat =
-        tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-    const textureFormat = glany.RGBA;
-    const textureType = glany.FLOAT;
-    const dataForUpload = new Float32Array([-1.2, -0.1, 0.1, 1.2]);
-
-    gl.bindTexture(tex2d, texture);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-    gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-    gl.texImage2D(
-        tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType,
-        dataForUpload);
-
-    const logicalShape: [number, number] = [2, 2];
-    const a = tf.tensor(
-        {texture, height, width, channels: 'RGBA'}, logicalShape, 'int32');
-
-    expect(a.shape).toEqual(logicalShape);
-    expect(a.dtype).toEqual('int32');
-    expectArraysClose(await a.data(), [-1, 0, 0, 1]);
-
-    gl.deleteTexture(texture);
-  });
-
-  it('throws for string dtype', async () => {
-    // In this test we create a WebGL texture using the GL context from the
-    // WebGL backend. Then we create a tensor from that texture, and ensure that
-    // we can get the expected result.
-
-    const width = 1;
-    const height = 2;
-
-    const webGlBackend = tf.backend() as MathBackendWebGL;
-    const gl = webGlBackend.gpgpu.gl;
-    const texture = gl.createTexture();
-    const tex2d = gl.TEXTURE_2D;
-    // tslint:disable-next-line:no-any
-    const glany = gl as any;
-    const internalFormat =
-        tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-    const textureFormat = glany.RGBA;
-    const textureType = glany.FLOAT;
-    const dataForUpload = new Float32Array([-2, -1, 0, 1, 2, 3, 4, 5]);
-
-    gl.bindTexture(tex2d, texture);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-    gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-    gl.texImage2D(
-        tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType,
-        dataForUpload);
-
-    const logicalShape: [number, number] = [height, width];
-    const a = () => tf.tensor(
-        {texture, height, width, channels: 'RGBA'}, logicalShape, 'string');
-
-    expect(a).toThrowError();
-    gl.deleteTexture(texture);
-  });
-
-  it('pad zeros at the rear if texture size is smaller than tensor size',
-     async () => {
-       // In this test we create a WebGL texture using the GL context from the
-       // WebGL backend. Then we create a tensor from that texture, and ensure
-       // that we can get the expected result.
-
-       const width = 1;
-       const height = 1;
-
-       const webGlBackend = tf.backend() as MathBackendWebGL;
-       const gl = webGlBackend.gpgpu.gl;
-       const texture = gl.createTexture();
-       const tex2d = gl.TEXTURE_2D;
-       // tslint:disable-next-line:no-any
-       const glany = gl as any;
-       const internalFormat =
-           tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-       const textureFormat = glany.RGBA;
-       const textureType = glany.FLOAT;
-       const dataForUpload = new Float32Array([0, 1, 2, 3]);
-
-       gl.bindTexture(tex2d, texture);
-       gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-       gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-       gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-       gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-       gl.texImage2D(
-           tex2d, 0, internalFormat, width, height, 0, textureFormat,
-           textureType, dataForUpload);
-
-       const logicalShape: [number, number] = [3, 3];
-       const a = tf.tensor(
-           {texture, height, width, channels: 'RGBA'}, logicalShape, 'float32');
-
-       expect(a.shape).toEqual(logicalShape);
-       expect(a.dtype).toEqual('float32');
-       expectArraysClose(await a.data(), [0, 1, 2, 3, 0, 0, 0, 0, 0]);
-
-       gl.deleteTexture(texture);
-     });
-
-  it('works if tensor size is smaller than texture size', async () => {
-    // In this test we create a WebGL texture using the GL context from the
-    // WebGL backend. Then we create a tensor from that texture, and ensure
-    // that we can get the expected result.
-
-    const width = 2;
-    const height = 2;
-
-    const webGlBackend = tf.backend() as MathBackendWebGL;
-    const gl = webGlBackend.gpgpu.gl;
-    const texture = gl.createTexture();
-    const tex2d = gl.TEXTURE_2D;
-    // tslint:disable-next-line:no-any
-    const glany = gl as any;
-    const internalFormat =
-        tf.env().getNumber('WEBGL_VERSION') === 1 ? gl.RGBA : glany.RGBA32F;
-    const textureFormat = glany.RGBA;
-    const textureType = glany.FLOAT;
-    const dataForUpload = new Float32Array(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
-
-    gl.bindTexture(tex2d, texture);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-    gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-    gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-    gl.texImage2D(
-        tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType,
-        dataForUpload);
-
-    const logicalShape: [number, number] = [3, 3];
-    const a = tf.tensor(
-        {texture, height, width, channels: 'RGBA'}, logicalShape, 'float32');
-
-    expect(a.shape).toEqual(logicalShape);
-    expect(a.dtype).toEqual('float32');
-    expectArraysClose(await a.data(), [0, 1, 2, 3, 4, 5, 6, 7, 8]);
-    gl.deleteTexture(texture);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/backend_webgl_test_registry.ts b/tfjs-master/tfjs-backend-webgl/src/backend_webgl_test_registry.ts
deleted file mode 100644
index 0339dd1e8..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/backend_webgl_test_registry.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import {Constraints, registerTestEnv} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-export const WEBGL_ENVS: Constraints = {
-  predicate: testEnv => testEnv.backendName === 'webgl'
-};
-export const PACKED_ENVS: Constraints = {
-  flags: {'WEBGL_PACK': true}
-};
-
-export function registerTestEnvs() {
-  registerTestEnv({
-    name: 'webgl1',
-    backendName: 'webgl',
-    flags: {
-      'WEBGL_VERSION': 1,
-      'WEBGL_CPU_FORWARD': false,
-      'WEBGL_SIZE_UPLOAD_UNIFORM': 0
-    },
-    isDataSync: true
-  });
-
-  registerTestEnv({
-    name: 'webgl2',
-    backendName: 'webgl',
-    flags: {
-      'WEBGL_VERSION': 2,
-      'WEBGL_CPU_FORWARD': false,
-      'WEBGL_SIZE_UPLOAD_UNIFORM': 0
-    },
-    isDataSync: true
-  });
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/base.ts b/tfjs-master/tfjs-backend-webgl/src/base.ts
deleted file mode 100644
index f2412eab1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/base.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// base.ts is the webgl backend without auto kernel registration.
-
-import {device_util, registerBackend} from '@tensorflow/tfjs-core';
-import {MathBackendWebGL} from './backend_webgl';
-export {version as version_webgl} from './version';
-
-if (device_util.isBrowser()) {
-  registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);
-}
-
-// Export webgl utilities
-export * from './webgl';
-
-// Export forceHalfFlost under webgl namespace for the union bundle.
-import {forceHalfFloat} from './webgl';
-export const webgl = {forceHalfFloat};
diff --git a/tfjs-master/tfjs-backend-webgl/src/batchnorm_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/batchnorm_gpu.ts
deleted file mode 100644
index 487453932..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/batchnorm_gpu.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class BatchNormProgram implements GPGPUProgram {
-  variableNames: string[];
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(
-      xShape: number[], meanShape: number[], varianceShape: number[],
-      offsetShape: number[]|null, scaleShape: number[]|null,
-      varianceEpsilon: number) {
-    this.variableNames = ['x', 'mean', 'variance'];
-    backend_util.assertAndGetBroadcastShape(xShape, meanShape);
-    backend_util.assertAndGetBroadcastShape(xShape, varianceShape);
-
-    let offsetSnippet = '0.0';
-    if (offsetShape != null) {
-      backend_util.assertAndGetBroadcastShape(xShape, offsetShape);
-      this.variableNames.push('offset');
-      offsetSnippet = 'getOffsetAtOutCoords()';
-    }
-
-    let scaleSnippet = '1.0';
-    if (scaleShape != null) {
-      backend_util.assertAndGetBroadcastShape(xShape, scaleShape);
-      this.variableNames.push('scale');
-      scaleSnippet = 'getScaleAtOutCoords()';
-    }
-
-    this.outputShape = xShape;
-    this.userCode = `
-      void main() {
-        float x = getXAtOutCoords();
-        float mean = getMeanAtOutCoords();
-        float variance = getVarianceAtOutCoords();
-        float offset = ${offsetSnippet};
-        float scale = ${scaleSnippet};
-        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
-        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/batchnorm_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/batchnorm_packed_gpu.ts
deleted file mode 100644
index b5c4f72d6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/batchnorm_packed_gpu.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class BatchNormPackedProgram implements GPGPUProgram {
-  variableNames: string[];
-  outputShape: number[];
-  userCode: string;
-  packedInputs = true;
-  packedOutput = true;
-
-  constructor(
-      xShape: number[], meanShape: number[], varianceShape: number[],
-      offsetShape: number[]|null, scaleShape: number[]|null,
-      varianceEpsilon: number) {
-    this.variableNames = ['x', 'mean', 'variance'];
-    backend_util.assertAndGetBroadcastShape(xShape, meanShape);
-    backend_util.assertAndGetBroadcastShape(xShape, varianceShape);
-
-    let offsetSnippet = 'vec4(0.0)';
-    if (offsetShape != null) {
-      backend_util.assertAndGetBroadcastShape(xShape, offsetShape);
-      this.variableNames.push('offset');
-      offsetSnippet = 'getOffsetAtOutCoords()';
-    }
-
-    let scaleSnippet = 'vec4(1.0)';
-    if (scaleShape != null) {
-      backend_util.assertAndGetBroadcastShape(xShape, scaleShape);
-      this.variableNames.push('scale');
-      scaleSnippet = 'getScaleAtOutCoords()';
-    }
-
-    this.outputShape = xShape;
-    this.userCode = `
-      void main() {
-        vec4 offset = ${offsetSnippet};
-        vec4 scale = ${scaleSnippet};
-
-        vec4 x = getXAtOutCoords();
-        vec4 mean = getMeanAtOutCoords();
-        vec4 variance = getVarianceAtOutCoords();
-
-        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));
-
-        setOutput((x - mean) * inv + offset);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/binaryop_complex_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/binaryop_complex_gpu.ts
deleted file mode 100644
index 3b9d0002d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/binaryop_complex_gpu.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-// (Ar + Ai)(Br + Bi) =
-// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr
-// Yr = ArBr - AB
-// Yi = ArBi + AiBr
-export const COMPLEX_MULTIPLY = {
-  REAL: 'return areal * breal - aimag * bimag;',
-  IMAG: 'return areal * bimag + aimag * breal;'
-};
-
-export class BinaryOpComplexProgram implements GPGPUProgram {
-  variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];
-  userCode: string;
-  outputShape: number[];
-
-  constructor(op: string, aShape: number[], bShape: number[]) {
-    this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);
-
-    this.userCode = `
-      float binaryOpComplex(
-          float areal, float aimag, float breal, float bimag) {
-        ${op}
-      }
-
-      void main() {
-        float areal = getARealAtOutCoords();
-        float aimag = getAImagAtOutCoords();
-        float breal = getBRealAtOutCoords();
-        float bimag = getBImagAtOutCoords();
-        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/binaryop_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/binaryop_gpu.ts
deleted file mode 100644
index 39fc2858f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/binaryop_gpu.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-
-export const CHECK_NAN_SNIPPET = `
-  if (isnan(a)) return a;
-  if (isnan(b)) return b;
-`;
-
-export const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';
-export class BinaryOpProgram implements GPGPUProgram {
-  variableNames = ['A', 'B'];
-  outputShape: number[];
-  userCode: string;
-  enableShapeUniforms: boolean;
-
-  constructor(op: string, aShape: number[], bShape: number[]) {
-    this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-    this.userCode = `
-      float binaryOperation(float a, float b) {
-        ${op}
-      }
-
-      void main() {
-        float a = getAAtOutCoords();
-        float b = getBAtOutCoords();
-        setOutput(binaryOperation(a, b));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/binaryop_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/binaryop_packed_gpu.ts
deleted file mode 100644
index 41928d41e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/binaryop_packed_gpu.ts
+++ /dev/null
@@ -1,125 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, util} from '@tensorflow/tfjs-core';
-
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-import {getChannels} from './packing_util';
-import {getCoordsDataType} from './shader_compiler';
-
-export const CHECK_NAN_SNIPPET_PACKED = `
-  result.r = isNaN.r ? NAN : result.r;
-  result.g = isNaN.g ? NAN : result.g;
-  result.b = isNaN.b ? NAN : result.b;
-  result.a = isNaN.a ? NAN : result.a;
-`;
-
-export const ELU_DER = `
-  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
-  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`;
-
-export const NOT_EQUAL = `
-  return vec4(notEqual(a, b));
-`;
-
-export class BinaryOpPackedProgram implements GPGPUProgram {
-  variableNames = ['A', 'B'];
-  outputShape: number[];
-  userCode: string;
-  supportsBroadcasting = true;
-  packedInputs = true;
-  packedOutput = true;
-  enableShapeUniforms: boolean;
-
-  constructor(
-      op: string, aShape: number[], bShape: number[],
-      checkOutOfBounds = false) {
-    this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);
-    const rank = this.outputShape.length;
-    this.enableShapeUniforms = useShapeUniforms(rank);
-    let checkOutOfBoundsString = '';
-    if (checkOutOfBounds) {
-      if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {
-        checkOutOfBoundsString = `
-          result.y = 0.;
-          result.z = 0.;
-          result.w = 0.;
-        `;
-      } else {
-        const dtype = getCoordsDataType(rank);
-        checkOutOfBoundsString = `
-          ${dtype} coords = getOutputCoords();
-        `;
-        if (rank === 1) {
-          if (this.enableShapeUniforms) {
-            checkOutOfBoundsString += `
-            result.y = (coords + 1) >= outShape ? 0. : result.y;
-            result.z = 0.;
-            result.w = 0.;
-          `;
-          } else {
-            checkOutOfBoundsString += `
-            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
-            result.z = 0.;
-            result.w = 0.;
-          `;
-          }
-        } else {
-          const channels = getChannels('coords', rank);
-          if (this.enableShapeUniforms) {
-            checkOutOfBoundsString += `
-            bool nextRowOutOfBounds =
-              (${channels[rank - 2]} + 1) >= outShape[${rank} - 2];
-            bool nextColOutOfBounds =
-              (${channels[rank - 1]} + 1) >= outShape[${rank} - 1];
-            result.y = nextColOutOfBounds ? 0. : result.y;
-            result.z = nextRowOutOfBounds ? 0. : result.z;
-            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
-          `;
-          } else {
-            checkOutOfBoundsString += `
-            bool nextRowOutOfBounds =
-              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};
-            bool nextColOutOfBounds =
-              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};
-            result.y = nextColOutOfBounds ? 0. : result.y;
-            result.z = nextRowOutOfBounds ? 0. : result.z;
-            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
-          `;
-          }
-        }
-      }
-    }
-
-    this.userCode = `
-      vec4 binaryOperation(vec4 a, vec4 b) {
-        ${op}
-      }
-
-      void main() {
-        vec4 a = getAAtOutCoords();
-        vec4 b = getBAtOutCoords();
-
-        vec4 result = binaryOperation(a, b);
-        ${checkOutOfBoundsString}
-
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/canvas_util.ts b/tfjs-master/tfjs-backend-webgl/src/canvas_util.ts
deleted file mode 100644
index 3c44681af..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/canvas_util.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-
-const contexts: {[key: string]: WebGLRenderingContext} = {};
-
-const WEBGL_ATTRIBUTES: WebGLContextAttributes = {
-  alpha: false,
-  antialias: false,
-  premultipliedAlpha: false,
-  preserveDrawingBuffer: false,
-  depth: false,
-  stencil: false,
-  failIfMajorPerformanceCaveat: true
-};
-
-export function clearWebGLContext(webGLVersion: number) {
-  delete contexts[webGLVersion];
-}
-
-export function setWebGLContext(
-    webGLVersion: number, gl: WebGLRenderingContext) {
-  contexts[webGLVersion] = gl;
-}
-
-export function getWebGLContext(
-    webGLVersion: number,
-    customCanvas?: HTMLCanvasElement|OffscreenCanvas): WebGLRenderingContext {
-  if (!(webGLVersion in contexts) || customCanvas != null) {
-    const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas);
-    if (newCtx !== null) {
-      contexts[webGLVersion] = newCtx;
-    } else {
-      console.log('Could not get context for WebGL version', webGLVersion);
-      return null;
-    }
-  }
-  const gl = contexts[webGLVersion];
-  if (gl == null || gl.isContextLost()) {
-    delete contexts[webGLVersion];
-    return getWebGLContext(webGLVersion);
-  }
-
-  gl.disable(gl.DEPTH_TEST);
-  gl.disable(gl.STENCIL_TEST);
-  gl.disable(gl.BLEND);
-  gl.disable(gl.DITHER);
-  gl.disable(gl.POLYGON_OFFSET_FILL);
-  gl.disable(gl.SAMPLE_COVERAGE);
-  gl.enable(gl.SCISSOR_TEST);
-  gl.enable(gl.CULL_FACE);
-  gl.cullFace(gl.BACK);
-
-  return contexts[webGLVersion];
-}
-
-function createCanvas(webGLVersion: number) {
-  // Use canvas element for Safari, since its offscreen canvas does not support
-  // fencing.
-  if (!env().getBool('IS_SAFARI') && typeof OffscreenCanvas !== 'undefined' &&
-      webGLVersion === 2) {
-    return new OffscreenCanvas(300, 150);
-  } else if (typeof document !== 'undefined') {
-    return document.createElement('canvas');
-  } else {
-    throw new Error('Cannot create a canvas in this context');
-  }
-}
-
-function getWebGLRenderingContext(
-    webGLVersion: number,
-    customCanvas?: HTMLCanvasElement|OffscreenCanvas): WebGLRenderingContext {
-  if (webGLVersion !== 1 && webGLVersion !== 2) {
-    throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');
-  }
-  const canvas =
-      customCanvas == null ? createCanvas(webGLVersion) : customCanvas;
-
-  canvas.addEventListener('webglcontextlost', (ev: Event) => {
-    ev.preventDefault();
-    delete contexts[webGLVersion];
-  }, false);
-
-  if (env().getBool('SOFTWARE_WEBGL_ENABLED')) {
-    WEBGL_ATTRIBUTES.failIfMajorPerformanceCaveat = false;
-  }
-
-  if (webGLVersion === 1) {
-    return (
-        // tslint:disable-next-line
-        canvas.getContext('webgl', WEBGL_ATTRIBUTES) as WebGLRenderingContext ||
-        (canvas as HTMLCanvasElement)
-            .getContext('experimental-webgl',
-                        WEBGL_ATTRIBUTES) as WebGLRenderingContext);
-  }
-  return canvas.getContext('webgl2', WEBGL_ATTRIBUTES) as WebGLRenderingContext;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/canvas_util_test.ts b/tfjs-master/tfjs-backend-webgl/src/canvas_util_test.ts
deleted file mode 100644
index 6f8b5deab..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/canvas_util_test.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {BROWSER_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {getWebGLContext} from './canvas_util';
-
-describeWithFlags('canvas_util', BROWSER_ENVS, () => {
-  it('Returns a valid canvas', () => {
-    const canvas =
-        getWebGLContext(tf.env().getNumber('WEBGL_VERSION')).canvas as
-            // tslint:disable-next-line: no-any
-            any;
-    expect(
-        (canvas instanceof HTMLCanvasElement) ||
-        (canvas instanceof OffscreenCanvas))
-        .toBe(true);
-  });
-
-  it('Returns a valid gl context', () => {
-    const gl = getWebGLContext(tf.env().getNumber('WEBGL_VERSION'));
-    expect(gl.isContextLost()).toBe(false);
-  });
-
-  it('Returns a valid user defined canvas.', () => {
-    const webGLVersion = tf.env().getNumber('WEBGL_VERSION');
-
-    const customCanvas = document.createElement('canvas');
-    customCanvas.width = 10;
-    customCanvas.height = 10;
-
-    const gl = getWebGLContext(webGLVersion, customCanvas);
-
-    expect(gl).not.toBeNull();
-    expect(gl.canvas).toBe(customCanvas);
-  });
-});
-
-describeWithFlags('canvas_util webgl2', {flags: {WEBGL_VERSION: 2}}, () => {
-  it('is ok when the user requests webgl 1 canvas', () => {
-    const canvas = getWebGLContext(1).canvas;
-    expect(canvas.getContext != null).toBe(true);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/clip_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/clip_gpu.ts
deleted file mode 100644
index b53aad13c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/clip_gpu.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {UniformType} from './shader_compiler';
-
-export class ClipProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  outputShape: number[];
-  customUniforms = [
-    {name: 'minVal', type: 'float' as UniformType},
-    {name: 'maxVal', type: 'float' as UniformType}
-  ];
-
-  constructor(aShape: number[]) {
-    this.outputShape = aShape;
-    this.userCode = `
-
-      void main() {
-        float value = getAAtOutCoords();
-        if (isnan(value)) {
-          setOutput(value);
-          return;
-        }
-
-        setOutput(clamp(value, minVal, maxVal));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/clip_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/clip_packed_gpu.ts
deleted file mode 100644
index 08d78fd32..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/clip_packed_gpu.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {UniformType} from './shader_compiler';
-
-export class ClipPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  packedInputs = true;
-  packedOutput = true;
-  userCode: string;
-  outputShape: number[];
-  customUniforms = [
-    {name: 'minVal', type: 'float' as UniformType},
-    {name: 'maxVal', type: 'float' as UniformType}
-  ];
-
-  constructor(aShape: number[]) {
-    this.outputShape = aShape;
-    this.userCode = `
-      void main() {
-        vec4 value = getAAtOutCoords();
-
-        if (any(isnan(value))) {
-          setOutput(value);
-          return;
-        }
-
-        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/complex_abs_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/complex_abs_gpu.ts
deleted file mode 100644
index d9c4635d0..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/complex_abs_gpu.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ComplexAbsProgram implements GPGPUProgram {
-  variableNames = ['real', 'imag'];
-  userCode: string;
-  outputShape: number[];
-
-  constructor(shape: number[]) {
-    this.outputShape = shape;
-    this.userCode = `
-      void main() {
-        float re = abs(getRealAtOutCoords());
-        float im = abs(getImagAtOutCoords());
-        float mx = max(re, im);
-
-        // sadly the length function in glsl is not underflow-safe
-        // (at least not on Intel GPUs). So the safe solution is
-        // to ensure underflow-safety in all cases.
-        setOutput(
-          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
-        );
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/concat_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/concat_gpu.ts
deleted file mode 100644
index 5b38e8d5d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/concat_gpu.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ConcatProgram implements GPGPUProgram {
-  variableNames: string[];
-  outputShape: number[] = [];
-  userCode: string;
-
-  // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().
-  constructor(shapes: Array<[number, number]>) {
-    this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);
-    this.variableNames = shapes.map((_, i) => `T${i}`);
-
-    const offsets: number[] = new Array(shapes.length - 1);
-    offsets[0] = shapes[0][1];
-    for (let i = 1; i < offsets.length; i++) {
-      offsets[i] = offsets[i - 1] + shapes[i][1];
-    }
-
-    const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];
-    for (let i = 1; i < offsets.length; i++) {
-      const shift = offsets[i - 1];
-      snippets.push(
-          `else if (yC < ${offsets[i]}) ` +
-          `setOutput(getT${i}(yR, yC-${shift}));`);
-    }
-    const lastIndex = offsets.length;
-    const lastShift = offsets[offsets.length - 1];
-    snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);
-
-    this.userCode = `
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int yR = coords.x;
-        int yC = coords.y;
-
-        ${snippets.join('\n        ')}
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/concat_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/concat_packed_gpu.ts
deleted file mode 100644
index 72ab1a94a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/concat_packed_gpu.ts
+++ /dev/null
@@ -1,125 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getChannels} from './packing_util';
-import {getCoordsDataType} from './shader_compiler';
-
-export class ConcatPackedProgram implements GPGPUProgram {
-  variableNames: string[];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(shapes: number[][], axis: number) {
-    this.outputShape = backend_util.computeOutShape(shapes, axis);
-    const shape = this.outputShape;
-    const rank = shape.length;
-    const dtype = getCoordsDataType(rank);
-    const coords = getChannels('coords', rank);
-    const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);
-    this.variableNames = shapes.map((_, i) => `T${i}`);
-
-    const offsets: number[] = new Array(shapes.length - 1);
-    offsets[0] = shapes[0][axis];
-    for (let i = 1; i < offsets.length; i++) {
-      offsets[i] = offsets[i - 1] + shapes[i][axis];
-    }
-
-    const channel = channels[axis];
-    const lastChannels = channels.slice(-2);
-    const allChannels = channels.join();
-
-    let getValueSnippet = `if (${channel} < ${offsets[0]}) {
-        return getChannel(
-            getT0(${allChannels}), vec2(${lastChannels.join()}));
-        }`;
-    for (let i = 1; i < offsets.length; i++) {
-      const shift = offsets[i - 1];
-      // Note: the >= comparison below may seem unnecessary given the check
-      // above but is needed to workaround branch execution issues on some
-      // devices. It makes all the conditions exclusive without relying on
-      // execution order.
-      getValueSnippet += `
-        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {
-          return getChannel(
-            getT${i}(${shiftedChannels(channels, channel, shift)}),
-            vec2(${shiftedChannels(lastChannels, channel, shift)}));
-        }`;
-    }
-    const lastIndex = offsets.length;
-    const shift = offsets[offsets.length - 1];
-    getValueSnippet += `
-        return getChannel(
-          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),
-          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;
-
-    this.userCode = `
-      float getValue(${channels.map(x => 'int ' + x)}) {
-        ${getValueSnippet}
-      }
-
-      void main() {
-        ${dtype} coords = getOutputCoords();
-        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);
-
-        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;
-        if (${coords[rank - 1]} < ${shape[rank - 1]}) {
-          result.g = getValue(${coords});
-        }
-
-        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;
-        if (${coords[rank - 2]} < ${shape[rank - 2]}) {
-          result.a = getValue(${coords});
-        }
-
-        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;
-        if (${coords[rank - 2]} < ${shape[rank - 2]} &&
-            ${coords[rank - 1]} < ${shape[rank - 1]}) {
-          result.b = getValue(${coords});
-        }
-        setOutput(result);
-      }
-    `;
-  }
-}
-
-/**
- * Return an expression for coordinates into a vector where a given channel
- * will be offset by [shift].
- *
- * @param channels the channels to consider
- * @param channel the channel we want shifted
- * @param shift  the amount to subtract from the channel.
- *
- * @returns a string of the form 'x, y-[shift], z' where any one channel can
- * have the shift applied.
- */
-function shiftedChannels(channels: string[], channel: string, shift: number) {
-  const channelIdx = channels.indexOf(channel);
-  const res = channels.map((c, idx) => {
-    if (idx === channelIdx) {
-      return `${c} - ${shift}`;
-    } else {
-      return c;
-    }
-  });
-  return res.join();
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/conv_backprop_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/conv_backprop_gpu.ts
deleted file mode 100644
index 21cb0e8ba..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/conv_backprop_gpu.ts
+++ /dev/null
@@ -1,295 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class Conv2DDerFilterProgram implements GPGPUProgram {
-  variableNames = ['x', 'dy'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.filterShape;
-
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const padTop = convInfo.padInfo.top;
-    const padLeft = convInfo.padInfo.left;
-    const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-
-    this.userCode = `
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int wR = coords.x;
-        int wC = coords.y;
-        int d1 = coords.z;
-        int d2 = coords.w;
-
-        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
-        // ? = to be determined. : = across all values in that axis.
-        float dotProd = 0.0;
-
-        for (int b = 0; b < ${convInfo.batchSize}; b++) {
-          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
-            int xR = wR + yR * ${strideHeight} - ${padTop};
-
-            if (xR < 0 || xR >= ${convInfo.inHeight}) {
-              continue;
-            }
-
-            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
-              int xC = wC + yC * ${strideWidth} - ${padLeft};
-
-              if (xC < 0 || xC >= ${convInfo.inWidth}) {
-                continue;
-              }
-
-              ${isChannelsLast?
-             `float dyValue = getDy(b, yR, yC, d2);
-              float xValue = getX(b, xR, xC, d1);
-              dotProd += (xValue * dyValue);` :
-             `float dyValue = getDy(b, d2, yR, yC);
-              float xValue = getX(b, d1, xR, xC);
-              dotProd += (xValue * dyValue);`}
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
-
-export class Conv2DDerInputProgram implements GPGPUProgram {
-  variableNames = ['dy', 'W'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-
-    const padTop = filterHeight - 1 - convInfo.padInfo.top;
-    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
-
-    const rowDim = isChannelsLast ? 1 : 2;
-    const colDim = isChannelsLast ? 2 : 3;
-    const channelDim = isChannelsLast ? 3 : 1;
-
-    this.userCode = `
-      const ivec2 pads = ivec2(${padTop}, ${padLeft});
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords[0];
-        int d1 = coords[${channelDim}];
-
-        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;
-        int dyRCorner = dyCorner.x;
-        int dyCCorner = dyCorner.y;
-
-        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
-        // ? = to be determined. : = across all values in that axis.
-        float dotProd = 0.0;
-        for (int wR = 0; wR < ${filterHeight}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
-
-          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
-            continue;
-          }
-          int idyR = int(dyR);
-
-          int wRPerm = ${filterHeight} - 1 - wR;
-
-          for (int wC = 0; wC < ${filterWidth}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
-
-            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
-                fract(dyC) > 0.0) {
-              continue;
-            }
-            int idyC = int(dyC);
-
-            int wCPerm = ${filterWidth} - 1 - wC;
-
-            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
-
-              if (${isChannelsLast}) {
-                float xValue = getDy(batch, idyR, idyC, d2);
-                float wValue = getW(wRPerm, wCPerm, d1, d2);
-                dotProd += xValue * wValue;
-              } else {
-                float xValue = getDy(batch, d2, idyR, idyC);
-                float wValue = getW(wRPerm, wCPerm, d1, d2);
-                dotProd += xValue * wValue;
-              }
-
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
-
-export class Conv3DDerFilterProgram implements GPGPUProgram {
-  variableNames = ['x', 'dy'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.filterShape;
-
-    const strideDepth = convInfo.strideDepth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const padFront = convInfo.padInfo.front;
-    const padTop = convInfo.padInfo.top;
-    const padLeft = convInfo.padInfo.left;
-
-    this.userCode = `
-      void main() {
-        ivec5 coords = getOutputCoords();
-        int wF = coords.x;
-        int wR = coords.y;
-        int wC = coords.z;
-        int d1 = coords.w;
-        int d2 = coords.u;
-
-        float dotProd = 0.0;
-
-        for (int b = 0; b < ${convInfo.batchSize}; b++) {
-          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
-            int xF = wF + yF * ${strideDepth} - ${padFront};
-
-            if (xF < 0 || xF >= ${convInfo.inDepth}) {
-              continue;
-            }
-
-            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
-              int xR = wR + yR * ${strideHeight} - ${padTop};
-
-              if (xR < 0 || xR >= ${convInfo.inHeight}) {
-                continue;
-              }
-
-              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
-                int xC = wC + yC * ${strideWidth} - ${padLeft};
-
-                if (xC < 0 || xC >= ${convInfo.inWidth}) {
-                  continue;
-                }
-
-                float dyValue = getDy(b, yF, yR, yC, d2);
-                float xValue = getX(b, xF, xR, xC, d1);
-                dotProd += (xValue * dyValue);
-              }
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
-
-export class Conv3DDerInputProgram implements GPGPUProgram {
-  variableNames = ['dy', 'W'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.inShape;
-
-    const filterDepth = convInfo.filterDepth;
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-    const strideDepth = convInfo.strideDepth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-
-    const padFront = filterDepth - 1 - convInfo.padInfo.front;
-    const padTop = filterHeight - 1 - convInfo.padInfo.top;
-    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
-
-    this.userCode = `
-      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
-
-      void main() {
-        ivec5 coords = getOutputCoords();
-        int batch = coords.x;
-        int d1 = coords.u;
-
-
-        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
-        int dyFCorner = dyCorner.x;
-        int dyRCorner = dyCorner.y;
-        int dyCCorner = dyCorner.z;
-
-        float dotProd = 0.0;
-        for (int wF = 0; wF < ${filterDepth}; wF++) {
-          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;
-
-          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
-            continue;
-          }
-          int idyF = int(dyF);
-
-          int wFPerm = ${filterDepth} - 1 - wF;
-
-          for (int wR = 0; wR < ${filterHeight}; wR++) {
-            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
-
-            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
-              fract(dyR) > 0.0) {
-              continue;
-            }
-            int idyR = int(dyR);
-
-            int wRPerm = ${filterHeight} - 1 - wR;
-
-            for (int wC = 0; wC < ${filterWidth}; wC++) {
-              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
-
-              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
-                  fract(dyC) > 0.0) {
-                continue;
-              }
-              int idyC = int(dyC);
-
-              int wCPerm = ${filterWidth} - 1 - wC;
-
-              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
-                float xValue = getDy(batch, idyF, idyR, idyC, d2);
-                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
-                dotProd += xValue * wValue;
-              }
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/conv_backprop_gpu_depthwise.ts b/tfjs-master/tfjs-backend-webgl/src/conv_backprop_gpu_depthwise.ts
deleted file mode 100644
index cf11e3dbf..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/conv_backprop_gpu_depthwise.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class DepthwiseConv2DDerFilterProgram implements GPGPUProgram {
-  variableNames = ['x', 'dy'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.filterShape;
-
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const padTop = convInfo.padInfo.top;
-    const padLeft = convInfo.padInfo.left;
-    const channelMul = convInfo.outChannels / convInfo.inChannels;
-
-    this.userCode = `
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int wR = coords.x;
-        int wC = coords.y;
-        int d1 = coords.z;
-        int dm = coords.w;
-        int d2 = d1 * ${channelMul} + dm;
-
-        float dotProd = 0.0;
-
-        // TO DO: Vec4 over the batch size
-        for (int b = 0; b < ${convInfo.batchSize}; b++) {
-          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
-            int xR = wR + yR * ${strideHeight} - ${padTop};
-
-            if (xR < 0 || xR >= ${convInfo.inHeight}) {
-              continue;
-            }
-
-            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
-              int xC = wC + yC * ${strideWidth} - ${padLeft};
-
-              if (xC < 0 || xC >= ${convInfo.inWidth}) {
-                continue;
-              }
-
-              float dyValue = getDy(b, yR, yC, d2);
-              float xValue = getX(b, xR, xC, d1);
-              dotProd += (xValue * dyValue);
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
-
-export class DepthwiseConv2DDerInputProgram implements GPGPUProgram {
-  variableNames = ['dy', 'W'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-
-    const padTop = filterHeight - 1 - convInfo.padInfo.top;
-    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
-    const channelMul = convInfo.outChannels / convInfo.inChannels;
-
-    this.userCode = `
-      const ivec2 pads = ivec2(${padTop}, ${padLeft});
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords[0];
-        int d1 = coords[3];
-        ivec2 dyCorner = coords.yz - pads;
-        int dyRCorner = dyCorner.x;
-        int dyCCorner = dyCorner.y;
-
-        float dotProd = 0.0;
-
-        for (int wR = 0; wR < ${filterHeight}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
-
-          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
-            continue;
-          }
-          int idyR = int(dyR);
-
-          int wRPerm = ${filterHeight} - 1 - wR;
-
-          for (int wC = 0; wC < ${filterWidth}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
-
-            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
-                fract(dyC) > 0.0) {
-              continue;
-            }
-            int idyC = int(dyC);
-
-            int wCPerm = ${filterWidth} - 1 - wC;
-
-            // TO DO: Vec4 over the channelMul
-            for (int dm = 0; dm < ${channelMul}; dm++) {
-              int d2 = d1 * ${channelMul} + dm;
-              float xValue = getDy(batch, idyR, idyC, d2);
-              float wValue = getW(wRPerm, wCPerm, d1, dm);
-              dotProd += xValue * wValue;
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/conv_backprop_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/conv_backprop_packed_gpu.ts
deleted file mode 100644
index 059d17fa3..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/conv_backprop_packed_gpu.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-
-export class Conv2DDerInputPackedProgram implements GPGPUProgram {
-  variableNames = ['dy', 'W'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[];
-  userCode: string;
-  enableShapeUniforms: boolean;
-  customUniforms = [
-    {name: 'strides', type: 'vec2' as const },
-  ];
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-
-    const padTop = filterHeight - 1 - convInfo.padInfo.top;
-    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
-
-    this.userCode = `
-      const ivec2 pads = ivec2(${padTop}, ${padLeft});
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords[0];
-        int d1 = coords[3];
-
-        ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;
-        int dyRCorner = dyCorner.x;
-        int dyCCorner = dyCorner.y;
-
-        vec4 result = vec4(0.);
-        for (int wR = 0; wR < ${filterHeight}; wR++) {
-          float dyR = float(dyRCorner + wR) / strides[0];
-          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
-            continue;
-          }
-          int idyR = int(dyR);
-          int wRPerm = ${filterHeight} - 1 - wR;
-
-          for (int wC = 0; wC < ${filterWidth}; wC++) {
-            int wCPerm = ${filterWidth} - 1 - wC;
-
-            float dyC = float(dyCCorner + wC) / strides[1];
-            bool idyCVal = (dyC >= 0.0) && (dyC < ${convInfo.outWidth}.0)
-              && (fract(dyC) == 0.0);
-            int idyC = int(dyC);
-
-            float dyC2 = float(dyCCorner + wC + 1) / strides[1];
-            bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${convInfo.outWidth}.0)
-              && (fract(dyC2) == 0.0);
-            int idyC2 = int(dyC2);
-
-            if (idyCVal && idyCVal2) {
-              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) {
-                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
-                vec4 dySample = getDy(batch, idyR, idyC, d2);
-                vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?
-                  dySample : getDy(batch, idyR, idyC2, d2);
-
-                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
-                  dySample.xy : dySample.zw;
-                result.xy += vec2(dot(dyValue, wValue.xy),
-                  dot(dyValue, wValue.zw));
-
-                dyValue = mod(float(idyC2), 2.) == 0. ?
-                  dySample2.xy : dySample2.zw;
-                result.zw += vec2(dot(dyValue, wValue.xy),
-                  dot(dyValue, wValue.zw));
-              }
-            } else if (idyCVal) {
-              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) {
-                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
-                vec4 dySample = getDy(batch, idyR, idyC, d2);
-                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
-                  dySample.xy : dySample.zw;
-                result.xy += vec2(dot(dyValue, wValue.xy),
-                  dot(dyValue, wValue.zw));
-              }
-            } else if (idyCVal2) {
-              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) {
-                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
-                vec4 dySample = getDy(batch, idyR, idyC2, d2);
-                vec2 dyValue = mod(float(idyC2), 2.) == 0. ?
-                  dySample.xy : dySample.zw;
-                result.zw += vec2(dot(dyValue, wValue.xy),
-                  dot(dyValue, wValue.zw));
-              }
-            }
-          }
-        }
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/conv_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/conv_gpu.ts
deleted file mode 100644
index b998e08be..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/conv_gpu.ts
+++ /dev/null
@@ -1,326 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class Conv2DProgram implements GPGPUProgram {
-  variableNames = ['x', 'W'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, addBias = false,
-      activation: string = null, hasPreluActivationWeights = false,
-      hasLeakyreluAlpha = false) {
-    this.outputShape = convInfo.outShape;
-    const padTop = convInfo.padInfo.top;
-    const padLeft = convInfo.padInfo.left;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const dilationHeight = convInfo.dilationHeight;
-    const dilationWidth = convInfo.dilationWidth;
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-
-    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
-    const inputDepthVec4Remainder = convInfo.inChannels % 4;
-    const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-
-    const rowDim = isChannelsLast ? 1 : 2;
-    const colDim = isChannelsLast ? 2 : 3;
-    const channelDim = isChannelsLast ? 3 : 1;
-
-    let activationSnippet = '', applyActivationSnippet = '';
-    if (activation) {
-      if (hasPreluActivationWeights) {
-        activationSnippet = `float activation(float a) {
-          float b = getPreluActivationWeightsAtOutCoords();
-          ${activation}
-        }`;
-      } else if (hasLeakyreluAlpha) {
-        activationSnippet = `float activation(float a) {
-          float b = getLeakyreluAlphaAtOutCoords();
-          ${activation}
-        }`;
-      } else {
-        activationSnippet = `
-          float activation(float x) {
-            ${activation}
-          }
-        `;
-      }
-
-      applyActivationSnippet = `result = activation(result);`;
-    }
-
-    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-
-    if (hasPreluActivationWeights) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    if (hasLeakyreluAlpha) {
-      this.variableNames.push('leakyreluAlpha');
-    }
-
-    this.userCode = `
-      ${activationSnippet}
-
-      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
-      const ivec2 pads = ivec2(${padTop}, ${padLeft});
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords[0];
-        int d2 = coords[${channelDim}];
-
-        ivec2 xRCCorner =
-            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;
-        int xRCorner = xRCCorner.x;
-        int xCCorner = xRCCorner.y;
-
-        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
-        // ? = to be determined. : = across all values in that axis.
-        float dotProd = 0.0;
-        for (int wR = 0; wR < ${filterHeight}; wR++) {
-          int xR = xRCorner + wR * ${dilationHeight};
-
-          if (xR < 0 || xR >= ${convInfo.inHeight}) {
-            continue;
-          }
-
-          for (int wC = 0; wC < ${filterWidth}; wC++) {
-            int xC = xCCorner + wC * ${dilationWidth};
-
-            if (xC < 0 || xC >= ${convInfo.inWidth}) {
-              continue;
-            }
-
-            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
-              vec4 wValues = vec4(
-                getW(wR, wC, d1, d2),
-                getW(wR, wC, d1 + 1, d2),
-                getW(wR, wC, d1 + 2, d2),
-                getW(wR, wC, d1 + 3, d2)
-              );
-
-              if (${isChannelsLast}) {
-                vec4 xValues = vec4(
-                  getX(batch, xR, xC, d1),
-                  getX(batch, xR, xC, d1 + 1),
-                  getX(batch, xR, xC, d1 + 2),
-                  getX(batch, xR, xC, d1 + 3)
-                );
-                dotProd += dot(xValues, wValues);
-              } else {
-                vec4 xValues = vec4(
-                  getX(batch, d1, xR, xC),
-                  getX(batch, d1 + 1, xR, xC),
-                  getX(batch, d1 + 2, xR, xC),
-                  getX(batch, d1 + 3, xR, xC)
-                );
-                dotProd += dot(xValues, wValues);
-              }
-            }
-
-            if (${inputDepthVec4Remainder === 1}) {
-
-              if (${isChannelsLast}) {
-                dotProd +=
-                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *
-                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
-              } else {
-                dotProd +=
-                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *
-                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
-              }
-
-            } else if (${inputDepthVec4Remainder === 2}) {
-              vec2 wValues = vec2(
-                getW(wR, wC, ${inputDepthNearestVec4}, d2),
-                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
-              );
-
-              if (${isChannelsLast}) {
-                vec2 xValues = vec2(
-                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
-                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
-                );
-                dotProd += dot(xValues, wValues);
-              } else {
-                vec2 xValues = vec2(
-                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
-                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
-                );
-                dotProd += dot(xValues, wValues);
-              }
-
-            } else if (${inputDepthVec4Remainder === 3}) {
-              vec3 wValues = vec3(
-                getW(wR, wC, ${inputDepthNearestVec4}, d2),
-                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
-                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
-              );
-
-              if (${isChannelsLast}) {
-                vec3 xValues = vec3(
-                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
-                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
-                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
-                );
-                dotProd += dot(xValues, wValues);
-              } else {
-                vec3 xValues = vec3(
-                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
-                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
-                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
-                );
-                dotProd += dot(xValues, wValues);
-              }
-
-            }
-          }
-        }
-
-        float result = dotProd;
-        ${addBiasSnippet}
-        ${applyActivationSnippet}
-        setOutput(result);
-      }
-    `;
-  }
-}
-
-export class Conv3DProgram implements GPGPUProgram {
-  variableNames = ['x', 'W'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.outShape;
-    const padFront = convInfo.padInfo.front;
-    const padTop = convInfo.padInfo.top;
-    const padLeft = convInfo.padInfo.left;
-    const strideDepth = convInfo.strideDepth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const dilationDepth = convInfo.dilationDepth;
-    const dilationHeight = convInfo.dilationHeight;
-    const dilationWidth = convInfo.dilationWidth;
-    const filterDepth = convInfo.filterDepth;
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-
-    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
-    const inputDepthVec4Remainder = convInfo.inChannels % 4;
-
-    this.userCode = `
-      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${
-        strideWidth});
-      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
-
-      void main() {
-        ivec5 coords = getOutputCoords();
-        int batch = coords.x;
-        int d2 = coords.u;
-
-        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
-        int xFCorner = xFRCCorner.x;
-        int xRCorner = xFRCCorner.y;
-        int xCCorner = xFRCCorner.z;
-
-        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
-        // y(yF, yR, yC, d2). ? = to be determined. : = across all
-        // values in that axis.
-        float dotProd = 0.0;
-        for (int wF = 0; wF < ${filterDepth}; wF++) {
-          int xF = xFCorner + wF * ${dilationDepth};
-
-          if (xF < 0 || xF >= ${convInfo.inDepth}) {
-            continue;
-          }
-
-          for (int wR = 0; wR < ${filterHeight}; wR++) {
-            int xR = xRCorner + wR * ${dilationHeight};
-
-            if (xR < 0 || xR >= ${convInfo.inHeight}) {
-              continue;
-            }
-
-            for (int wC = 0; wC < ${filterWidth}; wC++) {
-              int xC = xCCorner + wC * ${dilationWidth};
-
-              if (xC < 0 || xC >= ${convInfo.inWidth}) {
-                continue;
-              }
-
-              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
-                vec4 xValues = vec4(
-                  getX(batch, xF, xR, xC, d1),
-                  getX(batch, xF, xR, xC, d1 + 1),
-                  getX(batch, xF, xR, xC, d1 + 2),
-                  getX(batch, xF, xR, xC, d1 + 3)
-                );
-                vec4 wValues = vec4(
-                  getW(wF, wR, wC, d1, d2),
-                  getW(wF, wR, wC, d1 + 1, d2),
-                  getW(wF, wR, wC, d1 + 2, d2),
-                  getW(wF, wR, wC, d1 + 3, d2)
-                );
-
-                dotProd += dot(xValues, wValues);
-              }
-
-              if (${inputDepthVec4Remainder === 1}) {
-                dotProd +=
-                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
-                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
-              } else if (${inputDepthVec4Remainder === 2}) {
-                vec2 xValues = vec2(
-                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
-                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
-                );
-                vec2 wValues = vec2(
-                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
-                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
-                );
-                dotProd += dot(xValues, wValues);
-              } else if (${inputDepthVec4Remainder === 3}) {
-                vec3 xValues = vec3(
-                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
-                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
-                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
-                );
-                vec3 wValues = vec3(
-                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
-                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
-                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
-                );
-                dotProd += dot(xValues, wValues);
-              }
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/conv_gpu_depthwise.ts b/tfjs-master/tfjs-backend-webgl/src/conv_gpu_depthwise.ts
deleted file mode 100644
index a4d13539d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/conv_gpu_depthwise.ts
+++ /dev/null
@@ -1,124 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-
-export class DepthwiseConv2DProgram implements GPGPUProgram {
-  variableNames = ['x', 'W'];
-  outputShape: number[];
-  userCode: string;
-  enableShapeUniforms: boolean;
-  customUniforms = [
-    {name: 'pads', type: 'ivec2' as const },
-    {name: 'strides', type: 'ivec2' as const },
-    {name: 'dilations', type: 'ivec2' as const },
-    {name: 'inDims', type: 'ivec2' as const },
-  ];
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, addBias = false,
-      activation: string = null, hasPreluActivation = false,
-      hasLeakyReluAlpha = false) {
-    this.outputShape = convInfo.outShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-    const channelMul = convInfo.outChannels / convInfo.inChannels;
-
-    let activationSnippet = '', applyActivationSnippet = '';
-    if (activation) {
-      if (hasPreluActivation) {
-        activationSnippet = `float activation(float a) {
-          float b = getPreluActivationWeightsAtOutCoords();
-          ${activation}
-        }`;
-      } else if (hasLeakyReluAlpha) {
-        activationSnippet = `float activation(float a) {
-          float b = getLeakyreluAlphaAtOutCoords();
-          ${activation}
-        }`;
-      } else {
-        activationSnippet = `
-          float activation(float x) {
-            ${activation}
-          }
-        `;
-      }
-
-      applyActivationSnippet = `result = activation(result);`;
-    }
-
-    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-
-    if (hasPreluActivation) {
-      this.variableNames.push('preluActivationWeights');
-    }
-    if (hasLeakyReluAlpha) {
-      this.variableNames.push('leakyreluAlpha');
-    }
-
-    this.userCode = `
-      ${activationSnippet}
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords.x;
-        ivec2 xRCCorner = coords.yz * strides - pads;
-        int d2 = coords.w;
-        int d1 = d2 / ${channelMul};
-        int q = d2 - d1 * ${channelMul};
-
-        int xRCorner = xRCCorner.x;
-        int xCCorner = xRCCorner.y;
-
-        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
-        // ? = to be determined. : = across all values in that axis.
-        float dotProd = 0.0;
-        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
-        for (int wR = 0; wR < ${filterHeight}; wR++) {
-          int xR = xRCorner + wR * dilations[0];
-
-          if (xR < 0 || xR >= inDims[0]) {
-            continue;
-          }
-
-          for (int wC = 0; wC < ${filterWidth}; wC++) {
-            int xC = xCCorner + wC * dilations[1];
-
-            if (xC < 0 || xC >= inDims[1]) {
-              continue;
-            }
-
-            float xVal = getX(batch, xR, xC, d1);
-            float wVal = getW(wR, wC, d1, q);
-            dotProd += xVal * wVal;
-          }
-        }
-
-        float result = dotProd;
-        ${addBiasSnippet}
-        ${applyActivationSnippet}
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/conv_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/conv_packed_gpu.ts
deleted file mode 100644
index 12154dfcd..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/conv_packed_gpu.ts
+++ /dev/null
@@ -1,411 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
- import {backend_util, util} from '@tensorflow/tfjs-core';
-
- import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-
- export class Conv2DPackedProgram implements GPGPUProgram {
-   variableNames = ['x', 'W'];
-   packedInputs = true;
-   packedOutput = true;
-   outputShape: number[];
-   userCode: string;
-   enableShapeUniforms: boolean;
-   customUniforms = [
-     {name: 'pads', type: 'ivec2' as const },
-     {name: 'strides', type: 'ivec2' as const },
-     {name: 'dilations', type: 'ivec2' as const },
-     {name: 'inDims', type: 'ivec2' as const },
-   ];
-
-   constructor(
-       convInfo: backend_util.Conv2DInfo, addBias = false,
-       activation: string = null, hasPreluActivation = false,
-       hasLeakyReluAlpha = false) {
-     this.outputShape = convInfo.outShape;
-     this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-     const padLeft = convInfo.padInfo.left;
-     const strideWidth = convInfo.strideWidth;
-     const dilationWidth = convInfo.dilationWidth;
-     const filterHeight = convInfo.filterHeight;
-     const filterWidth = convInfo.filterWidth;
-     const texelsAcross = filterWidth;
-
-     let mainLoop = `
-       int xR; int xC; int xCOffset;
-       vec4 wTexel; vec4 previous; vec4 final;`;
-
-     for (let c = 0; c < filterWidth; c++) {
-       mainLoop += `
-           vec4 xTexelC${c * 2};
-           int xTexelC${c * 2}Ready;
-           vec4 xTexelC${c * 2 + 1};
-           int xTexelC${c * 2 + 1}Ready;
-           vec4 xC${c};`;
-     }
-
-     /**
-      * This vectorized implementation works by gathering the values needed for
-      * each output channel's dot product into vec4's and then multiplying them
-      * all together (this happens in the final double for-loop below). Most of
-      * the main loop consists of constructing these vec4's with the minimum
-      * number of texture2D calls, which means making use of all four returned
-      * values from a texture2D call at once.
-      */
-     mainLoop += `
-     for (int r = 0; r < ${filterHeight}; r++) {
-      for (int d1 = 0; d1 < ${convInfo.inChannels}; d1 += 2) {
-       `;
-     for (let c = 0; c < filterWidth; c++) {
-       mainLoop += `
-           xTexelC${c * 2} = vec4(0.0);
-           xTexelC${c * 2}Ready = 0;
-           xTexelC${c * 2 + 1} = vec4(0.0);
-           xTexelC${c * 2 + 1}Ready = 0;
-           xC${c} = vec4(0.0);`;
-     }
-     mainLoop += `
-         xR = xRCorner + r * dilations[0];
-         if (xR >=0 && xR < inDims[0]) {
-       `;
-
-     for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {
-       const colIndex = texelC * 2;
-
-       mainLoop += `
-           xC = xCCorner + ${colIndex * dilationWidth};
-           `;
-
-       if (strideWidth === 1) {
-         if (colIndex < filterWidth) {
-           // If padding is odd, the outer texels have to be composed.
-           if (padLeft % 2 === 1) {
-             // TODO: Ensure vec4 previous does not result in redundant sample,
-             // and avoid setting xTexelRC's that exceed the boundary in the
-             // first place rather than resetting them to vec4(0)).
-
-             // To compute xCOffset:
-             // - If padding is odd, we must add 1 to ensure we ask for an
-             // even-numbered row.
-             // - We subtract 2 to access the previous texel.
-
-             mainLoop += `
-                 xCOffset = xC + 1;
-                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                 colIndex}Ready == 0) {
-                   xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
-
-                   // Need to manually clear unused channels in case
-                   // we're reading from recycled texture.
-                   if (xCOffset + 1 >= inDims[1]) {
-                     xTexelC${colIndex}.zw = vec2(0.0);
-                   }
-                   xTexelC${colIndex}Ready = 1;
-                 }
-               `;
-             // This texel has been read in previous iteration if the dilation
-             // is 1.
-             if (dilationWidth === 1 && colIndex > 0) {
-               mainLoop += `
-                 xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${
-                   colIndex}.xy);
-                 `;
-             } else {
-               mainLoop += `
-                   xCOffset = xC + 1 - 2;
-
-                   if (xCOffset >= 0 && xCOffset < inDims[1]) {
-                     previous = getX(batch, xR, xCOffset, d1);
-
-                     // Need to manually clear unused channels in case
-                     // we're reading from recycled texture.
-                     if (xCOffset + 1 >= inDims[1]) {
-                       previous.zw = vec2(0.0);
-                     }
-
-                     xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);
-                   } else {
-                     xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);
-                   }
-                   `;
-             }
-           } else {
-             // Padding is even, so xRC corresponds to a single texel.
-             mainLoop += `
-                 if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
-                   xTexelC${colIndex} = getX(batch, xR, xC, d1);
-                   if (xC + 1 >= inDims[1]) {
-                     xTexelC${colIndex}.zw = vec2(0.0);
-                   }
-                   xTexelC${colIndex}Ready = 1;
-                 }
-
-                 xC${colIndex} = xTexelC${colIndex};
-                 `;
-           }
-
-           if (colIndex + 1 < filterWidth) {
-             // If dilation is even, the second entry should match the first
-             // (either both are composed or both are single samples). But if
-             // dilation is odd, then the second entry should be the opposite
-             // of the first (if the first is composed, the second is a single
-             // sample, and vice versa.)
-
-             const nextTexelOffset = padLeft % 2 === 0 ?
-                 util.nearestLargerEven(dilationWidth) :
-                 dilationWidth;
-
-             if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||
-                 (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {
-               mainLoop += `
-                   xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};
-
-                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                   colIndex + 1}Ready == 0) {
-                     xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
-
-                     // Need to manually clear unused channels in case
-                     // we're reading from recycled texture.
-                     if (xCOffset + 1 >= inDims[1]) {
-                       xTexelC${colIndex + 1}.zw = vec2(0.0);
-                     }
-                     xTexelC${colIndex + 1}Ready = 1;
-                   }
-                   `;
-
-               // If dilation > 1 then the xRC's will not be able to share any
-               // values, so each xRC will require two unique calls to getX.
-               if (dilationWidth > 1) {
-                 mainLoop += `
-                     xCOffset -= 2;
-                     if (xCOffset >= 0 && xCOffset < inDims[1]) {
-                      previous = getX(batch, xR, xCOffset, d1);
-                      xC${colIndex + 1} = vec4(previous.zw, xTexelC${
-                        colIndex + 1}.xy);
-                     } else {
-                      xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${
-                        colIndex + 1}.xy);
-                     }
-                     `;
-               } else {
-                 mainLoop += `
-                     xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${
-                     colIndex + 1}.xy);
-                     `;
-               }
-
-             } else {
-               // If dilation is 1 and padding is odd, we have already read the
-               // texel when constructing the previous x value. Here we can
-               // simply skip the texture read.
-               if (nextTexelOffset === 1) {
-                 mainLoop += `
-                     xC${colIndex + 1} = xTexelC${colIndex};
-                     `;
-               } else {
-                 mainLoop += `
-                     xCOffset = xC + ${nextTexelOffset};
-
-                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                     colIndex + 1}Ready == 0) {
-                       xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
-                       if (xCOffset + 1 >= inDims[1]) {
-                         xTexelC${colIndex + 1}.zw = vec2(0.0);
-                       }
-                       xTexelC${colIndex + 1}Ready = 1;
-                     }
-
-                     xC${colIndex + 1} = xTexelC${colIndex + 1};
-                     `;
-               }
-             }
-           }
-         }
-       } else {  // stride === 2
-         if (colIndex < filterWidth) {
-           // Depending on whether padLeft is even or odd, we want either the
-           // xy or zw channels from X texels for xC${colIndex}. If padLeft is
-           // even, xC${colIndex +1} is simply the zw channels of texels we've
-           // already sampled. But if padLeft is odd, xC{$c + 1}.zw will
-           // need to come from the xy channels of a new texel, hence the `
-           // vec4
-           // final` initialized below.
-           if (padLeft % 2 === 1) {
-             mainLoop += `
-                 xCOffset = xC + 1 - strides[1];
-                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                 colIndex}Ready == 0) {
-                   xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
-                   // Need to manually clear unused channels in case
-                   // we're reading from recycled texture.
-                   if (xCOffset + 1 >= inDims[1]) {
-                     xTexelC${colIndex}.zw = vec2(0.0);
-                   }
-                   xTexelC${colIndex}Ready = 1;
-                 }
-
-                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${
-                 colIndex + 1}Ready == 0) {
-                   xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);
-                   // Need to manually clear unused channels in case
-                   // we're reading from recycled texture.
-                   if (xC + 2 >= inDims[1]) {
-                     xTexelC${colIndex + 1}.zw = vec2(0.0);
-                   }
-                   xTexelC${colIndex + 1}Ready = 1;
-                 }
-
-                 xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${
-                 colIndex + 1}.zw);
-               `;
-
-             if (colIndex + 1 < filterWidth) {
-               mainLoop += `
-                   final = vec4(0.0);
-                   xCOffset = xC + 1 + strides[1];
-                   if(xCOffset >= 0 && xCOffset < inDims[1]) {
-                     final = getX(batch, xR, xCOffset, d1);
-                   }
-                   xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);
-                 `;
-             }
-           } else {
-             mainLoop += `
-                 if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
-                   xTexelC${colIndex} = getX(batch, xR, xC, d1);
-                   if (xC + 1 >= inDims[1]) {
-                     xTexelC${colIndex}.zw = vec2(0.0);
-                   }
-                   xTexelC${colIndex}Ready = 1;
-                 }
-
-                 xCOffset = xC + strides[1];
-                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                 colIndex + 1}Ready == 0) {
-                   xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
-                   if (xCOffset + 1 >= inDims[1]) {
-                     xTexelC${colIndex + 1}.zw = vec2(0.);
-                   }
-                   xTexelC${colIndex + 1}Ready = 1;
-                 }
-
-                 xC${colIndex} = vec4(
-                   xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);
-               `;
-
-             if (colIndex + 1 < filterWidth) {
-               mainLoop += `
-                   xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${
-                   colIndex + 1}.zw);
-                 `;
-             }
-           }
-         }
-       }
-
-       // localize the dotProd accumulation within the loop, the theory is for
-       // GPU with limited cache, accumulate sum across large amount of
-       // veriables will cause lots of cache misses. (i.e. 5x5 filter will have
-       // 50 variables)
-       if (colIndex < filterWidth) {
-         mainLoop += `
-             wTexel = getW(r, ${colIndex}, d1, d2);
-             dotProd += xC${colIndex}.xxzz * vec4(wTexel.xy, wTexel.xy);
-             if(d1 + 1 < ${convInfo.inChannels}) {
-               dotProd += xC${colIndex}.yyww * vec4(wTexel.zw, wTexel.zw);
-             }
-           `;
-
-         if (colIndex + 1 < filterWidth) {
-           mainLoop += `
-               wTexel = getW(r, ${colIndex + 1}, d1, d2);
-               dotProd += xC${colIndex + 1}.xxzz * vec4(wTexel.xy, wTexel.xy);
-               if(d1 + 1 < ${convInfo.inChannels}) {
-                 dotProd += xC${colIndex + 1}.yyww * vec4(wTexel.zw, wTexel.zw);
-               }
-             `;
-         }
-       }
-     }
-     mainLoop += `
-     }
-   `;
-   mainLoop += `
-     }
-   `;
-   mainLoop += `
-     }
-   `;
-
-     let activationSnippet = '', applyActivationSnippet = '';
-     if (activation) {
-       if (hasPreluActivation) {
-         activationSnippet = `vec4 activation(vec4 a) {
-           vec4 b = getPreluActivationWeightsAtOutCoords();
-           ${activation}
-         }`;
-       } else if (hasLeakyReluAlpha) {
-         activationSnippet = `vec4 activation(vec4 a) {
-           vec4 b = getLeakyreluAlphaAtOutCoords();
-           ${activation}
-         }`;
-       } else {
-         activationSnippet = `vec4 activation(vec4 x) {
-           ${activation}
-         }`;
-       }
-
-       applyActivationSnippet = `result = activation(result);`;
-     }
-
-     const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
-     if (addBias) {
-       this.variableNames.push('bias');
-     }
-
-     if (hasPreluActivation) {
-       this.variableNames.push('preluActivationWeights');
-     }
-     if (hasLeakyReluAlpha) {
-       this.variableNames.push('leakyreluAlpha');
-     }
-
-     this.userCode = `
-       ${activationSnippet}
-
-       void main() {
-         ivec4 coords = getOutputCoords();
-         int batch = coords.x;
-         ivec2 xRCCorner = coords.yz * strides - pads;
-         int d2 = coords.w;
-         int xRCorner = xRCCorner.x;
-         int xCCorner = xRCCorner.y;
-
-         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
-         vec4 dotProd = vec4(0.000000000000001);
-
-         ${mainLoop}
-
-         vec4 result = dotProd - vec4(0.000000000000001);
-         ${addBiasSnippet}
-         ${applyActivationSnippet}
-         setOutput(result);
-       }
-     `;
-   }
- }
diff --git a/tfjs-master/tfjs-backend-webgl/src/conv_packed_gpu_depthwise.ts b/tfjs-master/tfjs-backend-webgl/src/conv_packed_gpu_depthwise.ts
deleted file mode 100644
index 8a66f865c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/conv_packed_gpu_depthwise.ts
+++ /dev/null
@@ -1,403 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, util} from '@tensorflow/tfjs-core';
-
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-
-export class DepthwiseConvPacked2DProgram implements GPGPUProgram {
-  variableNames = ['x', 'W'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[];
-  userCode: string;
-  enableShapeUniforms: boolean;
-  customUniforms = [
-    {name: 'pads', type: 'ivec2' as const },
-    {name: 'strides', type: 'ivec2' as const },
-    {name: 'dilations', type: 'ivec2' as const },
-    {name: 'inDims', type: 'ivec2' as const },
-  ];
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, addBias = false,
-      activation: string = null, hasPreluActivation = false,
-      hasLeakyReluAlpha = false) {
-    this.outputShape = convInfo.outShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-    const channelMul = convInfo.outChannels / convInfo.inChannels;
-    const padLeft = convInfo.padInfo.left;
-    const strideWidth = convInfo.strideWidth;
-    const dilationWidth = convInfo.dilationWidth;
-    const filterHeight = convInfo.filterHeight;
-    const filterWidth = convInfo.filterWidth;
-    const texelsAcross = filterWidth;
-
-    let mainLoop = `
-      int xR; int xC; int xCOffset;
-      vec4 wTexel; vec4 previous; vec4 final;`;
-
-    for (let c = 0; c < filterWidth; c++) {
-      mainLoop += `
-          vec4 xTexelC${c * 2};
-          int xTexelC${c * 2}Ready;
-          vec4 xTexelC${c * 2 + 1};
-          int xTexelC${c * 2 + 1}Ready;
-          vec4 xC${c};`;
-    }
-
-    /**
-     * This vectorized implementation works by gathering the values needed for
-     * each output channel's dot product into vec4's and then multiplying them
-     * all together (this happens in the final double for-loop below). Most of
-     * the main loop consists of constructing these vec4's with the minimum
-     * number of texture2D calls, which means making use of all four returned
-     * values from a texture2D call at once.
-     */
-    mainLoop += `
-    for (int r = 0; r < ${filterHeight}; r++) {
-      `;
-    for (let c = 0; c < filterWidth; c++) {
-      mainLoop += `
-          xTexelC${c * 2} = vec4(0.0);
-          xTexelC${c * 2}Ready = 0;
-          xTexelC${c * 2 + 1} = vec4(0.0);
-          xTexelC${c * 2 + 1}Ready = 0;
-          xC${c} = vec4(0.0);`;
-    }
-    mainLoop += `
-        xR = xRCorner + r * dilations[0];
-        if (xR >=0 && xR < inDims[0]) {
-      `;
-
-    for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {
-      const colIndex = texelC * 2;
-
-      mainLoop += `
-          xC = xCCorner + ${colIndex * dilationWidth};
-          `;
-
-      if (strideWidth === 1) {
-        if (colIndex < filterWidth) {
-          // If padding is odd, the outer texels have to be composed.
-          if (padLeft % 2 === 1) {
-            // TODO: Ensure vec4 previous does not result in redundant sample,
-            // and avoid setting xTexelRC's that exceed the boundary in the
-            // first place rather than resetting them to vec4(0)).
-
-            // To compute xCOffset:
-            // - If padding is odd, we must add 1 to ensure we ask for an
-            // even-numbered row.
-            // - We subtract 2 to access the previous texel.
-
-            mainLoop += `
-                xCOffset = xC + 1;
-                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                colIndex}Ready == 0) {
-                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
-
-                  // Need to manually clear unused channels in case
-                  // we're reading from recycled texture.
-                  if (xCOffset + 1 >= inDims[1]) {
-                    xTexelC${colIndex}.zw = vec2(0.0);
-                  }
-                  xTexelC${colIndex}Ready = 1;
-                }
-              `;
-            // This texel has been read in previous iteration if the dilation
-            // is 1.
-            if (dilationWidth === 1 && colIndex > 0) {
-              mainLoop += `
-                xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${
-                  colIndex}.xy);
-                `;
-            } else {
-              mainLoop += `
-                  xCOffset = xC + 1 - 2;
-
-                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
-                    previous = getX(batch, xR, xCOffset, d1);
-
-                    // Need to manually clear unused channels in case
-                    // we're reading from recycled texture.
-                    if (xCOffset + 1 >= inDims[1]) {
-                      previous.zw = vec2(0.0);
-                    }
-
-                    xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);
-                  } else {
-                    xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);
-                  }
-                  `;
-            }
-          } else {
-            // Padding is even, so xRC corresponds to a single texel.
-            mainLoop += `
-                if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
-                  xTexelC${colIndex} = getX(batch, xR, xC, d1);
-                  if (xC + 1 >= inDims[1]) {
-                    xTexelC${colIndex}.zw = vec2(0.0);
-                  }
-                  xTexelC${colIndex}Ready = 1;
-                }
-
-                xC${colIndex} = xTexelC${colIndex};
-                `;
-          }
-
-          if (colIndex + 1 < filterWidth) {
-            // If dilation is even, the second entry should match the first
-            // (either both are composed or both are single samples). But if
-            // dilation is odd, then the second entry should be the opposite
-            // of the first (if the first is composed, the second is a single
-            // sample, and vice versa.)
-
-            const nextTexelOffset = padLeft % 2 === 0 ?
-                util.nearestLargerEven(dilationWidth) :
-                dilationWidth;
-
-            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||
-                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {
-              mainLoop += `
-                  xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};
-
-                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                  colIndex + 1}Ready == 0) {
-                    xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
-
-                    // Need to manually clear unused channels in case
-                    // we're reading from recycled texture.
-                    if (xCOffset + 1 >= inDims[1]) {
-                      xTexelC${colIndex + 1}.zw = vec2(0.0);
-                    }
-                    xTexelC${colIndex + 1}Ready = 1;
-                  }
-                  `;
-
-              // If dilation > 1 then the xRC's will not be able to share any
-              // values, so each xRC will require two unique calls to getX.
-              if (dilationWidth > 1) {
-                mainLoop += `
-                    xCOffset -= 2;
-                    if (xCOffset >= 0 && xCOffset < inDims[1]) {
-                     previous = getX(batch, xR, xCOffset, d1);
-                     xC${colIndex + 1} = vec4(previous.zw, xTexelC${
-                       colIndex + 1}.xy);
-                    } else {
-                     xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${
-                       colIndex + 1}.xy);
-                    }
-                    `;
-              } else {
-                mainLoop += `
-                    xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${
-                    colIndex + 1}.xy);
-                    `;
-              }
-            } else {
-              // If dilation is 1 and padding is odd, we have already read the
-              // texel when constructing the previous x value. Here we can
-              // simply skip the texture read.
-              if (nextTexelOffset === 1) {
-                mainLoop += `
-                    xC${colIndex + 1} = xTexelC${colIndex};
-                    `;
-              } else {
-                mainLoop += `
-                    xCOffset = xC + ${nextTexelOffset};
-
-                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                    colIndex + 1}Ready == 0) {
-                      xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
-                      if (xCOffset + 1 >= inDims[1]) {
-                        xTexelC${colIndex + 1}.zw = vec2(0.0);
-                      }
-                      xTexelC${colIndex + 1}Ready = 1;
-                    }
-
-                    xC${colIndex + 1} = xTexelC${colIndex + 1};
-                    `;
-              }
-            }
-          }
-        }
-      } else {  // stride === 2
-        if (colIndex < filterWidth) {
-          // Depending on whether padLeft is even or odd, we want either the
-          // xy or zw channels from X texels for xC${colIndex}. If padLeft is
-          // even, xC${colIndex +1} is simply the zw channels of texels we've
-          // already sampled. But if padLeft is odd, xC{$c + 1}.zw will
-          // need to come from the xy channels of a new texel, hence the `
-          // vec4
-          // final` initialized below.
-          if (padLeft % 2 === 1) {
-            mainLoop += `
-                xCOffset = xC + 1 - strides[1];
-                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                colIndex}Ready == 0) {
-                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
-                  // Need to manually clear unused channels in case
-                  // we're reading from recycled texture.
-                  if (xCOffset + 1 >= inDims[1]) {
-                    xTexelC${colIndex}.zw = vec2(0.0);
-                  }
-                  xTexelC${colIndex}Ready = 1;
-                }
-
-                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${
-                colIndex + 1}Ready == 0) {
-                  xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);
-                  // Need to manually clear unused channels in case
-                  // we're reading from recycled texture.
-                  if (xC + 2 >= inDims[1]) {
-                    xTexelC${colIndex + 1}.zw = vec2(0.0);
-                  }
-                  xTexelC${colIndex + 1}Ready = 1;
-                }
-
-                xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${
-                colIndex + 1}.zw);
-              `;
-
-            if (colIndex + 1 < filterWidth) {
-              mainLoop += `
-                  final = vec4(0.0);
-                  xCOffset = xC + 1 + strides[1];
-                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
-                    final = getX(batch, xR, xCOffset, d1);
-                  }
-                  xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);
-                `;
-            }
-          } else {
-            mainLoop += `
-                if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
-                  xTexelC${colIndex} = getX(batch, xR, xC, d1);
-                  if (xC + 1 >= inDims[1]) {
-                    xTexelC${colIndex}.zw = vec2(0.0);
-                  }
-                  xTexelC${colIndex}Ready = 1;
-                }
-
-                xCOffset = xC + strides[1];
-                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${
-                colIndex + 1}Ready == 0) {
-                  xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
-                  if (xCOffset + 1 >= inDims[1]) {
-                    xTexelC${colIndex + 1}.zw = vec2(0.);
-                  }
-                  xTexelC${colIndex + 1}Ready = 1;
-                }
-
-                xC${colIndex} = vec4(
-                  xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);
-              `;
-
-            if (colIndex + 1 < filterWidth) {
-              mainLoop += `
-                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${
-                  colIndex + 1}.zw);
-                `;
-            }
-          }
-        }
-      }
-
-      // localize the dotProd accumulation within the loop, the theory is for
-      // GPU with limited cache, accumulate sum across large amount of
-      // veriables will cause lots of cache misses. (i.e. 5x5 filter will have
-      // 50 variables)
-      if (colIndex < filterWidth) {
-        mainLoop += `
-            wTexel = getW(r, ${colIndex}, d1, q);
-            dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);
-          `;
-
-        if (colIndex + 1 < filterWidth) {
-          mainLoop += `
-              wTexel = getW(r, ${colIndex + 1}, d1, q);
-              dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);
-            `;
-        }
-      }
-    }
-    mainLoop += `
-    }
-  `;
-    mainLoop += `
-      }
-    `;
-
-    let activationSnippet = '', applyActivationSnippet = '';
-    if (activation) {
-      if (hasPreluActivation) {
-        activationSnippet = `vec4 activation(vec4 a) {
-          vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${activation}
-        }`;
-      } else if (hasLeakyReluAlpha) {
-        activationSnippet = `vec4 activation(vec4 a) {
-          vec4 b = getLeakyreluAlphaAtOutCoords();
-          ${activation}
-        }`;
-      } else {
-        activationSnippet = `vec4 activation(vec4 x) {
-          ${activation}
-        }`;
-      }
-
-      applyActivationSnippet = `result = activation(result);`;
-    }
-
-    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-
-    if (hasPreluActivation) {
-      this.variableNames.push('preluActivationWeights');
-    }
-    if (hasLeakyReluAlpha) {
-      this.variableNames.push('leakyreluAlpha');
-    }
-
-    this.userCode = `
-      ${activationSnippet}
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords.x;
-        ivec2 xRCCorner = coords.yz * strides - pads;
-        int d2 = coords.w;
-        int d1 = d2 / ${channelMul};
-        int q = d2 - d1 * ${channelMul};
-        int xRCorner = xRCCorner.x;
-        int xCCorner = xRCCorner.y;
-
-        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
-        vec4 dotProd = vec4(0.000000000000001);
-
-        ${mainLoop}
-
-        vec4 result = dotProd - vec4(0.000000000000001);
-        ${addBiasSnippet}
-        ${applyActivationSnippet}
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/crop_and_resize_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/crop_and_resize_gpu.ts
deleted file mode 100644
index 10f779c18..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/crop_and_resize_gpu.ts
+++ /dev/null
@@ -1,127 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class CropAndResizeProgram implements GPGPUProgram {
-  variableNames = ['Image', 'Boxes', 'BoxInd'];
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(
-      imageShape: [number, number, number, number], boxShape: [number, number],
-      cropSize: [number, number], method: 'bilinear'|'nearest',
-      extrapolationValue: number) {
-    const [batch, imageHeight, imageWidth, depth] = imageShape;
-    const [numBoxes, ] = boxShape;
-    const [cropHeight, cropWidth] = cropSize;
-    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
-    const methodId = method === 'bilinear' ? 1 : 0;
-
-    const [inputHeightFloat, inputWidthFloat] =
-        [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];
-
-    const [heightRatio, heightScale, inY] = cropHeight > 1 ?
-        [
-          `${(imageHeight - 1) / (cropHeight - 1)}`,
-          '(y2-y1) * height_ratio',
-          `y1*${inputHeightFloat} + float(y)*(height_scale)`,
-        ] :
-        [
-          '0.0',
-          '0.0',
-          `0.5 * (y1+y2) * ${inputHeightFloat}`,
-        ];
-    const [widthRatio, widthScale, inX] = cropWidth > 1 ?
-        [
-          `${(imageWidth - 1) / (cropWidth - 1)}`,
-          '(x2-x1) * width_ratio',
-          `x1*${inputWidthFloat} + float(x)*(width_scale)`,
-        ] :
-        [
-          '0.0',
-          '0.0',
-          `0.5 * (x1+x2) * ${inputWidthFloat}`,
-        ];
-
-    // Reference implementation
-    // tslint:disable-next-line:max-line-length
-    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
-    this.userCode = `
-      const float height_ratio = float(${heightRatio});
-      const float width_ratio = float(${widthRatio});
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int y = coords[1];
-        int x = coords[2];
-        int d = coords[3];
-
-        // get box vals
-        float y1 = getBoxes(b,0);
-        float x1 = getBoxes(b,1);
-        float y2 = getBoxes(b,2);
-        float x2 = getBoxes(b,3);
-
-        // get image in batch index
-        int bInd = round(getBoxInd(b));
-        if(bInd < 0 || bInd >= ${batch}) {
-          return;
-        }
-
-        float height_scale = ${heightScale};
-        float width_scale = ${widthScale};
-
-        float in_y = ${inY};
-        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
-          setOutput(float(${extrapolationValue}));
-          return;
-        }
-        float in_x = ${inX};
-        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
-          setOutput(float(${extrapolationValue}));
-          return;
-        }
-
-        vec2 sourceFracIndexCR = vec2(in_x,in_y);
-        if(${methodId} == 1) {
-          // Compute the four integer indices.
-          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
-          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
-
-          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
-          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
-          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
-          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
-
-          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
-
-          float top = topLeft + (topRight - topLeft) * fracCR.x;
-          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
-          float newValue = top + (bottom - top) * fracCR.y;
-          setOutput(newValue);
-        } else {
-          // Compute the coordinators of nearest neighbor point.
-          ivec2 sourceNearestCR = ivec2(floor(
-            sourceFracIndexCR + vec2(0.5,0.5)));
-          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
-          setOutput(newValue);
-        }
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/cum_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/cum_gpu.ts
deleted file mode 100644
index 9e32dd886..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/cum_gpu.ts
+++ /dev/null
@@ -1,94 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType, UniformType} from './shader_compiler';
-
-export enum CumOpType {
-  Prod = '*',
-  Sum = '+',
-}
-
-export class CumProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  userCode: string;
-  customUniforms = [{name: 'index', type: 'float' as UniformType}];
-
-  constructor(
-      public op: CumOpType, public outputShape: number[], exclusive: boolean,
-      reverse: boolean) {
-    const rank = this.outputShape.length;
-    const initVal = this.op === CumOpType.Prod ? '1.0' : '0.0';
-    const val =
-        exclusive ? initVal : `getX(${getCoords(rank, 'coords', this.op)})`;
-    const length = this.outputShape[this.outputShape.length - 1];
-    let condition = '';
-    let idxString = '';
-    // When exclusive is set, the cum op becomes roll op that copies the
-    // value from the previous index based on the direction specified by the
-    // reverse flag.
-    if (exclusive) {
-      condition = reverse ? `end != ${length - 1}` : 'end != 0';
-      idxString = reverse ? 'end + 1' : 'end - 1';
-    } else {
-      condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';
-      idxString = (reverse ? 'end + pow2' : 'end - pow2');
-    }
-
-    this.userCode = `
-      void main() {
-        ${getCoordsDataType(rank)} coords = getOutputCoords();
-        int end = ${getFinalCoord(rank, 'coords', this.op)};
-        float val = ${val};
-        int pow2 = int(pow(2.0, index));
-        if (${condition}) {
-          int idx = ${idxString};
-          ${getFinalCoord(rank, 'coords', this.op)} = idx;
-          val ${this.op}= getX(${getCoords(rank, 'coords', this.op)});
-        }
-        setOutput(val);
-      }
-    `;
-  }
-}
-
-function getCoords(rank: number, name: string, op: CumOpType): string {
-  if (rank === 1) {
-    return `${name}`;
-  } else if (rank === 2) {
-    return `${name}.x, ${name}.y`;
-  } else if (rank === 3) {
-    return `${name}.x, ${name}.y, ${name}.z`;
-  } else if (rank === 4) {
-    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;
-  } else {
-    throw new Error(`Cumulative ${op} for rank ${rank} is not yet supported`);
-  }
-}
-
-function getFinalCoord(rank: number, name: string, op: CumOpType): string {
-  if (rank === 1) {
-    return `${name}`;
-  } else if (rank === 2) {
-    return `${name}.y`;
-  } else if (rank === 3) {
-    return `${name}.z`;
-  } else if (rank === 4) {
-    return `${name}.w`;
-  } else {
-    throw new Error(`Cumulative ${op} for rank ${rank} is not yet supported`);
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/decode_matrix_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/decode_matrix_gpu.ts
deleted file mode 100644
index 173812691..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/decode_matrix_gpu.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getGlslDifferences} from './glsl_version';
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-import * as shader_util from './shader_compiler_util';
-import {PackingScheme} from './tex_util';
-
-export class DecodeMatrixProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  outputShape: [number, number, number];
-  packedInputs = false;
-  packedOutput = true;
-  outPackingScheme = PackingScheme.DENSE;
-  enableShapeUniforms: boolean;
-  customUniforms = [{name: 'texShape', type: 'ivec2' as const }];
-
-  constructor(outputShape: [number, number, number]) {
-    const glsl = getGlslDifferences();
-    this.outputShape = outputShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-
-    this.userCode = `
-      ivec3 outCoordsFromFlatIndex(int index) {
-        ${
-        this.enableShapeUniforms ?
-            shader_util.getOutputLogicalCoordinatesFromFlatIndexByUniform(
-                ['r', 'c', 'd'], outputShape) :
-            shader_util.getLogicalCoordinatesFromFlatIndex(
-                ['r', 'c', 'd'], outputShape)}
-        return ivec3(r, c, d);
-      }
-
-      void main() {
-        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
-        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
-
-        vec4 result = vec4(0.);
-
-        for (int i=0; i<4; i++) {
-          int flatIndex = index + i;
-          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
-          result[i] = getA(rc.x, rc.y, rc.z);
-        }
-
-        ${glsl.output} = result;
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/decode_matrix_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/decode_matrix_packed_gpu.ts
deleted file mode 100644
index c3888024f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/decode_matrix_packed_gpu.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getGlslDifferences} from './glsl_version';
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-import * as shader_util from './shader_compiler_util';
-import {PackingScheme} from './tex_util';
-
-export class DecodeMatrixPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: [number, number, number];
-  outPackingScheme = PackingScheme.DENSE;
-  enableShapeUniforms: boolean;
-  customUniforms = [{name: 'texShape', type: 'ivec2' as const }];
-
-  constructor(outputShape: [number, number, number]) {
-    const glsl = getGlslDifferences();
-    this.outputShape = outputShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-
-    this.userCode = `
-      ivec3 outCoordsFromFlatIndex(int index) {
-        ${
-        this.enableShapeUniforms ?
-            shader_util.getOutputLogicalCoordinatesFromFlatIndexByUniform(
-                ['r', 'c', 'd'], outputShape) :
-            shader_util.getLogicalCoordinatesFromFlatIndex(
-                ['r', 'c', 'd'], outputShape)}
-        return ivec3(r, c, d);
-      }
-
-      void main() {
-        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
-        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
-
-        vec4 result = vec4(0.);
-
-        for (int i=0; i<4; i++) {
-          int flatIndex = index + i;
-          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
-          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
-        }
-
-        ${glsl.output} = result;
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/depth_to_space_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/depth_to_space_gpu.ts
deleted file mode 100644
index 0575cf4df..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/depth_to_space_gpu.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class DepthToSpaceProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[] = [];
-  userCode: string;
-  blockSize: number;
-  dataFormat: string;
-
-  constructor(
-      outputShape: number[], blockSize: number, dataFormat: 'NHWC'|'NCHW') {
-    this.outputShape = outputShape;
-    this.blockSize = blockSize;
-    this.dataFormat = dataFormat;
-    this.userCode = `
-    void main() {
-      ivec4 coords = getOutputCoords();
-      int b = coords[0];
-      int h = ${this.getHeightCoordString()};
-      int w = ${this.getWidthCoordString()};
-      int d = ${this.getDepthCoordString()};
-
-      int in_h = h / ${blockSize};
-      int offset_h = imod(h, ${blockSize});
-      int in_w = w / ${blockSize};
-      int offset_w = imod(w, ${blockSize});
-      int offset_d = (offset_h * ${blockSize} + offset_w) *
-        ${this.getOutputDepthSize()};
-      int in_d = d + offset_d;
-
-      float result = ${this.getInputSamplingString()};
-      setOutput(result);
-    }
-  `;
-  }
-
-  private getHeightCoordString(): string {
-    if (this.dataFormat === 'NHWC') {
-      return `coords[1]`;
-    } else {
-      return `coords[2]`;
-    }
-  }
-
-  private getWidthCoordString(): string {
-    if (this.dataFormat === 'NHWC') {
-      return `coords[2]`;
-    } else {
-      return `coords[3]`;
-    }
-  }
-
-  private getDepthCoordString(): string {
-    if (this.dataFormat === 'NHWC') {
-      return `coords[3]`;
-    } else {
-      return `coords[1]`;
-    }
-  }
-
-  private getOutputDepthSize(): number {
-    if (this.dataFormat === 'NHWC') {
-      return this.outputShape[3];
-    } else {
-      return this.outputShape[1];
-    }
-  }
-
-  private getInputSamplingString(): string {
-    if (this.dataFormat === 'NHWC') {
-      return `getX(b, in_h, in_w, in_d)`;
-    } else {
-      return `getX(b, in_d, in_h, in_w)`;
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/diag_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/diag_gpu.ts
deleted file mode 100644
index 8c38716a0..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/diag_gpu.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class DiagProgram implements GPGPUProgram {
-  variableNames = ['X'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(size: number) {
-    this.outputShape = [size, size];
-    this.userCode = `
-      void main() {
-          ivec2 coords = getOutputCoords();
-          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
-          setOutput(val);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/dilation_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/dilation_gpu.ts
deleted file mode 100644
index d0953159a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/dilation_gpu.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class Dilation2DProgram implements GPGPUProgram {
-  variableNames = ['x', 'W'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.outShape;
-
-    const {
-      inHeight,
-      inWidth,
-      padInfo,
-      strideHeight,
-      strideWidth,
-      filterHeight,
-      filterWidth,
-      dilationHeight,
-      dilationWidth
-    } = convInfo;
-
-    const {top: padTop, left: padLeft} = padInfo;
-
-    this.userCode = `
-      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
-      const ivec2 pads = ivec2(${padTop}, ${padLeft});
-      const float neg_infinity = -3.4e38;
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords.x;
-        int d1 = coords.w;
-        ivec2 outTopLeftCorner =
-            coords.yz * strides - pads;
-        int hBeg = outTopLeftCorner.x;
-        int wBeg = outTopLeftCorner.y;
-
-        float curVal = neg_infinity;
-        for (int h = 0; h < ${filterHeight}; h++) {
-          int hIn = hBeg + h * ${dilationHeight};
-
-          if (hIn >= 0 && hIn < ${inHeight}) {
-            for (int w = 0; w < ${filterWidth}; w++) {
-              int wIn = wBeg + w * ${dilationWidth};
-
-              if (wIn >= 0 && wIn < ${inWidth}) {
-                float xVal = getX(batch, hIn, wIn, d1);
-                float wVal = getW(h, w, d1);
-
-                float val = xVal + wVal;
-                if (val > curVal) {
-                  curVal = val;
-                }
-              }
-            }
-          }
-        }
-
-        float result = curVal;
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/encode_float_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/encode_float_gpu.ts
deleted file mode 100644
index d7d8fb193..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/encode_float_gpu.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getGlslDifferences} from './glsl_version';
-import {GPGPUProgram} from './gpgpu_math';
-import {ENCODE_FLOAT_SNIPPET} from './shader_compiler_util';
-import {TextureUsage} from './tex_util';
-
-export class EncodeFloatProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  outputShape: number[];
-  outTexUsage = TextureUsage.DOWNLOAD;
-
-  constructor(outputShape: number[]) {
-    const glsl = getGlslDifferences();
-    this.outputShape = outputShape;
-    this.userCode = `
-      ${ENCODE_FLOAT_SNIPPET}
-
-      void main() {
-        float x = getAAtOutCoords();
-        ${glsl.output} = encode_float(x);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/encode_float_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/encode_float_packed_gpu.ts
deleted file mode 100644
index 645e5bed0..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/encode_float_packed_gpu.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getGlslDifferences} from './glsl_version';
-import {GPGPUProgram} from './gpgpu_math';
-import {ENCODE_FLOAT_SNIPPET} from './shader_compiler_util';
-import {TextureUsage} from './tex_util';
-
-export class EncodeFloatPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  outputShape: number[];
-  packedInputs = true;
-  packedOutput = false;
-  outTexUsage = TextureUsage.DOWNLOAD;
-
-  constructor(outputShape: [number, number, number]) {
-    const glsl = getGlslDifferences();
-    this.outputShape = outputShape;
-    this.userCode = `
-      ${ENCODE_FLOAT_SNIPPET}
-
-      void main() {
-        ivec3 coords = getOutputCoords();
-        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
-        ${glsl.output} = encode_float(x);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/encode_matrix_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/encode_matrix_gpu.ts
deleted file mode 100644
index 5d3bc4eb5..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/encode_matrix_gpu.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getGlslDifferences} from './glsl_version';
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-import * as shader_util from './shader_compiler_util';
-
-const CHANNEL_CHAR_TO_INDEX_MAP: Record<string, number> = {
-  'R': 0,
-  'G': 1,
-  'B': 2,
-  'A': 3
-};
-
-export class EncodeMatrixProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  outputShape: number[];
-  enableShapeUniforms: boolean;
-  customUniforms = [{name: 'texShape', type: 'ivec2' as const }];
-
-  constructor(
-      outputShape: [number, number, number], inputIsUnsignedByte = false,
-      usedChannels = 'RGBA') {
-    const glsl = getGlslDifferences();
-    this.outputShape = outputShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-
-    let output = `result`;
-    if (inputIsUnsignedByte) {
-      output = `floor(result * 255. + 0.5)`;
-    }
-
-    let mainLoop = '';
-    for (let usedChannelIndex = 0; usedChannelIndex < usedChannels.length;
-         usedChannelIndex++) {
-      const curChannel = usedChannels[usedChannelIndex];
-      mainLoop += `
-          if(offset == ${usedChannelIndex}) {
-            result = values[${CHANNEL_CHAR_TO_INDEX_MAP[curChannel]}];
-          }`;
-    }
-
-    this.userCode = `
-      ${
-        this.enableShapeUniforms ? shader_util.getFlatIndexFrom3DOutput() :
-                                   shader_util.getFlatIndexFrom3D(outputShape)}
-
-      void main() {
-        ivec3 coords = getOutputCoords();
-        int flatIndex = getFlatIndex(coords);
-        float result = 0.;
-        int offset = imod(flatIndex, ${usedChannels.length});
-
-        flatIndex = idiv(flatIndex, ${usedChannels.length}, 1.);
-
-        int r = flatIndex / texShape[1];
-        if (r < texShape[0]) {
-          int c = imod(flatIndex, texShape[1]);
-          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
-          vec4 values = ${glsl.texture2D}(A, uv);
-          ${mainLoop}
-        }
-        ${glsl.output} = vec4(${output}, 0., 0., 0.);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/encode_matrix_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/encode_matrix_packed_gpu.ts
deleted file mode 100644
index d22b5bee0..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/encode_matrix_packed_gpu.ts
+++ /dev/null
@@ -1,117 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getGlslDifferences} from './glsl_version';
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-import * as shader_util from './shader_compiler_util';
-
-/*
-This is how the shader encodes a tensor with shape = [2, 3, 5]
-(indices are [batch, row, col]).
-
-000|001   002|003   004|xxx   020|021   022|023   024|xxx
--------   -------   -------   -------   -------   -------
-010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx
-
-100|101   102|103   104|xxx   120|121   122|123   124|xxx
--------   -------   -------   -------   -------   -------
-110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx
-
-Single texels contain only values from the same batch, and from adjacent rows
-and columns.
- */
-
-export class EncodeMatrixPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  outputShape: number[];
-  packedInputs = false;
-  packedOutput = true;
-  enableShapeUniforms: boolean;
-  customUniforms = [{name: 'texShape', type: 'ivec2' as const }];
-
-  constructor(
-      outputShape: [number, number, number], inputIsUnsignedByte = false) {
-    const glsl = getGlslDifferences();
-    this.outputShape = outputShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-
-    let mainLoop = '';
-    let output = 'result';
-    if (inputIsUnsignedByte) {
-      output = 'floor(result * 255. + 0.5)';
-    }
-
-    for (let row = 0; row <= 1; row++) {
-      for (let col = 0; col <= 1; col++) {
-        const channel = row * 2 + col;
-
-        mainLoop += `
-          localCoords = coords;
-          if(localCoords[2] + ${col} < ${
-            this.enableShapeUniforms ? 'outShape[2]' : `${outputShape[2]}`}) {
-          localCoords[2] += ${col};
-          if (localCoords[1] + ${row} < ${
-            this.enableShapeUniforms ? 'outShape[1]' : `${outputShape[1]}`}) {
-            localCoords[1] += ${row};
-
-            flatIndex = getFlatIndex(localCoords);
-            offset = imod(flatIndex, 4);
-
-            flatIndex = idiv(flatIndex, 4, 1.);
-
-            int r = flatIndex / texShape[1];
-            int c = imod(flatIndex, texShape[1]);
-            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
-            values = ${glsl.texture2D}(A, uv);
-
-            if (offset == 0) {
-              result[${channel}] = values[0];
-            } else if (offset == 1) {
-              result[${channel}] = values[1];
-            } else if (offset == 2) {
-              result[${channel}] = values[2];
-            } else {
-              result[${channel}] = values[3];
-            }
-          }
-        }
-        `;
-      }
-    }
-
-    this.userCode = `
-        ${
-        this.enableShapeUniforms ? shader_util.getFlatIndexFrom3DOutput() :
-                                   shader_util.getFlatIndexFrom3D(outputShape)}
-
-        void main() {
-          ivec3 coords = getOutputCoords();
-
-          vec4 result = vec4(0.);
-          int flatIndex, r, c, offset;
-          ivec3 localCoords;
-          vec2 uv;
-          vec4 values;
-
-          ${mainLoop}
-
-          ${glsl.output} = ${output};
-        }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/fft_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/fft_gpu.ts
deleted file mode 100644
index 4e6e150a7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/fft_gpu.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class FFTProgram implements GPGPUProgram {
-  variableNames = ['real', 'imag'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      component: 'real'|'imag', inputShape: [number, number],
-      inverse: boolean) {
-    const innerDim = inputShape[1];
-    this.outputShape = inputShape;
-
-    const exponentMultiplierSnippet =
-        inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;
-    const resultDenominator = inverse ? `${innerDim}.0` : '1.0';
-
-    let opString: string;
-    if (component === 'real') {
-      opString = 'return real * expR - imag * expI;';
-    } else if (component === 'imag') {
-      opString = 'return real * expI + imag * expR;';
-    } else {
-      throw new Error(
-          `FFT component must be either "real" or "imag", got ${component}.`);
-    }
-
-    this.userCode = `
-      const float exponentMultiplier = ${exponentMultiplierSnippet};
-
-      float unaryOpComplex(float real, float expR, float imag, float expI) {
-        ${opString}
-      }
-
-      float mulMatDFT(int batch, int index) {
-        float indexRatio = float(index) / float(${innerDim});
-        float exponentMultiplierTimesIndexRatio =
-            exponentMultiplier * indexRatio;
-
-        float result = 0.0;
-
-        for (int i = 0; i < ${innerDim}; i++) {
-          // x = (-2|2 * PI / N) * index * i;
-          float x = exponentMultiplierTimesIndexRatio * float(i);
-          float expR = cos(x);
-          float expI = sin(x);
-          float real = getReal(batch, i);
-          float imag = getImag(batch, i);
-
-          result +=
-              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};
-        }
-
-        return result;
-      }
-
-      void main() {
-        ivec2 coords = getOutputCoords();
-        setOutput(mulMatDFT(coords[0], coords[1]));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/fill_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/fill_gpu.ts
deleted file mode 100644
index b7fc19bc1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/fill_gpu.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {UniformType} from './shader_compiler';
-
-export class FillProgram implements GPGPUProgram {
-  variableNames: string[];
-  outputShape: number[] = [];
-  userCode: string;
-  customUniforms = [{name: 'value', type: 'float' as UniformType}];
-
-  constructor(shape: number[], value: number) {
-    this.variableNames = ['x'];
-    this.outputShape = shape;
-
-    this.userCode = `
-      void main() {
-        // Input can be obtained from uniform value.
-        setOutput(value);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/flags_webgl.ts b/tfjs-master/tfjs-backend-webgl/src/flags_webgl.ts
deleted file mode 100644
index 7bf7ad92a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/flags_webgl.ts
+++ /dev/null
@@ -1,290 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {device_util, env} from '@tensorflow/tfjs-core';
-
-import {getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled} from './webgl_util';
-
-const ENV = env();
-
-/**
- * This file contains WebGL-specific flag registrations.
- */
-
-/**
- * True if WebGL is supported.
- */
-ENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);
-
-/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */
-ENV.registerFlag('WEBGL_VERSION', () => {
-  if (isWebGLVersionEnabled(2)) {
-    return 2;
-  } else if (isWebGLVersionEnabled(1)) {
-    return 1;
-  }
-  return 0;
-});
-
-/** Whether to check for numerical representation problems. */
-ENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);
-
-ENV.registerFlag(
-    'WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);
-
-/** Whether the WebGL backend will sometimes forward ops to the CPU. */
-ENV.registerFlag('WEBGL_CPU_FORWARD', () => true);
-
-/** Whether the WebGL backend will always use f16 textures for rendering. */
-ENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);
-
-/** Whether to turn all packing related flags on. */
-ENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));
-
-/** Whether we will pack the batchnormalization op. */
-ENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether we will pack the clip op. */
-ENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether we will pack the depthwise conv op. */
-ENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether we will pack binary ops. */
-ENV.registerFlag(
-    'WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether we will pack unary ops. */
-ENV.registerFlag(
-    'WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether we will pack array ops. */
-ENV.registerFlag(
-    'WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether we will pack image ops. */
-ENV.registerFlag(
-    'WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether we will pack reduce ops. */
-ENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether packed WebGL kernels lazily unpack their outputs. */
-ENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether we will use the im2col algorithm to speed up convolutions. */
-ENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));
-
-/** Whether we will pack conv2dTranspose op. */
-ENV.registerFlag('WEBGL_PACK_CONV2DTRANSPOSE', () => ENV.getBool('WEBGL_PACK'));
-
-/** The maximum texture dimension. */
-ENV.registerFlag(
-    'WEBGL_MAX_TEXTURE_SIZE',
-    () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));
-
-/** The maximum texture dimension. */
-ENV.registerFlag(
-    'WEBGL_MAX_TEXTURES_IN_SHADER',
-    () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));
-
-/**
- * The disjoint_query_timer extension version.
- * 0: disabled, 1: EXT_disjoint_timer_query, 2:
- * EXT_disjoint_timer_query_webgl2.
- * In Firefox with WebGL 2.0,
- * EXT_disjoint_timer_query_webgl2 is not available, so we must use the
- * WebGL 1.0 extension.
- */
-ENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {
-  const webGLVersion = ENV.getNumber('WEBGL_VERSION');
-
-  if (webGLVersion === 0) {
-    return 0;
-  }
-  return getWebGLDisjointQueryTimerVersion(webGLVersion);
-});
-
-/**
- * Whether the timer object from the disjoint_query_timer extension gives
- * timing information that is reliable.
- */
-ENV.registerFlag(
-    'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE',
-    () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&
-        !device_util.isMobile());
-
-/**
- * Whether the device is physically capable of rendering to float32 textures.
- */
-ENV.registerFlag(
-    'WEBGL_RENDER_FLOAT32_CAPABLE',
-    () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));
-
-/**
- * Whether rendering to float32 textures is enabled. If disabled, renders to
- * float16 textures.
- */
-ENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {
-  return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?
-      false :
-      ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');
-});
-
-/**
- * Whether downloading float textures is enabled (16 or 32 bit). If disabled,
- * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.
- */
-ENV.registerFlag(
-    'WEBGL_DOWNLOAD_FLOAT_ENABLED',
-    () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));
-
-/** Whether the fence API is available. */
-ENV.registerFlag(
-    'WEBGL_FENCE_API_ENABLED',
-    () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));
-
-/**
- * Tensors with size <= than this will be uploaded as uniforms, not textures.
- */
-ENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {
-  // Use uniform uploads only when 32bit floats are supported. In
-  // 16bit
-  // environments there are problems with comparing a 16bit texture value
-  // with a 32bit uniform value.
-  const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');
-  return useUniforms ? 4 : 0;
-});
-
-/**
- * If the total number of bytes allocated on the GPU is greater than this
- * number, we will aggressively delete textures upon disposal with
- * gl.deleteMatrixTexture, rather than making them available for reuse.
- *
- * Default value -1 indicates that we will never aggressively delete textures.
- */
-ENV.registerFlag(
-    'WEBGL_DELETE_TEXTURE_THRESHOLD',
-    () => {
-      return -1;
-    },
-    threshold => {
-      if (!(typeof threshold === 'number')) {
-        throw new Error('WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but ' +
-            `got ${threshold}.`);
-      }
-      if (threshold < 0 && threshold !== -1) {
-        throw new Error(
-            `WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +
-            `delete) or at least 0, but got ${threshold}.`);
-      }
-    });
-
-/**
- * Trigger a manual GL command flush if the threshold of time has passed since
- * previous Kernel execution. This can be useful for Andorid device where GL
- * command flush are delayed un til the end of javascript task. This value is
- * measured in millisecond. Typically you want to set this value to close to 1.
- *
- * Default value 1 for mobile chrome, and -1 for rest cases. -1 indicates that
- * we will not enforce manual flush and depend on system default flush schedule.
- */
-ENV.registerFlag(
-    'WEBGL_FLUSH_THRESHOLD',
-    () => {
-      return device_util.isMobile() ? 1 : -1;
-    },
-    threshold => {
-      if (!(typeof threshold === 'number')) {
-        throw new Error('WEBGL_FLUSH_THRESHOLD must be a number but got ' +
-            `${threshold}.`);
-      }
-      if (threshold < 0 && threshold !== -1) {
-        throw new Error(
-            `WEBGL_FLUSH_THRESHOLD must be -1 (indicating never ` +
-            `manual flush) or at least 0, but got ${threshold}.`);
-      }
-    });
-
-/**
- * Threshold for input tensor size that determines whether WebGL backend will
- * delegate computation to CPU.
- *
- * Default value is 128.
- */
-ENV.registerFlag('CPU_HANDOFF_SIZE_THRESHOLD', () => 128);
-
-/** Whether we will use shapes uniforms. */
-ENV.registerFlag('WEBGL_USE_SHAPES_UNIFORMS', () => false);
-
-/**
- * Threshold for last dimension of input tensor that determines whether
- * WebGL backend for the Top K op will delegate computation to CPU. If input
- * is smaller than threshold then CPU will be used
- *
- * Default value is 100000.
- */
-ENV.registerFlag('TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD', () => 100000);
-
-/**
- * Threshold for K that determines whether
- * WebGL backend for the Top K op will delegate computation to CPU. If k
- * is larger than threshold then CPU will be used
- *
- * Default value is 128.
- */
-ENV.registerFlag('TOPK_K_CPU_HANDOFF_THRESHOLD', () => 128);
-
-/** Whether we will use the experimental conv op. */
-ENV.registerFlag('WEBGL_EXP_CONV', () => false);
-
-/**
- * If the device performance is low or if no hardware GPU is available, whether
- * software WebGL will be used.
- */
-ENV.registerFlag('SOFTWARE_WEBGL_ENABLED', () => ENV.getBool('IS_TEST'));
-
-/**
- * For narrow texture (physical height or physical width is 1), if the length of
- * any texture edges exceed the threshold, the texture will be reshaped to be
- * more squarish.
- *
- * This flag is used to help some GPUs that could not provide correct
- * interpolations for long skinny triangles. We found Mali GPU probably has this
- * problem: https://github.com/tensorflow/tfjs/issues/6775.
- */
-ENV.registerFlag('WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE', () => Infinity);
-
-/**
- * If the flag is set to true, the max size of the narrow texture will be auto
- * computed and it will be considerred as a threshold to reshape the narrow
- * texture to be more squarish.
- *
- * This flag is used to help some GPUs that could not provide correct
- * interpolations for long skinny triangles. We found Mali GPU probably has this
- * problem: https://github.com/tensorflow/tfjs/issues/6775.
- */
-ENV.registerFlag('WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE', () => false);
-
-/**
- * Whether to use the customized isnan. It's only useful for webgl2 since webgl1
- * doesn't have the builtin isnan.
- */
-ENV.registerFlag('WEBGL2_ISNAN_CUSTOM', () => false);
-
-/** Experimental flag, whether enter compile only phase. */
-ENV.registerFlag('ENGINE_COMPILE_ONLY', () => false);
diff --git a/tfjs-master/tfjs-backend-webgl/src/flags_webgl_test.ts b/tfjs-master/tfjs-backend-webgl/src/flags_webgl_test.ts
deleted file mode 100644
index 3a999c7de..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/flags_webgl_test.ts
+++ /dev/null
@@ -1,481 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {device_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {WEBGL_ENVS} from './backend_webgl_test_registry';
-import * as canvas_util from './canvas_util';
-import {forceHalfFloat, webgl_util} from './webgl';
-
-describe('WEBGL_FORCE_F16_TEXTURES', () => {
-  afterAll(() => tf.env().reset());
-
-  it('can be activated via forceHalfFloat utility', () => {
-    forceHalfFloat();
-    expect(tf.env().getBool('WEBGL_FORCE_F16_TEXTURES')).toBe(true);
-  });
-
-  it('turns off WEBGL_RENDER_FLOAT32_ENABLED', () => {
-    tf.env().reset();
-    forceHalfFloat();
-    expect(tf.env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')).toBe(false);
-  });
-});
-
-const RENDER_FLOAT32_ENVS = {
-  flags: {'WEBGL_RENDER_FLOAT32_CAPABLE': true},
-  predicate: WEBGL_ENVS.predicate
-};
-
-const RENDER_FLOAT16_ENVS = {
-  flags: {'WEBGL_RENDER_FLOAT32_CAPABLE': false},
-  predicate: WEBGL_ENVS.predicate
-};
-
-describeWithFlags('WEBGL_RENDER_FLOAT32_CAPABLE', RENDER_FLOAT32_ENVS, () => {
-  beforeEach(() => {
-    tf.env().reset();
-  });
-
-  afterAll(() => tf.env().reset());
-
-  it('should be independent of forcing f16 rendering', () => {
-    forceHalfFloat();
-    expect(tf.env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')).toBe(true);
-  });
-
-  it('if user is not forcing f16, device should render to f32', () => {
-    expect(tf.env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')).toBe(true);
-  });
-});
-
-describeWithFlags('WEBGL_RENDER_FLOAT32_CAPABLE', RENDER_FLOAT16_ENVS, () => {
-  beforeEach(() => {
-    tf.env().reset();
-  });
-
-  afterAll(() => tf.env().reset());
-
-  it('should be independent of forcing f16 rendering', () => {
-    forceHalfFloat();
-    expect(tf.env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')).toBe(false);
-  });
-
-  it('should be reflected in WEBGL_RENDER_FLOAT32_ENABLED', () => {
-    expect(tf.env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')).toBe(false);
-  });
-});
-
-describe('HAS_WEBGL', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('false when version is 0', () => {
-    tf.env().set('WEBGL_VERSION', 0);
-    expect(tf.env().getBool('HAS_WEBGL')).toBe(false);
-  });
-
-  it('true when version is 1', () => {
-    tf.env().set('WEBGL_VERSION', 1);
-    expect(tf.env().getBool('HAS_WEBGL')).toBe(true);
-  });
-
-  it('true when version is 2', () => {
-    tf.env().set('WEBGL_VERSION', 2);
-    expect(tf.env().getBool('HAS_WEBGL')).toBe(true);
-  });
-});
-
-describe('WEBGL_PACK', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when HAS_WEBGL is true', () => {
-    tf.env().set('HAS_WEBGL', true);
-    expect(tf.env().getBool('WEBGL_PACK')).toBe(true);
-  });
-
-  it('false when HAS_WEBGL is false', () => {
-    tf.env().set('HAS_WEBGL', false);
-    expect(tf.env().getBool('WEBGL_PACK')).toBe(false);
-  });
-});
-
-describe('WEBGL_PACK_NORMALIZATION', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    tf.env().set('WEBGL_PACK', true);
-    expect(tf.env().getBool('WEBGL_PACK_NORMALIZATION')).toBe(true);
-  });
-
-  it('false when WEBGL_PACK is false', () => {
-    tf.env().set('WEBGL_PACK', false);
-    expect(tf.env().getBool('WEBGL_PACK_NORMALIZATION')).toBe(false);
-  });
-});
-
-describe('WEBGL_PACK_CLIP', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    tf.env().set('WEBGL_PACK', true);
-    expect(tf.env().getBool('WEBGL_PACK_CLIP')).toBe(true);
-  });
-
-  it('false when WEBGL_PACK is false', () => {
-    tf.env().set('WEBGL_PACK', false);
-    expect(tf.env().getBool('WEBGL_PACK_CLIP')).toBe(false);
-  });
-});
-
-describe('WEBGL_PACK_DEPTHWISECONV', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    tf.env().set('WEBGL_PACK', true);
-    expect(tf.env().getBool('WEBGL_PACK_DEPTHWISECONV')).toBe(true);
-  });
-
-  it('false when WEBGL_PACK is false', () => {
-    tf.env().set('WEBGL_PACK', false);
-    expect(tf.env().getBool('WEBGL_PACK_DEPTHWISECONV')).toBe(false);
-  });
-});
-
-describe('WEBGL_PACK_BINARY_OPERATIONS', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    tf.env().set('WEBGL_PACK', true);
-    expect(tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS')).toBe(true);
-  });
-
-  it('false when WEBGL_PACK is false', () => {
-    tf.env().set('WEBGL_PACK', false);
-    expect(tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS')).toBe(false);
-  });
-});
-
-describe('WEBGL_PACK_ARRAY_OPERATIONS', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    tf.env().set('WEBGL_PACK', true);
-    expect(tf.env().getBool('WEBGL_PACK_ARRAY_OPERATIONS')).toBe(true);
-  });
-
-  it('false when WEBGL_PACK is false', () => {
-    tf.env().set('WEBGL_PACK', false);
-    expect(tf.env().getBool('WEBGL_PACK_ARRAY_OPERATIONS')).toBe(false);
-  });
-});
-
-describe('WEBGL_PACK_IMAGE_OPERATIONS', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    tf.env().set('WEBGL_PACK', true);
-    expect(tf.env().getBool('WEBGL_PACK_IMAGE_OPERATIONS')).toBe(true);
-  });
-
-  it('false when WEBGL_PACK is false', () => {
-    tf.env().set('WEBGL_PACK', false);
-    expect(tf.env().getBool('WEBGL_PACK_IMAGE_OPERATIONS')).toBe(false);
-  });
-});
-
-describe('WEBGL_PACK_REDUCE', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    tf.env().set('WEBGL_PACK', true);
-    expect(tf.env().getBool('WEBGL_PACK_REDUCE')).toBe(true);
-  });
-
-  it('false when WEBGL_PACK is false', () => {
-    tf.env().set('WEBGL_PACK', false);
-    expect(tf.env().getBool('WEBGL_PACK_REDUCE')).toBe(false);
-  });
-});
-
-describe('WEBGL_LAZILY_UNPACK', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    tf.env().set('WEBGL_PACK', true);
-    expect(tf.env().getBool('WEBGL_LAZILY_UNPACK')).toBe(true);
-  });
-
-  it('false when WEBGL_PACK is false', () => {
-    tf.env().set('WEBGL_PACK', false);
-    expect(tf.env().getBool('WEBGL_LAZILY_UNPACK')).toBe(false);
-  });
-});
-
-describe('WEBGL_CONV_IM2COL', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    tf.env().set('WEBGL_PACK', true);
-    expect(tf.env().getBool('WEBGL_CONV_IM2COL')).toBe(true);
-  });
-
-  it('false when WEBGL_PACK is false', () => {
-    tf.env().set('WEBGL_PACK', false);
-    expect(tf.env().getBool('WEBGL_CONV_IM2COL')).toBe(false);
-  });
-});
-
-describe('WEBGL_MAX_TEXTURE_SIZE', () => {
-  beforeEach(() => {
-    tf.env().reset();
-    webgl_util.resetMaxTextureSize();
-    canvas_util.setWebGLContext(2, {
-      MAX_TEXTURE_SIZE: 101,
-      isContextLost: () => false,
-      disable: (cap: number) => {},
-      enable: (cap: number) => {},
-      cullFace: (mode: number) => {},
-      getParameter: (param: number) => {
-        if (param === 101) {
-          return 50;
-        }
-        throw new Error(`Got undefined param ${param}.`);
-      }
-    } as unknown as WebGLRenderingContext);
-  });
-  afterAll(() => {
-    canvas_util.clearWebGLContext(2);
-    tf.env().reset();
-    webgl_util.resetMaxTextureSize();
-  });
-
-  it('is a function of gl.getParameter(MAX_TEXTURE_SIZE)', () => {
-    expect(tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE')).toBe(50);
-  });
-});
-
-describe('WEBGL_MAX_TEXTURES_IN_SHADER', () => {
-  let maxTextures: number;
-  beforeEach(() => {
-    tf.env().reset();
-    webgl_util.resetMaxTexturesInShader();
-
-    canvas_util.setWebGLContext(2, {
-      MAX_TEXTURE_IMAGE_UNITS: 101,
-      isContextLost: () => false,
-      disable: (cap: number) => {},
-      enable: (cap: number) => {},
-      cullFace: (mode: number) => {},
-      getParameter: (param: number) => {
-        if (param === 101) {
-          return maxTextures;
-        }
-        throw new Error(`Got undefined param ${param}.`);
-      }
-    } as unknown as WebGLRenderingContext);
-  });
-  afterAll(() => {
-    canvas_util.clearWebGLContext(2);
-    tf.env().reset();
-    webgl_util.resetMaxTexturesInShader();
-  });
-
-  it('is a function of gl.getParameter(MAX_TEXTURE_IMAGE_UNITS)', () => {
-    maxTextures = 10;
-    expect(tf.env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')).toBe(10);
-  });
-
-  it('is capped at 16', () => {
-    maxTextures = 20;
-    expect(tf.env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')).toBe(16);
-  });
-});
-
-describe('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => {
-  beforeEach(() => {
-    tf.env().reset();
-    device_util.mockIsMobile(undefined);
-  });
-  afterAll(() => {
-    tf.env().reset();
-    device_util.mockIsMobile(undefined);
-  });
-
-  it('disjoint query timer disabled', () => {
-    tf.env().set('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', 0);
-
-    expect(tf.env().getBool('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'))
-        .toBe(false);
-  });
-
-  it('disjoint query timer enabled, mobile', () => {
-    tf.env().set('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', 1);
-    device_util.mockIsMobile(true);
-
-    expect(tf.env().getBool('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'))
-        .toBe(false);
-  });
-
-  it('disjoint query timer enabled, not mobile', () => {
-    tf.env().set('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', 1);
-    device_util.mockIsMobile(false);
-
-    expect(tf.env().getBool('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'))
-        .toBe(true);
-  });
-});
-
-describe('WEBGL_SIZE_UPLOAD_UNIFORM', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('is 0 when there is no float32 bit support', () => {
-    tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', false);
-    expect(tf.env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')).toBe(0);
-  });
-
-  it('is > 0 when there is float32 bit support', () => {
-    tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);
-    expect(tf.env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')).toBeGreaterThan(0);
-  });
-});
-
-describeWithFlags('WEBGL_DELETE_TEXTURE_THRESHOLD', WEBGL_ENVS, () => {
-  it('should throw an error if given a negative value', () => {
-    expect(() => tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', -2)).toThrow();
-  });
-});
-
-describeWithFlags('WEBGL_FLUSH_THRESHOLD', WEBGL_ENVS, () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('should return the correct default value', () => {
-    if (device_util.isMobile()) {
-      expect(tf.env().getNumber('WEBGL_FLUSH_THRESHOLD')).toEqual(1);
-    } else {
-      expect(tf.env().getNumber('WEBGL_FLUSH_THRESHOLD')).toEqual(-1);
-    }
-  });
-  it('should throw an error if given a negative value', () => {
-    expect(() => tf.env().set('WEBGL_FLUSH_THRESHOLD', -2)).toThrow();
-  });
-});
-
-describe('CPU_HANDOFF_SIZE_THRESHOLD', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('returns correct value when CPU_HANDOFF_SIZE_THRESHOLD is set', () => {
-    tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 256);
-    expect(tf.env().getNumber('CPU_HANDOFF_SIZE_THRESHOLD')).toBe(256);
-  });
-
-  it('returns default when WEBGL_PACK is not set', () => {
-    expect(tf.env().getNumber('CPU_HANDOFF_SIZE_THRESHOLD')).toBe(128);
-  });
-});
-
-const LAST_DIM_FLAG = 'TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD';
-
-describeWithFlags(LAST_DIM_FLAG, WEBGL_ENVS, () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it(`returns correct value when ${LAST_DIM_FLAG} is set`, () => {
-    tf.env().set(LAST_DIM_FLAG, 256);
-    expect(tf.env().getNumber(LAST_DIM_FLAG)).toBe(256);
-  });
-
-  it(`returns default when ${LAST_DIM_FLAG} is not set`, () => {
-    expect(tf.env().getNumber(LAST_DIM_FLAG)).toBe(100000);
-  });
-});
-
-const K_FLAG = 'TOPK_K_CPU_HANDOFF_THRESHOLD';
-
-describeWithFlags(K_FLAG, WEBGL_ENVS, () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it(`returns correct value when ${K_FLAG} is set`, () => {
-    tf.env().set(K_FLAG, 256);
-    expect(tf.env().getNumber(K_FLAG)).toBe(256);
-  });
-
-  it(`returns default when ${K_FLAG} is not set`, () => {
-    expect(tf.env().getNumber(K_FLAG)).toBe(128);
-  });
-});
-
-describe('WEBGL_EXP_CONV', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('true when WEBGL_PACK is true', () => {
-    expect(tf.env().getBool('WEBGL_EXP_CONV')).toBe(false);
-  });
-});
-
-const MAX_SIZE_FOR_NARROR_TEX_FLAG = 'WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE';
-
-describeWithFlags(MAX_SIZE_FOR_NARROR_TEX_FLAG, WEBGL_ENVS, () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it(`returns correct value when ${MAX_SIZE_FOR_NARROR_TEX_FLAG} is set`,
-     () => {
-       tf.env().set(MAX_SIZE_FOR_NARROR_TEX_FLAG, 2048);
-       expect(tf.env().getNumber(MAX_SIZE_FOR_NARROR_TEX_FLAG)).toBe(2048);
-     });
-
-  it(`returns default when ${MAX_SIZE_FOR_NARROR_TEX_FLAG} is not set`, () => {
-    expect(tf.env().getNumber(MAX_SIZE_FOR_NARROR_TEX_FLAG)).toBe(Infinity);
-  });
-});
-
-const AUTO_SQUARIFY_NARROW_TEX_FLAG =
-    'WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE';
-
-describeWithFlags(AUTO_SQUARIFY_NARROW_TEX_FLAG, WEBGL_ENVS, () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it(`returns correct value when ${AUTO_SQUARIFY_NARROW_TEX_FLAG} is set`,
-     () => {
-       tf.env().set(AUTO_SQUARIFY_NARROW_TEX_FLAG, true);
-       expect(tf.env().getBool(AUTO_SQUARIFY_NARROW_TEX_FLAG)).toBe(true);
-     });
-
-  it(`returns default when ${AUTO_SQUARIFY_NARROW_TEX_FLAG} is not set`, () => {
-    expect(tf.env().getBool(AUTO_SQUARIFY_NARROW_TEX_FLAG)).toBe(false);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/flip_left_right_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/flip_left_right_gpu.ts
deleted file mode 100644
index 1b7fd05cc..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/flip_left_right_gpu.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class FlipLeftRightProgram implements GPGPUProgram {
-  variableNames = ['Image'];
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(imageShape: [number, number, number, number]) {
-    const imageWidth = imageShape[2];
-    this.outputShape = imageShape;
-
-    this.userCode = `
-        void main() {
-          ivec4 coords = getOutputCoords();
-          int x = coords[2];
-
-          int coordX = ${imageWidth} - x - 1;
-          float outputValue;
-          if(coordX >= 0 && coordX < ${imageWidth}) {
-            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
-          } else {
-            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
-          }
-          setOutput(outputValue);
-        }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/gather_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/gather_gpu.ts
deleted file mode 100644
index e867bf4ad..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/gather_gpu.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export type GatherShape = [number, number, number, number];
-
-export class GatherProgram implements GPGPUProgram {
-  variableNames = ['A', 'indices'];
-  outputShape: number[];
-  userCode: string;
-  rank: number;
-
-  constructor(aShape: GatherShape, outputShape: GatherShape) {
-    this.outputShape = outputShape;
-    this.rank = outputShape.length;
-    const dtype = getCoordsDataType(this.rank);
-    const sourceCoords = getSourceCoords(aShape, 2);
-
-    this.userCode = `
-      void main() {
-        ${dtype} resRC = getOutputCoords();
-        int index = int(getIndices(resRC.x, resRC.z));
-        float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0;
-        setOutput(inBounds * getA(${sourceCoords}));
-      }
-    `;
-  }
-}
-
-// The input and output are always flattened into rank 4 tensors.
-function getSourceCoords(aShape: GatherShape, axis: number): string {
-  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
-
-  const sourceCoords = [];
-  for (let i = 0; i < aShape.length; i++) {
-    if (i === 2) {
-      sourceCoords.push('index');
-    } else {
-      sourceCoords.push(`${currentCoords[i]}`);
-    }
-  }
-  return sourceCoords.join();
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/gather_nd_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/gather_nd_gpu.ts
deleted file mode 100644
index 507f9f743..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/gather_nd_gpu.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export class GatherNDProgram implements GPGPUProgram {
-  variableNames = ['x', 'indices'];
-  outputShape: number[];
-  userCode: string;
-  constructor(
-      private sliceDim: number, private strides: number[], shape: number[],
-      private paramsShape: number[]) {
-    this.outputShape = shape;
-    const dtype = getCoordsDataType(shape.length);
-
-    let mainLoop = `
-    int index;`;
-    for (let j = 0; j < this.sliceDim; j++) {
-      mainLoop += `
-          index = round(getIndices(coords[0], ${j}));
-          out_of_bounds = out_of_bounds || index < 0;
-          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[j]};
-          flattenIndex += index * ${this.strides[j]};`;
-    }
-
-    this.userCode = `
-         void main() {
-          ${dtype} coords = getOutputCoords();
-          int flattenIndex = 0;
-          bool out_of_bounds = false;
-
-          ${mainLoop}
-
-          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
-        }
-      `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/glsl_version.ts b/tfjs-master/tfjs-backend-webgl/src/glsl_version.ts
deleted file mode 100644
index 9b36a9c7e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/glsl_version.ts
+++ /dev/null
@@ -1,143 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {env} from '@tensorflow/tfjs-core';
-
-export type GLSL = {
-  version: string,
-  attribute: string,
-  varyingVs: string,
-  varyingFs: string,
-  texture2D: string,
-  output: string,
-  defineOutput: string,
-  defineSpecialNaN: string,
-  defineSpecialInf: string,
-  defineRound: string
-};
-
-export function getGlslDifferences(): GLSL {
-  let version: string;
-  let attribute: string;
-  let varyingVs: string;
-  let varyingFs: string;
-  let texture2D: string;
-  let output: string;
-  let defineOutput: string;
-  let defineSpecialNaN: string;
-  let defineSpecialInf: string;
-  let defineRound: string;
-
-  if (env().getNumber('WEBGL_VERSION') === 2) {
-    version = '#version 300 es';
-    attribute = 'in';
-    varyingVs = 'out';
-    varyingFs = 'in';
-    texture2D = 'texture';
-    output = 'outputColor';
-    defineOutput = 'out vec4 outputColor;';
-
-    // Use custom isnan definition to work across differences between
-    // implementations on various platforms. While this should happen in ANGLE
-    // we still see differences between android and windows (on chrome) when
-    // using isnan directly. Since WebGL2 supports uint type and
-    // floatBitsToUinT built-in function, we could implment isnan following
-    // IEEE 754 rules.
-    // NaN defination in IEEE 754-1985 is :
-    //   - sign = either 0 or 1.
-    //   - biased exponent = all 1 bits.
-    //   - fraction = anything except all 0 bits (since all 0 bits represents
-    //   infinity).
-    // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
-    defineSpecialNaN = env().getBool('WEBGL2_ISNAN_CUSTOM') ? `
-      bool isnan_custom(float val) {
-        uint floatToUint = floatBitsToUint(val);
-        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
-      }
-
-      bvec4 isnan_custom(vec4 val) {
-        return bvec4(isnan_custom(val.x),
-          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
-      }
-
-      #define isnan(value) isnan_custom(value)
-    ` :
-                                                              '';
-    // In webgl 2 we do not need to specify a custom isinf so there is no
-    // need for a special INFINITY constant.
-    defineSpecialInf = ``;
-    defineRound = `
-      #define round(value) newRound(value)
-      int newRound(float value) {
-        return int(floor(value + 0.5));
-      }
-
-      ivec4 newRound(vec4 value) {
-        return ivec4(floor(value + vec4(0.5)));
-      }
-    `;
-  } else {
-    version = '';
-    attribute = 'attribute';
-    varyingVs = 'varying';
-    varyingFs = 'varying';
-    texture2D = 'texture2D';
-    output = 'gl_FragColor';
-    defineOutput = '';
-    // WebGL1 has no built in isnan so we define one here.
-    defineSpecialNaN = `
-      #define isnan(value) isnan_custom(value)
-      bool isnan_custom(float val) {
-        return (val > 0. || val < 1. || val == 0.) ? false : true;
-      }
-      bvec4 isnan_custom(vec4 val) {
-        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
-      }
-    `;
-    defineSpecialInf = `
-      uniform float INFINITY;
-
-      bool isinf(float val) {
-        return abs(val) == INFINITY;
-      }
-      bvec4 isinf(vec4 val) {
-        return equal(abs(val), vec4(INFINITY));
-      }
-    `;
-    defineRound = `
-      int round(float value) {
-        return int(floor(value + 0.5));
-      }
-
-      ivec4 round(vec4 value) {
-        return ivec4(floor(value + vec4(0.5)));
-      }
-    `;
-  }
-
-  return {
-    version,
-    attribute,
-    varyingVs,
-    varyingFs,
-    texture2D,
-    output,
-    defineOutput,
-    defineSpecialNaN,
-    defineSpecialInf,
-    defineRound
-  };
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/gpgpu_context.ts b/tfjs-master/tfjs-backend-webgl/src/gpgpu_context.ts
deleted file mode 100644
index 2873b2648..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/gpgpu_context.ts
+++ /dev/null
@@ -1,710 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, PixelData, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {getWebGLContext, setWebGLContext} from './canvas_util';
-import * as gpgpu_util from './gpgpu_util';
-import * as tex_util from './tex_util';
-import {Texture, TextureConfig} from './tex_util';
-import {WebGL1DisjointQueryTimerExtension, WebGL2DisjointQueryTimerExtension, WebGLParallelCompilationExtension} from './webgl_types';
-import * as webgl_util from './webgl_util';
-
-export interface FenceContext {
-  query: WebGLQuery|WebGLSync;
-  isFencePassed(): boolean;
-}
-
-type WebGLVao = WebGLVertexArrayObject|WebGLVertexArrayObjectOES;
-
-export interface GPGPUContextProgram extends WebGLProgram {
-  vao: WebGLVao;
-}
-
-export class GPGPUContext {
-  gl: WebGLRenderingContext;
-  textureFloatExtension: {};
-  textureHalfFloatExtension: {};
-  colorBufferFloatExtension: {};
-  colorBufferHalfFloatExtension: {};
-  disjointQueryTimerExtension: WebGL2DisjointQueryTimerExtension|
-      WebGL1DisjointQueryTimerExtension;
-  parallelCompilationExtension: WebGLParallelCompilationExtension;
-  vertexBuffer: WebGLBuffer;
-  indexBuffer: WebGLBuffer;
-  framebuffer: WebGLFramebuffer;
-  outputTexture: WebGLTexture|null = null;
-  program: GPGPUContextProgram|null = null;
-  private disposed = false;
-  private disjoint: boolean;
-  private vertexShader: WebGLShader;
-  textureConfig: TextureConfig;
-
-  createVertexArray: () => WebGLVao | null;
-  bindVertexArray: (vao: WebGLVao|null) => void;
-  deleteVertexArray: (vao: WebGLVao|null) => void;
-  getVertexArray: () => WebGLVao | null;
-
-  constructor(gl?: WebGLRenderingContext) {
-    const glVersion = env().getNumber('WEBGL_VERSION');
-    if (gl != null) {
-      this.gl = gl;
-      setWebGLContext(glVersion, gl);
-    } else {
-      this.gl = getWebGLContext(glVersion);
-    }
-    gl = this.gl;
-
-    if (env().getNumber('WEBGL_VERSION') === 2) {
-      const gl2 = gl as WebGL2RenderingContext;
-      this.createVertexArray = () => {
-        return webgl_util.callAndCheck(gl2, () => gl2.createVertexArray());
-      };
-      this.bindVertexArray = (vao: WebGLVao|null) => {
-        return webgl_util.callAndCheck(
-            gl2, () => gl2.bindVertexArray(vao as WebGLVertexArrayObject));
-      };
-      this.deleteVertexArray = (vao: WebGLVao|null) => {
-        return webgl_util.callAndCheck(
-            gl2, () => gl2.deleteVertexArray(vao as WebGLVertexArrayObject));
-      };
-      this.getVertexArray = () => {
-        return webgl_util.callAndCheck(
-            gl2, () => gl2.getParameter(gl2.VERTEX_ARRAY_BINDING));
-      };
-    } else if (gl != null) {
-      const ext = gl.getExtension('OES_vertex_array_object');
-      if (ext == null) {
-        throw new Error(
-            'All WebGL1 implementations are expected to offer' +
-            ' OES_vertex_array_object.');
-      }
-      this.createVertexArray = () => {
-        return webgl_util.callAndCheck(gl, () => ext.createVertexArrayOES());
-      };
-      this.bindVertexArray = (vao: WebGLVao|null) => {
-        return webgl_util.callAndCheck(
-            gl, () => ext.bindVertexArrayOES(vao as WebGLVertexArrayObjectOES));
-      };
-      this.deleteVertexArray = (vao: WebGLVao|null) => {
-        return webgl_util.callAndCheck(
-            gl,
-            () => ext.deleteVertexArrayOES(vao as WebGLVertexArrayObjectOES));
-      };
-      this.getVertexArray = () => {
-        return webgl_util.callAndCheck(
-            gl, () => gl.getParameter(ext.VERTEX_ARRAY_BINDING_OES));
-      };
-    }
-
-    // WebGL 2.0 enables texture floats without an extension.
-    let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';
-    const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';
-    this.parallelCompilationExtension =
-        this.gl.getExtension('KHR_parallel_shader_compile');
-    if (env().getNumber('WEBGL_VERSION') === 1) {
-      const TEXTURE_FLOAT = 'OES_texture_float';
-      const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';
-
-      this.textureFloatExtension =
-          webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);
-      if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {
-        this.textureHalfFloatExtension =
-            webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);
-      } else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {
-        throw new Error(
-            'GL context does not support half float textures, yet the ' +
-            'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');
-      }
-
-      this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
-      if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
-        this.colorBufferHalfFloatExtension =
-            webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);
-      } else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {
-        throw new Error(
-            'GL context does not support color renderable half floats, yet ' +
-            'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');
-      }
-    } else {
-      COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';
-      if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {
-        this.colorBufferFloatExtension =
-            this.gl.getExtension(COLOR_BUFFER_FLOAT);
-      } else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
-        this.colorBufferHalfFloatExtension =
-            this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
-      } else {
-        throw new Error('GL context does not support color renderable floats');
-      }
-    }
-
-    this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);
-    this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);
-    this.framebuffer = webgl_util.createFramebuffer(this.gl);
-
-    this.textureConfig =
-        tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);
-  }
-
-  private get debug(): boolean {
-    return env().getBool('DEBUG');
-  }
-
-  public dispose() {
-    if (this.disposed) {
-      return;
-    }
-    if (this.program != null) {
-      console.warn(
-          'Disposing a GPGPUContext that still has a bound WebGLProgram.' +
-          ' This is probably a resource leak, delete the program with ' +
-          'GPGPUContext.deleteProgram before disposing.');
-    }
-    if (this.outputTexture != null) {
-      console.warn(
-          'Disposing a GPGPUContext that still has a bound output matrix ' +
-          'texture.  This is probably a resource leak, delete the output ' +
-          'matrix texture with GPGPUContext.deleteMatrixTexture before ' +
-          'disposing.');
-    }
-    const gl = this.gl;
-    webgl_util.callAndCheck(gl, () => gl.finish());
-    webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));
-    webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));
-    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));
-    webgl_util.callAndCheck(
-        gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));
-    webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));
-    this.disposed = true;
-  }
-
-  public createFloat32MatrixTexture(rows: number, columns: number): Texture {
-    this.throwIfDisposed();
-    return gpgpu_util.createFloat32MatrixTexture(
-        this.gl, rows, columns, this.textureConfig);
-  }
-
-  public createFloat16MatrixTexture(rows: number, columns: number): Texture {
-    this.throwIfDisposed();
-    return gpgpu_util.createFloat16MatrixTexture(
-        this.gl, rows, columns, this.textureConfig);
-  }
-
-  public createUnsignedBytesMatrixTexture(rows: number, columns: number):
-      Texture {
-    this.throwIfDisposed();
-    return gpgpu_util.createUnsignedBytesMatrixTexture(
-        this.gl, rows, columns, this.textureConfig);
-  }
-
-  public uploadPixelDataToTexture(
-      texture: WebGLTexture,
-      pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|
-      ImageBitmap) {
-    this.throwIfDisposed();
-    gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);
-  }
-
-  public uploadDenseMatrixToTexture(
-      texture: WebGLTexture, width: number, height: number, data: TypedArray) {
-    this.throwIfDisposed();
-    gpgpu_util.uploadDenseMatrixToTexture(
-        this.gl, texture, width, height, data, this.textureConfig);
-  }
-
-  public createFloat16PackedMatrixTexture(rows: number, columns: number):
-      Texture {
-    this.throwIfDisposed();
-    return gpgpu_util.createFloat16PackedMatrixTexture(
-        this.gl, rows, columns, this.textureConfig);
-  }
-
-  public createPackedMatrixTexture(rows: number, columns: number): Texture {
-    this.throwIfDisposed();
-    return gpgpu_util.createPackedMatrixTexture(
-        this.gl, rows, columns, this.textureConfig);
-  }
-
-  public deleteMatrixTexture(texture: WebGLTexture) {
-    this.throwIfDisposed();
-    if (this.outputTexture === texture) {
-      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
-      this.outputTexture = null;
-    }
-    webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));
-  }
-
-  public downloadByteEncodedFloatMatrixFromOutputTexture(
-      texture: WebGLTexture, rows: number, columns: number): Float32Array {
-    return this.downloadMatrixDriver(
-        texture,
-        () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(
-            this.gl, rows, columns, this.textureConfig));
-  }
-
-  public downloadPackedMatrixFromBuffer(
-      buffer: WebGLBuffer, batch: number, rows: number, columns: number,
-      physicalRows: number, physicalCols: number): Float32Array {
-    return gpgpu_util.downloadPackedMatrixFromBuffer(
-        this.gl, buffer, batch, rows, columns, physicalRows, physicalCols,
-        this.textureConfig);
-  }
-
-  public downloadFloat32MatrixFromBuffer(buffer: WebGLBuffer, size: number):
-      Float32Array {
-    return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);
-  }
-
-  public createBufferFromTexture(
-      texture: WebGLTexture, rows: number, columns: number): WebGLBuffer {
-    this.bindTextureToFrameBuffer(texture);
-    const result = gpgpu_util.createBufferFromOutputTexture(
-        this.gl as WebGL2RenderingContext, rows, columns, this.textureConfig);
-    this.unbindTextureToFrameBuffer();
-    return result;
-  }
-
-  public createAndWaitForFence(): Promise<void> {
-    const fenceContext = this.createFence(this.gl);
-    return this.pollFence(fenceContext);
-  }
-
-  private createFence(gl: WebGLRenderingContext): FenceContext {
-    let query: WebGLQuery|WebGLSync;
-    let isFencePassed: () => boolean;
-
-    if (env().getBool('WEBGL_FENCE_API_ENABLED')) {
-      const gl2 = gl as WebGL2RenderingContext;
-
-      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);
-      gl.flush();
-
-      isFencePassed = () => {
-        const status = gl2.clientWaitSync(sync, 0, 0);
-        return status === gl2.ALREADY_SIGNALED ||
-            status === gl2.CONDITION_SATISFIED;
-      };
-
-      query = sync;
-    } else if (
-        env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
-      query = this.beginQuery();
-      this.endQuery();
-      isFencePassed = () => this.isQueryAvailable(
-          query,
-          env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));
-    } else {
-      // If we have no way to fence, return true immediately. This will fire in
-      // WebGL 1.0 when there is no disjoint query timer. In this case, because
-      // the fence passes immediately, we'll immediately ask for a download of
-      // the texture, which will cause the UI thread to hang.
-      isFencePassed = () => true;
-    }
-
-    return {query, isFencePassed};
-  }
-
-  public downloadMatrixFromPackedTexture(
-      texture: WebGLTexture, physicalRows: number,
-      physicalCols: number): Float32Array {
-    return this.downloadMatrixDriver(
-        texture,
-        () => gpgpu_util.downloadMatrixFromPackedOutputTexture(
-            this.gl, physicalRows, physicalCols));
-  }
-
-  public createProgram(fragmentShader: WebGLShader): GPGPUContextProgram {
-    this.throwIfDisposed();
-    const gl = this.gl;
-    if (this.vertexShader == null) {
-      this.vertexShader = gpgpu_util.createVertexShader(gl);
-    }
-    const program: WebGLProgram = webgl_util.createProgram(gl);
-    webgl_util.callAndCheck(
-        gl, () => gl.attachShader(program, this.vertexShader));
-    webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));
-    webgl_util.linkProgram(gl, program);
-
-    const program2 = Object.assign(program, {vao: this.createVertexArray()});
-    if (this.debug) {
-      webgl_util.validateProgram(gl, program2);
-    }
-    return program2;
-  }
-
-  public buildVao(program: GPGPUContextProgram) {
-    this.setProgram(program);
-    this.bindVertexArray(program.vao);
-    const gl = this.gl;
-    // Bind index buffer, and vertex buffers based on program attrib
-    // locations.
-    webgl_util.callAndCheck(
-        gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, this.indexBuffer));
-    gpgpu_util.bindVertexProgramAttributeStreams(
-        gl, program, this.vertexBuffer);
-  }
-
-  public deleteProgram(program: GPGPUContextProgram) {
-    this.throwIfDisposed();
-    if (program === this.program) {
-      this.program = null;
-    }
-    if (program != null) {
-      webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));
-      this.deleteVertexArray(program.vao);
-    }
-  }
-
-  public setProgram(program: GPGPUContextProgram|null) {
-    this.throwIfDisposed();
-    this.program = program;
-
-    if (this.program != null) {
-      if (this.debug) {
-        webgl_util.validateProgram(this.gl, this.program);
-      }
-    }
-    webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));
-  }
-
-  public getUniformLocation(
-      program: WebGLProgram, uniformName: string,
-      shouldThrow = true): WebGLUniformLocation {
-    this.throwIfDisposed();
-    if (shouldThrow) {
-      return webgl_util.getProgramUniformLocationOrThrow(
-          this.gl, program, uniformName);
-    } else {
-      return webgl_util.getProgramUniformLocation(
-          this.gl, program, uniformName);
-    }
-  }
-
-  public getAttributeLocation(program: WebGLProgram, attribute: string):
-      number {
-    this.throwIfDisposed();
-    return webgl_util.callAndCheck(
-        this.gl, () => this.gl.getAttribLocation(program, attribute));
-  }
-
-  public getUniformLocationNoThrow(program: WebGLProgram, uniformName: string):
-      WebGLUniformLocation {
-    this.throwIfDisposed();
-    return this.gl.getUniformLocation(program, uniformName);
-  }
-
-  public setInputMatrixTexture(
-      inputMatrixTexture: WebGLTexture, uniformLocation: WebGLUniformLocation,
-      textureUnit: number) {
-    this.throwIfDisposed();
-    this.throwIfNoProgram();
-    webgl_util.bindTextureToProgramUniformSampler(
-        this.gl, inputMatrixTexture, uniformLocation, textureUnit);
-  }
-
-  public setOutputMatrixTexture(
-      outputMatrixTexture: WebGLTexture, rows: number, columns: number) {
-    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
-  }
-
-  public setOutputPackedMatrixTexture(
-      outputPackedMatrixTexture: WebGLTexture, rows: number, columns: number) {
-    this.throwIfDisposed();
-    const [width, height] =
-        tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);
-    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
-  }
-
-  public setOutputMatrixWriteRegion(
-      startRow: number, numRows: number, startColumn: number,
-      numColumns: number) {
-    this.setOutputMatrixWriteRegionDriver(
-        startColumn, startRow, numColumns, numRows);
-  }
-
-  public setOutputPackedMatrixWriteRegion(
-      startRow: number, numRows: number, startColumn: number,
-      numColumns: number) {
-    throw new Error('setOutputPackedMatrixWriteRegion not implemented.');
-  }
-
-  public debugValidate() {
-    if (this.program != null) {
-      webgl_util.validateProgram(this.gl, this.program);
-    }
-    webgl_util.validateFramebuffer(this.gl);
-  }
-
-  public executeProgram() {
-    this.throwIfDisposed();
-    this.throwIfNoProgram();
-    const gl = this.gl;
-    if (this.debug) {
-      const boundVao = this.getVertexArray();
-      console.assert(
-          boundVao === this.program.vao,
-          'VAO changed between setProgram and executeProgram!');
-
-      this.debugValidate();
-    }
-    webgl_util.callAndCheck(
-        gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));
-  }
-
-  public blockUntilAllProgramsCompleted() {
-    this.throwIfDisposed();
-    webgl_util.callAndCheck(this.gl, () => this.gl.finish());
-  }
-
-  private getQueryTimerExtension(): WebGL1DisjointQueryTimerExtension
-      |WebGL2DisjointQueryTimerExtension {
-    if (this.disjointQueryTimerExtension == null) {
-      this.disjointQueryTimerExtension =
-          webgl_util.getExtensionOrThrow(
-              this.gl,
-              env().getNumber(
-                  'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?
-                  'EXT_disjoint_timer_query_webgl2' :
-                  'EXT_disjoint_timer_query') as
-              WebGL1DisjointQueryTimerExtension |
-          WebGL2DisjointQueryTimerExtension;
-    }
-    return this.disjointQueryTimerExtension;
-  }
-
-  private getQueryTimerExtensionWebGL2(): WebGL2DisjointQueryTimerExtension {
-    return this.getQueryTimerExtension();
-  }
-
-  private getQueryTimerExtensionWebGL1(): WebGL1DisjointQueryTimerExtension {
-    return this.getQueryTimerExtension() as WebGL1DisjointQueryTimerExtension;
-  }
-
-  beginQuery(): WebGLQuery {
-    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
-      const gl2 = this.gl as WebGL2RenderingContext;
-      const ext = this.getQueryTimerExtensionWebGL2();
-
-      const query = gl2.createQuery();
-      gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);
-      return query;
-    }
-    const ext = this.getQueryTimerExtensionWebGL1();
-    const query = ext.createQueryEXT() as WebGLQuery;
-    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
-    return query;
-  }
-
-  endQuery() {
-    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
-      const gl2 = this.gl as WebGL2RenderingContext;
-      const ext = this.getQueryTimerExtensionWebGL2();
-      gl2.endQuery(ext.TIME_ELAPSED_EXT);
-      return;
-    }
-    const ext = this.getQueryTimerExtensionWebGL1();
-    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
-  }
-
-  public async waitForQueryAndGetTime(query: WebGLQuery): Promise<number> {
-    await util.repeatedTry(
-        () => this.disposed ||  // while testing contexts are created / disposed
-                                // in rapid succession, so without this check we
-                                // may poll for the query timer indefinitely
-            this.isQueryAvailable(
-                query,
-                env().getNumber(
-                    'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));
-    return this.getQueryTime(
-        query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));
-  }
-
-  private getQueryTime(query: WebGLQuery, queryTimerVersion: number): number {
-    if (queryTimerVersion === 0) {
-      return null;
-    }
-
-    if (queryTimerVersion === 2) {
-      const gl2 = this.gl as WebGL2RenderingContext;
-
-      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
-      // Return milliseconds.
-      return timeElapsedNanos / 1000000;
-    } else {
-      const ext = this.getQueryTimerExtensionWebGL1();
-
-      const timeElapsedNanos =
-          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
-      // Return milliseconds.
-      return timeElapsedNanos / 1000000;
-    }
-  }
-
-  private isQueryAvailable(query: WebGLQuery, queryTimerVersion: number):
-      boolean {
-    if (queryTimerVersion === 0) {
-      return true;
-    }
-
-    if (queryTimerVersion === 2) {
-      const gl2 = this.gl as WebGL2RenderingContext;
-      const ext = this.getQueryTimerExtensionWebGL2();
-
-      const available =
-          gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
-      if (this.disjoint == null) {
-        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
-      }
-
-      return available && !this.disjoint;
-    } else {
-      const ext = this.getQueryTimerExtensionWebGL1();
-
-      const available =
-          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
-      if (this.disjoint == null) {
-        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
-      }
-
-      return available && !this.disjoint;
-    }
-  }
-
-  pollFence(fenceContext: FenceContext) {
-    return new Promise<void>(resolve => {
-      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());
-    });
-  }
-
-  private itemsToPoll: PollItem[] = [];
-
-  pollItems(): void {
-    // Find the last query that has finished.
-    const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));
-    for (let i = 0; i <= index; ++i) {
-      const {resolveFn} = this.itemsToPoll[i];
-      resolveFn();
-    }
-    this.itemsToPoll = this.itemsToPoll.slice(index + 1);
-  }
-
-  private addItemToPoll(isDoneFn: () => boolean, resolveFn: () => void) {
-    this.itemsToPoll.push({isDoneFn, resolveFn});
-    if (this.itemsToPoll.length > 1) {
-      // We already have a running loop that polls.
-      return;
-    }
-    // Start a new loop that polls.
-    let scheduleFn = undefined;
-    if ('setTimeoutCustom' in env().platform) {
-      scheduleFn = env().platform.setTimeoutCustom.bind(env().platform);
-    }
-    util.repeatedTry(() => {
-      this.pollItems();
-      // End the loop if no more items to poll.
-      return this.itemsToPoll.length === 0;
-    }, () => 0, null, scheduleFn);
-  }
-
-  private bindTextureToFrameBuffer(texture: WebGLTexture) {
-    this.throwIfDisposed();
-    webgl_util.bindColorTextureToFramebuffer(
-        this.gl, texture, this.framebuffer);
-    if (this.debug) {
-      webgl_util.validateFramebuffer(this.gl);
-    }
-  }
-
-  private unbindTextureToFrameBuffer() {
-    if (this.outputTexture != null) {
-      webgl_util.bindColorTextureToFramebuffer(
-          this.gl, this.outputTexture, this.framebuffer);
-      if (this.debug) {
-        webgl_util.validateFramebuffer(this.gl);
-      }
-    } else {
-      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
-    }
-  }
-
-  private downloadMatrixDriver(
-      texture: WebGLTexture,
-      downloadAndDecode: () => Float32Array): Float32Array {
-    this.bindTextureToFrameBuffer(texture);
-    const result = downloadAndDecode();
-    this.unbindTextureToFrameBuffer();
-
-    return result;
-  }
-
-  private setOutputMatrixTextureDriver(
-      outputMatrixTextureMaybePacked: WebGLTexture, width: number,
-      height: number) {
-    this.throwIfDisposed();
-    const gl = this.gl;
-    webgl_util.bindColorTextureToFramebuffer(
-        gl, outputMatrixTextureMaybePacked, this.framebuffer);
-    if (this.debug) {
-      webgl_util.validateFramebuffer(gl);
-    }
-    this.outputTexture = outputMatrixTextureMaybePacked;
-    webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));
-    webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));
-  }
-
-  private setOutputMatrixWriteRegionDriver(
-      x: number, y: number, width: number, height: number) {
-    this.throwIfDisposed();
-    webgl_util.callAndCheck(
-        this.gl, () => this.gl.scissor(x, y, width, height));
-  }
-
-  private throwIfDisposed() {
-    if (this.disposed) {
-      throw new Error('Attempted to use disposed GPGPUContext.');
-    }
-  }
-
-  private throwIfNoProgram() {
-    if (this.program == null) {
-      throw new Error('No GPU program is currently set.');
-    }
-  }
-}
-
-type PollItem = {
-  isDoneFn: () => boolean,
-  resolveFn: () => void
-};
-
-/**
- * Finds the index of the last true element using linear search.
- * Note: We can't do binary search because Chrome expects us to explicitly
- * test all fences before download:
- * https://github.com/tensorflow/tfjs/issues/1145
- */
-export function linearSearchLastTrue(arr: Array<() => boolean>): number {
-  let i = 0;
-  for (; i < arr.length; ++i) {
-    const isDone = arr[i]();
-    if (!isDone) {
-      break;
-    }
-  }
-  return i - 1;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/gpgpu_context_test.ts b/tfjs-master/tfjs-backend-webgl/src/gpgpu_context_test.ts
deleted file mode 100644
index 6eaad6a2d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/gpgpu_context_test.ts
+++ /dev/null
@@ -1,282 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-// tslint:disable-next-line: no-imports-from-dist
-import {expectArraysEqual} from '@tensorflow/tfjs-core/dist/test_util';
-
-import {WEBGL_ENVS} from './backend_webgl_test_registry';
-import * as canvas_util from './canvas_util';
-import {getGlslDifferences} from './glsl_version';
-import {GPGPUContext, GPGPUContextProgram, linearSearchLastTrue} from './gpgpu_context';
-import * as tex_util from './tex_util';
-import {Texture} from './tex_util';
-import {createFragmentShader} from './webgl_util';
-
-const DOWNLOAD_FLOAT_ENVS = {
-  flags: {'WEBGL_DOWNLOAD_FLOAT_ENABLED': true},
-  predicate: WEBGL_ENVS.predicate
-};
-
-describeWithFlags(
-    'GPGPUContext setOutputMatrixTexture', DOWNLOAD_FLOAT_ENVS, () => {
-      let gpgpu: GPGPUContext;
-      let texture: WebGLTexture;
-      let gl: WebGLRenderingContext;
-
-      beforeEach(() => {
-        canvas_util.clearWebGLContext(tf.env().getNumber('WEBGL_VERSION'));
-        gl = canvas_util.getWebGLContext(tf.env().getNumber('WEBGL_VERSION'));
-        gpgpu = new GPGPUContext(gl);
-        // Silences debug warnings.
-        spyOn(console, 'warn');
-        tf.enableDebugMode();
-      });
-
-      afterEach(() => {
-        if (texture != null) {
-          gpgpu.deleteMatrixTexture(texture);
-        }
-        gpgpu.dispose();
-      });
-
-      it('sets the output texture property to the output texture', () => {
-        texture = gpgpu.createFloat32MatrixTexture(1, 1).texture;
-        gpgpu.setOutputMatrixTexture(texture, 1, 1);
-        expect(gpgpu.outputTexture).toBe(texture);
-      });
-
-      it('sets the gl viewport to the output texture dimensions', () => {
-        const columns = 456;
-        const rows = 123;
-        texture = gpgpu.createFloat32MatrixTexture(rows, columns).texture;
-        gpgpu.setOutputMatrixTexture(texture, rows, columns);
-        const expected = new Int32Array([0, 0, columns, rows]);
-        expect(gpgpu.gl.getParameter(gpgpu.gl.VIEWPORT)).toEqual(expected);
-      });
-    });
-
-describeWithFlags(
-    'GPGPUContext setOutputPackedMatrixTexture', DOWNLOAD_FLOAT_ENVS, () => {
-      let gpgpu: GPGPUContext;
-      let texture: Texture;
-      let gl: WebGLRenderingContext;
-
-      beforeEach(() => {
-        canvas_util.clearWebGLContext(tf.env().getNumber('WEBGL_VERSION'));
-        gl = canvas_util.getWebGLContext(tf.env().getNumber('WEBGL_VERSION'));
-        gpgpu = new GPGPUContext(gl);
-        // Silences debug warnings.
-        spyOn(console, 'warn');
-        tf.enableDebugMode();
-      });
-
-      afterEach(() => {
-        if (texture != null) {
-          gpgpu.deleteMatrixTexture(texture.texture);
-        }
-        gpgpu.dispose();
-      });
-
-      it('sets the output texture property to the output texture', () => {
-        texture = gpgpu.createPackedMatrixTexture(1, 1);
-        expectArraysEqual(texture.texShape, [1, 1]);
-        gpgpu.setOutputPackedMatrixTexture(texture.texture, 1, 1);
-        expect(gpgpu.outputTexture).toBe(texture.texture);
-      });
-
-      it('sets the gl viewport to the output packed texture dimensions', () => {
-        const columns = 456;
-        const rows = 123;
-        texture = gpgpu.createPackedMatrixTexture(rows, columns);
-        gpgpu.setOutputPackedMatrixTexture(texture.texture, rows, columns);
-        const [width, height] =
-            tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);
-        const expected = new Int32Array([0, 0, width, height]);
-        expect(gpgpu.gl.getParameter(gpgpu.gl.VIEWPORT)).toEqual(expected);
-        expectArraysEqual(texture.texShape, [height, width]);
-      });
-    });
-
-describeWithFlags(
-    'GPGPUContext setOutputMatrixWriteRegion', DOWNLOAD_FLOAT_ENVS, () => {
-      let gpgpu: GPGPUContext;
-      let program: GPGPUContextProgram;
-      let output: WebGLTexture;
-
-      beforeEach(() => {
-        gpgpu = new GPGPUContext();
-        // Silences debug warnings.
-        spyOn(console, 'warn');
-        tf.enableDebugMode();
-        const glsl = getGlslDifferences();
-        const src = `${glsl.version}
-          precision highp float;
-          ${glsl.defineOutput}
-          void main() {
-            ${glsl.output} = vec4(2,0,0,0);
-          }
-        `;
-        // tslint:disable-next-line: max-line-length
-        const fragmentShader = createFragmentShader(gpgpu.gl, src);
-        program = gpgpu.createProgram(fragmentShader);
-        output = gpgpu.createPackedMatrixTexture(4, 4).texture;
-        gpgpu.uploadDenseMatrixToTexture(output, 2, 2, new Float32Array(16));
-        gpgpu.setOutputMatrixTexture(output, 4, 4);
-        gpgpu.setProgram(program);
-      });
-
-      afterEach(() => {
-        gpgpu.deleteMatrixTexture(output);
-        gpgpu.deleteProgram(program);
-        gpgpu.dispose();
-      });
-
-      it('sets the scissor box to the requested parameters', () => {
-        gpgpu.setOutputMatrixWriteRegion(0, 1, 2, 3);
-        const scissorBox = gpgpu.gl.getParameter(gpgpu.gl.SCISSOR_BOX);
-        expect(scissorBox[0]).toEqual(2);
-        expect(scissorBox[1]).toEqual(0);
-        expect(scissorBox[2]).toEqual(3);
-        expect(scissorBox[3]).toEqual(1);
-      });
-    });
-
-describeWithFlags('GPGPUContext', DOWNLOAD_FLOAT_ENVS, () => {
-  let gpgpu: GPGPUContext;
-
-  beforeEach(() => {
-    gpgpu = new GPGPUContext();
-    // Silences debug warnings.
-    spyOn(console, 'warn');
-    tf.enableDebugMode();
-  });
-
-  afterEach(() => {
-    gpgpu.dispose();
-  });
-
-  it('throws an error if used after dispose', () => {
-    const gpgpuContext = new GPGPUContext();
-    gpgpuContext.dispose();
-    expect(gpgpuContext.dispose).toThrowError();
-  });
-
-  it('throws an error if validation is on and framebuffer incomplete', () => {
-    const glsl = getGlslDifferences();
-    const src = `${glsl.version}
-      precision highp float;
-      void main() {}
-    `;
-    const fragmentShader = createFragmentShader(gpgpu.gl, src);
-    const program = gpgpu.createProgram(fragmentShader);
-    const result = gpgpu.createFloat32MatrixTexture(1, 1).texture;
-    gpgpu.setOutputMatrixTexture(result, 1, 1);
-    gpgpu.setProgram(program);
-    gpgpu.deleteMatrixTexture(result);
-    expect(gpgpu.executeProgram).toThrowError();
-    gpgpu.deleteProgram(program);
-  });
-});
-
-describe('gpgpu_context linearSearchLastTrue', () => {
-  it('[false]', () => {
-    const a: boolean[] = [false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(-1);
-  });
-
-  it('[true]', () => {
-    const a: boolean[] = [true];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(0);
-  });
-
-  it('[false, false]', () => {
-    const a: boolean[] = [false, false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(-1);
-  });
-
-  it('[true, false]', () => {
-    const a: boolean[] = [true, false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(0);
-  });
-
-  it('[true, true]', () => {
-    const a: boolean[] = [true, true];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(1);
-  });
-
-  it('[false, false, false]', () => {
-    const a: boolean[] = [false, false, false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(-1);
-  });
-
-  it('[true, false, false]', () => {
-    const a: boolean[] = [true, false, false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(0);
-  });
-
-  it('[true, true, false]', () => {
-    const a: boolean[] = [true, true, false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(1);
-  });
-
-  it('[true, true, true]', () => {
-    const a: boolean[] = [true, true, true];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(2);
-  });
-
-  it('[false, false, false, false]', () => {
-    const a: boolean[] = [false, false, false, false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(-1);
-  });
-
-  it('[true, false, false, false]', () => {
-    const a: boolean[] = [true, false, false, false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(0);
-  });
-
-  it('[true, true, false, false]', () => {
-    const a: boolean[] = [true, true, false, false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(1);
-  });
-
-  it('[true, true, true, false]', () => {
-    const a: boolean[] = [true, true, true, false];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(2);
-  });
-
-  it('[true, true, true, true]', () => {
-    const a: boolean[] = [true, true, true, true];
-    const arr = a.map(x => () => x);
-    expect(linearSearchLastTrue(arr)).toBe(3);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/gpgpu_math.ts b/tfjs-master/tfjs-backend-webgl/src/gpgpu_math.ts
deleted file mode 100644
index bcf52ff96..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/gpgpu_math.ts
+++ /dev/null
@@ -1,480 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, env, Tensor, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {GPGPUContext, GPGPUContextProgram} from './gpgpu_context';
-import * as shader_compiler from './shader_compiler';
-import {InputInfo, ShapeInfo, UniformType} from './shader_compiler';
-import {PackingScheme, TextureData, TextureUsage} from './tex_util';
-import {createFragmentShader} from './webgl_util';
-
-export interface GPGPUProgram {
-  variableNames: string[];
-  outputShape: number[];
-  userCode: string;
-  enableShapeUniforms?: boolean;
-  /** If true, this program expects packed input textures. Defaults to false. */
-  packedInputs?: boolean;
-  /** If true, this program produces a packed texture. Defaults to false. */
-  packedOutput?: boolean;
-  /**
-   * Affects what type of texture we allocate for the output. Defaults to
-   * `TextureUsage.RENDER`.
-   */
-  outTexUsage?: TextureUsage;
-  /**
-   * The type of scheme to use when packing texels for the output values.
-   * See `PackingScheme` for details. Defaults to `PackingScheme.SHARED_BATCH`.
-   */
-  outPackingScheme?: PackingScheme;
-  customUniforms?:
-      Array<{name: string; arrayIndex?: number; type: UniformType;}>;
-}
-
-export interface GPGPUBinary extends GPGPUBinaryLocations {
-  webGLProgram: GPGPUContextProgram;
-  program: GPGPUProgram;
-  source: string;
-  fragmentShader: WebGLShader;
-  inShapeInfos: ShapeInfo[];
-  outShapeInfo: ShapeInfo;
-}
-
-export interface GPGPUBinaryLocations {
-  customUniformLocations?: WebGLUniformLocation[];
-  infLoc: WebGLUniformLocation;
-  nanLoc: WebGLUniformLocation;
-  outShapeLocation?: WebGLUniformLocation;
-  outShapeStridesLocation?: WebGLUniformLocation;
-  outTexShapeLocation?: WebGLUniformLocation;
-  variablesLocations?: GPGPUVariableLocations[];
-}
-
-export interface GPGPUVariableLocations {
-  name: string;
-  uniform: WebGLUniformLocation;
-  offset: WebGLUniformLocation;
-  shape?: WebGLUniformLocation;
-  texShape?: WebGLUniformLocation;
-}
-
-export interface TensorData {
-  shape: number[];
-  texData: TextureData;
-  isUniform: boolean;
-  // Available when we decide to upload as uniform instead of texture.
-  uniformValues?: TypedArray;
-}
-
-export function compileProgram<T extends Tensor, K extends Tensor>(
-    gpgpu: GPGPUContext, program: GPGPUProgram, inputs: TensorData[],
-    output: TensorData): GPGPUBinary {
-  const inputInfos: InputInfo[] = inputs.map((input, i) => {
-    const shapeInfo: ShapeInfo = {
-      logicalShape: input.shape,
-      texShape: input.isUniform ? null : input.texData.texShape,
-      isUniform: input.isUniform,
-      isPacked: input.isUniform ? false : input.texData.isPacked,
-      flatOffset: null
-    };
-    if (input.texData != null && input.texData.slice != null &&
-        input.texData.slice.flatOffset > 0) {
-      shapeInfo.flatOffset = input.texData.slice.flatOffset;
-    }
-    return {name: program.variableNames[i], shapeInfo};
-  });
-  const inShapeInfos = inputInfos.map(x => x.shapeInfo);
-  const outShapeInfo: ShapeInfo = {
-    logicalShape: output.shape,
-    texShape: output.texData.texShape,
-    isUniform: false,
-    isPacked: output.texData.isPacked,
-    flatOffset: null
-  };
-  const source = shader_compiler.makeShader(inputInfos, outShapeInfo, program);
-  const fragmentShader = createFragmentShader(gpgpu.gl, source);
-  const webGLProgram = gpgpu.createProgram(fragmentShader);
-
-  if (!env().get('ENGINE_COMPILE_ONLY')) {
-    gpgpu.buildVao(webGLProgram);
-    return {
-      program,
-      fragmentShader,
-      source,
-      webGLProgram,
-      inShapeInfos,
-      outShapeInfo,
-      ...getUniformLocations(gpgpu, program, webGLProgram)
-    };
-  } else {
-    return {
-      program,
-      fragmentShader,
-      source,
-      webGLProgram,
-      inShapeInfos,
-      outShapeInfo,
-      variablesLocations: null,
-      customUniformLocations: null,
-      infLoc: null,
-      nanLoc: null,
-      outShapeLocation: null,
-      outShapeStridesLocation: null,
-      outTexShapeLocation: null
-    };
-  }
-}
-
-export function getUniformLocations(
-    gpgpu: GPGPUContext, program: GPGPUProgram,
-    webGLProgram: WebGLProgram): GPGPUBinaryLocations {
-  const variablesLocations: GPGPUVariableLocations[] = [];
-  const customUniformLocations: WebGLUniformLocation[] = [];
-  let outShapeLocation: WebGLUniformLocation;
-  let outTexShapeLocation: WebGLUniformLocation;
-  let outShapeStridesLocation: WebGLUniformLocation;
-  let infLoc: WebGLUniformLocation = null;
-  let nanLoc: WebGLUniformLocation = null;
-
-  // Add special uniforms (NAN, INFINITY)
-  nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);
-  if (env().getNumber('WEBGL_VERSION') === 1) {
-    infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);
-  }
-
-  // Add user-defined uniforms
-  const shouldThrow = false;
-  for (const varName of program.variableNames) {
-    const varLocs: GPGPUVariableLocations = {
-      name: varName,
-      uniform: gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow),
-      offset: gpgpu.getUniformLocation(
-          webGLProgram, `offset${varName}`, shouldThrow),
-    };
-    if (program.enableShapeUniforms) {
-      varLocs.shape = gpgpu.getUniformLocation(
-          webGLProgram, `${varName}Shape`, shouldThrow);
-      varLocs.texShape = gpgpu.getUniformLocation(
-          webGLProgram, `${varName}TexShape`, shouldThrow);
-    }
-
-    variablesLocations.push(varLocs);
-  }
-
-  if (program.enableShapeUniforms) {
-    outShapeLocation =
-        gpgpu.getUniformLocation(webGLProgram, 'outShape', shouldThrow);
-    outShapeStridesLocation =
-        gpgpu.getUniformLocation(webGLProgram, 'outShapeStrides', shouldThrow);
-    outTexShapeLocation =
-        gpgpu.getUniformLocation(webGLProgram, 'outTexShape', shouldThrow);
-  }
-
-  if (program.customUniforms) {
-    for (const d of program.customUniforms) {
-      customUniformLocations.push(
-          gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow));
-    }
-  }
-
-  return {
-    variablesLocations,
-    customUniformLocations,
-    infLoc,
-    nanLoc,
-    outShapeLocation,
-    outShapeStridesLocation,
-    outTexShapeLocation
-  };
-}
-
-function validateBinaryAndProgram(
-    shapeInfos: ShapeInfo[], inputs: TensorData[]) {
-  if (shapeInfos.length !== inputs.length) {
-    throw Error(
-        `Binary was compiled with ${shapeInfos.length} inputs, but ` +
-        `was executed with ${inputs.length} inputs`);
-  }
-
-  shapeInfos.forEach((s, i) => {
-    const shapeA = s.logicalShape;
-    const input = inputs[i];
-    const shapeB = input.shape;
-
-    if (!util.arraysEqual(shapeA, shapeB)) {
-      throw Error(
-          `Binary was compiled with different shapes than ` +
-          `the current args. Shapes ${shapeA} and ${shapeB} must match`);
-    }
-    // The input is uploaded as uniform.
-    if (s.isUniform && input.isUniform) {
-      return;
-    }
-
-    const texShapeA = s.texShape;
-    const texShapeB = input.isUniform ? null : input.texData.texShape;
-    if (!util.arraysEqual(texShapeA, texShapeB)) {
-      throw Error(
-          `Binary was compiled with different texture shapes than the` +
-          ` current args. Shape ${texShapeA} and ${texShapeB} must match`);
-    }
-  });
-}
-
-export function runProgram<T extends Tensor, K extends Tensor>(
-    gpgpu: GPGPUContext, binary: GPGPUBinary, inputs: TensorData[],
-    output: TensorData, customUniformValues?: number[][]): void {
-  if (!binary.program.enableShapeUniforms) {
-    validateBinaryAndProgram(binary.inShapeInfos, inputs);
-    validateBinaryAndProgram([binary.outShapeInfo], [output]);
-  }
-
-  const outTex = output.texData.texture;
-  const outTexShape = output.texData.texShape;
-  if (output.texData.isPacked) {
-    gpgpu.setOutputPackedMatrixTexture(
-        outTex.texture, outTexShape[0], outTexShape[1]);
-  } else {
-    gpgpu.setOutputMatrixTexture(
-        outTex.texture, outTexShape[0], outTexShape[1]);
-  }
-  gpgpu.setProgram(binary.webGLProgram);
-  gpgpu.bindVertexArray(binary.webGLProgram.vao);
-
-  // Set special uniforms (NAN, INFINITY)
-  if (env().getNumber('WEBGL_VERSION') === 1) {
-    if (binary.infLoc !== null) {
-      gpgpu.gl.uniform1f(binary.infLoc, Infinity);
-    }
-  }
-  if (binary.nanLoc !== null) {
-    gpgpu.gl.uniform1f(binary.nanLoc, NaN);
-  }
-
-  // Set user-defined inputs
-  for (let i = 0; i < inputs.length; ++i) {
-    const input = inputs[i];
-    const {
-      uniform: varLoc,
-      offset: varOffsetLoc,
-      shape: varShapeLoc,
-      texShape: varTexShapeLoc,
-    } = binary.variablesLocations[i];
-
-    if (varShapeLoc) {
-      const {uniformShape} = shader_compiler.getUniformInfoFromShape(
-          binary.program.packedInputs, input.shape, input.texData.texShape);
-      switch (uniformShape.length) {
-        case 1:
-          gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape));
-          break;
-        case 2:
-          gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape));
-          break;
-        case 3:
-          gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape));
-          break;
-        case 4:
-          gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape));
-          break;
-        default:
-          break;
-      }
-    }
-
-    if (varTexShapeLoc) {
-      gpgpu.gl.uniform2i(
-          varTexShapeLoc, input.texData.texShape[0], input.texData.texShape[1]);
-    }
-
-    if (varLoc == null) {
-      // The compiler inferred that this variable is not used in this shader.
-      continue;
-    }
-
-    if (input.isUniform) {
-      // Upload the values of the tensor as uniform.
-      if (util.sizeFromShape(input.shape) < 2) {
-        gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);
-      } else {
-        let vals = input.uniformValues;
-        if (!(vals instanceof Float32Array)) {
-          vals = new Float32Array(vals);
-        }
-        gpgpu.gl.uniform1fv(varLoc, vals);
-      }
-      continue;
-    }
-
-    // If the input was sliced, upload the flat offset index.
-    if (input.texData.slice != null && varOffsetLoc != null) {
-      gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);
-    }
-
-    gpgpu.setInputMatrixTexture(input.texData.texture.texture, varLoc, i);
-  }
-
-  const outShapeLoc = binary.outShapeLocation;
-  if (outShapeLoc) {
-    switch (output.shape.length) {
-      case 1:
-        gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape));
-        break;
-      case 2:
-        gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape));
-        break;
-      case 3:
-        gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape));
-        break;
-      case 4:
-        gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape));
-        break;
-      default:
-        break;
-    }
-  }
-  if (binary.outShapeStridesLocation) {
-    const strides = util.computeStrides(output.shape);
-    switch (output.shape.length) {
-      case 2:
-        gpgpu.gl.uniform1iv(
-            binary.outShapeStridesLocation, new Int32Array(strides));
-        break;
-      case 3:
-        gpgpu.gl.uniform2iv(
-            binary.outShapeStridesLocation, new Int32Array(strides));
-        break;
-      case 4:
-        gpgpu.gl.uniform3iv(
-            binary.outShapeStridesLocation, new Int32Array(strides));
-        break;
-      default:
-        break;
-    }
-  }
-  if (binary.outTexShapeLocation) {
-    gpgpu.gl.uniform2i(
-        binary.outTexShapeLocation, output.texData.texShape[0],
-        output.texData.texShape[1]);
-  }
-
-  if (binary.program.customUniforms && customUniformValues) {
-    for (let i = 0; i < binary.program.customUniforms.length; ++i) {
-      const d = binary.program.customUniforms[i];
-      const customLoc = binary.customUniformLocations[i];
-      const customValue = customUniformValues[i];
-      if (d.type === 'float') {
-        gpgpu.gl.uniform1fv(customLoc, customValue);
-      } else if (d.type === 'vec2') {
-        gpgpu.gl.uniform2fv(customLoc, customValue);
-      } else if (d.type === 'vec3') {
-        gpgpu.gl.uniform3fv(customLoc, customValue);
-      } else if (d.type === 'vec4') {
-        gpgpu.gl.uniform4fv(customLoc, customValue);
-      } else if (d.type === 'int') {
-        gpgpu.gl.uniform1iv(customLoc, customValue);
-      } else if (d.type === 'ivec2') {
-        gpgpu.gl.uniform2iv(customLoc, customValue);
-      } else if (d.type === 'ivec3') {
-        gpgpu.gl.uniform3iv(customLoc, customValue);
-      } else if (d.type === 'ivec4') {
-        gpgpu.gl.uniform4iv(customLoc, customValue);
-      } else {
-        throw Error(`uniform type ${d.type} is not supported yet.`);
-      }
-    }
-  }
-  gpgpu.executeProgram();
-}
-
-export function makeShaderKey(
-    program: GPGPUProgram, inputs: TensorData[], output: TensorData): string {
-  let keyInputs = '';
-  inputs.concat(output).forEach(x => {
-    const hasOffset = x.texData != null && x.texData.slice != null &&
-        x.texData.slice.flatOffset > 0;
-    // TODO: Remove the condition of !x.isUniform.
-    if (program.enableShapeUniforms && !x.isUniform) {
-      const xTexShape = x.texData.texShape;
-      const {useSqueezeShape, uniformShape, keptDims} =
-          shader_compiler.getUniformInfoFromShape(
-              program.packedInputs, x.shape, xTexShape);
-      let rank1 = '', rank2 = '', rank34 = '';
-      if (uniformShape.length === 1 && program.packedInputs) {
-        const packedTexShape =
-            [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)];
-        rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`;
-      } else if (uniformShape.length === 2 && !program.packedInputs) {
-        rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`;
-      } else if (uniformShape.length > 2 && !program.packedInputs) {
-        const strides = util.computeStrides(uniformShape);
-        rank34 = `${strides[0] === xTexShape[1]}_${
-            strides[strides.length - 1] === xTexShape[1]}`;
-      }
-      const xRank = x.shape.length;
-      const isLogicalShapTexShapeEqual =
-          uniformShape.length === 2 && util.arraysEqual(x.shape, xTexShape);
-      const isScalar = util.sizeFromShape(x.shape) === 1;
-      const broadcastDims =
-          backend_util.getBroadcastDims(x.shape, output.shape);
-      const isInOutTexShapeEqual = !program.packedInputs &&
-          xRank === output.shape.length &&
-          util.arraysEqual(xTexShape, output.texData.texShape);
-      const isTexShapeGreaterThanOne =
-          program.packedInputs || uniformShape.length > 2 ?
-          '' :
-          `${xTexShape[0] > 1}_${xTexShape[1] > 1}`;
-      // These key components are needed due to shader_compiler is embedding
-      // them in the shader.
-      // |xRank| is used to determine the coords length. See
-      // get[Packed]SamplerAtOutputCoords.
-      // |isInOutTexShapeEqual| is used to determine whether going to an
-      // optimization path in getSamplerAtOutputCoords.
-      // |useSqueezeShape| is extracted from squeezeInputInfo of
-      // getSampler[2|3|4]D/getPackedSampler3D.
-      // |isScalar| is extracted from isInputScalar/isOutputScalar in
-      // getPackedSamplerAtOutputCoords.
-      // |broadcastDims| is extracted from get[Packed]SamplerAtOutputCoords.
-      // |isLogicalShapTexShapeEqual| is used in
-      // getOutput[Packed]2DCoords/get[Packed]Sampler2D.
-      // |rank1| is used in getOutputPacked1DCoords.
-      // |rank2| is used in getOutput2DCoords.
-      // |rank34| is used in getSampler3D/getSampler4D.
-      // |isTexShapeGreaterThanOne| are used in
-      // getSampler[Scalar|1D|2D]/getOutput1DCoords.
-      keyInputs += `${xRank}_${isInOutTexShapeEqual}_${
-          useSqueezeShape ? keptDims : ''}_${uniformShape.length}_${isScalar}_${
-          broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${
-          rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`;
-    } else {
-      const texShape = x.isUniform ? 'uniform' : x.texData.texShape;
-      keyInputs += `${x.shape}_${texShape}_${hasOffset}`;
-    }
-  });
-  const keyUserCode = program.userCode;
-  let key = program.constructor.name;
-  // Fast string concat. See https://jsperf.com/string-concatenation/14.
-  key += '_' + keyInputs + '_' + keyUserCode +
-      `${env().getNumber('WEBGL_VERSION')}`;
-  return key;
-}
-
-export function useShapeUniforms(rank: number) {
-  // TODO: Remove the limitaion of rank <= 4.
-  return env().getBool('WEBGL_USE_SHAPES_UNIFORMS') && rank <= 4;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/gpgpu_util.ts b/tfjs-master/tfjs-backend-webgl/src/gpgpu_util.ts
deleted file mode 100644
index 3343ac9b6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/gpgpu_util.ts
+++ /dev/null
@@ -1,346 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, PixelData, TypedArray} from '@tensorflow/tfjs-core';
-
-import {getGlslDifferences} from './glsl_version';
-import * as tex_util from './tex_util';
-import {Texture, TextureConfig} from './tex_util';
-import * as webgl_util from './webgl_util';
-
-export function createVertexShader(gl: WebGLRenderingContext): WebGLShader {
-  const glsl = getGlslDifferences();
-  const vertexShaderSource = `${glsl.version}
-    precision highp float;
-    ${glsl.attribute} vec3 clipSpacePos;
-    ${glsl.attribute} vec2 uv;
-    ${glsl.varyingVs} vec2 resultUV;
-
-    void main() {
-      gl_Position = vec4(clipSpacePos, 1);
-      resultUV = uv;
-    }`;
-  return webgl_util.createVertexShader(gl, vertexShaderSource);
-}
-
-export function createVertexBuffer(gl: WebGLRenderingContext): WebGLBuffer {
-  // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]
-  const vertexArray = new Float32Array(
-      [-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
-  return webgl_util.createStaticVertexBuffer(gl, vertexArray);
-}
-
-export function createIndexBuffer(gl: WebGLRenderingContext): WebGLBuffer {
-  // OpenGL (and WebGL) have "CCW == front" winding
-  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);
-  return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);
-}
-
-function createAndConfigureTexture(
-    gl: WebGLRenderingContext, width: number, height: number,
-    internalFormat: number, textureFormat: number,
-    textureType: number): Texture {
-  webgl_util.validateTextureSize(width, height);
-  const texture = webgl_util.createTexture(gl);
-
-  const tex2d = gl.TEXTURE_2D;
-  webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));
-  webgl_util.callAndCheck(
-      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));
-  webgl_util.callAndCheck(
-      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));
-  webgl_util.callAndCheck(
-      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));
-  webgl_util.callAndCheck(
-      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));
-  if (env().getNumber('WEBGL_VERSION') === 1) {
-    webgl_util.callAndCheck(
-        gl,
-        () => gl.texImage2D(
-            tex2d, 0, internalFormat, width, height, 0, textureFormat,
-            textureType, null));
-  } else {
-    webgl_util.callAndCheck(
-        gl,
-        () => (gl as WebGL2RenderingContext)
-                  .texStorage2D(tex2d, 1, internalFormat, width, height));
-  }
-  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
-
-  return {texture, texShape: [height, width]};
-}
-
-export function getInternalFormatForFloat32MatrixTexture(
-    textureConfig: TextureConfig) {
-  return textureConfig.internalFormatFloat;
-}
-
-export function createFloat32MatrixTexture(
-    gl: WebGLRenderingContext, rows: number, columns: number,
-    textureConfig: TextureConfig): Texture {
-  const [width, height] =
-      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
-  return createAndConfigureTexture(
-      gl, width, height,
-      getInternalFormatForFloat32MatrixTexture(textureConfig),
-      textureConfig.textureFormatFloat, gl.FLOAT);
-}
-
-export function getInternalFormatForFloat16MatrixTexture(
-    textureConfig: TextureConfig) {
-  return textureConfig.internalFormatHalfFloat;
-}
-
-export function createFloat16MatrixTexture(
-    gl: WebGLRenderingContext, rows: number, columns: number,
-    textureConfig: TextureConfig): Texture {
-  const [width, height] =
-      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
-  return createAndConfigureTexture(
-      gl, width, height,
-      getInternalFormatForFloat16MatrixTexture(textureConfig),
-      textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
-}
-
-export function getInternalFormatForUnsignedBytesMatrixTexture(
-    textureConfig: TextureConfig) {
-  return textureConfig.downloadTextureFormat;
-}
-
-export function createUnsignedBytesMatrixTexture(
-    gl: WebGLRenderingContext, rows: number, columns: number,
-    textureConfig: TextureConfig): Texture {
-  const [width, height] =
-      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
-  return createAndConfigureTexture(
-      gl, width, height,
-      getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA,
-      gl.UNSIGNED_BYTE);
-}
-
-export function getInternalFormatForPackedMatrixTexture(
-    textureConfig: TextureConfig) {
-  return textureConfig.internalFormatPackedFloat;
-}
-
-export function createPackedMatrixTexture(
-    gl: WebGLRenderingContext, rows: number, columns: number,
-    textureConfig: TextureConfig): Texture {
-  const [width, height] =
-      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);
-  return createAndConfigureTexture(
-      gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig),
-      gl.RGBA, gl.FLOAT);
-}
-
-export function getInternalFormatForFloat16PackedMatrixTexture(
-    textureConfig: TextureConfig) {
-  return textureConfig.internalFormatPackedHalfFloat;
-}
-
-export function createFloat16PackedMatrixTexture(
-    gl: WebGLRenderingContext, rows: number, columns: number,
-    textureConfig: TextureConfig): Texture {
-  const [width, height] =
-      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);
-  return createAndConfigureTexture(
-      gl, width, height,
-      getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA,
-      textureConfig.textureTypeHalfFloat);
-}
-
-export function bindVertexProgramAttributeStreams(
-    gl: WebGLRenderingContext, program: WebGLProgram,
-    vertexBuffer: WebGLBuffer): boolean {
-  const posOffset = 0;               // x is the first buffer element
-  const uvOffset = 3 * 4;            // uv comes after [x y z]
-  const stride = (3 * 4) + (2 * 4);  // xyz + uv, each entry is 4-byte float.
-  webgl_util.callAndCheck(
-      gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));
-  const success = webgl_util.bindVertexBufferToProgramAttribute(
-      gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);
-  return success &&
-      webgl_util.bindVertexBufferToProgramAttribute(
-          gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);
-}
-
-export function uploadDenseMatrixToTexture(
-    gl: WebGLRenderingContext, texture: WebGLTexture, width: number,
-    height: number, data: TypedArray, textureConfig: TextureConfig) {
-  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
-
-  let dataForUpload: TypedArray, texelDataType: number, internalFormat: number;
-  if (data instanceof Uint8Array) {
-    dataForUpload = new Uint8Array(width * height * 4);
-    texelDataType = gl.UNSIGNED_BYTE;
-    internalFormat = gl.RGBA;
-  } else {
-    dataForUpload = new Float32Array(width * height * 4);
-    texelDataType = gl.FLOAT;
-    internalFormat = textureConfig.internalFormatPackedFloat;
-  }
-
-  dataForUpload.set(data);
-  if (env().getNumber('WEBGL_VERSION') === 2) {
-    webgl_util.callAndCheck(
-        gl,
-        () => gl.texSubImage2D(
-            gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType,
-            dataForUpload));
-  } else {
-    webgl_util.callAndCheck(
-        gl,
-        () => gl.texImage2D(
-            gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA,
-            texelDataType, dataForUpload));
-  }
-
-  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
-}
-
-export function uploadPixelDataToTexture(
-    gl: WebGLRenderingContext, texture: WebGLTexture,
-    pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|
-    HTMLVideoElement|ImageBitmap) {
-  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
-  if ((pixels as PixelData).data instanceof Uint8Array) {
-    if (env().getNumber('WEBGL_VERSION') === 2) {
-      webgl_util.callAndCheck(
-          gl,
-          () => gl.texSubImage2D(
-              gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA,
-              gl.UNSIGNED_BYTE, (pixels as PixelData).data));
-    } else {
-      webgl_util.callAndCheck(
-          gl,
-          () => gl.texImage2D(
-              gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0,
-              gl.RGBA, gl.UNSIGNED_BYTE, (pixels as PixelData).data));
-    }
-  } else {
-    if (env().getNumber('WEBGL_VERSION') === 2) {
-      webgl_util.callAndCheck(
-          gl,
-          () => gl.texSubImage2D(
-              gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE,
-              (pixels as ImageData | HTMLImageElement | HTMLCanvasElement |
-               HTMLVideoElement | ImageBitmap)));
-    } else {
-      webgl_util.callAndCheck(
-          gl,
-          () => gl.texImage2D(
-              gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE,
-              pixels as ImageData | HTMLImageElement | HTMLCanvasElement |
-                  HTMLVideoElement | ImageBitmap));
-    }
-  }
-
-  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
-}
-
-export function createBufferFromOutputTexture(
-    gl2: WebGL2RenderingContext, rows: number, columns: number,
-    textureConfig: TextureConfig): WebGLBuffer {
-  // Create and bind the buffer.
-  const buffer = gl2.createBuffer();
-  webgl_util.callAndCheck(
-      gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));
-
-  // Initialize the buffer to the size of the texture in bytes.
-  const bytesPerFloat = 4;
-  const valuesPerTexel = 4;
-  const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;
-
-  webgl_util.callAndCheck(
-      gl2,
-      () => gl2.bufferData(
-          gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));
-
-  // Enqueue a command on the GPU command queue to copy of texture into the
-  // buffer.
-  webgl_util.callAndCheck(
-      gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));
-
-  webgl_util.callAndCheck(
-      gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));
-
-  return buffer;
-}
-
-export function downloadFloat32MatrixFromBuffer(
-    gl: WebGLRenderingContext, buffer: WebGLBuffer,
-    size: number): Float32Array {
-  const gl2 = gl as WebGL2RenderingContext;
-
-  const downloadTarget = new Float32Array(size);
-
-  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);
-  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
-  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
-
-  return downloadTarget;
-}
-
-export function downloadByteEncodedFloatMatrixFromOutputTexture(
-    gl: WebGLRenderingContext, rows: number, columns: number,
-    textureConfig: TextureConfig) {
-  const [w, h] =
-      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
-
-  const numChannels = 4;
-  const downloadTarget = new Uint8Array(
-      tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
-
-  webgl_util.callAndCheck(
-      gl,
-      () => gl.readPixels(
-          0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE,
-          downloadTarget));
-
-  // By wrapping the buffer in a Float32Array, we use native browser IEEE 754
-  // decoding of the 4 bytes that back each 32 bit float.
-  return new Float32Array(downloadTarget.buffer);
-}
-
-export function downloadPackedMatrixFromBuffer(
-    gl: WebGLRenderingContext, buffer: WebGLBuffer, batch: number, rows: number,
-    cols: number, physicalRows: number, physicalCols: number,
-    textureConfig: TextureConfig): Float32Array {
-  const gl2 = gl as WebGL2RenderingContext;
-
-  const downloadTarget =
-      new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(
-          physicalRows, physicalCols));
-
-  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);
-  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
-  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
-
-  return downloadTarget;
-}
-
-export function downloadMatrixFromPackedOutputTexture(
-    gl: WebGLRenderingContext, physicalRows: number,
-    physicalCols: number): Float32Array {
-  const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);
-  webgl_util.callAndCheck(
-      gl,
-      () => gl.readPixels(
-          0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));
-
-  return packedRGBA;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/gpgpu_util_test.ts b/tfjs-master/tfjs-backend-webgl/src/gpgpu_util_test.ts
deleted file mode 100644
index e71d462d1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/gpgpu_util_test.ts
+++ /dev/null
@@ -1,133 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {WEBGL_ENVS} from './backend_webgl_test_registry';
-import {GPGPUContext} from './gpgpu_context';
-import * as gpgpu_util from './gpgpu_util';
-import * as tex_util from './tex_util';
-
-describeWithFlags('gpgpu_util createWebGLContext', WEBGL_ENVS, () => {
-  let gpgpu: GPGPUContext;
-
-  beforeEach(() => {
-    gpgpu = new GPGPUContext();
-  });
-
-  afterEach(() => {
-    gpgpu.dispose();
-  });
-
-  it('disables DEPTH_TEST and STENCIL_TEST', () => {
-    expect(gpgpu.gl.getParameter(gpgpu.gl.DEPTH_TEST)).toEqual(false);
-    expect(gpgpu.gl.getParameter(gpgpu.gl.STENCIL_TEST)).toEqual(false);
-  });
-
-  it('disables BLEND', () => {
-    expect(gpgpu.gl.getParameter(gpgpu.gl.BLEND)).toEqual(false);
-  });
-
-  it('disables DITHER, POLYGON_OFFSET_FILL', () => {
-    expect(gpgpu.gl.getParameter(gpgpu.gl.DITHER)).toEqual(false);
-    expect(gpgpu.gl.getParameter(gpgpu.gl.POLYGON_OFFSET_FILL)).toEqual(false);
-  });
-
-  it('enables CULL_FACE with BACK', () => {
-    expect(gpgpu.gl.getParameter(gpgpu.gl.CULL_FACE)).toEqual(true);
-    expect(gpgpu.gl.getParameter(gpgpu.gl.CULL_FACE_MODE))
-        .toEqual(gpgpu.gl.BACK);
-  });
-
-  it('enables SCISSOR_TEST', () => {
-    expect(gpgpu.gl.getParameter(gpgpu.gl.SCISSOR_TEST)).toEqual(true);
-  });
-});
-
-describeWithFlags('gpgpu_util createFloat32MatrixTexture', WEBGL_ENVS, () => {
-  it('sets the TEXTURE_WRAP S+T parameters to CLAMP_TO_EDGE', () => {
-    const gpgpu = new GPGPUContext();
-    const textureConfig = tex_util.getTextureConfig(gpgpu.gl);
-    const tex =
-        gpgpu_util.createFloat32MatrixTexture(gpgpu.gl, 32, 32, textureConfig)
-            .texture;
-    gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex);
-    expect(
-        gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_WRAP_S))
-        .toEqual(gpgpu.gl.CLAMP_TO_EDGE);
-    expect(
-        gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_WRAP_T))
-        .toEqual(gpgpu.gl.CLAMP_TO_EDGE);
-    gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, null);
-    gpgpu.deleteMatrixTexture(tex);
-    gpgpu.dispose();
-  });
-
-  it('sets the TEXTURE_[MIN|MAG]_FILTER parameters to NEAREST', () => {
-    const gpgpu = new GPGPUContext();
-    const textureConfig = tex_util.getTextureConfig(gpgpu.gl);
-    const tex =
-        gpgpu_util.createFloat32MatrixTexture(gpgpu.gl, 32, 32, textureConfig)
-            .texture;
-    gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex);
-    expect(gpgpu.gl.getTexParameter(
-               gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_MIN_FILTER))
-        .toEqual(gpgpu.gl.NEAREST);
-    expect(gpgpu.gl.getTexParameter(
-               gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_MAG_FILTER))
-        .toEqual(gpgpu.gl.NEAREST);
-    gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, null);
-    gpgpu.deleteMatrixTexture(tex);
-    gpgpu.dispose();
-  });
-});
-
-describeWithFlags('gpgpu_util createPackedMatrixTexture', WEBGL_ENVS, () => {
-  it('sets the TEXTURE_WRAP S+T parameters to CLAMP_TO_EDGE', () => {
-    const gpgpu = new GPGPUContext();
-    const textureConfig = tex_util.getTextureConfig(gpgpu.gl);
-    const tex =
-        gpgpu_util.createPackedMatrixTexture(gpgpu.gl, 32, 32, textureConfig);
-    gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex.texture);
-    expect(
-        gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_WRAP_S))
-        .toEqual(gpgpu.gl.CLAMP_TO_EDGE);
-    expect(
-        gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_WRAP_T))
-        .toEqual(gpgpu.gl.CLAMP_TO_EDGE);
-    gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, null);
-    gpgpu.deleteMatrixTexture(tex.texture);
-    gpgpu.dispose();
-  });
-
-  it('sets the TEXTURE_[MIN|MAG]_FILTER parameters to NEAREST', () => {
-    const gpgpu = new GPGPUContext();
-    const textureConfig = tex_util.getTextureConfig(gpgpu.gl);
-    const tex =
-        gpgpu_util.createPackedMatrixTexture(gpgpu.gl, 32, 32, textureConfig);
-    gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex.texture);
-    expect(gpgpu.gl.getTexParameter(
-               gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_MIN_FILTER))
-        .toEqual(gpgpu.gl.NEAREST);
-    expect(gpgpu.gl.getTexParameter(
-               gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_MAG_FILTER))
-        .toEqual(gpgpu.gl.NEAREST);
-    gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, null);
-    gpgpu.deleteMatrixTexture(tex.texture);
-    gpgpu.dispose();
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/im2col_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/im2col_packed_gpu.ts
deleted file mode 100644
index 70596d860..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/im2col_packed_gpu.ts
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getGlslDifferences} from './glsl_version';
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-
-export class Im2ColPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[];
-  userCode: string;
-  enableShapeUniforms: boolean;
-  customUniforms = [
-    {name: 'inputShape', type: 'ivec4' as const },
-    {name: 'pad', type: 'ivec2' as const },
-    {name: 'stride', type: 'ivec2' as const },
-    {name: 'dilation', type: 'ivec2' as const },
-    {name: 'inChannels', type: 'int' as const },
-    {name: 'itemsPerBlockRow', type: 'int' as const },
-    {name: 'outWidth', type: 'int' as const },
-  ];
-
-  constructor(outputShape: number[], convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = outputShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-    const {dataFormat} = convInfo;
-    const glsl = getGlslDifferences();
-    const isChannelsLast = dataFormat === 'channelsLast';
-    const rowDim = isChannelsLast ? 1 : 2;
-    const colDim = isChannelsLast ? 2 : 3;
-
-    const boundsCheckingSnippet = this.enableShapeUniforms ?
-        'if(blockIndex < outShape[2] && pos < outShape[1]) {' :
-        `if(blockIndex < ${outputShape[2]} && pos < ${outputShape[1]}) {`;
-    let unrolled = ``;
-
-    for (let row = 0; row <= 1; row++) {
-      for (let col = 0; col <= 1; col++) {
-        unrolled += `
-          blockIndex = rc.z + ${col};
-          pos = rc.y + ${row};
-
-          ${boundsCheckingSnippet}
-            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
-            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
-
-            if(d0 < inputShape[${rowDim}] && d0 >= 0) {
-              // Use custom imod instead mod. On Intel GPU, mod may generate
-              // unexpected value.
-              // https://github.com/tensorflow/tfjs/issues/5447
-              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
-              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
-                  inChannels);
-
-              if(d1 < inputShape[${colDim}] && d1 >= 0) {
-
-                ch = imod(pos, inChannels);
-
-                if (${isChannelsLast}) {
-                  innerDims = vec2(d1, ch);
-                  result[${row * 2 + col}] = getChannel(
-                    getA(rc.x, d0, int(innerDims.x),
-                    int(innerDims.y)), innerDims);
-                } else {
-                  innerDims = vec2(d0, d1);
-                  result[${row * 2 + col}] = getChannel(
-                    getA(rc.x, ch, int(innerDims.x),
-                    int(innerDims.y)), innerDims);
-                }
-              }
-            }
-          }
-        `;
-      }
-    }
-
-    this.userCode = `
-      void main() {
-        ivec3 rc = getOutputCoords();
-
-        vec4 result = vec4(0);
-
-        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
-        vec2 innerDims;
-
-        ${unrolled}
-
-        ${glsl.output} = result;
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/index.ts b/tfjs-master/tfjs-backend-webgl/src/index.ts
deleted file mode 100644
index abf89246e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/index.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// All exports from this package should be in base.
-export * from './base';
-import './register_all_kernels';
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/arg_min_max.ts b/tfjs-master/tfjs-backend-webgl/src/kernel_utils/arg_min_max.ts
deleted file mode 100644
index 57b94a25b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/arg_min_max.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, env, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {ArgMinMaxProgram} from '../argminmax_gpu';
-import {ArgMinMaxPackedProgram} from '../argminmax_packed_gpu';
-import {MathBackendWebGL} from '../backend_webgl';
-import {reshape} from '../kernels/Reshape';
-
-function argReduce(
-    backend: MathBackendWebGL, x: TensorInfo, reduceType: 'max'|'min',
-    bestIndicesA: TensorInfo = null): TensorInfo {
-  let batchSize = x.shape[0];
-  let inSize = x.shape[1];
-  if (bestIndicesA != null) {
-    batchSize = bestIndicesA.shape[0];
-    inSize = bestIndicesA.shape[1];
-  }
-  const windowSize = backend_util.computeOptimalWindowSize(inSize);
-  const reduceInfo =
-      {windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize)};
-  const program =
-      new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);
-  const inputs = [x];
-  if (bestIndicesA != null) {
-    inputs.push(bestIndicesA);
-  }
-  const output = backend.runWebGLProgram(program, inputs, 'int32');
-  // No need to run another GPGPU program.
-  if (output.shape[1] === 1) {
-    return output;
-  }
-  const result = argReduce(backend, x, reduceType, output);
-  backend.disposeIntermediateTensorInfo(output);
-  return result;
-}
-
-function argReducePacked(
-    backend: MathBackendWebGL, x: TensorInfo, reduceType: 'max'|'min',
-    bestIndicesA: TensorInfo = null): TensorInfo {
-  const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;
-  const inSize = inShape[inShape.length - 1];
-  const windowSize = backend_util.computeOptimalWindowSize(inSize);
-  const program = new ArgMinMaxPackedProgram(
-      inShape, windowSize, reduceType, bestIndicesA == null);
-  const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];
-  const output = backend.runWebGLProgram(program, inputs, 'int32');
-  if (output.shape.length === x.shape.length) {
-    const result = argReducePacked(backend, x, reduceType, output);
-    backend.disposeIntermediateTensorInfo(output);
-    return result;
-  }
-  return output;
-}
-
-export function argMinMaxReduce(
-    backend: MathBackendWebGL, x: TensorInfo, axis: number,
-    reduceType: 'min'|'max'): TensorInfo {
-  const axes = [axis];
-  backend_util.assertAxesAreInnerMostDims(
-      'arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes,
-      x.shape.length);
-  if (!env().getBool('WEBGL_PACK_REDUCE') || x.shape.length <= 2) {
-    const intermediateTensorInfos = [];
-    // Eagerly unpack x input since it is passed in to all the shaders which
-    // require unpacked inputs.
-    const xtexData = backend.texData.get(x.dataId);
-    const xIsPacked = xtexData !== null && xtexData.isPacked;
-    let xUnPacked = x;
-    if (xIsPacked) {
-      xUnPacked = backend.unpackTensor(x);
-      intermediateTensorInfos.push(xUnPacked);
-    }
-
-    const [outShape, reduceShape] =
-        backend_util.computeOutAndReduceShapes(xUnPacked.shape, axes);
-    const inSize = util.sizeFromShape(reduceShape);
-    const a2D = reshape(
-        {inputs: {x: xUnPacked}, backend, attrs: {shape: [-1, inSize]}});
-    intermediateTensorInfos.push(a2D);
-
-    const reduced = argReduce(backend, a2D, reduceType);
-    intermediateTensorInfos.push(reduced);
-    const reshaped =
-        reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});
-
-    intermediateTensorInfos.forEach(
-        t => backend.disposeIntermediateTensorInfo(t));
-    return reshaped;
-  }
-  return argReducePacked(backend, x, reduceType);
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/int.ts b/tfjs-master/tfjs-backend-webgl/src/kernel_utils/int.ts
deleted file mode 100644
index c7475ded9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/int.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {UnaryOpProgram} from '../unaryop_gpu';
-
-const TO_INT = `return float(int(x));`;
-
-export function int(input: TensorInfo, backend: MathBackendWebGL): TensorInfo {
-  const program = new UnaryOpProgram(input.shape, TO_INT);
-  const output = backend.runWebGLProgram(program, [input], 'int32');
-  return {dataId: output.dataId, shape: output.shape, dtype: output.dtype};
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/kernel_funcs_utils.ts b/tfjs-master/tfjs-backend-webgl/src/kernel_utils/kernel_funcs_utils.ts
deleted file mode 100644
index 73e073072..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/kernel_funcs_utils.ts
+++ /dev/null
@@ -1,224 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { backend_util, BinaryInputs, DataType, env, KernelFunc, TypedArray, UnaryInputs, upcastType} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {BinaryOpProgram} from '../binaryop_gpu';
-import {BinaryOpPackedProgram} from '../binaryop_packed_gpu';
-import {complex} from '../kernels/Complex';
-import {LEAKYRELU, LEAKYRELU_PACKED} from '../kernels/LeakyRelu';
-import {PRELU, PRELU_PACKED} from '../kernels/Prelu';
-import * as unary_op from '../unaryop_gpu';
-import {UnaryOpProgram} from '../unaryop_gpu';
-import * as unary_packed_op from '../unaryop_packed_gpu';
-import {UnaryOpPackedProgram} from '../unaryop_packed_gpu';
-
-import {SimpleBinaryKernelImplCPU, SimpleUnaryKernelImplCPU} from './shared';
-
-export const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;
-
-type UnaryKernelFuncConfig = {
-  opSnippet: string,
-  packedOpSnippet?: string,
-  cpuKernelImpl?: SimpleUnaryKernelImplCPU,
-  dtype?: DataType,
-};
-
-/**
- * Template that creates a `KernelFunc` for unary ops.
- * @param opSnippet Op snippet to create `UnaryOpProgram`.
- * @param packedOpSnippet Op snippet to create `UnaryOpPackedProgram`.
- * @param dtype Optional. If set, the result has this dtype. Otherwise, the
- *     result has the same dtype as the first input. This is mainly used in
- *     comparison kernels, such as Equal, Less, Greater, etc.
- */
-export function unaryKernelFunc(
-    {opSnippet, packedOpSnippet, cpuKernelImpl, dtype}: UnaryKernelFuncConfig):
-    KernelFunc {
-  return ({inputs, backend}) => {
-    const {x} = inputs as UnaryInputs;
-    const webglBackend = backend as MathBackendWebGL;
-
-    const $dtype = dtype || x.dtype;
-    if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {
-      const xData = webglBackend.texData.get(x.dataId);
-      const outValues = cpuKernelImpl(xData.values as TypedArray, $dtype);
-      return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);
-    }
-
-    const shouldUsePackedProgram =
-        env().getBool('WEBGL_PACK_UNARY_OPERATIONS') && packedOpSnippet != null;
-    let program: UnaryOpProgram|UnaryOpPackedProgram;
-    if (shouldUsePackedProgram) {
-      program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);
-    } else {
-      program = new UnaryOpProgram(x.shape, opSnippet);
-    }
-
-    return webglBackend.runWebGLProgram(program, [x], $dtype);
-  };
-}
-
-type BinaryKernelFuncConfig = {
-  opSnippet: string,
-  packedOpSnippet?: string,
-  checkOutOfBounds?: boolean,
-  supportsComplex?: boolean,
-  cpuKernelImpl?: SimpleBinaryKernelImplCPU,
-  dtype?: DataType
-};
-
-/**
- * Template that creates a `KernelFunc` for binary ops.
- * @param opSnippet Op snippet to create `BinaryOpProgram`.
- * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.
- * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true
- *     when creating BinaryOpPackedProgram.
- * @param dtype Optional. If set, the result has this dtype. Otherwise, the
- *     result has the same dtype as the first input. This is mainly used in
- *     comparison kernels, such as Equal, Less, Greater, etc.
- */
-export function binaryKernelFunc({
-  opSnippet,
-  packedOpSnippet,
-  checkOutOfBounds = false,
-  supportsComplex = false,
-  cpuKernelImpl,
-  dtype
-}: BinaryKernelFuncConfig): KernelFunc {
-  return ({inputs, backend}) => {
-    const {a, b} = inputs as BinaryInputs;
-    const webglBackend = backend as MathBackendWebGL;
-
-    if (supportsComplex && a.dtype === 'complex64') {
-      const aData = webglBackend.texData.get(a.dataId);
-      const bData = webglBackend.texData.get(b.dataId);
-
-      const [real, imag] = [
-        [aData.complexTensorInfos.real, bData.complexTensorInfos.real],
-        [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]
-      ].map(complexParts => {
-        const [aPart, bPart] = complexParts;
-
-        const aHandle = {
-          dataId: aPart.dataId,
-          dtype: aPart.dtype,
-          shape: a.shape
-        };
-        const bHandle = {
-          dataId: bPart.dataId,
-          dtype: bPart.dtype,
-          shape: b.shape
-        };
-
-        const program = new BinaryOpProgram(opSnippet, a.shape, b.shape);
-        return webglBackend.runWebGLProgram(
-            program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));
-      });
-
-      const complexOutput =
-          complex({inputs: {real, imag}, backend: webglBackend});
-
-      webglBackend.disposeIntermediateTensorInfo(real);
-      webglBackend.disposeIntermediateTensorInfo(imag);
-
-      // TODO(annxingyuan): Implement CPU forwarding for complex inputs.
-
-      return complexOutput;
-    }
-
-    const $dtype = dtype || upcastType(a.dtype, b.dtype);
-    if ((a.dtype === 'string' || b.dtype === 'string' ||
-         webglBackend.shouldExecuteOnCPU([a, b])) &&
-        cpuKernelImpl != null) {
-      const aVals = webglBackend.texData.get(a.dataId).values as TypedArray;
-      const bVals = webglBackend.texData.get(b.dataId).values as TypedArray;
-
-      const decodedAVals = a.dtype === 'string' ?
-          // tslint:disable-next-line: no-any
-          backend_util.fromUint8ToStringArray(aVals as any as Uint8Array[]) :
-          aVals;
-      const decodedBVals = a.dtype === 'string' ?
-          // tslint:disable-next-line: no-any
-          backend_util.fromUint8ToStringArray(bVals as any as Uint8Array[]) :
-          bVals;
-      const [outValues, outShape] =
-          cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);
-
-      const out = webglBackend.makeTensorInfo(outShape, $dtype);
-      const outData = webglBackend.texData.get(out.dataId);
-      outData.values = outValues;
-      return out;
-    }
-
-    const shouldUsePackedProgram =
-        env().getBool('WEBGL_PACK_BINARY_OPERATIONS') &&
-        packedOpSnippet != null;
-    let program: BinaryOpProgram|BinaryOpPackedProgram;
-    if (shouldUsePackedProgram) {
-      program = new BinaryOpPackedProgram(
-          packedOpSnippet, a.shape, b.shape, checkOutOfBounds);
-    } else {
-      program = new BinaryOpProgram(opSnippet, a.shape, b.shape);
-    }
-
-    return webglBackend.runWebGLProgram(program, [a, b], $dtype);
-  };
-}
-
-export function mapActivationToShaderProgram(
-    activation: backend_util.Activation, packed = false): string {
-  if (activation === 'linear') {
-    if (packed) {
-      return unary_packed_op.LINEAR;
-    }
-    return unary_op.LINEAR;
-  } else if (activation === 'relu') {
-    if (packed) {
-      return unary_packed_op.RELU;
-    }
-    return unary_op.RELU;
-  } else if (activation === 'elu') {
-    if (packed) {
-      return unary_packed_op.ELU;
-    }
-    return unary_op.ELU;
-  } else if (activation === 'relu6') {
-    if (packed) {
-      return unary_packed_op.RELU6;
-    }
-    return unary_op.RELU6;
-  } else if (activation === 'prelu') {
-    if (packed) {
-      return PRELU_PACKED;
-    }
-    return PRELU;
-  } else if (activation === 'leakyrelu') {
-    if (packed) {
-      return LEAKYRELU_PACKED;
-    }
-    return LEAKYRELU;
-  } else if (activation === 'sigmoid') {
-    if (packed) {
-      return unary_packed_op.SIGMOID;
-    }
-    return unary_op.SIGMOID;
-  }
-  throw new Error(`Activation ${
-      activation} has not been implemented for the WebGL backend.`);
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/reduce.ts b/tfjs-master/tfjs-backend-webgl/src/kernel_utils/reduce.ts
deleted file mode 100644
index b36f3374a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/reduce.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {MeanProgram} from '../mean_gpu';
-import {ReduceProgram} from '../reduce_gpu';
-
-type ReduceTypes = 'all'|'any'|'max'|'min'|'sum'|'prod'|'mean';
-
-// Returns an array of configuration objects that describe each stage of the
-// reduction.
-function getReductionStages(inShape: number[]):
-    Array<{inSize: number, windowSize: number, outSize: number}> {
-  const stages = [];
-
-  while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {
-    const outSize: number =
-        stages.length ? stages[stages.length - 1].outSize : inShape[1];
-    const windowSize = backend_util.computeOptimalWindowSize(outSize);
-    stages.push({
-      inSize: outSize,
-      windowSize,
-      outSize: Math.ceil(outSize / windowSize)
-    });
-  }
-
-  return stages;
-}
-
-export function reduce(
-    x: TensorInfo, dtype: DataType, reductionType: ReduceTypes,
-    backend: MathBackendWebGL): TensorInfo {
-  const reductionStages = getReductionStages(x.shape);
-
-  let result = x;
-  for (let i = 0; i < reductionStages.length; i++) {
-    const {inSize, windowSize, outSize} = reductionStages[i];
-
-    let program: ReduceProgram|MeanProgram;
-    let previousResult: TensorInfo;
-    if (reductionType === 'mean') {
-      program = i === 0 ?
-          new MeanProgram(
-              {windowSize, inSize, batchSize: x.shape[0], outSize}, inSize) :
-          new MeanProgram({windowSize, inSize, batchSize: x.shape[0], outSize});
-    } else {
-      program = new ReduceProgram(
-          {windowSize, inSize, batchSize: x.shape[0], outSize}, reductionType);
-    }
-
-    previousResult = result;
-    result = backend.runWebGLProgram(program, [result], dtype);
-
-    if (previousResult.dataId !== x.dataId) {
-      backend.disposeIntermediateTensorInfo(previousResult);
-    }
-  }
-
-  return result;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/reshape.ts b/tfjs-master/tfjs-backend-webgl/src/kernel_utils/reshape.ts
deleted file mode 100644
index 1f3ae73ef..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/reshape.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ReshapePackedProgram} from '../reshape_packed_gpu';
-import {getBatchDim, getRowsCols} from '../webgl_util';
-
-export function packedReshape(
-    input: TensorInfo, afterShape: number[],
-    backend: MathBackendWebGL): TensorInfo {
-  const input3DShape =
-      [getBatchDim(input.shape),
-       ...getRowsCols(input.shape)] as [number, number, number];
-  const input3D: TensorInfo = {
-    dtype: input.dtype,
-    shape: input3DShape,
-    dataId: input.dataId
-  };
-  const afterShapeAs3D =
-      [getBatchDim(afterShape),
-       ...getRowsCols(afterShape)] as [number, number, number];
-
-  const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
-  const preventEagerUnpackingOfOutput = true;
-  const customValues = [input3DShape];
-  const output = backend.runWebGLProgram(
-      program, [input3D], input.dtype, customValues,
-      preventEagerUnpackingOfOutput);
-  return {dataId: output.dataId, shape: afterShape, dtype: output.dtype};
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/shared.ts b/tfjs-master/tfjs-backend-webgl/src/kernel_utils/shared.ts
deleted file mode 100644
index 327a932a8..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernel_utils/shared.ts
+++ /dev/null
@@ -1,132 +0,0 @@
-
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Import shared functionality from tfjs-backend-cpu without triggering
-// side effects.
-// tslint:disable-next-line: no-imports-from-dist
-import * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {SimpleBinaryKernelImpl} from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {SimpleUnaryImpl} from '@tensorflow/tfjs-backend-cpu/dist/utils/unary_types';
-
-export type SimpleBinaryKernelImplCPU = SimpleBinaryKernelImpl;
-export type SimpleUnaryKernelImplCPU = SimpleUnaryImpl;
-const {
-  addImpl: addImplCPU,
-  bincountImpl: bincountImplCPU,
-  bincountReduceImpl: bincountReduceImplCPU,
-  bitwiseAndImpl: bitwiseAndImplCPU,
-  castImpl: castImplCPU,
-  ceilImpl: ceilImplCPU,
-  concatImpl: concatImplCPU,
-  equalImpl: equalImplCPU,
-  expImpl: expImplCPU,
-  expm1Impl: expm1ImplCPU,
-  floorImpl: floorImplCPU,
-  gatherNdImpl: gatherNdImplCPU,
-  gatherV2Impl: gatherV2ImplCPU,
-  greaterImpl: greaterImplCPU,
-  greaterEqualImpl: greaterEqualImplCPU,
-  lessImpl: lessImplCPU,
-  lessEqualImpl: lessEqualImplCPU,
-  linSpaceImpl: linSpaceImplCPU,
-  logImpl: logImplCPU,
-  maxImpl: maxImplCPU,
-  maximumImpl: maximumImplCPU,
-  minimumImpl: minimumImplCPU,
-  multiplyImpl: multiplyImplCPU,
-  negImpl: negImplCPU,
-  notEqualImpl: notEqualImplCPU,
-  prodImpl: prodImplCPU,
-  raggedGatherImpl: raggedGatherImplCPU,
-  raggedRangeImpl: raggedRangeImplCPU,
-  raggedTensorToTensorImpl: raggedTensorToTensorImplCPU,
-  rangeImpl: rangeImplCPU,
-  rsqrtImpl: rsqrtImplCPU,
-  scatterImpl: scatterImplCPU,
-  sigmoidImpl: sigmoidImplCPU,
-  simpleAbsImpl: simpleAbsImplCPU,
-  sliceImpl: sliceImplCPU,
-  sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU,
-  sparseReshapeImpl: sparseReshapeImplCPU,
-  sparseSegmentReductionImpl: sparseSegmentReductionImplCPU,
-  sqrtImpl: sqrtImplCPU,
-  staticRegexReplaceImpl: staticRegexReplaceImplCPU,
-  stridedSliceImpl: stridedSliceImplCPU,
-  stringNGramsImpl: stringNGramsImplCPU,
-  stringSplitImpl: stringSplitImplCPU,
-  stringToHashBucketFastImpl: stringToHashBucketFastImplCPU,
-  subImpl: subImplCPU,
-  tileImpl: tileImplCPU,
-  topKImpl: topKImplCPU,
-  transposeImpl: transposeImplCPU,
-  uniqueImpl: uniqueImplCPU,
-} = shared;
-
-export {
-  addImplCPU,
-  bincountImplCPU,
-  bincountReduceImplCPU,
-  bitwiseAndImplCPU,
-  castImplCPU,
-  ceilImplCPU,
-  concatImplCPU,
-  equalImplCPU,
-  expImplCPU,
-  expm1ImplCPU,
-  floorImplCPU,
-  gatherNdImplCPU,
-  gatherV2ImplCPU,
-  greaterEqualImplCPU,
-  greaterImplCPU,
-  lessEqualImplCPU,
-  lessImplCPU,
-  linSpaceImplCPU,
-  logImplCPU,
-  maxImplCPU,
-  maximumImplCPU,
-  minimumImplCPU,
-  multiplyImplCPU,
-  negImplCPU,
-  notEqualImplCPU,
-  prodImplCPU,
-  raggedGatherImplCPU,
-  raggedRangeImplCPU,
-  raggedTensorToTensorImplCPU,
-  scatterImplCPU,
-  sigmoidImplCPU,
-  simpleAbsImplCPU,
-  sliceImplCPU,
-  sparseFillEmptyRowsImplCPU,
-  sparseReshapeImplCPU,
-  sparseSegmentReductionImplCPU,
-  sqrtImplCPU,
-  staticRegexReplaceImplCPU,
-  stridedSliceImplCPU,
-  stringNGramsImplCPU,
-  stringSplitImplCPU,
-  stringToHashBucketFastImplCPU,
-  subImplCPU,
-  rangeImplCPU,
-  rsqrtImplCPU,
-  tileImplCPU,
-  topKImplCPU,
-  transposeImplCPU,
-  uniqueImplCPU,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Abs.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Abs.ts
deleted file mode 100644
index 010b205db..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Abs.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Abs, AbsInputs, env, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {simpleAbsImplCPU} from '../kernel_utils/shared';
-import {UnaryOpProgram} from '../unaryop_gpu';
-import {UnaryOpPackedProgram} from '../unaryop_packed_gpu';
-
-const ABS = `return abs(x);`;
-
-export function abs(args: {inputs: AbsInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  // TODO: handle cases when x is complex. Once the cpu implementation
-  // can handle complex values, refactor to use unaryKernelFunc.
-  if (backend.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {
-    const xData = backend.texData.get(x.dataId);
-    const outValues = simpleAbsImplCPU(xData.values as TypedArray);
-    return backend.makeTensorInfo(x.shape, x.dtype, outValues);
-  }
-
-  let program: UnaryOpProgram|UnaryOpPackedProgram;
-  if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
-    program = new UnaryOpPackedProgram(x.shape, ABS);
-  } else {
-    program = new UnaryOpProgram(x.shape, ABS);
-  }
-  return backend.runWebGLProgram(program, [x], x.dtype);
-}
-
-export const absConfig: KernelConfig = {
-  kernelName: Abs,
-  backendName: 'webgl',
-  kernelFunc: abs as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Acos.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Acos.ts
deleted file mode 100644
index 63d9f0022..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Acos.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acos, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {CHECK_NAN_SNIPPET} from '../unaryop_gpu';
-
-const ACOS = CHECK_NAN_SNIPPET + `
-  if (abs(x) > 1.) {
-    return NAN;
-  }
-  return acos(x);
-`;
-
-export const acos = unaryKernelFunc({opSnippet: ACOS});
-
-export const acosConfig: KernelConfig = {
-  kernelName: Acos,
-  backendName: 'webgl',
-  kernelFunc: acos,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Acosh.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Acosh.ts
deleted file mode 100644
index 6ed1356ee..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Acosh.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acosh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {CHECK_NAN_SNIPPET} from '../unaryop_gpu';
-
-const ACOSH = CHECK_NAN_SNIPPET + `
-  if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`;
-
-export const acosh = unaryKernelFunc({opSnippet: ACOSH});
-
-export const acoshConfig: KernelConfig = {
-  kernelName: Acosh,
-  backendName: 'webgl',
-  kernelFunc: acosh,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Add.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Add.ts
deleted file mode 100644
index c548b77e0..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Add.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Add, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {addImplCPU as cpuAdd} from '../kernel_utils/shared';
-
-const ADD = 'return a + b;';
-
-export const addKernelFunc = binaryKernelFunc({
-  opSnippet: ADD,
-  packedOpSnippet: ADD,
-  supportsComplex: true,
-  cpuKernelImpl: cpuAdd
-});
-
-export const addConfig: KernelConfig = {
-  kernelName: Add,
-  backendName: 'webgl',
-  kernelFunc: addKernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/AddN.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/AddN.ts
deleted file mode 100644
index 98d83e817..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/AddN.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AddN, AddNInputs, env, KernelConfig, KernelFunc, TensorInfo, upcastType} from '@tensorflow/tfjs-core';
-
-import {AddNProgram} from '../addn_gpu';
-import {AddNPackedProgram} from '../addn_packed_gpu';
-import {MathBackendWebGL} from '../backend_webgl';
-import {identity} from './Identity';
-
-export function addN(args: {inputs: AddNInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-
-  const tensors = inputs;
-  if (tensors.length === 1) {
-    return identity({inputs: {x: tensors[0]}, backend});
-  }
-
-  // Limit the number of uploaded textures for optimization.
-  if (tensors.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {
-    const midIndex = Math.floor(tensors.length / 2);
-    const leftSide = addN({inputs: tensors.slice(0, midIndex), backend});
-    const rightSide = addN({inputs: tensors.slice(midIndex), backend});
-    return addN({inputs: [leftSide, rightSide], backend});
-  }
-
-  const dtype =
-      tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));
-  const shapes = tensors.map(t => t.shape);
-  // We can make sure shapes are identical in op level.
-  const usePackedOp = env().getBool('WEBGL_PACK');
-  const program = usePackedOp ?
-      new AddNPackedProgram(tensors[0].shape, shapes) :
-      new AddNProgram(tensors[0].shape, shapes);
-  return backend.runWebGLProgram(program, tensors, dtype);
-}
-
-export const addNConfig: KernelConfig = {
-  kernelName: AddN,
-  backendName: 'webgl',
-  kernelFunc: addN as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/All.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/All.ts
deleted file mode 100644
index 3b1b8b0c4..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/All.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {All, AllAttrs, AllInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {reduce} from '../kernel_utils/reduce';
-
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function all(
-    args: {inputs: AllInputs, backend: MathBackendWebGL, attrs: AllAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  const xRank = x.shape.length;
-
-  const origAxes = util.parseAxisParam(axis, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-  let permutedX = x;
-  if (permutedAxes != null) {
-    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    axes = backend_util.getInnerMostAxes(axes.length, xRank);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('all', axes, xRank);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(permutedX.shape, axes);
-  const inSize = util.sizeFromShape(reduceShape);
-
-  const a2D =
-      reshape({inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});
-  const reduced = reduce(a2D, a2D.dtype, 'all', backend);
-
-  let res;
-  if (keepDims) {
-    const newShape = backend_util.expandShapeToKeepDim(outShape, origAxes);
-    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: newShape}});
-  } else {
-    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});
-  }
-
-  backend.disposeIntermediateTensorInfo(a2D);
-  backend.disposeIntermediateTensorInfo(reduced);
-
-  if (permutedAxes != null) {
-    backend.disposeIntermediateTensorInfo(permutedX);
-  }
-
-  return res;
-}
-
-export const allConfig: KernelConfig = {
-  kernelName: All,
-  backendName: 'webgl',
-  kernelFunc: all as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Any.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Any.ts
deleted file mode 100644
index 172cdc4d2..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Any.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Any, AnyAttrs, AnyInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {reduce} from '../kernel_utils/reduce';
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function any(
-    args: {inputs: AnyInputs, backend: MathBackendWebGL, attrs: AnyAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  const xRank = x.shape.length;
-
-  const origAxes = util.parseAxisParam(axis, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-  let permutedX = x;
-  if (permutedAxes != null) {
-    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    axes = backend_util.getInnerMostAxes(axes.length, xRank);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('any', axes, xRank);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(permutedX.shape, axes);
-  const inSize = util.sizeFromShape(reduceShape);
-
-  const a2D =
-      reshape({inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});
-  const reduced = reduce(a2D, a2D.dtype, 'any', backend);
-
-  let res;
-  if (keepDims) {
-    const newShape = backend_util.expandShapeToKeepDim(outShape, origAxes);
-    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: newShape}});
-  } else {
-    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});
-  }
-
-  backend.disposeIntermediateTensorInfo(a2D);
-  backend.disposeIntermediateTensorInfo(reduced);
-
-  if (permutedAxes != null) {
-    backend.disposeIntermediateTensorInfo(permutedX);
-  }
-
-  return res;
-}
-
-export const anyConfig: KernelConfig = {
-  kernelName: Any,
-  backendName: 'webgl',
-  kernelFunc: any as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMax.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMax.ts
deleted file mode 100644
index 5a8684966..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMax.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMax, ArgMaxAttrs, ArgMaxInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {argMinMaxReduce} from '../kernel_utils/arg_min_max';
-
-import {transpose} from './Transpose';
-
-export function argMax(
-    args:
-        {inputs: ArgMaxInputs, backend: MathBackendWebGL, attrs: ArgMaxAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis} = attrs;
-
-  let axes = util.parseAxisParam(axis, x.shape);
-  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
-  let $x = x;
-  const intermediateTensorInfos = [];
-  if (permutedAxes != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    intermediateTensorInfos.push($x);
-    axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('argMax', [axes[0]], $x.shape.length);
-  const out = argMinMaxReduce(backend, $x, axes[0], 'max');
-
-  intermediateTensorInfos.forEach(
-      t => backend.disposeIntermediateTensorInfo(t));
-  return out;
-}
-
-export const argMaxConfig: KernelConfig = {
-  kernelName: ArgMax,
-  backendName: 'webgl',
-  kernelFunc: argMax as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMax_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMax_test.ts
deleted file mode 100644
index 1b3bf044b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMax_test.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {WEBGL_ENVS} from '../backend_webgl_test_registry';
-
-describeWithFlags('ArgMax', WEBGL_ENVS, () => {
-  it('handles packed inputs', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-
-    // pack a using the add op which packs outputs
-    tf.env().set('WEBGL_PACK', true);
-    const aPacked = tf.addN([a, tf.zeros(a.shape)]);
-
-    tf.test_util.expectArraysEqual(await tf.argMax(aPacked).data(), [1, 0, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMin.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMin.ts
deleted file mode 100644
index a7583ae64..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMin.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMin, ArgMinAttrs, ArgMinInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {argMinMaxReduce} from '../kernel_utils/arg_min_max';
-import {transpose} from './Transpose';
-
-export function argMin(
-    args:
-        {inputs: ArgMinInputs, backend: MathBackendWebGL, attrs: ArgMinAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis} = attrs;
-
-  let axes = util.parseAxisParam(axis, x.shape);
-  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
-  let $x = x;
-  const intermediateTensorInfos = [];
-  if (permutedAxes != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    intermediateTensorInfos.push($x);
-    axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('argMin', [axes[0]], $x.shape.length);
-
-  const out = argMinMaxReduce(backend, $x, axes[0], 'min');
-
-  intermediateTensorInfos.forEach(
-      t => backend.disposeIntermediateTensorInfo(t));
-  return out;
-}
-
-export const argMinConfig: KernelConfig = {
-  kernelName: ArgMin,
-  backendName: 'webgl',
-  kernelFunc: argMin as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMin_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMin_test.ts
deleted file mode 100644
index e4ff5ffd1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ArgMin_test.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {WEBGL_ENVS} from '../backend_webgl_test_registry';
-
-describeWithFlags('ArgMin', WEBGL_ENVS, () => {
-  it('handles packed inputs', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-
-    // pack a using the add op which packs outputs
-    tf.env().set('WEBGL_PACK', true);
-    const aPacked = tf.addN([a, tf.zeros(a.shape)]);
-
-    tf.test_util.expectArraysEqual(await tf.argMin(aPacked).data(), [0, 1, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Asin.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Asin.ts
deleted file mode 100644
index 7291fda48..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Asin.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asin, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {CHECK_NAN_SNIPPET} from '../unaryop_gpu';
-
-const ASIN = CHECK_NAN_SNIPPET + `
-  if (abs(x) > 1.) {
-    return NAN;
-  }
-  return asin(x);
-`;
-
-export const asin = unaryKernelFunc({opSnippet: ASIN});
-
-export const asinConfig: KernelConfig = {
-  kernelName: Asin,
-  backendName: 'webgl',
-  kernelFunc: asin,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Asinh.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Asinh.ts
deleted file mode 100644
index e02be2f73..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Asinh.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asinh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {CHECK_NAN_SNIPPET} from '../unaryop_gpu';
-
-const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;
-
-export const asinh = unaryKernelFunc({opSnippet: ASINH});
-
-export const asinhConfig: KernelConfig = {
-  kernelName: Asinh,
-  backendName: 'webgl',
-  kernelFunc: asinh,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Atan.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Atan.ts
deleted file mode 100644
index 8fd4bf2ab..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Atan.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {CHECK_NAN_SNIPPET} from '../unaryop_gpu';
-
-const ATAN = CHECK_NAN_SNIPPET + `
-  return atan(x);
-`;
-
-export const atan = unaryKernelFunc({opSnippet: ATAN});
-
-export const atanConfig: KernelConfig = {
-  kernelName: Atan,
-  backendName: 'webgl',
-  kernelFunc: atan,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Atan2.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Atan2.ts
deleted file mode 100644
index 539321373..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Atan2.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan2} from '@tensorflow/tfjs-core';
-import {KernelConfig} from '@tensorflow/tfjs-core';
-import {CHECK_NAN_SNIPPET} from '../binaryop_gpu';
-import {CHECK_NAN_SNIPPET_PACKED} from '../binaryop_packed_gpu';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const ATAN2 = CHECK_NAN_SNIPPET + `
-  return atan(a, b);
-`;
-
-const ATAN2_PACKED = `
-  vec4 result = atan(a, b);
-  bvec4 isNaNA = isnan(a);
-  bvec4 isNaNB = isnan(b);
-  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
-  ` +
-    CHECK_NAN_SNIPPET_PACKED + `
-  return result;
-`;
-
-export const atan2 =
-    binaryKernelFunc({opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED});
-
-export const atan2Config: KernelConfig = {
-  kernelName: Atan2,
-  backendName: 'webgl',
-  kernelFunc: atan2,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Atanh.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Atanh.ts
deleted file mode 100644
index 582831a7b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Atanh.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atanh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {CHECK_NAN_SNIPPET} from '../unaryop_gpu';
-
-const ATANH = CHECK_NAN_SNIPPET + `
-  if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
-
-export const atanh = unaryKernelFunc({opSnippet: ATANH});
-
-export const atanhConfig: KernelConfig = {
-  kernelName: Atanh,
-  backendName: 'webgl',
-  kernelFunc: atanh,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPool.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPool.ts
deleted file mode 100644
index 3b0e76f7b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPool.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {AvgPool, AvgPoolAttrs, AvgPoolInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Pool2DProgram} from '../pool_gpu';
-import {assertNotComplex} from '../webgl_util';
-import {identity} from './Identity';
-
-export function avgPool(args: {
-  inputs: AvgPoolInputs,
-  backend: MathBackendWebGL,
-  attrs: AvgPoolAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  assertNotComplex(x, 'avgPool');
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const dilations = 1;
-
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in avgPool: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode);
-  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
-      util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
-    return identity({inputs: {x}, backend});
-  }
-  const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);
-  return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');
-}
-
-export const avgPoolConfig: KernelConfig = {
-  kernelName: AvgPool,
-  backendName: 'webgl',
-  kernelFunc: avgPool as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPool3D.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPool3D.ts
deleted file mode 100644
index 7dd55ea1e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPool3D.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Pool3DProgram} from '../pool_gpu';
-
-export function avgPool3D(args: {
-  inputs: AvgPool3DInputs,
-  backend: MathBackendWebGL,
-  attrs: AvgPool3DAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode, dataFormat} = attrs;
-  const dilations: [number, number, number] = [1, 1, 1];
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode, dataFormat);
-  const avgPoolProgram = new Pool3DProgram(convInfo, 'avg', false);
-  return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');
-}
-
-export const avgPool3DConfig: KernelConfig = {
-  kernelName: AvgPool3D,
-  backendName: 'webgl',
-  kernelFunc: avgPool3D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPool3DGrad.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPool3DGrad.ts
deleted file mode 100644
index f4c4054c7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPool3DGrad.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {AvgPool3DGrad, AvgPool3DGradAttrs, AvgPool3DGradInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {AvgPool3DBackpropProgram} from '../avg_pool_backprop_gpu';
-import {MathBackendWebGL} from '../backend_webgl';
-
-export function avgPool3DGrad(args: {
-  inputs: AvgPool3DGradInputs,
-  backend: MathBackendWebGL,
-  attrs: AvgPool3DGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const x = input;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const dilations: [number, number, number] = [1, 1, 1];
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode);
-  const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);
-  return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
-}
-
-export const avgPool3DGradConfig: KernelConfig = {
-  kernelName: AvgPool3DGrad,
-  backendName: 'webgl',
-  kernelFunc: avgPool3DGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPoolGrad.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPoolGrad.ts
deleted file mode 100644
index e5c80b6e7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/AvgPoolGrad.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {AvgPoolGrad, AvgPoolGradAttrs, AvgPoolGradInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {AvgPool2DBackpropProgram} from '../avg_pool_backprop_gpu';
-import {MathBackendWebGL} from '../backend_webgl';
-import {assertNotComplex} from '../webgl_util';
-
-export function avgPoolGrad(args: {
-  inputs: AvgPoolGradInputs,
-  backend: MathBackendWebGL,
-  attrs: AvgPoolGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const x = input;
-  assertNotComplex([dy, input], 'avgPoolGrad');
-  const {filterSize, strides, pad} = attrs;
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      1 /* dilations */, pad);
-  const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);
-  return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
-}
-
-export const avgPoolGradConfig: KernelConfig = {
-  kernelName: AvgPoolGrad,
-  backendName: 'webgl',
-  kernelFunc: avgPoolGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/BatchMatMul.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/BatchMatMul.ts
deleted file mode 100644
index 6c5480e08..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/BatchMatMul.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {batchMatMulImpl} from './BatchMatMul_impl';
-
-export function batchMatMul(args: {
-  inputs: BatchMatMulInputs,
-  attrs: BatchMatMulAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {a, b} = inputs;
-  const {transposeA, transposeB} = attrs;
-
-  return batchMatMulImpl({a, b, transposeA, transposeB, backend});
-}
-
-export const batchMatMulConfig: KernelConfig = {
-  kernelName: BatchMatMul,
-  backendName: 'webgl',
-  kernelFunc: batchMatMul as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/BatchMatMul_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/BatchMatMul_impl.ts
deleted file mode 100644
index 972bb7295..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/BatchMatMul_impl.ts
+++ /dev/null
@@ -1,187 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, broadcast_util, TensorInfo, upcastType, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {mapActivationToShaderProgram} from '../kernel_utils/kernel_funcs_utils';
-import {MatMulPackedProgram} from '../mulmat_packed_gpu';
-
-import {multiply} from './Multiply';
-import {reshape} from './Reshape';
-import {sum} from './Sum';
-import {transpose} from './Transpose';
-
-// Empirically determined minimal shared dimension in matmul before we forward
-// to a.mul(b).sum() in order to take advantage of GPU parallelism. See
-// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.
-export const MATMUL_SHARED_DIM_THRESHOLD = 1000;
-
-type BatchMatMulConfig = {
-  a: TensorInfo,
-  b: TensorInfo,
-  transposeA: boolean,
-  transposeB: boolean,
-  backend: MathBackendWebGL,
-  bias?: TensorInfo,
-  preluActivationWeights?: TensorInfo,
-  leakyreluAlpha?: number,
-  activation?: backend_util.Activation
-};
-
-export function batchMatMulImpl({
-  a,
-  b,
-  transposeA,
-  transposeB,
-  backend,
-  bias = null,
-  preluActivationWeights = null,
-  leakyreluAlpha = 0,
-  activation = null
-}: BatchMatMulConfig): TensorInfo {
-  const aRank = a.shape.length;
-  const bRank = b.shape.length;
-
-  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
-  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
-
-  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
-  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
-
-  const outerDimsA = a.shape.slice(0, -2);
-  const outerDimsB = b.shape.slice(0, -2);
-
-  const batchDimA = util.sizeFromShape(outerDimsA);
-  const batchDimB = util.sizeFromShape(outerDimsB);
-
-  const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape(
-      a.shape.slice(0, -2), b.shape.slice(0, -2));
-  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
-
-  util.assert(
-      innerShapeA === innerShapeB,
-      () => `Error in matMul: inner shapes (${innerShapeA}) and (` +
-          `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +
-          `${b.shape} and transposeA=${transposeA}` +
-          ` and transposeB=${transposeB} must match.`);
-
-  const a3dShape: [number, number, number] = transposeA ?
-      [batchDimA, innerShapeA, outerShapeA] :
-      [batchDimA, outerShapeA, innerShapeA];
-  const b3dShape: [number, number, number] = transposeB ?
-      [batchDimB, outerShapeB, innerShapeB] :
-      [batchDimB, innerShapeB, outerShapeB];
-
-  // The rest of the implementation is designed to operate on rank-3 tensors
-  const a3d = reshape({inputs: {x: a}, backend, attrs: {shape: a3dShape}});
-  const b3d = reshape({inputs: {x: b}, backend, attrs: {shape: b3dShape}});
-
-  const intermediates: TensorInfo[] = [a3d, b3d];
-
-  const batchDim = Math.max(batchDimA, batchDimB);
-  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
-
-  const hasBias = bias != null;
-  const hasPreluActivationWeights = preluActivationWeights != null;
-  const hasLeakyreluAlpha = activation === 'leakyrelu';
-  const fusedActivation = activation != null ?
-      mapActivationToShaderProgram(activation, true) :
-      null;
-  const containsFusedOps = hasBias || hasPreluActivationWeights ||
-      hasLeakyreluAlpha || fusedActivation != null;
-  let out: TensorInfo;
-
-  // Since the matrices are vectors, it is faster to call mul().sum()
-  // because sum() is O(sqrt(N)) due to divide-and-conquer.
-  if ((outerShapeA === 1 || outerShapeB === 1) &&
-      sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {
-    let aVec = a3d;
-    let bVec = b3d;
-    if (transposeA) {
-      aVec = transpose({inputs: {x: a3d}, backend, attrs: {perm: [0, 2, 1]}});
-      intermediates.push(aVec);
-    }
-    if (transposeB) {
-      bVec = transpose({inputs: {x: b3d}, backend, attrs: {perm: [0, 2, 1]}});
-      intermediates.push(bVec);
-    }
-
-    const shouldReshapeA = outerShapeB !== 1;
-    const shouldReshapeB = outerShapeB === 1;
-
-    let aVec3d = aVec;
-    if (shouldReshapeA) {
-      aVec3d = reshape({
-        inputs: {x: aVec},
-        backend,
-        attrs: {shape: [batchDim, sharedDim, 1]}
-      });
-
-      intermediates.push(aVec3d);
-    }
-
-    const axis = outerShapeB === 1 ? 2 : 1;
-
-    let bVec3d = bVec;
-    if (shouldReshapeB) {
-      bVec3d = reshape({
-        inputs: {x: bVec},
-        backend,
-        attrs: {shape: [batchDim, 1, sharedDim]}
-      });
-
-      intermediates.push(bVec3d);
-    }
-
-    const product = multiply({inputs: {a: aVec3d, b: bVec3d}, backend});
-    out = sum({inputs: {x: product}, backend, attrs: {axis, keepDims: true}});
-    intermediates.push(product);
-  } else {
-    const dtype = upcastType(a.dtype, b.dtype);
-
-    const program = new MatMulPackedProgram(
-        a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA,
-        transposeB, hasBias, fusedActivation, hasPreluActivationWeights,
-        hasLeakyreluAlpha);
-
-    const inputs: TensorInfo[] = [a3d, b3d];
-    if (bias != null) {
-      inputs.push(bias);
-    }
-    if (hasPreluActivationWeights) {
-      inputs.push(preluActivationWeights);
-    }
-    if (hasLeakyreluAlpha) {
-      const $leakyreluAlpha = backend.makeTensorInfo(
-          [], 'float32',
-          util.createScalarValue(leakyreluAlpha as unknown as 'float32', 'float32'));
-      inputs.push($leakyreluAlpha);
-      intermediates.push($leakyreluAlpha);
-    }
-
-    out = backend.runWebGLProgram(program, inputs, dtype);
-  }
-
-  const outReshaped =
-      reshape({inputs: {x: out}, backend, attrs: {shape: outShape}});
-  intermediates.push(out);
-  for (const i of intermediates) {
-    backend.disposeIntermediateTensorInfo(i);
-  }
-  return outReshaped;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/BatchNorm.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/BatchNorm.ts
deleted file mode 100644
index 57328613b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/BatchNorm.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {BatchNormProgram} from '../batchnorm_gpu';
-import {BatchNormPackedProgram} from '../batchnorm_packed_gpu';
-
-export const batchNorm: (params: {
-  inputs: FusedBatchNormInputs,
-  backend: MathBackendWebGL,
-  attrs: FusedBatchNormAttrs
-}) => TensorInfo = ({inputs, backend, attrs}) => {
-  const {x, mean, variance, offset, scale} = inputs;
-
-  util.assert(
-      mean.shape.length === variance.shape.length,
-      () => 'Batch normalization gradient requires mean and variance to have ' +
-          'equal ranks.');
-  util.assert(
-      offset == null || mean.shape.length === offset.shape.length,
-      () => 'Batch normalization gradient requires mean and offset to have ' +
-          'equal ranks.');
-  util.assert(
-      scale == null || mean.shape.length === scale.shape.length,
-      () => 'Batch normalization gradient requires mean and scale to have ' +
-          'equal ranks.');
-
-  let {varianceEpsilon} = attrs;
-  if (varianceEpsilon == null) {
-    varianceEpsilon = 0.001;
-  }
-
-  const finalInputs = [x, mean, variance];
-
-  let offsetShape = null;
-  if (offset != null) {
-    offsetShape = offset.shape;
-    finalInputs.push(offset);
-  }
-
-  let scaleShape = null;
-  if (scale != null) {
-    scaleShape = scale.shape;
-    finalInputs.push(scale);
-  }
-
-  const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?
-      new BatchNormPackedProgram(
-          x.shape, mean.shape, variance.shape, offsetShape, scaleShape,
-          varianceEpsilon) :
-      new BatchNormProgram(
-          x.shape, mean.shape, variance.shape, offsetShape, scaleShape,
-          varianceEpsilon);
-  const output =
-      backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);
-
-  return output;
-};
-
-export const batchNormConfig: KernelConfig = {
-  kernelName: FusedBatchNorm,
-  backendName: 'webgl',
-  kernelFunc: batchNorm as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/BatchToSpaceND.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/BatchToSpaceND.ts
deleted file mode 100644
index 2e7de7ea5..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/BatchToSpaceND.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-import {transpose} from './Transpose';
-
-export const batchToSpaceND = (args: {
-  inputs: BatchToSpaceNDInputs,
-  backend: MathBackendWebGL,
-  attrs: BatchToSpaceNDAttrs
-}): TensorInfo => {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockShape, crops} = attrs;
-
-  util.assert(
-      x.shape.length <= 4,
-      () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +
-          'implemented yet');
-  const prod = blockShape.reduce((a, b) => a * b);
-
-  const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);
-  const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);
-  const reshapedPermuted =
-      backend_util.getReshapedPermuted(x.shape, blockShape, prod);
-  const sliceBeginCoords =
-      backend_util.getSliceBeginCoords(crops, blockShape.length);
-  const sliceSize =
-      backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);
-
-  const toDispose = [];
-
-  const reshapedIntermediate =
-      reshape({inputs: {x}, backend, attrs: {shape: reshaped}});
-  const transposedIntermediate = transpose(
-      {inputs: {x: reshapedIntermediate}, backend, attrs: {perm: permuted}});
-  const reshapedIntermediate2 = reshape({
-    inputs: {x: transposedIntermediate},
-    backend,
-    attrs: {shape: reshapedPermuted}
-  });
-  const sliced = slice({
-    inputs: {x: reshapedIntermediate2},
-    backend,
-    attrs: {begin: sliceBeginCoords, size: sliceSize}
-  });
-
-  toDispose.push(reshapedIntermediate);
-  toDispose.push(transposedIntermediate);
-  toDispose.push(reshapedIntermediate2);
-
-  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-  return sliced;
-};
-
-export const batchToSpaceNDConfig: KernelConfig = {
-  kernelName: BatchToSpaceND,
-  backendName: 'webgl',
-  kernelFunc: batchToSpaceND as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Bincount.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Bincount.ts
deleted file mode 100644
index d5c8620d6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Bincount.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Bincount, BincountAttrs, BincountInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {bincountImplCPU} from '../kernel_utils/shared';
-
-export function bincount(args: {
-  inputs: BincountInputs,
-  backend: MathBackendWebGL,
-  attrs: BincountAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, weights} = inputs;
-  const {size} = attrs;
-
-  const xVals = backend.readSync(x.dataId) as TypedArray;
-  const weightsVals = backend.readSync(weights.dataId) as TypedArray;
-
-  const outVals =
-      bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
-
-  return backend.makeTensorInfo([size], weights.dtype, outVals);
-}
-
-export const bincountConfig: KernelConfig = {
-  kernelName: Bincount,
-  backendName: 'webgl',
-  kernelFunc: bincount as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/BitwiseAnd.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/BitwiseAnd.ts
deleted file mode 100644
index 0c72abeb2..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/BitwiseAnd.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BitwiseAnd, BitwiseAndInputs, env, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-import {MathBackendWebGL} from '../backend_webgl';
-import {BinaryOpProgram} from '../binaryop_gpu';
-import {BinaryOpPackedProgram} from '../binaryop_packed_gpu';
-import {bitwiseAndImplCPU as cpuBitwiseAnd} from '../kernel_utils/shared';
-
-export const BITWISEAND = `
-  int r = int(a.r) & int(b.r);
-  int g = int(a.g) & int(b.g);
-  int rb = int(a.b) & int(b.b);
-  int ra = int(a.a) & int(b.a);
-  return vec4(r, g, rb, ra);
-`;
-
-export const BITWISEAND_UNPACKED = `
-  return float(int(a.r) & int(b.r));
-`;
-
-export function bitwiseAnd(args: {
-  inputs: BitwiseAndInputs,
-  backend: MathBackendWebGL,
-}): TensorInfo {
-  const {inputs, backend} = args;
-  const {a, b} = inputs;
-  const shouldUsePackedProgram = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
-  const versionNumber = env().getNumber('WEBGL_VERSION');
-
-  // The type of a and b are ensured to be `int32` in core, therefore no need to
-  // consider other type situations.
-  if ((backend.shouldExecuteOnCPU([a, b])) || versionNumber === 1) {
-    const aVals = backend.texData.get(a.dataId).values as TypedArray;
-    const bVals = backend.texData.get(b.dataId).values as TypedArray;
-    const [outValues, outShape] =
-        cpuBitwiseAnd(a.shape, b.shape, aVals, bVals, a.dtype);
-
-    const out = backend.makeTensorInfo(outShape, a.dtype);
-    const outData = backend.texData.get(out.dataId);
-    outData.values = outValues;
-    return out;
-  }
-
-  let program: BinaryOpProgram|BinaryOpPackedProgram;
-  if (shouldUsePackedProgram) {
-    program = new BinaryOpPackedProgram(BITWISEAND, a.shape, b.shape, false);
-  } else {
-    program = new BinaryOpProgram(BITWISEAND_UNPACKED, a.shape, b.shape);
-  }
-
-  return backend.runWebGLProgram(program, [a, b], a.dtype);
-}
-
-export const bitwiseAndConfig: KernelConfig = {
-  kernelName: BitwiseAnd,
-  backendName: 'webgl',
-  kernelFunc: bitwiseAnd as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/BroadcastArgs.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/BroadcastArgs.ts
deleted file mode 100644
index 025555d47..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/BroadcastArgs.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BroadcastArgs, BroadcastArgsInputs, KernelConfig, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-import {MathBackendWebGL} from '../backend_webgl';
-
-export function broadcastArgs(args: {
-  inputs: BroadcastArgsInputs,
-  backend: MathBackendWebGL,
-}): TensorInfo {
-  const {inputs, backend} = args;
-  const {s0, s1} = inputs;
-
-  const s0Vals = backend.readSync(s0.dataId) as TypedArray;
-  const s1Vals = backend.readSync(s1.dataId) as TypedArray;
-
-  const broadcastShape = backend_util.assertAndGetBroadcastShape(
-      Array.from(s0Vals), Array.from(s1Vals));
-
-  return backend.makeTensorInfo(
-      [broadcastShape.length], 'int32', Int32Array.from(broadcastShape));
-}
-
-export const broadcastArgsConfig: KernelConfig = {
-  kernelName: BroadcastArgs,
-  backendName: 'webgl',
-  kernelFunc: broadcastArgs
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Cast.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Cast.ts
deleted file mode 100644
index 8a6da6406..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Cast.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '@tensorflow/tfjs-core';
-import {BinaryInputs, Cast, CastAttrs, CastInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {castImplCPU} from '../kernel_utils/shared';
-import {complex} from './Complex';
-import {identity} from './Identity';
-import {notEqual} from './NotEqual';
-import {real} from './Real';
-
-import {int} from '../kernel_utils/int';
-
-export function cast(
-    args: {inputs: CastInputs, backend: MathBackendWebGL, attrs: CastAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {dtype} = attrs;
-
-  // Casting to complex64.
-  if (dtype === 'complex64') {
-    if (x.dtype === 'complex64') {
-      return identity({inputs: {x}, backend});
-    }
-
-    // TODO(annxingyuan): Import kernel function once zeros is modularized.
-    const zerosTensor = tf.zeros(x.shape);
-    const floatX = cast({inputs: {x}, backend, attrs: {dtype: 'float32'}});
-
-    const result =
-        complex({inputs: {real: floatX, imag: zerosTensor}, backend});
-
-    zerosTensor.dispose();
-    backend.disposeIntermediateTensorInfo(floatX);
-
-    return result;
-  }
-
-  // Casting from complex64
-  if (x.dtype === 'complex64') {
-    const realPart = real({inputs: {input: x}, backend});
-    const result = cast({inputs: {x: realPart}, backend, attrs: {dtype}});
-    backend.disposeIntermediateTensorInfo(realPart);
-    return result;
-  }
-
-  if (!util.hasEncodingLoss(x.dtype, dtype)) {
-    // We don't change the underlying data, since we cast to higher
-    // precision.
-    const result = identity({inputs: {x}, backend});
-    return {dataId: result.dataId, shape: result.shape, dtype};
-  }
-
-  if (backend.shouldExecuteOnCPU([x])) {
-    const values = backend.texData.get(x.dataId).values as TypedArray;
-    const [resultShape, resultType, resultData] =
-        castImplCPU(values, x.shape, x.dtype, dtype);
-    return backend.makeTensorInfo(resultShape, resultType, resultData);
-  }
-
-  if (dtype === 'int32') {
-    return int(x, backend);
-  }
-
-  if (dtype === 'bool') {
-    const zerosTensorInfo = backend.makeTensorInfo(
-        [], 'bool', util.getTypedArrayFromDType('bool', 1));
-
-    const binaryInputs: BinaryInputs = {a: x, b: zerosTensorInfo};
-
-    const result = notEqual({inputs: binaryInputs, backend}) as TensorInfo;
-    backend.disposeIntermediateTensorInfo(zerosTensorInfo);
-    return result;
-  }
-
-  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
-}
-
-export const castConfig: KernelConfig = {
-  kernelName: Cast,
-  backendName: 'webgl',
-  kernelFunc: cast as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Ceil.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Ceil.ts
deleted file mode 100644
index 22bc06655..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Ceil.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Ceil, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {ceilImplCPU} from '../kernel_utils/shared';
-
-const CEIL = `return ceil(x);`;
-
-export const ceil = unaryKernelFunc(
-    {opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU});
-
-export const ceilConfig: KernelConfig = {
-  kernelName: Ceil,
-  backendName: 'webgl',
-  kernelFunc: ceil as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ClipByValue.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ClipByValue.ts
deleted file mode 100644
index 7fb789fdf..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ClipByValue.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ClipByValue, ClipByValueAttrs, ClipByValueInputs, env, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ClipProgram} from '../clip_gpu';
-import {ClipPackedProgram} from '../clip_packed_gpu';
-
-export function clipByValue(args: {
-  inputs: ClipByValueInputs,
-  backend: MathBackendWebGL,
-  attrs: ClipByValueAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {clipValueMin, clipValueMax} = attrs;
-
-  let program;
-  if (env().getBool('WEBGL_PACK_CLIP')) {
-    program = new ClipPackedProgram(x.shape);
-  } else {
-    program = new ClipProgram(x.shape);
-  }
-  const customValues = [[clipValueMin], [clipValueMax]];
-  return backend.runWebGLProgram(program, [x], x.dtype, customValues);
-}
-
-export const clipByValueConfig: KernelConfig = {
-  kernelName: ClipByValue,
-  backendName: 'webgl',
-  kernelFunc: clipByValue as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Complex.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Complex.ts
deleted file mode 100644
index 2900d6670..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Complex.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Complex, ComplexInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {identity} from './Identity';
-
-/**
- * In WebGL data is stored in GPU textures which can't be efficiently copied, so
- * complex tensors share data with their real and imaginary components. Complex
- * tensors' reference to the components is tracked by refCount on the individual
- * component. The refCounts are increased by the identity call.
- *
- * When a complex tensor is disposed, it will reduce the refCount on the
- * components by calling disposeData on each.
- */
-export function complex(
-    args: {inputs: ComplexInputs, backend: MathBackendWebGL}): TensorInfo {
-  const {inputs, backend} = args;
-  const {real, imag} = inputs;
-
-  const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');
-  const complex = backend.texData.get(complexInfo.dataId);
-
-  const realTensorInfo = identity({inputs: {x: real}, backend});
-
-  const imagTensorInfo = identity({inputs: {x: imag}, backend});
-
-  complex.complexTensorInfos = {real: realTensorInfo, imag: imagTensorInfo};
-
-  return complexInfo;
-}
-
-export const complexConfig: KernelConfig = {
-  kernelName: Complex,
-  backendName: 'webgl',
-  kernelFunc: complex as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ComplexAbs.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ComplexAbs.ts
deleted file mode 100644
index 999a36017..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ComplexAbs.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ComplexAbs, ComplexAbsInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ComplexAbsProgram} from '../complex_abs_gpu';
-
-// Returns a TensorInfo with the complex shape and the dataId of the
-// underlying part. We need to do this because a reshaped complex tensor is
-// not reflected in its parts.
-function makeComplexComponentTensorInfo(
-    complexTensor: TensorInfo, complexPart: TensorInfo): TensorInfo {
-  return {
-    dataId: complexPart.dataId,
-    dtype: complexPart.dtype,
-    shape: complexTensor.shape
-  };
-}
-
-export function complexAbs(
-    args: {inputs: ComplexAbsInputs, backend: MathBackendWebGL}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  const xData = backend.texData.get(x.dataId);
-
-  const program = new ComplexAbsProgram(x.shape);
-  const programInputs = [
-    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),
-    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag),
-  ];
-
-  return backend.runWebGLProgram(
-      program, programInputs, programInputs[0].dtype);
-}
-
-export const complexAbsConfig: KernelConfig = {
-  kernelName: ComplexAbs,
-  backendName: 'webgl',
-  kernelFunc: complexAbs as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Complex_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Complex_test.ts
deleted file mode 100644
index 4fbc5dc1f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Complex_test.ts
+++ /dev/null
@@ -1,352 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-
-const {expectArraysClose} = test_util;
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags, ALL_ENVS} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {WebGLMemoryInfo} from '../backend_webgl';
-
-const BYTES_PER_COMPLEX_ELEMENT = 4 * 2;
-describeWithFlags('complex64 memory', ALL_ENVS, () => {
-  it('usage', async () => {
-    const webglSizeUploadUniformFlagSaved =
-        tf.env().get('WEBGL_SIZE_UPLOAD_UNIFORM');
-    tf.env().set('WEBGL_SIZE_UPLOAD_UNIFORM', 4);
-
-    let numTensors = tf.memory().numTensors;
-    let numBytes = tf.memory().numBytes;
-    let numDataIds = tf.engine().backend.numDataIds();
-
-    const startTensors = numTensors;
-    const startNumBytes = numBytes;
-    const startNumBytesInGPU = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-    const startDataIds = numDataIds;
-
-    const real1 = tf.tensor1d([1]);
-    const imag1 = tf.tensor1d([2]);
-
-    // 2 new Tensors: real1, imag1, and two data buckets created.
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-    expect(tf.memory().numBytes).toBe(numBytes + BYTES_PER_COMPLEX_ELEMENT);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 2);
-    numTensors = tf.memory().numTensors;
-    numBytes = tf.memory().numBytes;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const complex1 = tf.complex(real1, imag1);
-
-    // 1 new complex Tensor and 1 new data bucket created. No new bytes
-    // allocated.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.memory().numBytes).toBe(numBytes);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 1);
-
-    numTensors = tf.memory().numTensors;
-    numBytes = tf.memory().numBytes;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const real2 = tf.tensor1d([3]);
-    const imag2 = tf.tensor1d([4]);
-
-    // 2 new Tensors: real2, imag2, and 2 new data buckets.
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-    expect(tf.memory().numBytes).toBe(numBytes + BYTES_PER_COMPLEX_ELEMENT);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 2);
-    numTensors = tf.memory().numTensors;
-    numBytes = tf.memory().numBytes;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const complex2 = tf.complex(real2, imag2);
-
-    // 1 new Tensor and 1 new data bucket.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    // numBytes stays the same because it is determined by tensor creation at
-    // the engine level, and we do not increment memory for complex tensors
-    // (complex tensors track their own memory).
-    expect(tf.memory().numBytes).toBe(numBytes);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 1);
-
-    numTensors = tf.memory().numTensors;
-    numBytes = tf.memory().numBytes;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const result = complex1.add(complex2);
-
-    // A complex tensor is created, which is composed of real and imag parts.
-    // They should not increase tensor count, only complex tensor does.
-    // 3 new data buckets created for complex, real and imag.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.memory().numBytes).toBe(numBytes);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 3);
-    // Two new 1x1 textures are created to compute the sum of real / imag
-    // components, respectively. No new textures are allocated for the inputs
-    // because they are beneath the uniform upload threshold.
-    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU)
-        .toBe(startNumBytesInGPU + BYTES_PER_COMPLEX_ELEMENT);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    expect(result.dtype).toBe('complex64');
-    expect(result.shape).toEqual([1]);
-    expectArraysClose(await result.data(), [4, 6]);
-
-    const real = tf.real(result);
-
-    // A new tensor is created. No new data buckets created.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.memory().numBytes).toBe(numBytes + 4);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds);
-    numTensors = tf.memory().numTensors;
-    numBytes = tf.memory().numBytes;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    expectArraysClose(await real.data(), [4]);
-
-    const imag = tf.imag(result);
-
-    // A new tensor is created. No new data buckets created.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.memory().numBytes).toBe(numBytes + 4);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds);
-
-    expectArraysClose(await imag.data(), [6]);
-
-    // After disposing, there should be no tensors.
-    real1.dispose();
-    imag1.dispose();
-    real2.dispose();
-    imag2.dispose();
-    complex1.dispose();
-    complex2.dispose();
-    result.dispose();
-    real.dispose();
-    imag.dispose();
-    expect(tf.memory().numTensors).toBe(startTensors);
-    expect(tf.memory().numBytes).toBe(startNumBytes);
-    expect(tf.engine().backend.numDataIds()).toBe(startDataIds);
-    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU)
-        .toBe(startNumBytesInGPU);
-
-    tf.env().set('WEBGL_SIZE_UPLOAD_UNIFORM', webglSizeUploadUniformFlagSaved);
-  });
-
-  it('Creating tf.real, tf.imag from complex.', async () => {
-    let numTensors = tf.memory().numTensors;
-    let numDataIds = tf.engine().backend.numDataIds();
-
-    const startTensors = numTensors;
-    const startDataIds = numDataIds;
-
-    const complex = tf.complex([3, 30], [4, 40]);
-
-    // 1 new tensor, 3 new data buckets.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds + 3);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const real = tf.real(complex);
-    const imag = tf.imag(complex);
-
-    // 2 new tensors, no new data buckets.
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds);
-
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    complex.dispose();
-
-    // 1 fewer tensor, 1 fewer data buckets.
-    expect(tf.memory().numTensors).toBe(numTensors - 1);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds - 1);
-    expectArraysClose(await real.data(), [3, 30]);
-    expectArraysClose(await imag.data(), [4, 40]);
-
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    real.dispose();
-    imag.dispose();
-
-    // Zero net tensors / data buckets.
-    expect(tf.memory().numTensors).toBe(startTensors);
-    expect(tf.engine().backend.numDataIds()).toBe(startDataIds);
-  });
-
-  it('tf.complex disposing underlying tensors', async () => {
-    const numTensors = tf.memory().numTensors;
-    const numDataIds = tf.engine().backend.numDataIds();
-
-    const real = tf.tensor1d([3, 30]);
-    const imag = tf.tensor1d([4, 40]);
-    expect(tf.memory().numTensors).toEqual(numTensors + 2);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 2);
-
-    const complex = tf.complex(real, imag);
-
-    // 1 new tensor is created for complex. real and imag data buckets created.
-    expect(tf.memory().numTensors).toEqual(numTensors + 3);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 3);
-
-    real.dispose();
-    imag.dispose();
-
-    expect(tf.memory().numTensors).toEqual(numTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 3);
-
-    expect(complex.dtype).toBe('complex64');
-    expect(complex.shape).toEqual(real.shape);
-    expectArraysClose(await complex.data(), [3, 4, 30, 40]);
-
-    complex.dispose();
-
-    expect(tf.memory().numTensors).toEqual(numTensors);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds);
-  });
-
-  it('reshape', async () => {
-    const memoryBefore = tf.memory();
-    const numDataIdsBefore = tf.engine().backend.numDataIds();
-    let numTensors = memoryBefore.numTensors;
-    let numDataIds = numDataIdsBefore;
-
-    const a = tf.complex([[1, 3, 5], [7, 9, 11]], [[2, 4, 6], [8, 10, 12]]);
-
-    // 1 new tensor, the complex64 tensor
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 1);
-    // 1 new tensor and 2 underlying data buckets for real and imag.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-    numTensors = tf.memory().numTensors;
-    numDataIds = tf.engine().backend.numDataIds();
-
-    const b = a.reshape([6]);
-    // 1 new tensor from the reshape.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-    // No new data buckets.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds);
-
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([6]);
-    expectArraysClose(await a.data(), await b.data());
-
-    b.dispose();
-    // 1 complex tensor should be disposed.
-    expect(tf.memory().numTensors).toBe(numTensors);
-    // Data buckets not deleted yet.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIds);
-
-    a.dispose();
-    // All the tensors should now be disposed.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors);
-    // The underlying memory should now be released.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore);
-  });
-
-  it('clone', async () => {
-    const memoryBefore = tf.memory();
-    const numDataIdsBefore = tf.engine().backend.numDataIds();
-
-    const a = tf.complex([[1, 3, 5], [7, 9, 11]], [[2, 4, 6], [8, 10, 12]]);
-
-    // 1 new tensor, the complex64 tensor
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 1);
-    // 1 new tensor and 2 underlying data buckets for real and imag.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-
-    const b = a.clone();
-
-    // 1 new tensor from the clone.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 2);
-    // No new data buckets.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-
-    expect(b.dtype).toBe('complex64');
-    expectArraysClose(await a.data(), await b.data());
-
-    b.dispose();
-
-    // 1 complex tensor should be disposed.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 1);
-    // Data bucket not deleted yet.
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore + 3);
-
-    a.dispose();
-
-    // All the tensors should now be disposed.
-    expect(tf.memory().numTensors).toBe(memoryBefore.numTensors);
-    expect(tf.engine().backend.numDataIds()).toBe(numDataIdsBefore);
-  });
-
-  it('Multiple complex tensors sharing same underlying components works',
-     async () => {
-       const numTensors = tf.memory().numTensors;
-       const numDataIds = tf.engine().backend.numDataIds();
-
-       const real = tf.tensor1d([1]);
-       const imag = tf.tensor1d([2]);
-
-       expect(tf.memory().numTensors).toEqual(numTensors + 2);
-       expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 2);
-
-       const complex1 = tf.complex(real, imag);
-       const complex2 = tf.complex(real, imag);
-
-       expect(tf.memory().numTensors).toEqual(numTensors + 4);
-       expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 4);
-
-       real.dispose();
-       expect(tf.memory().numTensors).toEqual(numTensors + 3);
-       expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 4);
-
-       complex1.dispose();
-       expect(tf.memory().numTensors).toEqual(numTensors + 2);
-       expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 3);
-
-       expectArraysClose(await complex2.data(), [1, 2]);
-     });
-
-  it('tidy should not have mem leak', async () => {
-    const numTensors = tf.memory().numTensors;
-    const numDataIds = tf.engine().backend.numDataIds();
-    const complex = tf.tidy(() => {
-      const real = tf.tensor1d([3, 30]);
-      const realReshape = tf.reshape(real, [2]);
-      const imag = tf.tensor1d([4, 40]);
-      const imagReshape = tf.reshape(imag, [2]);
-      expect(tf.memory().numTensors).toEqual(numTensors + 4);
-      expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 2);
-
-      const complex = tf.complex(realReshape, imagReshape);
-
-      // 1 new tensor is created for complex. real and imag data buckets
-      // created.
-      expect(tf.memory().numTensors).toEqual(numTensors + 5);
-      expect(tf.engine().backend.numDataIds()).toEqual(numDataIds + 3);
-
-      return complex;
-    });
-
-    complex.dispose();
-
-    expect(tf.memory().numTensors).toEqual(numTensors);
-    expect(tf.engine().backend.numDataIds()).toEqual(numDataIds);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Concat.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Concat.ts
deleted file mode 100644
index 04ee49e82..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Concat.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Concat, ConcatAttrs, ConcatInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {concatImpl} from './Concat_impl';
-import {identity} from './Identity';
-
-export function concat(
-    args:
-        {inputs: ConcatInputs, attrs: ConcatAttrs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {axis} = attrs;
-
-  const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];
-
-  const shapes = inputs.map(t => t.shape);
-  backend_util.assertParamsConsistent(shapes, $axis);
-
-  const outShape =
-      backend_util.computeOutShape(inputs.map(t => t.shape), $axis);
-
-  if (util.sizeFromShape(outShape) === 0) {
-    return backend.makeTensorInfo(outShape, inputs[0].dtype, []);
-  }
-
-  // Keep only non-empty tensors (ignore tensors with 0 in their shape).
-  const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);
-  if ($inputs.length === 1) {
-    return identity({inputs: {x: $inputs[0]}, backend});
-  }
-
-  return concatImpl($inputs, $axis, backend);
-}
-
-export const concatConfig: KernelConfig = {
-  kernelName: Concat,
-  backendName: 'webgl',
-  kernelFunc: concat as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Concat_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Concat_impl.ts
deleted file mode 100644
index 33ef86e8b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Concat_impl.ts
+++ /dev/null
@@ -1,166 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, ConcatInputs, env, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ConcatProgram} from '../concat_gpu';
-import {ConcatPackedProgram} from '../concat_packed_gpu';
-import {concatImplCPU} from '../kernel_utils/shared';
-import {CLONE, UnaryOpProgram} from '../unaryop_gpu';
-import {UnaryOpPackedProgram} from '../unaryop_packed_gpu';
-
-import {complex} from './Complex';
-import {imag} from './Imag';
-import {real} from './Real';
-import {reshape} from './Reshape';
-
-export function concatImpl(
-    inputs: ConcatInputs, axis: number, backend: MathBackendWebGL): TensorInfo {
-  const dtype = inputs[0].dtype;
-  if (dtype === 'complex64') {
-    const reals = inputs.map((t) => real({inputs: {input: t}, backend}));
-    const imags = inputs.map((t) => imag({inputs: {input: t}, backend}));
-
-    const realConcated = concatImpl(reals, axis, backend);
-    const imagConcated = concatImpl(imags, axis, backend);
-
-    const result =
-        complex({inputs: {real: realConcated, imag: imagConcated}, backend});
-
-    reals.forEach(r => backend.disposeIntermediateTensorInfo(r));
-    imags.forEach(i => backend.disposeIntermediateTensorInfo(i));
-    backend.disposeIntermediateTensorInfo(realConcated);
-    backend.disposeIntermediateTensorInfo(imagConcated);
-
-    return result;
-  }
-
-  let runOnCpu = backend.shouldExecuteOnCPU(inputs);
-
-  // Run on cpu if dtype is string. For string, the backend represents it
-  // as Uint8Array[], where each Uint8Array is a character. Given that the
-  // computation is only on the outer array, uploading the whole data onto
-  // gpu is wasteful. Also, currently webgl doesn't have a design to
-  // upload and retrieve Uint8Array[] between cpu and gpu. Therefore, we
-  // just run the kernel on cpu if dtype is string.
-  if (dtype === 'string') {
-    runOnCpu = true;
-  }
-
-  if (runOnCpu) {
-    // Any concat of n-dimensional tensors across any axis can be reduced to
-    // a concatenation of two-dimensional tensors across the axis 1 by first
-    // partitioning the axes of the original tensors into those less than the
-    // axis to be concatenated and the rest. Then reshape the tensors
-    // into a two-dimensional tensor by collapsing these two sets of axes and
-    // concatenate the resulting matrices across the axis 1, finally reshaping
-    // the result to have the proper shape.
-    const tensors2D = inputs.map(t => {
-      const innerSize = util.sizeFromShape(t.shape.slice(axis));
-      const shape = [-1, innerSize];
-      return reshape({inputs: {x: t}, backend, attrs: {shape}});
-    });
-
-    const inputsValShapes = tensors2D.map(t => {
-      return {vals: backend.readSync(t.dataId), shape: t.shape};
-    });
-
-    // Concats 2d tensors along axis=1.
-    const outShape =
-        backend_util.computeOutShape(tensors2D.map(t => t.shape), 1 /* axis */);
-    const simplyConcat = tensors2D[0].shape[0] === 1;
-    const outVals =
-        concatImplCPU(inputsValShapes, outShape, dtype, simplyConcat);
-
-    const finalOutShape =
-        backend_util.computeOutShape(inputs.map(t => t.shape), axis);
-
-    const outInfo = backend.makeTensorInfo(finalOutShape, dtype, outVals);
-
-    tensors2D.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-    return outInfo;
-  }
-
-  // Keep only non-empty tensors (ignore tensors with 0 in their shape).
-  const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);
-
-  const shouldPack: boolean = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') &&
-      $inputs[0].shape.length > 1;
-
-  if ($inputs.length === 1) {
-    // Clone tensor.
-    const program = shouldPack ?
-        new UnaryOpProgram(inputs[0].shape, CLONE) :
-        new UnaryOpPackedProgram(inputs[0].shape, CLONE);
-    return backend.runWebGLProgram(program, inputs, dtype);
-  }
-
-  const maxTexturesInShader = env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER');
-  if ($inputs.length > maxTexturesInShader) {
-    const reducedInputs = [];
-    for (let i = 0; i < $inputs.length; i += maxTexturesInShader) {
-      const subArray = $inputs.slice(i, i + maxTexturesInShader);
-      reducedInputs.push(concatImpl(subArray, axis, backend));
-    }
-    const result = concatImpl(reducedInputs, axis, backend);
-
-    for (const i of reducedInputs) {
-      backend.disposeIntermediateTensorInfo(i);
-    }
-
-    return result;
-  }
-
-  if (shouldPack) {
-    const program = new ConcatPackedProgram($inputs.map(t => t.shape), axis);
-    return backend.runWebGLProgram(program, $inputs, dtype);
-  }
-
-  const {tensors2D, outShape} = computeTensors2D($inputs, axis, backend);
-  const program =
-      new ConcatProgram(tensors2D.map(t => t.shape as [number, number]));
-  const result = backend.runWebGLProgram(program, tensors2D, dtype);
-
-  tensors2D.forEach(r => backend.disposeIntermediateTensorInfo(r));
-  const reshapedResult =
-      reshape({inputs: {x: result}, attrs: {shape: outShape}, backend});
-  backend.disposeIntermediateTensorInfo(result);
-
-  return reshapedResult;
-}
-
-function computeTensors2D(
-    inputs: ConcatInputs, axis: number, backend: MathBackendWebGL) {
-  // Any concat of n-dimensional tensors across any axis can be reduced to
-  // a concatenation of two-dimensional tensors across the axis 1 by first
-  // partitioning the axes of the original tensors into those less than the
-  // axis to be concatenated and the rest. Then reshape the tensors
-  // into a two-dimensional tensor by collapsing these two sets of axes and
-  // concatenate the resulting matrices across the axis 1, finally reshaping
-  // the result to have the proper shape.
-  const outShape = backend_util.computeOutShape(inputs.map(t => t.shape), axis);
-  const tensors2D = inputs.map(
-      x => reshape({
-        inputs: {x},
-        attrs: {shape: [-1, util.sizeFromShape(x.shape.slice(axis))]},
-        backend
-      }));
-
-  return {tensors2D, outShape};
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Concat_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Concat_test.ts
deleted file mode 100644
index 41f621b55..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Concat_test.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {expectArraysClose} from '@tensorflow/tfjs-core/test_util';
-
-import {WEBGL_ENVS} from '../backend_webgl_test_registry';
-
-describeWithFlags('Concat', WEBGL_ENVS, () => {
-  it('Works if input size is larger than WEBGL_MAX_TEXTURES_IN_SHADER',
-     async () => {
-       const x1 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-       const tensorNum = tf.env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER') + 1;
-       const input = [];
-       const expected = [];
-       for (let i = 0; i < tensorNum; i++) {
-         input.push(x1);
-         expected.push(1, 2, 3, 4);
-       }
-       const values = tf.concat(input, 0);
-       expect(values.shape).toEqual([tensorNum * 2, 2, 1]);
-       expectArraysClose(await values.data(), expected);
-     });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2D.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2D.ts
deleted file mode 100644
index ff7b467b9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2D.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2D, Conv2DAttrs, Conv2DInputs, env, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Conv2DProgram} from '../conv_gpu';
-import {Conv2DPackedProgram} from '../conv_packed_gpu';
-import {conv2dByMatMul, conv2dWithIm2Row} from './Conv2D_impl';
-import {reshape} from './Reshape';
-
-export function conv2d(
-    args:
-        {inputs: Conv2DInputs, attrs: Conv2DAttrs, backend: MathBackendWebGL}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dataFormat, dilations, dimRoundingMode} = attrs;
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, dilations, pad,
-      dimRoundingMode, false /* depthwise */, $dataFormat);
-  let out: TensorInfo;
-
-  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&
-      convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
-      convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
-      (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {
-    out = conv2dByMatMul({x, filter, convInfo, backend});
-  } else if (convInfo.strideWidth <= 2 && $dataFormat === 'channelsLast'
-    && env().getBool('WEBGL_EXP_CONV')
-    ) {
-    const program = new Conv2DPackedProgram(convInfo);
-    const customValues = [
-      [convInfo.padInfo.top, convInfo.padInfo.left],
-      [convInfo.strideHeight, convInfo.strideWidth],
-      [convInfo.dilationHeight, convInfo.dilationWidth],
-      [convInfo.inHeight, convInfo.inWidth]
-    ];
-    out =
-        backend.runWebGLProgram(program, [x, filter], 'float32', customValues);
-  } else if (env().getBool('WEBGL_CONV_IM2COL')) {
-    out = conv2dWithIm2Row({x, filter, convInfo, backend});
-  } else {
-    const program = new Conv2DProgram(convInfo);
-    out = backend.runWebGLProgram(program, [x, filter], 'float32');
-  }
-
-  const outReshaped =
-      reshape({inputs: {x: out}, backend, attrs: {shape: convInfo.outShape}});
-  backend.disposeIntermediateTensorInfo(out);
-
-  return outReshaped;
-}
-
-export const conv2DConfig: KernelConfig = {
-  kernelName: Conv2D,
-  backendName: 'webgl',
-  kernelFunc: conv2d as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2DBackpropFilter.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2DBackpropFilter.ts
deleted file mode 100644
index de6878b9e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2DBackpropFilter.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Conv2DDerFilterProgram} from '../conv_backprop_gpu';
-
-export function conv2DBackpropFilter(args: {
-  inputs: Conv2DBackpropFilterInputs,
-  attrs: Conv2DBackpropFilterAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, pad, dataFormat, dimRoundingMode, filterShape} = attrs;
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number], filterShape, strides,
-      1 /* dilations */, pad, dimRoundingMode, false /* depthwise */,
-      $dataFormat);
-
-  const program = new Conv2DDerFilterProgram(convInfo);
-  return backend.runWebGLProgram(program, [x, dy], 'float32');
-}
-
-export const conv2DBackpropFilterConfig: KernelConfig = {
-  kernelName: Conv2DBackpropFilter,
-  backendName: 'webgl',
-  kernelFunc: conv2DBackpropFilter as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2DBackpropInput.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2DBackpropInput.ts
deleted file mode 100644
index e01cce7ff..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2DBackpropInput.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs, env, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Conv2DDerInputProgram} from '../conv_backprop_gpu';
-import {Conv2DDerInputPackedProgram} from '../conv_backprop_packed_gpu';
-
-export function conv2DBackpropInput(args: {
-  inputs: Conv2DBackpropInputInputs,
-  attrs: Conv2DBackpropInputAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {inputShape, strides, pad, dataFormat, dimRoundingMode} = attrs;
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      inputShape, filter.shape as [number, number, number, number], strides,
-      1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);
-
-  if (env().getBool('WEBGL_PACK_CONV2DTRANSPOSE') &&
-      $dataFormat === 'channelsLast') {
-    const customValues = [
-      [convInfo.strideHeight, convInfo.strideWidth],
-    ];
-    const program = new Conv2DDerInputPackedProgram(convInfo);
-    return backend.runWebGLProgram(
-        program, [dy, filter], 'float32', customValues);
-  } else {
-    const program = new Conv2DDerInputProgram(convInfo);
-    return backend.runWebGLProgram(program, [dy, filter], 'float32');
-  }
-}
-
-export const conv2DBackpropInputConfig: KernelConfig = {
-  kernelName: Conv2DBackpropInput,
-  backendName: 'webgl',
-  kernelFunc: conv2DBackpropInput as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2D_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2D_impl.ts
deleted file mode 100644
index 3cc9c41f9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2D_impl.ts
+++ /dev/null
@@ -1,361 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-// import {assertAndGetBroadcastShape} from
-// '../../../tfjs-core/src/ops/broadcast_util';
-import {MathBackendWebGL} from '../backend_webgl';
-import {Im2ColPackedProgram} from '../im2col_packed_gpu';
-import {mapActivationToShaderProgram} from '../kernel_utils/kernel_funcs_utils';
-import {MatMulPackedProgram} from '../mulmat_packed_gpu';
-import * as webgl_util from '../webgl_util';
-
-import {batchMatMulImpl, MATMUL_SHARED_DIM_THRESHOLD} from './BatchMatMul_impl';
-import {identity} from './Identity';
-import {reshape} from './Reshape';
-
-type Conv2DConfig = {
-  x: TensorInfo,
-  filter: TensorInfo,
-  convInfo: backend_util.Conv2DInfo,
-  backend: MathBackendWebGL,
-  bias?: TensorInfo,
-  preluActivationWeights?: TensorInfo,
-  leakyreluAlpha?: number,
-  activation?: backend_util.Activation
-};
-
-// Both conv2dByMatMul and conv2dWithIm2Row fuse height and width into one
-// dimension to compute batchMatMul, so bias and activation weights are also
-// supposed to fuse the two dimensions into one.
-//
-// This function computes the target shape for fusing height and width
-// dimensions. Returning null means the shape is already compatible.
-//
-// Even though the bias is not supposed to be a 3-D or a 4-D (including
-// batch) tensor and PReLU activiation weights is not supposed to be a 4-D
-// tensor, we still need to support them, because we haven't disabled
-// them for NHWC format.
-// https://github.com/tensorflow/tfjs/blob/b53bd47e880367ae57493f0ea628abaf08db2d5d/tfjs-core/src/ops/fused/conv2d.ts#L181-L196
-function getShapeForBatchMatMul(
-    shape: number[], isChannelsLast: boolean): number[] {
-  const length = shape.length;
-  if (length >= 3) {
-    return isChannelsLast ?
-        [
-          ...shape.slice(0, -3) /* batch */,
-          shape[length - 3] * shape[length - 2] /* height * width */,
-          shape[length - 1] /* channel */
-        ] :
-        [
-          ...shape.slice(0, -3) /* batch */, shape[length - 3] /* channel */,
-          shape[length - 2] * shape[length - 1] /* height * width */
-        ];
-  } else if (!isChannelsLast && length === 1 && shape[0] > 1) {
-    return [shape[0], 1];
-  } else {
-    return null;
-  }
-}
-
-// For 1x1 kernels that iterate through every point in the input, convolution
-// can be expressed as matrix multiplication (without need for memory
-// remapping).
-export function conv2dByMatMul({
-  x,
-  filter,
-  convInfo,
-  backend,
-  bias = null,
-  preluActivationWeights = null,
-  leakyreluAlpha = 0,
-  activation = null
-}: Conv2DConfig) {
-  // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the
-  // result from 2D to 4D.
-  const xShape = x.shape;
-  const xTexData = backend.texData.get(x.dataId);
-  const sharedMatMulDim = convInfo.inChannels;
-  const outerShapeX = xShape[0] * xShape[1] * xShape[2];
-  const outerShapeFilter = convInfo.outChannels;
-  const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-  const transposeA = false;
-  const transposeB = false;
-
-  let out: TensorInfo;
-  const intermediates: TensorInfo[] = [];
-
-  if (preluActivationWeights != null) {
-    const targetShape =
-        getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
-    if (targetShape != null) {
-      preluActivationWeights = reshape({
-        inputs: {x: preluActivationWeights},
-        backend,
-        attrs: {shape: targetShape}
-      });
-      intermediates.push(preluActivationWeights);
-    }
-  }
-
-  if (bias != null) {
-    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
-    if (targetShape != null) {
-      bias = reshape({inputs: {x: bias}, backend, attrs: {shape: targetShape}});
-      intermediates.push(bias);
-    }
-  }
-
-  // TODO: Once reduction ops are packed, batchMatMul will always be packed
-  // and we can remove this condition.
-  const batchMatMulWillBeUnpacked =
-      (outerShapeX === 1 || outerShapeFilter === 1) &&
-      sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;
-
-  // The algorithm in the if condition assumes (1) the output will be packed,
-  // (2) x is packed, (3) x isChannelsLast, (4)  x's packed texture is already
-  // on GPU, (5) col is odd, (6) the width, height and inChannels are the same
-  // for xTexData.shape and xShape.
-  const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked &&
-      isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 &&
-      util.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));
-
-  if (canOptimize) {
-    // We avoid expensive packed 2x2 reshape by padding col count to next,
-    // even number. When col is odd, the result of packed batchMatMul is
-    // the same (has the same texture layout and and values in the texture) as
-    // it is for next even col. We make the odd-cols tensor to look like
-    // even-cols tensor before the operation and, after the batchMatMul,
-    // fix the even-cols result to have odd number of cols.
-    const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);
-    const xReshaped: TensorInfo = {
-      dataId: x.dataId,
-      shape: [1, targetShape, convInfo.inChannels],
-      dtype: x.dtype
-    };
-    // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.
-    // Decrementing col count, after batchMatMul->...->compileProgram leads to
-    // invalid col count within the reference in GPGPUBinary.inShapeInfos.
-    // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos
-    // in compileProgram method, but that would affect compilation of all
-    // programs - instead, provide a copy here, with even col count, before
-    // calling batchMatMul->...->compileProgram and after that, the original
-    // xTexData.shape is restored.
-    const originalXTexDataShape = xTexData.shape;
-    xTexData.shape = xTexData.shape.slice();
-    xTexData.shape[xTexData.shape.length - 2]++;
-    util.assert(
-        webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape),
-        () => `packed reshape ${xTexData.shape} to ${
-            xReshaped.shape} isn't free`);
-    const filterReshaped = reshape({
-      inputs: {x: filter},
-      backend,
-      attrs: {shape: [1, convInfo.inChannels, convInfo.outChannels]}
-    });
-    intermediates.push(filterReshaped);
-    const pointwiseConv = batchMatMulImpl({
-      a: xReshaped,
-      b: filterReshaped,
-      backend,
-      transposeA,
-      transposeB,
-      bias,
-      activation,
-      preluActivationWeights,
-      leakyreluAlpha
-    });
-
-    const pointwiseConvTexData = backend.texData.get(pointwiseConv.dataId);
-    util.assert(
-        pointwiseConvTexData.isPacked,
-        () => 'batchMatMul result is expected to be packed');
-    // Restore the input shape to original.
-    xTexData.shape = originalXTexDataShape;
-    // Set the output shape - there is no need for expensive reshape as data
-    // layout is already correct.
-    pointwiseConvTexData.shape = convInfo.outShape;
-
-    out = identity({inputs: {x: pointwiseConv}, backend});
-    out.shape = convInfo.outShape;
-
-    intermediates.push(pointwiseConv);
-  } else {
-    const numCols = convInfo.outHeight * convInfo.outWidth;
-    const xReshaped = reshape({
-      inputs: {x},
-      backend,
-      attrs: {
-        shape: isChannelsLast ?
-            [convInfo.batchSize, numCols, convInfo.inChannels] :
-            [convInfo.batchSize, convInfo.inChannels, numCols]
-      }
-    });
-    const filterReshaped = reshape({
-      inputs: {x: filter},
-      backend,
-      attrs: {shape: [1, convInfo.inChannels, convInfo.outChannels]}
-    });
-    const result = batchMatMulImpl({
-      a: isChannelsLast ? xReshaped : filterReshaped,
-      b: isChannelsLast ? filterReshaped : xReshaped,
-      transposeA: !isChannelsLast,
-      transposeB,
-      backend,
-      bias,
-      activation,
-      preluActivationWeights,
-      leakyreluAlpha
-    });
-
-    out = reshape(
-        {inputs: {x: result}, backend, attrs: {shape: convInfo.outShape}});
-
-    intermediates.push(xReshaped);
-    intermediates.push(filterReshaped);
-    intermediates.push(result);
-  }
-
-  for (const i of intermediates) {
-    backend.disposeIntermediateTensorInfo(i);
-  }
-
-  return out;
-}
-
-// Implements the im2row algorithm as outlined in "High Performance
-// Convolutional Neural Networks for Document Processing" (Suvisoft, 2006)
-export function conv2dWithIm2Row({
-  x,
-  filter,
-  convInfo,
-  backend,
-  bias = null,
-  preluActivationWeights = null,
-  leakyreluAlpha = 0,
-  activation = null
-}: Conv2DConfig) {
-  // Rearranges conv2d input so each block to be convolved over forms the
-  // column of a new matrix with shape [filterWidth * filterHeight *
-  // inChannels, outHeight * outWidth]. The filter is also rearranged so each
-  // output channel forms a row of a new matrix with shape [outChannels,
-  // filterWidth * filterHeight * inChannels]. The convolution is then
-  // computed by multiplying these matrices and reshaping the result.
-  const {
-    filterWidth,
-    filterHeight,
-    inChannels,
-    outWidth,
-    outHeight,
-    dataFormat
-  } = convInfo;
-
-  const isChannelsLast = dataFormat === 'channelsLast';
-
-  const sharedDim = filterWidth * filterHeight * inChannels;
-  const numCols = outHeight * outWidth;
-  const x2ColShape = [convInfo.batchSize, sharedDim, numCols];
-  const transposeA = true;
-  const transposeB = false;
-
-  const intermediates: TensorInfo[] = [];
-
-  if (preluActivationWeights != null) {
-    const targetShape =
-        getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
-    if (targetShape != null) {
-      preluActivationWeights = reshape({
-        inputs: {x: preluActivationWeights},
-        backend,
-        attrs: {shape: targetShape}
-      });
-      intermediates.push(preluActivationWeights);
-    }
-  }
-
-  if (bias != null) {
-    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
-    if (targetShape != null) {
-      bias = reshape({inputs: {x: bias}, backend, attrs: {shape: targetShape}});
-      intermediates.push(bias);
-    }
-  }
-
-  const w2Row = reshape({
-    inputs: {x: filter},
-    backend,
-    attrs: {shape: [1, sharedDim, util.sizeFromShape(filter.shape) / sharedDim]}
-  });
-  intermediates.push(w2Row);
-
-  const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);
-  const customValues = [
-    x.shape, [convInfo.padInfo.top, convInfo.padInfo.left],
-    [convInfo.strideHeight, convInfo.strideWidth],
-    [convInfo.dilationHeight, convInfo.dilationWidth], [convInfo.inChannels],
-    [convInfo.filterWidth * convInfo.inChannels], [convInfo.outWidth]
-  ];
-  const im2Col =
-      backend.runWebGLProgram(im2ColProgram, [x], 'float32', customValues);
-  const im2ColReshaped =
-      reshape({inputs: {x: im2Col}, backend, attrs: {shape: x2ColShape}});
-
-  intermediates.push(im2Col);
-  intermediates.push(im2ColReshaped);
-
-  const hasBias = bias != null;
-  const hasPreluActivationWeights = preluActivationWeights != null;
-  const hasLeakyreluAlpha = activation === 'leakyrelu';
-  const fusedActivation =
-      activation ? mapActivationToShaderProgram(activation, true) : null;
-  const matmulProgram = new MatMulPackedProgram(
-      isChannelsLast ? im2ColReshaped.shape as [number, number, number] :
-                       w2Row.shape as [number, number, number],
-      isChannelsLast ? w2Row.shape as [number, number, number] :
-                       im2ColReshaped.shape as [number, number, number],
-      isChannelsLast ? [convInfo.batchSize, numCols, convInfo.outChannels] :
-                       [convInfo.batchSize, convInfo.outChannels, numCols],
-      transposeA, transposeB, hasBias, fusedActivation,
-      hasPreluActivationWeights, hasLeakyreluAlpha);
-  const inputs: TensorInfo[] =
-      isChannelsLast ? [im2ColReshaped, w2Row] : [w2Row, im2ColReshaped];
-  if (bias) {
-    inputs.push(bias);
-  }
-  if (hasPreluActivationWeights) {
-    inputs.push(preluActivationWeights);
-  }
-  if (hasLeakyreluAlpha) {
-    const $leakyreluAlpha = backend.makeTensorInfo(
-        [], 'float32',
-        util.createScalarValue(leakyreluAlpha as unknown as 'float32',
-                               'float32'));
-    inputs.push($leakyreluAlpha);
-    intermediates.push($leakyreluAlpha);
-  }
-  const product = backend.runWebGLProgram(matmulProgram, inputs, 'float32');
-  const out = reshape(
-      {inputs: {x: product}, backend, attrs: {shape: convInfo.outShape}});
-
-  intermediates.push(product);
-  for (const i of intermediates) {
-    backend.disposeIntermediateTensorInfo(i);
-  }
-
-  return out;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2D_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2D_test.ts
deleted file mode 100644
index 9c816a30c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv2D_test.ts
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-const {expectArraysClose} = test_util;
-
-describeWithFlags('Conv2D WebGL Implementation ', ALL_ENVS, () => {
-  it('should work when width is odd and called multiple times.', async () => {
-    const filter = tf.tensor4d([-1, 3, 2, 1, 3, 4, 4, -2], [1, 1, 4, 2]);
-    const image = tf.tensor3d(
-        [
-          111, 112, 113, 114, 121, 122, 123, 124, 131, 132, 133, 134,
-          211, 212, 213, 214, 221, 222, 223, 224, 231, 232, 233, 234,
-          311, 312, 313, 314, 321, 322, 323, 324, 331, 332, 333, 334,
-
-        ],
-        [3, 3, 4]);
-
-    tf.conv2d(image, filter, 1, 'valid');
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const result = tf.conv2d(image, filter, 1, 'valid');
-    const resultData = await result.data();
-
-    const expected = [
-      908, 669, 988, 729, 1068, 789, 1708, 1269, 1788, 1329, 1868, 1389, 2508,
-      1869, 2588, 1929, 2668, 1989
-    ];
-
-    expectArraysClose(resultData, expected);
-  });
-
-  it('image is packed and isChannelFirst.', async () => {
-    const filter = tf.tensor4d([1], [1, 1, 1, 1]);
-    const image = tf.tensor3d([11, 12, 13, 21, 22, 23, 31, 32, 33], [1, 3, 3]);
-
-    // pack image.
-    tf.mul(image, 1);
-
-    tf.conv2d(image, filter, 1, 'valid', 'NCHW');
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const result = tf.conv2d(image, filter, 1, 'valid', 'NCHW');
-    const resultData = await result.data();
-
-    const expected = [11, 12, 13, 21, 22, 23, 31, 32, 33];
-
-    expectArraysClose(resultData, expected);
-  });
-
-  it('image is unpacked and isChannelFirst.', async () => {
-    const filter = tf.tensor4d([1], [1, 1, 1, 1]);
-    const image = tf.tensor3d([11, 12, 13, 21, 22, 23, 31, 32, 33], [1, 3, 3]);
-
-    tf.conv2d(image, filter, 1, 'valid', 'NCHW');
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const result = tf.conv2d(image, filter, 1, 'valid', 'NCHW');
-    const resultData = await result.data();
-
-    const expected = [11, 12, 13, 21, 22, 23, 31, 32, 33];
-
-    expectArraysClose(resultData, expected);
-  });
-
-  it('image is packed and isChannelLast.', async () => {
-    const filter = tf.tensor4d([1], [1, 1, 1, 1]);
-    const image = tf.tensor3d([11, 12, 13, 21, 22, 23, 31, 32, 33], [3, 3, 1]);
-
-    // pack image.
-    tf.mul(image, 1);
-
-    tf.conv2d(image, filter, 1, 'valid');
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const result = tf.conv2d(image, filter, 1, 'valid');
-    const resultData = await result.data();
-
-    const expected = [11, 12, 13, 21, 22, 23, 31, 32, 33];
-
-    expectArraysClose(resultData, expected);
-  });
-
-  it('image is unpacked and isChannelLast.', async () => {
-    const filter = tf.tensor4d([1], [1, 1, 1, 1]);
-    const image = tf.tensor3d([11, 12, 13, 21, 22, 23, 31, 32, 33], [3, 3, 1]);
-
-    tf.conv2d(image, filter, 1, 'valid');
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const result = tf.conv2d(image, filter, 1, 'valid');
-    const resultData = await result.data();
-
-    const expected = [11, 12, 13, 21, 22, 23, 31, 32, 33];
-
-    expectArraysClose(resultData, expected);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv3D.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Conv3D.ts
deleted file mode 100644
index fb073b415..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv3D.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3D, Conv3DAttrs, Conv3DInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Conv3DProgram} from '../conv_gpu';
-
-export function conv3D(
-    args:
-        {inputs: Conv3DInputs, attrs: Conv3DAttrs, backend: MathBackendWebGL}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  const convInfo = backend_util.computeConv3DInfo(
-      x.shape as [number, number, number, number, number],
-      filter.shape as [number, number, number, number, number], strides,
-      dilations, pad);
-
-  const program = new Conv3DProgram(convInfo);
-  return backend.runWebGLProgram(program, [x, filter], 'float32');
-}
-
-export const conv3DConfig: KernelConfig = {
-  kernelName: Conv3D,
-  backendName: 'webgl',
-  kernelFunc: conv3D as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv3DBackpropFilterV2.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Conv3DBackpropFilterV2.ts
deleted file mode 100644
index 68a363c9d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv3DBackpropFilterV2.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropFilterV2, Conv3DBackpropFilterV2Attrs, Conv3DBackpropFilterV2Inputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Conv3DDerFilterProgram} from '../conv_backprop_gpu';
-
-export function conv3DBackpropFilterV2(args: {
-  inputs: Conv3DBackpropFilterV2Inputs,
-  attrs: Conv3DBackpropFilterV2Attrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, pad, filterShape} = attrs;
-
-  const convInfo = backend_util.computeConv3DInfo(
-      x.shape as [number, number, number, number, number], filterShape, strides,
-      1 /* dilations */, pad);
-
-  const program = new Conv3DDerFilterProgram(convInfo);
-  return backend.runWebGLProgram(program, [x, dy], 'float32');
-}
-
-export const conv3DBackpropFilterV2Config: KernelConfig = {
-  kernelName: Conv3DBackpropFilterV2,
-  backendName: 'webgl',
-  kernelFunc: conv3DBackpropFilterV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv3DBackpropInputV2.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Conv3DBackpropInputV2.ts
deleted file mode 100644
index 8fc7f5fd6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Conv3DBackpropInputV2.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Conv3DDerInputProgram} from '../conv_backprop_gpu';
-
-export function conv3DBackpropInput(args: {
-  inputs: Conv3DBackpropInputV2Inputs,
-  attrs: Conv3DBackpropInputV2Attrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {pad, strides, inputShape} = attrs;
-
-  const convInfo = backend_util.computeConv3DInfo(
-      inputShape, filter.shape as [number, number, number, number, number],
-      strides, 1 /* dilations */, pad);
-
-  const program = new Conv3DDerInputProgram(convInfo);
-  return backend.runWebGLProgram(program, [dy, filter], 'float32');
-}
-
-export const conv3DBackpropInputConfig: KernelConfig = {
-  kernelName: Conv3DBackpropInputV2,
-  backendName: 'webgl',
-  kernelFunc: conv3DBackpropInput as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Cos.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Cos.ts
deleted file mode 100644
index 729f85354..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Cos.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cos, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET_PACKED} from '../binaryop_packed_gpu';
-import {CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const COS = CHECK_NAN_SNIPPET_UNARY + `
-  return cos(x);
-`;
-
-const COS_PACKED = `
-  vec4 result = cos(x);
-  bvec4 isNaN = isnan(x);
-  ${CHECK_NAN_SNIPPET_PACKED}
-  return result;
-`;
-
-export const cos =
-    unaryKernelFunc({opSnippet: COS, packedOpSnippet: COS_PACKED});
-
-export const cosConfig: KernelConfig = {
-  kernelName: Cos,
-  backendName: 'webgl',
-  kernelFunc: cos,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Cosh.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Cosh.ts
deleted file mode 100644
index 3ecf2f01e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Cosh.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cosh, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const COSH = `
-  float e2x = exp(-x);
-  return (e2x + 1.0 / e2x) / 2.0;
-`;
-
-export const cosh = unaryKernelFunc({opSnippet: COSH});
-
-export const coshConfig: KernelConfig = {
-  kernelName: Cosh,
-  backendName: 'webgl',
-  kernelFunc: cosh,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/CropAndResize.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/CropAndResize.ts
deleted file mode 100644
index 2949b5b38..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/CropAndResize.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {CropAndResizeProgram} from '../crop_and_resize_gpu';
-
-export const cropAndResize = (args: {
-  inputs: CropAndResizeInputs,
-  backend: MathBackendWebGL,
-  attrs: CropAndResizeAttrs
-}): TensorInfo => {
-  const {inputs, backend, attrs} = args;
-  const {image, boxes, boxInd} = inputs;
-  const {cropSize, method, extrapolationValue} = attrs;
-
-  const program = new CropAndResizeProgram(
-      image.shape as [number, number, number, number],
-      boxes.shape as [number, number], cropSize, method, extrapolationValue);
-  return backend.runWebGLProgram(program, [image, boxes, boxInd], 'float32');
-};
-
-export const cropAndResizeConfig: KernelConfig = {
-  kernelName: CropAndResize,
-  backendName: 'webgl',
-  kernelFunc: cropAndResize as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Cum_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Cum_impl.ts
deleted file mode 100644
index 2ad5db2eb..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Cum_impl.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {CumOpType, CumProgram} from '../cum_gpu';
-
-import {identity} from './Identity';
-import {transpose} from './Transpose';
-
-export function cumImpl(
-    op: CumOpType, x: TensorInfo, backend: MathBackendWebGL, axis: number,
-    exclusive: boolean, reverse: boolean): TensorInfo {
-  const xRank = x.shape.length;
-  const permutation = backend_util.getAxesPermutation([axis], xRank);
-  let permutedX = x;
-  if (permutation != null) {
-    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutation}});
-  }
-  const permutedAxis = backend_util.getInnerMostAxes(1, xRank)[0];
-
-  if (permutedAxis !== xRank - 1) {
-    throw new Error(
-        `WebGL cumprod shader expects an inner-most axis=${
-            x.shape.length - 1} ` +
-        `but got axis=${axis}`);
-  }
-  const size = permutedX.shape[permutedAxis];
-  let result = identity({inputs: {x: permutedX}, backend});
-  // Use cum parallel algorithm, inspired by:
-  // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda
-  // Note: although the algorithm is called sum, it works for any associtative
-  // operator with an identity.
-
-  for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {
-    const program = new CumProgram(op, permutedX.shape, false, reverse);
-    const customValues = [[i]];
-    const prevResult = result;
-    result =
-        backend.runWebGLProgram(program, [result], result.dtype, customValues);
-    backend.disposeIntermediateTensorInfo(prevResult);
-  }
-  // For exclusive cum, shift the end result in the direction of product or sum
-  // and add 1 for product or 0 for sum to the front index.
-  if (exclusive) {
-    const program = new CumProgram(op, permutedX.shape, exclusive, reverse);
-    const prevResult = result;
-    result = backend.runWebGLProgram(program, [result], result.dtype);
-    backend.disposeIntermediateTensorInfo(prevResult);
-  }
-
-  if (permutation != null) {
-    const reversePermutation = backend_util.getUndoAxesPermutation(permutation);
-    const reverseTransposedResult = transpose(
-        {inputs: {x: result}, backend, attrs: {perm: reversePermutation}});
-
-    backend.disposeIntermediateTensorInfo(result);
-    backend.disposeIntermediateTensorInfo(permutedX);
-
-    return reverseTransposedResult;
-  }
-
-  return result;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Cumprod.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Cumprod.ts
deleted file mode 100644
index cabe6d1b2..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Cumprod.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cumprod, CumprodAttrs, CumprodInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {CumOpType} from '../cum_gpu';
-import {cumImpl} from './Cum_impl';
-
-export function cumprod(args: {
-  inputs: CumprodInputs,
-  backend: MathBackendWebGL,
-  attrs: CumprodAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, exclusive, reverse} = attrs;
-
-  return cumImpl(CumOpType.Prod, x, backend, axis, exclusive, reverse);
-}
-
-export const cumprodConfig: KernelConfig = {
-  kernelName: Cumprod,
-  backendName: 'webgl',
-  kernelFunc: cumprod as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Cumsum.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Cumsum.ts
deleted file mode 100644
index 64b7772bd..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Cumsum.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cumsum, CumsumAttrs, CumsumInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {CumOpType} from '../cum_gpu';
-import {cumImpl} from './Cum_impl';
-
-export function cumsum(
-    args:
-        {inputs: CumsumInputs, backend: MathBackendWebGL, attrs: CumsumAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, exclusive, reverse} = attrs;
-  return cumImpl(CumOpType.Sum, x, backend, axis, exclusive, reverse);
-}
-
-export const cumsumConfig: KernelConfig = {
-  kernelName: Cumsum,
-  backendName: 'webgl',
-  kernelFunc: cumsum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/DenseBincount.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/DenseBincount.ts
deleted file mode 100644
index 17620bfb1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/DenseBincount.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DenseBincount, DenseBincountAttrs, DenseBincountInputs, KernelConfig, KernelFunc, Rank, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {bincountImplCPU, bincountReduceImplCPU} from '../kernel_utils/shared';
-
-export function denseBincount(args: {
-  inputs: DenseBincountInputs,
-  backend: MathBackendWebGL,
-  attrs: DenseBincountAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, weights} = inputs;
-  const {size, binaryOutput} = attrs;
-
-  if (x.shape.length === 1) {
-    const xVals = backend.readSync(x.dataId) as TypedArray;
-    const weightsVals = backend.readSync(weights.dataId) as TypedArray;
-
-    const outVals =
-        bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
-
-    return backend.makeTensorInfo([size], weights.dtype, outVals);
-  } else if (x.shape.length === 2) {
-    const xBuf = backend.bufferSync<Rank, 'float32'>(x);
-    const weightsBuf = backend.bufferSync<Rank, 'float32'>(weights);
-
-    const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);
-
-    return backend.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
-  }
-
-  throw new Error(
-      `Error in denseBincount: input must be at most rank 2, but got rank` +
-      `${x.shape.length}.`);
-}
-
-export const denseBincountConfig: KernelConfig = {
-  kernelName: DenseBincount,
-  backendName: 'webgl',
-  kernelFunc: denseBincount as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/DepthToSpace.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/DepthToSpace.ts
deleted file mode 100644
index 85d7dfc9f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/DepthToSpace.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {DepthToSpaceProgram} from '../depth_to_space_gpu';
-
-export function depthToSpace(args: {
-  inputs: DepthToSpaceInputs,
-  backend: MathBackendWebGL,
-  attrs: DepthToSpaceAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockSize, dataFormat} = attrs;
-
-  const batchSize = x.shape[0];
-  const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];
-  const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];
-  const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];
-
-  const outputHeight = inputHeight * blockSize;
-  const outputWidth = inputWidth * blockSize;
-  const outputDepth = inputDepth / (blockSize * blockSize);
-
-  const outputShape = (dataFormat === 'NHWC') ?
-      [batchSize, outputHeight, outputWidth, outputDepth] :
-      [batchSize, outputDepth, outputHeight, outputWidth];
-
-  const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);
-  return backend.runWebGLProgram(program, [x], x.dtype);
-}
-
-export const depthToSpaceConfig: KernelConfig = {
-  kernelName: DepthToSpace,
-  backendName: 'webgl',
-  kernelFunc: depthToSpace as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/DepthwiseConv2dNative.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/DepthwiseConv2dNative.ts
deleted file mode 100644
index c3900ceb3..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/DepthwiseConv2dNative.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs, env, KernelConfig, KernelFunc, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {DepthwiseConv2DProgram} from '../conv_gpu_depthwise';
-import {DepthwiseConvPacked2DProgram} from '../conv_packed_gpu_depthwise';
-
-export function depthwiseConv2dNative(args: {
-  inputs: DepthwiseConv2dNativeInputs,
-  attrs: DepthwiseConv2dNativeAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dilations, dimRoundingMode} = attrs;
-
-  let $dilations = dilations;
-  if ($dilations == null) {
-    $dilations = [1, 1];
-  }
-
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, $dilations),
-      () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +
-          `1. Got strides ${strides} and dilations '${$dilations}'`);
-
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, $dilations,
-      pad, dimRoundingMode, true /* depthwise */);
-
-  let program: DepthwiseConv2DProgram|DepthwiseConvPacked2DProgram;
-  if (env().getBool('WEBGL_PACK_DEPTHWISECONV') && convInfo.strideWidth <= 2 &&
-      convInfo.outChannels / convInfo.inChannels === 1) {
-    program = new DepthwiseConvPacked2DProgram(convInfo);
-  } else {
-    program = new DepthwiseConv2DProgram(convInfo);
-  }
-  const customValues = [
-    [convInfo.padInfo.top, convInfo.padInfo.left],
-    [convInfo.strideHeight, convInfo.strideWidth],
-    [convInfo.dilationHeight, convInfo.dilationWidth],
-    [convInfo.inHeight, convInfo.inWidth]
-  ];
-  return backend.runWebGLProgram(program, [x, filter], 'float32', customValues);
-}
-
-export const depthwiseConv2dNativeConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNative,
-  backendName: 'webgl',
-  kernelFunc: depthwiseConv2dNative as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts
deleted file mode 100644
index cafae4f1b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropFilterAttrs, DepthwiseConv2dNativeBackpropFilterInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {DepthwiseConv2DDerFilterProgram} from '../conv_backprop_gpu_depthwise';
-
-export function depthwiseConv2dNativeBackpropFilter(args: {
-  inputs: DepthwiseConv2dNativeBackpropFilterInputs,
-  attrs: DepthwiseConv2dNativeBackpropFilterAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, dilations, pad, dimRoundingMode, filterShape} = attrs;
-
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number], filterShape, strides,
-      dilations, pad, dimRoundingMode, true /* depthwise */);
-
-  const program = new DepthwiseConv2DDerFilterProgram(convInfo);
-  return backend.runWebGLProgram(program, [x, dy], 'float32');
-}
-
-export const depthwiseConv2dNativeBackpropFilterConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNativeBackpropFilter,
-  backendName: 'webgl',
-  kernelFunc: depthwiseConv2dNativeBackpropFilter as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/DepthwiseConv2dNativeBackpropInput.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/DepthwiseConv2dNativeBackpropInput.ts
deleted file mode 100644
index cc57122ac..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/DepthwiseConv2dNativeBackpropInput.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNativeBackpropInput, DepthwiseConv2dNativeBackpropInputAttrs, DepthwiseConv2dNativeBackpropInputInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {DepthwiseConv2DDerInputProgram} from '../conv_backprop_gpu_depthwise';
-
-export function depthwiseConv2dNativeBackpropInput(args: {
-  inputs: DepthwiseConv2dNativeBackpropInputInputs,
-  attrs: DepthwiseConv2dNativeBackpropInputAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {strides, dilations, pad, dimRoundingMode, inputShape} = attrs;
-
-  const convInfo = backend_util.computeConv2DInfo(
-      inputShape, filter.shape as [number, number, number, number], strides,
-      dilations, pad, dimRoundingMode, true /* depthwise */);
-
-  const program = new DepthwiseConv2DDerInputProgram(convInfo);
-  return backend.runWebGLProgram(program, [dy, filter], 'float32');
-}
-
-export const depthwiseConv2dNativeBackpropInputConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNativeBackpropInput,
-  backendName: 'webgl',
-  kernelFunc: depthwiseConv2dNativeBackpropInput as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Diag.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Diag.ts
deleted file mode 100644
index 6e7ae481f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Diag.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Diag, DiagInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {DiagProgram} from '../diag_gpu';
-import {reshape} from './Reshape';
-
-export function diag(args: {inputs: DiagInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  const outShape = [...x.shape, ...x.shape];
-  const xSize = util.sizeFromShape(x.shape);
-
-  const flat = reshape({inputs: {x}, backend, attrs: {shape: [xSize]}});
-
-  const program = new DiagProgram(xSize);
-  const res = backend.runWebGLProgram(program, [flat], flat.dtype);
-
-  const out = reshape({inputs: {x: res}, backend, attrs: {shape: outShape}});
-
-  backend.disposeIntermediateTensorInfo(flat);
-  backend.disposeIntermediateTensorInfo(res);
-
-  return out;
-}
-
-export const diagConfig: KernelConfig = {
-  kernelName: Diag,
-  backendName: 'webgl',
-  kernelFunc: diag as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Dilation2D.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Dilation2D.ts
deleted file mode 100644
index 6456aa76f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Dilation2D.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2D, Dilation2DAttrs, Dilation2DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Dilation2DProgram} from '../dilation_gpu';
-import {reshape} from './Reshape';
-
-export function dilation2D(args: {
-  inputs: Dilation2DInputs,
-  attrs: Dilation2DAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  const convInfo = backend_util.computeDilation2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number], strides, pad,
-      'NHWC' /* dataFormat */, dilations);
-  let out: TensorInfo;
-
-  const program = new Dilation2DProgram(convInfo);
-  out = backend.runWebGLProgram(program, [x, filter], 'float32');
-
-  const outReshaped =
-      reshape({inputs: {x: out}, backend, attrs: {shape: convInfo.outShape}});
-  backend.disposeIntermediateTensorInfo(out);
-
-  return outReshaped;
-}
-
-export const dilation2DConfig: KernelConfig = {
-  kernelName: Dilation2D,
-  backendName: 'webgl',
-  kernelFunc: dilation2D as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Einsum.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Einsum.ts
deleted file mode 100644
index 738684ff9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Einsum.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Einsum, EinsumAttrs, EinsumInputs, KernelConfig, KernelFunc, Tensor, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {multiply} from './Multiply';
-import {reshape} from './Reshape';
-import {sum} from './Sum';
-import {transpose} from './Transpose';
-
-export function einsum(
-    args:
-        {inputs: EinsumInputs, backend: MathBackendWebGL, attrs: EinsumAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {equation} = attrs;
-  const tensors = inputs as Tensor[];
-
-  const {allDims, summedDims, idDims} =
-      backend_util.decodeEinsumEquation(equation, tensors.length);
-  backend_util.checkEinsumDimSizes(allDims.length, idDims, tensors);
-  const {path, steps} = backend_util.getEinsumComputePath(summedDims, idDims);
-
-  const nSteps = steps.length;
-  let out: TensorInfo|null = null;
-  let numDimsRemaining = allDims.length;
-  const tensorsToDispose: TensorInfo[] = [];
-  for (let i = 0; i < nSteps; ++i) {
-    for (const idTerm of steps[i]) {
-      const {permutationIndices: perm, expandDims: dimsToExpand} =
-          backend_util.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
-      let x: TensorInfo;
-      if (backend_util.isIdentityPermutation(perm)) {
-        x = tensors[idTerm];
-      } else {
-        x = transpose({inputs: {x: tensors[idTerm]}, backend, attrs: {perm}});
-        tensorsToDispose.push(x);
-      }
-      const targetShape: number[] = x.shape.slice();
-      for (let k = 0; k < dimsToExpand.length; ++k) {
-        targetShape.splice(dimsToExpand[k], 0, 1);
-      }
-
-      if (!util.arraysEqual(x.shape, targetShape)) {
-        x = reshape({inputs: {x}, backend, attrs: {shape: targetShape}});
-        tensorsToDispose.push(x);
-      }
-      if (out === null) {
-        out = x;
-      } else {
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        out = multiply({inputs: {a: x, b: out}, backend}) as TensorInfo;
-        tensorsToDispose.push(out);
-      }
-    }
-    if (i < nSteps - 1) {
-      if (path[i] >= 0) {
-        out = sum({
-          inputs: {x: out},
-          backend,
-          attrs: {
-            axis: path[i] - (allDims.length - numDimsRemaining),
-            keepDims: false
-          }
-        });
-        tensorsToDispose.push(out);
-      }
-      numDimsRemaining--;
-    }
-  }
-
-  // Clean up intermediate tensors.
-  for (const tensorInfo of tensorsToDispose) {
-    if (tensorInfo === out) {
-      continue;
-    }
-    backend.disposeIntermediateTensorInfo(tensorInfo);
-  }
-
-  return out;
-}
-
-export const einsumConfig: KernelConfig = {
-  kernelName: Einsum,
-  backendName: 'webgl',
-  kernelFunc: einsum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Elu.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Elu.ts
deleted file mode 100644
index 7a57ef96d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Elu.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Elu, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
-
-const ELU_PACKED = `
-  vec4 result;
-
-  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
-  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
-  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
-  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
-
-  return result;
-`;
-
-const elu = unaryKernelFunc({opSnippet: ELU, packedOpSnippet: ELU_PACKED});
-
-export const eluConfig: KernelConfig = {
-  kernelName: Elu,
-  backendName: 'webgl',
-  kernelFunc: elu as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/EluGrad.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/EluGrad.ts
deleted file mode 100644
index bf1d21a0b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/EluGrad.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {EluGrad, EluGradInputs, env, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {BinaryOpProgram} from '../binaryop_gpu';
-import {BinaryOpPackedProgram} from '../binaryop_packed_gpu';
-
-const ELU_DER = `return (b >= 0.0) ? a : a * (b + 1.0);`;
-const ELU_DER_PACKED = `
-  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
-  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`;
-
-export const eluGrad =
-    (args: {inputs: EluGradInputs, backend: MathBackendWebGL}): TensorInfo => {
-      const {inputs, backend} = args;
-      const {dy, y} = inputs;
-
-      const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
-          new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) :
-          new BinaryOpProgram(ELU_DER, dy.shape, y.shape);
-      return backend.runWebGLProgram(program, [dy, y], dy.dtype);
-    };
-
-export const eluGradConfig: KernelConfig = {
-  kernelName: EluGrad,
-  backendName: 'webgl',
-  kernelFunc: eluGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Equal.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Equal.ts
deleted file mode 100644
index 3b64ff82d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Equal.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Equal, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {equalImplCPU} from '../kernel_utils/shared';
-const PACKED_EQUAL = `
-  return vec4(equal(a, b));
-`;
-
-const EQUAL = `return float(a == b);`;
-
-export const equal = binaryKernelFunc({
-  opSnippet: EQUAL,
-  packedOpSnippet: PACKED_EQUAL,
-  dtype: 'bool',
-  cpuKernelImpl: equalImplCPU,
-});
-
-export const equalConfig: KernelConfig = {
-  kernelName: Equal,
-  backendName: 'webgl',
-  kernelFunc: equal as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Erf.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Erf.ts
deleted file mode 100644
index d4dc3d779..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Erf.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Erf, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const ERF = `
-  // Error function is calculated approximately with elementary function.
-  // See "Handbook of Mathematical Functions with Formulas,
-  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
-  float p = ${backend_util.ERF_P};
-  float a1 = ${backend_util.ERF_A1};
-  float a2 = ${backend_util.ERF_A2};
-  float a3 = ${backend_util.ERF_A3};
-  float a4 = ${backend_util.ERF_A4};
-  float a5 = ${backend_util.ERF_A5};
-
-  float sign = sign(x);
-  x = abs(x);
-  float t = 1.0 / (1.0 + p * x);
-  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`;
-
-export const erf = unaryKernelFunc({opSnippet: ERF});
-
-export const erfConfig: KernelConfig = {
-  kernelName: Erf,
-  backendName: 'webgl',
-  kernelFunc: erf,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Exp.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Exp.ts
deleted file mode 100644
index 0602e7417..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Exp.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Exp, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {expImplCPU} from '../kernel_utils/shared';
-
-export const EXP = CHECK_NAN_SNIPPET_UNARY + `
-  return exp(x);
-`;
-
-const EXP_PACKED = `
-  vec4 result = exp(x);
-  bvec4 isNaN = isnan(x);
-  result.r = isNaN.r ? x.r : result.r;
-  result.g = isNaN.g ? x.g : result.g;
-  result.b = isNaN.b ? x.b : result.b;
-  result.a = isNaN.a ? x.a : result.a;
-
-  return result;
-`;
-
-export const exp = unaryKernelFunc({
-  opSnippet: EXP,
-  packedOpSnippet: EXP_PACKED,
-  cpuKernelImpl: expImplCPU,
-  dtype: 'float32',
-});
-
-export const expConfig: KernelConfig = {
-  kernelName: Exp,
-  backendName: 'webgl',
-  kernelFunc: exp as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ExpandDims.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ExpandDims.ts
deleted file mode 100644
index 121c97488..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ExpandDims.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ExpandDims, ExpandDimsAttrs, ExpandDimsInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {reshape} from './Reshape';
-
-export function expandDims(args: {
-  inputs: ExpandDimsInputs,
-  attrs: ExpandDimsAttrs,
-  backend: MathBackendWebGL
-}): TensorInfo {
-  const {inputs, attrs, backend} = args;
-  const {dim} = attrs;
-  const {input} = inputs;
-
-  const inputRank = input.shape.length;
-  const newShape = input.shape.slice();
-  let $dim = dim;
-  if (dim < 0) {
-    // Negative value is counted from the tail of rank.
-    util.assert(
-        -(inputRank + 1) <= dim,
-        () => `Axis must be in the interval [${- (inputRank + 1)}, ${
-            inputRank}]`);
-    $dim = inputRank + dim + 1;
-  }
-  newShape.splice($dim, 0, 1);
-
-  return reshape({inputs: {x: input}, backend, attrs: {shape: newShape}});
-}
-
-export const expandDimsConfig: KernelConfig = {
-  kernelName: ExpandDims,
-  backendName: 'webgl',
-  kernelFunc: expandDims as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Expm1.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Expm1.ts
deleted file mode 100644
index 78933ae90..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Expm1.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Expm1, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {expm1ImplCPU} from '../kernel_utils/shared';
-
-const EXPM1 = `return exp(x) - 1.0;`;
-
-export const expm1 = unaryKernelFunc(
-    {opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU});
-
-export const expm1Config: KernelConfig = {
-  kernelName: Expm1,
-  backendName: 'webgl',
-  kernelFunc: expm1 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/FFT.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/FFT.ts
deleted file mode 100644
index 953c1d9e4..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/FFT.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FFT, FFTInputs, KernelConfig, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {fftImpl} from './FFT_impl';
-
-export function fft(args: {inputs: FFTInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-
-  return fftImpl(input, false /* inverse */, backend);
-}
-
-export const fftConfig: KernelConfig = {
-  kernelName: FFT,
-  backendName: 'webgl',
-  kernelFunc: fft
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/FFT_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/FFT_impl.ts
deleted file mode 100644
index e2f042370..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/FFT_impl.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {FFTProgram} from '../fft_gpu';
-
-import {complex} from './Complex';
-import {reshape} from './Reshape';
-
-export function fftImpl(
-    x: TensorInfo, inverse: boolean, backend: MathBackendWebGL): TensorInfo {
-  const xData = backend.texData.get(x.dataId);
-
-  const inputSize = util.sizeFromShape(x.shape);
-  // Collapse all outer dimensions to a single batch dimension.
-  const innerDimensionSize = x.shape[x.shape.length - 1];
-  const batch = inputSize / innerDimensionSize;
-
-  const input2D = reshape(
-      {inputs: {x}, backend, attrs: {shape: [batch, innerDimensionSize]}});
-
-  const xShape = input2D.shape as [number, number];
-  const realProgram = new FFTProgram('real', xShape, inverse);
-  const imagProgram = new FFTProgram('imag', xShape, inverse);
-
-  const inputs = [
-    {
-      dataId: xData.complexTensorInfos.real.dataId,
-      dtype: xData.complexTensorInfos.real.dtype,
-      shape: xShape
-    },
-    {
-      dataId: xData.complexTensorInfos.imag.dataId,
-      dtype: xData.complexTensorInfos.imag.dtype,
-      shape: xShape
-    }
-  ];
-
-  const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');
-  const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');
-
-  const complexOutput =
-      complex({inputs: {real: realPart, imag: imagPart}, backend});
-
-  backend.disposeIntermediateTensorInfo(realPart);
-  backend.disposeIntermediateTensorInfo(imagPart);
-
-  const complexOutputReshaped =
-      reshape({inputs: {x: complexOutput}, backend, attrs: {shape: x.shape}});
-
-  backend.disposeIntermediateTensorInfo(input2D);
-  backend.disposeIntermediateTensorInfo(complexOutput);
-  return complexOutputReshaped;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Fill.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Fill.ts
deleted file mode 100644
index f0d6e6667..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Fill.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Fill, FillAttrs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {FillProgram} from '../fill_gpu';
-
-export function fill(args: {backend: MathBackendWebGL, attrs: FillAttrs}):
-    TensorInfo {
-  const {backend, attrs} = args;
-  const {shape, value} = attrs;
-  let {dtype} = attrs;
-
-  dtype = dtype || util.inferDtype(value);
-
-  if (dtype === 'string') {
-    // String type should be handled in CPU memory.
-    const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));
-    values.fill(value as string);
-    return backend.makeTensorInfo(shape, dtype, values);
-  } else {
-    const program = new FillProgram(shape, value as number);
-    const customValues = [[value as number]];
-    return backend.runWebGLProgram(program, [], dtype, customValues);
-  }
-}
-
-export const fillConfig: KernelConfig = {
-  kernelName: Fill,
-  backendName: 'webgl',
-  kernelFunc: fill as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/FlipLeftRight.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/FlipLeftRight.ts
deleted file mode 100644
index 1c58e3094..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/FlipLeftRight.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tensor4D} from '@tensorflow/tfjs-core';
-import {FlipLeftRight, FlipLeftRightInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {FlipLeftRightProgram} from '../flip_left_right_gpu';
-
-export const flipLeftRightConfig: KernelConfig = {
-  kernelName: FlipLeftRight,
-  backendName: 'webgl',
-  kernelFunc: ({inputs, backend}) => {
-    const {image} = inputs as FlipLeftRightInputs;
-    const webglBackend = backend as MathBackendWebGL;
-
-    const program = new FlipLeftRightProgram((image as Tensor4D).shape);
-    const output = webglBackend.runWebGLProgram(program, [image], image.dtype);
-    return output;
-  }
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Floor.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Floor.ts
deleted file mode 100644
index 92626f694..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Floor.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Floor, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {floorImplCPU} from '../kernel_utils/shared';
-
-const FLOOR = `return floor(x);`;
-
-export const floor = unaryKernelFunc(
-    {opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU});
-
-export const floorConfig: KernelConfig = {
-  kernelName: Floor,
-  backendName: 'webgl',
-  kernelFunc: floor,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/FloorDiv.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/FloorDiv.ts
deleted file mode 100644
index 24c2e3e6a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/FloorDiv.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FloorDiv, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-// We use native integer division to deal with floating point imprecision. Since
-// we implement floor division and glsl implements truncated division, we
-// correct for this by subtracting 1 from result when the result is negative and
-// there is a remainder.
-const INT_DIV = `
-  float s = sign(a) * sign(b);
-  int ia = round(a);
-  int ib = round(b);
-  if (ib != 0) {
-    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
-    return float(idiv(ia, ib, s));
-  } else {
-    return NAN;
-  }
-`;
-
-const INT_DIV_PACKED = `
-  ivec4 ia = round(a);
-  ivec4 ib = round(b);
-  bvec4 cond = notEqual(ib, ivec4(0));
-  ivec4 result = ivec4(0);
-  vec4 s = sign(a) * sign(b);
-
-  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
-  if (cond[0]) {
-    result[0] = idiv(ia[0], ib[0], s[0]);
-  }
-  if (cond[1]) {
-    result[1] = idiv(ia[1], ib[1], s[1]);
-  }
-  if (cond[2]) {
-    result[2] = idiv(ia[2], ib[2], s[2]);
-  }
-  if (cond[3]) {
-    result[3] = idiv(ia[3], ib[3], s[3]);
-  }
-  return vec4(result);
-`;
-
-export const floorDiv = binaryKernelFunc(
-    {opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: 'int32'});
-
-export const floorDivConfig: KernelConfig = {
-  kernelName: FloorDiv,
-  backendName: 'webgl',
-  kernelFunc: floorDiv as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/FromPixels.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/FromPixels.ts
deleted file mode 100644
index b32671dd6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/FromPixels.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-import {FromPixels, FromPixelsAttrs, FromPixelsInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {TextureUsage} from '../tex_util';
-
-import {FromPixelsProgram} from './FromPixels_utils/from_pixels_gpu';
-import {FromPixelsPackedProgram} from './FromPixels_utils/from_pixels_packed_gpu';
-
-export const fromPixelsConfig: KernelConfig = {
-  kernelName: FromPixels,
-  backendName: 'webgl',
-  kernelFunc: fromPixels as unknown as KernelFunc,
-};
-
-let fromPixels2DContext: CanvasRenderingContext2D;
-let willReadFrequently = env().getBool('CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU');
-
-function fromPixels(args: {
-  inputs: FromPixelsInputs,
-  backend: MathBackendWebGL,
-  attrs: FromPixelsAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  let {pixels} = inputs;
-  const {numChannels} = attrs;
-
-  const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&
-      pixels instanceof HTMLVideoElement;
-  const isImage = typeof (HTMLImageElement) !== 'undefined' &&
-      pixels instanceof HTMLImageElement;
-  const [width, height] = isVideo ?
-      [
-        (pixels as HTMLVideoElement).videoWidth,
-        (pixels as HTMLVideoElement).videoHeight
-      ] :
-      [pixels.width, pixels.height];
-
-  const texShape: [number, number] = [height, width];
-  const outShape = [height, width, numChannels];
-
-  if (isImage || isVideo) {
-    const newWillReadFrequently =
-        env().getBool('CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU');
-    if (fromPixels2DContext == null ||
-        newWillReadFrequently !== willReadFrequently) {
-      willReadFrequently = newWillReadFrequently;
-      fromPixels2DContext =
-          document.createElement('canvas').getContext(
-              '2d', {willReadFrequently});
-    }
-
-    fromPixels2DContext.canvas.width = width;
-    fromPixels2DContext.canvas.height = height;
-    fromPixels2DContext.drawImage(
-        pixels as HTMLVideoElement | HTMLImageElement | ImageBitmap, 0, 0,
-        width, height);
-    pixels = fromPixels2DContext.canvas;
-  }
-
-  const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');
-  // This is a byte texture with pixels.
-  backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;
-  backend.gpgpu.uploadPixelDataToTexture(
-      backend.getTexture(tempPixelHandle.dataId), pixels as ImageData);
-  const program = env().getBool('WEBGL_PACK') ?
-      new FromPixelsPackedProgram(outShape) :
-      new FromPixelsProgram(outShape);
-  const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');
-  backend.disposeData(tempPixelHandle.dataId);
-  return res;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/FromPixels_utils/from_pixels_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/FromPixels_utils/from_pixels_gpu.ts
deleted file mode 100644
index 8ffd6dcc3..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/FromPixels_utils/from_pixels_gpu.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getGlslDifferences} from '../../glsl_version';
-import {GPGPUProgram} from '../../gpgpu_math';
-
-export class FromPixelsProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  outputShape: number[];
-
-  constructor(outputShape: number[]) {
-    const glsl = getGlslDifferences();
-    const [height, width, ] = outputShape;
-    this.outputShape = outputShape;
-    this.userCode = `
-      void main() {
-        ivec3 coords = getOutputCoords();
-        int texR = coords[0];
-        int texC = coords[1];
-        int depth = coords[2];
-        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);
-
-        vec4 values = ${glsl.texture2D}(A, uv);
-        float value;
-        if (depth == 0) {
-          value = values.r;
-        } else if (depth == 1) {
-          value = values.g;
-        } else if (depth == 2) {
-          value = values.b;
-        } else if (depth == 3) {
-          value = values.a;
-        }
-
-        setOutput(floor(value * 255.0 + 0.5));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/FromPixels_utils/from_pixels_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/FromPixels_utils/from_pixels_packed_gpu.ts
deleted file mode 100644
index dedff6891..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/FromPixels_utils/from_pixels_packed_gpu.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getGlslDifferences} from '../../glsl_version';
-import {GPGPUProgram} from '../../gpgpu_math';
-
-export class FromPixelsPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  outputShape: number[];
-  packedInputs = false;
-  packedOutput = true;
-
-  constructor(outputShape: number[]) {
-    const glsl = getGlslDifferences();
-    const [height, width, ] = outputShape;
-    this.outputShape = outputShape;
-    this.userCode = `
-      void main() {
-        ivec3 coords = getOutputCoords();
-        int texR = coords[0];
-        int texC = coords[1];
-        int depth = coords[2];
-
-        vec4 result = vec4(0.);
-
-        for(int row=0; row<=1; row++) {
-          for(int col=0; col<=1; col++) {
-            texC = coords[1] + row;
-            depth = coords[2] + col;
-
-            vec2 uv = (vec2(texC, texR) + halfCR) /
-                       vec2(${width}.0, ${height}.0);
-            vec4 values = ${glsl.texture2D}(A, uv);
-            float value;
-            if (depth == 0) {
-              value = values.r;
-            } else if (depth == 1) {
-              value = values.g;
-            } else if (depth == 2) {
-              value = values.b;
-            } else if (depth == 3) {
-              value = values.a;
-            }
-
-            result[row * 2 + col] = floor(value * 255.0 + 0.5);
-          }
-        }
-
-        ${glsl.output} = result;
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/FusedConv2D.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/FusedConv2D.ts
deleted file mode 100644
index a142af98d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/FusedConv2D.ts
+++ /dev/null
@@ -1,165 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, env, FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Conv2DProgram} from '../conv_gpu';
-import {Conv2DPackedProgram} from '../conv_packed_gpu';
-import {mapActivationToShaderProgram} from '../kernel_utils/kernel_funcs_utils';
-
-import {conv2dByMatMul, conv2dWithIm2Row} from './Conv2D_impl';
-import {reshape} from './Reshape';
-
-export function fusedConv2d(args: {
-  inputs: FusedConv2DInputs,
-  attrs: FusedConv2DAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, bias, preluActivationWeights} = inputs;
-  const {
-    strides,
-    pad,
-    dataFormat,
-    dilations,
-    dimRoundingMode,
-    activation,
-    leakyreluAlpha
-  } = attrs;
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, dilations, pad,
-      dimRoundingMode, false /* depthwise */, $dataFormat);
-  let out: TensorInfo;
-  const intermediates: TensorInfo[] = [];
-
-  const hasBias = bias != null;
-  const hasPreluActivationWeights = preluActivationWeights != null;
-  const hasLeakyreluAlpha = activation === 'leakyrelu';
-
-  const prepareInputs = (): TensorInfo[] => {
-    const inputs: TensorInfo[] = [x, filter];
-
-    // If the input is a 1-D tensor, align it with the channels.
-    //
-    // For fusedConv2d, the inputs (x, W, bias, preluActivationWeights) are
-    // supposed to be aligned with the dataFormat. The 4-D tensor inputs or
-    // scalar inputs are originally aligned, but the 1-D tensor inputs are
-    // supposed to be aligned with the channels (only bias and PReLU activation
-    // weights could be a 1-D tensor).
-    const alignInputWithDataFormat =
-        (input: TensorInfo, dataFormat: 'NHWC'|'NCHW'): TensorInfo => {
-          if (dataFormat === 'NCHW' && input.shape.length === 1 &&
-              input.shape[0] !== 1) {
-            const alignedInput = reshape({
-              inputs: {x: input},
-              backend,
-              attrs: {shape: [input.shape[0], 1, 1]}
-            });
-            intermediates.push(alignedInput);
-            return alignedInput;
-          }
-          return input;
-        };
-
-    if (hasBias) {
-      inputs.push(alignInputWithDataFormat(bias, dataFormat));
-    }
-
-    if (hasPreluActivationWeights) {
-      inputs.push(alignInputWithDataFormat(preluActivationWeights, dataFormat));
-    }
-
-    if (hasLeakyreluAlpha) {
-      const $leakyreluAlpha = backend.makeTensorInfo(
-          [], 'float32',
-          util.createScalarValue(leakyreluAlpha as unknown as 'float32', 'float32'));
-      inputs.push($leakyreluAlpha);
-      intermediates.push($leakyreluAlpha);
-    }
-    return inputs;
-  };
-
-  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&
-      convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
-      convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
-      (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {
-    out = conv2dByMatMul({
-      x,
-      filter,
-      convInfo,
-      backend,
-      bias,
-      activation,
-      preluActivationWeights,
-      leakyreluAlpha
-    });
-  } else if (convInfo.strideWidth <= 2 && $dataFormat === 'channelsLast'
-    && env().getBool('WEBGL_EXP_CONV')
-    ) {
-      const fusedActivation =
-          activation ? mapActivationToShaderProgram(activation, true) : null;
-    const program = new Conv2DPackedProgram(
-      convInfo, hasBias, fusedActivation, hasPreluActivationWeights,
-      hasLeakyreluAlpha);
-    const customValues = [
-      [convInfo.padInfo.top, convInfo.padInfo.left],
-      [convInfo.strideHeight, convInfo.strideWidth],
-      [convInfo.dilationHeight, convInfo.dilationWidth],
-      [convInfo.inHeight, convInfo.inWidth]
-    ];
-    const inputs = prepareInputs();
-    out = backend.runWebGLProgram(program, inputs, 'float32', customValues);
-  } else if (env().getBool('WEBGL_CONV_IM2COL')) {
-    out = conv2dWithIm2Row({
-      x,
-      filter,
-      convInfo,
-      backend,
-      bias,
-      activation,
-      preluActivationWeights,
-      leakyreluAlpha
-    });
-  } else {
-    const fusedActivation =
-        activation ? mapActivationToShaderProgram(activation, false) : null;
-    const program = new Conv2DProgram(
-        convInfo, hasBias, fusedActivation, hasPreluActivationWeights,
-        hasLeakyreluAlpha);
-
-    const inputs = prepareInputs();
-    out = backend.runWebGLProgram(program, inputs, 'float32');
-  }
-
-  const outReshaped =
-      reshape({inputs: {x: out}, backend, attrs: {shape: convInfo.outShape}});
-
-  intermediates.push(out);
-  intermediates.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-  return outReshaped;
-}
-
-export const fusedConv2DConfig: KernelConfig = {
-  kernelName: FusedConv2D,
-  backendName: 'webgl',
-  kernelFunc: fusedConv2d as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/FusedDepthwiseConv2D.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/FusedDepthwiseConv2D.ts
deleted file mode 100644
index 0a57fb538..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/FusedDepthwiseConv2D.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, env, FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {DepthwiseConv2DProgram} from '../conv_gpu_depthwise';
-import {DepthwiseConvPacked2DProgram} from '../conv_packed_gpu_depthwise';
-import {mapActivationToShaderProgram} from '../kernel_utils/kernel_funcs_utils';
-
-export function fusedDepthwiseConv2D(args: {
-  inputs: FusedDepthwiseConv2DInputs,
-  attrs: FusedDepthwiseConv2DAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, bias, preluActivationWeights} = inputs;
-  const {strides, pad, dilations, dimRoundingMode, activation, leakyreluAlpha} =
-      attrs;
-
-  const intermediates: TensorInfo[] = [];
-
-  let $dilations = dilations;
-  if ($dilations == null) {
-    $dilations = [1, 1];
-  }
-
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, $dilations),
-      () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +
-          `1. Got strides ${strides} and dilations '${$dilations}'`);
-
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, $dilations,
-      pad, dimRoundingMode, true /* depthwise */);
-
-  const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&
-      convInfo.strideWidth <= 2 &&
-      convInfo.outChannels / convInfo.inChannels === 1;
-  const fusedActivation = activation ?
-      mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :
-      null;
-  const programInputs: TensorInfo[] = [x, filter];
-
-  const hasBias = bias != null;
-  const hasPreluActivationWeights = preluActivationWeights != null;
-  const hasLeakyreluAlpha = activation === 'leakyrelu';
-
-  if (hasBias) {
-    programInputs.push(bias);
-  }
-  if (hasPreluActivationWeights) {
-    programInputs.push(preluActivationWeights);
-  }
-  if (hasLeakyreluAlpha) {
-    const $leakyreluAlpha = backend.makeTensorInfo(
-        [], 'float32',
-        util.createScalarValue(leakyreluAlpha as unknown as 'float32',
-                               'float32'));
-    programInputs.push($leakyreluAlpha);
-    intermediates.push($leakyreluAlpha);
-  }
-
-  let program: DepthwiseConv2DProgram|DepthwiseConvPacked2DProgram;
-  if (shouldPackDepthwiseConv) {
-    program = new DepthwiseConvPacked2DProgram(
-        convInfo, hasBias, fusedActivation, hasPreluActivationWeights,
-        hasLeakyreluAlpha);
-  } else {
-    program = new DepthwiseConv2DProgram(
-        convInfo, hasBias, fusedActivation, hasPreluActivationWeights,
-        hasLeakyreluAlpha);
-  }
-  const customValues = [
-    [convInfo.padInfo.top, convInfo.padInfo.left],
-    [convInfo.strideHeight, convInfo.strideWidth],
-    [convInfo.dilationHeight, convInfo.dilationWidth],
-    [convInfo.inHeight, convInfo.inWidth]
-  ];
-  const result =
-      backend.runWebGLProgram(program, programInputs, 'float32', customValues);
-
-  intermediates.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-  return result;
-}
-
-export const fusedDepthwiseConv2DConfig: KernelConfig = {
-  kernelName: FusedDepthwiseConv2D,
-  backendName: 'webgl',
-  kernelFunc: fusedDepthwiseConv2D as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/GatherNd.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/GatherNd.ts
deleted file mode 100644
index 411b1e3d9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/GatherNd.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, GatherNd, GatherNdInputs, KernelConfig, KernelFunc, Rank, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {GatherNDProgram} from '../gather_nd_gpu';
-import {gatherNdImplCPU} from '../kernel_utils/shared';
-
-import {reshape} from './Reshape';
-
-export function gatherNd(
-    args: {inputs: GatherNdInputs, backend: MathBackendWebGL}): TensorInfo {
-  const {inputs, backend} = args;
-  const {params, indices} = inputs;
-
-  const indicesShape = indices.shape;
-  const sliceRank = indicesShape[indicesShape.length - 1];
-  const paramsSize = util.sizeFromShape(params.shape);
-
-  const [resultShape, numSlices, sliceSize, strides] =
-      backend_util.prepareAndValidate(params, indices);
-
-  const flattenIndices = reshape(
-      {inputs: {x: indices}, backend, attrs: {shape: [numSlices, sliceRank]}});
-  const flattenX = reshape({
-    inputs: {x: params},
-    backend,
-    attrs: {shape: [(util.sizeFromShape(params.shape) / sliceSize), sliceSize]}
-  });
-
-  if (backend.shouldExecuteOnCPU([params, indices]) ||
-      params.dtype === 'string') {
-    const indicesData = backend.readSync(indices.dataId) as TypedArray;
-    const paramsBuf = backend.bufferSync<Rank, 'float32'>(params);
-    const outValue = gatherNdImplCPU(
-        indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize,
-        strides, params.shape, paramsSize);
-
-    return backend.makeTensorInfo(resultShape, params.dtype, outValue.values);
-  }
-  const program =
-      new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize], 
-        params.shape);
-  const res = backend.runWebGLProgram(
-      program, [flattenX, flattenIndices], flattenX.dtype);
-
-  const reshaped =
-      reshape({inputs: {x: res}, backend, attrs: {shape: resultShape}});
-
-  backend.disposeIntermediateTensorInfo(flattenIndices);
-  backend.disposeIntermediateTensorInfo(flattenX);
-  backend.disposeIntermediateTensorInfo(res);
-
-  return reshaped;
-}
-
-export const gatherNdConfig: KernelConfig = {
-  kernelName: GatherNd,
-  backendName: 'webgl',
-  kernelFunc: gatherNd as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/GatherV2.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/GatherV2.ts
deleted file mode 100644
index d29e984a0..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/GatherV2.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, GatherV2, GatherV2Attrs, GatherV2Inputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util, env} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {GatherProgram, GatherShape} from '../gather_gpu';
-import {gatherV2ImplCPU} from '../kernel_utils/shared';
-
-import {reshape} from './Reshape';
-
-export function gatherV2(args: {
-  inputs: GatherV2Inputs,
-  backend: MathBackendWebGL,
-  attrs: GatherV2Attrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, indices} = inputs;
-  const {axis, batchDims} = attrs;
-
-  const parsedAxis = util.parseAxisParam(axis, x.shape)[0];
-  if (env().get('DEBUG')) {
-    // In debug mode, throw error when any index is out of bound.
-    // Otherwise, just fill out of bounds with zeroes.
-    const indicesVals = backend.readSync(indices.dataId) as TypedArray;
-    const axisDim = x.shape[parsedAxis];
-    for (let i = 0; i < indicesVals.length; ++i) {
-      const index = indicesVals[i];
-      util.assert(
-        index <= axisDim - 1 && index >= 0,
-        () =>
-          `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
-    }
-  }
-
-  const shapeInfo = backend_util.segment_util.collectGatherOpShapeInfo(
-      x, indices, parsedAxis, batchDims);
-
-  const indicesSize = util.sizeFromShape(indices.shape);
-
-  const toDispose = [];
-
-  const flattenX = reshape({
-    inputs: {x},
-    backend,
-    attrs: {
-      shape: [
-        shapeInfo.batchSize, shapeInfo.outerSize, shapeInfo.dimSize,
-        shapeInfo.sliceSize
-      ]
-    }
-  });
-
-  const flattenIndex = reshape({
-    inputs: {x: indices},
-    backend,
-    attrs: {shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize]}
-  });
-
-  toDispose.push(flattenX);
-  toDispose.push(flattenIndex);
-
-  const flattenOutputShape = [
-    shapeInfo.batchSize, shapeInfo.outerSize, indicesSize / shapeInfo.batchSize,
-    shapeInfo.sliceSize
-  ];
-
-  if (backend.shouldExecuteOnCPU([x, indices]) || x.dtype === 'string') {
-    const indicesBuf = backend.bufferSync(flattenIndex);
-    const xBuf = backend.bufferSync(flattenX);
-    const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);
-
-    toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-    return backend.makeTensorInfo(
-        shapeInfo.outputShape, outBuf.dtype, outBuf.values as TypedArray);
-  }
-
-  const program = new GatherProgram(flattenX.shape as GatherShape,
-                                    flattenOutputShape as GatherShape);
-  const res = backend.runWebGLProgram(
-      program, [flattenX, flattenIndex], flattenX.dtype);
-  toDispose.push(res);
-
-  const reshaped = reshape(
-      {inputs: {x: res}, backend, attrs: {shape: shapeInfo.outputShape}});
-  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));
-  return reshaped;
-}
-
-export const gatherV2Config: KernelConfig = {
-  kernelName: GatherV2,
-  backendName: 'webgl',
-  kernelFunc: gatherV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Greater.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Greater.ts
deleted file mode 100644
index 7633c54f7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Greater.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Greater, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {greaterImplCPU} from '../kernel_utils/shared';
-
-const GREATER = `return float(a > b);`;
-const GREATER_PACKED = `
-  return vec4(greaterThan(a, b));
-`;
-
-export const greater = binaryKernelFunc({
-  opSnippet: GREATER,
-  packedOpSnippet: GREATER_PACKED,
-  cpuKernelImpl: greaterImplCPU,
-  dtype: 'bool'
-});
-
-export const greaterConfig: KernelConfig = {
-  kernelName: Greater,
-  backendName: 'webgl',
-  kernelFunc: greater as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/GreaterEqual.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/GreaterEqual.ts
deleted file mode 100644
index ecfb8f0ed..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/GreaterEqual.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GreaterEqual, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {greaterEqualImplCPU} from '../kernel_utils/shared';
-
-const GREATER_EQUAL = `return float(a >= b);`;
-const GREATER_EQUAL_PACKED = `
-  return vec4(greaterThanEqual(a, b));
-`;
-
-export const greaterEqual = binaryKernelFunc({
-  opSnippet: GREATER_EQUAL,
-  packedOpSnippet: GREATER_EQUAL_PACKED,
-  dtype: 'bool',
-  cpuKernelImpl: greaterEqualImplCPU
-});
-
-export const greaterEqualConfig: KernelConfig = {
-  kernelName: GreaterEqual,
-  backendName: 'webgl',
-  kernelFunc: greaterEqual as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/IFFT.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/IFFT.ts
deleted file mode 100644
index e5775e785..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/IFFT.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IFFT, IFFTInputs, KernelConfig, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {fftImpl} from './FFT_impl';
-
-export function ifft(args: {inputs: IFFTInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-
-  return fftImpl(input, true /* inverse */, backend);
-}
-
-export const ifftConfig: KernelConfig = {
-  kernelName: IFFT,
-  backendName: 'webgl',
-  kernelFunc: ifft
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Identity.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Identity.ts
deleted file mode 100644
index b3b0b5441..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Identity.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Identity, IdentityInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-export function identity(
-    args: {inputs: IdentityInputs, backend: MathBackendWebGL}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  backend.incRef(x.dataId);
-
-  return {dataId: x.dataId, shape: x.shape, dtype: x.dtype};
-}
-
-export const identityConfig: KernelConfig = {
-  kernelName: Identity,
-  backendName: 'webgl',
-  kernelFunc: identity as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Imag.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Imag.ts
deleted file mode 100644
index 630706da5..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Imag.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Imag, ImagInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {identity} from './Identity';
-
-export function imag(args: {inputs: ImagInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-  const inputData = backend.texData.get(input.dataId);
-
-  return identity({inputs: {x: inputData.complexTensorInfos.imag}, backend});
-}
-
-export const imagConfig: KernelConfig = {
-  kernelName: Imag,
-  backendName: 'webgl',
-  kernelFunc: imag as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/IsFinite.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/IsFinite.ts
deleted file mode 100644
index 348ab22aa..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/IsFinite.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsFinite, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;
-
-export const isFinite = unaryKernelFunc({opSnippet: IS_FINITE, dtype: 'bool'});
-
-export const isFiniteConfig: KernelConfig = {
-  kernelName: IsFinite,
-  backendName: 'webgl',
-  kernelFunc: isFinite,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/IsInf.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/IsInf.ts
deleted file mode 100644
index 2621023cd..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/IsInf.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsInf, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const IS_INF = `return float(isinf(x));`;
-
-export const isInf = unaryKernelFunc({opSnippet: IS_INF, dtype: 'bool'});
-
-export const isInfConfig: KernelConfig = {
-  kernelName: IsInf,
-  backendName: 'webgl',
-  kernelFunc: isInf,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/IsNaN.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/IsNaN.ts
deleted file mode 100644
index e0589fdb0..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/IsNaN.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsNan, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const IS_NAN = `return float(isnan(x));`;
-
-export const isNaN = unaryKernelFunc({opSnippet: IS_NAN, dtype: 'bool'});
-
-export const isNaNConfig: KernelConfig = {
-  kernelName: IsNan,
-  backendName: 'webgl',
-  kernelFunc: isNaN,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/LRN.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/LRN.ts
deleted file mode 100644
index 1e7c5cb3c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/LRN.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, LRN, LRNAttrs, LRNInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {LRNProgram} from '../lrn_gpu';
-import {LRNPackedProgram} from '../lrn_packed_gpu';
-
-export const lrn =
-    (args: {inputs: LRNInputs, backend: MathBackendWebGL, attrs: LRNAttrs}):
-        TensorInfo => {
-          const {inputs, backend, attrs} = args;
-          const {x} = inputs;
-          const {depthRadius, bias, alpha, beta} = attrs;
-
-          const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?
-              new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) :
-              new LRNProgram(x.shape, depthRadius, bias, alpha, beta);
-          return backend.runWebGLProgram(program, [x], x.dtype);
-        };
-
-// tslint:disable-next-line: variable-name
-export const LRNConfig: KernelConfig = {
-  kernelName: LRN,
-  backendName: 'webgl',
-  kernelFunc: lrn as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/LRNGrad.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/LRNGrad.ts
deleted file mode 100644
index 099bcf2a9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/LRNGrad.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LRNGrad, LRNGradAttrs, LRNGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {LRNGradProgram} from '../lrn_grad_gpu';
-
-export const lrnGrad = (args: {
-  inputs: LRNGradInputs,
-  backend: MathBackendWebGL,
-  attrs: LRNGradAttrs
-}): TensorInfo => {
-  const {inputs, backend, attrs} = args;
-  const {x, y, dy} = inputs;
-  const {depthRadius, bias, alpha, beta} = attrs;
-
-  const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);
-  return backend.runWebGLProgram(program, [x, y, dy], x.dtype);
-};
-
-// tslint:disable-next-line: variable-name
-export const LRNGradConfig: KernelConfig = {
-  kernelName: LRNGrad,
-  backendName: 'webgl',
-  kernelFunc: lrnGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/LeakyRelu.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/LeakyRelu.ts
deleted file mode 100644
index 69f1b2700..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/LeakyRelu.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, LeakyRelu, LeakyReluAttrs, LeakyReluInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-import {MathBackendWebGL} from '../backend_webgl';
-import {BinaryOpProgram} from '../binaryop_gpu';
-import {BinaryOpPackedProgram} from '../binaryop_packed_gpu';
-
-export const LEAKYRELU = `return (a < 0.) ? b * a : a;`;
-export const LEAKYRELU_PACKED = `
-  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
-  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;
-
-export function leakyRelu(args: {
-  inputs: LeakyReluInputs,
-  backend: MathBackendWebGL,
-  attrs: LeakyReluAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {alpha} = attrs;
-
-  const $alpha = backend.makeTensorInfo(
-      [], 'float32',
-      util.createScalarValue(alpha as unknown as 'float32', 'float32'));
-
-  const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
-      new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) :
-      new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);
-  const result = backend.runWebGLProgram(program, [x, $alpha], 'float32');
-
-  backend.disposeIntermediateTensorInfo($alpha);
-
-  return result;
-}
-
-export const leakyReluConfig: KernelConfig = {
-  kernelName: LeakyRelu,
-  backendName: 'webgl',
-  kernelFunc: leakyRelu as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Less.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Less.ts
deleted file mode 100644
index a3cc1e828..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Less.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Less} from '@tensorflow/tfjs-core';
-
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {lessImplCPU} from '../kernel_utils/shared';
-
-const LESS = `return float(a < b);`;
-const LESS_PACKED = `
-  return vec4(lessThan(a, b));
-`;
-
-export const less = binaryKernelFunc({
-  opSnippet: LESS,
-  packedOpSnippet: LESS_PACKED,
-  cpuKernelImpl: lessImplCPU,
-  dtype: 'bool'
-});
-
-export const lessConfig: KernelConfig = {
-  kernelName: Less,
-  backendName: 'webgl',
-  kernelFunc: less as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/LessEqual.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/LessEqual.ts
deleted file mode 100644
index ae8d61947..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/LessEqual.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LessEqual} from '@tensorflow/tfjs-core';
-
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {lessEqualImplCPU} from '../kernel_utils/shared';
-
-export const LESS_EQUAL = `return float(a <= b);`;
-export const LESS_EQUAL_PACKED = `
-  return vec4(lessThanEqual(a, b));
-`;
-
-export const lessEqual = binaryKernelFunc({
-  opSnippet: LESS_EQUAL,
-  packedOpSnippet: LESS_EQUAL_PACKED,
-  cpuKernelImpl: lessEqualImplCPU,
-  dtype: 'bool'
-});
-
-export const lessEqualConfig: KernelConfig = {
-  kernelName: LessEqual,
-  backendName: 'webgl',
-  kernelFunc: lessEqual as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/LinSpace.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/LinSpace.ts
deleted file mode 100644
index 759cfae89..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/LinSpace.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LinSpace, LinSpaceAttrs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {linSpaceImplCPU} from '../kernel_utils/shared';
-
-export function linSpace(
-    args: {backend: MathBackendWebGL, attrs: LinSpaceAttrs}): TensorInfo {
-  const {backend, attrs} = args;
-  const {start, stop, num} = attrs;
-
-  // TODO: Use CPU implementation due to the precision problem in Safari.
-  const outVals = linSpaceImplCPU(start, stop, num);
-  return backend.makeTensorInfo([outVals.length], 'float32', outVals);
-}
-
-export const linSpaceConfig: KernelConfig = {
-  kernelName: LinSpace,
-  backendName: 'webgl',
-  kernelFunc: linSpace as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Log.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Log.ts
deleted file mode 100644
index 464aca16e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Log.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Log} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {logImplCPU} from '../kernel_utils/shared';
-
-// Windows chrome return 0 if the input is negative value. We will specifically
-// return NaN if the input is 0 to solve compatiblity issue.
-const LOG = CHECK_NAN_SNIPPET_UNARY + `
-  return x < 0.0 ? 0./0. : log(x);
-`;
-
-const LOG_PACKED = `
-  vec4 result = log(x);
-  bvec4 isNaN = isnan(x);
-  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
-  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
-  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
-  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
-  return result;
-`;
-
-export const log = unaryKernelFunc(
-    {opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU});
-
-export const logConfig: KernelConfig = {
-  kernelName: Log,
-  backendName: 'webgl',
-  kernelFunc: log as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Log1p.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Log1p.ts
deleted file mode 100644
index 0cae2831c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Log1p.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Log1p} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const LOG1P = CHECK_NAN_SNIPPET_UNARY + `
-  return log(1.0 + x);
-`;
-
-export const log1p = unaryKernelFunc({opSnippet: LOG1P});
-
-export const log1pConfig: KernelConfig = {
-  kernelName: Log1p,
-  backendName: 'webgl',
-  kernelFunc: log1p,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/LogicalAnd.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/LogicalAnd.ts
deleted file mode 100644
index ef721ce6d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/LogicalAnd.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LogicalAnd} from '@tensorflow/tfjs-core';
-
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;
-const LOGICAL_AND_PACKED = `
-  return vec4(
-    vec4(greaterThanEqual(a, vec4(1.0))) *
-    vec4(greaterThanEqual(b, vec4(1.0))));
-`;
-
-export const logicalAnd = binaryKernelFunc({
-  opSnippet: LOGICAL_AND,
-  packedOpSnippet: LOGICAL_AND_PACKED,
-  dtype: 'bool'
-});
-
-export const logicalAndConfig: KernelConfig = {
-  kernelName: LogicalAnd,
-  backendName: 'webgl',
-  kernelFunc: logicalAnd as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/LogicalNot.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/LogicalNot.ts
deleted file mode 100644
index 8aab1eaa1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/LogicalNot.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalNot} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const LOGICAL_NOT = `return float(!(x >= 1.0));`;
-
-export const logicalNot = unaryKernelFunc({opSnippet: LOGICAL_NOT});
-
-export const logicalNotConfig: KernelConfig = {
-  kernelName: LogicalNot,
-  backendName: 'webgl',
-  kernelFunc: logicalNot,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/LogicalOr.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/LogicalOr.ts
deleted file mode 100644
index a8832c80a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/LogicalOr.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LogicalOr} from '@tensorflow/tfjs-core';
-
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;
-const LOGICAL_OR_PACKED = `
-  return min(
-    vec4(greaterThanEqual(a, vec4(1.0))) +
-    vec4(greaterThanEqual(b, vec4(1.0))),
-    vec4(1.0));
-`;
-
-export const logicalOr = binaryKernelFunc(
-    {opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: 'bool'});
-
-export const logicalOrConfig: KernelConfig = {
-  kernelName: LogicalOr,
-  backendName: 'webgl',
-  kernelFunc: logicalOr as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Max.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Max.ts
deleted file mode 100644
index 100bedeca..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Max.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelFunc, Max, MaxAttrs, MaxInputs, TensorInfo} from '@tensorflow/tfjs-core';
-import {backend_util, KernelConfig, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {maxImplCPU} from '../kernel_utils/shared';
-
-import {maxImpl} from './Max_impl';
-import {transposeImpl, transposeImplCPU} from './Transpose_impl';
-
-export function max(
-    args: {inputs: MaxInputs, backend: MathBackendWebGL, attrs: MaxAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {reductionIndices, keepDims} = attrs;
-
-  const xRank = x.shape.length;
-
-  const origAxes = util.parseAxisParam(reductionIndices, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-  const maxInputIsTransposed = permutedAxes != null;
-  const shouldExecuteOnCPU = backend.shouldExecuteOnCPU([x]);
-
-  let maxInput = x;
-  if (maxInputIsTransposed) {
-    if (shouldExecuteOnCPU) {
-      const xTexData = backend.texData.get(maxInput.dataId);
-      const values = xTexData.values as TypedArray;
-
-      const newShape: number[] = new Array(xRank);
-      for (let i = 0; i < newShape.length; i++) {
-        newShape[i] = x.shape[permutedAxes[i]];
-      }
-      const maxInputValues =
-          transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
-
-      maxInput = backend.makeTensorInfo(newShape, x.dtype);
-      const maxInputData = backend.texData.get(maxInput.dataId);
-      maxInputData.values = maxInputValues;
-    } else {
-      maxInput = transposeImpl(x, permutedAxes, backend);
-    }
-
-    axes = backend_util.getInnerMostAxes(axes.length, xRank);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('max', axes, xRank);
-  const [maxOutShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(maxInput.shape, axes);
-
-  let outShape = maxOutShape;
-  if (keepDims) {
-    // rather than reshape at the end, set the target shape here.
-    outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);
-  }
-
-  let out;
-  if (shouldExecuteOnCPU) {
-    const xTexData = backend.texData.get(maxInput.dataId);
-    const values = xTexData.values as TypedArray;
-
-    const outValues =
-        maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);
-
-    out = backend.makeTensorInfo(outShape, x.dtype);
-    const outData = backend.texData.get(out.dataId);
-    outData.values = outValues;
-  } else {
-    out = maxImpl(maxInput, reduceShape, outShape, backend);
-  }
-
-  if (maxInputIsTransposed) {
-    backend.disposeIntermediateTensorInfo(maxInput);
-  }
-
-  return out;
-}
-
-export const maxConfig: KernelConfig = {
-  kernelName: Max,
-  backendName: 'webgl',
-  kernelFunc: max as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPool.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPool.ts
deleted file mode 100644
index ca029ecf7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPool.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, KernelConfig, KernelFunc, MaxPool, MaxPoolAttrs, MaxPoolInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Pool2DProgram} from '../pool_gpu';
-import {assertNotComplex} from '../webgl_util';
-import {identity} from './Identity';
-
-export function maxPool(args: {
-  inputs: MaxPoolInputs,
-  backend: MathBackendWebGL,
-  attrs: MaxPoolAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  assertNotComplex(x, 'maxPool');
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const dilations = 1;
-
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in maxPool: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode);
-  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
-      util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
-    return identity({inputs: {x}, backend});
-  }
-  const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);
-  return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);
-}
-
-export const maxPoolConfig: KernelConfig = {
-  kernelName: MaxPool,
-  backendName: 'webgl',
-  kernelFunc: maxPool as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPool3D.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPool3D.ts
deleted file mode 100644
index f7ad7eccc..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPool3D.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, KernelConfig, KernelFunc, MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Pool3DProgram} from '../pool_gpu';
-
-export function maxPool3d(args: {
-  inputs: MaxPool3DInputs,
-  backend: MathBackendWebGL,
-  attrs: MaxPool3DAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dataFormat, dimRoundingMode} = attrs;
-  const dilations: [number, number, number] = [1, 1, 1];
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode, dataFormat);
-  const maxPoolProgram = new Pool3DProgram(convInfo, 'max', false);
-  return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);
-}
-
-export const maxPool3DConfig: KernelConfig = {
-  kernelName: MaxPool3D,
-  backendName: 'webgl',
-  kernelFunc: maxPool3d as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPool3DGrad.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPool3DGrad.ts
deleted file mode 100644
index f7f83761c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPool3DGrad.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, KernelConfig, KernelFunc, MaxPool3DGrad, MaxPool3DGradAttrs, MaxPool3DGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {MaxPool3DBackpropProgram} from '../max_pool_backprop_gpu';
-import {Pool3DProgram} from '../pool_gpu';
-
-export function maxPool3DGrad(args: {
-  inputs: MaxPool3DGradInputs,
-  backend: MathBackendWebGL,
-  attrs: MaxPool3DGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const x = input;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const dilations: [number, number, number] = [1, 1, 1];
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode);
-
-  const maxPool3dPositionsProgram =
-      new Pool3DProgram(convInfo, 'max', true /* get positions */);
-  const maxPool3dPositions =
-      backend.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);
-  const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);
-  const result = backend.runWebGLProgram(
-      maxPoolBackpropProgram, [dy, maxPool3dPositions], x.dtype);
-  backend.disposeIntermediateTensorInfo(maxPool3dPositions);
-  return result;
-}
-
-export const maxPool3DGradConfig: KernelConfig = {
-  kernelName: MaxPool3DGrad,
-  backendName: 'webgl',
-  kernelFunc: maxPool3DGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPoolGrad.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPoolGrad.ts
deleted file mode 100644
index e8bf41f66..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPoolGrad.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, KernelConfig, KernelFunc, MaxPoolGrad, MaxPoolGradAttrs, MaxPoolGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {MaxPool2DBackpropProgram} from '../max_pool_backprop_gpu';
-import {Pool2DProgram} from '../pool_gpu';
-import {assertNotComplex} from '../webgl_util';
-
-export function maxPoolGrad(args: {
-  inputs: MaxPoolGradInputs,
-  backend: MathBackendWebGL,
-  attrs: MaxPoolGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input, output} = inputs;
-  const x = input;
-  assertNotComplex([input, output], 'maxPoolGrad');
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      1 /* dilations */, pad, dimRoundingMode);
-  const getPositions = true;
-  const maxPoolPositionsProgram =
-      new Pool2DProgram(convInfo, 'max', getPositions);
-  const maxPoolPositions: TensorInfo =
-      backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);
-
-  const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);
-  const result = backend.runWebGLProgram(
-      maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);
-  backend.disposeIntermediateTensorInfo(maxPoolPositions);
-  return result;
-}
-
-export const maxPoolGradConfig: KernelConfig = {
-  kernelName: MaxPoolGrad,
-  backendName: 'webgl',
-  kernelFunc: maxPoolGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPoolWithArgmax.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPoolWithArgmax.ts
deleted file mode 100644
index 9012c1d0d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPoolWithArgmax.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs} from '@tensorflow/tfjs-core';
-import {backend_util, KernelConfig, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {maxPoolWithArgmaxImpl} from './MaxPoolWithArgmax_impl';
-
-export const maxPoolWithArgmaxConfig: KernelConfig = {
-  kernelName: MaxPoolWithArgmax,
-  backendName: 'webgl',
-  kernelFunc: ({inputs, attrs, backend}) => {
-    const {x} = inputs as MaxPoolWithArgmaxInputs;
-    const {filterSize, strides, pad, includeBatchInIndex} =
-        attrs as unknown as MaxPoolWithArgmaxAttrs;
-    const webglBackend = backend as MathBackendWebGL;
-
-    util.assert(
-        x.shape.length === 4,
-        () => `Error in maxPool: input must be rank 4 but got rank ${
-            x.shape.length}.`);
-    const dilations: [number, number] = [1, 1];
-    util.assert(
-        backend_util.eitherStridesOrDilationsAreOne(strides, dilations),
-        () => 'Error in maxPool: Either strides or dilations must be 1. ' +
-            `Got strides ${strides} and dilations '${dilations}'`);
-
-    const convInfo = backend_util.computePool2DInfo(
-        x.shape as [number, number, number, number], filterSize, strides,
-        dilations, pad);
-
-    const [result, indexes] =
-        maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);
-    return [result, indexes];
-  }
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPoolWithArgmax_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPoolWithArgmax_impl.ts
deleted file mode 100644
index b6fbf5bff..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/MaxPoolWithArgmax_impl.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {Pool2DProgram} from '../pool_gpu';
-
-export function maxPoolWithArgmaxImpl(
-    x: TensorInfo, includeBatchInIndex: boolean,
-    convInfo: backend_util.Conv2DInfo,
-    backend: MathBackendWebGL): TensorInfo[] {
-  let program = new Pool2DProgram(convInfo, 'max', false);
-  const poolOutput = backend.runWebGLProgram(program, [x], 'float32');
-
-  program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);
-  const indexOutput = backend.runWebGLProgram(program, [x], 'float32');
-  return [poolOutput, indexOutput];
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Max_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Max_impl.ts
deleted file mode 100644
index d71086efd..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Max_impl.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {reduce} from '../kernel_utils/reduce';
-import {reshape} from '../kernels/Reshape';
-
-export function maxImpl(
-    x: TensorInfo, reduceShape: number[], outShape: number[],
-    backend: MathBackendWebGL): TensorInfo {
-  const inSize = util.sizeFromShape(reduceShape);
-  const xSize = util.sizeFromShape(x.shape);
-  const batchSize = xSize / inSize;
-  const reshapedInput =
-      reshape({inputs: {x}, attrs: {shape: [batchSize, inSize]}, backend});
-
-  const reduced = reduce(reshapedInput, x.dtype, 'max', backend);
-  const reshapedOutput =
-      reshape({inputs: {x: reduced}, attrs: {shape: outShape}, backend});
-
-  backend.disposeIntermediateTensorInfo(reshapedInput);
-  backend.disposeIntermediateTensorInfo(reduced);
-
-  return reshapedOutput;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Max_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Max_test.ts
deleted file mode 100644
index e323b3c8f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Max_test.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-describeWithFlags('Max', ALL_ENVS, () => {
-  it('does not have memory leak when calling reduce multiple times.',
-     async () => {
-       const beforeDataIds = tf.engine().backend.numDataIds();
-
-       // Input must be large enough to trigger multi-stage reduction.
-       const x = tf.ones([100, 100]);
-       const xMax = x.max();
-
-       const afterResDataIds = tf.engine().backend.numDataIds();
-       expect(afterResDataIds).toEqual(beforeDataIds + 2);
-
-       x.dispose();
-       xMax.dispose();
-
-       const afterDisposeDataIds = tf.engine().backend.numDataIds();
-       expect(afterDisposeDataIds).toEqual(beforeDataIds);
-     });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Maximum.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Maximum.ts
deleted file mode 100644
index 5c0176ba4..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Maximum.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Maximum} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET} from '../binaryop_gpu';
-import {CHECK_NAN_SNIPPET_PACKED} from '../binaryop_packed_gpu';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {maximumImplCPU} from '../kernel_utils/shared';
-
-const MAXIMUM = CHECK_NAN_SNIPPET + `
-  return max(a, b);
-`;
-
-const MAXIMUM_PACKED = `
-  vec4 result = vec4(max(a, b));
-  bvec4 isNaNA = isnan(a);
-  bvec4 isNaNB = isnan(b);
-  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
-  ` +
-    CHECK_NAN_SNIPPET_PACKED + `
-  return result;
-`;
-
-export const maximum = binaryKernelFunc({
-  opSnippet: MAXIMUM,
-  packedOpSnippet: MAXIMUM_PACKED,
-  cpuKernelImpl: maximumImplCPU
-});
-
-export const maximumConfig: KernelConfig = {
-  kernelName: Maximum,
-  backendName: 'webgl',
-  kernelFunc: maximum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Mean.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Mean.ts
deleted file mode 100644
index 9b0df6680..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Mean.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Mean, MeanAttrs, MeanInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {meanImpl} from './Mean_impl';
-import {transposeImpl, transposeImplCPU} from './Transpose_impl';
-
-export const meanConfig: KernelConfig = {
-  kernelName: Mean,
-  backendName: 'webgl',
-  kernelFunc: ({inputs, attrs, backend}) => {
-    const {x} = inputs as MeanInputs;
-    const {keepDims, axis} = attrs as unknown as MeanAttrs;
-    const webglBackend = backend as MathBackendWebGL;
-
-    const xRank = x.shape.length;
-    const origAxes = util.parseAxisParam(axis, x.shape);
-
-    let axes = origAxes;
-    const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-    const meanInputIsTransposed = permutedAxes != null;
-    const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);
-
-    const intermediates: TensorInfo[] = [];
-
-    let meanInput = x;
-    if (meanInputIsTransposed) {
-      if (shouldExecuteOnCPU) {
-        const xTexData = webglBackend.texData.get(meanInput.dataId);
-        const values = xTexData.values as TypedArray;
-
-        const newShape: number[] = new Array(xRank);
-        for (let i = 0; i < newShape.length; i++) {
-          newShape[i] = x.shape[permutedAxes[i]];
-        }
-        const meanInputValues =
-            transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
-
-        meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
-        const meanInputData = webglBackend.texData.get(meanInput.dataId);
-        meanInputData.values = meanInputValues;
-      } else {
-        meanInput = transposeImpl(x, permutedAxes, webglBackend);
-      }
-
-      intermediates.push(meanInput);
-      axes = backend_util.getInnerMostAxes(axes.length, xRank);
-    }
-
-    backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);
-    const [meanOutShape, reduceShape] =
-        backend_util.computeOutAndReduceShapes(meanInput.shape, axes);
-
-    let outShape = meanOutShape;
-    if (keepDims) {
-      // rather than reshape at the end, set the target shape here.
-      outShape = backend_util.expandShapeToKeepDim(meanOutShape, origAxes);
-    }
-
-    const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);
-    for (const i of intermediates) {
-      webglBackend.disposeIntermediateTensorInfo(i);
-    }
-
-    return out;
-  }
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Mean_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Mean_impl.ts
deleted file mode 100644
index bc31918fb..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Mean_impl.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {reduce} from '../kernel_utils/reduce';
-import {reshape} from '../kernels/Reshape';
-
-export function meanImpl(
-    x: TensorInfo, reduceShape: number[], outShape: number[],
-    backend: MathBackendWebGL): TensorInfo {
-  const inSize = util.sizeFromShape(reduceShape);
-  const xSize = util.sizeFromShape(x.shape);
-  const batchSize = xSize / inSize;
-  const reshapedInput =
-      reshape({inputs: {x}, attrs: {shape: [batchSize, inSize]}, backend});
-
-  const reduced = reduce(reshapedInput, 'float32', 'mean', backend);
-  const reshapedOutput =
-      reshape({inputs: {x: reduced}, attrs: {shape: outShape}, backend});
-
-  backend.disposeIntermediateTensorInfo(reshapedInput);
-  backend.disposeIntermediateTensorInfo(reduced);
-
-  return reshapedOutput;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Mean_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Mean_test.ts
deleted file mode 100644
index d2cd2f1e2..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Mean_test.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-
-const {expectArraysClose} = test_util;
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-describeWithFlags('Mean.', ALL_ENVS, () => {
-  it('does not have memory leak and works for large dimensions.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const a = tf.ones([1, 70000]);
-    const r = tf.mean(a);
-
-    expect(r.dtype).toBe('float32');
-    expectArraysClose(await r.data(), 1);
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 2);
-
-    a.dispose();
-    r.dispose();
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Min.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Min.ts
deleted file mode 100644
index c79e000e1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Min.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Min, MinAttrs, MinInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {reduce} from '../kernel_utils/reduce';
-
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function min(
-    args: {inputs: MinInputs, backend: MathBackendWebGL, attrs: MinAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  const xRank = x.shape.length;
-
-  const origAxes = util.parseAxisParam(axis, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-  let permutedX = x;
-  if (permutedAxes != null) {
-    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    axes = backend_util.getInnerMostAxes(axes.length, x.shape.length);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('min', axes, xRank);
-  const [outShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(permutedX.shape, axes);
-  const inSize = util.sizeFromShape(reduceShape);
-  const a2D =
-      reshape({inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});
-  const reduced = reduce(a2D, a2D.dtype, 'min', backend);
-
-  let res;
-  if (keepDims) {
-    const newShape = backend_util.expandShapeToKeepDim(outShape, origAxes);
-    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: newShape}});
-  } else {
-    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});
-  }
-
-  backend.disposeIntermediateTensorInfo(a2D);
-  backend.disposeIntermediateTensorInfo(reduced);
-
-  if (permutedAxes != null) {
-    backend.disposeIntermediateTensorInfo(permutedX);
-  }
-
-  return res;
-}
-
-export const minConfig: KernelConfig = {
-  kernelName: Min,
-  backendName: 'webgl',
-  kernelFunc: min as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Minimum.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Minimum.ts
deleted file mode 100644
index d503982a6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Minimum.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Minimum} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET} from '../binaryop_gpu';
-import {CHECK_NAN_SNIPPET_PACKED} from '../binaryop_packed_gpu';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {minimumImplCPU} from '../kernel_utils/shared';
-
-const MINIMUM = CHECK_NAN_SNIPPET + `
-  return min(a, b);
-`;
-
-const MINIMUM_PACKED = `
-  vec4 result = vec4(min(a, b));
-  bvec4 isNaNA = isnan(a);
-  bvec4 isNaNB = isnan(b);
-  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
-  ` +
-    CHECK_NAN_SNIPPET_PACKED + `
-  return result;
-`;
-
-export const minimum = binaryKernelFunc({
-  opSnippet: MINIMUM,
-  packedOpSnippet: MINIMUM_PACKED,
-  cpuKernelImpl: minimumImplCPU
-});
-
-export const minimumConfig: KernelConfig = {
-  kernelName: Minimum,
-  backendName: 'webgl',
-  kernelFunc: minimum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/MirrorPad.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/MirrorPad.ts
deleted file mode 100644
index 2c6ac647c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/MirrorPad.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, MirrorPad, MirrorPadAttrs, MirrorPadInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {MirrorPadProgram} from '../mirror_pad_gpu';
-import {MirrorPadPackedProgram} from '../mirror_pad_packed_gpu';
-
-export const mirrorPadKernelFunc: (params: {
-  inputs: MirrorPadInputs,
-  backend: MathBackendWebGL,
-  attrs: MirrorPadAttrs
-}) => TensorInfo = ({inputs, backend, attrs}) => {
-  const {x} = inputs;
-  const {paddings, mode} = attrs;
-
-  const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
-      new MirrorPadPackedProgram(x.shape, paddings, mode) :
-      new MirrorPadProgram(x.shape, paddings, mode);
-
-  const output = backend.runWebGLProgram(program, [x], x.dtype);
-
-  return output;
-};
-
-export const mirrorPadConfig: KernelConfig = {
-  kernelName: MirrorPad,
-  backendName: 'webgl',
-  kernelFunc: mirrorPadKernelFunc as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Mod.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Mod.ts
deleted file mode 100644
index c0ebda2f8..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Mod.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Mod} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET_PACKED} from '../binaryop_packed_gpu';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const MOD = `if (b == 0.0) return NAN;
-  return mod(a, b);`;
-
-const MOD_PACKED = `
-  vec4 result = mod(a, b);
-  bvec4 isNaN = equal(b, vec4(0.0));
-  ` +
-    CHECK_NAN_SNIPPET_PACKED + `
-  return result;
-`;
-
-export const mod = binaryKernelFunc({
-  opSnippet: MOD,
-  packedOpSnippet: MOD_PACKED,
-});
-
-export const modConfig: KernelConfig = {
-  kernelName: Mod,
-  backendName: 'webgl',
-  kernelFunc: mod as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Multinomial.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Multinomial.ts
deleted file mode 100644
index 160241d00..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Multinomial.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Multinomial, MultinomialAttrs, MultinomialInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {MultinomialProgram} from '../multinomial_gpu';
-
-import {softmax} from './Softmax';
-
-export function multinomial(args: {
-  inputs: MultinomialInputs,
-  backend: MathBackendWebGL,
-  attrs: MultinomialAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {logits} = inputs;
-  const {numSamples, seed, normalized} = attrs;
-
-  const probs = normalized ?
-      logits :
-      softmax(
-          {inputs: {logits}, backend, attrs: {dim: logits.shape.length - 1}});
-  const batchSize = probs.shape[0];
-  const numOutcomes = probs.shape[1];
-  const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);
-  const customValues = [[seed]];
-  const res = backend.runWebGLProgram(program, [probs], 'int32', customValues);
-  if (!normalized) {
-    backend.disposeIntermediateTensorInfo(probs);
-  }
-  return res;
-}
-
-export const multinomialConfig: KernelConfig = {
-  kernelName: Multinomial,
-  backendName: 'webgl',
-  kernelFunc: multinomial as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Multiply.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Multiply.ts
deleted file mode 100644
index ec5b94da1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Multiply.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BinaryInputs, env, KernelConfig, Multiply, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import * as binaryop_complex_gpu from '../binaryop_complex_gpu';
-import {BinaryOpComplexProgram} from '../binaryop_complex_gpu';
-import {BinaryOpProgram} from '../binaryop_gpu';
-import {BinaryOpPackedProgram} from '../binaryop_packed_gpu';
-import {multiplyImplCPU as cpuMultiply} from '../kernel_utils/shared';
-
-import {complex} from './Complex';
-
-const MUL = 'return a * b;';
-
-export function multiply(
-    args: {inputs: BinaryInputs, backend: MathBackendWebGL}): TensorInfo {
-  const {inputs, backend} = args;
-  const {a, b} = inputs;
-  const dtype = backend_util.upcastType(a.dtype, b.dtype);
-
-  if (a.dtype === 'complex64') {
-    const aData = backend.texData.get(a.dataId);
-    const bData = backend.texData.get(b.dataId);
-
-    const realProgram = new BinaryOpComplexProgram(
-        binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);
-    const imagProgram = new BinaryOpComplexProgram(
-        binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);
-
-    const inputs = [
-      {
-        dataId: aData.complexTensorInfos.real.dataId,
-        dtype: aData.complexTensorInfos.real.dtype,
-        shape: a.shape
-      },
-      {
-        dataId: aData.complexTensorInfos.imag.dataId,
-        dtype: aData.complexTensorInfos.imag.dtype,
-        shape: a.shape
-      },
-      {
-        dataId: bData.complexTensorInfos.real.dataId,
-        dtype: bData.complexTensorInfos.real.dtype,
-        shape: b.shape
-      },
-      {
-        dataId: bData.complexTensorInfos.imag.dataId,
-        dtype: bData.complexTensorInfos.imag.dtype,
-        shape: b.shape
-      }
-    ];
-
-    const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');
-    const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');
-
-    const complexOutput =
-        complex({inputs: {real: realPart, imag: imagPart}, backend});
-
-    backend.disposeIntermediateTensorInfo(realPart);
-    backend.disposeIntermediateTensorInfo(imagPart);
-
-    // TODO(annxingyuan): CPU forwarding for complex inputs.
-    return complexOutput;
-  }
-
-  if (backend.shouldExecuteOnCPU([a, b])) {
-    const aData = backend.texData.get(a.dataId);
-    const bData = backend.texData.get(b.dataId);
-    const [outValues, outShape] = cpuMultiply(
-        a.shape, b.shape, aData.values as TypedArray,
-        bData.values as TypedArray, dtype);
-
-    const out = backend.makeTensorInfo(outShape, dtype);
-    const outData = backend.texData.get(out.dataId);
-    outData.values = outValues;
-    return out;
-  }
-
-  let program: BinaryOpProgram|BinaryOpPackedProgram;
-  if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
-    program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);
-  } else {
-    program = new BinaryOpProgram(MUL, a.shape, b.shape);
-  }
-
-  return backend.runWebGLProgram(program, [a, b], dtype);
-}
-
-export const multiplyConfig: KernelConfig = {
-  kernelName: Multiply,
-  backendName: 'webgl',
-  kernelFunc: multiply
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Neg.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Neg.ts
deleted file mode 100644
index 9d24ec27b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Neg.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, Neg, NegInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {negImplCPU} from '../kernel_utils/shared';
-import {CHECK_NAN_SNIPPET, UnaryOpProgram} from '../unaryop_gpu';
-import {UnaryOpPackedProgram} from '../unaryop_packed_gpu';
-
-const NEG = CHECK_NAN_SNIPPET + `
-  return -x;
-`;
-
-const NEG_PACKED = `
-  vec4 result = -x;
-  bvec4 isNaN = isnan(x);
-
-  result.r = isNaN.r ? x.r : result.r;
-  result.g = isNaN.g ? x.g : result.g;
-  result.b = isNaN.b ? x.b : result.b;
-  result.a = isNaN.a ? x.a : result.a;
-
-  return result;
-`;
-
-// This doesn't use unaryKernelFunc because negImplCPU is not of type
-// SimpleUnaryKernelImplCPU.
-export function neg(args: {inputs: NegInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  if (backend.shouldExecuteOnCPU([x])) {
-    const xData = backend.texData.get(x.dataId);
-    const [outValues, newShape] =
-        negImplCPU(xData.values as TypedArray, x.shape, x.dtype);
-    return backend.makeTensorInfo(newShape, x.dtype, outValues);
-  }
-
-  let program: UnaryOpProgram|UnaryOpPackedProgram;
-  if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
-    program = new UnaryOpPackedProgram(x.shape, NEG_PACKED);
-  } else {
-    program = new UnaryOpProgram(x.shape, NEG);
-  }
-
-  return backend.runWebGLProgram(program, [x], x.dtype);
-}
-
-export const negConfig: KernelConfig = {
-  kernelName: Neg,
-  backendName: 'webgl',
-  kernelFunc: neg as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/NonMaxSuppressionV3.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/NonMaxSuppressionV3.ts
deleted file mode 100644
index 8dc607551..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/NonMaxSuppressionV3.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV3, NonMaxSuppressionV3Attrs, NonMaxSuppressionV3Inputs, TypedArray} from '@tensorflow/tfjs-core';
-
-const nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;
-import {MathBackendWebGL} from '../backend_webgl';
-
-export function nonMaxSuppressionV3(args: {
-  inputs: NonMaxSuppressionV3Inputs,
-  backend: MathBackendWebGL,
-  attrs: NonMaxSuppressionV3Attrs
-}) {
-  backend_util.warn(
-      'tf.nonMaxSuppression() in webgl locks the UI thread. ' +
-      'Call tf.nonMaxSuppressionAsync() instead');
-
-  const {inputs, backend, attrs} = args;
-  const {boxes, scores} = inputs;
-  const {maxOutputSize, iouThreshold, scoreThreshold} = attrs;
-
-  const boxesVals = backend.readSync(boxes.dataId) as TypedArray;
-  const scoresVals = backend.readSync(scores.dataId) as TypedArray;
-
-  const {selectedIndices} = nonMaxSuppressionV3Impl(
-      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
-
-  return backend.makeTensorInfo(
-      [selectedIndices.length], 'int32', new Int32Array(selectedIndices));
-}
-
-export const nonMaxSuppressionV3Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV3,
-  backendName: 'webgl',
-  kernelFunc: nonMaxSuppressionV3 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/NonMaxSuppressionV4.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/NonMaxSuppressionV4.ts
deleted file mode 100644
index 5b537b457..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/NonMaxSuppressionV4.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-const nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-export function nonMaxSuppressionV4(args: {
-  inputs: NonMaxSuppressionV4Inputs,
-  backend: MathBackendWebGL,
-  attrs: NonMaxSuppressionV4Attrs
-}): [TensorInfo, TensorInfo] {
-  backend_util.warn(
-      'tf.nonMaxSuppression() in webgl locks the UI thread. ' +
-      'Call tf.nonMaxSuppressionAsync() instead');
-
-  const {inputs, backend, attrs} = args;
-  const {boxes, scores} = inputs;
-  const {maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize} =
-      attrs;
-
-  const boxesVals = backend.readSync(boxes.dataId) as TypedArray;
-  const scoresVals = backend.readSync(scores.dataId) as TypedArray;
-
-  const {selectedIndices, validOutputs} = nonMaxSuppressionV4Impl(
-      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold,
-      padToMaxOutputSize);
-
-  return [
-    backend.makeTensorInfo(
-        [selectedIndices.length], 'int32', new Int32Array(selectedIndices)),
-    backend.makeTensorInfo([], 'int32', new Int32Array([validOutputs]))
-  ];
-}
-
-export const nonMaxSuppressionV4Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV4,
-  backendName: 'webgl',
-  kernelFunc: nonMaxSuppressionV4 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/NonMaxSuppressionV5.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/NonMaxSuppressionV5.ts
deleted file mode 100644
index ab947502d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/NonMaxSuppressionV5.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV5, NonMaxSuppressionV5Attrs, NonMaxSuppressionV5Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-const nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;
-import {MathBackendWebGL} from '../backend_webgl';
-
-export function nonMaxSuppressionV5(args: {
-  inputs: NonMaxSuppressionV5Inputs,
-  backend: MathBackendWebGL,
-  attrs: NonMaxSuppressionV5Attrs
-}): [TensorInfo, TensorInfo] {
-  backend_util.warn(
-      'tf.nonMaxSuppression() in webgl locks the UI thread. ' +
-      'Call tf.nonMaxSuppressionAsync() instead');
-
-  const {inputs, backend, attrs} = args;
-  const {boxes, scores} = inputs;
-  const {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} = attrs;
-
-  const boxesVals = backend.readSync(boxes.dataId) as TypedArray;
-  const scoresVals = backend.readSync(scores.dataId) as TypedArray;
-
-  const maxOutputSizeVal = maxOutputSize;
-  const iouThresholdVal = iouThreshold;
-  const scoreThresholdVal = scoreThreshold;
-  const softNmsSigmaVal = softNmsSigma;
-
-  const {selectedIndices, selectedScores} = nonMaxSuppressionV5Impl(
-      boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal,
-      scoreThresholdVal, softNmsSigmaVal);
-
-  return [
-    backend.makeTensorInfo(
-        [selectedIndices.length], 'int32', new Int32Array(selectedIndices)),
-    backend.makeTensorInfo(
-        [selectedScores.length], 'float32', new Float32Array(selectedScores))
-  ];
-}
-
-export const nonMaxSuppressionV5Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV5,
-  backendName: 'webgl',
-  kernelFunc: nonMaxSuppressionV5 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/NotEqual.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/NotEqual.ts
deleted file mode 100644
index 283bde4cf..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/NotEqual.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {NotEqual} from '@tensorflow/tfjs-core';
-import {KernelConfig} from '@tensorflow/tfjs-core';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {notEqualImplCPU} from '../kernel_utils/shared';
-
-const NOT_EQUAL = `return float(a != b);`;
-
-export const notEqual = binaryKernelFunc(
-    {opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: 'bool'});
-
-export const notEqualConfig: KernelConfig = {
-  kernelName: NotEqual,
-  backendName: 'webgl',
-  kernelFunc: notEqual,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/OneHot.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/OneHot.ts
deleted file mode 100644
index 752c3f68b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/OneHot.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, OneHot, OneHotAttrs, OneHotInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {OneHotProgram} from '../onehot_gpu';
-import {reshape} from './Reshape';
-
-export const oneHot = (args: {
-  inputs: OneHotInputs,
-  backend: MathBackendWebGL,
-  attrs: OneHotAttrs
-}): TensorInfo => {
-  const {inputs, backend, attrs} = args;
-  const {indices} = inputs;
-  const {dtype, depth, onValue, offValue} = attrs;
-
-  const indicesSize = util.sizeFromShape(indices.shape);
-  const program = new OneHotProgram(indicesSize, depth, onValue, offValue);
-  const reshaped =
-      reshape({inputs: {x: indices}, backend, attrs: {shape: [indicesSize]}});
-  const result = backend.runWebGLProgram(program, [reshaped], dtype);
-  backend.disposeIntermediateTensorInfo(reshaped);
-
-  const outShape = [...indices.shape, depth];
-  const out = reshape({inputs: {x: result}, backend, attrs: {shape: outShape}});
-  backend.disposeIntermediateTensorInfo(result);
-  return out;
-};
-
-export const oneHotConfig: KernelConfig = {
-  kernelName: OneHot,
-  backendName: 'webgl',
-  kernelFunc: oneHot as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/OnesLike.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/OnesLike.ts
deleted file mode 100644
index 2f5ac1275..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/OnesLike.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, OnesLike, OnesLikeInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {complex} from './Complex';
-import {fill} from './Fill';
-import {imag} from './Imag';
-import {real} from './Real';
-import {zerosLike} from './ZerosLike';
-
-export function onesLike(
-    args: {inputs: OnesLikeInputs, backend: MathBackendWebGL}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  if (x.dtype === 'string') {
-    throw new Error('onesLike is not supported under string dtype');
-  } else if (x.dtype === 'complex64') {
-    const realPart = real({inputs: {input: x}, backend});
-    const r = onesLike({inputs: {x: realPart}, backend});
-    const imagPart = imag({inputs: {input: x}, backend});
-    const i = zerosLike({inputs: {x: imagPart}, backend});
-
-    const result = complex({inputs: {real: r, imag: i}, backend});
-
-    backend.disposeIntermediateTensorInfo(realPart);
-    backend.disposeIntermediateTensorInfo(r);
-    backend.disposeIntermediateTensorInfo(imagPart);
-    backend.disposeIntermediateTensorInfo(i);
-
-    return result;
-  } else {
-    // TODO(cais, smilkov): Add WebGL shader for onesLike:
-    //   https://github.com/tensorflow/tfjs/issues/1293
-    return fill({attrs: {shape: x.shape, dtype: x.dtype, value: 1}, backend});
-  }
-}
-
-export const onesLikeConfig: KernelConfig = {
-  kernelName: OnesLike,
-  backendName: 'webgl',
-  kernelFunc: onesLike as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Pack.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Pack.ts
deleted file mode 100644
index caa7008e2..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Pack.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Pack, PackAttrs, PackInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {concat} from './Concat';
-import {expandDims} from './ExpandDims';
-
-export function pack(
-    args: {inputs: PackInputs, backend: MathBackendWebGL, attrs: PackAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {axis} = attrs;
-
-  if (inputs.length === 1) {
-    return expandDims(
-        {inputs: {input: inputs[0]}, backend, attrs: {dim: axis}});
-  }
-
-  const shape = inputs[0].shape;
-  const dtype = inputs[0].dtype;
-
-  inputs.forEach(t => {
-    util.assertShapesMatch(
-        shape, t.shape,
-        'All tensors passed to stack must have matching shapes');
-    util.assert(
-        dtype === t.dtype,
-        () => 'All tensors passed to stack must have matching dtypes');
-  });
-
-  const intermediateTensorInfos: TensorInfo[] = [];
-  const expandedTensors = inputs.map(t => {
-    const expandedT =
-        expandDims({inputs: {input: t}, backend, attrs: {dim: axis}});
-    intermediateTensorInfos.push(expandedT);
-    return expandedT;
-  });
-
-  const result = concat({inputs: expandedTensors, backend, attrs: {axis}});
-
-  intermediateTensorInfos.forEach(
-      t => backend.disposeIntermediateTensorInfo(t));
-
-  return result;
-}
-
-export const packConfig: KernelConfig = {
-  kernelName: Pack,
-  backendName: 'webgl',
-  kernelFunc: pack as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/PadV2.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/PadV2.ts
deleted file mode 100644
index e52a66855..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/PadV2.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, PadV2, PadV2Attrs, PadV2Inputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {PadProgram} from '../pad_gpu';
-import {PadPackedProgram} from '../pad_packed_gpu';
-import {fill} from './Fill';
-
-export const padV2 =
-    (args: {inputs: PadV2Inputs, backend: MathBackendWebGL, attrs: PadV2Attrs}):
-        TensorInfo => {
-          const {inputs, backend, attrs} = args;
-          const {x} = inputs;
-          const {paddings, constantValue} = attrs;
-
-          if (util.sizeFromShape(x.shape) === 0) {
-            // Short-circuit the computation, since x doesn't have value, only
-            // the shape is used to compute output shape to pad.
-            const outputShape = paddings.map(
-                (p, i) =>
-                    p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);
-            return fill({
-              backend,
-              attrs: {shape: outputShape, value: constantValue, dtype: x.dtype}
-            });
-          }
-
-          const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
-              new PadPackedProgram(x.shape, paddings, constantValue) :
-              new PadProgram(x.shape, paddings, constantValue);
-          const customValues = [[constantValue]];
-          return backend.runWebGLProgram(program, [x], x.dtype, customValues);
-        };
-
-export const padV2Config: KernelConfig = {
-  kernelName: PadV2,
-  backendName: 'webgl',
-  kernelFunc: padV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Pow.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Pow.ts
deleted file mode 100644
index f5ec3ab2f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Pow.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Pow} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET_PACKED} from '../binaryop_packed_gpu';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const POW = `
-  if(a < 0.0 && floor(b) < b){
-    return NAN;
-  }
-  if (b == 0.0) {
-    return 1.0;
-  }
-  return (round(mod(b, 2.0)) != 1) ?
-      pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`;
-
-const POW_PACKED = `
-  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
-  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
-  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
-  vec4 result = multiplier * pow(abs(a), b);
-
-  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
-  bvec4 isExpZero = equal(b, vec4(0.0));
-  result.r = isExpZero.r ? 1.0 : result.r;
-  result.g = isExpZero.g ? 1.0 : result.g;
-  result.b = isExpZero.b ? 1.0 : result.b;
-  result.a = isExpZero.a ? 1.0 : result.a;
-
-  bvec4 isNaN1 = lessThan(a, vec4(0.0));
-  bvec4 isNaN2 = lessThan(floor(b), b);
-  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
-  ` +
-    CHECK_NAN_SNIPPET_PACKED + `
-  return result;
-`;
-
-export const pow =
-    binaryKernelFunc({opSnippet: POW, packedOpSnippet: POW_PACKED});
-
-export const powConfig: KernelConfig = {
-  kernelName: Pow,
-  backendName: 'webgl',
-  kernelFunc: pow as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Prelu.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Prelu.ts
deleted file mode 100644
index d60d04088..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Prelu.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, Prelu, PreluInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {BinaryOpProgram} from '../binaryop_gpu';
-import {BinaryOpPackedProgram} from '../binaryop_packed_gpu';
-
-export const PRELU = `return (a < 0.) ? b * a : a;`;
-export const PRELU_PACKED = `
-  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
-  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;
-
-export function prelu(args: {inputs: PreluInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x, alpha} = inputs;
-
-  const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
-      new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) :
-      new BinaryOpProgram(PRELU, x.shape, alpha.shape);
-  return backend.runWebGLProgram(program, [x, alpha], 'float32');
-}
-
-export const preluConfig: KernelConfig = {
-  kernelName: Prelu,
-  backendName: 'webgl',
-  kernelFunc: prelu as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Prod.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Prod.ts
deleted file mode 100644
index ebf3fb83c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Prod.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Prod, ProdAttrs, ProdInputs, sumOutType, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {reduce} from '../kernel_utils/reduce';
-import {prodImplCPU} from '../kernel_utils/shared';
-
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function prod(
-    args: {inputs: ProdInputs, backend: MathBackendWebGL, attrs: ProdAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  const xRank = x.shape.length;
-  const toDispose = [];
-
-  const origAxes = util.parseAxisParam(axis, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-  let permutedX = x;
-  if (permutedAxes != null) {
-    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    axes = backend_util.getInnerMostAxes(axes.length, xRank);
-    toDispose.push(permutedX);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('prod', axes, xRank);
-
-  let res;
-  if (backend.shouldExecuteOnCPU([permutedX])) {
-    const xVals = backend.texData.get(permutedX.dataId).values as TypedArray;
-    const {outVals, outShape, outDtype} =
-        prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);
-    res = backend.makeTensorInfo(outShape, outDtype, outVals);
-  } else {
-    const [outShape, reduceShape] =
-        backend_util.computeOutAndReduceShapes(permutedX.shape, axes);
-    const inSize = util.sizeFromShape(reduceShape);
-    const a2D = reshape(
-        {inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});
-    const outputDType = sumOutType(x.dtype);
-    const reduced = reduce(a2D, outputDType, 'prod', backend);
-    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});
-
-    toDispose.push(a2D);
-    toDispose.push(reduced);
-  }
-
-  if (keepDims) {
-    toDispose.push(res);
-    const newShape = backend_util.expandShapeToKeepDim(res.shape, origAxes);
-    res = reshape({inputs: {x: res}, backend, attrs: {shape: newShape}});
-  }
-
-  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-  return res;
-}
-
-export const prodConfig: KernelConfig = {
-  kernelName: Prod,
-  backendName: 'webgl',
-  kernelFunc: prod as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/RaggedGather.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/RaggedGather.ts
deleted file mode 100644
index 97e1e187c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/RaggedGather.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, RaggedGather, RaggedGatherAttrs, RaggedGatherInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {raggedGatherImplCPU} from '../kernel_utils/shared';
-
-export function raggedGather(args: {
-  inputs: RaggedGatherInputs,
-  backend: MathBackendWebGL,
-  attrs: RaggedGatherAttrs
-}): TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {paramsNestedSplits, paramsDenseValues, indices} = inputs;
-  const {outputRaggedRank} = attrs;
-
-  const $paramsNestedSplits =
-      paramsNestedSplits.map(t => backend.readSync(t.dataId) as TypedArray);
-  const $paramsNestedSplitsShapes = paramsNestedSplits.map(t => t.shape);
-  const $paramsDenseValues =
-      backend.readSync(paramsDenseValues.dataId) as TypedArray;
-  const $indices = backend.readSync(indices.dataId) as TypedArray;
-
-  const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] =
-      raggedGatherImplCPU(
-          $paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues,
-          paramsDenseValues.shape, paramsDenseValues.dtype, $indices,
-          indices.shape, outputRaggedRank);
-
-  const outputNestedSplitsTensors = outputNestedSplits.map(
-      (splits) => backend.makeTensorInfo([splits.length], 'int32', splits));
-
-  const outputDenseValuesTensor = backend.makeTensorInfo(
-      outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues);
-
-  return outputNestedSplitsTensors.concat([outputDenseValuesTensor]);
-}
-
-export const raggedGatherConfig: KernelConfig = {
-  kernelName: RaggedGather,
-  backendName: 'webgl',
-  kernelFunc: raggedGather as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/RaggedRange.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/RaggedRange.ts
deleted file mode 100644
index e320a68b1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/RaggedRange.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, RaggedRange, RaggedRangeInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {raggedRangeImplCPU} from '../kernel_utils/shared';
-
-export function raggedRange(
-    args: {inputs: RaggedRangeInputs, backend: MathBackendWebGL}):
-    [TensorInfo, TensorInfo] {
-  const {inputs, backend} = args;
-  const {starts, limits, deltas} = inputs;
-
-  const $starts = backend.readSync(starts.dataId) as TypedArray;
-  const $limits = backend.readSync(limits.dataId) as TypedArray;
-  const $deltas = backend.readSync(deltas.dataId) as TypedArray;
-
-  const [rtNestedSplitsData, rtDenseValuesData] = raggedRangeImplCPU(
-      $starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas,
-      deltas.shape);
-
-  const rtNestedSplits = backend.makeTensorInfo(
-      [rtNestedSplitsData.length], 'int32', rtNestedSplitsData);
-  const rtDenseValues = backend.makeTensorInfo(
-      [rtDenseValuesData.length], starts.dtype, rtDenseValuesData);
-
-  return [rtNestedSplits, rtDenseValues];
-}
-
-export const raggedRangeConfig: KernelConfig = {
-  kernelName: RaggedRange,
-  backendName: 'webgl',
-  kernelFunc: raggedRange as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/RaggedTensorToTensor.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/RaggedTensorToTensor.ts
deleted file mode 100644
index 2c042c412..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/RaggedTensorToTensor.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, RaggedTensorToTensor, RaggedTensorToTensorAttrs, RaggedTensorToTensorInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {raggedTensorToTensorImplCPU} from '../kernel_utils/shared';
-
-export function raggedTensorToTensor(args: {
-  inputs: RaggedTensorToTensorInputs,
-  backend: MathBackendWebGL,
-  attrs: RaggedTensorToTensorAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {shape, values, defaultValue, rowPartitionTensors} = inputs;
-  const {rowPartitionTypes} = attrs;
-
-  const $shape = backend.readSync(shape.dataId) as TypedArray;
-  const $values = backend.readSync(values.dataId) as TypedArray;
-  const $defaultValue = backend.readSync(defaultValue.dataId) as TypedArray;
-  const $rowPartitionValues =
-      rowPartitionTensors.map(t => backend.readSync(t.dataId) as TypedArray);
-  const rowPartitionValuesShapes = rowPartitionTensors.map(t => t.shape);
-
-  const [outputShape, output] = raggedTensorToTensorImplCPU(
-      $shape, shape.shape, $values, values.shape, values.dtype, $defaultValue,
-      defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes,
-      rowPartitionTypes);
-  return backend.makeTensorInfo(outputShape, values.dtype, output);
-}
-
-export const raggedTensorToTensorConfig: KernelConfig = {
-  kernelName: RaggedTensorToTensor,
-  backendName: 'webgl',
-  kernelFunc: raggedTensorToTensor as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Range.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Range.ts
deleted file mode 100644
index 58c43a056..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Range.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Range, RangeAttrs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {rangeImplCPU} from '../kernel_utils/shared';
-
-export const range =
-    (args: {backend: MathBackendWebGL, attrs: RangeAttrs}): TensorInfo => {
-      const {backend, attrs} = args;
-      const {start, stop, step, dtype} = attrs;
-      const values = rangeImplCPU(start, stop, step, dtype);
-      return backend.makeTensorInfo([values.length], dtype, values);
-    };
-
-export const rangeConfig: KernelConfig = {
-  kernelName: Range,
-  backendName: 'webgl',
-  kernelFunc: range as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Real.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Real.ts
deleted file mode 100644
index bf7e28f93..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Real.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Real, RealInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {identity} from './Identity';
-
-export function real(args: {inputs: RealInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-  const inputData = backend.texData.get(input.dataId);
-
-  return identity({inputs: {x: inputData.complexTensorInfos.real}, backend});
-}
-
-export const realConfig: KernelConfig = {
-  kernelName: Real,
-  backendName: 'webgl',
-  kernelFunc: real as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/RealDiv.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/RealDiv.ts
deleted file mode 100644
index d0073d3d4..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/RealDiv.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {RealDiv} from '@tensorflow/tfjs-core';
-import {KernelConfig} from '@tensorflow/tfjs-core';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-// Without the equality check div produces 0.9999 for a = b, which when
-// floored can cause errors.
-const DIV = `
-if (a == b) {
-  return 1.0;
-};
-return a / b;`;
-
-// We do the same as in ./binaryop_gpu, with vec4 and ivec4.
-// On Linux, the vectorized implementation produces NaNs when a and b are 0.
-const DIV_PACKED = `
-  // vec4 one = vec4(equal(a, b));
-  // return one + (vec4(1.0) - one) * a / b;
-  vec4 result = a / b;
-  if(a.x == b.x) {
-    result.x = 1.;
-  }
-  if(a.y == b.y) {
-    result.y = 1.;
-  }
-  if(a.z == b.z) {
-    result.z = 1.;
-  }
-  if(a.w == b.w) {
-    result.w = 1.;
-  }
-
-  return result;
-`;
-
-export const realDiv = binaryKernelFunc(
-    {opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true});
-
-export const realDivConfig: KernelConfig = {
-  kernelName: RealDiv,
-  backendName: 'webgl',
-  kernelFunc: realDiv,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Reciprocal.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Reciprocal.ts
deleted file mode 100644
index 1cfcf642b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Reciprocal.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Reciprocal} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const RECIPROCAL = `return 1.0 / x;`;
-
-export const reciprocal = unaryKernelFunc({opSnippet: RECIPROCAL});
-
-export const reciprocalConfig: KernelConfig = {
-  kernelName: Reciprocal,
-  backendName: 'webgl',
-  kernelFunc: reciprocal,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Relu.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Relu.ts
deleted file mode 100644
index 3a1e465e2..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Relu.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Relu} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {CHECK_NAN_SNIPPET} from '../unaryop_gpu';
-
-const RELU = CHECK_NAN_SNIPPET + `
-  return (x < 0.0) ? 0.0 : x;
-`;
-
-const RELU_PACKED = `
-  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
-  bvec4 isNaN = isnan(x);
-
-  result.r = isNaN.r ? x.r : result.r;
-  result.g = isNaN.g ? x.g : result.g;
-  result.b = isNaN.b ? x.b : result.b;
-  result.a = isNaN.a ? x.a : result.a;
-
-  return result;
-`;
-
-export const relu =
-    unaryKernelFunc({opSnippet: RELU, packedOpSnippet: RELU_PACKED});
-
-export const reluConfig: KernelConfig = {
-  kernelName: Relu,
-  backendName: 'webgl',
-  kernelFunc: relu as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Relu6.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Relu6.ts
deleted file mode 100644
index 5faa51ba7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Relu6.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Relu6} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {CHECK_NAN_SNIPPET} from '../unaryop_gpu';
-
-const RELU6 = CHECK_NAN_SNIPPET + `
-  return (x < 0.0) ? 0.0 : min(6.0, x);
-`;
-
-const RELU6_PACKED = `
-  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
-  bvec4 isNaN = isnan(x);
-
-  result.r = isNaN.r ? x.r : result.r;
-  result.g = isNaN.g ? x.g : result.g;
-  result.b = isNaN.b ? x.b : result.b;
-  result.a = isNaN.a ? x.a : result.a;
-
-  return result;
-`;
-
-export const relu6 =
-    unaryKernelFunc({opSnippet: RELU6, packedOpSnippet: RELU6_PACKED});
-
-export const relu6Config: KernelConfig = {
-  kernelName: Relu6,
-  backendName: 'webgl',
-  kernelFunc: relu6 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Reshape.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Reshape.ts
deleted file mode 100644
index f8f5f5322..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Reshape.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Reshape, ReshapeAttrs, ReshapeInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {packedReshape} from '../kernel_utils/reshape';
-import {isReshapeFree} from '../webgl_util';
-
-export function reshape(args: {
-  inputs: ReshapeInputs,
-  backend: MathBackendWebGL,
-  attrs: ReshapeAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {shape} = attrs;
-  const webglBackend = backend;
-
-  const xSize = util.sizeFromShape(x.shape);
-  const $shape = util.inferFromImplicitShape(shape, xSize);
-  const $xSize = util.sizeFromShape($shape);
-
-  util.assert(
-      xSize === $xSize,
-      () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +
-          `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +
-          `shape must have the same number of elements.`);
-
-  const xTexData = webglBackend.texData.get(x.dataId);
-  if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&
-      !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {
-    return packedReshape(x, $shape, webglBackend);
-  }
-
-  webglBackend.incRef(x.dataId);
-
-  return {dataId: x.dataId, shape: $shape, dtype: x.dtype};
-}
-
-export const reshapeConfig: KernelConfig = {
-  kernelName: Reshape,
-  backendName: 'webgl',
-  kernelFunc: reshape as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Reshape_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Reshape_test.ts
deleted file mode 100644
index a1a1fc1ad..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Reshape_test.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {Tensor, test_util} from '@tensorflow/tfjs-core';
-
-const {expectArraysClose, expectArraysEqual} = test_util;
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags, ALL_ENVS} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-describeWithFlags('Reshape.', ALL_ENVS, () => {
-  it('does not have memory leak.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const x = tf.tensor1d([1, 1, 1, 1]);
-    const res =
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        tf.engine().runKernel('Reshape', {x}, {shape: [2, 2]}) as Tensor;
-
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-    expectArraysEqual(res.shape, [2, 2]);
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 1);
-
-    x.dispose();
-    res.dispose();
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-
-  it('does not have memory leak calling reshape twice.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    // Adding 1 new dataId.
-    const x = tf.tensor1d([1, 1, 1, 1]);
-
-    // Does not add new dataId;
-    const res =
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        tf.engine().runKernel('Reshape', {x}, {shape: [2, 2]}) as Tensor;
-
-    expectArraysEqual(res.shape, [2, 2]);
-
-    // Does not add new dataId.
-    const res2 =
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        tf.engine().runKernel('Reshape', {x: res}, {shape: [1, 4]}) as Tensor;
-    expectArraysEqual(res2.shape, [1, 4]);
-
-    const afterRes2DataIds = tf.engine().backend.numDataIds();
-    expect(afterRes2DataIds).toEqual(beforeDataIds + 1);
-
-    res.dispose();
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 1);
-
-    x.dispose();
-    res2.dispose();
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    // Should be able to dispose the dataId.
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-
-  it('does not leak when reshaping a shallowly sliced tensor', async () => {
-    const packedFlagSaved = tf.env().getBool('WEBGL_PACK');
-    tf.env().set('WEBGL_PACK', false);
-
-    const nBefore = tf.memory().numTensors;
-    const nBeforeDataIds = tf.engine().backend.numDataIds();
-
-    const a = tf.tensor1d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]);
-    const b = tf.slice(a, 0, 6);
-
-    await b.data();
-
-    let nAfter = tf.memory().numTensors;
-    let nAfterDataIds = tf.engine().backend.numDataIds();
-    expect(nAfter).toBe(nBefore + 2);
-    expect(nAfterDataIds).toBe(nBeforeDataIds + 2);
-
-    const c = tf.reshape(b, [2, 3]);
-    expectArraysClose(await c.data(), [0, 1, 2, 3, 4, 5]);
-
-    tf.dispose([a, b]);
-    nAfter = tf.memory().numTensors;
-    nAfterDataIds = tf.engine().backend.numDataIds();
-    expect(nAfter).toBe(nBefore + 1);
-    expect(nAfterDataIds).toBe(nBeforeDataIds + 1);
-
-    tf.dispose([c]);
-    nAfter = tf.memory().numTensors;
-    nAfterDataIds = tf.engine().backend.numDataIds();
-    expect(nAfter).toBe(nBefore);
-    expect(nAfterDataIds).toBe(nBeforeDataIds);
-
-    tf.env().set('WEBGL_PACK', packedFlagSaved);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeBilinear.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeBilinear.ts
deleted file mode 100644
index 6b35584fd..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeBilinear.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ResizeBilinearProgram} from '../resize_bilinear_gpu';
-import {ResizeBilinearPackedProgram} from '../resize_bilinear_packed_gpu';
-
-export function resizeBilinear(args: {
-  inputs: ResizeBilinearInputs,
-  backend: MathBackendWebGL,
-  attrs: ResizeBilinearAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images} = inputs;
-  const {alignCorners, halfPixelCenters, size} = attrs;
-
-  const [newHeight, newWidth] = size;
-
-  const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?
-      new ResizeBilinearPackedProgram(
-          images.shape as [number, number, number, number], newHeight, newWidth,
-          alignCorners, halfPixelCenters) :
-      new ResizeBilinearProgram(
-          images.shape as [number, number, number, number], newHeight, newWidth,
-          alignCorners, halfPixelCenters);
-  return backend.runWebGLProgram(program, [images], 'float32');
-}
-
-export const resizeBilinearConfig: KernelConfig = {
-  kernelName: ResizeBilinear,
-  backendName: 'webgl',
-  kernelFunc: resizeBilinear as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeBilinearGrad.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeBilinearGrad.ts
deleted file mode 100644
index 785a63f50..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeBilinearGrad.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeBilinearGrad, ResizeBilinearGradAttrs, ResizeBilinearGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ResizeBilinearBackpropProgram} from '../resize_bilinear_backprop_gpu';
-
-export function resizeBilinearGrad(args: {
-  inputs: ResizeBilinearGradInputs,
-  backend: MathBackendWebGL,
-  attrs: ResizeBilinearGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images, dy} = inputs;
-  const {alignCorners} = attrs;
-
-  const program = new ResizeBilinearBackpropProgram(
-      dy.shape as [number, number, number, number],
-      images.shape as [number, number, number, number], alignCorners);
-
-  return backend.runWebGLProgram(program, [dy], dy.dtype);
-}
-
-export const resizeBilinearGradConfig: KernelConfig = {
-  kernelName: ResizeBilinearGrad,
-  backendName: 'webgl',
-  kernelFunc: resizeBilinearGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeNearestNeighbor.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeNearestNeighbor.ts
deleted file mode 100644
index ec205a7ab..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeNearestNeighbor.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ResizeNearestNeighborProgram} from '../resize_nearest_neighbor_gpu';
-import {ResizeNearestNeighborPackedProgram} from '../resize_nearest_neighbor_packed_gpu';
-
-export function resizeNearestNeighbor(args: {
-  inputs: ResizeNearestNeighborInputs,
-  backend: MathBackendWebGL,
-  attrs: ResizeNearestNeighborAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images} = inputs;
-  const {alignCorners, halfPixelCenters, size} = attrs;
-
-  const [newHeight, newWidth] = size;
-
-  const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?
-      new ResizeNearestNeighborPackedProgram(
-          images.shape as [number, number, number, number], newHeight, newWidth,
-          alignCorners, halfPixelCenters) :
-      new ResizeNearestNeighborProgram(
-          images.shape as [number, number, number, number], newHeight, newWidth,
-          alignCorners, halfPixelCenters);
-  return backend.runWebGLProgram(program, [images], images.dtype);
-}
-
-export const resizeNearestNeighborConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighbor,
-  backendName: 'webgl',
-  kernelFunc: resizeNearestNeighbor as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeNearestNeighborGrad.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeNearestNeighborGrad.ts
deleted file mode 100644
index e287a0479..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ResizeNearestNeighborGrad.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeNearestNeighborGrad, ResizeNearestNeighborGradAttrs, ResizeNearestNeighborGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ResizeNearestNeigborBackpropProgram} from '../resize_nearest_neighbor_backprop_gpu';
-
-export function resizeNearestNeighborGrad(args: {
-  inputs: ResizeNearestNeighborGradInputs,
-  backend: MathBackendWebGL,
-  attrs: ResizeNearestNeighborGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images, dy} = inputs;
-  const {alignCorners} = attrs;
-
-  const program = new ResizeNearestNeigborBackpropProgram(
-      dy.shape as [number, number, number, number],
-      images.shape as [number, number, number, number], alignCorners);
-  return backend.runWebGLProgram(program, [dy], dy.dtype);
-}
-
-export const resizeNearestNeighborGradConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighborGrad,
-  backendName: 'webgl',
-  kernelFunc: resizeNearestNeighborGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Reverse.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Reverse.ts
deleted file mode 100644
index 7926b43c6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Reverse.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, Reverse, ReverseAttrs, ReverseInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ReverseProgram} from '../reverse_gpu';
-import {ReversePackedProgram} from '../reverse_packed_gpu';
-
-import {identity} from './Identity';
-
-export function reverse(args: {
-  inputs: ReverseInputs,
-  backend: MathBackendWebGL,
-  attrs: ReverseAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {dims} = attrs;
-
-  const xRank = x.shape.length;
-
-  const $dims = util.parseAxisParam(dims, x.shape);
-  if (xRank === 0) {
-    return identity({inputs: {x}, backend});
-  }
-
-  const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
-      new ReversePackedProgram(x.shape, $dims) :
-      new ReverseProgram(x.shape, $dims);
-
-  return backend.runWebGLProgram(program, [x], x.dtype);
-}
-
-export const reverseConfig: KernelConfig = {
-  kernelName: Reverse,
-  backendName: 'webgl',
-  kernelFunc: reverse as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/RotateWithOffset.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/RotateWithOffset.ts
deleted file mode 100644
index 5bec6d3eb..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/RotateWithOffset.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Tensor4D} from '@tensorflow/tfjs-core';
-import {RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {RotateProgram} from '../rotate_gpu';
-
-export const rotateWithOffsetConfig: KernelConfig = {
-  kernelName: RotateWithOffset,
-  backendName: 'webgl',
-  kernelFunc: ({inputs, attrs, backend}) => {
-    const {image} = inputs as RotateWithOffsetInputs;
-    const {radians, fillValue, center} =
-        attrs as unknown as RotateWithOffsetAttrs;
-    const webglBackend = backend as MathBackendWebGL;
-
-    const program = new RotateProgram((image as Tensor4D).shape, fillValue);
-    const [centerX, centerY] =
-        backend_util.getImageCenter(center, image.shape[1], image.shape[2]);
-    const customValues =
-        [[centerX, centerY, Math.sin(radians), Math.cos(radians)]];
-    const output = webglBackend.runWebGLProgram(
-        program, [image], image.dtype, customValues);
-    return output;
-  }
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Round.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Round.ts
deleted file mode 100644
index c29426364..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Round.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Round} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const ROUND = `
-  // OpenGL ES does not support round function.
-  // The algorithm is based on banker's rounding.
-  float base = floor(x);
-  if ((x - base) < 0.5) {
-    return floor(x);
-  } else if ((x - base) > 0.5) {
-    return ceil(x);
-  } else {
-    if (mod(base, 2.0) == 0.0) {
-      return base;
-    } else {
-      return base + 1.0;
-    }
-  }
-`;
-
-export const round = unaryKernelFunc({opSnippet: ROUND});
-
-export const roundConfig: KernelConfig = {
-  kernelName: Round,
-  backendName: 'webgl',
-  kernelFunc: round,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Rsqrt.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Rsqrt.ts
deleted file mode 100644
index dcea16dae..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Rsqrt.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Rsqrt} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {rsqrtImplCPU} from '../kernel_utils/shared';
-
-const RSQRT = `return inversesqrt(x);`;
-
-export const rsqrt =
-    unaryKernelFunc({opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU});
-
-export const rsqrtConfig: KernelConfig = {
-  kernelName: Rsqrt,
-  backendName: 'webgl',
-  kernelFunc: rsqrt as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/STFT_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/STFT_test.ts
deleted file mode 100644
index dc79027b6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/STFT_test.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-describeWithFlags('stft memory test', ALL_ENVS, () => {
-  it('should have no mem leak', async () => {
-    const win = 320;
-    const fft = 320;
-    const hop = 160;
-    const input = tf.zeros<tf.Rank.R1>([1760]);
-
-    const startTensors = tf.memory().numTensors;
-    const startDataIds = tf.engine().backend.numDataIds();
-    const result = await tf.signal.stft(input, win, hop, fft);
-
-    // 1 new tensor, 3 new data buckets.
-    expect(tf.memory().numTensors).toBe(startTensors + 1);
-    expect(tf.engine().backend.numDataIds()).toBe(startTensors + 3);
-
-    result.dispose();
-
-    // Zero net tensors / data buckets.
-    expect(tf.memory().numTensors).toBe(startTensors);
-    expect(tf.engine().backend.numDataIds()).toBe(startDataIds);
-    input.dispose();
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ScatterNd.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ScatterNd.ts
deleted file mode 100644
index 87642887a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ScatterNd.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, env, KernelConfig, KernelFunc, ScatterNd, ScatterNdAttrs, ScatterNdInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ScatterProgram} from '../scatter_gpu';
-import {ScatterPackedProgram} from '../scatter_packed_gpu';
-import {reshape} from './Reshape';
-
-export function scatterNd(args: {
-  inputs: ScatterNdInputs,
-  backend: MathBackendWebGL,
-  attrs: ScatterNdAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {indices, updates} = inputs;
-  const {shape} = attrs;
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(updates, indices, shape);
-
-  const flattenShape = [outputSize / sliceSize, sliceSize];
-
-  if (outputSize === 0) {
-    return backend.makeTensorInfo(shape, indices.dtype);
-  }
-
-  const flattenIndices = reshape(
-      {inputs: {x: indices}, backend, attrs: {shape: [numUpdates, sliceRank]}});
-  const flattenX = reshape(
-      {inputs: {x: updates}, backend, attrs: {shape: [numUpdates, sliceSize]}});
-
-  const defaultValue = backend.makeTensorInfo(
-      [], 'float32', new Float32Array([0]));  // scalar(0)
-  let program;
-  if (env().getBool('WEBGL_PACK')) {
-    program = new ScatterPackedProgram(
-        numUpdates, sliceRank, flattenIndices.shape.length,
-        flattenX.shape.length, strides, flattenShape);
-  } else {
-    program = new ScatterProgram(
-        numUpdates, sliceRank, flattenIndices.shape.length,
-        flattenX.shape.length, strides, flattenShape);
-  }
-  const res = backend.runWebGLProgram(
-      program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);
-
-  const reshaped = reshape({inputs: {x: res}, backend, attrs: {shape}});
-
-  backend.disposeIntermediateTensorInfo(flattenIndices);
-  backend.disposeIntermediateTensorInfo(flattenX);
-  backend.disposeIntermediateTensorInfo(res);
-  backend.disposeIntermediateTensorInfo(defaultValue);
-
-  return reshaped;
-}
-
-export const scatterNdConfig: KernelConfig = {
-  kernelName: ScatterNd,
-  backendName: 'webgl',
-  kernelFunc: scatterNd as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/SearchSorted.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/SearchSorted.ts
deleted file mode 100644
index 1a04611af..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/SearchSorted.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SearchSorted, SearchSortedAttrs, SearchSortedInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {SearchSortedProgram} from '../search_sorted_gpu';
-
-export function searchSorted(args: {
-  inputs: SearchSortedInputs,
-  backend: MathBackendWebGL,
-  attrs: SearchSortedAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {sortedSequence, values} = inputs;
-  const {side} = attrs;
-
-  const program = new SearchSortedProgram(
-      sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);
-  const customValues = [[sortedSequence.shape[1]]];
-  return backend.runWebGLProgram(
-      program, [sortedSequence, values], 'int32', customValues);
-}
-
-export const searchSortedConfig: KernelConfig = {
-  kernelName: SearchSorted,
-  backendName: 'webgl',
-  kernelFunc: searchSorted as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Select.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Select.ts
deleted file mode 100644
index 5d6209517..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Select.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Select, SelectInputs, TensorInfo, upcastType} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {SelectProgram} from '../select_gpu';
-
-export function select(args: {inputs: SelectInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {condition, t, e} = inputs;
-
-  const program =
-      new SelectProgram(condition.shape.length, t.shape, t.shape.length);
-  return backend.runWebGLProgram(
-      program, [condition, t, e], upcastType(t.dtype, e.dtype));
-}
-
-export const selectConfig: KernelConfig = {
-  kernelName: Select,
-  backendName: 'webgl',
-  kernelFunc: select as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Selu.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Selu.ts
deleted file mode 100644
index c2303f1b2..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Selu.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Selu} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const SELU = `
-  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
-  // see: https://arxiv.org/abs/1706.02515
-  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};
-  float scale = ${backend_util.SELU_SCALE};
-  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`;
-
-export const selu = unaryKernelFunc({opSnippet: SELU});
-
-export const seluConfig: KernelConfig = {
-  kernelName: Selu,
-  backendName: 'webgl',
-  kernelFunc: selu,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Sigmoid.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Sigmoid.ts
deleted file mode 100644
index a14808c0a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Sigmoid.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sigmoid} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {sigmoidImplCPU} from '../kernel_utils/shared';
-
-const SIGMOID = CHECK_NAN_SNIPPET_UNARY + `
-  return 1.0 / (1.0 + exp(-1.0 * x));
-`;
-
-const SIGMOID_PACKED = `
-  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
-  bvec4 isNaN = isnan(x);
-
-  result.r = isNaN.r ? x.r : result.r;
-  result.g = isNaN.g ? x.g : result.g;
-  result.b = isNaN.b ? x.b : result.b;
-  result.a = isNaN.a ? x.a : result.a;
-
-  return result;
-`;
-export const sigmoid = unaryKernelFunc({
-  opSnippet: SIGMOID,
-  packedOpSnippet: SIGMOID_PACKED,
-  cpuKernelImpl: sigmoidImplCPU
-});
-
-export const sigmoidConfig: KernelConfig = {
-  kernelName: Sigmoid,
-  backendName: 'webgl',
-  kernelFunc: sigmoid,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Sign.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Sign.ts
deleted file mode 100644
index dc3ad11c6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Sign.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sign} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-// Sign does not propagate NANs.
-const SIGN = `
-  if (isnan(x)) { return 0.0; }
-  return sign(x);
-`;
-
-export const sign = unaryKernelFunc({opSnippet: SIGN});
-
-export const signConfig: KernelConfig = {
-  kernelName: Sign,
-  backendName: 'webgl',
-  kernelFunc: sign,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Sin.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Sin.ts
deleted file mode 100644
index 77c7bf0cf..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Sin.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sin} from '@tensorflow/tfjs-core';
-
-import {CHECK_NAN_SNIPPET_PACKED} from '../binaryop_packed_gpu';
-import {CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const SIN = CHECK_NAN_SNIPPET_UNARY + `
-  return sin(x);
-`;
-
-const SIN_PACKED = `
-  vec4 result = sin(x);
-  bvec4 isNaN = isnan(x);
-  ${CHECK_NAN_SNIPPET_PACKED}
-  return result;
-`;
-
-export const sin =
-    unaryKernelFunc({opSnippet: SIN, packedOpSnippet: SIN_PACKED});
-
-export const sinConfig: KernelConfig = {
-  kernelName: Sin,
-  backendName: 'webgl',
-  kernelFunc: sin,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Sinh.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Sinh.ts
deleted file mode 100644
index 30c75f56c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Sinh.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sinh} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const SINH = `
-  float e2x = exp(x);
-  return (e2x - 1.0 / e2x) / 2.0;
-`;
-
-export const sinh = unaryKernelFunc({opSnippet: SINH});
-
-export const sinhConfig: KernelConfig = {
-  kernelName: Sinh,
-  backendName: 'webgl',
-  kernelFunc: sinh,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Slice.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Slice.ts
deleted file mode 100644
index e8c196b75..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Slice.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, Slice, slice_util, SliceAttrs, SliceInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {sliceImplCPU} from '../kernel_utils/shared';
-import {SliceProgram} from '../slice_gpu';
-import {SlicePackedProgram} from '../slice_packed_gpu';
-
-function shallowSlice(
-    x: TensorInfo, begin: number[], size: number[], backend: MathBackendWebGL) {
-  const xTexData = backend.texData.get(x.dataId);
-  const t = backend.makeTensorInfo(size, x.dtype);
-  const newTexData = backend.texData.get(t.dataId);
-  // Copy texture data from the original tensor.
-  Object.assign(newTexData, xTexData);
-  newTexData.refCount = 1;
-  newTexData.shape = size;
-  newTexData.dtype = x.dtype;
-  let flatOffset =
-      slice_util.computeFlatOffset(begin, util.computeStrides(x.shape));
-  if (xTexData.slice) {
-    // We are slicing an already sliced tensor, so we have to accumulate
-    // the offset.
-    flatOffset += xTexData.slice.flatOffset;
-  }
-  newTexData.slice = {
-    flatOffset,
-    // Point to the original dataId, which is used to do ref counting.
-    origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId
-  };
-
-  // Increase the ref count for that data bucket.
-  const refCount = backend.dataRefCount.get(newTexData.slice.origDataId) || 1;
-  backend.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);
-  return t;
-}
-
-export function slice(
-    args: {inputs: SliceInputs, backend: MathBackendWebGL, attrs: SliceAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {begin, size} = attrs;
-
-  const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);
-  slice_util.assertParamsValid(x, $begin, $size);
-
-  if (util.sizeFromShape($size) === 0) {
-    return backend.makeTensorInfo($size, x.dtype, []);
-  }
-
-  // Run on cpu if dtype is string. For string, the backend represents it
-  // as Uint8Array[], where each Uint8Array is a character. Given that the
-  // computation is only on the outer array, uploading the whole data onto
-  // gpu is wasteful. Also, currently webgl doesn't have a design to
-  // upload and retrieve Uint8Array[] between cpu and gpu. Therefore, we
-  // just run the kernel on cpu if dtype is string.
-  if (backend.shouldExecuteOnCPU([x]) || x.dtype === 'string') {
-    const xTexData = backend.texData.get(x.dataId);
-    const outValues = sliceImplCPU(
-        xTexData.values as TypedArray, $begin, $size, x.shape, x.dtype);
-    return backend.makeTensorInfo($size, x.dtype, outValues);
-  }
-
-  const {isPacked} = backend.texData.get(x.dataId);
-  const isContinous = slice_util.isSliceContinous(x.shape, $begin, $size);
-  if (isPacked || !isContinous) {
-    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
-        new SlicePackedProgram($size) :
-        new SliceProgram($size);
-    const customValues = [$begin];
-    return backend.runWebGLProgram(program, [x], x.dtype, customValues);
-  }
-  backend.uploadToGPU(x.dataId);
-  return shallowSlice(x, $begin, $size, backend);
-}
-
-export const sliceConfig: KernelConfig = {
-  kernelName: Slice,
-  backendName: 'webgl',
-  kernelFunc: slice as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Softmax.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Softmax.ts
deleted file mode 100644
index 9ba5770cb..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Softmax.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Softmax, SoftmaxAttrs, SoftmaxInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {exp} from './Exp';
-import {max} from './Max';
-import {realDiv} from './RealDiv';
-import {reshape} from './Reshape';
-import {sub} from './Sub';
-import {sum} from './Sum';
-
-export function softmax(args: {
-  inputs: SoftmaxInputs,
-  backend: MathBackendWebGL,
-  attrs: SoftmaxAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {logits} = inputs;
-  const {dim} = attrs;
-
-  const axes = util.parseAxisParam([dim], logits.shape);
-
-  const maxLogit = max({
-    inputs: {x: logits},
-    backend,
-    attrs: {reductionIndices: axes, keepDims: false}
-  });
-
-  const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);
-
-  const maxLogitsReshaped =
-      reshape({inputs: {x: maxLogit}, backend, attrs: {shape: expandedShape}});
-  const a =
-      sub({inputs: {a: logits, b: maxLogitsReshaped}, backend}) as TensorInfo;
-  const b = exp({inputs: {x: a}, backend}) as TensorInfo;
-  const sumExp =
-      sum({inputs: {x: b}, backend, attrs: {axis: axes, keepDims: false}});
-  const sumExpReshaped =
-      reshape({inputs: {x: sumExp}, backend, attrs: {shape: expandedShape}});
-
-  const res =
-      realDiv({inputs: {a: b, b: sumExpReshaped}, backend}) as TensorInfo;
-
-  backend.disposeIntermediateTensorInfo(maxLogit);
-  backend.disposeIntermediateTensorInfo(maxLogitsReshaped);
-  backend.disposeIntermediateTensorInfo(a);
-  backend.disposeIntermediateTensorInfo(b);
-  backend.disposeIntermediateTensorInfo(sumExp);
-  backend.disposeIntermediateTensorInfo(sumExpReshaped);
-
-  return res;
-}
-
-export const softmaxConfig: KernelConfig = {
-  kernelName: Softmax,
-  backendName: 'webgl',
-  kernelFunc: softmax as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Softplus.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Softplus.ts
deleted file mode 100644
index 2545658e7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Softplus.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Softplus} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const SOFTPLUS = `
-  float epsilon = 1.1920928955078125e-7;
-  float threshold = log(epsilon) + 2.0;
-
-  bool too_large = x > -threshold;
-  bool too_small = x < threshold;
-
-  float result;
-  float exp_x = exp(x);
-
-  if (too_large){
-    result = x;
-  }
-  else if (too_small){
-    result = exp_x;
-  }
-  else{
-    result = log(exp_x + 1.0);
-  }
-  return result;
-`;
-
-export const softplus = unaryKernelFunc({opSnippet: SOFTPLUS});
-
-export const softplusConfig: KernelConfig = {
-  kernelName: Softplus,
-  backendName: 'webgl',
-  kernelFunc: softplus,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/SpaceToBatchND.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/SpaceToBatchND.ts
deleted file mode 100644
index 6643b1ec9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/SpaceToBatchND.ts
+++ /dev/null
@@ -1,90 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {padV2} from './PadV2';
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export const spaceToBatchND = (args: {
-  inputs: SpaceToBatchNDInputs,
-  backend: MathBackendWebGL,
-  attrs: SpaceToBatchNDAttrs
-}): TensorInfo => {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockShape, paddings} = attrs;
-
-  util.assert(
-      x.shape.length <= 4,
-      () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +
-          'implemented yet');
-
-  const prod = blockShape.reduce((a, b) => a * b);
-
-  const completePaddings: Array<[number, number]> = [[0, 0]];
-  completePaddings.push(...paddings as Array<[number, number]>);
-  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
-    completePaddings.push([0, 0]);
-  }
-
-  const toDispose = [];
-
-  const paddedX = padV2({
-    inputs: {x},
-    backend,
-    attrs: {paddings: completePaddings, constantValue: 0}
-  });
-
-  const reshapedPaddedShape =
-      backend_util.getReshaped(paddedX.shape, blockShape, prod, false);
-
-  const permutedReshapedPaddedPermutation = backend_util.getPermuted(
-      reshapedPaddedShape.length, blockShape.length, false);
-
-  const flattenShape =
-      backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);
-
-  const reshapedPaddedX = reshape(
-      {inputs: {x: paddedX}, backend, attrs: {shape: reshapedPaddedShape}});
-
-  const paddedXT = transpose({
-    inputs: {x: reshapedPaddedX},
-    backend,
-    attrs: {perm: permutedReshapedPaddedPermutation}
-  });
-
-  const result =
-      reshape({inputs: {x: paddedXT}, backend, attrs: {shape: flattenShape}});
-
-  toDispose.push(paddedX);
-  toDispose.push(reshapedPaddedX);
-  toDispose.push(paddedXT);
-
-  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));
-
-  return result;
-};
-
-export const spaceToBatchNDConfig: KernelConfig = {
-  kernelName: SpaceToBatchND,
-  backendName: 'webgl',
-  kernelFunc: spaceToBatchND as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseFillEmptyRows.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/SparseFillEmptyRows.ts
deleted file mode 100644
index 437c86951..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseFillEmptyRows.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SparseFillEmptyRows, SparseFillEmptyRowsInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {sparseFillEmptyRowsImplCPU} from '../kernel_utils/shared';
-
-export function sparseFillEmptyRows(args: {
-  inputs: SparseFillEmptyRowsInputs,
-  backend: MathBackendWebGL
-}): [TensorInfo, TensorInfo, TensorInfo, TensorInfo] {
-  const {inputs, backend} = args;
-  const {indices, values, denseShape, defaultValue} = inputs;
-  if (denseShape.shape.length !== 1) {
-    throw new Error(`Dense shape must be a vector, saw:
-         ${denseShape.shape}`);
-  }
-  if (indices.shape.length !== 2) {
-    throw new Error(`Indices must be a matrix, saw:
-         ${indices.shape}`);
-  }
-  if (values.shape.length !== 1) {
-    throw new Error(`Values must be a vector, saw:
-         ${values.shape}`);
-  }
-  if (defaultValue.shape.length !== 0) {
-    throw new Error(`Default value must be a scalar, saw:
-        ${defaultValue.shape}`);
-  }
-
-  const $indices = backend.readSync(indices.dataId) as TypedArray;
-  const $values = backend.readSync(values.dataId) as TypedArray;
-  const $denseShape = backend.readSync(denseShape.dataId) as TypedArray;
-  const $defaultValue =
-      backend.readSync(defaultValue.dataId)[0] as number;
-
-  const [outputIndices, outputIndicesShape, outputValues,
-         emptyRowIndicator, reverseIndexMap] =
-      sparseFillEmptyRowsImplCPU(
-          $indices, indices.shape, indices.dtype, $values, values.dtype,
-          $denseShape, $defaultValue);
-  return [
-    backend.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
-    backend.makeTensorInfo(
-        [outputIndicesShape[0]], values.dtype, outputValues),
-    backend.makeTensorInfo(
-        [emptyRowIndicator.length], 'bool',
-        new Uint8Array(
-            emptyRowIndicator.map((value: boolean) => Number(value)))),
-    backend.makeTensorInfo(
-        [reverseIndexMap.length], indices.dtype,
-        new Int32Array(reverseIndexMap)),
-  ];
-}
-
-export const sparseFillEmptyRowsConfig: KernelConfig = {
-  kernelName: SparseFillEmptyRows,
-  backendName: 'webgl',
-  kernelFunc: sparseFillEmptyRows as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseReshape.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/SparseReshape.ts
deleted file mode 100644
index 7a6e5b846..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseReshape.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SparseReshape, SparseReshapeInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {sparseReshapeImplCPU} from '../kernel_utils/shared';
-
-export function sparseReshape(
-    args: {inputs: SparseReshapeInputs, backend: MathBackendWebGL}):
-    [TensorInfo, TensorInfo] {
-  const {inputs, backend} = args;
-  const {inputIndices, inputShape, newShape} = inputs;
-  if (inputIndices.shape.length !== 2) {
-    throw new Error(`Input indices should be a matrix but received shape ${
-        inputIndices.shape}`);
-  }
-  if (inputShape.shape.length !== 1) {
-    throw new Error(`Input shape should be a vector but received shape ${
-        inputShape.shape}`);
-  }
-
-  if (newShape.shape.length !== 1) {
-    throw new Error(
-        `Target shape should be a vector but received shape ${newShape.shape}`);
-  }
-
-  const $inputShape =
-      Array.from(backend.readSync(inputShape.dataId) as TypedArray);
-  const $inputIndices = backend.readSync(inputIndices.dataId) as TypedArray;
-  const targetShape =
-      Array.from(backend.readSync(newShape.dataId) as TypedArray);
-
-  const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU(
-      $inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape,
-      targetShape);
-  return [
-    backend.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
-    backend.makeTensorInfo(
-        [outputShape.length], newShape.dtype, new Int32Array(outputShape)),
-  ];
-}
-
-export const sparseReshapeConfig: KernelConfig = {
-  kernelName: SparseReshape,
-  backendName: 'webgl',
-  kernelFunc: sparseReshape,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseSegmentMean.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/SparseSegmentMean.ts
deleted file mode 100644
index dcd9dc7de..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseSegmentMean.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SparseSegmentMean, SparseSegmentMeanInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {sparseSegmentReductionImplCPU} from '../kernel_utils/shared';
-
-export function sparseSegmentMean(
-    args: {inputs: SparseSegmentMeanInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {data, indices, segmentIds} = inputs;
-  if (data.shape.length < 1) {
-    throw new Error(
-        `Data should be at least 1 dimensional but received scalar`);
-  }
-  if (indices.shape.length !== 1) {
-    throw new Error(`Indices should be a vector but received shape
-              ${indices.shape}`);
-  }
-  if (segmentIds.shape.length !== 1) {
-    throw new Error(`Segment ids should be a vector but received shape
-              ${segmentIds.shape}`);
-  }
-
-  const $data = backend.readSync(data.dataId) as TypedArray;
-  const $indices = backend.readSync(indices.dataId) as TypedArray;
-  const $segmentIds = backend.readSync(segmentIds.dataId) as TypedArray;
-
-  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU(
-      $data, data.shape, data.dtype, $indices, $segmentIds, true);
-  return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
-}
-
-export const sparseSegmentMeanConfig: KernelConfig = {
-  kernelName: SparseSegmentMean,
-  backendName: 'webgl',
-  kernelFunc: sparseSegmentMean as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseSegmentSum.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/SparseSegmentSum.ts
deleted file mode 100644
index 0421ef204..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseSegmentSum.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SparseSegmentSum, SparseSegmentSumInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {sparseSegmentReductionImplCPU} from '../kernel_utils/shared';
-
-export function sparseSegmentSum(
-    args: {inputs: SparseSegmentSumInputs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {data, indices, segmentIds} = inputs;
-  if (data.shape.length < 1) {
-    throw new Error(
-        `Data should be at least 1 dimensional but received scalar`);
-  }
-  if (indices.shape.length !== 1) {
-    throw new Error(`Indices should be a vector but received shape
-             ${indices.shape}`);
-  }
-  if (segmentIds.shape.length !== 1) {
-    throw new Error(`Segment ids should be a vector but received shape
-             ${segmentIds.shape}`);
-  }
-
-  const $data = backend.readSync(data.dataId) as TypedArray;
-  const $indices = backend.readSync(indices.dataId) as TypedArray;
-  const $segmentIds = backend.readSync(segmentIds.dataId) as TypedArray;
-
-  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU(
-      $data, data.shape, data.dtype, $indices, $segmentIds);
-  return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
-}
-
-export const sparseSegmentSumConfig: KernelConfig = {
-  kernelName: SparseSegmentSum,
-  backendName: 'webgl',
-  kernelFunc: sparseSegmentSum as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseToDense.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/SparseToDense.ts
deleted file mode 100644
index d0108e2ef..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/SparseToDense.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Rank, SparseToDense, SparseToDenseAttrs, SparseToDenseInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {scatterImplCPU} from '../kernel_utils/shared';
-import {ScatterProgram} from '../scatter_gpu';
-
-import {reshape} from './Reshape';
-
-export function sparseToDense(args: {
-  inputs: SparseToDenseInputs,
-  backend: MathBackendWebGL,
-  attrs: SparseToDenseAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {sparseIndices, sparseValues, defaultValue} = inputs;
-  const {outputShape} = attrs;
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);
-  const sumDupeIndices = false;
-
-  if (sparseValues.dtype === 'string') {
-    const indicesBuf = backend.bufferSync<Rank, 'int32'>(sparseIndices);
-    const updatesBuf = backend.bufferSync<Rank, 'string'>(sparseValues);
-    const $defaultValue = util.decodeString(
-        backend.readSync(defaultValue.dataId)[0] as Uint8Array);
-    const outBuf = scatterImplCPU(
-        indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates,
-        sliceRank, strides, $defaultValue, sumDupeIndices);
-    return backend.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
-  }
-  const program = new ScatterProgram(
-      numUpdates, sliceRank, sparseIndices.shape.length,
-      sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);
-
-  const res = backend.runWebGLProgram(
-      program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);
-
-  const reshaped =
-      reshape({inputs: {x: res}, backend, attrs: {shape: outputShape}});
-
-  backend.disposeIntermediateTensorInfo(res);
-  return reshaped;
-}
-
-export const sparseToDenseConfig: KernelConfig = {
-  kernelName: SparseToDense,
-  backendName: 'webgl',
-  kernelFunc: sparseToDense as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/SplitV.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/SplitV.ts
deleted file mode 100644
index b8658bca7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/SplitV.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, SplitV, SplitVAttrs, SplitVInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {slice} from './Slice';
-
-export function splitV(
-    args:
-        {inputs: SplitVInputs, backend: MathBackendWebGL, attrs: SplitVAttrs}):
-    TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {numOrSizeSplits, axis} = attrs;
-
-  const $axis = util.parseAxisParam(axis, x.shape)[0];
-  const splitSizes = backend_util.prepareSplitSize(x, numOrSizeSplits, $axis);
-
-  const xRank = x.shape.length;
-  const begin = new Array(xRank).fill(0);
-  const size = x.shape.slice();
-
-  return splitSizes.map(s => {
-    const sliceSize = [...size];
-    sliceSize[$axis] = s;
-    const sliceT =
-        slice({inputs: {x}, backend, attrs: {begin, size: sliceSize}});
-    begin[$axis] += s;
-    return sliceT;
-  });
-}
-
-export const splitVConfig: KernelConfig = {
-  kernelName: SplitV,
-  backendName: 'webgl',
-  kernelFunc: splitV as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Sqrt.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Sqrt.ts
deleted file mode 100644
index fb461725d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Sqrt.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Sqrt} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {sqrtImplCPU} from '../kernel_utils/shared';
-
-const SQRT = `return sqrt(x);`;
-
-export const sqrt = unaryKernelFunc(
-    {opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU});
-
-export const sqrtConfig: KernelConfig = {
-  kernelName: Sqrt,
-  backendName: 'webgl',
-  kernelFunc: sqrt as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Square.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Square.ts
deleted file mode 100644
index 030f7f6e9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Square.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Square} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const SQUARE = `return x * x;`;
-
-export const square = unaryKernelFunc({opSnippet: SQUARE});
-
-export const squareConfig: KernelConfig = {
-  kernelName: Square,
-  backendName: 'webgl',
-  kernelFunc: square,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/SquaredDifference.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/SquaredDifference.ts
deleted file mode 100644
index e8e658964..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/SquaredDifference.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SquaredDifference} from '@tensorflow/tfjs-core';
-
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';
-
-export const squaredDifference = binaryKernelFunc(
-    {opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE});
-
-export const squaredDifferenceConfig: KernelConfig = {
-  kernelName: SquaredDifference,
-  backendName: 'webgl',
-  kernelFunc: squaredDifference,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/StaticRegexReplace.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/StaticRegexReplace.ts
deleted file mode 100644
index e40ef192c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/StaticRegexReplace.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, NamedAttrMap, StaticRegexReplace, StaticRegexReplaceAttrs, StaticRegexReplaceInputs, TensorInfo} from '@tensorflow/tfjs-core';
-import {MathBackendWebGL} from '../backend_webgl';
-import {staticRegexReplaceImplCPU} from '../kernel_utils/shared';
-
-export function staticRegexReplace(args: {
-  inputs: StaticRegexReplaceInputs,
-  backend: MathBackendWebGL,
-  attrs: StaticRegexReplaceAttrs,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-
-  if (x.dtype !== 'string') {
-    throw new Error('Input must be of datatype string');
-  }
-
-  const $x = backend.readSync(x.dataId) as Uint8Array[];
-
-  const stringInput = backend_util.fromUint8ToStringArray($x);
-  const output = staticRegexReplaceImplCPU(stringInput, 'string',
-                                           attrs as unknown as NamedAttrMap);
-
-  return backend.makeTensorInfo(x.shape, 'string', output);
-}
-
-export const staticRegexReplaceConfig: KernelConfig = {
-  kernelName: StaticRegexReplace,
-  backendName: 'webgl',
-  kernelFunc: staticRegexReplace as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Step.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Step.ts
deleted file mode 100644
index ea87fb027..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Step.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Step, StepAttrs, TensorInfo, UnaryInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {CHECK_NAN_SNIPPET, UnaryOpProgram} from '../unaryop_gpu';
-
-export function step(
-    {inputs, attrs, backend}:
-        {inputs: UnaryInputs, attrs: StepAttrs, backend: MathBackendWebGL}):
-    TensorInfo {
-  const {x} = inputs;
-  const opSnippet = CHECK_NAN_SNIPPET + `
-    return x > 0.0 ? 1.0 : float(${attrs.alpha});
-  `;
-
-  const program = new UnaryOpProgram(x.shape, opSnippet);
-
-  return backend.runWebGLProgram(program, [x], x.dtype);
-}
-
-export const stepConfig: KernelConfig = {
-  kernelName: Step,
-  backendName: 'webgl',
-  kernelFunc: step as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/StridedSlice.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/StridedSlice.ts
deleted file mode 100644
index f7d84af89..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/StridedSlice.ts
+++ /dev/null
@@ -1,105 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, KernelConfig, KernelFunc, Rank, slice_util, StridedSlice, StridedSliceAttrs, StridedSliceInputs, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {stridedSliceImplCPU} from '../kernel_utils/shared';
-import {StridedSliceProgram} from '../strided_slice_gpu';
-
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-
-export function stridedSlice(args: {
-  inputs: StridedSliceInputs,
-  backend: MathBackendWebGL,
-  attrs: StridedSliceAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {
-    begin,
-    end,
-    strides,
-    beginMask,
-    endMask,
-    ellipsisMask,
-    newAxisMask,
-    shrinkAxisMask
-  } = attrs;
-
-  const {
-    finalShapeSparse,
-    finalShape,
-    isIdentity,
-    sliceDim0,
-    isSimpleSlice,
-    begin: $begin,
-    end: $end,
-    strides: $strides
-  } =
-      slice_util.sliceInfo(
-          x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,
-          newAxisMask, shrinkAxisMask);
-
-  let result;
-
-  if (isIdentity) {
-    // Optimization #1, slice is a no-op plus reshape
-    result = reshape({inputs: {x}, backend, attrs: {shape: finalShape}});
-  } else if (sliceDim0 || isSimpleSlice) {
-    // Optimization #2, slice is memory contiguous (only occurs in dim 0)
-    util.assert(
-        x.shape.length >= 1,
-        () => `Input must have rank at least 1, got: ${x.shape.length}`);
-
-    const size = slice_util.computeOutShape($begin, $end, $strides);
-    // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).
-    const sliced = slice({inputs: {x}, backend, attrs: {begin: $begin, size}});
-    result =
-        reshape({inputs: {x: sliced}, backend, attrs: {shape: finalShape}});
-    backend.disposeIntermediateTensorInfo(sliced);
-  } else {
-    const shouldExecuteOnCPU = backend.shouldExecuteOnCPU([x]);
-    if (shouldExecuteOnCPU) {
-      // tslint:disable-next-line: no-unnecessary-type-assertion
-      const values = backend.readSync(x.dataId) as TypedArray;
-      // tslint:disable-next-line: no-unnecessary-type-assertion
-      const xBuf = buffer(x.shape, x.dtype, values) as TensorBuffer<Rank>;
-      const resultValues =
-          stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);
-      result = backend.makeTensorInfo(finalShape, x.dtype, resultValues.values);
-    } else {
-      const program =
-          new StridedSliceProgram($begin, $strides, finalShapeSparse);
-      result = backend.runWebGLProgram(program, [x], x.dtype);
-    }
-  }
-
-  const resultReshaped =
-      reshape({inputs: {x: result}, backend, attrs: {shape: finalShape}});
-
-  backend.disposeIntermediateTensorInfo(result);
-
-  return resultReshaped;
-}
-
-export const stridedSliceConfig: KernelConfig = {
-  kernelName: StridedSlice,
-  backendName: 'webgl',
-  kernelFunc: stridedSlice as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/StringNGrams.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/StringNGrams.ts
deleted file mode 100644
index 7df34f3c4..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/StringNGrams.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringNGrams, StringNGramsAttrs, StringNGramsInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {stringNGramsImplCPU} from '../kernel_utils/shared';
-
-export function stringNGrams(args: {
-  inputs: StringNGramsInputs,
-  backend: MathBackendWebGL,
-  attrs: StringNGramsAttrs
-}): [TensorInfo, TensorInfo] {
-  const {inputs, backend, attrs} = args;
-  const {
-    separator,
-    nGramWidths,
-    leftPad,
-    rightPad,
-    padWidth,
-    preserveShortSequences
-  } = attrs;
-  const {data, dataSplits} = inputs;
-  const $data = backend.readSync(data.dataId) as Uint8Array[];
-  const $dataSplits = backend.readSync(dataSplits.dataId) as Int32Array;
-
-  const [nGrams, nGramsSplits] = stringNGramsImplCPU(
-      $data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth,
-      preserveShortSequences);
-  return [
-    backend.makeTensorInfo([nGrams.length], 'string', nGrams),
-    backend.makeTensorInfo(dataSplits.shape, 'int32', nGramsSplits),
-  ];
-}
-
-export const stringNGramsConfig: KernelConfig = {
-  kernelName: StringNGrams,
-  backendName: 'webgl',
-  kernelFunc: stringNGrams as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/StringSplit.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/StringSplit.ts
deleted file mode 100644
index 0373d34d6..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/StringSplit.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringSplit, StringSplitAttrs, StringSplitInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {stringSplitImplCPU} from '../kernel_utils/shared';
-
-export function stringSplit(args: {
-  inputs: StringSplitInputs,
-  backend: MathBackendWebGL,
-  attrs: StringSplitAttrs
-}): [TensorInfo, TensorInfo, TensorInfo] {
-  const {inputs, backend, attrs} = args;
-  const {skipEmpty} = attrs;
-  const {input, delimiter} = inputs;
-
-  if (input.dtype !== 'string') {
-    throw new Error('Input must be of datatype string');
-  }
-  if (input.shape.length !== 1) {
-    throw new Error(`Input must be a vector, got shape: ${input.shape}`);
-  }
-  if (delimiter.shape.length !== 0) {
-    throw new Error(
-        `Delimiter must be a scalar, got shape: ${delimiter.shape}`);
-  }
-
-  const $input = backend.readSync(input.dataId) as Uint8Array[];
-  const $delimiter = backend.readSync(delimiter.dataId)[0] as Uint8Array;
-
-  const [indices, values, shape] =
-      stringSplitImplCPU($input, $delimiter, skipEmpty);
-  const outputSize = values.length;
-  return [
-    backend.makeTensorInfo([outputSize, 2], 'int32', indices),
-    backend.makeTensorInfo([outputSize], 'string', values),
-    backend.makeTensorInfo([2], 'int32', new Int32Array(shape))
-  ];
-}
-
-export const stringSplitConfig: KernelConfig = {
-  kernelName: StringSplit,
-  backendName: 'webgl',
-  kernelFunc: stringSplit as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/StringToHashBucketFast.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/StringToHashBucketFast.ts
deleted file mode 100644
index 442acae9a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/StringToHashBucketFast.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringToHashBucketFast, StringToHashBucketFastAttrs, StringToHashBucketFastInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {stringToHashBucketFastImplCPU} from '../kernel_utils/shared';
-
-export function stringToHashBucketFast(args: {
-  inputs: StringToHashBucketFastInputs,
-  backend: MathBackendWebGL,
-  attrs: StringToHashBucketFastAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {numBuckets} = attrs;
-  const {input} = inputs;
-
-  if (input.dtype !== 'string') {
-    throw new Error('Input must be of datatype string');
-  }
-  if (numBuckets <= 0) {
-    throw new Error(`Number of buckets must be at least 1`);
-  }
-
-  const $input = backend.readSync(input.dataId) as Uint8Array[];
-
-  const output = stringToHashBucketFastImplCPU($input, numBuckets);
-  return backend.makeTensorInfo(input.shape, 'int32', output);
-}
-
-export const stringToHashBucketFastConfig: KernelConfig = {
-  kernelName: StringToHashBucketFast,
-  backendName: 'webgl',
-  kernelFunc: stringToHashBucketFast as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Sub.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Sub.ts
deleted file mode 100644
index 8d5ec7b7b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Sub.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sub} from '@tensorflow/tfjs-core';
-
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {subImplCPU as cpuSub} from '../kernel_utils/shared';
-
-const SUB = 'return a - b;';
-
-export const sub = binaryKernelFunc({
-  opSnippet: SUB,
-  packedOpSnippet: SUB,
-  supportsComplex: true,
-  cpuKernelImpl: cpuSub
-});
-
-export const subConfig: KernelConfig = {
-  kernelName: Sub,
-  backendName: 'webgl',
-  kernelFunc: sub
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Sum.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Sum.ts
deleted file mode 100644
index 986b30e7b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Sum.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Sum, SumAttrs, SumInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {sumImpl} from './Sum_impl';
-
-export function sum(
-    args: {inputs: SumInputs, attrs: SumAttrs, backend: MathBackendWebGL}) {
-  const {inputs, backend, attrs} = args;
-
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  return sumImpl(x, axis, keepDims, backend);
-}
-
-export const sumConfig: KernelConfig = {
-  kernelName: Sum,
-  backendName: 'webgl',
-  kernelFunc: sum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Sum_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Sum_impl.ts
deleted file mode 100644
index 5ac9d6084..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Sum_impl.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, sumOutType, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {reduce} from '../kernel_utils/reduce';
-import {reshape} from './Reshape';
-
-import {transposeImpl} from './Transpose_impl';
-
-export function sumImpl(
-    x: TensorInfo, axis: number|number[], keepDims: boolean,
-    backend: MathBackendWebGL): TensorInfo {
-  const reductionIndices = axis;
-
-  const xRank = x.shape.length;
-
-  const origAxes = util.parseAxisParam(reductionIndices, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-  const sumInputIsTransposed = permutedAxes != null;
-
-  let sumInput = x;
-  if (sumInputIsTransposed) {
-    sumInput = transposeImpl(x, permutedAxes, backend);
-
-    axes = backend_util.getInnerMostAxes(axes.length, xRank);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);
-  const [sumOutShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(sumInput.shape, axes);
-
-  let outShape = sumOutShape;
-  if (keepDims) {
-    // rather than reshape at the end, set the target shape here.
-    outShape = backend_util.expandShapeToKeepDim(sumOutShape, origAxes);
-  }
-
-  const inSize = util.sizeFromShape(reduceShape);
-  const xSize = util.sizeFromShape(x.shape);
-  const batchSize = xSize / inSize;
-  const reshapedInput = reshape(
-      {inputs: {x: sumInput}, attrs: {shape: [batchSize, inSize]}, backend});
-
-  const outType = sumOutType(x.dtype);
-
-  const reduced = reduce(reshapedInput, outType, 'sum', backend);
-  const out =
-      reshape({inputs: {x: reduced}, attrs: {shape: outShape}, backend});
-
-  backend.disposeIntermediateTensorInfo(reshapedInput);
-  backend.disposeIntermediateTensorInfo(reduced);
-  if (sumInputIsTransposed) {
-    backend.disposeIntermediateTensorInfo(sumInput);
-  }
-
-  return out;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Tan.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Tan.ts
deleted file mode 100644
index 6a050045e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Tan.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tan} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const TAN = `return tan(x);`;
-
-export const tan = unaryKernelFunc({opSnippet: TAN});
-
-export const tanConfig: KernelConfig = {
-  kernelName: Tan,
-  backendName: 'webgl',
-  kernelFunc: tan,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Tanh.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Tanh.ts
deleted file mode 100644
index 8866b54b7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Tanh.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tanh} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-const TANH = `
-  float e2x = exp(-2.0 * abs(x));
-  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`;
-
-export const tanh = unaryKernelFunc({opSnippet: TANH});
-
-export const tanhConfig: KernelConfig = {
-  kernelName: Tanh,
-  backendName: 'webgl',
-  kernelFunc: tanh,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/TensorScatterUpdate.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/TensorScatterUpdate.ts
deleted file mode 100644
index e719c2447..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/TensorScatterUpdate.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, TensorInfo, TensorScatterUpdate, TensorScatterUpdateAttrs, TensorScatterUpdateInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {ScatterProgram} from '../scatter_gpu';
-
-import {reshape} from './Reshape';
-
-export function tensorScatterUpdate(args: {
-  inputs: TensorScatterUpdateInputs,
-  backend: MathBackendWebGL,
-  attrs: TensorScatterUpdateAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {tensor, indices, updates} = inputs;
-  const {} = attrs;
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(updates, indices, tensor.shape);
-
-  const flattenShape = [outputSize / sliceSize, sliceSize];
-
-  if (outputSize === 0) {
-    return backend.makeTensorInfo(tensor.shape, indices.dtype);
-  }
-
-  const flattenIndices = reshape(
-      {inputs: {x: indices}, backend, attrs: {shape: [numUpdates, sliceRank]}});
-  const flattenX = reshape(
-      {inputs: {x: updates}, backend, attrs: {shape: [numUpdates, sliceSize]}});
-  const flattenTensor =
-      reshape({inputs: {x: tensor}, backend, attrs: {shape: flattenShape}});
-  const program = new ScatterProgram(
-      numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length,
-      strides, flattenShape, false, true);
-  const res = backend.runWebGLProgram(
-      program, [flattenX, flattenIndices, flattenTensor], flattenTensor.dtype);
-
-  const reshaped =
-      reshape({inputs: {x: res}, backend, attrs: {shape: tensor.shape}});
-
-  backend.disposeIntermediateTensorInfo(flattenIndices);
-  backend.disposeIntermediateTensorInfo(flattenX);
-  backend.disposeIntermediateTensorInfo(flattenTensor);
-  backend.disposeIntermediateTensorInfo(res);
-
-  return reshaped;
-}
-
-export const tensorScatterUpdateConfig: KernelConfig = {
-  kernelName: TensorScatterUpdate,
-  backendName: 'webgl',
-  kernelFunc: tensorScatterUpdate as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Tile.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Tile.ts
deleted file mode 100644
index 5438a2590..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Tile.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, KernelConfig, KernelFunc, TensorInfo, Tile, TileAttrs, TileInputs, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {tileImplCPU} from '../kernel_utils/shared';
-import {TileProgram} from '../tile_gpu';
-
-export function tile(
-    params: {inputs: TileInputs, backend: MathBackendWebGL, attrs: TileAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = params;
-  const {x} = inputs;
-  const {reps} = attrs;
-
-  // tile gpu program cannot handle rank > 5 case.
-  if (x.dtype === 'string' || x.shape.length > 5) {
-    // Even thought string tensor is always on CPU, just to be consistent on how
-    // to access tensor data.
-    const data = backend.readSync(x.dataId);
-    const value = x.dtype === 'string' ?
-        (data as Uint8Array[]).map(d => util.decodeString(d)) :
-        data as TypedArray;
-    const buf = buffer(x.shape, x.dtype, value);
-    const outBuf = tileImplCPU(buf, reps);
-    return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
-  }
-
-  const program = new TileProgram(x.shape, reps);
-  const output = backend.runWebGLProgram(program, [x], x.dtype);
-
-  return output;
-}
-
-export const tileConfig: KernelConfig = {
-  kernelName: Tile,
-  backendName: 'webgl',
-  kernelFunc: tile as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/TopK.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/TopK.ts
deleted file mode 100644
index 27afd36e4..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/TopK.ts
+++ /dev/null
@@ -1,193 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc, NumericDataType, TensorInfo, TopK, TopKAttrs, TopKInputs, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {topKImplCPU} from '../kernel_utils/shared';
-import {MergeProgram, SwapProgram} from '../top_k_gpu';
-import {fill} from './Fill';
-import {gatherV2} from './GatherV2';
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-
-function disposeIntermediateTensorInfoOrNull(
-    backend: MathBackendWebGL, tensorInfo: TensorInfo) {
-  if (tensorInfo !== null) {
-    backend.disposeIntermediateTensorInfo(tensorInfo);
-  }
-}
-
-function roundUpToPow2(num: number) {
-  let pow2 = 1;
-  while (pow2 < num) {
-    pow2 *= 2;
-  }
-  return pow2;
-}
-
-// Based on Algorithm 2 of Bitonic Top K, ref:
-// https://anilshanbhag.in/static/papers/gputopk_sigmod18.pdf
-export function topK(
-    args: {inputs: TopKInputs, backend: MathBackendWebGL, attrs: TopKAttrs}):
-    TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {k, sorted} = attrs;
-
-  // Empirically determined constant used to determine last dim threshold for
-  // handing off execution to the CPU.
-  const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD =
-      env().getNumber('TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD');
-
-  // Empirically determined constant used to determine k threshold for handing
-  // off execution to the CPU.
-  const TOPK_K_CPU_HANDOFF_THRESHOLD =
-      env().getNumber('TOPK_K_CPU_HANDOFF_THRESHOLD');
-
-  const xShape = x.shape;
-  const lastDim = xShape[xShape.length - 1];
-
-  if (backend.shouldExecuteOnCPU([x]) ||
-      lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD ||
-      k > TOPK_K_CPU_HANDOFF_THRESHOLD) {
-    const xVals = backend.readSync(x.dataId) as TypedArray;
-    const [allTopKVals, allTopKIndices] =
-        topKImplCPU(xVals, xShape, x.dtype as NumericDataType, k, sorted);
-
-    return [
-      backend.makeTensorInfo(
-          allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
-      backend.makeTensorInfo(
-          allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
-    ];
-  }
-
-  if (k === 0) {
-    xShape[xShape.length - 1] = 0;
-    return [
-      backend.makeTensorInfo(xShape, x.dtype, []),
-      backend.makeTensorInfo(xShape, 'int32', [])
-    ];
-  }
-
-  if (lastDim === 1 /* firstPass */) {
-    return [
-      x, fill({attrs: {shape: xShape, dtype: 'int32', value: 0}, backend})
-    ];
-  }
-
-  // Eagerly unpack x input since it is passed in to all the shaders which
-  // require unpacked inputs.
-  const xtexData = backend.texData.get(x.dataId);
-  const xIsPacked = xtexData !== null && xtexData.isPacked;
-  const xUnPacked = xIsPacked ? backend.unpackTensor(x) : x;
-
-  // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.
-  const xSize = util.sizeFromShape(xShape);
-  const batch = xSize / lastDim;
-  const x2D = reshape(
-      {inputs: {x: xUnPacked}, attrs: {shape: [batch, lastDim]}, backend});
-
-  if (xIsPacked) {
-    disposeIntermediateTensorInfoOrNull(backend, xUnPacked);
-  }
-
-  const kPow2 = roundUpToPow2(k);
-  const lastDimPow2 = roundUpToPow2(lastDim);
-
-  // Only the indices containing the top K are kept at every step to reduce
-  // number of outputs in the GPU algorithms, so once the final set of indices
-  // is computed then gather is used to grab the corresponding values
-  // from the original input.
-  let indices: TensorInfo = null;
-
-  // GPU algorithm always takes in an indices input but this input is not used
-  // on the first run of a GPU algorithm, therefore if indices is null we simply
-  // pass in x2D instead of it but the value will not actually be used
-  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];
-
-  const runSwap = (dir: number, inc: number, shape: number[]) => {
-    const inputs = getInputs();
-    const program = new SwapProgram(shape);
-    const fistPass = indices === null ? 1 : 0;
-    const customValues =
-        [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]];
-    const prevIndices = indices;
-    indices = backend.runWebGLProgram(program, inputs, 'int32', customValues);
-    disposeIntermediateTensorInfoOrNull(backend, prevIndices);
-  };
-
-  // Step 1: local sort
-  for (let len = 1; len < kPow2; len *= 2) {
-    const dir = len * 2;
-    for (let inc = len; inc >= 1; inc /= 2) {
-      runSwap(dir, inc, [batch, lastDimPow2]);
-    }
-  }
-
-  // Step 2: merge
-  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {
-    const inputs = getInputs();
-    const mergeProgram = new MergeProgram([batch, indicesSize / 2]);
-    const firstPass = indices === null ? 1 : 0;
-    const customValues = [[lastDim], [firstPass], [kPow2]];
-    const prevIndices = indices;
-    indices =
-        backend.runWebGLProgram(mergeProgram, inputs, 'int32', customValues);
-    disposeIntermediateTensorInfoOrNull(backend, prevIndices);
-
-    // Step 3: rebuild
-    const len = kPow2 / 2;
-    const dir = len * 2;
-    for (let inc = len; inc >= 1; inc /= 2) {
-      runSwap(dir, inc, indices.shape);
-    }
-  }
-
-  // Keep only the requested top K results instead of kPow2
-  let prevIndices = indices;
-  indices = slice(
-      {inputs: {x: indices}, backend, attrs: {begin: 0, size: [batch, k]}});
-  disposeIntermediateTensorInfoOrNull(backend, prevIndices);
-
-  // Gather values on last dimension
-  let values = gatherV2(
-      {inputs: {x: x2D, indices}, backend, attrs: {axis: 1, batchDims: 1}});
-  disposeIntermediateTensorInfoOrNull(backend, x2D);
-
-  // Reshape back to the original input shape, except that the last
-  // dimension is k.
-  const newShape = xShape.slice(0, -1);
-  newShape.push(k);
-
-  prevIndices = indices;
-  indices = reshape({inputs: {x: indices}, attrs: {shape: newShape}, backend});
-  disposeIntermediateTensorInfoOrNull(backend, prevIndices);
-
-  const prevValues = values;
-  values = reshape({inputs: {x: values}, attrs: {shape: newShape}, backend});
-  disposeIntermediateTensorInfoOrNull(backend, prevValues);
-
-  return [values, indices];
-}
-
-export const topKConfig: KernelConfig = {
-  kernelName: TopK,
-  backendName: 'webgl',
-  kernelFunc: topK as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/TopK_test.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/TopK_test.ts
deleted file mode 100644
index 4f213ae99..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/TopK_test.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {WEBGL_ENVS} from '../backend_webgl_test_registry';
-
-describeWithFlags('TopK', WEBGL_ENVS, () => {
-  it('handles packed inputs', async () => {
-    const a = tf.tensor1d([
-      1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2,
-      1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2,
-      1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1
-    ]);
-
-    // pack a using the add op which packs outputs
-    tf.env().set('WEBGL_PACK', true);
-    const aPacked = tf.addN([a, tf.zeros(a.shape)]);
-
-    const k = a.shape[0];
-    const {values, indices} = tf.topk(aPacked, k);
-
-    expect(values.shape).toEqual([k]);
-    expect(indices.shape).toEqual([k]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-
-    tf.test_util.expectArraysEqual(await values.data(), [
-      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-      2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
-    ]);
-    tf.test_util.expectArraysEqual(await indices.data(), [
-      2,  4,  8,  9,  12, 15, 18, 21, 23, 27, 30, 33, 38, 40, 41, 42, 43,
-      45, 47, 48, 49, 51, 52, 54, 55, 57, 61, 63, 0,  1,  3,  5,  6,  7,
-      10, 11, 13, 14, 16, 17, 19, 20, 22, 24, 25, 26, 28, 29, 31, 32, 34,
-      35, 36, 37, 39, 44, 46, 50, 53, 56, 58, 59, 60, 62, 64
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Transform.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Transform.ts
deleted file mode 100644
index fdd094754..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Transform.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Transform, TransformAttrs, TransformInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {TransformProgram} from '../transform_gpu';
-
-export function transform(args: {
-  inputs: TransformInputs,
-  backend: MathBackendWebGL,
-  attrs: TransformAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {image, transforms} = inputs;
-  const {interpolation, fillMode, fillValue, outputShape} = attrs;
-
-  const [batch, imageHeight, imageWidth, numChannels] = image.shape;
-  const [outHeight, outWidth] =
-      outputShape != null ? outputShape : [imageHeight, imageWidth];
-  const outShape =
-      [batch, outHeight, outWidth,
-       numChannels] as [number, number, number, number];
-
-  const program = new TransformProgram(
-      imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);
-  return backend.runWebGLProgram(program, [image, transforms], 'float32');
-}
-
-export const transformConfig: KernelConfig = {
-  kernelName: Transform,
-  backendName: 'webgl',
-  kernelFunc: transform as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Transpose.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Transpose.ts
deleted file mode 100644
index 60466ccb3..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Transpose.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Transpose, TransposeAttrs, TransposeInputs, TypedArray} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {transposeImpl} from './Transpose_impl';
-import {transposeImplCPU as cpuTranspose} from './Transpose_impl';
-
-export function transpose(args: {
-  inputs: TransposeInputs,
-  attrs: TransposeAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {perm} = attrs;
-  const webglBackend = backend;
-
-  const xRank = x.shape.length;
-
-  const newShape: number[] = new Array(xRank);
-  for (let i = 0; i < newShape.length; i++) {
-    newShape[i] = x.shape[perm[i]];
-  }
-
-  let out: TensorInfo;
-  if (webglBackend.shouldExecuteOnCPU([x])) {
-    const xTexData = webglBackend.texData.get(x.dataId);
-    const values = xTexData.values as TypedArray;
-    const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);
-
-    out = webglBackend.makeTensorInfo(newShape, x.dtype);
-    const outData = webglBackend.texData.get(out.dataId);
-    outData.values = outValues;
-  } else {
-    out = transposeImpl(x, perm, webglBackend);
-  }
-  return out;
-}
-
-export const transposeConfig: KernelConfig = {
-  kernelName: Transpose,
-  backendName: 'webgl',
-  kernelFunc: transpose as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Transpose_impl.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Transpose_impl.ts
deleted file mode 100644
index 1b0680bc7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Transpose_impl.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {transposeImplCPU} from '../kernel_utils/shared';
-import {TransposeProgram} from '../transpose_gpu';
-import {TransposePackedProgram} from '../transpose_packed_gpu';
-
-export function transposeImpl(
-    x: TensorInfo, perm: number[], backend: MathBackendWebGL): TensorInfo {
-  const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
-      new TransposePackedProgram(x.shape, perm) :
-      new TransposeProgram(x.shape, perm);
-  return backend.runWebGLProgram(program, [x], x.dtype);
-}
-
-export {transposeImplCPU};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Unique.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Unique.ts
deleted file mode 100644
index aa6dfcf42..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Unique.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Unique, UniqueAttrs, UniqueInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {uniqueImplCPU} from '../kernel_utils/shared';
-import {assertNotComplex} from '../webgl_util';
-
-export function unique(
-    args:
-        {inputs: UniqueInputs, attrs: UniqueAttrs, backend: MathBackendWebGL}):
-    TensorInfo[] {
-  const {inputs, attrs, backend} = args;
-  const {axis} = attrs;
-  const {x} = inputs;
-  assertNotComplex(x, 'unique');
-
-  // For now, always forward calculation to the CPU backend.
-  console.warn(
-      'WARNING: ',
-      'UI might be locked temporarily as data is being downloaded');
-  const values = backend.readSync(x.dataId);
-  const {outputValues, outputShape, indices} =
-      uniqueImplCPU(values, axis, x.shape, x.dtype);
-  return [
-    backend.makeTensorInfo(outputShape, x.dtype, outputValues),
-    backend.makeTensorInfo([indices.length], 'int32', indices),
-  ];
-}
-
-export const uniqueConfig: KernelConfig = {
-  kernelName: Unique,
-  backendName: 'webgl',
-  kernelFunc: unique as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/Unpack.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/Unpack.ts
deleted file mode 100644
index 6300bc032..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/Unpack.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Unpack, UnpackAttrs, UnpackInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-
-export function unpack(
-    args:
-        {inputs: UnpackInputs, backend: MathBackendWebGL, attrs: UnpackAttrs}):
-    TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {value} = inputs;
-  let {axis} = attrs;
-
-  if (axis < 0) {
-    axis += value.shape.length;
-  }
-
-  const x = value;
-  const xRank = x.shape.length;
-
-  const num = value.shape[axis];
-  const outShape: number[] = new Array(xRank - 1);
-  let outIndex = 0;
-  for (let i = 0; i < xRank; i++) {
-    if (i !== axis) {
-      outShape[outIndex++] = x.shape[i];
-    }
-  }
-
-  const toDispose = [];
-
-  const begin = new Array(xRank).fill(0);
-  const size = x.shape.slice();
-  size[axis] = 1;
-  const res: TensorInfo[] = new Array(num);
-  for (let i = 0; i < res.length; i++) {
-    begin[axis] = i;
-    const sliced = slice({inputs: {x}, backend, attrs: {begin, size}});
-    const reshaped =
-        reshape({inputs: {x: sliced}, backend, attrs: {shape: outShape}});
-    res[i] = reshaped;
-
-    toDispose.push(sliced);
-  }
-
-  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));
-  return res;
-}
-
-export const unpackConfig: KernelConfig = {
-  kernelName: Unpack,
-  backendName: 'webgl',
-  kernelFunc: unpack as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/UnsortedSegmentSum.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/UnsortedSegmentSum.ts
deleted file mode 100644
index df41fc993..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/UnsortedSegmentSum.ts
+++ /dev/null
@@ -1,114 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType, KernelConfig, KernelFunc, sumOutType, TensorInfo, UnsortedSegmentSum, UnsortedSegmentSumAttrs, UnsortedSegmentSumInputs, util} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {SegmentOpProgram} from '../segment_gpu';
-
-import {range} from './Range';
-import {reshape} from './Reshape';
-import {tile} from './Tile';
-import {transpose} from './Transpose';
-
-export function unsortedSegmentSum(args: {
-  inputs: UnsortedSegmentSumInputs,
-  backend: MathBackendWebGL,
-  attrs: UnsortedSegmentSumAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, segmentIds} = inputs;
-  const {numSegments} = attrs;
-
-  const xRank = x.shape.length;
-
-  const toDispose = [];
-
-  let axis = 0;
-  const permutation = backend_util.getAxesPermutation([axis], xRank);
-  let permutedX = x;
-  if (permutation != null) {
-    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutation}});
-    toDispose.push(permutedX);
-    axis = backend_util.getInnerMostAxes(1, xRank)[0];
-  }
-
-  const outShape = backend_util.segment_util.computeOutShape(
-      permutedX.shape, axis, numSegments);
-  const inSize = util.sizeFromShape([permutedX.shape[axis]]);
-  const a2D =
-      reshape({inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});
-  toDispose.push(a2D);
-
-  const outputDType = sumOutType(x.dtype);
-
-  const segOpCompute =
-      (x: TensorInfo, segOpType: 'unsortedSegmentSum', segmentIds: TensorInfo,
-       dtype: DataType, numSegments: number): TensorInfo => {
-        const batchSize = x.shape[0];
-        const inSize = x.shape[1];
-        const windowSize =
-            backend_util.segment_util.segOpComputeOptimalWindowSize(
-                inSize, numSegments);
-        const segOpInfo = {windowSize, inSize, batchSize, numSegments};
-        const program = new SegmentOpProgram(segOpInfo, segOpType);
-        const output = backend.compileAndRun(program, [x, segmentIds], dtype);
-        toDispose.push(output);
-        // No need to run another GPGPU program.
-        if (output.shape[1] === numSegments) {
-          return output;
-        }
-        const rangeInfo = range({
-          backend,
-          attrs: {start: 0, stop: numSegments, step: 1, dtype: 'float32'}
-        });
-        const tileInfo = tile({
-          inputs: {x: rangeInfo},
-          backend,
-          attrs: {reps: [inSize / windowSize]}
-        });
-
-        toDispose.push(rangeInfo);
-        toDispose.push(tileInfo);
-
-        const result =
-            segOpCompute(output, segOpType, tileInfo, dtype, numSegments);
-        return result;
-      };
-
-  const segOpResult = segOpCompute(
-      a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments);
-
-  const reshaped =
-      reshape({inputs: {x: segOpResult}, backend, attrs: {shape: outShape}});
-
-  let result = reshaped;
-  if (permutation != null) {
-    toDispose.push(reshaped);
-    const perm = backend_util.getUndoAxesPermutation(permutation);
-    result = transpose({inputs: {x: result}, backend, attrs: {perm}});
-  }
-
-  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));
-  return result;
-}
-
-export const unsortedSegmentSumConfig: KernelConfig = {
-  kernelName: UnsortedSegmentSum,
-  backendName: 'webgl',
-  kernelFunc: unsortedSegmentSum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/ZerosLike.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/ZerosLike.ts
deleted file mode 100644
index f40620be8..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/ZerosLike.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, ZerosLike, ZerosLikeInputs} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-
-import {complex} from './Complex';
-import {fill} from './Fill';
-import {imag} from './Imag';
-import {real} from './Real';
-
-export function zerosLike(
-    args: {inputs: ZerosLikeInputs, backend: MathBackendWebGL}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  if (x.dtype === 'complex64') {
-    const realPart = real({inputs: {input: x}, backend});
-    const r = zerosLike({inputs: {x: realPart}, backend});
-    const imagPart = imag({inputs: {input: x}, backend});
-    const i = zerosLike({inputs: {x: imagPart}, backend});
-
-    const result = complex({inputs: {real: r, imag: i}, backend});
-
-    backend.disposeIntermediateTensorInfo(realPart);
-    backend.disposeIntermediateTensorInfo(r);
-    backend.disposeIntermediateTensorInfo(imagPart);
-    backend.disposeIntermediateTensorInfo(i);
-
-    return result;
-  } else {
-    return fill({
-      attrs: {
-        shape: x.shape,
-        dtype: x.dtype,
-        value: x.dtype === 'string' ? '' : 0
-      },
-      backend
-    });
-  }
-}
-
-export const zerosLikeConfig: KernelConfig = {
-  kernelName: ZerosLike,
-  backendName: 'webgl',
-  kernelFunc: zerosLike as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/kernels/_FusedMatMul.ts b/tfjs-master/tfjs-backend-webgl/src/kernels/_FusedMatMul.ts
deleted file mode 100644
index 90ac1aa51..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/kernels/_FusedMatMul.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {MathBackendWebGL} from '../backend_webgl';
-import {batchMatMulImpl} from './BatchMatMul_impl';
-
-export function _fusedMatMul(args: {
-  inputs: _FusedMatMulInputs,
-  attrs: _FusedMatMulAttrs,
-  backend: MathBackendWebGL
-}) {
-  const {inputs, backend, attrs} = args;
-  const {a, b, bias, preluActivationWeights} = inputs;
-  const {transposeA, transposeB, activation, leakyreluAlpha} = attrs;
-
-  return batchMatMulImpl({
-    a,
-    b,
-    transposeA,
-    transposeB,
-    backend,
-    bias,
-    preluActivationWeights,
-    leakyreluAlpha,
-    activation
-  });
-}
-
-export const _fusedMatMulConfig: KernelConfig = {
-  kernelName: _FusedMatMul,
-  backendName: 'webgl',
-  kernelFunc: _fusedMatMul as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgl/src/lrn_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/lrn_gpu.ts
deleted file mode 100644
index 6cde2205d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/lrn_gpu.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class LRNProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(
-      xShape: number[], radius: number, bias: number, alpha: number,
-      beta: number) {
-    const rad = radius;
-    const maxD = xShape[3] - 1;
-    this.outputShape = xShape;
-
-    // optimize pow(bias + alpha * sum, -beta)
-    // src: https://github.com/tensorflow/tensorflow/..
-    // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
-    // tensorflow/core/kernels/mkl_lrn_op.cc#L320
-    let powOperator;
-    const basis = `float(${bias}) + float(${alpha}) * sum`;
-    if (beta === 0.5) {
-      powOperator = `inversesqrt(${basis})`;
-    } else if (beta === 1.0) {
-      powOperator = `1.0/(${basis})`;
-    } else {
-      powOperator = `exp(log(${basis}) * float(-${beta}));`;
-    }
-
-    this.userCode = `
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int r = coords[1];
-        int c = coords[2];
-        int d = coords[3];
-        float x = getX(b, r, c, d);
-        float sum = 0.0;
-        for (int j = -${rad}; j <= ${rad}; j++) {
-          int idx = d + j;
-          if (idx >= 0 && idx <=  ${maxD}) {
-            float z = getX(b, r, c, idx);
-            sum += z * z;
-          }
-        }
-        float val = x * ${powOperator};
-        setOutput(val);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/lrn_grad_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/lrn_grad_gpu.ts
deleted file mode 100644
index 998f535c8..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/lrn_grad_gpu.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class LRNGradProgram implements GPGPUProgram {
-  variableNames = ['inputImage', 'outputImage', 'dy'];
-  outputShape: number[] = [];
-  userCode: string;
-  depthRadius: number;
-  bias: number;
-  alpha: number;
-  beta: number;
-  depth: number;
-
-  constructor(
-      inputShape: number[], depthRadius: number, bias: number, alpha: number,
-      beta: number) {
-    this.outputShape = inputShape;
-    this.depth = inputShape[3];
-    this.depthRadius = depthRadius;
-    this.bias = bias;
-    this.alpha = alpha;
-    this.beta = beta;
-    this.userCode = `
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int r = coords[1];
-        int c = coords[2];
-
-        float result = 0.0;
-        for (int d = 0; d < ${this.depth}; ++d) {
-          int depthBegin = int(max(0.0, float(d - ${depthRadius})));
-          int depthEnd = int(min(float(${this.depth}),
-              float(d + ${depthRadius} + 1)));
-
-          const int MIN_DEPTH_BEGIN = 0;
-          const int MAX_DEPTH_END = ${this.depth};
-
-          float norm = 0.0;
-          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
-            if (k < depthBegin){
-              continue;
-            }
-            else if (k >= depthBegin && k < depthEnd) {
-              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
-            }
-            else {
-              break;
-            }
-          }
-
-          norm = float(${alpha}) * norm + float(${bias});
-
-          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
-            if (k < depthBegin){
-              continue;
-            }
-            else if (k >= depthBegin && k < depthEnd){
-              float dyi = -2.0 * float(${alpha})
-                * float(${beta})
-                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)
-                / norm;
-              if (k == d) {
-                dyi += pow(norm, -1.0 * ${beta});
-              }
-              if (k == coords[3]) {
-                dyi *= getDy(b, r, c, d);
-                result += dyi;
-              }
-            }
-            else {
-              break;
-            }
-          }
-      }
-      setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/lrn_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/lrn_packed_gpu.ts
deleted file mode 100644
index b2d83c863..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/lrn_packed_gpu.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class LRNPackedProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[] = [];
-  userCode: string;
-  packedInputs = true;
-  packedOutput = true;
-
-  constructor(
-      xShape: number[], radius: number, bias: number, alpha: number,
-      beta: number) {
-    const rad = radius;
-    const maxD = xShape[3] - 1;
-    this.outputShape = xShape;
-
-    // optimize pow(bias + alpha * sum, -beta)
-    // src: https://github.com/tensorflow/tensorflow/..
-    // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
-    // tensorflow/core/kernels/mkl_lrn_op.cc#L320
-    let powOperator;
-    const basis = `float(${bias}) + float(${alpha}) * sum`;
-    if (beta === 0.5) {
-      powOperator = `inversesqrt(${basis})`;
-    } else if (beta === 1.0) {
-      powOperator = `1.0/(${basis})`;
-    } else {
-      powOperator = `exp(log(${basis}) * float(-${beta}));`;
-    }
-
-    this.userCode = `
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords.x;
-        int r = coords.y;
-        int c = coords.z;
-        int d = coords.w;
-
-        bool hasNextCol = d < ${this.outputShape[3]};
-        bool hasNextRow = c < ${this.outputShape[2]};
-
-        vec4 sum = vec4(0.);
-        vec4 xFragAtOutputCoords = getX(b, r, c, d);
-
-        vec4 xAtOutputCoords = vec4(
-          getChannel(xFragAtOutputCoords, vec2(c, d)),
-          hasNextCol ?
-            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
-          hasNextRow ?
-            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
-          (hasNextRow && hasNextCol) ?
-            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
-        );
-
-        int firstChannel = d - ${rad};
-        vec2 cache = vec2(0.);
-        if(firstChannel >= 0){
-          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
-          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
-            if(hasNextRow){
-              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
-            }
-        }
-
-        ivec2 depth = ivec2(d, d + 1);
-        for (int j = - ${rad}; j <= ${rad}; j++) {
-          ivec2 idx = depth + j;
-          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
-          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));
-
-          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
-          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
-
-          if(depthInRange || depthPlusOneInRange){
-            vec4 z = vec4(0.);
-            vec4 xFragAtCurrentDepth;
-            z.xz = cache.xy;
-            if(depthPlusOneInRange && hasNextCol){
-              xFragAtCurrentDepth = idx.y != d ?
-                getX(b, r, c, idx.y) : xFragAtOutputCoords;
-              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
-              if(hasNextRow){
-                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
-              }
-            }
-            cache.xy = z.yw;
-            sum += z * z;
-          }
-        }
-        vec4 result = xAtOutputCoords * ${powOperator};
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/max_pool_backprop_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/max_pool_backprop_gpu.ts
deleted file mode 100644
index 69df54299..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/max_pool_backprop_gpu.ts
+++ /dev/null
@@ -1,177 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class MaxPool2DBackpropProgram implements GPGPUProgram {
-  variableNames = ['dy', 'maxPos'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const dilationHeight = convInfo.dilationHeight;
-    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-
-    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
-    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
-
-    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
-    this.userCode = `
-      const ivec2 pads = ivec2(${padTop}, ${padLeft});
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int d = coords[3];
-
-        ivec2 dyRCCorner = coords.yz - pads;
-        int dyRCorner = dyRCCorner.x;
-        int dyCCorner = dyRCCorner.y;
-
-        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
-        // ? = to be determined. : = across all values in that axis.
-        float dotProd = 0.0;
-        for (int wR = 0; wR < ${effectiveFilterHeight};
-          wR += ${dilationHeight}) {
-          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
-
-          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
-            continue;
-          }
-          int idyR = int(dyR);
-
-          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
-
-            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
-                fract(dyC) > 0.0) {
-              continue;
-            }
-            int idyC = int(dyC);
-
-            float dyValue = getDy(b, idyR, idyC, d);
-            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));
-
-            // Get the current value, check it against the value from the
-            // position matrix.
-            int curPosValue = wR * ${effectiveFilterWidth} + wC;
-            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
-
-            dotProd += dyValue * mask;
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
-
-export class MaxPool3DBackpropProgram implements GPGPUProgram {
-  variableNames = ['dy', 'maxPos'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.inShape;
-    const strideDepth = convInfo.strideDepth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const dilationDepth = convInfo.dilationDepth;
-    const dilationHeight = convInfo.dilationHeight;
-    const dilationWidth = convInfo.dilationWidth;
-    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
-    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-
-    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
-    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
-    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
-
-    const lastIndex =
-        effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;
-    this.userCode = `
-      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
-
-      void main() {
-        ivec5 coords = getOutputCoords();
-        int batch = coords.x;
-        int ch = coords.u;
-
-        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
-        int dyDCorner = dyCorner.x;
-        int dyRCorner = dyCorner.y;
-        int dyCCorner = dyCorner.z;
-
-        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
-        // dx(xD, xR, xC, ch).
-        // ? = to be determined. : = across all values in that axis.
-        float dotProd = 0.0;
-
-        for (int wD = 0; wD < ${effectiveFilterDepth};
-           wD += ${dilationDepth}) {
-          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
-
-          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
-            continue;
-          }
-          int idyD = int(dyD);
-
-          for (int wR = 0; wR < ${effectiveFilterHeight};
-              wR += ${dilationHeight}) {
-            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
-
-            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
-                fract(dyR) > 0.0) {
-              continue;
-            }
-            int idyR = int(dyR);
-
-            for (int wC = 0; wC < ${effectiveFilterWidth};
-                wC += ${dilationWidth}) {
-              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
-
-              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
-                  fract(dyC) > 0.0) {
-                continue;
-              }
-              int idyC = int(dyC);
-
-              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
-              int maxPosValue = ${lastIndex} -
-                  int(getMaxPos(batch, idyD, idyR, idyC, ch));
-
-              // Get the current value, check it against the value from the
-              // position matrix.
-              int curPosValue =
-                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
-                  wR * ${effectiveFilterWidth} + wC;
-              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
-
-              dotProd += dyValue * mask;
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/mean_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/mean_gpu.ts
deleted file mode 100644
index bc695e7d4..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/mean_gpu.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class MeanProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(reduceInfo: backend_util.ReduceInfo, divisor?: number) {
-    const {windowSize, batchSize, inSize, outSize} = reduceInfo;
-    this.outputShape = [batchSize, outSize];
-
-    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
-    const windowSizeVec4Remainder = windowSize % 4;
-
-    let updateSnippet = `sumValue += dot(values, ones);`;
-    if (divisor != null) {
-      const denominator = 1 / divisor;
-      updateSnippet = `sumValue += dot(values * ${
-          util.isInt(denominator) ? denominator.toPrecision(2) :
-                                    denominator}, ones);`;
-    }
-
-    let checkOutOfBounds = '';
-    if (inSize % windowSize > 0) {
-      checkOutOfBounds = `
-        if (inIdx < 0 || inIdx >= ${inSize}) {
-          return 0.0;
-        }
-      `;
-    }
-
-    this.userCode = `
-      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
-
-      float getValue(int batch, int inIdx) {
-        ${checkOutOfBounds}
-        return getX(batch, inIdx);
-      }
-
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int batch = coords[0];
-        int outIdx = coords[1];
-        int inOffset = outIdx * ${windowSize};
-
-        float sumValue = 0.0;
-
-        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
-          int inIdx = inOffset + i;
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2),
-            getValue(batch, inIdx + 3)
-          );
-
-          ${updateSnippet}
-        }
-
-        int inIdx = inOffset + ${windowSizeNearestVec4};
-        if (${windowSizeVec4Remainder === 1}) {
-          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
-
-          ${updateSnippet}
-        } else if (${windowSizeVec4Remainder === 2}) {
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1), 0.0, 0.0);
-
-          ${updateSnippet}
-        } else if (${windowSizeVec4Remainder === 3}) {
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2), 0.0);
-
-          ${updateSnippet}
-        }
-        setOutput(sumValue);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/mirror_pad_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/mirror_pad_gpu.ts
deleted file mode 100644
index f9f4faaf4..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/mirror_pad_gpu.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export class MirrorPadProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      xShape: number[], paddings: Array<[number, number]>,
-      mode: 'reflect'|'symmetric') {
-    this.outputShape = paddings.map(
-        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);
-    const rank = xShape.length;
-    const dtype = getCoordsDataType(rank);
-
-    const start = paddings.map(p => p[0]).join(',');
-    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');
-    const unpackedCoords =
-        ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);
-    const offset = mode === 'reflect' ? 0 : 1;
-
-    if (rank === 1) {
-      this.userCode = `
-        int start = ${start};
-        int end = ${end};
-
-        void main() {
-          int outC = getOutputCoords();
-          if (outC < start) {
-            outC = start * 2 - outC - ${offset};
-          } else if(outC >= end) {
-            outC = (end - 1) * 2 - outC + ${offset};
-          }
-          setOutput(getX(outC - start));
-        }
-      `;
-      return;
-    }
-    this.userCode = `
-      ${dtype} start = ${dtype}(${start});
-      ${dtype} end = ${dtype}(${end});
-
-      void main() {
-        ${dtype} outC = getOutputCoords();
-        for (int i = 0; i < ${rank}; i++) {
-          if (outC[i] < start[i]) {
-            outC[i] = start[i] * 2 - outC[i] - ${offset};
-          } else if(outC[i] >= end[i]) {
-            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
-          }
-        }
-        ${dtype} coords = outC - start;
-        setOutput(getX(${unpackedCoords}));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/mirror_pad_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/mirror_pad_packed_gpu.ts
deleted file mode 100644
index e684c1516..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/mirror_pad_packed_gpu.ts
+++ /dev/null
@@ -1,153 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getChannels} from './packing_util';
-import {getCoordsDataType} from './shader_compiler';
-
-/**
- * Example shader code for
- * `mirrorPad(tf.tensor1d([1, 2, 3], 'int32'), [[2, 2]], 'reflect')`
- * ```
- *    const int start = int(2);
- *    const int end = int(5);
- *
- *    void main() {
- *       int outputLoc = getOutputCoords();
- *       vec4 result = vec4(0.);
- *
- *       int rc = outputLoc;
- *
- *       int source = rc;
- *       if (source < start) {
- *         source = start * 2 - source - 0;
- *       } else if (source >= end) {
- *         source = (end - 1) * 2 - source + 0;
- *       }
- *       source -= start;
- *
- *       result[0] = getChannel(getX(source), source);
- *       rc += 1;
- *       if(rc < 6) {
- *          int source = rc;
- *          if (source < start) {
- *            source = start * 2 - source - 0;
- *          } else if (source >= end) {
- *            source = (end - 1) * 2 - source + 0;
- *          }
- *          source -= start;
- *
- *         result[1] = getChannel(getX(source), source);
- *       }
- *
- *       setOutput(result);
- *     }
- * ```
- */
-export class MirrorPadPackedProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      xShape: number[], paddings: Array<[number, number]>,
-      mode: 'reflect'|'symmetric') {
-    this.outputShape = paddings.map(
-        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);
-    const rank = xShape.length;
-    const dtype = getCoordsDataType(rank);
-
-    const start = paddings.map(p => p[0]).join(',');
-    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');
-    const coords = getChannels('rc', rank);
-    const source = getChannels('source', rank);
-    const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;
-    const innerDims =
-        rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;
-    const offset = mode === 'reflect' ? 0 : 1;
-
-    let mainLoop = '';
-    if (rank === 1) {
-      const padSetup = `
-        ${dtype} source = rc;
-        if (source < start) {
-          source = start * 2 - source - ${offset};
-        } else if (source >= end) {
-          source = (end - 1) * 2 - source + ${offset};
-        }
-        source -= start;
-      `;
-      mainLoop = `
-        ${dtype} rc = outputLoc;
-        ${padSetup}
-        result[0] = getChannel(getX(${source.join()}), ${innerDims});
-        ${coords[rank - 1]} += 1;
-        if(${cLimit}) {
-          ${padSetup}
-          result[1] = getChannel(getX(${source.join()}), ${innerDims});
-        }
-      `;
-    } else {
-      const padSetup = `
-        ${dtype} source = rc;
-        ${dtype} lt = ${dtype}(lessThan(source, start));
-        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));
-        ${dtype} orig = 1 - (lt + gte);
-        source = orig * source +
-                lt * (start * 2 - source - ${offset}) +
-                gte * ((end - 1) * 2 - source + ${offset});
-        source -= start;
-      `;
-
-      mainLoop = `
-        ${dtype} rc = outputLoc;
-        ${padSetup}
-        result[0] = getChannel(getX(${source.join()}), ${innerDims});
-        ${coords[rank - 1]} += 1;
-        if(${cLimit}) {
-          ${padSetup}
-          result[1] = getChannel(getX(${source.join()}), ${innerDims});
-        }
-        rc = outputLoc;
-        ${coords[rank - 2]} += 1;
-        if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {
-          ${padSetup}
-          result[2] = getChannel(getX(${source.join()}), ${innerDims});
-          ${coords[rank - 1]} += 1;
-          if(${cLimit}) {
-            ${padSetup}
-            result[3] = getChannel(getX(${source.join()}), ${innerDims});
-          }
-        }
-      `;
-    }
-
-    this.userCode = `
-      const ${dtype} start = ${dtype}(${start});
-      const ${dtype} end = ${dtype}(${end});
-
-      void main() {
-        ${dtype} outputLoc = getOutputCoords();
-        vec4 result = vec4(0.);
-        ${mainLoop}
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/mulmat_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/mulmat_packed_gpu.ts
deleted file mode 100644
index 3a19ded1e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/mulmat_packed_gpu.ts
+++ /dev/null
@@ -1,119 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-
-export class MatMulPackedProgram implements GPGPUProgram {
-  variableNames = ['matrixA', 'matrixB'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[];
-  userCode: string;
-  enableShapeUniforms: boolean;
-
-  constructor(
-      aShape: [number, number, number], bShape: [number, number, number],
-      outputShape: [number, number, number], transposeA = false,
-      transposeB = false, addBias = false, activation: string = null,
-      hasPreluActivation = false, hasLeakyreluActivation = false) {
-    this.outputShape = outputShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-
-    const sharedDim = transposeA ? aShape[1] : aShape[2];
-    const sharedDimensionPacked = Math.ceil(sharedDim / 2);
-
-    const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';
-    const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';
-    const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];
-    const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];
-
-    let activationSnippet = '', applyActivationSnippet = '';
-    if (activation) {
-      if (hasPreluActivation) {
-        activationSnippet = `vec4 activation(vec4 a) {
-          vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${activation}
-        }`;
-      } else if (hasLeakyreluActivation) {
-        activationSnippet = `vec4 activation(vec4 a) {
-          vec4 b = getLeakyreluAlphaAtOutCoords();
-          ${activation}
-        }`;
-      } else {
-        activationSnippet = `vec4 activation(vec4 x) {
-          ${activation}
-        }`;
-      }
-
-      applyActivationSnippet = `result = activation(result);`;
-    }
-
-    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-
-    if (hasPreluActivation) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    if (hasLeakyreluActivation) {
-      this.variableNames.push('leakyreluAlpha');
-    }
-
-    let batchASnippet = 'rc.x';
-    let batchBSnippet = 'rc.x';
-    if (aShape[0] < bShape[0]) {
-      batchASnippet = `imod(rc.x, ${aShape[0]})`;
-    } else if (bShape[0] < aShape[0]) {
-      batchBSnippet = `imod(rc.x, ${bShape[0]})`;
-    }
-
-    this.userCode = `
-      ${activationSnippet}
-      // Don't use uniform for sharedDimensionPacked for performance.
-      const float sharedDimension = ${sharedDimensionPacked}.0;
-
-      vec4 dot2x2ARowBCol(ivec3 rc) {
-        vec4 result = vec4(0);
-        int batchA = ${batchASnippet};
-        int batchB = ${batchBSnippet};
-        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
-          vec4 a = getMatrixA(batchA, ${aSample});
-          vec4 b = getMatrixB(batchB, ${bSample});
-
-          // These swizzled products need to be separately added.
-          // See: https://github.com/tensorflow/tfjs/issues/1735
-          result += (${aSwizzle[0]} * ${bSwizzle[0]});
-          result += (${aSwizzle[1]} * ${bSwizzle[1]});
-        }
-        return result;
-      }
-
-      void main() {
-        ivec3 rc = getOutputCoords();
-        vec4 result = dot2x2ARowBCol(rc);
-
-        ${addBiasSnippet}
-
-        ${applyActivationSnippet}
-
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/multinomial_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/multinomial_gpu.ts
deleted file mode 100644
index 429cb05e5..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/multinomial_gpu.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {UniformType} from './shader_compiler';
-
-export class MultinomialProgram implements GPGPUProgram {
-  variableNames = ['probs'];
-  outputShape: number[];
-  userCode: string;
-  customUniforms = [{name: 'seed', type: 'float' as UniformType}];
-
-  constructor(batchSize: number, numOutcomes: number, numSamples: number) {
-    this.outputShape = [batchSize, numSamples];
-
-    this.userCode = `
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int batch = coords[0];
-
-        float r = random(seed);
-        float cdf = 0.0;
-
-        for (int i = 0; i < ${numOutcomes - 1}; i++) {
-          cdf += getProbs(batch, i);
-
-          if (r < cdf) {
-            setOutput(float(i));
-            return;
-          }
-        }
-
-        // If no other event happened, last event happened.
-        setOutput(float(${numOutcomes - 1}));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/onehot_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/onehot_gpu.ts
deleted file mode 100644
index 9f1519857..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/onehot_gpu.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class OneHotProgram implements GPGPUProgram {
-  variableNames = ['indices'];
-  outputShape: number[];
-  userCode: string;
-
-  // Caching uniform location for speed.
-  seedLoc: WebGLUniformLocation;
-
-  constructor(
-      numIndices: number, depth: number, onValue: number, offValue: number) {
-    this.outputShape = [numIndices, depth];
-
-    this.userCode = `
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int index = round(getIndices(coords.x));
-        setOutput(mix(float(${offValue}), float(${onValue}),
-                      float(index == coords.y)));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/pack_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/pack_gpu.ts
deleted file mode 100644
index 5180732e3..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/pack_gpu.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-import {getChannels} from './packing_util';
-import {getCoordsDataType} from './shader_compiler';
-
-export class PackProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[];
-  userCode: string;
-  packedInputs = false;
-  packedOutput = true;
-  enableShapeUniforms: boolean;
-  rank: number;
-
-  constructor(
-      outputShape:
-          number[]) {  // TODO(https://github.com/tensorflow/tfjs/issues/893):
-                       // Only input / output 3D tensors.
-    this.outputShape = outputShape;
-    this.rank = outputShape.length;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-
-    if (this.rank === 0) {
-      this.userCode = `
-        void main() {
-          setOutput(vec4(getA(), 0., 0., 0.));
-        }
-      `;
-    } else {
-      const channels = getChannels('rc', this.rank);
-      const dtype = getCoordsDataType(this.rank);
-      const outOfBoundsCondition = this.getOutOfBoundsCondition(channels);
-      const setup = this.getSetup(channels);
-      const output = this.getOutput(channels);
-
-      this.userCode = `
-        void main() {
-          ${dtype} rc = getOutputCoords();
-
-          if(${outOfBoundsCondition}) {
-            setOutput(vec4(0));
-          } else {
-            ${setup}
-
-            setOutput(vec4(${output}));
-          }
-        }
-      `;
-    }
-  }
-
-  private getSourceCoordsArr(dims: string[]): string[] {
-    const coords = [];
-
-    for (let row = 0; row <= 1; row++) {
-      for (let col = 0; col <= 1; col++) {
-        let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;
-
-        for (let d = 2; d < this.rank; d++) {
-          coord = `${dims[dims.length - 1 - d]},` + coord;
-        }
-
-        coords.push(coord);
-      }
-    }
-    return coords;
-  }
-
-  private getOutOfBoundsCondition(dims: string[]): string {
-    if (this.rank === 1) {
-      return `rc > ${
-          this.enableShapeUniforms ? 'outShape' : this.outputShape[0]}`;
-    }
-
-    let cond = '';
-    for (let i = this.rank - 2; i < this.rank; i++) {
-      cond += `${dims[i]} >= ${
-          this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`;
-      if (i < this.rank - 1) {
-        cond += '||';
-      }
-    }
-
-    return cond;
-  }
-
-  private getSetup(dims: string[]): string {
-    if (this.rank === 1) {
-      return '';
-    }
-
-    const innerDims = dims.slice(-2);
-    const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` :
-                                           this.outputShape[this.rank - 1];
-    const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` :
-                                           this.outputShape[this.rank - 2];
-
-    return `
-      int r = ${innerDims[0]};
-      int c = ${innerDims[1]};
-      int rp1 = r + 1;
-      int cp1 = c + 1;
-
-      bool cEdge = cp1 >= ${col};
-      bool rEdge = rp1 >= ${row};
-    `;
-  }
-
-  private getOutput(dims: string[]): string {
-    const sourceCoords = this.getSourceCoordsArr(dims);
-    if (this.rank === 1) {
-      const outShape =
-          this.enableShapeUniforms ? 'outShape' : this.outputShape[0];
-      return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`;
-    }
-
-    return `getA(${sourceCoords[0]}),
-            cEdge ? 0. : getA(${sourceCoords[1]}),
-            rEdge ? 0. : getA(${sourceCoords[2]}),
-            rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/packing_util.ts b/tfjs-master/tfjs-backend-webgl/src/packing_util.ts
deleted file mode 100644
index cd0350708..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/packing_util.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export function getVecChannels(name: string, rank: number): string[] {
-  return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);
-}
-
-export function getChannels(name: string, rank: number): string[] {
-  if (rank === 1) {
-    return [name];
-  }
-  return getVecChannels(name, rank);
-}
-
-export function getSourceCoords(rank: number, dims: string[]): string {
-  if (rank === 1) {
-    return 'rc';
-  }
-
-  let coords = '';
-  for (let i = 0; i < rank; i++) {
-    coords += dims[i];
-    if (i < rank - 1) {
-      coords += ',';
-    }
-  }
-  return coords;
-}
\ No newline at end of file
diff --git a/tfjs-master/tfjs-backend-webgl/src/pad_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/pad_gpu.ts
deleted file mode 100644
index eb0d2b2d0..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/pad_gpu.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType, UniformType} from './shader_compiler';
-
-export class PadProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  userCode: string;
-  customUniforms = [{name: 'value', type: 'float' as UniformType}];
-
-  constructor(
-      xShape: number[], paddings: Array<[number, number]>,
-      constantValue: number) {
-    this.outputShape = paddings.map(
-        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);
-    const rank = xShape.length;
-    const type = getCoordsDataType(rank);
-
-    const start = paddings.map(p => p[0]).join(',');
-    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');
-    const unpackedCoords =
-        ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);
-
-    if (rank === 1) {
-      this.userCode = `
-        int start = ${start};
-        int end = ${end};
-
-        void main() {
-          int outC = getOutputCoords();
-          if (outC < start || outC >= end) {
-            setOutput(value);
-          } else {
-            setOutput(getX(outC - start));
-          }
-        }
-      `;
-      return;
-    }
-    this.userCode = `
-      ${type} start = ${type}(${start});
-      ${type} end = ${type}(${end});
-
-      void main() {
-        ${type} outC = getOutputCoords();
-        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
-          setOutput(value);
-        } else {
-          ${type} coords = outC - start;
-          setOutput(getX(${unpackedCoords}));
-        }
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/pad_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/pad_packed_gpu.ts
deleted file mode 100644
index 4f7c67ddf..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/pad_packed_gpu.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getChannels} from './packing_util';
-import {getCoordsDataType, UniformType} from './shader_compiler';
-
-export class PadPackedProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[];
-  userCode: string;
-  customUniforms = [{name: 'value', type: 'float' as UniformType}];
-
-  constructor(
-      xShape: number[], paddings: Array<[number, number]>,
-      constantValue: number) {
-    this.outputShape = paddings.map(
-        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);
-    const rank = xShape.length;
-    const dtype = getCoordsDataType(rank);
-
-    const start = paddings.map(p => p[0]).join(',');
-    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');
-    const coords = getChannels('rc', rank);
-    const source = getChannels('source', rank);
-    const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;
-    const innerDims =
-        rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;
-
-    const componentSetup = [
-      `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;
-       if(${cLimit}) {
-      `,
-      rank === 1 ? '' : `}
-       rc = outputLoc;
-       ${coords[rank - 2]} += 1;
-       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,
-      rank === 1 ? '' : `  ${coords[rank - 1]} += 1;
-         if(${cLimit}) {`
-    ];
-
-    const paddingArea = rank === 1 ?
-        'rc < start || rc >= end' :
-        'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';
-    let mainLoop = '';
-    for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {
-      mainLoop += `
-        ${componentSetup[i]}
-        if (${paddingArea}) {
-          result[${i}] = float(value);
-        } else {
-          ${dtype} source = rc - start;
-          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
-        }
-      `;
-    }
-    mainLoop += (rank === 1 ? `} ` : `}}`);
-
-    this.userCode = `
-      const ${dtype} start = ${dtype}(${start});
-      const ${dtype} end = ${dtype}(${end});
-
-      void main() {
-        ${dtype} outputLoc = getOutputCoords();
-        vec4 result = vec4(0.);
-        ${mainLoop}
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/pool_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/pool_gpu.ts
deleted file mode 100644
index f3ea8e4d7..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/pool_gpu.ts
+++ /dev/null
@@ -1,459 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class Pool2DProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, poolType: 'max'|'avg',
-      computePositions: boolean, flattenPositions = false,
-      includeBatchInIndex = false) {
-    if (poolType === 'avg' && computePositions) {
-      throw new Error('Cannot compute positions for average pool.');
-    }
-
-    const filterWidth = convInfo.filterWidth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const dilationHeight = convInfo.dilationHeight;
-    const dilationWidth = convInfo.dilationWidth;
-    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-
-    const padTop = convInfo.padInfo.top;
-    const padLeft = convInfo.padInfo.left;
-    this.outputShape = convInfo.outShape;
-
-    const isAvgPool = poolType === 'avg';
-    const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${
-        convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
-    const flattenPositionStr =
-        `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
-
-    let initializationValue = '0.0';
-    if (!isAvgPool) {
-      // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
-      initializationValue = '-1.0 / 1e-20';
-    }
-
-    if (computePositions) {
-      const compareOp = '>=';
-
-      this.userCode = `
-        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
-        const ivec2 pads = ivec2(${padTop}, ${padLeft});
-
-        void main() {
-          ivec4 coords = getOutputCoords();
-          int batch = coords[0];
-          int d = coords[3];
-
-          ivec2 xRCCorner = coords.yz * strides - pads;
-          int xRCorner = xRCCorner.x;
-          int xCCorner = xRCCorner.y;
-
-          // max/min x(?, ?, d) to get y(yR, yC, d).
-          // ? = to be determined
-          float minMaxValue = 0.0;
-          float minMaxValueFound = 0.0;
-          int minMaxPosition = 0;
-          float avgValue = 0.0;
-
-          for (int wR = 0; wR < ${effectiveFilterHeight};
-              wR += ${dilationHeight}) {
-            int xR = xRCorner + wR;
-
-            if (xR < 0 || xR >= ${convInfo.inHeight}) {
-              continue;
-            }
-
-            for (int wC = 0; wC < ${effectiveFilterWidth};
-                wC += ${dilationWidth}) {
-              int xC = xCCorner + wC;
-
-              if (xC < 0 || xC >= ${convInfo.inWidth}) {
-                continue;
-              }
-
-              float value = getX(batch, xR, xC, d);
-
-              // If a min / max value has already been found, use it. If not,
-              // use the current value.
-              float currMinMaxValue = mix(
-                  value, minMaxValue, minMaxValueFound);
-              if (value ${compareOp} currMinMaxValue) {
-                minMaxValue = value;
-                minMaxValueFound = 1.0;
-                minMaxPosition = ${
-          flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :
-                                                    flattenPositionStr) :
-                             `wR * ${effectiveFilterWidth} + wC`};
-              }
-            }
-          }
-          setOutput(float(minMaxPosition));
-        }
-      `;
-      return;
-    }
-
-    const compareOp = 'max';
-
-    let returnValue = `${poolType}(${poolType}(${poolType}(` +
-        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
-    if (poolType === 'avg') {
-      returnValue = `avgValue / max(count, 1.0)`;
-    }
-
-    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
-    const filterWidthVec4Remainder = filterWidth % 4;
-
-    const updateSnippet = `
-      if (${isAvgPool}) {
-        avgValue += dot(values, ones);
-      } else {
-        minMaxValue = ${compareOp}(values, minMaxValue);
-      }
-    `;
-
-    this.userCode = `
-      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
-      const ivec2 pads = ivec2(${padTop}, ${padLeft});
-      const float initializationValue = ${initializationValue};
-      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
-
-      float count = 0.0;
-
-      float getValue(int batch, int xR, int xC, int d) {
-        if (xC < 0 || xC >= ${convInfo.inWidth}) {
-          return initializationValue;
-        }
-        count += 1.0;
-        return getX(batch, xR, xC, d);
-      }
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords[0];
-        int d = coords[3];
-
-        ivec2 xRCCorner = coords.yz * strides - pads;
-        int xRCorner = xRCCorner.x;
-        int xCCorner = xRCCorner.y;
-
-        // max/min x(?, ?, d) to get y(yR, yC, d).
-        // ? = to be determined
-        vec4 minMaxValue = vec4(${initializationValue});
-        float avgValue = 0.0;
-        count = 0.0;
-
-        for (int wR = 0; wR < ${effectiveFilterHeight};
-            wR += ${dilationHeight}) {
-          int xR = xRCorner + wR;
-
-          if (xR < 0 || xR >= ${convInfo.inHeight}) {
-            continue;
-          }
-
-          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
-            int xC = xCCorner + wC * ${dilationWidth};
-
-            vec4 values = vec4(
-              getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${dilationWidth}, d),
-              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
-              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
-            );
-
-            ${updateSnippet}
-          }
-
-          int xC = xCCorner + ${filterWidthNearestVec4};
-          if (${filterWidthVec4Remainder === 1}) {
-            vec4 values = vec4(
-              getValue(batch, xR, xC, d),
-              initializationValue,
-              initializationValue,
-              initializationValue
-            );
-
-            ${updateSnippet}
-          } else if (${filterWidthVec4Remainder === 2}) {
-            vec4 values = vec4(
-              getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${dilationWidth}, d),
-              initializationValue,
-              initializationValue
-            );
-
-            ${updateSnippet}
-          } else if (${filterWidthVec4Remainder === 3}) {
-            vec4 values = vec4(
-              getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${dilationWidth}, d),
-              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
-              initializationValue
-            );
-
-            ${updateSnippet}
-          }
-        }
-        setOutput(${returnValue});
-      }
-    `;
-  }
-}
-
-export class Pool3DProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      convInfo: backend_util.Conv3DInfo, poolType: 'max'|'avg',
-      computePositions: boolean, flattenPositions = false,
-      includeBatchInIndex = false) {
-    if (poolType === 'avg' && computePositions) {
-      throw new Error('Cannot compute positions for average pool.');
-    }
-
-    const filterWidth = convInfo.filterWidth;
-    const strideDepth = convInfo.strideDepth;
-    const strideHeight = convInfo.strideHeight;
-    const strideWidth = convInfo.strideWidth;
-    const dilationDepth = convInfo.dilationDepth;
-    const dilationHeight = convInfo.dilationHeight;
-    const dilationWidth = convInfo.dilationWidth;
-    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
-    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
-    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
-
-    const padFront = convInfo.padInfo.front;
-    const padTop = convInfo.padInfo.top;
-    const padLeft = convInfo.padInfo.left;
-    this.outputShape = convInfo.outShape;
-
-    const isAvgPool = poolType === 'avg';
-
-    let initializationValue = '0.0';
-    if (!isAvgPool) {
-      // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
-      initializationValue = '-1.0 / 1e-20';
-    }
-
-    if (computePositions) {
-      const compareOp = '>=';
-
-      this.userCode = `
-        const ivec3 strides =
-            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
-        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
-
-        void main() {
-          ivec5 coords = getOutputCoords();
-          int batch = coords.x;
-          int ch = coords.u;
-
-          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
-          int xDCorner = xCorner.x;
-          int xRCorner = xCorner.y;
-          int xCCorner = xCorner.z;
-
-          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
-          // ? = to be determined
-          float minMaxValue = 0.0;
-          float minMaxValueFound = 0.0;
-          int minMaxPosition = 0;
-
-          for (int wD = 0; wD < ${effectiveFilterDepth};
-              wD += ${dilationDepth}) {
-            int xD = xDCorner + wD;
-
-            if (xD < 0 || xD >= ${convInfo.inDepth}) {
-              continue;
-            }
-
-            for (int wR = 0; wR < ${effectiveFilterHeight};
-                wR += ${dilationHeight}) {
-              int xR = xRCorner + wR;
-
-              if (xR < 0 || xR >= ${convInfo.inHeight}) {
-                continue;
-              }
-
-              for (int wC = 0; wC < ${effectiveFilterWidth};
-                  wC += ${dilationWidth}) {
-                int xC = xCCorner + wC;
-
-                if (xC < 0 || xC >= ${convInfo.inWidth}) {
-                  continue;
-                }
-
-                float value = getX(batch, xD, xR, xC, ch);
-
-                // If a min / max value has already been found, use it. If not,
-                // use the current value.
-                float currMinMaxValue = mix(
-                    value, minMaxValue, minMaxValueFound);
-                if (value ${compareOp} currMinMaxValue) {
-                  minMaxValue = value;
-                  minMaxValueFound = 1.0;
-                  minMaxPosition = ${
-          flattenPositions ?
-              (includeBatchInIndex ?
-                   `(((batch * ${convInfo.inDepth} + xD) * ${
-                       convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${
-                       convInfo.inChannels} + ch` :
-                   `((xD * ${convInfo.inHeight} + xR) * ${
-                       convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :
-              `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
-                      wR * ${effectiveFilterWidth} + wC`};
-                }
-              }
-            }
-          }
-          setOutput(float(minMaxPosition));
-        }
-      `;
-      return;
-    }
-
-    const compareOp = 'max';
-
-    let returnValue = `${poolType}(${poolType}(${poolType}(` +
-        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
-    if (poolType === 'avg') {
-      // Use `max(count, 1.0)` instead of `count` in case count === 0.0.
-      // If count === 0.0, `avgValue` is always 0.0 and we change `count`'s
-      // value to avoid dividing zero.
-      returnValue = `avgValue / max(count, 1.0)`;
-    }
-
-    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
-    const filterWidthVec4Remainder = filterWidth % 4;
-
-    const updateSnippet = `
-      if (${isAvgPool}) {
-        avgValue += dot(values, ones);
-      } else {
-        minMaxValue = ${compareOp}(values, minMaxValue);
-      }
-    `;
-
-    this.userCode = `
-      const ivec3 strides =
-        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
-      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
-      const float initializationValue = ${initializationValue};
-      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
-
-      float count = 0.0;
-
-      float getValue(int batch, int xD, int xR, int xC, int ch) {
-        if (xC < 0 || xC >= ${convInfo.inWidth}) {
-          return initializationValue;
-        }
-        count += 1.0;
-        return getX(batch, xD, xR, xC, ch);
-      }
-
-      void main() {
-        ivec5 coords = getOutputCoords();
-        int batch = coords.x;
-        int ch = coords.u;
-
-        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
-        int xDCorner = xCorner.x;
-        int xRCorner = xCorner.y;
-        int xCCorner = xCorner.z;
-
-        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
-        // ? = to be determined
-        vec4 minMaxValue = vec4(${initializationValue});
-        float avgValue = 0.0;
-        count = 0.0;
-
-        for (int wD = 0; wD < ${effectiveFilterDepth};
-            wD += ${dilationDepth}) {
-          int xD = xDCorner + wD;
-
-          if (xD < 0 || xD >= ${convInfo.inDepth}) {
-            continue;
-          }
-
-          for (int wR = 0; wR < ${effectiveFilterHeight};
-            wR += ${dilationHeight}) {
-            int xR = xRCorner + wR;
-
-            if (xR < 0 || xR >= ${convInfo.inHeight}) {
-              continue;
-            }
-
-            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
-              int xC = xCCorner + wC * ${dilationWidth};
-
-              vec4 values = vec4(
-                getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
-                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
-                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
-              );
-
-              ${updateSnippet}
-            }
-
-            int xC = xCCorner + ${filterWidthNearestVec4};
-            if (${filterWidthVec4Remainder === 1}) {
-              vec4 values = vec4(
-                getValue(batch, xD, xR, xC, ch),
-                initializationValue,
-                initializationValue,
-                initializationValue
-              );
-
-              ${updateSnippet}
-            } else if (${filterWidthVec4Remainder === 2}) {
-              vec4 values = vec4(
-                getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
-                initializationValue,
-                initializationValue
-              );
-
-              ${updateSnippet}
-            } else if (${filterWidthVec4Remainder === 3}) {
-              vec4 values = vec4(
-                getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
-                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
-                initializationValue
-              );
-
-              ${updateSnippet}
-            }
-          }
-        }
-        setOutput(${returnValue});
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/reduce_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/reduce_gpu.ts
deleted file mode 100644
index 86e748008..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/reduce_gpu.ts
+++ /dev/null
@@ -1,175 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ReduceProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      reduceInfo: backend_util.ReduceInfo,
-      reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod') {
-    const {windowSize, batchSize, inSize, outSize} = reduceInfo;
-    this.outputShape = [batchSize, outSize];
-
-    let initializationValue = '0.0';
-    let compareOp = ``;
-
-    if (reduceType === 'prod') {
-      initializationValue = '1.0';
-    } else if (reduceType === 'min') {
-      // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
-      initializationValue = '1.0 / 1e-20';
-      compareOp = `min`;
-    } else if (reduceType === 'max') {
-      // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
-      initializationValue = '-1.0 / 1e-20';
-      compareOp = `max`;
-    }
-
-    let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +
-        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
-
-    if (reduceType === 'sum') {
-      returnValue = `sumValue`;
-    } else if (reduceType === 'prod') {
-      returnValue = `prodValue`;
-    } else if (reduceType === 'all') {
-      returnValue = `allValue`;
-    } else if (reduceType === 'any') {
-      returnValue = `anyValue`;
-    }
-
-    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
-    const windowSizeVec4Remainder = windowSize % 4;
-
-    let updateSnippet = `
-      if (${reduceType === 'sum'}) {
-        sumValue += dot(values, ones);
-      } else if (${reduceType === 'prod'}) {
-        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
-        prodValue *= tmp[0] * tmp[1];
-      } else {
-        minMaxValue = ${compareOp}(values, minMaxValue);
-        if (${reduceType === 'min'} || ${reduceType === 'max'}) {
-          minMaxValue = ${compareOp}(values, minMaxValue);
-          bvec4 isNaN = isnan(values);
-          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
-            minMaxValue = vec4(NAN);
-          }
-        }
-      }
-    `;
-
-    let vecType = `vec4`;
-
-    if (reduceType === 'all') {
-      initializationValue = '1.0';
-      updateSnippet = `
-        bool reducedAllValue = all(values);
-        float floatedReducedAllValue = float(reducedAllValue);
-        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
-      `;
-      vecType = `bvec4`;
-    } else if (reduceType === 'any') {
-      initializationValue = '0.0';
-      updateSnippet = `
-        bool reducedAnyValue = any(values);
-        float floatedReducedAnyValue = float(reducedAnyValue);
-        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
-      `;
-      vecType = `bvec4`;
-    }
-
-    let checkOutOfBounds = '';
-    if (inSize % windowSize > 0) {
-      checkOutOfBounds = `
-        if (inIdx < 0 || inIdx >= ${inSize}) {
-          return initializationValue;
-        }
-      `;
-    }
-    this.userCode = `
-      const float initializationValue = ${initializationValue};
-      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
-
-      float getValue(int batch, int inIdx) {
-        ${checkOutOfBounds}
-        return getX(batch, inIdx);
-      }
-
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int batch = coords[0];
-        int outIdx = coords[1];
-        int inOffset = outIdx * ${windowSize};
-
-        vec4 minMaxValue = vec4(${initializationValue});
-        float prodValue = 1.0;
-        float sumValue = 0.0;
-        float allValue = 1.0;
-        float anyValue = 0.0;
-
-        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
-          int inIdx = inOffset + i;
-          ${vecType} values = ${vecType}(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2),
-            getValue(batch, inIdx + 3)
-          );
-
-          ${updateSnippet}
-        }
-
-        int inIdx = inOffset + ${windowSizeNearestVec4};
-        if (${windowSizeVec4Remainder === 1}) {
-          ${vecType} values = ${vecType}(
-            getValue(batch, inIdx),
-            initializationValue,
-            initializationValue,
-            initializationValue
-          );
-
-          ${updateSnippet}
-        } else if (${windowSizeVec4Remainder === 2}) {
-          ${vecType} values = ${vecType}(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            initializationValue,
-            initializationValue
-          );
-
-          ${updateSnippet}
-        } else if (${windowSizeVec4Remainder === 3}) {
-          ${vecType} values = ${vecType}(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2),
-            initializationValue
-          );
-
-          ${updateSnippet}
-        }
-        setOutput(${returnValue});
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/register_all_kernels.ts b/tfjs-master/tfjs-backend-webgl/src/register_all_kernels.ts
deleted file mode 100644
index 42b1e0e9a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/register_all_kernels.ts
+++ /dev/null
@@ -1,366 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, registerKernel} from '@tensorflow/tfjs-core';
-
-import {_fusedMatMulConfig} from './kernels/_FusedMatMul';
-import {absConfig} from './kernels/Abs';
-import {acosConfig} from './kernels/Acos';
-import {acoshConfig} from './kernels/Acosh';
-import {addConfig} from './kernels/Add';
-import {addNConfig} from './kernels/AddN';
-import {allConfig} from './kernels/All';
-import {anyConfig} from './kernels/Any';
-import {argMaxConfig} from './kernels/ArgMax';
-import {argMinConfig} from './kernels/ArgMin';
-import {asinConfig} from './kernels/Asin';
-import {asinhConfig} from './kernels/Asinh';
-import {atanConfig} from './kernels/Atan';
-import {atan2Config} from './kernels/Atan2';
-import {atanhConfig} from './kernels/Atanh';
-import {avgPoolConfig} from './kernels/AvgPool';
-import {avgPool3DConfig} from './kernels/AvgPool3D';
-import {avgPool3DGradConfig} from './kernels/AvgPool3DGrad';
-import {avgPoolGradConfig} from './kernels/AvgPoolGrad';
-import {batchMatMulConfig} from './kernels/BatchMatMul';
-import {batchNormConfig} from './kernels/BatchNorm';
-import {batchToSpaceNDConfig} from './kernels/BatchToSpaceND';
-import {bincountConfig} from './kernels/Bincount';
-import {bitwiseAndConfig} from './kernels/BitwiseAnd';
-import {broadcastArgsConfig} from './kernels/BroadcastArgs';
-import {castConfig} from './kernels/Cast';
-import {ceilConfig} from './kernels/Ceil';
-import {clipByValueConfig} from './kernels/ClipByValue';
-import {complexConfig} from './kernels/Complex';
-import {complexAbsConfig} from './kernels/ComplexAbs';
-import {concatConfig} from './kernels/Concat';
-import {conv2DConfig} from './kernels/Conv2D';
-import {conv2DBackpropFilterConfig} from './kernels/Conv2DBackpropFilter';
-import {conv2DBackpropInputConfig} from './kernels/Conv2DBackpropInput';
-import {conv3DConfig} from './kernels/Conv3D';
-import {conv3DBackpropFilterV2Config} from './kernels/Conv3DBackpropFilterV2';
-import {conv3DBackpropInputConfig} from './kernels/Conv3DBackpropInputV2';
-import {cosConfig} from './kernels/Cos';
-import {coshConfig} from './kernels/Cosh';
-import {cropAndResizeConfig} from './kernels/CropAndResize';
-import {cumprodConfig} from './kernels/Cumprod';
-import {cumsumConfig} from './kernels/Cumsum';
-import {denseBincountConfig} from './kernels/DenseBincount';
-import {depthToSpaceConfig} from './kernels/DepthToSpace';
-import {depthwiseConv2dNativeConfig} from './kernels/DepthwiseConv2dNative';
-import {depthwiseConv2dNativeBackpropFilterConfig} from './kernels/DepthwiseConv2dNativeBackpropFilter';
-import {depthwiseConv2dNativeBackpropInputConfig} from './kernels/DepthwiseConv2dNativeBackpropInput';
-import {diagConfig} from './kernels/Diag';
-import {dilation2DConfig} from './kernels/Dilation2D';
-import {einsumConfig} from './kernels/Einsum';
-import {eluConfig} from './kernels/Elu';
-import {eluGradConfig} from './kernels/EluGrad';
-import {equalConfig} from './kernels/Equal';
-import {erfConfig} from './kernels/Erf';
-import {expConfig} from './kernels/Exp';
-import {expandDimsConfig} from './kernels/ExpandDims';
-import {expm1Config} from './kernels/Expm1';
-import {fftConfig} from './kernels/FFT';
-import {fillConfig} from './kernels/Fill';
-import {flipLeftRightConfig} from './kernels/FlipLeftRight';
-import {floorConfig} from './kernels/Floor';
-import {floorDivConfig} from './kernels/FloorDiv';
-import {fromPixelsConfig} from './kernels/FromPixels';
-import {fusedConv2DConfig} from './kernels/FusedConv2D';
-import {fusedDepthwiseConv2DConfig} from './kernels/FusedDepthwiseConv2D';
-import {gatherNdConfig} from './kernels/GatherNd';
-import {gatherV2Config} from './kernels/GatherV2';
-import {greaterConfig} from './kernels/Greater';
-import {greaterEqualConfig} from './kernels/GreaterEqual';
-import {identityConfig} from './kernels/Identity';
-import {ifftConfig} from './kernels/IFFT';
-import {imagConfig} from './kernels/Imag';
-import {isFiniteConfig} from './kernels/IsFinite';
-import {isInfConfig} from './kernels/IsInf';
-import {isNaNConfig} from './kernels/IsNaN';
-import {leakyReluConfig} from './kernels/LeakyRelu';
-import {lessConfig} from './kernels/Less';
-import {lessEqualConfig} from './kernels/LessEqual';
-import {linSpaceConfig} from './kernels/LinSpace';
-import {logConfig} from './kernels/Log';
-import {log1pConfig} from './kernels/Log1p';
-import {logicalAndConfig} from './kernels/LogicalAnd';
-import {logicalNotConfig} from './kernels/LogicalNot';
-import {logicalOrConfig} from './kernels/LogicalOr';
-import {LRNConfig} from './kernels/LRN';
-import {LRNGradConfig} from './kernels/LRNGrad';
-import {maxConfig} from './kernels/Max';
-import {maximumConfig} from './kernels/Maximum';
-import {maxPoolConfig} from './kernels/MaxPool';
-import {maxPool3DConfig} from './kernels/MaxPool3D';
-import {maxPool3DGradConfig} from './kernels/MaxPool3DGrad';
-import {maxPoolGradConfig} from './kernels/MaxPoolGrad';
-import {maxPoolWithArgmaxConfig} from './kernels/MaxPoolWithArgmax';
-import {meanConfig} from './kernels/Mean';
-import {minConfig} from './kernels/Min';
-import {minimumConfig} from './kernels/Minimum';
-import {mirrorPadConfig} from './kernels/MirrorPad';
-import {modConfig} from './kernels/Mod';
-import {multinomialConfig} from './kernels/Multinomial';
-import {multiplyConfig} from './kernels/Multiply';
-import {negConfig} from './kernels/Neg';
-import {nonMaxSuppressionV3Config} from './kernels/NonMaxSuppressionV3';
-import {nonMaxSuppressionV4Config} from './kernels/NonMaxSuppressionV4';
-import {nonMaxSuppressionV5Config} from './kernels/NonMaxSuppressionV5';
-import {notEqualConfig} from './kernels/NotEqual';
-import {oneHotConfig} from './kernels/OneHot';
-import {onesLikeConfig} from './kernels/OnesLike';
-import {packConfig} from './kernels/Pack';
-import {padV2Config} from './kernels/PadV2';
-import {powConfig} from './kernels/Pow';
-import {preluConfig} from './kernels/Prelu';
-import {prodConfig} from './kernels/Prod';
-import {raggedGatherConfig} from './kernels/RaggedGather';
-import {raggedRangeConfig} from './kernels/RaggedRange';
-import {raggedTensorToTensorConfig} from './kernels/RaggedTensorToTensor';
-import {rangeConfig} from './kernels/Range';
-import {realConfig} from './kernels/Real';
-import {realDivConfig} from './kernels/RealDiv';
-import {reciprocalConfig} from './kernels/Reciprocal';
-import {reluConfig} from './kernels/Relu';
-import {relu6Config} from './kernels/Relu6';
-import {reshapeConfig} from './kernels/Reshape';
-import {resizeBilinearConfig} from './kernels/ResizeBilinear';
-import {resizeBilinearGradConfig} from './kernels/ResizeBilinearGrad';
-import {resizeNearestNeighborConfig} from './kernels/ResizeNearestNeighbor';
-import {resizeNearestNeighborGradConfig} from './kernels/ResizeNearestNeighborGrad';
-import {reverseConfig} from './kernels/Reverse';
-import {rotateWithOffsetConfig} from './kernels/RotateWithOffset';
-import {roundConfig} from './kernels/Round';
-import {rsqrtConfig} from './kernels/Rsqrt';
-import {scatterNdConfig} from './kernels/ScatterNd';
-import {searchSortedConfig} from './kernels/SearchSorted';
-import {selectConfig} from './kernels/Select';
-import {seluConfig} from './kernels/Selu';
-import {sigmoidConfig} from './kernels/Sigmoid';
-import {signConfig} from './kernels/Sign';
-import {sinConfig} from './kernels/Sin';
-import {sinhConfig} from './kernels/Sinh';
-import {sliceConfig} from './kernels/Slice';
-import {softmaxConfig} from './kernels/Softmax';
-import {softplusConfig} from './kernels/Softplus';
-import {spaceToBatchNDConfig} from './kernels/SpaceToBatchND';
-import {sparseFillEmptyRowsConfig} from './kernels/SparseFillEmptyRows';
-import {sparseReshapeConfig} from './kernels/SparseReshape';
-import {sparseSegmentMeanConfig} from './kernels/SparseSegmentMean';
-import {sparseSegmentSumConfig} from './kernels/SparseSegmentSum';
-import {sparseToDenseConfig} from './kernels/SparseToDense';
-import {splitVConfig} from './kernels/SplitV';
-import {sqrtConfig} from './kernels/Sqrt';
-import {squareConfig} from './kernels/Square';
-import {squaredDifferenceConfig} from './kernels/SquaredDifference';
-import {staticRegexReplaceConfig} from './kernels/StaticRegexReplace';
-import {stepConfig} from './kernels/Step';
-import {stridedSliceConfig} from './kernels/StridedSlice';
-import {stringNGramsConfig} from './kernels/StringNGrams';
-import {stringSplitConfig} from './kernels/StringSplit';
-import {stringToHashBucketFastConfig} from './kernels/StringToHashBucketFast';
-import {subConfig} from './kernels/Sub';
-import {sumConfig} from './kernels/Sum';
-import {tanConfig} from './kernels/Tan';
-import {tanhConfig} from './kernels/Tanh';
-import {tensorScatterUpdateConfig} from './kernels/TensorScatterUpdate';
-import {tileConfig} from './kernels/Tile';
-import {topKConfig} from './kernels/TopK';
-import {transformConfig} from './kernels/Transform';
-import {transposeConfig} from './kernels/Transpose';
-import {uniqueConfig} from './kernels/Unique';
-import {unpackConfig} from './kernels/Unpack';
-import {unsortedSegmentSumConfig} from './kernels/UnsortedSegmentSum';
-import {zerosLikeConfig} from './kernels/ZerosLike';
-
-// List all kernel configs here
-const kernelConfigs: KernelConfig[] = [
-  _fusedMatMulConfig,
-  absConfig,
-  acosConfig,
-  acoshConfig,
-  addConfig,
-  addNConfig,
-  allConfig,
-  anyConfig,
-  argMaxConfig,
-  argMinConfig,
-  asinConfig,
-  asinhConfig,
-  atanConfig,
-  atan2Config,
-  atanhConfig,
-  avgPoolConfig,
-  avgPool3DConfig,
-  avgPool3DGradConfig,
-  avgPoolGradConfig,
-  batchMatMulConfig,
-  batchNormConfig,
-  batchToSpaceNDConfig,
-  bincountConfig,
-  bitwiseAndConfig,
-  broadcastArgsConfig,
-  castConfig,
-  ceilConfig,
-  clipByValueConfig,
-  complexConfig,
-  complexAbsConfig,
-  concatConfig,
-  conv2DConfig,
-  conv2DBackpropFilterConfig,
-  conv2DBackpropInputConfig,
-  conv3DConfig,
-  conv3DBackpropFilterV2Config,
-  conv3DBackpropInputConfig,
-  cosConfig,
-  coshConfig,
-  cropAndResizeConfig,
-  cumprodConfig,
-  cumsumConfig,
-  denseBincountConfig,
-  depthToSpaceConfig,
-  depthwiseConv2dNativeConfig,
-  depthwiseConv2dNativeBackpropFilterConfig,
-  depthwiseConv2dNativeBackpropInputConfig,
-  diagConfig,
-  dilation2DConfig,
-  einsumConfig,
-  eluConfig,
-  eluGradConfig,
-  equalConfig,
-  erfConfig,
-  expConfig,
-  expandDimsConfig,
-  expm1Config,
-  fftConfig,
-  fillConfig,
-  flipLeftRightConfig,
-  floorConfig,
-  floorDivConfig,
-  fromPixelsConfig,
-  fusedConv2DConfig,
-  fusedDepthwiseConv2DConfig,
-  gatherNdConfig,
-  gatherV2Config,
-  greaterConfig,
-  greaterEqualConfig,
-  identityConfig,
-  ifftConfig,
-  imagConfig,
-  isFiniteConfig,
-  isInfConfig,
-  isNaNConfig,
-  leakyReluConfig,
-  lessConfig,
-  lessEqualConfig,
-  linSpaceConfig,
-  logConfig,
-  log1pConfig,
-  logicalAndConfig,
-  logicalNotConfig,
-  logicalOrConfig,
-  LRNConfig,
-  LRNGradConfig,
-  maxConfig,
-  maximumConfig,
-  maxPoolConfig,
-  maxPool3DConfig,
-  maxPool3DGradConfig,
-  maxPoolGradConfig,
-  maxPoolWithArgmaxConfig,
-  meanConfig,
-  minConfig,
-  minimumConfig,
-  mirrorPadConfig,
-  modConfig,
-  multinomialConfig,
-  multiplyConfig,
-  negConfig,
-  nonMaxSuppressionV3Config,
-  nonMaxSuppressionV4Config,
-  nonMaxSuppressionV5Config,
-  notEqualConfig,
-  oneHotConfig,
-  onesLikeConfig,
-  packConfig,
-  padV2Config,
-  powConfig,
-  preluConfig,
-  prodConfig,
-  raggedGatherConfig,
-  raggedRangeConfig,
-  raggedTensorToTensorConfig,
-  rangeConfig,
-  realConfig,
-  realDivConfig,
-  reciprocalConfig,
-  reluConfig,
-  relu6Config,
-  reshapeConfig,
-  resizeBilinearConfig,
-  resizeBilinearGradConfig,
-  resizeNearestNeighborConfig,
-  resizeNearestNeighborGradConfig,
-  reverseConfig,
-  rotateWithOffsetConfig,
-  roundConfig,
-  rsqrtConfig,
-  scatterNdConfig,
-  searchSortedConfig,
-  selectConfig,
-  seluConfig,
-  sigmoidConfig,
-  signConfig,
-  sinConfig,
-  sinhConfig,
-  sliceConfig,
-  softmaxConfig,
-  softplusConfig,
-  spaceToBatchNDConfig,
-  sparseFillEmptyRowsConfig,
-  sparseReshapeConfig,
-  sparseSegmentMeanConfig,
-  sparseSegmentSumConfig,
-  sparseToDenseConfig,
-  splitVConfig,
-  sqrtConfig,
-  squareConfig,
-  squaredDifferenceConfig,
-  staticRegexReplaceConfig,
-  stepConfig,
-  stridedSliceConfig,
-  stringNGramsConfig,
-  stringSplitConfig,
-  stringToHashBucketFastConfig,
-  subConfig,
-  sumConfig,
-  tanConfig,
-  tanhConfig,
-  tensorScatterUpdateConfig,
-  tileConfig,
-  topKConfig,
-  transformConfig,
-  transposeConfig,
-  uniqueConfig,
-  unpackConfig,
-  unsortedSegmentSumConfig,
-  zerosLikeConfig
-];
-
-for (const kernelConfig of kernelConfigs) {
-  registerKernel(kernelConfig);
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/reshape_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/reshape_packed_gpu.ts
deleted file mode 100644
index 29cae9cb0..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/reshape_packed_gpu.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-import * as shader_util from './shader_compiler_util';
-
-export class ReshapePackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[];
-  userCode: string;
-  enableShapeUniforms: boolean;
-  customUniforms = [{name: 'inputShape', type: 'ivec3' as const }];
-
-  constructor(outputShape: [number, number, number], inputShape: [
-    number, number, number
-  ]) {
-    this.outputShape = outputShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-
-    let mainLoop = ``;
-    for (let i = 0; i < 4; i++) {
-      let thisRC = `thisRC = rc;`;
-      if (i % 2 === 1) {
-        thisRC += `thisRC.z += 1;`;
-      }
-      if (i > 1) {
-        thisRC += `thisRC.y += 1;`;
-      }
-
-      mainLoop += `
-        ${thisRC}
-        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}
-          int flatIndex = getFlatIndex(thisRC);
-
-          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
-          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
-
-          result[${i}] =
-            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
-        ${i > 0 ? '}' : ''}
-      `;
-    }
-
-    this.userCode = `
-      ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)}
-      ${
-        this.enableShapeUniforms ? shader_util.getFlatIndexFrom3DOutput() :
-                                   shader_util.getFlatIndexFrom3D(outputShape)}
-
-      void main() {
-        ivec3 rc = getOutputCoords();
-
-        vec4 result = vec4(0.);
-
-        ivec3 thisRC;
-        int rows = ${this.enableShapeUniforms ? 'outShape[1]' : outputShape[1]};
-        int cols = ${this.enableShapeUniforms ? 'outShape[2]' : outputShape[2]};
-
-        ${mainLoop}
-
-        setOutput(result);
-      }
-    `;
-  }
-}
-
-function getReshapedInputCoords(
-    shape: [number, number, number], enableShapeUniforms: boolean): string {
-  const coordsFromIndexSnippet = enableShapeUniforms ?
-      shader_util.getLogicalCoordinatesFromFlatIndexByUniform(
-          ['r', 'c', 'd'], 'inputShape') :
-      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
-
-  return `
-    ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${coordsFromIndexSnippet}
-      return ivec3(r, c, d);
-    }
-  `;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/reshape_packed_test.ts b/tfjs-master/tfjs-backend-webgl/src/reshape_packed_test.ts
deleted file mode 100644
index 047177448..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/reshape_packed_test.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-const {expectArraysClose} = test_util;
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {PACKED_ENVS} from './backend_webgl_test_registry';
-
-describeWithFlags('expensive reshape', PACKED_ENVS, () => {
-  const cValues =
-      [46, 52, 58, 64, 70, 100, 115, 130, 145, 160, 154, 178, 202, 226, 250];
-  let c: tf.Tensor;
-
-  beforeEach(() => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
-    const b = tf.tensor2d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [3, 5]);
-    c = tf.matMul(a, b);
-  });
-
-  it('6d --> 1d', async () => {
-    const cAs6D = tf.reshape(c, [1, 1, 1, 3, 1, 5]);
-    const cAs1D = tf.reshape(cAs6D, [-1, cValues.length]);
-    expectArraysClose(await cAs1D.data(), cValues);
-  });
-  it('1d --> 2d', async () => {
-    const cAs1D = tf.reshape(c, [cValues.length]);
-    const cAs2D = tf.reshape(cAs1D, [5, -1]);
-    expectArraysClose(await cAs2D.data(), cValues);
-  });
-  it('2d --> 3d', async () => {
-    const cAs3D = tf.reshape(c, [3, 1, 5]);
-    expectArraysClose(await cAs3D.data(), cValues);
-  });
-  it('3d --> 4d', async () => {
-    const cAs3D = tf.reshape(c, [3, 1, 5]);
-    const cAs4D = tf.reshape(cAs3D, [3, 5, 1, 1]);
-    expectArraysClose(await cAs4D.data(), cValues);
-  });
-  it('4d --> 5d', async () => {
-    const cAs4D = tf.reshape(c, [3, 5, 1, 1]);
-    const cAs5D = tf.reshape(cAs4D, [1, 1, 1, 5, 3]);
-    expectArraysClose(await cAs5D.data(), cValues);
-  });
-  it('5d --> 6d', async () => {
-    const cAs5D = tf.reshape(c, [1, 1, 1, 5, 3]);
-    const cAs6D = tf.reshape(cAs5D, [3, 5, 1, 1, 1, 1]);
-    expectArraysClose(await cAs6D.data(), cValues);
-  });
-});
-
-describeWithFlags('expensive reshape with even columns', PACKED_ENVS, () => {
-  it('2 --> 4 columns', async () => {
-    const maxTextureSize = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-
-    let values: number[] = new Array<number>(16).fill(0);
-    values = values.map((d, i) => i + 1);
-    const a = tf.tensor2d(values, [8, 2]);
-    const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', 2);
-    // Setting WEBGL_MAX_TEXTURE_SIZE to 2 makes that [8, 2] tensor is packed
-    // to texture of width 2 by height 2. Indices are packed as:
-    // -------------
-    // | 0 1 | 4 5 |       // First row's four
-    // | 2 3 | 6 7 |       // pixels.
-    // -------------
-    // ...
-    const c = tf.matMul(a, b);
-    let cAs4D = c.reshape([2, 1, 2, 4]);
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', maxTextureSize);
-
-    // Execute non-packed operations to unpack tensor.
-    const webglPackFlagSaved = tf.env().getBool('WEBGL_PACK');
-    tf.env().set('WEBGL_PACK', false);
-    cAs4D = cAs4D.add(1);
-    cAs4D = cAs4D.add(-1);
-    tf.env().set('WEBGL_PACK', webglPackFlagSaved);
-
-    const result =
-        [7, 10, 15, 22, 23, 34, 31, 46, 39, 58, 47, 70, 55, 82, 63, 94];
-    expectArraysClose(await cAs4D.data(), result);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/resize_bilinear_backprop_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/resize_bilinear_backprop_gpu.ts
deleted file mode 100644
index 948727580..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/resize_bilinear_backprop_gpu.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ResizeBilinearBackpropProgram implements GPGPUProgram {
-  variableNames = ['dy'];
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(
-      dyShape: [number, number, number, number],
-      inputShape: [number, number, number, number], alignCorners: boolean) {
-    this.outputShape = inputShape;
-    const [, xHeight, xWidth, ] = inputShape;
-    const [, yHeight, yWidth] = dyShape;
-
-    // In the backwards pass, we want to find the pixels that were generated for
-    // each pixel in the input image the forward pass and add the corresponding
-    // coefficient from dy to the gradient (with some interpolation).
-
-    const effectiveXSize: [number, number] = [
-      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
-      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
-    ];
-
-    const effectiveYSize: [number, number] = [
-      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
-      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
-    ];
-
-    const heightScale = effectiveXSize[0] / effectiveYSize[0];
-    const widthScale = effectiveXSize[1] / effectiveYSize[1];
-
-    const invHeightScale = 1 / heightScale;
-    const invWidthScale = 1 / widthScale;
-
-    // This defines the size of the window of values around a particular
-    // index in dy that we want to search for contributions to dx.
-    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;
-    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;
-
-    this.userCode = `
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int d = coords[3];
-        int r = coords[1];
-        int c = coords[2];
-
-        float accumulator = 0.0;
-
-        const float heightScale = float(${heightScale});
-        const float widthScale = float(${widthScale});
-
-        const float invHeightScale = float(${invHeightScale});
-        const float invWidthScale = float(${invWidthScale});
-
-        const int winHeight = int(${winHeight});
-        const int winWidth = int(${winWidth});
-
-        // Compute bounds for where in dy we will look
-        float startRLerp = floor(float(r) * invHeightScale);
-        int startDyR = int(startRLerp - float(winHeight / 2));
-
-        float startCLerp = floor(float(c) * invWidthScale);
-        int startDyC = int(startCLerp - float(winWidth / 2));
-
-        // Loop over dy
-        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
-          int dyR = dyROffset + startDyR;
-
-          // Guard against the window exceeding the bounds of dy
-          if (dyR < 0 || dyR >= ${yHeight}) {
-            continue;
-          }
-
-          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
-            int dyC = dyCOffset + startDyC;
-
-            // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${yWidth}) {
-              continue;
-            }
-
-            float dxR = float(dyR) * heightScale;
-            int topDxRIndex = int(floor(dxR));
-            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));
-            float dxRLerp = dxR - float(topDxRIndex);
-            float inverseDxRLerp = 1.0 - dxRLerp;
-
-            float dxC = float(dyC) * widthScale;
-            int leftDxCIndex = int(floor(dxC));
-            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));
-            float dxCLerp = dxC - float(leftDxCIndex);
-            float inverseDxCLerp = 1.0 - dxCLerp;
-
-            if (r == topDxRIndex && c == leftDxCIndex) {
-              // topLeft
-              accumulator +=
-                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
-            }
-
-            if (r == topDxRIndex && c == rightDxCIndex) {
-              // topRight
-              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
-            }
-
-            if (r == bottomDxRIndex && c == leftDxCIndex) {
-              // bottomLeft
-              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
-            }
-
-            if (r == bottomDxRIndex && c == rightDxCIndex) {
-              // bottomRight
-              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
-            }
-          }
-        }
-        // End loop over dy
-
-        setOutput(accumulator);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/resize_bilinear_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/resize_bilinear_gpu.ts
deleted file mode 100644
index d19b54ec1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/resize_bilinear_gpu.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ResizeBilinearProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(
-      inputShape: [number, number, number, number], newHeight: number,
-      newWidth: number, alignCorners: boolean, halfPixelCenters: boolean) {
-    const [batch, oldHeight, oldWidth, depth] = inputShape;
-    this.outputShape = [batch, newHeight, newWidth, depth];
-
-    const effectiveInSize: [number, number] = [
-      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
-      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
-    ];
-
-    const effectiveOutSize: [number, number] = [
-      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
-      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
-    ];
-
-    let sourceFracIndexRC: string;
-    if (halfPixelCenters) {
-      sourceFracIndexRC =
-          `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC` +
-          ` - vec2(0.5)`;
-    } else {
-      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
-    }
-
-    this.userCode = `
-      const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${effectiveInSize[0] / effectiveOutSize[0]},
-          ${effectiveInSize[1] / effectiveOutSize[1]});
-      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int d = coords[3];
-        ivec2 yRC = coords.yz;
-
-        // Fractional source index.
-        vec2 sourceFracIndexRC = ${sourceFracIndexRC};
-
-        // Compute the four integer indices.
-        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
-        ivec2 sourceCeilRC = ivec2(
-          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
-
-        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
-        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
-        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
-        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
-
-        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
-
-        float top = topLeft + (topRight - topLeft) * fracRC.y;
-        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
-        float newValue = top + (bottom - top) * fracRC.x;
-
-        setOutput(newValue);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/resize_bilinear_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/resize_bilinear_packed_gpu.ts
deleted file mode 100644
index c734363ec..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/resize_bilinear_packed_gpu.ts
+++ /dev/null
@@ -1,130 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ResizeBilinearPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(
-      inputShape: [number, number, number, number], newHeight: number,
-      newWidth: number, alignCorners: boolean, halfPixelCenters: boolean) {
-    const [batch, oldHeight, oldWidth, depth] = inputShape;
-    this.outputShape = [batch, newHeight, newWidth, depth];
-
-    const effectiveInSize: [number, number] = [
-      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
-      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
-    ];
-
-    const effectiveOutSize: [number, number] = [
-      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
-      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
-    ];
-
-    let sourceFracIndexRC: string;
-    if (halfPixelCenters) {
-      sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * ` +
-          `effectiveInputOverOutputRatioRC - vec3(0.5)`;
-    } else {
-      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
-    }
-
-    this.userCode = `
-      const vec3 effectiveInputOverOutputRatioRC = vec3(
-          ${effectiveInSize[0] / effectiveOutSize[0]},
-          ${effectiveInSize[1] / effectiveOutSize[1]},
-          ${effectiveInSize[1] / effectiveOutSize[1]});
-      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
-                                     ${oldWidth}.0);
-
-      float getAValue(int b, int r, int c, int d) {
-        return getChannel(getA(b, r, c, d), vec2(c, d));
-      }
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int d = coords[3];
-        // Calculate values for next column in yRC.z.
-        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
-
-        // Fractional source index.
-        vec3 sourceFracIndexRC = ${sourceFracIndexRC};
-
-        // Compute the four integer indices.
-        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
-        ivec3 sourceCeilRC = ivec3(
-          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
-
-        // Should we calculate next column and row elements in 2x2 packed cell.
-        bool hasNextCol = d < ${depth - 1};
-        bool hasNextRow = coords.z < ${newWidth - 1};
-
-        // In parallel, construct four corners for all four components in
-        // packed 2x2 cell.
-        vec4 topLeft = vec4(
-          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
-          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
-                     : 0.0,
-          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
-                     : 0.0,
-          (hasNextRow && hasNextCol) ?
-            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
-
-        vec4 bottomLeft = vec4(
-          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
-          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
-                     : 0.0,
-          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
-                     : 0.0,
-          (hasNextRow && hasNextCol) ?
-            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
-
-        vec4 topRight = vec4(
-          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
-          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
-                     : 0.0,
-          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
-                     : 0.0,
-          (hasNextRow && hasNextCol) ?
-            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
-
-        vec4 bottomRight = vec4(
-          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
-          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
-                     : 0.0,
-          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
-                     : 0.0,
-          (hasNextRow && hasNextCol) ?
-            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
-
-        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
-
-        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
-        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
-        vec4 newValue = mix(top, bottom, fracRC.x);
-
-        setOutput(newValue);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/resize_nearest_neighbor_backprop_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/resize_nearest_neighbor_backprop_gpu.ts
deleted file mode 100644
index c711f6b7e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/resize_nearest_neighbor_backprop_gpu.ts
+++ /dev/null
@@ -1,129 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ResizeNearestNeigborBackpropProgram implements GPGPUProgram {
-  variableNames = ['dy'];
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(
-      dyShape: [number, number, number, number],
-      inputShape: [number, number, number, number], alignCorners: boolean) {
-    this.outputShape = inputShape;
-    const [, xHeight, xWidth, ] = inputShape;
-    const [, yHeight, yWidth] = dyShape;
-
-    // In the backwards pass, we want to find the pixels that were generated for
-    // each pixel in the input image the forward pass and add the corresponding
-    // coefficient from dy to the gradient (with some interpolation).
-
-    const effectiveXSize: [number, number] = [
-      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
-      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
-    ];
-
-    const effectiveYSize: [number, number] = [
-      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
-      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
-    ];
-
-    const heightScale = effectiveXSize[0] / effectiveYSize[0];
-    const widthScale = effectiveXSize[1] / effectiveYSize[1];
-
-    const invHeightScale = 1 / heightScale;
-    const invWidthScale = 1 / widthScale;
-
-    // This defines the size of the window of values around a particular
-    // index in dy that we want to search for contributions to dx.
-    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;
-    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;
-
-    this.userCode = `
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int d = coords[3];
-        int r = coords[1];
-        int c = coords[2];
-
-        float accumulator = 0.0;
-
-        const float heightScale = float(${heightScale});
-        const float widthScale = float(${widthScale});
-
-        const float invHeightScale = float(${invHeightScale});
-        const float invWidthScale = float(${invWidthScale});
-
-        const int winHeight = int(${winHeight});
-        const int winWidth = int(${winWidth});
-
-        // Compute bounds for where in dy we will look
-        float startRLerp = floor(float(r) * invHeightScale);
-        int startDyR = int(floor(startRLerp - float(winHeight / 2)));
-
-        float startCLerp = floor(float(c) * invWidthScale);
-        int startDyC = int(floor(startCLerp - float(winWidth / 2)));
-
-        // Loop over dy
-        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
-          int dyR = dyROffset + startDyR;
-
-          // Guard against the window exceeding the bounds of dy
-          if (dyR < 0 || dyR >= ${yHeight}) {
-            continue;
-          }
-
-          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
-            int dyC = dyCOffset + startDyC;
-
-            // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${yWidth}) {
-              continue;
-            }
-
-            float sourceFracRow =
-              float(${effectiveXSize[0]}) *
-                (float(dyR) / float(${effectiveYSize[0]}));
-
-            float sourceFracCol =
-                float(${effectiveXSize[1]}) *
-                  (float(dyC) / float(${effectiveYSize[1]}));
-
-            int sourceNearestRow = int(min(
-                float(int(${xHeight}) - 1),
-                ${alignCorners} ? float(round(sourceFracRow)) :
-                                  float(floor(sourceFracRow))));
-
-            int sourceNearestCol = int(min(
-                float(int(${xWidth}) - 1),
-                ${alignCorners} ? float(round(sourceFracCol)) :
-                                  float(floor(sourceFracCol))));
-
-            if (r == sourceNearestRow && c == sourceNearestCol) {
-              accumulator += getDy(b, dyR, dyC, d);
-            }
-          }
-        }
-        // End loop over dy
-
-        setOutput(accumulator);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/resize_nearest_neighbor_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/resize_nearest_neighbor_gpu.ts
deleted file mode 100644
index 44a470966..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/resize_nearest_neighbor_gpu.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ResizeNearestNeighborProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(
-      inputShape: [number, number, number, number], newHeight: number,
-      newWidth: number, alignCorners: boolean, halfPixelCenters: boolean) {
-    const [batch, oldHeight, oldWidth, depth] = inputShape;
-    this.outputShape = [batch, newHeight, newWidth, depth];
-
-    const effectiveInSize: [number, number] = [
-      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
-      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
-    ];
-
-    const effectiveOutSize: [number, number] = [
-      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
-      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
-    ];
-
-    // When align corners is false, we rounds the value with floor.
-    const roundBase = alignCorners ? '0.5' : '0.0';
-
-    let sourceFracIndexRC: string;
-    if (halfPixelCenters) {
-      sourceFracIndexRC =
-          `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC` +
-          `, vec2(0.0))`;
-    } else {
-      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
-    }
-    this.userCode = `
-      const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${effectiveInSize[0] / effectiveOutSize[0]},
-          ${effectiveInSize[1] / effectiveOutSize[1]});
-      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int d = coords[3];
-        ivec2 yRC = coords.yz;
-
-        // Fractional source index.
-        vec2 sourceFracIndexRC = ${sourceFracIndexRC};
-
-        // Compute the coordinators of nearest neighbor point.
-        ivec2 sourceNearestRC = ivec2(
-          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
-        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
-
-        setOutput(newValue);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/resize_nearest_neighbor_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/resize_nearest_neighbor_packed_gpu.ts
deleted file mode 100644
index fa691cf67..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/resize_nearest_neighbor_packed_gpu.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class ResizeNearestNeighborPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[] = [];
-  userCode: string;
-
-  constructor(
-      inputShape: [number, number, number, number], newHeight: number,
-      newWidth: number, alignCorners: boolean, halfPixelCenters: boolean) {
-    const [batch, oldHeight, oldWidth, depth] = inputShape;
-    this.outputShape = [batch, newHeight, newWidth, depth];
-
-    const effectiveInSize: [number, number] = [
-      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
-      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
-    ];
-
-    const effectiveOutSize: [number, number] = [
-      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
-      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
-    ];
-
-    // When align corners is false, we rounds the value with floor.
-    const roundBase = alignCorners ? '0.5' : '0.0';
-    let sourceFracIndexRC: string;
-    if (halfPixelCenters) {
-      sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * ` +
-          `effectiveInputOverOutputRatioRC, vec3(0.0))`;
-    } else {
-      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
-    }
-
-    this.userCode = `
-      const vec3 effectiveInputOverOutputRatioRC = vec3(
-          ${effectiveInSize[0] / effectiveOutSize[0]},
-          ${effectiveInSize[1] / effectiveOutSize[1]},
-          ${effectiveInSize[1] / effectiveOutSize[1]});
-      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
-                                     ${oldWidth}.0);
-
-      float getAValue(int b, int r, int c, int d) {
-        return getChannel(getA(b, r, c, d), vec2(c, d));
-      }
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int d = coords[3];
-        // Calculate values for next column in yRC.z.
-        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
-
-        // Fractional source index.
-        vec3 sourceFracIndexRC = ${sourceFracIndexRC};
-
-        // Compute the coordinators of nearest neighbor point.
-        ivec3 sourceNearestRC = ivec3(
-          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
-
-        // Should we calculate next column and row elements in 2x2 packed cell.
-        bool hasNextCol = d < ${depth - 1};
-        bool hasNextRow = coords.z < ${newWidth - 1};
-
-        vec4 newValue = vec4(
-          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
-          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
-                     : 0.0,
-          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
-                     : 0.0,
-          (hasNextRow && hasNextCol) ?
-            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
-
-        setOutput(newValue);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/reverse_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/reverse_gpu.ts
deleted file mode 100644
index 0f5a40ee3..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/reverse_gpu.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export class ReverseProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(xShape: number[], axis: number[]) {
-    const rank = xShape.length;
-    if (rank > 4) {
-      throw new Error(
-          `WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
-    }
-    this.outputShape = xShape;
-
-    if (rank === 1) {
-      this.userCode = `
-        void main() {
-          int coord = getOutputCoords();
-          setOutput(getX(${xShape[0]} - coord - 1));
-        }
-      `;
-      return;
-    }
-    const getInCoord = (i: number) => {
-      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
-        return `${xShape[i]} - coords[${i}] - 1`;
-      }
-      return `coords[${i}]`;
-    };
-    const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');
-    const type = getCoordsDataType(rank);
-
-    this.userCode = `
-      void main() {
-        ${type} coords = getOutputCoords();
-        setOutput(getX(${inCoords}));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/reverse_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/reverse_packed_gpu.ts
deleted file mode 100644
index 2e25744ff..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/reverse_packed_gpu.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getChannels} from './packing_util';
-import {getCoordsDataType} from './shader_compiler';
-
-export class ReversePackedProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  userCode: string;
-  packedInputs = true;
-  packedOutput = true;
-
-  constructor(xShape: number[], axis: number[]) {
-    const rank = xShape.length;
-    if (rank > 4) {
-      throw new Error(
-          `WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
-    }
-    this.outputShape = xShape;
-    const channels = getChannels('rc', rank);
-    const nextColumn =
-        `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;
-    const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;
-    const type = getCoordsDataType(rank);
-    if (rank === 1) {
-      this.userCode = `
-        void main(){
-          int rc = getOutputCoords();
-          vec4 result = vec4(0.);
-          result.r = getChannel(getX(${xShape[0]} - rc - 1),
-            ${xShape[0]} - rc - 1);
-          if(${nextColumn}){
-              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),
-                ${xShape[0]} - (rc  + 1) - 1);
-          }
-          setOutput(result);
-        }
-      `;
-    } else {
-      this.userCode = `
-        void main() {
-          ${type} rc = getOutputCoords();
-          vec4 result = vec4(0.);
-          result.r = ${getR(channels.slice())};
-          if(${nextColumn}){
-            result.g = ${getG(channels.slice())};
-          }
-          if(${nextRow}) {
-            result.b = ${getB(channels.slice())};
-            if(${nextColumn}) {
-              result.a = ${getA(channels.slice())};
-            }
-          }
-          setOutput(result);
-        }
-    `;
-    }
-
-    function getR(channels: string[]): string {
-      return getChannel(channels);
-    }
-
-    function getG(channels: string[]): string {
-      channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;
-      return getChannel(channels);
-    }
-
-    function getB(channels: string[]): string {
-      channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;
-      return getChannel(channels);
-    }
-
-    function getA(channels: string[]): string {
-      channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;
-      channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;
-      return getChannel(channels);
-    }
-
-    function getChannel(channels: string[]): string {
-      const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));
-      const inCoords = inCoordsArray.join(',');
-      const innerDims = inCoordsArray.slice(-2).join(',');
-      return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;
-    }
-
-    function getInCoord(i: number, channels1: string[]): string {
-      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
-        return `${xShape[i]} - ${channels1[i]} - 1`;
-      } else {
-        return `${channels1[i]}`;
-      }
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/rotate_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/rotate_gpu.ts
deleted file mode 100644
index d1b8e0bae..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/rotate_gpu.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {UniformType} from './shader_compiler';
-
-export class RotateProgram implements GPGPUProgram {
-  variableNames = ['Image'];
-  outputShape: number[] = [];
-  userCode: string;
-  customUniforms = [{name: 'params', type: 'vec4' as UniformType}];
-  constructor(
-      imageShape: [number, number, number, number],
-      fillValue: number|[number, number, number]) {
-    const imageHeight = imageShape[1];
-    const imageWidth = imageShape[2];
-    this.outputShape = imageShape;
-
-    let fillSnippet = '';
-    if (typeof fillValue === 'number') {
-      fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;
-    } else {
-      fillSnippet = `
-        vec3 fill = vec3(${fillValue.join(',')});
-        float outputValue = fill[coords[3]];`;
-    }
-
-    this.userCode = `
-        void main() {
-          ivec4 coords = getOutputCoords();
-          int x = coords[2];
-          int y = coords[1];
-          float coordXFloat = (float(x) - params[0]) * params[3] -
-            (float(y) - params[1]) * params[2];
-          float coordYFloat = (float(x) - params[0]) * params[2] +
-            (float(y) - params[1]) * params[3];
-          int coordX = int(round(coordXFloat + params[0]));
-          int coordY = int(round(coordYFloat + params[1]));
-          ${fillSnippet}
-          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${
-        imageHeight}) {
-            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
-          }
-          setOutput(outputValue);
-        }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/scatter_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/scatter_gpu.ts
deleted file mode 100644
index 0ed3a8048..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/scatter_gpu.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export class ScatterProgram implements GPGPUProgram {
-  variableNames = ['updates', 'indices', 'defaultValue'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      updateSize: number, sliceDim: number, indicesRank: number,
-      updatesRank: number, strides: number[], shape: number[],
-      summingDupeIndex = true, defaultIsTensor = false) {
-    this.outputShape = shape;
-    const stridesType = getCoordsDataType(strides.length);
-    const dtype = getCoordsDataType(shape.length);
-    let indicesString = '';
-    if (indicesRank === 1) {
-      indicesString = 'i';
-    } else if (indicesRank === 2) {
-      indicesString = 'i, j';
-    }
-    const indicesSnippet = `getIndices(${indicesString})`;
-
-    let updatesString = '';
-    if (updatesRank === 1) {
-      updatesString = 'i';
-    } else if (updatesRank === 2) {
-      updatesString = 'i, coords[1]';
-    }
-    const updatesSnippet = `getUpdates(${updatesString})`;
-
-    let defaultValuesString = '';
-    if (defaultIsTensor) {
-      defaultValuesString = 'coords[0], coords[1]';
-    }
-    const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`;
-
-    const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';
-    this.userCode = `
-        ${stridesType} strides = ${stridesType}(${strides});
-
-        void main() {
-          ${dtype} coords = getOutputCoords();
-          float sum = 0.0;
-          bool found = false;
-          for (int i = 0; i < ${updateSize}; i++) {
-            int flattenedIndex = 0;
-            for (int j = 0; j < ${sliceDim}; j++) {
-              int index = round(${indicesSnippet});
-              flattenedIndex += index * ${strideString};
-            }
-            if (flattenedIndex == coords[0]) {
-              sum += ${updatesSnippet};
-              found = true;
-            }
-          }
-          setOutput(mix(${defaultValueSnippet}, sum, float(found)));
-        }
-      `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/scatter_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/scatter_packed_gpu.ts
deleted file mode 100644
index 278e1be90..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/scatter_packed_gpu.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export class ScatterPackedProgram implements GPGPUProgram {
-  variableNames = ['updates', 'indices', 'defaultValue'];
-  outputShape: number[];
-  packedInputs = true;
-  packedOutput = true;
-  userCode: string;
-
-  constructor(
-      updateSize: number, sliceDim: number, indicesRank: number,
-      updatesRank: number, strides: number[], shape: number[],
-      summingDupeIndex = true, defaultIsTensor = false) {
-    this.outputShape = shape;
-    const stridesType = getCoordsDataType(strides.length);
-    const dtype = getCoordsDataType(shape.length);
-    let indicesString = '';
-    if (indicesRank === 1) {
-      indicesString = 'i';
-    } else if (indicesRank === 2) {
-      indicesString = 'i, j';
-    }
-    const indicesSnippet = `getIndices(${indicesString})`;
-
-    let updatesString = '';
-    if (updatesRank === 1) {
-      updatesString = 'i';
-    } else if (updatesRank === 2) {
-      updatesString = 'i, coords[1]';
-    }
-    const updatesSnippet = `getUpdates(${updatesString})`;
-
-    let defaultValuesString = '';
-    if (defaultIsTensor) {
-      defaultValuesString = 'coords[0], coords[1]';
-    }
-    const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`;
-
-    const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';
-    const strideString2 = sliceDim > 1 ? 'strides[j + 1]' : 'strides';
-
-    this.userCode = `
-        ${stridesType} strides = ${stridesType}(${strides});
-
-        void main() {
-          ${dtype} coords = getOutputCoords();
-          vec4 sum = vec4(0.);
-          vec4 found = vec4(0.);
-          for (int i = 0; i < ${updateSize}; i+=2) {
-            ivec2 flattenedIndex = ivec2(0);
-            for (int j = 0; j < ${sliceDim}; j+=2) {
-              ivec4 index = round(${indicesSnippet});
-              flattenedIndex += index.xz * ${strideString};
-              if (j + 1 < ${sliceDim}) {
-                flattenedIndex += index.yw * ${strideString2};
-              }
-            }
-            if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||
-                flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {
-              vec4 updVals = ${updatesSnippet};
-              if (flattenedIndex[0] == coords[0]) {
-                sum.xy += updVals.xy;
-                found.xy = vec2(1.);
-              } else if (flattenedIndex[0] == coords[0] + 1) {
-                sum.zw += updVals.xy;
-                found.zw = vec2(1.);
-              }
-              if (flattenedIndex[1] == coords[0]) {
-                sum.xy += updVals.zw;
-                found.xy = vec2(1.);
-              } else if (flattenedIndex[1] == coords[0] + 1) {
-                sum.zw += updVals.zw;
-                found.zw = vec2(1.);
-              }
-            }
-          }
-          setOutput(mix(${defaultValueSnippet}, sum, found));
-        }
-      `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/search_sorted_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/search_sorted_gpu.ts
deleted file mode 100644
index 3a9c01005..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/search_sorted_gpu.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-import {UniformType} from './shader_compiler';
-
-export class SearchSortedProgram implements GPGPUProgram {
-  variableNames = ['sortedSequence', 'values'];
-  outputShape: number[];
-  userCode: string;
-  customUniforms = [{name: 'numInputs', type: 'int' as UniformType}];
-
-  constructor(
-      batchSize: number, numInputs: number, numValues: number,
-      side: 'left'|'right') {
-    this.outputShape = [batchSize, numValues];
-
-    const webGL2LoopHead = 'while (left < right) {';
-    // WebGL1 doesn't accept non constant loop conditions, so upper bound loop
-    // iterations.
-    const webGL1LoopHead = `for (int i = 0; i < ${
-        Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`;
-    const loopHead = env().getNumber('WEBGL_VERSION') === 2 ? webGL2LoopHead :
-                                                              webGL1LoopHead;
-
-    // left corresponds to lower bound and right to upper bound.
-    const boundComparator = side === 'left' ? '<' : '<=';
-    this.userCode = `
-       int findBound(int batch, float value) {
-         int left = 0;
-         int right = numInputs;
-         int mid;
-         ${loopHead}
-           mid = (left + right) / 2;
-           if (getSortedSequence(batch, mid) ${boundComparator} value) {
-             left = mid + 1;
-           } else {
-             right = mid;
-           }
-         }
-         return right;
-       }
-
-       void main() {
-         ivec2 coords = getOutputCoords();
-         int batch = coords[0];
-         int valueIndex = coords[1];
-
-         float value = getValues(batch, valueIndex);
-
-         setOutput(float(findBound(batch, value)));
-       }
-     `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/segment_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/segment_gpu.ts
deleted file mode 100644
index f9cc32ac9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/segment_gpu.ts
+++ /dev/null
@@ -1,162 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {GPGPUProgram} from './gpgpu_math';
-
-export class SegmentOpProgram implements GPGPUProgram {
-  variableNames = ['x', 'segmentIds'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      segOpInfo: backend_util.segment_util.SegOpInfo,
-      segOpType: 'unsortedSegmentSum') {
-    const windowSize = segOpInfo.windowSize;
-    const batchSize = segOpInfo.batchSize;
-    const inSize = segOpInfo.inSize;
-    const numSegments = segOpInfo.numSegments;
-    const outSize = numSegments * Math.ceil(inSize / windowSize);
-    this.outputShape = [batchSize, outSize];
-
-    const initializationValue = '0.0';
-    const returnValue = `sumValue`;
-
-    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
-    const windowSizeVec4Remainder = windowSize % 4;
-
-    const updateSnippet = `
-        sumValue += dot(values, segFilter);
-    `;
-
-    let checkValueOutOfBounds = '';
-    if (inSize % windowSize > 0) {
-      checkValueOutOfBounds = `
-        if (inIdx < 0 || inIdx >= ${inSize}) {
-          return initializationValue;
-        }
-      `;
-    }
-
-    let checkSegmentIdOutOfBounds = '';
-    if (inSize % windowSize > 0) {
-      checkSegmentIdOutOfBounds = `
-        if (inIdx < 0 || inIdx >= ${inSize}) {
-          return -1.0;
-        }
-      `;
-    }
-
-    this.userCode = `
-      const float initializationValue = ${initializationValue};
-
-      float getValue(int batch, int inIdx) {
-        ${checkValueOutOfBounds}
-        return getX(batch, inIdx);
-      }
-
-      float getSegmentIdAtIndex(int inIdx) {
-        ${checkSegmentIdOutOfBounds}
-        return getSegmentIds(inIdx);
-      }
-
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int batch = coords[0];
-        int outIdx = coords[1];
-        int inOffset = int(floor(float(outIdx) / float(
-          ${numSegments})) * float(${windowSize}));
-        int currentSeg = int(mod(float(outIdx), float(${numSegments})));
-
-        float sumValue = 0.0;
-
-        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
-          int inIdx = inOffset + i;
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2),
-            getValue(batch, inIdx + 3)
-          );
-
-          vec4 segFilter = vec4(
-            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
-            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
-            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
-            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
-          );
-
-          ${updateSnippet}
-        }
-
-        int inIdx = inOffset + ${windowSizeNearestVec4};
-        if (${windowSizeVec4Remainder === 1}) {
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            initializationValue,
-            initializationValue,
-            initializationValue
-          );
-
-          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
-
-          vec4 segFilter = vec4(
-            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
-            0,
-            0,
-            0
-          );
-
-          ${updateSnippet}
-        } else if (${windowSizeVec4Remainder === 2}) {
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            initializationValue,
-            initializationValue
-          );
-
-          vec4 segFilter = vec4(
-            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
-            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
-              0,
-              0
-          );
-
-          ${updateSnippet}
-        } else if (${windowSizeVec4Remainder === 3}) {
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2),
-            initializationValue
-          );
-
-          vec4 segFilter = vec4(
-            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
-            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
-            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
-            0
-          );
-
-          ${updateSnippet}
-        }
-        setOutput(${returnValue});
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/select_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/select_gpu.ts
deleted file mode 100644
index 8cea29053..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/select_gpu.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export class SelectProgram implements GPGPUProgram {
-  variableNames = ['c', 'a', 'b'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(cRank: number, shape: number[], rank: number) {
-    this.outputShape = shape;
-
-    let cCoords;
-    let abCoords;
-    if (rank > 4) {
-      throw Error(`Where for rank ${rank} is not yet supported`);
-    }
-
-    if (rank === 1) {
-      abCoords = `resRC`;
-      cCoords = `resRC`;
-    } else {
-      const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
-      const cCoordVars = [];
-      const abCoordVars = [];
-      for (let i = 0; i < shape.length; i++) {
-        abCoordVars.push(`${currentCoords[i]}`);
-        if (i < cRank) {
-          cCoordVars.push(`${currentCoords[i]}`);
-        }
-      }
-      cCoords = cCoordVars.join();
-      abCoords = abCoordVars.join();
-    }
-
-    const dtype = getCoordsDataType(rank);
-
-    this.userCode = `
-      void main() {
-        ${dtype} resRC = getOutputCoords();
-        float cVal = getC(${cCoords});
-        if (cVal >= 1.0) {
-          setOutput(getA(${abCoords}));
-        } else {
-          setOutput(getB(${abCoords}));
-        }
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/setup_test.ts b/tfjs-master/tfjs-backend-webgl/src/setup_test.ts
deleted file mode 100644
index 0faa95d5a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/setup_test.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-// Register the backend.
-import './index';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-
-// tslint:disable-next-line: no-imports-from-dist
-import {parseTestEnvFromKarmaFlags, setTestEnvs, setupTestFilters, TEST_ENVS, TestFilter} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {registerTestEnvs} from './backend_webgl_test_registry';
-
-registerTestEnvs();
-
-const TEST_FILTERS: TestFilter[] = [];
-
-const customInclude = (testName: string) => {
-  const toExclude = [
-    'isBrowser: false',
-    'dilation gradient',
-    'throws when index is out of bound',
-    // otsu tests for threshold op is failing on windows
-    'method otsu',
-    'draw on canvas context',
-    // https://github.com/tensorflow/tfjs/issues/7618
-    'numbers exceed float32 precision',
-    // float32 inputs with nonzero fractional part should not be rounded
-    'floorDiv float32',
-  ];
-  for (const subStr of toExclude) {
-    if (testName.includes(subStr)) {
-      return false;
-    }
-  }
-  // TODO(msoulanille): Prefer TEST_FILTERS over customInclude.
-  return true;
-};
-setupTestFilters(TEST_FILTERS, customInclude);
-
-// Allow flags to override test envs
-// tslint:disable-next-line:no-any
-declare let __karma__: any;
-if (typeof __karma__ !== 'undefined') {
-  const testEnv = parseTestEnvFromKarmaFlags(__karma__.config.args, TEST_ENVS);
-  if (testEnv != null) {
-    setTestEnvs([testEnv]);
-  }
-}
-
-// These use 'require' because they must not be hoisted above
-// the preceding snippet that parses test environments.
-// Import and run tests from core.
-// tslint:disable-next-line:no-imports-from-dist
-// tslint:disable-next-line:no-require-imports
-require('@tensorflow/tfjs-core/dist/tests');
-// Import and run tests from webgl.
-// tslint:disable-next-line:no-imports-from-dist
-// tslint:disable-next-line:no-require-imports
-require('./tests');
diff --git a/tfjs-master/tfjs-backend-webgl/src/shader_compiler.ts b/tfjs-master/tfjs-backend-webgl/src/shader_compiler.ts
deleted file mode 100644
index b051e0974..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/shader_compiler.ts
+++ /dev/null
@@ -1,1915 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Please make sure the shaker key in makeShaderKey in gpgpu_math.ts is well
-// mapped if any shader source code is changed in this file.
-
-import {backend_util, util} from '@tensorflow/tfjs-core';
-const {getBroadcastDims} = backend_util;
-import {getGlslDifferences, GLSL} from './glsl_version';
-import * as shader_util from './shader_compiler_util';
-
-export type ShapeInfo = {
-  logicalShape: number[],
-  texShape: [number, number],
-  isUniform: boolean,
-  isPacked: boolean,
-  flatOffset: number
-};
-
-export type InputInfo = {
-  name: string,
-  shapeInfo: ShapeInfo
-};
-
-export type UniformType =
-    'float'|'vec2'|'vec3'|'vec4'|'int'|'ivec2'|'ivec3'|'ivec4';
-
-interface ProgramParams {
-  userCode: string;
-  enableShapeUniforms?: boolean;
-  packedInputs?: boolean;
-  customUniforms?:
-      Array<{name: string; arrayIndex?: number; type: UniformType;}>;
-}
-
-export function makeShader(
-    inputsInfo: InputInfo[], outputShape: ShapeInfo,
-    program: ProgramParams): string {
-  const prefixSnippets: string[] = [];
-  inputsInfo.forEach(x => {
-    const size = util.sizeFromShape(x.shapeInfo.logicalShape);
-
-    // Snippet when we decided to upload the values as uniform.
-    if (x.shapeInfo.isUniform) {
-      prefixSnippets.push(
-          `uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);
-    } else {
-      prefixSnippets.push(`uniform sampler2D ${x.name};`);
-      prefixSnippets.push(`uniform int offset${x.name};`);
-    }
-
-    if (program.enableShapeUniforms) {
-      const {uniformShape} = getUniformInfoFromShape(
-          program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape);
-      switch (uniformShape.length) {
-        case 1:
-          prefixSnippets.push(`uniform int ${x.name}Shape;`);
-          break;
-        case 2:
-          prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`);
-          break;
-        case 3:
-          prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`);
-          break;
-        case 4:
-          prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`);
-          break;
-        default:
-          break;
-      }
-      prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`);
-    }
-  });
-
-  if (program.enableShapeUniforms) {
-    switch (outputShape.logicalShape.length) {
-      case 1:
-        prefixSnippets.push(`uniform int outShape;`);
-        break;
-      case 2:
-        prefixSnippets.push(`uniform ivec2 outShape;`);
-        prefixSnippets.push(`uniform int outShapeStrides;`);
-        break;
-      case 3:
-        prefixSnippets.push(`uniform ivec3 outShape;`);
-        prefixSnippets.push(`uniform ivec2 outShapeStrides;`);
-        break;
-      case 4:
-        prefixSnippets.push(`uniform ivec4 outShape;`);
-        prefixSnippets.push(`uniform ivec3 outShapeStrides;`);
-        break;
-      default:
-        break;
-    }
-    prefixSnippets.push(`uniform ivec2 outTexShape;`);
-  }
-  if (program.customUniforms) {
-    program.customUniforms.forEach((d) => {
-      prefixSnippets.push(`uniform ${d.type} ${d.name}${
-          d.arrayIndex ? `[${d.arrayIndex}]` : ''};`);
-    });
-  }
-  const inputPrefixSnippet = prefixSnippets.join('\n');
-
-  const inputSamplingSnippet = inputsInfo
-                                   .map(
-                                       x => getInputSamplingSnippet(
-                                           x, outputShape, program.packedInputs,
-                                           program.enableShapeUniforms))
-                                   .join('\n');
-  const outTexShape = outputShape.texShape;
-  const glsl = getGlslDifferences();
-  const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);
-  let outputSamplingSnippet: string;
-  let floatTextureSetOutputSnippet: string;
-  let shaderPrefix = getShaderPrefix(glsl);
-
-  if (outputShape.isPacked) {
-    outputSamplingSnippet = getPackedOutputSamplingSnippet(
-        outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
-    floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);
-  } else {
-    outputSamplingSnippet = getOutputSamplingSnippet(
-        outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
-    floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);
-  }
-
-  if (program.packedInputs) {
-    shaderPrefix += SHADER_PACKED_PREFIX;
-  }
-
-  const source = [
-    shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,
-    inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet,
-    program.userCode
-  ].join('\n');
-  return source;
-}
-
-function getSamplerFromInInfo(
-    inInfo: InputInfo, enableShapeUniforms = false): string {
-  const shape = inInfo.shapeInfo.logicalShape;
-  switch (shape.length) {
-    case 0:
-      return getSamplerScalar(inInfo, enableShapeUniforms);
-    case 1:
-      return getSampler1D(inInfo, enableShapeUniforms);
-    case 2:
-      return getSampler2D(inInfo, enableShapeUniforms);
-    case 3:
-      return getSampler3D(inInfo, enableShapeUniforms);
-    case 4:
-      return getSampler4D(inInfo, enableShapeUniforms);
-    case 5:
-      return getSampler5D(inInfo);
-    case 6:
-      return getSampler6D(inInfo);
-    default:
-      throw new Error(
-          `${shape.length}-D input sampling` +
-          ` is not yet supported`);
-  }
-}
-
-function getPackedSamplerFromInInfo(
-    inInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const shape = inInfo.shapeInfo.logicalShape;
-  switch (shape.length) {
-    case 0:
-      return getPackedSamplerScalar(inInfo);
-    case 1:
-      return getPackedSampler1D(inInfo, enableShapeUniforms);
-    case 2:
-      return getPackedSampler2D(inInfo, enableShapeUniforms);
-    case 3:
-      return getPackedSampler3D(inInfo, enableShapeUniforms);
-    default:
-      return getPackedSamplerND(inInfo, enableShapeUniforms);
-  }
-}
-
-function getInputSamplingSnippet(
-    inInfo: InputInfo, outShapeInfo: ShapeInfo, usesPackedTextures = false,
-    enableShapeUniforms: boolean): string {
-  let res = '';
-  if (usesPackedTextures) {
-    res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms);
-  } else {
-    res += getSamplerFromInInfo(inInfo, enableShapeUniforms);
-  }
-
-  const inShape = inInfo.shapeInfo.logicalShape;
-  const outShape = outShapeInfo.logicalShape;
-  if (inShape.length <= outShape.length) {
-    if (usesPackedTextures) {
-      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);
-    } else {
-      res += getSamplerAtOutputCoords(inInfo, outShapeInfo);
-    }
-  }
-  return res;
-}
-
-function getPackedOutputSamplingSnippet(
-    outShape: number[], outTexShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  switch (outShape.length) {
-    case 0:
-      return getOutputScalarCoords();
-    case 1:
-      return getOutputPacked1DCoords(
-          outShape as [number], outTexShape, enableShapeUniforms);
-    case 2:
-      return getOutputPacked2DCoords(
-          outShape as [number, number], outTexShape, enableShapeUniforms);
-    case 3:
-      return getOutputPacked3DCoords(
-          outShape as [number, number, number], outTexShape,
-          enableShapeUniforms);
-    default:
-      return getOutputPackedNDCoords(
-          outShape, outTexShape, enableShapeUniforms);
-  }
-}
-
-function getOutputSamplingSnippet(
-    outShape: number[], outTexShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  switch (outShape.length) {
-    case 0:
-      return getOutputScalarCoords();
-    case 1:
-      return getOutput1DCoords(
-          outShape as [number], outTexShape, enableShapeUniforms);
-    case 2:
-      return getOutput2DCoords(
-          outShape as [number, number], outTexShape, enableShapeUniforms);
-    case 3:
-      return getOutput3DCoords(
-          outShape as [number, number, number], outTexShape,
-          enableShapeUniforms);
-    case 4:
-      return getOutput4DCoords(
-          outShape as [number, number, number, number], outTexShape,
-          enableShapeUniforms);
-    case 5:
-      return getOutput5DCoords(
-          outShape as [number, number, number, number, number], outTexShape);
-    case 6:
-      return getOutput6DCoords(
-          outShape as [number, number, number, number, number, number],
-          outTexShape);
-    default:
-      throw new Error(
-          `${outShape.length}-D output sampling is not yet supported`);
-  }
-}
-
-function getFloatTextureSampleSnippet(glsl: GLSL): string {
-  return `
-    float sampleTexture(sampler2D textureSampler, vec2 uv) {
-      return ${glsl.texture2D}(textureSampler, uv).r;
-    }
-  `;
-}
-
-function getFloatTextureSetRSnippet(glsl: GLSL): string {
-  return `
-    void setOutput(float val) {
-      ${glsl.output} = vec4(val, 0, 0, 0);
-    }
-  `;
-}
-
-function getFloatTextureSetRGBASnippet(glsl: GLSL): string {
-  return `
-    void setOutput(vec4 val) {
-      ${glsl.output} = val;
-    }
-  `;
-}
-
-function getShaderPrefix(glsl: GLSL): string {
-  const SHADER_PREFIX = `${glsl.version}
-    precision highp float;
-    precision highp int;
-    precision highp sampler2D;
-    ${glsl.varyingFs} vec2 resultUV;
-    ${glsl.defineOutput}
-    const vec2 halfCR = vec2(0.5, 0.5);
-
-    struct ivec5
-    {
-      int x;
-      int y;
-      int z;
-      int w;
-      int u;
-    };
-
-    struct ivec6
-    {
-      int x;
-      int y;
-      int z;
-      int w;
-      int u;
-      int v;
-    };
-
-    uniform float NAN;
-    ${glsl.defineSpecialNaN}
-    ${glsl.defineSpecialInf}
-    ${glsl.defineRound}
-
-    int imod(int x, int y) {
-      return x - y * (x / y);
-    }
-
-    int idiv(int a, int b, float sign) {
-      int res = a / b;
-      int mod = imod(a, b);
-      if (sign < 0. && mod != 0) {
-        res -= 1;
-      }
-      return res;
-    }
-
-    //Based on the work of Dave Hoskins
-    //https://www.shadertoy.com/view/4djSRW
-    #define HASHSCALE1 443.8975
-    float random(float seed){
-      vec2 p = resultUV * seed;
-      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
-      p3 += dot(p3, p3.yzx + 19.19);
-      return fract((p3.x + p3.y) * p3.z);
-    }
-
-    ${SAMPLE_1D_SNIPPET}
-    ${SAMPLE_2D_SNIPPET}
-    ${SAMPLE_3D_SNIPPET}
-  `;
-
-  return SHADER_PREFIX;
-}
-
-const SAMPLE_1D_SNIPPET = `
-vec2 uvFromFlat(int texNumR, int texNumC, int index) {
-  int texR = index / texNumC;
-  int texC = index - texR * texNumC;
-  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-}
-vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
-  int texelIndex = index / 2;
-  int texR = texelIndex / texNumC;
-  int texC = texelIndex - texR * texNumC;
-  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-}
-`;
-
-const SAMPLE_2D_SNIPPET = `
-vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
-  int texNumC, int row, int col) {
-  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
-  int texR = texelIndex / texNumC;
-  int texC = texelIndex - texR * texNumC;
-  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-}
-`;
-
-const SAMPLE_3D_SNIPPET = `
-vec2 packedUVfrom3D(int texNumR, int texNumC,
-    int texelsInBatch, int texelsInLogicalRow, int b,
-    int row, int col) {
-  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
-  int texR = index / texNumC;
-  int texC = index - texR * texNumC;
-  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-}
-`;
-
-const SHADER_PACKED_PREFIX = `
-  float getChannel(vec4 frag, vec2 innerDims) {
-    vec2 modCoord = mod(innerDims, 2.);
-    return modCoord.x == 0. ?
-      (modCoord.y == 0. ? frag.r : frag.g) :
-      (modCoord.y == 0. ? frag.b : frag.a);
-  }
-  float getChannel(vec4 frag, int dim) {
-    float modCoord = mod(float(dim), 2.);
-    return modCoord == 0. ? frag.r : frag.g;
-  }
-`;
-
-function getOutputScalarCoords() {
-  return `
-    int getOutputCoords() {
-      return 0;
-    }
-  `;
-}
-
-function getOutputPacked1DCoords(
-    shape: [number], texShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  const packedTexShape =
-      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
-  if (packedTexShape[0] === 1) {
-    if (enableShapeUniforms) {
-      return `
-      int getOutputCoords() {
-        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
-      }
-    `;
-    }
-
-    return `
-      int getOutputCoords() {
-        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
-      }
-    `;
-  }
-
-  if (packedTexShape[1] === 1) {
-    if (enableShapeUniforms) {
-      return `
-      int getOutputCoords() {
-        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
-      }
-    `;
-    }
-
-    return `
-      int getOutputCoords() {
-        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
-      }
-    `;
-  }
-
-  if (enableShapeUniforms) {
-    return `
-    int getOutputCoords() {
-      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(packedTexShape[0], packedTexShape[1]));
-      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
-    }
-  `;
-  }
-
-  return `
-    int getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
-      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
-    }
-  `;
-}
-
-function getOutput1DCoords(
-    shape: [number], texShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  if (texShape[0] === 1) {
-    if (enableShapeUniforms) {
-      return `
-      int getOutputCoords() {
-        return int(resultUV.x * float(outTexShape[1]));
-      }
-    `;
-    }
-    return `
-      int getOutputCoords() {
-        return int(resultUV.x * ${texShape[1]}.0);
-      }
-    `;
-  }
-  if (texShape[1] === 1) {
-    if (enableShapeUniforms) {
-      return `
-      int getOutputCoords() {
-        return int(resultUV.y * float(outTexShape[0]));
-      }
-    `;
-    }
-    return `
-      int getOutputCoords() {
-        return int(resultUV.y * ${texShape[0]}.0);
-      }
-    `;
-  }
-  if (enableShapeUniforms) {
-    return `
-    int getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(outTexShape[0], outTexShape[1]));
-      return resTexRC.x * outTexShape[1] + resTexRC.y;
-    }
-  `;
-  }
-  return `
-    int getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${texShape[0]}, ${texShape[1]}));
-      return resTexRC.x * ${texShape[1]} + resTexRC.y;
-    }
-  `;
-}
-
-function getOutputPacked3DCoords(
-    shape: [number, number, number], texShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  if (enableShapeUniforms) {
-    return `
-    ivec3 getOutputCoords() {
-      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
-      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
-      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(packedTexShape[0], packedTexShape[1]));
-      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
-
-      int b = index / texelsInBatch;
-      index -= b * texelsInBatch;
-
-      int r = 2 * (index / texelsInLogicalRow);
-      int c = imod(index, texelsInLogicalRow) * 2;
-
-      return ivec3(b, r, c);
-    }
-  `;
-  }
-
-  const packedTexShape =
-      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
-  const texelsInLogicalRow = Math.ceil(shape[2] / 2);
-  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
-
-  return `
-    ivec3 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
-      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
-
-      int b = index / ${texelsInBatch};
-      index -= b * ${texelsInBatch};
-
-      int r = 2 * (index / ${texelsInLogicalRow});
-      int c = imod(index, ${texelsInLogicalRow}) * 2;
-
-      return ivec3(b, r, c);
-    }
-  `;
-}
-
-function getOutput3DCoords(
-    shape: [number, number, number], texShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  if (enableShapeUniforms) {
-    const coordsFromIndexSnippet =
-        shader_util.getOutputLogicalCoordinatesFromFlatIndexByUniform(
-            ['r', 'c', 'd'], shape);
-
-    return `
-  ivec3 getOutputCoords() {
-    ivec2 resTexRC = ivec2(resultUV.yx *
-                           vec2(outTexShape[0], outTexShape[1]));
-    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-    ${coordsFromIndexSnippet}
-    return ivec3(r, c, d);
-  }
-`;
-  }
-  const coordsFromIndexSnippet =
-      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
-
-  return `
-    ivec3 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${texShape[0]}, ${texShape[1]}));
-      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
-      ${coordsFromIndexSnippet}
-      return ivec3(r, c, d);
-    }
-  `;
-}
-
-function getOutputPackedNDCoords(
-    shape: number[], texShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  if (enableShapeUniforms) {
-    // TODO: support 5d and 6d
-    return `
-    ivec4 getOutputCoords() {
-      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(packedTexShape[0], packedTexShape[1]));
-      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
-
-      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
-      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
-      int texelsInBatchN = texelsInBatch * outShape[1];
-
-      int b2 = index / texelsInBatchN;
-      index -= b2 * texelsInBatchN;
-
-      int b = index / texelsInBatch;
-      index -= b * texelsInBatch;
-
-      int r = 2 * (index / texelsInLogicalRow);
-      int c = imod(index, texelsInLogicalRow) * 2;
-
-      return ivec4(b2, b, r, c);
-    }
-  `;
-  }
-  const packedTexShape =
-      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
-
-  const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);
-  const texelsInBatch =
-      texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);
-  let texelsInBatchN = texelsInBatch;
-  let batches = ``;
-  let coords = 'b, r, c';
-
-  for (let b = 2; b < shape.length - 1; b++) {
-    texelsInBatchN *= shape[shape.length - b - 1];
-    batches = `
-      int b${b} = index / ${texelsInBatchN};
-      index -= b${b} * ${texelsInBatchN};
-    ` + batches;
-    coords = `b${b}, ` + coords;
-  }
-
-  return `
-    ivec${shape.length} getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
-      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
-
-      ${batches}
-
-      int b = index / ${texelsInBatch};
-      index -= b * ${texelsInBatch};
-
-      int r = 2 * (index / ${texelsInLogicalRow});
-      int c = imod(index, ${texelsInLogicalRow}) * 2;
-
-      return ivec${shape.length}(${coords});
-    }
-  `;
-}
-
-function getOutput4DCoords(
-    shape: [number, number, number, number], texShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  if (enableShapeUniforms) {
-    const coordsFromIndexSnippet =
-        shader_util.getOutputLogicalCoordinatesFromFlatIndexByUniform(
-            ['r', 'c', 'd', 'd2'], shape);
-
-    return `
-    ivec4 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-        vec2(outTexShape[0], outTexShape[1]));
-      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      ${coordsFromIndexSnippet}
-      return ivec4(r, c, d, d2);
-    }
-  `;
-  }
-  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(
-      ['r', 'c', 'd', 'd2'], shape);
-
-  return `
-    ivec4 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-        vec2(${texShape[0]}, ${texShape[1]}));
-      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
-      ${coordsFromIndexSnippet}
-      return ivec4(r, c, d, d2);
-    }
-  `;
-}
-
-function getOutput5DCoords(
-    shape: [number, number, number, number, number],
-    texShape: [number, number]): string {
-  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(
-      ['r', 'c', 'd', 'd2', 'd3'], shape);
-
-  return `
-    ivec5 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
-                             ${texShape[1]}));
-
-      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
-
-      ${coordsFromIndexSnippet}
-
-      ivec5 outShape = ivec5(r, c, d, d2, d3);
-      return outShape;
-    }
-  `;
-}
-
-function getOutput6DCoords(
-    shape: [number, number, number, number, number, number],
-    texShape: [number, number]): string {
-  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(
-      ['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);
-
-  return `
-    ivec6 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-        vec2(${texShape[0]}, ${texShape[1]}));
-      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
-
-      ${coordsFromIndexSnippet}
-
-      ivec6 result = ivec6(r, c, d, d2, d3, d4);
-      return result;
-    }
-  `;
-}
-
-function getOutputPacked2DCoords(
-    shape: [number, number], texShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  const packedTexShape =
-      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
-  if (util.arraysEqual(shape, texShape)) {
-    if (enableShapeUniforms) {
-      return `
-      ivec2 getOutputCoords() {
-        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
-        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
-      }
-    `;
-    }
-
-    return `
-      ivec2 getOutputCoords() {
-        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${
-        packedTexShape[1]}));
-      }
-    `;
-  }
-
-  // texels needed to accommodate a logical row
-  const texelsInLogicalRow = Math.ceil(shape[1] / 2);
-
-  /**
-   * getOutputCoords
-   *
-   * resTexRC: The rows and columns of the texels. If you move over one
-   * texel to the right in the packed texture, you are moving over one column
-   * (not two).
-   *
-   * index: The texel index
-   */
-  if (enableShapeUniforms) {
-    return `
-    ivec2 getOutputCoords() {
-      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
-      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(packedTexShape[0], packedTexShape[1]));
-
-      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
-      int r = 2 * (index / texelsInLogicalRow);
-      int c = imod(index, texelsInLogicalRow) * 2;
-
-      return ivec2(r, c);
-    }
-  `;
-  }
-
-  return `
-    ivec2 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
-
-      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
-      int r = 2 * (index / ${texelsInLogicalRow});
-      int c = imod(index, ${texelsInLogicalRow}) * 2;
-
-      return ivec2(r, c);
-    }
-  `;
-}
-
-function getOutput2DCoords(
-    shape: [number, number], texShape: [number, number],
-    enableShapeUniforms: boolean): string {
-  if (util.arraysEqual(shape, texShape)) {
-    if (enableShapeUniforms) {
-      return `
-      ivec2 getOutputCoords() {
-        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
-      }
-    `;
-    }
-    return `
-      ivec2 getOutputCoords() {
-        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
-      }
-    `;
-  }
-  if (shape[1] === 1) {
-    if (enableShapeUniforms) {
-      return `
-      ivec2 getOutputCoords() {
-        ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(outTexShape[0], outTexShape[1]));
-        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-        return ivec2(index, 0);
-      }
-    `;
-    }
-    return `
-      ivec2 getOutputCoords() {
-        ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(${texShape[0]}, ${texShape[1]}));
-        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
-        return ivec2(index, 0);
-      }
-    `;
-  }
-  if (shape[0] === 1) {
-    if (enableShapeUniforms) {
-      return `
-      ivec2 getOutputCoords() {
-        ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(outTexShape[0], outTexShape[1]));
-        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-        return ivec2(0, index);
-      }
-    `;
-    }
-    return `
-      ivec2 getOutputCoords() {
-        ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(${texShape[0]}, ${texShape[1]}));
-        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
-        return ivec2(0, index);
-      }
-    `;
-  }
-  if (enableShapeUniforms) {
-    return `
-    ivec2 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(outTexShape[0], outTexShape[1]));
-      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      int r = index / outShape[1];
-      int c = index - r * outShape[1];
-      return ivec2(r, c);
-    }
-  `;
-  }
-  return `
-    ivec2 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${texShape[0]}, ${texShape[1]}));
-      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
-      int r = index / ${shape[1]};
-      int c = index - r * ${shape[1]};
-      return ivec2(r, c);
-    }
-  `;
-}
-
-function getFlatOffsetUniformName(texName: string): string {
-  return `offset${texName}`;
-}
-
-function getPackedSamplerScalar(inputInfo: InputInfo): string {
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const glsl = getGlslDifferences();
-  return `
-    vec4 ${funcName}() {
-      return ${glsl.texture2D}(${texName}, halfCR);
-    }
-  `;
-}
-
-function getSamplerScalar(
-    inputInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  if (inputInfo.shapeInfo.isUniform) {
-    return `float ${funcName}() {return ${texName};}`;
-  }
-  const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;
-  if (texNumR === 1 && texNumC === 1) {
-    return `
-      float ${funcName}() {
-        return sampleTexture(${texName}, halfCR);
-      }
-    `;
-  }
-
-  const offset = getFlatOffsetUniformName(texName);
-  if (enableShapeUniforms) {
-    return `
-    float ${funcName}() {
-      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], ${
-        offset});
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-  }
-
-  const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;
-  return `
-    float ${funcName}() {
-      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-}
-
-function getPackedSampler1D(
-    inputInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const texShape = inputInfo.shapeInfo.texShape;
-  const glsl = getGlslDifferences();
-  if (enableShapeUniforms) {
-    return `
-    vec4 ${funcName}(int index) {
-      ivec2 packedTexShape = ivec2(ceil(float(${
-        texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
-      vec2 uv = packedUVfrom1D(
-        packedTexShape[0], packedTexShape[1], index);
-      return ${glsl.texture2D}(${texName}, uv);
-    }
-  `;
-  }
-  const packedTexShape =
-      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
-  return `
-    vec4 ${funcName}(int index) {
-      vec2 uv = packedUVfrom1D(
-        ${packedTexShape[0]}, ${packedTexShape[1]}, index);
-      return ${glsl.texture2D}(${texName}, uv);
-    }
-  `;
-}
-
-function getSampler1D(
-    inputInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-
-  if (inputInfo.shapeInfo.isUniform) {
-    // Uniform arrays will be less than 65505 (no risk of float16 overflow).
-    return `
-      float ${funcName}(int index) {
-        ${getUniformSampler(inputInfo)}
-      }
-    `;
-  }
-
-  const texShape = inputInfo.shapeInfo.texShape;
-  const tNumR = texShape[0];
-  const tNumC = texShape[1];
-
-  if (tNumC === 1 && tNumR === 1) {
-    return `
-      float ${funcName}(int index) {
-        return sampleTexture(${texName}, halfCR);
-      }
-    `;
-  }
-  const offset = getFlatOffsetUniformName(texName);
-  if (tNumC === 1) {
-    if (enableShapeUniforms) {
-      return `
-      float ${funcName}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / float(${
-          texName}TexShape[0]));
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-    }
-
-    return `
-      float ${funcName}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-  }
-  if (tNumR === 1) {
-    if (enableShapeUniforms) {
-      return `
-      float ${funcName}(int index) {
-        vec2 uv = vec2((float(index + ${offset}) + 0.5) / float(${
-          texName}TexShape[1]), 0.5);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-    }
-
-    return `
-      float ${funcName}(int index) {
-        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-  }
-
-  if (enableShapeUniforms) {
-    return `
-    float ${funcName}(int index) {
-      vec2 uv = uvFromFlat(${texName}TexShape[0], ${
-        texName}TexShape[1], index + ${offset});
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-  }
-
-  return `
-    float ${funcName}(int index) {
-      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-}
-
-function getPackedSampler2D(
-    inputInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const shape = inputInfo.shapeInfo.logicalShape;
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const texShape = inputInfo.shapeInfo.texShape;
-
-  const texNumR = texShape[0];
-  const texNumC = texShape[1];
-  const glsl = getGlslDifferences();
-  if (texShape != null && util.arraysEqual(shape, texShape)) {
-    if (enableShapeUniforms) {
-      return `
-      vec4 ${funcName}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${
-          texName}TexShape[0]);
-
-        return ${glsl.texture2D}(${texName}, uv);
-      }
-    `;
-    }
-    return `
-      vec4 ${funcName}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
-
-        return ${glsl.texture2D}(${texName}, uv);
-      }
-    `;
-  }
-
-  if (enableShapeUniforms) {
-    return `
-    vec4 ${funcName}(int row, int col) {
-      ivec2 packedTexShape = ivec2(ceil(float(${
-        texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
-      int valuesPerRow = int(ceil(float(${texName}Shape[1]) / 2.0));
-      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
-      return ${glsl.texture2D}(${texName}, uv);
-    }
-  `;
-  }
-  const packedTexShape =
-      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
-  const valuesPerRow = Math.ceil(shape[1] / 2);
-
-  return `
-    vec4 ${funcName}(int row, int col) {
-      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${
-      packedTexShape[1]}, row, col);
-      return ${glsl.texture2D}(${texName}, uv);
-    }
-  `;
-}
-
-function getSampler2D(
-    inputInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const shape = inputInfo.shapeInfo.logicalShape;
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const texShape = inputInfo.shapeInfo.texShape;
-
-  if (texShape != null && util.arraysEqual(shape, texShape)) {
-    if (enableShapeUniforms) {
-      return `
-      float ${funcName}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${
-          texName}TexShape[0]);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-    }
-
-    const texNumR = texShape[0];
-    const texNumC = texShape[1];
-    return `
-    float ${funcName}(int row, int col) {
-      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-  }
-
-  const {newShape, keptDims} = util.squeezeShape(shape);
-  const squeezedShape = newShape;
-  if (squeezedShape.length < shape.length) {
-    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
-    const params = ['row', 'col'];
-    return `
-      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
-      float ${funcName}(int row, int col) {
-        return ${funcName}(${getSqueezedParams(params, keptDims)});
-      }
-    `;
-  }
-
-  if (inputInfo.shapeInfo.isUniform) {
-    // Uniform arrays will be less than 65505 (no risk of float16 overflow).
-    return `
-      float ${funcName}(int row, int col) {
-        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
-        ${getUniformSampler(inputInfo)}
-      }
-    `;
-  }
-
-  const texNumR = texShape[0];
-  const texNumC = texShape[1];
-  const offset = getFlatOffsetUniformName(texName);
-  if (texNumC === 1) {
-    // index is used directly as physical (no risk of float16 overflow).
-    if (enableShapeUniforms) {
-      return `
-      float ${funcName}(int row, int col) {
-        float index = dot(vec3(row, col, ${offset}), vec3(${
-          texName}Shape[1], 1, 1));
-        vec2 uv = vec2(0.5, (index + 0.5) / float(${texName}TexShape[0]));
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-    }
-    return `
-    float ${funcName}(int row, int col) {
-      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
-      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-  }
-  if (texNumR === 1) {
-    // index is used directly as physical (no risk of float16 overflow).
-    if (enableShapeUniforms) {
-      return `
-      float ${funcName}(int row, int col) {
-        float index = dot(vec3(row, col, ${offset}), vec3(${
-          texName}Shape[1], 1, 1));
-        vec2 uv = vec2((index + 0.5) / float(${texName}TexShape[1]), 0.5);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-    }
-    return `
-    float ${funcName}(int row, int col) {
-      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
-      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-  }
-
-  if (enableShapeUniforms) {
-    return `
-      float ${funcName}(int row, int col) {
-        // Explicitly use integer operations as dot() only works on floats.
-        int index = row * ${texName}Shape[1] + col + ${offset};
-        vec2 uv = uvFromFlat(${texName}TexShape[0], ${
-        texName}TexShape[1], index);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-  }
-  return `
-  float ${funcName}(int row, int col) {
-    // Explicitly use integer operations as dot() only works on floats.
-    int index = row * ${shape[1]} + col + ${offset};
-    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
-    return sampleTexture(${texName}, uv);
-  }
-`;
-}
-
-function getPackedSampler3D(
-    inputInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const shape = inputInfo.shapeInfo.logicalShape;
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const texShape = inputInfo.shapeInfo.texShape;
-  const packedTexShape =
-      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
-
-  if (shape[0] === 1) {
-    const squeezedShape = shape.slice(1);
-    const keptDims = [1, 2];
-    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
-    const params = ['b', 'row', 'col'];
-    return `
-        ${getPackedSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
-        vec4 ${funcName}(int b, int row, int col) {
-          return ${funcName}(${getSqueezedParams(params, keptDims)});
-        }
-      `;
-  }
-
-  const glsl = getGlslDifferences();
-  if (enableShapeUniforms) {
-    return `
-    vec4 ${funcName}(int b, int row, int col) {
-      ivec2 packedTexShape = ivec2(ceil(float(${
-        texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
-      int valuesPerRow = int(ceil(float(${texName}Shape[2]) / 2.0));
-      int texelsInBatch = valuesPerRow * int(ceil(float(${
-        texName}Shape[1]) / 2.0));
-      vec2 uv = packedUVfrom3D(
-        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
-      return ${glsl.texture2D}(${texName}, uv);
-    }
-  `;
-  }
-
-  const texNumR = packedTexShape[0];
-  const texNumC = packedTexShape[1];
-
-  const valuesPerRow = Math.ceil(shape[2] / 2);
-  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
-
-  return `
-    vec4 ${funcName}(int b, int row, int col) {
-      vec2 uv = packedUVfrom3D(
-        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
-      return ${glsl.texture2D}(${texName}, uv);
-    }
-  `;
-}
-
-function getSampler3D(
-    inputInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const shape = inputInfo.shapeInfo.logicalShape;
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const stride0 = shape[1] * shape[2];
-  const stride1 = shape[2];
-
-  const {newShape, keptDims} = util.squeezeShape(shape);
-  const squeezedShape = newShape;
-  if (squeezedShape.length < shape.length) {
-    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
-    const params = ['row', 'col', 'depth'];
-    return `
-        ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
-        float ${funcName}(int row, int col, int depth) {
-          return ${funcName}(${getSqueezedParams(params, keptDims)});
-        }
-      `;
-  }
-
-  if (inputInfo.shapeInfo.isUniform) {
-    // Uniform arrays will be less than 65505 (no risk of float16 overflow).
-    return `
-      float ${funcName}(int row, int col, int depth) {
-        int index = round(dot(vec3(row, col, depth),
-                          vec3(${stride0}, ${stride1}, 1)));
-        ${getUniformSampler(inputInfo)}
-      }
-    `;
-  }
-
-  const texShape = inputInfo.shapeInfo.texShape;
-  const texNumR = texShape[0];
-  const texNumC = texShape[1];
-  const flatOffset = inputInfo.shapeInfo.flatOffset;
-  if (texNumC === stride0 && flatOffset == null) {
-    // texC is used directly as physical (no risk of float16 overflow).
-    if (enableShapeUniforms) {
-      return `
-      float ${funcName}(int row, int col, int depth) {
-        int stride1 = ${texName}Shape[2];
-        float texR = float(row);
-        float texC = dot(vec2(col, depth), vec2(stride1, 1));
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-    }
-    return `
-        float ${funcName}(int row, int col, int depth) {
-          float texR = float(row);
-          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
-          vec2 uv = (vec2(texC, texR) + halfCR) /
-                     vec2(${texNumC}.0, ${texNumR}.0);
-          return sampleTexture(${texName}, uv);
-        }
-      `;
-  }
-
-  if (texNumC === stride1 && flatOffset == null) {
-    // texR is used directly as physical (no risk of float16 overflow).
-    if (enableShapeUniforms) {
-      return `
-      float ${funcName}(int row, int col, int depth) {
-        float texR = dot(vec2(row, col), vec2(${texName}Shape[1], 1));
-        float texC = float(depth);
-        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texName}TexShape[1], ${
-          texName}TexShape[0]);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-    }
-    return `
-    float ${funcName}(int row, int col, int depth) {
-      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
-      float texC = float(depth);
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-  }
-
-  const offset = getFlatOffsetUniformName(texName);
-  if (enableShapeUniforms) {
-    return `
-    float ${funcName}(int row, int col, int depth) {
-      // Explicitly use integer operations as dot() only works on floats.
-      int stride0 = ${texName}Shape[1] * ${texName}Shape[2];
-      int stride1 = ${texName}Shape[2];
-      int index = row * stride0 + col * stride1 + depth + ${offset};
-      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);
-      return sampleTexture(${texName}, uv);
-    }
-    `;
-  }
-  return `
-      float ${funcName}(int row, int col, int depth) {
-        // Explicitly use integer operations as dot() only works on floats.
-        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
-        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
-        return sampleTexture(${texName}, uv);
-      }
-  `;
-}
-
-function getPackedSamplerND(
-    inputInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const glsl = getGlslDifferences();
-  if (enableShapeUniforms) {
-    // TODO: support 5d and 6d
-    return `
-    vec4 ${funcName}(int b2, int b, int row, int col) {
-      int valuesPerRow = int(ceil(float(${texName}Shape[3]) / 2.0));
-      int texelsInBatch = valuesPerRow * int(ceil(float(${
-        texName}Shape[2]) / 2.0));
-      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
-      texelsInBatch *= ${texName}Shape[1];
-      index = b2 * texelsInBatch + index;
-      ivec2 packedTexShape = ivec2(ceil(float(${
-        texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
-      int texR = index / packedTexShape[1];
-      int texC = index - texR * packedTexShape[1];
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${
-        glsl.texture2D}(${texName}, uv);
-    }
-  `;
-  }
-  const shape = inputInfo.shapeInfo.logicalShape;
-  const rank = shape.length;
-  const texShape = inputInfo.shapeInfo.texShape;
-  const packedTexShape =
-      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
-  const texNumR = packedTexShape[0];
-  const texNumC = packedTexShape[1];
-
-  const valuesPerRow = Math.ceil(shape[rank - 1] / 2);
-  let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);
-  let params = `int b, int row, int col`;
-  let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;
-  for (let b = 2; b < rank - 1; b++) {
-    params = `int b${b}, ` + params;
-    texelsInBatch *= shape[rank - b - 1];
-    index = `b${b} * ${texelsInBatch} + ` + index;
-  }
-  return `
-    vec4 ${funcName}(${params}) {
-      int index = ${index};
-      int texR = index / ${texNumC};
-      int texC = index - texR * ${texNumC};
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
-      return ${glsl.texture2D}(${texName}, uv);
-    }
-  `;
-}
-
-function getSampler4D(
-    inputInfo: InputInfo, enableShapeUniforms: boolean): string {
-  const shape = inputInfo.shapeInfo.logicalShape;
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const stride2 = shape[3];
-  const stride1 = shape[2] * stride2;
-  const stride0 = shape[1] * stride1;
-
-  const {newShape, keptDims} = util.squeezeShape(shape);
-  if (newShape.length < shape.length) {
-    const newInputInfo = squeezeInputInfo(inputInfo, newShape);
-    const params = ['row', 'col', 'depth', 'depth2'];
-    return `
-      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
-      float ${funcName}(int row, int col, int depth, int depth2) {
-        return ${funcName}(${getSqueezedParams(params, keptDims)});
-      }
-    `;
-  }
-
-  if (inputInfo.shapeInfo.isUniform) {
-    // Uniform arrays will be less than 65505 (no risk of float16 overflow).
-    return `
-      float ${funcName}(int row, int col, int depth, int depth2) {
-        int index = round(dot(vec4(row, col, depth, depth2),
-                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));
-        ${getUniformSampler(inputInfo)}
-      }
-    `;
-  }
-
-  const flatOffset = inputInfo.shapeInfo.flatOffset;
-  const texShape = inputInfo.shapeInfo.texShape;
-  const texNumR = texShape[0];
-  const texNumC = texShape[1];
-
-  const stride2Str = `int stride2 = ${texName}Shape[3];`;
-  const stride1Str = `int stride1 = ${texName}Shape[2] * stride2;`;
-  const stride0Str = `int stride0 = ${texName}Shape[1] * stride1;`;
-  if (texNumC === stride0 && flatOffset == null) {
-    // texC is used directly as physical (no risk of float16 overflow).
-    if (enableShapeUniforms) {
-      return `
-      float ${funcName}(int row, int col, int depth, int depth2) {
-        ${stride2Str}
-        ${stride1Str}
-        float texR = float(row);
-        float texC =
-            dot(vec3(col, depth, depth2),
-                vec3(stride1, stride2, 1));
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-    }
-    return `
-      float ${funcName}(int row, int col, int depth, int depth2) {
-        float texR = float(row);
-        float texC =
-            dot(vec3(col, depth, depth2),
-                vec3(${stride1}, ${stride2}, 1));
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${texNumC}.0, ${texNumR}.0);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-  }
-  if (texNumC === stride2 && flatOffset == null) {
-    // texR is used directly as physical (no risk of float16 overflow).
-    if (enableShapeUniforms) {
-      return `
-      float ${funcName}(int row, int col, int depth, int depth2) {
-        float texR = dot(vec3(row, col, depth),
-                         vec3(${texName}Shape[1] * ${texName}Shape[2], ${
-          texName}Shape[2], 1));
-        float texC = float(depth2);
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${texName}TexShape[1], ${texName}TexShape[0]);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-    }
-    return `
-      float ${funcName}(int row, int col, int depth, int depth2) {
-        float texR = dot(vec3(row, col, depth),
-                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));
-        float texC = float(depth2);
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${texNumC}.0, ${texNumR}.0);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-  }
-
-  const offset = getFlatOffsetUniformName(texName);
-  if (enableShapeUniforms) {
-    return `
-    float ${funcName}(int row, int col, int depth, int depth2) {
-      // Explicitly use integer operations as dot() only works on floats.
-      ${stride2Str}
-      ${stride1Str}
-      ${stride0Str}
-      int index = row * stride0 + col * stride1 +
-          depth * stride2 + depth2;
-      vec2 uv = uvFromFlat(${texName}TexShape[0], ${
-        texName}TexShape[1], index + ${offset});
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-  }
-  return `
-    float ${funcName}(int row, int col, int depth, int depth2) {
-      // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${stride0} + col * ${stride1} +
-          depth * ${stride2} + depth2;
-      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-}
-
-function getSampler5D(inputInfo: InputInfo): string {
-  const shape = inputInfo.shapeInfo.logicalShape;
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const stride3 = shape[4];
-  const stride2 = shape[3] * stride3;
-  const stride1 = shape[2] * stride2;
-  const stride0 = shape[1] * stride1;
-
-  const {newShape, keptDims} = util.squeezeShape(shape);
-  if (newShape.length < shape.length) {
-    const newInputInfo = squeezeInputInfo(inputInfo, newShape);
-    const params = ['row', 'col', 'depth', 'depth2', 'depth3'];
-    return `
-      ${getSamplerFromInInfo(newInputInfo)}
-      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
-        return ${funcName}(${getSqueezedParams(params, keptDims)});
-      }
-    `;
-  }
-
-  if (inputInfo.shapeInfo.isUniform) {
-    // Uniform arrays will be less than 65505 (no risk of float16 overflow).
-    return `
-      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
-        float index = dot(
-          vec4(row, col, depth, depth2),
-          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
-          depth3;
-        ${getUniformSampler(inputInfo)}
-      }
-    `;
-  }
-
-  const flatOffset = inputInfo.shapeInfo.flatOffset;
-  const texShape = inputInfo.shapeInfo.texShape;
-  const texNumR = texShape[0];
-  const texNumC = texShape[1];
-
-  if (texNumC === stride0 && flatOffset == null) {
-    // texC is used directly as physical (no risk of float16 overflow).
-    return `
-      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
-        int texR = row;
-        float texC = dot(vec4(col, depth, depth2, depth3),
-                         vec4(${stride1}, ${stride2}, ${stride3}, 1));
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${texNumC}.0, ${texNumR}.0);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-  }
-
-  if (texNumC === stride3 && flatOffset == null) {
-    // texR is used directly as physical (no risk of float16 overflow).
-    return `
-      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
-        float texR = dot(
-          vec4(row, col, depth, depth2),
-          vec4(${shape[1] * shape[2] * shape[3]},
-               ${shape[2] * shape[3]}, ${shape[3]}, 1));
-        int texC = depth3;
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${texNumC}.0, ${texNumR}.0);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-  }
-
-  const offset = getFlatOffsetUniformName(texName);
-  return `
-    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
-      // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
-          depth2 * ${stride3} + depth3 + ${offset};
-      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-}
-
-function getSampler6D(inputInfo: InputInfo): string {
-  const shape = inputInfo.shapeInfo.logicalShape;
-  const texName = inputInfo.name;
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-
-  const {newShape, keptDims} = util.squeezeShape(shape);
-  if (newShape.length < shape.length) {
-    const newInputInfo = squeezeInputInfo(inputInfo, newShape);
-    const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];
-    return `
-      ${getSamplerFromInInfo(newInputInfo)}
-      float ${funcName}(int row, int col, int depth,
-                    int depth2, int depth3, int depth4) {
-        return ${funcName}(${getSqueezedParams(params, keptDims)});
-      }
-    `;
-  }
-
-  const stride4 = shape[5];
-  const stride3 = shape[4] * stride4;
-  const stride2 = shape[3] * stride3;
-  const stride1 = shape[2] * stride2;
-  const stride0 = shape[1] * stride1;
-
-  if (inputInfo.shapeInfo.isUniform) {
-    // Uniform arrays will be less than 65505 (no risk of float16 overflow).
-    return `
-      float ${funcName}(int row, int col, int depth,
-                  int depth2, int depth3, int depth4) {
-        int index = round(dot(
-          vec4(row, col, depth, depth2),
-          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
-          dot(
-            vec2(depth3, depth4),
-            vec2(${stride4}, 1)));
-        ${getUniformSampler(inputInfo)}
-      }
-    `;
-  }
-
-  const flatOffset = inputInfo.shapeInfo.flatOffset;
-  const texShape = inputInfo.shapeInfo.texShape;
-  const texNumR = texShape[0];
-  const texNumC = texShape[1];
-  if (texNumC === stride0 && flatOffset == null) {
-    // texC is used directly as physical (no risk of float16 overflow).
-    return `
-      float ${funcName}(int row, int col, int depth,
-                    int depth2, int depth3, int depth4) {
-        int texR = row;
-        float texC = dot(vec4(col, depth, depth2, depth3),
-          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
-               float(depth4);
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${texNumC}.0, ${texNumR}.0);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-  }
-  if (texNumC === stride4 && flatOffset == null) {
-    // texR is used directly as physical (no risk of float16 overflow).
-    return `
-      float ${funcName}(int row, int col, int depth,
-                    int depth2, int depth3, int depth4) {
-        float texR = dot(vec4(row, col, depth, depth2),
-          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},
-               ${shape[2] * shape[3] * shape[4]},
-               ${shape[3] * shape[4]},
-               ${shape[4]})) + float(depth3);
-        int texC = depth4;
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${texNumC}.0, ${texNumR}.0);
-        return sampleTexture(${texName}, uv);
-      }
-    `;
-  }
-  const offset = getFlatOffsetUniformName(texName);
-  return `
-    float ${funcName}(int row, int col, int depth,
-                  int depth2, int depth3, int depth4) {
-      // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
-          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
-      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
-      return sampleTexture(${texName}, uv);
-    }
-  `;
-}
-
-function getUniformSampler(inputInfo: InputInfo): string {
-  const texName = inputInfo.name;
-  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);
-
-  if (inSize < 2) {
-    return `return ${texName};`;
-  }
-
-  return `
-    for (int i = 0; i < ${inSize}; i++) {
-      if (i == index) {
-        return ${texName}[i];
-      }
-    }
-  `;
-}
-
-function getPackedSamplerAtOutputCoords(
-    inputInfo: InputInfo, outShapeInfo: ShapeInfo) {
-  const texName = inputInfo.name;
-  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
-  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';
-  const inRank = inputInfo.shapeInfo.logicalShape.length;
-  const outRank = outShapeInfo.logicalShape.length;
-
-  const broadcastDims = getBroadcastDims(
-      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
-
-  const type = getCoordsDataType(outRank);
-  const rankDiff = outRank - inRank;
-  let coordsSnippet: string;
-  const fields = ['x', 'y', 'z', 'w', 'u', 'v'];
-
-  if (inRank === 0) {
-    coordsSnippet = '';
-  } else if (outRank < 2 && broadcastDims.length >= 1) {
-    coordsSnippet = 'coords = 0;';
-  } else {
-    coordsSnippet =
-        broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)
-            .join('\n');
-  }
-  let unpackedCoordsSnippet = '';
-  if (outRank < 2 && inRank > 0) {
-    unpackedCoordsSnippet = 'coords';
-  } else {
-    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape
-                                .map((s, i) => `coords.${fields[i + rankDiff]}`)
-                                .join(', ');
-  }
-
-  let output = `return outputValue;`;
-  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);
-  const isInputScalar = inSize === 1;
-  const outSize = util.sizeFromShape(outShapeInfo.logicalShape);
-  const isOutputScalar = outSize === 1;
-
-  if (inRank === 1 && !isInputScalar && !isOutputScalar) {
-    output = `
-      return vec4(outputValue.xy, outputValue.xy);
-    `;
-  } else if (isInputScalar && !isOutputScalar) {
-    if (outRank === 1) {
-      output = `
-        return vec4(outputValue.x, outputValue.x, 0., 0.);
-      `;
-    } else {
-      output = `
-        return vec4(outputValue.x);
-      `;
-    }
-  } else if (broadcastDims.length) {
-    const rows = inRank - 2;
-    const cols = inRank - 1;
-
-    if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {
-      output = `return vec4(outputValue.x);`;
-    } else if (broadcastDims.indexOf(rows) > -1) {
-      output = `return vec4(outputValue.x, outputValue.y, ` +
-          `outputValue.x, outputValue.y);`;
-    } else if (broadcastDims.indexOf(cols) > -1) {
-      output = `return vec4(outputValue.xx, outputValue.zz);`;
-    }
-  }
-
-  return `
-    vec4 ${funcName}() {
-      ${type} coords = getOutputCoords();
-      ${coordsSnippet}
-      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
-      ${output}
-    }
-  `;
-}
-
-function getSamplerAtOutputCoords(
-    inputInfo: InputInfo, outShapeInfo: ShapeInfo) {
-  const texName = inputInfo.name;
-  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
-  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';
-  const outTexShape = outShapeInfo.texShape;
-  const inTexShape = inputInfo.shapeInfo.texShape;
-  const inRank = inputInfo.shapeInfo.logicalShape.length;
-  const outRank = outShapeInfo.logicalShape.length;
-
-  if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&
-      inputInfo.shapeInfo.flatOffset == null &&
-      util.arraysEqual(inTexShape, outTexShape)) {
-    return `
-      float ${funcName}() {
-        return sampleTexture(${texName}, resultUV);
-      }
-    `;
-  }
-
-  const type = getCoordsDataType(outRank);
-  const broadcastDims = getBroadcastDims(
-      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
-  const rankDiff = outRank - inRank;
-  let coordsSnippet: string;
-  const fields = ['x', 'y', 'z', 'w', 'u', 'v'];
-
-  if (inRank === 0) {
-    coordsSnippet = '';
-  } else if (outRank < 2 && broadcastDims.length >= 1) {
-    coordsSnippet = 'coords = 0;';
-  } else {
-    coordsSnippet =
-        broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)
-            .join('\n');
-  }
-  let unpackedCoordsSnippet = '';
-  if (outRank < 2 && inRank > 0) {
-    unpackedCoordsSnippet = 'coords';
-  } else {
-    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape
-                                .map((s, i) => `coords.${fields[i + rankDiff]}`)
-                                .join(', ');
-  }
-
-  return `
-    float ${funcName}() {
-      ${type} coords = getOutputCoords();
-      ${coordsSnippet}
-      return get${texFuncSnippet}(${unpackedCoordsSnippet});
-    }
-  `;
-}
-
-export function getCoordsDataType(rank: number): string {
-  if (rank <= 1) {
-    return 'int';
-  } else if (rank === 2) {
-    return 'ivec2';
-  } else if (rank === 3) {
-    return 'ivec3';
-  } else if (rank === 4) {
-    return 'ivec4';
-  } else if (rank === 5) {
-    return 'ivec5';
-  } else if (rank === 6) {
-    return 'ivec6';
-  } else {
-    throw Error(`GPU for rank ${rank} is not yet supported`);
-  }
-}
-
-export function getUniformInfoFromShape(
-    isPacked: boolean, shape: number[], texShape: number[]) {
-  const {newShape, keptDims} = util.squeezeShape(shape);
-  const rank = shape.length;
-  const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1;
-  const squeezeShape = useSqueezePackedShape ? shape.slice(1) : newShape;
-  const useSqueezeShape =
-      (!isPacked && rank > 1 && !util.arraysEqual(shape, texShape) &&
-       newShape.length < rank) ||
-      useSqueezePackedShape;
-  const uniformShape = useSqueezeShape ? squeezeShape : shape;
-  return {useSqueezeShape, uniformShape, keptDims};
-}
-
-/** Returns a new input info (a copy) that has a squeezed logical shape. */
-export function squeezeInputInfo(
-    inInfo: InputInfo, squeezedShape: number[]): InputInfo {
-  // Deep copy.
-  const newInputInfo: InputInfo = JSON.parse(JSON.stringify(inInfo));
-  newInputInfo.shapeInfo.logicalShape = squeezedShape;
-  return newInputInfo;
-}
-
-function getSqueezedParams(params: string[], keptDims: number[]): string {
-  return keptDims.map(d => params[d]).join(', ');
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/shader_compiler_util.ts b/tfjs-master/tfjs-backend-webgl/src/shader_compiler_util.ts
deleted file mode 100644
index 7e744c73a..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/shader_compiler_util.ts
+++ /dev/null
@@ -1,184 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {util} from '@tensorflow/tfjs-core';
-
-/**
- * Produces GLSL code that derives logical coordinates from a flat
- * index. The code performs integer division with each stride and decrements
- * the index until the index equals the final dimension coordinate.
- */
-export function getLogicalCoordinatesFromFlatIndex(
-    coords: string[], shape: number[], index = 'index'): string {
-  const strides = util.computeStrides(shape);
-  return strides
-      .map((stride, i) => {
-        const line1 = `int ${coords[i]} = ${index} / ${stride}`;
-        const line2 = i === strides.length - 1 ?
-            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :
-            `index -= ${coords[i]} * ${stride}`;
-        return `${line1}; ${line2};`;
-      })
-      .join('');
-}
-
-export function getOutputLogicalCoordinatesFromFlatIndexByUniform(
-    coords: string[], shape: number[], index = 'index'): string {
-  const strides = util.computeStrides(shape);
-  return strides
-      .map((_, i) => {
-        const line1 = `int ${coords[i]} = ${index} / outShapeStrides[${i}]`;
-        const line2 = i === strides.length - 1 ?
-            `int ${coords[i + 1]} = ${index} - ${coords[i]} * outShapeStrides[${
-                i}]` :
-            `index -= ${coords[i]} * outShapeStrides[${i}]`;
-        return `${line1}; ${line2};`;
-      })
-      .join('');
-}
-
-// Produces GLSL code that computes strides.
-function symbolicallyComputeStrides(
-    indicesArr: number[], variableName: string): string[] {
-  const numCoords = indicesArr.length;
-  const shape = indicesArr.map(d => `${variableName}[${d}]`);
-  const strides = new Array(numCoords - 1);
-  strides[numCoords - 2] = shape[numCoords - 1];
-  for (let i = numCoords - 3; i >= 0; --i) {
-    strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`;
-  }
-
-  return strides;
-}
-
-export function getLogicalCoordinatesFromFlatIndexByUniform(
-    coords: string[], variableName: string, index = 'index'): string {
-  const indicesArray = coords.map((_, i) => i);
-  const strides = symbolicallyComputeStrides(indicesArray, variableName);
-  return strides
-      .map((_, i) => {
-        const line1 = `int ${coords[i]} = ${index} / ${strides[i]}`;
-        const line2 = i === strides.length - 1 ?
-            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${strides[i]}` :
-            `index -= ${coords[i]} * ${strides[i]}`;
-        return `${line1}; ${line2};`;
-      })
-      .join('');
-}
-
-function buildVec(x: string[]): string {
-  if (x.length === 1) {
-    return `${x[0]}`;
-  }
-  return `vec${x.length}(${x.join(',')})`;
-}
-
-/**
- * Produces GLSL code that computes the dot product of the input x and y
- * vectors. Handles splitting inputs into increments of vec4s when necessary.
- */
-export function dotify(x: string[], y: string[]): string {
-  if (x.length !== y.length) {
-    throw new Error(
-        `Vectors to be dotted must be of the same length -` +
-        `got ${x.length} and ${y.length}`);
-  }
-
-  const slices: string[] = [];
-  const nearestVec4 = Math.floor(x.length / 4);
-  const nearestVec4Remainder = x.length % 4;
-
-  for (let i = 0; i < nearestVec4; i++) {
-    const xSlice = x.slice(i * 4, i * 4 + 4);
-    const ySlice = y.slice(i * 4, i * 4 + 4);
-    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);
-  }
-
-  if (nearestVec4Remainder !== 0) {
-    let xSlice = x.slice(nearestVec4 * 4);
-    let ySlice = y.slice(nearestVec4 * 4);
-    if (xSlice.length === 1) {
-      xSlice = xSlice.map(d => `float(${d})`);
-      ySlice = ySlice.map(d => `float(${d})`);
-    }
-    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);
-  }
-
-  return slices.map((d, i) => `dot(${d})`).join('+');
-}
-
-/**
- * Produces GLSL that computes the flat index from 3D coordinates.
- */
-export function getFlatIndexFrom3D(shape: [number, number, number]): string {
-  const strides = util.computeStrides(shape).map(d => d.toString());
-
-  return `
-  int getFlatIndex(ivec3 coords) {
-    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;
-  }
-`;
-}
-
-export function getFlatIndexFrom3DOutput(): string {
-  return `
-  int getFlatIndex(ivec3 coords) {
-    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
-  }
-`;
-}
-
-export const ENCODE_FLOAT_SNIPPET = `
-  const float FLOAT_MAX = 1.70141184e38;
-  const float FLOAT_MIN = 1.17549435e-38;
-
-  lowp vec4 encode_float(highp float v) {
-    if (isnan(v)) {
-      return vec4(255, 255, 255, 255);
-    }
-
-    highp float av = abs(v);
-
-    if(av < FLOAT_MIN) {
-      return vec4(0.0, 0.0, 0.0, 0.0);
-    } else if(v > FLOAT_MAX) {
-      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
-    } else if(v < -FLOAT_MAX) {
-      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
-    }
-
-    highp vec4 c = vec4(0,0,0,0);
-
-    highp float e = floor(log2(av));
-    highp float m = exp2(fract(log2(av))) - 1.0;
-
-    c[2] = floor(128.0 * m);
-    m -= c[2] / 128.0;
-    c[1] = floor(32768.0 * m);
-    m -= c[1] / 32768.0;
-    c[0] = floor(8388608.0 * m);
-
-    highp float ebias = e + 127.0;
-    c[3] = floor(ebias / 2.0);
-    ebias -= c[3] * 2.0;
-    c[2] += floor(ebias) * 128.0;
-
-    c[3] += 128.0 * step(0.0, -v);
-
-    return c / 255.0;
-  }
-`;
diff --git a/tfjs-master/tfjs-backend-webgl/src/shader_compiler_util_test.ts b/tfjs-master/tfjs-backend-webgl/src/shader_compiler_util_test.ts
deleted file mode 100644
index 8613b5512..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/shader_compiler_util_test.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {WEBGL_ENVS} from './backend_webgl_test_registry';
-import {dotify, getLogicalCoordinatesFromFlatIndex} from './shader_compiler_util';
-
-describeWithFlags('shader compiler', WEBGL_ENVS, () => {
-  it('dotify takes two arrays of coordinates and produces' +
-         'the glsl that finds the dot product of those coordinates',
-     () => {
-       const coords1 = ['r', 'g', 'b', 'a'];
-       const coords2 = ['x', 'y', 'z', 'w'];
-
-       expect(dotify(coords1, coords2))
-           .toEqual('dot(vec4(r,g,b,a), vec4(x,y,z,w))');
-     });
-
-  it('dotify should split up arrays into increments of vec4s', () => {
-    const coords1 = ['a', 'b', 'c', 'd', 'e', 'f', 'g'];
-    const coords2 = ['h', 'i', 'j', 'k', 'l', 'm', 'n'];
-
-    expect(dotify(coords1, coords2))
-        .toEqual(
-            'dot(vec4(a,b,c,d), vec4(h,i,j,k))+dot(vec3(e,f,g), vec3(l,m,n))');
-  });
-
-  it('getLogicalCoordinatesFromFlatIndex produces glsl that takes' +
-         'a flat index and finds its coordinates within that shape',
-     () => {
-       const coords = ['r', 'c', 'd'];
-       const shape = [1, 2, 3];
-
-       expect(getLogicalCoordinatesFromFlatIndex(coords, shape))
-           .toEqual(
-               'int r = index / 6; index -= r * 6;' +
-               'int c = index / 3; int d = index - c * 3;');
-     });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/slice_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/slice_gpu.ts
deleted file mode 100644
index a55d06e2f..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/slice_gpu.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType, UniformType} from './shader_compiler';
-
-export class SliceProgram implements GPGPUProgram {
-  variableNames = ['source'];
-  outputShape: number[];
-  userCode: string;
-  rank: number;
-  customUniforms: Array<{name: string; arrayIndex: number; type: UniformType;}>;
-
-  constructor(destSize: number[]) {
-    this.outputShape = destSize;
-    this.rank = destSize.length;
-
-    const dtype = getCoordsDataType(this.rank);
-    this.customUniforms = [{name: 'start', arrayIndex: this.rank, type: 'int'}];
-    const sourceCoords = getCoords(this.rank);
-
-    let body: string;
-    const coordSum = destSize.map((_, i) => {
-      return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;
-    });
-    body = `
-        ${dtype} sourceLoc;
-        ${dtype} coords = getOutputCoords();
-        ${coordSum.join('\n')}
-      `;
-    this.userCode = `
-      void main() {
-        ${body}
-        setOutput(getSource(${sourceCoords}));
-      }
-    `;
-  }
-}
-
-const coords = ['x', 'y', 'z', 'w', 'u', 'v'];
-
-function getCoords(rank: number): string {
-  if (rank === 1) {
-    return 'sourceLoc';
-  } else if (rank <= 6) {
-    return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');
-  } else {
-    throw Error(`Slicing for rank ${rank} is not yet supported`);
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/slice_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/slice_packed_gpu.ts
deleted file mode 100644
index de0203980..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/slice_packed_gpu.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getChannels} from './packing_util';
-import {getCoordsDataType, UniformType} from './shader_compiler';
-
-export class SlicePackedProgram implements GPGPUProgram {
-  variableNames = ['source'];
-  packedInputs = true;
-  packedOutput = true;
-  outputShape: number[];
-  userCode: string;
-  rank: number;
-  customUniforms: Array<{name: string; arrayIndex: number; type: UniformType;}>;
-
-  constructor(destSize: number[]) {
-    this.outputShape = destSize;
-    this.rank = destSize.length;
-    this.customUniforms = [{name: 'start', arrayIndex: this.rank, type: 'int'}];
-    const dtype = getCoordsDataType(this.rank);
-    const coords = getChannels('coords', this.rank);
-    const sourceLoc = getChannels('sourceLoc', this.rank);
-
-    const innerDims =
-        this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;
-    const getChannel =
-        `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;
-    const upperRow = `
-      result.x = ${getChannel};
-      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {
-        ++${sourceLoc[this.rank - 1]};
-        result.y = ${getChannel};
-        --${sourceLoc[this.rank - 1]};
-      }
-    `;
-    const lowerRow = this.rank === 1 ? '' : `
-      --${coords[this.rank - 1]};
-      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {
-        ++${sourceLoc[this.rank - 2]};
-        result.z = ${getChannel};
-        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {
-          ++${sourceLoc[this.rank - 1]};
-          result.w = ${getChannel};
-        }
-      }
-    `;
-
-    const sourceLocSetup = this.rank <= 4 ?
-        `sourceLoc = coords +
-            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :
-        destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)
-            .join('\n');
-    this.userCode = `
-      void main() {
-        ${dtype} coords = getOutputCoords();
-        ${dtype} sourceLoc;
-        ${sourceLocSetup}
-        vec4 result = vec4(0.);
-        ${upperRow}
-        ${lowerRow}
-        setOutput(result);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/strided_slice_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/strided_slice_gpu.ts
deleted file mode 100644
index fd90b4340..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/strided_slice_gpu.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export class StridedSliceProgram implements GPGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(begin: number[], strides: number[], size: number[]) {
-    this.outputShape = size;
-    const rank = size.length;
-    const inputDtype = getCoordsDataType(size.length);
-    const dtype = getCoordsDataType(size.length);
-
-    let newCoords = '';
-    if (rank === 1) {
-      newCoords = 'coords * strides + begin';
-    } else {
-      let outputAxis = 0;
-      newCoords =
-          size.map((_, i) => {
-                outputAxis++;
-                return size.length === 1 ?
-                    `coords * strides[${i}] + begin[${i}]` :
-                    `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;
-              })
-              .join(',');
-    }
-
-    this.userCode = `
-      ${inputDtype} begin = ${inputDtype}(${begin});
-      ${inputDtype} strides = ${inputDtype}(${strides});
-
-      void main() {
-        ${dtype} coords = getOutputCoords();
-        setOutput(getX(${newCoords}));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/tex_util.ts b/tfjs-master/tfjs-backend-webgl/src/tex_util.ts
deleted file mode 100644
index 3c04ddc3b..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/tex_util.ts
+++ /dev/null
@@ -1,240 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataId, DataType, env, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-export enum PackingScheme {
-  /**
-   * All values in a single texel are densely packed without any constraints.
-   *
-   * This is how the shader encodes a tensor with shape = [2, 3, 4]
-   * (indices are [batch, row, col]).
-   *
-   * 000|001   010|011   020|021
-   * -------   -------   -------
-   * 002|003   012|013   022|023
-   *
-   * 100|101   110|111   120|121
-   * -------   -------   -------
-   * 102|103   112|113   122|123
-   *
-   */
-  DENSE,
-
-  /**
-   * Single texels contain only values from the same batch, and from adjacent
-   * rows and columns.
-   *
-   * This is how the shader encodes a tensor with shape = [2, 3, 5]
-   * (indices are [batch, row, col]).
-   *
-   * 000|001   002|003   004|xxx   020|021   022|023   024|xxx
-   * -------   -------   -------   -------   -------   -------
-   * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx
-   *
-   * 100|101   102|103   104|xxx   120|121   122|123   124|xxx
-   * -------   -------   -------   -------   -------   -------
-   * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx
-   *
-   */
-  SHARED_BATCH
-}
-
-export enum TextureUsage {
-  RENDER,
-  UPLOAD,
-  PIXELS,
-  DOWNLOAD
-}
-
-export enum PhysicalTextureType {
-  UNPACKED_FLOAT16,
-  UNPACKED_FLOAT32,
-  PACKED_4X1_UNSIGNED_BYTE,
-  PACKED_2X2_FLOAT32,
-  PACKED_2X2_FLOAT16
-}
-
-export interface Texture {
-  texture: WebGLTexture;
-  texShape: [number, number];
-}
-export interface TextureData {
-  // Required.
-  shape: number[];
-  dtype: DataType;
-
-  // Optional.
-  values?: backend_util.BackendValues;
-  texture?: Texture;
-  // For complex numbers, the real and imaginary parts are stored as their own
-  // individual tensorInfos, with a parent joining the two with the
-  // complexTensors field. When this is defined, texture will be null.
-  complexTensorInfos?: {real: TensorInfo, imag: TensorInfo};
-  /** [rows, columns] shape of the texture. */
-  texShape?: [number, number];
-  usage?: TextureUsage;
-  isPacked?: boolean;
-
-  refCount: number;
-
-  // Available when the tensor has been sliced.
-  slice?: {
-    // Offset in the 'flat index' space.
-    flatOffset: number;
-    // Used for counting how many sliced tensors point to the same texture.
-    origDataId: DataId;
-  };
-}
-
-export function getUnpackedMatrixTextureShapeWidthHeight(
-    rows: number, columns: number): [number, number] {
-  return [columns, rows];
-}
-
-export function getUnpackedArraySizeFromMatrixSize(
-    matrixSize: number, channelsPerTexture: number): number {
-  return matrixSize * channelsPerTexture;
-}
-
-export function getColorMatrixTextureShapeWidthHeight(
-    rows: number, columns: number): [number, number] {
-  return [columns * 4, rows];
-}
-
-/**
- * Get shape for densely packed RGBA texture.
- */
-export function getDenseTexShape(shape: number[]): [number, number] {
-  const size = util.sizeFromShape(shape);
-  const texelsNeeded = Math.ceil(size / 4);
-  return util.sizeToSquarishShape(texelsNeeded);
-}
-
-export function getMatrixSizeFromUnpackedArraySize(
-    unpackedSize: number, channelsPerTexture: number): number {
-  if (unpackedSize % channelsPerTexture !== 0) {
-    throw new Error(
-        `unpackedSize (${unpackedSize}) must be a multiple of ` +
-        `${channelsPerTexture}`);
-  }
-  return unpackedSize / channelsPerTexture;
-}
-
-export function decodeMatrixFromUnpackedColorRGBAArray(
-    unpackedArray: Float32Array, matrix: Float32Array, channels: number) {
-  const requiredSize = unpackedArray.length * channels / 4;
-  if (matrix.length < requiredSize) {
-    throw new Error(
-        `matrix length (${matrix.length}) must be >= ${requiredSize}`);
-  }
-  let dst = 0;
-  for (let src = 0; src < unpackedArray.length; src += 4) {
-    for (let c = 0; c < channels; c++) {
-      matrix[dst++] = unpackedArray[src + c];
-    }
-  }
-}
-
-export function getPackedMatrixTextureShapeWidthHeight(
-    rows: number, columns: number): [number, number] {
-  return [
-    Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))
-  ];
-}
-
-export function getPackedRGBAArraySizeFromMatrixShape(
-    rows: number, columns: number): number {
-  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
-  return w * h * 4;
-}
-
-export interface TextureConfig {
-  internalFormatFloat: number;
-  textureFormatFloat: number;
-  internalFormatPackedHalfFloat: number;
-  internalFormatHalfFloat: number;
-  internalFormatPackedFloat: number;
-
-  // The format to use during a gl.readPixels call.
-  downloadTextureFormat: number;
-  // How many channels need to be unpacked after a gl.readPixels call.
-  downloadUnpackNumChannels: number;
-
-  defaultNumChannels: number;
-  textureTypeHalfFloat: number;
-  textureTypeFloat: number;
-}
-
-export function getTextureConfig(
-    // tslint:disable-next-line:no-any
-    gl: WebGLRenderingContext, textureHalfFloatExtension?: any): TextureConfig {
-  // tslint:disable-next-line:no-any
-  const glany = gl as any;
-
-  let internalFormatFloat: number;
-  let internalFormatHalfFloat: number;
-  let internalFormatPackedHalfFloat: number;
-  let internalFormatPackedFloat: number;
-  let textureFormatFloat: number;
-
-  let downloadTextureFormat: number;
-  let downloadUnpackNumChannels: number;
-
-  let defaultNumChannels: number;
-  let textureTypeHalfFloat: number;
-  let textureTypeFloat: number;
-
-  if (env().getNumber('WEBGL_VERSION') === 2) {
-    internalFormatFloat = glany.R32F;
-    internalFormatHalfFloat = glany.R16F;
-    internalFormatPackedHalfFloat = glany.RGBA16F;
-    internalFormatPackedFloat = glany.RGBA32F;
-    textureFormatFloat = glany.RED;
-    downloadUnpackNumChannels = 4;
-    defaultNumChannels = 1;
-    textureTypeHalfFloat = glany.HALF_FLOAT;
-    textureTypeFloat = glany.FLOAT;
-    downloadTextureFormat = glany.RGBA8;
-  } else {
-    internalFormatFloat = gl.RGBA;
-    internalFormatHalfFloat = gl.RGBA;
-    internalFormatPackedHalfFloat = gl.RGBA;
-    internalFormatPackedFloat = glany.RGBA;
-    textureFormatFloat = gl.RGBA;
-    downloadUnpackNumChannels = 4;
-    defaultNumChannels = 4;
-    textureTypeHalfFloat = textureHalfFloatExtension != null ?
-        textureHalfFloatExtension.HALF_FLOAT_OES :
-        null;
-    textureTypeFloat = gl.FLOAT;
-    downloadTextureFormat = gl.RGBA;
-  }
-
-  return {
-    internalFormatFloat,
-    internalFormatHalfFloat,
-    internalFormatPackedHalfFloat,
-    internalFormatPackedFloat,
-    textureFormatFloat,
-    downloadTextureFormat,
-    downloadUnpackNumChannels,
-    defaultNumChannels,
-    textureTypeHalfFloat,
-    textureTypeFloat
-  };
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/tex_util_test.ts b/tfjs-master/tfjs-backend-webgl/src/tex_util_test.ts
deleted file mode 100644
index 867466552..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/tex_util_test.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import {test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-const {expectArraysClose} = test_util;
-import {WEBGL_ENVS} from './backend_webgl_test_registry';
-import * as tex_util from './tex_util';
-
-describe('tex_util getUnpackedMatrixTextureShapeWidthHeight', () => {
-  it('[1x1] => [1x1]', () => {
-    expect(tex_util.getUnpackedMatrixTextureShapeWidthHeight(1, 1)).toEqual([
-      1, 1
-    ]);
-  });
-
-  it('[MxN] => [NxM]', () => {
-    expect(tex_util.getUnpackedMatrixTextureShapeWidthHeight(123, 456))
-        .toEqual([456, 123]);
-  });
-});
-
-describe('tex_util getPackedMatrixTextureShapeWidthHeight', () => {
-  it('[1x1] => [1x1]', () => {
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(1, 1);
-    expect(shape).toEqual([1, 1]);
-  });
-
-  it('[1x2] => [1x1]', () => {
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(1, 2);
-    expect(shape).toEqual([1, 1]);
-  });
-
-  it('[2x1] => [1x1]', () => {
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(2, 1);
-    expect(shape).toEqual([1, 1]);
-  });
-
-  it('[2x2] => [1x1]', () => {
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(2, 2);
-    expect(shape).toEqual([1, 1]);
-  });
-
-  it('[3x3] => [2x2]', () => {
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(3, 3);
-    expect(shape).toEqual([2, 2]);
-  });
-
-  it('[4x3] => [2x2]', () => {
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(4, 3);
-    expect(shape).toEqual([2, 2]);
-  });
-
-  it('[3x4] => [2x2]', () => {
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(3, 4);
-    expect(shape).toEqual([2, 2]);
-  });
-
-  it('[4x4] => [2x2]', () => {
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(4, 4);
-    expect(shape).toEqual([2, 2]);
-  });
-
-  it('[1024x1024] => [512x512]', () => {
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(1024, 1024);
-    expect(shape).toEqual([512, 512]);
-  });
-
-  it('[MxN] => [ceil(N/2)xceil(M/2)]', () => {
-    const M = 123;
-    const N = 5013;
-    const shape = tex_util.getPackedMatrixTextureShapeWidthHeight(M, N);
-    expect(shape).toEqual([Math.ceil(N / 2), Math.ceil(M / 2)]);
-  });
-});
-
-describeWithFlags('tex_util getDenseTexShape', WEBGL_ENVS, () => {
-  it('basic', () => {
-    const shape = [1, 3, 3, 4];
-    const denseShape = tex_util.getDenseTexShape(shape);
-    expectArraysClose(denseShape, [3, 3]);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/texture_manager.ts b/tfjs-master/tfjs-backend-webgl/src/texture_manager.ts
deleted file mode 100644
index feecad280..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/texture_manager.ts
+++ /dev/null
@@ -1,287 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-
-import {GPGPUContext} from './gpgpu_context';
-import {getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture} from './gpgpu_util';
-import {getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, Texture, TextureConfig, TextureUsage} from './tex_util';
-
-export class TextureManager {
-  private numUsedTextures = 0;
-  private numFreeTextures = 0;
-  private _numBytesAllocated = 0;
-  // Number of bytes that have been allocated and available for reuse.
-  private _numBytesFree = 0;
-  private freeTextures: Record<string, Texture[]> = {};
-  private usedTextures: Record<string, Texture[]> = {};
-  private logEnabled = false;
-
-  constructor(private readonly gpgpu: GPGPUContext) {}
-
-  acquireTexture(
-      shapeRC: [number, number], usage: TextureUsage,
-      isPacked: boolean): Texture {
-    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
-
-    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
-    if (!(shapeKey in this.freeTextures)) {
-      this.freeTextures[shapeKey] = [];
-    }
-    if (!(shapeKey in this.usedTextures)) {
-      this.usedTextures[shapeKey] = [];
-    }
-
-    const texBytes = computeBytes(
-        shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig,
-        isPacked);
-
-    if (this.freeTextures[shapeKey].length > 0) {
-      this.numFreeTextures--;
-      this.numUsedTextures++;
-      this._numBytesFree -= texBytes;
-      this.log();
-      const newTexture = this.freeTextures[shapeKey].pop();
-      this.usedTextures[shapeKey].push(newTexture);
-      return newTexture;
-    }
-
-    let newTexture: Texture;
-    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {
-      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
-    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {
-      newTexture =
-          this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
-    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {
-      newTexture =
-          this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
-    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {
-      newTexture =
-          this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
-    } else if (
-        physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {
-      newTexture =
-          this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
-    }
-    this.usedTextures[shapeKey].push(newTexture);
-
-    this.numUsedTextures++;
-    this._numBytesAllocated += texBytes;
-    this.log();
-
-    return newTexture;
-  }
-
-  releaseTexture(
-      texture: Texture, shape: [number, number], logicalTexType: TextureUsage,
-      isPacked: boolean): void {
-    if (this.freeTextures == null) {
-      // Already disposed.
-      return;
-    }
-    const physicalTexType =
-        getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
-    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
-    if (!(shapeKey in this.freeTextures)) {
-      this.freeTextures[shapeKey] = [];
-    }
-
-    const texBytes = computeBytes(
-        shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig,
-        isPacked);
-    const deleteTexThreshold = env()
-        .getNumber('WEBGL_DELETE_TEXTURE_THRESHOLD');
-    if (deleteTexThreshold !== -1 &&
-        this._numBytesAllocated > deleteTexThreshold) {
-      this.gpgpu.deleteMatrixTexture(texture.texture);
-      this._numBytesAllocated -= texBytes;
-    } else {
-      this.freeTextures[shapeKey].push(texture);
-      this.numFreeTextures++;
-      this._numBytesFree += texBytes;
-    }
-
-    this.numUsedTextures--;
-
-    const texList = this.usedTextures[shapeKey];
-    const texIndex = texList && texList.indexOf(texture);
-    if (texIndex == null || texIndex < 0) {
-      throw new Error(
-          'Cannot release a texture that was never provided by this ' +
-          'texture manager');
-    }
-    texList[texIndex] = texList[texList.length - 1];
-    texList.pop();
-    this.log();
-  }
-
-  private log() {
-    if (!this.logEnabled) {
-      return;
-    }
-    const total = this.numFreeTextures + this.numUsedTextures;
-    console.log(
-        'Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`,
-        `(${total})`);
-    const freeRatio = this._numBytesFree / this._numBytesAllocated;
-    console.log(`Bytes allocated: ${this._numBytesAllocated}`);
-    console.log(`Bytes unused: ${this._numBytesFree} (${
-        Math.round(100 * freeRatio)}%)`);
-  }
-
-  get numBytesAllocated(): number {
-    return this._numBytesAllocated;
-  }
-
-  get numBytesFree(): number {
-    return this._numBytesFree;
-  }
-
-  getNumUsedTextures(): number {
-    return this.numUsedTextures;
-  }
-
-  getNumFreeTextures(): number {
-    return this.numFreeTextures;
-  }
-
-  dispose() {
-    if (this.freeTextures == null) {
-      // Already disposed.
-      return;
-    }
-    for (const texShape in this.freeTextures) {
-      this.freeTextures[texShape].forEach(tex => {
-        this.gpgpu.deleteMatrixTexture(tex.texture);
-      });
-    }
-    for (const texShape in this.usedTextures) {
-      this.usedTextures[texShape].forEach(tex => {
-        this.gpgpu.deleteMatrixTexture(tex.texture);
-      });
-    }
-    // TODO: Assign non-null value (empty object) to textures after disposed.
-    this.freeTextures = null;
-    this.usedTextures = null;
-    this.numUsedTextures = 0;
-    this.numFreeTextures = 0;
-    this._numBytesAllocated = 0;
-    this._numBytesFree = 0;
-  }
-}
-
-function numBytesForInternalFormat(
-    gl: WebGLRenderingContext, internalFormat: number): number {
-  // tslint:disable-next-line:no-any
-  const glany = gl as any;
-  if (internalFormat === glany.R32F) {
-    return 4;
-  } else if (internalFormat === glany.R16F) {
-    return 2;
-  } else if (internalFormat === glany.RGBA32F) {
-    return 16;
-  } else if (internalFormat === gl.RGBA) {
-    return 16;
-  } else if (internalFormat === glany.RGBA16F) {
-    return 8;
-  } else if (internalFormat === glany.RGBA8) {
-    return 4;
-  }
-  throw new Error(`Unknown internal format ${internalFormat}`);
-}
-
-export function computeBytes(
-    shape: [number, number], physicalTexType: PhysicalTextureType,
-    gl: WebGLRenderingContext, textureConfig: TextureConfig,
-    isPacked: boolean): number {
-  // It is not possible to infer packed status from the texture type because
-  // depending on the textureConfig, different  texture types may resolve to the
-  // same internal format (e.g. in WebGL1, the internal format for
-  // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`
-  // explicitly.
-  const internalFormat =
-      internalFormatForPhysicalTexType(physicalTexType, textureConfig);
-
-  let numElements: number;
-  if (isPacked) {
-    const [packedWidth, packedHeight] =
-        getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
-    numElements = packedWidth * packedHeight;
-
-  } else {
-    const [width, height] =
-        getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
-    numElements = width * height;
-  }
-
-  const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);
-  return numElements * bytesPerElement;
-}
-
-function internalFormatForPhysicalTexType(
-    physicalTexType: PhysicalTextureType,
-    textureConfig: TextureConfig): number {
-  switch (physicalTexType) {
-    case PhysicalTextureType.PACKED_2X2_FLOAT32:
-      return getInternalFormatForPackedMatrixTexture(textureConfig);
-    case PhysicalTextureType.PACKED_2X2_FLOAT16:
-      return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);
-    case PhysicalTextureType.UNPACKED_FLOAT32:
-      return getInternalFormatForFloat32MatrixTexture(textureConfig);
-    case PhysicalTextureType.UNPACKED_FLOAT16:
-      return getInternalFormatForFloat16MatrixTexture(textureConfig);
-    case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:
-      return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);
-    default:
-      throw new Error(`Unknown physical texture type ${physicalTexType}`);
-  }
-}
-
-function getPhysicalTextureForRendering(isPacked: boolean):
-    PhysicalTextureType {
-  if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {
-    if (isPacked) {
-      return PhysicalTextureType.PACKED_2X2_FLOAT32;
-    }
-    return PhysicalTextureType.UNPACKED_FLOAT32;
-  }
-
-  if (isPacked) {
-    return PhysicalTextureType.PACKED_2X2_FLOAT16;
-  }
-  return PhysicalTextureType.UNPACKED_FLOAT16;
-}
-
-function getPhysicalFromLogicalTextureType(
-    logicalTexType: TextureUsage, isPacked: boolean): PhysicalTextureType {
-  if (logicalTexType === TextureUsage.UPLOAD) {
-    return PhysicalTextureType.PACKED_2X2_FLOAT32;
-  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {
-    return getPhysicalTextureForRendering(isPacked);
-  } else if (
-      logicalTexType === TextureUsage.DOWNLOAD ||
-      logicalTexType === TextureUsage.PIXELS) {
-    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;
-  }
-  throw new Error(`Unknown logical texture type ${logicalTexType}`);
-}
-
-function getKeyFromTextureShape(
-    shapeRowsCol: [number, number], physicalTexType: PhysicalTextureType,
-    isPacked: boolean): string {
-  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/tile_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/tile_gpu.ts
deleted file mode 100644
index 7ab7505b3..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/tile_gpu.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export class TileProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[];
-  userCode: string;
-  rank: number;
-
-  constructor(aShape: number[], reps: number[]) {
-    const outputShape: number[] = new Array(aShape.length);
-    for (let i = 0; i < outputShape.length; i++) {
-      outputShape[i] = aShape[i] * reps[i];
-    }
-    this.outputShape = outputShape;
-    this.rank = outputShape.length;
-    const dtype = getCoordsDataType(this.rank);
-    const sourceCoords = getSourceCoords(aShape);
-
-    this.userCode = `
-      void main() {
-        ${dtype} resRC = getOutputCoords();
-        setOutput(getA(${sourceCoords}));
-      }
-    `;
-  }
-}
-
-function getSourceCoords(aShape: number[]): string {
-  const rank = aShape.length;
-  if (rank > 5) {
-    throw Error(`Tile for rank ${rank} is not yet supported`);
-  }
-  if (rank === 1) {
-    return `imod(resRC, ${aShape[0]})`;
-  }
-
-  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];
-
-  const sourceCoords = [];
-  for (let i = 0; i < aShape.length; i++) {
-    sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);
-  }
-  return sourceCoords.join();
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/top_k_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/top_k_gpu.ts
deleted file mode 100644
index 3d66e167e..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/top_k_gpu.ts
+++ /dev/null
@@ -1,152 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {GPGPUProgram} from './gpgpu_math';
-import {UniformType} from './shader_compiler';
-
-// Based on Algorithm 2 of Bitonic Top K, ref:
-// https://anilshanbhag.in/static/papers/gputopk_sigmod18.pdf
-// The original algorithm is based on computing the top K only, however
-// since for TFJS we require the indices of the top K values as well then the
-// algorithm found here is a bit modified. Rather than producing the values
-// at each step, the indices containing the top K are generated instead.
-// The output values are not generated to reduce the number of outputs in the
-// GPU, the values can easily be retrieved from the indices using a gather
-// op.
-export class SwapProgram implements GPGPUProgram {
-  variableNames = ['x', 'indices'];
-  outputShape: number[];
-  userCode: string;
-  // |n| Size of the original input of TopK.
-  // |firstPass|indicates if this is the first time swap is being used which
-  // means no indices input containing the top K is present yet.
-  // |inc| Swaps pairs of indices (0, inc), (1, inc + 1), (2, inc + 2) ...
-  customUniforms = [
-    {name: 'n', type: 'int' as UniformType},
-    {name: 'firstPass', type: 'int' as UniformType},
-    {name: 'negativeInf', type: 'float' as UniformType},
-    {name: 'dir', type: 'int' as UniformType},
-    {name: 'inc', type: 'int' as UniformType}
-  ];
-
-  /**
-   * @param shape desired output shape (can be larger than input shape, output
-   *                                    will be padded with -Infinity)
-   */
-  constructor(shape: number[]) {
-    this.outputShape = shape;
-
-    this.userCode = `
-       void main() {
-         ivec2 coords = getOutputCoords();
-         int batch = coords[0];
-         int elemIdx = coords[1];
-
-         // We compare elements pair-wise within a group of size 2 * inc.
-         // The comparing rule for each group alternates between ascending
-         // and descending. Within each group, we compare each pair at
-         // positions i and i+inc. To decide whether an element at position i
-         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
-         // inc, it is in the first half of the group, we denote it as x0,
-         // otherwise we denote it as x1.
-         // For example, as shown in the Bitonic top K paper referenced above,
-         // Figure5(a) shows that element[1] is in the
-         // second half of the group when group size is 2, but it is in the
-         // first half of the group when group size is 4.
-
-         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
-         int i = isFirstInPair ? elemIdx : elemIdx - inc;
-
-         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
-         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
-         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
-         float x1 = i1 < n ? getX(batch, i1) : negativeInf;
-
-         // Denotes which direction indices are in (ascending or descending).
-         bool reverse = imod(elemIdx, 2 * dir) >= dir;
-         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
-         if (reverse == isGreater) { // Elements in opposite order of direction
-           int iTemp = i0;
-           i0 = i1;
-           i1 = iTemp;
-         }
-         if (isFirstInPair) {
-            setOutput(float(i0));
-         } else {
-            setOutput(float(i1));
-         }
-       }
-     `;
-  }
-}
-
-export class MergeProgram implements GPGPUProgram {
-  variableNames = ['x', 'indices'];
-  outputShape: number[];
-  userCode: string;
-  // |n| Size of the original input of TopK
-  // |firstPass| indicates if this is the first time swap is being used which
-  // means no indices input containing the top K is present yet.
-  // |k| Top k elements desired
-  customUniforms = [
-    {name: 'n', type: 'int' as UniformType},
-    {name: 'firstPass', type: 'int' as UniformType},
-    {name: 'k', type: 'int' as UniformType}
-  ];
-
-  /**
-   * @param shape desired output shape (must be half of the input size)
-   */
-  constructor(shape: number[]) {
-    this.outputShape = shape;
-
-    this.userCode = `
-    void main() {
-         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
-         ivec2 coords = getOutputCoords();
-         int batch = coords[0];
-         int elemIdx = coords[1];
-
-         // The output size is half of the previous size.
-         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
-         // we only need to output the indices at positions |, the indices at
-         // positions _ can be thrown away, see Figure5(b) After Phase 2
-         // (Merge phase) in the Bitonic Top K paper referenced above.
-         // For example, the paper shows we only need to output the orange bars.
-         // The output sequence should look like this | | | | | | | |.
-         // Because the sequence is halved, to map the output index back
-         // to the previous sequence to find the corresponding value,
-         // we need to double the index. When we double the index,
-         // we basically interpolate a position, so 2i looks like
-         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
-         // of each 2k positions by - elemIdx % k. E.g. for output at
-         // index 4,5,6,7, we want to get the corresponding element at
-         // original index 8,9,10,11, for output at index 8,9,10,11,
-         // we want to get the corresponding element at original index
-         // 16,17,18,19, so on and so forth.
-
-         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
-         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
-         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
-
-         float x0 = getX(batch, i0);
-         float x1 = i1 < n ? getX(batch, i1) : x0;
-
-         setOutput(x0 >= x1 ? float(i0) : float(i1));
-       }
-     `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/transform_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/transform_gpu.ts
deleted file mode 100644
index 8b364933c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/transform_gpu.ts
+++ /dev/null
@@ -1,165 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-
-export class TransformProgram implements GPGPUProgram {
-  variableNames = ['Image', 'Transforms'];
-  outputShape: number[];
-  userCode: string;
-
-  constructor(
-      imageHeight: number, imageWidth: number,
-      interpolation: 'nearest'|'bilinear',
-      fillMode: 'constant'|'reflect'|'wrap'|'nearest', fillValue: number,
-      outShape: [number, number, number, number]) {
-    this.outputShape = outShape;
-    const interpolationModeId = interpolation === 'nearest' ? 1 : 2;
-    let fillModeId;
-    switch (fillMode) {
-      case 'constant':
-        fillModeId = 1;
-        break;
-      case 'reflect':
-        fillModeId = 2;
-        break;
-      case 'wrap':
-        fillModeId = 3;
-        break;
-      case 'nearest':
-        fillModeId = 4;
-        break;
-      default:
-        fillModeId = 1;
-        break;
-    }
-    this.userCode = `
-            float mapCoord(float outCoord, float len) {
-              float inCoord = outCoord;
-              if(${fillModeId} == 2) {
-                if (inCoord < 0.0) {
-                  if (len <= 1.0) {
-                    inCoord = 0.0;
-                  } else {
-                    float sz2 = 2.0 * len;
-                    if (inCoord < sz2) {
-                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
-                      inCoord;
-                    }
-                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
-                  }
-                } else if (inCoord > len - 1.0) {
-                  if (len <= 1.0) {
-                    inCoord = 0.0;
-                  } else {
-                    float sz2 = 2.0 * len;
-                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
-                    if (inCoord >= len) {
-                      inCoord = sz2 - inCoord - 1.0;
-                    }
-                  }
-                }
-                return clamp(inCoord, 0.0, len - 1.0);
-              } else if (${fillModeId} == 3) {
-                if (inCoord < 0.0) {
-                  if (len <= 1.0) {
-                    inCoord = 0.0;
-                  } else {
-                    float sz = len - 1.0;
-                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
-                  }
-                } else if (inCoord > len - 1.0) {
-                  if (len <= 1.0) {
-                    inCoord = 0.0;
-                  } else {
-                    float sz = len - 1.0;
-                    inCoord -= len * float(int(float(inCoord / sz)));
-                  }
-                }
-                return clamp(inCoord, 0.0, len - 1.0);
-              } else if (${fillModeId} == 4) {
-                return clamp(outCoord, 0.0, len - 1.0);
-              } else {
-                return outCoord;
-              }
-            }
-
-            float readWithFillValue(int batch, int coordY, int coordX,
-              int channel) {
-              float outputValue;
-              if (0 <= coordY && coordY < ${
-        imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {
-                  outputValue = getImage(batch, coordY, coordX, channel);
-              } else {
-                outputValue = float(${fillValue});
-              }
-              return outputValue;
-            }
-
-            void main() {
-              ivec4 coords = getOutputCoords();
-              float outputValue;
-              int batch = coords[0];
-              int x = coords[2];
-              int y = coords[1];
-              int channel = coords[3];
-              float xf = float(x);
-              float yf = float(y);
-              float a1 = getTransforms(batch, 0);
-              float a2 = getTransforms(batch, 1);
-              float a3 = getTransforms(batch, 2);
-              float b1 = getTransforms(batch, 3);
-              float b2 = getTransforms(batch, 4);
-              float b3 = getTransforms(batch, 5);
-              float c1 = getTransforms(batch, 6);
-              float c2 = getTransforms(batch, 7);
-              float projection = c1 * xf + c2 * yf + 1.0;
-              if (projection == 0.0) {
-                outputValue = float(${fillValue});
-              } else {
-                float inX = (a1 * xf + a2 * yf + a3) / projection;
-                float inY = (b1 * xf + b2 * yf + b3) / projection;
-                float mapX = mapCoord(inX, float(${imageWidth}));
-                float mapY = mapCoord(inY, float(${imageHeight}));
-
-                if (${interpolationModeId} == 1) {
-                  int coordY = int(round(mapY));
-                  int coordX = int(round(mapX));
-                  outputValue = readWithFillValue(batch, coordY, coordX,
-                    channel);
-                } else {
-                  float yFloor = floor(mapY);
-                  float xFloor = floor(mapX);
-                  float yCeil = yFloor + 1.0;
-                  float xCeil = xFloor + 1.0;
-                  float valueYFloor = (xCeil - mapX) *
-                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
-                  (mapX - xFloor) *
-                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
-                  float valueYCeil = (xCeil - mapX) *
-                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
-                  (mapX - xFloor) *
-                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
-                  outputValue = (yCeil - mapY) * valueYFloor +
-                  (mapY - yFloor) * valueYCeil;
-                }
-              }
-              setOutput(outputValue);
-            }
-        `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/transpose_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/transpose_gpu.ts
deleted file mode 100644
index b6da972a1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/transpose_gpu.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getCoordsDataType} from './shader_compiler';
-
-export class TransposeProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[];
-  userCode: string;
-  rank: number;
-
-  constructor(aShape: number[], newDim: number[]) {
-    const outputShape: number[] = new Array(aShape.length);
-    for (let i = 0; i < outputShape.length; i++) {
-      outputShape[i] = aShape[newDim[i]];
-    }
-    this.outputShape = outputShape;
-    this.rank = outputShape.length;
-    const dtype = getCoordsDataType(this.rank);
-    const switched = getSwitchedCoords(newDim);
-
-    this.userCode = `
-    void main() {
-      ${dtype} resRC = getOutputCoords();
-      setOutput(getA(${switched}));
-    }
-    `;
-  }
-}
-
-function getSwitchedCoords(newDim: number[]): string {
-  const rank = newDim.length;
-  if (rank > 6) {
-    throw Error(`Transpose for rank ${rank} is not yet supported`);
-  }
-  const originalOrder =
-      ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];
-  const switchedCoords = new Array(rank);
-  for (let i = 0; i < newDim.length; i++) {
-    switchedCoords[newDim[i]] = originalOrder[i];
-  }
-  return switchedCoords.join();
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/transpose_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/transpose_packed_gpu.ts
deleted file mode 100644
index 07312cf54..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/transpose_packed_gpu.ts
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram} from './gpgpu_math';
-import {getVecChannels} from './packing_util';
-import {getCoordsDataType} from './shader_compiler';
-
-export class TransposePackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[];
-  userCode: string;
-  rank: number;
-  packedInputs = true;
-  packedOutput = true;
-
-  constructor(aShape: number[], newDim: number[]) {
-    const outputShape: number[] = new Array(aShape.length);
-    for (let i = 0; i < outputShape.length; i++) {
-      outputShape[i] = aShape[newDim[i]];
-    }
-    this.outputShape = outputShape;
-    this.rank = outputShape.length;
-    if (this.rank > 6) {
-      throw Error(
-          `Packed transpose for rank ${this.rank} is not yet supported.`);
-    }
-    const dtype = getCoordsDataType(this.rank);
-
-    const outputOrder = getVecChannels('rc', this.rank);
-    const switchedOrder = new Array(this.rank);
-    for (let i = 0; i < newDim.length; i++) {
-      switchedOrder[newDim[i]] = outputOrder[i];
-    }
-    const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;
-    const nextColumn =
-        `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;
-    const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;
-
-    this.userCode = `
-    void main() {
-      ${dtype} rc = getOutputCoords();
-      vec4 result = vec4(0.);
-      result[0] = ${getc};
-      if(${nextColumn}) {
-        result[1] = ${getc};
-      }
-      --${outputOrder[this.rank - 1]};
-      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {
-        result[2] = ${getc};
-        if(${nextColumn}) {
-          result[3] = ${getc};
-        }
-      }
-      setOutput(result);
-    }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/unaryop_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/unaryop_gpu.ts
deleted file mode 100644
index 63b453fd1..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/unaryop_gpu.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-
-export class UnaryOpProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  outputShape: number[];
-  enableShapeUniforms: boolean;
-
-  constructor(aShape: number[], opSnippet: string) {
-    this.outputShape = aShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-    this.userCode = `
-      float unaryOperation(float x) {
-        ${opSnippet}
-      }
-
-      void main() {
-        float x = getAAtOutCoords();
-        float y = unaryOperation(x);
-
-        setOutput(y);
-      }
-    `;
-  }
-}
-
-export const CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;
-
-export const LINEAR = `return x;`;
-
-export const ABS = `return abs(x);`;
-
-export function STEP(alpha = 0.0) {
-  return CHECK_NAN_SNIPPET + `
-    return x > 0.0 ? 1.0 : float(${alpha});
-  `;
-}
-
-export const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
-export const RELU = CHECK_NAN_SNIPPET + `
-  return (x < 0.0) ? 0.0 : x;
-`;
-
-export const RELU6 = CHECK_NAN_SNIPPET + `
-  return (x < 0.0) ? 0.0 : min(6.0, x);
-`;
-
-export const CLONE = 'return x;';
-
-export const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;
diff --git a/tfjs-master/tfjs-backend-webgl/src/unaryop_packed_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/unaryop_packed_gpu.ts
deleted file mode 100644
index 1e2ccf039..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/unaryop_packed_gpu.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-
-export const LINEAR = `return x;`;
-
-export const ELU = `
-  vec4 result;
-
-  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
-  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
-  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
-  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
-
-  return result;
-`;
-
-export const RELU = `
-  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
-  bvec4 isNaN = isnan(x);
-
-  result.r = isNaN.r ? x.r : result.r;
-  result.g = isNaN.g ? x.g : result.g;
-  result.b = isNaN.b ? x.b : result.b;
-  result.a = isNaN.a ? x.a : result.a;
-
-  return result;
-`;
-
-export const RELU6 = `
-  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
-  bvec4 isNaN = isnan(x);
-
-  result.r = isNaN.r ? x.r : result.r;
-  result.g = isNaN.g ? x.g : result.g;
-  result.b = isNaN.b ? x.b : result.b;
-  result.a = isNaN.a ? x.a : result.a;
-
-  return result;
-`;
-
-export const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;
-
-export class UnaryOpPackedProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  userCode: string;
-  enableShapeUniforms: boolean;
-  outputShape: number[];
-  packedInputs = true;
-  packedOutput = true;
-
-  constructor(aShape: number[], opSnippet: string) {
-    this.outputShape = aShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-    this.userCode = `
-      vec4 unaryOperation(vec4 x) {
-        ${opSnippet}
-      }
-
-      void main() {
-        vec4 x = getAAtOutCoords();
-        vec4 y = unaryOperation(x);
-
-        setOutput(y);
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/unpack_gpu.ts b/tfjs-master/tfjs-backend-webgl/src/unpack_gpu.ts
deleted file mode 100644
index 79bb34475..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/unpack_gpu.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GPGPUProgram, useShapeUniforms} from './gpgpu_math';
-import {getChannels, getSourceCoords} from './packing_util';
-import {getCoordsDataType} from './shader_compiler';
-
-export class UnpackProgram implements GPGPUProgram {
-  variableNames = ['A'];
-  packedInputs = true;
-  packedOutput = false;
-  outputShape: number[];
-  userCode: string;
-  enableShapeUniforms: boolean;
-
-  constructor(outputShape: number[]) {
-    this.outputShape = outputShape;
-    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
-    const rank = outputShape.length;
-
-    const channels = getChannels('rc', rank);
-    const dtype = getCoordsDataType(rank);
-    const sourceCoords = getSourceCoords(rank, channels);
-    const innerDims = channels.slice(-2);
-    const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;
-
-    this.userCode = `
-      void main() {
-        ${dtype} rc = getOutputCoords();
-        vec4 packedInput = getA(${sourceCoords});
-
-        setOutput(getChannel(packedInput, ${coords}));
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/version.ts b/tfjs-master/tfjs-backend-webgl/src/version.ts
deleted file mode 100644
index 5fa574e7d..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.0';
-export {version};
diff --git a/tfjs-master/tfjs-backend-webgl/src/webgl.ts b/tfjs-master/tfjs-backend-webgl/src/webgl.ts
deleted file mode 100644
index 48b29b9ec..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/webgl.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-
-import * as gpgpu_util from './gpgpu_util';
-import * as webgl_util from './webgl_util';
-
-export {MathBackendWebGL, WebGLMemoryInfo, WebGLTimingInfo} from './backend_webgl';
-export {setWebGLContext} from './canvas_util';
-export {GPGPUContext} from './gpgpu_context';
-export {GPGPUProgram} from './gpgpu_math';
-// WebGL specific utils.
-export {gpgpu_util, webgl_util};
-
-/**
- * Enforce use of half precision textures if available on the platform.
- *
- * @doc {heading: 'Environment', namespace: 'webgl'}
- */
-export function forceHalfFloat(): void {
-  env().set('WEBGL_FORCE_F16_TEXTURES', true);
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/webgl_batchnorm_test.ts b/tfjs-master/tfjs-backend-webgl/src/webgl_batchnorm_test.ts
deleted file mode 100644
index 94857209c..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/webgl_batchnorm_test.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-const {expectArraysClose} = test_util;
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {PACKED_ENVS, WEBGL_ENVS} from './backend_webgl_test_registry';
-
-describeWithFlags('batchNorm', WEBGL_ENVS, () => {
-  it('should work for broadcasted inputs', async () => {
-    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const mean = tf.tensor4d([1], [1, 1, 1, 1]);
-    const variance = tf.tensor4d([1], [1, 1, 1, 1]);
-
-    const result = tf.batchNorm4d(x, mean, variance);
-    expectArraysClose(
-        await result.data(), [0.9995003, 2.9985011, 7.9960027, 21.9890079]);
-  });
-
-  it('should work when squarification results in zero padding', async () => {
-    const maxTextureSize = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', 5);
-
-    const x = tf.tensor3d(
-        [
-          0.49955603, 0.04158615, -1.09440524, 2.03854165, -0.61578344,
-          2.87533573, 1.18105987, 0.807462, 1.87888837, 2.26563962, -0.37040935,
-          1.35848753, -0.75347094, 0.15683117, 0.91925946, 0.34121279,
-          0.92717143, 1.89683965
-        ],
-        [2, 3, 3]);
-    const mean = tf.tensor1d([0.39745062, -0.48062894, 0.4847822]);
-    const variance = tf.tensor1d([0.32375343, 0.67117643, 1.08334653]);
-    const offset = tf.tensor1d([0.69398749, -1.29056387, 0.9429723]);
-    const scale = tf.tensor1d([-0.5607271, 0.9878457, 0.25181573]);
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon);
-
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', maxTextureSize);
-
-    expectArraysClose(await result.data(), [
-      0.59352049, -0.66135202, 0.5610874, -0.92077015, -1.45341019, 1.52106473,
-      -0.07704776, 0.26144429, 1.28010017, -1.14422404, -1.15776136, 1.15425493,
-      1.82644104, -0.52249442, 1.04803919, 0.74932291, 0.40568101, 1.2844412
-    ]);
-  });
-});
-
-describeWithFlags('batchnorm packed', PACKED_ENVS, () => {
-  it('should not leak memory', () => {
-    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const mean = tf.tensor1d([1, 2]);
-    const variance = tf.tensor1d([2, 3]);
-    const varianceEpsilon = .001;
-
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-    tf.batchNorm4d(x, mean, variance, undefined, undefined, varianceEpsilon);
-    const endNumBytes = tf.memory().numBytes;
-    const endNumTensors = tf.memory().numTensors;
-
-    expect(endNumBytes - startNumBytes).toEqual(16);
-    expect(endNumTensors - startNumTensors).toEqual(1);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/webgl_custom_op_test.ts b/tfjs-master/tfjs-backend-webgl/src/webgl_custom_op_test.ts
deleted file mode 100644
index fcfbae0e3..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/webgl_custom_op_test.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {engine, Tensor, TensorInfo, test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {WEBGL_ENVS} from './backend_webgl_test_registry';
-import {GPGPUProgram, MathBackendWebGL} from './webgl';
-
-describeWithFlags('custom-op webgl', WEBGL_ENVS, () => {
-  class SquareAndAddKernel implements GPGPUProgram {
-    variableNames = ['X'];
-    outputShape: number[];
-    userCode: string;
-    constructor(inputShape: number[]) {
-      this.outputShape = inputShape.slice();
-
-      this.userCode = `
-          void main() {
-            float x = getXAtOutCoords();
-            float value = x * x + x;
-            setOutput(value);
-          }
-        `;
-    }
-  }
-
-  class SquareAndAddBackpropKernel implements GPGPUProgram {
-    variableNames = ['X'];
-    outputShape: number[];
-    userCode: string;
-    constructor(inputShape: number[]) {
-      this.outputShape = inputShape.slice();
-
-      this.userCode = `
-          void main() {
-            float x = getXAtOutCoords();
-            float value = 2.0 * x + 1.0;
-            setOutput(value);
-          }
-        `;
-    }
-  }
-
-  function squareAndAdd<T extends tf.Tensor>(x: T): T {
-    const fn = tf.customGrad((x: T, save: tf.GradSaveFunc) => {
-      save([x]);
-      const webglBackend = tf.backend() as MathBackendWebGL;
-      const program = new SquareAndAddKernel(x.shape);
-      const backpropProgram = new SquareAndAddBackpropKernel(x.shape);
-
-      const outInfo: TensorInfo = webglBackend.compileAndRun(program, [x]);
-      const value = engine().makeTensorFromTensorInfo(outInfo) as T;
-
-      const gradFunc = (dy: T, saved: Tensor[]) => {
-        const [x] = saved;
-        const backInfo = webglBackend.compileAndRun(backpropProgram, [x]);
-        const back: T = engine().makeTensorFromTensorInfo(backInfo) as T;
-        return back.mul(dy);
-      };
-      return {value, gradFunc};
-    });
-    return fn(x);
-  }
-
-  it('lets users use custom operations', async () => {
-    const inputArr = [1, 2, 3, 4];
-    const input = tf.tensor(inputArr);
-    const output = squareAndAdd(input);
-    test_util.expectArraysClose(
-        await output.data(), inputArr.map(x => x * x + x));
-  });
-
-  it('lets users define gradients for operations', async () => {
-    const inputArr = [1, 2, 3, 4];
-    const input = tf.tensor(inputArr);
-    const grads = tf.valueAndGrad(x => squareAndAdd(x));
-    const {value, grad} = grads(input);
-    test_util.expectArraysClose(
-        await value.data(), inputArr.map(x => x * x + x));
-    test_util.expectArraysClose(
-        await grad.data(), inputArr.map(x => 2 * x + 1));
-  });
-
-  it('multiplies by dy parameter when it is passed', async () => {
-    const inputArr = [1, 2, 3, 4];
-    const input = tf.tensor(inputArr);
-    const grads = tf.valueAndGrad(x => squareAndAdd(x));
-    const {value, grad} = grads(input, tf.zerosLike(input));
-    test_util.expectArraysClose(
-        await value.data(), inputArr.map(x => x * x + x));
-    test_util.expectArraysClose(await grad.data(), inputArr.map(() => 0.0));
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/webgl_ops_test.ts b/tfjs-master/tfjs-backend-webgl/src/webgl_ops_test.ts
deleted file mode 100644
index 48ff09f25..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/webgl_ops_test.ts
+++ /dev/null
@@ -1,1063 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Rank, Tensor2D, Tensor3D, Tensor4D, test_util} from '@tensorflow/tfjs-core';
-const expectArraysClose = test_util.expectArraysClose;
-const expectArraysEqual = test_util.expectArraysEqual;
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import * as tf from '@tensorflow/tfjs-core';
-
-import {WebGLMemoryInfo} from './backend_webgl';
-import {PACKED_ENVS, WEBGL_ENVS} from './backend_webgl_test_registry';
-
-describeWithFlags('fromPixels + regular math op', WEBGL_ENVS, () => {
-  it('fromPixels + add', async () => {
-    const pixels = new ImageData(2, 2);
-    for (let i = 0; i < 8; i++) {
-      pixels.data[i] = 100;
-    }
-    for (let i = 8; i < 16; i++) {
-      pixels.data[i] = 250;
-    }
-
-    const a = tf.browser.fromPixels(pixels, 4);
-    const b = tf.scalar(20, 'int32');
-
-    const res = tf.add(a, b);
-
-    expectArraysEqual(await res.data(), [
-      120, 120, 120, 120, 120, 120, 120, 120, 270, 270, 270, 270, 270, 270, 270,
-      270
-    ]);
-  });
-});
-
-describeWithFlags('toPixels', WEBGL_ENVS, () => {
-  it('draws a rank-2 float32 tensor, canvas', done => {
-    const x = tf.tensor2d([.15, .2], [2, 1], 'float32');
-    const canvas = document.createElement('canvas');
-
-    tf.browser.toPixels(x, canvas).then(data => {
-      const expected = new Uint8ClampedArray([
-        Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255),
-        255, Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255),
-        255
-      ]);
-      expect(data).toEqual(expected);
-
-      const ctx = canvas.getContext('2d');
-      const imgData = ctx.getImageData(0, 0, 1, 2);
-
-      expect(imgData.data).toEqual(expected);
-      done();
-    });
-  });
-
-  it('draws a rank-2 int32 tensor, canvas', done => {
-    const x = tf.tensor2d([10, 20], [2, 1], 'int32');
-    const canvas = document.createElement('canvas');
-
-    tf.browser.toPixels(x, canvas).then(data => {
-      const expected =
-          new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
-      expect(data).toEqual(expected);
-
-      const ctx = canvas.getContext('2d');
-      const imgData = ctx.getImageData(0, 0, 1, 2);
-
-      expect(imgData.data).toEqual(expected);
-      done();
-    });
-  });
-
-  it('draws a rank-3 float32 tensor, 1 channel, canvas', done => {
-    const x = tf.tensor3d([.15, .2], [2, 1, 1], 'float32');
-    const canvas = document.createElement('canvas');
-
-    tf.browser.toPixels(x, canvas).then(data => {
-      const expected = new Uint8ClampedArray([
-        Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255),
-        255, Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255),
-        255
-      ]);
-      expect(data).toEqual(expected);
-
-      const ctx = canvas.getContext('2d');
-      const imgData = ctx.getImageData(0, 0, 1, 2);
-
-      expect(imgData.data).toEqual(expected);
-      done();
-    });
-  });
-
-  it('draws a rank-3 int32 tensor, 1 channel, canvas', done => {
-    const x = tf.tensor3d([10, 20], [2, 1, 1], 'int32');
-    const canvas = document.createElement('canvas');
-
-    tf.browser.toPixels(x, canvas).then(data => {
-      const expected =
-          new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
-      expect(data).toEqual(expected);
-
-      const ctx = canvas.getContext('2d');
-      const imgData = ctx.getImageData(0, 0, 1, 2);
-
-      expect(imgData.data).toEqual(expected);
-      done();
-    });
-  });
-
-  it('draws a rank-3 float32 tensor, 3 channel, canvas', done => {
-    const x =
-        tf.tensor3d([.05, .1001, .15, .20, .25, .3001], [2, 1, 3], 'float32');
-    const canvas = document.createElement('canvas');
-
-    tf.browser.toPixels(x, canvas).then(data => {
-      const expected = new Uint8ClampedArray([
-        Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),
-        255, Math.round(.2 * 255), Math.round(.25 * 255),
-        Math.round(.3001 * 255), 255
-      ]);
-      expect(data).toEqual(expected);
-
-      const ctx = canvas.getContext('2d');
-      const imgData = ctx.getImageData(0, 0, 1, 2);
-
-      expect(imgData.data).toEqual(expected);
-      done();
-    });
-  });
-
-  it('draws a rank-3 int32 tensor, 3 channel, canvas', done => {
-    const x = tf.tensor3d([10, 20, 30, 40, 50, 60], [2, 1, 3], 'int32');
-    const canvas = document.createElement('canvas');
-
-    tf.browser.toPixels(x, canvas).then(data => {
-      const expected =
-          new Uint8ClampedArray([10, 20, 30, 255, 40, 50, 60, 255]);
-      expect(data).toEqual(expected);
-
-      const ctx = canvas.getContext('2d');
-      const imgData = ctx.getImageData(0, 0, 1, 2);
-      expect(imgData.data).toEqual(expected);
-      done();
-    });
-  });
-
-  it('draws a rank-3 float32 tensor, 4 channel, canvas', done => {
-    // ImageData roundtrips are lossy because of pre-multiplied alphas, so we
-    // use an alpha = 1 to avoid losing precision on r, g, b channels in these
-    // tests https://www.w3.org/TR/2dcontext/
-    const x = tf.tensor3d(
-        [.05, .1001, .15, 1, .20, .25, .3001, 1], [2, 1, 4], 'float32');
-    const canvas = document.createElement('canvas');
-
-    tf.browser.toPixels(x, canvas).then(data => {
-      const expected = new Uint8ClampedArray([
-        Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),
-        255, Math.round(.20 * 255), Math.round(.25 * 255),
-        Math.round(.3001 * 255), 255
-      ]);
-      expect(data).toEqual(expected);
-
-      const ctx = canvas.getContext('2d');
-      const imgData = ctx.getImageData(0, 0, 1, 2);
-
-      expect(imgData.data).toEqual(expected);
-      done();
-    });
-  });
-
-  it('draws a rank-3 int32 tensor, 4 channel, canvas', done => {
-    // ImageData roundtrips are lossy because of pre-multiplied alphas, so we
-    // use an alpha = 1 to avoid losing precision on r, g, b channels in these
-    // tests https://www.w3.org/TR/2dcontext/
-    const x =
-        tf.tensor3d([10, 20, 30, 255, 50, 60, 70, 255], [2, 1, 4], 'int32');
-    const canvas = document.createElement('canvas');
-
-    tf.browser.toPixels(x, canvas).then(data => {
-      const expected =
-          new Uint8ClampedArray([10, 20, 30, 255, 50, 60, 70, 255]);
-      expect(data).toEqual(expected);
-
-      const ctx = canvas.getContext('2d');
-      const imgData = ctx.getImageData(0, 0, 1, 2);
-
-      expect(imgData.data).toEqual(expected);
-      done();
-    });
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[127], [100]];  // 2x1;
-    const canvas = document.createElement('canvas');
-
-    const data = await tf.browser.toPixels(x, canvas);
-    const expected =
-        new Uint8ClampedArray([127, 127, 127, 255, 100, 100, 100, 255]);
-    expect(data).toEqual(expected);
-
-    const ctx = canvas.getContext('2d');
-    const imgData = ctx.getImageData(0, 0, 1, 2);
-
-    expect(imgData.data).toEqual(expected);
-  });
-});
-
-describeWithFlags('depthToSpace', WEBGL_ENVS, () => {
-  it('tensor4d, input shape=[1, 4, 1, 1], blockSize=2, format=NCHW',
-     async () => {
-       const t = tf.tensor4d([1, 2, 3, 4], [1, 4, 1, 1]);
-       const blockSize = 2;
-       const dataFormat = 'NCHW';
-
-       const res = tf.depthToSpace(t, blockSize, dataFormat);
-       expect(res.shape).toEqual([1, 1, 2, 2]);
-       expectArraysClose(await res.data(), [1, 2, 3, 4]);
-     });
-
-  it('tensor4d, input shape=[1, 12, 1, 1], blockSize=2, format=NCHW',
-     async () => {
-       const t =
-           tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [1, 12, 1, 1]);
-       const blockSize = 2;
-       const dataFormat = 'NCHW';
-
-       const res = tf.depthToSpace(t, blockSize, dataFormat);
-       expect(res.shape).toEqual([1, 3, 2, 2]);
-       expectArraysClose(
-           await res.data(), [1, 4, 7, 10, 2, 5, 8, 11, 3, 6, 9, 12]);
-     });
-
-  it('tensor4d, input shape=[1, 4, 2, 2], blockSize=2, format=NCHW',
-     async () => {
-       const t = tf.tensor4d(
-           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-           [1, 4, 2, 2]);
-       const blockSize = 2;
-       const dataFormat = 'NCHW';
-
-       const res = tf.depthToSpace(t, blockSize, dataFormat);
-       expect(res.shape).toEqual([1, 1, 4, 4]);
-       expectArraysClose(
-           await res.data(),
-           [1, 5, 2, 6, 9, 13, 10, 14, 3, 7, 4, 8, 11, 15, 12, 16]);
-     });
-
-  it('tensor4d, input shape=[1, 8, 2, 2], blockSize=2, format=NCHW',
-     async () => {
-       const t = tf.tensor4d(
-           [
-             1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
-             17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
-           ],
-           [1, 8, 2, 2]);
-       const blockSize = 2;
-       const dataFormat = 'NCHW';
-
-       const res = tf.depthToSpace(t, blockSize, dataFormat);
-       expect(res.shape).toEqual([1, 2, 4, 4]);
-       expectArraysClose(await res.data(), [
-         1, 9,  2, 10, 17, 25, 18, 26, 3, 11, 4, 12, 19, 27, 20, 28,
-         5, 13, 6, 14, 21, 29, 22, 30, 7, 15, 8, 16, 23, 31, 24, 32
-       ]);
-     });
-});
-
-describeWithFlags('maximum', WEBGL_ENVS, () => {
-  it('works with squarification for large dimension', async () => {
-    const maxTextureSize = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', 5);
-    const a =
-        tf.tensor2d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], [2, 7]);
-    const b =
-        tf.tensor2d([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 7]);
-
-    const result = tf.maximum(a, b);
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', maxTextureSize);
-    expectArraysClose(
-        await result.data(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]);
-  });
-});
-
-describeWithFlags('div', PACKED_ENVS, () => {
-  it('works when unused channels are divided', async () => {
-    // Tests that the 0's in unused channels for input textures do not corrupt
-    // the result when swizzled with 3 / 3.
-    const a = tf.tensor2d([1], [1, 1]);
-    const b = tf.tensor2d([1], [1, 1]);
-
-    const c = tf.add(a, b).div(a);
-    const d = tf.add(a, b).div(a);
-
-    const result = c.matMul(d);
-    expectArraysClose(await result.data(), [4]);
-  });
-
-  it('works when unused channels in tensors with size > 1 are divided',
-     async () => {
-       const a = tf.tensor2d([1, 2, 3], [3, 1]);
-       const b = tf.tensor2d([1, 2, 3], [3, 1]);
-       const c = a.div(b);
-
-       const d = tf.tensor1d([1, 2, 3]);
-       const e = tf.tensor1d([1, 2, 3]);
-       const f = d.div(e).reshape([1, 3]);
-
-       const result = c.matMul(f);
-       expectArraysClose(await result.data(), [1, 1, 1, 1, 1, 1, 1, 1, 1]);
-     });
-});
-
-describeWithFlags('conv2d webgl', WEBGL_ENVS, () => {
-  it('packed input x=[2,1,2] f=[1,1,2,2] s=1 d=1 p=0', async () => {
-    const inputShape: [number, number, number] = [2, 1, 2];
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d([1, 2, 3, 4], [fSize, fSize, 2, 2]);
-
-    const webglLazilyUnpackFlagSaved = tf.env().getBool('WEBGL_LAZILY_UNPACK');
-    tf.env().set('WEBGL_LAZILY_UNPACK', true);
-    const webglPackBinaryOperationsFlagSaved =
-        tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
-    tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', true);
-
-    // First conv2D tests conv2D with non-packed input |x|, and the second uses
-    // packed input |result|.
-    const result = tf.conv2d(x, w, stride, pad);
-    const result1 = tf.conv2d(result, w, stride, pad);
-
-    tf.env().set('WEBGL_LAZILY_UNPACK', webglLazilyUnpackFlagSaved);
-    tf.env().set(
-        'WEBGL_PACK_BINARY_OPERATIONS', webglPackBinaryOperationsFlagSaved);
-
-    expectArraysClose(await result.data(), [7, 10, 15, 22]);
-    expectArraysClose(await result1.data(), [37, 54, 81, 118]);
-  });
-
-  it('tf.memory() packed input x=[1,1,1,2] f=[1,1,2,2] s=1 d=1 p=0',
-     async () => {
-       const startNumBytesInGPU =
-           (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-       const startNumBytes = tf.memory().numBytes;
-
-       const inputShape: [number, number, number, number] = [1, 1, 1, 2];
-       const fSize = 1;
-       const pad = 0;
-       const stride = 1;
-
-       const xInit = tf.tensor4d([0, 1], inputShape);
-       const w = tf.tensor4d([1, 2, 3, 4], [fSize, fSize, 2, 2]);
-
-       const webglLazilyUnpackFlagSaved =
-           tf.env().getBool('WEBGL_LAZILY_UNPACK');
-       tf.env().set('WEBGL_LAZILY_UNPACK', true);
-       const webglPackBinaryOperationsFlagSaved =
-           tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
-       tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', true);
-
-       const x = xInit.add<tf.Tensor4D>(1);
-       const result = tf.conv2d(x, w, stride, pad);
-
-       tf.env().set('WEBGL_LAZILY_UNPACK', webglLazilyUnpackFlagSaved);
-       tf.env().set(
-           'WEBGL_PACK_BINARY_OPERATIONS', webglPackBinaryOperationsFlagSaved);
-
-       expectArraysClose(await result.data(), [7, 10]);
-       result.dispose();
-       x.dispose();
-       xInit.dispose();
-       w.dispose();
-       expect(
-           (tf.memory() as WebGLMemoryInfo).numBytesInGPU - startNumBytesInGPU)
-           .toBe(0);
-       expect(tf.memory().numBytes - startNumBytes).toBe(0);
-     });
-});
-
-describeWithFlags('conv to matmul', PACKED_ENVS, () => {
-  it('im2col should not leak memory', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    const startNumBytes = tf.memory().numBytes;
-    tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-    const endNumBytes = tf.memory().numBytes;
-
-    expect(endNumBytes - startNumBytes).toEqual(4);
-  });
-
-  it('pointwise conv should work when matmul is unpacked', () => {
-    const inputDepth =
-        1001;  // this number must be greater than MATMUL_SHARED_DIM_THRESHOLD
-               // for matmul to be unpacked
-    const inputShape: [number, number, number] = [3, 3, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 'same';
-    const stride: [number, number] = [1, 1];
-
-    let x: tf.Tensor3D = tf.randomNormal(inputShape);
-    x = x.add(1);  // this packs x so we can test the case where we mistakenly
-                   // want to avoid expensive reshape in pointwise conv2d even
-                   // though matmul is unpacked
-    const w: tf.Tensor4D =
-        tf.randomNormal([fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv2d(x, w, stride, pad)).not.toThrow();
-  });
-});
-
-// For operations on non-trivial matrix sizes, we skip the CPU-only ENV and use
-// only WebGL ENVs.
-describeWithFlags('gramSchmidt-non-tiny', WEBGL_ENVS, () => {
-  it('8x16', async () => {
-    // Part of this test's point is that operation on a matrix of this size
-    // can complete in the timeout limit of the unit test.
-    const xs: Tensor2D = tf.randomUniform([8, 16]);
-    const y = tf.linalg.gramSchmidt(xs) as Tensor2D;
-    const yTransposed: Tensor2D = y.transpose();
-    expectArraysClose(
-        await y.matMul(yTransposed).data(), await tf.eye(8).data());
-  });
-});
-
-describeWithFlags('matmul webgl-only', WEBGL_ENVS, () => {
-  it('Matrix times vector, large matrix', async () => {
-    const maxTexSize = 16000;
-    const sharedDim = maxTexSize + 4;
-    const matrix = tf.buffer<Rank.R2>([2, sharedDim], 'float32');
-    matrix.set(1, 0, sharedDim - 3);
-    matrix.set(1, 0, sharedDim - 2);
-
-    const v = tf.buffer<Rank.R1>([sharedDim], 'float32');
-    v.set(1, sharedDim - 3);
-    v.set(1, sharedDim - 2);
-
-    const result = tf.dot(matrix.toTensor(), v.toTensor());
-    const expected = [2, 0];
-    expectArraysClose(await result.data(), expected);
-  });
-});
-
-describeWithFlags('matmul', PACKED_ENVS, () => {
-  it('should not leak memory', () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
-    const b = tf.tensor2d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [3, 5]);
-
-    const startNumBytes = tf.memory().numBytes;
-    tf.matMul(a, b);
-    const endNumBytes = tf.memory().numBytes;
-
-    expect(endNumBytes - startNumBytes).toEqual(60);
-  });
-
-  it('should work when input matrix dimensions are not divisible by 2',
-     async () => {
-       const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
-       const b = tf.tensor2d(
-           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [3, 5]);
-
-       const c = tf.matMul(a, b);
-
-       expect(c.shape).toEqual([3, 5]);
-       expectArraysClose(await c.data(), [
-         46, 52, 58, 64, 70, 100, 115, 130, 145, 160, 154, 178, 202, 226, 250
-       ]);
-     });
-
-  it('should work when output texture shape != physical shape', async () => {
-    const sharedDim = 16000;
-    const a = tf.buffer<Rank.R2>([2, sharedDim], 'float32');
-    const b = tf.buffer<Rank.R2>([sharedDim, 2], 'float32');
-
-    a.set(1, 0, sharedDim - 1);
-    a.set(1, 0, sharedDim - 2);
-    a.set(1, 1, sharedDim - 1);
-    b.set(1, sharedDim - 1, 0);
-    b.set(1, sharedDim - 2, 0);
-
-    const c = tf.matMul(a.toTensor(), b.toTensor());
-    const expected = [2, 0, 1, 0];
-    expectArraysClose(await c.data(), expected);
-  });
-
-  it('should work when input texture shapes != physical shape', async () => {
-    const maxTextureSize = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', 5);
-    const a = tf.tensor2d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [1, 12]);
-    const b = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [12, 1]);
-
-    const c = tf.matMul(a, b);
-
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', maxTextureSize);
-
-    expectArraysClose(await c.data(), [572]);
-  });
-
-  it('should work when squarification results in zero padding', async () => {
-    const maxTextureSize = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', 3);
-    const a = tf.tensor2d([1, 2], [1, 2]);
-    const b = tf.tensor2d(
-        [[0, 1, 2, 3, 4, 5, 6, 7, 8], [9, 10, 11, 12, 13, 14, 15, 16, 17]]);
-
-    const c = tf.matMul(a, b);
-
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', maxTextureSize);
-
-    expectArraysClose(await c.data(), [18, 21, 24, 27, 30, 33, 36, 39, 42]);
-  });
-
-  it('A x B', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    const c = tf.matMul(a, b);
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -3, 20]);
-  });
-
-  it('A x B^t', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
-
-    const transposeA = false;
-    const transposeB = true;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-
-    const expected = [7, 10, 16, 31];
-    expectArraysClose(await c.data(), expected);
-  });
-
-  it('A^t x B', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
-
-    const transposeA = true;
-    const transposeB = false;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-
-    const expected = [17, 12, 2, 22, 15, 4, 27, 18, 6];
-    expectArraysClose(await c.data(), expected);
-  });
-
-  it('A^t x B^t', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
-
-    const transposeA = true;
-    const transposeB = true;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-
-    const expected = [11, 13, 14, 20];
-    expectArraysClose(await c.data(), expected);
-  });
-
-  it('works when followed by an op that requires unpacked inputs', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    const c = tf.matMul(a, b);
-
-    const webglPackBinarySaved =
-        tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
-    tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', false);
-    const d = tf.add(c, 1);
-    tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', webglPackBinarySaved);
-
-    expectArraysClose(await d.data(), [1, 9, -2, 21]);
-  });
-
-  // tslint:disable-next-line:max-line-length
-  it('works when followed by a packed reshape that changes texture layout, and then an unpacked op',
-     async () => {
-       const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [9, 1]);
-       const b = tf.tensor2d([1], [1, 1]);
-       const c = tf.matMul(a, b);
-
-       const d = tf.reshape(c, [1, 3, 3, 1]);
-
-       const webglPackBinarySaved =
-           tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
-       tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', false);
-       const e = tf.add(d, 1);
-       tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', webglPackBinarySaved);
-
-       expectArraysClose(await e.data(), [2, 3, 4, 5, 6, 7, 8, 9, 10]);
-     });
-
-  it('works when preceded by an op that requires packed inputs', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    const c = tf.add(a, 1);
-    const d = tf.matMul(b, c);
-
-    expectArraysClose(await d.data(), [5, 6, 7, 4, 3, 2, 9, 12, 15]);
-  });
-});
-
-describeWithFlags('Reduction: webgl packed input', WEBGL_ENVS, () => {
-  it('argmax 3D, odd number of rows, axis = -1', async () => {
-    const webglLazilyUnpackFlagSaved = tf.env().getBool('WEBGL_LAZILY_UNPACK');
-    tf.env().set('WEBGL_LAZILY_UNPACK', true);
-    const webglPackBinaryOperationsFlagSaved =
-        tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
-    tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', true);
-
-    const a = tf.tensor3d([3, 2, 5, 100, -7, 2], [2, 1, 3]).add(1);
-    const r = tf.argMax(a, -1);
-    tf.env().set('WEBGL_LAZILY_UNPACK', webglLazilyUnpackFlagSaved);
-    tf.env().set(
-        'WEBGL_PACK_BINARY_OPERATIONS', webglPackBinaryOperationsFlagSaved);
-
-    expect(r.dtype).toBe('int32');
-    expectArraysEqual(await r.data(), [2, 0]);
-  });
-
-  it('argmin 4D, odd number of rows, axis = -1', async () => {
-    const webglLazilyUnpackFlagSaved = tf.env().getBool('WEBGL_LAZILY_UNPACK');
-    tf.env().set('WEBGL_LAZILY_UNPACK', true);
-    const webglPackBinaryOperationsFlagSaved =
-        tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
-    tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', true);
-
-    const a =
-        tf.tensor4d(
-              [3, 2, 5, 100, -7, 2, 8, 7, -5, 101, 7, -2, 100, -7, 2, 8, 7, -5],
-              [1, 2, 3, 3])
-            .add(1);
-    const r = tf.argMin(a, -1);
-    tf.env().set('WEBGL_LAZILY_UNPACK', webglLazilyUnpackFlagSaved);
-    tf.env().set(
-        'WEBGL_PACK_BINARY_OPERATIONS', webglPackBinaryOperationsFlagSaved);
-
-    expect(r.dtype).toBe('int32');
-    expectArraysEqual(await r.data(), [1, 1, 2, 2, 1, 2]);
-  });
-
-  it('should not leak memory when called after unpacked op', async () => {
-    const webglPackBinaryOperationsFlagSaved =
-        tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
-    tf.env().set('WEBGL_PACK_BINARY_OPERATIONS', false);
-
-    const a =
-        tf.tensor5d(
-              [3, 2, 5, 100, -7, 2, 8, 7, -5, 101, 7, -2, 100, -7, 2, 8, 7, -5],
-              [1, 2, 3, 1, 3])
-            .add(1);
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-    const r = tf.argMin(a, -1);
-    tf.env().set(
-        'WEBGL_PACK_BINARY_OPERATIONS', webglPackBinaryOperationsFlagSaved);
-    const endNumBytes = tf.memory().numBytes;
-    const endNumTensors = tf.memory().numTensors;
-    expect(endNumBytes - startNumBytes).toEqual(24);
-    expect(endNumTensors - startNumTensors).toEqual(1);
-    expect(r.dtype).toBe('int32');
-    expectArraysEqual(await r.data(), [1, 1, 2, 2, 1, 2]);
-  });
-});
-
-describeWithFlags('slice and memory usage', WEBGL_ENVS, () => {
-  let webglUploadUniformFlagSaved: boolean;
-  let webglCpuForwardFlagSaved: boolean;
-  beforeAll(() => {
-    webglUploadUniformFlagSaved = tf.env().getBool('WEBGL_SIZE_UPLOAD_UNIFORM');
-    webglCpuForwardFlagSaved = tf.env().getBool('WEBGL_CPU_FORWARD');
-    tf.env().set('WEBGL_SIZE_UPLOAD_UNIFORM', 0);
-    tf.env().set('WEBGL_CPU_FORWARD', false);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGL_SIZE_UPLOAD_UNIFORM', webglUploadUniformFlagSaved);
-    tf.env().set('WEBGL_CPU_FORWARD', webglCpuForwardFlagSaved);
-  });
-
-  it('slice a tensor, read it and check memory', async () => {
-    const getMem = () => tf.memory() as WebGLMemoryInfo;
-    expect(getMem().numBytesInGPU).toBe(0);
-
-    // Lazy upload won't increase gpu memory.
-    const a = tf.tensor([2, 3]);
-    expect(getMem().numBytesInGPU).toBe(0);
-
-    // Upload a to the GPU by running an op.
-    a.square().dispose();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Slicing does not allocate new memory.
-    const b = a.slice(0);
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Download a to the CPU but the texture remains on GPU
-    // since b points to it.
-    await a.data();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Dispose a, but the texture should still remain on the GPU
-    // since b points to it.
-    a.dispose();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Dispose b and expect 0 memory on GPU.
-    b.dispose();
-    expect(getMem().numBytesInGPU).toBe(0);
-  });
-
-  it('slice a tensor twice, read it and check memory', async () => {
-    const getMem = () => tf.memory() as WebGLMemoryInfo;
-    expect(getMem().numBytesInGPU).toBe(0);
-
-    // Lazy upload won't increase gpu memory.
-    const a = tf.tensor([2, 3]);
-    expect(getMem().numBytesInGPU).toBe(0);
-
-    // Upload a to the GPU by running an op.
-    a.square().dispose();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Slicing does not allocate new memory.
-    const b = a.slice(0);
-    const c = a.slice(0);
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Download a to the CPU but the texture remains on GPU
-    // since b points to it.
-    await a.data();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Dispose a, but the texture should still remain on the GPU
-    // since b points to it.
-    a.dispose();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Dispose b, but the texture should still remain on the GPU
-    // since c points to it.
-    b.dispose();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Download c to the CPU but the texture remains on GPU
-    // since c points to it.
-    await c.data();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Dispose c and expect 0 memory on GPU.
-    c.dispose();
-    expect(getMem().numBytesInGPU).toBe(0);
-  });
-
-  it('slice a sliced tensor, read it and check memory', async () => {
-    const getMem = () => tf.memory() as WebGLMemoryInfo;
-    expect(getMem().numBytesInGPU).toBe(0);
-
-    // Lazy upload won't increase gpu memory.
-    const a = tf.tensor([2, 3]);
-    expect(getMem().numBytesInGPU).toBe(0);
-
-    // Upload a to the GPU by running an op.
-    a.square().dispose();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Slicing does not allocate new memory.
-    const b = a.slice(0);
-    const c = b.slice(0);
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Download a to the CPU but the texture remains on GPU
-    // since b points to it.
-    await a.data();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Dispose a, but the texture should still remain on the GPU
-    // since b points to it.
-    a.dispose();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Dispose b, but the texture should still remain on the GPU
-    // since c points to it.
-    b.dispose();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Download c to the CPU but the texture remains on GPU
-    // since c points to it.
-    await c.data();
-    expect(getMem().numBytesInGPU).toBe(8);
-
-    // Dispose c and expect 0 memory on GPU.
-    c.dispose();
-    expect(getMem().numBytesInGPU).toBe(0);
-  });
-});
-
-describeWithFlags('slice a packed texture', WEBGL_ENVS, () => {
-  beforeAll(() => {
-    tf.env().set('WEBGL_PACK', true);
-  });
-
-  it('slice after a matmul', async () => {
-    const a = [[1, 2], [3, 4]];
-    const b = [[5, 6], [7, 8]];
-    // Matmul gives a packed tensor in webgl.
-    //  [19, 22]
-    //  [43, 50]
-    const c = tf.matMul(a, b);
-    expectArraysClose(await c.slice([0, 0]).data(), [19, 22, 43, 50]);
-    expectArraysClose(await c.slice([0, 1]).data(), [22, 50]);
-    expectArraysClose(await c.slice([1, 0]).data(), [43, 50]);
-    expectArraysClose(await c.slice([1, 1]).data(), [50]);
-  });
-});
-
-describeWithFlags('pointwise conv2d packed', WEBGL_ENVS, () => {
-  beforeAll(() => {
-    tf.env().set('WEBGL_SIZE_UPLOAD_UNIFORM', 0);
-  });
-
-  it('pointwise conv2d optimization with odd input size', async () => {
-    // We do special optimization in the webl backend which avoids an expensive
-    // reshape, when the following 3 conditions are met:
-    // 1) the input width/height is odd-shaped.
-    // 2) the input is already packed.
-    // 3) the filter size is 1x1, i.e. pointwise.
-    const inChannels = 1;
-    const outChannels = 2;
-    const oddInputSize = 3;
-    const x: Tensor3D = tf.ones([oddInputSize, oddInputSize, inChannels]);
-    const xPacked = tf.relu(x);
-    const pointwiseFilter: Tensor4D = tf.ones([1, 1, inChannels, outChannels]);
-    const strides = 1;
-    const pad = 'same';
-    const c = tf.conv2d(xPacked, pointwiseFilter, strides, pad);
-    expectArraysClose(
-        await c.data(), [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]);
-  });
-});
-
-describeWithFlags('relu', WEBGL_ENVS, () => {
-  it('works with squarification for prime number length vector', async () => {
-    const maxTextureSize = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', 5);
-    const a = tf.tensor1d([1, -2, 5, -3, -1, 4, 7]);
-    const result = tf.relu(a);
-
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', maxTextureSize);
-    expectArraysClose(await result.data(), [1, 0, 5, 0, 0, 4, 7]);
-  });
-});
-
-describeWithFlags('packed clip', PACKED_ENVS, () => {
-  it('should not leak memory', () => {
-    const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
-    const min = -1;
-    const max = 50;
-
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-    tf.clipByValue(a, min, max);
-    const endNumBytes = tf.memory().numBytes;
-    const endNumTensors = tf.memory().numTensors;
-
-    expect(endNumBytes - startNumBytes).toEqual(24);
-    expect(endNumTensors - startNumTensors).toEqual(1);
-  });
-
-  it('basic', async () => {
-    const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
-    const min = -1;
-    const max = 50;
-
-    const result = tf.clipByValue(a, min, max);
-
-    expectArraysClose(await result.data(), [3, -1, 0, 50, -1, 2]);
-  });
-
-  it('using extreme values', async () => {
-    const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
-    let result =
-        tf.clipByValue(a, Number.NEGATIVE_INFINITY, Number.POSITIVE_INFINITY);
-    expectArraysClose(await result.data(), [3, -1, 0, 100, -7, 2]);
-
-    result = tf.clipByValue(a, Number.MIN_VALUE, Number.MAX_VALUE);
-    expectArraysClose(
-        await result.data(),
-        [3, Number.MIN_VALUE, Number.MIN_VALUE, 100, Number.MIN_VALUE, 2]);
-  });
-
-  it('should work for scalars', async () => {
-    const a = tf.scalar(-4);
-    const min = -1;
-    const max = 50;
-
-    const result = tf.clipByValue(a, min, max);
-
-    expectArraysClose(await result.data(), [min]);
-  });
-
-  it('derivative: 1D tensor with max or min value', async () => {
-    const min = -1;
-    const max = 2;
-    const x = tf.tensor1d([-1, 1, 2, 3]);
-    const dy = tf.tensor1d([1, 10, 100, 1000]);
-    const gradients = tf.grad(x => x.clipByValue(min, max))(x, dy);
-
-    expect(gradients.shape).toEqual(x.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [1, 10, 100, 0]);
-  });
-});
-
-describeWithFlags('depthwiseConv2d packed', PACKED_ENVS, () => {
-  it('should not leak memory', () => {
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, 1]);
-    const w = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [2, 2, 1, 1],
-    );
-
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-
-    tf.depthwiseConv2d(x, w, 1, 'valid');
-
-    const endNumBytes = tf.memory().numBytes;
-    const endNumTensors = tf.memory().numTensors;
-
-    expect(endNumBytes - startNumBytes).toEqual(16);
-    expect(endNumTensors - startNumTensors).toEqual(1);
-  });
-});
-
-describeWithFlags('gather debug', WEBGL_ENVS, () => {
-  it('throws if index is out of bound if in debug mode', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    tf.enableDebugMode();
-    expect(() => tf.gather(t, tf.tensor1d([100], 'int32')))
-        .toThrowError(/GatherV2: the index value 100 is not in \[0, 1\]/);
-    expect(() => tf.gather(t, tf.tensor1d([-1], 'int32')))
-        .toThrowError(/GatherV2: the index value -1 is not in \[0, 1\]/);
-  });
-});
-
-describeWithFlags('gatherNd', WEBGL_ENVS, () => {
-  let webglCpuForwardFlagSaved: boolean;
-  beforeAll(() => {
-    webglCpuForwardFlagSaved = tf.env().getBool('WEBGL_CPU_FORWARD');
-    tf.env().set('WEBGL_CPU_FORWARD', false);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGL_CPU_FORWARD', webglCpuForwardFlagSaved);
-  });
-  it('throws for zero-rank input', async () => {
-    const x = tf.scalar(5, 'int32');
-    const ind = tf.tensor1d([5, 2, 1], 'int32');
-    expect(() => tf.gatherND(x, ind))
-        .toThrowError(
-            /expects the input to be rank 1 or higher, but the rank was 0/);
-  });
-
-  it('works for out of bounds indices', async () => {
-    const x = tf.tensor1d([1, 2], 'int32');
-    const ind = tf.tensor2d([-1, 0, 1, 2], [4, 1], 'int32');
-    const g = tf.gatherND(x, ind);
-    expectArraysEqual(await g.data(), [0, 1, 2, 0]);
-  });
-
-  it('works for out of bounds indices 1d', async () => {
-    const x = tf.tensor1d([...Array(4).keys()].map(e => e + 1), 'int32');
-    const indices = [0, 1, 2, 5];
-    const ind = tf.tensor2d(indices, [4, 1], 'int32');
-    const g = tf.gatherND(x, ind);
-    const expected = [1, 2, 3, 0];
-    expectArraysEqual(await g.data(), expected);
-  });
-
-  it('works for out of bounds indices 2d', async () => {
-    const x =
-        tf.tensor2d([...Array(4).keys()].map(e => e + 1), [2, 2], 'int32');
-    const indices = [];
-    for (let y = -1; y !== 3; y += 1) {
-      for (let x = -1; x !== 3; x += 1) {
-        indices.push(y);
-        indices.push(x);
-      }
-    }
-    const ind = tf.tensor2d(indices, [16, 2], 'int32');
-    const g = tf.gatherND(x, ind);
-    const expected = [0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0];
-    expect(g.shape).toEqual([16]);
-    expectArraysEqual(await g.data(), expected);
-  });
-
-  it('works for out of bounds indices 3d', async () => {
-    const x =
-        tf.tensor3d([...Array(8).keys()].map(e => e + 1), [2, 2, 2], 'int32');
-    const indices = [];
-    for (let z = -1; z !== 3; z += 1) {
-      for (let y = -1; y !== 3; y += 1) {
-        for (let x = -1; x !== 3; x += 1) {
-          indices.push(z);
-          indices.push(y);
-          indices.push(x);
-        }
-      }
-    }
-    const ind = tf.tensor2d(indices, [64, 3], 'int32');
-    const g = tf.gatherND(x, ind);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
-      2, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 7, 8, 0,
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    ];
-    expectArraysEqual(await g.data(), expected);
-  });
-
-  it('works for out of bounds indices 5d', async () => {
-    const x = tf.tensor5d(
-        [...Array(32).keys()].map(e => e + 1), [2, 2, 2, 2, 2], 'int32');
-    const indices = [0, 0, 0, 0, 0, 2, 1, 1, 1, 1];
-    const ind = tf.tensor2d(indices, [2, 5], 'int32');
-    const g = tf.gatherND(x, ind);
-    const expected = [1, 0];
-    expectArraysEqual(await g.data(), expected);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/webgl_topixels_test.ts b/tfjs-master/tfjs-backend-webgl/src/webgl_topixels_test.ts
deleted file mode 100644
index e0dc2bb91..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/webgl_topixels_test.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {WebGLMemoryInfo} from './backend_webgl';
-import {WEBGL_ENVS} from './backend_webgl_test_registry';
-
-describeWithFlags('toPixels', WEBGL_ENVS, () => {
-  it('does not leak memory', async () => {
-    const x = tf.tensor2d([[.1], [.2]], [2, 1]);
-    const startNumBytesInGPU = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-    await tf.browser.toPixels(x);
-    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU)
-        .toEqual(startNumBytesInGPU);
-  });
-
-  it('does not leak memory given a tensor-like object', async () => {
-    const x = [[10], [20]];  // 2x1;
-    const startNumBytesInGPU = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
-    await tf.browser.toPixels(x);
-    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU)
-        .toEqual(startNumBytesInGPU);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/src/webgl_types.ts b/tfjs-master/tfjs-backend-webgl/src/webgl_types.ts
deleted file mode 100644
index 2e340eef9..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/webgl_types.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO(nsthorat): Move these to the webgl official typings.
-export interface WebGL2DisjointQueryTimerExtension {
-  TIME_ELAPSED_EXT: number;
-  GPU_DISJOINT_EXT: number;
-}
-
-export interface WebGL1DisjointQueryTimerExtension {
-  TIME_ELAPSED_EXT: number;
-  QUERY_RESULT_AVAILABLE_EXT: number;
-  GPU_DISJOINT_EXT: number;
-  QUERY_RESULT_EXT: number;
-  createQueryEXT: () => {};
-  beginQueryEXT: (ext: number, query: WebGLQuery) => void;
-  endQueryEXT: (ext: number) => void;
-  deleteQueryEXT: (query: WebGLQuery) => void;
-  isQueryEXT: (query: WebGLQuery) => boolean;
-  getQueryObjectEXT:
-      (query: WebGLQuery, queryResultAvailableExt: number) => number;
-}
-
-export interface WebGLContextAttributes {
-  alpha?: boolean;
-  antialias?: boolean;
-  premultipliedAlpha?: boolean;
-  preserveDrawingBuffer?: boolean;
-  depth?: boolean;
-  stencil?: boolean;
-  failIfMajorPerformanceCaveat?: boolean;
-}
-
-export interface WebGLParallelCompilationExtension {
-  COMPLETION_STATUS_KHR: number;
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/webgl_util.ts b/tfjs-master/tfjs-backend-webgl/src/webgl_util.ts
deleted file mode 100644
index b9c621585..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/webgl_util.ts
+++ /dev/null
@@ -1,723 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {getWebGLContext} from './canvas_util';
-import {getTextureConfig} from './tex_util';
-
-export function callAndCheck<T>(gl: WebGLRenderingContext, func: () => T): T {
-  const returnValue = func();
-  if (env().getBool('DEBUG')) {
-    checkWebGLError(gl);
-  }
-  return returnValue;
-}
-
-function checkWebGLError(gl: WebGLRenderingContext) {
-  const error = gl.getError();
-  if (error !== gl.NO_ERROR) {
-    throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));
-  }
-}
-
-// https://en.wikipedia.org/wiki/Half-precision_floating-point_format
-const MIN_FLOAT16 = 5.96e-8;
-const MAX_FLOAT16 = 65504;
-
-export function canBeRepresented(num: number): boolean {
-  if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||
-      (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {
-    return true;
-  }
-  return false;
-}
-
-export function getWebGLErrorMessage(
-    gl: WebGLRenderingContext, status: number): string {
-  switch (status) {
-    case gl.NO_ERROR:
-      return 'NO_ERROR';
-    case gl.INVALID_ENUM:
-      return 'INVALID_ENUM';
-    case gl.INVALID_VALUE:
-      return 'INVALID_VALUE';
-    case gl.INVALID_OPERATION:
-      return 'INVALID_OPERATION';
-    case gl.INVALID_FRAMEBUFFER_OPERATION:
-      return 'INVALID_FRAMEBUFFER_OPERATION';
-    case gl.OUT_OF_MEMORY:
-      return 'OUT_OF_MEMORY';
-    case gl.CONTEXT_LOST_WEBGL:
-      return 'CONTEXT_LOST_WEBGL';
-    default:
-      return `Unknown error code ${status}`;
-  }
-}
-
-export function getExtensionOrThrow(
-    gl: WebGLRenderingContext, extensionName: string): {} {
-  return throwIfNull<{}>(
-      gl, () => gl.getExtension(extensionName),
-      'Extension "' + extensionName + '" not supported on this browser.');
-}
-
-export function createVertexShader(
-    gl: WebGLRenderingContext, vertexShaderSource: string): WebGLShader {
-  const vertexShader: WebGLShader = throwIfNull<WebGLShader>(
-      gl, () => gl.createShader(gl.VERTEX_SHADER),
-      'Unable to create vertex WebGLShader.');
-  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));
-  callAndCheck(gl, () => gl.compileShader(vertexShader));
-  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
-    console.log(gl.getShaderInfoLog(vertexShader));
-    throw new Error('Failed to compile vertex shader.');
-  }
-  return vertexShader;
-}
-
-export function createFragmentShader(
-    gl: WebGLRenderingContext, fragmentShaderSource: string): WebGLShader {
-  const fragmentShader: WebGLShader = throwIfNull<WebGLShader>(
-      gl, () => gl.createShader(gl.FRAGMENT_SHADER),
-      'Unable to create fragment WebGLShader.');
-  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));
-  callAndCheck(gl, () => gl.compileShader(fragmentShader));
-  if (env().get('ENGINE_COMPILE_ONLY')) {
-    return fragmentShader;
-  }
-  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
-    logShaderSourceAndInfoLog(
-        fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
-    throw new Error('Failed to compile fragment shader.');
-  }
-  return fragmentShader;
-}
-
-const lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
-export function logShaderSourceAndInfoLog(
-    shaderSource: string, shaderInfoLog: string) {
-  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
-  if (lineNumberRegexResult == null) {
-    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);
-    console.log(shaderSource);
-    return;
-  }
-
-  const lineNumber = +lineNumberRegexResult[1];
-
-  const shaderLines = shaderSource.split('\n');
-  const pad = shaderLines.length.toString().length + 2;
-  const linesWithLineNumbers = shaderLines.map(
-      (line, lineNumber) =>
-          util.rightPad((lineNumber + 1).toString(), pad) + line);
-  let maxLineLength = 0;
-  for (let i = 0; i < linesWithLineNumbers.length; i++) {
-    maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
-  }
-
-  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
-  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
-  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);
-
-  console.log(beforeErrorLines.join('\n'));
-  console.log(shaderInfoLog.split('\n')[0]);
-  console.log(
-      `%c ${util.rightPad(errorLine[0], maxLineLength)}`,
-      'border:1px solid red; background-color:#e3d2d2; color:#a61717');
-  console.log(afterErrorLines.join('\n'));
-}
-
-export function createProgram(gl: WebGLRenderingContext): WebGLProgram {
-  return throwIfNull<WebGLProgram>(
-      gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');
-}
-
-export function linkProgram(gl: WebGLRenderingContext, program: WebGLProgram) {
-  callAndCheck(gl, () => gl.linkProgram(program));
-  if (env().get('ENGINE_COMPILE_ONLY')) {
-    return;
-  }
-  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
-    console.log(gl.getProgramInfoLog(program));
-    throw new Error('Failed to link vertex and fragment shaders.');
-  }
-}
-
-/// validateProgram is effectively "If we `useProgram(program); drawArrays();`,
-/// give feedback in log about perf/correctness warnings or errors that would
-/// occur."
-/// So make sure we set up all vertex/texture/sampler/uniform data before
-/// calling validateProgram!
-export function validateProgram(
-    gl: WebGLRenderingContext, program: WebGLProgram) {
-  callAndCheck(gl, () => gl.validateProgram(program));
-  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
-    console.log(gl.getProgramInfoLog(program));
-    throw new Error('Shader program validation failed.');
-  }
-}
-
-export function createStaticVertexBuffer(
-    gl: WebGLRenderingContext, data: Float32Array): WebGLBuffer {
-  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(
-      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');
-  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));
-  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));
-  return buffer;
-}
-
-export function createStaticIndexBuffer(
-    gl: WebGLRenderingContext, data: Uint16Array): WebGLBuffer {
-  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(
-      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');
-  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));
-  callAndCheck(
-      gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));
-  return buffer;
-}
-
-export function getNumChannels(): number {
-  if (env().getNumber('WEBGL_VERSION') === 2) {
-    return 1;
-  }
-  return 4;
-}
-
-export function createTexture(gl: WebGLRenderingContext): WebGLTexture {
-  return throwIfNull<WebGLTexture>(
-      gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');
-}
-
-export function validateTextureSize(width: number, height: number) {
-  const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-  if ((width <= 0) || (height <= 0)) {
-    const requested = `[${width}x${height}]`;
-    throw new Error('Requested texture size ' + requested + ' is invalid.');
-  }
-  if ((width > maxTextureSize) || (height > maxTextureSize)) {
-    const requested = `[${width}x${height}]`;
-    const max = `[${maxTextureSize}x${maxTextureSize}]`;
-    throw new Error(
-        'Requested texture size ' + requested +
-        ' greater than WebGL maximum on this browser / GPU ' + max + '.');
-  }
-}
-
-export function createFramebuffer(gl: WebGLRenderingContext): WebGLFramebuffer {
-  return throwIfNull<WebGLFramebuffer>(
-      gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');
-}
-
-export function bindVertexBufferToProgramAttribute(
-    gl: WebGLRenderingContext, program: WebGLProgram, attribute: string,
-    buffer: WebGLBuffer, arrayEntriesPerItem: number, itemStrideInBytes: number,
-    itemOffsetInBytes: number): boolean {
-  const loc = gl.getAttribLocation(program, attribute);
-  if (loc === -1) {
-    // The GPU compiler decided to strip out this attribute because it's unused,
-    // thus no need to bind.
-    return false;
-  }
-  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));
-  callAndCheck(
-      gl,
-      () => gl.vertexAttribPointer(
-          loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes,
-          itemOffsetInBytes));
-  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));
-  return true;
-}
-
-export function bindTextureUnit(
-    gl: WebGLRenderingContext, texture: WebGLTexture, textureUnit: number) {
-  validateTextureUnit(gl, textureUnit);
-  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));
-  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
-}
-
-export function unbindTextureUnit(
-    gl: WebGLRenderingContext, textureUnit: number) {
-  validateTextureUnit(gl, textureUnit);
-  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));
-  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
-}
-
-export function getProgramUniformLocationOrThrow(
-    gl: WebGLRenderingContext, program: WebGLProgram,
-    uniformName: string): WebGLUniformLocation {
-  return throwIfNull<WebGLUniformLocation>(
-      gl, () => gl.getUniformLocation(program, uniformName),
-      'uniform "' + uniformName + '" not present in program.');
-}
-
-export function getProgramUniformLocation(
-    gl: WebGLRenderingContext, program: WebGLProgram,
-    uniformName: string): WebGLUniformLocation {
-  return gl.getUniformLocation(program, uniformName);
-}
-
-export function bindTextureToProgramUniformSampler(
-    gl: WebGLRenderingContext, texture: WebGLTexture,
-    uniformSamplerLocation: WebGLUniformLocation, textureUnit: number) {
-  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));
-  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));
-}
-
-export function bindCanvasToFramebuffer(gl: WebGLRenderingContext) {
-  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));
-  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));
-  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));
-}
-
-export function bindColorTextureToFramebuffer(
-    gl: WebGLRenderingContext, texture: WebGLTexture,
-    framebuffer: WebGLFramebuffer) {
-  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
-  callAndCheck(
-      gl,
-      () => gl.framebufferTexture2D(
-          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));
-}
-
-export function unbindColorTextureFromFramebuffer(
-    gl: WebGLRenderingContext, framebuffer: WebGLFramebuffer) {
-  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
-  callAndCheck(
-      gl,
-      () => gl.framebufferTexture2D(
-          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));
-}
-
-export function validateFramebuffer(gl: WebGLRenderingContext) {
-  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
-  if (status !== gl.FRAMEBUFFER_COMPLETE) {
-    throw new Error(
-        'Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));
-  }
-}
-
-export function getFramebufferErrorMessage(
-    gl: WebGLRenderingContext, status: number): string {
-  switch (status) {
-    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
-      return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';
-    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
-      return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';
-    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
-      return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';
-    case gl.FRAMEBUFFER_UNSUPPORTED:
-      return 'FRAMEBUFFER_UNSUPPORTED';
-    default:
-      return `unknown error ${status}`;
-  }
-}
-
-function throwIfNull<T>(
-    gl: WebGLRenderingContext, returnTOrNull: () => T | null,
-    failureMessage: string): T {
-  const tOrNull: T|null = callAndCheck(gl, () => returnTOrNull());
-  if (tOrNull == null) {
-    throw new Error(failureMessage);
-  }
-  return tOrNull;
-}
-
-function validateTextureUnit(gl: WebGLRenderingContext, textureUnit: number) {
-  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
-  const glTextureUnit = textureUnit + gl.TEXTURE0;
-  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
-    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;
-    throw new Error(`textureUnit must be in ${textureUnitRange}.`);
-  }
-}
-
-export function getBatchDim(shape: number[], dimsToSkip = 2): number {
-  return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
-}
-
-export function getRowsCols(shape: number[]): [number, number] {
-  if (shape.length === 0) {
-    throw Error('Cannot get rows and columns of an empty shape array.');
-  }
-
-  return [
-    shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]
-  ];
-}
-
-export function getShapeAs3D(shape: number[]): [number, number, number] {
-  let shapeAs3D: [number, number, number] = [1, 1, 1];
-  const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);
-  if (!isScalar) {
-    shapeAs3D =
-        [getBatchDim(shape), ...getRowsCols(shape)] as [number, number, number];
-  }
-  return shapeAs3D;
-}
-
-export function getTextureShapeFromLogicalShape(
-    logShape: number[], isPacked = false): [number, number] {
-  let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-  let maxSizeForNarrowTex =
-      env().getNumber('WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE');
-  if (maxSizeForNarrowTex === Infinity &&
-      env().getBool('WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE')) {
-    maxSizeForNarrowTex = maxTexSize / 2;
-  }
-
-  if (isPacked) {
-    maxTexSize = maxTexSize * 2;
-    maxSizeForNarrowTex = maxSizeForNarrowTex * 2;
-
-    // This logic ensures we accurately count the number of packed texels needed
-    // to accommodate the tensor. We can only pack values in the same texel if
-    // they are from adjacent pairs of rows/cols within the same batch. So if a
-    // tensor has 3 rows, we pretend it has 4 rows in order to account for the
-    // fact that the texels containing the third row are half empty.
-    logShape = logShape.map(
-        (d, i) => i >= logShape.length - 2 ?
-            util.nearestLargerEven(logShape[i]) :
-            logShape[i]);
-
-    // Packed texture height is at least 2 (the channel height of a single
-    // texel).
-    if (logShape.length === 1) {
-      logShape = [2, logShape[0]];
-    }
-  }
-
-  // If logical shape is 2, we don't squeeze, since we want to match physical.
-  if (logShape.length !== 2) {
-    const squeezeResult = util.squeezeShape(logShape);
-    logShape = squeezeResult.newShape;
-  }
-
-  let size = util.sizeFromShape(logShape);
-  let textureShape: [number, number] = null;
-  if (logShape.length <= 1 && size <= maxTexSize) {
-    textureShape = [1, size];
-  } else if (
-      logShape.length === 2 && logShape[0] <= maxTexSize &&
-      logShape[1] <= maxTexSize) {
-    textureShape = logShape as [number, number];
-  } else if (
-      logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&
-      logShape[2] <= maxTexSize) {
-    textureShape = [logShape[0] * logShape[1], logShape[2]];
-  } else if (
-      logShape.length === 3 && logShape[0] <= maxTexSize &&
-      logShape[1] * logShape[2] <= maxTexSize) {
-    textureShape = [logShape[0], logShape[1] * logShape[2]];
-  } else if (
-      logShape.length === 4 &&
-      logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&
-      logShape[3] <= maxTexSize) {
-    textureShape = [logShape[0] * logShape[1] * logShape[2], logShape[3]];
-  } else if (
-      logShape.length === 4 && logShape[0] <= maxTexSize &&
-      logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {
-    textureShape = [logShape[0], logShape[1] * logShape[2] * logShape[3]];
-  }
-
-  // true if one edge length is 1 (1 or 2, if packed), while another edge
-  // length exceeds maxSizeForNarrowTex.
-  const isLongNarrowTex = textureShape != null &&
-      Math.max(...textureShape) > maxSizeForNarrowTex &&
-      Math.min(...textureShape) <= (isPacked ? 2 : 1) &&
-      Math.min(...textureShape) > 0;
-
-  if (textureShape == null || isLongNarrowTex) {
-    if (isPacked) {
-      // For packed textures size equals the number of channels required to
-      // accommodate the texture data. However in order to squarify such that
-      // inner dimensions stay even, we rewrite size to equal the number of
-      // texels. Then in the return statement we rehydrate the squarified
-      // dimensions to channel units.
-
-      const batchDim = getBatchDim(logShape);
-      let rows = 2, cols = 2;
-      if (logShape.length) {
-        [rows, cols] = getRowsCols(logShape);
-      }
-      size = batchDim * (rows / 2) * (cols / 2);
-      textureShape =
-          util.sizeToSquarishShape(size).map(d => d * 2) as [number, number];
-    } else {
-      textureShape = util.sizeToSquarishShape(size);
-    }
-  }
-
-  return textureShape;
-}
-
-function isEven(n: number): boolean {
-  return n % 2 === 0;
-}
-
-/**
- * This determines whether reshaping a packed texture requires rearranging
- * the data within the texture, assuming 2x2 packing.
- */
-export function isReshapeFree(shape1: number[], shape2: number[]): boolean {
-  shape1 = shape1.slice(-2);
-  shape2 = shape2.slice(-2);
-
-  if (util.arraysEqual(shape1, shape2)) {
-    return true;
-  }
-
-  if (!shape1.length || !shape2.length) {  // One of the shapes is a scalar.
-    return true;
-  }
-
-  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||
-      shape2[1] === 0) {
-    return true;
-  }
-
-  if (shape1.length !== shape2.length) {  // One of the shapes is a vector.
-    const shape1Cols = shape1[shape1.length - 1];
-    const shape2Cols = shape2[shape2.length - 1];
-    if (shape1Cols === shape2Cols) {
-      return true;
-    }
-
-    if (isEven(shape1Cols) && isEven(shape2Cols) &&
-        (shape1[0] === 1 || shape2[0] === 1)) {
-      return true;
-    }
-  }
-  return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);
-}
-
-// We cache webgl params because the environment gets reset between
-// unit tests and we don't want to constantly query the WebGLContext for
-// MAX_TEXTURE_SIZE.
-let MAX_TEXTURE_SIZE: number;
-let MAX_TEXTURES_IN_SHADER: number;
-
-export function getWebGLMaxTextureSize(webGLVersion: number): number {
-  if (MAX_TEXTURE_SIZE == null) {
-    const gl = getWebGLContext(webGLVersion);
-    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
-  }
-  return MAX_TEXTURE_SIZE;
-}
-
-export function resetMaxTextureSize() {
-  MAX_TEXTURE_SIZE = null;
-}
-export function resetMaxTexturesInShader() {
-  MAX_TEXTURES_IN_SHADER = null;
-}
-
-export function getMaxTexturesInShader(webGLVersion: number): number {
-  if (MAX_TEXTURES_IN_SHADER == null) {
-    const gl = getWebGLContext(webGLVersion);
-    MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);
-  }
-  // We cap at 16 to avoid spurious runtime "memory exhausted" error.
-  return Math.min(16, MAX_TEXTURES_IN_SHADER);
-}
-
-export function getWebGLDisjointQueryTimerVersion(webGLVersion: number):
-    number {
-  if (webGLVersion === 0) {
-    return 0;
-  }
-
-  let queryTimerVersion: number;
-  const gl = getWebGLContext(webGLVersion);
-
-  if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&
-      webGLVersion === 2) {
-    queryTimerVersion = 2;
-  } else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {
-    queryTimerVersion = 1;
-  } else {
-    queryTimerVersion = 0;
-  }
-  return queryTimerVersion;
-}
-
-export function hasExtension(gl: WebGLRenderingContext, extensionName: string) {
-  const ext = gl.getExtension(extensionName);
-  return ext != null;
-}
-
-export function isWebGLVersionEnabled(webGLVersion: 1|2) {
-  try {
-    const gl = getWebGLContext(webGLVersion);
-    if (gl != null) {
-      return true;
-    }
-  } catch (e) {
-    console.log('Error when getting WebGL context: ', e);
-    return false;
-  }
-  return false;
-}
-
-export function isCapableOfRenderingToFloatTexture(webGLVersion: number):
-    boolean {
-  if (webGLVersion === 0) {
-    return false;
-  }
-
-  const gl = getWebGLContext(webGLVersion);
-
-  if (webGLVersion === 1) {
-    if (!hasExtension(gl, 'OES_texture_float')) {
-      return false;
-    }
-  } else {
-    if (!hasExtension(gl, 'EXT_color_buffer_float')) {
-      return false;
-    }
-  }
-
-  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
-  return isFrameBufferComplete;
-}
-
-/**
- * Check if we can download values from a float/half-float texture.
- *
- * Note that for performance reasons we use binding a texture to a framebuffer
- * as a proxy for ability to download float values later using readPixels. The
- * texture params of this texture will not match those in readPixels exactly
- * but if we are unable to bind some kind of float texture to the frameBuffer
- * then we definitely will not be able to read float values from it.
- */
-export function isDownloadFloatTextureEnabled(webGLVersion: number): boolean {
-  if (webGLVersion === 0) {
-    return false;
-  }
-
-  const gl = getWebGLContext(webGLVersion);
-
-  if (webGLVersion === 1) {
-    if (!hasExtension(gl, 'OES_texture_float')) {
-      return false;
-    }
-    if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {
-      return false;
-    }
-  } else {
-    if (hasExtension(gl, 'EXT_color_buffer_float')) {
-      return createFloatTextureAndBindToFramebuffer(gl);
-    }
-
-    const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';
-    if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {
-      const textureHalfFloatExtension =
-          gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
-      return createHalfFloatTextureAndBindToFramebuffer(
-          gl, textureHalfFloatExtension);
-    }
-
-    return false;
-  }
-
-  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
-  return isFrameBufferComplete;
-}
-
-function createFloatTextureAndBindToFramebuffer(gl: WebGLRenderingContext):
-    boolean {
-  const texConfig = getTextureConfig(gl);
-
-  const texture = gl.createTexture();
-  gl.bindTexture(gl.TEXTURE_2D, texture);
-
-  const width = 1;
-  const height = 1;
-  gl.texImage2D(
-      gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0,
-      texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);
-
-  const frameBuffer = gl.createFramebuffer();
-  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
-  gl.framebufferTexture2D(
-      gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
-
-  const isFrameBufferComplete =
-      gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
-
-  gl.bindTexture(gl.TEXTURE_2D, null);
-  gl.bindFramebuffer(gl.FRAMEBUFFER, null);
-  gl.deleteTexture(texture);
-  gl.deleteFramebuffer(frameBuffer);
-
-  return isFrameBufferComplete;
-}
-
-function createHalfFloatTextureAndBindToFramebuffer(
-    // tslint:disable-next-line:no-any
-    gl: WebGLRenderingContext, textureHalfFloatExtension: any): boolean {
-  const texConfig = getTextureConfig(gl, textureHalfFloatExtension);
-  const texture = gl.createTexture();
-  gl.bindTexture(gl.TEXTURE_2D, texture);
-
-  const width = 1;
-  const height = 1;
-  gl.texImage2D(
-      gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0,
-      texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);
-
-  const frameBuffer = gl.createFramebuffer();
-  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
-  gl.framebufferTexture2D(
-      gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
-
-  const isFrameBufferComplete =
-      gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
-
-  gl.bindTexture(gl.TEXTURE_2D, null);
-  gl.bindFramebuffer(gl.FRAMEBUFFER, null);
-  gl.deleteTexture(texture);
-  gl.deleteFramebuffer(frameBuffer);
-
-  return isFrameBufferComplete;
-}
-
-export function isWebGLFenceEnabled(webGLVersion: number) {
-  if (webGLVersion !== 2) {
-    return false;
-  }
-  const gl = getWebGLContext(webGLVersion);
-
-  // tslint:disable-next-line:no-any
-  const isEnabled = (gl as any).fenceSync != null;
-  return isEnabled;
-}
-
-export function assertNotComplex(
-    tensor: TensorInfo|TensorInfo[], opName: string): void {
-  if (!Array.isArray(tensor)) {
-    tensor = [tensor];
-  }
-  tensor.forEach(t => {
-    if (t != null) {
-      util.assert(
-          t.dtype !== 'complex64',
-          () => `${opName} does not support complex64 tensors ` +
-              'in the WebGL backend.');
-    }
-  });
-}
diff --git a/tfjs-master/tfjs-backend-webgl/src/webgl_util_test.ts b/tfjs-master/tfjs-backend-webgl/src/webgl_util_test.ts
deleted file mode 100644
index d57d0b181..000000000
--- a/tfjs-master/tfjs-backend-webgl/src/webgl_util_test.ts
+++ /dev/null
@@ -1,214 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {WEBGL_ENVS} from './backend_webgl_test_registry';
-import * as webgl_util from './webgl_util';
-
-describeWithFlags('getTextureShapeFromLogicalShape', WEBGL_ENVS, () => {
-  it('scalar', () => {
-    const texShape = webgl_util.getTextureShapeFromLogicalShape([]);
-    expect(texShape).toEqual([1, 1]);
-  });
-
-  it('1d', () => {
-    const texShape = webgl_util.getTextureShapeFromLogicalShape([4]);
-    expect(texShape).toEqual([1, 4]);
-  });
-
-  it('2d stays same', () => {
-    let texShape = webgl_util.getTextureShapeFromLogicalShape([5, 2]);
-    expect(texShape).toEqual([5, 2]);
-
-    texShape = webgl_util.getTextureShapeFromLogicalShape([5, 1]);
-    expect(texShape).toEqual([5, 1]);
-
-    texShape = webgl_util.getTextureShapeFromLogicalShape([1, 5]);
-    expect(texShape).toEqual([1, 5]);
-  });
-
-  it('3d 2x3x4', () => {
-    const texShape = webgl_util.getTextureShapeFromLogicalShape([2, 3, 4]);
-    expect(texShape).toEqual([6, 4]);
-  });
-
-  it('3d 3x256x256', () => {
-    const texShape = webgl_util.getTextureShapeFromLogicalShape([3, 256, 256]);
-    expect(texShape).toEqual([3 * 256, 256]);
-  });
-
-  it('3d 2x1x4 got squeezed', () => {
-    const texShape = webgl_util.getTextureShapeFromLogicalShape([2, 1, 4]);
-    expect(texShape).toEqual([2, 4]);
-  });
-
-  it('3d 1x8x2 got squeezed', () => {
-    const texShape = webgl_util.getTextureShapeFromLogicalShape([1, 8, 2]);
-    expect(texShape).toEqual([8, 2]);
-  });
-
-  it('4d 2x2x256x256 got squeezed', () => {
-    const texShape =
-        webgl_util.getTextureShapeFromLogicalShape([2, 2, 256, 256]);
-    expect(texShape).toEqual([2 * 2 * 256, 256]);
-  });
-
-  it('4d 1x8x1x3 got squeezed', () => {
-    const texShape = webgl_util.getTextureShapeFromLogicalShape([1, 8, 1, 3]);
-    expect(texShape).toEqual([8, 3]);
-  });
-
-  it('4d 1x3x1x8 got squeezed', () => {
-    const texShape = webgl_util.getTextureShapeFromLogicalShape([1, 3, 1, 8]);
-    expect(texShape).toEqual([3, 8]);
-  });
-});
-
-describeWithFlags('getTextureShapeFromLogicalShape packed', WEBGL_ENVS, () => {
-  it('textures less than 2x max size of platform preserve their shapes', () => {
-    const isPacked = true;
-    const logicalShape = [
-      2,
-      util.nearestLargerEven(tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE') + 1)
-    ];
-    const texShape =
-        webgl_util.getTextureShapeFromLogicalShape(logicalShape, isPacked);
-    expect(texShape).toEqual(logicalShape);
-  });
-
-  it('rows/columns do not get squeezed', () => {
-    const isPacked = true;
-    const logicalShape = [1, 1, 1];
-    const texShape =
-        webgl_util.getTextureShapeFromLogicalShape(logicalShape, isPacked);
-    expect(texShape).toEqual([2, 2]);
-  });
-
-  it('squarified texture shapes account for packing constraints', () => {
-    const isPacked = true;
-    const max = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', 5);
-    const logicalShape = [1, 12];
-    const texShape =
-        webgl_util.getTextureShapeFromLogicalShape(logicalShape, isPacked);
-
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', max);
-    expect(texShape).toEqual([6, 4]);
-  });
-
-  it('squarified long narrow texture shapes', () => {
-    const isPacked = true;
-    const max = tf.env().getNumber('WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE');
-
-    tf.env().set('WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE', 5);
-    const logicalShape = [1, 16];
-    const texShape =
-        webgl_util.getTextureShapeFromLogicalShape(logicalShape, isPacked);
-
-    tf.env().set('WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE', max);
-    expect(texShape).toEqual([6, 6]);
-  });
-
-  it('auto squarified long narrow texture shapes', () => {
-    const isPacked = true;
-    const max = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
-    const maxForNarrowTex =
-        tf.env().getNumber('WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE');
-    const autoSquarify =
-        tf.env().getNumber('WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE');
-
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', 6);
-    tf.env().set('WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE', true);
-    tf.env().set('WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE', Infinity);
-    const logicalShape = [1, 16];
-    const texShape =
-        webgl_util.getTextureShapeFromLogicalShape(logicalShape, isPacked);
-
-    tf.env().set('WEBGL_MAX_TEXTURE_SIZE', max);
-    tf.env().set('WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE', maxForNarrowTex);
-    tf.env().set('WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE', autoSquarify);
-    expect(texShape).toEqual([6, 6]);
-  });
-});
-
-describeWithFlags('isReshapeFree', WEBGL_ENVS, () => {
-  it('is free when shapes have the same inner dimensions', () => {
-    const before = [1, 2, 3];
-    const after = [5, 2, 3];
-    expect(webgl_util.isReshapeFree(before, after)).toBe(true);
-  });
-
-  it('is free when one of the shapes is a scalar', () => {
-    const before: number[] = [];
-    const after = [1, 2, 3];
-    expect(webgl_util.isReshapeFree(before, after)).toBe(true);
-  });
-
-  it('is free when one of the dimensions equals 0', () => {
-    const before = [1, 0];
-    const after = [1, 2, 3];
-    expect(webgl_util.isReshapeFree(before, after)).toBe(true);
-  });
-
-  it('is free when one shape is a vector and the final dimensions match',
-     () => {
-       const before = [9];
-       const after = [1, 1, 9];
-       expect(webgl_util.isReshapeFree(before, after)).toBe(true);
-     });
-
-  it('is free when one shape is a vector and the other has 1 row' +
-         'in every batch and the final dimensions are even',
-     () => {
-       const before = [10];
-       const after = [5, 1, 2];
-       expect(webgl_util.isReshapeFree(before, after)).toBe(true);
-     });
-
-  it('is not free when one shape is a vector and the final dimensions' +
-         'do not match and are not even',
-     () => {
-       const before = [18];
-       const after = [2, 1, 9];
-       expect(webgl_util.isReshapeFree(before, after)).toBe(false);
-     });
-
-  it('is free if the rows are divisible by two and the columns are the same',
-     () => {
-       const before = [1, 2, 3];
-       const after = [1, 4, 3];
-       expect(webgl_util.isReshapeFree(before, after)).toBe(true);
-     });
-
-  it('is not free when the inner dimensions are different and even', () => {
-    const before = [1, 2, 4];
-    const after = [1, 8, 10];
-    expect(webgl_util.isReshapeFree(before, after)).toBe(false);
-  });
-
-  it('is not free when the inner dimensions are different and not all even',
-     () => {
-       const before = [1, 2, 3];
-       const after = [1, 3, 2];
-       expect(webgl_util.isReshapeFree(before, after)).toBe(false);
-     });
-});
diff --git a/tfjs-master/tfjs-backend-webgl/tsconfig.json b/tfjs-master/tfjs-backend-webgl/tsconfig.json
deleted file mode 100644
index ecb732e21..000000000
--- a/tfjs-master/tfjs-backend-webgl/tsconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgl/yarn.lock b/tfjs-master/tfjs-backend-webgl/yarn.lock
deleted file mode 100644
index 2968534de..000000000
--- a/tfjs-master/tfjs-backend-webgl/yarn.lock
+++ /dev/null
@@ -1,50 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@babel/polyfill@^7.8.7":
-  version "7.12.1"
-  resolved "https://registry.npmjs.org/@babel/polyfill/-/polyfill-7.12.1.tgz#1f2d6371d1261bbd961f3c5d5909150e12d0bd96"
-  integrity sha512-X0pi0V6gxLi6lFZpGmeNa4zxtwEmCs42isWLNjZZDE0Y8yVfgu0T2OAHlzBbdYlqbW/YXVvoBHpATEM+goCj8g==
-  dependencies:
-    core-js "^2.6.5"
-    regenerator-runtime "^0.13.4"
-
-"@bazel/bazelisk@^1.12.0":
-  version "1.12.0"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.12.0.tgz#f08aebbf4afcb12684422450b0845dd6ef5cfe50"
-  integrity sha512-7oQusq1e4AIyFgotxVV7Pc40Et0QyvoVjujL+7/qV5Vrbfh0Nj3CfqSgl63weEyI4r0+K6RlGVsjfRuBi05p5w==
-
-"@tensorflow/tfjs-backend-cpu@link:../link-package/node_modules/@tensorflow/tfjs-backend-cpu":
-  version "0.0.0"
-  uid ""
-
-"@types/offscreencanvas@~2019.3.0":
-  version "2019.3.0"
-  resolved "https://registry.npmjs.org/@types/offscreencanvas/-/offscreencanvas-2019.3.0.tgz#3336428ec7e9180cf4566dfea5da04eb586a6553"
-  integrity sha512-esIJx9bQg+QYF0ra8GnvfianIY8qWB0GBx54PK5Eps6m+xTj86KLavHv6qDhzKcu5UUOgNfJ2pWaIIV7TRUd9Q==
-
-"@types/seedrandom@^2.4.28":
-  version "2.4.30"
-  resolved "https://registry.yarnpkg.com/@types/seedrandom/-/seedrandom-2.4.30.tgz#d2efe425869b84163c2d56e779dddadb9372cbfa"
-  integrity sha512-AnxLHewubLVzoF/A4qdxBGHCKifw8cY32iro3DQX9TPcetE95zBeVt3jnsvtvAUf1vwzMfwzp4t/L2yqPlnjkQ==
-
-"@types/webgl-ext@0.0.30":
-  version "0.0.30"
-  resolved "https://registry.npmjs.org/@types/webgl-ext/-/webgl-ext-0.0.30.tgz#0ce498c16a41a23d15289e0b844d945b25f0fb9d"
-  integrity sha512-LKVgNmBxN0BbljJrVUwkxwRYqzsAEPcZOe6S2T6ZaBDIrFp0qu4FNlpc5sM1tGbXUYFgdVQIoeLk1Y1UoblyEg==
-
-core-js@^2.6.5:
-  version "2.6.12"
-  resolved "https://registry.npmjs.org/core-js/-/core-js-2.6.12.tgz#d9333dfa7b065e347cc5682219d6f690859cc2ec"
-  integrity sha512-Kb2wC0fvsWfQrgk8HU5lW6U/Lcs8+9aaYcy4ZFc6DDlo4nZ7n70dEgE5rtR0oG6ufKDUnrwfWL1mXR5ljDatrQ==
-
-regenerator-runtime@^0.13.4:
-  version "0.13.7"
-  resolved "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.13.7.tgz#cac2dacc8a1ea675feaabaeb8ae833898ae46f55"
-  integrity sha512-a54FxoJDIr27pgf7IgeQGxmqUNYrcV338lf/6gH456HZ/PhX+5BcwHXG9ajESmwe6WRO0tAzRUrRmNONWgkrew==
-
-seedrandom@^3.0.5:
-  version "3.0.5"
-  resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-3.0.5.tgz#54edc85c95222525b0c7a6f6b3543d8e0b3aa0a7"
-  integrity sha512-8OwmbklUNzwezjGInmZ+2clQmExQPvomqjL7LFqOYqtmuxRgQYqOD3mHaU+MvZn5FLUeVxVfQjwLZW/n/JFuqg==
diff --git a/tfjs-master/tfjs-backend-webgpu/.npmignore b/tfjs-master/tfjs-backend-webgpu/.npmignore
deleted file mode 100644
index 6292b5fdb..000000000
--- a/tfjs-master/tfjs-backend-webgpu/.npmignore
+++ /dev/null
@@ -1,27 +0,0 @@
-.babelrc
-.DS_Store
-.idea/
-.rpt2_cache
-.travis.yml
-.vscode
-*.tgz
-*.txt
-**.yalc
-**yalc.lock
-cloudbuild.yml
-coverage/
-demo/
-dist/**/*_test.d.ts
-dist/**/*_test.js
-karma.conf.js
-node_modules/
-npm-debug.log
-package-lock.json
-package/
-rollup.config.js
-scripts/
-src/**/*_test.ts
-tsconfig.json
-tslint.json
-yarn-error.log
-yarn.lock
diff --git a/tfjs-master/tfjs-backend-webgpu/.npmrc b/tfjs-master/tfjs-backend-webgpu/.npmrc
deleted file mode 100644
index 43c97e719..000000000
--- a/tfjs-master/tfjs-backend-webgpu/.npmrc
+++ /dev/null
@@ -1 +0,0 @@
-package-lock=false
diff --git a/tfjs-master/tfjs-backend-webgpu/.vscode/settings.json b/tfjs-master/tfjs-backend-webgpu/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-backend-webgpu/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-backend-webgpu/BUILD.bazel b/tfjs-master/tfjs-backend-webgpu/BUILD.bazel
deleted file mode 100644
index b38b970e8..000000000
--- a/tfjs-master/tfjs-backend-webgpu/BUILD.bazel
+++ /dev/null
@@ -1,118 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//rules:copy_file.bzl", "copy_file")
-load("@build_bazel_rules_nodejs//:index.bzl", "pkg_npm")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-tfjs_bundle(
-    name = "tf-backend-webgpu",
-    entry_point = "//tfjs-backend-webgpu/src:index.ts",
-    external = [
-        "@tensorflow/tfjs-core",
-        "@webgpu/types",
-        "node-fetch",
-        "util",
-    ],
-    globals = {
-        "@tensorflow/tfjs-core": "tf",
-    },
-    umd_name = "tf",
-    deps = [
-        "//tfjs-backend-webgpu/src:tfjs-backend-webgpu_lib",
-        "//tfjs-backend-webgpu/src:tfjs-backend-webgpu_src_lib",
-    ],
-)
-
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-backend-webgpu/src:tfjs-backend-webgpu_lib",
-        "//tfjs-backend-webgpu/src:tfjs-backend-webgpu_src_lib",
-    ],
-    root = "src",
-)
-
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-backend-webgpu",
-        ":tf-backend-webgpu.es2017",
-        ":tf-backend-webgpu.es2017.min",
-        ":tf-backend-webgpu.fesm",
-        ":tf-backend-webgpu.fesm.min",
-        ":tf-backend-webgpu.min",
-        ":tf-backend-webgpu.node",
-    ],
-)
-
-copy_file(
-    name = "copy_miniprogram",
-    src = ":tf-backend-webgpu.min.js",
-    out = "dist/miniprogram/index.js",
-)
-
-copy_file(
-    name = "copy_miniprogram_map",
-    src = ":tf-backend-webgpu.min.js.map",
-    out = "dist/miniprogram/index.js.map",
-)
-
-pkg_npm(
-    name = "tfjs-backend-webgpu_pkg",
-    package_name = "@tensorflow/tfjs-backend-webgpu",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_miniprogram",
-        ":copy_miniprogram_map",
-        ":copy_src_to_dist",
-    ],
-)
-
-STATIC_FILES = [
-    # Listed here so sourcemaps are served
-    "//tfjs-backend-webgpu/src:tfjs-backend-webgpu_test_bundle",
-]
-
-tfjs_web_test(
-    name = "tfjs-backend-webgpu_test",
-    srcs = [
-        "//tfjs-backend-webgpu/src:tfjs-backend-webgpu_test_bundle",
-    ],
-    args = [
-        "--testEnv",
-        "webgpu",
-        "--flags",
-        '{"WEBGPU_CPU_FORWARD": false}',
-    ],
-    browsers = [
-        "bs_chrome_mac_webgpu",
-    ],
-    local_browser = select({
-        "@bazel_tools//src/conditions:linux_x86_64": "chrome_webgpu_linux",
-        "@bazel_tools//src/conditions:windows": "chrome_webgpu",
-        "//conditions:default": "chrome_webgpu",
-    }),
-    static_files = STATIC_FILES,
-)
diff --git a/tfjs-master/tfjs-backend-webgpu/README.md b/tfjs-master/tfjs-backend-webgpu/README.md
deleted file mode 100644
index 7f3975072..000000000
--- a/tfjs-master/tfjs-backend-webgpu/README.md
+++ /dev/null
@@ -1,87 +0,0 @@
-# Usage
-
-This package adds a GPU accelerated [WebGPU](https://www.w3.org/TR/webgpu/)
-backend to TensorFlow.js. It currently supports
-the following models:
-- BlazeFace
-- BodyPix
-- Face landmarks detection
-- HandPose
-- MobileNet
-- PoseDetection
-- Universal sentence encoder
-- AutoML Image classification
-- AutoML Object detection
-- Speech commands
-
-Google Chrome started to support WebGPU by default in M113 on May 2, 2023.
-
-
-## Importing the backend
-
-### Via NPM
-
-```js
-// Import @tensorflow/tfjs or @tensorflow/tfjs-core
-import * as tf from '@tensorflow/tfjs';
-// Add the WebGPU backend to the global backend registry.
-import '@tensorflow/tfjs-backend-webgpu';
-// Set the backend to WebGPU and wait for the module to be ready.
-tf.setBackend('webgpu').then(() => main());
-```
-
-### Via a script tag
-
-```html
-<!-- Import @tensorflow/tfjs or @tensorflow/tfjs-core -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
-
-<!-- Add the WebGPU backend to the global backend registry -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgpu/dist/tf-backend-webgpu.js"></script>
-<script>
-// Set the backend to WebGPU and wait for the module to be ready
-tf.setBackend('webgpu').then(() => main());
-</script>
-```
-
-# FAQ
-
-### When should I use the WebGPU backend?
-The mission of WebGPU backend is to achieve the best performance among all
-approaches. However, this target can not be met overnight, but we are committed
-to supporting it with rapid and continuous performance improvement. Many
-exciting features, like FP16, DP4A, will be brought in soon.
-
-### How many ops have you implemented?
-See [`register_all_kernels.ts`](https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-webgpu/src/register_all_kernels.ts)
-for an up-to-date list of supported ops. We love contributions. See the
-[contributing](https://github.com/tensorflow/tfjs/blob/master/CONTRIBUTING.md#adding-functionality)
-document for more info.
-
-### Do you support training?
-Maybe. There are still a decent number of ops that we are missing in WebGPU that
-are needed for gradient computation. At this point we are focused on making
-inference as fast as possible.
-
-### Do you work in node?
-Yes. If you run into issues, please let us know.
-
-### How do I give feedback?
-We'd love your feedback as we develop this backend! Please file an issue
-[here](https://github.com/tensorflow/tfjs/issues/new).
-
-# Development
-
-## Building
-
-```sh
-yarn build
-```
-
-## Testing
-Currently the Canary channel of Chrome is used for testing of the WebGPU
-backend:
-
-```sh
-yarn test  # --test_env=CHROME_CANARY_BIN=/path/to/chrome
-```
diff --git a/tfjs-master/tfjs-backend-webgpu/benchmarks/config.js b/tfjs-master/tfjs-backend-webgpu/benchmarks/config.js
deleted file mode 100644
index 64063ff58..000000000
--- a/tfjs-master/tfjs-backend-webgpu/benchmarks/config.js
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const SWATCHES = {
-  'webgpu_min': '#F1523E',
-  'webgpu_mean': '#F1523E',
-  'webgl_min': '#3f51b5',
-  'webgl_mean': '#3f51b5'
-};
-
-const STROKES = {
-  'webgpu_min': '2',
-  'webgpu_mean': '0',
-  'webgl_min': '2',
-  'webgl_mean': '0'
-};
-
-const TARGETS = ['canary'];
-const MOMENT_DISPLAY_FORMAT = 'MM/DD/YYYY';
-const MAX_NUM_LOGS = 50;
-const START_LOGGING_DATE = '2019-08-16';
-const CHART_HEIGHT = 200;
diff --git a/tfjs-master/tfjs-backend-webgpu/benchmarks/index.html b/tfjs-master/tfjs-backend-webgpu/benchmarks/index.html
deleted file mode 100644
index 711bb0767..000000000
--- a/tfjs-master/tfjs-backend-webgpu/benchmarks/index.html
+++ /dev/null
@@ -1,70 +0,0 @@
-<!-- Copyright 2019 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-============================================================================== -->
-
-<!DOCTYPE html>
-<html>
-
-<head>
-  <meta charset="utf-8">
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/moment.js/2.24.0/moment.min.js"></script>
-  <script src="https://code.getmdl.io/1.3.0/material.min.js"></script>
-  <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
-  <link rel="stylesheet" href="https://code.getmdl.io/1.3.0/material.indigo-pink.min.css">
-  <link href="./main.css" rel="stylesheet">
-  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-</head>
-
-<body>
-  <div id="container">
-    <h2>Tensorflow.js / WebGPU Benchmarks</h2>
-    <div class="time-selection">
-      <div class="time-selection-label">
-        <div class="description">Showing benchmarks for:</div>
-        <div class="start-date"></div>
-        <div class="description">to</div>
-        <div class="end-date"></div>
-      </div>
-      <div class="time-selection-edit-button">Edit</div>
-    </div>
-    <div class="mdl-tabs mdl-js-tabs mdl-js-ripple-effect">
-      <div class="mdl-tabs__tab-bar">
-      </div>
-    </div>
-  </div>
-  <div class="time-selection-modal">
-    <div class="modal-backdrop"></div>
-    <div class="modal-contents">
-      <div class="edit-time-wrapper">
-        <div class="instructions"></div>
-        <div class="range">
-          <div>Start:</div>
-          <input class="editable-start-date" placeholder="format: MM/DD/YYYY" type="text"></input>
-          <div>End:</div>
-          <input class="editable-end-date" placeholder="format: MM/DD/YYYY" type="text"></input>
-        </div>
-      </div>
-      <div class="controls">
-        <div class="modal-cancel-button">Cancel</div>
-        <div class="modal-submit-button">Submit</div>
-      </div>
-    </div>
-  </div>
-
-  <script src="./config.js"></script>
-  <script src="./util.js"></script>
-  <script src="./main.js"></script>
-</body>
-
-</html>
diff --git a/tfjs-master/tfjs-backend-webgpu/benchmarks/main.css b/tfjs-master/tfjs-backend-webgpu/benchmarks/main.css
deleted file mode 100644
index 8ef52beef..000000000
--- a/tfjs-master/tfjs-backend-webgpu/benchmarks/main.css
+++ /dev/null
@@ -1,235 +0,0 @@
-/* Copyright 2019 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-============================================================================== */
-
-#container {
-  margin-left: auto;
-  margin-right: auto;
-  max-width: 960px;
-}
-
-.color {
-  display: inline-block;
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-.detail-panel {
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-
-.graph-container {
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-.y-min {
-  font-size: 11px;
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-  background: #fafafa;
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diff --git a/tfjs-master/tfjs-backend-webgpu/benchmarks/main.js b/tfjs-master/tfjs-backend-webgpu/benchmarks/main.js
deleted file mode 100644
index 4478f2503..000000000
--- a/tfjs-master/tfjs-backend-webgpu/benchmarks/main.js
+++ /dev/null
@@ -1,193 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Create handles to DOM elements.
-const container = document.querySelector('#container');
-const tabsContainer = document.querySelector('.mdl-tabs__tab-bar');
-const tabs = document.querySelector('.mdl-tabs');
-const timeSelectionInstructions =
-    document.querySelector('.edit-time-wrapper .instructions');
-const editTimeButton = document.querySelector('.time-selection-edit-button');
-const cancelEditTimeButton = document.querySelector('.modal-cancel-button');
-const submitEditTimeButton = document.querySelector('.modal-submit-button');
-const startDateEl = document.querySelector('.start-date');
-const endDateEl = document.querySelector('.end-date');
-const startDateInput = document.querySelector('.editable-start-date');
-const endDateInput = document.querySelector('.editable-end-date');
-const modalBackdrop = document.querySelector('.modal-backdrop');
-
-const CHART_WIDTH = container.offsetWidth;
-const state = {
-  'activeTarget': 0,
-  'activeTest': 0
-};
-
-let startDate = moment(START_LOGGING_DATE, 'YYYY-MM-DD'),
-  endDate = moment(),
-  graphOffsetLeft = 0,
-  data = [];
-
-function resize() {
-  graphOffsetLeft = document.querySelector('.graph-container').offsetLeft;
-};
-window.addEventListener('resize', resize);
-
-function templateBenchmarksForTimePeriod(start, end) {
-  const logFiles = getLogFiles(start, end);
-  const files = logFiles['results'];
-
-  clearDisplay();
-
-  getDataForFiles(files).then(allResponses => {
-    const responses = [], dateFormats = [];
-    for (let i = 0; i < allResponses.length; i++) {
-      if (allResponses[i] != null) {
-        responses.push(allResponses[i]);
-        dateFormats.push(logFiles['formatted'][i]);
-      }
-    }
-
-    const processedResponses = [];
-    for (let i = 0; i < responses.length; i++) {
-      const response = responses[i];
-      const processedResponse = [];
-
-      for (let idx = 0; idx < response.length; idx++) {
-        const {name, backend, min, mean} = response[idx];
-        let testIndex = processedResponse.map(d => d.name).indexOf(name);
-
-        if (testIndex === -1) {
-          processedResponse.push({name: name, params: []});
-          testIndex = processedResponse.length - 1;
-        }
-
-        processedResponse[testIndex].params.push(
-            {name: `${backend}_min`, ms: min});
-        processedResponse[testIndex].params.push(
-            {name: `${backend}_mean`, ms: mean});
-      }
-      processedResponses.push(processedResponse);
-    }
-
-    data = TARGETS.map(name => ({name, tests: []}));
-    const targetIndex = 0;  // Hard coded - Canary is the only target for now.
-
-    // populate data
-    for (let i = 0; i < processedResponses.length; i++) {
-      const response = processedResponses[i];
-
-      for (let idx = 0; idx < response.length; idx++) {
-        const {name, params} = response[idx];
-        let testIndex = data[targetIndex].tests.map(d => d.name).indexOf(name);
-
-        if (testIndex === -1) {
-          data[targetIndex].tests.push({name: name, entries: []});
-          testIndex = data[targetIndex].tests.length - 1;
-        }
-
-        const timestamp = dateFormats[i];
-        data[targetIndex].tests[testIndex].entries.push({timestamp, params});
-      }
-    }
-
-    data.forEach((target, i) => {
-      const tab = getOrCreateTab(target.name);
-      const panel = getOrCreatePanel(`${name}-panel`);
-      if (i === 0) {
-        tab.classList.add('is-active');
-        panel.classList.add('is-active');
-      }
-
-      target.tests = target.tests.filter(test => test.entries.length > 1)
-                         .sort((a, b) => a.name.localeCompare(b.name));
-
-      target.tests.forEach((test, i) => {
-        const params = {};
-        test.entries.forEach(entry => {
-          entry.params.forEach(({name, ms}) => {
-            if (params[name] == null) {
-              params[name] = [];
-            }
-
-            params[name].push({ms})
-          });
-        });
-
-        const msArray = flatten(test.entries.map(d => d.params.map(p => p.ms)));
-        const max = Math.max(...msArray);
-        const increment = getIncrementForWidth(
-            CHART_WIDTH, test.entries.length, 20 /* minimum increment width */);
-        const xIncrement = CHART_WIDTH / (test.entries.length - 1);
-        panel.innerHTML +=
-            getTrendlinesHTML(test, params, max, increment, xIncrement, i);
-      });
-
-      tabsContainer.appendChild(tab);
-      tabs.appendChild(panel);
-
-      resize();
-    });
-  });
-}
-
-document.addEventListener('mousemove', e => {
-  if (e.target.classList.contains('graph')) {
-    state.activeTest = +e.target.getAttribute('data-index');
-
-    const entries =
-        data[state.activeTarget].tests[state.activeTest].entries;
-    const left = e.clientX - graphOffsetLeft;
-    const entryIndex = Math.max(
-        0,
-        Math.min(
-            entries.length - 1,
-            Math.floor((left / CHART_WIDTH) * entries.length)));
-
-    const parentNode = e.target.parentNode;
-    parentNode.querySelector('.detail-panel').style.left = left + 'px';
-    parentNode.querySelector('.detail-panel .contents').innerHTML =
-      `${entries[entryIndex].params.map(d =>
-        `<div class='label-wrapper'>
-          <div class='color'
-            style='background:
-              ${getSwatchBackground(SWATCHES[d.name], STROKES[d.name])}'>
-          </div>
-          <div class='label'>${d.ms}</div>
-        </div>`).join(' ')}`;
-  }
-});
-
-timeSelectionInstructions.innerHTML = `Enter dates in the format <span>${
-    MOMENT_DISPLAY_FORMAT}</span>, within the time range <span>${
-    startDate.format(MOMENT_DISPLAY_FORMAT)}</span> to <span>${
-    endDate.format(MOMENT_DISPLAY_FORMAT)}</span>.`;
-
-editTimeButton.addEventListener('click', () => openModal(startDate, endDate));
-
-cancelEditTimeButton.addEventListener('click', closeModal);
-submitEditTimeButton.addEventListener('click', () => {
-  closeModal();
-  startDate = moment(startDateInput.value, MOMENT_DISPLAY_FORMAT);
-  endDate = moment(endDateInput.value, MOMENT_DISPLAY_FORMAT);
-
-  templateTimeSelection(startDate, endDate);
-  templateBenchmarksForTimePeriod(startDate, endDate);
-});
-modalBackdrop.addEventListener('click', closeModal);
-
-templateTimeSelection(startDate, endDate);
-templateBenchmarksForTimePeriod(startDate, endDate);
diff --git a/tfjs-master/tfjs-backend-webgpu/benchmarks/util.js b/tfjs-master/tfjs-backend-webgpu/benchmarks/util.js
deleted file mode 100644
index 0fab0aba7..000000000
--- a/tfjs-master/tfjs-backend-webgpu/benchmarks/util.js
+++ /dev/null
@@ -1,156 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-function getSwatchBackground(swatch, stroke) {
-  let background = swatch;
-  if (stroke > 0) {
-    background = `repeating-linear-gradient(
-      to right,
-      ${swatch},
-      ${swatch} 2px,
-      white 2px,
-      white 4px
-    );`;
-  }
-  return background;
-}
-
-function getLogFiles(start, end) {
-  const daysElapsed = end.diff(start, 'd');
-  const results = [];
-  const formatted = [];
-  let interval = 1;
-  while (daysElapsed / interval > MAX_NUM_LOGS) {
-    interval += 1;
-  }
-
-  for (let i = 0; i <= daysElapsed; i += interval) {
-    const current = endDate.clone().subtract(i, 'days');
-    results.unshift(`${current.format('MM_DD_YYYY')}`);
-    formatted.unshift(current.format('M/DD'));
-  }
-
-  return {results, formatted};
-}
-
-async function getDataForFiles(files) {
-  return Promise.all(files.map(
-      d =>
-          fetch(
-              `https://storage.googleapis.com/learnjs-data/webgpu_benchmark_logs/${
-                  d}.json`)
-              .then(d => d.json())
-              .catch(err => console.log(err))));
-}
-
-function templateTimeSelection(start, end) {
-  startDateEl.innerHTML = start.format(MOMENT_DISPLAY_FORMAT);
-  endDateEl.innerHTML = end.format(MOMENT_DISPLAY_FORMAT);
-}
-
-function closeModal() {
-  container.classList.remove('show-modal');
-}
-
-function openModal(start, end) {
-  container.classList.add('show-modal');
-  startDateInput.value = start.format(MOMENT_DISPLAY_FORMAT);
-  endDateInput.value = end.format(MOMENT_DISPLAY_FORMAT);
-}
-
-function clearDisplay() {
-  tabsContainer.innerHTML = '';
-  // remove all panels
-  [].slice.call(document.querySelectorAll('.mdl-tabs__panel')).forEach(el => {
-    el.parentNode.removeChild(el);
-  });
-}
-
-function getOrCreateTab(name) {
-  let tab = document.querySelector(`[href='#${name}']`);
-  if (tab == null) {
-    tab = document.createElement('a');
-    tab.setAttribute('href', '#' + name);
-    tab.textContent = name;
-    tab.classList.add('mdl-tabs__tab');
-  }
-  return tab;
-}
-
-function getOrCreatePanel(id) {
-  let panel = document.querySelector(id);
-  if (panel == null) {
-    panel = document.createElement('div');
-    panel.classList.add('mdl-tabs__panel');
-    panel.id = id;
-  }
-  return panel;
-}
-
-function flatten(arr) {
-  return arr.reduce((acc, curr) => acc.concat(curr), []);
-}
-
-function getIncrementForWidth(width, length, minWidth) {
-  let increment = 1;
-  while ((width / ((length - 1) / increment)) < minWidth) {
-    increment *= 2;
-  }
-  return increment;
-}
-
-function getTrendlinesHTML(test, params, max, increment, xIncrement, i) {
-  return `<div class='test'>
-    <h4 class='test-name'>${test.name}</h4>
-    <div class='legend'>${Object.keys(params).map(param => {
-      const backgroundColor =
-          getSwatchBackground(SWATCHES[param], STROKES[param]);
-      return `<div class='swatch'>
-        <div class='color' style='background: ${backgroundColor}'></div>
-        <div class='label'>${param}</div>
-      </div>`;}).join(' ')}</div>
-    <div class='graph-container'>
-      <div style='height:${CHART_HEIGHT}px' class='y-axis-labels'>
-        <div class='y-max'>${max}ms</div>
-        <div class='y-min'>0ms</div>
-      </div>
-      <svg data-index=${i} class='graph'
-        width='${CHART_WIDTH}' height='${CHART_HEIGHT}'>
-        ${Object.keys(params).map((param) =>
-          `<path stroke-dasharray='${STROKES[param]}'
-              stroke='${SWATCHES[param]}'
-              d='M${params[param].map((d, i) =>
-                `${i * xIncrement}, ${CHART_HEIGHT * (1 - (d.ms / max))}`)
-                    .join('L')}'></path>`)}
-      </svg>
-      <div class='x-axis-labels'>
-        ${test.entries.map((d, i) => {
-          if (i % increment === 0) {
-            const left = (i / increment) *
-              (CHART_WIDTH / ((test.entries.length - 1) / increment));
-            return `<div class='x-label' style='left:${left}px'>
-                ${d.timestamp}</div>`;
-          }
-          return '';
-        }).join(' ')}</div>
-      <div class='detail-panel'>
-        <div class='line'></div>
-        <div class='contents'></div>
-      </div>
-    </div>
-  </div>`;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/karma.conf.js b/tfjs-master/tfjs-backend-webgpu/karma.conf.js
deleted file mode 100644
index 5f781b6ba..000000000
--- a/tfjs-master/tfjs-backend-webgpu/karma.conf.js
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const karmaTypescriptConfig = {
-  tsconfig: 'tsconfig.test.json',
-  // Disable coverage reports and instrumentation by default for tests
-  coverageOptions: {instrumentation: false},
-  reports: {},
-  bundlerOptions: {
-    transforms: [require('karma-typescript-es6-transform')({
-      presets: [
-        // ensure we get es5 by adding IE 11 as a target
-        ['@babel/env', {'targets': {'ie': '11'}, 'loose': true}]
-      ]
-    })],
-    // worker_node_test in tfjs-core contains a conditional require statement
-    // that confuses the bundler of karma-typescript.
-    ignore: ['./worker_node_test']
-  }
-};
-
-const devConfig = {
-  frameworks: ['jasmine', 'karma-typescript'],
-  files: [
-    {pattern: './node_modules/@babel/polyfill/dist/polyfill.js'},
-    'src/setup_test.ts',
-    {pattern: 'src/**/*.ts'},
-  ],
-  preprocessors: {'src/**/*.ts': ['karma-typescript']},
-  karmaTypescriptConfig,
-};
-
-module.exports = function(config) {
-  const args = [];
-  if (config.grep) {
-    args.push('--grep', config.grep);
-  }
-  if (config.flags) {
-    args.push('--flags', config.flags);
-  }
-  let exclude = [];
-  if (config.excludeTest != null) {
-    exclude.push(config.excludeTest);
-  }
-
-  config.set({
-    ...devConfig,
-    reporters: ['dots', 'karma-typescript'],
-    plugins: [
-      require('karma-chrome-launcher'),
-      require('karma-typescript'),
-      require('karma-jasmine'),
-      require('karma-jasmine-html-reporter'),
-    ],
-    exclude,
-    port: 9200,
-    colors: true,
-    autoWatch: false,
-    browsers: ['Chrome', 'chrome_webgpu'],
-    singleRun: true,
-    customLaunchers: {
-      chrome_webgpu: {
-        base: 'ChromeCanary',
-        flags: ['--disable-dawn-features=disallow_unsafe_apis'],
-      }
-    },
-    client: {jasmine: {random: false}, args: args}
-  })
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/package.json b/tfjs-master/tfjs-backend-webgpu/package.json
deleted file mode 100644
index 06c7ec90a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/package.json
+++ /dev/null
@@ -1,48 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-backend-webgpu",
-  "version": "0.0.1-alpha.4",
-  "main": "dist/tf-backend-webgpu.node.js",
-  "types": "dist/index.d.ts",
-  "jsnext:main": "dist/index.js",
-  "module": "dist/index.js",
-  "unpkg": "dist/tf-backend-webgpu.min.js",
-  "jsdelivr": "dist/tf-backend-webgpu.min.js",
-  "miniprogram": "dist/miniprogram",
-  "scripts": {
-    "benchmark": "yarn test --//:grep=benchmark",
-    "build-ci": "yarn build",
-    "build": "yarn --cwd .. bazel build tfjs-backend-webgpu:tfjs-backend-webgpu_pkg",
-    "bundle": "yarn build",
-    "bundle-ci": "yarn bundle",
-    "build-npm": "yarn build",
-    "publish-npm": "yarn --cwd .. bazel run tfjs-backend-webgpu:tfjs-backend-webgpu_pkg.publish",
-    "test": "yarn --cwd .. bazel test tfjs-backend-webgpu:tfjs-backend-webgpu_test --test_output=streamed",
-    "test-dev": "yarn --cwd .. bazel run tfjs-backend-webgpu:tfjs-backend-webgpu_test --test_output=streamed"
-  },
-  "license": "Apache-2.0",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-backend-webgpu"
-  },
-  "devDependencies": {
-    "@babel/polyfill": "^7.8.7",
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core",
-    "jasmine": "link:../node_modules/jasmine",
-    "jasmine-core": "link:../node_modules/jasmine-core",
-    "karma": "~6.4.0",
-    "karma-browserstack-launcher": "~1.6.0",
-    "karma-chrome-launcher": "~3.1.1",
-    "karma-commonjs": "^1.0.0",
-    "karma-jasmine": "~5.1.0",
-    "karma-typescript": "~5.5.3",
-    "karma-jasmine-html-reporter": "^2.0.0",
-    "karma-typescript-es6-transform": "^5.0.2"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs-backend-cpu": "link:../link-package/node_modules/@tensorflow/tfjs-backend-cpu"
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core"
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/perf/tune.html b/tfjs-master/tfjs-backend-webgpu/perf/tune.html
deleted file mode 100644
index 0b4c55057..000000000
--- a/tfjs-master/tfjs-backend-webgpu/perf/tune.html
+++ /dev/null
@@ -1,42 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <meta http-equiv="X-UA-Compatible" content="ie=edge">
-    <title>Document</title>
-    <style>
-      th {
-          cursor:pointer
-      }
-      tr:hover td {
-          background:#c2c2c5;
-          color:#151fb1;
-          border-top: 1px solid #22262e;
-      }
-      div.rerun {
-          display:inline-block;
-          color:#444;
-          border:1px solid #CCC;
-          background:#DDD;
-          box-shadow: 0 0 5px -1px rgba(0,0,0,0.2);
-          cursor:pointer;
-          vertical-align:middle;
-          max-width: 100px;
-          padding: 5px;
-          text-align: center;
-      }
-      div.rerun:active {
-          color:red;
-          box-shadow: 0 0 5px -1px rgba(0,0,0,0.6);
-      }
-    </style>
-</head>
-<body>
-    <script src="../../dist/bin/tfjs-core/tf-core.js"></script>
-    <script src="../../dist/bin/tfjs-backend-webgl/tf-backend-webgl.js"></script>
-    <script src="../../dist/bin/tfjs-backend-webgpu/tf-backend-webgpu.js"></script>
-    <script src="./tune.js"></script>
-    </script>
-</body>
-</html>
diff --git a/tfjs-master/tfjs-backend-webgpu/perf/tune.js b/tfjs-master/tfjs-backend-webgpu/perf/tune.js
deleted file mode 100644
index 8d691190f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/perf/tune.js
+++ /dev/null
@@ -1,458 +0,0 @@
-window.addEventListener("DOMContentLoaded", initPage);
-// append row to the HTML table
-function appendRow(result) {
-  let tbl = document.getElementById('my-table'), // table reference
-    rowNum = tbl.rows.length - 1,
-    row = tbl.insertRow(tbl.rows.length);      // append table row
-  // insert table cells to the new row
-  // 'Kernel name', 'Input', 'WebGPU', 'WebGL', 'WebGLComp', 'WebGPUProgram', 'WebGLProgram', 'Scale',
-  createCell(row.insertCell(0), result.name, result.name, `${result.name}-${rowNum}`);
-  createCell(row.insertCell(1), result.input, result.input, `input-${rowNum}`);
-  createCell(row.insertCell(2), result.webgpu, 'webgpu', `webgpu-${rowNum}`);
-  createCell(row.insertCell(3), result.webgl, 'webgl', `webgl-${rowNum}`);
-  createCell(row.insertCell(4), result.webglComp, 'webglComp', `webglComp-${rowNum}`);
-  createCell(row.insertCell(5), result.webgpuProgram, 'webgpuProgram', `webgpuProgram-${rowNum}`);
-  createCell(row.insertCell(6), result.webglProgram, 'webglProgram', `webglProgram-${rowNum}`);
-  createCell(row.insertCell(7), result.scale, `scale-${result.scale}`, `scale-${rowNum}`);
-  createCell(row.insertCell(8), 'Rerun', 'rerun', `rerun-${rowNum}`, rerun);
-}
-
-// Update result table by rerun
-function updateRow(result, rowNum) {
-  document.getElementById(`webgpu-${rowNum}`).innerHTML = result.webgpu;
-  document.getElementById(`webgl-${rowNum}`).innerHTML = result.webgl;
-  document.getElementById(`webglComp-${rowNum}`).innerHTML = result.webglComp;
-}
-
-// create DIV element and append to the table cell
-function createCell(cell, text, classes, id, onclick) {
-  let div = document.createElement('div'), // create DIV element
-    txt = document.createTextNode(text); // create text node
-  div.appendChild(txt);                    // append text node to the DIV
-  div.setAttribute('class', classes);        // set DIV class attribute
-  div.setAttribute('id', id);
-  div.setAttribute('value', text);
-  if (onclick) {
-    div.setAttribute('type', 'button');
-    let rowNum = id.split('-')[1];
-    div.onclick = function () { onclick(rowNum) };
-  }
-  cell.appendChild(div);                   // append DIV to the table cell
-}
-
-function initPage() {
-  // get the reference for the body
-  const mybody = document.getElementsByTagName("body")[0];
-  mybody.innerHTML = '';
-  const myMessage = document.createElement("p");
-  myMessage.id = 'message';
-  mybody.appendChild(myMessage);
-
-  // creates INFO labels
-  for (let info of INFO) {
-    const labelDiv = document.createElement('div');
-    const label = document.createElement('label');
-    label.innerHTML = `${info}`;
-    labelDiv.appendChild(label);
-    mybody.appendChild(labelDiv);
-  }
-
-  // creates Scales labels and checkbox
-  for (let item of [SCALES]) {
-    let backendDiv = document.createElement('div');
-    let labelClass = document.createElement("label");
-    labelClass.innerHTML = 'Scales';
-    backendDiv.appendChild(labelClass);
-    for (let i of item) {
-      let checkbox = document.createElement('input');
-      checkbox.type = 'checkbox';
-      if (currentScale.includes(i)) {
-        checkbox.checked = true;
-      }
-      checkbox.name = `scale-${i}`;
-      checkbox.value = i;
-      checkbox.className = `scaleCheckBox`;
-      checkbox.addEventListener('change', (event) => hideOrPresent(event));
-      let label = document.createElement("label");
-      label.innerHTML = i;
-      backendDiv.appendChild(checkbox);
-      backendDiv.appendChild(label);
-    }
-    mybody.appendChild(backendDiv);
-  }
-
-  // creates run button
-  let btn = document.createElement("button");
-  btn.id = 'run';
-  btn.innerHTML = "Run";
-  btn.style.background = 'orange';
-  btn.onclick = function () {
-    run();
-  };
-  document.body.appendChild(btn);
-
-  // creates <table> and <tbody> elements
-  const mytable = document.createElement("table");
-  mytable.id = 'my-table';
-  mytablebody = document.createElement("tbody");
-
-  // creates a <tr> element
-  mycurrent_row = document.createElement("tr");
-  mycurrent_row.style = 'background-color:#BDB76B;color:#ffffff;';
-  // creating all cells
-  ['Kernel name', 'Input', 'WebGPU', 'WebGL', 'WebGLComp %', 'WebGPUProgram', 'WebGLProgram', 'Scale(m*k*n)'].forEach((i) => {
-    // creates a <td> element
-    mycurrent_cell = document.createElement("th");
-    // creates a Text Node
-    currenttext = document.createTextNode(i);
-    // appends the Text Node we created into the cell <td>
-    mycurrent_cell.appendChild(currenttext);
-    // appends the cell <td> into the row <tr>
-    mycurrent_row.appendChild(mycurrent_cell);
-  });
-  // appends the row <tr> into <tbody>
-  mytablebody.appendChild(mycurrent_row);
-
-  // appends <tbody> into <table>
-  mytable.appendChild(mytablebody);
-  // appends <table> into <body>
-  mybody.appendChild(mytable);
-  // sets the border attribute of mytable to 2;
-  mytable.setAttribute("border", "2");
-
-  // Add sorting for tr
-  document.querySelectorAll('th').forEach(th => th.addEventListener('click', (() => {
-    const table = th.closest('table');
-    Array.from(table.querySelectorAll('tr:nth-child(n+2)'))
-      .sort(comparer(Array.from(th.parentNode.children).indexOf(th), this.asc = !this.asc))
-      .forEach(tr => table.appendChild(tr));
-  })));
-
-  run();
-}
-
-async function timeMatmul(backend, rowNum) {
-  await tf.setBackend(backend);
-  let tensors = [];
-  let tensorsWarmUp = [];
-  let tensorsToDispose = [];
-  let inputs = [];
-
-  // Prepare data
-  const warmA = tf.tensor2d(
-    Array.from({ length: warmUpSize * warmUpSize }, () => Math.floor(Math.random())),
-    [warmUpSize, warmUpSize]
-  );
-  const warmB = tf.tensor2d(
-    Array.from({ length: warmUpSize * warmUpSize }, () => Math.floor(Math.random())),
-    [warmUpSize, warmUpSize]
-  );
-  tensorsWarmUp = { tensorA: warmA, tensorB: warmB };
-  tensorsToDispose.push(warmA);
-  tensorsToDispose.push(warmB);
-  if (rowNum !== undefined) {
-    let input = document.getElementById(`input-${rowNum}`).getAttribute('value');
-    let m = input.split('[')[1].split(',')[0];
-    let k = input.split('[')[2].split(']')[0].split(',')[0];
-    let n = input.split('[')[2].split(']')[0].split(',')[1];
-    inputs = [`${m},${k},${n}`];
-  }
-  else {
-    inputs = INPUTS;
-  }
-  for (let i = 0; i < inputs.length; i++) {
-    let dimAOuter = parseInt(inputs[i].split(',')[0]);
-    let dimInner = parseInt(inputs[i].split(',')[1]);
-    let dimBOuter = parseInt(inputs[i].split(',')[2]);
-    const tensorA = tf.tensor2d(
-      Array.from({ length: dimAOuter * dimInner }, () => Math.floor(Math.random())),
-      [dimAOuter, dimInner]
-    );
-    const tensorB = tf.tensor2d(
-      Array.from({ length: dimInner * dimBOuter }, () => Math.floor(Math.random())),
-      [dimInner, dimBOuter]
-    );
-    tensors.push({ tensorA, tensorB });
-    tensorsToDispose.push(tensorA);
-    tensorsToDispose.push(tensorB);
-  }
-
-  tf.env().set('CHECK_COMPUTATION_FOR_ERRORS', false);
-  // Warmup, first run of each matmul shapes
-  for (let i = 0; i < inputs.length; i++) {
-    let result = tf.matMul(tensors[i].tensorA, tensors[i].tensorB);
-    await result.data();
-    result.dispose();
-  }
-  const profile_data = await tf.profile(() => {
-    // Warmup gpu and keep gpu frequency at a high level
-    document.getElementById('message').innerHTML =
-      `Warming up testing on ${backend} ...`;
-    for (let i = 0; i < numWarmUp; i++) {
-      let m = tf.matMul(tensorsWarmUp.tensorA, tensorsWarmUp.tensorB);
-      m.dispose();
-    }
-    // Collect result from here
-    for (let i = 0; i < inputs.length; i++) {
-      document.getElementById('message').innerHTML =
-        `Testing on ${backend} ...`;
-      for (let j = 0; j < numAvg; j++) {
-        const result = tf.matMul(tensors[i].tensorA, tensors[i].tensorB);
-        // Insert large shape between each test to keep gpu high frequency
-        const m = tf.matMul(tensorsWarmUp.tensorA, tensorsWarmUp.tensorB);
-        result.dispose();
-        m.dispose();
-      }
-    }
-  });
-  const profile_kernels = profile_data.kernels;
-
-  for (let tensor of tensorsToDispose) {
-    tensor.dispose();
-  }
-  return profile_kernels;
-}
-
-function drawTable(webgpu_kernels, webgl_kernels, rowNum) {
-  for (let i = numWarmUp; i < webgpu_kernels.length; i += numAvg * 2) {
-    let inputInfo;
-    webgpu_kernels[i].inputShapes.forEach((inputShape, index) => {
-      if (inputInfo == null) {
-        inputInfo = '';
-      } else {
-        inputInfo += '\n';
-      }
-      if (inputShape == null) {
-        inputInfo += `input${index}: null`;
-      } else {
-        inputInfo += `input${index}: ${inputShape.length}D[${inputShape}]`;
-      }
-    });
-
-    let mkn = webgpu_kernels[i].inputShapes[0][0] * webgpu_kernels[i].inputShapes[0][1] * webgpu_kernels[i].inputShapes[1][1];
-
-    const avgWebgpu = getAvgKernelTime(webgpu_kernels.slice(i, i + 2 * numAvg));
-    const avgWebgl = getAvgKernelTime(webgl_kernels.slice(i, i + 2 * numAvg));
-
-    const result = {};
-    result.name = webgpu_kernels[i].name;
-    result.input = `${inputInfo}`;
-    result.webgpu = `${parseFloat(avgWebgpu).toFixed(2)}`; // WebGPU
-    result.webgl = `${parseFloat(avgWebgl).toFixed(2)}`; // WebGL
-    result.webglComp = `${parseFloat((result.webgl / result.webgpu) * 100).toFixed(0)}`; // WebGLComp
-    result.scale = `${parseInt(mkn)}`;  // Scale
-    result.webgpuProgram = `${webgpu_kernels[i].extraInfo.split(',').map(x => x.split(':')[0])}`;  // WebGPUProgram
-    result.webglProgram = `${webgl_kernels[i].extraInfo.split(',').map(x => x.split(':')[0])}`;  // WebGLProgram
-
-    if (rowNum !== undefined) {
-      updateRow(result, rowNum);
-      // update gpu result
-      webglCompResults[rowNum] = result.webglComp;
-    } else {
-      appendRow(result);
-      // store gpu result
-      webglCompResults.push(result.webglComp);
-    }
-  }
-}
-
-function updateTable(avgWebgpu, avgWebgl, rowNum) {
-  const webglComp = `${parseFloat((avgWebgl / avgWebgpu) * 100).toFixed(0)}`;
-  const result = {};
-  result.webgpu = `${parseFloat(avgWebgpu).toFixed(2)}`;
-  result.webgl = `${parseFloat(avgWebgl).toFixed(2)}`;
-  result.webglComp = webglComp;
-  updateRow(result, rowNum);
-  webglCompResults[rowNum] = webglComp;
-
-}
-
-function updateColor() {
-  for (let i = 0; i <= webglCompResults.length - 1; i++) {
-    const webglCompValue = webglCompResults[i];
-    let r, g, b;
-    if (webglCompValue < 100) {
-      r = 255;
-      g = Math.floor(255 - 255 * ((100 - webglCompValue)) / 100);
-      b = Math.floor(255 - 255 * ((100 - webglCompValue)) / 100);
-    } else {
-      g = 255;
-      r = Math.floor(255 - 255 * ((webglCompValue - 100)) / 100);
-      b = Math.floor(255 - 255 * ((webglCompValue - 100)) / 100);
-    }
-    document.getElementById(`webglComp-${i}`).style = `background-color: rgb(${r}, ${g}, ${b})`;
-  }
-}
-
-function hideOrPresent(event) {
-  const value = event.target.name;
-  const nodes = document.getElementsByClassName(value);
-  for (let i = 0; i <= nodes.length - 1; i++) {
-    if (event.target.checked) {
-      nodes[i].parentNode.parentNode.style = ``;
-    } else {
-      nodes[i].parentNode.parentNode.style = `display:none`;
-    }
-  }
-}
-
-async function run() {
-  document.getElementById('run').disabled = true;
-  // remove results
-  let tableHeaderRowCount = 1;
-  let table = document.getElementById('my-table');
-  let rowCount = table.rows.length;
-  for (let i = rowCount; i > tableHeaderRowCount; i--) {
-    table.deleteRow(i - 1);
-  }
-  const scaleSelected = [];
-
-  // clear compared result
-  webglCompResults = [];
-
-  // define scale suite
-  let scales = document.querySelectorAll('.scaleCheckBox:checked');
-  for (let s of scales) {
-    scaleSelected.push(s.value);
-  }
-
-  INPUTS = defaultInputs;
-  for (let scale of scaleSelected) {
-    let mySet = getTestSet(scale);
-    INPUTS = INPUTS.concat([...mySet]);
-  }
-  const profile_webgl = await timeMatmul('webgl');
-  const profile_webgpu = await timeMatmul('webgpu');
-  drawTable(profile_webgpu, profile_webgl);
-  document.getElementById('message').innerHTML = 'Done!';
-  updateColor();
-  document.getElementById('run').disabled = false;
-}
-
-async function rerun(rowNum) {
-  const profile_webgl = await timeMatmul('webgl', rowNum);
-  const profile_webgpu = await timeMatmul('webgpu', rowNum);
-  const avgWebgl = getAvgKernelTime(profile_webgl.slice(-numAvg * 2));
-  const avgWebgpu = getAvgKernelTime(profile_webgpu.slice(-numAvg * 2));
-  updateTable(avgWebgpu, avgWebgl, rowNum);
-  document.getElementById('message').innerHTML = 'Done!';
-  updateColor();
-}
-
-const getCellValue = (tr, idx) => tr.children[idx].innerText || tr.children[idx].textContent;
-
-const comparer = (idx, asc) => (a, b) => ((v1, v2) =>
-  v1 !== '' && v2 !== '' && !isNaN(v1) && !isNaN(v2) ? v1 - v2 : v1.toString().localeCompare(v2)
-)(getCellValue(asc ? a : b, idx), getCellValue(asc ? b : a, idx));
-
-function getFactors(c) {
-  const y = [];
-  let min = 1;
-  let max = c;
-  while (min < max && min * min <= c) {
-    if (c % min === 0) {
-      y.push(min, c / min);
-      max = c / min;
-    }
-    min++;
-  }
-  return y;
-}
-
-function getTestSet(num) {
-  const factors = getFactors(num);
-  const mySet = new Set();
-  for (let i = 0; i < factors.length; i++) {
-    for (let j = 0; j < factors.length; j++) {
-      if (factors[i] * factors[j] < num && num % (factors[i] * factors[j]) === 0) {
-        let m = factors[i];
-        let n = factors[j];
-        let k = num / factors[i] / factors[j];
-        if (m > 15 && n > 15 && k > 15
-          && n < 2048 && k < 2048
-          && Math.sqrt(m) % 1 === 0) {
-          mySet.add(`${m},${k},${n}`);
-        }
-      }
-    }
-  }
-  return mySet;
-}
-
-function getPrimeFactor(num) {
-  const prime_factor = [];
-  for (let i = 2; i < num; i++) {
-    if (num % i == 0) {
-      prime_factor.push(i);
-      num /= i;
-      i -= 1;
-    }
-  }
-  prime_factor.push(num);
-  return prime_factor;
-}
-
-function getAvgKernelTime(kernels) {
-  const avg = kernels.reduce(
-    (a, b, index) => {
-      if (index % 2 === 0) {
-        return a + b.kernelTimeMs;
-      }
-      return a;
-    },
-    0,
-  ) / numAvg;
-  return avg;
-}
-
-let webglCompResults = [];
-
-let SCALES = [1016064, 5013504, 10838016, 33554432];
-let currentScale = [];
-let defaultInputs = [
-  '1, 1280, 1001',
-  '12544, 16, 64',
-  '196, 672, 112',
-  '1, 960, 1280',
-  '196, 112, 672',
-  '196, 480, 112',
-  '49, 960, 160',
-  '784, 40, 240',
-  '49, 672, 160',
-  '3136, 24, 72',
-  '3136, 72, 24',
-  '49, 160, 960',
-  '196, 80, 480',
-  '784, 120, 40',
-  '784, 40, 120',
-  '3136, 64, 24',
-  '784, 72, 40',
-  '1, 240, 960',
-  '196, 80, 200',
-  '1, 960, 240',
-  '196, 240, 80',
-  '12544, 16, 16',
-  '196, 200, 80',
-  '196, 80, 184',
-  '196, 184, 80',
-  '1, 672, 168',
-  '1, 168, 672',
-  '1, 480, 120',
-  '1, 32, 120',
-  '1, 120, 480',
-  '1, 120, 32',
-  '1, 24, 72',
-  '1, 72, 24',
-];
-
-let INPUTS = [];
-const numWarmUp = 1000;
-const numAvg = 20;
-const warmUpSize = 512;
-const INFO = [
-  '0. Run under flag: --enable-unsafe-webgpu --disable-dawn-features=disallow_unsafe_apis',
-  '1. Sortable by clicking table column title',
-  '2. Rerunable for every single line',
-  '3. Set checkBox below to define new test suite',
-  '4. Default workloads are used for MobileNetV3',
-];
diff --git a/tfjs-master/tfjs-backend-webgpu/rollup.config.js b/tfjs-master/tfjs-backend-webgpu/rollup.config.js
deleted file mode 100644
index 35ba82bba..000000000
--- a/tfjs-master/tfjs-backend-webgpu/rollup.config.js
+++ /dev/null
@@ -1,121 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import commonjs from '@rollup/plugin-commonjs';
-import resolve from '@rollup/plugin-node-resolve';
-import typescript from '@rollup/plugin-typescript';
-import visualizer from 'rollup-plugin-visualizer';
-import {getBrowserBundleConfigOptions} from '../rollup.config.helpers';
-
-const PREAMBLE = `/**
- * @license
- * Copyright ${(new Date).getFullYear()} Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */`;
-
-function config({
-  plugins = [],
-  output = {},
-  external = [],
-  visualize = false,
-  tsCompilerOptions = {}
-}) {
-  if (visualize) {
-    const filename = output.file + '.html';
-    plugins.push(visualizer(
-        {sourcemap: true, filename, template: 'sunburst', gzipSize: true}));
-    console.log(`Will output a bundle visualization in ${filename}`);
-  }
-
-  const defaultTsOptions = {
-    include: ['src/**/*.ts'],
-    module: 'ES2015',
-  };
-  const tsoptions = Object.assign({}, defaultTsOptions, tsCompilerOptions);
-
-  return {
-    input: 'src/index.ts',
-    plugins: [
-      typescript(tsoptions), resolve(),
-      // Polyfill require() from dependencies.
-      commonjs({
-        ignore: ['crypto'],
-        include: 'node_modules/**',
-      }),
-      ...plugins
-    ],
-    output: {
-      banner: PREAMBLE,
-      sourcemap: true,
-      globals: {'@tensorflow/tfjs-core': 'tf'},
-      ...output,
-    },
-    external: ['@tensorflow/tfjs-core'],
-    onwarn: warning => {
-      let {code} = warning;
-      if (code === 'CIRCULAR_DEPENDENCY' || code === 'CIRCULAR' ||
-          code === 'THIS_IS_UNDEFINED') {
-        return;
-      }
-      console.warn('WARNING: ', warning.toString());
-    }
-  };
-}
-
-module.exports = cmdOptions => {
-  const bundles = [];
-  const name = 'tf';
-  const extend = true;
-  const fileName = 'tf-backend-webgpu';
-
-  // Node
-  bundles.push(config({
-    output: {
-      format: 'cjs',
-      name,
-      extend,
-      file: `dist/${fileName}.node.js`,
-      freeze: false
-    },
-    tsCompilerOptions: {target: 'es5'}
-  }));
-
-  if (cmdOptions.ci) {
-    const browserBundles = getBrowserBundleConfigOptions(
-        config, name, fileName, PREAMBLE, cmdOptions.visualize, true /* CI */);
-    bundles.push(...browserBundles);
-  }
-
-  if (cmdOptions.npm) {
-    const browserBundles = getBrowserBundleConfigOptions(
-        config, name, fileName, PREAMBLE, cmdOptions.visualize, false /* CI */);
-    bundles.push(...browserBundles);
-  }
-
-  return bundles;
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/scripts/build-npm.sh b/tfjs-master/tfjs-backend-webgpu/scripts/build-npm.sh
deleted file mode 100644
index 0b772cdd9..000000000
--- a/tfjs-master/tfjs-backend-webgpu/scripts/build-npm.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn rimraf dist/
-yarn
-
-yarn build
-yarn rollup -c --visualize --npm
-
-# Use minified files for miniprogram
-mkdir dist/miniprogram
-cp dist/tf-backend-webgpu.min.js dist/miniprogram/index.js
-cp dist/tf-backend-webgpu.min.js.map dist/miniprogram/index.js.map
-
-echo "Stored standalone library at dist/tf-backend-webgpu(.min).js"
diff --git a/tfjs-master/tfjs-backend-webgpu/scripts/publish-npm.sh b/tfjs-master/tfjs-backend-webgpu/scripts/publish-npm.sh
deleted file mode 100644
index 4c3edf8d4..000000000
--- a/tfjs-master/tfjs-backend-webgpu/scripts/publish-npm.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-yarn build
-rollup -c
-npm publish --tag beta
diff --git a/tfjs-master/tfjs-backend-webgpu/src/BUILD.bazel b/tfjs-master/tfjs-backend-webgpu/src/BUILD.bazel
deleted file mode 100644
index 4d3da28cd..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/BUILD.bazel
+++ /dev/null
@@ -1,98 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("//tools:defaults.bzl", "esbuild", "ts_library")
-load("//tools:enumerate_tests.bzl", "enumerate_tests")
-
-package(default_visibility = ["//visibility:public"])
-
-TEST_SRCS = [
-    "**/*_test.ts",
-]
-
-filegroup(
-    name = "all_test_entrypoints",
-    srcs = glob(
-        ["**/*_test.ts"],
-        exclude = [
-            "setup_test.ts",
-        ],
-    ),
-)
-
-# Generates the 'tests.ts' file that imports all test entrypoints.
-enumerate_tests(
-    name = "tests",
-    srcs = [":all_test_entrypoints"],
-    root_path = "tfjs-backend-webgpu/src",
-)
-
-ts_library(
-    name = "tfjs-backend-webgpu_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-backend-webgpu/dist",
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "@npm//@types/offscreencanvas",
-        "@npm//@webgpu/types",
-    ],
-)
-
-ts_library(
-    name = "tfjs-backend-webgpu_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-backend-webgpu",
-    deps = [
-        ":tfjs-backend-webgpu_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-    ],
-)
-
-ts_library(
-    name = "tfjs-backend-webgpu_test_lib",
-    # testonly = True,
-    srcs = glob(TEST_SRCS) + [":tests"],
-    module_name = "@tensorflow/tfjs-backend-webgpu/dist",
-    deps = [
-        ":tfjs-backend-webgpu_lib",
-        ":tfjs-backend-webgpu_src_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-        "@npm//@webgpu/types",
-    ],
-)
-
-esbuild(
-    name = "tfjs-backend-webgpu_test_bundle",
-    testonly = True,
-    entry_point = "setup_test.ts",
-    external = [
-        "util",
-    ],
-    sources_content = True,
-    deps = [
-        ":tfjs-backend-webgpu_lib",
-        ":tfjs-backend-webgpu_test_lib",
-    ],
-)
diff --git a/tfjs-master/tfjs-backend-webgpu/src/activation_util.ts b/tfjs-master/tfjs-backend-webgpu/src/activation_util.ts
deleted file mode 100644
index bda3917a2..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/activation_util.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType, getBinaryOpString} from './binary_op_util';
-import {getUnaryOpString, UnaryOpType} from './unary_op_util';
-import {typeSnippet} from './webgpu_program';
-
-export function activationFnSnippet(
-    activation: backend_util.Activation, hasPreluActivationWeights = false,
-    packed = false, coordsLength = 3): string {
-  if (activation === null) {
-    return '';
-  }
-
-  let activationOpSnippet = '';
-  if (activation === 'linear') {
-    activationOpSnippet = getUnaryOpString(UnaryOpType.LINEAR);
-  } else if (activation === 'relu') {
-    activationOpSnippet = getUnaryOpString(UnaryOpType.RELU, packed);
-  } else if (activation === 'elu') {
-    activationOpSnippet = getUnaryOpString(UnaryOpType.ELU, packed);
-  } else if (activation === 'relu6') {
-    activationOpSnippet = getUnaryOpString(UnaryOpType.RELU6, packed);
-  } else if (activation === 'prelu') {
-    activationOpSnippet = getBinaryOpString(BinaryOpType.PRELU, packed);
-  } else if (activation === 'sigmoid') {
-    activationOpSnippet = getUnaryOpString(UnaryOpType.SIGMOID, packed);
-  } else if (activation === 'leakyrelu') {
-    activationOpSnippet = getUnaryOpString(UnaryOpType.LEAKYRELU, packed);
-  } else {
-    throw new Error(`Activation ${
-        activation} has not been implemented for the WebGPU backend.`);
-  }
-  const elementSize = packed ? 4 : 1;
-  const dataType = typeSnippet(elementSize);
-  let activationFnSnippet = '';
-  if (hasPreluActivationWeights) {
-    activationFnSnippet = `
-      fn activation(a : ${dataType}, coords : vec${coordsLength}<i32>) -> ${
-        dataType} {
-        let b = getPreluActivationWeightsByOutputCoords(coords);
-        ${activationOpSnippet}
-      }`;
-  } else {
-    activationFnSnippet = `
-      fn activation(a : ${dataType}, coords : vec${coordsLength}<i32>) -> ${
-        dataType} {
-        ${activationOpSnippet}
-      }`;
-  }
-  return activationFnSnippet;
-}
-
-export function biasActivationSnippet(
-    hasBias: boolean, activation: backend_util.Activation): string {
-  return `
-      ${hasBias ? 'value = value + getBiasByOutputCoords(coords);' : ''}
-      ${activation ? 'value = activation(value, coords);' : ''}
-      `;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/adapter_info.ts b/tfjs-master/tfjs-backend-webgpu/src/adapter_info.ts
deleted file mode 100644
index fa124b57c..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/adapter_info.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export class AdapterInfo {
-  private vendor: string;
-  private architecture: string;
-  public intelGPUGeneration: number;
-
-  constructor(adapterInfo: GPUAdapterInfo) {
-    if (adapterInfo) {
-      this.vendor = adapterInfo.vendor;
-      this.architecture = adapterInfo.architecture;
-      this.intelGPUGeneration = this.getIntelGPUGeneration();
-    }
-  }
-
-  private getIntelGPUGeneration() {
-    if (this.isIntel()) {
-      if (this.architecture.startsWith('gen')) {
-        return Number(this.architecture.match(/\d+/));
-      } else if (this.architecture.startsWith('xe')) {
-        return 12;
-      }
-    }
-    return 0;
-  }
-
-  isIntel(): boolean {
-    return this.vendor === 'intel';
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/addn_packed_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/addn_packed_webgpu.ts
deleted file mode 100644
index 30494fffb..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/addn_packed_webgpu.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class AddNPackedProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames: string[];
-  workPerThread = 1;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(shapes: number[][]) {
-    this.outputShape = shapes[0];
-    this.variableNames = shapes.map((_, i) => `T${i}`);
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        [this.workPerThread, 1, 1]);
-    this.shaderKey = 'addN';
-  }
-
-  getUserCode(): string {
-    const snippets: string[] = [];
-    // Get target elements from every input tensor.
-    this.variableNames.forEach(variable => {
-      snippets.push(`let v${variable} = get${variable}ByOutputCoords(coords);`);
-    });
-    // Calculate the sum of all elements.
-    const operation = this.variableNames
-                          .map(variable => {
-                            return `v${variable}`;
-                          })
-                          .join(' + ');
-
-    const userCode = `
-      ${main('index')} {
-        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
-          let flatIndex = index * ${this.workPerThread} + i;
-          if (flatIndex < uniforms.size) {
-            let coords = getCoordsFromIndex(flatIndex);
-            ${snippets.join('\n        ')}
-            setOutputAtIndex(flatIndex, ${operation});
-          }
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/argminmax_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/argminmax_webgpu.ts
deleted file mode 100644
index f70598ecd..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/argminmax_webgpu.ts
+++ /dev/null
@@ -1,167 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, util} from '@tensorflow/tfjs-core';
-import {getCoordsXYZ, getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ArgMinMaxProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  variableNames = ['x'];
-  uniforms = 'infinityValue : f32,';
-  inputShape: number[];
-  reductionFactor: number;
-  op: string;
-  size = true;
-  private type: string;
-
-  constructor(inputShape: number[], axis: number, reduceType: 'min'|'max') {
-    const axes = [axis];
-
-    this.op = reduceType === 'min' ? '<' : '>';
-
-    // |outShape| is the shape with the removed axis
-    const [outputShape, reduceShape] =
-        backend_util.computeOutAndReduceShapes(inputShape, axes);
-
-    this.outputShape = outputShape.length === 0 ? [1] : outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    // The shared algorithm is mainly used for large reduce size. It fully
-    // utilizes the threads in one workgroup to do the reduction. However,
-    // when the reduce size is very small, it's better to use the plain
-    // algorithm to reduce the number of workgroups to speedup. The threthold
-    // can be further tuned.
-    if (util.sizeFromShape(reduceShape) < 32) {
-      this.type = 'plain';
-      this.dispatch = computeDispatch(
-          this.dispatchLayout, this.outputShape, this.workgroupSize);
-    } else {
-      this.type = 'shared';
-      // A work group only outputs a data, so we transfer [1, 1, 1] to compute
-      // dispatch size.
-      this.dispatch =
-          computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);
-    }
-
-    this.inputShape = inputShape;
-    this.shaderKey = `argMinMax_${this.op}_${this.type}`;
-  }
-
-  getUserCode(): string {
-    const workgroupSizeX = this.workgroupSize[0];
-    const getInputShapeLastDim = () => {
-      if (this.inputShape.length === 1) {
-        return 'uniforms.xShape';
-      } else {
-        return `uniforms.xShape.${getCoordsXYZ(this.inputShape.length - 1)}`;
-      }
-    };
-
-    const splitOutputCoords = () => {
-      let snippet = '';
-      if (this.outputShape.length === 1) {
-        if (this.inputShape.length !== 1) {
-          snippet += 'outputCoords,';
-        }
-      } else {
-        for (let i = 0; i < this.outputShape.length; i++) {
-          snippet += `outputCoords.${getCoordsXYZ(i)},`;
-        }
-      }
-      return snippet;
-    };
-
-    if (this.type === 'shared') {
-      const sharedMemorySnippet = `
-      var<workgroup> xBestIndices : array<i32, ${workgroupSizeX}>;
-      var<workgroup> xBestValues : array<f32, ${workgroupSizeX}>;
-    `;
-      const userCode = `
-      fn DIV_CEIL(a : u32, b : u32) -> u32 {
-        return ((a - 1u) / b + 1u);
-      }
-
-      ${sharedMemorySnippet}
-
-      ${main('index')} {
-        let outputIndex = index / ${workgroupSizeX};
-        let reduceLength = ${getInputShapeLastDim()};
-
-        var bestIndex = i32(localId.x);
-        var bestValue = uniforms.infinityValue;
-        let outputCoords = getCoordsFromIndex(outputIndex);
-        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;
-            k = k + ${workgroupSizeX}) {
-          let candidate = getX(${splitOutputCoords()} k);
-          if (!isnan(candidate) && candidate ${this.op} bestValue) {
-            bestValue = candidate;
-            bestIndex = k;
-          }
-        }
-        xBestValues[localId.x] = bestValue;
-        xBestIndices[localId.x] = bestIndex;
-        workgroupBarrier();
-
-        var reduceSize = min(u32(reduceLength), ${workgroupSizeX}u);
-        for (var currentSize = reduceSize / 2u; reduceSize > 1u;
-            currentSize = reduceSize / 2u) {
-          let interval = DIV_CEIL(reduceSize, 2u);
-          if (localId.x < currentSize) {
-            let candidate = xBestValues[localId.x + interval];
-            if (candidate ${this.op} bestValue) {
-              bestValue = candidate;
-              xBestValues[localId.x] = bestValue;
-              xBestIndices[localId.x] = xBestIndices[localId.x + interval];
-            }
-          }
-          reduceSize = interval;
-          workgroupBarrier();
-        }
-
-        if (localId.x == 0u && outputIndex < uniforms.size) {
-          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
-        }
-      }
-    `;
-      return userCode;
-    } else {
-      const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let outputCoords = getCoordsFromIndex(index);
-          var bestIndex = 0;
-          var bestValue = getX(${splitOutputCoords()} 0);
-          let reduceLength = ${getInputShapeLastDim()};
-          for (var i = 1; i < reduceLength; i++) {
-            let candidate = getX(${splitOutputCoords()} i);
-            if (candidate ${this.op} bestValue) {
-              bestValue = candidate;
-              bestIndex = i;
-            }
-          }
-          setOutputAtIndexI32(index, bestIndex);
-        }
-      }
-      `;
-      return userCode;
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/avg_pool_backprop_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/avg_pool_backprop_webgpu.ts
deleted file mode 100644
index 3e70c96f9..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/avg_pool_backprop_webgpu.ts
+++ /dev/null
@@ -1,163 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class AvgPool2DBackpropProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['dy'];
-  uniforms =
-      `strides : vec2<i32>, pads : vec2<i32>, dilations : vec2<i32>, filterDims : vec2<i32>,
-       outHeight : i32, outWidth : i32, avgMultiplier : f32,`;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = `avgPool2DBackprop`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let batch = coords[0];
-        let d = coords[3];
-
-        let dyRCCorner = vec2<i32>(coords.yz) - uniforms.pads;
-        let dyRCorner = dyRCCorner.x;
-        let dyCCorner = dyRCCorner.y;
-
-        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
-        // ? = to be determined. : = across all values in that axis.
-        var dotProd = 0.0;
-        for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {
-          let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);
-
-          if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
-            continue;
-          }
-          let idyR = i32(dyR);
-
-          for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {
-            let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);
-
-            if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
-              continue;
-            }
-            let idyC = i32(dyC);
-
-            let dyValue = getDy(batch, idyR, idyC, d);
-
-            dotProd = dotProd + dyValue * uniforms.avgMultiplier;
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-    `;
-    return userCode;
-  }
-}
-
-export class AvgPool3DBackpropProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['dy'];
-  uniforms = `strides : vec3<i32>, pads : vec3<i32>, filterDims : vec3<i32>,
-       outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.inShape;
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = `avgPool3DBackprop`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let batch = coords.x;
-        let ch = coords.u;
-
-        let dyCorner = vec3<i32>(coords.y, coords.z, coords.w) - uniforms.pads;
-        let dyDCorner = dyCorner.x;
-        let dyRCorner = dyCorner.y;
-        let dyCCorner = dyCorner.z;
-
-        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
-        // dx(xD, xR, xC, ch).
-        // ? = to be determined. : = across all values in that axis.
-        var dotProd = 0.0;
-        for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {
-          let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);
-
-          if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {
-            continue;
-          }
-          let idyD = i32(dyD);
-
-          for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {
-            let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);
-
-            if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
-              continue;
-            }
-            let idyR = i32(dyR);
-
-            for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {
-              let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);
-
-              if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
-                continue;
-              }
-              let idyC = i32(dyC);
-
-              let dyValue = getDy(batch, idyD, idyR, idyC, ch);
-              dotProd += dyValue * uniforms.avgMultiplier;
-            }
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/backend_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/backend_webgpu.ts
deleted file mode 100644
index ceae66c51..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/backend_webgpu.ts
+++ /dev/null
@@ -1,1053 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import './flags_webgpu';
-
-import {backend_util, BackendValues, buffer, DataStorage, DataType, engine, env, GPUData, KernelBackend, Rank, RecursiveArray, ShapeMap, Tensor, TensorBuffer, TensorInfo, TimingInfo, TypedArray, util, WebGPUData} from '@tensorflow/tfjs-core';
-
-import {AdapterInfo} from './adapter_info';
-import {BufferManager} from './buffer_manager';
-import {TextureManager} from './texture_manager';
-import * as webgpu_program from './webgpu_program';
-import * as webgpu_util from './webgpu_util';
-
-export interface WebGPUMemoryInfo extends backend_util.MemoryInfo {
-  numBytesInGPU: number;
-  numBytesAllocatedInGPU: number;
-  unreliable: boolean;
-}
-
-type TensorData = {
-  values: BackendValues,
-  dtype: DataType,
-  shape: number[],
-  refCount: number,
-  resource?: GPUBuffer|GPUTexture|GPUExternalTexture,
-  // external is true means we use the resource provided by users directly
-  // (without a copy), so users should be responsible for its release.
-  external?: boolean,
-  // For complex numbers, the real and imaginary parts are stored as their own
-  // individual tensors, with a parent joining the two with the
-  // complexTensorInfos field.
-  complexTensorInfos?: {real: TensorInfo, imag: TensorInfo}
-};
-
-interface DataId {}
-
-export type WebGPUKernelInfo = {
-  name: string,
-  query: Promise<number>,
-};
-
-export type TimerNode = RecursiveArray<WebGPUKernelInfo>|WebGPUKernelInfo;
-
-export interface WebGPUTimingInfo extends TimingInfo {
-  uploadWaitMs: number;
-  downloadWaitMs: number;
-}
-
-type ProgramUniform = Array<{type: string; data: number[]}>;
-
-// Empirically determined constant used to determine size threshold for handing
-// off execution to the CPU.
-const CPU_HANDOFF_SIZE_THRESHOLD =
-    env().getNumber('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD');
-
-// Reshape dispatch, not to exceed device limits.
-const reshapeDispatch =
-    (device: GPUDevice,
-     program: webgpu_program.WebGPUProgram): [number, number, number] => {
-      const MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE =
-          device.limits.maxComputeWorkgroupsPerDimension;
-      const layout = program['dispatchLayout'];
-      const dispatch = program['dispatch'];
-      if (dispatch.every((d) => d <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE)) {
-        return dispatch;
-      }
-
-      util.assert(
-          dispatch[0] > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE &&
-              layout.y === undefined && layout.z === undefined,
-          () => 'Dispatch size exceeds WebGPU limits in Y or Z dimension.');
-
-      let dispatchAverage = Math.ceil(Math.sqrt(dispatch[0]));
-      if (dispatchAverage > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE) {
-        dispatchAverage = Math.ceil(Math.cbrt(dispatch[0]));
-        util.assert(
-            dispatchAverage <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE,
-            () => 'Total dispatch size exceeds WebGPU maximum.');
-        return [dispatchAverage, dispatchAverage, dispatchAverage];
-      } else {
-        return [dispatchAverage, dispatchAverage, 1];
-      }
-    };
-
-export class WebGPUBackend extends KernelBackend {
-  bufferManager: BufferManager;
-  adapterInfo: AdapterInfo;
-  device: GPUDevice;
-  queue: GPUQueue;
-  tensorMap: DataStorage<TensorData>;
-  textureManager: TextureManager;
-  thresholdToIncreaseWorkgroups: number;
-
-  private activeTimers: TimerNode[];
-  private commandEncoder: GPUCommandEncoder;
-  private computePassEncoder: GPUComputePassEncoder;
-  private commandQueueOwnedIds = new WeakSet<DataId>();
-  private dispatchCountInPass = 0;
-  private disposed = false;
-  private downloadWaitMs = 0;
-  private dummyCanvas: HTMLCanvasElement;
-  private dummyContext: GPUCanvasContext;
-  private tensorDataPendingDisposal: DataId[] = [];
-  private static nextDataId = 0;
-  private pipelineCache:
-      {[key: string]: GPUComputePipeline|Promise<GPUComputePipeline>};
-  private programTimersStack: TimerNode[];
-  private queryResolveBuffer: GPUBuffer = null;
-  private querySet: GPUQuerySet = null;
-  private querySetCount = 2;
-  private stagingPendingDisposal: GPUBuffer[] = [];
-  private supportTimestampQuery: boolean;
-  private uniformPendingDisposal: GPUBuffer[] = [];
-  private uploadWaitMs = 0;
-  private hasReadSyncWarned = false;
-  private hasTimestampQueryWarned = false;
-
-  private nextDataId(): number {
-    return WebGPUBackend.nextDataId++;
-  }
-
-  constructor(device: GPUDevice, adapterInfo?: GPUAdapterInfo) {
-    super();
-    if (!webgpu_util.isWebGPUSupported()) {
-      throw new Error('WebGPU is not supported on this device');
-    }
-    this.pipelineCache = {};
-    this.device = device;
-    this.queue = device.queue;
-    this.commandEncoder = null;
-    this.computePassEncoder = null;
-    this.adapterInfo = new AdapterInfo(adapterInfo);
-    this.supportTimestampQuery = this.device.features.has('timestamp-query');
-    this.thresholdToIncreaseWorkgroups =
-        this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8;
-
-    this.bufferManager = new BufferManager(this.device);
-    this.textureManager = new TextureManager(this.device);
-    this.tensorMap = new DataStorage(this, engine());
-
-    // Profiling tools like PIX needs this dummy canvas to
-    // trigger capturing a frame.
-    if (env().getBool('WEBGPU_USE_PROFILE_TOOL')) {
-      this.dummyCanvas = document.createElement('canvas');
-      this.dummyCanvas.width = 1;
-      this.dummyCanvas.height = 1;
-
-      this.dummyContext = this.dummyCanvas.getContext('webgpu');
-      this.dummyContext.configure({
-        device,
-        format: 'bgra8unorm',
-      });
-
-      document.body.appendChild(this.dummyCanvas);
-    }
-  }
-
-  override floatPrecision(): 32 {
-    return 32;
-  }
-
-  /**
-   * Dispose the memory if the dataId has 0 refCount. Return true if the memory
-   * is released or delayed in this backend, false if there are still
-   * references.
-   * @param dataId
-   * @oaram force Optional, remove the data regardless of refCount
-   */
-  override disposeData(dataId: DataId, force = false): boolean {
-    // No-op if already disposed.
-    if (!this.tensorMap.has(dataId)) {
-      return true;
-    }
-
-    const tensorData = this.tensorMap.get(dataId);
-    if (force) {
-      tensorData.refCount = 0;
-    } else {
-      tensorData.refCount--;
-    }
-
-    if (tensorData.refCount > 0) {
-      return false;
-    }
-
-    if (tensorData.complexTensorInfos != null) {
-      this.disposeData(tensorData.complexTensorInfos.real.dataId);
-      this.disposeData(tensorData.complexTensorInfos.imag.dataId);
-    }
-
-    if (this.commandQueueOwnedIds.has(dataId)) {
-      this.tensorDataPendingDisposal.push(dataId);
-      return true;
-    }
-
-    this.releaseResource(dataId);
-    this.tensorMap.delete(dataId);
-
-    return true;
-  }
-
-  override memory(): WebGPUMemoryInfo {
-    return {
-      numBytesInGPU: this.bufferManager.numBytesUsed,
-      numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated,
-      unreliable: false
-    } as WebGPUMemoryInfo;
-  }
-
-  private releaseResource(dataId: DataId) {
-    const tensorData = this.tensorMap.get(dataId);
-    if (!tensorData || !tensorData.resource) {
-      return;
-    }
-
-    // If tensor's resource is from external, do not release.
-    if (tensorData.external) {
-      tensorData.resource = null;
-      return;
-    }
-    if (tensorData.resource instanceof GPUBuffer) {
-      this.bufferManager.releaseBuffer(tensorData.resource);
-    } else if (tensorData.resource instanceof GPUTexture) {
-      this.textureManager.releaseTexture(tensorData.resource);
-    }
-    tensorData.resource = null;
-  }
-
-  /** Return refCount of a `TensorData`. */
-  override refCount(dataId: DataId): number {
-    if (this.tensorMap.has(dataId)) {
-      const tensorData = this.tensorMap.get(dataId);
-      return tensorData.refCount;
-    }
-    return 0;
-  }
-
-  /** Increase refCount of a `TensorData`. */
-  override incRef(dataId: DataId): void {
-    const tensorData = this.tensorMap.get(dataId);
-    tensorData.refCount++;
-  }
-
-  /** Decrease refCount of a `TensorData`. */
-  decRef(dataId: DataId): void {
-    if (this.tensorMap.has(dataId)) {
-      const tensorData = this.tensorMap.get(dataId);
-      tensorData.refCount--;
-    }
-  }
-
-  override write(values: BackendValues, shape: number[], dtype: DataType):
-      DataId {
-    if (dtype === 'complex64' && values != null) {
-      throw new Error(
-          `Cannot write to a complex64 dtype. ` +
-          `Please use tf.complex(real, imag).`);
-    }
-    const dataId = {id: this.nextDataId()};
-    this.tensorMap.set(dataId, {dtype, shape, values, refCount: 1});
-    return dataId;
-  }
-
-  override move(
-      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,
-      refCount: number): void {
-    if (dtype === 'complex64') {
-      throw new Error(
-          `Cannot write to a complex64 dtype. ` +
-          `Please use tf.complex(real, imag).`);
-    }
-    this.tensorMap.set(dataId, {dtype, shape, values, refCount});
-  }
-
-  submitQueue() {
-    this.queue.submit([this.commandEncoder.finish()]);
-    this.commandEncoder = null;
-    this.dispatchCountInPass = 0;
-
-    this.commandQueueOwnedIds = new WeakSet<DataId>();
-
-    this.tensorDataPendingDisposal.forEach(d => {
-      this.releaseResource(d);
-      this.tensorMap.delete(d);
-    });
-
-    this.uniformPendingDisposal.forEach(
-        b => this.bufferManager.releaseBuffer(b));
-    this.stagingPendingDisposal.forEach(
-        b => this.bufferManager.releaseBuffer(b, false));
-
-    this.tensorDataPendingDisposal = [];
-    this.uniformPendingDisposal = [];
-    this.stagingPendingDisposal = [];
-  }
-
-  ensureCommandEncoderReady() {
-    if (!this.commandEncoder) {
-      this.commandEncoder = this.device.createCommandEncoder();
-    }
-  }
-
-  endComputePassEncoder() {
-    if (this.computePassEncoder) {
-      this.computePassEncoder.end();
-      this.computePassEncoder = null;
-    }
-  }
-
-  // Check if parallel compilation is done.
-  async checkCompileCompletionAsync() {
-    let pipelines: GPUComputePipeline[];
-    try {
-      pipelines = await Promise.all(Object.values(this.pipelineCache));
-    } catch (e) {
-      // TODO: Add test case to catch this exception.
-      throw new Error(e.message);
-    }
-    Object.keys(this.pipelineCache).map((key, i) => {
-      this.pipelineCache[key] = pipelines[i];
-    });
-  }
-
-  public async getBufferData(buffer: GPUBuffer): Promise<ArrayBuffer> {
-    if (env().getBool('WEBGPU_ENGINE_COMPILE_ONLY')) {
-      console.warn(
-          'The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false');
-      return null;
-    }
-    const size = buffer.size;
-    const stagingBuffer = this.bufferManager.acquireBuffer(
-        size, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
-    this.ensureCommandEncoderReady();
-    this.endComputePassEncoder();
-    this.commandEncoder.copyBufferToBuffer(buffer, 0, stagingBuffer, 0, size);
-    this.submitQueue();
-
-    await stagingBuffer.mapAsync(GPUMapMode.READ);
-    const values = stagingBuffer.getMappedRange().slice(0);
-
-    stagingBuffer.unmap();
-    if (stagingBuffer != null) {
-      this.bufferManager.releaseBuffer(stagingBuffer);
-    }
-
-    // Need to get texture from swapChain to enable profiling tool
-    // to capture a frame
-    if (env().getBool('WEBGPU_USE_PROFILE_TOOL')) {
-      util.assert(
-          this.dummyContext !== undefined,
-          () => `Fail to get context for profiling tool`);
-      this.dummyContext.getCurrentTexture();
-    }
-
-    return values;
-  }
-
-  private convertAndCacheOnCPU(dataId: DataId, data: BackendValues):
-      BackendValues {
-    const tensorData = this.tensorMap.get(dataId);
-    tensorData.values = data;
-    return tensorData.values;
-  }
-
-  override readSync(dataId: object): BackendValues {
-    const tensorData = this.tensorMap.get(dataId);
-    const {values, complexTensorInfos} = tensorData;
-
-    if (values != null || tensorData.dtype === 'string') {
-      return values;
-    }
-
-    if (tensorData.dtype === 'complex64') {
-      const realValues =
-          this.readSync(complexTensorInfos.real.dataId) as Float32Array;
-      const imagValues =
-          this.readSync(complexTensorInfos.imag.dataId) as Float32Array;
-      const complexVals = util.convertBackendValuesAndArrayBuffer(
-          backend_util.mergeRealAndImagArrays(realValues, imagValues).buffer,
-          'float32');
-      this.convertAndCacheOnCPU(dataId, complexVals);
-      return complexVals;
-    }
-
-    if (!this.hasReadSyncWarned) {
-      this.hasReadSyncWarned = true;
-      console.warn(
-          `The performance of synchronously reading data from GPU to CPU is ` +
-          `poor on the webgpu backend, please use asynchronous APIs instead.`);
-    }
-
-    const alphaModes: GPUCanvasAlphaMode[] = ['opaque', 'premultiplied'];
-
-    const buffer = tensorData.resource as GPUBuffer;
-    const bufferSize = buffer.size;
-    util.assert(
-        bufferSize % 4 === 0,
-        () => 'Because there is 4 bytes for ' +
-            'one pixel, buffer size must be multiple of 4.');
-    const pixelsSize = bufferSize / 4;
-    const valsGPU = new ArrayBuffer(bufferSize);
-    // TODO: adjust the reading window size according the `bufferSize`.
-    const canvasWidth = 256, canvasHeight = 256;
-    const stagingDeviceStorage: OffscreenCanvas[] =
-        alphaModes.map(_ => new OffscreenCanvas(canvasWidth, canvasHeight));
-    const stagingHostStorage = new OffscreenCanvas(canvasWidth, canvasHeight);
-
-    this.endComputePassEncoder();
-    stagingDeviceStorage
-        .map((storage, index) => {
-          const context = storage.getContext('webgpu');
-          // TODO: use rgba8unorm format when this format is supported on Mac.
-          // https://bugs.chromium.org/p/chromium/issues/detail?id=1298618
-          context.configure({
-            device: this.device,
-            format: 'bgra8unorm',
-            usage: GPUTextureUsage.COPY_DST,
-            alphaMode: alphaModes[index],
-          });
-          return context.getCurrentTexture();
-        })
-        .map((texture, index) => {
-          const bytesPerRow = canvasWidth * 4;
-          const readDataGPUToCPU =
-              (width: number, height: number, offset: number) => {
-                this.ensureCommandEncoderReady();
-                this.commandEncoder.copyBufferToTexture(
-                    {
-                      buffer,
-                      bytesPerRow,
-                      offset,
-                    },
-                    {
-                      texture,
-                    },
-                    {
-                      width,
-                      height,
-                    });
-                this.submitQueue();
-
-                const context = stagingHostStorage.getContext('2d', {
-                  willReadFrequently: true,
-                });
-                context.clearRect(0, 0, width, height);
-                context.drawImage(stagingDeviceStorage[index], 0, 0);
-                const stagingValues =
-                    context.getImageData(0, 0, width, height).data;
-                const alphaMode = alphaModes[index];
-                const span =
-                    new Uint8ClampedArray(valsGPU, offset, width * height * 4);
-                for (let k = 0; k < span.length; k += 4) {
-                  if (alphaMode === 'premultiplied') {
-                    span[k + 3] = stagingValues[k + 3];
-                  } else {
-                    const value = stagingValues[k];
-                    span[k] = stagingValues[k + 2];
-                    span[k + 1] = stagingValues[k + 1];
-                    span[k + 2] = value;
-                  }
-                }
-              };
-
-          const fullyReadCount =
-              Math.floor(pixelsSize / (canvasWidth * canvasHeight));
-          let width = canvasWidth, height = canvasHeight, offset = 0;
-          for (let i = 0; i < fullyReadCount; i++) {
-            // Read the buffer data, which fully fill the whole canvas.
-            readDataGPUToCPU(width, height, offset);
-            offset += canvasWidth * canvasHeight * 4;
-          }
-
-          const remainSize = pixelsSize % (canvasWidth * canvasHeight);
-          height = Math.floor(remainSize / canvasWidth);
-          if (height > 0) {
-            // Read the buffer data, which fully fill certain rows of canvas.
-            readDataGPUToCPU(width, height, offset);
-            offset += height * (canvasWidth * 4);
-          }
-
-          width = remainSize % canvasWidth;
-          if (width > 0) {
-            // Read the buffer data, which not fully fill one row of canvas.
-            readDataGPUToCPU(width, 1, offset);
-          }
-        });
-
-    const vals =
-        util.convertBackendValuesAndArrayBuffer(valsGPU, tensorData.dtype);
-    this.convertAndCacheOnCPU(dataId, vals);
-    return vals;
-  }
-
-  override async read(dataId: object): Promise<BackendValues> {
-    if (!this.tensorMap.has(dataId)) {
-      throw new Error(`Tensor ${dataId} was not registered!`);
-    }
-    const tensorData = this.tensorMap.get(dataId);
-
-    const {values} = tensorData;
-
-    if (values != null) {
-      return values;
-    }
-
-    // Download the values from the GPU.
-    let vals: BackendValues;
-    if (tensorData.dtype === 'complex64') {
-      const ps = await Promise.all([
-        this.read(tensorData.complexTensorInfos.real.dataId),
-        this.read(tensorData.complexTensorInfos.imag.dataId)
-      ]);
-
-      const realValues = ps[0];
-      const imagValues = ps[1];
-      vals = backend_util.mergeRealAndImagArrays(
-          realValues as Float32Array, imagValues as Float32Array);
-    } else {
-      const data = await this.getBufferData(tensorData.resource as GPUBuffer);
-      vals = util.convertBackendValuesAndArrayBuffer(data, tensorData.dtype);
-    }
-    this.convertAndCacheOnCPU(dataId, vals);
-    return vals;
-  }
-
-  // The source GPUBuffer and destination GPUBuffer have the same size and
-  // usage.
-  private copyBuffer(srcBuffer: GPUBuffer) {
-    const size = srcBuffer.size;
-    const usage = srcBuffer.usage;
-    const dstBuffer = this.bufferManager.acquireBuffer(size, usage);
-    this.ensureCommandEncoderReady();
-    this.endComputePassEncoder();
-    this.commandEncoder.copyBufferToBuffer(srcBuffer, 0, dstBuffer, 0, size);
-    this.submitQueue();
-    return dstBuffer;
-  }
-
-  /**
-   * Create a TF.js tensor out of an existing WebGPU buffer.
-   */
-  override createTensorFromGPUData(
-      webGPUData: WebGPUData, shape: number[], dtype: DataType): Tensor {
-    let buffer = webGPUData.buffer;
-    if (dtype === 'complex64') {
-      throw new Error(`Cannot write to a complex64 dtype. `);
-    }
-    const dataId = {id: this.nextDataId()};
-    this.tensorMap.set(dataId, {
-      dtype,
-      shape,
-      values: null,
-      refCount: 1,
-      external: webGPUData.zeroCopy
-    });
-    const tensorData = this.tensorMap.get(dataId);
-    const size = webgpu_util.GPUBytesPerElement(tensorData.dtype) *
-        util.sizeFromShape(tensorData.shape);
-    if (webGPUData.buffer.size < size) {
-      throw new Error(`GPUBuffer size(${
-          webGPUData.buffer.size}) is smaller than tensor size(${size})!`);
-    } else if (
-        (webGPUData.buffer.usage &
-         (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) !==
-        (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) {
-      throw new Error(
-          'GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!');
-    }
-
-    // Do buffer copy by default.
-    if (webGPUData.zeroCopy !== true) {
-      buffer = this.copyBuffer(buffer);
-    }
-    tensorData.resource = buffer;
-    return engine().makeTensorFromDataId(dataId, shape, dtype, this);
-  }
-
-  /**
-   * Read tensor to a new GPUBuffer.
-   * @param dataId The source tensor.
-   */
-  override readToGPU(dataId: DataId): GPUData {
-    const srcTensorData = this.tensorMap.get(dataId);
-    const {values, dtype, shape, resource} = srcTensorData;
-
-    if (dtype === 'complex64') {
-      throw new Error('Does not support reading buffer for complex64 dtype.');
-    }
-
-    if (resource == null) {
-      if (values != null) {
-        throw new Error('Data is not on GPU but on CPU.');
-      } else {
-        throw new Error('There is no data on GPU or CPU.');
-      }
-    }
-
-    const srcBuffer = resource as GPUBuffer;
-    const size = srcBuffer.size;
-    const usage = srcBuffer.usage;
-    const buffer = this.bufferManager.acquireBuffer(size, usage);
-    this.ensureCommandEncoderReady();
-    this.endComputePassEncoder();
-    this.commandEncoder.copyBufferToBuffer(
-        resource as GPUBuffer, 0, buffer, 0, size);
-    this.submitQueue();
-
-    const tensorInfo = this.makeTensorInfo(shape, dtype);
-    // Make engine track this tensor, so that we can dispose it later.
-    const tensorRef = engine().makeTensorFromTensorInfo(tensorInfo);
-
-    const tensorData = this.tensorMap.get(tensorInfo.dataId);
-    tensorData.resource = buffer;
-
-    return {tensorRef, buffer};
-  }
-
-  bufferSync<R extends Rank, D extends DataType>(t: TensorInfo):
-      TensorBuffer<R, D> {
-    const data = this.readSync(t.dataId);
-    if (t.dtype === 'string') {
-      try {
-        // Decode the bytes into string.
-        const strings = (data as Uint8Array[]).map(d => util.decodeString(d));
-        return buffer(t.shape as ShapeMap[R], t.dtype, strings) as
-            TensorBuffer<R, D>;
-      } catch {
-        throw new Error('Failed to decode encoded string bytes into utf-8');
-      }
-    }
-    return buffer(t.shape as ShapeMap[R], t.dtype, data as TypedArray) as
-        TensorBuffer<R, D>;
-  }
-
-  override async time(f: () => void): Promise<WebGPUTimingInfo> {
-    if (!this.supportTimestampQuery && !this.hasTimestampQueryWarned) {
-      console.warn(
-          `This device doesn't support timestamp-query extension. ` +
-          `Start Chrome browser with flag ` +
-          `--enable-dawn-features=allow_unsafe_apis to try it again. ` +
-          `Otherwise, zero will be shown for the kernel time when profiling ` +
-          `mode is enabled.`);
-      this.hasTimestampQueryWarned = true;
-    }
-
-    const oldActiveTimers = this.activeTimers;
-    const newActiveTimers: TimerNode[] = [];
-
-    let outerMostTime = false;
-    if (this.programTimersStack == null) {
-      this.programTimersStack = newActiveTimers;
-      outerMostTime = true;
-    } else {
-      this.activeTimers.push(newActiveTimers);
-    }
-    this.activeTimers = newActiveTimers;
-
-    f();
-
-    const flattenedActiveTimerQueries =
-        util.flatten(this.activeTimers.map((d: WebGPUKernelInfo) => d.query))
-            .filter(d => d != null);
-    const flattenedActiveTimerNames =
-        util.flatten(this.activeTimers.map((d: WebGPUKernelInfo) => d.name))
-            .filter(d => d != null);
-
-    this.activeTimers = oldActiveTimers;
-
-    if (outerMostTime) {
-      this.programTimersStack = null;
-    }
-    const res: WebGPUTimingInfo = {
-      uploadWaitMs: this.uploadWaitMs,
-      downloadWaitMs: this.downloadWaitMs,
-      kernelMs: null,
-      wallMs: null
-    };
-
-    const kernelMs = await Promise.all(flattenedActiveTimerQueries);
-    res['kernelMs'] = util.sum(kernelMs);
-    res['getExtraProfileInfo'] = () =>
-        kernelMs.map((d, i) => ({name: flattenedActiveTimerNames[i], ms: d}))
-            .map(d => `${d.name}: ${d.ms}`)
-            .join(', ');
-    this.uploadWaitMs = 0;
-    this.downloadWaitMs = 0;
-    return res;
-  }
-
-  makeTensorInfo(
-      shape: number[], dtype: DataType,
-      values?: BackendValues|string[]): TensorInfo {
-    if (dtype === 'string' && values != null && values.length > 0 &&
-        util.isString(values[0])) {
-      values = (values as unknown as string[]).map(d => util.encodeString(d));
-    }
-    const dataId = this.write(values as BackendValues, shape, dtype);
-    return {dataId, shape, dtype};
-  }
-
-  private tensorToBinding(tensor?: TensorInfo): GPUBindingResource {
-    if (!tensor) {
-      return null;
-    }
-
-    const tensorData = this.tensorMap.get(tensor.dataId);
-    const resource = tensorData.resource;
-
-    if (resource instanceof GPUBuffer) {
-      return {buffer: resource};
-    }
-    if (resource instanceof GPUTexture) {
-      return resource.createView();
-    }
-    // GPUExternalTexture
-    return resource;
-  }
-
-  uploadToGPU(dataId: DataId): void {
-    const tensorData = this.tensorMap.get(dataId);
-    // Already on the GPU.
-    if (tensorData.resource != null) {
-      return;
-    }
-
-    const size = webgpu_util.GPUBytesPerElement(tensorData.dtype) *
-        util.sizeFromShape(tensorData.shape);
-    let buffer;
-    const usage = GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC |
-        GPUBufferUsage.COPY_DST;
-    if (tensorData.values) {
-      buffer = this.bufferManager.acquireBuffer(size, usage, true);
-      if (buffer.mapState === 'unmapped') {
-        const stagingBuffer = this.bufferManager.acquireBuffer(
-            size, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, true,
-            false);
-        const arrayBuffer = stagingBuffer.getMappedRange();
-        if (tensorData.dtype === 'int32' || tensorData.dtype === 'bool') {
-          new Int32Array(arrayBuffer).set(tensorData.values as TypedArray);
-        } else {
-          new Float32Array(arrayBuffer).set(tensorData.values as Float32Array);
-        }
-        stagingBuffer.unmap();
-        this.ensureCommandEncoderReady();
-        this.endComputePassEncoder();
-        this.commandEncoder.copyBufferToBuffer(
-            stagingBuffer, 0, buffer, 0, size);
-
-        this.stagingPendingDisposal.push(stagingBuffer);
-      } else {
-        const arrayBuffer = buffer.getMappedRange();
-        if (tensorData.dtype === 'int32' || tensorData.dtype === 'bool') {
-          new Int32Array(arrayBuffer).set(tensorData.values as TypedArray);
-        } else {
-          new Float32Array(arrayBuffer).set(tensorData.values as Float32Array);
-        }
-        buffer.unmap();
-      }
-
-      // Once uploaded, don't store the values on cpu.
-      tensorData.values = null;
-    } else {
-      buffer = this.bufferManager.acquireBuffer(size, usage);
-    }
-    tensorData.resource = buffer;
-  }
-
-  private makeUniforms(programUniform: ProgramUniform): GPUBindingResource {
-    let currentOffset = 0;
-    let preLength = 0;
-    const offsets: number[] = [];
-    let maxAlignmentOfField = 1;
-    programUniform.forEach((d) => {
-      if (d.data.length === 0) {
-        d.data = [1];
-      }
-      // https://www.w3.org/TR/WGSL/#alignof
-      let baseAlignment: number;
-      switch (d.data.length) {
-        case 1:
-          baseAlignment = 4;
-          break;
-        case 2:
-          baseAlignment = 8;
-          break;
-        case 3:
-          baseAlignment = 16;
-          break;
-        case 4:
-          baseAlignment = 16;
-          break;
-        case 5:
-          baseAlignment = 16;
-          break;
-        case 6:
-          baseAlignment = 16;
-          break;
-        default:
-          util.assert(false, () => `Unsupported ${d.data.length}D shape`);
-      }
-
-      if (preLength === 5 || preLength === 6) {
-        baseAlignment = 16;
-      }
-      if (baseAlignment > maxAlignmentOfField) {
-        maxAlignmentOfField = baseAlignment;
-      }
-      currentOffset = Math.ceil(currentOffset / baseAlignment) * baseAlignment;
-      preLength = d.data.length;
-      offsets.push(currentOffset);
-      currentOffset += d.data.length * 4;
-    });
-
-    currentOffset =
-        Math.ceil(currentOffset / maxAlignmentOfField) * maxAlignmentOfField;
-    const arrayBuffer = new ArrayBuffer(currentOffset);
-    programUniform.forEach((d, i) => {
-      const offset = offsets[i];
-      if (d.type === 'int32') {
-        new Int32Array(arrayBuffer, offset, d.data.length).set(d.data);
-      } else if (d.type === 'uint32') {
-        new Uint32Array(arrayBuffer, offset, d.data.length).set(d.data);
-      } else {
-        new Float32Array(arrayBuffer, offset, d.data.length).set(d.data);
-      }
-    });
-
-    const uniformBuffer = this.bufferManager.acquireBuffer(
-        currentOffset, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
-    this.queue.writeBuffer(uniformBuffer, 0, arrayBuffer, 0, currentOffset);
-    this.uniformPendingDisposal.push(uniformBuffer);
-
-    return {offset: 0, size: currentOffset, buffer: uniformBuffer};
-  }
-
-  public runWebGPUProgram(
-      program: webgpu_program.WebGPUProgram, inputs: TensorInfo[],
-      outputDtype: DataType, programDefinedUniform?: ProgramUniform,
-      output?: TensorInfo): TensorInfo {
-    if (!output) {
-      output = this.makeTensorInfo(program.outputShape, outputDtype);
-    }
-    if (util.sizeFromShape(output.shape) === 0) {
-      // Short-circuit the computation since the result is empty (has 0 in its
-      // shape).
-      this.tensorMap.get(output.dataId).values =
-          util.getTypedArrayFromDType(output.dtype as 'float32', 0);
-      return output;
-    }
-    this.uploadToGPU(output.dataId);
-    program.dispatch = reshapeDispatch(this.device, program);
-
-    const inputsData = inputs.map((input: TensorInfo, i: number) => {
-      if (input.dtype === 'complex64') {
-        throw new Error(
-            `GPGPUProgram does not support complex64 input. For complex64 ` +
-            `dtypes, please separate the program into real and imaginary ` +
-            `parts.`);
-      }
-      this.uploadToGPU(input.dataId);
-
-      return {
-        // Returning dtype from tensorMap because it reflects dtype
-        // of underlying buffer, rather than abstract dtype.
-        dtype: this.tensorMap.get(input.dataId).dtype,
-        shape: input.shape,
-        name: program.variableNames[i]
-      };
-    });
-
-    program.shaderKey =
-        webgpu_program.makeShaderKey(program, inputsData, output);
-
-    const parallelCompilation = env().getBool('WEBGPU_ENGINE_COMPILE_ONLY');
-    if (!(program.shaderKey in this.pipelineCache)) {
-      this.pipelineCache[program.shaderKey] = webgpu_program.compileProgram(
-          this.device, program, inputsData, output, parallelCompilation);
-    }
-    program.pipeline = this.pipelineCache[program.shaderKey];
-
-    if (!parallelCompilation) {
-      this.recordAndSubmit(program, output, inputs, programDefinedUniform);
-    }
-    return output;
-  }
-
-  private recordAndSubmit(
-      program: webgpu_program.WebGPUProgram, output: TensorInfo,
-      inputs: TensorInfo[], programDefinedUniform?: ProgramUniform) {
-    if (program.pipeline instanceof Promise) {
-      throw new Error(
-          'Please call checkCompileCompletionAsync to ensure parallel compilation is done!');
-    }
-    // There are six kinds of uniforms: NAN, INFINITY, shapes, shape strides,
-    // program size, program defined uniforms.
-    let programUniform: ProgramUniform = [];
-    let bufferShapes: number[][] = [];
-    const uniformsType = 'int32';
-    if (program.pixelsOpType == null) {
-      programUniform.push(
-          {type: 'float32', data: [NaN]}, {type: 'float32', data: [Infinity]});
-      bufferShapes = inputs.concat(output).map(d => d.shape);
-      const uniformsType = 'int32';
-      bufferShapes.map(d => {
-        programUniform.push({type: uniformsType, data: d});
-        const strides = util.computeStrides(d);
-        programUniform.push({type: uniformsType, data: strides});
-      });
-    } else {
-      const strides = util.computeStrides(output.shape);
-      programUniform.push({type: uniformsType, data: strides});
-    }
-    if (program.size) {
-      const size = util.sizeFromShape(program.outputShape);
-      programUniform.push({
-        type: uniformsType,
-        data: [program.outputComponent ? size / program.outputComponent : size]
-      });
-    }
-
-    if (programDefinedUniform) {
-      programUniform = [...programUniform, ...programDefinedUniform];
-    }
-    const bindings = [
-      this.tensorToBinding(output), ...inputs.map(t => this.tensorToBinding(t)),
-      this.makeUniforms(programUniform)
-    ];
-
-    inputs.forEach(input => {
-      this.commandQueueOwnedIds.add(input.dataId);
-    });
-    this.commandQueueOwnedIds.add(output.dataId);
-
-    const bindGroup = this.device.createBindGroup({
-      layout: program.pipeline.getBindGroupLayout(0),
-      entries: bindings.map((b, i) => ({binding: i, resource: b})),
-    });
-
-    const shouldTimeProgram = this.activeTimers != null;
-    this.ensureCommandEncoderReady();
-
-    const computePassDescriptor: GPUComputePassDescriptor = {};
-    if (shouldTimeProgram && this.supportTimestampQuery) {
-      this.endComputePassEncoder();
-      if (this.querySet == null) {
-        this.querySet = this.device.createQuerySet({
-          type: 'timestamp',
-          count: this.querySetCount,
-        });
-      }
-      computePassDescriptor.timestampWrites = {
-        querySet: this.querySet,
-        beginningOfPassWriteIndex: 0,
-        endOfPassWriteIndex: 1,
-      };
-      this.computePassEncoder =
-          this.commandEncoder.beginComputePass(computePassDescriptor);
-    } else if (!this.computePassEncoder) {
-      this.computePassEncoder =
-          this.commandEncoder.beginComputePass(computePassDescriptor);
-    }
-
-    this.computePassEncoder.setPipeline(program.pipeline);
-    this.computePassEncoder.setBindGroup(0, bindGroup);
-    this.computePassEncoder.dispatchWorkgroups(
-        program.dispatch[0], program.dispatch[1], program.dispatch[2]);
-    this.dispatchCountInPass++;
-
-    if (shouldTimeProgram ||
-        env().get('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE') as
-            number <= this.dispatchCountInPass ||
-        program.pixelsOpType === webgpu_program.PixelsOpType.DRAW) {
-      this.endComputePassEncoder();
-      if (shouldTimeProgram) {
-        this.activeTimers.push(
-            {name: program.constructor.name, query: this.getQueryTime()});
-      } else {
-        this.submitQueue();
-      }
-    }
-  }
-
-  async getQueryTime(): Promise<number> {
-    if (!this.supportTimestampQuery) {
-      return 0;
-    }
-
-    if (this.queryResolveBuffer == null) {
-      this.queryResolveBuffer = this.bufferManager.acquireBuffer(
-          this.querySetCount * 8,
-          GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST |
-              GPUBufferUsage.QUERY_RESOLVE);
-    }
-    this.commandEncoder.resolveQuerySet(
-        this.querySet, 0, this.querySetCount, this.queryResolveBuffer, 0);
-
-    const queryStagingBuffer = this.bufferManager.acquireBuffer(
-        this.querySetCount * 8,
-        GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
-
-    this.commandEncoder.copyBufferToBuffer(
-        this.queryResolveBuffer, 0, queryStagingBuffer, 0,
-        this.querySetCount * 8);
-
-    this.submitQueue();
-
-    await queryStagingBuffer.mapAsync(GPUMapMode.READ);
-    const arrayBuffer = new BigUint64Array(queryStagingBuffer.getMappedRange());
-    const time = Number(arrayBuffer[1] - arrayBuffer[0]) / 1000000;
-    queryStagingBuffer.unmap();
-    this.bufferManager.releaseBuffer(queryStagingBuffer);
-    return time;
-  }
-
-  shouldExecuteOnCPU(
-      inputs: TensorInfo[],
-      sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD): boolean {
-    return env().getBool('WEBGPU_CPU_FORWARD') &&
-        inputs.every(
-            input => this.tensorMap.get(input.dataId).resource == null &&
-                util.sizeFromShape(input.shape) < sizeThreshold);
-  }
-
-  override numDataIds() {
-    return this.tensorMap.numDataIds() - this.tensorDataPendingDisposal.length;
-  }
-
-  override dispose() {
-    if (this.disposed) {
-      return;
-    }
-    if (this.querySet != null) {
-      this.querySet.destroy();
-    }
-    this.bufferManager.dispose();
-    this.textureManager.dispose();
-    this.disposed = true;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/backend_webgpu_test.ts b/tfjs-master/tfjs-backend-webgpu/src/backend_webgpu_test.ts
deleted file mode 100644
index ed8149f40..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/backend_webgpu_test.ts
+++ /dev/null
@@ -1,688 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-
-import {GPUData, test_util} from '@tensorflow/tfjs-core';
-const {expectArraysEqual, expectArraysClose} = test_util;
-
-import {WebGPUBackend, WebGPUMemoryInfo} from './backend_webgpu';
-import {describeWebGPU} from './test_util';
-
-describeWebGPU('backend webgpu cpu forwarding turned on', () => {
-  let cpuForwardFlagSaved: boolean;
-
-  beforeAll(() => {
-    cpuForwardFlagSaved = tf.env().getBool('WEBGPU_CPU_FORWARD');
-
-    tf.env().set('WEBGPU_CPU_FORWARD', true);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGPU_CPU_FORWARD', cpuForwardFlagSaved);
-  });
-
-  it('should not allocate GPU memory when CPU forwarding', async () => {
-    const a = tf.tensor2d([2, 4, 6, 8], [2, 2]);
-    const b = tf.tensor2d([0.5, 0.5, 0.5, 0.5], [2, 2]);
-
-    const c = tf.mul(a, b);
-
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-    const startNumBytesInGPU = (tf.memory() as WebGPUMemoryInfo).numBytesInGPU;
-
-    expect(startNumBytes).toEqual(48);
-    expect(startNumTensors).toEqual(3);
-    expect(startNumBytesInGPU).toEqual(0);
-
-    const f = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const d = tf.matMul(c, f);
-
-    const dData = await d.data();
-    const endNumBytes = tf.memory().numBytes;
-    const endNumTensors = tf.memory().numTensors;
-    const endNumBytesInGPU = (tf.memory() as WebGPUMemoryInfo).numBytesInGPU;
-
-    expect(endNumBytes - startNumBytes).toEqual(48);
-    expect(endNumTensors - startNumTensors).toEqual(2);
-    expect(endNumBytesInGPU - startNumBytesInGPU).toEqual(64);
-
-    expectArraysClose(dData, new Float32Array([9, 12, 15, 19, 26, 33]));
-  });
-});
-
-describeWebGPU('backend webgpu', () => {
-  it('should not leak memory in delayed mode', async () => {
-    const savedFlag = tf.env().get('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE');
-    tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 15);
-    const a = tf.tensor2d([2, 4, 6, 8], [2, 2]);
-    const b = tf.tensor2d([0.5, 0.5, 0.5, 0.5], [2, 2]);
-
-    const c = tf.mul(a, b);
-
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-
-    const f = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const d = tf.matMul(c, f);
-
-    const dData = await d.data();
-    const endNumBytes = tf.memory().numBytes;
-    const endNumTensors = tf.memory().numTensors;
-
-    expect(endNumBytes - startNumBytes).toEqual(48);
-    expect(endNumTensors - startNumTensors).toEqual(2);
-
-    expectArraysClose(dData, new Float32Array([9, 12, 15, 19, 26, 33]));
-    tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', savedFlag);
-  });
-
-  it('should not leak memory in immediate mode', async () => {
-    const savedFlag = tf.env().get('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE');
-    tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 1);
-    const a = tf.tensor2d([2, 4, 6, 8], [2, 2]);
-    const b = tf.tensor2d([0.5, 0.5, 0.5, 0.5], [2, 2]);
-
-    const c = tf.mul(a, b);
-
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-
-    const f = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const d = tf.matMul(c, f);
-
-    const dData = await d.data();
-    const endNumBytes = tf.memory().numBytes;
-    const endNumTensors = tf.memory().numTensors;
-
-    expect(endNumBytes - startNumBytes).toEqual(48);
-    expect(endNumTensors - startNumTensors).toEqual(2);
-
-    expectArraysClose(dData, new Float32Array([9, 12, 15, 19, 26, 33]));
-    tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', savedFlag);
-  });
-
-  it('should recycle buffers in immediate mode', () => {
-    const savedFlag = tf.env().get('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE');
-    tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 1);
-    const backend = tf.backend() as WebGPUBackend;
-    const bufferManager = backend.bufferManager;
-    bufferManager.dispose();
-
-    const a = tf.tensor2d([2, 4, 6, 8], [2, 2]);
-    const b = tf.tensor2d([0.5, 0.5, 0.5, 0.5], [2, 2]);
-
-    const c = tf.mul(a, b);
-    const freeBuffersAfterFirstMul = bufferManager.getNumFreeBuffers();
-    const usedBuffersAfterFirstMul = bufferManager.getNumUsedBuffers();
-
-    const f = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    tf.matMul(c, f);
-    const freeBuffersAfterFirstMatMul = bufferManager.getNumFreeBuffers();
-    const usedBuffersAfterFirstMatMul = bufferManager.getNumUsedBuffers();
-    expect(freeBuffersAfterFirstMatMul - freeBuffersAfterFirstMul)
-        .toEqual(1);  // from released uniform
-    expect(usedBuffersAfterFirstMatMul - usedBuffersAfterFirstMul).toEqual(2);
-
-    const a2 = tf.tensor2d([2, 4, 6, 8], [2, 2]);
-    const b2 = tf.tensor2d([0.5, 0.5, 0.5, 0.5], [2, 2]);
-
-    const c2 = tf.mul(a2, b2);
-    const freeBuffersAfterSecondMul = bufferManager.getNumFreeBuffers();
-    const usedBuffersAfterSecondMul = bufferManager.getNumUsedBuffers();
-    expect(freeBuffersAfterSecondMul - freeBuffersAfterFirstMatMul)
-        .toEqual(0);  // released a uniform buffer and reused a buffer
-    expect(usedBuffersAfterSecondMul - usedBuffersAfterFirstMatMul).toEqual(3);
-
-    const f2 = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    tf.matMul(c2, f2);
-    const freeBuffersAfterSecondMatMul = bufferManager.getNumFreeBuffers();
-    const usedBuffersAfterSecondMatMul = bufferManager.getNumUsedBuffers();
-    expect(freeBuffersAfterSecondMatMul - freeBuffersAfterSecondMul).toEqual(0);
-    expect(usedBuffersAfterSecondMatMul - usedBuffersAfterSecondMul).toEqual(2);
-    tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', savedFlag);
-  });
-
-  it('should not recycle buffers in delayed mode', async () => {
-    const savedFlag = tf.env().get('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE');
-    tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 15);
-    const backend = tf.backend() as WebGPUBackend;
-    const bufferManager = backend.bufferManager;
-    bufferManager.dispose();
-
-    const a = tf.tensor2d([2, 4, 6, 8], [2, 2]);
-    const b = tf.tensor2d([0.5, 0.5, 0.5, 0.5], [2, 2]);
-
-    const c = tf.mul(a, b);
-    const freeBuffersAfterFirstMul = bufferManager.getNumFreeBuffers();
-    const usedBuffersAfterFirstMul = bufferManager.getNumUsedBuffers();
-
-    const f = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    tf.matMul(c, f);
-    const freeBuffersAfterFirstMatMul = bufferManager.getNumFreeBuffers();
-    const usedBuffersAfterFirstMatMul = bufferManager.getNumUsedBuffers();
-    expect(freeBuffersAfterFirstMatMul - freeBuffersAfterFirstMul).toEqual(0);
-    expect(usedBuffersAfterFirstMatMul - usedBuffersAfterFirstMul).toEqual(3);
-
-    const a2 = tf.tensor2d([2, 4, 6, 8], [2, 2]);
-    const b2 = tf.tensor2d([0.5, 0.5, 0.5, 0.5], [2, 2]);
-
-    const c2 = tf.mul(a2, b2);
-    const freeBuffersAfterSecondMul = bufferManager.getNumFreeBuffers();
-    const usedBuffersAfterSecondMul = bufferManager.getNumUsedBuffers();
-    expect(freeBuffersAfterSecondMul - freeBuffersAfterFirstMatMul).toEqual(0);
-    expect(usedBuffersAfterSecondMul - usedBuffersAfterFirstMatMul).toEqual(4);
-
-    const f2 = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const c3 = tf.matMul(c2, f2);
-    const freeBuffersAfterSecondMatMul = bufferManager.getNumFreeBuffers();
-    const usedBuffersAfterSecondMatMul = bufferManager.getNumUsedBuffers();
-    expect(freeBuffersAfterSecondMatMul - freeBuffersAfterSecondMul).toEqual(0);
-    expect(usedBuffersAfterSecondMatMul - usedBuffersAfterSecondMul).toEqual(3);
-
-    // Tests happen within a tidy so we need to read a tensor at the end of a
-    // test in delayed mode in order to force flush the disposal queue.
-    await c3.data();
-    tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', savedFlag);
-  });
-});
-
-describeWebGPU('backendWebGPU', () => {
-  let prevBackend: string;
-
-  beforeAll(() => {
-    prevBackend = tf.getBackend();
-  });
-
-  afterEach(() => {
-    tf.setBackend(prevBackend);
-    tf.removeBackend('test-storage');
-  });
-
-  it('lazily upload', async () => {
-    const adapter = await navigator.gpu.requestAdapter({});
-    const device = await adapter.requestDevice({});
-    const backend = new WebGPUBackend(device);
-    tf.registerBackend('test-storage', () => backend);
-    tf.setBackend('test-storage');
-
-    const bufferManager = backend.bufferManager;
-    const t = tf.tensor1d([1, 2, 3], 'float32');
-
-    expect(bufferManager.getNumUsedBuffers()).toBe(0);
-
-    backend.uploadToGPU(t.dataId);
-    expect(bufferManager.getNumUsedBuffers()).toBe(1);
-  });
-});
-
-describeWebGPU('keeping data on gpu ', () => {
-  let flag: boolean;
-
-  beforeAll(() => {
-    flag = tf.env().getBool('WEBGPU_CPU_FORWARD');
-    tf.env().set('WEBGPU_CPU_FORWARD', false);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGPU_CPU_FORWARD', flag);
-  });
-
-  it('has a valid buffer for dtype=float32.', async () => {
-    const webGPUBackend = (tf.backend() as WebGPUBackend);
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const size = 48;
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-    const res = b.dataToGPU();
-    expectArraysEqual(res.buffer.size, size);
-    if (res.tensorRef.dtype !== 'float32') {
-      throw new Error(
-          `Unexpected type. Actual: ${res.tensorRef.dtype}. ` +
-          `Expected: float32`);
-    }
-    const resData = await webGPUBackend.getBufferData(res.buffer);
-    const values = tf.util.convertBackendValuesAndArrayBuffer(
-        resData, res.tensorRef.dtype);
-    expectArraysEqual(values, data);
-  });
-
-  it('has a valid buffer for dtype=int32.', async () => {
-    const webGPUBackend = (tf.backend() as WebGPUBackend);
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const size = 48;
-    const a = tf.tensor(data, [1, 3, 4], 'int32');
-    const b = tf.tensor([0], [1], 'int32');
-    const c = tf.add(a, b);
-    const res = c.dataToGPU();
-    expectArraysEqual(res.buffer.size, size);
-    if (res.tensorRef.dtype !== 'int32') {
-      throw new Error(
-          `Unexpected type. Actual: ${res.tensorRef.dtype}. ` +
-          `Expected: float32`);
-    }
-    const resData = await webGPUBackend.getBufferData(res.buffer);
-    const values = tf.util.convertBackendValuesAndArrayBuffer(
-        resData, res.tensorRef.dtype);
-    expectArraysEqual(values, data);
-  });
-
-  it('has no memory leak.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-
-    const a = tf.tensor(data, [1, 3, 4]);
-    const b = tf.add(a, 0);
-
-    const webGPUBackend = tf.backend() as WebGPUBackend;
-    const startTensor = tf.memory().numTensors;
-    const startDataBuckets = webGPUBackend.numDataIds();
-
-    const res = b.dataToGPU();
-    res.tensorRef.dispose();
-
-    const endTensor = tf.memory().numTensors;
-    const endDataBuckets = webGPUBackend.numDataIds();
-
-    expect(endTensor).toEqual(startTensor);
-    expect(endDataBuckets).toEqual(startDataBuckets);
-  });
-
-  it('can be used in tidy.', async () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-
-    const webGPUBackend = tf.backend() as WebGPUBackend;
-    const startTensor = tf.memory().numTensors;
-    const startDataBuckets = webGPUBackend.numDataIds();
-
-    const result = tf.tidy(() => {
-      const a = tf.tensor(data, [1, 3, 4]);
-      const b = tf.add(a, 0);
-      return b.dataToGPU() as unknown as tf.Tensor;
-    });
-
-    const endTensor = tf.memory().numTensors;
-    const endDataBuckets = webGPUBackend.numDataIds();
-
-    expect(endTensor).toEqual(startTensor + 1);
-    expect(endDataBuckets).toEqual(startDataBuckets + 1);
-
-    const res = result as unknown as GPUData;
-    const resData = await webGPUBackend.getBufferData(res.buffer);
-    const values = tf.util.convertBackendValuesAndArrayBuffer(
-        resData, res.tensorRef.dtype);
-    expectArraysEqual(values, data);
-  });
-
-  it('tidy has no memory leak.', () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-
-    const webGPUBackend = tf.backend() as WebGPUBackend;
-    const startTensor = tf.memory().numTensors;
-    const startDataBuckets = webGPUBackend.numDataIds();
-
-    tf.tidy(() => {
-      const a = tf.tensor(data, [1, 3, 4]);
-      const b = tf.add(a, 0);
-      b.dataToGPU();
-      return b;
-    });
-
-    const endTensor = tf.memory().numTensors;
-    const endDataBuckets = webGPUBackend.numDataIds();
-
-    expect(endTensor).toEqual(startTensor + 1);
-    expect(endDataBuckets).toEqual(startDataBuckets + 1);
-  });
-});
-
-async function parallelCompilationCommon(webGPUBackend: WebGPUBackend) {
-  const startNumBytes = (tf.memory() as WebGPUMemoryInfo).numBytesInGPU;
-  const startTensor = tf.memory().numTensors;
-  const startDataBuckets = webGPUBackend.numDataIds();
-
-  const a1 = tf.tensor1d([1, 1, 1]);
-  const b1 = tf.tensor1d([1, 1, 1]);
-
-  // Parallel compile.
-  tf.env().set('WEBGPU_ENGINE_COMPILE_ONLY', true);
-  const c1 = tf.add(a1, b1);
-  await webGPUBackend.checkCompileCompletionAsync();
-
-  // Actual inference.
-  tf.env().set('WEBGPU_ENGINE_COMPILE_ONLY', false);
-  const c2 = tf.add(a1, b1);
-  expectArraysEqual(await c2.data(), [2, 2, 2]);
-
-  tf.dispose([a1, b1, c1, c2]);
-  const endNumBytes = (tf.memory() as WebGPUMemoryInfo).numBytesInGPU;
-  const endTensor = tf.memory().numTensors;
-  const endDataBuckets = webGPUBackend.numDataIds();
-
-  // We only check numBytesInGPU. For parallel compilation,
-  // numBytesInGPUAllocated will be more because of the two pass
-  // uploadToGPU, but they will all be freed, resulting in endNumbytes equal
-  // to startNumBytes.
-  expect(startNumBytes).toEqual(endNumBytes);
-  expect(startTensor).toEqual(endTensor);
-  expect(endDataBuckets).toEqual(startDataBuckets);
-}
-
-describeWebGPU('parallel compilation', () => {
-  let prevBackend: string;
-  let savedWebGPUCPUForward: boolean;
-  let savedEngineCompileOnly: boolean;
-  let webGPUBackend: WebGPUBackend;
-  const customWebGPUBackendName = 'test-parallel';
-
-  beforeAll(() => {
-    prevBackend = tf.getBackend();
-  });
-
-  beforeEach(async () => {
-    const adapter = await navigator.gpu.requestAdapter({});
-    const device = await adapter.requestDevice({});
-    webGPUBackend = new WebGPUBackend(device);
-
-    tf.copyRegisteredKernels('webgpu', customWebGPUBackendName);
-    tf.registerBackend(customWebGPUBackendName, () => webGPUBackend);
-    tf.setBackend('test-parallel');
-
-    savedWebGPUCPUForward = tf.env().get('WEBGPU_CPU_FORWARD') as boolean;
-    savedEngineCompileOnly =
-        tf.env().get('WEBGPU_ENGINE_COMPILE_ONLY') as boolean;
-    tf.env().set('WEBGPU_CPU_FORWARD', false);
-    await tf.ready();
-  });
-
-  afterEach(() => {
-    tf.env().set('WEBGPU_CPU_FORWARD', savedWebGPUCPUForward);
-    tf.env().set('WEBGPU_ENGINE_COMPILE_ONLY', savedEngineCompileOnly);
-    tf.setBackend(prevBackend);
-    tf.removeBackend(customWebGPUBackendName);
-  });
-
-  it('should work if pipeline cache not exist.', async () => {
-    await parallelCompilationCommon(webGPUBackend);
-  });
-
-  it('should work if pipeline cache exists.', async () => {
-    // This will create pipeline cache.
-    const a0 = tf.tensor1d([1, 1, 1]);
-    const b0 = tf.tensor1d([1, 1, 1]);
-    const c0 = tf.add(a0, b0);
-    const data = await c0.data();
-    expectArraysClose(data, [2, 2, 2]);
-
-    await parallelCompilationCommon(webGPUBackend);
-  });
-
-  it('should work when running parallel compile again', async () => {
-    // This will create pipeline cache.
-    const a0 = tf.tensor1d([1, 1, 1]);
-    const b0 = tf.tensor1d([1, 1, 1]);
-    const c0 = tf.add(a0, b0);
-    const data = await c0.data();
-    expectArraysClose(data, [2, 2, 2]);
-
-    await parallelCompilationCommon(webGPUBackend);
-    await parallelCompilationCommon(webGPUBackend);
-  });
-
-  it('should not work if not call checkCompileCompletionAsync', async () => {
-    const a1 = tf.tensor1d([1, 1, 1]);
-    const b1 = tf.tensor1d([1, 1, 1]);
-
-    // Parallel compile but not call await (tf.backend() as
-    // WebGPUBackend).checkCompileCompletionAsync().
-    tf.env().set('WEBGPU_ENGINE_COMPILE_ONLY', true);
-    tf.add(a1, b1);
-
-    // Actual inference.
-    tf.env().set('WEBGPU_ENGINE_COMPILE_ONLY', false);
-    expect(() => tf.add(a1, b1))
-        .toThrowError(
-            'Please call checkCompileCompletionAsync to ensure parallel compilation is done!');
-  });
-
-  it('read data is invalid if parallel compilation is true', async () => {
-    const a1 = tf.tensor1d([1, 1, 1]);
-    const b1 = tf.tensor1d([1, 1, 1]);
-
-    // Parallel compile.
-    tf.env().set('WEBGPU_ENGINE_COMPILE_ONLY', true);
-    const c1 = tf.add(a1, b1);
-    await (tf.backend() as WebGPUBackend).checkCompileCompletionAsync();
-    // Read data is invalid.
-    expectArraysClose((await c1.data()).length, 0);
-  });
-
-  it('checkCompileCompletionAsync is nop if parallel compilation is false',
-     async () => {
-       const a1 = tf.tensor1d([1, 1, 1]);
-       const b1 = tf.tensor1d([1, 1, 1]);
-       // If parallel compilation is false, checkCompileCompletionAsync is nop.
-       tf.env().set('WEBGPU_ENGINE_COMPILE_ONLY', false);
-       const c1 = tf.add(a1, b1);
-       await (tf.backend() as WebGPUBackend).checkCompileCompletionAsync();
-       expectArraysClose(await c1.data(), [2, 2, 2]);
-     });
-});
-
-function createStagingGPUBufferFromData(
-    device: GPUDevice, data: number[], dtype: tf.DataType) {
-  const bytesPerElement = 4;
-  const sizeInBytes = data.length * bytesPerElement;
-
-  const gpuWriteBuffer = device.createBuffer({
-    mappedAtCreation: true,
-    size: sizeInBytes,
-    usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC
-  });
-  const arrayBuffer = gpuWriteBuffer.getMappedRange();
-  if (dtype === 'float32') {
-    new Float32Array(arrayBuffer).set(data);
-  } else if (dtype === 'int32') {
-    new Int32Array(arrayBuffer).set(data);
-  } else {
-    throw new Error(
-        `Creating tensor from GPUBuffer only supports` +
-        `'float32'|'int32' dtype, while the dtype is ${dtype}.`);
-  }
-  gpuWriteBuffer.unmap();
-  return gpuWriteBuffer;
-}
-
-function createGPUBufferFromData(
-    device: GPUDevice, data: number[], dtype: tf.DataType,
-    bufferUsage = GPUBufferUsage.COPY_DST | GPUBufferUsage.STORAGE |
-        GPUBufferUsage.COPY_SRC) {
-  const bytesPerElement = 4;
-  const sizeInBytes = data.length * bytesPerElement;
-
-  const gpuWriteBuffer = createStagingGPUBufferFromData(device, data, dtype);
-  const gpuReadBuffer = device.createBuffer(
-      {mappedAtCreation: false, size: sizeInBytes, usage: bufferUsage});
-
-  const copyEncoder = device.createCommandEncoder();
-  copyEncoder.copyBufferToBuffer(
-      gpuWriteBuffer, 0, gpuReadBuffer, 0, sizeInBytes);
-  const copyCommands = copyEncoder.finish();
-  device.queue.submit([copyCommands]);
-  gpuWriteBuffer.destroy();
-  return gpuReadBuffer;
-}
-
-async function testCreateTensorFromGPUBuffer(
-    dtype: tf.DataType, useDefaultShapeAndType = false, zeroCopy = false) {
-  const webGPUBackend = tf.backend() as WebGPUBackend;
-  const device = webGPUBackend.device;
-  const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
-  const bData = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4];
-  const expected = [2, 4, 6, 8, 6, 8, 10, 12, 10, 12, 14, 16, 14, 16, 18, 20];
-  const aBuffer = createGPUBufferFromData(device, aData, dtype);
-  const shape: number[] = [aData.length];
-  const startNumBytes = tf.memory().numBytes;
-  const startNumTensors = tf.memory().numTensors;
-  const webGPUData = {buffer: aBuffer, zeroCopy};
-  const a = useDefaultShapeAndType ? tf.tensor(webGPUData) :
-                                     tf.tensor(webGPUData, shape, dtype);
-  if (zeroCopy !== true) {
-    aBuffer.destroy();
-  }
-  const b = tf.tensor(bData, shape, dtype);
-  const result = tf.add(a, b);
-  tf.test_util.expectArraysClose(await result.data(), expected);
-  a.dispose();
-  b.dispose();
-  result.dispose();
-  const endNumBytes = tf.memory().numBytes;
-  const endNumTensors = tf.memory().numTensors;
-  expect(endNumBytes - startNumBytes).toEqual(0);
-  expect(endNumTensors - startNumTensors).toEqual(0);
-  if (zeroCopy === true) {
-    aBuffer.destroy();
-  }
-}
-
-function createTensorFromGPUTest(zeroCopy = false) {
-  it('use default shape and data type(float32)', async () => {
-    await testCreateTensorFromGPUBuffer('float32', true, zeroCopy);
-  });
-
-  it('work for float32', async () => {
-    await testCreateTensorFromGPUBuffer('float32', false, zeroCopy);
-  });
-
-  it('work for int32', async () => {
-    await testCreateTensorFromGPUBuffer('int32', false, zeroCopy);
-  });
-
-  it('work for read', async () => {
-    const webGPUBackend = tf.backend() as WebGPUBackend;
-    const device = webGPUBackend.device;
-    const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
-    const dtype = 'float32';
-    const aBuffer = createGPUBufferFromData(device, aData, dtype);
-    const shape: number[] = [aData.length];
-    const a = tf.tensor({buffer: aBuffer, zeroCopy}, shape, dtype);
-    if (zeroCopy !== true) {
-      aBuffer.destroy();
-    }
-    await a.data();
-    if (zeroCopy === true) {
-      aBuffer.destroy();
-    }
-  });
-
-  it('two tensors share the same GPUBuffer', async () => {
-    const webGPUBackend = tf.backend() as WebGPUBackend;
-    const device = webGPUBackend.device;
-    const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
-    const dtype = 'float32';
-    const aBuffer = createGPUBufferFromData(device, aData, dtype);
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-    const shape: number[] = [aData.length];
-    const webGPUData = {buffer: aBuffer, zeroCopy};
-    const a = tf.tensor(webGPUData, shape, dtype);
-    const b = tf.tensor(webGPUData, shape, dtype);
-    if (zeroCopy !== true) {
-      aBuffer.destroy();
-    }
-    const result = tf.add(a, b);
-    const expected =
-        [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32];
-    tf.test_util.expectArraysClose(await result.data(), expected);
-    a.dispose();
-    b.dispose();
-    result.dispose();
-    const endNumBytes = tf.memory().numBytes;
-    const endNumTensors = tf.memory().numTensors;
-    expect(endNumBytes - startNumBytes).toEqual(0);
-    expect(endNumTensors - startNumTensors).toEqual(0);
-    if (zeroCopy === true) {
-      aBuffer.destroy();
-    }
-  });
-
-  it('GPUBuffer size is bigger than tensor size', async () => {
-    const webGPUBackend = tf.backend() as WebGPUBackend;
-    const device = webGPUBackend.device;
-    const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
-    const dtype = 'float32';
-    const aBuffer = createGPUBufferFromData(device, aData, dtype);
-    const startNumBytes = tf.memory().numBytes;
-    const startNumTensors = tf.memory().numTensors;
-    // GPUBuffer.size is bigger than shape size
-    const shape: number[] = [aData.length - 1];
-    const webGPUData = {buffer: aBuffer, zeroCopy};
-    const a = tf.tensor(webGPUData, shape, dtype);
-    const b = tf.tensor(webGPUData, shape, dtype);
-    if (zeroCopy !== true) {
-      aBuffer.destroy();
-    }
-    const result = tf.add(a, b);
-    const expected = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30];
-    tf.test_util.expectArraysClose(await result.data(), expected);
-    a.dispose();
-    b.dispose();
-    result.dispose();
-    const endNumBytes = tf.memory().numBytes;
-    const endNumTensors = tf.memory().numTensors;
-    expect(endNumBytes - startNumBytes).toEqual(0);
-    expect(endNumTensors - startNumTensors).toEqual(0);
-    if (zeroCopy === true) {
-      aBuffer.destroy();
-    }
-  });
-
-  it('throw when GPUBuffer size is smaller than tensor size', async () => {
-    const webGPUBackend = tf.backend() as WebGPUBackend;
-    const device = webGPUBackend.device;
-    const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
-    const dtype = 'float32';
-    const aBuffer = createGPUBufferFromData(device, aData, dtype);
-    // Throw when GPUBuffer.size is smaller than shape size
-    const shape: number[] = [aData.length + 1];
-    const a = () => tf.tensor({buffer: aBuffer}, shape, dtype);
-    expect(a).toThrowError();
-    aBuffer.destroy();
-  });
-
-  it('throw when GPUBuffer usage is not correct', async () => {
-    const webGPUBackend = tf.backend() as WebGPUBackend;
-    const device = webGPUBackend.device;
-    const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
-    const dtype = 'float32';
-    // Create a GPUBuffer without GPUBufferUsage.STORAGE.
-    const aBuffer = createStagingGPUBufferFromData(device, aData, dtype);
-    // Throw when GPUBuffer usage is not correct.
-    const shape: number[] = [aData.length];
-    const a = () => tf.tensor({buffer: aBuffer, zeroCopy}, shape, dtype);
-    expect(a).toThrowError();
-    aBuffer.destroy();
-  });
-}
-
-describeWebGPU('create tensor from GPUBuffer', () => {
-  createTensorFromGPUTest();
-});
-
-describeWebGPU('create tensor from GPUBuffer with zero copy', () => {
-  createTensorFromGPUTest(true);
-});
diff --git a/tfjs-master/tfjs-backend-webgpu/src/backend_webgpu_test_registry.ts b/tfjs-master/tfjs-backend-webgpu/src/backend_webgpu_test_registry.ts
deleted file mode 100644
index 461af317b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/backend_webgpu_test_registry.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import {registerTestEnv} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-registerTestEnv({
-  name: 'webgpu',
-  backendName: 'webgpu',
-  flags: {
-    'WEBGPU_CPU_FORWARD': false,
-  },
-  isDataSync: true
-});
diff --git a/tfjs-master/tfjs-backend-webgpu/src/base.ts b/tfjs-master/tfjs-backend-webgpu/src/base.ts
deleted file mode 100644
index 1de43149b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/base.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import './flags_webgpu';
-
-import {env, registerBackend} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from './backend_webgpu';
-import {isWebGPUSupported} from './webgpu_util';
-
-if (isWebGPUSupported()) {
-  registerBackend('webgpu', async () => {
-    const gpuDescriptor: GPURequestAdapterOptions = {
-      powerPreference: env().get('WEBGPU_USE_LOW_POWER_GPU') ?
-          'low-power' :
-          'high-performance'
-    };
-
-    const adapter = await navigator.gpu.requestAdapter(gpuDescriptor);
-    const deviceDescriptor: GPUDeviceDescriptor = {};
-
-    const requiredFeatures = [];
-    if (adapter.features.has('timestamp-query')) {
-      requiredFeatures.push('timestamp-query');
-    }
-    if (adapter.features.has('bgra8unorm-storage')) {
-      requiredFeatures.push(['bgra8unorm-storage']);
-    }
-    deviceDescriptor.requiredFeatures =
-        requiredFeatures as Iterable<GPUFeatureName>;
-
-    const adapterLimits = adapter.limits;
-    deviceDescriptor.requiredLimits = {
-      'maxComputeWorkgroupStorageSize':
-          adapterLimits.maxComputeWorkgroupStorageSize,
-      'maxComputeWorkgroupsPerDimension':
-          adapterLimits.maxComputeWorkgroupsPerDimension,
-      'maxStorageBufferBindingSize': adapterLimits.maxStorageBufferBindingSize,
-      'maxBufferSize': adapterLimits.maxBufferSize,
-      'maxComputeWorkgroupSizeX': adapterLimits.maxComputeWorkgroupSizeX,
-      'maxComputeInvocationsPerWorkgroup':
-          adapterLimits.maxComputeInvocationsPerWorkgroup,
-    };
-
-    const device: GPUDevice = await adapter.requestDevice(deviceDescriptor);
-    const adapterInfo = await adapter.requestAdapterInfo();
-    return new WebGPUBackend(device, adapterInfo);
-  }, 3 /*priority*/);
-}
-
-// Export webgpu utilities
-export * from './webgpu';
diff --git a/tfjs-master/tfjs-backend-webgpu/src/batchnorm_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/batchnorm_webgpu.ts
deleted file mode 100644
index 9f5199ded..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/batchnorm_webgpu.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class BatchNormProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y?: number[], z?: number[]};
-  dispatch: [number, number, number];
-  variableNames: string[];
-  uniforms = 'varianceEpsilon : f32,';
-  // This is an experimental value.
-  workgroupSize: [number, number, number] = [128, 1, 1];
-  offsetShape: number[]|null;
-  scaleShape: number[]|null;
-  varianceEpsilon: number;
-  size = true;
-
-  constructor(
-      xShape: number[], meanShape: number[], varianceShape: number[],
-      offsetShape: number[]|null, scaleShape: number[]|null) {
-    this.variableNames = ['x', 'mean', 'variance'];
-    backend_util.assertAndGetBroadcastShape(xShape, meanShape);
-    backend_util.assertAndGetBroadcastShape(xShape, varianceShape);
-    this.outputShape = xShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    if (offsetShape != null) {
-      backend_util.assertAndGetBroadcastShape(xShape, offsetShape);
-      this.variableNames.push('offset');
-    }
-    if (scaleShape != null) {
-      backend_util.assertAndGetBroadcastShape(xShape, scaleShape);
-      this.variableNames.push('scale');
-    }
-    this.offsetShape = offsetShape;
-    this.scaleShape = scaleShape;
-    this.shaderKey = 'batchNorm';
-  }
-
-  getUserCode(): string {
-    let offsetSnippet = '0.0';
-    if (this.offsetShape != null) {
-      offsetSnippet = 'getOffsetByOutputIndex(index)';
-    }
-
-    let scaleSnippet = '1.0';
-    if (this.scaleShape != null) {
-      scaleSnippet = 'getScaleByOutputIndex(index)';
-    }
-
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size)
-        {
-          let xValue = getXByOutputIndex(index);
-          let meanValue = getMeanByOutputIndex(index);
-          let varianValue = getVarianceByOutputIndex(index);
-          let offsetValue = ${offsetSnippet};
-          let scaleValue = ${scaleSnippet};
-          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
-          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
-        }
-      }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/binary_op_complex_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/binary_op_complex_webgpu.ts
deleted file mode 100644
index 2b11c0093..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/binary_op_complex_webgpu.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {BinaryOpType, getBinaryOpString} from './binary_op_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class BinaryOpComplexProgram implements WebGPUProgram {
-  variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [128, 1, 1];
-  op: BinaryOpType;
-  size = true;
-
-  constructor(op: BinaryOpType, aShape: number[], bShape: number[]) {
-    this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = `binaryOpComplex_${op}`;
-    this.op = op;
-  }
-
-  getUserCode(): string {
-    const opStr = getBinaryOpString(this.op, false);
-    const userCode = `
-      fn binaryOpComplex(
-          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
-        ${opStr}
-      }
-
-      ${main('index')} {
-        if(index < uniforms.size) {
-          let areal = getARealByOutputIndex(index);
-          let aimag = getAImagByOutputIndex(index);
-          let breal = getBRealByOutputIndex(index);
-          let bimag = getBImagByOutputIndex(index);
-          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/binary_op_util.ts b/tfjs-master/tfjs-backend-webgpu/src/binary_op_util.ts
deleted file mode 100644
index d462ed1ac..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/binary_op_util.ts
+++ /dev/null
@@ -1,282 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export enum BinaryOpType {
-  ADD,
-  ATAN2,
-  COMPLEX_MULTIPLY_IMAG,
-  COMPLEX_MULTIPLY_REAL,
-  DIV,
-  ELU_DER,
-  EQUAL,
-  FLOOR_DIV,
-  GREATER,
-  GREATER_EQUAL,
-  LESS,
-  LESS_EQUAL,
-  LOGICAL_AND,
-  LOGICAL_OR,
-  MAX,
-  MIN,
-  MOD,
-  MUL,
-  NOT_EQUAL,
-  POW,
-  PRELU,
-  SQUARED_DIFFERENCE,
-  SUB
-}
-
-const ADD = 'let resultTemp = a + b;';
-const ATAN2 = 'let resultTemp = atan2(a, b);';
-// (Ar + Ai)(Br + Bi) =
-// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr
-// Yr = ArBr - AB
-// Yi = ArBi + AiBr
-const COMPLEX_MULTIPLY_REAL = 'let resultTemp = areal * breal - aimag * bimag;';
-const COMPLEX_MULTIPLY_IMAG = 'let resultTemp = areal * bimag + aimag * breal;';
-const DIV = 'let resultTemp = a / b;';
-const ELU_DER = 'let resultTemp = select(a * (b + 1.0), a, b >= b - b);';
-const EQUAL = `
-  let zero = sign(a) * 0 + 0;
-  let one = sign(b) * 0 + 1;
-  let resultTemp = select(zero, one, a == b);
-`;
-const FLOOR_DIV = `
-  let remainder =
-      select(a % b, round(a % b), (round(a) == a) & (round(b) == b));
-  let quotient = (a - remainder) / b;
-  let resultTemp =
-      round(select(quotient, quotient - 1, sign(remainder) == -sign(b)));
-`;
-const GREATER = `
-  let zero = sign(a) * 0 + 0;
-  let one = sign(b) * 0 + 1;
-  let resultTemp = select(zero, one, a > b);
-`;
-const GREATER_EQUAL = `
-  let zero = sign(a) * 0 + 0;
-  let one = sign(b) * 0 + 1;
-  let resultTemp = select(zero, one, a >= b);
-`;
-const LESS = `
-  let zero = sign(a) * 0 + 0;
-  let one = sign(b) * 0 + 1;
-  let resultTemp = select(zero, one, a < b);
-`;
-const LESS_EQUAL = `
-  let zero = sign(a) * 0 + 0;
-  let one = sign(b) * 0 + 1;
-  let resultTemp = select(zero, one, a <= b);
-`;
-const LOGICAL_AND = 'return f32(a >= 1.0 && b >= 1.0);';
-const LOGICAL_AND_VEC4 = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
-  vec4<f32>(b >= vec4<f32>(1.0)));`;
-const LOGICAL_OR = 'return f32(a >= 1.0 || b >= 1.0);';
-const LOGICAL_OR_VEC4 = `return min(vec4<f32>(a >= vec4<f32>(1.0)) +
-  vec4<f32>(b >= vec4<f32>(1.0)), vec4<f32>(1.0));`;
-const MAX = 'let resultTemp = max(a, b);';
-const MIN = 'let resultTemp = min(a, b);';
-const MOD = `
-  let isNaN = b == 0.;
-  var resultTemp = a % b;
-  resultTemp = select((resultTemp + b) % b, resultTemp,
-      (a < 0. && b < 0.) || (a >= 0. && b > 0.));
-`;
-const MOD_VEC4 = `
-  let isNaN = !vec4<bool>(b);
-  var resultTemp = vec4<f32>(a % b);
-  if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {
-    resultTemp[0] = (resultTemp[0] + b[0]) % b[0];
-  }
-  if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {
-    resultTemp[1] = (resultTemp[1] + b[1]) % b[1];
-  }
-  if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {
-    resultTemp[2] = (resultTemp[2] + b[2]) % b[2];
-  }
-  if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {
-    resultTemp[3] = (resultTemp[3] + b[3]) % b[3];
-  }
-`;
-const MUL = 'let resultTemp = a * b;';
-const NOT_EQUAL = `
-  var resultTemp = f32(a != b);
-  let valueForNaN = 1.0;
-`;
-const NOT_EQUAL_VEC4 = `
-  var resultTemp = vec4<f32>(a != b);
-  let valueForNaN = 1.0;
-`;
-
-const POW = `
-  let isNaN = a < 0.0 && floor(b) < b;
-  if (b == 0.0) {
-    return 1.0;
-  }
-  var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),
-      round(abs(b) % 2.0) != 1.0);
-`;
-const POW_VEC4 = `
-  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
-  let isModRound1 = vec4<f32>(isModRound1Bool);
-  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
-  var resultTemp = multiplier * pow(abs(a), b);
-
-  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
-  let isExpZero = b == vec4<f32>(0.0);
-  if (isExpZero.r) {
-    resultTemp.r = 1.0;
-  }
-  if (isExpZero.g) {
-    resultTemp.g = 1.0;
-  }
-  if (isExpZero.b) {
-    resultTemp.b = 1.0;
-  }
-  if (isExpZero.a) {
-    resultTemp.a = 1.0;
-  }
-  let isNaN = (a < vec4<f32>(0.0)) & (floor(b) < b);
-`;
-
-const PRELU = `if (a < 0.0) { return b * a; }  return a;`;
-const PRELU_VEC4 = `
-  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
-  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-`;
-const SQUARED_DIFFERENCE = 'let resultTemp = (a - b) * (a - b);';
-const SUB = 'let resultTemp = a - b;';
-
-export function getBinaryOpString(
-    type: BinaryOpType, useVec4?: boolean): string {
-  let doOpSnippet: string;
-
-  // Ops with NaN check
-  do {
-    switch (type) {
-      case BinaryOpType.ATAN2:
-        doOpSnippet = ATAN2;
-        break;
-      case BinaryOpType.MAX:
-        doOpSnippet = MAX;
-        break;
-      case BinaryOpType.MIN:
-        doOpSnippet = MIN;
-        break;
-      case BinaryOpType.MOD:
-        doOpSnippet = useVec4 ? MOD_VEC4 : MOD;
-        break;
-      case BinaryOpType.NOT_EQUAL:
-        doOpSnippet = useVec4 ? NOT_EQUAL_VEC4 : NOT_EQUAL;
-        break;
-      case BinaryOpType.POW:
-        doOpSnippet = useVec4 ? POW_VEC4 : POW;
-        break;
-      default:
-        continue;
-    }
-
-    let isNaN: string;
-    let dTypeN: string;
-    let boolN: string;
-    if (useVec4) {
-      isNaN = 'isnanVec4';
-      dTypeN = 'vec4<f32>';
-      boolN = 'vec4<bool>';
-    } else {
-      isNaN = 'isnan';
-      dTypeN = 'f32';
-      boolN = 'bool';
-    }
-
-    return `
-      let aIsNaN = ${isNaN}(a);
-      let aPostLegalization = select(a, ${dTypeN}(42), aIsNaN);
-      let bIsNaN = ${isNaN}(b);
-      let bPostLegalization = select(b, ${dTypeN}(42), bIsNaN);
-      let isNaN = false;
-      let valueForNaN = uniforms.NAN;
-      {
-        let a = aPostLegalization;
-        let b = bPostLegalization;
-        ${doOpSnippet}
-        return select(
-            resultTemp, ${dTypeN}(valueForNaN),
-            ${boolN}(isNaN) | aIsNaN | bIsNaN);
-      }
-    `;
-  } while (false);
-
-  // Ops without NaN check
-  switch (type) {
-    case BinaryOpType.ADD:
-      doOpSnippet = ADD;
-      break;
-    case BinaryOpType.COMPLEX_MULTIPLY_IMAG:
-      doOpSnippet = COMPLEX_MULTIPLY_IMAG;
-      break;
-    case BinaryOpType.COMPLEX_MULTIPLY_REAL:
-      doOpSnippet = COMPLEX_MULTIPLY_REAL;
-      break;
-    case BinaryOpType.DIV:
-      doOpSnippet = DIV;
-      break;
-    case BinaryOpType.ELU_DER:
-      doOpSnippet = ELU_DER;
-      break;
-    case BinaryOpType.EQUAL:
-      doOpSnippet = EQUAL;
-      break;
-    case BinaryOpType.FLOOR_DIV:
-      doOpSnippet = FLOOR_DIV;
-      break;
-    case BinaryOpType.GREATER:
-      doOpSnippet = GREATER;
-      break;
-    case BinaryOpType.GREATER_EQUAL:
-      doOpSnippet = GREATER_EQUAL;
-      break;
-    case BinaryOpType.LESS:
-      doOpSnippet = LESS;
-      break;
-    case BinaryOpType.LESS_EQUAL:
-      doOpSnippet = LESS_EQUAL;
-      break;
-    case BinaryOpType.LOGICAL_AND:
-      return useVec4 ? LOGICAL_AND_VEC4 : LOGICAL_AND;
-    case BinaryOpType.LOGICAL_OR:
-      return useVec4 ? LOGICAL_OR_VEC4 : LOGICAL_OR;
-    case BinaryOpType.MUL:
-      doOpSnippet = MUL;
-      break;
-    case BinaryOpType.PRELU:
-      return useVec4 ? PRELU_VEC4 : PRELU;
-    case BinaryOpType.SQUARED_DIFFERENCE:
-      doOpSnippet = SQUARED_DIFFERENCE;
-      break;
-    case BinaryOpType.SUB:
-      doOpSnippet = SUB;
-      break;
-    default:
-      // throw new Error(`BinaryType ${type} is not implemented!`);
-  }
-  return `
-    ${doOpSnippet}
-    return resultTemp;
-  `;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/binary_op_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/binary_op_webgpu.ts
deleted file mode 100644
index 588fe9282..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/binary_op_webgpu.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, util} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType, getBinaryOpString} from './binary_op_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class BinaryOpProgram implements WebGPUProgram {
-  dispatch: [number, number, number];
-  dispatchLayout: {x: number[]};
-  outputComponent: number;
-  op: BinaryOpType;
-  outputShape: number[];
-  shaderKey: string;
-  size = true;
-  variableNames = ['A', 'B'];
-  workgroupSize: [number, number, number];
-  variableComponents: number[];
-
-  private lastDimensionSize: number;
-  private useSharedMemoryWithA: boolean;
-  private useSharedMemoryWithB: boolean;
-  private type: string;
-
-  constructor(op: BinaryOpType, aShape: number[], bShape: number[]) {
-    this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.op = op;
-
-    this.useSharedMemoryWithA =
-        aShape.length <= 1 && bShape.length > 1 && aShape[0] < 128;
-    this.useSharedMemoryWithB =
-        bShape.length <= 1 && aShape.length > 1 && bShape[0] < 128;
-
-    if (this.useSharedMemoryWithA || this.useSharedMemoryWithB) {
-      this.outputComponent = 1;
-      this.variableComponents = [1, 1];
-      // lastDimensionSize is used as sharedBuf array size, so can not be
-      // used as uniform.
-      this.lastDimensionSize =
-          this.useSharedMemoryWithB ? bShape[0] : aShape[0];
-      this.shaderKey = `binary_${op}_${this.lastDimensionSize}`;
-      this.type = 'shared';
-      // This is an experimental value when using shared memory.
-      // Note that the maximum of workgroup X dimension is 256.
-      this.workgroupSize = [256, 1, 1];
-    } else {
-      const aDivisibleBy4 =
-          aShape.length > 0 && aShape[aShape.length - 1] % 4 === 0;
-      const bDivisibleBy4 =
-          bShape.length > 0 && bShape[bShape.length - 1] % 4 === 0;
-      if (aDivisibleBy4 && bDivisibleBy4) {
-        this.outputComponent = 4;
-        this.variableComponents = [4, 4];
-      } else if (
-          (aDivisibleBy4 &&
-           (util.isScalarShape(bShape) || bShape[bShape.length - 1] === 1)) ||
-          (bDivisibleBy4 &&
-           (util.isScalarShape(aShape) || aShape[aShape.length - 1] === 1))) {
-        this.outputComponent = 4;
-        this.variableComponents = aDivisibleBy4 ? [4, 1] : [1, 4];
-      } else {
-        this.outputComponent = 1;
-        this.variableComponents = [1, 1];
-      }
-      this.type = 'nonshared';
-      this.shaderKey = `binary_${op}_${this.variableComponents}`;
-      // TODO(jiajia.qin@intel.com): Heuristically select a good work group
-      // size.
-      this.workgroupSize = [128, 1, 1];
-    }
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        [this.outputComponent, 1, 1]);
-  }
-
-  getUserCode(): string {
-    let userCode;
-    const dType = this.outputComponent === 4 ? 'vec4<f32>' : 'f32';
-    const opFnStr = `
-    fn binaryOperation(a : ${dType}, b : ${dType}) -> ${dType} {
-      ${getBinaryOpString(this.op, this.outputComponent === 4)}
-    };
-    `;
-
-    if (this.type === 'shared') {
-      const sharedIndexSnippet = this.lastDimensionSize > 1 ?
-          `coords[${this.outputShape.length - 1}]` :
-          '0';
-      const accessDataSnippet = this.useSharedMemoryWithB ?
-          `let a = getAByOutputIndex(index);
-          let b = sharedBuf[${sharedIndexSnippet}];` :
-          `let a = sharedBuf[${sharedIndexSnippet}];
-          let b = getBByOutputIndex(index);`;
-      userCode = `
-        ${opFnStr}
-        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
-        ${main('index')} {
-          // Fill in the shared memory buffer.
-          let localIndex = i32(localId.x);
-          if(localIndex < ${this.lastDimensionSize}) {
-            sharedBuf[localIndex] = f32(${
-          this.useSharedMemoryWithB ? 'B' : 'A'}[localIndex]);
-          }
-          workgroupBarrier();
-
-          if(index < uniforms.size) {
-            let coords = getCoordsFromIndex(index);
-            ${accessDataSnippet}
-            setOutputAtIndex(index, binaryOperation(a, b));
-          }
-        }
-        `;
-    } else {
-      userCode = `
-       ${opFnStr}
-       ${main('index')} {
-         if (index < uniforms.size) {
-           let coords = getCoordsFromIndex(index * ${this.outputComponent});
-           let a = ${dType}(getAByOutputCoords(coords));
-           let b = ${dType}(getBByOutputCoords(coords));
-           setOutputAtIndex(index, binaryOperation(a, b));
-         }
-       }
-       `;
-    }
-
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/bincount_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/bincount_webgpu.ts
deleted file mode 100644
index 22ca01513..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/bincount_webgpu.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {atomicAddSnippet} from './shader_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-const writeSnippet = `
-  fn bincount_write(index: i32, value: f32) {
-    ${atomicAddSnippet('&result[index]', 'value', 'float32')}
-  }
-`;
-
-const binaryWriteSnippet = `
-  fn bincount_write(index: i32, value: f32) {
-    atomicStore(&result[index], bitcast<i32>(value));
-  }
-`;
-
-export class BincountProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms = 'binCountSize : i32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  atomic = true;
-  hasWeights = true;
-  binaryOutput = false;
-  rank: number;
-
-  constructor(
-      shape: [number]|[number, number], hasWeights: boolean,
-      binaryOutput = false) {
-    this.outputShape = shape;
-    this.rank = shape.length;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.binaryOutput = binaryOutput;
-    if (binaryOutput) {
-      this.atomic = false;
-    }
-    this.hasWeights = hasWeights;
-    if (this.hasWeights) {
-      this.variableNames.push('w');
-    }
-    this.shaderKey =
-        `bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${this.binaryOutput ? binaryWriteSnippet : writeSnippet}
-  ${main('index')} {
-    ${
-        this.rank === 1 ?
-            `if (index < uniforms.xShape) {
-      let indexVal = i32(getX(index));
-      if (indexVal < uniforms.binCountSize) {
-        let value = ${
-                this.binaryOutput ? 1. :
-                                    (this.hasWeights ? 'getW(index)' : '1.')};
-        bincount_write(indexVal, value);
-      }
-    }` :
-            `let coord = getCoordsFromIndex(index);
-    if (coordsInBounds2D(coord, uniforms.xShape)) {
-      let indexVal = i32(getX(coord[0], coord[1]));
-      if (indexVal < uniforms.binCountSize) {
-        let value = ${
-                this.binaryOutput ?
-                    1. :
-                    (this.hasWeights ? 'getW(coord[0], coord[1])' : '1.')};
-        bincount_write(coord.x * uniforms.binCountSize + indexVal, value);
-      }
-    }`}
-  }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/broadcast_args_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/broadcast_args_webgpu.ts
deleted file mode 100644
index 1e0075437..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/broadcast_args_webgpu.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class BroadcastArgsProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['s0', 's1'];
-  uniforms = 's0Size : i32, s1Size : i32, ';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(shape: number) {
-    this.outputShape = [shape];
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'broadcastArgs';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-  ${main('index')} {
-    if (index < uniforms.size) {
-      var s0 = 1.0;
-      var s1 = 1.0;
-      let indexS0 = index - uniforms.size + uniforms.s0Size;
-      let indexS1 = index - uniforms.size + uniforms.s1Size;
-      if (indexS0 >= 0) {
-        s0 = getS0(indexS0);
-      }
-      if (indexS1 >= 0) {
-        s1 = getS1(indexS1);
-      }
-
-      if (s0 == 1.0) {
-        setOutputAtIndex(index, s1);
-      } else if (s1 == 1.0) {
-        setOutputAtIndex(index, s0);
-      } else if (s0 != s1) {
-        setOutputAtIndex(index, uniforms.NAN);
-      } else {
-        setOutputAtIndex(index, s0);
-      }
-    }
-  }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/buffer_manager.ts b/tfjs-master/tfjs-backend-webgpu/src/buffer_manager.ts
deleted file mode 100644
index bafcebcc3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/buffer_manager.ts
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export class BufferManager {
-  private numUsedBuffers = 0;
-  private numFreeBuffers = 0;
-  private freeBuffers: Map<string, GPUBuffer[]> = new Map();
-  private usedBuffers: Map<string, GPUBuffer[]> = new Map();
-
-  public numBytesUsed = 0;
-  public numBytesAllocated = 0;
-
-  constructor(private device: GPUDevice) {}
-
-  acquireBuffer(
-      size: number, usage: GPUBufferUsageFlags, mappedAtCreation = false,
-      reuse = true) {
-    let buffer;
-    const key = getBufferKey(size, usage);
-
-    if (reuse) {
-      if (!this.freeBuffers.has(key)) {
-        this.freeBuffers.set(key, []);
-      }
-
-      if (this.freeBuffers.get(key).length > 0) {
-        buffer = this.freeBuffers.get(key).pop();
-        this.numFreeBuffers--;
-      } else {
-        buffer = this.device.createBuffer({size, usage, mappedAtCreation});
-        this.numBytesAllocated += size;
-      }
-    } else {
-      buffer = this.device.createBuffer({size, usage, mappedAtCreation});
-      this.numBytesAllocated += size;
-    }
-
-    if (!this.usedBuffers.has(key)) {
-      this.usedBuffers.set(key, []);
-    }
-    this.usedBuffers.get(key).push(buffer);
-    this.numUsedBuffers++;
-    this.numBytesUsed += size;
-
-    return buffer;
-  }
-
-  releaseBuffer(buffer: GPUBuffer, reuse = true) {
-    if (this.freeBuffers.size === 0) {
-      return;
-    }
-
-    const size = buffer.size;
-    const usage = buffer.usage;
-
-    const key = getBufferKey(size, usage);
-    const bufferArray = this.usedBuffers.get(key);
-    const index = bufferArray.indexOf(buffer);
-    if (index < 0) {
-      throw new Error('Cannot find the buffer in buffer manager');
-    }
-    bufferArray[index] = bufferArray[bufferArray.length - 1];
-    bufferArray.pop();
-    this.numUsedBuffers--;
-    this.numBytesUsed -= size;
-
-    if (reuse) {
-      this.freeBuffers.get(key).push(buffer);
-      this.numFreeBuffers++;
-    } else {
-      buffer.destroy();
-      this.numBytesAllocated -= size;
-    }
-  }
-
-  getNumUsedBuffers(): number {
-    return this.numUsedBuffers;
-  }
-
-  getNumFreeBuffers(): number {
-    return this.numFreeBuffers;
-  }
-
-  dispose() {
-    this.freeBuffers.forEach((buffers, key) => {
-      buffers.forEach(buffer => {
-        buffer.destroy();
-      });
-    });
-
-    this.usedBuffers.forEach((buffers, key) => {
-      buffers.forEach(buffer => {
-        buffer.destroy();
-      });
-    });
-
-    this.freeBuffers = new Map();
-    this.usedBuffers = new Map();
-    this.numUsedBuffers = 0;
-    this.numFreeBuffers = 0;
-    this.numBytesUsed = 0;
-    this.numBytesAllocated = 0;
-  }
-}
-
-function getBufferKey(size: number, usage: GPUBufferUsageFlags) {
-  return `${size}_${usage}`;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/clip_vec4_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/clip_vec4_webgpu.ts
deleted file mode 100644
index 1cb5a2a0d..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/clip_vec4_webgpu.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ClipVec4Program implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  variableNames = ['A'];
-  uniforms = 'minVal : f32, maxVal : f32,';
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workPerThread = 4;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  outputComponent = 4;
-  size = true;
-
-  constructor(outputShape: number[]) {
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        [this.workPerThread, 1, 1]);
-    this.shaderKey = 'clipVec4';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if(index < uniforms.size) {
-          let value = getAByOutputIndex(index);
-          var clampedValue = clamp(
-              value, vec4<f32>(uniforms.minVal), vec4<f32>(uniforms.maxVal));
-          clampedValue = select(clampedValue, value, isnanVec4(value));
-          setOutputAtIndex(index, clampedValue);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/clip_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/clip_webgpu.ts
deleted file mode 100644
index 7e0746b64..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/clip_webgpu.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ClipProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  variableNames = ['A'];
-  uniforms = 'minVal : f32, maxVal : f32,';
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  minVal: number;
-  maxVal: number;
-  size = true;
-
-  constructor(outputShape: number[]) {
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'clip';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if(index < uniforms.size) {
-          let value = getAByOutputIndex(index);
-          if (isnan(value)) {
-            setOutputAtIndex(index, value);
-            return;
-          }
-          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/complex_abs_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/complex_abs_webgpu.ts
deleted file mode 100644
index b0c8482ba..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/complex_abs_webgpu.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ComplexAbsProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['real', 'imag'];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(shape: number[]) {
-    this.outputShape = shape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'complexAbs';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.size) {
-        let re = abs(getRealByOutputIndex(index));
-        let im = abs(getImagByOutputIndex(index));
-        let mx = max(re, im);
-
-        // The length function in wgsl may be not underflow-safe on some GPUs.
-        // So the safe solution is to ensure underflow-safety in all cases.
-        setOutputAtIndex(index, select(mx * length(vec2<f32>(1, min(re, im)/mx)), 0.0, mx == 0.0));
-      }
-    }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/concat_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/concat_webgpu.ts
deleted file mode 100644
index d3485d838..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/concat_webgpu.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ConcatProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames: string[];
-  uniforms = '';
-  workPerThread = 1;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-  offsetLength: number;
-
-  constructor(shapes: Array<[number, number]>) {
-    this.outputShape =
-        backend_util.computeOutShape(shapes, 1 /* axis */) as [number, number];
-    this.variableNames = shapes.map((_, i) => `T${i}`);
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        [this.workPerThread, 1, 1]);
-
-    this.offsetLength = shapes.length - 1;
-    for (let i = 0; i < this.offsetLength; i++) {
-      this.uniforms += `offset${i} : i32,`;
-    }
-    this.shaderKey = 'concat';
-  }
-
-  getUserCode(): string {
-    const snippets: string[] = [];
-    if (this.offsetLength > 0) {
-      snippets.push(
-          `if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }`);
-      for (let i = 1; i < this.offsetLength; i++) {
-        snippets.push(
-            `else if (yC < uniforms.offset${[i]}){ ` +
-            `setOutputAtCoords(coords.x, coords.y, getT${
-                i}(yR, yC - uniforms.offset${i - 1})); }`);
-      }
-      const lastIndex = this.offsetLength;
-      const lastShiftIndex = this.offsetLength - 1;
-      snippets.push(`else { setOutputAtCoords(coords.x, coords.y, getT${
-          lastIndex}(yR, yC - uniforms.offset${lastShiftIndex})); }`);
-    } else {
-      snippets.push(`setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));`);
-    }
-
-    const userCode = `
-      ${main('index')} {
-        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
-          let flatIndex = index * ${this.workPerThread} + i;
-          if(flatIndex < uniforms.size) {
-            let coords = getCoordsFromIndex(flatIndex);
-            let yR = coords.x;
-            let yC = coords.y;
-
-            ${snippets.join('\n        ')}
-          }
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/conv2d_mm_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/conv2d_mm_webgpu.ts
deleted file mode 100644
index 32791a0dd..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/conv2d_mm_webgpu.ts
+++ /dev/null
@@ -1,270 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {activationFnSnippet, biasActivationSnippet} from './activation_util';
-import {makeMatMulPackedSource, makeMatMulPackedVec4Source} from './matmul_packed_webgpu';
-import {typeSnippet, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, computeWorkgroupSizeForConv2d, computeWorkPerThreadForConv2d} from './webgpu_util';
-
-function conv2dCommonSnippet(
-    isChannelsLast: boolean, fitAOuter: boolean, fitBOuter: boolean,
-    fitInner: boolean, addBias = false,
-    activation: backend_util.Activation = null,
-    hasPreluActivationWeights = false, innerElementSizeX = 4,
-    innerElementSizeW = 4, innerElementSize = 4) {
-  const getXSnippet = (innerElementSize: number) => {
-    switch (innerElementSize) {
-      case 1:
-        return 'resData = f32(x[xIndex]);';
-      case 3:
-        return 'resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);';
-      case 4:
-        return 'resData = vec4<f32>(x[xIndex / 4]);';
-      default:
-        throw new Error(
-            `innerElementSize ${innerElementSize} is not supported.`);
-    }
-  };
-  const getWSnippet = (innerElementSize: number) => {
-    switch (innerElementSize) {
-      case 1:
-        return 'return f32(W[row * uniforms.wShape[3] + col]);';
-      case 4:
-        return 'return vec4<f32>(W[(row * uniforms.wShape[3] + col) / 4]);';
-      default:
-        throw new Error(
-            `innerElementSize ${innerElementSize} is not supported.`);
-    }
-  };
-  const coordASnippet = isChannelsLast ? `
-      let coord = vec4<i32>(batch, xRow, xCol, xCh);
-      ` :
-                                         `
-      let coord = vec4<i32>(batch, xCh, xRow, xCol);
-      `;
-
-  const coordResSnippet = isChannelsLast ? `
-      let coords = vec4<i32>(
-        batch,
-        row / outWidth,
-        row % outWidth,
-        col);
-      ` :
-                                           `
-      let coords = vec4<i32>(
-        batch,
-        row,
-        col / outWidth,
-        col % outWidth);
-      `;
-
-  const xHight = isChannelsLast ? 'uniforms.xShape[1]' : 'uniforms.xShape[2]';
-  const xWidth = isChannelsLast ? 'uniforms.xShape[2]' : 'uniforms.xShape[3]';
-  const row = isChannelsLast ? 'row' : 'col';
-  const col = isChannelsLast ? 'col' : 'row';
-  const readXSnippet = `
-      let inChannels = uniforms.wShape[2];
-      let outWidth = ${
-      isChannelsLast ? 'uniforms.outShape[2]' : 'uniforms.outShape[3]'};
-      let outRow = ${row} / outWidth;
-      let outCol = ${row} % outWidth;
-
-      let WRow = ${col} / (uniforms.filterDims[1] * inChannels);
-      let WCol = ${col} / inChannels % uniforms.filterDims[1];
-      let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];
-      let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];
-      let xCh = ${col} % inChannels;
-      var resData = ${typeSnippet(innerElementSizeX)}(0.0);
-      // The bounds checking is always needed since we use it to pad zero for
-      // the 'same' padding type.
-      if (xRow >= 0 && xRow < ${xHight} && xCol >= 0 && xCol < ${xWidth}) {
-        ${coordASnippet}
-        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
-        ${getXSnippet(innerElementSizeX)}
-      }
-      return resData;`;
-
-  const sampleX = isChannelsLast ? (fitAOuter && fitInner ? `
-      ${readXSnippet}` :
-                                                            `
-      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
-        ${readXSnippet}
-      }
-      return ${typeSnippet(innerElementSizeX)}(0.0);`) :
-                                   (fitInner && fitBOuter ? `
-      ${readXSnippet}` :
-                                                            `
-      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
-        ${readXSnippet}
-      }
-      return ${typeSnippet(innerElementSizeX)}(0.0);`);
-
-  const sampleW = `${getWSnippet(innerElementSizeW)}`;
-
-  const resType = typeSnippet(innerElementSize);
-  const aType = isChannelsLast ? typeSnippet(innerElementSizeX) :
-                                 typeSnippet(innerElementSizeW);
-  const bType = isChannelsLast ? typeSnippet(innerElementSizeW) :
-                                 typeSnippet(innerElementSizeX);
-  const userCode = `
-      ${
-      activationFnSnippet(
-          activation, hasPreluActivationWeights, innerElementSize === 4, 4)}
-      fn mm_readA(batch: i32, row : i32, col : i32) -> ${aType} {
-        ${isChannelsLast ? sampleX : sampleW}
-      }
-
-      fn mm_readB(batch: i32, row : i32, col : i32) -> ${bType} {
-        ${isChannelsLast ? sampleW : sampleX}
-      }
-
-      fn mm_write(batch: i32, row : i32, col : i32, valueIn : ${resType}) {
-        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
-        {
-        var value = valueIn;
-        let outWidth = ${
-      isChannelsLast ? 'uniforms.outShape[2]' : 'uniforms.outShape[3]'};
-        ${coordResSnippet}
-        ${biasActivationSnippet(addBias, activation)}
-        setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
-        }
-      }`;
-  return userCode;
-}
-
-export class Conv2DMMProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[], z: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'W'];
-  variableComponents: number[];
-  uniforms =
-      `filterDims : vec2<i32>, pads : vec2<i32>, strides : vec2<i32>, dilations : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
-  workgroupSize: [number, number, number];
-  elementsPerThread: [number, number, number];
-  addBias: boolean;
-  activation: backend_util.Activation;
-  hasPreluActivationWeights: boolean;
-  isChannelsLast: boolean;
-  fitAOuter: boolean;
-  fitBOuter: boolean;
-  fitInner: boolean;
-  tileAOuter: number;
-  tileBOuter: number;
-  tileInner: number;
-  innerElementSize: number;
-  isVec4?: boolean;
-  outputComponent: number;
-  private sequentialAccessByThreads: boolean;
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, dimAOuter: number, dimBOuter: number,
-      dimInner: number, addBias = false,
-      activation: backend_util.Activation = null,
-      hasPreluActivationWeights = false, sequentialAccessByThreads = false) {
-    this.outputShape = convInfo.outShape;
-    this.isChannelsLast = convInfo.dataFormat === 'channelsLast';
-    this.isVec4 =
-        (((convInfo.inChannels % 4 === 0 || convInfo.inChannels % 3 === 0) &&
-          this.isChannelsLast) ||
-         (convInfo.outWidth % 4 === 0 && !this.isChannelsLast)) &&
-        convInfo.outChannels % 4 === 0;
-    this.dispatchLayout = this.isChannelsLast ? {x: [3], y: [1, 2], z: [0]} :
-                                                {x: [2, 3], y: [1], z: [0]};
-    this.workgroupSize = computeWorkgroupSizeForConv2d(
-        this.dispatchLayout, this.outputShape, this.isVec4);
-    this.elementsPerThread = computeWorkPerThreadForConv2d(
-        this.dispatchLayout, this.outputShape, this.isVec4);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        this.elementsPerThread);
-
-    if (this.isVec4) {
-      this.outputComponent = 4;
-      if (this.isChannelsLast && convInfo.inChannels % 4 !== 0) {
-        this.innerElementSize = 3;
-        this.variableComponents = [1, 4];
-      } else {
-        this.innerElementSize = 4;
-        this.variableComponents = [4, 4];
-      }
-
-      if (addBias) {
-        this.variableNames.push('bias');
-        this.variableComponents.push(4);
-      }
-
-      if (hasPreluActivationWeights) {
-        this.variableNames.push('preluActivationWeights');
-        this.variableComponents.push(4);
-      }
-    } else {
-      this.innerElementSize = this.elementsPerThread[0];
-      if (addBias) {
-        this.variableNames.push('bias');
-      }
-
-      if (hasPreluActivationWeights) {
-        this.variableNames.push('preluActivationWeights');
-      }
-    }
-
-    this.sequentialAccessByThreads = sequentialAccessByThreads;
-    this.addBias = addBias;
-    this.activation = activation;
-    this.hasPreluActivationWeights = hasPreluActivationWeights;
-
-    this.tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1];
-    this.tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0];
-    this.tileInner = Math.max(
-        this.workgroupSize[0] * this.innerElementSize, this.workgroupSize[1]);
-
-    this.fitAOuter = dimAOuter % this.tileAOuter === 0;
-    this.fitBOuter = dimBOuter % this.tileBOuter === 0;
-    this.fitInner = dimInner % this.tileInner === 0;
-
-    this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${
-        this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${
-        this.innerElementSize}_${this.isChannelsLast}_${
-        this.sequentialAccessByThreads}`;
-  }
-
-  getUserCode(): string {
-    const matMulSource = this.isVec4 ?
-        makeMatMulPackedVec4Source(
-            this.elementsPerThread, this.workgroupSize, !this.isChannelsLast,
-            this.tileInner) :
-        makeMatMulPackedSource(
-            this.elementsPerThread, this.workgroupSize, !this.isChannelsLast,
-            this.tileInner, false, null, this.sequentialAccessByThreads);
-    const elementsSize =
-        this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1];
-    const userCode = `
-    ${
-        conv2dCommonSnippet(
-            this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner,
-            this.addBias, this.activation, this.hasPreluActivationWeights,
-            elementsSize[0], elementsSize[1], elementsSize[2])}
-    ${matMulSource}
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/conv2d_naive_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/conv2d_naive_webgpu.ts
deleted file mode 100644
index 90d936f6f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/conv2d_naive_webgpu.ts
+++ /dev/null
@@ -1,121 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {activationFnSnippet, biasActivationSnippet} from './activation_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch} from './webgpu_util';
-
-export class Conv2DNaiveProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[], z: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'W'];
-  uniforms =
-      'filterDims: vec2<i32>, pads: vec2<i32>, strides: vec2<i32>, dilations: vec2<i32>,';
-  workgroupSize: [number, number, number] = [4, 4, 8];
-  addBias: boolean;
-  activation: backend_util.Activation;
-  hasPreluActivationWeights: boolean;
-  isChannelsLast: boolean;
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, addBias = false,
-      activation: backend_util.Activation = null,
-      hasPreluActivationWeights = false) {
-    this.outputShape = convInfo.outShape;
-    this.isChannelsLast = convInfo.dataFormat === 'channelsLast';
-    this.dispatchLayout = this.isChannelsLast ? {x: [2], y: [1], z: [0, 3]} :
-                                                {x: [3], y: [2], z: [0, 1]};
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.addBias = addBias;
-    this.activation = activation;
-    this.hasPreluActivationWeights = hasPreluActivationWeights;
-
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-
-    if (hasPreluActivationWeights) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    this.shaderKey = `conv2dnaive_${this.activation}_${this.isChannelsLast}`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-       ${
-        activationFnSnippet(
-            this.activation, this.hasPreluActivationWeights, false, 4)}
-       fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{
-         let coords = vec4<i32>(batch, row, col, chan);
-         if (coordsInBounds4D(coords, uniforms.xShape)) {
-           return  getX(batch, row, col, chan);
-         } else {
-          return 0.0;
-         }
-       }
-       fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
-         let coords = vec4<i32>(row, col, xChannel, outChannel);
-         if(coordsInBounds4D(coords, uniforms.wShape)) {
-           return getW(row, col, xChannel, outChannel);
-          } else {
-            return 0.0;
-          }
-       }
-       fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {
-         let coords = ${
-        this.isChannelsLast ? `vec4<i32>(batch, row, col, chan);` :
-                              `vec4<i32>(batch, chan, row, col);`}
-         if (coordsInBounds4D(coords, uniforms.outShape)) {
-           var value = valueIn;
-           ${biasActivationSnippet(this.addBias, this.activation)}
-           setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);
-         }
-       }
-       ${main('index')} {
-         let coords = getOutputCoords();
-         let batch = coords[0];
-         let outChannel = ${this.isChannelsLast ? `coords[3];` : `coords[1];`}
-         let outRow = ${this.isChannelsLast ? `coords[1];` : `coords[2];`}
-         let outCol = ${this.isChannelsLast ? `coords[2];` : `coords[3];`}
-         var acc : f32 = 0.0;
-         for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
-           for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
-             let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];
-             let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];
-             for (var xChannel = 0; xChannel < ${
-        this.isChannelsLast ? `uniforms.xShape[3];` :
-                              `uniforms.xShape[1];`} xChannel = xChannel + 1) {
-               ${
-        this.isChannelsLast ? `let v = readInp(batch, xRow, xCol, xChannel);` :
-                              `let v = readInp(batch, xChannel, xRow, xCol);`}
-               let f = readFilt(row, col, xChannel, outChannel);
-               acc = acc + v * f;
-             }
-           }
-         }
-         writeResult(batch, outRow, outCol, outChannel, acc);
-       }
-     `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/conv3d_naive_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/conv3d_naive_webgpu.ts
deleted file mode 100644
index cb86c15a6..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/conv3d_naive_webgpu.ts
+++ /dev/null
@@ -1,129 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class Conv3DNaiveProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'W'];
-  uniforms =
-      'filterDims: vec3<i32>, pads: vec3<i32>, strides: vec3<i32>, dilations: vec3<i32>,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.outShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = `conv3dnaive`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getOutputCoords();
-        let batch = coords.x;
-        let d2 = coords.u;
-
-        let xFRCCorner = vec3<i32>(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;
-        let xFCorner = xFRCCorner.x;
-        let xRCorner = xFRCCorner.y;
-        let xCCorner = xFRCCorner.z;
-
-        let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;
-        let inputDepthVec4Remainder = uniforms.xShape.u % 4;
-
-        var dotProd = 0.0;
-        for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {
-          let xF = xFCorner + wF * uniforms.dilations[0];
-          if (xF < 0 || xF >= uniforms.xShape.y) {
-            continue;
-          }
-
-          for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {
-            let xR = xRCorner + wR * uniforms.dilations[1];
-            if (xR < 0 || xR >= uniforms.xShape.z) {
-              continue;
-            }
-
-            for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {
-              let xC = xCCorner + wC * uniforms.dilations[2];
-              if (xC < 0 || xC >= uniforms.xShape.w) {
-                continue;
-              }
-
-              for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {
-                let xValues = vec4<f32>(
-                  getX(batch, xF, xR, xC, d1),
-                  getX(batch, xF, xR, xC, d1 + 1),
-                  getX(batch, xF, xR, xC, d1 + 2),
-                  getX(batch, xF, xR, xC, d1 + 3)
-                );
-                let wValues = vec4<f32>(
-                  getW(wF, wR, wC, d1, d2),
-                  getW(wF, wR, wC, d1 + 1, d2),
-                  getW(wF, wR, wC, d1 + 2, d2),
-                  getW(wF, wR, wC, d1 + 3, d2)
-                );
-
-                dotProd += dot(xValues, wValues);
-              }
-
-              if (inputDepthVec4Remainder == 1) {
-                dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *
-                  getW(wF, wR, wC, inputDepthNearestVec4, d2);
-              } else if (inputDepthVec4Remainder == 2) {
-                let xValues = vec2<f32>(
-                  getX(batch, xF, xR, xC, inputDepthNearestVec4),
-                  getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)
-                );
-                let wValues = vec2<f32>(
-                  getW(wF, wR, wC, inputDepthNearestVec4, d2),
-                  getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)
-                );
-                dotProd += dot(xValues, wValues);
-              } else if (inputDepthVec4Remainder == 3) {
-                let xValues = vec3<f32>(
-                  getX(batch, xF, xR, xC, inputDepthNearestVec4),
-                  getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
-                  getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)
-                );
-                let wValues = vec3<f32>(
-                  getW(wF, wR, wC, inputDepthNearestVec4, d2),
-                  getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),
-                  getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)
-                );
-                dotProd += dot(xValues, wValues);
-              }
-            }
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }`;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/conv_backprop_depthwise_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/conv_backprop_depthwise_webgpu.ts
deleted file mode 100644
index 7d3cf269f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/conv_backprop_depthwise_webgpu.ts
+++ /dev/null
@@ -1,154 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class DepthwiseConv2DDerFilterProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'dy'];
-  uniforms =
-      `strides : vec2<i32>, pads : vec2<i32>, filterDims : vec2<i32>, outHeight : i32,
-      outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.filterShape;
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = `depthwise_conv2d_backprop_filter`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let wR = coords[0];
-        let wC = coords[1];
-        let d1 = coords[2];
-        let dm = coords[3];
-        let d2 = d1 * uniforms.channelMul + dm;
-
-        var dotProd = 0.0;
-        for (var b = 0; b < uniforms.batchSize; b++) {
-          for (var yR = 0; yR < uniforms.outHeight; yR++) {
-            let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];
-
-            if (xR < 0 || xR >= uniforms.inHeight) {
-              continue;
-            }
-
-            for (var yC = 0; yC < uniforms.outWidth; yC++) {
-              let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];
-
-              if (xC < 0 || xC >= uniforms.inWidth) {
-                continue;
-              }
-
-              let dyValue = getDy(b, yR, yC, d2);
-              let xValue = getX(b, xR, xC, d1);
-              dotProd += xValue * dyValue;
-            }
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-    `;
-    return userCode;
-  }
-}
-
-export class DepthwiseConv2DDerInputProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['dy', 'W'];
-  uniforms = `strides : vec2<i32>, pads : vec2<i32>, filterDims : vec2<i32>,
-       outHeight : i32, outWidth : i32, channelMul : i32,`;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = `depthwise_conv2d_backprop_input`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let batch = coords[0];
-        let d1 = coords[3];
-        let dyCorner = coords.yz - uniforms.pads;
-        let dyRCorner = dyCorner.x;
-        let dyCCorner = dyCorner.y;
-
-        var dotProd = 0.0;
-        for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {
-          let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);
-
-          if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
-            continue;
-          }
-
-          let idyR = i32(dyR);
-          let wRPerm = uniforms.filterDims[0] - 1 - wR;
-
-          for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {
-            let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);
-
-            if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
-              continue;
-            }
-
-            let idyC = i32(dyC);
-            let wCPerm = uniforms.filterDims[1] - 1 - wC;
-
-            for (var dm = 0; dm < uniforms.channelMul; dm++) {
-              let d2 = d1 * uniforms.channelMul + dm;
-              let xValue = getDy(batch, idyR, idyC, d2);
-              let wValue = getW(wRPerm, wCPerm, d1, dm);
-              dotProd += xValue * wValue;
-            }
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/conv_backprop_mm_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/conv_backprop_mm_webgpu.ts
deleted file mode 100644
index 492449f59..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/conv_backprop_mm_webgpu.ts
+++ /dev/null
@@ -1,161 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, util} from '@tensorflow/tfjs-core';
-
-import {makeMatMulPackedSource, makeMatMulPackedVec4Source} from './matmul_packed_webgpu';
-import {typeSnippet, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, computeWorkgroupSizeForConv2d, computeWorkPerThreadForConv2d} from './webgpu_util';
-
-function conv2dTransposeCommonSnippet(innerElementSize = 4) {
-  const getWSnippet = (innerElementSize: number) => {
-    switch (innerElementSize) {
-      case 1:
-        return 'return W[getIndexFromCoords4D(coord, uniforms.wShape)];';
-      case 4:
-        return `
-            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
-            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
-            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
-            let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];
-            let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];
-            let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];
-            let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];
-            return vec4<f32>(v0, v1, v2, v3);
-            `;
-      default:
-        throw new Error(
-            `innerElementSize ${innerElementSize} is not supported.`);
-    }
-  };
-
-  const readASnippet = `
-      let outRow = row / uniforms.outShape[2];
-      let outCol = row % uniforms.outShape[2];
-
-      let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
-      let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
-      let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);
-      let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);
-      if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
-        return ${typeSnippet(innerElementSize)}(0.0);
-      }
-      if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
-        return ${typeSnippet(innerElementSize)}(0.0);
-      }
-      let coord = vec4<i32>(
-          batch,
-          i32(xR),
-          i32(xC),
-          col % uniforms.outBackprop[3]);
-      return x[getIndexFromCoords4D(coord, uniforms.xShape)/${
-      innerElementSize}];`;
-
-  const sampleA = `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
-        ${readASnippet}
-      }
-      return ${typeSnippet(innerElementSize)}(0.0);`;
-
-  const userCode = `
-  fn mm_readA(batch: i32, row : i32, col : i32) -> ${
-      typeSnippet(innerElementSize)} {
-    ${sampleA}
-  }
-
-  fn mm_readB(batch: i32, row : i32, col : i32) -> ${
-      typeSnippet(innerElementSize)} {
-    let coordX = uniforms.filterDims.x - 1 -
-        row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
-    let coordY = uniforms.filterDims.y - 1 -
-        (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
-    if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
-        coordX >= 0 && coordY >= 0) {
-      let rowInner = row % uniforms.outBackprop[3];
-      let coord = vec4<i32>(coordX, coordY, col, rowInner);
-      ${getWSnippet(innerElementSize)}
-    }
-    return ${typeSnippet(innerElementSize)}(0.0);
-  }
-
-  fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${
-      typeSnippet(innerElementSize)}) {
-    if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {
-      var value = valueInput;
-      let outCoord = vec4<i32>(
-          batch,
-          row / uniforms.outShape[2],
-          row % uniforms.outShape[2],
-          col);
-      result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${
-      innerElementSize}] = value;
-    }
-  }`;
-  return userCode;
-}
-
-export class Conv2DDerInputMMProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[], z: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'W'];
-  variableComponents: number[];
-  uniforms =
-      'filterDims : vec2<i32>, pads : vec2<i32>, strides : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,';
-  workgroupSize: [number, number, number];
-  elementsPerThread: [number, number, number];
-  isVec4?: boolean;
-  outputComponent: number;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-
-    util.assert(
-        convInfo.dataFormat === 'channelsLast',
-        () => 'TODO: NCHW is unimplemented');
-    this.isVec4 =
-        convInfo.inChannels % 4 === 0 && convInfo.outChannels % 4 === 0;
-    this.dispatchLayout = {x: [3], y: [1, 2], z: [0]};
-    this.workgroupSize = computeWorkgroupSizeForConv2d(
-        this.dispatchLayout, this.outputShape, this.isVec4);
-    this.elementsPerThread = computeWorkPerThreadForConv2d(
-        this.dispatchLayout, this.outputShape, this.isVec4);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        this.elementsPerThread);
-
-    if (this.isVec4) {
-      this.outputComponent = 4;
-      this.variableComponents = [4, 1];
-    }
-
-    this.shaderKey =
-        `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`;
-  }
-
-  getUserCode(): string {
-    const matMulSource = this.isVec4 ?
-        makeMatMulPackedVec4Source(this.elementsPerThread, this.workgroupSize) :
-        makeMatMulPackedSource(this.elementsPerThread, this.workgroupSize);
-    const userCode = `
-    ${conv2dTransposeCommonSnippet(this.isVec4 ? 4 : 1)}
-    ${matMulSource}
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/conv_backprop_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/conv_backprop_webgpu.ts
deleted file mode 100644
index 1257c2cbe..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/conv_backprop_webgpu.ts
+++ /dev/null
@@ -1,428 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class Conv2DDerInputProgram implements WebGPUProgram {
-  variableNames = ['dy', 'W'];
-  uniforms =
-      'filterDims : vec2<i32>, pads : vec2<i32>, strides : vec2<i32>, outBackprop : vec4<i32>,';
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y?: number[], z?: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  isChannelsLast: boolean;
-  size = false;
-  isVec4 = false;
-  workPerThread = 1;
-  outputComponent: number;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-    this.isChannelsLast = convInfo.dataFormat === 'channelsLast';
-    this.isVec4 = this.isChannelsLast && convInfo.outChannels % 4 === 0 &&
-        convInfo.inChannels % 4 === 0;
-    if (this.isVec4) {
-      // TODO: Expand to any value.
-      this.workPerThread = 2;
-      this.outputComponent = 4;
-      this.workgroupSize = [4, 4, 4];
-      this.dispatchLayout = {x: [3], y: [2], z: [0, 1]};
-      this.dispatch = computeDispatch(
-          this.dispatchLayout, this.outputShape, this.workgroupSize,
-          [4, this.workPerThread, 1]);
-    } else {
-      this.size = true;
-      this.workPerThread = 1;
-      this.workgroupSize = [64, 1, 1];
-      this.dispatchLayout = flatDispatchLayout(this.outputShape);
-      this.dispatch = computeDispatch(
-          this.dispatchLayout, this.outputShape, this.workgroupSize);
-    }
-    this.shaderKey = `conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${
-        this.workPerThread}`;
-  }
-
-  getUserCode(): string {
-    const rowDim = this.isChannelsLast ? 1 : 2;
-    const colDim = this.isChannelsLast ? 2 : 3;
-    const channelDim = this.isChannelsLast ? 3 : 1;
-
-    const vec4Snippet = `
-    ${main()} {
-      let batch = i32(globalId.z) / uniforms.outShape[1];
-      let r = i32(globalId.z) % uniforms.outShape[1];
-      let c = i32(globalId.y) * ${this.workPerThread};
-      let d1 = i32(globalId.x) * 4;
-
-      let dyCorner = vec2<i32>(r, c) - uniforms.pads;
-
-      // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
-      // ? = to be determined. : = across all values in that axis.
-      var dotProd: array<vec4<f32>, ${this.workPerThread}>;
-      for (var i = 0; i < ${this.workPerThread}; i++) {
-        dotProd[i] = vec4<f32>(0.0);
-      }
-      for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
-        let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);
-        let wRPerm = uniforms.filterDims.x - 1 - wR;
-        if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||
-            fract(dyR) > 0.0) {
-          continue;
-        }
-        let idyR = i32(dyR);
-
-        for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
-          let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);
-          let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);
-          let wCPerm = uniforms.filterDims.y - 1 - wC;
-          var bDyCVal = true;
-          var bDyCVal2 = true;
-          if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
-              fract(dyC) > 0.0) {
-            bDyCVal = false;
-          }
-          if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||
-              fract(dyC2) > 0.0) {
-            bDyCVal2 = false;
-          }
-
-          let idyC = i32(dyC);
-          let idyC2 = i32(dyC2);
-          if (bDyCVal && bDyCVal2) {
-            let d2Length = uniforms.outBackprop[3];
-            for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {
-              let wValue0 = getW(wRPerm, wCPerm, d1, d2);
-              let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);
-              let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);
-              let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);
-              var xValue =  getDy(batch, idyR, idyC, d2);
-              let tmpval = vec4<f32>(dot(xValue, wValue0),
-                                     dot(xValue, wValue1),
-                                     dot(xValue, wValue2),
-                                     dot(xValue, wValue3));
-              dotProd[0] = dotProd[0] + tmpval;
-              xValue = getDy(batch, idyR, idyC2, d2);
-              dotProd[1] = dotProd[1] + vec4<f32>(dot(xValue, wValue0),
-                                                  dot(xValue, wValue1),
-                                                  dot(xValue, wValue2),
-                                                  dot(xValue, wValue3));
-            }
-          } else if (bDyCVal) {
-            let d2Length = uniforms.outBackprop[3];
-            for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {
-              let wValue0 = getW(wRPerm, wCPerm, d1, d2);
-              let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);
-              let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);
-              let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);
-              var xValue =  getDy(batch, idyR, idyC, d2);
-              let tmpval = vec4<f32>(dot(xValue, wValue0),
-                                     dot(xValue, wValue1),
-                                     dot(xValue, wValue2),
-                                     dot(xValue, wValue3));
-              dotProd[0] = dotProd[0] + tmpval;
-            }
-          } else if (bDyCVal2) {
-            let d2Length = uniforms.outBackprop[3];
-            for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {
-              let wValue0 = getW(wRPerm, wCPerm, d1, d2);
-              let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);
-              let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);
-              let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);
-              var xValue =  getDy(batch, idyR, idyC2, d2);
-              let tmpval = vec4<f32>(dot(xValue, wValue0),
-                                     dot(xValue, wValue1),
-                                     dot(xValue, wValue2),
-                                     dot(xValue, wValue3));
-              dotProd[1] = dotProd[1] + tmpval;
-            }
-          }
-        }
-      }
-
-      for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
-        let coords = vec4<i32>(batch, r, c + i, d1);
-        if (coordsInBounds4D(coords, uniforms.outShape)) {
-          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
-        }
-      }
-    }
-    `;
-    return this.isVec4 ?
-        `
-    ${vec4Snippet}
-    ` :
-        `
-    ${main('index')} {
-      if(index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let batch = coords[0];
-        let d1 = coords[${channelDim}];
-
-        let dyCorner = vec2<i32>(coords[${rowDim}], coords[${
-            colDim}]) - uniforms.pads;
-        let dyRCorner = dyCorner.x;
-        let dyCCorner = dyCorner.y;
-
-        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
-        // ? = to be determined. : = across all values in that axis.
-        var dotProd = 0.0;
-        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
-          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);
-          let wRPerm = uniforms.filterDims.x - 1 - wR;
-          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
-              wRPerm < 0) {
-            continue;
-          }
-          let idyR = i32(dyR);
-
-          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
-            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);
-            let wCPerm = uniforms.filterDims.y - 1 - wC;
-            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
-                fract(dyC) > 0.0 || wCPerm < 0) {
-              continue;
-            }
-            let idyC = i32(dyC);
-
-            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
-              let xValue = ${
-            this.isChannelsLast ? 'getDy(batch, idyR, idyC, d2)' :
-                                  'getDy(batch, d2, idyR, idyC)'};
-              let wValue = getW(wRPerm, wCPerm, d1, d2);
-              dotProd = dotProd + xValue * wValue;
-            }
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-  `;
-  }
-}
-
-export class Conv2DDerFilterProgram implements WebGPUProgram {
-  variableNames = ['x', 'dy'];
-  uniforms =
-      'pads : vec2<i32>, strides : vec2<i32>, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,';
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  isChannelsLast: boolean;
-  size = true;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.filterShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.isChannelsLast = convInfo.dataFormat === 'channelsLast';
-    this.shaderKey = `conv2DDerFilter_${this.isChannelsLast}`;
-  }
-
-  getUserCode(): string {
-    return `
-    ${main('index')} {
-      if(index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let wR = coords[0];
-        let wC = coords[1];
-        let d1 = coords[2];
-        let d2 = coords[3];
-
-        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
-        // ? = to be determined. : = across all values in that axis.
-        var dotProd = 0.0;
-        for (var b = 0; b < uniforms.batchSize; b = b + 1) {
-          for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {
-            let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];
-            if (xR < 0 || xR >= uniforms.inHeight) {
-              continue;
-            }
-
-            for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {
-              let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];
-
-              if (xC < 0 || xC >= uniforms.inWidth) {
-                continue;
-              }
-
-              if (${this.isChannelsLast}) {
-                let dyValue = getDy(b, yR, yC, d2);
-                let xValue = getX(b, xR, xC, d1);
-                dotProd = dotProd + xValue * dyValue;
-              } else {
-                let dyValue = getDy(b, d2, yR, yC);
-                let xValue = getX(b, d1, xR, xC);
-                dotProd = dotProd + xValue * dyValue;
-              }
-            }
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-  `;
-  }
-}
-
-export class Conv3DDerFilterProgram implements WebGPUProgram {
-  variableNames = ['x', 'dy'];
-  uniforms =
-      `pads : vec3<i32>, strides : vec3<i32>, batchSize : i32, outDepth : i32,
-       outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`;
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.filterShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = `conv3DDerFilter`;
-  }
-
-  getUserCode(): string {
-    return `
-    ${main('index')} {
-      if(index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let wF = coords.x;
-        let wR = coords.y;
-        let wC = coords.z;
-        let d1 = coords.w;
-        let d2 = coords.u;
-
-        var dotProd = 0.0;
-        for (var b = 0; b < uniforms.batchSize; b++) {
-          for (var yF = 0; yF < uniforms.outDepth; yF++) {
-            let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];
-            if (xF < 0 || xF >= uniforms.inDepth) {
-              continue;
-            }
-
-            for (var yR = 0; yR < uniforms.outHeight; yR++) {
-              let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];
-              if (xR < 0 || xR >= uniforms.inHeight) {
-                continue;
-              }
-
-              for (var yC = 0; yC < uniforms.outWidth; yC++) {
-                let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];
-                if (xC < 0 || xC >= uniforms.inWidth) {
-                  continue;
-                }
-
-                let dyValue = getDy(b, yF, yR, yC, d2);
-                let xValue = getX(b, xF, xR, xC, d1);
-                dotProd += xValue * dyValue;
-              }
-            }
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-  `;
-  }
-}
-
-export class Conv3DDerInputProgram implements WebGPUProgram {
-  variableNames = ['dy', 'W'];
-  uniforms = `filterDims : vec3<i32>, pads : vec3<i32>, strides : vec3<i32>,
-      outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`;
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.inShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = `conv3DDerInput`;
-  }
-
-  getUserCode(): string {
-    return `
-    ${main('index')} {
-      if(index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let batch = coords.x;
-        let d1 = coords.u;
-
-        let dyCorner = vec3<i32>(coords.y, coords.z, coords.w) - uniforms.pads;
-        let dyFCorner = dyCorner.x;
-        let dyRCorner = dyCorner.y;
-        let dyCCorner = dyCorner.z;
-
-        var dotProd = 0.0;
-        for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {
-          let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);
-          if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {
-            continue;
-          }
-          let idyF = i32(dyF);
-
-          let wFPerm = uniforms.filterDims[0] - 1 - wF;
-
-          for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {
-            let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);
-
-            if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
-              continue;
-            }
-            let idyR = i32(dyR);
-
-            let wRPerm = uniforms.filterDims[1] - 1 - wR;
-
-            for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {
-              let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);
-
-              if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
-                continue;
-              }
-              let idyC = i32(dyC);
-
-              let wCPerm = uniforms.filterDims[2] - 1 - wC;
-
-              for (var d2 = 0; d2 < uniforms.outChannels; d2++) {
-                let xValue = getDy(batch, idyF, idyR, idyC, d2);
-                let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
-                dotProd += xValue * wValue;
-              }
-            }
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-  `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/crop_and_resize_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/crop_and_resize_webgpu.ts
deleted file mode 100644
index 20a8276e4..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/crop_and_resize_webgpu.ts
+++ /dev/null
@@ -1,139 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class CropAndResizeProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['Image', 'Boxes', 'BoxInd'];
-  uniforms = 'extrapolationValue : f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  methodId: number;
-  cropHeightBiggerThan1: boolean;
-  cropWidthBiggerThan1: boolean;
-  size = true;
-
-  constructor(
-      channnel: number, boxShape: [number, number], cropSize: [number, number],
-      method: 'bilinear'|'nearest') {
-    const [numBoxes, ] = boxShape;
-    this.outputShape = [numBoxes, cropSize[0], cropSize[1], channnel];
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.methodId = method === 'bilinear' ? 1 : 0;
-    this.cropHeightBiggerThan1 = this.outputShape[1] > 1;
-    this.cropWidthBiggerThan1 = this.outputShape[2] > 1;
-    this.shaderKey = `cropAndResize_${this.methodId}_${
-        this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;
-  }
-
-  getUserCode(): string {
-    const [inputHeightFloat, inputWidthFloat] =
-        [`f32(uniforms.imageShape[1] - 1)`, `f32(uniforms.imageShape[2] - 1)`];
-
-    const [heightRatio, heightScale, inY] = this.cropHeightBiggerThan1 ?
-        [
-          `(${inputHeightFloat} / f32(uniforms.outShape[1] - 1))`,
-          '(y2-y1) * height_ratio',
-          `y1*${inputHeightFloat} + f32(y)*(height_scale)`,
-        ] :
-        [
-          '0.0',
-          '0.0',
-          `0.5 * (y1+y2) * ${inputHeightFloat}`,
-        ];
-    const [widthRatio, widthScale, inX] = this.cropWidthBiggerThan1 ?
-        [
-          `(${inputWidthFloat} / f32(uniforms.outShape[2] - 1))`,
-          '(x2-x1) * width_ratio',
-          `x1*${inputWidthFloat} + f32(x)*(width_scale)`,
-        ] :
-        [
-          '0.0',
-          '0.0',
-          `0.5 * (x1+x2) * ${inputWidthFloat}`,
-        ];
-
-    // Reference implementation
-    // tslint:disable-next-line:max-line-length
-    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let height_ratio = f32(${heightRatio});
-        let width_ratio = f32(${widthRatio});
-        let b = coords[0];
-        let y = coords[1];
-        let x = coords[2];
-        let d = coords[3];
-        // get box vals
-        let y1 = getBoxes(b, 0);
-        let x1 = getBoxes(b, 1);
-        let y2 = getBoxes(b, 2);
-        let x2 = getBoxes(b, 3);
-        // get image in batch index
-        let bInd = i32(round(getBoxInd(b)));
-        if(bInd < 0 || bInd >= uniforms.outShape[0]) {
-          return;
-        }
-        let height_scale = ${heightScale};
-        let width_scale = ${widthScale};
-        let in_y = ${inY};
-        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
-          setOutputAtIndex(index, uniforms.extrapolationValue);
-          return;
-        }
-        let in_x = ${inX};
-        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
-          setOutputAtIndex(index, uniforms.extrapolationValue);
-          return;
-        }
-        let sourceFracIndexCR = vec2<f32>(in_x,in_y);
-        if(${this.methodId} == 1) {
-          // Compute the four integer indices.
-          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
-          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
-          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
-          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
-          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
-          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
-          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
-          let top = topLeft + (topRight - topLeft) * fracCR.x;
-          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
-          let newValue = top + (bottom - top) * fracCR.y;
-          setOutputAtIndex(index, newValue);
-        } else {
-          // Compute the coordinators of nearest neighbor point.
-          let sourceNearestCR = vec2<i32>(floor(
-            sourceFracIndexCR + vec2<f32>(0.5,0.5)));
-          let newValue = getImage(
-            bInd, sourceNearestCR.y, sourceNearestCR.x, d);
-          setOutputAtIndex(index, newValue);
-        }
-      }
-    }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/cum_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/cum_webgpu.ts
deleted file mode 100644
index a7362f5a0..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/cum_webgpu.ts
+++ /dev/null
@@ -1,117 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export enum CumOpType {
-  Prod = '*',
-  Sum = '+',
-}
-
-export class CumProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  workgroupSize: [number, number, number];
-  // pow(i32, i32) is not supported, use pow(f32, f32) instead.
-  uniforms = 'index : f32,';
-  size = true;
-  exclusive: boolean;
-  reverse: boolean;
-  op: CumOpType;
-
-  constructor(
-      op: CumOpType, shape: number[], exclusive: boolean, reverse: boolean) {
-    this.workgroupSize = [128, 1, 1];
-    this.outputShape = shape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.exclusive = exclusive;
-    this.reverse = reverse;
-    this.op = op;
-    this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
-  }
-
-  getUserCode(): string {
-    const rank = this.outputShape.length;
-    const initVal = this.op === CumOpType.Prod ? '1.0' : '0.0';
-    const val = this.exclusive ? initVal :
-                                 `getX(${getCoords(rank, 'coords', this.op)})`;
-    const length = this.outputShape[this.outputShape.length - 1];
-    let condition = '';
-    let idxString = '';
-    // When exclusive is set, the cum op becomes roll op that copies the
-    // value from the previous index based on the direction specified by the
-    // reverse flag.
-    if (this.exclusive) {
-      condition = this.reverse ? `end != ${length - 1}` : 'end != 0';
-      idxString = this.reverse ? 'end + 1' : 'end - 1';
-    } else {
-      condition = this.reverse ? `end + pow2 < ${length}` : 'end >= pow2';
-      idxString = (this.reverse ? 'end + pow2' : 'end - pow2');
-    }
-    return `
-      ${main('index')} {
-       if (index < uniforms.size) {
-         var coords = getCoordsFromIndex(index);
-
-         let end = ${getFinalCoord(rank, 'coords', this.op)};
-         var val = ${val};
-         let pow2 = i32(pow(2.0, uniforms.index));
-         if (${condition}) {
-           let idx = ${idxString};
-           ${getFinalCoord(rank, 'coords', this.op)} = idx;
-           val ${this.op}= getX(${getCoords(rank, 'coords', this.op)});
-         }
-         setOutputAtIndex(index, val);
-       }
-      }
-    `;
-  }
-}
-
-function getCoords(rank: number, name: string, op: CumOpType): string {
-  if (rank === 1) {
-    return `${name}`;
-  } else if (rank === 2) {
-    return `${name}.x, ${name}.y`;
-  } else if (rank === 3) {
-    return `${name}.x, ${name}.y, ${name}.z`;
-  } else if (rank === 4) {
-    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;
-  } else {
-    throw Error(`Cumulative ${op} for rank ${rank} is not yet supported`);
-  }
-}
-
-function getFinalCoord(rank: number, name: string, op: CumOpType): string {
-  if (rank === 1) {
-    return `${name}`;
-  } else if (rank === 2) {
-    return `${name}.y`;
-  } else if (rank === 3) {
-    return `${name}.z`;
-  } else if (rank === 4) {
-    return `${name}.w`;
-  } else {
-    throw Error(`Cumulative ${op} for rank ${rank} is not yet supported`);
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/depth_to_space_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/depth_to_space_webgpu.ts
deleted file mode 100644
index cb9c9bbb6..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/depth_to_space_webgpu.ts
+++ /dev/null
@@ -1,105 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class DepthToSpaceProgram implements WebGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[];
-  dataFormat: string;
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-  uniforms = 'blockSize : i32,';
-
-  constructor(outputShape: number[], dataFormat: 'NHWC'|'NCHW') {
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = `depthToSpace_${dataFormat}`;
-    this.dataFormat = dataFormat;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          let b = coords[0];
-          let h = ${this.getHeightCoordString()};
-          let w = ${this.getWidthCoordString()};
-          let d = ${this.getDepthCoordString()};
-
-          let in_h = h / uniforms.blockSize;
-          let offset_h = h % uniforms.blockSize;
-          let in_w = w / uniforms.blockSize;
-          let offset_w = w % uniforms.blockSize;
-          let offset_d = (offset_h * uniforms.blockSize + offset_w) *
-            ${this.getOutputDepthSize()};
-          let in_d = d + offset_d;
-
-          let rlt = ${this.getInputSamplingString()};
-          setOutputAtIndex(index, rlt);
-        }
-      }`;
-    return userCode;
-  }
-
-  private getHeightCoordString(): string {
-    if (this.dataFormat === 'NHWC') {
-      return `coords[1]`;
-    } else {
-      return `coords[2]`;
-    }
-  }
-
-  private getWidthCoordString(): string {
-    if (this.dataFormat === 'NHWC') {
-      return `coords[2]`;
-    } else {
-      return `coords[3]`;
-    }
-  }
-
-  private getDepthCoordString(): string {
-    if (this.dataFormat === 'NHWC') {
-      return `coords[3]`;
-    } else {
-      return `coords[1]`;
-    }
-  }
-
-  private getOutputDepthSize(): string {
-    if (this.dataFormat === 'NHWC') {
-      return `uniforms.outShape[3]`;
-    } else {
-      return `uniforms.outShape[1]`;
-    }
-  }
-
-  private getInputSamplingString(): string {
-    if (this.dataFormat === 'NHWC') {
-      return `getX(b, in_h, in_w, in_d)`;
-    } else {
-      return `getX(b, in_d, in_h, in_w)`;
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/depthwise_conv2d_nchw_shared_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/depthwise_conv2d_nchw_shared_webgpu.ts
deleted file mode 100644
index ef1df6ea8..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/depthwise_conv2d_nchw_shared_webgpu.ts
+++ /dev/null
@@ -1,142 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {activationFnSnippet, biasActivationSnippet} from './activation_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch} from './webgpu_util';
-
-export class DepthwiseConv2DNCHWSharedProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[], z: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'W'];
-  uniforms = `pads : vec2<i32>, inDims : vec2<i32>,`;
-  workgroupSize: [number, number, number] = [16, 16, 1];
-  addBias: boolean;
-  activation: backend_util.Activation;
-  hasPreluActivation: boolean;
-  filterHeight: number;
-  filterWidth: number;
-
-  constructor(
-      outputShape: number[], filterHeight: number, filterWidth: number,
-      addBias = false, activation: backend_util.Activation = null,
-      hasPreluActivation = false) {
-    this.outputShape = outputShape;
-    this.dispatchLayout = {x: [3], y: [2], z: [0, 1]};
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-    if (hasPreluActivation) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    this.addBias = addBias;
-    this.activation = activation;
-    this.hasPreluActivation = hasPreluActivation;
-    this.filterHeight = filterHeight;
-    this.filterWidth = filterWidth;
-    this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${
-        this.filterWidth}`;
-  }
-
-  getUserCode(): string {
-    const filterSize = this.filterWidth * this.filterHeight;
-    const flatWorkgroupSize =
-        this.workgroupSize[0] * this.workgroupSize[1] * this.workgroupSize[2];
-    const tileAHeight = this.workgroupSize[1] + this.filterHeight - 1;
-    const tileAWidth = this.workgroupSize[0] + this.filterWidth - 1;
-
-    const userCode = `
-      ${activationFnSnippet(this.activation, this.hasPreluActivation, false, 4)}
-
-      var<workgroup> mm_Asub : array<array<f32, ${tileAWidth}>, ${tileAHeight}>;
-      var<workgroup> mm_Bsub : array<array<f32, ${this.filterWidth}>, ${
-        this.filterHeight}>;
-      fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {
-        var value = 0.0;
-        if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])
-        {
-          value = getX(batch, channel, row, col);
-        }
-        return value;
-      }
-
-      ${main()} {
-        let coords = getOutputCoords();
-        let batch = coords[0];
-        let xRCCorner = vec2<i32>(coords.zw) - uniforms.pads;
-        let channelMul = uniforms.wShape[3];
-        let d1 = coords[1] / channelMul;
-        let q = coords[1] % channelMul;
-
-        let inputRowStart = xRCCorner.x;
-        let inputColStart = xRCCorner.y;
-
-        let localRow = i32(localId.y);
-        let localCol = i32(localId.x);
-
-        // Load one tile of X into local memory.
-        for (var inputRow = localRow; inputRow < ${
-        tileAHeight}; inputRow = inputRow + ${this.workgroupSize[1]}) {
-          for (var inputCol = localCol; inputCol < ${
-        tileAWidth}; inputCol = inputCol + ${this.workgroupSize[0]}) {
-            let rowOffset = inputRow - localRow;
-            let colOffset = inputCol - localCol;
-            mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);
-          }
-        }
-
-        // Load one tile of W into local memory.
-        var wIndex = i32(localIndex);
-        ${
-        filterSize < flatWorkgroupSize ?
-            `if (wIndex < ${filterSize})` :
-            `for(; wIndex < ${filterSize}; wIndex = wIndex + ${
-                flatWorkgroupSize})`}
-
-        {
-          let wRow = wIndex / ${this.filterWidth};
-          let wCol = wIndex % ${this.filterWidth};
-          mm_Bsub[wRow][wCol] = getW(wRow, wCol, d1, q);
-        }
-
-        workgroupBarrier();
-
-        var value = 0.0;
-        for (var wR = 0; wR < ${this.filterHeight}; wR = wR + 1) {
-          for (var wC = 0; wC < ${this.filterWidth}; wC = wC + 1) {
-            let xVal = mm_Asub[localRow + wR][localCol + wC];
-            let wVal = mm_Bsub[wR][wC];
-            value = fma(xVal, wVal, value);
-          }
-        }
-        ${biasActivationSnippet(this.addBias, this.activation)}
-        if (coordsInBounds4D(coords, uniforms.outShape)) {
-          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/depthwise_conv2d_vec4_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/depthwise_conv2d_vec4_webgpu.ts
deleted file mode 100644
index 70ed0645b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/depthwise_conv2d_vec4_webgpu.ts
+++ /dev/null
@@ -1,143 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, util} from '@tensorflow/tfjs-core';
-import {activationFnSnippet, biasActivationSnippet} from './activation_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class DepthwiseConv2DVec4Program implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'W'];
-  uniforms = 'pads : vec2<i32>, inDims : vec2<i32>, virtualWidth : i32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  workPerThread = 4;
-  convInfo: backend_util.Conv2DInfo;
-  addBias: boolean;
-  activation: backend_util.Activation;
-  hasPreluActivation: boolean;
-  outputComponent = 4;
-  virtualWidth: number;
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, addBias = false,
-      activation: backend_util.Activation = null, hasPreluActivation = false) {
-    this.outputShape = convInfo.outShape;
-    this.virtualWidth = Math.ceil(this.outputShape[2] / this.workPerThread) *
-        this.workPerThread;
-    const virtualOutputShape = [
-      this.outputShape[0], this.outputShape[1], this.virtualWidth,
-      this.outputShape[3]
-    ];
-    this.dispatchLayout = flatDispatchLayout(virtualOutputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, virtualOutputShape, this.workgroupSize,
-        [this.outputComponent * this.workPerThread, 1, 1]);
-
-    util.assert(
-        convInfo.dataFormat === 'channelsLast',
-        () => 'TODO: NCHW is unimplemented');
-
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-    if (hasPreluActivation) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    this.convInfo = convInfo;
-    this.addBias = addBias;
-    this.activation = activation;
-    this.hasPreluActivation = hasPreluActivation;
-
-    this.shaderKey =
-        `depthwiseVec4_${activation}_${this.convInfo.filterHeight}_${
-            this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${
-            this.convInfo.strideWidth}_${this.workPerThread}`;
-  }
-
-  getUserCode(): string {
-    const xNumber = (this.workPerThread - 1) * this.convInfo.strideWidth +
-        this.convInfo.filterWidth;
-    const strideHeight = this.convInfo.strideHeight;
-    const strideWidth = this.convInfo.strideWidth;
-
-    const userCode = `
-      ${activationFnSnippet(this.activation, this.hasPreluActivation, true, 4)}
-      fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {
-        var value = vec4<f32>(0.0);
-        if (col >=0 && col < uniforms.inDims[1]) {
-          value = getX(batch, row, col, channel);
-        }
-        return value;
-      }
-
-      ${main('index')} {
-        let width0 = uniforms.outShape[3] / ${this.outputComponent};
-        let d1 = (index % width0) * ${this.outputComponent};
-        var index1 = index / width0;
-        let width1 = uniforms.virtualWidth / ${this.workPerThread};
-        let c = (index1 % width1) * ${this.workPerThread};
-        index1 = index1 / width1;
-        let r = index1 % uniforms.outShape[1];
-        let batch = index1 / uniforms.outShape[1];
-
-        let xRCCorner = vec2<i32>(r, c) * vec2<i32>(${strideHeight}, ${
-        strideWidth}) - uniforms.pads;
-
-        let xRCorner = xRCCorner.x;
-        let xCCorner = xRCCorner.y;
-        var xVals : array<vec4<f32>, ${xNumber}>;
-        var dotProd : array<vec4<f32>, ${this.workPerThread}>;
-        for (var i = 0; i < ${this.workPerThread}; i++) {
-          dotProd[i] = vec4<f32>(0.0);
-        }
-
-        // Use constant instead of uniform can give better performance.
-        for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
-          let xR = xRCorner + wR;
-          if (xR >=0 && xR < uniforms.inDims[0]) {
-            for (var i = 0; i < ${xNumber}; i++) {
-              xVals[i] = readX(batch, xR, xCCorner + i, d1);
-            }
-            for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
-              let wValue = getW(wR, wC, d1, 0);
-              for (var i = 0; i < ${this.workPerThread}; i++) {
-                dotProd[i] = fma(xVals[i * ${
-        strideWidth} + wC], wValue, dotProd[i]);
-              }
-            }
-          }
-        }
-
-        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
-          let coords = vec4<i32>(batch, r, c + i, d1);
-          if (coordsInBounds4D(coords, uniforms.outShape)) {
-            var value = dotProd[i];
-            ${biasActivationSnippet(this.addBias, this.activation)}
-            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
-          }
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/depthwise_conv2d_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/depthwise_conv2d_webgpu.ts
deleted file mode 100644
index 6325962e8..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/depthwise_conv2d_webgpu.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {activationFnSnippet, biasActivationSnippet} from './activation_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class DepthwiseConv2DProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y?: number[], z?: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'W'];
-  uniforms = `pads : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
-      filterWidth : i32, strides : vec2<i32>, dilations : vec2<i32>,`;
-  // This is an experimental value.
-  workgroupSize: [number, number, number] = [256, 1, 1];
-  convInfo: backend_util.Conv2DInfo;
-  addBias: boolean;
-  activation: backend_util.Activation;
-  hasPreluActivation: boolean;
-  isChannelsLast: boolean;
-  size = true;
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, addBias = false,
-      activation: backend_util.Activation = null, hasPreluActivation = false) {
-    this.outputShape = convInfo.outShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.isChannelsLast = convInfo.dataFormat === 'channelsLast';
-
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-    if (hasPreluActivation) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    this.convInfo = convInfo;
-    this.addBias = addBias;
-    this.activation = activation;
-    this.hasPreluActivation = hasPreluActivation;
-    this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`;
-  }
-
-  getUserCode(): string {
-    const getXSnippet = this.isChannelsLast ? 'getX(batch, xR, xC, d1);' :
-                                              'getX(batch, d1, xR, xC);';
-
-    const userCode = `
-      ${activationFnSnippet(this.activation, this.hasPreluActivation, false, 4)}
-
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getOutputCoords();
-          let batch = coords[0];
-          let xRCCorner = vec2<i32>(coords.${
-        this.isChannelsLast ? 'yz' : 'zw'}) * uniforms.strides - uniforms.pads;
-          let d2 = coords[${this.isChannelsLast ? 3 : 1}];
-          let channelMul = uniforms.wShape[3];
-          let d1 = d2 / channelMul;
-          let q = d2 % channelMul;
-
-          let inputRowStart = xRCCorner.x;
-          let inputColStart = xRCCorner.y;
-          let inputRowEnd = inputRowStart + uniforms.filterHeight *
-              uniforms.dilations[0];
-          let inputColEnd = inputColStart + uniforms.filterWidth *
-              uniforms.dilations[1];
-
-          // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get
-          // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all
-          // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.
-          // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.
-          var value = 0.0;
-
-          // Extract if checking out of for loop for performance.
-          if (inputRowStart >= 0 && inputColStart >= 0 &&
-            inputRowEnd < uniforms.inDims[0] &&
-                inputColEnd < uniforms.inDims[1]) {
-              for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
-                let xR = inputRowStart + wR * uniforms.dilations[0];
-
-                for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
-                  let xC = inputColStart + wC * uniforms.dilations[1];
-
-                  let xVal = ${getXSnippet};
-                  let wVal = getW(wR, wC, d1, q);
-                  value = value + xVal * wVal;
-                }
-              }
-            } else {
-              for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
-                let xR = inputRowStart + wR * uniforms.dilations[0];
-
-                if (xR < 0 || xR >= uniforms.inDims[0]) {
-                  continue;
-                }
-
-                for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
-                  let xC = inputColStart + wC * uniforms.dilations[1];
-
-                  if (xC < 0 || xC >= uniforms.inDims[1]) {
-                    continue;
-                  }
-
-                  let xVal = ${getXSnippet};
-                  let wVal = getW(wR, wC, d1, q);
-                  value = value + xVal * wVal;
-                }
-              }
-            }
-            ${biasActivationSnippet(this.addBias, this.activation)}
-          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/diag_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/diag_webgpu.ts
deleted file mode 100644
index 2896967e1..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/diag_webgpu.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class DiagProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(size: number) {
-    this.outputShape = [size, size];
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'diag';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getOutputCoords();
-          let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);
-          setOutputAtIndex(index, value);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/dilation_backprop_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/dilation_backprop_webgpu.ts
deleted file mode 100644
index 8fc61f88c..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/dilation_backprop_webgpu.ts
+++ /dev/null
@@ -1,181 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType} from '@tensorflow/tfjs-core';
-
-import {atomicAddSnippet} from './shader_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class Dilation2DBackpropInputProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'w', 'dy'];
-  uniforms =
-      'filterDims: vec2<i32>, pads: vec2<i32>, strides: vec2<i32>, dilations: vec2<i32>, dySize: i32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  atomic = true;
-  type: DataType;
-
-  constructor(convInfo: backend_util.Conv2DInfo, outputDtype: DataType) {
-    this.outputShape = convInfo.inShape;
-    this.dispatchLayout = flatDispatchLayout(convInfo.outShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, convInfo.outShape, this.workgroupSize);
-
-    if (outputDtype !== 'float32' && outputDtype !== 'int32') {
-      throw new Error(`Dilation2DBackpropInput only supports float32 and int32
-          types, does not support ${outputDtype} type.`);
-    }
-    this.type = outputDtype;
-    this.shaderKey = 'dilation2DBackpropInput';
-  }
-
-  getUserCode(): string {
-    // This implementation follows the TF c++ cuda implementation:
-    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/dilation_ops_gpu.cu.cc
-    const userCode = `
-       ${main('index')} {
-         if (index < uniforms.dySize) {
-           let coords = getDyCoordsFromIndex(index);
-           let b = coords[0];
-           let r = coords[1];
-           let c = coords[2];
-           let d = coords[3];
-
-           let dyCorner = vec2<i32>(r, c) * uniforms.strides - uniforms.pads;
-           var curVal = -3.4e38;  // neg_infinity
-           var xRMax = 0;
-           var xCMax = 0;
-
-           // In the case of multiple argmax branches, we only back-propagate
-           // along the last branch, i.e., the one with largest value of
-           // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling
-           // backward routines.
-           for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {
-             let xR = dyCorner.x + wR * uniforms.dilations[0];
-
-             if (xR >= 0 && xR < uniforms.xShape[1]) {
-               for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {
-                 let xC = dyCorner.y + wC * uniforms.dilations[1];
-
-                 if (xC >= 0 && xC < uniforms.xShape[2]) {
-                   let val = getX(b, xR, xC, d) + getW(wR, wC, d);
-                   if (val > curVal) {
-                     curVal = val;
-                     xRMax = xR;
-                     xCMax = xC;
-                   }
-                 }
-               }
-             }
-           }
-
-           let flatIndexIn = d + uniforms.xShape[3] *
-               (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));
-           let value = getDy(b, r, c, d);
-           ${
-        atomicAddSnippet(
-            '&result[flatIndexIn]', 'value', this.type as 'float32' | 'int32')}
-         }
-       }
-     `;
-    return userCode;
-  }
-}
-
-export class Dilation2DBackpropFilterProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'w', 'dy'];
-  uniforms =
-      'filterDims: vec2<i32>, pads: vec2<i32>, strides: vec2<i32>, dilations: vec2<i32>, dySize: i32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  atomic = true;
-  type: DataType;
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, shape: number[],
-      outputDtype: DataType) {
-    this.outputShape = convInfo.filterShape;
-    this.dispatchLayout = flatDispatchLayout(convInfo.outShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, convInfo.outShape, this.workgroupSize);
-
-    if (outputDtype !== 'float32' && outputDtype !== 'int32') {
-      throw new Error(`Dilation2DBackpropFilter only supports float32 and int32
-          types, does not support ${outputDtype} type.`);
-    }
-    this.type = outputDtype;
-    this.shaderKey = 'dilation2DBackpropFilter';
-  }
-
-  getUserCode(): string {
-    // This implementation follows the TF c++ cuda implementation:
-    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/dilation_ops_gpu.cu.cc
-    const userCode = `
-       ${main('index')} {
-         if (index < uniforms.dySize) {
-           let coords = getDyCoordsFromIndex(index);
-           let b = coords[0];
-           let r = coords[1];
-           let c = coords[2];
-           let d = coords[3];
-
-           let dyCorner = vec2<i32>(r, c) * uniforms.strides - uniforms.pads;
-           var curVal = -3.4e38;  // neg_infinity
-           var wRMax = 0;
-           var wCMax = 0;
-
-           // In the case of multiple argmax branches, we only back-propagate
-           // along the last branch, i.e., the one with largest value of
-           // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling
-           // backward routines.
-           for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {
-             let xR = dyCorner.x + wR * uniforms.dilations[0];
-
-             if (xR >= 0 && xR < uniforms.xShape[1]) {
-               for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {
-                 let xC = dyCorner.y + wC * uniforms.dilations[1];
-
-                 if (xC >= 0 && xC < uniforms.xShape[2]) {
-                   let val = getX(b, xR, xC, d) + getW(wR, wC, d);
-                   if (val > curVal) {
-                     curVal = val;
-                     wRMax = wR;
-                     wCMax = wC;
-                   }
-                 }
-               }
-             }
-           }
-
-           let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);
-           let value = getDy(b, r, c, d);
-           ${
-        atomicAddSnippet(
-            '&result[flatIndexIn]', 'value', this.type as 'float32' | 'int32')}
-         }
-       }
-     `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/dilation_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/dilation_webgpu.ts
deleted file mode 100644
index fd46bbd5b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/dilation_webgpu.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class Dilation2DProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'w'];
-  uniforms =
-      'filterDims: vec2<i32>, pads: vec2<i32>, strides: vec2<i32>, dilations: vec2<i32>';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.outShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'dilation2d';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-       ${main('index')} {
-         if (index < uniforms.size) {
-           let neg_infinity = -3.4e38;
-           let coords = getOutputCoords();
-           let batch = coords.x;
-           let d1 = coords.w;
-           let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;
-           let hBeg = outTopLeftCorner.x;
-           let wBeg = outTopLeftCorner.y;
-
-           var curVal = neg_infinity;
-           for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {
-             let hIn = hBeg + h * uniforms.dilations[0];
-
-             if (hIn >= 0 && hIn < uniforms.xShape[1]) {
-               for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {
-                 let wIn = wBeg + w * uniforms.dilations[1];
-
-                 if (wIn >= 0 && wIn < uniforms.xShape[2]) {
-                   let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);
-                   if (val > curVal) {
-                     curVal = val;
-                   }
-                 }
-               }
-             }
-           }
-
-           setOutputAtIndex(index, curVal);
-         }
-       }
-     `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/draw_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/draw_webgpu.ts
deleted file mode 100644
index 608e8099d..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/draw_webgpu.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType} from '@tensorflow/tfjs-core';
-
-import {getMainHeaderString as main, PixelsOpType, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class DrawProgram implements WebGPUProgram {
-  variableNames = ['Image'];
-  uniforms = 'alpha: f32,';
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  type: DataType;
-  textureFormat: GPUTextureFormat;
-  pixelsOpType = PixelsOpType.DRAW;
-  size = true;
-
-  constructor(
-      outShape: number[], type: DataType, textureFormat: GPUTextureFormat) {
-    this.outputShape = outShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.type = type;
-    this.textureFormat = textureFormat;
-    this.shaderKey = `draw_${type}_${textureFormat}`;
-  }
-
-  getUserCode(): string {
-    let calculateResult;
-    const value = this.type === 'float32' ? 'value' : 'value / 255.0';
-    calculateResult = `
-      if (uniforms.numChannels == 1) {
-        rgba[0] = ${value};
-        rgba[1] = ${value};
-        rgba[2] = ${value};
-      } else {
-        rgba[d] = ${value};
-      }`;
-
-    const userCode = `
-       @group(0) @binding(0) var outImage : texture_storage_2d<${
-        this.textureFormat}, write>;
-       ${main('index')} {
-         if (index < uniforms.size) {
-           var rgba = vec4<f32>(0.0, 0.0, 0.0, uniforms.alpha);
-           for (var d = 0; d < uniforms.numChannels; d = d + 1) {
-             let value = f32(inBuf[index * uniforms.numChannels + d]);
-             ${calculateResult}
-           }
-           rgba.x = rgba.x * rgba.w;
-           rgba.y = rgba.y * rgba.w;
-           rgba.z = rgba.z * rgba.w;
-           let coords = getCoordsFromIndex(index);
-           textureStore(outImage, vec2<i32>(coords.yx), rgba);
-         }
-       }
-      `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/fft_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/fft_webgpu.ts
deleted file mode 100644
index 30d977cc9..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/fft_webgpu.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class FFTProgram implements WebGPUProgram {
-  variableNames: string[] = ['real', 'imag'];
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  uniforms = 'exponentMultiplier : f32, denominator: f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-  component: string;
-
-  constructor(component: 'real'|'imag', shape: [number, number]) {
-    this.outputShape = shape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.component = component;
-    this.shaderKey = `fft_${component}`;
-  }
-
-  getUserCode(): string {
-    const opString = this.component === 'real' ?
-        'return real * expR - imag * expI;' :
-        'return real * expI + imag * expR;';
-    const userCode = `
-    fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {
-      ${opString}
-    }
-
-    fn mulMatDFT(batch: i32, index: i32) -> f32 {
-      let indexRatio = f32(index) / f32(uniforms.realShape[1]);
-      let exponentMultiplierTimesIndexRatio =
-          uniforms.exponentMultiplier * indexRatio;
-
-      var result = 0.0;
-
-      for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {
-        // x = (-2|2 * PI / N) * index * i;
-        let x = exponentMultiplierTimesIndexRatio * f32(i);
-        let expR = cos(x);
-        let expI = sin(x);
-        let real = getReal(batch, i);
-        let imag = getImag(batch, i);
-
-        result = result +
-            unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;
-      }
-
-      return result;
-    }
-
-    ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getOutputCoords();
-        setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));
-      }
-    }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/fill_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/fill_webgpu.ts
deleted file mode 100644
index 808a3e1b4..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/fill_webgpu.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class FillProgram implements WebGPUProgram {
-  variableNames: string[] = [];
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  uniforms = 'value : f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(shape: number[]) {
-    this.outputShape = shape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'fill';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.size) {
-        setOutputAtIndex(index, uniforms.value);
-      }
-    }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/flags_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/flags_webgpu.ts
deleted file mode 100644
index 959c93066..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/flags_webgpu.ts
+++ /dev/null
@@ -1,95 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-
-const ENV = env();
-
-/** The batched dispatching calls size in the device queue. */
-ENV.registerFlag('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', () => 15);
-
-/**
- * Whether we forward execution to the CPU backend if tensors are small and
- * reside on the CPU.
- */
-ENV.registerFlag('WEBGPU_CPU_FORWARD', () => true);
-
-/**
- * This flag is used to test different types of matmul programs.
- *
- * See MatMulProgramType in webgpu_util.ts for a list of available values.
- */
-ENV.registerFlag('WEBGPU_MATMUL_PROGRAM_TYPE', () => -1);
-
-/**
- * Whether to use conv2dTranspose_naive which directly implement the
- * conv2dTranspose logic rather than using a matmul to simulate.
- */
-ENV.registerFlag('WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE', () => true);
-
-/**
- * Whether we use low power GPU. Otherwise, a high performance GPU will be
- * requested.
- */
-ENV.registerFlag('WEBGPU_USE_LOW_POWER_GPU', () => false);
-
-/**
- * Threshold for input tensor size that determines whether WebGPU backend will
- * delegate computation to CPU.
- *
- * Default value is 1000.
- */
-ENV.registerFlag('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', () => 1000);
-
-/**
- * Whether to use a dummy canvas to make profiling tools like PIX work with
- * TFJS webgpu backend.
- */
-ENV.registerFlag('WEBGPU_USE_PROFILE_TOOL', () => false);
-
-/**
- * Whether to use import API.
- */
-ENV.registerFlag('WEBGPU_IMPORT_EXTERNAL_TEXTURE', () => true);
-
-/**
- * Whether to use conv2dNaive for debugging.
- */
-ENV.registerFlag('WEBGPU_USE_NAIVE_CONV2D_DEBUG', () => false);
-
-/**
- * Threshold to increase dispatched workgroups for matmul. If too few workgroups
- * are dispatched, it means the hardware may be in low occupancy.
- * -1 means it's not set by the user. A default strategy will be applied.
- */
-ENV.registerFlag(
-    'WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL', () => -1);
-
-/**
- * Whether we will run im2col as a separate shader for convolution.
- */
-ENV.registerFlag('WEBGPU_CONV_SEPARATE_IM2COL_SHADER', () => false);
-
-/**
- * A string used to match shader key. If any matches, print the related shader.
- * Seperated by comma. 'all' to print all. 'binary' to print binary(add, mul,
- * etc.). 'unary,conv2d' to print both unary and conv2d.
- */
-ENV.registerFlag('WEBGPU_PRINT_SHADER', () => '');
-
-/** Experimental flag, whether enter compile only phase. */
-ENV.registerFlag('WEBGPU_ENGINE_COMPILE_ONLY', () => false);
diff --git a/tfjs-master/tfjs-backend-webgpu/src/flip_left_right_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/flip_left_right_webgpu.ts
deleted file mode 100644
index 11adbe272..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/flip_left_right_webgpu.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class FlipLeftRightProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(imageShape: [number, number, number, number]) {
-    this.outputShape = imageShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = 'flipLeftRight';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          let coordX = uniforms.xShape[2] - coords[2] - 1;
-          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
-          setOutputAtIndex(index, outputValue);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/from_pixels_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/from_pixels_webgpu.ts
deleted file mode 100644
index 92a720f02..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/from_pixels_webgpu.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, PixelsOpType, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class FromPixelsProgram implements WebGPUProgram {
-  dispatch: [number, number, number];
-  dispatchLayout: {x: number[]};
-  pixelsOpType = PixelsOpType.FROM_PIXELS;
-  outputShape: number[] = [0];
-  shaderKey: string;
-  importVideo: boolean;
-  variableNames: string[] = [];
-  workgroupSize: [number, number, number] =
-      [256, 1, 1];  // The empirical value.
-
-  constructor(outputShape: number[], numChannels: number, importVideo = false) {
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        [numChannels, 1, 1]);
-
-    this.importVideo = importVideo;
-    this.shaderKey = `fromPixels_${this.importVideo}`;
-  }
-
-  getUserCode(): string {
-    const textureLoad = this.importVideo ?
-        'textureLoad(src, vec2<i32>(coords.yx));' :
-        'textureLoad(src, vec2<i32>(coords.yx), 0)';
-    const textureType =
-        this.importVideo ? 'texture_external' : 'texture_2d<f32>';
-    return `
-      @binding(1) @group(0) var src: ${textureType};
-      ${main('index')} {
-        let flatIndex = index * uniforms.numChannels;
-        if (flatIndex < uniforms.size) {
-          let coords = getCoordsFromIndex(flatIndex);
-          let values = ${textureLoad};
-          for (var i = 0; i < uniforms.numChannels; i = i + 1) {
-            result[flatIndex + i] = i32(floor(255.0 * values[i]));
-          }
-        }
-      }
-  `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/from_pixels_webgpu_test.ts b/tfjs-master/tfjs-backend-webgpu/src/from_pixels_webgpu_test.ts
deleted file mode 100644
index 5c1af6f0b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/from_pixels_webgpu_test.ts
+++ /dev/null
@@ -1,133 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-import {WebGPUBackend} from './backend_webgpu';
-import {describeWebGPU} from './test_util';
-
-describeWebGPU('fromPixels', () => {
-  let originalTimeout: number;
-  beforeAll(() => {
-    originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-  });
-  afterAll(() => {
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout;
-  });
-
-  // Device is lost on Linux
-  // tslint:disable-next-line: ban
-  xit('should behave well if WEBGPU_IMPORT_EXTERNAL_TEXTURE is true or false',
-      async () => {
-        const oldImportExternalTexture =
-            tf.env().getBool('WEBGPU_IMPORT_EXTERNAL_TEXTURE');
-        const backend = tf.backend() as WebGPUBackend;
-        const textureManager = backend.textureManager;
-        textureManager.dispose();
-
-        const source = document.createElement('source');
-        source.src =
-            // tslint:disable-next-line:max-line-length
-            'data:video/mp4;base64,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';
-        source.type = 'video/mp4';
-
-        const video = await test_util.createVideoElement(source);
-        document.body.appendChild(video);
-        await test_util.play(video);
-
-        {
-          tf.env().set('WEBGPU_IMPORT_EXTERNAL_TEXTURE', true);
-          const res = tf.browser.fromPixels(video);
-          expect(res.shape).toEqual([90, 160, 3]);
-          const data = await res.data();
-          expect(data.length).toEqual(90 * 160 * 3);
-          const freeTexturesAfterFromPixels =
-              textureManager.getNumFreeTextures();
-          expect(freeTexturesAfterFromPixels).toEqual(0);
-          const usedTexturesAfterFromPixels =
-              textureManager.getNumUsedTextures();
-          expect(usedTexturesAfterFromPixels).toEqual(0);
-        }
-
-        {
-          tf.env().set('WEBGPU_IMPORT_EXTERNAL_TEXTURE', false);
-          const res = tf.browser.fromPixels(video);
-          expect(res.shape).toEqual([90, 160, 3]);
-          const data = await res.data();
-          expect(data.length).toEqual(90 * 160 * 3);
-          const freeTexturesAfterFromPixels =
-              textureManager.getNumFreeTextures();
-          expect(freeTexturesAfterFromPixels).toEqual(1);
-          const usedTexturesAfterFromPixels =
-              textureManager.getNumUsedTextures();
-          expect(usedTexturesAfterFromPixels).toEqual(0);
-        }
-
-        document.body.removeChild(video);
-        tf.env().set(
-            'WEBGPU_IMPORT_EXTERNAL_TEXTURE', oldImportExternalTexture);
-      });
-
-  // Failing on Linux
-  // tslint:disable-next-line: ban
-  xit('should reuse texture when fromPixels have same input size', async () => {
-    const backend = tf.backend() as WebGPUBackend;
-    const textureManager = backend.textureManager;
-    textureManager.dispose();
-
-    {
-      const img = new Image(10, 10);
-      img.src = 'data:image/gif;base64' +
-          ',R0lGODlhAQABAIAAAP///wAAACH5BAEAAAAALAAAAAABAAEAAAICRAEAOw==';
-
-      await new Promise(resolve => {
-        img.onload = () => resolve(img);
-      });
-
-      const resImage = tf.browser.fromPixels(img);
-      expect(resImage.shape).toEqual([10, 10, 3]);
-
-      const dataImage = await resImage.data();
-      expect(dataImage[0]).toEqual(0);
-      expect(dataImage.length).toEqual(10 * 10 * 3);
-      const freeTexturesAfterFromPixels = textureManager.getNumFreeTextures();
-      expect(freeTexturesAfterFromPixels).toEqual(1);
-      const usedTexturesAfterFromPixels = textureManager.getNumUsedTextures();
-      expect(usedTexturesAfterFromPixels).toEqual(0);
-    }
-
-    {
-      const img = new Image(10, 10);
-      img.src =
-          'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAIAAAACUFjqAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAAABfSURBVChTY/gPBu8NLd/KqLxT1oZw4QAqDZSDoPeWDj9WrYUIAgG6NBAhm4FFGoIgxuCUBiKgMfikv1bW4pQGav334wdUGshBk/6SVQAUh0p/mzIDTQ6oFSGNHfz/DwAwi8mNzTi6rwAAAABJRU5ErkJggg==';
-      await new Promise(resolve => {
-        img.onload = () => resolve(img);
-      });
-      const resImage = tf.browser.fromPixels(img);
-      expect(resImage.shape).toEqual([10, 10, 3]);
-
-      const dataImage = await resImage.data();
-      expect(dataImage[0]).toEqual(255);
-      expect(dataImage.length).toEqual(10 * 10 * 3);
-      const freeTexturesAfterFromPixels = textureManager.getNumFreeTextures();
-      expect(freeTexturesAfterFromPixels).toEqual(1);
-      const usedTexturesAfterFromPixels = textureManager.getNumUsedTextures();
-      expect(usedTexturesAfterFromPixels).toEqual(0);
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgpu/src/gather_nd_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/gather_nd_webgpu.ts
deleted file mode 100644
index c2a348631..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/gather_nd_webgpu.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getCoordsDataType, getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class GatherNDProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames: string[] = ['A', 'indices'];
-  uniforms: string;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-  sliceDim: number;
-  constructor(sliceDim: number, shape: number[]) {
-    this.outputShape = shape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = `gathernd_${sliceDim}`;
-    this.sliceDim = sliceDim;
-    this.uniforms = `sliceDim : i32, strides : ${getCoordsDataType(sliceDim)},`;
-  }
-
-  getUserCode(): string {
-    let strideString;
-    if (this.sliceDim > 1) {
-      strideString = 'uniforms.strides[j]';
-    } else {
-      strideString = 'uniforms.strides';
-    }
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          var flattenIndex = 0;
-          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
-            let indexTemp = i32(round(getIndices(coords[0], j)));
-            let strideNum = ${strideString};
-            flattenIndex = flattenIndex + indexTemp * strideNum;
-          }
-
-          setOutputAtIndex(index, getA(flattenIndex, coords[1]));
-        }
-      }
-      `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/gather_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/gather_webgpu.ts
deleted file mode 100644
index 5dac58069..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/gather_webgpu.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class GatherProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames: string[] = ['A', 'indices'];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  aShape: number[];
-  size = true;
-
-  constructor(aShape: number[], outputShape: number[]) {
-    this.outputShape = aShape.slice();
-    this.aShape = aShape;
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = `gather`;
-  }
-
-  getUserCode(): string {
-    const sourceCoords = getSourceCoords(this.aShape);
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let resRC = getCoordsFromIndex(index);
-          let indexZ = i32(getIndices(resRC.x, resRC.z));
-          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
-          setOutputAtIndex(index, inBounds * getA(${sourceCoords}));
-        }
-      }
-    `;
-    return userCode;
-  }
-}
-
-// The input and output are always flattened into rank 4 tensors.
-function getSourceCoords(aShape: number[]): string {
-  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
-  const sourceCoords = [];
-  for (let i = 0; i < aShape.length; i++) {
-    if (i === 2) {
-      sourceCoords.push('indexZ');
-    } else {
-      sourceCoords.push(`${currentCoords[i]}`);
-    }
-  }
-  return sourceCoords.join();
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/im2col_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/im2col_webgpu.ts
deleted file mode 100644
index 9069cf717..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/im2col_webgpu.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class Im2ColProgram implements WebGPUProgram {
-  variableNames = ['x'];
-  uniforms =
-      `pads : vec2<i32>, strides : vec2<i32>, dilations : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
-       inChannels : i32,`;
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  isChannelsLast: boolean;
-  size = true;
-
-  constructor(outputShape: number[], isChannelsLast: boolean) {
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.isChannelsLast = isChannelsLast;
-    this.shaderKey = `im2col_${this.isChannelsLast}`;
-  }
-
-  getUserCode(): string {
-    const rowDim = this.isChannelsLast ? 1 : 2;
-    const colDim = this.isChannelsLast ? 2 : 3;
-
-    const row = this.isChannelsLast ? 'coords[1]' : 'coords[2]';
-    const col = this.isChannelsLast ? 'coords[2]' : 'coords[1]';
-    const getXSnippet = this.isChannelsLast ? 'getX(batch, xRow, xCol, ch)' :
-                                              'getX(batch, ch, xRow, xCol)';
-
-    const userCode = `
-    ${main('index')} {
-      let coords = getCoordsFromIndex(index);
-      if(index < uniforms.size) {
-        let batch = coords[0];
-        let row = ${row};
-        let col = ${col};
-        let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];
-        let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);
-        var value = 0.0;
-        if(xRow < uniforms.xShape[${rowDim}] && xRow >= 0) {
-          let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -
-              uniforms.pads[1];
-          let xCol = offsetX + uniforms.dilations[1] * ((col %
-              uniforms.itemsPerBlockRow) / uniforms.inChannels);
-          let ch = col % uniforms.inChannels;
-          if(xCol < uniforms.xShape[${colDim}] && xCol >= 0) {
-            value = ${getXSnippet};
-          }
-        }
-        setOutputAtIndex(index, value);
-      }
-    }
-   `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/index.ts b/tfjs-master/tfjs-backend-webgpu/src/index.ts
deleted file mode 100644
index 4bfecbe97..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/index.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export * from './base';
-import './register_all_kernels';
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/int.ts b/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/int.ts
deleted file mode 100644
index 0211f703f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/int.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorInfo} from '@tensorflow/tfjs-core';
-import {WebGPUBackend} from '../backend_webgpu';
-import {UnaryOpType} from '../unary_op_util';
-import {UnaryOpProgram} from '../unary_op_webgpu';
-
-export function int(input: TensorInfo, backend: WebGPUBackend): TensorInfo {
-  const program = new UnaryOpProgram(input.shape, UnaryOpType.TO_INT);
-  const output = backend.runWebGPUProgram(program, [input], 'int32');
-  return {dataId: output.dataId, shape: output.shape, dtype: output.dtype};
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/kernel_funcs_utils.ts b/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/kernel_funcs_utils.ts
deleted file mode 100644
index 82e0fd4df..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/kernel_funcs_utils.ts
+++ /dev/null
@@ -1,179 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BinaryInputs, DataType, KernelFunc, TensorInfo, TypedArray, UnaryInputs, upcastType} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {BinaryOpComplexProgram} from '../binary_op_complex_webgpu';
-import {BinaryOpType} from '../binary_op_util';
-import {BinaryOpProgram} from '../binary_op_webgpu';
-import {complex} from '../kernels/Complex';
-import {UnaryOpType} from '../unary_op_util';
-import {UnaryOpProgram} from '../unary_op_webgpu';
-
-import {SimpleBinaryKernelImplCPU, SimpleUnaryKernelImplCPU} from './shared';
-
-type UnaryKernelFuncConfig = {
-  opType: UnaryOpType,
-  cpuKernelImpl?: SimpleUnaryKernelImplCPU,
-  dtype?: DataType
-};
-
-/**
- * Template that creates a `KernelFunc` for unary ops.
- * @param opType Op type to create `UnaryOpProgram`.
- * @param cpuKernelImpl Optional. Shared functionality from tfjs-backend-cpu, it
- *     will be involved when necessary.
- * @param dtype Optional. If set, the result has this dtype. Otherwise, the
- *     result has the same dtype as the first input. This is mainly used in
- *     comparison kernels, such as Equal, Less, Greater, etc.
- */
-export function unaryKernelFunc(
-    {opType, cpuKernelImpl, dtype}: UnaryKernelFuncConfig): KernelFunc {
-  return ({inputs, backend}) => {
-    const {x} = inputs as UnaryInputs;
-    const webgpuBackend = backend as WebGPUBackend;
-
-    const $dtype = dtype || x.dtype;
-    if (webgpuBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {
-      const xData = webgpuBackend.tensorMap.get(x.dataId);
-      const outValues = cpuKernelImpl(xData.values as TypedArray, $dtype);
-      return webgpuBackend.makeTensorInfo(x.shape, $dtype, outValues);
-    }
-
-    const program: UnaryOpProgram = new UnaryOpProgram(x.shape, opType);
-    return webgpuBackend.runWebGPUProgram(program, [x], $dtype);
-  };
-}
-
-type BinaryKernelFuncConfig = {
-  opType: BinaryOpType,
-  cpuKernelImpl?: SimpleBinaryKernelImplCPU,
-  supportsComplex?: boolean,
-  dtype?: DataType
-};
-
-/**
- * Template that creates a `KernelFunc` for binary ops.
- * @param opType Op type to create `BinaryOpProgram`.
- * @param cpuKernelImpl Optional. Shared functionality from tfjs-backend-cpu, it
- *     will be involved when necessary.
- * @param dtype Optional. If set, the result has this dtype. Otherwise, the
- *     result has the same dtype as the first input. This is mainly used in
- *     comparison kernels, such as Equal, Less, Greater, etc.
- */
-export function binaryKernelFunc(
-    {opType, cpuKernelImpl, supportsComplex = false, dtype}:
-        BinaryKernelFuncConfig): KernelFunc {
-  return ({inputs, backend}) => {
-    const {a, b} = inputs as BinaryInputs;
-    const webgpuBackend = backend as WebGPUBackend;
-
-    if (supportsComplex && a.dtype === 'complex64') {
-      const aData = webgpuBackend.tensorMap.get(a.dataId);
-      const bData = webgpuBackend.tensorMap.get(b.dataId);
-      let real: TensorInfo, imag: TensorInfo;
-      if (opType !== BinaryOpType.MUL) {
-        [real, imag] = [
-          [aData.complexTensorInfos.real, bData.complexTensorInfos.real],
-          [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]
-        ].map(complexParts => {
-          const [aPart, bPart] = complexParts;
-
-          const aHandle = {
-            dataId: aPart.dataId,
-            dtype: aPart.dtype,
-            shape: a.shape
-          };
-          const bHandle = {
-            dataId: bPart.dataId,
-            dtype: bPart.dtype,
-            shape: b.shape
-          };
-
-          const program = new BinaryOpProgram(opType, a.shape, b.shape);
-          return webgpuBackend.runWebGPUProgram(
-              program, [aHandle, bHandle],
-              upcastType(aPart.dtype, bPart.dtype));
-        });
-      } else {
-        const realProgram = new BinaryOpComplexProgram(
-            BinaryOpType.COMPLEX_MULTIPLY_REAL, a.shape, b.shape);
-        const imagProgram = new BinaryOpComplexProgram(
-            BinaryOpType.COMPLEX_MULTIPLY_IMAG, a.shape, b.shape);
-
-        const inputs = [
-          {
-            dataId: aData.complexTensorInfos.real.dataId,
-            dtype: aData.complexTensorInfos.real.dtype,
-            shape: a.shape
-          },
-          {
-            dataId: aData.complexTensorInfos.imag.dataId,
-            dtype: aData.complexTensorInfos.imag.dtype,
-            shape: a.shape
-          },
-          {
-            dataId: bData.complexTensorInfos.real.dataId,
-            dtype: bData.complexTensorInfos.real.dtype,
-            shape: b.shape
-          },
-          {
-            dataId: bData.complexTensorInfos.imag.dataId,
-            dtype: bData.complexTensorInfos.imag.dtype,
-            shape: b.shape
-          }
-        ];
-
-        real = webgpuBackend.runWebGPUProgram(realProgram, inputs, 'float32');
-        imag = webgpuBackend.runWebGPUProgram(imagProgram, inputs, 'float32');
-      }
-
-      const complexOutput =
-          complex({inputs: {real, imag}, backend: webgpuBackend});
-
-      webgpuBackend.disposeData(real.dataId);
-      webgpuBackend.disposeData(imag.dataId);
-
-      // TODO: Implement CPU forwarding for complex inputs.
-
-      return complexOutput;
-    }
-
-    const $dtype = dtype || upcastType(a.dtype, b.dtype);
-    if ((a.dtype === 'string' || b.dtype === 'string' ||
-         webgpuBackend.shouldExecuteOnCPU([a, b])) &&
-        cpuKernelImpl != null) {
-      const aData = webgpuBackend.tensorMap.get(a.dataId).values as TypedArray;
-      const bData = webgpuBackend.tensorMap.get(b.dataId).values as TypedArray;
-      const decodedAVals = a.dtype === 'string' ?
-          // tslint:disable-next-line: no-any
-          backend_util.fromUint8ToStringArray(aData as any as Uint8Array[]) :
-          aData;
-      const decodedBVals = a.dtype === 'string' ?
-          // tslint:disable-next-line: no-any
-          backend_util.fromUint8ToStringArray(bData as any as Uint8Array[]) :
-          bData;
-      const [outValues, outShape] =
-          cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);
-
-      return webgpuBackend.makeTensorInfo(outShape, $dtype, outValues);
-    }
-    const program = new BinaryOpProgram(opType, a.shape, b.shape);
-    return webgpuBackend.runWebGPUProgram(program, [a, b], $dtype);
-  };
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/reduce.ts b/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/reduce.ts
deleted file mode 100644
index f6c82a5df..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/reduce.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType, sumOutType, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {reshape} from '../kernels/Reshape';
-import {transpose} from '../kernels/Transpose';
-import {ReduceProgram} from '../reduce_webgpu';
-
-import {maxImplCPU} from './shared';
-import {prodImplCPU} from './shared';
-
-type ReduceTypes = 'all'|'any'|'max'|'mean'|'min'|'prod'|'sum';
-const RETURN_TYPES: {[key in ReduceTypes]?: DataType} = {
-  'mean': 'float32',
-  'all': 'bool',
-  'any': 'bool',
-};
-
-export function reduce(
-    x: TensorInfo, axis: number|number[], keepDims: boolean,
-    reduceType: ReduceTypes, backend: WebGPUBackend): TensorInfo {
-  const xRank = x.shape.length;
-  const toDispose = [];
-
-  const origAxes = util.parseAxisParam(axis, x.shape);
-  let axes = origAxes;
-  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
-
-  let input = x;
-  if (permutedAxes != null) {
-    input = transpose({inputs: {x}, attrs: {perm: permutedAxes}, backend});
-    axes = backend_util.getInnerMostAxes(axes.length, xRank);
-    toDispose.push(input);
-  }
-
-  backend_util.assertAxesAreInnerMostDims(reduceType, axes, xRank);
-
-  const [reduceOutShape, reduceShape] =
-      backend_util.computeOutAndReduceShapes(input.shape, axes);
-  let resOutShape = reduceOutShape;
-  if (keepDims) {
-    // rather than reshape at the end, set the target shape here.
-    resOutShape = backend_util.expandShapeToKeepDim(reduceOutShape, origAxes);
-  }
-
-  let res;
-  if ((reduceType === 'max' || reduceType === 'prod') &&
-      backend.shouldExecuteOnCPU([input])) {
-    const xVals = backend.tensorMap.get(input.dataId).values as TypedArray;
-    switch (reduceType) {
-      case 'max':
-        const outValues = maxImplCPU(
-            xVals, util.sizeFromShape(reduceShape), resOutShape, x.dtype);
-        res = backend.makeTensorInfo(resOutShape, x.dtype, outValues);
-        break;
-      case 'prod':
-        const {outVals, outShape, outDtype} =
-            prodImplCPU(input.shape, input.dtype, xVals, axes);
-        res = backend.makeTensorInfo(outShape, outDtype, outVals);
-        break;
-      default:
-        throw new Error(
-            `${reduceType} CPU implementation is not yet supported.`);
-    }
-  } else {
-    const inSize = util.sizeFromShape(reduceShape);
-    const xSize = util.sizeFromShape(input.shape);
-    const batchSize = xSize / inSize;
-
-    const reduceInfo = {windowSize: inSize, inSize, batchSize, outSize: 1};
-    const dtype = RETURN_TYPES[reduceType] || sumOutType(x.dtype);
-    const uniformData = [
-      {type: 'int32', data: [inSize]},
-    ];
-    const program = new ReduceProgram(
-        reduceInfo, reduceType, backend.device.limits.maxComputeWorkgroupSizeX);
-    const reduced =
-        backend.runWebGPUProgram(program, [input], dtype, uniformData);
-    toDispose.push(reduced);
-
-    res = reshape({inputs: {x: reduced}, attrs: {shape: resOutShape}, backend});
-  }
-
-  toDispose.forEach(t => backend.disposeData(t.dataId));
-
-  return res;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/shared.ts b/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/shared.ts
deleted file mode 100644
index 2d7cbf1aa..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/shared.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Import shared functionality from tfjs-backend-cpu without triggering
-// side effects.
-// tslint:disable-next-line: no-imports-from-dist
-import * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {SimpleBinaryKernelImpl} from '@tensorflow/tfjs-backend-cpu/dist/shared';
-// tslint:disable-next-line: no-imports-from-dist
-import {SimpleUnaryImpl} from '@tensorflow/tfjs-backend-cpu/dist/utils/unary_types';
-
-export type SimpleBinaryKernelImplCPU = SimpleBinaryKernelImpl;
-export type SimpleUnaryKernelImplCPU = SimpleUnaryImpl;
-const {
-  addImpl: addImplCPU,
-  castImpl: castImplCPU,
-  ceilImpl: ceilImplCPU,
-  concatImpl: concatImplCPU,
-  equalImpl: equalImplCPU,
-  expImpl: expImplCPU,
-  expm1Impl: expm1ImplCPU,
-  floorImpl: floorImplCPU,
-  floorDivImpl: floorDivImplCPU,
-  gatherNdImpl: gatherNdImplCPU,
-  gatherV2Impl: gatherV2ImplCPU,
-  greaterEqualImpl: greaterEqualImplCPU,
-  greaterImpl: greaterImplCPU,
-  lessEqualImpl: lessEqualImplCPU,
-  lessImpl: lessImplCPU,
-  logImpl: logImplCPU,
-  maxImpl: maxImplCPU,
-  maximumImpl: maximumImplCPU,
-  minimumImpl: minimumImplCPU,
-  multiplyImpl: multiplyImplCPU,
-  negImpl: negImplCPU,
-  notEqualImpl: notEqualImplCPU,
-  prodImpl: prodImplCPU,
-  rangeImpl: rangeImplCPU,
-  rsqrtImpl: rsqrtImplCPU,
-  scatterImpl: scatterImplCPU,
-  simpleAbsImpl: simpleAbsImplCPU,
-  sliceImpl: sliceImplCPU,
-  stridedSliceImpl: stridedSliceImplCPU,
-  stringNGramsImpl: stringNGramsImplCPU,
-  subImpl: subImplCPU,
-  tileImpl: tileImplCPU,
-  topKImpl: topKImplCPU,
-  transposeImpl: transposeImplCPU,
-  uniqueImpl: uniqueImplCPU,
-} = shared;
-
-export {
-  addImplCPU,
-  castImplCPU,
-  ceilImplCPU,
-  concatImplCPU,
-  equalImplCPU,
-  expImplCPU,
-  expm1ImplCPU,
-  floorImplCPU,
-  floorDivImplCPU,
-  gatherNdImplCPU,
-  gatherV2ImplCPU,
-  greaterEqualImplCPU,
-  greaterImplCPU,
-  lessEqualImplCPU,
-  lessImplCPU,
-  logImplCPU,
-  maxImplCPU,
-  maximumImplCPU,
-  minimumImplCPU,
-  multiplyImplCPU,
-  prodImplCPU,
-  negImplCPU,
-  notEqualImplCPU,
-  scatterImplCPU,
-  simpleAbsImplCPU,
-  sliceImplCPU,
-  stridedSliceImplCPU,
-  stringNGramsImplCPU,
-  subImplCPU,
-  rangeImplCPU,
-  rsqrtImplCPU,
-  tileImplCPU,
-  topKImplCPU,
-  transposeImplCPU,
-  uniqueImplCPU,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/sparse_segment_reduce.ts b/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/sparse_segment_reduce.ts
deleted file mode 100644
index b14f175b3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernel_utils/sparse_segment_reduce.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {fill} from '../kernels/Fill';
-import {SparseSegmentIdCountProgram, SparseSegmentMeanProgram, SparseSegmentSumProgram} from '../sparse_segment_reduce_webgpu';
-import {WebGPUProgram} from '../webgpu_program';
-
-export function sparseSegmentReduce(
-    input: TensorInfo, indices: TensorInfo, segmentIds: TensorInfo,
-    isSum = false, backend: WebGPUBackend): TensorInfo {
-  const inputSize = util.sizeFromShape(input.shape);
-  const segmentSize = inputSize / input.shape[0];
-  const dtype = input.dtype;
-
-  // Note that the current implementation assumes that segmentIds values are
-  // sorted.
-  const numIndices = util.sizeFromShape(indices.shape);
-  const $segmentIds = backend.readSync(segmentIds.dataId) as TypedArray;
-  const lastSegmentIdPlusOne =
-      numIndices > 0 ? $segmentIds[numIndices - 1] + 1 : 0;
-  const outputRows = lastSegmentIdPlusOne;
-
-  let program: WebGPUProgram;
-  const outputShape = input.shape.slice();
-  outputShape[0] = outputRows;
-
-  const sparseSize = numIndices * segmentSize;
-  const sparseSegmentSum =
-      fill({backend, attrs: {shape: outputShape, value: 0, dtype}});
-  program = new SparseSegmentSumProgram(outputShape, sparseSize, dtype);
-  let uniformData = [
-    {type: 'int32', data: [segmentSize]}, {type: 'int32', data: [sparseSize]}
-  ];
-  const $sparseSegmentSum = backend.runWebGPUProgram(
-      program, [input, indices, segmentIds], dtype, uniformData,
-      sparseSegmentSum);
-
-  if (isSum) {
-    return $sparseSegmentSum;
-  }
-
-  const sparseSegmentIdCount =
-      fill({backend, attrs: {shape: [outputRows], value: 0, dtype: 'int32'}});
-  program = new SparseSegmentIdCountProgram(outputRows, segmentIds.shape);
-  const $sparseSegmentIdCount = backend.runWebGPUProgram(
-      program, [segmentIds], 'int32', null, sparseSegmentIdCount);
-
-  const sparseSegmentMean =
-      fill({backend, attrs: {shape: outputShape, value: 0, dtype}});
-  program = new SparseSegmentMeanProgram(outputShape, dtype);
-  uniformData = [{type: 'int32', data: [segmentSize]}];
-  const $sparseSegmentMean = backend.runWebGPUProgram(
-      program, [$sparseSegmentSum, $sparseSegmentIdCount], dtype, uniformData,
-      sparseSegmentMean);
-
-  backend.disposeData($sparseSegmentSum.dataId);
-  backend.disposeData($sparseSegmentIdCount.dataId);
-  return $sparseSegmentMean;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Abs.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Abs.ts
deleted file mode 100644
index 1ef3f0218..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Abs.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Abs, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {simpleAbsImplCPU} from '../kernel_utils/shared';
-import {UnaryOpType} from '../unary_op_util';
-
-export const abs =
-    unaryKernelFunc({opType: UnaryOpType.ABS, cpuKernelImpl: simpleAbsImplCPU});
-
-export const absConfig: KernelConfig = {
-  kernelName: Abs,
-  backendName: 'webgpu',
-  kernelFunc: abs
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Acos.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Acos.ts
deleted file mode 100644
index 757cdb632..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Acos.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acos, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const acos = unaryKernelFunc({opType: UnaryOpType.ACOS});
-
-export const acosConfig: KernelConfig = {
-  kernelName: Acos,
-  backendName: 'webgpu',
-  kernelFunc: acos
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Acosh.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Acosh.ts
deleted file mode 100644
index f0b2403bb..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Acosh.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acosh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const acosh = unaryKernelFunc({opType: UnaryOpType.ACOSH});
-
-export const acoshConfig: KernelConfig = {
-  kernelName: Acosh,
-  backendName: 'webgpu',
-  kernelFunc: acosh
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Add.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Add.ts
deleted file mode 100644
index 6e9f60858..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Add.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Add, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {addImplCPU as cpuAdd} from '../kernel_utils/shared';
-
-export const addKernelFunc = binaryKernelFunc(
-    {opType: BinaryOpType.ADD, cpuKernelImpl: cpuAdd, supportsComplex: true});
-
-export const addConfig: KernelConfig = {
-  kernelName: Add,
-  backendName: 'webgpu',
-  kernelFunc: addKernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/AddN.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/AddN.ts
deleted file mode 100644
index 4a3bf3dc3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/AddN.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AddN, AddNInputs, KernelConfig, KernelFunc, TensorInfo, upcastType} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {AddNPackedProgram} from '../addn_packed_webgpu';
-import {identity} from './Identity';
-
-export function addN(args: {inputs: AddNInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-
-  const tensors = inputs;
-  if (tensors.length === 1) {
-    return identity({inputs: {x: tensors[0]}, backend});
-  }
-
-  const dtype =
-      tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));
-  const shapes = tensors.map(t => t.shape);
-  const program = new AddNPackedProgram(shapes);
-  return backend.runWebGPUProgram(program, tensors, dtype);
-}
-
-export const addNConfig: KernelConfig = {
-  kernelName: AddN,
-  backendName: 'webgpu',
-  kernelFunc: addN as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/All.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/All.ts
deleted file mode 100644
index dd7f3c76f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/All.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {All, AllAttrs, AllInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {reduce} from '../kernel_utils/reduce';
-
-export function all(
-    args: {inputs: AllInputs, attrs: AllAttrs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {keepDims, axis} = attrs;
-
-  return reduce(x, axis, keepDims, 'all', backend);
-}
-
-export const allConfig: KernelConfig = {
-  kernelName: All,
-  backendName: 'webgpu',
-  kernelFunc: all as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Any.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Any.ts
deleted file mode 100644
index 3af616240..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Any.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Any, AnyAttrs, AnyInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {reduce} from '../kernel_utils/reduce';
-
-export function any(
-    args: {inputs: AnyInputs, attrs: AnyAttrs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {keepDims, axis} = attrs;
-
-  return reduce(x, axis, keepDims, 'any', backend);
-}
-
-export const anyConfig: KernelConfig = {
-  kernelName: Any,
-  backendName: 'webgpu',
-  kernelFunc: any as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ArgMax.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ArgMax.ts
deleted file mode 100644
index 70311832f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ArgMax.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMax, ArgMaxAttrs, ArgMaxInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {ArgMinMaxProgram} from '../argminmax_webgpu';
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {transpose} from './Transpose';
-
-export function argMax(
-    args: {inputs: ArgMaxInputs, backend: WebGPUBackend, attrs: ArgMaxAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis} = attrs;
-
-  let axes = util.parseAxisParam(axis, x.shape);
-  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
-  let $x = x;
-  const intermediateTensorInfos = [];
-  if (permutedAxes != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    intermediateTensorInfos.push($x);
-    axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('argMax', [axes[0]], $x.shape.length);
-  const program = new ArgMinMaxProgram($x.shape, axes[0], 'max');
-  const uniformData = [{type: 'float32', data: [Number.NEGATIVE_INFINITY]}];
-  const out = backend.runWebGPUProgram(program, [$x], 'int32', uniformData);
-  intermediateTensorInfos.forEach(t => backend.disposeData(t.dataId));
-  return out;
-}
-
-export const argMaxConfig: KernelConfig = {
-  kernelName: ArgMax,
-  backendName: 'webgpu',
-  kernelFunc: argMax as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ArgMin.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ArgMin.ts
deleted file mode 100644
index 61c504c9d..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ArgMin.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMin, ArgMinAttrs, ArgMinInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {ArgMinMaxProgram} from '../argminmax_webgpu';
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {transpose} from './Transpose';
-
-export function argMin(
-    args: {inputs: ArgMinInputs, backend: WebGPUBackend, attrs: ArgMinAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis} = attrs;
-
-  let axes = util.parseAxisParam(axis, x.shape);
-  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
-  let $x = x;
-  const intermediateTensorInfos = [];
-  if (permutedAxes != null) {
-    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});
-    intermediateTensorInfos.push($x);
-    axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);
-  }
-
-  backend_util.assertAxesAreInnerMostDims('argMin', [axes[0]], $x.shape.length);
-  const program = new ArgMinMaxProgram($x.shape, axes[0], 'min');
-  const uniformData = [{type: 'float32', data: [Number.POSITIVE_INFINITY]}];
-  const out = backend.runWebGPUProgram(program, [$x], 'int32', uniformData);
-  intermediateTensorInfos.forEach(t => backend.disposeData(t.dataId));
-  return out;
-}
-
-export const argMinConfig: KernelConfig = {
-  kernelName: ArgMin,
-  backendName: 'webgpu',
-  kernelFunc: argMin as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Asin.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Asin.ts
deleted file mode 100644
index 61dd375fc..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Asin.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asin, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const asin = unaryKernelFunc({opType: UnaryOpType.ASIN});
-
-export const asinConfig: KernelConfig = {
-  kernelName: Asin,
-  backendName: 'webgpu',
-  kernelFunc: asin
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Asinh.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Asinh.ts
deleted file mode 100644
index 1997d479e..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Asinh.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asinh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const asinh = unaryKernelFunc({opType: UnaryOpType.ASINH});
-
-export const asinhConfig: KernelConfig = {
-  kernelName: Asinh,
-  backendName: 'webgpu',
-  kernelFunc: asinh
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Atan.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Atan.ts
deleted file mode 100644
index 98537fb33..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Atan.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const atan = unaryKernelFunc({opType: UnaryOpType.ATAN});
-
-export const atanConfig: KernelConfig = {
-  kernelName: Atan,
-  backendName: 'webgpu',
-  kernelFunc: atan
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Atan2.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Atan2.ts
deleted file mode 100644
index ca4c4884f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Atan2.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan2, KernelConfig} from '@tensorflow/tfjs-core';
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-export const atan2 = binaryKernelFunc({opType: BinaryOpType.ATAN2});
-
-export const atan2Config: KernelConfig = {
-  kernelName: Atan2,
-  backendName: 'webgpu',
-  kernelFunc: atan2
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Atanh.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Atanh.ts
deleted file mode 100644
index d08bccdbd..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Atanh.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atanh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const atanh = unaryKernelFunc({opType: UnaryOpType.ATANH});
-
-export const atanhConfig: KernelConfig = {
-  kernelName: Atanh,
-  backendName: 'webgpu',
-  kernelFunc: atanh
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPool.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPool.ts
deleted file mode 100644
index b05f214a1..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPool.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {AvgPool, AvgPoolAttrs, AvgPoolInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {poolImpl} from './Pool_impl';
-
-export function avgPool(
-    args: {inputs: AvgPoolInputs, backend: WebGPUBackend, attrs: AvgPoolAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const dilations = 1;
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode);
-
-  return poolImpl(x, convInfo, 'avg', backend);
-}
-
-export const avgPoolConfig: KernelConfig = {
-  kernelName: AvgPool,
-  backendName: 'webgpu',
-  kernelFunc: avgPool as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPool3D.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPool3D.ts
deleted file mode 100644
index 909437b61..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPool3D.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Pool3DProgram} from '../pool_webgpu';
-
-export function avgPool3D(args: {
-  inputs: AvgPool3DInputs,
-  backend: WebGPUBackend,
-  attrs: AvgPool3DAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dataFormat, dimRoundingMode} = attrs;
-  const dilations: [number, number, number] = [1, 1, 1];
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode, dataFormat);
-  const avgPoolProgram = new Pool3DProgram(convInfo, 'avg');
-  const dimensions = [
-    {
-      type: 'int32',
-      data: [convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth]
-    },
-    {
-      type: 'int32',
-      data:
-          [convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left]
-    },
-    {
-      type: 'int32',
-      data: [convInfo.inDepth, convInfo.inHeight, convInfo.inWidth]
-    },
-    {
-      type: 'int32',
-      data: [
-        convInfo.effectiveFilterDepth, convInfo.effectiveFilterHeight,
-        convInfo.effectiveFilterWidth
-      ]
-    }
-  ];
-  return backend.runWebGPUProgram(avgPoolProgram, [x], x.dtype, dimensions);
-}
-
-export const avgPool3DConfig: KernelConfig = {
-  kernelName: AvgPool3D,
-  backendName: 'webgpu',
-  kernelFunc: avgPool3D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPool3DGrad.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPool3DGrad.ts
deleted file mode 100644
index ae9280fec..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPool3DGrad.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool3DGrad, AvgPool3DGradAttrs, AvgPool3DGradInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {AvgPool3DBackpropProgram} from '../avg_pool_backprop_webgpu';
-import {WebGPUBackend} from '../backend_webgpu';
-
-export function avgPool3DGrad(args: {
-  inputs: AvgPool3DGradInputs,
-  backend: WebGPUBackend,
-  attrs: AvgPool3DGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const x = input;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      1 /* dilations */, pad, dimRoundingMode);
-  const program = new AvgPool3DBackpropProgram(convInfo);
-  const avgMultiplier =
-      1 / (convInfo.filterDepth * convInfo.filterHeight * convInfo.filterWidth);
-  const uniformData = [
-    {
-      type: 'int32',
-      data: [convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth]
-    },
-    {
-      type: 'int32',
-      data: [
-        convInfo.effectiveFilterDepth - 1 - convInfo.padInfo.front,
-        convInfo.effectiveFilterHeight - 1 - convInfo.padInfo.top,
-        convInfo.effectiveFilterWidth - 1 - convInfo.padInfo.left
-      ]
-    },
-    {
-      type: 'int32',
-      data: [
-        convInfo.effectiveFilterDepth, convInfo.effectiveFilterHeight,
-        convInfo.effectiveFilterWidth
-      ]
-    },
-    {type: 'int32', data: [convInfo.outDepth]},
-    {type: 'int32', data: [convInfo.outHeight]},
-    {type: 'int32', data: [convInfo.outWidth]},
-    {type: 'float32', data: [avgMultiplier]}
-  ];
-  return backend.runWebGPUProgram(program, [dy], x.dtype, uniformData);
-}
-
-export const avgPool3DGradConfig: KernelConfig = {
-  kernelName: AvgPool3DGrad,
-  backendName: 'webgpu',
-  kernelFunc: avgPool3DGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPoolGrad.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPoolGrad.ts
deleted file mode 100644
index b021bcff8..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/AvgPoolGrad.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPoolGrad, AvgPoolGradAttrs, AvgPoolGradInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {AvgPool2DBackpropProgram} from '../avg_pool_backprop_webgpu';
-import {WebGPUBackend} from '../backend_webgpu';
-import {assertNotComplex} from '../webgpu_util';
-
-export function avgPoolGrad(args: {
-  inputs: AvgPoolGradInputs,
-  backend: WebGPUBackend,
-  attrs: AvgPoolGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const x = input;
-  assertNotComplex([dy, input], 'avgPoolGrad');
-  const {filterSize, strides, pad} = attrs;
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      1 /* dilations */, pad);
-  const program = new AvgPool2DBackpropProgram(convInfo);
-  const avgMultiplier = 1 / (convInfo.filterHeight * convInfo.filterWidth);
-  const uniformData = [
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]}, {
-      type: 'int32',
-      data: [
-        convInfo.effectiveFilterHeight - 1 - convInfo.padInfo.top,
-        convInfo.effectiveFilterWidth - 1 - convInfo.padInfo.left
-      ]
-    },
-    {type: 'int32', data: [convInfo.dilationHeight, convInfo.dilationWidth]}, {
-      type: 'int32',
-      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]
-    },
-    {type: 'int32', data: [convInfo.outHeight]},
-    {type: 'int32', data: [convInfo.outWidth]},
-    {type: 'float32', data: [avgMultiplier]}
-  ];
-  return backend.runWebGPUProgram(program, [dy], x.dtype, uniformData);
-}
-
-export const avgPoolGradConfig: KernelConfig = {
-  kernelName: AvgPoolGrad,
-  backendName: 'webgpu',
-  kernelFunc: avgPoolGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/BatchMatMul.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/BatchMatMul.ts
deleted file mode 100644
index 573dd38de..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/BatchMatMul.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {batchMatMulImpl} from './BatchMatMul_impl';
-
-export function batchMatMul(args: {
-  inputs: BatchMatMulInputs,
-  attrs: BatchMatMulAttrs,
-  backend: WebGPUBackend
-}) {
-  const {inputs, backend, attrs} = args;
-  const {a, b} = inputs;
-  const {transposeA, transposeB} = attrs;
-
-  return batchMatMulImpl({a, b, transposeA, transposeB, backend});
-}
-
-export const batchMatMulConfig: KernelConfig = {
-  kernelName: BatchMatMul,
-  backendName: 'webgpu',
-  kernelFunc: batchMatMul as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/BatchMatMul_impl.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/BatchMatMul_impl.ts
deleted file mode 100644
index f2db26387..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/BatchMatMul_impl.ts
+++ /dev/null
@@ -1,218 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, broadcast_util, env, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {MatMulPackedProgram} from '../matmul_packed_webgpu';
-import {MatMulReduceProgram} from '../matmul_reduce_webgpu';
-import {MatMulSmallOutputSizeProgram} from '../matmul_small_output_size_webgpu';
-import {BiasActivationProgram, MatMulSplitKProgram} from '../matmul_splitK_webgpu';
-import {WebGPUProgram} from '../webgpu_program';
-import {MatMulProgramType} from '../webgpu_util';
-
-import {fill} from './Fill';
-import {reshape} from './Reshape';
-
-type BatchMatMulConfig = {
-  a: TensorInfo,
-  b: TensorInfo,
-  transposeA: boolean,
-  transposeB: boolean,
-  backend: WebGPUBackend,
-  bias?: TensorInfo,
-  preluActivationWeights?: TensorInfo,
-  leakyreluAlpha?: number,
-  activation?: backend_util.Activation
-};
-
-export function batchMatMulImpl({
-  a,
-  b,
-  transposeA,
-  transposeB,
-  backend,
-  bias = null,
-  preluActivationWeights = null,
-  leakyreluAlpha = 0,
-  activation = null
-}: BatchMatMulConfig): TensorInfo {
-  const aRank = a.shape.length;
-  const bRank = b.shape.length;
-
-  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
-  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
-
-  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
-  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
-
-  const outerDimsA = a.shape.slice(0, -2);
-  const outerDimsB = b.shape.slice(0, -2);
-
-  const batchDimA = util.sizeFromShape(outerDimsA);
-  const batchDimB = util.sizeFromShape(outerDimsB);
-
-  const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape(
-      a.shape.slice(0, -2), b.shape.slice(0, -2));
-  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
-
-  util.assert(
-      innerShapeA === innerShapeB,
-      () => `Error in matMul: inner shapes (${innerShapeA}) and (` +
-          `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +
-          `${b.shape} and transposeA=${transposeA}` +
-          ` and transposeB=${transposeB} must match.`);
-
-  const a3dShape: [number, number, number] = transposeA ?
-      [batchDimA, innerShapeA, outerShapeA] :
-      [batchDimA, outerShapeA, innerShapeA];
-  const b3dShape: [number, number, number] = transposeB ?
-      [batchDimB, outerShapeB, innerShapeB] :
-      [batchDimB, innerShapeB, outerShapeB];
-
-  // The rest of the implementation is designed to operate on rank-3 tensors
-  const a3d = reshape({inputs: {x: a}, backend, attrs: {shape: a3dShape}});
-  const b3d = reshape({inputs: {x: b}, backend, attrs: {shape: b3dShape}});
-  const intermediates: TensorInfo[] = [a3d, b3d];
-
-  const batchDim = Math.max(batchDimA, batchDimB);
-
-  const inputs: TensorInfo[] = [a3d, b3d];
-  const dimensions = [
-    {type: 'int32', data: [outerShapeA]}, {type: 'int32', data: [outerShapeB]},
-    {type: 'int32', data: [innerShapeA]}
-  ];
-
-  let program: WebGPUProgram;
-  let out: TensorInfo;
-  const outputShape: [number, number, number] =
-      [batchDim, outerShapeA, outerShapeB];
-  let matmulProgramType = env().get('WEBGPU_MATMUL_PROGRAM_TYPE') as number;
-  if (matmulProgramType < 0) {
-    // Usually increasing workgroups is a good way to gain more performance for
-    // few workgroups by tiling 32x32 (default matmul algorithm). Currently,
-    // there are three ways to increase workgroups. 1) MatMulReduceProgram,
-    // which is used only when the output size is very small (128 for now). 2)
-    // MatMulSplitKProgram, increasing workgroups by spliting K. 3)
-    // MatMulSmallOutputSizeProgram, increasing workgroups by small tile size.
-    // For different devices, the minimum optimal workgroups may be different.
-    // So here we set a |thresholdToIncreaseWorkgroups| to indicate whether we
-    // need to increase workgroups. And the literal number is an empirical
-    // value.
-    const thresholdFlagValue =
-        env().getNumber('WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL');
-    const thresholdToIncreaseWorkgroups = thresholdFlagValue > 0 ?
-        thresholdFlagValue :
-        backend.thresholdToIncreaseWorkgroups;
-    const workgroupsBy32x32 =
-        batchDim * Math.ceil(outerShapeA / 32) * Math.ceil(outerShapeB / 32);
-    const hasFewWorkgroups =
-        workgroupsBy32x32 <= thresholdToIncreaseWorkgroups ||
-        (outerShapeA <= 8 &&
-         workgroupsBy32x32 <= thresholdToIncreaseWorkgroups * 2);
-    if (hasFewWorkgroups) {
-      if (batchDim * outerShapeA * outerShapeB <= 128) {
-        matmulProgramType = MatMulProgramType.MatMulReduceProgram;
-      } else if (batchDim === 1 && innerShapeB >= 2000) {
-        matmulProgramType = MatMulProgramType.MatMulSplitKProgram;
-      } else {
-        matmulProgramType = MatMulProgramType.MatMulSmallOutputSizeProgram;
-      }
-    } else {
-      matmulProgramType = MatMulProgramType.MatMulPackedProgram;
-    }
-  }
-
-  switch (matmulProgramType) {
-    case MatMulProgramType.MatMulReduceProgram:
-      program = new MatMulReduceProgram(
-          outputShape, transposeA, transposeB, bias, activation,
-          preluActivationWeights);
-      break;
-    case MatMulProgramType.MatMulSplitKProgram: {
-      // The output buffer must be initailzed to zero before using since we
-      // use atomicAdd in MatMulSplitKProgram.
-      out = fill(
-          {backend, attrs: {shape: outputShape, value: 0, dtype: a.dtype}});
-      program = new MatMulSplitKProgram(
-          outputShape, innerShapeB, transposeA, transposeB);
-      if (bias || activation) {
-        out =
-            backend.runWebGPUProgram(program, inputs, a.dtype, dimensions, out);
-        const biasActivationProgram = new BiasActivationProgram(
-            out.shape, bias, activation, preluActivationWeights);
-        let uniformData = null;
-        const activationInputs: TensorInfo[] = [out];
-        if (bias) {
-          activationInputs.push(bias);
-        }
-        if (preluActivationWeights) {
-          activationInputs.push(preluActivationWeights);
-        }
-        if (activation === 'leakyrelu') {
-          uniformData = [{type: 'float32', data: [leakyreluAlpha]}];
-          biasActivationProgram.uniforms += ' alpha : f32,';
-        }
-        const outActivated = backend.runWebGPUProgram(
-            biasActivationProgram, activationInputs, out.dtype, uniformData);
-        intermediates.push(out);
-        const outReshaped = reshape(
-            {inputs: {x: outActivated}, backend, attrs: {shape: outShape}});
-        intermediates.push(outActivated);
-        for (const i of intermediates) {
-          backend.disposeData(i.dataId);
-        }
-        return outReshaped;
-      }
-      break;
-    }
-    case MatMulProgramType.MatMulSmallOutputSizeProgram:
-      program = new MatMulSmallOutputSizeProgram(
-          a3dShape, b3dShape, outputShape, transposeA, transposeB, bias,
-          activation, preluActivationWeights);
-      break;
-    case MatMulProgramType.MatMulPackedProgram:
-      // Experiments show that sequential access is more friendly for Intel
-      // GPUs.
-      const sequentialAccessByThreads = backend.adapterInfo.isIntel();
-      program = new MatMulPackedProgram(
-          a3dShape, outputShape, transposeA, transposeB, bias, activation,
-          preluActivationWeights, sequentialAccessByThreads);
-      break;
-    default:
-      throw new Error(`Unsupported MatMulProgramType ${matmulProgramType}.`);
-  }
-
-  if (bias) {
-    inputs.push(bias);
-  }
-  if (preluActivationWeights) {
-    inputs.push(preluActivationWeights);
-  }
-  if (activation === 'leakyrelu') {
-    dimensions.push({type: 'float32', data: [leakyreluAlpha]});
-    program.uniforms += ' alpha : f32,';
-  }
-  out = backend.runWebGPUProgram(program, inputs, a.dtype, dimensions, out);
-  const outReshaped =
-      reshape({inputs: {x: out}, backend, attrs: {shape: outShape}});
-  intermediates.push(out);
-  for (const i of intermediates) {
-    backend.disposeData(i.dataId);
-  }
-  return outReshaped;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/BatchToSpaceND.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/BatchToSpaceND.ts
deleted file mode 100644
index 44e684af5..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/BatchToSpaceND.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-import {transpose} from './Transpose';
-
-export const batchToSpaceND = (args: {
-  inputs: BatchToSpaceNDInputs,
-  backend: WebGPUBackend,
-  attrs: BatchToSpaceNDAttrs
-}): TensorInfo => {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockShape, crops} = attrs;
-
-  util.assert(
-      x.shape.length <= 4,
-      () => 'batchToSpaceND for rank > 4 with a WebGPU backend not ' +
-          'implemented yet');
-  const prod = blockShape.reduce((a, b) => a * b);
-
-  const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);
-  const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);
-  const reshapedPermuted =
-      backend_util.getReshapedPermuted(x.shape, blockShape, prod);
-  const sliceBeginCoords =
-      backend_util.getSliceBeginCoords(crops, blockShape.length);
-  const sliceSize =
-      backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);
-
-  const toDispose = [];
-
-  const reshapedIntermediate =
-      reshape({inputs: {x}, backend, attrs: {shape: reshaped}});
-  const transposedIntermediate = transpose(
-      {inputs: {x: reshapedIntermediate}, backend, attrs: {perm: permuted}});
-  const reshapedIntermediate2 = reshape({
-    inputs: {x: transposedIntermediate},
-    backend,
-    attrs: {shape: reshapedPermuted}
-  });
-  const sliced = slice({
-    inputs: {x: reshapedIntermediate2},
-    backend,
-    attrs: {begin: sliceBeginCoords, size: sliceSize}
-  });
-
-  toDispose.push(reshapedIntermediate);
-  toDispose.push(transposedIntermediate);
-  toDispose.push(reshapedIntermediate2);
-
-  toDispose.forEach(t => backend.disposeData(t.dataId));
-
-  return sliced;
-};
-
-export const batchToSpaceNDConfig: KernelConfig = {
-  kernelName: BatchToSpaceND,
-  backendName: 'webgpu',
-  kernelFunc: batchToSpaceND as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Bincount.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Bincount.ts
deleted file mode 100644
index a287ecfb8..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Bincount.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Bincount, BincountAttrs, BincountInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {BincountProgram} from '../bincount_webgpu';
-
-import {fill} from './Fill';
-
-export function bincount(
-    args:
-        {inputs: BincountInputs, backend: WebGPUBackend, attrs: BincountAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, weights} = inputs;
-  const {size} = attrs;
-
-  const xSize = util.sizeFromShape(x.shape);
-  const weightsSize = util.sizeFromShape(weights.shape);
-  const hasWeights = weightsSize > 0;
-  const outputSize: [number] = [size];
-  const dtype = weights.dtype;
-
-  const output = fill({backend, attrs: {shape: outputSize, value: 0, dtype}});
-  const program = new BincountProgram([xSize], hasWeights);
-  const uniformData = [{type: 'int32', data: [size]}];
-  const bincountInputs: TensorInfo[] = hasWeights ? [x, weights] : [x];
-  const res = backend.runWebGPUProgram(
-      program, bincountInputs, dtype, uniformData, output);
-
-  return res;
-}
-
-export const bincountConfig: KernelConfig = {
-  kernelName: Bincount,
-  backendName: 'webgpu',
-  kernelFunc: bincount as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/BroadcastArgs.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/BroadcastArgs.ts
deleted file mode 100644
index 6340995c0..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/BroadcastArgs.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BroadcastArgs, BroadcastArgsInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {BroadcastArgsProgram} from '../broadcast_args_webgpu';
-
-export function broadcastArgs(args: {
-  inputs: BroadcastArgsInputs,
-  backend: WebGPUBackend,
-}): TensorInfo {
-  const {inputs, backend} = args;
-  const {s0, s1} = inputs;
-
-  if (backend.shouldExecuteOnCPU([s0, s1])) {
-    const s0TensorInfo = backend.tensorMap.get(s0.dataId);
-    const s1TensorInfo = backend.tensorMap.get(s1.dataId);
-    const s0Vals = s0TensorInfo.values as TypedArray;
-    const s1Vals = s1TensorInfo.values as TypedArray;
-    const broadcastShape = backend_util.assertAndGetBroadcastShape(
-        Array.from(s0Vals), Array.from(s1Vals));
-    return backend.makeTensorInfo(
-        [broadcastShape.length], 'int32', Int32Array.from(broadcastShape));
-  }
-
-  const s0Size = util.sizeFromShape(s0.shape);
-  const s1Size = util.sizeFromShape(s1.shape);
-  const outputSize = Math.max(s0Size, s1Size);
-
-  const program = new BroadcastArgsProgram(outputSize);
-  const uniformData =
-      [{type: 'int32', data: [s0Size]}, {type: 'int32', data: [s1Size]}];
-  return backend.runWebGPUProgram(program, [s0, s1], 'int32', uniformData);
-}
-
-export const broadcastArgsConfig: KernelConfig = {
-  kernelName: BroadcastArgs,
-  backendName: 'webgpu',
-  kernelFunc: broadcastArgs as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cast.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Cast.ts
deleted file mode 100644
index 57388b8b0..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cast.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '@tensorflow/tfjs-core';
-import {BinaryInputs, Cast, CastAttrs, CastInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {castImplCPU} from '../kernel_utils/shared';
-
-import {complex} from './Complex';
-import {identity} from './Identity';
-import {notEqual} from './NotEqual';
-import {real} from './Real';
-
-import {int} from '../kernel_utils/int';
-
-export function cast(
-    args: {inputs: CastInputs, backend: WebGPUBackend, attrs: CastAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {dtype} = attrs;
-
-  // Casting to complex64.
-  if (dtype === 'complex64') {
-    if (x.dtype === 'complex64') {
-      return identity({inputs: {x}, backend});
-    }
-
-    // TODO: Import kernel function once zeros is modularized.
-    const zerosTensor = tf.zeros(x.shape);
-    const floatX = cast({inputs: {x}, backend, attrs: {dtype: 'float32'}});
-
-    const result =
-        complex({inputs: {real: floatX, imag: zerosTensor}, backend});
-
-    zerosTensor.dispose();
-    backend.disposeData(floatX.dataId);
-
-    return result;
-  }
-
-  // Casting from complex64
-  if (x.dtype === 'complex64') {
-    const realPart = real({inputs: {input: x}, backend});
-    const result = cast({inputs: {x: realPart}, backend, attrs: {dtype}});
-    backend.disposeData(realPart.dataId);
-    return result;
-  }
-
-  if (!util.hasEncodingLoss(x.dtype, dtype)) {
-    // We don't change the underlying data, since we cast to higher
-    // precision.
-    const result = identity({inputs: {x}, backend});
-    return {dataId: result.dataId, shape: result.shape, dtype};
-  }
-
-  if (backend.shouldExecuteOnCPU([x])) {
-    const values = backend.tensorMap.get(x.dataId).values as TypedArray;
-    const [resultShape, resultType, resultData] =
-        castImplCPU(values, x.shape, x.dtype, dtype);
-    return backend.makeTensorInfo(resultShape, resultType, resultData);
-  }
-
-  if (dtype === 'int32') {
-    return int(x, backend);
-  }
-
-  if (dtype === 'bool') {
-    const zerosTensorInfo = backend.makeTensorInfo(
-        [], 'bool', util.getTypedArrayFromDType('bool', 1));
-
-    const binaryInputs: BinaryInputs = {a: x, b: zerosTensorInfo};
-
-    const result = notEqual({inputs: binaryInputs, backend}) as TensorInfo;
-    backend.disposeData(zerosTensorInfo.dataId);
-    return result;
-  }
-
-  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
-}
-
-export const castConfig: KernelConfig = {
-  kernelName: Cast,
-  backendName: 'webgpu',
-  kernelFunc: cast as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Ceil.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Ceil.ts
deleted file mode 100644
index d1c443f88..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Ceil.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Ceil, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {ceilImplCPU} from '../kernel_utils/shared';
-import {UnaryOpType} from '../unary_op_util';
-
-export const ceil =
-    unaryKernelFunc({opType: UnaryOpType.CEIL, cpuKernelImpl: ceilImplCPU});
-
-export const ceilConfig: KernelConfig = {
-  kernelName: Ceil,
-  backendName: 'webgpu',
-  kernelFunc: ceil
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ClipByValue.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ClipByValue.ts
deleted file mode 100644
index 03f9de350..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ClipByValue.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ClipByValue, ClipByValueAttrs, ClipByValueInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {ClipVec4Program} from '../clip_vec4_webgpu';
-import {ClipProgram} from '../clip_webgpu';
-
-export function clipByValue(args: {
-  inputs: ClipByValueInputs,
-  backend: WebGPUBackend,
-  attrs: ClipByValueAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {clipValueMin, clipValueMax} = attrs;
-
-  let program: ClipProgram|ClipVec4Program;
-  const uniformData = [
-    {type: 'float32', data: [clipValueMin]},
-    {type: 'float32', data: [clipValueMax]}
-  ];
-  if (util.sizeFromShape(x.shape) % 4 === 0) {
-    program = new ClipVec4Program(x.shape);
-  } else {
-    program = new ClipProgram(x.shape);
-  }
-  return backend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-}
-
-export const clipByValueConfig: KernelConfig = {
-  kernelName: ClipByValue,
-  backendName: 'webgpu',
-  kernelFunc: clipByValue as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Complex.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Complex.ts
deleted file mode 100644
index e2ed33c70..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Complex.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Complex, ComplexInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {identity} from './Identity';
-
-/**
- * Complex tensors share data with their real and imaginary components. Complex
- * tensors' reference to the components is tracked by refCount on the individual
- * component. The refCounts are increased by the identity call.
- *
- * When a complex tensor is disposed, it will reduce the refCount on the
- * components by calling disposeData on each.
- */
-export function complex(args: {inputs: ComplexInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {real, imag} = inputs;
-
-  const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');
-  const complex = backend.tensorMap.get(complexInfo.dataId);
-
-  const realTensorInfo = identity({inputs: {x: real}, backend});
-
-  const imagTensorInfo = identity({inputs: {x: imag}, backend});
-
-  complex.complexTensorInfos = {real: realTensorInfo, imag: imagTensorInfo};
-
-  return complexInfo;
-}
-
-export const complexConfig: KernelConfig = {
-  kernelName: Complex,
-  backendName: 'webgpu',
-  kernelFunc: complex as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ComplexAbs.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ComplexAbs.ts
deleted file mode 100644
index ea27ad0af..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ComplexAbs.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ComplexAbs, ComplexAbsInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {ComplexAbsProgram} from '../complex_abs_webgpu';
-
-// Returns a TensorInfo with the complex shape and the dataId of the
-// underlying part. We need to do this because a reshaped complex tensor is
-// not reflected in its parts.
-function makeComplexComponentTensorInfo(
-    complexTensor: TensorInfo, complexPart: TensorInfo): TensorInfo {
-  return {
-    dataId: complexPart.dataId,
-    dtype: complexPart.dtype,
-    shape: complexTensor.shape
-  };
-}
-
-export function complexAbs(
-    args: {inputs: ComplexAbsInputs, backend: WebGPUBackend}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  const xData = backend.tensorMap.get(x.dataId);
-
-  const program = new ComplexAbsProgram(x.shape);
-  const programInputs = [
-    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),
-    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag),
-  ];
-
-  return backend.runWebGPUProgram(
-      program, programInputs, programInputs[0].dtype);
-}
-
-export const complexAbsConfig: KernelConfig = {
-  kernelName: ComplexAbs,
-  backendName: 'webgpu',
-  kernelFunc: complexAbs as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Concat.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Concat.ts
deleted file mode 100644
index 7d1866463..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Concat.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Concat, ConcatAttrs, ConcatInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {concatImpl} from './Concat_impl';
-import {identity} from './Identity';
-
-export function concat(
-    args: {inputs: ConcatInputs, attrs: ConcatAttrs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {axis} = attrs;
-
-  const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];
-
-  const shapes = inputs.map(t => t.shape);
-  backend_util.assertParamsConsistent(shapes, $axis);
-
-  const outShape =
-      backend_util.computeOutShape(inputs.map(t => t.shape), $axis);
-  if (util.sizeFromShape(outShape) === 0) {
-    return backend.makeTensorInfo(outShape, inputs[0].dtype, []);
-  }
-
-  // Keep only non-empty tensors (ignore tensors with 0 in their shape).
-  const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);
-  if ($inputs.length === 1) {
-    return identity({inputs: {x: $inputs[0]}, backend});
-  }
-
-  return concatImpl($inputs, $axis, backend);
-}
-
-export const concatConfig: KernelConfig = {
-  kernelName: Concat,
-  backendName: 'webgpu',
-  kernelFunc: concat as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Concat_impl.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Concat_impl.ts
deleted file mode 100644
index 2d98f35bb..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Concat_impl.ts
+++ /dev/null
@@ -1,155 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, ConcatInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {ConcatProgram} from '../concat_webgpu';
-import {concatImplCPU} from '../kernel_utils/shared';
-
-import {complex} from './Complex';
-import {imag} from './Imag';
-import {real} from './Real';
-import {reshape} from './Reshape';
-
-export function concatImpl(
-    inputs: ConcatInputs, axis: number, backend: WebGPUBackend): TensorInfo {
-  const dtype = inputs[0].dtype;
-  if (dtype === 'complex64') {
-    const reals = inputs.map((t) => real({inputs: {input: t}, backend}));
-    const imags = inputs.map((t) => imag({inputs: {input: t}, backend}));
-
-    const realConcated = concatImpl(reals, axis, backend);
-    const imagConcated = concatImpl(imags, axis, backend);
-
-    const result =
-        complex({inputs: {real: realConcated, imag: imagConcated}, backend});
-
-    reals.forEach(r => backend.disposeData(r.dataId));
-    imags.forEach(i => backend.disposeData(i.dataId));
-    backend.disposeData(realConcated.dataId);
-    backend.disposeData(imagConcated.dataId);
-
-    return result;
-  }
-
-  let runOnCpu = backend.shouldExecuteOnCPU(inputs);
-
-  // Run on cpu if dtype is string. For string, the backend represents it
-  // as Uint8Array[], where each Uint8Array is a character. Given that the
-  // computation is only on the outer array, uploading the whole data onto
-  // gpu is wasteful. Also, currently webgpu doesn't have a design to
-  // upload and retrieve Uint8Array[] between cpu and gpu. Therefore, we
-  // just run the kernel on cpu if dtype is string.
-  if (dtype === 'string') {
-    runOnCpu = true;
-  }
-
-  if (runOnCpu) {
-    // Any concat of n-dimensional tensors across any axis can be reduced to
-    // a concatenation of two-dimensional tensors across the axis 1 by first
-    // partitioning the axes of the original tensors into those less than the
-    // axis to be concatenated and the rest. Then reshape the tensors
-    // into a two-dimensional tensor by collapsing these two sets of axes and
-    // concatenate the resulting matrices across the axis 1, finally reshaping
-    // the result to have the proper shape.
-    const tensors2D = inputs.map(t => {
-      const innerSize = util.sizeFromShape(t.shape.slice(axis));
-      const shape = [-1, innerSize];
-      return reshape({inputs: {x: t}, backend, attrs: {shape}});
-    });
-
-    const inputsValShapes = tensors2D.map(t => {
-      return {vals: backend.readSync(t.dataId), shape: t.shape};
-    });
-
-    // Concats 2d tensors along axis=1.
-    const outShape =
-        backend_util.computeOutShape(tensors2D.map(t => t.shape), 1 /* axis */);
-    const simplyConcat = tensors2D[0].shape[0] === 1;
-    const outVals =
-        concatImplCPU(inputsValShapes, outShape, dtype, simplyConcat);
-
-    const finalOutShape =
-        backend_util.computeOutShape(inputs.map(t => t.shape), axis);
-
-    const outInfo = backend.makeTensorInfo(finalOutShape, dtype, outVals);
-
-    tensors2D.forEach(t => backend.disposeData(t.dataId));
-
-    return outInfo;
-  }
-
-  // There is a storage buffer limitation in compute stage, one for output so
-  // the maximum for input is limits.maxStorageBuffersPerShaderStage - 1
-  const maxInputNum = backend.device.limits.maxStorageBuffersPerShaderStage - 1;
-  if (inputs.length > maxInputNum) {
-    const reducedInputs = [];
-    for (let i = 0; i < inputs.length; i += maxInputNum) {
-      const subArray = inputs.slice(i, i + maxInputNum);
-      reducedInputs.push(concatImpl(subArray, axis, backend));
-    }
-    const result = concatImpl(reducedInputs, axis, backend);
-
-    for (const i of reducedInputs) {
-      backend.disposeData(i.dataId);
-    }
-
-    return result;
-  }
-
-  const {tensors2D, outShape} = computeTensors2D(inputs, axis, backend);
-  const shapes = (tensors2D).map(t => t.shape as [number, number]);
-  const program = new ConcatProgram(shapes);
-
-  const uniformData: Array<{type: string; data: number[]}> = [];
-  const offsets: number[] = new Array(shapes.length - 1);
-  if (offsets.length > 0) {
-    offsets[0] = shapes[0][1];
-    uniformData.push({type: 'int32', data: [offsets[0]]});
-    for (let i = 1; i < offsets.length; i++) {
-      offsets[i] = offsets[i - 1] + shapes[i][1];
-      uniformData.push({type: 'int32', data: [offsets[i]]});
-    }
-  }
-
-  const res = backend.runWebGPUProgram(
-      program, tensors2D, tensors2D[0].dtype, uniformData);
-  tensors2D.forEach(r => backend.disposeData(r.dataId));
-
-  const reshapedResult =
-      reshape({inputs: {x: res}, backend, attrs: {shape: outShape}});
-  backend.disposeData(res.dataId);
-  return reshapedResult;
-}
-
-function computeTensors2D(
-    inputs: ConcatInputs, axis: number, backend: WebGPUBackend) {
-  const outShape = backend_util.computeOutShape(inputs.map(t => t.shape), axis);
-  const tensors2D = inputs.map(t => reshape({
-                                 inputs: {x: t},
-                                 backend,
-                                 attrs: {
-                                   shape: [
-                                     util.sizeFromShape(t.shape.slice(0, axis)),
-                                     util.sizeFromShape(t.shape.slice(axis))
-                                   ]
-                                 }
-                               }));
-
-  return {tensors2D, outShape};
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2D.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2D.ts
deleted file mode 100644
index 5ec5b4103..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2D.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2D, Conv2DAttrs, Conv2DInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {conv2DImpl} from './Conv2D_impl';
-
-export function conv2d(
-    args: {inputs: Conv2DInputs, attrs: Conv2DAttrs, backend: WebGPUBackend}) {
-  const {inputs, attrs, backend} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dataFormat, dilations, dimRoundingMode} = attrs;
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, dilations, pad,
-      dimRoundingMode, false /* depthwise */, $dataFormat);
-  return conv2DImpl({x, filter, convInfo, backend});
-}
-
-export const conv2DConfig: KernelConfig = {
-  kernelName: Conv2D,
-  backendName: 'webgpu',
-  kernelFunc: conv2d as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2DBackpropFilter.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2DBackpropFilter.ts
deleted file mode 100644
index 0783eeb8e..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2DBackpropFilter.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Conv2DDerFilterProgram} from '../conv_backprop_webgpu';
-
-export function conv2DBackpropFilter(args: {
-  inputs: Conv2DBackpropFilterInputs,
-  backend: WebGPUBackend,
-  attrs: Conv2DBackpropFilterAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, pad, dataFormat, dimRoundingMode, filterShape} = attrs;
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number], filterShape, strides,
-      1 /* dilations */, pad, dimRoundingMode, false /* depthwise */,
-      $dataFormat);
-
-  const program = new Conv2DDerFilterProgram(convInfo);
-  const uniformData = [
-    {type: 'int32', data: [convInfo.padInfo.top, convInfo.padInfo.left]},
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]},
-    {type: 'int32', data: [convInfo.batchSize]},
-    {type: 'int32', data: [convInfo.outHeight]},
-    {type: 'int32', data: [convInfo.outWidth]},
-    {type: 'int32', data: [convInfo.inHeight]},
-    {type: 'int32', data: [convInfo.inWidth]}
-  ];
-  return backend.runWebGPUProgram(program, [x, dy], x.dtype, uniformData);
-}
-
-export const conv2DBackpropFilterConfig: KernelConfig = {
-  kernelName: Conv2DBackpropFilter,
-  backendName: 'webgpu',
-  kernelFunc: conv2DBackpropFilter as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2DBackpropInput.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2DBackpropInput.ts
deleted file mode 100644
index 3c99755cb..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2DBackpropInput.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs, env, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Conv2DDerInputMMProgram} from '../conv_backprop_mm_webgpu';
-import {Conv2DDerInputProgram} from '../conv_backprop_webgpu';
-
-export function conv2DBackpropInput(args: {
-  inputs: Conv2DBackpropInputInputs,
-  attrs: Conv2DBackpropInputAttrs,
-  backend: WebGPUBackend
-}) {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {inputShape, strides, pad, dataFormat, dimRoundingMode} = attrs;
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      inputShape, filter.shape as [number, number, number, number], strides,
-      1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);
-
-  const dimensions = [
-    {type: 'int32', data: [convInfo.filterHeight, convInfo.filterWidth]},
-    {
-      type: 'int32',
-      data: [
-        convInfo.filterHeight - 1 - convInfo.padInfo.top,
-        convInfo.filterWidth - 1 - convInfo.padInfo.left
-      ]
-    },
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]},
-    {
-      type: 'int32',
-      data: [
-        convInfo.batchSize, convInfo.outHeight, convInfo.outWidth,
-        convInfo.outChannels
-      ]
-    },
-  ];
-  let program: Conv2DDerInputProgram|Conv2DDerInputMMProgram;
-  // TODO: Experiment when to use Conv2DDerInputMMProgram algorithm.
-  if (env().getBool('WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE') ||
-      convInfo.dataFormat !== 'channelsLast') {
-    program = new Conv2DDerInputProgram(convInfo);
-  } else {
-    program = new Conv2DDerInputMMProgram(convInfo);
-    const dimAOuter = convInfo.inHeight * convInfo.inWidth;
-    const dimBOuter = convInfo.inChannels;
-    const dimInner =
-        convInfo.filterHeight * convInfo.filterWidth * convInfo.outChannels;
-    dimensions.push(
-        {type: 'uint32', data: [dimAOuter]},
-        {type: 'uint32', data: [dimBOuter]},
-        {type: 'uint32', data: [dimInner]});
-  }
-  return backend.runWebGPUProgram(program, [dy, filter], 'float32', dimensions);
-}
-
-export const conv2DBackpropInputConfig: KernelConfig = {
-  kernelName: Conv2DBackpropInput,
-  backendName: 'webgpu',
-  kernelFunc: conv2DBackpropInput as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2D_impl.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2D_impl.ts
deleted file mode 100644
index 05c4c4d94..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv2D_impl.ts
+++ /dev/null
@@ -1,394 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, env, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Conv2DMMProgram} from '../conv2d_mm_webgpu';
-import {Conv2DNaiveProgram} from '../conv2d_naive_webgpu';
-import {Im2ColProgram} from '../im2col_webgpu';
-import {WebGPUProgram} from '../webgpu_program';
-
-import {batchMatMulImpl} from './BatchMatMul_impl';
-import {reshape} from './Reshape';
-
-type Conv2DConfig = {
-  x: TensorInfo,
-  filter: TensorInfo,
-  convInfo: backend_util.Conv2DInfo,
-  backend: WebGPUBackend,
-  bias?: TensorInfo,
-  preluActivationWeights?: TensorInfo,
-  leakyreluAlpha?: number,
-  activation?: backend_util.Activation
-};
-
-// conv2dByMatMul fuses height and width into one dimension to compute
-// batchMatMul, so bias and activation weights are also supposed to fuse the two
-// dimensions into one.
-//
-// This function computes the target shape for fusing height and width
-// dimensions. Returning null means the shape is already compatible.
-function getShapeForBatchMatMul(
-    shape: number[], isChannelsLast: boolean): number[] {
-  const length = shape.length;
-  if (length >= 3) {
-    return isChannelsLast ?
-        [
-          ...shape.slice(0, -3) /* batch */,
-          shape[length - 3] * shape[length - 2] /* height * width */,
-          shape[length - 1] /* channel */
-        ] :
-        [
-          ...shape.slice(0, -3) /* batch */, shape[length - 3] /* channel */,
-          shape[length - 2] * shape[length - 1] /* height * width */
-        ];
-  } else if (!isChannelsLast && length === 1 && shape[0] > 1) {
-    return [shape[0], 1];
-  } else {
-    return null;
-  }
-}
-
-// For 1x1 kernels that iterate through every point in the input, convolution
-// can be expressed as matrix multiplication (without need for memory
-// remapping).
-function conv2dByMatMul({
-  x,
-  filter,
-  convInfo,
-  backend,
-  bias = null,
-  preluActivationWeights = null,
-  leakyreluAlpha = 0,
-  activation = null
-}: Conv2DConfig) {
-  const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-  const transposeA = isChannelsLast ? false : true;
-  const transposeB = false;
-
-  const sameSize = isChannelsLast &&
-      convInfo.filterHeight === convInfo.inHeight &&
-      convInfo.filterWidth === convInfo.inWidth &&
-      convInfo.padInfo.type === 'VALID';
-  const intermediates: TensorInfo[] = [];
-  let xReshaped;
-  let filterReshaped;
-
-  if (sameSize) {
-    const sharedDim =
-        convInfo.inHeight * convInfo.inWidth * convInfo.inChannels;
-    xReshaped = reshape({
-      inputs: {x},
-      backend,
-      attrs: {shape: [1, convInfo.batchSize, sharedDim]}
-    });
-    filterReshaped = reshape({
-      inputs: {x: filter},
-      backend,
-      attrs: {shape: [1, sharedDim, convInfo.outChannels]}
-    });
-  } else {
-    xReshaped = reshape({
-      inputs: {x},
-      backend,
-      attrs: {
-        shape: isChannelsLast ?
-            [
-              convInfo.batchSize, convInfo.inHeight * convInfo.inWidth,
-              convInfo.inChannels
-            ] :
-            [
-              convInfo.batchSize, convInfo.inChannels,
-              convInfo.inHeight * convInfo.inWidth
-            ]
-      }
-    });
-    filterReshaped = reshape({
-      inputs: {x: filter},
-      backend,
-      attrs: {shape: [1, convInfo.inChannels, convInfo.outChannels]}
-    });
-  }
-  intermediates.push(xReshaped);
-  intermediates.push(filterReshaped);
-
-  if (preluActivationWeights != null) {
-    const targetShape =
-        getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
-    if (targetShape != null) {
-      preluActivationWeights = reshape({
-        inputs: {x: preluActivationWeights},
-        backend,
-        attrs: {shape: targetShape}
-      });
-      intermediates.push(preluActivationWeights);
-    }
-  }
-
-  if (bias != null) {
-    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
-    if (targetShape != null) {
-      bias = reshape({inputs: {x: bias}, backend, attrs: {shape: targetShape}});
-      intermediates.push(bias);
-    }
-  }
-
-  const result = batchMatMulImpl({
-    a: isChannelsLast ? xReshaped : filterReshaped,
-    b: isChannelsLast ? filterReshaped : xReshaped,
-    transposeA,
-    transposeB,
-    backend,
-    bias,
-    activation,
-    preluActivationWeights,
-    leakyreluAlpha
-  });
-  const out = reshape(
-      {inputs: {x: result}, backend, attrs: {shape: convInfo.outShape}});
-  intermediates.push(result);
-
-  for (const i of intermediates) {
-    backend.disposeData(i.dataId);
-  }
-
-  return out;
-}
-
-// Implements the im2col algorithm as outlined in "High Performance
-// Convolutional Neural Networks for Document Processing" (Suvisoft, 2006)
-function conv2dWithIm2Col({
-  x,
-  filter,
-  convInfo,
-  backend,
-  bias = null,
-  preluActivationWeights = null,
-  leakyreluAlpha = 0,
-  activation = null
-}: Conv2DConfig) {
-  // Rearranges conv2d input so each block to be convolved over forms the
-  // row of a new matrix with shape [outHeight * outWidth,
-  // filterWidth * filterHeight * inChannels]. The filter is also rearranged so
-  // each output channel forms a col of a new matrix with shape [
-  // filterWidth * filterHeight * inChannels, outChannels]. The convolution is
-  // then computed by multiplying these matrices and reshaping the result.
-  const {
-    filterWidth,
-    filterHeight,
-    inChannels,
-    strideWidth,
-    strideHeight,
-    padInfo,
-    outWidth,
-    outHeight,
-    dilationWidth,
-    dilationHeight,
-    dataFormat
-  } = convInfo;
-
-  const isChannelsLast = dataFormat === 'channelsLast';
-
-  const sharedDim = filterWidth * filterHeight * inChannels;
-  const numCols = outHeight * outWidth;
-  const x2ColShape = isChannelsLast ? [convInfo.batchSize, numCols, sharedDim] :
-                                      [convInfo.batchSize, sharedDim, numCols];
-
-  const im2ColProgram = new Im2ColProgram(x2ColShape, isChannelsLast);
-  const dimensions = [
-    {type: 'int32', data: [padInfo.top, padInfo.left]},      // Padding.
-    {type: 'int32', data: [strideHeight, strideWidth]},      // Stride.
-    {type: 'int32', data: [dilationHeight, dilationWidth]},  // Dilation.
-    {type: 'int32', data: [outWidth]},
-    {type: 'int32', data: [inChannels * filterWidth]},  // itemsPerBlockRow.
-    {type: 'int32', data: [inChannels]}
-  ];
-  const x2Col =
-      backend.runWebGPUProgram(im2ColProgram, [x], x.dtype, dimensions);
-
-  const intermediates: TensorInfo[] = [];
-  intermediates.push(x2Col);
-
-  const filterReshaped = reshape(
-      {inputs: {x: filter}, backend, attrs: {shape: [1, sharedDim, -1]}});
-  intermediates.push(filterReshaped);
-
-  if (preluActivationWeights != null) {
-    const targetShape =
-        getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
-    if (targetShape != null) {
-      preluActivationWeights = reshape({
-        inputs: {x: preluActivationWeights},
-        backend,
-        attrs: {shape: targetShape}
-      });
-      intermediates.push(preluActivationWeights);
-    }
-  }
-
-  if (bias != null) {
-    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
-    if (targetShape != null) {
-      bias = reshape({inputs: {x: bias}, backend, attrs: {shape: targetShape}});
-      intermediates.push(bias);
-    }
-  }
-
-  const transposeA = isChannelsLast ? false : true;
-  const transposeB = false;
-  const result = batchMatMulImpl({
-    a: isChannelsLast ? x2Col : filterReshaped,
-    b: isChannelsLast ? filterReshaped : x2Col,
-    transposeA,
-    transposeB,
-    backend,
-    bias,
-    activation,
-    preluActivationWeights,
-    leakyreluAlpha
-  });
-  const out = reshape(
-      {inputs: {x: result}, backend, attrs: {shape: convInfo.outShape}});
-  intermediates.push(result);
-  for (const i of intermediates) {
-    backend.disposeData(i.dataId);
-  }
-
-  return out;
-}
-
-export function conv2DImpl({
-  x,
-  filter,
-  convInfo,
-  backend,
-  bias = null,
-  preluActivationWeights = null,
-  leakyreluAlpha = 0,
-  activation = null
-}: Conv2DConfig) {
-  const hasBias = bias != null;
-  const hasPreluActivationWeights = preluActivationWeights != null;
-  const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-  const sameSize = isChannelsLast &&
-      convInfo.filterHeight === convInfo.inHeight &&
-      convInfo.filterWidth === convInfo.inWidth &&
-      convInfo.padInfo.type === 'VALID';
-  const useNaiveConv2d = env().getBool('WEBGPU_USE_NAIVE_CONV2D_DEBUG');
-
-  if (!useNaiveConv2d &&
-      (sameSize ||
-       (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&
-        convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
-        convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
-        (convInfo.padInfo.type === 'SAME' ||
-         convInfo.padInfo.type === 'VALID')))) {
-    return conv2dByMatMul({
-      x,
-      filter,
-      convInfo,
-      backend,
-      bias,
-      activation,
-      preluActivationWeights,
-      leakyreluAlpha
-    });
-  }
-
-  const thresholdFlagValue =
-      env().getNumber('WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL');
-  const thresholdToIncreaseWorkgroups = thresholdFlagValue > -1 ?
-      thresholdFlagValue :
-      backend.thresholdToIncreaseWorkgroups;
-  const workgroupsBy32x32 = convInfo.batchSize *
-      Math.ceil((convInfo.outHeight * convInfo.outWidth) / 32) *
-      Math.ceil(convInfo.outChannels / 32);
-  if (env().getBool('WEBGPU_CONV_SEPARATE_IM2COL_SHADER') ||
-      workgroupsBy32x32 <= thresholdToIncreaseWorkgroups) {
-    return conv2dWithIm2Col({
-      x,
-      filter,
-      convInfo,
-      backend,
-      bias,
-      preluActivationWeights,
-      leakyreluAlpha,
-      activation
-    });
-  }
-
-  let program: WebGPUProgram;
-  const padInfo = [convInfo.padInfo.top, convInfo.padInfo.left];
-  const dimensions = [
-    {type: 'int32', data: [convInfo.filterHeight, convInfo.filterWidth]},
-    {type: 'int32', data: [...padInfo]},
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]},
-    {type: 'int32', data: [convInfo.dilationHeight, convInfo.dilationWidth]}
-  ];
-  if (useNaiveConv2d) {
-    program = new Conv2DNaiveProgram(
-        convInfo, hasBias, activation, hasPreluActivationWeights);
-  } else {
-    const dimAOuter = isChannelsLast ? convInfo.outHeight * convInfo.outWidth :
-                                       convInfo.outChannels;
-    const dimBOuter = isChannelsLast ? convInfo.outChannels :
-                                       convInfo.outHeight * convInfo.outWidth;
-    const dimInner =
-        convInfo.filterHeight * convInfo.filterWidth * convInfo.inChannels;
-    dimensions.push(
-        {type: 'int32', data: [dimAOuter]}, {type: 'int32', data: [dimBOuter]},
-        {type: 'int32', data: [dimInner]});
-
-    // Experiments show that sequential access is more friendly for Intel GPUs.
-    const sequentialAccessByThreads = backend.adapterInfo.isIntel();
-    program = new Conv2DMMProgram(
-        convInfo, dimAOuter, dimBOuter, dimInner, hasBias, activation,
-        hasPreluActivationWeights, sequentialAccessByThreads);
-  }
-
-  const intermediates: TensorInfo[] = [];
-  const inputVar: TensorInfo[] = [x, filter];
-  if (hasBias) {
-    if (!isChannelsLast && bias.shape.length === 1) {
-      bias = reshape(
-          {inputs: {x: bias}, backend, attrs: {shape: [bias.shape[0], 1, 1]}});
-      intermediates.push(bias);
-    }
-    inputVar.push(bias);
-  }
-  if (hasPreluActivationWeights) {
-    if (!isChannelsLast && preluActivationWeights.shape.length === 1) {
-      preluActivationWeights = reshape({
-        inputs: {x: preluActivationWeights},
-        backend,
-        attrs: {shape: [preluActivationWeights.shape[0], 1, 1]}
-      });
-      intermediates.push(preluActivationWeights);
-    }
-    inputVar.push(preluActivationWeights);
-  }
-  if (activation === 'leakyrelu') {
-    dimensions.push({type: 'float32', data: [leakyreluAlpha]});
-    program.uniforms += ' alpha : f32,';
-  }
-  const out = backend.runWebGPUProgram(program, inputVar, x.dtype, dimensions);
-  for (const i of intermediates) {
-    backend.disposeData(i.dataId);
-  }
-  return out;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv3D.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv3D.ts
deleted file mode 100644
index a8bc7a6da..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv3D.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3D, Conv3DAttrs, Conv3DInputs, KernelConfig, KernelFunc, upcastType} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Conv3DNaiveProgram} from '../conv3d_naive_webgpu';
-
-export function conv3D(
-    args: {inputs: Conv3DInputs, attrs: Conv3DAttrs, backend: WebGPUBackend}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  const convInfo = backend_util.computeConv3DInfo(
-      x.shape as [number, number, number, number, number],
-      filter.shape as [number, number, number, number, number], strides,
-      dilations, pad);
-
-  const padInfo =
-      [convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left];
-  const dimensions = [
-    {
-      type: 'int32',
-      data: [convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth]
-    },
-    {type: 'int32', data: [...padInfo]}, {
-      type: 'int32',
-      data: [convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth]
-    },
-    {
-      type: 'int32',
-      data: [
-        convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth
-      ]
-    }
-  ];
-  const program = new Conv3DNaiveProgram(convInfo);
-  const dtype = upcastType(x.dtype, filter.dtype);
-  return backend.runWebGPUProgram(program, [x, filter], dtype, dimensions);
-}
-
-export const conv3DConfig: KernelConfig = {
-  kernelName: Conv3D,
-  backendName: 'webgpu',
-  kernelFunc: conv3D as {} as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv3DBackpropFilterV2.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv3DBackpropFilterV2.ts
deleted file mode 100644
index 22c22f026..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv3DBackpropFilterV2.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropFilterV2, Conv3DBackpropFilterV2Attrs, Conv3DBackpropFilterV2Inputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Conv3DDerFilterProgram} from '../conv_backprop_webgpu';
-
-export function conv3DBackpropFilterV2(args: {
-  inputs: Conv3DBackpropFilterV2Inputs,
-  attrs: Conv3DBackpropFilterV2Attrs,
-  backend: WebGPUBackend,
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, pad, filterShape} = attrs;
-
-  const convInfo = backend_util.computeConv3DInfo(
-      x.shape as [number, number, number, number, number], filterShape, strides,
-      1 /* dilations */, pad);
-
-  const program = new Conv3DDerFilterProgram(convInfo);
-  const uniformData = [
-    {
-      type: 'int32',
-      data:
-          [convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left]
-    },
-    {
-      type: 'int32',
-      data: [convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth]
-    },
-    {type: 'int32', data: [convInfo.batchSize]},
-    {type: 'int32', data: [convInfo.outDepth]},
-    {type: 'int32', data: [convInfo.outHeight]},
-    {type: 'int32', data: [convInfo.outWidth]},
-    {type: 'int32', data: [convInfo.inDepth]},
-    {type: 'int32', data: [convInfo.inHeight]},
-    {type: 'int32', data: [convInfo.inWidth]}
-  ];
-  return backend.runWebGPUProgram(program, [x, dy], dy.dtype, uniformData);
-}
-
-export const conv3DBackpropFilterV2Config: KernelConfig = {
-  kernelName: Conv3DBackpropFilterV2,
-  backendName: 'webgpu',
-  kernelFunc: conv3DBackpropFilterV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv3DBackpropInputV2.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv3DBackpropInputV2.ts
deleted file mode 100644
index ce64a916a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Conv3DBackpropInputV2.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Conv3DDerInputProgram} from '../conv_backprop_webgpu';
-
-export function conv3DBackpropInputV2(args: {
-  inputs: Conv3DBackpropInputV2Inputs,
-  attrs: Conv3DBackpropInputV2Attrs,
-  backend: WebGPUBackend
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {strides, pad, inputShape} = attrs;
-
-  const convInfo = backend_util.computeConv3DInfo(
-      inputShape, filter.shape as [number, number, number, number, number],
-      strides, 1 /* dilations */, pad);
-
-  const program = new Conv3DDerInputProgram(convInfo);
-  const uniformData = [
-    {
-      type: 'int32',
-      data: [convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth]
-    },
-    {
-      type: 'int32',
-      data: [
-        convInfo.filterDepth - 1 - convInfo.padInfo.front,
-        convInfo.filterHeight - 1 - convInfo.padInfo.top,
-        convInfo.filterWidth - 1 - convInfo.padInfo.left
-      ]
-    },
-    {
-      type: 'int32',
-      data: [convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth]
-    },
-    {type: 'int32', data: [convInfo.outDepth]},
-    {type: 'int32', data: [convInfo.outHeight]},
-    {type: 'int32', data: [convInfo.outWidth]},
-    {type: 'int32', data: [convInfo.outChannels]}
-  ];
-
-  return backend.runWebGPUProgram(program, [dy, filter], dy.dtype, uniformData);
-}
-
-export const conv3DBackpropInputV2Config: KernelConfig = {
-  kernelName: Conv3DBackpropInputV2,
-  backendName: 'webgpu',
-  kernelFunc: conv3DBackpropInputV2 as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cos.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Cos.ts
deleted file mode 100644
index 7c81be74b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cos.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cos, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const cos = unaryKernelFunc({opType: UnaryOpType.COS});
-
-export const cosConfig: KernelConfig = {
-  kernelName: Cos,
-  backendName: 'webgpu',
-  kernelFunc: cos
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cosh.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Cosh.ts
deleted file mode 100644
index 52cf7dba2..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cosh.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cosh, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const cosh = unaryKernelFunc({opType: UnaryOpType.COSH});
-
-export const coshConfig: KernelConfig = {
-  kernelName: Cosh,
-  backendName: 'webgpu',
-  kernelFunc: cosh
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/CropAndResize.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/CropAndResize.ts
deleted file mode 100644
index 19d89afda..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/CropAndResize.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {CropAndResizeProgram} from '../crop_and_resize_webgpu';
-
-export const cropAndResize = (args: {
-  inputs: CropAndResizeInputs,
-  backend: WebGPUBackend,
-  attrs: CropAndResizeAttrs
-}): TensorInfo => {
-  const {inputs, backend, attrs} = args;
-  const {image, boxes, boxInd} = inputs;
-  const {cropSize, method, extrapolationValue} = attrs;
-
-  const program = new CropAndResizeProgram(
-      image.shape[3], boxes.shape as [number, number], cropSize, method);
-  const uniformData = [{type: 'float32', data: [extrapolationValue]}];
-  return backend.runWebGPUProgram(
-      program, [image, boxes, boxInd], 'float32', uniformData);
-};
-
-export const cropAndResizeConfig: KernelConfig = {
-  kernelName: CropAndResize,
-  backendName: 'webgpu',
-  kernelFunc: cropAndResize as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cum_impl.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Cum_impl.ts
deleted file mode 100644
index e6deaa612..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cum_impl.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {CumOpType, CumProgram} from '../cum_webgpu';
-
-import {identity} from './Identity';
-import {transpose} from './Transpose';
-
-export function cumImpl(
-    op: CumOpType, x: TensorInfo, backend: WebGPUBackend, axis: number,
-    exclusive: boolean, reverse: boolean): TensorInfo {
-  const xRank = x.shape.length;
-  const permutation = backend_util.getAxesPermutation([axis], xRank);
-  let permutedX = x;
-  if (permutation != null) {
-    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutation}});
-  }
-  const permutedAxis = backend_util.getInnerMostAxes(1, xRank)[0];
-
-  if (permutedAxis !== xRank - 1) {
-    throw new Error(
-        `WebGPU cumprod shader expects an inner-most axis=${
-            x.shape.length - 1} ` +
-        `but got axis=${axis}`);
-  }
-  const size = permutedX.shape[permutedAxis];
-  let result = identity({inputs: {x: permutedX}, backend});
-  // Use cum parallel algorithm, inspired by:
-  // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda
-  // Note: although the algorithm is called sum, it works for any associtative
-  // operator with an identity.
-
-  for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {
-    const program = new CumProgram(op, permutedX.shape, false, reverse);
-    const prevResult = result;
-    const uniformData = [{type: 'float32', data: [i]}];
-    result =
-        backend.runWebGPUProgram(program, [result], result.dtype, uniformData);
-    backend.disposeData(prevResult.dataId);
-  }
-  // For exclusive cum, shift the end result in the direction of product or sum
-  // and add 1 for product or 0 for sum to the front index.
-  if (exclusive) {
-    const program = new CumProgram(op, permutedX.shape, exclusive, reverse);
-    const prevResult = result;
-    const uniformData = [{type: 'float32', data: [0]}];
-    result =
-        backend.runWebGPUProgram(program, [result], result.dtype, uniformData);
-    backend.disposeData(prevResult.dataId);
-  }
-
-  if (permutation != null) {
-    const reversePermutation = backend_util.getUndoAxesPermutation(permutation);
-    const reverseTransposedResult = transpose(
-        {inputs: {x: result}, backend, attrs: {perm: reversePermutation}});
-
-    backend.disposeData(result.dataId);
-    backend.disposeData(permutedX.dataId);
-
-    return reverseTransposedResult;
-  }
-
-  return result;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cumprod.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Cumprod.ts
deleted file mode 100644
index 22f6a3c73..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cumprod.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cumprod, CumprodAttrs, CumprodInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {CumOpType} from '../cum_webgpu';
-import {cumImpl} from './Cum_impl';
-
-export function cumprod(
-    args: {inputs: CumprodInputs, backend: WebGPUBackend, attrs: CumprodAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, exclusive, reverse} = attrs;
-  return cumImpl(CumOpType.Prod, x, backend, axis, exclusive, reverse);
-}
-
-export const cumprodConfig: KernelConfig = {
-  kernelName: Cumprod,
-  backendName: 'webgpu',
-  kernelFunc: cumprod as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cumsum.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Cumsum.ts
deleted file mode 100644
index ab25a4bf2..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Cumsum.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cumsum, CumsumAttrs, CumsumInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {CumOpType} from '../cum_webgpu';
-import {cumImpl} from './Cum_impl';
-
-export function cumsum(
-    args: {inputs: CumsumInputs, backend: WebGPUBackend, attrs: CumsumAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, exclusive, reverse} = attrs;
-  return cumImpl(CumOpType.Sum, x, backend, axis, exclusive, reverse);
-}
-
-export const cumsumConfig: KernelConfig = {
-  kernelName: Cumsum,
-  backendName: 'webgpu',
-  kernelFunc: cumsum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/DenseBincount.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/DenseBincount.ts
deleted file mode 100644
index 5152d7cb8..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/DenseBincount.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DenseBincount, DenseBincountAttrs, DenseBincountInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {BincountProgram} from '../bincount_webgpu';
-
-import {fill} from './Fill';
-
-export function denseBincount(args: {
-  inputs: DenseBincountInputs,
-  backend: WebGPUBackend,
-  attrs: DenseBincountAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, weights} = inputs;
-  const {size, binaryOutput} = attrs;
-
-  const xRankOne = x.shape.length === 1;
-  const weightsSize = util.sizeFromShape(weights.shape);
-  const hasWeights = weightsSize > 0;
-  const dtype = weights.dtype;
-  const xSize: [number]|[number, number] =
-      xRankOne ? [x.shape[0]] : [x.shape[0], x.shape[1]];
-  const outputSize: [number]|[number, number] =
-      xRankOne ? [size] : [x.shape[0], size];
-
-  const output = fill({backend, attrs: {shape: outputSize, value: 0, dtype}});
-  const program = new BincountProgram(xSize, hasWeights, binaryOutput);
-  const uniformData = [{type: 'int32', data: [size]}];
-  const bincountInputs: TensorInfo[] = hasWeights ? [x, weights] : [x];
-  const res = backend.runWebGPUProgram(
-      program, bincountInputs, dtype, uniformData, output);
-
-  return res;
-}
-
-export const denseBincountConfig: KernelConfig = {
-  kernelName: DenseBincount,
-  backendName: 'webgpu',
-  kernelFunc: denseBincount as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthToSpace.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthToSpace.ts
deleted file mode 100644
index 62fff9bf9..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthToSpace.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {DepthToSpaceProgram} from '../depth_to_space_webgpu';
-
-export function depthToSpace(args: {
-  inputs: DepthToSpaceInputs,
-  backend: WebGPUBackend,
-  attrs: DepthToSpaceAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockSize, dataFormat} = attrs;
-
-  const batchSize = x.shape[0];
-  const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];
-  const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];
-  const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];
-
-  const outputHeight = inputHeight * blockSize;
-  const outputWidth = inputWidth * blockSize;
-  const outputDepth = inputDepth / (blockSize * blockSize);
-
-  const outputShape = (dataFormat === 'NHWC') ?
-      [batchSize, outputHeight, outputWidth, outputDepth] :
-      [batchSize, outputDepth, outputHeight, outputWidth];
-
-  const uniformData = [
-    {type: 'int32', data: [blockSize]},
-  ];
-
-  const program = new DepthToSpaceProgram(outputShape, dataFormat);
-  return backend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-}
-
-export const depthToSpaceConfig: KernelConfig = {
-  kernelName: DepthToSpace,
-  backendName: 'webgpu',
-  kernelFunc: depthToSpace as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthwiseConv2dNative.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthwiseConv2dNative.ts
deleted file mode 100644
index 28fa2226b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthwiseConv2dNative.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {DepthwiseConv2DNCHWSharedProgram} from '../depthwise_conv2d_nchw_shared_webgpu';
-import {DepthwiseConv2DVec4Program} from '../depthwise_conv2d_vec4_webgpu';
-import {DepthwiseConv2DProgram} from '../depthwise_conv2d_webgpu';
-
-export function depthwiseConv2dNative(args: {
-  inputs: DepthwiseConv2dNativeInputs,
-  attrs: DepthwiseConv2dNativeAttrs,
-  backend: WebGPUBackend
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dataFormat, dilations, dimRoundingMode} = attrs;
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  let $dilations = dilations;
-  if ($dilations == null) {
-    $dilations = [1, 1];
-  }
-
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, $dilations,
-      pad, dimRoundingMode, true /* depthwise */, $dataFormat);
-  const dimensions = [
-    {type: 'int32', data: [convInfo.padInfo.top, convInfo.padInfo.left]},
-    {type: 'int32', data: [convInfo.inHeight, convInfo.inWidth]},
-  ];
-
-  const isChannelsLast = convInfo.dataFormat === 'channelsLast';
-  let program: DepthwiseConv2DProgram|DepthwiseConv2DVec4Program|
-      DepthwiseConv2DNCHWSharedProgram;
-  if (!isChannelsLast && convInfo.inHeight > 16 && convInfo.inWidth > 16 &&
-      convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
-      convInfo.dilationWidth === 1 && convInfo.dilationHeight === 1 &&
-      convInfo.inChannels === convInfo.outChannels) {
-    program = new DepthwiseConv2DNCHWSharedProgram(
-        convInfo.outShape, convInfo.filterHeight, convInfo.filterWidth);
-  } else if (
-      isChannelsLast && convInfo.outHeight > 4 && convInfo.outWidth > 4 &&
-      convInfo.strideWidth <= 2 &&
-      convInfo.inChannels === convInfo.outChannels &&
-      convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
-      convInfo.inChannels % 4 === 0) {
-    program = new DepthwiseConv2DVec4Program(convInfo);
-    dimensions.push({type: 'int32', data: [program.virtualWidth]});
-  } else {
-    program = new DepthwiseConv2DProgram(convInfo);
-    dimensions.push(
-        {type: 'int32', data: [convInfo.filterHeight]},
-        {type: 'int32', data: [convInfo.filterWidth]},
-        {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]}, {
-          type: 'int32',
-          data: [convInfo.dilationHeight, convInfo.dilationWidth]
-        });
-  }
-
-  return backend.runWebGPUProgram(program, [x, filter], x.dtype, dimensions);
-}
-
-export const depthwiseConv2dNativeConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNative,
-  backendName: 'webgpu',
-  kernelFunc: depthwiseConv2dNative as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts
deleted file mode 100644
index 385a0af48..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropFilterAttrs, DepthwiseConv2dNativeBackpropFilterInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {DepthwiseConv2DDerFilterProgram} from '../conv_backprop_depthwise_webgpu';
-
-export function depthwiseConv2dNativeBackpropFilter(args: {
-  inputs: DepthwiseConv2dNativeBackpropFilterInputs,
-  attrs: DepthwiseConv2dNativeBackpropFilterAttrs,
-  backend: WebGPUBackend
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, dy} = inputs;
-  const {strides, dilations, pad, dimRoundingMode, filterShape} = attrs;
-
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number], filterShape, strides,
-      dilations, pad, dimRoundingMode, true /* depthwise */);
-
-  const program = new DepthwiseConv2DDerFilterProgram(convInfo);
-  const uniformData = [
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]},
-    {type: 'int32', data: [convInfo.padInfo.top, convInfo.padInfo.left]},
-    {type: 'int32', data: [convInfo.filterHeight, convInfo.filterWidth]},
-    {type: 'int32', data: [convInfo.outHeight]},
-    {type: 'int32', data: [convInfo.outWidth]},
-    {type: 'int32', data: [convInfo.inHeight]},
-    {type: 'int32', data: [convInfo.inWidth]},
-    {type: 'int32', data: [convInfo.batchSize]},
-    {type: 'int32', data: [convInfo.outChannels / convInfo.inChannels]}
-  ];
-  return backend.runWebGPUProgram(program, [x, dy], 'float32', uniformData);
-}
-
-export const depthwiseConv2dNativeBackpropFilterConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNativeBackpropFilter,
-  backendName: 'webgpu',
-  kernelFunc: depthwiseConv2dNativeBackpropFilter as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthwiseConv2dNativeBackpropInput.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthwiseConv2dNativeBackpropInput.ts
deleted file mode 100644
index 67a91a970..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/DepthwiseConv2dNativeBackpropInput.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNativeBackpropInput, DepthwiseConv2dNativeBackpropInputAttrs, DepthwiseConv2dNativeBackpropInputInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {DepthwiseConv2DDerInputProgram} from '../conv_backprop_depthwise_webgpu';
-
-export function depthwiseConv2dNativeBackpropInput(args: {
-  inputs: DepthwiseConv2dNativeBackpropInputInputs,
-  attrs: DepthwiseConv2dNativeBackpropInputAttrs,
-  backend: WebGPUBackend
-}) {
-  const {inputs, backend, attrs} = args;
-  const {dy, filter} = inputs;
-  const {strides, dilations, pad, dimRoundingMode, inputShape} = attrs;
-
-  const convInfo = backend_util.computeConv2DInfo(
-      inputShape, filter.shape as [number, number, number, number], strides,
-      dilations, pad, dimRoundingMode, true /* depthwise */);
-
-  const program = new DepthwiseConv2DDerInputProgram(convInfo);
-  const uniformData = [
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]}, {
-      type: 'int32',
-      data: [
-        convInfo.filterHeight - 1 - convInfo.padInfo.top,
-        convInfo.filterWidth - 1 - convInfo.padInfo.left
-      ]
-    },
-    {type: 'int32', data: [convInfo.filterHeight, convInfo.filterWidth]},
-    {type: 'int32', data: [convInfo.outHeight]},
-    {type: 'int32', data: [convInfo.outWidth]},
-    {type: 'int32', data: [convInfo.outChannels / convInfo.inChannels]}
-  ];
-  return backend.runWebGPUProgram(program, [dy, filter], dy.dtype, uniformData);
-}
-
-export const depthwiseConv2dNativeBackpropInputConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNativeBackpropInput,
-  backendName: 'webgpu',
-  kernelFunc: depthwiseConv2dNativeBackpropInput as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Diag.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Diag.ts
deleted file mode 100644
index 776d42eac..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Diag.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Diag, DiagInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {DiagProgram} from '../diag_webgpu';
-import {reshape} from './Reshape';
-
-export function diag(args: {inputs: DiagInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  const outShape = [...x.shape, ...x.shape];
-  const xSize = util.sizeFromShape(x.shape);
-
-  const flat = reshape({inputs: {x}, backend, attrs: {shape: [xSize]}});
-
-  const program = new DiagProgram(xSize);
-  const res = backend.runWebGPUProgram(program, [flat], flat.dtype);
-
-  const out = reshape({inputs: {x: res}, backend, attrs: {shape: outShape}});
-
-  backend.disposeData(flat.dataId);
-  backend.disposeData(res.dataId);
-
-  return out;
-}
-
-export const diagConfig: KernelConfig = {
-  kernelName: Diag,
-  backendName: 'webgpu',
-  kernelFunc: diag as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Dilation2D.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Dilation2D.ts
deleted file mode 100644
index ea47a2bf9..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Dilation2D.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2D, Dilation2DAttrs, Dilation2DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Dilation2DProgram} from '../dilation_webgpu';
-
-export function dilation2D(args: {
-  inputs: Dilation2DInputs,
-  attrs: Dilation2DAttrs,
-  backend: WebGPUBackend
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  const convInfo = backend_util.computeDilation2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number], strides, pad,
-      'NHWC' /* dataFormat */, dilations);
-  const padInfo = [convInfo.padInfo.top, convInfo.padInfo.left];
-  const uniformData = [
-    {type: 'int32', data: [convInfo.filterHeight, convInfo.filterWidth]},
-    {type: 'int32', data: [...padInfo]},
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]},
-    {type: 'int32', data: [convInfo.dilationHeight, convInfo.dilationWidth]}
-  ];
-
-  const program = new Dilation2DProgram(convInfo);
-  const out =
-      backend.runWebGPUProgram(program, [x, filter], x.dtype, uniformData);
-
-  return out;
-}
-
-export const dilation2DConfig: KernelConfig = {
-  kernelName: Dilation2D,
-  backendName: 'webgpu',
-  kernelFunc: dilation2D as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Dilation2DBackpropFilter.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Dilation2DBackpropFilter.ts
deleted file mode 100644
index 3ade8ffd6..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Dilation2DBackpropFilter.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2DAttrs, Dilation2DBackpropFilter, Dilation2DBackpropFilterInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Dilation2DBackpropFilterProgram} from '../dilation_backprop_webgpu';
-import {fill} from './Fill';
-
-export function dilation2DBackpropFilter(args: {
-  inputs: Dilation2DBackpropFilterInputs,
-  attrs: Dilation2DAttrs,
-  backend: WebGPUBackend
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, dy} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  const convInfo = backend_util.computeDilation2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number], strides, pad,
-      'NHWC' /* dataFormat */, dilations);
-
-  const dtype = filter.dtype;
-  const program =
-      new Dilation2DBackpropFilterProgram(convInfo, filter.shape, dtype);
-  const uniformData = [
-    {type: 'int32', data: [convInfo.filterHeight, convInfo.filterWidth]},
-    {type: 'int32', data: [convInfo.padInfo.top, convInfo.padInfo.left]},
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]},
-    {type: 'int32', data: [convInfo.dilationHeight, convInfo.dilationWidth]},
-    {type: 'int32', data: [util.sizeFromShape(convInfo.outShape)]}
-  ];
-  const output = fill({backend, attrs: {shape: filter.shape, value: 0, dtype}});
-  return backend.runWebGPUProgram(
-      program, [x, filter, dy], dtype, uniformData, output);
-}
-
-export const dilation2DBackpropFilterConfig: KernelConfig = {
-  kernelName: Dilation2DBackpropFilter,
-  backendName: 'webgpu',
-  kernelFunc: dilation2DBackpropFilter as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Dilation2DBackpropInput.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Dilation2DBackpropInput.ts
deleted file mode 100644
index db4d14c7a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Dilation2DBackpropInput.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2DAttrs, Dilation2DBackpropInput, Dilation2DBackpropInputInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Dilation2DBackpropInputProgram} from '../dilation_backprop_webgpu';
-import {fill} from './Fill';
-
-export function dilation2DBackpropInput(args: {
-  inputs: Dilation2DBackpropInputInputs,
-  attrs: Dilation2DAttrs,
-  backend: WebGPUBackend
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, dy} = inputs;
-  const {strides, pad, dilations} = attrs;
-
-  const convInfo = backend_util.computeDilation2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number], strides, pad,
-      'NHWC' /* dataFormat */, dilations);
-
-  const dtype = x.dtype;
-  const program = new Dilation2DBackpropInputProgram(convInfo, dtype);
-  const uniformData = [
-    {type: 'int32', data: [convInfo.filterHeight, convInfo.filterWidth]},
-    {type: 'int32', data: [convInfo.padInfo.top, convInfo.padInfo.left]},
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]},
-    {type: 'int32', data: [convInfo.dilationHeight, convInfo.dilationWidth]},
-    {type: 'int32', data: [util.sizeFromShape(convInfo.outShape)]}
-  ];
-  const output =
-      fill({backend, attrs: {shape: convInfo.inShape, value: 0, dtype}});
-  return backend.runWebGPUProgram(
-      program, [x, filter, dy], dtype, uniformData, output);
-}
-
-export const dilation2DBackpropInputConfig: KernelConfig = {
-  kernelName: Dilation2DBackpropInput,
-  backendName: 'webgpu',
-  kernelFunc: dilation2DBackpropInput as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Draw.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Draw.ts
deleted file mode 100644
index 6ccbb7b36..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Draw.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use backend file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-import {Draw, DrawAttrs, DrawInputs,} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {DrawProgram} from '../draw_webgpu';
-
-export function draw(
-    args: {inputs: DrawInputs, backend: WebGPUBackend, attrs: DrawAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {image} = inputs;
-  const {canvas, options} = attrs;
-  const [height, width] = image.shape.slice(0, 2);
-  const {imageOptions} = options || {};
-  const alpha = imageOptions ?.alpha || 1;
-
-  //  'rgba8unorm' should work on macOS according to
-  //  https://bugs.chromium.org/p/chromium/issues/detail?id=1298618. But
-  //  failed on macOS/M2. So use 'bgra8unorm' first when available.
-  const format = backend.device.features.has('bgra8unorm-storage') ?
-      'bgra8unorm' :
-      'rgba8unorm';
-  const outShape = [height, width];
-  const program = new DrawProgram(outShape, image.dtype, format);
-  canvas.width = width;
-  canvas.height = height;
-  const backendName = 'webgpu';
-  let gpuContext = canvas.getContext(backendName);
-  let canvasWebGPU;
-  if (!gpuContext) {
-    canvasWebGPU = new OffscreenCanvas(width, height);
-    gpuContext = canvasWebGPU.getContext(backendName);
-  }
-  const numChannels = image.shape.length === 3 ? image.shape[2] : 1;
-  gpuContext.configure({
-    device: backend.device,
-    format,
-    usage: GPUTextureUsage.STORAGE_BINDING,
-    alphaMode: 'premultiplied'
-  });
-
-  const outputDtype = 'int32';
-  const output = backend.makeTensorInfo(outShape, outputDtype);
-  const info = backend.tensorMap.get(output.dataId);
-  info.resource = gpuContext.getCurrentTexture();
-  info.external = true;
-
-  const uniformData =
-      [{type: 'uint32', data: [numChannels]}, {type: 'float32', data: [alpha]}];
-  backend.runWebGPUProgram(program, [image], outputDtype, uniformData, output);
-
-  if (canvasWebGPU) {
-    const canvas2dContext = canvas.getContext('2d');
-    if (!canvas2dContext) {
-      throw new Error(
-          `Please make sure this canvas has only been used for 2d or webgpu context!`);
-    }
-    canvas2dContext.drawImage(canvasWebGPU, 0, 0);
-  }
-  backend.disposeData(output.dataId);
-  return image;
-}
-
-export const drawConfig: KernelConfig = {
-  kernelName: Draw,
-  backendName: 'webgpu',
-  kernelFunc: draw as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Einsum.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Einsum.ts
deleted file mode 100644
index 3641514ea..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Einsum.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Einsum, EinsumAttrs, EinsumInputs, KernelConfig, KernelFunc, Tensor, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {multiplyKernelFunc} from './Multiply';
-import {reshape} from './Reshape';
-import {sum} from './Sum';
-import {transpose} from './Transpose';
-
-export function einsum(
-    args: {inputs: EinsumInputs, backend: WebGPUBackend, attrs: EinsumAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {equation} = attrs;
-  const tensors = inputs as Tensor[];
-
-  const {allDims, summedDims, idDims} =
-      backend_util.decodeEinsumEquation(equation, tensors.length);
-  backend_util.checkEinsumDimSizes(allDims.length, idDims, tensors);
-  const {path, steps} = backend_util.getEinsumComputePath(summedDims, idDims);
-
-  const nSteps = steps.length;
-  let out: TensorInfo|null = null;
-  let numDimsRemaining = allDims.length;
-  const tensorsToDispose: TensorInfo[] = [];
-  for (let i = 0; i < nSteps; ++i) {
-    for (const idTerm of steps[i]) {
-      const {permutationIndices: perm, expandDims: dimsToExpand} =
-          backend_util.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
-      let x: TensorInfo;
-      if (backend_util.isIdentityPermutation(perm)) {
-        x = tensors[idTerm];
-      } else {
-        x = transpose({inputs: {x: tensors[idTerm]}, backend, attrs: {perm}});
-        tensorsToDispose.push(x);
-      }
-      const targetShape: number[] = x.shape.slice();
-      for (let k = 0; k < dimsToExpand.length; ++k) {
-        targetShape.splice(dimsToExpand[k], 0, 1);
-      }
-
-      if (!util.arraysEqual(x.shape, targetShape)) {
-        x = reshape({inputs: {x}, backend, attrs: {shape: targetShape}});
-        tensorsToDispose.push(x);
-      }
-      if (out === null) {
-        out = x;
-      } else {
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        out =
-            multiplyKernelFunc({inputs: {a: x, b: out}, backend}) as TensorInfo;
-        tensorsToDispose.push(out);
-      }
-    }
-    if (i < nSteps - 1) {
-      if (path[i] >= 0) {
-        out = sum({
-          inputs: {x: out},
-          backend,
-          attrs: {
-            axis: path[i] - (allDims.length - numDimsRemaining),
-            keepDims: false
-          }
-        });
-        tensorsToDispose.push(out);
-      }
-      numDimsRemaining--;
-    }
-  }
-
-  // Clean up intermediate tensors.
-  for (const tensorInfo of tensorsToDispose) {
-    if (tensorInfo === out) {
-      continue;
-    }
-    backend.disposeData(tensorInfo.dataId);
-  }
-
-  return out;
-}
-
-export const einsumConfig: KernelConfig = {
-  kernelName: Einsum,
-  backendName: 'webgpu',
-  kernelFunc: einsum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Elu.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Elu.ts
deleted file mode 100644
index 88eb23bea..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Elu.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Elu, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {UnaryOpType} from '../unary_op_util';
-
-export const elu = unaryKernelFunc({opType: UnaryOpType.ELU});
-
-export const eluConfig: KernelConfig = {
-  kernelName: Elu,
-  backendName: 'webgpu',
-  kernelFunc: elu
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/EluGrad.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/EluGrad.ts
deleted file mode 100644
index ed5b387b5..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/EluGrad.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {EluGrad, EluGradInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {BinaryOpType} from '../binary_op_util';
-import {BinaryOpProgram} from '../binary_op_webgpu';
-
-export const eluGrad =
-    (args: {inputs: EluGradInputs, backend: WebGPUBackend}): TensorInfo => {
-      const {inputs, backend} = args;
-      const {dy, y} = inputs;
-
-      const program =
-          new BinaryOpProgram(BinaryOpType.ELU_DER, dy.shape, y.shape);
-      return backend.runWebGPUProgram(program, [dy, y], dy.dtype);
-    };
-
-export const eluGradConfig: KernelConfig = {
-  kernelName: EluGrad,
-  backendName: 'webgpu',
-  kernelFunc: eluGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Equal.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Equal.ts
deleted file mode 100644
index dbd2db08c..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Equal.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Equal, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {equalImplCPU as cpuEqual} from '../kernel_utils/shared';
-
-export const equal = binaryKernelFunc(
-    {opType: BinaryOpType.EQUAL, dtype: 'bool', cpuKernelImpl: cpuEqual});
-
-export const equalConfig: KernelConfig = {
-  kernelName: Equal,
-  backendName: 'webgpu',
-  kernelFunc: equal
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Erf.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Erf.ts
deleted file mode 100644
index fdec94925..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Erf.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Erf, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const erf = unaryKernelFunc({opType: UnaryOpType.ERF});
-
-export const erfConfig: KernelConfig = {
-  kernelName: Erf,
-  backendName: 'webgpu',
-  kernelFunc: erf
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Exp.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Exp.ts
deleted file mode 100644
index ddee16237..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Exp.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Exp, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {expImplCPU} from '../kernel_utils/shared';
-import {UnaryOpType} from '../unary_op_util';
-
-export const exp = unaryKernelFunc({
-  opType: UnaryOpType.EXP,
-  cpuKernelImpl: expImplCPU,
-  dtype: 'float32',
-});
-
-export const expConfig: KernelConfig = {
-  kernelName: Exp,
-  backendName: 'webgpu',
-  kernelFunc: exp
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ExpandDims.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ExpandDims.ts
deleted file mode 100644
index 75a7e0416..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ExpandDims.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the License);
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ExpandDims, ExpandDimsAttrs, ExpandDimsInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {reshape} from './Reshape';
-
-export function expandDims(args: {
-  inputs: ExpandDimsInputs,
-  attrs: ExpandDimsAttrs,
-  backend: WebGPUBackend
-}): TensorInfo {
-  const {inputs, attrs, backend} = args;
-  const {dim} = attrs;
-  const {input} = inputs;
-
-  const inputRank = input.shape.length;
-  const newShape = input.shape.slice();
-  let $dim = dim;
-  if (dim < 0) {
-    // Negative value is counted from the tail of rank.
-    util.assert(
-        -(inputRank + 1) <= dim,
-        () => `Axis must be in the interval [${- (inputRank + 1)}, ${
-            inputRank}]`);
-    $dim = inputRank + dim + 1;
-  }
-  newShape.splice($dim, 0, 1);
-
-  return reshape({inputs: {x: input}, backend, attrs: {shape: newShape}});
-}
-
-export const expandDimsConfig: KernelConfig = {
-  kernelName: ExpandDims,
-  backendName: 'webgpu',
-  kernelFunc: expandDims as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Expm1.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Expm1.ts
deleted file mode 100644
index 8832fe635..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Expm1.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Expm1, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {expm1ImplCPU} from '../kernel_utils/shared';
-import {UnaryOpType} from '../unary_op_util';
-
-export const expm1 =
-    unaryKernelFunc({opType: UnaryOpType.EXPM1, cpuKernelImpl: expm1ImplCPU});
-
-export const expm1Config: KernelConfig = {
-  kernelName: Expm1,
-  backendName: 'webgpu',
-  kernelFunc: expm1
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/FFT.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/FFT.ts
deleted file mode 100644
index cb91cd66f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/FFT.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FFT, FFTInputs, KernelConfig, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {fftImpl} from './FFT_impl';
-
-export function fft(args: {inputs: FFTInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-
-  return fftImpl(input, false /* inverse */, backend);
-}
-
-export const fftConfig: KernelConfig = {
-  kernelName: FFT,
-  backendName: 'webgpu',
-  kernelFunc: fft
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/FFT_impl.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/FFT_impl.ts
deleted file mode 100644
index 4aa894a31..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/FFT_impl.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {FFTProgram} from '../fft_webgpu';
-
-import {complex} from './Complex';
-import {reshape} from './Reshape';
-
-export function fftImpl(
-    x: TensorInfo, inverse: boolean, backend: WebGPUBackend): TensorInfo {
-  const xData = backend.tensorMap.get(x.dataId);
-
-  const inputSize = util.sizeFromShape(x.shape);
-  // Collapse all outer dimensions to a single batch dimension.
-  const innerDimensionSize = x.shape[x.shape.length - 1];
-  const batch = inputSize / innerDimensionSize;
-
-  const toDispose = [];
-  const input2D = reshape(
-      {inputs: {x}, backend, attrs: {shape: [batch, innerDimensionSize]}});
-  toDispose.push(input2D);
-
-  const xShape = input2D.shape as [number, number];
-  const realProgram = new FFTProgram('real', xShape);
-  const imagProgram = new FFTProgram('imag', xShape);
-
-  const inputs = [
-    {
-      dataId: xData.complexTensorInfos.real.dataId,
-      dtype: xData.complexTensorInfos.real.dtype,
-      shape: xShape
-    },
-    {
-      dataId: xData.complexTensorInfos.imag.dataId,
-      dtype: xData.complexTensorInfos.imag.dtype,
-      shape: xShape
-    }
-  ];
-
-  const exponentMultiplier = inverse ? 2.0 * Math.PI : -2.0 * Math.PI;
-  const denominator = inverse ? xShape[1] : 1.0;
-  const uniformData = [
-    {type: 'float32', data: [exponentMultiplier]},
-    {type: 'float32', data: [denominator]}
-  ];
-
-  const realPart =
-      backend.runWebGPUProgram(realProgram, inputs, 'float32', uniformData);
-  toDispose.push(realPart);
-  const imagPart =
-      backend.runWebGPUProgram(imagProgram, inputs, 'float32', uniformData);
-  toDispose.push(imagPart);
-
-  const complexOutput =
-      complex({inputs: {real: realPart, imag: imagPart}, backend});
-  toDispose.push(complexOutput);
-
-  const complexOutputReshaped =
-      reshape({inputs: {x: complexOutput}, backend, attrs: {shape: x.shape}});
-
-  toDispose.forEach(t => backend.disposeData(t.dataId));
-
-  return complexOutputReshaped;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Fill.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Fill.ts
deleted file mode 100644
index b7e1bee27..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Fill.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Fill, FillAttrs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {FillProgram} from '../fill_webgpu';
-
-export function fill(args: {backend: WebGPUBackend, attrs: FillAttrs}):
-    TensorInfo {
-  const {backend, attrs} = args;
-  const {shape, value} = attrs;
-  let {dtype} = attrs;
-
-  dtype = dtype || util.inferDtype(value);
-
-  if (dtype === 'string') {
-    // String type should be handled in CPU memory.
-    const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));
-    values.fill(value as string);
-    return backend.makeTensorInfo(shape, dtype, values);
-  } else {
-    const program = new FillProgram(shape);
-    const uniformData = [{type: 'float32', data: [value as number]}];
-    return backend.runWebGPUProgram(program, [], dtype, uniformData);
-  }
-}
-
-export const fillConfig: KernelConfig = {
-  kernelName: Fill,
-  backendName: 'webgpu',
-  kernelFunc: fill as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/FlipLeftRight.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/FlipLeftRight.ts
deleted file mode 100644
index b4140bb41..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/FlipLeftRight.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tensor4D} from '@tensorflow/tfjs-core';
-import {FlipLeftRight, FlipLeftRightInputs} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {FlipLeftRightProgram} from '../flip_left_right_webgpu';
-
-export const flipLeftRightConfig: KernelConfig = {
-    kernelName: FlipLeftRight,
-    backendName: 'webgpu',
-    kernelFunc: ({inputs, backend}) => {
-      const {image} = inputs as FlipLeftRightInputs;
-      const webgpuBackend = backend as WebGPUBackend;
-
-      const program = new FlipLeftRightProgram((image as Tensor4D).shape);
-      const output =
-          webgpuBackend.runWebGPUProgram(program, [image], image.dtype);
-      return output;
-  }
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Floor.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Floor.ts
deleted file mode 100644
index 546d1f40f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Floor.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Floor, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {floorImplCPU} from '../kernel_utils/shared';
-import {UnaryOpType} from '../unary_op_util';
-
-export const floor =
-    unaryKernelFunc({opType: UnaryOpType.FLOOR, cpuKernelImpl: floorImplCPU});
-
-export const floorConfig: KernelConfig = {
-  kernelName: Floor,
-  backendName: 'webgpu',
-  kernelFunc: floor
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/FloorDiv.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/FloorDiv.ts
deleted file mode 100644
index b57b552f9..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/FloorDiv.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FloorDiv, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {floorDivImplCPU} from '../kernel_utils/shared';
-
-export const floorDiv = binaryKernelFunc({
-  opType: BinaryOpType.FLOOR_DIV,
-  cpuKernelImpl: floorDivImplCPU,
-  dtype: 'int32'
-});
-
-export const floorDivConfig: KernelConfig = {
-  kernelName: FloorDiv,
-  backendName: 'webgpu',
-  kernelFunc: floorDiv
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/FromPixels.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/FromPixels.ts
deleted file mode 100644
index 6c37aecd3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/FromPixels.ts
+++ /dev/null
@@ -1,141 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use backend file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-import {FromPixels, FromPixelsAttrs, FromPixelsInputs, util} from '@tensorflow/tfjs-core';
-import {backend_util, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {FromPixelsProgram} from '../from_pixels_webgpu';
-
-export const fromPixelsConfig: KernelConfig = {
-  kernelName: FromPixels,
-  backendName: 'webgpu',
-  kernelFunc: fromPixels as unknown as KernelFunc,
-};
-
-let fromPixels2DContext: CanvasRenderingContext2D;
-let willReadFrequently = env().getBool('CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU');
-
-export function fromPixels(args: {
-  inputs: FromPixelsInputs,
-  backend: WebGPUBackend,
-  attrs: FromPixelsAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  let {pixels} = inputs;
-  const {numChannels} = attrs;
-
-  if (pixels == null) {
-    throw new Error('pixels passed to tf.browser.fromPixels() can not be null');
-  }
-
-  const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&
-      pixels instanceof HTMLVideoElement;
-  const isImage = typeof (HTMLImageElement) !== 'undefined' &&
-      pixels instanceof HTMLImageElement;
-  const isCanvas = (typeof (HTMLCanvasElement) !== 'undefined' &&
-                    pixels instanceof HTMLCanvasElement) ||
-      (typeof (OffscreenCanvas) !== 'undefined' &&
-       pixels instanceof OffscreenCanvas);
-  const isImageBitmap =
-      typeof (ImageBitmap) !== 'undefined' && pixels instanceof ImageBitmap;
-
-  const [width, height] = isVideo ?
-      [
-        (pixels as HTMLVideoElement).videoWidth,
-        (pixels as HTMLVideoElement).videoHeight
-      ] :
-      [pixels.width, pixels.height];
-  const outputShape = [height, width, numChannels];
-
-  const importVideo =
-      env().getBool('WEBGPU_IMPORT_EXTERNAL_TEXTURE') && isVideo;
-  const isVideoOrImage = isVideo || isImage;
-  if (isImageBitmap || isCanvas || isVideoOrImage) {
-    let resource;
-    if (importVideo) {
-      resource = backend.device.importExternalTexture(
-          {source: pixels as HTMLVideoElement});
-    } else {
-      if (isVideoOrImage) {
-        const newWillReadFrequently =
-            env().getBool('CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU');
-        if (fromPixels2DContext == null ||
-            newWillReadFrequently !== willReadFrequently) {
-          willReadFrequently = newWillReadFrequently;
-          fromPixels2DContext = document.createElement('canvas').getContext(
-              '2d', {willReadFrequently});
-        }
-        fromPixels2DContext.canvas.width = width;
-        fromPixels2DContext.canvas.height = height;
-        fromPixels2DContext.drawImage(
-            pixels as HTMLVideoElement | HTMLImageElement, 0, 0, width, height);
-        pixels = fromPixels2DContext.canvas;
-      }
-
-      const usage = GPUTextureUsage.COPY_DST |
-          GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING;
-      const format = 'rgba8unorm' as GPUTextureFormat;
-      const texture = backend.textureManager.acquireTexture(
-          outputShape[1], outputShape[0], format, usage);
-      backend.queue.copyExternalImageToTexture(
-          {source: pixels as HTMLCanvasElement | ImageBitmap}, {texture},
-          [outputShape[1], outputShape[0]]);
-      resource = texture;
-    }
-
-    const size = util.sizeFromShape(outputShape);
-    const strides = util.computeStrides(outputShape);
-    const program =
-        new FromPixelsProgram(outputShape, numChannels, importVideo);
-
-    const uniformData = [
-      {type: 'uint32', data: [size]}, {type: 'uint32', data: [numChannels]},
-      {type: 'uint32', data: [...strides]}
-    ];
-    const input = backend.makeTensorInfo([height, width], 'int32');
-    const info = backend.tensorMap.get(input.dataId);
-    info.resource = resource;
-
-    const result =
-        backend.runWebGPUProgram(program, [input], 'int32', uniformData);
-    backend.disposeData(input.dataId);
-    return result;
-  }
-
-  // TODO: Encoding should happen on GPU once we no longer have to download
-  // image data to the CPU.
-  const imageData = (pixels as ImageData | backend_util.PixelData).data;
-  let pixelArray = imageData;
-  if (numChannels != null && numChannels !== 4) {
-    pixelArray = new Uint8Array(pixels.width * pixels.height * numChannels);
-
-    const dataLength = imageData.length;
-    let j = 0;
-    for (let i = 0; i < dataLength; i++) {
-      if (i % 4 < numChannels) {
-        pixelArray[j++] = imageData[i];
-      }
-    }
-  }
-
-  const output =
-      backend.makeTensorInfo(outputShape, 'int32', new Int32Array(pixelArray));
-  backend.uploadToGPU(output.dataId);
-  return output;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/FusedBatchNorm.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/FusedBatchNorm.ts
deleted file mode 100644
index 05fbe2835..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/FusedBatchNorm.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs, KernelConfig, Tensor} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {BatchNormProgram} from '../batchnorm_webgpu';
-
-export const fusedBatchNormConfig: KernelConfig = {
-  kernelName: FusedBatchNorm,
-  backendName: 'webgpu',
-  kernelFunc: ({inputs, attrs, backend}) => {
-    const {x, scale, offset, mean, variance} = inputs as FusedBatchNormInputs;
-    const {varianceEpsilon} = attrs as unknown as FusedBatchNormAttrs;
-    const webGPUBackend = backend as WebGPUBackend;
-    const batchNormInputs = [x as Tensor, mean as Tensor, variance as Tensor];
-    let offsetShape = null;
-    if (offset != null) {
-      offsetShape = offset.shape;
-      batchNormInputs.push(offset as Tensor);
-    }
-    let scaleShape = null;
-    if (scale != null) {
-      scaleShape = scale.shape;
-      batchNormInputs.push(scale as Tensor);
-    }
-    const program = new BatchNormProgram(
-        x.shape, mean.shape, variance.shape, offsetShape, scaleShape);
-    const uniformData = [{type: 'float32', data: [varianceEpsilon]}];
-    return webGPUBackend.runWebGPUProgram(
-        program, batchNormInputs, x.dtype, uniformData);
-  }
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/FusedConv2D.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/FusedConv2D.ts
deleted file mode 100644
index b2ce41f66..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/FusedConv2D.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {conv2DImpl} from './Conv2D_impl';
-
-export function fusedConv2d(args: {
-  inputs: FusedConv2DInputs,
-  attrs: FusedConv2DAttrs,
-  backend: WebGPUBackend
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, bias, preluActivationWeights} = inputs;
-  const {
-    strides,
-    pad,
-    dataFormat,
-    dilations,
-    dimRoundingMode,
-    activation,
-    leakyreluAlpha
-  } = attrs;
-
-  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, dilations, pad,
-      dimRoundingMode, false /* depthwise */, $dataFormat);
-
-  return conv2DImpl({
-    x,
-    filter,
-    convInfo,
-    backend,
-    bias,
-    preluActivationWeights,
-    leakyreluAlpha,
-    activation
-  });
-}
-
-export const fusedConv2DConfig: KernelConfig = {
-  kernelName: FusedConv2D,
-  backendName: 'webgpu',
-  kernelFunc: fusedConv2d as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/FusedDepthwiseConv2D.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/FusedDepthwiseConv2D.ts
deleted file mode 100644
index 9660d1367..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/FusedDepthwiseConv2D.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {DepthwiseConv2DVec4Program} from '../depthwise_conv2d_vec4_webgpu';
-import {DepthwiseConv2DProgram} from '../depthwise_conv2d_webgpu';
-
-export function fusedDepthwiseConv2D(args: {
-  inputs: FusedDepthwiseConv2DInputs,
-  attrs: FusedDepthwiseConv2DAttrs,
-  backend: WebGPUBackend
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x, filter, bias, preluActivationWeights} = inputs;
-  const {strides, pad, dilations, dimRoundingMode, activation, leakyreluAlpha} =
-      attrs;
-
-  let $dilations = dilations;
-  if ($dilations == null) {
-    $dilations = [1, 1];
-  }
-
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, $dilations),
-      () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +
-          `1. Got strides ${strides} and dilations '${$dilations}'`);
-
-  const convInfo = backend_util.computeConv2DInfo(
-      x.shape as [number, number, number, number],
-      filter.shape as [number, number, number, number], strides, $dilations,
-      pad, dimRoundingMode, true /* depthwise */);
-
-  const programInputs: TensorInfo[] = [x, filter];
-
-  const hasBias = bias != null;
-  const hasPreluActivationWeights = preluActivationWeights != null;
-
-  if (hasBias) {
-    programInputs.push(bias);
-  }
-  if (hasPreluActivationWeights) {
-    programInputs.push(preluActivationWeights);
-  }
-
-  const dimensions = [
-    {type: 'int32', data: [convInfo.padInfo.top, convInfo.padInfo.left]},
-    {type: 'int32', data: [convInfo.inHeight, convInfo.inWidth]},
-  ];
-
-  let program: DepthwiseConv2DProgram|DepthwiseConv2DVec4Program;
-  if (convInfo.outHeight > 4 && convInfo.outWidth > 4 &&
-      convInfo.strideWidth <= 2 &&
-      convInfo.inChannels === convInfo.outChannels &&
-      convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
-      convInfo.inChannels % 4 === 0) {
-    program = new DepthwiseConv2DVec4Program(
-        convInfo, hasBias, activation, hasPreluActivationWeights);
-    dimensions.push({type: 'int32', data: [program.virtualWidth]});
-  } else {
-    program = new DepthwiseConv2DProgram(
-        convInfo, hasBias, activation, hasPreluActivationWeights);
-    dimensions.push(
-        {type: 'int32', data: [convInfo.filterHeight]},
-        {type: 'int32', data: [convInfo.filterWidth]},
-        {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]}, {
-          type: 'int32',
-          data: [convInfo.dilationHeight, convInfo.dilationWidth]
-        });
-  }
-  if (activation === 'leakyrelu') {
-    dimensions.push({type: 'float32', data: [leakyreluAlpha]});
-    program.uniforms += ' alpha : f32,';
-  }
-  const result =
-      backend.runWebGPUProgram(program, programInputs, 'float32', dimensions);
-
-  return result;
-}
-
-export const fusedDepthwiseConv2DConfig: KernelConfig = {
-  kernelName: FusedDepthwiseConv2D,
-  backendName: 'webgpu',
-  kernelFunc: fusedDepthwiseConv2D as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/GatherNd.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/GatherNd.ts
deleted file mode 100644
index 558953388..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/GatherNd.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, GatherNd, GatherNdInputs, KernelConfig, KernelFunc, Rank, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {GatherNDProgram} from '../gather_nd_webgpu';
-import {gatherNdImplCPU} from '../kernel_utils/shared';
-
-import {reshape} from './Reshape';
-
-export function gatherNd(
-    args: {inputs: GatherNdInputs, backend: WebGPUBackend}): TensorInfo {
-  const {inputs, backend} = args;
-  const {params, indices} = inputs;
-
-  const indicesShape = indices.shape;
-  const sliceRank = indicesShape[indicesShape.length - 1];
-  const paramsSize = util.sizeFromShape(params.shape);
-
-  const [resultShape, numSlices, sliceSize, strides] =
-      backend_util.prepareAndValidate(params, indices);
-
-  const flattenIndices = reshape(
-      {inputs: {x: indices}, backend, attrs: {shape: [numSlices, sliceRank]}});
-  const flattenX = reshape({
-    inputs: {x: params},
-    backend,
-    attrs: {shape: [(util.sizeFromShape(params.shape) / sliceSize), sliceSize]}
-  });
-  if (backend.shouldExecuteOnCPU([params, indices]) ||
-      params.dtype === 'string') {
-    const indicesData = backend.readSync(indices.dataId) as TypedArray;
-    const paramsBuf = backend.bufferSync<Rank, 'float32'>(params);
-    const outValue = gatherNdImplCPU(
-        indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize,
-        strides, params.shape, paramsSize);
-
-    return backend.makeTensorInfo(resultShape, params.dtype, outValue.values);
-  }
-  const program = new GatherNDProgram(sliceRank, [numSlices, sliceSize]);
-  const uniformData =
-      [{type: 'int32', data: [sliceRank]}, {type: 'int32', data: strides}];
-  const res = backend.runWebGPUProgram(
-      program, [flattenX, flattenIndices], flattenX.dtype, uniformData);
-
-  const reshaped =
-      reshape({inputs: {x: res}, backend, attrs: {shape: resultShape}});
-
-  backend.disposeData(flattenIndices.dataId);
-  backend.disposeData(flattenX.dataId);
-  backend.disposeData(res.dataId);
-
-  return reshaped;
-}
-
-export const gatherNdConfig: KernelConfig = {
-  kernelName: GatherNd,
-  backendName: 'webgpu',
-  kernelFunc: gatherNd as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/GatherV2.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/GatherV2.ts
deleted file mode 100644
index 000057ccf..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/GatherV2.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, buffer, GatherV2, GatherV2Attrs, GatherV2Inputs, KernelConfig, KernelFunc, Rank, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {GatherProgram} from '../gather_webgpu';
-import {gatherV2ImplCPU} from '../kernel_utils/shared';
-
-import {reshape} from './Reshape';
-
-export function gatherV2(
-    args:
-        {inputs: GatherV2Inputs, backend: WebGPUBackend, attrs: GatherV2Attrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, indices} = inputs;
-  const {axis, batchDims} = attrs;
-
-  // Unlike WebGL, WebGPU won't check if index is out of bound by calling
-  // backend.readSync() function in debug mode.
-  const parsedAxis = util.parseAxisParam(axis, x.shape)[0];
-
-  const shapeInfo = backend_util.segment_util.collectGatherOpShapeInfo(
-      x, indices, parsedAxis, batchDims);
-
-  const indicesSize = util.sizeFromShape(indices.shape);
-
-  const toDispose = [];
-
-  const flattenX = reshape({
-    inputs: {x},
-    backend,
-    attrs: {
-      shape: [
-        shapeInfo.batchSize, shapeInfo.outerSize, shapeInfo.dimSize,
-        shapeInfo.sliceSize
-      ]
-    }
-  });
-
-  const flattenIndex = reshape({
-    inputs: {x: indices},
-    backend,
-    attrs: {shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize]}
-  });
-
-  toDispose.push(flattenX);
-  toDispose.push(flattenIndex);
-
-  const flattenOutputShape = [
-    shapeInfo.batchSize, shapeInfo.outerSize, indicesSize / shapeInfo.batchSize,
-    shapeInfo.sliceSize
-  ];
-
-  if (backend.shouldExecuteOnCPU([x, indices])) {
-    const indicesTensorData = backend.tensorMap.get(flattenIndex.dataId);
-    const indicesValues = indicesTensorData.values as TypedArray;
-    const indicesBuffer =
-        buffer(flattenIndex.shape, flattenIndex.dtype, indicesValues) as
-        TensorBuffer<Rank>;
-    const flattenXTensorData = backend.tensorMap.get(flattenX.dataId);
-    const xValues = flattenXTensorData.values as TypedArray;
-    const xBuffer =
-        buffer(flattenX.shape, flattenX.dtype, xValues) as TensorBuffer<Rank>;
-    const outBuf = gatherV2ImplCPU(xBuffer, indicesBuffer, flattenOutputShape);
-
-    toDispose.forEach(t => backend.disposeData(t.dataId));
-
-    return backend.makeTensorInfo(
-        shapeInfo.outputShape, outBuf.dtype, outBuf.values as TypedArray);
-  }
-
-  const program = new GatherProgram(flattenX.shape, flattenOutputShape);
-  const res = backend.runWebGPUProgram(
-      program, [flattenX, flattenIndex], flattenX.dtype);
-  toDispose.push(res);
-
-  const reshaped = reshape(
-      {inputs: {x: res}, backend, attrs: {shape: shapeInfo.outputShape}});
-  toDispose.forEach(t => backend.disposeData(t.dataId));
-  return reshaped;
-}
-
-export const gatherV2Config: KernelConfig = {
-  kernelName: GatherV2,
-  backendName: 'webgpu',
-  kernelFunc: gatherV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Greater.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Greater.ts
deleted file mode 100644
index 30dc419b7..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Greater.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Greater, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {greaterImplCPU as cpuGreater} from '../kernel_utils/shared';
-
-export const greater = binaryKernelFunc({
-  opType: BinaryOpType.GREATER,
-  cpuKernelImpl: cpuGreater,
-  dtype: 'bool',
-});
-
-export const greaterConfig: KernelConfig = {
-  kernelName: Greater,
-  backendName: 'webgpu',
-  kernelFunc: greater
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/GreaterEqual.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/GreaterEqual.ts
deleted file mode 100644
index 1bdeca26a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/GreaterEqual.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GreaterEqual, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {greaterEqualImplCPU as cpuGreaterEqual} from '../kernel_utils/shared';
-
-export const greaterEqual = binaryKernelFunc({
-  opType: BinaryOpType.GREATER_EQUAL,
-  dtype: 'bool',
-  cpuKernelImpl: cpuGreaterEqual
-});
-
-export const greaterEqualConfig: KernelConfig = {
-  kernelName: GreaterEqual,
-  backendName: 'webgpu',
-  kernelFunc: greaterEqual
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/IFFT.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/IFFT.ts
deleted file mode 100644
index b5c29e885..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/IFFT.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IFFT, IFFTInputs, KernelConfig, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {fftImpl} from './FFT_impl';
-
-export function ifft(args: {inputs: IFFTInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-
-  return fftImpl(input, true /* inverse */, backend);
-}
-
-export const ifftConfig: KernelConfig = {
-  kernelName: IFFT,
-  backendName: 'webgpu',
-  kernelFunc: ifft
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Identity.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Identity.ts
deleted file mode 100644
index 362d07e8a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Identity.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Identity, IdentityInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-import {WebGPUBackend} from '../backend_webgpu';
-
-export function identity(
-    args: {inputs: IdentityInputs, backend: WebGPUBackend}): TensorInfo {
-  const {inputs} = args;
-  const {x} = inputs;
-
-  args.backend.incRef(x.dataId);
-  return {dataId: x.dataId, shape: x.shape, dtype: x.dtype};
-}
-
-export const identityConfig: KernelConfig = {
-  kernelName: Identity,
-  backendName: 'webgpu',
-  kernelFunc: identity as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Imag.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Imag.ts
deleted file mode 100644
index 0c98652da..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Imag.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Imag, ImagInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {identity} from './Identity';
-
-export function imag(args: {inputs: ImagInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-  const inputData = backend.tensorMap.get(input.dataId);
-
-  return identity({inputs: {x: inputData.complexTensorInfos.imag}, backend});
-}
-
-export const imagConfig: KernelConfig = {
-  kernelName: Imag,
-  backendName: 'webgpu',
-  kernelFunc: imag as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/IsFinite.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/IsFinite.ts
deleted file mode 100644
index 4f4eb88e4..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/IsFinite.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsFinite, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const isFinite =
-    unaryKernelFunc({opType: UnaryOpType.IS_FINITE, dtype: 'bool'});
-
-export const isFiniteConfig: KernelConfig = {
-  kernelName: IsFinite,
-  backendName: 'webgpu',
-  kernelFunc: isFinite
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/IsInf.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/IsInf.ts
deleted file mode 100644
index 3d9d3c7e0..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/IsInf.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsInf, KernelConfig} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const isInf =
-    unaryKernelFunc({opType: UnaryOpType.IS_INF, dtype: 'bool'});
-
-export const isInfConfig: KernelConfig = {
-  kernelName: IsInf,
-  backendName: 'webgpu',
-  kernelFunc: isInf
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/IsNaN.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/IsNaN.ts
deleted file mode 100644
index e70592c29..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/IsNaN.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsNan, KernelConfig} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {UnaryOpType} from '../unary_op_util';
-
-export const isNaN =
-    unaryKernelFunc({opType: UnaryOpType.IS_NAN, dtype: 'bool'});
-
-export const isNaNConfig: KernelConfig = {
-  kernelName: IsNan,
-  backendName: 'webgpu',
-  kernelFunc: isNaN
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/LRN.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/LRN.ts
deleted file mode 100644
index 3290a9adc..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/LRN.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LRN, LRNAttrs, LRNInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {LRNProgram, LRNSharedProgram} from '../lrn_webgpu';
-
-export function lrn(
-    args: {inputs: LRNInputs, backend: WebGPUBackend, attrs: LRNAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {depthRadius, bias, alpha, beta} = attrs;
-
-  // When the adjacent channels is less than or equal to 16, which could cover
-  // most cases, we use shared memory version to get better performance.
-  // The theoretical adjacent channels may be very large, but the shared memory
-  // size of hardware is limited, so we use the naive version when the adjacent
-  // channels is large.
-  let program: LRNProgram|LRNSharedProgram;
-  if (depthRadius > 16) {
-    program = new LRNProgram(x.shape);
-  } else {
-    program = new LRNSharedProgram(x.shape, depthRadius);
-  }
-  const uniformData = [
-    {type: 'int32', data: [depthRadius]}, {type: 'float32', data: [bias]},
-    {type: 'float32', data: [alpha]}, {type: 'float32', data: [beta]}
-  ];
-  const res = backend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-
-  return res;
-}
-
-export const lrnConfig: KernelConfig = {
-  kernelName: LRN,
-  backendName: 'webgpu',
-  kernelFunc: lrn as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/LRNGrad.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/LRNGrad.ts
deleted file mode 100644
index 63fcfccf2..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/LRNGrad.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LRNGrad, LRNGradAttrs, LRNGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {LRNGradProgram} from '../lrn_grad_webgpu';
-
-export function lrnGrad(
-    args: {inputs: LRNGradInputs, backend: WebGPUBackend, attrs: LRNGradAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, y, dy} = inputs;
-  const {depthRadius, bias, alpha, beta} = attrs;
-
-  const program = new LRNGradProgram(x.shape);
-  const uniformData = [
-    {type: 'int32', data: [depthRadius]}, {type: 'float32', data: [bias]},
-    {type: 'float32', data: [alpha]}, {type: 'float32', data: [beta]}
-  ];
-  const res =
-      backend.runWebGPUProgram(program, [x, y, dy], x.dtype, uniformData);
-
-  return res;
-}
-
-export const lrnGradConfig: KernelConfig = {
-  kernelName: LRNGrad,
-  backendName: 'webgpu',
-  kernelFunc: lrnGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/LeakyRelu.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/LeakyRelu.ts
deleted file mode 100644
index 61d4bc812..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/LeakyRelu.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LeakyRelu, LeakyReluAttrs, LeakyReluInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {UnaryOpType} from '../unary_op_util';
-import {UnaryOpProgram} from '../unary_op_webgpu';
-
-export function leakyRelu(args: {
-  inputs: LeakyReluInputs,
-  backend: WebGPUBackend,
-  attrs: LeakyReluAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {alpha} = attrs;
-  const uniformData = [{type: 'float32', data: [alpha]}];
-  const program =
-      new UnaryOpProgram(x.shape, UnaryOpType.LEAKYRELU, 'alpha : f32,');
-  return backend.runWebGPUProgram(program, [x], 'float32', uniformData);
-}
-
-export const leakyReluConfig: KernelConfig = {
-  kernelName: LeakyRelu,
-  backendName: 'webgpu',
-  kernelFunc: leakyRelu as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Less.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Less.ts
deleted file mode 100644
index 470a978be..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Less.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Less} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {lessImplCPU as cpuLess} from '../kernel_utils/shared';
-
-export const less = binaryKernelFunc(
-    {opType: BinaryOpType.LESS, dtype: 'bool', cpuKernelImpl: cpuLess});
-
-export const lessConfig: KernelConfig = {
-  kernelName: Less,
-  backendName: 'webgpu',
-  kernelFunc: less
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/LessEqual.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/LessEqual.ts
deleted file mode 100644
index a56f6774a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/LessEqual.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LessEqual} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {lessEqualImplCPU as cpuLessEqual} from '../kernel_utils/shared';
-
-export const lessEqual = binaryKernelFunc({
-  opType: BinaryOpType.LESS_EQUAL,
-  dtype: 'bool',
-  cpuKernelImpl: cpuLessEqual
-});
-
-export const lessEqualConfig: KernelConfig = {
-  kernelName: LessEqual,
-  backendName: 'webgpu',
-  kernelFunc: lessEqual
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/LinSpace.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/LinSpace.ts
deleted file mode 100644
index 15f4699a0..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/LinSpace.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, LinSpace, LinSpaceAttrs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {LinSpaceProgram} from '../lin_space_webgpu';
-
-export function linSpace(args: {backend: WebGPUBackend, attrs: LinSpaceAttrs}):
-    TensorInfo {
-  const {backend, attrs} = args;
-  const {start, stop, num} = attrs;
-  const step = (stop - start) / (num - 1);
-
-  const program = new LinSpaceProgram(num);
-  const uniformData =
-      [{type: 'float32', data: [start]}, {type: 'float32', data: [step]}];
-  return backend.runWebGPUProgram(program, [], 'float32', uniformData);
-}
-
-export const linSpaceConfig: KernelConfig = {
-  kernelName: LinSpace,
-  backendName: 'webgpu',
-  kernelFunc: linSpace as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Log.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Log.ts
deleted file mode 100644
index a5fc64583..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Log.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Log} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {logImplCPU} from '../kernel_utils/shared';
-import {UnaryOpType} from '../unary_op_util';
-
-export const log =
-    unaryKernelFunc({opType: UnaryOpType.LOG, cpuKernelImpl: logImplCPU});
-
-export const logConfig: KernelConfig = {
-  kernelName: Log,
-  backendName: 'webgpu',
-  kernelFunc: log
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Log1p.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Log1p.ts
deleted file mode 100644
index 2a9e68eb1..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Log1p.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Log1p} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const log1p = unaryKernelFunc({opType: UnaryOpType.LOG1P});
-
-export const log1pConfig: KernelConfig = {
-  kernelName: Log1p,
-  backendName: 'webgpu',
-  kernelFunc: log1p
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/LogicalAnd.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/LogicalAnd.ts
deleted file mode 100644
index e8931f0d5..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/LogicalAnd.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalAnd} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-export const logicalAnd =
-    binaryKernelFunc({opType: BinaryOpType.LOGICAL_AND, dtype: 'bool'});
-
-export const logicalAndConfig: KernelConfig = {
-  kernelName: LogicalAnd,
-  backendName: 'webgpu',
-  kernelFunc: logicalAnd
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/LogicalNot.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/LogicalNot.ts
deleted file mode 100644
index 44b5c9a2c..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/LogicalNot.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalNot} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const logicalNot = unaryKernelFunc({opType: UnaryOpType.LOGICAL_NOT});
-
-export const logicalNotConfig: KernelConfig = {
-  kernelName: LogicalNot,
-  backendName: 'webgpu',
-  kernelFunc: logicalNot
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/LogicalOr.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/LogicalOr.ts
deleted file mode 100644
index 543ecf4b5..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/LogicalOr.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalOr} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-export const logicalOr = binaryKernelFunc({opType: BinaryOpType.LOGICAL_OR});
-
-export const logicalOrConfig: KernelConfig = {
-  kernelName: LogicalOr,
-  backendName: 'webgpu',
-  kernelFunc: logicalOr
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Max.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Max.ts
deleted file mode 100644
index 544abd3d7..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Max.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Max, MaxAttrs, MaxInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {reduce} from '../kernel_utils/reduce';
-
-export function max(
-    args: {inputs: MaxInputs, backend: WebGPUBackend, attrs: MaxAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {reductionIndices, keepDims} = attrs;
-
-  return reduce(x, reductionIndices, keepDims, 'max', backend);
-}
-
-export const maxConfig: KernelConfig = {
-  kernelName: Max,
-  backendName: 'webgpu',
-  kernelFunc: max as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPool.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPool.ts
deleted file mode 100644
index f8715ee02..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPool.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, KernelConfig, KernelFunc, MaxPool, MaxPoolAttrs, MaxPoolInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {poolImpl} from './Pool_impl';
-
-export function maxPool(
-    args: {inputs: MaxPoolInputs, backend: WebGPUBackend, attrs: MaxPoolAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const dilations = 1;
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode);
-
-  return poolImpl(x, convInfo, 'max', backend);
-}
-
-export const maxPoolConfig: KernelConfig = {
-  kernelName: MaxPool,
-  backendName: 'webgpu',
-  kernelFunc: maxPool as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPool3D.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPool3D.ts
deleted file mode 100644
index 0fbec5f7e..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPool3D.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, KernelConfig, KernelFunc, MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Pool3DProgram} from '../pool_webgpu';
-
-export function maxPool3d(args: {
-  inputs: MaxPool3DInputs,
-  backend: WebGPUBackend,
-  attrs: MaxPool3DAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {filterSize, strides, pad, dataFormat, dimRoundingMode} = attrs;
-  const dilations: [number, number, number] = [1, 1, 1];
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode, dataFormat);
-  const maxPoolProgram = new Pool3DProgram(convInfo, 'max');
-  const dimensions = [
-    {
-      type: 'int32',
-      data: [convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth]
-    },
-    {
-      type: 'int32',
-      data:
-          [convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left]
-    },
-    {
-      type: 'int32',
-      data: [convInfo.inDepth, convInfo.inHeight, convInfo.inWidth]
-    },
-    {
-      type: 'int32',
-      data: [
-        convInfo.effectiveFilterDepth, convInfo.effectiveFilterHeight,
-        convInfo.effectiveFilterWidth
-      ]
-    }
-  ];
-  return backend.runWebGPUProgram(maxPoolProgram, [x], x.dtype, dimensions);
-}
-
-export const maxPool3DConfig: KernelConfig = {
-  kernelName: MaxPool3D,
-  backendName: 'webgpu',
-  kernelFunc: maxPool3d as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPool3DGrad.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPool3DGrad.ts
deleted file mode 100644
index 41d8c3afb..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPool3DGrad.ts
+++ /dev/null
@@ -1,102 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, MaxPool3DGrad, MaxPool3DGradAttrs, MaxPool3DGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {MaxPool3DBackpropProgram} from '../max_pool_backprop_webgpu';
-import {Pool3DProgram} from '../pool_webgpu';
-
-export function maxPool3DGrad(args: {
-  inputs: MaxPool3DGradInputs,
-  backend: WebGPUBackend,
-  attrs: MaxPool3DGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input} = inputs;
-  const x = input;
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-  const dilations: [number, number, number] = [1, 1, 1];
-
-  const convInfo = backend_util.computePool3DInfo(
-      x.shape as [number, number, number, number, number], filterSize, strides,
-      dilations, pad, dimRoundingMode);
-
-  const maxPool3dPositionsProgram =
-      new Pool3DProgram(convInfo, 'max', true /* get positions */);
-  let uniformData = [
-    {
-      type: 'int32',
-      data: [convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth]
-    },
-    {
-      type: 'int32',
-      data:
-          [convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left]
-    },
-    {
-      type: 'int32',
-      data: [convInfo.inDepth, convInfo.inHeight, convInfo.inWidth]
-    },
-    {
-      type: 'int32',
-      data: [
-        convInfo.effectiveFilterDepth, convInfo.effectiveFilterHeight,
-        convInfo.effectiveFilterWidth
-      ]
-    }
-  ];
-  const maxPool3dPositions = backend.runWebGPUProgram(
-      maxPool3dPositionsProgram, [x], 'int32', uniformData);
-
-  const maxPool3dBackpropProgram = new MaxPool3DBackpropProgram(convInfo);
-  uniformData = [
-    {
-      type: 'int32',
-      data: [convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth]
-    },
-    {
-      type: 'int32',
-      data: [
-        convInfo.effectiveFilterDepth - 1 - convInfo.padInfo.front,
-        convInfo.effectiveFilterHeight - 1 - convInfo.padInfo.top,
-        convInfo.effectiveFilterWidth - 1 - convInfo.padInfo.left
-      ]
-    },
-    {
-      type: 'int32',
-      data: [
-        convInfo.effectiveFilterDepth, convInfo.effectiveFilterHeight,
-        convInfo.effectiveFilterWidth
-      ]
-    },
-    {type: 'int32', data: [convInfo.outDepth]},
-    {type: 'int32', data: [convInfo.outHeight]},
-    {type: 'int32', data: [convInfo.outWidth]}
-  ];
-  const result = backend.runWebGPUProgram(
-      maxPool3dBackpropProgram, [dy, maxPool3dPositions], x.dtype, uniformData);
-  backend.disposeData(maxPool3dPositions.dataId);
-
-  return result;
-}
-
-export const maxPool3DGradConfig: KernelConfig = {
-  kernelName: MaxPool3DGrad,
-  backendName: 'webgpu',
-  kernelFunc: maxPool3DGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPoolGrad.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPoolGrad.ts
deleted file mode 100644
index f1deb622c..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPoolGrad.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, MaxPoolGrad, MaxPoolGradAttrs, MaxPoolGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {MaxPool2DBackpropProgram} from '../max_pool_backprop_webgpu';
-import {Pool2DProgram} from '../pool_webgpu';
-import {assertNotComplex} from '../webgpu_util';
-
-export function maxPoolGrad(args: {
-  inputs: MaxPoolGradInputs,
-  backend: WebGPUBackend,
-  attrs: MaxPoolGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {dy, input, output} = inputs;
-  const x = input;
-  assertNotComplex([input, output], 'maxPoolGrad');
-  const {filterSize, strides, pad, dimRoundingMode} = attrs;
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      1 /* dilations */, pad, dimRoundingMode);
-
-  const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', true);
-  let uniformData = [
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]},
-    {type: 'int32', data: [convInfo.padInfo.top, convInfo.padInfo.left]},
-    {type: 'int32', data: [convInfo.dilationHeight, convInfo.dilationWidth]},
-    {type: 'int32', data: [convInfo.inHeight, convInfo.inWidth]}, {
-      type: 'int32',
-      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]
-    }
-  ];
-  const maxPoolPositions = backend.runWebGPUProgram(
-      maxPoolPositionsProgram, [x], 'int32', uniformData);
-
-  const maxPoolBackpropProgram = new MaxPool2DBackpropProgram(convInfo);
-  uniformData = [
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]}, {
-      type: 'int32',
-      data: [
-        convInfo.effectiveFilterHeight - 1 - convInfo.padInfo.top,
-        convInfo.effectiveFilterWidth - 1 - convInfo.padInfo.left
-      ]
-    },
-    {type: 'int32', data: [convInfo.dilationHeight, convInfo.dilationWidth]}, {
-      type: 'int32',
-      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]
-    },
-    {type: 'int32', data: [convInfo.outHeight]},
-    {type: 'int32', data: [convInfo.outWidth]}
-  ];
-  const result = backend.runWebGPUProgram(
-      maxPoolBackpropProgram, [dy, maxPoolPositions], x.dtype, uniformData);
-  backend.disposeData(maxPoolPositions.dataId);
-
-  return result;
-}
-
-export const maxPoolGradConfig: KernelConfig = {
-  kernelName: MaxPoolGrad,
-  backendName: 'webgpu',
-  kernelFunc: maxPoolGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPoolWithArgmax.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPoolWithArgmax.ts
deleted file mode 100644
index e5006ba7a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/MaxPoolWithArgmax.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs} from '@tensorflow/tfjs-core';
-import {backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {Pool2DProgram} from '../pool_webgpu';
-
-export function maxPoolWithArgmax(args: {
-  inputs: MaxPoolWithArgmaxInputs,
-  attrs: MaxPoolWithArgmaxAttrs,
-  backend: WebGPUBackend
-}): TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {filterSize, strides, pad, includeBatchInIndex} = attrs;
-  const {x} = inputs;
-
-  util.assert(
-      x.shape.length === 4,
-      () => `Error in maxPool: input must be rank 4 but got rank ${
-          x.shape.length}.`);
-  const dilations: [number, number] = [1, 1];
-  util.assert(
-      backend_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in maxPool: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-
-  const convInfo = backend_util.computePool2DInfo(
-      x.shape as [number, number, number, number], filterSize, strides,
-      dilations, pad);
-
-  const uniformData = [
-    {type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]},
-    {type: 'int32', data: [convInfo.padInfo.top, convInfo.padInfo.left]},
-    {type: 'int32', data: [convInfo.dilationHeight, convInfo.dilationWidth]},
-    {type: 'int32', data: [convInfo.inHeight, convInfo.inWidth]}, {
-      type: 'int32',
-      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]
-    }
-  ];
-  let program = new Pool2DProgram(convInfo, 'max', false);
-  const poolOutput =
-      backend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-
-  program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);
-  const indexOutput =
-      backend.runWebGPUProgram(program, [x], 'int32', uniformData);
-  return [poolOutput, indexOutput];
-}
-
-export const maxPoolWithArgmaxConfig: KernelConfig = {
-  kernelName: MaxPoolWithArgmax,
-  backendName: 'webgpu',
-  kernelFunc: maxPoolWithArgmax as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Maximum.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Maximum.ts
deleted file mode 100644
index 85a8bce83..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Maximum.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Maximum} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {maximumImplCPU as cpuMaximum} from '../kernel_utils/shared';
-
-export const maximum = binaryKernelFunc({
-  opType: BinaryOpType.MAX,
-  cpuKernelImpl: cpuMaximum,
-});
-
-export const maximumConfig: KernelConfig = {
-  kernelName: Maximum,
-  backendName: 'webgpu',
-  kernelFunc: maximum
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Mean.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Mean.ts
deleted file mode 100644
index 80c1b418f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Mean.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Mean, MeanAttrs, MeanInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {reduce} from '../kernel_utils/reduce';
-
-export function mean(
-    args: {inputs: MeanInputs, attrs: MeanAttrs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {keepDims, axis} = attrs;
-
-  return reduce(x, axis, keepDims, 'mean', backend);
-}
-
-export const meanConfig: KernelConfig = {
-  kernelName: Mean,
-  backendName: 'webgpu',
-  kernelFunc: mean as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Min.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Min.ts
deleted file mode 100644
index 0ce7d7919..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Min.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Min, MinAttrs, MinInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {reduce} from '../kernel_utils/reduce';
-
-export function min(
-    args: {inputs: MinInputs, backend: WebGPUBackend, attrs: MinAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  return reduce(x, axis, keepDims, 'min', backend);
-}
-
-export const minConfig: KernelConfig = {
-  kernelName: Min,
-  backendName: 'webgpu',
-  kernelFunc: min as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Minimum.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Minimum.ts
deleted file mode 100644
index 2d2c79745..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Minimum.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Minimum} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {minimumImplCPU as cpuMinimum} from '../kernel_utils/shared';
-
-export const minimum = binaryKernelFunc({
-  opType: BinaryOpType.MIN,
-  cpuKernelImpl: cpuMinimum,
-});
-
-export const minimumConfig: KernelConfig = {
-  kernelName: Minimum,
-  backendName: 'webgpu',
-  kernelFunc: minimum
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/MirrorPad.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/MirrorPad.ts
deleted file mode 100644
index 0a85ef33b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/MirrorPad.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, MirrorPad, MirrorPadAttrs, MirrorPadInputs} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {MirrorPadProgram} from '../mirror_pad_webgpu';
-
-export const mirrorPadConfig: KernelConfig = {
-  kernelName: MirrorPad,
-  backendName: 'webgpu',
-  kernelFunc: ({inputs, attrs, backend}) => {
-    const {x} = inputs as MirrorPadInputs;
-    const {paddings, mode} = attrs as unknown as MirrorPadAttrs;
-    const webGPUBackend = backend as WebGPUBackend;
-
-    const uniformData = paddings.map(p => {
-      return {type: 'int32', data: [p[0], p[1]]};
-    });
-    const program = new MirrorPadProgram(x.shape, paddings, mode);
-    const output =
-        webGPUBackend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-
-    return output;
-  }
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Mod.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Mod.ts
deleted file mode 100644
index 7a2a4f6f8..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Mod.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Mod} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-export const mod = binaryKernelFunc({opType: BinaryOpType.MOD});
-
-export const modConfig: KernelConfig = {
-  kernelName: Mod,
-  backendName: 'webgpu',
-  kernelFunc: mod
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Multinomial.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Multinomial.ts
deleted file mode 100644
index 3f5b0d4de..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Multinomial.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Multinomial, MultinomialAttrs, MultinomialInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {MultinomialProgram} from '../multinomial_webgpu';
-
-import {softmax} from './Softmax';
-
-export function multinomial(args: {
-  inputs: MultinomialInputs,
-  backend: WebGPUBackend,
-  attrs: MultinomialAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {logits} = inputs;
-  const {numSamples, seed, normalized} = attrs;
-
-  const probs = normalized ?
-      logits :
-      softmax(
-          {inputs: {logits}, backend, attrs: {dim: logits.shape.length - 1}});
-  const batchSize = probs.shape[0];
-  const numOutcomes = probs.shape[1];
-  const program = new MultinomialProgram(batchSize, numSamples);
-  const uniformData =
-      [{type: 'float32', data: [seed]}, {type: 'int32', data: [numOutcomes]}];
-  const res = backend.runWebGPUProgram(program, [probs], 'int32', uniformData);
-  if (!normalized) {
-    backend.disposeData(probs.dataId);
-  }
-  return res;
-}
-
-export const multinomialConfig: KernelConfig = {
-  kernelName: Multinomial,
-  backendName: 'webgpu',
-  kernelFunc: multinomial as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Multiply.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Multiply.ts
deleted file mode 100644
index 4a125a3e6..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Multiply.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Multiply} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {multiplyImplCPU as cpuMultiply} from '../kernel_utils/shared';
-
-export const multiplyKernelFunc = binaryKernelFunc({
-  opType: BinaryOpType.MUL,
-  cpuKernelImpl: cpuMultiply,
-  supportsComplex: true
-});
-
-export const multiplyConfig: KernelConfig = {
-  kernelName: Multiply,
-  backendName: 'webgpu',
-  kernelFunc: multiplyKernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Neg.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Neg.ts
deleted file mode 100644
index 6b348e421..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Neg.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Neg, NegInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {negImplCPU} from '../kernel_utils/shared';
-
-import {UnaryOpType} from '../unary_op_util';
-import {UnaryOpProgram} from '../unary_op_webgpu';
-
-// This doesn't use unaryKernelFunc because negImplCPU is not of type
-// SimpleUnaryKernelImplCPU.
-export function neg(args: {inputs: NegInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  if (backend.shouldExecuteOnCPU([x])) {
-    const xData = backend.tensorMap.get(x.dataId);
-    const [outValues, newShape] =
-        negImplCPU(xData.values as TypedArray, x.shape, x.dtype);
-    return backend.makeTensorInfo(newShape, x.dtype, outValues);
-  }
-
-  const program = new UnaryOpProgram(x.shape, UnaryOpType.NEG);
-
-  return backend.runWebGPUProgram(program, [x], x.dtype);
-}
-
-export const negConfig: KernelConfig = {
-  kernelName: Neg,
-  backendName: 'webgpu',
-  kernelFunc: neg as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/NonMaxSuppressionV3.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/NonMaxSuppressionV3.ts
deleted file mode 100644
index 2ea2e3716..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/NonMaxSuppressionV3.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV3, NonMaxSuppressionV3Attrs, NonMaxSuppressionV3Inputs, TypedArray} from '@tensorflow/tfjs-core';
-import {WebGPUBackend} from '../backend_webgpu';
-
-export function nonMaxSuppressionV3(args: {
-  inputs: NonMaxSuppressionV3Inputs,
-  backend: WebGPUBackend,
-  attrs: NonMaxSuppressionV3Attrs
-}) {
-  console.warn(
-      'tf.nonMaxSuppression() in webgpu locks the UI thread. ' +
-      'Call tf.nonMaxSuppressionAsync() instead');
-
-  const {inputs, backend, attrs} = args;
-  const {boxes, scores} = inputs;
-  const {maxOutputSize, iouThreshold, scoreThreshold} = attrs;
-
-  const boxesVals = backend.readSync(boxes.dataId) as TypedArray;
-  const scoresVals = backend.readSync(scores.dataId) as TypedArray;
-
-  const {selectedIndices} = kernel_impls.nonMaxSuppressionV3Impl(
-      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
-
-  return backend.makeTensorInfo(
-      [selectedIndices.length], 'int32', new Int32Array(selectedIndices));
-}
-
-export const nonMaxSuppressionV3Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV3,
-  backendName: 'webgpu',
-  kernelFunc: nonMaxSuppressionV3 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/NonMaxSuppressionV5.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/NonMaxSuppressionV5.ts
deleted file mode 100644
index 5b7b42887..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/NonMaxSuppressionV5.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV5, NonMaxSuppressionV5Attrs, NonMaxSuppressionV5Inputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-export type TypedArray = Float32Array|Int32Array|Uint8Array;
-
-export function nonMaxSuppressionV5(args: {
-  inputs: NonMaxSuppressionV5Inputs,
-  backend: WebGPUBackend,
-  attrs: NonMaxSuppressionV5Attrs
-}): [TensorInfo, TensorInfo] {
-  console.warn(
-      'tf.nonMaxSuppression() in webgpu locks the UI thread. ' +
-      'Call tf.nonMaxSuppressionAsync() instead');
-
-  const {inputs, backend, attrs} = args;
-  const {boxes, scores} = inputs;
-  const {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} = attrs;
-
-  const boxesVals = backend.readSync(boxes.dataId) as TypedArray;
-  const scoresVals = backend.readSync(scores.dataId) as TypedArray;
-
-  const maxOutputSizeVal = maxOutputSize;
-  const iouThresholdVal = iouThreshold;
-  const scoreThresholdVal = scoreThreshold;
-  const softNmsSigmaVal = softNmsSigma;
-
-  const {selectedIndices, selectedScores} =
-      kernel_impls.nonMaxSuppressionV5Impl(
-          boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal,
-          scoreThresholdVal, softNmsSigmaVal);
-
-  return [
-    backend.makeTensorInfo(
-        [selectedIndices.length], 'int32', new Int32Array(selectedIndices)),
-    backend.makeTensorInfo(
-        [selectedScores.length], 'float32', new Float32Array(selectedScores))
-  ];
-}
-
-export const nonMaxSuppressionV5Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV5,
-  backendName: 'webgpu',
-  kernelFunc: nonMaxSuppressionV5 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/NotEqual.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/NotEqual.ts
deleted file mode 100644
index b563e51a9..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/NotEqual.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, NotEqual} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {notEqualImplCPU as cpuNotEqual} from '../kernel_utils/shared';
-
-export const notEqual = binaryKernelFunc({
-  opType: BinaryOpType.NOT_EQUAL,
-  dtype: 'bool',
-  cpuKernelImpl: cpuNotEqual
-});
-
-export const notEqualConfig: KernelConfig = {
-  kernelName: NotEqual,
-  backendName: 'webgpu',
-  kernelFunc: notEqual
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/OneHot.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/OneHot.ts
deleted file mode 100644
index 084ce524d..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/OneHot.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, OneHot, OneHotAttrs, OneHotInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {OneHotProgram} from '../onehot_webgpu';
-import {reshape} from './Reshape';
-
-export function oneHot(
-    args: {inputs: OneHotInputs, backend: WebGPUBackend, attrs: OneHotAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {indices} = inputs;
-  const {dtype, depth, onValue, offValue} = attrs;
-
-  const indicesSize = util.sizeFromShape(indices.shape);
-  const program = new OneHotProgram(indicesSize, depth);
-  const reshaped =
-      reshape({inputs: {x: indices}, backend, attrs: {shape: [indicesSize]}});
-
-  const uniformData =
-      [{type: 'float32', data: [onValue]}, {type: 'float32', data: [offValue]}];
-  const result =
-      backend.runWebGPUProgram(program, [reshaped], dtype, uniformData);
-  backend.disposeData(reshaped.dataId);
-
-  const outShape = [...indices.shape, depth];
-  const out = reshape({inputs: {x: result}, backend, attrs: {shape: outShape}});
-  backend.disposeData(result.dataId);
-
-  return out;
-}
-
-export const oneHotConfig: KernelConfig = {
-  kernelName: OneHot,
-  backendName: 'webgpu',
-  kernelFunc: oneHot as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/OnesLike.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/OnesLike.ts
deleted file mode 100644
index ff7478e6a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/OnesLike.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, OnesLike, OnesLikeInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {complex} from './Complex';
-import {fill} from './Fill';
-import {imag} from './Imag';
-import {real} from './Real';
-import {zerosLike} from './ZerosLike';
-
-export function onesLike(
-    args: {inputs: OnesLikeInputs, backend: WebGPUBackend}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-
-  if (x.dtype === 'string') {
-    throw new Error('onesLike is not supported under string dtype');
-  } else if (x.dtype === 'complex64') {
-    const realPart = real({inputs: {input: x}, backend});
-    const r = onesLike({inputs: {x: realPart}, backend});
-    const imagPart = imag({inputs: {input: x}, backend});
-    const i = zerosLike({inputs: {x: imagPart}, backend});
-
-    const result = complex({inputs: {real: r, imag: i}, backend});
-
-    backend.disposeData(realPart.dataId);
-    backend.disposeData(r.dataId);
-    backend.disposeData(imagPart.dataId);
-    backend.disposeData(i.dataId);
-
-    return result;
-  } else {
-    return fill({attrs: {shape: x.shape, dtype: x.dtype, value: 1}, backend});
-  }
-}
-
-export const onesLikeConfig: KernelConfig = {
-  kernelName: OnesLike,
-  backendName: 'webgpu',
-  kernelFunc: onesLike as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Pack.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Pack.ts
deleted file mode 100644
index a801db914..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Pack.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Pack, PackAttrs, PackInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {concat} from './Concat';
-import {expandDims} from './ExpandDims';
-
-export function pack(
-    args: {inputs: PackInputs, backend: WebGPUBackend, attrs: PackAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {axis} = attrs;
-
-  if (inputs.length === 1) {
-    return expandDims(
-        {inputs: {input: inputs[0]}, backend, attrs: {dim: axis}});
-  }
-
-  const shape = inputs[0].shape;
-  const dtype = inputs[0].dtype;
-
-  inputs.forEach(t => {
-    util.assertShapesMatch(
-        shape, t.shape,
-        'All tensors passed to stack must have matching shapes');
-    util.assert(
-        dtype === t.dtype,
-        () => 'All tensors passed to stack must have matching dtypes');
-  });
-
-  const intermediateTensorInfos: TensorInfo[] = [];
-  const expandedTensors = inputs.map(t => {
-    const expandedT =
-        expandDims({inputs: {input: t}, backend, attrs: {dim: axis}});
-    intermediateTensorInfos.push(expandedT);
-    return expandedT;
-  });
-
-  const result = concat({inputs: expandedTensors, backend, attrs: {axis}});
-
-  intermediateTensorInfos.forEach(t => backend.disposeData(t.dataId));
-
-  return result;
-}
-
-export const packConfig: KernelConfig = {
-  kernelName: Pack,
-  backendName: 'webgpu',
-  kernelFunc: pack as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/PadV2.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/PadV2.ts
deleted file mode 100644
index 3ea31229c..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/PadV2.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, PadV2, PadV2Attrs, PadV2Inputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {identity} from './Identity';
-import {PadProgram} from '../pad_webgpu';
-import {fill} from './Fill';
-
-export const padV2 =
-    (args: {inputs: PadV2Inputs,
-            backend: WebGPUBackend,
-            attrs: PadV2Attrs}): TensorInfo => {
-      const {inputs, backend, attrs} = args;
-      const {x} = inputs;
-      const {paddings, constantValue} = attrs;
-      if (paddings.every(p => util.arraysEqual(p, [0, 0]))) {
-        return identity({inputs: {x}, backend});
-      }
-      if (util.sizeFromShape(x.shape) === 0) {
-        // Short-circuit the computation, since x doesn't have value, only
-        // the shape is used to compute output shape to pad.
-        const outputShape = paddings.map(
-            (p, i) =>
-                p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);
-        return fill({
-          backend,
-          attrs: {shape: outputShape, value: constantValue, dtype: x.dtype}
-        });
-      }
-      const uniformData = [{type: 'float32', data: [constantValue]}];
-      paddings.map(p => uniformData.push({type: 'int32', data: [p[0], p[1]]}));
-      const program = new PadProgram(x.shape, paddings);
-      return backend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-    };
-
-export const padV2Config: KernelConfig = {
-  kernelName: PadV2,
-  backendName: 'webgpu',
-  kernelFunc: padV2 as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Pool_impl.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Pool_impl.ts
deleted file mode 100644
index be41c060f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Pool_impl.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {backend_util, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {PoolWithFilterSizeEqualsOneProgram} from '../pool_filtersizeone_webgpu';
-import {Pool2DProgram} from '../pool_webgpu';
-
-import {identity} from './Identity';
-import {max} from './Max';
-import {mean} from './Mean';
-import {reshape} from './Reshape';
-
-type PoolType = 'max'|'avg';
-export function poolImpl(
-    x: TensorInfo, convInfo: backend_util.Conv2DInfo, poolType: PoolType,
-    backend: WebGPUBackend): TensorInfo {
-  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
-      util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
-    return identity({inputs: {x}, backend});
-  }
-
-  if (convInfo.filterWidth === convInfo.inWidth &&
-      convInfo.filterHeight === convInfo.inHeight && convInfo.batchSize === 1 &&
-      convInfo.padInfo.type === 'VALID') {
-    const length = x.shape.length;
-    const reshapeX = reshape({
-      inputs: {x},
-      backend,
-      attrs: {
-        shape: [
-          x.shape[length - 3] * x.shape[length - 2] /* height * width */,
-          x.shape[length - 1] /* channel */
-        ]
-      }
-    });
-    let reduceX;
-    if (poolType === 'avg') {
-      reduceX = mean(
-          {inputs: {x: reshapeX}, backend, attrs: {axis: 0, keepDims: false}});
-    } else {
-      util.assert(poolType === 'max', () => `Invalid pool type ${poolType}`);
-      reduceX = max({
-        inputs: {x: reshapeX},
-        backend,
-        attrs: {reductionIndices: 0, keepDims: false}
-      });
-    }
-
-    const result = reshape(
-        {inputs: {x: reduceX}, backend, attrs: {shape: convInfo.outShape}});
-    backend.disposeData(reshapeX.dataId);
-    backend.disposeData(reduceX.dataId);
-    return result;
-  }
-
-  let program: Pool2DProgram|PoolWithFilterSizeEqualsOneProgram;
-  const dimensions =
-      [{type: 'int32', data: [convInfo.strideHeight, convInfo.strideWidth]}];
-  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) {
-    program = new PoolWithFilterSizeEqualsOneProgram(convInfo);
-  } else {
-    if (poolType === 'avg') {
-      program = new Pool2DProgram(convInfo, 'avg');
-    } else {
-      util.assert(poolType === 'max', () => `Invalid pool type ${poolType}`);
-      program = new Pool2DProgram(convInfo, 'max');
-    }
-
-    dimensions.push(
-        {type: 'int32', data: [convInfo.padInfo.top, convInfo.padInfo.left]}, {
-          type: 'int32',
-          data: [convInfo.dilationHeight, convInfo.dilationWidth]
-        },
-        {type: 'int32', data: [convInfo.inHeight, convInfo.inWidth]}, {
-          type: 'int32',
-          data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]
-        });
-  }
-
-  return backend.runWebGPUProgram(program, [x], x.dtype, dimensions);
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Pow.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Pow.ts
deleted file mode 100644
index 0064645cd..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Pow.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Pow} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-export const pow = binaryKernelFunc({
-  opType: BinaryOpType.POW,
-});
-
-export const powConfig: KernelConfig = {
-  kernelName: Pow,
-  backendName: 'webgpu',
-  kernelFunc: pow
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Prelu.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Prelu.ts
deleted file mode 100644
index 4b1a384ce..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Prelu.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Prelu, PreluInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {BinaryOpType} from '../binary_op_util';
-import {BinaryOpProgram} from '../binary_op_webgpu';
-
-export function prelu(args: {inputs: PreluInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {x, alpha} = inputs;
-
-  const program = new BinaryOpProgram(BinaryOpType.PRELU, x.shape, alpha.shape);
-  return backend.runWebGPUProgram(program, [x, alpha], 'float32');
-}
-
-export const preluConfig: KernelConfig = {
-  kernelName: Prelu,
-  backendName: 'webgpu',
-  kernelFunc: prelu as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Prod.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Prod.ts
deleted file mode 100644
index 33f9df15d..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Prod.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Prod, ProdAttrs, ProdInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {reduce} from '../kernel_utils/reduce';
-
-export function prod(
-    args: {inputs: ProdInputs, backend: WebGPUBackend, attrs: ProdAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  return reduce(x, axis, keepDims, 'prod', backend);
-}
-
-export const prodConfig: KernelConfig = {
-  kernelName: Prod,
-  backendName: 'webgpu',
-  kernelFunc: prod as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Range.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Range.ts
deleted file mode 100644
index 2cc96047f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Range.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Range, RangeAttrs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {rangeImplCPU} from '../kernel_utils/shared';
-
-export const range =
-    (args: {backend: WebGPUBackend, attrs: RangeAttrs}): TensorInfo => {
-      const {backend, attrs} = args;
-      const {start, stop, step, dtype} = attrs;
-      const values = rangeImplCPU(start, stop, step, dtype);
-      return backend.makeTensorInfo([values.length], dtype, values);
-    };
-
-export const rangeConfig: KernelConfig = {
-  kernelName: Range,
-  backendName: 'webgpu',
-  kernelFunc: range as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Real.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Real.ts
deleted file mode 100644
index d93959823..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Real.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Real, RealInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {identity} from './Identity';
-
-export function real(args: {inputs: RealInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {input} = inputs;
-  const inputData = backend.tensorMap.get(input.dataId);
-
-  return identity({inputs: {x: inputData.complexTensorInfos.real}, backend});
-}
-
-export const realConfig: KernelConfig = {
-  kernelName: Real,
-  backendName: 'webgpu',
-  kernelFunc: real as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/RealDiv.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/RealDiv.ts
deleted file mode 100644
index e11888b7c..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/RealDiv.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, RealDiv} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-export const realDiv = binaryKernelFunc({opType: BinaryOpType.DIV});
-
-export const realDivConfig: KernelConfig = {
-  kernelName: RealDiv,
-  backendName: 'webgpu',
-  kernelFunc: realDiv as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Reciprocal.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Reciprocal.ts
deleted file mode 100644
index f5cc4c88b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Reciprocal.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Reciprocal} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {UnaryOpType} from '../unary_op_util';
-
-export const reciprocal = unaryKernelFunc({opType: UnaryOpType.RECIPROCAL});
-
-export const reciprocalConfig: KernelConfig = {
-  kernelName: Reciprocal,
-  backendName: 'webgpu',
-  kernelFunc: reciprocal
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Relu.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Relu.ts
deleted file mode 100644
index e8543576e..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Relu.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Relu} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {UnaryOpType} from '../unary_op_util';
-
-export const relu = unaryKernelFunc({opType: UnaryOpType.RELU});
-
-export const reluConfig: KernelConfig = {
-  kernelName: Relu,
-  backendName: 'webgpu',
-  kernelFunc: relu
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Relu6.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Relu6.ts
deleted file mode 100644
index 5b6234365..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Relu6.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Relu6} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {UnaryOpType} from '../unary_op_util';
-
-export const relu6 = unaryKernelFunc({opType: UnaryOpType.RELU6});
-
-export const relu6Config: KernelConfig = {
-  kernelName: Relu6,
-  backendName: 'webgpu',
-  kernelFunc: relu6
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Reshape.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Reshape.ts
deleted file mode 100644
index 81329caba..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Reshape.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Reshape, ReshapeAttrs, ReshapeInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-export function reshape(
-    args: {inputs: ReshapeInputs, backend: WebGPUBackend, attrs: ReshapeAttrs}):
-    TensorInfo {
-  const {inputs, attrs} = args;
-  const {x} = inputs;
-  const {shape} = attrs;
-
-  const xSize = util.sizeFromShape(x.shape);
-  const $shape = util.inferFromImplicitShape(shape, xSize);
-  const $xSize = util.sizeFromShape($shape);
-
-  util.assert(
-      xSize === $xSize,
-      () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +
-          `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +
-          `shape must have the same number of elements.`);
-
-  // Backend needs to track refCount for the dataId for reshape op
-  args.backend.incRef(x.dataId);
-  return {dataId: x.dataId, shape: $shape, dtype: x.dtype};
-}
-
-export const reshapeConfig: KernelConfig = {
-  kernelName: Reshape,
-  backendName: 'webgpu',
-  kernelFunc: reshape as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeBilinear.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeBilinear.ts
deleted file mode 100644
index 84b7a5d87..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeBilinear.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {ResizeBilinearProgram} from '../resize_bilinear_webgpu';
-
-export function resizeBilinear(args: {
-  inputs: ResizeBilinearInputs,
-  backend: WebGPUBackend,
-  attrs: ResizeBilinearAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images} = inputs;
-  const {alignCorners, size, halfPixelCenters} = attrs;
-
-  const [newHeight, newWidth] = size;
-  const adjustHeight = alignCorners && newHeight > 1 ? 1.0 : 0.0;
-  const adjustWidth = alignCorners && newWidth > 1 ? 1.0 : 0.0;
-  const halfPixelCentersValue = halfPixelCenters ? 0.5 : 0.0;
-  const uniformData = [
-    {type: 'float32', data: [adjustHeight, adjustWidth]},
-    {type: 'float32', data: [halfPixelCentersValue]}
-  ];
-
-  const program = new ResizeBilinearProgram(
-      images.shape as [number, number, number, number], newHeight, newWidth);
-
-  return backend.runWebGPUProgram(program, [images], 'float32', uniformData);
-}
-
-export const resizeBilinearConfig: KernelConfig = {
-  kernelName: ResizeBilinear,
-  backendName: 'webgpu',
-  kernelFunc: resizeBilinear as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeBilinearGrad.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeBilinearGrad.ts
deleted file mode 100644
index 7af86c719..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeBilinearGrad.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeBilinearGrad, ResizeBilinearGradAttrs, ResizeBilinearGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {ResizeBilinearBackpropProgram} from '../resize_bilinear_backprop_webgpu';
-
-export function resizeBilinearGrad(args: {
-  inputs: ResizeBilinearGradInputs,
-  backend: WebGPUBackend,
-  attrs: ResizeBilinearGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images, dy} = inputs;
-  const {alignCorners} = attrs;
-
-  const [, xHeight, xWidth, ] =
-      images.shape as [number, number, number, number];
-  const [, yHeight, yWidth] = dy.shape as [number, number, number, number];
-
-  const effectiveXSize: [number, number] = [
-    (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
-    (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
-  ];
-
-  const effectiveYSize: [number, number] = [
-    (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
-    (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
-  ];
-
-  const heightScale = effectiveXSize[0] / effectiveYSize[0];
-  const widthScale = effectiveXSize[1] / effectiveYSize[1];
-
-  const invHeightScale = 1 / heightScale;
-  const invWidthScale = 1 / widthScale;
-
-  // This defines the size of the window of values around a particular
-  // index in dy that we want to search for contributions to dx.
-  const winHeight = (Math.ceil(invHeightScale) * 2) + 2;
-  const winWidth = (Math.ceil(invWidthScale) * 2) + 2;
-
-  const program = new ResizeBilinearBackpropProgram(
-      images.shape as [number, number, number, number], alignCorners);
-  const uniformData = [
-    {type: 'int32', data: effectiveXSize},
-    {type: 'int32', data: effectiveYSize},
-    {type: 'float32', data: [heightScale]},
-    {type: 'float32', data: [widthScale]},
-    {type: 'float32', data: [invHeightScale]},
-    {type: 'float32', data: [invWidthScale]},
-    {type: 'int32', data: [winHeight]}, {type: 'int32', data: [winWidth]}
-  ];
-  return backend.runWebGPUProgram(program, [dy], dy.dtype, uniformData);
-}
-
-export const resizeBilinearGradConfig: KernelConfig = {
-  kernelName: ResizeBilinearGrad,
-  backendName: 'webgpu',
-  kernelFunc: resizeBilinearGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeNearestNeighbor.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeNearestNeighbor.ts
deleted file mode 100644
index c860f888c..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeNearestNeighbor.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {ResizeNearestNeighborProgram} from '../resize_nearest_neighbor_webgpu';
-
-export function resizeNearestNeighbor(args: {
-  inputs: ResizeNearestNeighborInputs,
-  backend: WebGPUBackend,
-  attrs: ResizeNearestNeighborAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images} = inputs;
-  const {alignCorners, halfPixelCenters, size} = attrs;
-
-  const [newHeight, newWidth] = size;
-  const adjustHeight = alignCorners && newHeight > 1 ? 1.0 : 0.0;
-  const adjustWidth = alignCorners && newWidth > 1 ? 1.0 : 0.0;
-  // When align corners is false, we rounds the value with floor.
-  const roundBase = alignCorners ? 0.5 : 0.0;
-  const uniformData = [
-    {type: 'float32', data: [adjustHeight, adjustWidth]},
-    {type: 'float32', data: [roundBase]}
-  ];
-
-  const program = new ResizeNearestNeighborProgram(
-      images.shape as [number, number, number, number], newHeight, newWidth,
-      halfPixelCenters);
-  return backend.runWebGPUProgram(program, [images], images.dtype, uniformData);
-}
-
-export const resizeNearestNeighborConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighbor,
-  backendName: 'webgpu',
-  kernelFunc: resizeNearestNeighbor as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeNearestNeighborGrad.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeNearestNeighborGrad.ts
deleted file mode 100644
index 19df64f0d..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ResizeNearestNeighborGrad.ts
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, ResizeNearestNeighborGrad, ResizeNearestNeighborGradAttrs, ResizeNearestNeighborGradInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {ResizeNearestNeigborBackpropProgram} from '../resize_nearest_neighbor_backprop_webgpu';
-
-export function resizeNearestNeighborGrad(args: {
-  inputs: ResizeNearestNeighborGradInputs,
-  backend: WebGPUBackend,
-  attrs: ResizeNearestNeighborGradAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {images, dy} = inputs;
-  const {alignCorners} = attrs;
-
-  const [, xHeight, xWidth] = images.shape as [number, number, number, number];
-  const [, yHeight, yWidth] = dy.shape as [number, number, number, number];
-
-  const effectiveXSize: [number, number] = [
-    (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
-    (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
-  ];
-
-  const effectiveYSize: [number, number] = [
-    (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
-    (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
-  ];
-
-  const heightScale = effectiveXSize[0] / effectiveYSize[0];
-  const widthScale = effectiveXSize[1] / effectiveYSize[1];
-
-  const invHeightScale = 1 / heightScale;
-  const invWidthScale = 1 / widthScale;
-
-  // This defines the size of the window of values around a particular
-  // index in dy that we want to search for contributions to dx.
-  const winHeight = (Math.ceil(invHeightScale) * 2) + 2;
-  const winWidth = (Math.ceil(invWidthScale) * 2) + 2;
-
-  const program = new ResizeNearestNeigborBackpropProgram(
-      images.shape as [number, number, number, number], alignCorners);
-  const uniformData = [
-    {type: 'int32', data: effectiveXSize},
-    {type: 'int32', data: effectiveYSize},
-    {type: 'float32', data: [invHeightScale]},
-    {type: 'float32', data: [invWidthScale]},
-    {type: 'int32', data: [winHeight]}, {type: 'int32', data: [winWidth]}
-  ];
-  return backend.runWebGPUProgram(program, [dy], dy.dtype, uniformData);
-}
-
-export const resizeNearestNeighborGradConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighborGrad,
-  backendName: 'webgpu',
-  kernelFunc: resizeNearestNeighborGrad as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Reverse.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Reverse.ts
deleted file mode 100644
index 5a6f26812..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Reverse.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Reverse, ReverseAttrs, ReverseInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {ReverseProgram} from '../reverse_webgpu';
-
-import {identity} from './Identity';
-import {reshape} from './Reshape';
-
-export function reverse(
-    args: {inputs: ReverseInputs, backend: WebGPUBackend, attrs: ReverseAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {dims} = attrs;
-
-  const xRank = x.shape.length;
-  if (xRank === 0) {
-    return identity({inputs: {x}, backend});
-  }
-
-  const xShape = x.shape;
-  const xShape4D: [number, number, number, number] = [1, 1, 1, 1];
-  xShape.forEach((d, i) => {
-    const index = i + 4 - xRank;
-    xShape4D[index] = d;
-  });
-
-  const axes = util.parseAxisParam(dims, x.shape);
-  const dims4D: [number, number, number, number] = [0, 0, 0, 0];
-  axes.forEach(ax => {
-    const index = ax + 4 - xRank;
-    dims4D[index] = 1;
-  });
-  const uniformData = [{type: 'int32', data: dims4D}];
-
-  const xReshaped = reshape({inputs: {x}, backend, attrs: {shape: xShape4D}});
-
-  const program = new ReverseProgram(xShape4D);
-  const values = backend.runWebGPUProgram(
-      program, [xReshaped], xReshaped.dtype, uniformData);
-  backend.disposeData(xReshaped.dataId);
-
-  const result =
-      reshape({inputs: {x: values}, backend, attrs: {shape: xShape}});
-  backend.disposeData(values.dataId);
-
-  return result;
-}
-
-export const reverseConfig: KernelConfig = {
-  kernelName: Reverse,
-  backendName: 'webgpu',
-  kernelFunc: reverse as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/RotateWithOffset.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/RotateWithOffset.ts
deleted file mode 100644
index fd8da3b3a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/RotateWithOffset.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Tensor4D} from '@tensorflow/tfjs-core';
-import {RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {RotateProgram} from '../rotate_webgpu';
-
-export const rotateWithOffsetConfig: KernelConfig = {
-    kernelName: RotateWithOffset,
-    backendName: 'webgpu',
-    kernelFunc: ({inputs, attrs, backend}) => {
-      const {image} = inputs as RotateWithOffsetInputs;
-      const {radians, fillValue, center} =
-          attrs as unknown as RotateWithOffsetAttrs;
-      const webgpuBackend = backend as WebGPUBackend;
-
-      const program = new RotateProgram((image as Tensor4D).shape, fillValue);
-      const [centerX, centerY] =
-          backend_util.getImageCenter(center, image.shape[1], image.shape[2]);
-      const uniformData = [
-            {type: 'float32', data: [centerX]},
-            {type: 'float32', data: [centerY]},
-            {type: 'float32', data: [Math.sin(radians)]},
-            {type: 'float32', data: [Math.cos(radians)]}
-          ];
-
-      if (typeof fillValue === 'number') {
-        uniformData.push(
-            {type: 'float32', data: [Number.parseFloat(fillValue.toFixed(2))]});
-      } else {
-        uniformData.push({type: 'float32', data: fillValue});
-      }
-
-      const output = webgpuBackend.runWebGPUProgram(
-          program, [image], image.dtype, uniformData);
-      return output;
-   }
- };
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Round.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Round.ts
deleted file mode 100644
index de95f634b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Round.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Round} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const round = unaryKernelFunc({opType: UnaryOpType.ROUND});
-
-export const roundConfig: KernelConfig = {
-  kernelName: Round,
-  backendName: 'webgpu',
-  kernelFunc: round
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Rsqrt.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Rsqrt.ts
deleted file mode 100644
index a6cba8cc5..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Rsqrt.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Rsqrt} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {rsqrtImplCPU} from '../kernel_utils/shared';
-import {UnaryOpType} from '../unary_op_util';
-
-export const rsqrt =
-    unaryKernelFunc({opType: UnaryOpType.RSQRT, cpuKernelImpl: rsqrtImplCPU});
-
-export const rsqrtConfig: KernelConfig = {
-  kernelName: Rsqrt,
-  backendName: 'webgpu',
-  kernelFunc: rsqrt
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ScatterNd.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ScatterNd.ts
deleted file mode 100644
index 9c148b420..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ScatterNd.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, ScatterNd, ScatterNdAttrs, ScatterNdInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {ScatterProgram} from '../scatter_webgpu';
-
-import {fill} from './Fill';
-import {reshape} from './Reshape';
-
-export function scatterNd(args: {
-  inputs: ScatterNdInputs,
-  backend: WebGPUBackend,
-  attrs: ScatterNdAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {indices, updates} = inputs;
-  const {shape} = attrs;
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(updates, indices, shape);
-
-  const flattenShape = [outputSize / sliceSize, sliceSize];
-
-  if (outputSize === 0) {
-    return backend.makeTensorInfo(shape, indices.dtype);
-  }
-
-  const flattenIndices = reshape(
-      {inputs: {x: indices}, backend, attrs: {shape: [numUpdates, sliceRank]}});
-  const flattenX = reshape(
-      {inputs: {x: updates}, backend, attrs: {shape: [numUpdates, sliceSize]}});
-
-  const type = flattenX.dtype;
-  const output =
-      fill({backend, attrs: {shape: flattenShape, value: 0, dtype: type}});
-  const size = util.sizeFromShape(flattenX.shape);
-  const uniformData = [
-    {type: 'int32', data: [sliceRank]}, {type: 'int32', data: strides},
-    {type: 'int32', data: [size]}
-  ];
-  const program = new ScatterProgram(
-      flattenX.shape, sliceRank, flattenIndices.shape.length,
-      flattenX.shape.length, strides, flattenShape, type);
-  const res = backend.runWebGPUProgram(
-      program, [flattenX, flattenIndices], type, uniformData, output);
-
-  const reshaped = reshape({inputs: {x: res}, backend, attrs: {shape}});
-
-  backend.disposeData(flattenIndices.dataId);
-  backend.disposeData(flattenX.dataId);
-  backend.disposeData(res.dataId);
-
-  return reshaped;
-}
-
-export const scatterNdConfig: KernelConfig = {
-  kernelName: ScatterNd,
-  backendName: 'webgpu',
-  kernelFunc: scatterNd as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/SearchSorted.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/SearchSorted.ts
deleted file mode 100644
index b7ef60bf6..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/SearchSorted.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SearchSorted, SearchSortedAttrs, SearchSortedInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {SearchSortedProgram} from '../search_sorted_webgpu';
-
-export function searchSorted(args: {
-  inputs: SearchSortedInputs,
-  backend: WebGPUBackend,
-  attrs: SearchSortedAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {sortedSequence, values} = inputs;
-  const {side} = attrs;
-
-  const program =
-      new SearchSortedProgram([values.shape[0], values.shape[1]], side);
-  const uniformData = [{type: 'int32', data: [sortedSequence.shape[1]]}];
-  return backend.runWebGPUProgram(
-      program, [sortedSequence, values], 'int32', uniformData);
-}
-
-export const searchSortedConfig: KernelConfig = {
-  kernelName: SearchSorted,
-  backendName: 'webgpu',
-  kernelFunc: searchSorted as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Select.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Select.ts
deleted file mode 100644
index 1dc8256cf..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Select.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Select, SelectInputs, TensorInfo, upcastType} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {SelectProgram} from '../select_webgpu';
-
-export function select(args: {inputs: SelectInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {condition, t, e} = inputs;
-
-  const program =
-      new SelectProgram(condition.shape.length, t.shape, t.shape.length);
-  return backend.runWebGPUProgram(
-      program, [condition, t, e], upcastType(t.dtype, e.dtype));
-}
-
-export const selectConfig: KernelConfig = {
-  kernelName: Select,
-  backendName: 'webgpu',
-  kernelFunc: select as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Selu.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Selu.ts
deleted file mode 100644
index 612778efb..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Selu.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Selu} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const selu = unaryKernelFunc({opType: UnaryOpType.SELU});
-
-export const seluConfig: KernelConfig = {
-  kernelName: Selu,
-  backendName: 'webgpu',
-  kernelFunc: selu
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sigmoid.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Sigmoid.ts
deleted file mode 100644
index 4d415a446..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sigmoid.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sigmoid} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {UnaryOpType} from '../unary_op_util';
-
-export const sigmoid = unaryKernelFunc({opType: UnaryOpType.SIGMOID});
-
-export const sigmoidConfig: KernelConfig = {
-  kernelName: Sigmoid,
-  backendName: 'webgpu',
-  kernelFunc: sigmoid,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sign.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Sign.ts
deleted file mode 100644
index f1cd3dfe9..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sign.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sign} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const sign = unaryKernelFunc({opType: UnaryOpType.SIGN});
-
-export const signConfig: KernelConfig = {
-  kernelName: Sign,
-  backendName: 'webgpu',
-  kernelFunc: sign
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sin.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Sin.ts
deleted file mode 100644
index 008df3822..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sin.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sin} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const sin = unaryKernelFunc({opType: UnaryOpType.SIN});
-
-export const sinConfig: KernelConfig = {
-  kernelName: Sin,
-  backendName: 'webgpu',
-  kernelFunc: sin
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sinh.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Sinh.ts
deleted file mode 100644
index 068125a2e..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sinh.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sinh} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const sinh = unaryKernelFunc({opType: UnaryOpType.SINH});
-
-export const sinhConfig: KernelConfig = {
-  kernelName: Sinh,
-  backendName: 'webgpu',
-  kernelFunc: sinh
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Slice.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Slice.ts
deleted file mode 100644
index d96826501..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Slice.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Slice, slice_util, SliceAttrs, SliceInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {sliceImplCPU} from '../kernel_utils/shared';
-import {SliceProgram} from '../slice_webgpu';
-
-export function slice(
-    args: {inputs: SliceInputs, backend: WebGPUBackend, attrs: SliceAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {begin, size} = attrs;
-
-  const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);
-  slice_util.assertParamsValid(x, $begin, $size);
-
-  if (backend.shouldExecuteOnCPU([x]) || x.dtype === 'string') {
-    const xTensorData = backend.tensorMap.get(x.dataId);
-    const outValues = sliceImplCPU(
-        xTensorData.values as TypedArray, $begin, $size, x.shape, x.dtype);
-    return backend.makeTensorInfo($size, x.dtype, outValues);
-  }
-
-  if (util.sizeFromShape($size) === 0) {
-    return backend.makeTensorInfo($size, x.dtype, []);
-  }
-
-  // TODO(xing.xu): Add shadow slice support.
-  const program = new SliceProgram($begin, $size);
-  const uniformData = [{type: 'int32', data: $begin}];
-  return backend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-}
-
-export const sliceConfig: KernelConfig = {
-  kernelName: Slice,
-  backendName: 'webgpu',
-  kernelFunc: slice as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Softmax.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Softmax.ts
deleted file mode 100644
index 5ebd5beb5..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Softmax.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Softmax, SoftmaxAttrs, SoftmaxInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {SoftmaxProgram} from '../softmax_webgpu';
-
-import {reshape} from './Reshape';
-
-export function softmax(
-    args: {inputs: SoftmaxInputs, backend: WebGPUBackend, attrs: SoftmaxAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {logits} = inputs;
-  const {dim} = attrs;
-
-  const logitsReshaped = reshape({
-    inputs: {x: logits},
-    backend,
-    attrs: {
-      shape: [
-        util.sizeFromShape(logits.shape) / logits.shape[dim], logits.shape[dim]
-      ]
-    }
-  });
-  const program = new SoftmaxProgram(logitsReshaped.shape);
-  const res = backend.runWebGPUProgram(program, [logitsReshaped], logits.dtype);
-  const resReshaped =
-      reshape({inputs: {x: res}, backend, attrs: {shape: logits.shape}});
-  backend.disposeData(logitsReshaped.dataId);
-  backend.disposeData(res.dataId);
-  return resReshaped;
-}
-
-export const softmaxConfig: KernelConfig = {
-  kernelName: Softmax,
-  backendName: 'webgpu',
-  kernelFunc: softmax as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Softplus.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Softplus.ts
deleted file mode 100644
index 6c8a74c2a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Softplus.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Softplus} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-import {UnaryOpType} from '../unary_op_util';
-
-export const softplus = unaryKernelFunc({opType: UnaryOpType.SOFTPLUS});
-
-export const softplusConfig: KernelConfig = {
-  kernelName: Softplus,
-  backendName: 'webgpu',
-  kernelFunc: softplus
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/SpaceToBatchND.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/SpaceToBatchND.ts
deleted file mode 100644
index c5d695afc..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/SpaceToBatchND.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {SpaceToBatchNDProgram} from '../space_to_batchND_webgpu';
-
-import {reshape} from './Reshape';
-
-export const spaceToBatchND = (args: {
-  inputs: SpaceToBatchNDInputs,
-  backend: WebGPUBackend,
-  attrs: SpaceToBatchNDAttrs
-}): TensorInfo => {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {blockShape, paddings} = attrs;
-
-  util.assert(
-      x.shape.length <= 4,
-      () => 'spaceToBatchND for rank > 4 with a WebGPU backend not ' +
-          'implemented yet');
-
-  const prod = blockShape.reduce((a, b) => a * b);
-
-  const completePaddings: Array<[number, number]> = [[0, 0]];
-  completePaddings.push(...paddings as Array<[number, number]>);
-  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
-    completePaddings.push([0, 0]);
-  }
-
-  const paddedXShape = completePaddings.map(
-      (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);
-  const reshapedPaddedShape =
-      backend_util.getReshaped(paddedXShape, blockShape, prod, false);
-
-  const permutedReshapedPaddedPermutation = backend_util.getPermuted(
-      reshapedPaddedShape.length, blockShape.length, false);
-
-  const flattenShape =
-      backend_util.getReshapedPermuted(paddedXShape, blockShape, prod, false);
-
-  const paddedXShapeStrides = util.computeStrides(paddedXShape);
-  const program = new SpaceToBatchNDProgram(
-      x.shape, paddedXShape, completePaddings, reshapedPaddedShape,
-      permutedReshapedPaddedPermutation, paddedXShapeStrides.length);
-  const uniformData = [
-    {type: 'int32', data: reshapedPaddedShape},
-    {type: 'int32', data: paddedXShapeStrides}
-  ];
-  completePaddings.map(
-      p => uniformData.push({type: 'int32', data: [p[0], p[1]]}));
-  const paddedXT = backend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-  const result =
-      reshape({inputs: {x: paddedXT}, backend, attrs: {shape: flattenShape}});
-  backend.disposeData(paddedXT.dataId);
-  return result;
-};
-
-export const spaceToBatchNDConfig: KernelConfig = {
-  kernelName: SpaceToBatchND,
-  backendName: 'webgpu',
-  kernelFunc: spaceToBatchND as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/SparseSegmentMean.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/SparseSegmentMean.ts
deleted file mode 100644
index 599595942..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/SparseSegmentMean.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SparseSegmentMean, SparseSegmentMeanInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {sparseSegmentReduce} from '../kernel_utils/sparse_segment_reduce';
-
-export function sparseSegmentMean(
-    args: {inputs: SparseSegmentMeanInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {data, indices, segmentIds} = inputs;
-
-  return sparseSegmentReduce(data, indices, segmentIds, false, backend);
-}
-
-export const sparseSegmentMeanConfig: KernelConfig = {
-  kernelName: SparseSegmentMean,
-  backendName: 'webgpu',
-  kernelFunc: sparseSegmentMean as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/SparseSegmentSum.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/SparseSegmentSum.ts
deleted file mode 100644
index 0d572efcc..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/SparseSegmentSum.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, SparseSegmentSum, SparseSegmentSumInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {sparseSegmentReduce} from '../kernel_utils/sparse_segment_reduce';
-
-export function sparseSegmentSum(
-    args: {inputs: SparseSegmentSumInputs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {inputs, backend} = args;
-  const {data, indices, segmentIds} = inputs;
-
-  return sparseSegmentReduce(data, indices, segmentIds, true, backend);
-}
-
-export const sparseSegmentSumConfig: KernelConfig = {
-  kernelName: SparseSegmentSum,
-  backendName: 'webgpu',
-  kernelFunc: sparseSegmentSum as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/SparseToDense.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/SparseToDense.ts
deleted file mode 100644
index e784f3975..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/SparseToDense.ts
+++ /dev/null
@@ -1,136 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, Rank, SparseToDense, SparseToDenseAttrs, SparseToDenseInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {scatterImplCPU} from '../kernel_utils/shared';
-import {ScatterProgram} from '../scatter_webgpu';
-
-import {identity} from './Identity';
-import {reshape} from './Reshape';
-import {tile} from './Tile';
-
-export function sparseToDense(args: {
-  inputs: SparseToDenseInputs,
-  backend: WebGPUBackend,
-  attrs: SparseToDenseAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {sparseIndices, sparseValues, defaultValue} = inputs;
-  const {outputShape} = attrs;
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);
-
-  const sumDupeIndices = false;
-  if (sparseValues.dtype === 'string') {
-    const indicesBuf = backend.bufferSync<Rank, 'int32'>(sparseIndices);
-    const updatesBuf = backend.bufferSync<Rank, 'string'>(sparseValues);
-    const $defaultValue = util.decodeString(
-        backend.readSync(defaultValue.dataId)[0] as Uint8Array);
-    const outBuf = scatterImplCPU(
-        indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates,
-        sliceRank, strides, $defaultValue, sumDupeIndices);
-    return backend.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
-  }
-
-  const flattenShape = [outputSize / sliceSize, sliceSize];
-
-  const $sparseIndices = reshape({
-    inputs: {x: sparseIndices},
-    backend,
-    attrs: {shape: [numUpdates, sliceRank]}
-  });
-  const $sparseValues = sparseValues.shape.length ?
-      reshape({
-        inputs: {x: sparseValues},
-        backend,
-        attrs: {shape: [numUpdates, sliceSize]}
-      }) :
-      identity({inputs: {x: sparseValues}, backend});
-
-  const type = $sparseValues.dtype;
-  const zero =
-      backend.makeTensorInfo([], type, util.makeZerosTypedArray(1, type));
-
-  // Fill output tensor with the default value.
-  const $defaultValue = reshape({
-    inputs: {x: defaultValue},
-    backend,
-    attrs: {shape: Array(flattenShape.length).fill(1)}
-  });
-  const $denseValues =
-      tile({inputs: {x: $defaultValue}, backend, attrs: {reps: flattenShape}});
-
-  const size = util.sizeFromShape([numUpdates, sliceSize]);
-  const uniformData = [
-    {type: 'int32', data: [sliceRank]},
-    {type: 'int32', data: strides},
-    {type: 'int32', data: [size]},
-  ];
-
-  switch (numUpdates) {
-    case 0:
-      break;
-    case 1:
-      if (true) {
-        const program = new ScatterProgram(
-            [numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length,
-            $sparseValues.shape.length, strides, flattenShape, type,
-            sumDupeIndices);
-        backend.runWebGPUProgram(
-            program, [$sparseValues, $sparseIndices], type, uniformData,
-            $denseValues);
-      }
-      break;
-    default:
-      if (true) {
-        // First replace the default value with 0 at indices.
-        const program = new ScatterProgram(
-            [numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length,
-            zero.shape.length, strides, flattenShape, type, sumDupeIndices);
-        backend.runWebGPUProgram(
-            program, [zero, $sparseIndices], type, uniformData, $denseValues);
-      }
-      {
-        // Then replace 0 with the (sum of) sparse value(s) at indices.
-        const program = new ScatterProgram(
-            [numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length,
-            $sparseValues.shape.length, strides, flattenShape, type);
-        backend.runWebGPUProgram(
-            program, [$sparseValues, $sparseIndices], type, uniformData,
-            $denseValues);
-      }
-  }
-
-  const denseValues = reshape(
-      {inputs: {x: $denseValues}, backend, attrs: {shape: outputShape}});
-
-  backend.disposeData($sparseIndices.dataId);
-  backend.disposeData($sparseValues.dataId);
-  backend.disposeData($defaultValue.dataId);
-  backend.disposeData(zero.dataId);
-  backend.disposeData($denseValues.dataId);
-  return denseValues;
-}
-
-export const sparseToDenseConfig: KernelConfig = {
-  kernelName: SparseToDense,
-  backendName: 'webgpu',
-  kernelFunc: sparseToDense as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/SplitV.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/SplitV.ts
deleted file mode 100644
index 78f627f89..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/SplitV.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, SplitV, SplitVAttrs, SplitVInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {slice} from './Slice';
-
-export function splitV(
-    args: {inputs: SplitVInputs, backend: WebGPUBackend, attrs: SplitVAttrs}):
-    TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {numOrSizeSplits, axis} = attrs;
-
-  const $axis = util.parseAxisParam(axis, x.shape)[0];
-  const splitSizes = backend_util.prepareSplitSize(x, numOrSizeSplits, $axis);
-
-  const xRank = x.shape.length;
-  const begin = new Array(xRank).fill(0);
-  const size = x.shape.slice();
-
-  return splitSizes.map(s => {
-    const sliceSize = [...size];
-    sliceSize[$axis] = s;
-    const sliceT =
-        slice({inputs: {x}, backend, attrs: {begin, size: sliceSize}});
-    begin[$axis] += s;
-    return sliceT;
-  });
-}
-
-export const splitVConfig: KernelConfig = {
-  kernelName: SplitV,
-  backendName: 'webgpu',
-  kernelFunc: splitV as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sqrt.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Sqrt.ts
deleted file mode 100644
index 327b033c1..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sqrt.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sqrt} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {UnaryOpType} from '../unary_op_util';
-
-export const sqrt = unaryKernelFunc({opType: UnaryOpType.SQRT});
-
-export const sqrtConfig: KernelConfig = {
-  kernelName: Sqrt,
-  backendName: 'webgpu',
-  kernelFunc: sqrt
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Square.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Square.ts
deleted file mode 100644
index 9251e75e4..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Square.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Square, SquareInputs} from '@tensorflow/tfjs-core';
-import {WebGPUBackend} from '../backend_webgpu';
-import {UnaryOpProgram} from '../unary_op_webgpu';
-import {UnaryOpType} from '../unary_op_util';
-
-export const squareConfig: KernelConfig = {
-  kernelName: Square,
-  backendName: 'webgpu',
-  kernelFunc: ({inputs, backend}) => {
-    const {x} = inputs as SquareInputs;
-    const webGPUBackend = backend as WebGPUBackend;
-    const program = new UnaryOpProgram(x.shape, UnaryOpType.SQUARE);
-    return webGPUBackend.runWebGPUProgram(program, [x], x.dtype);
-  }
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/SquaredDifference.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/SquaredDifference.ts
deleted file mode 100644
index 54f31aef8..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/SquaredDifference.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SquaredDifference} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-
-export const squaredDifference = binaryKernelFunc({
-  opType: BinaryOpType.SQUARED_DIFFERENCE,
-});
-
-export const squaredDifferenceConfig: KernelConfig = {
-  kernelName: SquaredDifference,
-  backendName: 'webgpu',
-  kernelFunc: squaredDifference
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Step.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Step.ts
deleted file mode 100644
index a497b34e4..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Step.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Step, StepAttrs, TensorInfo, UnaryInputs} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {UnaryOpType} from '../unary_op_util';
-import {UnaryOpProgram} from '../unary_op_webgpu';
-
-export function step(
-    {inputs, attrs, backend}:
-        {inputs: UnaryInputs, attrs: StepAttrs, backend: WebGPUBackend}):
-    TensorInfo {
-  const {x} = inputs;
-  const program =
-      new UnaryOpProgram(x.shape, UnaryOpType.STEP, 'stepAlpha : f32,');
-  const uniformData = [{type: 'float32', data: [attrs.alpha]}];
-  return backend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-}
-
-export const stepConfig: KernelConfig = {
-  kernelName: Step,
-  backendName: 'webgpu',
-  kernelFunc: step as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/StridedSlice.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/StridedSlice.ts
deleted file mode 100644
index 48dd4b349..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/StridedSlice.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, KernelConfig, KernelFunc, Rank, slice_util, StridedSlice, StridedSliceAttrs, StridedSliceInputs, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {stridedSliceImplCPU} from '../kernel_utils/shared';
-
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-import {StridedSliceProgram} from '../strided_slice_webgpu';
-
-export function stridedSlice(args: {
-  inputs: StridedSliceInputs,
-  backend: WebGPUBackend,
-  attrs: StridedSliceAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {
-    begin,
-    end,
-    strides,
-    beginMask,
-    endMask,
-    ellipsisMask,
-    newAxisMask,
-    shrinkAxisMask
-  } = attrs;
-
-  const {
-    finalShapeSparse,
-    finalShape,
-    isIdentity,
-    sliceDim0,
-    isSimpleSlice,
-    begin: $begin,
-    end: $end,
-    strides: $strides
-  } =
-      slice_util.sliceInfo(
-          x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,
-          newAxisMask, shrinkAxisMask);
-
-  let result;
-
-  if (isIdentity) {
-    // Optimization #1, slice is a no-op plus reshape
-    result = reshape({inputs: {x}, backend, attrs: {shape: finalShape}});
-  } else if (sliceDim0 || isSimpleSlice) {
-    // Optimization #2, slice is memory contiguous (only occurs in dim 0)
-    util.assert(
-        x.shape.length >= 1,
-        () => `Input must have rank at least 1, got: ${x.shape.length}`);
-
-    const size = slice_util.computeOutShape($begin, $end, $strides);
-    // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).
-    const sliced = slice({inputs: {x}, backend, attrs: {begin: $begin, size}});
-    result =
-        reshape({inputs: {x: sliced}, backend, attrs: {shape: finalShape}});
-    backend.disposeData(sliced.dataId);
-  } else {
-    const shouldExecuteOnCPU = backend.shouldExecuteOnCPU([x]);
-    if (shouldExecuteOnCPU) {
-      const values = backend.readSync(x.dataId) as TypedArray;
-      const xBuf = buffer(x.shape, x.dtype, values) as TensorBuffer<Rank>;
-      const resultValues =
-          stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);
-      result = backend.makeTensorInfo(finalShape, x.dtype, resultValues.values);
-    } else {
-      const program = new StridedSliceProgram(finalShapeSparse);
-      const uniformData =
-          [{type: 'int32', data: $begin}, {type: 'int32', data: $strides}];
-      const resultValues =
-          backend.runWebGPUProgram(program, [x], x.dtype, uniformData);
-      result = reshape(
-          {inputs: {x: resultValues}, backend, attrs: {shape: finalShape}});
-      backend.disposeData(resultValues.dataId);
-    }
-  }
-
-  return result;
-}
-
-export const stridedSliceConfig: KernelConfig = {
-  kernelName: StridedSlice,
-  backendName: 'webgpu',
-  kernelFunc: stridedSlice as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/StringNGrams.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/StringNGrams.ts
deleted file mode 100644
index d784f2699..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/StringNGrams.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, StringNGrams, StringNGramsAttrs, StringNGramsInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {stringNGramsImplCPU} from '../kernel_utils/shared';
-
-export function stringNGrams(args: {
-  inputs: StringNGramsInputs,
-  backend: WebGPUBackend,
-  attrs: StringNGramsAttrs
-}): [TensorInfo, TensorInfo] {
-  const {inputs, backend, attrs} = args;
-  const {
-    separator,
-    nGramWidths,
-    leftPad,
-    rightPad,
-    padWidth,
-    preserveShortSequences
-  } = attrs;
-  const {data, dataSplits} = inputs;
-  const $data = backend.readSync(data.dataId) as Uint8Array[];
-  const $dataSplits = backend.readSync(dataSplits.dataId) as Int32Array;
-
-  const [nGrams, nGramsSplits] = stringNGramsImplCPU(
-      $data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth,
-      preserveShortSequences);
-  return [
-    backend.makeTensorInfo([nGrams.length], 'string', nGrams),
-    backend.makeTensorInfo(dataSplits.shape, 'int32', nGramsSplits),
-  ];
-}
-
-export const stringNGramsConfig: KernelConfig = {
-  kernelName: StringNGrams,
-  backendName: 'webgpu',
-  kernelFunc: stringNGrams as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sub.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Sub.ts
deleted file mode 100644
index eedf4f4be..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sub.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sub} from '@tensorflow/tfjs-core';
-
-import {BinaryOpType} from '../binary_op_util';
-import {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {subImplCPU as cpuSub} from '../kernel_utils/shared';
-
-export const sub = binaryKernelFunc(
-    {opType: BinaryOpType.SUB, cpuKernelImpl: cpuSub, supportsComplex: true});
-
-export const subConfig: KernelConfig = {
-  kernelName: Sub,
-  backendName: 'webgpu',
-  kernelFunc: sub
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sum.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Sum.ts
deleted file mode 100644
index 9ac537dc3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Sum.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Sum, SumAttrs, SumInputs, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {reduce} from '../kernel_utils/reduce';
-
-export function sum(
-    args: {inputs: SumInputs, backend: WebGPUBackend, attrs: SumAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {axis, keepDims} = attrs;
-
-  return reduce(x, axis, keepDims, 'sum', backend);
-}
-
-export const sumConfig: KernelConfig = {
-  kernelName: Sum,
-  backendName: 'webgpu',
-  kernelFunc: sum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Tan.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Tan.ts
deleted file mode 100644
index 54ad016e3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Tan.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tan} from '@tensorflow/tfjs-core';
-
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {UnaryOpType} from '../unary_op_util';
-
-export const tan = unaryKernelFunc({opType: UnaryOpType.TAN});
-
-export const tanConfig: KernelConfig = {
-  kernelName: Tan,
-  backendName: 'webgpu',
-  kernelFunc: tan
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Tanh.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Tanh.ts
deleted file mode 100644
index 19555c8ec..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Tanh.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tanh} from '@tensorflow/tfjs-core';
-import {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';
-import {UnaryOpType} from '../unary_op_util';
-
-export const tanh = unaryKernelFunc({opType: UnaryOpType.TANH});
-
-export const tanhConfig: KernelConfig = {
-  kernelName: Tanh,
-  backendName: 'webgpu',
-  kernelFunc: tanh
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/TensorScatterUpdate.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/TensorScatterUpdate.ts
deleted file mode 100644
index 9043f5013..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/TensorScatterUpdate.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, TensorInfo, TensorScatterUpdate, TensorScatterUpdateAttrs, TensorScatterUpdateInputs, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {ScatterProgram} from '../scatter_webgpu';
-
-import {reshape} from './Reshape';
-import {tile} from './Tile';
-
-export function tensorScatterUpdate(args: {
-  inputs: TensorScatterUpdateInputs,
-  backend: WebGPUBackend,
-  attrs: TensorScatterUpdateAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {tensor, indices, updates} = inputs;
-  const {} = attrs;
-
-  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
-      backend_util.calculateShapes(updates, indices, tensor.shape);
-
-  const flattenShape = [outputSize / sliceSize, sliceSize];
-
-  if (outputSize === 0) {
-    return backend.makeTensorInfo(tensor.shape, indices.dtype);
-  }
-
-  const toDispose = [];
-
-  const flattenIndices = reshape(
-      {inputs: {x: indices}, backend, attrs: {shape: [numUpdates, sliceRank]}});
-  toDispose.push(flattenIndices);
-  const flattenX = reshape(
-      {inputs: {x: updates}, backend, attrs: {shape: [numUpdates, sliceSize]}});
-  toDispose.push(flattenX);
-  const flattenTensor =
-      reshape({inputs: {x: tensor}, backend, attrs: {shape: flattenShape}});
-  toDispose.push(flattenTensor);
-  const output = tile({
-    inputs: {x: flattenTensor},
-    backend,
-    attrs: {reps: Array(flattenShape.length).fill(1)}
-  });
-  const program = new ScatterProgram(
-      [numUpdates, sliceSize], sliceRank, flattenIndices.shape.length,
-      flattenX.shape.length, strides, flattenShape, tensor.dtype, false);
-  const size = util.sizeFromShape([numUpdates, sliceSize]);
-  const uniformData = [
-    {type: 'int32', data: [sliceRank]},
-    {type: 'int32', data: strides},
-    {type: 'int32', data: [size]},
-  ];
-  const res = backend.runWebGPUProgram(
-      program, [flattenX, flattenIndices], flattenTensor.dtype, uniformData,
-      output);
-  toDispose.push(res);
-
-  const reshaped =
-      reshape({inputs: {x: res}, backend, attrs: {shape: tensor.shape}});
-
-  toDispose.forEach(t => backend.disposeData(t.dataId));
-
-  return reshaped;
-}
-
-export const tensorScatterUpdateConfig: KernelConfig = {
-  kernelName: TensorScatterUpdate,
-  backendName: 'webgpu',
-  kernelFunc: tensorScatterUpdate as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Tile.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Tile.ts
deleted file mode 100644
index 0ea3ff488..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Tile.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {buffer, KernelConfig, KernelFunc, TensorInfo, Tile, TileAttrs, TileInputs, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {tileImplCPU} from '../kernel_utils/shared';
-import {TileProgram} from '../tile_webgpu';
-
-export function tile(
-    params: {inputs: TileInputs, backend: WebGPUBackend, attrs: TileAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = params;
-  const {x} = inputs;
-  const {reps} = attrs;
-
-  // tile gpu program cannot handle rank >= 5 case.
-  if (backend.shouldExecuteOnCPU([x]) || x.dtype === 'string' ||
-      x.shape.length >= 5) {
-    // Even thought string tensor is always on CPU, just to be consistent on how
-    // to access tensor data.
-    const data = backend.readSync(x.dataId);
-    const value = x.dtype === 'string' ?
-        (data as Uint8Array[]).map(d => util.decodeString(d)) :
-        data as TypedArray;
-    const buf = buffer(x.shape, x.dtype, value);
-    const outBuf = tileImplCPU(buf, reps);
-    return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
-  }
-
-  const program = new TileProgram(x.shape, reps);
-  const output = backend.runWebGPUProgram(program, [x], x.dtype);
-
-  return output;
-}
-
-export const tileConfig: KernelConfig = {
-  kernelName: Tile,
-  backendName: 'webgpu',
-  kernelFunc: tile as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/TopK.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/TopK.ts
deleted file mode 100644
index 7a6085732..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/TopK.ts
+++ /dev/null
@@ -1,180 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, NumericDataType, TensorInfo, TopK, TopKAttrs, TopKInputs, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {topKImplCPU} from '../kernel_utils/shared';
-import {MergeProgram, SwapProgram} from '../top_k_webgpu';
-import {fill} from './Fill';
-import {gatherV2} from './GatherV2';
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-
-function disposeIntermediateTensorInfoOrNull(
-    backend: WebGPUBackend, tensorInfo: TensorInfo) {
-  if (tensorInfo !== null) {
-    backend.disposeData(tensorInfo.dataId);
-  }
-}
-
-function roundUpToPow2(num: number) {
-  let pow2 = 1;
-  while (pow2 < num) {
-    pow2 *= 2;
-  }
-  return pow2;
-}
-
-// Based on Algorithm 2 of Bitonic Top K, ref:
-// https://anilshanbhag.in/static/papers/gputopk_sigmod18.pdf
-export function topK(
-    args: {inputs: TopKInputs, backend: WebGPUBackend, attrs: TopKAttrs}):
-    TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {k, sorted}= attrs;
-
-  const xShape = x.shape;
-  const lastDim = xShape[xShape.length - 1];
-
-  if (backend.shouldExecuteOnCPU([x])) {
-    const xVals = backend.readSync(x.dataId) as TypedArray;
-    const [allTopKVals, allTopKIndices] =
-        topKImplCPU(xVals, xShape, x.dtype as NumericDataType, k, sorted);
-
-    return [
-      backend.makeTensorInfo(
-          allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
-      backend.makeTensorInfo(
-          allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
-    ];
-  }
-
-  if (k === 0) {
-    xShape[xShape.length - 1] = 0;
-    return [
-      backend.makeTensorInfo(xShape, x.dtype, []),
-      backend.makeTensorInfo(xShape, 'int32', [])
-    ];
-  }
-
-  if (lastDim === 1 /* firstPass */) {
-    return [
-      x, fill({attrs: {shape: xShape, dtype: 'int32', value: 0}, backend})
-    ];
-  }
-
-  // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.
-  const xSize = util.sizeFromShape(xShape);
-  const batch = xSize / lastDim;
-  const x2D = reshape({inputs: {x}, attrs: {shape: [batch, lastDim]}, backend});
-
-  const kPow2 = roundUpToPow2(k);
-  const lastDimPow2 = roundUpToPow2(lastDim);
-
-  // Only the indices containing the top K are kept at every step to reduce
-  // number of outputs in the GPU algorithms, so once the final set of indices
-  // is computed then gather is used to grab the corresponding values
-  // from the original input.
-  let indices: TensorInfo = null;
-
-  // GPU algorithm always takes in an indices input but this input is not used
-  // on the first run of a GPU algorithm, therefore if indices is null we simply
-  // pass in x2D instead of it but the value will not actually be used
-  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];
-
-  const runSwap = (dir: number, inc: number, shape: number[]) => {
-    const inputs = getInputs();
-    const program = new SwapProgram(shape);
-    const firstPass = indices === null ? 1 : 0;
-    const uniformDataSwap = [
-        {type: 'int32', data: [lastDim]},
-        {type: 'int32', data: [firstPass]},
-        {type: 'float32', data: [Number.NEGATIVE_INFINITY]},
-        {type: 'int32', data: [dir]},
-        {type: 'int32', data: [inc]}
-    ];
-    const prevIndices = indices;
-    indices = backend.runWebGPUProgram(
-        program, inputs, 'int32', uniformDataSwap);
-    disposeIntermediateTensorInfoOrNull(backend, prevIndices);
-  };
-
-  // Step 1: local sort
-  for (let len = 1; len < kPow2; len *= 2) {
-    const dir = len * 2;
-    for (let inc = len; inc >= 1; inc /= 2) {
-      runSwap(dir, inc, [batch, lastDimPow2]);
-    }
-  }
-
-  // Step 2: merge
-  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {
-    const inputs = getInputs();
-    const mergeProgram = new MergeProgram([batch, indicesSize / 2]);
-    const firstPass = indices === null ? 1 : 0;
-    const uniformDataMerge = [
-        {type: 'int32', data: [lastDim]},
-        {type: 'int32', data: [firstPass]},
-        {type: 'int32', data: [kPow2]}
-    ];
-    const prevIndices = indices;
-    indices = backend.runWebGPUProgram(
-        mergeProgram, inputs, 'int32', uniformDataMerge);
-    disposeIntermediateTensorInfoOrNull(backend, prevIndices);
-
-    // Step 3: rebuild
-    const len = kPow2 / 2;
-    const dir = len * 2;
-    for (let inc = len; inc >= 1; inc /= 2) {
-      runSwap(dir, inc, indices.shape);
-    }
-  }
-
-  // Keep only the requested top K results instead of kPow2
-  let prevIndices = indices;
-  indices = slice(
-      {inputs: {x: indices}, backend, attrs: {begin: 0, size: [batch, k]}});
-  disposeIntermediateTensorInfoOrNull(backend, prevIndices);
-
-  // Gather values on last dimension
-  let values = gatherV2(
-      {inputs: {x: x2D, indices}, backend, attrs: {axis: 1, batchDims: 1}});
-  disposeIntermediateTensorInfoOrNull(backend, x2D);
-
-  // Reshape back to the original input shape, except that the last
-  // dimension is k.
-  const newShape = xShape.slice(0, -1);
-  newShape.push(k);
-
-  prevIndices = indices;
-  indices = reshape({inputs: {x: indices}, attrs: {shape: newShape}, backend});
-  disposeIntermediateTensorInfoOrNull(backend, prevIndices);
-
-  const prevValues = values;
-  values = reshape({inputs: {x: values}, attrs: {shape: newShape}, backend});
-  disposeIntermediateTensorInfoOrNull(backend, prevValues);
-
-  return [values, indices];
-}
-
-export const topKConfig: KernelConfig = {
-  kernelName: TopK,
-  backendName: 'webgpu',
-  kernelFunc: topK as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Transform.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Transform.ts
deleted file mode 100644
index b3d6fd160..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Transform.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Transform, TransformAttrs, TransformInputs} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {TransformProgram} from '../transform_webgpu';
-
-export function transform(args: {
-  inputs: TransformInputs,
-  backend: WebGPUBackend,
-  attrs: TransformAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {image, transforms} = inputs;
-  const {interpolation, fillMode, fillValue, outputShape} = attrs;
-
-  const [batch, imageHeight, imageWidth, numChannels] = image.shape;
-  const [outHeight, outWidth] =
-      outputShape != null ? outputShape : [imageHeight, imageWidth];
-  const outShape =
-      [batch, outHeight, outWidth,
-       numChannels] as [number, number, number, number];
-
-  const program = new TransformProgram(outShape);
-  const interpolationModeId = interpolation === 'nearest' ? 1 : 2;
-  let fillModeId: number;
-  switch (fillMode) {
-    case 'constant':
-      fillModeId = 1;
-      break;
-    case 'reflect':
-      fillModeId = 2;
-      break;
-    case 'wrap':
-      fillModeId = 3;
-      break;
-    case 'nearest':
-      fillModeId = 4;
-      break;
-    default:
-      fillModeId = 1;
-      break;
-  }
-  const uniformData = [
-    {type: 'int32', data: [interpolationModeId]},
-    {type: 'int32', data: [fillModeId]}, {type: 'float32', data: [fillValue]}
-  ];
-  return backend.runWebGPUProgram(
-      program, [image, transforms], 'float32', uniformData);
-}
-
-export const transformConfig: KernelConfig = {
-  kernelName: Transform,
-  backendName: 'webgpu',
-  kernelFunc: transform as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Transpose.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Transpose.ts
deleted file mode 100644
index 2f3a6ead3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Transpose.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Transpose, TransposeAttrs, TransposeInputs, TypedArray, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {transposeImplCPU as cpuTranspose} from '../kernel_utils/shared';
-
-import {TransposeSharedProgram} from '../transpose_shared_webgpu';
-import {TransposeProgram} from '../transpose_webgpu';
-
-export function transpose(args: {
-  inputs: TransposeInputs,
-  attrs: TransposeAttrs,
-  backend: WebGPUBackend
-}) {
-  const {inputs, backend, attrs} = args;
-  const {x} = inputs;
-  const {perm} = attrs;
-  const webgpuBackend = backend;
-
-  const xRank = x.shape.length;
-  const newShape: number[] = new Array(xRank);
-  for (let i = 0; i < newShape.length; i++) {
-    newShape[i] = x.shape[perm[i]];
-  }
-  if (backend.shouldExecuteOnCPU([x])) {
-    const xData = webgpuBackend.tensorMap.get(x.dataId);
-    const values = xData.values as TypedArray;
-    const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);
-    return backend.makeTensorInfo(newShape, x.dtype, outValues);
-  }
-  if (x.shape.length === 2 && util.arraysEqual(perm, [1, 0])) {
-    const program = new TransposeSharedProgram(x.shape, perm);
-    return webgpuBackend.runWebGPUProgram(program, [x], x.dtype);
-  }
-  const program = new TransposeProgram(x.shape, perm);
-  return webgpuBackend.runWebGPUProgram(program, [x], x.dtype);
-}
-
-export const transposeConfig: KernelConfig = {
-  kernelName: Transpose,
-  backendName: 'webgpu',
-  kernelFunc: transpose as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/Unpack.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/Unpack.ts
deleted file mode 100644
index e77026967..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/Unpack.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, Unpack, UnpackAttrs, UnpackInputs} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {reshape} from './Reshape';
-import {slice} from './Slice';
-
-export function unpack(
-    args:
-        {inputs: UnpackInputs, backend: WebGPUBackend, attrs: UnpackAttrs}):
-    TensorInfo[] {
-  const {inputs, backend, attrs} = args;
-  const {value} = inputs;
-  let {axis} = attrs;
-
-  if (axis < 0) {
-    axis += value.shape.length;
-  }
-
-  const x = value;
-  const xRank = x.shape.length;
-
-  const num = value.shape[axis];
-  const outShape: number[] = new Array(xRank - 1);
-  let outIndex = 0;
-  for (let i = 0; i < xRank; i++) {
-    if (i !== axis) {
-      outShape[outIndex++] = x.shape[i];
-    }
-  }
-
-  const toDispose = [];
-
-  const begin = new Array(xRank).fill(0);
-  const size = x.shape.slice();
-  size[axis] = 1;
-  const res: TensorInfo[] = new Array(num);
-  for (let i = 0; i < res.length; i++) {
-    begin[axis] = i;
-    const sliced = slice({inputs: {x}, backend, attrs: {begin, size}});
-    const reshaped =
-        reshape({inputs: {x: sliced}, backend, attrs: {shape: outShape}});
-    res[i] = reshaped;
-
-    toDispose.push(sliced);
-  }
-
-  toDispose.forEach(t => backend.disposeData(t.dataId));
-  return res;
-}
-
-export const unpackConfig: KernelConfig = {
-  kernelName: Unpack,
-  backendName: 'webgpu',
-  kernelFunc: unpack as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/UnsortedSegmentSum.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/UnsortedSegmentSum.ts
deleted file mode 100644
index 670a5382e..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/UnsortedSegmentSum.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, KernelFunc, TensorInfo, UnsortedSegmentSum, UnsortedSegmentSumAttrs, UnsortedSegmentSumInputs, util} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {UnsortedSegmentSumProgram} from '../unsorted_segment_sum_webgpu';
-
-import {fill} from './Fill';
-import {reshape} from './Reshape';
-import {transpose} from './Transpose';
-
-export function unsortedSegmentSum(args: {
-  inputs: UnsortedSegmentSumInputs,
-  backend: WebGPUBackend,
-  attrs: UnsortedSegmentSumAttrs
-}): TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {x, segmentIds} = inputs;
-  const {numSegments} = attrs;
-
-  const xRank = x.shape.length;
-
-  const toDispose = [];
-
-  let axis = 0;
-  const permutation = backend_util.getAxesPermutation([axis], xRank);
-  let permutedX = x;
-  if (permutation != null) {
-    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutation}});
-    toDispose.push(permutedX);
-    axis = backend_util.getInnerMostAxes(1, xRank)[0];
-  }
-
-  const outShape = backend_util.segment_util.computeOutShape(
-      permutedX.shape, axis, numSegments);
-  const inSize = util.sizeFromShape([permutedX.shape[axis]]);
-  const a2D =
-      reshape({inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});
-  toDispose.push(a2D);
-
-  const dtype = x.dtype;
-  const shape = [a2D.shape[0], numSegments];
-  const output = fill({backend, attrs: {shape, value: 0, dtype}});
-  const program = new UnsortedSegmentSumProgram(a2D.shape, shape, dtype);
-  const uniformData = [
-    {type: 'int32', data: [numSegments]},
-    {type: 'int32', data: [util.sizeFromShape(a2D.shape)]}
-  ];
-  const segResult = backend.runWebGPUProgram(
-      program, [a2D, segmentIds], dtype, uniformData, output);
-
-  const reshaped =
-      reshape({inputs: {x: segResult}, backend, attrs: {shape: outShape}});
-  toDispose.push(segResult);
-  let result = reshaped;
-  if (permutation != null) {
-    toDispose.push(reshaped);
-    const perm = backend_util.getUndoAxesPermutation(permutation);
-    result = transpose({inputs: {x: result}, backend, attrs: {perm}});
-  }
-
-  toDispose.forEach(t => backend.disposeData(t.dataId));
-  return result;
-}
-
-export const unsortedSegmentSumConfig: KernelConfig = {
-  kernelName: UnsortedSegmentSum,
-  backendName: 'webgpu',
-  kernelFunc: unsortedSegmentSum as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/ZerosLike.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/ZerosLike.ts
deleted file mode 100644
index ad64794b1..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/ZerosLike.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, TensorInfo, ZerosLike, ZerosLikeInputs} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-
-import {complex} from './Complex';
-import {fill} from './Fill';
-import {imag} from './Imag';
-import {real} from './Real';
-
-export function zerosLike(
-    args: {inputs: ZerosLikeInputs, backend: WebGPUBackend}): TensorInfo {
-  const {inputs, backend} = args;
-  const {x} = inputs;
-  if (x.dtype === 'complex64') {
-    const realPart = real({inputs: {input: x}, backend});
-    const r = zerosLike({inputs: {x: realPart}, backend});
-    const imagPart = imag({inputs: {input: x}, backend});
-    const i = zerosLike({inputs: {x: imagPart}, backend});
-
-    const result = complex({inputs: {real: r, imag: i}, backend});
-
-    backend.disposeData(realPart.dataId);
-    backend.disposeData(r.dataId);
-    backend.disposeData(imagPart.dataId);
-    backend.disposeData(i.dataId);
-
-    return result;
-  } else {
-    return fill({
-      attrs: {
-        shape: x.shape,
-        dtype: x.dtype,
-        value: x.dtype === 'string' ? '' : 0
-      },
-      backend
-    });
-  }
-}
-
-export const zerosLikeConfig: KernelConfig = {
-  kernelName: ZerosLike,
-  backendName: 'webgpu',
-  kernelFunc: zerosLike as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/_FusedMatMul.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/_FusedMatMul.ts
deleted file mode 100644
index d377480b1..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/_FusedMatMul.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';
-
-import {WebGPUBackend} from '../backend_webgpu';
-import {batchMatMulImpl} from './BatchMatMul_impl';
-
-export function _fusedMatMul(args: {
-  inputs: _FusedMatMulInputs,
-  attrs: _FusedMatMulAttrs,
-  backend: WebGPUBackend
-}) {
-  const {inputs, backend, attrs} = args;
-  const {a, b, bias, preluActivationWeights} = inputs;
-  const {transposeA, transposeB, activation, leakyreluAlpha} = attrs;
-
-  return batchMatMulImpl({
-    a,
-    b,
-    transposeA,
-    transposeB,
-    backend,
-    bias,
-    preluActivationWeights,
-    leakyreluAlpha,
-    activation
-  });
-}
-
-export const _fusedMatMulConfig: KernelConfig = {
-  kernelName: _FusedMatMul,
-  backendName: 'webgpu',
-  kernelFunc: _fusedMatMul as unknown as KernelFunc,
-};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/conv2d_webgpu_test.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/conv2d_webgpu_test.ts
deleted file mode 100644
index 0eb818715..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/conv2d_webgpu_test.ts
+++ /dev/null
@@ -1,861 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-import {describeWebGPU} from '../test_util';
-
-function generateCaseInputs(totalSizeTensor: number, totalSizeFilter: number) {
-  const inp = new Array(totalSizeTensor);
-  const filt = new Array(totalSizeFilter);
-
-  for (let i = 0; i < totalSizeTensor; i++) {
-    inp[i] = i + 1;
-  }
-  for (let i = 0; i < totalSizeFilter; i++) {
-    filt[i] = i + 1;
-  }
-
-  return {input: inp, filter: filt};
-}
-
-describeWebGPU('im2col as separate shader', () => {
-  let flag: boolean;
-  beforeAll(() => {
-    flag = tf.env().getBool('WEBGPU_CONV_SEPARATE_IM2COL_SHADER');
-    tf.env().set('WEBGPU_CONV_SEPARATE_IM2COL_SHADER', true);
-  });
-
-  afterAll(() => {
-    tf.env().set('WEBGPU_CONV_SEPARATE_IM2COL_SHADER', flag);
-  });
-
-  it('x=[4,4,1] f=[2,2,1,1] s=1 d=2 p=0', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const fSizeDilated = 3;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 2;
-    const noDilation = 1;
-
-    const x = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 2], [fSize, fSize, inputDepth, outputDepth]);
-    // adding a dilation rate is equivalent to using a filter
-    // with 0s for the dilation rate
-    const wDilated = tf.tensor4d(
-        [3, 0, 1, 0, 0, 0, 5, 0, 2],
-        [fSizeDilated, fSizeDilated, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-    const expectedResult =
-        tf.conv2d(x, wDilated, stride, pad, dataFormat, noDilation);
-
-    expect(result.shape).toEqual(expectedResult.shape);
-    test_util.expectArraysClose(
-        await result.data(), await expectedResult.data());
-    expect(result.shape).toEqual(expectedResult.shape);
-    expect(result.dtype).toBe(expectedResult.dtype);
-  });
-
-  it('x=[1,4,4,1] f=[1,1,1,3] s=2 d=1 p=same', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 3;
-    const fSize = 1;
-    const pad = 'same';
-    const stride: [number, number] = [2, 2];
-
-    const x = tf.tensor3d(
-        [
-          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
-        ],
-        inputShape);
-    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    test_util.expectArraysClose(
-        await result.data(),
-        [10, 5, 10, 50, 25, 50, -10, -5, -10, -50, -25, -50]);
-  });
-
-  it('x=[2,2,1] f=[1,1,1,2] s=1 d=1 p=0', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    test_util.expectArraysClose(await result.data(), [2, 4, 6, 8]);
-  });
-
-  it('x=[3,3,2] f=[2,2,2,1] s=1 d=1 p=valid', async () => {
-    const pad = 'valid';
-    const stride = 1;
-
-    const x = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90],
-        [3, 3, 2]);
-    const w = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8], [2, 2, 2, 1]);
-    const result = tf.conv2d(x, w, stride, pad);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([2, 2, 1]);
-    test_util.expectArraysClose(
-        resultData, new Float32Array([25.6, 53.5, 157.0, 220.9]));
-  });
-
-  it('x=[4,2,1] f=[4,2,1,1] s=1 d=1 p=same', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2, inputDepth]);
-    const w =
-        tf.tensor4d([3, 1, 5, 0, 2, 7, 8, 9], [4, 2, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-    const resultData = await result.data();
-    expect(result.shape).toEqual([4, 2, 1]);
-    test_util.expectArraysClose(
-        resultData, [133, 66, 200, 102, 108, 58, 56, 58]);
-  });
-
-  it('x=[2,2,1] f=[2,2,1,1] s=1 d=1 p=same', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([2, 2, 1]);
-    test_util.expectArraysClose(resultData, new Float32Array([20, 26, 13, 12]));
-  });
-
-  it('x=[2,2,1] f=[2,2,1,1] s=1 d=1 p=0', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-    test_util.expectArraysClose(await result.data(), [20]);
-  });
-
-  it('x=[1,3,6,1] f=[2,2,1,1] s=[1,2] d=1 p=valid', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number, number] = [1, 3, 6, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'valid';
-    const stride: [number, number] = [1, 2];
-
-    const inputs = generateCaseInputs(1 * 3 * 6 * inputDepth, fSize * fSize);
-    const x = tf.tensor4d(inputs.input, inputShape);
-    const w =
-        tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    test_util.expectArraysClose(
-        await result.data(), [58.0, 78.0, 98.0, 118.0, 138.0, 158.0]);
-  });
-});
-
-describeWebGPU('conv2d vec4', () => {
-  it('conv2d vec4 follows a conv2d vec4 with inChannels=3', async () => {
-    const pad = 'valid';
-    const stride = 1;
-
-    const inputData = [];
-    for (let i = 0; i < 4 * 4 * 3; i++) {
-      inputData.push(i % 5);
-    }
-    const wData1 = [];
-    for (let i = 0; i < 2 * 2 * 3 * 4; i++) {
-      wData1.push(i % 5);
-    }
-    const wData2 = [];
-    for (let i = 0; i < 2 * 2 * 4 * 4; i++) {
-      wData2.push(i % 5);
-    }
-
-    const x = tf.tensor3d(inputData, [4, 4, 3]);
-    const w = tf.tensor4d(wData1, [2, 2, 3, 4]);
-    const result = tf.conv2d(x, w, stride, pad);
-    const resultData = await result.data();
-    expect(result.shape).toEqual([3, 3, 4]);
-    test_util.expectArraysClose(
-        resultData, new Float32Array([
-          53, 50, 47, 34, 30, 33, 51, 59, 62, 46, 35, 39,
-          61, 32, 38, 59, 53, 50, 47, 34, 30, 33, 51, 59,
-          34, 49, 59, 59, 61, 32, 38, 59, 53, 50, 47, 34
-        ]));
-
-    const w2 = tf.tensor4d(wData2, [2, 2, 4, 4]);
-    const result2 = tf.conv2d(result, w2, stride, pad);
-    expect(result2.shape).toEqual([2, 2, 4]);
-    const result2Data = await result2.data();
-    test_util.expectArraysClose(
-        result2Data, new Float32Array([
-          1516, 1447, 1383, 1389, 1221, 1423, 1535, 1522, 1341, 1416, 1516,
-          1656, 1516, 1447, 1383, 1389
-        ]));
-  });
-
-  it('x=[1,9,9,3] f=[3,3,3,4] s=[2,2] d=1 p=same', async () => {
-    const inputDepth = 3;
-    const xSize = 9;
-    const inputShape: [number, number, number, number] =
-        [1, xSize, xSize, inputDepth];
-    const outputDepth = 4;
-    const fSize = 3;
-    const pad = 'same';
-    const stride: [number, number] = [2, 2];
-
-    const inputData = [];
-    for (let i = 0; i < xSize * xSize * inputDepth; i++) {
-      inputData.push(i % 5);
-    }
-
-    const wData = [];
-    for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
-      wData.push(i % 5);
-    }
-
-    const x = tf.tensor4d(inputData, inputShape);
-    const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 5, 5, 4]);
-    const resData = await result.data();
-    test_util.expectArraysClose(
-        resData, new Float32Array([
-          53,  35,  42,  39,  54,  58,  62,  86,  74,  61,  78, 90,  59,
-          74,  74,  69,  52,  56,  50,  44,  75,  87,  69,  71, 115, 97,
-          114, 91,  104, 108, 102, 106, 123, 99,  95,  126, 59, 64,  79,
-          69,  66,  70,  69,  63,  101, 116, 111, 116, 115, 97, 114, 91,
-          104, 108, 102, 106, 71,  68,  75,  87,  87,  63,  59, 75,  97,
-          90,  108, 111, 101, 116, 111, 116, 115, 97,  114, 91, 63,  72,
-          66,  70,  46,  61,  61,  36,  83,  79,  55,  76,  80, 54,  58,
-          62,  67,  74,  61,  78,  42,  39,  51,  53
-        ]));
-  });
-
-  it('bool cast float32 + conv2d', async () => {
-    const im2colFlag = tf.env().getBool('WEBGPU_CONV_SEPARATE_IM2COL_SHADER');
-    const thresholdFlag =
-        tf.env().getBool('WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL');
-    tf.env().set('WEBGPU_CONV_SEPARATE_IM2COL_SHADER', false);
-    // Setting the threshold to 0 is like skipping the corresponding
-    // optimization.
-    tf.env().set('WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL', 0);
-    const a1 = tf.tensor4d([3, 2, 3, 4, 3, 2], [1, 2, 3, 1]);
-    const b1 = tf.tensor4d([2, 2, 2, 1, 4, 1], [1, 2, 3, 1]);
-    // Generate a bool tensor and have data in GPU.
-    const a = tf.greater(a1, b1);
-    // Cast the bool tensor to a float32 tensor.
-    const x = tf.cast(a, 'float32') as tf.Tensor4D;
-    const w = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 1, 2]);
-    const res = tf.conv2d(x, w, 1, 0);
-    const resData = await res.data();
-    test_util.expectArraysClose(resData, new Float32Array([6, 8, 10, 12]));
-    tf.env().set('WEBGPU_CONV_SEPARATE_IM2COL_SHADER', im2colFlag);
-    tf.env().set(
-        'WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL', thresholdFlag);
-  });
-
-  it('x=[1,9,9,3] f=[3,3,3,4] s=[2,2] d=1 p=valid NCHW', async () => {
-    const inputDepth = 3;
-    const xSize = 9;
-    const inputShape: [number, number, number, number] =
-        [1, xSize, xSize, inputDepth];
-    const outputDepth = 4;
-    const fSize = 3;
-    const pad = 'valid';
-    const stride: [number, number] = [2, 2];
-
-    const inputData = [];
-    for (let i = 0; i < xSize * xSize * inputDepth; i++) {
-      inputData.push(i % 5);
-    }
-
-    const wData = [];
-    for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
-      wData.push(i % 5);
-    }
-
-    const x = tf.tensor4d(inputData, inputShape);
-    const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 4, 4, 4]);
-    const resData = await result.data();
-    test_util.expectArraysClose(
-        resData, new Float32Array([
-          115, 97,  114, 91,  104, 108, 102, 106, 123, 99,  95,  126, 97,
-          90,  108, 111, 101, 116, 111, 116, 115, 97,  114, 91,  104, 108,
-          102, 106, 123, 99,  95,  126, 97,  90,  108, 111, 101, 116, 111,
-          116, 115, 97,  114, 91,  104, 108, 102, 106, 123, 99,  95,  126,
-          97,  90,  108, 111, 101, 116, 111, 116, 115, 97,  114, 91
-        ]));
-  });
-
-  it('x=[1,5,5,6] f=[3,3,6,4] s=[2,2] d=1 p=same', async () => {
-    const inputDepth = 6;
-    const xSize = 5;
-    const inputShape: [number, number, number, number] =
-        [1, xSize, xSize, inputDepth];
-    const outputDepth = 4;
-    const fSize = 3;
-    const pad = 'same';
-    const stride: [number, number] = [2, 2];
-
-    const inputData = [];
-    for (let i = 0; i < xSize * xSize * inputDepth; i++) {
-      inputData.push(i % 5);
-    }
-
-    const wData = [];
-    for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
-      wData.push(i % 5);
-    }
-
-    const x = tf.tensor4d(inputData, inputShape);
-    const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 3, 3, 4]);
-    const resData = await result.data();
-    test_util.expectArraysClose(
-        resData, new Float32Array([
-          92,  74,  86,  73,  140, 132, 164, 156, 124, 123, 97,  106,
-          115, 118, 136, 124, 232, 220, 228, 196, 180, 146, 147, 173,
-          73,  95,  92,  74,  156, 128, 140, 132, 106, 90,  124, 123
-        ]));
-  });
-
-  it('conv2d x=[1,8,8,3] f=[3,3,3,64] s=[2,2] d=1 p=valid Conv2DMMVec4Program remainder != 0',
-     async () => {
-       const inputDepth = 3;
-       const xSize = 8;
-       const inputShape: [number, number, number, number] =
-           [1, xSize, xSize, inputDepth];
-       const outputDepth = 64;
-       const fSize = 3;
-       const pad = 'valid';
-       const stride: [number, number] = [2, 2];
-
-       const inputData = [];
-       for (let i = 0; i < xSize * xSize * inputDepth; i++) {
-         inputData.push(i % 5);
-       }
-
-       const wData = [];
-       for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
-         wData.push(i % 5);
-       }
-
-       const x = tf.tensor4d(inputData, inputShape);
-       const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
-
-       const result = tf.conv2d(x, w, stride, pad);
-       expect(result.shape).toEqual([1, 3, 3, 64]);
-       test_util.expectArraysClose(
-           await result.data(), new Float32Array([
-             104, 125, 126, 102, 53,  104, 125, 126, 102, 53,  104, 125, 126,
-             102, 53,  104, 125, 126, 102, 53,  104, 125, 126, 102, 53,  104,
-             125, 126, 102, 53,  104, 125, 126, 102, 53,  104, 125, 126, 102,
-             53,  104, 125, 126, 102, 53,  104, 125, 126, 102, 53,  104, 125,
-             126, 102, 53,  104, 125, 126, 102, 53,  104, 125, 126, 102, 133,
-             126, 104, 57,  110, 133, 126, 104, 57,  110, 133, 126, 104, 57,
-             110, 133, 126, 104, 57,  110, 133, 126, 104, 57,  110, 133, 126,
-             104, 57,  110, 133, 126, 104, 57,  110, 133, 126, 104, 57,  110,
-             133, 126, 104, 57,  110, 133, 126, 104, 57,  110, 133, 126, 104,
-             57,  110, 133, 126, 104, 57,  110, 133, 126, 104, 57,  137, 102,
-             57,  112, 142, 137, 102, 57,  112, 142, 137, 102, 57,  112, 142,
-             137, 102, 57,  112, 142, 137, 102, 57,  112, 142, 137, 102, 57,
-             112, 142, 137, 102, 57,  112, 142, 137, 102, 57,  112, 142, 137,
-             102, 57,  112, 142, 137, 102, 57,  112, 142, 137, 102, 57,  112,
-             142, 137, 102, 57,  112, 142, 137, 102, 57,  112, 116, 53,  110,
-             142, 149, 116, 53,  110, 142, 149, 116, 53,  110, 142, 149, 116,
-             53,  110, 142, 149, 116, 53,  110, 142, 149, 116, 53,  110, 142,
-             149, 116, 53,  110, 142, 149, 116, 53,  110, 142, 149, 116, 53,
-             110, 142, 149, 116, 53,  110, 142, 149, 116, 53,  110, 142, 149,
-             116, 53,  110, 142, 149, 116, 53,  110, 142, 50,  104, 133, 137,
-             116, 50,  104, 133, 137, 116, 50,  104, 133, 137, 116, 50,  104,
-             133, 137, 116, 50,  104, 133, 137, 116, 50,  104, 133, 137, 116,
-             50,  104, 133, 137, 116, 50,  104, 133, 137, 116, 50,  104, 133,
-             137, 116, 50,  104, 133, 137, 116, 50,  104, 133, 137, 116, 50,
-             104, 133, 137, 116, 50,  104, 133, 137, 104, 125, 126, 102, 53,
-             104, 125, 126, 102, 53,  104, 125, 126, 102, 53,  104, 125, 126,
-             102, 53,  104, 125, 126, 102, 53,  104, 125, 126, 102, 53,  104,
-             125, 126, 102, 53,  104, 125, 126, 102, 53,  104, 125, 126, 102,
-             53,  104, 125, 126, 102, 53,  104, 125, 126, 102, 53,  104, 125,
-             126, 102, 53,  104, 125, 126, 102, 133, 126, 104, 57,  110, 133,
-             126, 104, 57,  110, 133, 126, 104, 57,  110, 133, 126, 104, 57,
-             110, 133, 126, 104, 57,  110, 133, 126, 104, 57,  110, 133, 126,
-             104, 57,  110, 133, 126, 104, 57,  110, 133, 126, 104, 57,  110,
-             133, 126, 104, 57,  110, 133, 126, 104, 57,  110, 133, 126, 104,
-             57,  110, 133, 126, 104, 57,  137, 102, 57,  112, 142, 137, 102,
-             57,  112, 142, 137, 102, 57,  112, 142, 137, 102, 57,  112, 142,
-             137, 102, 57,  112, 142, 137, 102, 57,  112, 142, 137, 102, 57,
-             112, 142, 137, 102, 57,  112, 142, 137, 102, 57,  112, 142, 137,
-             102, 57,  112, 142, 137, 102, 57,  112, 142, 137, 102, 57,  112,
-             142, 137, 102, 57,  112, 116, 53,  110, 142, 149, 116, 53,  110,
-             142, 149, 116, 53,  110, 142, 149, 116, 53,  110, 142, 149, 116,
-             53,  110, 142, 149, 116, 53,  110, 142, 149, 116, 53,  110, 142,
-             149, 116, 53,  110, 142, 149, 116, 53,  110, 142, 149, 116, 53,
-             110, 142, 149, 116, 53,  110, 142, 149, 116, 53,  110, 142, 149,
-             116, 53,  110, 142
-           ]));
-     });
-
-  it('conv2d x=[1,8,8,64] f=[3,3,64,64] s=[1,1] d=1 p=same Conv2DMMVec4Program fitA == true and fitB == true',
-     async () => {
-       const inputDepth = 64;
-       const xSize = 8;
-       const inputShape: [number, number, number, number] =
-           [1, xSize, xSize, inputDepth];
-       const outputDepth = 64;
-       const fSize = 3;
-       const pad = 'same';
-       const stride: [number, number] = [1, 1];
-
-       const inputData = [];
-       for (let i = 0; i < xSize * xSize * inputDepth; i++) {
-         inputData.push(i % 5);
-       }
-
-       const wData = [];
-       for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
-         wData.push(i % 5);
-       }
-
-       const x = tf.tensor4d(inputData, inputShape);
-       const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
-
-       const result = tf.conv2d(x, w, stride, pad);
-       expect(result.shape).toEqual([1, 8, 8, 64]);
-       test_util.expectArraysClose(
-           await result.data(), new Float32Array([
-             514,  1026, 1283, 1275, 1022, 514,  1026, 1283, 1275, 1022, 514,
-             1026, 1283, 1275, 1022, 514,  1026, 1283, 1275, 1022, 514,  1026,
-             1283, 1275, 1022, 514,  1026, 1283, 1275, 1022, 514,  1026, 1283,
-             1275, 1022, 514,  1026, 1283, 1275, 1022, 514,  1026, 1283, 1275,
-             1022, 514,  1026, 1283, 1275, 1022, 514,  1026, 1283, 1275, 1022,
-             514,  1026, 1283, 1275, 1022, 514,  1026, 1283, 1275, 1524, 770,
-             1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536,
-             1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917,
-             1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913,
-             1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524,
-             770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,
-             1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1540, 767,  1534,
-             1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916,
-             1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913,
-             1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540,
-             767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,
-             1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534,
-             1916, 1913, 1540, 767,  1534, 1917, 1925, 1538, 766,  1534, 1917,
-             1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925,
-             1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538,
-             766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,
-             1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534,
-             1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917,
-             1925, 1538, 766,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924,
-             1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538,
-             767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,
-             1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536,
-             1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925,
-             1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924,
-             1538, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540,
-             770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,
-             1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540,
-             1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925,
-             1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925,
-             1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1524,
-             770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,
-             1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536,
-             1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917,
-             1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913,
-             1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524,
-             770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1276, 1022, 513,
-             1024, 1275, 1276, 1022, 513,  1024, 1275, 1276, 1022, 513,  1024,
-             1275, 1276, 1022, 513,  1024, 1275, 1276, 1022, 513,  1024, 1275,
-             1276, 1022, 513,  1024, 1275, 1276, 1022, 513,  1024, 1275, 1276,
-             1022, 513,  1024, 1275, 1276, 1022, 513,  1024, 1275, 1276, 1022,
-             513,  1024, 1275, 1276, 1022, 513,  1024, 1275, 1276, 1022, 513,
-             1024, 1275, 1276, 1022, 513,  1024, 1916, 1913, 1540, 767,  1534,
-             1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916,
-             1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913,
-             1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540,
-             767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,
-             1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534,
-             1916, 1913, 1540, 767,  2300, 2875, 2875, 2300, 1150, 2300, 2875,
-             2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875,
-             2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300,
-             1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150,
-             2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300,
-             2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875,
-             2875, 2300, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887,
-             2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316,
-             1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150,
-             2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304,
-             2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883,
-             2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887,
-             2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300,
-             1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153,
-             2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306,
-             2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884,
-             2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887,
-             2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2875, 2302,
-             1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154,
-             2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306,
-             2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883,
-             2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875,
-             2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302,
-             1154, 2306, 2883, 2875, 2302, 1154, 2306, 2875, 2876, 2302, 1153,
-             2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304,
-             2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875,
-             2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876,
-             2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302,
-             1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153,
-             2304, 2875, 2876, 2302, 1153, 2300, 2875, 2875, 2300, 1150, 2300,
-             2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875,
-             2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875,
-             2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300,
-             1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150,
-             2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300,
-             2875, 2875, 2300, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917,
-             1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925,
-             1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538,
-             766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,
-             1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534,
-             1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917,
-             1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925,
-             1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540,
-             770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,
-             1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540,
-             1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925,
-             1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 2875,
-             2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302,
-             1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154,
-             2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306,
-             2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883,
-             2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875,
-             2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2875, 2876, 2302,
-             1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153,
-             2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304,
-             2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875,
-             2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876,
-             2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302,
-             1153, 2304, 2875, 2876, 2302, 1153, 2300, 2875, 2875, 2300, 1150,
-             2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300,
-             2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875,
-             2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875,
-             2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300,
-             1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150,
-             2300, 2875, 2875, 2300, 1150, 2304, 2883, 2887, 2316, 1150, 2304,
-             2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883,
-             2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887,
-             2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316,
-             1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150,
-             2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304,
-             2883, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884,
-             2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887,
-             2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300,
-             1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153,
-             2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306,
-             2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884,
-             2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875,
-             2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302,
-             1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154,
-             2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306,
-             2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883,
-             2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 1917, 1913,
-             1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524,
-             770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,
-             1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536,
-             1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917,
-             1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913,
-             1524, 770,  1536, 1917, 1913, 1524, 770,  1534, 1917, 1925, 1538,
-             766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,
-             1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534,
-             1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917,
-             1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925,
-             1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538,
-             766,  1534, 1917, 1925, 1538, 1150, 2304, 2883, 2887, 2316, 1150,
-             2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304,
-             2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883,
-             2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887,
-             2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316,
-             1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150,
-             2304, 2883, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306,
-             2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884,
-             2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887,
-             2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300,
-             1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153,
-             2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306,
-             2884, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883,
-             2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875,
-             2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302,
-             1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154,
-             2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306,
-             2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2875,
-             2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876,
-             2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302,
-             1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153,
-             2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304,
-             2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875,
-             2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2300, 2875, 2875,
-             2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300,
-             1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150,
-             2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300,
-             2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875,
-             2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875,
-             2300, 1150, 2300, 2875, 2875, 2300, 1150, 2304, 2883, 2887, 2316,
-             1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150,
-             2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304,
-             2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883,
-             2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887,
-             2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316,
-             1150, 2304, 2883, 2887, 1538, 767,  1536, 1925, 1924, 1538, 767,
-             1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536,
-             1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925,
-             1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924,
-             1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538,
-             767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,
-             1536, 1925, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536,
-             1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917,
-             1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913,
-             1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524,
-             770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,
-             1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536,
-             2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875,
-             2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876,
-             2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302,
-             1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153,
-             2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304,
-             2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2300, 2875,
-             2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875,
-             2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300,
-             1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150,
-             2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300,
-             2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875,
-             2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2304, 2883, 2887,
-             2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316,
-             1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150,
-             2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304,
-             2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883,
-             2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887,
-             2316, 1150, 2304, 2883, 2887, 2300, 1153, 2306, 2884, 2887, 2300,
-             1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153,
-             2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306,
-             2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884,
-             2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887,
-             2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300,
-             1153, 2306, 2884, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154,
-             2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306,
-             2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883,
-             2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875,
-             2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302,
-             1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154,
-             2306, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304,
-             2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875,
-             2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876,
-             2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302,
-             1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153,
-             2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 1534,
-             1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916,
-             1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913,
-             1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540,
-             767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,
-             1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534,
-             1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1536, 1925,
-             1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924,
-             1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538,
-             767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,
-             1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536,
-             1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925,
-             1924, 1538, 767,  1536, 1925, 1924, 2300, 1153, 2306, 2884, 2887,
-             2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300,
-             1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153,
-             2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306,
-             2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884,
-             2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887,
-             2300, 1153, 2306, 2884, 2875, 2302, 1154, 2306, 2883, 2875, 2302,
-             1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154,
-             2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306,
-             2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883,
-             2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875,
-             2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302,
-             1154, 2306, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153,
-             2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304,
-             2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875,
-             2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876,
-             2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302,
-             1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153,
-             2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300,
-             2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875,
-             2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875,
-             2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300,
-             1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150,
-             2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2304,
-             2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883,
-             2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887,
-             2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316,
-             1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150,
-             2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304,
-             2883, 2887, 2316, 1150, 2304, 2883, 2887, 2300, 1153, 2306, 2884,
-             2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887,
-             2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300,
-             1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153,
-             2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306,
-             2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884,
-             2887, 2300, 1153, 2306, 2884, 1925, 1540, 770,  1540, 1925, 1925,
-             1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540,
-             770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,
-             1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540,
-             1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925,
-             1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925,
-             1540, 770,  1540, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540,
-             767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,
-             1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534,
-             1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916,
-             1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913,
-             1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540,
-             767,  2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150,
-             2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300,
-             2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875,
-             2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875,
-             2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300,
-             1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150,
-             2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304,
-             2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883,
-             2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887,
-             2316, 1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316,
-             1150, 2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2316, 1150,
-             2304, 2883, 2887, 2316, 1150, 2304, 2883, 2887, 2300, 1153, 2306,
-             2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884,
-             2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887,
-             2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300,
-             1153, 2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153,
-             2306, 2884, 2887, 2300, 1153, 2306, 2884, 2887, 2300, 1153, 2306,
-             2884, 2887, 2300, 1153, 2306, 2884, 2875, 2302, 1154, 2306, 2883,
-             2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875,
-             2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302,
-             1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154,
-             2306, 2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306,
-             2883, 2875, 2302, 1154, 2306, 2883, 2875, 2302, 1154, 2306, 2883,
-             2875, 2302, 1154, 2306, 2875, 2876, 2302, 1153, 2304, 2875, 2876,
-             2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302,
-             1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153,
-             2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304,
-             2875, 2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875,
-             2876, 2302, 1153, 2304, 2875, 2876, 2302, 1153, 2304, 2875, 2876,
-             2302, 1153, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300,
-             1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150,
-             2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300,
-             2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875,
-             2875, 2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875,
-             2300, 1150, 2300, 2875, 2875, 2300, 1150, 2300, 2875, 2875, 2300,
-             766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,
-             1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534,
-             1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917,
-             1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925,
-             1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538,
-             766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1036, 510,
-             1024, 1283, 1287, 1036, 510,  1024, 1283, 1287, 1036, 510,  1024,
-             1283, 1287, 1036, 510,  1024, 1283, 1287, 1036, 510,  1024, 1283,
-             1287, 1036, 510,  1024, 1283, 1287, 1036, 510,  1024, 1283, 1287,
-             1036, 510,  1024, 1283, 1287, 1036, 510,  1024, 1283, 1287, 1036,
-             510,  1024, 1283, 1287, 1036, 510,  1024, 1283, 1287, 1036, 510,
-             1024, 1283, 1287, 1036, 510,  1024, 1283, 1924, 1538, 767,  1536,
-             1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925,
-             1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924,
-             1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538,
-             767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,
-             1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536,
-             1925, 1924, 1538, 767,  1536, 1925, 1925, 1540, 770,  1540, 1925,
-             1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925,
-             1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540,
-             770,  1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,
-             1540, 1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540,
-             1925, 1925, 1540, 770,  1540, 1925, 1925, 1540, 770,  1540, 1925,
-             1925, 1540, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913,
-             1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524,
-             770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,
-             1536, 1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536,
-             1917, 1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917,
-             1913, 1524, 770,  1536, 1917, 1913, 1524, 770,  1536, 1917, 1913,
-             1524, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540,
-             767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,
-             1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534,
-             1916, 1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916,
-             1913, 1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913,
-             1540, 767,  1534, 1916, 1913, 1540, 767,  1534, 1916, 1913, 1538,
-             766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,
-             1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534,
-             1917, 1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917,
-             1925, 1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925,
-             1538, 766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1925, 1538,
-             766,  1534, 1917, 1925, 1538, 766,  1534, 1917, 1924, 1538, 767,
-             1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536,
-             1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925,
-             1924, 1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924,
-             1538, 767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538,
-             767,  1536, 1925, 1924, 1538, 767,  1536, 1925, 1924, 1538, 767,
-             1536, 1925, 1924, 1538, 767,  1536, 1284, 1287, 1020, 513,  1026,
-             1284, 1287, 1020, 513,  1026, 1284, 1287, 1020, 513,  1026, 1284,
-             1287, 1020, 513,  1026, 1284, 1287, 1020, 513,  1026, 1284, 1287,
-             1020, 513,  1026, 1284, 1287, 1020, 513,  1026, 1284, 1287, 1020,
-             513,  1026, 1284, 1287, 1020, 513,  1026, 1284, 1287, 1020, 513,
-             1026, 1284, 1287, 1020, 513,  1026, 1284, 1287, 1020, 513,  1026,
-             1284, 1287, 1020, 513
-           ]));
-     });
-});
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/depthwise_conv2d_webgpu_test.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/depthwise_conv2d_webgpu_test.ts
deleted file mode 100644
index 2f20699d7..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/depthwise_conv2d_webgpu_test.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-import {describeWebGPU} from '../test_util';
-
-const expectArraysClose = test_util.expectArraysClose;
-
-describeWebGPU('depthwise conv2d nchw', () => {
-  it('input=1x1x3x3,f=2,s=1,d=1,p=valid,chMul=1', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d([0, 1, 2, 3, 4, 5, 6, 7, 8], [1, inDepth, 3, 3]);
-    const w = tf.tensor4d(
-        [2, 3, 4, 5],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NCHW');
-    expect(result.shape).toEqual([1, 1, 2, 2]);
-    const expected = [35, 49, 77, 91];
-    const resValue = await result.data();
-    expectArraysClose(resValue, expected);
-  });
-
-  it('input=1x1x3x3,f=2,s=1,d=2,p=valid,chMul=1', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 2;
-
-    const x = tf.tensor4d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 2, 4, 6, 8, 0, 1, 3, 5, 7],
-        [1, inDepth, 3, 3]);
-    const w = tf.tensor4d(
-        [2, 6, 3, 7, 4, 8, 5, 9],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NCHW');
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [35, 49, 77, 91, 104, 75, 117, 110];
-    const resValue = await result.data();
-    expectArraysClose(resValue, expected);
-  });
-
-  it('input=1x1x5x5,f=5,s=1,d=2,p=same,chMul=1', async () => {
-    const fSize = 5;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7,
-          8, 0, 1, 2, 3, 4, 5, 6, 4, 5, 6, 7, 8, 0, 1, 2, 3,
-          4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3
-        ],
-        [1, inDepth, 5, 5]);
-    const w = tf.tensor4d(
-        [
-          3, 2, 1, 0, 3, 2, 1, 0, 4, 5, 3, 2, 1, 0, 3, 4, 2,
-          1, 0, 5, 4, 3, 2, 1, 0, 0, 3, 4, 2, 1, 0, 5, 4, 3,
-          2, 1, 0, 3, 2, 1, 0, 3, 2, 1, 0, 4, 5, 3, 2, 1
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NCHW');
-    expect(result.shape).toEqual([1, 2, 5, 5]);
-    const resValue = await result.data();
-    const expected = [
-      47,  85, 91,  95,  54,  93,  122, 109, 101, 91,  99,  145, 203,
-      166, 82, 105, 135, 144, 153, 121, 66,  95,  128, 95,  81,  69,
-      75,  97, 107, 57,  109, 134, 202, 166, 107, 126, 119, 212, 148,
-      116, 66, 104, 139, 85,  55,  52,  82,  122, 66,  47
-    ];
-    expectArraysClose(resValue, expected);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/pool_webgpu_test.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/pool_webgpu_test.ts
deleted file mode 100644
index 7dc3b7a3f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/pool_webgpu_test.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-import {describeWebGPU} from '../test_util';
-
-const expectArraysClose = test_util.expectArraysClose;
-
-describeWebGPU('pool', () => {
-  // For PoolWithFilterSizeEqualsOneProgram. This case will fail wasm, so keep
-  // it here. Wasm bug: https://github.com/tensorflow/tfjs/issues/5471.
-  // TODO(xing.xu): https://github.com/tensorflow/tfjs/issues/5506.
-  it('x=[4,4,1] f=[1,1] s=2 d=1', async () => {
-    // Feed forward.
-    const a = tf.tensor3d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
-
-    const windowShape = 1;
-    const padding = 0;
-    const dilationRate: number = undefined;
-    const strides = 2;
-
-    const result =
-        tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [0, 2, 8, 10]);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgpu/src/kernels/precision_webgpu_test.ts b/tfjs-master/tfjs-backend-webgpu/src/kernels/precision_webgpu_test.ts
deleted file mode 100644
index 9c9eacfd5..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/kernels/precision_webgpu_test.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-import {describeWebGPU} from '../test_util';
-
-const expectArraysClose = test_util.expectArraysClose;
-
-describeWebGPU('precision', () => {
-  it('float precision', async () => {
-    const batch = 262145;
-    const size = batch * 4 * 4 * 4;
-
-    const aData = new Float32Array(size);
-    const bData = new Float32Array(size / 4);
-    for (let i = 0; i < size; i++) {
-      aData[i] = i - 100;
-      if (i % 4 === 0) {
-        const iB = i / 4;
-        bData[iB] = iB - 100;
-      }
-    }
-
-    const aTensor = tf.tensor4d(aData, [batch, 4, 4, 4]);
-    const bTensor = tf.tensor4d(bData, [batch, 4, 4, 1]);
-
-    const gpuData = await tf.add(aTensor, bTensor).data();
-    const expected = [
-      20971312, 20971312, 20971316, 20971316, 20971316, 20971318, 20971320,
-      20971320, 20971322, 20971324, 20971324, 20971326, 20971328, 20971328,
-      20971330, 20971332, 20971332, 20971332, 20971336, 20971336
-    ];
-    expectArraysClose(Array.from(gpuData).slice(16777210, 16777230), expected);
-  });
-});
diff --git a/tfjs-master/tfjs-backend-webgpu/src/lin_space_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/lin_space_webgpu.ts
deleted file mode 100644
index 07668c2b4..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/lin_space_webgpu.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class LinSpaceProgram implements WebGPUProgram {
-  variableNames: string[] = [];
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  uniforms = 'start : f32, step : f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(shape: number) {
-    this.outputShape = [shape];
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'linSpace';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/lrn_grad_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/lrn_grad_webgpu.ts
deleted file mode 100644
index b065477f5..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/lrn_grad_webgpu.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class LRNGradProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['inputImage', 'outputImage', 'dy'];
-  uniforms = 'depthRadius : i32, bias : f32, alpha : f32, beta : f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(inputShape: number[]) {
-    this.outputShape = inputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = 'lrn_grad';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getOutputCoords();
-        let b = coords[0];
-        let r = coords[1];
-        let c = coords[2];
-
-        let MIN_DEPTH_BEGIN = 0;
-        let MAX_DEPTH_END = uniforms.outShape[3];
-        var result = 0.0;
-        for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {
-          let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);
-          let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);
-
-          var norm = 0.0;
-          for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {
-            if (k < depthBegin) {
-              continue;
-            } else if (k >= depthBegin && k < depthEnd) {
-              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
-            } else {
-              break;
-            }
-          }
-
-          norm = uniforms.alpha * norm + uniforms.bias;
-
-          for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {
-            if (k < depthBegin) {
-              continue;
-            } else if (k >= depthBegin && k < depthEnd) {
-              var dyi = -2.0 * uniforms.alpha * uniforms.beta
-                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;
-              if (k == d) {
-                dyi += pow(norm, -1.0 * uniforms.beta);
-              }
-              if (k == coords[3]) {
-                dyi *= getDy(b, r, c, d);
-                result += dyi;
-              }
-            } else {
-              break;
-            }
-          }
-        }
-
-        setOutputAtIndex(index, result);
-      }
-    }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/lrn_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/lrn_webgpu.ts
deleted file mode 100644
index 75773872d..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/lrn_webgpu.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-const powOperatorSnippet = `
-  var powValue = 0.0;
-  let basis = uniforms.bias + uniforms.alpha * sum;
-  if (uniforms.beta == 0.5) {
-    powValue = inverseSqrt(basis);
-  } else if (uniforms.beta == 1.0) {
-    powValue = 1.0 / basis;
-  } else {
-    powValue = exp(log(basis) * (-uniforms.beta));
-  }
-`;
-
-export class LRNProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms = 'radius : i32, bias : f32, alpha : f32, beta : f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(xShape: number[]) {
-    this.outputShape = xShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = 'lrn';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getOutputCoords();
-        let b = coords[0];
-        let r = coords[1];
-        let c = coords[2];
-        let d = coords[3];
-
-        let x = getX(b, r, c, d);
-        var sum = 0.0;
-        for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {
-          let idx = d + i;
-          if (idx >= 0 && idx < uniforms.xShape[3]) {
-            let z = getX(b, r, c, idx);
-            sum = sum + z * z;
-          }
-        }
-        ${powOperatorSnippet}
-
-        setOutputAtIndex(index, x * powValue);
-      }
-    }
-  `;
-    return userCode;
-  }
-}
-
-export class LRNSharedProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[], z: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms = 'radius : i32, bias : f32, alpha : f32, beta : f32,';
-  workgroupSize: [number, number, number] = [256, 1, 1];
-  maxAllowRadius = 16;
-  elementsPerWorkgroup: number;
-
-  constructor(xShape: number[], radius: number) {
-    util.assert(
-        radius <= this.maxAllowRadius,
-        () => `Radius must be less than or equal to ${
-            this.maxAllowRadius}, current radius is ${radius}`);
-
-    this.outputShape = xShape;
-    // The reason why not using this.workgroupSize[0] + 2 * maxAllowRadius here
-    // is to make sure that there is only one time global memory load access for
-    // each thread.
-    this.elementsPerWorkgroup = this.workgroupSize[0] - 2 * this.maxAllowRadius;
-    this.dispatchLayout = {x: [3], y: [2], z: [0, 1]};
-    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [
-      this.elementsPerWorkgroup, this.workgroupSize[1], this.workgroupSize[2]
-    ]);
-    this.shaderKey = 'lrn_shared';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    var <workgroup>lrnSub: array<f32, ${this.workgroupSize[0]}>;
-    const elementsPerWorkgroup = ${this.elementsPerWorkgroup};
-    const maxAllowRadius = ${this.maxAllowRadius};
-
-    ${main()} {
-      let localDepth = i32(localId.x);
-      let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;
-      let xDepth = workgroupDepth + localDepth - maxAllowRadius;
-      let b = i32(globalId.z) / uniforms.xShape[1];
-      let r = i32(globalId.z) - b * uniforms.xShape[1];
-      let c = i32(globalId.y);
-      let d = workgroupDepth + localDepth;
-
-      var x = 0.0;
-      if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {
-        x = getX(b, r, c, xDepth);
-      }
-      lrnSub[localDepth] = x;
-      workgroupBarrier();
-
-      if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {
-        var sum = 0.0;
-        let index = localDepth + maxAllowRadius;
-        for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {
-          let z = lrnSub[index + i];
-          sum = sum + z * z;
-        }
-        ${powOperatorSnippet}
-
-        setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);
-      }
-    } `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/matmul_packed_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/matmul_packed_webgpu.ts
deleted file mode 100644
index bfcf25bc3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/matmul_packed_webgpu.ts
+++ /dev/null
@@ -1,600 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {activationFnSnippet, biasActivationSnippet} from './activation_util';
-import {getMainHeaderString as main, typeSnippet, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, computeWorkgroupInfoForMatMul} from './webgpu_util';
-
-export function matMulReadFnSource(
-    transposeA: boolean, transposeB: boolean, fitAOuter = false,
-    fitBOuter = false, fitInner = false, component = 1) {
-  util.assert(
-      transposeA && component === 1 || !transposeA,
-      () => `transposeA ${transposeA} is not compatible with component size ${
-          component}`);
-  const sampleA = `
-      ${
-      transposeA ? `value = getA(batch, col, row);` :
-                   `value = getA(batch, row, col);`}
-
-    `;
-  const sampleB = transposeB ? `value = getB(batch, col, row);` :
-                               `value = getB(batch, row, col);`;
-
-  return `
-  fn mm_readA(batch: i32, row: i32, col: i32) -> ${typeSnippet(component)} {
-    var value = ${typeSnippet(component)}(0.0);
-    ${
-      fitAOuter && fitInner ?
-          sampleA :
-          `
-    ${
-              transposeA ?
-                  `if(row < uniforms.dimAOuter && col < uniforms.dimInner)` :
-                  `if(row < uniforms.aShape[1] && col < uniforms.aShape[2])`}
-    {
-      ${sampleA}
-    }
-    `}
-    return value;
-  }
-
-  fn mm_readB(batch: i32, row: i32, col: i32) -> ${typeSnippet(component)} {
-    var value = ${typeSnippet(component)}(0.0);
-    ${sampleB}
-    return value;
-  }
-  `;
-}
-
-export function matMulReadWriteFnSource(
-    hasBias: boolean, activation: backend_util.Activation, transposeA: boolean,
-    transposeB: boolean, fitAOuter = false, fitBOuter = false, fitInner = false,
-    component = 1) {
-  return `
-  ${
-      matMulReadFnSource(
-          transposeA, transposeB, fitAOuter, fitBOuter, fitInner, component)}
-  fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${
-      typeSnippet(component)}) {
-    ${
-      fitAOuter && fitBOuter ?
-          '' :
-          'if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)'}
-    {
-      var value = valueIn;
-      let coords = vec3<i32>(batch, row, col);
-      ${biasActivationSnippet(hasBias, activation)}
-      setOutputAtCoords(coords[0], coords[1], coords[2], value);
-    }
-  }
-  `;
-}
-
-const writeDataToSubAVec4Snippet =
-    (transpose: boolean, innerElementSize: number) => {
-      if (transpose) {
-        return `
-        mm_Asub[inputRow][inputCol] = mm_readA(batchA,
-          kStart + inputRow,
-          globalRowStart + inputCol * ${innerElementSize});
-        `;
-
-      } else {
-        return `
-        mm_Asub[inputRow][inputCol] = mm_readA(batchA,
-          globalRow + innerRow,
-          kStart + inputCol * ${innerElementSize});
-        `;
-      }
-    };
-
-const calculateResultSnippet =
-    (transposeA: boolean, innerElementSize: number, rowPerThread: number,
-     tileInner: number) => {
-      if (transposeA) {
-        return `
-      for (var k = 0; k < ${tileInner}; k++) {
-        let BCached0 = mm_Bsub[k][tileCol];
-        let ACached0 = mm_Asub[k][localRow];
-        for (var i = 0; i < ${rowPerThread}; i++) {
-          acc[i] = fma(BCached0, vec4<f32>(ACached0[i]), acc[i]);
-        }
-      }`;
-      } else {
-        let bCachedStr = '';
-        let accStr = '';
-        for (let i = 0; i < innerElementSize; i++) {
-          bCachedStr += `let BCached${i} = mm_Bsub[k * ${innerElementSize} + ${
-              i}][tileCol];`;
-          accStr +=
-              `acc[i] = fma(BCached${i}, vec4<f32>(ACached[${i}]), acc[i]);`;
-        }
-        return `
-      for (var k = 0; k < ${tileInner / innerElementSize}; k++) {
-        ${bCachedStr}
-        for (var i = 0; i < ${rowPerThread}; i++) {
-          let ACached = mm_Asub[tileRow + i][k];
-          ${accStr}
-        }
-      }`;
-      }
-    };
-
-export function makeMatMulPackedVec4Source(
-    workPerThread: number[], workgroupSize: [number, number, number],
-    transposeA = false, tileInner = 32, splitK = false, splitedDimInner = 32,
-    broadcastBatch = false): string {
-  const tileAOuter = workgroupSize[1] * workPerThread[1];
-  const tileBOuter = workgroupSize[0] * workPerThread[0];
-  const tileAWidth = transposeA ? tileAOuter : tileInner;
-  const tileAHight = transposeA ? tileInner : tileAOuter;
-  const innerElementSize = tileAWidth / workgroupSize[0];
-  const rowPerThreadB = tileInner / workgroupSize[1];
-  const rowPerThread = workPerThread[1];
-  const colPerThread = workPerThread[0];
-  util.assert(
-      ((transposeA && innerElementSize === 4 && workPerThread[1] === 4) ||
-       (!transposeA && (innerElementSize === 3 || innerElementSize === 4))) &&
-          tileAWidth % workgroupSize[0] === 0 &&
-          tileInner % workgroupSize[1] === 0 && workPerThread[0] === 4,
-      () => `If transposeA ${transposeA} is true, innerElementSize ${
-          innerElementSize} and workPerThread[1] ${workPerThread[1]} must be 4.
-          Otherwise, innerElementSize ${innerElementSize} must be 3 or 4.
-      tileAWidth ${tileAWidth} must be divisible by workgroupSize[0]${
-          workgroupSize[0]}. tileInner ${
-          tileInner} must be divisible by workgroupSize[1] ${
-          workgroupSize[1]}. colPerThread ${workPerThread[0]} must be 4.`);
-  return `
-  var<workgroup> mm_Asub : array<array<vec${innerElementSize}<f32>, ${
-      tileAWidth / innerElementSize}>, ${tileAHight}>;
-  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${
-      tileBOuter / workPerThread[0]}>, ${tileInner}>;
-
-  ${main()} {
-    let localRow = i32(localId.y);
-    let tileRow = localRow * ${rowPerThread};
-    let tileCol = i32(localId.x);
-
-    let globalRow = i32(globalId.y) * ${rowPerThread};
-    let globalCol = i32(globalId.x) * ${colPerThread};
-    let batch = ${splitK ? '0' : 'i32(globalId.z)'};
-    let batchA = ${
-      splitK || !broadcastBatch ? 'batch' : 'batch % uniforms.aShape[0]'};
-    let batchB = ${
-      splitK || !broadcastBatch ? 'batch' : 'batch % uniforms.bShape[0]'};
-    let globalRowStart = i32(workgroupId.y) * ${tileAOuter};
-
-    let numTiles = ${
-      splitK ? `${Math.ceil(splitedDimInner / tileInner)}` :
-               `(uniforms.dimInner - 1) / ${tileInner} + 1`};
-    var kStart = ${splitK ? `i32(globalId.z) * ${splitedDimInner}` : '0'};
-
-    var acc: array<vec4<f32>, ${rowPerThread}>;
-
-    // Loop over shared dimension.
-    let tileRowB = localRow * ${rowPerThreadB};
-    for (var t = 0; t < numTiles; t++) {
-        // Load one tile of A into local memory.
-        for (var innerRow = 0; innerRow < ${rowPerThread}; innerRow++) {
-            let inputRow = tileRow + innerRow;
-            let inputCol = tileCol;
-            ${writeDataToSubAVec4Snippet(transposeA, innerElementSize)}
-        }
-
-        // Load one tile of B into local memory.
-        for (var innerRow = 0; innerRow < ${rowPerThreadB}; innerRow++) {
-            let inputRow = tileRowB + innerRow;
-            let inputCol = tileCol;
-            mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);
-        }
-        kStart = kStart + ${tileInner};
-        workgroupBarrier();
-
-        // Compute acc values for a single thread.
-        ${
-      calculateResultSnippet(
-          transposeA, innerElementSize, rowPerThread, tileInner)}
-        workgroupBarrier();
-    }
-
-    for (var innerRow = 0; innerRow < ${rowPerThread}; innerRow++) {
-        mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
-    }
-  }`;
-}
-
-const writeDataToSubASnippet = (transpose: boolean) => {
-  if (transpose) {
-    return `
-        mm_Asub[inputRow][inputCol] = mm_readA(batchA,
-          kStart + inputRow,
-          globalRowStart + inputCol);
-        `;
-
-  } else {
-    return `
-        mm_Asub[inputRow][inputCol] = mm_readA(batchA,
-          globalRowStart + inputRow,
-          kStart + inputCol);
-        `;
-  }
-};
-
-const readDataFromSubASnippet = (transposeA: boolean) => {
-  return transposeA ? 'let ACached = mm_Asub[k][tileRow + innerRow];' :
-
-                      'let ACached = mm_Asub[tileRow + innerRow][k];';
-};
-
-// sequentialAccessByThreads means sequential data in memory is accessed by
-// threads, instead of a single thread (default behavior).
-export function makeMatMulPackedSource(
-    workPerThread: number[], workgroupSize: [number, number, number],
-    transposeA = false, tileInner = 32, splitK = false, splitedDimInner = 32,
-    sequentialAccessByThreads = false, broadcastBatch = false): string {
-  const tileAOuter = workPerThread[1] * workgroupSize[1];
-  const tileBOuter = workPerThread[0] * workgroupSize[0];
-  const tileAWidth = transposeA ? tileAOuter : tileInner;
-  const tileAHight = transposeA ? tileInner : tileAOuter;
-  util.assert(
-      tileAHight % workgroupSize[1] === 0 &&
-          tileAWidth % workgroupSize[0] === 0 &&
-          tileInner % workgroupSize[1] === 0,
-      () => `tileAHight ${tileAHight} must be divisible by workgroupSize[1]${
-          workgroupSize[1]}, tileAWidth ${
-          tileAWidth} must be divisible by workgroupSize[0]${
-          workgroupSize[0]}, tileInner ${
-          tileInner} must be divisible by workgroupSize[1]${workgroupSize[1]}`);
-  const rowPerThreadA = tileAHight / workgroupSize[1];
-  const colPerThreadA = tileAWidth / workgroupSize[0];
-  const rowPerThreadB = tileInner / workgroupSize[1];
-  const rowPerThread = workPerThread[1];
-  const colPerThread = workPerThread[0];
-  const matmulSnippet = sequentialAccessByThreads ?
-      `
-      let localRow = i32(localId.y);
-      let localCol = i32(localId.x);
-      let globalRowStart = i32(workgroupId.y) * ${tileAOuter};
-      let globalColStart = i32(workgroupId.x) * ${tileBOuter};
-
-      // Loop over shared dimension.
-      for (var t = 0; t < numTiles; t++) {
-        // Load one tile of A into local memory.
-        for (var inputRow = localRow; inputRow < ${
-          tileAHight}; inputRow = inputRow + ${workgroupSize[1]}) {
-          for (var inputCol = localCol; inputCol < ${
-          tileAWidth}; inputCol = inputCol + ${workgroupSize[0]}) {
-            ${writeDataToSubASnippet(transposeA)}
-          }
-        }
-        // Load one tile of B into local memory.
-        for (var inputRow = localRow; inputRow < ${
-          tileInner}; inputRow = inputRow + ${workgroupSize[1]}) {
-              for (var inputCol = localCol; inputCol < ${
-          tileBOuter}; inputCol = inputCol + ${workgroupSize[0]}) {
-            mm_Bsub[inputRow][inputCol] = mm_readB(batchB,
-              kStart + inputRow,
-              globalColStart + inputCol);
-          }
-        }
-        kStart = kStart + ${tileInner};
-        workgroupBarrier();
-
-        // Compute acc values for a single thread.
-        var BCached : array<f32, ${colPerThread}>;
-        for (var k = 0; k < ${tileInner}; k++) {
-          for (var inner = 0; inner < ${colPerThread}; inner++) {
-            BCached[inner] = mm_Bsub[k][localCol + inner * ${workgroupSize[0]}];
-          }
-          for (var innerRow = 0; innerRow < ${rowPerThread}; innerRow++) {
-            let ACached = ${
-          transposeA ?
-              `mm_Asub[k][localRow + innerRow * ${workgroupSize[1]}];` :
-              `mm_Asub[localRow + innerRow * ${workgroupSize[1]}][k];`}
-            for (var innerCol = 0; innerCol < ${colPerThread}; innerCol++) {
-              acc[innerRow][innerCol] =
-                  fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);
-            }
-          }
-        }
-        workgroupBarrier();
-      }
-      for (var innerRow = 0; innerRow < ${rowPerThread}; innerRow++) {
-        let gRow = globalRowStart + localRow + innerRow * ${workgroupSize[1]};
-        for (var innerCol = 0; innerCol < ${colPerThread}; innerCol++) {
-          let gCol = globalColStart + localCol + innerCol * ${workgroupSize[0]};
-          mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
-        }
-      }
-      ` :
-      `
-  let tileRow = i32(localId.y) * ${rowPerThread};
-  let tileCol = i32(localId.x) * ${colPerThread};
-
-  let globalRow = i32(globalId.y) * ${rowPerThread};
-  let globalCol = i32(globalId.x) * ${colPerThread};
-  let globalRowStart = i32(workgroupId.y) * ${tileAOuter};
-
-  let tileRowA = i32(localId.y) * ${rowPerThreadA};
-  let tileColA = i32(localId.x) * ${colPerThreadA};
-  let tileRowB = i32(localId.y) * ${rowPerThreadB};
-  // Loop over shared dimension.
-  for (var t = 0; t < numTiles; t++) {
-    // Load one tile of A into local memory.
-    for (var innerRow = 0; innerRow < ${rowPerThreadA}; innerRow++) {
-      for (var innerCol = 0; innerCol < ${colPerThreadA}; innerCol++) {
-        let inputRow = tileRowA + innerRow;
-        let inputCol = tileColA + innerCol;
-        ${writeDataToSubASnippet(transposeA)}
-      }
-    }
-
-    // Load one tile of B into local memory.
-    for (var innerRow = 0; innerRow < ${rowPerThreadB}; innerRow++) {
-      for (var innerCol = 0; innerCol < ${colPerThread}; innerCol++) {
-        let inputRow = tileRowB + innerRow;
-        let inputCol = tileCol + innerCol;
-        mm_Bsub[inputRow][inputCol] = mm_readB(batchB,
-          kStart + inputRow,
-          globalCol + innerCol);
-      }
-    }
-    kStart = kStart + ${tileInner};
-    workgroupBarrier();
-
-    // Compute acc values for a single thread.
-    var BCached : array<f32, ${colPerThread}>;
-    for (var k = 0; k < ${tileInner}; k++) {
-      for (var inner = 0; inner < ${colPerThread}; inner++) {
-        BCached[inner] = mm_Bsub[k][tileCol + inner];
-      }
-
-      for (var innerRow = 0; innerRow < ${rowPerThread}; innerRow++) {
-        ${readDataFromSubASnippet(transposeA)}
-        for (var innerCol = 0; innerCol < ${colPerThread}; innerCol++) {
-          acc[innerRow][innerCol] =
-              fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);
-        }
-      }
-    }
-
-    workgroupBarrier();
-  }
-
-  for (var innerRow = 0; innerRow < ${rowPerThread}; innerRow++) {
-    for (var innerCol = 0; innerCol < ${colPerThread}; innerCol++) {
-      mm_write(batch, globalRow + innerRow, globalCol + innerCol,
-          acc[innerRow][innerCol]);
-    }
-  }
-  `;
-
-  return `
-    var<workgroup> mm_Asub : array<array<f32, ${tileAWidth}>, ${tileAHight}>;
-    var<workgroup> mm_Bsub : array<array<f32, ${tileBOuter}>, ${tileInner}>;
-
-    ${main()} {
-      let batch = ${splitK ? '0' : 'i32(globalId.z)'};
-      let batchA = ${
-      splitK || !broadcastBatch ? 'batch' : 'batch % uniforms.aShape[0]'};
-      let batchB = ${
-      splitK || !broadcastBatch ? 'batch' : 'batch % uniforms.bShape[0]'};
-      let numTiles = ${
-      splitK ? `${Math.ceil(splitedDimInner / tileInner)}` :
-               `(uniforms.dimInner - 1) / ${tileInner} + 1`};
-      var kStart = ${splitK ? `i32(globalId.z) * ${splitedDimInner}` : '0'};
-
-      var acc : array<array<f32, ${colPerThread}>, ${rowPerThread}>;
-
-      // Without this initialization strange values show up in acc.
-      for (var innerRow = 0; innerRow < ${rowPerThread}; innerRow++) {
-        for (var innerCol = 0; innerCol < ${colPerThread}; innerCol++) {
-          acc[innerRow][innerCol] = 0.0;
-        }
-      }
-      ${matmulSnippet}
-    }
-  `;
-}
-
-const readVectorASnippet = (transpose: boolean) => {
-  return transpose ? `
-      mm_readA(batchA, colA, globalRow),
-      mm_readA(batchA, colA + 1, globalRow),
-      mm_readA(batchA, colA + 2, globalRow),
-      mm_readA(batchA, colA + 3, globalRow)
-  ` :
-                     `
-      mm_readA(batchA, globalRow, colA),
-      mm_readA(batchA, globalRow, colA + 1),
-      mm_readA(batchA, globalRow, colA + 2),
-      mm_readA(batchA, globalRow, colA + 3)
-  `;
-};
-
-export function makeVectorMatrixProductSource(
-    workgroupSize: [number, number, number], transposeA = false): string {
-  util.assert(
-      workgroupSize[1] === 1 && workgroupSize[2] === 1,
-      () => `A linear work group size is required. But got ${workgroupSize}.`);
-  const tileSize = workgroupSize[0] * 4;
-  return `
-    var<workgroup> mm_Asub : array<vec4<f32>, ${workgroupSize[0]}>;
-
-    ${main()} {
-      let tileCol = i32(localId.x);
-      let globalCol = i32(globalId.x);
-      let globalRow = i32(globalId.y);
-
-      let numTiles = (uniforms.dimInner - 1) / ${tileSize} + 1;
-      let batch = i32(globalId.z);
-      let batchA = batch % uniforms.aShape[0];
-      let batchB = batch % uniforms.bShape[0];
-      // Without this initialization strange values show up in acc.
-      var acc = 0.0;
-
-      // Loop over shared dimension.
-      for (var t = 0; t < numTiles; t++) {
-        // Load one tile of A into local memory.
-        let colA = t * ${tileSize} + tileCol * 4;
-        mm_Asub[tileCol] = vec4<f32>(${readVectorASnippet(transposeA)});
-        workgroupBarrier();
-
-        // Compute acc values for a single thread.
-        for (var k = 0; k < ${tileSize / 4}; k++) {
-          let rowB = t * ${tileSize} + k * 4;
-          let BCached = vec4<f32>(mm_readB(batchB, rowB, globalCol),
-                              mm_readB(batchB, rowB + 1, globalCol),
-                              mm_readB(batchB, rowB + 2, globalCol),
-                              mm_readB(batchB, rowB + 3, globalCol));
-
-          let ACached = mm_Asub[k];
-          acc = acc + dot(ACached, BCached);
-        }
-
-        workgroupBarrier();
-      }
-
-      mm_write(batch, globalRow, globalCol, acc);
-    }
-  `;
-}
-
-export class MatMulPackedProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[], z: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['A', 'B'];
-  uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
-  workgroupSize: [number, number, number];
-  elementsPerThread: [number, number, number];
-  transposeA: boolean;
-  transposeB: boolean;
-  addBias: boolean;
-  activation: backend_util.Activation;
-  hasPreluActivationWeights: boolean;
-  fitAOuter: boolean;
-  fitBOuter: boolean;
-  fitInner: boolean;
-  tileInner: number;
-  isVectorA: boolean;
-  isVec4: boolean;
-  outputComponent: number;
-  private sequentialAccessByThreads: boolean;
-
-  constructor(
-      aShape: [number, number, number], outputShape: [number, number, number],
-      transposeA = false, transposeB = false, bias: TensorInfo = null,
-      activation: backend_util.Activation = null,
-      preluActivationWeights: TensorInfo = null,
-      sequentialAccessByThreads = false) {
-    this.outputShape = outputShape;
-    this.dispatchLayout = {x: [2], y: [1], z: [0]};
-    const dimInner = transposeA ? aShape[1] : aShape[2];
-    this.isVec4 = ((dimInner % 4 === 0 && !transposeA) ||
-                   (outputShape[1] % 4 === 0 && transposeA)) &&
-        outputShape[2] % 4 === 0 && !transposeB;
-    this.outputComponent = this.isVec4 ? 4 : 1;
-    this.isVectorA = outputShape[1] === 1 && !transposeA;
-
-    if (!this.isVec4 && this.isVectorA) {
-      // For makeVectorMatrixProductSource
-      this.elementsPerThread = [1, 1, 1];
-      this.workgroupSize = [32, 1, 1];
-    } else {
-      const workgroupInfo = computeWorkgroupInfoForMatMul(
-          outputShape[1], dimInner, outputShape[2], transposeA);
-      this.workgroupSize = workgroupInfo.workgroupSize;
-      this.elementsPerThread = workgroupInfo.elementsPerThread;
-    }
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        this.elementsPerThread);
-
-    const addBias = bias != null;
-    const hasPreluActivationWeights = preluActivationWeights != null;
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-
-    if (hasPreluActivationWeights) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    this.sequentialAccessByThreads = sequentialAccessByThreads;
-    this.transposeA = transposeA;
-    this.transposeB = transposeB;
-    this.addBias = addBias;
-    this.activation = activation;
-    this.hasPreluActivationWeights = hasPreluActivationWeights;
-    [this.fitAOuter, this.fitBOuter, this.fitInner] =
-        this.getShapeFit(outputShape[1], outputShape[2], dimInner);
-    this.shaderKey = `matMulPacked_${this.elementsPerThread}_${transposeA}_${
-        transposeB}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${
-        this.fitInner}_${this.isVec4}_${this.isVectorA}_${
-        this.sequentialAccessByThreads}`;
-  }
-
-  getShapeFit(dimAOuter: number, dimBOuter: number, dimInner: number):
-      boolean[] {
-    const tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1];
-    const tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0];
-
-    if (!this.isVec4 && this.isVectorA) {
-      // For makeVectorMatrixProductSource
-      this.tileInner = this.workgroupSize[0] * 4;
-    } else {
-      this.tileInner = tileBOuter;
-    }
-
-    const fitAOuter = dimAOuter % tileAOuter === 0;
-    const fitBOuter = dimBOuter % tileBOuter === 0;
-    const fitInner = dimInner % this.tileInner === 0;
-    return [fitAOuter, fitBOuter, fitInner];
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${
-        activationFnSnippet(
-            this.activation, this.hasPreluActivationWeights, this.isVec4)}
-      ${
-        matMulReadWriteFnSource(
-            this.addBias, this.activation,
-            false /* transposeA is implemented in makeMatMulPackedSource */,
-            this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner,
-            this.isVec4 ? 4 : 1)}
-      ${
-        this.isVec4 ?
-            makeMatMulPackedVec4Source(
-                this.elementsPerThread, this.workgroupSize, this.transposeA,
-                this.tileInner, false, null, true) :
-            (this.isVectorA ? makeVectorMatrixProductSource(
-                                  this.workgroupSize, this.transposeA) :
-                              makeMatMulPackedSource(
-                                  this.elementsPerThread, this.workgroupSize,
-                                  this.transposeA, this.tileInner, false, null,
-                                  this.sequentialAccessByThreads, true))}
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/matmul_reduce_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/matmul_reduce_webgpu.ts
deleted file mode 100644
index 7392ef353..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/matmul_reduce_webgpu.ts
+++ /dev/null
@@ -1,115 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, TensorInfo} from '@tensorflow/tfjs-core';
-
-import {activationFnSnippet} from './activation_util';
-import {matMulReadWriteFnSource} from './matmul_packed_webgpu';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch} from './webgpu_util';
-
-export function makeMatMulReduceSource(workgroupSizeX: number): string {
-  return `
-    var<workgroup> sumValues : array<f32, ${workgroupSizeX}>;
-    ${main()} {
-      let coords = getOutputCoords();
-      let batch = coords[0];
-      let batchA = batch % uniforms.aShape[0];
-      let batchB = batch % uniforms.bShape[0];
-      let row = coords[1];
-      let col = coords[2];
-      var sum = 0.0;
-      let Length = uniforms.dimInner;
-      for (var k = i32(localId.x); k < Length; k = k + ${workgroupSizeX}) {
-        let dataA = mm_readA(batchA, row, k);
-        let dataB = mm_readB(batchB, k, col);
-        sum = sum + dataA * dataB;
-      }
-      sumValues[localId.x] = sum;
-      workgroupBarrier();
-
-      for(var currentSize = ${workgroupSizeX / 2}u; currentSize > 1u;
-          currentSize = currentSize / 2u) {
-        if (localId.x < currentSize)
-        {
-          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
-        }
-        workgroupBarrier();
-      }
-
-      if (localId.x == 0u) {
-        sum = sumValues[0] + sumValues[1];
-        mm_write(batch, row, col, sum);
-      }
-    }
-  `;
-}
-
-export class MatMulReduceProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[], z: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['A', 'B'];
-  uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
-  workgroupSize: [number, number, number] = [256, 1, 1];
-  transposeA: boolean;
-  transposeB: boolean;
-  addBias: boolean;
-  activation: backend_util.Activation;
-  hasPreluActivationWeights: boolean;
-
-  constructor(
-      outputShape: [number, number, number], transposeA = false,
-      transposeB = false, bias: TensorInfo = null,
-      activation: backend_util.Activation = null,
-      preluActivationWeights: TensorInfo = null) {
-    this.outputShape = outputShape;
-    this.dispatchLayout = {x: [], y: [1, 2], z: [0]};
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    const addBias = bias != null;
-    const hasPreluActivationWeights = preluActivationWeights != null;
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-
-    if (hasPreluActivationWeights) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    this.transposeA = transposeA;
-    this.transposeB = transposeB;
-    this.addBias = addBias;
-    this.activation = activation;
-    this.hasPreluActivationWeights = hasPreluActivationWeights;
-    this.shaderKey =
-        `matMulReduce_${this.activation}_${transposeA}_${transposeB}`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${activationFnSnippet(this.activation, this.hasPreluActivationWeights)}
-      ${
-        matMulReadWriteFnSource(
-            this.addBias, this.activation, this.transposeA, this.transposeB)}
-      ${makeMatMulReduceSource(this.workgroupSize[0])}
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/matmul_small_output_size_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/matmul_small_output_size_webgpu.ts
deleted file mode 100644
index 5aae1d45f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/matmul_small_output_size_webgpu.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, TensorInfo} from '@tensorflow/tfjs-core';
-import {activationFnSnippet} from './activation_util';
-import {matMulReadWriteFnSource} from './matmul_packed_webgpu';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-
-export function makeMatMulSmallOutputSizeSource(
-    workgroupSize: [number, number, number]): string {
-  const tileAOuter = workgroupSize[1];
-  const tileBOuter = workgroupSize[0];
-  const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;
-  return `
-  var<workgroup> mm_Asub : array<array<f32, ${tileInner}>, ${tileAOuter}>;
-  var<workgroup> mm_Bsub : array<array<f32, ${tileBOuter}>, ${tileInner}>;
-
-  // If the output size is small for matrix multiplication, avoid to use vec4
-  // and handle some elements per thread to optimally utilize the ALU.
-  // Read data from global memory to registers firstly, then store them into
-  // shared memory, so it is instruction-Level parallelism for arithmetic
-  // operations and others handle IO operations between barrier api, makes ALU
-  // and load/store units work simultaneously, could improves the performance.
-  ${main()} {
-    let tileRow = i32(localId.y);
-    let tileCol = i32(localId.x);
-    let globalRow = i32(globalId.y);
-    let globalCol = i32(globalId.x);
-    let batch = i32(globalId.z);
-    let batchA = batch % uniforms.aShape[0];
-    let batchB = batch % uniforms.bShape[0];
-
-    // uniforms.dimInner should be greater than 0.
-    let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1;
-    var acc = 0.0;
-
-    var globalColA = tileCol;
-    var globalRowB = 0;
-    var regA = mm_readA(batchA, globalRow, globalColA);
-    var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);
-    var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);
-    globalColA = globalColA + ${tileInner};
-    globalRowB = globalRowB + ${tileInner};
-
-    for (var t = 0; t < numTiles; t = t + 1) {
-      mm_Asub[tileRow][tileCol] = regA;
-      mm_Bsub[2 * tileRow][tileCol] = regB0;
-      mm_Bsub[2 * tileRow + 1][tileCol] = regB1;
-
-      workgroupBarrier();
-
-      regA = mm_readA(batchA, globalRow, globalColA);
-      regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);
-      regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);
-      globalColA = globalColA + ${tileInner};
-      globalRowB = globalRowB + ${tileInner};
-
-      for (var k = 0; k < ${tileInner}; k = k + 1) {
-        acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];
-      }
-      workgroupBarrier();
-    }
-
-    mm_write(batch, globalRow, globalCol, acc);
-  }
-  `;
-}
-
-export class MatMulSmallOutputSizeProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[], z: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['A', 'B'];
-  uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
-  workgroupSize: [number, number, number] = [16, 8, 1];
-  transposeA: boolean;
-  transposeB: boolean;
-  addBias: boolean;
-  activation: backend_util.Activation;
-  hasPreluActivationWeights: boolean;
-
-  constructor(
-      aShape: [number, number, number], bShape: [number, number, number],
-      outputShape: [number, number, number], transposeA = false,
-      transposeB = false, bias: TensorInfo = null,
-      activation: backend_util.Activation = null,
-      preluActivationWeights: TensorInfo = null) {
-    this.outputShape = outputShape;
-
-    this.dispatchLayout = {x: [2], y: [1], z: [0]};
-    this.dispatch = [
-      Math.ceil(outputShape[2] / this.workgroupSize[0]),
-      Math.ceil(outputShape[1] / this.workgroupSize[1]), outputShape[0]
-    ];
-
-    const addBias = bias != null;
-    if (addBias) {
-      this.variableNames.push('bias');
-    }
-
-    const hasPreluActivationWeights = preluActivationWeights != null;
-    if (hasPreluActivationWeights) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    this.transposeA = transposeA;
-    this.transposeB = transposeB;
-    this.addBias = addBias;
-    this.activation = activation;
-    this.hasPreluActivationWeights = hasPreluActivationWeights;
-    this.shaderKey =
-        `matMulSmallOutputSize_${this.activation}_${transposeA}_${transposeB}`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${activationFnSnippet(this.activation, this.hasPreluActivationWeights)}
-      ${
-        matMulReadWriteFnSource(
-            this.addBias, this.activation, this.transposeA, this.transposeB)}
-      ${makeMatMulSmallOutputSizeSource(this.workgroupSize)}
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/matmul_splitK_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/matmul_splitK_webgpu.ts
deleted file mode 100644
index 65d42ef81..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/matmul_splitK_webgpu.ts
+++ /dev/null
@@ -1,158 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {activationFnSnippet, biasActivationSnippet} from './activation_util';
-import {makeMatMulPackedSource, makeMatMulPackedVec4Source, matMulReadFnSource} from './matmul_packed_webgpu';
-import {atomicAddSnippet} from './shader_util';
-import {getMainHeaderString as main, typeSnippet, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class MatMulSplitKProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[], z: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['A', 'B'];
-  uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
-  workgroupSize: [number, number, number] = [8, 8, 1];
-  elementsPerThread: [number, number, number];
-  transposeA: boolean;
-  transposeB: boolean;
-  atomic = true;
-  outputComponent: number;
-  splitedDimInner = 128;
-
-  constructor(
-      outputShape: [number, number, number], dimInner: number,
-      transposeA = false, transposeB = false) {
-    util.assert(
-        outputShape[0] === 1,
-        () => 'MatMulSplitKProgram only supports batch = 1.');
-    this.outputShape = outputShape;
-    this.dispatchLayout = {x: [2], y: [1], z: [0, 3]};
-    const isVec4 = (transposeA && this.outputShape[1] % 4 === 0 ||
-                    !transposeA && dimInner % 4 === 0) &&
-        this.outputShape[2] % 4 === 0;
-    this.elementsPerThread = [4, 4, this.splitedDimInner];
-    this.outputComponent = isVec4 ? 4 : 1;
-    if (!isVec4) {
-      if (this.outputShape[1] < 16) {
-        this.elementsPerThread[1] = 1;
-      }
-      if (this.outputShape[2] < 16) {
-        this.elementsPerThread[0] = 1;
-      }
-    }
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout,
-        [
-          this.outputShape[0], this.outputShape[1], this.outputShape[2],
-          dimInner
-        ],
-        this.workgroupSize, this.elementsPerThread);
-
-    this.transposeA = transposeA;
-    this.transposeB = transposeB;
-    this.shaderKey = `matMulSplitK_${transposeA}_${transposeB}_${
-        this.elementsPerThread}_${this.outputComponent}`;
-  }
-
-  getUserCode(): string {
-    const component = this.outputComponent;
-    const userCode = `
-      ${
-        matMulReadFnSource(
-            false, this.transposeB, false, false, false, component)}
-      fn mm_write(batch: i32, row : i32, col : i32, value : ${
-        typeSnippet(component)}) {
-        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {
-          let coords = vec3<i32>(batch, row, col);
-          let flatIndex = getOutputIndexFromCoords(coords);
-          // The problem is that we should initialize output to zero before using.
-          // Otherwise, the original value will be added to the result.
-          for (var i = 0; i < ${component}; i = i + 1) {
-            ${
-        atomicAddSnippet(
-            '&result[flatIndex + i]', `${component > 1 ? 'value[i]' : 'value'}`,
-            'float32')}
-          }
-        }
-      }
-      ${
-        component === 4 ? makeMatMulPackedVec4Source(
-                              this.elementsPerThread, this.workgroupSize,
-                              this.transposeA, 32, true, this.splitedDimInner) :
-                          makeMatMulPackedSource(
-                              this.elementsPerThread, this.workgroupSize,
-                              this.transposeA, 32, true, this.splitedDimInner)}
-    `;
-    return userCode;
-  }
-}
-
-export class BiasActivationProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  uniforms = '';
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-  private addBias: boolean;
-  private activation: backend_util.Activation;
-  private hasPreluActivationWeights: boolean;
-
-  constructor(
-      outputShape: number[], bias: TensorInfo = null,
-      activation: backend_util.Activation = null,
-      preluActivationWeights: TensorInfo = null) {
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.addBias = bias != null;
-    this.hasPreluActivationWeights = preluActivationWeights != null;
-    this.activation = activation;
-    if (this.addBias) {
-      this.variableNames.push('bias');
-    }
-
-    if (this.hasPreluActivationWeights) {
-      this.variableNames.push('preluActivationWeights');
-    }
-
-    this.shaderKey = `biasActivation_${activation}`;
-  }
-
-  getUserCode(): string {
-    return `
-    ${activationFnSnippet(this.activation, this.hasPreluActivationWeights)}
-    ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        var value = getXByOutputIndex(index);
-        ${biasActivationSnippet(this.addBias, this.activation)}
-        setOutputAtIndex(index, value);
-      }
-    }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/matmul_test.ts b/tfjs-master/tfjs-backend-webgpu/src/matmul_test.ts
deleted file mode 100644
index ad7312af0..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/matmul_test.ts
+++ /dev/null
@@ -1,1971 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {MatMulProgramType} from './webgpu_util';
-
-const {expectArraysClose} = test_util;
-const MATMUL_SHARED_DIM_THRESHOLD = 1000;
-function matmulTest(programType: MatMulProgramType) {
-  return () => {
-    let savedMatmulFlag = -1;
-    beforeAll(() => {
-      savedMatmulFlag = tf.env().get('WEBGPU_MATMUL_PROGRAM_TYPE') as number;
-      tf.env().set('WEBGPU_MATMUL_PROGRAM_TYPE', programType);
-    });
-    afterAll(() => {
-      tf.env().set('WEBGPU_MATMUL_PROGRAM_TYPE', savedMatmulFlag);
-    });
-
-    it('it works in delayed mode.', async () => {
-      const savedFlag = tf.env().get('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE');
-      tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 15);
-      const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-      const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-
-      const c = tf.matMul(a, b);
-
-      const f = tf.tensor2d([0, 1, 0.5, 0, 0.25, 2], [2, 3]);
-      const d = tf.mul(c, f);
-
-      const dData = await d.data();
-      test_util.expectArraysClose(
-          dData, new Float32Array([0, 12, 7.5, 0, 6.5, 66]));
-      tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', savedFlag);
-    });
-
-    it('it works in immediate mode.', async () => {
-      const savedFlag = tf.env().get('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE');
-      tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 1);
-      const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-      const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-
-      const c = tf.matMul(a, b);
-
-      const f = tf.tensor2d([0, 1, 0.5, 0, 0.25, 2], [2, 3]);
-      const d = tf.mul(c, f);
-
-      const dData = await d.data();
-      test_util.expectArraysClose(
-          dData, new Float32Array([0, 12, 7.5, 0, 6.5, 66]));
-      tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', savedFlag);
-    });
-
-    // tslint:disable-next-line:max-line-length
-    it('matMul works when we do not check coords because tiles fit perfectly into input dimensions',
-       async () => {
-         const inputData = [
-           0,   1,   2,   3,   4,   5,   6,   7,   8,   9,   10,  11,  12,  13,
-           14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
-           28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
-           42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
-           56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
-           70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
-           84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
-           98,  99,  100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
-           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
-           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
-           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
-           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
-           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
-           182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,
-           196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,
-           210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,
-           224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,
-           238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251,
-           252, 253, 254, 255
-         ];
-         const a = tf.tensor2d(inputData, [16, 16]);
-         const b = tf.tensor2d(inputData, [16, 16]);
-         const expected = new Float32Array([
-           19840,  19960,  20080,  20200,  20320,  20440,  20560,  20680,
-           20800,  20920,  21040,  21160,  21280,  21400,  21520,  21640,
-           50560,  50936,  51312,  51688,  52064,  52440,  52816,  53192,
-           53568,  53944,  54320,  54696,  55072,  55448,  55824,  56200,
-           81280,  81912,  82544,  83176,  83808,  84440,  85072,  85704,
-           86336,  86968,  87600,  88232,  88864,  89496,  90128,  90760,
-           112000, 112888, 113776, 114664, 115552, 116440, 117328, 118216,
-           119104, 119992, 120880, 121768, 122656, 123544, 124432, 125320,
-           142720, 143864, 145008, 146152, 147296, 148440, 149584, 150728,
-           151872, 153016, 154160, 155304, 156448, 157592, 158736, 159880,
-           173440, 174840, 176240, 177640, 179040, 180440, 181840, 183240,
-           184640, 186040, 187440, 188840, 190240, 191640, 193040, 194440,
-           204160, 205816, 207472, 209128, 210784, 212440, 214096, 215752,
-           217408, 219064, 220720, 222376, 224032, 225688, 227344, 229000,
-           234880, 236792, 238704, 240616, 242528, 244440, 246352, 248264,
-           250176, 252088, 254000, 255912, 257824, 259736, 261648, 263560,
-           265600, 267768, 269936, 272104, 274272, 276440, 278608, 280776,
-           282944, 285112, 287280, 289448, 291616, 293784, 295952, 298120,
-           296320, 298744, 301168, 303592, 306016, 308440, 310864, 313288,
-           315712, 318136, 320560, 322984, 325408, 327832, 330256, 332680,
-           327040, 329720, 332400, 335080, 337760, 340440, 343120, 345800,
-           348480, 351160, 353840, 356520, 359200, 361880, 364560, 367240,
-           357760, 360696, 363632, 366568, 369504, 372440, 375376, 378312,
-           381248, 384184, 387120, 390056, 392992, 395928, 398864, 401800,
-           388480, 391672, 394864, 398056, 401248, 404440, 407632, 410824,
-           414016, 417208, 420400, 423592, 426784, 429976, 433168, 436360,
-           419200, 422648, 426096, 429544, 432992, 436440, 439888, 443336,
-           446784, 450232, 453680, 457128, 460576, 464024, 467472, 470920,
-           449920, 453624, 457328, 461032, 464736, 468440, 472144, 475848,
-           479552, 483256, 486960, 490664, 494368, 498072, 501776, 505480,
-           480640, 484600, 488560, 492520, 496480, 500440, 504400, 508360,
-           512320, 516280, 520240, 524200, 528160, 532120, 536080, 540040
-         ]);
-
-         const c = tf.matMul(a, b);
-         const cData = await c.data();
-         test_util.expectArraysClose(cData, expected);
-       });
-
-    // TODO: Make this test much smaller by controlling tile size with a flag.
-    it('matMul A x B multiple tiles', async () => {
-      const a = tf.tensor2d(
-          [
-            2, 7, 5, 1, 5, 1, 3, 2, 8, 8, 2, 0, 0, 0, 4, 0, 8, 4, 0, 5, 4, 1, 4,
-            4, 2, 4, 3, 4, 4, 7, 4, 5, 0, 1, 4, 5, 5, 6, 6, 1, 9, 0, 0, 9, 0, 0,
-            5, 3, 2, 2, 3, 4, 1, 1, 2, 4, 3, 4, 1, 6, 9, 2, 6, 5, 6, 5, 3, 6, 9,
-            9, 7, 8, 8, 4, 8, 1, 4, 5, 1, 5, 8, 1, 9, 3, 7, 2, 2, 0, 5, 4, 3, 1,
-            6, 1, 8, 4, 2, 9, 9, 6, 9, 0, 2, 3, 2, 3, 3, 9, 4, 8, 5, 6, 9, 0, 2,
-            1, 5, 5, 8, 4, 9, 9, 2, 1, 5, 9, 6, 7, 9, 9, 3, 5, 3, 5, 7, 9, 8, 8,
-            9, 0, 5, 8, 4, 5, 6, 1, 7, 8, 7, 2, 9, 9, 1, 1, 7, 9, 0, 4, 7, 7, 8,
-            8, 2, 3, 8, 3, 3, 8, 7, 8, 3, 2, 5, 9, 1, 4, 4, 6, 4, 5, 8, 7, 9, 7,
-            4, 4, 5, 6, 7, 1, 2, 0, 9, 9, 0, 3, 4, 7, 3, 6, 9, 3, 6, 0, 7, 0, 9,
-            5, 4, 0, 2, 8, 4, 7, 6, 9, 6, 4, 2, 9, 8, 2, 5, 6, 2, 6, 2, 3, 3, 4,
-            2, 5, 0, 3, 3, 2, 5, 4, 7, 3, 3, 8, 1, 4, 7, 8, 5, 3, 0, 0, 7, 7, 0,
-            4, 1, 8, 3, 6, 9, 2, 6, 8, 8, 6, 1, 6, 0, 9, 2, 1, 4, 7, 4, 1, 5, 1,
-            5, 4, 8, 0, 3, 3, 0, 6, 9, 3, 0, 6, 5, 3, 6, 4, 6, 1, 0, 2, 8, 1, 4,
-            3, 9, 9, 3, 4, 1, 1, 1, 1, 9, 4, 0, 4, 1, 3, 3, 4, 3, 2, 3, 9, 6, 2,
-            1, 3, 3, 8, 0, 9, 4, 0, 6, 8, 5, 7, 3, 1, 2, 3, 5, 4, 3, 9, 1, 6, 4,
-            0, 6, 7, 8, 0, 2, 2, 0, 1, 8, 6, 6, 3, 6, 7, 2, 3, 2, 7, 9, 0, 1, 9,
-            8, 7, 8, 4, 6, 4, 6, 5, 8, 8, 9, 5, 5, 1, 7, 3, 0, 2, 2, 3, 8, 1, 8,
-            1, 2, 3, 6, 0, 1, 3, 0, 8, 9, 9, 6, 8, 8, 1, 5, 1, 7, 8, 8, 3, 3, 4,
-            7, 3, 9, 3, 3, 7, 9, 8, 3, 8, 6, 8, 4, 2, 2, 5, 0, 4, 0, 7, 7, 0, 0,
-            3, 1, 8, 3, 2, 0, 1, 6, 5, 3, 9, 6, 7, 5, 6, 5, 4, 2, 7, 0, 3, 2, 8,
-            3, 5, 8, 3, 5, 1, 3, 8, 2, 1, 5, 3, 3, 3, 5, 8, 3, 2, 0, 7, 4, 9, 4,
-            5, 0, 8, 7, 3, 2, 1, 8, 4, 5, 7, 0, 7, 0, 7, 4, 6, 1, 6, 1, 9, 7, 2,
-            5, 6, 0, 6, 3, 5, 3, 9, 9, 5, 8, 2, 9, 2, 8, 6, 3, 1, 9, 7, 4, 7, 8,
-            6, 3, 6, 1, 5, 5, 6, 0, 6, 6, 3, 1, 8, 9, 1, 2, 0, 3, 8, 4, 0, 0, 5,
-            1, 0, 7, 2, 5, 3, 7, 7, 8, 1, 3, 4, 5, 8, 4, 7, 6, 4, 7, 6, 9, 9, 2,
-            9, 1, 7, 3, 2, 3, 0, 1, 6, 8, 5, 9, 2, 4, 4, 9, 5, 0, 2, 2, 7, 5, 3,
-            6, 2, 0, 8, 3, 8, 2, 0, 1, 5, 8, 5, 2, 8, 6, 0, 2, 3, 7, 0, 9, 2, 0,
-            7, 0, 9, 4, 0, 2, 5, 4, 2, 7, 3, 2, 9, 1, 2, 6, 3, 3, 5, 3, 6, 5, 4,
-            0, 8, 6, 9, 8, 5, 3, 1, 5, 1, 2, 3, 2, 9, 7, 3, 1, 0, 6, 5, 6, 2, 7,
-            5, 1, 4, 9, 0, 9, 7, 1, 7, 5, 0, 5, 7, 7, 9, 0, 0, 7, 7, 6, 3, 3, 0,
-            0, 8, 6, 4, 8, 2, 9, 2, 3, 4, 1, 4, 5, 3, 1, 6, 4, 5, 1, 7, 0, 5, 6,
-            4, 1, 4, 4, 9, 1, 1, 6, 1, 2, 9, 4, 7, 7, 7, 7, 5, 5, 0, 5, 1, 0, 6,
-            1, 6, 1, 7, 4, 0, 5, 3, 4, 4, 4, 7, 9, 1, 6, 8, 6, 4, 7, 7, 3, 6, 5,
-            8, 0, 0, 1, 4, 6, 7, 4, 7, 5, 3, 3, 4, 5, 9, 8, 8, 6, 3, 2, 8, 7, 2,
-            7, 5, 9, 9, 2, 6, 9, 4, 0, 5, 8, 0, 3, 0, 1, 3, 1, 7, 2, 1, 9, 2, 3,
-            4, 5, 6, 5, 2, 7, 9, 4, 9, 8, 8, 0, 8, 3, 4, 6, 6, 6, 6, 7, 8, 8, 3,
-            3, 3, 2, 0, 7, 6, 4, 9, 5, 1, 0, 1, 7, 9, 3, 4, 2, 9, 7, 0, 5, 5, 6,
-            3, 1, 9, 2, 7, 2, 8, 5, 9, 7, 9, 1, 9, 4, 4, 4, 0, 1, 1, 1, 8, 5, 3,
-            3, 4, 7, 9, 3, 3, 6, 4, 4, 5, 0, 5, 6, 4, 4, 5, 5, 9, 7, 8, 6, 0, 6,
-            3, 4, 4, 9, 6, 7, 7, 7, 5, 3, 2, 3, 3, 7, 0, 5, 9, 5, 6, 7, 6, 0, 6,
-            2, 7, 7, 0, 7, 6, 6, 7, 3, 6, 8, 9, 7, 8, 1, 7, 8, 9, 0, 0, 3, 8, 2,
-            2, 9, 1, 0, 7, 7, 3, 4, 2, 4, 7, 0, 2, 4, 2, 3, 5, 8, 5, 4, 4, 2, 3,
-            1, 7, 0, 4, 1, 8, 8, 2, 9, 7, 2, 1, 8, 1, 8, 6, 2, 8, 9, 9, 2, 4, 3,
-            6, 1, 3, 7, 4, 1, 1, 3, 2, 0, 1, 6, 2, 1, 5, 0, 9, 9, 1, 8, 4, 8, 7,
-            9, 5, 9, 8, 8, 1, 3, 4, 7, 5, 4, 9, 0, 9, 9, 3, 5, 1, 6, 6, 8, 9, 0,
-            0, 0, 7, 5, 1, 3, 7, 9, 0, 4, 6, 3, 7, 7, 3, 8, 6, 4, 5, 7, 1, 0, 0,
-            8, 0, 2, 1, 5, 4, 0, 3, 0, 0, 7, 2, 2, 3, 0, 9, 0, 4, 4, 8, 9, 1, 5,
-            8, 3, 2, 1, 8, 9, 8, 1, 1, 0, 8, 3, 2, 3, 0, 6, 2, 2, 3, 9, 5, 7, 0,
-            1, 0, 3, 5, 4, 4, 5, 5, 1, 0, 2, 6, 3, 4, 7, 0, 7, 7, 9, 0, 1, 4, 9,
-            9, 2, 1, 7, 4, 2, 1, 1, 1, 0, 2, 7, 4, 1, 8, 1, 7, 8, 1, 1, 5, 3, 5,
-            4, 2, 1, 0, 5, 9, 9, 6, 8, 4, 9, 9, 0, 5, 2, 9, 8, 1, 4, 0, 2, 9, 6,
-            7, 2, 0, 4, 0, 1, 1, 9, 5, 9, 0, 4, 0, 7, 1, 3, 8, 0, 5, 3, 4, 9, 8,
-            7, 8, 8, 5, 6, 8, 6, 6, 6, 1, 3, 4, 7, 6, 9, 6, 3, 1, 3, 8, 4, 4, 0,
-            1, 9, 5, 9, 1, 6, 8, 6, 0, 1, 8, 1, 8, 9, 9, 0, 6, 0, 2, 6, 2, 6, 9,
-            1, 0, 2, 7, 0, 9, 2, 3, 9, 0, 2, 9, 9, 5, 4, 1, 3, 5, 4, 2, 6, 5, 9,
-            8, 0, 8, 3, 4, 7, 4, 4, 5, 6, 8, 4, 7, 4, 1, 9, 5, 6, 3, 8, 3, 4, 0,
-            3, 6, 1, 1, 6, 8, 6, 6, 4, 8, 9, 8, 8, 9, 0, 1, 5, 9, 7, 9, 0, 6, 4,
-            4, 5, 8, 4, 2, 8, 1, 3, 2, 1, 5, 6, 6, 6, 2, 9, 0, 8, 1, 8, 2, 6, 4,
-            3, 0, 3, 8, 6, 5, 7, 6, 8, 1, 5, 6, 7, 7, 5, 6, 7, 0, 0, 8, 3, 6, 0,
-            7, 2, 8, 8, 4, 7, 8, 7, 5, 6, 2, 7, 3, 0, 1, 0, 1, 9, 7, 7, 0, 1, 8,
-            0, 2, 6, 2, 6, 4, 6, 0, 0, 8, 9, 7, 4, 1, 5, 6, 2, 5, 3, 8, 4, 0, 4,
-            1, 5, 9, 8, 1, 8, 0, 0, 0, 0, 3, 8, 8, 9, 6, 1, 2, 3, 3, 9, 9, 6, 4,
-            3, 1, 7, 2, 8, 4, 5, 6, 6, 3, 6, 7, 6, 5, 5, 7, 9, 7, 9, 9, 9, 8, 8,
-            0, 7, 0, 6, 6, 9, 6, 3, 5, 2, 3, 2, 6, 8, 1, 6, 0, 8, 6, 8, 6, 0, 4,
-            6, 8, 1, 7, 2, 5, 3, 2, 2, 5, 1, 5, 2, 6, 8, 5, 0, 5, 3, 9, 2, 4, 1,
-            6, 5, 5, 7, 7, 8, 2, 1, 7, 5, 7, 2, 8, 0, 6, 7, 2, 7, 0, 6, 9, 9, 7,
-            7, 5, 1, 4, 5, 3, 0, 3, 2, 0, 0, 0, 7, 7, 8, 9, 0, 0, 7, 8, 5, 2, 9,
-            6, 2, 2, 1, 8, 3, 1, 1, 7, 7, 1, 0, 7, 3, 4, 0, 3, 7, 6, 2, 7, 2, 6,
-            9, 4, 9, 5, 4, 6, 6, 6, 5, 6, 9, 6, 8, 6, 1, 3, 3, 2, 2, 7, 4, 6, 8,
-            3, 3, 1, 3, 4, 8, 7, 6, 7, 6, 0, 7, 2, 0, 1, 6, 3, 3, 9, 2, 0, 3, 9,
-            3, 9, 2, 8, 6, 2, 8, 0, 2, 2, 5, 6, 5, 7, 4, 2, 5, 6, 4, 8, 9, 0, 6,
-            5, 2, 7, 3, 9, 0, 4, 2, 4, 5, 2, 8, 9, 1, 2, 3, 7, 2, 7, 2, 5, 0, 1,
-            5, 0, 5, 1, 7, 4, 4, 5, 7, 0, 7, 2, 7, 1, 6, 5, 6, 2, 1, 6, 1, 3, 0,
-            4, 6, 3, 9, 5, 1, 1, 3, 9, 5, 5, 8, 6, 9, 0, 4, 0, 6, 8, 7, 3, 6, 4,
-            5, 6, 2, 4, 5, 1, 0, 7, 3, 3, 3, 5, 2, 0, 2, 7, 5, 6, 2, 0, 4, 8, 0,
-            8, 6, 9, 5, 3, 0, 5, 0, 5, 3, 9, 5, 6, 8, 2, 9, 9, 6, 1, 5, 9, 5, 6,
-            2, 8, 9, 3, 5, 4, 0, 1, 8, 2, 4, 4, 6, 4, 5, 8, 6, 0, 1, 3, 3, 6, 8,
-            6, 1, 7, 1, 5, 5, 7, 2, 3, 2, 0, 2, 0, 7, 4, 6, 0, 0, 5, 6, 6, 0, 2,
-            2, 0, 5, 3, 9, 9, 5, 1, 5, 9, 8, 4, 3, 4, 2, 1, 4, 7, 9, 5, 5, 9, 9,
-            1, 8, 2, 8, 0, 7, 9, 8, 8, 8, 0, 8, 1, 3, 9, 2, 0, 9, 5, 5, 6, 2, 5,
-            5, 6, 7, 0, 4, 9, 5, 3, 0, 6, 6, 2, 1, 6, 7, 4, 5, 3, 0, 9, 7, 8, 5,
-            7, 9, 0, 8, 6, 9, 6, 7, 3, 2, 2, 1, 0, 6, 7, 3, 7, 7, 4, 6, 7, 6, 3,
-            3, 9, 9, 4, 7, 9, 2, 6, 1, 5, 5, 9, 3, 5, 8, 3, 8, 5, 8, 5, 3, 2, 7,
-            0, 6, 5, 5, 5, 9, 2, 1, 2, 4, 9, 8, 8, 7, 3, 7, 5, 6, 3, 6, 5, 9, 2,
-            6, 4, 4, 7, 3, 0, 5, 3, 6, 6, 6, 9, 4, 3, 3, 0, 0, 3, 6, 7, 8, 6, 6,
-            6, 8, 4, 9, 4, 2, 6, 5, 5, 1, 5, 5, 7, 0, 5, 5, 5, 0, 8, 9, 9, 0, 7,
-            6, 5, 5, 8, 7, 8, 3, 1, 8, 4, 6, 6, 6, 1, 7, 0, 9, 5, 8, 7, 7, 4, 5,
-            3, 1, 7, 4, 6, 6, 6, 5, 7, 5, 3, 1, 0, 2, 7, 8, 8, 4, 6, 3, 2, 3, 6,
-            3, 2, 0, 0, 1, 4, 7, 4, 3, 7, 9, 0, 9, 3, 4, 3, 8, 6, 3, 5, 8, 1, 0,
-            1, 6, 0, 1, 8, 9, 5, 8, 2, 7, 7, 1, 6, 7, 4, 1, 0, 3, 1, 6, 5, 5, 0,
-            9, 4, 7, 1, 4, 3, 4, 2, 4, 5, 0, 6, 4, 8, 7, 0, 6, 5, 3, 9, 3, 1, 3,
-            9, 3, 5, 1, 8, 2, 7, 1, 3, 8, 7, 3, 5, 6, 6, 2, 7, 7, 1, 3, 7, 1, 6,
-            5, 6, 3, 1, 7, 0, 5, 2, 6, 7, 3, 2, 5, 9, 4, 8, 1, 8, 0, 0, 0, 8, 5,
-            2, 0, 8, 3, 6, 6, 3, 7, 8, 0, 0, 7, 3, 9, 1, 6, 0, 0, 2, 4, 3, 3, 9,
-            0, 8, 3, 3, 9, 5, 4, 5, 3, 8, 4, 7, 2, 2, 9, 9, 7, 6, 4, 4, 4, 5, 2,
-            6, 3, 9, 5, 3, 5, 8, 2, 9, 9, 4, 4, 7, 3, 3, 9, 0, 3, 3, 3, 5, 5, 8,
-            3, 9, 2, 0, 9, 3, 6, 8, 0, 2, 6, 0, 6, 6, 0, 5, 9, 6, 5, 7, 0, 4, 3,
-            1, 9, 6, 4, 0, 6, 6, 7, 3, 7, 9, 7, 7, 3, 1, 4, 2, 9, 9, 5, 5, 3, 3,
-            1, 8, 2, 0, 5, 7, 4, 9, 3, 3, 0, 5, 7, 0, 9, 0, 9, 9, 2, 4, 2, 3, 7,
-            1, 3, 1, 2, 5, 0, 3, 8, 6, 3, 1, 5, 1, 1, 0, 1, 7, 9, 4, 8, 0, 1, 7,
-            9, 3, 8, 3, 0, 5, 1, 1, 5, 5, 7, 6, 3, 6, 0, 4, 6, 5, 2, 2, 3, 6, 6,
-            2, 7, 1, 9, 9, 1, 1, 8, 0, 1, 2, 1, 4, 4, 3, 9, 3, 6, 1, 4, 3, 3, 9,
-            3, 4, 8, 1, 7, 8, 8, 8, 2, 3, 7, 4, 4, 2, 8, 1, 5, 6, 1, 1, 3, 0, 7,
-            1, 8, 4, 7, 2, 6, 9, 1, 9, 0, 0, 5, 1, 7, 3, 4, 1, 9, 8, 7, 4, 0, 8,
-            3, 6, 8, 5, 7, 6, 6, 0, 2, 8, 8, 2, 6, 6, 8, 3, 5, 9, 9, 9, 2, 4, 2,
-            1, 1, 0, 1, 8, 4, 5, 6, 9, 5, 8, 3, 1, 1, 6, 7, 5, 3, 0, 6, 3, 0, 1,
-            9, 9, 1, 7, 9, 4, 0, 3, 1, 9, 7, 3, 8, 5, 9, 6, 5, 6, 8, 6, 2, 4, 8,
-            3, 5, 5, 3, 4, 0, 1, 4, 0, 0, 7, 4, 0, 4, 0, 8, 3, 4, 6, 1, 1, 9, 0,
-            0, 7, 7, 0, 3, 0, 8, 5, 7, 3, 3, 4, 3, 1, 2, 2, 0, 5, 4, 2, 8, 0, 9,
-            0, 2, 6, 1, 9, 3, 1, 9, 3, 5, 4, 1, 1, 3, 4, 5, 9, 8, 3, 6, 3, 2, 4,
-            9, 0, 3, 5, 4, 9, 7, 0, 3, 9, 8, 9, 2, 9, 5, 8, 5, 6, 5, 2, 2, 5, 9,
-            9, 2, 3, 5, 2, 2, 4, 0, 6, 7, 0, 8, 5, 9, 4, 9, 8, 2, 5, 0, 2, 1, 1,
-            0, 0, 0, 3, 7, 0, 7, 9, 8, 1, 1, 4, 8, 6, 2, 6, 9, 7, 7, 9, 0, 4, 5,
-            9, 8, 4, 3, 8, 1, 1, 1, 5, 9, 0, 1, 2, 2, 4, 9, 1, 4, 7, 8, 4, 7, 3,
-            4, 5, 8, 6, 0, 9, 2, 6, 9, 5, 8, 8, 1, 3, 6, 2, 0, 8, 1, 0, 1, 8, 7,
-            5, 7, 4, 0, 4, 8, 9, 7, 3, 5, 2, 4, 6, 8, 2, 0, 9, 7, 9, 5, 4, 8, 4,
-            2, 2, 8, 7, 3, 0, 6, 6, 1, 4, 9, 7, 7, 1, 8, 2, 2, 0, 1, 8, 2, 5, 7,
-            4, 7, 7, 1, 8, 5, 5, 6, 8, 2, 7, 5, 3, 1, 7, 9, 9, 1, 9, 6, 8, 7, 7,
-            8, 1, 9, 5, 5, 5, 2, 4, 6, 2, 7, 7, 3, 2, 1, 4, 0, 7, 7, 4, 6, 9, 5,
-            0, 7, 0, 0, 1, 3, 1, 7, 8, 7, 3, 2, 3, 9, 0, 5, 6, 9, 9, 1, 4, 2, 3,
-            5, 0, 7, 5, 6, 5, 1, 6, 8, 7, 8, 3, 2, 3, 4, 2, 1, 9, 2, 8, 9, 9, 4,
-            4, 8, 6, 3, 0, 4, 5, 7, 9, 2, 3, 8, 0, 8, 3, 3, 8, 1, 2, 2, 8, 1, 5,
-            1, 3, 1, 3, 7, 8, 6, 3, 2, 8, 8, 3, 1, 0, 7, 2, 3, 1, 1, 5, 1, 7, 0,
-            1, 1, 5, 9, 6, 8, 3, 7, 3, 9, 0, 3, 5, 4, 9, 0, 6, 9, 5, 9, 6, 4, 2,
-            3, 7, 9, 3, 4, 0, 9, 2, 2, 1, 9, 9, 8, 2, 3, 2, 5, 6, 3, 7, 6, 3, 5,
-            5, 7, 6, 3, 1, 6, 8, 3, 9, 1, 8, 2, 6, 5, 9, 1, 0, 1, 0, 3, 6, 4, 1,
-            2, 1, 0, 4, 9, 8, 6, 3, 1, 2, 4, 2, 5, 9, 6, 6, 4, 3, 0, 1, 0, 6, 7,
-            2, 7, 9, 5, 4, 0, 4, 6, 6, 5, 2, 4, 2, 4, 5, 1, 6, 0, 3, 2, 0, 0, 1,
-            3, 0, 3, 7, 5, 8, 1, 3, 8, 3, 7, 0, 1, 1, 0, 4, 5, 6, 2, 6, 5, 6, 6,
-            4, 8, 8, 8, 5, 2, 7, 1, 2, 2, 3, 2, 8, 3, 4, 3, 2, 3, 9, 7, 6, 3, 9,
-            8, 1, 2, 6, 7, 0, 2, 1, 5, 1, 4, 3, 3, 4, 8, 6, 6, 5, 8, 0, 0, 8, 5,
-            6, 3, 5, 4, 5, 6, 7, 6, 9, 5, 0, 0, 2, 1, 3, 0, 0, 0, 4, 0, 9, 1, 1,
-            7, 0, 6, 0, 7, 3, 6, 7, 0, 9, 3, 2, 0, 0, 3, 9, 2, 0, 5, 2, 0, 1, 6,
-            2, 8, 6, 8, 0, 8, 1, 2, 6, 4, 7, 0, 7, 3, 4, 8, 5, 1, 8, 8, 4, 0, 9,
-            1, 5, 6, 0, 9, 2, 7, 6, 9, 5, 5, 6, 0, 4, 3, 9, 6, 5, 7, 7, 7, 7, 1,
-            0, 1, 3, 8, 3, 6, 4, 6, 5, 9, 9, 8, 4, 6, 0, 7, 3, 0, 8, 5, 5, 2, 4,
-            5, 8, 6, 3, 5, 8, 5, 4, 8, 0, 8, 3, 4, 5, 7, 2, 0, 1, 3, 2, 5, 4, 5,
-            0, 3, 3, 2, 9, 9, 5, 8, 4, 7, 7, 6, 4, 9, 3, 5, 8, 0, 6, 8, 7, 0, 6,
-            9, 1, 3, 4, 9, 5, 2, 7, 3, 4, 0, 0, 2, 0, 6, 0, 8, 4, 9, 3, 2, 4, 3,
-            4, 9, 7, 6, 1, 5, 5, 0, 2, 7, 2, 4, 3, 9, 2, 6, 5, 9, 3, 9, 3, 6, 6,
-            8, 8, 5, 7, 7, 3, 0, 8, 9, 9, 0, 8, 6, 2, 4, 6, 1, 1, 0, 0, 5, 6, 0,
-            3, 5, 1, 0, 7, 6, 9, 3, 1, 0, 4, 4, 9, 2, 6, 4, 3, 2, 8, 5, 9, 0, 3,
-            8, 0, 9, 6, 1, 8, 7, 3, 6, 3, 9, 8, 4, 5, 6, 7, 6, 5, 6, 0, 3, 8, 9,
-            1, 9, 5, 0, 3, 3, 0, 5, 9, 2, 4, 2, 6, 3, 4, 7, 2, 1, 8, 1, 4, 9, 9,
-            9, 2, 9, 6, 9, 9, 6, 4, 5, 4, 7, 3, 8, 3, 5, 5, 5, 3, 7, 4, 5, 0, 0,
-            6, 5, 0, 3, 5, 4, 7, 3, 2, 1, 4, 3, 5, 6, 3, 3, 2, 9, 6, 0, 8, 5, 6,
-            8, 3, 7, 6, 4, 2, 7, 1, 2, 4, 7, 2, 0, 4, 2, 7, 5, 7, 3, 0, 0, 4, 7,
-            9, 0, 6, 3, 8, 5, 3, 8, 0, 2, 4, 0, 9, 6, 4, 4, 8, 0, 5, 9, 5, 8, 2,
-            3, 8, 8, 5, 2, 2, 1, 3, 3, 2, 0, 6, 4, 6, 3, 4, 2, 0, 2, 6, 6, 6, 2,
-            5, 0, 0, 5, 6, 0, 8, 7, 4, 2, 7, 4, 9, 8, 3, 2, 2, 7, 7, 5, 7, 3, 4,
-            7, 4, 8, 0, 5, 2, 0, 1, 0, 2, 2, 5, 7, 5, 4, 1, 9, 4, 6, 9, 8, 3, 2,
-            9, 4, 0, 7, 6, 8, 7, 1, 5, 1, 9, 1, 5, 1, 0, 4, 9, 5, 3, 6, 6, 4, 0,
-            8, 1, 9, 6, 8, 3, 3, 0, 0, 7, 6, 5, 5, 7, 4, 8, 1, 2, 3, 4, 2, 5, 1,
-            7, 4, 6, 5, 5, 0, 4, 5, 8, 5, 2, 6, 5, 4, 5, 1, 4, 3, 8, 8, 2, 9, 4,
-            6, 7, 3, 3, 3, 5, 2, 5, 7, 1, 7, 0, 2, 1, 0, 1, 3, 9, 6, 3, 7, 6, 7,
-            4, 5, 2, 5, 4, 5, 6, 5, 3, 0, 8, 3, 4, 7, 5, 6, 8, 4, 1, 3, 6, 0, 0,
-            7, 6, 2, 4, 0, 1, 9, 5, 2, 3, 1, 0, 3, 2, 4, 7, 4, 1, 3, 8, 0, 7, 3,
-            4, 3, 8, 6, 9, 8, 0, 2, 9, 2, 1, 6, 7, 5, 2, 4, 6, 9, 6, 1, 4, 3, 5,
-            0, 4, 8, 9, 4, 8, 9, 9, 6, 1, 0, 4, 4, 2, 4, 6, 4, 5, 8, 9, 7, 4, 1,
-            6, 2, 7, 9, 7, 6, 4, 1, 1, 5, 9, 3, 4, 8, 3, 5, 6, 3, 5, 6, 0, 8, 7,
-            1, 7, 9, 8, 3, 8, 8, 2, 9, 8, 5, 9, 9, 3, 5, 7, 1, 6, 7, 8, 5, 0, 8,
-            8, 9, 8, 3, 4, 1, 8, 7, 9, 9, 7, 5, 6, 2, 5, 1, 5, 5, 8, 1, 0, 5, 9,
-            8, 5, 4, 8, 3, 8, 2, 2, 0, 4, 3, 3, 6, 9, 5, 2, 2, 4, 0, 7, 6, 1, 4,
-            9, 9, 9, 5, 3, 8, 1, 7, 7, 3, 2, 2, 5, 7, 7, 3, 4, 0, 0, 9, 5, 1, 4,
-            8, 4, 3, 3, 4, 1, 2, 9, 1, 6, 4, 4, 3, 8, 1, 3, 4, 0, 3, 7, 8, 6, 1,
-            5, 9, 6, 2, 0, 5, 6, 8, 8, 5, 0, 5, 0, 3, 5, 8, 8, 3, 8, 1, 0, 0, 6,
-            1, 5, 5, 3, 3, 4, 7, 8, 8, 8, 5, 4, 9, 6, 1, 3, 2, 0, 0, 1, 0, 8, 4,
-            7, 0, 5, 9, 7, 5, 1, 4, 1, 0, 1, 1, 8, 1, 7, 6, 0, 8, 2, 4, 3, 5, 2,
-            1, 3, 8, 6, 1, 8, 7, 7, 9, 7, 9, 6, 1, 0, 0, 9, 8, 6, 8, 4, 8, 1, 2,
-            1, 0, 5, 4, 3, 8, 6, 8, 1, 7, 8, 0, 5, 7, 5, 3, 3, 1, 4, 4, 8, 2, 2,
-            0, 3, 0, 1, 9, 5, 5, 3, 0, 4, 1, 8, 7, 1, 3, 9, 7, 2, 8, 3, 4, 8, 8,
-            8, 4, 0, 7, 5, 4, 0, 2, 7, 7, 2, 8, 2, 9, 3, 7, 5, 5, 0, 0, 9, 9, 8,
-            2, 9, 3, 8, 6, 0, 6, 0, 1, 8, 5, 3, 8, 8, 5, 4, 1, 2, 0, 2, 7, 7, 0,
-            0, 1, 8, 6, 6, 6, 5, 4, 9, 6, 4, 3, 6, 7, 2, 3, 5, 1, 7, 2, 0, 8, 6,
-            7, 4, 4, 1, 5, 2, 7, 1, 7, 4, 8, 5, 9, 9, 7, 1, 7, 7, 4, 1, 2, 1, 7,
-            3, 0, 9, 7, 1, 1, 1, 1, 6, 4, 1, 4, 4, 4, 9, 3, 0, 5, 1, 0, 9, 6, 4,
-            8, 6, 2, 2, 3, 0, 4, 5, 4, 6, 8, 1, 7, 8, 1, 4, 2, 7, 5, 7, 2, 2, 4,
-            0, 1, 6, 9, 5, 4, 9, 6, 8, 7, 6, 5, 1, 4, 4, 0, 7, 9, 9, 6, 5, 4, 5,
-            7, 4, 2, 5, 2, 9, 1, 1, 6, 3, 5, 1, 9, 9, 9, 4, 9, 4, 1, 2, 5, 3, 9,
-            5, 8, 2, 9, 3, 6, 6, 7, 6, 1, 5, 8, 1, 1, 9, 0, 3, 3, 8, 1, 2, 7, 1,
-            7, 1, 8, 7, 8, 5, 9, 4, 7, 1, 8, 7, 5, 6, 2, 5, 6, 0, 2, 9, 0, 7, 3,
-            6, 4, 0, 0, 9, 2, 9, 8, 1, 3, 6, 7, 0, 6, 1, 8, 0, 1, 1, 9, 6, 6, 6,
-            7, 8, 6, 5, 2, 4, 2, 9, 6, 2, 8, 8, 3, 4, 9, 0, 9, 5, 4, 6, 2, 1, 0,
-            7, 9, 3, 7, 0, 2, 1, 5, 8, 4, 9, 5, 2, 5, 4, 4, 6, 6, 7, 2, 5, 2, 6,
-            2, 3, 0, 8, 0, 9, 8, 7, 4, 9, 3, 9, 4, 9, 8, 0, 4, 4, 8, 6, 8, 3, 3,
-            3, 0, 7, 8, 3, 3, 8, 3, 7, 4, 7, 6, 0, 3, 7, 8, 5, 6, 0, 3, 8, 0, 4,
-            8, 8, 8, 4, 3, 9, 2, 3, 4, 0, 8, 7, 6, 7, 2, 1, 0, 7, 6, 9, 1, 6, 0,
-            5, 7, 5, 9, 0, 4, 8, 7, 8, 2, 1, 4, 8, 2, 8, 5, 4, 3, 4, 6, 1, 2, 3,
-            3, 1, 2, 2, 4, 6, 2, 3, 5, 6, 0, 5, 0, 8, 6, 7, 5, 2, 3, 4, 8, 7, 7,
-            3, 8, 8, 2, 4, 8, 0, 1
-          ],
-          [65, 67]);
-
-      const b = tf.tensor2d(
-          [
-            0, 3, 3, 3, 7, 3, 3, 3, 2, 9, 8, 4, 8, 5, 2, 5, 3, 0, 7, 0, 7, 9, 3,
-            5, 7, 9, 6, 1, 0, 2, 9, 5, 6, 0, 9, 2, 6, 1, 1, 1, 6, 8, 6, 8, 8, 7,
-            2, 0, 8, 5, 7, 3, 4, 7, 0, 1, 5, 8, 1, 5, 0, 3, 3, 4, 9, 1, 6, 9, 0,
-            7, 9, 7, 5, 2, 6, 2, 0, 0, 3, 7, 2, 8, 8, 6, 8, 3, 6, 0, 3, 3, 2, 6,
-            2, 0, 3, 5, 8, 9, 9, 1, 6, 6, 0, 3, 7, 0, 9, 9, 7, 0, 2, 9, 7, 6, 9,
-            1, 1, 7, 9, 7, 2, 0, 6, 4, 2, 3, 6, 6, 3, 3, 9, 0, 1, 3, 4, 2, 2, 3,
-            7, 4, 8, 4, 2, 1, 5, 5, 6, 1, 1, 2, 1, 1, 0, 7, 3, 8, 2, 8, 1, 3, 9,
-            4, 8, 0, 3, 6, 7, 9, 2, 5, 4, 6, 1, 9, 6, 1, 4, 2, 3, 2, 7, 7, 0, 3,
-            6, 1, 2, 8, 4, 3, 4, 1, 4, 3, 1, 3, 2, 8, 2, 5, 6, 0, 7, 0, 3, 4, 7,
-            3, 9, 9, 0, 1, 0, 9, 3, 6, 5, 9, 7, 2, 3, 5, 1, 9, 3, 5, 2, 4, 5, 6,
-            2, 1, 5, 5, 6, 8, 6, 0, 8, 1, 8, 6, 6, 8, 7, 6, 9, 7, 9, 4, 9, 8, 2,
-            9, 8, 5, 3, 1, 3, 2, 4, 6, 4, 8, 7, 5, 3, 0, 0, 9, 1, 7, 6, 3, 1, 3,
-            6, 2, 0, 2, 3, 3, 7, 1, 5, 8, 4, 6, 5, 9, 8, 8, 5, 9, 1, 0, 4, 6, 2,
-            4, 9, 9, 2, 6, 6, 9, 9, 8, 9, 9, 8, 0, 5, 8, 4, 8, 3, 9, 2, 0, 6, 4,
-            9, 9, 5, 5, 7, 9, 3, 3, 8, 9, 3, 2, 2, 8, 8, 8, 8, 8, 5, 2, 9, 4, 0,
-            0, 2, 2, 8, 5, 1, 3, 8, 5, 6, 7, 4, 4, 0, 2, 1, 4, 9, 4, 8, 9, 6, 7,
-            9, 2, 9, 6, 1, 5, 8, 4, 9, 1, 0, 0, 6, 2, 8, 5, 8, 7, 0, 5, 8, 4, 8,
-            5, 4, 7, 1, 7, 9, 0, 4, 3, 5, 3, 9, 8, 7, 8, 0, 7, 8, 1, 9, 3, 6, 6,
-            9, 1, 4, 6, 3, 6, 3, 2, 1, 7, 8, 3, 4, 4, 7, 0, 0, 1, 9, 0, 8, 8, 3,
-            8, 8, 3, 3, 7, 0, 6, 6, 9, 9, 7, 5, 6, 4, 2, 1, 2, 0, 2, 4, 4, 3, 7,
-            3, 8, 0, 9, 0, 1, 5, 0, 5, 2, 1, 5, 4, 2, 6, 0, 9, 1, 2, 1, 7, 4, 4,
-            3, 9, 9, 8, 4, 4, 8, 0, 0, 9, 0, 9, 8, 7, 4, 5, 4, 4, 9, 1, 1, 9, 6,
-            0, 8, 3, 4, 5, 5, 2, 1, 9, 8, 8, 0, 3, 1, 5, 7, 3, 0, 2, 7, 9, 2, 5,
-            9, 1, 1, 1, 3, 1, 1, 4, 7, 3, 2, 4, 3, 3, 9, 7, 0, 9, 4, 2, 5, 6, 2,
-            0, 0, 3, 2, 3, 8, 0, 7, 0, 9, 9, 0, 6, 8, 2, 7, 3, 9, 6, 5, 5, 1, 3,
-            1, 9, 2, 0, 8, 3, 2, 6, 6, 8, 4, 6, 6, 1, 5, 5, 9, 0, 0, 1, 4, 0, 6,
-            4, 4, 7, 4, 9, 8, 1, 6, 5, 5, 6, 3, 5, 7, 9, 2, 7, 1, 1, 5, 3, 3, 6,
-            6, 9, 6, 2, 0, 1, 5, 0, 8, 7, 6, 9, 9, 1, 3, 9, 4, 5, 4, 0, 8, 3, 3,
-            1, 4, 6, 7, 0, 6, 3, 8, 8, 4, 9, 8, 8, 5, 0, 8, 7, 1, 5, 0, 8, 0, 8,
-            7, 0, 2, 8, 6, 3, 4, 2, 3, 6, 0, 3, 5, 7, 1, 6, 1, 8, 0, 0, 0, 4, 6,
-            7, 5, 3, 2, 2, 3, 5, 9, 4, 0, 3, 5, 0, 4, 7, 4, 1, 9, 6, 9, 7, 2, 3,
-            3, 3, 0, 9, 3, 1, 2, 3, 5, 9, 6, 2, 6, 8, 3, 1, 8, 1, 1, 0, 7, 3, 4,
-            2, 9, 0, 6, 9, 5, 6, 2, 4, 3, 0, 5, 6, 2, 1, 4, 5, 1, 5, 2, 3, 5, 5,
-            0, 5, 2, 9, 4, 4, 0, 8, 4, 3, 9, 6, 5, 9, 0, 2, 9, 3, 1, 0, 5, 9, 9,
-            3, 5, 6, 0, 7, 0, 3, 6, 6, 7, 5, 2, 0, 8, 3, 6, 2, 4, 6, 5, 1, 0, 6,
-            5, 6, 3, 2, 0, 8, 5, 9, 7, 7, 1, 8, 2, 6, 1, 7, 4, 2, 8, 6, 1, 3, 1,
-            3, 4, 7, 2, 0, 5, 5, 6, 4, 8, 9, 2, 9, 9, 1, 4, 3, 2, 2, 3, 3, 0, 1,
-            1, 0, 9, 2, 8, 3, 0, 5, 5, 6, 3, 9, 9, 2, 6, 2, 3, 8, 4, 3, 7, 7, 1,
-            4, 7, 2, 4, 4, 5, 5, 8, 2, 9, 8, 4, 9, 4, 0, 9, 8, 1, 9, 6, 8, 4, 8,
-            5, 6, 2, 9, 7, 3, 7, 2, 7, 9, 2, 8, 4, 3, 5, 7, 5, 1, 6, 9, 0, 0, 7,
-            3, 9, 6, 0, 6, 9, 2, 9, 0, 9, 1, 8, 8, 8, 9, 4, 3, 8, 7, 8, 5, 9, 1,
-            1, 9, 1, 7, 3, 3, 7, 1, 5, 5, 9, 6, 1, 8, 6, 7, 1, 9, 0, 1, 4, 4, 4,
-            6, 9, 4, 7, 2, 9, 5, 9, 9, 2, 0, 5, 4, 5, 3, 7, 5, 2, 7, 4, 2, 4, 0,
-            9, 0, 1, 7, 0, 3, 2, 3, 9, 9, 6, 7, 4, 6, 8, 1, 2, 5, 2, 2, 3, 4, 3,
-            7, 7, 4, 6, 9, 9, 1, 6, 1, 9, 4, 5, 4, 7, 3, 0, 0, 6, 0, 7, 8, 3, 4,
-            5, 6, 1, 6, 8, 0, 8, 5, 5, 7, 4, 2, 6, 0, 4, 2, 9, 7, 3, 9, 8, 6, 8,
-            4, 4, 9, 1, 8, 9, 1, 3, 3, 5, 9, 5, 0, 9, 5, 5, 5, 6, 2, 1, 4, 3, 9,
-            5, 1, 8, 9, 7, 8, 7, 3, 1, 9, 3, 7, 3, 2, 9, 4, 5, 8, 7, 9, 6, 7, 2,
-            3, 9, 6, 2, 3, 1, 0, 2, 7, 2, 9, 2, 5, 5, 9, 4, 4, 7, 4, 4, 6, 9, 2,
-            1, 8, 7, 7, 8, 8, 4, 3, 1, 6, 2, 2, 2, 8, 1, 5, 5, 5, 3, 5, 0, 8, 1,
-            1, 9, 2, 9, 4, 8, 3, 4, 3, 5, 1, 2, 6, 4, 5, 6, 6, 8, 9, 1, 4, 9, 1,
-            3, 1, 5, 5, 0, 1, 8, 6, 9, 3, 0, 9, 4, 7, 4, 4, 8, 5, 9, 8, 8, 4, 5,
-            9, 7, 7, 0, 6, 0, 0, 7, 6, 4, 8, 2, 0, 0, 6, 8, 8, 1, 4, 7, 8, 5, 5,
-            3, 9, 6, 8, 7, 8, 2, 4, 9, 0, 5, 3, 3, 5, 9, 8, 1, 9, 8, 2, 7, 2, 1,
-            7, 4, 3, 5, 4, 9, 8, 1, 2, 0, 2, 7, 8, 4, 3, 2, 8, 7, 2, 6, 6, 2, 9,
-            2, 9, 1, 3, 4, 8, 2, 2, 0, 8, 3, 2, 3, 9, 3, 0, 1, 3, 4, 2, 9, 5, 4,
-            0, 5, 9, 4, 8, 5, 8, 4, 5, 3, 7, 2, 1, 8, 2, 8, 9, 0, 4, 3, 9, 3, 6,
-            3, 5, 1, 1, 1, 0, 5, 0, 8, 2, 7, 4, 3, 4, 0, 5, 3, 4, 0, 8, 5, 3, 1,
-            1, 9, 3, 1, 8, 6, 1, 2, 2, 2, 6, 8, 3, 7, 7, 8, 9, 1, 0, 1, 9, 0, 1,
-            6, 7, 3, 6, 2, 7, 0, 7, 8, 2, 8, 8, 5, 0, 4, 3, 8, 5, 0, 1, 8, 0, 7,
-            1, 1, 8, 3, 7, 7, 1, 6, 1, 4, 9, 3, 3, 0, 0, 0, 2, 7, 8, 6, 4, 7, 7,
-            2, 0, 9, 7, 8, 4, 2, 3, 7, 2, 2, 7, 4, 0, 4, 8, 8, 8, 8, 3, 8, 4, 4,
-            4, 7, 8, 0, 3, 4, 5, 8, 1, 5, 5, 7, 8, 9, 7, 1, 7, 3, 4, 3, 5, 0, 8,
-            7, 1, 4, 5, 0, 6, 3, 9, 4, 4, 8, 9, 6, 3, 0, 5, 7, 1, 4, 9, 2, 0, 9,
-            1, 3, 1, 2, 7, 8, 9, 6, 2, 8, 3, 4, 7, 9, 2, 7, 4, 1, 0, 7, 2, 1, 1,
-            9, 5, 6, 9, 5, 8, 4, 6, 3, 1, 9, 6, 1, 9, 1, 6, 0, 4, 5, 7, 9, 3, 5,
-            4, 4, 7, 2, 1, 8, 2, 7, 6, 6, 1, 8, 4, 6, 4, 7, 4, 7, 9, 9, 7, 5, 7,
-            2, 4, 0, 7, 2, 3, 2, 0, 1, 0, 7, 8, 4, 1, 0, 2, 8, 8, 0, 8, 9, 9, 8,
-            4, 3, 2, 4, 5, 4, 5, 5, 1, 6, 3, 6, 5, 1, 4, 9, 9, 5, 6, 0, 9, 9, 8,
-            2, 6, 3, 3, 4, 1, 2, 9, 3, 7, 2, 2, 0, 7, 6, 4, 6, 7, 8, 7, 9, 8, 0,
-            9, 4, 7, 2, 5, 0, 6, 7, 7, 0, 4, 0, 0, 0, 4, 1, 8, 3, 5, 9, 8, 5, 5,
-            1, 4, 6, 3, 5, 3, 5, 1, 8, 5, 3, 4, 2, 8, 3, 4, 9, 1, 8, 4, 4, 4, 8,
-            7, 5, 3, 0, 1, 4, 6, 3, 3, 0, 3, 1, 5, 8, 6, 6, 7, 3, 9, 9, 2, 4, 1,
-            1, 9, 9, 5, 1, 9, 4, 9, 4, 7, 2, 8, 0, 6, 0, 3, 7, 2, 7, 6, 9, 6, 8,
-            5, 8, 9, 7, 4, 1, 5, 5, 2, 7, 8, 6, 6, 7, 3, 6, 9, 1, 9, 4, 9, 9, 2,
-            6, 3, 2, 9, 8, 9, 4, 8, 3, 0, 5, 9, 9, 2, 1, 7, 2, 2, 1, 8, 8, 7, 0,
-            9, 8, 8, 0, 8, 4, 3, 2, 2, 7, 9, 9, 7, 5, 8, 9, 8, 7, 4, 7, 9, 1, 0,
-            7, 6, 2, 4, 4, 0, 1, 9, 7, 4, 5, 9, 2, 2, 5, 1, 3, 2, 2, 9, 2, 2, 8,
-            7, 6, 0, 7, 5, 6, 5, 6, 0, 0, 5, 6, 7, 2, 7, 1, 2, 1, 7, 0, 0, 2, 6,
-            3, 8, 1, 7, 1, 8, 7, 7, 5, 1, 0, 8, 4, 7, 8, 9, 1, 5, 4, 1, 9, 3, 3,
-            8, 4, 5, 7, 9, 4, 7, 3, 8, 9, 5, 3, 2, 7, 3, 9, 7, 9, 8, 2, 8, 0, 1,
-            7, 8, 2, 1, 0, 7, 5, 1, 3, 7, 2, 4, 8, 8, 4, 6, 8, 2, 7, 6, 8, 2, 0,
-            9, 5, 4, 0, 2, 2, 1, 1, 4, 4, 9, 3, 0, 7, 1, 6, 4, 4, 9, 9, 2, 3, 6,
-            3, 8, 7, 3, 3, 1, 0, 0, 1, 9, 6, 7, 5, 5, 8, 4, 1, 3, 6, 3, 6, 5, 6,
-            3, 7, 8, 1, 4, 0, 0, 2, 4, 1, 3, 6, 8, 1, 2, 9, 3, 9, 7, 8, 4, 0, 6,
-            4, 3, 3, 9, 8, 9, 1, 7, 1, 5, 8, 1, 4, 3, 2, 0, 6, 5, 9, 2, 6, 2, 5,
-            8, 8, 8, 0, 7, 4, 1, 1, 3, 6, 0, 8, 3, 6, 2, 4, 6, 1, 7, 5, 3, 8, 0,
-            6, 3, 7, 5, 5, 2, 3, 2, 7, 6, 9, 3, 8, 4, 5, 5, 4, 0, 9, 9, 7, 8, 6,
-            7, 1, 8, 7, 1, 5, 0, 0, 9, 5, 8, 7, 7, 3, 4, 2, 1, 4, 1, 9, 1, 1, 9,
-            8, 6, 3, 5, 5, 0, 8, 9, 1, 7, 5, 1, 7, 8, 0, 0, 4, 0, 3, 7, 1, 4, 4,
-            7, 4, 3, 3, 8, 7, 1, 5, 1, 1, 0, 9, 5, 0, 5, 9, 7, 0, 5, 7, 4, 6, 0,
-            2, 4, 0, 4, 8, 1, 6, 1, 5, 7, 5, 5, 1, 8, 2, 3, 8, 5, 8, 8, 9, 0, 4,
-            1, 2, 5, 9, 0, 0, 8, 1, 7, 4, 8, 8, 3, 3, 2, 4, 8, 1, 7, 6, 0, 1, 6,
-            6, 7, 1, 5, 9, 1, 7, 8, 1, 5, 9, 5, 8, 2, 4, 6, 0, 3, 4, 2, 3, 8, 4,
-            4, 9, 7, 3, 7, 3, 8, 9, 1, 7, 2, 2, 6, 5, 1, 9, 2, 8, 1, 6, 4, 3, 1,
-            0, 6, 4, 3, 1, 0, 5, 0, 5, 9, 9, 2, 7, 1, 0, 0, 3, 4, 7, 1, 5, 9, 2,
-            8, 7, 8, 0, 9, 0, 8, 8, 6, 8, 3, 1, 3, 5, 4, 3, 9, 5, 2, 5, 5, 1, 2,
-            4, 7, 8, 3, 1, 9, 2, 4, 9, 1, 2, 8, 9, 9, 6, 1, 5, 8, 8, 4, 2, 3, 8,
-            3, 8, 7, 0, 7, 0, 1, 6, 3, 2, 5, 0, 0, 5, 6, 0, 6, 8, 4, 9, 9, 3, 8,
-            5, 7, 3, 5, 4, 7, 3, 4, 7, 8, 2, 3, 8, 5, 1, 4, 3, 4, 8, 0, 7, 5, 8,
-            5, 4, 0, 7, 0, 7, 2, 2, 0, 0, 0, 1, 6, 9, 8, 2, 9, 5, 1, 2, 9, 3, 9,
-            1, 7, 3, 7, 2, 9, 8, 6, 1, 7, 2, 4, 2, 3, 2, 7, 8, 0, 0, 9, 0, 8, 2,
-            1, 1, 7, 6, 7, 5, 4, 7, 0, 2, 8, 4, 8, 2, 0, 0, 5, 5, 3, 6, 5, 9, 4,
-            6, 1, 6, 1, 1, 3, 6, 2, 5, 4, 1, 7, 6, 9, 7, 4, 6, 2, 7, 8, 0, 8, 6,
-            7, 0, 3, 8, 7, 4, 0, 8, 4, 9, 0, 1, 6, 8, 7, 4, 2, 7, 8, 5, 2, 9, 4,
-            4, 2, 0, 2, 8, 0, 8, 1, 2, 4, 1, 3, 1, 8, 2, 9, 8, 6, 9, 6, 5, 1, 9,
-            5, 4, 2, 1, 5, 9, 2, 2, 2, 6, 4, 4, 8, 4, 0, 1, 4, 2, 7, 4, 6, 1, 8,
-            8, 3, 4, 1, 6, 2, 2, 4, 9, 5, 8, 5, 2, 7, 0, 9, 4, 5, 2, 3, 2, 4, 8,
-            0, 3, 3, 4, 4, 0, 3, 6, 8, 6, 4, 3, 1, 9, 9, 8, 0, 3, 3, 5, 0, 6, 8,
-            4, 0, 2, 5, 7, 0, 0, 5, 8, 2, 5, 3, 5, 9, 5, 8, 3, 9, 5, 0, 6, 7, 9,
-            4, 5, 8, 7, 8, 9, 8, 2, 9, 0, 4, 6, 5, 5, 8, 9, 7, 7, 7, 4, 9, 3, 9,
-            3, 4, 0, 9, 3, 5, 3, 4, 3, 9, 3, 4, 1, 3, 5, 8, 0, 6, 8, 1, 3, 6, 5,
-            5, 9, 9, 6, 9, 7, 8, 2, 6, 0, 4, 3, 3, 7, 2, 9, 2, 3, 6, 0, 3, 0, 2,
-            7, 4, 5, 2, 3, 5, 7, 9, 9, 8, 0, 9, 3, 1, 6, 1, 7, 6, 4, 9, 0, 8, 5,
-            8, 8, 4, 3, 1, 5, 8, 4, 8, 0, 3, 8, 3, 2, 8, 7, 9, 0, 0, 3, 3, 4, 5,
-            2, 5, 7, 3, 7, 8, 1, 7, 1, 6, 6, 7, 7, 8, 8, 1, 2, 2, 7, 6, 6, 7, 2,
-            6, 8, 7, 1, 6, 2, 5, 9, 9, 1, 3, 7, 1, 0, 6, 3, 8, 5, 9, 8, 3, 3, 9,
-            0, 8, 4, 0, 6, 0, 6, 1, 9, 2, 7, 5, 7, 5, 2, 3, 6, 2, 7, 4, 6, 3, 9,
-            5, 4, 7, 3, 1, 7, 4, 6, 3, 4, 3, 1, 9, 7, 5, 2, 0, 9, 1, 6, 3, 9, 1,
-            9, 8, 0, 7, 0, 9, 9, 1, 2, 2, 4, 1, 5, 7, 7, 5, 7, 8, 2, 6, 0, 9, 3,
-            9, 8, 6, 4, 4, 7, 4, 0, 6, 0, 3, 0, 9, 4, 6, 8, 9, 4, 2, 8, 5, 7, 9,
-            3, 5, 3, 1, 1, 7, 2, 5, 7, 1, 6, 2, 6, 5, 5, 9, 1, 4, 4, 8, 6, 8, 1,
-            2, 8, 6, 7, 1, 8, 7, 6, 9, 4, 6, 4, 1, 8, 9, 9, 4, 6, 3, 1, 0, 1, 4,
-            7, 8, 1, 0, 4, 6, 5, 6, 4, 2, 8, 8, 0, 3, 8, 6, 6, 1, 2, 8, 7, 6, 8,
-            6, 0, 4, 2, 2, 7, 9, 4, 3, 2, 7, 7, 5, 1, 5, 5, 6, 6, 2, 7, 2, 0, 3,
-            7, 2, 5, 7, 1, 2, 8, 6, 6, 7, 8, 1, 0, 0, 1, 9, 7, 8, 7, 1, 2, 6, 1,
-            8, 4, 2, 6, 5, 1, 5, 7, 7, 7, 3, 3, 0, 8, 7, 9, 9, 8, 9, 8, 3, 3, 1,
-            9, 9, 1, 9, 2, 5, 2, 2, 7, 8, 7, 3, 0, 2, 3, 4, 8, 6, 4, 2, 8, 8, 0,
-            8, 9, 3, 1, 1, 0, 9, 5, 1, 3, 7, 4, 8, 7, 3, 7, 8, 0, 1, 1, 9, 4, 3,
-            9, 7, 3, 4, 1, 8, 5, 2, 9, 1, 6, 8, 6, 3, 2, 1, 4, 8, 9, 7, 9, 2, 1,
-            6, 8, 9, 0, 2, 3, 9, 6, 1, 4, 0, 2, 2, 2, 3, 5, 5, 4, 0, 2, 7, 8, 2,
-            5, 9, 3, 9, 5, 0, 5, 2, 8, 5, 7, 5, 2, 3, 5, 4, 2, 9, 4, 1, 0, 7, 0,
-            7, 3, 1, 3, 8, 8, 3, 2, 2, 1, 4, 8, 8, 3, 9, 7, 4, 2, 6, 4, 8, 1, 6,
-            3, 3, 2, 3, 0, 2, 0, 0, 3, 8, 9, 1, 7, 7, 5, 5, 2, 7, 6, 7, 7, 0, 2,
-            8, 1, 5, 0, 7, 9, 3, 9, 6, 8, 7, 1, 3, 6, 8, 4, 7, 8, 2, 9, 8, 9, 2,
-            3, 9, 4, 8, 8, 9, 4, 7, 1, 3, 9, 2, 4, 8, 8, 2, 8, 1, 3, 2, 3, 6, 6,
-            4, 7, 5, 3, 9, 2, 0, 8, 2, 9, 9, 0, 5, 5, 3, 5, 5, 6, 7, 0, 3, 0, 6,
-            5, 8, 7, 0, 4, 3, 9, 7, 7, 1, 2, 2, 8, 8, 4, 3, 1, 8, 7, 2, 7, 2, 6,
-            5, 6, 4, 7, 8, 0, 6, 6, 2, 3, 0, 3, 3, 7, 2, 6, 9, 7, 6, 4, 8, 5, 5,
-            9, 3, 7, 9, 5, 3, 1, 1, 9, 2, 4, 8, 1, 7, 3, 5, 9, 8, 5, 6, 6, 5, 7,
-            5, 7, 4, 5, 6, 1, 2, 7, 9, 0, 6, 0, 2, 3, 9, 8, 7, 4, 3, 1, 2, 4, 9,
-            7, 4, 8, 3, 0, 3, 2, 7, 7, 0, 0, 7, 2, 0, 6, 0, 2, 9, 2, 6, 5, 7, 8,
-            9, 8, 3, 4, 7, 2, 0, 0, 7, 5, 9, 9, 6, 7, 5, 2, 7, 4, 2, 9, 6, 6, 3,
-            1, 2, 0, 8, 3, 2, 8, 0, 5, 1, 0, 9, 1, 6, 3, 1, 3, 7, 7, 5, 7, 4, 4,
-            4, 1, 9, 2, 6, 7, 1, 3, 6, 2, 5, 8, 2, 4, 0, 7, 4, 3, 6, 7, 4, 9, 4,
-            9, 5, 0, 5, 1, 0, 0, 9, 1, 4, 8, 2, 9, 6, 7, 5, 3, 5, 0, 8, 3, 4, 0,
-            7, 5, 8, 5, 9, 4, 0, 2, 7, 1, 9, 9, 6, 1, 2, 8, 5, 7, 8, 5, 0, 4, 9,
-            6, 7, 6, 6, 5, 6, 2, 6, 8, 0, 9, 7, 3, 3, 3, 4, 6, 5, 9, 0, 1, 6, 8,
-            9, 3, 3, 9, 2, 0, 2, 1, 0, 0, 8, 7, 6, 3, 4, 4, 9, 7, 5, 8, 5, 1, 5,
-            2, 3, 6, 2, 0, 9, 1, 3, 0, 3, 3, 6, 9, 4, 2, 0, 2, 3, 1, 9, 0, 2, 5,
-            2, 0, 8, 6, 0, 9, 8, 8, 8, 3, 1, 4, 5, 0, 3, 0, 4, 5, 4, 9, 2, 8, 2,
-            1, 9, 0, 8, 2, 2, 3, 2, 5, 6, 3, 1, 5, 1, 9, 0, 4, 6, 4, 4, 8, 3, 8,
-            9, 9, 3, 9, 9, 2, 2, 1, 7, 3, 8, 7, 6, 9, 0, 4, 2, 7, 8, 4, 9, 3, 1,
-            3, 6, 1, 3, 7, 8, 0, 5, 7, 3, 3, 2, 8, 9, 4, 8, 6, 6, 9, 4, 2, 5, 7,
-            9, 3, 8, 2, 5, 6, 9, 3, 4, 3, 3, 2, 0, 0, 4, 7, 7, 5, 9, 5, 5, 6, 9,
-            4, 9, 3, 1, 5, 3, 1, 5, 3, 2, 2, 3, 4, 4, 7, 5, 0, 5, 4, 3, 3, 2, 1,
-            4, 1, 8, 5, 1, 7, 7, 7, 8, 6, 5, 2, 3, 2, 1, 3, 1, 0, 2, 0, 9, 8, 6,
-            5, 3, 4, 6, 6, 7, 5, 8, 1, 4, 8, 1, 4, 5, 7, 2, 1, 9, 1, 7, 1, 4, 5,
-            9, 8, 3, 1, 3, 8, 6, 3, 0, 0, 0, 6, 7, 4, 3, 4, 3, 6, 5, 0, 9, 0, 9,
-            4, 2, 8, 4, 1, 7, 3, 9, 7, 6, 5, 2, 8, 0, 8, 3, 8, 6, 2, 3, 3, 0, 8,
-            0, 2, 9, 7, 7, 1, 4, 7, 3, 8, 2, 0, 1, 6, 2, 4, 1, 5, 1, 2, 7, 2, 0,
-            3, 1, 6, 0, 4, 4, 5, 5, 6, 6, 5, 1, 7, 7, 0, 8, 6, 3, 5, 8, 4, 4, 8,
-            2, 6, 3, 8, 1, 2, 9, 4, 3, 7, 4, 2, 9, 4, 0, 1, 4, 7, 8, 0, 3, 1, 2,
-            0, 8, 5, 6, 5, 7, 6, 3, 9, 8, 7, 1, 2, 4, 2, 4, 2, 2, 3, 2, 3, 3, 2,
-            5, 7, 5, 5, 1, 0, 6, 2, 2, 3, 6, 3, 3, 4, 2, 1, 8, 4, 2, 8, 7, 6, 1,
-            2, 2, 5, 9, 7, 1, 5, 2, 8, 6, 5, 5, 1, 7, 2, 6, 5, 7, 8, 3, 3, 5, 8,
-            6, 4, 8, 0, 0, 4, 2, 6, 3, 2, 8, 3, 1, 6, 8, 4, 8, 9, 3, 8, 3, 3, 6,
-            5, 3, 4, 9, 0, 3, 4, 2, 8, 1, 9, 8, 0, 6, 6, 5, 7, 9, 3, 0, 3, 9, 2,
-            9, 1, 1, 3, 2, 7, 7, 6, 8, 2, 8, 9, 0, 6, 2, 5, 1, 6, 5, 7, 9, 2, 1,
-            2, 1, 3, 8, 2, 9, 9, 8, 0, 4, 7, 3, 6, 3, 8, 4, 4, 3, 9, 7, 1, 5, 0,
-            1, 3, 4, 7, 0, 6, 9, 6, 2, 1, 1, 2, 3, 2, 0, 9, 8, 8, 2, 9, 4, 8, 1,
-            7, 9, 7, 8, 2, 6, 2, 5, 6, 1, 5, 7, 4, 4, 4, 4, 0, 1, 4, 9, 1, 5, 4,
-            4, 7, 1, 8, 3, 3, 8, 6, 9, 4, 4, 0, 6, 6, 9, 4, 2, 8, 5, 6, 7, 2, 3,
-            2, 2, 9, 9, 7, 6, 1, 4, 0, 2, 4, 4, 7, 2, 2, 7, 5, 5, 4, 1, 9, 0, 9,
-            4, 1, 8, 6, 9, 6, 3, 6, 4, 9, 5, 1, 0, 4, 0, 4, 6, 7, 1, 6, 3, 3, 1,
-            5, 3, 7, 7, 4, 4, 7, 4, 3, 5, 5, 9, 6, 3, 3, 3, 6, 8, 3, 5, 7, 2, 7,
-            6, 4, 3, 1, 6, 8, 8, 4, 9, 5, 9, 1, 0, 4, 8, 5, 7, 5, 7, 1, 8, 2, 3,
-            1, 5, 1, 9, 7, 9, 0, 3, 9, 3, 2, 5, 1, 9, 9, 9, 9, 4, 8, 7, 4, 2, 4,
-            9, 3, 4, 6, 7, 9, 0, 0, 3, 6, 9, 8, 1, 8, 1, 9, 1, 5, 1, 3, 5, 2, 4,
-            8, 8, 7, 8, 0, 3, 0, 6, 9, 4, 5, 9, 3, 8, 8, 7, 9, 7, 9, 7, 6, 7, 5,
-            5, 4, 0, 9, 9, 1, 0, 7, 5, 9, 7, 8, 0, 2, 0, 2, 4, 4, 0, 4, 7, 5, 1,
-            9, 7, 1, 2, 6, 7, 4, 1, 5, 8, 6, 8, 6, 6, 1, 9, 4, 4, 9, 9, 7, 6, 8,
-            4, 9, 3, 6, 6, 1, 1, 9, 6, 9, 1, 9, 2, 5, 1, 5, 6, 0, 1, 1, 2, 1, 1,
-            6, 8, 6, 6, 9, 1, 6, 1, 3, 2, 2, 5, 4, 2, 6, 0, 2, 2, 5, 0, 1, 2, 5,
-            4, 7, 2, 4, 8, 6, 5, 0, 8, 1, 0, 8, 1, 4, 3, 9, 0, 6, 3, 7, 3, 4, 5,
-            6, 0, 9, 7, 3, 1, 5, 7, 8, 0, 3, 2, 9, 3, 0, 3, 3, 1, 6, 4, 0, 6, 9,
-            4, 9, 9, 1, 9, 6, 7, 3, 3, 3, 5, 8, 8, 9, 3, 6, 9, 1, 7, 1, 5, 8, 4,
-            2, 3, 8, 7, 1, 3, 4, 0, 7, 5, 5, 5, 1, 4, 4, 2, 7, 8, 1, 9, 3, 1, 5,
-            9, 6, 0, 6, 4, 7, 2, 1, 2, 3, 2, 4, 5, 6, 1, 5, 0, 1, 2, 7, 2, 5, 7,
-            6, 9, 9, 5, 9, 4, 5, 4, 1, 4, 0, 3, 9, 9, 1, 0, 4, 5, 4, 7, 7, 1, 0,
-            3, 5, 3, 5, 0, 9, 9, 7, 8, 9, 3, 0, 2, 0, 5, 3, 1, 3, 4, 0, 2, 6, 4,
-            6, 4, 7, 4, 1, 8, 4, 8, 6, 1, 1, 0, 2, 3, 0, 5, 1, 9, 2, 1, 0, 9, 3,
-            0, 0, 4, 4, 7, 5
-          ],
-          [67, 66]);
-
-      const c = tf.matMul(a, b);
-      const cData = await c.data();
-
-      expect(c.shape).toEqual([65, 66]);
-      test_util.expectArraysClose(cData, [
-        1017, 1086, 958,  925,  1117, 1018, 1092, 1180, 1055, 1141, 1061, 970,
-        1200, 969,  1066, 1007, 1241, 909,  980,  1141, 1084, 889,  971,  1042,
-        1087, 985,  1019, 929,  1112, 1239, 1155, 966,  984,  1154, 1146, 1117,
-        1264, 1059, 960,  1265, 1024, 1071, 1288, 1086, 1219, 1070, 1168, 962,
-        1098, 1041, 937,  1002, 1102, 1110, 940,  1120, 1028, 1062, 1162, 1049,
-        904,  1065, 1056, 1230, 853,  1013, 1579, 1519, 1552, 1377, 1478, 1375,
-        1397, 1820, 1672, 1626, 1633, 1374, 1514, 1420, 1371, 1357, 1689, 1346,
-        1378, 1497, 1424, 1387, 1495, 1601, 1455, 1594, 1716, 1375, 1459, 1523,
-        1601, 1615, 1583, 1462, 1669, 1600, 1879, 1527, 1370, 1692, 1496, 1541,
-        1894, 1470, 1784, 1530, 1792, 1410, 1615, 1713, 1465, 1420, 1671, 1637,
-        1472, 1473, 1421, 1389, 1638, 1330, 1329, 1531, 1475, 1720, 1592, 1416,
-        1759, 1748, 1467, 1582, 1749, 1484, 1636, 1844, 1601, 1643, 1683, 1514,
-        1721, 1438, 1580, 1511, 1716, 1480, 1477, 1671, 1765, 1478, 1577, 1617,
-        1638, 1612, 1633, 1449, 1690, 1733, 1758, 1591, 1810, 1655, 1911, 1447,
-        1939, 1687, 1401, 1655, 1669, 1624, 2213, 1648, 1867, 1745, 1872, 1551,
-        1799, 1824, 1505, 1542, 1635, 1826, 1590, 1596, 1598, 1416, 1645, 1470,
-        1352, 1511, 1773, 1780, 1593, 1534, 1424, 1329, 1326, 1238, 1426, 1276,
-        1175, 1584, 1398, 1429, 1525, 1254, 1505, 1137, 1213, 1242, 1575, 1174,
-        1169, 1379, 1433, 1121, 1357, 1371, 1322, 1193, 1412, 1197, 1294, 1392,
-        1362, 1403, 1576, 1291, 1458, 1340, 1664, 1247, 1186, 1359, 1283, 1290,
-        1624, 1326, 1520, 1423, 1613, 1339, 1417, 1480, 1399, 1185, 1427, 1356,
-        1397, 1303, 1248, 1208, 1419, 1125, 1158, 1330, 1203, 1532, 1220, 1325,
-        1231, 1121, 1095, 974,  1179, 953,  1085, 1392, 1102, 1154, 1314, 1045,
-        1204, 999,  1059, 1089, 1305, 1053, 973,  1190, 1200, 937,  1097, 1184,
-        1143, 1131, 1182, 1121, 1219, 1233, 1134, 1251, 1063, 1151, 1209, 1144,
-        1362, 1261, 1026, 1398, 1007, 1093, 1363, 1082, 1316, 1070, 1237, 1068,
-        1220, 1238, 1094, 1076, 1152, 1202, 1114, 1221, 1159, 1184, 1200, 983,
-        905,  1127, 1203, 1310, 924,  1198, 1435, 1411, 1119, 1250, 1485, 1265,
-        1245, 1584, 1370, 1411, 1452, 1162, 1425, 1150, 1254, 1227, 1508, 1200,
-        1171, 1468, 1419, 1218, 1239, 1186, 1198, 1255, 1368, 1223, 1478, 1354,
-        1299, 1267, 1336, 1300, 1620, 1226, 1577, 1435, 1317, 1482, 1259, 1231,
-        1568, 1326, 1524, 1329, 1477, 1231, 1404, 1388, 1254, 1184, 1301, 1414,
-        1385, 1393, 1283, 1292, 1357, 1211, 1062, 1243, 1309, 1487, 1240, 1286,
-        1508, 1506, 1214, 1284, 1419, 1317, 1234, 1545, 1556, 1442, 1439, 1175,
-        1441, 1304, 1355, 1308, 1519, 1154, 1267, 1275, 1411, 1240, 1460, 1292,
-        1292, 1197, 1451, 1181, 1371, 1391, 1339, 1293, 1392, 1465, 1514, 1363,
-        1644, 1279, 1137, 1500, 1311, 1502, 1699, 1402, 1518, 1464, 1522, 1181,
-        1545, 1604, 1299, 1460, 1273, 1425, 1256, 1251, 1342, 1278, 1416, 1213,
-        1231, 1246, 1322, 1521, 1200, 1295, 1665, 1411, 1342, 1406, 1607, 1287,
-        1335, 1776, 1378, 1636, 1504, 1329, 1573, 1227, 1453, 1305, 1655, 1318,
-        1224, 1443, 1547, 1345, 1425, 1369, 1400, 1191, 1581, 1284, 1484, 1511,
-        1430, 1342, 1553, 1520, 1580, 1386, 1709, 1419, 1258, 1477, 1379, 1567,
-        1765, 1446, 1611, 1399, 1693, 1281, 1469, 1561, 1331, 1398, 1403, 1473,
-        1353, 1394, 1426, 1348, 1403, 1235, 1166, 1333, 1390, 1653, 1174, 1298,
-        1620, 1389, 1235, 1201, 1390, 1262, 1361, 1576, 1483, 1387, 1324, 1099,
-        1354, 1125, 1234, 1196, 1394, 1142, 1135, 1366, 1253, 1085, 1226, 1238,
-        1221, 1155, 1295, 1242, 1355, 1375, 1379, 1277, 1474, 1359, 1552, 1296,
-        1469, 1220, 1141, 1337, 1206, 1230, 1476, 1198, 1483, 1465, 1536, 1115,
-        1447, 1378, 1203, 1311, 1280, 1305, 1345, 1361, 1277, 1292, 1220, 1231,
-        1135, 1270, 1280, 1362, 1185, 1240, 1250, 1243, 1229, 1124, 1187, 1209,
-        1044, 1315, 1281, 1378, 1319, 1148, 1331, 1043, 1075, 1152, 1410, 1077,
-        1234, 1224, 1159, 1124, 1192, 1244, 1225, 1205, 1436, 1178, 1158, 1239,
-        1225, 1214, 1334, 1206, 1427, 1202, 1473, 1268, 1066, 1366, 1225, 1302,
-        1523, 1167, 1418, 1158, 1397, 1043, 1281, 1254, 1078, 1199, 1326, 1370,
-        1295, 1212, 1054, 1196, 1233, 1148, 1066, 1250, 1185, 1411, 1196, 1096,
-        1375, 1351, 1218, 1097, 1459, 1128, 1351, 1564, 1357, 1400, 1371, 1180,
-        1343, 1277, 1168, 1242, 1487, 1079, 1065, 1292, 1304, 1169, 1262, 1131,
-        1224, 1213, 1317, 1118, 1298, 1396, 1314, 1160, 1294, 1368, 1568, 1263,
-        1668, 1198, 981,  1346, 1113, 1362, 1544, 1403, 1545, 1254, 1492, 1206,
-        1403, 1467, 1234, 1263, 1216, 1249, 1214, 1370, 1332, 1327, 1287, 1101,
-        1083, 1192, 1464, 1396, 1112, 1380, 1572, 1428, 1350, 1355, 1516, 1294,
-        1407, 1742, 1510, 1569, 1602, 1191, 1450, 1302, 1421, 1338, 1574, 1213,
-        1125, 1440, 1362, 1199, 1307, 1322, 1422, 1449, 1479, 1335, 1427, 1526,
-        1547, 1321, 1502, 1483, 1598, 1440, 1669, 1406, 1233, 1483, 1331, 1409,
-        1575, 1450, 1561, 1422, 1530, 1231, 1587, 1621, 1258, 1295, 1338, 1465,
-        1495, 1489, 1454, 1504, 1375, 1264, 1249, 1235, 1371, 1548, 1235, 1260,
-        1578, 1523, 1356, 1263, 1589, 1304, 1291, 1747, 1575, 1527, 1549, 1407,
-        1424, 1201, 1369, 1408, 1645, 1203, 1322, 1370, 1488, 1225, 1387, 1362,
-        1296, 1398, 1553, 1328, 1459, 1621, 1399, 1497, 1568, 1522, 1556, 1517,
-        1872, 1516, 1257, 1576, 1393, 1467, 1766, 1468, 1617, 1512, 1512, 1312,
-        1678, 1615, 1444, 1448, 1438, 1550, 1513, 1298, 1487, 1466, 1514, 1263,
-        1266, 1271, 1474, 1747, 1270, 1418, 1704, 1559, 1474, 1575, 1725, 1490,
-        1461, 1846, 1711, 1777, 1693, 1471, 1652, 1478, 1447, 1557, 1734, 1361,
-        1398, 1645, 1604, 1397, 1644, 1460, 1488, 1497, 1639, 1450, 1546, 1625,
-        1611, 1644, 1671, 1655, 1753, 1536, 1912, 1628, 1449, 1774, 1562, 1618,
-        1928, 1526, 1843, 1649, 1800, 1510, 1694, 1791, 1477, 1531, 1564, 1718,
-        1643, 1452, 1569, 1442, 1670, 1525, 1425, 1476, 1623, 1747, 1486, 1595,
-        1248, 1134, 1095, 972,  1315, 1142, 1148, 1371, 1321, 1256, 1176, 1080,
-        1188, 1024, 1138, 1098, 1351, 1086, 1039, 1286, 1181, 892,  975,  1051,
-        1118, 1131, 1258, 1088, 1197, 1231, 1238, 994,  1228, 1203, 1176, 1218,
-        1446, 1103, 1053, 1160, 1056, 1068, 1314, 1191, 1275, 1269, 1291, 1075,
-        1228, 1265, 1185, 1204, 1111, 1287, 1124, 1257, 1115, 1196, 1051, 981,
-        1001, 1261, 1155, 1245, 1051, 1148, 1350, 1655, 1178, 1205, 1437, 1279,
-        1376, 1558, 1514, 1487, 1483, 1280, 1492, 1181, 1209, 1460, 1621, 1113,
-        1453, 1345, 1425, 1138, 1307, 1335, 1380, 1369, 1429, 1251, 1365, 1479,
-        1469, 1342, 1461, 1258, 1722, 1327, 1687, 1380, 1233, 1386, 1341, 1386,
-        1641, 1441, 1515, 1497, 1570, 1275, 1566, 1550, 1290, 1408, 1483, 1568,
-        1340, 1378, 1353, 1363, 1272, 1241, 1227, 1266, 1557, 1501, 1354, 1345,
-        1440, 1161, 1078, 1038, 1201, 1212, 1115, 1370, 1272, 1340, 1242, 1054,
-        1261, 931,  1079, 1078, 1378, 1039, 1018, 1073, 1285, 991,  1110, 1133,
-        1119, 997,  1212, 1060, 1122, 1303, 1114, 1206, 1330, 1235, 1266, 1165,
-        1395, 1046, 1052, 1124, 1183, 1131, 1495, 1132, 1310, 1250, 1293, 1200,
-        1218, 1193, 1060, 1139, 1208, 1298, 1317, 1053, 1086, 1079, 1120, 1006,
-        1070, 1108, 1159, 1322, 985,  1136, 1524, 1463, 1256, 1184, 1500, 1327,
-        1405, 1638, 1388, 1430, 1444, 1176, 1537, 1318, 1180, 1245, 1594, 1107,
-        1070, 1370, 1322, 1272, 1309, 1325, 1465, 1258, 1487, 1339, 1404, 1587,
-        1466, 1395, 1427, 1467, 1601, 1377, 1794, 1464, 1106, 1529, 1206, 1482,
-        1660, 1415, 1680, 1351, 1658, 1304, 1435, 1596, 1260, 1319, 1378, 1387,
-        1400, 1406, 1397, 1327, 1561, 1345, 1131, 1272, 1510, 1472, 1181, 1443,
-        1491, 1531, 1197, 1316, 1411, 1354, 1403, 1749, 1527, 1462, 1493, 1290,
-        1571, 1331, 1162, 1305, 1458, 1271, 1269, 1367, 1448, 1107, 1377, 1314,
-        1331, 1333, 1375, 1366, 1412, 1537, 1480, 1293, 1423, 1420, 1601, 1243,
-        1804, 1414, 1279, 1431, 1413, 1392, 1804, 1420, 1630, 1359, 1579, 1302,
-        1557, 1609, 1430, 1263, 1343, 1480, 1324, 1517, 1437, 1213, 1466, 1322,
-        1262, 1352, 1555, 1436, 1238, 1477, 1546, 1674, 1285, 1258, 1479, 1313,
-        1358, 1631, 1482, 1504, 1530, 1354, 1575, 1221, 1376, 1298, 1658, 1189,
-        1237, 1485, 1489, 1310, 1366, 1486, 1493, 1446, 1520, 1335, 1522, 1539,
-        1647, 1452, 1474, 1498, 1815, 1379, 1763, 1537, 1363, 1582, 1335, 1341,
-        1827, 1449, 1611, 1515, 1611, 1328, 1594, 1618, 1361, 1347, 1524, 1630,
-        1521, 1509, 1447, 1298, 1568, 1349, 1259, 1320, 1590, 1690, 1301, 1392,
-        1548, 1330, 1358, 1253, 1601, 1308, 1364, 1610, 1476, 1547, 1485, 1290,
-        1564, 1083, 1348, 1145, 1482, 1172, 1212, 1272, 1545, 1226, 1316, 1378,
-        1328, 1273, 1417, 1211, 1304, 1404, 1395, 1335, 1530, 1552, 1600, 1395,
-        1528, 1330, 1260, 1402, 1288, 1405, 1762, 1336, 1495, 1487, 1400, 1337,
-        1521, 1490, 1204, 1326, 1362, 1521, 1447, 1251, 1320, 1343, 1483, 1258,
-        1073, 1324, 1438, 1557, 1247, 1255, 1541, 1590, 1368, 1439, 1673, 1432,
-        1545, 1754, 1526, 1555, 1574, 1355, 1641, 1368, 1523, 1466, 1738, 1378,
-        1319, 1694, 1605, 1289, 1527, 1396, 1491, 1268, 1520, 1423, 1547, 1672,
-        1586, 1487, 1640, 1597, 1761, 1497, 1831, 1477, 1291, 1750, 1398, 1467,
-        1912, 1509, 1732, 1555, 1734, 1354, 1625, 1596, 1400, 1428, 1588, 1584,
-        1557, 1535, 1529, 1450, 1563, 1544, 1309, 1516, 1582, 1660, 1412, 1317,
-        1238, 1298, 1211, 1321, 1435, 1212, 1218, 1685, 1275, 1379, 1385, 1214,
-        1436, 1289, 1298, 1102, 1498, 1175, 1127, 1497, 1508, 1267, 1307, 1357,
-        1330, 1335, 1438, 1127, 1374, 1441, 1279, 1375, 1413, 1289, 1377, 1323,
-        1622, 1468, 1121, 1406, 1389, 1246, 1665, 1427, 1511, 1402, 1584, 1283,
-        1393, 1325, 1158, 1219, 1421, 1358, 1380, 1430, 1261, 1234, 1448, 1066,
-        1037, 1192, 1370, 1480, 1300, 1296, 1378, 1382, 1300, 1243, 1254, 1325,
-        1216, 1541, 1417, 1472, 1382, 1139, 1390, 1134, 1231, 1234, 1539, 1151,
-        1300, 1322, 1408, 1149, 1190, 1198, 1321, 1288, 1522, 1226, 1310, 1471,
-        1408, 1253, 1301, 1401, 1563, 1203, 1568, 1376, 1231, 1465, 1256, 1250,
-        1565, 1369, 1517, 1334, 1501, 1121, 1494, 1462, 1191, 1292, 1257, 1490,
-        1300, 1374, 1312, 1330, 1414, 1261, 1144, 1370, 1345, 1485, 1256, 1217,
-        1326, 1396, 1122, 1061, 1434, 1331, 1255, 1483, 1400, 1373, 1299, 1049,
-        1382, 1213, 1148, 1297, 1493, 1072, 1131, 1346, 1333, 1124, 1228, 1302,
-        1303, 1364, 1332, 1205, 1225, 1497, 1301, 1323, 1370, 1245, 1595, 1253,
-        1561, 1303, 1146, 1298, 1197, 1275, 1478, 1258, 1444, 1447, 1529, 1252,
-        1429, 1345, 1255, 1269, 1344, 1373, 1341, 1393, 1265, 1255, 1267, 1192,
-        1081, 1266, 1280, 1393, 1233, 1315, 1449, 1368, 1300, 1266, 1373, 1245,
-        1280, 1623, 1384, 1554, 1432, 1176, 1451, 1283, 1370, 1104, 1390, 1101,
-        1143, 1336, 1441, 1339, 1422, 1415, 1483, 1312, 1499, 1260, 1257, 1384,
-        1324, 1351, 1391, 1392, 1474, 1338, 1513, 1310, 1147, 1528, 1253, 1241,
-        1701, 1310, 1501, 1409, 1467, 1155, 1400, 1499, 1203, 1231, 1252, 1378,
-        1335, 1333, 1238, 1188, 1423, 1209, 1105, 1304, 1452, 1556, 1258, 1178,
-        1272, 1456, 1125, 1095, 1353, 1133, 1121, 1383, 1313, 1381, 1451, 1144,
-        1380, 1146, 1012, 1212, 1417, 1018, 1085, 1287, 1239, 1032, 1191, 1217,
-        1283, 1199, 1366, 1226, 1288, 1428, 1415, 1169, 1331, 1280, 1396, 1245,
-        1596, 1280, 1146, 1361, 1268, 1253, 1551, 1302, 1338, 1246, 1431, 1130,
-        1486, 1373, 1185, 1241, 1189, 1384, 1184, 1296, 1352, 1259, 1363, 1177,
-        1050, 1180, 1210, 1436, 1114, 1256, 1632, 1461, 1415, 1322, 1474, 1415,
-        1471, 1916, 1663, 1626, 1569, 1387, 1541, 1310, 1530, 1386, 1726, 1309,
-        1341, 1427, 1481, 1333, 1438, 1519, 1468, 1545, 1523, 1466, 1426, 1619,
-        1545, 1535, 1585, 1446, 1656, 1575, 1943, 1491, 1364, 1645, 1397, 1431,
-        1841, 1485, 1650, 1508, 1649, 1436, 1559, 1813, 1608, 1347, 1557, 1641,
-        1586, 1569, 1463, 1450, 1587, 1246, 1373, 1437, 1516, 1746, 1383, 1616,
-        1719, 1645, 1450, 1405, 1522, 1454, 1419, 1883, 1583, 1691, 1617, 1492,
-        1800, 1442, 1633, 1457, 1722, 1304, 1379, 1552, 1825, 1495, 1611, 1524,
-        1519, 1485, 1752, 1437, 1532, 1630, 1553, 1544, 1653, 1736, 1781, 1452,
-        1795, 1614, 1389, 1532, 1589, 1568, 2020, 1577, 1753, 1714, 1848, 1433,
-        1722, 1693, 1430, 1602, 1618, 1739, 1640, 1563, 1544, 1501, 1522, 1388,
-        1409, 1443, 1634, 1807, 1467, 1484, 1611, 1589, 1547, 1421, 1635, 1359,
-        1402, 1797, 1567, 1662, 1769, 1438, 1587, 1414, 1611, 1496, 1788, 1359,
-        1363, 1486, 1676, 1319, 1491, 1553, 1557, 1430, 1785, 1403, 1576, 1666,
-        1585, 1495, 1540, 1595, 1742, 1432, 1890, 1444, 1354, 1781, 1362, 1530,
-        1879, 1630, 1769, 1504, 1661, 1296, 1730, 1593, 1382, 1456, 1566, 1629,
-        1566, 1626, 1519, 1559, 1449, 1407, 1258, 1467, 1503, 1766, 1416, 1372,
-        1406, 1337, 1185, 1092, 1354, 1131, 1090, 1465, 1212, 1393, 1363, 1183,
-        1386, 1217, 1246, 1043, 1421, 1150, 1052, 1209, 1351, 1155, 1181, 1267,
-        1228, 1126, 1332, 1168, 1278, 1209, 1325, 1155, 1323, 1338, 1342, 1149,
-        1574, 1166, 1163, 1313, 1215, 1266, 1658, 1260, 1380, 1296, 1346, 1250,
-        1385, 1203, 1165, 1255, 1282, 1291, 1228, 1304, 1202, 1134, 1277, 1069,
-        1009, 1166, 1271, 1434, 1057, 1104, 1301, 1347, 1176, 1160, 1377, 1129,
-        1359, 1574, 1328, 1359, 1261, 1018, 1411, 1174, 1305, 1218, 1485, 1083,
-        1120, 1398, 1329, 1168, 1258, 1314, 1389, 1347, 1352, 1224, 1391, 1404,
-        1494, 1281, 1342, 1323, 1454, 1148, 1570, 1355, 1211, 1519, 1106, 1106,
-        1605, 1237, 1521, 1431, 1500, 1200, 1494, 1460, 1194, 1169, 1304, 1362,
-        1353, 1263, 1203, 1143, 1354, 1293, 1149, 1347, 1392, 1470, 1250, 1270,
-        1505, 1545, 1340, 1220, 1514, 1245, 1442, 1607, 1501, 1546, 1570, 1300,
-        1454, 1279, 1390, 1392, 1654, 1325, 1183, 1435, 1576, 1125, 1301, 1327,
-        1337, 1381, 1455, 1303, 1410, 1497, 1569, 1474, 1412, 1591, 1595, 1312,
-        1751, 1441, 1255, 1485, 1367, 1321, 1703, 1422, 1537, 1376, 1543, 1323,
-        1657, 1503, 1385, 1307, 1384, 1494, 1471, 1495, 1454, 1313, 1436, 1347,
-        1163, 1447, 1509, 1579, 1248, 1342, 1489, 1378, 1253, 1272, 1442, 1287,
-        1278, 1624, 1429, 1389, 1398, 1172, 1462, 1345, 1301, 1217, 1510, 1282,
-        1106, 1457, 1433, 1176, 1341, 1331, 1331, 1343, 1397, 1347, 1405, 1457,
-        1459, 1389, 1471, 1406, 1566, 1489, 1645, 1404, 1204, 1511, 1294, 1417,
-        1751, 1263, 1530, 1434, 1563, 1356, 1530, 1583, 1384, 1389, 1468, 1443,
-        1278, 1315, 1451, 1237, 1422, 1206, 1191, 1331, 1358, 1641, 1187, 1236,
-        1272, 1202, 1111, 1073, 1256, 943,  1118, 1375, 1158, 1163, 1271, 1059,
-        1342, 1032, 1034, 1015, 1248, 1014, 969,  1202, 1144, 991,  1162, 1228,
-        1217, 1029, 1290, 1125, 1158, 1129, 1188, 1146, 1292, 1183, 1315, 1054,
-        1359, 1128, 901,  1336, 1102, 1160, 1476, 1138, 1284, 1212, 1441, 1017,
-        1244, 1276, 1090, 1198, 1154, 1278, 1107, 1167, 1162, 951,  1171, 1062,
-        1044, 1045, 1155, 1273, 1056, 1253, 1536, 1525, 1348, 1385, 1478, 1268,
-        1375, 1726, 1509, 1669, 1621, 1462, 1534, 1350, 1497, 1311, 1606, 1205,
-        1390, 1567, 1582, 1373, 1628, 1604, 1285, 1410, 1569, 1298, 1349, 1452,
-        1515, 1627, 1557, 1517, 1667, 1411, 1847, 1610, 1296, 1620, 1490, 1511,
-        1895, 1504, 1609, 1448, 1630, 1247, 1726, 1654, 1439, 1375, 1584, 1561,
-        1569, 1634, 1442, 1329, 1621, 1432, 1246, 1413, 1500, 1752, 1393, 1406,
-        1240, 1157, 1171, 1294, 1313, 1073, 1179, 1589, 1288, 1423, 1360, 1112,
-        1350, 1178, 1357, 1113, 1422, 1147, 1118, 1353, 1242, 1059, 1270, 1228,
-        1201, 1245, 1297, 1008, 1256, 1346, 1245, 1179, 1345, 1368, 1385, 1191,
-        1517, 1296, 1145, 1336, 1273, 1191, 1502, 1384, 1391, 1262, 1523, 1224,
-        1417, 1361, 1173, 972,  1294, 1239, 1282, 1329, 1195, 1104, 1351, 1162,
-        1115, 1133, 1235, 1430, 1156, 1067, 1250, 1201, 1268, 1283, 1299, 1070,
-        1086, 1462, 1265, 1336, 1417, 1296, 1300, 1233, 1302, 1236, 1427, 1185,
-        1142, 1433, 1299, 1114, 1425, 1298, 1116, 1263, 1457, 1220, 1211, 1165,
-        1303, 1307, 1263, 1221, 1380, 1134, 1596, 1255, 1248, 1396, 1298, 1255,
-        1556, 1321, 1302, 1222, 1442, 1120, 1424, 1448, 1300, 1109, 1366, 1358,
-        1339, 1408, 1270, 1225, 1403, 1122, 1072, 1253, 1169, 1420, 1233, 1154,
-        1368, 1426, 1245, 1048, 1401, 1242, 1266, 1481, 1397, 1321, 1256, 1221,
-        1433, 1103, 1123, 1238, 1465, 1067, 1243, 1320, 1220, 1097, 1172, 1234,
-        1334, 1312, 1503, 1281, 1276, 1348, 1453, 1178, 1479, 1304, 1441, 1257,
-        1602, 1284, 1166, 1415, 1283, 1277, 1613, 1311, 1495, 1455, 1409, 1056,
-        1500, 1419, 1311, 1347, 1309, 1413, 1325, 1232, 1239, 1177, 1244, 1154,
-        1129, 1286, 1262, 1394, 1245, 1139, 1707, 1657, 1377, 1465, 1391, 1324,
-        1401, 1842, 1617, 1663, 1545, 1338, 1506, 1325, 1483, 1395, 1627, 1353,
-        1336, 1422, 1585, 1300, 1451, 1407, 1435, 1416, 1544, 1432, 1506, 1646,
-        1604, 1475, 1611, 1519, 1689, 1300, 1840, 1557, 1299, 1537, 1513, 1452,
-        1859, 1569, 1827, 1640, 1769, 1358, 1543, 1728, 1411, 1316, 1479, 1572,
-        1553, 1438, 1506, 1269, 1560, 1387, 1420, 1294, 1472, 1660, 1415, 1370,
-        1492, 1410, 1263, 1313, 1665, 1278, 1329, 1687, 1492, 1574, 1431, 1245,
-        1518, 1314, 1449, 1300, 1586, 1203, 1134, 1458, 1571, 1264, 1307, 1403,
-        1494, 1402, 1546, 1279, 1434, 1504, 1590, 1362, 1458, 1481, 1734, 1388,
-        1758, 1511, 1298, 1546, 1265, 1427, 1734, 1461, 1594, 1440, 1542, 1272,
-        1520, 1660, 1308, 1293, 1431, 1508, 1523, 1487, 1499, 1330, 1447, 1320,
-        1113, 1380, 1420, 1625, 1469, 1385, 1410, 1520, 1232, 1170, 1427, 1109,
-        1281, 1567, 1371, 1503, 1385, 1274, 1453, 1228, 1350, 1230, 1557, 1212,
-        1208, 1482, 1421, 1137, 1228, 1440, 1329, 1361, 1463, 1402, 1336, 1410,
-        1456, 1391, 1366, 1475, 1673, 1258, 1577, 1436, 1195, 1546, 1275, 1346,
-        1699, 1387, 1456, 1327, 1510, 1195, 1467, 1419, 1208, 1280, 1413, 1493,
-        1463, 1540, 1445, 1251, 1335, 1187, 1071, 1364, 1387, 1599, 1273, 1263,
-        1522, 1385, 1268, 1340, 1510, 1267, 1283, 1559, 1429, 1447, 1563, 1152,
-        1455, 1100, 1255, 1308, 1508, 1304, 1175, 1278, 1372, 1083, 1236, 1424,
-        1273, 1169, 1540, 1263, 1473, 1487, 1466, 1315, 1535, 1383, 1545, 1344,
-        1636, 1303, 1225, 1402, 1262, 1322, 1717, 1223, 1510, 1482, 1598, 1234,
-        1599, 1397, 1262, 1408, 1330, 1399, 1354, 1337, 1278, 1282, 1297, 1331,
-        1092, 1186, 1297, 1409, 1264, 1330, 1232, 1201, 984,  1019, 1146, 996,
-        963,  1329, 1181, 1239, 1177, 972,  1150, 1087, 1042, 1056, 1201, 1006,
-        929,  1144, 1170, 1049, 1058, 1128, 1024, 1219, 1219, 1007, 1104, 1219,
-        1149, 1097, 1197, 1311, 1254, 1061, 1378, 1164, 836,  1294, 1110, 1146,
-        1510, 1129, 1325, 1160, 1190, 1000, 1327, 1143, 1033, 1101, 1103, 1214,
-        1206, 1056, 1146, 998,  1111, 1002, 929,  987,  1226, 1343, 1015, 966,
-        1361, 1307, 1148, 1274, 1428, 1312, 1165, 1505, 1407, 1348, 1288, 1118,
-        1335, 1191, 1258, 1232, 1487, 1072, 1174, 1287, 1389, 1219, 1257, 1296,
-        1311, 1343, 1396, 1261, 1258, 1489, 1249, 1410, 1405, 1150, 1504, 1331,
-        1612, 1316, 1050, 1450, 1231, 1326, 1589, 1251, 1512, 1411, 1482, 1198,
-        1433, 1429, 1249, 1337, 1218, 1426, 1365, 1257, 1296, 1219, 1299, 1203,
-        1103, 1207, 1410, 1473, 1257, 1278, 1234, 1158, 1162, 984,  1187, 1163,
-        1142, 1360, 1207, 1173, 1058, 1164, 1287, 927,  1099, 1142, 1279, 1023,
-        1036, 1177, 1197, 1067, 1135, 1255, 1183, 1159, 1151, 1012, 1097, 1124,
-        1157, 1130, 1189, 1230, 1232, 1233, 1442, 1113, 992,  1331, 1178, 1195,
-        1454, 1201, 1348, 1138, 1298, 992,  1154, 1209, 1066, 1188, 1305, 1215,
-        1188, 1221, 1104, 1053, 1191, 1002, 968,  1131, 1140, 1302, 1052, 1055,
-        1579, 1537, 1246, 1301, 1496, 1260, 1336, 1718, 1460, 1499, 1491, 1246,
-        1512, 1275, 1439, 1351, 1648, 1175, 1188, 1337, 1471, 1236, 1371, 1454,
-        1359, 1368, 1394, 1346, 1493, 1608, 1488, 1526, 1476, 1547, 1576, 1289,
-        1795, 1563, 1278, 1619, 1290, 1455, 1834, 1285, 1598, 1462, 1533, 1292,
-        1649, 1571, 1284, 1440, 1353, 1424, 1636, 1414, 1432, 1337, 1446, 1345,
-        1230, 1322, 1538, 1550, 1210, 1393, 1463, 1526, 1243, 1299, 1427, 1263,
-        1319, 1546, 1461, 1499, 1497, 1269, 1379, 1281, 1331, 1223, 1534, 1171,
-        1248, 1352, 1486, 1169, 1395, 1347, 1247, 1324, 1472, 1367, 1384, 1425,
-        1496, 1567, 1531, 1475, 1484, 1288, 1716, 1412, 1208, 1424, 1239, 1289,
-        1703, 1298, 1482, 1542, 1553, 1273, 1643, 1560, 1259, 1406, 1347, 1607,
-        1532, 1294, 1294, 1212, 1447, 1322, 1250, 1340, 1445, 1578, 1303, 1296,
-        1555, 1566, 1232, 1283, 1518, 1393, 1293, 1631, 1575, 1520, 1523, 1184,
-        1416, 1279, 1344, 1293, 1574, 1270, 1229, 1286, 1449, 1368, 1471, 1482,
-        1449, 1443, 1552, 1430, 1420, 1368, 1455, 1511, 1476, 1268, 1685, 1437,
-        1921, 1412, 1299, 1643, 1257, 1430, 1818, 1447, 1685, 1381, 1608, 1158,
-        1562, 1625, 1354, 1363, 1320, 1504, 1482, 1507, 1359, 1260, 1560, 1369,
-        1225, 1434, 1425, 1659, 1419, 1419, 1531, 1696, 1102, 1243, 1486, 1364,
-        1261, 1619, 1443, 1455, 1517, 1151, 1503, 1255, 1215, 1286, 1515, 1283,
-        1136, 1332, 1478, 1104, 1301, 1333, 1390, 1355, 1434, 1335, 1396, 1486,
-        1485, 1231, 1476, 1414, 1738, 1286, 1749, 1386, 1224, 1433, 1375, 1349,
-        1649, 1449, 1520, 1401, 1656, 1209, 1498, 1343, 1317, 1336, 1263, 1528,
-        1376, 1468, 1399, 1337, 1427, 1354, 1205, 1263, 1390, 1507, 1324, 1346,
-        1366, 1368, 1219, 1130, 1323, 1177, 1246, 1517, 1321, 1418, 1329, 1211,
-        1279, 1151, 1384, 1083, 1433, 1181, 1126, 1336, 1451, 1116, 1312, 1242,
-        1306, 1255, 1264, 1142, 1303, 1344, 1346, 1160, 1334, 1351, 1458, 1305,
-        1604, 1304, 1164, 1377, 1258, 1199, 1555, 1351, 1427, 1311, 1388, 1140,
-        1359, 1326, 1176, 1116, 1304, 1309, 1432, 1466, 1236, 1194, 1362, 1096,
-        1196, 1276, 1203, 1529, 1063, 1233, 1412, 1333, 1315, 1207, 1375, 1122,
-        1180, 1513, 1390, 1444, 1399, 1305, 1355, 1109, 1248, 1330, 1534, 1252,
-        1190, 1368, 1317, 1090, 1336, 1314, 1246, 1181, 1440, 1177, 1324, 1375,
-        1347, 1255, 1365, 1412, 1473, 1271, 1590, 1248, 1194, 1526, 1368, 1279,
-        1705, 1348, 1472, 1277, 1476, 1153, 1481, 1412, 1320, 1215, 1227, 1384,
-        1251, 1251, 1242, 1229, 1497, 1190, 1134, 1347, 1318, 1524, 1238, 1294,
-        1437, 1465, 1218, 1282, 1365, 1284, 1280, 1539, 1428, 1407, 1296, 1229,
-        1463, 1299, 1215, 1230, 1509, 1138, 1244, 1448, 1432, 1125, 1389, 1249,
-        1351, 1250, 1424, 1177, 1318, 1447, 1283, 1307, 1461, 1400, 1494, 1221,
-        1643, 1325, 1123, 1467, 1290, 1322, 1627, 1380, 1476, 1307, 1565, 1210,
-        1401, 1396, 1286, 1246, 1317, 1374, 1363, 1357, 1284, 1163, 1432, 1145,
-        1215, 1406, 1461, 1522, 1199, 1286, 1266, 1313, 1081, 1091, 1227, 1095,
-        1082, 1344, 1166, 1319, 1199, 1105, 1274, 1183, 1232, 1097, 1437, 1075,
-        1039, 1225, 1245, 1038, 1117, 1212, 1236, 1148, 1352, 1136, 1265, 1296,
-        1282, 1183, 1152, 1325, 1395, 1119, 1471, 1202, 1132, 1296, 1170, 1209,
-        1496, 1208, 1306, 1151, 1417, 1165, 1274, 1249, 998,  1132, 1150, 1326,
-        1201, 1348, 1186, 1155, 1250, 1129, 1044, 1185, 1204, 1450, 1142, 1174,
-        1708, 1579, 1418, 1431, 1637, 1474, 1386, 1708, 1633, 1650, 1654, 1333,
-        1540, 1327, 1383, 1312, 1789, 1259, 1333, 1449, 1590, 1277, 1367, 1499,
-        1490, 1379, 1642, 1351, 1473, 1700, 1496, 1524, 1631, 1453, 1683, 1451,
-        1699, 1543, 1288, 1647, 1415, 1455, 1704, 1494, 1672, 1532, 1664, 1409,
-        1554, 1592, 1340, 1373, 1481, 1706, 1544, 1521, 1514, 1439, 1579, 1368,
-        1332, 1494, 1468, 1620, 1355, 1405, 1692, 1700, 1486, 1480, 1806, 1522,
-        1557, 1823, 1703, 1790, 1883, 1414, 1765, 1355, 1531, 1542, 1806, 1318,
-        1570, 1560, 1795, 1430, 1576, 1619, 1663, 1551, 1613, 1422, 1530, 1734,
-        1738, 1571, 1743, 1592, 1891, 1502, 1942, 1586, 1521, 1624, 1463, 1502,
-        1952, 1580, 1751, 1784, 1820, 1606, 1829, 1805, 1601, 1425, 1716, 1821,
-        1640, 1581, 1562, 1546, 1563, 1641, 1328, 1494, 1700, 1806, 1567, 1569,
-        1447, 1238, 1155, 1199, 1268, 1088, 1217, 1544, 1316, 1332, 1333, 1164,
-        1339, 1135, 1184, 1154, 1342, 1146, 1156, 1226, 1242, 1085, 1201, 1226,
-        1192, 1252, 1302, 1121, 1303, 1295, 1380, 1249, 1291, 1232, 1395, 1161,
-        1563, 1369, 1117, 1195, 1288, 1256, 1551, 1153, 1506, 1254, 1555, 1219,
-        1320, 1408, 1265, 1034, 1219, 1345, 1255, 1242, 1200, 1096, 1412, 1127,
-        1209, 1253, 1339, 1436, 1226, 1177, 1449, 1488, 1239, 1163, 1459, 1295,
-        1434, 1414, 1402, 1489, 1337, 1173, 1321, 1226, 1360, 1292, 1522, 1112,
-        1288, 1372, 1330, 1216, 1324, 1272, 1371, 1398, 1436, 1276, 1260, 1459,
-        1530, 1236, 1491, 1399, 1588, 1221, 1686, 1401, 1257, 1407, 1130, 1322,
-        1580, 1292, 1491, 1409, 1470, 1201, 1534, 1527, 1281, 1287, 1376, 1490,
-        1402, 1442, 1272, 1255, 1311, 1409, 1143, 1352, 1431, 1555, 1234, 1191,
-        1341, 1206, 1169, 1179, 1426, 1222, 1235, 1566, 1237, 1410, 1442, 1143,
-        1339, 1115, 1274, 1323, 1555, 1135, 1102, 1279, 1277, 1113, 1267, 1226,
-        1243, 1276, 1297, 1188, 1203, 1373, 1252, 1263, 1323, 1333, 1398, 1284,
-        1662, 1244, 1115, 1416, 1191, 1322, 1535, 1319, 1415, 1155, 1415, 1267,
-        1371, 1429, 1189, 1224, 1212, 1377, 1378, 1334, 1331, 1271, 1235, 1187,
-        1064, 1171, 1333, 1473, 1134, 1220, 1592, 1529, 1386, 1437, 1543, 1418,
-        1423, 1742, 1590, 1687, 1604, 1302, 1567, 1287, 1299, 1281, 1536, 1403,
-        1353, 1352, 1502, 1325, 1422, 1499, 1447, 1350, 1647, 1499, 1578, 1529,
-        1581, 1429, 1600, 1461, 1599, 1519, 1728, 1372, 1300, 1684, 1394, 1555,
-        1914, 1459, 1677, 1555, 1644, 1115, 1657, 1588, 1263, 1475, 1417, 1547,
-        1309, 1357, 1379, 1428, 1466, 1383, 1189, 1446, 1399, 1601, 1362, 1292,
-        1243, 1332, 1139, 1040, 1241, 1272, 1185, 1448, 1242, 1319, 1317, 1027,
-        1398, 1068, 1168, 1157, 1443, 942,  1070, 1196, 1218, 1161, 1217, 1127,
-        1279, 1128, 1313, 1212, 1145, 1388, 1349, 1206, 1277, 1323, 1454, 1238,
-        1656, 1206, 1063, 1342, 1165, 1187, 1459, 1255, 1448, 1219, 1453, 1120,
-        1383, 1316, 1258, 1203, 1214, 1292, 1300, 1219, 1250, 1176, 1283, 1266,
-        1012, 1167, 1228, 1390, 1104, 1080, 1470, 1543, 1305, 1416, 1610, 1364,
-        1374, 1670, 1606, 1678, 1648, 1409, 1593, 1392, 1395, 1445, 1721, 1255,
-        1479, 1643, 1629, 1384, 1537, 1521, 1502, 1499, 1546, 1373, 1536, 1714,
-        1550, 1528, 1584, 1494, 1729, 1532, 1746, 1698, 1435, 1656, 1511, 1556,
-        1842, 1476, 1681, 1523, 1729, 1313, 1658, 1629, 1486, 1329, 1611, 1682,
-        1546, 1539, 1543, 1401, 1497, 1395, 1289, 1419, 1530, 1754, 1438, 1507,
-        1497, 1526, 1234, 1248, 1584, 1325, 1324, 1619, 1346, 1517, 1615, 1066,
-        1484, 1226, 1263, 1220, 1529, 1252, 1106, 1380, 1432, 1038, 1318, 1453,
-        1424, 1348, 1527, 1373, 1444, 1498, 1440, 1401, 1495, 1456, 1565, 1394,
-        1617, 1386, 1207, 1602, 1272, 1291, 1664, 1363, 1476, 1329, 1545, 1301,
-        1480, 1300, 1084, 1281, 1342, 1481, 1473, 1423, 1385, 1349, 1426, 1303,
-        1166, 1293, 1393, 1520, 1253, 1365, 1679, 1597, 1419, 1337, 1438, 1364,
-        1396, 1821, 1586, 1580, 1537, 1285, 1580, 1269, 1246, 1364, 1607, 1272,
-        1351, 1503, 1378, 1211, 1363, 1419, 1447, 1383, 1556, 1393, 1467, 1652,
-        1626, 1461, 1487, 1555, 1600, 1402, 1798, 1397, 1191, 1467, 1488, 1423,
-        1715, 1407, 1730, 1639, 1720, 1395, 1584, 1628, 1438, 1493, 1380, 1516,
-        1384, 1385, 1454, 1433, 1489, 1295, 1279, 1375, 1481, 1587, 1311, 1414,
-        1316, 1458, 1177, 1206, 1404, 1185, 1172, 1516, 1389, 1400, 1466, 1256,
-        1398, 1235, 1235, 1363, 1604, 1178, 1142, 1250, 1410, 1141, 1289, 1417,
-        1268, 1381, 1399, 1276, 1308, 1426, 1502, 1389, 1282, 1317, 1560, 1305,
-        1742, 1300, 1216, 1397, 1255, 1352, 1747, 1263, 1471, 1287, 1396, 1297,
-        1534, 1502, 1306, 1252, 1380, 1464, 1356, 1397, 1359, 1291, 1445, 1183,
-        1164, 1281, 1375, 1472, 1217, 1327
-      ]);
-    });
-
-    it('A x B vec4', async () => {
-      const a = tf.tensor3d(
-          [
-            2, 1, 3,  2,  1, 1, 1, 5,  6,  7, 8, 1, 2,  2,  1, 9, 11, 10, 1,
-            1, 3, 2,  1,  1, 2, 1, 3,  2,  1, 1, 1, 5,  6,  7, 8, 1,  2,  2,
-            1, 9, 11, 10, 1, 1, 3, 2,  1,  1, 2, 1, 3,  2,  1, 1, 1,  5,  6,
-            7, 8, 1,  2,  2, 1, 9, 11, 10, 1, 1, 3, 2,  1,  1, 2, 1,  3,  2,
-            1, 1, 1,  5,  6, 7, 8, 1,  2,  2, 1, 9, 11, 10, 1, 1, 3,  2,  1,
-            1, 2, 1,  3,  2, 1, 1, 1,  5,  6, 7, 8, 1,  2,  2, 1, 9,  11, 10,
-            1, 1, 3,  2,  1, 1, 2, 1,  3,  2, 1, 1, 1,  5
-          ],
-          [1, 32, 4]);
-
-      const b = tf.tensor3d(
-          [
-            2, 1, 3,  2,  1, 1, 1, 5,  6,  7, 8, 1, 2,  2,  1, 9, 11, 10, 1,
-            1, 3, 2,  1,  1, 2, 1, 3,  2,  1, 1, 1, 5,  6,  7, 8, 1,  2,  2,
-            1, 9, 11, 10, 1, 1, 3, 2,  1,  1, 2, 1, 3,  2,  1, 1, 1,  5,  6,
-            7, 8, 1,  2,  2, 1, 9, 11, 10, 1, 1, 3, 2,  1,  1, 2, 1,  3,  2,
-            1, 1, 1,  5,  6, 7, 8, 1,  2,  2, 1, 9, 11, 10, 1, 1, 3,  2,  1,
-            1, 2, 1,  3,  2, 1, 1, 1,  5,  6, 7, 8, 1,  2,  2, 1, 9,  11, 10,
-            1, 1, 3,  2,  1, 1, 2, 1,  3,  2, 1, 1, 1,  5
-          ],
-          [1, 4, 32]);
-      const c = tf.matMul(a, b);
-      expect(c.shape).toEqual([1, 32, 32]);
-      test_util.expectArraysClose(await c.data(), [
-        47,  41,  23,  12, 15, 12, 8,  32,  41,  41,  42,  11, 14, 13, 8,  52,
-        64,  62,  31,  9,  19, 15, 8,  36,  47,  41,  23,  12, 15, 12, 8,  32,
-        29,  23,  27,  14, 11, 10, 8,  40,  49,  53,  52,  9,  16, 15, 8,  60,
-        74,  68,  17,  9,  21, 15, 8,  20,  29,  23,  27,  14, 11, 10, 8,  40,
-        144, 136, 85,  29, 45, 37, 22, 106, 135, 127, 87,  30, 43, 36, 22, 110,
-        139, 133, 92,  29, 44, 37, 22, 114, 144, 136, 85,  29, 45, 37, 22, 106,
-        45,  35,  50,  25, 18, 17, 14, 74,  90,  98,  93,  15, 29, 27, 14, 106,
-        131, 119, 25,  16, 37, 26, 14, 30,  45,  35,  50,  25, 18, 17, 14, 74,
-        95,  92,  117, 35, 35, 34, 23, 151, 184, 185, 109, 24, 55, 45, 23, 123,
-        158, 137, 50,  33, 48, 36, 23, 71,  95,  92,  117, 35, 35, 34, 23, 151,
-        31,  28,  29,  11, 11, 10, 7,  39,  48,  49,  37,  8,  15, 13, 7,  43,
-        54,  49,  18,  9,  16, 12, 7,  23,  31,  28,  29,  11, 11, 10, 7,  39,
-        47,  41,  23,  12, 15, 12, 8,  32,  41,  41,  42,  11, 14, 13, 8,  52,
-        64,  62,  31,  9,  19, 15, 8,  36,  47,  41,  23,  12, 15, 12, 8,  32,
-        29,  23,  27,  14, 11, 10, 8,  40,  49,  53,  52,  9,  16, 15, 8,  60,
-        74,  68,  17,  9,  21, 15, 8,  20,  29,  23,  27,  14, 11, 10, 8,  40,
-        144, 136, 85,  29, 45, 37, 22, 106, 135, 127, 87,  30, 43, 36, 22, 110,
-        139, 133, 92,  29, 44, 37, 22, 114, 144, 136, 85,  29, 45, 37, 22, 106,
-        45,  35,  50,  25, 18, 17, 14, 74,  90,  98,  93,  15, 29, 27, 14, 106,
-        131, 119, 25,  16, 37, 26, 14, 30,  45,  35,  50,  25, 18, 17, 14, 74,
-        95,  92,  117, 35, 35, 34, 23, 151, 184, 185, 109, 24, 55, 45, 23, 123,
-        158, 137, 50,  33, 48, 36, 23, 71,  95,  92,  117, 35, 35, 34, 23, 151,
-        31,  28,  29,  11, 11, 10, 7,  39,  48,  49,  37,  8,  15, 13, 7,  43,
-        54,  49,  18,  9,  16, 12, 7,  23,  31,  28,  29,  11, 11, 10, 7,  39,
-        47,  41,  23,  12, 15, 12, 8,  32,  41,  41,  42,  11, 14, 13, 8,  52,
-        64,  62,  31,  9,  19, 15, 8,  36,  47,  41,  23,  12, 15, 12, 8,  32,
-        29,  23,  27,  14, 11, 10, 8,  40,  49,  53,  52,  9,  16, 15, 8,  60,
-        74,  68,  17,  9,  21, 15, 8,  20,  29,  23,  27,  14, 11, 10, 8,  40,
-        144, 136, 85,  29, 45, 37, 22, 106, 135, 127, 87,  30, 43, 36, 22, 110,
-        139, 133, 92,  29, 44, 37, 22, 114, 144, 136, 85,  29, 45, 37, 22, 106,
-        45,  35,  50,  25, 18, 17, 14, 74,  90,  98,  93,  15, 29, 27, 14, 106,
-        131, 119, 25,  16, 37, 26, 14, 30,  45,  35,  50,  25, 18, 17, 14, 74,
-        95,  92,  117, 35, 35, 34, 23, 151, 184, 185, 109, 24, 55, 45, 23, 123,
-        158, 137, 50,  33, 48, 36, 23, 71,  95,  92,  117, 35, 35, 34, 23, 151,
-        31,  28,  29,  11, 11, 10, 7,  39,  48,  49,  37,  8,  15, 13, 7,  43,
-        54,  49,  18,  9,  16, 12, 7,  23,  31,  28,  29,  11, 11, 10, 7,  39,
-        47,  41,  23,  12, 15, 12, 8,  32,  41,  41,  42,  11, 14, 13, 8,  52,
-        64,  62,  31,  9,  19, 15, 8,  36,  47,  41,  23,  12, 15, 12, 8,  32,
-        29,  23,  27,  14, 11, 10, 8,  40,  49,  53,  52,  9,  16, 15, 8,  60,
-        74,  68,  17,  9,  21, 15, 8,  20,  29,  23,  27,  14, 11, 10, 8,  40,
-        144, 136, 85,  29, 45, 37, 22, 106, 135, 127, 87,  30, 43, 36, 22, 110,
-        139, 133, 92,  29, 44, 37, 22, 114, 144, 136, 85,  29, 45, 37, 22, 106,
-        45,  35,  50,  25, 18, 17, 14, 74,  90,  98,  93,  15, 29, 27, 14, 106,
-        131, 119, 25,  16, 37, 26, 14, 30,  45,  35,  50,  25, 18, 17, 14, 74,
-        95,  92,  117, 35, 35, 34, 23, 151, 184, 185, 109, 24, 55, 45, 23, 123,
-        158, 137, 50,  33, 48, 36, 23, 71,  95,  92,  117, 35, 35, 34, 23, 151,
-        31,  28,  29,  11, 11, 10, 7,  39,  48,  49,  37,  8,  15, 13, 7,  43,
-        54,  49,  18,  9,  16, 12, 7,  23,  31,  28,  29,  11, 11, 10, 7,  39,
-        47,  41,  23,  12, 15, 12, 8,  32,  41,  41,  42,  11, 14, 13, 8,  52,
-        64,  62,  31,  9,  19, 15, 8,  36,  47,  41,  23,  12, 15, 12, 8,  32,
-        29,  23,  27,  14, 11, 10, 8,  40,  49,  53,  52,  9,  16, 15, 8,  60,
-        74,  68,  17,  9,  21, 15, 8,  20,  29,  23,  27,  14, 11, 10, 8,  40,
-        144, 136, 85,  29, 45, 37, 22, 106, 135, 127, 87,  30, 43, 36, 22, 110,
-        139, 133, 92,  29, 44, 37, 22, 114, 144, 136, 85,  29, 45, 37, 22, 106,
-        45,  35,  50,  25, 18, 17, 14, 74,  90,  98,  93,  15, 29, 27, 14, 106,
-        131, 119, 25,  16, 37, 26, 14, 30,  45,  35,  50,  25, 18, 17, 14, 74,
-        95,  92,  117, 35, 35, 34, 23, 151, 184, 185, 109, 24, 55, 45, 23, 123,
-        158, 137, 50,  33, 48, 36, 23, 71,  95,  92,  117, 35, 35, 34, 23, 151,
-        31,  28,  29,  11, 11, 10, 7,  39,  48,  49,  37,  8,  15, 13, 7,  43,
-        54,  49,  18,  9,  16, 12, 7,  23,  31,  28,  29,  11, 11, 10, 7,  39,
-        47,  41,  23,  12, 15, 12, 8,  32,  41,  41,  42,  11, 14, 13, 8,  52,
-        64,  62,  31,  9,  19, 15, 8,  36,  47,  41,  23,  12, 15, 12, 8,  32,
-        29,  23,  27,  14, 11, 10, 8,  40,  49,  53,  52,  9,  16, 15, 8,  60,
-        74,  68,  17,  9,  21, 15, 8,  20,  29,  23,  27,  14, 11, 10, 8,  40
-      ]);
-    });
-
-    it('A x B vec4 A is a vector', async () => {
-      const a = tf.tensor3d([2, 1, 3, 2], [1, 1, 4]);
-
-      const b = tf.tensor3d(
-          [
-            2, 1, 3,  2,  1, 1, 1, 5,  6,  7, 8, 1, 2,  2,  1, 9, 11, 10, 1,
-            1, 3, 2,  1,  1, 2, 1, 3,  2,  1, 1, 1, 5,  6,  7, 8, 1,  2,  2,
-            1, 9, 11, 10, 1, 1, 3, 2,  1,  1, 2, 1, 3,  2,  1, 1, 1,  5,  6,
-            7, 8, 1,  2,  2, 1, 9, 11, 10, 1, 1, 3, 2,  1,  1, 2, 1,  3,  2,
-            1, 1, 1,  5,  6, 7, 8, 1,  2,  2, 1, 9, 11, 10, 1, 1, 3,  2,  1,
-            1, 2, 1,  3,  2, 1, 1, 1,  5,  6, 7, 8, 1,  2,  2, 1, 9,  11, 10,
-            1, 1, 3,  2,  1, 1, 2, 1,  3,  2, 1, 1, 1,  5
-          ],
-          [1, 4, 32]);
-      const c = tf.matMul(a, b);
-      expect(c.shape).toEqual([1, 1, 32]);
-      test_util.expectArraysClose(await c.data(), [
-        47, 41, 23, 12, 15, 12, 8, 32, 41, 41, 42, 11, 14, 13, 8, 52,
-        64, 62, 31, 9,  19, 15, 8, 36, 47, 41, 23, 12, 15, 12, 8, 32
-      ]);
-    });
-
-    it('A^t x B vec4', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d([1, 0, 2, 4, 3, 0, 5, 6], [2, 4]);
-
-      const transposeA = true;
-      const transposeB = false;
-      const c = tf.matMul(a, b, transposeA, transposeB);
-      const result = await c.data();
-      const expected =
-          [16, 0, 27, 34, 20, 0, 34, 44, 24, 0, 41, 54, 28, 0, 48, 64];
-      test_util.expectArraysClose(result, expected);
-    });
-
-    it('fused A x B vec4 with relu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d(
-          [0, 1, -3, 2, 2, 1, 1, 0, 2, 4, 3, 0, 5, -6, 7, -8], [4, 4]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'relu'});
-
-      expect(c.shape).toEqual([2, 4]);
-      expectArraysClose(await c.data(), [30, 0, 36, 0, 66, 0, 68, 0]);
-    });
-
-    it('fused A x B vec4 with elu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d(
-          [0, 1, -3, 2, 2, 1, 1, 0, 2, 4, 3, 0, 5, -6, 7, -8], [4, 4]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'elu'});
-
-      expect(c.shape).toEqual([2, 4]);
-      expectArraysClose(
-          await c.data(), [30, -0.9999, 36, -1, 66, -0.9999, 68, -1]);
-    });
-
-    it('fused A x B vec4 with relu6', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d(
-          [0, 1, -3, 2, 2, 1, 1, 0, 2, 4, 3, 0, 5, -6, 7, -8], [4, 4]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'relu6'});
-
-      expect(c.shape).toEqual([2, 4]);
-      expectArraysClose(await c.data(), [6, 0, 6, 0, 6, 0, 6, 0]);
-    });
-
-    it('fused A x B vec4 with prelu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d(
-          [0, 1, -3, 2, 2, 1, 1, 0, 2, 4, 3, 0, 5, -6, 7, -8], [4, 4]);
-      const alpha = tf.tensor2d([0.5, 0.5, 0.5, 0.5], [1, 4]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul({
-        a,
-        b,
-        transposeA,
-        transposeB,
-        bias: null,
-        activation: 'prelu',
-        preluActivationWeights: alpha
-      });
-
-      expect(c.shape).toEqual([2, 4]);
-      expectArraysClose(await c.data(), [30, -4.5, 36, -15, 66, -4.5, 68, -27]);
-    });
-
-    it('fused A x B vec4 with leakyrelu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d(
-          [0, 1, -3, 2, 2, 1, 1, 0, 2, 4, 3, 0, 5, -6, 7, -8], [4, 4]);
-      const alpha = 0.3;
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul({
-        a,
-        b,
-        transposeA,
-        transposeB,
-        bias: null,
-        activation: 'leakyrelu',
-        leakyreluAlpha: alpha
-      });
-
-      expect(c.shape).toEqual([2, 4]);
-      expectArraysClose(await c.data(), [
-        30, -2.700000047683716, 36, -9, 66, -2.700000047683716, 68,
-        -16.200000762939453
-      ]);
-    });
-
-    it('fused A x B vec4 with leakyrelu not provided.', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d(
-          [0, 1, -3, 2, 2, 1, 1, 0, 2, 4, 3, 0, 5, -6, 7, -8], [4, 4]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'leakyrelu'});
-
-      expect(c.shape).toEqual([2, 4]);
-      // leakyRelu should use default alpha=0.2.
-      expectArraysClose(await c.data(), [
-        30, -1.8000000715255737, 36, -6, 66, -1.8000000715255737, 68,
-        -10.800000190734863
-      ]);
-    });
-
-    it('fused A x B with sigmoid', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d(
-          [0, 1, -3, 2, 2, 1, 1, 0, 2, 4, 3, 0, 5, -6, 7, -8], [4, 4]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'sigmoid'});
-
-      expect(c.shape).toEqual([2, 4]);
-      expectArraysClose(await c.data(), [
-        1, 0.00012339462409727275, 1, 9.35763443186792e-14, 1,
-        0.00012339462409727275, 1, 3.5326268130932535e-24
-      ]);
-    });
-
-    it('fused A x B vec4 with 2d bias and relu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d(
-          [0, 1, -3, 2, 2, 1, 1, 0, 2, 4, 3, 0, 5, -6, 7, -8], [4, 4]);
-      const c = tf.tensor2d([1, 1, 1, 1, 1, 1, 1, 1], [2, 4]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const d = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: 'relu'});
-
-      expect(d.shape).toEqual([2, 4]);
-      expectArraysClose(await d.data(), [31, 0, 37, 0, 67, 0, 69, 0]);
-    });
-
-    it('fused A x B vec4 with relu and broadcasted bias', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      const b = tf.tensor2d(
-          [0, 1, -3, 2, 2, 1, 1, 0, 2, 4, 3, 0, 5, -6, 7, -8], [4, 4]);
-      const c = tf.tensor1d([1, 1, 1, 1]);
-      const act: tf.fused.Activation = 'relu';
-      const transposeA = false;
-      const transposeB = false;
-
-      const d = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: act});
-
-      expect(d.shape).toEqual([2, 4]);
-      expectArraysClose(await d.data(), [31, 0, 37, 0, 67, 0, 69, 0]);
-    });
-
-    // Below cases are from mat_mul_test.ts in tfjs-core.
-    it('A x B', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-      const c = tf.matMul(a, b);
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 8, -3, 20]);
-    });
-
-    it('A x B with M/N/K divisible by 4. [8,4]x[4,8]', async () => {
-      const a = tf.tensor2d(
-          [
-            1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
-            17, 18, 19, 20, 21, 22, 23, 24, 1, 2,  3,  4,  5,  6,  7,  8
-          ],
-          [8, 4]);
-      const b = tf.tensor2d(
-          [
-            0,  1,  -3, 2, 1,  -1, 0, 5,  6, 7, 8, 0, -2, -2, 1, 9,
-            11, 10, 0,  1, -3, 2,  1, -1, 1, 2, 3, 4, 5,  6,  7, 8
-          ],
-          [4, 8]);
-
-      const c = tf.matMul(a, b);
-      const cData = await c.data();
-
-      expect(c.shape).toEqual([8, 8]);
-      expectArraysClose(cData, [
-        49,  53,  25,  21,  8,   25,  33,  52,  121, 133, 57,  49,  12,
-        45,  69,  136, 193, 213, 89,  77,  16,  65,  105, 220, 265, 293,
-        121, 105, 20,  85,  141, 304, 337, 373, 153, 133, 24,  105, 177,
-        388, 409, 453, 185, 161, 28,  125, 213, 472, 49,  53,  25,  21,
-        8,   25,  33,  52,  121, 133, 57,  49,  12,  45,  69,  136
-      ]);
-    });
-
-    it('A x B with large K. [16,2048]x[2048,16]', async () => {
-      const a = tf.tensor2d(new Array(16 * 2048).fill(1), [16, 2048]);
-      const b = tf.tensor2d(new Array(2048 * 16).fill(1), [2048, 16]);
-
-      const c = tf.matMul(a, b);
-      const cData = await c.data();
-
-      expect(c.shape).toEqual([16, 16]);
-      expectArraysClose(cData, new Array(16 * 16).fill(2048));
-    });
-
-    it('matmul followed by mul', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-      const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-
-      const c = tf.matMul(a, b);
-
-      const f = tf.tensor2d([0, 1, 0.5, 0, 0.25, 2], [2, 3]);
-      const d = tf.mul(c, f);
-
-      const dData = await d.data();
-
-      expect(d.shape).toEqual([2, 3]);
-      expectArraysClose(dData, [0, 12, 7.5, 0, 6.5, 66]);
-    });
-
-    it('A x B^t', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
-
-      const transposeA = false;
-      const transposeB = true;
-      const c = tf.matMul(a, b, transposeA, transposeB);
-
-      const expected = [7, 10, 16, 31];
-      expectArraysClose(await c.data(), expected);
-    });
-
-    it('A^t x B', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
-
-      const transposeA = true;
-      const transposeB = false;
-      const c = tf.matMul(a, b, transposeA, transposeB);
-
-      const expected = [17, 12, 2, 22, 15, 4, 27, 18, 6];
-      expectArraysClose(await c.data(), expected);
-    });
-
-    it('A^t x B^t', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-      const b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
-
-      const transposeA = true;
-      const transposeB = true;
-      const c = tf.matMul(a, b, transposeA, transposeB);
-
-      const expected = [11, 13, 14, 20];
-      expectArraysClose(await c.data(), expected);
-    });
-
-    it('Vector times matrix', async () => {
-      const v = tf.tensor1d([2, 3]);
-      const matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-      const result = tf.dot(v, matrix);
-
-      const expected = [11, 16];
-      expectArraysClose(await result.data(), expected);
-    });
-
-    it('Vector times matrix with implicit reshape', async () => {
-      const v = tf.tensor1d([2, 3]);
-
-      const matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-      const result = tf.dot(v, matrix);
-
-      const expected = [11, 16];
-      expectArraysClose(await result.data(), expected);
-    });
-
-    it('Matrix times vector', async () => {
-      const matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-      const v = tf.tensor1d([2, 3]);
-      const result = tf.dot(matrix, v);
-
-      const expected = [8, 18];
-      expectArraysClose(await result.data(), expected);
-    });
-
-    it('accepts a tensor-like object', async () => {
-      const a = [[1, 2, 3], [4, 5, 6]];     // 2x3
-      const b = [[0, 1], [-3, 2], [2, 1]];  // 3x2
-      const c = tf.matMul(a, b);
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 8, -3, 20]);
-    });
-
-    it('accepts a tensor-like object chained', async () => {
-      const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3]);  // 2x3
-      const b = [[0, 1], [-3, 2], [2, 1]];                    // 3x2
-      const c = a.matMul(b);
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 8, -3, 20]);
-    });
-
-    it('a * b where a has zero in its shape', async () => {
-      const a = tf.tensor2d([], [0, 3]);
-      const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-      const c = tf.matMul(a, b);
-      expect(c.shape).toEqual([0, 2]);
-      expect(c.rank).toBe(2);
-      expect(c.size).toBe(0);
-      expectArraysClose(await c.data(), []);
-    });
-
-    it('(a * b) * c where a has zero in its shape, so a*b does also',
-       async () => {
-         const a = tf.tensor2d([], [0, 3]);
-         const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-         const ab = tf.matMul(a, b);
-         expect(ab.shape).toEqual([0, 2]);
-         expectArraysClose(await ab.data(), []);
-         const c = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-         const res = tf.matMul(ab, c);
-         expect(res.shape).toEqual([0, 3]);
-         expectArraysClose(await res.data(), []);
-       });
-
-    it('fused A x B', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-      const c = tf.fused.matMul({a, b});
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 8, -3, 20]);
-    });
-
-    it('fused A x B with relu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'relu'});
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 8, 0, 20]);
-    });
-
-    it('fused A x B with elu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'elu'});
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 8, -0.9502, 20]);
-    });
-
-    it('fused A x B with relu6', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'relu6'});
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 6, 0, 6]);
-    });
-
-    it('fused A x B with prelu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const alpha = tf.tensor2d([0.5, 0.5], [1, 2]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul({
-        a,
-        b,
-        transposeA,
-        transposeB,
-        bias: null,
-        activation: 'prelu',
-        preluActivationWeights: alpha
-      });
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 8, -1.5, 20]);
-    });
-
-    it('fused A x B with leakyrelu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const alpha = 0.3;
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul({
-        a,
-        b,
-        transposeA,
-        transposeB,
-        bias: null,
-        activation: 'leakyrelu',
-        leakyreluAlpha: alpha
-      });
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 8, -0.9000000357627869, 20]);
-    });
-
-    it('fused A x B with leakyrelu not provided.', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'leakyrelu'});
-
-      expect(c.shape).toEqual([2, 2]);
-      // leakyRelu should use default alpha=0.2.
-      expectArraysClose(await c.data(), [0, 8, -0.6000000238418579, 20]);
-    });
-
-    it('fused A x B with sigmoid', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'sigmoid'});
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0.5, 0.99966455, 0.04742587, 1]);
-    });
-
-    it('fused A x B with relu transpose', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [2, 3]);
-      const transposeA = false;
-      const transposeB = true;
-
-      const c = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'relu'});
-
-      expect(c.shape).toEqual([2, 2]);
-      expectArraysClose(await c.data(), [0, 9, 0, 24]);
-    });
-
-    it('fused A x B with 2d bias and relu', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const d = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: 'relu'});
-
-      expect(d.shape).toEqual([2, 2]);
-      expectArraysClose(await d.data(), [1, 9, 0, 21]);
-    });
-
-    it('fused A x B with relu and broadcasted bias', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const c = tf.tensor1d([1, 1]);
-      const act: tf.fused.Activation = 'relu';
-      const transposeA = false;
-      const transposeB = false;
-
-      const d = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: act});
-
-      expect(d.shape).toEqual([2, 2]);
-      expectArraysClose(await d.data(), [1, 9, 0, 21]);
-    });
-
-    it('fused A x B with elu and broadcasted bias', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const c = tf.tensor1d([1, 1]);
-      const act: tf.fused.Activation = 'elu';
-      const transposeA = false;
-      const transposeB = false;
-
-      const d = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: act});
-
-      expect(d.shape).toEqual([2, 2]);
-      expectArraysClose(await d.data(), [1, 9, -0.8647, 21]);
-    });
-
-    it('fused A x B with elu and broadcasted shape', async () => {
-      const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const c = tf.tensor1d([1, 1]);
-      const act: tf.fused.Activation = 'elu';
-      const transposeA = false;
-      const transposeB = false;
-
-      const d = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: act});
-
-      expect(d.shape).toEqual([1, 2, 2]);
-      expectArraysClose(await d.data(), [1, 9, -0.8647, 21]);
-    });
-
-    it('fused A x B with 2d bias only', async () => {
-      const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-      const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-      const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
-      const transposeA = false;
-      const transposeB = false;
-
-      const d = tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: 'linear'});
-
-      expect(d.shape).toEqual([2, 2]);
-      expectArraysClose(await d.data(), [1, 9, -2, 21]);
-    });
-  };
-}
-
-// Below cases are from [fused_]mat_mul_test.ts in tfjs-core.
-function matmulBatchTest(programType: MatMulProgramType) {
-  return () => {
-    let savedMatmulFlag = -1;
-    beforeAll(() => {
-      savedMatmulFlag = tf.env().get('WEBGPU_MATMUL_PROGRAM_TYPE') as number;
-      tf.env().set('WEBGPU_MATMUL_PROGRAM_TYPE', programType);
-    });
-    afterAll(() => {
-      tf.env().set('WEBGPU_MATMUL_PROGRAM_TYPE', savedMatmulFlag);
-    });
-
-    it('fused A x B with relu and broadcasted bias different rank',
-       async () => {
-         const a =
-             tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [2, 2, 3]);
-         const b =
-             tf.tensor3d([0, 1, -3, 2, 2, 1, 0, 1, -3, 2, 2, 1], [2, 3, 2]);
-         const c = tf.tensor2d([1, 2], [1, 2]);
-         const act: tf.fused.Activation = 'relu';
-         const transposeA = false;
-         const transposeB = false;
-
-         const d = tf.fused.matMul(
-             {a, b, transposeA, transposeB, bias: c, activation: act});
-
-         expect(d.shape).toEqual([2, 2, 2]);
-         expectArraysClose(await d.data(), [2, 6, 0, 18, 0, 30, 0, 42]);
-       });
-
-    it('broadcast with unequal batch dims', async () => {
-      const a = tf.tensor3d(
-          [
-            2, 1, 3, 2, 1,  1,  1, 5, 6, 7, 8, 1,
-            2, 2, 1, 9, 11, 10, 1, 1, 3, 2, 1, 1
-          ],
-          [4, 3, 2]);
-      const b = tf.tensor3d([1, 0.5], [1, 2, 1]);
-      const c = tf.matMul(a, b);
-      expect(c.shape).toEqual([4, 3, 1]);
-      expectArraysClose(
-          await c.data(),
-          [2.5, 4, 1.5, 3.5, 9.5, 8.5, 3, 5.5, 16, 1.5, 4, 1.5]);
-    });
-
-    it('broadcast with unequal ranks', async () => {
-      const a = tf.tensor5d(
-          [
-            2, 1, 3, 2, 1,  1,  1, 5, 6, 7, 8, 1,
-            2, 2, 1, 9, 11, 10, 1, 1, 3, 2, 1, 1
-          ],
-          [1, 2, 2, 3, 2]);
-      const b = tf.tensor2d([1, 0.5], [2, 1]);
-      const c = tf.matMul(a, b);
-      expect(c.shape).toEqual([1, 2, 2, 3, 1]);
-      expectArraysClose(
-          await c.data(),
-          [2.5, 4, 1.5, 3.5, 9.5, 8.5, 3, 5.5, 16, 1.5, 4, 1.5]);
-    });
-
-    it('batched matmul with the matrices being vectors', async () => {
-      const batch = 3;
-      const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-      const values = new Float32Array(batch * sharedDim);
-      values[10] = 2;
-
-      const a = tf.tensor(values, [batch, 1, sharedDim]);
-      const b = tf.tensor(values, [batch, sharedDim, 1]);
-      const result = tf.matMul(a, b);
-      expect(result.shape).toEqual([batch, 1, 1]);
-      expectArraysClose(await result.data(), [4, 0, 0]);
-    });
-
-    it('batched matmul called twice so memory of output is reused',
-       async () => {
-         const batch = 3;
-         const n = 2;
-         const vals = new Float32Array(batch * n * n);
-         vals[0] = 2;
-         vals[4] = 3;
-         vals[8] = 4;
-
-         const a = tf.tensor(vals, [batch, n, n]);
-         const b = tf.tensor(vals, [batch, n, n]);
-         const result = tf.matMul(a, b);
-         expect(result.shape).toEqual([batch, n, n]);
-         expectArraysClose(
-             await result.data(), [4, 0, 0, 0, 9, 0, 0, 0, 16, 0, 0, 0]);
-         // Dispose the first output, so memory of the second output (which has
-         // the same shape), could be reused.
-         result.dispose();
-
-         const vals2 = new Float32Array(batch * n * n);
-         vals2[3] = 2;
-         vals2[7] = 3;
-         vals2[11] = 4;
-         const a2 = tf.tensor(vals2, [batch, n, n]);
-         const b2 = tf.tensor(vals2, [batch, n, n]);
-         const result2 = tf.matMul(a2, b2);
-         expect(result2.shape).toEqual([batch, n, n]);
-         expectArraysClose(
-             await result2.data(), [0, 0, 0, 4, 0, 0, 0, 9, 0, 0, 0, 16]);
-       });
-
-    it('batched matmul with the matrices being vectors transposedA',
-       async () => {
-         const batch = 3;
-         const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-         const values = new Float32Array(batch * sharedDim);
-         values[10] = 2;
-
-         const a = tf.tensor(values, [batch, sharedDim, 1]);
-         const b = tf.tensor(values, [batch, sharedDim, 1]);
-         const transposeA = true;
-         const transposeB = false;
-         const result = tf.matMul(a, b, transposeA, transposeB);
-         expect(result.shape).toEqual([batch, 1, 1]);
-         expectArraysClose(await result.data(), [4, 0, 0]);
-       });
-
-    it('batched matmul with the matrices being vectors transposedB',
-       async () => {
-         const batch = 3;
-         const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-         const values = new Float32Array(batch * sharedDim);
-         values[10] = 2;
-
-         const a = tf.tensor(values, [batch, 1, sharedDim]);
-         const b = tf.tensor(values, [batch, 1, sharedDim]);
-         const transposeA = false;
-         const transposeB = true;
-         const result = tf.matMul(a, b, transposeA, transposeB);
-         expect(result.shape).toEqual([batch, 1, 1]);
-         expectArraysClose(await result.data(), [4, 0, 0]);
-       });
-
-    it('batched matmul with matrix x vector', async () => {
-      const batch = 3;
-      const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-      const values = new Float32Array(batch * sharedDim);
-      values[10] = 2;
-
-      const a = tf.ones([batch, 2, sharedDim]);
-      const b = tf.tensor(values, [batch, sharedDim, 1]);
-      const result = tf.matMul(a, b);
-      expect(result.shape).toEqual([batch, 2, 1]);
-      expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-    });
-
-    it('batched matmul with matrix x vector transposedA', async () => {
-      const batch = 3;
-      const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-      const values = new Float32Array(batch * sharedDim);
-      values[10] = 2;
-
-      const a = tf.ones([batch, sharedDim, 2]);
-      const b = tf.tensor(values, [batch, sharedDim, 1]);
-      const transposeA = true;
-      const transposeB = false;
-      const result = tf.matMul(a, b, transposeA, transposeB);
-      expect(result.shape).toEqual([batch, 2, 1]);
-      expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-    });
-
-    it('batched matmul with matrix x vector transposedB', async () => {
-      const batch = 3;
-      const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-      const values = new Float32Array(batch * sharedDim);
-      values[10] = 2;
-
-      const a = tf.ones([batch, 2, sharedDim]);
-      const b = tf.tensor(values, [batch, 1, sharedDim]);
-      const transposeA = false;
-      const transposeB = true;
-      const result = tf.matMul(a, b, transposeA, transposeB);
-      expect(result.shape).toEqual([batch, 2, 1]);
-      expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-    });
-
-    it('batched matmul with vector x matrix', async () => {
-      const batch = 3;
-      const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-      const values = new Float32Array(batch * sharedDim);
-      values[10] = 2;
-
-      const a = tf.tensor(values, [batch, 1, sharedDim]);
-      const b = tf.ones([batch, sharedDim, 2]);
-      const result = tf.matMul(a, b);
-      expect(result.shape).toEqual([batch, 1, 2]);
-      expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-    });
-
-    it('batched matmul with vector x matrix transposedA', async () => {
-      const batch = 3;
-      const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-      const values = new Float32Array(batch * sharedDim);
-      values[10] = 2;
-
-      const a = tf.tensor(values, [batch, sharedDim, 1]);
-      const b = tf.ones([batch, sharedDim, 2]);
-      const transposeA = true;
-      const transposeB = false;
-      const result = tf.matMul(a, b, transposeA, transposeB);
-      expect(result.shape).toEqual([batch, 1, 2]);
-      expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-    });
-
-    it('batched matmul with vector x matrix transposedB', async () => {
-      const batch = 3;
-      const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-      const values = new Float32Array(batch * sharedDim);
-      values[10] = 2;
-
-      const a = tf.tensor(values, [batch, 1, sharedDim]);
-      const b = tf.ones([batch, 2, sharedDim]);
-      const transposeA = false;
-      const transposeB = true;
-      const result = tf.matMul(a, b, transposeA, transposeB);
-      expect(result.shape).toEqual([batch, 1, 2]);
-      expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-    });
-
-    it('A x B', async () => {
-      const a = tf.tensor3d(
-          [
-            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-            -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-          ],
-          [5, 2, 3]);
-      const b = tf.tensor3d(
-          [
-            -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-            5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-          ],
-          [5, 3, 2]);
-
-      const c = tf.matMul(a, b);
-      expect(c.shape).toEqual([5, 2, 2]);
-      expectArraysClose(await c.data(), [
-        87, 20, -6,  -32, -24, -50, -36, -5, 24, 98,
-        70, 33, -64, 47,  -42, -28, -71, 24, 37, 5
-      ]);
-    });
-
-    it('A x B in 4D', async () => {
-      const a = tf.tensor4d(
-          [
-            -2, 3,  5,  -5, 3,  9,  -3, -5, 1,   1,  -9, 9,   -6, 6,  -8,
-            -7, -1, 3,  9,  -7, -7, 2,  10, -6,  -8, -6, 9,   -6, 4,  -1,
-            9,  -6, 10, 8,  -9, 5,  -8, -7, 0,   2,  -5, -1,  -9, -4, 3,
-            -2, 6,  -4, 7,  1,  -5, -4, 9,  -8,  -6, -8, 4,   -1, 4,  3,
-            -7, 8,  -7, 5,  -3, -2, -4, 9,  2,   -1, 1,  -10, -3, 5,  -4,
-            6,  -8, -8, 9,  -3, -5, 10, 3,  -3,  -3, 9,  3,   -3, 2,  -8,
-            10, 1,  9,  -2, -2, -3, -4, 6,  -10, -1, 8,  -8,  7,  3,  -2,
-            3,  6,  -2, -2, -4, 1,  -5, -4, 0,   5,  1,  9,   -8, -2, -1
-          ],
-          [4, 5, 2, 3]);
-      const b = tf.tensor4d(
-          [
-            -4, -3, -2, -6, 6,  -1, -4, -1, 7,  -4, 8,  -9,  -9, 0,   -1,
-            -4, -6, -7, -3, -4, -7, 6,  -8, 1,  -2, 1,  -1,  -3, 8,   -5,
-            9,  -2, 5,  9,  -2, 2,  -5, -5, -8, -1, -2, -3,  -2, -10, 6,
-            -3, 0,  1,  6,  7,  1,  2,  -4, -5, 2,  -5, -7,  9,  3,   -6,
-            6,  4,  -4, 6,  10, -3, -2, 8,  10, -8, 10, -1,  -9, -7,  -8,
-            -3, 1,  1,  -2, -9, -7, -6, -1, 0,  7,  -9, -7,  -5, 0,   -4,
-            -4, -7, 2,  4,  6,  6,  -4, -6, -8, 3,  -8, -9,  6,  9,   -4,
-            1,  -1, 0,  8,  9,  0,  -5, 3,  -1, 5,  0,  -10, 7,  -2,  6
-          ],
-          [4, 5, 3, 2]);
-
-      const transposeA = false;
-      const transposeB = false;
-      const c = tf.matMul(a, b, transposeA, transposeB);
-
-      expectArraysClose(await c.data(), [
-        32,  -17, 68,  -12,  -15, 14,  5,   -46, 96,  32,  46,  -17, 78,   -85,
-        -28, 46,  94,  -35,  0,   -13, 31,  -52, 17,  -87, 96,  47,  32,   -2,
-        -6,  105, 40,  -2,   63,  76,  17,  30,  56,  -66, -21, 23,  -144, 41,
-        22,  8,   118, -106, -88, -6,  -17, 2,   2,   -26, 8,   -63, -38,  -108,
-        -84, -30, -35, 49,   16,  -12, -14, -12, 48,  132, 4,   102, 32,   66,
-        -4,  33,  -13, 1,    -40, -25, -3,  61,  -18, -20
-      ]);
-    });
-
-    it('A x B^t', async () => {
-      const a = tf.tensor3d(
-          [
-            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-            -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-          ],
-          [5, 2, 3]);
-      const b = tf.tensor3d(
-          [
-            -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-            5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-          ],
-          [5, 2, 3]);
-
-      const transposeA = false;
-      const transposeB = true;
-      const c = tf.matMul(a, b, transposeA, transposeB);
-      expect(c.shape).toEqual([5, 2, 2]);
-      expectArraysClose(await c.data(), [
-        66, 35, -48,  14, -45, -33, -12, 7,  -76, 64,
-        3,  66, -119, -9, -64, -60, -76, 48, 33,  -16
-      ]);
-    });
-
-    it('A^t x B', async () => {
-      const a = tf.tensor3d(
-          [
-            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-            -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-          ],
-          [5, 2, 3]);
-      const b = tf.tensor3d(
-          [
-            -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-            5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-          ],
-          [5, 2, 3]);
-
-      const transposeA = true;
-      const transposeB = false;
-      const c = tf.matMul(a, b, transposeA, transposeB);
-
-      expectArraysClose(await c.data(), [
-        40,  -36, 5,   40,  34, 5,   48,  80, 6,  -6, 21,  -48, -23, -20, -50,
-        -12, -21, -12, -58, 15, -96, 23,  6,  39, 20, 109, 42,  -67, 45,  -40,
-        76,  -52, 40,  -15, 1,  -60, -58, -3, 36, 40, -6,  -24, 51,  -33, -28
-      ]);
-    });
-
-    it('A^t x B in 4D', async () => {
-      const a = tf.tensor4d(
-          [
-            -2, 3,  5,  -5, 3,  9,  -3, -5, 1,   1,  -9, 9,   -6, 6,  -8,
-            -7, -1, 3,  9,  -7, -7, 2,  10, -6,  -8, -6, 9,   -6, 4,  -1,
-            9,  -6, 10, 8,  -9, 5,  -8, -7, 0,   2,  -5, -1,  -9, -4, 3,
-            -2, 6,  -4, 7,  1,  -5, -4, 9,  -8,  -6, -8, 4,   -1, 4,  3,
-            -7, 8,  -7, 5,  -3, -2, -4, 9,  2,   -1, 1,  -10, -3, 5,  -4,
-            6,  -8, -8, 9,  -3, -5, 10, 3,  -3,  -3, 9,  3,   -3, 2,  -8,
-            10, 1,  9,  -2, -2, -3, -4, 6,  -10, -1, 8,  -8,  7,  3,  -2,
-            3,  6,  -2, -2, -4, 1,  -5, -4, 0,   5,  1,  9,   -8, -2, -1
-          ],
-          [4, 5, 2, 3]);
-      const b = tf.tensor4d(
-          [
-            -4, -3, -2, -6, 6,  -1, -4, -1, 7,  -4, 8,  -9,  -9, 0,   -1,
-            -4, -6, -7, -3, -4, -7, 6,  -8, 1,  -2, 1,  -1,  -3, 8,   -5,
-            9,  -2, 5,  9,  -2, 2,  -5, -5, -8, -1, -2, -3,  -2, -10, 6,
-            -3, 0,  1,  6,  7,  1,  2,  -4, -5, 2,  -5, -7,  9,  3,   -6,
-            6,  4,  -4, 6,  10, -3, -2, 8,  10, -8, 10, -1,  -9, -7,  -8,
-            -3, 1,  1,  -2, -9, -7, -6, -1, 0,  7,  -9, -7,  -5, 0,   -4,
-            -4, -7, 2,  4,  6,  6,  -4, -6, -8, 3,  -8, -9,  6,  9,   -4,
-            1,  -1, 0,  8,  9,  0,  -5, 3,  -1, 5,  0,  -10, 7,  -2,  6
-          ],
-          [4, 5, 2, 3]);
-
-      const transposeA = true;
-      const transposeB = false;
-      const c = tf.matMul(a, b, transposeA, transposeB);
-
-      expectArraysClose(await c.data(), [
-        38,  -24, 9,   -30, 9,   -9,  -74,  39,  -19,  8,    11,  -30, 56,  -67,
-        46,  -40, 71,  -74, 82,  42,  55,   -50, 6,    1,    60,  -18, -13, -15,
-        -52, -61, 81,  -52, 59,  -15, 76,   43,  34,   -56,  38,  0,   26,  -14,
-        -15, 1,   -4,  153, -34, 61,  -135, 30,  -48,  135,  -30, 60,  38,  36,
-        58,  40,  45,  71,  1,   2,   3,    24,  90,   -56,  -10, 40,  -18, 6,
-        -30, 14,  34,  65,  27,  24,  -29,  -44, -46,  -3,   35,  -21, 27,  48,
-        20,  52,  32,  35,  -11, -46, -12,  22,  13,   30,   2,   -23, -54, -48,
-        34,  16,  -42, -39, -26, 82,  89,   76,  -84,  30,   9,   27,  30,  -21,
-        -43, -48, 60,  20,  24,  -78, -91,  -63, -12,  24,   21,  28,  48,  35,
-        -6,  27,  33,  53,  -81, -71, 61,   -27, 11,   -48,  -82, 8,   -12, -19,
-        -10, -48, -81, 0,   13,  32,  41,   0,   -100, -120, 16,  124, 152, 45,
-        60,  -28, 24,  21,  -12, -14, -16,  8,   9,    -33,  5,   -12, -48, 4,
-        8,   9,   0,   -31, 16,  -98, -9,   4,   -22,  38,   2,   -96
-      ]);
-    });
-
-    it('A^t x B^t', async () => {
-      const a = tf.tensor3d(
-          [
-            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-            -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-          ],
-          [5, 3, 2]);
-      const b = tf.tensor3d(
-          [
-            -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-            5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-          ],
-          [5, 2, 3]);
-
-      const transposeA = true;
-      const transposeB = true;
-      const c = tf.matMul(a, b, transposeA, transposeB);
-      expectArraysClose(await c.data(), [
-        66,  42, 16,  -56, -12, 6,   -30, 19,  -1, 102,
-        -94, 14, -56, 32,  100, -56, -47, -11, 5,  -31
-      ]);
-    });
-  };
-}
-
-for (let i = 0; i < MatMulProgramType.MatMulMax; i++) {
-  describeWithFlags(`matmul ${MatMulProgramType[i]}`, ALL_ENVS, matmulTest(i));
-  // Skip MatMulSplitKProgram since it doesn't support batch > 1;
-  if (i !== MatMulProgramType.MatMulSplitKProgram) {
-    describeWithFlags(
-        `matmulBatch ${MatMulProgramType[i]}`, ALL_ENVS, matmulBatchTest(i));
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/max_pool_backprop_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/max_pool_backprop_webgpu.ts
deleted file mode 100644
index 94089af1f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/max_pool_backprop_webgpu.ts
+++ /dev/null
@@ -1,178 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class MaxPool2DBackpropProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['dy', 'maxPos'];
-  uniforms =
-      `strides : vec2<i32>, pads : vec2<i32>, dilations : vec2<i32>, filterDims : vec2<i32>,
-       outHeight : i32, outWidth : i32`;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.inShape;
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'maxPool2DBackprop';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let batch = coords[0];
-        let d = coords[3];
-
-        let dyRCCorner = vec2<i32>(coords.yz) - uniforms.pads;
-        let dyRCorner = dyRCCorner.x;
-        let dyCCorner = dyRCCorner.y;
-
-        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
-        // ? = to be determined. : = across all values in that axis.
-        var dotProd = 0.0;
-        let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;
-        for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {
-          let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);
-
-          if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
-            continue;
-          }
-          let idyR = i32(dyR);
-
-          for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {
-            let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);
-
-            if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
-              continue;
-            }
-            let idyC = i32(dyC);
-
-            let dyValue = getDy(batch, idyR, idyC, d);
-            let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));
-
-            // Get the current value, check it against the value from the
-            // position matrix.
-            let curPosValue = wR * uniforms.filterDims[1] + wC;
-            let mask = select(0.0, 1.0, maxPosValue == curPosValue);
-            dotProd += dyValue * mask;
-          }
-        }
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-    `;
-    return userCode;
-  }
-}
-
-export class MaxPool3DBackpropProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['dy', 'maxPos'];
-  uniforms = `strides : vec3<i32>, pads : vec3<i32>, filterDims : vec3<i32>,
-      outDepth : i32, outHeight : i32, outWidth : i32`;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv3DInfo) {
-    this.outputShape = convInfo.inShape;
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'maxPool3DBackprop';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-        let batch = coords.x;
-        let ch = coords.u;
-
-        let dyCorner = vec3<i32>(coords.y, coords.z, coords.w) - uniforms.pads;
-        let dyDCorner = dyCorner.x;
-        let dyRCorner = dyCorner.y;
-        let dyCCorner = dyCorner.z;
-
-        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
-        // dx(xD, xR, xC, ch).
-        // ? = to be determined. : = across all values in that axis.
-        var dotProd = 0.0;
-        let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;
-
-        for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {
-          let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);
-
-          if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {
-            continue;
-          }
-          let idyD = i32(dyD);
-
-          for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {
-            let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);
-
-            if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
-              continue;
-            }
-            let idyR = i32(dyR);
-
-            for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {
-              let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);
-
-              if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
-                continue;
-              }
-              let idyC = i32(dyC);
-
-              let dyValue = getDy(batch, idyD, idyR, idyC, ch);
-              let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));
-
-              // Get the current value, check it against the value from the
-              // position matrix.
-              let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;
-              let mask = select(0.0, 1.0, maxPosValue == curPosValue);
-              dotProd += dyValue * mask;
-            }
-          }
-        }
-
-        setOutputAtIndex(index, dotProd);
-      }
-    }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/mirror_pad_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/mirror_pad_webgpu.ts
deleted file mode 100644
index c92a9db6f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/mirror_pad_webgpu.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getCoordsDataType, getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class MirrorPadProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  uniforms = '';
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  xShape: number[];
-  offset: number;
-  size = true;
-
-  constructor(
-      xShape: number[], paddings: Array<[number, number]>,
-      mode: 'reflect'|'symmetric') {
-    this.outputShape = paddings.map(
-        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.xShape = xShape;
-    paddings.map((_, i) => {
-      this.uniforms += ` pad${i} : vec2<i32>,`;
-    });
-    this.offset = mode === 'reflect' ? 0 : 1;
-    this.shaderKey = `mirrorPad_${mode}`;
-  }
-
-  getUserCode(): string {
-    const rank = this.xShape.length;
-    // The length of paddings are same with the rank of the input tensor.
-    const start = this.xShape.map((_, i) => `uniforms.pad${i}[0]`).join(',');
-    const end = this.xShape
-                    .map(
-                        (_, i) => `uniforms.pad${i}[0] + uniforms.xShape${
-                            rank > 1 ? `[${i}]` : ''}`)
-                    .join(',');
-
-    const shaderStart = rank === 1 ? 'start' : 'start[i]';
-    const shaderEnd = rank === 1 ? 'end' : 'end[i]';
-    const shaderOutC = rank === 1 ? 'outC' : 'outC[i]';
-    const dtype = getCoordsDataType(rank);
-    const unpackedCoords = rank > 1 ?
-        ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank) :
-        'coords';
-
-    return `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let start = ${dtype}(${start});
-          let end = ${dtype}(${end});
-          var outC = getCoordsFromIndex(index);
-          for (var i = 0; i < ${rank}; i = i + 1) {
-            if (${shaderOutC} < ${shaderStart}) {
-              ${shaderOutC} = ${shaderStart} * 2 - ${shaderOutC} - ${
-        this.offset};
-            } else if(${shaderOutC} >= ${shaderEnd}) {
-              ${shaderOutC} = (${shaderEnd} - 1) * 2 - ${shaderOutC} + ${
-        this.offset};
-            }
-          }
-          let coords = outC - start;
-          setOutputAtIndex(index, getX(${unpackedCoords}));
-        }
-      }
-    `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/multinomial_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/multinomial_webgpu.ts
deleted file mode 100644
index 2f50abdc3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/multinomial_webgpu.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class MultinomialProgram implements WebGPUProgram {
-  variableNames: string[] = ['probs'];
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  uniforms = 'seed : f32, numOutcomes: i32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(batchSize: number, numSamples: number) {
-    this.outputShape = [batchSize, numSamples];
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'multinomial';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    //Based on the work of Dave Hoskins
-    //https://www.shadertoy.com/view/4djSRW
-    fn random (seed : f32, resultUV : vec2<f32>) -> f32 {
-      let HASHSCALE1 = 443.8975;
-      let p = resultUV * seed;
-      var p3  = fract(vec3<f32>(p.xyx) * HASHSCALE1);
-      p3 = p3 + dot(p3, p3.yzx + 19.19);
-      return fract((p3.x + p3.y) * p3.z);
-    }
-
-    ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getOutputCoords();
-        let batch = coords[0];
-
-        let resUV = vec2<f32>(f32(coords[1]) / f32(uniforms.outShape[1]),
-            f32(coords[0]) / f32(uniforms.outShape[0]));
-        let r = random(uniforms.seed, resUV);
-        var cdf = 0.0;
-        for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {
-          cdf = cdf + getProbs(batch, i);
-
-          if (r < cdf) {
-            setOutputAtIndexI32(index, i);
-            return;
-          }
-        }
-
-        // If no other event happened, last event happened.
-        setOutputAtIndexI32(index, uniforms.numOutcomes - 1);
-      }
-    }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/onehot_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/onehot_webgpu.ts
deleted file mode 100644
index aced559bc..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/onehot_webgpu.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class OneHotProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms = 'onValue : f32, offValue : f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(numIndices: number, depth: number) {
-    this.outputShape = [numIndices, depth];
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = 'onehot';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if(index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,
-                                      f32(i32(round(getX(coords.x))) == coords.y)));
-        }
-      }
-    `;
-
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/pad_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/pad_webgpu.ts
deleted file mode 100644
index f999a4538..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/pad_webgpu.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getCoordsDataType, getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export function padCommon(shape: number[], fillZero = false): string {
-  const rank = shape.length;
-  const type = getCoordsDataType(rank);
-  const start = shape.map((_, i) => `uniforms.pad${i}[0]`).join(',');
-  const end = shape
-                  .map(
-                      (_, i) => `uniforms.pad${i}[0] + uniforms.xShape${
-                          rank > 1 ? `[${i}]` : ''}`)
-                  .join(',');
-  const startValue = rank > 1 ? `${type}(${start})` : `${start}`;
-  const endValue = rank > 1 ? `${type}(${end})` : `${end}`;
-
-  const leftPadCondition =
-      rank > 1 ? `any(paddedCoords < start)` : `paddedCoords < start`;
-  const rightPadCondition =
-      rank > 1 ? `any(paddedCoords >= end)` : `paddedCoords >= end`;
-
-  const unpackedCoords = rank > 1 ?
-      ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank) :
-      'coords';
-  return `
-        let start = ${startValue};
-        let end = ${endValue};
-        if (${leftPadCondition} || ${rightPadCondition}) {
-          setOutputAtIndex(index, ${fillZero ? 0.0 : 'uniforms.constantValue'});
-        } else {
-          let coords = paddedCoords - start;
-          setOutputAtIndex(index, getX(${unpackedCoords}));
-        }
-  `;
-}
-
-export class PadProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms = 'constantValue : f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  xShape: number[];
-  size = true;
-
-  constructor(xShape: number[], paddings: Array<[number, number]>) {
-    this.outputShape = paddings.map(
-        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    paddings.map((_, i) => {
-      this.uniforms += ` pad${i} : vec2<i32>,`;
-    });
-    this.xShape = xShape;
-    this.shaderKey = 'pad';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let paddedCoords = getCoordsFromIndex(index);
-          ${padCommon(this.xShape)}
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/pool_filtersizeone_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/pool_filtersizeone_webgpu.ts
deleted file mode 100644
index ccd7e47e9..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/pool_filtersizeone_webgpu.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class PoolWithFilterSizeEqualsOneProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms = `strides : vec2<i32>,`;
-  workgroupSize: [number, number, number] = [256, 1, 1];
-  size = true;
-
-  constructor(convInfo: backend_util.Conv2DInfo) {
-    this.outputShape = convInfo.outShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = 'poolWithFilterSizeEqualsOne';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          let batch = coords[0];
-          let d = coords[3];
-
-          let xRCCorner = coords.yz * uniforms.strides;
-          let xRCorner = xRCCorner.x;
-          let xCCorner = xRCCorner.y;
-
-          let value = getX(batch, xRCorner, xCCorner, d);
-          setOutputAtIndex(index, value);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/pool_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/pool_webgpu.ts
deleted file mode 100644
index 78b74ee4c..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/pool_webgpu.ts
+++ /dev/null
@@ -1,246 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class Pool2DProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms =
-      `strides : vec2<i32>, pads : vec2<i32>, dilations : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,`;
-  // TODO(jiajia.qin@intel.com): Dynamically choose different workgroupSize for
-  // different output shapes.
-  workgroupSize: [number, number, number] = [128, 1, 1];
-  poolType: 'max'|'avg';
-  size = true;
-  computePositions: boolean;
-  flattenPositions: boolean;
-  includeBatchIndex: boolean;
-
-  constructor(
-      convInfo: backend_util.Conv2DInfo, poolType: 'max'|'avg',
-      computePositions = false, flattenPositions = false,
-      includeBatchIndex = false) {
-    if (poolType === 'avg' && computePositions) {
-      throw new Error('Cannot compute positions for average pool.');
-    }
-
-    this.outputShape = convInfo.outShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.poolType = poolType;
-    this.computePositions = computePositions;
-    this.flattenPositions = flattenPositions;
-    this.includeBatchIndex = includeBatchIndex;
-    this.shaderKey = `pool2D_${poolType}_${computePositions}_${
-        flattenPositions}_${includeBatchIndex}`;
-  }
-
-  getUserCode(): string {
-    let updateSnippet: string;
-    if (this.poolType === 'avg') {
-      updateSnippet = `resultValue = resultValue + value; count = count + 1.0;`;
-    } else if (this.computePositions) {
-      const positionStr = this.flattenPositions ?
-          (this.includeBatchIndex ?
-               `((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d` :
-               `(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d`) :
-          `wR * uniforms.filterDims.y + wC`;
-      updateSnippet = `let currMaxValue = mix(value, maxValue, maxValueFound);
-      if (value >= currMaxValue) {
-        maxValue = value;
-        maxValueFound = 1.0;
-        maxPosition = ${positionStr};
-      }`;
-    } else {
-      updateSnippet = `resultValue = max(value, resultValue);`;
-    }
-
-    let returnValue = `resultValue`;
-    if (this.poolType === 'avg') {
-      returnValue = `resultValue / max(count, 1.0)`;
-    }
-
-    const userCode = `
-      ${main('index')} {
-      if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-          let batch = coords[0];
-          let d = coords[3];
-          let xRCCorner = vec2<i32>(coords.yz) * uniforms.strides - uniforms.pads;
-          let xRCorner = xRCCorner.x;
-          let xCCorner = xRCCorner.y;
-
-          ${
-        this.computePositions ?
-            `var maxValue = 0.0;
-            var maxValueFound = 0.0;
-            var maxPosition = 0;` :
-            `var resultValue = ${
-                this.poolType === 'avg' ? '0.0' : '-1.0 / pow(10.0, -20.0)'};`}
-
-          var count = 0.0;
-          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {
-            let xR = xRCorner + wR;
-
-            if (xR < 0 || xR >= uniforms.convDims.x) {
-              continue;
-            }
-
-            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {
-              let xC = xCCorner + wC;
-              if (xC < 0 || xC >= uniforms.convDims.y) {
-                continue;
-              }
-
-              let value = getX(batch, xR, xC, d);
-              ${updateSnippet}
-            }
-          }
-
-          ${
-        this.computePositions ? `setOutputAtIndexI32(index, maxPosition);` :
-                                `setOutputAtIndex(index, ${returnValue});`}
-        }
-      }
-    `;
-    return userCode;
-  }
-}
-
-export class Pool3DProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms =
-      `strides : vec3<i32>, pads : vec3<i32>, convDims : vec3<i32>, filterDims : vec3<i32>,`;
-  workgroupSize: [number, number, number] = [128, 1, 1];
-  poolType: 'max'|'avg';
-  size = true;
-  computePositions: boolean;
-  flattenPositions: boolean;
-  includeBatchIndex: boolean;
-
-  constructor(
-      convInfo: backend_util.Conv3DInfo, poolType: 'max'|'avg',
-      computePositions = false, flattenPositions = false,
-      includeBatchIndex = false) {
-    if (poolType === 'avg' && computePositions) {
-      throw new Error('Cannot compute positions for average pool.');
-    }
-
-    this.outputShape = convInfo.outShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.poolType = poolType;
-    this.computePositions = computePositions;
-    this.flattenPositions = flattenPositions;
-    this.includeBatchIndex = includeBatchIndex;
-    this.shaderKey = `pool3D_${poolType}_${computePositions}_${
-        flattenPositions}_${includeBatchIndex}`;
-  }
-
-  getUserCode(): string {
-    let updateSnippet: string;
-    if (this.poolType === 'avg') {
-      updateSnippet = `resultValue += value; count += 1.0;`;
-    } else if (this.computePositions) {
-      const positionStr = this.flattenPositions ?
-          (this.includeBatchIndex ?
-               `(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch` :
-               `((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch`) :
-          `wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC`;
-      updateSnippet = `let currMaxValue = mix(value, maxValue, maxValueFound);
-      if (value >= currMaxValue) {
-        maxValue = value;
-        maxValueFound = 1.0;
-        maxPosition = ${positionStr};
-      }`;
-    } else {
-      updateSnippet = `resultValue = max(value, resultValue);`;
-    }
-
-    let returnValue = `resultValue`;
-    if (this.poolType === 'avg') {
-      returnValue = `resultValue / max(count, 1.0)`;
-    }
-
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          let batch = coords.x;
-          let ch = coords.u;
-
-          let xCorner = vec3<i32>(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;
-          let xDCorner = xCorner.x;
-          let xRCorner = xCorner.y;
-          let xCCorner = xCorner.z;
-
-          ${
-        this.computePositions ?
-            `var maxValue = 0.0;
-            var maxValueFound = 0.0;
-            var maxPosition = 0;` :
-            `var resultValue = ${
-                this.poolType === 'avg' ? '0.0' : '-1.0 / pow(10.0, -20.0)'};`}
-
-          var count = 0.0;
-          for (var wD = 0; wD < uniforms.filterDims.x; wD++) {
-            let xD = xDCorner + wD;
-            if (xD < 0 || xD >= uniforms.convDims.x) {
-              continue;
-            }
-
-            for (var wR = 0; wR < uniforms.filterDims.y; wR++) {
-              let xR = xRCorner + wR;
-              if (xR < 0 || xR >= uniforms.convDims.y) {
-                continue;
-              }
-
-              for (var wC = 0; wC < uniforms.filterDims.z; wC++) {
-                let xC = xCCorner + wC;
-                if (xC < 0 || xC >= uniforms.convDims.z) {
-                  continue;
-                }
-
-                let value = getX(batch, xD, xR, xC, ch);
-                ${updateSnippet}
-              }
-            }
-          }
-
-          ${
-        this.computePositions ? `setOutputAtIndexI32(index, maxPosition);` :
-                                `setOutputAtIndex(index, ${returnValue});`}
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/reduce_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/reduce_webgpu.ts
deleted file mode 100644
index ab0d86207..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/reduce_webgpu.ts
+++ /dev/null
@@ -1,145 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ReduceProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number];
-  variableNames = ['x'];
-  uniforms = 'reduceSize : i32,';
-  reduceType: 'all'|'any'|'max'|'mean'|'min'|'prod'|'sum';
-  inputShape: number[];
-  size = true;
-
-  constructor(
-      reduceInfo: backend_util.ReduceInfo,
-      reduceType: 'all'|'any'|'max'|'mean'|'min'|'prod'|'sum',
-      maxComputeWorkgroupSizeX: number) {
-    this.inputShape = [reduceInfo.batchSize, reduceInfo.inSize];
-    const [outputShape, ] =
-        backend_util.computeOutAndReduceShapes(this.inputShape, [1]);
-    this.outputShape = outputShape.length === 0 ? [1] : outputShape;
-    // If reduceSize |reduceInfo.inSize| is very large, the I/O accessing will
-    // become the bottleneck. Increasing workgroupSize can reduce the times of
-    // accessing global memory. The threshold value is just to make sure the
-    // reduceSize is large enough for a bigger workgroupSize.
-    if (reduceInfo.inSize >= 32768 && maxComputeWorkgroupSizeX >= 512) {
-      this.workgroupSize = [512, 1, 1];
-    } else if (reduceInfo.inSize >= 4096) {
-      this.workgroupSize = [256, 1, 1];
-    } else {
-      this.workgroupSize = [64, 1, 1];
-    }
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    // A work group only outputs a data, so we transfer [1, 1, 1] to compute
-    // dispatch size.
-    this.dispatch =
-        computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);
-
-    this.reduceType = reduceType;
-    this.shaderKey = `reduce_${reduceType}`;
-  }
-
-  getUserCode(): string {
-    let reduceOp = ``;
-    let initValue = '0.0';
-    const workgroupSizeX = this.workgroupSize[0];
-    if (this.reduceType === 'min' || this.reduceType === 'max') {
-      reduceOp = `
-         if (isnan(candidate)) {
-          bestValue = uniforms.NAN;
-         } else if (!isnan(bestValue) && candidate ${
-          this.reduceType === 'min' ? '<' : '>'} bestValue)
-           {  bestValue = candidate; }`;
-      initValue = 'f32(x[offset])';
-    } else if (this.reduceType === 'sum' || this.reduceType === 'mean') {
-      reduceOp = ' bestValue = bestValue + candidate; ';
-    } else if (this.reduceType === 'prod') {
-      reduceOp = ' bestValue = bestValue * candidate; ';
-      initValue = '1.0';
-    } else if (this.reduceType === 'all') {
-      reduceOp = ' bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ';
-      initValue = '1.0';
-    } else if (this.reduceType === 'any') {
-      reduceOp = ' bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ';
-      initValue = '0.0';
-    }
-
-    const outputSnippet = this.reduceType === 'mean' ?
-        // tslint:disable-next-line:max-line-length
-        `setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));` :
-        `setOutputAtIndex(outputIndex, bestValue);`;
-
-    const sharedMemorySnippet = `
-         var<workgroup> xBestValues : array<f32, ${workgroupSizeX}>;
-       `;
-
-    const userCode = `
-       fn DIV_CEIL(a : u32, b : u32) -> u32 {
-        return ((a - 1u) / b + 1u);
-       }
-
-       ${sharedMemorySnippet}
-       fn getOffset(outputIndex : i32) -> i32 {
-         let outputCoords = getCoordsFromIndex(outputIndex);
-         let offset = ${
-        this.outputShape.length === 1 ?
-            'outputCoords' :
-            'outputCoords[0]'} * uniforms.reduceSize;
-          return offset;
-       }
-       ${main('index')} {
-         let outputIndex = index / ${workgroupSizeX};
-         let offset = getOffset(outputIndex);
-         var bestValue = ${initValue};
-         let Length = uniforms.reduceSize;
-         let WorkPerThread = DIV_CEIL(u32(Length), ${workgroupSizeX}u);
-         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
-             k = k + ${workgroupSizeX}) {
-           let candidate = f32(x[offset + k]);
-           ${reduceOp}
-         }
-         xBestValues[localId.x] = bestValue;
-         workgroupBarrier();
-
-         var reduceSize = min(u32(Length), ${workgroupSizeX}u);
-         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
-             currentSize = reduceSize / 2u) {
-           let interval = DIV_CEIL(reduceSize, 2u);
-           if (localId.x < currentSize) {
-            let candidate = xBestValues[localId.x + interval];
-            ${reduceOp}
-            xBestValues[localId.x] = bestValue;
-           }
-           reduceSize = interval;
-           workgroupBarrier();
-         }
-
-         if (localId.x == 0u && outputIndex < uniforms.size) {
-          ${outputSnippet}
-        }
-       }
-     `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/register_all_kernels.ts b/tfjs-master/tfjs-backend-webgpu/src/register_all_kernels.ts
deleted file mode 100644
index 26d2ef3a1..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/register_all_kernels.ts
+++ /dev/null
@@ -1,350 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {KernelConfig, registerKernel} from '@tensorflow/tfjs-core';
-
-import {_fusedMatMulConfig} from './kernels/_FusedMatMul';
-import {absConfig} from './kernels/Abs';
-import {acosConfig} from './kernels/Acos';
-import {acoshConfig} from './kernels/Acosh';
-import {addConfig} from './kernels/Add';
-import {addNConfig} from './kernels/AddN';
-import {allConfig} from './kernels/All';
-import {anyConfig} from './kernels/Any';
-import {argMaxConfig} from './kernels/ArgMax';
-import {argMinConfig} from './kernels/ArgMin';
-import {asinConfig} from './kernels/Asin';
-import {asinhConfig} from './kernels/Asinh';
-import {atanConfig} from './kernels/Atan';
-import {atan2Config} from './kernels/Atan2';
-import {atanhConfig} from './kernels/Atanh';
-import {avgPoolConfig} from './kernels/AvgPool';
-import {avgPool3DConfig} from './kernels/AvgPool3D';
-import {avgPool3DGradConfig} from './kernels/AvgPool3DGrad';
-import {avgPoolGradConfig} from './kernels/AvgPoolGrad';
-import {batchMatMulConfig} from './kernels/BatchMatMul';
-import {batchToSpaceNDConfig} from './kernels/BatchToSpaceND';
-import {bincountConfig} from './kernels/Bincount';
-import {broadcastArgsConfig} from './kernels/BroadcastArgs';
-import {castConfig} from './kernels/Cast';
-import {ceilConfig} from './kernels/Ceil';
-import {clipByValueConfig} from './kernels/ClipByValue';
-import {complexConfig} from './kernels/Complex';
-import {complexAbsConfig} from './kernels/ComplexAbs';
-import {concatConfig} from './kernels/Concat';
-import {conv2DConfig} from './kernels/Conv2D';
-import {conv2DBackpropFilterConfig} from './kernels/Conv2DBackpropFilter';
-import {conv2DBackpropInputConfig} from './kernels/Conv2DBackpropInput';
-import {conv3DConfig} from './kernels/Conv3D';
-import {conv3DBackpropFilterV2Config} from './kernels/Conv3DBackpropFilterV2';
-import {conv3DBackpropInputV2Config} from './kernels/Conv3DBackpropInputV2';
-import {cosConfig} from './kernels/Cos';
-import {coshConfig} from './kernels/Cosh';
-import {cropAndResizeConfig} from './kernels/CropAndResize';
-import {cumprodConfig} from './kernels/Cumprod';
-import {cumsumConfig} from './kernels/Cumsum';
-import {denseBincountConfig} from './kernels/DenseBincount';
-import {depthToSpaceConfig} from './kernels/DepthToSpace';
-import {depthwiseConv2dNativeConfig} from './kernels/DepthwiseConv2dNative';
-import {depthwiseConv2dNativeBackpropFilterConfig} from './kernels/DepthwiseConv2dNativeBackpropFilter';
-import {depthwiseConv2dNativeBackpropInputConfig} from './kernels/DepthwiseConv2dNativeBackpropInput';
-import {diagConfig} from './kernels/Diag';
-import {dilation2DConfig} from './kernels/Dilation2D';
-import {dilation2DBackpropFilterConfig} from './kernels/Dilation2DBackpropFilter';
-import {dilation2DBackpropInputConfig} from './kernels/Dilation2DBackpropInput';
-import {drawConfig} from './kernels/Draw';
-import {einsumConfig} from './kernels/Einsum';
-import {eluConfig} from './kernels/Elu';
-import {eluGradConfig} from './kernels/EluGrad';
-import {equalConfig} from './kernels/Equal';
-import {erfConfig} from './kernels/Erf';
-import {expConfig} from './kernels/Exp';
-import {expandDimsConfig} from './kernels/ExpandDims';
-import {expm1Config} from './kernels/Expm1';
-import {fftConfig} from './kernels/FFT';
-import {fillConfig} from './kernels/Fill';
-import {flipLeftRightConfig} from './kernels/FlipLeftRight';
-import {floorConfig} from './kernels/Floor';
-import {floorDivConfig} from './kernels/FloorDiv';
-import {fromPixelsConfig} from './kernels/FromPixels';
-import {fusedBatchNormConfig} from './kernels/FusedBatchNorm';
-import {fusedConv2DConfig} from './kernels/FusedConv2D';
-import {fusedDepthwiseConv2DConfig} from './kernels/FusedDepthwiseConv2D';
-import {gatherNdConfig} from './kernels/GatherNd';
-import {gatherV2Config} from './kernels/GatherV2';
-import {greaterConfig} from './kernels/Greater';
-import {greaterEqualConfig} from './kernels/GreaterEqual';
-import {identityConfig} from './kernels/Identity';
-import {ifftConfig} from './kernels/IFFT';
-import {imagConfig} from './kernels/Imag';
-import {isFiniteConfig} from './kernels/IsFinite';
-import {isInfConfig} from './kernels/IsInf';
-import {isNaNConfig} from './kernels/IsNaN';
-import {leakyReluConfig} from './kernels/LeakyRelu';
-import {lessConfig} from './kernels/Less';
-import {lessEqualConfig} from './kernels/LessEqual';
-import {linSpaceConfig} from './kernels/LinSpace';
-import {logConfig} from './kernels/Log';
-import {log1pConfig} from './kernels/Log1p';
-import {logicalAndConfig} from './kernels/LogicalAnd';
-import {logicalNotConfig} from './kernels/LogicalNot';
-import {logicalOrConfig} from './kernels/LogicalOr';
-import {lrnConfig} from './kernels/LRN';
-import {lrnGradConfig} from './kernels/LRNGrad';
-import {maxConfig} from './kernels/Max';
-import {maximumConfig} from './kernels/Maximum';
-import {maxPoolConfig} from './kernels/MaxPool';
-import {maxPool3DConfig} from './kernels/MaxPool3D';
-import {maxPool3DGradConfig} from './kernels/MaxPool3DGrad';
-import {maxPoolGradConfig} from './kernels/MaxPoolGrad';
-import {maxPoolWithArgmaxConfig} from './kernels/MaxPoolWithArgmax';
-import {meanConfig} from './kernels/Mean';
-import {minConfig} from './kernels/Min';
-import {minimumConfig} from './kernels/Minimum';
-import {mirrorPadConfig} from './kernels/MirrorPad';
-import {modConfig} from './kernels/Mod';
-import {multinomialConfig} from './kernels/Multinomial';
-import {multiplyConfig} from './kernels/Multiply';
-import {negConfig} from './kernels/Neg';
-import {nonMaxSuppressionV3Config} from './kernels/NonMaxSuppressionV3';
-import {nonMaxSuppressionV5Config} from './kernels/NonMaxSuppressionV5';
-import {notEqualConfig} from './kernels/NotEqual';
-import {oneHotConfig} from './kernels/OneHot';
-import {onesLikeConfig} from './kernels/OnesLike';
-import {packConfig} from './kernels/Pack';
-import {padV2Config} from './kernels/PadV2';
-import {powConfig} from './kernels/Pow';
-import {preluConfig} from './kernels/Prelu';
-import {prodConfig} from './kernels/Prod';
-import {rangeConfig} from './kernels/Range';
-import {realConfig} from './kernels/Real';
-import {realDivConfig} from './kernels/RealDiv';
-import {reciprocalConfig} from './kernels/Reciprocal';
-import {reluConfig} from './kernels/Relu';
-import {relu6Config} from './kernels/Relu6';
-import {reshapeConfig} from './kernels/Reshape';
-import {resizeBilinearConfig} from './kernels/ResizeBilinear';
-import {resizeBilinearGradConfig} from './kernels/ResizeBilinearGrad';
-import {resizeNearestNeighborConfig} from './kernels/ResizeNearestNeighbor';
-import {resizeNearestNeighborGradConfig} from './kernels/ResizeNearestNeighborGrad';
-import {reverseConfig} from './kernels/Reverse';
-import {rotateWithOffsetConfig} from './kernels/RotateWithOffset';
-import {roundConfig} from './kernels/Round';
-import {rsqrtConfig} from './kernels/Rsqrt';
-import {scatterNdConfig} from './kernels/ScatterNd';
-import {searchSortedConfig} from './kernels/SearchSorted';
-import {selectConfig} from './kernels/Select';
-import {seluConfig} from './kernels/Selu';
-import {sigmoidConfig} from './kernels/Sigmoid';
-import {signConfig} from './kernels/Sign';
-import {sinConfig} from './kernels/Sin';
-import {sinhConfig} from './kernels/Sinh';
-import {sliceConfig} from './kernels/Slice';
-import {softmaxConfig} from './kernels/Softmax';
-import {softplusConfig} from './kernels/Softplus';
-import {spaceToBatchNDConfig} from './kernels/SpaceToBatchND';
-import {sparseSegmentMeanConfig} from './kernels/SparseSegmentMean';
-import {sparseSegmentSumConfig} from './kernels/SparseSegmentSum';
-import {sparseToDenseConfig} from './kernels/SparseToDense';
-import {splitVConfig} from './kernels/SplitV';
-import {sqrtConfig} from './kernels/Sqrt';
-import {squareConfig} from './kernels/Square';
-import {squaredDifferenceConfig} from './kernels/SquaredDifference';
-import {stepConfig} from './kernels/Step';
-import {stridedSliceConfig} from './kernels/StridedSlice';
-import {stringNGramsConfig} from './kernels/StringNGrams';
-import {subConfig} from './kernels/Sub';
-import {sumConfig} from './kernels/Sum';
-import {tanConfig} from './kernels/Tan';
-import {tanhConfig} from './kernels/Tanh';
-import {tensorScatterUpdateConfig} from './kernels/TensorScatterUpdate';
-import {tileConfig} from './kernels/Tile';
-import {topKConfig} from './kernels/TopK';
-import {transformConfig} from './kernels/Transform';
-import {transposeConfig} from './kernels/Transpose';
-import {unpackConfig} from './kernels/Unpack';
-import {unsortedSegmentSumConfig} from './kernels/UnsortedSegmentSum';
-import {zerosLikeConfig} from './kernels/ZerosLike';
-
-// List all kernel configs here
-const kernelConfigs: KernelConfig[] = [
-  _fusedMatMulConfig,
-  absConfig,
-  acosConfig,
-  acoshConfig,
-  addConfig,
-  addNConfig,
-  allConfig,
-  anyConfig,
-  argMaxConfig,
-  argMinConfig,
-  asinConfig,
-  asinhConfig,
-  atanConfig,
-  atan2Config,
-  atanhConfig,
-  avgPoolConfig,
-  avgPool3DConfig,
-  avgPool3DGradConfig,
-  avgPoolGradConfig,
-  batchMatMulConfig,
-  batchToSpaceNDConfig,
-  bincountConfig,
-  broadcastArgsConfig,
-  castConfig,
-  ceilConfig,
-  clipByValueConfig,
-  complexConfig,
-  complexAbsConfig,
-  concatConfig,
-  conv2DConfig,
-  conv2DBackpropFilterConfig,
-  conv2DBackpropInputConfig,
-  conv3DConfig,
-  conv3DBackpropFilterV2Config,
-  conv3DBackpropInputV2Config,
-  cosConfig,
-  coshConfig,
-  cropAndResizeConfig,
-  cumprodConfig,
-  cumsumConfig,
-  denseBincountConfig,
-  depthToSpaceConfig,
-  depthwiseConv2dNativeBackpropFilterConfig,
-  depthwiseConv2dNativeBackpropInputConfig,
-  depthwiseConv2dNativeConfig,
-  diagConfig,
-  dilation2DConfig,
-  dilation2DBackpropFilterConfig,
-  dilation2DBackpropInputConfig,
-  drawConfig,
-  einsumConfig,
-  eluConfig,
-  eluGradConfig,
-  equalConfig,
-  erfConfig,
-  expConfig,
-  expandDimsConfig,
-  expm1Config,
-  fftConfig,
-  fillConfig,
-  flipLeftRightConfig,
-  fromPixelsConfig,
-  floorConfig,
-  floorDivConfig,
-  fusedBatchNormConfig,
-  fusedConv2DConfig,
-  fusedDepthwiseConv2DConfig,
-  gatherNdConfig,
-  gatherV2Config,
-  greaterConfig,
-  greaterEqualConfig,
-  identityConfig,
-  ifftConfig,
-  imagConfig,
-  isFiniteConfig,
-  isInfConfig,
-  isNaNConfig,
-  leakyReluConfig,
-  lessConfig,
-  lessEqualConfig,
-  linSpaceConfig,
-  log1pConfig,
-  logConfig,
-  logicalAndConfig,
-  logicalNotConfig,
-  logicalOrConfig,
-  lrnConfig,
-  lrnGradConfig,
-  maxConfig,
-  maximumConfig,
-  maxPoolConfig,
-  maxPoolGradConfig,
-  maxPool3DConfig,
-  maxPool3DGradConfig,
-  maxPoolWithArgmaxConfig,
-  meanConfig,
-  minConfig,
-  minimumConfig,
-  mirrorPadConfig,
-  modConfig,
-  multinomialConfig,
-  multiplyConfig,
-  negConfig,
-  nonMaxSuppressionV3Config,
-  nonMaxSuppressionV5Config,
-  notEqualConfig,
-  oneHotConfig,
-  onesLikeConfig,
-  packConfig,
-  padV2Config,
-  powConfig,
-  preluConfig,
-  prodConfig,
-  rangeConfig,
-  realConfig,
-  realDivConfig,
-  reciprocalConfig,
-  reluConfig,
-  relu6Config,
-  reshapeConfig,
-  resizeBilinearConfig,
-  resizeBilinearGradConfig,
-  resizeNearestNeighborConfig,
-  resizeNearestNeighborGradConfig,
-  reverseConfig,
-  rotateWithOffsetConfig,
-  roundConfig,
-  rsqrtConfig,
-  scatterNdConfig,
-  searchSortedConfig,
-  selectConfig,
-  seluConfig,
-  sigmoidConfig,
-  signConfig,
-  sinConfig,
-  sinhConfig,
-  sliceConfig,
-  stepConfig,
-  stridedSliceConfig,
-  stringNGramsConfig,
-  softmaxConfig,
-  softplusConfig,
-  spaceToBatchNDConfig,
-  sparseSegmentMeanConfig,
-  sparseSegmentSumConfig,
-  sparseToDenseConfig,
-  splitVConfig,
-  sqrtConfig,
-  squareConfig,
-  squaredDifferenceConfig,
-  subConfig,
-  sumConfig,
-  tanConfig,
-  tanhConfig,
-  tensorScatterUpdateConfig,
-  tileConfig,
-  topKConfig,
-  transformConfig,
-  transposeConfig,
-  unpackConfig,
-  unsortedSegmentSumConfig,
-  zerosLikeConfig
-];
-
-for (const kernelConfig of kernelConfigs) {
-  registerKernel(kernelConfig);
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/resize_bilinear_backprop_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/resize_bilinear_backprop_webgpu.ts
deleted file mode 100644
index d24a46ed3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/resize_bilinear_backprop_webgpu.ts
+++ /dev/null
@@ -1,124 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ResizeBilinearBackpropProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['dy'];
-  uniforms =
-      `effectiveXSize : vec2<i32>, effectiveYSize : vec2<i32>, heightScale : f32, widthScale : f32,
-       invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  alignCorners: boolean;
-  size = true;
-
-  constructor(
-      inputShape: [number, number, number, number], alignCorners: boolean) {
-    this.outputShape = inputShape;
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.alignCorners = alignCorners;
-    this.shaderKey = `resizeBilinearBackprop_${alignCorners}`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getOutputCoords();
-          let b = coords[0];
-          let d = coords[3];
-          let r = coords[1];
-          let c = coords[2];
-
-          var accumulator = 0.0;
-
-          // Compute bounds for where in dy we will look
-          let startRLerp = floor(f32(r) * uniforms.invHeightScale);
-          let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));
-
-          let startCLerp = floor(f32(c) * uniforms.invWidthScale);
-          let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));
-
-          // Loop over dy
-          for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {
-            let dyR = startDyR + dyROffset;
-
-            // Guard against the window exceeding the bounds of dy
-            if (dyR < 0 || dyR >= uniforms.dyShape[1]) {
-              continue;
-            }
-
-            for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {
-              let dyC = startDyC + dyCOffset;
-
-              // Guard against the window exceeding the bounds of dy
-              if (dyC < 0 || dyC >= uniforms.dyShape[2]) {
-                continue;
-              }
-
-              let dxR = f32(dyR) * uniforms.heightScale;
-              let topDxRIndex = i32(floor(dxR));
-              let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));
-              let dxRLerp = dxR - f32(topDxRIndex);
-              let inverseDxRLerp = 1.0 - dxRLerp;
-
-              let dxC = f32(dyC) * uniforms.widthScale;
-              let leftDxCIndex = i32(floor(dxC));
-              let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));
-              let dxCLerp = dxC - f32(leftDxCIndex);
-              let inverseDxCLerp = 1.0 - dxCLerp;
-
-              if (r == topDxRIndex && c == leftDxCIndex) {
-                // topLeft
-                accumulator +=
-                  getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
-              }
-
-              if (r == topDxRIndex && c == rightDxCIndex) {
-                // topRight
-                accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
-              }
-
-              if (r == bottomDxRIndex && c == leftDxCIndex) {
-                // bottomLeft
-                accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
-              }
-
-              if (r == bottomDxRIndex && c == rightDxCIndex) {
-                // bottomRight
-                accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
-              }
-            }
-          }
-          // End loop over dy
-
-          setOutputAtIndex(index, accumulator);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/resize_bilinear_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/resize_bilinear_webgpu.ts
deleted file mode 100644
index ca07e81a6..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/resize_bilinear_webgpu.ts
+++ /dev/null
@@ -1,91 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ResizeBilinearProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms = 'adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(
-      inputShape: [number, number, number, number], newHeight: number,
-      newWidth: number) {
-    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.shaderKey = `resizeBilinear`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-        let coords = getCoordsFromIndex(index);
-          let b = coords[0];
-          let d = coords[3];
-          let rc = coords.yz;
-
-          let effectiveInSize = vec2<f32>(
-            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
-            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
-
-          let effectiveOutSize = vec2<f32>(
-            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
-            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
-
-          let effectiveInputOverOutputRatioRC =
-              effectiveInSize / effectiveOutSize;
-
-          // Fractional source index
-          let sourceFracIndexRC =
-            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
-            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
-
-          // Compute the four integer indices.
-          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
-          let sourceCeilRC = vec2<i32>(
-            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
-
-          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
-          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
-          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
-          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
-
-          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
-
-          let top = topLeft + (topRight - topLeft) * fracRC.y;
-          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
-          let newValue = top + (bottom - top) * fracRC.x;
-
-          setOutputAtIndex(index, newValue);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/resize_nearest_neighbor_backprop_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/resize_nearest_neighbor_backprop_webgpu.ts
deleted file mode 100644
index ec44ce575..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/resize_nearest_neighbor_backprop_webgpu.ts
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ResizeNearestNeigborBackpropProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['dy'];
-  uniforms =
-      `effectiveXSize : vec2<i32>, effectiveYSize : vec2<i32>, invHeightScale : f32, invWidthScale : f32,
-       winHeight : i32, winWidth : i32,`;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  alignCorners: boolean;
-  size = true;
-
-  constructor(
-      inputShape: [number, number, number, number], alignCorners: boolean) {
-    this.outputShape = inputShape;
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.alignCorners = alignCorners;
-    this.shaderKey = `resizeNearestNeigborBackprop_${alignCorners}`;
-  }
-
-  getUserCode(): string {
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getOutputCoords();
-          let b = coords[0];
-          let d = coords[3];
-          let r = coords[1];
-          let c = coords[2];
-
-          var accumulator = 0.0;
-
-          // Compute bounds for where in dy we will look
-          let startRLerp = floor(f32(r) * uniforms.invHeightScale);
-          let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));
-
-          let startCLerp = floor(f32(c) * uniforms.invWidthScale);
-          let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));
-
-          // Loop over dy
-          for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {
-            let dyR = startDyR + dyROffset;
-
-            // Guard against the window exceeding the bounds of dy
-            if (dyR < 0 || dyR >= uniforms.dyShape[1]) {
-              continue;
-            }
-
-            for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {
-              let dyC = startDyC + dyCOffset;
-
-              // Guard against the window exceeding the bounds of dy
-              if (dyC < 0 || dyC >= uniforms.dyShape[2]) {
-                continue;
-              }
-
-              let sourceFracRow = f32(uniforms.effectiveXSize[0]) *
-                  (f32(dyR) / f32(uniforms.effectiveYSize[0]));
-
-              let sourceFracCol = f32(uniforms.effectiveXSize[1]) *
-                  (f32(dyC) / f32(uniforms.effectiveYSize[1]));
-
-              let sourceNearestRow =
-                  i32(min(f32(uniforms.outShape[1] - 1),
-                  ${
-        this.alignCorners ? 'floor(sourceFracRow + 0.5)' :
-                            'floor(sourceFracRow)'}));
-
-              let sourceNearestCol =
-                  i32(min(f32(uniforms.outShape[2] - 1),
-                  ${
-        this.alignCorners ? 'floor(sourceFracCol + 0.5)' :
-                            'floor(sourceFracCol)'}));
-
-              if (r == sourceNearestRow && c == sourceNearestCol) {
-                accumulator += getDy(b, dyR, dyC, d);
-              }
-            }
-          }
-          // End loop over dy
-
-          setOutputAtIndex(index, accumulator);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/resize_nearest_neighbor_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/resize_nearest_neighbor_webgpu.ts
deleted file mode 100644
index 5076a008a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/resize_nearest_neighbor_webgpu.ts
+++ /dev/null
@@ -1,90 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ResizeNearestNeighborProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms = 'adjustHeightWidth : vec2<f32>, roundBase : f32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  halfPixelCenters: boolean;
-  size = true;
-
-  constructor(
-      inputShape: [number, number, number, number], newHeight: number,
-      newWidth: number, halfPixelCenters: boolean) {
-    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];
-
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.halfPixelCenters = halfPixelCenters;
-    this.shaderKey = `resizeNearest_${halfPixelCenters}`;
-  }
-
-  getUserCode(): string {
-    let sourceFracIndexRC: string;
-    if (this.halfPixelCenters) {
-      sourceFracIndexRC =
-          `max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC` +
-          `, vec2<f32>(0.0))`;
-    } else {
-      sourceFracIndexRC = `vec2<f32>(rc) * effectiveInputOverOutputRatioRC`;
-    }
-
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          let b = coords[0];
-          let d = coords[3];
-          let rc = coords.yz;
-
-          let effectiveInSize = vec2<f32>(
-            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
-            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
-
-          let effectiveOutSize = vec2<f32>(
-            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
-            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
-
-          let effectiveInputOverOutputRatioRC =
-              effectiveInSize / effectiveOutSize;
-
-          // Fractional source index
-          let sourceFracIndexRC = ${sourceFracIndexRC};
-
-          // Compute the coordinators of nearest neighbor point.
-          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
-          let sourceNearestRC = vec2<i32>(
-            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
-          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
-
-          setOutputAtIndex(index, newValue);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/reverse_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/reverse_webgpu.ts
deleted file mode 100644
index e76c086ed..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/reverse_webgpu.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ReverseProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms: string;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(xShape: [number, number, number, number]) {
-    this.outputShape = xShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.uniforms = ` axis : vec4<i32>,`;
-    this.shaderKey = 'reverse';
-  }
-
-  getUserCode(): string {
-    const reverseCoordsSnippet = `
-      // Using uniform variables as judging conditions, so the function has
-      // coherent execution within all threads.
-      fn getReverseCoords(coords : vec4<i32>) -> vec4<i32> {
-        var reverseCoords = coords;
-        if (uniforms.axis[0] == 1) {
-          reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;
-        }
-        if (uniforms.axis[1] == 1) {
-          reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;
-        }
-        if (uniforms.axis[2] == 1) {
-          reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;
-        }
-        if (uniforms.axis[3] == 1) {
-          reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;
-        }
-
-        return reverseCoords;
-      }
-    `;
-    const userCode = `
-      ${reverseCoordsSnippet}
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          let reverseCoords = getReverseCoords(coords);
-          setOutputAtIndex(index, getX(reverseCoords[0],
-              reverseCoords[1], reverseCoords[2], reverseCoords[3]));
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/rotate_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/rotate_webgpu.ts
deleted file mode 100644
index 8755c1681..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/rotate_webgpu.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class RotateProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x'];
-  uniforms: string;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  fillSnippet: string;
-  size = true;
-
-  constructor(
-      imageShape: [number, number, number, number],
-      fillValue: number|[number, number, number]) {
-    this.outputShape = imageShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
-          cosRadians : f32,`;
-    this.shaderKey = 'rotate';
-    this.outputShape = imageShape;
-
-    if (typeof fillValue === 'number') {
-      this.uniforms += ` fillValue : f32,`;
-      this.fillSnippet = `var outputValue = uniforms.fillValue;`;
-      this.shaderKey += '_float';
-    } else {
-      this.uniforms += ` fillValue : vec3<f32>,`;
-      this.fillSnippet = `var outputValue = uniforms.fillValue[coords[3]];`;
-      this.shaderKey += '_vec3';
-    }
-  }
-
-  getUserCode(): string {
-    const userCode = `
-        ${main('index')} {
-          if (index < uniforms.size) {
-            let coords = getCoordsFromIndex(index);
-            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
-                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
-                uniforms.sinRadians;
-            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
-                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
-                uniforms.cosRadians;
-            let coordX = i32(round(coordXFloat + uniforms.centerX));
-            let coordY = i32(round(coordYFloat + uniforms.centerY));
-            ${this.fillSnippet}
-            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
-                coordY < uniforms.xShape[1]) {
-              outputValue = getX(coords[0], coordY, coordX, coords[3]);
-            }
-            setOutputAtIndex(index, outputValue);
-          }
-        }
-      `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/scatter_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/scatter_webgpu.ts
deleted file mode 100644
index 224020303..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/scatter_webgpu.ts
+++ /dev/null
@@ -1,125 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType} from '@tensorflow/tfjs-core';
-
-import {atomicAddSnippet} from './shader_util';
-import {dataTypeToGPUType, getCoordsDataType, getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class ScatterProgram implements WebGPUProgram {
-  variableNames = ['updates', 'indices'];
-  uniforms: string;
-  outputShape: number[];
-  sumDupeIndices: boolean;
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  updatesRank: number;
-  indicesRank: number;
-  sliceDimGreaterThanOne: boolean;
-  atomic = true;
-  type: DataType;
-
-  constructor(
-      flattenXShape: number[], sliceDim: number, indicesRank: number,
-      updatesRank: number, strides: number[], shape: number[],
-      outputDtype: DataType, sumDupeIndices = true) {
-    this.outputShape = shape;
-    this.type = outputDtype;
-    this.sumDupeIndices = sumDupeIndices;
-    this.dispatchLayout = flatDispatchLayout(flattenXShape);
-    // Dispatching based on |updates| shape instead of output shape.
-    this.dispatch =
-        computeDispatch(this.dispatchLayout, flattenXShape, this.workgroupSize);
-    this.sliceDimGreaterThanOne = sliceDim > 1;
-    this.shaderKey =
-        `scatter_${indicesRank}_${updatesRank}_${this.sliceDimGreaterThanOne}_${
-            outputDtype}_${sumDupeIndices}_${strides.length}`;
-    const stridesType = getCoordsDataType(strides.length);
-    this.uniforms =
-        `sliceDim : i32, strides: ${stridesType}, updatesSize: i32,`;
-    this.updatesRank = updatesRank;
-    this.indicesRank = indicesRank;
-  }
-
-  getUserCode(): string {
-    let indicesString = '';
-    if (this.indicesRank === 1) {
-      indicesString = 'coords[0]';
-    } else if (this.indicesRank === 2) {
-      indicesString = 'coords[0], j';
-    }
-    const indicesSnippet = `getIndices(${indicesString})`;
-
-    const strideString = this.sliceDimGreaterThanOne ? 'uniforms.strides[j]' :
-                                                       'uniforms.strides';
-
-    let outCoordsString = '';
-    let getUpdatesCoordsFromFlatIndex = '';
-    if (this.dispatchLayout.x.length === 1) {
-      outCoordsString = 'flattenedIndex';
-      getUpdatesCoordsFromFlatIndex = `
-      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
-        return index;
-      }
-      `;
-    } else if (this.dispatchLayout.x.length === 2) {
-      outCoordsString = 'vec2<i32>(flattenedIndex, coords[1])';
-      getUpdatesCoordsFromFlatIndex = `
-      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
-        // N.B. |updates| could be a scalar tensor, conceptually representing a
-        // 2D tensor with all values equal to that. By design, its size must be
-        // the same as |outShape[1]| in one dimension, and |indicesShape[0]|
-        // gives the other.
-        let sliceSize = uniforms.outShape[1];
-        let d0 = index / sliceSize;
-        let d1 = index - d0 * sliceSize;
-        return vec2<i32>(d0, d1);
-      }
-      `;
-    }
-    const updatesString =
-        Array.from({length: this.updatesRank}, (_, idx) => `coords[${idx}]`);
-    const updatesSnippet = `getUpdates(${updatesString.join(', ')})`;
-
-    const userCode = `
-    ${getUpdatesCoordsFromFlatIndex}
-      ${main('index')} {
-        if (index < uniforms.updatesSize) {
-          let coords = getUpdatesCoordsFromFlatIndex(index);
-          var flattenedIndex = 0;
-          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
-            let indexInside = i32(round(${indicesSnippet}));
-            flattenedIndex = flattenedIndex + indexInside * ${strideString};
-          }
-          let updateValue =
-              ${dataTypeToGPUType(this.type)}(${updatesSnippet});
-          let flatIndex = getOutputIndexFromCoords(${outCoordsString});
-
-          ${
-        this.sumDupeIndices ?
-            atomicAddSnippet(
-                '&result[flatIndex]', 'updateValue',
-                this.type as 'float32' | 'int32') :
-            `atomicStore(&result[flatIndex], bitcast<i32>(updateValue));`}
-        }
-      }`;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/search_sorted_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/search_sorted_webgpu.ts
deleted file mode 100644
index 4d17667ac..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/search_sorted_webgpu.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class SearchSortedProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['sortedSequence', 'values'];
-  uniforms = 'numInputs : i32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-  side: string;
-
-  constructor(outputShape: [number, number], side: 'left'|'right') {
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.side = side;
-    this.shaderKey = `search_sorted_${side}`;
-  }
-
-  getUserCode(): string {
-    const boundComparator = this.side === 'left' ? '<' : '<=';
-    const userCode = `
-      fn findBound(batch: i32, value: f32) -> i32 {
-        var left = i32(0);
-        var right = uniforms.numInputs;
-        while (left < right) {
-          var mid = (left + right) / 2;
-          if (getSortedSequence(batch, mid) ${boundComparator} value) {
-            left = mid + 1;
-          } else {
-            right = mid;
-          }
-        }
-        return right;
-      }
-
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          let value = getValuesByOutputIndex(index);
-          setOutputAtIndexI32(index, findBound(coords[0], value));
-        }
-      }
-    `;
-
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/select_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/select_webgpu.ts
deleted file mode 100644
index 324e9fba6..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/select_webgpu.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class SelectProgram implements WebGPUProgram {
-  variableNames = ['c', 'a', 'b'];
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  cRank: number;
-  rank: number;
-  size = true;
-
-  constructor(cRank: number, shape: number[], rank: number) {
-    this.outputShape = shape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-
-    this.cRank = cRank;
-    this.rank = rank;
-    this.shaderKey = 'select';
-  }
-
-  getUserCode(): string {
-    // TODO(WGSL): below code can be merged with getUserCode.
-    let cCoords;
-    let abCoords;
-    if (this.rank > 4) {
-      throw Error(`Where for rank ${this.rank} is not yet supported`);
-    }
-
-    if (this.rank === 1) {
-      abCoords = `resRC`;
-      cCoords = `resRC`;
-    } else {
-      const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
-      const cCoordVars = [];
-      const abCoordVars = [];
-      for (let i = 0; i < this.outputShape.length; i++) {
-        abCoordVars.push(`${currentCoords[i]}`);
-        if (i < this.cRank) {
-          cCoordVars.push(`${currentCoords[i]}`);
-        }
-      }
-      cCoords = cCoordVars.join();
-      abCoords = abCoordVars.join();
-    }
-
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let resRC = getCoordsFromIndex(index);
-          let cVal = getC(${cCoords});
-          if (cVal >= 1.0) {
-            setOutputAtIndex(index, getA(${abCoords}));
-          } else {
-            setOutputAtIndex(index, getB(${abCoords}));
-          }
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/setup_test.ts b/tfjs-master/tfjs-backend-webgpu/src/setup_test.ts
deleted file mode 100644
index f881a35fb..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/setup_test.ts
+++ /dev/null
@@ -1,218 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Register the backend.
-import './index';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-import './backend_webgpu_test_registry';
-// tslint:disable-next-line: no-imports-from-dist
-import {parseTestEnvFromKarmaFlags, setTestEnvs, setupTestFilters, TEST_ENVS, TestFilter} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-const TEST_FILTERS: TestFilter[] = [
-  // skip specific test cases for supported kernels
-  {
-    startsWith: 'cumsum ',
-    excludes: [
-      'gradient',  // gradient function not found.
-    ]
-  },
-  {
-    startsWith: 'exp ',
-    excludes: [
-      'int32',  // TODO: fix precision problem.
-    ]
-  },
-  {
-    startsWith: 'gather ',
-    excludes: [
-      'throws when index is out of bound',
-    ]
-  },
-  {
-    startsWith: 'nonMaxSuppression ',
-    excludes: [
-      'NonMaxSuppressionPadded'  // NonMaxSuppressionV4 not yet implemented.
-    ]
-  },
-  {
-    startsWith: 'prod ',
-    excludes: [
-      'gradients',  // Not yet implemented
-    ]
-  },
-  {
-    startsWith: 'cos ',
-    excludes: [
-      'gradients',             // Failing on MacOS
-      'gradient with clones',  // Failing on MacOS
-    ],
-  },
-  {
-    startsWith: 'tan ',
-    excludes: [
-      'gradients',  // Failing on MacOS
-      //'gradient with clones', // Failing on MacOS
-      // https://github.com/tensorflow/tfjs/issues/7618
-      'numbers exceed float32 precision',
-    ],
-  },
-  {
-    startsWith: 'acosh ',
-    excludes: [
-      'propagates NaNs',       // Failing on MacOS
-      'gradient with clones',  // Failing on MacOS
-    ],
-  },
-  {
-    startsWith: 'asinh ',
-    excludes: [
-      'propagates NaNs',  // Failing on MacOS
-      //'gradient with clones', // Failing on MacOS
-    ],
-  },
-  {
-    startsWith: 'atanh ',
-    excludes: [
-      'propagates NaNs',  // Failing on MacOS
-      //'gradient with clones', // Failing on MacOS
-    ],
-  },
-  {
-    startsWith: 'sigmoid ',
-    excludes: [
-      'propagates NaNs',  // Failing on MacOS
-      //'gradient with clones', // Failing on MacOS
-    ],
-  },
-  {
-    startsWith: 'unsortedSegmentSum ',
-    excludes: [
-      'ignores negative segmentIds',  // Failing on MacOS
-    ],
-  },
-  {
-    startsWith: 'log ',
-    excludes: [
-      'log propagates NaNs',  // Failing on MacOS
-    ],
-  },
-  {
-    startsWith: 'softmax ',
-    excludes: [
-      'Propagates NaNs',  // Failing on MacOS
-    ],
-  },
-  {
-    startsWith: 'fromPixels ',
-    excludes: [
-      'HTMLVideoElement',        // Device is lost on Linux
-      'canvas and image match',  // Failing on Linux
-    ],
-  },
-  {
-    startsWith: 'sign ',
-    excludes: [
-      // Failing on Linux
-      'basic',
-      'does not propagate NaNs',
-      'accepts a tensor-like object',
-    ],
-  },
-  {
-    startsWith: 'broadcastArgs ',
-    excludes: [
-      'error',  // Currently, cannot transfer the error from gpu to cpu
-    ],
-  },
-  {
-    startsWith: 'tensor.data ',
-    excludes: [
-      '.data() postpones disposal of tensor',
-      'calling .data() twice works',
-    ],
-  },
-  {
-    startsWith: 'bitwiseAnd',
-    excludes: [
-      'bitwiseAnd',
-    ],
-  },
-  {
-    startsWith: 'sparseSegmentMean',
-    excludes: [
-      'throw error',  // Currently, cannot transfer the error from gpu to cpu
-    ],
-  },
-  {
-    startsWith: 'sparseSegmentSum',
-    excludes: [
-      // Currently, cannot transfer the error from gpu to cpu
-      'segments invalid',
-      'indices invalid',
-    ],
-  },
-
-  // exclude unsupported kernels and to be fixed cases
-  {
-    include: ' webgpu ',
-    excludes: [
-      'raggedGather ',
-      'raggedRange ',
-      'raggedTensorToTensor ',
-      'method otsu',  // round
-      'sparseFillEmptyRows ',
-      'sparseReshape ',
-      'staticRegexReplace ',
-      'stringSplit ',
-      'stringToHashBucketFast ',
-      'unique ',
-    ]
-  },
-];
-
-const customInclude = (testName: string) => {
-  // Include webgpu specific tests.
-  if (testName.startsWith('webgpu')) {
-    return true;
-  }
-  return false;
-};
-setupTestFilters(TEST_FILTERS, customInclude);
-
-// Allow flags to override test envs
-// tslint:disable-next-line:no-any
-declare let __karma__: any;
-if (typeof __karma__ !== 'undefined') {
-  const testEnv = parseTestEnvFromKarmaFlags(__karma__.config.args, TEST_ENVS);
-  if (testEnv != null) {
-    setTestEnvs([testEnv]);
-  }
-}
-
-// These use 'require' because they must not be hoisted above
-// the preceding snippet that parses test environments.
-// Import and run tests from core.
-// tslint:disable-next-line:no-imports-from-dist
-// tslint:disable-next-line:no-require-imports
-require('@tensorflow/tfjs-core/dist/tests');
-// Import and run tests from webgpu.
-// tslint:disable-next-line:no-imports-from-dist
-// tslint:disable-next-line:no-require-imports
-require('./tests');
diff --git a/tfjs-master/tfjs-backend-webgpu/src/shader_util.ts b/tfjs-master/tfjs-backend-webgpu/src/shader_util.ts
deleted file mode 100644
index 98a5782e3..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/shader_util.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Generates WGSL that computes strides.
-export function symbolicallyComputeStrides(
-    indicesArr: number[], variableName: string): string[] {
-  if (Math.max(...indicesArr) > 5) {
-    throw new Error('Cannot symbolically compute strides for rank > 6 tensor.');
-  }
-
-  const numCoords = indicesArr.length;
-  const indicesStr = 'xyzwuv';
-  const shape = indicesArr.map(d => `${variableName}.${indicesStr[d]}`);
-  const strides = new Array(numCoords - 1);
-  strides[numCoords - 2] = shape[numCoords - 1];
-  for (let i = numCoords - 3; i >= 0; --i) {
-    strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`;
-  }
-
-  return strides;
-}
-
-export const atomicAddSnippet =
-    (ptr: string, v: string, type: 'int32'|'float32') => {
-      if (type === 'int32') {
-        return `atomicAdd(${ptr}, bitcast<i32>(${v}));`;
-      } else {
-        // atomicAdd only supports uint/int type. For float, we use
-        // atomicCompareExchangeWeak to simulate.
-        return `
-          {
-            var oldValue = 0;
-            loop {
-              let newValueF32 = bitcast<f32>(oldValue) + (${v});
-              let newValue = bitcast<i32>(newValueF32);
-              let res = atomicCompareExchangeWeak(${ptr}, oldValue, newValue);
-              if res.exchanged {
-                break;
-              }
-              oldValue = res.old_value;
-            }
-          }`;
-      }
-    };
diff --git a/tfjs-master/tfjs-backend-webgpu/src/shader_util_test.ts b/tfjs-master/tfjs-backend-webgpu/src/shader_util_test.ts
deleted file mode 100644
index 161e335dd..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/shader_util_test.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {symbolicallyComputeStrides} from './shader_util';
-
-describe('shader util', () => {
-  it('symbolicallyComputeStrides takes in array of dimensions ' +
-         'and returns GLSL to compute strides for those dimensions',
-     () => {
-       const layout = [0, 2, 1];
-       const strides = symbolicallyComputeStrides(layout, 'output');
-       expect(strides[0]).toEqual('(output[1] * output[2])');
-       expect(strides[1]).toEqual('output[1]');
-     });
-
-  it('symbolicallyComputeStrides throws if given a dimension ' +
-         'that cannot be accessed from a GLSL data type',
-     () => {
-       const layout = [0, 5, 2];
-       expect(() => symbolicallyComputeStrides(layout, 'output'))
-           .toThrowError();
-     });
-});
\ No newline at end of file
diff --git a/tfjs-master/tfjs-backend-webgpu/src/slice_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/slice_webgpu.ts
deleted file mode 100644
index e8d15af66..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/slice_webgpu.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getCoordsDataType, getCoordsXYZ, getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class SliceProgram implements WebGPUProgram {
-  variableNames = ['source'];
-  uniforms: string;
-  outputShape: number[];
-  shaderKey: string;
-  rank: number;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workPerThread = 1;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  start: number[];
-  size = true;
-
-  constructor(start: number[], destSize: number[]) {
-    this.outputShape = destSize;
-    this.rank = destSize.length;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        [this.workPerThread, 1, 1]);
-
-    this.start = start;
-    this.uniforms = `start : ${getCoordsDataType(start.length)}, `;
-    this.shaderKey = 'slice';
-  }
-
-  getUserCode(): string {
-    const dtype = getCoordsDataType(this.rank);
-    const sourceCoords = getCoords(this.rank);
-    let coordSum;
-    if (this.start.length === 1) {
-      coordSum = this.outputShape.map((_, i) => {
-        return `sourceLoc = uniforms.start + coords;`;
-      });
-    } else {
-      coordSum = this.outputShape.map((_, i) => {
-        return `sourceLoc.${coords[i]} = uniforms.start.${
-            getCoordsXYZ(i)} + coords.${coords[i]};`;
-      });
-    }
-
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          var sourceLoc : ${dtype};
-          let coords = getCoordsFromIndex(index);
-          ${coordSum.join('\n')}
-          setOutputAtIndex(index, getSource(${sourceCoords}));
-        }
-      }
-    `;
-    return userCode;
-  }
-}
-
-const coords = ['x', 'y', 'z', 'w', 'u', 'v'];
-
-function getCoords(rank: number): string {
-  if (rank === 1) {
-    return 'sourceLoc';
-  } else if (rank <= 6) {
-    return coords.slice(0, rank).map(coord => `sourceLoc.${coord}`).join(',');
-  } else {
-    throw Error(`Slicing for rank ${rank} is not yet supported`);
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/softmax_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/softmax_webgpu.ts
deleted file mode 100644
index fed491dbb..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/softmax_webgpu.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {flatDispatchLayout} from './webgpu_util';
-
-export class SoftmaxProgram implements WebGPUProgram {
-  variableNames = ['logits'];
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number];
-
-  constructor(outputShape: number[]) {
-    this.outputShape = outputShape;  // [rows, cols]
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = [this.outputShape[0], 1, 1];
-    if (this.outputShape[1] >= 4096) {
-      this.workgroupSize = [256, 1, 1];
-    } else {
-      this.workgroupSize = [64, 1, 1];
-    }
-    this.shaderKey = 'softmax';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    var<workgroup> buf : array<f32, ${this.workgroupSize[0]}>;
-    var<workgroup> rowMaxShared : f32;
-    var<workgroup> rowSumShared : f32;
-    const blockSize = ${this.workgroupSize[0]};
-    ${main('index')} {
-      let row = index / blockSize;
-      let tid = i32(localId.x);
-      let cols = uniforms.outShape[1];
-
-      var threadMax = -3.402823e+38f;
-      for (var col = tid; col < cols; col += blockSize) {
-        let value = getLogits(row, col);
-        threadMax = max(threadMax, value);
-      }
-      if (tid < cols) {
-        buf[tid] = threadMax;
-      }
-      workgroupBarrier();
-
-      var reduceSize = min(cols, blockSize);
-      for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {
-        reduceSize = currSize + (reduceSize & 1);
-        if (tid < currSize) {
-          buf[tid] = max(buf[tid], buf[tid + reduceSize]);
-        }
-        workgroupBarrier();
-      }
-
-      if (tid == 0) {
-        rowMaxShared = buf[0];
-      }
-      workgroupBarrier();
-
-      var threadSum = 0.0;
-      for (var col = tid; col < cols; col += blockSize) {
-        let subExp = exp(getLogits(row, col) - rowMaxShared);
-        threadSum += subExp;
-      }
-      buf[tid] = threadSum;
-      workgroupBarrier();
-
-      for (var currSize = blockSize >> 1;  currSize > 0; currSize = currSize >> 1) {
-        if (tid < currSize) {
-          buf[tid] = buf[tid] + buf[tid + currSize];
-        }
-        workgroupBarrier();
-      }
-
-      if (tid == 0) {
-        rowSumShared = buf[0];
-      }
-      workgroupBarrier();
-
-      for (var col = tid; col < cols; col += blockSize) {
-        let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;
-        setOutputAtCoords(row, col, value);
-      }
-  }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/space_to_batchND_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/space_to_batchND_webgpu.ts
deleted file mode 100644
index 72b5f24b2..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/space_to_batchND_webgpu.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {padCommon} from './pad_webgpu';
-import {getSwitchedCoords} from './transpose_webgpu';
-import {getCoordsDataType, getCoordsFromIndexSnippet, getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class SpaceToBatchNDProgram implements WebGPUProgram {
-  variableNames = ['x'];
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  uniforms = '';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  newDim: number[];
-  xShape: number[];
-  paddedXShape: number[];
-  size = true;
-
-  constructor(
-      xShape: number[], paddedXShape: number[],
-      paddings: Array<[number, number]>, reshapedPaddedXShape: number[],
-      newDim: number[], paddedXShapeStridesShapeLength: number) {
-    const outputShape: number[] = new Array(reshapedPaddedXShape.length);
-    for (let i = 0; i < outputShape.length; i++) {
-      outputShape[i] = reshapedPaddedXShape[newDim[i]];
-    }
-    this.outputShape = outputShape;
-    this.newDim = newDim;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.xShape = xShape;
-    this.paddedXShape = paddedXShape;
-    this.uniforms += `reshapedPaddedXShape : ${
-        getCoordsDataType(
-            reshapedPaddedXShape.length)}, paddedXShapeStrides : ${
-        getCoordsDataType(paddedXShapeStridesShapeLength)}, `;
-    paddings.map((_, i) => {
-      this.uniforms += ` pad${i} : vec2<i32>,`;
-    });
-    this.shaderKey = `spaceToBatchND_${newDim}`;
-  }
-
-  getUserCode(): string {
-    const dtype = getCoordsDataType(this.outputShape.length);
-    const switched = getSwitchedCoords(this.newDim);
-
-    const userCode = `
-      ${getCoordsFromIndexSnippet(this.paddedXShape, 'PaddedX')}
-      ${main('index')} {
-        if(index < uniforms.size) {
-          let coords = getCoordsFromIndex(index);
-          let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${
-        dtype}(${switched}), uniforms.reshapedPaddedXShape);
-          let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex);
-          ${padCommon(this.xShape, true)}
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/sparse_segment_reduce_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/sparse_segment_reduce_webgpu.ts
deleted file mode 100644
index a25cd81ed..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/sparse_segment_reduce_webgpu.ts
+++ /dev/null
@@ -1,135 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType} from '@tensorflow/tfjs-core';
-
-import {atomicAddSnippet} from './shader_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class SparseSegmentSumProgram implements WebGPUProgram {
-  variableNames = ['input', 'indices', 'segmentIds'];
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  uniforms = 'segmentSize : i32, sparseSize : i32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  atomic = true;
-  type: DataType;
-
-  constructor(outShape: number[], sparseSize: number, outputDtype: DataType) {
-    this.outputShape = outShape;
-    this.type = outputDtype;
-    this.dispatchLayout = flatDispatchLayout([sparseSize]);
-    this.dispatch =
-        computeDispatch(this.dispatchLayout, [sparseSize], this.workgroupSize);
-
-    this.shaderKey = 'sparseSegmentSum';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.sparseSize) {
-        let indexInSegmentIds = index / uniforms.segmentSize;
-        let indexInSegment = index % uniforms.segmentSize;
-        let indexInInput = indices[indexInSegmentIds];
-        let segmentId = segmentIds[indexInSegmentIds];
-
-        let value = input[indexInInput * uniforms.segmentSize + indexInSegment];
-        let outIndex = segmentId * uniforms.segmentSize + indexInSegment;
-        ${
-        atomicAddSnippet(
-            '&result[outIndex]', 'value', this.type as 'float32' | 'int32')}
-      }
-    }
-  `;
-    return userCode;
-  }
-}
-
-export class SparseSegmentIdCountProgram implements WebGPUProgram {
-  variableNames = ['segmentIds'];
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  atomic = true;
-
-  constructor(outShape: number, segmentIdsShape: number[]) {
-    this.outputShape = [outShape];
-    this.dispatchLayout = flatDispatchLayout(segmentIdsShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, segmentIdsShape, this.workgroupSize);
-
-    this.shaderKey = 'sparseSegmentIdCountProgram';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.segmentIdsShape) {
-        let segmentId = segmentIds[index];
-        ${atomicAddSnippet('&result[segmentId]', '1', 'int32')}
-      }
-    }
-  `;
-    return userCode;
-  }
-}
-
-export class SparseSegmentMeanProgram implements WebGPUProgram {
-  variableNames = ['segmentSum', 'sameSegmentIdCount'];
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  uniforms = 'segmentSize : i32';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-  type: DataType;
-
-  constructor(outShape: number[], outputDtype: DataType) {
-    this.outputShape = outShape;
-    this.type = outputDtype;
-    this.dispatchLayout = flatDispatchLayout(outShape);
-    this.dispatch =
-        computeDispatch(this.dispatchLayout, outShape, this.workgroupSize);
-
-    this.shaderKey = 'sparseSegmentMean';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.size) {
-        let segmentId = index / uniforms.segmentSize;
-        let count = sameSegmentIdCount[segmentId];
-        if (count != 0) {
-          ${
-        this.type === 'float32' ?
-            'setOutputAtIndex(index, segmentSum[index] / f32(count));' :
-            'setOutputAtIndexI32(index, segmentSum[index] / count);'}
-        }
-      }
-    }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/strided_slice_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/strided_slice_webgpu.ts
deleted file mode 100644
index 707906dad..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/strided_slice_webgpu.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getCoordsDataType, getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class StridedSliceProgram implements WebGPUProgram {
-  variableNames = ['x'];
-  uniforms: string;
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  // TODO(xing.xu): Increase the workPerThread.
-  workPerThread = 1;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(destSize: number[]) {
-    this.outputShape = destSize;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        [this.workPerThread, 1, 1]);
-
-    const dtype = getCoordsDataType(this.outputShape.length);
-    this.uniforms = `begin : ${dtype},  strides : ${dtype}, `;
-    this.shaderKey = 'stridedSlice';
-  }
-
-  getUserCode(): string {
-    const rank = this.outputShape.length;
-    let newCoords = '';
-    if (rank === 1) {
-      newCoords = 'coords * uniforms.strides + uniforms.begin';
-    } else {
-      let outputAxis = 0;
-      newCoords =
-          this.outputShape
-              .map((_, i) => {
-                outputAxis++;
-                return this.outputShape.length === 1 ?
-                    `coords * uniforms.strides[${i}] + uniforms.begin[${i}]` :
-                    `coords[${outputAxis - 1}] * uniforms.strides[${
-                        i}] + uniforms.begin[${i}]`;
-              })
-              .join(',');
-    }
-
-    const userCode = `
-       ${main('index')} {
-         if (index < uniforms.size) {
-           let coords = getCoordsFromIndex(index);
-           setOutputAtIndex(index, getX(${newCoords}));
-         }
-       }
-     `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/test_util.ts b/tfjs-master/tfjs-backend-webgpu/src/test_util.ts
deleted file mode 100644
index 77fefed75..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/test_util.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags, TestEnv} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-export function describeWebGPU(name: string, tests: (env: TestEnv) => void) {
-  describeWithFlags('webgpu ' + name, ALL_ENVS, tests);
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/texture_manager.ts b/tfjs-master/tfjs-backend-webgpu/src/texture_manager.ts
deleted file mode 100644
index 0bdc27612..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/texture_manager.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export class TextureManager {
-  private numUsedTextures = 0;
-  private numFreeTextures = 0;
-  private freeTextures: Map<string, GPUTexture[]> = new Map();
-  private usedTextures: Map<string, GPUTexture[]> = new Map();
-
-  public numBytesUsed = 0;
-  public numBytesAllocated = 0;
-
-  constructor(private device: GPUDevice) {}
-
-  acquireTexture(
-      width: number, height: number, format: GPUTextureFormat,
-      usage: GPUTextureUsageFlags) {
-    const bytesPerElement = getBytesPerElement(format);
-    const byteSize = width * height * bytesPerElement;
-    const key = getTextureKey(width, height, format, usage);
-    if (!this.freeTextures.has(key)) {
-      this.freeTextures.set(key, []);
-    }
-
-    if (!this.usedTextures.has(key)) {
-      this.usedTextures.set(key, []);
-    }
-
-    this.numBytesUsed += byteSize;
-    this.numUsedTextures++;
-
-    if (this.freeTextures.get(key).length > 0) {
-      this.numFreeTextures--;
-
-      const newTexture = this.freeTextures.get(key).shift();
-      this.usedTextures.get(key).push(newTexture);
-      return newTexture;
-    }
-
-    this.numBytesAllocated += byteSize;
-
-    const newTexture = this.device.createTexture({
-      size: [width, height],
-      format,
-      usage,
-    });
-    this.usedTextures.get(key).push(newTexture);
-
-    return newTexture;
-  }
-
-  releaseTexture(texture: GPUTexture) {
-    if (this.freeTextures.size === 0) {
-      return;
-    }
-
-    const width = texture.width;
-    const height = texture.height;
-    const format = texture.format;
-    const usage = texture.usage;
-
-    const key = getTextureKey(width, height, format, usage);
-    if (!this.freeTextures.has(key)) {
-      this.freeTextures.set(key, []);
-    }
-
-    this.freeTextures.get(key).push(texture);
-    this.numFreeTextures++;
-    this.numUsedTextures--;
-
-    const textureList = this.usedTextures.get(key);
-    const textureIndex = textureList.indexOf(texture);
-    if (textureIndex < 0) {
-      throw new Error(
-          'Cannot release a texture that was never provided by this ' +
-          'texture manager');
-    }
-    textureList.splice(textureIndex, 1);
-    const bytesPerElement = getBytesPerElement(format);
-    const byteSize = width * height * bytesPerElement;
-    this.numBytesUsed -= byteSize;
-  }
-
-  getNumUsedTextures(): number {
-    return this.numUsedTextures;
-  }
-
-  getNumFreeTextures(): number {
-    return this.numFreeTextures;
-  }
-
-  dispose() {
-    this.freeTextures.forEach((textures, key) => {
-      textures.forEach(texture => {
-        texture.destroy();
-      });
-    });
-
-    this.usedTextures.forEach((textures, key) => {
-      textures.forEach(texture => {
-        texture.destroy();
-      });
-    });
-
-    this.freeTextures = new Map();
-    this.usedTextures = new Map();
-    this.numUsedTextures = 0;
-    this.numFreeTextures = 0;
-    this.numBytesUsed = 0;
-    this.numBytesAllocated = 0;
-  }
-}
-
-function getTextureKey(
-    width: number, height: number, format: GPUTextureFormat,
-    usage: GPUTextureUsageFlags) {
-  return `${width}_${height}_${format}_${usage}`;
-}
-
-function getBytesPerElement(format: GPUTextureFormat) {
-  if (format === 'rgba8unorm') {
-    return 16;
-  } else {
-    throw new Error(`${format} is not supported!`);
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/tile_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/tile_webgpu.ts
deleted file mode 100644
index 9ac75c4e2..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/tile_webgpu.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class TileProgram implements WebGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-  rank: number;
-
-  constructor(aShape: number[], reps: number[]) {
-    const outputShape: number[] = new Array(aShape.length);
-    for (let i = 0; i < outputShape.length; i++) {
-      outputShape[i] = aShape[i] * reps[i];
-    }
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.rank = this.outputShape.length;
-    this.shaderKey = 'tile';
-  }
-
-  getUserCode(): string {
-    const sourceCoords = getSourceCoords(this.rank, 'uniforms.');
-
-    const userCode = `
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let resRC = getCoordsFromIndex(index);
-          setOutputAtIndex(index, getA(${sourceCoords}));
-        }
-      }
-    `;
-    return userCode;
-  }
-}
-
-function getSourceCoords(rank: number, uniformPrefix = ''): string {
-  if (rank >= 5) {
-    throw Error(`Tile for rank ${rank} is not yet supported`);
-  }
-  if (rank === 1) {
-    return `(resRC % ${uniformPrefix}aShape)`;
-  }
-
-  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
-  const sourceCoords = [];
-  for (let i = 0; i < rank; i++) {
-    sourceCoords.push(`(${currentCoords[i]} % ${uniformPrefix}aShape[${i}])`);
-  }
-  return sourceCoords.join();
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/top_k_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/top_k_webgpu.ts
deleted file mode 100644
index 1858a17ec..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/top_k_webgpu.ts
+++ /dev/null
@@ -1,210 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-// Based on Algorithm 2 of Bitonic Top K, ref:
-// https://anilshanbhag.in/static/papers/gputopk_sigmod18.pdf
-// The original algorithm is based on computing the top K only, however
-// since for TFJS we require the indices of the top K values as well then the
-// algorithm found here is a bit modified. Rather than producing the values
-// at each step, the indices containing the top K are generated instead.
-// The output values are not generated to reduce the number of outputs in the
-// GPU, the values can easily be retrieved from the indices using a gather
-// op.
-
-export class SwapProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'indices'];
-  uniforms: string;
-  workgroupSize: [number, number, number] = [256, 1, 1];
-  size = true;
-
-  constructor(shape: number[]) {
-    this.outputShape = shape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
-        dir : i32, inc : i32,`;
-    this.shaderKey = 'swap';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-        ${main('index')} {
-          if (index < uniforms.size) {
-            let outC = getCoordsFromIndex(index);
-            let batch = outC[0];
-            let elemIdx = outC[1];
-            // We compare elements pair-wise within a group of size 2 * inc.
-            // The comparing rule for each group alternates between ascending
-            // and descending. Within each group, we compare each pair at
-            // positions i and i+inc. To decide whether an element at position i
-            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
-            // inc, it is in the first half of the group, we denote it as x0,
-            // otherwise we denote it as x1.
-            // For example, as shown in the Bitonic top K paper referenced
-            // above, Figure5(a) shows that element[1] is in the second half of
-            // the group when group size is 2, but it is in the first half of
-            // the group when group size is 4.
-            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
-            var i = 0;
-            if (isFirstInPair) {
-              i = elemIdx;
-            } else {
-              i = elemIdx - uniforms.inc;
-            }
-
-            var i0 = 0;
-            if (uniforms.firstPass == 1) {
-              i0 = i;
-            } else {
-              i0 = i32(getIndices(batch, i));
-            }
-
-            var i1 = 0;
-            if (uniforms.firstPass == 1) {
-              i1 = i + uniforms.inc;
-            } else {
-              i1 = i32(getIndices(batch, i + uniforms.inc));
-            }
-
-            var x0 = f32(0.0);
-            var x1 = f32(0.0);
-            if (i0 < uniforms.inputSize) {
-              x0 = getX(batch, i0);
-            } else {
-              x0 = uniforms.negativeInf;
-            }
-            if (i1 < uniforms.inputSize) {
-              x1 = getX(batch, i1);
-            } else {
-              x1 = uniforms.negativeInf;
-            }
-
-            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
-            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
-            if (reverse == isGreater) {
-              // Elements in opposite order of direction
-              let iTemp = i0;
-              i0 = i1;
-              i1 = iTemp;
-            }
-            if (isFirstInPair) {
-              setOutputAtIndex(index, f32(i0));
-            } else {
-              setOutputAtIndex(index, f32(i1));
-            }
-          }
-        }
-      `;
-    return userCode;
-  }
-}
-
-export class MergeProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'indices'];
-  uniforms: string;
-  workgroupSize: [number, number, number] = [256, 1, 1];
-  size = true;
-
-  constructor(shape: number[]) {
-    this.outputShape = shape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    // |n| Size of the original input of TopK
-    // |firstPass| indicates if this is the first time swap is being used which
-    // means no indices input containing the top K is present yet.
-    // |k| Top k elements desired
-    this.uniforms = `inputSize : i32, firstPass : i32, k : i32,`;
-    this.shaderKey = 'merge';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-        ${main('index')} {
-          if (index < uniforms.size) {
-            let outC = getCoordsFromIndex(index);
-            let batch = outC[0];
-            let elemIdx = outC[1];
-            // The output size is half of the previous size.
-            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _
-            // (k=4), we only need to output the indices at positions |, the
-            // indices at positions _ can be thrown away, see Figure5(b) After
-            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced
-            // above.
-            // For example, the paper shows we only need to output the orange
-            // bars. The output sequence should look like this | | | | | | | |.
-            // Because the sequence is halved, to map the output index back to
-            // the previous sequence to find the corresponding value, we need
-            // to double the index. When we double the index, we basically
-            // interpolate a position, so 2i looks like
-            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
-            // position of each 2k positions by - elemIdx % k. E.g. for output
-            // at index 4,5,6,7, we want to get the corresponding element at
-            // original index 8,9,10,11, for output at index 8,9,10,11,
-            // we want to get the corresponding element at original index
-            // 16,17,18,19, so on and so forth.
-
-            var i = 0;
-            if (elemIdx < uniforms.k) {
-              i = elemIdx;
-            } else {
-              i = elemIdx * 2 - elemIdx % uniforms.k;
-            }
-            var i0 = 0;
-            if (uniforms.firstPass == 1) {
-              i0 = i;
-            } else {
-              i0 = i32(getIndices(batch, i));
-            }
-            var i1 = 0;
-            if (uniforms.firstPass == 1) {
-              i1 = i + uniforms.k;
-            } else {
-              i1 = i32(getIndices(batch, i + uniforms.k));
-            }
-
-            let x0 = getX(batch, i0);
-            var x1 = f32(0.0);
-            if (i1 < uniforms.inputSize) {
-              x1 = getX(batch, i1);
-            } else {
-              x1 = x0;
-            }
-
-            if (x0 >= x1) {
-              setOutputAtIndex(index, f32(i0));
-            } else {
-              setOutputAtIndex(index, f32(i1));
-            }
-          }
-        }
-      `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/transform_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/transform_webgpu.ts
deleted file mode 100644
index 8cd780c6e..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/transform_webgpu.ts
+++ /dev/null
@@ -1,159 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class TransformProgram implements WebGPUProgram {
-  variableNames = ['Image', 'Transforms'];
-  outputShape: number[];
-  uniforms = 'interpolationModeId : i32, fillModeId : i32, fillValue : f32,';
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  size = true;
-
-  constructor(outShape: [number, number, number, number]) {
-    this.outputShape = outShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.shaderKey = 'transform';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-          fn mapCoord(outCoord : f32, len : f32) -> f32{
-            var inCoord = outCoord;
-            if(uniforms.fillModeId == 2) {
-              if (inCoord < 0.0) {
-                if (len <= 1.0) {
-                  inCoord = 0.0;
-                } else {
-                  let sz2 = 2.0 * len;
-                  if (inCoord < sz2) {
-                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
-                    inCoord;
-                  }
-                  if (inCoord < -len) {
-                    inCoord = inCoord + sz2;
-                  } else {
-                    inCoord = -inCoord - 1.0;
-                  }
-                }
-              } else if (inCoord > len - 1.0) {
-                if (len <= 1.0) {
-                  inCoord = 0.0;
-                } else {
-                  let sz2 = 2.0 * len;
-                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
-                  if (inCoord >= len) {
-                    inCoord = sz2 - inCoord - 1.0;
-                  }
-                }
-              }
-              return clamp(inCoord, 0.0, len - 1.0);
-            } else if (uniforms.fillModeId == 3) {
-              if (inCoord < 0.0) {
-                if (len <= 1.0) {
-                  inCoord = 0.0;
-                } else {
-                  let sz = len - 1.0;
-                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
-                }
-              } else if (inCoord > len - 1.0) {
-                if (len <= 1.0) {
-                  inCoord = 0.0;
-                } else {
-                  let sz = len - 1.0;
-                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
-                }
-              }
-              return clamp(inCoord, 0.0, len - 1.0);
-            } else if (uniforms.fillModeId == 4) {
-              return clamp(outCoord, 0.0, len - 1.0);
-            }
-            return outCoord;
-          }
-          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
-            channel : i32) -> f32 {
-            var outputValue : f32;
-            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
-                outputValue = getImage(batch, coordY, coordX, channel);
-            } else {
-              outputValue = uniforms.fillValue;
-            }
-            return outputValue;
-          }
-
-          ${main('index')} {
-            if (index < uniforms.size) {
-              let coords = getCoordsFromIndex(index);
-              var outputValue : f32;
-              let batch = coords[0];
-              let x = coords[2];
-              let y = coords[1];
-              let channel = coords[3];
-              let xf = f32(x);
-              let yf = f32(y);
-              let a1 = getTransforms(batch, 0);
-              let a2 = getTransforms(batch, 1);
-              let a3 = getTransforms(batch, 2);
-              let b1 = getTransforms(batch, 3);
-              let b2 = getTransforms(batch, 4);
-              let b3 = getTransforms(batch, 5);
-              let c1 = getTransforms(batch, 6);
-              let c2 = getTransforms(batch, 7);
-              let projection = c1 * xf + c2 * yf + 1.0;
-              if (projection == 0.0) {
-                outputValue = uniforms.fillValue;
-              } else {
-                let inX = (a1 * xf + a2 * yf + a3) / projection;
-                let inY = (b1 * xf + b2 * yf + b3) / projection;
-                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
-                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
-
-                if (uniforms.interpolationModeId == 1) {
-                  let coordY = i32(round(mapY));
-                  let coordX = i32(round(mapX));
-                  outputValue = readWithFillValue(batch, coordY, coordX,
-                    channel);
-                } else {
-                  let yFloor = floor(mapY);
-                  let xFloor = floor(mapX);
-                  let yCeil = yFloor + 1.0;
-                  let xCeil = xFloor + 1.0;
-                  let valueYFloor = (xCeil - mapX) *
-                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
-                  (mapX - xFloor) *
-                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
-                  let valueYCeil = (xCeil - mapX) *
-                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
-                  (mapX - xFloor) *
-                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
-                  outputValue = (yCeil - mapY) * valueYFloor +
-                  (mapY - yFloor) * valueYCeil;
-                }
-              }
-              setOutputAtIndex(index, outputValue);
-            }
-          }
-        `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/transpose_shared_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/transpose_shared_webgpu.ts
deleted file mode 100644
index 26a92ad3a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/transpose_shared_webgpu.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {util} from '@tensorflow/tfjs-core';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch} from './webgpu_util';
-
-export class TransposeSharedProgram implements WebGPUProgram {
-  variableNames = ['A'];
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[], y: number[]};
-  dispatch: [number, number, number];
-  // Note that the maximum number of workgroup invocations by webgpu is 256.
-  workgroupSize: [number, number, number] = [16, 16, 1];
-
-  constructor(aShape: number[], newDim: number[]) {
-    const outputShape: number[] = new Array(aShape.length);
-    for (let i = 0; i < outputShape.length; i++) {
-      outputShape[i] = aShape[newDim[i]];
-    }
-    this.outputShape = outputShape;
-    this.dispatchLayout = {x: [0], y: [1]};
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize, [1, 1, 1]);
-
-    this.shaderKey = 'transposeShared';
-  }
-
-  getUserCode(): string {
-    util.assert(
-        this.workgroupSize[0] === this.workgroupSize[1],
-        () => `Must be a square tile, current tile shape is ${
-            this.workgroupSize[0]} x ${this.workgroupSize[1]}`);
-    const tileSize = this.workgroupSize[0];
-    const userCode = `
-      var<workgroup> tile : array<array<f32, ${this.workgroupSize[0] + 1}>, ${
-        this.workgroupSize[0]}>;
-      ${main()} {
-        var x = i32(workgroupId.x) * ${tileSize} + i32(localId.x);
-        var y = i32(workgroupId.y) * ${tileSize} + i32(localId.y);
-        let width = uniforms.outShape[0];
-        let height = uniforms.outShape[1];
-        if (x < width && y < height) {
-          tile[localId.y][localId.x] = f32(A[y * width + x]);
-        }
-        workgroupBarrier();
-
-        x = i32(workgroupId.y) * ${tileSize} + i32(localId.x);
-        y = i32(workgroupId.x) * ${tileSize} + i32(localId.y);
-        if (x < height && y < width) {
-          setOutputAtIndex((y * height + x), tile[localId.x]
-            [localId.y]);
-        }
-      }
-    `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/transpose_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/transpose_webgpu.ts
deleted file mode 100644
index 8c86b4f30..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/transpose_webgpu.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getCoordsDataType, getCoordsXYZ, getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class TransposeProgram implements WebGPUProgram {
-  variableNames = ['A'];
-  shaderKey: string;
-  outputShape: number[];
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  workPerThread = 1;
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  newDim: number[];
-  size = true;
-
-  constructor(aShape: number[], newDim: number[]) {
-    const outputShape: number[] = new Array(aShape.length);
-    for (let i = 0; i < outputShape.length; i++) {
-      outputShape[i] = aShape[newDim[i]];
-    }
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize,
-        [this.workPerThread, 1, 1]);
-
-    this.newDim = newDim;
-    this.shaderKey = `transpose_${newDim}`;
-  }
-
-  getUserCode(): string {
-    const dtype = getCoordsDataType(this.outputShape.length);
-    const switched = getSwitchedCoords(this.newDim);
-
-    const userCode = `
-      ${main('index')} {
-        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
-          let flatIndex = index * ${this.workPerThread} + i;
-          if(flatIndex < uniforms.size) {
-            let coords = getCoordsFromIndex(flatIndex);
-            setOutputAtIndex(flatIndex, A[getIndexFromCoords${
-        this.outputShape.length}D(
-              ${dtype}(${switched}), uniforms.aShape)]);
-          }
-        }
-      }
-    `;
-    return userCode;
-  }
-}
-
-export function getSwitchedCoords(newDim: number[]): string {
-  const rank = newDim.length;
-  if (rank > 6) {
-    throw Error(`Transpose for rank ${rank} is not yet supported`);
-  }
-  const switchedCoords = new Array(rank);
-  for (let i = 0; i < newDim.length; i++) {
-    switchedCoords[newDim[i]] = `coords.${getCoordsXYZ(i)}`;
-  }
-
-  return switchedCoords.join();
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/unary_op_util.ts b/tfjs-master/tfjs-backend-webgpu/src/unary_op_util.ts
deleted file mode 100644
index debf2a014..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/unary_op_util.ts
+++ /dev/null
@@ -1,307 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs-core';
-
-export enum UnaryOpType {
-  ABS,
-  ACOS,
-  ACOSH,
-  ASIN,
-  ASINH,
-  ATAN,
-  ATANH,
-  CEIL,
-  COS,
-  COSH,
-  ELU,
-  ERF,
-  EXP,
-  EXPM1,
-  FLOOR,
-  IS_FINITE,
-  IS_INF,
-  IS_NAN,
-  LINEAR,
-  LOG,
-  LOG1P,
-  LOGICAL_NOT,
-  NEG,
-  RELU,
-  RELU6,
-  LEAKYRELU,
-  RECIPROCAL,
-  ROUND,
-  RSQRT,
-  SELU,
-  SIGMOID,
-  SIGN,
-  SIN,
-  SINH,
-  SOFTPLUS,
-  SQRT,
-  SQUARE,
-  STEP,
-  TAN,
-  TANH,
-  TO_INT
-}
-
-const ABS = `return abs(a);`;
-const ACOS = `
-  if (abs(a) > 1.) {
-    return uniforms.NAN;
-  }
-  return acos(a);
-`;
-const ACOSH = `
-  if (a < 1.) {
-    return uniforms.NAN;
-  }
-  return acosh(a);
-`;
-const ASIN = `
-  if (abs(a) > 1.) {
-    return uniforms.NAN;
-  }
-  return asin(a);
-`;
-const ASINH = `return asinh(a);`;
-const ATAN = `
-  if (isnan(a)) {
-    return uniforms.NAN;
-  }
-  return atan(a);
-`;
-const ATANH = `
-  if (abs(a) > 1.) {
-    return uniforms.NAN;
-  }
-  if (a == 1.) {
-    return uniforms.INFINITY;
-  }
-  if (a == -1.) {
-    return -uniforms.INFINITY;
-  }
-  return atanh(a);
-`;
-const CEIL = `return ceil(a);`;
-const COS = `return cos(a);`;
-const COSH = `
-  let e2x = exp(-a);
-  return (e2x + 1.0 / e2x) / 2.0;
-`;
-const EXPM1 = `return exp(a) - 1.0;`;
-const ELU = `if (a >= 0.0) { return a; }  return (exp(a) - 1.0);`;
-const ELU_VEC4 = `
-  var resFloat = exp(a) - vec4<f32>(1.0);
-  if (a.r >= 0.0) {
-    resFloat.r = a.r;
-  }
-  if (a.g >= 0.0) {
-    resFloat.g = a.g;
-  }
-  if (a.b >= 0.0) {
-    resFloat.b = a.b;
-  }
-  if (a.a >= 0.0) {
-    resFloat.a = a.a;
-  }
-  return resFloat;
-`;
-const ERF = `
-  // Error function is calculated approximately with elementary function.
-  // See "Handbook of Mathematical Functions with Formulas,
-  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
-  let p = ${backend_util.ERF_P};
-  let a1 = ${backend_util.ERF_A1};
-  let a2 = ${backend_util.ERF_A2};
-  let a3 = ${backend_util.ERF_A3};
-  let a4 = ${backend_util.ERF_A4};
-  let a5 = ${backend_util.ERF_A5};
-
-  let sign = sign(a);
-  let absA = abs(a);
-  let t = 1.0 / (1.0 + p * absA);
-  return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));
-`;
-const EXP = `return exp(a);`;
-const FLOOR = `return floor(a);`;
-const IS_FINITE = `return f32(!isnan(a) && !isinf(a));`;
-const IS_INF = `return f32(isinf(a));`;
-const IS_NAN = `return f32(isnan(a));`;
-const LINEAR = `return a;`;
-const LOG = `if (a < 0.0) { return uniforms.NAN; }
-  return log(a);`;
-const LOG1P = `
-  if (isnan(a)) { return a; }
-  return log(1.0 + a);
-`;
-const LOGICAL_NOT = `return f32(!(a >= 1.0));`;
-const NEG = `return -a;`;
-const LEAKYRELU = `if (a < 0.0) { return uniforms.alpha * a; } return a;`;
-const LEAKYRELU_VEC4 = `
-  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
-  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-`;
-const RECIPROCAL = `return 1.0 / a;`;
-const RELU = `return select(a, 0.0, a < 0.0);`;
-const RELU6 = 'return clamp(a, 0.0, 6.0);';
-const RELU6_VEC4 =
-    'return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));';
-const RELU_VEC4 = `
-  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
-`;
-const ROUND = `return round(a);`;
-const RSQRT = `return inverseSqrt(a);`;
-// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
-// See: https://arxiv.org/abs/1706.02515
-const SELU = `
-  if (a >= 0.0) {
-    return ${backend_util.SELU_SCALE} * a;
-  } else {
-    return ${backend_util.SELU_SCALEALPHA} * (exp(a) - 1.0);
-  }
-`;
-const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * a));`;
-const SIGN = `return sign(a);`;
-const SIN = `return sin(a);`;
-const SINH = `
-  let e2x = exp(a);
-  return (e2x - 1.0 / e2x) / 2.0;
-`;
-const SOFTPLUS = `
-  let epsilon = 1.1920928955078125e-7;
-  let threshold = log(epsilon) + 2.0;
-
-  let too_large = a > -threshold;
-  let too_small = a < threshold;
-  let exp_a = exp(a);
-
-  if (too_large) {
-    return a;
-  } else if (too_small) {
-    return exp_a;
-  } else {
-    return log(exp_a + 1.0);
-  }
-`;
-const SQRT = `return sqrt(a);`;
-const SQUARE = `return a * a;`;
-const STEP = `
-  if (isnan(a)) {
-    return a;
-  }
-
-  return select(uniforms.stepAlpha, 1.0, a > 0.0);
-`;
-const TAN = `return tan(a);`;
-const TANH = `
-  let e2x = exp(-2.0 * abs(a));
-  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`;
-const TO_INT = `return f32(i32((a)));`;
-
-export function getUnaryOpString(type: UnaryOpType, useVec4?: boolean): string {
-  switch (type) {
-    case UnaryOpType.ABS:
-      return ABS;
-    case UnaryOpType.ACOS:
-      return ACOS;
-    case UnaryOpType.ACOSH:
-      return ACOSH;
-    case UnaryOpType.ASIN:
-      return ASIN;
-    case UnaryOpType.ASINH:
-      return ASINH;
-    case UnaryOpType.ATAN:
-      return ATAN;
-    case UnaryOpType.ATANH:
-      return ATANH;
-    case UnaryOpType.COS:
-      return COS;
-    case UnaryOpType.COSH:
-      return COSH;
-    case UnaryOpType.CEIL:
-      return CEIL;
-    case UnaryOpType.ELU:
-      return useVec4 ? ELU_VEC4 : ELU;
-    case UnaryOpType.ERF:
-      return ERF;
-    case UnaryOpType.EXP:
-      return EXP;
-    case UnaryOpType.EXPM1:
-      return EXPM1;
-    case UnaryOpType.FLOOR:
-      return FLOOR;
-    case UnaryOpType.IS_FINITE:
-      return IS_FINITE;
-    case UnaryOpType.IS_INF:
-      return IS_INF;
-    case UnaryOpType.IS_NAN:
-      return IS_NAN;
-    case UnaryOpType.LINEAR:
-      return LINEAR;
-    case UnaryOpType.LOG:
-      return LOG;
-    case UnaryOpType.LOG1P:
-      return LOG1P;
-    case UnaryOpType.LOGICAL_NOT:
-      return LOGICAL_NOT;
-    case UnaryOpType.NEG:
-      return NEG;
-    case UnaryOpType.LEAKYRELU:
-      return useVec4 ? LEAKYRELU_VEC4 : LEAKYRELU;
-    case UnaryOpType.RECIPROCAL:
-      return RECIPROCAL;
-    case UnaryOpType.RELU:
-      return useVec4 ? RELU_VEC4 : RELU;
-    case UnaryOpType.RELU6:
-      return useVec4 ? RELU6_VEC4 : RELU6;
-    case UnaryOpType.ROUND:
-      return ROUND;
-    case UnaryOpType.RSQRT:
-      return RSQRT;
-    case UnaryOpType.SELU:
-      return SELU;
-    case UnaryOpType.SIGMOID:
-      return SIGMOID;
-    case UnaryOpType.SIGN:
-      return SIGN;
-    case UnaryOpType.SIN:
-      return SIN;
-    case UnaryOpType.SINH:
-      return SINH;
-    case UnaryOpType.SOFTPLUS:
-      return SOFTPLUS;
-    case UnaryOpType.SQRT:
-      return SQRT;
-    case UnaryOpType.SQUARE:
-      return SQUARE;
-    case UnaryOpType.STEP:
-      return STEP;
-    case UnaryOpType.TAN:
-      return TAN;
-    case UnaryOpType.TANH:
-      return TANH;
-    case UnaryOpType.TO_INT:
-      return TO_INT;
-
-    default:
-      throw new Error(`BinaryType ${type} is not implemented!`);
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/unary_op_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/unary_op_webgpu.ts
deleted file mode 100644
index ad41e0d7f..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/unary_op_webgpu.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getUnaryOpString, UnaryOpType} from './unary_op_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class UnaryOpProgram implements WebGPUProgram {
-  outputShape: number[];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['A'];
-  workgroupSize: [number, number, number];
-  op: UnaryOpType;
-  uniforms?: string;
-  size = true;
-
-  constructor(outputShape: number[], op: UnaryOpType, uniforms = '') {
-    // TODO(jiajia.qin@intel.com): Heuristically select a good work group size.
-    const workgroupSizeX = 128;
-    this.workgroupSize = [workgroupSizeX, 1, 1];
-    this.outputShape = outputShape;
-    this.dispatchLayout = flatDispatchLayout(this.outputShape);
-    this.dispatch = computeDispatch(
-        this.dispatchLayout, this.outputShape, this.workgroupSize);
-    this.op = op;
-    if (uniforms !== '') {
-      this.uniforms = uniforms;
-    }
-    this.shaderKey = `unary_${op}`;
-  }
-
-  getUserCode(): string {
-    return `
-      fn unaryOperation(a : f32) -> f32 {
-        ${getUnaryOpString(this.op, false)}
-      }
-      ${main('index')} {
-        if (index < uniforms.size) {
-          let a = getAByOutputIndex(index);
-          setOutputAtIndex(index, unaryOperation(a));
-        }
-      }
-      `;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/unsorted_segment_sum_webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/unsorted_segment_sum_webgpu.ts
deleted file mode 100644
index d6c82842a..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/unsorted_segment_sum_webgpu.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType} from '@tensorflow/tfjs-core';
-
-import {atomicAddSnippet} from './shader_util';
-import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
-import {computeDispatch, flatDispatchLayout} from './webgpu_util';
-
-export class UnsortedSegmentSumProgram implements WebGPUProgram {
-  outputShape: number[] = [];
-  shaderKey: string;
-  dispatchLayout: {x: number[]};
-  dispatch: [number, number, number];
-  variableNames = ['x', 'segmentIds'];
-  uniforms = 'numSegments : i32, xSize: i32,';
-  workgroupSize: [number, number, number] = [64, 1, 1];
-  atomic = true;
-  type: DataType;
-
-  constructor(inShape: number[], outShape: number[], outputDtype: DataType) {
-    this.outputShape = outShape;
-    this.dispatchLayout = flatDispatchLayout(inShape);
-    this.dispatch =
-        computeDispatch(this.dispatchLayout, inShape, this.workgroupSize);
-    if (outputDtype !== 'float32' && outputDtype !== 'int32') {
-      throw new Error(`UnsortedSegmentSum only supports float32 and int32
-              types, does not support ${outputDtype} type.`);
-    }
-    this.type = outputDtype;
-    this.shaderKey = 'unsortedSegmentSum';
-  }
-
-  getUserCode(): string {
-    const userCode = `
-    ${main('index')} {
-      if (index < uniforms.xSize) {
-        let coords = getXCoordsFromIndex(index);
-        let b = coords[0];
-        let inCol = coords[1];
-
-        let segmentId = i32(getSegmentIds(inCol));
-        if (segmentId >= 0) {
-          let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;
-          let value = getX(b, inCol);
-
-          ${
-        atomicAddSnippet(
-            '&result[flatIndex]', 'value', this.type as 'float32' | 'int32')}
-        }
-      }
-    }
-  `;
-    return userCode;
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/webgpu.ts b/tfjs-master/tfjs-backend-webgpu/src/webgpu.ts
deleted file mode 100644
index 8c484e1b0..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/webgpu.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as webgpu_util from './webgpu_util';
-export {WebGPUBackend} from './backend_webgpu';
-export {WebGPUProgram} from './webgpu_program';
-export {webgpu_util};
diff --git a/tfjs-master/tfjs-backend-webgpu/src/webgpu_program.ts b/tfjs-master/tfjs-backend-webgpu/src/webgpu_program.ts
deleted file mode 100644
index ddaeddc6b..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/webgpu_program.ts
+++ /dev/null
@@ -1,853 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DataType, DataTypeMap, env, Rank, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-import {symbolicallyComputeStrides} from './shader_util';
-
-export enum PixelsOpType {
-  FROM_PIXELS,
-  DRAW
-}
-
-export interface WebGPUProgram {
-  // Whether to use atomic built-in functions.
-  atomic?: boolean;
-  // dispatch specifies geometry of thread groups - derived from dispatchLayout.
-  dispatch: [number, number, number];
-  // dispatchLayout enumerates how tensor dimensions are distributed among
-  // dispatch x,y,z dimensions.
-  dispatchLayout: {x: number[], y?: number[], z?: number[]};
-  // By default, the output data component is 1.
-  outputComponent?: number;
-  outputShape: number[];
-  pixelsOpType?: PixelsOpType;
-  // The unique key to distinguish different shader source code.
-  shaderKey: string;
-  // Whether to use output size for bounds checking.
-  size?: boolean;
-  uniforms?: string;
-  variableNames: string[];
-  // Describe each variable's component and must have one-one mapping with
-  // variableNames. If not set, all variables component will be same with output
-  // component member.
-  variableComponents?: number[];
-  // workgroupSize.x * workgroupSize.y * workgroupSize.z = the number of threads
-  // in a thread group. Individual dimensions determines thread layout within
-  // the group.
-  workgroupSize: [number, number, number];
-  // Size of register cache in one dimension (assumes square cache).
-  // Each thread writes to workPerThread * workPerThread locations in the output
-  // buffer.
-  workPerThread?: number;
-  pipeline?: GPUComputePipeline|Promise<GPUComputePipeline>;
-  getUserCode: () => string;
-}
-
-export const compileProgram =
-    (device: GPUDevice, program: WebGPUProgram, inputsData: InputInfo[],
-     output: TensorInfo, parallelCompilation: boolean): GPUComputePipeline|
-    Promise<GPUComputePipeline> => {
-      const outputData = {dtype: output.dtype, shape: output.shape};
-      const source = makeShader(inputsData, outputData, program);
-      const module = device.createShaderModule(
-          {code: source, label: program.constructor.name});
-
-      let printShaderString = env().get('WEBGPU_PRINT_SHADER') as string;
-      if (printShaderString !== '') {
-        printShaderString = printShaderString.toLowerCase();
-        const printShaderArray = printShaderString.split(',');
-        if (printShaderString === 'all' ||
-            printShaderArray.some(
-                item => program.shaderKey.toLowerCase().includes(item))) {
-          console.group(program.shaderKey);
-          console.debug(source);
-          console.groupEnd();
-        }
-      }
-
-      if (parallelCompilation) {
-        return device.createComputePipelineAsync({
-          compute: {module, entryPoint: '_start'},
-          label: program.constructor.name,
-          layout: 'auto'
-        });
-      } else {
-        return device.createComputePipeline({
-          compute: {module, entryPoint: '_start'},
-          label: program.constructor.name,
-          layout: 'auto'
-        });
-      }
-    };
-
-export const typeSnippet = (component: number, type = 'f32') => {
-  switch (component) {
-    case 1:
-      return `${type}`;
-    case 2:
-      return `vec2<${type}>`;
-    case 3:
-      return `vec3<${type}>`;
-    case 4:
-      return `vec4<${type}>`;
-    default:
-      throw new Error(`${component}-component ${type} is not supported.`);
-  }
-};
-
-export function getCoordsDataType(rank: number): string {
-  if (rank <= 1) {
-    return 'i32';
-  } else if (rank === 2) {
-    return `vec2<i32>`;
-  } else if (rank === 3) {
-    return `vec3<i32>`;
-  } else if (rank === 4) {
-    return `vec4<i32>`;
-  } else if (rank === 5) {
-    return `vec5`;
-  } else if (rank === 6) {
-    return `vec6`;
-  } else {
-    throw Error(`GPU for rank ${rank} is not yet supported`);
-  }
-}
-
-export function getCoordsXYZ(index: number): string {
-  if (index === 0) {
-    return 'x';
-  } else if (index === 1) {
-    return 'y';
-  } else if (index === 2) {
-    return 'z';
-  } else if (index === 3) {
-    return 'w';
-  } else if (index === 4) {
-    return 'u';
-  } else if (index === 5) {
-    return 'v';
-  } else {
-    throw Error(`Index ${index} is not yet supported`);
-  }
-}
-
-export function getMainHeaderString(): string;
-export function getMainHeaderString(index: string): string;
-export function getMainHeaderString(...params: string[]): string {
-  let snippet: string;
-  switch (params.length) {
-    case 0:
-      snippet = `
-        fn main()
-      `;
-      break;
-    case 1:
-      snippet = `
-        fn main(${params[0]} : i32)
-      `;
-      break;
-    default:
-      throw Error('Unreachable');
-  }
-  return snippet;
-}
-
-export function getStartHeaderString(
-    useGlobalIndex: boolean, program: WebGPUProgram): string {
-  let snippet: string;
-  snippet = `
-     ${getWorkgroupSizeString(program)}
-      fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
-                @builtin(global_invocation_id) GlobalId : vec3<u32>,
-                @builtin(local_invocation_index) LocalIndex: u32,
-                @builtin(workgroup_id) WorkgroupId : vec3<u32>,
-                @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {
-        localId = LocalId;
-        localIndex = LocalIndex;
-        globalId = GlobalId;
-        numWorkgroups = NumWorkgroups;
-        workgroupId = WorkgroupId;
-        ${useGlobalIndex ? `main(getGlobalIndex());` : `main();`};
-      }
-    `;
-  return snippet;
-}
-
-export function getWorkgroupSizeString(program: WebGPUProgram): string {
-  return `
-  @compute @workgroup_size(${program.workgroupSize[0]}, ${
-      program.workgroupSize[1]}, ${program.workgroupSize[2]})
-`;
-}
-
-function makeShader(
-    inputInfo: InputInfo[], outputData: {dtype: DataType, shape: number[]},
-    program: WebGPUProgram): string {
-  const prefixSnippets: string[] = [];
-  const flatWorkgroupSize = program.workgroupSize[0] *
-      program.workgroupSize[1] * program.workgroupSize[2];
-  program.outputComponent =
-      program.outputComponent ? program.outputComponent : 1;
-  prefixSnippets.push(`
-
-      var<private> localId: vec3<u32>;
-      var<private> localIndex: u32;
-      var<private> globalId: vec3<u32>;
-      var<private> numWorkgroups: vec3<u32>;
-      var<private> workgroupId: vec3<u32>;
-
-      // Only used when the y/z dimension of workgroup size is 1.
-      fn getGlobalIndex() -> i32 {
-        ${
-      isFlatDispatch(program) ?
-          `  return i32(globalId.x);` :
-          `  return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +
-                workgroupId.y * numWorkgroups.x + workgroupId.x) * ${
-              flatWorkgroupSize}u +
-                localIndex);
-        `}
-      }
-    `);
-
-  if (program.pixelsOpType != null) {
-    const inoutSnippet = program.pixelsOpType === PixelsOpType.FROM_PIXELS ?
-        `@group(0) @binding(0) var<storage, read_write> result: array<${
-            dataTypeToGPUType(outputData.dtype, program.outputComponent)}>;` :
-        `@group(0) @binding(1) var<storage, read> inBuf : array<${
-            dataTypeToGPUType(inputInfo[0].dtype, program.outputComponent)}>;`;
-    const outShapeStridesType =
-        outputData.shape.length === 3 ? 'vec2<i32>' : 'i32';
-    prefixSnippets.push(`
-        struct Uniform {
-          outShapeStrides : ${outShapeStridesType},
-          size            : i32,
-          numChannels     : i32,
-          alpha           : f32,
-        };
-
-        ${inoutSnippet}
-        @group(0) @binding(2) var<uniform> uniforms: Uniform;
-      `);
-    const useGlobalIndex = isFlatDispatchLayout(program);
-    return [
-      commonSnippet,
-      prefixSnippets.join('\n'),
-      getCoordsFromIndexSnippet(outputData.shape),
-      program.getUserCode(),
-      getStartHeaderString(useGlobalIndex, program),
-    ].join('\n');
-  }
-
-  let stridesLength: number;
-  let stridesDataType: string;
-  let uniformDeclaration = 'struct Uniforms { NAN : f32, INFINITY : f32, ';
-  program.variableNames.forEach((x, i) => {
-    const perDataType = getCoordsDataType(inputInfo[i].shape.length);
-    uniformDeclaration +=
-        `${x.charAt(0).toLowerCase() + x.slice(1)}Shape : ${perDataType}, `;
-    stridesLength = inputInfo[i].shape.length - 1;
-    stridesDataType = getCoordsDataType(stridesLength);
-    uniformDeclaration +=
-        `${x.charAt(0).toLowerCase() + x.slice(1)}ShapeStrides: ${
-            stridesDataType}, `;
-  });
-  const outputDataType = getCoordsDataType(outputData.shape.length);
-  uniformDeclaration += `outShape : ${outputDataType}, `;
-  stridesLength = outputData.shape.length - 1;
-  stridesDataType = getCoordsDataType(stridesLength);
-  uniformDeclaration += `
-         outShapeStrides: ${stridesDataType}, `;
-
-  if (program.size) {
-    uniformDeclaration += 'size : i32, ';
-  }
-
-  if (program.uniforms) {
-    uniformDeclaration += program.uniforms;
-  }
-  uniformDeclaration += '};';
-  uniformDeclaration = insertAlignment(uniformDeclaration);
-
-  prefixSnippets.push(uniformDeclaration);
-
-  // Output buffer.
-  if (program.atomic) {
-    prefixSnippets.push(`
-      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
-    `);
-  } else {
-    prefixSnippets.push(`
-      @group(0) @binding(0) var<storage, read_write> result: array<${
-        dataTypeToGPUType(outputData.dtype, program.outputComponent)}>;
-    `);
-  }
-  program.variableNames.forEach((x, i) => {
-    prefixSnippets.push(`
-      @group(0) @binding(${1 + i}) var<storage, read> ${x}: array<${
-        program.variableComponents ?
-            dataTypeToGPUType(
-                inputInfo[i].dtype, program.variableComponents[i]) :
-            dataTypeToGPUType(inputInfo[i].dtype, program.outputComponent)}>;
-        `);
-  });
-
-  if (uniformDeclaration !== '') {
-    prefixSnippets.push(`
-      @group(0) @binding(${
-        1 + program.variableNames.length}) var<uniform> uniforms: Uniforms;
-      `);
-  }
-
-  const coordsSnippet =
-      getOutputCoordsSnippet(outputData.shape, program.dispatchLayout);
-
-  const sources = [
-    commonSnippet, prefixSnippets.join('\n') + isInfSnippet,
-    getCoordsFromIndexSnippet(outputData.shape), coordsSnippet,
-    getOutputIndexFromCoordsSnippet(outputData.shape.length)
-  ];
-  if (!program.atomic) {
-    sources.push(setOutputSnippet(
-        outputData.shape, outputData.dtype, program.outputComponent));
-  }
-
-  program.variableNames.forEach((x, i) => {
-    sources.push(`${getCoordsFromIndexSnippet(inputInfo[i].shape, x)}`);
-  });
-
-  const inputSnippet =
-      inputInfo
-          .map(
-              (x, i) => getInputSnippet(
-                  x, outputData.shape,
-                  program.variableComponents ? program.variableComponents[i] :
-                                               program.outputComponent,
-                  program.dispatchLayout.x.length === outputData.shape.length))
-          .join('\n');
-  sources.push(inputSnippet);
-  sources.push(program.getUserCode());
-  const useGlobalIndex = isFlatDispatchLayout(program);
-  sources.push(getStartHeaderString(useGlobalIndex, program));
-  const source = sources.join('\n');
-  return source;
-}
-
-export function makeShaderKey<R extends Rank>(
-    program: WebGPUProgram, inputsData: InputInfo[],
-    output: TensorInfo): string {
-  let key = program.shaderKey;
-  if (program.pixelsOpType != null) {
-    return key;
-  }
-
-  const shapes: number[][] = [];
-  const types: Array<keyof DataTypeMap> = [];
-  inputsData.forEach(element => {
-    shapes.push(element.shape);
-    types.push(element.dtype);
-  });
-  shapes.push(output.shape);
-  types.push(output.dtype);
-
-  const broadcastDims =
-      inputsData.map(d => backend_util.getBroadcastDims(d.shape, output.shape));
-  const inputShapesEqualsOutShape =
-      inputsData.map(d => util.arraysEqual(d.shape, output.shape)).join('_');
-  const broadcastDimsKey = broadcastDims.map(d => d.join('_')).join(';');
-
-  const flatDispatchString = isFlatDispatch(program) ? 'flatDispatch' : '';
-
-  key += '_' + (program.workgroupSize ? program.workgroupSize.join(',') : '') +
-      shapes.map(shape => shape.length).join(',') + types.join(',') +
-      program.variableNames.join(',') + broadcastDimsKey +
-      inputShapesEqualsOutShape + flatDispatchString;
-
-  return key;
-}
-
-const commonSnippet = `
-  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
-  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
-
-  // Checks whether coordinates lie within the bounds of the shape.
-  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
-    return all(coord >= vec2<i32>(0)) && all(coord < shape);
-  }
-  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
-    return all(coord >= vec3<i32>(0)) && all(coord < shape);
-  }
-  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
-    return all(coord >= vec4<i32>(0)) && all(coord < shape);
-  }
-
-  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
-    return coord;
-  }
-  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
-    return dot(coords, vec2<i32>(shape.y, 1));
-  }
-  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
-    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
-  }
-  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
-    return dot(coords, vec4<i32>(
-        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
-  }
-  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {
-    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);
-    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;
-  }
-  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {
-    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);
-    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;
-  }
-
-  // NaN defination in IEEE 754-1985 is :
-  //   - sign = either 0 or 1.
-  //   - biased exponent = all 1 bits.
-  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).
-  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
-  fn isnan(val: f32) -> bool {
-    let floatToUint: u32 = bitcast<u32>(val);
-    return (floatToUint & 0x7fffffffu) > 0x7f800000u;
-  }
-  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
-    let floatToUint: vec4<u32> = bitcast<vec4<u32>>(val);
-    return (floatToUint & vec4<u32>(0x7fffffffu)) > vec4<u32>(0x7f800000u);
-  }
-`;
-
-const isInfSnippet = `
-  fn isinf(val: f32) -> bool {
-    return abs(val) == uniforms.INFINITY;
-  }
-`;
-
-type InputInfo = {
-  dtype: DataType; shape: number[]; name: string;
-};
-
-/**
- * Derives logical coordinates from a flat index. Performs integer division
- * with each stride and decrements the index until the index equals the final
- * dimension coordinate.
- */
-export function getCoordsFromIndexSnippet(shape: number[], name = ''): string {
-  const rank = shape.length;
-  const funcName = name !== '' ?
-      `get${name.charAt(0).toUpperCase() + name.slice(1)}CoordsFromIndex` :
-      'getCoordsFromIndex';
-  const stridesName = name !== '' ?
-      `${name.charAt(0).toLowerCase() + name.slice(1)}ShapeStrides` :
-      `outShapeStrides`;
-
-  if (rank <= 1) {
-    return `fn ${funcName}(index : i32) -> i32 { return index; }`;
-  }
-
-  const strides = util.computeStrides(shape);
-  const dtype = getCoordsDataType(rank);
-
-  const coords: string[] = [];
-  for (let i = 0; i < rank; i++) {
-    coords.push(`d${i}`);
-  }
-
-  if (strides.length === 1) {
-    return `    fn ${funcName}(index : i32) -> vec2<i32> {
-      let d0 = index / uniforms.${
-        stridesName}; let d1 = index - d0 * uniforms.${stridesName};
-      return vec2<i32>(d0, d1);
-    }`;
-  }
-  let snippet;
-  snippet = 'var index2 = index;' +
-      strides
-          .map((_, i) => {
-            const line1 = `let ${coords[i]} = index2 / uniforms.${
-                stridesName}.${getCoordsXYZ(i)}`;
-            const line2 = i === strides.length - 1 ?
-                `let ${coords[i + 1]} = index2 - ${coords[i]} * uniforms.${
-                    stridesName}.${getCoordsXYZ(i)}` :
-                `index2 = index2 - ${coords[i]} * uniforms.${stridesName}.${
-                    getCoordsXYZ(i)}`;
-            return `${line1}; ${line2};`;
-          })
-          .join('');
-
-  return `
-    fn ${funcName}(index : i32) -> ${dtype} {
-      ${snippet}
-      return ${dtype}(${coords.join(',')});
-    }
-  `;
-}
-
-function getInputAtCoordsSnippet(
-    inputInfo: InputInfo, component: number): string {
-  const texName = inputInfo.name;
-  const rank = inputInfo.shape.length;
-  const type = getCoordsDataType(rank);
-  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
-  const dims = ['d0', 'd1', 'd2', 'd3', 'd4', 'd5'].slice(0, rank);
-  const inputs = dims.map(d => `${d} : i32`).join(', ');
-
-  if (rank < 1) {
-    return `
-      fn ${funcName}() -> ${typeSnippet(component)} {
-        return ${typeSnippet(component)}(${texName}[0]);
-      }
-    `;
-  }
-
-  const shapeStr =
-      `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;
-  let rankStr = `${rank}D`;
-  if (rank === 0) {
-    rankStr = '1D';
-  }
-
-  return `
-    fn ${funcName}(${inputs}) -> ${typeSnippet(component)} {
-      return ${typeSnippet(component)}(${texName}[getIndexFromCoords${
-      rankStr}(${type}(${dims.join(',')}),
-        ${shapeStr})${component === 1 ? '' : ` / ${component}`}]);
-    }
-   `;
-}
-
-function getInputByOutputSnippet(
-    inputInfo: InputInfo, outShape: number[], component: number,
-    isFlatDispatchLayout: boolean): string {
-  const texName = inputInfo.name;
-  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
-
-  const funcName = 'get' + texFuncSnippet + 'ByOutput';
-
-  const inRank = inputInfo.shape.length;
-  const outRank = outShape.length;
-  const type = getCoordsDataType(outRank);
-
-  // If the inShape equals the outShape and the dispatch layout is flat, we can
-  // directly use |gl_GlobalInvocationID.x| as the index and don't need coords
-  // conversion between these two shapes.
-  if (util.arraysEqual(inputInfo.shape, outShape) && isFlatDispatchLayout) {
-    return `
-    fn ${funcName}Index(globalIndex : i32) -> ${typeSnippet(component)} {
-      return ${typeSnippet(component)}(${texName}[globalIndex]);
-    }
-
-    fn ${funcName}Coords(coords : ${type}) -> ${typeSnippet(component)} {
-      return ${typeSnippet(component)}(${texName}[${
-        outRank > 1 ? 'getOutputIndexFromCoords(coords)' :
-                      'coords'}${component === 1 ? '' : ` / ${component}`}]);
-    }
-    `;
-  }
-
-  const broadcastDims =
-      backend_util.getBroadcastDims(inputInfo.shape, outShape);
-  const rankDiff = outRank - inRank;
-
-  let coordsSnippet = '';
-
-  if (inRank === 0) {
-    return `
-    fn ${funcName}Index(globalIndex : i32) -> ${typeSnippet(component)}{
-      return get${texFuncSnippet}();
-    }
-
-    fn ${funcName}Coords(coords : ${type}) -> ${typeSnippet(component)}{
-      return get${texFuncSnippet}();
-    }
-  `;
-  } else {
-    if (outRank < 2 && broadcastDims.length >= 1) {
-      coordsSnippet = 'coords = 0;';
-    } else {
-      coordsSnippet =
-          broadcastDims.map(d => `coords.${getCoordsXYZ(d + rankDiff)} = 0;`)
-              .join('\n');
-    }
-  }
-
-  let unpackedCoordsSnippet = '';
-  if (outRank < 2 && inRank > 0) {
-    unpackedCoordsSnippet = 'coords';
-  } else {
-    if (outRank > 1) {
-      const coordsType = getCoordsDataType(inRank);
-      const coordsValues =
-          inputInfo.shape.map((s, i) => `coords.${getCoordsXYZ(i + rankDiff)}`)
-              .join(', ');
-      unpackedCoordsSnippet = `${coordsType}(${coordsValues})`;
-    } else {
-      unpackedCoordsSnippet = 'coords';
-    }
-  }
-
-  const shapeStr =
-      `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;
-  const rankStr = `${inRank}D`;
-
-  return `
-  fn ${funcName}Index(globalIndex : i32) -> ${typeSnippet(component)} {
-    var coords = getCoordsFromIndex(globalIndex);
-    ${coordsSnippet}
-    return ${typeSnippet(component)}(${texName}[getIndexFromCoords${rankStr}(${
-      unpackedCoordsSnippet}, ${shapeStr})${
-      component === 1 ? '' : ` / ${component}`}]);
-  }
-
-  fn ${funcName}Coords(coordsIn : ${type}) -> ${typeSnippet(component)} {
-    var coords = coordsIn;
-    ${coordsSnippet}
-    return ${typeSnippet(component)}(${texName}[getIndexFromCoords${rankStr}(${
-      unpackedCoordsSnippet}, ${shapeStr})${
-      component === 1 ? '' : ` / ${component}`}]);
-  }
-`;
-}
-
-function getInputSnippet(
-    inputInfo: InputInfo, outShape: number[], component: number,
-    isFlatDispatchLayout: boolean): string {
-  let res = getInputAtCoordsSnippet(inputInfo, component);
-
-  const inShape = inputInfo.shape;
-  if (inShape.length <= outShape.length) {
-    res += getInputByOutputSnippet(
-        inputInfo, outShape, component, isFlatDispatchLayout);
-  }
-
-  return res;
-}
-
-/**
- * Generates getOutputCoords() function that computes output coordinates
- * from dispatch geometry to reduce arithmetic.
- */
-function getOutputCoordsSnippet(
-    outShape: number[],
-    dispatchLayout: {x: number[], y?: number[], z?: number[]}): string {
-  const {x, y = [], z = []} = dispatchLayout;
-
-  const outRank = outShape.length;
-  const rank = x.length + y.length + z.length;
-  // getOutputCoords is only meaningful when the output rank is same with
-  // dispatch layout rank.
-  if (rank !== outRank) {
-    return '';
-  }
-
-  if (x.length === outRank) {
-    const dtype = getCoordsDataType(outRank);
-    const snippet = `fn getOutputCoords() -> ${dtype}{
-    let globalIndex = getGlobalIndex();
-    return getCoordsFromIndex(globalIndex);
-  }
-  `;
-    return snippet;
-  }
-
-  let gatherDimensionsStr = '';
-  const dims = [x, y, z];
-
-  for (let i = 0; i < dims.length; i++) {
-    const arr = dims[i];
-
-    if (arr.length === 0) {
-      continue;
-    }
-
-    if (arr.length === 1) {
-      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i}]);`;
-    } else {
-      const strides = symbolicallyComputeStrides(arr, 'uniforms.outShape');
-      gatherDimensionsStr += `var index${i} = i32(globalId[${i}]);`;
-      for (let j = 0; j < strides.length; j++) {
-        gatherDimensionsStr += `let d${arr[j]} = index${i} / ${strides[j]};`;
-
-        if (j === strides.length - 1) {
-          gatherDimensionsStr += `let d${arr[j + 1]} = ` +
-              `index${i} - d${arr[j]} * ${strides[j]};`;
-        } else {
-          gatherDimensionsStr +=
-              `index${i} = index${i} - d${arr[j]} * ${strides[j]};`;
-        }
-      }
-    }
-  }
-
-  const dimensions = [];
-  for (let i = 0; i < rank; i++) {
-    dimensions.push(`d${i}`);
-  }
-
-  const dtype = getCoordsDataType(rank);
-  let snippet = `fn getOutputCoords() -> ${dtype} {
-  ${gatherDimensionsStr}
-`;
-  if (dimensions.length === 0) {
-    snippet += `return ${dtype}(0); }`;
-  } else {
-    snippet += `return ${dtype}(${dimensions.join(',')}); }`;
-  }
-
-  return snippet;
-}
-
-function getOutputIndexFromCoordsSnippet(outRank: number) {
-  let snippet = '';
-  switch (outRank) {
-    case 0:
-    case 1:
-      snippet += `
-        fn getOutputIndexFromCoords(coords : i32) -> i32 {
-          return coords;
-        }
-        `;
-      break;
-    case 2:
-      snippet += `
-        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
-          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
-        }
-        `;
-      break;
-    case 3:
-      snippet += `
-        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
-          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
-        }
-        `;
-      break;
-    case 4:
-      snippet += `
-        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
-          return dot(coords, vec4<i32>(
-            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
-        }
-        `;
-      break;
-    case 5:
-      snippet += `
-        fn getOutputIndexFromCoords(coords : vec5) -> i32 {
-          return coords.x * uniforms.outShapeStrides.x +
-              coords.y * uniforms.outShapeStrides.y +
-              coords.z * uniforms.outShapeStrides.z +
-              coords.w * uniforms.outShapeStrides.w +
-              coords.u;
-        }
-        `;
-      break;
-    case 6:
-      snippet += `
-        fn getOutputIndexFromCoords(coords : vec6) -> i32 {
-          return coords.x * uniforms.outShapeStrides.x +
-              coords.y * uniforms.outShapeStrides.y +
-              coords.z * uniforms.outShapeStrides.z +
-              coords.w * uniforms.outShapeStrides.w +
-              coords.u * uniforms.outShapeStrides.u +
-              coords.v;
-        }
-        `;
-      break;
-    default:
-      util.assert(false, () => `Unsupported ${outRank}D shape`);
-      break;
-  }
-  return snippet;
-}
-
-function isFlatDispatch(program: WebGPUProgram): boolean {
-  return program.dispatch[1] === 1 && program.dispatch[2] === 1;
-}
-
-export function dataTypeToGPUType(type: DataType, component = 1) {
-  if (type === 'float32') {
-    return typeSnippet(component, 'f32');
-  } else if (type === 'int32' || type === 'bool') {
-    return typeSnippet(component, 'i32');
-  }
-  throw new Error(`type ${type} is not supported.`);
-}
-
-function setOutputSnippet(
-    outShape: number[], outBufferType: DataType, component: number): string {
-  const outRank = outShape.length;
-  const gpuType = dataTypeToGPUType(outBufferType, component);
-  let snippet =
-      `fn setOutputAtIndex(flatIndex : i32, value : ${typeSnippet(component)}) {
-      result[flatIndex] = ${gpuType}(value);
-    }
-
-    fn setOutputAtIndexI32(flatIndex : i32, value : ${
-          typeSnippet(component, 'i32')}) {
-      result[flatIndex] = ${gpuType}(value);
-    }
-    `;
-  if (outRank >= 2) {
-    const dims = ['d0', 'd1', 'd2', 'd3', 'd4', 'd5'].slice(0, outRank);
-    const type = getCoordsDataType(outRank);
-
-    snippet += `
-      fn setOutputAtCoords(${dims.map(d => `${d} : i32`).join(', ')}, value : ${
-        typeSnippet(component)}) {
-        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(', ')}));
-        setOutputAtIndex(flatIndex${
-        component === 1 ? '' : ` / ${component}`}, value);
-      }
-      fn setOutputAtCoordsI32(${
-        dims.map(d => `${d} : i32`).join(', ')}, value : ${
-        typeSnippet(component, 'i32')}) {
-        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(', ')}));
-        setOutputAtIndexI32(flatIndex${
-        component === 1 ? '' : ` / ${component}`}, value);
-      }
-    `;
-  }
-
-  return snippet;
-}
-
-function insertAlignment(uniformShader: string) {
-  // insert alignment when current pattern is vec5 or vec6
-  const curInsertRe = /(\w+)\s*:\s*vec(5|6)/g;
-  uniformShader = uniformShader.replace(curInsertRe, (match) => {
-    return '@align(16) ' + match;
-  });
-
-  // insert alignment when previous pattern is vec5 or vec6
-  const preInsertRe = /vec(5|6)\s*,\s*(\w+)/g;
-  uniformShader = uniformShader.replace(preInsertRe, (_, p1, p2) => {
-    return `vec${p1}, @align(16) ${p2}`;
-  });
-  return uniformShader;
-}
-function isFlatDispatchLayout(program: WebGPUProgram): boolean {
-  if (program.dispatchLayout.hasOwnProperty('y') &&
-      program.dispatchLayout.y.length !== 0) {
-    return false;
-  }
-  if (program.dispatchLayout.hasOwnProperty('z') &&
-      program.dispatchLayout.z.length !== 0) {
-    return false;
-  }
-  return true;
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/webgpu_util.ts b/tfjs-master/tfjs-backend-webgpu/src/webgpu_util.ts
deleted file mode 100644
index bf0f01d73..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/webgpu_util.ts
+++ /dev/null
@@ -1,183 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {DataType, TensorInfo, util} from '@tensorflow/tfjs-core';
-
-const arrayProduct = (arr: number[]) => {
-  let product = 1;
-  for (let i = 0; i < arr.length; i++) {
-    product *= arr[i];
-  }
-  return product;
-};
-
-export function tilesFitEvenlyIntoShape(
-    tileSize: number[], shape: number[]): boolean {
-  if (tileSize.length !== shape.length) {
-    throw new Error(
-        `Cannot compute whether rank ${tileSize.length}` +
-        ` tiles fit evenly into rank ${shape.length} shape` +
-        ` - ranks must match.`);
-  }
-  return shape.every(
-      (dim: number, dimIdx: number) => dim % tileSize[dimIdx] === 0);
-}
-
-// Computes dispatch geometry based on layout of output dimensions and
-// workgroupSize.
-export function computeDispatch(
-    layout: {x: number[], y?: number[], z?: number[]}, outputShape: number[],
-    workgroupSize: [number, number, number] = [1, 1, 1],
-    elementsPerThread: [number, number, number] =
-        [1, 1, 1]): [number, number, number] {
-  const [dispatchX, dispatchY, dispatchZ] = [
-    Math.ceil(
-        arrayProduct(layout.x.map(d => outputShape[d])) /
-        (workgroupSize[0] * elementsPerThread[0])),
-    layout.y ? Math.ceil(
-                   arrayProduct(layout.y.map(d => outputShape[d])) /
-                   (workgroupSize[1] * elementsPerThread[1])) :
-               1,
-    layout.z ? Math.ceil(
-                   arrayProduct(layout.z.map(d => outputShape[d])) /
-                   (workgroupSize[2] * elementsPerThread[2])) :
-               1
-  ];
-  return [dispatchX, dispatchY, dispatchZ];
-}
-
-export type WorkgroupInfo = {
-  workgroupSize: [number, number, number],
-  elementsPerThread: [number, number, number],
-};
-
-export function computeWorkgroupInfoForMatMul(
-    dimAOuter: number, dimInner: number, dimBOuter: number,
-    transposeA = false): WorkgroupInfo {
-  // These are experimental values. Usually, we need to adjust the work group
-  // size based on the input shapes to improve the EU occupancy.
-  // TODO: WebGPU limits the maximum allowed shared memory size as 16K. To make
-  // sure it doesn't exceed this limitations. Temporarily reduce the work group
-  // size to [8, 8, 1] and the work per thread size is [4, 4, 1]. But we should
-  // revisit it and find the balance between work group size and work per thread
-  // size.
-  const workgroupSize: [number, number, number] = [8, 8, 1];
-  const elementsPerThread: [number, number, number] = [4, 4, 1];
-
-  if (!transposeA) {
-    if (dimAOuter <= 8) {
-      elementsPerThread[1] = 1;
-    }
-
-    if (dimInner <= 16 && dimBOuter <= 16) {
-      workgroupSize[0] = 4;
-    }
-  }
-
-  return {workgroupSize, elementsPerThread};
-}
-
-export function computeWorkgroupSizeForConv2d(
-    layout: {x: number[], y?: number[], z?: number[]}, outputShape: number[],
-    isVec4 = false): [number, number, number] {
-  if (isVec4) {
-    return [8, 8, 1];
-  }
-
-  const dim0 = arrayProduct(layout.x.map(d => outputShape[d]));
-  const dim1 = arrayProduct(layout.y.map(d => outputShape[d]));
-  // TODO(jiajia.qin@intel.com): More fine tune based on outputShape.
-  // These are experimental values. Usually, we need to adjust the work group
-  // size based on the output shape. For example, when one dimension is smaller
-  // than 4, it will be wasteful if we assign a larger size for this dimension,
-  // which results lots of threads doing useless work and reduces parallelism
-  // of hardware threads. But it is always a balance between work group size
-  // and shared memory. If one dimension is too small, such as 1, shared memory
-  // will won't be fully utilized.
-  if (dim0 <= 4) {
-    return [4, 16, 1];
-  }
-  if (dim1 <= 4) {
-    return [16, 4, 1];
-  }
-
-  return [16, 16, 1];
-}
-
-export function computeWorkPerThreadForConv2d(
-    layout: {x: number[], y?: number[], z?: number[]}, outputShape: number[],
-    isVec4 = false): [number, number, number] {
-  if (isVec4) {
-    return [4, 4, 1];
-  }
-
-  const dim0 = arrayProduct(layout.x.map(d => outputShape[d]));
-  const dim1 = arrayProduct(layout.y.map(d => outputShape[d]));
-  // TODO(jiajia.qin@intel.com): More fine tune based on outputShape.
-  // The following conditions correspond to the values set in
-  // computeWorkgroupSizeForConv2d.
-  if (dim0 <= 4) {
-    return [1, 2, 1];
-  }
-  if (dim1 <= 4) {
-    return [2, 1, 1];
-  }
-
-  return [2, 2, 1];
-}
-
-export function flatDispatchLayout(shape: number[]) {
-  return {x: shape.map((d, i) => i)};
-}
-
-export function GPUBytesPerElement(dtype: DataType): number {
-  if (dtype === 'float32' || dtype === 'int32' || dtype === 'bool' ||
-      dtype === 'string') {
-    return 4;
-  } else if (dtype === 'complex64') {
-    return 8;
-  } else {
-    throw new Error(`Unknown dtype ${dtype}`);
-  }
-}
-
-export function isWebGPUSupported(): boolean {
-  return !!(typeof globalThis !== 'undefined' && (globalThis.navigator)
-    && (globalThis.navigator.gpu));
-}
-
-export function assertNotComplex(
-    tensor: TensorInfo|TensorInfo[], opName: string): void {
-  if (!Array.isArray(tensor)) {
-    tensor = [tensor];
-  }
-  tensor.forEach(t => {
-    if (t != null) {
-      util.assert(
-          t.dtype !== 'complex64',
-          () => `${opName} does not support complex64 tensors ` +
-              'in the WebGPU backend.');
-    }
-  });
-}
-
-export enum MatMulProgramType {
-  MatMulReduceProgram,
-  MatMulSplitKProgram,
-  MatMulSmallOutputSizeProgram,
-  MatMulPackedProgram,
-  MatMulMax
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/src/webgpu_util_test.ts b/tfjs-master/tfjs-backend-webgpu/src/webgpu_util_test.ts
deleted file mode 100644
index c2a50b5c1..000000000
--- a/tfjs-master/tfjs-backend-webgpu/src/webgpu_util_test.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {computeDispatch} from './webgpu_util';
-
-describe('webgpu util', () => {
-  it('computeDispatch returns dispatch dimensions based on layout of ' +
-         'output dimensions and workgroupSize.',
-     () => {
-       const layout = {x: [0], y: [1], z: [2, 3]};
-       const outputShape = [1, 2, 3, 2];
-
-       const workgroupSize = [2, 2, 1] as [number, number, number];
-
-       const dispatch = computeDispatch(layout, outputShape, workgroupSize);
-       expect(dispatch).toEqual([1, 1, 6]);
-     });
-
-  it('computeDispatch returns dispatch dimensions based on layout of ' +
-         'output dimensions, workgroupSize, and elementsPerThread.',
-     () => {
-       const layout = {x: [0], y: [1], z: [2, 3]};
-       const outputShape = [4, 8, 12, 2];
-
-       const workgroupSize = [2, 1, 1] as [number, number, number];
-       const elementsPerThread = [2, 2, 3] as [number, number, number];
-
-       const dispatch = computeDispatch(
-           layout, outputShape, workgroupSize, elementsPerThread);
-       expect(dispatch).toEqual([1, 4, 8]);
-     });
-});
diff --git a/tfjs-master/tfjs-backend-webgpu/tsconfig.json b/tfjs-master/tfjs-backend-webgpu/tsconfig.json
deleted file mode 100644
index 37483bf58..000000000
--- a/tfjs-master/tfjs-backend-webgpu/tsconfig.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "extends": "../tsconfig_base.json",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "baseUrl": ".",
-    "paths": {},
-    "outDir": "./dist",
-    "target": "es2017"
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/tsconfig.test.json b/tfjs-master/tfjs-backend-webgpu/tsconfig.test.json
deleted file mode 100644
index 646e86c21..000000000
--- a/tfjs-master/tfjs-backend-webgpu/tsconfig.test.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "extends": "../tsconfig_ts_library",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "module": "commonjs",
-    "baseUrl": ".",
-    "paths": {},
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-backend-webgpu/yarn.lock b/tfjs-master/tfjs-backend-webgpu/yarn.lock
deleted file mode 100644
index 7332b5f63..000000000
--- a/tfjs-master/tfjs-backend-webgpu/yarn.lock
+++ /dev/null
@@ -1,3198 +0,0 @@
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-  resolved "https://registry.yarnpkg.com/minimalistic-crypto-utils/-/minimalistic-crypto-utils-1.0.1.tgz#f6c00c1c0b082246e5c4d99dfb8c7c083b2b582a"
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-  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.1.2.tgz#19cd194bfd3e428f049a70817c038d89ab4be35b"
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-  version "1.2.6"
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-  version "0.5.5"
-  resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.5.tgz#d91cefd62d1436ca0f41620e251288d420099def"
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-  version "0.6.3"
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-  version "1.1.73"
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-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/normalize-path/-/normalize-path-3.0.0.tgz#0dcd69ff23a1c9b11fd0978316644a0388216a65"
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-  version "4.1.1"
-  resolved "https://registry.yarnpkg.com/object-assign/-/object-assign-4.1.1.tgz#2109adc7965887cfc05cbbd442cac8bfbb360863"
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-  version "1.11.0"
-  resolved "https://registry.yarnpkg.com/object-inspect/-/object-inspect-1.11.0.tgz#9dceb146cedd4148a0d9e51ab88d34cf509922b1"
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-  version "1.1.5"
-  resolved "https://registry.yarnpkg.com/object-is/-/object-is-1.1.5.tgz#b9deeaa5fc7f1846a0faecdceec138e5778f53ac"
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-  version "1.1.1"
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-  version "4.1.2"
-  resolved "https://registry.yarnpkg.com/object.assign/-/object.assign-4.1.2.tgz#0ed54a342eceb37b38ff76eb831a0e788cb63940"
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-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/on-finished/-/on-finished-2.3.0.tgz#20f1336481b083cd75337992a16971aa2d906947"
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-  version "1.4.0"
-  resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
-  integrity sha1-WDsap3WWHUsROsF9nFC6753Xa9E=
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-    wrappy "1"
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-  version "0.3.0"
-  resolved "https://registry.yarnpkg.com/os-browserify/-/os-browserify-0.3.0.tgz#854373c7f5c2315914fc9bfc6bd8238fdda1ec27"
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-  version "3.2.0"
-  resolved "https://registry.yarnpkg.com/pad/-/pad-3.2.0.tgz#be7a1d1cb6757049b4ad5b70e71977158fea95d1"
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-  version "1.0.11"
-  resolved "https://registry.yarnpkg.com/pako/-/pako-1.0.11.tgz#6c9599d340d54dfd3946380252a35705a6b992bf"
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-  version "5.1.6"
-  resolved "https://registry.yarnpkg.com/parse-asn1/-/parse-asn1-5.1.6.tgz#385080a3ec13cb62a62d39409cb3e88844cdaed4"
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-  version "1.3.3"
-  resolved "https://registry.yarnpkg.com/parseurl/-/parseurl-1.3.3.tgz#9da19e7bee8d12dff0513ed5b76957793bc2e8d4"
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-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/path-browserify/-/path-browserify-1.0.1.tgz#d98454a9c3753d5790860f16f68867b9e46be1fd"
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-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
-  integrity sha1-F0uSaHNVNP+8es5r9TpanhtcX18=
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-  version "1.0.7"
-  resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.7.tgz#fbc114b60ca42b30d9daf5858e4bd68bbedb6735"
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-  version "0.0.11"
-  resolved "https://registry.yarnpkg.com/pause-stream/-/pause-stream-0.0.11.tgz#fe5a34b0cbce12b5aa6a2b403ee2e73b602f1445"
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-    through "~2.3"
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-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/pbkdf2/-/pbkdf2-3.1.2.tgz#dd822aa0887580e52f1a039dc3eda108efae3075"
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-  version "0.11.10"
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-  version "1.2.0"
-  resolved "https://registry.yarnpkg.com/ps-tree/-/ps-tree-1.2.0.tgz#5e7425b89508736cdd4f2224d028f7bb3f722ebd"
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-  version "4.0.3"
-  resolved "https://registry.yarnpkg.com/public-encrypt/-/public-encrypt-4.0.3.tgz#4fcc9d77a07e48ba7527e7cbe0de33d0701331e0"
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-  resolved "https://registry.yarnpkg.com/punycode/-/punycode-1.3.2.tgz#9653a036fb7c1ee42342f2325cceefea3926c48d"
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-  resolved "https://registry.yarnpkg.com/q/-/q-1.5.1.tgz#7e32f75b41381291d04611f1bf14109ac00651d7"
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-  version "1.2.0"
-  resolved "https://registry.yarnpkg.com/qjobs/-/qjobs-1.2.0.tgz#c45e9c61800bd087ef88d7e256423bdd49e5d071"
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-  version "6.7.0"
-  resolved "https://registry.yarnpkg.com/qs/-/qs-6.7.0.tgz#41dc1a015e3d581f1621776be31afb2876a9b1bc"
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-  version "2.4.0"
-  resolved "https://registry.yarnpkg.com/raw-body/-/raw-body-2.4.0.tgz#a1ce6fb9c9bc356ca52e89256ab59059e13d0332"
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-  resolved "https://registry.yarnpkg.com/regenerate-unicode-properties/-/regenerate-unicode-properties-8.2.0.tgz#e5de7111d655e7ba60c057dbe9ff37c87e65cdec"
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-  resolved "https://registry.yarnpkg.com/ua-parser-js/-/ua-parser-js-0.7.33.tgz#1d04acb4ccef9293df6f70f2c3d22f3030d8b532"
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-
-unicode-canonical-property-names-ecmascript@^1.0.4:
-  version "1.0.4"
-  resolved "https://registry.yarnpkg.com/unicode-canonical-property-names-ecmascript/-/unicode-canonical-property-names-ecmascript-1.0.4.tgz#2619800c4c825800efdd8343af7dd9933cbe2818"
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-  resolved "https://registry.yarnpkg.com/unicode-match-property-ecmascript/-/unicode-match-property-ecmascript-1.0.4.tgz#8ed2a32569961bce9227d09cd3ffbb8fed5f020c"
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-
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-  version "1.1.0"
-  resolved "https://registry.yarnpkg.com/unicode-property-aliases-ecmascript/-/unicode-property-aliases-ecmascript-1.1.0.tgz#dd57a99f6207bedff4628abefb94c50db941c8f4"
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-
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-
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/unpipe/-/unpipe-1.0.0.tgz#b2bf4ee8514aae6165b4817829d21b2ef49904ec"
-  integrity sha1-sr9O6FFKrmFltIF4KdIbLvSZBOw=
-
-url@^0.11.0:
-  version "0.11.0"
-  resolved "https://registry.yarnpkg.com/url/-/url-0.11.0.tgz#3838e97cfc60521eb73c525a8e55bfdd9e2e28f1"
-  integrity sha1-ODjpfPxgUh63PFJajlW/3Z4uKPE=
-  dependencies:
-    punycode "1.3.2"
-    querystring "0.2.0"
-
-util-deprecate@^1.0.1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
-  integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=
-
-util@^0.12.0, util@^0.12.1:
-  version "0.12.4"
-  resolved "https://registry.yarnpkg.com/util/-/util-0.12.4.tgz#66121a31420df8f01ca0c464be15dfa1d1850253"
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-    is-arguments "^1.0.4"
-    is-generator-function "^1.0.7"
-    is-typed-array "^1.1.3"
-    safe-buffer "^5.1.2"
-    which-typed-array "^1.1.2"
-
-utils-merge@1.0.1:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/utils-merge/-/utils-merge-1.0.1.tgz#9f95710f50a267947b2ccc124741c1028427e713"
-  integrity sha1-n5VxD1CiZ5R7LMwSR0HBAoQn5xM=
-
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-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/vary/-/vary-1.1.2.tgz#2299f02c6ded30d4a5961b0b9f74524a18f634fc"
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-
-vm-browserify@^1.1.2:
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/vm-browserify/-/vm-browserify-1.1.2.tgz#78641c488b8e6ca91a75f511e7a3b32a86e5dda0"
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-
-void-elements@^2.0.0:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/void-elements/-/void-elements-2.0.1.tgz#c066afb582bb1cb4128d60ea92392e94d5e9dbec"
-  integrity sha1-wGavtYK7HLQSjWDqkjkulNXp2+w=
-
-wcwidth@^1.0.1:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/wcwidth/-/wcwidth-1.0.1.tgz#f0b0dcf915bc5ff1528afadb2c0e17b532da2fe8"
-  integrity sha1-8LDc+RW8X/FSivrbLA4XtTLaL+g=
-  dependencies:
-    defaults "^1.0.3"
-
-which-boxed-primitive@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/which-boxed-primitive/-/which-boxed-primitive-1.0.2.tgz#13757bc89b209b049fe5d86430e21cf40a89a8e6"
-  integrity sha512-bwZdv0AKLpplFY2KZRX6TvyuN7ojjr7lwkg6ml0roIy9YeuSr7JS372qlNW18UQYzgYK9ziGcerWqZOmEn9VNg==
-  dependencies:
-    is-bigint "^1.0.1"
-    is-boolean-object "^1.1.0"
-    is-number-object "^1.0.4"
-    is-string "^1.0.5"
-    is-symbol "^1.0.3"
-
-which-typed-array@^1.1.2:
-  version "1.1.4"
-  resolved "https://registry.yarnpkg.com/which-typed-array/-/which-typed-array-1.1.4.tgz#8fcb7d3ee5adf2d771066fba7cf37e32fe8711ff"
-  integrity sha512-49E0SpUe90cjpoc7BOJwyPHRqSAd12c10Qm2amdEZrJPCY2NDxaW01zHITrem+rnETY3dwrbH3UUrUwagfCYDA==
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-    call-bind "^1.0.0"
-    es-abstract "^1.18.0-next.1"
-    foreach "^2.0.5"
-    function-bind "^1.1.1"
-    has-symbols "^1.0.1"
-    is-typed-array "^1.1.3"
-
-which@^1.2.1:
-  version "1.3.1"
-  resolved "https://registry.yarnpkg.com/which/-/which-1.3.1.tgz#a45043d54f5805316da8d62f9f50918d3da70b0a"
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-  dependencies:
-    isexe "^2.0.0"
-
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-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
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-
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-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
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-  version "8.2.3"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-8.2.3.tgz#63a56456db1b04367d0b721a0b80cae6d8becbba"
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-
-xtend@^4.0.2:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/xtend/-/xtend-4.0.2.tgz#bb72779f5fa465186b1f438f674fa347fdb5db54"
-  integrity sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==
-
-y18n@^5.0.5:
-  version "5.0.8"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.8.tgz#7f4934d0f7ca8c56f95314939ddcd2dd91ce1d55"
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-
-yargs-parser@^20.2.2:
-  version "20.2.9"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
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-
-yargs@^16.1.1:
-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
diff --git a/tfjs-master/tfjs-converter/.babelrc b/tfjs-master/tfjs-converter/.babelrc
deleted file mode 100644
index 9772b939a..000000000
--- a/tfjs-master/tfjs-converter/.babelrc
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "presets": [
-    [
-      "env",
-      {
-        "modules": false
-      }
-    ]
-  ],
-  "plugins": [
-    "external-helpers"
-  ]
-}
diff --git a/tfjs-master/tfjs-converter/.npmignore b/tfjs-master/tfjs-converter/.npmignore
deleted file mode 100644
index 43a845310..000000000
--- a/tfjs-master/tfjs-converter/.npmignore
+++ /dev/null
@@ -1,35 +0,0 @@
-.babelrc
-.DS_Store
-.idea/
-.pylintrc
-.rpt2_cache
-.travis.yml
-.vscode
-*.tgz
-*.txt
-**.yalc
-**yalc.lock
-cloudbuild.yml
-coverage/
-demo/
-DEVELOPMENT.md
-dist/**/*_test.d.ts
-dist/**/*_test.js
-docs/
-ISSUE_TEMPLATE.md
-karma.conf.js
-node_modules/
-npm-debug.log
-package-lock.json
-package/
-python/
-rollup.config.google.js
-rollup.config.js
-scripts/
-src/**/*_test.ts
-test_results
-tsconfig.json
-tslint.json
-yarn-error.log
-yarn.lock
-.git
diff --git a/tfjs-master/tfjs-converter/.vscode/launch.json b/tfjs-master/tfjs-converter/.vscode/launch.json
deleted file mode 100644
index df98d811f..000000000
--- a/tfjs-master/tfjs-converter/.vscode/launch.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "version": "0.2.0",
-  "configurations": [
-    {
-      "name": "Python: Attach",
-      "type": "python",
-      "request": "attach",
-      "port": 5724,
-      "host": "localhost"
-    }
-  ],
-  "compounds": []
-}
diff --git a/tfjs-master/tfjs-converter/.vscode/settings.json b/tfjs-master/tfjs-converter/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-converter/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-converter/.vscode/tasks.json b/tfjs-master/tfjs-converter/.vscode/tasks.json
deleted file mode 100644
index 5edb3db96..000000000
--- a/tfjs-master/tfjs-converter/.vscode/tasks.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/tasks.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-converter/BUILD.bazel b/tfjs-master/tfjs-converter/BUILD.bazel
deleted file mode 100644
index ade7cd6c1..000000000
--- a/tfjs-master/tfjs-converter/BUILD.bazel
+++ /dev/null
@@ -1,149 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//rules:copy_file.bzl", "copy_file")
-load("@build_bazel_rules_nodejs//:index.bzl", "js_library", "nodejs_test", "pkg_npm")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-
-package(default_visibility = ["//visibility:public"])
-
-# Allow typescript rules in any package to reference this file
-exports_files([
-    "package.json",
-    "README.md",
-])
-
-nodejs_test(
-    name = "tfjs-converter_test",
-    data = [
-        ":package_json",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-converter/metadata:kernels_to_ops",
-        "//tfjs-converter/src:tfjs-converter_test_lib",
-    ],
-    entry_point = "//tfjs-converter/src:run_tests.ts",
-    link_workspace_root = True,
-    tags = ["ci"],
-    # This fixes a race condition between this test and test_snippets_test where
-    # the wrong runfiles will be linked to node_modules/@tensorflow/tfjs-converter/dist.
-    # TODO(mattSoulanille): Assess whether we should apply this to all nodejs_test targets.
-    templated_args = ["--bazel_patch_module_resolver"],
-)
-
-# Defined here because chdir must be a subdirectory of the directory the rule is
-# defined in.
-nodejs_test(
-    name = "test_snippets_test",
-    chdir = "tfjs-converter",
-    data = [
-        ":tsconfig.json",
-        "//:tsconfig.json",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-converter/scripts:test_snippets_lib",
-        "//tfjs-converter/src:all_srcs",
-    ],
-    entry_point = "//tfjs-converter/scripts:test_snippets.ts",
-    link_workspace_root = True,
-    tags = [
-        "ci",
-        # Run the test locally since it makes http requests.
-        "no-remote-exec",
-        "requires-network",
-    ],
-)
-
-tfjs_bundle(
-    name = "tf-converter",
-    entry_point = "//tfjs-converter/src:index.ts",
-    external = [
-        "@tensorflow/tfjs-core",
-    ],
-    globals = {
-        "@tensorflow/tfjs-core": "tf",
-    },
-    umd_name = "tf",
-    deps = [
-        "//tfjs-converter/src:tfjs-converter_lib",
-        "//tfjs-converter/src:tfjs-converter_src_lib",
-    ],
-)
-
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-converter/src:tfjs-converter_lib",
-        "//tfjs-converter/src:tfjs-converter_src_lib",
-    ],
-    root = "src",
-)
-
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-converter",
-        ":tf-converter.es2017",
-        ":tf-converter.es2017.min",
-        ":tf-converter.fesm",
-        ":tf-converter.fesm.min",
-        ":tf-converter.min",
-        ":tf-converter.node",
-    ],
-)
-
-copy_file(
-    name = "copy_miniprogram",
-    src = ":tf-converter.min.js",
-    out = "dist/miniprogram/index.js",
-)
-
-copy_file(
-    name = "copy_miniprogram_map",
-    src = ":tf-converter.min.js.map",
-    out = "dist/miniprogram/index.js.map",
-)
-
-pkg_npm(
-    name = "tfjs-converter_pkg",
-    package_name = "@tensorflow/tfjs-converter",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_miniprogram",
-        ":copy_miniprogram_map",
-        ":copy_src_to_dist",
-        "//tfjs-converter/metadata:kernels_to_ops",
-    ],
-)
-
-js_library(
-    name = "package_json",
-    package_name = "tfjs-converter",
-    srcs = [
-        ":package.json",
-    ],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        ":test_snippets_test",
-        ":tfjs-converter_test",
-    ],
-)
diff --git a/tfjs-master/tfjs-converter/DEVELOPMENT.md b/tfjs-master/tfjs-converter/DEVELOPMENT.md
deleted file mode 100644
index 1f8b3b7f9..000000000
--- a/tfjs-master/tfjs-converter/DEVELOPMENT.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# Notes for TensorFlow.js-Converter Developers
-
-## Python Development
-
-There are some Python libraries, binary and tests in the `python/` directory.
-
-It is recommended to do your Python development and testing in a
-[virtualenv](https://virtualenv.pypa.io/en/stable/) or
-[pipenv](https://docs.pipenv.org/).
-
-As a prerequisite, install the following dependencies for python testing
-```sh
-cd python
-pip install -r requirements.txt
-```
-
-For Python linter, install `pylint`, e.g.,
-* `apt-get install -y pylint`
-
-To run the Python linter:
-```sh
-cd python
-pylint tensorflowjs
-```
-
-To run the python unit tests, there are two options. You can choose the one that
-you prefer.
-
-1. Run the tests using the `run-python-tests.sh` script:
-
-   ```sh
-   cd python
-   ./run-python-tests.sh
-   ```
-
-2. Run the tests using Bazel. See bazel installation guide
-   [here](https://docs.bazel.build/versions/master/install.html). Once bazel
-   is installed, do:
-
-   ```sh
-   cd python
-   bazel test tensorflowjs/...
-   ```
-
-Be sure to run the tests under **both** Python 2 and Python 3.
-
-### Building and testing the tensorflowjs pip package
-
-```sh
-cd python
-
-# You need to specify a folder where the pip wheel file will be stored, e.g.,
-./build-pip-package.sh /tmp/my_tensorflowjs_pip
-
-# If the script succeeds, you can use `pip install` to install the pip package:
-
-pip install --force-reinstall \
-  /tmp/my_tensorflowjs_pip/tensorflowjs-0.0.1-py2-none-any.whl
-```
-
-`build-pip-package.sh` provides a flag (`--test`) with which you can run a
-test-on-install after building the pip package. Make sure you are using a
-`virutalenv` or `pipenv` to avoid changing your base environmnet.
-
-```sh
-./build-pip-package.sh --test /tmp/my_tensorflowjs_pip
-```
diff --git a/tfjs-master/tfjs-converter/README.md b/tfjs-master/tfjs-converter/README.md
deleted file mode 100644
index 6b4d69e81..000000000
--- a/tfjs-master/tfjs-converter/README.md
+++ /dev/null
@@ -1,563 +0,0 @@
-# Getting started
-
-**TensorFlow.js converter** is an open source library to load a pretrained
-TensorFlow
-[SavedModel](https://www.tensorflow.org/guide/saved_model)
-or [TensorFlow Hub module](https://www.tensorflow.org/hub/)
-into the browser and run inference through
-[TensorFlow.js](https://js.tensorflow.org).
-
-__Note__: _Session bundle format have been deprecated.
-
-A 2-step process to import your model:
-
-1. A python pip package to convert a TensorFlow SavedModel or TensorFlow Hub
-module to a web friendly format. If you already have a converted model, or are
-using an already hosted model (e.g. MobileNet), skip this step.
-2. [JavaScript API](./src/executor/graph_model.ts), for loading and running
-inference.
-
-## Step 1: Converting a [TensorFlow SavedModel](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md), [TensorFlow Hub module](https://www.tensorflow.org/hub/), [Keras HDF5](https://keras.io/getting_started/faq/#what-are-my-options-for-saving-models), [tf.keras SavedModel](https://www.tensorflow.org/api_docs/python/tf/keras/saving/save_model), or [Flax/JAX model](http://github.com/google/flax) to a web-friendly format
-
-__0. Please make sure that you run in a Docker container or a virtual environment.__
-
- The script pulls its own subset of TensorFlow, which might conflict with the
- existing TensorFlow/Keras installation.
-
-__Note__: *Check that [`tf-nightly-cpu-2.0-preview`](https://pypi.org/project/tf-nightly-cpu-2.0-preview/#files) is available for your platform.*
-
-Most of the times, this means that you have to use Python 3.6.8 in your local
-environment. To force Python 3.6.8 in your local project, you can install
-[`pyenv`](https://github.com/pyenv/pyenv) and proceed as follows in the target
-directory:
-
-```bash
-pyenv install 3.6.8
-pyenv local 3.6.8
-```
-
-Now, you can
-[create and activate](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)
-a `venv` virtual environment in your current folder:
-
-```bash
-virtualenv --no-site-packages venv
-. venv/bin/activate
-```
-
-__1. Install the TensorFlow.js pip package:__
-
-Install the library with interactive CLI:
-```bash
- pip install tensorflowjs[wizard]
-```
-
-__2. Run the converter script provided by the pip package:__
-
-There are three way to trigger the model conversion, explain below:
-
-- The conversion wizard: `tensorflowjs_wizard` ([go to section](https://github.com/tensorflow/tfjs/blob/master/tfjs-converter/README.md#conversion-wizard-tensorflowjs_wizard))
-- Regular conversion script: `tensorflowjs_converter` ([go to section](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter#regular-conversion-script-tensorflowjs_converter))
-- Calling a converter function in Python (Flax/JAX) ([go to section](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter#calling-a-converter-function-in-python-flaxjax))
-
-## Conversion wizard: `tensorflowjs_wizard`
-
-To start the conversion wizard:
-```bash
-tensorflowjs_wizard
-```
-
-This tool will walk you through the conversion process and provide you with
-details explanations for each choice you need to make. Behind the scene it calls
-the converter script (`tensorflowjs_converter`) in pip package. This is the
-recommended way to convert a single model.
-
-There is also a dry run mode for the wizard, which will not perform the actual
-conversion but only generate the command for `tensorflowjs_converter` command.
-This generated command can be used in your own shell script.
-
-Here is an screen capture of the wizard in action. ![wizard](./tensorflowjs_wizard.gif)
-```bash
-tensorflowjs_wizard --dryrun
-```
-
-To convert a batch of models or integrate the conversion process into your own
-script, you should use the tensorflowjs_converter script.
-
-## Regular conversion script: `tensorflowjs_converter`
-
-The converter expects a __TensorFlow SavedModel__, __TensorFlow Hub module__,
-__TensorFlow.js JSON__ format, __Keras HDF5 model__, or __tf.keras SavedModel__
-for input.
-
-* __TensorFlow SavedModel__ example:
-
-```bash
-tensorflowjs_converter \
-    --input_format=tf_saved_model \
-    --output_format=tfjs_graph_model \
-    --signature_name=serving_default \
-    --saved_model_tags=serve \
-    /mobilenet/saved_model \
-    /mobilenet/web_model
-```
-* __TensorFlow Frozen Model__ example:
-
-__Note:__ Frozen model is a deprecated format and support is added for backward compatibility purpose.
-
-```bash
-$ tensorflowjs_converter \
-    --input_format=tf_frozen_model \
-    --output_node_names='MobilenetV1/Predictions/Reshape_1' \
-    /mobilenet/frozen_model.pb \
-    /mobilenet/web_model
-```
-
-* __Tensorflow Hub module__ example:
-
-```bash
-tensorflowjs_converter \
-    --input_format=tf_hub \
-    'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/1' \
-    /mobilenet/web_model
-```
-
-* __Keras HDF5 model__ example:
-
-```bash
-tensorflowjs_converter \
-    --input_format=keras \
-    /tmp/my_keras_model.h5 \
-    /tmp/my_tfjs_model
-```
-
-* __tf.keras SavedModel__ example:
-
-```bash
-tensorflowjs_converter \
-    --input_format=keras_saved_model \
-    /tmp/my_tf_keras_saved_model/1542211770 \
-    /tmp/my_tfjs_model
-```
-
-Note that the input path used above is a subfolder that has a Unix epoch
-time (1542211770) and is generated automatically by tensorflow when it
-saved a tf.keras model in the SavedModel format.
-
-### Conversion Flags
-
-|Positional Arguments | Description |
-|---|---|
-|`input_path`  | Full path of the saved model directory or TensorFlow Hub module handle or path.|
-|`output_path` | Path for all output artifacts.|
-
-
-| Options | Description
-|---|---|
-|`--input_format`     | The format of input model, use `tf_saved_model` for SavedModel, `tf_hub` for TensorFlow Hub module, `tfjs_layers_model` for TensorFlow.js JSON format, and `keras` for Keras HDF5. |
-|`--output_format`| The desired output format.  Must be `tfjs_layers_model`, `tfjs_graph_model` or `keras`. Not all pairs of input-output formats are supported.  Please file a [github issue](https://github.com/tensorflow/tfjs/issues) if your desired input-output pair is not supported.|
-|<nobr>`--saved_model_tags`</nobr> | Only applicable to SavedModel conversion. Tags of the MetaGraphDef to load, in comma separated format. If there are no tags defined in the saved model, set it to empty string `saved_model_tags=''`. Defaults to `serve`.|
-|`--signature_name`   | Only applicable to TensorFlow SavedModel and Hub module conversion, signature to load. Defaults to `serving_default` for SavedModel and `default` for Hub module. See https://www.tensorflow.org/hub/common_signatures/.|
-|`--strip_debug_ops`   | Strips out TensorFlow debug operations `Print`, `Assert`, `CheckNumerics`. Defaults to `True`.|
-|`--quantization_bytes`  | (Deprecated) How many bytes to optionally quantize/compress the weights to. Valid values are 1 and 2. which will quantize int32 and float32 to 1 or 2 bytes respectively. The default (unquantized) size is 4 bytes.|
-|`--quantize_float16`  | Comma separated list of node names to apply float16 quantization. You can also use wildcard symbol (\*) to apply quantization to multiple nodes (e.g., conv/\*/weights). When the flag is provided without any nodes the default behavior will match all nodes. |
-|`--quantize_uint8`  | Comma separated list of node names to apply 1-byte affine quantization. You can also use wildcard symbol (\*) to apply quantization to multiple nodes (e.g., conv/\*/weights). When the flag is provided without any nodes the default behavior will match all nodes. |
-|`--quantize_uint16`  | Comma separated list of node names to apply 2-byte affine quantization. You can also use wildcard symbol (\*) to apply quantization to multiple nodes (e.g., conv/\*/weights). When the flag is provided without any nodes the default behavior will match all nodes. |
-|`--weight_shard_size_bytes` | Shard size (in bytes) of the weight files. Only supported when `output_format` is `tfjs_layers_model` or `tfjs_graph_model`. Default size is 4 MB (4194304 bytes).|
-|<nobr>`--output_node_names`</nobr>| Only applicable to Frozen Model. The names of the output nodes, separated by commas.|
-|<nobr>`--control_flow_v2`</nobr>| Only applicable to TF 2.x Saved Model. This flag improve performance on models with control flow ops, default to False.|
-|<nobr>`--metadata`</nobr>| Comma separated list of metadata json file paths, indexed by name. Prefer absolute path. Example: 'metadata1:/metadata1.json,metadata2:/metadata2.json'.|
-|<nobr>`--use_structured_outputs_names`</nobr>| Changes output of graph model to match the structured_outputs format instead of list format. Defaults to `False`.|
-
-__Note: If you want to convert TensorFlow session bundle, you can install older versions of the tensorflowjs pip package, i.e. `pip install tensorflowjs==0.8.6`.__
-
-### Format Conversion Support Tables
-
-Note: Unless stated otherwise, we can infer the value of `--output_format` from the
-value of `--input_format`. So the `--output_format` flag can be omitted in
-most cases.
-
-#### Python-to-JavaScript
-
-| `--input_format` | `--output_format` | Description |
-|---|---|---|
-| `keras` | `tfjs_layers_model` | Convert a keras or tf.keras HDF5 model file to TensorFlow.js Layers model format. Use [`tf.loadLayersModel()`](https://js.tensorflow.org/api/latest/#loadLayersModel) to load the model in JavaScript. The loaded model supports the full inference and training (e.g., transfer learning) features of the original keras or tf.keras model. |
-| `keras` | `tfjs_graph_model` | Convert a keras or tf.keras HDF5 model file to TensorFlow.js Graph model format. Use [`tf.loadGraphModel()`](https://js.tensorflow.org/api/latest/#loadGraphModel) to load the converted model in JavaScript. The loaded model supports only inference, but the speed of inference is generally faster than that of a tfjs_layers_model (see above row) thanks to the graph optimization performed by TensorFlow. Another limitation of this conversion route is that it does not support some layer types (e.g., recurrent layers such as LSTM) yet. |
-| `keras_saved_model` | `tfjs_layers_model` | Convert a tf.keras SavedModel model file (from [`tf.contrib.saved_model.save_keras_model`](https://www.tensorflow.org/api_docs/python/tf/contrib/saved_model/save_keras_model)) to TensorFlow.js Layers model format. Use [`tf.loadLayersModel()`](https://js.tensorflow.org/api/latest/#loadLayersModel) to load the model in JavaScript. |
-| `tf_hub` | `tfjs_graph_model` | Convert a [TF-Hub](https://www.tensorflow.org/hub) model file to TensorFlow.js graph model format. Use [`tf.loadGraphModel()`](https://js.tensorflow.org/api/latest/#loadGraphModel) to load the converted model in JavaScript. |
-| `tf_saved_model` | `tfjs_graph_model` | Convert a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model#build_and_load_a_savedmodel) to TensorFlow.js graph model format. Use [`tf.loadGraphModel()`](https://js.tensorflow.org/api/latest/#loadGraphModel) to load the converted model in JavaScript. |
-| `tf_frozen_model` | `tfjs_graph_model` | Convert a [Frozen Model](https://medium.com/@sebastingarcaacosta/how-to-export-a-tensorflow-2-x-keras-model-to-a-frozen-and-optimized-graph-39740846d9eb) to TensorFlow.js graph model format. Use [`tf.loadGraphModel()`](https://js.tensorflow.org/api/latest/#loadGraphModel) to load the converted model in JavaScript. |
-
-#### JavaScript-to-Python
-
-| `--input_format` | `--output_format` | Description |
-|---|---|---|
-| `tfjs_layers_model` | `keras` | Convert a TensorFlow.js Layers model (JSON + binary weight file(s)) to a Keras HDF5 model file. Use [`keras.model.load_model()`](https://keras.io/getting-started/faq/#savingloading-whole-models-architecture-weights-optimizer-state) or [`tf.keras.models.load_model()`](https://www.tensorflow.org/api_docs/python/tf/keras/models/load_model) to load the converted model in Python. |
-| `tfjs_layers_model` | `keras_saved_model` | Convert a TensorFlow.js Layers model (JSON + binary weight file(s)) to the tf.keras SavedModel format. This format is useful for subsequent uses such as [TensorFlow Serving](https://www.tensorflow.org/tfx/serving/serving_basic) and [conversion to TFLite](https://www.tensorflow.org/lite/convert). |
-
-#### JavaScript-to-JavaScript
-
-##### Converting tfjs_layers_model to tfjs_layers_model with weight sharding and quantization
-
-The tfjs_layers_model-to-tfjs_layer_model conversion option serves the following
-purposes:
-
-1. It allows you to shard the binary weight file into multiple small shards
-   to facilitate browser caching. This step is necessary for models with
-   large-sized weights saved from TensorFlow.js (either browser or Node.js),
-   because TensorFlow.js puts all weights in a single weight file
-   ('group1-shard1of1.bin'). To shard the weight file, do
-
-   ```sh
-   tensorflowjs_converter \
-       --input_format tfjs_layers_model \
-       --output_format tfjs_layers_model \
-       original_model/model.json \
-       sharded_model/
-   ```
-
-    The command above creates shards of size 4 MB (4194304 bytes) by default.
-    Alternative shard sizes can be specified using the
-    `--weight_shard_size_bytes` flag.
-
-2. It allows you to reduce the on-the-wire size of the weights through
-   16- or 8-bit quantization. For example:
-
-   ```sh
-   tensorflowjs_converter \
-      --input_format tfjs_layers_model \
-      --output_format tfjs_layers_model \
-      --quantize_uint16 \
-      original_model/model.json
-      quantized_model/
-   ```
-
-##### Converting tfjs_layers_model to tfjs_graph_model
-
-Converting a `tfjs_layers_model` to a `tfjs_graph_model` usually leads to
-faster inference speed in the browser and Node.js, thanks to the graph
-optimization that goes into generating the tfjs_graph_models. For more details,
-see the following document on TensorFlow's Grappler:
-["TensorFlow Graph Optimizations" by R. Larsen an T. Shpeisman](https://ai.google/research/pubs/pub48051).
-
-There are two caveats:
-
-1. The model that results from this conversion does not support further
-   training.
-2. Certain layer types (e.g., recurrent layers such as LSTM) are not supported
-   yet.
-
-See example command below:
-
-```sh
- tensorflowjs_converter \
-    --input_format tfjs_layers_model \
-    --output_format tfjs_graph_model \
-    my_layers_model/model.json
-    my_graph_model/
-```
-
-tfjs_layers_model-to-tfjs_graph_model also support weight quantization.
-
-### Web-friendly format
-
-The conversion script above produces 2 types of files:
-
-* `model.json` (the dataflow graph and weight manifest file)
-* `group1-shard\*of\*` (collection of binary weight files)
-
-For example, here is the MobileNet model converted and served in
-following location:
-
-```html
-  https://storage.cloud.google.com/tfjs-models/savedmodel/mobilenet_v1_1.0_224/model.json
-  https://storage.cloud.google.com/tfjs-models/savedmodel/mobilenet_v1_1.0_224/group1-shard1of5
-  ...
-  https://storage.cloud.google.com/tfjs-models/savedmodel/mobilenet_v1_1.0_224/group1-shard5of5
-```
-
-## Calling a Converter Function in Python (Flax/JAX)
-
-You can also convert your model to web format in Python by calling one of the
-conversion functions. This is currently the only way to convert a Flax or JAX
-model, since no standard serialization format exists to store a Module (only the
-checkpoints).
-
-Here we provide an example of how to convert a Flax function using the
-conversion function `tfjs.jax_conversion.convert_jax()`.
-
-```py
-import numpy as np
-from flax import linen as nn
-from jax import random
-import jax.numpy as jnp
-from tensorflowjs.converters import jax_conversion
-
-module = nn.Dense(features=4)
-inputs = jnp.ones((3, 4))
-params = module.init(random.PRNKey(0), inputs)['params']
-
-jax_conversion.convert_jax(
-  apply_fn=module.apply,
-  params=params,
-  input_signatures=[((3, 4), np.float32)],
-  model_dir=tfjs_model_dir)
-```
-
-Note that when using dynamic shapes, an additional argument `polymorphic_shapes`
-should be provided specifying values for the dynamic ("polymorphic")
-dimensions). So in order to convert the same model as before, but now with a
-dynamic first dimension, one should call `convert_jax` as follows:
-
-```py
-jax_conversion.convert_jax(
-  apply_fn=module.apply,
-  params=params,
-  input_signatures=[((None, 4), np.float32)],
-  polymorphic_shapes=["(b, 4)"],
-  model_dir=tfjs_model_dir)
-```
-
-See
-[here](https://github.com/google/jax/tree/main/jax/experimental/jax2tf#shape-polymorphic-conversion)
-for more details on the exact syntax for this argument.
-
-When converting JAX models, you can also pass any [options that
-`convert_tf_saved_model`
-uses](https://github.com/tensorflow/tfjs/blob/master/tfjs-converter/python/tensorflowjs/converters/tf_saved_model_conversion_v2.py#L951-L974).
-For example, to quantize a model's weights, pass the `quantization_dtype_map`
-option listing the weights that should be quantized.
-
-```py
-jax_conversion.convert_jax(
-  apply_fn=module.apply,
-  params=params,
-  input_signatures=[((3, 4), np.float32)],
-  model_dir=tfjs_model_dir,
-  quantization_dtype_map={'float16': '*'})
-```
-
-## Step 2: Loading and running in the browser
-
-If the original model was a `SavedModel`, use
-[`tf.loadGraphModel()`](https://js.tensorflow.org/api/latest/#loadGraphModel).
-If it was Keras, use
-[`tf.loadLayersModel()`](https://js.tensorflow.org/api/latest/#loadLayersModel):
-
-```typescript
-import * as tf from '@tensorflow/tfjs';
-
-const MODEL_URL = 'https://.../mobilenet/model.json';
-
-// For Keras use tf.loadLayersModel().
-const model = await tf.loadGraphModel(MODEL_URL);
-const cat = document.getElementById('cat');
-model.predict(tf.browser.fromPixels(cat));
-```
-
-See our API docs for the
-[`predict()`](https://js.tensorflow.org/api/latest/#tf.GraphModel.predict)
-method. To see what other methods exist on a `Model`, see
-[`tf.LayersModel`](https://js.tensorflow.org/api/latest/#class:LayersModel)
-and [`tf.GraphModel`](https://js.tensorflow.org/api/latest/#class:GraphModel).
-Also check out our working [MobileNet demo](./demo/mobilenet/README.md).
-
-If your server requests credentials for accessing the model files, you can
-provide the optional RequestOption param.
-
-```typescript
-const model = await loadGraphModel(MODEL_URL,
-    {credentials: 'include'});
-```
-
-Please see
-[fetch() documentation](https://developer.mozilla.org/en-US/docs/Web/API/WindowOrWorkerGlobalScope/fetch)
-for details.
-
-### Native File System
-
-TensorFlow.js can be used from Node.js. See the
-[tfjs-node project](https://github.com/tensorflow/tfjs-node) for more details.
-Unlike web browsers, Node.js can access the local file system directly.
-Therefore, you can load the same frozen model from local file system into
-a Node.js program running TensorFlow.js. This is done by calling
-`loadGraphModel` with the path to the model files:
-
-```js
-// Load the tfjs-node binding
-import * as tf from '@tensorflow/tfjs-node';
-
-const MODEL_PATH = 'file:///tmp/mobilenet/model.json';
-const model = await tf.loadGraphModel(MODEL_PATH);
-```
-
-You can also load the remote model files the same way as in browser, but you
-might need to polyfill
-the fetch() method.
-
-## Supported operations
-
-Currently TensorFlow.js only supports a limited set of TensorFlow Ops. See the
-[full list](./docs/supported_ops.md).
-If your model uses unsupported ops, the `tensorflowjs_converter` script will
-fail and produce a list of the unsupported ops in your model. Please file issues
-to let us know what ops you need support with.
-
-## Manual forward pass and direct weights loading
-
-If you want to manually write the forward pass with the ops API, you can load
-the weights directly as a map from weight names to tensors:
-
-```js
-import * as tf from '@tensorflow/tfjs';
-
-const modelUrl = "https://example.org/model/model.json";
-
-const response = await fetch(modelUrl);
-this.weightManifest = (await response.json())['weightsManifest'];
-const weightMap = await tf.io.loadWeights(
-        this.weightManifest, "https://example.org/model");
-```
-
-`weightMap` maps a weight name to a tensor. You can use it to manually implement
-the forward pass of the model:
-
-```js
-const input = tf.tensor(...);
-tf.matMul(weightMap['fc1/weights'], input).add(weightMap['fc1/bias']);
-```
-
-## FAQ
-
-__1. What TensorFlow models does the converter currently support?__
-
-Image-based models (MobileNet, SqueezeNet, add more if you tested) are the most
-supported. Models with control flow ops (e.g. RNNs) are also supported.
-The tensorflowjs_converter script will validate the model you have and show a
-list of unsupported ops in your model. See [this list](./docs/supported_ops.md)
-for which ops are currently supported.
-
-__2. Will model with large weights work?__
-
-While the browser supports loading 100-500MB models, the page load time,
-the inference time and the user experience would not be great. We recommend
-using models that are designed for edge devices (e.g. phones). These models are
-usually smaller than 30MB.
-
-__3. Will the model and weight files be cached in the browser?__
-
-Yes, we are splitting the weights into files of 4MB chunks, which enable the
-browser to cache them automatically. If the model architecture is less than 4MB
-(most models are), it will also be cached.
-
-__4. Can I quantize the weights over the wire?__
-
-Yes, you can use the --quantize_{float16, uint8, uint16} flags to compress
-weights with 1 byte integer quantization (`uint8`) or 2 byte integer
-(`uint16`)/float (`float16`) quantization.
-Quantizing to float16 may provide better accuracy over
-2 byte affine integer scaling (`uint16`). 1-byte affine quantization,
-i.e., `uint8` provides a 4x size reduction at the cost of accuracy.
-For example, we can quantize our MobileNet model using float16 quantization:
-
-```
-tensorflowjs_converter
-    --quantize_float16 \
-    --input_format=tf_hub \
-    'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/1' \
-    /mobilenet/web_model
-```
-
-You can also quantize specific weights as well as weight groupings using
-a wildcard replacement. For example,
-```
-tensorflowjs_converter
-    --quantize_float16="conv/*/weights"
-```
-which will quantize all weights that match the pattern conv/*/weights.
-This will exclude biases and any weights that don't begin with conv/.
-This can be a powerful tool to reduce model size while trying to maximize
-performance.
-
-__5. Why is the predict() method for inference so much slower on the first call than the subsequent calls?__
-
-The time of first call also includes the compilation time of WebGL shader
-programs for the model. After the first call the shader programs are cached,
-which makes the subsequent calls much faster. You can warm up the cache by
-calling the predict method with an all zero inputs, right after the completion
-of the model loading.
-
-__6. I have a model converted with a previous version of TensorFlow.js converter (0.15.x), that is in .pb format. How do I convert it to the new JSON format?__
-
-You can use the built-in migration tool to convert the models generated by
-previous versions. Here are the steps:
-
-```bash
-git clone git@github.com:tensorflow/tfjs-converter.git
-cd tfjs-converter
-yarn
-yarn ts-node tools/pb2json_converter.ts pb_model_directory/ json_model_directory/
-```
-
-`pb_model_directory` is the directory where the model generated by previous
-version is located.
-`json_model_directory` is the destination directory for the converted model.
-
-
-## Development
-
-To build **TensorFlow.js converter** from source, we need to prepare the dev environment and clone the project.
-
-Bazel builds Python from source, so we install the dependencies required to build it. Since we will be using pip and C extensions, we also install the ssl, foreign functions, and zlib development packages. On debian, this is done with:
-
-```bash
-sudo apt-get build-dep python3
-sudo apt install libssl-dev libffi-dev zlib1g-dev
-```
-
-See the [python developer guide](https://devguide.python.org/setup/#install-dependencies) for instructions on installing these for other platforms.
-
-Then, we clone the project and install dependencies with:
-
-```bash
-git clone https://github.com/tensorflow/tfjs.git
-cd tfjs
-yarn # Installs dependencies.
-```
-
-We recommend using [Visual Studio Code](https://code.visualstudio.com/) for
-development. Make sure to install
-[TSLint VSCode extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode.vscode-typescript-tslint-plugin)
-and the npm [clang-format](https://github.com/angular/clang-format) `1.2.2`
-or later with the
-[Clang-Format VSCode extension](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)
-for auto-formatting.
-
-Before submitting a pull request, make sure the code passes all the tests and is
-clean of lint errors:
-
-```bash
-cd tfjs-converter
-yarn test
-yarn lint
-```
-
-To run a subset of tests and/or on a specific browser:
-
-```bash
-yarn test --browsers=Chrome --grep='execute'
-> ...
-> Chrome 64.0.3282 (Linux 0.0.0): Executed 39 of 39 SUCCESS (0.129 secs / 0 secs)
-```
-
-To run the tests once and exit the karma process (helpful on Windows):
-
-```bash
-yarn test --single-run
-```
-
-To run all the python tests
-
-```bash
-yarn run-python-tests
-```
diff --git a/tfjs-master/tfjs-converter/cloudbuild.yml b/tfjs-master/tfjs-converter/cloudbuild.yml
deleted file mode 100644
index d3bd56ed5..000000000
--- a/tfjs-master/tfjs-converter/cloudbuild.yml
+++ /dev/null
@@ -1,41 +0,0 @@
-
-steps:
-
-# Install common dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  entrypoint: 'yarn'
-  id: 'yarn-common'
-  args: ['install']
-
-# Install tfjs-converter dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-converter'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-  waitFor: ['yarn-common']
-
-# Create python pips
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'create-pips'
-  entrypoint: 'bash'
-  args:
-    - './tfjs-converter/scripts/create_python_pips.sh'
-  waitFor: ['yarn-common']
-
-# Run python tests.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-converter/python'
-  entrypoint: 'bash'
-  id: 'test-python-pip'
-  args: ['./build-pip-package.sh', '--test', '/tmp/tfjs-pips']
-  waitFor: ['create-pips']
-
-timeout: 1800s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  substitution_option: 'ALLOW_LOOSE'
-  machineType: 'N1_HIGHCPU_8'
diff --git a/tfjs-master/tfjs-converter/cloudbuild_nightly.yml b/tfjs-master/tfjs-converter/cloudbuild_nightly.yml
deleted file mode 100644
index 658b37a1a..000000000
--- a/tfjs-master/tfjs-converter/cloudbuild_nightly.yml
+++ /dev/null
@@ -1,37 +0,0 @@
-steps:
-
-# Install common dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  entrypoint: 'yarn'
-  id: 'yarn-common'
-  args: ['install']
-
-# Install converter dependencies
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-converter'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-  waitFor: ['yarn-common']
-
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'create-pips'
-  entrypoint: 'bash'
-  args:
-    - './tfjs-converter/scripts/create_python_pips.sh'
-  waitFor: ['yarn-common']
-
-# Run python tests.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-converter/python'
-  entrypoint: 'bash'
-  args: ['./build-pip-package.sh', '--test-nightly', '/tmp/tfjs-pips']
-  waitFor: ['create-pips']
-
-timeout: 4800s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/tfjs-converter/demo/control_flow/README.md b/tfjs-master/tfjs-converter/demo/control_flow/README.md
deleted file mode 100644
index f83cb360a..000000000
--- a/tfjs-master/tfjs-converter/demo/control_flow/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-# TensorFlow SavedModel Import Demo
-
-This demo imports a TensorFlow model that is generated by tf.while_loop
-for inference in the browser. The model was pre-converted to TensorFlow.js
-format and hosted on Google Cloud, using the steps in
-the repo's [README.md](../../README.md).
-
-The following commands will start a web server on `localhost:1234` and open
-a browser page with the demo.
-
-The demo allows you to change the conditions of the loop interactively and observe the execution result right in the browser.
-
-```bash
-cd demo # If not already in the demo directory.
-yarn # Installs dependencies.
-yarn control_flow # Starts a web server and opens a page. Also watches for changes.
-```
diff --git a/tfjs-master/tfjs-converter/demo/control_flow/index.html b/tfjs-master/tfjs-converter/demo/control_flow/index.html
deleted file mode 100644
index 0f2ce4410..000000000
--- a/tfjs-master/tfjs-converter/demo/control_flow/index.html
+++ /dev/null
@@ -1,55 +0,0 @@
-<!-- Copyright 2018 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-
-<!doctype html>
-<html>
-
-<head></head>
-<link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.9.0/styles/default.min.css">
-<script src="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.9.0/highlight.min.js"></script>
-<script>hljs.initHighlightingOnLoad();</script>
-<style>
-  body {
-    font-family: "Roboto", "Helvetica", "Arial", sans-serif;
-    margin-top: 50px;
-    margin-left: 50px;
-  }
-</style>
-
-<body>
-  <h1>TensorFlow.js Converter: ControlFlow</h1>
-  <h3>This example uses a Tensorflow model that is generated by tf.while_loop method.</h3>
-
-  <p>
-    You can change the conditions of the loop and press the run button to
-    observe the execution result.
-  </p>
-
-  <pre>
-        <code class="javascript">
-        var output = 0; <br>
-        <label>for (i = </label><input id="init" type="text" value="0" size="3">; <label>i &lt; </label><input id="loop" type="text" value="10" size="5">; i++) {<br>
-          <label>for (j = 0; j &lt; </label><input id="loop2" type="text" value="10" size="5">; j++) {<br>
-            <label>output += </label><input id="inc" type="text" value="1" size="3">;<br>
-        }<br>
-        console.log(output+1);<br>
-    </code></pre>
-  <button id="run">Run</button>
-  <div id="result"></div>
-</body>
-
-<script type="module" src="loop_model.js"></script>
-
-</html>
diff --git a/tfjs-master/tfjs-converter/demo/control_flow/loop_model.js b/tfjs-master/tfjs-converter/demo/control_flow/loop_model.js
deleted file mode 100644
index 9d7b76b73..000000000
--- a/tfjs-master/tfjs-converter/demo/control_flow/loop_model.js
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-const GOOGLE_CLOUD_STORAGE_DIR =
-    'https://storage.googleapis.com/tfjs-models/savedmodel/';
-const MODEL_FILE_URL = 'nested_loop/model.json';
-const OUTPUT_NODE_NAME = 'Add';
-
-export class LoopModel {
-  constructor() {}
-
-  async load() {
-    this.model = await tf.loadGraphModel(
-      GOOGLE_CLOUD_STORAGE_DIR + MODEL_FILE_URL);
-  }
-
-  dispose() {
-    if (this.model) {
-      this.model.dispose();
-    }
-  }
-
-  async predict(init, loop, loop2, inc) {
-    const dict = {
-      'init': tf.scalar(init, 'int32'),
-      'times': tf.scalar(loop, 'int32'),
-      'times2': tf.scalar(loop2, 'int32'),
-      'inc': tf.scalar(inc, 'int32')
-    };
-    return this.model.executeAsync(dict, OUTPUT_NODE_NAME);
-  }
-}
-
-window.onload = async () => {
-  const resultElement = document.getElementById('result');
-
-  resultElement.innerText = 'Loading Control Flow model...';
-
-  const loopModel = new LoopModel();
-  console.time('Loading of model');
-  await loopModel.load();
-  console.timeEnd('Loading of model');
-  resultElement.innerText = 'Model loaded.';
-
-  const runBtn = document.getElementById('run');
-  runBtn.onclick = async () => {
-    const init = document.getElementById('init').value;
-    const loop = document.getElementById('loop').value;
-    const loop2 = document.getElementById('loop2').value;
-    const inc = document.getElementById('inc').value;
-    console.time('prediction');
-    const result = await loopModel.predict(init, loop, loop2, inc);
-    console.timeEnd('prediction');
-
-    resultElement.innerText = "output = " + result.dataSync()[0];
-  };
-};
diff --git a/tfjs-master/tfjs-converter/demo/mobilenet/README.md b/tfjs-master/tfjs-converter/demo/mobilenet/README.md
deleted file mode 100644
index 795f8dd7a..000000000
--- a/tfjs-master/tfjs-converter/demo/mobilenet/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
-# TensorFlow SavedModel Import Demo
-
-This demo imports the
-[MobileNet v2.0](https://www.tensorflow.org/hub/modules/google/imagenet/mobilenet_v2_100_224/classification/1)
-model for inference in the browser. The model was pre-converted to TensorFlow.js
-format and hosted on Google Cloud, using the steps in
-the repo's [README.md](../../README.md).
-
-The following commands will start a web server on `localhost:1234` and open
-a browser page with the demo.
-
-```bash
-cd demo # If not already in the demo directory.
-yarn # Installs dependencies.
-yarn mobilenet # Starts a web server and opens a page. Also watches for changes.
-```
diff --git a/tfjs-master/tfjs-converter/demo/mobilenet/cat.jpg b/tfjs-master/tfjs-converter/demo/mobilenet/cat.jpg
deleted file mode 100644
index fe0ccb13c..000000000
Binary files a/tfjs-master/tfjs-converter/demo/mobilenet/cat.jpg and /dev/null differ
diff --git a/tfjs-master/tfjs-converter/demo/mobilenet/imagenet_classes.js b/tfjs-master/tfjs-converter/demo/mobilenet/imagenet_classes.js
deleted file mode 100644
index c1ab1ff48..000000000
--- a/tfjs-master/tfjs-converter/demo/mobilenet/imagenet_classes.js
+++ /dev/null
@@ -1,1041 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export const IMAGENET_CLASSES = {
-  0: 'background',
-  1: 'tench, Tinca tinca',
-  2: 'goldfish, Carassius auratus',
-  3: 'great white shark, white shark, man-eater,' +
-      ' man-eating shark, Carcharodon carcharias',
-  4: 'tiger shark, Galeocerdo cuvieri',
-  5: 'hammerhead, hammerhead shark',
-  6: 'electric ray, crampfish, numbfish, torpedo',
-  7: 'stingray',
-  8: 'cock',
-  9: 'hen',
-  10: 'ostrich, Struthio camelus',
-  11: 'brambling, Fringilla montifringilla',
-  12: 'goldfinch, Carduelis carduelis',
-  13: 'house finch, linnet, Carpodacus mexicanus',
-  14: 'junco, snowbird',
-  15: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',
-  16: 'robin, American robin, Turdus migratorius',
-  17: 'bulbul',
-  18: 'jay',
-  19: 'magpie',
-  20: 'chickadee',
-  21: 'water ouzel, dipper',
-  22: 'kite',
-  23: 'bald eagle, American eagle, Haliaeetus leucocephalus',
-  24: 'vulture',
-  25: 'great grey owl, great gray owl, Strix nebulosa',
-  26: 'European fire salamander, Salamandra salamandra',
-  27: 'common newt, Triturus vulgaris',
-  28: 'eft',
-  29: 'spotted salamander, Ambystoma maculatum',
-  30: 'axolotl, mud puppy, Ambystoma mexicanum',
-  31: 'bullfrog, Rana catesbeiana',
-  32: 'tree frog, tree-frog',
-  33: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',
-  34: 'loggerhead, loggerhead turtle, Caretta caretta',
-  35: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',
-  36: 'mud turtle',
-  37: 'terrapin',
-  38: 'box turtle, box tortoise',
-  39: 'banded gecko',
-  40: 'common iguana, iguana, Iguana iguana',
-  41: 'American chameleon, anole, Anolis carolinensis',
-  42: 'whiptail, whiptail lizard',
-  43: 'agama',
-  44: 'frilled lizard, Chlamydosaurus kingi',
-  45: 'alligator lizard',
-  46: 'Gila monster, Heloderma suspectum',
-  47: 'green lizard, Lacerta viridis',
-  48: 'African chameleon, Chamaeleo chamaeleon',
-  49: 'Komodo dragon, Komodo lizard, dragon lizard,' +
-      ' giant lizard, Varanus komodoensis',
-  50: 'African crocodile, Nile crocodile, Crocodylus niloticus',
-  51: 'American alligator, Alligator mississipiensis',
-  52: 'triceratops',
-  53: 'thunder snake, worm snake, Carphophis amoenus',
-  54: 'ringneck snake, ring-necked snake, ring snake',
-  55: 'hognose snake, puff adder, sand viper',
-  56: 'green snake, grass snake',
-  57: 'king snake, kingsnake',
-  58: 'garter snake, grass snake',
-  59: 'water snake',
-  60: 'vine snake',
-  61: 'night snake, Hypsiglena torquata',
-  62: 'boa constrictor, Constrictor constrictor',
-  63: 'rock python, rock snake, Python sebae',
-  64: 'Indian cobra, Naja naja',
-  65: 'green mamba',
-  66: 'sea snake',
-  67: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',
-  68: 'diamondback, diamondback rattlesnake, Crotalus adamanteus',
-  69: 'sidewinder, horned rattlesnake, Crotalus cerastes',
-  70: 'trilobite',
-  71: 'harvestman, daddy longlegs, Phalangium opilio',
-  72: 'scorpion',
-  73: 'black and gold garden spider, Argiope aurantia',
-  74: 'barn spider, Araneus cavaticus',
-  75: 'garden spider, Aranea diademata',
-  76: 'black widow, Latrodectus mactans',
-  77: 'tarantula',
-  78: 'wolf spider, hunting spider',
-  79: 'tick',
-  80: 'centipede',
-  81: 'black grouse',
-  82: 'ptarmigan',
-  83: 'ruffed grouse, partridge, Bonasa umbellus',
-  84: 'prairie chicken, prairie grouse, prairie fowl',
-  85: 'peacock',
-  86: 'quail',
-  87: 'partridge',
-  88: 'African grey, African gray, Psittacus erithacus',
-  89: 'macaw',
-  90: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
-  91: 'lorikeet',
-  92: 'coucal',
-  93: 'bee eater',
-  94: 'hornbill',
-  95: 'hummingbird',
-  96: 'jacamar',
-  97: 'toucan',
-  98: 'drake',
-  99: 'red-breasted merganser, Mergus serrator',
-  100: 'goose',
-  101: 'black swan, Cygnus atratus',
-  102: 'tusker',
-  103: 'echidna, spiny anteater, anteater',
-  104: 'platypus, duckbill, duckbilled platypus, duck-billed platypus,' +
-      ' Ornithorhynchus anatinus',
-  105: 'wallaby, brush kangaroo',
-  106: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',
-  107: 'wombat',
-  108: 'jelly fish',
-  109: 'sea anemone, anemone',
-  110: 'brain coral',
-  111: 'flatworm, platyhelminth',
-  112: 'nematode, nematode worm, roundworm',
-  113: 'conch',
-  114: 'snail',
-  115: 'slug',
-  116: 'sea slug, nudibranch',
-  117: 'chiton, coat-of-mail shell, sea cradle, polyplacophore',
-  118: 'chambered nautilus, pearly nautilus, nautilus',
-  119: 'Dungeness crab, Cancer magister',
-  120: 'rock crab, Cancer irroratus',
-  121: 'fiddler crab',
-  122: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab,' +
-      ' Paralithodes camtschatica',
-  123: 'American lobster, Northern lobster, Maine lobster, Homarus americanus',
-  124: 'spiny lobster, langouste, rock lobster, crawfish, crayfish,' +
-      ' sea crawfish',
-  125: 'crayfish, crawfish, crawdad, crawdaddy',
-  126: 'hermit crab',
-  127: 'isopod',
-  128: 'white stork, Ciconia ciconia',
-  129: 'black stork, Ciconia nigra',
-  130: 'spoonbill',
-  131: 'flamingo',
-  132: 'little blue heron, Egretta caerulea',
-  133: 'American egret, great white heron, Egretta albus',
-  134: 'bittern',
-  135: 'crane',
-  136: 'limpkin, Aramus pictus',
-  137: 'European gallinule, Porphyrio porphyrio',
-  138: 'American coot, marsh hen, mud hen, water hen, Fulica americana',
-  139: 'bustard',
-  140: 'ruddy turnstone, Arenaria interpres',
-  141: 'red-backed sandpiper, dunlin, Erolia alpina',
-  142: 'redshank, Tringa totanus',
-  143: 'dowitcher',
-  144: 'oystercatcher, oyster catcher',
-  145: 'pelican',
-  146: 'king penguin, Aptenodytes patagonica',
-  147: 'albatross, mollymawk',
-  148: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus,' +
-      ' Eschrichtius robustus',
-  149: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',
-  150: 'dugong, Dugong dugon',
-  151: 'sea lion',
-  152: 'Chihuahua',
-  153: 'Japanese spaniel',
-  154: 'Maltese dog, Maltese terrier, Maltese',
-  155: 'Pekinese, Pekingese, Peke',
-  156: 'Shih-Tzu',
-  157: 'Blenheim spaniel',
-  158: 'papillon',
-  159: 'toy terrier',
-  160: 'Rhodesian ridgeback',
-  161: 'Afghan hound, Afghan',
-  162: 'basset, basset hound',
-  163: 'beagle',
-  164: 'bloodhound, sleuthhound',
-  165: 'bluetick',
-  166: 'black-and-tan coonhound',
-  167: 'Walker hound, Walker foxhound',
-  168: 'English foxhound',
-  169: 'redbone',
-  170: 'borzoi, Russian wolfhound',
-  171: 'Irish wolfhound',
-  172: 'Italian greyhound',
-  173: 'whippet',
-  174: 'Ibizan hound, Ibizan Podenco',
-  175: 'Norwegian elkhound, elkhound',
-  176: 'otterhound, otter hound',
-  177: 'Saluki, gazelle hound',
-  178: 'Scottish deerhound, deerhound',
-  179: 'Weimaraner',
-  180: 'Staffordshire bullterrier, Staffordshire bull terrier',
-  181: 'American Staffordshire terrier, Staffordshire terrier,' +
-      ' American pit bull terrier, pit bull terrier',
-  182: 'Bedlington terrier',
-  183: 'Border terrier',
-  184: 'Kerry blue terrier',
-  185: 'Irish terrier',
-  186: 'Norfolk terrier',
-  187: 'Norwich terrier',
-  188: 'Yorkshire terrier',
-  189: 'wire-haired fox terrier',
-  190: 'Lakeland terrier',
-  191: 'Sealyham terrier, Sealyham',
-  192: 'Airedale, Airedale terrier',
-  193: 'cairn, cairn terrier',
-  194: 'Australian terrier',
-  195: 'Dandie Dinmont, Dandie Dinmont terrier',
-  196: 'Boston bull, Boston terrier',
-  197: 'miniature schnauzer',
-  198: 'giant schnauzer',
-  199: 'standard schnauzer',
-  200: 'Scotch terrier, Scottish terrier, Scottie',
-  201: 'Tibetan terrier, chrysanthemum dog',
-  202: 'silky terrier, Sydney silky',
-  203: 'soft-coated wheaten terrier',
-  204: 'West Highland white terrier',
-  205: 'Lhasa, Lhasa apso',
-  206: 'flat-coated retriever',
-  207: 'curly-coated retriever',
-  208: 'golden retriever',
-  209: 'Labrador retriever',
-  210: 'Chesapeake Bay retriever',
-  211: 'German short-haired pointer',
-  212: 'vizsla, Hungarian pointer',
-  213: 'English setter',
-  214: 'Irish setter, red setter',
-  215: 'Gordon setter',
-  216: 'Brittany spaniel',
-  217: 'clumber, clumber spaniel',
-  218: 'English springer, English springer spaniel',
-  219: 'Welsh springer spaniel',
-  220: 'cocker spaniel, English cocker spaniel, cocker',
-  221: 'Sussex spaniel',
-  222: 'Irish water spaniel',
-  223: 'kuvasz',
-  224: 'schipperke',
-  225: 'groenendael',
-  226: 'malinois',
-  227: 'briard',
-  228: 'kelpie',
-  229: 'komondor',
-  230: 'Old English sheepdog, bobtail',
-  231: 'Shetland sheepdog, Shetland sheep dog, Shetland',
-  232: 'collie',
-  233: 'Border collie',
-  234: 'Bouvier des Flandres, Bouviers des Flandres',
-  235: 'Rottweiler',
-  236: 'German shepherd, German shepherd dog, German police dog, alsatian',
-  237: 'Doberman, Doberman pinscher',
-  238: 'miniature pinscher',
-  239: 'Greater Swiss Mountain dog',
-  240: 'Bernese mountain dog',
-  241: 'Appenzeller',
-  242: 'EntleBucher',
-  243: 'boxer',
-  244: 'bull mastiff',
-  245: 'Tibetan mastiff',
-  246: 'French bulldog',
-  247: 'Great Dane',
-  248: 'Saint Bernard, St Bernard',
-  249: 'Eskimo dog, husky',
-  250: 'malamute, malemute, Alaskan malamute',
-  251: 'Siberian husky',
-  252: 'dalmatian, coach dog, carriage dog',
-  253: 'affenpinscher, monkey pinscher, monkey dog',
-  254: 'basenji',
-  255: 'pug, pug-dog',
-  256: 'Leonberg',
-  257: 'Newfoundland, Newfoundland dog',
-  258: 'Great Pyrenees',
-  259: 'Samoyed, Samoyede',
-  260: 'Pomeranian',
-  261: 'chow, chow chow',
-  262: 'keeshond',
-  263: 'Brabancon griffon',
-  264: 'Pembroke, Pembroke Welsh corgi',
-  265: 'Cardigan, Cardigan Welsh corgi',
-  266: 'toy poodle',
-  267: 'miniature poodle',
-  268: 'standard poodle',
-  269: 'Mexican hairless',
-  270: 'timber wolf, grey wolf, gray wolf, Canis lupus',
-  271: 'white wolf, Arctic wolf, Canis lupus tundrarum',
-  272: 'red wolf, maned wolf, Canis rufus, Canis niger',
-  273: 'coyote, prairie wolf, brush wolf, Canis latrans',
-  274: 'dingo, warrigal, warragal, Canis dingo',
-  275: 'dhole, Cuon alpinus',
-  276: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',
-  277: 'hyena, hyaena',
-  278: 'red fox, Vulpes vulpes',
-  279: 'kit fox, Vulpes macrotis',
-  280: 'Arctic fox, white fox, Alopex lagopus',
-  281: 'grey fox, gray fox, Urocyon cinereoargenteus',
-  282: 'tabby, tabby cat',
-  283: 'tiger cat',
-  284: 'Persian cat',
-  285: 'Siamese cat, Siamese',
-  286: 'Egyptian cat',
-  287: 'cougar, puma, catamount, mountain lion, painter, panther,' +
-      ' Felis concolor',
-  288: 'lynx, catamount',
-  289: 'leopard, Panthera pardus',
-  290: 'snow leopard, ounce, Panthera uncia',
-  291: 'jaguar, panther, Panthera onca, Felis onca',
-  292: 'lion, king of beasts, Panthera leo',
-  293: 'tiger, Panthera tigris',
-  294: 'cheetah, chetah, Acinonyx jubatus',
-  295: 'brown bear, bruin, Ursus arctos',
-  296: 'American black bear, black bear, Ursus americanus,' +
-      ' Euarctos americanus',
-  297: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',
-  298: 'sloth bear, Melursus ursinus, Ursus ursinus',
-  299: 'mongoose',
-  300: 'meerkat, mierkat',
-  301: 'tiger beetle',
-  302: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',
-  303: 'ground beetle, carabid beetle',
-  304: 'long-horned beetle, longicorn, longicorn beetle',
-  305: 'leaf beetle, chrysomelid',
-  306: 'dung beetle',
-  307: 'rhinoceros beetle',
-  308: 'weevil',
-  309: 'fly',
-  310: 'bee',
-  311: 'ant, emmet, pismire',
-  312: 'grasshopper, hopper',
-  313: 'cricket',
-  314: 'walking stick, walkingstick, stick insect',
-  315: 'cockroach, roach',
-  316: 'mantis, mantid',
-  317: 'cicada, cicala',
-  318: 'leafhopper',
-  319: 'lacewing, lacewing fly',
-  320: 'dragonfly, darning needle, devil\'s darning needle, sewing' +
-      'needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk',
-  321: 'damselfly',
-  322: 'admiral',
-  323: 'ringlet, ringlet butterfly',
-  324: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
-  325: 'cabbage butterfly',
-  326: 'sulphur butterfly, sulfur butterfly',
-  327: 'lycaenid, lycaenid butterfly',
-  328: 'starfish, sea star',
-  329: 'sea urchin',
-  330: 'sea cucumber, holothurian',
-  331: 'wood rabbit, cottontail, cottontail rabbit',
-  332: 'hare',
-  333: 'Angora, Angora rabbit',
-  334: 'hamster',
-  335: 'porcupine, hedgehog',
-  336: 'fox squirrel, eastern fox squirrel, Sciurus niger',
-  337: 'marmot',
-  338: 'beaver',
-  339: 'guinea pig, Cavia cobaya',
-  340: 'sorrel',
-  341: 'zebra',
-  342: 'hog, pig, grunter, squealer, Sus scrofa',
-  343: 'wild boar, boar, Sus scrofa',
-  344: 'warthog',
-  345: 'hippopotamus, hippo, river horse, Hippopotamus amphibius',
-  346: 'ox',
-  347: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',
-  348: 'bison',
-  349: 'ram, tup',
-  350: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky ' +
-      'Mountain sheep, Ovis canadensis',
-  351: 'ibex, Capra ibex',
-  352: 'hartebeest',
-  353: 'impala, Aepyceros melampus',
-  354: 'gazelle',
-  355: 'Arabian camel, dromedary, Camelus dromedarius',
-  356: 'llama',
-  357: 'weasel',
-  358: 'mink',
-  359: 'polecat, fitch, foulmart, foumart, Mustela putorius',
-  360: 'black-footed ferret, ferret, Mustela nigripes',
-  361: 'otter',
-  362: 'skunk, polecat, wood pussy',
-  363: 'badger',
-  364: 'armadillo',
-  365: 'three-toed sloth, ai, Bradypus tridactylus',
-  366: 'orangutan, orang, orangutang, Pongo pygmaeus',
-  367: 'gorilla, Gorilla gorilla',
-  368: 'chimpanzee, chimp, Pan troglodytes',
-  369: 'gibbon, Hylobates lar',
-  370: 'siamang, Hylobates syndactylus, Symphalangus syndactylus',
-  371: 'guenon, guenon monkey',
-  372: 'patas, hussar monkey, Erythrocebus patas',
-  373: 'baboon',
-  374: 'macaque',
-  375: 'langur',
-  376: 'colobus, colobus monkey',
-  377: 'proboscis monkey, Nasalis larvatus',
-  378: 'marmoset',
-  379: 'capuchin, ringtail, Cebus capucinus',
-  380: 'howler monkey, howler',
-  381: 'titi, titi monkey',
-  382: 'spider monkey, Ateles geoffroyi',
-  383: 'squirrel monkey, Saimiri sciureus',
-  384: 'Madagascar cat, ring-tailed lemur, Lemur catta',
-  385: 'indri, indris, Indri indri, Indri brevicaudatus',
-  386: 'Indian elephant, Elephas maximus',
-  387: 'African elephant, Loxodonta africana',
-  388: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',
-  389: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',
-  390: 'barracouta, snoek',
-  391: 'eel',
-  392: 'coho, cohoe, coho salmon, blue jack, silver salmon, ' +
-      'Oncorhynchus kisutch',
-  393: 'rock beauty, Holocanthus tricolor',
-  394: 'anemone fish',
-  395: 'sturgeon',
-  396: 'gar, garfish, garpike, billfish, Lepisosteus osseus',
-  397: 'lionfish',
-  398: 'puffer, pufferfish, blowfish, globefish',
-  399: 'abacus',
-  400: 'abaya',
-  401: 'academic gown, academic robe, judge\'s robe',
-  402: 'accordion, piano accordion, squeeze box',
-  403: 'acoustic guitar',
-  404: 'aircraft carrier, carrier, flattop, attack aircraft carrier',
-  405: 'airliner',
-  406: 'airship, dirigible',
-  407: 'altar',
-  408: 'ambulance',
-  409: 'amphibian, amphibious vehicle',
-  410: 'analog clock',
-  411: 'apiary, bee house',
-  412: 'apron',
-  413: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin,' +
-      ' ashbin, dustbin, trash barrel, trash bin',
-  414: 'assault rifle, assault gun',
-  415: 'backpack, back pack, knapsack, packsack, rucksack, haversack',
-  416: 'bakery, bakeshop, bakehouse',
-  417: 'balance beam, beam',
-  418: 'balloon',
-  419: 'ballpoint, ballpoint pen, ballpen, Biro',
-  420: 'Band Aid',
-  421: 'banjo',
-  422: 'bannister, banister, balustrade, balusters, handrail',
-  423: 'barbell',
-  424: 'barber chair',
-  425: 'barbershop',
-  426: 'barn',
-  427: 'barometer',
-  428: 'barrel, cask',
-  429: 'barrow, garden cart, lawn cart, wheelbarrow',
-  430: 'baseball',
-  431: 'basketball',
-  432: 'bassinet',
-  433: 'bassoon',
-  434: 'bathing cap, swimming cap',
-  435: 'bath towel',
-  436: 'bathtub, bathing tub, bath, tub',
-  437: 'beach wagon, station wagon, wagon, estate car, beach waggon,' +
-      ' station waggon, waggon',
-  438: 'beacon, lighthouse, beacon light, pharos',
-  439: 'beaker',
-  440: 'bearskin, busby, shako',
-  441: 'beer bottle',
-  442: 'beer glass',
-  443: 'bell cote, bell cot',
-  444: 'bib',
-  445: 'bicycle-built-for-two, tandem bicycle, tandem',
-  446: 'bikini, two-piece',
-  447: 'binder, ring-binder',
-  448: 'binoculars, field glasses, opera glasses',
-  449: 'birdhouse',
-  450: 'boathouse',
-  451: 'bobsled, bobsleigh, bob',
-  452: 'bolo tie, bolo, bola tie, bola',
-  453: 'bonnet, poke bonnet',
-  454: 'bookcase',
-  455: 'bookshop, bookstore, bookstall',
-  456: 'bottlecap',
-  457: 'bow',
-  458: 'bow tie, bow-tie, bowtie',
-  459: 'brass, memorial tablet, plaque',
-  460: 'brassiere, bra, bandeau',
-  461: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',
-  462: 'breastplate, aegis, egis',
-  463: 'broom',
-  464: 'bucket, pail',
-  465: 'buckle',
-  466: 'bulletproof vest',
-  467: 'bullet train, bullet',
-  468: 'butcher shop, meat market',
-  469: 'cab, hack, taxi, taxicab',
-  470: 'caldron, cauldron',
-  471: 'candle, taper, wax light',
-  472: 'cannon',
-  473: 'canoe',
-  474: 'can opener, tin opener',
-  475: 'cardigan',
-  476: 'car mirror',
-  477: 'carousel, carrousel, merry-go-round, roundabout, whirligig',
-  478: 'carpenter\'s kit, tool kit',
-  479: 'carton',
-  480: 'car wheel',
-  481: 'cash machine, cash dispenser, automated teller machine, automatic' +
-      ' teller machine, automated teller, automatic teller, ATM',
-  482: 'cassette',
-  483: 'cassette player',
-  484: 'castle',
-  485: 'catamaran',
-  486: 'CD player',
-  487: 'cello, violoncello',
-  488: 'cellular telephone, cellular phone, cellphone, cell, mobile phone',
-  489: 'chain',
-  490: 'chainlink fence',
-  491: 'chain mail, ring mail, mail, chain armor, chain armour,' +
-      ' ring armor, ring armour',
-  492: 'chain saw, chainsaw',
-  493: 'chest',
-  494: 'chiffonier, commode',
-  495: 'chime, bell, gong',
-  496: 'china cabinet, china closet',
-  497: 'Christmas stocking',
-  498: 'church, church building',
-  499: 'cinema, movie theater, movie theatre, movie house, picture palace',
-  500: 'cleaver, meat cleaver, chopper',
-  501: 'cliff dwelling',
-  502: 'cloak',
-  503: 'clog, geta, patten, sabot',
-  504: 'cocktail shaker',
-  505: 'coffee mug',
-  506: 'coffeepot',
-  507: 'coil, spiral, volute, whorl, helix',
-  508: 'combination lock',
-  509: 'computer keyboard, keypad',
-  510: 'confectionery, confectionary, candy store',
-  511: 'container ship, containership, container vessel',
-  512: 'convertible',
-  513: 'corkscrew, bottle screw',
-  514: 'cornet, horn, trumpet, trump',
-  515: 'cowboy boot',
-  516: 'cowboy hat, ten-gallon hat',
-  517: 'cradle',
-  518: 'crane',
-  519: 'crash helmet',
-  520: 'crate',
-  521: 'crib, cot',
-  522: 'Crock Pot',
-  523: 'croquet ball',
-  524: 'crutch',
-  525: 'cuirass',
-  526: 'dam, dike, dyke',
-  527: 'desk',
-  528: 'desktop computer',
-  529: 'dial telephone, dial phone',
-  530: 'diaper, nappy, napkin',
-  531: 'digital clock',
-  532: 'digital watch',
-  533: 'dining table, board',
-  534: 'dishrag, dishcloth',
-  535: 'dishwasher, dish washer, dishwashing machine',
-  536: 'disk brake, disc brake',
-  537: 'dock, dockage, docking facility',
-  538: 'dogsled, dog sled, dog sleigh',
-  539: 'dome',
-  540: 'doormat, welcome mat',
-  541: 'drilling platform, offshore rig',
-  542: 'drum, membranophone, tympan',
-  543: 'drumstick',
-  544: 'dumbbell',
-  545: 'Dutch oven',
-  546: 'electric fan, blower',
-  547: 'electric guitar',
-  548: 'electric locomotive',
-  549: 'entertainment center',
-  550: 'envelope',
-  551: 'espresso maker',
-  552: 'face powder',
-  553: 'feather boa, boa',
-  554: 'file, file cabinet, filing cabinet',
-  555: 'fireboat',
-  556: 'fire engine, fire truck',
-  557: 'fire screen, fireguard',
-  558: 'flagpole, flagstaff',
-  559: 'flute, transverse flute',
-  560: 'folding chair',
-  561: 'football helmet',
-  562: 'forklift',
-  563: 'fountain',
-  564: 'fountain pen',
-  565: 'four-poster',
-  566: 'freight car',
-  567: 'French horn, horn',
-  568: 'frying pan, frypan, skillet',
-  569: 'fur coat',
-  570: 'garbage truck, dustcart',
-  571: 'gasmask, respirator, gas helmet',
-  572: 'gas pump, gasoline pump, petrol pump, island dispenser',
-  573: 'goblet',
-  574: 'go-kart',
-  575: 'golf ball',
-  576: 'golfcart, golf cart',
-  577: 'gondola',
-  578: 'gong, tam-tam',
-  579: 'gown',
-  580: 'grand piano, grand',
-  581: 'greenhouse, nursery, glasshouse',
-  582: 'grille, radiator grille',
-  583: 'grocery store, grocery, food market, market',
-  584: 'guillotine',
-  585: 'hair slide',
-  586: 'hair spray',
-  587: 'half track',
-  588: 'hammer',
-  589: 'hamper',
-  590: 'hand blower, blow dryer, blow drier, hair dryer, hair drier',
-  591: 'hand-held computer, hand-held microcomputer',
-  592: 'handkerchief, hankie, hanky, hankey',
-  593: 'hard disc, hard disk, fixed disk',
-  594: 'harmonica, mouth organ, harp, mouth harp',
-  595: 'harp',
-  596: 'harvester, reaper',
-  597: 'hatchet',
-  598: 'holster',
-  599: 'home theater, home theatre',
-  600: 'honeycomb',
-  601: 'hook, claw',
-  602: 'hoopskirt, crinoline',
-  603: 'horizontal bar, high bar',
-  604: 'horse cart, horse-cart',
-  605: 'hourglass',
-  606: 'iPod',
-  607: 'iron, smoothing iron',
-  608: 'jack-o\'-lantern',
-  609: 'jean, blue jean, denim',
-  610: 'jeep, landrover',
-  611: 'jersey, T-shirt, tee shirt',
-  612: 'jigsaw puzzle',
-  613: 'jinrikisha, ricksha, rickshaw',
-  614: 'joystick',
-  615: 'kimono',
-  616: 'knee pad',
-  617: 'knot',
-  618: 'lab coat, laboratory coat',
-  619: 'ladle',
-  620: 'lampshade, lamp shade',
-  621: 'laptop, laptop computer',
-  622: 'lawn mower, mower',
-  623: 'lens cap, lens cover',
-  624: 'letter opener, paper knife, paperknife',
-  625: 'library',
-  626: 'lifeboat',
-  627: 'lighter, light, igniter, ignitor',
-  628: 'limousine, limo',
-  629: 'liner, ocean liner',
-  630: 'lipstick, lip rouge',
-  631: 'Loafer',
-  632: 'lotion',
-  633: 'loudspeaker, speaker, speaker unit, loudspeaker system, ' +
-      'speaker system',
-  634: 'loupe, jeweler\'s loupe',
-  635: 'lumbermill, sawmill',
-  636: 'magnetic compass',
-  637: 'mailbag, postbag',
-  638: 'mailbox, letter box',
-  639: 'maillot',
-  640: 'maillot, tank suit',
-  641: 'manhole cover',
-  642: 'maraca',
-  643: 'marimba, xylophone',
-  644: 'mask',
-  645: 'matchstick',
-  646: 'maypole',
-  647: 'maze, labyrinth',
-  648: 'measuring cup',
-  649: 'medicine chest, medicine cabinet',
-  650: 'megalith, megalithic structure',
-  651: 'microphone, mike',
-  652: 'microwave, microwave oven',
-  653: 'military uniform',
-  654: 'milk can',
-  655: 'minibus',
-  656: 'miniskirt, mini',
-  657: 'minivan',
-  658: 'missile',
-  659: 'mitten',
-  660: 'mixing bowl',
-  661: 'mobile home, manufactured home',
-  662: 'Model T',
-  663: 'modem',
-  664: 'monastery',
-  665: 'monitor',
-  666: 'moped',
-  667: 'mortar',
-  668: 'mortarboard',
-  669: 'mosque',
-  670: 'mosquito net',
-  671: 'motor scooter, scooter',
-  672: 'mountain bike, all-terrain bike, off-roader',
-  673: 'mountain tent',
-  674: 'mouse, computer mouse',
-  675: 'mousetrap',
-  676: 'moving van',
-  677: 'muzzle',
-  678: 'nail',
-  679: 'neck brace',
-  680: 'necklace',
-  681: 'nipple',
-  682: 'notebook, notebook computer',
-  683: 'obelisk',
-  684: 'oboe, hautboy, hautbois',
-  685: 'ocarina, sweet potato',
-  686: 'odometer, hodometer, mileometer, milometer',
-  687: 'oil filter',
-  688: 'organ, pipe organ',
-  689: 'oscilloscope, scope, cathode-ray oscilloscope, CRO',
-  690: 'overskirt',
-  691: 'oxcart',
-  692: 'oxygen mask',
-  693: 'packet',
-  694: 'paddle, boat paddle',
-  695: 'paddlewheel, paddle wheel',
-  696: 'padlock',
-  697: 'paintbrush',
-  698: 'pajama, pyjama, pj\'s, jammies',
-  699: 'palace',
-  700: 'panpipe, pandean pipe, syrinx',
-  701: 'paper towel',
-  702: 'parachute, chute',
-  703: 'parallel bars, bars',
-  704: 'park bench',
-  705: 'parking meter',
-  706: 'passenger car, coach, carriage',
-  707: 'patio, terrace',
-  708: 'pay-phone, pay-station',
-  709: 'pedestal, plinth, footstall',
-  710: 'pencil box, pencil case',
-  711: 'pencil sharpener',
-  712: 'perfume, essence',
-  713: 'Petri dish',
-  714: 'photocopier',
-  715: 'pick, plectrum, plectron',
-  716: 'pickelhaube',
-  717: 'picket fence, paling',
-  718: 'pickup, pickup truck',
-  719: 'pier',
-  720: 'piggy bank, penny bank',
-  721: 'pill bottle',
-  722: 'pillow',
-  723: 'ping-pong ball',
-  724: 'pinwheel',
-  725: 'pirate, pirate ship',
-  726: 'pitcher, ewer',
-  727: 'plane, carpenter\'s plane, woodworking plane',
-  728: 'planetarium',
-  729: 'plastic bag',
-  730: 'plate rack',
-  731: 'plow, plough',
-  732: 'plunger, plumber\'s helper',
-  733: 'Polaroid camera, Polaroid Land camera',
-  734: 'pole',
-  735: 'police van, police wagon, paddy wagon, patrol wagon, wagon,' +
-      ' black Maria',
-  736: 'poncho',
-  737: 'pool table, billiard table, snooker table',
-  738: 'pop bottle, soda bottle',
-  739: 'pot, flowerpot',
-  740: 'potter\'s wheel',
-  741: 'power drill',
-  742: 'prayer rug, prayer mat',
-  743: 'printer',
-  744: 'prison, prison house',
-  745: 'projectile, missile',
-  746: 'projector',
-  747: 'puck, hockey puck',
-  748: 'punching bag, punch bag, punching ball, punchball',
-  749: 'purse',
-  750: 'quill, quill pen',
-  751: 'quilt, comforter, comfort, puff',
-  752: 'racer, race car, racing car',
-  753: 'racket, racquet',
-  754: 'radiator',
-  755: 'radio, wireless',
-  756: 'radio telescope, radio reflector',
-  757: 'rain barrel',
-  758: 'recreational vehicle, RV, R.V.',
-  759: 'reel',
-  760: 'reflex camera',
-  761: 'refrigerator, icebox',
-  762: 'remote control, remote',
-  763: 'restaurant, eating house, eating place, eatery',
-  764: 'revolver, six-gun, six-shooter',
-  765: 'rifle',
-  766: 'rocking chair, rocker',
-  767: 'rotisserie',
-  768: 'rubber eraser, rubber, pencil eraser',
-  769: 'rugby ball',
-  770: 'rule, ruler',
-  771: 'running shoe',
-  772: 'safe',
-  773: 'safety pin',
-  774: 'saltshaker, salt shaker',
-  775: 'sandal',
-  776: 'sarong',
-  777: 'sax, saxophone',
-  778: 'scabbard',
-  779: 'scale, weighing machine',
-  780: 'school bus',
-  781: 'schooner',
-  782: 'scoreboard',
-  783: 'screen, CRT screen',
-  784: 'screw',
-  785: 'screwdriver',
-  786: 'seat belt, seatbelt',
-  787: 'sewing machine',
-  788: 'shield, buckler',
-  789: 'shoe shop, shoe-shop, shoe store',
-  790: 'shoji',
-  791: 'shopping basket',
-  792: 'shopping cart',
-  793: 'shovel',
-  794: 'shower cap',
-  795: 'shower curtain',
-  796: 'ski',
-  797: 'ski mask',
-  798: 'sleeping bag',
-  799: 'slide rule, slipstick',
-  800: 'sliding door',
-  801: 'slot, one-armed bandit',
-  802: 'snorkel',
-  803: 'snowmobile',
-  804: 'snowplow, snowplough',
-  805: 'soap dispenser',
-  806: 'soccer ball',
-  807: 'sock',
-  808: 'solar dish, solar collector, solar furnace',
-  809: 'sombrero',
-  810: 'soup bowl',
-  811: 'space bar',
-  812: 'space heater',
-  813: 'space shuttle',
-  814: 'spatula',
-  815: 'speedboat',
-  816: 'spider web, spider\'s web',
-  817: 'spindle',
-  818: 'sports car, sport car',
-  819: 'spotlight, spot',
-  820: 'stage',
-  821: 'steam locomotive',
-  822: 'steel arch bridge',
-  823: 'steel drum',
-  824: 'stethoscope',
-  825: 'stole',
-  826: 'stone wall',
-  827: 'stopwatch, stop watch',
-  828: 'stove',
-  829: 'strainer',
-  830: 'streetcar, tram, tramcar, trolley, trolley car',
-  831: 'stretcher',
-  832: 'studio couch, day bed',
-  833: 'stupa, tope',
-  834: 'submarine, pigboat, sub, U-boat',
-  835: 'suit, suit of clothes',
-  836: 'sundial',
-  837: 'sunglass',
-  838: 'sunglasses, dark glasses, shades',
-  839: 'sunscreen, sunblock, sun blocker',
-  840: 'suspension bridge',
-  841: 'swab, swob, mop',
-  842: 'sweatshirt',
-  843: 'swimming trunks, bathing trunks',
-  844: 'swing',
-  845: 'switch, electric switch, electrical switch',
-  846: 'syringe',
-  847: 'table lamp',
-  848: 'tank, army tank, armored combat vehicle, armoured combat vehicle',
-  849: 'tape player',
-  850: 'teapot',
-  851: 'teddy, teddy bear',
-  852: 'television, television system',
-  853: 'tennis ball',
-  854: 'thatch, thatched roof',
-  855: 'theater curtain, theatre curtain',
-  856: 'thimble',
-  857: 'thresher, thrasher, threshing machine',
-  858: 'throne',
-  859: 'tile roof',
-  860: 'toaster',
-  861: 'tobacco shop, tobacconist shop, tobacconist',
-  862: 'toilet seat',
-  863: 'torch',
-  864: 'totem pole',
-  865: 'tow truck, tow car, wrecker',
-  866: 'toyshop',
-  867: 'tractor',
-  868: 'trailer truck, tractor trailer, trucking rig, rig,' +
-      ' articulated lorry, semi',
-  869: 'tray',
-  870: 'trench coat',
-  871: 'tricycle, trike, velocipede',
-  872: 'trimaran',
-  873: 'tripod',
-  874: 'triumphal arch',
-  875: 'trolleybus, trolley coach, trackless trolley',
-  876: 'trombone',
-  877: 'tub, vat',
-  878: 'turnstile',
-  879: 'typewriter keyboard',
-  880: 'umbrella',
-  881: 'unicycle, monocycle',
-  882: 'upright, upright piano',
-  883: 'vacuum, vacuum cleaner',
-  884: 'vase',
-  885: 'vault',
-  886: 'velvet',
-  887: 'vending machine',
-  888: 'vestment',
-  889: 'viaduct',
-  890: 'violin, fiddle',
-  891: 'volleyball',
-  892: 'waffle iron',
-  893: 'wall clock',
-  894: 'wallet, billfold, notecase, pocketbook',
-  895: 'wardrobe, closet, press',
-  896: 'warplane, military plane',
-  897: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',
-  898: 'washer, automatic washer, washing machine',
-  899: 'water bottle',
-  900: 'water jug',
-  901: 'water tower',
-  902: 'whiskey jug',
-  903: 'whistle',
-  904: 'wig',
-  905: 'window screen',
-  906: 'window shade',
-  907: 'Windsor tie',
-  908: 'wine bottle',
-  909: 'wing',
-  910: 'wok',
-  911: 'wooden spoon',
-  912: 'wool, woolen, woollen',
-  913: 'worm fence, snake fence, snake-rail fence, Virginia fence',
-  914: 'wreck',
-  915: 'yawl',
-  916: 'yurt',
-  917: 'web site, website, internet site, site',
-  918: 'comic book',
-  919: 'crossword puzzle, crossword',
-  920: 'street sign',
-  921: 'traffic light, traffic signal, stoplight',
-  922: 'book jacket, dust cover, dust jacket, dust wrapper',
-  923: 'menu',
-  924: 'plate',
-  925: 'guacamole',
-  926: 'consomme',
-  927: 'hot pot, hotpot',
-  928: 'trifle',
-  929: 'ice cream, icecream',
-  930: 'ice lolly, lolly, lollipop, popsicle',
-  931: 'French loaf',
-  932: 'bagel, beigel',
-  933: 'pretzel',
-  934: 'cheeseburger',
-  935: 'hotdog, hot dog, red hot',
-  936: 'mashed potato',
-  937: 'head cabbage',
-  938: 'broccoli',
-  939: 'cauliflower',
-  940: 'zucchini, courgette',
-  941: 'spaghetti squash',
-  942: 'acorn squash',
-  943: 'butternut squash',
-  944: 'cucumber, cuke',
-  945: 'artichoke, globe artichoke',
-  946: 'bell pepper',
-  947: 'cardoon',
-  948: 'mushroom',
-  949: 'Granny Smith',
-  950: 'strawberry',
-  951: 'orange',
-  952: 'lemon',
-  953: 'fig',
-  954: 'pineapple, ananas',
-  955: 'banana',
-  956: 'jackfruit, jak, jack',
-  957: 'custard apple',
-  958: 'pomegranate',
-  959: 'hay',
-  960: 'carbonara',
-  961: 'chocolate sauce, chocolate syrup',
-  962: 'dough',
-  963: 'meat loaf, meatloaf',
-  964: 'pizza, pizza pie',
-  965: 'potpie',
-  966: 'burrito',
-  967: 'red wine',
-  968: 'espresso',
-  969: 'cup',
-  970: 'eggnog',
-  971: 'alp',
-  972: 'bubble',
-  973: 'cliff, drop, drop-off',
-  974: 'coral reef',
-  975: 'geyser',
-  976: 'lakeside, lakeshore',
-  977: 'promontory, headland, head, foreland',
-  978: 'sandbar, sand bar',
-  979: 'seashore, coast, seacoast, sea-coast',
-  980: 'valley, vale',
-  981: 'volcano',
-  982: 'ballplayer, baseball player',
-  983: 'groom, bridegroom',
-  984: 'scuba diver',
-  985: 'rapeseed',
-  986: 'daisy',
-  987: 'yellow lady\'s slipper, yellow lady-slipper, Cypripedium' +
-      ' calceolus, Cypripedium parviflorum',
-  988: 'corn',
-  989: 'acorn',
-  990: 'hip, rose hip, rosehip',
-  991: 'buckeye, horse chestnut, conker',
-  992: 'coral fungus',
-  993: 'agaric',
-  994: 'gyromitra',
-  995: 'stinkhorn, carrion fungus',
-  996: 'earthstar',
-  997: 'hen-of-the-woods, hen of the woods, Polyporus' +
-      ' frondosus, Grifola frondosa',
-  998: 'bolete',
-  999: 'ear, spike, capitulum',
-  1000: 'toilet tissue, toilet paper, bathroom tissue'
-};
diff --git a/tfjs-master/tfjs-converter/demo/mobilenet/index.html b/tfjs-master/tfjs-converter/demo/mobilenet/index.html
deleted file mode 100644
index 8811f9424..000000000
--- a/tfjs-master/tfjs-converter/demo/mobilenet/index.html
+++ /dev/null
@@ -1,34 +0,0 @@
-<!-- Copyright 2018 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-
-<!doctype html>
-<html>
-  <head></head>
-  <style>
-    body {
-      font-family: "Roboto", "Helvetica", "Arial", sans-serif;
-      margin-top: 50px;
-      margin-left: 50px;
-    }
-  </style>
-
-  <body>
-    <h1>TensorFlow.js Converter: MobileNet</h1>
-    <img id="cat"></img>
-    <div id="result"></div>
-  </body>
-
-  <script type="module" src="index.js"></script>
-</html>
diff --git a/tfjs-master/tfjs-converter/demo/mobilenet/index.js b/tfjs-master/tfjs-converter/demo/mobilenet/index.js
deleted file mode 100644
index 341309770..000000000
--- a/tfjs-master/tfjs-converter/demo/mobilenet/index.js
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import 'babel-polyfill';
-import * as tf from '@tensorflow/tfjs';
-import {MobileNet} from './mobilenet';
-import imageURL from './cat.jpg';
-
-const cat = document.getElementById('cat');
-cat.onload = async () => {
-  const resultElement = document.getElementById('result');
-
-  resultElement.innerText = 'Loading MobileNet...';
-
-  const mobileNet = new MobileNet();
-  console.time('Loading of model');
-  await mobileNet.load();
-  console.timeEnd('Loading of model');
-
-  const pixels = tf.browser.fromPixels(cat);
-
-  console.time('First prediction');
-  let result = mobileNet.predict(pixels);
-  const topK = mobileNet.getTopKClasses(result, 5);
-  console.timeEnd('First prediction');
-
-  resultElement.innerText = '';
-  topK.forEach(x => {
-    resultElement.innerText += `${x.value.toFixed(3)}: ${x.label}\n`;
-  });
-
-  console.time('Subsequent predictions');
-  result = mobileNet.predict(pixels);
-  mobileNet.getTopKClasses(result, 5);
-  console.timeEnd('Subsequent predictions');
-
-  mobileNet.dispose();
-};
-cat.src = imageURL;
diff --git a/tfjs-master/tfjs-converter/demo/mobilenet/mobilenet.js b/tfjs-master/tfjs-converter/demo/mobilenet/mobilenet.js
deleted file mode 100644
index 061c85649..000000000
--- a/tfjs-master/tfjs-converter/demo/mobilenet/mobilenet.js
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-
-import {IMAGENET_CLASSES} from './imagenet_classes';
-
-const GOOGLE_CLOUD_STORAGE_DIR =
-    'https://storage.googleapis.com/tfjs-models/savedmodel/';
-const MODEL_FILE_URL = 'mobilenet_v2_1.0_224/model.json';
-const INPUT_NODE_NAME = 'images';
-const OUTPUT_NODE_NAME = 'module_apply_default/MobilenetV2/Logits/output';
-const PREPROCESS_DIVISOR = tf.scalar(255 / 2);
-
-export class MobileNet {
-  constructor() {}
-
-  async load() {
-    this.model = await tf.loadGraphModel(
-        GOOGLE_CLOUD_STORAGE_DIR + MODEL_FILE_URL);
-  }
-
-  dispose() {
-    if (this.model) {
-      this.model.dispose();
-    }
-  }
-  /**
-   * Infer through MobileNet. This does standard ImageNet pre-processing before
-   * inferring through the model. This method returns named activations as well
-   * as softmax logits.
-   *
-   * @param input un-preprocessed input Array.
-   * @return The softmax logits.
-   */
-  predict(input) {
-    const preprocessedInput = tf.div(
-        tf.sub(input.asType('float32'), PREPROCESS_DIVISOR),
-        PREPROCESS_DIVISOR);
-    const reshapedInput =
-        preprocessedInput.reshape([1, ...preprocessedInput.shape]);
-    return this.model.execute(
-        {[INPUT_NODE_NAME]: reshapedInput}, OUTPUT_NODE_NAME);
-  }
-
-  getTopKClasses(logits, topK) {
-    const predictions = tf.tidy(() => {
-      return tf.softmax(logits);
-    });
-
-    const values = predictions.dataSync();
-    predictions.dispose();
-
-    let predictionList = [];
-    for (let i = 0; i < values.length; i++) {
-      predictionList.push({value: values[i], index: i});
-    }
-    predictionList = predictionList
-                         .sort((a, b) => {
-                           return b.value - a.value;
-                         })
-                         .slice(0, topK);
-
-    return predictionList.map(x => {
-      return {label: IMAGENET_CLASSES[x.index], value: x.value};
-    });
-  }
-}
diff --git a/tfjs-master/tfjs-converter/demo/package.json b/tfjs-master/tfjs-converter/demo/package.json
deleted file mode 100644
index ae249255b..000000000
--- a/tfjs-master/tfjs-converter/demo/package.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "name": "tfjs-converter-demo",
-  "version": "0.0.1",
-  "description": "Imports mobilenet model using the tfc.js converter",
-  "main": "index.js",
-  "license": "Apache-2.0",
-  "private": true,
-  "dependencies": {
-    "@tensorflow/tfjs": "0.15.3",
-    "babel-polyfill": "^6.26.0"
-  },
-  "scripts": {
-    "mobilenet": "NODE_ENV=development parcel mobilenet/index.html --no-hmr --open",
-    "ssd": "NODE_ENV=development parcel ssd/index.html --no-hmr --open",
-    "control_flow": "NODE_ENV=development parcel control_flow/index.html --no-hmr --open"
-  },
-  "devDependencies": {
-    "@babel/core": "^7.0.0-0",
-    "@babel/plugin-transform-runtime": "^7.3.4",
-    "@babel/polyfill": "~7.2.5",
-    "@babel/preset-env": "~7.3.4",
-    "@babel/runtime": "~7.3.4",
-    "buffer": "^6.0.3",
-    "clang-format": "~1.2.2",
-    "crypto-browserify": "^3.12.0",
-    "events": "^3.3.0",
-    "parcel": "~2.3.2",
-    "process": "^0.11.10",
-    "stream-browserify": "^3.0.0"
-  },
-  "browserslist": "> 0.25%, not dead"
-}
diff --git a/tfjs-master/tfjs-converter/demo/ssd/classes.js b/tfjs-master/tfjs-converter/demo/ssd/classes.js
deleted file mode 100644
index b814deaaf..000000000
--- a/tfjs-master/tfjs-converter/demo/ssd/classes.js
+++ /dev/null
@@ -1,400 +0,0 @@
-export const CLASSES = [{
-  name: "/m/01g317",
-  id: 1,
-  display_name: "person",
-},
-{
-  name: "/m/0199g",
-  id: 2,
-  display_name: "bicycle",
-},
-{
-  name: "/m/0k4j",
-  id: 3,
-  display_name: "car",
-},
-{
-  name: "/m/04_sv",
-  id: 4,
-  display_name: "motorcycle",
-},
-{
-  name: "/m/05czz6l",
-  id: 5,
-  display_name: "airplane",
-},
-{
-  name: "/m/01bjv",
-  id: 6,
-  display_name: "bus",
-},
-{
-  name: "/m/07jdr",
-  id: 7,
-  display_name: "train",
-},
-{
-  name: "/m/07r04",
-  id: 8,
-  display_name: "truck",
-},
-{
-  name: "/m/019jd",
-  id: 9,
-  display_name: "boat",
-},
-{
-  name: "/m/015qff",
-  id: 10,
-  display_name: "traffic light",
-},
-{
-  name: "/m/01pns0",
-  id: 11,
-  display_name: "fire hydrant",
-},
-{
-  name: "/m/02pv19",
-  id: 13,
-  display_name: "stop sign",
-},
-{
-  name: "/m/015qbp",
-  id: 14,
-  display_name: "parking meter",
-},
-{
-  name: "/m/0cvnqh",
-  id: 15,
-  display_name: "bench",
-},
-{
-  name: "/m/015p6",
-  id: 16,
-  display_name: "bird",
-},
-{
-  name: "/m/01yrx",
-  id: 17,
-  display_name: "cat",
-},
-{
-  name: "/m/0bt9lr",
-  id: 18,
-  display_name: "dog",
-},
-{
-  name: "/m/03k3r",
-  id: 19,
-  display_name: "horse",
-},
-{
-  name: "/m/07bgp",
-  id: 20,
-  display_name: "sheep",
-},
-{
-  name: "/m/01xq0k1",
-  id: 21,
-  display_name: "cow",
-},
-{
-  name: "/m/0bwd_0j",
-  id: 22,
-  display_name: "elephant",
-},
-{
-  name: "/m/01dws",
-  id: 23,
-  display_name: "bear",
-},
-{
-  name: "/m/0898b",
-  id: 24,
-  display_name: "zebra",
-},
-{
-  name: "/m/03bk1",
-  id: 25,
-  display_name: "giraffe",
-},
-{
-  name: "/m/01940j",
-  id: 27,
-  display_name: "backpack",
-},
-{
-  name: "/m/0hnnb",
-  id: 28,
-  display_name: "umbrella",
-},
-{
-  name: "/m/080hkjn",
-  id: 31,
-  display_name: "handbag",
-},
-{
-  name: "/m/01rkbr",
-  id: 32,
-  display_name: "tie",
-},
-{
-  name: "/m/01s55n",
-  id: 33,
-  display_name: "suitcase",
-},
-{
-  name: "/m/02wmf",
-  id: 34,
-  display_name: "frisbee",
-},
-{
-  name: "/m/071p9",
-  id: 35,
-  display_name: "skis",
-},
-{
-  name: "/m/06__v",
-  id: 36,
-  display_name: "snowboard",
-},
-{
-  name: "/m/018xm",
-  id: 37,
-  display_name: "sports ball",
-},
-{
-  name: "/m/02zt3",
-  id: 38,
-  display_name: "kite",
-},
-{
-  name: "/m/03g8mr",
-  id: 39,
-  display_name: "baseball bat",
-},
-{
-  name: "/m/03grzl",
-  id: 40,
-  display_name: "baseball glove",
-},
-{
-  name: "/m/06_fw",
-  id: 41,
-  display_name: "skateboard",
-},
-{
-  name: "/m/019w40",
-  id: 42,
-  display_name: "surfboard",
-},
-{
-  name: "/m/0dv9c",
-  id: 43,
-  display_name: "tennis racket",
-},
-{
-  name: "/m/04dr76w",
-  id: 44,
-  display_name: "bottle",
-},
-{
-  name: "/m/09tvcd",
-  id: 46,
-  display_name: "wine glass",
-},
-{
-  name: "/m/08gqpm",
-  id: 47,
-  display_name: "cup",
-},
-{
-  name: "/m/0dt3t",
-  id: 48,
-  display_name: "fork",
-},
-{
-  name: "/m/04ctx",
-  id: 49,
-  display_name: "knife",
-},
-{
-  name: "/m/0cmx8",
-  id: 50,
-  display_name: "spoon",
-},
-{
-  name: "/m/04kkgm",
-  id: 51,
-  display_name: "bowl",
-},
-{
-  name: "/m/09qck",
-  id: 52,
-  display_name: "banana",
-},
-{
-  name: "/m/014j1m",
-  id: 53,
-  display_name: "apple",
-},
-{
-  name: "/m/0l515",
-  id: 54,
-  display_name: "sandwich",
-},
-{
-  name: "/m/0cyhj_",
-  id: 55,
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-},
-{
-  name: "/m/0hkxq",
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-},
-{
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-},
-{
-  name: "/m/01b9xk",
-  id: 58,
-  display_name: "hot dog",
-},
-{
-  name: "/m/0663v",
-  id: 59,
-  display_name: "pizza",
-},
-{
-  name: "/m/0jy4k",
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-  display_name: "donut",
-},
-{
-  name: "/m/0fszt",
-  id: 61,
-  display_name: "cake",
-},
-{
-  name: "/m/01mzpv",
-  id: 62,
-  display_name: "chair",
-},
-{
-  name: "/m/02crq1",
-  id: 63,
-  display_name: "couch",
-},
-{
-  name: "/m/03fp41",
-  id: 64,
-  display_name: "potted plant",
-},
-{
-  name: "/m/03ssj5",
-  id: 65,
-  display_name: "bed",
-},
-{
-  name: "/m/04bcr3",
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-  display_name: "dining table",
-},
-{
-  name: "/m/09g1w",
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-  name: "/m/07c52",
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diff --git a/tfjs-master/tfjs-converter/demo/ssd/image1.jpg b/tfjs-master/tfjs-converter/demo/ssd/image1.jpg
deleted file mode 100644
index 8886504c2..000000000
Binary files a/tfjs-master/tfjs-converter/demo/ssd/image1.jpg and /dev/null differ
diff --git a/tfjs-master/tfjs-converter/demo/ssd/image2.jpg b/tfjs-master/tfjs-converter/demo/ssd/image2.jpg
deleted file mode 100644
index 2ff570d1c..000000000
Binary files a/tfjs-master/tfjs-converter/demo/ssd/image2.jpg and /dev/null differ
diff --git a/tfjs-master/tfjs-converter/demo/ssd/index.html b/tfjs-master/tfjs-converter/demo/ssd/index.html
deleted file mode 100644
index 303916875..000000000
--- a/tfjs-master/tfjs-converter/demo/ssd/index.html
+++ /dev/null
@@ -1,29 +0,0 @@
-<!-- Copyright 2018 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-<!doctype html>
-<html>
-
-<body>
-  <h1>TensorFlow.js converter: ssd_mobilenet_v1_coco_2018_01_28</h1>
-  <button type="button" id="run">Run</button>
-  <button type="button" id="toggle">Toggle Image</button>
-<div>
-  <img id="image"></img>
-  <canvas id="canvas" width="600" height="399"></canvas>
-</div>
-</body>
-
-<script type="module" src="index.js"></script>
-</html>
diff --git a/tfjs-master/tfjs-converter/demo/ssd/index.js b/tfjs-master/tfjs-converter/demo/ssd/index.js
deleted file mode 100644
index 302aa8a6e..000000000
--- a/tfjs-master/tfjs-converter/demo/ssd/index.js
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import 'babel-polyfill';
-import * as tf from '@tensorflow/tfjs';
-import {CLASSES} from './classes';
-import imageURL from './image1.jpg';
-import image2URL from './image2.jpg';
-
-const GOOGLE_CLOUD_STORAGE_DIR =
-    'https://storage.googleapis.com/tfjs-models/savedmodel/';
-const MODEL_URL = GOOGLE_CLOUD_STORAGE_DIR + 'ssdlite_mobilenet_v2/model.json';
-
-let modelPromise;
-
-window.onload = () => modelPromise = tf.loadGraphModel(MODEL_URL);
-
-const button = document.getElementById('toggle');
-button.onclick = () => {
-  image.src = image.src.endsWith(imageURL) ? image2URL : imageURL;
-};
-
-const image = document.getElementById('image');
-image.src = imageURL;
-
-const runButton = document.getElementById('run');
-runButton.onclick = async () => {
-  const model = await modelPromise;
-  const pixels = tf.browser.fromPixels(image);
-  console.log('model loaded');
-  console.time('predict1');
-  const res1 = await model.executeAsync(pixels.reshape([1, ...pixels.shape]));
-  res1.map(t => t.dataSync());
-  console.timeEnd('predict1');
-  console.time('predict2');
-  const res2 = await model.executeAsync(pixels.reshape([1, ...pixels.shape]));
-  const boxes = res2[0].dataSync();
-  const scores = res2[1].dataSync();
-  console.timeEnd('predict2');
-};
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deleted file mode 100644
index 1b4692858..000000000
--- a/tfjs-master/tfjs-converter/demo/yarn.lock
+++ /dev/null
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-supports-preserve-symlinks-flag@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz#6eda4bd344a3c94aea376d4cc31bc77311039e09"
-  integrity sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==
-
-svgo@^2.4.0, svgo@^2.7.0:
-  version "2.8.0"
-  resolved "https://registry.yarnpkg.com/svgo/-/svgo-2.8.0.tgz#4ff80cce6710dc2795f0c7c74101e6764cfccd24"
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-  dependencies:
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-    commander "^7.2.0"
-    css-select "^4.1.3"
-    css-tree "^1.1.3"
-    csso "^4.2.0"
-    picocolors "^1.0.0"
-    stable "^0.1.8"
-
-terser@^5.2.0:
-  version "5.14.2"
-  resolved "https://registry.yarnpkg.com/terser/-/terser-5.14.2.tgz#9ac9f22b06994d736174f4091aa368db896f1c10"
-  integrity sha512-oL0rGeM/WFQCUd0y2QrWxYnq7tfSuKBiqTjRPWrRgB46WD/kiwHwF8T23z78H6Q6kGCuuHcPB+KULHRdxvVGQA==
-  dependencies:
-    "@jridgewell/source-map" "^0.3.2"
-    acorn "^8.5.0"
-    commander "^2.20.0"
-    source-map-support "~0.5.20"
-
-timsort@^0.3.0:
-  version "0.3.0"
-  resolved "https://registry.yarnpkg.com/timsort/-/timsort-0.3.0.tgz#405411a8e7e6339fe64db9a234de11dc31e02bd4"
-  integrity sha1-QFQRqOfmM5/mTbmiNN4R3DHgK9Q=
-
-to-fast-properties@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/to-fast-properties/-/to-fast-properties-2.0.0.tgz#dc5e698cbd079265bc73e0377681a4e4e83f616e"
-  integrity sha1-3F5pjL0HkmW8c+A3doGk5Og/YW4=
-
-trim-right@^1.0.1:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/trim-right/-/trim-right-1.0.1.tgz#cb2e1203067e0c8de1f614094b9fe45704ea6003"
-  integrity sha1-yy4SAwZ+DI3h9hQJS5/kVwTqYAM=
-
-type-fest@^0.20.2:
-  version "0.20.2"
-  resolved "https://registry.yarnpkg.com/type-fest/-/type-fest-0.20.2.tgz#1bf207f4b28f91583666cb5fbd327887301cd5f4"
-  integrity sha512-Ne+eE4r0/iWnpAxD852z3A+N0Bt5RN//NjJwRd2VFHEmrywxf5vsZlh4R6lixl6B+wz/8d+maTSAkN1FIkI3LQ==
-
-util-deprecate@^1.0.1, util-deprecate@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
-  integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=
-
-utility-types@^3.10.0:
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/utility-types/-/utility-types-3.10.0.tgz#ea4148f9a741015f05ed74fd615e1d20e6bed82b"
-  integrity sha512-O11mqxmi7wMKCo6HKFt5AhO4BwY3VV68YU07tgxfz8zJTIxr4BpsezN49Ffwy9j3ZpwwJp4fkRwjRzq3uWE6Rg==
-
-v8-compile-cache@^2.0.0:
-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/v8-compile-cache/-/v8-compile-cache-2.3.0.tgz#2de19618c66dc247dcfb6f99338035d8245a2cee"
-  integrity sha512-l8lCEmLcLYZh4nbunNZvQCJc5pv7+RCwa8q/LdUx8u7lsWvPDKmpodJAJNwkAhJC//dFY48KuIEmjtd4RViDrA==
-
-weak-lru-cache@^1.2.2:
-  version "1.2.2"
-  resolved "https://registry.yarnpkg.com/weak-lru-cache/-/weak-lru-cache-1.2.2.tgz#fdbb6741f36bae9540d12f480ce8254060dccd19"
-  integrity sha512-DEAoo25RfSYMuTGc9vPJzZcZullwIqRDSI9LOy+fkCJPi6hykCnfKaXTuPBDuXAUcqHXyOgFtHNp/kB2FjYHbw==
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-xxhash-wasm@^0.4.2:
-  version "0.4.2"
-  resolved "https://registry.yarnpkg.com/xxhash-wasm/-/xxhash-wasm-0.4.2.tgz#752398c131a4dd407b5132ba62ad372029be6f79"
-  integrity sha512-/eyHVRJQCirEkSZ1agRSCwriMhwlyUcFkXD5TPVSLP+IPzjsqMVzZwdoczLp1SoQU0R3dxz1RpIK+4YNQbCVOA==
-
-yaml@^1.10.0, yaml@^1.10.2:
-  version "1.10.2"
-  resolved "https://registry.yarnpkg.com/yaml/-/yaml-1.10.2.tgz#2301c5ffbf12b467de8da2333a459e29e7920e4b"
-  integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==
diff --git a/tfjs-master/tfjs-converter/docs/supported_ops.md b/tfjs-master/tfjs-converter/docs/supported_ops.md
deleted file mode 100644
index 43ae0fe40..000000000
--- a/tfjs-master/tfjs-converter/docs/supported_ops.md
+++ /dev/null
@@ -1,381 +0,0 @@
-# Supported Tensorflow Ops
-
-## Operations - Arithmetic
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Add|add|
-|AddN|addN|
-|AddV2|AddV2|
-|BiasAdd|BiasAdd|
-|Div|div|
-|DivNoNan|divNoNan|
-|FloorDiv|floorDiv|
-|FloorMod|FloorMod|
-|Maximum|maximum|
-|Minimum|minimum|
-|Mod|mod|
-|Mul|mul|
-|Pow|pow|
-|RealDiv|RealDiv|
-|SquaredDifference|squaredDifference|
-|Sub|sub|
-
-## Operations - Basic math
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Abs|abs|
-|Acos|acos|
-|Acosh|acosh|
-|Asin|asin|
-|Asinh|asinh|
-|Atan|atan|
-|Atan2|atan2|
-|Atanh|atanh|
-|Ceil|ceil|
-|ClipByValue|clipByValue|
-|Complex|Complex|
-|ComplexAbs|ComplexAbs|
-|Cos|cos|
-|Cosh|cosh|
-|Elu|elu|
-|Erf|erf|
-|Exp|exp|
-|Expm1|expm1|
-|Floor|floor|
-|Imag|Imag|
-|LeakyRelu|leakyRelu|
-|Log|log|
-|Log1p|log1p|
-|Neg|neg|
-|Prelu|prelu|
-|Prod|Prod|
-|Real|Real|
-|Reciprocal|reciprocal|
-|Relu|relu|
-|Relu6|relu6|
-|Round|round|
-|Rsqrt|rsqrt|
-|Selu|selu|
-|Sigmoid|sigmoid|
-|Sign|sign|
-|Sin|sin|
-|Sinh|sinh|
-|Softplus|softplus|
-|Sqrt|sqrt|
-|Square|square|
-|Tan|tan|
-|Tanh|tanh|
-|IsFinite|isFinite|
-|IsInf|isInf|
-|IsNan|isNaN|
-|Not mapped|logSigmoid|
-|Not mapped|step|
-
-## Operations - Control Flow
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|EmptyTensorList|EmptyTensorList|
-|Enter|Enter|
-|Exit|Exit|
-|If|If|
-|LoopCond|LoopCond|
-|Merge|Merge|
-|NextIteration|NextIteration|
-|StatelessIf|StatelessIf|
-|StatelessWhile|StatelessWhile|
-|Switch|Switch|
-|TensorArrayCloseV3|TensorArrayCloseV3|
-|TensorArrayConcatV3|TensorArrayConcatV3|
-|TensorArrayGatherV3|TensorArrayGatherV3|
-|TensorArrayReadV3|TensorArrayReadV3|
-|TensorArrayScatterV3|TensorArrayScatterV3|
-|TensorArraySizeV3|TensorArraySizeV3|
-|TensorArraySplitV3|TensorArraySplitV3|
-|TensorArrayV3|TensorArrayV3|
-|TensorArrayWriteV3|TensorArrayWriteV3|
-|TensorListConcat|TensorListConcat|
-|TensorListConcatV2|TensorListConcatV2|
-|TensorListFromTensor|TensorListFromTensor|
-|TensorListGather|TensorListGather|
-|TensorListGetItem|TensorListGetItem|
-|TensorListLength|TensorListLength|
-|TensorListPopBack|TensorListPopBack|
-|TensorListPushBack|TensorListPushBack|
-|TensorListReserve|TensorListReserve|
-|TensorListResize|TensorListResize|
-|TensorListScatter|TensorListScatter|
-|TensorListScatterV2|TensorListScatterV2|
-|TensorListSetItem|TensorListSetItem|
-|TensorListSplit|TensorListSplit|
-|TensorListStack|TensorListStack|
-|While|While|
-
-## Operations - Convolution
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|_FusedConv2D|_FusedConv2D|
-|AvgPool|AvgPool|
-|AvgPool3D|avgPool3d|
-|Conv1D|conv1d|
-|Conv2D|conv2d|
-|Conv2DBackpropInput|Conv2DBackpropInput|
-|Conv3D|conv3d|
-|DepthwiseConv2d|depthwiseConv2d|
-|DepthwiseConv2dNative|DepthwiseConv2dNative|
-|Dilation2D|Dilation2D|
-|FusedDepthwiseConv2dNative|FusedDepthwiseConv2dNative|
-|MaxPool|MaxPool|
-|MaxPool3D|maxPool3d|
-|MaxPoolWithArgmax|maxPoolWithArgmax|
-|Not mapped|conv2dTranspose|
-|Not mapped|conv3dTranspose|
-|Not mapped|pool|
-|Not mapped|separableConv2d|
-
-## Tensors - Creation
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Fill|fill|
-|LinSpace|linspace|
-|Multinomial|Multinomial|
-|OneHot|oneHot|
-|Ones|ones|
-|OnesLike|onesLike|
-|RandomStandardNormal|RandomStandardNormal|
-|RandomUniform|RandomUniform|
-|Range|range|
-|TruncatedNormal|truncatedNormal|
-|Zeros|zeros|
-|ZerosLike|zerosLike|
-|Not mapped|eye|
-
-## Operations - Dynamic
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|ListDiff|ListDiff|
-|NonMaxSuppressionV2|NonMaxSuppressionV2|
-|NonMaxSuppressionV3|NonMaxSuppressionV3|
-|NonMaxSuppressionV4|NonMaxSuppressionV4|
-|NonMaxSuppressionV5|NonMaxSuppressionV5|
-|Where|Where|
-
-## Operations - Evaluation
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|LowerBound|LowerBound|
-|TopKV2|TopKV2|
-|Unique|Unique|
-|UniqueV2|UniqueV2|
-|UpperBound|UpperBound|
-|Not mapped|confusionMatrix|
-|Not mapped|inTopKAsync|
-|Not mapped|topk|
-
-## Tensorflow - Graph
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Const|Const|
-|FakeQuantWithMinMaxVars|FakeQuantWithMinMaxVars|
-|Identity|Identity|
-|IdentityN|IdentityN|
-|NoOp|NoOp|
-|Placeholder|Placeholder|
-|PlaceholderWithDefault|PlaceholderWithDefault|
-|Print|Print|
-|Rank|Rank|
-|Shape|Shape|
-|ShapeN|ShapeN|
-|Size|Size|
-|Snapshot|Snapshot|
-|StopGradient|StopGradient|
-
-## Operations - Logical
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|BitwiseAnd|bitwiseAnd|
-|Equal|equal|
-|Greater|greater|
-|GreaterEqual|greaterEqual|
-|Less|less|
-|LessEqual|lessEqual|
-|LogicalAnd|logicalAnd|
-|LogicalNot|logicalNot|
-|LogicalOr|logicalOr|
-|NotEqual|notEqual|
-|Select|Select|
-|SelectV2|SelectV2|
-|Not mapped|logicalXor|
-
-## Operations - Hashtable
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|HashTable|HashTable|
-|HashTableV2|HashTableV2|
-|LookupTableFind|LookupTableFind|
-|LookupTableFindV2|LookupTableFindV2|
-|LookupTableImport|LookupTableImport|
-|LookupTableImportV2|LookupTableImportV2|
-|LookupTableSize|LookupTableSize|
-|LookupTableSizeV2|LookupTableSizeV2|
-
-## Operations - Images
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|CropAndResize|cropAndResize|
-|ResizeBilinear|resizeBilinear|
-|ResizeNearestNeighbor|resizeNearestNeighbor|
-|Not mapped|flipLeftRight|
-|Not mapped|rotateWithOffset|
-|ImageProjectiveTransformV3|transform|
-
-## Operations - Matrices
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|_FusedMatMul|_FusedMatMul|
-|BatchMatMul|BatchMatMul|
-|BatchMatMulV2|BatchMatMulV2|
-|Einsum|Einsum|
-|MatMul|matMul|
-|Transpose|transpose|
-|MatrixBandPart|MatrixBandPart|
-|Not mapped|dot|
-|Not mapped|norm|
-|Not mapped|outerProduct|
-
-## Operations - Moving Average
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Not mapped|movingAverage|
-
-## Operations - Normalization
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|EuclideanNorm|EuclideanNorm|
-|FusedBatchNorm|FusedBatchNorm|
-|FusedBatchNormV2|FusedBatchNormV2|
-|FusedBatchNormV3|FusedBatchNormV3|
-|LogSoftmax|logSoftmax|
-|LRN|LRN|
-|Softmax|softmax|
-|SparseToDense|sparseToDense|
-|Not mapped|batchNorm|
-|Not mapped|moments|
-
-## Operations - Reduction
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|All|all|
-|Any|any|
-|ArgMax|argMax|
-|ArgMin|argMin|
-|Bincount|bincount|
-|DenseBincount|denseBincount|
-|Max|max|
-|Mean|mean|
-|Min|min|
-|Prod|prod|
-|Sum|sum|
-|Not mapped|logSumExp|
-
-## Tensors - RNN
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-
-## Operations - Scan
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Cumprod|cumprod|
-|Cumsum|cumsum|
-
-## Operations - Segment
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Not mapped|unsortedSegmentSum|
-
-## Tensors - Slicing and Joining
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Concat|concat|
-|ConcatV2|ConcatV2|
-|Gather|gather|
-|GatherNd|GatherNd|
-|GatherV2|GatherV2|
-|Pack|Pack|
-|Reverse|reverse|
-|ReverseV2|ReverseV2|
-|ScatterNd|ScatterNd|
-|Slice|slice|
-|SparseToDense|SparseToDense|
-|Split|split|
-|SplitV|SplitV|
-|StridedSlice|StridedSlice|
-|Tile|tile|
-|Unpack|Unpack|
-|Not mapped|booleanMaskAsync|
-|Not mapped|stack|
-|Not mapped|unstack|
-
-## Operations - Spectral
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|FFT|fft|
-|IFFT|ifft|
-|IRFFT|irfft|
-|RFFT|rfft|
-
-## Operations - Signal
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Not mapped|frame|
-|Not mapped|hammingWindow|
-|Not mapped|hannWindow|
-|Not mapped|stft|
-
-## Operations - Linear Algebra
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|Not mapped|bandPart|
-|Not mapped|gramSchmidt|
-|Not mapped|qr|
-
-## Tensors - Transformations
-
-|Tensorflow Op Name|Tensorflow.js Op Name|
-|---|---|
-|BatchToSpaceND|batchToSpaceND|
-|BroadcastArgs|broadcastArgs|
-|BroadcastTo|broadcastTo|
-|Cast|cast|
-|DepthToSpace|depthToSpace|
-|ExpandDims|expandDims|
-|MirrorPad|MirrorPad|
-|Pad|pad|
-|PadV2|PadV2|
-|Reshape|reshape|
-|EnsureShape|ensureShape|
-|SpaceToBatchND|spaceToBatchND|
-|Squeeze|squeeze|
-|Not mapped|setdiff1dAsync|
-
diff --git a/tfjs-master/tfjs-converter/metadata/BUILD.bazel b/tfjs-master/tfjs-converter/metadata/BUILD.bazel
deleted file mode 100644
index 2d31cf5ff..000000000
--- a/tfjs-master/tfjs-converter/metadata/BUILD.bazel
+++ /dev/null
@@ -1,11 +0,0 @@
-load("//tfjs-converter/scripts:kernels_to_ops.bzl", "kernels_to_ops")
-
-package(default_visibility = ["//visibility:public"])
-
-kernels_to_ops(
-    name = "kernels_to_ops",
-    srcs = [
-        "//tfjs-converter/src:all_srcs",
-    ],
-    out = "kernel2op.json",
-)
diff --git a/tfjs-master/tfjs-converter/package.json b/tfjs-master/tfjs-converter/package.json
deleted file mode 100644
index b350a90b2..000000000
--- a/tfjs-master/tfjs-converter/package.json
+++ /dev/null
@@ -1,55 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-converter",
-  "version": "0.0.0",
-  "description": "Tensorflow model converter for javascript",
-  "main": "dist/tf-converter.node.js",
-  "jsnext:main": "dist/index.js",
-  "module": "dist/index.js",
-  "types": "dist/index.d.ts",
-  "unpkg": "dist/tf-converter.min.js",
-  "jsdelivr": "dist/tf-converter.min.js",
-  "miniprogram": "dist/miniprogram",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-converter"
-  },
-  "license": "Apache-2.0",
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core"
-  },
-  "devDependencies": {
-    "@bazel/bazelisk": "^1.12.0",
-    "@bazel/ibazel": "^0.16.2",
-    "@tensorflow/tfjs-backend-cpu": "link:../link-package/node_modules/@tensorflow/tfjs-backend-cpu",
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core",
-    "@types/argparse": "^1.0.38",
-    "@types/long": "~3.0.32",
-    "@types/node-fetch": "1.6.9",
-    "argparse": "^1.0.10",
-    "node-fetch": "~2.6.1",
-    "opn": "~5.1.0",
-    "protobufjs": "~7.2.4",
-    "ts-node": "~8.8.2",
-    "typescript": "5.0.4",
-    "yalc": "~1.0.0-pre.50"
-  },
-  "scripts": {
-    "build": "bazel build :tfjs-converter_pkg",
-    "publish-npm": "bazel run :tfjs-converter_pkg.publish",
-    "test": "bazel test :tests",
-    "test-dev": "ibazel test :tests",
-    "test-debug": "bazel run :tfjs-converter_test --config=debug",
-    "test-converter": "bazel test :tfjs-converter_test",
-    "test-converter-debug": "yarn test-debug",
-    "test-snippets": "bazel test :test_snippets_test --test_output=all",
-    "gen-doc": "ts-node -s ./scripts/gen_doc.ts",
-    "model-summary": "ts-node -s ./tools/model_summary.ts",
-    "pb2json": "ts-node -s ./tools/pb2json_converter.ts",
-    "build-pip-package": "cd python && ./build-pip-package.sh --test /tmp/tfjs-pips",
-    "run-python-tests": "bazel test python/..."
-  },
-  "resolutions": {
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-converter/python/.pylintrc b/tfjs-master/tfjs-converter/python/.pylintrc
deleted file mode 100644
index be7b93763..000000000
--- a/tfjs-master/tfjs-converter/python/.pylintrc
+++ /dev/null
@@ -1,425 +0,0 @@
-[MASTER]
-
-# A comma-separated list of package or module names from where C extensions may
-# be loaded. Extensions are loading into the active Python interpreter and may
-# run arbitrary code
-extension-pkg-whitelist=
-
-# Add files or directories to the blacklist. They should be base names, not
-# paths.
-ignore=CVS
-
-# Add files or directories matching the regex patterns to the blacklist. The
-# regex matches against base names, not paths.
-ignore-patterns=.*_pb2
-
-# Python code to execute, usually for sys.path manipulation such as
-# pygtk.require().
-#init-hook=
-
-# Use multiple processes to speed up Pylint.
-jobs=1
-
-# List of plugins (as comma separated values of python modules names) to load,
-# usually to register additional checkers.
-load-plugins=
-
-# Pickle collected data for later comparisons.
-persistent=yes
-
-# Specify a configuration file.
-#rcfile=
-
-# Allow loading of arbitrary C extensions. Extensions are imported into the
-# active Python interpreter and may run arbitrary code.
-unsafe-load-any-extension=no
-
-
-[MESSAGES CONTROL]
-
-# Only show warnings with the listed confidence levels. Leave empty to show
-# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED
-confidence=
-
-# Disable the message, report, category or checker with the given id(s). You
-# can either give multiple identifiers separated by comma (,) or put this
-# option multiple times (only on the command line, not in the configuration
-# file where it should appear only once).You can also use "--disable=all" to
-# disable everything first and then reenable specific checks. For example, if
-# you want to run only the similarities checker, you can use "--disable=all
-# --enable=similarities". If you want to run only the classes checker, but have
-# no Warning level messages displayed, use"--disable=all --enable=classes
-# --disable=W"
-disable=print-statement,parameter-unpacking,unpacking-in-except,old-raise-syntax,backtick,long-suffix,old-ne-operator,old-octal-literal,import-star-module-level,raw-checker-failed,bad-inline-option,locally-disabled,locally-enabled,file-ignored,suppressed-message,useless-suppression,deprecated-pragma,apply-builtin,basestring-builtin,buffer-builtin,cmp-builtin,coerce-builtin,execfile-builtin,file-builtin,long-builtin,raw_input-builtin,reduce-builtin,standarderror-builtin,unicode-builtin,xrange-builtin,coerce-method,delslice-method,getslice-method,setslice-method,no-absolute-import,old-division,dict-iter-method,dict-view-method,next-method-called,metaclass-assignment,indexing-exception,raising-string,reload-builtin,oct-method,hex-method,nonzero-method,cmp-method,input-builtin,round-builtin,intern-builtin,unichr-builtin,map-builtin-not-iterating,zip-builtin-not-iterating,range-builtin-not-iterating,filter-builtin-not-iterating,using-cmp-argument,eq-without-hash,div-method,idiv-method,rdiv-method,exception-message-attribute,invalid-str-codec,sys-max-int,bad-python3-import,deprecated-string-function,deprecated-str-translate-call,duplicate-code,invalid-name,missing-docstring,fixme,no-member,protected-access,no-self-use,no-name-in-module,bad-option-value,no-else-return,too-few-public-methods,too-many-branches,too-many-arguments,too-many-public-methods,too-many-locals,too-many-return-statements,too-many-instance-attributes,not-context-manager,blacklisted-name,import-error
-
-# Enable the message, report, category or checker with the given id(s). You can
-# either give multiple identifier separated by comma (,) or put this option
-# multiple time (only on the command line, not in the configuration file where
-# it should appear only once). See also the "--disable" option for examples.
-enable=
-
-
-[REPORTS]
-
-# Python expression which should return a note less than 10 (10 is the highest
-# note). You have access to the variables errors warning, statement which
-# respectively contain the number of errors / warnings messages and the total
-# number of statements analyzed. This is used by the global evaluation report
-# (RP0004).
-evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
-
-# Template used to display messages. This is a python new-style format string
-# used to format the message information. See doc for all details
-#msg-template=
-
-# Set the output format. Available formats are text, parseable, colorized, json
-# and msvs (visual studio).You can also give a reporter class, eg
-# mypackage.mymodule.MyReporterClass.
-output-format=text
-
-# Tells whether to display a full report or only the messages
-reports=no
-
-# Activate the evaluation score.
-score=yes
-
-
-[REFACTORING]
-
-# Maximum number of nested blocks for function / method body
-max-nested-blocks=5
-
-
-[BASIC]
-
-# Naming hint for argument names
-argument-name-hint=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-# Regular expression matching correct argument names
-argument-rgx=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-# Naming hint for attribute names
-attr-name-hint=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-# Regular expression matching correct attribute names
-attr-rgx=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-# Bad variable names which should always be refused, separated by a comma
-bad-names=foo,bar,baz,toto,tutu,tata
-
-# Naming hint for class attribute names
-class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
-
-# Regular expression matching correct class attribute names
-class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
-
-# Naming hint for class names
-class-name-hint=[A-Z_][a-zA-Z0-9]+$
-
-# Regular expression matching correct class names
-class-rgx=[A-Z_][a-zA-Z0-9]+$
-
-# Naming hint for constant names
-const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$
-
-# Regular expression matching correct constant names
-const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$
-
-# Minimum line length for functions/classes that require docstrings, shorter
-# ones are exempt.
-docstring-min-length=10
-
-# Naming hint for function names
-function-name-hint=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-# Regular expression matching correct function names
-function-rgx=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-# Good variable names which should always be accepted, separated by a comma
-good-names=i,j,k,ex,Run,_
-
-# Include a hint for the correct naming format with invalid-name
-include-naming-hint=yes
-
-# Naming hint for inline iteration names
-inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$
-
-# Regular expression matching correct inline iteration names
-inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$
-
-# Naming hint for method names
-method-name-hint=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-# Regular expression matching correct method names
-method-rgx=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-# Naming hint for module names
-module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
-
-# Regular expression matching correct module names
-module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
-
-# Colon-delimited sets of names that determine each other's naming style when
-# the name regexes allow several styles.
-name-group=
-
-# Regular expression which should only match function or class names that do
-# not require a docstring.
-no-docstring-rgx=^_
-
-# List of decorators that produce properties, such as abc.abstractproperty. Add
-# to this list to register other decorators that produce valid properties.
-property-classes=abc.abstractproperty
-
-# Naming hint for variable names
-variable-name-hint=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-# Regular expression matching correct variable names
-variable-rgx=(([a-z][a-z0-9_]{2,30})|(_[a-z0-9_]*))$
-
-
-[TYPECHECK]
-
-# List of decorators that produce context managers, such as
-# contextlib.contextmanager. Add to this list to register other decorators that
-# produce valid context managers.
-contextmanager-decorators=contextlib.contextmanager,tensorflow.python.util.tf_contextlib.contextmanager
-
-# List of members which are set dynamically and missed by pylint inference
-# system, and so shouldn't trigger E1101 when accessed. Python regular
-# expressions are accepted.
-generated-members=
-
-# Tells whether missing members accessed in mixin class should be ignored. A
-# mixin class is detected if its name ends with "mixin" (case insensitive).
-ignore-mixin-members=yes
-
-# This flag controls whether pylint should warn about no-member and similar
-# checks whenever an opaque object is returned when inferring. The inference
-# can return multiple potential results while evaluating a Python object, but
-# some branches might not be evaluated, which results in partial inference. In
-# that case, it might be useful to still emit no-member and other checks for
-# the rest of the inferred objects.
-ignore-on-opaque-inference=yes
-
-# List of class names for which member attributes should not be checked (useful
-# for classes with dynamically set attributes). This supports the use of
-# qualified names.
-ignored-classes=optparse.Values,thread._local,_thread._local
-
-# List of module names for which member attributes should not be checked
-# (useful for modules/projects where namespaces are manipulated during runtime
-# and thus existing member attributes cannot be deduced by static analysis. It
-# supports qualified module names, as well as Unix pattern matching.
-ignored-modules=six,six.moves,*_pb2
-
-# Show a hint with possible names when a member name was not found. The aspect
-# of finding the hint is based on edit distance.
-missing-member-hint=yes
-
-# The minimum edit distance a name should have in order to be considered a
-# similar match for a missing member name.
-missing-member-hint-distance=1
-
-# The total number of similar names that should be taken in consideration when
-# showing a hint for a missing member.
-missing-member-max-choices=1
-
-
-[MISCELLANEOUS]
-
-# List of note tags to take in consideration, separated by a comma.
-notes=FIXME,XXX,TODO
-
-
-[VARIABLES]
-
-# List of additional names supposed to be defined in builtins. Remember that
-# you should avoid to define new builtins when possible.
-additional-builtins=
-
-# Tells whether unused global variables should be treated as a violation.
-allow-global-unused-variables=yes
-
-# List of strings which can identify a callback function by name. A callback
-# name must start or end with one of those strings.
-callbacks=cb_,_cb
-
-# A regular expression matching the name of dummy variables (i.e. expectedly
-# not used).
-dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
-
-# Argument names that match this expression will be ignored. Default to name
-# with leading underscore
-ignored-argument-names=_.*|^ignored_|^unused_
-
-# Tells whether we should check for unused import in __init__ files.
-init-import=no
-
-# List of qualified module names which can have objects that can redefine
-# builtins.
-redefining-builtins-modules=six.moves,future.builtins
-
-
-[FORMAT]
-
-# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
-expected-line-ending-format=
-
-# Regexp for a line that is allowed to be longer than the limit.
-ignore-long-lines=^\s*(# )?<?https?://\S+>?$
-
-# Number of spaces of indent required inside a hanging  or continued line.
-indent-after-paren=4
-
-# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
-# tab).
-indent-string='  '
-
-# Maximum number of characters on a single line.
-max-line-length=80
-
-# Maximum number of lines in a module
-max-module-lines=10000
-
-# List of optional constructs for which whitespace checking is disabled. `dict-
-# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
-# `trailing-comma` allows a space between comma and closing bracket: (a, ).
-# `empty-line` allows space-only lines.
-no-space-check=trailing-comma,dict-separator
-
-# Allow the body of a class to be on the same line as the declaration if body
-# contains single statement.
-single-line-class-stmt=no
-
-# Allow the body of an if to be on the same line as the test if there is no
-# else.
-single-line-if-stmt=no
-
-
-[SPELLING]
-
-# Spelling dictionary name. Available dictionaries: none. To make it working
-# install python-enchant package.
-spelling-dict=
-
-# List of comma separated words that should not be checked.
-spelling-ignore-words=
-
-# A path to a file that contains private dictionary; one word per line.
-spelling-private-dict-file=
-
-# Tells whether to store unknown words to indicated private dictionary in
-# --spelling-private-dict-file option instead of raising a message.
-spelling-store-unknown-words=no
-
-
-[LOGGING]
-
-# Logging modules to check that the string format arguments are in logging
-# function parameter format
-logging-modules=logging
-
-
-[SIMILARITIES]
-
-# Ignore comments when computing similarities.
-ignore-comments=yes
-
-# Ignore docstrings when computing similarities.
-ignore-docstrings=yes
-
-# Ignore imports when computing similarities.
-ignore-imports=yes
-
-# Minimum lines number of a similarity.
-min-similarity-lines=4
-
-
-[CLASSES]
-
-# List of method names used to declare (i.e. assign) instance attributes.
-defining-attr-methods=__init__,__new__,setUp
-
-# List of member names, which should be excluded from the protected access
-# warning.
-exclude-protected=_asdict,_fields,_replace,_source,_make
-
-# List of valid names for the first argument in a class method.
-valid-classmethod-first-arg=cls
-
-# List of valid names for the first argument in a metaclass class method.
-valid-metaclass-classmethod-first-arg=mcs
-
-
-[IMPORTS]
-
-# Allow wildcard imports from modules that define __all__.
-allow-wildcard-with-all=no
-
-# Analyse import fallback blocks. This can be used to support both Python 2 and
-# 3 compatible code, which means that the block might have code that exists
-# only in one or another interpreter, leading to false positives when analysed.
-analyse-fallback-blocks=no
-
-# Deprecated modules which should not be used, separated by a comma
-deprecated-modules=regsub,TERMIOS,Bastion,rexec
-
-# Create a graph of external dependencies in the given file (report RP0402 must
-# not be disabled)
-ext-import-graph=
-
-# Create a graph of every (i.e. internal and external) dependencies in the
-# given file (report RP0402 must not be disabled)
-import-graph=
-
-# Create a graph of internal dependencies in the given file (report RP0402 must
-# not be disabled)
-int-import-graph=
-
-# Force import order to recognize a module as part of the standard
-# compatibility libraries.
-known-standard-library=
-
-# Force import order to recognize a module as part of a third party library.
-known-third-party=enchant
-
-
-[DESIGN]
-
-# Maximum number of arguments for function / method
-max-args=5
-
-# Maximum number of attributes for a class (see R0902).
-max-attributes=7
-
-# Maximum number of boolean expressions in a if statement
-max-bool-expr=5
-
-# Maximum number of branch for function / method body
-max-branches=12
-
-# Maximum number of locals for function / method body
-max-locals=15
-
-# Maximum number of parents for a class (see R0901).
-max-parents=7
-
-# Maximum number of public methods for a class (see R0904).
-max-public-methods=20
-
-# Maximum number of return / yield for function / method body
-max-returns=6
-
-# Maximum number of statements in function / method body
-max-statements=80
-
-# Minimum number of public methods for a class (see R0903).
-min-public-methods=2
-
-
-[EXCEPTIONS]
-
-# Exceptions that will emit a warning when being caught. Defaults to
-# "Exception"
-overgeneral-exceptions=Exception
diff --git a/tfjs-master/tfjs-converter/python/BUILD.bazel b/tfjs-master/tfjs-converter/python/BUILD.bazel
deleted file mode 100644
index 3df31fc75..000000000
--- a/tfjs-master/tfjs-converter/python/BUILD.bazel
+++ /dev/null
@@ -1,88 +0,0 @@
-load("@rules_python//python:packaging.bzl", "py_package", "py_wheel")
-load("@rules_python//python:pip.bzl", "compile_pip_requirements")
-
-package(default_visibility = ["//visibility:public"])
-
-licenses(["notice"])  # Apache 2.0
-
-CONSOLE_SCRIPTS = {
-    "tensorflowjs_converter": "tensorflowjs.converters.converter:pip_main",
-    "tensorflowjs_wizard": "tensorflowjs.converters.wizard:pip_main",
-}
-
-compile_pip_requirements(
-    name = "tensorflowjs_deps_requirements",
-    extra_args = ["--allow-unsafe"],  # Allow pinning setuptools
-    requirements_in = "requirements.txt",
-    requirements_txt = "requirements_lock.txt",
-)
-
-compile_pip_requirements(
-    name = "tensorflowjs_dev_deps_requirements",
-    data = [
-        ":requirements.txt",
-    ],
-    extra_args = ["--allow-unsafe"],  # Allow pinning setuptools
-    requirements_in = "requirements-dev.txt",
-    requirements_txt = "requirements-dev_lock.txt",
-)
-
-py_package(
-    name = "tensorflowjs_pkg",
-    # Only include these Python packages.
-    packages = ["tfjs-converter/python/tensorflowjs"],
-    deps = ["//tfjs-converter/python/tensorflowjs"],
-)
-
-py_wheel(
-    name = "python3_wheel",
-    author = "Google LLC",
-    author_email = "opensource@google.com",
-    classifiers = [
-        "Development Status :: 3 - Alpha",
-        "Intended Audience :: Developers",
-        "Intended Audience :: Education",
-        "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: Apache Software License",
-        "Programming Language :: JavaScript",
-        "Programming Language :: Python :: 2",
-        "Programming Language :: Python :: 3",
-        "Topic :: Scientific/Engineering",
-        "Topic :: Scientific/Engineering :: Mathematics",
-        "Topic :: Scientific/Engineering :: Artificial Intelligence",
-        "Topic :: Software Development",
-        "Topic :: Software Development :: Libraries",
-        "Topic :: Software Development :: Libraries :: Python Modules",
-    ],
-    console_scripts = CONSOLE_SCRIPTS,
-    description_file = ":README.md",
-    distribution = "tensorflowjs",
-    extra_requires = {
-        "PyInquirer": ["PyInquirer==1.0.3"],
-        "all": ["PyInquirer==1.0.3"],
-        "wizard": ["PyInquirer==1.0.3"],
-    },
-    homepage = "https://js.tensorflow.org/",
-    license = "Apache 2.0",
-    python_tag = "py3",
-    requires = [
-        "flax>=0.7.2",
-        "importlib_resources>=5.9.0",
-        "jax>=0.4.13",
-        "jaxlib>=0.4.13",
-        "tensorflow>=2.13.0,<3",
-        "tensorflow-decision-forests>=1.5.0",
-        "six>=1.16.0,<2",
-        "tensorflow-hub>=0.14.0",
-        "packaging~=23.1",
-    ],
-    strip_path_prefixes = [
-        "tfjs-converter/python",
-    ],
-    version = "0.0.0",
-    #keywords='tensorflow javascript machine deep learning converter',
-    deps = [
-        ":tensorflowjs_pkg",
-        "//tfjs-converter/python/tensorflowjs",
-    ],
-)
diff --git a/tfjs-master/tfjs-converter/python/MANIFEST.in b/tfjs-master/tfjs-converter/python/MANIFEST.in
deleted file mode 100644
index f9bd1455b..000000000
--- a/tfjs-master/tfjs-converter/python/MANIFEST.in
+++ /dev/null
@@ -1 +0,0 @@
-include requirements.txt
diff --git a/tfjs-master/tfjs-converter/python/README.md b/tfjs-master/tfjs-converter/python/README.md
deleted file mode 100644
index 25849cd7c..000000000
--- a/tfjs-master/tfjs-converter/python/README.md
+++ /dev/null
@@ -1,63 +0,0 @@
-# tensorflowjs: The Python Package for TensorFlow.js
-
-The **tensorflowjs** pip package contains libraries and tools for
-[TensorFlow.js](https://js.tensorflow.org).
-
-Use following command to install the library with support of interactive CLI:
-```bash
-pip install tensorflowjs[wizard]
-```
-
-Then, run the following to see a list of CLI options
-
-```bash
-tensorflowjs_converter --help
-```
-
-or, use the wizard
-
-```bash
-tensorflowjs_wizard
-```
-
-Alternatively, run the converter via its Bazel target. This must be run from withing the tfjs repo:
-
-```bash
-yarn bazel run //tfjs-converter/python/tensorflowjs/converters:converter -- --help
-```
-
-## Development
-
-The python tests are run with Bazel.
-
-```bash
-yarn bazel test //tfjs-converter/python/...
-```
-
-Alternatively, run `yarn run-python-tests` to run the above command.
-
-To debug a specific test case, use the `--test_filter` option. For example,
-
-```bash
-yarn bazel test //tfjs-converter/python/tensorflowjs/converters:tf_saved_model_conversion_v2_test --test_filter=ConvertTest.test_convert_saved_model_v1
-```
-
-Interactive debugging with breakpoints is supported by `debugpy` in VSCode.
-To enable debugging, put this code at the top of the test file you want to
-debug.
-
-```python
-import debugpy
-debugpy.listen(('localhost', 5724))
-print("Waiting for debugger to connect. See tfjs-converter python README")
-debugpy.wait_for_client()
-```
-
-You may also need to add the following dependency to the test target in the
-Bazel `BUILD` file if it's not already present.
-```starlark
-"//tfjs-converter/python/tensorflowjs:expect_debugpy_installed"
-```
-
-Then, run the test with `bazel run --config=debugpy` and connect
-the VSCode debugger by selecting the `Python: Attach (Converter)` option.
diff --git a/tfjs-master/tfjs-converter/python/build-pip-package.sh b/tfjs-master/tfjs-converter/python/build-pip-package.sh
deleted file mode 100644
index 130e69b06..000000000
--- a/tfjs-master/tfjs-converter/python/build-pip-package.sh
+++ /dev/null
@@ -1,262 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# Build pip package for keras_model_converter.
-#
-# Run this script outside a virtualenv, as this script will activate a
-# virtualenv for python3 to generate the wheel files.
-#
-# Usage:
-#   build-pip-package.sh \
-#       [--test] [--upload] [--upload-to-test] [--confirm-upload] <DEST_DIR>
-#
-# Positional arguments:
-#   DEST_DIR: Destination directory for writing the pip wheels.
-#
-# Optional argumnets:
-#   --test: Test the pip packages by installing it (inside virtualenv)
-#           and running test_pip_package.py against the install.
-#   --test-nightly: Test the pip packages by installing it (inside virtualenv)
-#           and running test_pip_package.py and test_pip_nightly_package.py
-#           against the install.
-#   --build: Create the pip packages.
-#   --upload:         Upload the py2 and py3 wheels to prod PyPI.
-#   --upload-to-test: Upload the py2 and py3 wheels to test PyPI, mutually
-#                     exclusive with --upload.
-#   --confirm-upload: Do not prompt for yes/no before uploading to test or
-#                     prod PyPI. Use with care!
-#
-# N.B. For pypi/twine authentication, you need to have the file ~/.pypirc,
-#   with content formatted like below:
-# ```
-# [distutils]
-# index-servers =
-#     pypi-warehouse
-#     test-warehouse
-#
-# [test-warehouse]
-#     repository = https://test.pypi.org/legacy/
-#     username:<PYPIUSERNAME>
-#     password:<PYPIPWD>
-#
-# [pypi-warehouse]
-#     repository = https://upload.pypi.org/legacy/
-#     username:<PYPIUSERNAME>
-#     password:<PYPIPWD>
-# ```
-
-set -e
-
-function print_usage() {
-  echo "Usage:"
-  echo "  build-pip-packages.sh \\"
-  echo "      [--test] [--test-nightly] [--build] [--upload] [--upload-to-test] [--confirm-upload] <OUTPUT_DIR>"
-  echo
-}
-
-SCRIPTS_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-
-if [[ $# == 0 ]]; then
-  print_usage
-  exit 1
-fi
-
-RUN_TEST=0
-RUN_TEST_NIGHTLY=0
-UPLOAD_TO_PROD_PYPI=0
-UPLOAD_TO_TEST_PYPI=0
-CONFIRM_UPLOAD=0
-BUILD=0
-DEST_DIR=""
-while true; do
-  if [[ "$1" == "--test" ]]; then
-    RUN_TEST=1
-  elif [[ "$1" == "--test-nightly" ]]; then
-    RUN_TEST_NIGHTLY=1
-  elif [[ "$1" == "--upload" ]]; then
-    UPLOAD_TO_PROD_PYPI=1
-  elif [[ "$1" == "--upload-to-test" ]]; then
-    UPLOAD_TO_TEST_PYPI=1
-  elif [[ "$1" == "--confirm-upload" ]]; then
-    CONFIRM_UPLOAD=1
-  elif [[ "$1" == "--build" ]]; then
-    BUILD=1
-  elif [[ "$1" != --* ]]; then
-    DEST_DIR="$1"
-  else
-    print_usage
-    exit 1
-  fi
-  shift
-
-  if [[ -z "$1" ]]; then
-    break
-  fi
-done
-
-if [[ "${UPLOAD_TO_TEST_PYPI}" == 1 && "${UPLOAD_TO_PROD_PYPI}" == 1 ]]; then
-  echo "ERROR: Do not use --upload and --upload-to-test together."
-  exit 1
-fi
-
-if [[ -z "${DEST_DIR}" ]]; then
-  print_usage
-  exit 1
-fi
-
-if [[ -f "${DEST_DIR}" || -d "${DEST_DIR}" ]]; then
-  echo "ERROR: ${DEST_DIR} already exists. Please specify a new DEST_DIR."
-  exit 1
-fi
-
-mkdir -p "${DEST_DIR}"
-DEST_DIR="$(cd "${DEST_DIR}" 2>/dev/null && pwd -P)"
-
-pip install virtualenv
-
-# Check virtualenv is on path.
-if [[ -z "$(which virtualenv)" ]]; then
-  echo "ERROR: Cannot find virtualenv on path. Install virtualenv first."
-  exit 1
-fi
-
-# Create virtualenv for python3; build (and test) the wheel inside it.
-VENV_PYTHON_BINS="python3"
-for VENV_PYTHON_BIN in ${VENV_PYTHON_BINS}; do
-  if [[ -z "$(which "${VENV_PYTHON_BIN}")" ]]; then
-    echo "ERROR: Unable to find ${VENV_PYTHON_BIN} on path."
-    exit 1
-  fi
-
-  TMP_VENV_DIR="$(mktemp -d)"
-  virtualenv -p "${VENV_PYTHON_BIN}" "${TMP_VENV_DIR}"
-  source "${TMP_VENV_DIR}/bin/activate"
-
-  echo
-  echo "Looking for wheel for ${VENV_PYTHON_BIN}: $(python --version 2>&1) ..."
-  echo "The wheel should be build with 'bazel build python3_wheel' command"
-  echo
-
-  pushd "${TMP_DIR}" > /dev/null
-  echo
-
-  WHEELS=$(ls ../../dist/bin/tfjs-converter/python/*py${VENV_PYTHON_BIN: -1}*.whl)
-  cp ../../dist/bin/tfjs-converter/python/*py${VENV_PYTHON_BIN: -1}*.whl "${DEST_DIR}/"
-
-  WHEEL_PATH=""
-  echo
-  echo "Generated wheel file(s) in ${DEST_DIR} :"
-  for WHEEL in ${WHEELS}; do
-    WHEEL_BASE_NAME="$(basename "${WHEEL}")"
-    echo "  ${WHEEL_BASE_NAME}"
-    WHEEL_PATH="${DEST_DIR}/${WHEEL_BASE_NAME}"
-  done
-
-  # Run test on install.
-  if [[ "${RUN_TEST}" == "1" || "${RUN_TEST_NIGHTLY}" == 1 ]]; then
-    echo
-    echo "Running test-on-install for $(python --version 2>&1) ..."
-    echo
-
-    pip uninstall -y tensorflowjs || \
-      echo "It appears that tensorflowjs is not installed."
-
-    echo
-    echo "Installing tensorflowjs from wheel at path: ${WHEEL_PATH} ..."
-    echo
-
-    TEST_ON_INSTALL_DIR="$(mktemp -d)"
-
-    cp "${SCRIPTS_DIR}/test_pip_package.py" "${TEST_ON_INSTALL_DIR}"
-    if [[ "${RUN_TEST_NIGHTLY}" == 1 ]]; then
-      cp "${SCRIPTS_DIR}/test_nightly_pip_package.py" "${TEST_ON_INSTALL_DIR}"
-    fi
-
-    pushd "${TEST_ON_INSTALL_DIR}" > /dev/null
-
-    pip install "${WHEEL_PATH}[wizard]"
-    echo "Successfully installed ${WHEEL_PATH} for $(python --version 2>&1)."
-    echo
-
-    python test_pip_package.py
-
-    if [[ "${RUN_TEST_NIGHTLY}" == 1 ]]; then
-      python test_nightly_pip_package.py
-    fi
-
-    popd > /dev/null
-
-    rm -rf "${TEST_ON_INSTALL_DIR}"
-
-    echo
-    echo "Test-on-install for $(python --version 2>&1) PASSED."
-    echo
-    echo "Your pip wheel for $(python --version 2>&1) is at:"
-    echo "  ${WHEEL_PATH}"
-  fi
-
-  popd > /dev/null
-
-  deactivate
-  rm -rf "${TMP_VENV_DIR}"
-done
-
-if [[ "${UPLOAD_TO_PROD_PYPI}" == 1 || "${UPLOAD_TO_TEST_PYPI}" == 1 ]]; then
-  if [[ "${UPLOAD_TO_PROD_PYPI}" == 1 ]]; then
-    UPLOAD_DEST="pypi-warehouse"
-  else
-    UPLOAD_DEST="test-warehouse"
-  fi
-
-  pushd "${DEST_DIR}" > /dev/null
-  echo "Found wheel files to upload to ${UPLOAD_DEST}:"
-  ls ./*.whl
-  echo
-
-  TO_UPLOAD=0
-  if [[ "${CONFIRM_UPLOAD}" == 0 ]]; then
-    while true; do
-      read -r -p "Do you wish to proceed with the upload to ${UPLOAD_DEST}? (y/n): " yn
-        case $yn in
-          [Yy]* ) TO_UPLOAD=1; break;;
-          [Nn]* ) exit;;
-          * ) echo "Please answer y or n.";;
-        esac
-    done
-  else
-    TO_UPLOAD=1
-  fi
-
-  if [[ "${TO_UPLOAD}" == 1 ]]; then
-    echo "Proceeding with the upload ..."
-    echo
-
-    # Create a virtualenv and install twine in it for uploading.
-    TMP_VENV_DIR="$(mktemp -d)"
-    virtualenv -p "${VENV_PYTHON_BIN}" "${TMP_VENV_DIR}"
-    source "${TMP_VENV_DIR}/bin/activate"
-    pip install twine
-
-    twine upload -r "${UPLOAD_DEST}" ./*.whl
-
-    deactivate
-    rm -rf "${TMP_VENV_DIR}"
-  fi
-
-  popd > /dev/null
-fi
-
-rm -rf "${TMP_DIR}"
diff --git a/tfjs-master/tfjs-converter/python/extra-requirements.txt b/tfjs-master/tfjs-converter/python/extra-requirements.txt
deleted file mode 100644
index 25f974647..000000000
--- a/tfjs-master/tfjs-converter/python/extra-requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-PyInquirer==1.0.3: wizard
diff --git a/tfjs-master/tfjs-converter/python/requirements-dev.txt b/tfjs-master/tfjs-converter/python/requirements-dev.txt
deleted file mode 100644
index adcb0732e..000000000
--- a/tfjs-master/tfjs-converter/python/requirements-dev.txt
+++ /dev/null
@@ -1,5 +0,0 @@
--r requirements.txt
-PyInquirer==1.0.3
-pylint==2.5.0; python_version > '3.0'
-setuptools==65.6.3
-debugpy==1.6.5
diff --git a/tfjs-master/tfjs-converter/python/requirements-dev_lock.txt b/tfjs-master/tfjs-converter/python/requirements-dev_lock.txt
deleted file mode 100644
index f99ce9fce..000000000
--- a/tfjs-master/tfjs-converter/python/requirements-dev_lock.txt
+++ /dev/null
@@ -1,905 +0,0 @@
-#
-# This file is autogenerated by pip-compile with Python 3.8
-# by the following command:
-#
-#    bazel run //tfjs-converter/python:tensorflowjs_dev_deps_requirements.update
-#
-absl-py==1.3.0 \
-    --hash=sha256:34995df9bd7a09b3b8749e230408f5a2a2dd7a68a0d33c12a3d0cb15a041a507 \
-    --hash=sha256:463c38a08d2e4cef6c498b76ba5bd4858e4c6ef51da1a5a1f27139a022e20248
-    # via
-    #   chex
-    #   optax
-    #   orbax-checkpoint
-    #   tensorboard
-    #   tensorflow
-    #   tensorflow-decision-forests
-astroid==2.5 \
-    --hash=sha256:87ae7f2398b8a0ae5638ddecf9987f081b756e0e9fc071aeebdca525671fc4dc \
-    --hash=sha256:b31c92f545517dcc452f284bc9c044050862fbe6d93d2b3de4a215a6b384bf0d
-    # via pylint
-astunparse==1.6.3 \
-    --hash=sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872 \
-    --hash=sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8
-    # via tensorflow
-cached-property==1.5.2 \
-    --hash=sha256:9fa5755838eecbb2d234c3aa390bd80fbd3ac6b6869109bfc1b499f7bd89a130 \
-    --hash=sha256:df4f613cf7ad9a588cc381aaf4a512d26265ecebd5eb9e1ba12f1319eb85a6a0
-    # via orbax-checkpoint
-cachetools==5.2.0 \
-    --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \
-    --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db
-    # via google-auth
-certifi==2022.12.7 \
-    --hash=sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3 \
-    --hash=sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18
-    # via requests
-charset-normalizer==2.1.1 \
-    --hash=sha256:5a3d016c7c547f69d6f81fb0db9449ce888b418b5b9952cc5e6e66843e9dd845 \
-    --hash=sha256:83e9a75d1911279afd89352c68b45348559d1fc0506b054b346651b5e7fee29f
-    # via requests
-chex==0.1.5 \
-    --hash=sha256:686858320f8f220c82a6c7eeb54dcdcaa4f3d7f66690dacd13a24baa1ee8299e \
-    --hash=sha256:b3321184850d5fc29b2eca63087cdbdd83a1b3e4f33c1314ff8b3b8bd67abbca
-    # via optax
-commonmark==0.9.1 \
-    --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \
-    --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9
-    # via rich
-debugpy==1.6.5 \
-    --hash=sha256:048368f121c08b00bbded161e8583817af5055982d2722450a69efe2051621c2 \
-    --hash=sha256:0f9afcc8cad6424695f3356dc9a7406d5b18e37ee2e73f34792881a44b02cc50 \
-    --hash=sha256:15bc5febe0edc79726517b1f8d57d7ac7c784567b5ba804aab8b1c9d07a57018 \
-    --hash=sha256:17039e392d6f38388a68bd02c5f823b32a92142a851e96ba3ec52aeb1ce9d900 \
-    --hash=sha256:286ae0c2def18ee0dc8a61fa76d51039ca8c11485b6ed3ef83e3efe8a23926ae \
-    --hash=sha256:377391341c4b86f403d93e467da8e2d05c22b683f08f9af3e16d980165b06b90 \
-    --hash=sha256:500dd4a9ff818f5c52dddb4a608c7de5371c2d7d905c505eb745556c579a9f11 \
-    --hash=sha256:5e55e6c79e215239dd0794ee0bf655412b934735a58e9d705e5c544f596f1603 \
-    --hash=sha256:62a06eb78378292ba6c427d861246574dc8b84471904973797b29dd33c7c2495 \
-    --hash=sha256:696165f021a6a17da08163eaae84f3faf5d8be68fb78cd78488dd347e625279c \
-    --hash=sha256:74e4eca42055759032e3f1909d1374ba1d729143e0c2729bb8cb5e8b5807c458 \
-    --hash=sha256:7e84d9e4420122384cb2cc762a00b4e17cbf998022890f89b195ce178f78ff47 \
-    --hash=sha256:8116e40a1cd0593bd2aba01d4d560ee08f018da8e8fbd4cbd24ff09b5f0e41ef \
-    --hash=sha256:8f3fab217fe7e2acb2d90732af1a871947def4e2b6654945ba1ebd94bd0bea26 \
-    --hash=sha256:947c686e8adb46726f3d5f19854f6aebf66c2edb91225643c7f44b40b064a235 \
-    --hash=sha256:9984fc00ab372c97f63786c400107f54224663ea293daab7b365a5b821d26309 \
-    --hash=sha256:9e809ef787802c808995e5b6ade714a25fa187f892b41a412d418a15a9c4a432 \
-    --hash=sha256:b5a74ecebe5253344501d9b23f74459c46428b30437fa9254cfb8cb129943242
-    # via -r tfjs-converter/python/requirements-dev.txt
-dm-tree==0.1.7 \
-    --hash=sha256:0f01743cc2247170e64798c6b4b31853717054bf9ceec47a1b1b8c2a4baf5792 \
-    --hash=sha256:1379a02df36e2bbff9819ceafa55ccd436b15af398803f781f372f8ead7ed871 \
-    --hash=sha256:1410fa2f2cc8dc7c01386f4e93ddeeb56765574ffafb632a9b6bd96496195b10 \
-    --hash=sha256:20f24cad4decbf4c1f176a959d16e877c73df33b07d7d1f078a5b8abe72f79f8 \
-    --hash=sha256:2a843608e078d1622ebb5e50962a8c718d3fa1ab9461b95a12395a803545b2f5 \
-    --hash=sha256:30fec8aca5b92823c0e796a2f33b875b4dccd470b57e91e6c542405c5f77fd2a \
-    --hash=sha256:3166304411d14c50a5da1c583e24d6069b44de0c9e06479cb36cdf048a466945 \
-    --hash=sha256:3b00885c21267934a3d3c68660811d3f891c9539fd53712f5b2423c6d74bf1e6 \
-    --hash=sha256:3ca0a58e219b7b0bc201fea4679971188d0a9028a2543c16803a84e8f8c7eb2c \
-    --hash=sha256:3fae437135b6cbbdd51e96488a35e78c3617defa0b65265e7e8752d506f933fd \
-    --hash=sha256:4992ac5c42af1d73042cd2d3af4e7892d3750e6c1bb8e5a4f81534aa6515f350 \
-    --hash=sha256:51b9bdf1109b47cc22884b1919e6fe38edf28b5aa02e7c661bb760a0e7cf0157 \
-    --hash=sha256:57edb6fbd88fcdd9908547cbf21045a9d663c0d9e5983dca7e6f9cf8b6584bb5 \
-    --hash=sha256:7f1f3dca9d669f3c09654ff6d69cfafd86a7f967c3095405b2692ee8d8ef3cfd \
-    --hash=sha256:7fa0740b7fbae2c3a43a3114a514891b5d6c383050828f36aa1816cf40f73a6a \
-    --hash=sha256:91c6240e47c9d80dbd7de5a29a2ca663143717a72c613130ba8ac4354fa741a9 \
-    --hash=sha256:98fce150ceebb0a818f0eace1616004031cfa5e3375f50599ad790ff52414ba9 \
-    --hash=sha256:9edc1783a08d87c4e130781f55cbd904d6a564f7cce7dfb63f9ef3bee8e38209 \
-    --hash=sha256:a085f500b295a6bf439c538e9058c7798ecb8c7d0dc916291f3d8d79d6124d17 \
-    --hash=sha256:b4364fc9a5721a2b840ac8ea75b8f58b430bec9fdc8b99304d2aecb3cfe46b1b \
-    --hash=sha256:d377bd621b485db42c4aeea0eabbd8f6274b89a9c338c2c1bf69a40c3b86a1fd \
-    --hash=sha256:f3e2bd9b9c05d1a0039f7c128d8b055c8a05708ef569cdbbeec0a2946e425bd4
-    # via chex
-etils==1.3.0 \
-    --hash=sha256:0a695ec45a982ae7c9deb437f1f251346d88b43ca59be67e961f61fe8bc8cae4 \
-    --hash=sha256:809a92ff72f12149441492cf4d9a26b56a4741dffb4dfb9c4c7b7afe055c2d28
-    # via orbax-checkpoint
-flatbuffers==23.5.26 \
-    --hash=sha256:9ea1144cac05ce5d86e2859f431c6cd5e66cd9c78c558317c7955fb8d4c78d89 \
-    --hash=sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1
-    # via tensorflow
-flax==0.7.2 \
-    --hash=sha256:261c7b93e6d15ad80e2cedd2edb797d41b0b3c7805a54254de72a2366dc80148 \
-    --hash=sha256:7f023ece0b8b0d03019d2841dbe780e33b81ec42268bdc3e5d24c8fb0582fd7b
-    # via -r tfjs-converter/python/requirements.txt
-gast==0.4.0 \
-    --hash=sha256:40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1 \
-    --hash=sha256:b7adcdd5adbebf1adf17378da5ba3f543684dbec47b1cda1f3997e573cd542c4
-    # via tensorflow
-google-auth==2.15.0 \
-    --hash=sha256:6897b93556d8d807ad70701bb89f000183aea366ca7ed94680828b37437a4994 \
-    --hash=sha256:72f12a6cfc968d754d7bdab369c5c5c16032106e52d32c6dfd8484e4c01a6d1f
-    # via
-    #   google-auth-oauthlib
-    #   tensorboard
-google-auth-oauthlib==1.0.0 \
-    --hash=sha256:95880ca704928c300f48194d1770cf5b1462835b6e49db61445a520f793fd5fb \
-    --hash=sha256:e375064964820b47221a7e1b7ee1fd77051b6323c3f9e3e19785f78ab67ecfc5
-    # via tensorboard
-google-pasta==0.2.0 \
-    --hash=sha256:4612951da876b1a10fe3960d7226f0c7682cf901e16ac06e473b267a5afa8954 \
-    --hash=sha256:b32482794a366b5366a32c92a9a9201b107821889935a02b3e51f6b432ea84ed \
-    --hash=sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e
-    # via tensorflow
-grpcio==1.51.1 \
-    --hash=sha256:094e64236253590d9d4075665c77b329d707b6fca864dd62b144255e199b4f87 \
-    --hash=sha256:0dc5354e38e5adf2498312f7241b14c7ce3484eefa0082db4297189dcbe272e6 \
-    --hash=sha256:0e1a9e1b4a23808f1132aa35f968cd8e659f60af3ffd6fb00bcf9a65e7db279f \
-    --hash=sha256:0fb93051331acbb75b49a2a0fd9239c6ba9528f6bdc1dd400ad1cb66cf864292 \
-    --hash=sha256:16c71740640ba3a882f50b01bf58154681d44b51f09a5728180a8fdc66c67bd5 \
-    --hash=sha256:172405ca6bdfedd6054c74c62085946e45ad4d9cec9f3c42b4c9a02546c4c7e9 \
-    --hash=sha256:17ec9b13cec4a286b9e606b48191e560ca2f3bbdf3986f91e480a95d1582e1a7 \
-    --hash=sha256:22b011674090594f1f3245960ced7386f6af35485a38901f8afee8ad01541dbd \
-    --hash=sha256:24ac1154c4b2ab4a0c5326a76161547e70664cd2c39ba75f00fc8a2170964ea2 \
-    --hash=sha256:257478300735ce3c98d65a930bbda3db172bd4e00968ba743e6a1154ea6edf10 \
-    --hash=sha256:29cb97d41a4ead83b7bcad23bdb25bdd170b1e2cba16db6d3acbb090bc2de43c \
-    --hash=sha256:2b170eaf51518275c9b6b22ccb59450537c5a8555326fd96ff7391b5dd75303c \
-    --hash=sha256:31bb6bc7ff145e2771c9baf612f4b9ebbc9605ccdc5f3ff3d5553de7fc0e0d79 \
-    --hash=sha256:3c2b3842dcf870912da31a503454a33a697392f60c5e2697c91d133130c2c85d \
-    --hash=sha256:3f9b0023c2c92bebd1be72cdfca23004ea748be1813a66d684d49d67d836adde \
-    --hash=sha256:471d39d3370ca923a316d49c8aac66356cea708a11e647e3bdc3d0b5de4f0a40 \
-    --hash=sha256:49d680356a975d9c66a678eb2dde192d5dc427a7994fb977363634e781614f7c \
-    --hash=sha256:4c4423ea38a7825b8fed8934d6d9aeebdf646c97e3c608c3b0bcf23616f33877 \
-    --hash=sha256:506b9b7a4cede87d7219bfb31014d7b471cfc77157da9e820a737ec1ea4b0663 \
-    --hash=sha256:538d981818e49b6ed1e9c8d5e5adf29f71c4e334e7d459bf47e9b7abb3c30e09 \
-    --hash=sha256:59dffade859f157bcc55243714d57b286da6ae16469bf1ac0614d281b5f49b67 \
-    --hash=sha256:5a6ebcdef0ef12005d56d38be30f5156d1cb3373b52e96f147f4a24b0ddb3a9d \
-    --hash=sha256:5dca372268c6ab6372d37d6b9f9343e7e5b4bc09779f819f9470cd88b2ece3c3 \
-    --hash=sha256:6df3b63538c362312bc5fa95fb965069c65c3ea91d7ce78ad9c47cab57226f54 \
-    --hash=sha256:6f0b89967ee11f2b654c23b27086d88ad7bf08c0b3c2a280362f28c3698b2896 \
-    --hash=sha256:75e29a90dc319f0ad4d87ba6d20083615a00d8276b51512e04ad7452b5c23b04 \
-    --hash=sha256:7942b32a291421460d6a07883033e392167d30724aa84987e6956cd15f1a21b9 \
-    --hash=sha256:9235dcd5144a83f9ca6f431bd0eccc46b90e2c22fe27b7f7d77cabb2fb515595 \
-    --hash=sha256:97d67983189e2e45550eac194d6234fc38b8c3b5396c153821f2d906ed46e0ce \
-    --hash=sha256:9ff42c5620b4e4530609e11afefa4a62ca91fa0abb045a8957e509ef84e54d30 \
-    --hash=sha256:a8a0b77e992c64880e6efbe0086fe54dfc0bbd56f72a92d9e48264dcd2a3db98 \
-    --hash=sha256:aacb54f7789ede5cbf1d007637f792d3e87f1c9841f57dd51abf89337d1b8472 \
-    --hash=sha256:bc59f7ba87972ab236f8669d8ca7400f02a0eadf273ca00e02af64d588046f02 \
-    --hash=sha256:cc2bece1737b44d878cc1510ea04469a8073dbbcdd762175168937ae4742dfb3 \
-    --hash=sha256:cd3baccea2bc5c38aeb14e5b00167bd4e2373a373a5e4d8d850bd193edad150c \
-    --hash=sha256:dad6533411d033b77f5369eafe87af8583178efd4039c41d7515d3336c53b4f1 \
-    --hash=sha256:e223a9793522680beae44671b9ed8f6d25bbe5ddf8887e66aebad5e0686049ef \
-    --hash=sha256:e473525c28251558337b5c1ad3fa969511e42304524a4e404065e165b084c9e4 \
-    --hash=sha256:e4ef09f8997c4be5f3504cefa6b5c6cc3cf648274ce3cede84d4342a35d76db6 \
-    --hash=sha256:e6dfc2b6567b1c261739b43d9c59d201c1b89e017afd9e684d85aa7a186c9f7a \
-    --hash=sha256:eacad297ea60c72dd280d3353d93fb1dcca952ec11de6bb3c49d12a572ba31dd \
-    --hash=sha256:f1158bccbb919da42544a4d3af5d9296a3358539ffa01018307337365a9a0c64 \
-    --hash=sha256:f1fec3abaf274cdb85bf3878167cfde5ad4a4d97c68421afda95174de85ba813 \
-    --hash=sha256:f96ace1540223f26fbe7c4ebbf8a98e3929a6aa0290c8033d12526847b291c0f \
-    --hash=sha256:fbdbe9a849854fe484c00823f45b7baab159bdd4a46075302281998cb8719df5
-    # via
-    #   tensorboard
-    #   tensorflow
-h5py==3.7.0 \
-    --hash=sha256:03d64fb86bb86b978928bad923b64419a23e836499ec6363e305ad28afd9d287 \
-    --hash=sha256:04e2e1e2fc51b8873e972a08d2f89625ef999b1f2d276199011af57bb9fc7851 \
-    --hash=sha256:0798a9c0ff45f17d0192e4d7114d734cac9f8b2b2c76dd1d923c4d0923f27bb6 \
-    --hash=sha256:0a047fddbe6951bce40e9cde63373c838a978c5e05a011a682db9ba6334b8e85 \
-    --hash=sha256:0d8de8cb619fc597da7cf8cdcbf3b7ff8c5f6db836568afc7dc16d21f59b2b49 \
-    --hash=sha256:1fcb11a2dc8eb7ddcae08afd8fae02ba10467753a857fa07a404d700a93f3d53 \
-    --hash=sha256:3fcf37884383c5da64846ab510190720027dca0768def34dd8dcb659dbe5cbf3 \
-    --hash=sha256:43fed4d13743cf02798a9a03a360a88e589d81285e72b83f47d37bb64ed44881 \
-    --hash=sha256:63beb8b7b47d0896c50de6efb9a1eaa81dbe211f3767e7dd7db159cea51ba37a \
-    --hash=sha256:6776d896fb90c5938de8acb925e057e2f9f28755f67ec3edcbc8344832616c38 \
-    --hash=sha256:9e2ad2aa000f5b1e73b5dfe22f358ca46bf1a2b6ca394d9659874d7fc251731a \
-    --hash=sha256:9e7535df5ee3dc3e5d1f408fdfc0b33b46bc9b34db82743c82cd674d8239b9ad \
-    --hash=sha256:a9351d729ea754db36d175098361b920573fdad334125f86ac1dd3a083355e20 \
-    --hash=sha256:c038399ce09a58ff8d89ec3e62f00aa7cb82d14f34e24735b920e2a811a3a426 \
-    --hash=sha256:d77af42cb751ad6cc44f11bae73075a07429a5cf2094dfde2b1e716e059b3911 \
-    --hash=sha256:e5b7820b75f9519499d76cc708e27242ccfdd9dfb511d6deb98701961d0445aa \
-    --hash=sha256:ed43e2cc4f511756fd664fb45d6b66c3cbed4e3bd0f70e29c37809b2ae013c44 \
-    --hash=sha256:f084bbe816907dfe59006756f8f2d16d352faff2d107f4ffeb1d8de126fc5dc7 \
-    --hash=sha256:f514b24cacdd983e61f8d371edac8c1b780c279d0acb8485639e97339c866073 \
-    --hash=sha256:f73307c876af49aa869ec5df1818e9bb0bdcfcf8a5ba773cc45a4fba5a286a5c
-    # via tensorflow
-idna==3.4 \
-    --hash=sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4 \
-    --hash=sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2
-    # via requests
-importlib-metadata==5.1.0 \
-    --hash=sha256:d5059f9f1e8e41f80e9c56c2ee58811450c31984dfa625329ffd7c0dad88a73b \
-    --hash=sha256:d84d17e21670ec07990e1044a99efe8d615d860fd176fc29ef5c306068fda313
-    # via
-    #   jax
-    #   markdown
-importlib-resources==5.10.1 \
-    --hash=sha256:32bb095bda29741f6ef0e5278c42df98d135391bee5f932841efc0041f748dc3 \
-    --hash=sha256:c09b067d82e72c66f4f8eb12332f5efbebc9b007c0b6c40818108c9870adc363
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   orbax-checkpoint
-isort==4.3.21 \
-    --hash=sha256:54da7e92468955c4fceacd0c86bd0ec997b0e1ee80d97f67c35a78b719dccab1 \
-    --hash=sha256:6e811fcb295968434526407adb8796944f1988c5b65e8139058f2014cbe100fd
-    # via pylint
-jax==0.4.13 \
-    --hash=sha256:03bfe6749dfe647f16f15f6616638adae6c4a7ca7167c75c21961ecfd3a3baaa
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   chex
-    #   flax
-    #   optax
-    #   orbax-checkpoint
-jaxlib==0.4.13 \
-    --hash=sha256:19ae4c316b17a49342432c69f7f89f190b975333f3f9e9e175f686a651bc7347 \
-    --hash=sha256:411334d903df07dc1ace8d52fc53c17f6bc1d55aff7f6e0e5cf61ec149f758a0 \
-    --hash=sha256:49690fcdd26560515fd15399fc3a44777e0bfc5db5c48fe76ff7bc7228e8b2fb \
-    --hash=sha256:522635d5e159401a386c79f1236c218c1f68fbb4ca6648115c3ad3c2c3f518ab \
-    --hash=sha256:532ebc4fb11386282ad63b83941d4557f4038c1144acf026f1f8565f64c7e9c0 \
-    --hash=sha256:8000c0d15c107328e8f7b7b3ac91dd822f5c287a80231882b620503ed141fa89 \
-    --hash=sha256:839173b2e9593f5e9a6d3c42852cd15070fe80a939246efbb5cf40eec815de89 \
-    --hash=sha256:a259bb35429bfbd3b76e43019dfc8f7d6ea94bb217400b78f7d0824ce07a58ac \
-    --hash=sha256:b5c0a9737efd95fe18fd7715ce30dfce476546705ea8934aad6731777a9631a5 \
-    --hash=sha256:bebb4cf001f180dc431f9604daf930c2d9cc778e4dda26f401ac939b7bac912e \
-    --hash=sha256:c230ef85712e608d0f048869766a5a63afeb2e72309943db0df9f959ab17307f \
-    --hash=sha256:d19c05c15f962e098d49b45e2758aacf19330d192ec5395f9ef136f62db90edc \
-    --hash=sha256:ea1bc9811ef7d73a15e3213115e88fe7f5d14b59d95027bea9fccc98e5a14af8 \
-    --hash=sha256:f4e9e34e5d8a6556f62fead14aee0b1614c2c6296f0078d8e6139d6aff109649 \
-    --hash=sha256:fde66a93e9be89d99e5792f677ed8e319667d6b2396865b1c52c1312844c47f9
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   chex
-    #   optax
-    #   orbax-checkpoint
-keras==2.13.1 \
-    --hash=sha256:5ce5f706f779fa7330e63632f327b75ce38144a120376b2ae1917c00fa6136af \
-    --hash=sha256:5df12cc241a015a11b65ddb452c0eeb2744fce21d9b54ba48db87492568ccc68
-    # via tensorflow
-lazy-object-proxy==1.8.0 \
-    --hash=sha256:0c1c7c0433154bb7c54185714c6929acc0ba04ee1b167314a779b9025517eada \
-    --hash=sha256:14010b49a2f56ec4943b6cf925f597b534ee2fe1f0738c84b3bce0c1a11ff10d \
-    --hash=sha256:4e2d9f764f1befd8bdc97673261b8bb888764dfdbd7a4d8f55e4fbcabb8c3fb7 \
-    --hash=sha256:4fd031589121ad46e293629b39604031d354043bb5cdf83da4e93c2d7f3389fe \
-    --hash=sha256:5b51d6f3bfeb289dfd4e95de2ecd464cd51982fe6f00e2be1d0bf94864d58acd \
-    --hash=sha256:6850e4aeca6d0df35bb06e05c8b934ff7c533734eb51d0ceb2d63696f1e6030c \
-    --hash=sha256:6f593f26c470a379cf7f5bc6db6b5f1722353e7bf937b8d0d0b3fba911998858 \
-    --hash=sha256:71d9ae8a82203511a6f60ca5a1b9f8ad201cac0fc75038b2dc5fa519589c9288 \
-    --hash=sha256:7e1561626c49cb394268edd00501b289053a652ed762c58e1081224c8d881cec \
-    --hash=sha256:8f6ce2118a90efa7f62dd38c7dbfffd42f468b180287b748626293bf12ed468f \
-    --hash=sha256:ae032743794fba4d171b5b67310d69176287b5bf82a21f588282406a79498891 \
-    --hash=sha256:afcaa24e48bb23b3be31e329deb3f1858f1f1df86aea3d70cb5c8578bfe5261c \
-    --hash=sha256:b70d6e7a332eb0217e7872a73926ad4fdc14f846e85ad6749ad111084e76df25 \
-    --hash=sha256:c219a00245af0f6fa4e95901ed28044544f50152840c5b6a3e7b2568db34d156 \
-    --hash=sha256:ce58b2b3734c73e68f0e30e4e725264d4d6be95818ec0a0be4bb6bf9a7e79aa8 \
-    --hash=sha256:d176f392dbbdaacccf15919c77f526edf11a34aece58b55ab58539807b85436f \
-    --hash=sha256:e20bfa6db17a39c706d24f82df8352488d2943a3b7ce7d4c22579cb89ca8896e \
-    --hash=sha256:eac3a9a5ef13b332c059772fd40b4b1c3d45a3a2b05e33a361dee48e54a4dad0 \
-    --hash=sha256:eb329f8d8145379bf5dbe722182410fe8863d186e51bf034d2075eb8d85ee25b
-    # via astroid
-libclang==14.0.6 \
-    --hash=sha256:206d2789e4450a37d054e63b70451a6fc1873466397443fa13de2b3d4adb2796 \
-    --hash=sha256:2e4303e04517fcd11173cb2e51a7070eed71e16ef45d4e26a82c5e881cac3d27 \
-    --hash=sha256:5dd3c6fca1b007d308a4114afa8e4e9d32f32b2572520701d45fcc626ac5cd6c \
-    --hash=sha256:7b06fc76bd1e67c8b04b5719bf2ac5d6a323b289b245dfa9e468561d99538188 \
-    --hash=sha256:8791cf3c3b087c373a6d61e9199da7a541da922c9ddcfed1122090586b996d6e \
-    --hash=sha256:9052a8284d8846984f6fa826b1d7460a66d3b23a486d782633b42b6e3b418789 \
-    --hash=sha256:cfb0e892ebb5dff6bd498ab5778adb8581f26a00fd8347b3c76c989fe2fd04f7 \
-    --hash=sha256:e2add1703129b2abe066fb1890afa880870a89fd6ab4ec5d2a7a8dc8d271677e \
-    --hash=sha256:e429853939423f276a25140b0b702442d7da9a09e001c05e48df888336947614 \
-    --hash=sha256:ea03c12675151837660cdd5dce65bd89320896ac3421efef43a36678f113ce95
-    # via tensorflow
-markdown==3.4.1 \
-    --hash=sha256:08fb8465cffd03d10b9dd34a5c3fea908e20391a2a90b88d66362cb05beed186 \
-    --hash=sha256:3b809086bb6efad416156e00a0da66fe47618a5d6918dd688f53f40c8e4cfeff
-    # via tensorboard
-markupsafe==2.1.1 \
-    --hash=sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003 \
-    --hash=sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88 \
-    --hash=sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5 \
-    --hash=sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7 \
-    --hash=sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a \
-    --hash=sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603 \
-    --hash=sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1 \
-    --hash=sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135 \
-    --hash=sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247 \
-    --hash=sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6 \
-    --hash=sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601 \
-    --hash=sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77 \
-    --hash=sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02 \
-    --hash=sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e \
-    --hash=sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63 \
-    --hash=sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f \
-    --hash=sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980 \
-    --hash=sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b \
-    --hash=sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812 \
-    --hash=sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff \
-    --hash=sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96 \
-    --hash=sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1 \
-    --hash=sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925 \
-    --hash=sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a \
-    --hash=sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6 \
-    --hash=sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e \
-    --hash=sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f \
-    --hash=sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4 \
-    --hash=sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f \
-    --hash=sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3 \
-    --hash=sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c \
-    --hash=sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a \
-    --hash=sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417 \
-    --hash=sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a \
-    --hash=sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a \
-    --hash=sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37 \
-    --hash=sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452 \
-    --hash=sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933 \
-    --hash=sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a \
-    --hash=sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7
-    # via werkzeug
-mccabe==0.6.1 \
-    --hash=sha256:ab8a6258860da4b6677da4bd2fe5dc2c659cff31b3ee4f7f5d64e79735b80d42 \
-    --hash=sha256:dd8d182285a0fe56bace7f45b5e7d1a6ebcbf524e8f3bd87eb0f125271b8831f
-    # via pylint
-ml-dtypes==0.2.0 \
-    --hash=sha256:022d5a4ee6be14569c2a9d1549e16f1ec87ca949681d0dca59995445d5fcdd5b \
-    --hash=sha256:1749b60348da71fd3c2ab303fdbc1965958dc50775ead41f5669c932a341cafd \
-    --hash=sha256:32107e7fa9f62db9a5281de923861325211dfff87bd23faefb27b303314635ab \
-    --hash=sha256:35b984cddbe8173b545a0e3334fe56ea1a5c3eb67c507f60d0cfde1d3fa8f8c2 \
-    --hash=sha256:36d28b8861a8931695e5a31176cad5ae85f6504906650dea5598fbec06c94606 \
-    --hash=sha256:50845af3e9a601810751b55091dee6c2562403fa1cb4e0123675cf3a4fc2c17a \
-    --hash=sha256:6488eb642acaaf08d8020f6de0a38acee7ac324c1e6e92ee0c0fea42422cb797 \
-    --hash=sha256:75015818a7fccf99a5e8ed18720cb430f3e71a8838388840f4cdf225c036c983 \
-    --hash=sha256:80d304c836d73f10605c58ccf7789c171cc229bfb678748adfb7cea2510dfd0e \
-    --hash=sha256:832a019a1b6db5c4422032ca9940a990fa104eee420f643713241b3a518977fa \
-    --hash=sha256:8faaf0897942c8253dd126662776ba45f0a5861968cf0f06d6d465f8a7bc298a \
-    --hash=sha256:bc29a0524ef5e23a7fbb8d881bdecabeb3fc1d19d9db61785d077a86cb94fab2 \
-    --hash=sha256:df6a76e1c8adf484feb138ed323f9f40a7b6c21788f120f7c78bec20ac37ee81 \
-    --hash=sha256:e70047ec2c83eaee01afdfdabee2c5b0c133804d90d0f7db4dd903360fcc537c \
-    --hash=sha256:e85ba8e24cf48d456e564688e981cf379d4c8e644db0a2f719b78de281bac2ca \
-    --hash=sha256:f00c71c8c63e03aff313bc6a7aeaac9a4f1483a921a6ffefa6d4404efd1af3d0 \
-    --hash=sha256:f08c391c2794f2aad358e6f4c70785a9a7b1df980ef4c232b3ccd4f6fe39f719
-    # via
-    #   jax
-    #   jaxlib
-msgpack==1.0.4 \
-    --hash=sha256:002b5c72b6cd9b4bafd790f364b8480e859b4712e91f43014fe01e4f957b8467 \
-    --hash=sha256:0a68d3ac0104e2d3510de90a1091720157c319ceeb90d74f7b5295a6bee51bae \
-    --hash=sha256:0df96d6eaf45ceca04b3f3b4b111b86b33785683d682c655063ef8057d61fd92 \
-    --hash=sha256:0dfe3947db5fb9ce52aaea6ca28112a170db9eae75adf9339a1aec434dc954ef \
-    --hash=sha256:0e3590f9fb9f7fbc36df366267870e77269c03172d086fa76bb4eba8b2b46624 \
-    --hash=sha256:11184bc7e56fd74c00ead4f9cc9a3091d62ecb96e97653add7a879a14b003227 \
-    --hash=sha256:112b0f93202d7c0fef0b7810d465fde23c746a2d482e1e2de2aafd2ce1492c88 \
-    --hash=sha256:1276e8f34e139aeff1c77a3cefb295598b504ac5314d32c8c3d54d24fadb94c9 \
-    --hash=sha256:1576bd97527a93c44fa856770197dec00d223b0b9f36ef03f65bac60197cedf8 \
-    --hash=sha256:1e91d641d2bfe91ba4c52039adc5bccf27c335356055825c7f88742c8bb900dd \
-    --hash=sha256:26b8feaca40a90cbe031b03d82b2898bf560027160d3eae1423f4a67654ec5d6 \
-    --hash=sha256:2999623886c5c02deefe156e8f869c3b0aaeba14bfc50aa2486a0415178fce55 \
-    --hash=sha256:2a2df1b55a78eb5f5b7d2a4bb221cd8363913830145fad05374a80bf0877cb1e \
-    --hash=sha256:2bb8cdf50dd623392fa75525cce44a65a12a00c98e1e37bf0fb08ddce2ff60d2 \
-    --hash=sha256:2cc5ca2712ac0003bcb625c96368fd08a0f86bbc1a5578802512d87bc592fe44 \
-    --hash=sha256:35bc0faa494b0f1d851fd29129b2575b2e26d41d177caacd4206d81502d4c6a6 \
-    --hash=sha256:3c11a48cf5e59026ad7cb0dc29e29a01b5a66a3e333dc11c04f7e991fc5510a9 \
-    --hash=sha256:449e57cc1ff18d3b444eb554e44613cffcccb32805d16726a5494038c3b93dab \
-    --hash=sha256:462497af5fd4e0edbb1559c352ad84f6c577ffbbb708566a0abaaa84acd9f3ae \
-    --hash=sha256:4733359808c56d5d7756628736061c432ded018e7a1dff2d35a02439043321aa \
-    --hash=sha256:48f5d88c99f64c456413d74a975bd605a9b0526293218a3b77220a2c15458ba9 \
-    --hash=sha256:49565b0e3d7896d9ea71d9095df15b7f75a035c49be733051c34762ca95bbf7e \
-    --hash=sha256:4ab251d229d10498e9a2f3b1e68ef64cb393394ec477e3370c457f9430ce9250 \
-    --hash=sha256:4d5834a2a48965a349da1c5a79760d94a1a0172fbb5ab6b5b33cbf8447e109ce \
-    --hash=sha256:4dea20515f660aa6b7e964433b1808d098dcfcabbebeaaad240d11f909298075 \
-    --hash=sha256:545e3cf0cf74f3e48b470f68ed19551ae6f9722814ea969305794645da091236 \
-    --hash=sha256:63e29d6e8c9ca22b21846234913c3466b7e4ee6e422f205a2988083de3b08cae \
-    --hash=sha256:6916c78f33602ecf0509cc40379271ba0f9ab572b066bd4bdafd7434dee4bc6e \
-    --hash=sha256:6a4192b1ab40f8dca3f2877b70e63799d95c62c068c84dc028b40a6cb03ccd0f \
-    --hash=sha256:6c9566f2c39ccced0a38d37c26cc3570983b97833c365a6044edef3574a00c08 \
-    --hash=sha256:76ee788122de3a68a02ed6f3a16bbcd97bc7c2e39bd4d94be2f1821e7c4a64e6 \
-    --hash=sha256:7760f85956c415578c17edb39eed99f9181a48375b0d4a94076d84148cf67b2d \
-    --hash=sha256:77ccd2af37f3db0ea59fb280fa2165bf1b096510ba9fe0cc2bf8fa92a22fdb43 \
-    --hash=sha256:81fc7ba725464651190b196f3cd848e8553d4d510114a954681fd0b9c479d7e1 \
-    --hash=sha256:85f279d88d8e833ec015650fd15ae5eddce0791e1e8a59165318f371158efec6 \
-    --hash=sha256:9667bdfdf523c40d2511f0e98a6c9d3603be6b371ae9a238b7ef2dc4e7a427b0 \
-    --hash=sha256:a75dfb03f8b06f4ab093dafe3ddcc2d633259e6c3f74bb1b01996f5d8aa5868c \
-    --hash=sha256:ac5bd7901487c4a1dd51a8c58f2632b15d838d07ceedaa5e4c080f7190925bff \
-    --hash=sha256:aca0f1644d6b5a73eb3e74d4d64d5d8c6c3d577e753a04c9e9c87d07692c58db \
-    --hash=sha256:b17be2478b622939e39b816e0aa8242611cc8d3583d1cd8ec31b249f04623243 \
-    --hash=sha256:c1683841cd4fa45ac427c18854c3ec3cd9b681694caf5bff04edb9387602d661 \
-    --hash=sha256:c23080fdeec4716aede32b4e0ef7e213c7b1093eede9ee010949f2a418ced6ba \
-    --hash=sha256:d5b5b962221fa2c5d3a7f8133f9abffc114fe218eb4365e40f17732ade576c8e \
-    --hash=sha256:d603de2b8d2ea3f3bcb2efe286849aa7a81531abc52d8454da12f46235092bcb \
-    --hash=sha256:e83f80a7fec1a62cf4e6c9a660e39c7f878f603737a0cdac8c13131d11d97f52 \
-    --hash=sha256:eb514ad14edf07a1dbe63761fd30f89ae79b42625731e1ccf5e1f1092950eaa6 \
-    --hash=sha256:eba96145051ccec0ec86611fe9cf693ce55f2a3ce89c06ed307de0e085730ec1 \
-    --hash=sha256:ed6f7b854a823ea44cf94919ba3f727e230da29feb4a99711433f25800cf747f \
-    --hash=sha256:f0029245c51fd9473dc1aede1160b0a29f4a912e6b1dd353fa6d317085b219da \
-    --hash=sha256:f5d869c18f030202eb412f08b28d2afeea553d6613aee89e200d7aca7ef01f5f \
-    --hash=sha256:fb62ea4b62bfcb0b380d5680f9a4b3f9a2d166d9394e9bbd9666c0ee09a3645c \
-    --hash=sha256:fcb8a47f43acc113e24e910399376f7277cf8508b27e5b88499f053de6b115a8
-    # via
-    #   flax
-    #   orbax-checkpoint
-nest-asyncio==1.5.7 \
-    --hash=sha256:5301c82941b550b3123a1ea772ba9a1c80bad3a182be8c1a5ae6ad3be57a9657 \
-    --hash=sha256:6a80f7b98f24d9083ed24608977c09dd608d83f91cccc24c9d2cba6d10e01c10
-    # via orbax-checkpoint
-numpy==1.23.5 \
-    --hash=sha256:01dd17cbb340bf0fc23981e52e1d18a9d4050792e8fb8363cecbf066a84b827d \
-    --hash=sha256:06005a2ef6014e9956c09ba07654f9837d9e26696a0470e42beedadb78c11b07 \
-    --hash=sha256:09b7847f7e83ca37c6e627682f145856de331049013853f344f37b0c9690e3df \
-    --hash=sha256:0aaee12d8883552fadfc41e96b4c82ee7d794949e2a7c3b3a7201e968c7ecab9 \
-    --hash=sha256:0cbe9848fad08baf71de1a39e12d1b6310f1d5b2d0ea4de051058e6e1076852d \
-    --hash=sha256:1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a \
-    --hash=sha256:33161613d2269025873025b33e879825ec7b1d831317e68f4f2f0f84ed14c719 \
-    --hash=sha256:5039f55555e1eab31124a5768898c9e22c25a65c1e0037f4d7c495a45778c9f2 \
-    --hash=sha256:522e26bbf6377e4d76403826ed689c295b0b238f46c28a7251ab94716da0b280 \
-    --hash=sha256:56e454c7833e94ec9769fa0f86e6ff8e42ee38ce0ce1fa4cbb747ea7e06d56aa \
-    --hash=sha256:58f545efd1108e647604a1b5aa809591ccd2540f468a880bedb97247e72db387 \
-    --hash=sha256:5e05b1c973a9f858c74367553e236f287e749465f773328c8ef31abe18f691e1 \
-    --hash=sha256:7903ba8ab592b82014713c491f6c5d3a1cde5b4a3bf116404e08f5b52f6daf43 \
-    --hash=sha256:8969bfd28e85c81f3f94eb4a66bc2cf1dbdc5c18efc320af34bffc54d6b1e38f \
-    --hash=sha256:92c8c1e89a1f5028a4c6d9e3ccbe311b6ba53694811269b992c0b224269e2398 \
-    --hash=sha256:9c88793f78fca17da0145455f0d7826bcb9f37da4764af27ac945488116efe63 \
-    --hash=sha256:a7ac231a08bb37f852849bbb387a20a57574a97cfc7b6cabb488a4fc8be176de \
-    --hash=sha256:abdde9f795cf292fb9651ed48185503a2ff29be87770c3b8e2a14b0cd7aa16f8 \
-    --hash=sha256:af1da88f6bc3d2338ebbf0e22fe487821ea4d8e89053e25fa59d1d79786e7481 \
-    --hash=sha256:b2a9ab7c279c91974f756c84c365a669a887efa287365a8e2c418f8b3ba73fb0 \
-    --hash=sha256:bf837dc63ba5c06dc8797c398db1e223a466c7ece27a1f7b5232ba3466aafe3d \
-    --hash=sha256:ca51fcfcc5f9354c45f400059e88bc09215fb71a48d3768fb80e357f3b457e1e \
-    --hash=sha256:ce571367b6dfe60af04e04a1834ca2dc5f46004ac1cc756fb95319f64c095a96 \
-    --hash=sha256:d208a0f8729f3fb790ed18a003f3a57895b989b40ea4dce4717e9cf4af62c6bb \
-    --hash=sha256:dbee87b469018961d1ad79b1a5d50c0ae850000b639bcb1b694e9981083243b6 \
-    --hash=sha256:e9f4c4e51567b616be64e05d517c79a8a22f3606499941d97bb76f2ca59f982d \
-    --hash=sha256:f063b69b090c9d918f9df0a12116029e274daf0181df392839661c4c7ec9018a \
-    --hash=sha256:f9a909a8bae284d46bbfdefbdd4a262ba19d3bc9921b1e76126b1d21c3c34135
-    # via
-    #   chex
-    #   flax
-    #   h5py
-    #   jax
-    #   jaxlib
-    #   ml-dtypes
-    #   opt-einsum
-    #   optax
-    #   orbax-checkpoint
-    #   pandas
-    #   scipy
-    #   tensorboard
-    #   tensorflow
-    #   tensorflow-decision-forests
-    #   tensorflow-hub
-    #   tensorstore
-oauthlib==3.2.2 \
-    --hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
-    --hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
-    # via requests-oauthlib
-opt-einsum==3.3.0 \
-    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
-    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
-    # via
-    #   jax
-    #   tensorflow
-optax==0.1.4 \
-    --hash=sha256:12fcf33bd682f9a162a3deb097f864130c3224d76771af2ba09410de80399a9b \
-    --hash=sha256:fb7a0550d57a6636164a3de25986a8a19be8ff6431fcdf1225b4e05175810f22
-    # via flax
-orbax-checkpoint==0.2.3 \
-    --hash=sha256:155e0a2dceef2901122e66585171e1dff4f4a4d9d2abe43a2b514279b9a3dabd \
-    --hash=sha256:a001bf48f1cebc635b07263fa546473ea48be3e278c50d5ade880b9aafb96f8a
-    # via flax
-packaging==23.1 \
-    --hash=sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61 \
-    --hash=sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   tensorflow
-pandas==1.5.2 \
-    --hash=sha256:0183cb04a057cc38fde5244909fca9826d5d57c4a5b7390c0cc3fa7acd9fa883 \
-    --hash=sha256:1fc87eac0541a7d24648a001d553406f4256e744d92df1df8ebe41829a915028 \
-    --hash=sha256:220b98d15cee0b2cd839a6358bd1f273d0356bf964c1a1aeb32d47db0215488b \
-    --hash=sha256:2552bffc808641c6eb471e55aa6899fa002ac94e4eebfa9ec058649122db5824 \
-    --hash=sha256:315e19a3e5c2ab47a67467fc0362cb36c7c60a93b6457f675d7d9615edad2ebe \
-    --hash=sha256:344021ed3e639e017b452aa8f5f6bf38a8806f5852e217a7594417fb9bbfa00e \
-    --hash=sha256:375262829c8c700c3e7cbb336810b94367b9c4889818bbd910d0ecb4e45dc261 \
-    --hash=sha256:457d8c3d42314ff47cc2d6c54f8fc0d23954b47977b2caed09cd9635cb75388b \
-    --hash=sha256:4aed257c7484d01c9a194d9a94758b37d3d751849c05a0050c087a358c41ad1f \
-    --hash=sha256:530948945e7b6c95e6fa7aa4be2be25764af53fba93fe76d912e35d1c9ee46f5 \
-    --hash=sha256:5ae7e989f12628f41e804847a8cc2943d362440132919a69429d4dea1f164da0 \
-    --hash=sha256:71f510b0efe1629bf2f7c0eadb1ff0b9cf611e87b73cd017e6b7d6adb40e2b3a \
-    --hash=sha256:73f219fdc1777cf3c45fde7f0708732ec6950dfc598afc50588d0d285fddaefc \
-    --hash=sha256:8092a368d3eb7116e270525329a3e5c15ae796ccdf7ccb17839a73b4f5084a39 \
-    --hash=sha256:82ae615826da838a8e5d4d630eb70c993ab8636f0eff13cb28aafc4291b632b5 \
-    --hash=sha256:9608000a5a45f663be6af5c70c3cbe634fa19243e720eb380c0d378666bc7702 \
-    --hash=sha256:a40dd1e9f22e01e66ed534d6a965eb99546b41d4d52dbdb66565608fde48203f \
-    --hash=sha256:b4f5a82afa4f1ff482ab8ded2ae8a453a2cdfde2001567b3ca24a4c5c5ca0db3 \
-    --hash=sha256:c009a92e81ce836212ce7aa98b219db7961a8b95999b97af566b8dc8c33e9519 \
-    --hash=sha256:c218796d59d5abd8780170c937b812c9637e84c32f8271bbf9845970f8c1351f \
-    --hash=sha256:cc3cd122bea268998b79adebbb8343b735a5511ec14efb70a39e7acbc11ccbdc \
-    --hash=sha256:d0d8fd58df5d17ddb8c72a5075d87cd80d71b542571b5f78178fb067fa4e9c72 \
-    --hash=sha256:e18bc3764cbb5e118be139b3b611bc3fbc5d3be42a7e827d1096f46087b395eb \
-    --hash=sha256:e2b83abd292194f350bb04e188f9379d36b8dfac24dd445d5c87575f3beaf789 \
-    --hash=sha256:e7469271497960b6a781eaa930cba8af400dd59b62ec9ca2f4d31a19f2f91090 \
-    --hash=sha256:e9dbacd22555c2d47f262ef96bb4e30880e5956169741400af8b306bbb24a273 \
-    --hash=sha256:f6257b314fc14958f8122779e5a1557517b0f8e500cfb2bd53fa1f75a8ad0af2
-    # via tensorflow-decision-forests
-prompt-toolkit==1.0.14 \
-    --hash=sha256:7281b5199235adaef6980942840c43753e4ab20dfe41338da634fb41c194f9d8 \
-    --hash=sha256:82c7f8e07d7a0411ff5367a5a8ff520f0112b9179f3e599ee8ad2ad9b943d911 \
-    --hash=sha256:cc66413b1b4b17021675d9f2d15d57e640b06ddfd99bb724c73484126d22622f
-    # via pyinquirer
-protobuf==4.22.3 \
-    --hash=sha256:13233ee2b9d3bd9a5f216c1fa2c321cd564b93d8f2e4f521a85b585447747997 \
-    --hash=sha256:23452f2fdea754a8251d0fc88c0317735ae47217e0d27bf330a30eec2848811a \
-    --hash=sha256:52f0a78141078077cfe15fe333ac3e3a077420b9a3f5d1bf9b5fe9d286b4d881 \
-    --hash=sha256:70659847ee57a5262a65954538088a1d72dfc3e9882695cab9f0c54ffe71663b \
-    --hash=sha256:7760730063329d42a9d4c4573b804289b738d4931e363ffbe684716b796bde51 \
-    --hash=sha256:7cf56e31907c532e460bb62010a513408e6cdf5b03fb2611e4b67ed398ad046d \
-    --hash=sha256:8b54f56d13ae4a3ec140076c9d937221f887c8f64954673d46f63751209e839a \
-    --hash=sha256:d14fc1a41d1a1909998e8aff7e80d2a7ae14772c4a70e4bf7db8a36690b54425 \
-    --hash=sha256:d4b66266965598ff4c291416be429cef7989d8fae88b55b62095a2331511b3fa \
-    --hash=sha256:e0e630d8e6a79f48c557cd1835865b593d0547dce221c66ed1b827de59c66c97 \
-    --hash=sha256:ecae944c6c2ce50dda6bf76ef5496196aeb1b85acb95df5843cd812615ec4b61 \
-    --hash=sha256:f08aa300b67f1c012100d8eb62d47129e53d1150f4469fd78a29fa3cb68c66f2 \
-    --hash=sha256:f2f4710543abec186aee332d6852ef5ae7ce2e9e807a3da570f36de5a732d88e
-    # via
-    #   tensorboard
-    #   tensorflow
-    #   tensorflow-hub
-pyasn1==0.4.8 \
-    --hash=sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d \
-    --hash=sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba
-    # via
-    #   pyasn1-modules
-    #   rsa
-pyasn1-modules==0.2.8 \
-    --hash=sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e \
-    --hash=sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74
-    # via google-auth
-pygments==2.13.0 \
-    --hash=sha256:56a8508ae95f98e2b9bdf93a6be5ae3f7d8af858b43e02c5a2ff083726be40c1 \
-    --hash=sha256:f643f331ab57ba3c9d89212ee4a2dabc6e94f117cf4eefde99a0574720d14c42
-    # via
-    #   pyinquirer
-    #   rich
-pyinquirer==1.0.3 \
-    --hash=sha256:c9a92d68d7727fbd886a7908c08fd9e9773e5dc211bf5cbf836ba90d366dee51
-    # via -r tfjs-converter/python/requirements-dev.txt
-pylint==2.5.0 ; python_version > "3.0" \
-    --hash=sha256:588e114e3f9a1630428c35b7dd1c82c1c93e1b0e78ee312ae4724c5e1a1e0245 \
-    --hash=sha256:bd556ba95a4cf55a1fc0004c00cf4560b1e70598a54a74c6904d933c8f3bd5a8
-    # via -r tfjs-converter/python/requirements-dev.txt
-python-dateutil==2.8.2 \
-    --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
-    --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
-    # via pandas
-pytz==2022.6 \
-    --hash=sha256:222439474e9c98fced559f1709d89e6c9cbf8d79c794ff3eb9f8800064291427 \
-    --hash=sha256:e89512406b793ca39f5971bc999cc538ce125c0e51c27941bef4568b460095e2
-    # via pandas
-pyyaml==6.0 \
-    --hash=sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf \
-    --hash=sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293 \
-    --hash=sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b \
-    --hash=sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57 \
-    --hash=sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b \
-    --hash=sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4 \
-    --hash=sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07 \
-    --hash=sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba \
-    --hash=sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9 \
-    --hash=sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287 \
-    --hash=sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513 \
-    --hash=sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0 \
-    --hash=sha256:432557aa2c09802be39460360ddffd48156e30721f5e8d917f01d31694216782 \
-    --hash=sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0 \
-    --hash=sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92 \
-    --hash=sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f \
-    --hash=sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2 \
-    --hash=sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc \
-    --hash=sha256:81957921f441d50af23654aa6c5e5eaf9b06aba7f0a19c18a538dc7ef291c5a1 \
-    --hash=sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c \
-    --hash=sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86 \
-    --hash=sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4 \
-    --hash=sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c \
-    --hash=sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34 \
-    --hash=sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b \
-    --hash=sha256:afa17f5bc4d1b10afd4466fd3a44dc0e245382deca5b3c353d8b757f9e3ecb8d \
-    --hash=sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c \
-    --hash=sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb \
-    --hash=sha256:bfaef573a63ba8923503d27530362590ff4f576c626d86a9fed95822a8255fd7 \
-    --hash=sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737 \
-    --hash=sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3 \
-    --hash=sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d \
-    --hash=sha256:d4b0ba9512519522b118090257be113b9468d804b19d63c71dbcf4a48fa32358 \
-    --hash=sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53 \
-    --hash=sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78 \
-    --hash=sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803 \
-    --hash=sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a \
-    --hash=sha256:dbad0e9d368bb989f4515da330b88a057617d16b6a8245084f1b05400f24609f \
-    --hash=sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174 \
-    --hash=sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5
-    # via
-    #   flax
-    #   orbax-checkpoint
-regex==2022.10.31 \
-    --hash=sha256:052b670fafbe30966bbe5d025e90b2a491f85dfe5b2583a163b5e60a85a321ad \
-    --hash=sha256:0653d012b3bf45f194e5e6a41df9258811ac8fc395579fa82958a8b76286bea4 \
-    --hash=sha256:0a069c8483466806ab94ea9068c34b200b8bfc66b6762f45a831c4baaa9e8cdd \
-    --hash=sha256:0cf0da36a212978be2c2e2e2d04bdff46f850108fccc1851332bcae51c8907cc \
-    --hash=sha256:131d4be09bea7ce2577f9623e415cab287a3c8e0624f778c1d955ec7c281bd4d \
-    --hash=sha256:144486e029793a733e43b2e37df16a16df4ceb62102636ff3db6033994711066 \
-    --hash=sha256:1ddf14031a3882f684b8642cb74eea3af93a2be68893901b2b387c5fd92a03ec \
-    --hash=sha256:1eba476b1b242620c266edf6325b443a2e22b633217a9835a52d8da2b5c051f9 \
-    --hash=sha256:20f61c9944f0be2dc2b75689ba409938c14876c19d02f7585af4460b6a21403e \
-    --hash=sha256:22960019a842777a9fa5134c2364efaed5fbf9610ddc5c904bd3a400973b0eb8 \
-    --hash=sha256:22e7ebc231d28393dfdc19b185d97e14a0f178bedd78e85aad660e93b646604e \
-    --hash=sha256:23cbb932cc53a86ebde0fb72e7e645f9a5eec1a5af7aa9ce333e46286caef783 \
-    --hash=sha256:29c04741b9ae13d1e94cf93fca257730b97ce6ea64cfe1eba11cf9ac4e85afb6 \
-    --hash=sha256:2bde29cc44fa81c0a0c8686992c3080b37c488df167a371500b2a43ce9f026d1 \
-    --hash=sha256:2cdc55ca07b4e70dda898d2ab7150ecf17c990076d3acd7a5f3b25cb23a69f1c \
-    --hash=sha256:370f6e97d02bf2dd20d7468ce4f38e173a124e769762d00beadec3bc2f4b3bc4 \
-    --hash=sha256:395161bbdbd04a8333b9ff9763a05e9ceb4fe210e3c7690f5e68cedd3d65d8e1 \
-    --hash=sha256:44136355e2f5e06bf6b23d337a75386371ba742ffa771440b85bed367c1318d1 \
-    --hash=sha256:44a6c2f6374e0033873e9ed577a54a3602b4f609867794c1a3ebba65e4c93ee7 \
-    --hash=sha256:4919899577ba37f505aaebdf6e7dc812d55e8f097331312db7f1aab18767cce8 \
-    --hash=sha256:4b4b1fe58cd102d75ef0552cf17242705ce0759f9695334a56644ad2d83903fe \
-    --hash=sha256:4bdd56ee719a8f751cf5a593476a441c4e56c9b64dc1f0f30902858c4ef8771d \
-    --hash=sha256:4bf41b8b0a80708f7e0384519795e80dcb44d7199a35d52c15cc674d10b3081b \
-    --hash=sha256:4cac3405d8dda8bc6ed499557625585544dd5cbf32072dcc72b5a176cb1271c8 \
-    --hash=sha256:4fe7fda2fe7c8890d454f2cbc91d6c01baf206fbc96d89a80241a02985118c0c \
-    --hash=sha256:50921c140561d3db2ab9f5b11c5184846cde686bb5a9dc64cae442926e86f3af \
-    --hash=sha256:5217c25229b6a85049416a5c1e6451e9060a1edcf988641e309dbe3ab26d3e49 \
-    --hash=sha256:5352bea8a8f84b89d45ccc503f390a6be77917932b1c98c4cdc3565137acc714 \
-    --hash=sha256:542e3e306d1669b25936b64917285cdffcd4f5c6f0247636fec037187bd93542 \
-    --hash=sha256:543883e3496c8b6d58bd036c99486c3c8387c2fc01f7a342b760c1ea3158a318 \
-    --hash=sha256:586b36ebda81e6c1a9c5a5d0bfdc236399ba6595e1397842fd4a45648c30f35e \
-    --hash=sha256:597f899f4ed42a38df7b0e46714880fb4e19a25c2f66e5c908805466721760f5 \
-    --hash=sha256:5a260758454580f11dd8743fa98319bb046037dfab4f7828008909d0aa5292bc \
-    --hash=sha256:5aefb84a301327ad115e9d346c8e2760009131d9d4b4c6b213648d02e2abe144 \
-    --hash=sha256:5e6a5567078b3eaed93558842346c9d678e116ab0135e22eb72db8325e90b453 \
-    --hash=sha256:5ff525698de226c0ca743bfa71fc6b378cda2ddcf0d22d7c37b1cc925c9650a5 \
-    --hash=sha256:61edbca89aa3f5ef7ecac8c23d975fe7261c12665f1d90a6b1af527bba86ce61 \
-    --hash=sha256:659175b2144d199560d99a8d13b2228b85e6019b6e09e556209dfb8c37b78a11 \
-    --hash=sha256:6a9a19bea8495bb419dc5d38c4519567781cd8d571c72efc6aa959473d10221a \
-    --hash=sha256:6b30bddd61d2a3261f025ad0f9ee2586988c6a00c780a2fb0a92cea2aa702c54 \
-    --hash=sha256:6ffd55b5aedc6f25fd8d9f905c9376ca44fcf768673ffb9d160dd6f409bfda73 \
-    --hash=sha256:702d8fc6f25bbf412ee706bd73019da5e44a8400861dfff7ff31eb5b4a1276dc \
-    --hash=sha256:74bcab50a13960f2a610cdcd066e25f1fd59e23b69637c92ad470784a51b1347 \
-    --hash=sha256:75f591b2055523fc02a4bbe598aa867df9e953255f0b7f7715d2a36a9c30065c \
-    --hash=sha256:763b64853b0a8f4f9cfb41a76a4a85a9bcda7fdda5cb057016e7706fde928e66 \
-    --hash=sha256:76c598ca73ec73a2f568e2a72ba46c3b6c8690ad9a07092b18e48ceb936e9f0c \
-    --hash=sha256:78d680ef3e4d405f36f0d6d1ea54e740366f061645930072d39bca16a10d8c93 \
-    --hash=sha256:7b280948d00bd3973c1998f92e22aa3ecb76682e3a4255f33e1020bd32adf443 \
-    --hash=sha256:7db345956ecce0c99b97b042b4ca7326feeec6b75facd8390af73b18e2650ffc \
-    --hash=sha256:7dbdce0c534bbf52274b94768b3498abdf675a691fec5f751b6057b3030f34c1 \
-    --hash=sha256:7ef6b5942e6bfc5706301a18a62300c60db9af7f6368042227ccb7eeb22d0892 \
-    --hash=sha256:7f5a3ffc731494f1a57bd91c47dc483a1e10048131ffb52d901bfe2beb6102e8 \
-    --hash=sha256:8a45b6514861916c429e6059a55cf7db74670eaed2052a648e3e4d04f070e001 \
-    --hash=sha256:8ad241da7fac963d7573cc67a064c57c58766b62a9a20c452ca1f21050868dfa \
-    --hash=sha256:8b0886885f7323beea6f552c28bff62cbe0983b9fbb94126531693ea6c5ebb90 \
-    --hash=sha256:8ca88da1bd78990b536c4a7765f719803eb4f8f9971cc22d6ca965c10a7f2c4c \
-    --hash=sha256:8e0caeff18b96ea90fc0eb6e3bdb2b10ab5b01a95128dfeccb64a7238decf5f0 \
-    --hash=sha256:957403a978e10fb3ca42572a23e6f7badff39aa1ce2f4ade68ee452dc6807692 \
-    --hash=sha256:9af69f6746120998cd9c355e9c3c6aec7dff70d47247188feb4f829502be8ab4 \
-    --hash=sha256:9c94f7cc91ab16b36ba5ce476f1904c91d6c92441f01cd61a8e2729442d6fcf5 \
-    --hash=sha256:a37d51fa9a00d265cf73f3de3930fa9c41548177ba4f0faf76e61d512c774690 \
-    --hash=sha256:a3a98921da9a1bf8457aeee6a551948a83601689e5ecdd736894ea9bbec77e83 \
-    --hash=sha256:a3c1ebd4ed8e76e886507c9eddb1a891673686c813adf889b864a17fafcf6d66 \
-    --hash=sha256:a5f9505efd574d1e5b4a76ac9dd92a12acb2b309551e9aa874c13c11caefbe4f \
-    --hash=sha256:a8ff454ef0bb061e37df03557afda9d785c905dab15584860f982e88be73015f \
-    --hash=sha256:a9d0b68ac1743964755ae2d89772c7e6fb0118acd4d0b7464eaf3921c6b49dd4 \
-    --hash=sha256:aa62a07ac93b7cb6b7d0389d8ef57ffc321d78f60c037b19dfa78d6b17c928ee \
-    --hash=sha256:ac741bf78b9bb432e2d314439275235f41656e189856b11fb4e774d9f7246d81 \
-    --hash=sha256:ae1e96785696b543394a4e3f15f3f225d44f3c55dafe3f206493031419fedf95 \
-    --hash=sha256:b683e5fd7f74fb66e89a1ed16076dbab3f8e9f34c18b1979ded614fe10cdc4d9 \
-    --hash=sha256:b7a8b43ee64ca8f4befa2bea4083f7c52c92864d8518244bfa6e88c751fa8fff \
-    --hash=sha256:b8e38472739028e5f2c3a4aded0ab7eadc447f0d84f310c7a8bb697ec417229e \
-    --hash=sha256:bfff48c7bd23c6e2aec6454aaf6edc44444b229e94743b34bdcdda2e35126cf5 \
-    --hash=sha256:c14b63c9d7bab795d17392c7c1f9aaabbffd4cf4387725a0ac69109fb3b550c6 \
-    --hash=sha256:c27cc1e4b197092e50ddbf0118c788d9977f3f8f35bfbbd3e76c1846a3443df7 \
-    --hash=sha256:c28d3309ebd6d6b2cf82969b5179bed5fefe6142c70f354ece94324fa11bf6a1 \
-    --hash=sha256:c670f4773f2f6f1957ff8a3962c7dd12e4be54d05839b216cb7fd70b5a1df394 \
-    --hash=sha256:ce6910b56b700bea7be82c54ddf2e0ed792a577dfaa4a76b9af07d550af435c6 \
-    --hash=sha256:d0213671691e341f6849bf33cd9fad21f7b1cb88b89e024f33370733fec58742 \
-    --hash=sha256:d03fe67b2325cb3f09be029fd5da8df9e6974f0cde2c2ac6a79d2634e791dd57 \
-    --hash=sha256:d0e5af9a9effb88535a472e19169e09ce750c3d442fb222254a276d77808620b \
-    --hash=sha256:d243b36fbf3d73c25e48014961e83c19c9cc92530516ce3c43050ea6276a2ab7 \
-    --hash=sha256:d26166acf62f731f50bdd885b04b38828436d74e8e362bfcb8df221d868b5d9b \
-    --hash=sha256:d403d781b0e06d2922435ce3b8d2376579f0c217ae491e273bab8d092727d244 \
-    --hash=sha256:d8716f82502997b3d0895d1c64c3b834181b1eaca28f3f6336a71777e437c2af \
-    --hash=sha256:e4f781ffedd17b0b834c8731b75cce2639d5a8afe961c1e58ee7f1f20b3af185 \
-    --hash=sha256:e613a98ead2005c4ce037c7b061f2409a1a4e45099edb0ef3200ee26ed2a69a8 \
-    --hash=sha256:ef4163770525257876f10e8ece1cf25b71468316f61451ded1a6f44273eedeb5
-    # via pyinquirer
-requests==2.28.1 \
-    --hash=sha256:7c5599b102feddaa661c826c56ab4fee28bfd17f5abca1ebbe3e7f19d7c97983 \
-    --hash=sha256:8fefa2a1a1365bf5520aac41836fbee479da67864514bdb821f31ce07ce65349
-    # via
-    #   requests-oauthlib
-    #   tensorboard
-requests-oauthlib==1.3.1 \
-    --hash=sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5 \
-    --hash=sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a
-    # via google-auth-oauthlib
-rich==12.6.0 \
-    --hash=sha256:a4eb26484f2c82589bd9a17c73d32a010b1e29d89f1604cd9bf3a2097b81bb5e \
-    --hash=sha256:ba3a3775974105c221d31141f2c116f4fd65c5ceb0698657a11e9f295ec93fd0
-    # via flax
-rsa==4.9 \
-    --hash=sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7 \
-    --hash=sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21
-    # via google-auth
-scipy==1.9.3 \
-    --hash=sha256:06d2e1b4c491dc7d8eacea139a1b0b295f74e1a1a0f704c375028f8320d16e31 \
-    --hash=sha256:0d54222d7a3ba6022fdf5773931b5d7c56efe41ede7f7128c7b1637700409108 \
-    --hash=sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0 \
-    --hash=sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b \
-    --hash=sha256:2318bef588acc7a574f5bfdff9c172d0b1bf2c8143d9582e05f878e580a3781e \
-    --hash=sha256:4db5b30849606a95dcf519763dd3ab6fe9bd91df49eba517359e450a7d80ce2e \
-    --hash=sha256:545c83ffb518094d8c9d83cce216c0c32f8c04aaf28b92cc8283eda0685162d5 \
-    --hash=sha256:5a04cd7d0d3eff6ea4719371cbc44df31411862b9646db617c99718ff68d4840 \
-    --hash=sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58 \
-    --hash=sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523 \
-    --hash=sha256:83b89e9586c62e787f5012e8475fbb12185bafb996a03257e9675cd73d3736dd \
-    --hash=sha256:83c06e62a390a9167da60bedd4575a14c1f58ca9dfde59830fc42e5197283dab \
-    --hash=sha256:90453d2b93ea82a9f434e4e1cba043e779ff67b92f7a0e85d05d286a3625df3c \
-    --hash=sha256:abaf921531b5aeaafced90157db505e10345e45038c39e5d9b6c7922d68085cb \
-    --hash=sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096 \
-    --hash=sha256:c68db6b290cbd4049012990d7fe71a2abd9ffbe82c0056ebe0f01df8be5436b0 \
-    --hash=sha256:cff3a5295234037e39500d35316a4c5794739433528310e117b8a9a0c76d20fc \
-    --hash=sha256:d01e1dd7b15bd2449c8bfc6b7cc67d630700ed655654f0dfcf121600bad205c9 \
-    --hash=sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c \
-    --hash=sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95 \
-    --hash=sha256:fbc5c05c85c1a02be77b1ff591087c83bc44579c6d2bd9fb798bb64ea5e1a027
-    # via
-    #   jax
-    #   jaxlib
-setuptools==65.6.3 \
-    --hash=sha256:57f6f22bde4e042978bcd50176fdb381d7c21a9efa4041202288d3737a0c6a54 \
-    --hash=sha256:a7620757bf984b58deaf32fc8a4577a9bbc0850cf92c20e1ce41c38c19e5fb75
-    # via
-    #   -r tfjs-converter/python/requirements-dev.txt
-    #   tensorboard
-    #   tensorflow
-six==1.16.0 \
-    --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
-    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   astunparse
-    #   google-auth
-    #   google-pasta
-    #   prompt-toolkit
-    #   python-dateutil
-    #   tensorflow
-    #   tensorflow-decision-forests
-tensorboard==2.13.0 \
-    --hash=sha256:ab69961ebddbddc83f5fa2ff9233572bdad5b883778c35e4fe94bf1798bd8481
-    # via tensorflow
-tensorboard-data-server==0.7.0 \
-    --hash=sha256:64aa1be7c23e80b1a42c13b686eb0875bb70f5e755f4d2b8de5c1d880cf2267f \
-    --hash=sha256:753d4214799b31da7b6d93837959abebbc6afa86e69eacf1e9a317a48daa31eb \
-    --hash=sha256:eb7fa518737944dbf4f0cf83c2e40a7ac346bf91be2e6a0215de98be74e85454
-    # via tensorboard
-tensorflow==2.13.0 \
-    --hash=sha256:00060c5516a61e30c51936084ebc37091d116efe9ae74b2818cbd8b2006218e7 \
-    --hash=sha256:06559eeaa69e6561cccbe2d02b015bcec663e875c8bbc4643f55692476e52147 \
-    --hash=sha256:076d953a1508dc58bf95f30f58bcc9ee364b1353c61e143cb20c2dada91afb05 \
-    --hash=sha256:11ad6a7ff49b4a690fbf37a5adaf28ba2686350a859c5f13c58dc8d2cc670375 \
-    --hash=sha256:19ee67901702b26787ad685cca65730c163c101c0c2f238a2584d714e0fa8c25 \
-    --hash=sha256:2822ac48c38d69b7fb104e606dacbd763c4bf5d3b20791f25be16a5076d01912 \
-    --hash=sha256:5e0fdadec59de3d11c5b5129ddc38e739bde7aa13095b82e19d4380e14d04999 \
-    --hash=sha256:6fff426661d286a4c634da44275d2ea2b951f392f3e65c8603681e7cb040586a \
-    --hash=sha256:72d68b8c2f382e2d01b956c8ba516c0a7d5dad98111dd351bf82bfa646aa1c72 \
-    --hash=sha256:7a08c0e2938ed5b642a8787678123827477b81d316055d5073fff82fa183eb82 \
-    --hash=sha256:89125443e998548059c4e4a129dfab2b1ff7f2fd4c8eaed97842c3cd9b663101 \
-    --hash=sha256:948003b5a23b72b3d89746d729e62ec5f01e47460f05521b2211d95069f569ba \
-    --hash=sha256:9c04bc3023b6c4cfb9ee9759c3f03f21993891b4c345df52eb5519204fbf28c0 \
-    --hash=sha256:b2978b39e8b3919059b5fd9e28508d50a77965d06ed0b537ed71c97de22dabdf \
-    --hash=sha256:cbb83561bb7d55859eaefc70c674e58713d4e10c10927423ed836a5289bbfa86 \
-    --hash=sha256:de77306c0c22c9d8754f54700752ac3a1efee895c5357308e6594436404bfbc0 \
-    --hash=sha256:e0cf94d36ceaba8f158c6e15404a81fd5b3aa4cb04147c674cf55bd1aec78154 \
-    --hash=sha256:e8f0b69ee2f800399fc6bc7ec55fecfa33662d136e425485959d90638f32a32a \
-    --hash=sha256:fa7abe265cc3ebccc9b405a280bf674824c6d85df5e6ccfa985987b3c9d265b4 \
-    --hash=sha256:fb2ff1129c93e853c19897d6a22ed0ec56387f5c6290ec03dec1c6f7b80bc396
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   tensorflow-decision-forests
-tensorflow-decision-forests==1.5.0 \
-    --hash=sha256:04fd913627d08fe54514b179c612e87eebf55f1448bf01951660985dfa14a6e1 \
-    --hash=sha256:1209a2832ac65f8f74bd9d0c1d58f3f8b771e7fa5c9d504c547842311647b7d4 \
-    --hash=sha256:22e3835acbfbd5356bb2f8e0c973dfc40ef80a7924b793e90c811158448cfe77 \
-    --hash=sha256:43ffd4fba1c3376f58a9dcee943df80f0cff6e47224d109ad0389a723c74947c \
-    --hash=sha256:4a0df3a3be5751594d49f5a8f99977b553cf1c42f320b952ac2a2f67b85283f5 \
-    --hash=sha256:5ec4297eb5e7c4110cf8aae89e9b08b9ad2cb725e3e63c89c78304c0d7235d24 \
-    --hash=sha256:804f6bed277b5c5b6d2bd85738a64973d5d3e8e6ac06abf6098545740245cedc \
-    --hash=sha256:a43af2a5a8c34e550bf549c6cad96da271979efc5a8ec988f6f76cc90770415a \
-    --hash=sha256:d137241dad8e884d0c937aa8769fe0768324804e9ba666a78b7b5f2f536a0bd2 \
-    --hash=sha256:d685e92abe44920ee6d89394ec4e075bb1ada7402f673566146e1b476a576e96 \
-    --hash=sha256:f5d8c3730578bda55a8f520ae39b0c9b2560d69bd53b57882e5371c1a82ba098 \
-    --hash=sha256:fbd403acf736bb9b4afd2985d9056e6d5043fc4b9a31bd05e5fcae2b1d413dc3
-    # via -r tfjs-converter/python/requirements.txt
-tensorflow-estimator==2.13.0 \
-    --hash=sha256:6f868284eaa654ae3aa7cacdbef2175d0909df9fcf11374f5166f8bf475952aa
-    # via tensorflow
-tensorflow-hub==0.14.0 \
-    --hash=sha256:519c6b56c4d304667fbd8ce66bd637e6a750c901215468db2cc6bfd0739bb0b0
-    # via -r tfjs-converter/python/requirements.txt
-tensorflow-io-gcs-filesystem==0.28.0 \
-    --hash=sha256:00cf6a92f1f9f90b2ba2d728870bcd2a70b116316d0817ab0b91dd390c25b3fd \
-    --hash=sha256:22753dc28c949bfaf29b573ee376370762c88d80330fe95cfb291261eb5e927a \
-    --hash=sha256:366e1eff8dbd6b64333d7061e2a8efd081ae4742614f717ced08d8cc9379eb50 \
-    --hash=sha256:52988659f405166df79905e9859bc84ae2a71e3ff61522ba32a95e4dce8e66d2 \
-    --hash=sha256:5fbef5836e70026245d8d9e692c44dae2c6dbc208c743d01f5b7a2978d6b6bc6 \
-    --hash=sha256:698d7f89e09812b9afeb47c3860797343a22f997c64ab9dab98132c61daa8a7d \
-    --hash=sha256:6d95f306ff225c5053fd06deeab3e3a2716357923cb40c44d566c11be779caa3 \
-    --hash=sha256:9484893779324b2d34874b0aacf3b824eb4f22d782e75df029cbccab2e607974 \
-    --hash=sha256:a6670e0da16c884267e896ea5c3334d6fd319bd6ff7cf917043a9f3b2babb1b3 \
-    --hash=sha256:b6e2d275020fb4d1a952cd3fa546483f4e46ad91d64e90d3458e5ca3d12f6477 \
-    --hash=sha256:bbf245883aa52ec687b66d0fcbe0f5f0a92d98c0b1c53e6a736039a3548d29a1 \
-    --hash=sha256:bfed720fc691d3f45802a7bed420716805aef0939c11cebf25798906201f626e \
-    --hash=sha256:c5d99f56c12a349905ff684142e4d2df06ae68ecf50c4aad5449a5f81731d858 \
-    --hash=sha256:cc062ce13ec95fb64b1fd426818a6d2b0e5be9692bc0e43a19cce115b6da4336 \
-    --hash=sha256:f76cbe1a784841c223f6861e5f6c7e53aa6232cb626d57e76881a0638c365de6
-    # via tensorflow
-tensorstore==0.1.41 \
-    --hash=sha256:025a62bb9122364885e90469af05fec2f62ad05f46ff46d9eae1d76ad9125563 \
-    --hash=sha256:2aa81581f768382a38584698a3fcb07a533fc391067467326656f24ab019cba1 \
-    --hash=sha256:2c4c578e82866b8f764de871ff7e0a81fe0949ac3565d8d2eb10f29e43020a52 \
-    --hash=sha256:2d65ea0fd5ac96a9d577f16bb917ae8a0a121d2093472bfb7bd762b1e32c753b \
-    --hash=sha256:5168f7f71e51da7d6cc85a11cd5d102d9eae750d5f5a3ee90cc9ebae10226621 \
-    --hash=sha256:620ad460023eeeae721e2e25a2a3e2b608f09cd169c1f68af7043c6d44e88cbf \
-    --hash=sha256:634c78fd62cd6e5357291ccb9671e43262f818f9cf7cc58f701b5bd80d1c1ef7 \
-    --hash=sha256:6b3b14616f9141b12e61c0c46d1c954927f7f307498d8b9d2261ff2bd4005bbd \
-    --hash=sha256:74e317ef7cba8c0208c5d8d9f1406eac37f58e8f92b3f7caa9a72b8b118b1c09 \
-    --hash=sha256:803fa2bcbc93f43fe0b3b5b70d78882d3b266a70d419acfc7fdd515f89cba79b \
-    --hash=sha256:8b5dbc0e809c90377527e0f65829d6abcdf5c69f892f433ed2cb8508d4ba519a \
-    --hash=sha256:8df13f990acc58889160eff5b2e1df029cdfffdf020ce5044e655242c1016bb1 \
-    --hash=sha256:91549a16b1ef2d6bc5ac8f28eed32737001fcfe33309f1ba126cd4c1e08b971b \
-    --hash=sha256:96fb62a880bf25da7e12ad4bba00a82deb2daf6f59050e8db6f0b04107120799 \
-    --hash=sha256:a99b87b65dfca65a830503bdfd2e5168a69b5290807cb8e922fa5a1acea2edec \
-    --hash=sha256:bbd58cedddce29216703a63ea42db010b6151c7bc05ac741af50aa31e31491fb \
-    --hash=sha256:c400aa46fc814edd69c72fcdf202dbd8c666ae684b534e81350a3a30ab16bdfc
-    # via
-    #   flax
-    #   orbax-checkpoint
-termcolor==2.1.1 \
-    --hash=sha256:67cee2009adc6449c650f6bcf3bdeed00c8ba53a8cda5362733c53e0a39fb70b \
-    --hash=sha256:fa852e957f97252205e105dd55bbc23b419a70fec0085708fc0515e399f304fd
-    # via tensorflow
-toml==0.10.2 \
-    --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \
-    --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f
-    # via pylint
-toolz==0.12.0 \
-    --hash=sha256:2059bd4148deb1884bb0eb770a3cde70e7f954cfbbdc2285f1f2de01fd21eb6f \
-    --hash=sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194
-    # via chex
-typing-extensions==4.4.0 \
-    --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa \
-    --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e
-    # via
-    #   flax
-    #   optax
-    #   orbax-checkpoint
-    #   rich
-    #   tensorflow
-urllib3==1.26.13 \
-    --hash=sha256:47cc05d99aaa09c9e72ed5809b60e7ba354e64b59c9c173ac3018642d8bb41fc \
-    --hash=sha256:c083dd0dce68dbfbe1129d5271cb90f9447dea7d52097c6e0126120c521ddea8
-    # via requests
-wcwidth==0.2.5 \
-    --hash=sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784 \
-    --hash=sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83
-    # via prompt-toolkit
-werkzeug==2.2.2 \
-    --hash=sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f \
-    --hash=sha256:f979ab81f58d7318e064e99c4506445d60135ac5cd2e177a2de0089bfd4c9bd5
-    # via tensorboard
-wheel==0.38.4 \
-    --hash=sha256:965f5259b566725405b05e7cf774052044b1ed30119b5d586b2703aafe8719ac \
-    --hash=sha256:b60533f3f5d530e971d6737ca6d58681ee434818fab630c83a734bb10c083ce8
-    # via
-    #   astunparse
-    #   tensorboard
-    #   tensorflow-decision-forests
-wrapt==1.12.1 \
-    --hash=sha256:b62ffa81fb85f4332a4f609cab4ac40709470da05643a082ec1eb88e6d9b97d7
-    # via
-    #   astroid
-    #   tensorflow
-wurlitzer==3.0.3 \
-    --hash=sha256:224f5fe70618be3872c05dfddc8c457191ec1870654596279fcc1edadebe3e5b \
-    --hash=sha256:ffcc584109f5ecd5244abfde4534f22140f8735a4890ce1abd90b4e503f5f427
-    # via tensorflow-decision-forests
-zipp==3.11.0 \
-    --hash=sha256:83a28fcb75844b5c0cdaf5aa4003c2d728c77e05f5aeabe8e95e56727005fbaa \
-    --hash=sha256:a7a22e05929290a67401440b39690ae6563279bced5f314609d9d03798f56766
-    # via
-    #   importlib-metadata
-    #   importlib-resources
diff --git a/tfjs-master/tfjs-converter/python/requirements.txt b/tfjs-master/tfjs-converter/python/requirements.txt
deleted file mode 100644
index 81b412a90..000000000
--- a/tfjs-master/tfjs-converter/python/requirements.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-flax>=0.7.2
-importlib_resources>=5.9.0
-jax>=0.4.13
-jaxlib>=0.4.13
-tensorflow>=2.13.0,<3
-tensorflow-decision-forests>=1.5.0
-six>=1.16.0,<2
-tensorflow-hub>=0.14.0
-packaging~=23.1
diff --git a/tfjs-master/tfjs-converter/python/requirements_lock.txt b/tfjs-master/tfjs-converter/python/requirements_lock.txt
deleted file mode 100644
index 88cc454e8..000000000
--- a/tfjs-master/tfjs-converter/python/requirements_lock.txt
+++ /dev/null
@@ -1,802 +0,0 @@
-#
-# This file is autogenerated by pip-compile with Python 3.8
-# by the following command:
-#
-#    bazel run //tfjs-converter/python:tensorflowjs_deps_requirements.update
-#
-absl-py==1.3.0 \
-    --hash=sha256:34995df9bd7a09b3b8749e230408f5a2a2dd7a68a0d33c12a3d0cb15a041a507 \
-    --hash=sha256:463c38a08d2e4cef6c498b76ba5bd4858e4c6ef51da1a5a1f27139a022e20248
-    # via
-    #   chex
-    #   optax
-    #   orbax-checkpoint
-    #   tensorboard
-    #   tensorflow
-    #   tensorflow-decision-forests
-astunparse==1.6.3 \
-    --hash=sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872 \
-    --hash=sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8
-    # via tensorflow
-cached-property==1.5.2 \
-    --hash=sha256:9fa5755838eecbb2d234c3aa390bd80fbd3ac6b6869109bfc1b499f7bd89a130 \
-    --hash=sha256:df4f613cf7ad9a588cc381aaf4a512d26265ecebd5eb9e1ba12f1319eb85a6a0
-    # via orbax-checkpoint
-cachetools==5.2.0 \
-    --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \
-    --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db
-    # via google-auth
-certifi==2022.12.7 \
-    --hash=sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3 \
-    --hash=sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18
-    # via requests
-charset-normalizer==2.1.1 \
-    --hash=sha256:5a3d016c7c547f69d6f81fb0db9449ce888b418b5b9952cc5e6e66843e9dd845 \
-    --hash=sha256:83e9a75d1911279afd89352c68b45348559d1fc0506b054b346651b5e7fee29f
-    # via requests
-chex==0.1.5 \
-    --hash=sha256:686858320f8f220c82a6c7eeb54dcdcaa4f3d7f66690dacd13a24baa1ee8299e \
-    --hash=sha256:b3321184850d5fc29b2eca63087cdbdd83a1b3e4f33c1314ff8b3b8bd67abbca
-    # via optax
-colorama==0.4.6 \
-    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
-    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
-    # via rich
-commonmark==0.9.1 \
-    --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \
-    --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9
-    # via rich
-dm-tree==0.1.7 \
-    --hash=sha256:0f01743cc2247170e64798c6b4b31853717054bf9ceec47a1b1b8c2a4baf5792 \
-    --hash=sha256:1379a02df36e2bbff9819ceafa55ccd436b15af398803f781f372f8ead7ed871 \
-    --hash=sha256:1410fa2f2cc8dc7c01386f4e93ddeeb56765574ffafb632a9b6bd96496195b10 \
-    --hash=sha256:20f24cad4decbf4c1f176a959d16e877c73df33b07d7d1f078a5b8abe72f79f8 \
-    --hash=sha256:2a843608e078d1622ebb5e50962a8c718d3fa1ab9461b95a12395a803545b2f5 \
-    --hash=sha256:30fec8aca5b92823c0e796a2f33b875b4dccd470b57e91e6c542405c5f77fd2a \
-    --hash=sha256:3166304411d14c50a5da1c583e24d6069b44de0c9e06479cb36cdf048a466945 \
-    --hash=sha256:3b00885c21267934a3d3c68660811d3f891c9539fd53712f5b2423c6d74bf1e6 \
-    --hash=sha256:3ca0a58e219b7b0bc201fea4679971188d0a9028a2543c16803a84e8f8c7eb2c \
-    --hash=sha256:3fae437135b6cbbdd51e96488a35e78c3617defa0b65265e7e8752d506f933fd \
-    --hash=sha256:4992ac5c42af1d73042cd2d3af4e7892d3750e6c1bb8e5a4f81534aa6515f350 \
-    --hash=sha256:51b9bdf1109b47cc22884b1919e6fe38edf28b5aa02e7c661bb760a0e7cf0157 \
-    --hash=sha256:57edb6fbd88fcdd9908547cbf21045a9d663c0d9e5983dca7e6f9cf8b6584bb5 \
-    --hash=sha256:7f1f3dca9d669f3c09654ff6d69cfafd86a7f967c3095405b2692ee8d8ef3cfd \
-    --hash=sha256:7fa0740b7fbae2c3a43a3114a514891b5d6c383050828f36aa1816cf40f73a6a \
-    --hash=sha256:91c6240e47c9d80dbd7de5a29a2ca663143717a72c613130ba8ac4354fa741a9 \
-    --hash=sha256:98fce150ceebb0a818f0eace1616004031cfa5e3375f50599ad790ff52414ba9 \
-    --hash=sha256:9edc1783a08d87c4e130781f55cbd904d6a564f7cce7dfb63f9ef3bee8e38209 \
-    --hash=sha256:a085f500b295a6bf439c538e9058c7798ecb8c7d0dc916291f3d8d79d6124d17 \
-    --hash=sha256:b4364fc9a5721a2b840ac8ea75b8f58b430bec9fdc8b99304d2aecb3cfe46b1b \
-    --hash=sha256:d377bd621b485db42c4aeea0eabbd8f6274b89a9c338c2c1bf69a40c3b86a1fd \
-    --hash=sha256:f3e2bd9b9c05d1a0039f7c128d8b055c8a05708ef569cdbbeec0a2946e425bd4
-    # via chex
-etils==0.9.0 \
-    --hash=sha256:489103e9e499a566765c60458ee15d185cf0065f2060a4d16a68f8f46962ed0d \
-    --hash=sha256:635d6f7d1c519eb194304228543a4c5c7df0e6b58243302473e34c18cf720588
-    # via orbax-checkpoint
-flatbuffers==23.5.26 \
-    --hash=sha256:9ea1144cac05ce5d86e2859f431c6cd5e66cd9c78c558317c7955fb8d4c78d89 \
-    --hash=sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1
-    # via tensorflow
-flax==0.7.2 \
-    --hash=sha256:261c7b93e6d15ad80e2cedd2edb797d41b0b3c7805a54254de72a2366dc80148 \
-    --hash=sha256:7f023ece0b8b0d03019d2841dbe780e33b81ec42268bdc3e5d24c8fb0582fd7b
-    # via -r tfjs-converter/python/requirements.txt
-gast==0.4.0 \
-    --hash=sha256:40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1 \
-    --hash=sha256:b7adcdd5adbebf1adf17378da5ba3f543684dbec47b1cda1f3997e573cd542c4
-    # via tensorflow
-google-auth==2.15.0 \
-    --hash=sha256:6897b93556d8d807ad70701bb89f000183aea366ca7ed94680828b37437a4994 \
-    --hash=sha256:72f12a6cfc968d754d7bdab369c5c5c16032106e52d32c6dfd8484e4c01a6d1f
-    # via
-    #   google-auth-oauthlib
-    #   tensorboard
-google-auth-oauthlib==1.0.0 \
-    --hash=sha256:95880ca704928c300f48194d1770cf5b1462835b6e49db61445a520f793fd5fb \
-    --hash=sha256:e375064964820b47221a7e1b7ee1fd77051b6323c3f9e3e19785f78ab67ecfc5
-    # via tensorboard
-google-pasta==0.2.0 \
-    --hash=sha256:4612951da876b1a10fe3960d7226f0c7682cf901e16ac06e473b267a5afa8954 \
-    --hash=sha256:b32482794a366b5366a32c92a9a9201b107821889935a02b3e51f6b432ea84ed \
-    --hash=sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e
-    # via tensorflow
-grpcio==1.51.1 \
-    --hash=sha256:094e64236253590d9d4075665c77b329d707b6fca864dd62b144255e199b4f87 \
-    --hash=sha256:0dc5354e38e5adf2498312f7241b14c7ce3484eefa0082db4297189dcbe272e6 \
-    --hash=sha256:0e1a9e1b4a23808f1132aa35f968cd8e659f60af3ffd6fb00bcf9a65e7db279f \
-    --hash=sha256:0fb93051331acbb75b49a2a0fd9239c6ba9528f6bdc1dd400ad1cb66cf864292 \
-    --hash=sha256:16c71740640ba3a882f50b01bf58154681d44b51f09a5728180a8fdc66c67bd5 \
-    --hash=sha256:172405ca6bdfedd6054c74c62085946e45ad4d9cec9f3c42b4c9a02546c4c7e9 \
-    --hash=sha256:17ec9b13cec4a286b9e606b48191e560ca2f3bbdf3986f91e480a95d1582e1a7 \
-    --hash=sha256:22b011674090594f1f3245960ced7386f6af35485a38901f8afee8ad01541dbd \
-    --hash=sha256:24ac1154c4b2ab4a0c5326a76161547e70664cd2c39ba75f00fc8a2170964ea2 \
-    --hash=sha256:257478300735ce3c98d65a930bbda3db172bd4e00968ba743e6a1154ea6edf10 \
-    --hash=sha256:29cb97d41a4ead83b7bcad23bdb25bdd170b1e2cba16db6d3acbb090bc2de43c \
-    --hash=sha256:2b170eaf51518275c9b6b22ccb59450537c5a8555326fd96ff7391b5dd75303c \
-    --hash=sha256:31bb6bc7ff145e2771c9baf612f4b9ebbc9605ccdc5f3ff3d5553de7fc0e0d79 \
-    --hash=sha256:3c2b3842dcf870912da31a503454a33a697392f60c5e2697c91d133130c2c85d \
-    --hash=sha256:3f9b0023c2c92bebd1be72cdfca23004ea748be1813a66d684d49d67d836adde \
-    --hash=sha256:471d39d3370ca923a316d49c8aac66356cea708a11e647e3bdc3d0b5de4f0a40 \
-    --hash=sha256:49d680356a975d9c66a678eb2dde192d5dc427a7994fb977363634e781614f7c \
-    --hash=sha256:4c4423ea38a7825b8fed8934d6d9aeebdf646c97e3c608c3b0bcf23616f33877 \
-    --hash=sha256:506b9b7a4cede87d7219bfb31014d7b471cfc77157da9e820a737ec1ea4b0663 \
-    --hash=sha256:538d981818e49b6ed1e9c8d5e5adf29f71c4e334e7d459bf47e9b7abb3c30e09 \
-    --hash=sha256:59dffade859f157bcc55243714d57b286da6ae16469bf1ac0614d281b5f49b67 \
-    --hash=sha256:5a6ebcdef0ef12005d56d38be30f5156d1cb3373b52e96f147f4a24b0ddb3a9d \
-    --hash=sha256:5dca372268c6ab6372d37d6b9f9343e7e5b4bc09779f819f9470cd88b2ece3c3 \
-    --hash=sha256:6df3b63538c362312bc5fa95fb965069c65c3ea91d7ce78ad9c47cab57226f54 \
-    --hash=sha256:6f0b89967ee11f2b654c23b27086d88ad7bf08c0b3c2a280362f28c3698b2896 \
-    --hash=sha256:75e29a90dc319f0ad4d87ba6d20083615a00d8276b51512e04ad7452b5c23b04 \
-    --hash=sha256:7942b32a291421460d6a07883033e392167d30724aa84987e6956cd15f1a21b9 \
-    --hash=sha256:9235dcd5144a83f9ca6f431bd0eccc46b90e2c22fe27b7f7d77cabb2fb515595 \
-    --hash=sha256:97d67983189e2e45550eac194d6234fc38b8c3b5396c153821f2d906ed46e0ce \
-    --hash=sha256:9ff42c5620b4e4530609e11afefa4a62ca91fa0abb045a8957e509ef84e54d30 \
-    --hash=sha256:a8a0b77e992c64880e6efbe0086fe54dfc0bbd56f72a92d9e48264dcd2a3db98 \
-    --hash=sha256:aacb54f7789ede5cbf1d007637f792d3e87f1c9841f57dd51abf89337d1b8472 \
-    --hash=sha256:bc59f7ba87972ab236f8669d8ca7400f02a0eadf273ca00e02af64d588046f02 \
-    --hash=sha256:cc2bece1737b44d878cc1510ea04469a8073dbbcdd762175168937ae4742dfb3 \
-    --hash=sha256:cd3baccea2bc5c38aeb14e5b00167bd4e2373a373a5e4d8d850bd193edad150c \
-    --hash=sha256:dad6533411d033b77f5369eafe87af8583178efd4039c41d7515d3336c53b4f1 \
-    --hash=sha256:e223a9793522680beae44671b9ed8f6d25bbe5ddf8887e66aebad5e0686049ef \
-    --hash=sha256:e473525c28251558337b5c1ad3fa969511e42304524a4e404065e165b084c9e4 \
-    --hash=sha256:e4ef09f8997c4be5f3504cefa6b5c6cc3cf648274ce3cede84d4342a35d76db6 \
-    --hash=sha256:e6dfc2b6567b1c261739b43d9c59d201c1b89e017afd9e684d85aa7a186c9f7a \
-    --hash=sha256:eacad297ea60c72dd280d3353d93fb1dcca952ec11de6bb3c49d12a572ba31dd \
-    --hash=sha256:f1158bccbb919da42544a4d3af5d9296a3358539ffa01018307337365a9a0c64 \
-    --hash=sha256:f1fec3abaf274cdb85bf3878167cfde5ad4a4d97c68421afda95174de85ba813 \
-    --hash=sha256:f96ace1540223f26fbe7c4ebbf8a98e3929a6aa0290c8033d12526847b291c0f \
-    --hash=sha256:fbdbe9a849854fe484c00823f45b7baab159bdd4a46075302281998cb8719df5
-    # via
-    #   tensorboard
-    #   tensorflow
-h5py==3.7.0 \
-    --hash=sha256:03d64fb86bb86b978928bad923b64419a23e836499ec6363e305ad28afd9d287 \
-    --hash=sha256:04e2e1e2fc51b8873e972a08d2f89625ef999b1f2d276199011af57bb9fc7851 \
-    --hash=sha256:0798a9c0ff45f17d0192e4d7114d734cac9f8b2b2c76dd1d923c4d0923f27bb6 \
-    --hash=sha256:0a047fddbe6951bce40e9cde63373c838a978c5e05a011a682db9ba6334b8e85 \
-    --hash=sha256:0d8de8cb619fc597da7cf8cdcbf3b7ff8c5f6db836568afc7dc16d21f59b2b49 \
-    --hash=sha256:1fcb11a2dc8eb7ddcae08afd8fae02ba10467753a857fa07a404d700a93f3d53 \
-    --hash=sha256:3fcf37884383c5da64846ab510190720027dca0768def34dd8dcb659dbe5cbf3 \
-    --hash=sha256:43fed4d13743cf02798a9a03a360a88e589d81285e72b83f47d37bb64ed44881 \
-    --hash=sha256:63beb8b7b47d0896c50de6efb9a1eaa81dbe211f3767e7dd7db159cea51ba37a \
-    --hash=sha256:6776d896fb90c5938de8acb925e057e2f9f28755f67ec3edcbc8344832616c38 \
-    --hash=sha256:9e2ad2aa000f5b1e73b5dfe22f358ca46bf1a2b6ca394d9659874d7fc251731a \
-    --hash=sha256:9e7535df5ee3dc3e5d1f408fdfc0b33b46bc9b34db82743c82cd674d8239b9ad \
-    --hash=sha256:a9351d729ea754db36d175098361b920573fdad334125f86ac1dd3a083355e20 \
-    --hash=sha256:c038399ce09a58ff8d89ec3e62f00aa7cb82d14f34e24735b920e2a811a3a426 \
-    --hash=sha256:d77af42cb751ad6cc44f11bae73075a07429a5cf2094dfde2b1e716e059b3911 \
-    --hash=sha256:e5b7820b75f9519499d76cc708e27242ccfdd9dfb511d6deb98701961d0445aa \
-    --hash=sha256:ed43e2cc4f511756fd664fb45d6b66c3cbed4e3bd0f70e29c37809b2ae013c44 \
-    --hash=sha256:f084bbe816907dfe59006756f8f2d16d352faff2d107f4ffeb1d8de126fc5dc7 \
-    --hash=sha256:f514b24cacdd983e61f8d371edac8c1b780c279d0acb8485639e97339c866073 \
-    --hash=sha256:f73307c876af49aa869ec5df1818e9bb0bdcfcf8a5ba773cc45a4fba5a286a5c
-    # via tensorflow
-idna==3.4 \
-    --hash=sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4 \
-    --hash=sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2
-    # via requests
-importlib-metadata==5.1.0 \
-    --hash=sha256:d5059f9f1e8e41f80e9c56c2ee58811450c31984dfa625329ffd7c0dad88a73b \
-    --hash=sha256:d84d17e21670ec07990e1044a99efe8d615d860fd176fc29ef5c306068fda313
-    # via
-    #   jax
-    #   markdown
-importlib-resources==5.9.0 \
-    --hash=sha256:5481e97fb45af8dcf2f798952625591c58fe599d0735d86b10f54de086a61681 \
-    --hash=sha256:f78a8df21a79bcc30cfd400bdc38f314333de7c0fb619763f6b9dabab8268bb7
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   orbax-checkpoint
-jax==0.4.13 \
-    --hash=sha256:03bfe6749dfe647f16f15f6616638adae6c4a7ca7167c75c21961ecfd3a3baaa
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   chex
-    #   flax
-    #   optax
-    #   orbax-checkpoint
-jaxlib==0.4.13 \
-    --hash=sha256:19ae4c316b17a49342432c69f7f89f190b975333f3f9e9e175f686a651bc7347 \
-    --hash=sha256:411334d903df07dc1ace8d52fc53c17f6bc1d55aff7f6e0e5cf61ec149f758a0 \
-    --hash=sha256:49690fcdd26560515fd15399fc3a44777e0bfc5db5c48fe76ff7bc7228e8b2fb \
-    --hash=sha256:522635d5e159401a386c79f1236c218c1f68fbb4ca6648115c3ad3c2c3f518ab \
-    --hash=sha256:532ebc4fb11386282ad63b83941d4557f4038c1144acf026f1f8565f64c7e9c0 \
-    --hash=sha256:8000c0d15c107328e8f7b7b3ac91dd822f5c287a80231882b620503ed141fa89 \
-    --hash=sha256:839173b2e9593f5e9a6d3c42852cd15070fe80a939246efbb5cf40eec815de89 \
-    --hash=sha256:a259bb35429bfbd3b76e43019dfc8f7d6ea94bb217400b78f7d0824ce07a58ac \
-    --hash=sha256:b5c0a9737efd95fe18fd7715ce30dfce476546705ea8934aad6731777a9631a5 \
-    --hash=sha256:bebb4cf001f180dc431f9604daf930c2d9cc778e4dda26f401ac939b7bac912e \
-    --hash=sha256:c230ef85712e608d0f048869766a5a63afeb2e72309943db0df9f959ab17307f \
-    --hash=sha256:d19c05c15f962e098d49b45e2758aacf19330d192ec5395f9ef136f62db90edc \
-    --hash=sha256:ea1bc9811ef7d73a15e3213115e88fe7f5d14b59d95027bea9fccc98e5a14af8 \
-    --hash=sha256:f4e9e34e5d8a6556f62fead14aee0b1614c2c6296f0078d8e6139d6aff109649 \
-    --hash=sha256:fde66a93e9be89d99e5792f677ed8e319667d6b2396865b1c52c1312844c47f9
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   chex
-    #   optax
-    #   orbax-checkpoint
-keras==2.13.1 \
-    --hash=sha256:5ce5f706f779fa7330e63632f327b75ce38144a120376b2ae1917c00fa6136af \
-    --hash=sha256:5df12cc241a015a11b65ddb452c0eeb2744fce21d9b54ba48db87492568ccc68
-    # via tensorflow
-libclang==14.0.6 \
-    --hash=sha256:206d2789e4450a37d054e63b70451a6fc1873466397443fa13de2b3d4adb2796 \
-    --hash=sha256:2e4303e04517fcd11173cb2e51a7070eed71e16ef45d4e26a82c5e881cac3d27 \
-    --hash=sha256:5dd3c6fca1b007d308a4114afa8e4e9d32f32b2572520701d45fcc626ac5cd6c \
-    --hash=sha256:7b06fc76bd1e67c8b04b5719bf2ac5d6a323b289b245dfa9e468561d99538188 \
-    --hash=sha256:8791cf3c3b087c373a6d61e9199da7a541da922c9ddcfed1122090586b996d6e \
-    --hash=sha256:9052a8284d8846984f6fa826b1d7460a66d3b23a486d782633b42b6e3b418789 \
-    --hash=sha256:cfb0e892ebb5dff6bd498ab5778adb8581f26a00fd8347b3c76c989fe2fd04f7 \
-    --hash=sha256:e2add1703129b2abe066fb1890afa880870a89fd6ab4ec5d2a7a8dc8d271677e \
-    --hash=sha256:e429853939423f276a25140b0b702442d7da9a09e001c05e48df888336947614 \
-    --hash=sha256:ea03c12675151837660cdd5dce65bd89320896ac3421efef43a36678f113ce95
-    # via tensorflow
-markdown==3.4.1 \
-    --hash=sha256:08fb8465cffd03d10b9dd34a5c3fea908e20391a2a90b88d66362cb05beed186 \
-    --hash=sha256:3b809086bb6efad416156e00a0da66fe47618a5d6918dd688f53f40c8e4cfeff
-    # via tensorboard
-markupsafe==2.1.1 \
-    --hash=sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003 \
-    --hash=sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88 \
-    --hash=sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5 \
-    --hash=sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7 \
-    --hash=sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a \
-    --hash=sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603 \
-    --hash=sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1 \
-    --hash=sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135 \
-    --hash=sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247 \
-    --hash=sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6 \
-    --hash=sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601 \
-    --hash=sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77 \
-    --hash=sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02 \
-    --hash=sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e \
-    --hash=sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63 \
-    --hash=sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f \
-    --hash=sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980 \
-    --hash=sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b \
-    --hash=sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812 \
-    --hash=sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff \
-    --hash=sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96 \
-    --hash=sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1 \
-    --hash=sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925 \
-    --hash=sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a \
-    --hash=sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6 \
-    --hash=sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e \
-    --hash=sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f \
-    --hash=sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4 \
-    --hash=sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f \
-    --hash=sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3 \
-    --hash=sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c \
-    --hash=sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a \
-    --hash=sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417 \
-    --hash=sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a \
-    --hash=sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a \
-    --hash=sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37 \
-    --hash=sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452 \
-    --hash=sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933 \
-    --hash=sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a \
-    --hash=sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7
-    # via werkzeug
-ml-dtypes==0.2.0 \
-    --hash=sha256:022d5a4ee6be14569c2a9d1549e16f1ec87ca949681d0dca59995445d5fcdd5b \
-    --hash=sha256:1749b60348da71fd3c2ab303fdbc1965958dc50775ead41f5669c932a341cafd \
-    --hash=sha256:32107e7fa9f62db9a5281de923861325211dfff87bd23faefb27b303314635ab \
-    --hash=sha256:35b984cddbe8173b545a0e3334fe56ea1a5c3eb67c507f60d0cfde1d3fa8f8c2 \
-    --hash=sha256:36d28b8861a8931695e5a31176cad5ae85f6504906650dea5598fbec06c94606 \
-    --hash=sha256:50845af3e9a601810751b55091dee6c2562403fa1cb4e0123675cf3a4fc2c17a \
-    --hash=sha256:6488eb642acaaf08d8020f6de0a38acee7ac324c1e6e92ee0c0fea42422cb797 \
-    --hash=sha256:75015818a7fccf99a5e8ed18720cb430f3e71a8838388840f4cdf225c036c983 \
-    --hash=sha256:80d304c836d73f10605c58ccf7789c171cc229bfb678748adfb7cea2510dfd0e \
-    --hash=sha256:832a019a1b6db5c4422032ca9940a990fa104eee420f643713241b3a518977fa \
-    --hash=sha256:8faaf0897942c8253dd126662776ba45f0a5861968cf0f06d6d465f8a7bc298a \
-    --hash=sha256:bc29a0524ef5e23a7fbb8d881bdecabeb3fc1d19d9db61785d077a86cb94fab2 \
-    --hash=sha256:df6a76e1c8adf484feb138ed323f9f40a7b6c21788f120f7c78bec20ac37ee81 \
-    --hash=sha256:e70047ec2c83eaee01afdfdabee2c5b0c133804d90d0f7db4dd903360fcc537c \
-    --hash=sha256:e85ba8e24cf48d456e564688e981cf379d4c8e644db0a2f719b78de281bac2ca \
-    --hash=sha256:f00c71c8c63e03aff313bc6a7aeaac9a4f1483a921a6ffefa6d4404efd1af3d0 \
-    --hash=sha256:f08c391c2794f2aad358e6f4c70785a9a7b1df980ef4c232b3ccd4f6fe39f719
-    # via
-    #   jax
-    #   jaxlib
-msgpack==1.0.4 \
-    --hash=sha256:002b5c72b6cd9b4bafd790f364b8480e859b4712e91f43014fe01e4f957b8467 \
-    --hash=sha256:0a68d3ac0104e2d3510de90a1091720157c319ceeb90d74f7b5295a6bee51bae \
-    --hash=sha256:0df96d6eaf45ceca04b3f3b4b111b86b33785683d682c655063ef8057d61fd92 \
-    --hash=sha256:0dfe3947db5fb9ce52aaea6ca28112a170db9eae75adf9339a1aec434dc954ef \
-    --hash=sha256:0e3590f9fb9f7fbc36df366267870e77269c03172d086fa76bb4eba8b2b46624 \
-    --hash=sha256:11184bc7e56fd74c00ead4f9cc9a3091d62ecb96e97653add7a879a14b003227 \
-    --hash=sha256:112b0f93202d7c0fef0b7810d465fde23c746a2d482e1e2de2aafd2ce1492c88 \
-    --hash=sha256:1276e8f34e139aeff1c77a3cefb295598b504ac5314d32c8c3d54d24fadb94c9 \
-    --hash=sha256:1576bd97527a93c44fa856770197dec00d223b0b9f36ef03f65bac60197cedf8 \
-    --hash=sha256:1e91d641d2bfe91ba4c52039adc5bccf27c335356055825c7f88742c8bb900dd \
-    --hash=sha256:26b8feaca40a90cbe031b03d82b2898bf560027160d3eae1423f4a67654ec5d6 \
-    --hash=sha256:2999623886c5c02deefe156e8f869c3b0aaeba14bfc50aa2486a0415178fce55 \
-    --hash=sha256:2a2df1b55a78eb5f5b7d2a4bb221cd8363913830145fad05374a80bf0877cb1e \
-    --hash=sha256:2bb8cdf50dd623392fa75525cce44a65a12a00c98e1e37bf0fb08ddce2ff60d2 \
-    --hash=sha256:2cc5ca2712ac0003bcb625c96368fd08a0f86bbc1a5578802512d87bc592fe44 \
-    --hash=sha256:35bc0faa494b0f1d851fd29129b2575b2e26d41d177caacd4206d81502d4c6a6 \
-    --hash=sha256:3c11a48cf5e59026ad7cb0dc29e29a01b5a66a3e333dc11c04f7e991fc5510a9 \
-    --hash=sha256:449e57cc1ff18d3b444eb554e44613cffcccb32805d16726a5494038c3b93dab \
-    --hash=sha256:462497af5fd4e0edbb1559c352ad84f6c577ffbbb708566a0abaaa84acd9f3ae \
-    --hash=sha256:4733359808c56d5d7756628736061c432ded018e7a1dff2d35a02439043321aa \
-    --hash=sha256:48f5d88c99f64c456413d74a975bd605a9b0526293218a3b77220a2c15458ba9 \
-    --hash=sha256:49565b0e3d7896d9ea71d9095df15b7f75a035c49be733051c34762ca95bbf7e \
-    --hash=sha256:4ab251d229d10498e9a2f3b1e68ef64cb393394ec477e3370c457f9430ce9250 \
-    --hash=sha256:4d5834a2a48965a349da1c5a79760d94a1a0172fbb5ab6b5b33cbf8447e109ce \
-    --hash=sha256:4dea20515f660aa6b7e964433b1808d098dcfcabbebeaaad240d11f909298075 \
-    --hash=sha256:545e3cf0cf74f3e48b470f68ed19551ae6f9722814ea969305794645da091236 \
-    --hash=sha256:63e29d6e8c9ca22b21846234913c3466b7e4ee6e422f205a2988083de3b08cae \
-    --hash=sha256:6916c78f33602ecf0509cc40379271ba0f9ab572b066bd4bdafd7434dee4bc6e \
-    --hash=sha256:6a4192b1ab40f8dca3f2877b70e63799d95c62c068c84dc028b40a6cb03ccd0f \
-    --hash=sha256:6c9566f2c39ccced0a38d37c26cc3570983b97833c365a6044edef3574a00c08 \
-    --hash=sha256:76ee788122de3a68a02ed6f3a16bbcd97bc7c2e39bd4d94be2f1821e7c4a64e6 \
-    --hash=sha256:7760f85956c415578c17edb39eed99f9181a48375b0d4a94076d84148cf67b2d \
-    --hash=sha256:77ccd2af37f3db0ea59fb280fa2165bf1b096510ba9fe0cc2bf8fa92a22fdb43 \
-    --hash=sha256:81fc7ba725464651190b196f3cd848e8553d4d510114a954681fd0b9c479d7e1 \
-    --hash=sha256:85f279d88d8e833ec015650fd15ae5eddce0791e1e8a59165318f371158efec6 \
-    --hash=sha256:9667bdfdf523c40d2511f0e98a6c9d3603be6b371ae9a238b7ef2dc4e7a427b0 \
-    --hash=sha256:a75dfb03f8b06f4ab093dafe3ddcc2d633259e6c3f74bb1b01996f5d8aa5868c \
-    --hash=sha256:ac5bd7901487c4a1dd51a8c58f2632b15d838d07ceedaa5e4c080f7190925bff \
-    --hash=sha256:aca0f1644d6b5a73eb3e74d4d64d5d8c6c3d577e753a04c9e9c87d07692c58db \
-    --hash=sha256:b17be2478b622939e39b816e0aa8242611cc8d3583d1cd8ec31b249f04623243 \
-    --hash=sha256:c1683841cd4fa45ac427c18854c3ec3cd9b681694caf5bff04edb9387602d661 \
-    --hash=sha256:c23080fdeec4716aede32b4e0ef7e213c7b1093eede9ee010949f2a418ced6ba \
-    --hash=sha256:d5b5b962221fa2c5d3a7f8133f9abffc114fe218eb4365e40f17732ade576c8e \
-    --hash=sha256:d603de2b8d2ea3f3bcb2efe286849aa7a81531abc52d8454da12f46235092bcb \
-    --hash=sha256:e83f80a7fec1a62cf4e6c9a660e39c7f878f603737a0cdac8c13131d11d97f52 \
-    --hash=sha256:eb514ad14edf07a1dbe63761fd30f89ae79b42625731e1ccf5e1f1092950eaa6 \
-    --hash=sha256:eba96145051ccec0ec86611fe9cf693ce55f2a3ce89c06ed307de0e085730ec1 \
-    --hash=sha256:ed6f7b854a823ea44cf94919ba3f727e230da29feb4a99711433f25800cf747f \
-    --hash=sha256:f0029245c51fd9473dc1aede1160b0a29f4a912e6b1dd353fa6d317085b219da \
-    --hash=sha256:f5d869c18f030202eb412f08b28d2afeea553d6613aee89e200d7aca7ef01f5f \
-    --hash=sha256:fb62ea4b62bfcb0b380d5680f9a4b3f9a2d166d9394e9bbd9666c0ee09a3645c \
-    --hash=sha256:fcb8a47f43acc113e24e910399376f7277cf8508b27e5b88499f053de6b115a8
-    # via
-    #   flax
-    #   orbax-checkpoint
-nest-asyncio==1.5.7 \
-    --hash=sha256:5301c82941b550b3123a1ea772ba9a1c80bad3a182be8c1a5ae6ad3be57a9657 \
-    --hash=sha256:6a80f7b98f24d9083ed24608977c09dd608d83f91cccc24c9d2cba6d10e01c10
-    # via orbax-checkpoint
-numpy==1.23.5 \
-    --hash=sha256:01dd17cbb340bf0fc23981e52e1d18a9d4050792e8fb8363cecbf066a84b827d \
-    --hash=sha256:06005a2ef6014e9956c09ba07654f9837d9e26696a0470e42beedadb78c11b07 \
-    --hash=sha256:09b7847f7e83ca37c6e627682f145856de331049013853f344f37b0c9690e3df \
-    --hash=sha256:0aaee12d8883552fadfc41e96b4c82ee7d794949e2a7c3b3a7201e968c7ecab9 \
-    --hash=sha256:0cbe9848fad08baf71de1a39e12d1b6310f1d5b2d0ea4de051058e6e1076852d \
-    --hash=sha256:1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a \
-    --hash=sha256:33161613d2269025873025b33e879825ec7b1d831317e68f4f2f0f84ed14c719 \
-    --hash=sha256:5039f55555e1eab31124a5768898c9e22c25a65c1e0037f4d7c495a45778c9f2 \
-    --hash=sha256:522e26bbf6377e4d76403826ed689c295b0b238f46c28a7251ab94716da0b280 \
-    --hash=sha256:56e454c7833e94ec9769fa0f86e6ff8e42ee38ce0ce1fa4cbb747ea7e06d56aa \
-    --hash=sha256:58f545efd1108e647604a1b5aa809591ccd2540f468a880bedb97247e72db387 \
-    --hash=sha256:5e05b1c973a9f858c74367553e236f287e749465f773328c8ef31abe18f691e1 \
-    --hash=sha256:7903ba8ab592b82014713c491f6c5d3a1cde5b4a3bf116404e08f5b52f6daf43 \
-    --hash=sha256:8969bfd28e85c81f3f94eb4a66bc2cf1dbdc5c18efc320af34bffc54d6b1e38f \
-    --hash=sha256:92c8c1e89a1f5028a4c6d9e3ccbe311b6ba53694811269b992c0b224269e2398 \
-    --hash=sha256:9c88793f78fca17da0145455f0d7826bcb9f37da4764af27ac945488116efe63 \
-    --hash=sha256:a7ac231a08bb37f852849bbb387a20a57574a97cfc7b6cabb488a4fc8be176de \
-    --hash=sha256:abdde9f795cf292fb9651ed48185503a2ff29be87770c3b8e2a14b0cd7aa16f8 \
-    --hash=sha256:af1da88f6bc3d2338ebbf0e22fe487821ea4d8e89053e25fa59d1d79786e7481 \
-    --hash=sha256:b2a9ab7c279c91974f756c84c365a669a887efa287365a8e2c418f8b3ba73fb0 \
-    --hash=sha256:bf837dc63ba5c06dc8797c398db1e223a466c7ece27a1f7b5232ba3466aafe3d \
-    --hash=sha256:ca51fcfcc5f9354c45f400059e88bc09215fb71a48d3768fb80e357f3b457e1e \
-    --hash=sha256:ce571367b6dfe60af04e04a1834ca2dc5f46004ac1cc756fb95319f64c095a96 \
-    --hash=sha256:d208a0f8729f3fb790ed18a003f3a57895b989b40ea4dce4717e9cf4af62c6bb \
-    --hash=sha256:dbee87b469018961d1ad79b1a5d50c0ae850000b639bcb1b694e9981083243b6 \
-    --hash=sha256:e9f4c4e51567b616be64e05d517c79a8a22f3606499941d97bb76f2ca59f982d \
-    --hash=sha256:f063b69b090c9d918f9df0a12116029e274daf0181df392839661c4c7ec9018a \
-    --hash=sha256:f9a909a8bae284d46bbfdefbdd4a262ba19d3bc9921b1e76126b1d21c3c34135
-    # via
-    #   chex
-    #   flax
-    #   h5py
-    #   jax
-    #   jaxlib
-    #   ml-dtypes
-    #   opt-einsum
-    #   optax
-    #   orbax-checkpoint
-    #   pandas
-    #   scipy
-    #   tensorboard
-    #   tensorflow
-    #   tensorflow-decision-forests
-    #   tensorflow-hub
-    #   tensorstore
-oauthlib==3.2.2 \
-    --hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
-    --hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
-    # via requests-oauthlib
-opt-einsum==3.3.0 \
-    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
-    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
-    # via
-    #   jax
-    #   tensorflow
-optax==0.1.4 \
-    --hash=sha256:12fcf33bd682f9a162a3deb097f864130c3224d76771af2ba09410de80399a9b \
-    --hash=sha256:fb7a0550d57a6636164a3de25986a8a19be8ff6431fcdf1225b4e05175810f22
-    # via flax
-orbax-checkpoint==0.2.3 \
-    --hash=sha256:155e0a2dceef2901122e66585171e1dff4f4a4d9d2abe43a2b514279b9a3dabd \
-    --hash=sha256:a001bf48f1cebc635b07263fa546473ea48be3e278c50d5ade880b9aafb96f8a
-    # via flax
-packaging==23.1 \
-    --hash=sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61 \
-    --hash=sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   tensorflow
-pandas==1.5.2 \
-    --hash=sha256:0183cb04a057cc38fde5244909fca9826d5d57c4a5b7390c0cc3fa7acd9fa883 \
-    --hash=sha256:1fc87eac0541a7d24648a001d553406f4256e744d92df1df8ebe41829a915028 \
-    --hash=sha256:220b98d15cee0b2cd839a6358bd1f273d0356bf964c1a1aeb32d47db0215488b \
-    --hash=sha256:2552bffc808641c6eb471e55aa6899fa002ac94e4eebfa9ec058649122db5824 \
-    --hash=sha256:315e19a3e5c2ab47a67467fc0362cb36c7c60a93b6457f675d7d9615edad2ebe \
-    --hash=sha256:344021ed3e639e017b452aa8f5f6bf38a8806f5852e217a7594417fb9bbfa00e \
-    --hash=sha256:375262829c8c700c3e7cbb336810b94367b9c4889818bbd910d0ecb4e45dc261 \
-    --hash=sha256:457d8c3d42314ff47cc2d6c54f8fc0d23954b47977b2caed09cd9635cb75388b \
-    --hash=sha256:4aed257c7484d01c9a194d9a94758b37d3d751849c05a0050c087a358c41ad1f \
-    --hash=sha256:530948945e7b6c95e6fa7aa4be2be25764af53fba93fe76d912e35d1c9ee46f5 \
-    --hash=sha256:5ae7e989f12628f41e804847a8cc2943d362440132919a69429d4dea1f164da0 \
-    --hash=sha256:71f510b0efe1629bf2f7c0eadb1ff0b9cf611e87b73cd017e6b7d6adb40e2b3a \
-    --hash=sha256:73f219fdc1777cf3c45fde7f0708732ec6950dfc598afc50588d0d285fddaefc \
-    --hash=sha256:8092a368d3eb7116e270525329a3e5c15ae796ccdf7ccb17839a73b4f5084a39 \
-    --hash=sha256:82ae615826da838a8e5d4d630eb70c993ab8636f0eff13cb28aafc4291b632b5 \
-    --hash=sha256:9608000a5a45f663be6af5c70c3cbe634fa19243e720eb380c0d378666bc7702 \
-    --hash=sha256:a40dd1e9f22e01e66ed534d6a965eb99546b41d4d52dbdb66565608fde48203f \
-    --hash=sha256:b4f5a82afa4f1ff482ab8ded2ae8a453a2cdfde2001567b3ca24a4c5c5ca0db3 \
-    --hash=sha256:c009a92e81ce836212ce7aa98b219db7961a8b95999b97af566b8dc8c33e9519 \
-    --hash=sha256:c218796d59d5abd8780170c937b812c9637e84c32f8271bbf9845970f8c1351f \
-    --hash=sha256:cc3cd122bea268998b79adebbb8343b735a5511ec14efb70a39e7acbc11ccbdc \
-    --hash=sha256:d0d8fd58df5d17ddb8c72a5075d87cd80d71b542571b5f78178fb067fa4e9c72 \
-    --hash=sha256:e18bc3764cbb5e118be139b3b611bc3fbc5d3be42a7e827d1096f46087b395eb \
-    --hash=sha256:e2b83abd292194f350bb04e188f9379d36b8dfac24dd445d5c87575f3beaf789 \
-    --hash=sha256:e7469271497960b6a781eaa930cba8af400dd59b62ec9ca2f4d31a19f2f91090 \
-    --hash=sha256:e9dbacd22555c2d47f262ef96bb4e30880e5956169741400af8b306bbb24a273 \
-    --hash=sha256:f6257b314fc14958f8122779e5a1557517b0f8e500cfb2bd53fa1f75a8ad0af2
-    # via tensorflow-decision-forests
-protobuf==4.22.3 \
-    --hash=sha256:13233ee2b9d3bd9a5f216c1fa2c321cd564b93d8f2e4f521a85b585447747997 \
-    --hash=sha256:23452f2fdea754a8251d0fc88c0317735ae47217e0d27bf330a30eec2848811a \
-    --hash=sha256:52f0a78141078077cfe15fe333ac3e3a077420b9a3f5d1bf9b5fe9d286b4d881 \
-    --hash=sha256:70659847ee57a5262a65954538088a1d72dfc3e9882695cab9f0c54ffe71663b \
-    --hash=sha256:7760730063329d42a9d4c4573b804289b738d4931e363ffbe684716b796bde51 \
-    --hash=sha256:7cf56e31907c532e460bb62010a513408e6cdf5b03fb2611e4b67ed398ad046d \
-    --hash=sha256:8b54f56d13ae4a3ec140076c9d937221f887c8f64954673d46f63751209e839a \
-    --hash=sha256:d14fc1a41d1a1909998e8aff7e80d2a7ae14772c4a70e4bf7db8a36690b54425 \
-    --hash=sha256:d4b66266965598ff4c291416be429cef7989d8fae88b55b62095a2331511b3fa \
-    --hash=sha256:e0e630d8e6a79f48c557cd1835865b593d0547dce221c66ed1b827de59c66c97 \
-    --hash=sha256:ecae944c6c2ce50dda6bf76ef5496196aeb1b85acb95df5843cd812615ec4b61 \
-    --hash=sha256:f08aa300b67f1c012100d8eb62d47129e53d1150f4469fd78a29fa3cb68c66f2 \
-    --hash=sha256:f2f4710543abec186aee332d6852ef5ae7ce2e9e807a3da570f36de5a732d88e
-    # via
-    #   tensorboard
-    #   tensorflow
-    #   tensorflow-hub
-pyasn1==0.4.8 \
-    --hash=sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d \
-    --hash=sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba
-    # via
-    #   pyasn1-modules
-    #   rsa
-pyasn1-modules==0.2.8 \
-    --hash=sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e \
-    --hash=sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74
-    # via google-auth
-pygments==2.13.0 \
-    --hash=sha256:56a8508ae95f98e2b9bdf93a6be5ae3f7d8af858b43e02c5a2ff083726be40c1 \
-    --hash=sha256:f643f331ab57ba3c9d89212ee4a2dabc6e94f117cf4eefde99a0574720d14c42
-    # via rich
-python-dateutil==2.8.2 \
-    --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
-    --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
-    # via pandas
-pytz==2022.6 \
-    --hash=sha256:222439474e9c98fced559f1709d89e6c9cbf8d79c794ff3eb9f8800064291427 \
-    --hash=sha256:e89512406b793ca39f5971bc999cc538ce125c0e51c27941bef4568b460095e2
-    # via pandas
-pyyaml==6.0 \
-    --hash=sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf \
-    --hash=sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293 \
-    --hash=sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b \
-    --hash=sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57 \
-    --hash=sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b \
-    --hash=sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4 \
-    --hash=sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07 \
-    --hash=sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba \
-    --hash=sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9 \
-    --hash=sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287 \
-    --hash=sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513 \
-    --hash=sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0 \
-    --hash=sha256:432557aa2c09802be39460360ddffd48156e30721f5e8d917f01d31694216782 \
-    --hash=sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0 \
-    --hash=sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92 \
-    --hash=sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f \
-    --hash=sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2 \
-    --hash=sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc \
-    --hash=sha256:81957921f441d50af23654aa6c5e5eaf9b06aba7f0a19c18a538dc7ef291c5a1 \
-    --hash=sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c \
-    --hash=sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86 \
-    --hash=sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4 \
-    --hash=sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c \
-    --hash=sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34 \
-    --hash=sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b \
-    --hash=sha256:afa17f5bc4d1b10afd4466fd3a44dc0e245382deca5b3c353d8b757f9e3ecb8d \
-    --hash=sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c \
-    --hash=sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb \
-    --hash=sha256:bfaef573a63ba8923503d27530362590ff4f576c626d86a9fed95822a8255fd7 \
-    --hash=sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737 \
-    --hash=sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3 \
-    --hash=sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d \
-    --hash=sha256:d4b0ba9512519522b118090257be113b9468d804b19d63c71dbcf4a48fa32358 \
-    --hash=sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53 \
-    --hash=sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78 \
-    --hash=sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803 \
-    --hash=sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a \
-    --hash=sha256:dbad0e9d368bb989f4515da330b88a057617d16b6a8245084f1b05400f24609f \
-    --hash=sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174 \
-    --hash=sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5
-    # via
-    #   flax
-    #   orbax-checkpoint
-requests==2.28.1 \
-    --hash=sha256:7c5599b102feddaa661c826c56ab4fee28bfd17f5abca1ebbe3e7f19d7c97983 \
-    --hash=sha256:8fefa2a1a1365bf5520aac41836fbee479da67864514bdb821f31ce07ce65349
-    # via
-    #   requests-oauthlib
-    #   tensorboard
-requests-oauthlib==1.3.1 \
-    --hash=sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5 \
-    --hash=sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a
-    # via google-auth-oauthlib
-rich==11.2.0 \
-    --hash=sha256:1a6266a5738115017bb64a66c59c717e7aa047b3ae49a011ede4abdeffc6536e \
-    --hash=sha256:d5f49ad91fb343efcae45a2b2df04a9755e863e50413623ab8c9e74f05aee52b
-    # via flax
-rsa==4.9 \
-    --hash=sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7 \
-    --hash=sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21
-    # via google-auth
-scipy==1.9.3 \
-    --hash=sha256:06d2e1b4c491dc7d8eacea139a1b0b295f74e1a1a0f704c375028f8320d16e31 \
-    --hash=sha256:0d54222d7a3ba6022fdf5773931b5d7c56efe41ede7f7128c7b1637700409108 \
-    --hash=sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0 \
-    --hash=sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b \
-    --hash=sha256:2318bef588acc7a574f5bfdff9c172d0b1bf2c8143d9582e05f878e580a3781e \
-    --hash=sha256:4db5b30849606a95dcf519763dd3ab6fe9bd91df49eba517359e450a7d80ce2e \
-    --hash=sha256:545c83ffb518094d8c9d83cce216c0c32f8c04aaf28b92cc8283eda0685162d5 \
-    --hash=sha256:5a04cd7d0d3eff6ea4719371cbc44df31411862b9646db617c99718ff68d4840 \
-    --hash=sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58 \
-    --hash=sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523 \
-    --hash=sha256:83b89e9586c62e787f5012e8475fbb12185bafb996a03257e9675cd73d3736dd \
-    --hash=sha256:83c06e62a390a9167da60bedd4575a14c1f58ca9dfde59830fc42e5197283dab \
-    --hash=sha256:90453d2b93ea82a9f434e4e1cba043e779ff67b92f7a0e85d05d286a3625df3c \
-    --hash=sha256:abaf921531b5aeaafced90157db505e10345e45038c39e5d9b6c7922d68085cb \
-    --hash=sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096 \
-    --hash=sha256:c68db6b290cbd4049012990d7fe71a2abd9ffbe82c0056ebe0f01df8be5436b0 \
-    --hash=sha256:cff3a5295234037e39500d35316a4c5794739433528310e117b8a9a0c76d20fc \
-    --hash=sha256:d01e1dd7b15bd2449c8bfc6b7cc67d630700ed655654f0dfcf121600bad205c9 \
-    --hash=sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c \
-    --hash=sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95 \
-    --hash=sha256:fbc5c05c85c1a02be77b1ff591087c83bc44579c6d2bd9fb798bb64ea5e1a027
-    # via
-    #   jax
-    #   jaxlib
-setuptools==65.6.3 \
-    --hash=sha256:57f6f22bde4e042978bcd50176fdb381d7c21a9efa4041202288d3737a0c6a54 \
-    --hash=sha256:a7620757bf984b58deaf32fc8a4577a9bbc0850cf92c20e1ce41c38c19e5fb75
-    # via
-    #   tensorboard
-    #   tensorflow
-six==1.16.0 \
-    --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
-    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   astunparse
-    #   google-auth
-    #   google-pasta
-    #   python-dateutil
-    #   tensorflow
-    #   tensorflow-decision-forests
-tensorboard==2.13.0 \
-    --hash=sha256:ab69961ebddbddc83f5fa2ff9233572bdad5b883778c35e4fe94bf1798bd8481
-    # via tensorflow
-tensorboard-data-server==0.7.0 \
-    --hash=sha256:64aa1be7c23e80b1a42c13b686eb0875bb70f5e755f4d2b8de5c1d880cf2267f \
-    --hash=sha256:753d4214799b31da7b6d93837959abebbc6afa86e69eacf1e9a317a48daa31eb \
-    --hash=sha256:eb7fa518737944dbf4f0cf83c2e40a7ac346bf91be2e6a0215de98be74e85454
-    # via tensorboard
-tensorflow==2.13.0 \
-    --hash=sha256:00060c5516a61e30c51936084ebc37091d116efe9ae74b2818cbd8b2006218e7 \
-    --hash=sha256:06559eeaa69e6561cccbe2d02b015bcec663e875c8bbc4643f55692476e52147 \
-    --hash=sha256:076d953a1508dc58bf95f30f58bcc9ee364b1353c61e143cb20c2dada91afb05 \
-    --hash=sha256:11ad6a7ff49b4a690fbf37a5adaf28ba2686350a859c5f13c58dc8d2cc670375 \
-    --hash=sha256:19ee67901702b26787ad685cca65730c163c101c0c2f238a2584d714e0fa8c25 \
-    --hash=sha256:2822ac48c38d69b7fb104e606dacbd763c4bf5d3b20791f25be16a5076d01912 \
-    --hash=sha256:5e0fdadec59de3d11c5b5129ddc38e739bde7aa13095b82e19d4380e14d04999 \
-    --hash=sha256:6fff426661d286a4c634da44275d2ea2b951f392f3e65c8603681e7cb040586a \
-    --hash=sha256:72d68b8c2f382e2d01b956c8ba516c0a7d5dad98111dd351bf82bfa646aa1c72 \
-    --hash=sha256:7a08c0e2938ed5b642a8787678123827477b81d316055d5073fff82fa183eb82 \
-    --hash=sha256:89125443e998548059c4e4a129dfab2b1ff7f2fd4c8eaed97842c3cd9b663101 \
-    --hash=sha256:948003b5a23b72b3d89746d729e62ec5f01e47460f05521b2211d95069f569ba \
-    --hash=sha256:9c04bc3023b6c4cfb9ee9759c3f03f21993891b4c345df52eb5519204fbf28c0 \
-    --hash=sha256:b2978b39e8b3919059b5fd9e28508d50a77965d06ed0b537ed71c97de22dabdf \
-    --hash=sha256:cbb83561bb7d55859eaefc70c674e58713d4e10c10927423ed836a5289bbfa86 \
-    --hash=sha256:de77306c0c22c9d8754f54700752ac3a1efee895c5357308e6594436404bfbc0 \
-    --hash=sha256:e0cf94d36ceaba8f158c6e15404a81fd5b3aa4cb04147c674cf55bd1aec78154 \
-    --hash=sha256:e8f0b69ee2f800399fc6bc7ec55fecfa33662d136e425485959d90638f32a32a \
-    --hash=sha256:fa7abe265cc3ebccc9b405a280bf674824c6d85df5e6ccfa985987b3c9d265b4 \
-    --hash=sha256:fb2ff1129c93e853c19897d6a22ed0ec56387f5c6290ec03dec1c6f7b80bc396
-    # via
-    #   -r tfjs-converter/python/requirements.txt
-    #   tensorflow-decision-forests
-tensorflow-decision-forests==1.5.0 \
-    --hash=sha256:04fd913627d08fe54514b179c612e87eebf55f1448bf01951660985dfa14a6e1 \
-    --hash=sha256:1209a2832ac65f8f74bd9d0c1d58f3f8b771e7fa5c9d504c547842311647b7d4 \
-    --hash=sha256:22e3835acbfbd5356bb2f8e0c973dfc40ef80a7924b793e90c811158448cfe77 \
-    --hash=sha256:43ffd4fba1c3376f58a9dcee943df80f0cff6e47224d109ad0389a723c74947c \
-    --hash=sha256:4a0df3a3be5751594d49f5a8f99977b553cf1c42f320b952ac2a2f67b85283f5 \
-    --hash=sha256:5ec4297eb5e7c4110cf8aae89e9b08b9ad2cb725e3e63c89c78304c0d7235d24 \
-    --hash=sha256:804f6bed277b5c5b6d2bd85738a64973d5d3e8e6ac06abf6098545740245cedc \
-    --hash=sha256:a43af2a5a8c34e550bf549c6cad96da271979efc5a8ec988f6f76cc90770415a \
-    --hash=sha256:d137241dad8e884d0c937aa8769fe0768324804e9ba666a78b7b5f2f536a0bd2 \
-    --hash=sha256:d685e92abe44920ee6d89394ec4e075bb1ada7402f673566146e1b476a576e96 \
-    --hash=sha256:f5d8c3730578bda55a8f520ae39b0c9b2560d69bd53b57882e5371c1a82ba098 \
-    --hash=sha256:fbd403acf736bb9b4afd2985d9056e6d5043fc4b9a31bd05e5fcae2b1d413dc3
-    # via -r tfjs-converter/python/requirements.txt
-tensorflow-estimator==2.13.0 \
-    --hash=sha256:6f868284eaa654ae3aa7cacdbef2175d0909df9fcf11374f5166f8bf475952aa
-    # via tensorflow
-tensorflow-hub==0.14.0 \
-    --hash=sha256:519c6b56c4d304667fbd8ce66bd637e6a750c901215468db2cc6bfd0739bb0b0
-    # via -r tfjs-converter/python/requirements.txt
-tensorflow-io-gcs-filesystem==0.28.0 \
-    --hash=sha256:00cf6a92f1f9f90b2ba2d728870bcd2a70b116316d0817ab0b91dd390c25b3fd \
-    --hash=sha256:22753dc28c949bfaf29b573ee376370762c88d80330fe95cfb291261eb5e927a \
-    --hash=sha256:366e1eff8dbd6b64333d7061e2a8efd081ae4742614f717ced08d8cc9379eb50 \
-    --hash=sha256:52988659f405166df79905e9859bc84ae2a71e3ff61522ba32a95e4dce8e66d2 \
-    --hash=sha256:5fbef5836e70026245d8d9e692c44dae2c6dbc208c743d01f5b7a2978d6b6bc6 \
-    --hash=sha256:698d7f89e09812b9afeb47c3860797343a22f997c64ab9dab98132c61daa8a7d \
-    --hash=sha256:6d95f306ff225c5053fd06deeab3e3a2716357923cb40c44d566c11be779caa3 \
-    --hash=sha256:9484893779324b2d34874b0aacf3b824eb4f22d782e75df029cbccab2e607974 \
-    --hash=sha256:a6670e0da16c884267e896ea5c3334d6fd319bd6ff7cf917043a9f3b2babb1b3 \
-    --hash=sha256:b6e2d275020fb4d1a952cd3fa546483f4e46ad91d64e90d3458e5ca3d12f6477 \
-    --hash=sha256:bbf245883aa52ec687b66d0fcbe0f5f0a92d98c0b1c53e6a736039a3548d29a1 \
-    --hash=sha256:bfed720fc691d3f45802a7bed420716805aef0939c11cebf25798906201f626e \
-    --hash=sha256:c5d99f56c12a349905ff684142e4d2df06ae68ecf50c4aad5449a5f81731d858 \
-    --hash=sha256:cc062ce13ec95fb64b1fd426818a6d2b0e5be9692bc0e43a19cce115b6da4336 \
-    --hash=sha256:f76cbe1a784841c223f6861e5f6c7e53aa6232cb626d57e76881a0638c365de6
-    # via tensorflow
-tensorstore==0.1.41 \
-    --hash=sha256:025a62bb9122364885e90469af05fec2f62ad05f46ff46d9eae1d76ad9125563 \
-    --hash=sha256:2aa81581f768382a38584698a3fcb07a533fc391067467326656f24ab019cba1 \
-    --hash=sha256:2c4c578e82866b8f764de871ff7e0a81fe0949ac3565d8d2eb10f29e43020a52 \
-    --hash=sha256:2d65ea0fd5ac96a9d577f16bb917ae8a0a121d2093472bfb7bd762b1e32c753b \
-    --hash=sha256:5168f7f71e51da7d6cc85a11cd5d102d9eae750d5f5a3ee90cc9ebae10226621 \
-    --hash=sha256:620ad460023eeeae721e2e25a2a3e2b608f09cd169c1f68af7043c6d44e88cbf \
-    --hash=sha256:634c78fd62cd6e5357291ccb9671e43262f818f9cf7cc58f701b5bd80d1c1ef7 \
-    --hash=sha256:6b3b14616f9141b12e61c0c46d1c954927f7f307498d8b9d2261ff2bd4005bbd \
-    --hash=sha256:74e317ef7cba8c0208c5d8d9f1406eac37f58e8f92b3f7caa9a72b8b118b1c09 \
-    --hash=sha256:803fa2bcbc93f43fe0b3b5b70d78882d3b266a70d419acfc7fdd515f89cba79b \
-    --hash=sha256:8b5dbc0e809c90377527e0f65829d6abcdf5c69f892f433ed2cb8508d4ba519a \
-    --hash=sha256:8df13f990acc58889160eff5b2e1df029cdfffdf020ce5044e655242c1016bb1 \
-    --hash=sha256:91549a16b1ef2d6bc5ac8f28eed32737001fcfe33309f1ba126cd4c1e08b971b \
-    --hash=sha256:96fb62a880bf25da7e12ad4bba00a82deb2daf6f59050e8db6f0b04107120799 \
-    --hash=sha256:a99b87b65dfca65a830503bdfd2e5168a69b5290807cb8e922fa5a1acea2edec \
-    --hash=sha256:bbd58cedddce29216703a63ea42db010b6151c7bc05ac741af50aa31e31491fb \
-    --hash=sha256:c400aa46fc814edd69c72fcdf202dbd8c666ae684b534e81350a3a30ab16bdfc
-    # via
-    #   flax
-    #   orbax-checkpoint
-termcolor==2.1.1 \
-    --hash=sha256:67cee2009adc6449c650f6bcf3bdeed00c8ba53a8cda5362733c53e0a39fb70b \
-    --hash=sha256:fa852e957f97252205e105dd55bbc23b419a70fec0085708fc0515e399f304fd
-    # via tensorflow
-toolz==0.12.0 \
-    --hash=sha256:2059bd4148deb1884bb0eb770a3cde70e7f954cfbbdc2285f1f2de01fd21eb6f \
-    --hash=sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194
-    # via chex
-typing-extensions==4.4.0 \
-    --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa \
-    --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e
-    # via
-    #   flax
-    #   optax
-    #   orbax-checkpoint
-    #   tensorflow
-urllib3==1.26.13 \
-    --hash=sha256:47cc05d99aaa09c9e72ed5809b60e7ba354e64b59c9c173ac3018642d8bb41fc \
-    --hash=sha256:c083dd0dce68dbfbe1129d5271cb90f9447dea7d52097c6e0126120c521ddea8
-    # via requests
-werkzeug==2.2.2 \
-    --hash=sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f \
-    --hash=sha256:f979ab81f58d7318e064e99c4506445d60135ac5cd2e177a2de0089bfd4c9bd5
-    # via tensorboard
-wheel==0.38.4 \
-    --hash=sha256:965f5259b566725405b05e7cf774052044b1ed30119b5d586b2703aafe8719ac \
-    --hash=sha256:b60533f3f5d530e971d6737ca6d58681ee434818fab630c83a734bb10c083ce8
-    # via
-    #   astunparse
-    #   tensorboard
-    #   tensorflow-decision-forests
-wrapt==1.14.1 \
-    --hash=sha256:00b6d4ea20a906c0ca56d84f93065b398ab74b927a7a3dbd470f6fc503f95dc3 \
-    --hash=sha256:01c205616a89d09827986bc4e859bcabd64f5a0662a7fe95e0d359424e0e071b \
-    --hash=sha256:02b41b633c6261feff8ddd8d11c711df6842aba629fdd3da10249a53211a72c4 \
-    --hash=sha256:07f7a7d0f388028b2df1d916e94bbb40624c59b48ecc6cbc232546706fac74c2 \
-    --hash=sha256:11871514607b15cfeb87c547a49bca19fde402f32e2b1c24a632506c0a756656 \
-    --hash=sha256:1b376b3f4896e7930f1f772ac4b064ac12598d1c38d04907e696cc4d794b43d3 \
-    --hash=sha256:21ac0156c4b089b330b7666db40feee30a5d52634cc4560e1905d6529a3897ff \
-    --hash=sha256:257fd78c513e0fb5cdbe058c27a0624c9884e735bbd131935fd49e9fe719d310 \
-    --hash=sha256:2b39d38039a1fdad98c87279b48bc5dce2c0ca0d73483b12cb72aa9609278e8a \
-    --hash=sha256:2cf71233a0ed05ccdabe209c606fe0bac7379fdcf687f39b944420d2a09fdb57 \
-    --hash=sha256:2fe803deacd09a233e4762a1adcea5db5d31e6be577a43352936179d14d90069 \
-    --hash=sha256:3232822c7d98d23895ccc443bbdf57c7412c5a65996c30442ebe6ed3df335383 \
-    --hash=sha256:34aa51c45f28ba7f12accd624225e2b1e5a3a45206aa191f6f9aac931d9d56fe \
-    --hash=sha256:36f582d0c6bc99d5f39cd3ac2a9062e57f3cf606ade29a0a0d6b323462f4dd87 \
-    --hash=sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d \
-    --hash=sha256:40e7bc81c9e2b2734ea4bc1aceb8a8f0ceaac7c5299bc5d69e37c44d9081d43b \
-    --hash=sha256:43ca3bbbe97af00f49efb06e352eae40434ca9d915906f77def219b88e85d907 \
-    --hash=sha256:4fcc4649dc762cddacd193e6b55bc02edca674067f5f98166d7713b193932b7f \
-    --hash=sha256:5a0f54ce2c092aaf439813735584b9537cad479575a09892b8352fea5e988dc0 \
-    --hash=sha256:5a9a0d155deafd9448baff28c08e150d9b24ff010e899311ddd63c45c2445e28 \
-    --hash=sha256:5b02d65b9ccf0ef6c34cba6cf5bf2aab1bb2f49c6090bafeecc9cd81ad4ea1c1 \
-    --hash=sha256:60db23fa423575eeb65ea430cee741acb7c26a1365d103f7b0f6ec412b893853 \
-    --hash=sha256:642c2e7a804fcf18c222e1060df25fc210b9c58db7c91416fb055897fc27e8cc \
-    --hash=sha256:6a9a25751acb379b466ff6be78a315e2b439d4c94c1e99cb7266d40a537995d3 \
-    --hash=sha256:6b1a564e6cb69922c7fe3a678b9f9a3c54e72b469875aa8018f18b4d1dd1adf3 \
-    --hash=sha256:6d323e1554b3d22cfc03cd3243b5bb815a51f5249fdcbb86fda4bf62bab9e164 \
-    --hash=sha256:6e743de5e9c3d1b7185870f480587b75b1cb604832e380d64f9504a0535912d1 \
-    --hash=sha256:709fe01086a55cf79d20f741f39325018f4df051ef39fe921b1ebe780a66184c \
-    --hash=sha256:7b7c050ae976e286906dd3f26009e117eb000fb2cf3533398c5ad9ccc86867b1 \
-    --hash=sha256:7d2872609603cb35ca513d7404a94d6d608fc13211563571117046c9d2bcc3d7 \
-    --hash=sha256:7ef58fb89674095bfc57c4069e95d7a31cfdc0939e2a579882ac7d55aadfd2a1 \
-    --hash=sha256:80bb5c256f1415f747011dc3604b59bc1f91c6e7150bd7db03b19170ee06b320 \
-    --hash=sha256:81b19725065dcb43df02b37e03278c011a09e49757287dca60c5aecdd5a0b8ed \
-    --hash=sha256:833b58d5d0b7e5b9832869f039203389ac7cbf01765639c7309fd50ef619e0b1 \
-    --hash=sha256:88bd7b6bd70a5b6803c1abf6bca012f7ed963e58c68d76ee20b9d751c74a3248 \
-    --hash=sha256:8ad85f7f4e20964db4daadcab70b47ab05c7c1cf2a7c1e51087bfaa83831854c \
-    --hash=sha256:8c0ce1e99116d5ab21355d8ebe53d9460366704ea38ae4d9f6933188f327b456 \
-    --hash=sha256:8d649d616e5c6a678b26d15ece345354f7c2286acd6db868e65fcc5ff7c24a77 \
-    --hash=sha256:903500616422a40a98a5a3c4ff4ed9d0066f3b4c951fa286018ecdf0750194ef \
-    --hash=sha256:9736af4641846491aedb3c3f56b9bc5568d92b0692303b5a305301a95dfd38b1 \
-    --hash=sha256:988635d122aaf2bdcef9e795435662bcd65b02f4f4c1ae37fbee7401c440b3a7 \
-    --hash=sha256:9cca3c2cdadb362116235fdbd411735de4328c61425b0aa9f872fd76d02c4e86 \
-    --hash=sha256:9e0fd32e0148dd5dea6af5fee42beb949098564cc23211a88d799e434255a1f4 \
-    --hash=sha256:9f3e6f9e05148ff90002b884fbc2a86bd303ae847e472f44ecc06c2cd2fcdb2d \
-    --hash=sha256:a85d2b46be66a71bedde836d9e41859879cc54a2a04fad1191eb50c2066f6e9d \
-    --hash=sha256:a9a52172be0b5aae932bef82a79ec0a0ce87288c7d132946d645eba03f0ad8a8 \
-    --hash=sha256:aa31fdcc33fef9eb2552cbcbfee7773d5a6792c137b359e82879c101e98584c5 \
-    --hash=sha256:b014c23646a467558be7da3d6b9fa409b2c567d2110599b7cf9a0c5992b3b471 \
-    --hash=sha256:b21bb4c09ffabfa0e85e3a6b623e19b80e7acd709b9f91452b8297ace2a8ab00 \
-    --hash=sha256:b5901a312f4d14c59918c221323068fad0540e34324925c8475263841dbdfe68 \
-    --hash=sha256:b9b7a708dd92306328117d8c4b62e2194d00c365f18eff11a9b53c6f923b01e3 \
-    --hash=sha256:d1967f46ea8f2db647c786e78d8cc7e4313dbd1b0aca360592d8027b8508e24d \
-    --hash=sha256:d52a25136894c63de15a35bc0bdc5adb4b0e173b9c0d07a2be9d3ca64a332735 \
-    --hash=sha256:d77c85fedff92cf788face9bfa3ebaa364448ebb1d765302e9af11bf449ca36d \
-    --hash=sha256:d79d7d5dc8a32b7093e81e97dad755127ff77bcc899e845f41bf71747af0c569 \
-    --hash=sha256:dbcda74c67263139358f4d188ae5faae95c30929281bc6866d00573783c422b7 \
-    --hash=sha256:ddaea91abf8b0d13443f6dac52e89051a5063c7d014710dcb4d4abb2ff811a59 \
-    --hash=sha256:dee0ce50c6a2dd9056c20db781e9c1cfd33e77d2d569f5d1d9321c641bb903d5 \
-    --hash=sha256:dee60e1de1898bde3b238f18340eec6148986da0455d8ba7848d50470a7a32fb \
-    --hash=sha256:e2f83e18fe2f4c9e7db597e988f72712c0c3676d337d8b101f6758107c42425b \
-    --hash=sha256:e3fb1677c720409d5f671e39bac6c9e0e422584e5f518bfd50aa4cbbea02433f \
-    --hash=sha256:ee2b1b1769f6707a8a445162ea16dddf74285c3964f605877a20e38545c3c462 \
-    --hash=sha256:ee6acae74a2b91865910eef5e7de37dc6895ad96fa23603d1d27ea69df545015 \
-    --hash=sha256:ef3f72c9666bba2bab70d2a8b79f2c6d2c1a42a7f7e2b0ec83bb2f9e383950af
-    # via tensorflow
-wurlitzer==3.0.3 \
-    --hash=sha256:224f5fe70618be3872c05dfddc8c457191ec1870654596279fcc1edadebe3e5b \
-    --hash=sha256:ffcc584109f5ecd5244abfde4534f22140f8735a4890ce1abd90b4e503f5f427
-    # via tensorflow-decision-forests
-zipp==3.11.0 \
-    --hash=sha256:83a28fcb75844b5c0cdaf5aa4003c2d728c77e05f5aeabe8e95e56727005fbaa \
-    --hash=sha256:a7a22e05929290a67401440b39690ae6563279bced5f314609d9d03798f56766
-    # via
-    #   importlib-metadata
-    #   importlib-resources
diff --git a/tfjs-master/tfjs-converter/python/run-python-tests.sh b/tfjs-master/tfjs-converter/python/run-python-tests.sh
deleted file mode 100644
index 24c86a311..000000000
--- a/tfjs-master/tfjs-converter/python/run-python-tests.sh
+++ /dev/null
@@ -1,74 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# A script that runs all Python unit tests in tfjs-layers.
-
-function print_usage() {
-  echo "Usage:"
-  echo "  run-python-tests.sh <requirments_file>"
-  echo
-}
-
-set -e
-
-SCRIPTS_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-
-TEST_FILES="$(find "${SCRIPTS_DIR}" -name '*_test.py')"
-
-pip install virtualenv
-
-TMP_VENV_DIR="$(mktemp -u).venv"
-virtualenv -p "python" "${TMP_VENV_DIR}"
-source "${TMP_VENV_DIR}/local/bin/activate"
-
-# There is one argument (requirements_file), please update this constant when
-# you adding more arguments.
-ARGS_COUNT=1
-
-# Default requirements file name.
-REQ_FILE="${SCRIPTS_DIR}/requirements-dev.txt"
-
-# Show the usage message if there are too many arguments.
-if [[ $# > ARGS_COUNT ]]; then
-  print_usage
-  exit 1
-fi
-
-# Use the user specified requirements file name.
-if [[ $# == 1 ]]; then
-  REQ_FILE=$1
-fi
-pip install -r "${REQ_FILE}"
-
-# Run pylint for tensorflowjs directory
-cd "${SCRIPTS_DIR}"
-pylint --rcfile=.pylintrc tensorflowjs
-
-export PYTHONPATH=".:${PYTHONPATH}"
-for TEST_FILE in ${TEST_FILES}; do
-  echo
-  echo "====== Running test: ${TEST_FILE} ======"
-  echo
-  python "${TEST_FILE}"
-done
-
-echo
-echo "All tests passed."
-echo
-
-# Clean up
-deactivate
-rm -rf "${TMP_VENV_DIR}"
diff --git a/tfjs-master/tfjs-converter/python/setup.cfg b/tfjs-master/tfjs-converter/python/setup.cfg
deleted file mode 100644
index b88034e41..000000000
--- a/tfjs-master/tfjs-converter/python/setup.cfg
+++ /dev/null
@@ -1,2 +0,0 @@
-[metadata]
-description-file = README.md
diff --git a/tfjs-master/tfjs-converter/python/setup.py b/tfjs-master/tfjs-converter/python/setup.py
deleted file mode 100644
index d04a2e1e8..000000000
--- a/tfjs-master/tfjs-converter/python/setup.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# Copyright 2018 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Build pip wheel for model_converter."""
-
-import os
-import setuptools
-from tensorflowjs import version
-
-DIR_NAME = os.path.dirname(__file__)
-
-def _get_requirements(file):
-  "Reads the requirements file and returns the packages"
-  with open(os.path.join(DIR_NAME, file), 'r') as requirements:
-    return requirements.readlines()
-
-def get_extra_requires(path, add_all=True):
-    import re
-    from collections import defaultdict
-
-    with open(path) as fp:
-        extra_deps = defaultdict(set)
-        for k in fp:
-            if k.strip() and not k.startswith('#'):
-                tags = set()
-                if ':' in k:
-                    k, v = k.split(':')
-                    tags.update(vv.strip() for vv in v.split(','))
-                tags.add(re.split('[<=>]', k)[0])
-                for t in tags:
-                    extra_deps[t].add(k)
-
-        # add tag `all` at the end
-        if add_all:
-            extra_deps['all'] = set(vv for v in extra_deps.values() for vv in v)
-
-    return extra_deps
-
-CONSOLE_SCRIPTS = [
-    'tensorflowjs_converter = tensorflowjs.converters.converter:pip_main',
-    'tensorflowjs_wizard = tensorflowjs.converters.wizard:pip_main',
-]
-
-setuptools.setup(
-    name='tensorflowjs',
-    version=version.version,
-    description='Python Libraries and Tools for TensorFlow.js',
-    url='https://js.tensorflow.org/',
-    author='Google LLC',
-    author_email='opensource@google.com',
-    classifiers=[
-        'Development Status :: 3 - Alpha',
-        'Intended Audience :: Developers',
-        'Intended Audience :: Education',
-        'Intended Audience :: Science/Research',
-        'License :: OSI Approved :: Apache Software License',
-        'Programming Language :: JavaScript',
-        'Programming Language :: Python :: 2',
-        'Programming Language :: Python :: 3',
-        'Topic :: Scientific/Engineering',
-        'Topic :: Scientific/Engineering :: Mathematics',
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-        'Topic :: Software Development',
-        'Topic :: Software Development :: Libraries',
-        'Topic :: Software Development :: Libraries :: Python Modules',
-    ],
-    py_modules=[
-        'tensorflowjs',
-        'tensorflowjs.version',
-        'tensorflowjs.quantization',
-        'tensorflowjs.read_weights',
-        'tensorflowjs.resource_loader',
-        'tensorflowjs.write_weights',
-        'tensorflowjs.converters',
-        'tensorflowjs.converters.common',
-        'tensorflowjs.converters.converter',
-        'tensorflowjs.converters.fold_batch_norms',
-        'tensorflowjs.converters.fuse_depthwise_conv2d',
-        'tensorflowjs.converters.fuse_prelu',
-        'tensorflowjs.converters.graph_rewrite_util',
-        'tensorflowjs.converters.keras_h5_conversion',
-        'tensorflowjs.converters.keras_tfjs_loader',
-        'tensorflowjs.converters.jax_conversion',
-        'tensorflowjs.converters.tf_saved_model_conversion_v2',
-        'tensorflowjs.converters.wizard',
-    ],
-    include_package_data=True,
-    packages=['tensorflowjs/op_list'],
-    package_data={
-        'tensorflowjs/op_list': ['*.json']
-    },
-    install_requires=_get_requirements('requirements.txt'),
-    extras_require=get_extra_requires('extra-requirements.txt'),
-    entry_points={
-        'console_scripts': CONSOLE_SCRIPTS,
-    },
-    license='Apache 2.0',
-    keywords='tensorflow javascript machine deep learning converter',
-)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/BUILD.bazel b/tfjs-master/tfjs-converter/python/tensorflowjs/BUILD.bazel
deleted file mode 100644
index 2c3f02d7b..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/BUILD.bazel
+++ /dev/null
@@ -1,212 +0,0 @@
-load("@rules_python//python:defs.bzl", "py_library", "py_test")
-load("@tensorflowjs_dev_deps//:requirements.bzl", "requirement")
-
-package(
-    default_visibility = [
-        ":__subpackages__",
-    ],
-)
-
-licenses(["notice"])
-
-exports_files([
-    "LICENSE",
-])
-
-py_library(
-    name = "tensorflowjs",
-    srcs = ["__init__.py"],
-    data = [
-        "//tfjs-converter/python/tensorflowjs/op_list:ops",
-    ],
-    srcs_version = "PY3",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":resource_loader",
-        ":version",
-        ":write_weights",
-        "//tfjs-converter/python/tensorflowjs/converters",
-    ],
-)
-
-py_library(
-    name = "expect_flax_installed",
-    # This is a dummy rule used as a Flax dependency in open-source.
-    # We expect JAX to already be installed on the system, e.g. via
-    # `pip install flax`.
-    deps = [requirement("flax")],
-)
-
-py_library(
-    name = "expect_h5py_installed",
-    # This is a dummy rule used as a h5py dependency in open-source.
-    # We expect h5py to already be installed on the system, e.g. via
-    # `pip install h5py`.
-    deps = [requirement("h5py")],
-)
-
-py_library(
-    name = "expect_jax_installed",
-    # This is a dummy rule used as a JAX dependency in open-source.
-    # We expect JAX to already be installed on the system, e.g. via
-    # `pip install jax`.
-    deps = [
-        requirement("jax"),
-        requirement("importlib_resources"),
-    ],
-)
-
-py_library(
-    name = "expect_jax2tf_installed",
-    # This is a dummy rule used as a jax2tf dependency in open-source.
-    # We expect jax2tf to already be installed on the system, e.g. via
-    # `pip install jax`.
-    deps = [requirement("jax")],
-)
-
-py_library(
-    name = "expect_numpy_installed",
-    # This is a dummy rule used as a numpy dependency in open-source.
-    # We expect numpy to already be installed on the system, e.g. via
-    # `pip install numpy`.
-    deps = [requirement("numpy")],
-)
-
-py_library(
-    name = "expect_tensorflow_installed",
-    # This is a dummy rule used as a tensorflow dependency in open-source.
-    # We expect tensorflow to already be installed on the system, e.g. via
-    # `pip install tensorflow` or `pip install tensorflow-gpu`.
-    deps = [requirement("tensorflow")],
-)
-
-py_library(
-    name = "expect_tensorflow_decision_forests_installed",
-    # This is a dummy rule used as a tensorflow dependency in open-source.
-    # We expect tensorflow-decision-forests to already be installed on
-    # the system, e.g. via
-    # `pip install tensorflow-decision-forests`.
-    deps = [requirement("tensorflow-decision-forests")],
-)
-
-py_library(
-    name = "expect_tensorflow_hub_installed",
-    # This is a dummy rule used as a tensorflow_hub dependency in open-source.
-    # We expect tensorflow to already be installed on the system, e.g. via
-    # `pip install tensorflow` or `pip install tensorflow-gpu`.
-    deps = [requirement("tensorflow-hub")],
-)
-
-py_library(
-    name = "expect_PyInquirer_installed",
-    # This is a dummy rule used as a PyInquirer dependency in open-source.
-    # We expect PyInquirer to already be installed on the system, e.g. via
-    # `pip install PyInquirer`.
-    deps = [requirement("PyInquirer")],
-)
-
-py_library(
-    name = "expect_packaging_installed",
-    deps = [requirement("packaging")],
-)
-
-py_library(
-    name = "expect_debugpy_installed",
-    # debugpy is used for interactive debugging with vscode
-    deps = [requirement("debugpy")],
-)
-
-py_library(
-    name = "quantization",
-    srcs = ["quantization.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":expect_numpy_installed",
-    ],
-)
-
-py_library(
-    name = "write_weights",
-    srcs = ["write_weights.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":expect_numpy_installed",
-        ":expect_tensorflow_installed",
-        ":quantization",
-        ":read_weights",
-    ],
-)
-
-py_library(
-    name = "read_weights",
-    srcs = ["read_weights.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":expect_numpy_installed",
-        ":quantization",
-    ],
-)
-
-py_library(
-    name = "resource_loader",
-    srcs = ["resource_loader.py"],
-    srcs_version = "PY3",
-)
-
-py_library(
-    name = "version",
-    srcs = ["version.py"],
-    srcs_version = "PY3",
-    deps = [],
-)
-
-py_test(
-    name = "quantization_test",
-    srcs = ["quantization_test.py"],
-    imports = [".."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":expect_numpy_installed",
-        ":quantization",
-    ],
-)
-
-py_test(
-    name = "write_weights_test",
-    srcs = ["write_weights_test.py"],
-    imports = [".."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":expect_numpy_installed",
-        ":expect_tensorflow_installed",
-        ":write_weights",
-    ],
-)
-
-py_test(
-    name = "read_weights_test",
-    srcs = ["read_weights_test.py"],
-    imports = [".."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":expect_numpy_installed",
-        ":expect_tensorflow_installed",
-        ":read_weights",
-        ":write_weights",
-    ],
-)
-
-py_test(
-    name = "resource_loader_test",
-    srcs = ["resource_loader_test.py"],
-    data = ["//tfjs-converter/python/tensorflowjs/op_list:ops"],
-    imports = [".."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":resource_loader",
-    ],
-)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/__init__.py b/tfjs-master/tfjs-converter/python/tensorflowjs/__init__.py
deleted file mode 100644
index 946f94954..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-# pylint: disable=unused-imports
-from tensorflowjs import converters
-from tensorflowjs import quantization
-from tensorflowjs import version
-
-__version__ = version.version
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/BUILD.bazel b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/BUILD.bazel
deleted file mode 100644
index df2bfac33..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/BUILD.bazel
+++ /dev/null
@@ -1,354 +0,0 @@
-load("@rules_python//python:defs.bzl", "py_library", "py_test")
-
-package(
-    default_visibility = [
-        "//tfjs-converter/python/tensorflowjs:__subpackages__",
-    ],
-    licenses = ["notice"],  # Apache 2.0
-)
-
-py_library(
-    name = "converters",
-    srcs = ["__init__.py"],
-    srcs_version = "PY3",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":converter",
-        ":jax_conversion",
-        ":keras_h5_conversion",
-        ":keras_tfjs_loader",
-        ":tf_saved_model_conversion_v2",
-        ":wizard",
-    ],
-)
-
-py_library(
-    name = "common",
-    srcs = ["common.py"],
-    srcs_version = "PY3",
-    deps = ["//tfjs-converter/python/tensorflowjs:version"],
-)
-
-py_library(
-    name = "tf_module_mapper",
-    srcs = ["tf_module_mapper.py"],
-    srcs_version = "PY3",
-    deps = [],
-)
-
-py_library(
-    name = "keras_h5_conversion",
-    srcs = ["keras_h5_conversion.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":common",
-        "//tfjs-converter/python/tensorflowjs:expect_h5py_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:write_weights",
-    ],
-)
-
-py_test(
-    name = "keras_h5_conversion_test",
-    srcs = ["keras_h5_conversion_test.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":keras_h5_conversion",
-        "//tfjs-converter/python/tensorflowjs:expect_h5py_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-        "//tfjs-converter/python/tensorflowjs:version",
-    ],
-)
-
-py_library(
-    name = "keras_tfjs_loader",
-    srcs = ["keras_tfjs_loader.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":keras_h5_conversion",
-        ":tf_module_mapper",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:read_weights",
-    ],
-)
-
-py_test(
-    name = "keras_tfjs_loader_test",
-    srcs = ["keras_tfjs_loader_test.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":keras_h5_conversion",
-        ":keras_tfjs_loader",
-        ":tf_module_mapper",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_library(
-    name = "graph_rewrite_util",
-    srcs = ["graph_rewrite_util.py"],
-    srcs_version = "PY3",
-    deps = ["//tfjs-converter/python/tensorflowjs:version"],
-)
-
-py_library(
-    name = "fuse_prelu",
-    srcs = ["fuse_prelu.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":graph_rewrite_util",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_test(
-    name = "fuse_prelu_test",
-    srcs = ["fuse_prelu_test.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":fuse_prelu",
-        ":tf_saved_model_conversion_v2",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_library(
-    name = "fold_batch_norms",
-    srcs = ["fold_batch_norms.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":graph_rewrite_util",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_test(
-    name = "fold_batch_norms_test",
-    srcs = ["fold_batch_norms_test.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":fold_batch_norms",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_library(
-    name = "fuse_depthwise_conv2d",
-    srcs = ["fuse_depthwise_conv2d.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":graph_rewrite_util",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_test(
-    name = "fuse_depthwise_conv2d_test",
-    srcs = ["fuse_depthwise_conv2d_test.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":fuse_depthwise_conv2d",
-        ":graph_rewrite_util",
-        ":tf_saved_model_conversion_v2",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_library(
-    name = "normalize_bias_add",
-    srcs = ["normalize_bias_add.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":graph_rewrite_util",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_test(
-    name = "normalize_bias_add_test",
-    srcs = ["normalize_bias_add_test.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":graph_rewrite_util",
-        ":normalize_bias_add",
-        ":tf_saved_model_conversion_v2",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_test(
-    name = "tf_saved_model_conversion_v2_test",
-    srcs = ["tf_saved_model_conversion_v2_test.py"],
-    data = ["//tfjs-converter/python/tensorflowjs/op_list:ops"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":tf_saved_model_conversion_v2",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_hub_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_library(
-    name = "tf_saved_model_conversion_v2",
-    srcs = ["tf_saved_model_conversion_v2.py"],
-    data = ["//tfjs-converter/python/tensorflowjs/op_list:ops"],
-    srcs_version = "PY3",
-    deps = [
-        ":common",
-        ":fold_batch_norms",
-        ":fuse_depthwise_conv2d",
-        ":fuse_prelu",
-        ":graph_rewrite_util",
-        ":normalize_bias_add",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_packaging_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_decision_forests_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_hub_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-        "//tfjs-converter/python/tensorflowjs:resource_loader",
-        "//tfjs-converter/python/tensorflowjs:version",
-        "//tfjs-converter/python/tensorflowjs:write_weights",
-    ],
-)
-
-py_test(
-    name = "jax_conversion_test",
-    srcs = ["jax_conversion_test.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":jax_conversion",
-        "//tfjs-converter/python/tensorflowjs:expect_flax_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_jax_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_library(
-    name = "jax_conversion",
-    srcs = ["jax_conversion.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":tf_saved_model_conversion_v2",
-        "//tfjs-converter/python/tensorflowjs:expect_jax2tf_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_jax_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_test(
-    name = "wizard_test",
-    srcs = ["wizard_test.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":wizard",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-    ],
-)
-
-py_binary(
-    name = "wizard",
-    srcs = ["wizard.py"],
-    srcs_version = "PY3",
-    deps = [
-        ":common",
-        ":converter",
-        "//tfjs-converter/python/tensorflowjs:expect_PyInquirer_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_h5py_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_library(
-    name = "converter_lib",
-    srcs = ["converter.py"],
-    srcs_version = "PY3",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":common",
-        ":keras_h5_conversion",
-        ":keras_tfjs_loader",
-        ":tf_saved_model_conversion_v2",
-        "//tfjs-converter/python/tensorflowjs:version",
-    ],
-)
-
-py_binary(
-    name = "converter",
-    srcs = ["converter.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":common",
-        ":keras_h5_conversion",
-        ":keras_tfjs_loader",
-        ":tf_module_mapper",
-        ":tf_saved_model_conversion_v2",
-        "//tfjs-converter/python/tensorflowjs:expect_h5py_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-        "//tfjs-converter/python/tensorflowjs:version",
-    ],
-)
-
-py_binary(
-    name = "generate_test_model",
-    testonly = True,
-    srcs = ["generate_test_model.py"],
-    srcs_version = "PY3",
-    deps = [
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-    ],
-)
-
-py_test(
-    name = "converter_test",
-    srcs = ["converter_test.py"],
-    imports = ["../.."],
-    srcs_version = "PY3",
-    tags = ["ci"],
-    deps = [
-        ":converter",
-        ":keras_tfjs_loader",
-        "//tfjs-converter/python/tensorflowjs:expect_numpy_installed",
-        "//tfjs-converter/python/tensorflowjs:expect_tensorflow_installed",
-        "//tfjs-converter/python/tensorflowjs:version",
-    ],
-)
-
-sh_test(
-    name = "converter_binary_test",
-    srcs = ["converter_binary_test.sh"],
-    data = [
-        ":converter",
-        ":generate_test_model",
-    ],
-    tags = ["ci"],
-)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/__init__.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/__init__.py
deleted file mode 100644
index 96cb3e358..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-# pylint: disable=unused-imports,line-too-long
-from tensorflowjs.converters.converter import convert
-from tensorflowjs.converters.keras_h5_conversion import save_keras_model
-from tensorflowjs.converters.keras_tfjs_loader import deserialize_keras_model
-from tensorflowjs.converters.keras_tfjs_loader import load_keras_model
-from tensorflowjs.converters.tf_saved_model_conversion_v2 import convert_tf_saved_model
-from tensorflowjs.converters.jax_conversion import convert_jax
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/common.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/common.py
deleted file mode 100644
index 0dc4d6695..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/common.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-from tensorflowjs import version
-
-
-# File name for the indexing JSON file in an artifact directory.
-ARTIFACT_MODEL_JSON_FILE_NAME = 'model.json'
-ASSETS_DIRECTORY_NAME = 'assets'
-
-# JSON string keys for fields of the indexing JSON.
-ARTIFACT_MODEL_TOPOLOGY_KEY = 'modelTopology'
-ARTIFACT_MODEL_INITIALIZER = 'modelInitializer'
-ARTIFACT_WEIGHTS_MANIFEST_KEY = 'weightsManifest'
-
-FORMAT_KEY = 'format'
-TFJS_GRAPH_MODEL_FORMAT = 'graph-model'
-TFJS_LAYERS_MODEL_FORMAT = 'layers-model'
-
-GENERATED_BY_KEY = 'generatedBy'
-CONVERTED_BY_KEY = 'convertedBy'
-
-SIGNATURE_KEY = 'signature'
-INITIALIZER_SIGNATURE_KEY = 'initializerSignature'
-USER_DEFINED_METADATA_KEY = 'userDefinedMetadata'
-STRUCTURED_OUTPUTS_KEYS_KEY = 'structuredOutputKeys'
-RESOURCE_ID_KEY = 'resourceId'
-
-# Model formats.
-KERAS_SAVED_MODEL = 'keras_saved_model'
-KERAS_MODEL = 'keras'
-KERAS_KERAS_MODEL = 'keras_keras'
-TF_SAVED_MODEL = 'tf_saved_model'
-TF_HUB_MODEL = 'tf_hub'
-TFJS_GRAPH_MODEL = 'tfjs_graph_model'
-TFJS_LAYERS_MODEL = 'tfjs_layers_model'
-TF_FROZEN_MODEL = 'tf_frozen_model'
-
-# CLI argument strings.
-INPUT_PATH = 'input_path'
-OUTPUT_PATH = 'output_path'
-INPUT_FORMAT = 'input_format'
-OUTPUT_FORMAT = 'output_format'
-OUTPUT_NODE = 'output_node_names'
-SIGNATURE_NAME = 'signature_name'
-SAVED_MODEL_TAGS = 'saved_model_tags'
-QUANTIZATION_BYTES = 'quantization_bytes'
-QUANTIZATION_TYPE_FLOAT16 = 'quantize_float16'
-QUANTIZATION_TYPE_UINT8 = 'quantize_uint8'
-QUANTIZATION_TYPE_UINT16 = 'quantize_uint16'
-SPLIT_WEIGHTS_BY_LAYER = 'split_weights_by_layer'
-VERSION = 'version'
-SKIP_OP_CHECK = 'skip_op_check'
-STRIP_DEBUG_OPS = 'strip_debug_ops'
-USE_STRUCTURED_OUTPUTS_NAMES = 'use_structured_outputs_names'
-WEIGHT_SHARD_SIZE_BYTES = 'weight_shard_size_bytes'
-CONTROL_FLOW_V2 = 'control_flow_v2'
-EXPERIMENTS = 'experiments'
-METADATA = 'metadata'
-
-def get_converted_by():
-  """Get the convertedBy string for storage in model artifacts."""
-  return 'TensorFlow.js Converter v%s' % version.version
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/converter.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/converter.py
deleted file mode 100644
index d0100e28d..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/converter.py
+++ /dev/null
@@ -1,966 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Artifact conversion to and from Python TensorFlow and tf.keras."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import json
-import os
-import shutil
-import sys
-import tempfile
-
-import h5py
-import tensorflow.compat.v1 as tf1
-import tensorflow.compat.v2 as tf
-
-from tensorflowjs import quantization
-from tensorflowjs import version
-from tensorflowjs.converters import common
-from tensorflowjs.converters import keras_h5_conversion as conversion
-from tensorflowjs.converters import keras_tfjs_loader
-from tensorflowjs.converters import tf_saved_model_conversion_v2
-from zipfile import ZipFile, is_zipfile
-
-
-def dispatch_keras_h5_to_tfjs_layers_model_conversion(
-    h5_path, output_dir=None, quantization_dtype_map=None,
-    split_weights_by_layer=False,
-    weight_shard_size_bytes=1024 * 1024 * 4,
-    metadata=None):
-  """Converts a Keras HDF5 saved-model file to TensorFlow.js format.
-
-  Auto-detects saved_model versus weights-only and generates the correct
-  json in either case. This function accepts Keras HDF5 files in two formats:
-    - A weights-only HDF5 (e.g., generated with Keras Model's `save_weights()`
-      method),
-    - A topology+weights combined HDF5 (e.g., generated with
-      `tf.keras.model.save_model`).
-
-  Args:
-    h5_path: path to an HDF5 file containing keras model data as a `str`.
-    output_dir: Output directory to which the TensorFlow.js-format model JSON
-      file and weights files will be written. If the directory does not exist,
-      it will be created.
-    quantization_dtype_map: A mapping from dtype (`uint8`, `uint16`, `float16`)
-      to weights. The weight mapping supports wildcard substitution.
-    split_weights_by_layer: Whether to split the weights into separate weight
-      groups (corresponding to separate binary weight files) layer by layer
-      (Default: `False`).
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    metadata: User defined metadata map.
-
-  Returns:
-    (model_json, groups)
-      model_json: a json dictionary (empty if unused) for model topology.
-        If `h5_path` points to a weights-only HDF5 file, this return value
-        will be `None`.
-      groups: an array of weight_groups as defined in tfjs weights_writer.
-  """
-  if not os.path.exists(h5_path):
-    raise ValueError('Nonexistent path to HDF5 file: %s' % h5_path)
-  if os.path.isdir(h5_path):
-    raise ValueError(
-        'Expected path to point to an HDF5 file, but it points to a '
-        'directory: %s' % h5_path)
-
-  h5_file = h5py.File(h5_path, 'r')
-  if 'layer_names' in h5_file.attrs:
-    model_json = None
-    groups = conversion.h5_weights_to_tfjs_format(
-        h5_file, split_by_layer=split_weights_by_layer)
-  else:
-    model_json, groups = conversion.h5_merged_saved_model_to_tfjs_format(
-        h5_file, split_by_layer=split_weights_by_layer)
-
-  if output_dir:
-    if os.path.isfile(output_dir):
-      raise ValueError(
-          'Output path "%s" already exists as a file' % output_dir)
-    if not os.path.isdir(output_dir):
-      os.makedirs(output_dir)
-    conversion.write_artifacts(
-        model_json, groups, output_dir, quantization_dtype_map,
-        weight_shard_size_bytes=weight_shard_size_bytes, metadata=metadata)
-
-  return model_json, groups
-
-def dispatch_keras_keras_to_tfjs_layers_model_conversion(
-    v3_path,
-    output_dir=None,
-    quantization_dtype_map=None,
-    split_weights_by_layer=False,
-    weight_shard_size_bytes=1024 * 1024 * 4,
-    metadata=None,
-):
-  """Converts a Keras v3 .keras file to TensorFlow.js format.
-
-  Args:
-    v3_path: path to an .keras file containing keras model data as a `str`.
-    output_dir: Output directory to which the TensorFlow.js-format model JSON
-      file and weights files will be written. If the directory does not exist,
-      it will be created.
-    quantization_dtype_map: A mapping from dtype (`uint8`, `uint16`, `float16`)
-      to weights. The weight mapping supports wildcard substitution.
-    split_weights_by_layer: Whether to split the weights into separate weight
-      groups (corresponding to separate binary weight files) layer by layer
-      (Default: `False`).
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    metadata: User defined metadata map.
-
-  Returns:
-    (model_json, groups)
-      model_json: a json dictionary (empty if unused) for model topology.
-      groups: an array of weight_groups as defined in tfjs weights_writer.
-  """
-  if not os.path.exists(v3_path):
-      raise ValueError("Nonexistent path to .keras file: %s" % v3_path)
-  if os.path.isdir(v3_path):
-      raise ValueError(
-          "Expected path to point to an .keras file, but it points to a "
-          "directory: %s" % v3_path
-      )
-  file_path = str(v3_path)
-  if not file_path.endswith(".keras"):
-      raise ValueError(
-          "Invalid `filepath` argument: expected a `.keras` extension. "
-          f"Received: filepath={file_path}"
-      )
-  with ZipFile(v3_path, "r") as zip_file:
-      zip_file.extractall(path=os.path.dirname(v3_path))
-  dir_path = os.path.dirname(file_path)
-  meta_data_json_path = os.path.join(dir_path, "metadata.json")
-  config_json_path = os.path.join(dir_path, "config.json")
-  model_weights_path = os.path.join(dir_path, "model.weights.h5")
-  h5_file = h5py.File(model_weights_path, "r")
-  with open(config_json_path, "rt") as conf:
-      try:
-          config_file = json.load(conf)
-      except (ValueError, IOError):
-          raise ValueError(
-              "The input path is expected to contain valid JSON content, "
-              "but cannot read valid JSON content from %s." % config_json_path
-          )
-
-  with open(meta_data_json_path, "rt") as meta_json:
-      try:
-          meta_file = json.load(meta_json)
-      except (ValueError, IOError):
-          raise ValueError(
-              "The input path is expected to contain valid JSON content, "
-              "but cannot read valid JSON content from %s." % meta_data_json_path
-          )
-
-  model_json, groups = conversion.h5_v3_merged_saved_model_to_tfjs_format(
-      h5_file, meta_file, config_file, split_by_layer=split_weights_by_layer
-  )
-
-  if output_dir:
-    if os.path.isfile(output_dir):
-      raise ValueError(
-          'Output path "%s" already exists as a file' % output_dir)
-    if not os.path.isdir(output_dir):
-      os.makedirs(output_dir)
-    conversion.write_artifacts(
-      model_json, groups, output_dir, quantization_dtype_map,
-      weight_shard_size_bytes=weight_shard_size_bytes, metadata=metadata)
-
-  return model_json, groups
-
-def dispatch_keras_h5_to_tfjs_graph_model_conversion(
-    h5_path, output_dir=None,
-    quantization_dtype_map=None,
-    skip_op_check=False,
-    strip_debug_ops=False,
-    use_structured_outputs_names=False,
-    weight_shard_size_bytes=1024 * 1024 * 4,
-    control_flow_v2=False,
-    experiments=False,
-    metadata=None):
-  """
-  Convert a keras HDF5-format model to tfjs GraphModel artifacts.
-
-  Args:
-    h5_path: Path to the HDF5-format file that contains the model saved from
-      keras or tf.keras.
-    output_dir: The destination to which the tfjs GraphModel artifacts will be
-      written.
-    quantization_dtype_map: A mapping from dtype (`uint8`, `uint16`, `float16`)
-      to weights. The weight mapping supports wildcard substitution.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to allow unsupported debug ops.
-    use_structured_outputs_names: Bool whether output of graph model will follow
-      the structured_outputs format.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    control_flow_v2: Bool whether to enable control flow v2 ops.
-    experiments: Bool enable experimental features.
-    metadata: User defined metadata map.
-  """
-
-  if not os.path.exists(h5_path):
-    raise ValueError('Nonexistent path to HDF5 file: %s' % h5_path)
-  if os.path.isdir(h5_path):
-    raise ValueError(
-        'Expected path to point to an HDF5 file, but it points to a '
-        'directory: %s' % h5_path)
-
-  temp_savedmodel_dir = tempfile.mktemp(suffix='.savedmodel')
-  model = tf.keras.models.load_model(h5_path, compile=False)
-  model.save(temp_savedmodel_dir, include_optimizer=False, save_format='tf')
-
-  # NOTE(cais): This cannot use `tf.compat.v1` because
-  #   `convert_tf_saved_model()` works only in v2.
-  tf_saved_model_conversion_v2.convert_tf_saved_model(
-      temp_savedmodel_dir, output_dir,
-      signature_def='serving_default',
-      saved_model_tags='serve',
-      quantization_dtype_map=quantization_dtype_map,
-      skip_op_check=skip_op_check,
-      strip_debug_ops=strip_debug_ops,
-      use_structured_outputs_names=use_structured_outputs_names,
-      weight_shard_size_bytes=weight_shard_size_bytes,
-      control_flow_v2=control_flow_v2,
-      experiments=experiments,
-      metadata=metadata)
-
-  # Clean up the temporary SavedModel directory.
-  shutil.rmtree(temp_savedmodel_dir)
-
-
-def dispatch_keras_saved_model_to_tensorflowjs_conversion(
-    keras_saved_model_path, output_dir, quantization_dtype_map=None,
-    split_weights_by_layer=False,
-    weight_shard_size_bytes=1024 * 1024 * 4,
-    metadata=None):
-  """Converts keras model saved in the SavedModel format to tfjs format.
-
-  Note that the SavedModel format exists in keras, but not in
-  keras-team/tf.keras.
-
-  Args:
-    keras_saved_model_path: path to a folder in which the
-      assets/saved_model.json can be found. This is usually a subfolder
-      that is under the folder passed to
-      `tf.keras.models.save_model()` and has a Unix epoch time
-      as its name (e.g., 1542212752).
-    output_dir: Output directory to which the TensorFlow.js-format model JSON
-      file and weights files will be written. If the directory does not exist,
-      it will be created.
-    quantization_dtype_map: A mapping from dtype (`uint8`, `uint16`, `float16`)
-      to weights. The weight mapping supports wildcard substitution.
-    split_weights_by_layer: Whether to split the weights into separate weight
-      groups (corresponding to separate binary weight files) layer by layer
-      (Default: `False`).
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    metadata: User defined metadata map.
-  """
-  with tf.Graph().as_default(), tf.compat.v1.Session():
-    model = tf.keras.models.load_model(keras_saved_model_path)
-
-    # Save model temporarily in HDF5 format.
-    temp_h5_path = tempfile.mktemp(suffix='.h5')
-    model.save(temp_h5_path, save_format='h5')
-    assert os.path.isfile(temp_h5_path)
-
-    dispatch_keras_h5_to_tfjs_layers_model_conversion(
-        temp_h5_path,
-        output_dir,
-        quantization_dtype_map=quantization_dtype_map,
-        split_weights_by_layer=split_weights_by_layer,
-        weight_shard_size_bytes=weight_shard_size_bytes,
-        metadata=metadata)
-
-    # Delete temporary .h5 file.
-    os.remove(temp_h5_path)
-
-def dispatch_tensorflowjs_to_keras_h5_conversion(config_json_path, h5_path):
-  """Converts a TensorFlow.js Layers model format to Keras H5.
-
-  Args:
-    config_json_path: Path to the JSON file that includes the model's
-      topology and weights manifest, in tensorflowjs format.
-    h5_path: Path for the to-be-created Keras HDF5 model file.
-
-  Raises:
-    ValueError, if `config_json_path` is not a path to a valid JSON
-      file, or if h5_path points to an existing directory.
-  """
-  if os.path.isdir(config_json_path):
-    raise ValueError(
-        'For input_type=tfjs_layers_model & output_format=keras, '
-        'the input path should be a model.json '
-        'file, but received a directory.')
-  if os.path.isdir(h5_path):
-    raise ValueError(
-        'For input_type=tfjs_layers_model & output_format=keras, '
-        'the output path should be the path to an HDF5 file, '
-        'but received an existing directory (%s).' % h5_path)
-
-  # Verify that config_json_path points to a JSON file.
-  with open(config_json_path, 'rt') as f:
-    try:
-      json.load(f)
-    except (ValueError, IOError):
-      raise ValueError(
-          'For input_type=tfjs_layers_model & output_format=keras, '
-          'the input path is expected to contain valid JSON content, '
-          'but cannot read valid JSON content from %s.' % config_json_path)
-
-  with tf.Graph().as_default(), tf.compat.v1.Session():
-    model = keras_tfjs_loader.load_keras_model(config_json_path)
-    model.save(h5_path)
-
-def dispatch_tensorflowjs_to_keras_keras_conversion(config_json_path, v3_path):
-  """Converts a TensorFlow.js Layers model format to Keras V3 format.
-
-  Args:
-    config_json_path: Path to the JSON file that includes the model's
-      topology and weights manifest, in tensorflowjs format.
-    v3_path: Path for the to-be-created Keras V3 model file.
-
-  Raises:
-    ValueError, if `config_json_path` is not a path to a valid JSON
-      file.
-  """
-  if os.path.isdir(config_json_path):
-    raise ValueError(
-        'For input_type=tfjs_layers_model & output_format=keras_keras, '
-        'the input path should be a model.json '
-        'file, but received a directory.')
-  if os.path.isdir(v3_path):
-    raise ValueError(
-        'For input_type=tfjs_layers_model & output_format=keras_keras, '
-        'the output path should be the path to a .keras file, '
-        'but received an existing directory (%s).' % v3_path)
-
-  # Verify that config_json_path points to a JSON file.
-  with open(config_json_path, 'rt') as f:
-    try:
-      json.load(f)
-    except (ValueError, IOError):
-      raise ValueError(
-          'For input_type=tfjs_layers_model & output_format=keras_keras, '
-          'the input path is expected to contain valid JSON content, '
-          'but cannot read valid JSON content from %s.' % config_json_path)
-
-  model = keras_tfjs_loader.load_keras_keras_model(config_json_path)
-  tf.keras.saving.save_model(model, v3_path, save_format="keras")
-
-
-def dispatch_tensorflowjs_to_keras_saved_model_conversion(
-    config_json_path, keras_saved_model_path):
-  """Converts a TensorFlow.js Layers model format to a tf.keras SavedModel.
-
-  Args:
-    config_json_path: Path to the JSON file that includes the model's
-      topology and weights manifest, in tensorflowjs format.
-    keras_saved_model_path: Path for the to-be-created Keras SavedModel.
-
-  Raises:
-    ValueError, if `config_json_path` is not a path to a valid JSON
-      file, or if h5_path points to an existing directory.
-  """
-  if os.path.isdir(config_json_path):
-    raise ValueError(
-        'For input_type=tfjs_layers_model & output_format=keras_saved_model, '
-        'the input path should be a model.json '
-        'file, but received a directory.')
-
-  # Verify that config_json_path points to a JSON file.
-  with open(config_json_path, 'rt') as f:
-    try:
-      json.load(f)
-    except (ValueError, IOError):
-      raise ValueError(
-          'For input_type=tfjs_layers_model & output_format=keras, '
-          'the input path is expected to contain valid JSON content, '
-          'but cannot read valid JSON content from %s.' % config_json_path)
-
-  with tf.Graph().as_default(), tf.compat.v1.Session():
-    model = keras_tfjs_loader.load_keras_model(config_json_path)
-    tf.keras.models.save_model(
-        model, keras_saved_model_path, save_format='tf')
-
-
-def dispatch_tensorflowjs_to_tensorflowjs_conversion(
-    config_json_path,
-    output_dir_path,
-    quantization_dtype_map=None,
-    weight_shard_size_bytes=1024 * 1024 * 4):
-  """Converts a Keras Model from tensorflowjs format to H5.
-
-  Args:
-    config_json_path: Path to the JSON file that includes the model's
-      topology and weights manifest, in tensorflowjs format.
-    output_dir_path: Path to output directory in which the result of the
-      conversion will be saved.
-    quantization_dtype_map: A mapping from dtype (`uint8`, `uint16`, `float16`)
-      to weights. The weight mapping supports wildcard substitution.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-
-  Raises:
-    ValueError, if `config_json_path` is not a path to a valid JSON
-      file, or if h5_path points to an existing directory.
-    ValueError, if `output_dir_path` exists and is a file (instead of
-      a directory).
-  """
-  if os.path.isdir(config_json_path):
-    raise ValueError(
-        'For input_type=tfjs_layers_model, '
-        'the input path should be a model.json '
-        'file, but received a directory.')
-  # TODO(cais): Assert output_dir_path doesn't exist or is the path to
-  # a directory (not a file).
-
-  # Verify that config_json_path points to a JSON file.
-  with open(config_json_path, 'rt') as f:
-    try:
-      json.load(f)
-    except (ValueError, IOError):
-      raise ValueError(
-          'For input_type=tfjs_layers_model, '
-          'the input path is expected to contain valid JSON content, '
-          'but cannot read valid JSON content from %s.' % config_json_path)
-
-  temp_h5_path = tempfile.mktemp(suffix='.h5')
-  with tf.Graph().as_default(), tf.compat.v1.Session():
-    model = keras_tfjs_loader.load_keras_model(config_json_path)
-    model.save(temp_h5_path)
-    dispatch_tensorflowjs_to_keras_h5_conversion(config_json_path, temp_h5_path)
-
-  with tf.Graph().as_default(), tf.compat.v1.Session():
-    dispatch_keras_h5_to_tfjs_layers_model_conversion(
-        temp_h5_path, output_dir_path,
-        quantization_dtype_map=quantization_dtype_map,
-        weight_shard_size_bytes=weight_shard_size_bytes)
-    # TODO(cais): Support weight quantization.
-
-  # Clean up the temporary H5 file.
-  os.remove(temp_h5_path)
-
-
-def dispatch_tfjs_layers_model_to_tfjs_graph_conversion(
-    config_json_path,
-    output_dir_path,
-    quantization_dtype_map=None,
-    skip_op_check=False,
-    strip_debug_ops=False,
-    weight_shard_size_bytes=1024 * 1024 * 4,
-    control_flow_v2=False,
-    experiments=False,
-    metadata=None):
-  """Converts a TensorFlow.js Layers Model to TensorFlow.js Graph Model.
-
-  This conversion often benefits speed of inference, due to the graph
-  optimization that goes into generating the Graph Model.
-
-  Args:
-    config_json_path: Path to the JSON file that includes the model's
-      topology and weights manifest, in tensorflowjs format.
-    output_dir_path: Path to output directory in which the result of the
-      conversion will be saved.
-    quantization_dtype_map: A mapping from dtype (`uint8`, `uint16`, `float16`)
-      to weights. The weight mapping supports wildcard substitution.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to allow unsupported debug ops.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    control_flow_v2: Bool whether to enable control flow v2 ops.
-    experiments: Bool enable experimental features.
-    metadata: User defined metadata map.
-  Raises:
-    ValueError, if `config_json_path` is not a path to a valid JSON
-      file, or if h5_path points to an existing directory.
-    ValueError, if `output_dir_path` exists and is a file (instead of
-      a directory).
-  """
-  if os.path.isdir(config_json_path):
-    raise ValueError(
-        'For input_type=tfjs_layers_model, '
-        'the input path should be a model.json '
-        'file, but received a directory.')
-  # TODO(cais): Assert output_dir_path doesn't exist or is the path to
-  # a directory (not a file).
-
-  # Verify that config_json_path points to a JSON file.
-  with open(config_json_path, 'rt') as f:
-    try:
-      json.load(f)
-    except (ValueError, IOError):
-      raise ValueError(
-          'For input_type=tfjs_layers_model, '
-          'the input path is expected to contain valid JSON content, '
-          'but cannot read valid JSON content from %s.' % config_json_path)
-
-  temp_h5_path = tempfile.mktemp(suffix='.h5')
-
-  model = keras_tfjs_loader.load_keras_model(config_json_path)
-  model.save(temp_h5_path)
-  dispatch_keras_h5_to_tfjs_graph_model_conversion(
-      temp_h5_path, output_dir_path,
-      quantization_dtype_map=quantization_dtype_map,
-      skip_op_check=skip_op_check,
-      strip_debug_ops=strip_debug_ops,
-      weight_shard_size_bytes=weight_shard_size_bytes,
-      control_flow_v2=control_flow_v2,
-      experiments=experiments,
-      metadata=metadata)
-
-  # Clean up temporary HDF5 file.
-  os.remove(temp_h5_path)
-
-
-def _standardize_input_output_formats(input_format, output_format):
-  """Standardize input and output formats.
-
-  Args:
-    input_format: Input format as a string.
-    output_format: Output format as a string.
-
-  Returns:
-    A `tuple` of two strings:
-      (standardized_input_format, standardized_output_format).
-  """
-  input_format_is_keras = (
-      input_format in [common.KERAS_MODEL, common.KERAS_SAVED_MODEL])
-  input_format_is_tf = (
-      input_format in [common.TF_SAVED_MODEL,
-                       common.TF_FROZEN_MODEL, common.TF_HUB_MODEL])
-  if output_format is None:
-    # If no explicit output_format is provided, infer it from input format.
-    if input_format_is_keras:
-      output_format = common.TFJS_LAYERS_MODEL
-    elif input_format_is_tf:
-      output_format = common.TFJS_GRAPH_MODEL
-    elif input_format == common.TFJS_LAYERS_MODEL:
-      output_format = common.KERAS_MODEL
-
-  return (input_format, output_format)
-
-def _parse_quantization_dtype_map(float16, uint8, uint16, quantization_bytes):
-  quantization_dtype_map = {}
-
-  if quantization_bytes:
-    print(
-        'Warning: --quantization_bytes will be deprecated in a future release\n'
-        'Please consider using --quantize_uint8, --quantize_uint16, '
-        '--quantize_float16.', file=sys.stderr)
-    if float16 is not None or uint8 is not None or uint16 is not None:
-      raise ValueError(
-          '--quantization_bytes cannot be used with the new quantization flags')
-
-    dtype = quantization.QUANTIZATION_BYTES_TO_DTYPES[quantization_bytes]
-    quantization_dtype_map[dtype] = True
-
-  if float16 is not None:
-    quantization_dtype_map[quantization.QUANTIZATION_DTYPE_FLOAT16] = \
-      float16.split(',') if isinstance(float16, str) else float16
-  if uint8 is not None:
-    quantization_dtype_map[quantization.QUANTIZATION_DTYPE_UINT8] = \
-      uint8.split(',') if isinstance(uint8, str) else uint8
-  if uint16 is not None:
-    quantization_dtype_map[quantization.QUANTIZATION_DTYPE_UINT16] = \
-      uint16.split(',') if isinstance(uint16, str) else uint16
-
-  return quantization_dtype_map
-
-def _parse_metadata_map(metadata_arg):
-  if metadata_arg:
-    metadata_map = {}
-    metadata_list = metadata_arg.split(',')
-    for metadata in metadata_list:
-      [key, path] = metadata.split(':')
-      with open(path, 'rt') as f:
-        try:
-          metadata_json = json.load(f)
-          metadata_map[key] = metadata_json
-        except (ValueError, IOError):
-          raise ValueError(
-              'Loading metadata error: '
-              'the path is expected to contain valid JSON content, '
-              'but cannot read valid JSON content from %s.' % path)
-    if not metadata_map:
-      metadata_map = None
-    return metadata_map
-
-  return None
-
-def _dispatch_converter(input_format,
-                        output_format,
-                        args,
-                        quantization_dtype_map,
-                        weight_shard_size_bytes,
-                        metadata_map):
-  # TODO(cais, piyu): More conversion logics can be added as additional
-  #   branches below.
-  if (input_format == common.KERAS_MODEL and
-      output_format == common.TFJS_LAYERS_MODEL):
-    dispatch_keras_h5_to_tfjs_layers_model_conversion(
-        args.input_path, output_dir=args.output_path,
-        quantization_dtype_map=quantization_dtype_map,
-        split_weights_by_layer=args.split_weights_by_layer,
-        weight_shard_size_bytes=weight_shard_size_bytes,
-        metadata=metadata_map)
-  elif (input_format == common.KERAS_KERAS_MODEL and
-        output_format == common.TFJS_LAYERS_MODEL):
-    dispatch_keras_keras_to_tfjs_layers_model_conversion(
-        args.input_path, output_dir=args.output_path,
-        quantization_dtype_map=quantization_dtype_map,
-        split_weights_by_layer=args.split_weights_by_layer,
-        weight_shard_size_bytes=weight_shard_size_bytes,
-        metadata=metadata_map)
-  elif (input_format == common.KERAS_MODEL and
-        output_format == common.TFJS_GRAPH_MODEL):
-    dispatch_keras_h5_to_tfjs_graph_model_conversion(
-        args.input_path, output_dir=args.output_path,
-        quantization_dtype_map=quantization_dtype_map,
-        skip_op_check=args.skip_op_check,
-        strip_debug_ops=args.strip_debug_ops,
-        use_structured_outputs_names=args.use_structured_outputs_names,
-        weight_shard_size_bytes=weight_shard_size_bytes,
-        control_flow_v2=args.control_flow_v2,
-        experiments=args.experiments,
-        metadata=metadata_map)
-  elif (input_format == common.KERAS_SAVED_MODEL and
-        output_format == common.TFJS_LAYERS_MODEL):
-    dispatch_keras_saved_model_to_tensorflowjs_conversion(
-        args.input_path, args.output_path,
-        quantization_dtype_map=quantization_dtype_map,
-        split_weights_by_layer=args.split_weights_by_layer,
-        weight_shard_size_bytes=weight_shard_size_bytes,
-        metadata=metadata_map)
-  elif (input_format == common.TF_SAVED_MODEL and
-        output_format == common.TFJS_GRAPH_MODEL):
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        args.input_path, args.output_path,
-        signature_def=args.signature_name,
-        saved_model_tags=args.saved_model_tags,
-        quantization_dtype_map=quantization_dtype_map,
-        skip_op_check=args.skip_op_check,
-        strip_debug_ops=args.strip_debug_ops,
-        use_structured_outputs_names=args.use_structured_outputs_names,
-        weight_shard_size_bytes=weight_shard_size_bytes,
-        control_flow_v2=args.control_flow_v2,
-        experiments=args.experiments,
-        metadata=metadata_map)
-  elif (input_format == common.TF_HUB_MODEL and
-        output_format == common.TFJS_GRAPH_MODEL):
-    tf_saved_model_conversion_v2.convert_tf_hub_module(
-        args.input_path, args.output_path,
-        signature=args.signature_name,
-        saved_model_tags=args.saved_model_tags,
-        quantization_dtype_map=quantization_dtype_map,
-        skip_op_check=args.skip_op_check,
-        strip_debug_ops=args.strip_debug_ops,
-        use_structured_outputs_names=args.use_structured_outputs_names,
-        weight_shard_size_bytes=weight_shard_size_bytes,
-        control_flow_v2=args.control_flow_v2,
-        experiments=args.experiments,
-        metadata=metadata_map)
-  elif (input_format == common.TFJS_LAYERS_MODEL and
-        output_format == common.KERAS_MODEL):
-    dispatch_tensorflowjs_to_keras_h5_conversion(args.input_path,
-                                                 args.output_path)
-  elif (input_format == common.TFJS_LAYERS_MODEL and
-        output_format == common.KERAS_KERAS_MODEL):
-    dispatch_tensorflowjs_to_keras_keras_conversion(args.input_path,
-                                                 args.output_path)
-  elif (input_format == common.TFJS_LAYERS_MODEL and
-        output_format == common.KERAS_SAVED_MODEL):
-    dispatch_tensorflowjs_to_keras_saved_model_conversion(args.input_path,
-                                                          args.output_path)
-  elif (input_format == common.TFJS_LAYERS_MODEL and
-        output_format == common.TFJS_LAYERS_MODEL):
-    dispatch_tensorflowjs_to_tensorflowjs_conversion(
-        args.input_path, args.output_path,
-        quantization_dtype_map=quantization_dtype_map,
-        weight_shard_size_bytes=weight_shard_size_bytes)
-  elif (input_format == common.TFJS_LAYERS_MODEL and
-        output_format == common.TFJS_GRAPH_MODEL):
-    dispatch_tfjs_layers_model_to_tfjs_graph_conversion(
-        args.input_path, args.output_path,
-        quantization_dtype_map=quantization_dtype_map,
-        skip_op_check=args.skip_op_check,
-        strip_debug_ops=args.strip_debug_ops,
-        weight_shard_size_bytes=weight_shard_size_bytes,
-        control_flow_v2=args.control_flow_v2,
-        experiments=args.experiments,
-        metadata=metadata_map)
-  elif (input_format == common.TF_FROZEN_MODEL and
-        output_format == common.TFJS_GRAPH_MODEL):
-    tf_saved_model_conversion_v2.convert_tf_frozen_model(
-        args.input_path, args.output_node_names, args.output_path,
-        quantization_dtype_map=quantization_dtype_map,
-        skip_op_check=args.skip_op_check,
-        strip_debug_ops=args.strip_debug_ops,
-        weight_shard_size_bytes=weight_shard_size_bytes,
-        experiments=args.experiments,
-        metadata=metadata_map)
-  else:
-    raise ValueError(
-        'Unsupported input_format - output_format pair: %s - %s' %
-        (input_format, output_format))
-
-def get_arg_parser():
-  """
-  Create the argument parser for the converter binary.
-  """
-
-  parser = argparse.ArgumentParser('TensorFlow.js model converters.')
-  parser.add_argument(
-      common.INPUT_PATH,
-      nargs='?',
-      type=str,
-      help='Path to the input file or directory. For input format "keras", '
-      'an HDF5 (.h5) file is expected. For input format "tensorflow", '
-      'a SavedModel directory, frozen model file, '
-      'or TF-Hub module is expected.')
-  parser.add_argument(
-      common.OUTPUT_PATH,
-      nargs='?',
-      type=str,
-      help='Path for all output artifacts.')
-  parser.add_argument(
-      '--%s' % common.INPUT_FORMAT,
-      type=str,
-      required=False,
-      default=common.TF_SAVED_MODEL,
-      choices=set([common.KERAS_MODEL, common.KERAS_SAVED_MODEL, common.KERAS_KERAS_MODEL,
-                   common.TF_SAVED_MODEL, common.TF_HUB_MODEL,
-                   common.TFJS_LAYERS_MODEL, common.TF_FROZEN_MODEL]),
-      help='Input format. '
-      'For "keras", the input path can be one of the two following formats:\n'
-      '  - A topology+weights combined HDF5 (e.g., generated with'
-      '    `tf.keras.model.save_model()` method).\n'
-      '  - A weights-only HDF5 (e.g., generated with Keras Model\'s '
-      '    `save_weights()` method). \n'
-      'For "keras_saved_model", the input_path must point to a subfolder '
-      'under the saved model folder that is passed as the argument '
-      'to tf.contrib.save_model.save_keras_model(). '
-      'The subfolder is generated automatically by tensorflow when '
-      'saving keras model in the SavedModel format. It is usually named '
-      'as a Unix epoch time (e.g., 1542212752).\n'
-      'For "tf" formats, a SavedModel, frozen model, '
-      ' or TF-Hub module is expected.')
-  parser.add_argument(
-      '--%s' % common.OUTPUT_FORMAT,
-      type=str,
-      required=False,
-      choices=set([common.KERAS_MODEL, common.KERAS_SAVED_MODEL,
-                   common.TFJS_LAYERS_MODEL, common.TFJS_GRAPH_MODEL, common.KERAS_KERAS_MODEL]),
-      help='Output format. Default: tfjs_graph_model.')
-  parser.add_argument(
-      '--%s' % common.SIGNATURE_NAME,
-      type=str,
-      default=None,
-      help='Signature of the SavedModel Graph or TF-Hub module to load. '
-      'Applicable only if input format is "tf_hub" or "tf_saved_model".')
-  parser.add_argument(
-      '--%s' % common.SAVED_MODEL_TAGS,
-      type=str,
-      default='serve',
-      help='Tags of the MetaGraphDef to load, in comma separated string '
-      'format. Defaults to "serve". Applicable only if input format is '
-      '"tf_saved_model".')
-  parser.add_argument(
-      '--%s' % common.QUANTIZATION_TYPE_FLOAT16,
-      type=str,
-      default=None,
-      const=True,
-      nargs='?',
-      help='Comma separated list of node names to apply float16 quantization. '
-      'You can also use wildcard symbol (*) to apply quantization to multiple '
-      'nodes (e.g., conv/*/weights). When the flag is provided without any '
-      'nodes the default behavior will match all nodes.')
-  parser.add_argument(
-      '--%s' % common.QUANTIZATION_TYPE_UINT8,
-      type=str,
-      default=None,
-      const=True,
-      nargs='?',
-      help='Comma separated list of node names to apply 1-byte affine '
-      'quantization. You can also use wildcard symbol (*) to apply '
-      'quantization to multiple nodes (e.g., conv/*/weights). When the flag is '
-      'provided without any nodes the default behavior will match all nodes.')
-  parser.add_argument(
-      '--%s' % common.QUANTIZATION_TYPE_UINT16,
-      type=str,
-      default=None,
-      const=True,
-      nargs='?',
-      help='Comma separated list of node names to apply 2-byte affine '
-      'quantization. You can also use wildcard symbol (*) to apply '
-      'quantization to multiple nodes (e.g., conv/*/weights). When the flag is '
-      'provided without any nodes the default behavior will match all nodes.')
-  parser.add_argument(
-      '--%s' % common.QUANTIZATION_BYTES,
-      type=int,
-      choices=set(quantization.QUANTIZATION_BYTES_TO_DTYPES.keys()),
-      help='(Deprecated) How many bytes to optionally quantize/compress the '
-      'weights to. 1- and 2-byte quantizaton is supported. The default '
-      '(unquantized) size is 4 bytes.')
-  parser.add_argument(
-      '--%s' % common.SPLIT_WEIGHTS_BY_LAYER,
-      action='store_true',
-      help='Applicable to keras input_format only: Whether the weights from '
-      'different layers are to be stored in separate weight groups, '
-      'corresponding to separate binary weight files. Default: False.')
-  parser.add_argument(
-      '--%s' % common.VERSION,
-      '-v',
-      dest='show_version',
-      action='store_true',
-      help='Show versions of tensorflowjs and its dependencies')
-  parser.add_argument(
-      '--%s' % common.SKIP_OP_CHECK,
-      action='store_true',
-      help='Skip op validation for TensorFlow model conversion.')
-  parser.add_argument(
-      '--%s' % common.STRIP_DEBUG_OPS,
-      type=bool,
-      default=True,
-      help='Strip debug ops (Print, Assert, CheckNumerics) from graph.')
-  parser.add_argument(
-      '--%s' % common.USE_STRUCTURED_OUTPUTS_NAMES,
-      type=bool,
-      default=False,
-      help='TFJS graph outputs become a tensor map with the same structure as '
-      'the TF graph structured_outputs (only supported for structured_outputs '
-      'of the form {key1: tensor1, key2: tensor2...})')
-  parser.add_argument(
-      '--%s' % common.WEIGHT_SHARD_SIZE_BYTES,
-      type=int,
-      default=None,
-      help='Shard size (in bytes) of the weight files. Currently applicable '
-      'only when output_format is tfjs_layers_model or tfjs_graph_model.')
-  parser.add_argument(
-      '--output_node_names',
-      type=str,
-      help='The names of the output nodes, separated by commas. E.g., '
-      '"logits,activations". Applicable only if input format is '
-      '"tf_frozen_model".')
-  parser.add_argument(
-      '--%s' % common.CONTROL_FLOW_V2,
-      type=bool,
-      default=False,
-      help='Enable control flow v2 ops, this would improve inference '
-      'performance on models with branches or loops.')
-  parser.add_argument(
-      '--%s' % common.EXPERIMENTS,
-      type=bool,
-      default=False,
-      help='Enable experimental features, you should only enable this flag '
-      'when using Python3 and TensorFlow nightly build.')
-  parser.add_argument(
-      '--%s' % common.METADATA,
-      type=str,
-      help='Attach user defined metadata in format key:path/metadata.json '
-      'Separate multiple metadata files by comma.'
-  )
-  return parser
-
-def convert(arguments):
-  args = get_arg_parser().parse_args(arguments)
-
-  if args.show_version:
-    print('\ntensorflowjs %s\n' % version.version)
-    print('Dependency versions:')
-    print('  keras %s' % tf.keras.__version__)
-    print('  tensorflow %s' % tf.__version__)
-    return
-
-  if not args.input_path:
-    raise ValueError(
-        'Missing input_path argument. For usage, use the --help flag.')
-  if not args.output_path:
-    raise ValueError(
-        'Missing output_path argument. For usage, use the --help flag.')
-
-  if args.input_path is None:
-    raise ValueError(
-        'Error: The input_path argument must be set. '
-        'Run with --help flag for usage information.')
-
-  input_format, output_format = _standardize_input_output_formats(
-      args.input_format, args.output_format)
-
-  weight_shard_size_bytes = 1024 * 1024 * 4
-  if args.weight_shard_size_bytes is not None:
-    if (output_format not in
-        (common.TFJS_LAYERS_MODEL, common.TFJS_GRAPH_MODEL)):
-      raise ValueError(
-          'The --weight_shard_size_bytes flag is only supported when '
-          'output_format is tfjs_layers_model or tfjs_graph_model.')
-
-    if not (isinstance(args.weight_shard_size_bytes, int) and
-            args.weight_shard_size_bytes > 0):
-      raise ValueError(
-          'Expected weight_shard_size_bytes to be a positive integer, '
-          'but got %s' % args.weight_shard_size_bytes)
-    weight_shard_size_bytes = args.weight_shard_size_bytes
-
-  quantization_dtype_map = _parse_quantization_dtype_map(
-      args.quantize_float16,
-      args.quantize_uint8,
-      args.quantize_uint16,
-      args.quantization_bytes
-  )
-
-  if (not args.output_node_names and input_format == common.TF_FROZEN_MODEL):
-    raise ValueError(
-        'The --output_node_names flag is required for "tf_frozen_model"')
-
-  if (args.signature_name and input_format not in
-      (common.TF_SAVED_MODEL, common.TF_HUB_MODEL)):
-    raise ValueError(
-        'The --signature_name flag is applicable only to "tf_saved_model" and '
-        '"tf_hub" input format, but the current input format is '
-        '"%s".' % input_format)
-
-  if (args.control_flow_v2 and output_format != common.TFJS_GRAPH_MODEL):
-    raise ValueError(
-        'The --control_flow_v2 flag is applicable only to "tfjs_graph_model" '
-        'as output format, but the current  output format '
-        'is "%s"' % input_format, output_format)
-
-  metadata_map = _parse_metadata_map(args.metadata)
-
-  _dispatch_converter(input_format, output_format, args, quantization_dtype_map,
-                      weight_shard_size_bytes, metadata_map)
-
-def pip_main():
-  """Entry point for pip-packaged binary.
-
-  Note that pip-packaged binary calls the entry method without
-  any arguments, which is why this method is needed in addition to the
-  `main` method below.
-  """
-  main([' '.join(sys.argv[1:])])
-
-
-def main(argv):
-  convert(argv[0].split(' '))
-
-
-if __name__ == '__main__':
-  tf1.app.run(main=main, argv=[' '.join(sys.argv[1:])])
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/converter_binary_test.sh b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/converter_binary_test.sh
deleted file mode 100644
index 75cdb6ff7..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/converter_binary_test.sh
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-GENERATE_BIN="${TEST_SRCDIR}/tfjs/tfjs-converter/python/tensorflowjs/converters/generate_test_model"
-CONVERTER_BIN="${TEST_SRCDIR}/tfjs/tfjs-converter/python/tensorflowjs/converters/converter"
-
-# 1. Test tf_saved_model --> tfjs_graph_model conversion.
-SAVED_MODEL_DIR="$(mktemp -d)"
-echo "Genearting TF SavedModel for testing..."
-"${GENERATE_BIN}" "${SAVED_MODEL_DIR}" --model_type tf_saved_model
-echo "Done genearting TF SavedModel for testing at ${SAVED_MODEL_DIR}"
-
-OUTPUT_DIR="${SAVED_MODEL_DIR}_converted"
-"${CONVERTER_BIN}" \
-    --input_format tf_saved_model \
-    --output_format tfjs_graph_model \
-    "${SAVED_MODEL_DIR}" \
-    "${OUTPUT_DIR}"
-
-if [[ ! -d "${OUTPUT_DIR}" ]]; then
-  echo "ERROR: Failed to find conversion output directory: ${OUTPUT_DIR}" 1>&2
-  exit 1
-fi
-
-# Clean up files.
-rm -rf "${SAVED_MODEL_DIR}" "${OUTPUT_DIR}"
-
-# 2. Test keras HDF5 --> tfjs_layers_model conversion.
-KERAS_H5_PATH="$(mktemp).h5"
-echo "Genearting Keras HDF5 model for testing..."
-"${GENERATE_BIN}" "${KERAS_H5_PATH}" --model_type tf_keras_h5
-echo "Done genearting Keras HDF5 model for testing at ${KERAS_H5_PATH}"
-
-OUTPUT_H5_PATH="${KERAS_H5_PATH}_converted.h5"
-"${CONVERTER_BIN}" \
-    --input_format keras \
-    --output_format tfjs_layers_model \
-    "${KERAS_H5_PATH}" \
-    "${OUTPUT_H5_PATH}"
-
-if [[ ! -d "${OUTPUT_H5_PATH}" ]]; then
-  echo "ERROR: Failed to find conversion output directory: ${OUTPUT_H5_PATH}" 1>&2
-  exit 1
-fi
-
-# Clean up files.
-rm -rf "${KERAS_H5_PATH}" "${OUTPUT_H5_PATH}"
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/converter_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/converter_test.py
deleted file mode 100644
index 397e94d2c..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/converter_test.py
+++ /dev/null
@@ -1,804 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for artifact conversion to and from Python tf.keras."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import glob
-import json
-import os
-import shutil
-import tempfile
-import unittest
-import keras
-
-import numpy as np
-import tensorflow.compat.v2 as tf
-
-from tensorflowjs import version
-from tensorflowjs.converters import converter
-from tensorflowjs.converters import keras_tfjs_loader
-
-
-# TODO(adarob): Add tests for quantization option.
-
-
-class ConvertH5WeightsTest(unittest.TestCase):
-
-  def setUp(self):
-    self._tmp_dir = tempfile.mkdtemp()
-    super(ConvertH5WeightsTest, self).setUp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(ConvertH5WeightsTest, self).tearDown()
-
-  def testWeightsOnly(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      input_tensor = tf.keras.layers.Input((3,))
-      dense1 = tf.keras.layers.Dense(
-          4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
-          name='MyDense1')(input_tensor)
-      output = tf.keras.layers.Dense(
-          2, use_bias=False, kernel_initializer='ones', name='MyDense2')(dense1)
-      model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
-      h5_path = os.path.join(self._tmp_dir, 'MyModel.h5')
-      model.save_weights(h5_path)
-
-    # Load the saved weights as a JSON string.
-    model_json, groups = (
-        converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-            h5_path, output_dir=self._tmp_dir))
-    self.assertIsNone(model_json)
-
-    # Check the loaded weights.
-    self.assertEqual(1, len(groups))
-    self.assertEqual(3, len(groups[0]))
-    # contents of weights are verified in tests of the library code
-
-    # Check the content of the output directory.
-    output_json = json.load(
-        open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
-    self.assertEqual(model_json, output_json['modelTopology'])
-    self.assertIsInstance(output_json['weightsManifest'], list)
-    self.assertTrue(glob.glob(os.path.join(self._tmp_dir, 'group*-*')))
-
-  def testConvertSavedKerasModelNoSplitByLayer(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      input_tensor = tf.keras.layers.Input((3,))
-      dense1 = tf.keras.layers.Dense(
-          4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
-          name='MergedDense1')(input_tensor)
-      output = tf.keras.layers.Dense(
-          2, use_bias=False,
-          kernel_initializer='ones', name='MergedDense2')(dense1)
-      model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
-      h5_path = os.path.join(self._tmp_dir, 'MyModelMerged.h5')
-      model.save(h5_path)
-
-    # Load the saved weights as a JSON string.
-    model_json, groups = (
-        converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-            h5_path, output_dir=self._tmp_dir))
-    # check the model topology was stored
-    self.assertIsInstance(model_json['model_config'], dict)
-    self.assertIsInstance(model_json['model_config']['config'], dict)
-    self.assertIn('layers', model_json['model_config']['config'])
-    # Check the loaded weights.
-    self.assertEqual(keras.__version__, model_json['keras_version'])
-    self.assertEqual('tensorflow', model_json['backend'])
-    self.assertEqual(1, len(groups))
-    self.assertEqual(3, len(groups[0]))
-    # contents of weights are verified in tests of the library code
-
-    # Check the content of the output directory.
-    output_json = json.load(
-        open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
-    self.assertEqual(model_json, output_json['modelTopology'])
-    self.assertIsInstance(output_json['weightsManifest'], list)
-    self.assertTrue(glob.glob(os.path.join(self._tmp_dir, 'group*-*')))
-
-  def testConvertSavedKerasModelSplitByLayer(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      input_tensor = tf.keras.layers.Input((3,))
-      dense1 = tf.keras.layers.Dense(
-          4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
-          name='MergedDense1')(input_tensor)
-      output = tf.keras.layers.Dense(
-          2, use_bias=False,
-          kernel_initializer='ones', name='MergedDense2')(dense1)
-      model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
-      h5_path = os.path.join(self._tmp_dir, 'MyModelMerged.h5')
-      model.save(h5_path)
-
-    # Load the saved weights as a JSON string.
-    model_json, groups = (
-        converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-            h5_path, output_dir=self._tmp_dir, split_weights_by_layer=True))
-    # check the model topology was stored
-    self.assertIsInstance(model_json['model_config'], dict)
-    self.assertIsInstance(model_json['model_config']['config'], dict)
-    self.assertIn('layers', model_json['model_config']['config'])
-
-    # Check the loaded weights.
-    self.assertEqual(keras.__version__, model_json['keras_version'])
-    self.assertEqual('tensorflow', model_json['backend'])
-    self.assertEqual(2, len(groups))
-    self.assertEqual(2, len(groups[0]))
-    self.assertEqual(1, len(groups[1]))
-    # contents of weights are verified in tests of the library code
-
-    # Check the content of the output directory.
-    output_json = json.load(
-        open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
-    self.assertEqual(model_json, output_json['modelTopology'])
-    self.assertIsInstance(output_json['weightsManifest'], list)
-    self.assertTrue(glob.glob(os.path.join(self._tmp_dir, 'group*-*')))
-
-  def testConvertSavedKerasModeltoTfLayersModelSharded(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      sequential_model = tf.keras.models.Sequential([
-          tf.keras.layers.Dense(
-              3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-              name='Dense1')])
-      h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-      sequential_model.save(h5_path)
-
-      weights = sequential_model.get_weights()
-      total_weight_bytes = sum(np.size(w) for w in weights) * 4
-
-      # Due to the shard size, there ought to be 4 shards after conversion.
-      weight_shard_size_bytes = int(total_weight_bytes * 0.3)
-
-      # Convert Keras model to tfjs_layers_model format.
-      output_dir = os.path.join(self._tmp_dir, 'sharded_tfjs')
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, output_dir,
-          weight_shard_size_bytes=weight_shard_size_bytes)
-
-      weight_files = sorted(glob.glob(os.path.join(output_dir, 'group*.bin')))
-      self.assertEqual(len(weight_files), 4)
-      weight_file_sizes = [os.path.getsize(f) for f in weight_files]
-      self.assertEqual(sum(weight_file_sizes), total_weight_bytes)
-      self.assertEqual(weight_file_sizes[0], weight_file_sizes[1])
-      self.assertEqual(weight_file_sizes[0], weight_file_sizes[2])
-      self.assertLess(weight_file_sizes[3], weight_file_sizes[0])
-
-  def testConvertWeightsFromSequentialModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      sequential_model = tf.keras.models.Sequential([
-          tf.keras.layers.Dense(
-              3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-              name='Dense1'),
-          tf.keras.layers.Dense(
-              1, use_bias=False, kernel_initializer='ones', name='Dense2')])
-      h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-      sequential_model.save_weights(h5_path)
-
-    # Load the saved weights as a JSON string.
-    model_json, groups = (
-        converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-            h5_path, output_dir=self._tmp_dir))
-    self.assertIsNone(model_json)
-
-    # Check the loaded weights.
-    self.assertEqual(1, len(groups))
-    self.assertEqual(3, len(groups[0]))
-    # contents of weights are verified in tests of the library code
-
-    # Check the content of the output directory.
-    output_json = json.load(
-        open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
-    self.assertEqual(model_json, output_json['modelTopology'])
-    self.assertIsInstance(output_json['weightsManifest'], list)
-    self.assertTrue(glob.glob(os.path.join(self._tmp_dir, 'group*-*')))
-
-  def testUserDefinedMetadata(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      sequential_model = tf.keras.models.Sequential([
-          tf.keras.layers.Dense(
-              3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-              name='Dense1'),
-          tf.keras.layers.Dense(
-              1, use_bias=False, kernel_initializer='ones', name='Dense2')])
-      h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-      sequential_model.save_weights(h5_path)
-
-    metadata_json = {'a': 1}
-    converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-        h5_path, output_dir=self._tmp_dir, metadata={'key': metadata_json})
-
-    # Check the content of the output directory.
-    output_json = json.load(
-        open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
-    self.assertEqual(metadata_json, output_json['userDefinedMetadata']['key'])
-
-  def testConvertModelForNonexistentDirCreatesDir(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      output_dir = os.path.join(self._tmp_dir, 'foo_model')
-      sequential_model = tf.keras.models.Sequential([
-          tf.keras.layers.Dense(
-              3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-              name='Dense1')])
-      h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-      sequential_model.save_weights(h5_path)
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, output_dir=output_dir)
-
-    # Check the content of the output directory.
-    output_json = json.load(
-        open(os.path.join(output_dir, 'model.json'), 'rt'))
-    self.assertIsNone(output_json['modelTopology'])
-    self.assertIsInstance(output_json['weightsManifest'], list)
-    self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))
-
-  def testOutpuDirAsAnExistingFileLeadsToValueError(self):
-    output_path = os.path.join(self._tmp_dir, 'foo_model')
-    with open(output_path, 'wt') as f:
-      f.write('\n')
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      sequential_model = tf.keras.models.Sequential([
-          tf.keras.layers.Dense(
-              3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-              name='Dense1')])
-      h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-      sequential_model.save_weights(h5_path)
-
-    with self.assertRaisesRegexp(  # pylint: disable=deprecated-method
-        ValueError, r'already exists as a file'):
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, output_dir=output_path)
-
-  def testTensorflowjsToKerasConversionSucceeds(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      sequential_model = tf.keras.models.Sequential([
-          tf.keras.layers.Dense(
-              3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-              name='Dense1'),
-          tf.keras.layers.Dense(
-              1, use_bias=False, kernel_initializer='ones', name='Dense2')])
-      h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-      sequential_model.use_legacy_config = True
-      sequential_model.save(h5_path)
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, output_dir=self._tmp_dir)
-      old_model_json = sequential_model.to_json()
-
-    # Convert the tensorflowjs artifacts to a new H5 file.
-    new_h5_path = os.path.join(self._tmp_dir, 'new.h5')
-    converter.dispatch_tensorflowjs_to_keras_h5_conversion(
-        os.path.join(self._tmp_dir, 'model.json'), new_h5_path)
-
-    # Load the new H5 and compare the model JSONs.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      new_model = tf.keras.models.load_model(new_h5_path)
-      new_model.use_legacy_config = True
-      self.assertEqual(old_model_json, new_model.to_json())
-
-  def testTensorflowjsToKerasConversionFailsOnDirInputPath(self):
-    with self.assertRaisesRegexp(  # pylint: disable=deprecated-method
-        ValueError, r'input path should be a model\.json file'):
-      converter.dispatch_tensorflowjs_to_keras_h5_conversion(
-          self._tmp_dir, os.path.join(self._tmp_dir, 'new.h5'))
-
-  def testTensorflowjsToKerasConversionFailsOnExistingDirOutputPath(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      sequential_model = tf.keras.models.Sequential([
-          tf.keras.layers.Dense(
-              3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-              name='Dense1'),
-          tf.keras.layers.Dense(
-              1, use_bias=False, kernel_initializer='ones', name='Dense2')])
-      h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-      sequential_model.save(h5_path)
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, output_dir=self._tmp_dir)
-
-    with self.assertRaisesRegexp(  # pylint: disable=deprecated-method
-        ValueError, r'but received an existing directory'):
-      converter.dispatch_tensorflowjs_to_keras_h5_conversion(
-          os.path.join(self._tmp_dir, 'model.json'), self._tmp_dir)
-
-  def testTensorflowjsToKerasConversionFailsOnInvalidJsonFile(self):
-    fake_json_path = os.path.join(self._tmp_dir, 'fake.json')
-    with open(fake_json_path, 'wt') as f:
-      f.write('__invalid_json_content__')
-
-    with self.assertRaisesRegexp(  # pylint: disable=deprecated-method
-        ValueError, r'cannot read valid JSON content from'):
-      converter.dispatch_tensorflowjs_to_keras_h5_conversion(
-          fake_json_path, os.path.join(self._tmp_dir, 'model.h5'))
-
-
-class ConvertKerasToTfGraphModelTest(tf.test.TestCase):
-
-  def setUp(self):
-    super(ConvertKerasToTfGraphModelTest, self).setUp()
-    self._tmp_dir = tempfile.mkdtemp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(ConvertKerasToTfGraphModelTest, self).tearDown()
-
-  def testConvertKerasModelToTfGraphModel(self):
-    output_dir = os.path.join(self._tmp_dir, 'foo_model')
-    sequential_model = tf.keras.models.Sequential([
-        tf.keras.layers.Dense(
-            3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-            name='Dense1')])
-    h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-    sequential_model.save(h5_path)
-    converter.dispatch_keras_h5_to_tfjs_graph_model_conversion(
-        h5_path, output_dir=output_dir)
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(output_dir, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-    weights_manifest = model_json['weightsManifest']
-    self.assertEqual(len(weights_manifest), 1)
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))
-
-  def testConvertKerasModelToTfGraphModelSharded(self):
-    output_dir = os.path.join(self._tmp_dir, 'foo_model')
-    sequential_model = tf.keras.models.Sequential([
-        tf.keras.layers.Dense(
-            3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-            name='Dense1')])
-    h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-    sequential_model.save(h5_path)
-
-    # Do initial conversion without sharding.
-    converter.dispatch_keras_h5_to_tfjs_graph_model_conversion(
-        h5_path, output_dir)
-    weight_files = glob.glob(os.path.join(output_dir, 'group*.bin'))
-
-    # Get size of weights in bytes after graph optimizations.
-    optimized_total_weight = sum([os.path.getsize(f) for f in weight_files])
-
-    # Due to the shard size, there ought to be 4 shards after conversion.
-    weight_shard_size_bytes = int(optimized_total_weight * 0.3)
-
-    output_dir = os.path.join(self._tmp_dir, 'sharded_model')
-    # Convert Keras model again with shard argument set.
-    converter.dispatch_keras_h5_to_tfjs_graph_model_conversion(
-        h5_path, output_dir,
-        weight_shard_size_bytes=weight_shard_size_bytes)
-
-    weight_files = sorted(glob.glob(os.path.join(output_dir, 'group*.bin')))
-    self.assertEqual(len(weight_files), 4)
-    weight_file_sizes = [os.path.getsize(f) for f in weight_files]
-    self.assertEqual(sum(weight_file_sizes), optimized_total_weight)
-    self.assertEqual(weight_file_sizes[0], weight_file_sizes[1])
-    self.assertEqual(weight_file_sizes[0], weight_file_sizes[2])
-    self.assertLess(weight_file_sizes[3], weight_file_sizes[0])
-
-  def testUserDefinedMetadata(self):
-    output_dir = os.path.join(self._tmp_dir, 'foo_model')
-    sequential_model = tf.keras.models.Sequential([
-        tf.keras.layers.Dense(
-            3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-            name='Dense1')])
-    h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-    sequential_model.save(h5_path)
-    metadata_json = {'a': 1}
-    converter.dispatch_keras_h5_to_tfjs_graph_model_conversion(
-        h5_path, output_dir=output_dir, metadata={'key': metadata_json})
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(output_dir, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertEqual(metadata_json, model_json['userDefinedMetadata']['key'])
-
-class ConvertTfKerasSavedModelTest(tf.test.TestCase):
-
-  def setUp(self):
-    super(ConvertTfKerasSavedModelTest, self).setUp()
-    self._tmp_dir = tempfile.mkdtemp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(ConvertTfKerasSavedModelTest, self).tearDown()
-
-  def _createSimpleSequentialModel(self):
-    model = tf.keras.Sequential()
-    model.add(tf.keras.layers.Reshape([2, 3], input_shape=[6]))
-    model.add(tf.keras.layers.LSTM(10))
-    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
-    model.compile(optimizer='adam', loss='binary_crossentropy')
-    model.predict(tf.ones((1, 6)), steps=1)
-    tf.keras.backend.set_learning_phase(0)
-    return model
-
-  def _createNestedSequentialModel(self):
-    model = tf.keras.Sequential()
-    model.add(tf.keras.layers.Dense(6, input_shape=[10], activation='relu'))
-    model.add(self._createSimpleSequentialModel())
-    model.compile(optimizer='adam', loss='binary_crossentropy')
-    model.predict(tf.ones((1, 10)), steps=1)
-    return model
-
-  def _createFunctionalModelWithWeights(self):
-    input1 = tf.keras.Input(shape=[8])
-    input2 = tf.keras.Input(shape=[10])
-    y = tf.keras.layers.Concatenate()([input1, input2])
-    y = tf.keras.layers.Dense(4, activation='softmax')(y)
-    model = tf.keras.Model([input1, input2], y)
-    model.compile(optimizer='adam', loss='binary_crossentropy')
-    model.predict([tf.ones((1, 8)), tf.ones((1, 10))], steps=1)
-    return model
-
-  def testConvertTfKerasSequentialSavedAsSavedModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-      old_model_json = json.loads(model.to_json())
-      old_weights = model.get_weights()
-      tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_saved_model_to_tensorflowjs_conversion(
-          self._tmp_dir, tfjs_output_dir)
-
-      # Verify the size of the weight file.
-      weight_path = glob.glob(os.path.join(tfjs_output_dir, 'group*-*'))[0]
-      weight_file_bytes = os.path.getsize(weight_path)
-      model_weight_bytes = sum(w.size * 4 for w in model.get_weights())
-      self.assertEqual(weight_file_bytes, model_weight_bytes)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # Load the converted mode back.
-      model_json_path = os.path.join(tfjs_output_dir, 'model.json')
-      model_prime = keras_tfjs_loader.load_keras_model(model_json_path)
-      new_weights = model_prime.get_weights()
-
-      # Check the equality of the old and new model JSONs.
-      self.assertEqual(old_model_json, json.loads(model_prime.to_json()))
-
-      # Check the equality of the old and new weights.
-      self.assertAllClose(old_weights, new_weights)
-
-  def testConvertTfKerasSequentialCompiledAndSavedAsSavedModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-
-      old_model_json = json.loads(model.to_json())
-      old_weights = model.get_weights()
-      tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_saved_model_to_tensorflowjs_conversion(
-          self._tmp_dir, tfjs_output_dir)
-
-      # Verify the size of the weight file.
-      weight_path = glob.glob(os.path.join(tfjs_output_dir, 'group*-*'))[0]
-      weight_file_bytes = os.path.getsize(weight_path)
-      model_weight_bytes = sum(w.size * 4 for w in model.get_weights())
-      self.assertEqual(weight_file_bytes, model_weight_bytes)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # Load the converted mode back.
-      model_json_path = os.path.join(tfjs_output_dir, 'model.json')
-      model_prime = keras_tfjs_loader.load_keras_model(model_json_path)
-      new_weights = model_prime.get_weights()
-
-      # Check the equality of the old and new model JSONs.
-      self.assertEqual(old_model_json, json.loads(model_prime.to_json()))
-
-      # Check the equality of the old and new weights.
-      self.assertAllClose(old_weights, new_weights)
-
-  def testWrongConverterRaisesCorrectErrorMessage(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-      tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      # Use wrong dispatcher.
-      with self.assertRaisesRegexp(  # pylint: disable=deprecated-method
-          ValueError,
-          r'Expected path to point to an HDF5 file, but it points to a '
-          r'directory'):
-        converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-            self._tmp_dir, tfjs_output_dir)
-
-  def testConvertTfKerasNestedSequentialSavedAsSavedModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createNestedSequentialModel()
-      old_model_json = json.loads(model.to_json())
-      old_weights = model.get_weights()
-      tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_saved_model_to_tensorflowjs_conversion(
-          self._tmp_dir, tfjs_output_dir)
-
-      # Verify the size of the weight file.
-      weight_path = glob.glob(os.path.join(tfjs_output_dir, 'group*-*'))[0]
-      weight_file_bytes = os.path.getsize(weight_path)
-      model_weight_bytes = sum(w.size * 4 for w in model.get_weights())
-      self.assertEqual(weight_file_bytes, model_weight_bytes)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # Load the converted mode back.
-      model_json_path = os.path.join(tfjs_output_dir, 'model.json')
-      model_prime = keras_tfjs_loader.load_keras_model(model_json_path)
-      new_weights = model_prime.get_weights()
-
-      # Check the equality of the old and new model JSONs.
-      self.assertEqual(old_model_json, json.loads(model_prime.to_json()))
-
-      # Check the equality of the old and new weights.
-      self.assertAllClose(old_weights, new_weights)
-
-  def testConvertTfKerasFunctionalModelWithWeightsSavedAsSavedModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createFunctionalModelWithWeights()
-      old_model_json = json.loads(model.to_json())
-      old_weights = model.get_weights()
-      tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_saved_model_to_tensorflowjs_conversion(
-          self._tmp_dir, tfjs_output_dir)
-
-      # Verify the size of the weight file.
-      weight_path = glob.glob(os.path.join(tfjs_output_dir, 'group*-*'))[0]
-      weight_file_bytes = os.path.getsize(weight_path)
-      model_weight_bytes = sum(w.size * 4 for w in model.get_weights())
-      self.assertEqual(weight_file_bytes, model_weight_bytes)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # Load the converted mode back.
-      model_json_path = os.path.join(tfjs_output_dir, 'model.json')
-      model_prime = keras_tfjs_loader.load_keras_model(model_json_path)
-      new_weights = model_prime.get_weights()
-
-      # Check the equality of the old and new model JSONs.
-      self.assertEqual(old_model_json, json.loads(model_prime.to_json()))
-
-      # Check the equality of the old and new weights.
-      self.assertAllClose(old_weights, new_weights)
-
-  def testConvertTfKerasSequentialSavedAsSavedModelWithQuantization(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-      tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_saved_model_to_tensorflowjs_conversion(
-          self._tmp_dir, tfjs_output_dir,
-          quantization_dtype_map={'uint16': '*'})
-
-      # Verify the size of the weight file.
-      weight_path = glob.glob(os.path.join(tfjs_output_dir, 'group*-*'))[0]
-      weight_file_bytes = os.path.getsize(weight_path)
-      # Each uint16 number has 2 bytes.
-      bytes_per_num = 2
-      model_weight_bytes = sum(
-          w.size * bytes_per_num for w in model.get_weights())
-      self.assertEqual(weight_file_bytes, model_weight_bytes)
-
-  def testConvertTfjsLayersModelToShardedWeights(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-      weights = model.get_weights()
-      total_weight_bytes = sum(np.size(w) for w in weights) * 4
-
-      # Save the keras model to a .h5 file.
-      h5_path = os.path.join(self._tmp_dir, 'model.h5')
-      model.save(h5_path)
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, tfjs_output_dir)
-
-      weight_shard_size_bytes = int(total_weight_bytes * 0.3)
-      # Due to the shard size, there ought to be 4 shards after conversion.
-
-      # Convert the tfjs model to another tfjs model, with a specified weight
-      # shard size.
-      sharded_model_path = os.path.join(self._tmp_dir, 'sharded_model')
-      converter.dispatch_tensorflowjs_to_tensorflowjs_conversion(
-          os.path.join(tfjs_output_dir, 'model.json'), sharded_model_path,
-          weight_shard_size_bytes=weight_shard_size_bytes)
-
-      # Check the number of sharded files and their sizes.
-      weight_files = sorted(
-          glob.glob(os.path.join(sharded_model_path, 'group*.bin')))
-      self.assertEqual(len(weight_files), 4)
-      weight_file_sizes = [os.path.getsize(f) for f in weight_files]
-      self.assertEqual(sum(weight_file_sizes), total_weight_bytes)
-      self.assertEqual(weight_file_sizes[0], weight_file_sizes[1])
-      self.assertEqual(weight_file_sizes[0], weight_file_sizes[2])
-      self.assertLess(weight_file_sizes[3], weight_file_sizes[0])
-
-  def testConvertTfjsLayersModelWithShardSizeGreaterThanTotalWeightSize(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-      weights = model.get_weights()
-      total_weight_bytes = sum(np.size(w) for w in weights) * 4
-
-      # Save the keras model to a .h5 file.
-      h5_path = os.path.join(self._tmp_dir, 'model.h5')
-      model.save(h5_path)
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, tfjs_output_dir)
-
-      weight_shard_size_bytes = int(total_weight_bytes * 2)
-      # Due to the shard size, there ought to be 1 shard after conversion.
-
-      # Convert the tfjs model to another tfjs model, with a specified weight
-      # shard size.
-      sharded_model_path = os.path.join(self._tmp_dir, 'sharded_model')
-      converter.dispatch_tensorflowjs_to_tensorflowjs_conversion(
-          os.path.join(tfjs_output_dir, 'model.json'), sharded_model_path,
-          weight_shard_size_bytes=weight_shard_size_bytes)
-
-      # Check the number of sharded files and their sizes.
-      weight_files = sorted(
-          glob.glob(os.path.join(sharded_model_path, 'group*.bin')))
-      self.assertEqual(len(weight_files), 1)
-      weight_file_sizes = [os.path.getsize(f) for f in weight_files]
-      self.assertEqual(sum(weight_file_sizes), total_weight_bytes)
-
-  def testTfjsLayer2TfjsLayersConversionWithExistingFilePathFails(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-
-      # Save the keras model to a .h5 file.
-      h5_path = os.path.join(self._tmp_dir, 'model.h5')
-      model.save(h5_path)
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, tfjs_output_dir)
-
-      # Convert the tfjs model to another tfjs model, with a specified weight
-      # shard size.
-      sharded_model_path = os.path.join(self._tmp_dir, 'sharded_model')
-      with open(sharded_model_path, 'wt') as f:
-        # Create a fie at the path to elicit the error.
-        f.write('hello')
-      with self.assertRaisesRegexp(  # pylint: disable=deprecated-method
-          ValueError, r'already exists as a file'):
-        converter.dispatch_tensorflowjs_to_tensorflowjs_conversion(
-            os.path.join(tfjs_output_dir, 'model.json'), sharded_model_path)
-
-  def testConvertTfjsLayersModelWithUint16Quantization(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-      weights = model.get_weights()
-      total_weight_bytes = sum(np.size(w) for w in weights) * 4
-
-      # Save the keras model to a .h5 file.
-      h5_path = os.path.join(self._tmp_dir, 'model.h5')
-      model.save(h5_path)
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, tfjs_output_dir)
-
-      weight_shard_size_bytes = int(total_weight_bytes * 2)
-      # Due to the shard size, there ought to be 1 shard after conversion.
-
-      # Convert the tfjs model to another tfjs model, with quantization.
-      sharded_model_path = os.path.join(self._tmp_dir, 'sharded_model')
-      converter.dispatch_tensorflowjs_to_tensorflowjs_conversion(
-          os.path.join(tfjs_output_dir, 'model.json'), sharded_model_path,
-          quantization_dtype_map={'uint16': '*'},
-          weight_shard_size_bytes=weight_shard_size_bytes)
-
-      # Check the number of quantized files and their sizes.
-      weight_files = sorted(
-          glob.glob(os.path.join(sharded_model_path, 'group*.bin')))
-      self.assertEqual(len(weight_files), 1)
-      weight_file_size = os.path.getsize(weight_files[0])
-
-      # The size of the saved weight file should reflect the result of the
-      # uint16 quantization.
-      self.assertEqual(weight_file_size, total_weight_bytes / 2)
-
-  def testConvertTfjsLayersModelWithUint8Quantization(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-      weights = model.get_weights()
-      total_weight_bytes = sum(np.size(w) for w in weights) * 4
-
-      # Save the keras model to a .h5 file.
-      h5_path = os.path.join(self._tmp_dir, 'model.h5')
-      model.save(h5_path)
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, tfjs_output_dir)
-
-      weight_shard_size_bytes = int(total_weight_bytes * 2)
-      # Due to the shard size, there ought to be 1 shard after conversion.
-
-      # Convert the tfjs model to another tfjs model, with quantization.
-      sharded_model_path = os.path.join(self._tmp_dir, 'sharded_model')
-      converter.dispatch_tensorflowjs_to_tensorflowjs_conversion(
-          os.path.join(tfjs_output_dir, 'model.json'), sharded_model_path,
-          quantization_dtype_map={'uint8': '*'},
-          weight_shard_size_bytes=weight_shard_size_bytes)
-
-      # Check the number of quantized files and their sizes.
-      weight_files = sorted(
-          glob.glob(os.path.join(sharded_model_path, 'group*.bin')))
-      self.assertEqual(len(weight_files), 1)
-      weight_file_size = os.path.getsize(weight_files[0])
-
-      # The size of the saved weight file should reflect the result of the
-      # uint16 quantization.
-      self.assertEqual(weight_file_size, total_weight_bytes / 4)
-
-  def testConvertTfjsLayersModelToKerasSavedModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = self._createSimpleSequentialModel()
-
-      # Save the keras model to a .h5 file.
-      h5_path = os.path.join(self._tmp_dir, 'model.h5')
-      model.save(h5_path)
-
-      # Convert the keras SavedModel to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      converter.dispatch_keras_h5_to_tfjs_layers_model_conversion(
-          h5_path, tfjs_output_dir)
-
-    # Convert the tfjs LayersModel to tf.keras SavedModel.
-    keras_saved_model_dir = os.path.join(self._tmp_dir, 'saved_model')
-    converter.dispatch_tensorflowjs_to_keras_saved_model_conversion(
-        os.path.join(tfjs_output_dir, 'model.json'), keras_saved_model_dir)
-
-    # Check the files of the keras SavedModel.
-    files = glob.glob(os.path.join(keras_saved_model_dir, '*'))
-    self.assertIn(os.path.join(keras_saved_model_dir, 'saved_model.pb'), files)
-    self.assertIn(os.path.join(keras_saved_model_dir, 'variables'), files)
-    self.assertIn(os.path.join(keras_saved_model_dir, 'assets'), files)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fold_batch_norms.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fold_batch_norms.py
deleted file mode 100644
index f76079001..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fold_batch_norms.py
+++ /dev/null
@@ -1,272 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Folding the BatchNorm op into Conv2D."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import math
-
-import numpy as np
-from tensorflow.core.framework import attr_value_pb2
-from tensorflow.core.framework import graph_pb2
-from tensorflow.core.framework import node_def_pb2
-from tensorflow.python.framework import tensor_util
-from tensorflow.python.platform import tf_logging
-
-from tensorflowjs.converters import graph_rewrite_util
-
-INPUT_ORDER = {
-    # Order of inputs for BatchNormWithGlobalNormalization.
-    "BatchNormWithGlobalNormalization": [
-        "conv_op", "mean_op", "var_op", "beta_op", "gamma_op"
-    ],
-    # Order of inputs for FusedBatchNorm.
-    "FusedBatchNorm": ["conv_op", "gamma_op", "beta_op", "mean_op", "var_op"],
-    # Order of inputs for FusedBatchNormV3.
-    "FusedBatchNormV3": ["conv_op", "gamma_op", "beta_op", "mean_op", "var_op"]
-}
-# Name of the attribute epsilon value is stored in.
-EPSILON_ATTR = {
-    "BatchNormWithGlobalNormalization": "variance_epsilon",
-    "FusedBatchNorm": "epsilon",
-    "FusedBatchNormV3": "epsilon"
-}
-
-# pylint: disable=R0915
-def fold_batch_norms(input_graph_def):
-  """Removes batch normalization ops by folding them into convolutions.
-
-  Batch normalization during training has multiple dynamic parameters that are
-  updated, but once the graph is finalized these become constants. That means
-  there's an opportunity to reduce the computations down to a scale and
-  addition, rather than the more expensive multiple ops, and even bake the
-  scaling into the convolution weights. This function identifies the typical
-  pattern of batch normalization subgraphs, and performs the transformation to
-  fold the computations down into a simpler form. It currently only supports
-  batch normalization that's performed by the BatchNormWithGlobalNormalization
-  FusedBatchNorm and FusedBatchNormV3 ops, and will need to be extended in the
-  future to handle the newer style.
-
-  Args:
-    input_graph_def: A GraphDef containing a model.
-
-  Returns:
-    Modified graph with BN ops removed, and modified weights.
-
-  Raises:
-    ValueError: If the graph is badly formed with duplicate node names.
-  """
-  input_node_map = {}
-  for node in input_graph_def.node:
-    if node.name not in input_node_map:
-      input_node_map[node.name] = node
-    else:
-      raise ValueError("Duplicate node names detected for ", node.name)
-
-  nodes_to_skip = {}
-  new_ops = []
-  for node in input_graph_def.node:
-    if (node.op not in ("BatchNormWithGlobalNormalization",
-                        "FusedBatchNorm", "FusedBatchNormV3")):
-      continue
-
-    bias = None
-    conv_op = graph_rewrite_util.node_from_map(
-        input_node_map,
-        node.input[INPUT_ORDER[node.op].index("conv_op")])
-    # There might be an Add/BiasAdd op between the conv and the batchnorm,
-    # which we can fold into the mean param of the batchnorm.
-    if conv_op.op in ['BiasAdd', 'Add', 'AddV2']:
-      add_op = conv_op
-      # Follow the first input of the add to get to the conv.
-      conv_op = graph_rewrite_util.node_from_map(
-          input_node_map, add_op.input[0])
-      bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[1])
-      if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
-        # Follow the second input of the add to get to the conv.
-        conv_op = graph_rewrite_util.node_from_map(
-            input_node_map, add_op.input[1])
-        bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[0])
-    if bias and bias.op != 'Const':
-      tf_logging.warning("The bias %s after the conv %s was not a constant. "
-                         "Maybe because freeze_graph wasn't "
-                         "run first?" % (bias.name, conv_op.name))
-      continue
-    if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
-      tf_logging.warning("Didn't find expected Conv2D or DepthwiseConv2dNative"
-                         " input to '%s'" % node.name)
-      continue
-    weights_op = graph_rewrite_util.node_from_map(
-        input_node_map, conv_op.input[1])
-    if weights_op.op != "Const":
-      tf_logging.warning("Didn't find expected conv Constant input to '%s',"
-                         " found %s instead. Maybe because freeze_graph wasn't"
-                         " run first?" % (conv_op.name, weights_op))
-      continue
-    weights = graph_rewrite_util.values_from_const(weights_op)
-    if conv_op.op == "Conv2D":
-      channel_count = weights.shape[3]
-    elif conv_op.op == "DepthwiseConv2dNative":
-      channel_count = weights.shape[2] * weights.shape[3]
-
-    mean_op = graph_rewrite_util.node_from_map(
-        input_node_map,
-        node.input[INPUT_ORDER[node.op].index("mean_op")])
-    if mean_op.op != "Const":
-      tf_logging.warning("Didn't find expected mean Constant input to '%s',"
-                         " found %s instead. Maybe because freeze_graph wasn't"
-                         " run first?" % (node.name, mean_op))
-      continue
-    mean_value = graph_rewrite_util.values_from_const(mean_op)
-    if bias is not None:
-      # Adjust the mean of the batchnorm based on the add op in-between the conv
-      # and the batchnorm.
-      mean_value = mean_value - graph_rewrite_util.values_from_const(bias)
-    if mean_value.shape != (channel_count,):
-      tf_logging.warning("Incorrect shape for mean, found %s, expected %s,"
-                         " for node %s" % (str(mean_value.shape), str(
-                             (channel_count,)), node.name))
-      continue
-
-    var_op = graph_rewrite_util.node_from_map(
-        input_node_map,
-        node.input[INPUT_ORDER[node.op].index("var_op")])
-    if var_op.op != "Const":
-      tf_logging.warning("Didn't find expected var Constant input to '%s',"
-                         " found %s instead. Maybe because freeze_graph wasn't"
-                         " run first?" % (node.name, var_op))
-      continue
-    var_value = graph_rewrite_util.values_from_const(var_op)
-    if var_value.shape != (channel_count,):
-      tf_logging.warning("Incorrect shape for var, found %s, expected %s,"
-                         " for node %s" % (str(var_value.shape), str(
-                             (channel_count,)), node.name))
-      continue
-
-    beta_op = graph_rewrite_util.node_from_map(
-        input_node_map,
-        node.input[INPUT_ORDER[node.op].index("beta_op")])
-    if beta_op.op != "Const":
-      tf_logging.warning("Didn't find expected beta Constant input to '%s',"
-                         " found %s instead. Maybe because freeze_graph wasn't"
-                         " run first?" % (node.name, beta_op))
-      continue
-    beta_value = graph_rewrite_util.values_from_const(beta_op)
-    if beta_value.shape != (channel_count,):
-      tf_logging.warning("Incorrect shape for beta, found %s, expected %s,"
-                         " for node %s" % (str(beta_value.shape), str(
-                             (channel_count,)), node.name))
-      continue
-
-    gamma_op = graph_rewrite_util.node_from_map(
-        input_node_map,
-        node.input[INPUT_ORDER[node.op].index("gamma_op")])
-    if gamma_op.op != "Const":
-      tf_logging.warning("Didn't find expected gamma Constant input to '%s',"
-                         " found %s instead. Maybe because freeze_graph wasn't"
-                         " run first?" % (node.name, gamma_op))
-      continue
-    gamma_value = graph_rewrite_util.values_from_const(gamma_op)
-    if gamma_value.shape != (channel_count,):
-      tf_logging.warning("Incorrect shape for gamma, found %s, expected %s,"
-                         " for node %s" % (str(gamma_value.shape), str(
-                             (channel_count,)), node.name))
-      continue
-
-    variance_epsilon_value = node.attr[EPSILON_ATTR[node.op]].f
-    nodes_to_skip[node.name] = True
-    nodes_to_skip[weights_op.name] = True
-    nodes_to_skip[conv_op.name] = True
-    if bias is not None:
-      nodes_to_skip[add_op.name] = True
-
-    if scale_after_normalization(node):
-      scale_value = (
-          (1.0 / np.vectorize(math.sqrt)(var_value + variance_epsilon_value)) *
-          gamma_value)
-    else:
-      scale_value = (
-          1.0 / np.vectorize(math.sqrt)(var_value + variance_epsilon_value))
-    offset_value = (-mean_value * scale_value) + beta_value
-    scaled_weights = np.copy(weights)
-    it = np.nditer(
-        scaled_weights, flags=["multi_index"], op_flags=["readwrite"])
-    if conv_op.op == "Conv2D":
-      while not it.finished:
-        current_scale = scale_value[it.multi_index[3]]
-        it[0] *= current_scale
-        it.iternext()
-    elif conv_op.op == "DepthwiseConv2dNative":
-      channel_multiplier = weights.shape[3]
-      while not it.finished:
-        current_scale = scale_value[it.multi_index[2] * channel_multiplier +
-                                    it.multi_index[3]]
-        it[0] *= current_scale
-        it.iternext()
-    scaled_weights_op = node_def_pb2.NodeDef()
-    scaled_weights_op.op = "Const"
-    scaled_weights_op.name = conv_op.name + '_weights'
-    scaled_weights_op.attr["dtype"].CopyFrom(weights_op.attr["dtype"])
-    scaled_weights_op.attr["value"].CopyFrom(
-        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
-            scaled_weights, weights.dtype.type, weights.shape)))
-    # Replace the weights node with scaled weights node
-    for i, weights_node in enumerate(conv_op.input):
-      if weights_node == weights_op.name:
-        conv_op.input[i] = scaled_weights_op.name
-
-    new_conv_op = node_def_pb2.NodeDef()
-    new_conv_op.CopyFrom(conv_op)
-    offset_op = node_def_pb2.NodeDef()
-    offset_op.op = "Const"
-    offset_op.name = conv_op.name + "_bn_offset"
-    offset_op.attr["dtype"].CopyFrom(mean_op.attr["dtype"])
-    offset_op.attr["value"].CopyFrom(
-        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
-            offset_value, mean_value.dtype.type, offset_value.shape)))
-    bias_add_op = node_def_pb2.NodeDef()
-    bias_add_op.op = "BiasAdd"
-    bias_add_op.name = node.name
-    bias_add_op.attr["T"].CopyFrom(conv_op.attr["T"])
-    bias_add_op.attr["data_format"].CopyFrom(conv_op.attr["data_format"])
-    bias_add_op.input.extend([new_conv_op.name, offset_op.name])
-    new_ops.extend([scaled_weights_op, new_conv_op, offset_op, bias_add_op])
-
-  result_graph_def = graph_pb2.GraphDef()
-  for node in input_graph_def.node:
-    if node.name in nodes_to_skip:
-      continue
-    new_node = node_def_pb2.NodeDef()
-    new_node.CopyFrom(node)
-    retained_input = []
-    for input_node in new_node.input:
-      if not input_node.startswith('^') or input_node[1:] not in nodes_to_skip:
-        retained_input.append(input_node)
-    new_node.input[:] = retained_input
-
-    result_graph_def.node.extend([new_node])
-
-  result_graph_def.node.extend(new_ops)
-  result_graph_def.library.CopyFrom(input_graph_def.library)
-  result_graph_def.versions.CopyFrom(input_graph_def.versions)
-  return result_graph_def
-
-# Whether to scale by gamma after normalization.
-def scale_after_normalization(node):
-  if node.op == "BatchNormWithGlobalNormalization":
-    return node.attr["scale_after_normalization"].b
-  return True
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fold_batch_norms_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fold_batch_norms_test.py
deleted file mode 100644
index c641e0abb..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fold_batch_norms_test.py
+++ /dev/null
@@ -1,269 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for batch norm folding."""
-
-import os
-import shutil
-import tempfile
-
-import numpy as np
-import tensorflow.compat.v1 as tf1
-import tensorflow.compat.v2 as tf
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import importer
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import test_util
-from tensorflow.python.ops import gen_nn_ops
-from tensorflow.python.ops import nn_ops
-from tensorflowjs.converters import fold_batch_norms
-
-class FoldBatchNormsTest(tf.test.TestCase):
-  def setUp(self):
-    super(FoldBatchNormsTest, self).setUp()
-    self._tmp_dir = tempfile.mkdtemp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(FoldBatchNormsTest, self).tearDown()
-
-  def testFoldBatchNorms(self):
-    with tf1.Session() as sess:
-      inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6]
-      input_op = constant_op.constant(
-          np.array(inputs), shape=[1, 1, 6, 2], dtype=dtypes.float32)
-      weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4]
-      weights_op = constant_op.constant(
-          np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32)
-      conv_op = nn_ops.conv2d(
-          input_op, weights_op, [1, 1, 1, 1], padding="SAME", name="conv_op")
-      mean_op = constant_op.constant(
-          np.array([10, 20]), shape=[2], dtype=dtypes.float32)
-      variance_op = constant_op.constant(
-          np.array([0.25, 0.5]), shape=[2], dtype=dtypes.float32)
-      beta_op = constant_op.constant(
-          np.array([0.1, 0.6]), shape=[2], dtype=dtypes.float32)
-      gamma_op = constant_op.constant(
-          np.array([1.0, 2.0]), shape=[2], dtype=dtypes.float32)
-      test_util.set_producer_version(ops.get_default_graph(), 8)
-      gen_nn_ops._batch_norm_with_global_normalization(
-          conv_op,
-          mean_op,
-          variance_op,
-          beta_op,
-          gamma_op,
-          0.00001,
-          False,
-          name="output")
-      original_graph_def = sess.graph_def
-      original_result = sess.run(["output:0"])
-    optimized_graph_def = fold_batch_norms.fold_batch_norms(
-        original_graph_def)
-    with tf1.Session() as sess:
-      _ = importer.import_graph_def(
-          optimized_graph_def, input_map={}, name="optimized")
-      optimized_result = sess.run(["optimized/output:0"])
-
-    self.assertAllClose(original_result, optimized_result)
-
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("BatchNormWithGlobalNormalization", node.op)
-
-  def testFoldFusedBatchNorms(self):
-    for data_format, conv2d_func in [
-        ("NHWC", nn_ops.conv2d), ("NCHW", nn_ops.conv2d),
-        ("NHWC", nn_ops.depthwise_conv2d_native),
-        ("NCHW", nn_ops.depthwise_conv2d_native)
-    ]:
-      with tf1.Session() as sess:
-        inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6]
-        input_op = constant_op.constant(
-            np.array(inputs),
-            shape=[1, 1, 6, 2] if data_format == "NHWC" else [1, 2, 1, 6],
-            dtype=dtypes.float32)
-        if conv2d_func == nn_ops.conv2d:
-          weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4]
-          weights_op = constant_op.constant(
-              np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32)
-        else:
-          weights = [1, 2, 0.3, 0.4]
-          weights_op = constant_op.constant(
-              np.array(weights), shape=[1, 2, 2, 1], dtype=dtypes.float32)
-        conv_op = conv2d_func(
-            input_op,
-            weights_op, [1, 1, 1, 1],
-            padding="SAME",
-            data_format=data_format,
-            name="conv_op")
-        mean_op = constant_op.constant(
-            np.array([10, 20]), shape=[2], dtype=dtypes.float32)
-        variance_op = constant_op.constant(
-            np.array([0.25, 0.5]), shape=[2], dtype=dtypes.float32)
-        beta_op = constant_op.constant(
-            np.array([0.1, 0.6]), shape=[2], dtype=dtypes.float32)
-        gamma_op = constant_op.constant(
-            np.array([1.0, 2.0]), shape=[2], dtype=dtypes.float32)
-        ops.get_default_graph().graph_def_versions.producer = 9
-        gen_nn_ops._fused_batch_norm(
-            conv_op,
-            gamma_op,
-            beta_op,
-            mean_op,
-            variance_op,
-            0.00001,
-            is_training=False,
-            data_format=data_format,
-            name="output")
-        original_graph_def = sess.graph_def
-        original_result = sess.run(["output:0"])
-      optimized_graph_def = fold_batch_norms.fold_batch_norms(
-          original_graph_def)
-    with tf1.Session() as sess:
-      _ = importer.import_graph_def(
-          optimized_graph_def, input_map={}, name="optimized")
-      optimized_result = sess.run(["optimized/output:0"])
-
-      self.assertAllClose(
-          original_result, optimized_result, rtol=1e-04, atol=1e-06)
-
-      for node in optimized_graph_def.node:
-        self.assertNotEqual("FusedBatchNorm", node.op)
-
-  def testFoldFusedBatchNormsV3(self):
-    for data_format, conv2d_func in [
-        ("NHWC", nn_ops.conv2d), ("NCHW", nn_ops.conv2d),
-        ("NHWC", nn_ops.depthwise_conv2d_native),
-        ("NCHW", nn_ops.depthwise_conv2d_native)
-    ]:
-      with tf1.Session() as sess:
-        _generate_fused_batchnorm(data_format, conv2d_func)
-        original_graph_def = sess.graph_def
-        original_result = sess.run(["output:0"])
-      optimized_graph_def = fold_batch_norms.fold_batch_norms(
-          original_graph_def)
-    with tf1.Session() as sess:
-      _ = importer.import_graph_def(
-          optimized_graph_def, input_map={}, name="optimized")
-      optimized_result = sess.run(["optimized/output:0"])
-
-      self.assertAllClose(
-          original_result, optimized_result, rtol=1e-04, atol=1e-06)
-
-      for node in optimized_graph_def.node:
-        self.assertNotEqual("FusedBatchNormV3", node.op)
-
-
-  def testFoldFusedBatchNormWithBias(self):
-    for data_format, conv2d_func in [
-        ("NHWC", nn_ops.conv2d),
-        ("NHWC", nn_ops.depthwise_conv2d_native),
-    ]:
-      graph = tf1.Graph()
-      with tf1.Session(graph=graph) as sess:
-        count = 1
-        add_bias = True
-        _generate_fused_batchnorm(data_format, conv2d_func, count, add_bias)
-        original_graph_def = sess.graph_def
-        original_result = sess.run(["output:0"])
-      optimized_graph_def = fold_batch_norms.fold_batch_norms(
-          original_graph_def)
-    with tf1.Session() as sess:
-      _ = importer.import_graph_def(
-          optimized_graph_def, input_map={}, name="optimized")
-      optimized_result = sess.run(["optimized/output:0"])
-
-      self.assertAllClose(
-          original_result, optimized_result, rtol=1e-04, atol=1e-06)
-
-      bias_nodes = [
-          node for node in optimized_graph_def.node if node.op == 'BiasAdd'
-      ]
-      self.assertEqual(len(bias_nodes), 1)
-      for node in optimized_graph_def.node:
-        self.assertNotEqual("FusedBatchNormV3", node.op)
-
-  def testFoldFusedBatchNormsWithSharedWeights(self):
-    for data_format, conv2d_func in [
-        ("NHWC", nn_ops.conv2d), ("NCHW", nn_ops.conv2d),
-        ("NHWC", nn_ops.depthwise_conv2d_native),
-        ("NCHW", nn_ops.depthwise_conv2d_native)
-    ]:
-      with tf1.Session() as sess:
-        _generate_fused_batchnorm(data_format, conv2d_func, 2)
-        original_graph_def = sess.graph_def
-        original_result = sess.run(["output:0"])
-      optimized_graph_def = fold_batch_norms.fold_batch_norms(
-          original_graph_def)
-    with tf1.Session() as sess:
-      _ = importer.import_graph_def(
-          optimized_graph_def, input_map={}, name="optimized")
-      optimized_result = sess.run(["optimized/output:0"])
-
-      self.assertAllClose(
-          original_result, optimized_result, rtol=1e-04, atol=1e-06)
-
-      for node in optimized_graph_def.node:
-        self.assertNotEqual("FusedBatchNormV3", node.op)
-
-def _generate_fused_batchnorm(data_format, conv2d_func, count=1,
-                              add_bias=False):
-  inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6]
-  input_op = constant_op.constant(
-      np.array(inputs),
-      shape=[1, 1, 6, 2] if data_format == "NHWC" else [1, 2, 1, 6],
-      dtype=dtypes.float32)
-  if conv2d_func == nn_ops.conv2d: # pylint: disable=W0143
-    weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4]
-    weights_op = constant_op.constant(
-        np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32)
-  else:
-    weights = [1, 2, 0.3, 0.4]
-    weights_op = constant_op.constant(
-        np.array(weights), shape=[1, 2, 2, 1], dtype=dtypes.float32)
-  mean_op = constant_op.constant(
-      np.array([10, 20]), shape=[2], dtype=dtypes.float32)
-  variance_op = constant_op.constant(
-      np.array([0.25, 0.5]), shape=[2], dtype=dtypes.float32)
-  beta_op = constant_op.constant(
-      np.array([0.1, 0.6]), shape=[2], dtype=dtypes.float32)
-  gamma_op = constant_op.constant(
-      np.array([1.0, 2.0]), shape=[2], dtype=dtypes.float32)
-  ops.get_default_graph().graph_def_versions.producer = 9
-  for _ in range(count):
-    conv_op = conv2d_func(
-        input_op,
-        weights_op, [1, 1, 1, 1],
-        padding="SAME",
-        data_format=data_format,
-        name="conv_op")
-    if add_bias:
-      out_channels = conv_op.shape[3]
-      bias = constant_op.constant(
-          np.array([1.0]*out_channels),
-          shape=[out_channels], dtype=dtypes.float32)
-      conv_op = nn_ops.bias_add(conv_op, bias)
-    gen_nn_ops.fused_batch_norm_v3(
-        conv_op,
-        gamma_op,
-        beta_op,
-        mean_op,
-        variance_op,
-        0.00001,
-        is_training=False,
-        data_format=data_format,
-        name="output")
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_depthwise_conv2d.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_depthwise_conv2d.py
deleted file mode 100644
index 8276f80c9..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_depthwise_conv2d.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the 'License');
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an 'AS IS' BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""
- This transformation rule tries to identify following transformation
-  DepthwiseConv2dNative + BiasAdd + Activation => FusedDepthwiseConv2dNative
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflowjs.converters import graph_rewrite_util
-
-def _is_supported_activation(node):
-  return node.op == 'Relu' or node.op == 'Relu6' or node.op == 'Elu'
-
-def _find_contraction_with_activation(node, node_map):
-  if not _is_supported_activation(node):
-    return False
-
-  # And input to the activation node must match ContractionWithBiasAdd pattern.
-  if len(node.input) != 1:
-    return False
-
-  conv2d_node = graph_rewrite_util.node_from_map(node_map, node.input[0])
-  if conv2d_node.op != 'DepthwiseConv2dNative':
-    return False
-
-  return {'contraction': conv2d_node, 'bias': None, 'activation': node}
-
-def _find_contraction_with_bias(node, node_map):
-  if node.op != 'BiasAdd':
-    return False
-
-  # Input to the BiasAdd must be a DepthwiseConv2dNative.
-  if not node.input:
-    return False
-
-  conv2d_node = graph_rewrite_util.node_from_map(node_map, node.input[0])
-  if conv2d_node.op != 'DepthwiseConv2dNative':
-    return False
-
-  return {'contraction': conv2d_node, 'bias': node, 'activation': None}
-
-def _find_contraction_with_bias_and_activation(node, node_map):
-  if not _is_supported_activation(node):
-    return False
-
-  # And input to the activation node must match ContractionWithBiasAdd pattern.
-  if len(node.input) != 1:
-    return False
-
-  bias_add = graph_rewrite_util.node_from_map(node_map, node.input[0])
-
-  match = _find_contraction_with_bias(bias_add, node_map)
-  if not match:
-    return False
-
-  match['activation'] = node
-  return match
-
-def _add_fused_contraction_node(contraction, bias_add, activation,
-                                inputs_to_remove, nodes_to_skip):
-  fused_op = contraction
-  fused_op.op = graph_rewrite_util.FUSED_DEPTHWISE_CONV2D
-  fused_op.attr['num_args'].i = 0
-  if bias_add:
-    fused_op.input.extend([bias_add.input[1]])
-
-    fused_op.attr['fused_ops'].list.s.extend([b'BiasAdd'])
-    fused_op.attr['num_args'].i = fused_op.attr['num_args'].i + 1
-    bias_add.input[:] = [contraction.name]
-    inputs_to_remove.append(bias_add)
-    nodes_to_skip[bias_add.name] = True
-  else:
-    fused_op.attr['fused_ops'].list.s.extend([b'NoOp'])
-
-  if activation:
-    fused_op.attr['fused_ops'].list.s.extend([activation.op.encode('ascii')])
-    nodes_to_skip[activation.name] = True
-    activation.input[:] = [contraction.name]
-    inputs_to_remove.append(activation)
-
-
-def fuse_depthwise_conv2d(input_graph_def):
-  """Modifies the provided graph by fusing a set of ops into a single
-  _FusedDepthwiseConv2d op.
-
-  DepthwiseConv2dNative + BiasAdd + Activation => _FusedDepthwiseConv2dNative
-
-  Args:
-    input_graph_def: A GraphDef containing a model.
-
-  Returns:
-    Modified graph with FusedDepthwiseConv2dNative ops generated, and modified
-    weights.
-
-  Raises:
-    ValueError: If the graph is badly formed with duplicate node names.
-  """
-  # Two passes approach, first find pattern of
-  #   DepthwiseConv2dNative + BiasAdd + Activation
-  # Then find pattern of
-  #   DepthwiseConv2dNative + BiasAdd
-  graph_def = _fuse_depthwise_conv2d_with_match_function(
-      input_graph_def, _find_contraction_with_bias_and_activation)
-  graph_def = _fuse_depthwise_conv2d_with_match_function(
-      graph_def, _find_contraction_with_bias)
-  graph_def = _fuse_depthwise_conv2d_with_match_function(
-      graph_def, _find_contraction_with_activation)
-  return graph_def
-
-def _fuse_depthwise_conv2d_with_match_function(input_graph_def, match_function):
-  """Modifies the provided graph by fusing a set of ops into a single
-  _FusedDepthwiseConv2d op.
-
-  DepthwiseConv2dNative + BiasAdd + Activation => _FusedDepthwiseConv2dNative
-
-  Args:
-    input_graph_def: A GraphDef containing a model.
-    match_function: A Function that matches the pattern and return a dict of
-      contraction, bias_add and activation nodes.
-
-  Returns:
-    Modified graph with FusedDepthwiseConv2dNative ops generated, and modified
-    weights.
-
-  Raises:
-    ValueError: If the graph is badly formed with duplicate node names.
-  """
-  input_node_map = {}
-  for node in input_graph_def.node:
-    if node.name not in input_node_map:
-      input_node_map[node.name] = node
-    else:
-      raise ValueError('Duplicate node names detected for ', node.name)
-
-  nodes_to_skip = {}
-  inputs_to_remove = []
-  for node in input_graph_def.node:
-    nodes = match_function(node, input_node_map)
-    if nodes:
-      _add_fused_contraction_node(nodes['contraction'], nodes['bias'],
-                                  nodes['activation'], inputs_to_remove,
-                                  nodes_to_skip)
-
-  if nodes_to_skip or inputs_to_remove:
-    return graph_rewrite_util.cleanup_graph_def(
-        input_graph_def, nodes_to_skip, inputs_to_remove)
-
-  # No pattern detected
-  return input_graph_def
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_depthwise_conv2d_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_depthwise_conv2d_test.py
deleted file mode 100644
index b86850df7..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_depthwise_conv2d_test.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for depthwise conv2d op fusing."""
-
-import os
-import shutil
-import tempfile
-
-import tensorflow.compat.v2 as tf
-
-from tensorflowjs.converters import fuse_depthwise_conv2d
-from tensorflowjs.converters import graph_rewrite_util
-from tensorflowjs.converters import tf_saved_model_conversion_v2
-
-
-class FuseDepthwiseConv2dTest(tf.test.TestCase):
-  def setUp(self):
-    super(FuseDepthwiseConv2dTest, self).setUp()
-    self._tmp_dir = tempfile.mkdtemp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(FuseDepthwiseConv2dTest, self).tearDown()
-
-  def testFuseDepthwiseConv2dNativeWithBias(self):
-    layers = [
-        tf.keras.layers.DepthwiseConv2D(
-            1, bias_initializer=tf.initializers.constant(0.25))
-    ]
-    model = tf.keras.Sequential(layers)
-    tf.keras.backend.set_learning_phase(0)
-    input_tensor = tf.constant([1.0, 1.0], shape=[1, 1, 1, 2])
-
-    @tf.function
-    def execute_model(tensor):
-      return model(tensor)
-
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        execute_model.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-
-    optimized_graph_def = fuse_depthwise_conv2d.fuse_depthwise_conv2d(graph_def)
-
-    depthwise_conv2d_count = 0
-    depthwise_conv2d = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("BiasAdd", node.op)
-      self.assertNotEqual("DepthwiseConv2dNative", node.op)
-      if node.op == graph_rewrite_util.FUSED_DEPTHWISE_CONV2D:
-        depthwise_conv2d_count += 1
-        depthwise_conv2d = node
-    self.assertEqual(depthwise_conv2d_count, 1)
-    self.assertEqual(depthwise_conv2d.attr['fused_ops'].list.s, [b'BiasAdd'])
-    self.assertEqual(depthwise_conv2d.attr['num_args'].i, 1)
-
-  def testFuseDepthwiseConv2dNativeWithBiasAndActivation(self):
-    layers = [
-        tf.keras.layers.DepthwiseConv2D(
-            1, bias_initializer=tf.initializers.constant(0.25)),
-        tf.keras.layers.ReLU()
-    ]
-    model = tf.keras.Sequential(layers)
-    tf.keras.backend.set_learning_phase(0)
-    input_tensor = tf.constant([1.0, 1.0], shape=[1, 1, 1, 2])
-
-    @tf.function
-    def execute_model(tensor):
-      return model(tensor)
-
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        execute_model.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-
-    optimized_graph_def = fuse_depthwise_conv2d.fuse_depthwise_conv2d(graph_def)
-    depthwise_conv2d_count = 0
-    depthwise_conv2d = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("BiasAdd", node.op)
-      self.assertNotEqual("DepthwiseConv2dNative", node.op)
-      self.assertNotEqual("Relu", node.op)
-      if node.op == graph_rewrite_util.FUSED_DEPTHWISE_CONV2D:
-        depthwise_conv2d_count += 1
-        depthwise_conv2d = node
-    self.assertEqual(depthwise_conv2d_count, 1)
-    self.assertEqual(
-        depthwise_conv2d.attr['fused_ops'].list.s, [b'BiasAdd', b'Relu'])
-    self.assertEqual(depthwise_conv2d.attr['num_args'].i, 1)
-
-  def testFuseDepthwiseConv2dNativeWithActivation(self):
-    layers = [
-        tf.keras.layers.DepthwiseConv2D(1, use_bias=False),
-        tf.keras.layers.ReLU()
-    ]
-    model = tf.keras.Sequential(layers)
-    tf.keras.backend.set_learning_phase(0)
-    input_tensor = tf.constant([1.0, 1.0], shape=[1, 1, 1, 2])
-
-    @tf.function
-    def execute_model(tensor):
-      return model(tensor)
-
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        execute_model.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-
-    optimized_graph_def = fuse_depthwise_conv2d.fuse_depthwise_conv2d(graph_def)
-    depthwise_conv2d_count = 0
-    depthwise_conv2d = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("BiasAdd", node.op)
-      self.assertNotEqual("DepthwiseConv2dNative", node.op)
-      self.assertNotEqual("Relu", node.op)
-      if node.op == graph_rewrite_util.FUSED_DEPTHWISE_CONV2D:
-        depthwise_conv2d_count += 1
-        depthwise_conv2d = node
-    self.assertEqual(depthwise_conv2d_count, 1)
-    self.assertEqual(
-        depthwise_conv2d.attr['fused_ops'].list.s, [b'NoOp', b'Relu'])
-    self.assertEqual(depthwise_conv2d.attr['num_args'].i, 0)
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_prelu.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_prelu.py
deleted file mode 100644
index 764bf56cc..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_prelu.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the 'License');
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an 'AS IS' BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""
- This transformation rule tries to identify the PRelu structure generated by
- Keras, and convert it to a single op.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.core.framework import attr_value_pb2
-from tensorflow.python.framework import tensor_util
-
-from tensorflowjs.converters import graph_rewrite_util
-
-def fuse_ops_for_prelu(input_graph_def):
-  """Modifies the provided graph by fusing a set of ops into a single Prelu op.
-  The formula of PReLU is:
-  f(x) = alpha * x for x < 0, f(x) = x for x >= 0.
-
-  `x` is the input, and `alpha` is a trainable tensor which can be broadcasted
-  to the shape of `x`.
-
-  There's no native PRelu op in TensorFlow, so Keras generates the following
-  structure which does the equivalent calculation:
-  f(x) = Relu(x) + (-alpha * Relu(-x))
-
-  Practically, alpha is always a constant in the inference graph, and grappler
-  can have other graph transformations which fold the activation functions to
-  other ops. Therefore, we're looking for the structure:
-
-  f(x) = Relu(x) + (negative_alpha * Neg(x, activation=Relu))
-
-  Args:
-    input_graph_def: A GraphDef containing a model.
-
-  Returns:
-    Modified graph with Prelu ops generated, and modified weights.
-
-  Raises:
-    ValueError: If the graph is badly formed with duplicate node names.
-  """
-  input_node_map = {}
-  for node in input_graph_def.node:
-    if node.name not in input_node_map:
-      input_node_map[node.name] = node
-    else:
-      raise ValueError('Duplicate node names detected for ', node.name)
-
-  nodes_to_skip = {}
-  inputs_to_remove = []
-  updated_alpha = []
-  for node in input_graph_def.node:
-    if (node.op not in ('Add', 'AddV2') or len(node.input) != 2):
-      continue
-
-    relu_input_op = graph_rewrite_util.node_from_map(
-        input_node_map, node.input[0])
-    if (not relu_input_op or relu_input_op.op != 'Relu'):
-      continue
-
-    mul_op = graph_rewrite_util.node_from_map(input_node_map, node.input[1])
-    if (not mul_op or mul_op.op != 'Mul'):
-      continue
-
-    neg_alpha_op = None
-    for name in mul_op.input:
-      op = graph_rewrite_util.node_from_map(input_node_map, name)
-      if op.op == 'Const':
-        neg_alpha_op = op
-        break
-
-    if not neg_alpha_op:
-      continue
-
-    alpha_tensor_name = neg_alpha_op.name
-
-    relu_neg_input_op = None
-    for name in mul_op.input:
-      op = graph_rewrite_util.node_from_map(input_node_map, name)
-      if op.op == 'Relu':
-        relu_neg_input_op = op
-        break
-
-    if (not relu_neg_input_op or len(relu_neg_input_op.input) != 1 or
-        relu_neg_input_op.op != 'Relu'):
-      continue
-
-    # This detects a Neg op followed by a separated Relu op.
-    neg_input_op = graph_rewrite_util.node_from_map(
-        input_node_map, relu_neg_input_op.input[0])
-    if (not neg_input_op or len(neg_input_op.input) != 1 or
-        neg_input_op.op != 'Neg'):
-      continue
-    final_input_op = neg_input_op
-
-    if relu_input_op.input[0] != final_input_op.input[0]:
-      continue
-
-    relu_input_op.op = 'Prelu'
-    relu_input_op.input.extend([alpha_tensor_name])
-    # Remove the T attr that is defined in Relu op, since our custom Prelu op
-    # definition does not have that.
-    del relu_input_op.attr['T']
-
-    node.op = 'Identity'
-    del node.input[:]
-    node.input.append(relu_input_op.name)
-    _create_alpha_node(neg_alpha_op, updated_alpha)
-
-    nodes_to_skip[mul_op.name] = True
-    nodes_to_skip[relu_neg_input_op.name] = True
-    nodes_to_skip[neg_input_op.name] = True
-    nodes_to_skip[node.name] = True
-    inputs_to_remove.append(node)
-
-  return graph_rewrite_util.cleanup_graph_def(
-      input_graph_def, nodes_to_skip, inputs_to_remove)
-
-def _create_alpha_node(neg_alpha_op, updated_alpha):
-  if neg_alpha_op.name not in updated_alpha:
-    alpha_value = -graph_rewrite_util.values_from_const(neg_alpha_op)
-    neg_alpha_op.attr['value'].CopyFrom(
-        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
-            alpha_value, alpha_value.dtype.type, alpha_value.shape)))
-    updated_alpha.append(neg_alpha_op.name)
-
-def fuse_prelu_with_fused_conv2d_or_matmul(input_graph_def):
-  """Tensorflow does not support Prelu op, and the grappler remap optimizer
-  will not fuse the prelu op with _FusedConv2D op. This method searches for
-  the pattern and fuse the (_FusedConv2D||FusedDepthwiseConv2dNative + Prelu)
-  nodes into a single _FusedConv2D||FusedDepthwiseConv2dNative op with
-  activation information.
-
-  Args:
-    input_graph_def: A GraphDef containing a model.
-
-  Returns:
-    Modified graph with Prelu ops fused with _FusedConv2D or
-    FusedDepthwiseConv2dNative as activation function
-
-  Raises:
-    ValueError: If the graph is badly formed with duplicate node names.
-  """
-  input_node_map = {}
-  nodes_to_skip = {}
-  inputs_to_remove = []
-  for node in input_graph_def.node:
-    if node.name not in input_node_map:
-      input_node_map[node.name] = node
-    else:
-      raise ValueError('Duplicate node names detected for ', node.name)
-
-  for node in input_graph_def.node:
-    if node.op != 'Prelu':
-      continue
-
-    fused_op = graph_rewrite_util.node_from_map(
-        input_node_map, node.input[0])
-    if (not fused_op or
-        (fused_op.op != '_FusedConv2D'
-         and fused_op.op != '_FusedMatMul'
-         and fused_op.op != 'FusedDepthwiseConv2dNative') or
-        len(fused_op.attr['fused_ops'].list.s) > 1):
-      continue
-
-    alpha_tensor_name = node.input[1]
-
-    fused_op.input.extend([alpha_tensor_name])
-    fused_op.attr['fused_ops'].list.s.extend([b'Prelu'])
-    fused_op.attr['num_args'].i = fused_op.attr['num_args'].i + 1
-    node.op = 'Identity'
-    node.input[:] = [node.input[0]]
-    nodes_to_skip[node.name] = True
-    inputs_to_remove.append(node)
-
-  return graph_rewrite_util.cleanup_graph_def(
-      input_graph_def, nodes_to_skip, inputs_to_remove)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_prelu_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_prelu_test.py
deleted file mode 100644
index ffceb14d1..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/fuse_prelu_test.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for prelu op fusing."""
-
-import os
-import shutil
-import tempfile
-
-import tensorflow.compat.v2 as tf
-from tensorflow.core.protobuf import config_pb2
-from tensorflow.core.protobuf import meta_graph_pb2
-from tensorflow.python.eager import def_function
-from tensorflow.python.framework import constant_op
-from tensorflow.python.ops import variables
-from tensorflow.python.trackable import autotrackable
-
-from tensorflowjs.converters import fuse_depthwise_conv2d
-from tensorflowjs.converters import fuse_prelu
-from tensorflowjs.converters import tf_saved_model_conversion_v2
-from tensorflowjs.converters import graph_rewrite_util
-
-class FusePreluTest(tf.test.TestCase):
-  def setUp(self):
-    super(FusePreluTest, self).setUp()
-    self._tmp_dir = tempfile.mkdtemp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(FusePreluTest, self).tearDown()
-
-  def testFusePrelu(self):
-    layers = [
-        tf.keras.layers.PReLU(
-            alpha_initializer=tf.initializers.constant(0.25)),
-        tf.keras.layers.PReLU(
-            alpha_initializer=tf.initializers.constant(0.25))
-    ]
-    model = tf.keras.Sequential(layers)
-    tf.keras.backend.set_learning_phase(0)
-    input_tensor = tf.constant([1.0, 1.0])
-
-    @tf.function
-    def execute_model(tensor):
-      return model(tensor)
-
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        execute_model.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-    for node in graph_def.node:
-      if node.op == 'Conv2D':
-        node.device = "/CPU:0"
-
-    config = config_pb2.ConfigProto()
-    rewriter_config = config.graph_options.rewrite_options
-    rewriter_config.optimizers[:] = [
-        'pruning', 'constfold', 'arithmetic', 'dependency', 'pruning',
-        'remap', 'constfold', 'arithmetic', 'dependency'
-    ]
-
-    for output in ['Identity']:
-      graph.add_to_collection('train_op', graph.get_operation_by_name(output))
-
-    signature = meta_graph_pb2.SignatureDef()
-    graph_def = tf_saved_model_conversion_v2._run_grappler(
-        config, graph_def, graph, signature)
-
-    optimized_graph_def = fuse_prelu.fuse_ops_for_prelu(graph_def)
-
-    prelu_op_count = 0
-    value = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("Relu", node.op)
-      if node.op == 'Prelu':
-        prelu_op_count += 1
-      if node.op == 'Const':
-        value = graph_rewrite_util.values_from_const(node)
-    self.assertEqual(prelu_op_count, 2)
-    self.assertEqual(value, [0.25])
-
-  def testFusePreluWithConv2d(self):
-    layers = [
-        tf.keras.layers.Conv2D(
-            16, [3, 3], padding='same', use_bias=True,
-            bias_initializer=tf.initializers.constant(0.25)),
-        tf.keras.layers.PReLU()
-    ]
-    model = tf.keras.Sequential(layers)
-    tf.keras.backend.set_learning_phase(0)
-    input_tensor = tf.constant([1.0, 1.0], shape=[1, 2, 1, 1])
-
-    @tf.function
-    def execute_model(tensor):
-      return model(tensor)
-
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        execute_model.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-
-    for node in graph_def.node:
-      if node.op == 'Conv2D':
-        node.device = "/CPU:0"
-
-    config = config_pb2.ConfigProto()
-    rewriter_config = config.graph_options.rewrite_options
-    rewriter_config.optimizers[:] = [
-        'pruning', 'constfold', 'arithmetic', 'dependency', 'pruning', 'remap',
-        'constfold', 'arithmetic', 'dependency'
-    ]
-
-    for output in ['Identity']:
-      graph.add_to_collection('train_op', graph.get_operation_by_name(output))
-
-    signature = meta_graph_pb2.SignatureDef()
-    graph_def = tf_saved_model_conversion_v2._run_grappler(
-        config, graph_def, graph, signature)
-    graph_def = fuse_prelu.fuse_ops_for_prelu(graph_def)
-
-    optimized_graph_def = fuse_prelu.fuse_prelu_with_fused_conv2d_or_matmul(
-        graph_def)
-
-    conv2d_op = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("Prelu", node.op)
-      if node.op == '_FusedConv2D':
-        conv2d_op = node
-    self.assertNotEqual(conv2d_op, None)
-    self.assertEqual(conv2d_op.attr['fused_ops'].list.s, [b'BiasAdd', b'Prelu'])
-    self.assertEqual(conv2d_op.attr['num_args'].i, 2)
-
-  def testFusePreluWithMatMul(self):
-    layers = [
-        tf.keras.layers.Dense(
-            2, use_bias=True,
-            kernel_initializer=tf.initializers.constant(0.25),
-            bias_initializer=tf.initializers.constant(0.25)),
-        tf.keras.layers.PReLU()
-    ]
-    model = tf.keras.Sequential(layers)
-    tf.keras.backend.set_learning_phase(0)
-    input_tensor = tf.constant([1.0, 1.0], shape=[1, 2])
-
-    @tf.function
-    def execute_model(tensor):
-      return model(tensor)
-
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        execute_model.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-    for node in graph_def.node:
-      if node.op == 'MatMul':
-        node.device = "/CPU:0"
-
-    config = config_pb2.ConfigProto()
-    rewriter_config = config.graph_options.rewrite_options
-    rewriter_config.optimizers[:] = [
-        'pruning', 'constfold', 'arithmetic', 'dependency', 'pruning', 'remap',
-        'constfold', 'arithmetic', 'dependency'
-    ]
-
-    for output in ['Identity']:
-      graph.add_to_collection('train_op', graph.get_operation_by_name(output))
-
-    signature = meta_graph_pb2.SignatureDef()
-    graph_def = tf_saved_model_conversion_v2._run_grappler(
-        config, graph_def, graph, signature)
-    graph_def = fuse_prelu.fuse_ops_for_prelu(graph_def)
-    optimized_graph_def = fuse_prelu.fuse_prelu_with_fused_conv2d_or_matmul(
-        graph_def)
-
-    matmul_op = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("Prelu", node.op)
-      if node.op == '_FusedMatMul':
-        matmul_op = node
-    self.assertNotEqual(matmul_op, None)
-    self.assertEqual(matmul_op.attr['fused_ops'].list.s, [b'BiasAdd', b'Prelu'])
-    self.assertEqual(matmul_op.attr['num_args'].i, 2)
-
-  def testFusePreluWithDepthwiseConv2d(self):
-    layers = [
-        tf.keras.layers.DepthwiseConv2D(
-            1, bias_initializer=tf.initializers.constant(0.25)),
-        tf.keras.layers.PReLU()
-    ]
-    model = tf.keras.Sequential(layers)
-    tf.keras.backend.set_learning_phase(0)
-    input_tensor = tf.constant([1.0, 1.0], shape=[1, 2, 1, 1])
-
-    @tf.function
-    def execute_model(tensor):
-      return model(tensor)
-
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        execute_model.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-
-    for node in graph_def.node:
-      if node.op == 'Conv2D':
-        node.device = "/CPU:0"
-
-    config = config_pb2.ConfigProto()
-    rewriter_config = config.graph_options.rewrite_options
-    rewriter_config.optimizers[:] = [
-        'pruning', 'constfold', 'arithmetic', 'dependency', 'pruning', 'remap',
-        'constfold', 'arithmetic', 'dependency'
-    ]
-
-    for output in ['Identity']:
-      graph.add_to_collection('train_op', graph.get_operation_by_name(output))
-
-    signature = meta_graph_pb2.SignatureDef()
-    graph_def = tf_saved_model_conversion_v2._run_grappler(
-        config, graph_def, graph, signature)
-    graph_def = fuse_prelu.fuse_ops_for_prelu(graph_def)
-    graph_def = fuse_depthwise_conv2d.fuse_depthwise_conv2d(graph_def)
-
-    optimized_graph_def = fuse_prelu.fuse_prelu_with_fused_conv2d_or_matmul(
-        graph_def)
-
-    conv2d_op = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("Prelu", node.op)
-      if node.op == 'FusedDepthwiseConv2dNative':
-        conv2d_op = node
-    self.assertNotEqual(conv2d_op, None)
-    self.assertEqual(conv2d_op.attr['fused_ops'].list.s, [b'BiasAdd', b'Prelu'])
-    self.assertEqual(conv2d_op.attr['num_args'].i, 2)
-
-  def testNonPreluPattern(self):
-    """Test a basic model with functions to make sure functions are inlined."""
-    input_data = constant_op.constant(1., shape=[1])
-    root = autotrackable.AutoTrackable()
-    root.v1 = variables.Variable(3.)
-    root.v2 = variables.Variable(2.)
-
-    root.f = def_function.function(lambda x: tf.nn.relu(root.v1) + root.v2 * 2.0)
-    to_save = root.f.get_concrete_function(input_data)
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        root.f.get_concrete_function(input_data))
-    graph_def = graph.as_graph_def()
-    graph_def = fuse_prelu.fuse_ops_for_prelu(graph_def)
-    const_op = None
-    for node in graph_def.node:
-      self.assertNotEqual("Prelu", node.op)
-      if node.op == 'Const':
-        const_op = node
-    self.assertNotEqual(const_op, None)
-    self.assertEqual(const_op.attr['value'].tensor.float_val, [2.0])
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/generate_test_model.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/generate_test_model.py
deleted file mode 100644
index ab8e990fc..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/generate_test_model.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""A binary that generates saved model artifacts for testing."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import os
-import sys
-
-import tensorflow.compat.v2 as tf
-
-def parse_args():
-  parser = argparse.ArgumentParser(
-      'Generates saved model artifacts for testing.')
-  parser.add_argument(
-      'output_path',
-      type=str,
-      help='Model output path.')
-  parser.add_argument(
-      '--model_type',
-      type=str,
-      required=True,
-      choices=set(['tf_keras_h5', 'tf_saved_model']),
-      help='Model format to generate.')
-  return parser.parse_known_args()
-
-
-def main(_):
-
-  if args.model_type == 'tf_keras_h5':
-    model = tf.keras.Sequential()
-    model.add(tf.keras.layers.Dense(5, activation='relu', input_shape=(8,)))
-    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
-    model.save(os.path.join(args.output_path))
-  elif args.model_type == 'tf_saved_model':
-    class TimesThreePlusOne(tf.Module):
-
-      @tf.function(input_signature=[
-          tf.TensorSpec(shape=None, dtype=tf.float32)])
-      def compute(self, x):
-        return x * 3.0 + 1.0
-
-    tf.saved_model.save(TimesThreePlusOne(), args.output_path)
-  else:
-    raise ValueError('Unrecognized model type: %s' % args.model_type)
-
-
-if __name__ == '__main__':
-  args, unparsed = parse_args()
-  tf.compat.v1.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/graph_rewrite_util.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/graph_rewrite_util.py
deleted file mode 100644
index b2816d3d7..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/graph_rewrite_util.py
+++ /dev/null
@@ -1,139 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-import re
-
-from tensorflow.core.framework import graph_pb2
-from tensorflow.core.framework import node_def_pb2
-from tensorflow.python.framework import tensor_util
-
-# Custom op name for fused depthwise conv2d
-FUSED_DEPTHWISE_CONV2D = 'FusedDepthwiseConv2dNative'
-# The grappler op name for fused MatMul which starts with '_'
-FUSED_MATMUL = '_FusedMatMul'
-FUSED_CONV2D = '_FusedConv2D'
-
-def node_from_map(node_map, name):
-  """Pulls a node def from a dictionary for a given name.
-
-  Args:
-    node_map: Dictionary containing an entry indexed by name for every node.
-    name: Identifies the node we want to find.
-
-  Returns:
-    NodeDef of the node with the given name.
-
-  Raises:
-    ValueError: If the node isn't present in the dictionary.
-  """
-  stripped_name = node_name_from_input(name)
-  if stripped_name not in node_map:
-    raise ValueError("No node named '%s' found in map." % name)
-  return node_map[stripped_name]
-
-
-def values_from_const(node_def):
-  """Extracts the values from a const NodeDef as a numpy ndarray.
-
-  Args:
-    node_def: Const NodeDef that has the values we want to access.
-
-  Returns:
-    Numpy ndarray containing the values.
-
-  Raises:
-    ValueError: If the node isn't a Const.
-  """
-  if node_def.op != "Const":
-    raise ValueError(
-        "Node named '%s' should be a Const op for values_from_const." %
-        node_def.name)
-  input_tensor = node_def.attr["value"].tensor
-  tensor_value = tensor_util.MakeNdarray(input_tensor)
-  return tensor_value
-
-# Whether to scale by gamma after normalization.
-def scale_after_normalization(node):
-  if node.op == "BatchNormWithGlobalNormalization":
-    return node.attr["scale_after_normalization"].b
-  return True
-
-def node_name_from_input(node_name):
-  """Strips off ports and other decorations to get the underlying node name."""
-  if node_name.startswith("^"):
-    node_name = node_name[1:]
-  m = re.search(r"(.*):\d+$", node_name)
-  if m:
-    node_name = m.group(1)
-  return node_name
-
-def cleanup_graph_def(input_graph_def, nodes_to_skip, inputs_to_remove):
-  """Clean up the graph def by removing the skipped nodes and clean up the nodes
-    with inputs that have been removed.
-
-  Args:
-    input_graph_def: GraphDef object to be cleaned.
-    node_to_skip: Dict with node names to be skipped.
-    inputs_to_remove: List of nodes to be removed from inputs of all nodes.
-  Returns:
-    GraphDef that has been cleaned.
-
-  """
-  result_graph_def = graph_pb2.GraphDef()
-  for node in input_graph_def.node:
-    if node.name in nodes_to_skip:
-      continue
-    new_node = node_def_pb2.NodeDef()
-    new_node.CopyFrom(node)
-    for value in inputs_to_remove:
-      for i, input_node in enumerate(new_node.input):
-        if input_node == value.name:
-          new_node.input[i] = value.input[0]
-    result_graph_def.node.extend([new_node])
-  result_graph_def.library.CopyFrom(input_graph_def.library)
-  result_graph_def.versions.CopyFrom(input_graph_def.versions)
-  return result_graph_def
-
-def rename_constants(node_list, prefix):
-  """Update all constants name by adding a prefix.
-
-  Args:
-    node_list: NodeDef list to update.
-    prefix: string to add to the constant name.
-  Returns:
-    NodeDef list that has been updated.
-
-  """
-  nodes = []
-  constant_names = [node.name for node in node_list if node.op == 'Const']
-  for node in node_list:
-    new_node = node_def_pb2.NodeDef()
-    new_node.CopyFrom(node)
-    nodes.append(new_node)
-    if node.op == 'Const':
-      new_node.name = prefix + '/' + node.name
-    else:
-      for i, input_node in enumerate(new_node.input):
-        for name in constant_names:
-          if input_node.startswith(name):
-            new_node.input[i] = prefix + '/' + input_node
-  return nodes
-
-def get_output_node_names(node_map, target):
-  output_node_names = []
-  for name, node in node_map.items():
-    for input_name in node.input:
-      if target == input_name:
-        output_node_names.append(name)
-  return output_node_names
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/jax_conversion.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/jax_conversion.py
deleted file mode 100644
index ea0c7004d..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/jax_conversion.py
+++ /dev/null
@@ -1,151 +0,0 @@
-# Copyright 2022 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Converts a JAX function to TensorFlow.js web format."""
-import tempfile
-from typing import Any, Callable, Optional, Sequence, Tuple, Union
-
-from jax.experimental import jax2tf
-from jax.experimental.jax2tf import shape_poly
-import tensorflow as tf
-from tensorflowjs.converters import tf_saved_model_conversion_v2 as saved_model_conversion
-
-
-_TF_SERVING_KEY = tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY
-Array = Any
-DType = Any
-PolyShape = shape_poly.PolyShape
-
-
-class _ReusableSavedModelWrapper(tf.train.Checkpoint):
-  """Wraps a function and its parameters for saving to a SavedModel.
-
-  Implements the interface described at
-  https://www.tensorflow.org/hub/reusable_saved_models.
-  """
-
-  def __init__(self, tf_graph, param_vars):
-    """Initializes a _ReusableSavedModelWrapper.
-
-    Args:
-      tf_graph: a tf.function taking one argument (the inputs), which can be
-         be tuples/lists/dictionaries of np.ndarray or tensors. The function
-         may have references to the tf.Variables in `param_vars`.
-      param_vars: the parameters, as tuples/lists/dictionaries of tf.Variable,
-         to be saved as the variables of the SavedModel.
-    """
-    super().__init__()
-    self.variables = tf.nest.flatten(param_vars)
-    self.trainable_variables = [v for v in self.variables if v.trainable]
-    # If you intend to prescribe regularization terms for users of the model,
-    # add them as @tf.functions with no inputs to this list. Else drop this.
-    self.regularization_losses = []
-    self.__call__ = tf_graph
-
-
-def convert_jax(
-    apply_fn: Callable[..., Any],
-    params: Array,
-    *,
-    input_signatures: Sequence[Tuple[Sequence[Union[int, None]], DType]],
-    model_dir: str,
-    polymorphic_shapes: Optional[Sequence[Union[str, PolyShape]]] = None,
-    **tfjs_converter_params):
-  """Converts a JAX function `jax_apply_fn` and model parameters to a TensorflowJS model.
-
-  Example usage for a Flax Module:
-
-  ```
-  import numpy as np
-  from flax import linen as nn
-  from jax import random
-  import jax.numpy as jnp
-  from tensorflowjs.converters.jax_conversion import convert_jax
-
-  module = nn.Dense(features=4)
-  inputs = jnp.ones((3, 4))
-  params = module.init(random.PRNKey(0), inputs)['params']
-
-  convert_jax(
-    apply_fn=module.apply,
-    params=params,
-    input_signatures=[((3, 4), np.float32)],
-    model_dir=tfjs_model_dir)
-  ```
-
-  Note that when using dynamic shapes, an additional argument
-  `polymorphic_shapes` should be provided specifying values for the dynamic
-  ("polymorphic") dimensions). See here for more details:
-  https://github.com/google/jax/tree/main/jax/experimental/jax2tf#shape-polymorphic-conversion
-
-  This is an adaption of the original implementation in jax2tf here:
-  https://github.com/google/jax/blob/main/jax/experimental/jax2tf/examples/saved_model_lib.py
-
-  Arguments:
-    apply_fn: A JAX function that has one or more arguments, of which the first
-      argument are the model parameters. This function typically is the forward
-      pass of the network (e.g., `Module.apply()` in Flax).
-    params: A Pytree containing the parameters of the module. These will all be
-      converted to TF.Variables.
-    input_signatures: the input signatures for the second and remaining
-      arguments to `apply_fn` (the input). A signature must be a
-      `tensorflow.TensorSpec` instance, or a (nested) tuple/list/dictionary
-      thereof with a structure matching the second argument of `apply_fn`.
-    model_dir: Directory where the TensorflowJS model will be written to.
-    polymorphic_shapes: If given then it will be used as the
-      `polymorphic_shapes` argument for the second parameter of `apply_fn`. In
-      this case, a single `input_signatures` is supported, and should have
-      `None` in the polymorphic (dynamic) dimensions.
-  """
-  if polymorphic_shapes is not None:
-    # If polymorphic shapes are provided, add a polymorphic spec for the
-    # first argument to `apply_fn`, which are the parameters.
-    polymorphic_shapes = [None, *polymorphic_shapes]
-
-  tf_fn = jax2tf.convert(
-      apply_fn,
-      # Gradients must be included as 'PreventGradient' is not supported.
-      with_gradient=True,
-      polymorphic_shapes=polymorphic_shapes,
-      # Do not use TFXLA Ops because these aren't supported by TFjs, but use
-      # workarounds instead. More information:
-      # https://github.com/google/jax/tree/main/jax/experimental/jax2tf#tensorflow-xla-ops
-      enable_xla=False)
-
-  # Create tf.Variables for the parameters. If you want more useful variable
-  # names, you can use `tree.map_structure_with_path` from the `dm-tree`
-  # package.
-  param_vars = tf.nest.map_structure(
-      lambda param: tf.Variable(param, trainable=True), params)
-  # Do not use TF's jit compilation on the function.
-  tf_graph = tf.function(
-      lambda *xs: tf_fn(param_vars, *xs), autograph=False, jit_compile=False)
-
-  # This signature is needed for TensorFlow Serving use.
-  signatures = {
-      _TF_SERVING_KEY: tf_graph.get_concrete_function(*input_signatures)
-  }
-
-  wrapper = _ReusableSavedModelWrapper(tf_graph, param_vars)
-  saved_model_options = tf.saved_model.SaveOptions(
-      experimental_custom_gradients=True)
-
-  with tempfile.TemporaryDirectory() as saved_model_dir:
-    tf.saved_model.save(
-        wrapper,
-        saved_model_dir,
-        signatures=signatures,
-        options=saved_model_options)
-    saved_model_conversion.convert_tf_saved_model(saved_model_dir, model_dir,
-                                                  **tfjs_converter_params)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/jax_conversion_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/jax_conversion_test.py
deleted file mode 100644
index cf7e49a14..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/jax_conversion_test.py
+++ /dev/null
@@ -1,168 +0,0 @@
-# Copyright 2022 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for converting JAX to TensorFlow.js web format."""
-import functools
-
-from flax import linen as nn
-from jax import random
-import jax.numpy as jnp
-import tensorflow as tf
-from tensorflowjs.converters import jax_conversion
-import os
-import json
-
-class FlaxModule(nn.Module):
-  """A simple Flax Module containing a few Dense layers and ReLUs."""
-
-  @nn.compact
-  def __call__(self, x):
-    x = nn.Dense(features=20)(x)
-    x = nn.relu(x)
-    for _ in range(5):
-      x = nn.Dense(features=10)(x)
-      x = nn.relu(x)
-
-    x = nn.Dense(features=2)(x)
-    x = nn.sigmoid(x)
-    return x
-
-
-class FlaxModuleBatchNorm(nn.Module):
-  """A simple CNN-like Flax model with BatchNorm."""
-
-  @nn.compact
-  def __call__(self, x, *, training=True):
-    x = nn.Conv(features=32, kernel_size=(3, 3))(x)
-    x = nn.BatchNorm(use_running_average=not training)(x)
-    x = nn.relu(x)
-    x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))
-    x = nn.Conv(features=64, kernel_size=(3, 3))(x)
-    x = nn.BatchNorm(use_running_average=not training)(x)
-    x = nn.relu(x)
-    x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))
-    x = x.reshape((x.shape[0], -1))  # flatten
-    x = nn.Dense(features=256)(x)
-    x = nn.relu(x)
-    x = nn.Dense(features=10)(x)
-    return x
-
-
-class JaxConversionTest(tf.test.TestCase):
-
-  def test_convert_simple(self):
-    apply_fn = lambda params, x: jnp.sum(x) * params['w']
-    jax_conversion.convert_jax(
-        apply_fn,
-        {'w': 0.5},
-        input_signatures=[tf.TensorSpec((2, 3), tf.float32)],
-        model_dir=self.get_temp_dir())
-
-  def test_convert_quantize(self):
-    apply_fn = lambda params, x: jnp.sum(x) * params['w']
-    model_dir = self.get_temp_dir()
-    jax_conversion.convert_jax(
-        apply_fn,
-        {'w': 0.5},
-        input_signatures=[tf.TensorSpec((2, 3), tf.float32)],
-        model_dir=model_dir,
-        quantization_dtype_map = {'float16': '*'})
-
-    with open(os.path.join(model_dir, 'model.json'), 'rt') as model:
-      model_json = json.load(model)
-      quantization = model_json['weightsManifest'][0]['weights'][1]['quantization']
-      self.assertEqual(quantization['dtype'], 'float16')
-
-  def test_convert_poly(self):
-    apply_fn = lambda params, x: jnp.sum(x) * params['w']
-    jax_conversion.convert_jax(
-        apply_fn,
-        {'w': 0.5},
-        input_signatures=[tf.TensorSpec((None, 3), tf.float32)],
-        polymorphic_shapes=['(b, 3)'],
-        model_dir=self.get_temp_dir(),
-        strip_debug_ops=True)
-
-  def test_convert_tf_poly_mismatch_raises(self):
-    apply_fn = lambda params, x: jnp.sum(x) * params['w']
-    with self.assertRaisesRegex(
-        ValueError, 'syntax error in polymorphic shape.* in dimension.* Parsed.*, remaining.*'):
-      jax_conversion.convert_jax(
-          apply_fn,
-          {'w': 0.5},
-          input_signatures=[tf.TensorSpec((None, 3), tf.float32)],
-          polymorphic_shapes=['(b, 4)'],
-          model_dir=self.get_temp_dir())
-
-  def test_convert_multiargs(self):
-    apply_fn = lambda params, x, y: jnp.sum(x) * jnp.sum(y) * params['w']
-    jax_conversion.convert_jax(
-        apply_fn,
-        {'w': 0.5},
-        input_signatures=[tf.TensorSpec((2, 3), tf.float32),
-                          tf.TensorSpec((5, 6), tf.float32)],
-        model_dir=self.get_temp_dir())
-
-  def test_convert_multiarg_poly(self):
-    apply_fn = lambda params, x, y: jnp.sum(x) * jnp.sum(y) * params['w']
-    jax_conversion.convert_jax(
-        apply_fn,
-        {'w': 0.5},
-        input_signatures=[tf.TensorSpec((None, 3), tf.float32),
-                          tf.TensorSpec((None, 6), tf.float32)],
-        polymorphic_shapes=['(b, 3)', '(b, 6)'],
-        model_dir=self.get_temp_dir(),
-        strip_debug_ops=True)
-
-  def test_convert_flax(self):
-    m, x = FlaxModule(), jnp.zeros((3, 4))
-    variables = m.init(random.PRNGKey(0), x)
-    jax_conversion.convert_jax(
-        m.apply,
-        variables,
-        input_signatures=[tf.TensorSpec((3, 4), tf.float32)],
-        model_dir=self.get_temp_dir())
-
-  def test_convert_flax_poly(self):
-    m, x = FlaxModule(), jnp.zeros((3, 4))
-    variables = m.init(random.PRNGKey(0), x)
-    jax_conversion.convert_jax(
-        m.apply,
-        variables,
-        input_signatures=[tf.TensorSpec((None, 4), tf.float32)],
-        polymorphic_shapes=['(b, 4)'],
-        model_dir=self.get_temp_dir(),
-        strip_debug_ops=True)
-
-  # TODO(marcvanzee): This test currently fails due to
-  # https://github.com/google/jax/issues/11804.
-  # This issue is fixed in JAX, but only will be part of jax>0.3.16. Once JAX
-  # releases a new version we can re-enable this test. If you install JAX from
-  # Github this will work fine.
-  # def test_convert_flax_bn(self):
-  #   m, x = FlaxModuleBatchNorm(), jnp.zeros((1, 32, 32, 3))
-  #   variables = m.init(random.PRNGKey(0), x)
-  #   # Note: if we don't pass training=False here, we will get an error during
-  #   # conversion since `batch_stats` is mutated while it is not passed as a
-  #   # mutable variable collections (we currently do not support this).
-  #   apply_fn = functools.partial(m.apply, training=False)
-  #   jax_conversion.convert_jax(
-  #       apply_fn,
-  #       variables,
-  #       input_signatures=[tf.TensorSpec((1, 32, 32, 3), tf.float32)],
-  #       model_dir=self.get_temp_dir())
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_h5_conversion.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_h5_conversion.py
deleted file mode 100644
index cb0219b21..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_h5_conversion.py
+++ /dev/null
@@ -1,472 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Library for converting from hdf5 to json + binary weights.
-
-Used primarily to convert saved weights, or saved_models from their
-hdf5 format to a JSON + binary weights format that the TS codebase can use.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import os
-import tempfile
-
-import six
-import h5py
-import numpy as np
-
-from tensorflowjs import write_weights  # pylint: disable=import-error
-from tensorflowjs.converters import common
-
-
-def normalize_weight_name(weight_name):
-  """Remove suffix ":0" (if present) from weight name."""
-  name = as_text(weight_name)
-  if name.endswith(':0'):
-    # Python TensorFlow weight names ends with the output slot, which is
-    # not applicable to TensorFlow.js.
-    name = name[:-2]
-  return name
-
-
-def as_text(bytes_or_text, encoding='utf-8'):
-  if isinstance(bytes_or_text, six.text_type):
-    return bytes_or_text
-  elif isinstance(bytes_or_text, bytes):
-    return bytes_or_text.decode(encoding)
-  else:
-    raise TypeError('Expected binary or unicode string, got %r' %
-                    bytes_or_text)
-
-
-def _convert_h5_group(group):
-  """Construct a weights group entry.
-
-  Args:
-    group: The HDF5 group data, possibly nested.
-
-  Returns:
-    An array of weight groups (see `write_weights` in TensorFlow.js).
-  """
-  group_out = []
-  if 'weight_names' in group.attrs:
-    # This is a leaf node in namespace (e.g., 'Dense' in 'foo/bar/Dense').
-    names = group.attrs['weight_names'].tolist()
-
-    if not names:
-      return group_out
-
-    names = [as_text(name) for name in names]
-    weight_values = [
-        np.array(group[weight_name]) for weight_name in names]
-    group_out += [{
-        'name': normalize_weight_name(weight_name),
-        'data': weight_value
-    } for (weight_name, weight_value) in zip(names, weight_values)]
-  else:
-    # This is *not* a leaf level in the namespace (e.g., 'foo' in
-    # 'foo/bar/Dense').
-    for key in group.keys():
-      # Call this method recursively.
-      group_out += _convert_h5_group(group[key])
-
-  return group_out
-
-def _convert_v3_group(group, actual_layer_name):
-  """Construct a weights group entry.
-
-  Args:
-    group: The HDF5 group data, possibly nested.
-
-  Returns:
-    An array of weight groups (see `write_weights` in TensorFlow.js).
-  """
-  group_out = []
-  list_of_folder = [as_text(name) for name in group]
-  if 'vars' in list_of_folder:
-    names = group['vars']
-    if not names:
-      return group_out
-    name_list = [as_text(name) for name in names]
-    weight_values = [np.array(names[weight_name]) for weight_name in name_list]
-    name_list = [os.path.join(actual_layer_name, item) for item in name_list]
-    group_out += [{
-    'name': normalize_weight_name(weight_name),
-    'data': weight_value
-    } for (weight_name, weight_value) in zip(name_list, weight_values)]
-  else:
-    for key in list_of_folder:
-      group_out += _convert_v3_group(group[key], actual_layer_name)
-  return group_out
-
-
-def _check_version(h5file):
-  """Check version compatiility.
-
-  Args:
-    h5file: An h5file object.
-
-  Raises:
-    ValueError: if the KerasVersion of the HDF5 file is unsupported.
-  """
-  keras_version = as_text(h5file.attrs['keras_version'])
-  if keras_version.split('.')[0] != '2':
-    raise ValueError(
-        'Expected Keras version 2; got Keras version %s' % keras_version)
-
-
-def _initialize_output_dictionary(h5file):
-  """Prepopulate required fields for all data foramts.
-
-  Args:
-    h5file: Valid h5file object.
-
-  Returns:
-    A dictionary with common fields sets, shared across formats.
-  """
-  out = dict()
-  out['keras_version'] = as_text(h5file.attrs['keras_version'])
-  out['backend'] = as_text(h5file.attrs['backend'])
-  return out
-
-
-def _ensure_h5file(h5file):
-  if not isinstance(h5file, h5py.File):
-    return h5py.File(h5file, "r")
-  else:
-    return h5file
-
-
-def _ensure_json_dict(item):
-  return item if isinstance(item, dict) else json.loads(as_text(item))
-
-def _discard_v3_keys(json_dict, keys_to_delete):
-  if isinstance(json_dict, dict):
-    keys = list(json_dict.keys())
-    for key in keys:
-      if key in keys_to_delete:
-        del json_dict[key]
-      else:
-        _discard_v3_keys(json_dict[key], keys_to_delete)
-  elif isinstance(json_dict, list):
-    for item in json_dict:
-      _discard_v3_keys(item, keys_to_delete)
-
-
-# https://github.com/tensorflow/tfjs/issues/1255, b/124791387
-# In tensorflow version 1.13 and some alpha and nightly-preview versions,
-# the following layers have different class names in their serialization.
-# This issue should be fixed in later releases. But we include the logic
-# to translate them anyway, for users who use those versions of tensorflow.
-_CLASS_NAME_MAP = {
-    'BatchNormalizationV1': 'BatchNormalization',
-    'UnifiedGRU': 'GRU',
-    'UnifiedLSTM': 'LSTM'
-}
-
-
-def translate_class_names(input_object):
-  """Perform class name replacement.
-
-  Beware that this method modifies the input object in-place.
-  """
-  if not isinstance(input_object, dict):
-    return
-  for key in input_object:
-    value = input_object[key]
-    if key == 'class_name' and value in _CLASS_NAME_MAP:
-      input_object[key] = _CLASS_NAME_MAP[value]
-    elif isinstance(value, dict):
-      translate_class_names(value)
-    elif isinstance(value, (tuple, list)):
-      for item in value:
-        translate_class_names(item)
-
-
-def h5_merged_saved_model_to_tfjs_format(h5file, split_by_layer=False):
-  """Load topology & weight values from HDF5 file and convert.
-
-  The HDF5 file is one generated by Keras' save_model method or model.save()
-
-  N.B.:
-  1) This function works only on HDF5 values from Keras version 2.
-  2) This function does not perform conversion for special weights including
-      ConvLSTM2D and CuDNNLSTM.
-
-  Args:
-    h5file: An instance of h5py.File, or the path to an h5py file.
-    split_by_layer: (Optional) whether the weights of different layers are
-      to be stored in separate weight groups (Default: `False`).
-
-  Returns:
-    (model_json, groups)
-      model_json: a JSON dictionary holding topology and system metadata.
-      group: an array of group_weights as defined in tfjs write_weights.
-
-  Raises:
-    ValueError: If the Keras version of the HDF5 file is not supported.
-  """
-  h5file = _ensure_h5file(h5file)
-  try:
-    _check_version(h5file)
-  except ValueError:
-    print("""failed to lookup keras version from the file,
-    this is likely a weight only file""")
-  model_json = _initialize_output_dictionary(h5file)
-
-  model_json['model_config'] = _ensure_json_dict(
-      h5file.attrs['model_config'])
-  translate_class_names(model_json['model_config'])
-  if 'training_config' in h5file.attrs:
-    model_json['training_config'] = _ensure_json_dict(
-        h5file.attrs['training_config'])
-
-  groups = [] if split_by_layer else [[]]
-
-  model_weights = h5file['model_weights']
-  layer_names = [as_text(n) for n in model_weights]
-  for layer_name in layer_names:
-    layer = model_weights[layer_name]
-    group = _convert_h5_group(layer)
-    if group:
-      if split_by_layer:
-        groups.append(group)
-      else:
-        groups[0] += group
-  return model_json, groups
-
-def h5_v3_merged_saved_model_to_tfjs_format(h5file, meta_file, config_file,split_by_layer=False):
-  """Load topology & weight values from HDF5 file and convert.
-
-  The HDF5 weights file is one generated by Keras's save_model method or model.save()
-
-  N.B.:
-  1) This function works only on HDF5 values from Keras version 3.
-  2) This function does not perform conversion for special weights including
-      ConvLSTM2D and CuDNNLSTM.
-
-  Args:
-    h5file: An instance of h5py.File, or the path to an h5py file.
-    split_by_layer: (Optional) whether the weights of different layers are
-      to be stored in separate weight groups (Default: `False`).
-
-  Returns:
-    (model_json, groups)
-      model_json: a JSON dictionary holding topology and system metadata.
-      group: an array of group_weights as defined in tfjs write_weights.
-
-  Raises:
-    ValueError: If the Keras version of the HDF5 file is not supported.
-  """
-  h5file = _ensure_h5file(h5file)
-  model_json = dict()
-  model_json['keras_version'] = meta_file['keras_version']
-
-  keys_to_remove = ["module", "registered_name", "date_saved"]
-  config = _ensure_json_dict(config_file)
-  _discard_v3_keys(config, keys_to_remove)
-  model_json['model_config'] = config
-  translate_class_names(model_json['model_config'])
-  if 'training_config' in h5file.attrs:
-    model_json['training_config'] = _ensure_json_dict(
-        h5file.attrs['training_config'])
-
-  groups = [] if split_by_layer else [[]]
-
-  _convert_v3_group_structure_to_weights(groups=groups, group=h5file, split_by_layer=split_by_layer)
-  return model_json, groups
-
-def _convert_v3_group_structure_to_weights(groups, group, split_by_layer, indent=""):
-  for key in group.keys():
-    if isinstance(group[key], h5py.Group):
-      _convert_v3_group_structure_to_weights(groups, group[key], split_by_layer, indent + key + "/")
-    elif isinstance(group[key], h5py.Dataset):
-      group_of_weights = dict()
-      for key in group.keys():
-        group_of_weights[str(indent + key)] = group[key]
-      group_out = _convert_group(group_of_weights)
-      if split_by_layer:
-        groups.append(group_out)
-      else:
-        groups[0] += group_out
-      break
-
-
-def _convert_group(group_dict):
-  group_out = []
-  for key in group_dict.keys():
-    name = key
-    weights_value = np.array(group_dict[key])
-    group_out += [{'name': name, 'data' : weights_value}]
-
-  return group_out
-
-
-def h5_weights_to_tfjs_format(h5file, split_by_layer=False):
-  """Load weight values from a Keras HDF5 file and to a binary format.
-
-  The HDF5 file is one generated by Keras' Model.save_weights() method.
-
-  N.B.:
-  1) This function works only on HDF5 values from Keras version 2.
-  2) This function does not perform conversion for special weights including
-      ConvLSTM2D and CuDNNLSTM.
-
-  Args:
-    h5file: An instance of h5py.File, or the path to an h5py file.
-    split_by_layer: (Optional) whether the weights of different layers are
-      to be stored in separate weight groups (Default: `False`).
-
-  Returns:
-    An array of group_weights as defined in tfjs write_weights.
-
-  Raises:
-    ValueError: If the Keras version of the HDF5 file is not supported
-  """
-  h5file = _ensure_h5file(h5file)
-  try:
-    _check_version(h5file)
-  except ValueError:
-    print("""failed to lookup keras version from the file,
-    this is likely a weight only file""")
-
-  groups = [] if split_by_layer else [[]]
-
-  # pylint: disable=not-an-iterable
-  layer_names = [as_text(n) for n in h5file.attrs['layer_names']]
-  # pylint: enable=not-an-iterable
-  for layer_name in layer_names:
-    layer = h5file[layer_name]
-    group = _convert_h5_group(layer)
-    if group:
-      if split_by_layer:
-        groups.append(group)
-      else:
-        groups[0] += group
-  return groups
-
-
-def _get_generated_by(topology):
-  if topology is None:
-    return None
-  elif 'keras_version' in topology:
-    return 'keras v%s' % topology['keras_version']
-  else:
-    return None
-
-
-def write_artifacts(topology,
-                    weights,
-                    output_dir,
-                    quantization_dtype_map=None,
-                    weight_shard_size_bytes=1024 * 1024 * 4,
-                    metadata=None):
-  """Writes weights and topology to the output_dir.
-
-  If `topology` is Falsy (e.g., `None`), only emit weights to output_dir.
-
-  Args:
-    topology: a JSON dictionary, representing the Keras config.
-    weights: an array of weight groups (as defined in tfjs write_weights).
-    output_dir: the directory to hold all the contents.
-    quantization_dtype_map: (Optional) A mapping from dtype
-      (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-      supports wildcard substitution.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    metadata: User defined metadata map.
-  """
-  # TODO(cais, nielsene): This method should allow optional arguments of
-  #   `write_weights.write_weights` (e.g., shard size) and forward them.
-  # We write the topology after since write_weights makes no promises about
-  # preserving directory contents.
-  if not (isinstance(weight_shard_size_bytes, int) and
-          weight_shard_size_bytes > 0):
-    raise ValueError(
-        'Expected weight_shard_size_bytes to be a positive integer, '
-        'but got %s' % weight_shard_size_bytes)
-
-  if os.path.isfile(output_dir):
-    raise ValueError(
-        'Path "%d" already exists as a file (not a directory).' % output_dir)
-
-  model_json = {
-      common.FORMAT_KEY: common.TFJS_LAYERS_MODEL_FORMAT,
-      common.GENERATED_BY_KEY: _get_generated_by(topology),
-      common.CONVERTED_BY_KEY: common.get_converted_by()
-  }
-
-  if metadata:
-    model_json[common.USER_DEFINED_METADATA_KEY] = metadata
-
-  model_json[common.ARTIFACT_MODEL_TOPOLOGY_KEY] = topology or None
-  weights_manifest = write_weights.write_weights(
-      weights, output_dir, write_manifest=False,
-      quantization_dtype_map=quantization_dtype_map,
-      shard_size_bytes=weight_shard_size_bytes)
-  assert isinstance(weights_manifest, list)
-  model_json[common.ARTIFACT_WEIGHTS_MANIFEST_KEY] = weights_manifest
-
-  model_json_path = os.path.join(
-      output_dir, common.ARTIFACT_MODEL_JSON_FILE_NAME)
-  with open(model_json_path, 'wt') as f:
-    json.dump(model_json, f)
-
-
-def save_keras_model(model, artifacts_dir, quantization_dtype_map=None,
-                     weight_shard_size_bytes=1024 * 1024 * 4, metadata=None):
-  r"""Save a Keras model and its weights in TensorFlow.js format.
-
-  Args:
-    model: An instance of `keras.Model`.
-    artifacts_dir: The directory in which the artifacts will be saved.
-      The artifacts to be saved include:
-        - model.json: A JSON representing the model. It has the following
-          fields:
-          - 'modelTopology': A JSON object describing the topology of the model,
-            along with additional information such as training. It is obtained
-            through calling `model.save()`.
-          - 'weightsManifest': A TensorFlow.js-format JSON manifest for the
-            model's weights.
-        - files containing weight values in groups, with the file name pattern
-          group(\d+)-shard(\d+)of(\d+).
-      If the directory does not exist, this function will attempt to create it.
-    quantization_dtype_map: (Optional) A mapping from dtype
-      (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-      supports wildcard substitution.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    metadata: User defined metadata map.
-
-  Raises:
-    ValueError: If `artifacts_dir` already exists as a file (not a directory).
-  """
-  temp_h5_path = tempfile.mktemp() + '.h5'
-  model.save(temp_h5_path)
-  topology_json, weight_groups = (
-      h5_merged_saved_model_to_tfjs_format(temp_h5_path))
-  if os.path.isfile(artifacts_dir):
-    raise ValueError('Path "%s" already exists as a file.' % artifacts_dir)
-  if not os.path.isdir(artifacts_dir):
-    os.makedirs(artifacts_dir)
-  write_artifacts(
-      topology_json, weight_groups, artifacts_dir,
-      quantization_dtype_map=quantization_dtype_map,
-      weight_shard_size_bytes=weight_shard_size_bytes,
-      metadata=metadata)
-  os.remove(temp_h5_path)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_h5_conversion_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_h5_conversion_test.py
deleted file mode 100644
index f3c95e3aa..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_h5_conversion_test.py
+++ /dev/null
@@ -1,549 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for artifact conversion to and from Python keras."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import glob
-import json
-import os
-import shutil
-import tempfile
-import unittest
-import six
-import keras
-
-import h5py
-import numpy as np
-import tensorflow.compat.v2 as tf
-
-from tensorflowjs import version
-from tensorflowjs.converters import keras_h5_conversion as conversion
-
-
-class ConvertH5WeightsTest(unittest.TestCase):
-
-  def setUp(self):
-    self._tmp_dir = tempfile.mkdtemp()
-    super(ConvertH5WeightsTest, self).setUp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(ConvertH5WeightsTest, self).tearDown()
-
-  def testConvertWeightsFromSimpleModelNoSplitByLayer(self):
-    input_tensor = tf.keras.layers.Input((3,))
-    dense1 = tf.keras.layers.Dense(
-        4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
-        name='MyDense10')(input_tensor)
-    output = tf.keras.layers.Dense(
-        2, use_bias=False, kernel_initializer='ones', name='MyDense20')(dense1)
-    model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
-    h5_path = os.path.join(self._tmp_dir, 'MyModel.h5')
-    model.save_weights(h5_path)
-
-    # Load the saved weights as a JSON string.
-    groups = conversion.h5_weights_to_tfjs_format(h5py.File(h5_path))
-
-    # Check the loaded weights.
-    # Due to the default `split_by_layer=True`, there should be only one weight
-    # group.
-    self.assertEqual(1, len(groups))
-    self.assertEqual(3, len(groups[0]))
-    kernel1 = groups[0][0]
-    self.assertEqual('MyDense10/kernel', kernel1['name'])
-    self.assertEqual('float32', kernel1['data'].dtype)
-    self.assertEqual((3, 4), kernel1['data'].shape)
-    self.assertTrue(np.allclose(np.ones([3, 4]), kernel1['data']))
-    bias1 = groups[0][1]
-    self.assertEqual('MyDense10/bias', bias1['name'])
-    self.assertEqual('float32', bias1['data'].dtype)
-    self.assertEqual((4,), bias1['data'].shape)
-    self.assertTrue(np.allclose(np.zeros([4]), bias1['data']))
-    kernel2 = groups[0][2]
-    self.assertEqual('MyDense20/kernel', kernel2['name'])
-    self.assertEqual('float32', kernel2['data'].dtype)
-    self.assertEqual((4, 2), kernel2['data'].shape)
-    self.assertTrue(np.allclose(np.ones([4, 2]), kernel2['data']))
-
-  def testConvertWeightsFromSimpleModelSplitByLayer(self):
-    input_tensor = tf.keras.layers.Input((3,))
-    dense1 = tf.keras.layers.Dense(
-        4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
-        name='MyDense30')(input_tensor)
-    output = tf.keras.layers.Dense(
-        2, use_bias=False, kernel_initializer='ones', name='MyDense40')(dense1)
-    model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
-    h5_path = os.path.join(self._tmp_dir, 'MyModel.h5')
-    model.save_weights(h5_path)
-
-    # Load the saved weights as a JSON string.
-    groups = conversion.h5_weights_to_tfjs_format(h5py.File(h5_path),
-                                                  split_by_layer=True)
-
-    # Check the loaded weights.
-    # Due to `split_by_layer=True` and the fact that the model has two layers,
-    # there should be two weight groups.
-    self.assertEqual(2, len(groups))
-    self.assertEqual(2, len(groups[0]))
-    kernel1 = groups[0][0]
-    self.assertEqual('MyDense30/kernel', kernel1['name'])
-    self.assertEqual('float32', kernel1['data'].dtype)
-    self.assertEqual((3, 4), kernel1['data'].shape)
-    self.assertTrue(np.allclose(np.ones([3, 4]), kernel1['data']))
-    bias1 = groups[0][1]
-    self.assertEqual('MyDense30/bias', bias1['name'])
-    self.assertEqual('float32', bias1['data'].dtype)
-    self.assertEqual((4,), bias1['data'].shape)
-    self.assertTrue(np.allclose(np.zeros([4]), bias1['data']))
-
-    self.assertEqual(1, len(groups[1]))
-    kernel2 = groups[1][0]
-    self.assertEqual('MyDense40/kernel', kernel2['name'])
-    self.assertEqual('float32', kernel2['data'].dtype)
-    self.assertEqual((4, 2), kernel2['data'].shape)
-    self.assertTrue(np.allclose(np.ones([4, 2]), kernel2['data']))
-
-  def testConvertModelWithNestedLayerNames(self):
-    model = tf.keras.Sequential()
-
-    # Add a layer with a nested layer name, i.e., a layer name with slash(es)
-    # in it.
-    model.add(tf.keras.layers.Dense(2, input_shape=[12], name='dense'))
-    model.add(tf.keras.layers.Dense(8, name='foo/dense'))
-    model.add(tf.keras.layers.Dense(4, name='foo/bar/dense'))
-    tfjs_path = os.path.join(self._tmp_dir, 'nested_layer_names_model')
-    conversion.save_keras_model(model, tfjs_path)
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'layers-model')
-    self.assertEqual(model_json['generatedBy'],
-                     'keras v%s' % keras.__version__)
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-
-    self.assertTrue(model_json['modelTopology'])
-    weights_manifest = model_json['weightsManifest']
-    weight_shapes = dict()
-    for group in weights_manifest:
-      for weight in group['weights']:
-        weight_shapes[weight['name']] = weight['shape']
-    self.assertEqual(
-        sorted(['dense/kernel', 'dense/bias', 'foo/dense/kernel',
-                'foo/dense/bias', 'foo/bar/dense/kernel',
-                'foo/bar/dense/bias']),
-        sorted(list(weight_shapes.keys())))
-    self.assertEqual([12, 2], weight_shapes['dense/kernel'])
-    self.assertEqual([2], weight_shapes['dense/bias'])
-    self.assertEqual([2, 8], weight_shapes['foo/dense/kernel'])
-    self.assertEqual([8], weight_shapes['foo/dense/bias'])
-    self.assertEqual([8, 4], weight_shapes['foo/bar/dense/kernel'])
-    self.assertEqual([4], weight_shapes['foo/bar/dense/bias'])
-
-  def testConvertMergedModelFromSimpleModelNoSplitByLayer(self):
-    input_tensor = tf.keras.layers.Input((3,))
-    dense1 = tf.keras.layers.Dense(
-        4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
-        name='MergedDense10')(input_tensor)
-    output = tf.keras.layers.Dense(
-        2, use_bias=False,
-        kernel_initializer='ones', name='MergedDense20')(dense1)
-    model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
-    h5_path = os.path.join(self._tmp_dir, 'MyModelMerged.h5')
-    model.save(h5_path)
-    # Ensure matching legacy serialization format
-    model.use_legacy_config = True
-
-    if six.PY3:
-      config_json = json.loads(model.to_json())
-    else:
-      config_json = json.loads(model.to_json(), encoding='utf8')
-
-    # Load the saved weights as a JSON string.
-    out, groups = conversion.h5_merged_saved_model_to_tfjs_format(
-        h5py.File(h5_path))
-    saved_topology = out['model_config']
-
-    # check the model topology was stored
-    self.assertEqual(config_json['class_name'], saved_topology['class_name'])
-    self.assertEqual(config_json['config'], saved_topology['config'])
-
-    # Check the loaded weights.
-    # By default, all weights of the model ought to be put in the same group.
-    self.assertEqual(1, len(groups))
-    self.assertEqual(keras.__version__, out['keras_version'])
-    self.assertEqual('tensorflow', out['backend'])
-    weight_group = groups[0]
-    self.assertEqual(3, len(weight_group))
-    kernel1 = weight_group[0]
-    self.assertEqual('MergedDense10/kernel', kernel1['name'])
-    self.assertEqual('float32', kernel1['data'].dtype)
-    self.assertEqual((3, 4), kernel1['data'].shape)
-    self.assertTrue(np.allclose(np.ones([3, 4]), kernel1['data']))
-    bias1 = weight_group[1]
-    self.assertEqual('MergedDense10/bias', bias1['name'])
-    self.assertEqual('float32', bias1['data'].dtype)
-    self.assertEqual((4,), bias1['data'].shape)
-    self.assertTrue(np.allclose(np.zeros([4]), bias1['data']))
-    kernel2 = weight_group[2]
-    self.assertEqual('MergedDense20/kernel', kernel2['name'])
-    self.assertEqual('float32', kernel2['data'].dtype)
-    self.assertEqual((4, 2), kernel2['data'].shape)
-    self.assertTrue(np.allclose(np.ones([4, 2]), kernel2['data']))
-
-  def testConvertMergedModelFromSimpleModelSplitByLayer(self):
-    input_tensor = tf.keras.layers.Input((3,))
-    dense1 = tf.keras.layers.Dense(
-        4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
-        name='MergedDense30')(input_tensor)
-    output = tf.keras.layers.Dense(
-        2, use_bias=False,
-        kernel_initializer='ones', name='MergedDense40')(dense1)
-    model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
-    h5_path = os.path.join(self._tmp_dir, 'MyModelMerged.h5')
-    model.save(h5_path)
-    # Ensure matching legacy serialization format
-    model.use_legacy_config = True
-
-    if six.PY3:
-      config_json = json.loads(model.to_json())
-    else:
-      config_json = json.loads(model.to_json(), encoding='utf8')
-
-    # Load the saved weights as a JSON string.
-    out, groups = conversion.h5_merged_saved_model_to_tfjs_format(
-        h5py.File(h5_path), split_by_layer=True)
-    saved_topology = out['model_config']
-
-    # check the model topology was stored
-    self.assertEqual(config_json['class_name'], saved_topology['class_name'])
-    self.assertEqual(config_json['config'], saved_topology['config'])
-
-    # Check the loaded weights.
-    # Due to `split_by_layer=True`, there ought to be two weight groups,
-    # because the model has two layers.
-    self.assertEqual(2, len(groups))
-    self.assertEqual(keras.__version__, out['keras_version'])
-    self.assertEqual('tensorflow', out['backend'])
-    self.assertEqual(2, len(groups[0]))
-    kernel1 = groups[0][0]
-    self.assertEqual('MergedDense30/kernel', kernel1['name'])
-    self.assertEqual('float32', kernel1['data'].dtype)
-    self.assertEqual((3, 4), kernel1['data'].shape)
-    self.assertTrue(np.allclose(np.ones([3, 4]), kernel1['data']))
-    bias1 = groups[0][1]
-    self.assertEqual('MergedDense30/bias', bias1['name'])
-    self.assertEqual('float32', bias1['data'].dtype)
-    self.assertEqual((4,), bias1['data'].shape)
-    self.assertTrue(np.allclose(np.zeros([4]), bias1['data']))
-    self.assertEqual(1, len(groups[1]))
-    kernel2 = groups[1][0]
-    self.assertEqual('MergedDense40/kernel', kernel2['name'])
-    self.assertEqual('float32', kernel2['data'].dtype)
-    self.assertEqual((4, 2), kernel2['data'].shape)
-    self.assertTrue(np.allclose(np.ones([4, 2]), kernel2['data']))
-
-  def testConvertWeightsFromSequentialModelNoSplitByLayer(self):
-    sequential_model = tf.keras.models.Sequential([
-        tf.keras.layers.Dense(
-            3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-            name='Dense10'),
-        tf.keras.layers.Dense(
-            1, use_bias=False, kernel_initializer='ones', name='Dense20')])
-    h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-    sequential_model.save_weights(h5_path)
-
-    # Load the saved weights as a JSON string.
-    groups = conversion.h5_weights_to_tfjs_format(h5py.File(h5_path))
-
-    # Check the loaded weights.
-    # Due to the default `split_by_layer=False`, there should be only one weight
-    # group.
-    self.assertEqual(1, len(groups))
-    self.assertEqual(3, len(groups[0]))
-    kernel1 = groups[0][0]
-    self.assertEqual('Dense10/kernel', kernel1['name'])
-    self.assertEqual('float32', kernel1['data'].dtype)
-    self.assertEqual((2, 3), kernel1['data'].shape)
-    self.assertTrue(np.allclose(np.ones([2, 3]).tolist(), kernel1['data']))
-    bias1 = groups[0][1]
-    self.assertEqual('Dense10/bias', bias1['name'])
-    self.assertEqual('float32', bias1['data'].dtype)
-    self.assertEqual((3,), bias1['data'].shape)
-    self.assertTrue(np.allclose(np.zeros([3]).tolist(), bias1['data']))
-    kernel2 = groups[0][2]
-    self.assertEqual('Dense20/kernel', kernel2['name'])
-    self.assertEqual('float32', kernel2['data'].dtype)
-    self.assertEqual((3, 1), kernel2['data'].shape)
-    self.assertTrue(np.allclose(np.ones([3, 1]).tolist(), kernel2['data']))
-
-  def testConvertWeightsFromSequentialModelSplitByLayer(self):
-    sequential_model = tf.keras.models.Sequential([
-        tf.keras.layers.Dense(
-            3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
-            name='Dense30'),
-        tf.keras.layers.Dense(
-            1, use_bias=False, kernel_initializer='ones', name='Dense40')])
-    h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
-    sequential_model.save_weights(h5_path)
-
-    # Load the saved weights as a JSON string.
-    groups = conversion.h5_weights_to_tfjs_format(h5py.File(h5_path),
-                                                  split_by_layer=True)
-
-    # Check the loaded weights.
-    # Due to the default `split_by_layer=True`, there should be two weight
-    # gropus, because the model has two layers.
-    self.assertEqual(2, len(groups))
-    self.assertEqual(2, len(groups[0]))
-    kernel1 = groups[0][0]
-    self.assertEqual('Dense30/kernel', kernel1['name'])
-    self.assertEqual('float32', kernel1['data'].dtype)
-    self.assertEqual((2, 3), kernel1['data'].shape)
-    self.assertTrue(np.allclose(np.ones([2, 3]).tolist(), kernel1['data']))
-    bias1 = groups[0][1]
-    self.assertEqual('Dense30/bias', bias1['name'])
-    self.assertEqual('float32', bias1['data'].dtype)
-    self.assertEqual((3,), bias1['data'].shape)
-    self.assertTrue(np.allclose(np.zeros([3]).tolist(), bias1['data']))
-
-    self.assertEqual(1, len(groups[1]))
-    kernel2 = groups[1][0]
-    self.assertEqual('Dense40/kernel', kernel2['name'])
-    self.assertEqual('float32', kernel2['data'].dtype)
-    self.assertEqual((3, 1), kernel2['data'].shape)
-    self.assertTrue(np.allclose(np.ones([3, 1]).tolist(), kernel2['data']))
-
-  def testSaveModelSucceedsForNonSequentialModel(self):
-    t_input = tf.keras.Input([2])
-    dense_layer = tf.keras.layers.Dense(3)
-    t_output = dense_layer(t_input)
-    model = tf.keras.Model(t_input, t_output)
-    conversion.save_keras_model(model, self._tmp_dir)
-
-    # Verify the content of the artifacts output directory.
-    self.assertTrue(
-        os.path.isfile(os.path.join(self._tmp_dir, 'group1-shard1of1.bin')))
-    model_json = json.load(
-        open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
-
-    topology_json = model_json['modelTopology']
-    self.assertIn('keras_version', topology_json)
-    self.assertIn('backend', topology_json)
-    self.assertIn('model_config', topology_json)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertIsInstance(weights_manifest, list)
-    self.assertEqual(1, len(weights_manifest))
-    self.assertIn('paths', weights_manifest[0])
-
-  def testSaveModelSucceedsForTfKerasNonSequentialModel(self):
-    t_input = tf.keras.Input([2])
-    dense_layer = tf.keras.layers.Dense(3)
-    t_output = dense_layer(t_input)
-    model = tf.keras.Model(t_input, t_output)
-
-    # `tf.keras.Model`s must be compiled before they can be saved.
-    model.compile(loss='mean_squared_error', optimizer='sgd')
-
-    conversion.save_keras_model(model, self._tmp_dir)
-
-    # Verify the content of the artifacts output directory.
-    self.assertTrue(
-        os.path.isfile(os.path.join(self._tmp_dir, 'group1-shard1of1.bin')))
-    model_json = json.load(
-        open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
-
-    topology_json = model_json['modelTopology']
-    self.assertIn('keras_version', topology_json)
-    self.assertIn('backend', topology_json)
-    self.assertIn('model_config', topology_json)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertIsInstance(weights_manifest, list)
-    self.assertEqual(1, len(weights_manifest))
-    self.assertIn('paths', weights_manifest[0])
-
-  def testSaveModelSucceedsForNestedKerasModel(self):
-    inner_model = tf.keras.Sequential([
-        tf.keras.layers.Dense(4, input_shape=[3], activation='relu'),
-        tf.keras.layers.Dense(3, activation='tanh')])
-    outer_model = tf.keras.Sequential()
-    outer_model.add(inner_model)
-    outer_model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
-
-    conversion.save_keras_model(outer_model, self._tmp_dir)
-
-    # Verify the content of the artifacts output directory.
-    self.assertTrue(
-        os.path.isfile(os.path.join(self._tmp_dir, 'group1-shard1of1.bin')))
-    model_json = json.load(
-        open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
-
-    topology_json = model_json['modelTopology']
-    self.assertIn('keras_version', topology_json)
-    self.assertIn('backend', topology_json)
-    self.assertIn('model_config', topology_json)
-
-    # Verify that all the layers' weights are present.
-    weights_manifest = model_json['weightsManifest']
-    self.assertIsInstance(weights_manifest, list)
-    weight_entries = []
-    for group in weights_manifest:
-      weight_entries.extend(group['weights'])
-    self.assertEqual(6, len(weight_entries))
-
-  def testSaveModelSucceedsForTfKerasSequentialModel(self):
-    model = tf.keras.Sequential([tf.keras.layers.Dense(1, input_shape=[2])])
-
-    # `tf.keras.Model`s must be compiled before they can be saved.
-    model.compile(loss='mean_squared_error', optimizer='sgd')
-
-    conversion.save_keras_model(model, self._tmp_dir)
-
-    # Verify the content of the artifacts output directory.
-    self.assertTrue(
-        os.path.isfile(os.path.join(self._tmp_dir, 'group1-shard1of1.bin')))
-    model_json = json.load(
-        open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
-
-    topology_json = model_json['modelTopology']
-    self.assertIn('keras_version', topology_json)
-    self.assertIn('backend', topology_json)
-    self.assertIn('model_config', topology_json)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertIsInstance(weights_manifest, list)
-    self.assertEqual(1, len(weights_manifest))
-    self.assertIn('paths', weights_manifest[0])
-
-  def testSavedModelSucceedsForExistingDirAndSequential(self):
-    artifacts_dir = os.path.join(self._tmp_dir, 'artifacts')
-    os.makedirs(artifacts_dir)
-    model = tf.keras.Sequential()
-    model.add(tf.keras.layers.Dense(3, input_shape=[2]))
-    conversion.save_keras_model(model, artifacts_dir)
-
-    # Verify the content of the artifacts output directory.
-    self.assertTrue(
-        os.path.isfile(os.path.join(artifacts_dir, 'group1-shard1of1.bin')))
-    model_json = json.load(
-        open(os.path.join(artifacts_dir, 'model.json'), 'rt'))
-
-    topology_json = model_json['modelTopology']
-    self.assertIn('keras_version', topology_json)
-    self.assertIn('backend', topology_json)
-    self.assertIn('model_config', topology_json)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertIsInstance(weights_manifest, list)
-    self.assertEqual(1, len(weights_manifest))
-    self.assertIn('paths', weights_manifest[0])
-
-  def testSavedModelSucceedsForCustomShardSize(self):
-    model = tf.keras.Sequential([
-        tf.keras.layers.Dense(1, input_shape=[2], activation='relu'),
-        tf.keras.layers.Dense(3, activation='tanh')
-    ])
-
-    weights = model.get_weights()
-    total_weight_bytes = sum(np.size(w) for w in weights) * 4
-
-    # Due to the shard size, there ought to be 4 shards after conversion.
-    weight_shard_size_bytes = int(total_weight_bytes * 0.3)
-
-    # Convert Keras model to tfjs_layers_model format.
-    conversion.save_keras_model(model, self._tmp_dir,
-                                weight_shard_size_bytes=weight_shard_size_bytes)
-
-    weight_files = sorted(glob.glob(os.path.join(self._tmp_dir, 'group*.bin')))
-    self.assertEqual(len(weight_files), 4)
-    weight_file_sizes = [os.path.getsize(f) for f in weight_files]
-    self.assertEqual(sum(weight_file_sizes), total_weight_bytes)
-    self.assertEqual(weight_file_sizes[0], weight_file_sizes[1])
-    self.assertEqual(weight_file_sizes[0], weight_file_sizes[2])
-    self.assertLess(weight_file_sizes[3], weight_file_sizes[0])
-
-  def testSavedModelRaisesErrorIfArtifactsDirExistsAsAFile(self):
-    artifacts_dir = os.path.join(self._tmp_dir, 'artifacts')
-    with open(artifacts_dir, 'wt') as f:
-      f.write('foo\n')
-    t_input = tf.keras.Input([2])
-    dense_layer = tf.keras.layers.Dense(3)
-    t_output = dense_layer(t_input)
-    model = tf.keras.Model(t_input, t_output)
-    with self.assertRaisesRegexp(  # pylint: disable=deprecated-method
-        ValueError, r'already exists as a file'):
-      conversion.save_keras_model(model, artifacts_dir)
-
-  def testTranslateBatchNormalizationV1ClassName(self):
-    # The config JSON of a model consisting of a "BatchNormalizationV1" layer.
-    # pylint: disable=line-too-long
-    json_object = json.loads(
-        '{"class_name": "Sequential", "keras_version": "2.2.4-tf", "config": {"layers": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "GlorotUniform", "config": {"dtype": "float32", "seed": null}}, "name": "dense", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 10, "batch_input_shape": [null, 3], "use_bias": true, "activity_regularizer": null}}, {"class_name": "BatchNormalizationV1", "config": {"beta_constraint": null, "gamma_initializer": {"class_name": "Ones", "config": {"dtype": "float32"}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "name": "batch_normalization_v1", "dtype": "float32", "trainable": true, "moving_variance_initializer": {"class_name": "Ones", "config": {"dtype": "float32"}}, "beta_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "scale": true, "axis": [1], "epsilon": 0.001, "gamma_constraint": null, "gamma_regularizer": null, "beta_regularizer": null, "momentum": 0.99, "center": true}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "GlorotUniform", "config": {"dtype": "float32", "seed": null}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "linear", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 1, "use_bias": true, "activity_regularizer": null}}], "name": "sequential"}, "backend": "tensorflow"}')
-    # pylint: enable=line-too-long
-    conversion.translate_class_names(json_object)
-    # Some class names should not have been changed be translate_class_names().
-    self.assertEqual(json_object['class_name'], 'Sequential')
-    self.assertEqual(json_object['keras_version'], '2.2.4-tf')
-    self.assertEqual(json_object['config']['layers'][0]['class_name'], 'Dense')
-    # The translation should have happend:
-    #     BatchNormalizationV1 --> BatchNormalization.
-    self.assertEqual(
-        json_object['config']['layers'][1]['class_name'], 'BatchNormalization')
-    self.assertEqual(json_object['config']['layers'][2]['class_name'], 'Dense')
-
-    # Assert that converted JSON can be reconstituted as a model object.
-    model = tf.keras.models.model_from_json(json.dumps(json_object))
-    self.assertIsInstance(model, tf.keras.Sequential)
-    self.assertEqual(model.input_shape, (None, 3))
-    self.assertEqual(model.output_shape, (None, 1))
-    self.assertEqual(model.layers[0].units, 10)
-    self.assertEqual(model.layers[2].units, 1)
-
-  def testTranslateUnifiedGRUAndLSTMClassName(self):
-    # The config JSON of a model consisting of a "UnifiedGRU" layer
-    # and a "UnifiedLSTM" layer.
-    # pylint: disable=line-too-long
-    json_object = json.loads(
-        '{"class_name": "Sequential", "keras_version": "2.2.4-tf", "config": {"layers": [{"class_name": "UnifiedGRU", "config": {"recurrent_activation": "sigmoid", "dtype": "float32", "trainable": true, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"seed": null, "gain": 1.0}}, "use_bias": true, "bias_regularizer": null, "return_state": false, "unroll": false, "activation": "tanh", "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4, 3], "activity_regularizer": null, "recurrent_dropout": 0.0, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "kernel_constraint": null, "time_major": false, "dropout": 0.0, "stateful": false, "reset_after": true, "recurrent_regularizer": null, "name": "unified_gru", "bias_constraint": null, "go_backwards": false, "implementation": 1, "kernel_regularizer": null, "return_sequences": true, "recurrent_constraint": null}}, {"class_name": "UnifiedLSTM", "config": {"recurrent_activation": "sigmoid", "dtype": "float32", "trainable": true, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"seed": null, "gain": 1.0}}, "use_bias": true, "bias_regularizer": null, "return_state": false, "unroll": false, "activation": "tanh", "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 2, "unit_forget_bias": true, "activity_regularizer": null, "recurrent_dropout": 0.0, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "kernel_constraint": null, "time_major": false, "dropout": 0.0, "stateful": false, "recurrent_regularizer": null, "name": "unified_lstm", "bias_constraint": null, "go_backwards": false, "implementation": 1, "kernel_regularizer": null, "return_sequences": false, "recurrent_constraint": null}}], "name": "sequential"}, "backend": "tensorflow"}')
-    # pylint: enable=line-too-long
-    conversion.translate_class_names(json_object)
-    # Some class names should not have been changed be translate_class_names().
-    self.assertEqual(json_object['class_name'], 'Sequential')
-    self.assertEqual(json_object['keras_version'], '2.2.4-tf')
-    # The translation should have happend:
-    #     UnifiedGRU --> GRU.
-    #     UnifiedLSTM --> LSTM.
-    self.assertEqual(json_object['config']['layers'][0]['class_name'], 'GRU')
-    self.assertEqual(json_object['config']['layers'][1]['class_name'], 'LSTM')
-
-    # Assert that converted JSON can be reconstituted as a model object.
-    model = tf.keras.models.model_from_json(json.dumps(json_object))
-    self.assertIsInstance(model, tf.keras.Sequential)
-    self.assertEqual(model.input_shape, (None, 4, 3))
-    self.assertEqual(model.output_shape, (None, 2))
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_tfjs_loader.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_tfjs_loader.py
deleted file mode 100644
index c6b1a5f6b..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_tfjs_loader.py
+++ /dev/null
@@ -1,368 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Library for loading a Keras model from TensorFlow.js format."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import os
-import uuid
-import zipfile
-import datetime
-import six
-import tensorflow.compat.v2 as tf
-from tensorflowjs.converters import tf_module_mapper
-from tensorflowjs.converters import keras_h5_conversion
-from tensorflowjs.converters.tf_module_mapper import TFCLASS_MODULE_MAP
-from tensorflowjs import read_weights
-
-_CONFIG_FILENAME = "config.json"
-_METADATA_FILENAME = "metadata.json"
-_VARS_FNAME = "model.weights"
-
-
-def _deserialize_keras_model(model_topology_json,
-                             weight_entries=None,
-                             use_unique_name_scope=False):
-  """Internal helper method for deserializing a Keras Model.
-
-  Args:
-    model_topology_json: content of the JSON containing model topology, in
-      Keras (i.e., tfjs-layers) format. It can be any of the following types:
-      - A JSON object, i.e., a `dict`.
-      - A `str` or `buffer`, in which case it will be parsed as a JSON object.
-      - A `file` object or `file`-like object containing the JSON, in which
-        case it will be read with the `read()` method and the content parsed
-        as a JSON object.
-    weight_entries: Weight entries, in tensorflow.js format, as a `list`.
-    use_unique_name_scope: Use a unique ID as the name scope for the loaded
-      model. This may facilitate loading of multiple Keras models in the
-      same TensorFlow Graph or Session context. Default: `False`.
-  """
-  if isinstance(model_topology_json, (six.string_types, bytes)):
-    model_topology_json = json.loads(tf.compat.as_text(model_topology_json))
-  elif not isinstance(model_topology_json, dict):
-    model_topology_json = json.load(model_topology_json)
-
-  if 'model_config' in model_topology_json:
-    model_topology_json = model_topology_json['model_config']
-  unique_name_scope = uuid.uuid4().hex if use_unique_name_scope else None
-  with tf.compat.v1.name_scope(unique_name_scope):
-    model = tf.keras.models.model_from_json(json.dumps(model_topology_json))
-
-  if weight_entries:
-    weights_dict = dict()
-    for weight_entry in weight_entries:
-      weights_dict[weight_entry['name']] = weight_entry['data']
-
-    # Collect weight names from the model, in the same order as the internal
-    # ordering of model.set_weights() used below.
-    weight_names = []
-    for layer in model.layers:
-      for w in layer.weights:
-        weight_names.append(
-            keras_h5_conversion.normalize_weight_name(
-                w.name[len(unique_name_scope) + 1:])
-            if use_unique_name_scope
-            else keras_h5_conversion.normalize_weight_name(w.name))
-
-    # Prepare list of weight values for calling set_weights().
-    weights_list = []
-
-    for name in weight_names:
-      if name in weights_dict:
-        weights_list.append(weights_dict[name])
-      else:
-        # TF 2.2.0 added cell name to the weight name in the format of
-        # layer_name/cell_name/weight_name, we need to remove
-        # the inner cell name.
-        tokens = name.split('/')
-        shorten_name = '/'.join(tokens[0:-2] + [tokens[-1]])
-        weights_list.append(weights_dict[shorten_name])
-
-    model.set_weights(weights_list)
-
-  return model
-
-def _deserialize_keras_keras_model(model_topology_json,
-                             weight_entries=None,
-                             use_unique_name_scope=False):
-  """Internal helper method for deserializing a Keras V3 Model.
-
-  Args:
-    model_topology_json: content of the JSON containing model topology, in
-      Keras (i.e., tfjs-layers) format. It can be any of the following types:
-      - A JSON object, i.e., a `dict`.
-      - A `str` or `buffer`, in which case it will be parsed as a JSON object.
-      - A `file` object or `file`-like object containing the JSON, in which
-        case it will be read with the `read()` method and the content parsed
-        as a JSON object.
-    weight_entries: Weight entries, in tensorflow.js format, as a `list`.
-    use_unique_name_scope: Use a unique ID as the name scope for the loaded
-      model. This may facilitate loading of multiple Keras models in the
-      same TensorFlow Graph or Session context. Default: `False`.
-  """
-  if isinstance(model_topology_json, (six.string_types, bytes)):
-    model_topology_json = json.loads(tf.compat.as_text(model_topology_json))
-  elif not isinstance(model_topology_json, dict):
-    model_topology_json = json.load(model_topology_json)
-
-  if 'model_config' in model_topology_json:
-    # Build the map between class and its corresponding module in TF.
-    _generate_v3_keys(model_topology_json['model_config'])
-    model_topology_json = model_topology_json['model_config']
-
-  model = tf.keras.models.model_from_json(json.dumps(model_topology_json))
-
-  if weight_entries:
-    weights_dict = dict()
-    for weight_entry in weight_entries:
-      weights_dict[weight_entry['name']] = weight_entry['data']
-
-    # Collect weight names from the model, in the same order as the internal
-    # ordering of model.set_weights() used below.
-
-    weight_names = []
-    for layer in model.layers:
-      for index, w in enumerate(layer.weights):
-        weight_names.append(layer.name + '/' + str(index))
-
-
-    # Prepare list of weight values for calling set_weights().
-    weights_list = []
-
-    for name in weight_names:
-      if name in weights_dict:
-        weights_list.append(weights_dict[name])
-      else:
-        raise Exception(f"${name} does not exist in weights entries.")
-
-    model.set_weights(weights_list)
-
-  return model
-
-def _check_config_json(config_json):
-  if not isinstance(config_json, dict):
-    raise TypeError(
-        'The JSON content is required to be a `dict`, but found %s.' %
-        type(config_json))
-  if 'modelTopology' not in config_json:
-    raise KeyError('Field "modelTopology" is missing from the JSON content.')
-
-
-def _get_weights_manifest_from_config_json(config_json):
-  if 'weightsManifest' not in config_json:
-    raise KeyError(
-        'Field "weightsManifest" is missing from the JSON content.')
-  return config_json['weightsManifest']
-
-def _generate_v3_keys(config):
-  if isinstance(config, dict):
-    list_of_keys = list(config.keys())
-    for key in list_of_keys:
-      _generate_v3_keys(config[key])
-    if 'class_name' in list_of_keys:
-      config['module'] = tf_module_mapper.get_module_path(config['class_name'])
-      # Put registred name as None since we do not support
-      # custom object saving when we save the model.
-      config['registered_name'] = None
-
-  elif isinstance(config, list):
-    for item in config:
-      _generate_v3_keys(item)
-
-
-def deserialize_keras_model(config_json,
-                            weight_data=None,
-                            use_unique_name_scope=False):
-  """Deserialize a Keras Model from buffers or file-like objects.
-
-  Args:
-    config: content of the JSON containing model topology and weights manifest,
-      in Keras (i.e., tfjs-layers) format. It can be one of the following
-      types:
-      - A JSON object, i.e., a `dict`.
-      - A `str` or `buffer`, in which case it will be parsed as a JSON object.
-      - A `file` object or `file`-like object containing the JSON, in which
-        case it will be read with the `read()` method and the content parsed
-        as a JSON object.
-    weight_data: a `list` of `buffer`s or a `list` of `file`-like objects
-      (e.g., `io.BytesIO`) containing the binary weight values.
-      If `None`, the weights of the model will not be loaded (i.e., only the
-      topology of the model will be loaded).
-    use_unique_name_scope: Use a unique ID as the name scope for the loaded
-      model. This may facilitate loading of multiple Keras models in the
-      same TensorFlow Graph or Session context. Default: `False`.
-  """
-  if isinstance(config_json, (six.string_types, bytes)):
-    config_json = json.loads(tf.compat.as_text(config_json))
-  elif not isinstance(config_json, dict):
-    config_json = json.load(config_json)
-  _check_config_json(config_json)
-  model_topology_json = config_json['modelTopology']
-
-  weight_entries = None
-  if weight_data:
-    weights_manifest = _get_weights_manifest_from_config_json(config_json)
-    if not isinstance(weight_data, list):
-      raise ValueError(
-          'weight_data must be a list, but is %s' % type(weight_data))
-    if hasattr(weight_data[0], 'read'):
-      # weight_data is a list of file-like objects.
-      weight_data = [d.read() for d in weight_data]
-    weight_entries = read_weights.decode_weights(weights_manifest,
-                                                 weight_data,
-                                                 flatten=True)
-
-  return _deserialize_keras_model(model_topology_json,
-                                  weight_entries=weight_entries,
-                                  use_unique_name_scope=use_unique_name_scope)
-
-
-def load_keras_model(config_json_path,
-                     weights_path_prefix=None,
-                     weights_data_buffers=None,
-                     load_weights=True,
-                     use_unique_name_scope=False):
-  """Load a Keras Model from TensorFlow.js-format artifacts from file system
-
-  Args:
-    config_json_path: Path to the TensorFlow.js-format JSON file that includes
-      the model topology and weights manifest.
-    weights_path_prefix: Optional path prefix for the weights files.
-      If not specified (`None`), will assume the prefix is the same directory
-      as the dirname of `config_json_path`.
-    weights_data_buffers: A buffer of a `list` of buffers containing the weight
-      values concatenated and sharded in the order as specified by the
-      weights manifest at `config_json_path`. This argument is mutually
-      exclusive with `weights_path_prefix`.
-    load_weights: Whether the weights are to be loaded according
-      to the weights manifest at `config_json_path`. Default: `True`.
-    use_unique_name_scope: Use a unique ID as the name scope for the loaded
-      model. This may facilitate loading of multiple Keras models in the
-      same TensorFlow Graph or Session context. Default: `False`.
-
-  Returns:
-    The loaded instance of `tf.keras.Model`.
-
-  Raises:
-    TypeError, if the format of the JSON content of `config_json_path` has an
-      invalid format.
-    KeyError, if required keys do not exist in the JSON content of
-      `config_json_path`.
-    ValueError, if both `weights_data_buffers` and `weights_path_prefix` are
-      provided.
-  """
-  if weights_data_buffers and weights_path_prefix:
-    raise ValueError(
-        'The arguments weights_data_buffers and weights_path_prefix are '
-        'mutually exclusive and should not be both specified.')
-
-  with open(config_json_path, 'rt') as f:
-    config_json = json.load(f)
-    _check_config_json(config_json)
-
-  weight_entries = None
-  if load_weights:
-    weights_manifest = _get_weights_manifest_from_config_json(config_json)
-
-    if not weights_data_buffers and not weights_path_prefix:
-      weights_path_prefix = os.path.dirname(
-          os.path.realpath(config_json_path))
-    if not os.path.isdir(weights_path_prefix):
-      raise ValueError(
-          'Weights path prefix is not an existing directory: %s' %
-          weights_path_prefix)
-    if weights_path_prefix:
-      weight_entries = read_weights.read_weights(weights_manifest,
-                                                 weights_path_prefix,
-                                                 flatten=True)
-    else:
-      weight_entries = read_weights.decode_weights(weights_manifest,
-                                                   weights_data_buffers,
-                                                   flatten=True)
-
-  return _deserialize_keras_model(config_json['modelTopology'],
-                                  weight_entries=weight_entries,
-                                  use_unique_name_scope=use_unique_name_scope)
-
-def load_keras_keras_model(config_json_path,
-                     weights_path_prefix=None,
-                     weights_data_buffers=None,
-                     load_weights=True,
-                     use_unique_name_scope=False):
-  """Load a Keras Model from TensorFlow.js-format artifacts from file system
-
-  Args:
-    config_json_path: Path to the TensorFlow.js-format JSON file that includes
-      the model topology and weights manifest.
-    weights_path_prefix: Optional path prefix for the weights files.
-      If not specified (`None`), will assume the prefix is the same directory
-      as the dirname of `config_json_path`.
-    weights_data_buffers: A buffer of a `list` of buffers containing the weight
-      values concatenated and sharded in the order as specified by the
-      weights manifest at `config_json_path`. This argument is mutually
-      exclusive with `weights_path_prefix`.
-    load_weights: Whether the weights are to be loaded according
-      to the weights manifest at `config_json_path`. Default: `True`.
-    use_unique_name_scope: Use a unique ID as the name scope for the loaded
-      model. This may facilitate loading of multiple Keras models in the
-      same TensorFlow Graph or Session context. Default: `False`.
-
-  Returns:
-    The loaded instance of `tf.keras.Model`.
-
-  Raises:
-    TypeError, if the format of the JSON content of `config_json_path` has an
-      invalid format.
-    KeyError, if required keys do not exist in the JSON content of
-      `config_json_path`.
-    ValueError, if both `weights_data_buffers` and `weights_path_prefix` are
-      provided.
-  """
-  if weights_data_buffers and weights_path_prefix:
-    raise ValueError(
-        'The arguments weights_data_buffers and weights_path_prefix are '
-        'mutually exclusive and should not be both specified.')
-
-  with open(config_json_path, 'rt') as f:
-    config_json = json.load(f)
-    _check_config_json(config_json)
-
-  weight_entries = None
-  if load_weights:
-    weights_manifest = _get_weights_manifest_from_config_json(config_json)
-
-    if not weights_data_buffers and not weights_path_prefix:
-      weights_path_prefix = os.path.dirname(
-          os.path.realpath(config_json_path))
-    if not os.path.isdir(weights_path_prefix):
-      raise ValueError(
-          'Weights path prefix is not an existing directory: %s' %
-          weights_path_prefix)
-    if weights_path_prefix:
-      weight_entries = read_weights.read_weights(weights_manifest,
-                                                 weights_path_prefix,
-                                                 flatten=True)
-    else:
-      weight_entries = read_weights.decode_weights(weights_manifest,
-                                                   weights_data_buffers,
-                                                   flatten=True)
-
-  return _deserialize_keras_keras_model(config_json['modelTopology'],
-                                     weight_entries=weight_entries,
-                                     use_unique_name_scope=use_unique_name_scope)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_tfjs_loader_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_tfjs_loader_test.py
deleted file mode 100644
index a9a166d71..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/keras_tfjs_loader_test.py
+++ /dev/null
@@ -1,486 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for keras_tfjs_loader."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import glob
-import io
-import json
-import os
-import shutil
-import tempfile
-
-import numpy as np
-import tensorflow.compat.v2 as tf
-
-from tensorflowjs.converters import keras_h5_conversion
-from tensorflowjs.converters import keras_tfjs_loader
-
-
-class LoadKerasModelTest(tf.test.TestCase):
-
-  def setUp(self):
-    self._tmp_dir = tempfile.mkdtemp()
-    super(LoadKerasModelTest, self).setUp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    tf.compat.v1.reset_default_graph()
-    super(LoadKerasModelTest, self).tearDown()
-
-  def _saveKerasModelForTest(self, path):
-    model = tf.keras.Sequential()
-    model.use_legacy_config = True
-    model.add(tf.keras.layers.Dense(
-        2, input_shape=[12], bias_initializer='random_normal', name='dense'))
-    model.add(tf.keras.layers.Dense(
-        8, bias_initializer='random_normal', name='foo/dense'))
-    model.add(tf.keras.layers.Dense(
-        4, bias_initializer='random_normal', name='foo/bar/dense'))
-    keras_h5_conversion.save_keras_model(model, path)
-    return model
-
-
-  def _saveRNNKerasModelForTest(self, path):
-    model = tf.keras.Sequential()
-    model.use_legacy_config = True
-    model.add(tf.keras.layers.Embedding(100, 20, input_shape=[10]))
-    model.add(tf.keras.layers.SimpleRNN(4))
-    keras_h5_conversion.save_keras_model(model, path)
-    return model
-
-  def testLoadKerasModelAndWeights(self):
-    """Test loading of model and its weights."""
-    # Use separate tf.Graph and tf.compat.v1.Session contexts to
-    # prevent name collision.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-      model1_weight_values = model1.get_weights()
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.load_keras_model(
-          os.path.join(tfjs_path, 'model.json'))
-      model2.use_legacy_config = True
-      # Verify the equality of all the weight values.
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertAllClose(model1_weight_value, model2_weight_value)
-
-      # The two model JSONs should match exactly.
-      self.assertEqual(model1.to_json(), model2.to_json())
-
-  def testLoadKerasRNNModelAndWeights(self):
-    """Test loading of model and its weights."""
-    # Use separate tf.Graph and tf.compat.v1.Session contexts to
-    # prevent name collision.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveRNNKerasModelForTest(tfjs_path)
-      model1_weight_values = model1.get_weights()
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.load_keras_model(
-          os.path.join(tfjs_path, 'model.json'))
-      model2.use_legacy_config = True
-      # Verify the equality of all the weight values.
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertAllClose(model1_weight_value, model2_weight_value)
-
-      # The two model JSONs should match exactly.
-      self.assertEqual(model1.to_json(), model2.to_json())
-
-  def testDeserializeKerasModelTopologyOnlyFromBytesIO(self):
-    """Test loading of model (only topology) from a BytesIO object."""
-    # Use separate tf.Graph and tf.compat.v1.Session contexts to prevent
-    # name collision.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-
-    # Read the content of model.json into a BytesIO
-    buff = io.BytesIO()
-    buff_writer = io.BufferedWriter(buff)
-    with open(os.path.join(tfjs_path, 'model.json'), 'rb') as f:
-      buff_writer.write(f.read())
-    buff_writer.flush()
-    buff_writer.seek(0)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.deserialize_keras_model(buff.read())
-      model2.use_legacy_config = True
-
-      # The two model JSONs should match exactly.
-      self.assertEqual(model1.to_json(), model2.to_json())
-
-  def testDeserializeKerasModelTopologyOnlyFromJSONDict(self):
-    """Test loading of model (only topology) from a JSON Dict."""
-    # Use separate tf.Graph and tf.compat.v1.Session contexts to prevent
-    # name collision.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-
-    # Read the content of model.json into a BytesIO
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      config_json = json.load(f)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.deserialize_keras_model(config_json)
-      model2.use_legacy_config = True
-      # The two model JSONs should match exactly.
-      self.assertEqual(model1.to_json(), model2.to_json())
-
-  def testDeserializeKerasModelTopologyAndWeightsFromBuffers(self):
-    """Test loading of model and its weights from buffers."""
-    # Use separate tf.Graph and tf.compat.v1.Session contexts to prevent
-    # name collision.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-      model1_weight_values = model1.get_weights()
-
-    # Read the content of model.json into a BytesIO object.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rb') as f:
-      json_buff = f.read()
-
-    weight_paths = sorted(glob.glob(os.path.join(tfjs_path, 'group*')))
-    weight_buffers = []
-    for path in weight_paths:
-      with open(path, 'rb') as f:
-        weight_buffers.append(f.read())
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.deserialize_keras_model(
-          json_buff, weight_data=weight_buffers)
-      model2.use_legacy_config = True
-
-      # Verify the equality of all the weight values.
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertAllClose(model1_weight_value, model2_weight_value)
-
-      # The two model JSONs should match exactly.
-      self.assertEqual(model1.to_json(), model2.to_json())
-
-  def testDeserializeKerasModelTopologyAndWeightsFromFileObjects(self):
-    """Test loading of model and its weights using file objects."""
-    # Use separate tf.Graph and tf.compat.v1.Session contexts to prevent
-    # name collision.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-      model1_weight_values = model1.get_weights()
-
-    # Read the content of model.json into a file object.
-    json_file = open(os.path.join(tfjs_path, 'model.json'), 'rt')
-
-    weight_paths = sorted(glob.glob(os.path.join(tfjs_path, 'group*')))
-    weight_files = [open(path, 'rb') for path in weight_paths]
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.deserialize_keras_model(
-          json_file, weight_files)
-      model2.use_legacy_config = True
-
-      # Verify the equality of all the weight values.
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertAllClose(model1_weight_value, model2_weight_value)
-
-      # The two model JSONs should match exactly.
-      self.assertEqual(model1.to_json(), model2.to_json())
-
-    json_file.close()
-    for f in weight_files:
-      f.close()
-
-  def testLoadKerasModelWithCurrentWorkingDirectoryRelativePath(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-      model1_weight_values = model1.get_weights()
-
-    os.chdir(tfjs_path)
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # Use a relative path under the current working directory.
-      model2 = keras_tfjs_loader.load_keras_model('model.json')
-      model2.use_legacy_config = True
-      # Verify the equality of all the weight values.
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertAllClose(model1_weight_value, model2_weight_value)
-
-      # The two model JSONs should match exactly.
-      self.assertEqual(model1.to_json(), model2.to_json())
-    os.chdir("/")
-
-  def testLoadKerasModelWithoutWeights(self):
-    """Test loading of model topology only, without loading weight values."""
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-      model1_weight_values = model1.get_weights()
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.load_keras_model(
-          os.path.join(tfjs_path, 'model.json'), load_weights=False)
-      model2.use_legacy_config = True
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertEqual(model1_weight_value.dtype,
-                         model2_weight_value.dtype)
-        self.assertEqual(model1_weight_value.shape,
-                         model2_weight_value.shape)
-        # The weights should be unequal, because `load_weights=False`.
-        self.assertNotEqual(model1_weight_value.tobytes(),
-                            model2_weight_value.tobytes())
-
-      self.assertEqual(model1.to_json(), model2.to_json())
-
-  def testLoadKerasModelFromNonDefaultWeightsPathWorks(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-      model1_weight_values = model1.get_weights()
-
-      # Move the model.json file to a different folder.
-      model_json_dir = os.path.join(tfjs_path, 'model_json_dir')
-      os.makedirs(model_json_dir)
-      new_model_json_path = os.path.join(model_json_dir, 'model.json')
-      shutil.move(os.path.join(tfjs_path, 'model.json'), new_model_json_path)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.load_keras_model(
-          new_model_json_path, weights_path_prefix=tfjs_path)
-      model2.use_legacy_config = True
-      # Verify the equality of all the weight values.
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertAllClose(model1_weight_value, model2_weight_value)
-
-      self.assertEqual(model1.to_json(), model2.to_json())
-
-  def testLoadKerasModelWithUniqueNameScopeInTheSameGraphContext(self):
-    """Test enabling unique name scope during model loading."""
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-      model1_weight_values = model1.get_weights()
-
-      model2 = keras_tfjs_loader.load_keras_model(
-          os.path.join(tfjs_path, 'model.json'), use_unique_name_scope=True)
-
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertAllClose(model1_weight_value, model2_weight_value)
-
-      # Verify that model1's weight names are suffixes of model2's weight names.
-      model1_weight_names = [w.name for w in model1.weights]
-      model2_weight_names = [w.name for w in model2.weights]
-      self.assertEqual(len(model1_weight_names), len(model2_weight_names))
-      for name1, name2 in zip(model1_weight_names, model2_weight_names):
-        self.assertTrue(name2.endswith(name1))
-
-  def testLoadKerasModelFromDataBuffers(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      model1 = self._saveKerasModelForTest(tfjs_path)
-      model1_weight_values = model1.get_weights()
-
-      with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-        weights_manifest = json.load(f)['weightsManifest']
-
-      data_buffers = []
-      for group in weights_manifest:
-        data_buffer = b''
-        for path in group['paths']:
-          with open(os.path.join(tfjs_path, path), 'rb') as f:
-            data_buffer += f.read()
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.load_keras_model(
-          os.path.join(tfjs_path, 'model.json'),
-          weights_data_buffers=data_buffers)
-      model2.use_legacy_config = True
-      # Verify the equality of all the weight values.
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertAllClose(model1_weight_value, model2_weight_value)
-
-      self.assertEqual(model1.to_json(), model2.to_json())
-
-  def testLoadNestedKerasModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      inner_model = tf.keras.Sequential([
-          tf.keras.layers.Dense(4, input_shape=[3], activation='relu'),
-          tf.keras.layers.Dense(3, activation='tanh')])
-      outer_model = tf.keras.Sequential()
-      outer_model.add(inner_model)
-      outer_model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
-
-      x = np.ones([1, 3], dtype=np.float32)
-      predict_out = outer_model.predict(x)
-
-      save_dir = os.path.join(self._tmp_dir, 'nested_model')
-      keras_h5_conversion.save_keras_model(outer_model, save_dir)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.load_keras_model(
-          os.path.join(save_dir, 'model.json'), use_unique_name_scope=True)
-      self.assertAllClose(predict_out, model2.predict(x))
-
-  def testLoadNestedTfKerasModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      inner_model = tf.keras.Sequential([
-          tf.keras.layers.Dense(4, input_shape=[3], activation='relu'),
-          tf.keras.layers.Dense(3, activation='tanh')])
-      outer_model = tf.keras.Sequential()
-      outer_model.add(inner_model)
-      outer_model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
-      outer_model.compile(loss='binary_crossentropy', optimizer='sgd')
-
-      x = np.ones([1, 3], dtype=np.float32)
-      predict_out = outer_model.predict(x)
-
-      save_dir = os.path.join(self._tmp_dir, 'nested_model')
-      keras_h5_conversion.save_keras_model(outer_model, save_dir)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.load_keras_model(
-          os.path.join(save_dir, 'model.json'), use_unique_name_scope=True)
-      self.assertAllClose(predict_out, model2.predict(x))
-
-  def testLoadKerasModeFromNonexistentWeightsPathRaisesError(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      self._saveKerasModelForTest(tfjs_path)
-      with self.assertRaises(ValueError):
-        keras_tfjs_loader.load_keras_model(
-            os.path.join(tfjs_path, 'model.json'),
-            weights_path_prefix=os.path.join(self._tmp_dir, 'nonexistent'))
-
-  def testUsingBothWeightsDataBuffersAndWeightsPathPrefixRaisesError(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      self._saveKerasModelForTest(tfjs_path)
-
-      with self.assertRaises(ValueError):
-        keras_tfjs_loader.load_keras_model(
-            os.path.join(tfjs_path, 'model.json'),
-            weights_data_buffers=[b'foo'], weights_path_prefix='bar')
-
-  def testInvalidJSONRaisesError(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      tfjs_path = os.path.join(self._tmp_dir, 'model_for_test')
-      self._saveKerasModelForTest(tfjs_path)
-
-      # Make some changes to the model.json file content to create an invalid
-      # file content.
-      model_json_path = os.path.join(tfjs_path, 'model.json')
-      with open(model_json_path, 'rt') as f:
-        model_json_content = f.read()
-      with open(model_json_path, 'wt') as f:
-        f.write('[' + model_json_content + ']')
-
-      with self.assertRaises(ValueError):
-        keras_tfjs_loader.load_keras_model(
-            model_json_path,
-            weights_data_buffers=[b'foo'], weights_path_prefix='bar')
-
-  def testLoadFunctionalKerasModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      input1 = tf.keras.Input([4])
-      x1 = tf.keras.layers.Dense(2, activation='relu')(input1)
-      x1 = tf.keras.layers.BatchNormalization()(x1)
-
-      input2 = tf.keras.Input([10])
-      x2 = tf.keras.layers.Dense(5, activation='relu')(input2)
-      x2 = tf.keras.layers.BatchNormalization()(x2)
-
-      y = tf.keras.layers.Concatenate()([x1, x2])
-      y = tf.keras.layers.Dense(1, activation='sigmoid')(y)
-
-      model = tf.keras.Model([input1, input2], y)
-      model.compile(loss='binary_crossentropy', optimizer='sgd')
-
-      input1_val = np.ones([1, 4])
-      input2_val = np.ones([1, 10])
-      predict_out = model.predict([input1_val, input2_val])
-
-      save_dir = os.path.join(self._tmp_dir, 'functional_model')
-      keras_h5_conversion.save_keras_model(model, save_dir)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.load_keras_model(
-          os.path.join(save_dir, 'model.json'))
-      self.assertAllClose(
-          predict_out, model2.predict([input1_val, input2_val]))
-
-  def testLoadFunctionalTfKerasModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      input1 = tf.keras.Input([4])
-      x1 = tf.keras.layers.Dense(2, activation='relu')(input1)
-      x1 = tf.keras.layers.BatchNormalization()(x1)
-
-      input2 = tf.keras.Input([10])
-      x2 = tf.keras.layers.Dense(5, activation='relu')(input2)
-      x2 = tf.keras.layers.BatchNormalization()(x2)
-
-      y = tf.keras.layers.Concatenate()([x1, x2])
-      y = tf.keras.layers.Dense(1, activation='sigmoid')(y)
-
-      model = tf.keras.Model([input1, input2], y)
-      model.compile(loss='binary_crossentropy', optimizer='sgd')
-
-      input1_val = np.ones([1, 4])
-      input2_val = np.ones([1, 10])
-      predict_out = model.predict([input1_val, input2_val])
-
-      save_dir = os.path.join(self._tmp_dir, 'functional_model')
-      keras_h5_conversion.save_keras_model(model, save_dir)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model2 = keras_tfjs_loader.load_keras_model(
-          os.path.join(save_dir, 'model.json'))
-      self.assertAllClose(
-          predict_out, model2.predict([input1_val, input2_val]))
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/normalize_bias_add.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/normalize_bias_add.py
deleted file mode 100644
index 328ed564c..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/normalize_bias_add.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Normalize BiasAdd op to be fused."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflowjs.converters import graph_rewrite_util
-
-def normalize_bias_add_op(input_graph_def):
-  """Convert AddV2 ops and Add ops to BiasAdd if they could be fused with the
-  ancestor node.
-
-  Grappler and the TFJS's fusing pass for DepthwiseConv2D can only fuse the
-  BiasAdd op, but some AddV2 ops in the graph have the same functionality and
-  can be fused with MatMul, Conv2D and DepthwiseConv2D ops. This function
-  finds which AddV2 and Add ops in the graph can be fused and converts them
-  to BiasAdd, which will be fused in the following passes. The AddV2 and Add ops
-  must satisfy the following conditions to be fused:
-    * The parent node has to be MatMul, Conv2D or DepthwiseConv.
-    * The current node is the only child of the parent node (MatMul, Conv2D or
-    DepthwiseConv).
-
-  Args:
-    input_graph_def: A GraphDef containing a model.
-
-  Returns:
-    Modified graph with fusable AddV2 and Add converted to BiasAdd.
-
-  Raises:
-    ValueError: If the graph is badly formed with duplicate node names.
-  """
-  input_node_map = {}
-  for node in input_graph_def.node:
-    if node.name not in input_node_map:
-      input_node_map[node.name] = node
-    else:
-      raise ValueError('Duplicate node names detected for ', node.name)
-
-  for node in input_graph_def.node:
-    if node.op == 'AddV2' or node.op == 'Add':
-      ancestor_node_name = node.input[0]
-      ancestor_node = graph_rewrite_util.node_from_map(input_node_map,
-                                                       ancestor_node_name)
-      if (ancestor_node.op == 'Conv2D' \
-            or ancestor_node.op == 'DepthwiseConv2dNative'
-            or ancestor_node.op == 'MatMul') \
-          and len(graph_rewrite_util.get_output_node_names(input_node_map, ancestor_node_name)) == 1:
-            node.op = 'BiasAdd'
-            node.attr['data_format'].s = bytes('NHWC', 'utf-8')
-  return input_graph_def
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/normalize_bias_add_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/normalize_bias_add_test.py
deleted file mode 100644
index 6ef32902c..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/normalize_bias_add_test.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for depthwise conv2d op fusing."""
-
-import os
-import shutil
-import tempfile
-
-import tensorflow.compat.v2 as tf
-from tensorflow.python.framework import dtypes
-
-from tensorflowjs.converters import normalize_bias_add
-from tensorflowjs.converters import graph_rewrite_util
-from tensorflowjs.converters import tf_saved_model_conversion_v2
-
-
-class NormalizeBiasAddTest(tf.test.TestCase):
-  def setUp(self):
-    super(NormalizeBiasAddTest, self).setUp()
-    self._tmp_dir = tempfile.mkdtemp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(NormalizeBiasAddTest, self).tearDown()
-
-  def testFuseConv2DWithAddV2(self):
-    @tf.function
-    def conv2d_addV2(x):
-      filter = tf.ones([1, 1, 1, 1])
-      bias = tf.constant([100], dtype=dtypes.float32)
-      res = tf.raw_ops.Conv2D(
-        input=x, filter=filter, strides=[1, 1, 1, 1], padding="VALID")
-      res = tf.raw_ops.AddV2(x=res, y=bias)
-      return res
-
-    input_tensor = tf.constant([1.0], shape=[1, 1, 1, 1])
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        conv2d_addV2.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-
-    optimized_graph_def = normalize_bias_add.normalize_bias_add_op(graph_def)
-
-    bias_add_count = 0
-    bias_add = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("AddV2", node.op)
-      if node.op == "BiasAdd":
-        bias_add_count += 1
-        bias_add = node
-    self.assertEqual(bias_add_count, 1)
-    self.assertEqual(bias_add.attr['data_format'].s, b'NHWC')
-
-  def testFuseDepthwiseConv2dNativeWithAddV2(self):
-    @tf.function
-    def depthwise_addV2(x):
-      filter = tf.ones([1, 1, 1, 1])
-      bias = tf.constant([100], dtype=dtypes.float32)
-      res = tf.raw_ops.DepthwiseConv2dNative(
-        input=x, filter=filter, strides=[1, 1, 1, 1], padding="VALID")
-      res = tf.raw_ops.AddV2(x=res, y=bias)
-      return res
-
-    input_tensor = tf.constant([1.0], shape=[1, 1, 1, 1])
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        depthwise_addV2.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-
-    optimized_graph_def = normalize_bias_add.normalize_bias_add_op(graph_def)
-
-    bias_add_count = 0
-    bias_add = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("AddV2", node.op)
-      if node.op == "BiasAdd":
-        bias_add_count += 1
-        bias_add = node
-    self.assertEqual(bias_add_count, 1)
-    self.assertEqual(bias_add.attr['data_format'].s, b'NHWC')
-
-  def testMatmulWithAddV2(self):
-    @tf.function
-    def matmul_addV2(x):
-      y = tf.ones([1, 1])
-      bias = tf.constant([100], dtype=dtypes.float32)
-      res = tf.raw_ops.MatMul(a=x, b=y)
-      res = tf.raw_ops.AddV2(x=res, y=bias)
-      return res
-
-    input_tensor = tf.constant([1.0], shape=[1, 1])
-    graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
-        matmul_addV2.get_concrete_function(input_tensor))
-    graph_def = graph.as_graph_def()
-
-    optimized_graph_def = normalize_bias_add.normalize_bias_add_op(graph_def)
-
-    bias_add_count = 0
-    bias_add = None
-    for node in optimized_graph_def.node:
-      self.assertNotEqual("AddV2", node.op)
-      if node.op == "BiasAdd":
-        bias_add_count += 1
-        bias_add = node
-    self.assertEqual(bias_add_count, 1)
-    self.assertEqual(bias_add.attr['data_format'].s, b'NHWC')
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_module_mapper.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_module_mapper.py
deleted file mode 100644
index 9f7c79bb8..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_module_mapper.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-import tensorflow.keras as keras
-import inspect
-
-from tensorflow.python.util import tf_export
-
-
-TFCLASS_MODULE_MAP = {}
-MODULE = keras
-
-def _build_class_module_map(keras_module):
-    """Build the map between TFJS classes and corresponding module path in TF.
-
-    Args:
-      keras_module: keras module used to go through all the classes
-    """
-    for name, obj in inspect.getmembers(keras_module):
-        if inspect.isclass(obj):
-            # Retrive the module path from tensorflow.
-            parts = str(tf_export.get_canonical_name_for_symbol(obj, api_name='keras')).split(".")
-            # Map the class name with module path exclude the class name.
-            TFCLASS_MODULE_MAP[name] = ".".join(parts[:-1])
-
-        elif inspect.ismodule(obj):
-            _build_class_module_map(obj)
-
-def get_module_path(key):
-    """Get the module path base on input key
-
-    Args:
-      key: The name of the class we want to get module path.
-    Return:
-      RESULT_MAP[key]: the corresponding module path in TF.
-    """
-    if not TFCLASS_MODULE_MAP:
-        _build_class_module_map(MODULE)
-    if key not in TFCLASS_MODULE_MAP:
-        raise KeyError(f"Cannot find the module path for {key} class.")
-    return TFCLASS_MODULE_MAP[key]
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_module_mapper_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_module_mapper_test.py
deleted file mode 100644
index c62bc779c..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_module_mapper_test.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for build_module_map."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflowjs.converters.tf_module_mapper as bm
-import tensorflow.compat.v2 as tf
-
-class TfModuleMapperTest(tf.test.TestCase):
-  def setUp(self):
-    bm.build_map()
-    super(TfModuleMapperTest, self).setUp()
-
-  def tearDown(self):
-    super(TfModuleMapperTest, self).tearDown()
-
-  def testUnsupportClassInMap(self):
-    non_exist_class_name = 'FakeClass'
-
-    with self.assertRaises(KeyError):
-      bm.get_module_path(non_exist_class_name)
-
-  def testDenseClassInMap(self):
-    class_name = 'Dense'
-
-    self.assertEqual('keras.layers', bm.get_module_path(class_name))
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_saved_model_conversion_v2.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_saved_model_conversion_v2.py
deleted file mode 100644
index 014abe2b7..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_saved_model_conversion_v2.py
+++ /dev/null
@@ -1,1229 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Convert Tensorflow SavedModel to TensorFlow.js web format."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import os
-import shutil
-import tempfile
-from zipfile import ZipFile
-
-# Required to load saved models that use TFDF.
-import tensorflow_decision_forests
-import tensorflow as tf
-from tensorflow.core.framework import function_pb2
-from tensorflow.core.framework import graph_pb2
-from tensorflow.core.framework import node_def_pb2
-from tensorflow.core.protobuf import config_pb2
-from tensorflow.core.protobuf import device_properties_pb2
-from tensorflow.core.protobuf import meta_graph_pb2
-from tensorflow.io import gfile
-from tensorflow.python.checkpoint.trackable_view import TrackableView
-from tensorflow.python.eager import context
-from tensorflow.python.framework import convert_to_constants
-from tensorflow.python.grappler import cluster as gcluster
-from tensorflow.python.grappler import tf_optimizer
-from tensorflow.python.keras.saving.saving_utils import trace_model_call
-from tensorflow.python.keras.saving.saving_utils import def_function
-from tensorflow.python.keras.saving.saving_utils import model_input_signature
-from tensorflow.python.saved_model.load import load
-from tensorflow.python.saved_model import loader
-from tensorflow.python.tools.saved_model_utils import get_meta_graph_def
-from tensorflow.python.training.saver import export_meta_graph
-from tensorflow.saved_model.experimental import TrackableResource
-from google.protobuf.json_format import MessageToDict
-import tensorflow_hub as hub
-from packaging import version
-
-from tensorflowjs import write_weights
-from tensorflowjs.converters import common
-from tensorflowjs.converters import normalize_bias_add
-from tensorflowjs.converters import fold_batch_norms
-from tensorflowjs.converters import fuse_prelu
-from tensorflowjs.converters import fuse_depthwise_conv2d
-from tensorflowjs.converters import graph_rewrite_util
-from tensorflowjs import resource_loader
-
-CLEARED_TENSOR_FIELDS = (
-    'tensor_content', 'half_val', 'float_val', 'double_val', 'int_val',
-    'string_val', 'scomplex_val', 'int64_val', 'bool_val',
-    'resource_handle_val', 'variant_val', 'uint32_val', 'uint64_val')
-
-_HUB_V1_MODULE_PB = "tfhub_module.pb"
-
-def load_graph(graph_filename):
-  """Loads GraphDef. Returns Python Graph object.
-
-  Args:
-    graph_filename: string File name for the frozen graph.
-  """
-  with tf.compat.v1.gfile.Open(graph_filename, 'rb') as f:
-    graph_def = tf.compat.v1.GraphDef()
-    graph_def.ParseFromString(f.read())
-
-  with tf.Graph().as_default() as graph:
-    # Set name to empty to avoid using the default name 'import'.
-    tf.import_graph_def(graph_def, name='')
-
-  return graph
-
-def get_cluster():
-  """Grappler optimization configuration for GPU."""
-  named_device = device_properties_pb2.NamedDevice()
-  named_device.name = '/GPU:0'
-  named_device.properties.type = 'GPU'
-  named_device.properties.environment['architecture'] = '4'
-  cluster = gcluster.Cluster(devices=[named_device])
-  return cluster
-
-def validate(graph_def, skip_op_check, strip_debug_ops):
-  """Validate if the node's op is compatible with TensorFlow.js.
-
-  Args:
-    graph_def: tf.GraphDef TensorFlow GraphDef proto object, which represents
-      the model topology.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to allow unsupported debug ops.
-  """
-  nodes = [] + list(graph_def.node)
-  for func in graph_def.library.function:
-    nodes.extend(list(func.node_def))
-
-  if skip_op_check:
-    return set()
-  ops = []
-  for filename in resource_loader.list_dir('op_list'):
-    if os.path.splitext(filename)[1] == '.json':
-      with resource_loader.open_file(os.path.join('op_list',
-                                                  filename)) as json_data:
-        ops += json.load(json_data)
-
-  names = {x['tfOpName'] for x in ops}
-  if strip_debug_ops:
-    names = names.union({'Assert', 'CheckNumerics', 'Print'})
-  not_supported = {x.op for x in [x for x in nodes if x.op not in names]}
-  return not_supported
-
-def _run_grappler(config, graph_def, graph, signature_def):
-  meta_graph = export_meta_graph(
-      graph_def=graph_def, graph=graph)
-
-  meta_graph.signature_def["not_used_key"].CopyFrom(signature_def)
-
-  return tf_optimizer.OptimizeGraph(
-      config, meta_graph, cluster=get_cluster())
-
-def optimize_graph(graph, signature_def,
-                   skip_op_check=False, strip_debug_ops=False,
-                   experiments=False):
-  """Takes a Python Graph object and optimizes the graph.
-
-  Args:
-    graph: The frozen graph to optimize.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to strip debug ops.
-  """
-
-  # Add a collection 'train_op' so that Grappler knows the outputs.
-  for _, output in signature_def.outputs.items():
-    name = output.name.split(':')[0]
-    graph.add_to_collection('train_op', graph.get_operation_by_name(name))
-
-  graph_def = graph.as_graph_def()
-
-  unsupported = validate(graph_def, skip_op_check,
-                         strip_debug_ops)
-  if unsupported:
-    raise ValueError('Unsupported Ops in the model before optimization\n' +
-                     ', '.join(unsupported))
-
-  # first pass of grappler optimization, this is needed for batch norm folding.
-  config = config_pb2.ConfigProto()
-  rewriter_config = config.graph_options.rewrite_options
-  rewriter_config.optimizers[:] = [
-      'pruning', 'constfold', 'arithmetic', 'dependency', 'pruning',
-      'constfold', 'arithmetic', 'dependency'
-  ]
-  if experiments:
-    rewriter_config.experimental_disable_compressed_tensor_optimization = True
-
-  if strip_debug_ops:
-    rewriter_config.optimizers.insert(0, 'debug_stripper')
-
-  optimized_graph = _run_grappler(config, graph_def, graph, signature_def)
-
-  # batch norm folding
-  optimized_graph = fold_batch_norms.fold_batch_norms(optimized_graph)
-
-  optimized_graph = normalize_bias_add.normalize_bias_add_op(optimized_graph)
-
-  # set the device to CPU for all Conv2d and MatMul nodes, since grappler
-  # remap optimizer only support FusedConv2D and FusedMatMul for CPU.
-  for node in optimized_graph.node:
-    if node.op == 'Conv2D' or node.op == 'MatMul':
-      node.device = '/device:CPU:0'
-
-  # rerun grappler to fuse conv2d/matmul
-  config.graph_options.rewrite_options.optimizers[:] = [
-      'remap',
-      'constfold', 'arithmetic', 'dependency'
-  ]
-
-  optimized_graph = _run_grappler(config, optimized_graph, graph, signature_def)
-
-  optimized_graph = _remove_unused_control_flow_inputs(optimized_graph)
-
-  # Because TF break the Prelu op into 6 ops, for performance we are
-  # fusing those ops into a single prelu
-  optimized_graph = fuse_prelu.fuse_ops_for_prelu(optimized_graph)
-
-  # Because grappler does not support DepthwiseConv2d fusing, we have
-  # implemented it here.
-  optimized_graph = fuse_depthwise_conv2d.fuse_depthwise_conv2d(optimized_graph)
-
-  # Since the grappler remap optimizer doe snot support prelu as the activation
-  # function for _FusedConv2D op, we are doing it manually here.
-  optimized_graph = fuse_prelu.fuse_prelu_with_fused_conv2d_or_matmul(
-      optimized_graph)
-
-  unsupported = validate(optimized_graph, skip_op_check,
-                         strip_debug_ops)
-  if unsupported:
-    raise ValueError('Unsupported Ops in the model after optimization\n' +
-                     ', '.join(unsupported))
-
-  return optimized_graph
-
-def extract_const_nodes(nodes):
-  """Takes a list of nodes and extract the weights. Return weight manifest
-  object.
-
-  Args:
-    nodes: list of tf.NodeDef TensorFlow NodeDef proto object.
-  """
-  constants = [node for node in nodes if node.op == 'Const']
-  const_inputs = {}
-  # removed the conditional inputs for constants
-  for const in constants:
-    const_inputs[const.name] = const.input[:]
-    del const.input[:]
-
-  const_manifest = []
-
-  for const in constants:
-    const_manifest.append({
-        'name': const.name,
-        'data': graph_rewrite_util.values_from_const(const)
-    })
-    # Restore the conditional inputs
-    const.input[:] = const_inputs[const.name]
-
-    # Remove the binary array from tensor and save it to the external file.
-    for field_name in CLEARED_TENSOR_FIELDS:
-      const.attr["value"].tensor.ClearField(field_name)
-
-  return const_manifest
-
-def extract_weights(graph_def, initializer_graph_def=None):
-  """Takes a Python GraphDef object and extract the weights.
-
-  Args:
-    graph_def: tf.GraphDef TensorFlow GraphDef proto object, which represents
-      the model topology.
-    initializer_graph_def: tf.GraphDef proto object for initializer graph.
-  """
-  global_manifest = extract_const_nodes(graph_def.node)
-
-  function_manifests = []
-  for func in graph_def.library.function:
-    nodes = graph_rewrite_util.rename_constants(
-        func.node_def, func.signature.name)
-    del func.node_def[:]
-    func.node_def.extend(nodes)
-    function_manifests += extract_const_nodes(func.node_def)
-
-  initializer_manifests = []
-  if initializer_graph_def:
-    initializer_manifests = extract_const_nodes(initializer_graph_def.node)
-
-  return [global_manifest + function_manifests + initializer_manifests]
-
-def write_artifacts(topology,
-                    weights,
-                    output_graph,
-                    tf_version,
-                    signature_def,
-                    quantization_dtype_map=None,
-                    weight_shard_size_bytes=1024 * 1024 * 4,
-                    initializer_graph_def=None,
-                    initializer_signature_def=None,
-                    resource_ids_maps=None,
-                    metadata=None):
-  """Writes weights and topology to the output_dir.
-
-  If `topology` is Falsy (e.g., `None`), only emit weights to output_dir.
-
-  Args:
-    topology: tf.GraphDef TensorFlow GraphDef proto object, which represents
-      the model topology.
-    weights: an array of weight groups (as defined in tfjs write_weights).
-    output_graph: the output file name to hold all the contents.
-    tf_version: Tensorflow version of the input graph.
-    signature_def: the SignatureDef of the inference graph.
-    quantization_dtype_map: A mapping from dtype
-      (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-      supports wildcard substitution.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    initializer_graph_def: tf.GraphDef proto object for initializer graph.
-    initializer_signature_def: the SignatureDef of the initializer graph.
-    resource_ids_maps: Tuple of two dictionaries, one
-      mapping inference input names to resource id, and the other
-      mapping initializer output names to resource id.
-    metadata: User defined metadata map.
-  """
-  model_json = {
-      common.FORMAT_KEY: common.TFJS_GRAPH_MODEL_FORMAT,
-      # TODO(piyu): Add tensorflow version below by using `meta_info_def`.
-      common.GENERATED_BY_KEY: tf_version,
-      common.CONVERTED_BY_KEY: common.get_converted_by(),
-      common.SIGNATURE_KEY: MessageToDict(signature_def),
-  }
-  model_json[common.ARTIFACT_MODEL_TOPOLOGY_KEY] = topology or None
-
-  if metadata:
-    model_json[common.USER_DEFINED_METADATA_KEY] = metadata
-
-  if initializer_graph_def and initializer_graph_def.node:
-    model_json[common.ARTIFACT_MODEL_INITIALIZER] = MessageToDict(
-        initializer_graph_def)
-    if initializer_signature_def:
-      model_json[common.INITIALIZER_SIGNATURE_KEY] = MessageToDict(
-          initializer_signature_def)
-
-  # Assign resource ids to inference inputs and initializer outputs. In
-  # TensorFlow, both inference and initializer graphs have a reference
-  # to the common resource (so initializer runs on reference, and then inference
-  # graph uses it). We are doing something similar but instead of assigning
-  # a reference to the resource in the serialized graph, we assign the id
-  # of the resource, and then we can recreate the common reference in javascript
-  # by matching resource ids.
-  if resource_ids_maps is not None:
-    model_input_to_resource_id, init_output_to_resource_id = resource_ids_maps
-    signature_inputs = model_json[common.SIGNATURE_KEY]['inputs']
-    initializer_signature_outputs = model_json[common.INITIALIZER_SIGNATURE_KEY]['outputs']
-
-    for (input, resource_id) in model_input_to_resource_id.items():
-      if input in signature_inputs:
-        signature_inputs[input][common.RESOURCE_ID_KEY] = resource_id
-
-    for (output, resource_id) in init_output_to_resource_id.items():
-      if output in initializer_signature_outputs:
-        initializer_signature_outputs[output][common.RESOURCE_ID_KEY] = resource_id
-
-
-  weights_manifest = write_weights.write_weights(
-      weights, os.path.dirname(output_graph), write_manifest=False,
-      quantization_dtype_map=quantization_dtype_map,
-      shard_size_bytes=weight_shard_size_bytes)
-  assert isinstance(weights_manifest, list)
-  model_json[common.ARTIFACT_WEIGHTS_MANIFEST_KEY] = weights_manifest
-
-  with gfile.GFile(output_graph, 'w') as f:
-    json.dump(model_json, f)
-
-def _remove_unused_control_flow_inputs(input_graph_def):
-  result_graph_def = graph_pb2.GraphDef()
-  for node in input_graph_def.node:
-    if (node.op == 'Placeholder' and
-        node.name.startswith('unused_control_flow_input')):
-      continue
-    new_node = node_def_pb2.NodeDef()
-    new_node.CopyFrom(node)
-    result_graph_def.node.extend([new_node])
-  result_graph_def.library.CopyFrom(input_graph_def.library)
-  result_graph_def.versions.CopyFrom(input_graph_def.versions)
-  return result_graph_def
-
-def _check_signature_in_model(saved_model, signature_name):
-  if signature_name not in saved_model.signatures:
-    raise ValueError("Signature '%s' does not exist. The following signatures "
-                     "are available: %s" % (signature_name,
-                                            saved_model.signatures.keys()))
-
-def _copy_assets(saved_model_dir, output_dir):
-  input_assets_path = os.path.join(saved_model_dir, common.ASSETS_DIRECTORY_NAME)
-
-  if gfile.exists(input_assets_path) and gfile.isdir(input_assets_path):
-
-    tmp_dir = tempfile.mkdtemp()
-    zip_path = gfile.join(tmp_dir, common.ASSETS_DIRECTORY_NAME + '.zip')
-
-    with ZipFile(zip_path, 'w') as archive:
-      for (input_dir_path, _, file_names) in gfile.walk(input_assets_path):
-
-        relative_dir_path = os.path.relpath(input_dir_path, input_assets_path)
-
-        for file_name in file_names:
-
-          input_file_path = gfile.join(input_dir_path, file_name)
-          relative_file_path = gfile.join(relative_dir_path, file_name)
-
-          with gfile.GFile(input_file_path, 'rb') as input_file:
-            with archive.open(relative_file_path, 'w') as relative_file:
-              shutil.copyfileobj(input_file, relative_file)
-
-    output_assets_path = gfile.join(output_dir, common.ASSETS_DIRECTORY_NAME + '.zip')
-    gfile.copy(zip_path, output_assets_path, overwrite=True)
-
-    if gfile.isdir(tmp_dir):
-      gfile.rmtree(tmp_dir)
-
-def _is_assets_required(model_ops):
-  # TFDF stores the necessary files for its binary in the assets folder.
-  # Check if any TFDF ops are used in the model.
-  with resource_loader.open_file('op_list/tfdf.json') as tfdf_json:
-    ops = json.load(tfdf_json)
-    opNames = frozenset([x['tfOpName'] for x in ops])
-    return not opNames.isdisjoint(model_ops)
-
-def _get_frozen_graph_ops(frozen_graph):
-  if frozen_graph is None:
-    return []
-  return [node.op for node in frozen_graph.as_graph_def().node]
-
-
-def _freeze_saved_model_v1(saved_model_dir, saved_model_tags,
-                           output_node_names):
-  """Freeze the graph by converting variables to constants for 1.x saved model.
-
-  Args:
-    saved_model_dir: dir where saved model files are stored.
-    saved_model_tags: inference graph tag.
-    output_node_names: List of name strings for the result nodes of the graph.
-
-  Returns:
-    A freezed and optimized graph.
-    Nullable. A freezed and optimized initializer graph.
-    Nullable. A list of output node names of initializer.
-  """
-  # v1 loader need empty list if there are no saved_model tags.
-  if not saved_model_tags:
-    saved_model_tags = []
-
-  g = tf.Graph()
-  with g.as_default():
-    with tf.compat.v1.Session() as sess:
-      meta_graph = loader.load(sess, saved_model_tags, saved_model_dir)
-
-      meta_graph_def = g.as_graph_def()
-      if not meta_graph_def.HasField('library'):
-        meta_graph_def.library.CopyFrom(function_pb2.FunctionDefLibrary())
-
-      frozen_graph_def = tf.compat.v1.graph_util.convert_variables_to_constants(
-          sess, meta_graph_def, output_node_names)
-
-      frozen_graph = tf.Graph()
-      with frozen_graph.as_default():
-        tf.import_graph_def(frozen_graph_def, name='')
-
-      frozen_initializer_graph = None
-      initializer_output_names = None
-      # Only support table initializers for now.
-      if meta_graph.collection_def and meta_graph.collection_def[
-          'table_initializer']:
-        initializer_output_names = meta_graph.collection_def[
-            'table_initializer'].node_list.value
-        # This will use grappler to extract a subgraph with the
-        # table initializer ops as the outputs.
-        frozen_initializer_graph_def = (tf.compat.v1.graph_util
-                                        .convert_variables_to_constants(
-                                            sess, meta_graph_def,
-                                            initializer_output_names))
-        frozen_initializer_graph = tf.Graph()
-        with frozen_initializer_graph.as_default():
-          tf.import_graph_def(frozen_initializer_graph_def, name='')
-
-      return frozen_graph, frozen_initializer_graph
-
-def _freeze_saved_model_v2(concrete_func, control_flow_v2=False):
-  if version.parse(tf.__version__) < version.parse('2.2.0'):
-    return convert_to_constants.convert_variables_to_constants_v2(
-        concrete_func, lower_control_flow=not control_flow_v2).graph
-
-  return convert_to_constants.convert_variables_to_constants_v2(
-      concrete_func, lower_control_flow=not control_flow_v2,
-      aggressive_inlining=True).graph
-
-def _find_signature_def_name(tensor, signature_map):
-  if not signature_map:
-    return tensor.name
-
-  tensor_shape_str = tensor.shape.as_proto().SerializeToString()
-  names = []
-  for key in signature_map:
-    tensor_info = signature_map[key]
-    signature_shape_str = tensor_info.tensor_shape.SerializeToString()
-    if (tensor_info.dtype == tensor.dtype and
-        tensor_shape_str == signature_shape_str):
-      names.append(key)
-
-  if not names or len(names) > 1:
-    return tensor.name
-  else:
-    return names[0]
-
-def _build_signature_def(frozen_graph, input_nodes, output_nodes,
-                         signature_def=None):
-  signature = meta_graph_pb2.SignatureDef()
-  for input_tensor in input_nodes:
-    op_name = input_tensor.name.split(':')[0]
-    # The graph freezing may turn the original inputs into constants, or remove
-    # them from the graph, so we need to ignore those.
-    try:
-      op = frozen_graph.get_operation_by_name(op_name)
-      if op.type != 'Const':
-        name = input_tensor.name
-        if hasattr(signature_def, 'inputs'):
-          name = _find_signature_def_name(input_tensor, signature_def.inputs)
-        signature.inputs[name].name = input_tensor.name
-        signature.inputs[name].dtype = input_tensor.dtype.as_datatype_enum
-        signature.inputs[name].tensor_shape.CopyFrom(
-            input_tensor.shape.as_proto())
-    except KeyError:
-      # The original input was removed when the graph was frozen.
-      continue
-  for output_tensor in output_nodes:
-    if hasattr(output_tensor, 'name'):
-      name = output_tensor.name
-      if hasattr(signature_def, 'inputs'):
-        name = _find_signature_def_name(output_tensor, signature_def.outputs)
-      signature.outputs[name].name = output_tensor.name
-      signature.outputs[name].dtype = output_tensor.dtype.as_datatype_enum
-      signature.outputs[name].tensor_shape.CopyFrom(
-          output_tensor.shape.as_proto())
-    else: #just the tensor name string array
-      signature.outputs[output_tensor].name = output_tensor
-  return signature
-
-def convert_tf_frozen_model(frozen_model_path,
-                            output_node_names,
-                            output_dir,
-                            quantization_dtype_map=None,
-                            skip_op_check=False,
-                            strip_debug_ops=False,
-                            weight_shard_size_bytes=1024 * 1024 * 4,
-                            experiments=False,
-                            metadata=None):
-  """Convert frozen model and check the model compatibility with Tensorflow.js.
-  Optimize and convert the model to Tensorflow.js format, when the model passes
-  the compatiblity check.
-  Args:
-    frozen_model_path: string The path to frozen model.
-    output_node_names: string The names of the output nodes, comma separated.
-    output_dir: string The name of the output directory. The directory
-      will consist of
-      - a file named 'model.json'
-      - possibly sharded binary weight files.
-    quantization_dtype_map: A mapping from dtype
-      (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-      supports wildcard substitution.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to strip debug ops.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    experiments: Bool enable experimental features.
-    metadata: User defined metadata map.
-  """
-
-  if not os.path.exists(output_dir):
-    os.makedirs(output_dir)
-  output_graph = os.path.join(output_dir, common.ARTIFACT_MODEL_JSON_FILE_NAME)
-
-  graph = load_graph(frozen_model_path)
-  signature = _build_signature_def(
-      graph, [], output_node_names.split(','))
-
-  optimized_graph = optimize_graph(graph, signature,
-                                   skip_op_check=skip_op_check,
-                                   strip_debug_ops=strip_debug_ops,
-                                   experiments=experiments)
-
-  weights = extract_weights(optimized_graph)
-
-  write_artifacts(MessageToDict(optimized_graph),
-                 weights,
-                 output_graph, tf.__version__,
-                 signature,
-                 quantization_dtype_map=quantization_dtype_map,
-                 weight_shard_size_bytes=weight_shard_size_bytes,
-                 metadata=metadata)
-
-def _load_model(saved_model_dir, saved_model_tags):
-  model = None
-  # Ensure any graphs created in eager mode are able to run.
-  with context.eager_mode():
-    if saved_model_tags:
-      model = load(saved_model_dir, saved_model_tags)
-    else:
-      model = load(saved_model_dir)
-  return model
-
-def _find_signature(saved_model_dir, saved_model_tags, signature_def):
-  meta_graph = get_meta_graph_def(saved_model_dir, saved_model_tags)
-  signature_def_map = meta_graph.signature_def
-  if signature_def not in signature_def_map.keys():
-    raise ValueError('Signature "%s" does not exist in the saved model'
-                     % (signature_def))
-
-  return signature_def_map[signature_def]
-
-def _get_resource_initializer_concrete_function(model):
-  """Create a tf.function that creates and initializes all the resources used by the model.
-  For more information on resources, please see the TensorFlow code:
-  https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/trackable/resource.py#L232
-  Args:
-    model: Loaded saved model.
-
-  Returns:
-    Nullable. A concrete function.
-  """
-  trackable_view = TrackableView(model)
-  model_resources = [obj for obj in trackable_view.descendants() if isinstance(obj, TrackableResource)]
-
-  if not model_resources:
-    return None
-
-  # A list holding tuples of (TrackableResource, captured_input_index) where
-  # TrackableResource represents one resource in the model
-  # (a hash table for example), and captured_input_index is the resource
-  # initialization function's captured input index corresponding
-  # to the TrackableResource. Captured inputs are simply inputs not provided
-  # directly be user, but by the model.
-  model_resources_with_captured_input_index = []
-  for model_resource in model_resources:
-    # A runtime id that is unique across different resources, and constant
-    # across graphs.
-    resource_handle_id = model_resource.resource_handle._id
-    # the _initialize function initializes the resource, so one of its captured
-    # inputs must be the resource, so search for that input.
-    captured_inputs = model_resource._initialize.get_concrete_function()._captured_inputs
-    for captured_input_index in range(len(captured_inputs)):
-      if captured_inputs[captured_input_index]._id == resource_handle_id:
-        model_resources_with_captured_input_index.append((model_resource, captured_input_index))
-
-  @tf.function()
-  def resource_initializer():
-    # Recreate resources to capture them in this tf.function.
-    new_resources = []
-    for (model_resource, captured_input_index) in model_resources_with_captured_input_index:
-      # Make a new resource (that is identical to the old, but captured in
-      # this functon only).
-      new_resource = model_resource._create_resource()
-      new_resources.append(new_resource)
-
-      # Since we precomputed the captured input corresponding to this resource,
-      # we can directly replace it with the copy new_resource. If we don't do
-      # this, then _initialize will not get capture in this graph since the
-      # old resource was already initialized in TF model load.
-      model_resource._initialize.get_concrete_function()._captured_inputs[captured_input_index] = new_resource
-      model_resource._initialize()
-
-    return new_resources
-
-  # Add resource_initializer to the output graph.
-  return resource_initializer.get_concrete_function()
-
-def _get_resource_ids_maps(model, concrete_func, resource_init_concrete_func):
-  """Generates dictionaries that map tensor names to the loaded saved model resource id,
-     allowing for matching of initializer outputs to inference inputs.
-
-  Args:
-    model: Loaded saved model.
-    concrete_func: Concrete function of the inference graph.
-    resource_init_concrete_func: Concrete function of the initializer graph.
-
-  Returns:
-    A dictionary mapping inference input names to resource id.
-    A dictionary mapping initializer output names to resource id.
-  """
-  trackable_view = TrackableView(model)
-  model_resources = [obj for obj in trackable_view.descendants() if isinstance(obj, TrackableResource)]
-
-
-  # Each resource has a unique runtime resource id associated with it which
-  # can be used across graphs, so we extract it here from inference
-  # graph for use later.
-  resource_id_to_captured_input_index = {
-    captured_input._id : captured_input_index for \
-    captured_input_index, captured_input in \
-    enumerate(concrete_func._captured_inputs)
-  }
-  # Captured inputs always come after user provided inputs.
-  captured_input_index_offset = len(concrete_func.inputs) - len(concrete_func._captured_inputs)
-
-  model_input_to_resource_id = {}
-  init_output_to_resource_id = {}
-  for i, resource in enumerate(model_resources):
-    _id = resource.resource_handle._id
-    # Get input from inference graph corresponding to this resource.
-    captured_input_index = resource_id_to_captured_input_index[_id]
-    model_input = concrete_func.inputs[captured_input_index + captured_input_index_offset]
-
-    # Get output from initializer graph corresponding to this resource.
-    init_output = resource_init_concrete_func.outputs[i]
-
-    # Match both with the same id (initializer output will be passed in to
-    # corresponding input in inference input).
-    model_input_to_resource_id[model_input.name] = _id
-    init_output_to_resource_id[init_output.name] = _id
-
-  return (model_input_to_resource_id, init_output_to_resource_id)
-
-def _convert_tf_saved_model(output_dir,
-                            saved_model_dir=None,
-                            keras_model=None,
-                            signature_def='serving_default',
-                            saved_model_tags='serve',
-                            quantization_dtype_map=None,
-                            skip_op_check=False,
-                            strip_debug_ops=False,
-                            use_structured_outputs_names=False,
-                            weight_shard_size_bytes=1024 * 1024 * 4,
-                            control_flow_v2=False,
-                            experiments=False,
-                            metadata=None,
-                            frozen_graph_dir=None):
-  """Take a SavedModel or KerasModel and convert to Tensorflow.js graph model.
-
-  Args:
-    output_dir: string The name of the output directory. The directory
-      will consist of
-      - a file named 'model.json'
-      - possibly sharded binary weight files.
-    saved_model_dir: string The saved model directory.
-    : string The names of the output nodes, comma separated.
-    keras_model: An in-memory Keras model object.
-    signature_def: string Tagset of the SignatureDef to load. Defaults to
-      'serving_default'.
-    saved_model_tags: tags of the GraphDef to load. Defaults to 'serve'.
-    quantization_dtype_map: A mapping from dtype
-      (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-      supports wildcard substitution.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to strip debug ops.
-    use_structured_outputs_names: Bool whether output of graph model will follow
-      the structured_outputs format.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    control_flow_v2: Bool whether to enable control flow v2 ops.
-    experiments: Bool enable experimental features.
-    metadata: User defined metadata map.
-    frozen_graph_dir: The directory to keep the intermediate frozen graph of
-      model.
-  """
-  if signature_def is None:
-    signature_def = 'serving_default'
-
-  if not gfile.exists(output_dir):
-    gfile.makedirs(output_dir)
-  output_graph = os.path.join(
-      output_dir, common.ARTIFACT_MODEL_JSON_FILE_NAME)
-
-  saved_model_tags_list = None
-  if saved_model_tags:
-    saved_model_tags_list = saved_model_tags.split(',')
-
-  model = None
-  concrete_func = None
-  saved_model_sigature = None
-  if saved_model_dir:
-    saved_model_sigature = _find_signature(saved_model_dir, saved_model_tags,
-                                           signature_def)
-    model = _load_model(saved_model_dir, saved_model_tags_list)
-    _check_signature_in_model(model, signature_def)
-    concrete_func = model.signatures[signature_def]
-  elif keras_model:
-    model = keras_model
-    input_signature = None
-    # If the model's call is not a `tf.function`, then we need to first get its
-    # input signature from `model_input_signature` method. We can't directly
-    # call `trace_model_call` because otherwise the batch dimension is set
-    # to None.
-    if not isinstance(model.call, def_function.Function):
-      # Pass `keep_original_batch_size=True` will ensure that we get an input
-      # signature including the batch dimension specified by the user.
-      input_signature = model_input_signature(
-          model, keep_original_batch_size=True)
-    func = trace_model_call(model, input_signature)
-    concrete_func = func.get_concrete_function()
-  else:
-    raise Exception('Provide either a saved model or keras model to convert.')
-
-  output_node_names = []
-  for output_tensor in concrete_func.outputs:
-    output_node_names.append(output_tensor.name.split(':')[0])
-
-  num_outputs = len(output_node_names)
-  structured_outputs = concrete_func.structured_outputs
-  if use_structured_outputs_names and structured_outputs is not None:
-    if not isinstance(structured_outputs, dict):
-      raise Exception('Converter only supports dict structured_outputs.')
-
-    # As per tensorflow/python/util/nest.py: "If `structure` is or contains a
-    # dict instance, the keys will be sorted to pack the flat sequence
-    # in deterministic order."
-    sorted_keys = sorted(structured_outputs.keys())
-
-    # Check if structure is a simple dictionary.
-    # We don't support anything more complex due to the GraphModel.predict
-    # function return type in typescript.
-    test_sequence = list(range(num_outputs))
-    actual_structure = tf.nest.pack_sequence_as(
-        structured_outputs, test_sequence, True)
-    expected_structure = dict(zip(sorted_keys, test_sequence))
-    if actual_structure != expected_structure:
-      raise Exception('Converter only supports structured_outputs of form '
-                      '{"key1": value1, "key2":value2 ... })')
-
-    metadata = metadata or {}
-    metadata[common.STRUCTURED_OUTPUTS_KEYS_KEY] = sorted_keys
-
-  # TensorFlow doesn't encode the saved model version in the graph in a
-  # reliable way. Try to freeze the graph using V2 utils. If that fails, freeze
-  # the graph using V1 utils.
-  frozen_initializer_graph = None
-  resource_ids_maps = None
-  try:
-    frozen_graph = _freeze_saved_model_v2(concrete_func, control_flow_v2)
-    resource_initializer_concrete_func = _get_resource_initializer_concrete_function(model)
-
-    if resource_initializer_concrete_func:
-      frozen_initializer_graph = _freeze_saved_model_v2(resource_initializer_concrete_func, control_flow_v2)
-      resource_ids_maps = _get_resource_ids_maps(model, concrete_func, resource_initializer_concrete_func)
-
-  except BaseException:
-    if saved_model_dir:
-      (frozen_graph,
-       frozen_initializer_graph) = _freeze_saved_model_v1(saved_model_dir,
-                                                          saved_model_tags_list,
-                                                          output_node_names)
-    else:
-      print('Can not freeze saved model v1.')
-      return
-
-  if frozen_graph_dir:
-    output_graph = os.path.join(frozen_graph_dir,
-                                common.ARTIFACT_MODEL_JSON_FILE_NAME)
-    frozen_file = output_graph + '.frozen'
-    with tf.compat.v1.gfile.GFile(frozen_file, 'wb') as f:
-      f.write(frozen_graph.as_graph_def().SerializeToString())
-
-  signature = _build_signature_def(
-      frozen_graph, concrete_func.inputs, concrete_func.outputs, saved_model_sigature)
-
-  define_transform_graph_func()
-
-  tf_version = None
-  try:
-    tf_version = model.tensorflow_version
-  except: # pylint: disable=W0702
-    # keras model does not have tensorflow_version, hard code to the latest
-    # tensorflow version.
-    tf_version = tf.__version__
-
-  if saved_model_dir:
-      model_ops = set(_get_frozen_graph_ops(frozen_graph)) |\
-                  set(_get_frozen_graph_ops(frozen_initializer_graph))
-      if _is_assets_required(model_ops):
-        _copy_assets(saved_model_dir, output_dir)
-
-  optimized_graph = optimize_graph(frozen_graph, signature,
-                                   skip_op_check=skip_op_check,
-                                   strip_debug_ops=strip_debug_ops,
-                                   experiments=experiments)
-
-  initializer_graph_def = None
-  initializer_signature_def = None
-  if frozen_initializer_graph:
-    initializer_graph_def = frozen_initializer_graph.as_graph_def()
-    if hasattr(frozen_initializer_graph, 'outputs'):
-      initializer_signature_def = _build_signature_def(frozen_initializer_graph, [], frozen_initializer_graph.outputs)
-
-  weights = extract_weights(optimized_graph, initializer_graph_def)
-
-  write_artifacts(MessageToDict(optimized_graph),
-      weights,
-      output_graph,
-      tf_version, signature,
-      quantization_dtype_map=quantization_dtype_map,
-      weight_shard_size_bytes=weight_shard_size_bytes,
-      initializer_graph_def=initializer_graph_def,
-      initializer_signature_def=initializer_signature_def,
-      resource_ids_maps=resource_ids_maps,
-      metadata=metadata)
-
-def define_transform_graph_func():
-  """Check if the TransformGraph is available to be imported, this package is
-  available in g3 but not in oss version of TensorFlow.
-  """
-
-  transform_graph_available = True
-  try:
-    from tensorflow.tools.graph_transforms import TransformGraph # pylint: disable=C0415
-  except: # pylint: disable=W0702
-    transform_graph_available = False
-
-  # Define the strip graph functions when TransformGraph is available, this will
-  # strip the unused nodes from the graph.
-  if transform_graph_available:
-    def _strip_unused_nodes(frozen_graph, concrete_func, output_node_names):
-      # Find the names of the input nodes needed to extract the minimal
-      # inference graph. This is particularly useful for cases when the concrete
-      # function contains nodes that do not contribute the inference computation
-      # defined by the input/output pair. This would also eliminate op
-      # unsupported error caused by nodes outside of the minial infrerence
-      # graph.
-      input_node_names = []
-      input_tensors = {}
-      for input_tensor in concrete_func.inputs:
-        if input_tensor.dtype != 'resource':
-          op_name = input_tensor.name.split(':')[0]
-          # The graph freezing may turn the original inputs into constants, or
-          # remove them from the graph, so we need to ignore those.
-          try:
-            op = frozen_graph.get_operation_by_name(op_name)
-            if op.type != 'Const':
-              input_node_names.append(op_name)
-              input_tensors[op_name] = input_tensor
-          except KeyError:
-            # The original input was removed when the graph was frozen.
-            continue
-
-      graph_transformations = ['strip_unused_nodes']
-      stripped_graph_def = TransformGraph(
-          frozen_graph.as_graph_def(), input_node_names, output_node_names,
-          graph_transformations)
-
-      # The transform graph library cannot support input nodes that has dynamic
-      # shape, this code will update the dtype and shape based on the
-      # input tensor manually.
-      for node in stripped_graph_def.node:
-        if node.name in input_tensors:
-          if node.attr['shape'] and node.attr['shape'].shape:
-            node.attr['shape'].shape.CopyFrom(
-                input_tensors[node.name].shape.as_proto())
-          if node.attr['dtype'] and node.attr['dtype'].type:
-            node.attr['dtype'].type = input_tensors[
-                node.name].dtype.as_datatype_enum
-
-      with tf.Graph().as_default() as stripped_graph:
-        tf.import_graph_def(stripped_graph_def, name='')
-        return stripped_graph
-
-def convert_tf_saved_model(saved_model_dir,
-                           output_dir, signature_def='serving_default',
-                           saved_model_tags='serve',
-                           quantization_dtype_map=None,
-                           skip_op_check=False,
-                           strip_debug_ops=False,
-                           use_structured_outputs_names=False,
-                           weight_shard_size_bytes=1024 * 1024 * 4,
-                           control_flow_v2=False,
-                           experiments=False,
-                           metadata=None,
-                           frozen_graph_dir=None):
-  """Freeze the SavedModel and check the model compatibility with Tensorflow.js.
-
-  Optimize and convert the model to Tensorflow.js format, when the model passes
-  the compatiblity check.
-
-  Args:
-    saved_model_dir: string The saved model directory.
-    : string The names of the output nodes, comma separated.
-    output_dir: string The name of the output directory. The directory
-      will consist of
-      - a file named 'model.json'
-      - possibly sharded binary weight files.
-    signature_def: string Tagset of the SignatureDef to load. Defaults to
-      'serving_default'.
-    saved_model_tags: tags of the GraphDef to load. Defaults to 'serve'.
-    quantization_dtype_map: A mapping from dtype
-      (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-      supports wildcard substitution.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to strip debug ops.
-    use_structured_outputs_names: Bool whether output of graph model will follow
-      the structured_outputs format.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    control_flow_v2: Bool whether to enable control flow v2 ops.
-    experiments: Bool enable experimental features.
-    metadata: User defined metadata map.
-    frozen_graph_dir: The directory to keep the intermediate frozen graph of
-      model.
-  """
-  _convert_tf_saved_model(output_dir, saved_model_dir=saved_model_dir,
-                          signature_def=signature_def,
-                          saved_model_tags=saved_model_tags,
-                          quantization_dtype_map=quantization_dtype_map,
-                          skip_op_check=skip_op_check,
-                          strip_debug_ops=strip_debug_ops,
-                          use_structured_outputs_names=
-                          use_structured_outputs_names,
-                          weight_shard_size_bytes=weight_shard_size_bytes,
-                          control_flow_v2=control_flow_v2,
-                          experiments=experiments,
-                          metadata=metadata,
-                          frozen_graph_dir=frozen_graph_dir)
-
-def load_and_initialize_hub_module(module_path, signature='default'):
-  """Loads graph of a TF-Hub module and initializes it into a session.
-
-  Args:
-    module_path: string Path to TF-Hub module.
-    signature: string Signature to use when creating the apply graph.
-
-  Return:
-    graph: tf.Graph Graph of the module.
-    session: tf.Session Session with initialized variables and tables.
-    inputs: dict Dictionary of input tensors.
-    outputs: dict Dictionary of output tensors.
-
-  Raises:
-    ValueError: If signature contains a SparseTensor on input or output.
-  """
-  graph = tf.Graph()
-  with graph.as_default():
-    tf.compat.v1.logging.info('Importing %s', module_path)
-    module = hub.Module(module_path)
-
-    signature_inputs = module.get_input_info_dict(signature)
-    signature_outputs = module.get_output_info_dict(signature)
-    # First check there are no SparseTensors in input or output.
-    for key, info in list(signature_inputs.items()) + list(
-        signature_outputs.items()):
-      if info.is_sparse:
-        raise ValueError(
-            'Signature "%s" has a SparseTensor on input/output "%s".'
-            ' SparseTensors are not supported.' % (signature, key))
-
-    # Create placeholders to represent the input of the provided signature.
-    inputs = {}
-    for input_key, input_info in signature_inputs.items():
-      inputs[input_key] = tf.compat.v1.placeholder(
-          shape=input_info.get_shape(), dtype=input_info.dtype, name=input_key)
-
-    outputs = module(inputs=inputs, signature=signature, as_dict=True)
-
-    session = tf.compat.v1.Session(graph=graph)
-    session.run(tf.compat.v1.global_variables_initializer())
-    session.run(tf.compat.v1.tables_initializer())
-
-  return graph, session, inputs, outputs
-
-
-def convert_tf_hub_module_v1(module_path, output_dir,
-                             signature='default', quantization_dtype_map=None,
-                             skip_op_check=False, strip_debug_ops=False,
-                             weight_shard_size_bytes=1024 * 1024 * 4,
-                             experiments=False,
-                             metadata=None):
-  """Freeze the TF-Hub module and check compatibility with Tensorflow.js.
-
-  Optimize and convert the TF-Hub module to Tensorflow.js format, if it passes
-  the compatiblity check.
-
-  Args:
-    module_path: string Path to the module.
-    output_dir: string The name of the output directory. The directory
-      will consist of
-      - a file named 'model.json'
-      - possibly sharded binary weight files.
-    signature: string Signature to load.
-    quantization_dtype_map: A mapping from dtype
-      (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-      supports wildcard substitution.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to strip debug ops.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    experiments: Bool enable experimental features.
-    metadata: User defined metadata map.
-  """
-
-  if signature is None:
-    signature = 'default'
-
-  if not os.path.exists(output_dir):
-    os.makedirs(output_dir)
-
-  graph, sess, inputs, outputs = load_and_initialize_hub_module(
-      module_path, signature)
-
-  input_node_names = []
-  output_node_names = []
-
-  for input_tensor in inputs.values():
-    input_node_names.append(input_tensor.name.split(':')[0])
-  for output_tensor in outputs.values():
-    output_node_names.append(output_tensor.name.split(':')[0])
-
-  print('Creating a model with inputs %s and outputs %s.' % (input_node_names,
-                                                             output_node_names))
-
-  frozen_graph_def = tf.compat.v1.graph_util.convert_variables_to_constants(
-      sess, graph.as_graph_def(), output_node_names)
-
-  output_graph = os.path.join(output_dir, common.ARTIFACT_MODEL_JSON_FILE_NAME)
-  frozen_file = output_graph + '.frozen'
-  try:
-    with tf.compat.v1.gfile.GFile(frozen_file, 'wb') as f:
-      f.write(frozen_graph_def.SerializeToString())
-
-    frozen_graph = load_graph(frozen_file)
-    signature = _build_signature_def(frozen_graph,
-                                     inputs.values(), outputs.values())
-
-    optimized_graph = optimize_graph(frozen_graph, signature,
-                                     skip_op_check=skip_op_check,
-                                     strip_debug_ops=strip_debug_ops,
-                                     experiments=experiments)
-
-    weights = extract_weights(optimized_graph)
-
-    write_artifacts(MessageToDict(optimized_graph), weights,
-                   output_graph, tf.__version__, signature,
-                   quantization_dtype_map=quantization_dtype_map,
-                   weight_shard_size_bytes=weight_shard_size_bytes,
-                   metadata=metadata)
-  finally:
-    # Clean up the temp files.
-    if os.path.exists(frozen_file):
-      os.remove(frozen_file)
-
-
-def convert_tf_hub_module(module_handle, output_dir,
-                          signature='default', saved_model_tags='serve',
-                          quantization_dtype_map=None,
-                          skip_op_check=False, strip_debug_ops=False,
-                          use_structured_outputs_names=False,
-                          weight_shard_size_bytes=1024 * 1024 * 4,
-                          control_flow_v2=False,
-                          experiments=False,
-                          metadata=None):
-  """Conversion for TF Hub modules V1 and V2.
-
-  See convert_tf_hub_module and convert_tf_saved_model.
-
-  Args:
-    module_path: string Path to the module.
-    output_dir: string The name of the output directory. The directory
-      will consist of
-      - a file named 'model.json'
-      - possibly sharded binary weight files.
-    signature: string Signature to load.
-    saved_model_tags: tags of the GraphDef to load. Defaults to ''.
-    quantization_dtype_map: A mapping from dtype
-      (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-      supports wildcard substitution.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to strip debug ops.
-    use_structured_outputs_names: Bool whether output of graph model will follow
-      the structured_outputs format.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    control_flow_v2: Bool whether to enable control flow v2 ops.
-    experiments: Bool enable experimental features.
-    metadata: User defined metadata map.
-  """
-  module_path = hub.resolve(module_handle)
-  # TODO(vbardiovskyg): We can remove this v1 code path once loading of all v1
-  # modules is fixed on the TF side, or once the modules we cannot load become
-  # replaced with newer versions.
-  if gfile.exists(os.path.join(module_path, _HUB_V1_MODULE_PB)):
-    print("Loading the module using TF 1.X interface from %s." % module_path)
-    convert_tf_hub_module_v1(module_path, output_dir, signature,
-                             quantization_dtype_map,
-                             skip_op_check, strip_debug_ops,
-                             weight_shard_size_bytes,
-                             experiments=experiments,
-                             metadata=metadata)
-  else:
-    print("Loading the module using TF 2.X interface from %s." % module_path)
-    if signature is None:
-      signature = 'default'
-    convert_tf_saved_model(saved_model_dir=module_path,
-                           output_dir=output_dir,
-                           signature_def=signature,
-                           saved_model_tags=saved_model_tags,
-                           quantization_dtype_map=quantization_dtype_map,
-                           skip_op_check=skip_op_check,
-                           strip_debug_ops=strip_debug_ops,
-                           use_structured_outputs_names=
-                           use_structured_outputs_names,
-                           weight_shard_size_bytes=weight_shard_size_bytes,
-                           control_flow_v2=control_flow_v2,
-                           experiments=experiments,
-                           metadata=metadata)
-
-def convert_keras_model_to_graph_model(keras_model,
-                                       output_dir,
-                                       saved_model_tags='serve',
-                                       quantization_dtype_map=None,
-                                       skip_op_check=False,
-                                       strip_debug_ops=False,
-                                       use_structured_outputs_names=False,
-                                       weight_shard_size_bytes=1024 * 1024 * 4,
-                                       control_flow_v2=False,
-                                       experiments=False,
-                                       metadata=None):
-  """Convert an in-memory keras model to Tensorflow.js graph model format.
-
-  Args:
-    keras_model: Keras Model object.
-    output_dir: string The name of the output directory. The directory
-      will consist of
-      - a file named 'model.json'
-      - possibly sharded binary weight files.
-    saved_model_tags: tags of the GraphDef to load. Defaults to 'serve'.
-    quantization_dtype_map: A mapping from dtype
-      (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-      supports wildcard substitution.
-    skip_op_check: Bool whether to skip the op check.
-    strip_debug_ops: Bool whether to strip debug ops.
-    use_structured_outputs_names: Bool whether output of graph model will follow
-      the structured_outputs format.
-    weight_shard_size_bytes: Shard size (in bytes) of the weight files.
-      The size of each weight file will be <= this value.
-    control_flow_v2: Bool whether to enable control flow v2 ops.
-    experiments: Bool enable experimental features.
-    metadata: User defined metadata map.
-  """
-  _convert_tf_saved_model(output_dir, keras_model=keras_model,
-                          saved_model_tags=saved_model_tags,
-                          quantization_dtype_map=quantization_dtype_map,
-                          skip_op_check=skip_op_check,
-                          strip_debug_ops=strip_debug_ops,
-                          use_structured_outputs_names=
-                          use_structured_outputs_names,
-                          weight_shard_size_bytes=weight_shard_size_bytes,
-                          control_flow_v2=control_flow_v2,
-                          experiments=experiments,
-                          metadata=metadata)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_saved_model_conversion_v2_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_saved_model_conversion_v2_test.py
deleted file mode 100644
index 8eb000d3d..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/tf_saved_model_conversion_v2_test.py
+++ /dev/null
@@ -1,1428 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for artifact conversion to and from Tensorflow SavedModel v2."""
-
-import base64
-import glob
-import json
-import os
-import shutil
-import tempfile
-import unittest
-import numpy as np
-
-import tensorflow.compat.v2 as tf
-from tensorflow_decision_forests.keras import GradientBoostedTreesModel
-from tensorflow.python.eager import def_function
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import tensor_spec
-from tensorflow.python.ops import variables
-from tensorflow.python.trackable import autotrackable
-from tensorflow.python.tools import freeze_graph
-from tensorflow.python.saved_model.save import save
-import tensorflow_hub as hub
-from tensorflowjs import version
-from tensorflowjs.converters import graph_rewrite_util
-from tensorflowjs.converters import tf_saved_model_conversion_v2
-from tensorflowjs.converters.common import ASSETS_DIRECTORY_NAME
-
-SAVED_MODEL_DIR = 'saved_model'
-HUB_MODULE_DIR = 'hub_module'
-FROZEN_MODEL_DIR = 'frozen_model'
-
-class ConvertTest(tf.test.TestCase):
-  def setUp(self):
-    super(ConvertTest, self).setUp()
-    self._tmp_dir = tempfile.mkdtemp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(ConvertTest, self).tearDown()
-
-  def _create_saved_model_v1(self):
-    """Create a TensorFlow SavedModel for testing."""
-
-    graph = tf.Graph()
-    with graph.as_default():
-      x = tf.compat.v1.constant([[37.0, -23.0], [1.0, 4.0]])
-      w = tf.compat.v1.get_variable('w', shape=[2, 2])
-      y = tf.compat.v1.matmul(x, w)
-      output = tf.compat.v1.nn.softmax(y)
-      init_op = w.initializer
-
-      # Create a builder.
-      save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-      builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(save_dir)
-
-      with tf.compat.v1.Session() as sess:
-        # Run the initializer on `w`.
-        sess.run(init_op)
-
-        builder.add_meta_graph_and_variables(
-            sess, [tf.compat.v1.saved_model.tag_constants.SERVING],
-            signature_def_map={
-                'serving_default':
-                    tf.compat.v1.saved_model \
-                        .signature_def_utils.predict_signature_def(
-                            inputs={'x': x},
-                            outputs={'output': output})
-            },
-            assets_collection=None)
-
-      builder.save()
-
-  def _create_saved_model_v1_with_hashtable(self):
-    """Create a TensorFlow SavedModel V1 with unused hash table for testing."""
-
-    graph = tf.Graph()
-    with graph.as_default():
-      x = tf.compat.v1.placeholder('int32', [None, 2, 2])
-      t = tf.compat.v1.to_float(x)
-      w = tf.compat.v1.get_variable('w', shape=[2, 2])
-      output = tf.compat.v1.matmul(t, w)
-      init_op = w.initializer
-
-      # Add a hash table that is not used by the output.
-      keys = tf.constant(['key'])
-      values = tf.constant([1])
-      initializer = tf.lookup.KeyValueTensorInitializer(keys, values)
-      table = tf.lookup.StaticHashTable(initializer, -1)
-
-      # Create a builder.
-      save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-      builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(
-          save_dir)
-
-      with tf.compat.v1.Session() as sess:
-        # Run the initializer on `w`.
-        sess.run(init_op)
-        table.lookup(keys)
-        builder.add_meta_graph_and_variables(
-            sess, [tf.compat.v1.saved_model.tag_constants.SERVING],
-            signature_def_map={
-                'serving_default':
-                    tf.compat.v1.saved_model \
-                        .signature_def_utils.predict_signature_def(
-                            inputs={'t': t},
-                            outputs={'output': output})
-            },
-            assets_collection=None)
-
-      builder.save()
-
-  def _create_saved_model_v2_with_hashtable(self):
-    """Create a TensorFlow SavedModel V2 with hash table for testing."""
-
-    class Table(tf.Module):
-        def __init__(self):
-            super(Table, self).__init__()
-            keys = tf.constant(['a', 'b'])
-            vals= tf.constant([0, 1])
-            init = tf.lookup.KeyValueTensorInitializer(keys, vals)
-            self.table = tf.lookup.StaticHashTable(init, -1)
-
-        def initializeTable(self):
-            @tf.function
-            def lookup(input):
-                return self.table.lookup(input)
-
-            return lookup
-
-    model = Table()
-    concrete_fn = model.initializeTable().get_concrete_function(
-      input=tf.TensorSpec([None], tf.string))
-
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf.saved_model.save(model, save_dir, signatures={"serving_default": concrete_fn})
-
-  def _create_saved_model_with_fusable_conv2d(self, use_bias):
-    """Test a basic model with fusable conv2d."""
-    layers = [
-        tf.keras.layers.Conv2D(
-            16, [3, 3], padding='same', use_bias=use_bias),
-        tf.keras.layers.BatchNormalization(),
-        tf.keras.layers.ReLU()
-    ]
-    model = tf.keras.Sequential(layers)
-    model.predict(tf.ones((1, 224, 224, 3)))
-    tf.keras.backend.set_learning_phase(0)
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf.saved_model.save(model, save_dir)
-
-  def _create_saved_model_with_fusable_depthwise_conv2d(self):
-    """Test a basic model with fusable depthwise conv2d."""
-    layers = [
-        tf.keras.layers.DepthwiseConv2D(
-            1, use_bias=True,
-            bias_initializer=tf.initializers.constant(0.25)),
-        tf.keras.layers.ReLU()
-    ]
-    model = tf.keras.Sequential(layers)
-    model.predict(tf.ones((1, 2, 2, 3)))
-    tf.keras.backend.set_learning_phase(0)
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf.saved_model.save(model, save_dir)
-
-  def _create_saved_model_with_fusable_addV2(self):
-    """Test a basic model with fusable addV2."""
-    @tf.function
-    def conv2d_addV2_depthwise_addV2(x):
-      filter = tf.ones([1, 1, 1, 1])
-      bias = tf.constant([100], dtype=dtypes.float32)
-      res = tf.raw_ops.Conv2D(
-        input=x, filter=filter, strides=[1, 1, 1, 1], padding="VALID")
-      res = tf.raw_ops.AddV2(x=res, y=bias)
-      res = tf.raw_ops.DepthwiseConv2dNative(
-        input=res, filter=filter, strides=[1, 1, 1, 1], padding="VALID")
-      res = tf.raw_ops.AddV2(x=res, y=bias)
-      return res
-    root = autotrackable.AutoTrackable()
-    root.f = conv2d_addV2_depthwise_addV2
-    to_save = root.f.get_concrete_function(
-        tensor_spec.TensorSpec([1, 1, 1, 1], dtypes.float32))
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    save(root, save_dir, to_save)
-
-  def _create_saved_model_with_unfusable_addV2(self):
-    """Test a basic model with fusable addV2."""
-    @tf.function
-    def addV2_conv2d(x):
-      bias = tf.constant([100], dtype=dtypes.float32)
-      filter = tf.ones([1, 1, 1, 1])
-      res = tf.raw_ops.AddV2(x=x, y=bias)
-      res = tf.raw_ops.Conv2D(
-        input=res, filter=filter, strides=[1, 1, 1, 1], padding="VALID")
-      return res
-    root = autotrackable.AutoTrackable()
-    root.f = addV2_conv2d
-    to_save = root.f.get_concrete_function(
-        tensor_spec.TensorSpec([1, 1, 1, 1], dtypes.float32))
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    save(root, save_dir, to_save)
-
-  def _create_saved_model_with_prelu(self):
-    """Test a basic model with fusable conv2d."""
-    layers = [
-        tf.keras.layers.Conv2D(
-            16, [3, 3], padding='same', use_bias=True,
-            bias_initializer=tf.initializers.constant(0.25)),
-        tf.keras.layers.PReLU(alpha_initializer=tf.initializers.constant(0.25)),
-        tf.keras.layers.DepthwiseConv2D(
-            1, use_bias=True,
-            bias_initializer=tf.initializers.constant(0.25)),
-        tf.keras.layers.PReLU(alpha_initializer=tf.initializers.constant(0.25))
-    ]
-    model = tf.keras.Sequential(layers)
-    model.predict(tf.ones((1, 224, 224, 3)))
-    tf.keras.backend.set_learning_phase(0)
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf.saved_model.save(model, save_dir)
-
-  def _create_saved_model_with_unfusable_prelu(self):
-    """Test a basic model with unfusable prelu."""
-    layers = [
-        tf.keras.layers.ReLU(),
-        tf.keras.layers.PReLU(alpha_initializer=tf.initializers.constant(0.25))
-    ]
-    model = tf.keras.Sequential(layers)
-    model.predict(tf.ones((1, 224, 3)))
-    tf.keras.backend.set_learning_phase(0)
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf.saved_model.save(model, save_dir)
-
-  def _create_saved_model(self):
-    """Test a basic model with functions to make sure functions are inlined."""
-    input_data = constant_op.constant(1., shape=[1])
-    root = autotrackable.AutoTrackable()
-    root.v1 = variables.Variable(3.)
-    root.v2 = variables.Variable(2.)
-    root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
-    to_save = root.f.get_concrete_function(input_data)
-
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    save(root, save_dir, to_save)
-
-  def _create_saved_model_with_fusable_matmul(self):
-    """Test a fusable matmul model."""
-    input_data = constant_op.constant(1., shape=[1, 1])
-    bias_data = constant_op.constant(1., shape=[1])
-    root = autotrackable.AutoTrackable()
-    root.v2 = variables.Variable([[2.]])
-    root.f = def_function.function(
-        lambda x: tf.nn.relu(tf.nn.bias_add(tf.matmul(x, root.v2),
-                                            bias_data)))
-    to_save = root.f.get_concrete_function(input_data)
-
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    save(root, save_dir, to_save)
-
-  def _create_saved_model_with_control_flow(self):
-    """Test a basic model with control flow to inlined."""
-    @tf.function
-    def find_next_odd(v):
-      v1 = v + 1
-      while tf.equal(v1 % 2, 0):
-        v1 = v1 + 1
-      return v1
-    root = autotrackable.AutoTrackable()
-    root.f = find_next_odd
-    to_save = root.f.get_concrete_function(
-        tensor_spec.TensorSpec([], dtypes.int32))
-
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    save(root, save_dir, to_save)
-
-  def _create_saved_model_with_tfdf(self):
-    """Test a basic TFDF model."""
-    P = 5
-    NUM_EXAMPLES = 10
-    NUM_FEATURES = 4
-
-    x_train = np.random.uniform(size=(NUM_EXAMPLES, NUM_FEATURES))
-    y_train = np.random.uniform(size=NUM_EXAMPLES) > 0.5
-    w_train = y_train * (P - 1) + 1  # 1 or p depending on the class.
-
-    model = GradientBoostedTreesModel()
-    model.fit(x=x_train, y=y_train, sample_weight=w_train)
-
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    model.save(save_dir)
-
-  def _create_unsupported_saved_model(self):
-    root = autotrackable.AutoTrackable()
-    root.w = variables.Variable(tf.random.uniform([2, 2]))
-
-    @def_function.function
-    def exported_function(x):
-      root.x = constant_op.constant([[37.0, -23.0], [1.0, 4.0]])
-      root.y = tf.matmul(root.x, root.w)
-      # unsupported op: linalg.diag
-      root.z = tf.linalg.diag(root.y)
-      return root.z * x
-
-    root.f = exported_function
-    to_save = root.f.get_concrete_function(
-        tensor_spec.TensorSpec([], dtypes.float32))
-
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    save(root, save_dir, to_save)
-
-  def _create_saved_model_with_debug_ops(self):
-    root = autotrackable.AutoTrackable()
-    root.w = variables.Variable(tf.random.uniform([2, 2]))
-
-    @def_function.function
-    def exported_function(x):
-      root.x = constant_op.constant([[37.0, -23.0], [1.0, 4.0]])
-      root.y = tf.matmul(root.x, root.w)
-      tf.compat.v1.Print(root.x, [root.x])
-      tf.compat.v1.Assert(tf.greater(tf.reduce_max(root.x), 0), [root.x])
-      tf.compat.v1.check_numerics(root.x, 'NaN found')
-      return root.y * x
-
-    root.f = exported_function
-    to_save = root.f.get_concrete_function(
-        tensor_spec.TensorSpec([], dtypes.float32))
-
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    save(root, save_dir, to_save)
-
-  def _create_saved_model_with_structured_outputs(self):
-    def create_input(name):
-      return tf.keras.layers.Input(name=name, shape=(1,), dtype=tf.float32)
-
-    input1 = create_input("input1")
-    input3 = create_input("input3")
-    input2 = create_input("input2")
-
-    output1 = tf.keras.layers.Dense(1, name='a')
-    output1 = output1(tf.keras.layers.concatenate([input1, input3], axis=1))
-    output2 = tf.keras.layers.Dense(1, name='b')(input2)
-    output3 = tf.keras.layers.Multiply(name='c')([output1, output2])
-
-    inputs = {
-        "input1": input1,
-        "input3": input3,
-        "input2": input2
-    }
-
-    outputs = {
-        "a": output1,
-        "c": output3,
-        "b": output2
-    }
-
-    model = tf.keras.Model(inputs=inputs, outputs=outputs)
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf.saved_model.save(model, save_dir)
-
-  def _create_hub_module(self):
-    # Module function that doubles its input.
-    def double_module_fn():
-      w = tf.Variable([2.0, 4.0])
-      x = tf.compat.v1.placeholder(dtype=tf.float32)
-      hub.add_signature(inputs=x, outputs=x*w)
-    graph = tf.Graph()
-    with graph.as_default():
-      spec = hub.create_module_spec(double_module_fn)
-      m = hub.Module(spec)
-    # Export the module.
-    with tf.compat.v1.Session(graph=graph) as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
-      m.export(os.path.join(self._tmp_dir, HUB_MODULE_DIR), sess)
-
-  def create_frozen_model(self):
-    graph = tf.Graph()
-    saved_model_dir = os.path.join(self._tmp_dir, FROZEN_MODEL_DIR)
-    with graph.as_default():
-      x = tf.constant([[37.0, -23.0], [1.0, 4.0]])
-      w = tf.Variable(tf.random.uniform([2, 2]))
-      y = tf.matmul(x, w)
-      tf.nn.softmax(y)
-      init_op = w.initializer
-
-      # Create a builder
-      builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(
-          saved_model_dir)
-
-      with tf.compat.v1.Session() as sess:
-        # Run the initializer on `w`.
-        sess.run(init_op)
-
-        builder.add_meta_graph_and_variables(
-            sess, [tf.compat.v1.saved_model.tag_constants.SERVING],
-            signature_def_map=None,
-            assets_collection=None)
-
-      builder.save()
-
-    frozen_file = os.path.join(self._tmp_dir, FROZEN_MODEL_DIR, 'model.frozen')
-    freeze_graph.freeze_graph(
-        '',
-        '',
-        True,
-        '',
-        "Softmax",
-        '',
-        '',
-        frozen_file,
-        True,
-        '',
-        saved_model_tags=tf.compat.v1.saved_model.tag_constants.SERVING,
-        input_saved_model_dir=saved_model_dir)
-
-  def test_convert_saved_model_v1(self):
-    self._create_saved_model_v1()
-
-    input_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    output_dir = os.path.join(input_dir, 'js')
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        input_dir,
-        output_dir
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'js')
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))
-
-  def test_convert_saved_model_v1_with_hashtable(self):
-    self._create_saved_model_v1_with_hashtable()
-
-    input_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    output_dir = os.path.join(input_dir, 'js')
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        input_dir,
-        output_dir
-    )
-
-    expected_weights_manifest = [{
-        'paths': ['group1-shard1of1.bin'],
-        'weights': [
-            {'dtype': 'float32', 'name': 'w', 'shape': [2, 2]},
-            {'dtype': 'string', 'name': 'Const', 'shape': [1]},
-            {'dtype': 'int32', 'name': 'Const_1', 'shape': [1]}
-        ]}]
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'js')
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-    self.assertTrue(model_json['modelInitializer'])
-
-    for node in model_json['modelTopology']['node']:
-      if node['name'] == 'ToFloat' and node['op'] == 'Placeholder':
-        self.assertEqual(node['attr']['shape'],
-                         {'shape': {'dim': [
-                             {'size': '-1'}, {'size': '2'}, {'size': '2'}]}})
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertEqual(weights_manifest, expected_weights_manifest)
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))
-
-  def test_convert_saved_model_v2_with_hashtable(self):
-    self._create_saved_model_v2_with_hashtable()
-
-    input_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    output_dir = os.path.join(input_dir, 'js')
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        input_dir,
-        output_dir
-    )
-
-    expected_signature = {
-      'inputs': {
-        'input': {
-          'name': 'input:0',
-          'dtype': 'DT_STRING',
-          'tensorShape': {'dim': [{'size': '-1'}]}
-        },
-        'unknown:0': {
-          'name': 'unknown:0',
-          'dtype': 'DT_RESOURCE',
-          'tensorShape': {},
-          'resourceId': None
-        }
-    },
-      'outputs': {
-        'output_0': {
-          'name': 'Identity:0',
-          'dtype': 'DT_INT32',
-          'tensorShape': {'dim': [{'size': '-1'}]}
-        }
-      }
-    }
-
-    expected_initializer_signature = {
-      'outputs': {
-        'Identity:0': {
-          'name': 'Identity:0',
-          'dtype': 'DT_RESOURCE',
-          'tensorShape': {},
-          'resourceId': None
-        }
-      }
-    }
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'js')
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-
-    # Check resource ids match which indicates the initializer output is mapped
-    # to the inference input.
-    signature_resource_id = model_json['signature']['inputs']['unknown:0']['resourceId']
-    initializer_resource_id = model_json['initializerSignature']['outputs']['Identity:0']['resourceId']
-    self.assertTrue(signature_resource_id)
-    self.assertEqual(signature_resource_id, initializer_resource_id)
-
-    # Update expected signatures with resourceId since it is a runtime value.
-    expected_signature['inputs']['unknown:0']['resourceId'] = signature_resource_id
-    expected_initializer_signature['outputs']['Identity:0']['resourceId'] = signature_resource_id
-    self.assertEqual(model_json['signature'], expected_signature)
-    self.assertEqual(model_json['initializerSignature'], expected_initializer_signature)
-
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    model_ops = [node['op'] for node in model_json['modelTopology']['node']]
-    self.assertIn('LookupTableFindV2', model_ops)
-
-    self.assertTrue(model_json['modelInitializer'])
-    initializer_ops = [node['op'] for node in model_json['modelInitializer']['node']]
-    self.assertIn('HashTableV2', initializer_ops)
-    self.assertIn('LookupTableImportV2', initializer_ops)
-
-    weights_manifest = model_json['weightsManifest'][0]
-    self.assertEqual(weights_manifest['paths'], ['group1-shard1of1.bin'])
-    self.assertEqual(weights_manifest['weights'][0],
-                     {'name': 'unknown_0', 'shape': [], 'dtype': 'int32'})
-    # Only check weights and dtype since name may vary between TF versions.
-    self.assertEqual(weights_manifest['weights'][1]['shape'], [2])
-    self.assertEqual(weights_manifest['weights'][1]['dtype'], 'string')
-    self.assertEqual(weights_manifest['weights'][2]['shape'], [2])
-    self.assertEqual(weights_manifest['weights'][2]['dtype'], 'int32')
-
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))
-
-  def test_convert_saved_model_v1_with_metadata(self):
-    self._create_saved_model_v1()
-
-    input_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    output_dir = os.path.join(input_dir, 'js')
-
-    metadata_json = {'a': 1}
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        input_dir,
-        output_dir,
-        metadata={'key': metadata_json}
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'js')
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertEqual(metadata_json, model_json['userDefinedMetadata']['key'])
-
-  def test_convert_saved_model(self):
-    self._create_saved_model()
-
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-
-  def test_convert_saved_model_with_frozen_file(self):
-    self._create_saved_model()
-
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        frozen_graph_dir=os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    )
-
-    frozen_file_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR,
-                                    'model.json.frozen')
-    # Check model.json.frozen exist.
-    self.assertTrue(
-        glob.glob(frozen_file_path))
-
-  def test_convert_saved_model_with_metadata(self):
-    self._create_saved_model()
-
-    metadata_json = {'a': 1}
-
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        metadata={'key': metadata_json}
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertEqual(metadata_json, model_json['userDefinedMetadata']['key'])
-
-  def test_convert_saved_model_with_fused_conv2d(self):
-    for use_bias in [True, False]:
-      self._create_saved_model_with_fusable_conv2d(use_bias)
-      tf_saved_model_conversion_v2.convert_tf_saved_model(
-          os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-          os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-      )
-
-      tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-      # Check model.json and weights manifest.
-      with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-        model_json = json.load(f)
-      self.assertTrue(model_json['modelTopology'])
-      self.assertIsNot(model_json['modelTopology']['versions'], None)
-      signature = model_json['signature']
-      self.assertIsNot(signature, None)
-      self.assertIsNot(signature['inputs'], None)
-      self.assertIsNot(signature['outputs'], None)
-
-      nodes = model_json['modelTopology']['node']
-
-      fused_op = None
-      for node in nodes:
-        self.assertNotIn('BatchNorm', node['op'])
-        self.assertNotIn('Relu', node['op'])
-        self.assertNotIn('BiasAdd', node['op'])
-        if node['op'] == '_FusedConv2D':
-          fused_op = node
-      self.assertIsNot(fused_op, None)
-      self.assertEqual(
-          base64.b64decode(fused_op['attr']['fused_ops']['list']['s'][0]),
-          b'BiasAdd')
-      self.assertEqual(
-          base64.b64decode(fused_op['attr']['fused_ops']['list']['s'][1]),
-          b'Relu')
-
-      # Check meta-data in the artifact JSON.
-      self.assertEqual(model_json['format'], 'graph-model')
-      self.assertEqual(
-          model_json['convertedBy'],
-          'TensorFlow.js Converter v%s' % version.version)
-      self.assertEqual(model_json['generatedBy'],
-                       tf.__version__)
-      self.assertTrue(
-          glob.glob(
-              os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_with_fused_matmul(self):
-    self._create_saved_model_with_fusable_matmul()
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    nodes = model_json['modelTopology']['node']
-    fused_op = None
-    for node in nodes:
-      self.assertNotEqual(node['op'], 'MatMul')
-      self.assertNotIn('Relu', node['op'])
-      self.assertNotIn('BiasAdd', node['op'])
-      if node['op'] == graph_rewrite_util.FUSED_MATMUL:
-        fused_op = node
-    self.assertIsNot(fused_op, None)
-    self.assertIsNot(fused_op['attr']['transpose_a'], None)
-    self.assertIsNot(fused_op['attr']['transpose_b'], None)
-    self.assertEqual(
-        base64.b64decode(fused_op['attr']['fused_ops']['list']['s'][0]),
-        b'BiasAdd')
-    self.assertEqual(
-        base64.b64decode(fused_op['attr']['fused_ops']['list']['s'][1]),
-        b'Relu')
-
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_with_fused_depthwise_conv2d(self):
-    self._create_saved_model_with_fusable_depthwise_conv2d()
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    nodes = model_json['modelTopology']['node']
-
-    fused_op = None
-    for node in nodes:
-      self.assertNotIn('BatchNorm', node['op'])
-      self.assertNotIn('Relu', node['op'])
-      self.assertNotIn('BiasAdd', node['op'])
-      if node['op'] == graph_rewrite_util.FUSED_DEPTHWISE_CONV2D:
-        fused_op = node
-    self.assertIsNot(fused_op, None)
-    self.assertIsNot(fused_op['attr']['dilations'], None)
-    self.assertIsNot(fused_op['attr']['strides'], None)
-    self.assertEqual(
-        base64.b64decode(fused_op['attr']['fused_ops']['list']['s'][0]),
-        b'BiasAdd')
-    self.assertEqual(
-        base64.b64decode(fused_op['attr']['fused_ops']['list']['s'][1]),
-        b'Relu')
-
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_with_unfusable_addV2(self):
-    self._create_saved_model_with_unfusable_addV2()
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    nodes = model_json['modelTopology']['node']
-
-    # check if AddV2 op exists
-    addV2_op = None
-    for node in nodes:
-      if node['op'] == 'AddV2':
-        addV2_op = node
-        break
-    self.assertTrue(addV2_op)
-
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_with_fusable_addV2(self):
-    self._create_saved_model_with_fusable_addV2()
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    nodes = model_json['modelTopology']['node']
-
-    # Check if AddV2 is fused to Conv2D and Depthwise ops.
-    fused_conv2d_op = None
-    fused_depthwise_op = None
-    for node in nodes:
-      self.assertNotEqual('Conv2D', node['op'])
-      self.assertNotEqual('DepthwiseConv2dNative', node['op'])
-      self.assertNotEqual('AddV2', node['op'])
-      self.assertNotEqual('BiasAdd', node['op'])
-      if node['op'] == graph_rewrite_util.FUSED_CONV2D:
-        fused_conv2d_op = node
-      elif node['op'] == graph_rewrite_util.FUSED_DEPTHWISE_CONV2D:
-        fused_depthwise_op = node
-    self.assertIsNot(fused_conv2d_op, None)
-    self.assertIsNot(fused_depthwise_op, None)
-    fused_conv2d_ops = list(map(base64.b64decode,
-                         fused_conv2d_op['attr']['fused_ops']['list']['s']))
-    self.assertEqual(fused_conv2d_ops, [b'BiasAdd'])
-    self.assertEqual(fused_conv2d_op['attr']['num_args']['i'], '1')
-    fused_depthwise_ops = list(
-        map(base64.b64decode,
-            fused_depthwise_op['attr']['fused_ops']['list']['s']))
-    self.assertEqual(fused_depthwise_ops, [b'BiasAdd'])
-    self.assertEqual(fused_depthwise_op['attr']['num_args']['i'], '1')
-
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_with_prelu(self):
-    self._create_saved_model_with_prelu()
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    nodes = model_json['modelTopology']['node']
-
-    prelu_op = None
-    fused_op = None
-    depthwise_fused_op = None
-    for node in nodes:
-      if node['op'] == 'Prelu':
-        prelu_op = node
-      if node['op'] == graph_rewrite_util.FUSED_CONV2D:
-        fused_op = node
-      if node['op'] == graph_rewrite_util.FUSED_DEPTHWISE_CONV2D:
-        depthwise_fused_op = node
-    self.assertTrue(prelu_op is None)
-    self.assertIsNot(fused_op, None)
-    self.assertIsNot(depthwise_fused_op, None)
-
-    fused_ops = list(map(base64.b64decode,
-                         fused_op['attr']['fused_ops']['list']['s']))
-    self.assertEqual(fused_ops, [b'BiasAdd', b'Prelu'])
-    self.assertEqual(fused_op['attr']['num_args']['i'], '2')
-    depthwise_fused_ops = list(
-        map(base64.b64decode,
-            depthwise_fused_op['attr']['fused_ops']['list']['s']))
-    self.assertEqual(depthwise_fused_ops, [b'BiasAdd', b'Prelu'])
-    self.assertEqual(depthwise_fused_op['attr']['num_args']['i'], '2')
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_with_unfusable_prelu(self):
-    self._create_saved_model_with_unfusable_prelu()
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    nodes = model_json['modelTopology']['node']
-
-    prelu_op = None
-    for node in nodes:
-      if node['op'] == 'Prelu':
-        prelu_op = node
-        break
-
-    self.assertTrue(prelu_op)
-
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_with_control_flow(self):
-    self._create_saved_model_with_control_flow()
-
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_with_control_flow_v2(self):
-    self._create_saved_model_with_control_flow()
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        tfjs_path, tfjs_path, control_flow_v2=True
-    )
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-
-    add_y_weight = None
-    for weight in weights_manifest[0]['weights']:
-      if 'add/y' in weight['name']:
-        add_y_weight = weight
-
-    self.assertIsNot(add_y_weight, None)
-    self.assertFalse(add_y_weight['name'].startswith('add/y'))
-
-    nodes = model_json['modelTopology']['node']
-
-    while_op = None
-    for node in nodes:
-      self.assertNotIn('Merge', node['op'])
-      self.assertNotIn('Switch', node['op'])
-      if node['op'] == 'StatelessWhile':
-        while_op = node
-    self.assertIsNot(while_op, None)
-    # Check meta-data in the artifact JSON.
-    self.assertEqual(model_json['format'], 'graph-model')
-    self.assertEqual(
-        model_json['convertedBy'],
-        'TensorFlow.js Converter v%s' % version.version)
-    self.assertEqual(model_json['generatedBy'],
-                     tf.__version__)
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_with_tfdf(self):
-    self._create_saved_model_with_tfdf()
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        tfjs_path, tfjs_path, skip_op_check=True
-    )
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-
-    # Check TFDF ops are present.
-    model_ops = [node['op'] for node in model_json['modelTopology']['node']]
-    self.assertIn('SimpleMLInferenceOpWithHandle', model_ops)
-
-    initializer_ops = [node['op'] for node in model_json['modelInitializer']['node']]
-    self.assertIn('SimpleMLCreateModelResource', initializer_ops)
-    self.assertIn('SimpleMLLoadModelFromPathWithHandle', initializer_ops)
-
-    # Check assets containing TFDF files were copied over.
-    self.assertTrue(
-        os.path.exists(
-            os.path.join(tfjs_path, ASSETS_DIRECTORY_NAME + '.zip')))
-
-  def test_convert_saved_model_sharded(self):
-    self._create_saved_model()
-    model_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-
-    # Do initial conversion without sharding.
-    tf_saved_model_conversion_v2.convert_tf_saved_model(model_path, tfjs_path)
-    weight_files = glob.glob(os.path.join(tfjs_path, 'group*.bin'))
-
-    # Get size of weights in bytes after graph optimizations.
-    optimized_total_weight = sum([os.path.getsize(f) for f in weight_files])
-
-    # Due to the shard size, there ought to be 2 shards after conversion.
-    weight_shard_size_bytes = int(optimized_total_weight * 0.8)
-
-    tfjs_path = os.path.join(self._tmp_dir, 'sharded_model')
-    # Convert Saved Model again with shard argument set.
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        model_path, tfjs_path,
-        weight_shard_size_bytes=weight_shard_size_bytes)
-
-    weight_files = sorted(glob.glob(os.path.join(tfjs_path, 'group*.bin')))
-    self.assertEqual(len(weight_files), 2)
-    weight_file_sizes = [os.path.getsize(f) for f in weight_files]
-
-    self.assertEqual(sum(weight_file_sizes), optimized_total_weight)
-    self.assertLess(weight_file_sizes[1], weight_file_sizes[0])
-
-  def test_optimizer_add_unsupported_op(self):
-    self._create_unsupported_saved_model()
-    with self.assertRaisesRegexp(  # pylint: disable=deprecated-method
-        ValueError, r'^Unsupported Ops'):
-      tf_saved_model_conversion_v2.convert_tf_saved_model(
-          os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-          os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-      )
-
-  def test_convert_saved_model_skip_op_check(self):
-    self._create_unsupported_saved_model()
-
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR), skip_op_check=True
-    )
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  # (TODO: piyu) disable this test, need to change
-  # convert_variables_to_constants_v2 to set function_optimization=aggressive.
-  @unittest.skip('not supported')
-  def test_convert_saved_model_strip_debug_ops(self):
-    self._create_saved_model_with_debug_ops()
-
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        strip_debug_ops=True)
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_saved_model_structured_outputs_true(self):
-    self._create_saved_model_with_structured_outputs()
-
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        use_structured_outputs_names=True)
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    self.assertEqual(["a", "b", "c"],
-                     model_json['userDefinedMetadata']['structuredOutputKeys'])
-
-  def test_convert_saved_model_structured_outputs_false(self):
-    self._create_saved_model_with_structured_outputs()
-
-    tf_saved_model_conversion_v2.convert_tf_saved_model(
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR),
-        os.path.join(self._tmp_dir, SAVED_MODEL_DIR))
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertIs(model_json.get('userDefinedMetadata'), None)
-
-  def test_convert_hub_module_v1(self):
-    self._create_hub_module()
-    module_path = os.path.join(self._tmp_dir, HUB_MODULE_DIR)
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-
-    tf_saved_model_conversion_v2.convert_tf_hub_module(module_path, tfjs_path)
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_hub_module_v1_sharded(self):
-    self._create_hub_module()
-    module_path = os.path.join(self._tmp_dir, HUB_MODULE_DIR)
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-
-    # Do initial conversion without sharding.
-    tf_saved_model_conversion_v2.convert_tf_hub_module(module_path, tfjs_path)
-    weight_files = glob.glob(os.path.join(tfjs_path, 'group*.bin'))
-
-    # Get size of weights in bytes after graph optimizations.
-    optimized_total_weight = sum([os.path.getsize(f) for f in weight_files])
-
-    # Due to the shard size, there ought to be 3 shards after conversion.
-    weight_shard_size_bytes = int(optimized_total_weight * 0.4)
-
-    tfjs_path = os.path.join(self._tmp_dir, 'sharded_model')
-    # Convert Hub model again with shard argument set.
-    tf_saved_model_conversion_v2.convert_tf_hub_module(
-        module_path, tfjs_path,
-        weight_shard_size_bytes=weight_shard_size_bytes)
-
-    weight_files = sorted(glob.glob(os.path.join(tfjs_path, 'group*.bin')))
-    self.assertEqual(len(weight_files), 3)
-    weight_file_sizes = [os.path.getsize(f) for f in weight_files]
-
-    self.assertEqual(sum(weight_file_sizes), optimized_total_weight)
-    self.assertEqual(weight_file_sizes[0], weight_file_sizes[1])
-    self.assertLess(weight_file_sizes[2], weight_file_sizes[0])
-
-  def test_convert_hub_module_v1_with_metadata(self):
-    self._create_hub_module()
-    module_path = os.path.join(self._tmp_dir, HUB_MODULE_DIR)
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-
-    metadata_json = {'a': 1}
-    tf_saved_model_conversion_v2.convert_tf_hub_module(
-        module_path, tfjs_path, metadata={'key': metadata_json})
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertEqual(metadata_json, model_json['userDefinedMetadata']['key'])
-
-  def test_convert_hub_module_v2(self):
-    self._create_saved_model()
-    module_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-
-    tf_saved_model_conversion_v2.convert_tf_hub_module(
-        module_path, tfjs_path, "serving_default", "serve")
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-  def test_convert_hub_module_v2_with_metadata(self):
-    self._create_saved_model()
-    module_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-
-    metadata_json = {'a': 1}
-    tf_saved_model_conversion_v2.convert_tf_hub_module(
-        module_path, tfjs_path, "serving_default", "serve",
-        metadata={'key': metadata_json})
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertEqual(metadata_json, model_json['userDefinedMetadata']['key'])
-
-  def test_convert_frozen_model(self):
-    self.create_frozen_model()
-    print(glob.glob(
-        os.path.join(self._tmp_dir, FROZEN_MODEL_DIR, '*')))
-
-    tf_saved_model_conversion_v2.convert_tf_frozen_model(
-        os.path.join(self._tmp_dir, FROZEN_MODEL_DIR, 'model.frozen'),
-        'Softmax',
-        os.path.join(self._tmp_dir, FROZEN_MODEL_DIR))
-
-    tfjs_path = os.path.join(self._tmp_dir, FROZEN_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    # frozen model signature has no input nodes.
-    self.assertIsNot(signature['outputs'], None)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, FROZEN_MODEL_DIR, 'group*-*')))
-
-  def test_convert_frozen_model_with_metadata(self):
-    self.create_frozen_model()
-    print(glob.glob(
-        os.path.join(self._tmp_dir, FROZEN_MODEL_DIR, '*')))
-
-    metadata_json = {'a': 1}
-    tf_saved_model_conversion_v2.convert_tf_frozen_model(
-        os.path.join(self._tmp_dir, FROZEN_MODEL_DIR, 'model.frozen'),
-        'Softmax',
-        os.path.join(self._tmp_dir, FROZEN_MODEL_DIR),
-        metadata={'key': metadata_json})
-
-    tfjs_path = os.path.join(self._tmp_dir, FROZEN_MODEL_DIR)
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertEqual(metadata_json, model_json['userDefinedMetadata']['key'])
-
-  def test_convert_keras_model_to_saved_model(self):
-    keras_model = tf.keras.Sequential(
-        [tf.keras.layers.Dense(1, input_shape=[2])])
-
-    tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf_saved_model_conversion_v2.convert_keras_model_to_graph_model(
-        keras_model, tfjs_path)
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    self.assertIsNot(signature['inputs'], None)
-    self.assertIsNot(signature['outputs'], None)
-
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/wizard.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/wizard.py
deleted file mode 100644
index 4a3767068..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/wizard.py
+++ /dev/null
@@ -1,658 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Interactive command line tool for tensorflow.js model conversion."""
-
-from __future__ import print_function, unicode_literals
-
-import json
-import os
-import re
-import sys
-import tempfile
-import traceback
-
-try:
-  import PyInquirer
-except ImportError:
-  sys.exit("""Please install PyInquirer using following command:
-              pip install PyInquirer==1.0.3""")
-
-import h5py
-import tensorflow.compat.v2 as tf
-from tensorflow.core.framework import types_pb2
-from tensorflow.python.saved_model import loader_impl
-from tensorflowjs.converters import converter
-from tensorflowjs.converters import common
-
-# regex for recognizing valid url for TFHub module.
-TFHUB_VALID_URL_REGEX = re.compile(
-    # http:// or https://
-    r'^(http)s?://', re.IGNORECASE)
-
-# prompt style
-prompt_style = PyInquirer.style_from_dict({
-    PyInquirer.Token.Separator: '#6C6C6C',
-    PyInquirer.Token.QuestionMark: '#FF9D00 bold',
-    PyInquirer.Token.Selected: '#5F819D',
-    PyInquirer.Token.Pointer: '#FF9D00 bold',
-    PyInquirer.Token.Instruction: '',  # default
-    PyInquirer.Token.Answer: '#5F819D bold',
-    PyInquirer.Token.Question: '',
-})
-
-
-def value_in_list(answers, key, values):
-  """Determine user's answer for the key is in the value list.
-  Args:
-    answer: Dict of user's answers to the questions.
-    key: question key.
-    values: List of values to check from.
-  """
-  try:
-    value = answers[key]
-    return value in values
-  except KeyError:
-    return False
-
-
-def get_tfjs_model_type(model_file):
-  with open(model_file) as f:
-    data = json.load(f)
-    print("====", data)
-    if 'format' in data:
-      return data['format']
-    else: # Default to layers model
-      return common.TFJS_LAYERS_MODEL_FORMAT
-
-
-def detect_saved_model(input_path):
-  if os.path.exists(os.path.join(input_path, 'assets', 'saved_model.json')):
-    return common.KERAS_SAVED_MODEL
-  saved_model = loader_impl.parse_saved_model(input_path)
-  graph_def = saved_model.meta_graphs[0].object_graph_def
-  if graph_def.nodes:
-    if 'tf_keras' in graph_def.nodes[0].user_object.identifier:
-      return common.KERAS_SAVED_MODEL
-  return common.TF_SAVED_MODEL
-
-
-def detect_input_format(input_path):
-  """Determine the input format from model's input path or file.
-  Args:
-    input_path: string of the input model path
-  returns:
-    string: detected input format
-    string: normalized input path
-  """
-  input_path = input_path.strip()
-  detected_input_format = None
-  if re.match(TFHUB_VALID_URL_REGEX, input_path):
-    detected_input_format = common.TF_HUB_MODEL
-  elif os.path.isdir(input_path):
-    if (any(fname.lower().endswith('saved_model.pb')
-            for fname in os.listdir(input_path))):
-      detected_input_format = detect_saved_model(input_path)
-    else:
-      for fname in os.listdir(input_path):
-        fname = fname.lower()
-        if fname.endswith('model.json'):
-          filename = os.path.join(input_path, fname)
-          if get_tfjs_model_type(filename) == common.TFJS_LAYERS_MODEL_FORMAT:
-            input_path = os.path.join(input_path, fname)
-            detected_input_format = common.TFJS_LAYERS_MODEL
-            break
-  elif os.path.isfile(input_path):
-    if h5py.is_hdf5(input_path):
-      detected_input_format = common.KERAS_MODEL
-    elif input_path.endswith('saved_model.pb'):
-      input_path = os.path.dirname(input_path)
-      detected_input_format = detect_saved_model(input_path)
-    elif (input_path.endswith('model.json') and
-          get_tfjs_model_type(input_path) == common.TFJS_LAYERS_MODEL_FORMAT):
-      detected_input_format = common.TFJS_LAYERS_MODEL
-
-  return detected_input_format, input_path
-
-
-def input_path_message(answers):
-  """Determine question for model's input path.
-  Args:
-    answer: Dict of user's answers to the questions
-  """
-  answer = answers[common.INPUT_FORMAT]
-  message = 'The original path seems to be wrong, '
-  if answer == common.KERAS_MODEL:
-    return message + 'what is the path of input HDF5 file?'
-  elif answer == common.TF_HUB_MODEL:
-    return message + ("what is the TFHub module URL? \n"
-                      "(i.e. https://tfhub.dev/google/imagenet/"
-                      "mobilenet_v1_100_224/classification/1)")
-  else:
-    return message + 'what is the directory that contains the model?'
-
-
-def validate_input_path(input_path, input_format):
-  """Validate the input path for given input format.
-  Args:
-    input_path: input path of the model.
-    input_format: model format string.
-  """
-  path = os.path.expanduser(input_path.strip())
-  if not path:
-    return 'Please enter a valid path'
-  if input_format == common.TF_HUB_MODEL:
-    if not re.match(TFHUB_VALID_URL_REGEX, path):
-      return """This is not an valid URL for TFHub module: %s,
-        We expect a URL that starts with http(s)://""" % path
-  elif not os.path.exists(path):
-    return 'Nonexistent path for the model: %s' % path
-  if input_format in (common.KERAS_SAVED_MODEL, common.TF_SAVED_MODEL):
-    is_dir = os.path.isdir(path)
-    if not is_dir and not path.endswith('saved_model.pb'):
-      return 'The path provided is not a directory or pb file: %s' % path
-    if (is_dir and
-        not any(f.endswith('saved_model.pb') for f in os.listdir(path))):
-      return 'Did not find a .pb file inside the directory: %s' % path
-    if input_format == common.KERAS_SAVED_MODEL:
-      if detect_saved_model(input_path) != common.KERAS_SAVED_MODEL:
-        return 'This is a saved model but not a keras saved model: %s' % path
-
-  if input_format == common.TFJS_LAYERS_MODEL:
-    is_dir = os.path.isdir(path)
-    if not is_dir and not path.endswith('model.json'):
-      return 'The path provided is not a directory or json file: %s' % path
-    if is_dir and not any(f.endswith('model.json') for f in os.listdir(path)):
-      return 'Did not find the model.json file inside the directory: %s' % path
-  if input_format == common.KERAS_MODEL:
-    if not h5py.is_hdf5(path):
-      return 'The path provided is not a keras model file: %s' % path
-  return True
-
-
-def expand_input_path(input_path):
-  """Expand the relative input path to absolute path, and add layers model file
-  name to the end if input format is `tfjs_layers_model`.
-  Args:
-    input_path: input path of the model.
-  Returns:
-    string: return expanded input path.
-  """
-  input_path = os.path.expanduser(input_path.strip())
-  is_dir = os.path.isdir(input_path)
-  if is_dir:
-    for fname in os.listdir(input_path):
-      if fname.endswith('.json'):
-        filename = os.path.join(input_path, fname)
-        return filename
-  return input_path
-
-
-def output_path_exists(output_path):
-  """Check the existence of the output path.
-  Args:
-    output_path: input path of the model.
-  Returns:
-    bool: return true when the output directory exists.
-  """
-  if os.path.exists(output_path):
-    return True
-  return False
-
-
-def generate_arguments(params):
-  """Generate the tensorflowjs command string for the selected params.
-  Args:
-    params: user selected parameters for the conversion.
-  Returns:
-    list: the argument list for converter.
-  """
-  args = []
-  not_param_list = [common.INPUT_PATH, common.OUTPUT_PATH,
-                    'overwrite_output_path', 'quantize']
-  no_false_param = [common.SPLIT_WEIGHTS_BY_LAYER, common.SKIP_OP_CHECK]
-  for key, value in sorted(params.items()):
-    if key not in not_param_list and value is not None:
-      if key in no_false_param:
-        if value is True:
-          args.append('--%s' % (key))
-      else:
-        args.append('--%s=%s' % (key, value))
-
-  args.append(params[common.INPUT_PATH])
-  args.append(params[common.OUTPUT_PATH])
-  return args
-
-
-def is_saved_model(input_format):
-  """Check if the input path contains saved model.
-  Args:
-    input_format: input model format.
-  Returns:
-    bool: whether this is for a saved model conversion.
-  """
-  return input_format == common.TF_SAVED_MODEL
-
-def available_output_formats(answers):
-  """Generate the output formats for given input format.
-  Args:
-    ansowers: user selected parameter dict.
-  """
-  input_format = answers[common.INPUT_FORMAT]
-  if input_format == common.KERAS_MODEL:
-    return [{
-        'key': 'g', # shortcut key for the option
-        'name': 'Tensorflow.js Graph Model',
-        'value': common.TFJS_GRAPH_MODEL,
-    }, {
-        'key': 'l',
-        'name': 'TensoFlow.js Layers Model',
-        'value': common.TFJS_LAYERS_MODEL,
-    }]
-  if input_format == common.TFJS_LAYERS_MODEL:
-    return [{
-        'key': 'k', # shortcut key for the option
-        'name': 'Keras Model',
-        'value': common.KERAS_MODEL,
-    }, {
-        'key': 's',
-        'name': 'Keras Saved Model',
-        'value': common.KERAS_SAVED_MODEL,
-    }, {
-        'key': 'l',
-        'name': 'TensoFlow.js Layers Model',
-        'value': common.TFJS_LAYERS_MODEL,
-    }]
-  return []
-
-
-def available_tags(answers):
-  """Generate the available saved model tags from the proto file.
-  Args:
-    ansowers: user selected parameter dict.
-  """
-  if is_saved_model(answers[common.INPUT_FORMAT]):
-    saved_model = loader_impl.parse_saved_model(answers[common.INPUT_PATH])
-    tags = []
-    for meta_graph in saved_model.meta_graphs:
-      tags.append(",".join(meta_graph.meta_info_def.tags))
-    return tags
-  return []
-
-
-def available_signature_names(answers):
-  """Generate the available saved model signatures from the proto file
-    and selected tags.
-  Args:
-    ansowers: user selected parameter dict.
-  """
-  if (is_saved_model(answers[common.INPUT_FORMAT]) and
-      common.SAVED_MODEL_TAGS in answers):
-    path = answers[common.INPUT_PATH]
-    tags = answers[common.SAVED_MODEL_TAGS]
-    saved_model = loader_impl.parse_saved_model(path)
-    for meta_graph in saved_model.meta_graphs:
-      if tags == ",".join(meta_graph.meta_info_def.tags):
-        signatures = []
-        for key in meta_graph.signature_def:
-          input_nodes = meta_graph.signature_def[key].inputs
-          output_nodes = meta_graph.signature_def[key].outputs
-          signatures.append(
-              {'value': key,
-               'name': format_signature(key, input_nodes, output_nodes)})
-        return signatures
-  return []
-
-
-def format_signature(name, input_nodes, output_nodes):
-  string = "signature name: %s\n" % name
-  string += "        inputs: %s" % format_nodes(input_nodes)
-  string += "        outputs: %s" % format_nodes(output_nodes)
-  return string
-
-
-def format_nodes(nodes):
-  string = "%s of %s\n" % (3 if len(nodes) > 3 else len(nodes), len(nodes))
-  count = 0
-  for key in nodes:
-    value = nodes[key]
-    string += "              name: %s, " % value.name
-    string += "dtype: %s, " % types_pb2.DataType.Name(value.dtype)
-    if value.tensor_shape.unknown_rank:
-      string += "shape: Unknown\n"
-    else:
-      string += "shape: %s\n" % [x.size for x in value.tensor_shape.dim]
-    count += 1
-    if count >= 3:
-      break
-  return string
-
-
-def input_format_string(base, target_format, detected_format):
-  if target_format == detected_format:
-    return base + ' *'
-  else:
-    return base
-
-
-def input_format_message(detected_input_format):
-  message = 'What is your input model format? '
-  if detected_input_format:
-    message += '(auto-detected format is marked with *)'
-  else:
-    message += '(model format cannot be detected.) '
-  return message
-
-def update_output_path(output_path, params):
-  output_path = os.path.expanduser(output_path.strip())
-  if (common.OUTPUT_FORMAT in params and
-      params[common.OUTPUT_FORMAT] == common.KERAS_MODEL):
-    if os.path.isdir(output_path):
-      output_path = os.path.join(output_path, 'model.H5')
-  return output_path
-
-def input_formats(detected_format):
-  formats = [{
-      'key': 'k',
-      'name': input_format_string('Keras (HDF5)', common.KERAS_MODEL,
-                                  detected_format),
-      'value': common.KERAS_MODEL
-  }, {
-      'key': 'e',
-      'name': input_format_string('Tensorflow Keras Saved Model',
-                                  common.KERAS_SAVED_MODEL,
-                                  detected_format),
-      'value': common.KERAS_SAVED_MODEL,
-  }, {
-      'key': 's',
-      'name': input_format_string('Tensorflow Saved Model',
-                                  common.TF_SAVED_MODEL,
-                                  detected_format),
-      'value': common.TF_SAVED_MODEL,
-  }, {
-      'key': 'h',
-      'name': input_format_string('TFHub Module',
-                                  common.TF_HUB_MODEL,
-                                  detected_format),
-      'value': common.TF_HUB_MODEL,
-  }, {
-      'key': 'l',
-      'name': input_format_string('TensoFlow.js Layers Model',
-                                  common.TFJS_LAYERS_MODEL,
-                                  detected_format),
-      'value': common.TFJS_LAYERS_MODEL,
-  }]
-  formats.sort(key=lambda x: x['value'] != detected_format)
-  return formats
-
-
-def run(dryrun):
-  print('Welcome to TensorFlow.js Converter.')
-  input_path = [{
-      'type': 'input',
-      'name': common.INPUT_PATH,
-      'message': 'Please provide the path of model file or '
-                 'the directory that contains model files. \n'
-                 'If you are converting TFHub module please provide the URL.',
-      'filter': os.path.expanduser,
-      'validate':
-          lambda path: 'Please enter a valid path' if not path else True
-  }]
-
-  input_params = PyInquirer.prompt(input_path, style=prompt_style)
-  detected_input_format, normalized_path = detect_input_format(
-      input_params[common.INPUT_PATH])
-  input_params[common.INPUT_PATH] = normalized_path
-
-  formats = [
-      {
-          'type': 'list',
-          'name': common.INPUT_FORMAT,
-          'message': input_format_message(detected_input_format),
-          'choices': input_formats(detected_input_format)
-      }, {
-          'type': 'list',
-          'name': common.OUTPUT_FORMAT,
-          'message': 'What is your output format?',
-          'choices': available_output_formats,
-          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
-                                                (common.KERAS_MODEL,
-                                                 common.TFJS_LAYERS_MODEL))
-      }
-  ]
-  format_params = PyInquirer.prompt(formats, input_params, style=prompt_style)
-  message = input_path_message(format_params)
-
-  questions = [
-      {
-          'type': 'input',
-          'name': common.INPUT_PATH,
-          'message': message,
-          'filter': expand_input_path,
-          'validate': lambda value: validate_input_path(
-              value, format_params[common.INPUT_FORMAT]),
-          'when': lambda answers: (not detected_input_format)
-      },
-      {
-          'type': 'list',
-          'name': common.SAVED_MODEL_TAGS,
-          'choices': available_tags,
-          'message': 'What is tags for the saved model?',
-          'when': lambda answers: (is_saved_model(answers[common.INPUT_FORMAT])
-                                   and
-                                   (common.OUTPUT_FORMAT not in format_params
-                                    or format_params[common.OUTPUT_FORMAT] ==
-                                    common.TFJS_GRAPH_MODEL))
-      },
-      {
-          'type': 'list',
-          'name': common.SIGNATURE_NAME,
-          'message': 'What is signature name of the model?',
-          'choices': available_signature_names,
-          'when': lambda answers: (is_saved_model(answers[common.INPUT_FORMAT])
-                                   and
-                                   (common.OUTPUT_FORMAT not in format_params
-                                    or format_params[common.OUTPUT_FORMAT] ==
-                                    common.TFJS_GRAPH_MODEL))
-      },
-      {
-          'type': 'list',
-          'name': 'quantize',
-          'message': 'Do you want to compress the model? '
-                     '(this will decrease the model precision.)',
-          'choices': [{
-              'name': 'No compression (Higher accuracy)',
-              'value': None
-          }, {
-              'name': 'float16 quantization '
-                      '(2x smaller, Minimal accuracy loss)',
-              'value': 'float16'
-          }, {
-              'name': 'uint16 affine quantization (2x smaller, Accuracy loss)',
-              'value': 'uint16'
-          }, {
-              'name': 'uint8 affine quantization (4x smaller, Accuracy loss)',
-              'value': 'uint8'
-          }]
-      },
-      {
-          'type': 'input',
-          'name': common.QUANTIZATION_TYPE_FLOAT16,
-          'message': 'Please enter the layers to apply float16 quantization '
-                     '(2x smaller, minimal accuracy tradeoff).\n'
-                     'Supports wildcard expansion with *, e.g., conv/*/weights',
-          'default': '*',
-          'when': lambda answers:
-                  value_in_list(answers, 'quantize', ('float16'))
-      },
-      {
-          'type': 'input',
-          'name': common.QUANTIZATION_TYPE_UINT8,
-          'message': 'Please enter the layers to apply affine 1-byte integer '
-                     'quantization (4x smaller, accuracy tradeoff).\n'
-                     'Supports wildcard expansion with *, e.g., conv/*/weights',
-          'default': '*',
-          'when': lambda answers:
-                  value_in_list(answers, 'quantize', ('uint8'))
-      },
-      {
-          'type': 'input',
-          'name': common.QUANTIZATION_TYPE_UINT16,
-          'message': 'Please enter the layers to apply affine 2-byte integer '
-                     'quantization (2x smaller, accuracy tradeoff).\n'
-                     'Supports wildcard expansion with *, e.g., conv/*/weights',
-          'default': '*',
-          'when': lambda answers:
-                  value_in_list(answers, 'quantize', ('uint16'))
-      },
-      {
-          'type': 'input',
-          'name': common.WEIGHT_SHARD_SIZE_BYTES,
-          'message': 'Please enter shard size (in bytes) of the weight files?',
-          'default': str(4 * 1024 * 1024),
-          'validate':
-              lambda size: ('Please enter a positive integer' if not
-                            (size.isdigit() and int(size) > 0) else True),
-          'when': lambda answers: (value_in_list(answers, common.OUTPUT_FORMAT,
-                                                 (common.TFJS_LAYERS_MODEL,
-                                                  common.TFJS_GRAPH_MODEL)) or
-                                   value_in_list(answers, common.INPUT_FORMAT,
-                                                 (common.TF_SAVED_MODEL,
-                                                  common.TF_HUB_MODEL)))
-      },
-      {
-          'type': 'confirm',
-          'name': common.SPLIT_WEIGHTS_BY_LAYER,
-          'message': 'Do you want to split weights by layers?',
-          'default': False,
-          'when': lambda answers: (value_in_list(answers, common.OUTPUT_FORMAT,
-                                                 (common.TFJS_LAYERS_MODEL)) and
-                                   value_in_list(answers, common.INPUT_FORMAT,
-                                                 (common.KERAS_MODEL,
-                                                  common.KERAS_SAVED_MODEL)))
-      },
-      {
-          'type': 'confirm',
-          'name': common.SKIP_OP_CHECK,
-          'message': 'Do you want to skip op validation? \n'
-                     'This will allow conversion of unsupported ops, \n'
-                     'you can implement them as custom ops in tfjs-converter.',
-          'default': False,
-          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
-                                                (common.TF_SAVED_MODEL,
-                                                 common.TF_HUB_MODEL))
-      },
-      {
-          'type': 'confirm',
-          'name': common.STRIP_DEBUG_OPS,
-          'message': 'Do you want to strip debug ops? \n'
-                     'This will improve model execution performance.',
-          'default': True,
-          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
-                                                (common.TF_SAVED_MODEL,
-                                                 common.TF_HUB_MODEL))
-      },
-      {
-          'type': 'confirm',
-          'name': common.CONTROL_FLOW_V2,
-          'message': 'Do you want to enable Control Flow V2 ops? \n'
-                     'This will improve branch and loop execution performance.',
-          'default': True,
-          'when': lambda answers: value_in_list(answers, common.INPUT_FORMAT,
-                                                (common.TF_SAVED_MODEL,
-                                                 common.TF_HUB_MODEL))
-      },
-      {
-          'type': 'input',
-          'name': common.METADATA,
-          'message': 'Do you want to provide metadata? \n'
-                     'Provide your own metadata in the form: \n'
-                     'metadata_key:path/metadata.json \n'
-                     'Separate multiple metadata by comma.'
-      }
-  ]
-  params = PyInquirer.prompt(questions, format_params, style=prompt_style)
-
-  output_options = [
-      {
-          'type': 'input',
-          'name': common.OUTPUT_PATH,
-          'message': 'Which directory do you want to save '
-                     'the converted model in?',
-          'filter': lambda path: update_output_path(path, params),
-          'validate': lambda path: len(path) > 0
-      },
-      {
-          'type': 'confirm',
-          'message': 'The output already directory exists, '
-                     'do you want to overwrite it?',
-          'name': 'overwrite_output_path',
-          'default': False,
-          'when': lambda ans: output_path_exists(ans[common.OUTPUT_PATH])
-      }
-  ]
-
-  while (common.OUTPUT_PATH not in params or
-         output_path_exists(params[common.OUTPUT_PATH]) and
-         not params['overwrite_output_path']):
-    params = PyInquirer.prompt(output_options, params, style=prompt_style)
-
-  arguments = generate_arguments(params)
-  print('converter command generated:')
-  print('tensorflowjs_converter %s' % ' '.join(arguments))
-  print('\n\n')
-
-  log_file = os.path.join(tempfile.gettempdir(), 'converter_error.log')
-  if not dryrun:
-    try:
-      converter.convert(arguments)
-      print('\n\nFile(s) generated by conversion:')
-
-      print("Filename {0:25} Size(bytes)".format(''))
-      total_size = 0
-      output_path = params[common.OUTPUT_PATH]
-      if os.path.isfile(output_path):
-        output_path = os.path.dirname(output_path)
-      for basename in sorted(os.listdir(output_path)):
-        filename = os.path.join(output_path, basename)
-        size = os.path.getsize(filename)
-        print("{0:35} {1}".format(basename, size))
-        total_size += size
-      print("Total size:{0:24} {1}".format('', total_size))
-    except BaseException:
-      exc_type, exc_value, exc_traceback = sys.exc_info()
-      lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
-      with open(log_file, 'a') as writer:
-        writer.write(''.join(line for line in lines))
-      print('Conversion failed, please check error log file %s.' % log_file)
-
-def pip_main():
-  """Entry point for pip-packaged binary.
-
-  Note that pip-packaged binary calls the entry method without
-  any arguments, which is why this method is needed in addition to the
-  `main` method below.
-  """
-  main([' '.join(sys.argv[1:])])
-
-
-def main(argv):
-  if argv[0] and not argv[0] == '--dryrun':
-    print("Usage: tensorflowjs_wizard [--dryrun]")
-    sys.exit(1)
-  dry_run = argv[0] == '--dryrun'
-  run(dry_run)
-
-if __name__ == '__main__':
-  tf.app.run(main=main, argv=[' '.join(sys.argv[1:])])
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/wizard_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/converters/wizard_test.py
deleted file mode 100644
index dce959230..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/converters/wizard_test.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import unittest
-import tempfile
-import json
-import os
-import shutil
-import tensorflow.compat.v2 as tf
-from tensorflow.python.eager import def_function
-from tensorflow.python.ops import variables
-from tensorflow.python.trackable import autotrackable
-from tensorflow.python.saved_model import save
-
-from tensorflowjs.converters import wizard
-
-SAVED_MODEL_DIR = 'saved_model'
-SAVED_MODEL_NAME = 'saved_model.pb'
-HD5_FILE_NAME = 'test.h5'
-LAYERS_MODEL_NAME = 'model.json'
-
-
-class CliTest(unittest.TestCase):
-  def setUp(self):
-    self._tmp_dir = tempfile.mkdtemp()
-    super(CliTest, self).setUp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(CliTest, self).tearDown()
-
-  def _create_layers_model(self):
-    data = {'format': 'layers-model'}
-    filename = os.path.join(self._tmp_dir, 'model.json')
-    with open(filename, 'a') as model_file:
-      json.dump(data, model_file)
-
-  def _create_hd5_file(self):
-    input_tensor = tf.keras.layers.Input((3,))
-    dense1 = tf.keras.layers.Dense(
-        4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
-        name='MyDense10')(input_tensor)
-    output = tf.keras.layers.Dense(
-        2, use_bias=False, kernel_initializer='ones', name='MyDense20')(dense1)
-    model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
-    h5_path = os.path.join(self._tmp_dir, HD5_FILE_NAME)
-    print(h5_path)
-    model.save_weights(h5_path)
-
-  def _create_keras_saved_model(self):
-    model = tf.keras.Sequential()
-    model.add(tf.keras.layers.Reshape([2, 3], input_shape=[6]))
-    model.add(tf.keras.layers.LSTM(10))
-    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    tf.keras.models.save_model(model, save_dir)
-
-  def _create_saved_model(self):
-    """Test a basic model with functions to make sure functions are inlined."""
-    input_data = tf.constant(1., shape=[1])
-    root = autotrackable.AutoTrackable()
-    root.v1 = variables.Variable(3.)
-    root.v2 = variables.Variable(2.)
-    root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
-    to_save = root.f.get_concrete_function(input_data)
-
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    save.save(root, save_dir, to_save)
-
-  def testOfValues(self):
-    answers = {'input_path': 'abc', 'input_format': '123'}
-    self.assertEqual(True, wizard.value_in_list(answers, 'input_path', ['abc']))
-    self.assertEqual(False, wizard.value_in_list(answers,
-                                                 'input_path', ['abd']))
-    self.assertEqual(False, wizard.value_in_list(answers,
-                                                 'input_format2', ['abc']))
-
-  def testInputPathMessage(self):
-    answers = {'input_format': 'keras'}
-    self.assertEqual("The original path seems to be wrong, "
-                     "what is the path of input HDF5 file?",
-                     wizard.input_path_message(answers))
-
-    answers = {'input_format': 'tf_hub'}
-    self.assertEqual("The original path seems to be wrong, "
-                     "what is the TFHub module URL? \n"
-                     "(i.e. https://tfhub.dev/google/imagenet/"
-                     "mobilenet_v1_100_224/classification/1)",
-                     wizard.input_path_message(answers))
-
-    answers = {'input_format': 'tf_saved_model'}
-    self.assertEqual("The original path seems to be wrong, "
-                     "what is the directory that contains the model?",
-                     wizard.input_path_message(answers))
-
-  def testValidateInputPathForTFHub(self):
-    self.assertNotEqual(True,
-                        wizard.validate_input_path(self._tmp_dir, 'tf_hub'))
-    self.assertEqual(True,
-                     wizard.validate_input_path("https://tfhub.dev/mobilenet",
-                                                'tf_hub'))
-
-  def testValidateInputPathForSavedModel(self):
-    self.assertNotEqual(True, wizard.validate_input_path(
-        self._tmp_dir, 'tf_saved_model'))
-    self._create_saved_model()
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    self.assertEqual(True, wizard.validate_input_path(
-        save_dir, 'tf_saved_model'))
-
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR, SAVED_MODEL_NAME)
-    self.assertEqual(True, wizard.validate_input_path(
-        save_dir, 'tf_saved_model'))
-
-  def testValidateInputPathForKerasSavedModel(self):
-    self.assertNotEqual(True, wizard.validate_input_path(
-        self._tmp_dir, 'keras_saved_model'))
-    self._create_keras_saved_model()
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    self.assertEqual(True, wizard.validate_input_path(
-        save_dir, 'keras_saved_model'))
-
-  def testValidateInputPathForKerasModel(self):
-    self.assertNotEqual(True,
-                        wizard.validate_input_path(self._tmp_dir, 'keras'))
-    self._create_hd5_file()
-    save_dir = os.path.join(self._tmp_dir, HD5_FILE_NAME)
-    self.assertEqual(True, wizard.validate_input_path(
-        save_dir, 'keras'))
-
-  def testValidateInputPathForLayersModel(self):
-    self.assertNotEqual(True,
-                        wizard.validate_input_path(self._tmp_dir, 'keras'))
-    self._create_layers_model()
-    save_dir = os.path.join(self._tmp_dir)
-    self.assertEqual(True, wizard.validate_input_path(
-        save_dir, 'tfjs_layers_model'))
-
-    save_dir = os.path.join(self._tmp_dir, 'model.json')
-    self.assertEqual(True, wizard.validate_input_path(
-        save_dir, 'tfjs_layers_model'))
-
-  def testOutputPathExist(self):
-    self.assertEqual(True, wizard.output_path_exists(self._tmp_dir))
-    output_dir = os.path.join(self._tmp_dir, 'test')
-    self.assertNotEqual(True, wizard.output_path_exists(output_dir))
-
-  def testAvailableTags(self):
-    self._create_saved_model()
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    self.assertEqual(['serve'], wizard.available_tags(
-        {'input_path': save_dir,
-         'input_format': 'tf_saved_model'}))
-
-  def testAvailableSignatureNames(self):
-    self._create_saved_model()
-    save_dir = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
-    self.assertEqual(sorted(['__saved_model_init_op', 'serving_default']),
-                     sorted(
-                         [x['value'] for x in wizard.available_signature_names(
-                             {'input_path': save_dir,
-                              'input_format': 'tf_saved_model',
-                              'saved_model_tags': 'serve'})]))
-
-  def testGenerateCommandForSavedModel(self):
-    options = {'input_format': 'tf_saved_model',
-               'input_path': 'tmp/saved_model',
-               'saved_model_tags': 'test',
-               'signature_name': 'test_default',
-               'quantize_float16': 'conv/*/weights',
-               'weight_shard_size_bytes': '4194304',
-               'skip_op_check': False,
-               'strip_debug_ops': True,
-               'control_flow_v2': True,
-               'metadata': 'metadata_key:metadata.json',
-               'output_path': 'tmp/web_model'}
-
-    self.assertEqual(['--control_flow_v2=True',
-                      '--input_format=tf_saved_model',
-                      '--metadata=metadata_key:metadata.json',
-                      '--quantize_float16=conv/*/weights',
-                      '--saved_model_tags=test',
-                      '--signature_name=test_default',
-                      '--strip_debug_ops=True',
-                      '--weight_shard_size_bytes=4194304',
-                      'tmp/saved_model', 'tmp/web_model'],
-                     wizard.generate_arguments(options))
-
-  def testGenerateCommandForKerasSavedModel(self):
-    options = {'input_format': 'tf_keras_saved_model',
-               'output_format': 'tfjs_layers_model',
-               'input_path': 'tmp/saved_model',
-               'saved_model_tags': 'test',
-               'signature_name': 'test_default',
-               'weight_shard_size_bytes': '100',
-               'quantize_float16': 'conv/*/weights',
-               'skip_op_check': True,
-               'strip_debug_ops': False,
-               'control_flow_v2': False,
-               'output_path': 'tmp/web_model'}
-
-    self.assertEqual(['--control_flow_v2=False',
-                      '--input_format=tf_keras_saved_model',
-                      '--output_format=tfjs_layers_model',
-                      '--quantize_float16=conv/*/weights',
-                      '--saved_model_tags=test',
-                      '--signature_name=test_default', '--skip_op_check',
-                      '--strip_debug_ops=False',
-                      '--weight_shard_size_bytes=100',
-                      'tmp/saved_model', 'tmp/web_model'],
-                     wizard.generate_arguments(options))
-
-  def testGenerateCommandForKerasModel(self):
-    options = {'input_format': 'keras',
-               'input_path': 'tmp/model.HD5',
-               'weight_shard_size_bytes': '100',
-               'quantize_uint16': 'conv/*/weights',
-               'output_path': 'tmp/web_model'}
-
-    self.assertEqual(['--input_format=keras',
-                      '--quantize_uint16=conv/*/weights',
-                      '--weight_shard_size_bytes=100',
-                      'tmp/model.HD5', 'tmp/web_model'],
-                     wizard.generate_arguments(options))
-
-  def testGenerateCommandForLayerModel(self):
-    options = {'input_format': 'tfjs_layers_model',
-               'output_format': 'keras',
-               'input_path': 'tmp/model.json',
-               'quantize_uint8': 'conv/*/weights',
-               'weight_shard_size_bytes': '100',
-               'output_path': 'tmp/web_model'}
-
-    self.assertEqual(['--input_format=tfjs_layers_model',
-                      '--output_format=keras',
-                      '--quantize_uint8=conv/*/weights',
-                      '--weight_shard_size_bytes=100',
-                      'tmp/model.json',
-                      'tmp/web_model'],
-                     wizard.generate_arguments(options))
-
-
-if __name__ == '__main__':
-  unittest.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/BUILD.bazel b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/BUILD.bazel
deleted file mode 100644
index 41805d22a..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/BUILD.bazel
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-package(
-    licenses = ["notice"],  # Apache 2.0
-)
-
-filegroup(
-    name = "ops",
-    srcs = glob(["*.json"]),
-    visibility = ["//visibility:public"],
-)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/arithmetic.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/arithmetic.json
deleted file mode 100644
index a87d8425f..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/arithmetic.json
+++ /dev/null
@@ -1,380 +0,0 @@
-[
-  {
-    "tfOpName": "Add",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "AddV2",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "AddN",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "end": 0,
-        "name": "tensors",
-        "type": "tensors"
-      }
-    ]
-  },
-  {
-    "tfOpName": "BiasAdd",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      },
-      {
-        "tfName": "data_format",
-        "name": "dataFormat",
-        "type": "string",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Sub",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "RealDiv",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Div",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "DivNoNan",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "FloorDiv",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Mul",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Maximum",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Minimum",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Pow",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "SquaredDifference",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Mod",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "FloorMod",
-    "category": "arithmetic",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "a",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "b",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  }
-]
\ No newline at end of file
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/basic_math.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/basic_math.json
deleted file mode 100644
index d63b255c6..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/basic_math.json
+++ /dev/null
@@ -1,881 +0,0 @@
-[
-  {
-    "tfOpName": "Abs",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Acos",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Asin",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Atan",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Atan2",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "y",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Ceil",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "ClipByValue",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "clipValueMin",
-        "type": "number"
-      },
-      {
-        "start": 2,
-        "name": "clipValueMax",
-        "type": "number"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Complex",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "real",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "imag",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "ComplexAbs",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Cos",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Cosh",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Elu",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Exp",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Floor",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Log",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Imag",
-    "category": "basic_math",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/control.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/control.json
deleted file mode 100644
index ef5f531af..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/control.json
+++ /dev/null
@@ -1,864 +0,0 @@
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/convolution.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/convolution.json
deleted file mode 100644
index e64f53b4e..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/convolution.json
+++ /dev/null
@@ -1,689 +0,0 @@
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deleted file mode 100644
index 3b2476b97..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/creation.json
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/dynamic.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/dynamic.json
deleted file mode 100644
index 9a028b506..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/dynamic.json
+++ /dev/null
@@ -1,188 +0,0 @@
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/evaluation.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/evaluation.json
deleted file mode 100644
index 3a66b1051..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/evaluation.json
+++ /dev/null
@@ -1,84 +0,0 @@
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/graph.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/graph.json
deleted file mode 100644
index bb32c430d..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/graph.json
+++ /dev/null
@@ -1,198 +0,0 @@
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/hash_table.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/hash_table.json
deleted file mode 100644
index 7d07f2a40..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/hash_table.json
+++ /dev/null
@@ -1,260 +0,0 @@
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/image.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/image.json
deleted file mode 100644
index 6664ef6d6..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/image.json
+++ /dev/null
@@ -1,146 +0,0 @@
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\ No newline at end of file
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/logical.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/logical.json
deleted file mode 100644
index fb9a4e297..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/logical.json
+++ /dev/null
@@ -1,287 +0,0 @@
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/matrices.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/matrices.json
deleted file mode 100644
index d8a9f246d..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/matrices.json
+++ /dev/null
@@ -1,250 +0,0 @@
-[
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/normalization.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/normalization.json
deleted file mode 100644
index fd460f0da..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/normalization.json
+++ /dev/null
@@ -1,220 +0,0 @@
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/ragged.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/ragged.json
deleted file mode 100644
index cf1f8c71f..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/ragged.json
+++ /dev/null
@@ -1,86 +0,0 @@
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/reduction.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/reduction.json
deleted file mode 100644
index 486a75461..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/reduction.json
+++ /dev/null
@@ -1,306 +0,0 @@
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diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/slice_join.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/slice_join.json
deleted file mode 100644
index 724ae5a3d..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/slice_join.json
+++ /dev/null
@@ -1,419 +0,0 @@
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-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "axis",
-        "name": "axis",
-        "type": "number",
-        "defaultValue": 0
-      },
-      {
-        "tfName": "num",
-        "name": "num",
-        "type": "number",
-        "defaultValue": 0,
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "Tile",
-    "category": "slice_join",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "reps",
-        "type": "number[]"
-      }
-    ]
-  },
-  {
-    "tfOpName": "Split",
-    "category": "slice_join",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "axis",
-        "type": "number",
-        "defaultValue": 0
-      },
-      {
-        "start": 1,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "num_split",
-        "name": "numOrSizeSplits",
-        "type": "number",
-        "defaultValue": 1
-      }
-    ]
-  },
-  {
-    "tfOpName": "SplitV",
-    "category": "slice_join",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "numOrSizeSplits",
-        "type": "number[]"
-      },
-      {
-        "start": 2,
-        "name": "axis",
-        "type": "number",
-        "defaultValue": 0
-      }
-    ]
-  },
-  {
-    "tfOpName": "ScatterNd",
-    "category": "slice_join",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "indices",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "values",
-        "type": "tensor"
-      },
-      {
-        "start": 2,
-        "name": "shape",
-        "type": "number[]"
-      }
-    ]
-  },
-  {
-    "tfOpName": "GatherNd",
-    "category": "slice_join",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "indices",
-        "type": "tensor"
-      }
-    ]
-  },
-  {
-    "tfOpName": "SparseToDense",
-    "category": "slice_join",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "sparseIndices",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "outputShape",
-        "type": "number[]"
-      },
-      {
-        "start": 2,
-        "name": "sparseValues",
-        "type": "tensor"
-      },
-      {
-        "start": 3,
-        "name": "defaultValue",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "validate_indices",
-        "name": "validateIndices",
-        "type": "bool",
-        "defaultValue": false,
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "TensorScatterUpdate",
-    "category": "slice_join",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "tensor",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "indices",
-        "type": "tensor"
-      },
-      {
-        "start": 2,
-        "name": "values",
-        "type": "tensor"
-      }
-    ]
-  }
-]
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/sparse.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/sparse.json
deleted file mode 100644
index 51f98afed..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/sparse.json
+++ /dev/null
@@ -1,99 +0,0 @@
-[
-  {
-    "tfOpName": "SparseFillEmptyRows",
-    "category": "sparse",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "indices",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "values",
-        "type": "tensor"
-      },
-      {
-        "start": 2,
-        "name": "denseShape",
-        "type": "tensor"
-      },
-      {
-        "start": 3,
-        "name": "defaultValue",
-        "type": "tensor"
-      }
-    ]
-  },
-  {
-    "tfOpName": "SparseReshape",
-    "category": "sparse",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "inputIndices",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "inputShape",
-        "type": "tensor"
-      },
-      {
-        "start": 2,
-        "name": "newShape",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "T",
-        "name": "dtype",
-        "type": "dtype",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "SparseSegmentMean",
-    "category": "sparse",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "data",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "indices",
-        "type": "tensor"
-      },
-      {
-        "start": 2,
-        "name": "segmentIds",
-        "type": "tensor"
-      }
-    ]
-  },
-  {
-    "tfOpName": "SparseSegmentSum",
-    "category": "sparse",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "data",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "indices",
-        "type": "tensor"
-      },
-      {
-        "start": 2,
-        "name": "segmentIds",
-        "type": "tensor"
-      }
-    ]
-  }
-]
\ No newline at end of file
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/spectral.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/spectral.json
deleted file mode 100644
index e852669f6..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/spectral.json
+++ /dev/null
@@ -1,58 +0,0 @@
-[
-  {
-    "tfOpName": "FFT",
-    "category": "spectral",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ]
-  },
-  {
-    "tfOpName": "IFFT",
-    "category": "spectral",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ]
-  },
-  {
-    "tfOpName": "RFFT",
-    "category": "spectral",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "fft_length",
-        "type": "number",
-        "notSupported": true
-      }
-    ]
-  },
-  {
-    "tfOpName": "IRFFT",
-    "category": "spectral",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "fft_length",
-        "type": "number",
-        "notSupported": true
-      }
-    ]
-  }
-]
\ No newline at end of file
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/string.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/string.json
deleted file mode 100644
index 1df8caba1..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/string.json
+++ /dev/null
@@ -1,128 +0,0 @@
-[
-  {
-    "tfOpName": "StaticRegexReplace",
-    "category": "string",
-    "inputs": [
-      {
-      "start": 0,
-        "name": "input",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "pattern",
-        "name": "pattern",
-        "type": "string"
-      },
-      {
-        "tfName": "rewrite",
-        "name": "rewrite",
-        "type": "string"
-      },
-      {
-        "tfName": "replace_global",
-        "name": "replaceGlobal",
-        "type": "bool"
-      }
-    ]
-  },
-  {
-    "tfOpName": "StringNGrams",
-    "category": "string",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "data",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "dataSplits",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "separator",
-        "name": "separator",
-        "type": "string"
-      },
-      {
-        "tfName": "ngram_widths",
-        "name": "nGramWidths",
-        "type": "number[]"
-      },
-      {
-        "tfName": "left_pad",
-        "name": "leftPad",
-        "type": "string"
-      },
-      {
-        "tfName": "right_pad",
-        "name": "rightPad",
-        "type": "string"
-      },
-      {
-        "tfName": "pad_width",
-        "name": "padWidth",
-        "type": "number"
-      },
-      {
-        "tfName": "preserve_short_sequences",
-        "name": "preserveShortSequences",
-        "type": "bool"
-      }
-    ],
-    "outputs": [
-      "ngrams",
-      "ngrams_splits"
-    ]
-  },
-  {
-    "tfOpName": "StringSplit",
-    "category": "string",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "input",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "delimiter",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "skip_empty",
-        "name": "skipEmpty",
-        "type": "bool"
-      }
-    ],
-    "outputs": [
-      "indices",
-      "values",
-      "shape"
-    ]
-  },
-  {
-    "tfOpName": "StringToHashBucketFast",
-    "category": "string",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "input",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "num_buckets",
-        "name": "numBuckets",
-        "type": "number"
-      }
-    ]
-  }
-]
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/tfdf.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/tfdf.json
deleted file mode 100644
index 385438c00..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/tfdf.json
+++ /dev/null
@@ -1,11 +0,0 @@
-[
-  {
-    "tfOpName": "SimpleMLCreateModelResource"
-  },
-  {
-    "tfOpName": "SimpleMLLoadModelFromPathWithHandle"
-  },
-  {
-    "tfOpName": "SimpleMLInferenceOpWithHandle"
-  }
-]
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/transformation.json b/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/transformation.json
deleted file mode 100644
index c3023e669..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/op_list/transformation.json
+++ /dev/null
@@ -1,261 +0,0 @@
-[
-  {
-    "tfOpName": "Cast",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "SrcT",
-        "name": "sdtype",
-        "type": "dtype",
-        "notSupported": true
-      },
-      {
-        "tfName": "DstT",
-        "name": "dtype",
-        "type": "dtype"
-      }
-    ]
-  },
-  {
-    "tfOpName": "ExpandDims",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "axis",
-        "type": "number"
-      }
-    ]
-  },
-  {
-    "tfOpName": "MirrorPad",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "padding",
-        "type": "number[]"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "mode",
-        "name": "mode",
-        "type": "string"
-      }
-    ]
-  },
-  {
-    "tfOpName": "Pad",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "padding",
-        "type": "number[]"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "constant_value",
-        "name": "constantValue",
-        "type": "number",
-        "defaultValue": 0
-      }
-    ]
-  },
-  {
-    "tfOpName": "PadV2",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "padding",
-        "type": "number[]"
-      },
-      {
-        "start": 2,
-        "name": "constantValue",
-        "type": "number",
-        "defaultValue": 0
-      }
-    ]
-  },
-  {
-    "tfOpName": "Reshape",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "shape",
-        "type": "number[]"
-      }
-    ]
-  },
-  {
-    "tfOpName": "EnsureShape",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "shape",
-        "type": "number[]"
-      }
-    ]
-  },
-  {
-    "tfOpName": "Squeeze",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "axis",
-        "tfDeprecatedName": "squeeze_dims",
-        "name": "axis",
-        "type": "number[]"
-      }
-    ]
-  },
-  {
-    "tfOpName": "SpaceToBatchND",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "blockShape",
-        "type": "number[]"
-      },
-      {
-        "start": 2,
-        "name": "paddings",
-        "type": "number[]"
-      }
-    ]
-  },
-  {
-    "tfOpName": "BatchToSpaceND",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "blockShape",
-        "type": "number[]"
-      },
-      {
-        "start": 2,
-        "name": "crops",
-        "type": "number[]"
-      }
-    ]
-  },
-  {
-    "tfOpName": "DepthToSpace",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      }
-    ],
-    "attrs": [
-      {
-        "tfName": "block_size",
-        "name": "blockSize",
-        "type": "number"
-      },
-      {
-        "tfName": "data_format",
-        "name": "dataFormat",
-        "type": "string"
-      }
-    ]
-  },
-  {
-    "tfOpName": "BroadcastTo",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "x",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "shape",
-        "type": "number[]"
-      }
-    ],
-    "attrs": []
-  },
-  {
-    "tfOpName": "BroadcastArgs",
-    "category": "transformation",
-    "inputs": [
-      {
-        "start": 0,
-        "name": "s0",
-        "type": "tensor"
-      },
-      {
-        "start": 1,
-        "name": "s1",
-        "type": "tensor"
-      }
-    ],
-    "attrs": []
-  }
-]
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/quantization.py b/tfjs-master/tfjs-converter/python/tensorflowjs/quantization.py
deleted file mode 100644
index 93fa83767..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/quantization.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import fnmatch
-import numpy as np
-
-QUANTIZATION_DTYPE_FLOAT16 = 'float16'
-QUANTIZATION_DTYPE_UINT8 = 'uint8'
-QUANTIZATION_DTYPE_UINT16 = 'uint16'
-
-QUANTIZATION_BYTES_TO_DTYPES = {1: QUANTIZATION_DTYPE_UINT8,
-                                2: QUANTIZATION_DTYPE_UINT16}
-QUANTIZATION_OPTION_TO_DTYPES = {QUANTIZATION_DTYPE_UINT8: np.uint8,
-                                 QUANTIZATION_DTYPE_UINT16: np.uint16,
-                                 QUANTIZATION_DTYPE_FLOAT16: np.float16}
-
-
-def map_layers_to_quantization_dtype(names, quantization_dtype_map):
-  """Maps node names to their quantization dtypes.
-
-  Given a quantization_dtype_map which maps dtypes `uint8`, `uint16`, `float16`
-  to node patterns, e.g., conv/*/weights we construct a new mapping for each
-  individual node name to its dtype, e.g., conv/1/weight -> `uint8`.
-  A dtype in the map can also be a boolean, signaling a fallthrough dtype.
-  There can only be one fallthrough dtype in the map. A fallthrough dtype
-  will convert all weights that don't match any pattern to the provided dtype.
-
-  Args:
-    names: Array of node names.
-    quantization_dtype_map: A mapping from dtype (`uint8`, `uint16`, `float16`)
-      to weights. The weight mapping supports wildcard substitution.
-
-  Returns:
-    quantization_dtype: A mapping from each node name which matches
-    an entry in quantization_dtype_map to its corresponding dtype.
-
-  Raises:
-    ValueError: - If multiple dtypes match the same node name
-                - If more than one fallthrough is provided
-  """
-  if quantization_dtype_map is None:
-    return {}
-
-  fallthrough = None
-  quantization_dtype = {}
-  for dtype_name, patterns in quantization_dtype_map.items():
-    # Record fallthrough if there is one
-    if isinstance(patterns, bool) and patterns:
-      # Only one fallthrough is supported
-      if fallthrough is not None:
-        raise ValueError(
-            'More than one quantization fallthrough provided, '
-            'exactly one is supported')
-      fallthrough = dtype_name
-      continue
-    if isinstance(patterns, str):
-      patterns = list([patterns])
-
-    # Record matched weights for dtype
-    for pattern in patterns:
-      for match in fnmatch.filter(names, pattern):
-        dtype = QUANTIZATION_OPTION_TO_DTYPES[dtype_name]
-        if match in quantization_dtype and quantization_dtype[match] != dtype:
-          raise ValueError(
-              'Two quantization values %s, %s match the same node %s' %
-              (dtype, quantization_dtype[match], match))
-        quantization_dtype[match] = dtype
-
-  # Catch all remaining names with fallthrough
-  if fallthrough is not None:
-    nameset = set(names)
-    fallthrough_names = nameset - set(quantization_dtype.keys())
-    for name in fallthrough_names:
-      quantization_dtype[name] = QUANTIZATION_OPTION_TO_DTYPES[fallthrough]
-
-  return quantization_dtype
-
-def quantize_weights(data, quantization_dtype):
-  """Quantizes the weights by linearly re-scaling across available bits.
-
-  The weights are quantized by linearly re-scaling the values between the
-  minimum and maximum value, and representing them with the number of bits
-  provided by the `quantization_dtype`.
-
-  In order to guarantee that 0 is perfectly represented by one of the quantized
-  values, the range is "nudged" in the same manner as in TF-Lite.
-
-  Weights can be de-quantized by multiplying by the returned `scale` and adding
-  `min`.
-
-  Args:
-    data: A numpy array of dtype 'float32' or 'int32'.
-    quantization_dtype: A numpy dtype to quantize weights to. Only np.float16,
-      np.uint8, and np.uint16 are supported.
-
-  Returns:
-    quantized_data: The quantized weights as a numpy array with dtype
-      `quantization_dtype`.
-    metadata: A dictionary with the corresponding metadata for the quantization
-      type. There is no metadata associated with float16.
-      For affine quantization there are two associated metadata values:
-        scale: The linearly scaling constant used for quantization.
-        min_val: The minimum value of the linear range.
-  Raises:
-    ValueError: if `quantization_dtype` is not a valid type.
-  """
-  if quantization_dtype in [np.uint8, np.uint16]:
-    # Compute the min and max for the group.
-    min_val = data.min().astype(np.float64)
-    max_val = data.max().astype(np.float64)
-    if min_val == max_val:
-      # If there is only a single value, we can represent everything as zeros.
-      quantized_data = np.zeros_like(data, dtype=quantization_dtype)
-      scale = 1.0
-    else:
-      # Quantize data.
-      scale, min_val, max_val = _get_affine_quantization_range(
-          min_val, max_val, quantization_dtype)
-      quantized_data = np.round(
-          (data.clip(min_val, max_val) - min_val) / scale).astype(
-              quantization_dtype)
-
-    return quantized_data, {'min': min_val, 'scale': scale}
-  elif quantization_dtype == np.float16:
-    if data.dtype != np.float32:
-      raise ValueError(
-          'Invalid data dtype %r\n'
-          'float16 quantization only supports float32 dtype' % data.dtype)
-    quantized_data = data.astype(np.float16)
-    return quantized_data, {}
-  else:
-    raise ValueError('Invalid `quantization_dtype`: %r' % quantization_dtype)
-
-
-
-def dequantize_weights(data, metadata, original_dtype=np.float32):
-  dtype = data.dtype
-
-  if dtype in [np.uint8, np.uint16]:
-    if not ('scale' in metadata and 'min' in metadata):
-      raise ValueError(
-          'Missing metadata min or scale for dtype %s' % dtype.name)
-    scale = metadata['scale']
-    min_val = metadata['min']
-    if original_dtype == np.int32:
-      return np.round(data * scale + min_val).astype(original_dtype)
-    else:
-      return (data * scale + min_val).astype(original_dtype)
-  elif dtype == np.float16:
-    if original_dtype != np.float32:
-      raise ValueError(
-          'Invalid data dtype %r\n'
-          'float16 quantization only supports float32 dtype' % data.dtype)
-    return data.astype(original_dtype)
-  else:
-    raise ValueError(
-        'Invalid dtype %s for dequantization\n'
-        'Supported dtypes are uint8, uint16, float16' % dtype.name)
-
-def _get_affine_quantization_range(min_val, max_val, quantization_dtype):
-  """Computes quantization range to ensure that zero is represented if covered.
-
-  Gymnastics with nudged zero point is to ensure that real zero maps to an
-  integer, which is required for e.g. zero-padding in convolutional layers.
-
-  Based on `NudgeQuantizationRange` in
-  tensorflow/contrib/lite/kernels/internal/quantization_util.h, except we do not
-  nudge if 0 is not in the range.
-
-  Args:
-    min_val: The actual minimum value of the data.
-    max_val: The actual maximum value of the data.
-    quantization_dtype: A numpy dtype to quantize weights to. Only np.uint8 and
-      np.uint16 are supported.
-
-  Returns:
-    scale: The linear scaling constant used for quantization.
-    nudged_min: The adjusted minimum value to ensure zero is represented, if
-      covered.
-    nudged_max: The adjusted maximum value to ensure zero is represented, if
-      covered.
-  Raises:
-    ValueError: if `quantization_dtype` is not a valid type.
-  """
-  if quantization_dtype not in [np.uint8, np.uint16]:
-    raise ValueError('Invalid `quantization_dtype`: %r' % quantization_dtype)
-
-  quant_max = np.iinfo(quantization_dtype).max
-  scale = (max_val - min_val) / quant_max
-
-  if min_val <= 0 <= max_val:
-    quantized_zero_point = (0 - min_val) / scale
-    nudged_zero_point = np.round(quantized_zero_point)
-
-    # Solve `0 = nudged_zero_point * scale + nudged_min` for `nudged_min`.
-    nudged_min = -nudged_zero_point * scale
-    nudged_max = quant_max * scale + nudged_min
-  else:
-    nudged_min, nudged_max = min_val, max_val
-
-  return scale, nudged_min, nudged_max
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/quantization_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/quantization_test.py
deleted file mode 100644
index caab4415f..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/quantization_test.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import unittest
-
-import numpy as np
-
-from tensorflowjs import quantization
-
-class TestQuantizationUtil(unittest.TestCase):
-
-  def assertDictContainsSubsetAlmostEqual(self, d1, d2):
-    self.assertIsInstance(d1, dict)
-    self.assertIsInstance(d2, dict)
-
-    d1_keys = set(d1.keys())
-    d2_keys = set(d2.keys())
-
-    self.assertTrue(d2_keys.issubset(d1_keys))
-
-    for key in d2_keys:
-      self.assertAlmostEqual(d1[key], d2[key])
-
-
-  def _runQuantizeTest(
-      self, range_min, range_max, data_dtype, quantization_dtype,
-      expected_metadata):
-    d = np.arange(range_min, range_max + 1, dtype=data_dtype)
-    q, metadata = quantization.quantize_weights(d, quantization_dtype)
-
-    self.assertDictContainsSubsetAlmostEqual(metadata, expected_metadata)
-    self.assertEqual(q.dtype, quantization_dtype)
-
-    de_q = quantization.dequantize_weights(
-        q, metadata, data_dtype)
-    if data_dtype != np.float32:
-      np.testing.assert_allclose(de_q, d)
-    else:
-      np.testing.assert_array_almost_equal(de_q, d, decimal=2)
-
-    if quantization_dtype in [np.uint8, np.uint16]:
-      s = metadata['scale']
-      m = metadata['min']
-      if range_min <= 0 <= range_max:
-        d_0 = np.zeros(1, data_dtype)
-        q_0 = np.round((d_0 - m) / s).astype(quantization_dtype)
-        self.assertEqual(
-            quantization.dequantize_weights(q_0, metadata, data_dtype), d_0)
-
-  def testAffineQuantizeAllEqual(self):
-    d = np.ones(5, dtype=np.float32)
-    q, metadata = quantization.quantize_weights(d, np.uint8)
-    assert 'scale' in metadata and 'min' in metadata
-    self.assertEqual(metadata['scale'], 1.0)
-    self.assertEqual(q.dtype, np.uint8)
-
-    de_q = quantization.dequantize_weights(q, metadata, np.float32)
-    np.testing.assert_array_equal(de_q, d)
-
-  def testFloatQuantizeAllEqual(self):
-    d = np.ones(5, dtype=np.float32)
-    q, metadata = quantization.quantize_weights(d, np.float16)
-    self.assertDictEqual(metadata, {})
-
-    self.assertEqual(q.dtype, np.float16)
-    de_q = quantization.dequantize_weights(q, metadata, np.float32)
-    np.testing.assert_array_equal(de_q, d)
-
-  def testAffineQuantizeNegativeFloats(self):
-    self._runQuantizeTest(
-        -3, -1, np.float32, np.uint8,
-        expected_metadata={'scale': 2/255})
-    self._runQuantizeTest(
-        -3, -1, np.float32, np.uint16,
-        expected_metadata={'scale': 2/65536})
-
-  def testAffineQuantizeNegativeAndZeroFloats(self):
-    self._runQuantizeTest(
-        -3, 0, np.float32, np.uint8,
-        expected_metadata={'scale': 3/255})
-    self._runQuantizeTest(
-        -3, 0, np.float32, np.uint16,
-        expected_metadata={'scale': 3/65536})
-
-  def testAffineQuantizeNegativeAndPositiveFloats(self):
-    self._runQuantizeTest(
-        -3, 3, np.float32, np.uint8,
-        expected_metadata={'scale': 6/255})
-    self._runQuantizeTest(
-        -3, 3, np.float32, np.uint16,
-        expected_metadata={'scale': 6/65536})
-
-  def testAffineQuantizeZeroAndPositiveFloats(self):
-    self._runQuantizeTest(
-        0, 3, np.float32, np.uint8,
-        expected_metadata={'scale': 3/255})
-    self._runQuantizeTest(
-        0, 3, np.float32, np.uint16,
-        expected_metadata={'scale': 3/65536})
-
-  def testAffineQuantizePositiveFloats(self):
-    self._runQuantizeTest(
-        1, 3, np.float32, np.uint8,
-        expected_metadata={'scale': 2/255})
-    self._runQuantizeTest(
-        1, 3, np.float32, np.uint16,
-        expected_metadata={'scale': 2/65536})
-
-  def testAffineQuantizeNormalizedFloats(self):
-    data = np.array(
-        [-0.29098126, -0.24776903, -0.27248842, 0.23848203], dtype=np.float32)
-    q, metadata = quantization.quantize_weights(data, np.uint16)
-    de_q = quantization.dequantize_weights(q, metadata, data.dtype)
-    np.testing.assert_array_almost_equal(de_q, data, decimal=5)
-
-  def testAffineQuantizeNegativeInts(self):
-    self._runQuantizeTest(
-        -3, -1, np.int32, np.uint8,
-        expected_metadata={'scale': 2/255})
-    self._runQuantizeTest(
-        -3, -1, np.int32, np.uint16,
-        expected_metadata={'scale': 2/65536})
-
-  def testAffineQuantizeNegativeAndZeroInts(self):
-    self._runQuantizeTest(
-        -3, 0, np.int32, np.uint8,
-        expected_metadata={'scale': 3/255})
-    self._runQuantizeTest(
-        -3, 0, np.int32, np.uint16,
-        expected_metadata={'scale': 3/65536})
-
-  def testAffineQuantizeNegativeAndPositiveInts(self):
-    self._runQuantizeTest(
-        -3, 3, np.int32, np.uint8,
-        expected_metadata={'scale': 6/255})
-    self._runQuantizeTest(
-        -3, 3, np.int32, np.uint16,
-        expected_metadata={'scale': 6/65536})
-
-  def testAffineQuantizeZeroAndPositiveInts(self):
-    self._runQuantizeTest(
-        0, 3, np.int32, np.uint8,
-        expected_metadata={'scale': 3/255})
-    self._runQuantizeTest(
-        0, 3, np.int32, np.uint16,
-        expected_metadata={'scale': 3/65536})
-
-  def testAffineQuantizePositiveInts(self):
-    self._runQuantizeTest(
-        1, 3, np.int32, np.uint8,
-        expected_metadata={'scale': 2/255})
-    self._runQuantizeTest(
-        1, 3, np.int32, np.uint16,
-        expected_metadata={'scale': 2/65536})
-
-  def testInvalidQuantizationTypes(self):
-    # Invalid quantization type
-    with self.assertRaises(ValueError):
-      quantization.quantize_weights(np.array([]), bool)
-
-    # Invalid data dtype for float16 quantization
-    with self.assertRaises(ValueError):
-      d = np.ones(1, dtype=np.int32)
-      quantization.quantize_weights(d, np.float16)
-
-  def testInvalidDequantizationTypes(self):
-    # Invalid metadata for affine quantization
-    with self.assertRaises(ValueError):
-      d = np.ones(1, dtype=np.uint8)
-      quantization.dequantize_weights(np.array([]), {})
-
-    # Invalid target dtype for float16 quantization
-    with self.assertRaises(ValueError):
-      d = np.ones(1, dtype=np.float16)
-      quantization.dequantize_weights(d, {}, np.int32)
-
-    # Invalid dequantization type
-    with self.assertRaises(ValueError):
-      d = np.ones(1, dtype=bool)
-      quantization.dequantize_weights(d, {})
-
-  def testMapLayerFallthrough(self):
-    names = ['conv/0/weight', 'conv/0/bias', 'conv/1/weight', 'conv/1/bias']
-    quantization_dtype_map = {'float16': ['conv/0/*'], 'uint8': True}
-    dtype_map = quantization.map_layers_to_quantization_dtype(
-        names, quantization_dtype_map)
-
-    self.assertDictEqual(dtype_map, {
-        'conv/0/weight': np.float16,
-        'conv/0/bias': np.float16,
-        'conv/1/weight': np.uint8,
-        'conv/1/bias': np.uint8
-    })
-
-  def testMapLayerConflictingMap(self):
-    names = ['conv/0/weight', 'conv/0/bias', 'conv/1/weight', 'conv/1/bias']
-    quantization_dtype_map = {'float16': ['conv/0/*'], 'uint8': ['conv/0/bias']}
-
-    with self.assertRaises(ValueError):
-      quantization.map_layers_to_quantization_dtype(
-          names, quantization_dtype_map)
-
-
-  def testMapLayerStringToList(self):
-    names = ['conv/0/weight', 'conv/0/bias', 'conv/1/weight', 'conv/1/bias']
-    quantization_dtype_map = {'float16': '*'}
-
-
-    dtype_map = quantization.map_layers_to_quantization_dtype(
-        names, quantization_dtype_map)
-
-    self.assertDictEqual(dtype_map, {
-        'conv/0/weight': np.float16,
-        'conv/0/bias': np.float16,
-        'conv/1/weight': np.float16,
-        'conv/1/bias': np.float16
-    })
-
-  def testMapLayerNoDtypeMap(self):
-    names = ['conv/0/weight', 'conv/0/bias', 'conv/1/weight', 'conv/1/bias']
-    quantization_dtype_map = {}
-    dtype_map = quantization.map_layers_to_quantization_dtype(
-        names, quantization_dtype_map)
-
-    self.assertDictEqual(dtype_map, {})
-
-  def testMapLayerExactlyOneFallthrough(self):
-    names = ['conv/0/weight', 'conv/0/bias', 'conv/1/weight', 'conv/1/bias']
-    quantization_dtype_map = {'float16': True, 'uint8': True}
-
-    with self.assertRaises(ValueError):
-      quantization.map_layers_to_quantization_dtype(
-          names, quantization_dtype_map)
-
-
-
-if __name__ == '__main__':
-  unittest.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/read_weights.py b/tfjs-master/tfjs-converter/python/tensorflowjs/read_weights.py
deleted file mode 100644
index ee8d7b96d..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/read_weights.py
+++ /dev/null
@@ -1,202 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Read weights stored in TensorFlow.js-format binary files."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import io
-import os
-
-import numpy as np
-from tensorflowjs import quantization
-
-_INPUT_DTYPES = [np.float16, np.float32, np.int32, np.complex64,
-                 np.uint8, np.uint16, object, bool]
-
-# Number of bytes used to encode the length of a string in a string tensor.
-STRING_LENGTH_NUM_BYTES = 4
-# The data type used to encode the length of a string in a string tensor.
-STRING_LENGTH_DTYPE = np.dtype('uint32').newbyteorder('<')
-
-def read_weights(weights_manifest, base_path, flatten=False):
-  """Load weight values according to a TensorFlow.js weights manifest.
-
-  Args:
-    weights_manifest: A TensorFlow.js-format weights manifest (a JSON array).
-    base_path: Base path prefix for the weights files.
-    flatten: Whether all the weight groups in the return value are to be
-      flattened as a single weights group. Default: `False`.
-
-  Returns:
-    If `flatten` is `False`, a `list` of weight groups. Each group is an array
-    of weight entries. Each entry is a dict that maps a unique name to a numpy
-    array, for example:
-        entry = {
-          'name': 'weight1',
-          'data': np.array([1, 2, 3], 'float32')
-        }
-
-        Weights groups would then look like:
-        weight_groups = [
-          [group_0_entry1, group_0_entry2],
-          [group_1_entry1, group_1_entry2],
-        ]
-    If `flatten` is `True`, returns a single weight group.
-  """
-  if not isinstance(weights_manifest, list):
-    raise ValueError(
-        'weights_manifest should be a `list`, but received %s' %
-        type(weights_manifest))
-
-  data_buffers = []
-  for group in weights_manifest:
-    buff = io.BytesIO()
-    buff_writer = io.BufferedWriter(buff)
-    for path in group['paths']:
-      with open(os.path.join(base_path, path), 'rb') as f:
-        buff_writer.write(f.read())
-    buff_writer.flush()
-    buff_writer.seek(0)
-    data_buffers.append(buff.read())
-  return decode_weights(weights_manifest, data_buffers, flatten=flatten)
-
-
-def _deserialize_string_array(data_buffer, offset, shape):
-  """Deserializes bytes into np.array of dtype `object` which holds strings.
-
-  Each string value is preceded by 4 bytes which denote a 32-bit unsigned
-  integer in little endian that specifies the byte length of the following
-  string. This is followed by the actual string bytes. If the tensor has no
-  strings there will be no bytes reserved. Empty strings will still take 4 bytes
-  for the length.
-
-  For example, a tensor that has 2 strings will be encoded as
-  [byte length of s1][bytes of s1...][byte length of s2][bytes of s2...]
-
-  where byte length always takes 4 bytes.
-
-  Args:
-    data_buffer: A buffer of bytes containing the serialized data.
-    offset: The byte offset in that buffer that denotes the start of the tensor.
-    shape: The logical shape of the tensor.
-
-  Returns:
-    A tuple of (np.array, offset) where the np.array contains the encoded
-    strings, and the offset contains the new offset (the byte position in the
-    buffer at the end of the string data).
-  """
-  size = int(np.prod(shape))
-  if size == 0:
-    return (np.array([], 'object').reshape(shape),
-            offset + STRING_LENGTH_NUM_BYTES)
-  vals = []
-  for _ in range(size):
-    byte_length = np.frombuffer(
-        data_buffer[offset:offset + STRING_LENGTH_NUM_BYTES],
-        STRING_LENGTH_DTYPE)[0]
-    offset += STRING_LENGTH_NUM_BYTES
-    string = data_buffer[offset:offset + byte_length]
-    vals.append(string)
-    offset += byte_length
-  return np.array(vals, 'object').reshape(shape), offset
-
-
-def _deserialize_numeric_array(data_buffer, offset, dtype, shape):
-  weight_numel = 1
-  for dim in shape:
-    weight_numel *= dim
-  return np.frombuffer(
-      data_buffer, dtype=dtype, count=weight_numel,
-      offset=offset).reshape(shape)
-
-def decode_weights(weights_manifest, data_buffers, flatten=False):
-  """Load weight values from buffer(s) according to a weights manifest.
-
-  Args:
-    weights_manifest: A TensorFlow.js-format weights manifest (a JSON array).
-    data_buffers: A buffer or a `list` of buffers containing the weights values
-      in binary format, concatenated in the order specified in
-      `weights_manifest`. If a `list` of buffers, the length of the `list`
-      must match the length of `weights_manifest`. A single buffer is
-      interpreted as a `list` of one buffer and is valid only if the length of
-      `weights_manifest` is `1`.
-    flatten: Whether all the weight groups in the return value are to be
-      flattened as a single weight groups. Default: `False`.
-
-  Returns:
-    If `flatten` is `False`, a `list` of weight groups. Each group is an array
-    of weight entries. Each entry is a dict that maps a unique name to a numpy
-    array, for example:
-        entry = {
-          'name': 'weight1',
-          'data': np.array([1, 2, 3], 'float32')
-        }
-
-        Weights groups would then look like:
-        weight_groups = [
-          [group_0_entry1, group_0_entry2],
-          [group_1_entry1, group_1_entry2],
-        ]
-    If `flatten` is `True`, returns a single weight group.
-
-  Raises:
-    ValueError: if the lengths of `weights_manifest` and `data_buffers` do not
-      match.
-  """
-  if not isinstance(data_buffers, list):
-    data_buffers = [data_buffers]
-  if len(weights_manifest) != len(data_buffers):
-    raise ValueError(
-        'Mismatch in the length of weights_manifest (%d) and the length of '
-        'data buffers (%d)' % (len(weights_manifest), len(data_buffers)))
-
-  out = []
-  for group, data_buffer in zip(weights_manifest, data_buffers):
-    offset = 0
-    out_group = []
-
-    for weight in group['weights']:
-      quant_info = weight.get('quantization', None)
-      name = weight['name']
-      if weight['dtype'] == 'string':
-        # String array.
-        dtype = object
-      elif quant_info:
-        # Quantized array.
-        dtype = np.dtype(quant_info['dtype'])
-      else:
-        # Regular numeric array.
-        dtype = np.dtype(weight['dtype'])
-      shape = weight['shape']
-      if dtype not in _INPUT_DTYPES:
-        raise NotImplementedError('Unsupported data type: %s' % dtype)
-      if weight['dtype'] == 'string':
-        value, offset = _deserialize_string_array(data_buffer, offset, shape)
-      else:
-        value = _deserialize_numeric_array(data_buffer, offset, dtype, shape)
-        offset += dtype.itemsize * value.size
-      if quant_info:
-        value = quantization.dequantize_weights(
-            value, quant_info, np.dtype(weight['dtype']))
-      out_group.append({'name': name, 'data': value})
-
-    if flatten:
-      out += out_group
-    else:
-      out.append(out_group)
-
-  return out
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/read_weights_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/read_weights_test.py
deleted file mode 100644
index 9e9d46ec6..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/read_weights_test.py
+++ /dev/null
@@ -1,406 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import shutil
-
-import tempfile
-
-import numpy as np
-import tensorflow as tf
-
-from tensorflowjs import read_weights
-from tensorflowjs import write_weights
-
-
-class ReadWeightsTest(tf.test.TestCase):
-  def setUp(self):
-    self._tmp_dir = tempfile.mkdtemp()
-    super(ReadWeightsTest, self).setUp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(ReadWeightsTest, self).tearDown()
-
-  def testReadOneGroup(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }, {
-            'name': 'weight2',
-            'data': np.array([1 + 1j, 2 + 2j, 3 + 3j])
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    self.assertEqual(2, len(read_output[0]))
-    self.assertEqual('weight1', read_output[0][0]['name'])
-    self.assertTrue(
-        np.allclose(groups[0][0]['data'], read_output[0][0]['data']))
-    self.assertEqual('weight2', read_output[0][1]['name'])
-    self.assertTrue(
-        np.allclose(groups[0][1]['data'], read_output[0][1]['data']))
-  def testReadOneGroupString(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([['test', 'a'], ['b', 'c']], 'object')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    self.assertEqual(1, len(read_output[0]))
-    self.assertEqual('weight1', read_output[0][0]['name'])
-    np.testing.assert_array_equal(
-        read_output[0][0]['data'],
-        np.array([[u'test'.encode('utf-8'), u'a'.encode('utf-8')],
-                  [u'b'.encode('utf-8'), u'c'.encode('utf-8')]], 'object'))
-
-
-  def testReadCyrillicStringUnicodeAndEncoded(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            # String is stored as unicode.
-            'data': np.array([u'здраво'], 'object')
-        }, {
-            'name': 'weight2',
-            # String is stored encoded.
-            'data': np.array([u'поздрав'.encode('utf-8')], 'object')
-        }, {
-            'name': 'weight3',
-            # Let np choose the dtype (string or bytes depending on py version).
-            'data': np.array([u'здраво'.encode('utf-8')])
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    group = read_output[0]
-    self.assertEqual(3, len(group))
-
-    weight1 = group[0]
-    self.assertEqual('weight1', weight1['name'])
-    np.testing.assert_array_equal(
-        weight1['data'],
-        np.array([u'здраво'.encode('utf-8')], 'object'))
-
-    weight2 = group[1]
-    self.assertEqual('weight2', weight2['name'])
-    np.testing.assert_array_equal(
-        weight2['data'],
-        np.array([u'поздрав'.encode('utf-8')], 'object'))
-
-    weight3 = group[2]
-    self.assertEqual('weight3', weight3['name'])
-    np.testing.assert_array_equal(
-        weight3['data'],
-        np.array([u'здраво'.encode('utf-8')]))
-
-  def testReadEastAsianStringUnicodeAndEncoded(self):
-    # Each string tensor uses different encoding.
-    groups = [
-        [{
-            'name': 'weight1',
-            # Decoded.
-            'data': np.array([u'语言处理'], 'object')
-        }, {
-            'name': 'weight2',
-            # Encoded as utf-16.
-            'data': np.array([u'语言处理'.encode('utf-16')], 'object')
-        }, {
-            'name': 'weight3',
-            # Encoded as utf-8.
-            'data': np.array([u'语言处理'.encode('utf-8')], 'object')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    group = read_output[0]
-    self.assertEqual(3, len(group))
-
-    weight1 = group[0]
-    self.assertEqual('weight1', weight1['name'])
-    np.testing.assert_array_equal(
-        weight1['data'],
-        np.array([u'语言处理'.encode('utf-8')], 'object'))
-
-    weight2 = group[1]
-    self.assertEqual('weight2', weight2['name'])
-    np.testing.assert_array_equal(
-        weight2['data'],
-        np.array([u'语言处理'.encode('utf-16')], 'object'))
-
-    weight3 = group[2]
-    self.assertEqual('weight3', weight3['name'])
-    np.testing.assert_array_equal(
-        weight3['data'],
-        np.array([u'语言处理'.encode('utf-8')], 'object'))
-
-  def testReadOneGroupStringWithShards(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array(['test', 'a', 'c'], 'object')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir,
-                                           shard_size_bytes=4)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    self.assertEqual(1, len(read_output[0]))
-    self.assertEqual('weight1', read_output[0][0]['name'])
-    np.testing.assert_array_equal(read_output[0][0]['data'],
-                                  np.array([u'test'.encode('utf-8'),
-                                            u'a'.encode('utf-8'),
-                                            u'c'.encode('utf-8')], 'object'))
-
-  def testReadOneGroupEmptyStrings(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array(['', ''], 'object')
-        }, {
-            'name': 'weight2',
-            'data': np.array([], 'object')
-        }, {
-            'name': 'weight3',
-            'data': np.array([[]], 'object')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    group = read_output[0]
-    self.assertEqual(3, len(group))
-
-    weight1 = group[0]
-    self.assertEqual('weight1', weight1['name'])
-    np.testing.assert_array_equal(
-        weight1['data'],
-        np.array([u''.encode('utf-8'), u''.encode('utf-8')], 'object'))
-
-    weight2 = group[1]
-    self.assertEqual('weight2', weight2['name'])
-    np.testing.assert_array_equal(
-        weight2['data'],
-        np.array([], 'object'))
-
-    weight3 = group[2]
-    self.assertEqual('weight3', weight3['name'])
-    np.testing.assert_array_equal(
-        weight3['data'],
-        np.array([[]], 'object'))
-
-  def testReadOneGroupFlattened(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(
-        manifest, self._tmp_dir, flatten=True)
-    self.assertEqual(1, len(read_output))
-    self.assertEqual('weight1', read_output[0]['name'])
-    self.assertTrue(np.allclose(groups[0][0]['data'], read_output[0]['data']))
-
-  def testReadOneGroupWithInt32DataFlattened(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }, {
-            'name': 'weight2',
-            'data': np.array([10, 20, 30], 'int32')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(
-        manifest, self._tmp_dir, flatten=True)
-    self.assertEqual(2, len(read_output))
-    self.assertEqual('weight1', read_output[0]['name'])
-    self.assertTrue(np.allclose(groups[0][0]['data'], read_output[0]['data']))
-    self.assertEqual('weight2', read_output[1]['name'])
-    self.assertTrue(np.allclose(groups[0][1]['data'], read_output[1]['data']))
-
-  def testReadTwoGroupsFlattend(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }],
-        [{
-            'name': 'weight2',
-            'data': np.array([10, 20, 30], 'int32')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(
-        manifest, self._tmp_dir, flatten=True)
-    self.assertEqual(2, len(read_output))
-    self.assertEqual('weight1', read_output[0]['name'])
-    self.assertTrue(np.allclose(groups[0][0]['data'], read_output[0]['data']))
-    self.assertEqual('weight2', read_output[1]['name'])
-    self.assertTrue(np.allclose(groups[1][0]['data'], read_output[1]['data']))
-
-  def testReadOneGroupWithShards(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.random.rand(1, 100).astype(np.float32)
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    self.assertEqual(1, len(read_output[0]))
-    self.assertEqual('weight1', read_output[0][0]['name'])
-    self.assertTrue(
-        np.allclose(groups[0][0]['data'], read_output[0][0]['data']))
-
-  def testReadWeightsWithIncorrectTypeInWeightsManifestRaisesError(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.random.rand(1, 100).astype(np.float32)
-        }]
-    ]
-
-    write_weights.write_weights(groups, self._tmp_dir)
-
-    with self.assertRaises(ValueError):
-      read_weights.read_weights(groups[0][0], self._tmp_dir)
-
-
-  def testReadAffineQuantizedWeights(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([0, 1, 2, 3], 'float32')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, self._tmp_dir, quantization_dtype_map={'uint8': '*'})
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    self.assertEqual(1, len(read_output[0]))
-    self.assertEqual('weight1', read_output[0][0]['name'])
-    self.assertEqual(read_output[0][0]['data'].dtype, np.float32)
-    self.assertTrue(
-        np.allclose(groups[0][0]['data'], read_output[0][0]['data']))
-
-  def testReadFloat16QuantizedWeights(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([0, 1, 2, 3], 'float32')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, self._tmp_dir, quantization_dtype_map={'float16': '*'})
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    self.assertEqual(1, len(read_output[0]))
-    self.assertEqual('weight1', read_output[0][0]['name'])
-    self.assertEqual(read_output[0][0]['data'].dtype, np.float32)
-    self.assertTrue(
-        np.allclose(groups[0][0]['data'], read_output[0][0]['data']))
-
-  def testReadBoolWeights(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([True, False, True], 'bool')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    self.assertEqual(1, len(read_output[0]))
-    self.assertEqual('weight1', read_output[0][0]['name'])
-    np.testing.assert_array_equal(read_output[0][0]['data'],
-                                  np.array([True, False, True], 'bool'))
-
-  def testReadStringScalar(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array(u'abc'.encode('utf-8'), 'object')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(groups, self._tmp_dir)
-
-    # Read the weights using `read_weights`.
-    read_output = read_weights.read_weights(manifest, self._tmp_dir)
-    self.assertEqual(1, len(read_output))
-    self.assertEqual(1, len(read_output[0]))
-    self.assertEqual('weight1', read_output[0][0]['name'])
-    np.testing.assert_array_equal(read_output[0][0]['data'],
-                                  np.array(u'abc'.encode('utf-8'), 'object'))
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/resource_loader.py b/tfjs-master/tfjs-converter/python/tensorflowjs/resource_loader.py
deleted file mode 100644
index 645fac394..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/resource_loader.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Resource management library."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-
-
-def open_file(path):
-  """Opens the file at given path, where path is relative to tensorflowjs/.
-
-  Args:
-    path: a string resource path relative to tensorflowjs/.
-
-  Returns:
-    An open file of that resource.
-
-  Raises:
-    IOError: If the path is not found, or the resource can't be opened.
-  """
-  path = os.path.join(os.path.dirname(os.path.abspath(__file__)), path)
-  return open(path)
-
-
-def list_dir(path):
-  """List the files inside a dir where path is relative to tensorflowjs/.
-
-  Args:
-    path: a string path to a resource directory relative to tensorflowjs/.
-
-  Returns:
-    A list of files inside that directory.
-
-  Raises:
-    IOError: If the path is not found, or the resource can't be read.
-  """
-  path = os.path.join(os.path.dirname(os.path.abspath(__file__)), path)
-  return os.listdir(path)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/resource_loader_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/resource_loader_test.py
deleted file mode 100644
index 28761065b..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/resource_loader_test.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import unittest
-from os import path
-
-from tensorflowjs import resource_loader
-
-class ResourceLoaderTest(unittest.TestCase):
-
-  def testListingFilesInOpList(self):
-    files = resource_loader.list_dir('op_list')
-    self.assertGreater(len(files), 0)
-    for filename in files:
-      self.assertTrue(filename == 'BUILD' or filename.endswith('.json'))
-
-  def testReadingFileInOpList(self):
-    file_path = path.join('op_list', 'arithmetic.json')
-    with resource_loader.open_file(file_path) as f:
-      data = json.load(f)
-      first_op = data[0]
-      self.assertIn('tfOpName', first_op)
-      self.assertIn('category', first_op)
-
-  def testReadingNonExistentFileRaisesError(self):
-    with self.assertRaises(IOError):
-      resource_loader.open_file('___non_existent')
-
-if __name__ == '__main__':
-  unittest.main()
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/version.py b/tfjs-master/tfjs-converter/python/tensorflowjs/version.py
deleted file mode 100644
index 545de7143..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/version.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# @license See the LICENSE file.
-
-# This code is auto-generated, do not modify this file!
-version = '1.7.0'
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/write_weights.py b/tfjs-master/tfjs-converter/python/tensorflowjs/write_weights.py
deleted file mode 100644
index 8dd616295..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/write_weights.py
+++ /dev/null
@@ -1,416 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-import io
-import json
-import math
-import os
-
-import numpy as np
-import tensorflow as tf
-
-from tensorflowjs import quantization
-from tensorflowjs import read_weights
-
-_OUTPUT_DTYPES = [np.float16, np.float32, np.int32, np.complex64,
-                  np.uint8, np.uint16, bool, object]
-_AUTO_DTYPE_CONVERSION = {
-    np.dtype(np.float16): np.float32,
-    np.dtype(np.float64): np.float32,
-    np.dtype(np.int64): np.int32,
-    np.dtype(np.complex128): np.complex64}
-
-def write_weights(
-    weight_groups, write_dir, shard_size_bytes=1024 * 1024 * 4,
-    write_manifest=True, quantization_dtype_map=None):
-  """Writes weights to a binary format on disk for ingestion by JavaScript.
-
-    Weights are organized into groups. When writing to disk, the bytes from all
-    weights in each group are concatenated together and then split into shards
-    (default is 4MB). This means that large weights (> shard_size) get sharded
-    and small weights (< shard_size) will be packed. If the bytes can't be split
-    evenly into shards, there will be a leftover shard that is smaller than the
-    shard size.
-
-    Weights are optionally quantized to either 8 or 16 bits for compression,
-    which is enabled via the `quantization_dtype_map`.
-
-    Args:
-      weight_groups: An list of groups. Each group is an array of weight
-        entries. Each entry is a dict that maps a unique name to a numpy array,
-        for example:
-        entry = {
-          'name': 'weight1',
-          'data': np.array([1, 2, 3], 'float32')
-        }
-
-        Weights groups would then look like:
-        weight_groups = [
-          [group_0_entry1, group_0_entry2],
-          [group_1_entry1, group_1_entry2],
-        ]
-
-        The 'name' must be unique across all groups and all entries. The 'data'
-        field must be a numpy ndarray.
-      write_dir: A directory to write the files to.
-      shard_size_bytes: The size of shards in bytes. Defaults to 4MB, which is
-        the max file size for caching for all major browsers.
-      write_manifest: Whether to write the manifest JSON to disk. Defaults to
-        True.
-      quantization_dtype_map: (Optional) A mapping from dtype
-        (`uint8`, `uint16`, `float16`) to weights names. The weight mapping
-        supports wildcard substitution.
-    Returns:
-      The weights manifest JSON dict.
-
-      An example manifest with 2 groups, 2 weights, and each weight sharded
-      into 2:
-
-      The manifest JSON looks like the following:
-      [{
-        'paths': ['group1-shard1of2', 'group1-shard2of2'],
-        'weights': [{
-          'name': 'weight1',
-          'shape': [1000, 1000],
-          'dtype': 'float32'
-        }]
-      }, {
-        'paths': ['group2-shard1of2', 'group2-shard2of2'],
-        'weights': [{
-          'name': 'weight2',
-          'shape': [2000, 2000],
-          'dtype': 'float32'
-        }]
-      }]
-      or, if quantization is used:
-      [{
-        'paths': ['group1-shard1of2', 'group1-shard2of2'],
-        'weights': [{
-          'name': 'weight1',
-          'shape': [1000, 1000],
-          'dtype': 'float32'
-          'quantization': {'min': -0.1, 'scale': 0.01, 'dtype': 'uint8'}
-        }]
-      }, {
-        'paths': ['group2-shard1of2', 'group2-shard2of2'],
-        'weights': [{
-          'name': 'weight2',
-          'shape': [2000, 2000],
-          'dtype': 'float32',
-          'quantization': {'dtype': 'float16'}
-        }]
-      }]
-  """
-  _assert_weight_groups_valid(weight_groups)
-  _assert_shard_size_bytes_valid(shard_size_bytes)
-  _assert_no_duplicate_weight_names(weight_groups)
-
-  manifest = []
-
-  for group_index, group in enumerate(weight_groups):
-    for e in group:
-      _auto_convert_weight_entry(e)
-    names = [entry['name'] for entry in group]
-    quantization_dtype = quantization.map_layers_to_quantization_dtype(
-        names, quantization_dtype_map)
-
-    group = [
-        _quantize_entry(e, quantization_dtype[e['name']])
-        if e['name'] in quantization_dtype else e for e in group
-    ]
-    group_bytes, total_bytes, _ = _stack_group_bytes(group)
-
-    shard_filenames = _shard_group_bytes_to_disk(
-        write_dir, group_index, group_bytes, total_bytes, shard_size_bytes)
-
-    weights_entries = _get_weights_manifest_for_group(group)
-    manifest_entry = {
-        'paths': shard_filenames,
-        'weights': weights_entries
-    }
-    manifest.append(manifest_entry)
-
-  if write_manifest:
-    manifest_path = os.path.join(write_dir, 'weights_manifest.json')
-    with tf.io.gfile.GFile(manifest_path, 'wb') as f:
-      f.write(json.dumps(manifest).encode())
-
-  return manifest
-
-
-def _quantize_entry(entry, quantization_dtype):
-  """Quantizes the weights in the entry, returning a new entry.
-
-  The weights are quantized by linearly re-scaling the values between the
-  minimum and maximum value, and representing them with the number of bits
-  provided by the `quantization_dtype`.
-
-  In order to guarantee that 0 is perfectly represented by one of the quanzitzed
-  values, the range is "nudged" in the same manner as in TF-Lite.
-
-  Args:
-    entry: A weight entries to quantize.
-    quantization_dtype: An numpy dtype to quantize weights to.
-        Only np.uint8, np.uint16, and np.float16 are supported.
-
-  Returns:
-    A new entry containing the quantized data and additional quantization info,
-    for example:
-        original_entry = {
-          'name': 'weight1',
-          'data': np.array([0, -0.1, 1.2], 'float32')
-        }
-        quantized_entry = {
-          'name': 'weight1',
-          'data': np.array([20, 0, 255], 'uint8')
-          'quantization': {'min': -0.10196078817, 'scale': 0.00509803940852,
-                           'dtype': 'uint8', 'original_dtype': 'float32'}
-        }
-  """
-  data = entry['data']
-  # Only float32 tensors are quantized.
-  if data.dtype != 'float32':
-    return entry
-  quantized_data, metadata = quantization.quantize_weights(
-      data, quantization_dtype)
-  metadata.update({'original_dtype': data.dtype.name})
-  quantized_entry = entry.copy()
-  quantized_entry['data'] = quantized_data
-  quantized_entry['quantization'] = metadata
-  return quantized_entry
-
-
-def _serialize_string_array(data):
-  """Serializes a numpy array of dtype `string` into bytes.
-
-  Each string value is preceded by 4 bytes which denote a 32-bit unsigned
-  integer in little endian that specifies the byte length of the following
-  string. This is followed by the actual string bytes. If the tensor has no
-  strings there will be no bytes reserved. Empty strings will still take 4 bytes
-  for the length.
-
-  For example, a tensor that has 2 strings will be encoded as
-  [byte length of s1][bytes of s1...][byte length of s2][bytes of s2...]
-
-  where byte length always takes 4 bytes.
-
-  Args:
-    data: A numpy array of dtype `string`.
-
-  Returns:
-    bytes of the entire string tensor to be serialized on disk.
-  """
-  strings = data.flatten().tolist()
-
-  string_bytes = io.BytesIO()
-  bytes_writer = io.BufferedWriter(string_bytes)
-
-  for x in strings:
-    encoded = x if isinstance(x, bytes) else x.encode('utf-8')
-    length_as_bytes = np.array(len(encoded),
-                               read_weights.STRING_LENGTH_DTYPE).tobytes()
-    bytes_writer.write(length_as_bytes)
-    bytes_writer.write(encoded)
-  bytes_writer.flush()
-  string_bytes.seek(0)
-  return string_bytes.read()
-
-def _serialize_numeric_array(data):
-  """Serializes a numeric numpy array into bytes.
-
-  Args:
-    data: A numeric numpy array.
-
-  Returns:
-    bytes of the array to be serialized on disk.
-  """
-  return data.tobytes()
-
-def _stack_group_bytes(group):
-  """Stacks the bytes for a weight group into a flat byte array.
-
-  Args:
-    group: A list of weight entries.
-  Returns:
-    A type: (group_bytes, total_bytes, weights_entries, group_bytes_writer)
-    group_bytes: The stacked bytes for the group, as a BytesIO() stream.
-    total_bytes: A number representing the total size of the byte buffer.
-    groups_bytes_writer: The io.BufferedWriter object. Returned so that
-      group_bytes does not get garbage collected and closed.
-
-  """
-  group_bytes = io.BytesIO()
-  group_bytes_writer = io.BufferedWriter(group_bytes)
-  total_bytes = 0
-
-  for entry in group:
-    _assert_valid_weight_entry(entry)
-    data = entry['data']
-
-    if data.dtype == object:
-      data_bytes = _serialize_string_array(data)
-    else:
-      data_bytes = _serialize_numeric_array(data)
-    group_bytes_writer.write(data_bytes)
-    total_bytes += len(data_bytes)
-
-  group_bytes_writer.flush()
-  group_bytes.seek(0)
-
-  # NOTE: We must return the bytes writer here, otherwise it goes out of scope
-  # and python closes the IO operation.
-  return (group_bytes, total_bytes, group_bytes_writer)
-
-
-def _shard_group_bytes_to_disk(
-    write_dir, group_index, group_bytes, total_bytes, shard_size_bytes):
-  """Shards the concatenated bytes for a group to disk.
-
-  Args:
-    write_dir: The directory to write the files to.
-    group_index: The index for the group.
-    group_bytes: An io.BytesIO() object representing the byte array.
-    total_bytes: The total number of bytes of the stream.
-    shard_size_bytes: The size of shards in bytes. If None, the whole byte
-        array will be written as one shard.
-  Returns:
-    A list of filenames that were written to disk.
-  """
-  if shard_size_bytes is None:
-    shard_size_bytes = total_bytes
-
-  num_shards = int(math.ceil(float(total_bytes) / shard_size_bytes))
-
-  filenames = []
-  for i in range(num_shards):
-    shard = group_bytes.read(shard_size_bytes)
-
-    filename = 'group%d-shard%dof%d.bin' % (group_index + 1, i + 1, num_shards)
-    filenames.append(filename)
-    filepath = os.path.join(write_dir, filename)
-
-    # Write the shard to disk.
-    with tf.io.gfile.GFile(filepath, 'wb') as f:
-      f.write(shard)
-
-  return filenames
-
-
-def _get_weights_manifest_for_group(group):
-  """Gets the weights entries manifest JSON for a group.
-
-  Args:
-    group: A list of weight entries.
-  Returns:
-    An list of manifest entries (dicts) to be written in the weights manifest.
-  """
-  weights_entries = []
-  for entry in group:
-    is_quantized = 'quantization' in entry
-    dtype = (entry['quantization']['original_dtype']
-             if is_quantized else entry['data'].dtype.name)
-    var_manifest = {
-        'name': entry['name'],
-        'shape': list(entry['data'].shape),
-        'dtype': dtype
-    }
-    # String arrays have dtype 'object' and need extra metadata to parse.
-    if dtype == 'object':
-      var_manifest['dtype'] = 'string'
-    if is_quantized:
-      manifest = {'dtype': entry['data'].dtype.name}
-      manifest.update(entry['quantization'])
-      var_manifest['quantization'] = manifest
-    weights_entries.append(var_manifest)
-  return weights_entries
-
-
-def _assert_no_duplicate_weight_names(weight_groups):
-  weight_names = set()
-  for group in weight_groups:
-    for entry in group:
-      name = entry['name']
-      if name in weight_names:
-        raise Exception(
-            'Error dumping weights, duplicate weight name ' + name)
-      weight_names.add(name)
-
-
-def _auto_convert_weight_entry(entry):
-  data = entry['data']
-  if data.dtype in _AUTO_DTYPE_CONVERSION:
-    entry['data'] = data.astype(_AUTO_DTYPE_CONVERSION[data.dtype])
-    print('weight ' + entry['name'] + ' with shape ' + str(data.shape) +
-          ' and dtype ' + data.dtype.name + ' was auto converted to the type ' +
-          np.dtype(_AUTO_DTYPE_CONVERSION[data.dtype]).name)
-
-
-def _assert_valid_weight_entry(entry):
-  if 'name' not in entry:
-    raise ValueError('Error dumping weight, no name field found.')
-  if 'data' not in entry:
-    raise ValueError('Error dumping weight, no data field found.')
-
-  name = entry['name']
-  data = entry['data']
-
-  # String tensors can be backed by different numpy dtypes, thus we consolidate
-  # to a single 'object' dtype.
-  if data.dtype.name.startswith('str') or data.dtype.name.startswith('bytes'):
-    data = data.astype(object)
-    entry['data'] = data
-
-
-  if not (data.dtype in _OUTPUT_DTYPES or data.dtype in _AUTO_DTYPE_CONVERSION):
-    raise ValueError('Error dumping weight ' + name + ', dtype ' +
-                     data.dtype.name + ' not supported.')
-
-  if not isinstance(data, np.ndarray):
-    raise ValueError('Error dumping weight ' + name + ', data ' +
-                     'must be a numpy ndarray.')
-
-
-def _assert_weight_groups_valid(weight_groups):
-  if not isinstance(weight_groups, list):
-    raise Exception('weight_groups must be a list of groups')
-  if not weight_groups:
-    raise ValueError('weight_groups must have more than one list element')
-  for i, weight_group in enumerate(weight_groups):
-    if not isinstance(weight_group, list):
-      raise ValueError(
-          'weight_groups[' + i + '] must be a list of weight entries')
-    for j, weights in enumerate(weight_group):
-      if 'name' not in weights:
-        raise ValueError(
-            'weight_groups[' + i + '][' + j + '] has no string field \'name\'')
-      if 'data' not in weights:
-        raise ValueError(
-            'weight_groups[' + i + '][' + j + '] has no numpy ' +
-            'array field \'data\'')
-      if not isinstance(weights['data'], np.ndarray):
-        raise ValueError(
-            'weight_groups[' + i + '][' + j + '][\'data\'] is not a numpy ' +
-            'array')
-
-
-def _assert_shard_size_bytes_valid(shard_size_bytes):
-  if shard_size_bytes <= 0:
-    raise ValueError(
-        'shard_size_bytes must be greater than 0, but got %s' %
-        shard_size_bytes)
-  if not isinstance(shard_size_bytes, int):
-    raise ValueError(
-        'shard_size_bytes must be an integer, but got %s' %
-        shard_size_bytes)
diff --git a/tfjs-master/tfjs-converter/python/tensorflowjs/write_weights_test.py b/tfjs-master/tfjs-converter/python/tensorflowjs/write_weights_test.py
deleted file mode 100644
index 11e7d8154..000000000
--- a/tfjs-master/tfjs-converter/python/tensorflowjs/write_weights_test.py
+++ /dev/null
@@ -1,789 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-import os
-import shutil
-
-import numpy as np
-import tensorflow as tf
-
-from tensorflowjs import write_weights
-
-TMP_DIR = '/tmp/write_weights_test/'
-
-
-class TestWriteWeights(tf.test.TestCase):
-  def setUp(self):
-    if not os.path.isdir(TMP_DIR):
-      os.makedirs(TMP_DIR)
-
-  def tearDown(self):
-    if os.path.isdir(TMP_DIR):
-      shutil.rmtree(TMP_DIR)
-
-  def test_1_group_1_weight(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=4 * 4)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'float32'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    weight1 = np.fromfile(weights_path, 'float32')
-    np.testing.assert_array_equal(weight1, np.array([1, 2, 3], 'float32'))
-
-  def test_1_group_1_weight_bool(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([True, False, True], 'bool')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=4 * 4)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'bool'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    weight1 = np.fromfile(weights_path, 'bool')
-    np.testing.assert_array_equal(
-        weight1, np.array([True, False, True], 'bool'))
-
-  def test_1_group_1_weight_string(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([['здраво', 'end'], ['test', 'a']], 'object')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=4 * 1024 * 1024)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [2, 2],
-                'dtype': 'string'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    with open(weights_path, 'rb') as f:
-      weight_bytes = f.read()
-
-      self.assertEqual(len(weight_bytes), 36)
-      # 'здраво'
-      size = np.frombuffer(weight_bytes[:4], 'uint32')[0]
-      self.assertEqual(size, 12)  # 6 cyrillic chars (2 bytes each).
-      string = weight_bytes[4:16].decode('utf-8')
-      self.assertEqual(string, u'здраво')
-      # 'end'
-      size = np.frombuffer(weight_bytes[16:20], 'uint32')[0]
-      self.assertEqual(size, 3)  # 3 ascii chars.
-      string = weight_bytes[20:23].decode('utf-8')
-      self.assertEqual(string, u'end')
-      # 'test'
-      size = np.frombuffer(weight_bytes[23:27], 'uint32')[0]
-      self.assertEqual(size, 4)  # 4 ascii chars.
-      string = weight_bytes[27:31].decode('utf-8')
-      self.assertEqual(string, u'test')
-      # 'a'
-      size = np.frombuffer(weight_bytes[31:35], 'uint32')[0]
-      self.assertEqual(size, 1)  # 4 ascii chars.
-      string = weight_bytes[35:36].decode('utf-8')
-      self.assertEqual(string, u'a')
-
-
-  def test_1_group_1_weight_string_empty(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([''], 'object')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=4 * 1024 * 1024)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [1],
-                'dtype': 'string'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    with open(weights_path, 'rb') as f:
-      weight_bytes = f.read()
-      self.assertEqual(len(weight_bytes), 4)
-      size = np.frombuffer(weight_bytes[:4], 'uint32')[0]
-      self.assertEqual(size, 0)  # Empty string.
-
-  def test_1_group_1_weight_string_unicode(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([[u'здраво', u'end'], [u'test', u'a']], 'object')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=4 * 1024 * 1024)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [2, 2],
-                'dtype': 'string'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    with open(weights_path, 'rb') as f:
-      weight_bytes = f.read()
-
-      self.assertEqual(len(weight_bytes), 36)
-      # 'здраво'
-      size = np.frombuffer(weight_bytes[:4], 'uint32')[0]
-      self.assertEqual(size, 12)  # 6 cyrillic chars (2 bytes each).
-      string = weight_bytes[4:16].decode('utf-8')
-      self.assertEqual(string, u'здраво')
-      # 'end'
-      size = np.frombuffer(weight_bytes[16:20], 'uint32')[0]
-      self.assertEqual(size, 3)  # 3 ascii chars.
-      string = weight_bytes[20:23].decode('utf-8')
-      self.assertEqual(string, u'end')
-      # 'test'
-      size = np.frombuffer(weight_bytes[23:27], 'uint32')[0]
-      self.assertEqual(size, 4)  # 4 ascii chars.
-      string = weight_bytes[27:31].decode('utf-8')
-      self.assertEqual(string, u'test')
-      # 'a'
-      size = np.frombuffer(weight_bytes[31:35], 'uint32')[0]
-      self.assertEqual(size, 1)  # 4 ascii chars.
-      string = weight_bytes[35:36].decode('utf-8')
-      self.assertEqual(string, u'a')
-
-  def test_1_group_1_weight_string_sharded(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array(['helloworld'], 'object')
-        }]
-    ]
-
-    # The array takes up 14 bytes across 3 shards when shard size is 5 bytes.
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=5)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': [
-                'group1-shard1of3.bin',
-                'group1-shard2of3.bin',
-                'group1-shard3of3.bin'
-            ],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [1],
-                'dtype': 'string'
-            }]
-        }])
-
-    weight_bytes = bytes()
-    with open(os.path.join(TMP_DIR, 'group1-shard1of3.bin'), 'rb') as f:
-      weight_bytes += f.read()
-    with open(os.path.join(TMP_DIR, 'group1-shard2of3.bin'), 'rb') as f:
-      weight_bytes += f.read()
-    with open(os.path.join(TMP_DIR, 'group1-shard3of3.bin'), 'rb') as f:
-      weight_bytes += f.read()
-
-    self.assertEqual(len(weight_bytes), 14)
-    size = np.frombuffer(weight_bytes[:4], 'uint32')[0]
-    self.assertEqual(size, 10)  # 10 ascii chars.
-    string = weight_bytes[4:14].decode('utf-8')
-    self.assertEqual(string, u'helloworld')
-
-  def test_1_group_1_weight_complex(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1 + 1j, 2 + 2j, 3 + 3j], 'complex')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=6 * 4)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'complex64'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    weight1 = np.fromfile(weights_path, 'complex64')
-    np.testing.assert_array_equal(
-        weight1, np.array([1 + 1j, 2 + 2j, 3 + 3j], 'complex64'))
-
-  def test_1_group_3_weights_packed_multi_dtype(self):
-    # Each string tensor uses different encoding.
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }, {
-            'name': 'weight2',
-            'data': np.array([
-                u'hello'.encode('utf-16'), u'end'.encode('utf-16')], 'object')
-        }, {
-            'name': 'weight3',
-            'data': np.array([u'здраво'.encode('windows-1251')], 'object')
-        }, {
-            'name': 'weight4',
-            'data': np.array([u'语言处理'.encode('utf-8')], 'object')
-        }, {
-            'name': 'weight5',
-            'data': np.array([4, 5, 6], 'float32')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=4 * 1024 * 1024)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'float32'
-            }, {
-                'name': 'weight2',
-                'shape': [2],
-                'dtype': 'string'
-            }, {
-                'name': 'weight3',
-                'shape': [1],
-                'dtype': 'string'
-            }, {
-                'name': 'weight4',
-                'shape': [1],
-                'dtype': 'string'
-            }, {
-                'name': 'weight5',
-                'shape': [3],
-                'dtype': 'float32'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    with open(weights_path, 'rb') as f:
-      weight_bytes = f.read()
-      self.assertEqual(len(weight_bytes), 78)
-
-      # [1, 2, 3]
-      weight1 = np.frombuffer(weight_bytes[:12], 'float32')
-      np.testing.assert_array_equal(weight1, np.array([1, 2, 3], 'float32'))
-
-      # 'hello'
-      size = np.frombuffer(weight_bytes[12:16], 'uint32')[0]
-      self.assertEqual(size, 12)  # 5 ascii chars in utf-16.
-      string = weight_bytes[16:28].decode('utf-16')
-      self.assertEqual(string, u'hello')
-
-      # 'end'
-      size = np.frombuffer(weight_bytes[28:32], 'uint32')[0]
-      self.assertEqual(size, 8)  # 3 ascii chars in utf-16.
-      string = weight_bytes[32:40].decode('utf-16')
-      self.assertEqual(string, u'end')
-
-      # 'здраво'
-      size = np.frombuffer(weight_bytes[40:44], 'uint32')[0]
-      self.assertEqual(size, 6)  # 6 cyrillic chars in windows-1251.
-      string = weight_bytes[44:50].decode('windows-1251')
-      self.assertEqual(string, u'здраво')
-
-      # '语言处理'
-      size = np.frombuffer(weight_bytes[50:54], 'uint32')[0]
-      self.assertEqual(size, 12)  # 4 east asian chars in utf-8.
-      string = weight_bytes[54:66].decode('utf-8')
-      self.assertEqual(string, u'语言处理')
-
-      weight5 = np.frombuffer(weight_bytes[66:], 'float32')
-      np.testing.assert_array_equal(weight5, np.array([4, 5, 6], 'float32'))
-
-  def test_1_group_1_weight_sharded(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }]
-    ]
-    # Shard size is smaller than the size of the array so it gets split between
-    # multiple files.
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=2 * 4)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of2.bin', 'group1-shard2of2.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'float32'
-            }]
-        }])
-
-    shard_1_path = os.path.join(TMP_DIR, 'group1-shard1of2.bin')
-    shard_1 = np.fromfile(shard_1_path, 'float32')
-    np.testing.assert_array_equal(shard_1, np.array([1, 2], 'float32'))
-
-    shard_2_path = os.path.join(TMP_DIR, 'group1-shard2of2.bin')
-    shard_2 = np.fromfile(shard_2_path, 'float32')
-    np.testing.assert_array_equal(shard_2, np.array([3], 'float32'))
-
-  def test_1_group_2_weights_packed(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }, {
-            'name': 'weight2',
-            'data': np.array([4, 5], 'float32')
-        }]
-    ]
-
-    # Shard size is larger than the sum of the two weights so they get packed.
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=8 * 4)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'float32'
-            }, {
-                'name': 'weight2',
-                'shape': [2],
-                'dtype': 'float32'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    weights = np.fromfile(weights_path, 'float32')
-    np.testing.assert_array_equal(weights, np.array([1, 2, 3, 4, 5], 'float32'))
-
-  def test_1_group_2_packed_sharded_multi_dtype(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'int32')
-        }, {
-            'name': 'weight2',
-            'data': np.array([True, False, False, True], 'bool')
-        }, {
-            'name': 'weight3',
-            'data': np.array([4.1, 5.1], 'float32')
-        }]
-    ]
-
-    # Shard size is smaller than the sum of the weights so they get packed and
-    # then sharded. The two buffers will get sharded into 3 files, with shapes
-    # [2], [2], and [1]. The second shard is a mixed dtype.
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=2 * 4)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of3.bin',
-                      'group1-shard2of3.bin',
-                      'group1-shard3of3.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'int32'
-            }, {
-                'name': 'weight2',
-                'shape': [4],
-                'dtype': 'bool'
-            }, {
-                'name': 'weight3',
-                'shape': [2],
-                'dtype': 'float32'
-            }]
-        }])
-
-    shard_1_path = os.path.join(TMP_DIR, 'group1-shard1of3.bin')
-    shard_1 = np.fromfile(shard_1_path, 'int32')
-    np.testing.assert_array_equal(shard_1, np.array([1, 2], 'int32'))
-
-    # Shard 2 has a mixed dtype so we parse the bytes directly.
-    shard_2_path = os.path.join(TMP_DIR, 'group1-shard2of3.bin')
-    with open(shard_2_path, 'rb') as f:
-      shard_2_bytes = f.read()
-    self.assertEqual(len(shard_2_bytes), 8)
-    shard_2_int = np.frombuffer(shard_2_bytes[:4], 'int32')
-    np.testing.assert_array_equal(shard_2_int, np.array([3], 'int32'))
-    shard_2_bool = np.frombuffer(shard_2_bytes[4:], 'bool')
-    np.testing.assert_array_equal(
-        shard_2_bool, np.array([True, False, False, True], 'bool'))
-
-    shard_3_path = os.path.join(TMP_DIR, 'group1-shard3of3.bin')
-    shard_3 = np.fromfile(shard_3_path, 'float32')
-    np.testing.assert_array_equal(shard_3, np.array([4.1, 5.1], 'float32'))
-
-  def test_2_groups_4_weights_sharded_packed(self):
-    groups = [
-        # Group 1
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float64')
-        }, {
-            'name': 'weight2',
-            'data': np.array([[4, 5], [6, 7]], 'float32')
-        }, {
-            'name': 'weight5',
-            'data': np.array([1], 'float16')
-        }],
-        # Group 2
-        [{
-            'name': 'weight3',
-            'data': np.array([1, 2, 3, 4], 'int64')
-        }, {
-            'name': 'weight4',
-            'data': np.array([[1.1, 1.2], [1.3, 1.4], [1.5, 1.6]], 'float32')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=4 * 4)
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of2.bin', 'group1-shard2of2.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'float32'
-            }, {
-                'name': 'weight2',
-                'shape': [2, 2],
-                'dtype': 'float32'
-            }, {
-                'name': 'weight5',
-                'shape': [1],
-                'dtype': 'float32'
-            }]
-        }, {
-            'paths': ['group2-shard1of3.bin',
-                      'group2-shard2of3.bin',
-                      'group2-shard3of3.bin'],
-            'weights': [{
-                'name': 'weight3',
-                'shape': [4],
-                'dtype': 'int32'
-            }, {
-                'name': 'weight4',
-                'shape': [3, 2],
-                'dtype': 'float32'
-            }]
-        }])
-
-    group0_shard_1_path = os.path.join(TMP_DIR, 'group1-shard1of2.bin')
-    group0_shard_1 = np.fromfile(group0_shard_1_path, 'float32')
-    np.testing.assert_array_equal(
-        group0_shard_1, np.array([1, 2, 3, 4], 'float32'))
-
-    group0_shard_2_path = os.path.join(TMP_DIR, 'group1-shard2of2.bin')
-    group0_shard_2 = np.fromfile(group0_shard_2_path, 'float32')
-    np.testing.assert_array_equal(
-        group0_shard_2, np.array([5, 6, 7, 1], 'float32'))
-
-    group1_shard_1_path = os.path.join(TMP_DIR, 'group2-shard1of3.bin')
-    group1_shard_1 = np.fromfile(group1_shard_1_path, 'int32')
-    np.testing.assert_array_equal(
-        group1_shard_1, np.array([1, 2, 3, 4], 'int32'))
-
-    group2_shard_2_path = os.path.join(TMP_DIR, 'group2-shard2of3.bin')
-    group2_shard_2 = np.fromfile(group2_shard_2_path, 'float32')
-    np.testing.assert_array_equal(
-        group2_shard_2, np.array([1.1, 1.2, 1.3, 1.4], 'float32'))
-
-    group2_shard_3_path = os.path.join(TMP_DIR, 'group2-shard3of3.bin')
-    group2_shard_3 = np.fromfile(group2_shard_3_path, 'float32')
-    np.testing.assert_array_equal(
-        group2_shard_3, np.array([1.5, 1.6], 'float32'))
-
-  def test_no_write_manfest(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, write_manifest=False)
-
-    self.assertFalse(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json exists, but expected not to exist')
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'float32'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    weight1 = np.fromfile(weights_path, 'float32')
-    np.testing.assert_array_equal(weight1, np.array([1, 2, 3], 'float32'))
-
-  def test_no_weights_groups_throws(self):
-    groups = None
-    with self.assertRaises(Exception):
-      write_weights.write_weights(groups, TMP_DIR)
-
-  def test_empty_groups_throws(self):
-    groups = []
-    with self.assertRaises(Exception):
-      write_weights.write_weights(groups, TMP_DIR)
-
-  def test_non_grouped_weights_throws(self):
-    groups = [{
-        'name': 'weight1',
-        'data': np.array([1, 2, 3], 'float32')
-    }]
-
-    with self.assertRaises(Exception):
-      write_weights.write_weights(groups, TMP_DIR)
-
-  def test_bad_weights_entry_throws_no_name(self):
-    groups = [
-        [{
-            'noname': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }]
-    ]
-
-    with self.assertRaises(Exception):
-      write_weights.write_weights(groups, TMP_DIR)
-
-  def test_bad_weights_entry_throws_no_data(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'nodata': np.array([1, 2, 3], 'float32')
-        }]
-    ]
-
-    with self.assertRaises(Exception):
-      write_weights.write_weights(groups, TMP_DIR)
-
-  def test_duplicate_weight_name_throws(self):
-    groups = [
-        [{
-            'name': 'duplicate',
-            'data': np.array([1, 2, 3], 'float32')
-        }], [{
-            'name': 'duplicate',
-            'data': np.array([4, 5, 6], 'float32')
-        }]
-    ]
-
-    with self.assertRaises(Exception):
-      write_weights.write_weights(groups, TMP_DIR)
-
-  def test_quantize_group(self):
-    groups = [
-        [{
-            'name': 'weight1',
-            'data': np.array([1, 2, 3], 'float32')
-        }, {
-            'name': 'weight2',
-            'data': np.array([4, 5], 'int32')
-        }, {
-            'name': 'weight3',
-            'data': np.array([6, 7], 'float64')
-        }, {
-            'name': 'weight4',
-            'data': np.array(['hello'], object)
-        }]
-    ]
-
-    manifest = write_weights.write_weights(
-        groups, TMP_DIR, shard_size_bytes=1024,
-        quantization_dtype_map={
-            'float16': 'weight1',
-            'uint8': 'weight3'
-        })
-
-    self.assertTrue(
-        os.path.isfile(os.path.join(TMP_DIR, 'weights_manifest.json')),
-        'weights_manifest.json does not exist')
-    self.assertEqual(
-        manifest,
-        [{
-            'paths': ['group1-shard1of1.bin'],
-            'weights': [{
-                'name': 'weight1',
-                'shape': [3],
-                'dtype': 'float32',
-                'quantization': {
-                    'original_dtype': 'float32',
-                    'dtype': 'float16'
-                }
-            }, {
-                'name': 'weight2',
-                'shape': [2],
-                'dtype': 'int32'
-            }, {
-                'name': 'weight3',
-                'shape': [2],
-                'dtype': 'float32',
-                'quantization': {
-                    'min': 6.0,
-                    'scale': 1/255.0,
-                    'original_dtype': 'float32',
-                    'dtype': 'uint8'
-                }
-            }, {
-                'name': 'weight4',
-                'shape': [1],
-                'dtype': 'string'
-            }]
-        }])
-
-    weights_path = os.path.join(TMP_DIR, 'group1-shard1of1.bin')
-    with open(weights_path, 'rb') as f:
-      weight_bytes = f.read()
-      self.assertEqual(len(weight_bytes), 25)
-      w1 = np.frombuffer(weight_bytes[:6], 'float16')
-      np.testing.assert_array_equal(w1, np.array([1, 2, 3], 'float16'))
-
-      w2 = np.frombuffer(weight_bytes[6:14], 'int32')
-      np.testing.assert_array_equal(w2, np.array([4, 5], 'int32'))
-
-      w3 = np.frombuffer(weight_bytes[14:16], 'uint8')
-      np.testing.assert_array_equal(w3, np.array([0, 255], 'uint8'))
-
-      size = np.frombuffer(weight_bytes[16:20], 'uint32')[0]
-      self.assertEqual(size, 5)  # 5 ascii letters.
-      w4 = weight_bytes[20:].decode('utf-8')
-      self.assertEqual(w4, u'hello')
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/test_nightly_pip_package.py b/tfjs-master/tfjs-converter/python/test_nightly_pip_package.py
deleted file mode 100644
index adb4ec9c5..000000000
--- a/tfjs-master/tfjs-converter/python/test_nightly_pip_package.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# Copyright 2019 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Test the Python API and shell binary of the tensorflowjs pip package."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import glob
-import os
-import shutil
-import subprocess
-import tempfile
-
-import tensorflow.compat.v2 as tf
-from tensorflow.python.saved_model.save import save
-
-class APIAndShellTest(tf.test.TestCase):
-  """Nightly tests for the Python API of the pip package."""
-
-  def setUp(self):
-    # Make sure this file is not being run from the source directory, to
-    # avoid picking up source files.
-    if os.path.isdir(
-        os.path.join(os.path.dirname(__file__), 'tensorflowjs')):
-      self.fail('Do not run this test from the Python source directory. '
-                'This file is intended to be run on pip install.')
-
-    self._tmp_dir = tempfile.mkdtemp()
-    super(APIAndShellTest, self).setUp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(APIAndShellTest, self).tearDown()
-
-  def testConvertTfHubMobileNetV2ToTfjsGraphModel(self):
-    # 1. Convert tfhub mobilenet v2 module.
-    tfhub_url = (
-        'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224'
-        '/classification/3'
-    )
-    graph_model_output_dir = os.path.join(self._tmp_dir, 'tfjs_graph')
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tf_hub',
-        tfhub_url, graph_model_output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 2. Check the files that belong to the conversion result.
-    files = glob.glob(os.path.join(graph_model_output_dir, '*'))
-    self.assertIn(os.path.join(graph_model_output_dir, 'model.json'), files)
-    weight_files = glob.glob(
-        os.path.join(graph_model_output_dir, 'group*.bin'))
-    self.assertEqual(len(weight_files), 4)
-
-  def testConvertMobileNetV2ModelToTfjsGraphModel(self):
-    """create the keras mobilenet v2 model."""
-    # 1. Create a saved model from keras mobilenet v2.
-    model = tf.keras.applications.MobileNetV2()
-
-    save_dir = os.path.join(self._tmp_dir, 'mobilenetv2')
-    save(model, save_dir)
-
-    # 2. Convert to graph model.
-    graph_model_output_dir = os.path.join(self._tmp_dir, 'tfjs_graph')
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tf_saved_model',
-        save_dir, graph_model_output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 3. Check the files that belong to the conversion result.
-    files = glob.glob(os.path.join(graph_model_output_dir, '*'))
-    self.assertIn(os.path.join(graph_model_output_dir, 'model.json'), files)
-    weight_files = glob.glob(
-        os.path.join(graph_model_output_dir, 'group*.bin'))
-    self.assertEqual(len(weight_files), 4)
-
-  def testConvertMobileNetV2Hdf5ToTfjsGraphModel(self):
-    # 1. Create a model for testing.
-    model = tf.keras.applications.MobileNetV2()
-
-    h5_path = os.path.join(self._tmp_dir, 'model.h5')
-    model.save(h5_path)
-
-    # 2. Convert the keras hdf5 model to tfjs_layers_model format.
-    graph_model_output_dir = os.path.join(self._tmp_dir, 'tfjs_graph')
-    # Implicit value of --output_format: tfjs_layers_model
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'keras',
-        '--output_format', 'tfjs_graph_model',
-        h5_path, graph_model_output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 3. Check the files that belong to the conversion result.
-    files = glob.glob(os.path.join(graph_model_output_dir, '*'))
-    self.assertIn(os.path.join(graph_model_output_dir, 'model.json'), files)
-    weight_files = glob.glob(
-        os.path.join(graph_model_output_dir, 'group*.bin'))
-    self.assertEqual(len(weight_files), 4)
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/test_pip_package.py b/tfjs-master/tfjs-converter/python/test_pip_package.py
deleted file mode 100644
index 313323ab2..000000000
--- a/tfjs-master/tfjs-converter/python/test_pip_package.py
+++ /dev/null
@@ -1,1034 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Test the Python API and shell binary of the tensorflowjs pip package."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import glob
-import json
-import os
-import shutil
-import subprocess
-import sys
-import tempfile
-
-import numpy as np
-import tensorflow.compat.v2 as tf
-from tensorflow.compat.v1 import saved_model
-from tensorflow.python.eager import def_function
-from tensorflow.python.framework import constant_op
-from tensorflow.python.ops import variables
-from tensorflow.python.tools import freeze_graph
-from tensorflow.python.trackable import autotrackable
-from tensorflow.python.saved_model.save import save
-import tensorflow_hub as hub
-
-import tensorflowjs as tfjs
-
-
-def _createKerasModel(layer_name_prefix, h5_path=None):
-  """Create a Keras model for testing.
-
-  Args:
-    layer_name_prefix: A prefix string for layer names. This helps avoid
-      clashes in layer names between different test methods.
-    h5_path: Optional string path for a HDF5 (.h5) file to save the model
-      in.
-
-  Returns:
-    An instance of tf.keras.Model.
-  """
-  input_tensor = tf.keras.layers.Input((3, ))
-  dense1 = tf.keras.layers.Dense(
-      4,
-      use_bias=True,
-      kernel_initializer='ones',
-      bias_initializer='zeros',
-      name=layer_name_prefix + '1')(input_tensor)
-  output = tf.keras.layers.Dense(
-      2,
-      use_bias=False,
-      kernel_initializer='ones',
-      name=layer_name_prefix + '2')(dense1)
-  model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
-  model.compile(optimizer='adam', loss='binary_crossentropy')
-  model.predict(tf.ones((1, 3)), steps=1)
-
-  if h5_path:
-    model.save(h5_path, save_format='h5')
-  return model
-
-def _createTensorFlowSavedModelV1(name_scope, save_path):
-  """Create a TensorFlow SavedModel for testing.
-  Args:
-    name_scope: Name scope to create the model under. This helps avoid
-      op and variable name clashes between different test methods.
-    save_path: The directory path in which to save the model.
-  """
-  graph = tf.Graph()
-  with graph.as_default():
-    with tf.compat.v1.name_scope(name_scope):
-      x = tf.compat.v1.constant([[37.0, -23.0], [1.0, 4.0]])
-      w = tf.compat.v1.get_variable('w', shape=[2, 2])
-      y = tf.compat.v1.matmul(x, w)
-      output = tf.compat.v1.nn.softmax(y)
-      init_op = w.initializer
-
-      # Create a builder.
-      builder = saved_model.builder.SavedModelBuilder(save_path)
-
-      with tf.compat.v1.Session() as sess:
-        # Run the initializer on `w`.
-        sess.run(init_op)
-
-        builder.add_meta_graph_and_variables(
-            sess, [saved_model.tag_constants.SERVING],
-            signature_def_map={
-                "serving_default":
-                    saved_model.signature_def_utils.predict_signature_def(
-                        inputs={"x": x},
-                        outputs={"output": output})
-            },
-            assets_collection=None)
-
-      builder.save()
-
-def _createTensorFlowSavedModel(save_path):
-  """Create a TensorFlow SavedModel for testing.
-
-  Args:
-    save_path: The directory path in which to save the model.
-  """
-
-  input_data = constant_op.constant(1., shape=[1])
-  root = autotrackable.AutoTrackable()
-  root.v1 = variables.Variable(3.)
-  root.v2 = variables.Variable(2.)
-  root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
-  to_save = root.f.get_concrete_function(input_data)
-
-  save(root, save_path, to_save)
-
-
-def _create_hub_module(save_path):
-  """Create a TensorFlow Hub module for testing.
-
-  Args:
-    save_path: The directory path in which to save the model.
-  """
-  # Module function that doubles its input.
-  def double_module_fn():
-    w = tf.Variable([2.0, 4.0])
-    x = tf.compat.v1.placeholder(dtype=tf.float32)
-    hub.add_signature(inputs=x, outputs=x*w)
-  graph = tf.Graph()
-  with graph.as_default():
-    spec = hub.create_module_spec(double_module_fn)
-    m = hub.Module(spec)
-  # Export the module.
-  with tf.compat.v1.Session(graph=graph) as sess:
-    sess.run(tf.compat.v1.global_variables_initializer())
-    m.export(save_path, sess)
-
-def _create_frozen_model(save_path):
-  graph = tf.Graph()
-  saved_model_dir = os.path.join(save_path)
-  with graph.as_default():
-    x = tf.constant([[37.0, -23.0], [1.0, 4.0]])
-    w = tf.Variable(tf.random.uniform([2, 2]))
-    y = tf.matmul(x, w)
-    tf.nn.softmax(y)
-    init_op = w.initializer
-
-    # Create a builder
-    builder = saved_model.builder.SavedModelBuilder(
-        saved_model_dir)
-
-    with tf.compat.v1.Session() as sess:
-      # Run the initializer on `w`.
-      sess.run(init_op)
-
-      builder.add_meta_graph_and_variables(
-          sess, [saved_model.tag_constants.SERVING],
-          signature_def_map=None,
-          assets_collection=None)
-
-    builder.save()
-
-  frozen_file = os.path.join(save_path, 'frozen.pb')
-  freeze_graph.freeze_graph(
-      '',
-      '',
-      True,
-      '',
-      "Softmax",
-      '',
-      '',
-      frozen_file,
-      True,
-      '',
-      saved_model_tags=saved_model.tag_constants.SERVING,
-      input_saved_model_dir=saved_model_dir)
-class APIAndShellTest(tf.test.TestCase):
-  """Tests for the Python API of the pip package."""
-
-  @classmethod
-  def setUpClass(cls):
-    cls.class_tmp_dir = tempfile.mkdtemp()
-    cls.tf_saved_model_dir = os.path.join(cls.class_tmp_dir, 'tf_saved_model')
-    cls.tf_saved_model_v1_dir = os.path.join(
-                cls.class_tmp_dir, 'tf_saved_model_v1')
-    cls.tf_frozen_model_dir = os.path.join(cls.class_tmp_dir, 'tf_frozen_model')
-    _createTensorFlowSavedModel(cls.tf_saved_model_dir)
-    _createTensorFlowSavedModelV1('b', cls.tf_saved_model_v1_dir)
-    _create_frozen_model(cls.tf_frozen_model_dir)
-    cls.tf_hub_module_dir = os.path.join(cls.class_tmp_dir, 'tf_hub_module')
-    _create_hub_module(cls.tf_hub_module_dir)
-
-  @classmethod
-  def tearDownClass(cls):
-    shutil.rmtree(cls.class_tmp_dir)
-
-  def setUp(self):
-    # Make sure this file is not being run from the source directory, to
-    # avoid picking up source files.
-    if os.path.isdir(
-        os.path.join(os.path.dirname(__file__), 'tensorflowjs')):
-      self.fail('Do not run this test from the Python source directory. '
-                'This file is intended to be run on pip install.')
-
-    self._tmp_dir = tempfile.mkdtemp()
-    super(APIAndShellTest, self).setUp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(APIAndShellTest, self).tearDown()
-
-  def testVersionString(self):
-    self.assertEqual(2, tfjs.__version__.count('.'))
-
-  def testSaveKerasModel(self):
-    with self.test_session():
-      # First create a toy keras model.
-      model = _createKerasModel('MergedDense')
-
-      tfjs.converters.save_keras_model(model, self._tmp_dir)
-
-      # Briefly check the model topology.
-      with open(os.path.join(self._tmp_dir, 'model.json')) as f:
-        json_content = json.load(f)
-      model_json = json_content['modelTopology']
-      self.assertIsInstance(model_json['model_config'], dict)
-      self.assertIsInstance(model_json['model_config']['config'], dict)
-      self.assertIn('layers', model_json['model_config']['config'])
-
-      weights_manifest = json_content['weightsManifest']
-      self.assertIsInstance(weights_manifest, list)
-
-      # Briefly check the weights manifest.
-      weight_shapes = dict()
-      weight_dtypes = dict()
-      for manifest_item in weights_manifest:
-        for weight in manifest_item['weights']:
-          weight_name = weight['name']
-          weight_shapes[weight_name] = weight['shape']
-          weight_dtypes[weight_name] = weight['dtype']
-
-      self.assertEqual(
-          sorted(list(weight_shapes.keys())),
-          sorted([
-              'MergedDense1/kernel', 'MergedDense1/bias',
-              'MergedDense2/kernel'
-          ]))
-      self.assertEqual(weight_shapes['MergedDense1/kernel'], [3, 4])
-      self.assertEqual(weight_shapes['MergedDense1/bias'], [4])
-      self.assertEqual(weight_shapes['MergedDense2/kernel'], [4, 2])
-      self.assertEqual(weight_dtypes['MergedDense1/kernel'], 'float32')
-      self.assertEqual(weight_dtypes['MergedDense1/bias'], 'float32')
-      self.assertEqual(weight_dtypes['MergedDense2/kernel'], 'float32')
-
-  def testLoadKerasModel(self):
-    # Use separate tf.Graph and tf.compat.v1.Session contexts
-    # to prevent name collision.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # First create a toy keras model.
-      model1 = _createKerasModel('MergedDense')
-      tfjs.converters.save_keras_model(model1, self._tmp_dir)
-      model1_weight_values = model1.get_weights()
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # Load the model from saved artifacts.
-      model2 = tfjs.converters.load_keras_model(
-          os.path.join(self._tmp_dir, 'model.json'))
-
-      # Compare the loaded model with the original one.
-      model2_weight_values = model2.get_weights()
-      self.assertEqual(len(model1_weight_values), len(model2_weight_values))
-      for model1_weight_value, model2_weight_value in zip(
-          model1_weight_values, model2_weight_values):
-        self.assertAllClose(model1_weight_value, model2_weight_value)
-
-    # Check the content of the output directory.
-    self.assertTrue(glob.glob(os.path.join(self._tmp_dir, 'group*-*')))
-
-  def testInvalidInputFormatRaisesError(self):
-    process = subprocess.Popen(
-        [
-            'tensorflowjs_converter', '--input_format',
-            'nonsensical_format', self._tmp_dir, self._tmp_dir
-        ],
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE)
-    _, stderr = process.communicate()
-    self.assertGreater(process.returncode, 0)
-    self.assertIn(b'--input_format', tf.compat.as_bytes(stderr))
-
-  def testMissingInputPathRaisesError(self):
-    process = subprocess.Popen(
-        [
-            'tensorflowjs_converter'
-        ],
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE)
-    _, stderr = process.communicate()
-    self.assertGreater(process.returncode, 0)
-    self.assertIn(b'input_path', tf.compat.as_bytes(stderr))
-
-  def testKerasH5ConversionWorksFromCLI(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # First create a toy keras model.
-      os.makedirs(os.path.join(self._tmp_dir, 'keras_h5'))
-      h5_path = os.path.join(self._tmp_dir, 'keras_h5', 'model.h5')
-      _createKerasModel('MergedDenseForCLI', h5_path)
-
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'keras', h5_path,
-          self._tmp_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      # Briefly check the model topology.
-      with open(os.path.join(self._tmp_dir, 'model.json'), 'rt') as f:
-        json_content = json.load(f)
-      model_json = json_content['modelTopology']
-      self.assertIsInstance(model_json['model_config'], dict)
-      self.assertIsInstance(model_json['model_config']['config'], dict)
-      self.assertIn('layers', model_json['model_config']['config'])
-
-      weights_manifest = json_content['weightsManifest']
-      self.assertIsInstance(weights_manifest, list)
-
-      # Briefly check the weights manifest.
-      weight_shapes = dict()
-      weight_dtypes = dict()
-      for manifest_item in weights_manifest:
-        for weight in manifest_item['weights']:
-          weight_name = weight['name']
-          weight_shapes[weight_name] = weight['shape']
-          weight_dtypes[weight_name] = weight['dtype']
-
-      self.assertEqual(
-          sorted(list(weight_shapes.keys())),
-          sorted([
-              'MergedDenseForCLI1/kernel', 'MergedDenseForCLI1/bias',
-              'MergedDenseForCLI2/kernel'
-          ]))
-      self.assertEqual(weight_shapes['MergedDenseForCLI1/kernel'], [3, 4])
-      self.assertEqual(weight_shapes['MergedDenseForCLI1/bias'], [4])
-      self.assertEqual(weight_shapes['MergedDenseForCLI2/kernel'], [4, 2])
-      self.assertEqual(weight_dtypes['MergedDenseForCLI1/kernel'], 'float32')
-      self.assertEqual(weight_dtypes['MergedDenseForCLI1/bias'], 'float32')
-      self.assertEqual(weight_dtypes['MergedDenseForCLI2/kernel'], 'float32')
-
-      # Verify that there is only one weight group due to the default
-      # non-split_weights_by_layer behavior. The model is a small one, which
-      # does not exceed the 4-MB shard size limit. Therefore, there should
-      # be only one weight file.
-      self.assertEqual(
-          1, len(glob.glob(os.path.join(self._tmp_dir, 'group*'))))
-
-  def testKerasH5ConversionSplitWeightsByLayerWorksFromCLI(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # First create a toy keras model.
-      os.makedirs(os.path.join(self._tmp_dir, 'keras_h5'))
-      h5_path = os.path.join(self._tmp_dir, 'keras_h5', 'model.h5')
-      _createKerasModel('MergedDenseForCLI', h5_path)
-
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'keras',
-          '--split_weights_by_layer', h5_path, self._tmp_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      # Briefly check the model topology.
-      with open(os.path.join(self._tmp_dir, 'model.json'), 'rt') as f:
-        json_content = json.load(f)
-      model_json = json_content['modelTopology']
-      self.assertIsInstance(model_json['model_config'], dict)
-      self.assertIsInstance(model_json['model_config']['config'], dict)
-      self.assertIn('layers', model_json['model_config']['config'])
-
-      weights_manifest = json_content['weightsManifest']
-      self.assertIsInstance(weights_manifest, list)
-
-      # Briefly check the weights manifest.
-      weight_shapes = dict()
-      weight_dtypes = dict()
-      for manifest_item in weights_manifest:
-        for weight in manifest_item['weights']:
-          weight_name = weight['name']
-          weight_shapes[weight_name] = weight['shape']
-          weight_dtypes[weight_name] = weight['dtype']
-
-      self.assertEqual(
-          sorted(list(weight_shapes.keys())),
-          sorted([
-              'MergedDenseForCLI1/kernel', 'MergedDenseForCLI1/bias',
-              'MergedDenseForCLI2/kernel'
-          ]))
-      self.assertEqual(weight_shapes['MergedDenseForCLI1/kernel'], [3, 4])
-      self.assertEqual(weight_shapes['MergedDenseForCLI1/bias'], [4])
-      self.assertEqual(weight_shapes['MergedDenseForCLI2/kernel'], [4, 2])
-      self.assertEqual(weight_dtypes['MergedDenseForCLI1/kernel'], 'float32')
-      self.assertEqual(weight_dtypes['MergedDenseForCLI1/bias'], 'float32')
-      self.assertEqual(weight_dtypes['MergedDenseForCLI2/kernel'], 'float32')
-
-      # Verify that there are two weight groups due to the optional flag
-      # --split_weights_by_layer behavior. The model is a small one. None of
-      # the layers should have weight sizes exceeding the 4-MB shard size
-      # limit.
-      self.assertEqual(
-          2, len(glob.glob(os.path.join(self._tmp_dir, 'group*'))))
-
-  def testKerasH5ConversionWithSignatureNameErrors(self):
-    process = subprocess.Popen(
-        [
-            'tensorflowjs_converter', '--input_format', 'keras',
-            '--signature_name', 'bar',
-            os.path.join(self._tmp_dir, 'foo.h5'),
-            os.path.join(self._tmp_dir, 'output')
-        ],
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE)
-    _, stderr = process.communicate()
-    self.assertGreater(process.returncode, 0)
-    self.assertIn(
-        b'The --signature_name flag is applicable only to',
-        tf.compat.as_bytes(stderr))
-
-  def testConvertTFSavedModelV1WithCommandLineWorks(self):
-    output_dir = os.path.join(self._tmp_dir)
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tf_saved_model',
-        '--output_format', 'tfjs_graph_model',
-        self.tf_saved_model_v1_dir, output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    weights = [{
-        'paths': ['group1-shard1of1.bin'],
-        'weights': [{'dtype': 'float32', 'name': 'w', 'shape': [2, 2]}]}]
-
-    # Load the saved weights as a JSON string.
-    output_json = json.load(
-        open(os.path.join(output_dir, 'model.json'), 'rt'))
-    self.assertEqual(output_json['weightsManifest'], weights)
-
-    # Check the content of the output directory.
-    self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))
-
-
-  def testConvertTFHubModuleWithCommandLineWorks(self):
-    output_dir = os.path.join(self._tmp_dir)
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tf_hub',
-        self.tf_hub_module_dir, output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    weights = [{
-        'paths': ['group1-shard1of1.bin'],
-        'weights': [{
-            'shape': [2],
-            'name': 'module/Variable',
-            'dtype': 'float32'
-        }]
-    }]
-    # Load the saved weights as a JSON string.
-    output_json = json.load(
-        open(os.path.join(output_dir, 'model.json'), 'rt'))
-    self.assertEqual(output_json['weightsManifest'], weights)
-
-    # Check the content of the output directory.
-    self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))
-
-  def testConvertTFSavedModelWithCommandLineWorks(self):
-    output_dir = os.path.join(self._tmp_dir)
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tf_saved_model',
-        '--output_format', 'tfjs_graph_model',
-        self.tf_saved_model_dir, output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    weights = [{
-        'paths': ['group1-shard1of1.bin'],
-        'weights': [{
-            'dtype': 'float32',
-            'shape': [],
-            'name': 'StatefulPartitionedCall/mul'
-        }]
-    }]
-
-    # Load the saved weights as a JSON string.
-    output_json = json.load(
-        open(os.path.join(output_dir, 'model.json'), 'rt'))
-    weights_manifest = output_json['weightsManifest']
-    self.assertEqual(len(weights_manifest), len(weights))
-    if sys.version_info[0] < 3:
-      self.assertItemsEqual(weights_manifest[0]['paths'],
-                            weights[0]['paths'])
-      self.assertItemsEqual(weights_manifest[0]['weights'],
-                            weights[0]['weights'])
-    else:
-      self.assertCountEqual(weights_manifest[0]['paths'],
-                            weights[0]['paths'])
-      self.assertCountEqual(weights_manifest[0]['weights'],
-                            weights[0]['weights'])
-
-    # Check the content of the output directory.
-    self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))
-
-  def testConvertTFSavedModelIntoShardedWeights(self):
-    output_dir = os.path.join(self._tmp_dir, 'tfjs_model')
-    # Do initial conversion without sharding.
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tf_saved_model',
-        '--output_format', 'tfjs_graph_model',
-        self.tf_saved_model_dir, output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    weight_files = glob.glob(os.path.join(output_dir, 'group*.bin'))
-
-    # Get size of weights in bytes after graph optimizations.
-    optimized_total_weight = sum([os.path.getsize(f) for f in weight_files])
-    # Due to the shard size, there ought to be 2 shards after conversion.
-    weight_shard_size_bytes = int(optimized_total_weight * 0.8)
-
-    output_dir = os.path.join(self._tmp_dir, 'sharded_model')
-    # Convert Saved Model again with shard argument set.
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tf_saved_model',
-        '--output_format', 'tfjs_graph_model',
-        '--weight_shard_size_bytes', str(weight_shard_size_bytes),
-        self.tf_saved_model_dir, output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    weight_files = sorted(glob.glob(os.path.join(output_dir, 'group*.bin')))
-    self.assertEqual(len(weight_files), 2)
-    weight_file_sizes = [os.path.getsize(f) for f in weight_files]
-    self.assertEqual(sum(weight_file_sizes), optimized_total_weight)
-    self.assertLess(weight_file_sizes[1], weight_file_sizes[0])
-
-  def testConvertTFFrozenModelWithCommandLineWorks(self):
-    output_dir = os.path.join(self._tmp_dir)
-    frozen_file = os.path.join(self.tf_frozen_model_dir, 'frozen.pb')
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tf_frozen_model',
-        '--output_format', 'tfjs_graph_model', '--output_node_names',
-        'Softmax',
-        frozen_file, output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # Check model.json and weights manifest.
-    with open(os.path.join(output_dir, 'model.json'), 'rt') as f:
-      model_json = json.load(f)
-    self.assertTrue(model_json['modelTopology'])
-    self.assertIsNot(model_json['modelTopology']['versions'], None)
-    signature = model_json['signature']
-    self.assertIsNot(signature, None)
-    # frozen model signature has no inputs
-    self.assertIsNot(signature['outputs'], None)
-
-    weights_manifest = model_json['weightsManifest']
-    weights_manifest = model_json['weightsManifest']
-    self.assertCountEqual(weights_manifest[0]['paths'],
-                          ['group1-shard1of1.bin'])
-    self.assertIn('weights', weights_manifest[0])
-    self.assertTrue(
-        glob.glob(
-            os.path.join(self._tmp_dir, 'group*-*')))
-
-    # Check the content of the output directory.
-    self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))
-
-  def testConvertTensorflowjsArtifactsToKerasH5(self):
-    # 1. Create a toy keras model and save it as an HDF5 file.
-    os.makedirs(os.path.join(self._tmp_dir, 'keras_h5'))
-    h5_path = os.path.join(self._tmp_dir, 'keras_h5', 'model.h5')
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = _createKerasModel('MergedDenseForCLI', h5_path)
-      model_json = model.to_json()
-
-    # 2. Convert the HDF5 file to tensorflowjs format.
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'keras', h5_path,
-        self._tmp_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 3. Convert the tensorflowjs artifacts back to HDF5.
-    new_h5_path = os.path.join(self._tmp_dir, 'model_2.h5')
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tfjs_layers_model',
-        '--output_format', 'keras',
-        os.path.join(self._tmp_dir, 'model.json'), new_h5_path])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 4. Load the model back from the new HDF5 file and compare with the
-    #    original model.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model_2 = tf.keras.models.load_model(new_h5_path)
-      model_2_json = model_2.to_json()
-      self.assertEqual(model_json, model_2_json)
-
-  def testLoadTensorflowjsArtifactsAsKerasModel(self):
-    # 1. Create a toy keras model and save it as an HDF5 file.
-    os.makedirs(os.path.join(self._tmp_dir, 'keras_h5'))
-    h5_path = os.path.join(self._tmp_dir, 'keras_h5', 'model.h5')
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model = _createKerasModel('MergedDenseForCLI', h5_path)
-      model_json = model.to_json()
-
-    # 2. Convert the HDF5 file to tensorflowjs format.
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'keras', h5_path,
-        self._tmp_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 3. Load the tensorflowjs artifacts as a tf.keras.Model instance.
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      model_2 = tfjs.converters.load_keras_model(
-          os.path.join(self._tmp_dir, 'model.json'))
-      model_2_json = model_2.to_json()
-      self.assertEqual(model_json, model_2_json)
-
-  def testVersion(self):
-    process = subprocess.Popen(
-        ['tensorflowjs_converter', '--version'],
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE)
-    stdout, _ = process.communicate()
-    self.assertEqual(0, process.returncode)
-    self.assertIn(
-        tf.compat.as_bytes('tensorflowjs %s' % tfjs.__version__),
-        tf.compat.as_bytes(stdout))
-
-    process = subprocess.Popen(
-        ['tensorflowjs_converter', '-v'],
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE)
-    stdout, _ = process.communicate()
-    self.assertEqual(0, process.returncode)
-    self.assertIn(
-        tf.compat.as_bytes('tensorflowjs %s' % tfjs.__version__),
-        tf.compat.as_bytes(stdout))
-
-
-class ConvertTfKerasSavedModelTest(tf.test.TestCase):
-
-  def setUp(self):
-    super(ConvertTfKerasSavedModelTest, self).setUp()
-    self._tmp_dir = tempfile.mkdtemp()
-
-  def tearDown(self):
-    if os.path.isdir(self._tmp_dir):
-      shutil.rmtree(self._tmp_dir)
-    super(ConvertTfKerasSavedModelTest, self).tearDown()
-
-  def _createSimpleSequentialModel(self):
-    model = tf.keras.Sequential()
-    model.add(tf.keras.layers.Reshape([2, 3], input_shape=[6]))
-    model.add(tf.keras.layers.LSTM(10))
-    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
-    model.compile(optimizer='adam', loss='binary_crossentropy')
-    model.predict(tf.ones((1, 6)), steps=1)
-    return model
-
-  def _createNestedSequentialModel(self):
-    model = tf.keras.Sequential()
-    model.add(tf.keras.layers.Dense(6, input_shape=[10], activation='relu'))
-    model.add(self._createSimpleSequentialModel())
-    model.compile(optimizer='adam', loss='binary_crossentropy')
-    model.predict(tf.ones((1, 10)), steps=1)
-    return model
-
-  def _createFunctionalModelWithWeights(self):
-    input1 = tf.keras.Input(shape=[8])
-    input2 = tf.keras.Input(shape=[10])
-    y = tf.keras.layers.Concatenate()([input1, input2])
-    y = tf.keras.layers.Dense(4, activation='softmax')(y)
-    model = tf.keras.Model([input1, input2], y)
-    model.compile(optimizer='adam', loss='binary_crossentropy')
-    model.predict([tf.ones((1, 8)), tf.ones((1, 10))], steps=1)
-    return model
-
-  def testConvertTfKerasNestedSequentialSavedModelIntoTfjsFormat(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      x = np.random.randn(8, 10)
-
-      # 1. Run the model.predict(), store the result. Then saved the model
-      #    as a SavedModel.
-      model = self._createNestedSequentialModel()
-      y = model.predict(x)
-
-      tf.keras.models.save_model(model, self._tmp_dir)
-
-      # 2. Convert the keras saved model to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      # Implicit value of --output_format: tfjs_layers_model
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'keras_saved_model',
-          self._tmp_dir, tfjs_output_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      model_json_path = os.path.join(tfjs_output_dir, 'model.json')
-      self.assertTrue(os.path.isfile(model_json_path))
-
-      # 3. Convert the tfjs model to keras h5 format.
-      new_h5_path = os.path.join(self._tmp_dir, 'new_h5.h5')
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'tfjs_layers_model',
-          '--output_format', 'keras', model_json_path, new_h5_path])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      self.assertTrue(os.path.isfile(new_h5_path))
-
-      # 4. Load the model back and assert on the equality of the predict
-      #    results.
-      model_prime = tf.keras.models.load_model(new_h5_path)
-      new_y = model_prime.predict(x)
-      self.assertAllClose(y, new_y)
-
-  def testConvertTfKerasFunctionalSavedModelIntoTfjsFormat(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      x1 = np.random.randn(4, 8)
-      x2 = np.random.randn(4, 10)
-
-      # 1. Run the model.predict(), store the result. Then saved the model
-      #    as a SavedModel.
-      model = self._createFunctionalModelWithWeights()
-      y = model.predict([x1, x2])
-
-      tf.keras.models.save_model(model, self._tmp_dir)
-
-      # 2. Convert the keras saved model to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      # Use explicit --output_format value: tfjs_layers_model
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'keras_saved_model',
-          '--output_format', 'tfjs_layers_model',
-          self._tmp_dir, tfjs_output_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      model_json_path = os.path.join(tfjs_output_dir, 'model.json')
-      self.assertTrue(os.path.isfile(model_json_path))
-
-      # 3. Convert the tfjs model to keras h5 format.
-      new_h5_path = os.path.join(self._tmp_dir, 'new_h5.h5')
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'tfjs_layers_model',
-          '--output_format', 'keras', model_json_path, new_h5_path])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      self.assertTrue(os.path.isfile(new_h5_path))
-
-      # 4. Load the model back and assert on the equality of the predict
-      #    results.
-      model_prime = tf.keras.models.load_model(new_h5_path)
-      new_y = model_prime.predict([x1, x2])
-      self.assertAllClose(y, new_y)
-
-  def testUsingIncorrectKerasSavedModelRaisesError(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # 1. Run the model.predict(), store the result. Then saved the model
-      #    as a SavedModel.
-      model = self._createNestedSequentialModel()
-      tf.keras.models.save_model(model, self._tmp_dir)
-
-      # 2. Convert the keras saved model to tfjs format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      # Use incorrect --input_format value: keras
-      process = subprocess.Popen(
-          [
-              'tensorflowjs_converter', '--input_format', 'keras',
-              self._tmp_dir, tfjs_output_dir
-          ],
-          stdout=subprocess.PIPE,
-          stderr=subprocess.PIPE)
-      _, stderr = process.communicate()
-      self.assertIn(
-          b'Expected path to point to an HDF5 file, '
-          b'but it points to a directory', tf.compat.as_bytes(stderr))
-
-  def testConvertTfjsLayersModelIntoShardedWeights(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      x = np.random.randn(8, 10)
-
-      # 1. Run the model.predict(), store the result. Then saved the model
-      #    as a SavedModel.
-      model = self._createNestedSequentialModel()
-      y = model.predict(x)
-
-      weights = model.get_weights()
-      total_weight_bytes = sum(np.size(w) for w in weights) * 4
-
-      tf.keras.models.save_model(model, self._tmp_dir)
-
-      # 2. Convert the keras saved model to tfjs_layers_model format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      # Implicit value of --output_format: tfjs_layers_model
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'keras_saved_model',
-          self._tmp_dir, tfjs_output_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      # 3. Convert the tfjs_layers_model to another tfjs_layers_model,
-      #    with sharded weights.
-      weight_shard_size_bytes = int(total_weight_bytes * 0.3)
-      # Due to the shard size, there ought to be 4 shards after conversion.
-      sharded_model_dir = os.path.join(self._tmp_dir, 'tfjs_sharded')
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'tfjs_layers_model',
-          '--output_format', 'tfjs_layers_model',
-          '--weight_shard_size_bytes', str(weight_shard_size_bytes),
-          os.path.join(tfjs_output_dir, 'model.json'), sharded_model_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      # 4. Check the sharded weight files and their sizes.
-      weight_files = sorted(
-          glob.glob(os.path.join(sharded_model_dir, 'group*.bin')))
-      self.assertEqual(len(weight_files), 4)
-      weight_file_sizes = [os.path.getsize(f) for f in weight_files]
-      self.assertEqual(sum(weight_file_sizes), total_weight_bytes)
-      self.assertEqual(weight_file_sizes[0], weight_file_sizes[1])
-      self.assertEqual(weight_file_sizes[0], weight_file_sizes[2])
-      self.assertLess(weight_file_sizes[3], weight_file_sizes[0])
-
-      # 5. Convert the sharded tfjs_layers_model back into a keras h5 file.
-      new_h5_path = os.path.join(self._tmp_dir, 'new_h5.h5')
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'tfjs_layers_model',
-          os.path.join(sharded_model_dir, 'model.json'), new_h5_path
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # 6. Load the keras model and check the predict() output is close to
-      #    before.
-      new_model = tf.keras.models.load_model(new_h5_path)
-      new_y = new_model.predict(x)
-      self.assertAllClose(new_y, y)
-
-  def testConvertTfjsLayersModelWithLegacyQuantization(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # 1. Saved the model as a SavedModel.
-      model = self._createNestedSequentialModel()
-
-      weights = model.get_weights()
-      total_weight_bytes = sum(np.size(w) for w in weights) * 4
-
-      tf.keras.models.save_model(model, self._tmp_dir)
-
-      # 2. Convert the keras saved model to tfjs_layers_model format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      # Implicit value of --output_format: tfjs_layers_model
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'keras_saved_model',
-          self._tmp_dir, tfjs_output_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      # 3. Convert the tfjs_layers_model to another tfjs_layers_model,
-      #    with uint16 quantization.
-      sharded_model_dir = os.path.join(self._tmp_dir, 'tfjs_sharded')
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'tfjs_layers_model',
-          '--output_format', 'tfjs_layers_model',
-          '--quantization_bytes', '2',
-          os.path.join(tfjs_output_dir, 'model.json'), sharded_model_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      # 4. Check the quantized weight file and its size.
-      weight_files = sorted(
-          glob.glob(os.path.join(sharded_model_dir, 'group*.bin')))
-      self.assertEqual(len(weight_files), 1)
-      weight_file_size = os.path.getsize(weight_files[0])
-      # The size of the weight file should reflect the uint16 quantization.
-      self.assertEqual(weight_file_size, total_weight_bytes // 2)
-
-
-  def testConvertTfjsLayersModelWithQuantization(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # 1. Saved the model as a SavedModel.
-      model = self._createNestedSequentialModel()
-
-      weights = model.get_weights()
-      total_weight_bytes = sum(np.size(w) for w in weights) * 4
-
-      tf.keras.models.save_model(model, self._tmp_dir)
-
-      # 2. Convert the keras saved model to tfjs_layers_model format.
-      tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
-      # Implicit value of --output_format: tfjs_layers_model
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'keras_saved_model',
-          self._tmp_dir, tfjs_output_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      # 3. Convert the tfjs_layers_model to another tfjs_layers_model,
-      #    with uint16 quantization.
-      sharded_model_dir = os.path.join(self._tmp_dir, 'tfjs_sharded')
-      process = subprocess.Popen([
-          'tensorflowjs_converter', '--input_format', 'tfjs_layers_model',
-          '--output_format', 'tfjs_layers_model',
-          '--quantize_uint16', '*',
-          os.path.join(tfjs_output_dir, 'model.json'), sharded_model_dir
-      ])
-      process.communicate()
-      self.assertEqual(0, process.returncode)
-
-      # 4. Check the quantized weight file and its size.
-      weight_files = sorted(
-          glob.glob(os.path.join(sharded_model_dir, 'group*.bin')))
-      self.assertEqual(len(weight_files), 1)
-      weight_file_size = os.path.getsize(weight_files[0])
-      # The size of the weight file should reflect the uint16 quantization.
-      self.assertEqual(weight_file_size, total_weight_bytes // 2)
-
-  def testConvertTfjsLayersModelToTfjsGraphModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # 1. Create a model for testing.
-      model = tf.keras.Sequential()
-      model.add(tf.keras.layers.Dense(10, activation='relu', input_shape=[4]))
-      model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
-      model.compile(optimizer='adam', loss='binary_crossentropy')
-      model.predict(tf.ones((1, 4)), steps=1)
-
-      h5_path = os.path.join(self._tmp_dir, 'model.h5')
-      model.save(h5_path, save_format='h5')
-
-    # 2. Convert the keras saved model to tfjs_layers_model format.
-    layers_model_output_dir = os.path.join(self._tmp_dir, 'tfjs_layers')
-    # Implicit value of --output_format: tfjs_layers_model
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'keras',
-        h5_path, layers_model_output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 3. Convert the tfjs_layers_model to another tfjs_graph_model.
-    graph_model_dir = os.path.join(self._tmp_dir, 'tfjs_graph')
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tfjs_layers_model',
-        '--output_format', 'tfjs_graph_model',
-        os.path.join(layers_model_output_dir, 'model.json'), graph_model_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 4. Check the model.json and weight file and its size.
-    self.assertTrue(os.path.isfile(os.path.join(graph_model_dir, 'model.json')))
-    weight_files = sorted(
-        glob.glob(os.path.join(graph_model_dir, 'group*.bin')))
-    self.assertEqual(len(weight_files), 1)
-
-  def testConvertTfjsLayersModelToKerasSavedModel(self):
-    with tf.Graph().as_default(), tf.compat.v1.Session():
-      # 1. Create a model for testing.
-      model = tf.keras.Sequential()
-      model.add(tf.keras.layers.Dense(10, activation='relu', input_shape=[4]))
-      model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
-      model.compile(optimizer='adam', loss='binary_crossentropy')
-      model.predict(tf.ones((1, 4)), steps=1)
-
-      h5_path = os.path.join(self._tmp_dir, 'model.h5')
-      model.save(h5_path, save_format='h5')
-
-    # 2. Convert the keras saved model to tfjs_layers_model format.
-    layers_model_output_dir = os.path.join(self._tmp_dir, 'tfjs_layers')
-    # Implicit value of --output_format: tfjs_layers_model
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'keras',
-        h5_path, layers_model_output_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 3. Convert the tfjs_layers_model to another keras_saved_model.
-    keras_saved_model_dir = os.path.join(self._tmp_dir, 'keras_saved_model')
-    process = subprocess.Popen([
-        'tensorflowjs_converter', '--input_format', 'tfjs_layers_model',
-        '--output_format', 'keras_saved_model',
-        os.path.join(layers_model_output_dir, 'model.json'),
-        keras_saved_model_dir
-    ])
-    process.communicate()
-    self.assertEqual(0, process.returncode)
-
-    # 4. Check the files that belong to the conversion result.
-    files = glob.glob(os.path.join(keras_saved_model_dir, '*'))
-    self.assertIn(os.path.join(keras_saved_model_dir, 'saved_model.pb'), files)
-    self.assertIn(os.path.join(keras_saved_model_dir, 'variables'), files)
-    self.assertIn(os.path.join(keras_saved_model_dir, 'assets'), files)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-converter/python/update_locked_deps.sh b/tfjs-master/tfjs-converter/python/update_locked_deps.sh
deleted file mode 100644
index cd7345e28..000000000
--- a/tfjs-master/tfjs-converter/python/update_locked_deps.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2022 Google LLC.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-
-# This script updates the pip requirements lock files that pin the installed
-# versions of pypi packages.
-
-bazel run //tfjs-converter/python:tensorflowjs_deps_requirements.update
-bazel run //tfjs-converter/python:tensorflowjs_dev_deps_requirements.update
diff --git a/tfjs-master/tfjs-converter/scripts/BUILD.bazel b/tfjs-master/tfjs-converter/scripts/BUILD.bazel
deleted file mode 100644
index 6834cf9c2..000000000
--- a/tfjs-master/tfjs-converter/scripts/BUILD.bazel
+++ /dev/null
@@ -1,65 +0,0 @@
-load("@build_bazel_rules_nodejs//:index.bzl", "nodejs_binary")
-load("//tools:defaults.bzl", "ts_library")
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files(
-    ["op.template.ts"],
-    visibility = ["//visibility:public"],
-)
-
-ts_library(
-    name = "test_snippets_lib",
-    testonly = True,
-    srcs = [
-        "test_snippets.ts",
-    ],
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-converter/src:tfjs-converter_lib",
-        "//tfjs-core/scripts/test_snippets:test_snippets_util_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-    ],
-)
-
-ts_library(
-    name = "gen_op_lib",
-    srcs = [
-        ":gen_op.ts",
-    ],
-    deps = [
-        "@npm//@types/argparse",
-        "@npm//@types/node",
-        "@npm//argparse",
-    ],
-)
-
-nodejs_binary(
-    name = "gen_op_bin",
-    data = [
-        ":gen_op_lib",
-    ],
-    entry_point = "gen_op.ts",
-)
-
-ts_library(
-    name = "kernels_to_ops_lib",
-    srcs = [
-        ":kernels_to_ops.ts",
-    ],
-    deps = [
-        "@npm//@types/argparse",
-        "@npm//@types/node",
-        "@npm//argparse",
-        "@npm//ts-morph",
-    ],
-)
-
-nodejs_binary(
-    name = "kernels_to_ops_bin",
-    data = [
-        ":kernels_to_ops_lib",
-    ],
-    entry_point = "kernels_to_ops.ts",
-)
diff --git a/tfjs-master/tfjs-converter/scripts/build-npm.sh b/tfjs-master/tfjs-converter/scripts/build-npm.sh
deleted file mode 100644
index 2bc67b4a9..000000000
--- a/tfjs-master/tfjs-converter/scripts/build-npm.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2018 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Exit immediately if a command exits with a non-zero status.
-set -e
-
-yarn rimraf dist/
-yarn
-yarn build-ci
-yarn rollup -c --visualize --npm
-
-echo "Stored standalone library at dist/tf-converter(.min).js"
-npm pack
diff --git a/tfjs-master/tfjs-converter/scripts/cloud_funcs/README.md b/tfjs-master/tfjs-converter/scripts/cloud_funcs/README.md
deleted file mode 100644
index d17ac4deb..000000000
--- a/tfjs-master/tfjs-converter/scripts/cloud_funcs/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-This directory contains the following Google Cloud Function.
-
-### `trigger_nightly`
-Programatically triggers a Cloud Build on master. This function is called by the Cloud Scheduler at 3am EST every day (configurable via the Cloud Scheduler UI).
-You can also trigger the function manually via the Cloud UI.
-
-Command to re-deploy:
-```sh
-gcloud functions deploy converter_python_nightly \
-  --runtime nodejs8 \
-  --trigger-topic converter_python_nightly
-```
-
-### The pipeline
-
-The pipeline looks like this:
-
-1) At 3am, Cloud Scheduler writes to `nightly` topic
-2) That triggers the `nightly` function, which starts a build programatically
-3) That build runs and writes its status to `cloud-builds` topic
-4) That triggers the `send_email` function, which sends email and chat with the build status.
diff --git a/tfjs-master/tfjs-converter/scripts/cloud_funcs/trigger_nightly/index.js b/tfjs-master/tfjs-converter/scripts/cloud_funcs/trigger_nightly/index.js
deleted file mode 100644
index 65e64d6fd..000000000
--- a/tfjs-master/tfjs-converter/scripts/cloud_funcs/trigger_nightly/index.js
+++ /dev/null
@@ -1,29 +0,0 @@
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const {google} = require('googleapis');
-
-module.exports.converter_python_nightly = async data => {
-  const cloudbuild = google.cloudbuild('v1');
-  const auth = await google.auth.getClient(
-      {scopes: ['https://www.googleapis.com/auth/cloud-platform']});
-  google.options({auth});
-  const resp = await cloudbuild.projects.triggers.run({
-    'projectId': 'learnjs-174218',
-    'triggerId': 'tfjs-converter-nightly-test',
-    'resource': {'branchName': 'master'}
-  });
-  console.log(resp);
-};
diff --git a/tfjs-master/tfjs-converter/scripts/cloud_funcs/trigger_nightly/package.json b/tfjs-master/tfjs-converter/scripts/cloud_funcs/trigger_nightly/package.json
deleted file mode 100644
index 38b313a61..000000000
--- a/tfjs-master/tfjs-converter/scripts/cloud_funcs/trigger_nightly/package.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-  "name": "cloudbuild",
-  "version": "1.0.0",
-  "description": "",
-  "main": "index.js",
-  "dependencies": {
-    "googleapis": "^39.2.0"
-  }
-}
diff --git a/tfjs-master/tfjs-converter/scripts/create_python_pips.sh b/tfjs-master/tfjs-converter/scripts/create_python_pips.sh
deleted file mode 100644
index 19a46b106..000000000
--- a/tfjs-master/tfjs-converter/scripts/create_python_pips.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Exit the script on any command with non 0 return code
-set -e
-
-yarn bazel build tfjs-converter/python:python3_wheel
diff --git a/tfjs-master/tfjs-converter/scripts/gen_doc.ts b/tfjs-master/tfjs-converter/scripts/gen_doc.ts
deleted file mode 100644
index 02fdd499a..000000000
--- a/tfjs-master/tfjs-converter/scripts/gen_doc.ts
+++ /dev/null
@@ -1,177 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as fs from 'fs';
-
-import * as arithmetic from '../src/operations/op_list/arithmetic';
-import * as basicMath from '../src/operations/op_list/basic_math';
-import * as control from '../src/operations/op_list/control';
-import * as convolution from '../src/operations/op_list/convolution';
-import * as creation from '../src/operations/op_list/creation';
-import * as dynamic from '../src/operations/op_list/dynamic';
-import * as evaluation from '../src/operations/op_list/evaluation';
-import * as graph from '../src/operations/op_list/graph';
-import * as hashtable from '../src/operations/op_list/hash_table';
-import * as image from '../src/operations/op_list/image';
-import * as logical from '../src/operations/op_list/logical';
-import * as matrices from '../src/operations/op_list/matrices';
-import * as normalization from '../src/operations/op_list/normalization';
-import * as reduction from '../src/operations/op_list/reduction';
-import * as sliceJoin from '../src/operations/op_list/slice_join';
-import * as sparse from '../src/operations/op_list/sparse';
-import * as spectral from '../src/operations/op_list/spectral';
-import * as string from '../src/operations/op_list/string';
-import * as transformation from '../src/operations/op_list/transformation';
-import {OpMapper} from '../src/operations/types';
-
-// tfjs-core api file is generated by tfjs-website project and should be
-// manually copied over before running this script.
-const JSON_DIR = './tfjs-core.json';
-const DOC_DIR = './docs/';
-const ops = [
-  arithmetic, basicMath, control, convolution, creation, dynamic, evaluation,
-  graph, hashtable, image, logical, matrices, normalization, reduction,
-  sliceJoin, sparse, spectral, string, transformation
-];
-
-async function genDoc() {
-  const json = JSON.parse(fs.readFileSync(JSON_DIR, 'utf8'));
-  // tslint:disable-next-line:no-any
-  const coreApis = json.docs.headings.reduce((list: Array<{}>, h: any) => {
-    return h.subheadings ? list.concat(h.subheadings.reduce(
-                               // tslint:disable-next-line:no-any
-                               (sublist: Array<{}>, sub: any) => {
-                                 return sublist.concat(sub.symbols);
-                               },
-                               [])) :
-                           list;
-  }, []);
-  const output: string[] = [];
-
-  output.push('# Supported Tensorflow Ops\n\n');
-
-  generateTable(
-      'Operations', 'Arithmetic', (arithmetic.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Basic math', (basicMath.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Control Flow', (control.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Convolution', (convolution.json as {}) as OpMapper[],
-      output, coreApis);
-  generateTable(
-      'Tensors', 'Creation', (creation.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Dynamic', (dynamic.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Evaluation', (evaluation.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Tensorflow', 'Graph', (graph.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Hashtable', (hashtable.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Images', (image.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Linear Algebra', [] as OpMapper[], output, coreApis);
-  generateTable(
-      'Operations', 'Logical', (logical.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Matrices', (matrices.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Moving Average', [] as OpMapper[], output, coreApis);
-  generateTable(
-      'Operations', 'Normalization', (normalization.json as {}) as OpMapper[],
-      output, coreApis);
-  generateTable(
-      'Operations', 'Reduction', (reduction.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable('Tensors', 'RNN', [] as OpMapper[], output, coreApis);
-  generateTable('Operations', 'Scan', [] as OpMapper[], output, coreApis);
-  generateTable('Operations', 'Segment', [] as OpMapper[], output, coreApis);
-  generateTable('Operations', 'Signal', [] as OpMapper[], output, coreApis);
-  generateTable(
-      'Tensors', 'Slicing and Joining', (sliceJoin.json as {}) as OpMapper[],
-      output, coreApis);
-  generateTable(
-      'Operations', 'Sparse', (sparse.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'Spectral', (spectral.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Operations', 'String', (string.json as {}) as OpMapper[], output,
-      coreApis);
-  generateTable(
-      'Tensors', 'Transformations', (transformation.json as {}) as OpMapper[],
-      output, coreApis);
-
-  console.log(process.cwd());
-  fs.writeFileSync(DOC_DIR + 'supported_ops.md', output.join(''));
-
-  console.log(
-      `Supported Ops written to ${DOC_DIR + 'supported_ops.md'}\n` +
-      `Found ${ops.reduce((sum, cat) => sum += cat.json.length, 0)} ops\n`);
-}
-
-function findCoreOps(heading: string, subHeading: string, coreApis: Array<{}>) {
-  return coreApis.filter(
-      // tslint:disable-next-line:no-any
-      (op: any) => op.docInfo.heading === heading &&
-          op.docInfo.subheading === subHeading);
-}
-
-function generateTable(
-    heading: string, subHeading: string, ops: OpMapper[], output: string[],
-    coreApis: Array<{}>) {
-  const coreOps = findCoreOps(heading, subHeading, coreApis);
-  output.push(`## ${heading} - ${subHeading}\n\n`);
-  output.push('|Tensorflow Op Name|Tensorflow.js Op Name|\n');
-  output.push('|---|---|\n');
-  ops.sort((a, b) => a.tfOpName.localeCompare(b.tfOpName)).forEach(element => {
-    const coreOp = coreOps.find(
-        (op: any) => op.symbolName.toLocaleLowerCase() ===
-            element.tfOpName.toLocaleLowerCase());
-
-    output.push(`|${element.tfOpName}|${
-        coreOp ? (coreOp as any).symbolName : element.tfOpName}|\n`);
-  });
-
-  coreOps
-      // tslint:disable-next-line:no-any
-      .sort((a: any, b: any) => a.symbolName.localeCompare(b.symbolName))
-      // tslint:disable-next-line:no-any
-      .forEach((element: any) => {
-        if (!ops.find(
-                op => op.tfOpName.toLocaleLowerCase() ===
-                    element.symbolName.toLocaleLowerCase())) {
-          output.push(`|Not mapped|${element.symbolName}|\n`);
-        }
-      });
-  output.push('\n');
-}
-
-genDoc();
diff --git a/tfjs-master/tfjs-converter/scripts/gen_op.bzl b/tfjs-master/tfjs-converter/scripts/gen_op.bzl
deleted file mode 100644
index 7e3370611..000000000
--- a/tfjs-master/tfjs-converter/scripts/gen_op.bzl
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//lib:paths.bzl", "paths")
-load("@build_bazel_rules_nodejs//:providers.bzl", "run_node")
-
-def _gen_op_impl(ctx):
-    files = [f for s in ctx.attr.srcs for f in s.files.to_list()]
-    outputs = []
-
-    for f in files:
-        ts_filename = f.basename[:-len(f.extension)] + "ts"
-        dest_path = paths.join(ctx.attr.dest_dir, ts_filename)
-        output_file = ctx.actions.declare_file(dest_path)
-        outputs.append(output_file)
-        run_node(
-            ctx,
-            executable = "gen_op_bin",
-            inputs = [f],
-            outputs = [output_file],
-            arguments = [
-                f.path,
-                output_file.path,
-            ],
-        )
-
-    return [DefaultInfo(files = depset(outputs))]
-
-gen_op = rule(
-    implementation = _gen_op_impl,
-    attrs = {
-        "dest_dir": attr.string(
-            mandatory = True,
-            doc = "Output directory for the generated .ts files relative to the BUILD file",
-        ),
-        "gen_op_bin": attr.label(
-            executable = True,
-            cfg = "exec",
-            default = Label("@//tfjs-converter/scripts:gen_op_bin"),
-            doc = "The script that generates ts ops from json",
-        ),
-        "srcs": attr.label_list(
-            allow_files = [".json"],
-            doc = "The json files to generate the ops from",
-        ),
-    },
-)
diff --git a/tfjs-master/tfjs-converter/scripts/gen_op.ts b/tfjs-master/tfjs-converter/scripts/gen_op.ts
deleted file mode 100644
index 39d66d8ef..000000000
--- a/tfjs-master/tfjs-converter/scripts/gen_op.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-import * as argparse from 'argparse';
-import * as fs from 'fs';
-
-const parser = new argparse.ArgumentParser();
-
-parser.addArgument(
-  'json', {help: 'Path to json input file'});
-
-parser.addArgument(
-  'out', {help: 'Path to write output'});
-
-const {json, out} = parser.parseArgs() as {
-  json: string,
-  out: string,
-};
-
-const jsonContents = fs.readFileSync(json, 'utf8').replace(/"/g, '\'');
-const tsContents = `
-/**
- * @license
- * Copyright ${new Date().getFullYear()} Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {OpMapper} from '../types';
-
-export const json: OpMapper[] = ${jsonContents};
-`;
-
-fs.writeFileSync(out, tsContents);
diff --git a/tfjs-master/tfjs-converter/scripts/kernels_to_ops.bzl b/tfjs-master/tfjs-converter/scripts/kernels_to_ops.bzl
deleted file mode 100644
index 6b119a2bd..000000000
--- a/tfjs-master/tfjs-converter/scripts/kernels_to_ops.bzl
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@build_bazel_rules_nodejs//:providers.bzl", "run_node")
-
-def _kernels_to_ops_impl(ctx):
-    output_file = ctx.outputs.out
-    run_node(
-        ctx,
-        executable = "kernels_to_ops_bin",
-        inputs = ctx.files.srcs,
-        outputs = [output_file],
-        arguments = [
-            "--out",
-            output_file.path,
-        ],
-    )
-
-    return [DefaultInfo(files = depset([output_file]))]
-
-kernels_to_ops = rule(
-    implementation = _kernels_to_ops_impl,
-    attrs = {
-        "kernels_to_ops_bin": attr.label(
-            executable = True,
-            cfg = "exec",
-            default = Label("@//tfjs-converter/scripts:kernels_to_ops_bin"),
-            doc = "The script that generates the kernel2op.json metadata file",
-        ),
-        "out": attr.output(
-            mandatory = True,
-            doc = "Output label for the generated .json file",
-        ),
-        "srcs": attr.label_list(
-            doc = "The files in the ts project",
-        ),
-    },
-)
diff --git a/tfjs-master/tfjs-converter/scripts/kernels_to_ops.ts b/tfjs-master/tfjs-converter/scripts/kernels_to_ops.ts
deleted file mode 100644
index 9ff1f6998..000000000
--- a/tfjs-master/tfjs-converter/scripts/kernels_to_ops.ts
+++ /dev/null
@@ -1,159 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This script generates a mapping of Kernel Names to op names as defined by
- * the converter source code. This allows a couple of things for modular builds
- *   1. From a model.json file we can create imports for the ops the converter
- *      will call.
- *   2. From those ops we could validate that the kernels we add to the modular
- *      build match the names of kernels in model.json (this is not necessary
- *      but is potentially useful for alignment).
- *
- * This can also be used to keep our supported ops list up to date.
- *
- * The approach used is to parse the source code of the converter executors
- * (src/operations/executors) for the following kind pattern.
- *   case 'BiasAdd':
- *   case 'AddV2':
- *   case 'Add': {
- *     return [tfc.add(
- *         (getParamValue('a', node, tensorMap, context) as tfc.Tensor),
- *         getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
- *   }
- *
- * Case matchers represent kernel names and tfc.*(...) represent the tfjs op(s)
- * that are called. This example shows that we need to support fallthrough case
- * statements as well.
- *
- */
-
-import * as argparse from 'argparse';
-import * as fs from 'fs';
-import {CaseClause, CaseOrDefaultClause, Project, SourceFile, SwitchStatement, SyntaxKind} from 'ts-morph';
-
-const parser = new argparse.ArgumentParser();
-
-parser.addArgument(
-    '--out', {help: 'Path to output JSON to create', required: true});
-
-const project = new Project({});
-
-function getSwitchStatement(source: SourceFile): SwitchStatement {
-  // Each executor only has one switch statment.
-  let switchStatement: SwitchStatement;
-  source.forEachDescendant((node) => {
-    if (node.getKindName() === 'SwitchStatement') {
-      switchStatement = node as SwitchStatement;
-    }
-  });
-  return switchStatement;
-}
-
-type KernelMapping = {
-  [key: string]: string[]
-};
-
-function getKernelMappingForFile(source: SourceFile) {
-  const switchStatement = getSwitchStatement(source);
-  if (switchStatement == null) {
-    throw new Error('No switch statment found in executor');
-  }
-  const caseClauses = switchStatement.getClauses();
-
-  const kernelsToOp: KernelMapping = {};
-  let currentClauseGroup: string[] = [];
-
-  // Loop through clauses until you reach one that has a block or return.
-  // This allows us to coalesce fallthrough case blocks in a switch statement.
-  caseClauses.forEach((caseClause: CaseOrDefaultClause) => {
-    if (caseClause instanceof CaseClause) {
-      let kernelName;
-      caseClause.forEachChild(clausePart => {
-        const kind = clausePart.getKindName();
-        if (kind === 'StringLiteral') {
-          kernelName = clausePart.getText().replace(/\'/g, '');
-          currentClauseGroup.push(kernelName);
-        }
-        if (kind === 'Block' || kind === 'ReturnStatement') {
-          // We have reached a code block, all the previously captured
-          // kernels use this block as their execution path.
-
-          // Parse the code block and determing all the tfc.*() function calls
-          // used.
-          const callExprs =
-              clausePart.getDescendantsOfKind(SyntaxKind.CallExpression);
-          const tfOpsCallExprs =
-              callExprs.filter(expr => expr.getText().match(/ops/));
-          const tfSymbols: Set<string> = new Set();
-          for (const tfOpsCall of tfOpsCallExprs) {
-            const tfOpsCallStr = tfOpsCall.getText();
-            const functionCallMatcher = /(ops\.([\w\.]*)\()/g;
-            const matches = tfOpsCallStr.match(functionCallMatcher);
-            if (matches != null && matches.length > 0) {
-              for (const match of matches) {
-                // extract the method name (and any namespaces used to call it)
-                const symbolMatcher = /(ops\.([\w\.]*)\()/;
-                const symbol = match.match(symbolMatcher)[2];
-                tfSymbols.add(symbol);
-              }
-            }
-          }
-          for (const kern of currentClauseGroup) {
-            kernelsToOp[kern] = Array.from(tfSymbols);
-          }
-          // Reset the clause tracker as we are moving to a new set of kernels
-          currentClauseGroup = [];
-        }
-      });
-    }
-  });
-
-  return kernelsToOp;
-}
-
-function getKernelMapping() {
-  const sourceFiles = project.getSourceFiles();
-  const kernelsToOp: KernelMapping = {};
-
-  for (const sourceFile of sourceFiles) {
-    const mapping = getKernelMappingForFile(sourceFile);
-    Object.assign(kernelsToOp, mapping);
-  }
-  return kernelsToOp;
-}
-
-async function run(outputFilePath: string) {
-  const EXECUTORS_PATH = 'tfjs-converter/src/operations/executors/*_executor.ts';
-  project.addSourceFilesAtPaths(EXECUTORS_PATH);
-
-  const kernelMapping = getKernelMapping();
-
-  const pairs: Array<[string, string[]]> = Object.entries(kernelMapping).sort();
-  const sortedKernelMapping: KernelMapping = {};
-  pairs.forEach(([k, v]) => {
-    sortedKernelMapping[k] = v;
-  });
-  const replacer: null = null;
-  const space = 2;
-  fs.writeFileSync(
-      outputFilePath, JSON.stringify(sortedKernelMapping, replacer, space),
-      {encoding: 'utf8'});
-}
-
-const args = parser.parseArgs();
-run(args.out);
diff --git a/tfjs-master/tfjs-converter/scripts/tag-version b/tfjs-master/tfjs-converter/scripts/tag-version
deleted file mode 100644
index 19f0194ff..000000000
--- a/tfjs-master/tfjs-converter/scripts/tag-version
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2018 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-
-// Run this script from the base directory (not the script directory):
-// ./scripts/tag-version
-
-var fs = require('fs');
-var exec = require('child_process').exec;
-
-var version = JSON.parse(fs.readFileSync('package.json', 'utf8')).version;
-var tag = `v${version}`;
-
-exec(`git tag ${tag}`, err => {
-  if (err) {
-    throw new Error(`Could not git tag with ${tag}: ${err.message}.`);
-  }
-  console.log(`Successfully tagged with ${tag}.`);
-});
-
-exec(`git push --tags`, err => {
-  if (err) {
-    throw new Error(`Could not push git tags: ${err.message}.`);
-  }
-  console.log(`Successfully pushed tags.`);
-});
diff --git a/tfjs-master/tfjs-converter/scripts/test_snippets.ts b/tfjs-master/tfjs-converter/scripts/test_snippets.ts
deleted file mode 100644
index 839b1c7a1..000000000
--- a/tfjs-master/tfjs-converter/scripts/test_snippets.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import '@tensorflow/tfjs-backend-cpu';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {parseAndEvaluateSnippets} from '@tensorflow/tfjs-core/dist/scripts/test_snippets/util';
-
-import * as tfconv from '../src/index';
-
-const tf = {
-  ...tfconv,
-  ...tfc
-};
-parseAndEvaluateSnippets(tf);
diff --git a/tfjs-master/tfjs-converter/scripts/tsconfig.json b/tfjs-master/tfjs-converter/scripts/tsconfig.json
deleted file mode 100644
index c5e8c9340..000000000
--- a/tfjs-master/tfjs-converter/scripts/tsconfig.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "extends": "../tsconfig.test",
-  "compilerOptions": {
-    "module": "commonjs"
-  }
-}
diff --git a/tfjs-master/tfjs-converter/src/BUILD.bazel b/tfjs-master/tfjs-converter/src/BUILD.bazel
deleted file mode 100644
index 941640fb7..000000000
--- a/tfjs-master/tfjs-converter/src/BUILD.bazel
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("//tfjs-converter/scripts:gen_op.bzl", "gen_op")
-load("//tools:defaults.bzl", "ts_library")
-
-package(default_visibility = ["//visibility:public"])
-
-TEST_SRCS = [
-    "**/*_test.ts",
-    "run_tests.ts",
-    "operations/executors/spy_ops.ts",
-]
-
-# Used for test-snippets
-filegroup(
-    name = "all_srcs",
-    srcs = glob(["**/*.ts"]),
-)
-
-gen_op(
-    name = "ops_ts_files",
-    srcs = [
-        "//tfjs-converter/python/tensorflowjs/op_list:ops",
-    ],
-    dest_dir = "operations/op_list/",
-)
-
-ts_library(
-    name = "tfjs-converter_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ) + [":ops_ts_files"],
-    module_name = "@tensorflow/tfjs-converter/dist",
-    deps = [
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "@npm//@types/seedrandom",
-        "@npm//seedrandom",
-    ],
-)
-
-ts_library(
-    name = "tfjs-converter_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-converter",
-    deps = [
-        ":tfjs-converter_src_lib",
-    ],
-)
-
-ts_library(
-    name = "tfjs-converter_test_lib",
-    testonly = True,
-    srcs = glob(TEST_SRCS),
-    deps = [
-        ":tfjs-converter_lib",
-        ":tfjs-converter_src_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "@npm//@types/jasmine",
-        "@npm//ajv",
-        "@npm//jasmine",
-    ],
-)
diff --git a/tfjs-master/tfjs-converter/src/data/api.proto b/tfjs-master/tfjs-converter/src/data/api.proto
deleted file mode 100644
index edf7f69fd..000000000
--- a/tfjs-master/tfjs-converter/src/data/api.proto
+++ /dev/null
@@ -1,347 +0,0 @@
-// api.proto
-// Definition of various TensorFlow protobuf messages for use with the TensorFlow API.
-//
-// Assembled from these relevant proto sources:
-// https://github.com/google/protobuf/blob/master/src/google/protobuf/any.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/attr_value.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/versions.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/node_def.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/saver.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/saved_model.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/function.proto
-
-syntax = "proto3";
-package tensorflow;
-
-message Any {
-  string type_url = 1;
-  bytes value = 2;
-}
-
-enum DataType {
-  // Not a legal value for DataType.  Used to indicate a DataType field
-  // has not been set.
-  DT_INVALID = 0;
-
-  // Data types that all computation devices are expected to be
-  // capable to support.
-  DT_FLOAT = 1;
-  DT_DOUBLE = 2;
-  DT_INT32 = 3;
-  DT_UINT8 = 4;
-  DT_INT16 = 5;
-  DT_INT8 = 6;
-  DT_STRING = 7;
-  DT_COMPLEX64 = 8;  // Single-precision complex
-  DT_INT64 = 9;
-  DT_BOOL = 10;
-  DT_QINT8 = 11;     // Quantized int8
-  DT_QUINT8 = 12;    // Quantized uint8
-  DT_QINT32 = 13;    // Quantized int32
-  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
-
-  // Do not use!  These are only for parameters.  Every enum above
-  // should have a corresponding value below (verified by types_test).
-  DT_FLOAT_REF = 101;
-  DT_DOUBLE_REF = 102;
-  DT_INT32_REF = 103;
-  DT_UINT8_REF = 104;
-  DT_INT16_REF = 105;
-  DT_INT8_REF = 106;
-  DT_STRING_REF = 107;
-  DT_COMPLEX64_REF = 108;
-  DT_INT64_REF = 109;
-  DT_BOOL_REF = 110;
-  DT_QINT8_REF = 111;
-  DT_QUINT8_REF = 112;
-  DT_QINT32_REF = 113;
-  DT_BFLOAT16_REF = 114;
-}
-
-message TensorShape {
-  // One dimension of the tensor.
-  message Dim {
-    // Size of the tensor in that dimension.
-    int64 size = 1;
-
-    // Optional name of the tensor dimension.
-    string name = 2;
-  }
-
-  // Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x
-  // 40 2D tensor.  The names are optional.
-  //
-  // The order of entries in "dim" matters: It indicates the layout of the
-  // values in the tensor in-memory representation.
-  //
-  // The first entry in "dim" is the outermost dimension used to layout the
-  // values, the last entry is the innermost dimension.  This matches the
-  // in-memory layout of RowMajor Eigen tensors.
-  repeated Dim dim = 2;
-
-  bool unknown_rank = 3;
-}
-
-message Tensor {
-  DataType dtype = 1;
-
-  // Shape of the tensor.  TODO(touts): sort out the 0-rank issues.
-  TensorShape tensor_shape = 2;
-
-  // Only one of the representations below is set, one of "tensor_contents" and
-  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
-  // contain repeated fields it would require another extra set of messages.
-
-  // Version number.
-  //
-  // In version 0, if the "repeated xxx" representations contain only one
-  // element, that element is repeated to fill the shape.  This makes it easy
-  // to represent a constant Tensor with a single value.
-  int32 version_number = 3;
-
-  // Serialized content from TensorBase::Serialize() This representation can be
-  // used for all tensor types.
-  bytes tensor_content = 4;
-
-  // Type specific representations that make it easy to create tensor protos in
-  // all languages.  Only the representation corresponding to "dtype" can
-  // be set.  The values hold the flattened representation of the tensor in
-  // row major order.
-
-  // DT_FLOAT.
-  repeated float float_val = 5 [packed = true];
-
-  // DT_DOUBLE.
-  repeated double double_val = 6 [packed = true];
-
-  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
-  repeated int32 int_val = 7 [packed = true];
-
-  // DT_STRING
-  repeated bytes string_val = 8;
-
-  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
-  // and imaginary parts of i-th single precision complex.
-  repeated float scomplex_val = 9 [packed = true];
-
-  // DT_INT64
-  repeated int64 int64_val = 10 [packed = true];
-
-  // DT_BOOL
-  repeated bool bool_val = 11 [packed = true];
-
-  // DT_UINT32
-  repeated uint32 uint32_val = 16 [packed = true];
-
-  // DT_UINT64
-  repeated uint64 uint64_val = 17 [packed = true];
-}
-
-message AttrValue {
-  message ListValue {
-    repeated bytes s = 2;
-    repeated int64 i = 3 [packed = true];
-    repeated float f = 4 [packed = true];
-    repeated bool b = 5 [packed = true];
-    repeated DataType type = 6 [packed = true];
-    repeated TensorShape shape = 7;
-    repeated Tensor tensor = 8;
-    repeated NameAttrList func = 9;
-  }
-
-  oneof value {
-    ListValue list = 1;
-    bytes s = 2;
-    int64 i = 3;
-    float f = 4;
-    bool b = 5;
-    DataType type = 6;
-    TensorShape shape = 7;
-    Tensor tensor = 8;
-    string placeholder = 9;
-    NameAttrList func = 10;
-  }
-}
-
-message NameAttrList {
-  string name = 1;
-  map<string, AttrValue> attr = 2;
-}
-
-message NodeDef {
-  string name = 1;
-  string op = 2;
-  repeated string input = 3;
-  string device = 4;
-  map<string, AttrValue> attr = 5;
-}
-
-message VersionDef {
-  int32 producer = 1;
-  int32 min_consumer = 2;
-  repeated int32 bad_consumers = 3;
-}
-
-message GraphDef {
-  repeated NodeDef node = 1;
-  VersionDef versions = 4;
-  FunctionDefLibrary library = 2;
-}
-
-message CollectionDef {
-  message NodeList {
-    repeated string value = 1;
-  }
-  message BytesList {
-    repeated bytes value = 1;
-  }
-  message Int64List {
-    repeated int64 value = 1 [packed = true];
-  }
-  message FloatList {
-    repeated float value = 1 [packed = true];
-  }
-  message AnyList {
-    repeated Any value = 1;
-  }
-
-  oneof kind {
-    NodeList node_list = 1;
-    BytesList bytes_list = 2;
-    Int64List int64_list = 3;
-    FloatList float_list = 4;
-    AnyList any_list = 5;
-  }
-}
-
-message SaverDef {
-  string filename_tensor_name = 1;
-  string save_tensor_name = 2;
-  string restore_op_name = 3;
-  int32 max_to_keep = 4;
-  bool sharded = 5;
-  float keep_checkpoint_every_n_hours = 6;
-
-  enum CheckpointFormatVersion {
-    LEGACY = 0;
-    V1 = 1;
-    V2 = 2;
-  }
-  CheckpointFormatVersion version = 7;
-}
-
-message TensorInfo {
-  message CooSparse {
-    string values_tensor_name = 1;
-    string indices_tensor_name = 2;
-    string dense_shape_tensor_name = 3;
-  }
-
-  oneof encoding {
-    string name = 1;
-    CooSparse coo_sparse = 4;
-  }
-  DataType dtype = 2;
-  TensorShape tensor_shape = 3;
-}
-
-message SignatureDef {
-  map<string, TensorInfo> inputs = 1;
-  map<string, TensorInfo> outputs = 2;
-  string method_name = 3;
-}
-
-message AssetFileDef {
-  TensorInfo tensor_info = 1;
-  string filename = 2;
-}
-
-message OpDef {
-  string name = 1;
-
-  message ArgDef {
-    string name = 1;
-    string description = 2;
-    DataType type = 3;
-    string type_attr = 4;    // if specified, attr must have type "type"
-    string number_attr = 5;  // if specified, attr must have type "int"
-    string type_list_attr = 6;
-    bool is_ref = 16;
-  }
-  repeated ArgDef input_arg = 2;
-  repeated ArgDef output_arg = 3;
-
-  message AttrDef {
-    string name = 1;
-    string type = 2;
-    AttrValue default_value = 3;
-    string description = 4;
-    bool has_minimum = 5;
-    int64 minimum = 6;
-    AttrValue allowed_values = 7;
-  }
-  repeated AttrDef attr = 4;
-
-  message OpDeprecation {
-    int32 version = 1;
-    string explanation = 2;
-  }
-  OpDeprecation deprecation = 8;
-
-  string summary = 5;
-  string description = 6;
-  bool is_commutative = 18;
-  bool is_aggregate = 16;  // for things like add
-  bool is_stateful = 17;  // for things like variables, queue
-  bool allows_uninitialized_input = 19;  // for Assign, etc.
-}
-
-message OpList {
-  repeated OpDef op = 1;
-}
-
-message MetaGraphDef {
-  message MetaInfoDef {
-    string meta_graph_version = 1;
-    OpList stripped_op_list = 2;
-    Any any_info = 3;
-    repeated string tags = 4;
-    string tensorflow_version = 5;
-    string tensorflow_git_version = 6;
-  }
-  MetaInfoDef meta_info_def = 1;
-  GraphDef graph_def = 2;
-  SaverDef saver_def = 3;
-  map<string, CollectionDef> collection_def = 4;
-  map<string, SignatureDef> signature_def = 5;
-  repeated AssetFileDef asset_file_def = 6;
-}
-
-message SavedModel {
-  int64 saved_model_schema_version = 1;
-  repeated MetaGraphDef meta_graphs = 2;
-}
-
-message FunctionDefLibrary {
-  repeated FunctionDef function = 1;
-  repeated GradientDef gradient = 2;
-}
-
-message FunctionDef {
-  OpDef signature = 1;
-  map<string, AttrValue> attr = 5;
-  reserved 2;
-  repeated NodeDef node_def = 3;
-  map<string, string> ret = 4;
-}
-
-message GradientDef {
-  string function_name = 1;  // The function name.
-  string gradient_func = 2;  // The gradient function's name.
-}
diff --git a/tfjs-master/tfjs-converter/src/data/compiled_api.ts b/tfjs-master/tfjs-converter/src/data/compiled_api.ts
deleted file mode 100644
index d5d0b4f17..000000000
--- a/tfjs-master/tfjs-converter/src/data/compiled_api.ts
+++ /dev/null
@@ -1,581 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-/* tslint:disable */
-
-/** Properties of an Any. */
-export declare interface IAny {
-  /** Any typeUrl */
-  typeUrl?: (string|null);
-
-  /** Any value */
-  value?: (Uint8Array|null);
-}
-
-/** DataType enum. */
-export enum DataType {
-  // Not a legal value for DataType.  Used to indicate a DataType field
-  // has not been set.
-  DT_INVALID = 0,
-
-  // Data types that all computation devices are expected to be
-  // capable to support.
-  DT_FLOAT = 1,
-  DT_DOUBLE = 2,
-  DT_INT32 = 3,
-  DT_UINT8 = 4,
-  DT_INT16 = 5,
-  DT_INT8 = 6,
-  DT_STRING = 7,
-  DT_COMPLEX64 = 8,  // Single-precision complex
-  DT_INT64 = 9,
-  DT_BOOL = 10,
-  DT_QINT8 = 11,     // Quantized int8
-  DT_QUINT8 = 12,    // Quantized uint8
-  DT_QINT32 = 13,    // Quantized int32
-  DT_BFLOAT16 = 14,  // Float32 truncated to 16 bits.  Only for cast ops.
-  DT_QINT16 = 15,    // Quantized int16
-  DT_QUINT16 = 16,   // Quantized uint16
-  DT_UINT16 = 17,
-  DT_COMPLEX128 = 18,  // Double-precision complex
-  DT_HALF = 19,
-  DT_RESOURCE = 20,
-  DT_VARIANT = 21,  // Arbitrary C++ data types
-  DT_UINT32 = 22,
-  DT_UINT64 = 23,
-
-  // Do not use!  These are only for parameters.  Every enum above
-  // should have a corresponding value below (verified by types_test).
-  DT_FLOAT_REF = 101,
-  DT_DOUBLE_REF = 102,
-  DT_INT32_REF = 103,
-  DT_UINT8_REF = 104,
-  DT_INT16_REF = 105,
-  DT_INT8_REF = 106,
-  DT_STRING_REF = 107,
-  DT_COMPLEX64_REF = 108,
-  DT_INT64_REF = 109,
-  DT_BOOL_REF = 110,
-  DT_QINT8_REF = 111,
-  DT_QUINT8_REF = 112,
-  DT_QINT32_REF = 113,
-  DT_BFLOAT16_REF = 114,
-  DT_QINT16_REF = 115,
-  DT_QUINT16_REF = 116,
-  DT_UINT16_REF = 117,
-  DT_COMPLEX128_REF = 118,
-  DT_HALF_REF = 119,
-  DT_RESOURCE_REF = 120,
-  DT_VARIANT_REF = 121,
-  DT_UINT32_REF = 122,
-  DT_UINT64_REF = 123,
-}
-
-/** Properties of a TensorShape. */
-export declare interface ITensorShape {
-  /** TensorShape dim */
-  dim?: (TensorShape.IDim[]|null);
-
-  /** TensorShape unknownRank */
-  unknownRank?: (boolean|null);
-}
-
-export namespace TensorShape {
-  /** Properties of a Dim. */
-  export declare interface IDim {
-    /** Dim size */
-    size?: (number|string|null);
-
-    /** Dim name */
-    name?: (string|null);
-  }
-}
-
-/** Properties of a Tensor. */
-export declare interface ITensor {
-  /** Tensor dtype */
-  dtype?: (DataType|null);
-
-  /** Tensor tensorShape */
-  tensorShape?: (ITensorShape|null);
-
-  /** Tensor versionNumber */
-  versionNumber?: (number|null);
-
-  /** Tensor tensorContent */
-  tensorContent?: (Uint8Array|null);
-
-  /** Tensor floatVal */
-  floatVal?: (number[]|null);
-
-  /** Tensor doubleVal */
-  doubleVal?: (number[]|null);
-
-  /** Tensor intVal */
-  intVal?: (number[]|null);
-
-  /** Tensor stringVal */
-  stringVal?: (Uint8Array[]|null);
-
-  /** Tensor scomplexVal */
-  scomplexVal?: (number[]|null);
-
-  /** Tensor int64Val */
-  int64Val?: ((number | string)[]|null);
-
-  /** Tensor boolVal */
-  boolVal?: (boolean[]|null);
-
-  /** Tensor uint32Val */
-  uint32Val?: (number[]|null);
-
-  /** Tensor uint64Val */
-  uint64Val?: ((number | string)[]|null);
-}
-
-/** Properties of an AttrValue. */
-export declare interface IAttrValue {
-  /** AttrValue list */
-  list?: (AttrValue.IListValue|null);
-
-  /** AttrValue s */
-  s?: (string|null);
-
-  /** AttrValue i */
-  i?: (number|string|null);
-
-  /** AttrValue f */
-  f?: (number|null);
-
-  /** AttrValue b */
-  b?: (boolean|null);
-
-  /** AttrValue type */
-  type?: (DataType|null);
-
-  /** AttrValue shape */
-  shape?: (ITensorShape|null);
-
-  /** AttrValue tensor */
-  tensor?: (ITensor|null);
-
-  /** AttrValue placeholder */
-  placeholder?: (string|null);
-
-  /** AttrValue func */
-  func?: (INameAttrList|null);
-}
-
-export namespace AttrValue {
-  /** Properties of a ListValue. */
-  export declare interface IListValue {
-    /** ListValue s */
-    s?: (string[]|null);
-
-    /** ListValue i */
-    i?: ((number | string)[]|null);
-
-    /** ListValue f */
-    f?: (number[]|null);
-
-    /** ListValue b */
-    b?: (boolean[]|null);
-
-    /** ListValue type */
-    type?: (DataType[]|null);
-
-    /** ListValue shape */
-    shape?: (ITensorShape[]|null);
-
-    /** ListValue tensor */
-    tensor?: (ITensor[]|null);
-
-    /** ListValue func */
-    func?: (INameAttrList[]|null);
-  }
-}
-
-/** Properties of a NameAttrList. */
-export declare interface INameAttrList {
-  /** NameAttrList name */
-  name?: (string|null);
-
-  /** NameAttrList attr */
-  attr?: ({[k: string]: IAttrValue}|null);
-}
-
-/** Properties of a NodeDef. */
-export declare interface INodeDef {
-  /** NodeDef name */
-  name?: (string|null);
-
-  /** NodeDef op */
-  op?: (string|null);
-
-  /** NodeDef input */
-  input?: (string[]|null);
-
-  /** NodeDef device */
-  device?: (string|null);
-
-  /** NodeDef attr */
-  attr?: ({[k: string]: IAttrValue}|null);
-}
-
-/** Properties of a VersionDef. */
-export declare interface IVersionDef {
-  /** VersionDef producer */
-  producer?: (number|null);
-
-  /** VersionDef minConsumer */
-  minConsumer?: (number|null);
-
-  /** VersionDef badConsumers */
-  badConsumers?: (number[]|null);
-}
-
-/** Properties of a GraphDef. */
-export declare interface IGraphDef {
-  /** GraphDef node */
-  node?: (INodeDef[]|null);
-
-  /** GraphDef versions */
-  versions?: (IVersionDef|null);
-
-  /** GraphDef library */
-  library?: (IFunctionDefLibrary|null);
-}
-
-/** Properties of a CollectionDef. */
-export declare interface ICollectionDef {
-  /** CollectionDef nodeList */
-  nodeList?: (CollectionDef.INodeList|null);
-
-  /** CollectionDef bytesList */
-  bytesList?: (CollectionDef.IBytesList|null);
-
-  /** CollectionDef int64List */
-  int64List?: (CollectionDef.IInt64List|null);
-
-  /** CollectionDef floatList */
-  floatList?: (CollectionDef.IFloatList|null);
-
-  /** CollectionDef anyList */
-  anyList?: (CollectionDef.IAnyList|null);
-}
-
-export namespace CollectionDef {
-  /** Properties of a NodeList. */
-  export declare interface INodeList {
-    /** NodeList value */
-    value?: (string[]|null);
-  }
-
-  /** Properties of a BytesList. */
-  export declare interface IBytesList {
-    /** BytesList value */
-    value?: (Uint8Array[]|null);
-  }
-
-  /** Properties of an Int64List. */
-  export declare interface IInt64List {
-    /** Int64List value */
-    value?: ((number | string)[]|null);
-  }
-
-  /** Properties of a FloatList. */
-  export declare interface IFloatList {
-    /** FloatList value */
-    value?: (number[]|null);
-  }
-
-  /** Properties of an AnyList. */
-  export declare interface IAnyList {
-    /** AnyList value */
-    value?: (IAny[]|null);
-  }
-}
-
-/** Properties of a SaverDef. */
-export declare interface ISaverDef {
-  /** SaverDef filenameTensorName */
-  filenameTensorName?: (string|null);
-
-  /** SaverDef saveTensorName */
-  saveTensorName?: (string|null);
-
-  /** SaverDef restoreOpName */
-  restoreOpName?: (string|null);
-
-  /** SaverDef maxToKeep */
-  maxToKeep?: (number|null);
-
-  /** SaverDef sharded */
-  sharded?: (boolean|null);
-
-  /** SaverDef keepCheckpointEveryNHours */
-  keepCheckpointEveryNHours?: (number|null);
-
-  /** SaverDef version */
-  version?: (SaverDef.CheckpointFormatVersion|null);
-}
-
-export namespace SaverDef {
-  /** CheckpointFormatVersion enum. */
-  export enum CheckpointFormatVersion {'LEGACY' = 0, 'V1' = 1, 'V2' = 2}
-}
-
-/** Properties of a TensorInfo. */
-export declare interface ITensorInfo {
-  /** TensorInfo name */
-  name?: (string|null);
-
-  /** TensorInfo cooSparse */
-  cooSparse?: (TensorInfo.ICooSparse|null);
-
-  /** TensorInfo dtype */
-  dtype?: (DataType|string|null);
-
-  /** TensorInfo tensorShape */
-  tensorShape?: (ITensorShape|null);
-
-  /** Resource id tensor was originally assigned to.  */
-  resourceId?: (number|null);
-}
-
-export namespace TensorInfo {
-  /** Properties of a CooSparse. */
-  export declare interface ICooSparse {
-    /** CooSparse valuesTensorName */
-    valuesTensorName?: (string|null);
-
-    /** CooSparse indicesTensorName */
-    indicesTensorName?: (string|null);
-
-    /** CooSparse denseShapeTensorName */
-    denseShapeTensorName?: (string|null);
-  }
-}
-
-/** Properties of a SignatureDef. */
-export declare interface ISignatureDef {
-  /** SignatureDef inputs */
-  inputs?: ({[k: string]: ITensorInfo}|null);
-
-  /** SignatureDef outputs */
-  outputs?: ({[k: string]: ITensorInfo}|null);
-
-  /** SignatureDef methodName */
-  methodName?: (string|null);
-}
-
-/** Properties of an AssetFileDef. */
-export declare interface IAssetFileDef {
-  /** AssetFileDef tensorInfo */
-  tensorInfo?: (ITensorInfo|null);
-
-  /** AssetFileDef filename */
-  filename?: (string|null);
-}
-
-/** Properties of an OpDef. */
-export declare interface IOpDef {
-  /** OpDef name */
-  name?: (string|null);
-
-  /** OpDef inputArg */
-  inputArg?: (OpDef.IArgDef[]|null);
-
-  /** OpDef outputArg */
-  outputArg?: (OpDef.IArgDef[]|null);
-
-  /** OpDef attr */
-  attr?: (OpDef.IAttrDef[]|null);
-
-  /** OpDef deprecation */
-  deprecation?: (OpDef.IOpDeprecation|null);
-
-  /** OpDef summary */
-  summary?: (string|null);
-
-  /** OpDef description */
-  description?: (string|null);
-
-  /** OpDef isCommutative */
-  isCommutative?: (boolean|null);
-
-  /** OpDef isAggregate */
-  isAggregate?: (boolean|null);
-
-  /** OpDef isStateful */
-  isStateful?: (boolean|null);
-
-  /** OpDef allowsUninitializedInput */
-  allowsUninitializedInput?: (boolean|null);
-}
-
-export namespace OpDef {
-  /** Properties of an ArgDef. */
-  export declare interface IArgDef {
-    /** ArgDef name */
-    name?: (string|null);
-
-    /** ArgDef description */
-    description?: (string|null);
-
-    /** ArgDef type */
-    type?: (DataType|null);
-
-    /** ArgDef typeAttr */
-    typeAttr?: (string|null);
-
-    /** ArgDef numberAttr */
-    numberAttr?: (string|null);
-
-    /** ArgDef typeListAttr */
-    typeListAttr?: (string|null);
-
-    /** ArgDef isRef */
-    isRef?: (boolean|null);
-  }
-
-  /** Properties of an AttrDef. */
-  export declare interface IAttrDef {
-    /** AttrDef name */
-    name?: (string|null);
-
-    /** AttrDef type */
-    type?: (string|null);
-
-    /** AttrDef defaultValue */
-    defaultValue?: (IAttrValue|null);
-
-    /** AttrDef description */
-    description?: (string|null);
-
-    /** AttrDef hasMinimum */
-    hasMinimum?: (boolean|null);
-
-    /** AttrDef minimum */
-    minimum?: (number|string|null);
-
-    /** AttrDef allowedValues */
-    allowedValues?: (IAttrValue|null);
-  }
-
-  /** Properties of an OpDeprecation. */
-  export declare interface IOpDeprecation {
-    /** OpDeprecation version */
-    version?: (number|null);
-
-    /** OpDeprecation explanation */
-    explanation?: (string|null);
-  }
-}
-
-/** Properties of an OpList. */
-export declare interface IOpList {
-  /** OpList op */
-  op?: (IOpDef[]|null);
-}
-
-/** Properties of a MetaGraphDef. */
-export declare interface IMetaGraphDef {
-  /** MetaGraphDef metaInfoDef */
-  metaInfoDef?: (MetaGraphDef.IMetaInfoDef|null);
-
-  /** MetaGraphDef graphDef */
-  graphDef?: (IGraphDef|null);
-
-  /** MetaGraphDef saverDef */
-  saverDef?: (ISaverDef|null);
-
-  /** MetaGraphDef collectionDef */
-  collectionDef?: ({[k: string]: ICollectionDef}|null);
-
-  /** MetaGraphDef signatureDef */
-  signatureDef?: ({[k: string]: ISignatureDef}|null);
-
-  /** MetaGraphDef assetFileDef */
-  assetFileDef?: (IAssetFileDef[]|null);
-}
-
-export namespace MetaGraphDef {
-  /** Properties of a MetaInfoDef. */
-  export declare interface IMetaInfoDef {
-    /** MetaInfoDef metaGraphVersion */
-    metaGraphVersion?: (string|null);
-
-    /** MetaInfoDef strippedOpList */
-    strippedOpList?: (IOpList|null);
-
-    /** MetaInfoDef anyInfo */
-    anyInfo?: (IAny|null);
-
-    /** MetaInfoDef tags */
-    tags?: (string[]|null);
-
-    /** MetaInfoDef tensorflowVersion */
-    tensorflowVersion?: (string|null);
-
-    /** MetaInfoDef tensorflowGitVersion */
-    tensorflowGitVersion?: (string|null);
-  }
-}
-
-/** Properties of a SavedModel. */
-export declare interface ISavedModel {
-  /** SavedModel savedModelSchemaVersion */
-  savedModelSchemaVersion?: (number|string|null);
-
-  /** SavedModel metaGraphs */
-  metaGraphs?: (IMetaGraphDef[]|null);
-}
-
-/** Properties of a FunctionDefLibrary. */
-export declare interface IFunctionDefLibrary {
-  /** FunctionDefLibrary function */
-  'function'?: (IFunctionDef[]|null);
-
-  /** FunctionDefLibrary gradient */
-  gradient?: (IGradientDef[]|null);
-}
-
-/** Properties of a FunctionDef. */
-export declare interface IFunctionDef {
-  /** FunctionDef signature */
-  signature?: (IOpDef|null);
-
-  /** FunctionDef attr */
-  attr?: ({[k: string]: IAttrValue}|null);
-
-  /** FunctionDef nodeDef */
-  nodeDef?: (INodeDef[]|null);
-
-  /** FunctionDef ret */
-  ret?: ({[k: string]: string}|null);
-}
-
-/** Properties of a GradientDef. */
-export declare interface IGradientDef {
-  /** GradientDef functionName */
-  functionName?: (string|null);
-
-  /** GradientDef gradientFunc */
-  gradientFunc?: (string|null);
-}
diff --git a/tfjs-master/tfjs-converter/src/data/types.ts b/tfjs-master/tfjs-converter/src/data/types.ts
deleted file mode 100644
index 122cf16d4..000000000
--- a/tfjs-master/tfjs-converter/src/data/types.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {DataType, Tensor} from '@tensorflow/tfjs-core';
-
-import {HashTable} from '../executor/hash_table';
-import {TensorArray} from '../executor/tensor_array';
-import {TensorList} from '../executor/tensor_list';
-
-export type NamedTensorMap = {
-  [key: string]: Tensor
-};
-
-export type NamedTensorsMap = {
-  [key: string]: Tensor[]
-};
-
-export type TensorArrayMap = {
-  [key: number]: TensorArray
-};
-
-export type TensorListMap = {
-  [key: number]: TensorList
-};
-
-export type HashTableMap = {
-  [key: number]: HashTable
-};
-
-export interface TensorInfo {
-  name: string;
-  shape?: number[];
-  dtype: DataType;
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/execution_context.ts b/tfjs-master/tfjs-converter/src/executor/execution_context.ts
deleted file mode 100644
index 5ba437ecc..000000000
--- a/tfjs-master/tfjs-converter/src/executor/execution_context.ts
+++ /dev/null
@@ -1,188 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor} from '@tensorflow/tfjs-core';
-
-import {NamedTensorsMap, TensorArrayMap, TensorListMap} from '../data/types';
-
-import {TensorArray} from './tensor_array';
-import {TensorList} from './tensor_list';
-import {FunctionExecutor} from './types';
-
-export interface ExecutionContextInfo {
-  id: number;           // the unique id of the context info
-  frameName: string;    // The frame name of the loop, this comes from
-                        // the TensorFlow NodeDef.
-  iterationId: number;  // The iteration id of the loop
-}
-
-/**
- * ExecutionContext captures the runtime environment of the node. It keeps
- * track of the current frame and iteration for the control flow ops.
- *
- * For example, typical Dynamic RNN model may contain loops, for which
- * TensorFlow will generate graphs with Enter/Exit nodes to control the
- * current execution frame, and NextIteration Nodes for iteration id increment.
- * For model with branch logic, TensorFLow will generate Switch/Merge ops.
- */
-export class ExecutionContext {
-  private rootContext = {id: 0, frameName: '', iterationId: 0};
-  private contexts: ExecutionContextInfo[] = [this.rootContext];
-  private lastId = 0;
-  private _currentContextIds: string[];
-
-  constructor(
-      readonly weightMap: NamedTensorsMap = {},
-      readonly tensorArrayMap: TensorArrayMap = {},
-      readonly tensorListMap: TensorListMap = {},
-      readonly functionMap: {[key: string]: FunctionExecutor} = {},
-      readonly parseNodeNameCache?: Map<string, [string, number, string?]>) {
-    this.generateCurrentContextIds();
-  }
-
-  private newFrame(id: number, frameName: string) {
-    return {id, frameName, iterationId: 0};
-  }
-
-  /**
-   * Set the current context
-   * @param contexts: ExecutionContextInfo[] the current path of execution
-   * frames
-   */
-  set currentContext(contexts: ExecutionContextInfo[]) {
-    if (this.contexts !== contexts) {
-      this.contexts = contexts;
-      this.generateCurrentContextIds();
-    }
-  }
-
-  get currentContext(): ExecutionContextInfo[] {
-    return this.contexts;
-  }
-
-  /**
-   * Returns the current context in string format.
-   */
-  get currentContextId(): string {
-    return this._currentContextIds[0];
-  }
-
-  /**
-   * Returns the current context and all parent contexts in string format.
-   * This allow access to the nodes in the current and parent frames.
-   */
-  get currentContextIds(): string[] {
-    return this._currentContextIds;
-  }
-
-  private generateCurrentContextIds() {
-    const names = [];
-    for (let i = 0; i < this.contexts.length - 1; i++) {
-      const contexts = this.contexts.slice(0, this.contexts.length - i);
-      names.push(this.contextIdforContexts(contexts));
-    }
-    names.push('');
-    this._currentContextIds = names;
-  }
-
-  private contextIdforContexts(contexts: ExecutionContextInfo[]) {
-    return contexts ?
-        contexts
-            .map(
-                context => (context.id === 0 && context.iterationId === 0) ?
-                    '' :
-                    `${context.frameName}-${context.iterationId}`)
-            .join('/') :
-        '';
-  }
-
-  /**
-   * Enter a new frame, a new context is pushed on the current context list.
-   * @param frameId new frame id
-   */
-  enterFrame(frameId: string) {
-    if (this.contexts) {
-      this.lastId++;
-      this.contexts = this.contexts.slice();
-      this.contexts.push(this.newFrame(this.lastId, frameId));
-      this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));
-    }
-  }
-
-  /**
-   * Exit the current frame, the last context is removed from the current
-   * context list.
-   */
-  exitFrame() {
-    if (this.contexts && this.contexts.length > 1) {
-      this.contexts = this.contexts.slice();
-      this.contexts.splice(-1);
-      this.currentContextIds.shift();
-    } else {
-      throw new Error('Cannot exit frame, the context is empty');
-    }
-  }
-
-  /**
-   * Enter the next iteration of a loop, the iteration id of last context is
-   * increased.
-   */
-  nextIteration() {
-    if (this.contexts && this.contexts.length > 0) {
-      this.contexts = this.contexts.slice();
-      this.lastId++;
-      const context =
-          Object.assign({}, this.contexts[this.contexts.length - 1]);
-      context.iterationId += 1;
-      context.id = this.lastId;
-      this.contexts.splice(-1, 1, context);
-      this._currentContextIds.splice(
-          0, 1, this.contextIdforContexts(this.contexts));
-    } else {
-      throw new Error('Cannot increase frame iteration, the context is empty');
-    }
-  }
-
-  getWeight(name: string): Tensor[] {
-    return this.weightMap[name];
-  }
-
-  addTensorArray(tensorArray: TensorArray) {
-    this.tensorArrayMap[tensorArray.id] = tensorArray;
-  }
-
-  getTensorArray(id: number): TensorArray {
-    return this.tensorArrayMap[id];
-  }
-
-  addTensorList(tensorList: TensorList) {
-    this.tensorListMap[tensorList.id] = tensorList;
-  }
-
-  getTensorList(id: number): TensorList {
-    return this.tensorListMap[id];
-  }
-
-  dispose(keepIds: Set<number>) {
-    for (const key in this.tensorArrayMap) {
-      this.tensorArrayMap[key].clearAndClose(keepIds);
-    }
-
-    for (const key in this.tensorListMap) {
-      this.tensorListMap[key].clearAndClose(keepIds);
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/execution_context_test.ts b/tfjs-master/tfjs-converter/src/executor/execution_context_test.ts
deleted file mode 100644
index 40821fd09..000000000
--- a/tfjs-master/tfjs-converter/src/executor/execution_context_test.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ExecutionContext} from './execution_context';
-import {TensorArray} from './tensor_array';
-
-let context: ExecutionContext;
-let tensorArray: TensorArray;
-describe('ExecutionContext', () => {
-  beforeEach(() => {
-    context = new ExecutionContext({}, {}, {});
-  });
-
-  it('should initialize', () => {
-    expect(context.currentContext).toEqual([
-      {id: 0, frameName: '', iterationId: 0}
-    ]);
-    expect(context.currentContextId).toEqual('');
-  });
-
-  describe('tensor array', () => {
-    beforeEach(() => {
-      tensorArray = new TensorArray('', 'float32', 10, [1], true, true, true);
-    });
-
-    it('should be able to add tensor array', () => {
-      context.addTensorArray(tensorArray);
-      expect(context.getTensorArray(tensorArray.id)).toBe(tensorArray);
-    });
-
-    it('should be able to read tensor array', () => {
-      expect(context.getTensorArray(tensorArray.id)).toBeUndefined();
-    });
-  });
-
-  describe('enterFrame', () => {
-    it('should add new Frame', () => {
-      context.enterFrame('1');
-      expect(context.currentContextId).toEqual('/1-0');
-      expect(context.currentContext).toEqual([
-        {id: 0, frameName: '', iterationId: 0},
-        {id: 1, frameName: '1', iterationId: 0}
-      ]);
-    });
-  });
-
-  describe('exitFrame', () => {
-    it('should remove Frame', () => {
-      context.enterFrame('1');
-      context.exitFrame();
-
-      expect(context.currentContextId).toEqual('');
-      expect(context.currentContext).toEqual([
-        {id: 0, frameName: '', iterationId: 0}
-      ]);
-    });
-
-    it('should remember previous Frame', () => {
-      context.enterFrame('1');
-      context.nextIteration();
-      context.enterFrame('2');
-      context.exitFrame();
-
-      expect(context.currentContextId).toEqual('/1-1');
-      expect(context.currentContext).toEqual([
-        {id: 0, frameName: '', iterationId: 0},
-        {id: 2, frameName: '1', iterationId: 1}
-      ]);
-    });
-  });
-
-  describe('nextIteration', () => {
-    it('should increate iteration', () => {
-      context.enterFrame('1');
-      context.nextIteration();
-
-      expect(context.currentContextId).toEqual('/1-1');
-      expect(context.currentContext).toEqual([
-        {id: 0, frameName: '', iterationId: 0},
-        {id: 2, frameName: '1', iterationId: 1}
-      ]);
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/executor/graph_executor.ts b/tfjs-master/tfjs-converter/src/executor/graph_executor.ts
deleted file mode 100644
index 52f7d481e..000000000
--- a/tfjs-master/tfjs-converter/src/executor/graph_executor.ts
+++ /dev/null
@@ -1,773 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, env, keep, NamedTensorMap, Tensor, tidy, util} from '@tensorflow/tfjs-core';
-
-import {ISignatureDef} from '../data/compiled_api';
-import {NamedTensorsMap, TensorArrayMap, TensorInfo, TensorListMap} from '../data/types';
-import {getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContext, parseNodeName} from '../operations/executors/utils';
-import {executeOp} from '../operations/operation_executor';
-import {Graph, Node} from '../operations/types';
-
-import {ExecutionContext, ExecutionContextInfo} from './execution_context';
-import {getExecutionSubgraph, getNodeLiveUntilMap, getNodesInTopologicalOrder, isControlFlow} from './model_analysis';
-import {ResourceManager} from './resource_manager';
-import {FunctionExecutor} from './types';
-
-interface NodeWithContexts {
-  contexts: ExecutionContextInfo[];
-  node: Node;
-}
-
-export class GraphExecutor implements FunctionExecutor {
-  private compiledMap = new Map<string, ReturnType<typeof this.compile>>();
-  private parseNodeNameCache = new Map<string, [string, number, string?]>();
-  private _weightMap: NamedTensorsMap = {};
-  private _weightIds: number[];
-  private _signature: ISignatureDef;
-  private _inputs: Node[];
-  private _outputs: Node[];
-  private _initNodes: Node[];  // Internal init nodes to start initialization.
-  private SEPARATOR = ',';
-  private _functions: {[key: string]: Graph} = {};
-  private _functionExecutorMap: {[key: string]: FunctionExecutor} = {};
-  private _resourceManager: ResourceManager;
-  private clonedTensorsMap: NamedTensorsMap;
-  private keepIntermediateTensors = false;
-
-  get weightIds(): number[] {
-    return this.parent ? this.parent.weightIds : this._weightIds;
-  }
-
-  get functionExecutorMap(): {[key: string]: FunctionExecutor} {
-    return this.parent ? this.parent.functionExecutorMap :
-                         this._functionExecutorMap;
-  }
-
-  get weightMap(): NamedTensorsMap {
-    return this.parent ? this.parent.weightMap : this._weightMap;
-  }
-
-  set weightMap(weightMap: NamedTensorsMap) {
-    const weightIds = Object.keys(weightMap).map(
-        key => weightMap[key].map(tensor => tensor.id));
-    this._weightIds = [].concat(...weightIds);
-    this._weightMap = weightMap;
-  }
-
-  /**
-   * Set `ResourceManager` shared by executors of a model.
-   * @param resourceManager: `ResourceManager` of the `GraphModel`.
-   */
-  set resourceManager(resourceManager: ResourceManager) {
-    this._resourceManager = resourceManager;
-  }
-
-  get inputs(): TensorInfo[] {
-    return this._inputs.map(node => {
-      return {
-        name: node.name,
-        shape: node.attrParams['shape'] ?
-            node.attrParams['shape'].value as number[] :
-            undefined,
-        dtype: node.attrParams['dtype'] ?
-            node.attrParams['dtype'].value as DataType :
-            undefined
-      };
-    });
-  }
-
-  get outputs(): TensorInfo[] {
-    return this._outputs.map(node => {
-      return {
-        name: node.name,
-        shape: node.attrParams['shape'] ?
-            node.attrParams['shape'].value as number[] :
-            undefined,
-        dtype: node.attrParams['dtype'] ?
-            node.attrParams['dtype'].value as DataType :
-            undefined
-      };
-    });
-  }
-
-  get inputNodes(): string[] {
-    return this._inputs.map(node => node.signatureKey || node.name);
-  }
-
-  get outputNodes(): string[] {
-    return this._outputs.map((node) => {
-      const name = node.signatureKey || node.name;
-      return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;
-    });
-  }
-
-  get functions(): {[key: string]: ISignatureDef} {
-    return Object.keys(this._functions).reduce((map, key) => {
-      map[key] = this._functions[key].signature;
-      return map;
-    }, {} as {[key: string]: ISignatureDef});
-  }
-
-  /**
-   *
-   * @param graph Graph the model or function graph to be executed.
-   * @param parent When building function exector you need to set the parent
-   * executor. Since the weights and function executor maps are set at parant
-   * level, that function executor can access the function maps and weight maps
-   * through the parent.
-   */
-  constructor(private graph: Graph, private parent?: GraphExecutor) {
-    this._outputs = graph.outputs;
-    this._inputs = graph.inputs;
-    this._initNodes = graph.initNodes;
-    this._signature = graph.signature;
-    this._functions = graph.functions;
-    // create sub-graph executors
-    if (graph.functions != null) {
-      Object.keys(graph.functions).forEach(name => {
-        this._functionExecutorMap[name] =
-            new GraphExecutor(graph.functions[name], this);
-      });
-    }
-  }
-
-  private getCompilationKey(inputs: Node[], outputs: Node[]): string {
-    const sortedInputs = inputs.map(node => node.name).sort();
-    const sortedOutputs = outputs.map(node => node.name).sort();
-    return sortedInputs.join(this.SEPARATOR) + '--' +
-        sortedOutputs.join(this.SEPARATOR);
-  }
-
-  /**
-   * Compiles the inference graph and returns the minimal set of nodes that are
-   * required for execution, in the correct execution order.
-   * @returns {Object} compilation The compile result.
-   * @returns {Node[]} compilation.orderedNodes Nodes in the correct execution
-   *     order.
-   * @returns {Map<string, Node[]>} compilation.nodeLiveUntilMap A map from node
-   *     to disposable nodes after its execution. That is, for a node `x`,
-   *     `nodeLiveUntilMap[x]` indicates all nodes whose intermediate
-   *     tensors should be disposed after `x` is executed.
-   */
-  private compile(inputs: NamedTensorMap, outputs: Node[]):
-      {orderedNodes: Node[], nodeLiveUntilMap: Map<string, Node[]>} {
-    const executionInfo =
-        getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);
-    const {missingInputs, dynamicNode, syncInputs} = executionInfo;
-    if (dynamicNode != null) {
-      throw new Error(
-          `This execution contains the node '${dynamicNode.name}', which has ` +
-          `the dynamic op '${dynamicNode.op}'. Please use ` +
-          `model.executeAsync() instead. Alternatively, to avoid the ` +
-          `dynamic ops, specify the inputs [${syncInputs}]`);
-    }
-
-    if (missingInputs.length > 0) {
-      const outNames = outputs.map(n => n.name);
-      const inNames = Object.keys(inputs);
-      throw new Error(
-          `Cannot compute the outputs [${outNames}] from the provided inputs ` +
-          `[${inNames}]. Missing the following inputs: [${missingInputs}]`);
-    }
-
-    const orderedNodes = getNodesInTopologicalOrder(this.graph, executionInfo);
-    const nodeLiveUntilMap = getNodeLiveUntilMap(orderedNodes);
-    return {orderedNodes, nodeLiveUntilMap};
-  }
-
-  private cloneAndKeepTensor(tensor: Tensor) {
-    if (tensor == null) {
-      return null;
-    }
-    const clone = tensor.clone();
-    // Keep the clone because`model.execute()` may be called within
-    // a `tidy()`, but the user may inspect these tensors after the
-    // tidy.
-    keep(clone);
-    return clone;
-  }
-
-  private cloneTensorList(tensors: Tensor[]) {
-    if (!tensors) {
-      return null;
-    }
-    const clonedTensor = tensors.map(tensor => {
-      return this.cloneAndKeepTensor(tensor);
-    });
-    return clonedTensor;
-  }
-
-  private cloneTensorMap(tensorsMap: NamedTensorsMap): NamedTensorsMap {
-    return Object.fromEntries(
-        Object.entries(tensorsMap).map(([name, tensorsList]) => {
-          return [name, this.cloneTensorList(tensorsList)];
-        }));
-  }
-
-  /**
-   * Executes the inference for given input tensors.
-   * @param inputs Tensor map for the model inputs, keyed by the input node
-   * names.
-   * @param outputs Optional. output node name from the Tensorflow model, if
-   * no outputs are specified, the default outputs of the model would be used.
-   * You can inspect intermediate nodes of the model by adding them to the
-   * outputs array.
-   */
-  execute(inputs: NamedTensorMap, outputs?: string[]): Tensor[] {
-    // Dispose any tensors from a prior run to avoid leaking them.
-    this.disposeIntermediateTensors();
-    inputs = this.mapInputs(inputs);
-    const names = Object.keys(inputs).sort();
-    this.checkInputs(inputs);
-    this.checkInputShapeAndType(inputs);
-    outputs = this.mapOutputs(outputs);
-    this.checkOutputs(outputs);
-    const inputNodes =
-        names.map(name => this.graph.nodes[parseNodeName(name)[0]]);
-    const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);
-    const outputNodeNameSet = new Set(outputNodeNames);
-    let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);
-    // If no outputs are specified, then use the default outputs of the model.
-    if (outputNodes.length === 0) {
-      outputNodes = this._outputs;
-    }
-
-    const compilationKey = this.getCompilationKey(inputNodes, outputNodes);
-
-    // Do nothing if the compiled graph cache contains the input.
-    let compilation = this.compiledMap.get(compilationKey);
-    if (compilation == null) {
-      compilation = this.compile(inputs, outputNodes);
-      this.compiledMap.set(compilationKey, compilation);
-    }
-
-    // Keep tensors if KEEP_INTERMEDIATE_TENSORS is on.
-    try {
-      this.keepIntermediateTensors = env().getBool('KEEP_INTERMEDIATE_TENSORS');
-    } catch (e) {
-      this.keepIntermediateTensors = false;
-      console.warn(e.message);
-    }
-    const tensorArrayMap: TensorArrayMap = {};
-    const tensorListMap: TensorListMap = {};
-
-    return tidy(() => {
-      const context = new ExecutionContext(
-          this.weightMap, tensorArrayMap, tensorListMap,
-          this.functionExecutorMap, this.parseNodeNameCache);
-      const tensorsMap: NamedTensorsMap = {...this.weightMap};
-      if (this.keepIntermediateTensors) {
-        this.clonedTensorsMap = this.cloneTensorMap(this.weightMap);
-      }
-
-      Object.keys(inputs).forEach(name => {
-        const [nodeName, index] = parseNodeName(name, context);
-        const tensors: Tensor[] = [];
-        tensors[index] = inputs[name];
-        tensorsMap[nodeName] = tensors;
-        if (this.keepIntermediateTensors) {
-          this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors);
-        }
-      });
-
-      const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
-      const {orderedNodes, nodeLiveUntilMap} = compilation;
-      for (const node of orderedNodes) {
-        if (tensorsMap[node.name]) {
-          continue;
-        }
-        const tensors =
-            executeOp(node, tensorsMap, context, this._resourceManager) as
-            Tensor[];
-        if (util.isPromise(tensors)) {
-          throw new Error(
-              `The execution of the op '${node.op}' returned a promise. ` +
-              `Please use model.executeAsync() instead.`);
-        }
-        tensorsMap[node.name] = tensors;
-        if (this.keepIntermediateTensors) {
-          this.clonedTensorsMap[node.name] = this.cloneTensorList(tensors);
-        }
-        this.checkTensorForDisposalWithNodeLiveUntilInfo(
-            node, tensorsMap, context, tensorsToKeep, outputNodeNameSet,
-            nodeLiveUntilMap.get(node.name));
-      }
-
-      // dispose the context for the root executor
-      if (this.parent == null) {
-        context.dispose(tensorsToKeep);
-      }
-
-      return outputs.map(name => getTensor(name, tensorsMap, context));
-    });
-  }
-
-  private getFrozenTensorIds(tensorMap: NamedTensorsMap): Set<number> {
-    const ids = [].concat.apply(
-        [],
-        Object.keys(tensorMap)
-            .map(key => tensorMap[key])
-            .map(tensors => tensors.map(tensor => tensor.id)));
-    return new Set(ids);
-  }
-
-  private checkTensorForDisposal(
-      nodeName: string, node: Node, tensorMap: NamedTensorsMap,
-      context: ExecutionContext, tensorsToKeep: Set<number>,
-      outputNodeNameSet: Set<string>,
-      intermediateTensorConsumerCount: {[key: string]: number}) {
-    // Skip output nodes and any control flow nodes, since its dependency is
-    // tricky to track correctly.
-    if (isControlFlow(node) || outputNodeNameSet.has(nodeName)) {
-      return;
-    }
-
-    for (const tensor of tensorMap[nodeName]) {
-      if (tensor == null) {
-        continue;
-      }
-      intermediateTensorConsumerCount[tensor.id] =
-          (intermediateTensorConsumerCount[tensor.id] || 0) +
-          node.children.length;
-    }
-
-    for (const input of node.inputs) {
-      // Skip any control flow nodes, since its dependency is tricky to track
-      // correctly.
-      if (isControlFlow(input)) {
-        continue;
-      }
-
-      const tensors =
-          getTensorsForCurrentContext(input.name, tensorMap, context);
-      if (tensors == null) {
-        continue;
-      }
-
-      for (const tensor of tensors) {
-        if (!tensor || tensor.kept || tensorsToKeep.has(tensor.id)) {
-          continue;
-        }
-
-        // Only intermediate nodes' tensors have counts set, not marked as
-        // kept, and not in `tensorsToKeep`.
-        // Input and weight nodes' tensors should exist in `tensorsToKeep`.
-        // Output and control flow nodes' tensors should never have count set.
-        const count = intermediateTensorConsumerCount[tensor.id];
-        if (count === 1) {
-          tensor.dispose();
-          delete intermediateTensorConsumerCount[tensor.id];
-        } else if (count != null) {
-          intermediateTensorConsumerCount[tensor.id]--;
-        }
-      }
-    }
-  }
-
-  private checkTensorForDisposalWithNodeLiveUntilInfo(
-      node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-      tensorsToKeep: Set<number>, outputNodeNameSet: Set<string>,
-      liveUntilNodes?: Node[]) {
-    function isNonDisposableNode(node: Node) {
-      // Skip output nodes and any control flow nodes, since its dependency is
-      // tricky to track correctly.
-      return isControlFlow(node) || outputNodeNameSet.has(node.name);
-    }
-
-    if (isControlFlow(node) || liveUntilNodes == null) {
-      return;
-    }
-
-    for (const nodeToDispose of liveUntilNodes) {
-      if (isNonDisposableNode(nodeToDispose)) {
-        continue;
-      }
-      const tensors = getTensorsForCurrentContext(
-          nodeToDispose.name, tensorMap, context);
-      for (const tensor of tensors) {
-        if (!tensor || tensor.kept || tensorsToKeep.has(tensor.id)) {
-          continue;
-        }
-        tensor.dispose();
-      }
-    }
-  }
-
-  /**
-   * Executes the inference for given input tensors in Async fashion.
-   * @param inputs Tensor map for the model inputs, keyed by the input node
-   * names.
-   * @param outputs output node name from the Tensorflow model, if no outputs
-   * are specified, the default outputs of the model would be used. You can
-   * inspect intermediate nodes of the model by adding them to the outputs
-   * array.
-   */
-  async executeAsync(inputs: NamedTensorMap, outputs?: string[]):
-      Promise<Tensor[]> {
-    return this._executeAsync(inputs, outputs);
-  }
-
-  disposeIntermediateTensors() {
-    if (!this.clonedTensorsMap) {
-      return;
-    }
-    Object.values(this.clonedTensorsMap).forEach(tensorsList => {
-      for (const tensor of tensorsList) {
-        if (tensor && !tensor.isDisposed) {
-          tensor.dispose();
-        }
-      }
-    });
-
-    this.clonedTensorsMap = null;
-  }
-
-  getIntermediateTensors(): NamedTensorsMap {
-    return this.clonedTensorsMap;
-  }
-
-  /**
-   * Executes the inference for given input tensors in Async fashion.
-   * @param inputs Tensor map for the model inputs, keyed by the input node
-   * names.
-   * @param outputs Optional. output node name from the Tensorflow model,
-   * if no outputs are specified, the default outputs of the model would be
-   * used. You can inspect intermediate nodes of the model by adding them to
-   * the outputs array.
-   * @param isFunctionExecution Optional. Flag for executing a function.
-   * @param tensorArrayMap Optional, global TensorArray map by id. Used for
-   * function execution.
-   * @param tensorArrayMap Optinal global TensorList map by id. Used for
-   * function execution.
-   */
-  private async _executeAsync(
-      inputs: NamedTensorMap, outputs?: string[], isFunctionExecution = false,
-      tensorArrayMap: TensorArrayMap = {},
-      tensorListMap: TensorListMap = {}): Promise<Tensor[]> {
-    // Dispose any tensors from a prior run to avoid leaking them.
-    this.disposeIntermediateTensors();
-    if (!isFunctionExecution) {
-      inputs = this.mapInputs(inputs);
-      this.checkInputs(inputs);
-      this.checkInputShapeAndType(inputs);
-      outputs = this.mapOutputs(outputs);
-      this.checkOutputs(outputs);
-    }
-
-    // Keep tensors if KEEP_INTERMEDIATE_TENSORS is on.
-    try {
-      this.keepIntermediateTensors = env().getBool('KEEP_INTERMEDIATE_TENSORS');
-    } catch (e) {
-      this.keepIntermediateTensors = false;
-      console.warn(e.message);
-    }
-
-    const context = new ExecutionContext(
-        this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap,
-        this.parseNodeNameCache);
-
-    if (this.keepIntermediateTensors) {
-      this.clonedTensorsMap = this.cloneTensorMap(this.weightMap);
-    }
-
-    // Graph with control flow op requires runtime evaluation of the execution
-    // order, while without control flow the execution order is pre-determined
-    // in the compile method.
-    const tensorsMap = await this.executeWithControlFlow(
-        inputs, context, outputs, isFunctionExecution);
-    const results = outputs.map(name => getTensor(name, tensorsMap, context));
-
-    // dispose all the intermediate tensors
-    const outputIds = results.map(t => t.id);
-    const inputIds = Object.keys(inputs).map(name => inputs[name].id);
-    const keepIds =
-        new Set<number>([...outputIds, ...inputIds, ...this.weightIds]);
-
-    Object.values(tensorsMap).forEach(tensorsList => {
-      tensorsList.forEach(tensor => {
-        if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {
-          tensor.dispose();
-        }
-      });
-    });
-
-    // dispose the context for the root executor
-    if (this.parent == null) {
-      context.dispose(keepIds);
-    }
-
-    return results;
-  }
-
-  async executeFunctionAsync(
-      inputs: Tensor[], tensorArrayMap: TensorArrayMap,
-      tensorListMap: TensorListMap): Promise<Tensor[]> {
-    const mappedInputs = inputs.reduce((map, tensor, index) => {
-      map[this.inputs[index].name] = tensor;
-      return map;
-    }, {} as NamedTensorMap);
-
-    return this._executeAsync(
-        mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);
-  }
-
-  /**
-   * When there are control flow nodes in the graph, the graph execution use
-   * ExecutionContext to keep track of the frames and loop iterators.
-   * @param inputs placeholder tensors for the graph.
-   * @param context the execution context object for current execution.
-   * @param outputNames Optional. output node name from the Tensorflow model,
-   * if no outputs are specified, the default outputs of the model would be
-   * used. You can inspect intermediate nodes of the model by adding them to
-   * the outputs array.
-   * @param isFunctionExecution Flag for executing a function.
-   */
-  private async executeWithControlFlow(
-      inputs: NamedTensorMap, context: ExecutionContext, outputNames?: string[],
-      isFunctionExecution?: boolean): Promise<NamedTensorsMap> {
-    const names = Object.keys(inputs);
-    const inputNodes =
-        names.map(name => this.graph.nodes[parseNodeName(name)[0]]);
-    const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);
-    const outputNodeNameSet = new Set(outputNodeNames);
-    let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);
-
-    // If no outputs are specified, then use the default outputs of the model.
-    if (outputNodes.length === 0) {
-      outputNodes = this._outputs;
-    }
-
-    const {usedNodes, missingInputs, dynamicNode, syncInputs} =
-        getExecutionSubgraph(
-            inputs, outputNodes, this.weightMap, this._initNodes);
-
-    // First nodes to execute include inputNodes, weights, and initNodes.
-    const stack: NodeWithContexts[] = [
-      ...inputNodes, ...this.graph.weights, ...(this._initNodes || [])
-    ].map(node => {
-      return {node, contexts: context.currentContext};
-    });
-    const tensorsMap: NamedTensorsMap = {...this.weightMap};
-    Object.keys(inputs).forEach(name => {
-      const [nodeName, index] = parseNodeName(name);
-      const tensors: Tensor[] = [];
-      tensors[index] = inputs[name];
-      tensorsMap[nodeName] = tensors;
-    });
-    const intermediateTensorConsumerCount: {[key: number]: number} = {};
-    const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
-    const added: {[key: string]: boolean} = {};
-    while (stack.length > 0) {
-      const promises = this.processStack(
-          inputNodes, stack, context, tensorsMap, added, tensorsToKeep,
-          outputNodeNameSet, intermediateTensorConsumerCount, usedNodes);
-      await Promise.all(promises);
-    }
-    if (dynamicNode == null && !isFunctionExecution) {
-      console.warn(
-          `This model execution did not contain any nodes with control flow ` +
-          `or dynamic output shapes. You can use model.execute() instead.`);
-    }
-    const missingOutputs =
-        outputNodes
-            .filter(
-                node => !isControlFlow(node) &&
-                    !getTensor(node.name, tensorsMap, context))
-            .map(node => node.name);
-    if (missingOutputs.length > 0) {
-      let alternativeMsg = '';
-      if (dynamicNode != null) {
-        alternativeMsg =
-            `Alternatively, to avoid the dynamic ops, use model.execute() ` +
-            `and specify the inputs [${syncInputs}]`;
-      }
-      throw new Error(
-          `Cannot compute the outputs [${missingOutputs}] from the provided ` +
-          `inputs [${names}]. Consider providing the following inputs: ` +
-          `[${missingInputs}]. ${alternativeMsg}`);
-    }
-    return tensorsMap;
-  }
-
-  private processStack(
-      inputNodes: Node[], stack: NodeWithContexts[], context: ExecutionContext,
-      tensorMap: NamedTensorsMap, added: {[key: string]: boolean},
-      tensorsToKeep: Set<number>, outputNodeNameSet: Set<string>,
-      intermediateTensorConsumerCount: {[key: number]: number},
-      usedNodes: Set<string>) {
-    const promises: Array<Promise<Tensor[]>> = [];
-    while (stack.length > 0) {
-      const item = stack.pop();
-      context.currentContext = item.contexts;
-      let nodeName = '';
-      // The tensor of the Enter op with isConstant set should be set
-      // in the parent scope, so it will be available as constant for the
-      // whole loop.
-      if (item.node.op === 'Enter' &&
-          getParamValue('isConstant', item.node, tensorMap, context)) {
-        [nodeName] = getNodeNameAndIndex(item.node.name, context);
-      }
-
-      // only process nodes that are not in the tensorMap yet, this include
-      // inputNodes and internal initNodes.
-      if (tensorMap[item.node.name] == null) {
-        const tensors =
-            executeOp(item.node, tensorMap, context, this._resourceManager);
-        if (!nodeName) {
-          [nodeName] = getNodeNameAndIndex(item.node.name, context);
-        }
-        const currentContext = context.currentContext;
-        if (util.isPromise(tensors)) {
-          promises.push(tensors.then(t => {
-            tensorMap[nodeName] = t;
-            if (this.keepIntermediateTensors) {
-              this.clonedTensorsMap[nodeName] = this.cloneTensorList(t);
-            }
-            context.currentContext = currentContext;
-            this.checkTensorForDisposal(
-                nodeName, item.node, tensorMap, context, tensorsToKeep,
-                outputNodeNameSet, intermediateTensorConsumerCount);
-            this.processChildNodes(
-                item.node, stack, context, tensorMap, added, usedNodes);
-            return t;
-          }));
-        } else {
-          tensorMap[nodeName] = tensors;
-          if (this.keepIntermediateTensors) {
-            this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors);
-          }
-          this.checkTensorForDisposal(
-              nodeName, item.node, tensorMap, context, tensorsToKeep,
-              outputNodeNameSet, intermediateTensorConsumerCount);
-          this.processChildNodes(
-              item.node, stack, context, tensorMap, added, usedNodes);
-        }
-      } else {
-        this.processChildNodes(
-            item.node, stack, context, tensorMap, added, usedNodes);
-      }
-    }
-    return promises;
-  }
-
-  private processChildNodes(
-      node: Node, stack: NodeWithContexts[], context: ExecutionContext,
-      tensorMap: NamedTensorsMap, added: {[key: string]: boolean},
-      usedNodes: Set<string>) {
-    node.children.forEach((childNode) => {
-      const [nodeName, ] = getNodeNameAndIndex(childNode.name, context);
-      if (added[nodeName] || !usedNodes.has(childNode.name)) {
-        return;
-      }
-      // Merge op can be pushed if any of its inputs has value.
-      if (childNode.op === 'Merge') {
-        if (childNode.inputNames.some(name => {
-              return !!getTensor(name, tensorMap, context);
-            })) {
-          added[nodeName] = true;
-          stack.push({contexts: context.currentContext, node: childNode});
-        }
-      } else  // Otherwise all inputs must to have value.
-          if (childNode.inputNames.every(name => {
-                return !!getTensor(name, tensorMap, context);
-              })) {
-        added[nodeName] = true;
-        stack.push({contexts: context.currentContext, node: childNode});
-      }
-    });
-  }
-
-  /**
-   * Releases the memory used by the weight tensors.
-   */
-  dispose() {
-    Object.keys(this.weightMap)
-        .forEach(
-            key => this.weightMap[key].forEach(tensor => tensor.dispose()));
-  }
-
-  private checkInputShapeAndType(inputs: NamedTensorMap) {
-    Object.keys(inputs).forEach(name => {
-      const input = inputs[name];
-      const [nodeName, ] = parseNodeName(name);
-      const node = this.graph.nodes[nodeName];
-      if (node.attrParams['shape'] && node.attrParams['shape'].value) {
-        const shape = node.attrParams['shape'].value as number[];
-        const match = shape.length === input.shape.length &&
-            input.shape.every(
-                (dim, index) => shape[index] === -1 || shape[index] === dim);
-        util.assert(
-            match,
-            () => `The shape of dict['${node.name}'] provided in ` +
-                `model.execute(dict) must be [${shape}], but was ` +
-                `[${input.shape}]`);
-      }
-      if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {
-        util.assert(
-            input.dtype === node.attrParams['dtype'].value as string,
-            () => `The dtype of dict['${node.name}'] provided in ` +
-                `model.execute(dict) must be ` +
-                `${node.attrParams['dtype'].value}, but was ${input.dtype}`);
-      }
-    });
-  }
-
-  private mapInputs(inputs: NamedTensorMap) {
-    const result: NamedTensorMap = {};
-    for (const inputName in inputs) {
-      const tensor = this._signature ?.inputs ?.[inputName];
-      if (tensor != null) {
-        result[tensor.name] = inputs[inputName];
-      } else {
-        result[inputName] = inputs[inputName];
-      }
-    }
-    return result;
-  }
-
-  private checkInputs(inputs: NamedTensorMap) {
-    const notInGraph = Object.keys(inputs).filter(name => {
-      const [nodeName] = parseNodeName(name);
-      return this.graph.nodes[nodeName] == null;
-    });
-    if (notInGraph.length > 0) {
-      throw new Error(
-          `The dict provided in model.execute(dict) has ` +
-          `keys: [${notInGraph}] that are not part of graph`);
-    }
-  }
-
-  private mapOutputs(outputs: string[]) {
-    return outputs.map(name => {
-      const tensor = this._signature ?.outputs ?.[name];
-      if (tensor != null) {
-        return tensor.name;
-      }
-      return name;
-    }, {});
-  }
-
-  private checkOutputs(outputs: string[]): void {
-    outputs.forEach(name => {
-      const [normalizedName] = parseNodeName(name);
-      if (!this.graph.nodes[normalizedName]) {
-        throw new Error(`The output '${name}' is not found in the graph`);
-      }
-    });
-  }
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/graph_executor_test.ts b/tfjs-master/tfjs-converter/src/executor/graph_executor_test.ts
deleted file mode 100644
index 2623a8a82..000000000
--- a/tfjs-master/tfjs-converter/src/executor/graph_executor_test.ts
+++ /dev/null
@@ -1,718 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tfc from '@tensorflow/tfjs-core';
-
-import * as tensorflow from '../data/compiled_api';
-import {createNumberAttr} from '../operations/executors/test_helper';
-import {createTensorAttr} from '../operations/executors/test_helper';
-import {Graph, Node} from '../operations/types';
-
-import {GraphExecutor} from './graph_executor';
-
-let executor: GraphExecutor;
-let inputNode: Node;
-let constNode: Node;
-let intermediateNode: Node;
-let intermediateNode2: Node;
-let rsqrtNode: Node;
-let outputNode: Node;
-let graph: Graph;
-let graphWithControlFlow: Graph;
-let constTensor: tfc.Tensor;
-
-const SIGNATURE: tensorflow.ISignatureDef = {
-  inputs: {
-    x: {name: 'input', dtype: tensorflow.DataType.DT_INT32, tensorShape: {}}
-  },
-  outputs: {
-    add: {name: 'output', dtype: tensorflow.DataType.DT_FLOAT, tensorShape: {}}
-  }
-};
-
-describe('GraphExecutor', () => {
-  beforeEach(() => {
-    inputNode = {
-      inputNames: [],
-      inputs: [],
-      children: [],
-      signatureKey: 'x',
-      name: 'input',
-      op: 'Placeholder',
-      category: 'graph',
-      attrParams: {},
-      inputParams: {}
-    };
-    constNode = {
-      inputNames: [],
-      inputs: [],
-      children: [],
-      name: 'const',
-      op: 'Const',
-      category: 'graph',
-      attrParams: {},
-      inputParams: {}
-
-    };
-    intermediateNode = {
-      inputNames: ['input', 'const'],
-      inputs: [inputNode, constNode],
-      children: [],
-      name: 'intermediate',
-      op: 'Add',
-      category: 'arithmetic',
-      inputParams: {'a': createTensorAttr(0), 'b': createTensorAttr(1)},
-      attrParams: {}
-    };
-    intermediateNode2 = {
-      inputNames: ['intermediate', 'const'],
-      inputs: [intermediateNode, constNode],
-      children: [],
-      name: 'intermediate2',
-      op: 'Add',
-      category: 'arithmetic',
-      inputParams: {'a': createTensorAttr(0), 'b': createTensorAttr(1)},
-      attrParams: {}
-    };
-    outputNode = {
-      inputNames: ['intermediate2', 'const'],
-      inputs: [intermediateNode2, constNode],
-      children: [],
-      name: 'output',
-      signatureKey: 'add',
-      op: 'Add',
-      category: 'arithmetic',
-      inputParams: {'a': createTensorAttr(0), 'b': createTensorAttr(1)},
-      attrParams: {}
-    };
-    graph = {
-      inputs: [inputNode],
-      nodes: {
-        'input': inputNode,
-        'const': constNode,
-        'intermediate': intermediateNode,
-        'intermediate2': intermediateNode2,
-        'output': outputNode
-      },
-      outputs: [outputNode],
-      weights: [constNode],
-      placeholders: [inputNode],
-      functions: {
-        while_body: {
-          inputs: [inputNode],
-          nodes: {
-            'input': inputNode,
-            'const': constNode,
-            'intermediate': intermediateNode,
-            'intermediate2': intermediateNode2,
-            'output': outputNode
-          },
-          outputs: [outputNode],
-          weights: [constNode],
-          placeholders: [inputNode],
-          signature: SIGNATURE
-        }
-      },
-      signature: SIGNATURE
-    };
-    inputNode.children.push(intermediateNode);
-    constNode.children.push(intermediateNode, intermediateNode2, outputNode);
-    intermediateNode.children.push(intermediateNode2);
-    intermediateNode2.children.push(outputNode);
-    executor = new GraphExecutor(graph);
-    constTensor = tfc.scalar(2.0);
-    executor.weightMap = {'const': [constTensor]};
-  });
-  afterEach(() => {});
-
-  describe('execute graph', () => {
-    describe('initialization', () => {
-      it('should expose input names', () => {
-        expect(executor.inputNodes).toEqual(['x']);
-      });
-
-      it('should expose output names', () => {
-        expect(executor.outputNodes).toEqual(['add']);
-      });
-
-      it('should expose inputs', () => {
-        inputNode.attrParams['shape'] = {value: [1], type: 'shape'};
-        inputNode.attrParams['dtype'] = {value: 'float32', type: 'dtype'};
-        expect(executor.inputs).toEqual([
-          {name: 'input', shape: [1], dtype: 'float32'}
-        ]);
-      });
-
-      it('should expose outputs', () => {
-        outputNode.attrParams['shape'] = {value: [1, 1], type: 'shape'};
-        outputNode.attrParams['dtype'] = {value: 'int32', type: 'dtype'};
-        expect(executor.outputs).toEqual([
-          {name: 'output', shape: [1, 1], dtype: 'int32'}
-        ]);
-      });
-
-      it('should expose functions', () => {
-        expect(executor.functions).toEqual({while_body: SIGNATURE});
-      });
-    });
-
-    describe('graph level', () => {
-      describe('execute', () => {
-        it('should execute the op', async () => {
-          const inputTensor = tfc.scalar(1);
-          const result = executor.execute({input: inputTensor}, ['output']);
-          tfc.test_util.expectArraysClose(await result[0].data(), [7.0]);
-        });
-
-        it('should allow output intermediate nodes', async () => {
-          const inputTensor = tfc.scalar(1);
-          const result = executor.execute(
-              {input: inputTensor}, ['output', 'intermediate']);
-          tfc.test_util.expectArraysClose(await result[1].data(), [3.0]);
-          tfc.test_util.expectArraysClose(await result[0].data(), [7.0]);
-        });
-
-        it('should allow output multiple intermediate nodes', async () => {
-          const inputTensor = tfc.scalar(1);
-          const result = executor.execute(
-              {input: inputTensor},
-              ['output', 'intermediate', 'intermediate2']);
-          tfc.test_util.expectArraysClose(await result[1].data(), [3.0]);
-          tfc.test_util.expectArraysClose(await result[2].data(), [5.0]);
-          tfc.test_util.expectArraysClose(await result[0].data(), [7.0]);
-        });
-
-        it('should allow feed intermediate nodes', async () => {
-          const intermediateTensor = tfc.scalar(1);
-          const result =
-              executor.execute({intermediate: intermediateTensor}, ['output']);
-          tfc.test_util.expectArraysClose(await result[0].data(), [5.0]);
-        });
-
-        it('should skip noop', async () => {
-          const inputNode: Node = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'input',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-
-          const noopNode: Node = {
-            inputNames: ['input'],
-            inputs: [inputNode],
-            children: [],
-            name: 'noop',
-            op: 'NoOp',
-            category: 'graph',
-            inputParams: {},
-            attrParams: {}
-          };
-
-          const packNode: Node = {
-            // Even though `noop` is an input, it should be excluded during
-            // execution
-            inputNames: ['input', 'noop'],
-            inputs: [inputNode, noopNode],
-            children: [],
-            name: 'pack',
-            op: 'Pack',
-            category: 'slice_join',
-            inputParams: {
-              tensors: {
-                // this range matches all the tensors in the input
-                'type': 'tensors',
-                'inputIndexStart': 0,
-                'inputIndexEnd': 0,
-              }
-            },
-            attrParams: {axis: createNumberAttr(0)}
-          };
-          inputNode.children.push(noopNode, packNode);
-          noopNode.children.push(packNode);
-
-          const graph: Graph = {
-            inputs: [inputNode],
-            nodes: {
-              'input': inputNode,
-              'noop': noopNode,
-              'pack': packNode,
-            },
-            outputs: [packNode],
-            placeholders: [inputNode],
-            weights: []
-          };
-
-          const executor = new GraphExecutor(graph);
-          const inputTensor = tfc.tensor1d([123, 456]);
-          const result = executor.execute({input: inputTensor}, ['pack']);
-          tfc.test_util.expectArraysClose(await result[0].data(), [123, 456]);
-        });
-
-        describe('strict input check', () => {
-          it('should throw exception if missing inputs', () => {
-            expect(() => executor.execute({}, ['output']))
-                .toThrowError(
-                    'Cannot compute the outputs [output] from the provided ' +
-                    'inputs []. Missing the following inputs: [input]');
-          });
-
-          it('should throw exception if contains extra inputs', () => {
-            const inputTensor = tfc.scalar(1);
-            expect(
-                () => executor.execute(
-                    {test: inputTensor, input: inputTensor}, ['output']))
-                .toThrowError(
-                    'The dict provided in model.execute(dict) has keys: ' +
-                    '[test] that are not part of graph');
-          });
-
-          it('should throw exception if inputs shapes mismatch', () => {
-            inputNode.attrParams['shape'] = {value: [1, 1], type: 'shape'};
-            const inputTensor = tfc.tensor1d([1], 'float32');
-            expect(() => executor.execute({input: inputTensor}, ['output']))
-                .toThrow(new Error(
-                    'The shape of dict[\'input\'] provided' +
-                    ' in model.execute(dict) must be [1,1], but was [1]'));
-          });
-
-          it('should throw exception for dtype mismatch', () => {
-            inputNode.attrParams['dtype'] = {value: 'int32', type: 'dtype'};
-            const inputTensor = tfc.tensor1d([1], 'float32');
-            expect(() => executor.execute({input: inputTensor}, ['output']))
-                .toThrow(new Error(
-                    'The dtype of dict[\'input\'] provided' +
-                    ' in model.execute(dict) must be int32, but was float32'));
-          });
-        });
-
-        describe('outputs check', () => {
-          it('should reject missing outputs', () => {
-            const inputTensor = tfc.tensor1d([1], 'float32');
-            expect(() => executor.execute({input: inputTensor}, ['missing']))
-                .toThrowError(/The output 'missing' is not found in the graph/);
-          });
-
-          it('should reject missing outputs with child tensors', () => {
-            const inputTensor = tfc.tensor1d([1], 'float32');
-            expect(() => executor.execute({input: inputTensor}, ['missing:0']))
-                .toThrowError(
-                    /The output 'missing:0' is not found in the graph/);
-          });
-
-          it('should accept existing outputs', () => {
-            const inputTensor = tfc.tensor1d([1], 'float32');
-            const res = executor.execute({input: inputTensor}, ['output']);
-            expect(res).not.toBeNull();
-          });
-
-          it('should accept existing outputs with child tensors', () => {
-            const inputTensor = tfc.tensor1d([1], 'float32');
-            const res = executor.execute({input: inputTensor}, ['output:0']);
-            expect(res).not.toBeNull();
-          });
-        });
-
-        it('should not throw exception if inputs shapes is dynamic', () => {
-          inputNode.attrParams['shape'] = {value: [-1, 1, 1, 1], type: 'shape'};
-          const inputTensor = tfc.tensor4d([1, 1], [2, 1, 1, 1], 'float32');
-          const res = executor.execute({input: inputTensor}, ['output']);
-          expect(res).not.toBeNull();
-        });
-
-        it('should not have mem leak when add index', async () => {
-          const inputTensor = tfc.tensor4d([1, 1], [2, 1, 1, 1], 'float32');
-          const numTensors: number = tfc.memory().numTensors;
-
-          const res = executor.execute({input: inputTensor}, ['output:0']);
-          expect(res).not.toBeNull();
-          expect(tfc.memory().numTensors).toEqual(numTensors + 1);
-        });
-      });
-
-      describe('executeAsync', () => {
-        beforeEach(() => {
-          inputNode = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'input',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          constNode = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'const',
-            op: 'Const',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          intermediateNode = {
-            inputNames: ['input', 'const'],
-            inputs: [inputNode, constNode],
-            children: [],
-            name: 'intermediate',
-            op: 'Add',
-            category: 'arithmetic',
-            inputParams: {'a': createTensorAttr(0), 'b': createTensorAttr(1)},
-            attrParams: {}
-          };
-          rsqrtNode = {
-            inputNames: ['intermediate'],
-            inputs: [intermediateNode],
-            children: [],
-            name: 'rsqrt',
-            op: 'Rsqrt',
-            category: 'basic_math',
-            inputParams: {'x': createTensorAttr(0)},
-            attrParams: {}
-          };
-          outputNode = {
-            inputNames: ['const', 'rsqrt'],
-            inputs: [constNode, rsqrtNode],
-            children: [],
-            name: 'output',
-            op: 'Switch',
-            category: 'control',
-            inputParams:
-                {'pred': createTensorAttr(0), 'data': createTensorAttr(1)},
-            attrParams: {}
-          };
-          inputNode.children.push(intermediateNode);
-          constNode.children.push(intermediateNode, outputNode);
-          intermediateNode.children.push(rsqrtNode);
-          rsqrtNode.children.push(outputNode);
-          graphWithControlFlow = {
-            inputs: [constNode, inputNode],
-            nodes: {
-              'input': inputNode,
-              'const': constNode,
-              'intermediate': intermediateNode,
-              'rsqrt': rsqrtNode,
-              'output': outputNode
-            },
-            outputs: [outputNode],
-            weights: [constNode],
-            placeholders: [inputNode]
-          };
-
-          executor = new GraphExecutor(graphWithControlFlow);
-          executor.weightMap = {const : [constTensor]};
-        });
-
-        it('should execute control flow graph', async () => {
-          const inputTensor = tfc.scalar(1);
-
-          const result =
-              await executor.executeAsync({input: inputTensor}, ['output:1']);
-          tfc.test_util.expectArraysClose(await result[0].data(), [0.57735]);
-        });
-
-        it('should allow output intermediate nodes', async () => {
-          const inputTensor = tfc.scalar(1);
-          const result = await executor.executeAsync(
-              {input: inputTensor}, ['intermediate']);
-          tfc.test_util.expectArraysClose(await result[0].data(), [3.0]);
-        });
-
-        it('should be able to execute control flow graph ' +
-               'with intermediate node more than once',
-           async () => {
-             const inputTensor = tfc.scalar(1);
-
-             const result = await executor.executeAsync(
-                 {intermediate: inputTensor}, ['output:1']);
-             tfc.test_util.expectArraysClose(await result[0].data(), [1]);
-             const result2 = await executor.executeAsync(
-                 {intermediate: inputTensor}, ['output:1']);
-             tfc.test_util.expectArraysClose(await result2[0].data(), [1]);
-           });
-
-        it('should not have mem leak', async () => {
-          const inputTensor = tfc.scalar(1);
-          const numTensors: number = tfc.memory().numTensors;
-
-          await executor.executeAsync({input: inputTensor}, ['output:1']);
-          expect(tfc.memory().numTensors).toEqual(numTensors + 1);
-        });
-      });
-
-      describe('controlFlowV2_if', () => {
-        beforeEach(() => {
-          inputNode = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'input',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          const inputNode2: Node = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'x',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          const inputNode3: Node = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'y',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          outputNode = {
-            inputNames: ['input', 'x', 'y'],
-            inputs: [inputNode, inputNode2, inputNode3],
-            children: [],
-            name: 'output',
-            op: 'StatelessIf',
-            category: 'control',
-            attrParams: {
-              'thenBranch': {'value': 'trueFunc', 'type': 'func'},
-              'elseBranch': {'value': 'falseFunc', 'type': 'func'}
-            },
-            inputParams: {
-              'cond': {'type': 'tensor', 'inputIndexStart': 0},
-              'args':
-                  {'type': 'tensors', 'inputIndexStart': 1, 'inputIndexEnd': 0}
-            }
-          };
-          inputNode.children.push(outputNode);
-          inputNode2.children.push(outputNode);
-          inputNode3.children.push(outputNode);
-          const xNode: Node = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'x',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          const yNode: Node = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'y',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          const trueFuncGraph: Graph = {
-            inputs: [xNode, yNode],
-            nodes: {'x': xNode, 'y': yNode},
-            outputs: [xNode],
-            weights: [],
-            placeholders: [xNode, yNode],
-          };
-          const falseFuncGraph: Graph = {
-            inputs: [xNode, yNode],
-            nodes: {'x': xNode, 'y': yNode},
-            outputs: [yNode],
-            weights: [],
-            placeholders: [xNode, yNode],
-          };
-          graphWithControlFlow = {
-            inputs: [inputNode, inputNode2, inputNode3],
-            nodes: {
-              'input': inputNode,
-              'x': inputNode2,
-              'y': inputNode3,
-              'output': outputNode
-            },
-            outputs: [outputNode],
-            weights: [],
-            placeholders: [inputNode, inputNode2, inputNode3],
-            functions: {trueFunc: trueFuncGraph, falseFunc: falseFuncGraph}
-          };
-
-          executor = new GraphExecutor(graphWithControlFlow);
-          executor.weightMap = {};
-        });
-
-        it('should execute control flow v2 graph', async () => {
-          const condTensor = tfc.scalar(true, 'bool');
-          const condTensor2 = tfc.scalar(false, 'bool');
-          const trueTensor = tfc.scalar(1, 'int32');
-          const falseTensor = tfc.scalar(0, 'int32');
-
-          let result = await executor.executeAsync(
-              {input: condTensor, x: trueTensor, y: falseTensor}, ['output']);
-          tfc.test_util.expectArraysClose(await result[0].data(), 1);
-          result = await executor.executeAsync(
-              {input: condTensor2, x: trueTensor, y: falseTensor}, ['output']);
-          tfc.test_util.expectArraysClose(await result[0].data(), 0);
-        });
-        it('should not have mem leak', async () => {
-          const condTensor = tfc.scalar(true, 'bool');
-          const trueTensor = tfc.scalar(1, 'int32');
-          const falseTensor = tfc.scalar(0, 'int32');
-          const numTensors: number = tfc.memory().numTensors;
-
-          await executor.executeAsync(
-              {input: condTensor, x: trueTensor, y: falseTensor}, ['output']);
-          expect(tfc.memory().numTensors).toEqual(numTensors);
-        });
-      });
-
-      describe('controlFlowV2_while', () => {
-        beforeEach(() => {
-          const inputNode2: Node = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'x',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          const inputNode3: Node = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'y',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          outputNode = {
-            inputNames: ['x', 'y'],
-            inputs: [inputNode2, inputNode3],
-            children: [],
-            name: 'output',
-            op: 'StatelessWhile',
-            category: 'control',
-            attrParams: {
-              'cond': {'value': 'condFunc', 'type': 'func'},
-              'body': {'value': 'bodyFunc', 'type': 'func'}
-            },
-            inputParams: {
-              'args':
-                  {'type': 'tensors', 'inputIndexStart': 0, 'inputIndexEnd': 0}
-            }
-          };
-          inputNode2.children.push(outputNode);
-          inputNode3.children.push(outputNode);
-          const xNode: Node = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'x',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          const yNode: Node = {
-            inputNames: [],
-            inputs: [],
-            children: [],
-            name: 'y',
-            op: 'Placeholder',
-            category: 'graph',
-            attrParams: {},
-            inputParams: {}
-          };
-          const addNode: Node = {
-            inputNames: ['x', 'y'],
-            inputs: [xNode, yNode],
-            children: [],
-            name: 'add',
-            op: 'Add',
-            category: 'arithmetic',
-            inputParams: {'a': createTensorAttr(0), 'b': createTensorAttr(1)},
-            attrParams: {}
-          };
-          xNode.children.push(addNode);
-          yNode.children.push(addNode);
-          const bodyFunc: Graph = {
-            inputs: [xNode, yNode],
-            nodes: {'x': xNode, 'y': yNode, add: addNode},
-            outputs: [addNode, yNode],
-            weights: [],
-            placeholders: [xNode, yNode],
-          };
-          const condFunc: Graph = {
-            inputs: [xNode, yNode],
-            nodes: {'x': xNode, 'y': yNode},
-            outputs: [xNode],
-            weights: [],
-            placeholders: [xNode, yNode],
-          };
-          graphWithControlFlow = {
-            inputs: [inputNode2, inputNode3],
-            nodes: {'x': inputNode2, 'y': inputNode3, 'output': outputNode},
-            outputs: [outputNode],
-            weights: [],
-            placeholders: [inputNode2, inputNode3],
-            functions: {condFunc, bodyFunc}
-          };
-
-          executor = new GraphExecutor(graphWithControlFlow);
-          executor.weightMap = {};
-        });
-
-        it('should execute control flow v2 graph', async () => {
-          const trueTensor = tfc.scalar(-1, 'int32');
-          const falseTensor = tfc.scalar(1, 'int32');
-
-          const result = await executor.executeAsync(
-              {x: trueTensor, y: falseTensor}, ['output']);
-          tfc.test_util.expectArraysClose(await result[0].data(), 0);
-        });
-        it('should not have mem leak', async () => {
-          const trueTensor = tfc.scalar(-1, 'int32');
-          const falseTensor = tfc.scalar(1, 'int32');
-          const numTensors: number = tfc.memory().numTensors;
-
-          await executor.executeAsync(
-              {x: trueTensor, y: falseTensor}, ['output']);
-          expect(tfc.memory().numTensors).toEqual(numTensors + 1);
-        });
-        it('should not have mem leak when add index', async () => {
-          const trueTensor = tfc.scalar(-1, 'int32');
-          const falseTensor = tfc.scalar(1, 'int32');
-          const numTensors: number = tfc.memory().numTensors;
-
-          await executor.executeAsync(
-              {x: trueTensor, y: falseTensor}, ['output:0']);
-          expect(tfc.memory().numTensors).toEqual(numTensors + 1);
-        });
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/executor/graph_model.ts b/tfjs-master/tfjs-converter/src/executor/graph_model.ts
deleted file mode 100644
index ef9c8ad57..000000000
--- a/tfjs-master/tfjs-converter/src/executor/graph_model.ts
+++ /dev/null
@@ -1,684 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {dispose, InferenceModel, io, ModelPredictConfig, NamedTensorMap, Tensor, util} from '@tensorflow/tfjs-core';
-
-import * as tensorflow from '../data/compiled_api';
-import {NamedTensorsMap, TensorInfo} from '../data/types';
-import {OperationMapper} from '../operations/operation_mapper';
-
-import {GraphExecutor} from './graph_executor';
-import {ResourceManager} from './resource_manager';
-
-export const TFHUB_SEARCH_PARAM = '?tfjs-format=file';
-export const DEFAULT_MODEL_NAME = 'model.json';
-type Url = string|io.IOHandler|io.IOHandlerSync;
-type UrlIOHandler<T extends Url> = T extends string ? io.IOHandler : T;
-
-/**
- * A `tf.GraphModel` is a directed, acyclic graph built from a
- * SavedModel GraphDef and allows inference execution.
- *
- * A `tf.GraphModel` can only be created by loading from a model converted from
- * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
- * the command line converter tool and loaded via `tf.loadGraphModel`.
- *
- * @doc {heading: 'Models', subheading: 'Classes'}
- */
-export class GraphModel<ModelURL extends Url = string | io.IOHandler> implements
-    InferenceModel {
-  private executor: GraphExecutor;
-  private version = 'n/a';
-  private handler: UrlIOHandler<ModelURL>;
-  private artifacts: io.ModelArtifacts;
-  private initializer: GraphExecutor;
-  private resourceIdToCapturedInput: {[key: number]: Tensor};
-  private resourceManager: ResourceManager;
-  private signature: tensorflow.ISignatureDef;
-  private initializerSignature: tensorflow.ISignatureDef;
-  private structuredOutputKeys: string[];
-  private readonly io: typeof io;
-
-  // Returns the version information for the tensorflow model GraphDef.
-  get modelVersion(): string {
-    return this.version;
-  }
-
-  get inputNodes(): string[] {
-    return this.executor.inputNodes;
-  }
-
-  get outputNodes(): string[] {
-    return this.executor.outputNodes;
-  }
-
-  get inputs(): TensorInfo[] {
-    return this.executor.inputs;
-  }
-
-  get outputs(): TensorInfo[] {
-    return this.executor.outputs;
-  }
-
-  get weights(): NamedTensorsMap {
-    return this.executor.weightMap;
-  }
-
-  get metadata(): {} {
-    return this.artifacts.userDefinedMetadata;
-  }
-
-  get modelSignature(): {} {
-    return this.signature;
-  }
-
-  get modelStructuredOutputKeys(): {} {
-    return this.structuredOutputKeys;
-  }
-
-  /**
-   * @param modelUrl url for the model, or an `io.IOHandler`.
-   * @param weightManifestUrl url for the weight file generated by
-   * scripts/convert.py script.
-   * @param requestOption options for Request, which allows to send credentials
-   * and custom headers.
-   * @param onProgress Optional, progress callback function, fired periodically
-   * before the load is completed.
-   */
-  constructor(
-      private modelUrl: ModelURL, private loadOptions: io.LoadOptions = {},
-      tfio = io) {
-    this.io = tfio;
-    if (loadOptions == null) {
-      this.loadOptions = {};
-    }
-    this.resourceManager = new ResourceManager();
-  }
-
-  private findIOHandler() {
-    type IOHandler = UrlIOHandler<ModelURL>;
-    const path = this.modelUrl;
-    if ((path as io.IOHandler).load != null) {
-      // Path is an IO Handler.
-      this.handler = path as IOHandler;
-    } else if (this.loadOptions.requestInit != null) {
-      this.handler = this.io.browserHTTPRequest(
-                         path as string, this.loadOptions) as IOHandler;
-    } else {
-      const handlers =
-          this.io.getLoadHandlers(path as string, this.loadOptions);
-      if (handlers.length === 0) {
-        // For backward compatibility: if no load handler can be found,
-        // assume it is a relative http path.
-        handlers.push(
-            this.io.browserHTTPRequest(path as string, this.loadOptions));
-      } else if (handlers.length > 1) {
-        throw new Error(
-            `Found more than one (${handlers.length}) load handlers for ` +
-            `URL '${[path]}'`);
-      }
-      this.handler = handlers[0] as IOHandler;
-    }
-  }
-
-  /**
-   * Loads the model and weight files, construct the in memory weight map and
-   * compile the inference graph.
-   */
-  load(): UrlIOHandler<ModelURL> extends io.IOHandlerSync? boolean:
-                                             Promise<boolean> {
-    type IOHandler = UrlIOHandler<ModelURL>;
-    this.findIOHandler();
-    if (this.handler.load == null) {
-      throw new Error(
-          'Cannot proceed with model loading because the IOHandler provided ' +
-          'does not have the `load` method implemented.');
-    }
-
-    type Result =
-        IOHandler extends io.IOHandlerSync ? boolean : Promise<boolean>;
-
-    const loadResult = this.handler.load() as ReturnType<IOHandler['load']>;
-    if (util.isPromise(loadResult)) {
-      return loadResult.then(artifacts => this.loadSync(artifacts)) as Result;
-    }
-
-    return this.loadSync(loadResult) as Result;
-  }
-
-  /**
-   * Synchronously construct the in memory weight map and
-   * compile the inference graph.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
-   */
-  loadSync(artifacts: io.ModelArtifacts) {
-    this.artifacts = artifacts;
-    const graph = this.artifacts.modelTopology as tensorflow.IGraphDef;
-
-    let signature = this.artifacts.signature;
-    if (this.artifacts.userDefinedMetadata != null) {
-      const metadata = this.artifacts.userDefinedMetadata;
-      if (metadata.signature != null) {
-        signature = metadata.signature;
-      }
-
-      if (metadata.structuredOutputKeys != null) {
-        this.structuredOutputKeys = metadata.structuredOutputKeys as string[];
-      }
-    }
-    this.signature = signature;
-
-    this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;
-    const weightMap = this.io.decodeWeights(
-        this.artifacts.weightData, this.artifacts.weightSpecs);
-    this.executor = new GraphExecutor(
-        OperationMapper.Instance.transformGraph(graph, this.signature));
-    this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);
-    // Attach a model-level resourceManager to each executor to share resources,
-    // such as `HashTable`.
-    this.executor.resourceManager = this.resourceManager;
-
-    if (artifacts.modelInitializer != null &&
-        (artifacts.modelInitializer as tensorflow.IGraphDef).node != null) {
-      const initializer =
-          OperationMapper.Instance.transformGraph(artifacts.modelInitializer);
-      this.initializer = new GraphExecutor(initializer);
-      this.initializer.weightMap = this.executor.weightMap;
-      // Attach a model-level resourceManager to the initializer, the
-      // hashTables created from when executing the initializer will be stored
-      // in the resourceManager.
-      this.initializer.resourceManager = this.resourceManager;
-      this.initializerSignature = artifacts.initializerSignature;
-    }
-
-    return true;
-  }
-
-  /**
-   * Save the configuration and/or weights of the GraphModel.
-   *
-   * An `IOHandler` is an object that has a `save` method of the proper
-   * signature defined. The `save` method manages the storing or
-   * transmission of serialized data ("artifacts") that represent the
-   * model's topology and weights onto or via a specific medium, such as
-   * file downloads, local storage, IndexedDB in the web browser and HTTP
-   * requests to a server. TensorFlow.js provides `IOHandler`
-   * implementations for a number of frequently used saving mediums, such as
-   * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
-   * for more details.
-   *
-   * This method also allows you to refer to certain types of `IOHandler`s
-   * as URL-like string shortcuts, such as 'localstorage://' and
-   * 'indexeddb://'.
-   *
-   * Example 1: Save `model`'s topology and weights to browser [local
-   * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
-   * then load it back.
-   *
-   * ```js
-   * const modelUrl =
-   *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
-   * const model = await tf.loadGraphModel(modelUrl);
-   * const zeros = tf.zeros([1, 224, 224, 3]);
-   * model.predict(zeros).print();
-   *
-   * const saveResults = await model.save('localstorage://my-model-1');
-   *
-   * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
-   * console.log('Prediction from loaded model:');
-   * model.predict(zeros).print();
-   * ```
-   *
-   * @param handlerOrURL An instance of `IOHandler` or a URL-like,
-   * scheme-based string shortcut for `IOHandler`.
-   * @param config Options for saving the model.
-   * @returns A `Promise` of `SaveResult`, which summarizes the result of
-   * the saving, such as byte sizes of the saved artifacts for the model's
-   *   topology and weight values.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
-   */
-  async save(handlerOrURL: io.IOHandler|string, config?: io.SaveConfig):
-      Promise<io.SaveResult> {
-    if (typeof handlerOrURL === 'string') {
-      const handlers = this.io.getSaveHandlers(handlerOrURL);
-      if (handlers.length === 0) {
-        throw new Error(
-            `Cannot find any save handlers for URL '${handlerOrURL}'`);
-      } else if (handlers.length > 1) {
-        throw new Error(
-            `Found more than one (${handlers.length}) save handlers for ` +
-            `URL '${handlerOrURL}'`);
-      }
-      handlerOrURL = handlers[0];
-    }
-    if (handlerOrURL.save == null) {
-      throw new Error(
-          'GraphModel.save() cannot proceed because the IOHandler ' +
-          'provided does not have the `save` attribute defined.');
-    }
-
-    return handlerOrURL.save(this.artifacts);
-  }
-
-  private addStructuredOutputNames(outputTensors: Tensor|Tensor[]) {
-    if (this.structuredOutputKeys) {
-      const outputTensorsArray =
-          outputTensors instanceof Tensor ? [outputTensors] : outputTensors;
-      const outputTensorMap: NamedTensorMap = {};
-
-      outputTensorsArray.forEach(
-          (outputTensor, i) => outputTensorMap[this.structuredOutputKeys[i]] =
-              outputTensor);
-
-      return outputTensorMap;
-    }
-    return outputTensors;
-  }
-
-  /**
-   * Execute the inference for the input tensors.
-   *
-   * @param input The input tensors, when there is single input for the model,
-   * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
-   * inputs params should be in either `tf.Tensor`[] if the input order is
-   * fixed, or otherwise NamedTensorMap format.
-   *
-   * For model with multiple inputs, we recommend you use NamedTensorMap as the
-   * input type, if you use `tf.Tensor`[], the order of the array needs to
-   * follow the
-   * order of inputNodes array. @see {@link GraphModel.inputNodes}
-   *
-   * You can also feed any intermediate nodes using the NamedTensorMap as the
-   * input type. For example, given the graph
-   *    InputNode => Intermediate => OutputNode,
-   * you can execute the subgraph Intermediate => OutputNode by calling
-   *    model.execute('IntermediateNode' : tf.tensor(...));
-   *
-   * This is useful for models that uses tf.dynamic_rnn, where the intermediate
-   * state needs to be fed manually.
-   *
-   * For batch inference execution, the tensors for each input need to be
-   * concatenated together. For example with mobilenet, the required input shape
-   * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
-   * If we are provide a batched data of 100 images, the input tensor should be
-   * in the shape of [100, 244, 244, 3].
-   *
-   * @param config Prediction configuration for specifying the batch size.
-   * Currently the batch size option is ignored for graph model.
-   *
-   * @returns Inference result tensors. If the model is converted and it
-   * originally had structured_outputs in tensorflow, then a NamedTensorMap
-   * will be returned matching the structured_outputs. If no structured_outputs
-   * are present, the output will be single `tf.Tensor` if the model has single
-   * output node, otherwise Tensor[].
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  predict(inputs: Tensor|Tensor[]|NamedTensorMap, config?: ModelPredictConfig):
-      Tensor|Tensor[]|NamedTensorMap {
-    const outputTensors = this.execute(inputs, this.outputNodes);
-    return this.addStructuredOutputNames(outputTensors);
-  }
-
-  /**
-   * Execute the inference for the input tensors in async fashion, use this
-   * method when your model contains control flow ops.
-   *
-   * @param input The input tensors, when there is single input for the model,
-   * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
-   * inputs params should be in either `tf.Tensor`[] if the input order is
-   * fixed, or otherwise NamedTensorMap format.
-   *
-   * For model with multiple inputs, we recommend you use NamedTensorMap as the
-   * input type, if you use `tf.Tensor`[], the order of the array needs to
-   * follow the
-   * order of inputNodes array. @see {@link GraphModel.inputNodes}
-   *
-   * You can also feed any intermediate nodes using the NamedTensorMap as the
-   * input type. For example, given the graph
-   *    InputNode => Intermediate => OutputNode,
-   * you can execute the subgraph Intermediate => OutputNode by calling
-   *    model.execute('IntermediateNode' : tf.tensor(...));
-   *
-   * This is useful for models that uses tf.dynamic_rnn, where the intermediate
-   * state needs to be fed manually.
-   *
-   * For batch inference execution, the tensors for each input need to be
-   * concatenated together. For example with mobilenet, the required input shape
-   * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
-   * If we are provide a batched data of 100 images, the input tensor should be
-   * in the shape of [100, 244, 244, 3].
-   *
-   * @param config Prediction configuration for specifying the batch size.
-   * Currently the batch size option is ignored for graph model.
-   *
-   * @returns A Promise of inference result tensors. If the model is converted
-   * and it originally had structured_outputs in tensorflow, then a
-   * NamedTensorMap will be returned matching the structured_outputs. If no
-   * structured_outputs are present, the output will be single `tf.Tensor` if
-   * the model has single output node, otherwise Tensor[].
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  async predictAsync(
-      inputs: Tensor|Tensor[]|NamedTensorMap,
-      config?: ModelPredictConfig): Promise<Tensor|Tensor[]|NamedTensorMap> {
-    const outputTensors = await this.executeAsync(inputs, this.outputNodes);
-    return this.addStructuredOutputNames(outputTensors);
-  }
-
-  private normalizeInputs(inputs: Tensor|Tensor[]|
-                          NamedTensorMap): NamedTensorMap {
-    if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {
-      // The input is already a NamedTensorMap.
-      const signatureInputs = this.signature?.inputs;
-      if (signatureInputs != null) {
-        for (const input in signatureInputs) {
-          const tensor = signatureInputs[input];
-          if (tensor.resourceId != null) {
-            inputs[input] = this.resourceIdToCapturedInput[tensor.resourceId];
-          }
-        }
-      }
-      return inputs;
-    }
-    inputs = Array.isArray(inputs) ? inputs : [inputs];
-
-    const numCapturedInputs =
-        Object.keys(this.resourceIdToCapturedInput).length;
-    if (inputs.length + numCapturedInputs !== this.inputNodes.length) {
-      throw new Error(`Input tensor count mismatch, the graph model has ${
-          this.inputNodes.length -
-          numCapturedInputs} non-resource placeholders, while there are ${
-          inputs.length} input tensors provided.`);
-    }
-
-    let inputIndex = 0;
-    return this.inputNodes.reduce((map, inputName) => {
-      const resourceId = this.signature?.inputs?.[inputName]?.resourceId;
-      if (resourceId != null) {
-        map[inputName] = this.resourceIdToCapturedInput[resourceId];
-      } else {
-        map[inputName] = (inputs as Tensor[])[inputIndex++];
-      }
-      return map;
-    }, {} as NamedTensorMap);
-  }
-
-  private normalizeOutputs(outputs: string|string[]): string[] {
-    outputs = outputs || this.outputNodes;
-    return !Array.isArray(outputs) ? [outputs] : outputs;
-  }
-
-  private executeInitializerGraph() {
-    if (this.initializer == null) {
-      return [];
-    }
-    if (this.initializerSignature == null) {
-      return this.initializer.execute({}, []);
-    } else {
-      return this.initializer.execute(
-          {}, Object.keys(this.initializerSignature.outputs));
-    }
-  }
-
-  private async executeInitializerGraphAsync() {
-    if (this.initializer == null) {
-      return [];
-    }
-    if (this.initializerSignature == null) {
-      return this.initializer.executeAsync({}, []);
-    } else {
-      return this.initializer.executeAsync(
-          {}, Object.keys(this.initializerSignature.outputs));
-    }
-  }
-
-  private setResourceIdToCapturedInput(outputs: Tensor[]) {
-    this.resourceIdToCapturedInput = {};
-
-    if (this.initializerSignature) {
-      const signatureOutputs = this.initializerSignature.outputs;
-      const outputNames = Object.keys(signatureOutputs);
-      for (let i = 0; i < outputNames.length; i++) {
-        const outputName = outputNames[i];
-        const tensorInfo = signatureOutputs[outputName];
-        this.resourceIdToCapturedInput[tensorInfo.resourceId] = outputs[i];
-      }
-    }
-  }
-
-  /**
-   * Executes inference for the model for given input tensors.
-   * @param inputs tensor, tensor array or tensor map of the inputs for the
-   * model, keyed by the input node names.
-   * @param outputs output node name from the TensorFlow model, if no
-   * outputs are specified, the default outputs of the model would be used.
-   * You can inspect intermediate nodes of the model by adding them to the
-   * outputs array.
-   *
-   * @returns A single tensor if provided with a single output or no outputs
-   * are provided and there is only one default output, otherwise return a
-   * tensor array. The order of the tensor array is the same as the outputs
-   * if provided, otherwise the order of outputNodes attribute of the model.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs?: string|string[]):
-      Tensor|Tensor[] {
-    if (this.resourceIdToCapturedInput == null) {
-      this.setResourceIdToCapturedInput(this.executeInitializerGraph());
-    }
-    inputs = this.normalizeInputs(inputs);
-    outputs = this.normalizeOutputs(outputs);
-    const result = this.executor.execute(inputs, outputs);
-    return result.length > 1 ? result : result[0];
-  }
-
-  /**
-   * Executes inference for the model for given input tensors in async
-   * fashion, use this method when your model contains control flow ops.
-   * @param inputs tensor, tensor array or tensor map of the inputs for the
-   * model, keyed by the input node names.
-   * @param outputs output node name from the TensorFlow model, if no outputs
-   * are specified, the default outputs of the model would be used. You can
-   * inspect intermediate nodes of the model by adding them to the outputs
-   * array.
-   *
-   * @returns A Promise of single tensor if provided with a single output or
-   * no outputs are provided and there is only one default output, otherwise
-   * return a tensor map.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  async executeAsync(
-      inputs: Tensor|Tensor[]|NamedTensorMap,
-      outputs?: string|string[]): Promise<Tensor|Tensor[]> {
-    if (this.resourceIdToCapturedInput == null) {
-      this.setResourceIdToCapturedInput(
-          await this.executeInitializerGraphAsync());
-    }
-    inputs = this.normalizeInputs(inputs);
-    outputs = this.normalizeOutputs(outputs);
-    const result = await this.executor.executeAsync(inputs, outputs);
-    return result.length > 1 ? result : result[0];
-  }
-
-  /**
-   * Get intermediate tensors for model debugging mode (flag
-   * KEEP_INTERMEDIATE_TENSORS is true).
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  getIntermediateTensors(): NamedTensorsMap {
-    return this.executor.getIntermediateTensors();
-  }
-
-  /**
-   * Dispose intermediate tensors for model debugging mode (flag
-   * KEEP_INTERMEDIATE_TENSORS is true).
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  disposeIntermediateTensors() {
-    this.executor.disposeIntermediateTensors();
-  }
-
-  private convertTensorMapToTensorsMap(map: NamedTensorMap): NamedTensorsMap {
-    return Object.keys(map).reduce((newMap: NamedTensorsMap, key) => {
-      newMap[key] = [map[key]];
-      return newMap;
-    }, {});
-  }
-
-  /**
-   * Releases the memory used by the weight tensors and resourceManager.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  dispose() {
-    this.executor.dispose();
-
-    if (this.initializer) {
-      this.initializer.dispose();
-      if (this.resourceIdToCapturedInput) {
-        dispose(this.resourceIdToCapturedInput);
-      }
-    }
-
-    this.resourceManager.dispose();
-  }
-}
-
-/**
- * Load a graph model given a URL to the model definition.
- *
- * Example of loading MobileNetV2 from a URL and making a prediction with a
- * zeros input:
- *
- * ```js
- * const modelUrl =
- *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
- * const model = await tf.loadGraphModel(modelUrl);
- * const zeros = tf.zeros([1, 224, 224, 3]);
- * model.predict(zeros).print();
- * ```
- *
- * Example of loading MobileNetV2 from a TF Hub URL and making a prediction
- * with a zeros input:
- *
- * ```js
- * const modelUrl =
- *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
- * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
- * const zeros = tf.zeros([1, 224, 224, 3]);
- * model.predict(zeros).print();
- * ```
- * @param modelUrl The url or an `io.IOHandler` that loads the model.
- * @param options Options for the HTTP request, which allows to send
- *     credentials
- *    and custom headers.
- *
- * @doc {heading: 'Models', subheading: 'Loading'}
- */
-export async function loadGraphModel(
-    modelUrl: string|io.IOHandler, options: io.LoadOptions = {},
-    tfio = io): Promise<GraphModel> {
-  if (modelUrl == null) {
-    throw new Error(
-        'modelUrl in loadGraphModel() cannot be null. Please provide a url ' +
-        'or an IOHandler that loads the model');
-  }
-  if (options == null) {
-    options = {};
-  }
-
-  if (options.fromTFHub && typeof modelUrl === 'string') {
-    modelUrl = getTFHubUrl(modelUrl);
-  }
-  const model = new GraphModel(modelUrl, options, tfio);
-  await model.load();
-  return model;
-}
-
-/**
- * Load a graph model given a synchronous IO handler with a 'load' method.
- *
- * @param modelSource The `io.IOHandlerSync` that loads the model, or the
- *     `io.ModelArtifacts` that encode the model, or a tuple of
- *     `[io.ModelJSON, ArrayBuffer]` of which the first element encodes the
- *      model and the second contains the weights.
- *
- * @doc {heading: 'Models', subheading: 'Loading'}
- */
-export function loadGraphModelSync(
-    modelSource: io.IOHandlerSync|
-    io.ModelArtifacts|[io.ModelJSON, /* Weights */ ArrayBuffer]):
-    GraphModel<io.IOHandlerSync> {
-  if (modelSource == null) {
-    throw new Error(
-        'modelUrl in loadGraphModelSync() cannot be null. Please provide ' +
-        'model artifacts or an IOHandler that loads the model');
-  }
-
-  let ioHandler: io.IOHandlerSync;
-  if (modelSource instanceof Array) {
-    const [modelJSON, weights] = modelSource;
-    if (!modelJSON) {
-      throw new Error('modelJSON must be the first element of the array');
-    }
-    if (!weights || !(weights instanceof ArrayBuffer)) {
-      throw new Error(
-          'An ArrayBuffer of weights must be the second element of' +
-          ' the array');
-    }
-    if (!('modelTopology' in modelJSON)) {
-      throw new Error('Model JSON is missing \'modelTopology\'');
-    }
-    if (!('weightsManifest' in modelJSON)) {
-      throw new Error('Model JSON is missing \'weightsManifest\'');
-    }
-
-    const weightSpecs = io.getWeightSpecs(modelJSON.weightsManifest);
-    const modelArtifacts =
-        io.getModelArtifactsForJSONSync(modelJSON, weightSpecs, weights);
-    ioHandler = io.fromMemorySync(modelArtifacts);
-  } else if ('load' in modelSource) {
-    // Then modelSource is already an IOHandlerSync.
-    ioHandler = modelSource;
-  } else if (
-      'modelTopology' in modelSource && 'weightSpecs' in modelSource &&
-      'weightData' in modelSource) {
-    // modelSource is of type ModelArtifacts.
-    ioHandler = io.fromMemorySync(modelSource);
-  } else {
-    throw new Error('Unknown model format');
-  }
-
-  const model = new GraphModel(ioHandler);
-  model.load();
-  return model;
-}
-
-function getTFHubUrl(modelUrl: string): string {
-  if (!modelUrl.endsWith('/')) {
-    modelUrl = (modelUrl) + '/';
-  }
-  return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/graph_model_test.ts b/tfjs-master/tfjs-converter/src/executor/graph_model_test.ts
deleted file mode 100644
index 2b6d4ca10..000000000
--- a/tfjs-master/tfjs-converter/src/executor/graph_model_test.ts
+++ /dev/null
@@ -1,1255 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tfc from '@tensorflow/tfjs-core';
-import {io, scalar, Tensor} from '@tensorflow/tfjs-core';
-
-import * as tensorflow from '../data/compiled_api';
-import {deregisterOp, registerOp} from '../operations/custom_op/register';
-import {RecursiveSpy, spyOnAllFunctions} from '../operations/executors/spy_ops';
-import {GraphNode} from '../operations/types';
-
-import {GraphModel, loadGraphModel, loadGraphModelSync} from './graph_model';
-import {HASH_TABLE_MODEL_V2} from './test_data/hash_table_v2_model_loader';
-import {STRUCTURED_OUTPUTS_MODEL} from './test_data/structured_outputs_model_loader';
-
-const HOST = 'http://example.org';
-const MODEL_URL = `${HOST}/model.json`;
-const RELATIVE_MODEL_URL = '/path/model.pb';
-let model: GraphModel;
-const bias = tfc.tensor1d([1], 'int32');
-
-const weightsManifest: tfc.io.WeightsManifestEntry[] =
-    [{'name': 'Const', 'dtype': 'int32', 'shape': [1]}];
-
-const weightsManifestWithInitializer: tfc.io.WeightsManifestEntry[] = [
-  {'dtype': 'string', 'name': 'transform/keys', 'shape': [1]},
-  {'dtype': 'float32', 'name': 'transform/values', 'shape': [1]}
-];
-
-const SIMPLE_MODEL: tensorflow.IGraphDef = {
-  node: [
-    {
-      name: 'Input',
-      op: 'Placeholder',
-      attr: {
-        dtype: {
-          type: tensorflow.DataType.DT_INT32,
-        },
-        shape: {shape: {dim: [{size: -1}, {size: 1}]}}
-      }
-    },
-    {
-      name: 'Const',
-      op: 'Const',
-      attr: {
-        dtype: {type: tensorflow.DataType.DT_INT32},
-        value: {
-          tensor: {
-            dtype: tensorflow.DataType.DT_INT32,
-            tensorShape: {dim: [{size: 1}]},
-          }
-        },
-        index: {i: 0},
-        length: {i: 4}
-      }
-    },
-    {name: 'Add1', op: 'Add', input: ['Input', 'Const'], attr: {}},
-    {name: 'Add', op: 'Add', input: ['Add1', 'Const'], attr: {}}
-  ],
-  versions: {producer: 1.0, minConsumer: 3}
-};
-
-const CONTROL_FLOW_MODEL: tensorflow.IGraphDef = {
-  node: [
-    {
-      name: 'Input',
-      op: 'Placeholder',
-      attr: {
-        dtype: {
-          type: tensorflow.DataType.DT_INT32,
-        },
-        shape: {shape: {dim: [{size: -1}, {size: 1}]}}
-      }
-    },
-    {name: 'Enter', op: 'Enter', attr: {}, input: ['Input']},
-  ],
-  versions: {producer: 1.0, minConsumer: 3}
-};
-
-const DYNAMIC_SHAPE_MODEL: tensorflow.IGraphDef = {
-  node: [
-    {
-      name: 'Input',
-      op: 'Placeholder',
-      attr: {
-        dtype: {
-          type: tensorflow.DataType.DT_BOOL,
-        },
-        shape: {shape: {dim: [{size: -1}, {size: 1}]}}
-      }
-    },
-    {name: 'Where', op: 'Where', attr: {}, input: ['Input']}
-  ],
-  versions: {producer: 1.0, minConsumer: 3}
-};
-
-const SIGNATURE: tensorflow.ISignatureDef = {
-  inputs: {x: {name: 'Input:0', dtype: tensorflow.DataType.DT_INT32}},
-  outputs: {y: {name: 'Add:0', dtype: tensorflow.DataType.DT_INT32}}
-};
-const SIMPLE_HTTP_MODEL_LOADER = {
-  load: async () => {
-    return {
-      modelTopology: SIMPLE_MODEL,
-      weightSpecs: weightsManifest,
-      weightData: bias.dataSync(),
-      format: 'tfjs-graph-model',
-      generatedBy: '1.15',
-      convertedBy: '1.3.1',
-      userDefinedMetadata: {signature: SIGNATURE}
-    };
-  }
-};
-
-const NO_INPUT_SIGNATURE_MODEL_LOADER = {
-  load: async () => {
-    return {
-      modelTopology: SIMPLE_MODEL,
-      weightSpecs: weightsManifest,
-      weightData: bias.dataSync(),
-      format: 'tfjs-graph-model',
-      generatedBy: '1.15',
-      convertedBy: '1.3.1',
-      userDefinedMetadata: {signature: {outputs: SIGNATURE.outputs}}
-    };
-  }
-};
-
-const CUSTOM_OP_MODEL: tensorflow.IGraphDef = {
-  node: [
-    {
-      name: 'Input',
-      op: 'Placeholder',
-      attr: {
-        dtype: {
-          type: tensorflow.DataType.DT_INT32,
-        },
-        shape: {shape: {dim: [{size: -1}, {size: 1}]}}
-      }
-    },
-    {
-      name: 'Const',
-      op: 'Const',
-      attr: {
-        dtype: {type: tensorflow.DataType.DT_INT32},
-        value: {
-          tensor: {
-            dtype: tensorflow.DataType.DT_INT32,
-            tensorShape: {dim: [{size: 1}]},
-          }
-        },
-        index: {i: 0},
-        length: {i: 4}
-      }
-    },
-    {name: 'Add1', op: 'Add', input: ['Input', 'Const'], attr: {}},
-    {name: 'CustomOp', op: 'CustomOp', input: ['Add1'], attr: {}}
-  ],
-  versions: {producer: 1.0, minConsumer: 3}
-};
-
-const CUSTOM_HTTP_MODEL_LOADER = {
-  load: async () => {
-    return {
-      modelTopology: CUSTOM_OP_MODEL,
-      weightSpecs: weightsManifest,
-      weightData: bias.dataSync(),
-      format: 'tfjs-graph-model',
-      generatedBy: '1.15',
-      convertedBy: '1.3.1'
-    };
-  }
-};
-
-const CONTROL_SIGNATURE: tensorflow.ISignatureDef = {
-  inputs: {x: {name: 'Input:0', dtype: tensorflow.DataType.DT_INT32}},
-  outputs: {y: {name: 'Enter:0', dtype: tensorflow.DataType.DT_INT32}}
-};
-const CONTROL_FLOW_HTTP_MODEL_LOADER = {
-  load: async () => {
-    return {
-      modelTopology: CONTROL_FLOW_MODEL,
-      weightSpecs: weightsManifest,
-      weightData: bias.dataSync(),
-      userDefinedMetadata: {signature: CONTROL_SIGNATURE},
-      format: 'tfjs-graph-model',
-      generatedBy: '1.15',
-      convertedBy: '1.3.1'
-    };
-  }
-};
-
-const STRUCTURED_OUTPUTS_MODEL_LOADER = {
-  load: async () => {
-    return STRUCTURED_OUTPUTS_MODEL;
-  }
-};
-
-const INITIALIZER_GRAPHDEF: tensorflow.IGraphDef = {
-  node: [
-    {
-      name: 'transform/values',
-      op: 'Const',
-      attr: {
-        dtype: {type: tensorflow.DataType.DT_FLOAT},
-        value: {
-          tensor: {
-            dtype: tensorflow.DataType.DT_FLOAT,
-            tensorShape: {dim: [{size: 1}]}
-          }
-        }
-      }
-    },
-    {
-      name: 'transform/keys',
-      op: 'Const',
-      attr: {
-        dtype: {type: tensorflow.DataType.DT_STRING},
-        value: {
-          tensor: {
-            dtype: tensorflow.DataType.DT_STRING,
-            tensorShape: {dim: [{size: 1}]}
-          }
-        }
-      }
-    },
-    {
-      name: 'transform/hash_table',
-      op: 'HashTableV2',
-      attr: {
-        value_dtype: {type: tensorflow.DataType.DT_FLOAT},
-        use_node_name_sharing: {b: false},
-        key_dtype: {type: tensorflow.DataType.DT_STRING},
-        container: {s: ''},
-        shared_name: {s: 'dGFibGVuYW1l' /* base64 'tablename' */}
-      }
-    },
-    {
-      name: 'transform/key_value_init/LookupTableImportV2',
-      op: 'LookupTableImportV2',
-      input: ['transform/hash_table', 'transform/keys', 'transform/values'],
-      attr: {
-        Tin: {type: tensorflow.DataType.DT_FLOAT},
-        Tout: {type: tensorflow.DataType.DT_STRING}
-      }
-    }
-  ],
-  versions: {producer: 1.0, minConsumer: 3}
-};
-
-const HASH_TABLE_MODEL: tensorflow.IGraphDef = {
-  node: [
-    {
-      name: 'Input',
-      op: 'Placeholder',
-      attr: {
-        dtype: {
-          type: tensorflow.DataType.DT_STRING,
-        },
-        shape: {shape: {dim: [{size: 1}]}}
-      }
-    },
-    {
-      name: 'Input_1',
-      op: 'Placeholder',
-      attr: {
-        dtype: {
-          type: tensorflow.DataType.DT_FLOAT,
-        },
-        shape: {shape: {dim: [{size: 1}]}}
-      }
-    },
-    {
-      name: 'transform/hash_table',
-      op: 'HashTableV2',
-      input: [],
-      attr: {
-        value_dtype: {type: tensorflow.DataType.DT_FLOAT},
-        use_node_name_sharing: {b: false},
-        key_dtype: {type: tensorflow.DataType.DT_STRING},
-        container: {s: ''},
-        shared_name: {s: 'dGFibGVuYW1l' /* base64 'tablename' */}
-      }
-    },
-    {
-      name: 'LookupTableFindV2',
-      op: 'LookupTableFindV2',
-      input: ['transform/hash_table', 'Input', 'Input_1'],
-      attr: {}
-    }
-  ],
-  versions: {producer: 1.0, minConsumer: 3}
-};
-
-const HASH_TABLE_SIGNATURE: tensorflow.ISignatureDef = {
-  inputs: {
-    keys: {name: 'Input:0', dtype: tensorflow.DataType.DT_STRING},
-    defaultValues: {name: 'Input_1:0', dtype: tensorflow.DataType.DT_FLOAT}
-  },
-  outputs: {
-    values: {name: 'LookupTableFindV2:0', dtype: tensorflow.DataType.DT_FLOAT}
-  }
-};
-const HASHTABLE_V1_HTTP_MODEL_LOADER = {
-  load: async () => {
-    return {
-      modelTopology: HASH_TABLE_MODEL,
-      weightSpecs: weightsManifestWithInitializer,
-      weightData: new ArrayBuffer(16),
-      format: 'tfjs-graph-model',
-      generatedBy: '1.15',
-      convertedBy: '2.4',
-      userDefinedMetadata: {signature: HASH_TABLE_SIGNATURE},
-      modelInitializer: INITIALIZER_GRAPHDEF
-    };
-  }
-};
-
-const HASHTABLE_V2_MODEL_LOADER = {
-  load: async () => {
-    return HASH_TABLE_MODEL_V2;
-  }
-};
-
-class IOHandlerForTest implements tfc.io.IOHandler {
-  savedArtifacts: tfc.io.ModelArtifacts;
-
-  async save(modelArtifacts: tfc.io.ModelArtifacts):
-      Promise<tfc.io.SaveResult> {
-    this.savedArtifacts = modelArtifacts;
-    return {modelArtifactsInfo: null};
-  }
-}
-
-describe('loadSync', () => {
-  let artifacts: io.ModelArtifacts;
-
-  beforeEach(() => {
-    model = new GraphModel(MODEL_URL);
-    artifacts = {
-      format: 'graph-model',
-      generatedBy: '0.0.0',
-      modelTopology: SIMPLE_MODEL,
-      weightSpecs: weightsManifest,
-      weightData: new Int32Array([5]).buffer
-    };
-  });
-
-  it('Can load old model.', () => {
-    artifacts.convertedBy = 'TensorFlow.js Converter v1.3.2';
-    artifacts.userDefinedMetadata = {signature: SIGNATURE};
-    const loaded = model.loadSync(artifacts);
-
-    expect(loaded).toBe(true);
-    expect(model.modelSignature).toEqual(SIGNATURE);
-  });
-
-  it('Can load new model.', () => {
-    artifacts.convertedBy = 'TensorFlow.js Converter v2.8.0';
-    artifacts.signature = SIGNATURE;
-    const loaded = model.loadSync(artifacts);
-
-    expect(loaded).toBe(true);
-    expect(model.modelSignature).toEqual(SIGNATURE);
-  });
-
-  it('Can load model without signature.', () => {
-    const loaded = model.loadSync(artifacts);
-
-    expect(loaded).toBe(true);
-    expect(model.modelSignature).toBeUndefined();
-  });
-
-  it('Can load model with structured_outputs.', () => {
-    artifacts.convertedBy = 'TensorFlow.js Converter v3.19.0';
-    artifacts.userDefinedMetadata = {structuredOutputKeys: ['a', 'b', 'c']};
-    const loaded = model.loadSync(artifacts);
-
-    expect(loaded).toBe(true);
-    expect(model.modelStructuredOutputKeys).toEqual(['a', 'b', 'c']);
-  });
-
-  it('Can load model with different convertedBy language.', () => {
-    artifacts.convertedBy = '1.3.2';
-    artifacts.userDefinedMetadata = {signature: SIGNATURE};
-    const loaded = model.loadSync(artifacts);
-
-    expect(loaded).toBe(true);
-    expect(model.modelSignature).toEqual(SIGNATURE);
-  });
-});
-
-describe('loadGraphModel', () => {
-  let spyIo: RecursiveSpy<typeof io>;
-
-  beforeEach(() => {
-    spyIo = spyOnAllFunctions(io);
-  });
-
-  it('Pass a custom io handler', async () => {
-    const customLoader: tfc.io.IOHandler = {
-      load: async () => {
-        return {
-          modelTopology: SIMPLE_MODEL,
-          weightSpecs: weightsManifest,
-          weightData: new Int32Array([5]).buffer,
-        };
-      }
-    };
-    const model = await loadGraphModel(customLoader);
-    expect(model).toBeDefined();
-    const bias = model.weights['Const'][0];
-    expect(bias.dtype).toBe('int32');
-    expect(bias.dataSync()).toEqual(new Int32Array([5]));
-  });
-
-  it('Expect an error when moderUrl is null', async () => {
-    let errorMsg = 'no error';
-    try {
-      await loadGraphModel(null);
-    } catch (err) {
-      errorMsg = err.message;
-    }
-    expect(errorMsg).toMatch(/modelUrl in loadGraphModel\(\) cannot be null/);
-  });
-
-  it('Pass a fetchFunc', async () => {
-    const fetchFunc = () => {};
-    spyIo.getLoadHandlers.and.returnValue([CUSTOM_HTTP_MODEL_LOADER]);
-    await loadGraphModel(MODEL_URL, {fetchFunc}, spyIo);
-    expect(spyIo.getLoadHandlers).toHaveBeenCalledWith(MODEL_URL, {fetchFunc});
-  });
-});
-
-describe('loadGraphModelSync', () => {
-  function checkModel(model: GraphModel<unknown>) {
-    expect(model).toBeDefined();
-    const bias = model.weights['Const'][0];
-    expect(bias.dtype).toBe('int32');
-    expect(bias.dataSync()).toEqual(new Int32Array([5]));
-  }
-
-  it('Pass a custom io handler', () => {
-    const customLoader: tfc.io.IOHandlerSync = {
-      load: () => {
-        return {
-          modelTopology: SIMPLE_MODEL,
-          weightSpecs: weightsManifest,
-          weightData: new Int32Array([5]).buffer,
-        };
-      }
-    };
-    const model = loadGraphModelSync(customLoader);
-    checkModel(model);
-  });
-
-  it('Pass the model artifacts directly', () => {
-    const modelArtifacts: tfc.io.ModelArtifacts = {
-      modelTopology: SIMPLE_MODEL,
-      weightSpecs: weightsManifest,
-      weightData: new Int32Array([5]).buffer,
-    };
-    const model = loadGraphModelSync(modelArtifacts);
-    checkModel(model);
-  });
-
-  it('Pass the model JSON and weights', () => {
-    const modelJson: tfc.io.ModelJSON = {
-      modelTopology: SIMPLE_MODEL,
-      weightsManifest: [{paths: [], weights: weightsManifest}],
-    };
-    const weights = new Int32Array([5]).buffer;
-    const model = loadGraphModelSync([modelJson, weights]);
-    checkModel(model);
-  });
-
-  it('Throws an error if ModelJSON is passed without weights', () => {
-    const modelJson: tfc.io.ModelJSON = {
-      modelTopology: SIMPLE_MODEL,
-      weightsManifest: [{paths: [], weights: weightsManifest}],
-    };
-    expect(() => {
-      return loadGraphModelSync(
-          [modelJson] as unknown as [io.ModelJSON, ArrayBuffer]);
-    })
-        .toThrowMatching(
-            err => err.message.includes('weights must be the second element'));
-  });
-
-  it('Throws an error if modelJSON is missing \'modelTopology\'', () => {
-    const badInput = {
-      weightsManifest: [{paths: [] as string[], weights: weightsManifest}],
-    };
-    const weights = new Int32Array([5]).buffer;
-    expect(() => {
-      return loadGraphModelSync([badInput as io.ModelJSON, weights]);
-    })
-        .toThrowMatching(
-            err => err.message.includes('missing \'modelTopology\''));
-  });
-
-  it('Throws an error if modelJSON is missing \'weightsManifest\'', () => {
-    const badInput = {
-      modelTopology: SIMPLE_MODEL,
-    };
-    const weights = new Int32Array([5]).buffer;
-    expect(() => {
-      return loadGraphModelSync([badInput as io.ModelJSON, weights]);
-    })
-        .toThrowMatching(
-            err => err.message.includes('missing \'weightsManifest\''));
-  });
-
-  it('Throws an error if modelSource is an unknown format', () => {
-    const badInput = {foo: 'bar'};
-    expect(() => {
-      return loadGraphModelSync(badInput as io.ModelArtifacts);
-    }).toThrowMatching(err => err.message.includes('Unknown model format'));
-  });
-
-  it('Expect an error when moderUrl is null', () => {
-    let errorMsg = 'no error';
-    try {
-      loadGraphModelSync(null);
-    } catch (err) {
-      errorMsg = err.message;
-    }
-    expect(errorMsg).toMatch(
-        /modelUrl in loadGraphModelSync\(\) cannot be null/);
-  });
-});
-
-describe('Model', () => {
-  let spyIo: RecursiveSpy<typeof io>;
-
-  beforeEach(() => {
-    spyIo = spyOnAllFunctions(io);
-    model = new GraphModel(MODEL_URL, undefined, spyIo);
-  });
-
-  describe('custom model', () => {
-    beforeEach(() => {
-      spyIo.getLoadHandlers.and.returnValue([CUSTOM_HTTP_MODEL_LOADER]);
-      registerOp('CustomOp', (nodeValue: GraphNode) => {
-        const x = nodeValue.inputs[0];
-        return [tfc.add(x, scalar(1, 'int32'))];
-      });
-    });
-    afterEach(() => deregisterOp('CustomOp'));
-    it('load', async () => {
-      const loaded = await model.load();
-      expect(loaded).toBe(true);
-    });
-
-    describe('predict', () => {
-      it('should generate the output for single tensor', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.predict(input);
-        expect((output as tfc.Tensor).dataSync()[0]).toEqual(3);
-      });
-    });
-
-    describe('save', () => {
-      it('should call the save io handler', async () => {
-        await model.load();
-        const handler = new IOHandlerForTest();
-
-        await model.save(handler);
-        expect(handler.savedArtifacts.format).toEqual('tfjs-graph-model');
-        expect(handler.savedArtifacts.generatedBy).toEqual('1.15');
-        expect(handler.savedArtifacts.convertedBy).toEqual('1.3.1');
-        expect(handler.savedArtifacts.modelTopology).toEqual(CUSTOM_OP_MODEL);
-        expect(handler.savedArtifacts.weightSpecs).toEqual(weightsManifest);
-        tfc.test_util.expectArraysClose(
-            new Int32Array(io.CompositeArrayBuffer.join(
-                handler.savedArtifacts.weightData)), bias.dataSync());
-      });
-    });
-  });
-
-  describe('simple model', () => {
-    beforeEach(() => {
-      spyIo.getLoadHandlers.and.returnValue([SIMPLE_HTTP_MODEL_LOADER]);
-      spyIo.browserHTTPRequest.and.returnValue(SIMPLE_HTTP_MODEL_LOADER);
-    });
-    it('load', async () => {
-      const loaded = await model.load();
-      expect(loaded).toBe(true);
-    });
-
-    describe('save', () => {
-      it('should call the save io handler', async () => {
-        await model.load();
-        const handler = new IOHandlerForTest();
-
-        await model.save(handler);
-        expect(handler.savedArtifacts.format).toEqual('tfjs-graph-model');
-        expect(handler.savedArtifacts.generatedBy).toEqual('1.15');
-        expect(handler.savedArtifacts.convertedBy).toEqual('1.3.1');
-        expect(handler.savedArtifacts.modelTopology).toEqual(SIMPLE_MODEL);
-        expect(handler.savedArtifacts.userDefinedMetadata).toEqual({
-          signature: SIGNATURE
-        });
-        expect(handler.savedArtifacts.weightSpecs).toEqual(weightsManifest);
-        tfc.test_util.expectArraysClose(
-            new Int32Array(io.CompositeArrayBuffer.join(
-                handler.savedArtifacts.weightData)), bias.dataSync());
-      });
-    });
-
-    describe('predict', () => {
-      it('should generate the output for single tensor', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.predict(input);
-        expect((output as tfc.Tensor).dataSync()[0]).toEqual(3);
-      });
-
-      it('should support signature keys', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.predict({x: input});
-        expect((output as tfc.Tensor).dataSync()[0]).toEqual(3);
-      });
-
-      it('should generate the output for tensor array', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.predict([input]);
-        expect((output as tfc.Tensor).dataSync()[0]).toEqual(3);
-      });
-
-      it('should generate the output for tensor map', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.predict({'Input': input});
-        expect((output as tfc.Tensor).dataSync()[0]).toEqual(3);
-      });
-
-      it('should throw error if wrong signature key is used', async () => {
-        await model.load();
-        const input = tfc.tensor1d([1], 'int32');
-        expect(() => model.predict({x1: input})).toThrow();
-      });
-
-      it('should throw error if input size mismatch', async () => {
-        await model.load();
-        const input = tfc.tensor1d([1], 'int32');
-        expect(() => model.predict([input, input])).toThrow();
-      });
-
-      it('should throw exception if inputs shapes do not match', () => {
-        const input = tfc.tensor2d([1, 1], [1, 2], 'int32');
-        expect(() => model.predict([input])).toThrow();
-      });
-
-      it('should throw exception if inputs dtype does not match graph', () => {
-        const input = tfc.tensor1d([1], 'float32');
-        expect(() => model.predict([input])).toThrow();
-      });
-    });
-
-    describe('execute', () => {
-      it('should generate the default output', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.execute({'Input': input});
-        expect((output as tfc.Tensor).dataSync()[0]).toEqual(3);
-      });
-      it('should allow signature keys', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.execute({x: input}, ['y']);
-        expect((output as tfc.Tensor).dataSync()[0]).toEqual(3);
-      });
-      it('should generate the output array', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.execute({'Input': input}, ['Add', 'Const']);
-        expect(Array.isArray(output)).toBeTruthy();
-        expect((output as tfc.Tensor[])[0].dataSync()[0]).toEqual(3);
-        expect((output as tfc.Tensor[])[1].dataSync()[0]).toEqual(1);
-      });
-      it('should throw exception if inputs shapes do not match', () => {
-        const input = tfc.tensor2d([1, 1], [1, 2], 'int32');
-        expect(() => model.execute([input])).toThrow();
-      });
-
-      it('should throw exception if inputs dtype does not match graph', () => {
-        const input = tfc.tensor2d([1, 1], [2, 1], 'float32');
-        expect(() => model.predict([input])).toThrow();
-      });
-
-      it('should throw error if input size mismatch', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-
-        expect(() => model.execute([input, input])).toThrow();
-      });
-
-      it('should allow feed intermediate node', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.execute({'Add1': input}) as tfc.Tensor;
-        tfc.test_util.expectArraysClose(await output.data(), [2, 2]);
-      });
-      it('should allow feed intermediate node with index', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.execute({'Add1:0': input}) as tfc.Tensor;
-        tfc.test_util.expectArraysClose(await output.data(), [2, 2]);
-      });
-    });
-
-    describe('dispose', () => {
-      it('should dispose the weights', async () => {
-        const numOfTensors = tfc.memory().numTensors;
-        model = new GraphModel(MODEL_URL, undefined, spyIo);
-
-        await model.load();
-        model.dispose();
-
-        expect(tfc.memory().numTensors).toEqual(numOfTensors);
-      });
-    });
-
-    describe('getVersion', () => {
-      it('should return the version info from the tf model', async () => {
-        await model.load();
-        expect(model.modelVersion).toEqual('1.3');
-      });
-    });
-
-    describe('relative path', () => {
-      beforeEach(() => {
-        model = new GraphModel(RELATIVE_MODEL_URL, undefined, spyIo);
-      });
-
-      it('load', async () => {
-        const loaded = await model.load();
-        expect(loaded).toBe(true);
-      });
-    });
-
-    it('should loadGraphModel', async () => {
-      const model = await loadGraphModel(MODEL_URL, undefined, spyIo);
-      expect(model).not.toBeUndefined();
-    });
-
-    it('should loadGraphModel with request options', async () => {
-      const model = await loadGraphModel(
-          MODEL_URL, {requestInit: {credentials: 'include'}}, spyIo);
-      expect(spyIo.browserHTTPRequest).toHaveBeenCalledWith(MODEL_URL, {
-        requestInit: {credentials: 'include'}
-      });
-      expect(model).not.toBeUndefined();
-    });
-
-    it('should call loadGraphModel for TfHub Module', async () => {
-      const url = `${HOST}/model/1`;
-      const model = await loadGraphModel(url, {fromTFHub: true}, spyIo);
-      expect(model).toBeDefined();
-    });
-
-    describe('InferenceModel interface', () => {
-      it('should expose inputs', async () => {
-        await model.load();
-        expect(model.inputs).toEqual([
-          {name: 'Input', shape: [-1, 1], dtype: 'int32'}
-        ]);
-      });
-      it('should expose outputs', async () => {
-        await model.load();
-        expect(model.outputs).toEqual([
-          {name: 'Add', shape: undefined, dtype: undefined}
-        ]);
-      });
-    });
-  });
-
-  describe('no signature input model', () => {
-    beforeEach(() => {
-      spyIo.getLoadHandlers.and.returnValue([NO_INPUT_SIGNATURE_MODEL_LOADER]);
-      spyIo.browserHTTPRequest.and.returnValue(NO_INPUT_SIGNATURE_MODEL_LOADER);
-    });
-
-    it('load', async () => {
-      const loaded = await model.load();
-      expect(loaded).toBe(true);
-    });
-
-    describe('predict', () => {
-      it('should generate default output', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.execute({'Input': input});
-        expect((output as tfc.Tensor).dataSync()[0]).toEqual(3);
-      });
-    });
-
-    describe('execute', () => {
-      it('should generate default output', async () => {
-        await model.load();
-        const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-        const output = model.execute(input);
-        expect((output as tfc.Tensor).dataSync()[0]).toEqual(3);
-      });
-    });
-  });
-
-  describe('structured outputs model', () => {
-    beforeEach(() => {
-      spyIo.getLoadHandlers.and.returnValue([STRUCTURED_OUTPUTS_MODEL_LOADER]);
-      spyIo.browserHTTPRequest.and.returnValue(STRUCTURED_OUTPUTS_MODEL_LOADER);
-    });
-    it('load', async () => {
-      const loaded = await model.load();
-      expect(loaded).toBe(true);
-    });
-
-    describe('save', () => {
-      it('should call the save io handler', async () => {
-        await model.load();
-        const handler = new IOHandlerForTest();
-
-        await model.save(handler);
-        expect(handler.savedArtifacts.format)
-            .toEqual(STRUCTURED_OUTPUTS_MODEL.format);
-        expect(handler.savedArtifacts.generatedBy)
-            .toEqual(STRUCTURED_OUTPUTS_MODEL.generatedBy);
-        expect(handler.savedArtifacts.convertedBy)
-            .toEqual(STRUCTURED_OUTPUTS_MODEL.convertedBy);
-        expect(handler.savedArtifacts.modelTopology)
-            .toEqual(STRUCTURED_OUTPUTS_MODEL.modelTopology);
-        expect(handler.savedArtifacts.userDefinedMetadata)
-            .toEqual(STRUCTURED_OUTPUTS_MODEL.userDefinedMetadata);
-      });
-    });
-
-    describe('predict', () => {
-      it('should support structured outputs', async () => {
-        await model.load();
-
-        const input = tfc.tensor2d([[1]]);
-        const output =
-            model.predict({input1: input, input2: input, input3: input}) as
-            tfc.NamedTensorMap;
-        expect(Object.keys(output)).toEqual(['a', 'b', 'c']);
-      });
-    });
-
-    describe('execute', () => {
-      it('should generate the list output', async () => {
-        await model.load();
-        const input = tfc.tensor2d([[1]]);
-        const output =
-            model.execute({input1: input, input2: input, input3: input});
-        expect(Array.isArray(output)).toBeTruthy();
-      });
-    });
-
-    describe('dispose', () => {
-      it('should dispose the weights', async () => {
-        const numOfTensors = tfc.memory().numTensors;
-        await model.load();
-        model.dispose();
-
-        expect(tfc.memory().numTensors).toEqual(numOfTensors);
-      });
-    });
-  });
-
-  describe('control flow model', () => {
-    beforeEach(() => {
-      spyIo.getLoadHandlers.and.returnValue([CONTROL_FLOW_HTTP_MODEL_LOADER]);
-      spyIo.browserHTTPRequest.and.returnValue(CONTROL_FLOW_HTTP_MODEL_LOADER);
-    });
-
-    describe('save', () => {
-      it('should call the save io handler', async () => {
-        await model.load();
-        const handler = new IOHandlerForTest();
-
-        await model.save(handler);
-        expect(handler.savedArtifacts.format).toEqual('tfjs-graph-model');
-        expect(handler.savedArtifacts.generatedBy).toEqual('1.15');
-        expect(handler.savedArtifacts.convertedBy).toEqual('1.3.1');
-        expect(handler.savedArtifacts.modelTopology)
-            .toEqual(CONTROL_FLOW_MODEL);
-        expect(handler.savedArtifacts.userDefinedMetadata).toEqual({
-          signature: CONTROL_SIGNATURE
-        });
-        expect(handler.savedArtifacts.weightSpecs).toEqual(weightsManifest);
-        tfc.test_util.expectArraysClose(
-            new Int32Array(io.CompositeArrayBuffer.join(handler.savedArtifacts
-                .weightData)), bias.dataSync());
-      });
-    });
-
-    it('should throw error if call predict directly', async () => {
-      await model.load();
-      const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-
-      expect(() => model.predict([input])).toThrow();
-    });
-
-    it('should throw error if call execute directly', async () => {
-      await model.load();
-      const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-
-      expect(() => model.predict([input])).toThrow();
-    });
-
-    it('should be success if call executeAsync', async () => {
-      await model.load();
-      const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-      const res = await model.executeAsync([input]);
-      expect(res).not.toBeNull();
-    });
-
-    it('should be success if call executeAsync with signature keys',
-       async () => {
-         await model.load();
-         const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-         const res = await model.executeAsync({x: input}, ['y']);
-         expect(res).not.toBeNull();
-       });
-
-    it('should allow feed intermediate node with executeAsync', async () => {
-      await model.load();
-      const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-      const res = await model.executeAsync({Enter: input});
-      expect(res).not.toBeNull();
-    });
-  });
-  const DYNAMIC_SIGNATURE: tensorflow.ISignatureDef = {
-    inputs: {x: {name: 'Input:0', dtype: tensorflow.DataType.DT_INT32}},
-    outputs: {y: {name: 'Where:0', dtype: tensorflow.DataType.DT_INT32}}
-  };
-  const DYNAMIC_HTTP_MODEL_LOADER = {
-    load: async () => {
-      return {
-        modelTopology: DYNAMIC_SHAPE_MODEL,
-        weightSpecs: weightsManifest,
-        weightData: bias.dataSync(),
-        userDefinedMetadata: {signature: DYNAMIC_SIGNATURE}
-      };
-    }
-  };
-  const DYNAMIC_HTTP_MODEL_NEW_LOADER = {
-    load: async () => {
-      return {
-        convertedBy: '2.8',
-        modelTopology: DYNAMIC_SHAPE_MODEL,
-        weightSpecs: weightsManifest,
-        weightData: bias.dataSync(),
-        signature: DYNAMIC_SIGNATURE,
-        userDefinedMetadata: {metadata1: {a: '1'}}
-      };
-    }
-  };
-  describe('dynamic shape model', () => {
-    beforeEach(() => {
-      spyIo.getLoadHandlers.and.returnValue([DYNAMIC_HTTP_MODEL_LOADER]);
-      spyIo.browserHTTPRequest.and.returnValue(DYNAMIC_HTTP_MODEL_LOADER);
-    });
-
-    it('should throw error if call predict directly', async () => {
-      await model.load();
-      const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-
-      expect(() => model.predict([input])).toThrow();
-    });
-
-    it('should throw error if call execute directly', async () => {
-      await model.load();
-      const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-
-      expect(() => model.execute([input])).toThrow();
-    });
-
-    it('should be success if call executeAsync', async () => {
-      await model.load();
-      const input = tfc.tensor2d([1, 1], [2, 1], 'bool');
-      const res = await model.executeAsync([input]);
-      expect(res).not.toBeNull();
-    });
-
-    it('should be success if call executeAsync with signature key',
-       async () => {
-         await model.load();
-         const input = tfc.tensor2d([1, 1], [2, 1], 'bool');
-         const res = await model.executeAsync({x: input}, ['y']);
-         expect(res).not.toBeNull();
-       });
-
-    it('should allow feed intermediate node with executeAsync', async () => {
-      await model.load();
-      const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-      const res = await model.executeAsync({Where: input});
-      expect(res).not.toBeNull();
-    });
-  });
-  describe('dynamic shape model with metadata', () => {
-    beforeEach(() => {
-      spyIo.getLoadHandlers.and.returnValue([DYNAMIC_HTTP_MODEL_NEW_LOADER]);
-      spyIo.browserHTTPRequest.and.returnValue(DYNAMIC_HTTP_MODEL_NEW_LOADER);
-    });
-
-    it('should be success if call executeAsync with signature key',
-       async () => {
-         await model.load();
-         const input = tfc.tensor2d([1, 1], [2, 1], 'bool');
-         const res = await model.executeAsync({x: input}, ['y']);
-         expect(res).not.toBeNull();
-         expect(model.metadata).toEqual({metadata1: {a: '1'}});
-       });
-
-    it('should allow feed intermediate node with executeAsync', async () => {
-      await model.load();
-      const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-      const res = await model.executeAsync({Where: input});
-      expect(res).not.toBeNull();
-    });
-  });
-
-  describe('Hashtable V1 model', () => {
-    beforeEach(() => {
-      spyIo.getLoadHandlers.and.returnValue([HASHTABLE_V1_HTTP_MODEL_LOADER]);
-      spyIo.browserHTTPRequest.and.returnValue(HASHTABLE_V1_HTTP_MODEL_LOADER);
-    });
-    it('should be successful if call executeAsync', async () => {
-      await model.load();
-      const keys = tfc.tensor1d(['a'], 'string');
-      const defaultValues = tfc.tensor1d([0]);
-      const res = await model.executeAsync({keys, defaultValues});
-      expect(res).not.toBeNull();
-    });
-  });
-
-  describe('Hashtable V2 model', () => {
-    beforeEach(() => {
-      spyIo.getLoadHandlers.and.returnValue([HASHTABLE_V2_MODEL_LOADER]);
-      spyIo.browserHTTPRequest.and.returnValue(HASHTABLE_V2_MODEL_LOADER);
-    });
-    it('load', async () => {
-      const loaded = await model.load();
-      expect(loaded).toBe(true);
-    });
-
-    describe('execute', () => {
-      it('should be successful if call executeAsync', async () => {
-        await model.load();
-        const res = await model.executeAsync(
-                        {'input': tfc.tensor1d(['a', 'b', 'c'])}) as Tensor;
-        expect(Array.from(res.dataSync())).toEqual([0, 1, -1]);
-      });
-    });
-
-    describe('dispose', () => {
-      it('should dispose the weights', async () => {
-        const startTensors = tfc.memory().numTensors;
-
-        await model.load();
-
-        const input = tfc.tensor1d(['a', 'b', 'c']);
-        const output = await model.executeAsync({input}) as Tensor;
-        input.dispose();
-        output.dispose();
-
-        model.dispose();
-
-        expect(tfc.memory().numTensors).toEqual(startTensors);
-      });
-    });
-  });
-});
-
-describe('Graph execution gives actionable errors', () => {
-  it('executeAsync warns when there are no dynamic ops', async () => {
-    const customLoader: tfc.io.IOHandler = {
-      load: async () => ({
-        modelTopology: SIMPLE_MODEL,
-        weightSpecs: weightsManifest,
-        weightData: new Int32Array([5]).buffer,
-      })
-    };
-    const model = await loadGraphModel(customLoader);
-    spyOn(console, 'warn');
-    const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-    await model.executeAsync(input);
-    expect(console.warn).toHaveBeenCalledTimes(1);
-    expect(console.warn)
-        .toHaveBeenCalledWith(
-            'This model execution did not contain any nodes with control ' +
-            'flow or dynamic output shapes. You can use model.execute() ' +
-            'instead.');
-  });
-
-  it('executeAsync does not warn when there are dynamic ops', async () => {
-    const customLoader: tfc.io.IOHandler = {
-      load: async () => ({
-        modelTopology: CONTROL_FLOW_MODEL,
-        weightSpecs: weightsManifest,
-        weightData: new Int32Array([5]).buffer,
-      })
-    };
-    const model = await loadGraphModel(customLoader);
-    spyOn(console, 'warn');
-    const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-    await model.executeAsync(input);
-    expect(console.warn).toHaveBeenCalledTimes(0);
-  });
-
-  it('executeAsync warns when the subgraph has no dynamic ops', async () => {
-    const graphDef: tensorflow.IGraphDef = {
-      node: [
-        {name: 'input', op: 'Placeholder'},
-        {name: 'intermediate', op: 'Enter', input: ['input']},
-        {name: 'intermediate2', op: 'Sqrt', input: ['intermediate']},
-        {name: 'output', op: 'Square', input: ['intermediate2']},
-      ],
-      versions: {producer: 1.0, minConsumer: 3}
-    };
-    const customLoader: tfc.io.IOHandler = {
-      load: async () => ({
-        modelTopology: graphDef,
-        weightSpecs: weightsManifest,
-        weightData: new Int32Array([5]).buffer,
-      })
-    };
-    const model = await loadGraphModel(customLoader);
-    spyOn(console, 'warn');
-    const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-    await model.executeAsync({'intermediate2': input});
-    expect(console.warn).toHaveBeenCalledTimes(1);
-    expect(console.warn)
-        .toHaveBeenCalledWith(
-            'This model execution did not contain any nodes with control ' +
-            'flow or dynamic output shapes. You can use model.execute() ' +
-            'instead.');
-  });
-
-  it('executeAsync works when the subgraph has no unknown ops', async () => {
-    const graphDef: tensorflow.IGraphDef = {
-      node: [
-        {name: 'input', op: 'Placeholder'},
-        {name: 'intermediate', op: 'Unknown', input: ['input']},
-        {name: 'intermediate2', op: 'Sqrt', input: ['intermediate']},
-        {name: 'output', op: 'Square', input: ['intermediate2']},
-      ],
-      versions: {producer: 1.0, minConsumer: 3}
-    };
-    const customLoader: tfc.io.IOHandler = {
-      load: async () => ({
-        modelTopology: graphDef,
-        weightSpecs: weightsManifest,
-        weightData: new Int32Array([5]).buffer,
-      })
-    };
-    const model = await loadGraphModel(customLoader);
-    spyOn(console, 'warn');
-    const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-    await model.executeAsync({'intermediate2': input});
-  });
-
-  it('executeAsync throws when the subgraph has unknown ops', async () => {
-    const graphDef: tensorflow.IGraphDef = {
-      node: [
-        {name: 'input', op: 'Placeholder'},
-        {name: 'intermediate', op: 'Unknown', input: ['input']},
-        {name: 'intermediate2', op: 'Sqrt', input: ['intermediate']},
-        {name: 'output', op: 'Square', input: ['intermediate2']},
-      ],
-      versions: {producer: 1.0, minConsumer: 3}
-    };
-    const customLoader: tfc.io.IOHandler = {
-      load: async () => ({
-        modelTopology: graphDef,
-        weightSpecs: weightsManifest,
-        weightData: new Int32Array([5]).buffer,
-      })
-    };
-    const model = await loadGraphModel(customLoader);
-    const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-    try {
-      await model.executeAsync({'input': input});
-      throw new Error('Previous line should throw');
-    } catch (ex) {
-      expect((ex as Error).message)
-          .toBe(
-              'Unknown op \'Unknown\'. File an issue at ' +
-              'https://github.com/tensorflow/tfjs/issues so we can add it, ' +
-              'or register a custom execution with tf.registerOp()');
-    }
-  });
-
-  it('execute fails when there are dynamic ops', async () => {
-    const customLoader: tfc.io.IOHandler = {
-      load: async () => ({
-        modelTopology: CONTROL_FLOW_MODEL,
-        weightSpecs: weightsManifest,
-        weightData: new Int32Array([5]).buffer,
-      })
-    };
-    const model = await loadGraphModel(customLoader);
-    const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-    expect(() => model.execute(input))
-        .toThrowError(/This execution contains the node 'Enter'/);
-  });
-
-  it('execute does not warn when there are no dynamic ops', async () => {
-    const customLoader: tfc.io.IOHandler = {
-      load: async () => ({
-        modelTopology: SIMPLE_MODEL,
-        weightSpecs: weightsManifest,
-        weightData: new Int32Array([5]).buffer,
-      })
-    };
-    const model = await loadGraphModel(customLoader);
-    spyOn(console, 'warn');
-    const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-    model.execute(input);
-    expect(console.warn).toHaveBeenCalledTimes(0);
-  });
-
-  it('execute err when there are no dynamic ops', async () => {
-    const customLoader: tfc.io.IOHandler = {
-      load: async () => ({
-        modelTopology: SIMPLE_MODEL,
-        weightSpecs: weightsManifest,
-        weightData: new Int32Array([5]).buffer,
-      })
-    };
-    const model = await loadGraphModel(customLoader);
-    spyOn(console, 'warn');
-    const input = tfc.tensor2d([1, 1], [2, 1], 'int32');
-    model.execute(input);
-    expect(console.warn).toHaveBeenCalledTimes(0);
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/executor/hash_table.ts b/tfjs-master/tfjs-converter/src/executor/hash_table.ts
deleted file mode 100644
index 9552349fe..000000000
--- a/tfjs-master/tfjs-converter/src/executor/hash_table.ts
+++ /dev/null
@@ -1,165 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {DataType, keep, scalar, stack, Tensor, tidy, unstack, util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-/**
- * Hashtable contains a set of tensors, which can be accessed by key.
- */
-export class HashTable {
-  readonly handle: Tensor;
-
-  // tslint:disable-next-line: no-any
-  private tensorMap: Map<any, Tensor>;
-
-  get id() {
-    return this.handle.id;
-  }
-
-  /**
-   * Constructor of HashTable. Creates a hash table.
-   *
-   * @param keyDType `dtype` of the table keys.
-   * @param valueDType `dtype` of the table values.
-   */
-  constructor(readonly keyDType: DataType, readonly valueDType: DataType) {
-    this.handle = scalar(0);
-    // tslint:disable-next-line: no-any
-    this.tensorMap = new Map<any, Tensor>();
-
-    keep(this.handle);
-  }
-
-  /**
-   * Dispose the tensors and handle and clear the hashtable.
-   */
-  clearAndClose() {
-    this.tensorMap.forEach(value => value.dispose());
-    this.tensorMap.clear();
-    this.handle.dispose();
-  }
-
-  /**
-   * The number of items in the hash table.
-   */
-  size(): number {
-    return this.tensorMap.size;
-  }
-
-  /**
-   * The number of items in the hash table as a rank-0 tensor.
-   */
-  tensorSize(): Tensor {
-    return tfOps.scalar(this.size(), 'int32');
-  }
-
-  /**
-   * Replaces the contents of the table with the specified keys and values.
-   * @param keys Keys to store in the hashtable.
-   * @param values Values to store in the hashtable.
-   */
-  async import(keys: Tensor, values: Tensor): Promise<Tensor> {
-    this.checkKeyAndValueTensor(keys, values);
-
-    // We only store the primitive values of the keys, this allows lookup
-    // to be O(1).
-    const $keys = await keys.data();
-
-    // Clear the hashTable before inserting new values.
-    this.tensorMap.forEach(value => value.dispose());
-    this.tensorMap.clear();
-
-    return tidy(() => {
-      const $values = unstack(values);
-
-      const keysLength = $keys.length;
-      const valuesLength = $values.length;
-
-      util.assert(
-          keysLength === valuesLength,
-          () => `The number of elements doesn't match, keys has ` +
-              `${keysLength} elements, the values has ${valuesLength} ` +
-              `elements.`);
-
-      for (let i = 0; i < keysLength; i++) {
-        const key = $keys[i];
-        const value = $values[i];
-
-        keep(value);
-        this.tensorMap.set(key, value);
-      }
-
-      return this.handle;
-    });
-  }
-
-  /**
-   * Looks up keys in a hash table, outputs the corresponding values.
-   *
-   * Performs batch lookups, for every element in the key tensor, `find`
-   * stacks the corresponding value into the return tensor.
-   *
-   * If an element is not present in the table, the given `defaultValue` is
-   * used.
-   *
-   * @param keys Keys to look up. Must have the same type as the keys of the
-   *     table.
-   * @param defaultValue The scalar `defaultValue` is the value output for keys
-   *     not present in the table. It must also be of the same type as the
-   *     table values.
-   */
-  async find(keys: Tensor, defaultValue: Tensor): Promise<Tensor> {
-    this.checkKeyAndValueTensor(keys, defaultValue);
-
-    const $keys = await keys.data();
-
-    return tidy(() => {
-      const result: Tensor[] = [];
-
-      for (let i = 0; i < $keys.length; i++) {
-        const key = $keys[i];
-
-        const value = this.findWithDefault(key, defaultValue);
-        result.push(value);
-      }
-
-      return stack(result);
-    });
-  }
-
-  // tslint:disable-next-line: no-any
-  private findWithDefault(key: any, defaultValue: Tensor): Tensor {
-    const result = this.tensorMap.get(key);
-
-    return result != null ? result : defaultValue;
-  }
-
-  private checkKeyAndValueTensor(key: Tensor, value: Tensor) {
-    if (key.dtype !== this.keyDType) {
-      throw new Error(
-          `Expect key dtype ${this.keyDType}, but got ` +
-          `${key.dtype}`);
-    }
-
-    if (value.dtype !== this.valueDType) {
-      throw new Error(
-          `Expect value dtype ${this.valueDType}, but got ` +
-          `${value.dtype}`);
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/model_analysis.ts b/tfjs-master/tfjs-converter/src/executor/model_analysis.ts
deleted file mode 100644
index 450e73071..000000000
--- a/tfjs-master/tfjs-converter/src/executor/model_analysis.ts
+++ /dev/null
@@ -1,347 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {NamedTensorMap} from '@tensorflow/tfjs-core';
-
-import {NamedTensorsMap} from '../data/types';
-import {parseNodeName} from '../operations/executors/utils';
-import {Graph, Node} from '../operations/types';
-
-export interface ExecutionInfo {
-  inputs: NamedTensorMap;
-  outputs: Node[];
-  usedNodes: Set<string>;
-  missingInputs: string[];
-  dynamicNode: Node;
-  syncInputs: string[];
-}
-
-/**
- * Given graph inputs and desired outputs, find the minimal set of nodes
- * to execute in order to compute the outputs. In addition return other useful
- * info such:
- * - Missing inputs needed to compute the output.
- * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).
- * - Alternative inputs in order to avoid async (dynamic op) execution.
- */
-export function getExecutionSubgraph(
-    inputs: NamedTensorMap, outputs: Node[], weightMap: NamedTensorsMap,
-    initNodes?: Node[]): ExecutionInfo {
-  const usedNodes = new Set<string>();
-  const missingInputs: string[] = [];
-  let dynamicNode: Node = null;
-  let syncInputs: string[] = null;
-
-  // Start with the outputs, going backwards and find all the nodes that are
-  // needed to compute those outputs.
-  const seen = new Set<string>();
-  const inputNodeNames =
-      new Set(Object.keys(inputs).map((name) => parseNodeName(name)[0]));
-
-  initNodes = initNodes || [];
-  const initNodeNames =
-      new Set(initNodes.map((node) => parseNodeName(node.name)[0]));
-
-  const frontier = [...outputs];
-  while (frontier.length > 0) {
-    const node = frontier.pop();
-    if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {
-      if (dynamicNode == null) {
-        dynamicNode = node;
-        syncInputs = dynamicNode.children.map(child => child.name)
-                         .filter(name => usedNodes.has(name));
-      }
-    }
-    usedNodes.add(node.name);
-
-    // Weights are dead end since we already have their values.
-    if (weightMap[node.name] != null) {
-      continue;
-    }
-    // This node is a dead end since it's one of the user-provided inputs.
-    if (inputNodeNames.has(node.name)) {
-      continue;
-    }
-    // This node is a dead end since it doesn't have any inputs.
-    if (initNodeNames.has(node.name)) {
-      continue;
-    }
-    if (node.inputs.length === 0) {
-      missingInputs.push(node.name);
-      continue;
-    }
-    node.inputs.forEach(input => {
-      // Don't add to the frontier if it is already there.
-      if (seen.has(input.name)) {
-        return;
-      }
-      seen.add(input.name);
-      frontier.push(input);
-    });
-  }
-  return {inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs};
-}
-
-/**
- * Given the execution info, return a list of nodes in topological order that
- * need to be executed to compute the output.
- */
-export function getNodesInTopologicalOrder(
-    graph: Graph, executionInfo: ExecutionInfo): Node[] {
-  const {usedNodes, inputs} = executionInfo;
-  const inputNodes = Object.keys(inputs)
-                         .map(name => parseNodeName(name)[0])
-                         .map(name => graph.nodes[name]);
-  const initNodes = graph.initNodes || [];
-
-  const isUsed = (node: Node|string) =>
-      usedNodes.has(typeof node === 'string' ? node : node.name);
-
-  function unique(nodes: Node[]): Node[] {
-    return [...new Map(nodes.map((node) => [node.name, node])).values()];
-  }
-  const predefinedNodes = unique([
-                            ...inputNodes,
-                            ...graph.weights,
-                            ...initNodes,
-                          ]).filter(isUsed);
-  const allNodes = unique([
-                     ...predefinedNodes,
-                     ...Object.values(graph.nodes),
-                   ]).filter(isUsed);
-  const nameToNode =
-      new Map<string, Node>(allNodes.map((node) => [node.name, node]));
-
-  const inCounts: Record<string, number> = {};
-  for (const node of allNodes) {
-    inCounts[node.name] = inCounts[node.name] || 0;
-    for (const child of node.children) {
-      // When the child is unused, set in counts to infinity so that it will
-      // never be decreased to 0 and added to the execution list.
-      if (!isUsed(child)) {
-        inCounts[child.name] = Number.POSITIVE_INFINITY;
-      }
-      inCounts[child.name] = (inCounts[child.name] || 0) + 1;
-    }
-  }
-
-  // Build execution order for all used nodes regardless whether they are
-  // predefined or not.
-  const frontier = Object.entries(inCounts)
-                       .filter(([, inCount]) => inCount === 0)
-                       .map(([name]) => name);
-  const orderedNodeNames = [...frontier];
-  while (frontier.length > 0) {
-    const nodeName = frontier.pop();
-    const node = nameToNode.get(nodeName)!;
-    for (const child of node.children.filter(isUsed)) {
-      if (--inCounts[child.name] === 0) {
-        orderedNodeNames.push(child.name);
-        frontier.push(child.name);
-      }
-    }
-  }
-
-  const orderedNodes = orderedNodeNames.map((name) => nameToNode.get(name));
-  const filteredOrderedNodes =
-      filterPredefinedReachableNodes(orderedNodes, predefinedNodes);
-
-  // TODO: Turn validation on/off with tf env flag.
-  validateNodesExecutionOrder(filteredOrderedNodes, predefinedNodes);
-
-  return filteredOrderedNodes;
-}
-
-/**
- * This is a helper function of `getNodesInTopologicalOrder`.
- * Returns ordered nodes reachable by at least one predefined node.
- * This can help us filter out redundant nodes from the returned node list.
- * For example:
- * If we have four nodes with dependencies like this:
- *   a --> b --> c --> d
- * when node `c` is predefined (e.g. given as an input tensor), we can
- * skip node `a` and `b` since their outputs will never be used.
- *
- * @param orderedNodes Graph nodes in execution order.
- * @param predefinedNodes Graph inputs, weights, and init nodes. Nodes in this
- *     list must have distinct names.
- */
-function filterPredefinedReachableNodes(
-    orderedNodes: Node[], predefinedNodes: Node[]) {
-  const nameToNode =
-      new Map<string, Node>(orderedNodes.map((node) => [node.name, node]));
-
-  // TODO: Filter out more nodes when >=2 nodes are predefined in a path.
-  const stack = predefinedNodes.map((node) => node.name);
-  const predefinedReachableNodeNames = new Set(stack);
-  // Perform a DFS starting from the set of all predefined nodes
-  // to find the set of all nodes reachable from the predefined nodes.
-  while (stack.length > 0) {
-    const nodeName = stack.pop();
-    const node = nameToNode.get(nodeName)!;
-    for (const child of node.children) {
-      if (!nameToNode.has(child.name) ||
-          predefinedReachableNodeNames.has(child.name)) {
-        continue;
-      }
-      predefinedReachableNodeNames.add(child.name);
-      stack.push(child.name);
-    }
-  }
-
-  // Filter out unreachable nodes and build the ordered node list.
-  const filteredOrderedNodes = orderedNodes.filter(
-      (node) => predefinedReachableNodeNames.has(node.name));
-
-  return filteredOrderedNodes;
-}
-
-class NodesExecutionOrderError extends Error {
-  constructor(message: string) {
-    super(`NodesExecutionOrderError: ${message}`);
-  }
-}
-
-/**
- * This is a helper function of `getNodesInTopologicalOrder`.
- * Validates property: given nodes `a` and `b`, Order(a) > Order(b) if `a`
- * is a child of `b`. This function throws an error if validation fails.
- *
- * @param orderedNodes Graph nodes in execution order.
- * @param predefinedNodes Graph inputs, weights, and init nodes. Nodes in this
- *     list must have distinct names.
- */
-function validateNodesExecutionOrder(
-    orderedNodes: Node[], predefinedNodes: Node[]) {
-  const nodeNameToOrder = new Map<string, number>(
-      orderedNodes.map((node, order) => [node.name, order]));
-  const predefinedNodeNames = new Set(predefinedNodes.map((node) => node.name));
-  const isPredefined = (node: Node|string) =>
-      predefinedNodeNames.has(typeof node === 'string' ? node : node.name);
-  const willBeExecutedNodeNames =
-      new Set(orderedNodes.map((node) => node.name));
-  const willBeExecuted = (node: Node|string) =>
-      willBeExecutedNodeNames.has(typeof node === 'string' ? node : node.name);
-
-  for (const node of orderedNodes) {
-    for (const child of node.children.filter(willBeExecuted)) {
-      if (!nodeNameToOrder.has(child.name)) {
-        throw new NodesExecutionOrderError(
-            `Child ${child.name} of node ${node.name} is unreachable.`);
-      }
-      if (nodeNameToOrder.get(node.name) > nodeNameToOrder.get(child.name)) {
-        throw new NodesExecutionOrderError(`Node ${
-            node.name} is scheduled to run after its child ${child.name}.`);
-      }
-    }
-    if (!isPredefined(node)) {
-      for (const input of node.inputs) {
-        if (!nodeNameToOrder.has(input.name)) {
-          throw new NodesExecutionOrderError(
-              `Input ${input.name} of node ${node.name} is unreachable.`);
-        }
-        if (nodeNameToOrder.get(input.name) > nodeNameToOrder.get(node.name)) {
-          throw new NodesExecutionOrderError(`Node ${
-              node.name} is scheduled to run before its input ${input.name}.`);
-        }
-      }
-    }
-  }
-}
-
-/**
- * Given the execution info, return a map from node name to the disposable
- * node name list after its execution.
- *
- * @returns A map from node name to disposable nodes after its
- *     execution. That is, for a node `x`, `nodeLiveUntilMap[x]` indicates
- *     all nodes which their intermediate tensors should be disposed after `x`
- *     being executed.
- */
-export function getNodeLiveUntilMap(orderedNodes: Node[]): Map<string, Node[]> {
-  const nodeNameToOrder = new Map<string, number>(
-      orderedNodes.map((node, order) => [node.name, order]));
-
-  const INF_LIFE = Number.MAX_SAFE_INTEGER;
-  // Make control flow nodes (and consequently their direct parents)
-  // live forever since they're tricky to track correctly.
-  const selfLifespans = orderedNodes.map(
-      (node, nodeOrder) => isControlFlow(node) ? INF_LIFE : nodeOrder);
-  const getSelfLifeSpan = (node: Node) => {
-    const selfLife = selfLifespans[nodeNameToOrder.get(node.name)!];
-    if (selfLife == null) {
-      // If nodeToOrder does not contain the node, it is unused or
-      // unreachable in graph.
-      return -1;
-    }
-    return selfLife;
-  };
-
-  // `liveUntil[i]` points to the last node in the `orderedNodes` array that
-  // may depend on tensors from node `i`. It indicates that all the
-  // intermediate tensors from `orderedNodes[i]` should be disposed after
-  // `orderedNodes[liveUntil[i]]` is executed.
-  // A node lives long enough to pass on its tensors to its children.
-  // It lives until at least `max(node's position, children's positions)`.
-  const liveUntilOrders = orderedNodes.map((node, nodeOrder) => {
-    return node.children.map(getSelfLifeSpan)
-        .reduce((a, b) => Math.max(a, b), selfLifespans[nodeOrder]);
-  });
-
-  // liveUntilMap:
-  // - Key: Name of a node `x`
-  // - Values: All nodes whose intermediate tensors should be disposed
-  //           after `x` is executed.
-  const liveUntilMap = new Map<string, Node[]>();
-  for (let nodeOrder = 0; nodeOrder < orderedNodes.length; ++nodeOrder) {
-    const liveUntilOrder = liveUntilOrders[nodeOrder];
-    if (liveUntilOrder === INF_LIFE) {
-      continue;
-    }
-    const node = orderedNodes[nodeOrder];
-    const liveUntilNode = orderedNodes[liveUntilOrder];
-    if (!liveUntilMap.has(liveUntilNode.name)) {
-      liveUntilMap.set(liveUntilNode.name, []);
-    }
-    liveUntilMap.get(liveUntilNode.name)!.push(node);
-  }
-  return liveUntilMap;
-}
-
-const CONTROL_FLOW_OPS = new Set([
-  'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',
-  'StatelessWhile', 'if', 'While'
-]);
-const DYNAMIC_SHAPE_OPS = new Set([
-  'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'
-]);
-const HASH_TABLE_OPS = new Set([
-  'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2',
-  'LookupTableFind', 'LookupTableFindV2', 'LookupTableSize', 'LookupTableSizeV2'
-]);
-
-export function isControlFlow(node: Node) {
-  return CONTROL_FLOW_OPS.has(node.op);
-}
-
-export function isDynamicShape(node: Node) {
-  return DYNAMIC_SHAPE_OPS.has(node.op);
-}
-
-export function isHashTable(node: Node) {
-  return HASH_TABLE_OPS.has(node.op);
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/model_analysis_test.ts b/tfjs-master/tfjs-converter/src/executor/model_analysis_test.ts
deleted file mode 100644
index 18c1da78f..000000000
--- a/tfjs-master/tfjs-converter/src/executor/model_analysis_test.ts
+++ /dev/null
@@ -1,136 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {NamedTensorMap, scalar} from '@tensorflow/tfjs-core';
-
-import {IGraphDef} from '../data/compiled_api';
-import {OperationMapper} from '../operations/operation_mapper';
-
-import {getExecutionSubgraph} from './model_analysis';
-
-describe('getExecutionInfo', () => {
-  it('2 disconnected subgraphs, no dynamic ops', () => {
-    const weightMap = {};
-    const graphDef: IGraphDef = {
-      node: [
-        {name: 'input', op: 'Placeholder'},
-        {name: 'intermediate', op: 'Add', input: ['input', 'input']},
-        {name: 'output', op: 'Square', input: ['intermediate']},
-        {name: 'input2', op: 'Const'},                    // Unrelated to input.
-        {name: 'output2', op: 'Sqrt', input: ['input2']}  // Related to input2.
-      ],
-      versions: {producer: 1.0, minConsumer: 3}
-    };
-    const graph = OperationMapper.Instance.transformGraph(graphDef);
-
-    // input --> output
-    let inputs: NamedTensorMap = {'input': scalar(0)};
-    let outputs = [graph.nodes['output']];
-    let executionInfo = getExecutionSubgraph(inputs, outputs, weightMap);
-    expect(executionInfo.inputs).toBe(inputs);
-    expect(executionInfo.outputs).toBe(outputs);
-    expect(executionInfo.dynamicNode).toBeFalsy();
-    expect(executionInfo.missingInputs).toEqual([]);
-    expect(executionInfo.syncInputs).toBeFalsy();
-    expect(executionInfo.usedNodes).toContain('input');
-    expect(executionInfo.usedNodes).toContain('intermediate');
-    expect(executionInfo.usedNodes).toContain('output');
-    expect(executionInfo.usedNodes.size).toBe(3);
-
-    // input --> intermediate
-    inputs = {'input': scalar(0)};
-    outputs = [graph.nodes['intermediate']];
-    executionInfo = getExecutionSubgraph(inputs, outputs, weightMap);
-    expect(executionInfo.inputs).toBe(inputs);
-    expect(executionInfo.outputs).toBe(outputs);
-    expect(executionInfo.dynamicNode).toBeFalsy();
-    expect(executionInfo.missingInputs).toEqual([]);
-    expect(executionInfo.syncInputs).toBeFalsy();
-    expect(executionInfo.usedNodes).toContain('input');
-    expect(executionInfo.usedNodes).toContain('intermediate');
-    expect(executionInfo.usedNodes.size).toBe(2);
-
-    // input2 --> output2
-    inputs = {'input2': scalar(0)};
-    outputs = [graph.nodes['output2']];
-    executionInfo = getExecutionSubgraph(inputs, outputs, weightMap);
-    expect(executionInfo.inputs).toBe(inputs);
-    expect(executionInfo.outputs).toBe(outputs);
-    expect(executionInfo.dynamicNode).toBeFalsy();
-    expect(executionInfo.missingInputs).toEqual([]);
-    expect(executionInfo.syncInputs).toBeFalsy();
-    expect(executionInfo.usedNodes).toContain('input2');
-    expect(executionInfo.usedNodes).toContain('output2');
-    expect(executionInfo.usedNodes.size).toBe(2);
-
-    // input --> output2 is disconnected.
-    inputs = {'input': scalar(0)};
-    outputs = [graph.nodes['output2']];
-    executionInfo = getExecutionSubgraph(inputs, outputs, weightMap);
-    expect(executionInfo.inputs).toBe(inputs);
-    expect(executionInfo.outputs).toBe(outputs);
-    expect(executionInfo.dynamicNode).toBeFalsy();
-    expect(executionInfo.missingInputs).toEqual(['input2']);
-    expect(executionInfo.syncInputs).toBeFalsy();
-    expect(executionInfo.usedNodes).toContain('output2');
-    expect(executionInfo.usedNodes).toContain('input2');
-    expect(executionInfo.usedNodes.size).toBe(2);
-
-    // input2 --> output is disconnected.
-    inputs = {'input2': scalar(0)};
-    outputs = [graph.nodes['output']];
-    executionInfo = getExecutionSubgraph(inputs, outputs, weightMap);
-    expect(executionInfo.inputs).toBe(inputs);
-    expect(executionInfo.outputs).toBe(outputs);
-    expect(executionInfo.dynamicNode).toBeFalsy();
-    expect(executionInfo.missingInputs).toEqual(['input']);
-    expect(executionInfo.syncInputs).toBeFalsy();
-    expect(executionInfo.usedNodes).toContain('input');
-    expect(executionInfo.usedNodes).toContain('intermediate');
-    expect(executionInfo.usedNodes).toContain('output');
-    expect(executionInfo.usedNodes.size).toBe(3);
-  });
-
-  it('Async graph', () => {
-    const weightMap = {};
-    const graphDef: IGraphDef = {
-      node: [
-        {name: 'input', op: 'Placeholder'},
-        {name: 'intermediate', op: 'Enter', input: ['input']},
-        {name: 'intermediate2', op: 'Const', input: ['intermediate']},
-        {name: 'output', op: 'Square', input: ['intermediate2']},
-      ],
-      versions: {producer: 1.0, minConsumer: 3}
-    };
-    const graph = OperationMapper.Instance.transformGraph(graphDef);
-
-    // input --> output
-    const inputs: NamedTensorMap = {'input': scalar(0)};
-    const outputs = [graph.nodes['output']];
-    const executionInfo = getExecutionSubgraph(inputs, outputs, weightMap);
-    expect(executionInfo.inputs).toBe(inputs);
-    expect(executionInfo.outputs).toBe(outputs);
-    expect(executionInfo.dynamicNode).toBe(graph.nodes['intermediate']);
-    expect(executionInfo.missingInputs).toEqual([]);
-    expect(executionInfo.syncInputs).toEqual(['intermediate2']);
-    expect(executionInfo.usedNodes).toContain('input');
-    expect(executionInfo.usedNodes).toContain('intermediate');
-    expect(executionInfo.usedNodes).toContain('intermediate2');
-    expect(executionInfo.usedNodes).toContain('output');
-    expect(executionInfo.usedNodes.size).toBe(4);
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/executor/resource_manager.ts b/tfjs-master/tfjs-converter/src/executor/resource_manager.ts
deleted file mode 100644
index add272007..000000000
--- a/tfjs-master/tfjs-converter/src/executor/resource_manager.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {HashTableMap, NamedTensorMap} from '../data/types';
-import {HashTable} from './hash_table';
-
-/**
- * Contains global resources of a model.
- */
-export class ResourceManager {
-  constructor(
-      readonly hashTableNameToHandle: NamedTensorMap = {},
-      readonly hashTableMap: HashTableMap = {}) {}
-
-  /**
-   * Register a `HashTable` in the resource manager.
-   *
-   * The `HashTable` can be retrieved by `resourceManager.getHashTableById`,
-   * where id is the table handle tensor's id.
-   *
-   * @param name Op node name that creates the `HashTable`.
-   * @param hashTable The `HashTable` to be added to resource manager.
-   */
-  addHashTable(name: string, hashTable: HashTable) {
-    this.hashTableNameToHandle[name] = hashTable.handle;
-    this.hashTableMap[hashTable.id] = hashTable;
-  }
-
-  /**
-   * Get the table handle by node name.
-   * @param name Op node name that creates the `HashTable`. This name is also
-   *     used in the inputs list of lookup and import `HashTable` ops.
-   */
-  getHashTableHandleByName(name: string) {
-    return this.hashTableNameToHandle[name];
-  }
-
-  /**
-   * Get the actual `HashTable` by its handle tensor's id.
-   * @param id The id of the handle tensor.
-   */
-  getHashTableById(id: number): HashTable {
-    return this.hashTableMap[id];
-  }
-
-  /**
-   * Dispose `ResourceManager`, including its hashTables and tensors in them.
-   */
-  dispose() {
-    for (const key in this.hashTableMap) {
-      this.hashTableMap[key].clearAndClose();
-      delete this.hashTableMap[key];
-    }
-
-    for (const name in this.hashTableNameToHandle) {
-      this.hashTableNameToHandle[name].dispose();
-      delete this.hashTableNameToHandle[name];
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/tensor_array.ts b/tfjs-master/tfjs-converter/src/executor/tensor_array.ts
deleted file mode 100644
index b69553ffb..000000000
--- a/tfjs-master/tfjs-converter/src/executor/tensor_array.ts
+++ /dev/null
@@ -1,316 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {concat, DataType, keep, reshape, scalar, slice, stack, Tensor, tensor, tidy, unstack} from '@tensorflow/tfjs-core';
-
-import {assertShapesMatchAllowUndefinedSize} from './tensor_utils';
-
-export interface TensorWithState {
-  tensor?: Tensor;
-  written?: boolean;
-  read?: boolean;
-  cleared?: boolean;
-}
-/**
- * The TensorArray object keeps an array of Tensors.  It
- * allows reading from the array and writing to the array.
- */
-export class TensorArray {
-  private tensors: TensorWithState[] = [];
-  private closed_ = false;
-  readonly idTensor: Tensor;
-  constructor(
-      readonly name: string, readonly dtype: DataType, private maxSize: number,
-      private elementShape: number[], readonly identicalElementShapes: boolean,
-      readonly dynamicSize: boolean, readonly clearAfterRead: boolean) {
-    this.idTensor = scalar(0);
-    keep(this.idTensor);
-  }
-
-  get id() {
-    return this.idTensor.id;
-  }
-
-  get closed() {
-    return this.closed_;
-  }
-
-  /**
-   * Dispose the tensors and idTensor and mark the TensoryArray as closed.
-   */
-  clearAndClose(keepIds?: Set<number>) {
-    this.tensors.forEach(tensor => {
-      if (keepIds == null || !keepIds.has(tensor.tensor.id)) {
-        tensor.tensor.dispose();
-      }
-    });
-    this.tensors = [];
-    this.closed_ = true;
-    this.idTensor.dispose();
-  }
-
-  size(): number {
-    return this.tensors.length;
-  }
-
-  /**
-   * Read the value at location index in the TensorArray.
-   * @param index Number the index to read from.
-   */
-  read(index: number): Tensor {
-    if (this.closed_) {
-      throw new Error(`TensorArray ${this.name} has already been closed.`);
-    }
-
-    if (index < 0 || index >= this.size()) {
-      throw new Error(`Tried to read from index ${index}, but array size is: ${
-          this.size()}`);
-    }
-
-    const tensorWithState = this.tensors[index];
-    if (tensorWithState.cleared) {
-      throw new Error(
-          `TensorArray ${this.name}: Could not read index ${
-              index} twice because it was cleared after a previous read ` +
-          `(perhaps try setting clear_after_read = false?).`);
-    }
-
-    if (this.clearAfterRead) {
-      tensorWithState.cleared = true;
-    }
-
-    tensorWithState.read = true;
-    return tensorWithState.tensor;
-  }
-
-  /**
-   * Helper method to read multiple tensors from the specified indices.
-   */
-  readMany(indices: number[]): Tensor[] {
-    return indices.map(index => this.read(index));
-  }
-
-  /**
-   * Write value into the index of the TensorArray.
-   * @param index number the index to write to.
-   * @param tensor
-   */
-  write(index: number, tensor: Tensor) {
-    if (this.closed_) {
-      throw new Error(`TensorArray ${this.name} has already been closed.`);
-    }
-
-    if (index < 0 || !this.dynamicSize && index >= this.maxSize) {
-      throw new Error(`Tried to write to index ${
-          index}, but array is not resizeable and size is: ${this.maxSize}`);
-    }
-
-    const t = this.tensors[index] || {};
-
-    if (tensor.dtype !== this.dtype) {
-      throw new Error(`TensorArray ${
-          this.name}: Could not write to TensorArray index ${index},
-          because the value dtype is ${
-          tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);
-    }
-
-    // Set the shape for the first time write to unknow shape tensor array
-    if (this.size() === 0 &&
-        (this.elementShape == null || this.elementShape.length === 0)) {
-      this.elementShape = tensor.shape;
-    }
-
-    assertShapesMatchAllowUndefinedSize(
-        this.elementShape, tensor.shape,
-        `TensorArray ${this.name}: Could not write to TensorArray index ${
-            index}.`);
-
-    if (t.read) {
-      throw new Error(
-          `TensorArray ${this.name}: Could not write to TensorArray index ${
-              index}, because it has already been read.`);
-    }
-
-    if (t.written) {
-      throw new Error(
-          `TensorArray ${this.name}: Could not write to TensorArray index ${
-              index}, because it has already been written.`);
-    }
-
-    t.tensor = tensor;
-    keep(tensor);
-    t.written = true;
-
-    this.tensors[index] = t;
-  }
-
-  /**
-   * Helper method to write multiple tensors to the specified indices.
-   */
-  writeMany(indices: number[], tensors: Tensor[]) {
-    if (indices.length !== tensors.length) {
-      throw new Error(
-          `TensorArray ${this.name}: could not write multiple tensors,` +
-          `because the index size: ${
-              indices.length} is not the same as tensors size: ${
-              tensors.length}.`);
-    }
-
-    indices.forEach((i, index) => this.write(i, tensors[index]));
-  }
-
-  /**
-   * Return selected values in the TensorArray as a packed Tensor. All of
-   * selected values must have been written and their shapes must all match.
-   * @param [indices] number[] Optional. Taking values in [0, max_value). If the
-   *    TensorArray is not dynamic, max_value=size(). If not specified returns
-   *    all tensors in the original order.
-   * @param [dtype]
-   */
-  gather(indices?: number[], dtype?: DataType): Tensor {
-    if (!!dtype && dtype !== this.dtype) {
-      throw new Error(`TensorArray dtype is ${
-          this.dtype} but gather requested dtype ${dtype}`);
-    }
-
-    if (!indices) {
-      indices = [];
-      for (let i = 0; i < this.size(); i++) {
-        indices.push(i);
-      }
-    } else {
-      indices = indices.slice(0, this.size());
-    }
-
-    if (indices.length === 0) {
-      return tensor([], [0].concat(this.elementShape));
-    }
-
-    // Read all the PersistentTensors into a vector to keep track of
-    // their memory.
-    const tensors = this.readMany(indices);
-
-    assertShapesMatchAllowUndefinedSize(
-        this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');
-
-    return stack(tensors, 0);
-  }
-
-  /**
-   * Return the values in the TensorArray as a concatenated Tensor.
-   */
-  concat(dtype?: DataType): Tensor {
-    if (!!dtype && dtype !== this.dtype) {
-      throw new Error(`TensorArray dtype is ${
-          this.dtype} but concat requested dtype ${dtype}`);
-    }
-
-    if (this.size() === 0) {
-      return tensor([], [0].concat(this.elementShape));
-    }
-
-    const indices = [];
-    for (let i = 0; i < this.size(); i++) {
-      indices.push(i);
-    }
-    // Collect all the tensors from the tensors array.
-    const tensors = this.readMany(indices);
-
-    assertShapesMatchAllowUndefinedSize(
-        this.elementShape, tensors[0].shape,
-        `TensorArray shape mismatch: tensor array shape (${
-            this.elementShape}) vs first tensor shape (${tensors[0].shape})`);
-
-    return concat(tensors, 0);
-  }
-
-  /**
-   * Scatter the values of a Tensor in specific indices of a TensorArray.
-   * @param indices nummber[] values in [0, max_value). If the
-   *    TensorArray is not dynamic, max_value=size().
-   * @param tensor Tensor input tensor.
-   */
-  scatter(indices: number[], tensor: Tensor) {
-    if (tensor.dtype !== this.dtype) {
-      throw new Error(`TensorArray dtype is ${
-          this.dtype} but tensor has dtype ${tensor.dtype}`);
-    }
-
-    if (indices.length !== tensor.shape[0]) {
-      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${
-          indices.length} vs. ${tensor.shape[0]}`);
-    }
-
-    const maxIndex = Math.max(...indices);
-
-    if (!this.dynamicSize && maxIndex >= this.maxSize) {
-      throw new Error(
-          `Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);
-    }
-
-    this.writeMany(indices, unstack(tensor, 0));
-  }
-
-  /**
-   * Split the values of a Tensor into the TensorArray.
-   * @param length number[] with the lengths to use when splitting value along
-   *    its first dimension.
-   * @param tensor Tensor, the tensor to split.
-   */
-  split(length: number[], tensor: Tensor) {
-    if (tensor.dtype !== this.dtype) {
-      throw new Error(`TensorArray dtype is ${
-          this.dtype} but tensor has dtype ${tensor.dtype}`);
-    }
-    let totalLength = 0;
-    const cumulativeLengths = length.map(len => {
-      totalLength += len;
-      return totalLength;
-    });
-
-    if (totalLength !== tensor.shape[0]) {
-      throw new Error(`Expected sum of lengths to be equal to
-          tensor.shape[0], but sum of lengths is
-        ${totalLength}, and tensor's shape is: ${tensor.shape}`);
-    }
-
-    if (!this.dynamicSize && length.length !== this.maxSize) {
-      throw new Error(
-          `TensorArray's size is not equal to the size of lengths (${
-              this.maxSize} vs. ${length.length}), ` +
-          'and the TensorArray is not marked as dynamically resizeable');
-    }
-
-    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;
-    const tensors: Tensor[] = [];
-    tidy(() => {
-      tensor = reshape(tensor, [1, totalLength, elementPerRow]);
-      for (let i = 0; i < length.length; ++i) {
-        const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];
-        const indices = [0, previousLength, 0];
-        const sizes = [1, length[i], elementPerRow];
-        tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);
-      }
-      return tensors;
-    });
-    const indices = [];
-    for (let i = 0; i < length.length; i++) {
-      indices[i] = i;
-    }
-    this.writeMany(indices, tensors);
-  }
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/tensor_array_test.ts b/tfjs-master/tfjs-converter/src/executor/tensor_array_test.ts
deleted file mode 100644
index 0e3d77387..000000000
--- a/tfjs-master/tfjs-converter/src/executor/tensor_array_test.ts
+++ /dev/null
@@ -1,286 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {memory, Tensor, tensor2d, tensor3d} from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-
-import {TensorArray} from './tensor_array';
-
-let tensorArray: TensorArray;
-let tensor: Tensor;
-let tensor2: Tensor;
-const NAME = 'TA1';
-const DTYPE = 'int32';
-const SIZE = 10;
-const SHAPE = [1, 1];
-const IDENTICAL_SHAPE = true;
-const DYNAMIC_SIZE = false;
-const CLEAR_AFTER_READ = false;
-
-describe('TensorArray', () => {
-  beforeEach(() => {
-    tensorArray = new TensorArray(
-        NAME, DTYPE, SIZE, SHAPE, IDENTICAL_SHAPE, DYNAMIC_SIZE,
-        CLEAR_AFTER_READ);
-    tensor = tensor2d([1], [1, 1], 'int32');
-    tensor2 = tensor2d([2], [1, 1], 'int32');
-  });
-  afterEach(() => tensorArray.clearAndClose());
-
-  it('should initialize', () => {
-    expect(tensorArray.size()).toEqual(0);
-    expect(tensorArray.name).toEqual('TA1');
-    expect(tensorArray.dynamicSize).toBeFalsy();
-    expect(tensorArray.closed).toBeFalsy();
-  });
-
-  it('should close', () => {
-    const numOfTensors = memory().numTensors;
-    const size = tensorArray.size();
-    tensorArray.clearAndClose();
-    expect(tensorArray.size()).toBe(0);
-    expect(tensorArray.closed).toBeTruthy();
-
-    // disposed the tensor in the array and idTensor of the array
-    expect(memory().numTensors).toEqual(numOfTensors - size - 1);
-  });
-
-  it('should not dispose keep tensors when close', () => {
-    const numOfTensors = memory().numTensors;
-    tensorArray.write(0, tensor);
-    tensorArray.write(1, tensor2);
-    const size = tensorArray.size();
-    const keepIds = new Set([tensor.id]);
-    tensorArray.clearAndClose(keepIds);
-    expect(tensorArray.size()).toBe(0);
-    expect(tensorArray.closed).toBeTruthy();
-    expect(tensor.isDisposed).toBeFalsy();
-    expect(tensor2.isDisposed).toBeTruthy();
-    // disposed the tensor in the array and idTensor of the array
-    expect(memory().numTensors).toEqual(numOfTensors - size);
-  });
-
-  describe('write', () => {
-    it('should add new tensor', () => {
-      tensorArray.write(0, tensor);
-      expect(tensorArray.size()).toBe(1);
-    });
-    it('should add multiple tensors', () => {
-      tensorArray.writeMany([0, 1], [tensor, tensor2]);
-      expect(tensorArray.size()).toBe(2);
-    });
-    it('should fail if dtype does not match', () => {
-      const tensor = tensor2d([1], [1, 1], 'float32');
-      expect(() => tensorArray.write(0, tensor)).toThrow();
-    });
-    it('should fail if shape does not match', () => {
-      const tensor = tensor2d([1, 2], [2, 1], 'int32');
-      expect(() => tensorArray.write(0, tensor)).toThrow();
-    });
-    it('should allow unknown shape', () => {
-      const unknownShape: number[] = undefined;
-      tensorArray = new TensorArray(
-          NAME, DTYPE, SIZE, unknownShape, IDENTICAL_SHAPE, DYNAMIC_SIZE,
-          CLEAR_AFTER_READ);
-      const tensor = tensor2d([1, 2], [2, 1], 'int32');
-      tensorArray.write(0, tensor);
-      expect(tensorArray.read(0)).not.toBeNull();
-    });
-    it('should fail if the index has already been written', () => {
-      tensorArray.write(0, tensor);
-      expect(() => tensorArray.write(0, tensor)).toThrow();
-    });
-    it('should fail if the index greater than array size', () => {
-      expect(() => tensorArray.write(11, tensor)).toThrow();
-    });
-    it('should fail if the array is closed', () => {
-      tensorArray.clearAndClose();
-      expect(() => tensorArray.write(0, tensor)).toThrow();
-    });
-    it('should create no new tensors', () => {
-      const numTensors = memory().numTensors;
-      tensorArray.write(0, tensor);
-      expect(memory().numTensors).toEqual(numTensors);
-    });
-  });
-
-  describe('read', () => {
-    beforeEach(() => {
-      tensorArray.writeMany([0, 1], [tensor, tensor2]);
-    });
-
-    it('should read the correct index', () => {
-      expect(tensorArray.read(0)).toBe(tensor);
-      expect(tensorArray.read(1)).toBe(tensor2);
-    });
-    it('should read the multiple indices', () => {
-      expect(tensorArray.readMany([0, 1])).toEqual([tensor, tensor2]);
-    });
-    it('should failed if index is out of bound', () => {
-      expect(() => tensorArray.read(3)).toThrow();
-      expect(() => tensorArray.read(-1)).toThrow();
-    });
-    it('should failed if array is closed', () => {
-      tensorArray.clearAndClose();
-      expect(() => tensorArray.read(0)).toThrow();
-    });
-    it('should create no new tensors', () => {
-      const numTensors = memory().numTensors;
-      tensorArray.read(0);
-      tensorArray.read(1);
-      expect(memory().numTensors).toEqual(numTensors);
-    });
-  });
-
-  describe('gather', () => {
-    beforeEach(() => {
-      tensorArray.writeMany([0, 1], [tensor, tensor2]);
-    });
-
-    it('should return default packed tensors', async () => {
-      const gathered = tensorArray.gather();
-      expect(gathered.shape).toEqual([2, 1, 1]);
-      test_util.expectArraysClose(await gathered.data(), [1, 2]);
-    });
-
-    it('should return packed tensors when indices is specified', async () => {
-      const gathered = tensorArray.gather([1, 0]);
-      expect(gathered.shape).toEqual([2, 1, 1]);
-      test_util.expectArraysClose(await gathered.data(), [2, 1]);
-    });
-    it('should return when indices longer than available tensors', async () => {
-      const gathered = tensorArray.gather([1, 0, 2, 3]);
-      expect(gathered.shape).toEqual([2, 1, 1]);
-      test_util.expectArraysClose(await gathered.data(), [2, 1]);
-    });
-    it('should fail if dtype is not matched', () => {
-      expect(() => tensorArray.gather([0, 1], 'float32')).toThrow();
-    });
-    it('should create one new tensor', () => {
-      const numTensors: number = memory().numTensors;
-      tensorArray.gather();
-      expect(memory().numTensors).toEqual(numTensors + 1);
-    });
-  });
-
-  describe('concat', () => {
-    beforeEach(() => {
-      tensorArray.writeMany([0, 1], [tensor, tensor2]);
-    });
-
-    it('should return default concat tensors', async () => {
-      const concat = tensorArray.concat();
-      expect(concat.shape).toEqual([2, 1]);
-      test_util.expectArraysClose(await concat.data(), [1, 2]);
-    });
-
-    it('should fail if dtype is not matched', () => {
-      expect(() => tensorArray.concat('float32')).toThrow();
-    });
-
-    it('should create one new tensor', () => {
-      const numTensors: number = memory().numTensors;
-      tensorArray.concat();
-      expect(memory().numTensors).toEqual(numTensors + 1);
-    });
-  });
-
-  describe('scatter', () => {
-    it('should scatter the input tensor', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      tensorArray.scatter([0, 1, 2], input);
-      expect(tensorArray.size()).toEqual(3);
-    });
-
-    it('should fail if indices and tensor shapes do not matched', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      expect(() => tensorArray.scatter([1, 2], input)).toThrow();
-    });
-
-    it('should not fail if tensor array shape is -1', () => {
-      tensorArray = new TensorArray(
-          NAME, DTYPE, SIZE, [-1, 1], IDENTICAL_SHAPE, DYNAMIC_SIZE,
-          CLEAR_AFTER_READ);
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      tensorArray.scatter([1, 2, 3], input);
-      const res = tensorArray.gather([1]);
-      expect(res).not.toBeNull();
-    });
-
-    it('should fail if max index > array max size', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      expect(() => tensorArray.scatter([1, 2, 11], input)).toThrow();
-    });
-
-    it('should fail if dtype is not matched', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'float32');
-      expect(() => tensorArray.scatter([0, 1, 2], input)).toThrow();
-    });
-    it('should create three new tensors', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      const numTensors: number = memory().numTensors;
-      tensorArray.scatter([0, 1, 2], input);
-      expect(memory().numTensors).toEqual(numTensors + 3);
-    });
-  });
-
-  describe('split', () => {
-    beforeEach(() => {
-      tensorArray = new TensorArray(
-          NAME, DTYPE, 3, [2, 2], IDENTICAL_SHAPE, DYNAMIC_SIZE,
-          CLEAR_AFTER_READ);
-    });
-
-    it('should split the input tensor', () => {
-      const input =
-          tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 2, 2], 'int32');
-      tensorArray.split([1, 1, 1], input);
-      expect(tensorArray.size()).toEqual(3);
-    });
-
-    it('should fail if indices and tensor shapes do not matched', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      expect(() => tensorArray.split([1, 1], input)).toThrow();
-    });
-
-    it('should not fail if indices and tensor shapes is -1', () => {
-      tensorArray = new TensorArray(
-          NAME, DTYPE, SIZE, [-1, 1], IDENTICAL_SHAPE, DYNAMIC_SIZE,
-          CLEAR_AFTER_READ);
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      expect(() => tensorArray.split([1, 1], input)).toThrow();
-    });
-
-    it('should fail if length > array max size', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      expect(() => tensorArray.split([1, 1, 1, 1], input)).toThrow();
-    });
-
-    it('should fail if dtype is not matched', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'float32');
-      expect(() => tensorArray.split([1, 1, 1], input)).toThrow();
-    });
-
-    it('should create three new tensors', () => {
-      const input =
-          tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 2, 2], 'int32');
-      const numTensors: number = memory().numTensors;
-      tensorArray.split([1, 1, 1], input);
-      expect(memory().numTensors).toEqual(numTensors + 3);
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/executor/tensor_list.ts b/tfjs-master/tfjs-converter/src/executor/tensor_list.ts
deleted file mode 100644
index 577ada6d1..000000000
--- a/tfjs-master/tfjs-converter/src/executor/tensor_list.ts
+++ /dev/null
@@ -1,426 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {concat, DataType, keep, reshape, scalar, slice, stack, Tensor, tensor, tidy, unstack} from '@tensorflow/tfjs-core';
-
-import {assertShapesMatchAllowUndefinedSize, inferElementShape, mergeElementShape} from './tensor_utils';
-
-/**
- * TensorList stores a container of `tf.Tensor` objects, which are accessible
- * via tensors field.
- *
- * In order to get a copy of the underlying list, use the copy method:
- * ```
- *    TensorList b = a.copy();
- *    b.tensors().pushBack(t);  // This does not modify a.tensors().
- * ```
- *
- * Note that this is not a deep copy: the memory locations of the underlying
- * tensors will still point to the same locations of the corresponding tensors
- * in the original.
- */
-
-export class TensorList {
-  readonly idTensor: Tensor;
-  maxNumElements: number;
-
-  get id() {
-    return this.idTensor.id;
-  }
-  /**
-   *
-   * @param tensors list of tensors
-   * @param elementShape shape of each tensor, this can be a single number (any
-   * shape is allowed) or partial shape (dim = -1).
-   * @param elementDtype data type of each tensor
-   * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1
-   *   meaning that the size of `tensors` is unbounded.
-   */
-  constructor(
-      readonly tensors: Tensor[], readonly elementShape: number|number[],
-      readonly elementDtype: DataType, maxNumElements = -1) {
-    if (tensors != null) {
-      tensors.forEach(tensor => {
-        if (elementDtype !== tensor.dtype) {
-          throw new Error(`Invalid data types; op elements ${
-              elementDtype}, but list elements ${tensor.dtype}`);
-        }
-        assertShapesMatchAllowUndefinedSize(
-            elementShape, tensor.shape, 'TensorList shape mismatch: ');
-
-        keep(tensor);
-      });
-    }
-    this.idTensor = scalar(0);
-    this.maxNumElements = maxNumElements;
-    keep(this.idTensor);
-  }
-
-  /**
-   * Get a new TensorList containing a copy of the underlying tensor container.
-   */
-  copy(): TensorList {
-    return new TensorList(
-        [...this.tensors], this.elementShape, this.elementDtype);
-  }
-
-  /**
-   * Dispose the tensors and idTensor and clear the tensor list.
-   */
-  clearAndClose(keepIds?: Set<number>) {
-    this.tensors.forEach(tensor => {
-      if (keepIds == null || !keepIds.has(tensor.id)) {
-        tensor.dispose();
-      }
-    });
-    this.tensors.length = 0;
-    this.idTensor.dispose();
-  }
-  /**
-   * The size of the tensors in the tensor list.
-   */
-  size() {
-    return this.tensors.length;
-  }
-
-  /**
-   * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)
-   * tf.Tensor.
-   * @param elementShape shape of each tensor
-   * @param elementDtype data type of each tensor
-   * @param numElements the number of elements to stack
-   */
-  stack(elementShape: number[], elementDtype: DataType, numElements = -1):
-      Tensor {
-    if (elementDtype !== this.elementDtype) {
-      throw new Error(`Invalid data types; op elements ${
-          elementDtype}, but list elements ${this.elementDtype}`);
-    }
-    if (numElements !== -1 && this.tensors.length !== numElements) {
-      throw new Error(`Operation expected a list with ${
-          numElements} elements but got a list with ${
-          this.tensors.length} elements.`);
-    }
-    assertShapesMatchAllowUndefinedSize(
-        elementShape, this.elementShape, 'TensorList shape mismatch: ');
-    const outputElementShape =
-        inferElementShape(this.elementShape, this.tensors, elementShape);
-    return tidy(() => {
-      const reshapedTensors =
-          this.tensors.map(tensor => reshape(tensor, outputElementShape));
-      return stack(reshapedTensors, 0);
-    });
-  }
-
-  /**
-   * Pop a tensor from the end of the list.
-   * @param elementShape shape of the tensor
-   * @param elementDtype data type of the tensor
-   */
-  popBack(elementShape: number[], elementDtype: DataType): Tensor {
-    if (elementDtype !== this.elementDtype) {
-      throw new Error(`Invalid data types; op elements ${
-          elementDtype}, but list elements ${this.elementDtype}`);
-    }
-
-    if (this.size() === 0) {
-      throw new Error('Trying to pop from an empty list.');
-    }
-    const outputElementShape =
-        inferElementShape(this.elementShape, this.tensors, elementShape);
-    const tensor = this.tensors.pop();
-    tensor.kept = false;
-
-    assertShapesMatchAllowUndefinedSize(
-        tensor.shape, elementShape, 'TensorList shape mismatch: ');
-
-    return reshape(tensor, outputElementShape);
-  }
-
-  /**
-   * Push a tensor to the end of the list.
-   * @param tensor Tensor to be pushed.
-   */
-  pushBack(tensor: Tensor) {
-    if (tensor.dtype !== this.elementDtype) {
-      throw new Error(`Invalid data types; op elements ${
-          tensor.dtype}, but list elements ${this.elementDtype}`);
-    }
-
-    assertShapesMatchAllowUndefinedSize(
-        tensor.shape, this.elementShape, 'TensorList shape mismatch: ');
-
-    if (this.maxNumElements === this.size()) {
-      throw new Error(`Trying to push element into a full list.`);
-    }
-    keep(tensor);
-    this.tensors.push(tensor);
-  }
-
-  /**
-   * Update the size of the list.
-   * @param size the new size of the list.
-   */
-  resize(size: number) {
-    if (size < 0) {
-      throw new Error(
-          `TensorListResize expects size to be non-negative. Got: ${size}`);
-    }
-
-    if (this.maxNumElements !== -1 && size > this.maxNumElements) {
-      throw new Error(`TensorListResize input size ${
-          size} is greater maxNumElement ${this.maxNumElements}.`);
-    }
-
-    const destTensorList: TensorList = new TensorList(
-        [], this.elementShape, this.elementDtype, this.maxNumElements);
-    destTensorList.tensors.length = size;
-    for (let i = 0; i < Math.min(this.tensors.length, size); ++i) {
-      destTensorList.tensors[i] = this.tensors[i];
-    }
-    return destTensorList;
-  }
-
-  /**
-   * Retrieve the element at the provided index
-   * @param elementShape shape of the tensor
-   * @param elementDtype dtype of the tensor
-   * @param elementIndex index of the tensor
-   */
-  getItem(elementIndex: number, elementShape: number[], elementDtype: DataType):
-      Tensor {
-    if (elementDtype !== this.elementDtype) {
-      throw new Error(`Invalid data types; op elements ${
-          elementDtype}, but list elements ${this.elementDtype}`);
-    }
-    if (elementIndex < 0 || elementIndex > this.tensors.length) {
-      throw new Error(`Trying to access element ${
-          elementIndex} in a list with ${this.tensors.length} elements.`);
-    }
-
-    if (this.tensors[elementIndex] == null) {
-      throw new Error(`element at index ${elementIndex} is null.`);
-    }
-
-    assertShapesMatchAllowUndefinedSize(
-        this.tensors[elementIndex].shape, elementShape,
-        'TensorList shape mismatch: ');
-    const outputElementShape =
-        inferElementShape(this.elementShape, this.tensors, elementShape);
-    return reshape(this.tensors[elementIndex], outputElementShape);
-  }
-
-  /**
-   * Set the tensor at the index
-   * @param elementIndex index of the tensor
-   * @param tensor the tensor to be inserted into the list
-   */
-  setItem(elementIndex: number, tensor: Tensor) {
-    if (tensor.dtype !== this.elementDtype) {
-      throw new Error(`Invalid data types; op elements ${
-          tensor.dtype}, but list elements ${this.elementDtype}`);
-    }
-
-    if (elementIndex < 0 ||
-        this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {
-      throw new Error(`Trying to set element ${
-          elementIndex} in a list with max ${this.maxNumElements} elements.`);
-    }
-
-    assertShapesMatchAllowUndefinedSize(
-        this.elementShape, tensor.shape, 'TensorList shape mismatch: ');
-    keep(tensor);
-
-    // dispose the previous value if it is replacing.
-    if (this.tensors[elementIndex] != null) {
-      this.tensors[elementIndex].kept = false;
-    }
-
-    this.tensors[elementIndex] = tensor;
-  }
-
-  /**
-   * Return selected values in the TensorList as a stacked Tensor. All of
-   * selected values must have been written and their shapes must all match.
-   * @param indices indices of tensors to gather
-   * @param elementDtype output tensor dtype
-   * @param elementShape output tensor element shape
-   */
-  gather(indices: number[], elementDtype: DataType, elementShape: number[]):
-      Tensor {
-    if (elementDtype !== this.elementDtype) {
-      throw new Error(`Invalid data types; op elements ${
-          elementDtype}, but list elements ${this.elementDtype}`);
-    }
-
-    assertShapesMatchAllowUndefinedSize(
-        this.elementShape, elementShape, 'TensorList shape mismatch: ');
-
-    // When indices is greater than the size of the list, indices beyond the
-    // size of the list are ignored.
-    indices = indices.slice(0, this.size());
-    const outputElementShape =
-        inferElementShape(this.elementShape, this.tensors, elementShape);
-    if (indices.length === 0) {
-      return tensor([], [0].concat(outputElementShape));
-    }
-
-    return tidy(() => {
-      const tensors =
-          indices.map(i => reshape(this.tensors[i], outputElementShape));
-      return stack(tensors, 0);
-    });
-  }
-
-  /**
-   * Return the values in the TensorList as a concatenated Tensor.
-   * @param elementDtype output tensor dtype
-   * @param elementShape output tensor element shape
-   */
-  concat(elementDtype: DataType, elementShape: number[]): Tensor {
-    if (!!elementDtype && elementDtype !== this.elementDtype) {
-      throw new Error(`TensorList dtype is ${
-          this.elementDtype} but concat requested dtype ${elementDtype}`);
-    }
-
-    assertShapesMatchAllowUndefinedSize(
-        this.elementShape, elementShape, 'TensorList shape mismatch: ');
-    const outputElementShape =
-        inferElementShape(this.elementShape, this.tensors, elementShape);
-
-    if (this.size() === 0) {
-      return tensor([], [0].concat(outputElementShape));
-    }
-    return tidy(() => {
-      const tensors = this.tensors.map(t => reshape(t, outputElementShape));
-      return concat(tensors, 0);
-    });
-  }
-}
-
-/**
- * Creates a TensorList which, when stacked, has the value of tensor.
- * @param tensor from tensor
- * @param elementShape output tensor element shape
- */
-export function fromTensor(
-    tensor: Tensor, elementShape: number[], elementDtype: DataType) {
-  const dtype = tensor.dtype;
-  if (tensor.shape.length < 1) {
-    throw new Error(
-        `Tensor must be at least a vector, but saw shape: ${tensor.shape}`);
-  }
-  if (tensor.dtype !== elementDtype) {
-    throw new Error(`Invalid data types; op elements ${
-        tensor.dtype}, but list elements ${elementDtype}`);
-  }
-  const tensorElementShape = tensor.shape.slice(1);
-  assertShapesMatchAllowUndefinedSize(
-      tensorElementShape, elementShape, 'TensorList shape mismatch: ');
-  const tensorList: Tensor[] = unstack(tensor);
-  return new TensorList(tensorList, elementShape, dtype);
-}
-
-/**
- * Return a TensorList of the given size with empty elements.
- * @param elementShape the shape of the future elements of the list
- * @param elementDtype the desired type of elements in the list
- * @param numElements the number of elements to reserve
- * @param maxNumElements the maximum number of elements in th list
- */
-export function reserve(
-    elementShape: number[], elementDtype: DataType, numElements: number,
-    maxNumElements: number) {
-  return new TensorList([], elementShape, elementDtype, maxNumElements);
-}
-
-/**
- * Put tensors at specific indices of a stacked tensor into a TensorList.
- * @param indices list of indices on how to scatter the tensor.
- * @param tensor input tensor.
- * @param elementShape the shape of the future elements of the list
- * @param numElements the number of elements to scatter
- */
-export function scatter(
-    tensor: Tensor, indices: number[], elementShape: number[],
-    numElements?: number): TensorList {
-  if (indices.length !== tensor.shape[0]) {
-    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${
-        indices.length} vs. ${tensor.shape[0]}`);
-  }
-
-  const maxIndex = Math.max(...indices);
-
-  if (numElements != null && numElements !== -1 && maxIndex >= numElements) {
-    throw new Error(
-        `Max index must be < array size (${maxIndex}  vs. ${numElements})`);
-  }
-
-  const list = new TensorList([], elementShape, tensor.dtype, numElements);
-  const tensors = unstack(tensor, 0);
-  indices.forEach((value, index) => {
-    list.setItem(value, tensors[index]);
-  });
-  return list;
-}
-
-/**
- * Split the values of a Tensor into a TensorList.
- * @param length the lengths to use when splitting value along
- *    its first dimension.
- * @param tensor the tensor to split.
- * @param elementShape the shape of the future elements of the list
- */
-export function split(
-    tensor: Tensor, length: number[], elementShape: number[]) {
-  let totalLength = 0;
-  const cumulativeLengths = length.map(len => {
-    totalLength += len;
-    return totalLength;
-  });
-
-  if (totalLength !== tensor.shape[0]) {
-    throw new Error(`Expected sum of lengths to be equal to
-          tensor.shape[0], but sum of lengths is
-        ${totalLength}, and tensor's shape is: ${tensor.shape}`);
-  }
-
-  const shapeWithoutFirstDim = tensor.shape.slice(1);
-  const outputElementShape =
-      mergeElementShape(shapeWithoutFirstDim, elementShape);
-  const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;
-  const tensors: Tensor[] = tidy(() => {
-    const tensors = [];
-    tensor = reshape(tensor, [1, totalLength, elementPerRow]);
-    for (let i = 0; i < length.length; ++i) {
-      const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];
-      const indices = [0, previousLength, 0];
-      const sizes = [1, length[i], elementPerRow];
-      tensors[i] = reshape(
-          slice(tensor, indices, sizes), outputElementShape as number[]);
-    }
-    tensor.dispose();
-    return tensors;
-  });
-
-  const list = new TensorList([], elementShape, tensor.dtype, length.length);
-
-  for (let i = 0; i < tensors.length; i++) {
-    list.setItem(i, tensors[i]);
-  }
-  return list;
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/tensor_list_test.ts b/tfjs-master/tfjs-converter/src/executor/tensor_list_test.ts
deleted file mode 100644
index 8cf5bc6cd..000000000
--- a/tfjs-master/tfjs-converter/src/executor/tensor_list_test.ts
+++ /dev/null
@@ -1,345 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {memory, Tensor, tensor2d, tensor3d, test_util} from '@tensorflow/tfjs-core';
-
-import {reserve, scatter, split, TensorList} from './tensor_list';
-
-let tensorList: TensorList;
-let tensor: Tensor;
-let tensor2: Tensor;
-const DTYPE = 'int32';
-const SIZE = 10;
-const SHAPE = [1, 1];
-
-describe('TensorList', () => {
-  beforeEach(() => {
-    tensorList = new TensorList([], SHAPE, DTYPE, SIZE);
-    tensor = tensor2d([1], [1, 1], 'int32');
-    tensor2 = tensor2d([2], [1, 1], 'int32');
-  });
-
-  it('should initialize', () => {
-    expect(tensorList.size()).toEqual(0);
-    expect(tensorList.elementDtype).toEqual(DTYPE);
-    expect(tensorList.maxNumElements).toEqual(SIZE);
-    expect(tensorList.elementShape).toEqual(SHAPE);
-  });
-
-  it('should allow scalar shape', () => {
-    tensorList = new TensorList([], -1, DTYPE, SIZE);
-    expect(tensorList.size()).toEqual(0);
-    expect(tensorList.elementDtype).toEqual(DTYPE);
-    expect(tensorList.maxNumElements).toEqual(SIZE);
-    expect(tensorList.elementShape).toEqual(-1);
-  });
-
-  it('should not dispose keep tensors when close', () => {
-    const numOfTensors = memory().numTensors;
-    tensorList.pushBack(tensor);
-    tensorList.pushBack(tensor2);
-    const size = tensorList.size();
-    const keepIds = new Set([tensor.id]);
-    tensorList.clearAndClose(keepIds);
-    expect(tensorList.size()).toBe(0);
-    expect(tensor.isDisposed).toBeFalsy();
-    expect(tensor2.isDisposed).toBeTruthy();
-    // disposed the tensor in the array and idTensor of the array
-    expect(memory().numTensors).toEqual(numOfTensors - size);
-  });
-
-  describe('pushBack', () => {
-    it('should add new tensor', () => {
-      tensorList.pushBack(tensor);
-      expect(tensorList.size()).toBe(1);
-    });
-    it('should fail if dtype does not match', () => {
-      const tensor = tensor2d([1], [1, 1], 'float32');
-      expect(() => tensorList.pushBack(tensor)).toThrow();
-    });
-    it('should fail if shape does not match', () => {
-      const tensor = tensor2d([1, 2], [2, 1], 'int32');
-      expect(() => tensorList.pushBack(tensor)).toThrow();
-    });
-    it('should not fail for multiple push', () => {
-      tensorList.pushBack(tensor);
-      expect(() => tensorList.pushBack(tensor)).not.toThrow();
-    });
-    it('should fail if greater than array size', () => {
-      tensorList.maxNumElements = 1;
-      tensorList.pushBack(tensor);
-      expect(() => tensorList.pushBack(tensor)).toThrow();
-    });
-    it('should not fail for wildcard shape', () => {
-      tensorList = new TensorList([], [-1, 1], DTYPE, SIZE);
-      const tensor = tensor2d([1], [1, 1], 'int32');
-      tensorList.pushBack(tensor);
-      expect(tensorList.size()).toBe(1);
-    });
-    it('should create no new tensors', () => {
-      const numTensors = memory().numTensors;
-      tensorList.pushBack(tensor);
-      expect(memory().numTensors).toEqual(numTensors);
-    });
-  });
-
-  describe('popBack', () => {
-    it('should add new tensor', () => {
-      tensorList.pushBack(tensor);
-      tensorList.popBack(SHAPE, DTYPE);
-      expect(tensorList.size()).toBe(0);
-    });
-    it('should fail if dtype does not match', () => {
-      tensorList.pushBack(tensor);
-      expect(() => tensorList.popBack(SHAPE, 'float32')).toThrow();
-    });
-    it('should fail if shape does not match', () => {
-      expect(() => tensorList.popBack([2, 1], DTYPE)).toThrow();
-    });
-    it('should not fail for multiple push', () => {
-      tensorList.pushBack(tensor);
-      tensorList.pushBack(tensor2);
-      tensorList.popBack(SHAPE, DTYPE);
-      expect(() => tensorList.popBack(SHAPE, DTYPE)).not.toThrow();
-    });
-    it('should fail if greater than array size', () => {
-      expect(() => tensorList.popBack(SHAPE, DTYPE)).toThrow();
-    });
-    it('should create no new tensors', () => {
-      tensorList.pushBack(tensor);
-      const numTensors = memory().numTensors;
-      const tensorPoped = tensorList.popBack(SHAPE, DTYPE);
-      expect(tensorPoped.kept).toBeFalsy();
-      // a new reshaped tensor
-      expect(memory().numTensors).toEqual(numTensors + 1);
-    });
-    it('should not fail for wildcard shape', () => {
-      tensorList = new TensorList([], [-1, 1], DTYPE, SIZE);
-      const tensor = tensor2d([1], [1, 1], DTYPE);
-      tensorList.pushBack(tensor);
-      tensorList.popBack([-1, 1], DTYPE);
-      expect(tensorList.size()).toBe(0);
-    });
-  });
-  describe('setItem', () => {
-    it('should add new tensor', () => {
-      tensorList.setItem(0, tensor);
-      expect(tensorList.size()).toBe(1);
-    });
-    it('should fail if dtype does not match', () => {
-      const tensor = tensor2d([1], [1, 1], 'float32');
-      expect(() => tensorList.setItem(0, tensor)).toThrow();
-    });
-    it('should fail if shape does not match', () => {
-      const tensor = tensor2d([1, 2], [2, 1], 'int32');
-      expect(() => tensorList.setItem(0, tensor)).toThrow();
-    });
-    it('should not fail if the index has already been written', () => {
-      tensorList.setItem(0, tensor);
-      expect(() => tensorList.setItem(0, tensor)).not.toThrow();
-    });
-    it('should fail if the index greater than array size', () => {
-      expect(() => tensorList.setItem(11, tensor)).toThrow();
-    });
-    it('should not fail for wildcard shape', () => {
-      tensorList = new TensorList([], [-1, 1], DTYPE, SIZE);
-      tensorList.setItem(0, tensor);
-      expect(tensorList.size()).toBe(1);
-    });
-    it('should create no new tensors', () => {
-      const numTensors = memory().numTensors;
-      tensorList.setItem(0, tensor);
-      expect(memory().numTensors).toEqual(numTensors);
-    });
-    it('should remove kept flag for replaced tensor', () => {
-      tensorList = new TensorList([], [-1, 1], DTYPE, SIZE);
-      tensorList.setItem(0, tensor);
-      expect(tensor.kept).toBeTruthy();
-      tensorList.setItem(0, tensor2);
-      expect(tensor.kept).toBeFalsy();
-      expect(tensor2.kept).toBeTruthy();
-    });
-  });
-
-  describe('getItem', () => {
-    beforeEach(() => {
-      tensorList.setItem(0, tensor);
-      tensorList.setItem(1, tensor2);
-    });
-
-    it('should read the correct index', async () => {
-      test_util.expectArraysEqual(
-          await tensorList.getItem(0, SHAPE, DTYPE).data(),
-          await tensor.data());
-      test_util.expectArraysEqual(
-          await tensorList.getItem(1, SHAPE, DTYPE).data(),
-          await tensor2.data());
-    });
-
-    it('should failed if index is out of bound', () => {
-      expect(() => tensorList.getItem(3, SHAPE, DTYPE)).toThrow();
-      expect(() => tensorList.getItem(-1, SHAPE, DTYPE)).toThrow();
-    });
-    it('should create no new tensors', () => {
-      const numTensors = memory().numTensors;
-      const tensor1 = tensorList.getItem(0, SHAPE, DTYPE);
-      const tensor2 = tensorList.getItem(1, SHAPE, DTYPE);
-
-      tensor1.dispose();
-      tensor2.dispose();
-      // 2 reshape tensors
-      expect(memory().numTensors).toEqual(numTensors);
-    });
-    it('should not fail for wildcard shape', async () => {
-      const tensor3 = tensorList.getItem(0, [-1, 1], DTYPE);
-      test_util.expectArraysEqual(await tensor3.data(), await tensor.data());
-    });
-  });
-
-  describe('reserve', () => {
-    it('should create a tensor list', async () => {
-      const tensorList = reserve([1, 1], 'float32', 10, 10);
-      expect(tensorList.maxNumElements).toEqual(10);
-      expect(tensorList.elementDtype).toEqual('float32');
-      expect(tensorList.elementShape).toEqual([1, 1]);
-    });
-    it('should not fail for wildcard shape', async () => {
-      const tensorList = reserve([-1, 1], 'float32', 10, 10);
-      expect(tensorList.maxNumElements).toEqual(10);
-      expect(tensorList.elementDtype).toEqual('float32');
-      expect(tensorList.elementShape).toEqual([-1, 1]);
-    });
-  });
-
-  describe('concat', () => {
-    beforeEach(() => {
-      tensorList.setItem(0, tensor);
-      tensorList.setItem(1, tensor2);
-    });
-
-    it('should return default concat tensors', async () => {
-      const concat = tensorList.concat(DTYPE, SHAPE);
-      expect(concat.shape).toEqual([2, 1]);
-      test_util.expectArraysClose(await concat.data(), [1, 2]);
-    });
-    it('should not fail for wildcard shape', async () => {
-      const concat = tensorList.concat(DTYPE, [-1, 1]);
-      expect(concat.shape).toEqual([2, 1]);
-      test_util.expectArraysClose(await concat.data(), [1, 2]);
-    });
-    it('should fail if dtype is not matched', () => {
-      expect(() => tensorList.concat('float32', SHAPE)).toThrow();
-    });
-
-    it('should create one new tensor', () => {
-      const numTensors: number = memory().numTensors;
-      tensorList.concat(DTYPE, SHAPE);
-      expect(memory().numTensors).toEqual(numTensors + 1);
-    });
-  });
-
-  describe('gather', () => {
-    beforeEach(() => {
-      tensorList.setItem(0, tensor);
-      tensorList.setItem(1, tensor2);
-    });
-
-    it('should return packed tensors when indices is specified', async () => {
-      const gathered = tensorList.gather([1, 0], DTYPE, SHAPE);
-      expect(gathered.shape).toEqual([2, 1, 1]);
-      test_util.expectArraysClose(await gathered.data(), [2, 1]);
-    });
-    it('should return when indices longer than available tensors', async () => {
-      const gathered = tensorList.gather([1, 0, 2, 3], DTYPE, SHAPE);
-      expect(gathered.shape).toEqual([2, 1, 1]);
-      test_util.expectArraysClose(await gathered.data(), [2, 1]);
-    });
-    it('should fail if dtype is not matched', () => {
-      expect(() => tensorList.gather([0, 1], 'float32', SHAPE)).toThrow();
-    });
-    it('should create one new tensor', () => {
-      const numTensors: number = memory().numTensors;
-      tensorList.gather([0, 1], DTYPE, SHAPE);
-      expect(memory().numTensors).toEqual(numTensors + 1);
-    });
-    it('should not fail for wildcard shape', async () => {
-      const numTensors: number = memory().numTensors;
-      tensorList.gather([0, 1], DTYPE, [-1, 1]);
-      expect(memory().numTensors).toEqual(numTensors + 1);
-    });
-  });
-
-  describe('scatter', () => {
-    it('should scatter the input tensor', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      const list = scatter(input, [0, 1, 2], [1, 1], 3);
-      expect(list.size()).toEqual(3);
-    });
-
-    it('should fail if indices and tensor shapes do not matched', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      expect(() => scatter(input, [1, 2], [1, 1], 2)).toThrow();
-    });
-
-    it('should fail if max index > array max size', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      expect(() => scatter(input, [0, 1, 11], [1, 1], 3)).toThrow();
-    });
-
-    it('should create three new tensors', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      const numTensors: number = memory().numTensors;
-      scatter(input, [0, 1, 2], [1, 1], 3);
-      // Three tensors in the list and the idTensor
-      expect(memory().numTensors).toEqual(numTensors + 3 + 1);
-    });
-    it('should not fail for wildcard shape', async () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      const list = scatter(input, [0, 1, 2], [-1, 1], 3);
-      expect(list.size()).toEqual(3);
-    });
-  });
-
-  describe('split', () => {
-    it('should split the input tensor', () => {
-      const input =
-          tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 2, 2], 'int32');
-      const list = split(input, [1, 1, 1], [2, 2]);
-      expect(list.size()).toEqual(3);
-    });
-
-    it('should fail if indices and tensor shapes do not matched', () => {
-      const input = tensor3d([1, 2, 3], [3, 1, 1], 'int32');
-      expect(() => split(input, [1, 1], [1, 1])).toThrow();
-    });
-
-    it('should create three new tensors', () => {
-      const input =
-          tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 2, 2], 'int32');
-      const numTensors: number = memory().numTensors;
-      split(input, [1, 1, 1], [2, 2]);
-      // Three tensors in the list and the idTensor
-      expect(memory().numTensors).toEqual(numTensors + 3 + 1);
-    });
-    it('should not fail for wildcard shape', async () => {
-      const input =
-          tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 2, 2], 'int32');
-      const list = split(input, [1, 1, 1], [-1, 2]);
-      expect(list.size()).toEqual(3);
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/executor/tensor_utils.ts b/tfjs-master/tfjs-converter/src/executor/tensor_utils.ts
deleted file mode 100644
index 14ef48d4f..000000000
--- a/tfjs-master/tfjs-converter/src/executor/tensor_utils.ts
+++ /dev/null
@@ -1,113 +0,0 @@
-
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-/**
- * This differs from util.assertShapesMatch in that it allows values of
- * negative one, an undefined size of a dimensinon, in a shape to match
- * anything.
- */
-
-import {Tensor, util} from '@tensorflow/tfjs-core';
-
-/**
- * Used by TensorList and TensorArray to verify if elementShape matches, support
- * negative value as the dim shape.
- * @param shapeA
- * @param shapeB
- * @param errorMessagePrefix
- */
-export function assertShapesMatchAllowUndefinedSize(
-    shapeA: number|number[], shapeB: number|number[],
-    errorMessagePrefix = ''): void {
-  // constant shape means unknown rank
-  if (typeof shapeA === 'number' || typeof shapeB === 'number') {
-    return;
-  }
-  util.assert(
-      shapeA.length === shapeB.length,
-      () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-  for (let i = 0; i < shapeA.length; i++) {
-    const dim0 = shapeA[i];
-    const dim1 = shapeB[i];
-    util.assert(
-        dim0 < 0 || dim1 < 0 || dim0 === dim1,
-        () =>
-            errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-  }
-}
-
-export function fullDefinedShape(elementShape: number|number[]): boolean {
-  if (typeof elementShape === 'number' || elementShape.some(dim => dim < 0)) {
-    return false;
-  }
-  return true;
-}
-/**
- * Generate the output element shape from the list elementShape, list tensors
- * and input param.
- * @param listElementShape
- * @param tensors
- * @param elementShape
- */
-export function inferElementShape(
-    listElementShape: number|number[], tensors: Tensor[],
-    elementShape: number|number[]): number[] {
-  let partialShape = mergeElementShape(listElementShape, elementShape);
-  const notfullDefinedShape = !fullDefinedShape(partialShape);
-  if (notfullDefinedShape && tensors.length === 0) {
-    throw new Error(
-        `Tried to calculate elements of an empty list` +
-        ` with non-fully-defined elementShape: ${partialShape}`);
-  }
-  if (notfullDefinedShape) {
-    tensors.forEach(tensor => {
-      partialShape = mergeElementShape(tensor.shape, partialShape);
-    });
-  }
-  if (!fullDefinedShape(partialShape)) {
-    throw new Error(`Non-fully-defined elementShape: ${partialShape}`);
-  }
-  return partialShape as number[];
-}
-
-export function mergeElementShape(
-    elementShapeA: number|number[], elementShapeB: number|number[]): number|
-    number[] {
-  if (typeof elementShapeA === 'number') {
-    return elementShapeB;
-  }
-  if (typeof elementShapeB === 'number') {
-    return elementShapeA;
-  }
-
-  if (elementShapeA.length !== elementShapeB.length) {
-    throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${
-        elementShapeB}`);
-  }
-
-  const result: number[] = [];
-  for (let i = 0; i < elementShapeA.length; ++i) {
-    const dim0 = elementShapeA[i];
-    const dim1 = elementShapeB[i];
-    if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {
-      throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${
-          elementShapeB}`);
-    }
-    result[i] = dim0 >= 0 ? dim0 : dim1;
-  }
-  return result;
-}
diff --git a/tfjs-master/tfjs-converter/src/executor/test_data/hash_table_v2_model_loader.ts b/tfjs-master/tfjs-converter/src/executor/test_data/hash_table_v2_model_loader.ts
deleted file mode 100644
index 622bda4e6..000000000
--- a/tfjs-master/tfjs-converter/src/executor/test_data/hash_table_v2_model_loader.ts
+++ /dev/null
@@ -1,342 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-export const HASH_TABLE_MODEL_V2 = {
-  modelTopology: {
-    node: [
-      {
-        name: 'unknown_0',
-        op: 'Const',
-        attr: {
-          value: {tensor: {dtype: 'DT_INT32', tensorShape: {}}},
-          dtype: {type: 'DT_INT32'}
-        }
-      },
-      {
-        name: 'input',
-        op: 'Placeholder',
-        attr:
-            {shape: {shape: {dim: [{size: '-1'}]}}, dtype: {type: 'DT_STRING'}}
-      },
-      {
-        name: 'unknown',
-        op: 'Placeholder',
-        attr: {shape: {shape: {}}, dtype: {type: 'DT_RESOURCE'}}
-      },
-      {
-        name: 'StatefulPartitionedCall/None_Lookup/LookupTableFindV2',
-        op: 'LookupTableFindV2',
-        input: ['unknown', 'input', 'unknown_0'],
-        attr: {
-          Tout: {type: 'DT_INT32'},
-          Tin: {type: 'DT_STRING'},
-          _has_manual_control_dependencies: {b: true}
-        }
-      },
-      {
-        name: 'Identity',
-        op: 'Identity',
-        input: ['StatefulPartitionedCall/None_Lookup/LookupTableFindV2'],
-        attr: {T: {type: 'DT_INT32'}}
-      }
-    ],
-    library: {},
-    versions: {producer: 1240}
-  },
-  format: 'graph-model',
-  generatedBy: '2.11.0-dev20220822',
-  convertedBy: 'TensorFlow.js Converter v1.7.0',
-  weightSpecs: [
-    {name: 'unknown_0', shape: [], dtype: 'int32'},
-    {name: '114', shape: [2], dtype: 'string'},
-    {name: '116', shape: [2], dtype: 'int32'}
-  ],
-  'weightData':
-      new Uint8Array([
-        0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x61, 0x01, 0x00,
-        0x00, 0x00, 0x62, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00
-      ]).buffer,
-
-  signature: {
-    inputs: {
-      input: {
-        name: 'input:0',
-        dtype: 'DT_STRING',
-        tensorShape: {dim: [{size: '-1'}]}
-      },
-      'unknown:0': {
-        name: 'unknown:0',
-        dtype: 'DT_RESOURCE',
-        tensorShape: {},
-        resourceId: 66
-      }
-    },
-    outputs: {
-      output_0: {
-        name: 'Identity:0',
-        dtype: 'DT_INT32',
-        tensorShape: {dim: [{size: '-1'}]}
-      }
-    }
-  },
-  modelInitializer: {
-    node: [
-      {
-        name: 'Func/StatefulPartitionedCall/input_control_node/_0',
-        op: 'NoOp',
-        attr: {_has_manual_control_dependencies: {b: true}}
-      },
-      {
-        name: '114',
-        op: 'Const',
-        attr: {
-          value:
-              {tensor: {dtype: 'DT_STRING', tensorShape: {dim: [{size: '2'}]}}},
-          _has_manual_control_dependencies: {b: true},
-          dtype: {type: 'DT_STRING'}
-        }
-      },
-      {
-        name: '116',
-        op: 'Const',
-        attr: {
-          _has_manual_control_dependencies: {b: true},
-          dtype: {type: 'DT_INT32'},
-          value:
-              {tensor: {dtype: 'DT_INT32', tensorShape: {dim: [{size: '2'}]}}}
-        }
-      },
-      {
-        name:
-            'Func/StatefulPartitionedCall/StatefulPartitionedCall/input_control_node/_9',
-        op: 'NoOp',
-        input: ['^Func/StatefulPartitionedCall/input_control_node/_0'],
-        attr: {_has_manual_control_dependencies: {b: true}}
-      },
-      {
-        name: 'StatefulPartitionedCall/StatefulPartitionedCall/hash_table',
-        op: 'HashTableV2',
-        input: [
-          '^Func/StatefulPartitionedCall/StatefulPartitionedCall/input_control_node/_9'
-        ],
-        attr: {
-          container: {s: ''},
-          use_node_name_sharing: {b: true},
-          _has_manual_control_dependencies: {b: true},
-          shared_name: {s: 'OTVfbG9hZF8xXzUy'},
-          value_dtype: {type: 'DT_INT32'},
-          key_dtype: {type: 'DT_STRING'}
-        }
-      },
-      {
-        name:
-            'Func/StatefulPartitionedCall/StatefulPartitionedCall/output_control_node/_11',
-        op: 'NoOp',
-        input: ['^StatefulPartitionedCall/StatefulPartitionedCall/hash_table'],
-        attr: {_has_manual_control_dependencies: {b: true}}
-      },
-      {
-        name: 'Func/StatefulPartitionedCall/output_control_node/_2',
-        op: 'NoOp',
-        input: [
-          '^Func/StatefulPartitionedCall/StatefulPartitionedCall/output_control_node/_11'
-        ],
-        attr: {_has_manual_control_dependencies: {b: true}}
-      },
-      {
-        name: 'StatefulPartitionedCall/StatefulPartitionedCall/NoOp',
-        op: 'NoOp',
-        input: ['^StatefulPartitionedCall/StatefulPartitionedCall/hash_table'],
-        attr: {
-          _acd_function_control_output: {b: true},
-          _has_manual_control_dependencies: {b: true}
-        }
-      },
-      {
-        name: 'StatefulPartitionedCall/StatefulPartitionedCall/Identity',
-        op: 'Identity',
-        input: [
-          'StatefulPartitionedCall/StatefulPartitionedCall/hash_table',
-          '^StatefulPartitionedCall/StatefulPartitionedCall/NoOp'
-        ],
-        attr: {T: {type: 'DT_RESOURCE'}}
-      },
-      {
-        name: 'Func/StatefulPartitionedCall/StatefulPartitionedCall/output/_10',
-        op: 'Identity',
-        input: ['StatefulPartitionedCall/StatefulPartitionedCall/Identity'],
-        attr: {T: {type: 'DT_RESOURCE'}}
-      },
-      {
-        name: 'StatefulPartitionedCall/NoOp',
-        op: 'NoOp',
-        input: [
-          '^Func/StatefulPartitionedCall/StatefulPartitionedCall/output_control_node/_11'
-        ],
-        attr: {
-          _has_manual_control_dependencies: {b: true},
-          _acd_function_control_output: {b: true}
-        }
-      },
-      {
-        name: 'StatefulPartitionedCall/Identity',
-        op: 'Identity',
-        input: [
-          'Func/StatefulPartitionedCall/StatefulPartitionedCall/output/_10',
-          '^StatefulPartitionedCall/NoOp'
-        ],
-        attr: {T: {type: 'DT_RESOURCE'}}
-      },
-      {
-        name: 'Func/StatefulPartitionedCall/output/_1',
-        op: 'Identity',
-        input: ['StatefulPartitionedCall/Identity'],
-        attr: {
-          T: {type: 'DT_RESOURCE'},
-          _has_manual_control_dependencies: {b: true}
-        }
-      },
-      {
-        name: 'Func/StatefulPartitionedCall_1/input_control_node/_3',
-        op: 'NoOp',
-        input: ['^114', '^116', '^Func/StatefulPartitionedCall/output/_1'],
-        attr: {_has_manual_control_dependencies: {b: true}}
-      },
-      {
-        name: 'Func/StatefulPartitionedCall_1/input/_4',
-        op: 'Identity',
-        input: [
-          'Func/StatefulPartitionedCall/output/_1',
-          '^Func/StatefulPartitionedCall_1/input_control_node/_3'
-        ],
-        attr: {T: {type: 'DT_RESOURCE'}}
-      },
-      {
-        name:
-            'Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input_control_node/_12',
-        op: 'NoOp',
-        input: ['^Func/StatefulPartitionedCall_1/input_control_node/_3'],
-        attr: {_has_manual_control_dependencies: {b: true}}
-      },
-      {
-        name:
-            'Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input/_13',
-        op: 'Identity',
-        input: [
-          'Func/StatefulPartitionedCall_1/input/_4',
-          '^Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input_control_node/_12'
-        ],
-        attr: {T: {type: 'DT_RESOURCE'}}
-      },
-      {
-        name: 'Func/StatefulPartitionedCall_1/input/_5',
-        op: 'Identity',
-        input: ['114', '^Func/StatefulPartitionedCall_1/input_control_node/_3'],
-        attr: {T: {type: 'DT_STRING'}}
-      },
-      {
-        name:
-            'Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input/_14',
-        op: 'Identity',
-        input: [
-          'Func/StatefulPartitionedCall_1/input/_5',
-          '^Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input_control_node/_12'
-        ],
-        attr: {T: {type: 'DT_STRING'}}
-      },
-      {
-        name: 'Func/StatefulPartitionedCall_1/input/_6',
-        op: 'Identity',
-        input: ['116', '^Func/StatefulPartitionedCall_1/input_control_node/_3'],
-        attr: {T: {type: 'DT_INT32'}}
-      },
-      {
-        name:
-            'Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input/_15',
-        op: 'Identity',
-        input: [
-          'Func/StatefulPartitionedCall_1/input/_6',
-          '^Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input_control_node/_12'
-        ],
-        attr: {T: {type: 'DT_INT32'}}
-      },
-      {
-        name:
-            'StatefulPartitionedCall_1/StatefulPartitionedCall/key_value_init94/LookupTableImportV2',
-        op: 'LookupTableImportV2',
-        input: [
-          'Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input/_13',
-          'Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input/_14',
-          'Func/StatefulPartitionedCall_1/StatefulPartitionedCall/input/_15'
-        ],
-        attr: {
-          Tout: {type: 'DT_INT32'},
-          Tin: {type: 'DT_STRING'},
-          _has_manual_control_dependencies: {b: true}
-        }
-      },
-      {
-        name:
-            'Func/StatefulPartitionedCall_1/StatefulPartitionedCall/output_control_node/_17',
-        op: 'NoOp',
-        input: [
-          '^StatefulPartitionedCall_1/StatefulPartitionedCall/key_value_init94/LookupTableImportV2'
-        ],
-        attr: {_has_manual_control_dependencies: {b: true}}
-      },
-      {
-        name: 'Func/StatefulPartitionedCall_1/output_control_node/_8',
-        op: 'NoOp',
-        input: [
-          '^Func/StatefulPartitionedCall_1/StatefulPartitionedCall/output_control_node/_17'
-        ],
-        attr: {_has_manual_control_dependencies: {b: true}}
-      },
-      {
-        name: 'NoOp',
-        op: 'NoOp',
-        input: [
-          '^Func/StatefulPartitionedCall/output_control_node/_2',
-          '^Func/StatefulPartitionedCall_1/output_control_node/_8'
-        ],
-        attr: {
-          _has_manual_control_dependencies: {b: true},
-          _acd_function_control_output: {b: true}
-        }
-      },
-      {
-        name: 'Identity',
-        op: 'Identity',
-        input: [
-          'Func/StatefulPartitionedCall/output/_1',
-          '^Func/StatefulPartitionedCall_1/output_control_node/_8', '^NoOp'
-        ],
-        attr: {T: {type: 'DT_RESOURCE'}}
-      }
-    ],
-    versions: {producer: 1240}
-  },
-  initializerSignature: {
-    outputs: {
-      'Identity:0': {
-        name: 'Identity:0',
-        dtype: 'DT_RESOURCE',
-        tensorShape: {},
-        resourceId: 66
-      }
-    }
-  }
-};
diff --git a/tfjs-master/tfjs-converter/src/executor/test_data/structured_outputs_model_loader.ts b/tfjs-master/tfjs-converter/src/executor/test_data/structured_outputs_model_loader.ts
deleted file mode 100644
index 2eb3ded12..000000000
--- a/tfjs-master/tfjs-converter/src/executor/test_data/structured_outputs_model_loader.ts
+++ /dev/null
@@ -1,210 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export const STRUCTURED_OUTPUTS_MODEL = {
-  'modelTopology': {
-    'node': [
-      {
-        'name': 'StatefulPartitionedCall/model/concatenate/concat/axis',
-        'op': 'Const',
-        'attr': {
-          'value': {'tensor': {'dtype': 'DT_INT32', 'tensorShape': {}}},
-          'dtype': {'type': 'DT_INT32'}
-        }
-      },
-      {
-        'name': 'StatefulPartitionedCall/model/a/MatMul/ReadVariableOp',
-        'op': 'Const',
-        'attr': {
-          'dtype': {'type': 'DT_FLOAT'},
-          'value': {
-            'tensor': {
-              'dtype': 'DT_FLOAT',
-              'tensorShape': {'dim': [{'size': '2'}, {'size': '1'}]}
-            }
-          }
-        }
-      },
-      {
-        'name': 'StatefulPartitionedCall/model/b/MatMul/ReadVariableOp',
-        'op': 'Const',
-        'attr': {
-          'value': {
-            'tensor': {
-              'dtype': 'DT_FLOAT',
-              'tensorShape': {'dim': [{'size': '1'}, {'size': '1'}]}
-            }
-          },
-          'dtype': {'type': 'DT_FLOAT'}
-        }
-      },
-      {
-        'name': 'input1',
-        'op': 'Placeholder',
-        'attr': {
-          'dtype': {'type': 'DT_FLOAT'},
-          'shape': {'shape': {'dim': [{'size': '-1'}, {'size': '1'}]}}
-        }
-      },
-      {
-        'name': 'input2',
-        'op': 'Placeholder',
-        'attr': {
-          'dtype': {'type': 'DT_FLOAT'},
-          'shape': {'shape': {'dim': [{'size': '-1'}, {'size': '1'}]}}
-        }
-      },
-      {
-        'name': 'input3',
-        'op': 'Placeholder',
-        'attr': {
-          'shape': {'shape': {'dim': [{'size': '-1'}, {'size': '1'}]}},
-          'dtype': {'type': 'DT_FLOAT'}
-        }
-      },
-      {
-        'name': 'StatefulPartitionedCall/model/b/MatMul',
-        'op': 'MatMul',
-        'input':
-            ['input2', 'StatefulPartitionedCall/model/b/MatMul/ReadVariableOp'],
-        'device': '/device:CPU:0',
-        'attr': {
-          'transpose_b': {'b': false},
-          'transpose_a': {'b': false},
-          'T': {'type': 'DT_FLOAT'}
-        }
-      },
-      {
-        'name': 'StatefulPartitionedCall/model/concatenate/concat',
-        'op': 'ConcatV2',
-        'input': [
-          'input1', 'input3',
-          'StatefulPartitionedCall/model/concatenate/concat/axis'
-        ],
-        'attr': {
-          'Tidx': {'type': 'DT_INT32'},
-          'T': {'type': 'DT_FLOAT'},
-          'N': {'i': '2'}
-        }
-      },
-      {
-        'name': 'Identity_1',
-        'op': 'Identity',
-        'input': ['StatefulPartitionedCall/model/b/MatMul'],
-        'attr': {'T': {'type': 'DT_FLOAT'}}
-      },
-      {
-        'name': 'StatefulPartitionedCall/model/a/MatMul',
-        'op': 'MatMul',
-        'input': [
-          'StatefulPartitionedCall/model/concatenate/concat',
-          'StatefulPartitionedCall/model/a/MatMul/ReadVariableOp'
-        ],
-        'device': '/device:CPU:0',
-        'attr': {
-          'T': {'type': 'DT_FLOAT'},
-          'transpose_b': {'b': false},
-          'transpose_a': {'b': false}
-        }
-      },
-      {
-        'name': 'Identity',
-        'op': 'Identity',
-        'input': ['StatefulPartitionedCall/model/a/MatMul'],
-        'attr': {'T': {'type': 'DT_FLOAT'}}
-      },
-      {
-        'name': 'StatefulPartitionedCall/model/c/mul',
-        'op': 'Mul',
-        'input': [
-          'StatefulPartitionedCall/model/a/MatMul',
-          'StatefulPartitionedCall/model/b/MatMul'
-        ],
-        'attr': {'T': {'type': 'DT_FLOAT'}}
-      },
-      {
-        'name': 'Identity_2',
-        'op': 'Identity',
-        'input': ['StatefulPartitionedCall/model/c/mul'],
-        'attr': {'T': {'type': 'DT_FLOAT'}}
-      }
-    ],
-    'library': {},
-    'versions': {'producer': 898}
-  },
-  'format': 'graph-model',
-  'generatedBy': '2.7.3',
-  'convertedBy': 'TensorFlow.js Converter v1.7.0',
-  'weightSpecs': [
-    {
-      'name': 'StatefulPartitionedCall/model/concatenate/concat/axis',
-      'shape': [],
-      'dtype': 'int32'
-    },
-    {
-      'name': 'StatefulPartitionedCall/model/a/MatMul/ReadVariableOp',
-      'shape': [2, 1],
-      'dtype': 'float32'
-    },
-    {
-      'name': 'StatefulPartitionedCall/model/b/MatMul/ReadVariableOp',
-      'shape': [1, 1],
-      'dtype': 'float32'
-    }
-  ],
-  'weightData': new Uint8Array([
-                  0x01, 0x00, 0x00, 0x00, 0x70, 0x3d, 0x72, 0x3e, 0x3d, 0xd2,
-                  0x12, 0xbf, 0x0c, 0xfb, 0x94, 0x3e
-                ]).buffer,
-  'signature': {
-    'inputs': {
-      'input1:0': {
-        'name': 'input1:0',
-        'dtype': 'DT_FLOAT',
-        'tensorShape': {'dim': [{'size': '-1'}, {'size': '1'}]}
-      },
-      'input3:0': {
-        'name': 'input3:0',
-        'dtype': 'DT_FLOAT',
-        'tensorShape': {'dim': [{'size': '-1'}, {'size': '1'}]}
-      },
-      'input2:0': {
-        'name': 'input2:0',
-        'dtype': 'DT_FLOAT',
-        'tensorShape': {'dim': [{'size': '-1'}, {'size': '1'}]}
-      }
-    },
-    'outputs': {
-      'Identity_1:0': {
-        'name': 'Identity_1:0',
-        'dtype': 'DT_FLOAT',
-        'tensorShape': {'dim': [{'size': '-1'}, {'size': '1'}]}
-      },
-      'Identity:0': {
-        'name': 'Identity:0',
-        'dtype': 'DT_FLOAT',
-        'tensorShape': {'dim': [{'size': '-1'}, {'size': '1'}]}
-      },
-      'Identity_2:0': {
-        'name': 'Identity_2:0',
-        'dtype': 'DT_FLOAT',
-        'tensorShape': {'dim': [{'size': '-1'}, {'size': '1'}]}
-      }
-    }
-  },
-  'userDefinedMetadata': {'structuredOutputKeys': ['a', 'b', 'c']}
-};
diff --git a/tfjs-master/tfjs-converter/src/executor/types.ts b/tfjs-master/tfjs-converter/src/executor/types.ts
deleted file mode 100644
index d225f4ac8..000000000
--- a/tfjs-master/tfjs-converter/src/executor/types.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-
-import {NamedTensorsMap, TensorArrayMap, TensorListMap} from '../data/types';
-
-/**
- *
- */
-export interface FunctionExecutor {
-  executeFunctionAsync(
-      inputs: Tensor[], tensorArrayMap: TensorArrayMap,
-      tensorListMap: TensorListMap): Promise<Tensor[]>;
-  weightMap: NamedTensorsMap;
-}
diff --git a/tfjs-master/tfjs-converter/src/flags.ts b/tfjs-master/tfjs-converter/src/flags.ts
deleted file mode 100644
index 56f868259..000000000
--- a/tfjs-master/tfjs-converter/src/flags.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-
-const ENV = env();
-
-/** Whether to keep intermediate tensors. */
-ENV.registerFlag('KEEP_INTERMEDIATE_TENSORS', () => false, debugValue => {
-  if (debugValue) {
-    console.warn(
-        'Keep intermediate tensors is ON. This will print the values of all ' +
-        'intermediate tensors during model inference. Not all models ' +
-        'support this mode. For details, check e2e/benchmarks/ ' +
-        'model_config.js. This significantly impacts performance.');
-  }
-});
diff --git a/tfjs-master/tfjs-converter/src/index.ts b/tfjs-master/tfjs-converter/src/index.ts
deleted file mode 100644
index e6abafdd3..000000000
--- a/tfjs-master/tfjs-converter/src/index.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import './flags';
-
-export {IAttrValue, INameAttrList, INodeDef, ITensor, ITensorShape} from './data/compiled_api';
-export {GraphModel, loadGraphModel, loadGraphModelSync} from './executor/graph_model';
-export {deregisterOp, registerOp} from './operations/custom_op/register';
-export {GraphNode, OpExecutor} from './operations/types';
-export {version as version_converter} from './version';
diff --git a/tfjs-master/tfjs-converter/src/metadata_test.ts b/tfjs-master/tfjs-converter/src/metadata_test.ts
deleted file mode 100644
index 16cb03219..000000000
--- a/tfjs-master/tfjs-converter/src/metadata_test.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// This test checks the metadata.json to make sure that only the kernels that
-// we know we don't map to tfjs ops have empty entries in metadata
-// kernel2op.json
-describe('kernel2op metadata file', () => {
-  it('has kernel2op.json', () => {
-    expect(() => {
-      // tslint:disable-next-line:no-require-imports
-      require('tfjs-converter/metadata/kernel2op.json');
-    }).not.toThrow();
-  });
-
-  it('only known unmapped kernel are unmmapped', () => {
-    const knownUnmappedKernels = [
-      'Const',
-      'EmptyTensorList',
-      'Enter',
-      'Exit',
-      'FakeQuantWithMinMaxVars',
-      'Identity',
-      'IdentityN',
-      'If',
-      'LoopCond',
-      'Merge',
-      'NextIteration',
-      'Placeholder',
-      'PlaceholderWithDefault',
-      'Print',
-      'Snapshot',
-      'StatelessIf',
-      'StatelessWhile',
-      'StopGradient',
-      'Switch',
-      'TensorArrayCloseV3',
-      'TensorArrayConcatV3',
-      'TensorArrayGatherV3',
-      'TensorArrayReadV3',
-      'TensorArrayScatterV3',
-      'TensorArraySizeV3',
-      'TensorArraySplitV3',
-      'TensorArrayV3',
-      'TensorArrayWriteV3',
-      'TensorListConcat',
-      'TensorListConcatV2',
-      'TensorListFromTensor',
-      'TensorListGather',
-      'TensorListGetItem',
-      'TensorListLength',
-      'TensorListPopBack',
-      'TensorListPushBack',
-      'TensorListReserve',
-      'TensorListResize',
-      'TensorListScatter',
-      'TensorListScatterV2',
-      'TensorListSetItem',
-      'TensorListSplit',
-      'TensorListStack',
-      'While',
-      'HashTable',
-      'HashTableV2',
-      'LookupTableImport',
-      'LookupTableImportV2',
-      'LookupTableFind',
-      'LookupTableFindV2',
-      'LookupTableSize',
-      'LookupTableSizeV2',
-      'InitializeTable',
-      'InitializeTableV2',
-    ];
-    // tslint:disable-next-line:no-require-imports
-    const kernel2op = require('tfjs-converter/metadata/kernel2op.json');
-    const kernels: string[] = Object.keys(kernel2op);
-
-    for (const kernelName of kernels) {
-      const tfOps = kernel2op[kernelName];
-      if (knownUnmappedKernels.includes(kernelName)) {
-        expect(tfOps.length)
-            .toEqual(0, `Kernel "${kernelName}" is expected to be unmapped but
-              instead maps to ${tfOps}`);
-      } else {
-        expect(tfOps.length)
-            .toBeGreaterThan(
-                0, `Kernel ${kernelName} is expected to be mapped to a list
-                of tf ops`);
-      }
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/custom_op/node_value_impl.ts b/tfjs-master/tfjs-converter/src/operations/custom_op/node_value_impl.ts
deleted file mode 100644
index 349811c14..000000000
--- a/tfjs-master/tfjs-converter/src/operations/custom_op/node_value_impl.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, Tensor} from '@tensorflow/tfjs-core';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {getTensor} from '../executors/utils';
-import {getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam} from '../operation_mapper';
-import {GraphNode, Node, ValueType} from '../types';
-
-/**
- * Helper class for lookup inputs and params for nodes in the model graph.
- */
-export class NodeValueImpl implements GraphNode {
-  public readonly inputs: Tensor[] = [];
-  public readonly attrs: {[key: string]: ValueType} = {};
-  constructor(
-      private node: Node, private tensorMap: NamedTensorsMap,
-      private context: ExecutionContext) {
-    this.inputs = node.inputNames.map(name => this.getInput(name));
-    if (node.rawAttrs != null) {
-      this.attrs = Object.keys(node.rawAttrs)
-                       .reduce((attrs: {[key: string]: ValueType}, key) => {
-                         attrs[key] = this.getAttr(key);
-                         return attrs;
-                       }, {});
-    }
-  }
-
-  /**
-   * Return the value of the attribute or input param.
-   * @param name String: name of attribute or input param.
-   */
-  private getInput(name: string): Tensor {
-    return getTensor(name, this.tensorMap, this.context);
-  }
-
-  /**
-   * Return the value of the attribute or input param.
-   * @param name String: name of attribute or input param.
-   */
-  private getAttr(name: string, defaultValue?: ValueType): ValueType {
-    const value = this.node.rawAttrs[name];
-    if (value.tensor != null) {
-      return getTensor(name, this.tensorMap, this.context);
-    }
-    if (value.i != null || value.f != null) {
-      return getNumberParam(this.node.rawAttrs, name, defaultValue as number);
-    }
-    if (value.s != null) {
-      return getStringParam(this.node.rawAttrs, name, defaultValue as string);
-    }
-    if (value.b != null) {
-      return getBoolParam(this.node.rawAttrs, name, defaultValue as boolean);
-    }
-    if (value.shape != null) {
-      return getTensorShapeParam(
-          this.node.rawAttrs, name, defaultValue as number[]);
-    }
-    if (value.type != null) {
-      return getDtypeParam(this.node.rawAttrs, name, defaultValue as DataType);
-    }
-    if (value.list != null) {
-      if (value.list.i != null || value.list.f != null) {
-        return getNumericArrayParam(
-            this.node.rawAttrs, name, defaultValue as number[]);
-      }
-      if (value.list.s != null) {
-        return getStringArrayParam(
-            this.node.rawAttrs, name, defaultValue as string[]);
-      }
-      if (value.list.shape != null) {
-        return getTensorShapeArrayParam(
-            this.node.rawAttrs, name, defaultValue as number[][]);
-      }
-      if (value.list.b != null) {
-        return getBoolArrayParam(
-            this.node.rawAttrs, name, defaultValue as boolean[]);
-      }
-      if (value.list.type != null) {
-        return getDtypeArrayParam(
-            this.node.rawAttrs, name, defaultValue as DataType[]);
-      }
-    }
-
-    return defaultValue;
-  }
-}
diff --git a/tfjs-master/tfjs-converter/src/operations/custom_op/node_value_impl_test.ts b/tfjs-master/tfjs-converter/src/operations/custom_op/node_value_impl_test.ts
deleted file mode 100644
index ab304eee0..000000000
--- a/tfjs-master/tfjs-converter/src/operations/custom_op/node_value_impl_test.ts
+++ /dev/null
@@ -1,115 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {scalar, test_util} from '@tensorflow/tfjs-core';
-
-import * as tensorflow from '../../data/compiled_api';
-import {ExecutionContext} from '../../executor/execution_context';
-import {Node} from '../types';
-
-import {NodeValueImpl} from './node_value_impl';
-
-const NODE: Node = {
-  name: 'test',
-  op: 'const',
-  category: 'custom',
-  inputNames: ['a', 'b'],
-  inputs: [],
-  inputParams: {},
-  attrParams: {},
-  children: [],
-  rawAttrs: {
-    c: {tensor: {}},
-    d: {i: 3},
-    e: {s: 'TkhXQw=='},
-    f: {type: tensorflow.DataType.DT_FLOAT},
-    g: {b: true},
-    h: {f: 4.5},
-    i: {list: {i: [3, 6, 0]}},
-    j: {list: {f: [4.5, 5.5, 0.0]}},
-    k: {list: {s: ['TkhXQw==', 'TkhXQw==', '']}},
-    l: {
-      list:
-          {type: [tensorflow.DataType.DT_FLOAT, tensorflow.DataType.DT_INT32]}
-    },
-    m: {shape: {dim: [{name: 'a', size: 1}, {name: 'b', size: 2}]}},
-    n: {
-      list: {
-        shape: [
-          {dim: [{name: 'a', size: 1}, {name: 'b', size: 2}]},
-          {dim: [{name: 'c', size: 2}, {name: 'd', size: 3}]}
-        ]
-      }
-    },
-    o: {list: {b: [true, false]}}
-  }
-};
-const TENSOR_MAP = {
-  'a': [scalar(1)],
-  'b': [scalar(2)],
-  'test': [scalar(3)]
-};
-
-let nodeValue: NodeValueImpl;
-describe('NodeValueImpl', () => {
-  beforeEach(() => {
-    nodeValue =
-        new NodeValueImpl(NODE, TENSOR_MAP, new ExecutionContext({}, {}, {}));
-  });
-  describe('getInput', () => {
-    it('should find tensor from tensormap', async () => {
-      const result = nodeValue.inputs[0];
-      test_util.expectArraysClose(await result.data(), [1]);
-
-      const result2 = nodeValue.inputs[1];
-      test_util.expectArraysClose(await result2.data(), [2]);
-    });
-  });
-  describe('getAttr', () => {
-    it('should parse number', () => {
-      expect(nodeValue.attrs['d']).toEqual(3);
-      expect(nodeValue.attrs['h']).toEqual(4.5);
-    });
-    it('should parse number[]', () => {
-      expect(nodeValue.attrs['i']).toEqual([3, 6, 0]);
-      expect(nodeValue.attrs['j']).toEqual([4.5, 5.5, 0.0]);
-    });
-    it('should parse string', () => {
-      expect(nodeValue.attrs['e']).toEqual('nhwc');
-    });
-    it('should parse string[]', () => {
-      expect(nodeValue.attrs['k']).toEqual(['nhwc', 'nhwc', '']);
-    });
-    it('should parse boolean', () => {
-      expect(nodeValue.attrs['g']).toEqual(true);
-    });
-    it('should parse boolean[]', () => {
-      expect(nodeValue.attrs['o']).toEqual([true, false]);
-    });
-    it('should parse dtype', () => {
-      expect(nodeValue.attrs['f']).toEqual('float32');
-    });
-    it('should parse dtype[]', () => {
-      expect(nodeValue.attrs['l']).toEqual(['float32', 'int32']);
-    });
-    it('should parse tensor shape', () => {
-      expect(nodeValue.attrs['m']).toEqual([1, 2]);
-    });
-    it('should parse tensor shape[]', () => {
-      expect(nodeValue.attrs['n']).toEqual([[1, 2], [2, 3]]);
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/custom_op/register.ts b/tfjs-master/tfjs-converter/src/operations/custom_op/register.ts
deleted file mode 100644
index a913636d7..000000000
--- a/tfjs-master/tfjs-converter/src/operations/custom_op/register.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {OpExecutor, OpMapper} from '../types';
-
-const CUSTOM_OPS: {[key: string]: OpMapper} = {};
-
-/**
- * Register an Op for graph model executor. This allows you to register
- * TensorFlow custom op or override existing op.
- *
- * Here is an example of registering a new MatMul Op.
- * ```js
- * const customMatmul = (node) =>
- *    tf.matMul(
- *        node.inputs[0], node.inputs[1],
- *        node.attrs['transpose_a'], node.attrs['transpose_b']);
- *
- * tf.registerOp('MatMul', customMatmul);
- * ```
- * The inputs and attrs of the node object are based on the TensorFlow op
- * registry.
- *
- * @param name The Tensorflow Op name.
- * @param opFunc An op function which is called with the current graph node
- * during execution and needs to return a tensor or a list of tensors. The node
- * has the following attributes:
- *    - attr: A map from attribute name to its value
- *    - inputs: A list of input tensors
- *
- * @doc {heading: 'Models', subheading: 'Op Registry'}
- */
-export function registerOp(name: string, opFunc: OpExecutor) {
-  const opMapper: OpMapper = {
-    tfOpName: name,
-    category: 'custom',
-    inputs: [],
-    attrs: [],
-    customExecutor: opFunc
-  };
-
-  CUSTOM_OPS[name] = opMapper;
-}
-
-/**
- * Retrieve the OpMapper object for the registered op.
- *
- * @param name The Tensorflow Op name.
- *
- * @doc {heading: 'Models', subheading: 'Op Registry'}
- */
-export function getRegisteredOp(name: string): OpMapper {
-  return CUSTOM_OPS[name];
-}
-
-/**
- * Deregister the Op for graph model executor.
- *
- * @param name The Tensorflow Op name.
- *
- * @doc {heading: 'Models', subheading: 'Op Registry'}
- */
-export function deregisterOp(name: string) {
-  delete CUSTOM_OPS[name];
-}
diff --git a/tfjs-master/tfjs-converter/src/operations/custom_op/register_test.ts b/tfjs-master/tfjs-converter/src/operations/custom_op/register_test.ts
deleted file mode 100644
index 65998ad70..000000000
--- a/tfjs-master/tfjs-converter/src/operations/custom_op/register_test.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {scalar} from '@tensorflow/tfjs-core';
-
-import {deregisterOp, getRegisteredOp, registerOp} from './register';
-
-describe('register custom op', () => {
-  describe('registerCustomOp', () => {
-    it('should auto fill missing fields', () => {
-      const executor = () => scalar(1);
-      registerOp('newOp', executor);
-      const opMapper = getRegisteredOp('newOp');
-      expect(opMapper.tfOpName).toEqual('newOp');
-      expect(opMapper.category).toEqual('custom');
-      expect(opMapper.inputs).toEqual([]);
-      expect(opMapper.attrs).toEqual([]);
-      expect(opMapper.customExecutor).toEqual(executor);
-      deregisterOp('newOp');
-    });
-  });
-  describe('deregisterOp', () => {
-    it('should remove the custom op', () => {
-      registerOp('newOp', () => scalar(1));
-      expect(getRegisteredOp('newOp')).toBeDefined();
-      deregisterOp('newOp');
-      expect(getRegisteredOp('newOp')).not.toBeDefined();
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/arithmetic_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/arithmetic_executor.ts
deleted file mode 100644
index 35e8f590b..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/arithmetic_executor.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap,
-     context: ExecutionContext, ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'BiasAdd':
-        case 'AddV2':
-        case 'Add': {
-          return [ops.add(
-              (getParamValue('a', node, tensorMap, context) as Tensor),
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'AddN': {
-          return [ops.addN((
-              getParamValue('tensors', node, tensorMap, context) as Tensor[]))];
-        }
-        case 'FloorMod':
-        case 'Mod':
-          return [ops.mod(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        case 'Mul':
-          return [ops.mul(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        case 'RealDiv':
-        case 'Div': {
-          return [ops.div(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'DivNoNan': {
-          return [ops.divNoNan(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'FloorDiv': {
-          return [ops.floorDiv(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'Sub': {
-          return [ops.sub(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'Minimum': {
-          return [ops.minimum(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'Maximum': {
-          return [ops.maximum(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'Pow': {
-          return [ops.pow(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'SquaredDifference': {
-          return [ops.squaredDifference(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'arithmetic';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/arithmetic_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/arithmetic_executor_test.ts
deleted file mode 100644
index 6b07ec604..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/arithmetic_executor_test.ts
+++ /dev/null
@@ -1,95 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import {Node} from '../types';
-
-import {executeOp} from './arithmetic_executor';
-import {createTensorAttr, createTensorsAttr, uncapitalize} from './test_helper';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-
-describe('arithmetic', () => {
-  let node: Node;
-  const input1 = [tfOps.scalar(1)];
-  const input2 = [tfOps.scalar(1)];
-  const input3 = [tfOps.scalar(4)];
-  const context = new ExecutionContext({}, {}, {});
-
-  beforeEach(() => {
-    node = {
-      name: 'test',
-      op: '',
-      category: 'arithmetic',
-      inputNames: ['input1', 'input2'],
-      inputs: [],
-      inputParams: {a: createTensorAttr(0), b: createTensorAttr(1)},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    let spyOps: RecursiveSpy<typeof tfOps>;
-    let spyOpsAsTfOps: typeof tfOps;
-
-    beforeEach(() => {
-      spyOps = spyOnAllFunctions(tfOps);
-      spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    });
-
-    (['Add', 'Mul', 'Div', 'Sub', 'Maximum', 'Minimum', 'Pow',
-      'SquaredDifference', 'Mod', 'FloorDiv', 'DivNoNan'] as const)
-         .forEach((op => {
-           it('should call tfOps.' + op, () => {
-             node.op = op;
-             executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-             expect(spyOps[uncapitalize(op)])
-               .toHaveBeenCalledWith(input1[0], input2[0]);
-           });
-         }));
-
-    it('AddV2', async () => {
-
-      node.op = 'AddV2';
-      const res = executeOp(node, {input1, input2}, context,
-                            spyOpsAsTfOps) as Tensor[];
-      expect(spyOps.add).toHaveBeenCalledWith(input1[0], input2[0]);
-      expect(res[0].dtype).toBe('float32');
-      expect(res[0].shape).toEqual([]);
-      test_util.expectArraysClose(await res[0].data(), 2);
-    });
-
-    it('AddN', async () => {
-      node.op = 'AddN';
-      node.inputParams = {tensors: createTensorsAttr(0, 0)};
-      node.inputNames = ['input1', 'input2', 'input3'];
-      const res =
-        executeOp(node, {input1, input2, input3}, context,
-                  spyOpsAsTfOps) as Tensor[];
-      expect(spyOps.addN)
-        .toHaveBeenCalledWith([input1[0], input2[0], input3[0]]);
-      expect(res[0].dtype).toBe('float32');
-      expect(res[0].shape).toEqual([]);
-      test_util.expectArraysClose(await res[0].data(), [6]);
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/basic_math_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/basic_math_executor.ts
deleted file mode 100644
index 530cd7757..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/basic_math_executor.ts
+++ /dev/null
@@ -1,182 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue, getTensor} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-     ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'Abs':
-        case 'ComplexAbs':
-          return [ops.abs(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Acos':
-          return [ops.acos(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Acosh':
-          return [ops.acosh(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Asin':
-          return [ops.asin(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Asinh':
-          return [ops.asinh(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Atan':
-          return [ops.atan(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Atan2':
-          return [ops.atan2(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('y', node, tensorMap, context) as Tensor)];
-        case 'Atanh':
-          return [ops.atanh(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Ceil':
-          return [ops.ceil(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Complex':
-          return [ops.complex(
-              getParamValue('real', node, tensorMap, context) as Tensor,
-              getParamValue('imag', node, tensorMap, context) as Tensor)];
-        case 'Cos':
-          return [ops.cos(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Cosh':
-          return [ops.cosh(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Elu':
-          return [ops.elu(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Erf':
-          return [ops.erf(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Exp':
-          return [ops.exp(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Expm1': {
-          return [ops.expm1(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Floor':
-          return [ops.floor(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Log':
-          return [ops.log(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Log1p': {
-          return [ops.log1p(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Imag':
-          return [ops.imag(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-
-        case 'Neg':
-          return [ops.neg(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Reciprocal': {
-          return [ops.reciprocal(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Real':
-          return [ops.real(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Relu':
-          return [ops.relu(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Round': {
-          return [ops.round(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Selu':
-          return [ops.selu(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Sigmoid':
-          return [ops.sigmoid(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Sin':
-          return [ops.sin(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Sign': {
-          return [ops.sign(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Sinh': {
-          return [ops.sinh(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Softplus': {
-          return [ops.softplus(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Sqrt': {
-          return [ops.sqrt(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Square': {
-          return [ops.square(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Tanh': {
-          return [ops.tanh(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'Tan':
-          return [ops.tan(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'ClipByValue':
-          return [ops.clipByValue(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('clipValueMin', node, tensorMap, context) as number,
-              getParamValue('clipValueMax', node, tensorMap, context) as
-                  number)];
-        case 'Relu6':
-          return [ops.relu6(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        case 'Rsqrt':
-          return [ops.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];
-        case 'LeakyRelu':
-          return [ops.leakyRelu(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('alpha', node, tensorMap, context) as number)];
-        case 'Prelu':
-          return [ops.prelu(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('alpha', node, tensorMap, context) as Tensor)];
-        case 'IsNan':
-          return [ops.isNaN(getTensor(node.inputNames[0], tensorMap, context))];
-        case 'IsInf':
-          return [ops.isInf(getTensor(node.inputNames[0], tensorMap, context))];
-        case 'IsFinite':
-          return [ops.isFinite(
-              getTensor(node.inputNames[0], tensorMap, context))];
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'basic_math';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/basic_math_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/basic_math_executor_test.ts
deleted file mode 100644
index 5d545d4a2..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/basic_math_executor_test.ts
+++ /dev/null
@@ -1,255 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {scalar} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as basic_math from '../op_list/basic_math';
-import {Node} from '../types';
-
-import {executeOp} from './basic_math_executor';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createNumberAttr, createNumberAttrFromIndex, createTensorAttr, uncapitalize, validateParam} from './test_helper';
-
-describe('basic math', () => {
-  let node: Node;
-  const input1 = [scalar(1)];
-  const context = new ExecutionContext({}, {}, {});
-
-  beforeEach(() => {
-    node = {
-      name: 'test',
-      op: '',
-      category: 'basic_math',
-      inputNames: ['input1'],
-      inputs: [],
-      inputParams: {x: createTensorAttr(0)},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    let spyOps: RecursiveSpy<typeof tfOps>;
-    let spyOpsAsTfOps: typeof tfOps;
-
-    beforeEach(() => {
-      spyOps = spyOnAllFunctions(tfOps);
-      spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    });
-
-    ([
-      'Abs',      'Acos',  'Asin',    'Atan',  'Ceil',  'Cos',   'Cosh',
-      'Elu',      'Exp',   'Floor',   'Log',   'Imag',  'Neg',   'Real',
-      'Relu',     'Selu',  'Sigmoid', 'Sin',   'Sinh',  'Sqrt',  'Square',
-      'Tanh',     'Tan',   'Sign',    'Round', 'Expm1', 'Log1p', 'Reciprocal',
-      'Softplus', 'Asinh', 'Acosh',   'Atanh', 'Erf'
-    ] as const )
-        .forEach(op => {
-          it('should call tfOps.' + op, () => {
-            node.op = op;
-            spyOps[uncapitalize(op)].and.returnValue({});
-            executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-            expect(spyOps[uncapitalize(op)]).toHaveBeenCalledWith(input1[0]);
-          });
-          it('should match op def', () => {
-            node.op = op;
-
-            expect(validateParam(node, basic_math.json)).toBeTruthy();
-          });
-        });
-    describe('Relu6', () => {
-      it('should call tfOps.relu6', () => {
-        node.op = 'Relu6';
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.relu6).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match op def', () => {
-        node.op = 'Relu6';
-
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('ClipByValue', () => {
-      it('should call tfOps.clipByValue', () => {
-        node.op = 'ClipByValue';
-        node.inputNames = ['input1', 'input2', 'input3'];
-        node.inputParams['clipValueMin'] = createNumberAttrFromIndex(1);
-        node.inputParams['clipValueMax'] = createNumberAttrFromIndex(2);
-        const input2 = [tfOps.scalar(2)];
-        const input3 = [tfOps.scalar(3)];
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.clipByValue).toHaveBeenCalledWith(input1[0], 2, 3);
-      });
-      it('should match op def', () => {
-        node.op = 'ClipByValue';
-        node.inputParams['clipValueMin'] = createNumberAttrFromIndex(1);
-        node.inputParams['clipValueMax'] = createNumberAttrFromIndex(2);
-
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('Rsqrt', () => {
-      it('should call tfOps.rsqrt', () => {
-        const input1 = [tfOps.scalar(1)];
-        node.op = 'Rsqrt';
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.rsqrt).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match op def', () => {
-        node.op = 'Rsqrt';
-
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('LeakyRelu', () => {
-      it('should call tfOps.leakyRelu', () => {
-        node.op = 'LeakyRelu';
-        node.attrParams['alpha'] = createNumberAttr(1);
-        node.inputNames = ['input1'];
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.leakyRelu).toHaveBeenCalledWith(input1[0], 1);
-      });
-      it('should match op def', () => {
-        node.op = 'LeakyRelu';
-        node.attrParams['alpha'] = createNumberAttr(1);
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('Prelu', () => {
-      it('should call tfOps.Prelu', () => {
-        node.op = 'Prelu';
-        node.inputParams['x'] = createTensorAttr(0);
-        node.inputParams['alpha'] = createTensorAttr(1);
-        node.inputNames = ['input1', 'input2'];
-        const input2 = [tfOps.scalar(1)];
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.prelu).toHaveBeenCalledWith(input1[0], input2[0]);
-      });
-      it('should match op def', () => {
-        node.op = 'Prelu';
-        node.inputParams['x'] = createTensorAttr(0);
-        node.inputParams['alpha'] = createTensorAttr(1);
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('Atan2', () => {
-      it('should call tfOps.atan2', () => {
-        node.op = 'Atan2';
-        node.inputParams['y'] = createTensorAttr(1);
-        node.inputNames = ['input1', 'input2'];
-        const input2 = [tfOps.scalar(2)];
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.atan2).toHaveBeenCalledWith(input1[0], input2[0]);
-      });
-      it('should match op def', () => {
-        node.op = 'Atan2';
-        node.inputParams['y'] = createTensorAttr(1);
-
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('ComplexAbs', () => {
-      it('should call tfOps.abs', () => {
-        node.op = 'ComplexAbs';
-        node.inputNames = ['input1'];
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.abs).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match op def', () => {
-        node.op = 'ComplexAbs';
-
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('Complex', () => {
-      it('should call tfOps.complex', () => {
-        node.op = 'Complex';
-        node.inputParams = {
-          real: createTensorAttr(0),
-          imag: createTensorAttr(1)
-        };
-        const input2 = [tfOps.scalar(2)];
-        node.inputNames = ['input1', 'input2'];
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.complex).toHaveBeenCalledWith(input1[0], input2[0]);
-      });
-      it('should match op def', () => {
-        node.op = 'Complex';
-        node.inputParams = {
-          real: createTensorAttr(0),
-          imag: createTensorAttr(1)
-        };
-
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('IsNan', () => {
-      it('should call tfOps.isNaN', () => {
-        node.op = 'IsNan';
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.isNaN).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match op def', () => {
-        node.op = 'IsNan';
-
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('IsInf', () => {
-      it('should call tfOps.isInf', () => {
-        node.op = 'IsInf';
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.isInf).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match op def', () => {
-        node.op = 'IsInf';
-
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-    describe('IsFinite', () => {
-      it('should call tfOps.isFinite', () => {
-        node.op = 'IsFinite';
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.isFinite).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match op def', () => {
-        node.op = 'IsFinite';
-
-        expect(validateParam(node, basic_math.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/control_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/control_executor.ts
deleted file mode 100644
index 0fa6bed98..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/control_executor.ts
+++ /dev/null
@@ -1,392 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, scalar, Tensor} from '@tensorflow/tfjs-core';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {TensorArray} from '../../executor/tensor_array';
-import {fromTensor, reserve, scatter, split} from '../../executor/tensor_list';
-import {InternalOpAsyncExecutor, Node} from '../types';
-
-import {cloneTensor, getParamValue, getTensor} from './utils';
-
-export const executeOp: InternalOpAsyncExecutor = async(
-    node: Node, tensorMap: NamedTensorsMap,
-    context: ExecutionContext): Promise<Tensor[]> => {
-  switch (node.op) {
-    case 'If':
-    case 'StatelessIf': {
-      const thenFunc =
-          getParamValue('thenBranch', node, tensorMap, context) as string;
-      const elseFunc =
-          getParamValue('elseBranch', node, tensorMap, context) as string;
-      const cond = getParamValue('cond', node, tensorMap, context) as Tensor;
-      const args = getParamValue('args', node, tensorMap, context) as Tensor[];
-      const condValue = await cond.data();
-      if (condValue[0]) {
-        return context.functionMap[thenFunc].executeFunctionAsync(
-            args, context.tensorArrayMap, context.tensorListMap);
-      } else {
-        return context.functionMap[elseFunc].executeFunctionAsync(
-            args, context.tensorArrayMap, context.tensorListMap);
-      }
-    }
-    case 'While':
-    case 'StatelessWhile': {
-      const bodyFunc =
-          getParamValue('body', node, tensorMap, context) as string;
-      const condFunc =
-          getParamValue('cond', node, tensorMap, context) as string;
-      const args = getParamValue('args', node, tensorMap, context) as Tensor[];
-
-      // Calculate the condition of the loop
-      const condResult =
-          (await context.functionMap[condFunc].executeFunctionAsync(
-              args, context.tensorArrayMap, context.tensorListMap));
-      const argIds = args.map(tensor => tensor.id);
-      let condValue = await condResult[0].data();
-      // Dispose the intermediate tensors for condition function
-      condResult.forEach(tensor => {
-        if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {
-          tensor.dispose();
-        }
-      });
-
-      let result: Tensor[] = args;
-
-      while (condValue[0]) {
-        // Record the previous result for intermediate tensor tracking
-        const origResult = result;
-        // Execution the body of the loop
-        result = await context.functionMap[bodyFunc].executeFunctionAsync(
-            result, context.tensorArrayMap, context.tensorListMap);
-        const resultIds = result.map(tensor => tensor.id);
-
-        // Dispose the intermediate tensor for body function that is not global
-        // kept, not input/output of the body function
-        origResult.forEach(tensor => {
-          if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&
-              resultIds.indexOf(tensor.id) === -1) {
-            tensor.dispose();
-          }
-        });
-
-        // Recalcuate the condition of the loop using the latest results.
-        const condResult =
-            (await context.functionMap[condFunc].executeFunctionAsync(
-                result, context.tensorArrayMap, context.tensorListMap));
-        condValue = await condResult[0].data();
-        // Dispose the intermediate tensors for condition function
-        condResult.forEach(tensor => {
-          if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&
-              resultIds.indexOf(tensor.id) === -1) {
-            tensor.dispose();
-          }
-        });
-      }
-      return result;
-    }
-    case 'LoopCond': {
-      const pred = getParamValue('pred', node, tensorMap, context) as Tensor;
-      return [cloneTensor(pred)];
-    }
-    case 'Switch': {
-      const pred = getParamValue('pred', node, tensorMap, context) as Tensor;
-      let data = getParamValue('data', node, tensorMap, context) as Tensor;
-      if (!data.kept) {
-        data = cloneTensor(data);
-      }
-      // Outputs nodes :0 => false, :1 => true
-      return (await pred.data())[0] ? [undefined, data] : [data, undefined];
-    }
-    case 'Merge': {
-      const inputName = node.inputNames.find(
-          name => getTensor(name, tensorMap, context) !== undefined);
-      if (inputName) {
-        const data = getTensor(inputName, tensorMap, context);
-        return [cloneTensor(data)];
-      }
-      return undefined;
-    }
-    case 'Enter': {
-      const frameId =
-          getParamValue('frameName', node, tensorMap, context) as string;
-      const data = getParamValue('tensor', node, tensorMap, context) as Tensor;
-      context.enterFrame(frameId);
-      return [cloneTensor(data)];
-    }
-    case 'Exit': {
-      const data = getParamValue('tensor', node, tensorMap, context) as Tensor;
-      context.exitFrame();
-      return [cloneTensor(data)];
-    }
-    case 'NextIteration': {
-      const data = getParamValue('tensor', node, tensorMap, context) as Tensor;
-      context.nextIteration();
-      return [cloneTensor(data)];
-    }
-    case 'TensorArrayV3': {
-      const size = getParamValue('size', node, tensorMap, context) as number;
-      const dtype =
-          getParamValue('dtype', node, tensorMap, context) as DataType;
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-      const dynamicSize =
-          getParamValue('dynamicSize', node, tensorMap, context) as boolean;
-      const clearAfterRead =
-          getParamValue('clearAfterRead', node, tensorMap, context) as boolean;
-      const identicalElementShapes =
-          getParamValue('identicalElementShapes', node, tensorMap, context) as
-          boolean;
-      const name = getParamValue('name', node, tensorMap, context) as string;
-      const tensorArray = new TensorArray(
-          name, dtype, size, elementShape, identicalElementShapes, dynamicSize,
-          clearAfterRead);
-      context.addTensorArray(tensorArray);
-      return [tensorArray.idTensor, scalar(1.0)];
-    }
-    case 'TensorArrayWriteV3': {
-      const id =
-          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;
-      const index = getParamValue('index', node, tensorMap, context) as number;
-      const writeTensor =
-          getParamValue('tensor', node, tensorMap, context) as Tensor;
-      const writeTensorArray = context.getTensorArray(id.id);
-      writeTensorArray.write(index, writeTensor);
-      return [writeTensorArray.idTensor];
-    }
-    case 'TensorArrayReadV3': {
-      const readId =
-          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;
-      const readIndex =
-          getParamValue('index', node, tensorMap, context) as number;
-      const readTensorArray = context.getTensorArray(readId.id);
-      return [readTensorArray.read(readIndex)];
-    }
-    case 'TensorArrayGatherV3': {
-      const gatherId =
-          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;
-      const gatherIndices =
-          getParamValue('indices', node, tensorMap, context) as number[];
-      const gatherDtype =
-          getParamValue('dtype', node, tensorMap, context) as DataType;
-      const gatherTensorArray = context.getTensorArray(gatherId.id);
-      return [gatherTensorArray.gather(gatherIndices, gatherDtype)];
-    }
-    case 'TensorArrayScatterV3': {
-      const scatterId =
-          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;
-      const scatterIndices =
-          getParamValue('indices', node, tensorMap, context) as number[];
-      const scatterTensor =
-          getParamValue('tensor', node, tensorMap, context) as Tensor;
-      const scatterTensorArray = context.getTensorArray(scatterId.id);
-      scatterTensorArray.scatter(scatterIndices, scatterTensor);
-      return [scatterTensorArray.idTensor];
-    }
-    case 'TensorArrayConcatV3': {
-      const concatId =
-          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;
-      const concatTensorArray = context.getTensorArray(concatId.id);
-      const concatDtype =
-          getParamValue('dtype', node, tensorMap, context) as DataType;
-      return [concatTensorArray.concat(concatDtype)];
-    }
-    case 'TensorArraySplitV3': {
-      const splitId =
-          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;
-      const splitTensor =
-          getParamValue('tensor', node, tensorMap, context) as Tensor;
-      const lengths =
-          getParamValue('lengths', node, tensorMap, context) as number[];
-      const splitTensorArray = context.getTensorArray(splitId.id);
-      splitTensorArray.split(lengths, splitTensor);
-      return [splitTensorArray.idTensor];
-    }
-    case 'TensorArraySizeV3': {
-      const sizeId =
-          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;
-      const sizeTensorArray = context.getTensorArray(sizeId.id);
-      return [scalar(sizeTensorArray.size(), 'int32')];
-    }
-    case 'TensorArrayCloseV3': {
-      const closeId =
-          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;
-      const closeTensorArray = context.getTensorArray(closeId.id);
-      closeTensorArray.clearAndClose();
-      return [closeTensorArray.idTensor];
-    }
-    case 'TensorListSetItem': {
-      const idTensor =
-          getParamValue('tensorListId', node, tensorMap, context) as Tensor;
-      const index = getParamValue('index', node, tensorMap, context) as number;
-      const writeTensor =
-          getParamValue('tensor', node, tensorMap, context) as Tensor;
-      const tensorList = context.getTensorList(idTensor.id);
-      tensorList.setItem(index, writeTensor);
-      return [tensorList.idTensor];
-    }
-    case 'TensorListGetItem': {
-      const idTensor =
-          getParamValue('tensorListId', node, tensorMap, context) as Tensor;
-      const readIndex =
-          getParamValue('index', node, tensorMap, context) as number;
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-
-      const elementDType =
-          getParamValue('elementDType', node, tensorMap, context) as DataType;
-      const tensorList = context.getTensorList(idTensor.id);
-      return [tensorList.getItem(readIndex, elementShape, elementDType)];
-    }
-    case 'TensorListScatterV2':
-    case 'TensorListScatter': {
-      const scatterIndices =
-          getParamValue('indices', node, tensorMap, context) as number[];
-      const scatterTensor =
-          getParamValue('tensor', node, tensorMap, context) as Tensor;
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-      const numElements =
-          getParamValue('numElements', node, tensorMap, context) as number;
-      const tensorList =
-          scatter(scatterTensor, scatterIndices, elementShape, numElements);
-      context.addTensorList(tensorList);
-      return [tensorList.idTensor];
-    }
-    case 'TensorListReserve':
-    case 'EmptyTensorList': {
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-      const elementDtype =
-          getParamValue('elementDType', node, tensorMap, context) as DataType;
-      let numElementsParam;
-
-      if (node.op === 'TensorListReserve') {
-        numElementsParam = 'numElements';
-      } else {
-        numElementsParam = 'maxNumElements';
-      }
-
-      const numElements =
-          getParamValue(numElementsParam, node, tensorMap, context) as number;
-      const maxNumElements = node.op === 'TensorListReserve' ? -1 : numElements;
-      const tensorList =
-          reserve(elementShape, elementDtype, numElements, maxNumElements);
-      context.addTensorList(tensorList);
-      return [tensorList.idTensor];
-    }
-    case 'TensorListGather': {
-      const gatherId =
-          getParamValue('tensorListId', node, tensorMap, context) as Tensor;
-      const gatherIndices =
-          getParamValue('indices', node, tensorMap, context) as number[];
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-      const elementDtype =
-          getParamValue('elementDType', node, tensorMap, context) as DataType;
-      const tensorList = context.getTensorList(gatherId.id);
-      return [tensorList.gather(gatherIndices, elementDtype, elementShape)];
-    }
-    case 'TensorListStack': {
-      const idTensor =
-          getParamValue('tensorListId', node, tensorMap, context) as Tensor;
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-      const elementDtype =
-          getParamValue('elementDType', node, tensorMap, context) as DataType;
-      const numElements =
-          getParamValue('numElements', node, tensorMap, context) as number;
-      const tensorList = context.getTensorList(idTensor.id);
-      return [tensorList.stack(elementShape, elementDtype, numElements)];
-    }
-    case 'TensorListFromTensor': {
-      const tensor =
-          getParamValue('tensor', node, tensorMap, context) as Tensor;
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-      const elementDtype =
-          getParamValue('elementDType', node, tensorMap, context) as DataType;
-      const tensorList = fromTensor(tensor, elementShape, elementDtype);
-      context.addTensorList(tensorList);
-      return [tensorList.idTensor];
-    }
-    case 'TensorListConcat':
-    case 'TensorListConcatV2': {
-      const concatId =
-          getParamValue('tensorListId', node, tensorMap, context) as Tensor;
-      const tensorList = context.getTensorList(concatId.id);
-      const concatDtype =
-          getParamValue('dtype', node, tensorMap, context) as DataType;
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-      return [tensorList.concat(concatDtype, elementShape)];
-    }
-    case 'TensorListPushBack': {
-      const idTensor =
-          getParamValue('tensorListId', node, tensorMap, context) as Tensor;
-      const writeTensor =
-          getParamValue('tensor', node, tensorMap, context) as Tensor;
-      const tensorList = context.getTensorList(idTensor.id);
-      tensorList.pushBack(writeTensor);
-      return [tensorList.idTensor];
-    }
-    case 'TensorListPopBack': {
-      const idTensor =
-          getParamValue('tensorListId', node, tensorMap, context) as Tensor;
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-      const elementDType =
-          getParamValue('elementDType', node, tensorMap, context) as DataType;
-      const tensorList = context.getTensorList(idTensor.id);
-      return [tensorList.popBack(elementShape, elementDType)];
-    }
-    case 'TensorListSplit': {
-      const splitTensor =
-          getParamValue('tensor', node, tensorMap, context) as Tensor;
-      const elementShape =
-          getParamValue('elementShape', node, tensorMap, context) as number[];
-      const lengths =
-          getParamValue('lengths', node, tensorMap, context) as number[];
-
-      const tensorList = split(splitTensor, lengths, elementShape);
-      context.addTensorList(tensorList);
-      return [tensorList.idTensor];
-    }
-    case 'TensorListLength': {
-      const idTensor =
-          getParamValue('tensorListId', node, tensorMap, context) as Tensor;
-      const tensorList = context.getTensorList(idTensor.id);
-      return [scalar(tensorList.size(), 'int32')];
-    }
-    case 'TensorListResize': {
-      const idTensor =
-          getParamValue('tensorListId', node, tensorMap, context) as Tensor;
-      const size = getParamValue('size', node, tensorMap, context) as number;
-
-      const srcTensorList = context.getTensorList(idTensor.id);
-      const destTensorList = srcTensorList.resize(size);
-      context.addTensorList(destTensorList);
-      return [destTensorList.idTensor];
-    }
-    default:
-      throw TypeError(`Node type ${node.op} is not implemented`);
-  }
-};
-
-export const CATEGORY = 'control';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/control_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/control_executor_test.ts
deleted file mode 100644
index 79c9c8d18..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/control_executor_test.ts
+++ /dev/null
@@ -1,1105 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {scalar, Tensor, tensor1d, tensor2d, test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import {GraphExecutor} from '../../executor/graph_executor';
-import {TensorArray} from '../../executor/tensor_array';
-import {TensorList} from '../../executor/tensor_list';
-import * as control from '../op_list/control';
-import {Graph, Node} from '../types';
-
-import {executeOp} from './control_executor';
-import {createBoolAttr, createDtypeAttr, createNumberAttrFromIndex, createNumericArrayAttrFromIndex, createShapeAttrFromIndex, createStrAttr, createTensorAttr, createTensorsAttr, createTensorShapeAttr, validateParam} from './test_helper';
-
-describe('control', () => {
-  let node: Node;
-  let input1: Tensor[];
-  let input2: Tensor[];
-  const context = new ExecutionContext({}, {}, {});
-
-  beforeEach(() => {
-    node = {
-      name: 'test',
-      op: '',
-      category: 'control',
-      inputNames: ['input1', 'pred'],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-    input1 = [tfOps.scalar(1, 'int32')];
-    input2 = [tfOps.scalar(0, 'bool')];
-  });
-
-  afterEach(() => {
-    input1[0].dispose();
-    input2[0].dispose();
-  });
-
-  describe('executeOp', () => {
-    describe('Switch', () => {
-      it('should set the output condition is true', async () => {
-        node.op = 'Switch';
-        node.inputParams['pred'] = createTensorAttr(1);
-        node.inputParams['data'] = createTensorAttr(0);
-
-        const pred = [tfOps.scalar(true)];
-        const result = await executeOp(node, {pred, input1}, context);
-        expect(result[0]).toBeUndefined();
-        test_util.expectArraysEqual(
-            await result[1].array(), await input1[0].array());
-      });
-      it('should set the output condition is false', async () => {
-        node.op = 'Switch';
-        node.inputParams['pred'] = createTensorAttr(1);
-        node.inputParams['data'] = createTensorAttr(0);
-
-        const pred = [tfOps.scalar(false)];
-        const result = await executeOp(node, {pred, input1}, context);
-        test_util.expectArraysEqual(
-            await result[0].array(), await input1[0].array());
-        expect(result[1]).toBeUndefined();
-      });
-      it('should match json def', () => {
-        node.op = 'Switch';
-        node.inputParams['pred'] = createTensorAttr(1);
-        node.inputParams['data'] = createTensorAttr(0);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-    describe('Merge', () => {
-      it('should return the first available input', async () => {
-        node.op = 'Merge';
-
-        const pred = [tfOps.scalar(true)];
-        test_util.expectArraysEqual(
-            await (await executeOp(node, {pred: undefined, input1}, context))[0]
-                .array(),
-            await input1[0].array());
-        test_util.expectArraysEqual(
-            await (await executeOp(node, {pred, input1: undefined}, context))[0]
-                .array(),
-            await pred[0].array());
-      });
-      it('should return undefined if no inputs are available', async () => {
-        node.op = 'Merge';
-        expect(await executeOp(
-                   node, {pred: undefined, input1: undefined}, context))
-            .toEqual(undefined);
-      });
-      it('should match json def', () => {
-        node.op = 'Merge';
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('Enter', () => {
-      it('should call enterFrame on context', async () => {
-        spyOn(context, 'enterFrame');
-        node.op = 'Enter';
-        node.inputParams['tensor'] = createTensorAttr(0);
-        node.attrParams['frameName'] = createStrAttr('test');
-        node.inputNames = ['input1'];
-
-        test_util.expectArraysEqual(
-            await (await executeOp(node, {input1}, context))[0].array(),
-            await input1[0].array());
-        expect(context.enterFrame).toHaveBeenCalled();
-      });
-      it('should match json def', () => {
-        node.op = 'Enter';
-        node.inputParams['tensor'] = createTensorAttr(0);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-    describe('Exit', () => {
-      it('should call existFrame on context', async () => {
-        spyOn(context, 'exitFrame');
-        node.op = 'Exit';
-        node.inputParams['tensor'] = createTensorAttr(0);
-        node.inputNames = ['input1'];
-
-        test_util.expectArraysEqual(
-            await (await executeOp(node, {input1}, context))[0].array(),
-            await input1[0].array());
-        expect(context.exitFrame).toHaveBeenCalled();
-      });
-      it('should match json def', () => {
-        node.op = 'Exit';
-        node.inputParams['tensor'] = createTensorAttr(0);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-    describe('NextIteration', () => {
-      it('should call nextIteration on context', async () => {
-        spyOn(context, 'nextIteration');
-        node.op = 'NextIteration';
-        node.inputParams['tensor'] = createTensorAttr(0);
-        node.inputNames = ['input1'];
-
-        test_util.expectArraysEqual(
-            await (await executeOp(node, {input1}, context))[0].array(),
-            await input1[0].array());
-        expect(context.nextIteration).toHaveBeenCalled();
-      });
-      it('should match json def', () => {
-        node.op = 'NextIteration';
-        node.inputParams['tensor'] = createTensorAttr(0);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('TensorArrayV3', () => {
-      it('should create new tensor on the context', async () => {
-        node.op = 'TensorArrayV3';
-        node.inputParams['size'] = createNumberAttrFromIndex(0);
-        node.attrParams['name'] = createStrAttr('');
-        node.attrParams['dtype'] = createDtypeAttr('int32');
-        node.attrParams['elementShape'] = createTensorShapeAttr([10, 10]);
-        node.attrParams['dynamicSize'] = createBoolAttr(false);
-        node.attrParams['clearAfterRead'] = createBoolAttr(true);
-        node.attrParams['identicalElementShapes'] = createBoolAttr(true);
-        node.inputNames = ['input1'];
-
-        const tensorId = (await executeOp(node, {input1}, context))[0];
-        expect(context.getTensorArray(tensorId.id)).toBeDefined();
-      });
-      it('should match json def', () => {
-        node.op = 'TensorArrayV3';
-        node.inputParams['size'] = createNumberAttrFromIndex(0);
-        node.attrParams['name'] = createStrAttr('');
-        node.attrParams['dtype'] = createDtypeAttr('int32');
-        node.attrParams['elementShape'] = createTensorShapeAttr([10, 10]);
-        node.attrParams['dynamicSize'] = createBoolAttr(false);
-        node.attrParams['clearAfterRead'] = createBoolAttr(true);
-        node.attrParams['identicalElementShapes'] = createBoolAttr(true);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('TensorArrayWriteV3', () => {
-      it('should write the tensor to tensorArray', async () => {
-        const tensorArray =
-            new TensorArray('', 'int32', 5, [], true, false, true);
-        context.addTensorArray(tensorArray);
-        node.op = 'TensorArrayWriteV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['index'] = createNumberAttrFromIndex(1);
-        node.inputParams['tensor'] = createTensorAttr(2);
-        node.inputNames = ['input2', 'input3', 'input1'];
-        const input2 = [tensorArray.idTensor];
-        const input3 = [scalar(0)];
-        await executeOp(node, {input1, input2, input3}, context);
-
-        expect(tensorArray.size()).toEqual(1);
-      });
-      it('should match json def', () => {
-        node.op = 'TensorArrayWriteV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['index'] = createNumberAttrFromIndex(1);
-        node.inputParams['tensor'] = createTensorAttr(2);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('TensorArrayReadV3', () => {
-      it('should read the tensor from tensorArray', async () => {
-        const tensorArray =
-            new TensorArray('', 'int32', 5, [3], true, false, true);
-        const input4 = tensor1d([0, 0, 0], 'int32');
-        tensorArray.write(0, input4);
-        context.addTensorArray(tensorArray);
-        node.op = 'TensorArrayReadV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['index'] = createNumberAttrFromIndex(1);
-        node.inputNames = ['input2', 'input3'];
-        const input2 = [tensorArray.idTensor];
-        const input3 = [scalar(0)];
-        const read = await executeOp(node, {input1, input2, input3}, context);
-
-        test_util.expectArraysClose(
-            await read[0].array(), await input4.array());
-      });
-      it('should match json def', () => {
-        node.op = 'TensorArrayReadV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['index'] = createNumberAttrFromIndex(1);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('TensorArrayGatherV3', () => {
-      it('should gather the tensors from tensorArray', async () => {
-        const tensorArray =
-            new TensorArray('', 'int32', 5, [3], true, false, true);
-        const input4 = tensor1d([0, 0, 0], 'int32');
-        const input5 = tensor1d([1, 1, 1], 'int32');
-        tensorArray.writeMany([0, 1], [input4, input5]);
-        context.addTensorArray(tensorArray);
-        node.op = 'TensorArrayGatherV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-        node.attrParams['dtype'] = createDtypeAttr('int32');
-        node.inputNames = ['input2', 'input3'];
-        const input2 = [tensorArray.idTensor];
-        const input3 = [tensor1d([0, 1])];
-        const gather = await executeOp(node, {input2, input3}, context);
-        expect(gather.length).toEqual(1);
-        expect(gather[0].shape).toEqual([2, 3]);
-        test_util.expectArraysClose(
-            gather[0].dataSync(), new Int32Array([0, 0, 0, 1, 1, 1]));
-      });
-      it('should match json def', () => {
-        node.op = 'TensorArrayGatherV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-        node.attrParams['dtype'] = createDtypeAttr('int32');
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('TensorArrayScatterV3', () => {
-      it('should scatter the tensor to tensorArray', async () => {
-        const tensorArray =
-            new TensorArray('', 'int32', 5, [3], true, false, true);
-        const input4 = [tensor2d([0, 0, 0, 1, 1, 1], [2, 3], 'int32')];
-        context.addTensorArray(tensorArray);
-        node.op = 'TensorArrayScatterV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-        node.inputParams['tensor'] = createTensorAttr(2);
-        node.inputNames = ['input2', 'input3', 'input4'];
-        const input2 = [tensorArray.idTensor];
-        const input3 = [tensor1d([0, 1], 'int32')];
-        await executeOp(node, {input2, input3, input4}, context);
-
-        expect(tensorArray.size()).toEqual(2);
-      });
-
-      it('should match json def', () => {
-        node.op = 'TensorArrayScatterV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-        node.inputParams['tensor'] = createTensorAttr(2);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('TensorArraySplitV3', () => {
-      it('should split the tensor to tensorArray', async () => {
-        const tensorArray =
-            new TensorArray('', 'int32', 2, [3], true, false, true);
-        const input4 = [tensor2d([0, 0, 0, 1, 1, 1], [2, 3], 'int32')];
-        context.addTensorArray(tensorArray);
-        node.op = 'TensorArraySplitV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['tensor'] = createTensorAttr(1);
-        node.inputParams['lengths'] = createNumericArrayAttrFromIndex(2);
-        node.inputNames = ['input2', 'input4', 'input3'];
-        const input2 = [tensorArray.idTensor];
-        const input3 = [tensor1d([1, 1], 'int32')];
-        await executeOp(node, {input2, input3, input4}, context);
-
-        expect(tensorArray.size()).toEqual(2);
-      });
-      it('should match json def', () => {
-        node.op = 'TensorArraySplitV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputParams['tensor'] = createTensorAttr(1);
-        node.inputParams['lengths'] = createNumericArrayAttrFromIndex(2);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('TensorArrayConcatV3', () => {
-      it('should concat the tensors from tensorArray', async () => {
-        const tensorArray =
-            new TensorArray('', 'int32', 5, [3], true, false, true);
-        const input4 = tensor1d([0, 0, 0], 'int32');
-        const input5 = tensor1d([1, 1, 1], 'int32');
-        tensorArray.writeMany([0, 1], [input4, input5]);
-        context.addTensorArray(tensorArray);
-        node.op = 'TensorArrayConcatV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.attrParams['dtype'] = createDtypeAttr('int32');
-        node.inputNames = ['input2'];
-        const input2 = [tensorArray.idTensor];
-        const concat = await executeOp(node, {input2}, context);
-        expect(concat.length).toEqual(1);
-        expect(concat[0].shape).toEqual([6]);
-        test_util.expectArraysClose(
-            concat[0].dataSync(), new Int32Array([0, 0, 0, 1, 1, 1]));
-      });
-      it('should match json def', () => {
-        node.op = 'TensorArrayConcatV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.attrParams['dtype'] = createDtypeAttr('int32');
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('TensorArraySizeV3', () => {
-      it('should get the size of tensorArray', async () => {
-        const tensorArray =
-            new TensorArray('', 'int32', 5, [3], true, false, true);
-        const input4 = tensor1d([0, 0, 0], 'int32');
-        const input5 = tensor1d([1, 1, 1], 'int32');
-        tensorArray.writeMany([0, 1], [input4, input5]);
-        context.addTensorArray(tensorArray);
-        node.op = 'TensorArraySizeV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputNames = ['input2'];
-        const input2 = [tensorArray.idTensor];
-        const size = await executeOp(node, {input2}, context);
-        expect(size.length).toEqual(1);
-        expect(size[0].shape).toEqual([]);
-        test_util.expectArraysClose(size[0].dataSync(), new Int32Array([2]));
-      });
-      it('should match json def', () => {
-        node.op = 'TensorArraySizeV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-
-    describe('TensorArrayCloseV3', () => {
-      it('should close the tensorArray', async () => {
-        const tensorArray =
-            new TensorArray('', 'int32', 5, [3], true, false, true);
-        const input4 = tensor1d([0, 0, 0], 'int32');
-        const input5 = tensor1d([1, 1, 1], 'int32');
-        tensorArray.writeMany([0, 1], [input4, input5]);
-        context.addTensorArray(tensorArray);
-        node.op = 'TensorArrayCloseV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-        node.inputNames = ['input2'];
-        const input2 = [tensorArray.idTensor];
-        await executeOp(node, {input2}, context);
-        expect(tensorArray.closed).toBeTruthy();
-      });
-      it('should match json def', () => {
-        node.op = 'TensorArrayCloseV3';
-        node.inputParams['tensorArrayId'] = createTensorAttr(0);
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-  });
-  describe('StatelessWhile', () => {
-    it('should set the output', async () => {
-      node.op = 'StatelessWhile';
-      node.inputNames = ['input1', 'input2'];
-      node.inputParams['args'] = createTensorsAttr(0, 0);
-      node.attrParams['cond'] = {'value': 'condFunc', 'type': 'func'};
-      node.attrParams['body'] = {'value': 'bodyFunc', 'type': 'func'};
-
-      const cond = [tfOps.scalar(false)];
-      const graph: Graph = {
-        inputs: [],
-        nodes: {
-
-        },
-        outputs: [],
-        weights: [],
-        placeholders: [],
-        functions: {},
-        signature: {}
-      };
-      const condExecutor = new GraphExecutor(graph);
-      let firstTime = true;
-      spyOn(condExecutor, 'executeFunctionAsync').and.callFake(async () => {
-        if (firstTime) {
-          firstTime = false;
-          return input1;
-        }
-        return input2;
-      });
-      const bodyExecutor = new GraphExecutor(graph);
-      const input3 = [tfOps.scalar(3, 'int32')];
-      spyOn(bodyExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input3));
-      context.functionMap['bodyFunc'] = bodyExecutor;
-      context.functionMap['condFunc'] = condExecutor;
-      const result = await executeOp(node, {cond, input1, input2}, context);
-
-      test_util.expectArraysEqual(
-          await result[0].array(), await input3[0].array());
-    });
-
-    it('should match json def', () => {
-      node.op = 'StatelessWhile';
-      node.inputNames = ['input1', 'input2'];
-      node.inputParams['args'] = createTensorsAttr(0, 0);
-      node.attrParams['cond'] = {'value': 'condFunc', 'type': 'func'};
-      node.attrParams['body'] = {'value': 'bodyFunc', 'type': 'func'};
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-  describe('While', () => {
-    it('should set the output', async () => {
-      node.op = 'While';
-      node.inputNames = ['input1', 'input2'];
-      node.inputParams['args'] = createTensorsAttr(0, 0);
-      node.attrParams['cond'] = {'value': 'condFunc', 'type': 'func'};
-      node.attrParams['body'] = {'value': 'bodyFunc', 'type': 'func'};
-
-      const cond = [tfOps.scalar(false)];
-      const graph: Graph = {
-        inputs: [],
-        nodes: {
-
-        },
-        outputs: [],
-        weights: [],
-        placeholders: [],
-        functions: {},
-        signature: {}
-      };
-      const condExecutor = new GraphExecutor(graph);
-      let firstTime = true;
-      spyOn(condExecutor, 'executeFunctionAsync').and.callFake(async () => {
-        if (firstTime) {
-          firstTime = false;
-          return input1;
-        }
-        return input2;
-      });
-      const bodyExecutor = new GraphExecutor(graph);
-      const input3 = [tfOps.scalar(3, 'int32')];
-      spyOn(bodyExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input3));
-      context.functionMap['bodyFunc'] = bodyExecutor;
-      context.functionMap['condFunc'] = condExecutor;
-      const result = await executeOp(node, {cond, input1, input2}, context);
-
-      test_util.expectArraysEqual(
-          await result[0].array(), await input3[0].array());
-    });
-
-    it('should match json def', () => {
-      node.op = 'While';
-      node.inputNames = ['input1', 'input2'];
-      node.inputParams['args'] = createTensorsAttr(0, 0);
-      node.attrParams['cond'] = {'value': 'condFunc', 'type': 'func'};
-      node.attrParams['body'] = {'value': 'bodyFunc', 'type': 'func'};
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-  describe('StatelessIf', () => {
-    it('should set the output condition is true', async () => {
-      node.op = 'StatelessIf';
-      node.inputNames = ['cond', 'input1', 'input2'];
-      node.inputParams['args'] = createTensorsAttr(1, 0);
-      node.inputParams['cond'] = createTensorAttr(0);
-      node.attrParams['thenBranch'] = {'value': 'thenFunc', 'type': 'func'};
-      node.attrParams['elseBranch'] = {'value': 'elseFunc', 'type': 'func'};
-
-      const cond = [tfOps.scalar(true)];
-      const graph: Graph = {
-        inputs: [],
-        nodes: {
-
-        },
-        outputs: [],
-        weights: [],
-        placeholders: [],
-        functions: {},
-        signature: {}
-      };
-      const thenExecutor = new GraphExecutor(graph);
-      spyOn(thenExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input1));
-      const elseExecutor = new GraphExecutor(graph);
-      spyOn(elseExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input2));
-      context.functionMap['thenFunc'] = thenExecutor;
-      context.functionMap['elseFunc'] = elseExecutor;
-      const result = await executeOp(node, {cond, input1, input2}, context);
-
-      test_util.expectArraysEqual(
-          await result[0].array(), await input1[0].array());
-    });
-    it('should set the output condition is false', async () => {
-      node.op = 'StatelessIf';
-      node.inputNames = ['cond', 'input1'];
-      node.inputParams['args'] = createTensorsAttr(1, 0);
-      node.inputParams['cond'] = createTensorAttr(0);
-      node.attrParams['thenBranch'] = {'value': 'thenFunc', 'type': 'func'};
-      node.attrParams['elseBranch'] = {'value': 'elseFunc', 'type': 'func'};
-
-      const cond = [tfOps.scalar(false)];
-      const graph: Graph = {
-        inputs: [],
-        nodes: {
-
-        },
-        outputs: [],
-        weights: [],
-        placeholders: [],
-        functions: {},
-        signature: {}
-      };
-      const thenExecutor = new GraphExecutor(graph);
-      spyOn(thenExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input1));
-      const elseExecutor = new GraphExecutor(graph);
-      spyOn(elseExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input2));
-      context.functionMap['thenFunc'] = thenExecutor;
-      context.functionMap['elseFunc'] = elseExecutor;
-      const result = await executeOp(node, {cond, input1, input2}, context);
-
-      test_util.expectArraysEqual(
-          await result[0].array(), await input2[0].array());
-    });
-    it('should match json def', () => {
-      node.op = 'StatelessIf';
-      node.inputNames = ['cond', 'input1'];
-      node.inputParams['args'] = createTensorsAttr(1, 0);
-      node.inputParams['cond'] = createTensorAttr(0);
-      node.attrParams['thenBranch'] = {'value': 'thenFunc', 'type': 'func'};
-      node.attrParams['elseBranch'] = {'value': 'elseFunc', 'type': 'func'};
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-  describe('If', () => {
-    it('should set the output condition is true', async () => {
-      node.op = 'If';
-      node.inputNames = ['cond', 'input1', 'input2'];
-      node.inputParams['args'] = createTensorsAttr(1, 0);
-      node.inputParams['cond'] = createTensorAttr(0);
-      node.attrParams['thenBranch'] = {'value': 'thenFunc', 'type': 'func'};
-      node.attrParams['elseBranch'] = {'value': 'elseFunc', 'type': 'func'};
-
-      const cond = [tfOps.scalar(true)];
-      const graph: Graph = {
-        inputs: [],
-        nodes: {
-
-        },
-        outputs: [],
-        weights: [],
-        placeholders: [],
-        functions: {},
-        signature: {}
-      };
-      const thenExecutor = new GraphExecutor(graph);
-      spyOn(thenExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input1));
-      const elseExecutor = new GraphExecutor(graph);
-      spyOn(elseExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input2));
-      context.functionMap['thenFunc'] = thenExecutor;
-      context.functionMap['elseFunc'] = elseExecutor;
-      const result = await executeOp(node, {cond, input1, input2}, context);
-
-      test_util.expectArraysEqual(
-          await result[0].array(), await input1[0].array());
-    });
-    it('should set the output condition is false', async () => {
-      node.op = 'If';
-      node.inputNames = ['cond', 'input1'];
-      node.inputParams['args'] = createTensorsAttr(1, 0);
-      node.inputParams['cond'] = createTensorAttr(0);
-      node.attrParams['thenBranch'] = {'value': 'thenFunc', 'type': 'func'};
-      node.attrParams['elseBranch'] = {'value': 'elseFunc', 'type': 'func'};
-
-      const cond = [tfOps.scalar(false)];
-      const graph: Graph = {
-        inputs: [],
-        nodes: {
-
-        },
-        outputs: [],
-        weights: [],
-        placeholders: [],
-        functions: {},
-        signature: {}
-      };
-      const thenExecutor = new GraphExecutor(graph);
-      spyOn(thenExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input1));
-      const elseExecutor = new GraphExecutor(graph);
-      spyOn(elseExecutor, 'executeFunctionAsync').and
-        .returnValue(Promise.resolve(input2));
-      context.functionMap['thenFunc'] = thenExecutor;
-      context.functionMap['elseFunc'] = elseExecutor;
-      const result = await executeOp(node, {cond, input1, input2}, context);
-
-      test_util.expectArraysEqual(
-          await result[0].array(), await input2[0].array());
-    });
-    it('should match json def', () => {
-      node.op = 'If';
-      node.inputNames = ['cond', 'input1'];
-      node.inputParams['args'] = createTensorsAttr(1, 0);
-      node.inputParams['cond'] = createTensorAttr(0);
-      node.attrParams['thenBranch'] = {'value': 'thenFunc', 'type': 'func'};
-      node.attrParams['elseBranch'] = {'value': 'elseFunc', 'type': 'func'};
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-  describe('TensorListReserve', () => {
-    it('should create new tensor on the context', async () => {
-      node.op = 'TensorListReserve';
-      node.inputParams['elementShape'] = createNumericArrayAttrFromIndex(0);
-      node.inputParams['numElements'] = createNumberAttrFromIndex(1);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      node.inputNames = ['input4', 'input1'];
-      const input4 = [tensor1d([10, 10], 'int32')];
-      const tensorListId =
-          (await executeOp(node, {input1, input4}, context))[0];
-      const tensorList = context.getTensorList(tensorListId.id);
-      expect(tensorList.elementDtype).toEqual('int32');
-      expect(tensorList.elementShape).toEqual([10, 10]);
-      expect(tensorList.maxNumElements).toEqual(-1);
-    });
-    it('should match json def', () => {
-      node.op = 'TensorListReserve';
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(0);
-      node.inputParams['numElements'] = createNumberAttrFromIndex(1);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('EmptyTensorList', () => {
-    it('should create new tensor on the context', async () => {
-      node.op = 'EmptyTensorList';
-      node.inputParams['elementShape'] = createNumericArrayAttrFromIndex(0);
-      node.inputParams['maxNumElements'] = createNumberAttrFromIndex(1);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      node.inputNames = ['input4', 'input1'];
-      const input4 = [tensor1d([10, 10], 'int32')];
-      const tensorListId =
-          (await executeOp(node, {input1, input4}, context))[0];
-      const tensorList = context.getTensorList(tensorListId.id);
-      expect(tensorList.elementDtype).toEqual('int32');
-      expect(tensorList.elementShape).toEqual([10, 10]);
-      expect(tensorList.maxNumElements).toEqual(1);
-    });
-    it('should match json def', () => {
-      node.op = 'EmptyTensorList';
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(0);
-      node.inputParams['maxNumElements'] = createNumberAttrFromIndex(1);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListConcat', () => {
-    it('should concat the tensors from tensorList', async () => {
-      const input4 = tensor1d([0, 0, 0], 'int32');
-      const input5 = tensor1d([1, 1, 1], 'int32');
-      const tensorList = new TensorList([input4, input5], [3], 'int32', 5);
-      context.addTensorList(tensorList);
-      node.op = 'TensorListConcat';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      node.attrParams['elementShape'] = createTensorShapeAttr([3]);
-      node.inputNames = ['input2'];
-      const input2 = [tensorList.idTensor];
-      const concat = await executeOp(node, {input2}, context);
-      expect(concat.length).toEqual(1);
-      expect(concat[0].shape).toEqual([6]);
-      test_util.expectArraysClose(
-          concat[0].dataSync(), new Int32Array([0, 0, 0, 1, 1, 1]));
-    });
-    it('should match json def', () => {
-      node.op = 'TensorListConcat';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      node.attrParams['elementShape'] = createTensorShapeAttr([3]);
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListConcatV2', () => {
-    it('should concat the tensors from tensorList', async () => {
-      const input4 = tensor1d([0, 0, 0], 'int32');
-      const input5 = tensor1d([1, 1, 1], 'int32');
-      const tensorList = new TensorList([input4, input5], [3], 'int32', 5);
-      context.addTensorList(tensorList);
-      node.op = 'TensorListConcatV2';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      node.attrParams['elementShape'] = createTensorShapeAttr([3]);
-      node.inputNames = ['input2'];
-      const input2 = [tensorList.idTensor];
-      const concat = await executeOp(node, {input2}, context);
-      expect(concat.length).toEqual(1);
-      expect(concat[0].shape).toEqual([6]);
-      test_util.expectArraysClose(
-          concat[0].dataSync(), new Int32Array([0, 0, 0, 1, 1, 1]));
-    });
-    it('should match json def', () => {
-      node.op = 'TensorListConcatV2';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      node.attrParams['elementShape'] = createTensorShapeAttr([3]);
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListScatter', () => {
-    it('should scatter the tensor to tensorList', async () => {
-      const input4 = [tensor2d([0, 0, 0, 1, 1, 1], [2, 3], 'int32')];
-      node.op = 'TensorListScatter';
-      node.inputParams['tensor'] = createTensorAttr(0);
-      node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(2);
-      node.inputNames = ['input4', 'input2', 'input3'];
-      const input2 = [tensor1d([0, 1], 'int32')];
-      const input3 = [tensor1d([3], 'int32')];
-      const tensorListId =
-          (await executeOp(node, {input2, input3, input4}, context))[0];
-      const tensorList = context.getTensorList(tensorListId.id);
-      expect(tensorList.size()).toEqual(2);
-    });
-
-    it('should match json def', () => {
-      node.op = 'TensorListScatter';
-      node.inputParams['tensor'] = createTensorAttr(0);
-      node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(2);
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListScatterV2', () => {
-    it('should scatter the tensor to tensorList', async () => {
-      const input4 = [tensor2d([0, 0, 0, 1, 1, 1], [2, 3], 'int32')];
-      node.op = 'TensorListScatterV2';
-      node.inputParams['tensor'] = createTensorAttr(0);
-      node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(2);
-      node.inputParams['numElements'] = createNumberAttrFromIndex(3);
-      node.inputNames = ['input4', 'input2', 'input3', 'input5'];
-      const input2 = [tensor1d([0, 1], 'int32')];
-      const input3 = [tensor1d([3], 'int32')];
-      const input5 = [tensor1d([2], 'int32')];
-      const tensorListId =
-          (await executeOp(node, {input2, input3, input4, input5}, context))[0];
-      const tensorList = context.getTensorList(tensorListId.id);
-      expect(tensorList.size()).toEqual(2);
-    });
-
-    it('should match json def', () => {
-      node.op = 'TensorListScatterV2';
-      node.inputParams['tensor'] = createTensorAttr(0);
-      node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(2);
-      node.inputParams['numElements'] = createNumberAttrFromIndex(3);
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListSetItem', () => {
-    it('should write the tensor to tensorArray', async () => {
-      const tensorList = new TensorList([], [], 'int32', 5);
-      context.addTensorList(tensorList);
-      node.op = 'TensorListSetItem';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['index'] = createNumberAttrFromIndex(1);
-      node.inputParams['tensor'] = createTensorAttr(2);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      node.inputNames = ['input2', 'input3', 'input1'];
-      const input2 = [tensorList.idTensor];
-      const input3 = [scalar(0)];
-      await executeOp(node, {input1, input2, input3}, context);
-
-      expect(tensorList.size()).toEqual(1);
-    });
-    it('should match json def', () => {
-      node.op = 'TensorListSetItem';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['index'] = createNumberAttrFromIndex(1);
-      node.inputParams['tensor'] = createTensorAttr(2);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListGetItem', () => {
-    it('should read the tensor from tensorList', async () => {
-      const tensorList = new TensorList([], [3], 'int32', 5);
-      const input4 = tensor1d([0, 0, 0], 'int32');
-      tensorList.setItem(0, input4);
-      context.addTensorList(tensorList);
-      node.op = 'TensorListGetItem';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['index'] = createNumberAttrFromIndex(1);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(2);
-      node.inputNames = ['input2', 'input3', 'input5'];
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      const input2 = [tensorList.idTensor];
-      const input3 = [scalar(0)];
-      const input5 = [tensor1d([3], 'int32')];
-      const read = await executeOp(node, {input5, input2, input3}, context);
-
-      test_util.expectArraysClose(await read[0].array(), await input4.array());
-    });
-    it('should match json def', () => {
-      node.op = 'TensorListGetItem';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['index'] = createNumberAttrFromIndex(1);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(2);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListPushBack', () => {
-    it('should write the tensor to tensorArray', async () => {
-      const tensorList = new TensorList([], [], 'int32', 5);
-      context.addTensorList(tensorList);
-      node.op = 'TensorListPushBack';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['tensor'] = createTensorAttr(1);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      node.inputNames = ['input2', 'input1'];
-      const input2 = [tensorList.idTensor];
-      await executeOp(node, {input1, input2}, context);
-
-      expect(tensorList.size()).toEqual(1);
-    });
-    it('should match json def', () => {
-      node.op = 'TensorListPushBack';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['tensor'] = createTensorAttr(1);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListPopBack', () => {
-    it('should read the tensor from tensorList', async () => {
-      const tensorList = new TensorList([], [3], 'int32', 5);
-      const input4 = tensor1d([0, 0, 0], 'int32');
-      tensorList.setItem(0, input4);
-      context.addTensorList(tensorList);
-      node.op = 'TensorListPopBack';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(1);
-      node.inputNames = ['input2', 'input5'];
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      const input2 = [tensorList.idTensor];
-      const input5 = [tensor1d([3], 'int32')];
-      const read = await executeOp(node, {input5, input2}, context);
-
-      test_util.expectArraysClose(await read[0].array(), await input4.array());
-    });
-    it('should match json def', () => {
-      node.op = 'TensorListPopBack';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(1);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListStack', () => {
-    it('should read the tensor from tensorList', async () => {
-      const tensorList = new TensorList([], [3], 'int32', 5);
-      const input4 = tensor1d([0, 0, 0], 'int32');
-      tensorList.setItem(0, input4);
-      context.addTensorList(tensorList);
-      node.op = 'TensorListStack';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(1);
-      node.inputNames = ['input2', 'input5'];
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-      const input2 = [tensorList.idTensor];
-      const input5 = [tensor1d([3], 'int32')];
-      const read = await executeOp(node, {input5, input2}, context);
-
-      test_util.expectArraysClose(
-          await read[0].array(), [await input4.array()]);
-    });
-    it('should match json def', () => {
-      node.op = 'TensorListStack';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(1);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-
-    describe('TensorListGather', () => {
-      it('should read the tensor from tensorList', async () => {
-        const tensorList = new TensorList([], [3], 'int32', 5);
-        const input4 = tensor1d([0, 0, 0], 'int32');
-        tensorList.setItem(0, input4);
-        const input6 = tensor1d([1, 1, 1], 'int32');
-        tensorList.setItem(1, input6);
-        context.addTensorList(tensorList);
-        node.op = 'TensorListGather';
-        node.inputParams['tensorListId'] = createTensorAttr(0);
-        node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-        node.inputParams['elementShape'] = createShapeAttrFromIndex(2);
-        node.inputNames = ['input2', 'input3', 'input5'];
-        node.attrParams['elementDType'] = createDtypeAttr('int32');
-        const input2 = [tensorList.idTensor];
-        const input3 = [tensor1d([0, 1], 'int32')];
-
-        const input5 = [tensor1d([3], 'int32')];
-        const read = await executeOp(node, {input5, input2, input3}, context);
-
-        test_util.expectArraysClose(
-            await read[0].array(),
-            [await input4.array(), await input6.array()]);
-      });
-      it('should match json def', () => {
-        node.op = 'TensorListGather';
-        node.inputParams['tensorListId'] = createTensorAttr(0);
-        node.inputParams['indices'] = createNumericArrayAttrFromIndex(1);
-        node.inputParams['elementShape'] = createShapeAttrFromIndex(2);
-        node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-        expect(validateParam(node, control.json)).toBeTruthy();
-      });
-    });
-  });
-
-  describe('TensorListSplit', () => {
-    it('should scatter the tensor to tensorList', async () => {
-      const input4 = [tensor2d([0, 0, 0, 1, 1, 1], [2, 3], 'int32')];
-      node.op = 'TensorListSplit';
-      node.inputParams['tensor'] = createTensorAttr(0);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(1);
-      node.inputParams['lengths'] = createNumericArrayAttrFromIndex(2);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-      node.inputNames = ['input4', 'input2', 'input3'];
-      const input2 = [tensor1d([3], 'int32')];
-      const input3 = [tensor1d([1, 1], 'int32')];
-      const idTensor =
-          (await executeOp(node, {input2, input3, input4}, context))[0];
-      const tensorList = context.getTensorList(idTensor.id);
-      expect(tensorList.size()).toEqual(2);
-    });
-
-    it('should match json def', () => {
-      node.op = 'TensorListSplit';
-      node.inputParams['tensor'] = createTensorAttr(0);
-      node.inputParams['elementShape'] = createShapeAttrFromIndex(1);
-      node.inputParams['lengths'] = createNumericArrayAttrFromIndex(2);
-      node.attrParams['elementDType'] = createDtypeAttr('int32');
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListLength', () => {
-    it('should get the size of tensorList', async () => {
-      const input4 = tensor1d([0, 0, 0], 'int32');
-      const input5 = tensor1d([1, 1, 1], 'int32');
-      const tensorList = new TensorList([input4, input5], [3], 'int32', 5);
-      context.addTensorList(tensorList);
-      node.op = 'TensorListLength';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputNames = ['input2'];
-      const input2 = [tensorList.idTensor];
-      const size = await executeOp(node, {input2}, context);
-      expect(size.length).toEqual(1);
-      expect(size[0].shape).toEqual([]);
-      test_util.expectArraysClose(size[0].dataSync(), [2]);
-    });
-
-    it('should match json def', () => {
-      node.op = 'TensorListLength';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-
-  describe('TensorListResize', () => {
-    it('should match the size when reducing the size', async () => {
-      const input4 = tensor1d([0, 0, 0], 'int32');
-      const input5 = tensor1d([1, 1, 1], 'int32');
-      const tensorList = new TensorList([input4, input5], [3], 'int32', 5);
-      context.addTensorList(tensorList);
-      node.op = 'TensorListResize';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['size'] = createNumberAttrFromIndex(1);
-      node.inputNames = ['input2', 'input3'];
-      const input2 = [tensorList.idTensor];
-      const input3 = [scalar(1)];
-      const tensorListId =
-          (await executeOp(node, {input2, input3}, context))[0];
-      const destTensorList = context.getTensorList(tensorListId.id);
-      expect(destTensorList.size()).toEqual(1);
-    });
-
-    it('should match the size when increasing the size', async () => {
-      const input4 = tensor1d([0, 0, 0], 'int32');
-      const input5 = tensor1d([1, 1, 1], 'int32');
-      const srcTensorList = new TensorList([input4, input5], [3], 'int32', 5);
-      context.addTensorList(srcTensorList);
-      node.op = 'TensorListResize';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['size'] = createNumberAttrFromIndex(1);
-      node.inputNames = ['input2', 'input3'];
-      const input2 = [srcTensorList.idTensor];
-      const input3 = [scalar(3)];
-      const destTensorListId =
-          (await executeOp(node, {input2, input3}, context))[0];
-      const destTensorList = context.getTensorList(destTensorListId.id);
-      expect(destTensorList.size()).toEqual(3);
-    });
-
-    it('should match json def', () => {
-      node.op = 'TensorListResize';
-      node.inputParams['tensorListId'] = createTensorAttr(0);
-      node.inputParams['size'] = createNumberAttrFromIndex(1);
-
-      expect(validateParam(node, control.json)).toBeTruthy();
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/convolution_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/convolution_executor.ts
deleted file mode 100644
index 18658dc06..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/convolution_executor.ts
+++ /dev/null
@@ -1,320 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Rank, Tensor, Tensor3D, Tensor4D, Tensor5D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getPadding, getParamValue} from './utils';
-
-function fusedConvAndDepthWiseParams(
-    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) {
-  const [extraOp, activationFunc] =
-      (getParamValue('fusedOps', node, tensorMap, context) as string[]);
-
-  const isBiasAdd = extraOp === 'biasadd';
-  const noBiasAdd = !isBiasAdd;
-  const isPrelu = activationFunc === 'prelu';
-  const isBatchNorm = extraOp === 'fusedbatchnorm';
-
-  const numArgs =
-      (getParamValue('numArgs', node, tensorMap, context) as number);
-  if (isBiasAdd) {
-    if (isPrelu && numArgs !== 2) {
-      throw new Error(
-          'FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +
-          'must have two extra arguments: bias and alpha.');
-    }
-    if (!isPrelu && isBiasAdd && numArgs !== 1) {
-      throw new Error(
-          'FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +
-          'one extra argument: bias.');
-    }
-  }
-  if (isBatchNorm) {
-    throw new Error(
-        'FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported');
-  }
-  const stride = getParamValue('strides', node, tensorMap, context) as number[];
-  const pad = getPadding(node, tensorMap, context);
-  const dataFormat =
-      (getParamValue('dataFormat', node, tensorMap, context) as string)
-          .toUpperCase();
-  const dilations =
-      getParamValue('dilations', node, tensorMap, context) as number[];
-  let [biasArg, preluArg] =
-      getParamValue('args', node, tensorMap, context) as Tensor[];
-  if (noBiasAdd) {
-    preluArg = biasArg;
-    biasArg = undefined;
-  }
-  const leakyreluAlpha =
-      getParamValue('leakyreluAlpha', node, tensorMap, context) as number;
-
-  return {
-    stride,
-    pad,
-    dataFormat,
-    dilations,
-    biasArg,
-    preluArg,
-    activationFunc,
-    leakyreluAlpha
-  };
-}
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap,
-     context: ExecutionContext, ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'Conv1D': {
-          const stride =
-              getParamValue('stride', node, tensorMap, context) as number;
-          const pad = getParamValue('pad', node, tensorMap, context);
-          const dataFormat =
-              (getParamValue('dataFormat', node, tensorMap, context) as string)
-                  .toUpperCase();
-          const dilation =
-              getParamValue('dilation', node, tensorMap, context) as number;
-          return [ops.conv1d(
-              getParamValue('x', node, tensorMap, context) as Tensor3D,
-              getParamValue('filter', node, tensorMap, context) as Tensor3D,
-              stride, pad as 'valid' | 'same', dataFormat as 'NWC' | 'NCW',
-              dilation)];
-        }
-        case 'Conv2D': {
-          const stride =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getPadding(node, tensorMap, context);
-          const dataFormat =
-              (getParamValue('dataFormat', node, tensorMap, context) as string)
-                  .toUpperCase();
-          const dilations =
-              getParamValue('dilations', node, tensorMap, context) as number[];
-          return [ops.conv2d(
-              getParamValue('x', node, tensorMap, context) as Tensor3D |
-                  Tensor4D,
-              getParamValue('filter', node, tensorMap, context) as Tensor4D,
-              [stride[1], stride[2]], pad as 'valid' | 'same',
-              dataFormat as 'NHWC' | 'NCHW', [dilations[1], dilations[2]])];
-        }
-        case '_FusedConv2D': {
-          const {
-            stride,
-            pad,
-            dataFormat,
-            dilations,
-            biasArg,
-            preluArg,
-            activationFunc,
-            leakyreluAlpha
-          } = fusedConvAndDepthWiseParams(node, tensorMap, context);
-
-          return [ops.fused.conv2d({
-            x: getParamValue('x', node, tensorMap, context) as Tensor3D |
-                Tensor4D,
-            filter: getParamValue('filter', node, tensorMap, context) as
-                Tensor4D,
-            strides: [stride[1], stride[2]],
-            pad: pad as 'valid' | 'same',
-            dataFormat: dataFormat as 'NHWC' | 'NCHW',
-            dilations: [dilations[1], dilations[2]],
-            bias: biasArg,
-            activation: activationFunc as tfOps.fused.Activation,
-            preluActivationWeights: preluArg,
-            leakyreluAlpha
-          })];
-        }
-
-        case 'FusedDepthwiseConv2dNative': {
-          const {
-            stride,
-            pad,
-            dataFormat,
-            dilations,
-            biasArg,
-            preluArg,
-            activationFunc,
-            leakyreluAlpha,
-          } = fusedConvAndDepthWiseParams(node, tensorMap, context);
-
-          return [ops.fused.depthwiseConv2d({
-            x: getParamValue('x', node, tensorMap, context) as Tensor3D |
-                Tensor4D,
-            filter: getParamValue('filter', node, tensorMap, context) as
-                Tensor4D,
-            strides: [stride[1], stride[2]],
-            pad: pad as 'valid' | 'same',
-            dataFormat: dataFormat as 'NHWC' | 'NCHW',
-            dilations: [dilations[1], dilations[2]],
-            bias: biasArg,
-            activation: activationFunc as tfOps.fused.Activation,
-            preluActivationWeights: preluArg,
-            leakyreluAlpha
-          })];
-        }
-        case 'Conv2DBackpropInput':
-        case 'Conv2dTranspose': {
-          const shape = getParamValue(
-                            'outputShape', node, tensorMap,
-                            context) as [number, number, number] |
-              [number, number, number, number];
-          const stride =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getPadding(node, tensorMap, context);
-          return [ops.conv2dTranspose(
-              getParamValue('x', node, tensorMap, context) as Tensor3D |
-                  Tensor4D,
-              getParamValue('filter', node, tensorMap, context) as Tensor4D,
-              shape, [stride[1], stride[2]], pad as 'valid' | 'same')];
-        }
-        case 'DepthwiseConv2dNative':
-        case 'DepthwiseConv2d': {
-          const stride =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getPadding(node, tensorMap, context);
-          const dilations =
-              getParamValue('dilations', node, tensorMap, context) as number[];
-          const dataFormat =
-              (getParamValue('dataFormat', node, tensorMap, context) as string)
-                  .toUpperCase();
-
-          return [ops.depthwiseConv2d(
-              getParamValue('input', node, tensorMap, context) as Tensor3D |
-                  Tensor4D,
-              getParamValue('filter', node, tensorMap, context) as Tensor4D,
-              [stride[1], stride[2]], pad as 'valid' | 'same',
-              dataFormat as 'NHWC' | 'NCHW', [dilations[1], dilations[2]])];
-        }
-        case 'Conv3D': {
-          const stride =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getParamValue('pad', node, tensorMap, context);
-          const dataFormat =
-              (getParamValue('dataFormat', node, tensorMap, context) as string)
-                  .toUpperCase();
-          const dilations =
-              getParamValue('dilations', node, tensorMap, context) as number[];
-          return [ops.conv3d(
-              getParamValue('x', node, tensorMap, context) as Tensor4D |
-                  Tensor<Rank.R5>,
-              getParamValue('filter', node, tensorMap, context) as
-                  Tensor<Rank.R5>,
-              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same',
-              dataFormat as 'NDHWC' | 'NCDHW',
-              [dilations[1], dilations[2], dilations[3]])];
-        }
-        case 'AvgPool': {
-          const stride =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getParamValue('pad', node, tensorMap, context);
-          const kernelSize =
-              getParamValue('kernelSize', node, tensorMap, context) as number[];
-
-          return [ops.avgPool(
-              getParamValue('x', node, tensorMap, context) as Tensor3D |
-                  Tensor4D,
-              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],
-              pad as 'valid' | 'same')];
-        }
-        case 'MaxPool': {
-          const stride =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getParamValue('pad', node, tensorMap, context);
-          const kernelSize =
-              getParamValue('kernelSize', node, tensorMap, context) as number[];
-
-          return [ops.maxPool(
-              getParamValue('x', node, tensorMap, context) as Tensor3D |
-                  Tensor4D,
-              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],
-              pad as 'valid' | 'same')];
-        }
-        case 'MaxPoolWithArgmax': {
-          const stride =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getParamValue('pad', node, tensorMap, context);
-          const kernelSize =
-              getParamValue('kernelSize', node, tensorMap, context) as number[];
-          const includeBatchInIndex =
-              getParamValue('includeBatchInIndex', node, tensorMap, context) as
-              boolean;
-          const {result, indexes} = ops.maxPoolWithArgmax(
-              getParamValue('x', node, tensorMap, context) as Tensor4D,
-              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],
-              pad as 'valid' | 'same', includeBatchInIndex);
-          return [result, indexes];
-        }
-        case 'AvgPool3D': {
-          const stride =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getParamValue('pad', node, tensorMap, context);
-          const kernelSize =
-              getParamValue('kernelSize', node, tensorMap, context) as number[];
-
-          return [ops.avgPool3d(
-              getParamValue('x', node, tensorMap, context) as Tensor5D,
-              [kernelSize[1], kernelSize[2], kernelSize[3]],
-              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same')];
-        }
-
-        case 'MaxPool3D': {
-          const stride =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getParamValue('pad', node, tensorMap, context);
-          const kernelSize =
-              getParamValue('kernelSize', node, tensorMap, context) as number[];
-
-          return [ops.maxPool3d(
-              getParamValue('x', node, tensorMap, context) as Tensor5D,
-              [kernelSize[1], kernelSize[2], kernelSize[3]],
-              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same')];
-        }
-
-        case 'Dilation2D': {
-          const strides =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const pad = getParamValue('pad', node, tensorMap, context);
-          const dilations =
-              getParamValue('dilations', node, tensorMap, context) as number[];
-
-          // strides: [1, stride_height, stride_width, 1].
-          const strideHeight = strides[1];
-          const strideWidth = strides[2];
-
-          // dilations: [1, dilation_height, dilation_width, 1].
-          const dilationHeight = dilations[1];
-          const dilationWidth = dilations[2];
-
-          return [ops.dilation2d(
-              getParamValue('x', node, tensorMap, context) as Tensor3D |
-                  Tensor4D,
-              getParamValue('filter', node, tensorMap, context) as Tensor3D,
-              [strideHeight, strideWidth], pad as 'valid' | 'same',
-              [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];
-        }
-
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'convolution';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/convolution_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/convolution_executor_test.ts
deleted file mode 100644
index ef3c3c8e3..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/convolution_executor_test.ts
+++ /dev/null
@@ -1,692 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import {Node} from '../types';
-
-import {executeOp} from './convolution_executor';
-import {RecursiveSpy} from './spy_ops';
-import {createNumberAttr, createNumericArrayAttr, createStrArrayAttr, createStrAttr, createTensorAttr, createTensorsAttr} from './test_helper';
-import {createBoolAttr} from './test_helper';
-
-describe('convolution', () => {
-  let node: Node;
-  const input = [tfOps.scalar(1)];
-  const context = new ExecutionContext({}, {}, {});
-
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    node = {
-      name: 'test',
-      op: '',
-      category: 'convolution',
-      inputNames: ['input'],
-      inputs: [],
-      inputParams: {x: createTensorAttr(0)},
-      attrParams: {},
-      children: []
-    };
-    spyOps =
-        Object.fromEntries(Object.keys(tfOps).map((op: keyof typeof tfOps) => {
-          if (op === 'fused') {
-            return [
-              op, {
-                conv2d: jasmine.createSpy(op),
-                depthwiseConv2d: jasmine.createSpy(op),
-                matMul: jasmine.createSpy(op),
-              }
-            ];
-          }
-          const spy = jasmine.createSpy(op);
-          return [op, spy] as const ;
-        })) as unknown as typeof spyOps;
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-  });
-
-  describe('executeOp', () => {
-    describe('AvgPool', () => {
-      it('should call tfOps.avgPool', () => {
-        node.op = 'AvgPool';
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['kernelSize'] = createNumericArrayAttr([1, 2, 2, 1]);
-
-        executeOp(node, {input}, context, spyOpsAsTfOps);
-
-        expect(spyOps.avgPool)
-            .toHaveBeenCalledWith(input[0], [2, 2], [2, 2], 'same');
-      });
-    });
-
-    describe('maxPool', () => {
-      it('should call tfOps.maxPool', () => {
-        node.op = 'MaxPool';
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['kernelSize'] = createNumericArrayAttr([1, 2, 2, 1]);
-
-        executeOp(node, {input}, context, spyOpsAsTfOps);
-
-        expect(spyOps.maxPool)
-            .toHaveBeenCalledWith(input[0], [2, 2], [2, 2], 'same');
-      });
-    });
-    describe('Conv2d', () => {
-      it('should call tfOps.conv2d', () => {
-        node.op = 'Conv2D';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(1.0)];
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.conv2d)
-            .toHaveBeenCalledWith(
-                input1[0], input2[0], [2, 2], 'same', 'NHWC', [2, 2]);
-      });
-      it('should support explicit padding', () => {
-        node.op = 'Conv2D';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('explicit');
-        node.attrParams['explicitPaddings'] =
-            createNumericArrayAttr([0, 0, 1, 1, 2, 2, 0, 0]);
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(1.0)];
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.conv2d)
-            .toHaveBeenCalledWith(
-                input1[0], input2[0], [2, 2], [[0, 0], [1, 1], [2, 2], [0, 0]],
-                'NHWC', [2, 2]);
-      });
-    });
-    describe('Conv2DBackpropInput', () => {
-      it('should call tfOps.conv2dTranspose', () => {
-        node.op = 'Conv2DBackpropInput';
-        node.attrParams['outputShape'] = createNumericArrayAttr([1, 2, 2, 2]);
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(1.0)];
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.conv2dTranspose)
-            .toHaveBeenCalledWith(
-                input1[0], input2[0], [1, 2, 2, 2], [2, 2], 'same');
-      });
-      it('should support explicit padding', () => {
-        node.op = 'Conv2DBackpropInput';
-        node.attrParams['outputShape'] = createNumericArrayAttr([1, 2, 2, 2]);
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('explicit');
-        node.attrParams['explicitPaddings'] =
-            createNumericArrayAttr([0, 0, 1, 1, 2, 2, 0, 0]);
-
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(1.0)];
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.conv2dTranspose)
-            .toHaveBeenCalledWith(
-                input1[0],
-                input2[0],
-                [1, 2, 2, 2],
-                [2, 2],
-                [[0, 0], [1, 1], [2, 2], [0, 0]],
-            );
-      });
-    });
-    describe('Conv1D', () => {
-      it('should call tfOps.conv1d', () => {
-        node.op = 'Conv1D';
-        node.category = 'convolution';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.attrParams['stride'] = createNumberAttr(1);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['dataFormat'] = createStrAttr('NWC');
-        node.attrParams['dilation'] = createNumberAttr(1);
-
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(1.0)];
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.conv1d)
-            .toHaveBeenCalledWith(input1[0], input2[0], 1, 'same', 'NWC', 1);
-      });
-    });
-
-    describe('DepthwiseConv2d', () => {
-      it('should call tfOps.depthwiseConv2d', () => {
-        node.op = 'DepthwiseConv2d';
-        node.category = 'convolution';
-        node.inputParams['input'] = createTensorAttr(0);
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(1.0)];
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.depthwiseConv2d)
-            .toHaveBeenCalledWith(
-                input1[0], input2[0], [2, 2], 'same', 'NHWC', [2, 2]);
-      });
-      it('support explicit padding', () => {
-        node.op = 'DepthwiseConv2d';
-        node.category = 'convolution';
-        node.inputParams['input'] = createTensorAttr(0);
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('explicit');
-        node.attrParams['explicitPaddings'] =
-            createNumericArrayAttr([0, 0, 1, 1, 2, 2, 0, 0]);
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(1.0)];
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.depthwiseConv2d)
-            .toHaveBeenCalledWith(
-                input1[0], input2[0], [2, 2], [[0, 0], [1, 1], [2, 2], [0, 0]],
-                'NHWC', [2, 2]);
-      });
-    });
-
-    describe('Conv3d', () => {
-      it('should call tfOps.conv3d', () => {
-        node.op = 'Conv3D';
-        node.category = 'convolution';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 2, 1]);
-
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(1.0)];
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.conv3d)
-            .toHaveBeenCalledWith(
-                input1[0], input2[0], [2, 2, 2], 'same', 'NHWC', [2, 2, 2]);
-      });
-    });
-
-    describe('AvgPool3D', () => {
-      it('should call tfOps.avgPool3d', () => {
-        node.op = 'AvgPool3D';
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['kernelSize'] = createNumericArrayAttr([1, 2, 2, 2, 1]);
-
-        executeOp(node, {input}, context, spyOpsAsTfOps);
-
-        expect(spyOps.avgPool3d)
-            .toHaveBeenCalledWith(input[0], [2, 2, 2], [2, 2, 2], 'same');
-      });
-    });
-
-    describe('MaxPool3D', () => {
-      it('should call tfOps.maxPool3d', () => {
-        node.op = 'MaxPool3D';
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['kernelSize'] = createNumericArrayAttr([1, 2, 2, 2, 1]);
-
-        executeOp(node, {input}, context, spyOpsAsTfOps);
-
-        expect(spyOps.maxPool3d)
-            .toHaveBeenCalledWith(input[0], [2, 2, 2], [2, 2, 2], 'same');
-      });
-    });
-
-    describe('MaxPoolWithArgmax', () => {
-      it('should call tfOps.maxPoolWithArgmax', () => {
-        node.op = 'MaxPoolWithArgmax';
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['kernelSize'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['dataFormat'] = createStrAttr('NDHWC');
-        node.attrParams['includeBatchInIndex'] = createBoolAttr(true);
-        spyOps.maxPoolWithArgmax.and.returnValue(
-            {result: 'fake', indexes: 'fake'});
-
-        executeOp(node, {input}, context, spyOpsAsTfOps);
-
-        expect(spyOps.maxPoolWithArgmax)
-            .toHaveBeenCalledWith(input[0], [2, 2], [2, 2], 'same', true);
-      });
-    });
-
-    describe('_FusedConv2d', () => {
-      it('with bias and activation func', () => {
-        node.op = '_FusedConv2D';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.inputParams['args'] = createTensorsAttr(2, 0);
-        node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd', 'relu']);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['numArgs'] = createNumberAttr(1);
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(2.0)];
-        const input3 = [tfOps.scalar(3.0)];
-
-        node.inputNames = ['input1', 'input2', 'input3'];
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fused.conv2d).toHaveBeenCalledWith({
-          x: input1[0],
-          filter: input2[0],
-          strides: [2, 2],
-          pad: 'same',
-          dataFormat: 'NHWC',
-          dilations: [2, 2],
-          bias: input3[0],
-          activation: 'relu',
-          preluActivationWeights: undefined,
-          leakyreluAlpha: undefined
-        });
-      });
-      it('should support explicit padding', () => {
-        node.op = '_FusedConv2D';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.inputParams['args'] = createTensorsAttr(2, 0);
-        node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd', 'relu']);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('explicit');
-        node.attrParams['explicitPaddings'] =
-            createNumericArrayAttr([0, 0, 1, 1, 2, 2, 0, 0]);
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['numArgs'] = createNumberAttr(1);
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(2.0)];
-        const input3 = [tfOps.scalar(3.0)];
-
-        node.inputNames = ['input1', 'input2', 'input3'];
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fused.conv2d).toHaveBeenCalledWith({
-          x: input1[0],
-          filter: input2[0],
-          strides: [2, 2],
-          pad: [[0, 0], [1, 1], [2, 2], [0, 0]],
-          dataFormat: 'NHWC',
-          dilations: [2, 2],
-          bias: input3[0],
-          activation: 'relu',
-          preluActivationWeights: undefined,
-          leakyreluAlpha: undefined
-        });
-      });
-      it('with bias and prelu activation func', () => {
-        node.op = '_FusedConv2D';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.inputParams['args'] = createTensorsAttr(2, 0);
-        node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd', 'prelu']);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['numArgs'] = createNumberAttr(2);
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(2.0)];
-        const input3 = [tfOps.scalar(3.0)];
-        const input4 = [tfOps.scalar(4.0)];
-        node.inputNames = ['input1', 'input2', 'input3', 'input4'];
-        executeOp(
-            node, {input1, input2, input3, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fused.conv2d).toHaveBeenCalledWith({
-          x: input1[0],
-          filter: input2[0],
-          strides: [2, 2],
-          pad: 'same',
-          dataFormat: 'NHWC',
-          dilations: [2, 2],
-          bias: input3[0],
-          activation: 'prelu',
-          preluActivationWeights: input4[0],
-          leakyreluAlpha: undefined
-        });
-      });
-      it('with bias and leakyrelu activation func', () => {
-        node.op = '_FusedConv2D';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.inputParams['args'] = createTensorsAttr(2, 0);
-        node.attrParams['fusedOps'] =
-            createStrArrayAttr(['biasadd', 'leakyrelu']);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['numArgs'] = createNumberAttr(1);
-        node.attrParams['leakyreluAlpha'] = createNumberAttr(0.3);
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(2.0)];
-        const input3 = [tfOps.scalar(3.0)];
-        node.inputNames = ['input1', 'input2', 'input3'];
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fused.conv2d).toHaveBeenCalledWith({
-          x: input1[0],
-          filter: input2[0],
-          strides: [2, 2],
-          pad: 'same',
-          dataFormat: 'NHWC',
-          dilations: [2, 2],
-          bias: input3[0],
-          activation: 'leakyrelu',
-          preluActivationWeights: undefined,
-          leakyreluAlpha: 0.3
-        });
-      });
-
-      it('bias add', () => {
-        node.op = '_FusedConv2D';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.inputParams['args'] = createTensorsAttr(2, 0);
-        node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd']);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['numArgs'] = createNumberAttr(1);
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(2.0)];
-        const input3 = [tfOps.scalar(3.0)];
-
-        node.inputNames = ['input1', 'input2', 'input3'];
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fused.conv2d).toHaveBeenCalledWith({
-          x: input1[0],
-          filter: input2[0],
-          strides: [2, 2],
-          pad: 'same',
-          dataFormat: 'NHWC',
-          dilations: [2, 2],
-          bias: input3[0],
-          activation: undefined,
-          preluActivationWeights: undefined,
-          leakyreluAlpha: undefined
-        });
-      });
-      it('fail with batchnorm', () => {
-        node.op = '_FusedConv2D';
-        node.inputParams['filter'] = createTensorAttr(1);
-        node.inputParams['args'] = createTensorsAttr(2, 0);
-        node.attrParams['fusedOps'] = createStrArrayAttr(['fusedbatchnorm']);
-        node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['pad'] = createStrAttr('same');
-        node.attrParams['dataFormat'] = createStrAttr('NHWC');
-        node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-        node.attrParams['numArgs'] = createNumberAttr(1);
-        const input1 = [tfOps.scalar(1.0)];
-        const input2 = [tfOps.scalar(2.0)];
-        const input3 = [tfOps.scalar(3.0)];
-
-        node.inputNames = ['input1', 'input2', 'input3'];
-        expect(
-            () => executeOp(
-                node, {input1, input2, input3}, context, spyOpsAsTfOps))
-            .toThrow();
-      });
-    });
-  });
-  describe('FusedDepthwiseConv2d', () => {
-    it('support explicit padding', () => {
-      node.op = 'FusedDepthwiseConv2dNative';
-      node.inputParams['filter'] = createTensorAttr(1);
-      node.inputParams['args'] = createTensorsAttr(2, 0);
-      node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd', 'relu']);
-      node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['pad'] = createStrAttr('explicit');
-      node.attrParams['explicitPaddings'] =
-          createNumericArrayAttr([0, 0, 1, 1, 2, 2, 0, 0]);
-      node.attrParams['dataFormat'] = createStrAttr('NHWC');
-      node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['numArgs'] = createNumberAttr(1);
-      const input1 = [tfOps.scalar(1.0)];
-      const input2 = [tfOps.scalar(2.0)];
-      const input3 = [tfOps.scalar(3.0)];
-
-      node.inputNames = ['input1', 'input2', 'input3'];
-      executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-      expect(spyOps.fused.depthwiseConv2d).toHaveBeenCalledWith({
-        x: input1[0],
-        filter: input2[0],
-        strides: [2, 2],
-        pad: [[0, 0], [1, 1], [2, 2], [0, 0]],
-        dataFormat: 'NHWC',
-        dilations: [2, 2],
-        bias: input3[0],
-        activation: 'relu',
-        preluActivationWeights: undefined,
-        leakyreluAlpha: undefined
-      });
-    });
-    it('with only activation func', () => {
-      node.op = 'FusedDepthwiseConv2dNative';
-      node.inputParams['filter'] = createTensorAttr(1);
-      node.inputParams['args'] = createTensorsAttr(2, 0);
-      node.attrParams['fusedOps'] = createStrArrayAttr(['noop', 'prelu']);
-      node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['pad'] = createStrAttr('same');
-      node.attrParams['dataFormat'] = createStrAttr('NHWC');
-      node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['numArgs'] = createNumberAttr(1);
-      const input1 = [tfOps.scalar(1.0)];
-      const input2 = [tfOps.scalar(2.0)];
-      const input3 = [tfOps.scalar(3.0)];
-      node.inputNames = ['input1', 'input2', 'input3'];
-      executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-      expect(spyOps.fused.depthwiseConv2d).toHaveBeenCalledWith({
-        x: input1[0],
-        filter: input2[0],
-        strides: [2, 2],
-        pad: 'same',
-        dataFormat: 'NHWC',
-        dilations: [2, 2],
-        bias: undefined,
-        activation: 'prelu',
-        preluActivationWeights: input3[0],
-        leakyreluAlpha: undefined
-      });
-    });
-    it('with bias and activation func', () => {
-      node.op = 'FusedDepthwiseConv2dNative';
-      node.inputParams['filter'] = createTensorAttr(1);
-      node.inputParams['args'] = createTensorsAttr(2, 0);
-      node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd', 'relu']);
-      node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['pad'] = createStrAttr('same');
-      node.attrParams['dataFormat'] = createStrAttr('NHWC');
-      node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['numArgs'] = createNumberAttr(1);
-      const input1 = [tfOps.scalar(1.0)];
-      const input2 = [tfOps.scalar(2.0)];
-      const input3 = [tfOps.scalar(3.0)];
-
-      node.inputNames = ['input1', 'input2', 'input3'];
-      executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-      expect(spyOps.fused.depthwiseConv2d).toHaveBeenCalledWith({
-        x: input1[0],
-        filter: input2[0],
-        strides: [2, 2],
-        pad: 'same',
-        dataFormat: 'NHWC',
-        dilations: [2, 2],
-        bias: input3[0],
-        activation: 'relu',
-        preluActivationWeights: undefined,
-        leakyreluAlpha: undefined
-      });
-    });
-    it('with bias and prelu activation func', () => {
-      node.op = 'FusedDepthwiseConv2dNative';
-      node.inputParams['filter'] = createTensorAttr(1);
-      node.inputParams['args'] = createTensorsAttr(2, 0);
-      node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd', 'prelu']);
-      node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['pad'] = createStrAttr('same');
-      node.attrParams['dataFormat'] = createStrAttr('NHWC');
-      node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['numArgs'] = createNumberAttr(2);
-      const input1 = [tfOps.scalar(1.0)];
-      const input2 = [tfOps.scalar(2.0)];
-      const input3 = [tfOps.scalar(3.0)];
-      const input4 = [tfOps.scalar(4.0)];
-      node.inputNames = ['input1', 'input2', 'input3', 'input4'];
-      executeOp(node, {input1, input2, input3, input4}, context, spyOpsAsTfOps);
-
-      expect(spyOps.fused.depthwiseConv2d).toHaveBeenCalledWith({
-        x: input1[0],
-        filter: input2[0],
-        strides: [2, 2],
-        pad: 'same',
-        dataFormat: 'NHWC',
-        dilations: [2, 2],
-        bias: input3[0],
-        activation: 'prelu',
-        preluActivationWeights: input4[0],
-        leakyreluAlpha: undefined
-      });
-    });
-    it('with bias and leakyrelu activation func', () => {
-      node.op = 'FusedDepthwiseConv2dNative';
-      node.inputParams['filter'] = createTensorAttr(1);
-      node.inputParams['args'] = createTensorsAttr(2, 0);
-      node.attrParams['fusedOps'] =
-          createStrArrayAttr(['biasadd', 'leakyrelu']);
-      node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['pad'] = createStrAttr('same');
-      node.attrParams['dataFormat'] = createStrAttr('NHWC');
-      node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['numArgs'] = createNumberAttr(1);
-      node.attrParams['leakyreluAlpha'] = createNumberAttr(0.3);
-      const input1 = [tfOps.scalar(1.0)];
-      const input2 = [tfOps.scalar(2.0)];
-      const input3 = [tfOps.scalar(3.0)];
-      node.inputNames = ['input1', 'input2', 'input3'];
-      executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-      expect(spyOps.fused.depthwiseConv2d).toHaveBeenCalledWith({
-        x: input1[0],
-        filter: input2[0],
-        strides: [2, 2],
-        pad: 'same',
-        dataFormat: 'NHWC',
-        dilations: [2, 2],
-        bias: input3[0],
-        activation: 'leakyrelu',
-        preluActivationWeights: undefined,
-        leakyreluAlpha: 0.3
-      });
-    });
-
-    it('bias add', () => {
-      node.op = 'FusedDepthwiseConv2dNative';
-      node.inputParams['filter'] = createTensorAttr(1);
-      node.inputParams['args'] = createTensorsAttr(2, 0);
-      node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd']);
-      node.attrParams['strides'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['pad'] = createStrAttr('same');
-      node.attrParams['dataFormat'] = createStrAttr('NHWC');
-      node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-      node.attrParams['numArgs'] = createNumberAttr(1);
-      const input1 = [tfOps.scalar(1.0)];
-      const input2 = [tfOps.scalar(2.0)];
-      const input3 = [tfOps.scalar(3.0)];
-
-      node.inputNames = ['input1', 'input2', 'input3'];
-      executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-      expect(spyOps.fused.depthwiseConv2d).toHaveBeenCalledWith({
-        x: input1[0],
-        filter: input2[0],
-        strides: [2, 2],
-        pad: 'same',
-        dataFormat: 'NHWC',
-        dilations: [2, 2],
-        bias: input3[0],
-        activation: undefined,
-        preluActivationWeights: undefined,
-        leakyreluAlpha: undefined
-      });
-    });
-  });
-
-  describe('dilation2d', () => {
-    it('should call tfOps.dilation2d', () => {
-      node.op = 'Dilation2D';
-      node.inputParams['filter'] = createTensorAttr(1);
-      node.attrParams['strides'] = createNumericArrayAttr([1, 1, 1, 1]);
-      node.attrParams['pad'] = createStrAttr('same');
-      node.attrParams['dilations'] = createNumericArrayAttr([1, 2, 2, 1]);
-
-      const input1 = [tfOps.scalar(1.0)];
-      const input2 = [tfOps.scalar(1.0)];
-      node.inputNames = ['input1', 'input2'];
-
-      executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-      expect(spyOps.dilation2d)
-          .toHaveBeenCalledWith(
-              input1[0], input2[0], [1, 1], 'same', [2, 2], 'NHWC');
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/creation_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/creation_executor.ts
deleted file mode 100644
index a13d37a7b..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/creation_executor.ts
+++ /dev/null
@@ -1,143 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, Tensor, Tensor1D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-     ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'Fill': {
-          const shape =
-              getParamValue('shape', node, tensorMap, context) as number[];
-          const dtype =
-              getParamValue('dtype', node, tensorMap, context) as DataType;
-          const value =
-              getParamValue('value', node, tensorMap, context) as number;
-          return [ops.fill(shape, value, dtype)];
-        }
-        case 'LinSpace': {
-          const start =
-              getParamValue('start', node, tensorMap, context) as number;
-          const stop =
-              getParamValue('stop', node, tensorMap, context) as number;
-          const num = getParamValue('num', node, tensorMap, context) as number;
-          return [ops.linspace(start, stop, num)];
-        }
-        case 'Multinomial': {
-          const logits =
-              getParamValue('logits', node, tensorMap, context) as Tensor1D;
-          const numSamples =
-              getParamValue('numSamples', node, tensorMap, context) as number;
-          const seed =
-              getParamValue('seed', node, tensorMap, context) as number;
-          return [ops.multinomial(logits, numSamples, seed)];
-        }
-        case 'OneHot': {
-          const indices =
-              getParamValue('indices', node, tensorMap, context) as Tensor1D;
-          const depth =
-              getParamValue('depth', node, tensorMap, context) as number;
-          const onValue =
-              getParamValue('onValue', node, tensorMap, context) as number;
-          const offValue =
-              getParamValue('offValue', node, tensorMap, context) as number;
-          const dtype =
-              getParamValue('dtype', node, tensorMap, context) as DataType;
-          return [ops.oneHot(indices, depth, onValue, offValue, dtype)];
-        }
-        case 'Ones': {
-          return [ops.ones(
-              getParamValue('shape', node, tensorMap, context) as number[],
-              getParamValue('dtype', node, tensorMap, context) as DataType)];
-        }
-        case 'OnesLike': {
-          return [ops.onesLike(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'RandomStandardNormal': {
-          return [ops.randomStandardNormal(
-              getParamValue('shape', node, tensorMap, context) as number[],
-              getParamValue('dtype', node, tensorMap, context) as 'float32' |
-                  'int32',
-              getParamValue('seed', node, tensorMap, context) as number)];
-        }
-        case 'RandomUniform': {
-          return [ops.randomUniform(
-              // tslint:disable-next-line:no-any
-              getParamValue('shape', node, tensorMap, context) as any,
-              getParamValue('minval', node, tensorMap, context) as number,
-              getParamValue('maxval', node, tensorMap, context) as number,
-              getParamValue('dtype', node, tensorMap, context) as DataType)];
-        }
-        case 'RandomUniformInt': {
-          return [ops.randomUniformInt(
-              getParamValue('shape', node, tensorMap, context) as number[],
-              getParamValue('minval', node, tensorMap, context) as number,
-              getParamValue('maxval', node, tensorMap, context) as number,
-              getParamValue('seed', node, tensorMap, context) as number)];
-        }
-        case 'Range': {
-          const start =
-              getParamValue('start', node, tensorMap, context) as number;
-          const stop =
-              getParamValue('stop', node, tensorMap, context) as number;
-          const step =
-              getParamValue('step', node, tensorMap, context) as number;
-          return [ops.range(
-              start, stop, step,
-              getParamValue('dtype', node, tensorMap, context) as 'float32' |
-                  'int32')];
-        }
-        case 'TruncatedNormal': {
-          const shape =
-              getParamValue('shape', node, tensorMap, context) as number[];
-          const mean =
-              getParamValue('mean', node, tensorMap, context) as number;
-          const stdDev =
-              getParamValue('stdDev', node, tensorMap, context) as number;
-          const seed =
-              getParamValue('seed', node, tensorMap, context) as number;
-          return [ops.truncatedNormal(
-              shape, mean, stdDev,
-              getParamValue('dtype', node, tensorMap, context) as 'float32' |
-                  'int32',
-              seed)];
-        }
-        case 'Zeros': {
-          return [ops.zeros(
-              getParamValue('shape', node, tensorMap, context) as number[],
-              getParamValue('dtype', node, tensorMap, context) as DataType)];
-        }
-        case 'ZerosLike': {
-          return [ops.zerosLike(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'creation';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/creation_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/creation_executor_test.ts
deleted file mode 100644
index 10733f8d5..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/creation_executor_test.ts
+++ /dev/null
@@ -1,332 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as creation from '../op_list/creation';
-import {Node} from '../types';
-
-import {executeOp} from './creation_executor';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createDtypeAttr, createNumberAttr, createNumberAttrFromIndex, createNumericArrayAttrFromIndex, createTensorAttr, validateParam} from './test_helper';
-
-describe('creation', () => {
-  let node: Node;
-  const input1 = [tfOps.tensor1d([1, 2, 3])];
-  const input2 = [tfOps.scalar(1)];
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-
-    node = {
-      name: 'test',
-      op: '',
-      category: 'creation',
-      inputNames: ['input1', 'input2'],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('Fill', () => {
-      it('should call tfOps.fill', () => {
-        node.op = 'Fill';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputParams['value'] = createNumberAttrFromIndex(1);
-        node.attrParams['dtype'] = createDtypeAttr('int32');
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fill).toHaveBeenCalledWith([1, 2, 3], 1, 'int32');
-      });
-      it('should match json def', () => {
-        node.op = 'Fill';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputParams['value'] = createNumberAttrFromIndex(1);
-        node.attrParams['dtype'] = createDtypeAttr('int32');
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('LinSpace', () => {
-      it('should call tfOps.linspace', () => {
-        node.op = 'LinSpace';
-        node.inputParams['start'] = createNumberAttrFromIndex(0);
-        node.inputParams['stop'] = createNumberAttrFromIndex(1);
-        node.inputParams['num'] = createNumberAttrFromIndex(2);
-        node.inputNames = ['input', 'input2', 'input3'];
-        const input = [tfOps.scalar(0)];
-        const input3 = [tfOps.scalar(2)];
-        executeOp(node, {input, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.linspace).toHaveBeenCalledWith(0, 1, 2);
-      });
-      it('should match json def', () => {
-        node.op = 'LinSpace';
-        node.inputParams['start'] = createNumberAttrFromIndex(0);
-        node.inputParams['stop'] = createNumberAttrFromIndex(1);
-        node.inputParams['num'] = createNumberAttrFromIndex(2);
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('OneHot', () => {
-      it('should call tfOps.oneHot', () => {
-        node.op = 'OneHot';
-        node.inputParams['indices'] = createTensorAttr(0);
-        node.inputParams['depth'] = createNumberAttrFromIndex(1);
-        node.inputParams['onValue'] = createNumberAttrFromIndex(2);
-        node.inputParams['offValue'] = createNumberAttrFromIndex(3);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        node.inputNames = ['input', 'input2', 'input3', 'input4'];
-        const input = [tfOps.tensor1d([0])];
-        const input3 = [tfOps.scalar(2)];
-        const input4 = [tfOps.scalar(3)];
-        spyOps.oneHot.and.returnValue({});
-        executeOp(
-            node, {input, input2, input3, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.oneHot)
-            .toHaveBeenCalledWith(input[0], 1, 2, 3, 'float32');
-      });
-      it('should match json def', () => {
-        node.op = 'OneHot';
-        node.inputParams['indices'] = createTensorAttr(0);
-        node.inputParams['depth'] = createNumberAttrFromIndex(1);
-        node.inputParams['onValue'] = createNumberAttrFromIndex(2);
-        node.inputParams['offValue'] = createNumberAttrFromIndex(3);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('Ones', () => {
-      it('should call tfOps.ones', () => {
-        node.op = 'Ones';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.ones).toHaveBeenCalledWith([1, 2, 3], 'float32');
-      });
-      it('should match json def', () => {
-        node.op = 'Ones';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('OnesLike', () => {
-      it('should call tfOps.onesLike', () => {
-        node.op = 'OnesLike';
-        node.inputParams['x'] = createTensorAttr(0);
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.onesLike).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match json def', () => {
-        node.op = 'OnesLike';
-        node.inputParams['x'] = createTensorAttr(0);
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('Range', () => {
-      it('should call tfOps.range', () => {
-        node.op = 'Range';
-        node.inputParams['start'] = createNumberAttrFromIndex(0);
-        node.inputParams['stop'] = createNumberAttrFromIndex(1);
-        node.inputParams['step'] = createNumberAttrFromIndex(2);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        node.inputNames = ['input', 'input2', 'input3'];
-        const input = [tfOps.scalar(0)];
-        const input3 = [tfOps.scalar(2)];
-        executeOp(node, {input, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.range).toHaveBeenCalledWith(0, 1, 2, 'float32');
-      });
-      it('should match json def', () => {
-        node.op = 'Range';
-        node.inputParams['start'] = createNumberAttrFromIndex(0);
-        node.inputParams['stop'] = createNumberAttrFromIndex(1);
-        node.inputParams['step'] = createNumberAttrFromIndex(2);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('RandomStandardNormal', () => {
-      it('should call tfOps.randomStandardNormal', () => {
-        node.op = 'RandomStandardNormal';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputNames = ['input1'];
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        node.attrParams['seed'] = createNumberAttr(0);
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.randomStandardNormal)
-            .toHaveBeenCalledWith([1, 2, 3], 'float32', 0);
-      });
-      it('should match json def', () => {
-        node.op = 'RandomStandardNormal';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputNames = ['input1'];
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        node.attrParams['seed'] = createNumberAttr(0);
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('RandomUniform', () => {
-      it('should call tfOps.randomUniform', () => {
-        node.op = 'RandomUniform';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputNames = ['input1'];
-        node.attrParams['maxval'] = createNumberAttr(1);
-        node.attrParams['minval'] = createNumberAttr(0);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        node.attrParams['seed'] = createNumberAttr(0);
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.randomUniform)
-            .toHaveBeenCalledWith([1, 2, 3], 0, 1, 'float32');
-      });
-      it('should match json def', () => {
-        node.op = 'RandomUniform';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputNames = ['input1'];
-        node.attrParams['maxval'] = createNumberAttr(1);
-        node.attrParams['minval'] = createNumberAttr(0);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        node.attrParams['seed'] = createNumberAttr(0);
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('RandomUniformInt', () => {
-      it('should call tfOps.randomUniformInt', () => {
-        node.op = 'RandomUniformInt';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputNames = ['input1'];
-        node.attrParams['maxval'] = createNumberAttr(1);
-        node.attrParams['minval'] = createNumberAttr(0);
-        node.attrParams['seed'] = createNumberAttr(456);
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.randomUniformInt)
-            .toHaveBeenCalledWith([1, 2, 3], 0, 1, 456);
-      });
-      it('should match json def', () => {
-        node.op = 'RandomUniformInt';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputNames = ['input1'];
-        node.attrParams['maxval'] = createNumberAttr(1);
-        node.attrParams['minval'] = createNumberAttr(0);
-        node.attrParams['seed'] = createNumberAttr(456);
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('TruncatedNormal', () => {
-      it('should call tfOps.truncatedNormal', () => {
-        node.op = 'TruncatedNormal';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputNames = ['input1'];
-        node.attrParams['stdDev'] = createNumberAttr(1);
-        node.attrParams['mean'] = createNumberAttr(0);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        node.attrParams['seed'] = createNumberAttr(0);
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.truncatedNormal)
-            .toHaveBeenCalledWith([1, 2, 3], 0, 1, 'float32', 0);
-      });
-      it('should match json def', () => {
-        node.op = 'TruncatedNormal';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.inputNames = ['input1'];
-        node.attrParams['stdDev'] = createNumberAttr(1);
-        node.attrParams['mean'] = createNumberAttr(0);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        node.attrParams['seed'] = createNumberAttr(0);
-
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('Zeros', () => {
-      it('should call tfOps.zeros', () => {
-        node.op = 'Zeros';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.zeros).toHaveBeenCalledWith([1, 2, 3], 'float32');
-      });
-      it('should match json def', () => {
-        node.op = 'Zeros';
-        node.inputParams['shape'] = createNumericArrayAttrFromIndex(0);
-        node.attrParams['dtype'] = createDtypeAttr('float32');
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('ZerosLike', () => {
-      it('should call tfOps.zerosLike', () => {
-        node.op = 'ZerosLike';
-        node.inputParams['x'] = createTensorAttr(0);
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.zerosLike).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match json def', () => {
-        node.op = 'ZerosLike';
-        node.inputParams['x'] = createTensorAttr(0);
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-    describe('Multinomial', () => {
-      it('should call tfOps.multinomial', () => {
-        node.op = 'Multinomial';
-        node.inputParams['logits'] = createTensorAttr(0);
-        node.inputParams['numSamples'] = createNumberAttrFromIndex(1);
-        node.attrParams['seed'] = createNumberAttr(2);
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.multinomial).toHaveBeenCalledWith(input1[0], 1, 2);
-      });
-      it('should match json def', () => {
-        node.op = 'Multinomial';
-        node.inputParams['logits'] = createTensorAttr(0);
-        node.inputParams['numSamples'] = createNumberAttrFromIndex(1);
-        node.attrParams['seed'] = createNumberAttr(2);
-        expect(validateParam(node, creation.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/dynamic_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/dynamic_executor.ts
deleted file mode 100644
index 801ec2ebf..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/dynamic_executor.ts
+++ /dev/null
@@ -1,114 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import { ResourceManager } from '../../executor/resource_manager';
-import {InternalOpAsyncExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-function nmsParams(
-    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) {
-  const boxes = getParamValue('boxes', node, tensorMap, context) as Tensor;
-  const scores = getParamValue('scores', node, tensorMap, context) as Tensor;
-  const maxOutputSize =
-      getParamValue('maxOutputSize', node, tensorMap, context) as number;
-  const iouThreshold =
-      getParamValue('iouThreshold', node, tensorMap, context) as number;
-  const scoreThreshold =
-      getParamValue('scoreThreshold', node, tensorMap, context) as number;
-  const softNmsSigma =
-      getParamValue('softNmsSigma', node, tensorMap, context) as number;
-
-  return {
-    boxes,
-    scores,
-    maxOutputSize,
-    iouThreshold,
-    scoreThreshold,
-    softNmsSigma
-  };
-}
-
-export const executeOp: InternalOpAsyncExecutor = async(
-    node: Node, tensorMap: NamedTensorsMap,
-    context: ExecutionContext, resourceManager: ResourceManager,
-    ops = tfOps): Promise<Tensor[]> => {
-  switch (node.op) {
-    case 'NonMaxSuppressionV5': {
-      const {
-        boxes,
-        scores,
-        maxOutputSize,
-        iouThreshold,
-        scoreThreshold,
-        softNmsSigma
-      } = nmsParams(node, tensorMap, context);
-
-      const result = await ops.image.nonMaxSuppressionWithScoreAsync(
-          boxes as Tensor2D, scores as Tensor1D, maxOutputSize, iouThreshold,
-          scoreThreshold, softNmsSigma);
-
-      return [result.selectedIndices, result.selectedScores];
-    }
-    case 'NonMaxSuppressionV4': {
-      const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} =
-          nmsParams(node, tensorMap, context);
-
-      const padToMaxOutputSize =
-          getParamValue('padToMaxOutputSize', node, tensorMap, context) as
-          boolean;
-
-      const result = await ops.image.nonMaxSuppressionPaddedAsync(
-          boxes as Tensor2D, scores as Tensor1D, maxOutputSize, iouThreshold,
-          scoreThreshold, padToMaxOutputSize);
-
-      return [result.selectedIndices, result.validOutputs];
-    }
-    case 'NonMaxSuppressionV3':
-    case 'NonMaxSuppressionV2': {
-      const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} =
-          nmsParams(node, tensorMap, context);
-
-      return [await ops.image.nonMaxSuppressionAsync(
-          boxes as Tensor2D, scores as Tensor1D, maxOutputSize, iouThreshold,
-          scoreThreshold)];
-    }
-    case 'Where': {
-      const condition = ops.cast(
-          (getParamValue('condition', node, tensorMap, context) as Tensor),
-          'bool');
-      const result = [await ops.whereAsync(condition)];
-      condition.dispose();
-      return result;
-    }
-    case 'ListDiff': {
-      return ops.setdiff1dAsync(
-          getParamValue('x', node, tensorMap, context) as Tensor,
-          getParamValue('y', node, tensorMap, context) as Tensor);
-    }
-    default:
-      throw TypeError(`Node type ${node.op} is not implemented`);
-  }
-};
-
-export const CATEGORY = 'dynamic';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/dynamic_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/dynamic_executor_test.ts
deleted file mode 100644
index 5e052d629..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/dynamic_executor_test.ts
+++ /dev/null
@@ -1,259 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {memory} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as dynamic from '../op_list/dynamic';
-import {Node} from '../types';
-
-import {executeOp} from './dynamic_executor';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createBoolAttr, createNumberAttrFromIndex, createTensorAttr, validateParam} from './test_helper';
-
-describe('dynamic', () => {
-  let node: Node;
-  const input1 = [tfOps.tensor1d([1])];
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    node = {
-      name: 'input1',
-      op: '',
-      category: 'dynamic',
-      inputNames: ['input1'],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('NonMaxSuppressionV2', () => {
-      it('should return input', () => {
-        node.op = 'NonMaxSuppressionV2';
-        node.inputParams['boxes'] = createTensorAttr(0);
-        node.inputParams['scores'] = createTensorAttr(1);
-        node.inputParams['maxOutputSize'] = createNumberAttrFromIndex(2);
-        node.inputParams['iouThreshold'] = createNumberAttrFromIndex(3);
-        node.inputParams['scoreThreshold'] = createNumberAttrFromIndex(4);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4', 'input5'];
-        const input2 = [tfOps.tensor1d([1])];
-        const input3 = [tfOps.tensor1d([1])];
-        const input4 = [tfOps.tensor1d([1])];
-        const input5 = [tfOps.tensor1d([1])];
-        spyOps.image.nonMaxSuppressionAsync.and.returnValue({});
-
-        const result = executeOp(
-            node, {input1, input2, input3, input4, input5}, context, undefined,
-            spyOpsAsTfOps);
-        expect(spyOps.image.nonMaxSuppressionAsync)
-            .toHaveBeenCalledWith(input1[0], input2[0], 1, 1, 1);
-        expect(result instanceof Promise).toBeTruthy();
-      });
-      it('should match json def', () => {
-        node.op = 'NonMaxSuppressionV2';
-        node.inputParams['boxes'] = createTensorAttr(0);
-        node.inputParams['scores'] = createTensorAttr(1);
-        node.inputParams['maxOutputSize'] = createNumberAttrFromIndex(2);
-        node.inputParams['iouThreshold'] = createNumberAttrFromIndex(3);
-        node.inputParams['scoreThreshold'] = createNumberAttrFromIndex(4);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4', 'input5'];
-
-        expect(validateParam(node, dynamic.json, 'NonMaxSuppressionV3'))
-            .toBeTruthy();
-      });
-    });
-    describe('NonMaxSuppressionV3', () => {
-      it('should return input', () => {
-        node.op = 'NonMaxSuppressionV3';
-        node.inputParams['boxes'] = createTensorAttr(0);
-        node.inputParams['scores'] = createTensorAttr(1);
-        node.inputParams['maxOutputSize'] = createNumberAttrFromIndex(2);
-        node.inputParams['iouThreshold'] = createNumberAttrFromIndex(3);
-        node.inputParams['scoreThreshold'] = createNumberAttrFromIndex(4);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4', 'input5'];
-        const input2 = [tfOps.tensor1d([1])];
-        const input3 = [tfOps.tensor1d([1])];
-        const input4 = [tfOps.tensor1d([1])];
-        const input5 = [tfOps.tensor1d([1])];
-        spyOps.image.nonMaxSuppressionAsync.and.returnValue({});
-
-        const result = executeOp(
-            node, {input1, input2, input3, input4, input5}, context, undefined,
-            spyOpsAsTfOps);
-        expect(spyOps.image.nonMaxSuppressionAsync)
-            .toHaveBeenCalledWith(input1[0], input2[0], 1, 1, 1);
-        expect(result instanceof Promise).toBeTruthy();
-      });
-      it('should match json def', () => {
-        node.op = 'NonMaxSuppressionV3';
-        node.inputParams['boxes'] = createTensorAttr(0);
-        node.inputParams['scores'] = createTensorAttr(1);
-        node.inputParams['maxOutputSize'] = createNumberAttrFromIndex(2);
-        node.inputParams['iouThreshold'] = createNumberAttrFromIndex(3);
-        node.inputParams['scoreThreshold'] = createNumberAttrFromIndex(4);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4', 'input5'];
-
-        expect(validateParam(node, dynamic.json, 'NonMaxSuppressionV3'))
-            .toBeTruthy();
-      });
-    });
-
-    describe('NonMaxSuppressionV4', () => {
-      it('should return input', () => {
-        node.op = 'NonMaxSuppressionV4';
-        node.inputParams['boxes'] = createTensorAttr(0);
-        node.inputParams['scores'] = createTensorAttr(1);
-        node.inputParams['maxOutputSize'] = createNumberAttrFromIndex(2);
-        node.inputParams['iouThreshold'] = createNumberAttrFromIndex(3);
-        node.inputParams['scoreThreshold'] = createNumberAttrFromIndex(4);
-        node.attrParams['padToMaxOutputSize'] = createBoolAttr(true);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4', 'input5'];
-        const input2 = [tfOps.tensor1d([1])];
-        const input3 = [tfOps.tensor1d([1])];
-        const input4 = [tfOps.tensor1d([1])];
-        const input5 = [tfOps.tensor1d([1])];
-
-        spyOps.image.nonMaxSuppressionPaddedAsync.and.returnValue({});
-
-        const result = executeOp(
-            node, {input1, input2, input3, input4, input5}, context, undefined,
-            spyOpsAsTfOps);
-        expect(spyOps.image.nonMaxSuppressionPaddedAsync)
-            .toHaveBeenCalledWith(input1[0], input2[0], 1, 1, 1, true);
-        expect(result instanceof Promise).toBeTruthy();
-      });
-      it('should match json def', () => {
-        node.op = 'NonMaxSuppressionV4';
-        node.inputParams['boxes'] = createTensorAttr(0);
-        node.inputParams['scores'] = createTensorAttr(1);
-        node.inputParams['maxOutputSize'] = createNumberAttrFromIndex(2);
-        node.inputParams['iouThreshold'] = createNumberAttrFromIndex(3);
-        node.inputParams['scoreThreshold'] = createNumberAttrFromIndex(4);
-        node.attrParams['padToMaxOutputSize'] = createBoolAttr(true);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4', 'input5'];
-
-        expect(validateParam(node, dynamic.json, 'NonMaxSuppressionV4'))
-            .toBeTruthy();
-      });
-    });
-
-    describe('NonMaxSuppressionV5', () => {
-      it('should return input', () => {
-        node.op = 'NonMaxSuppressionV5';
-        node.inputParams['boxes'] = createTensorAttr(0);
-        node.inputParams['scores'] = createTensorAttr(1);
-        node.inputParams['maxOutputSize'] = createNumberAttrFromIndex(2);
-        node.inputParams['iouThreshold'] = createNumberAttrFromIndex(3);
-        node.inputParams['scoreThreshold'] = createNumberAttrFromIndex(4);
-        node.inputParams['softNmsSigma'] = createNumberAttrFromIndex(5);
-        node.inputNames =
-            ['input1', 'input2', 'input3', 'input4', 'input5', 'input6'];
-        const input2 = [tfOps.tensor1d([1])];
-        const input3 = [tfOps.tensor1d([1])];
-        const input4 = [tfOps.tensor1d([1])];
-        const input5 = [tfOps.tensor1d([1])];
-        const input6 = [tfOps.tensor1d([1])];
-        spyOps.image.nonMaxSuppressionWithScoreAsync.and.returnValue({});
-        const result = executeOp(
-            node, {input1, input2, input3, input4, input5, input6}, context,
-            undefined, spyOpsAsTfOps);
-        expect(spyOps.image.nonMaxSuppressionWithScoreAsync)
-            .toHaveBeenCalledWith(input1[0], input2[0], 1, 1, 1, 1);
-        expect(result instanceof Promise).toBeTruthy();
-      });
-      it('should match json def', () => {
-        node.op = 'NonMaxSuppressionV5';
-        node.inputParams['boxes'] = createTensorAttr(0);
-        node.inputParams['scores'] = createTensorAttr(1);
-        node.inputParams['maxOutputSize'] = createNumberAttrFromIndex(2);
-        node.inputParams['iouThreshold'] = createNumberAttrFromIndex(3);
-        node.inputParams['scoreThreshold'] = createNumberAttrFromIndex(4);
-        node.inputParams['softNmsSigma'] = createNumberAttrFromIndex(5);
-        node.inputNames =
-            ['input1', 'input2', 'input3', 'input4', 'input5', 'input6'];
-
-        expect(validateParam(node, dynamic.json, 'NonMaxSuppressionV5'))
-            .toBeTruthy();
-      });
-    });
-
-    describe('Where', () => {
-      it('should call tfOps.whereAsync', async () => {
-        node.op = 'Where';
-        node.inputParams = {'condition': createTensorAttr(0)};
-        const input1 = [tfOps.scalar(1)];
-        // spyOn(tfOps, 'whereAsync');
-
-        const result =
-            executeOp(node, {input1}, context, undefined, spyOpsAsTfOps);
-        expect(spyOps.whereAsync.calls.mostRecent().args[0].dtype)
-            .toEqual('bool');
-        expect(spyOps.whereAsync.calls.mostRecent().args[0].arraySync())
-            .toEqual(1);
-        expect(result instanceof Promise).toBeTruthy();
-      });
-      it('should match json def', () => {
-        node.op = 'Where';
-        node.inputParams = {'condition': createTensorAttr(0)};
-
-        expect(validateParam(node, dynamic.json)).toBeTruthy();
-      });
-      it('should not have memory leak', async () => {
-        node.op = 'Where';
-        node.inputParams = {'condition': createTensorAttr(0)};
-        const input1 = [tfOps.scalar(1)];
-
-        const prevCount = memory().numTensors;
-        await executeOp(node, {input1}, context);
-        const afterCount = memory().numTensors;
-        expect(afterCount).toEqual(prevCount + 1);
-      });
-    });
-
-    describe('ListDiff', () => {
-      it('should call tfOps.setdiff1dAsync', async () => {
-        node.op = 'ListDiff';
-        node.inputNames = ['input1', 'input2'];
-        node.inputParams = {'x': createTensorAttr(0), 'y': createTensorAttr(1)};
-        const input1 = [tfOps.scalar(1)];
-        const input2 = [tfOps.scalar(1)];
-        spyOps.setdiff1dAsync.and.returnValue({});
-
-        const result = executeOp(
-            node, {input1, input2}, context, undefined, spyOpsAsTfOps);
-        expect(spyOps.setdiff1dAsync)
-            .toHaveBeenCalledWith(input1[0], input2[0]);
-        expect(result instanceof Promise).toBeTruthy();
-      });
-      it('should match json def', () => {
-        node.op = 'ListDiff';
-        node.inputNames = ['input1', 'input2'];
-        node.inputParams = {'x': createTensorAttr(0), 'y': createTensorAttr(1)};
-
-        expect(validateParam(node, dynamic.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/evaluation_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/evaluation_executor.ts
deleted file mode 100644
index 6020624ac..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/evaluation_executor.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-     ops = tfOps):
-        Tensor[] => {
-          switch (node.op) {
-            case 'LowerBound': {
-              const sortedSequence =
-                  getParamValue('sortedSequence', node, tensorMap, context) as
-                  Tensor;
-              const values =
-                  getParamValue('values', node, tensorMap, context) as Tensor;
-              return [ops.lowerBound(sortedSequence, values)];
-            }
-            case 'TopKV2': {
-              const x = getParamValue('x', node, tensorMap, context) as Tensor;
-              const k = getParamValue('k', node, tensorMap, context) as number;
-              const sorted =
-                  getParamValue('sorted', node, tensorMap, context) as boolean;
-              const result = ops.topk(x, k, sorted);
-              return [result.values, result.indices];
-            }
-            case 'UpperBound': {
-              const sortedSequence =
-                  getParamValue('sortedSequence', node, tensorMap, context) as
-                  Tensor;
-              const values =
-                  getParamValue('values', node, tensorMap, context) as Tensor;
-              return [ops.upperBound(sortedSequence, values)];
-            }
-            case 'Unique': {
-              const x = getParamValue('x', node, tensorMap, context) as Tensor;
-              const result = ops.unique(x);
-              return [result.values, result.indices];
-            }
-            case 'UniqueV2': {
-              const x = getParamValue('x', node, tensorMap, context) as Tensor;
-              const axis =
-                  getParamValue('axis', node, tensorMap, context) as number;
-              const result = ops.unique(x, axis);
-              return [result.values, result.indices];
-            }
-            default:
-              throw TypeError(`Node type ${node.op} is not implemented`);
-          }
-        };
-
-export const CATEGORY = 'evaluation';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/evaluation_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/evaluation_executor_test.ts
deleted file mode 100644
index 919fdf0a0..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/evaluation_executor_test.ts
+++ /dev/null
@@ -1,139 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import {Node} from '../types';
-
-import {executeOp} from './evaluation_executor';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createBoolAttr, createNumberAttrFromIndex, createTensorAttr} from './test_helper';
-
-describe('evaluation', () => {
-  let node: Node;
-  const input1 = [tfOps.tensor1d([1])];
-  const input2 = [tfOps.scalar(1)];
-  const context = new ExecutionContext({}, {}, {});
-
-  beforeEach(() => {
-    node = {
-      name: 'input1',
-      op: '',
-      category: 'evaluation',
-      inputNames: ['input1', 'input2'],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    let spyOps: RecursiveSpy<typeof tfOps>;
-    let spyOpsAsTfOps: typeof tfOps;
-
-    beforeEach(() => {
-      spyOps = spyOnAllFunctions(tfOps);
-      spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    });
-
-    describe('LowerBound', () => {
-      it('should return input', () => {
-        node.op = 'LowerBound';
-        node.inputParams['sortedSequence'] = createTensorAttr(0);
-        node.inputParams['values'] = createTensorAttr(1);
-        node.inputNames = ['sortedSequence', 'values'];
-
-        const sortedSequence = [tfOps.tensor2d(
-            [0., 3., 8., 9., 10., 1., 2., 3., 4., 5.], [2, 5], 'int32')];
-        const values = [tfOps.tensor2d(
-            [
-              9.8,
-              2.1,
-              4.3,
-              0.1,
-              6.6,
-              4.5,
-            ],
-            [2, 3], 'float32')];
-        executeOp(node, {sortedSequence, values}, context, spyOpsAsTfOps);
-        expect(spyOps.lowerBound)
-            .toHaveBeenCalledWith(sortedSequence[0], values[0]);
-      });
-    });
-
-    describe('TopKV2', () => {
-      it('should return input', () => {
-        node.op = 'TopKV2';
-        node.inputParams['x'] = createTensorAttr(0);
-        node.inputParams['k'] = createNumberAttrFromIndex(1);
-        node.attrParams['sorted'] = createBoolAttr(true);
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-        expect(spyOps.topk).toHaveBeenCalledWith(input1[0], 1, true);
-      });
-    });
-
-    describe('UpperBound', () => {
-      it('should return input', () => {
-        node.op = 'UpperBound';
-        node.inputParams['sortedSequence'] = createTensorAttr(0);
-        node.inputParams['values'] = createTensorAttr(1);
-        node.inputNames = ['sortedSequence', 'values'];
-
-        const sortedSequence = [tfOps.tensor2d(
-            [0., 3., 8., 9., 10., 1., 2., 3., 4., 5.], [2, 5], 'int32')];
-        const values = [tfOps.tensor2d(
-            [
-              9.8,
-              2.1,
-              4.3,
-              0.1,
-              6.6,
-              4.5,
-            ],
-            [2, 3], 'float32')];
-        executeOp(node, {sortedSequence, values}, context, spyOpsAsTfOps);
-        expect(spyOps.upperBound)
-            .toHaveBeenCalledWith(sortedSequence[0], values[0]);
-      });
-    });
-
-    describe('Unique', () => {
-      it('should get called correctly', () => {
-        node.op = 'Unique';
-        node.inputParams['x'] = createTensorAttr(0);
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-        expect(spyOps.unique).toHaveBeenCalledWith(input1[0]);
-      });
-    });
-
-    describe('UniqueV2', () => {
-      it('should get called correctly', () => {
-        node.op = 'UniqueV2';
-        node.inputParams['x'] = createTensorAttr(0);
-        node.inputParams['axis'] = createNumberAttrFromIndex(1);
-        const xInput = [tfOps.tensor2d([[1], [2]])];
-        const axisInput = [tfOps.scalar(1)];
-        executeOp(
-            node, {'input1': xInput, 'input2': axisInput}, context,
-            spyOpsAsTfOps);
-        expect(spyOps.unique).toHaveBeenCalledWith(xInput[0], 1);
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/graph_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/graph_executor.ts
deleted file mode 100644
index f6ba57e76..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/graph_executor.ts
+++ /dev/null
@@ -1,94 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {cloneTensor, getParamValue, getTensor} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap,
-     context: ExecutionContext, ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'Const': {
-          return tensorMap[node.name];
-        }
-        case 'PlaceholderWithDefault':
-          const def =
-              getParamValue('default', node, tensorMap, context) as Tensor;
-          return [getTensor(node.name, tensorMap, context) || def];
-        case 'Placeholder':
-          return [getTensor(node.name, tensorMap, context)];
-        case 'Identity':
-        case 'StopGradient':
-        case 'FakeQuantWithMinMaxVars': {  // This op is currently ignored.
-          const data = getParamValue('x', node, tensorMap, context) as Tensor;
-          return [cloneTensor(data)];
-        }
-        case 'IdentityN':
-          return (getParamValue('x', node, tensorMap, context) as Tensor[])
-              .map((t: Tensor) => cloneTensor(t));
-        case 'Snapshot':
-          const snapshot =
-              (getParamValue('x', node, tensorMap, context) as Tensor);
-          return [cloneTensor(snapshot)];
-        case 'Shape':
-          return [ops.tensor1d(
-              (getParamValue('x', node, tensorMap, context) as Tensor).shape,
-              'int32')];
-        case 'ShapeN':
-          return (getParamValue('x', node, tensorMap, context) as Tensor[])
-              .map((t: Tensor) => ops.tensor1d(t.shape));
-        case 'Size':
-          return [ops.scalar(
-              (getParamValue('x', node, tensorMap, context) as Tensor).size,
-              'int32')];
-        case 'Rank':
-          return [ops.scalar(
-              (getParamValue('x', node, tensorMap, context) as Tensor).rank,
-              'int32')];
-        case 'NoOp':
-          return [ops.scalar(1)];
-        case 'Print':
-          const input = getParamValue('x', node, tensorMap, context) as Tensor;
-          const data =
-              getParamValue('data', node, tensorMap, context) as Tensor[];
-          const message =
-              getParamValue('message', node, tensorMap, context) as string;
-          const summarize =
-              getParamValue('summarize', node, tensorMap, context) as number;
-          console.warn(
-              'The graph has a tf.print() operation,' +
-              'usually used for debugging, which slows down performance.');
-          console.log(message);
-          for (let i = 0; i < data.length; i++) {
-            console.log(Array.prototype.slice.call(data[i].dataSync())
-                            .slice(0, summarize));
-          }
-          return [input];
-
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'graph';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/graph_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/graph_executor_test.ts
deleted file mode 100644
index 1e32e4bee..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/graph_executor_test.ts
+++ /dev/null
@@ -1,193 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor} from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import {Node} from '../types';
-
-import {executeOp} from './graph_executor';
-import {createNumberAttr, createStrAttr, createTensorAttr, createTensorsAttr} from './test_helper';
-
-describe('graph', () => {
-  let node: Node;
-  const input1 = [tfOps.tensor1d([1])];
-  const input2 = [tfOps.tensor1d([1])];
-  const input3 = [tfOps.tensor3d([1, 1, 1, 2, 2, 2], [1, 2, 3])];
-  const context = new ExecutionContext({}, {}, {});
-
-  beforeEach(() => {
-    node = {
-      name: 'input1',
-      op: '',
-      category: 'graph',
-      inputNames: [],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('Const', () => {
-      it('should return input', () => {
-        node.op = 'Const';
-        expect(executeOp(node, {input1}, context)).toEqual(input1);
-      });
-    });
-    describe('placeholder', () => {
-      it('should return input', () => {
-        node.op = 'Placeholder';
-        expect(executeOp(node, {input1}, context)).toEqual(input1);
-      });
-      it('should return default if input not set', () => {
-        node.inputNames = ['input2'];
-        node.op = 'PlaceholderWithDefault';
-        node.inputParams.default = createTensorAttr(0);
-        expect(executeOp(node, {input2}, context)).toEqual(input2);
-      });
-    });
-    describe('Identity', () => {
-      it('should return input', async () => {
-        node.inputNames = ['input'];
-        node.inputParams.x = createTensorAttr(0);
-        node.op = 'Identity';
-        test_util.expectArraysEqual(
-            await (executeOp(node, {input: input1}, context) as Tensor[])[0]
-                .array(),
-            await input1[0].array());
-      });
-    });
-    describe('IdentityN', () => {
-      it('should return inputs', () => {
-        node.inputNames = ['input1', 'input3'];
-        node.inputParams.x = createTensorsAttr(0, 0);
-        node.op = 'IdentityN';
-        expect(
-            (executeOp(node, {input1, input3}, context) as Tensor[]).map(t => {
-              return Array.prototype.slice.call(t.dataSync());
-            }))
-            .toEqual([[1], [1, 1, 1, 2, 2, 2]]);
-      });
-    });
-    describe('Snapshot', () => {
-      it('should return input', async () => {
-        node.inputNames = ['input'];
-        node.inputParams.x = createTensorAttr(0);
-        node.op = 'Snapshot';
-        const result =
-            (executeOp(node, {input: input1}, context) as Tensor[])[0];
-        expect(result.rank).toEqual(input1[0].rank);
-        test_util.expectArraysClose(await result.data(), [1]);
-      });
-    });
-    describe('Shape', () => {
-      it('should return shape', () => {
-        node.inputNames = ['input'];
-        node.inputParams.x = createTensorAttr(0);
-        node.op = 'Shape';
-        expect(Array.prototype.slice.call(
-                   (executeOp(node, {input: input3}, context) as Tensor[])[0]
-                       .dataSync()))
-            .toEqual([1, 2, 3]);
-      });
-    });
-    describe('ShapeN', () => {
-      it('should return shapeN', () => {
-        node.inputNames = ['input1', 'input3'];
-        node.inputParams.x = createTensorsAttr(0, 0);
-        node.op = 'ShapeN';
-        expect(
-            (executeOp(node, {input1, input3}, context) as Tensor[]).map(t => {
-              return Array.prototype.slice.call(t.dataSync());
-            }))
-            .toEqual([[1], [1, 2, 3]]);
-      });
-    });
-    describe('Size', () => {
-      it('should return size', () => {
-        node.inputNames = ['input'];
-        node.inputParams.x = createTensorAttr(0);
-        node.op = 'Size';
-        expect(Array.prototype.slice.call(
-                   (executeOp(node, {input: input3}, context) as Tensor[])[0]
-                       .dataSync()))
-            .toEqual([6]);
-      });
-    });
-    describe('Rank', () => {
-      it('should return rank', () => {
-        node.inputNames = ['input'];
-        node.inputParams.x = createTensorAttr(0);
-        node.op = 'Rank';
-        expect(Array.prototype.slice.call(
-                   (executeOp(node, {input: input3}, context) as Tensor[])[0]
-                       .dataSync()))
-            .toEqual([3]);
-      });
-    });
-    describe('NoOp', () => {
-      it('should return empty', async () => {
-        node.op = 'NoOp';
-        test_util.expectArraysClose(
-            await (executeOp(node, {}, context) as Tensor[])[0].data(), [1]);
-      });
-    });
-  });
-  describe('Print', () => {
-    it('should return empty', () => {
-      node.op = 'Print';
-      node.inputNames = ['input1', 'input2'];
-      node.inputParams.x = createTensorAttr(0);
-      node.inputParams.data = createTensorsAttr(1, 2);
-      node.attrParams.message = createStrAttr('message');
-      node.attrParams.summarize = createNumberAttr(1);
-      spyOn(console, 'log').and.callThrough();
-      spyOn(console, 'warn').and.callThrough();
-
-      expect(executeOp(node, {input1, input2}, context)).toEqual(input1);
-      expect(console.warn).toHaveBeenCalled();
-      expect(console.log).toHaveBeenCalledWith('message');
-      expect(console.log).toHaveBeenCalledWith([1]);
-    });
-  });
-  describe('StopGradient', () => {
-    it('should return input', async () => {
-      node.inputNames = ['input'];
-      node.inputParams.x = createTensorAttr(0);
-      node.op = 'StopGradient';
-      test_util.expectArraysClose(
-          await (executeOp(node, {input: input1}, context) as Tensor[])[0]
-              .array(),
-          await input1[0].array());
-    });
-  });
-  describe('FakeQuantWithMinMaxVars', () => {
-    it('should return input', async () => {
-      node.inputNames = ['input'];
-      node.inputParams.x = createTensorAttr(0);
-      node.op = 'FakeQuantWithMinMaxVars';
-      test_util.expectArraysClose(
-          await (executeOp(node, {input: input1}, context) as Tensor[])[0]
-              .array(),
-          await input1[0].array());
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/hash_table_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/hash_table_executor.ts
deleted file mode 100644
index 6a040abc5..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/hash_table_executor.ts
+++ /dev/null
@@ -1,91 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, Tensor} from '@tensorflow/tfjs-core';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {HashTable} from '../../executor/hash_table';
-import {ResourceManager} from '../../executor/resource_manager';
-import {InternalOpAsyncExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpAsyncExecutor = async(
-    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-    resourceManager: ResourceManager): Promise<Tensor[]> => {
-  switch (node.op) {
-    case 'HashTable':
-    case 'HashTableV2': {
-      const existingTableHandle =
-          resourceManager.getHashTableHandleByName(node.name);
-      // Table is shared with initializer.
-      if (existingTableHandle != null) {
-        return [existingTableHandle];
-      } else {
-        const keyDType =
-            getParamValue('keyDType', node, tensorMap, context) as DataType;
-        const valueDType =
-            getParamValue('valueDType', node, tensorMap, context) as DataType;
-
-        const hashTable = new HashTable(keyDType, valueDType);
-        resourceManager.addHashTable(node.name, hashTable);
-        return [hashTable.handle];
-      }
-    }
-    case 'InitializeTable':
-    case 'InitializeTableV2':
-    case 'LookupTableImport':
-    case 'LookupTableImportV2': {
-      const handle = getParamValue(
-                         'tableHandle', node, tensorMap, context,
-                         resourceManager) as Tensor;
-      const keys = getParamValue('keys', node, tensorMap, context) as Tensor;
-      const values =
-          getParamValue('values', node, tensorMap, context) as Tensor;
-
-      const hashTable = resourceManager.getHashTableById(handle.id);
-
-      return [await hashTable.import(keys, values)];
-    }
-    case 'LookupTableFind':
-    case 'LookupTableFindV2': {
-      const handle = getParamValue(
-                         'tableHandle', node, tensorMap, context,
-                         resourceManager) as Tensor;
-      const keys = getParamValue('keys', node, tensorMap, context) as Tensor;
-      const defaultValue =
-          getParamValue('defaultValue', node, tensorMap, context) as Tensor;
-
-      const hashTable = resourceManager.getHashTableById(handle.id);
-      return [await hashTable.find(keys, defaultValue)];
-    }
-    case 'LookupTableSize':
-    case 'LookupTableSizeV2': {
-      const handle = getParamValue(
-                         'tableHandle', node, tensorMap, context,
-                         resourceManager) as Tensor;
-
-      const hashTable = resourceManager.getHashTableById(handle.id);
-      return [hashTable.tensorSize()];
-    }
-    default:
-      throw TypeError(`Node type ${node.op} is not implemented`);
-  }
-};
-
-export const CATEGORY = 'hash_table';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/hash_table_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/hash_table_executor_test.ts
deleted file mode 100644
index 64cc3d12e..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/hash_table_executor_test.ts
+++ /dev/null
@@ -1,515 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-import {memory, test_util} from '@tensorflow/tfjs-core';
-
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import {HashTable} from '../../executor/hash_table';
-import {ResourceManager} from '../../executor/resource_manager';
-import * as hashTable from '../op_list/hash_table';
-import {Node} from '../types';
-
-import {executeOp} from './hash_table_executor';
-import {createDtypeAttr, createTensorAttr, validateParam} from './test_helper';
-
-describe('hash_table', () => {
-  let node: Node;
-  const context = new ExecutionContext({}, {}, {});
-  let resourceManager: ResourceManager;
-
-  beforeEach(() => {
-    node = {
-      name: 'test',
-      op: '',
-      category: 'hash_table',
-      inputNames: [],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-    resourceManager = new ResourceManager();
-  });
-
-  afterEach(() => {
-    resourceManager.dispose();
-  });
-
-  describe('executeOp', () => {
-    describe('HashTable', () => {
-      it('should create new tensor on the resourceManager.', async () => {
-        node.op = 'HashTable';
-        node.attrParams['keyDType'] = createDtypeAttr('string');
-        node.attrParams['valueDType'] = createDtypeAttr('float32');
-
-        const before = memory().numTensors;
-        const handle = (await executeOp(node, {}, context, resourceManager))[0];
-        const after = memory().numTensors;
-        // 1 handle is created.
-        expect(after).toBe(before + 1);
-        expect(resourceManager.getHashTableById(handle.id)).toBeDefined();
-        expect(Object.keys(resourceManager.hashTableMap).length).toBe(1);
-      });
-      it('should match json def.', () => {
-        node.op = 'HashTable';
-        node.attrParams['keyDType'] = createDtypeAttr('string');
-        node.attrParams['valueDType'] = createDtypeAttr('float32');
-
-        expect(validateParam(node, hashTable.json)).toBeTruthy();
-      });
-    });
-
-    describe('HashTableV2', () => {
-      it('should create new tensor on the resourceManager.', async () => {
-        node.op = 'HashTableV2';
-        node.attrParams['keyDType'] = createDtypeAttr('string');
-        node.attrParams['valueDType'] = createDtypeAttr('float32');
-
-        const before = memory().numTensors;
-        const handle = (await executeOp(node, {}, context, resourceManager))[0];
-        const after = memory().numTensors;
-        // 1 handle is created.
-        expect(after).toBe(before + 1);
-        expect(resourceManager.getHashTableById(handle.id)).toBeDefined();
-        expect(Object.keys(resourceManager.hashTableMap).length).toBe(1);
-      });
-      it('should match json def.', () => {
-        node.op = 'HashTableV2';
-        node.attrParams['keyDType'] = createDtypeAttr('string');
-        node.attrParams['valueDType'] = createDtypeAttr('float32');
-
-        expect(validateParam(node, hashTable.json)).toBeTruthy();
-      });
-    });
-
-    for (const opName of ['LookupTableImport', 'LookupTableImportV2',
-                          'InitializeTable', 'InitializeTableV2']) {
-      describe(opName, () => {
-        it('should return hashTable handle.', async () => {
-          const hashTable = new HashTable('string', 'float32');
-
-          resourceManager.addHashTable('hashtable', hashTable);
-
-          node.op = opName;
-          node.inputParams['tableHandle'] = createTensorAttr(0);
-          node.inputParams['keys'] = createTensorAttr(1);
-          node.inputParams['values'] = createTensorAttr(2);
-          node.inputNames = ['hashtable', 'input3', 'input5'];
-          const input3 = [tfOps.tensor1d(['a'], 'string')];
-          const input5 = [tfOps.tensor1d([5.5])];
-
-          const before = memory().numTensors;
-          (await executeOp(node, {input3, input5}, context, resourceManager));
-          const after = memory().numTensors;
-          expect(after).toBe(before + 1);
-        });
-
-        it('should throw if dtype doesnot match.', async () => {
-          const hashTable = new HashTable('string', 'float32');
-
-          resourceManager.addHashTable('hashtable', hashTable);
-
-          node.op = opName;
-          node.inputParams['tableHandle'] = createTensorAttr(0);
-          node.inputParams['keys'] = createTensorAttr(1);
-          node.inputParams['values'] = createTensorAttr(2);
-          node.inputNames = ['hashtable', 'input3', 'input5'];
-          const input3 = [tfOps.tensor1d([1])];
-          const input5 = [tfOps.tensor1d([5.5])];
-
-          const before = memory().numTensors;
-          try {
-            await executeOp(node, {input3, input5}, context, resourceManager);
-            fail('Should fail, succeed unexpectedly.');
-          } catch (err) {
-            expect(err).toMatch(/Expect key dtype/);
-          }
-          const after = memory().numTensors;
-          expect(after).toBe(before);
-        });
-
-        it('should throw if length of keys and values doesnot match.',
-           async () => {
-             const hashTable = new HashTable('string', 'float32');
-
-             resourceManager.addHashTable('hashtable', hashTable);
-
-             node.op = opName;
-             node.inputParams['tableHandle'] = createTensorAttr(0);
-             node.inputParams['keys'] = createTensorAttr(1);
-             node.inputParams['values'] = createTensorAttr(2);
-             node.inputNames = ['hashtable', 'input3', 'input5'];
-             const input3 = [tfOps.tensor1d(['a', 'b'])];
-             const input5 = [tfOps.tensor1d([5.5])];
-
-             const before = memory().numTensors;
-             try {
-               await executeOp(node, {input3, input5}, context,
-                               resourceManager);
-               fail('Should fail, succeed unexpectedly.');
-             } catch (err) {
-               expect(err).toMatch(/The number of elements doesn't match/);
-             }
-             const after = memory().numTensors;
-             expect(after).toBe(before);
-           });
-
-        it('should match json def.', () => {
-          node.op = opName;
-          node.inputParams['tableHandle'] = createTensorAttr(0);
-          node.inputParams['keys'] = createTensorAttr(1);
-          node.inputParams['values'] = createTensorAttr(2);
-
-          expect(validateParam(node, hashTable.json)).toBeTruthy();
-        });
-      });
-    }
-
-    describe('LookupTableFind', () => {
-      it('should find the value from hashtable.', async () => {
-        const hashTable = new HashTable('string', 'float32');
-
-        resourceManager.addHashTable('hashtable', hashTable);
-
-        const importNode: Node = {
-          name: 'importv2',
-          op: 'LookupTableImportV2',
-          category: 'hash_table',
-          inputNames: ['hashtable', 'keys', 'values'],
-          inputs: [],
-          inputParams: {
-            tableHandle: createTensorAttr(0),
-            keys: createTensorAttr(1),
-            values: createTensorAttr(2)
-          },
-          attrParams: {},
-          children: []
-        };
-
-        const keys = [tfOps.tensor1d(['a'], 'string')];
-        const values = [tfOps.tensor1d([5.5])];
-
-        (await executeOp(importNode, {keys, values}, context, resourceManager));
-
-        node.op = 'LookupTableFind';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputParams['keys'] = createTensorAttr(1);
-        node.inputParams['defaultValue'] = createTensorAttr(2);
-        node.inputNames = ['hashtable', 'input3', 'input5'];
-        const input3 = [tfOps.tensor1d(['a', 'b'], 'string')];
-        const input5 = [tfOps.scalar(0)];
-
-        const before = memory().numTensors;
-
-        const result =
-            (await executeOp(node, {input3, input5}, context, resourceManager));
-        const after = memory().numTensors;
-        test_util.expectArraysClose(await result[0].data(), [5.5, 0]);
-
-        // Create a result tensor.
-        expect(after).toBe(before + 1);
-      });
-      it('should throw if dtype doesnot match.', async () => {
-        const hashTable = new HashTable('string', 'float32');
-
-        resourceManager.addHashTable('hashtable', hashTable);
-
-        const importNode: Node = {
-          name: 'importv2',
-          op: 'LookupTableImportV2',
-          category: 'hash_table',
-          inputNames: ['hashtable', 'keys', 'values'],
-          inputs: [],
-          inputParams: {
-            tableHandle: createTensorAttr(0),
-            keys: createTensorAttr(1),
-            values: createTensorAttr(2)
-          },
-          attrParams: {},
-          children: []
-        };
-
-        const keys = [tfOps.tensor1d(['a'], 'string')];
-        const values = [tfOps.tensor1d([5.5])];
-
-        (await executeOp(importNode, {keys, values}, context, resourceManager));
-
-        node.op = 'LookupTableFind';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputParams['keys'] = createTensorAttr(1);
-        node.inputParams['defaultValue'] = createTensorAttr(2);
-        node.inputNames = ['hashtable', 'input3', 'input5'];
-        const input3 = [tfOps.tensor1d([1, 2], 'float32')];
-        const input5 = [tfOps.scalar(0)];
-
-        const before = memory().numTensors;
-        try {
-          await executeOp(node, {input3, input5}, context, resourceManager);
-          fail('Shoudl fail, succeed unexpectedly.');
-        } catch (err) {
-          expect(err).toMatch(/Expect key dtype/);
-        }
-        const after = memory().numTensors;
-        expect(after).toBe(before);
-      });
-      it('should match json def.', () => {
-        node.op = 'LookupTableFind';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputParams['keys'] = createTensorAttr(1);
-        node.inputParams['defaultValue'] = createTensorAttr(2);
-
-        expect(validateParam(node, hashTable.json)).toBeTruthy();
-      });
-    });
-
-    describe('LookupTableFindV2', () => {
-      it('should find the value from hashtable.', async () => {
-        const hashTable = new HashTable('string', 'float32');
-
-        resourceManager.addHashTable('hashtable', hashTable);
-
-        const importNode: Node = {
-          name: 'importv2',
-          op: 'LookupTableImportV2',
-          category: 'hash_table',
-          inputNames: ['hashtable', 'keys', 'values'],
-          inputs: [],
-          inputParams: {
-            tableHandle: createTensorAttr(0),
-            keys: createTensorAttr(1),
-            values: createTensorAttr(2)
-          },
-          attrParams: {},
-          children: []
-        };
-
-        const keys = [tfOps.tensor1d(['a'], 'string')];
-        const values = [tfOps.tensor1d([5.5])];
-
-        (await executeOp(importNode, {keys, values}, context, resourceManager));
-
-        node.op = 'LookupTableFindV2';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputParams['keys'] = createTensorAttr(1);
-        node.inputParams['defaultValue'] = createTensorAttr(2);
-        node.inputNames = ['hashtable', 'input3', 'input5'];
-        const input3 = [tfOps.tensor1d(['a', 'b'], 'string')];
-        const input5 = [tfOps.scalar(0)];
-
-        const before = memory().numTensors;
-
-        const result =
-            (await executeOp(node, {input3, input5}, context, resourceManager));
-        const after = memory().numTensors;
-        test_util.expectArraysClose(await result[0].data(), [5.5, 0]);
-
-        // Create a result tensor.
-        expect(after).toBe(before + 1);
-      });
-      it('should throw if dtype doesnot match.', async () => {
-        const hashTable = new HashTable('string', 'float32');
-
-        resourceManager.addHashTable('hashtable', hashTable);
-
-        const importNode: Node = {
-          name: 'importv2',
-          op: 'LookupTableImportV2',
-          category: 'hash_table',
-          inputNames: ['hashtable', 'keys', 'values'],
-          inputs: [],
-          inputParams: {
-            tableHandle: createTensorAttr(0),
-            keys: createTensorAttr(1),
-            values: createTensorAttr(2)
-          },
-          attrParams: {},
-          children: []
-        };
-
-        const keys = [tfOps.tensor1d(['a'], 'string')];
-        const values = [tfOps.tensor1d([5.5])];
-
-        (await executeOp(importNode, {keys, values}, context, resourceManager));
-
-        node.op = 'LookupTableFindV2';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputParams['keys'] = createTensorAttr(1);
-        node.inputParams['defaultValue'] = createTensorAttr(2);
-        node.inputNames = ['hashtable', 'input3', 'input5'];
-        const input3 = [tfOps.tensor1d([1, 2], 'float32')];
-        const input5 = [tfOps.scalar(0)];
-
-        const before = memory().numTensors;
-        try {
-          await executeOp(node, {input3, input5}, context, resourceManager);
-          fail('Shoudl fail, succeed unexpectedly.');
-        } catch (err) {
-          expect(err).toMatch(/Expect key dtype/);
-        }
-        const after = memory().numTensors;
-        expect(after).toBe(before);
-      });
-      it('should match json def.', () => {
-        node.op = 'LookupTableFindV2';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputParams['keys'] = createTensorAttr(1);
-        node.inputParams['defaultValue'] = createTensorAttr(2);
-
-        expect(validateParam(node, hashTable.json)).toBeTruthy();
-      });
-    });
-    describe('LookupTableSize', () => {
-      it('should return 0 if the hashtable is empty.', async () => {
-        const hashTable = new HashTable('string', 'float32');
-        resourceManager.addHashTable('hashtablesize', hashTable);
-        const importNode: Node = {
-          name: 'importv2',
-          op: 'LookupTableImportV2',
-          category: 'hash_table',
-          inputNames: ['hashtablesize', 'keys', 'values'],
-          inputs: [],
-          inputParams: {
-            tableHandle: createTensorAttr(0),
-            keys: createTensorAttr(1),
-            values: createTensorAttr(2)
-          },
-          attrParams: {},
-          children: []
-        };
-        // Import empty tensors
-        const keys = [tfOps.tensor1d([], 'string')];
-        const values = [tfOps.tensor1d([])];
-        (await executeOp(importNode, {keys, values}, context, resourceManager));
-
-        node.op = 'LookupTableSize';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputNames = ['hashtablesize'];
-        const before = memory().numTensors;
-        const result = await executeOp(node, {}, context, resourceManager);
-        const after = memory().numTensors;
-        const size = await result[0].data();
-
-        test_util.expectArraysClose(size, [0]);
-        expect(after).toBe(before + 1);
-      });
-      it('should return the number of elements in the hashtable.', async () => {
-        const hashTable = new HashTable('string', 'float32');
-        resourceManager.addHashTable('hashtablesize', hashTable);
-        const importNode: Node = {
-          name: 'importv2',
-          op: 'LookupTableImportV2',
-          category: 'hash_table',
-          inputNames: ['hashtablesize', 'keys', 'values'],
-          inputs: [],
-          inputParams: {
-            tableHandle: createTensorAttr(0),
-            keys: createTensorAttr(1),
-            values: createTensorAttr(2)
-          },
-          attrParams: {},
-          children: []
-        };
-        // Import tensors with 2 elements
-        const keys = [tfOps.tensor1d(['a', 'b'], 'string')];
-        const values = [tfOps.tensor1d([5.5, 10])];
-        (await executeOp(importNode, {keys, values}, context, resourceManager));
-
-        node.op = 'LookupTableSize';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputNames = ['hashtablesize'];
-        const before = memory().numTensors;
-        const result = await executeOp(node, {}, context, resourceManager);
-        const after = memory().numTensors;
-        const size = await result[0].data();
-
-        test_util.expectArraysClose(size, [2]);
-        expect(after).toBe(before + 1);
-      });
-    });
-    describe('LookupTableSizeV2', () => {
-      it('should return 0 if the hashtable is empty.', async () => {
-        const hashTable = new HashTable('string', 'float32');
-        resourceManager.addHashTable('hashtablesize', hashTable);
-        const importNode: Node = {
-          name: 'importv2',
-          op: 'LookupTableImportV2',
-          category: 'hash_table',
-          inputNames: ['hashtablesize', 'keys', 'values'],
-          inputs: [],
-          inputParams: {
-            tableHandle: createTensorAttr(0),
-            keys: createTensorAttr(1),
-            values: createTensorAttr(2)
-          },
-          attrParams: {},
-          children: []
-        };
-        // Import empty tensors
-        const keys = [tfOps.tensor1d([], 'string')];
-        const values = [tfOps.tensor1d([])];
-        (await executeOp(importNode, {keys, values}, context, resourceManager));
-
-        node.op = 'LookupTableSizeV2';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputNames = ['hashtablesize'];
-        const before = memory().numTensors;
-        const result = await executeOp(node, {}, context, resourceManager);
-        const after = memory().numTensors;
-        const size = await result[0].data();
-
-        test_util.expectArraysClose(size, [0]);
-        expect(after).toBe(before + 1);
-      });
-      it('should return the number of elements in the hashtable.', async () => {
-        const hashTable = new HashTable('string', 'float32');
-        resourceManager.addHashTable('hashtablesize', hashTable);
-        const importNode: Node = {
-          name: 'importv2',
-          op: 'LookupTableImportV2',
-          category: 'hash_table',
-          inputNames: ['hashtablesize', 'keys', 'values'],
-          inputs: [],
-          inputParams: {
-            tableHandle: createTensorAttr(0),
-            keys: createTensorAttr(1),
-            values: createTensorAttr(2)
-          },
-          attrParams: {},
-          children: []
-        };
-        // Import tensors with 2 elements
-        const keys = [tfOps.tensor1d(['a', 'b'], 'string')];
-        const values = [tfOps.tensor1d([5.5, 10])];
-        (await executeOp(importNode, {keys, values}, context, resourceManager));
-
-        node.op = 'LookupTableSizeV2';
-        node.inputParams['tableHandle'] = createTensorAttr(0);
-        node.inputNames = ['hashtablesize'];
-        const before = memory().numTensors;
-        const result = await executeOp(node, {}, context, resourceManager);
-        const after = memory().numTensors;
-        const size = await result[0].data();
-
-        test_util.expectArraysClose(size, [2]);
-        expect(after).toBe(before + 1);
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/image_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/image_executor.ts
deleted file mode 100644
index 87e29d40d..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/image_executor.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap,
-     context: ExecutionContext, ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'ResizeBilinear': {
-          const images =
-              getParamValue('images', node, tensorMap, context) as Tensor;
-          const size =
-              getParamValue('size', node, tensorMap, context) as number[];
-          const alignCorners =
-              getParamValue('alignCorners', node, tensorMap, context) as
-              boolean;
-          const halfPixelCenters =
-              getParamValue('halfPixelCenters', node, tensorMap, context) as
-              boolean;
-          return [ops.image.resizeBilinear(
-              images as Tensor3D | Tensor4D, [size[0], size[1]], alignCorners,
-              halfPixelCenters)];
-        }
-        case 'ResizeNearestNeighbor': {
-          const images =
-              getParamValue('images', node, tensorMap, context) as Tensor;
-          const size =
-              getParamValue('size', node, tensorMap, context) as number[];
-          const alignCorners =
-              getParamValue('alignCorners', node, tensorMap, context) as
-              boolean;
-          const halfPixelCenters =
-              getParamValue('halfPixelCenters', node, tensorMap, context) as
-              boolean;
-          return [ops.image.resizeNearestNeighbor(
-              images as Tensor3D | Tensor4D, [size[0], size[1]], alignCorners,
-              halfPixelCenters)];
-        }
-        case 'CropAndResize': {
-          const image =
-              getParamValue('image', node, tensorMap, context) as Tensor;
-          const boxes =
-              getParamValue('boxes', node, tensorMap, context) as Tensor;
-          const boxInd =
-              getParamValue('boxInd', node, tensorMap, context) as Tensor;
-          const cropSize =
-              getParamValue('cropSize', node, tensorMap, context) as number[];
-          const method =
-              getParamValue('method', node, tensorMap, context) as string;
-          const extrapolationValue =
-              getParamValue('extrapolationValue', node, tensorMap, context) as
-              number;
-          return [ops.image.cropAndResize(
-              image as Tensor4D, boxes as Tensor2D, boxInd as Tensor1D,
-              cropSize as [number, number], method as 'bilinear' | 'nearest',
-              extrapolationValue)];
-        }
-        case 'ImageProjectiveTransformV3': {
-          const images =
-              getParamValue('images', node, tensorMap, context) as Tensor;
-          const transforms =
-              getParamValue('transforms', node, tensorMap, context) as Tensor;
-          const outputShape =
-              getParamValue('outputShape', node, tensorMap, context) as
-              number[];
-          const fillValue =
-              getParamValue('fillValue', node, tensorMap, context) as number;
-          const interpolation =
-              getParamValue('interpolation', node, tensorMap, context) as
-              string;
-          const fillMode =
-              getParamValue('fillMode', node, tensorMap, context) as string;
-          return [ops.image.transform(
-              images as Tensor4D,
-              transforms as Tensor2D,
-              interpolation.toLowerCase() as 'bilinear' | 'nearest',
-              fillMode.toLowerCase() as 'constant' | 'reflect' | 'wrap' | 'nearest',
-              fillValue,
-              outputShape as [number, number])];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'image';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/image_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/image_executor_test.ts
deleted file mode 100644
index ef73eb979..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/image_executor_test.ts
+++ /dev/null
@@ -1,174 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as image from '../op_list/image';
-import {Node} from '../types';
-
-import {executeOp} from './image_executor';
-import {createBoolAttr, createNumberAttr, createNumberAttrFromIndex, createNumericArrayAttrFromIndex, createStrAttr, createTensorAttr, validateParam} from './test_helper';
-import {spyOnAllFunctions, RecursiveSpy} from './spy_ops';
-
-describe('image', () => {
-  let node: Node;
-  const input1 = [tfOps.tensor1d([1])];
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    node = {
-      name: 'input1',
-      op: '',
-      category: 'image',
-      inputNames: ['input1'],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('ResizeBilinear', () => {
-      it('should return input', () => {
-        node.op = 'ResizeBilinear';
-        node.inputParams['images'] = createTensorAttr(0);
-        node.inputParams['size'] = createNumericArrayAttrFromIndex(1);
-        node.attrParams['alignCorners'] = createBoolAttr(true);
-        node.attrParams['halfPixelCenters'] = createBoolAttr(true);
-        node.inputNames = ['input1', 'input2'];
-        const input2 = [tfOps.tensor1d([1, 2])];
-        spyOps.image.resizeBilinear.and.returnValue({});
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-        expect(spyOps.image.resizeBilinear)
-            .toHaveBeenCalledWith(input1[0], [1, 2], true, true);
-      });
-      it('should match json def', () => {
-        node.op = 'ResizeBilinear';
-        node.inputParams['images'] = createTensorAttr(0);
-        node.inputParams['size'] = createNumericArrayAttrFromIndex(1);
-        node.attrParams['alignCorners'] = createBoolAttr(true);
-        node.attrParams['halfPixelCenters'] = createBoolAttr(true);
-
-        expect(validateParam(node, image.json)).toBeTruthy();
-      });
-    });
-    describe('ResizeNearestNeighbor', () => {
-      it('should return input', () => {
-        node.op = 'ResizeNearestNeighbor';
-        node.inputParams['images'] = createTensorAttr(0);
-        node.inputParams['size'] = createNumericArrayAttrFromIndex(1);
-        node.attrParams['alignCorners'] = createBoolAttr(true);
-        node.attrParams['halfPixelCenters'] = createBoolAttr(true);
-        node.inputNames = ['input1', 'input2'];
-        const input2 = [tfOps.tensor1d([1, 2])];
-        spyOps.image.resizeNearestNeighbor.and.returnValue({});
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-        expect(spyOps.image.resizeNearestNeighbor)
-            .toHaveBeenCalledWith(input1[0], [1, 2], true, true);
-      });
-      it('should match json def', () => {
-        node.op = 'ResizeNearestNeighbor';
-        node.inputParams['images'] = createTensorAttr(0);
-        node.inputParams['size'] = createNumericArrayAttrFromIndex(1);
-        node.attrParams['alignCorners'] = createBoolAttr(true);
-        node.attrParams['halfPixelCenters'] = createBoolAttr(true);
-
-        expect(validateParam(node, image.json)).toBeTruthy();
-      });
-    });
-    describe('CropAndResize', () => {
-      it('should return input', () => {
-        node.op = 'CropAndResize';
-        node.inputParams['image'] = createTensorAttr(0);
-        node.inputParams['boxes'] = createTensorAttr(1);
-        node.inputParams['boxInd'] = createTensorAttr(2);
-        node.inputParams['cropSize'] = createNumericArrayAttrFromIndex(3);
-        node.attrParams['method'] = createStrAttr('bilinear');
-        node.attrParams['extrapolationValue'] = createNumberAttr(0.5);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4'];
-
-        spyOps.image.cropAndResize.and.returnValue({});
-        const input2 = [tfOps.tensor1d([2])];
-        const input3 = [tfOps.tensor1d([3])];
-        const input4 = [tfOps.tensor1d([4, 5])];
-
-        executeOp(node, {input1, input2, input3, input4}, context,
-                  spyOpsAsTfOps);
-        expect(spyOps.image.cropAndResize)
-            .toHaveBeenCalledWith(
-                input1[0], input2[0], input3[0], [4, 5], 'bilinear', 0.5);
-      });
-
-      it('should match json def', () => {
-        node.op = 'CropAndResize';
-        node.inputParams['image'] = createTensorAttr(0);
-        node.inputParams['boxes'] = createTensorAttr(1);
-        node.inputParams['boxInd'] = createTensorAttr(2);
-        node.inputParams['cropSize'] = createNumericArrayAttrFromIndex(3);
-        node.attrParams['method'] = createStrAttr('bilinear');
-        node.attrParams['extrapolationValue'] = createNumberAttr(0.5);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4'];
-
-        expect(validateParam(node, image.json)).toBeTruthy();
-      });
-    });
-    describe('ImageProjectiveTransformV3', () => {
-      it('should return input', () => {
-        node.op = 'ImageProjectiveTransformV3';
-        node.inputParams['images'] = createTensorAttr(0);
-        node.inputParams['transforms'] = createTensorAttr(1);
-        node.inputParams['outputShape'] = createNumericArrayAttrFromIndex(2);
-        node.inputParams['fillValue'] = createNumberAttrFromIndex(3);
-        node.attrParams['interpolation'] = createStrAttr('bilinear');
-        node.attrParams['fillMode'] = createStrAttr('constant');
-        node.inputNames = ['input1', 'input2', 'input3', 'input4'];
-
-        const input1 = [tfOps.tensor4d([1], [1, 1, 1, 1])];
-        const input2 = [tfOps.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [1, 8])];
-        const input3 = [tfOps.tensor1d([4, 5])];
-        const input4 = [tfOps.scalar(3)];
-
-        executeOp(node, {input1, input2, input3, input4}, context,
-                  spyOpsAsTfOps);
-        expect(spyOps.image.transform)
-            .toHaveBeenCalledWith(
-                input1[0], input2[0], 'bilinear', 'constant', 3, [4, 5]);
-      });
-
-      it('should match json def', () => {
-        node.op = 'ImageProjectiveTransformV3';
-        node.inputParams['images'] = createTensorAttr(0);
-        node.inputParams['transforms'] = createTensorAttr(1);
-        node.inputParams['outputShape'] = createNumericArrayAttrFromIndex(2);
-        node.inputParams['fillValue'] = createNumberAttrFromIndex(3);
-        node.attrParams['interpolation'] = createStrAttr('bilinear');
-        node.attrParams['fillMode'] = createStrAttr('constant');
-        node.inputNames = ['input1', 'input2', 'input3', 'input4'];
-
-        expect(validateParam(node, image.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/logical_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/logical_executor.ts
deleted file mode 100644
index 108e0a678..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/logical_executor.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-     ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'Equal': {
-          return [ops.equal(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'NotEqual': {
-          return [ops.notEqual(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'Greater': {
-          return [ops.greater(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'GreaterEqual': {
-          return [ops.greaterEqual(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'Less': {
-          return [ops.less(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'LessEqual': {
-          return [ops.lessEqual(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'LogicalAnd': {
-          return [ops.logicalAnd(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'LogicalNot': {
-          return [ops.logicalNot(
-              getParamValue('a', node, tensorMap, context) as Tensor)];
-        }
-        case 'LogicalOr': {
-          return [ops.logicalOr(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'Select':
-        case 'SelectV2': {
-          return [ops.where(
-              getParamValue('condition', node, tensorMap, context) as Tensor,
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        case 'BitwiseAnd': {
-          return [ops.bitwiseAnd(
-              getParamValue('a', node, tensorMap, context) as Tensor,
-              getParamValue('b', node, tensorMap, context) as Tensor)];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'logical';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/logical_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/logical_executor_test.ts
deleted file mode 100644
index f8c3c6cc1..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/logical_executor_test.ts
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import {Node} from '../types';
-
-import {executeOp} from './logical_executor';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createTensorAttr, uncapitalize} from './test_helper';
-
-describe('logical', () => {
-  let node: Node;
-  const input1 = [tfOps.scalar(1)];
-  const input2 = [tfOps.scalar(2)];
-  const context = new ExecutionContext({}, {}, {});
-
-  beforeEach(() => {
-    node = {
-      name: 'test',
-      op: '',
-      category: 'logical',
-      inputNames: ['input1', 'input2'],
-      inputs: [],
-      inputParams: {a: createTensorAttr(0), b: createTensorAttr(1)},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    let spyOps: RecursiveSpy<typeof tfOps>;
-    let spyOpsAsTfOps: typeof tfOps;
-
-    beforeEach(() => {
-      spyOps = spyOnAllFunctions(tfOps);
-      spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    });
-
-    ([
-      'Equal', 'NotEqual', 'Greater', 'GreaterEqual', 'Less', 'LessEqual',
-      'LogicalAnd', 'LogicalOr', 'BitwiseAnd'
-    ] as const )
-        .forEach(op => {
-          it('should call tfOps.' + op, () => {
-            node.op = op;
-            spyOps[uncapitalize(op)].and.returnValue({});
-            executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-            expect(spyOps[uncapitalize(op)])
-                .toHaveBeenCalledWith(input1[0], input2[0]);
-          });
-        });
-    describe('LogicalNot', () => {
-      it('should call tfOps.logicalNot', () => {
-        node.op = 'LogicalNot';
-        spyOps.logicalNot.and.returnValue({});
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.logicalNot).toHaveBeenCalledWith(input1[0]);
-      });
-    });
-
-    describe('Select', () => {
-      it('should call tfOps.where', () => {
-        node.op = 'Select';
-        node.inputNames = ['input1', 'input2', 'input3'];
-        node.inputParams.condition = createTensorAttr(2);
-        const input3 = [tfOps.scalar(1)];
-        spyOps.where.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.where)
-            .toHaveBeenCalledWith(input3[0], input1[0], input2[0]);
-      });
-    });
-
-    describe('SelectV2', () => {
-      it('should call tfOps.where', () => {
-        node.op = 'SelectV2';
-        node.inputNames = ['input1', 'input2', 'input3'];
-        node.inputParams.condition = createTensorAttr(2);
-        const input3 = [tfOps.scalar(1)];
-        spyOps.where.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.where)
-            .toHaveBeenCalledWith(input3[0], input1[0], input2[0]);
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/matrices_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/matrices_executor.ts
deleted file mode 100644
index df03a7ebb..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/matrices_executor.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Scalar, Tensor, Tensor2D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-     ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'BatchMatMul':
-        case 'BatchMatMulV2':
-        case 'MatMul':
-          return [ops.matMul(
-              getParamValue('a', node, tensorMap, context) as Tensor2D,
-              getParamValue('b', node, tensorMap, context) as Tensor2D,
-              getParamValue('transposeA', node, tensorMap, context) as boolean,
-              getParamValue('transposeB', node, tensorMap, context) as
-                  boolean)];
-
-        case 'Einsum':
-          return [ops.einsum(
-              getParamValue('equation', node, tensorMap, context) as string,
-              ...getParamValue('tensors', node, tensorMap, context) as
-                  Tensor[])];
-
-        case 'Transpose':
-          return [ops.transpose(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('perm', node, tensorMap, context) as number[])];
-
-        case '_FusedMatMul':
-          const [extraOp, activationFunc] =
-              (getParamValue('fusedOps', node, tensorMap, context) as string[]);
-
-          const isBiasAdd = extraOp === 'biasadd';
-          const isPrelu = activationFunc === 'prelu';
-
-          const numArgs =
-              (getParamValue('numArgs', node, tensorMap, context) as number);
-          const leakyreluAlpha =
-              getParamValue('leakyreluAlpha', node, tensorMap, context) as
-              number;
-
-          if (isBiasAdd) {
-            if (isPrelu && numArgs !== 2) {
-              throw new Error(
-                  'Fused MatMul with BiasAdd and Prelu must have two ' +
-                  'extra arguments: bias and alpha.');
-            }
-            if (!isPrelu && numArgs !== 1) {
-              throw new Error(
-                  'Fused MatMul with BiasAdd must have one extra argument: bias.');
-            }
-          }
-          const [biasArg, preluArg] =
-              getParamValue('args', node, tensorMap, context) as Tensor[];
-          return [ops.fused.matMul({
-            a: getParamValue('a', node, tensorMap, context) as Tensor2D,
-            b: getParamValue('b', node, tensorMap, context) as Tensor2D,
-            transposeA: getParamValue('transposeA', node, tensorMap, context) as
-                boolean,
-            transposeB: getParamValue('transposeB', node, tensorMap, context) as
-                boolean,
-            bias: biasArg,
-            activation: activationFunc as tfOps.fused.Activation,
-            preluActivationWeights: preluArg,
-            leakyreluAlpha
-          })];
-
-        case 'MatrixBandPart':
-          return [ops.linalg.bandPart(
-              getParamValue('a', node, tensorMap, context) as Tensor2D,
-              getParamValue('numLower', node, tensorMap, context) as Scalar,
-              getParamValue('numUpper', node, tensorMap, context) as Scalar)];
-
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'matrices';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/matrices_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/matrices_executor_test.ts
deleted file mode 100644
index 19d06eae7..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/matrices_executor_test.ts
+++ /dev/null
@@ -1,218 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as matrices from '../op_list/matrices';
-import {Node} from '../types';
-
-import {executeOp} from './matrices_executor';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createBoolAttr, createNumberAttr, createNumericArrayAttr, createStrArrayAttr, createStrAttr, createTensorAttr, createTensorsAttr, validateParam} from './test_helper';
-
-describe('matrices', () => {
-  let node: Node;
-  const input1 = [tfOps.scalar(1)];
-  const input2 = [tfOps.scalar(2)];
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    node = {
-      name: 'test',
-      op: '',
-      category: 'matrices',
-      inputNames: ['input1', 'input2'],
-      inputs: [],
-      inputParams: {a: createTensorAttr(0), b: createTensorAttr(1)},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('MatMul', () => {
-      it('should call tfOps.matMul', () => {
-        node.op = 'MatMul';
-        node.attrParams.transposeA = createBoolAttr(true);
-        node.attrParams.transposeB = createBoolAttr(false);
-        spyOps.matMul.and.returnValue({});
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.matMul)
-            .toHaveBeenCalledWith(input1[0], input2[0], true, false);
-      });
-    });
-    describe('_FusedMatMul', () => {
-      it('should call tfOps.fused.matMul', () => {
-        node.op = '_FusedMatMul';
-        node.inputParams['args'] = createTensorsAttr(2, 0);
-        node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd', 'relu']);
-        node.attrParams['numArgs'] = createNumberAttr(1);
-        node.attrParams.transposeA = createBoolAttr(true);
-        node.attrParams.transposeB = createBoolAttr(false);
-        const input3 = [tfOps.scalar(3.0)];
-        node.inputNames = ['input1', 'input2', 'input3'];
-        spyOps.fused.matMul.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fused.matMul).toHaveBeenCalledWith({
-          a: input1[0],
-          b: input2[0],
-          transposeA: true,
-          transposeB: false,
-          bias: input3[0],
-          activation: 'relu',
-          preluActivationWeights: undefined,
-          leakyreluAlpha: undefined
-        });
-      });
-      it('should call tfOps.fused.matMul - prelu activation', () => {
-        node.op = '_FusedMatMul';
-        node.inputParams['args'] = createTensorsAttr(2, 0);
-        node.attrParams['fusedOps'] = createStrArrayAttr(['biasadd', 'prelu']);
-        node.attrParams['numArgs'] = createNumberAttr(2);
-        node.attrParams.transposeA = createBoolAttr(true);
-        node.attrParams.transposeB = createBoolAttr(false);
-        const input3 = [tfOps.scalar(3.0)];
-        const input4 = [tfOps.scalar(4.0)];
-        node.inputNames = ['input1', 'input2', 'input3', 'input4'];
-        spyOps.fused.matMul.and.returnValue({});
-        executeOp(
-            node, {input1, input2, input3, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fused.matMul).toHaveBeenCalledWith({
-          a: input1[0],
-          b: input2[0],
-          transposeA: true,
-          transposeB: false,
-          bias: input3[0],
-          activation: 'prelu',
-          preluActivationWeights: input4[0],
-          leakyreluAlpha: undefined
-        });
-      });
-      it('should call tfOps.fused.matMul - leakyrelu activation', () => {
-        node.op = '_FusedMatMul';
-        node.inputParams['args'] = createTensorsAttr(2, 0);
-        node.attrParams['fusedOps'] =
-            createStrArrayAttr(['biasadd', 'leakyrelu']);
-        node.attrParams['numArgs'] = createNumberAttr(1);
-        node.attrParams.transposeA = createBoolAttr(true);
-        node.attrParams.transposeB = createBoolAttr(false);
-        node.attrParams.leakyreluAlpha = createNumberAttr(0.3);
-        const input3 = [tfOps.scalar(3.0)];
-        node.inputNames = ['input1', 'input2', 'input3'];
-        spyOps.fused.matMul.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fused.matMul).toHaveBeenCalledWith({
-          a: input1[0],
-          b: input2[0],
-          transposeA: true,
-          transposeB: false,
-          bias: input3[0],
-          activation: 'leakyrelu',
-          preluActivationWeights: undefined,
-          leakyreluAlpha: 0.3
-        });
-      });
-      it('should match json def.', () => {
-        node.op = '_FusedMatMul';
-
-        node.attrParams['fusedOps'] =
-            createStrArrayAttr(['biasadd', 'leakyrelu']);
-        node.attrParams['numArgs'] = createNumberAttr(1);
-        node.attrParams.transposeA = createBoolAttr(true);
-        node.attrParams.transposeB = createBoolAttr(false);
-        node.attrParams.leakyreluAlpha = createNumberAttr(0.3);
-
-        expect(validateParam(node, matrices.json)).toBeTruthy();
-      });
-    });
-    describe('BatchMatMul', () => {
-      it('should call tfOps.matMul', () => {
-        node.op = 'BatchMatMul';
-        node.attrParams.transposeA = createBoolAttr(true);
-        node.attrParams.transposeB = createBoolAttr(false);
-        spyOps.matMul.and.returnValue({});
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.matMul)
-            .toHaveBeenCalledWith(input1[0], input2[0], true, false);
-      });
-    });
-    describe('BatchMatMulV2', () => {
-      it('should call tfOps.matMul', () => {
-        node.op = 'BatchMatMulV2';
-        node.attrParams.transposeA = createBoolAttr(true);
-        node.attrParams.transposeB = createBoolAttr(false);
-        spyOps.matMul.and.returnValue({});
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.matMul)
-            .toHaveBeenCalledWith(input1[0], input2[0], true, false);
-      });
-    });
-    describe('Einsum', () => {
-      it('should call tfOps.einsum', () => {
-        node.op = 'Einsum';
-        node.inputParams = {tensors: createTensorsAttr(0, 0)};
-        node.inputNames = ['input1', 'input2'];
-        node.attrParams.equation = createStrAttr(',->');
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-        const res = executeOp(node, {input1, input2}, context) as Tensor[];
-        expect(spyOps.einsum).toHaveBeenCalledWith(',->', input1[0], input2[0]);
-        expect(res[0].dtype).toBe('float32');
-        expect(res[0].shape).toEqual([]);
-      });
-    });
-    describe('Transpose', () => {
-      it('should call tfOps.transpose', () => {
-        node.op = 'Transpose';
-        node.inputNames = ['input1', 'input2', 'input3'];
-        node.inputParams.x = createTensorAttr(0);
-        node.attrParams.perm = createNumericArrayAttr([1, 2]);
-        spyOps.transpose.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.transpose).toHaveBeenCalledWith(input1[0], [1, 2]);
-      });
-    });
-    describe('MatrixBandPart', () => {
-      it('should call tfOps.linalg.bandPart', () => {
-        node.op = 'MatrixBandPart';
-        node.inputNames = ['input1', 'input2', 'input3'];
-        node.inputParams.a = createTensorAttr(0);
-        node.inputParams.numLower = createTensorAttr(1);
-        node.inputParams.numUpper = createTensorAttr(2);
-        const input3 = [tfOps.scalar(3.0)];
-        spyOps.linalg.bandPart.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.linalg.bandPart)
-            .toHaveBeenCalledWith(input1[0], input2[0], input3[0]);
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/normalization_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/normalization_executor.ts
deleted file mode 100644
index 56abb03ee..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/normalization_executor.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor3D, Tensor4D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap,
-     context: ExecutionContext, ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'EuclideanNorm':
-          return [ops.euclideanNorm(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('axis', node, tensorMap, context) as number[],
-              getParamValue('keepDims', node, tensorMap, context) as boolean)];
-        case 'FusedBatchNorm':
-        case 'FusedBatchNormV2': {
-          return [ops.batchNorm(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('mean', node, tensorMap, context) as Tensor,
-              getParamValue('variance', node, tensorMap, context) as Tensor,
-              getParamValue('offset', node, tensorMap, context) as Tensor,
-              getParamValue('scale', node, tensorMap, context) as Tensor,
-              getParamValue('epsilon', node, tensorMap, context) as number)];
-        }
-        case 'FusedBatchNormV3': {
-          return [ops.batchNorm(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('mean', node, tensorMap, context) as Tensor,
-              getParamValue('variance', node, tensorMap, context) as Tensor,
-              getParamValue('offset', node, tensorMap, context) as Tensor,
-              getParamValue('scale', node, tensorMap, context) as Tensor,
-              getParamValue('epsilon', node, tensorMap, context) as number)];
-        }
-        case 'LRN': {
-          return [ops.localResponseNormalization(
-              getParamValue('x', node, tensorMap, context) as Tensor3D |
-                  Tensor4D,
-              getParamValue('radius', node, tensorMap, context) as number,
-              getParamValue('bias', node, tensorMap, context) as number,
-              getParamValue('alpha', node, tensorMap, context) as number,
-              getParamValue('beta', node, tensorMap, context) as number)];
-        }
-        case 'Softmax': {
-          return [ops.softmax(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        case 'LogSoftmax': {
-          return [ops.logSoftmax(
-              getParamValue('x', node, tensorMap, context) as Tensor)];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'normalization';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/normalization_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/normalization_executor_test.ts
deleted file mode 100644
index 1e53b9346..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/normalization_executor_test.ts
+++ /dev/null
@@ -1,192 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as normalization from '../op_list/normalization';
-import {Node} from '../types';
-
-import {executeOp} from './normalization_executor';
-import {createBoolAttr, createNumberAttr, createNumericArrayAttrFromIndex, createTensorAttr, validateParam} from './test_helper';
-import {spyOnAllFunctions, RecursiveSpy} from './spy_ops';
-
-describe('normalization', () => {
-  let node: Node;
-  const input1 = [tfOps.scalar(1)];
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    node = {
-      name: 'test',
-      op: '',
-      category: 'normalization',
-      inputNames: ['input1'],
-      inputs: [],
-      inputParams: {x: createTensorAttr(0)},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('EuclideanNorm', () => {
-      it('should call tfOps.euclideanNorm', () => {
-        node.op = 'EuclideanNorm';
-        node.inputParams['axis'] = createNumericArrayAttrFromIndex(1);
-        node.attrParams.keepDims = createBoolAttr(false);
-        node.inputNames = ['input1', 'input2'];
-        const input2 = [tfOps.tensor1d([2])];
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.euclideanNorm).toHaveBeenCalledWith(input1[0], [2],
-            false);
-      });
-      it('should match json def', () => {
-        node.op = 'EuclideanNorm';
-        delete node.inputParams.x;
-        node.inputParams.axis = createNumericArrayAttrFromIndex(1);
-        node.attrParams.keepDims = createBoolAttr(false);
-
-        expect(validateParam(node, normalization.json)).toBeTruthy();
-      });
-    });
-    describe('FusedBatchNorm', () => {
-      it('should call tfOps.batchNorm', () => {
-        node.op = 'FusedBatchNorm';
-        node.inputParams.scale = createTensorAttr(1);
-        node.inputParams.offset = createTensorAttr(2);
-        node.inputParams.mean = createTensorAttr(3);
-        node.inputParams.variance = createTensorAttr(4);
-        node.attrParams.epsilon = createNumberAttr(5);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4', 'input5'];
-        const input2 = [tfOps.scalar(1)];
-        const input3 = [tfOps.scalar(2)];
-        const input4 = [tfOps.scalar(3)];
-        const input5 = [tfOps.scalar(4)];
-        executeOp(node, {input1, input2, input3, input4, input5}, context,
-                  spyOpsAsTfOps);
-
-        expect(spyOps.batchNorm)
-            .toHaveBeenCalledWith(
-                input1[0], input4[0], input5[0], input3[0], input2[0], 5);
-      });
-    });
-    describe('FusedBatchNormV2', () => {
-      it('should call tfOps.batchNorm', () => {
-        node.op = 'FusedBatchNormV2';
-        node.inputParams.scale = createTensorAttr(1);
-        node.inputParams.offset = createTensorAttr(2);
-        node.inputParams.mean = createTensorAttr(3);
-        node.inputParams.variance = createTensorAttr(4);
-        node.attrParams.epsilon = createNumberAttr(5);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4', 'input5'];
-        const input2 = [tfOps.scalar(1)];
-        const input3 = [tfOps.scalar(2)];
-        const input4 = [tfOps.scalar(3)];
-        const input5 = [tfOps.scalar(4)];
-        executeOp(node, {input1, input2, input3, input4, input5}, context,
-                  spyOpsAsTfOps);
-
-        expect(spyOps.batchNorm)
-            .toHaveBeenCalledWith(
-                input1[0], input4[0], input5[0], input3[0], input2[0], 5);
-      });
-    });
-    describe('FusedBatchNormV3', () => {
-      it('should call tfOps.batchNorm', () => {
-        node.op = 'FusedBatchNormV3';
-        node.inputParams.scale = createTensorAttr(1);
-        node.inputParams.offset = createTensorAttr(2);
-        node.inputParams.mean = createTensorAttr(3);
-        node.inputParams.variance = createTensorAttr(4);
-        node.attrParams.epsilon = createNumberAttr(5);
-        node.inputNames = ['input1', 'input2', 'input3', 'input4', 'input5'];
-        const input2 = [tfOps.scalar(1)];
-        const input3 = [tfOps.scalar(2)];
-        const input4 = [tfOps.scalar(3)];
-        const input5 = [tfOps.scalar(4)];
-        executeOp(node, {input1, input2, input3, input4, input5}, context,
-                  spyOpsAsTfOps);
-
-        expect(spyOps.batchNorm)
-            .toHaveBeenCalledWith(
-                input1[0], input4[0], input5[0], input3[0], input2[0], 5);
-      });
-    });
-    describe('LRN', () => {
-      it('should call tfOps.localResponseNormalization', () => {
-        node.op = 'LRN';
-        node.attrParams.radius = createNumberAttr(1);
-        node.attrParams.bias = createNumberAttr(2);
-        node.attrParams.alpha = createNumberAttr(3);
-        node.attrParams.beta = createNumberAttr(4);
-        spyOps.localResponseNormalization.and.returnValue({});
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.localResponseNormalization)
-            .toHaveBeenCalledWith(input1[0], 1, 2, 3, 4);
-      });
-      it('should match json def', () => {
-        node.op = 'LRN';
-        node.attrParams.radius = createNumberAttr(1);
-        node.attrParams.bias = createNumberAttr(2);
-        node.attrParams.alpha = createNumberAttr(3);
-        node.attrParams.beta = createNumberAttr(4);
-
-        expect(validateParam(node, normalization.json)).toBeTruthy();
-      });
-    });
-
-    describe('Softmax', () => {
-      it('should call tfOps.softmax', () => {
-        node.op = 'Softmax';
-        spyOps.softmax.and.returnValue({});
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.softmax).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match json def', () => {
-        node.op = 'Softmax';
-
-        expect(validateParam(node, normalization.json)).toBeTruthy();
-      });
-    });
-
-    describe('LogSoftmax', () => {
-      it('should call tfOps.logSoftmax', () => {
-        node.op = 'LogSoftmax';
-        spyOps.logSoftmax.and.returnValue({});
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.logSoftmax).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match json def', () => {
-        node.op = 'LogSoftmax';
-
-        expect(validateParam(node, normalization.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/ragged_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/ragged_executor.ts
deleted file mode 100644
index a9c773894..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/ragged_executor.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor1D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-     ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'RaggedGather': {
-          const {
-            outputNestedSplits,
-            outputDenseValues,
-          } =
-              ops.raggedGather(
-                  getParamValue(
-                      'paramsNestedSplits', node, tensorMap, context) as
-                      Tensor[],
-                  getParamValue(
-                      'paramsDenseValues', node, tensorMap, context) as Tensor,
-                  getParamValue('indices', node, tensorMap, context) as Tensor,
-                  getParamValue('outputRaggedRank', node, tensorMap, context) as
-                      number);
-          return outputNestedSplits.concat(outputDenseValues);
-        }
-        case 'RaggedRange': {
-          const {rtNestedSplits, rtDenseValues} = ops.raggedRange(
-              getParamValue('starts', node, tensorMap, context) as Tensor,
-              getParamValue('limits', node, tensorMap, context) as Tensor,
-              getParamValue('splits', node, tensorMap, context) as Tensor);
-          return [rtNestedSplits, rtDenseValues];
-        }
-        case 'RaggedTensorToTensor': {
-          return [ops.raggedTensorToTensor(
-              getParamValue('shape', node, tensorMap, context) as Tensor,
-              getParamValue('values', node, tensorMap, context) as Tensor1D,
-              getParamValue('defaultValue', node, tensorMap, context) as Tensor,
-              getParamValue('rowPartitionTensors', node, tensorMap, context) as
-                  Tensor[],
-              getParamValue('rowPartitionTypes', node, tensorMap, context) as
-                  string[])];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'ragged';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/ragged_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/ragged_executor_test.ts
deleted file mode 100644
index 3ec537c60..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/ragged_executor_test.ts
+++ /dev/null
@@ -1,182 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor, test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as ragged from '../op_list/ragged';
-import {Node} from '../types';
-
-import {executeOp} from './ragged_executor';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createNumberAttr, createStrArrayAttr, createTensorAttr, createTensorsAttr, validateParam} from './test_helper';
-
-describe('ragged', () => {
-  let node: Node;
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    node = {
-      name: 'test',
-      op: '',
-      category: 'ragged',
-      inputNames: [],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('RaggedGather', () => {
-      const OUTPUT_RAGGED_RANK = 2;
-      beforeEach(() => {
-        node.op = 'RaggedGather';
-        node.inputParams = {
-          paramsNestedSplits: createTensorsAttr(0, 2),
-          paramsDenseValues: createTensorAttr(2),
-          indices: createTensorAttr(3),
-        };
-        node.attrParams = {
-          outputRaggedRank: createNumberAttr(OUTPUT_RAGGED_RANK)
-        };
-      });
-
-      it('should call tfOps.ragged.raggedGather', async () => {
-        node.inputNames = [
-          'paramsNestedSplits1', 'paramsNestedSplits2', 'paramsDenseValues',
-          'indices'
-        ];
-
-        const paramsNestedSplits1 =
-            [tfOps.tensor1d([0, 1, 3, 3, 5, 6], 'int32')];
-        const paramsNestedSplits2 =
-            [tfOps.tensor1d([0, 0, 2, 3, 5, 8, 9], 'int32')];
-        const paramsDenseValues =
-            [tfOps.tensor1d([.1, .2, .3, .4, .5, .6, .7, .8, .9])];
-        const indices = [tfOps.tensor1d([2, 1, 0, 2, 3], 'int32')];
-
-        const result = executeOp(
-                           node, {
-                             paramsNestedSplits1,
-                             paramsNestedSplits2,
-                             paramsDenseValues,
-                             indices
-                           },
-                           context, spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.raggedGather)
-            .toHaveBeenCalledWith(
-                [paramsNestedSplits1[0], paramsNestedSplits2[0]],
-                paramsDenseValues[0], indices[0], OUTPUT_RAGGED_RANK);
-
-        test_util.expectArraysClose(await result[0].data(), [0, 0, 2, 3, 3, 5]);
-        test_util.expectArraysClose(await result[1].data(), [0, 2, 3, 3, 5, 8]);
-
-        test_util.expectArraysClose(
-            await result[2].data(), [.1, .2, .3, .4, .5, .6, .7, .8]);
-      });
-    });
-
-    it('should match json def', () => {
-      expect(validateParam(node, ragged.json)).toBeTruthy();
-    });
-
-    describe('RaggedRange', () => {
-      beforeEach(() => {
-        node.op = 'RaggedRange';
-        node.inputParams = {
-          starts: createTensorAttr(0),
-          limits: createTensorAttr(1),
-          splits: createTensorAttr(2),
-        };
-      });
-
-      it('should call tfOps.ragged.raggedRange', async () => {
-        node.inputNames = ['starts', 'limits', 'splits'];
-
-        const starts = [tfOps.tensor1d([0, 5, 8, 5], 'int32')];
-        const limits = [tfOps.tensor1d([8, 7, 8, 1], 'int32')];
-        const splits = [tfOps.tensor1d([2, 1, 1, -1], 'int32')];
-        const result =
-            executeOp(node, {starts, limits, splits}, context, spyOpsAsTfOps) as
-            Tensor[];
-
-        expect(spyOps.raggedRange)
-            .toHaveBeenCalledWith(starts[0], limits[0], splits[0]);
-        test_util.expectArraysClose(await result[0].data(), [0, 4, 6, 6, 10]);
-        test_util.expectArraysClose(
-            await result[1].data(), [0, 2, 4, 6, 5, 6, 5, 4, 3, 2]);
-      });
-
-      it('should match json def', () => {
-        expect(validateParam(node, ragged.json)).toBeTruthy();
-      });
-    });
-
-    describe('RaggedTensorToTensor', () => {
-      const types = ['FIRST_DIM_SIZE', 'VALUE_ROWIDS'];
-      beforeEach(() => {
-        node.op = 'RaggedTensorToTensor';
-        node.inputParams = {
-          shape: createTensorAttr(0),
-          values: createTensorAttr(1),
-          defaultValue: createTensorAttr(2),
-          rowPartitionTensors: createTensorsAttr(3, 0)
-        };
-        node.attrParams = {rowPartitionTypes: createStrArrayAttr(types)};
-      });
-
-      it('should call tfOps.ragged.raggedTensorToTensor', async () => {
-        node.inputNames = [
-          'shape', 'values', 'defaultValue', 'rowPartition1', 'rowPartition2'
-        ];
-
-        const shape = [tfOps.tensor1d([4, 4], 'int32')];
-        const values = [tfOps.tensor1d([.1, .2, .3, .4, .5, .6, .7, .8, .9])];
-        const defaultValue = [tfOps.scalar(1.5)];
-        const rowPartition1 = [tfOps.scalar(4, 'int32')];
-        const rowPartition2 =
-            [tfOps.tensor1d([0, 0, 0, 2, 2, 2, 2, 3, 3], 'int32')];
-
-        const result =
-            executeOp(
-                node,
-                {shape, values, defaultValue, rowPartition1, rowPartition2},
-                context, spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.raggedTensorToTensor)
-            .toHaveBeenCalledWith(
-                shape[0], values[0], defaultValue[0],
-                [rowPartition1[0], rowPartition2[0]], types);
-        test_util.expectArraysClose(await result[0].data(), [
-          .1, .2, .3, 1.5, 1.5, 1.5, 1.5, 1.5, .4, .5, .6, .7, .8, .9, 1.5, 1.5
-        ]);
-      });
-
-      it('should match json def', () => {
-        expect(validateParam(node, ragged.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/reduction_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/reduction_executor.ts
deleted file mode 100644
index f2d9e2c5c..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/reduction_executor.ts
+++ /dev/null
@@ -1,157 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap,
-     context: ExecutionContext, ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'Max': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number[];
-          const keepDims =
-              getParamValue('keepDims', node, tensorMap, context) as boolean;
-          return [ops.max(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis,
-              keepDims)];
-        }
-        case 'Mean': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number[];
-          const keepDims =
-              getParamValue('keepDims', node, tensorMap, context) as boolean;
-          return [ops.mean(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis,
-              keepDims)];
-        }
-        case 'Min': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number[];
-          const keepDims =
-              getParamValue('keepDims', node, tensorMap, context) as boolean;
-          return [ops.min(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis,
-              keepDims)];
-        }
-        case 'Sum': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number[];
-          const keepDims =
-              getParamValue('keepDims', node, tensorMap, context) as boolean;
-          return [ops.sum(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis,
-              keepDims)];
-        }
-        case 'All': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number[];
-          const keepDims =
-              getParamValue('keepDims', node, tensorMap, context) as boolean;
-          return [ops.all(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis,
-              keepDims)];
-        }
-        case 'Any': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number[];
-          const keepDims =
-              getParamValue('keepDims', node, tensorMap, context) as boolean;
-          return [ops.any(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis,
-              keepDims)];
-        }
-        case 'ArgMax': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number;
-          return [ops.argMax(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis)];
-        }
-        case 'ArgMin': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number;
-          return [ops.argMin(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis)];
-        }
-        case 'Prod': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number[];
-          const keepDims =
-              getParamValue('keepDims', node, tensorMap, context) as boolean;
-          return [ops.prod(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis,
-              keepDims)];
-        }
-        case 'Cumprod': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number;
-          const exclusive =
-              getParamValue('exclusive', node, tensorMap, context) as boolean;
-          const reverse =
-              getParamValue('reverse', node, tensorMap, context) as boolean;
-          return [ops.cumprod(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis,
-              exclusive, reverse)];
-        }
-        case 'Cumsum': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number;
-          const exclusive =
-              getParamValue('exclusive', node, tensorMap, context) as boolean;
-          const reverse =
-              getParamValue('reverse', node, tensorMap, context) as boolean;
-          return [ops.cumsum(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis,
-              exclusive, reverse)];
-        }
-        case 'Bincount':
-          const x = getParamValue('x', node, tensorMap, context) as Tensor1D;
-          const weights =
-              getParamValue('weights', node, tensorMap, context) as Tensor1D;
-          const size =
-              getParamValue('size', node, tensorMap, context) as number;
-
-          return [ops.bincount(x, weights, size)];
-        case 'DenseBincount': {
-          const x = getParamValue('x', node, tensorMap, context) as Tensor1D |
-              Tensor2D;
-          const weights =
-              getParamValue('weights', node, tensorMap, context) as Tensor1D |
-              Tensor2D;
-          const size =
-              getParamValue('size', node, tensorMap, context) as number;
-
-          const binaryOutput =
-              getParamValue('binaryOutput', node, tensorMap, context) as
-              boolean;
-
-          return [ops.denseBincount(x, weights, size, binaryOutput)];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'reduction';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/reduction_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/reduction_executor_test.ts
deleted file mode 100644
index 06d0c1491..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/reduction_executor_test.ts
+++ /dev/null
@@ -1,169 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as reduction from '../op_list/reduction';
-import {Node} from '../types';
-
-import {executeOp} from './reduction_executor';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createBoolAttr, createNumberAttr, createNumberAttrFromIndex, createNumericArrayAttrFromIndex, createTensorAttr, uncapitalize, validateParam} from './test_helper';
-
-describe('reduction', () => {
-  let node: Node;
-  const input1 = [tfOps.scalar(1)];
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    node = {
-      name: 'test',
-      op: '',
-      category: 'reduction',
-      inputNames: ['input1'],
-      inputs: [],
-      inputParams: {x: createTensorAttr(0)},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    (['Max', 'Mean', 'Min', 'Sum', 'All', 'Any', 'Prod'] as const )
-        .forEach(op => {
-          it('should call tfOps.' + op, () => {
-            node.op = op;
-            node.attrParams.keepDims = createBoolAttr(true);
-            node.attrParams.axis = createNumberAttr(1);
-            spyOps[uncapitalize(op)].and.returnValue({});
-            executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-            expect(spyOps[uncapitalize(op)])
-                .toHaveBeenCalledWith(input1[0], 1, true);
-          });
-        });
-    describe('ArgMax', () => {
-      it('should call tfOps.argMax', () => {
-        node.op = 'ArgMax';
-        node.attrParams.keepDims = createBoolAttr(true);
-        node.attrParams.axis = createNumberAttr(1);
-        spyOps.argMax.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.argMax).toHaveBeenCalledWith(input1[0], 1);
-      });
-    });
-    describe('ArgMin', () => {
-      it('should call tfOps.argMin', () => {
-        node.op = 'ArgMin';
-        node.attrParams.keepDims = createBoolAttr(true);
-        node.attrParams.axis = createNumberAttr(1);
-        spyOps.argMin.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.argMin).toHaveBeenCalledWith(input1[0], 1);
-      });
-    });
-    describe('Cumprod', () => {
-      it('should call tfOps.cumprod', () => {
-        node.op = 'Cumprod';
-        node.attrParams.exclusive = createBoolAttr(true);
-        node.attrParams.reverse = createBoolAttr(false);
-        node.inputNames = ['input1', 'input2'];
-        node.inputParams.axis = createNumberAttrFromIndex(1);
-        const input2 = [tfOps.scalar(2)];
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.cumprod).toHaveBeenCalledWith(input1[0], 2, true, false);
-      });
-    });
-    describe('Cumsum', () => {
-      it('should call tfOps.cumsum', () => {
-        node.op = 'Cumsum';
-        node.attrParams.exclusive = createBoolAttr(true);
-        node.attrParams.reverse = createBoolAttr(false);
-        node.inputNames = ['input1', 'input2'];
-        node.inputParams.axis = createNumberAttrFromIndex(1);
-        const input2 = [tfOps.scalar(2)];
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.cumsum).toHaveBeenCalledWith(input1[0], 2, true, false);
-      });
-    });
-    describe('Bincount', () => {
-      it('should call tfOps.bincount', () => {
-        node.op = 'Bincount';
-        node.inputNames = ['input4', 'input3', 'input2'];
-        node.inputParams.size = createNumberAttrFromIndex(1);
-        node.inputParams.weights = createTensorAttr(2);
-        const input4 = [tfOps.tensor1d([1, 1], 'int32')];
-        const input3 = [tfOps.scalar(2)];
-        const input2 = [tfOps.tensor1d([])];
-        executeOp(node, {input4, input3, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.bincount).toHaveBeenCalledWith(input4[0], input2[0], 2);
-      });
-      it('should match json def for bincount.', () => {
-        node.op = 'Bincount';
-        node.inputParams.size = createNumberAttrFromIndex(1);
-        node.inputParams.weights = createTensorAttr(2);
-
-        expect(validateParam(node, reduction.json, 'Bincount')).toBeTruthy();
-      });
-    });
-    describe('DenseBincount', () => {
-      it('should call tfOps.denseBincount', () => {
-        node.op = 'DenseBincount';
-        node.inputNames = ['input4', 'input3', 'input2'];
-        node.inputParams.x = createTensorAttr(0);
-        node.inputParams.size = createNumberAttrFromIndex(1);
-        node.inputParams.weights = createTensorAttr(2);
-        node.attrParams.binaryOutput = createBoolAttr(true);
-        const input4 = [tfOps.tensor1d([1, 1], 'int32')];
-        const input3 = [tfOps.scalar(2)];
-        const input2 = [tfOps.tensor1d([])];
-        executeOp(node, {input4, input3, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.denseBincount)
-            .toHaveBeenCalledWith(input4[0], input2[0], 2, true);
-      });
-      it('should match json def for denseBincount.', () => {
-        node.op = 'DenseBincount';
-        node.inputParams.size = createNumberAttrFromIndex(1);
-        node.inputParams.weights = createTensorAttr(2);
-        node.attrParams.binaryOutput = createBoolAttr(true);
-
-        expect(validateParam(node, reduction.json, 'DenseBincount'))
-            .toBeTruthy();
-      });
-    });
-    describe('Prod', () => {
-      it('should match op def', () => {
-        node.op = 'Prod';
-        node.inputParams['axis'] = createNumericArrayAttrFromIndex(1);
-        node.attrParams['keepDims'] = createBoolAttr(true);
-
-        expect(validateParam(node, reduction.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/slice_join_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/slice_join_executor.ts
deleted file mode 100644
index 524a5bce9..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/slice_join_executor.ts
+++ /dev/null
@@ -1,202 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Scalar, Tensor, Tensor1D, tidy, util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-     ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'ConcatV2':
-        case 'Concat': {
-          const n = getParamValue('n', node, tensorMap, context) as number;
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number;
-          let inputs =
-              getParamValue('tensors', node, tensorMap, context) as Tensor[];
-          inputs = inputs.slice(0, n);
-          return [ops.concat(inputs, axis)];
-        }
-        case 'Gather': {
-          const input = getParamValue('x', node, tensorMap, context) as Tensor;
-          const indices =
-              getParamValue('indices', node, tensorMap, context) as Tensor1D;
-          return [ops.gather(input, ops.cast(indices, 'int32'), 0)];
-        }
-        case 'GatherV2': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number;
-          const batchDims =
-              getParamValue('batchDims', node, tensorMap, context) as number;
-          const input = getParamValue('x', node, tensorMap, context) as Tensor;
-          const indices =
-              getParamValue('indices', node, tensorMap, context) as Tensor1D;
-          return [ops.gather(
-              input, ops.cast(indices, 'int32'), axis, batchDims)];
-        }
-        case 'Reverse': {
-          const dims =
-              getParamValue('dims', node, tensorMap, context) as boolean[];
-          const axis = [];
-          for (let i = 0; i < dims.length; i++) {
-            if (dims[i]) {
-              axis.push(i);
-            }
-          }
-          const input = getParamValue('x', node, tensorMap, context) as Tensor;
-          return [ops.reverse(input, axis)];
-        }
-        case 'ReverseV2': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number[];
-          const input = getParamValue('x', node, tensorMap, context) as Tensor;
-          return [ops.reverse(input, axis)];
-        }
-        case 'Slice': {
-          // tslint:disable-next-line:no-any
-          const begin = getParamValue('begin', node, tensorMap, context) as any;
-          // tslint:disable-next-line:no-any
-          const size = getParamValue('size', node, tensorMap, context) as any;
-          return [ops.slice(
-              getParamValue('x', node, tensorMap, context) as Tensor, begin,
-              size)];
-        }
-        case 'StridedSlice': {
-          const begin =
-              getParamValue('begin', node, tensorMap, context) as number[];
-          const end =
-              getParamValue('end', node, tensorMap, context) as number[];
-          const strides =
-              getParamValue('strides', node, tensorMap, context) as number[];
-          const beginMask =
-              getParamValue('beginMask', node, tensorMap, context) as number;
-          const endMask =
-              getParamValue('endMask', node, tensorMap, context) as number;
-          const ellipsisMask =
-              getParamValue('ellipsisMask', node, tensorMap, context) as number;
-          const newAxisMask =
-              getParamValue('newAxisMask', node, tensorMap, context) as number;
-          const shrinkAxisMask =
-              getParamValue('shrinkAxisMask', node, tensorMap, context) as
-              number;
-          const tensor = getParamValue('x', node, tensorMap, context) as Tensor;
-
-          return [ops.stridedSlice(
-              tensor, begin, end, strides, beginMask, endMask, ellipsisMask,
-              newAxisMask, shrinkAxisMask)];
-        }
-        case 'Pack': {
-          return tidy(() => {
-            const axis =
-                getParamValue('axis', node, tensorMap, context) as number;
-            const tensors =
-                getParamValue('tensors', node, tensorMap, context) as Tensor[];
-            // Reshape the tensors to the first tensor's shape if they don't
-            // match.
-            const shape = tensors[0].shape;
-            const squeezedShape = ops.squeeze(tensors[0]).shape;
-            const mapped = tensors.map(tensor => {
-              const sameShape = util.arraysEqual(tensor.shape, shape);
-              if (!sameShape &&
-                  !util.arraysEqual(ops.squeeze(tensor).shape, squeezedShape)) {
-                throw new Error('the input tensors shape does not match');
-              }
-              return sameShape ? tensor : ops.reshape(tensor, shape);
-            });
-            return [ops.stack(mapped, axis)];
-          });
-        }
-        case 'Unpack': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number;
-          const tensor =
-              getParamValue('tensor', node, tensorMap, context) as Tensor;
-          return ops.unstack(tensor, axis);
-        }
-        case 'Tile': {
-          const reps =
-              getParamValue('reps', node, tensorMap, context) as number[];
-          return [ops.tile(
-              getParamValue('x', node, tensorMap, context) as Tensor, reps)];
-        }
-        case 'Split':
-        case 'SplitV': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number;
-          const numOrSizeSplits =
-              getParamValue('numOrSizeSplits', node, tensorMap, context) as
-                  number |
-              number[];
-          const tensor = getParamValue('x', node, tensorMap, context) as Tensor;
-
-          return ops.split(tensor, numOrSizeSplits, axis);
-        }
-        case 'ScatterNd': {
-          const indices =
-              getParamValue('indices', node, tensorMap, context) as Tensor;
-          const values =
-              getParamValue('values', node, tensorMap, context) as Tensor;
-          const shape =
-              getParamValue('shape', node, tensorMap, context) as number[];
-          return [ops.scatterND(indices, values, shape)];
-        }
-        case 'GatherNd': {
-          const x = getParamValue('x', node, tensorMap, context) as Tensor;
-          const indices =
-              getParamValue('indices', node, tensorMap, context) as Tensor;
-          return [ops.gatherND(x, indices)];
-        }
-        case 'SparseToDense': {
-          const indices =
-              getParamValue('sparseIndices', node, tensorMap, context) as
-              Tensor;
-          const shape =
-              getParamValue('outputShape', node, tensorMap, context) as
-              number[];
-          const sparseValues =
-              getParamValue('sparseValues', node, tensorMap, context) as Tensor;
-          const defaultValue =
-              getParamValue('defaultValue', node, tensorMap, context) as Scalar;
-          return [ops.sparseToDense(
-              indices, sparseValues, shape,
-              sparseValues.dtype === defaultValue.dtype ?
-                  defaultValue :
-                  ops.cast(defaultValue, sparseValues.dtype))];
-        }
-        case 'TensorScatterUpdate': {
-          const indices =
-              getParamValue('indices', node, tensorMap, context) as Tensor;
-          const values =
-              getParamValue('values', node, tensorMap, context) as Tensor;
-          const tensor =
-              getParamValue('tensor', node, tensorMap, context) as Tensor;
-          return [ops.tensorScatterUpdate(tensor, indices, values)];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'slice_join';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/slice_join_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/slice_join_executor_test.ts
deleted file mode 100644
index 04132f2cc..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/slice_join_executor_test.ts
+++ /dev/null
@@ -1,486 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as slice_join from '../op_list/slice_join';
-import {Node} from '../types';
-
-import {executeOp} from './slice_join_executor';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createBooleanArrayAttrFromIndex, createNumberAttr, createNumberAttrFromIndex, createNumericArrayAttrFromIndex, createTensorAttr, createTensorsAttr, validateParam} from './test_helper';
-
-describe('slice join', () => {
-  let node: Node;
-  const input1 = [tfOps.scalar(1)];
-  const input2 = [tfOps.scalar(2)];
-  const input3 = [tfOps.scalar(3)];
-  const input4 = [tfOps.tensor1d([3])];
-  const input5 = [tfOps.tensor1d([3, 4])];
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-  });
-
-  describe('multi-tensor ops', () => {
-    beforeEach(() => {
-      node = {
-        name: 'test',
-        op: '',
-        category: 'slice_join',
-        inputNames: ['input1', 'input2', 'input3'],
-        inputs: [],
-        inputParams: {},
-        attrParams: {},
-        children: []
-      };
-    });
-    describe('executeOp', () => {
-      it('Concat', () => {
-        node.op = 'Concat';
-        node.inputParams.tensors = createTensorsAttr(1, 0);
-        node.inputParams.axis = createNumberAttrFromIndex(0);
-        node.attrParams.n = createNumberAttr(2);
-        spyOps.concat.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.concat).toHaveBeenCalledWith([input2[0], input3[0]], 1);
-      });
-      it('Concat when input length and n mismatch', () => {
-        node.op = 'Concat';
-        node.inputParams.tensors = createTensorsAttr(0, -1);
-        node.inputParams.axis = createNumberAttrFromIndex(-1);
-        node.attrParams.n = createNumberAttr(1);
-        spyOps.concat.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.concat).toHaveBeenCalledWith([input1[0]], 3);
-      });
-      it('should match json def for Concat', () => {
-        node.op = 'Concat';
-        node.inputParams.tensors = createTensorsAttr(1, 0);
-        node.inputParams.axis = createNumberAttrFromIndex(0);
-        node.attrParams.n = createNumberAttr(2);
-
-        expect(validateParam(node, slice_join.json, 'Concat')).toBeTruthy();
-      });
-      it('ConcatV2', () => {
-        node.op = 'ConcatV2';
-        node.inputParams.tensors = createTensorsAttr(0, -1);
-        node.inputParams.axis = createNumberAttrFromIndex(-1);
-        node.attrParams.n = createNumberAttr(2);
-        spyOps.concat.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.concat).toHaveBeenCalledWith([input1[0], input2[0]], 3);
-      });
-      it('ConcatV2 when input length and n mismatch', () => {
-        node.op = 'ConcatV2';
-        node.inputParams.tensors = createTensorsAttr(0, -1);
-        node.inputParams.axis = createNumberAttrFromIndex(-1);
-        node.attrParams.n = createNumberAttr(1);
-        spyOps.concat.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.concat).toHaveBeenCalledWith([input1[0]], 3);
-      });
-      it('should match json def for ConcatV2', () => {
-        node.op = 'ConcatV2';
-        node.inputParams.tensors = createTensorsAttr(0, -1);
-        node.inputParams.axis = createNumberAttrFromIndex(-1);
-        node.attrParams.n = createNumberAttr(3);
-
-        expect(validateParam(node, slice_join.json, 'ConcatV2')).toBeTruthy();
-      });
-      it('should call tfOps.unstack', () => {
-        node.op = 'Unpack';
-        node.inputParams.tensor = createTensorAttr(0);
-        node.attrParams.axis = createNumberAttr(4);
-        spyOps.unstack.and.returnValue({});
-
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.unstack).toHaveBeenCalledWith(input1[0], 4);
-      });
-      it('should match json def for unstack', () => {
-        node.op = 'Unpack';
-        node.inputParams.tensor = createTensorAttr(0);
-        node.attrParams.axis = createNumberAttr(4);
-
-        expect(validateParam(node, slice_join.json)).toBeTruthy();
-      });
-      it('should call tfOps.stack', () => {
-        node.op = 'Pack';
-        node.inputParams.tensors = createTensorsAttr(0, 0);
-        node.attrParams.axis = createNumberAttr(4);
-        spyOps.stack.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.stack.calls.mostRecent().args[0][0]).toEqual(input1[0]);
-        expect(spyOps.stack.calls.mostRecent().args[0][1]).toEqual(input2[0]);
-        expect(spyOps.stack.calls.mostRecent().args[0][2]).toEqual(input3[0]);
-        expect(spyOps.stack.calls.mostRecent().args[1]).toEqual(4);
-      });
-      it('should match json def for unstack', () => {
-        node.op = 'Pack';
-        node.inputParams.tensors = createTensorsAttr(0, 0);
-        node.attrParams.axis = createNumberAttr(4);
-
-        expect(validateParam(node, slice_join.json)).toBeTruthy();
-      });
-      it('should reshape tensors for tfOps.stack', () => {
-        node.op = 'Pack';
-        node.inputNames = ['input1', 'input2', 'input3', 'input4'];
-        node.inputParams.tensors = createTensorsAttr(0, 0);
-        node.attrParams.axis = createNumberAttr(4);
-        spyOps.stack.and.returnValue({});
-        executeOp(
-            node, {input1, input2, input3, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.stack.calls.mostRecent().args[0][0]).toEqual(input1[0]);
-        expect(spyOps.stack.calls.mostRecent().args[0][1]).toEqual(input2[0]);
-        expect(spyOps.stack.calls.mostRecent().args[0][2]).toEqual(input3[0]);
-        expect(spyOps.stack.calls.mostRecent().args[0][3].shape).toEqual([]);
-        expect(spyOps.stack.calls.mostRecent().args[1]).toEqual(4);
-      });
-      it('should raise error if tensors shape does not match for tfOps.stack',
-         () => {
-           node.op = 'Pack';
-           node.inputNames = ['input1', 'input2', 'input3', 'input5'];
-           node.inputParams.tensors = createTensorsAttr(0, 0);
-           node.attrParams.axis = createNumberAttr(4);
-           expect(
-               () => executeOp(node, {input1, input2, input3, input5}, context))
-               .toThrow(new Error('the input tensors shape does not match'));
-         });
-    });
-  });
-  describe('single-tensor ops', () => {
-    beforeEach(() => {
-      node = {
-        name: 'test',
-        op: '',
-        category: 'slice_join',
-        inputNames: ['input1'],
-        inputs: [],
-        inputParams: {x: createTensorAttr(0)},
-        attrParams: {},
-        children: []
-      };
-    });
-    describe('executeOp', () => {
-      it('should call tfOps.reverse', () => {
-        node.op = 'Reverse';
-        node.inputParams.dims = createBooleanArrayAttrFromIndex(1);
-        node.inputNames = ['input1', 'input6'];
-        const input6 = [tfOps.tensor1d([false, true], 'bool')];
-        spyOps.reverse.and.returnValue({});
-        executeOp(node, {input1, input6}, context, spyOpsAsTfOps);
-
-        expect(spyOps.reverse).toHaveBeenCalledWith(input1[0], [1]);
-      });
-      it('should match json def for reverse', () => {
-        node.op = 'Reverse';
-        node.inputParams.dims = createBooleanArrayAttrFromIndex(1);
-
-        expect(validateParam(node, slice_join.json, 'Reverse')).toBeTruthy();
-      });
-      it('should call tfOps.reverse', () => {
-        node.op = 'ReverseV2';
-        node.inputParams.axis = createNumericArrayAttrFromIndex(1);
-        node.inputNames = ['input1', 'input4'];
-        spyOps.reverse.and.returnValue({});
-        executeOp(node, {input1, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.reverse).toHaveBeenCalledWith(input1[0], [3]);
-      });
-      it('should match json def for reverse', () => {
-        node.op = 'ReverseV2';
-        node.inputParams.axis = createNumericArrayAttrFromIndex(1);
-
-        expect(validateParam(node, slice_join.json, 'ReverseV2')).toBeTruthy();
-      });
-      it('should call tfOps.tile', () => {
-        node.op = 'Tile';
-        node.inputParams.reps = createNumericArrayAttrFromIndex(1);
-        node.inputNames = ['input1', 'input4'];
-        spyOps.tile.and.returnValue({});
-        executeOp(node, {input1, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.tile).toHaveBeenCalledWith(input1[0], [3]);
-      });
-      it('should match json def for tile', () => {
-        node.op = 'Tile';
-        node.inputParams.reps = createNumericArrayAttrFromIndex(1);
-
-        expect(validateParam(node, slice_join.json)).toBeTruthy();
-      });
-      it('should call tfOps.slice', () => {
-        node.op = 'Slice';
-        node.inputParams.begin = createNumericArrayAttrFromIndex(1);
-        node.inputParams.size = createNumericArrayAttrFromIndex(2);
-        const input6 = [tfOps.tensor1d([2], 'int32')];
-        node.inputNames = ['input1', 'input6', 'input4'];
-        spyOps.slice.and.returnValue({});
-
-        executeOp(node, {input1, input6, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.slice).toHaveBeenCalledWith(input1[0], [2], [3]);
-      });
-      it('should match json def for slice', () => {
-        node.op = 'Slice';
-        node.inputParams.begin = createNumericArrayAttrFromIndex(1);
-        node.inputParams.size = createNumericArrayAttrFromIndex(2);
-
-        expect(validateParam(node, slice_join.json)).toBeTruthy();
-      });
-      it('should call tfOps.stridedSlice', () => {
-        node.op = 'StridedSlice';
-        node.inputParams.begin = createNumericArrayAttrFromIndex(1);
-        node.inputParams.end = createNumericArrayAttrFromIndex(2);
-        node.inputParams.strides = createNumericArrayAttrFromIndex(3);
-        node.attrParams.beginMask = createNumberAttr(4);
-        node.attrParams.endMask = createNumberAttr(5);
-        node.attrParams.ellipsisMask = createNumberAttr(1);
-        node.attrParams.newAxisMask = createNumberAttr(2);
-        node.attrParams.shrinkAxisMask = createNumberAttr(3);
-        node.inputNames = ['input1', 'input6', 'input7', 'input4'];
-        const input6 = [tfOps.tensor1d([2], 'int32')];
-        const input7 = [tfOps.tensor1d([3], 'int32')];
-        executeOp(
-            node, {input1, input6, input7, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.stridedSlice)
-            .toHaveBeenCalledWith(input1[0], [2], [3], [3], 4, 5, 1, 2, 3);
-      });
-      it('should match json def for stridedSlice', () => {
-        node.op = 'StridedSlice';
-        node.inputParams.begin = createNumericArrayAttrFromIndex(1);
-        node.inputParams.end = createNumericArrayAttrFromIndex(2);
-        node.inputParams.strides = createNumericArrayAttrFromIndex(3);
-        node.attrParams.beginMask = createNumberAttr(4);
-        node.attrParams.endMask = createNumberAttr(5);
-        node.attrParams.ellipsisMask = createNumberAttr(1);
-        node.attrParams.newAxisMask = createNumberAttr(2);
-        node.attrParams.shrinkAxisMask = createNumberAttr(3);
-
-        expect(validateParam(node, slice_join.json)).toBeTruthy();
-      });
-      it('should call tfOps.gather', () => {
-        node.op = 'Gather';
-        node.inputParams.indices = createTensorAttr(1);
-        const input5 = [tfOps.scalar(2, 'int32')];
-        node.inputNames = ['input1', 'input5'];
-        spyOps.gather.and.returnValue({});
-        executeOp(node, {input1, input5, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.gather)
-            .toHaveBeenCalledWith(
-                input1[0], jasmine.objectContaining({dataId: input5[0].dataId}),
-                0);
-      });
-      it('should match json def for gather', () => {
-        node.op = 'Gather';
-        node.inputParams.indices = createTensorAttr(1);
-
-        expect(validateParam(node, slice_join.json, 'Gather')).toBeTruthy();
-      });
-      it('should call tfOps.gather', () => {
-        node.op = 'GatherV2';
-        node.inputParams.indices = createTensorAttr(1);
-        node.inputParams.axis = createNumberAttrFromIndex(2);
-        node.attrParams.batchDims = createNumberAttr(1);
-        const input5 = [tfOps.scalar(2, 'int32')];
-        node.inputNames = ['input1', 'input5', 'input3'];
-        spyOps.gather.and.returnValue({});
-        executeOp(node, {input1, input5, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.gather)
-            .toHaveBeenCalledWith(
-                input1[0], jasmine.objectContaining({dataId: input5[0].dataId}),
-                3, 1);
-      });
-
-      it('should make indices param of int32 dtype', () => {
-        node.op = 'Gather';
-        node.inputParams.indices = createTensorAttr(1);
-        node.inputNames = ['input1', 'input5'];
-        const input5 = [tfOps.scalar(2, 'float32')];
-        spyOps.gather.and.returnValue({});
-        executeOp(node, {input1, input5}, context, spyOpsAsTfOps);
-
-        expect(spyOps.gather)
-            .toHaveBeenCalledWith(
-                input1[0], jasmine.objectContaining({dtype: 'int32'}), 0);
-      });
-      it('should match json def for gather', () => {
-        node.op = 'GatherV2';
-        node.inputParams.indices = createTensorAttr(1);
-        node.inputParams.axis = createNumberAttrFromIndex(2);
-        node.attrParams.batchDims = createNumberAttr(1);
-
-        expect(validateParam(node, slice_join.json, 'GatherV2')).toBeTruthy();
-      });
-      it('should call tfOps.split', () => {
-        node.op = 'Split';
-        node.inputParams.axis = createNumberAttrFromIndex(0);
-        node.inputParams.x = createTensorAttr(1);
-        node.attrParams.numOrSizeSplits = createNumberAttr(2);
-        node.inputNames = ['input1', 'input2'];
-        spyOps.split.and.returnValue({});
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.split).toHaveBeenCalledWith(input2[0], 2, 1);
-      });
-      it('should match json def for split', () => {
-        node.op = 'Split';
-        node.inputParams.axis = createNumberAttrFromIndex(0);
-        node.inputParams.x = createTensorAttr(1);
-        node.attrParams.numOrSizeSplits = createNumberAttr(2);
-
-        expect(validateParam(node, slice_join.json, 'Split')).toBeTruthy();
-      });
-      it('should call tfOps.split', () => {
-        node.op = 'SplitV';
-        node.inputParams.x = createTensorAttr(0);
-        node.inputParams.numOrSizeSplits = createNumericArrayAttrFromIndex(1);
-        node.inputParams.axis = createNumberAttrFromIndex(2);
-        node.inputNames = ['input1', 'input2', 'input3'];
-        spyOps.split.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.split).toHaveBeenCalledWith(input1[0], 2, 3);
-      });
-      it('should match json def for split', () => {
-        node.op = 'SplitV';
-        node.inputParams.x = createTensorAttr(0);
-        node.inputParams.numOrSizeSplits = createNumericArrayAttrFromIndex(1);
-        node.inputParams.axis = createNumberAttrFromIndex(2);
-
-        expect(validateParam(node, slice_join.json, 'SplitV')).toBeTruthy();
-      });
-      it('should call tfOps.scatterND', () => {
-        node.op = 'ScatterNd';
-        node.inputParams.indices = createTensorAttr(0);
-        node.inputParams.values = createTensorAttr(1);
-        node.inputParams.shape = createNumericArrayAttrFromIndex(2);
-        node.inputNames = ['input1', 'input2', 'input4'];
-        spyOps.scatterND.and.returnValue({});
-        executeOp(node, {input1, input2, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.scatterND).toHaveBeenCalledWith(input1[0], input2[0], [
-          3
-        ]);
-      });
-      it('should match json def for scatterND', () => {
-        node.op = 'ScatterNd';
-        delete node.inputParams.x;
-        node.inputParams.indices = createTensorAttr(0);
-        node.inputParams.values = createTensorAttr(1);
-        node.inputParams.shape = createNumericArrayAttrFromIndex(2);
-
-        expect(validateParam(node, slice_join.json)).toBeTruthy();
-      });
-      it('should call tfOps.gatherND', () => {
-        node.op = 'GatherNd';
-        node.inputParams.x = createTensorAttr(0);
-        node.inputParams.indices = createTensorAttr(1);
-        node.inputNames = ['input1', 'input2'];
-        spyOps.gatherND.and.returnValue({});
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.gatherND).toHaveBeenCalledWith(input1[0], input2[0]);
-      });
-      it('should match json def for gatherND', () => {
-        node.op = 'GatherNd';
-        node.inputParams.x = createTensorAttr(0);
-        node.inputParams.indices = createTensorAttr(1);
-
-        expect(validateParam(node, slice_join.json)).toBeTruthy();
-      });
-      it('should call tfOps.sparseToDense', () => {
-        node.op = 'SparseToDense';
-        node.inputParams.sparseIndices = createTensorAttr(0);
-        node.inputParams.outputShape = createNumericArrayAttrFromIndex(1);
-        node.inputParams.sparseValues = createTensorAttr(2);
-        node.inputParams.defaultValue = createTensorAttr(3);
-        node.inputParams.indices = createTensorAttr(1);
-        node.inputNames = ['input1', 'input4', 'input3', 'input2'];
-        spyOps.sparseToDense.and.returnValue({});
-        executeOp(
-            node, {input1, input2, input3, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.sparseToDense)
-            .toHaveBeenCalledWith(input1[0], input3[0], [3], input2[0]);
-      });
-      it('should make defaultValue of same dtype as sparseValues', () => {
-        node.op = 'SparseToDense';
-        node.inputParams.sparseIndices = createTensorAttr(0);
-        node.inputParams.outputShape = createNumericArrayAttrFromIndex(1);
-        node.inputParams.sparseValues = createTensorAttr(2);
-        node.inputParams.defaultValue = createTensorAttr(3);
-        node.inputParams.indices = createTensorAttr(1);
-        const input5 = [tfOps.scalar(5, 'int32')];
-        node.inputNames = ['input1', 'input4', 'input3', 'input5'];
-        spyOps.sparseToDense.and.returnValue({});
-        executeOp(
-            node, {input1, input5, input3, input4}, context, spyOpsAsTfOps);
-
-        expect(spyOps.sparseToDense)
-            .toHaveBeenCalledWith(
-                input1[0], input3[0], [3],
-                jasmine.objectContaining({dtype: 'float32'}));
-      });
-      it('should match json def for sparseToDense', () => {
-        node.op = 'SparseToDense';
-        node.inputParams = {};
-        node.inputParams.sparseIndices = createTensorAttr(0);
-        node.inputParams.outputShape = createNumericArrayAttrFromIndex(1);
-        node.inputParams.sparseValues = createTensorAttr(2);
-        node.inputParams.defaultValue = createTensorAttr(3);
-
-        expect(validateParam(node, slice_join.json)).toBeTruthy();
-      });
-      it('should call tfOps.tensorScatterUpdate', () => {
-        node.op = 'TensorScatterUpdate';
-        node.inputParams.tensor = createTensorAttr(0);
-        node.inputParams.indices = createTensorAttr(1);
-        node.inputParams.values = createTensorAttr(2);
-        node.inputNames = ['input3', 'input1', 'input2'];
-        spyOps.tensorScatterUpdate.and.returnValue({});
-        executeOp(node, {input3, input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.tensorScatterUpdate)
-            .toHaveBeenCalledWith(input3[0], input1[0], input2[0]);
-      });
-      it('should match json def for tensorScatterUpdate', () => {
-        node.op = 'TensorScatterUpdate';
-        delete node.inputParams.x;
-        node.inputParams.tensor = createTensorAttr(0);
-        node.inputParams.indices = createTensorAttr(1);
-        node.inputParams.values = createTensorAttr(2);
-
-        expect(validateParam(node, slice_join.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/sparse_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/sparse_executor.ts
deleted file mode 100644
index 3cab1601d..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/sparse_executor.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Scalar, Tensor, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap,
-     context: ExecutionContext, ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'SparseFillEmptyRows': {
-          const {
-            outputIndices,
-            outputValues,
-            emptyRowIndicator,
-            reverseIndexMap
-          } =
-              ops.sparse.sparseFillEmptyRows(
-                  getParamValue('indices', node, tensorMap, context) as
-                      Tensor2D,
-                  getParamValue('values', node, tensorMap, context) as Tensor1D,
-                  getParamValue('denseShape', node, tensorMap, context) as
-                      Tensor1D,
-                  getParamValue('defaultValue', node, tensorMap, context) as
-                      Scalar);
-          return [
-            outputIndices, outputValues, emptyRowIndicator, reverseIndexMap
-          ];
-        }
-        case 'SparseReshape': {
-          const {outputIndices, outputShape} = ops.sparse.sparseReshape(
-              getParamValue('inputIndices', node, tensorMap, context) as
-                  Tensor2D,
-              getParamValue('inputShape', node, tensorMap, context) as Tensor1D,
-              getParamValue('newShape', node, tensorMap, context) as Tensor1D);
-          return [outputIndices, outputShape];
-        }
-        case 'SparseSegmentMean': {
-          const outputData = ops.sparse.sparseSegmentMean(
-              getParamValue('data', node, tensorMap, context) as Tensor,
-              getParamValue('indices', node, tensorMap, context) as Tensor1D,
-              getParamValue('segmentIds', node, tensorMap, context) as
-                  Tensor1D);
-          return [outputData];
-        }
-        case 'SparseSegmentSum': {
-          const outputData = ops.sparse.sparseSegmentSum(
-              getParamValue('data', node, tensorMap, context) as Tensor,
-              getParamValue('indices', node, tensorMap, context) as Tensor1D,
-              getParamValue('segmentIds', node, tensorMap, context) as
-                  Tensor1D);
-          return [outputData];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'sparse';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/sparse_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/sparse_executor_test.ts
deleted file mode 100644
index f42a3fc07..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/sparse_executor_test.ts
+++ /dev/null
@@ -1,198 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor, test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as sparse from '../op_list/sparse';
-import {Node} from '../types';
-
-import {executeOp} from './sparse_executor';
-import {createTensorAttr, validateParam} from './test_helper';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-
-describe('sparse', () => {
-  let node: Node;
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    node = {
-      name: 'test',
-      op: '',
-      category: 'sparse',
-      inputNames: [],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('SparseFillEmptyRows', () => {
-      it('should call tfOps.sparse.sparseFillEmptyRows', async () => {
-        node.op = 'SparseFillEmptyRows';
-        node.inputParams = {
-          indices: createTensorAttr(0),
-          values: createTensorAttr(1),
-          denseShape: createTensorAttr(2),
-          defaultValue: createTensorAttr(3)
-        };
-        node.inputNames = ['indices', 'values', 'denseShape', 'defaultValue'];
-
-        const indices = [tfOps.tensor2d(
-            [0, 0, 1, 0, 1, 3, 1, 4, 3, 2, 3, 3], [6, 2], 'int32')];
-        const values = [tfOps.tensor1d([0, 10, 13, 14, 32, 33], 'int32')];
-        const denseShape = [tfOps.tensor1d([5, 6], 'int32')];
-        const defaultValue = [tfOps.scalar(-1, 'int32')];
-        const result = executeOp(
-                           node, {indices, values, denseShape, defaultValue},
-                           context, spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.sparse.sparseFillEmptyRows)
-            .toHaveBeenCalledWith(
-                indices[0], values[0], denseShape[0], defaultValue[0]);
-        test_util.expectArraysClose(
-            await result[0].data(),
-            [0, 0, 1, 0, 1, 3, 1, 4, 2, 0, 3, 2, 3, 3, 4, 0]);
-        test_util.expectArraysClose(
-            await result[1].data(), [0, 10, 13, 14, -1, 32, 33, -1]);
-        test_util.expectArraysClose(await result[2].data(), [0, 0, 1, 0, 1]);
-        test_util.expectArraysClose(await result[3].data(), [0, 1, 2, 3, 5, 6]);
-      });
-      it('should match json def', () => {
-        node.op = 'SparseFillEmptyRows';
-        node.inputParams = {
-          indices: createTensorAttr(0),
-          values: createTensorAttr(1),
-          denseShape: createTensorAttr(2),
-          defaultValue: createTensorAttr(3)
-        };
-
-        expect(validateParam(node, sparse.json)).toBeTruthy();
-      });
-    });
-    describe('SparseReshape', () => {
-      it('should call tfOps.sparse.sparseReshape', async () => {
-        node.op = 'SparseReshape';
-        node.inputParams = {
-          inputIndices: createTensorAttr(0),
-          inputShape: createTensorAttr(1),
-          newShape: createTensorAttr(2)
-        };
-        node.inputNames = ['inputIndices', 'inputShape', 'newShape'];
-
-        const inputIndices = [tfOps.tensor2d(
-            [0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 2, 3], [5, 3], 'int32')];
-        const inputShape = [tfOps.tensor1d([2, 3, 6], 'int32')];
-        const newShape = [tfOps.tensor1d([9, -1], 'int32')];
-        const result =
-            executeOp(node, {inputIndices, inputShape, newShape}, context,
-                      spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.sparse.sparseReshape)
-            .toHaveBeenCalledWith(inputIndices[0], inputShape[0], newShape[0]);
-        test_util.expectArraysClose(
-            await result[0].data(), [0, 0, 0, 1, 1, 2, 4, 2, 8, 1]);
-        test_util.expectArraysClose(await result[1].data(), [9, 4]);
-      });
-
-      it('should match json def', () => {
-        node.op = 'SparseReshape';
-        node.inputParams = {
-          inputIndices: createTensorAttr(0),
-          inputShape: createTensorAttr(1),
-          newShape: createTensorAttr(2)
-        };
-
-        expect(validateParam(node, sparse.json)).toBeTruthy();
-      });
-    });
-    describe('SparseSegmentMean', () => {
-      it('should call tfOps.sparse.sparseSegmentMean', async () => {
-        node.op = 'SparseSegmentMean';
-        node.inputParams = {
-          data: createTensorAttr(0),
-          indices: createTensorAttr(1),
-          segmentIds: createTensorAttr(2)
-        };
-        node.inputNames = ['data', 'indices', 'segmentIds'];
-
-        const data = [tfOps.tensor2d(
-            [1, 2, 3, 4, -1, -2, -3, -4, 6, 7, 8, 9], [3, 4], 'float32')];
-        const indices = [tfOps.tensor1d([0, 1, 2], 'int32')];
-        const segmentIds = [tfOps.tensor1d([0, 1, 1], 'int32')];
-        const result =
-            executeOp(node, {data, indices, segmentIds}, context,
-                      spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.sparse.sparseSegmentMean)
-            .toHaveBeenCalledWith(data[0], indices[0], segmentIds[0]);
-        test_util.expectArraysClose(
-            await result[0].data(), [1.0, 2.0, 3.0, 4.0, 2.5, 2.5, 2.5, 2.5]);
-      });
-      it('should match json def', () => {
-        node.op = 'SparseSegmentMean';
-        node.inputParams = {
-          data: createTensorAttr(0),
-          indices: createTensorAttr(1),
-          segmentIds: createTensorAttr(2)
-        };
-
-        expect(validateParam(node, sparse.json)).toBeTruthy();
-      });
-    });
-    describe('SparseSegmentSum', () => {
-      it('should call tfOps.sparse.sparseSegmentSum', async () => {
-        node.op = 'SparseSegmentSum';
-        node.inputParams = {
-          data: createTensorAttr(0),
-          indices: createTensorAttr(1),
-          segmentIds: createTensorAttr(2)
-        };
-        node.inputNames = ['data', 'indices', 'segmentIds'];
-
-        const data = [tfOps.tensor2d(
-            [1, 2, 3, 4, -1, -2, -3, -4, 5, 6, 7, 8], [3, 4], 'int32')];
-        const indices = [tfOps.tensor1d([0, 1], 'int32')];
-        const segmentIds = [tfOps.tensor1d([0, 0], 'int32')];
-        const result =
-            executeOp(node, {data, indices, segmentIds}, context,
-                      spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.sparse.sparseSegmentSum)
-            .toHaveBeenCalledWith(data[0], indices[0], segmentIds[0]);
-        test_util.expectArraysClose(await result[0].data(), [0, 0, 0, 0]);
-      });
-      it('should match json def', () => {
-        node.op = 'SparseSegmentSum';
-        node.inputParams = {
-          data: createTensorAttr(0),
-          indices: createTensorAttr(1),
-          segmentIds: createTensorAttr(2)
-        };
-
-        expect(validateParam(node, sparse.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/spectral_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/spectral_executor.ts
deleted file mode 100644
index 9a1f49b93..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/spectral_executor.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-     ops = tfOps): Tensor[] => {
-          switch (node.op) {
-            case 'FFT': {
-              return [ops.fft(
-                  getParamValue('x', node, tensorMap, context) as Tensor)];
-            }
-            case 'IFFT': {
-              return [ops.ifft(
-                  getParamValue('x', node, tensorMap, context) as Tensor)];
-            }
-            case 'RFFT': {
-              return [ops.rfft(
-                  getParamValue('x', node, tensorMap, context) as Tensor)];
-            }
-            case 'IRFFT': {
-              return [ops.irfft(
-                  getParamValue('x', node, tensorMap, context) as Tensor)];
-            }
-            default:
-              throw TypeError(`Node type ${node.op} is not implemented`);
-          }
-        };
-
-export const CATEGORY = 'spectral';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/spectral_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/spectral_executor_test.ts
deleted file mode 100644
index 5e4d0a794..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/spectral_executor_test.ts
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as spectral from '../op_list/spectral';
-import {Node} from '../types';
-
-import {executeOp} from './spectral_executor';
-import {createTensorAttr, validateParam} from './test_helper';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-
-describe('spectral', () => {
-  let node: Node;
-  const input1 = [tfOps.scalar(1)];
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    node = {
-      name: 'test',
-      op: '',
-      category: 'spectral',
-      inputNames: ['input1'],
-      inputs: [],
-      inputParams: {x: createTensorAttr(0)},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('FFT', () => {
-      it('should call tfOps.fft', () => {
-        node.op = 'FFT';
-        spyOps.fft.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.fft).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match json def', () => {
-        node.op = 'FFT';
-
-        expect(validateParam(node, spectral.json)).toBeTruthy();
-      });
-    });
-    describe('IFFT', () => {
-      it('should call tfOps.ifft', () => {
-        node.op = 'IFFT';
-        spyOps.ifft.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.ifft).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match json def', () => {
-        node.op = 'IFFT';
-
-        expect(validateParam(node, spectral.json)).toBeTruthy();
-      });
-    });
-    describe('RFFT', () => {
-      it('should call tfOps.rfft', () => {
-        node.op = 'RFFT';
-        spyOps.rfft.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.rfft).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match json def', () => {
-        node.op = 'RFFT';
-
-        expect(validateParam(node, spectral.json)).toBeTruthy();
-      });
-    });
-    describe('IRFFT', () => {
-      it('should call tfOps.irfft', () => {
-        node.op = 'IRFFT';
-        spyOps.irfft.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.irfft).toHaveBeenCalledWith(input1[0]);
-      });
-      it('should match json def', () => {
-        node.op = 'IRFFT';
-
-        expect(validateParam(node, spectral.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/spy_ops.ts b/tfjs-master/tfjs-converter/src/operations/executors/spy_ops.ts
deleted file mode 100644
index ae1ef9936..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/spy_ops.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// The opposite of Extract<T, U>
-type Without<T, U> = T extends U ? never : T;
-
-// Do not spy on CompositeArrayBuffer because it is a class constructor.
-type NotSpiedOn = 'CompositeArrayBuffer';
-
-export type RecursiveSpy<T> =
-  T extends Function ? jasmine.Spy :
-  {[K in Without<keyof T, NotSpiedOn>]: RecursiveSpy<T[K]>} &
-  {[K in Extract<keyof T, NotSpiedOn>]: T[K]};
-
-export function spyOnAllFunctions<T>(obj: T): RecursiveSpy<T> {
-  return Object.fromEntries(Object.entries(obj).map(([key, val]) => {
-    // TODO(mattSoulanille): Do not hard code this
-    if (key === 'CompositeArrayBuffer') {
-      return val;
-    }
-    if (val instanceof Function) {
-      return [key, jasmine.createSpy(`${key} spy`, val).and.callThrough()];
-    } else if (val instanceof Array) {
-      return [key, val];
-    } else if (val instanceof Object) {
-      return [key, spyOnAllFunctions(val)];
-    }
-    return [key, val];
-  })) as RecursiveSpy<T>;
-}
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/string_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/string_executor.ts
deleted file mode 100644
index 85b5a4942..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/string_executor.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Scalar, Tensor, Tensor1D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap,
-     context: ExecutionContext, ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'StaticRegexReplace': {
-          return [ops.string.staticRegexReplace(
-            getParamValue('input', node, tensorMap, context) as Tensor,
-            getParamValue('pattern', node, tensorMap, context) as string,
-            getParamValue('rewrite', node, tensorMap, context) as string,
-            getParamValue('replaceGlobal', node, tensorMap, context) as boolean,
-          )];
-        }
-        case 'StringNGrams': {
-          const {nGrams, nGramsSplits} = ops.string.stringNGrams(
-              getParamValue('data', node, tensorMap, context) as Tensor1D,
-              getParamValue('dataSplits', node, tensorMap, context) as Tensor,
-              getParamValue('separator', node, tensorMap, context) as string,
-              getParamValue('nGramWidths', node, tensorMap, context) as
-                  number[],
-              getParamValue('leftPad', node, tensorMap, context) as string,
-              getParamValue('rightPad', node, tensorMap, context) as string,
-              getParamValue('padWidth', node, tensorMap, context) as number,
-              getParamValue(
-                  'preserveShortSequences', node, tensorMap, context) as
-                  boolean);
-          return [nGrams, nGramsSplits];
-        }
-        case 'StringSplit': {
-          const {indices, values, shape} = ops.string.stringSplit(
-              getParamValue('input', node, tensorMap, context) as Tensor1D,
-              getParamValue('delimiter', node, tensorMap, context) as Scalar,
-              getParamValue('skipEmpty', node, tensorMap, context) as boolean);
-          return [indices, values, shape];
-        }
-        case 'StringToHashBucketFast': {
-          const output = ops.string.stringToHashBucketFast(
-              getParamValue('input', node, tensorMap, context) as Tensor,
-              getParamValue('numBuckets', node, tensorMap, context) as number);
-          return [output];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'string';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/string_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/string_executor_test.ts
deleted file mode 100644
index 55792db2a..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/string_executor_test.ts
+++ /dev/null
@@ -1,176 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor, test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import * as string from '../op_list/string';
-import {Node} from '../types';
-
-import {executeOp} from './string_executor';
-import {createBoolAttr, createNumberAttr, createNumericArrayAttr, createStrAttr, createTensorAttr, validateParam} from './test_helper';
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-
-describe('string', () => {
-  let node: Node;
-  const context = new ExecutionContext({}, {}, {});
-  let spyOps: RecursiveSpy<typeof tfOps>;
-  let spyOpsAsTfOps: typeof tfOps;
-
-  beforeEach(() => {
-    spyOps = spyOnAllFunctions(tfOps);
-    spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    node = {
-      name: 'test',
-      op: '',
-      category: 'string',
-      inputNames: [],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: [],
-      outputs: []
-    };
-  });
-
-  describe('executeOp', () => {
-    describe('StaticRegexReplace', () => {
-      it('should call tfOps.string.staticRegexReplace', async () => {
-        node.op = 'StaticRegexReplace';
-        node.inputParams = {
-          input: createTensorAttr(0),
-        };
-        node.attrParams = {
-          pattern: createStrAttr('foo'),
-          rewrite: createStrAttr('bar'),
-          replaceGlobal: createBoolAttr(true),
-        };
-        node.inputNames = ['input'];
-
-        const input = [tfOps.tensor1d(['a', 'b', 'foo', 'd'])];
-        const result = executeOp(node, {input}, context,
-                                 spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.string.staticRegexReplace)
-            .toHaveBeenCalledWith(input[0], 'foo', 'bar', true);
-
-        test_util.expectArraysEqual(
-            await result[0].data(), ['a', 'b', 'bar', 'd']);
-      });
-    });
-    describe('StringNGrams', () => {
-      it('should call tfOps.string.stringNGrams', async () => {
-        node.op = 'StringNGrams';
-        node.inputParams = {
-          data: createTensorAttr(0),
-          dataSplits: createTensorAttr(1)
-        };
-        node.attrParams = {
-          separator: createStrAttr('|'),
-          nGramWidths: createNumericArrayAttr([3]),
-          leftPad: createStrAttr('LP'),
-          rightPad: createStrAttr('RP'),
-          padWidth: createNumberAttr(-1),
-          preserveShortSequences: createBoolAttr(false)
-        };
-        node.inputNames = ['data', 'dataSplits'];
-        node.outputs = ['ngrams', 'ngrams_splits'];
-
-        const data = [tfOps.tensor1d(['a', 'b', 'c', 'd', 'e', 'f'], 'string')];
-        const dataSplits = [tfOps.tensor1d([0, 4, 6], 'int32')];
-        const result = executeOp(node, {data, dataSplits}, context,
-                                 spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.string.stringNGrams)
-            .toHaveBeenCalledWith(
-                data[0], dataSplits[0], '|', [3], 'LP', 'RP', -1, false);
-        test_util.expectArraysEqual(await result[0].data(), [
-          'LP|LP|a', 'LP|a|b', 'a|b|c', 'b|c|d', 'c|d|RP', 'd|RP|RP',  // 0
-          'LP|LP|e', 'LP|e|f', 'e|f|RP', 'f|RP|RP'                     // 1
-        ]);
-        test_util.expectArraysEqual(await result[1].data(), [0, 6, 10]);
-      });
-      it('should match json def', () => {
-        node.op = 'StringNGrams';
-        node.inputParams = {
-          data: createTensorAttr(0),
-          dataSplits: createTensorAttr(1)
-        };
-        node.outputs = ['ngrams', 'ngrams_splits'];
-        expect(validateParam(node, string.json)).toBeTruthy();
-      });
-    });
-    describe('StringSplit', () => {
-      it('should call tfOps.string.stringSplit', async () => {
-        node.op = 'StringSplit';
-        node.inputParams = {
-          input: createTensorAttr(0),
-          delimiter: createTensorAttr(1)
-        };
-        node.attrParams = {skipEmpty: createBoolAttr(false)};
-        node.inputNames = ['input', 'delimiter'];
-        node.outputs = ['indices', 'values', 'shape'];
-
-        const input = [tfOps.tensor1d(['#a', 'b#', '#c#'], 'string')];
-        const delimiter = [tfOps.scalar('#', 'string')];
-        const result = executeOp(node, {input, delimiter}, context,
-                                 spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.string.stringSplit)
-            .toHaveBeenCalledWith(input[0], delimiter[0], false);
-        test_util.expectArraysEqual(
-            await result[0].data(), [0, 0, 0, 1, 1, 0, 1, 1, 2, 0, 2, 1, 2, 2]);
-        test_util.expectArraysEqual(
-            await result[1].data(), ['', 'a', 'b', '', '', 'c', '']);
-        test_util.expectArraysEqual(await result[2].data(), [3, 3]);
-      });
-      it('should match json def', () => {
-        node.op = 'StringSplit';
-        node.inputParams = {
-          input: createTensorAttr(0),
-          delimiter: createTensorAttr(1)
-        };
-        node.outputs = ['indices', 'values', 'shape'];
-
-        expect(validateParam(node, string.json)).toBeTruthy();
-      });
-    });
-    describe('StringToHashBucketFast', () => {
-      it('should call tfOps.string.stringToHashBucketFast', async () => {
-        node.op = 'StringToHashBucketFast';
-        node.inputParams = {input: createTensorAttr(0)};
-        node.attrParams = {numBuckets: createNumberAttr(10)};
-        node.inputNames = ['input'];
-
-        const input = [tfOps.tensor1d(['a', 'b', 'c', 'd'], 'string')];
-        const result = executeOp(node, {input}, context,
-                                 spyOpsAsTfOps) as Tensor[];
-
-        expect(spyOps.string.stringToHashBucketFast)
-            .toHaveBeenCalledWith(input[0], 10);
-        test_util.expectArraysClose(await result[0].data(), [9, 2, 2, 5]);
-      });
-      it('should match json def', () => {
-        node.op = 'StringToHashBucketFast';
-        node.inputParams = {input: createTensorAttr(0)};
-
-        expect(validateParam(node, string.json)).toBeTruthy();
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/test_helper.ts b/tfjs-master/tfjs-converter/src/operations/executors/test_helper.ts
deleted file mode 100644
index 25a004fcc..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/test_helper.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {InputParamValue, OpMapper, ParamValue} from '../types';
-import {Node} from '../types';
-
-export function createNumberAttr(value: number): ParamValue {
-  return {value, type: 'number'};
-}
-
-export function createNumberAttrFromIndex(inputIndex: number): InputParamValue {
-  return {inputIndexStart: inputIndex, type: 'number'};
-}
-
-export function createStrAttr(str: string): ParamValue {
-  return {value: str, type: 'string'};
-}
-
-export function createStrArrayAttr(strs: string[]): ParamValue {
-  return {value: strs, type: 'string[]'};
-}
-
-export function createBoolAttr(value: boolean): ParamValue {
-  return {value, type: 'bool'};
-}
-
-export function createTensorShapeAttr(value: number[]): ParamValue {
-  return {value, type: 'shape'};
-}
-
-export function createShapeAttrFromIndex(inputIndex: number): InputParamValue {
-  return {inputIndexStart: inputIndex, type: 'shape'};
-}
-
-export function createNumericArrayAttr(value: number[]): ParamValue {
-  return {value, type: 'number[]'};
-}
-
-export function createNumericArrayAttrFromIndex(inputIndex: number):
-    InputParamValue {
-  return {inputIndexStart: inputIndex, type: 'number[]'};
-}
-
-export function createBooleanArrayAttrFromIndex(inputIndex: number):
-    InputParamValue {
-  return {inputIndexStart: inputIndex, type: 'bool[]'};
-}
-
-export function createTensorAttr(index: number): InputParamValue {
-  return {inputIndexStart: index, type: 'tensor'};
-}
-
-export function createTensorsAttr(
-    index: number, paramLength: number): InputParamValue {
-  return {inputIndexStart: index, inputIndexEnd: paramLength, type: 'tensors'};
-}
-
-export function createDtypeAttr(dtype: string): ParamValue {
-  return {value: dtype, type: 'dtype'};
-}
-
-export function validateParam(
-    node: Node, opMappers: OpMapper[], tfOpName?: string) {
-  const opMapper = tfOpName != null ?
-      opMappers.find(mapper => mapper.tfOpName === tfOpName) :
-      opMappers.find(mapper => mapper.tfOpName === node.op);
-  const matched = Object.keys(node.inputParams).every(key => {
-    const value = node.inputParams[key];
-    const def = opMapper.inputs.find(param => param.name === key);
-    return def && def.type === value.type &&
-        def.start === value.inputIndexStart && def.end === value.inputIndexEnd;
-  }) &&
-      Object.keys(node.attrParams).every(key => {
-        const value = node.attrParams[key];
-        const def = opMapper.attrs.find(param => param.name === key);
-        return def && def.type === value.type;
-      });
-  if (!matched) {
-    console.log('node = ', node);
-    console.log('opMapper = ', opMapper);
-  }
-  return matched;
-}
-
-export function uncapitalize<Name extends string>(name: Name): Uncapitalize<Name> {
-  return name.charAt(0).toLowerCase() + name.slice(1) as Uncapitalize<Name>;
-}
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/transformation_executor.ts b/tfjs-master/tfjs-converter/src/operations/executors/transformation_executor.ts
deleted file mode 100644
index 0244f8cb6..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/transformation_executor.ts
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor4D} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {InternalOpExecutor, Node} from '../types';
-
-import {getParamValue} from './utils';
-
-export const executeOp: InternalOpExecutor =
-    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-     ops = tfOps): Tensor[] => {
-      switch (node.op) {
-        case 'Cast': {
-          return [ops.cast(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('dtype', node, tensorMap, context) as 'int32' |
-                  'float32' | 'bool')];
-        }
-        case 'ExpandDims': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number;
-          return [ops.expandDims(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis)];
-        }
-        case 'Squeeze': {
-          const axis =
-              getParamValue('axis', node, tensorMap, context) as number[];
-          return [ops.squeeze(
-              getParamValue('x', node, tensorMap, context) as Tensor, axis)];
-        }
-
-        case 'Reshape': {
-          return [ops.reshape(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('shape', node, tensorMap, context) as number[])];
-        }
-        case 'EnsureShape': {
-          return [ops.ensureShape(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('shape', node, tensorMap, context) as number[])];
-        }
-        case 'MirrorPad': {
-          return [ops.mirrorPad(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('padding', node, tensorMap, context) as
-                  Array<[number, number]>,
-              getParamValue('mode', node, tensorMap, context) as 'reflect' |
-                  'symmetric')];
-        }
-        case 'PadV2':
-        case 'Pad': {
-          return [ops.pad(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('padding', node, tensorMap, context) as
-                  Array<[number, number]>,
-              getParamValue('constantValue', node, tensorMap, context) as
-                  number)];
-        }
-        case 'SpaceToBatchND': {
-          const blockShape =
-              getParamValue('blockShape', node, tensorMap, context) as number[];
-          const paddings =
-              getParamValue('paddings', node, tensorMap, context) as number[][];
-          return [ops.spaceToBatchND(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              blockShape, paddings)];
-        }
-        case 'BatchToSpaceND': {
-          const blockShape =
-              getParamValue('blockShape', node, tensorMap, context) as number[];
-          const crops =
-              getParamValue('crops', node, tensorMap, context) as number[][];
-          return [ops.batchToSpaceND(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              blockShape, crops)];
-        }
-        case 'DepthToSpace': {
-          const blockSize =
-              getParamValue('blockSize', node, tensorMap, context) as number;
-          const dataFormat =
-              (getParamValue('dataFormat', node, tensorMap, context) as
-               string).toUpperCase() as 'NHWC' |
-              'NCHW';
-          return [ops.depthToSpace(
-              getParamValue('x', node, tensorMap, context) as Tensor4D,
-              blockSize, dataFormat)];
-        }
-        case 'BroadcastTo': {
-          return [ops.broadcastTo(
-              getParamValue('x', node, tensorMap, context) as Tensor,
-              getParamValue('shape', node, tensorMap, context) as number[])];
-        }
-        case 'BroadcastArgs': {
-          return [ops.broadcastArgs(
-              getParamValue('s0', node, tensorMap, context) as Tensor,
-              getParamValue('s1', node, tensorMap, context) as Tensor)];
-        }
-        default:
-          throw TypeError(`Node type ${node.op} is not implemented`);
-      }
-    };
-
-export const CATEGORY = 'transformation';
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/transformation_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/executors/transformation_executor_test.ts
deleted file mode 100644
index dec5c09f1..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/transformation_executor_test.ts
+++ /dev/null
@@ -1,217 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// tslint:disable-next-line: no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import {ExecutionContext} from '../../executor/execution_context';
-import {Node} from '../types';
-
-import {RecursiveSpy, spyOnAllFunctions} from './spy_ops';
-import {createDtypeAttr, createNumberAttr, createNumericArrayAttrFromIndex, createStrAttr, createTensorAttr} from './test_helper';
-import {executeOp} from './transformation_executor';
-
-describe('transformation', () => {
-  let node: Node;
-  const input1 = [tfOps.scalar(1)];
-  const input2 = [tfOps.tensor1d([1, 1])];
-  const context = new ExecutionContext({}, {}, {});
-
-  beforeEach(() => {
-    node = {
-      name: 'test',
-      op: '',
-      category: 'transformation',
-      inputNames: ['input1'],
-      inputs: [],
-      inputParams: {x: createTensorAttr(0)},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    let spyOps: RecursiveSpy<typeof tfOps>;
-    let spyOpsAsTfOps: typeof tfOps;
-
-    beforeEach(() => {
-      spyOps = spyOnAllFunctions(tfOps);
-      spyOpsAsTfOps = spyOps as unknown as typeof tfOps;
-    });
-
-    describe('Cast', () => {
-      it('should call tfOps.cast', () => {
-        node.op = 'Cast';
-        node.attrParams.dtype = createDtypeAttr('float32');
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.cast).toHaveBeenCalledWith(input1[0], 'float32');
-      });
-    });
-    describe('ExpandDims', () => {
-      it('should call tfOps.expandDims', () => {
-        node.op = 'ExpandDims';
-        node.attrParams.axis = createNumberAttr(1);
-        spyOps.expandDims.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.expandDims).toHaveBeenCalledWith(input1[0], 1);
-      });
-    });
-    describe('MirrorPad', () => {
-      it('should call tfc.mirrorPad', () => {
-        node.op = 'MirrorPad';
-        node.inputParams.padding = createNumericArrayAttrFromIndex(1);
-        node.attrParams.mode = createStrAttr('reflect');
-        node.inputNames = ['input1', 'input3'];
-        const input3 = [tfOps.tensor2d([1, 1, 2, 2], [2, 2])];
-        spyOps.mirrorPad.and.returnValue({});
-        executeOp(node, {input1, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.mirrorPad)
-            .toHaveBeenCalledWith(input1[0], [[1, 1], [2, 2]], 'reflect');
-      });
-    });
-    describe('Pad', () => {
-      it('should call tfOps.pad', () => {
-        node.op = 'Pad';
-        node.inputParams.padding = createNumericArrayAttrFromIndex(1);
-        node.attrParams.constantValue = createNumberAttr(1);
-        node.inputNames = ['input1', 'input3'];
-        const input3 = [tfOps.tensor2d([1, 1, 2, 2], [2, 2])];
-        spyOps.pad.and.returnValue({});
-        executeOp(node, {input1, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.pad).toHaveBeenCalledWith(input1[0], [[1, 1], [2, 2]], 1);
-      });
-    });
-    describe('PadV2', () => {
-      it('should call tfOps.pad', () => {
-        node.op = 'PadV2';
-        node.inputParams.padding = createNumericArrayAttrFromIndex(1);
-        node.attrParams.constantValue = createNumberAttr(1);
-        node.inputNames = ['input1', 'input3'];
-        const input3 = [tfOps.tensor2d([1, 1, 2, 2], [2, 2])];
-        spyOps.pad.and.returnValue({});
-        executeOp(node, {input1, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.pad).toHaveBeenCalledWith(input1[0], [[1, 1], [2, 2]], 1);
-      });
-    });
-    describe('Reshape', () => {
-      it('should call tfOps.reshape', () => {
-        node.op = 'Reshape';
-        node.inputParams.shape = createNumericArrayAttrFromIndex(1);
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.reshape).toHaveBeenCalledWith(input1[0], [1, 1]);
-      });
-    });
-    describe('EnsureShape', () => {
-      it('should call tfOps.ensureShape', () => {
-        node.op = 'EnsureShape';
-        node.inputParams.shape = createNumericArrayAttrFromIndex(1);
-        node.inputNames = ['input2', 'input3'];
-        const input3 = [tfOps.tensor1d([2])];
-
-        executeOp(node, {input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.ensureShape).toHaveBeenCalledWith(input2[0], [2]);
-      });
-    });
-    describe('Squeeze', () => {
-      it('should call tfOps.squeeze', () => {
-        node.op = 'Squeeze';
-        node.attrParams.axis = createNumberAttr(1);
-        spyOps.squeeze.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.squeeze).toHaveBeenCalledWith(input1[0], 1);
-      });
-    });
-    describe('SpaceToBatchND', () => {
-      it('should call tfOps.spaceToBatchND', () => {
-        node.op = 'SpaceToBatchND';
-        node.inputParams.blockShape = createNumericArrayAttrFromIndex(1);
-        node.inputParams.paddings = createNumericArrayAttrFromIndex(2);
-        node.inputNames = ['input1', 'input2', 'input3'];
-        const input2 = [tfOps.tensor1d([1, 1, 2, 2])];
-        const input3 = [tfOps.tensor2d([1, 2, 2, 3, 2, 3, 3, 4], [4, 2])];
-        spyOps.spaceToBatchND.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.spaceToBatchND)
-            .toHaveBeenCalledWith(
-                input1[0], [1, 1, 2, 2], [[1, 2], [2, 3], [2, 3], [3, 4]]);
-      });
-    });
-    describe('BatchToSpaceND', () => {
-      it('should call tfOps.batchToSpaceND', () => {
-        node.op = 'BatchToSpaceND';
-        node.inputParams.blockShape = createNumericArrayAttrFromIndex(1);
-        node.inputParams.crops = createNumericArrayAttrFromIndex(2);
-        node.inputNames = ['input1', 'input2', 'input3'];
-        const input2 = [tfOps.tensor1d([1, 1, 2, 2])];
-        const input3 = [tfOps.tensor2d([1, 2, 2, 3, 2, 3, 3, 4], [4, 2])];
-        spyOps.batchToSpaceND.and.returnValue({});
-        executeOp(node, {input1, input2, input3}, context, spyOpsAsTfOps);
-
-        expect(spyOps.batchToSpaceND)
-            .toHaveBeenCalledWith(
-                input1[0], [1, 1, 2, 2], [[1, 2], [2, 3], [2, 3], [3, 4]]);
-      });
-    });
-    describe('DepthToSpace', () => {
-      it('should call tfOps.depthToSpace', () => {
-        node.op = 'DepthToSpace';
-        node.attrParams.blockSize = createNumberAttr(1);
-        node.attrParams.dataFormat = createStrAttr('nhwc');
-        node.inputNames = ['input1'];
-        spyOps.depthToSpace.and.returnValue({});
-        executeOp(node, {input1}, context, spyOpsAsTfOps);
-
-        expect(spyOps.depthToSpace).toHaveBeenCalledWith(input1[0], 1, 'NHWC');
-      });
-    });
-    describe('BroadcastTo', () => {
-      it('should call tfOps.broadcastTo', () => {
-        node.op = 'BroadcastTo';
-        node.inputParams.shape = createNumericArrayAttrFromIndex(1);
-        node.inputNames = ['input1', 'input2'];
-
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.broadcastTo).toHaveBeenCalledWith(input1[0], [1, 1]);
-      });
-    });
-    describe('BroadcastArgs', () => {
-      it('should call tfOps.broadcastArgs', () => {
-        node.op = 'BroadcastArgs';
-        node.inputParams.s0 = createTensorAttr(0);
-        node.inputParams.s1 = createTensorAttr(1);
-        node.inputNames = ['input1', 'input2'];
-        const input1 = [tfOps.tensor1d([1, 1])];
-        const input2 = [tfOps.tensor1d([1, 1])];
-        spyOps.broadcastArgs.and.returnValue({});
-        executeOp(node, {input1, input2}, context, spyOpsAsTfOps);
-
-        expect(spyOps.broadcastArgs).toHaveBeenCalledWith(input1[0], input2[0]);
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/executors/utils.ts b/tfjs-master/tfjs-converter/src/operations/executors/utils.ts
deleted file mode 100644
index e206893be..000000000
--- a/tfjs-master/tfjs-converter/src/operations/executors/utils.ts
+++ /dev/null
@@ -1,191 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {clone, Tensor, util} from '@tensorflow/tfjs-core';
-
-import {NamedTensorsMap} from '../../data/types';
-import {ExecutionContext} from '../../executor/execution_context';
-import {ResourceManager} from '../../executor/resource_manager';
-import {Node, ValueType} from '../types';
-
-export function getParamValue(
-    paramName: string, node: Node, tensorMap: NamedTensorsMap,
-    context: ExecutionContext, resourceManager?: ResourceManager): ValueType {
-  const inputParam = node.inputParams[paramName];
-  if (inputParam && inputParam.inputIndexStart !== undefined) {
-    const start = inputParam.inputIndexStart;
-    const end = inputParam.inputIndexEnd === 0 ?
-        undefined :
-        (inputParam.inputIndexEnd === undefined ? start + 1 :
-                                                  inputParam.inputIndexEnd);
-    const shiftedStart = start < 0 ? node.inputNames.length + start : start;
-    if (inputParam.type === 'tensor') {
-      return getTensor(
-          node.inputNames[shiftedStart], tensorMap, context, resourceManager);
-    }
-    if (inputParam.type === 'tensors') {
-      // TODO(mattSoulanille): This filters out NoOp nodes during execution, but
-      // these should really never be in the execution graph in the first place.
-      // They're necessary for ordering the graph, but should not be visible
-      // during execution. Perhaps have different sets of children, one for
-      // control dependencies and another for real dependencies.
-      const inputs = node.inputs.slice(start, end);
-      const inputNames = node.inputNames.slice(start, end)
-        .filter((_name, index) => inputs[index]?.op !== 'NoOp');
-
-      return inputNames.map(
-          name => getTensor(name, tensorMap, context, resourceManager));
-    }
-    const tensor = getTensor(
-        node.inputNames[shiftedStart], tensorMap, context, resourceManager);
-    const data = tensor.dataSync();
-    return inputParam.type === 'number' ?
-        data[0] :
-        util.toNestedArray(tensor.shape, data);
-  }
-  const attrParam = node.attrParams[paramName];
-  return attrParam && attrParam.value;
-}
-
-/**
- * Retrieve the tensor from tensorsMap based on input name.
- * @param name Node input name
- * @param tensorsMap Tensors map keyed by the node
- * @param context contains tensors and information for running the current node.
- * @param resourceManager Optional. Contains global resources of the model.
- */
-export function getTensor(
-    name: string, tensorsMap: NamedTensorsMap, context: ExecutionContext,
-    resourceManager?: ResourceManager): Tensor {
-  const [nodeName, index] = parseNodeName(name, context);
-
-  if (resourceManager != null) {
-    const tensor = resourceManager.getHashTableHandleByName(nodeName);
-    if (tensor != null) {
-      return tensor;
-    }
-  }
-
-  const contextId = context.currentContextIds.find(contextId => {
-    return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];
-  });
-
-  return contextId !== undefined ?
-      tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :
-      undefined;
-}
-
-/**
- * Retrieve the tensors based on input name for current context.
- * @param name Node input name
- * @param tensorsMap Tensors map keyed by the node
- */
-export function getTensorsForCurrentContext(
-    name: string, tensorsMap: NamedTensorsMap,
-    context: ExecutionContext): Tensor[] {
-  return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];
-}
-
-/**
- * Returns the node name, outputName and index from the Node input name.
- * @param inputName The input name of the node, in format of
- * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is
- * default to 0.
- * If the input name contains output name i.e. StringSplit:indices:0, it will
- * return ['StringSplit', 0, 'indices'].
- */
-export function getNodeNameAndIndex(
-    inputName: string, context?: ExecutionContext): [string, number, string] {
-  const [nodeName, index, outputName] = parseNodeName(inputName, context);
-
-  return [
-    getNodeNameWithContextId(nodeName, context && context.currentContextId),
-    index, outputName
-  ];
-}
-
-function getNodeNameWithContextId(name: string, contextId?: string): string {
-  return !!contextId ? `${name}-${contextId}` : name;
-}
-
-export function parseNodeName(
-    name: string, context?: ExecutionContext): [string, number, string?] {
-  if (name === '') {
-    return ['', 0, undefined];
-  }
-
-  const isCacheEnabled = context != null && context.parseNodeNameCache != null;
-  if (isCacheEnabled) {
-    const cachedResult = context.parseNodeNameCache.get(name);
-    if (cachedResult != null) {
-      return cachedResult;
-    }
-  }
-  const parts = name.split(':');
-  let result: [string, number, string?];
-  if (parts.length === 1) {
-    result = [name, 0, undefined];
-  } else {
-    const nodeName = parts[0];
-    const outputName = parts.length === 3 ? parts[1] : undefined;
-    const index = Number(parts[parts.length - 1]);
-    result = [nodeName, index, outputName];
-  }
-  if (isCacheEnabled) {
-    context.parseNodeNameCache.set(name, result);
-  }
-  return result;
-}
-
-export function split(arr: number[], size: number) {
-  const res = [];
-  for (let i = 0; i < arr.length; i += size) {
-    res.push(arr.slice(i, i + size));
-  }
-  return res;
-}
-export function getPadding(
-    node: Node, tensorMap: NamedTensorsMap,
-    context: ExecutionContext): ValueType {
-  let pad = getParamValue('pad', node, tensorMap, context);
-  if (pad === 'explicit') {
-    // This is 1d array, we need to convert it to 2d array
-    pad = getParamValue('explicitPaddings', node, tensorMap, context);
-    const explicitPadding: [
-      [number, number], [number, number], [number, number], [number, number]
-    ] = [[0, 0], [0, 0], [0, 0], [0, 0]];
-    for (let i = 0; i < 4; i++) {
-      explicitPadding[i][0] = (pad as number[])[i * 2];
-      explicitPadding[i][1] = (pad as number[])[i * 2 + 1];
-    }
-    return explicitPadding;
-  }
-  return pad;
-}
-
-/**
- *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to
- *  avoid disposal. This is important for TensorArray and TensorList ops, since
- *  internally they use a tensor as the id for TensorArray and TensorList, and
- * to simplify lookup, they also use Tensor.id as the key to the internal map.
- * These id tensors have been marked as kept in the backend, we need avoid clone
- * them in order to create new Tensor.id.
- * @param tensor
- */
-export function cloneTensor(tensor: Tensor): Tensor {
-  return tensor.kept ? tensor : clone(tensor);
-}
diff --git a/tfjs-master/tfjs-converter/src/operations/op_mapper_schema.ts b/tfjs-master/tfjs-converter/src/operations/op_mapper_schema.ts
deleted file mode 100644
index 1ec2ddd57..000000000
--- a/tfjs-master/tfjs-converter/src/operations/op_mapper_schema.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export const json = {
-  '$schema': 'http://json-schema.org/draft-07/schema#',
-  'definitions': {
-    'OpMapper': {
-      'type': 'object',
-      'properties': {
-        'tfOpName': {'type': 'string'},
-        'category': {'$ref': '#/definitions/Category'},
-        'inputs': {
-          'type': 'array',
-          'items': {'$ref': '#/definitions/InputParamMapper'}
-        },
-        'attrs': {
-          'type': 'array',
-          'items': {'$ref': '#/definitions/AttrParamMapper'}
-        },
-        'customExecutor': {'$ref': '#/definitions/OpExecutor'},
-        'outputs': {'type': 'array'}
-      },
-      'required': ['tfOpName'],
-      'additionalProperties': false
-    },
-    'Category': {
-      'type': 'string',
-      'enum': [
-        'arithmetic',    'basic_math',     'control',    'convolution',
-        'custom',        'dynamic',        'evaluation', 'image',
-        'creation',      'graph',          'logical',    'matrices',
-        'normalization', 'ragged',         'reduction',  'slice_join',
-        'spectral',      'transformation', 'sparse',     'string'
-      ]
-    },
-    'InputParamMapper': {
-      'type': 'object',
-      'properties': {
-        'name': {'type': 'string'},
-        'type': {'$ref': '#/definitions/ParamTypes'},
-        'defaultValue': {
-          'anyOf': [
-            {'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}},
-            {'type': 'number'}, {'type': 'array', 'items': {'type': 'number'}},
-            {'type': 'boolean'}, {'type': 'array', 'items': {'type': 'boolean'}}
-          ]
-        },
-        'notSupported': {'type': 'boolean'},
-        'start': {'type': 'number'},
-        'end': {'type': 'number'}
-      },
-      'required': ['name', 'start', 'type'],
-      'additionalProperties': false
-    },
-    'ParamTypes': {
-      'type': 'string',
-      'enum': [
-        'number', 'string', 'number[]', 'bool', 'shape', 'tensor', 'tensors',
-        'dtype', 'string[]', 'func', 'dtype[]', 'bool[]'
-      ]
-    },
-    'AttrParamMapper': {
-      'type': 'object',
-      'properties': {
-        'name': {'type': 'string'},
-        'type': {'$ref': '#/definitions/ParamTypes'},
-        'defaultValue': {
-          'anyOf': [
-            {'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}},
-            {'type': 'number'}, {'type': 'array', 'items': {'type': 'number'}},
-            {'type': 'boolean'}, {'type': 'array', 'items': {'type': 'boolean'}}
-          ]
-        },
-        'notSupported': {'type': 'boolean'},
-        'tfName': {'type': 'string'},
-        'tfDeprecatedName': {'type': 'string'}
-      },
-      'required': ['name', 'tfName', 'type'],
-      'additionalProperties': false
-    },
-    'OpExecutor': {'type': 'object', 'additionalProperties': false}
-  },
-  'items': {'$ref': '#/definitions/OpMapper'},
-  'type': 'array'
-};
diff --git a/tfjs-master/tfjs-converter/src/operations/operation_executor.ts b/tfjs-master/tfjs-converter/src/operations/operation_executor.ts
deleted file mode 100644
index 3a75d0343..000000000
--- a/tfjs-master/tfjs-converter/src/operations/operation_executor.ts
+++ /dev/null
@@ -1,124 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-
-import {NamedTensorsMap} from '../data/types';
-import {ExecutionContext} from '../executor/execution_context';
-import {ResourceManager} from '../executor/resource_manager';
-
-import {NodeValueImpl} from './custom_op/node_value_impl';
-import {getRegisteredOp} from './custom_op/register';
-import * as arithmetic from './executors/arithmetic_executor';
-import * as basicMath from './executors/basic_math_executor';
-import * as control from './executors/control_executor';
-import * as convolution from './executors/convolution_executor';
-import * as creation from './executors/creation_executor';
-import * as dynamic from './executors/dynamic_executor';
-import * as evaluation from './executors/evaluation_executor';
-import * as graph from './executors/graph_executor';
-import * as hashTable from './executors/hash_table_executor';
-import * as image from './executors/image_executor';
-import * as logical from './executors/logical_executor';
-import * as matrices from './executors/matrices_executor';
-import * as normalization from './executors/normalization_executor';
-import * as ragged from './executors/ragged_executor';
-import * as reduction from './executors/reduction_executor';
-import * as sliceJoin from './executors/slice_join_executor';
-import * as sparse from './executors/sparse_executor';
-import * as spectral from './executors/spectral_executor';
-import * as string from './executors/string_executor';
-import * as transformation from './executors/transformation_executor';
-import {Node} from './types';
-
-/**
- * Executes the op defined by the node object.
- * @param node
- * @param tensorMap contains tensors for executed nodes and weights
- * @param context contains tensors and information for running the current node.
- * @param resourceManager Optional. Contains global resources of the model.
- */
-export function executeOp(
-    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-    resourceManager?: ResourceManager, tidy = tfc.tidy): tfc.Tensor[]|
-    Promise<tfc.Tensor[]> {
-  const value =
-      ((node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) => {
-        switch (node.category) {
-          case 'arithmetic':
-            return tidy(() => arithmetic.executeOp(node, tensorMap, context));
-          case 'basic_math':
-            return tidy(() => basicMath.executeOp(node, tensorMap, context));
-          case 'control':
-            return control.executeOp(node, tensorMap, context);
-          case 'convolution':
-            return tidy(() => convolution.executeOp(node, tensorMap, context));
-          case 'creation':
-            return tidy(() => creation.executeOp(node, tensorMap, context));
-          case 'dynamic':
-            return dynamic.executeOp(node, tensorMap, context);
-          case 'evaluation':
-            return tidy(() => evaluation.executeOp(node, tensorMap, context));
-          case 'image':
-            return tidy(() => image.executeOp(node, tensorMap, context));
-          case 'graph':
-            return tidy(() => graph.executeOp(node, tensorMap, context));
-          case 'logical':
-            return tidy(() => logical.executeOp(node, tensorMap, context));
-          case 'matrices':
-            return tidy(() => matrices.executeOp(node, tensorMap, context));
-          case 'normalization':
-            return tidy(
-                () => normalization.executeOp(node, tensorMap, context));
-          case 'ragged':
-            return tidy(() => ragged.executeOp(node, tensorMap, context));
-          case 'reduction':
-            return tidy(() => reduction.executeOp(node, tensorMap, context));
-          case 'slice_join':
-            return tidy(() => sliceJoin.executeOp(node, tensorMap, context));
-          case 'sparse':
-            return tidy(() => sparse.executeOp(node, tensorMap, context));
-          case 'spectral':
-            return tidy(() => spectral.executeOp(node, tensorMap, context));
-          case 'string':
-            return tidy(() => string.executeOp(node, tensorMap, context));
-          case 'transformation':
-            return tidy(
-                () => transformation.executeOp(node, tensorMap, context));
-          case 'hash_table':
-            return hashTable.executeOp(
-                node, tensorMap, context, resourceManager);
-          case 'custom':
-            const opMapper = getRegisteredOp(node.op);
-            if (opMapper && opMapper.customExecutor) {
-              return opMapper.customExecutor(
-                  new NodeValueImpl(node, tensorMap, context));
-            } else {
-              throw TypeError(`Custom op ${node.op} is not registered.`);
-            }
-          default:
-            throw TypeError(
-                `Unknown op '${node.op}'. File an issue at ` +
-                `https://github.com/tensorflow/tfjs/issues so we can add it` +
-                `, or register a custom execution with tf.registerOp()`);
-        }
-      })(node, tensorMap, context);
-  if (tfc.util.isPromise(value)) {
-    return value.then((data) => [].concat(data));
-  }
-  return [].concat(value);
-}
diff --git a/tfjs-master/tfjs-converter/src/operations/operation_executor_test.ts b/tfjs-master/tfjs-converter/src/operations/operation_executor_test.ts
deleted file mode 100644
index e9979bea1..000000000
--- a/tfjs-master/tfjs-converter/src/operations/operation_executor_test.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {add, mul, scalar, Tensor, test_util} from '@tensorflow/tfjs-core';
-
-import {ExecutionContext} from '../executor/execution_context';
-import {ResourceManager} from '../executor/resource_manager';
-
-import {deregisterOp, registerOp} from './custom_op/register';
-import * as arithmetic from './executors/arithmetic_executor';
-import * as basic_math from './executors/basic_math_executor';
-import * as control from './executors/control_executor';
-import * as convolution from './executors/convolution_executor';
-import * as creation from './executors/creation_executor';
-import * as dynamic from './executors/dynamic_executor';
-import * as evaluation from './executors/evaluation_executor';
-import * as graph from './executors/graph_executor';
-import * as hash_table from './executors/hash_table_executor';
-import * as image from './executors/image_executor';
-import * as logical from './executors/logical_executor';
-import * as matrices from './executors/matrices_executor';
-import * as normalization from './executors/normalization_executor';
-import * as reduction from './executors/reduction_executor';
-import * as slice_join from './executors/slice_join_executor';
-import * as sparse from './executors/sparse_executor';
-import * as spectral from './executors/spectral_executor';
-import * as string from './executors/string_executor';
-import * as transformation from './executors/transformation_executor';
-import {executeOp} from './operation_executor';
-import {Node} from './types';
-
-describe('OperationExecutor', () => {
-  let node: Node;
-  const context = new ExecutionContext({}, {}, {});
-
-  beforeEach(() => {
-    node = {
-      name: 'test',
-      op: 'const',
-      category: 'graph',
-      inputNames: [],
-      inputs: [],
-      inputParams: {},
-      attrParams: {},
-      children: []
-    };
-  });
-
-  describe('executeOp', () => {
-    [arithmetic, basic_math, control, convolution, creation, dynamic,
-     evaluation, graph, image, logical, matrices, normalization, reduction,
-     slice_join, sparse, spectral, string, transformation]
-        .forEach(category => {
-          it('should call ' + category.CATEGORY + ' executor', () => {
-            spyOn(category, 'executeOp');
-            node.category = category.CATEGORY;
-            executeOp(node, {}, context);
-            expect(category.executeOp).toHaveBeenCalledWith(node, {}, context);
-          });
-        });
-    [arithmetic, basic_math, convolution, creation, evaluation, graph, image,
-     logical, matrices, normalization, reduction, slice_join, sparse, spectral,
-     string, transformation]
-        .forEach(category => {
-          it('should call tidy around executor', () => {
-            const tidySpy = jasmine.createSpy('tidy spy', tfc.tidy);
-
-            node.category = category.CATEGORY;
-            executeOp(node, {}, context, undefined, tidySpy as typeof tfc.tidy);
-            expect(tidySpy).toHaveBeenCalled();
-          });
-        });
-
-    it('hash_table executor should have been called.', () => {
-      const resourceManager = new ResourceManager();
-      spyOn(hash_table, 'executeOp');
-      node.category = hash_table.CATEGORY;
-      executeOp(node, {}, context, resourceManager);
-      expect(hash_table.executeOp)
-          .toHaveBeenCalledWith(node, {}, context, resourceManager);
-    });
-  });
-
-  describe('custom op executeOp', () => {
-    it('should throw exception if custom op is not registered', () => {
-      node.category = 'custom';
-      expect(() => executeOp(node, {}, context))
-          .toThrowError('Custom op const is not registered.');
-    });
-  });
-
-  describe('custom op executeOp', () => {
-    it('should call the registered custom op', async () => {
-      registerOp('const', () => [scalar(1)]);
-      registerOp('const2', () => [scalar(2)]);
-      node.category = 'custom';
-      const result = executeOp(node, {}, context) as Tensor[];
-      test_util.expectArraysClose(await result[0].data(), [1]);
-      deregisterOp('const');
-      deregisterOp('const2');
-    });
-
-    it('should handle custom op with inputs and attrs', async () => {
-      registerOp('const', (node) => {
-        const a = node.inputs[0];
-        const b = node.inputs[1];
-        const attrC = node.attrs['c'] as Tensor;
-        const attrD = node.attrs['d'] as number;
-        return [add(mul(attrC.dataSync()[0], a), mul(attrD, b))];
-      });
-
-      node.category = 'custom';
-      node.inputNames = ['a', 'b'];
-      node.rawAttrs = {c: {tensor: {}}, d: {i: 3}};
-      const result = executeOp(
-                         node, {a: [scalar(1)], b: [scalar(2)], c: [scalar(2)]},
-                         context) as Tensor[];
-      // result = 2 * 1 + 3 * 2
-      test_util.expectArraysClose(await result[0].data(), [8]);
-      deregisterOp('const');
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/operation_mapper.ts b/tfjs-master/tfjs-converter/src/operations/operation_mapper.ts
deleted file mode 100644
index a0e2e0787..000000000
--- a/tfjs-master/tfjs-converter/src/operations/operation_mapper.ts
+++ /dev/null
@@ -1,607 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, env} from '@tensorflow/tfjs-core';
-
-import * as tensorflow from '../data/compiled_api';
-
-import {getRegisteredOp} from './custom_op/register';
-import {getNodeNameAndIndex} from './executors/utils';
-import * as arithmetic from './op_list/arithmetic';
-import * as basicMath from './op_list/basic_math';
-import * as control from './op_list/control';
-import * as convolution from './op_list/convolution';
-import * as creation from './op_list/creation';
-import * as dynamic from './op_list/dynamic';
-import * as evaluation from './op_list/evaluation';
-import * as graph from './op_list/graph';
-import * as hashTable from './op_list/hash_table';
-import * as image from './op_list/image';
-import * as logical from './op_list/logical';
-import * as matrices from './op_list/matrices';
-import * as normalization from './op_list/normalization';
-import * as reduction from './op_list/reduction';
-import * as sliceJoin from './op_list/slice_join';
-import * as sparse from './op_list/sparse';
-import * as spectral from './op_list/spectral';
-import * as string from './op_list/string';
-import * as transformation from './op_list/transformation';
-import {Graph, InputParamValue, Node, OpMapper, ParamValue} from './types';
-
-export class OperationMapper {
-  private static _instance: OperationMapper;
-
-  private opMappers: {[key: string]: OpMapper};
-
-  // Singleton instance for the mapper
-  public static get Instance() {
-    return this._instance || (this._instance = new this());
-  }
-
-  // Loads the op mapping from the JSON file.
-  private constructor() {
-    const ops = [
-      arithmetic, basicMath, control, convolution, creation, dynamic,
-      evaluation, graph, hashTable, image, logical, matrices, normalization,
-      reduction, sliceJoin, sparse, spectral, string, transformation
-    ];
-    const mappersJson: OpMapper[] = [].concat(...ops.map(op => op.json));
-
-    this.opMappers = mappersJson.reduce<{[key: string]: OpMapper}>(
-        (map, mapper: OpMapper) => {
-          map[mapper.tfOpName] = mapper;
-          return map;
-        },
-        {});
-  }
-
-  // Converts the model inference graph from Tensorflow GraphDef to local
-  // representation for TensorFlow.js API
-  transformGraph(
-      graph: tensorflow.IGraphDef,
-      signature: tensorflow.ISignatureDef = {}): Graph {
-    const tfNodes = graph.node;
-    const placeholders: Node[] = [];
-    const weights: Node[] = [];
-    const initNodes: Node[] = [];
-    const nodes = tfNodes.reduce<{[key: string]: Node}>((map, node) => {
-      map[node.name] = this.mapNode(node);
-      if (node.op.startsWith('Placeholder')) {
-        placeholders.push(map[node.name]);
-      } else if (node.op === 'Const') {
-        weights.push(map[node.name]);
-      } else if (node.input == null || node.input.length === 0) {
-        initNodes.push(map[node.name]);
-      }
-      return map;
-    }, {});
-
-    let inputs: Node[] = [];
-    const outputs: Node[] = [];
-    let inputNodeNameToKey: {[key: string]: string} = {};
-    let outputNodeNameToKey: {[key: string]: string} = {};
-    if (signature != null) {
-      inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);
-      outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);
-    }
-    const allNodes = Object.keys(nodes);
-    allNodes.forEach(key => {
-      const node = nodes[key];
-      node.inputNames.forEach((name, index) => {
-        const [nodeName, , outputName] = getNodeNameAndIndex(name);
-        const inputNode = nodes[nodeName];
-        if (inputNode.outputs != null) {
-          const outputIndex = inputNode.outputs.indexOf(outputName);
-          if (outputIndex !== -1) {
-            const inputName = `${nodeName}:${outputIndex}`;
-            // update the input name to use the mapped output index directly.
-            node.inputNames[index] = inputName;
-          }
-        }
-        node.inputs.push(inputNode);
-        inputNode.children.push(node);
-      });
-    });
-
-    // if signature has not outputs set, add any node that does not have
-    // outputs.
-    if (Object.keys(outputNodeNameToKey).length === 0) {
-      allNodes.forEach(key => {
-        const node = nodes[key];
-        if (node.children.length === 0) {
-          outputs.push(node);
-        }
-      });
-    } else {
-      Object.keys(outputNodeNameToKey).forEach(name => {
-        const [nodeName, ] = getNodeNameAndIndex(name);
-        const node = nodes[nodeName];
-        if (node != null) {
-          node.signatureKey = outputNodeNameToKey[name];
-          outputs.push(node);
-        }
-      });
-    }
-
-    if (Object.keys(inputNodeNameToKey).length > 0) {
-      Object.keys(inputNodeNameToKey).forEach(name => {
-        const [nodeName, ] = getNodeNameAndIndex(name);
-        const node = nodes[nodeName];
-        if (node) {
-          node.signatureKey = inputNodeNameToKey[name];
-          inputs.push(node);
-        }
-      });
-    } else {
-      inputs = placeholders;
-    }
-
-    let functions = {};
-    if (graph.library != null && graph.library.function != null) {
-      functions = graph.library.function.reduce((functions, func) => {
-        functions[func.signature.name] = this.mapFunction(func);
-        return functions;
-      }, {} as {[key: string]: Graph});
-    }
-
-    const result: Graph =
-        {nodes, inputs, outputs, weights, placeholders, signature, functions};
-
-    if (initNodes.length > 0) {
-      result.initNodes = initNodes;
-    }
-
-    return result;
-  }
-
-  private mapSignatureEntries(entries: {[k: string]: tensorflow.ITensorInfo}) {
-    return Object.keys(entries || {})
-        .reduce<{[key: string]: string}>((prev, curr) => {
-          prev[entries[curr].name] = curr;
-          return prev;
-        }, {});
-  }
-
-  private mapNode(node: tensorflow.INodeDef): Node {
-    // Unsupported ops will cause an error at run-time (not parse time), since
-    // they may not be used by the actual execution subgraph.
-    const mapper =
-        getRegisteredOp(node.op) || this.opMappers[node.op] || {} as OpMapper;
-    if (node.attr == null) {
-      node.attr = {};
-    }
-
-    const newNode: Node = {
-      name: node.name,
-      op: node.op,
-      category: mapper.category,
-      inputNames:
-          (node.input ||
-           []).map(input => input.startsWith('^') ? input.slice(1) : input),
-      inputs: [],
-      children: [],
-      inputParams: {},
-      attrParams: {},
-      rawAttrs: node.attr,
-      outputs: mapper.outputs
-    };
-
-    if (mapper.inputs != null) {
-      newNode.inputParams =
-          mapper.inputs.reduce<{[key: string]: InputParamValue}>(
-              (map, param) => {
-                map[param.name] = {
-                  type: param.type,
-                  inputIndexStart: param.start,
-                  inputIndexEnd: param.end
-                };
-                return map;
-              },
-              {});
-    }
-    if (mapper.attrs != null) {
-      newNode.attrParams =
-          mapper.attrs.reduce<{[key: string]: ParamValue}>((map, param) => {
-            const type = param.type;
-            let value = undefined;
-            switch (param.type) {
-              case 'string':
-                value = getStringParam(
-                    node.attr, param.tfName, param.defaultValue as string);
-
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getStringParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as string);
-                }
-                break;
-              case 'string[]':
-                value = getStringArrayParam(
-                    node.attr, param.tfName, param.defaultValue as string[]);
-
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getStringArrayParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as string[]);
-                }
-                break;
-              case 'number':
-                value = getNumberParam(
-                    node.attr, param.tfName,
-                    (param.defaultValue || 0) as number);
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getNumberParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as number);
-                }
-                break;
-              case 'number[]':
-                value = getNumericArrayParam(
-                    node.attr, param.tfName, param.defaultValue as number[]);
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getNumericArrayParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as number[]);
-                }
-                break;
-              case 'bool':
-                value = getBoolParam(
-                    node.attr, param.tfName, param.defaultValue as boolean);
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getBoolParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as boolean);
-                }
-                break;
-              case 'bool[]':
-                value = getBoolArrayParam(
-                    node.attr, param.tfName, param.defaultValue as boolean[]);
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getBoolArrayParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as boolean[]);
-                }
-                break;
-              case 'shape':
-                value = getTensorShapeParam(
-                    node.attr, param.tfName, param.defaultValue as number[]);
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getTensorShapeParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as number[]);
-                }
-                break;
-              case 'shape[]':
-                value = getTensorShapeArrayParam(
-                    node.attr, param.tfName, param.defaultValue as number[][]);
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getTensorShapeArrayParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as number[][]);
-                }
-                break;
-              case 'dtype':
-                value = getDtypeParam(
-                    node.attr, param.tfName, param.defaultValue as DataType);
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getDtypeParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as DataType);
-                }
-                break;
-              case 'dtype[]':
-                value = getDtypeArrayParam(
-                    node.attr, param.tfName, param.defaultValue as DataType[]);
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getDtypeArrayParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as DataType[]);
-                }
-                break;
-              case 'func':
-                value = getFuncParam(
-                    node.attr, param.tfName, param.defaultValue as string);
-                if (value === undefined && !!param.tfDeprecatedName) {
-                  value = getFuncParam(
-                      node.attr, param.tfDeprecatedName,
-                      param.defaultValue as string);
-                }
-                break;
-              case 'tensor':
-              case 'tensors':
-                break;
-              default:
-                throw new Error(
-                    `Unsupported param type: ${param.type} for op: ${node.op}`);
-            }
-            map[param.name] = {value, type};
-            return map;
-          }, {});
-    }
-    return newNode;
-  }
-
-  // map the TFunctionDef to TFJS graph object
-  private mapFunction(functionDef: tensorflow.IFunctionDef): Graph {
-    const tfNodes = functionDef.nodeDef;
-    const placeholders: Node[] = [];
-    const weights: Node[] = [];
-    let nodes: {[key: string]: Node} = {};
-    if (tfNodes != null) {
-      nodes = tfNodes.reduce<{[key: string]: Node}>((map, node) => {
-        map[node.name] = this.mapNode(node);
-        if (node.op === 'Const') {
-          weights.push(map[node.name]);
-        }
-        return map;
-      }, {});
-    }
-    const inputs: Node[] = [];
-    const outputs: Node[] = [];
-
-    functionDef.signature.inputArg.forEach(arg => {
-      const [nodeName, ] = getNodeNameAndIndex(arg.name);
-      const node: Node = {
-        name: nodeName,
-        op: 'Placeholder',
-        inputs: [],
-        inputNames: [],
-        category: 'graph',
-        inputParams: {},
-        attrParams: {dtype: {value: parseDtypeParam(arg.type), type: 'dtype'}},
-        children: []
-      };
-      node.signatureKey = arg.name;
-      inputs.push(node);
-      nodes[nodeName] = node;
-    });
-
-    const allNodes = Object.keys(nodes);
-    allNodes.forEach(key => {
-      const node = nodes[key];
-      node.inputNames.forEach((name, index) => {
-        const [nodeName, , outputName] = getNodeNameAndIndex(name);
-        const inputNode = nodes[nodeName];
-        if (inputNode.outputs != null) {
-          const outputIndex = inputNode.outputs.indexOf(outputName);
-          if (outputIndex !== -1) {
-            const inputName = `${nodeName}:${outputIndex}`;
-            // update the input name to use the mapped output index directly.
-            node.inputNames[index] = inputName;
-          }
-        }
-        node.inputs.push(inputNode);
-        inputNode.children.push(node);
-      });
-    });
-
-    const returnNodeMap = functionDef.ret;
-
-    functionDef.signature.outputArg.forEach(output => {
-      const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);
-      const node = nodes[nodeName];
-      if (node != null) {
-        node.defaultOutput = index;
-        outputs.push(node);
-      }
-    });
-
-    const signature = this.mapArgsToSignature(functionDef);
-    return {nodes, inputs, outputs, weights, placeholders, signature};
-  }
-
-  private mapArgsToSignature(functionDef: tensorflow.IFunctionDef):
-      tensorflow.ISignatureDef {
-    return {
-      methodName: functionDef.signature.name,
-      inputs: functionDef.signature.inputArg.reduce(
-          (map, arg) => {
-            map[arg.name] = this.mapArgToTensorInfo(arg);
-            return map;
-          },
-          {} as {[key: string]: tensorflow.ITensorInfo}),
-      outputs: functionDef.signature.outputArg.reduce(
-          (map, arg) => {
-            map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);
-            return map;
-          },
-          {} as {[key: string]: tensorflow.ITensorInfo}),
-    };
-  }
-
-  private mapArgToTensorInfo(
-      arg: tensorflow.OpDef.IArgDef,
-      nameMap?: {[key: string]: string}): tensorflow.ITensorInfo {
-    let name = arg.name;
-    if (nameMap != null) {
-      name = nameMap[name];
-    }
-    return {name, dtype: arg.type};
-  }
-}
-
-export function decodeBase64(text: string): string {
-  const global = env().global;
-  if (typeof global.atob !== 'undefined') {
-    return global.atob(text);
-  } else if (typeof Buffer !== 'undefined') {
-    return new Buffer(text, 'base64').toString();
-  } else {
-    throw new Error(
-        'Unable to decode base64 in this environment. ' +
-        'Missing built-in atob() or Buffer()');
-  }
-}
-
-export function parseStringParam(s: []|string, keepCase: boolean): string {
-  const value =
-      Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);
-  return keepCase ? value : value.toLowerCase();
-}
-
-export function getStringParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string, def: string,
-    keepCase = false): string {
-  const param = attrs[name];
-  if (param != null) {
-    return parseStringParam(param.s, keepCase);
-  }
-  return def;
-}
-
-export function getBoolParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,
-    def: boolean): boolean {
-  const param = attrs[name];
-  return param ? param.b : def;
-}
-
-export function getNumberParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,
-    def: number): number {
-  const param = attrs[name] || {};
-  const value =
-      param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);
-  return (typeof value === 'number') ? value : parseInt(value, 10);
-}
-
-export function parseDtypeParam(value: string|tensorflow.DataType): DataType {
-  if (typeof (value) === 'string') {
-    // tslint:disable-next-line:no-any
-    value = tensorflow.DataType[value as any];
-  }
-  switch (value) {
-    case tensorflow.DataType.DT_FLOAT:
-    case tensorflow.DataType.DT_HALF:
-      return 'float32';
-    case tensorflow.DataType.DT_INT32:
-    case tensorflow.DataType.DT_INT64:
-    case tensorflow.DataType.DT_INT8:
-    case tensorflow.DataType.DT_UINT8:
-      return 'int32';
-    case tensorflow.DataType.DT_BOOL:
-      return 'bool';
-    case tensorflow.DataType.DT_DOUBLE:
-      return 'float32';
-    case tensorflow.DataType.DT_STRING:
-      return 'string';
-    default:
-      // Unknown dtype error will happen at runtime (instead of parse time),
-      // since these nodes might not be used by the actual subgraph execution.
-      return null;
-  }
-}
-
-export function getFuncParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,
-    def: string): string {
-  const param = attrs[name];
-  if (param && param.func) {
-    return param.func.name;
-  }
-  return def;
-}
-
-export function getDtypeParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,
-    def: DataType): DataType {
-  const param = attrs[name];
-  if (param && param.type) {
-    return parseDtypeParam(param.type);
-  }
-  return def;
-}
-
-export function getDtypeArrayParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,
-    def: DataType[]): DataType[] {
-  const param = attrs[name];
-  if (param && param.list && param.list.type) {
-    return param.list.type.map(v => parseDtypeParam(v));
-  }
-  return def;
-}
-
-export function parseTensorShapeParam(shape: tensorflow.ITensorShape): number[]|
-    undefined {
-  if (shape.unknownRank) {
-    return undefined;
-  }
-  if (shape.dim != null) {
-    return shape.dim.map(
-        dim =>
-            (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));
-  }
-  return [];
-}
-
-export function getTensorShapeParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,
-    def?: number[]): number[]|undefined {
-  const param = attrs[name];
-  if (param && param.shape) {
-    return parseTensorShapeParam(param.shape);
-  }
-  return def;
-}
-
-export function getNumericArrayParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,
-    def: number[]): number[] {
-  const param = attrs[name];
-  if (param) {
-    return ((param.list.f && param.list.f.length ? param.list.f :
-                                                   param.list.i) ||
-            [])
-        .map(v => (typeof v === 'number') ? v : parseInt(v, 10));
-  }
-  return def;
-}
-
-export function getStringArrayParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string, def: string[],
-    keepCase = false): string[] {
-  const param = attrs[name];
-  if (param && param.list && param.list.s) {
-    return param.list.s.map((v) => {
-      return parseStringParam(v, keepCase);
-    });
-  }
-  return def;
-}
-
-export function getTensorShapeArrayParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,
-    def: number[][]): number[][] {
-  const param = attrs[name];
-  if (param && param.list && param.list.shape) {
-    return param.list.shape.map((v) => {
-      return parseTensorShapeParam(v);
-    });
-  }
-  return def;
-}
-
-export function getBoolArrayParam(
-    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,
-    def: boolean[]): boolean[] {
-  const param = attrs[name];
-  if (param && param.list && param.list.b) {
-    return param.list.b;
-  }
-  return def;
-}
diff --git a/tfjs-master/tfjs-converter/src/operations/operation_mapper_test.ts b/tfjs-master/tfjs-converter/src/operations/operation_mapper_test.ts
deleted file mode 100644
index 522d95382..000000000
--- a/tfjs-master/tfjs-converter/src/operations/operation_mapper_test.ts
+++ /dev/null
@@ -1,557 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tensorflow from '../data/compiled_api';
-
-import * as arithmetic from './op_list/arithmetic';
-import * as basicMath from './op_list/basic_math';
-import * as control from './op_list/control';
-import * as convolution from './op_list/convolution';
-import * as creation from './op_list/creation';
-import * as dynamic from './op_list/dynamic';
-import * as evaluation from './op_list/evaluation';
-import * as graph from './op_list/graph';
-import * as hashTable from './op_list/hash_table';
-import * as image from './op_list/image';
-import * as logical from './op_list/logical';
-import * as matrices from './op_list/matrices';
-import * as normalization from './op_list/normalization';
-import * as reduction from './op_list/reduction';
-import * as sliceJoin from './op_list/slice_join';
-import * as sparse from './op_list/sparse';
-import * as spectral from './op_list/spectral';
-import * as string from './op_list/string';
-import * as transformation from './op_list/transformation';
-import {OperationMapper} from './operation_mapper';
-import {Graph} from './types';
-
-const ops = [
-  arithmetic, basicMath, control, convolution, creation, dynamic, evaluation,
-  graph, hashTable, image, logical, matrices, normalization, reduction,
-  sliceJoin, sparse, spectral, string, transformation
-];
-const mapper: OperationMapper = OperationMapper.Instance;
-let convertedGraph: Graph;
-
-const SIMPLE_MODEL: tensorflow.IGraphDef = {
-  node: [
-    {
-      name: 'image_placeholder',
-      op: 'Placeholder',
-      attr: {
-        dtype: {
-          type: tensorflow.DataType.DT_FLOAT,
-        },
-        shape: {shape: {dim: [{size: '3'}, {size: 3}, {size: '3'}, {size: 1}]}}
-      }
-    },
-    {
-      name: 'Const',
-      op: 'Const',
-      attr: {
-        dtype: {type: tensorflow.DataType.DT_INT32},
-        value: {
-          tensor: {
-            dtype: tensorflow.DataType.DT_INT32,
-            tensorShape: {dim: [{size: 3}, {size: 3}, {size: 1}, {size: 1}]},
-            intVal: [0, 0, 0, 0, 1, 0, 0, 0, 0]
-          }
-        }
-      }
-    },
-    {
-      name: 'Shape',
-      op: 'Const',
-      attr: {
-        dtype: {type: tensorflow.DataType.DT_INT32},
-        value: {
-          tensor: {
-            dtype: tensorflow.DataType.DT_INT32,
-            tensorShape: {dim: [{size: 3}, {size: 1}, {size: 1}, {size: 1}]},
-            intVal: [1, 1, 1]
-          }
-        }
-      }
-    },
-    {
-      name: 'Value',
-      op: 'Const',
-      attr: {dtype: {type: tensorflow.DataType.DT_INT32}, value: {i: 1}}
-    },
-    {name: 'Fill', op: 'Fill', input: ['Shape', 'Value'], attr: {}},
-    {
-      name: 'Conv2D',
-      op: 'Conv2D',
-      input: ['image_placeholder', 'Const'],
-      attr: {
-        T: {type: tensorflow.DataType.DT_FLOAT},
-        dataFormat: {s: 'TkhXQw=='},
-        padding: {s: 'U0FNRQ=='},
-        strides: {list: {f: [], i: [1, 2, 2, 1]}},
-        useCudnnOnGpu: {b: true}
-      }
-    },
-    {
-      name: 'BiasAdd',
-      op: 'BiasAdd',
-      input: ['Conv2D', 'Shape'],
-      attr:
-          {T: {type: tensorflow.DataType.DT_FLOAT}, dataFormat: {s: 'TkhXQw=='}}
-    },
-    {
-      name: 'Cast',
-      op: 'Cast',
-      input: ['BiasAdd'],
-      attr: {DstT: {type: tensorflow.DataType.DT_INT64}}
-    },
-    {
-      name: 'Squeeze',
-      op: 'Squeeze',
-      input: ['Cast'],
-      attr: {squeeze_dims: {list: {i: ['1', '2']}}}
-    },
-    {
-      name: 'Squeeze2',
-      op: 'Squeeze',
-      input: ['BiasAdd'],
-      attr: {squeeze_dims: {list: {}}}
-    },
-    {
-      name: 'Split',
-      op: 'Split',
-      input: ['image_placeholder'],
-      attr: {num_split: {i: 3} as tensorflow.IAttrValue}
-    },
-    {name: 'LogicalNot', op: 'LogicalNot', input: ['image_placeholder']},
-    {
-      name: 'FusedBatchNorm',
-      op: 'FusedBatchNorm',
-      input: ['image_placeholder'],
-      attr: {epsilon: {f: 0.0001} as tensorflow.IAttrValue}
-    },
-    {
-      name: 'Cast2',
-      op: 'Cast',
-      input: ['BiasAdd'],
-      attr: {DstT: {type: tensorflow.DataType.DT_UINT8}}
-    },
-    {
-      name: 'Cast3',
-      op: 'Cast',
-      input: ['BiasAdd'],
-      attr: {DstT: {type: tensorflow.DataType.DT_HALF}}
-    },
-  ],
-  library: {
-    function: [
-      {
-        signature: {
-          name: '__inference_while_cond_10_49_frozen',
-          inputArg: [
-            {name: 'while_loop_counter', type: tensorflow.DataType.DT_INT32}, {
-              name: 'while_maximum_iterations',
-              type: tensorflow.DataType.DT_INT32
-            },
-            {name: 'placeholder', type: tensorflow.DataType.DT_INT32},
-            {name: 'less_y', type: tensorflow.DataType.DT_INT32}, {
-              name: 'while_cond_10___redundant_placeholder0',
-              type: tensorflow.DataType.DT_INT32
-            }
-          ],
-          outputArg: [{name: 'identity', type: tensorflow.DataType.DT_BOOL}]
-        },
-        nodeDef: [{
-          name: 'Less',
-          op: 'Less',
-          input: ['placeholder', 'less_y'],
-          attr: {T: {type: tensorflow.DataType.DT_INT32}}
-        }],
-        ret: {identity: 'Less:z:0'}
-      },
-      {
-        signature: {
-          name: '__inference_while_body_11_40_frozen',
-          inputArg: [
-            {name: 'while_loop_counter', type: tensorflow.DataType.DT_INT32}, {
-              name: 'while_maximum_iterations',
-              type: tensorflow.DataType.DT_INT32
-            },
-            {name: 'placeholder', type: tensorflow.DataType.DT_INT32},
-            {name: 'y_0', type: tensorflow.DataType.DT_INT32},
-            {name: 'add_1_z_0', type: tensorflow.DataType.DT_INT32}
-          ],
-          outputArg: [
-            {name: 'identity', type: tensorflow.DataType.DT_INT32},
-            {name: 'identity_1', type: tensorflow.DataType.DT_INT32},
-            {name: 'identity_2', type: tensorflow.DataType.DT_INT32},
-            {name: 'y', type: tensorflow.DataType.DT_INT32},
-            {name: 'add_1_z', type: tensorflow.DataType.DT_INT32}
-          ]
-        },
-        nodeDef: [
-          {
-            name: 'add_2/y',
-            op: 'Const',
-            attr: {
-              dtype: {type: tensorflow.DataType.DT_INT32},
-              value: {
-                tensor: {
-                  dtype: tensorflow.DataType.DT_INT32,
-                  tensorShape: {},
-                  intVal: [1]
-                }
-              }
-            }
-          },
-          {
-            name: 'add',
-            op: 'AddV2',
-            input: ['placeholder', 'y_0'],
-            attr: {T: {type: tensorflow.DataType.DT_INT32}}
-          },
-          {
-            name: 'add_2',
-            op: 'AddV2',
-            input: ['add_2/y:output:0', 'while_loop_counter'],
-            attr: {T: {type: tensorflow.DataType.DT_INT32}}
-          },
-          {
-            name: 'add_1',
-            op: 'AddV2',
-            input: ['add:z:0', 'add_1_z_0'],
-            attr: {T: {type: tensorflow.DataType.DT_INT32}}
-          }
-        ],
-        ret: {
-          identity_1: 'while_maximum_iterations',
-          identity_2: 'add_1:z:0',
-          y: 'y_0',
-          identity: 'add_2:z:0',
-          add_1_z: 'add_1_z_0'
-        }
-      }
-    ]
-  },
-  versions: {producer: 1.0}
-};
-
-const SIGNATURE: tensorflow.ISignatureDef = {
-  inputs: {
-    image: {
-      name: 'image_placeholder',
-      dtype: tensorflow.DataType.DT_INT32,
-      tensorShape: {
-
-      }
-    }
-  },
-  outputs: {
-    squeeze:
-        {name: 'Squeeze', dtype: tensorflow.DataType.DT_FLOAT, tensorShape: {}}
-  }
-};
-
-describe('completeness check', () => {
-  it('should convert all op categories', () => {
-    ops.forEach(op => {
-      op.json.forEach(tfOp => {
-        const graph = {
-          node: [{name: tfOp.tfOpName, op: tfOp.tfOpName, attr: {}}]
-        };
-        convertedGraph = mapper.transformGraph(graph);
-        expect(Object.keys(convertedGraph.nodes)).toEqual([tfOp.tfOpName]);
-        const node = convertedGraph.nodes[tfOp.tfOpName];
-        expect(node.op).toEqual(tfOp.tfOpName);
-        expect(node.category).withContext(`Op: ${node.op}, category`)
-          .toEqual(tfOp.category);
-      });
-    });
-  });
-  it('should convert op with outputs field', () => {
-    const name = 'string split';
-    const op = 'StringSplit';
-    const graph = {node: [{name, op: 'StringSplit', attr: {}}]};
-    convertedGraph = mapper.transformGraph(graph);
-    expect(Object.keys(convertedGraph.nodes)).toEqual([name]);
-    expect(convertedGraph.nodes[name].op).toEqual(op);
-    expect(convertedGraph.nodes[name].outputs).toEqual([
-      'indices', 'values', 'shape'
-    ]);
-  });
-});
-describe('operationMapper without signature', () => {
-  beforeEach(() => {
-    convertedGraph = mapper.transformGraph(SIMPLE_MODEL);
-  });
-  afterEach(() => {});
-
-  describe('transform graph', () => {
-    describe('graph level', () => {
-      it('should find the graph input nodes', () => {
-        expect(convertedGraph.inputs.map(node => node.name)).toEqual([
-          'image_placeholder'
-        ]);
-      });
-
-      it('should find the graph output nodes', () => {
-        expect(convertedGraph.outputs.map(node => node.name)).toEqual([
-          'Fill', 'Squeeze', 'Squeeze2', 'Split', 'LogicalNot',
-          'FusedBatchNorm', 'Cast2', 'Cast3'
-        ]);
-      });
-
-      it('should find the graph weight nodes', () => {
-        expect(convertedGraph.weights.map(node => node.name)).toEqual([
-          'Const', 'Shape', 'Value'
-        ]);
-      });
-
-      it('should convert nodes', () => {
-        expect(Object.keys(convertedGraph.nodes)).toEqual([
-          'image_placeholder', 'Const', 'Shape', 'Value', 'Fill', 'Conv2D',
-          'BiasAdd', 'Cast', 'Squeeze', 'Squeeze2', 'Split', 'LogicalNot',
-          'FusedBatchNorm', 'Cast2', 'Cast3'
-        ]);
-      });
-    });
-
-    describe('function level', () => {
-      it('should convert the functions', () => {
-        expect(Object.keys(convertedGraph.functions)).toEqual([
-          '__inference_while_cond_10_49_frozen',
-          '__inference_while_body_11_40_frozen'
-        ]);
-      });
-      it('should find the graph input nodes', () => {
-        expect(convertedGraph.functions['__inference_while_cond_10_49_frozen']
-                   .inputs.map(node => node.name))
-            .toEqual([
-              'while_loop_counter', 'while_maximum_iterations', 'placeholder',
-              'less_y', 'while_cond_10___redundant_placeholder0'
-            ]);
-      });
-
-      it('should find the graph output nodes', () => {
-        expect(convertedGraph.functions['__inference_while_cond_10_49_frozen']
-                   .outputs.map(node => node.name))
-            .toEqual(['Less']);
-      });
-
-      it('should find the graph weight nodes', () => {
-        expect(convertedGraph.functions['__inference_while_cond_10_49_frozen']
-                   .weights.map(node => node.name))
-            .toEqual([]);
-      });
-
-      it('should convert nodes', () => {
-        expect(Object.keys(convertedGraph
-                               .functions['__inference_while_cond_10_49_frozen']
-                               .nodes))
-            .toEqual([
-              'Less', 'while_loop_counter', 'while_maximum_iterations',
-              'placeholder', 'less_y', 'while_cond_10___redundant_placeholder0'
-            ]);
-      });
-      it('should convert signature', () => {
-        expect(convertedGraph.functions['__inference_while_cond_10_49_frozen']
-                   .signature)
-            .toEqual({
-              methodName: '__inference_while_cond_10_49_frozen',
-              inputs: {
-                while_loop_counter: {
-                  name: 'while_loop_counter',
-                  dtype: tensorflow.DataType.DT_INT32
-                },
-                while_maximum_iterations: {
-                  name: 'while_maximum_iterations',
-                  dtype: tensorflow.DataType.DT_INT32
-                },
-                placeholder:
-                    {name: 'placeholder', dtype: tensorflow.DataType.DT_INT32},
-                less_y: {name: 'less_y', dtype: tensorflow.DataType.DT_INT32},
-                while_cond_10___redundant_placeholder0: {
-                  name: 'while_cond_10___redundant_placeholder0',
-                  dtype: tensorflow.DataType.DT_INT32
-                }
-              },
-              outputs: {
-                identity: {name: 'Less:z:0', dtype: tensorflow.DataType.DT_BOOL}
-              }
-            });
-      });
-    });
-    describe('node level', () => {
-      it('should find the input nodes', () => {
-        expect(convertedGraph.nodes['Fill'].inputs.map(node => node.name))
-            .toEqual(['Shape', 'Value']);
-      });
-      it('should find the children nodes', () => {
-        expect(convertedGraph.nodes['image_placeholder'].children.map(
-                   node => node.name))
-            .toEqual(['Conv2D', 'Split', 'LogicalNot', 'FusedBatchNorm']);
-      });
-
-      it('should map the input params', () => {
-        expect(
-            convertedGraph.nodes['Fill'].inputParams['shape'].inputIndexStart)
-            .toEqual(0);
-        expect(
-            convertedGraph.nodes['Fill'].inputParams['value'].inputIndexStart)
-            .toEqual(1);
-      });
-
-      it('should map the attribute params', () => {
-        expect(convertedGraph.nodes['Conv2D'].attrParams['strides'].value)
-            .toEqual([1, 2, 2, 1]);
-        expect(convertedGraph.nodes['Conv2D'].attrParams['pad'].value)
-            .toEqual('same');
-        expect(convertedGraph.nodes['Conv2D'].attrParams['useCudnnOnGpu'].value)
-            .toEqual(true);
-        expect(
-            convertedGraph.nodes['Split'].attrParams['numOrSizeSplits'].value)
-            .toEqual(3);
-        expect(
-            convertedGraph.nodes['FusedBatchNorm'].attrParams['epsilon'].value)
-            .toEqual(0.0001);
-        expect(convertedGraph.nodes['Squeeze2'].attrParams['axis'].value)
-            .toEqual([]);
-      });
-
-      it('should map the placeholder attribute params', () => {
-        expect(
-            convertedGraph.nodes['image_placeholder'].attrParams['shape'].value)
-            .toEqual([3, 3, 3, 1]);
-        expect(
-            convertedGraph.nodes['image_placeholder'].attrParams['dtype'].value)
-            .toEqual('float32');
-      });
-      it('should map params with deprecated name', () => {
-        expect(convertedGraph.nodes['Squeeze'].attrParams['axis'].value)
-            .toEqual([1, 2]);
-      });
-      it('should map params with int64 dtype', () => {
-        expect(convertedGraph.nodes['Cast'].attrParams['dtype'].value)
-            .toEqual('int32');
-      });
-    });
-  });
-});
-describe('operationMapper with signature', () => {
-  beforeEach(() => {
-    convertedGraph = mapper.transformGraph(SIMPLE_MODEL, SIGNATURE);
-  });
-  afterEach(() => {});
-
-  describe('transform graph', () => {
-    describe('graph level', () => {
-      it('should find the graph input nodes', () => {
-        expect(convertedGraph.inputs.map(node => node.name)).toEqual([
-          'image_placeholder'
-        ]);
-        expect(convertedGraph.inputs.map(node => node.signatureKey)).toEqual([
-          'image'
-        ]);
-      });
-
-      it('should find the graph output nodes', () => {
-        expect(convertedGraph.outputs.map(node => node.name)).toEqual([
-          'Squeeze'
-        ]);
-        expect(convertedGraph.outputs.map(node => node.signatureKey)).toEqual([
-          'squeeze'
-        ]);
-      });
-
-      it('should find the graph weight nodes', () => {
-        expect(convertedGraph.weights.map(node => node.name)).toEqual([
-          'Const', 'Shape', 'Value'
-        ]);
-      });
-
-      it('should convert nodes', () => {
-        expect(Object.keys(convertedGraph.nodes)).toEqual([
-          'image_placeholder', 'Const', 'Shape', 'Value', 'Fill', 'Conv2D',
-          'BiasAdd', 'Cast', 'Squeeze', 'Squeeze2', 'Split', 'LogicalNot',
-          'FusedBatchNorm', 'Cast2', 'Cast3'
-        ]);
-      });
-    });
-
-    describe('node level', () => {
-      it('should find the input nodes', () => {
-        expect(convertedGraph.nodes['Fill'].inputs.map(node => node.name))
-            .toEqual(['Shape', 'Value']);
-      });
-      it('should find the children nodes', () => {
-        expect(convertedGraph.nodes['image_placeholder'].children.map(
-                   node => node.name))
-            .toEqual(['Conv2D', 'Split', 'LogicalNot', 'FusedBatchNorm']);
-      });
-
-      it('should map the input params', () => {
-        expect(
-            convertedGraph.nodes['Fill'].inputParams['shape'].inputIndexStart)
-            .toEqual(0);
-        expect(
-            convertedGraph.nodes['Fill'].inputParams['value'].inputIndexStart)
-            .toEqual(1);
-      });
-
-      it('should map the attribute params', () => {
-        expect(convertedGraph.nodes['Conv2D'].attrParams['strides'].value)
-            .toEqual([1, 2, 2, 1]);
-        expect(convertedGraph.nodes['Conv2D'].attrParams['pad'].value)
-            .toEqual('same');
-        expect(convertedGraph.nodes['Conv2D'].attrParams['useCudnnOnGpu'].value)
-            .toEqual(true);
-        expect(
-            convertedGraph.nodes['Split'].attrParams['numOrSizeSplits'].value)
-            .toEqual(3);
-        expect(
-            convertedGraph.nodes['FusedBatchNorm'].attrParams['epsilon'].value)
-            .toEqual(0.0001);
-        expect(convertedGraph.nodes['Squeeze2'].attrParams['axis'].value)
-            .toEqual([]);
-      });
-
-      it('should map the placeholder attribute params', () => {
-        expect(
-            convertedGraph.nodes['image_placeholder'].attrParams['shape'].value)
-            .toEqual([3, 3, 3, 1]);
-        expect(
-            convertedGraph.nodes['image_placeholder'].attrParams['dtype'].value)
-            .toEqual('float32');
-      });
-      it('should map params with deprecated name', () => {
-        expect(convertedGraph.nodes['Squeeze'].attrParams['axis'].value)
-            .toEqual([1, 2]);
-      });
-      it('should map params with int64 dtype', () => {
-        expect(convertedGraph.nodes['Cast'].attrParams['dtype'].value)
-            .toEqual('int32');
-      });
-      it('should map params with uint8 dtype', () => {
-        expect(convertedGraph.nodes['Cast2'].attrParams['dtype'].value)
-            .toEqual('int32');
-      });
-      it('should map params with half dtype', () => {
-        expect(convertedGraph.nodes['Cast3'].attrParams['dtype'].value)
-            .toEqual('float32');
-      });
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-converter/src/operations/types.ts b/tfjs-master/tfjs-converter/src/operations/types.ts
deleted file mode 100644
index 4510edccc..000000000
--- a/tfjs-master/tfjs-converter/src/operations/types.ts
+++ /dev/null
@@ -1,143 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line:no-imports-from-dist
-import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
-
-import * as tensorflow from '../data/compiled_api';
-import {NamedTensorsMap} from '../data/types';
-import {ExecutionContext} from '../executor/execution_context';
-import {ResourceManager} from '../executor/resource_manager';
-
-export type ParamType = 'number'|'string'|'string[]'|'number[]'|'bool'|'bool[]'|
-    'shape'|'shape[]'|'tensor'|'tensors'|'dtype'|'dtype[]'|'func';
-export type Category = 'arithmetic'|'basic_math'|'control'|'convolution'|
-    'creation'|'custom'|'dynamic'|'evaluation'|'graph'|'hash_table'|'image'|
-    'logical'|'matrices'|'normalization'|'ragged'|'reduction'|'slice_join'|
-    'sparse'|'spectral'|'string'|'transformation';
-
-// For mapping input or attributes of NodeDef into TensorFlow.js op param.
-export declare interface ParamMapper {
-  // tensorflow.js name for the field, it should be in camelcase format.
-  name: string;
-  type: ParamType;
-  defaultValue?: ValueType;
-  notSupported?: boolean;
-}
-
-// For mapping the input of TensorFlow NodeDef into TensorFlow.js Op param.
-export declare interface InputParamMapper extends ParamMapper {
-  // The first number is the starting index of the param, the second number is
-  // the length of the param. If the length value is positive number, it
-  // represents the true length of the param. Otherwise, it represents a
-  // variable length, the value is the index go backward from the end of the
-  // array.
-  // For example `[0, 5]`: this param is the array of input tensors starting at
-  // index 0 and with the length of 5.
-  // For example `[1, -1]`: this param is the array of input tensors starting at
-  // index 1 and with the `inputs.length - 1`.
-  // Zero-based index at where in the input array this param starts.
-  // A negative index can be used, indicating an offset from the end of the
-  // sequence. slice(-2) extracts the last two elements in the sequence.
-  start: number;
-  // Zero-based index before where in the input array the param ends. The
-  // mapping is up to but not including end. For example, start = 1, end = 4
-  // includes the second element through the fourth element (elements indexed 1,
-  // 2, and 3). A negative index can be used, indicating an offset from the end
-  // of the sequence. start = 2, end = -1 includes the third element through the
-  // second-to-last element in the sequence. If end is omitted, end is set to
-  // start + 1, the mapping only include the single element at start index. If
-  // end is set to 0, the mapping is through the end of the input array
-  // (arr.length). If end is greater than the length of the inputs, mapping
-  // inncludes through to the end of the sequence (arr.length).
-  end?: number;
-}
-
-// For mapping the attributes of TensorFlow NodeDef into TensorFlow.js op param.
-export declare interface AttrParamMapper extends ParamMapper {
-  // TensorFlow attribute name, this should be set if the tensorflow attribute
-  // name is different form the tensorflow.js name.
-  tfName?: string;
-  // TensorFlow deprecated attribute name, this is used to support old models.
-  tfDeprecatedName?: string;
-}
-
-export interface InternalOpExecutor {
-  (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-   ops?: typeof tfOps): Tensor|Tensor[];
-}
-
-export interface InternalOpAsyncExecutor {
-  (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,
-   resourceManager?: ResourceManager, ops?: typeof tfOps): Promise<Tensor[]>;
-}
-
-export declare interface OpMapper {
-  tfOpName: string;
-  category?: Category;
-  inputs?: InputParamMapper[];
-  attrs?: AttrParamMapper[];
-  outputs?: string[];
-  customExecutor?: OpExecutor;
-}
-
-export declare interface Node {
-  signatureKey?: string;
-  name: string;
-  op: string;
-  category: Category;
-  inputNames: string[];
-  inputs: Node[];
-  inputParams: {[key: string]: InputParamValue};
-  attrParams: {[key: string]: ParamValue};
-  children: Node[];
-  rawAttrs?: {[k: string]: tensorflow.IAttrValue};
-  defaultOutput?: number;
-  outputs?: string[];
-}
-
-export declare interface Graph {
-  nodes: {[key: string]: Node};
-  placeholders: Node[];
-  inputs: Node[];
-  outputs: Node[];
-  weights: Node[];
-  signature?: tensorflow.ISignatureDef;
-  functions?: {[key: string]: Graph};
-  initNodes?: Node[];
-}
-
-export type ValueType = string|string[]|number|number[]|number[][]|boolean|
-    boolean[]|Tensor|Tensor[];
-export declare interface ParamValue {
-  value?: ValueType;
-  type: ParamType;
-}
-
-export declare interface InputParamValue extends ParamValue {
-  inputIndexStart?: number;
-  inputIndexEnd?: number;
-}
-
-export interface OpExecutor {
-  (node: GraphNode): Tensor|Tensor[]|Promise<Tensor|Tensor[]>;
-}
-
-export interface GraphNode {
-  inputs: Tensor[];
-  attrs: {[key: string]: ValueType};
-}
diff --git a/tfjs-master/tfjs-converter/src/run_tests.ts b/tfjs-master/tfjs-converter/src/run_tests.ts
deleted file mode 100644
index f362f5511..000000000
--- a/tfjs-master/tfjs-converter/src/run_tests.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Use the CPU backend for running tests.
-import '@tensorflow/tfjs-backend-cpu';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line:no-imports-from-dist
-import {setTestEnvs} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-// tslint:disable-next-line:no-require-imports
-const jasmineCtor = require('jasmine');
-// tslint:disable-next-line:no-require-imports
-
-Error.stackTraceLimit = Infinity;
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-setTestEnvs([{name: 'test-converter', backendName: 'cpu', flags: {}}]);
-
-const unitTests = 'tfjs-converter/src/**/*_test.js';
-
-const runner = new jasmineCtor();
-runner.loadConfig({spec_files: [unitTests], random: false});
-runner.execute();
diff --git a/tfjs-master/tfjs-converter/src/version.ts b/tfjs-master/tfjs-converter/src/version.ts
deleted file mode 100644
index 5fa574e7d..000000000
--- a/tfjs-master/tfjs-converter/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.0';
-export {version};
diff --git a/tfjs-master/tfjs-converter/src/version_test.ts b/tfjs-master/tfjs-converter/src/version_test.ts
deleted file mode 100644
index 1a989f182..000000000
--- a/tfjs-master/tfjs-converter/src/version_test.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC.
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {version_converter} from './index';
-
-describe('tfjs-core version consistency', () => {
-  it('dev-peer match', () => {
-    const tfjsCoreDevDepVersion =
-        // tslint:disable-next-line:no-require-imports
-        require('tfjs-converter/package.json').devDependencies['@tensorflow/tfjs-core'];
-
-    const tfjsCorePeerDepVersion =
-        // tslint:disable-next-line:no-require-imports
-        require('tfjs-converter/package.json').peerDependencies['@tensorflow/tfjs-core'];
-    expect(tfjsCoreDevDepVersion).toEqual(tfjsCorePeerDepVersion);
-  });
-
-  it('version.ts matches package version', () => {
-    // tslint:disable-next-line:no-require-imports
-    const expected = require('tfjs-converter/package.json').version;
-    expect(version_converter).toBe(expected);
-  });
-});
diff --git a/tfjs-master/tfjs-converter/tensorflowjs_wizard.gif b/tfjs-master/tfjs-converter/tensorflowjs_wizard.gif
deleted file mode 100644
index 4c88e4deb..000000000
Binary files a/tfjs-master/tfjs-converter/tensorflowjs_wizard.gif and /dev/null differ
diff --git a/tfjs-master/tfjs-converter/tools/compiled_api.d.ts b/tfjs-master/tfjs-converter/tools/compiled_api.d.ts
deleted file mode 100644
index e2c9c649f..000000000
--- a/tfjs-master/tfjs-converter/tools/compiled_api.d.ts
+++ /dev/null
@@ -1,1607 +0,0 @@
-import * as $protobuf from 'protobufjs';
-
-/** Namespace tensorflow. */
-export namespace tensorflow {
-  /** Properties of an Any. */
-  interface IAny {
-    /** Any typeUrl */
-    typeUrl?: (string|null);
-
-    /** Any value */
-    value?: (Uint8Array|null);
-  }
-
-  /** Represents an Any. */
-  class Any implements IAny {
-    /**
-     * Constructs a new Any.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IAny);
-
-    /** Any typeUrl. */
-    public typeUrl: string;
-
-    /** Any value. */
-    public value: Uint8Array;
-
-    /**
-     * Decodes an Any message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns Any
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.Any;
-  }
-
-  /** DataType enum. */
-  enum DataType {
-    // Not a legal value for DataType.  Used to indicate a DataType field
-    // has not been set.
-    DT_INVALID = 0,
-
-    // Data types that all computation devices are expected to be
-    // capable to support.
-    DT_FLOAT = 1,
-    DT_DOUBLE = 2,
-    DT_INT32 = 3,
-    DT_UINT8 = 4,
-    DT_INT16 = 5,
-    DT_INT8 = 6,
-    DT_STRING = 7,
-    DT_COMPLEX64 = 8,  // Single-precision complex
-    DT_INT64 = 9,
-    DT_BOOL = 10,
-    DT_QINT8 = 11,     // Quantized int8
-    DT_QUINT8 = 12,    // Quantized uint8
-    DT_QINT32 = 13,    // Quantized int32
-    DT_BFLOAT16 = 14,  // Float32 truncated to 16 bits.  Only for cast ops.
-    DT_QINT16 = 15,    // Quantized int16
-    DT_QUINT16 = 16,   // Quantized uint16
-    DT_UINT16 = 17,
-    DT_COMPLEX128 = 18,  // Double-precision complex
-    DT_HALF = 19,
-    DT_RESOURCE = 20,
-    DT_VARIANT = 21,  // Arbitrary C++ data types
-    DT_UINT32 = 22,
-    DT_UINT64 = 23,
-
-    // Do not use!  These are only for parameters.  Every enum above
-    // should have a corresponding value below (verified by types_test).
-    DT_FLOAT_REF = 101,
-    DT_DOUBLE_REF = 102,
-    DT_INT32_REF = 103,
-    DT_UINT8_REF = 104,
-    DT_INT16_REF = 105,
-    DT_INT8_REF = 106,
-    DT_STRING_REF = 107,
-    DT_COMPLEX64_REF = 108,
-    DT_INT64_REF = 109,
-    DT_BOOL_REF = 110,
-    DT_QINT8_REF = 111,
-    DT_QUINT8_REF = 112,
-    DT_QINT32_REF = 113,
-    DT_BFLOAT16_REF = 114,
-    DT_QINT16_REF = 115,
-    DT_QUINT16_REF = 116,
-    DT_UINT16_REF = 117,
-    DT_COMPLEX128_REF = 118,
-    DT_HALF_REF = 119,
-    DT_RESOURCE_REF = 120,
-    DT_VARIANT_REF = 121,
-    DT_UINT32_REF = 122,
-    DT_UINT64_REF = 123,
-  }
-
-  /** Properties of a TensorShape. */
-  interface ITensorShape {
-    /** TensorShape dim */
-    dim?: (tensorflow.TensorShape.IDim[]|null);
-
-    /** TensorShape unknownRank */
-    unknownRank?: (boolean|null);
-  }
-
-  /** Represents a TensorShape. */
-  class TensorShape implements ITensorShape {
-    /**
-     * Constructs a new TensorShape.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.ITensorShape);
-
-    /** TensorShape dim. */
-    public dim: tensorflow.TensorShape.IDim[];
-
-    /** TensorShape unknownRank. */
-    public unknownRank: boolean;
-
-    /**
-     * Decodes a TensorShape message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns TensorShape
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.TensorShape;
-  }
-
-  namespace TensorShape {
-
-    /** Properties of a Dim. */
-    interface IDim {
-      /** Dim size */
-      size?: (number|Long|null);
-
-      /** Dim name */
-      name?: (string|null);
-    }
-
-    /** Represents a Dim. */
-    class Dim implements IDim {
-      /**
-       * Constructs a new Dim.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.TensorShape.IDim);
-
-      /** Dim size. */
-      public size: (number|Long);
-
-      /** Dim name. */
-      public name: string;
-
-      /**
-       * Decodes a Dim message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns Dim
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.TensorShape.Dim;
-    }
-  }
-
-  /** Properties of a Tensor. */
-  interface ITensor {
-    /** Tensor dtype */
-    dtype?: (tensorflow.DataType|null);
-
-    /** Tensor tensorShape */
-    tensorShape?: (tensorflow.ITensorShape|null);
-
-    /** Tensor versionNumber */
-    versionNumber?: (number|null);
-
-    /** Tensor tensorContent */
-    tensorContent?: (Uint8Array|null);
-
-    /** Tensor floatVal */
-    floatVal?: (number[]|null);
-
-    /** Tensor doubleVal */
-    doubleVal?: (number[]|null);
-
-    /** Tensor intVal */
-    intVal?: (number[]|null);
-
-    /** Tensor stringVal */
-    stringVal?: (Uint8Array[]|null);
-
-    /** Tensor scomplexVal */
-    scomplexVal?: (number[]|null);
-
-    /** Tensor int64Val */
-    int64Val?: ((number | Long)[]|null);
-
-    /** Tensor boolVal */
-    boolVal?: (boolean[]|null);
-
-    /** Tensor uint32Val */
-    uint32Val?: (number[]|null);
-
-    /** Tensor uint64Val */
-    uint64Val?: ((number | Long)[]|null);
-  }
-
-  /** Represents a Tensor. */
-  class Tensor implements ITensor {
-    /**
-     * Constructs a new Tensor.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.ITensor);
-
-    /** Tensor dtype. */
-    public dtype: tensorflow.DataType;
-
-    /** Tensor tensorShape. */
-    public tensorShape?: (tensorflow.ITensorShape|null);
-
-    /** Tensor versionNumber. */
-    public versionNumber: number;
-
-    /** Tensor tensorContent. */
-    public tensorContent: Uint8Array;
-
-    /** Tensor floatVal. */
-    public floatVal: number[];
-
-    /** Tensor doubleVal. */
-    public doubleVal: number[];
-
-    /** Tensor intVal. */
-    public intVal: number[];
-
-    /** Tensor stringVal. */
-    public stringVal: Uint8Array[];
-
-    /** Tensor scomplexVal. */
-    public scomplexVal: number[];
-
-    /** Tensor int64Val. */
-    public int64Val: (number|Long)[];
-
-    /** Tensor boolVal. */
-    public boolVal: boolean[];
-
-    /** Tensor uint32Val. */
-    public uint32Val: number[];
-
-    /** Tensor uint64Val. */
-    public uint64Val: (number|Long)[];
-
-    /**
-     * Decodes a Tensor message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns Tensor
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.Tensor;
-  }
-
-  /** Properties of an AttrValue. */
-  interface IAttrValue {
-    /** AttrValue list */
-    list?: (tensorflow.AttrValue.IListValue|null);
-
-    /** AttrValue s */
-    s?: (Uint8Array|null);
-
-    /** AttrValue i */
-    i?: (number|Long|null);
-
-    /** AttrValue f */
-    f?: (number|null);
-
-    /** AttrValue b */
-    b?: (boolean|null);
-
-    /** AttrValue type */
-    type?: (tensorflow.DataType|null);
-
-    /** AttrValue shape */
-    shape?: (tensorflow.ITensorShape|null);
-
-    /** AttrValue tensor */
-    tensor?: (tensorflow.ITensor|null);
-
-    /** AttrValue placeholder */
-    placeholder?: (string|null);
-
-    /** AttrValue func */
-    func?: (tensorflow.INameAttrList|null);
-  }
-
-  /** Represents an AttrValue. */
-  class AttrValue implements IAttrValue {
-    /**
-     * Constructs a new AttrValue.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IAttrValue);
-
-    /** AttrValue list. */
-    public list?: (tensorflow.AttrValue.IListValue|null);
-
-    /** AttrValue s. */
-    public s: Uint8Array;
-
-    /** AttrValue i. */
-    public i: (number|Long);
-
-    /** AttrValue f. */
-    public f: number;
-
-    /** AttrValue b. */
-    public b: boolean;
-
-    /** AttrValue type. */
-    public type: tensorflow.DataType;
-
-    /** AttrValue shape. */
-    public shape?: (tensorflow.ITensorShape|null);
-
-    /** AttrValue tensor. */
-    public tensor?: (tensorflow.ITensor|null);
-
-    /** AttrValue placeholder. */
-    public placeholder: string;
-
-    /** AttrValue func. */
-    public func?: (tensorflow.INameAttrList|null);
-
-    /** AttrValue value. */
-    public value?: ('list'|'s'|'i'|'f'|'b'|'type'|'shape'|'tensor'|
-                    'placeholder'|'func');
-
-    /**
-     * Decodes an AttrValue message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns AttrValue
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.AttrValue;
-  }
-
-  namespace AttrValue {
-
-    /** Properties of a ListValue. */
-    interface IListValue {
-      /** ListValue s */
-      s?: (Uint8Array[]|null);
-
-      /** ListValue i */
-      i?: ((number | Long)[]|null);
-
-      /** ListValue f */
-      f?: (number[]|null);
-
-      /** ListValue b */
-      b?: (boolean[]|null);
-
-      /** ListValue type */
-      type?: (tensorflow.DataType[]|null);
-
-      /** ListValue shape */
-      shape?: (tensorflow.ITensorShape[]|null);
-
-      /** ListValue tensor */
-      tensor?: (tensorflow.ITensor[]|null);
-
-      /** ListValue func */
-      func?: (tensorflow.INameAttrList[]|null);
-    }
-
-    /** Represents a ListValue. */
-    class ListValue implements IListValue {
-      /**
-       * Constructs a new ListValue.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.AttrValue.IListValue);
-
-      /** ListValue s. */
-      public s: Uint8Array[];
-
-      /** ListValue i. */
-      public i: (number|Long)[];
-
-      /** ListValue f. */
-      public f: number[];
-
-      /** ListValue b. */
-      public b: boolean[];
-
-      /** ListValue type. */
-      public type: tensorflow.DataType[];
-
-      /** ListValue shape. */
-      public shape: tensorflow.ITensorShape[];
-
-      /** ListValue tensor. */
-      public tensor: tensorflow.ITensor[];
-
-      /** ListValue func. */
-      public func: tensorflow.INameAttrList[];
-
-      /**
-       * Decodes a ListValue message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns ListValue
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.AttrValue.ListValue;
-    }
-  }
-
-  /** Properties of a NameAttrList. */
-  interface INameAttrList {
-    /** NameAttrList name */
-    name?: (string|null);
-
-    /** NameAttrList attr */
-    attr?: ({[k: string]: tensorflow.IAttrValue}|null);
-  }
-
-  /** Represents a NameAttrList. */
-  class NameAttrList implements INameAttrList {
-    /**
-     * Constructs a new NameAttrList.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.INameAttrList);
-
-    /** NameAttrList name. */
-    public name: string;
-
-    /** NameAttrList attr. */
-    public attr: {[k: string]: tensorflow.IAttrValue};
-
-    /**
-     * Decodes a NameAttrList message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns NameAttrList
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.NameAttrList;
-  }
-
-  /** Properties of a NodeDef. */
-  interface INodeDef {
-    /** NodeDef name */
-    name?: (string|null);
-
-    /** NodeDef op */
-    op?: (string|null);
-
-    /** NodeDef input */
-    input?: (string[]|null);
-
-    /** NodeDef device */
-    device?: (string|null);
-
-    /** NodeDef attr */
-    attr?: ({[k: string]: tensorflow.IAttrValue}|null);
-  }
-
-  /** Represents a NodeDef. */
-  class NodeDef implements INodeDef {
-    /**
-     * Constructs a new NodeDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.INodeDef);
-
-    /** NodeDef name. */
-    public name: string;
-
-    /** NodeDef op. */
-    public op: string;
-
-    /** NodeDef input. */
-    public input: string[];
-
-    /** NodeDef device. */
-    public device: string;
-
-    /** NodeDef attr. */
-    public attr: {[k: string]: tensorflow.IAttrValue};
-
-    /**
-     * Decodes a NodeDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns NodeDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.NodeDef;
-  }
-
-  /** Properties of a VersionDef. */
-  interface IVersionDef {
-    /** VersionDef producer */
-    producer?: (number|null);
-
-    /** VersionDef minConsumer */
-    minConsumer?: (number|null);
-
-    /** VersionDef badConsumers */
-    badConsumers?: (number[]|null);
-  }
-
-  /** Represents a VersionDef. */
-  class VersionDef implements IVersionDef {
-    /**
-     * Constructs a new VersionDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IVersionDef);
-
-    /** VersionDef producer. */
-    public producer: number;
-
-    /** VersionDef minConsumer. */
-    public minConsumer: number;
-
-    /** VersionDef badConsumers. */
-    public badConsumers: number[];
-
-    /**
-     * Decodes a VersionDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns VersionDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.VersionDef;
-  }
-
-  /** Properties of a GraphDef. */
-  interface IGraphDef {
-    /** GraphDef node */
-    node?: (tensorflow.INodeDef[]|null);
-
-    /** GraphDef versions */
-    versions?: (tensorflow.IVersionDef|null);
-
-    /** GraphDef library */
-    library?: (tensorflow.IFunctionDefLibrary|null);
-  }
-
-  /** Represents a GraphDef. */
-  class GraphDef implements IGraphDef {
-    /**
-     * Constructs a new GraphDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IGraphDef);
-
-    /** GraphDef node. */
-    public node: tensorflow.INodeDef[];
-
-    /** GraphDef versions. */
-    public versions?: (tensorflow.IVersionDef|null);
-
-    /** GraphDef library. */
-    public library?: (tensorflow.IFunctionDefLibrary|null);
-
-    /**
-     * Decodes a GraphDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns GraphDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.GraphDef;
-  }
-
-  /** Properties of a CollectionDef. */
-  interface ICollectionDef {
-    /** CollectionDef nodeList */
-    nodeList?: (tensorflow.CollectionDef.INodeList|null);
-
-    /** CollectionDef bytesList */
-    bytesList?: (tensorflow.CollectionDef.IBytesList|null);
-
-    /** CollectionDef int64List */
-    int64List?: (tensorflow.CollectionDef.IInt64List|null);
-
-    /** CollectionDef floatList */
-    floatList?: (tensorflow.CollectionDef.IFloatList|null);
-
-    /** CollectionDef anyList */
-    anyList?: (tensorflow.CollectionDef.IAnyList|null);
-  }
-
-  /** Represents a CollectionDef. */
-  class CollectionDef implements ICollectionDef {
-    /**
-     * Constructs a new CollectionDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.ICollectionDef);
-
-    /** CollectionDef nodeList. */
-    public nodeList?: (tensorflow.CollectionDef.INodeList|null);
-
-    /** CollectionDef bytesList. */
-    public bytesList?: (tensorflow.CollectionDef.IBytesList|null);
-
-    /** CollectionDef int64List. */
-    public int64List?: (tensorflow.CollectionDef.IInt64List|null);
-
-    /** CollectionDef floatList. */
-    public floatList?: (tensorflow.CollectionDef.IFloatList|null);
-
-    /** CollectionDef anyList. */
-    public anyList?: (tensorflow.CollectionDef.IAnyList|null);
-
-    /** CollectionDef kind. */
-    public kind?: ('nodeList'|'bytesList'|'int64List'|'floatList'|'anyList');
-
-    /**
-     * Decodes a CollectionDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns CollectionDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.CollectionDef;
-  }
-
-  namespace CollectionDef {
-
-    /** Properties of a NodeList. */
-    interface INodeList {
-      /** NodeList value */
-      value?: (string[]|null);
-    }
-
-    /** Represents a NodeList. */
-    class NodeList implements INodeList {
-      /**
-       * Constructs a new NodeList.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.CollectionDef.INodeList);
-
-      /** NodeList value. */
-      public value: string[];
-
-      /**
-       * Decodes a NodeList message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns NodeList
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.CollectionDef.NodeList;
-    }
-
-    /** Properties of a BytesList. */
-    interface IBytesList {
-      /** BytesList value */
-      value?: (Uint8Array[]|null);
-    }
-
-    /** Represents a BytesList. */
-    class BytesList implements IBytesList {
-      /**
-       * Constructs a new BytesList.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.CollectionDef.IBytesList);
-
-      /** BytesList value. */
-      public value: Uint8Array[];
-
-      /**
-       * Decodes a BytesList message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns BytesList
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.CollectionDef.BytesList;
-    }
-
-    /** Properties of an Int64List. */
-    interface IInt64List {
-      /** Int64List value */
-      value?: ((number | Long)[]|null);
-    }
-
-    /** Represents an Int64List. */
-    class Int64List implements IInt64List {
-      /**
-       * Constructs a new Int64List.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.CollectionDef.IInt64List);
-
-      /** Int64List value. */
-      public value: (number|Long)[];
-
-      /**
-       * Decodes an Int64List message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns Int64List
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.CollectionDef.Int64List;
-    }
-
-    /** Properties of a FloatList. */
-    interface IFloatList {
-      /** FloatList value */
-      value?: (number[]|null);
-    }
-
-    /** Represents a FloatList. */
-    class FloatList implements IFloatList {
-      /**
-       * Constructs a new FloatList.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.CollectionDef.IFloatList);
-
-      /** FloatList value. */
-      public value: number[];
-
-      /**
-       * Decodes a FloatList message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns FloatList
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.CollectionDef.FloatList;
-    }
-
-    /** Properties of an AnyList. */
-    interface IAnyList {
-      /** AnyList value */
-      value?: (tensorflow.IAny[]|null);
-    }
-
-    /** Represents an AnyList. */
-    class AnyList implements IAnyList {
-      /**
-       * Constructs a new AnyList.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.CollectionDef.IAnyList);
-
-      /** AnyList value. */
-      public value: tensorflow.IAny[];
-
-      /**
-       * Decodes an AnyList message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns AnyList
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.CollectionDef.AnyList;
-    }
-  }
-
-  /** Properties of a SaverDef. */
-  interface ISaverDef {
-    /** SaverDef filenameTensorName */
-    filenameTensorName?: (string|null);
-
-    /** SaverDef saveTensorName */
-    saveTensorName?: (string|null);
-
-    /** SaverDef restoreOpName */
-    restoreOpName?: (string|null);
-
-    /** SaverDef maxToKeep */
-    maxToKeep?: (number|null);
-
-    /** SaverDef sharded */
-    sharded?: (boolean|null);
-
-    /** SaverDef keepCheckpointEveryNHours */
-    keepCheckpointEveryNHours?: (number|null);
-
-    /** SaverDef version */
-    version?: (tensorflow.SaverDef.CheckpointFormatVersion|null);
-  }
-
-  /** Represents a SaverDef. */
-  class SaverDef implements ISaverDef {
-    /**
-     * Constructs a new SaverDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.ISaverDef);
-
-    /** SaverDef filenameTensorName. */
-    public filenameTensorName: string;
-
-    /** SaverDef saveTensorName. */
-    public saveTensorName: string;
-
-    /** SaverDef restoreOpName. */
-    public restoreOpName: string;
-
-    /** SaverDef maxToKeep. */
-    public maxToKeep: number;
-
-    /** SaverDef sharded. */
-    public sharded: boolean;
-
-    /** SaverDef keepCheckpointEveryNHours. */
-    public keepCheckpointEveryNHours: number;
-
-    /** SaverDef version. */
-    public version: tensorflow.SaverDef.CheckpointFormatVersion;
-
-    /**
-     * Decodes a SaverDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns SaverDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.SaverDef;
-  }
-
-  namespace SaverDef {
-
-    /** CheckpointFormatVersion enum. */
-    enum CheckpointFormatVersion { LEGACY = 0, V1 = 1, V2 = 2 }
-  }
-
-  /** Properties of a TensorInfo. */
-  interface ITensorInfo {
-    /** TensorInfo name */
-    name?: (string|null);
-
-    /** TensorInfo cooSparse */
-    cooSparse?: (tensorflow.TensorInfo.ICooSparse|null);
-
-    /** TensorInfo dtype */
-    dtype?: (tensorflow.DataType|null);
-
-    /** TensorInfo tensorShape */
-    tensorShape?: (tensorflow.ITensorShape|null);
-  }
-
-  /** Represents a TensorInfo. */
-  class TensorInfo implements ITensorInfo {
-    /**
-     * Constructs a new TensorInfo.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.ITensorInfo);
-
-    /** TensorInfo name. */
-    public name: string;
-
-    /** TensorInfo cooSparse. */
-    public cooSparse?: (tensorflow.TensorInfo.ICooSparse|null);
-
-    /** TensorInfo dtype. */
-    public dtype: tensorflow.DataType;
-
-    /** TensorInfo tensorShape. */
-    public tensorShape?: (tensorflow.ITensorShape|null);
-
-    /** TensorInfo encoding. */
-    public encoding?: ('name'|'cooSparse');
-
-    /**
-     * Decodes a TensorInfo message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns TensorInfo
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.TensorInfo;
-  }
-
-  namespace TensorInfo {
-
-    /** Properties of a CooSparse. */
-    interface ICooSparse {
-      /** CooSparse valuesTensorName */
-      valuesTensorName?: (string|null);
-
-      /** CooSparse indicesTensorName */
-      indicesTensorName?: (string|null);
-
-      /** CooSparse denseShapeTensorName */
-      denseShapeTensorName?: (string|null);
-    }
-
-    /** Represents a CooSparse. */
-    class CooSparse implements ICooSparse {
-      /**
-       * Constructs a new CooSparse.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.TensorInfo.ICooSparse);
-
-      /** CooSparse valuesTensorName. */
-      public valuesTensorName: string;
-
-      /** CooSparse indicesTensorName. */
-      public indicesTensorName: string;
-
-      /** CooSparse denseShapeTensorName. */
-      public denseShapeTensorName: string;
-
-      /**
-       * Decodes a CooSparse message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns CooSparse
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.TensorInfo.CooSparse;
-    }
-  }
-
-  /** Properties of a SignatureDef. */
-  interface ISignatureDef {
-    /** SignatureDef inputs */
-    inputs?: ({[k: string]: tensorflow.ITensorInfo}|null);
-
-    /** SignatureDef outputs */
-    outputs?: ({[k: string]: tensorflow.ITensorInfo}|null);
-
-    /** SignatureDef methodName */
-    methodName?: (string|null);
-  }
-
-  /** Represents a SignatureDef. */
-  class SignatureDef implements ISignatureDef {
-    /**
-     * Constructs a new SignatureDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.ISignatureDef);
-
-    /** SignatureDef inputs. */
-    public inputs: {[k: string]: tensorflow.ITensorInfo};
-
-    /** SignatureDef outputs. */
-    public outputs: {[k: string]: tensorflow.ITensorInfo};
-
-    /** SignatureDef methodName. */
-    public methodName: string;
-
-    /**
-     * Decodes a SignatureDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns SignatureDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.SignatureDef;
-  }
-
-  /** Properties of an AssetFileDef. */
-  interface IAssetFileDef {
-    /** AssetFileDef tensorInfo */
-    tensorInfo?: (tensorflow.ITensorInfo|null);
-
-    /** AssetFileDef filename */
-    filename?: (string|null);
-  }
-
-  /** Represents an AssetFileDef. */
-  class AssetFileDef implements IAssetFileDef {
-    /**
-     * Constructs a new AssetFileDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IAssetFileDef);
-
-    /** AssetFileDef tensorInfo. */
-    public tensorInfo?: (tensorflow.ITensorInfo|null);
-
-    /** AssetFileDef filename. */
-    public filename: string;
-
-    /**
-     * Decodes an AssetFileDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns AssetFileDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.AssetFileDef;
-  }
-
-  /** Properties of an OpDef. */
-  interface IOpDef {
-    /** OpDef name */
-    name?: (string|null);
-
-    /** OpDef inputArg */
-    inputArg?: (tensorflow.OpDef.IArgDef[]|null);
-
-    /** OpDef outputArg */
-    outputArg?: (tensorflow.OpDef.IArgDef[]|null);
-
-    /** OpDef attr */
-    attr?: (tensorflow.OpDef.IAttrDef[]|null);
-
-    /** OpDef deprecation */
-    deprecation?: (tensorflow.OpDef.IOpDeprecation|null);
-
-    /** OpDef summary */
-    summary?: (string|null);
-
-    /** OpDef description */
-    description?: (string|null);
-
-    /** OpDef isCommutative */
-    isCommutative?: (boolean|null);
-
-    /** OpDef isAggregate */
-    isAggregate?: (boolean|null);
-
-    /** OpDef isStateful */
-    isStateful?: (boolean|null);
-
-    /** OpDef allowsUninitializedInput */
-    allowsUninitializedInput?: (boolean|null);
-  }
-
-  /** Represents an OpDef. */
-  class OpDef implements IOpDef {
-    /**
-     * Constructs a new OpDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IOpDef);
-
-    /** OpDef name. */
-    public name: string;
-
-    /** OpDef inputArg. */
-    public inputArg: tensorflow.OpDef.IArgDef[];
-
-    /** OpDef outputArg. */
-    public outputArg: tensorflow.OpDef.IArgDef[];
-
-    /** OpDef attr. */
-    public attr: tensorflow.OpDef.IAttrDef[];
-
-    /** OpDef deprecation. */
-    public deprecation?: (tensorflow.OpDef.IOpDeprecation|null);
-
-    /** OpDef summary. */
-    public summary: string;
-
-    /** OpDef description. */
-    public description: string;
-
-    /** OpDef isCommutative. */
-    public isCommutative: boolean;
-
-    /** OpDef isAggregate. */
-    public isAggregate: boolean;
-
-    /** OpDef isStateful. */
-    public isStateful: boolean;
-
-    /** OpDef allowsUninitializedInput. */
-    public allowsUninitializedInput: boolean;
-
-    /**
-     * Decodes an OpDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns OpDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.OpDef;
-  }
-
-  namespace OpDef {
-
-    /** Properties of an ArgDef. */
-    interface IArgDef {
-      /** ArgDef name */
-      name?: (string|null);
-
-      /** ArgDef description */
-      description?: (string|null);
-
-      /** ArgDef type */
-      type?: (tensorflow.DataType|null);
-
-      /** ArgDef typeAttr */
-      typeAttr?: (string|null);
-
-      /** ArgDef numberAttr */
-      numberAttr?: (string|null);
-
-      /** ArgDef typeListAttr */
-      typeListAttr?: (string|null);
-
-      /** ArgDef isRef */
-      isRef?: (boolean|null);
-    }
-
-    /** Represents an ArgDef. */
-    class ArgDef implements IArgDef {
-      /**
-       * Constructs a new ArgDef.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.OpDef.IArgDef);
-
-      /** ArgDef name. */
-      public name: string;
-
-      /** ArgDef description. */
-      public description: string;
-
-      /** ArgDef type. */
-      public type: tensorflow.DataType;
-
-      /** ArgDef typeAttr. */
-      public typeAttr: string;
-
-      /** ArgDef numberAttr. */
-      public numberAttr: string;
-
-      /** ArgDef typeListAttr. */
-      public typeListAttr: string;
-
-      /** ArgDef isRef. */
-      public isRef: boolean;
-
-      /**
-       * Decodes an ArgDef message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns ArgDef
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.OpDef.ArgDef;
-    }
-
-    /** Properties of an AttrDef. */
-    interface IAttrDef {
-      /** AttrDef name */
-      name?: (string|null);
-
-      /** AttrDef type */
-      type?: (string|null);
-
-      /** AttrDef defaultValue */
-      defaultValue?: (tensorflow.IAttrValue|null);
-
-      /** AttrDef description */
-      description?: (string|null);
-
-      /** AttrDef hasMinimum */
-      hasMinimum?: (boolean|null);
-
-      /** AttrDef minimum */
-      minimum?: (number|Long|null);
-
-      /** AttrDef allowedValues */
-      allowedValues?: (tensorflow.IAttrValue|null);
-    }
-
-    /** Represents an AttrDef. */
-    class AttrDef implements IAttrDef {
-      /**
-       * Constructs a new AttrDef.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.OpDef.IAttrDef);
-
-      /** AttrDef name. */
-      public name: string;
-
-      /** AttrDef type. */
-      public type: string;
-
-      /** AttrDef defaultValue. */
-      public defaultValue?: (tensorflow.IAttrValue|null);
-
-      /** AttrDef description. */
-      public description: string;
-
-      /** AttrDef hasMinimum. */
-      public hasMinimum: boolean;
-
-      /** AttrDef minimum. */
-      public minimum: (number|Long);
-
-      /** AttrDef allowedValues. */
-      public allowedValues?: (tensorflow.IAttrValue|null);
-
-      /**
-       * Decodes an AttrDef message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns AttrDef
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.OpDef.AttrDef;
-    }
-
-    /** Properties of an OpDeprecation. */
-    interface IOpDeprecation {
-      /** OpDeprecation version */
-      version?: (number|null);
-
-      /** OpDeprecation explanation */
-      explanation?: (string|null);
-    }
-
-    /** Represents an OpDeprecation. */
-    class OpDeprecation implements IOpDeprecation {
-      /**
-       * Constructs a new OpDeprecation.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.OpDef.IOpDeprecation);
-
-      /** OpDeprecation version. */
-      public version: number;
-
-      /** OpDeprecation explanation. */
-      public explanation: string;
-
-      /**
-       * Decodes an OpDeprecation message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns OpDeprecation
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.OpDef.OpDeprecation;
-    }
-  }
-
-  /** Properties of an OpList. */
-  interface IOpList {
-    /** OpList op */
-    op?: (tensorflow.IOpDef[]|null);
-  }
-
-  /** Represents an OpList. */
-  class OpList implements IOpList {
-    /**
-     * Constructs a new OpList.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IOpList);
-
-    /** OpList op. */
-    public op: tensorflow.IOpDef[];
-
-    /**
-     * Decodes an OpList message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns OpList
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.OpList;
-  }
-
-  /** Properties of a MetaGraphDef. */
-  interface IMetaGraphDef {
-    /** MetaGraphDef metaInfoDef */
-    metaInfoDef?: (tensorflow.MetaGraphDef.IMetaInfoDef|null);
-
-    /** MetaGraphDef graphDef */
-    graphDef?: (tensorflow.IGraphDef|null);
-
-    /** MetaGraphDef saverDef */
-    saverDef?: (tensorflow.ISaverDef|null);
-
-    /** MetaGraphDef collectionDef */
-    collectionDef?: ({[k: string]: tensorflow.ICollectionDef}|null);
-
-    /** MetaGraphDef signatureDef */
-    signatureDef?: ({[k: string]: tensorflow.ISignatureDef}|null);
-
-    /** MetaGraphDef assetFileDef */
-    assetFileDef?: (tensorflow.IAssetFileDef[]|null);
-  }
-
-  /** Represents a MetaGraphDef. */
-  class MetaGraphDef implements IMetaGraphDef {
-    /**
-     * Constructs a new MetaGraphDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IMetaGraphDef);
-
-    /** MetaGraphDef metaInfoDef. */
-    public metaInfoDef?: (tensorflow.MetaGraphDef.IMetaInfoDef|null);
-
-    /** MetaGraphDef graphDef. */
-    public graphDef?: (tensorflow.IGraphDef|null);
-
-    /** MetaGraphDef saverDef. */
-    public saverDef?: (tensorflow.ISaverDef|null);
-
-    /** MetaGraphDef collectionDef. */
-    public collectionDef: {[k: string]: tensorflow.ICollectionDef};
-
-    /** MetaGraphDef signatureDef. */
-    public signatureDef: {[k: string]: tensorflow.ISignatureDef};
-
-    /** MetaGraphDef assetFileDef. */
-    public assetFileDef: tensorflow.IAssetFileDef[];
-
-    /**
-     * Decodes a MetaGraphDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns MetaGraphDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.MetaGraphDef;
-  }
-
-  namespace MetaGraphDef {
-
-    /** Properties of a MetaInfoDef. */
-    interface IMetaInfoDef {
-      /** MetaInfoDef metaGraphVersion */
-      metaGraphVersion?: (string|null);
-
-      /** MetaInfoDef strippedOpList */
-      strippedOpList?: (tensorflow.IOpList|null);
-
-      /** MetaInfoDef anyInfo */
-      anyInfo?: (tensorflow.IAny|null);
-
-      /** MetaInfoDef tags */
-      tags?: (string[]|null);
-
-      /** MetaInfoDef tensorflowVersion */
-      tensorflowVersion?: (string|null);
-
-      /** MetaInfoDef tensorflowGitVersion */
-      tensorflowGitVersion?: (string|null);
-    }
-
-    /** Represents a MetaInfoDef. */
-    class MetaInfoDef implements IMetaInfoDef {
-      /**
-       * Constructs a new MetaInfoDef.
-       * @param [p] Properties to set
-       */
-      constructor(p?: tensorflow.MetaGraphDef.IMetaInfoDef);
-
-      /** MetaInfoDef metaGraphVersion. */
-      public metaGraphVersion: string;
-
-      /** MetaInfoDef strippedOpList. */
-      public strippedOpList?: (tensorflow.IOpList|null);
-
-      /** MetaInfoDef anyInfo. */
-      public anyInfo?: (tensorflow.IAny|null);
-
-      /** MetaInfoDef tags. */
-      public tags: string[];
-
-      /** MetaInfoDef tensorflowVersion. */
-      public tensorflowVersion: string;
-
-      /** MetaInfoDef tensorflowGitVersion. */
-      public tensorflowGitVersion: string;
-
-      /**
-       * Decodes a MetaInfoDef message from the specified reader or buffer.
-       * @param r Reader or buffer to decode from
-       * @param [l] Message length if known beforehand
-       * @returns MetaInfoDef
-       * @throws {Error} If the payload is not a reader or valid buffer
-       * @throws {$protobuf.util.ProtocolError} If required fields are missing
-       */
-      public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-          tensorflow.MetaGraphDef.MetaInfoDef;
-    }
-  }
-
-  /** Properties of a SavedModel. */
-  interface ISavedModel {
-    /** SavedModel savedModelSchemaVersion */
-    savedModelSchemaVersion?: (number|Long|null);
-
-    /** SavedModel metaGraphs */
-    metaGraphs?: (tensorflow.IMetaGraphDef[]|null);
-  }
-
-  /** Represents a SavedModel. */
-  class SavedModel implements ISavedModel {
-    /**
-     * Constructs a new SavedModel.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.ISavedModel);
-
-    /** SavedModel savedModelSchemaVersion. */
-    public savedModelSchemaVersion: (number|Long);
-
-    /** SavedModel metaGraphs. */
-    public metaGraphs: tensorflow.IMetaGraphDef[];
-
-    /**
-     * Decodes a SavedModel message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns SavedModel
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.SavedModel;
-  }
-
-  /** Properties of a FunctionDefLibrary. */
-  interface IFunctionDefLibrary {
-    /** FunctionDefLibrary function */
-    'function'?: (tensorflow.IFunctionDef[]|null);
-
-    /** FunctionDefLibrary gradient */
-    gradient?: (tensorflow.IGradientDef[]|null);
-  }
-
-  /** Represents a FunctionDefLibrary. */
-  class FunctionDefLibrary implements IFunctionDefLibrary {
-    /**
-     * Constructs a new FunctionDefLibrary.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IFunctionDefLibrary);
-
-    /** FunctionDefLibrary function. */
-    public function: tensorflow.IFunctionDef[];
-
-    /** FunctionDefLibrary gradient. */
-    public gradient: tensorflow.IGradientDef[];
-
-    /**
-     * Decodes a FunctionDefLibrary message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns FunctionDefLibrary
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.FunctionDefLibrary;
-  }
-
-  /** Properties of a FunctionDef. */
-  interface IFunctionDef {
-    /** FunctionDef signature */
-    signature?: (tensorflow.IOpDef|null);
-
-    /** FunctionDef attr */
-    attr?: ({[k: string]: tensorflow.IAttrValue}|null);
-
-    /** FunctionDef nodeDef */
-    nodeDef?: (tensorflow.INodeDef[]|null);
-
-    /** FunctionDef ret */
-    ret?: ({[k: string]: string}|null);
-  }
-
-  /** Represents a FunctionDef. */
-  class FunctionDef implements IFunctionDef {
-    /**
-     * Constructs a new FunctionDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IFunctionDef);
-
-    /** FunctionDef signature. */
-    public signature?: (tensorflow.IOpDef|null);
-
-    /** FunctionDef attr. */
-    public attr: {[k: string]: tensorflow.IAttrValue};
-
-    /** FunctionDef nodeDef. */
-    public nodeDef: tensorflow.INodeDef[];
-
-    /** FunctionDef ret. */
-    public ret: {[k: string]: string};
-
-    /**
-     * Decodes a FunctionDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns FunctionDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.FunctionDef;
-  }
-
-  /** Properties of a GradientDef. */
-  interface IGradientDef {
-    /** GradientDef functionName */
-    functionName?: (string|null);
-
-    /** GradientDef gradientFunc */
-    gradientFunc?: (string|null);
-  }
-
-  /** Represents a GradientDef. */
-  class GradientDef implements IGradientDef {
-    /**
-     * Constructs a new GradientDef.
-     * @param [p] Properties to set
-     */
-    constructor(p?: tensorflow.IGradientDef);
-
-    /** GradientDef functionName. */
-    public functionName: string;
-
-    /** GradientDef gradientFunc. */
-    public gradientFunc: string;
-
-    /**
-     * Decodes a GradientDef message from the specified reader or buffer.
-     * @param r Reader or buffer to decode from
-     * @param [l] Message length if known beforehand
-     * @returns GradientDef
-     * @throws {Error} If the payload is not a reader or valid buffer
-     * @throws {$protobuf.util.ProtocolError} If required fields are missing
-     */
-    public static decode(r: ($protobuf.Reader|Uint8Array), l?: number):
-        tensorflow.GradientDef;
-  }
-}
diff --git a/tfjs-master/tfjs-converter/tools/compiled_api.js b/tfjs-master/tfjs-converter/tools/compiled_api.js
deleted file mode 100644
index 89e3c9b5e..000000000
--- a/tfjs-master/tfjs-converter/tools/compiled_api.js
+++ /dev/null
@@ -1,1723 +0,0 @@
-/*eslint-disable block-scoped-var, no-redeclare, no-control-regex, no-prototype-builtins*/
-"use strict";
-
-var $protobuf = require("protobufjs/minimal");
-
-var $Reader = $protobuf.Reader, $util = $protobuf.util;
-
-var $root = $protobuf.roots["default"] || ($protobuf.roots["default"] = {});
-
-$root.tensorflow = (function() {
-
-    var tensorflow = {};
-
-    tensorflow.Any = (function() {
-
-        function Any(p) {
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        Any.prototype.typeUrl = "";
-        Any.prototype.value = $util.newBuffer([]);
-
-        Any.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.Any();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.typeUrl = r.string();
-                    break;
-                case 2:
-                    m.value = r.bytes();
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return Any;
-    })();
-
-    tensorflow.DataType = (function() {
-        var valuesById = {}, values = Object.create(valuesById);
-        values[valuesById[0] = "DT_INVALID"] = 0;
-        values[valuesById[1] = "DT_FLOAT"] = 1;
-        values[valuesById[2] = "DT_DOUBLE"] = 2;
-        values[valuesById[3] = "DT_INT32"] = 3;
-        values[valuesById[4] = "DT_UINT8"] = 4;
-        values[valuesById[5] = "DT_INT16"] = 5;
-        values[valuesById[6] = "DT_INT8"] = 6;
-        values[valuesById[7] = "DT_STRING"] = 7;
-        values[valuesById[8] = "DT_COMPLEX64"] = 8;
-        values[valuesById[9] = "DT_INT64"] = 9;
-        values[valuesById[10] = "DT_BOOL"] = 10;
-        values[valuesById[11] = "DT_QINT8"] = 11;
-        values[valuesById[12] = "DT_QUINT8"] = 12;
-        values[valuesById[13] = "DT_QINT32"] = 13;
-        values[valuesById[14] = "DT_BFLOAT16"] = 14;
-        values[valuesById[101] = "DT_FLOAT_REF"] = 101;
-        values[valuesById[102] = "DT_DOUBLE_REF"] = 102;
-        values[valuesById[103] = "DT_INT32_REF"] = 103;
-        values[valuesById[104] = "DT_UINT8_REF"] = 104;
-        values[valuesById[105] = "DT_INT16_REF"] = 105;
-        values[valuesById[106] = "DT_INT8_REF"] = 106;
-        values[valuesById[107] = "DT_STRING_REF"] = 107;
-        values[valuesById[108] = "DT_COMPLEX64_REF"] = 108;
-        values[valuesById[109] = "DT_INT64_REF"] = 109;
-        values[valuesById[110] = "DT_BOOL_REF"] = 110;
-        values[valuesById[111] = "DT_QINT8_REF"] = 111;
-        values[valuesById[112] = "DT_QUINT8_REF"] = 112;
-        values[valuesById[113] = "DT_QINT32_REF"] = 113;
-        values[valuesById[114] = "DT_BFLOAT16_REF"] = 114;
-        return values;
-    })();
-
-    tensorflow.TensorShape = (function() {
-
-        function TensorShape(p) {
-            this.dim = [];
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        TensorShape.prototype.dim = $util.emptyArray;
-        TensorShape.prototype.unknownRank = false;
-
-        TensorShape.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.TensorShape();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 2:
-                    if (!(m.dim && m.dim.length))
-                        m.dim = [];
-                    m.dim.push($root.tensorflow.TensorShape.Dim.decode(r, r.uint32()));
-                    break;
-                case 3:
-                    m.unknownRank = r.bool();
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        TensorShape.Dim = (function() {
-
-            function Dim(p) {
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            Dim.prototype.size = $util.Long ? $util.Long.fromBits(0,0,false) : 0;
-            Dim.prototype.name = "";
-
-            Dim.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.TensorShape.Dim();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        m.size = r.int64();
-                        break;
-                    case 2:
-                        m.name = r.string();
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return Dim;
-        })();
-
-        return TensorShape;
-    })();
-
-    tensorflow.Tensor = (function() {
-
-        function Tensor(p) {
-            this.floatVal = [];
-            this.doubleVal = [];
-            this.intVal = [];
-            this.stringVal = [];
-            this.scomplexVal = [];
-            this.int64Val = [];
-            this.boolVal = [];
-            this.uint32Val = [];
-            this.uint64Val = [];
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        Tensor.prototype.dtype = 0;
-        Tensor.prototype.tensorShape = null;
-        Tensor.prototype.versionNumber = 0;
-        Tensor.prototype.tensorContent = $util.newBuffer([]);
-        Tensor.prototype.floatVal = $util.emptyArray;
-        Tensor.prototype.doubleVal = $util.emptyArray;
-        Tensor.prototype.intVal = $util.emptyArray;
-        Tensor.prototype.stringVal = $util.emptyArray;
-        Tensor.prototype.scomplexVal = $util.emptyArray;
-        Tensor.prototype.int64Val = $util.emptyArray;
-        Tensor.prototype.boolVal = $util.emptyArray;
-        Tensor.prototype.uint32Val = $util.emptyArray;
-        Tensor.prototype.uint64Val = $util.emptyArray;
-
-        Tensor.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.Tensor();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.dtype = r.int32();
-                    break;
-                case 2:
-                    m.tensorShape = $root.tensorflow.TensorShape.decode(r, r.uint32());
-                    break;
-                case 3:
-                    m.versionNumber = r.int32();
-                    break;
-                case 4:
-                    m.tensorContent = r.bytes();
-                    break;
-                case 5:
-                    if (!(m.floatVal && m.floatVal.length))
-                        m.floatVal = [];
-                    if ((t & 7) === 2) {
-                        var c2 = r.uint32() + r.pos;
-                        while (r.pos < c2)
-                            m.floatVal.push(r.float());
-                    } else
-                        m.floatVal.push(r.float());
-                    break;
-                case 6:
-                    if (!(m.doubleVal && m.doubleVal.length))
-                        m.doubleVal = [];
-                    if ((t & 7) === 2) {
-                        var c2 = r.uint32() + r.pos;
-                        while (r.pos < c2)
-                            m.doubleVal.push(r.double());
-                    } else
-                        m.doubleVal.push(r.double());
-                    break;
-                case 7:
-                    if (!(m.intVal && m.intVal.length))
-                        m.intVal = [];
-                    if ((t & 7) === 2) {
-                        var c2 = r.uint32() + r.pos;
-                        while (r.pos < c2)
-                            m.intVal.push(r.int32());
-                    } else
-                        m.intVal.push(r.int32());
-                    break;
-                case 8:
-                    if (!(m.stringVal && m.stringVal.length))
-                        m.stringVal = [];
-                    m.stringVal.push(r.bytes());
-                    break;
-                case 9:
-                    if (!(m.scomplexVal && m.scomplexVal.length))
-                        m.scomplexVal = [];
-                    if ((t & 7) === 2) {
-                        var c2 = r.uint32() + r.pos;
-                        while (r.pos < c2)
-                            m.scomplexVal.push(r.float());
-                    } else
-                        m.scomplexVal.push(r.float());
-                    break;
-                case 10:
-                    if (!(m.int64Val && m.int64Val.length))
-                        m.int64Val = [];
-                    if ((t & 7) === 2) {
-                        var c2 = r.uint32() + r.pos;
-                        while (r.pos < c2)
-                            m.int64Val.push(r.int64());
-                    } else
-                        m.int64Val.push(r.int64());
-                    break;
-                case 11:
-                    if (!(m.boolVal && m.boolVal.length))
-                        m.boolVal = [];
-                    if ((t & 7) === 2) {
-                        var c2 = r.uint32() + r.pos;
-                        while (r.pos < c2)
-                            m.boolVal.push(r.bool());
-                    } else
-                        m.boolVal.push(r.bool());
-                    break;
-                case 16:
-                    if (!(m.uint32Val && m.uint32Val.length))
-                        m.uint32Val = [];
-                    if ((t & 7) === 2) {
-                        var c2 = r.uint32() + r.pos;
-                        while (r.pos < c2)
-                            m.uint32Val.push(r.uint32());
-                    } else
-                        m.uint32Val.push(r.uint32());
-                    break;
-                case 17:
-                    if (!(m.uint64Val && m.uint64Val.length))
-                        m.uint64Val = [];
-                    if ((t & 7) === 2) {
-                        var c2 = r.uint32() + r.pos;
-                        while (r.pos < c2)
-                            m.uint64Val.push(r.uint64());
-                    } else
-                        m.uint64Val.push(r.uint64());
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return Tensor;
-    })();
-
-    tensorflow.AttrValue = (function() {
-
-        function AttrValue(p) {
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        AttrValue.prototype.list = null;
-        AttrValue.prototype.s = $util.newBuffer([]);
-        AttrValue.prototype.i = $util.Long ? $util.Long.fromBits(0,0,false) : 0;
-        AttrValue.prototype.f = 0;
-        AttrValue.prototype.b = false;
-        AttrValue.prototype.type = 0;
-        AttrValue.prototype.shape = null;
-        AttrValue.prototype.tensor = null;
-        AttrValue.prototype.placeholder = "";
-        AttrValue.prototype.func = null;
-
-        var $oneOfFields;
-
-        Object.defineProperty(AttrValue.prototype, "value", {
-            get: $util.oneOfGetter($oneOfFields = ["list", "s", "i", "f", "b", "type", "shape", "tensor", "placeholder", "func"]),
-            set: $util.oneOfSetter($oneOfFields)
-        });
-
-        AttrValue.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.AttrValue();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.list = $root.tensorflow.AttrValue.ListValue.decode(r, r.uint32());
-                    break;
-                case 2:
-                    m.s = r.bytes();
-                    break;
-                case 3:
-                    m.i = r.int64();
-                    break;
-                case 4:
-                    m.f = r.float();
-                    break;
-                case 5:
-                    m.b = r.bool();
-                    break;
-                case 6:
-                    m.type = r.int32();
-                    break;
-                case 7:
-                    m.shape = $root.tensorflow.TensorShape.decode(r, r.uint32());
-                    break;
-                case 8:
-                    m.tensor = $root.tensorflow.Tensor.decode(r, r.uint32());
-                    break;
-                case 9:
-                    m.placeholder = r.string();
-                    break;
-                case 10:
-                    m.func = $root.tensorflow.NameAttrList.decode(r, r.uint32());
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        AttrValue.ListValue = (function() {
-
-            function ListValue(p) {
-                this.s = [];
-                this.i = [];
-                this.f = [];
-                this.b = [];
-                this.type = [];
-                this.shape = [];
-                this.tensor = [];
-                this.func = [];
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            ListValue.prototype.s = $util.emptyArray;
-            ListValue.prototype.i = $util.emptyArray;
-            ListValue.prototype.f = $util.emptyArray;
-            ListValue.prototype.b = $util.emptyArray;
-            ListValue.prototype.type = $util.emptyArray;
-            ListValue.prototype.shape = $util.emptyArray;
-            ListValue.prototype.tensor = $util.emptyArray;
-            ListValue.prototype.func = $util.emptyArray;
-
-            ListValue.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.AttrValue.ListValue();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 2:
-                        if (!(m.s && m.s.length))
-                            m.s = [];
-                        m.s.push(r.bytes());
-                        break;
-                    case 3:
-                        if (!(m.i && m.i.length))
-                            m.i = [];
-                        if ((t & 7) === 2) {
-                            var c2 = r.uint32() + r.pos;
-                            while (r.pos < c2)
-                                m.i.push(r.int64());
-                        } else
-                            m.i.push(r.int64());
-                        break;
-                    case 4:
-                        if (!(m.f && m.f.length))
-                            m.f = [];
-                        if ((t & 7) === 2) {
-                            var c2 = r.uint32() + r.pos;
-                            while (r.pos < c2)
-                                m.f.push(r.float());
-                        } else
-                            m.f.push(r.float());
-                        break;
-                    case 5:
-                        if (!(m.b && m.b.length))
-                            m.b = [];
-                        if ((t & 7) === 2) {
-                            var c2 = r.uint32() + r.pos;
-                            while (r.pos < c2)
-                                m.b.push(r.bool());
-                        } else
-                            m.b.push(r.bool());
-                        break;
-                    case 6:
-                        if (!(m.type && m.type.length))
-                            m.type = [];
-                        if ((t & 7) === 2) {
-                            var c2 = r.uint32() + r.pos;
-                            while (r.pos < c2)
-                                m.type.push(r.int32());
-                        } else
-                            m.type.push(r.int32());
-                        break;
-                    case 7:
-                        if (!(m.shape && m.shape.length))
-                            m.shape = [];
-                        m.shape.push($root.tensorflow.TensorShape.decode(r, r.uint32()));
-                        break;
-                    case 8:
-                        if (!(m.tensor && m.tensor.length))
-                            m.tensor = [];
-                        m.tensor.push($root.tensorflow.Tensor.decode(r, r.uint32()));
-                        break;
-                    case 9:
-                        if (!(m.func && m.func.length))
-                            m.func = [];
-                        m.func.push($root.tensorflow.NameAttrList.decode(r, r.uint32()));
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return ListValue;
-        })();
-
-        return AttrValue;
-    })();
-
-    tensorflow.NameAttrList = (function() {
-
-        function NameAttrList(p) {
-            this.attr = {};
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        NameAttrList.prototype.name = "";
-        NameAttrList.prototype.attr = $util.emptyObject;
-
-        NameAttrList.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.NameAttrList(), k;
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.name = r.string();
-                    break;
-                case 2:
-                    r.skip().pos++;
-                    if (m.attr === $util.emptyObject)
-                        m.attr = {};
-                    k = r.string();
-                    r.pos++;
-                    m.attr[k] = $root.tensorflow.AttrValue.decode(r, r.uint32());
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return NameAttrList;
-    })();
-
-    tensorflow.NodeDef = (function() {
-
-        function NodeDef(p) {
-            this.input = [];
-            this.attr = {};
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        NodeDef.prototype.name = "";
-        NodeDef.prototype.op = "";
-        NodeDef.prototype.input = $util.emptyArray;
-        NodeDef.prototype.device = "";
-        NodeDef.prototype.attr = $util.emptyObject;
-
-        NodeDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.NodeDef(), k;
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.name = r.string();
-                    break;
-                case 2:
-                    m.op = r.string();
-                    break;
-                case 3:
-                    if (!(m.input && m.input.length))
-                        m.input = [];
-                    m.input.push(r.string());
-                    break;
-                case 4:
-                    m.device = r.string();
-                    break;
-                case 5:
-                    r.skip().pos++;
-                    if (m.attr === $util.emptyObject)
-                        m.attr = {};
-                    k = r.string();
-                    r.pos++;
-                    m.attr[k] = $root.tensorflow.AttrValue.decode(r, r.uint32());
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return NodeDef;
-    })();
-
-    tensorflow.VersionDef = (function() {
-
-        function VersionDef(p) {
-            this.badConsumers = [];
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        VersionDef.prototype.producer = 0;
-        VersionDef.prototype.minConsumer = 0;
-        VersionDef.prototype.badConsumers = $util.emptyArray;
-
-        VersionDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.VersionDef();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.producer = r.int32();
-                    break;
-                case 2:
-                    m.minConsumer = r.int32();
-                    break;
-                case 3:
-                    if (!(m.badConsumers && m.badConsumers.length))
-                        m.badConsumers = [];
-                    if ((t & 7) === 2) {
-                        var c2 = r.uint32() + r.pos;
-                        while (r.pos < c2)
-                            m.badConsumers.push(r.int32());
-                    } else
-                        m.badConsumers.push(r.int32());
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return VersionDef;
-    })();
-
-    tensorflow.GraphDef = (function() {
-
-        function GraphDef(p) {
-            this.node = [];
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        GraphDef.prototype.node = $util.emptyArray;
-        GraphDef.prototype.versions = null;
-        GraphDef.prototype.library = null;
-
-        GraphDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.GraphDef();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    if (!(m.node && m.node.length))
-                        m.node = [];
-                    m.node.push($root.tensorflow.NodeDef.decode(r, r.uint32()));
-                    break;
-                case 4:
-                    m.versions = $root.tensorflow.VersionDef.decode(r, r.uint32());
-                    break;
-                case 2:
-                    m.library = $root.tensorflow.FunctionDefLibrary.decode(r, r.uint32());
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return GraphDef;
-    })();
-
-    tensorflow.CollectionDef = (function() {
-
-        function CollectionDef(p) {
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        CollectionDef.prototype.nodeList = null;
-        CollectionDef.prototype.bytesList = null;
-        CollectionDef.prototype.int64List = null;
-        CollectionDef.prototype.floatList = null;
-        CollectionDef.prototype.anyList = null;
-
-        var $oneOfFields;
-
-        Object.defineProperty(CollectionDef.prototype, "kind", {
-            get: $util.oneOfGetter($oneOfFields = ["nodeList", "bytesList", "int64List", "floatList", "anyList"]),
-            set: $util.oneOfSetter($oneOfFields)
-        });
-
-        CollectionDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.CollectionDef();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.nodeList = $root.tensorflow.CollectionDef.NodeList.decode(r, r.uint32());
-                    break;
-                case 2:
-                    m.bytesList = $root.tensorflow.CollectionDef.BytesList.decode(r, r.uint32());
-                    break;
-                case 3:
-                    m.int64List = $root.tensorflow.CollectionDef.Int64List.decode(r, r.uint32());
-                    break;
-                case 4:
-                    m.floatList = $root.tensorflow.CollectionDef.FloatList.decode(r, r.uint32());
-                    break;
-                case 5:
-                    m.anyList = $root.tensorflow.CollectionDef.AnyList.decode(r, r.uint32());
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        CollectionDef.NodeList = (function() {
-
-            function NodeList(p) {
-                this.value = [];
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            NodeList.prototype.value = $util.emptyArray;
-
-            NodeList.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.CollectionDef.NodeList();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        if (!(m.value && m.value.length))
-                            m.value = [];
-                        m.value.push(r.string());
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return NodeList;
-        })();
-
-        CollectionDef.BytesList = (function() {
-
-            function BytesList(p) {
-                this.value = [];
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            BytesList.prototype.value = $util.emptyArray;
-
-            BytesList.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.CollectionDef.BytesList();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        if (!(m.value && m.value.length))
-                            m.value = [];
-                        m.value.push(r.bytes());
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return BytesList;
-        })();
-
-        CollectionDef.Int64List = (function() {
-
-            function Int64List(p) {
-                this.value = [];
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            Int64List.prototype.value = $util.emptyArray;
-
-            Int64List.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.CollectionDef.Int64List();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        if (!(m.value && m.value.length))
-                            m.value = [];
-                        if ((t & 7) === 2) {
-                            var c2 = r.uint32() + r.pos;
-                            while (r.pos < c2)
-                                m.value.push(r.int64());
-                        } else
-                            m.value.push(r.int64());
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return Int64List;
-        })();
-
-        CollectionDef.FloatList = (function() {
-
-            function FloatList(p) {
-                this.value = [];
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            FloatList.prototype.value = $util.emptyArray;
-
-            FloatList.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.CollectionDef.FloatList();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        if (!(m.value && m.value.length))
-                            m.value = [];
-                        if ((t & 7) === 2) {
-                            var c2 = r.uint32() + r.pos;
-                            while (r.pos < c2)
-                                m.value.push(r.float());
-                        } else
-                            m.value.push(r.float());
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return FloatList;
-        })();
-
-        CollectionDef.AnyList = (function() {
-
-            function AnyList(p) {
-                this.value = [];
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            AnyList.prototype.value = $util.emptyArray;
-
-            AnyList.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.CollectionDef.AnyList();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        if (!(m.value && m.value.length))
-                            m.value = [];
-                        m.value.push($root.tensorflow.Any.decode(r, r.uint32()));
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return AnyList;
-        })();
-
-        return CollectionDef;
-    })();
-
-    tensorflow.SaverDef = (function() {
-
-        function SaverDef(p) {
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        SaverDef.prototype.filenameTensorName = "";
-        SaverDef.prototype.saveTensorName = "";
-        SaverDef.prototype.restoreOpName = "";
-        SaverDef.prototype.maxToKeep = 0;
-        SaverDef.prototype.sharded = false;
-        SaverDef.prototype.keepCheckpointEveryNHours = 0;
-        SaverDef.prototype.version = 0;
-
-        SaverDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.SaverDef();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.filenameTensorName = r.string();
-                    break;
-                case 2:
-                    m.saveTensorName = r.string();
-                    break;
-                case 3:
-                    m.restoreOpName = r.string();
-                    break;
-                case 4:
-                    m.maxToKeep = r.int32();
-                    break;
-                case 5:
-                    m.sharded = r.bool();
-                    break;
-                case 6:
-                    m.keepCheckpointEveryNHours = r.float();
-                    break;
-                case 7:
-                    m.version = r.int32();
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        SaverDef.CheckpointFormatVersion = (function() {
-            var valuesById = {}, values = Object.create(valuesById);
-            values[valuesById[0] = "LEGACY"] = 0;
-            values[valuesById[1] = "V1"] = 1;
-            values[valuesById[2] = "V2"] = 2;
-            return values;
-        })();
-
-        return SaverDef;
-    })();
-
-    tensorflow.TensorInfo = (function() {
-
-        function TensorInfo(p) {
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        TensorInfo.prototype.name = "";
-        TensorInfo.prototype.cooSparse = null;
-        TensorInfo.prototype.dtype = 0;
-        TensorInfo.prototype.tensorShape = null;
-
-        var $oneOfFields;
-
-        Object.defineProperty(TensorInfo.prototype, "encoding", {
-            get: $util.oneOfGetter($oneOfFields = ["name", "cooSparse"]),
-            set: $util.oneOfSetter($oneOfFields)
-        });
-
-        TensorInfo.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.TensorInfo();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.name = r.string();
-                    break;
-                case 4:
-                    m.cooSparse = $root.tensorflow.TensorInfo.CooSparse.decode(r, r.uint32());
-                    break;
-                case 2:
-                    m.dtype = r.int32();
-                    break;
-                case 3:
-                    m.tensorShape = $root.tensorflow.TensorShape.decode(r, r.uint32());
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        TensorInfo.CooSparse = (function() {
-
-            function CooSparse(p) {
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            CooSparse.prototype.valuesTensorName = "";
-            CooSparse.prototype.indicesTensorName = "";
-            CooSparse.prototype.denseShapeTensorName = "";
-
-            CooSparse.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.TensorInfo.CooSparse();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        m.valuesTensorName = r.string();
-                        break;
-                    case 2:
-                        m.indicesTensorName = r.string();
-                        break;
-                    case 3:
-                        m.denseShapeTensorName = r.string();
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return CooSparse;
-        })();
-
-        return TensorInfo;
-    })();
-
-    tensorflow.SignatureDef = (function() {
-
-        function SignatureDef(p) {
-            this.inputs = {};
-            this.outputs = {};
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        SignatureDef.prototype.inputs = $util.emptyObject;
-        SignatureDef.prototype.outputs = $util.emptyObject;
-        SignatureDef.prototype.methodName = "";
-
-        SignatureDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.SignatureDef(), k;
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    r.skip().pos++;
-                    if (m.inputs === $util.emptyObject)
-                        m.inputs = {};
-                    k = r.string();
-                    r.pos++;
-                    m.inputs[k] = $root.tensorflow.TensorInfo.decode(r, r.uint32());
-                    break;
-                case 2:
-                    r.skip().pos++;
-                    if (m.outputs === $util.emptyObject)
-                        m.outputs = {};
-                    k = r.string();
-                    r.pos++;
-                    m.outputs[k] = $root.tensorflow.TensorInfo.decode(r, r.uint32());
-                    break;
-                case 3:
-                    m.methodName = r.string();
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return SignatureDef;
-    })();
-
-    tensorflow.AssetFileDef = (function() {
-
-        function AssetFileDef(p) {
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        AssetFileDef.prototype.tensorInfo = null;
-        AssetFileDef.prototype.filename = "";
-
-        AssetFileDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.AssetFileDef();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.tensorInfo = $root.tensorflow.TensorInfo.decode(r, r.uint32());
-                    break;
-                case 2:
-                    m.filename = r.string();
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return AssetFileDef;
-    })();
-
-    tensorflow.OpDef = (function() {
-
-        function OpDef(p) {
-            this.inputArg = [];
-            this.outputArg = [];
-            this.attr = [];
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        OpDef.prototype.name = "";
-        OpDef.prototype.inputArg = $util.emptyArray;
-        OpDef.prototype.outputArg = $util.emptyArray;
-        OpDef.prototype.attr = $util.emptyArray;
-        OpDef.prototype.deprecation = null;
-        OpDef.prototype.summary = "";
-        OpDef.prototype.description = "";
-        OpDef.prototype.isCommutative = false;
-        OpDef.prototype.isAggregate = false;
-        OpDef.prototype.isStateful = false;
-        OpDef.prototype.allowsUninitializedInput = false;
-
-        OpDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.OpDef();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.name = r.string();
-                    break;
-                case 2:
-                    if (!(m.inputArg && m.inputArg.length))
-                        m.inputArg = [];
-                    m.inputArg.push($root.tensorflow.OpDef.ArgDef.decode(r, r.uint32()));
-                    break;
-                case 3:
-                    if (!(m.outputArg && m.outputArg.length))
-                        m.outputArg = [];
-                    m.outputArg.push($root.tensorflow.OpDef.ArgDef.decode(r, r.uint32()));
-                    break;
-                case 4:
-                    if (!(m.attr && m.attr.length))
-                        m.attr = [];
-                    m.attr.push($root.tensorflow.OpDef.AttrDef.decode(r, r.uint32()));
-                    break;
-                case 8:
-                    m.deprecation = $root.tensorflow.OpDef.OpDeprecation.decode(r, r.uint32());
-                    break;
-                case 5:
-                    m.summary = r.string();
-                    break;
-                case 6:
-                    m.description = r.string();
-                    break;
-                case 18:
-                    m.isCommutative = r.bool();
-                    break;
-                case 16:
-                    m.isAggregate = r.bool();
-                    break;
-                case 17:
-                    m.isStateful = r.bool();
-                    break;
-                case 19:
-                    m.allowsUninitializedInput = r.bool();
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        OpDef.ArgDef = (function() {
-
-            function ArgDef(p) {
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            ArgDef.prototype.name = "";
-            ArgDef.prototype.description = "";
-            ArgDef.prototype.type = 0;
-            ArgDef.prototype.typeAttr = "";
-            ArgDef.prototype.numberAttr = "";
-            ArgDef.prototype.typeListAttr = "";
-            ArgDef.prototype.isRef = false;
-
-            ArgDef.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.OpDef.ArgDef();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        m.name = r.string();
-                        break;
-                    case 2:
-                        m.description = r.string();
-                        break;
-                    case 3:
-                        m.type = r.int32();
-                        break;
-                    case 4:
-                        m.typeAttr = r.string();
-                        break;
-                    case 5:
-                        m.numberAttr = r.string();
-                        break;
-                    case 6:
-                        m.typeListAttr = r.string();
-                        break;
-                    case 16:
-                        m.isRef = r.bool();
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return ArgDef;
-        })();
-
-        OpDef.AttrDef = (function() {
-
-            function AttrDef(p) {
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            AttrDef.prototype.name = "";
-            AttrDef.prototype.type = "";
-            AttrDef.prototype.defaultValue = null;
-            AttrDef.prototype.description = "";
-            AttrDef.prototype.hasMinimum = false;
-            AttrDef.prototype.minimum = $util.Long ? $util.Long.fromBits(0,0,false) : 0;
-            AttrDef.prototype.allowedValues = null;
-
-            AttrDef.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.OpDef.AttrDef();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        m.name = r.string();
-                        break;
-                    case 2:
-                        m.type = r.string();
-                        break;
-                    case 3:
-                        m.defaultValue = $root.tensorflow.AttrValue.decode(r, r.uint32());
-                        break;
-                    case 4:
-                        m.description = r.string();
-                        break;
-                    case 5:
-                        m.hasMinimum = r.bool();
-                        break;
-                    case 6:
-                        m.minimum = r.int64();
-                        break;
-                    case 7:
-                        m.allowedValues = $root.tensorflow.AttrValue.decode(r, r.uint32());
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return AttrDef;
-        })();
-
-        OpDef.OpDeprecation = (function() {
-
-            function OpDeprecation(p) {
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            OpDeprecation.prototype.version = 0;
-            OpDeprecation.prototype.explanation = "";
-
-            OpDeprecation.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.OpDef.OpDeprecation();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        m.version = r.int32();
-                        break;
-                    case 2:
-                        m.explanation = r.string();
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return OpDeprecation;
-        })();
-
-        return OpDef;
-    })();
-
-    tensorflow.OpList = (function() {
-
-        function OpList(p) {
-            this.op = [];
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        OpList.prototype.op = $util.emptyArray;
-
-        OpList.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.OpList();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    if (!(m.op && m.op.length))
-                        m.op = [];
-                    m.op.push($root.tensorflow.OpDef.decode(r, r.uint32()));
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return OpList;
-    })();
-
-    tensorflow.MetaGraphDef = (function() {
-
-        function MetaGraphDef(p) {
-            this.collectionDef = {};
-            this.signatureDef = {};
-            this.assetFileDef = [];
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        MetaGraphDef.prototype.metaInfoDef = null;
-        MetaGraphDef.prototype.graphDef = null;
-        MetaGraphDef.prototype.saverDef = null;
-        MetaGraphDef.prototype.collectionDef = $util.emptyObject;
-        MetaGraphDef.prototype.signatureDef = $util.emptyObject;
-        MetaGraphDef.prototype.assetFileDef = $util.emptyArray;
-
-        MetaGraphDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.MetaGraphDef(), k;
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.metaInfoDef = $root.tensorflow.MetaGraphDef.MetaInfoDef.decode(r, r.uint32());
-                    break;
-                case 2:
-                    m.graphDef = $root.tensorflow.GraphDef.decode(r, r.uint32());
-                    break;
-                case 3:
-                    m.saverDef = $root.tensorflow.SaverDef.decode(r, r.uint32());
-                    break;
-                case 4:
-                    r.skip().pos++;
-                    if (m.collectionDef === $util.emptyObject)
-                        m.collectionDef = {};
-                    k = r.string();
-                    r.pos++;
-                    m.collectionDef[k] = $root.tensorflow.CollectionDef.decode(r, r.uint32());
-                    break;
-                case 5:
-                    r.skip().pos++;
-                    if (m.signatureDef === $util.emptyObject)
-                        m.signatureDef = {};
-                    k = r.string();
-                    r.pos++;
-                    m.signatureDef[k] = $root.tensorflow.SignatureDef.decode(r, r.uint32());
-                    break;
-                case 6:
-                    if (!(m.assetFileDef && m.assetFileDef.length))
-                        m.assetFileDef = [];
-                    m.assetFileDef.push($root.tensorflow.AssetFileDef.decode(r, r.uint32()));
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        MetaGraphDef.MetaInfoDef = (function() {
-
-            function MetaInfoDef(p) {
-                this.tags = [];
-                if (p)
-                    for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                        if (p[ks[i]] != null)
-                            this[ks[i]] = p[ks[i]];
-            }
-
-            MetaInfoDef.prototype.metaGraphVersion = "";
-            MetaInfoDef.prototype.strippedOpList = null;
-            MetaInfoDef.prototype.anyInfo = null;
-            MetaInfoDef.prototype.tags = $util.emptyArray;
-            MetaInfoDef.prototype.tensorflowVersion = "";
-            MetaInfoDef.prototype.tensorflowGitVersion = "";
-
-            MetaInfoDef.decode = function decode(r, l) {
-                if (!(r instanceof $Reader))
-                    r = $Reader.create(r);
-                var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.MetaGraphDef.MetaInfoDef();
-                while (r.pos < c) {
-                    var t = r.uint32();
-                    switch (t >>> 3) {
-                    case 1:
-                        m.metaGraphVersion = r.string();
-                        break;
-                    case 2:
-                        m.strippedOpList = $root.tensorflow.OpList.decode(r, r.uint32());
-                        break;
-                    case 3:
-                        m.anyInfo = $root.tensorflow.Any.decode(r, r.uint32());
-                        break;
-                    case 4:
-                        if (!(m.tags && m.tags.length))
-                            m.tags = [];
-                        m.tags.push(r.string());
-                        break;
-                    case 5:
-                        m.tensorflowVersion = r.string();
-                        break;
-                    case 6:
-                        m.tensorflowGitVersion = r.string();
-                        break;
-                    default:
-                        r.skipType(t & 7);
-                        break;
-                    }
-                }
-                return m;
-            };
-
-            return MetaInfoDef;
-        })();
-
-        return MetaGraphDef;
-    })();
-
-    tensorflow.SavedModel = (function() {
-
-        function SavedModel(p) {
-            this.metaGraphs = [];
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        SavedModel.prototype.savedModelSchemaVersion = $util.Long ? $util.Long.fromBits(0,0,false) : 0;
-        SavedModel.prototype.metaGraphs = $util.emptyArray;
-
-        SavedModel.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.SavedModel();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.savedModelSchemaVersion = r.int64();
-                    break;
-                case 2:
-                    if (!(m.metaGraphs && m.metaGraphs.length))
-                        m.metaGraphs = [];
-                    m.metaGraphs.push($root.tensorflow.MetaGraphDef.decode(r, r.uint32()));
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return SavedModel;
-    })();
-
-    tensorflow.FunctionDefLibrary = (function() {
-
-        function FunctionDefLibrary(p) {
-            this["function"] = [];
-            this.gradient = [];
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        FunctionDefLibrary.prototype["function"] = $util.emptyArray;
-        FunctionDefLibrary.prototype.gradient = $util.emptyArray;
-
-        FunctionDefLibrary.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.FunctionDefLibrary();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    if (!(m["function"] && m["function"].length))
-                        m["function"] = [];
-                    m["function"].push($root.tensorflow.FunctionDef.decode(r, r.uint32()));
-                    break;
-                case 2:
-                    if (!(m.gradient && m.gradient.length))
-                        m.gradient = [];
-                    m.gradient.push($root.tensorflow.GradientDef.decode(r, r.uint32()));
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return FunctionDefLibrary;
-    })();
-
-    tensorflow.FunctionDef = (function() {
-
-        function FunctionDef(p) {
-            this.attr = {};
-            this.nodeDef = [];
-            this.ret = {};
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        FunctionDef.prototype.signature = null;
-        FunctionDef.prototype.attr = $util.emptyObject;
-        FunctionDef.prototype.nodeDef = $util.emptyArray;
-        FunctionDef.prototype.ret = $util.emptyObject;
-
-        FunctionDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.FunctionDef(), k;
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.signature = $root.tensorflow.OpDef.decode(r, r.uint32());
-                    break;
-                case 5:
-                    r.skip().pos++;
-                    if (m.attr === $util.emptyObject)
-                        m.attr = {};
-                    k = r.string();
-                    r.pos++;
-                    m.attr[k] = $root.tensorflow.AttrValue.decode(r, r.uint32());
-                    break;
-                case 3:
-                    if (!(m.nodeDef && m.nodeDef.length))
-                        m.nodeDef = [];
-                    m.nodeDef.push($root.tensorflow.NodeDef.decode(r, r.uint32()));
-                    break;
-                case 4:
-                    r.skip().pos++;
-                    if (m.ret === $util.emptyObject)
-                        m.ret = {};
-                    k = r.string();
-                    r.pos++;
-                    m.ret[k] = r.string();
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return FunctionDef;
-    })();
-
-    tensorflow.GradientDef = (function() {
-
-        function GradientDef(p) {
-            if (p)
-                for (var ks = Object.keys(p), i = 0; i < ks.length; ++i)
-                    if (p[ks[i]] != null)
-                        this[ks[i]] = p[ks[i]];
-        }
-
-        GradientDef.prototype.functionName = "";
-        GradientDef.prototype.gradientFunc = "";
-
-        GradientDef.decode = function decode(r, l) {
-            if (!(r instanceof $Reader))
-                r = $Reader.create(r);
-            var c = l === undefined ? r.len : r.pos + l, m = new $root.tensorflow.GradientDef();
-            while (r.pos < c) {
-                var t = r.uint32();
-                switch (t >>> 3) {
-                case 1:
-                    m.functionName = r.string();
-                    break;
-                case 2:
-                    m.gradientFunc = r.string();
-                    break;
-                default:
-                    r.skipType(t & 7);
-                    break;
-                }
-            }
-            return m;
-        };
-
-        return GradientDef;
-    })();
-
-    return tensorflow;
-})();
-
-module.exports = $root;
diff --git a/tfjs-master/tfjs-converter/tools/model_summary.ts b/tfjs-master/tfjs-converter/tools/model_summary.ts
deleted file mode 100644
index c6fddf384..000000000
--- a/tfjs-master/tfjs-converter/tools/model_summary.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as fs from 'fs';
-
-function summarize(argv: string[]) {
-  if (argv.length < 3) {
-    console.log('Usage: yarn model-summary model_file');
-    return;
-  }
-
-  const sourcePath = process.argv[2];
-  console.log('reading pb model file: ' + sourcePath);
-  const rawdata = fs.readFileSync(sourcePath);
-  const model = JSON.parse(rawdata.toString());
-  if (model.format !== 'graph-model') {
-    console.log('This tool only supports TFJS Graph models.');
-    return;
-  }
-  // tslint:disable-next-line: no-any
-  let nodes: any[] = model['modelTopology']['node'];
-  const library = model['modelTopology']['library'];
-  if (library != null) {
-    const functions = library['function'];
-
-    // tslint:disable-next-line: no-any
-    if (functions != null) {
-      functions.forEach((func: any) => nodes = nodes.concat(func['nodeDef']));
-    }
-  }
-
-  const opCount: {[key: string]: number} = {};
-  nodes.forEach(opNode => {
-    let count = 0;
-    const op = opNode['op'];
-    if (opCount[op]) {
-      count = opCount[op];
-    }
-    opCount[op] = count + 1;
-  });
-
-  const keys = Object.keys(opCount).sort();
-  keys.forEach(key => console.log(`${key}: ${opCount[key]}`));
-  console.log(`Total ops = ${nodes.length}`);
-}
-
-summarize(process.argv);
diff --git a/tfjs-master/tfjs-converter/tools/pb2json_converter.ts b/tfjs-master/tfjs-converter/tools/pb2json_converter.ts
deleted file mode 100644
index 61db9f153..000000000
--- a/tfjs-master/tfjs-converter/tools/pb2json_converter.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as fs from 'fs';
-import * as Long from 'long';
-import * as path from 'path';
-
-import {tensorflow} from './compiled_api';
-
-function replacer(key: string, value: any) {
-  if (value instanceof Long) {
-    return (value as Long).toString();
-  }
-  if (value instanceof Uint8Array) {
-    return Array.from(value);
-  }
-  return value;
-}
-const PB_MODEL_FILENAME = 'tensorflowjs_model.pb';
-const WEIGHT_FILENAME = 'weights_manifest.json';
-const JSON_MODEL_FILENAME = 'model.json';
-function convert(argv: string[]) {
-  if (argv.length < 4) {
-    console.log(
-        'Usage: ts-node pb2json.ts pb_model_directory json_model_directory');
-    return;
-  }
-
-  const sourcePath = process.argv[2];
-  console.log('reading pb model directory: ' + sourcePath);
-  fs.readdir(sourcePath, (err, files) => {
-    if (![PB_MODEL_FILENAME, WEIGHT_FILENAME].every(
-            file => files.indexOf(file) !== -1)) {
-      console.log(
-          'Please make sure the pb model directory contains ' +
-          'tensorflowjs_model.pb and weights_manifest.json files.');
-      return;
-    }
-
-    const modelFile = path.join(sourcePath, PB_MODEL_FILENAME);
-    console.log('reading pb file: ' + modelFile);
-    const buffer = fs.readFileSync(modelFile);
-
-    const modelTopology = tensorflow.GraphDef.decode(new Uint8Array(buffer));
-
-    const manifestFile = path.join(sourcePath, WEIGHT_FILENAME);
-    console.log('reading manifest file: ' + manifestFile);
-    const weightsManifest = JSON.parse(fs.readFileSync(manifestFile, 'utf8'));
-
-    const destPath = process.argv[3];
-
-    if (!fs.existsSync(destPath)) {
-      fs.mkdirSync(destPath);
-    }
-    const destModelFile = path.join(destPath, JSON_MODEL_FILENAME);
-    console.log('writing json file: ' + destModelFile);
-    fs.writeFileSync(
-        destModelFile,
-        JSON.stringify({modelTopology, weightsManifest}, replacer));
-
-    files.forEach(file => {
-      if (file !== PB_MODEL_FILENAME && file !== WEIGHT_FILENAME) {
-        fs.copyFile(
-            path.join(sourcePath, file), path.join(destPath, file), (err) => {
-              if (err) throw err;
-              console.log(`Weight file: ${file} copied.`);
-            });
-      }
-    });
-  });
-}
-
-convert(process.argv);
diff --git a/tfjs-master/tfjs-converter/tools/tsconfig.json b/tfjs-master/tfjs-converter/tools/tsconfig.json
deleted file mode 100644
index dcad96028..000000000
--- a/tfjs-master/tfjs-converter/tools/tsconfig.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "compilerOptions": {
-    "module": "commonjs",
-    "target": "es5"
-  }
-}
diff --git a/tfjs-master/tfjs-converter/tsconfig.json b/tfjs-master/tfjs-converter/tsconfig.json
deleted file mode 100644
index 5ea8addec..000000000
--- a/tfjs-master/tfjs-converter/tsconfig.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist",
-    "downlevelIteration": true
-  }
-}
diff --git a/tfjs-master/tfjs-converter/yarn.lock b/tfjs-master/tfjs-converter/yarn.lock
deleted file mode 100644
index a0535ff08..000000000
--- a/tfjs-master/tfjs-converter/yarn.lock
+++ /dev/null
@@ -1,507 +0,0 @@
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-# yarn lockfile v1
-
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-
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-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/npm-normalize-package-bin/-/npm-normalize-package-bin-1.0.1.tgz#6e79a41f23fd235c0623218228da7d9c23b8f6e2"
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-once@^1.3.0:
-  version "1.4.0"
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-  dependencies:
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-
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-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/opn/-/opn-5.1.0.tgz#72ce2306a17dbea58ff1041853352b4a8fc77519"
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-
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-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
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-
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-  version "7.2.4"
-  resolved "https://registry.yarnpkg.com/protobufjs/-/protobufjs-7.2.4.tgz#3fc1ec0cdc89dd91aef9ba6037ba07408485c3ae"
-  integrity sha512-AT+RJgD2sH8phPmCf7OUZR8xGdcJRga4+1cOaXJ64hvcSkVhNcRHOwIxUatPH15+nj59WAGTDv3LSGZPEQbJaQ==
-  dependencies:
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-    "@protobufjs/base64" "^1.1.2"
-    "@protobufjs/codegen" "^2.0.4"
-    "@protobufjs/eventemitter" "^1.1.0"
-    "@protobufjs/fetch" "^1.1.0"
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-    "@protobufjs/utf8" "^1.1.0"
-    "@types/node" ">=13.7.0"
-    long "^5.0.0"
-
-require-directory@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/require-directory/-/require-directory-2.1.1.tgz#8c64ad5fd30dab1c976e2344ffe7f792a6a6df42"
-  integrity sha512-fGxEI7+wsG9xrvdjsrlmL22OMTTiHRwAMroiEeMgq8gzoLC/PQr7RsRDSTLUg/bZAZtF+TVIkHc6/4RIKrui+Q==
-
-source-map-support@^0.5.6:
-  version "0.5.19"
-  resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.19.tgz#a98b62f86dcaf4f67399648c085291ab9e8fed61"
-  integrity sha512-Wonm7zOCIJzBGQdB+thsPar0kYuCIzYvxZwlBa87yi/Mdjv7Tip2cyVbLj5o0cFPN4EVkuTwb3GDDyUx2DGnGw==
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-    buffer-from "^1.0.0"
-    source-map "^0.6.0"
-
-source-map@^0.6.0:
-  version "0.6.1"
-  resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
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-
-sprintf-js@~1.0.2:
-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/sprintf-js/-/sprintf-js-1.0.3.tgz#04e6926f662895354f3dd015203633b857297e2c"
-  integrity sha1-BOaSb2YolTVPPdAVIDYzuFcpfiw=
-
-string-width@^4.1.0, string-width@^4.2.0:
-  version "4.2.3"
-  resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.3.tgz#269c7117d27b05ad2e536830a8ec895ef9c6d010"
-  integrity sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==
-  dependencies:
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-    is-fullwidth-code-point "^3.0.0"
-    strip-ansi "^6.0.1"
-
-strip-ansi@^6.0.0, strip-ansi@^6.0.1:
-  version "6.0.1"
-  resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-6.0.1.tgz#9e26c63d30f53443e9489495b2105d37b67a85d9"
-  integrity sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==
-  dependencies:
-    ansi-regex "^5.0.1"
-
-supports-color@^7.1.0:
-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.2.0.tgz#1b7dcdcb32b8138801b3e478ba6a51caa89648da"
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-  dependencies:
-    has-flag "^4.0.0"
-
-tr46@~0.0.3:
-  version "0.0.3"
-  resolved "https://registry.yarnpkg.com/tr46/-/tr46-0.0.3.tgz#8184fd347dac9cdc185992f3a6622e14b9d9ab6a"
-  integrity sha1-gYT9NH2snNwYWZLzpmIuFLnZq2o=
-
-ts-node@~8.8.2:
-  version "8.8.2"
-  resolved "https://registry.yarnpkg.com/ts-node/-/ts-node-8.8.2.tgz#0b39e690bee39ea5111513a9d2bcdc0bc121755f"
-  integrity sha512-duVj6BpSpUpD/oM4MfhO98ozgkp3Gt9qIp3jGxwU2DFvl/3IRaEAvbLa8G60uS7C77457e/m5TMowjedeRxI1Q==
-  dependencies:
-    arg "^4.1.0"
-    diff "^4.0.1"
-    make-error "^1.1.1"
-    source-map-support "^0.5.6"
-    yn "3.1.1"
-
-typescript@4.9.4:
-  version "4.9.4"
-  resolved "https://registry.yarnpkg.com/typescript/-/typescript-4.9.4.tgz#a2a3d2756c079abda241d75f149df9d561091e78"
-  integrity sha512-Uz+dTXYzxXXbsFpM86Wh3dKCxrQqUcVMxwU54orwlJjOpO3ao8L7j5lH+dWfTwgCwIuM9GQ2kvVotzYJMXTBZg==
-
-universalify@^0.1.0:
-  version "0.1.2"
-  resolved "https://registry.yarnpkg.com/universalify/-/universalify-0.1.2.tgz#b646f69be3942dabcecc9d6639c80dc105efaa66"
-  integrity sha512-rBJeI5CXAlmy1pV+617WB9J63U6XcazHHF2f2dbJix4XzpUF0RS3Zbj0FGIOCAva5P/d/GBOYaACQ1w+0azUkg==
-
-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
-
-whatwg-url@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
-  integrity sha1-lmRU6HZUYuN2RNNib2dCzotwll0=
-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
-  dependencies:
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-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==
-
-y18n@^5.0.5:
-  version "5.0.8"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.8.tgz#7f4934d0f7ca8c56f95314939ddcd2dd91ce1d55"
-  integrity sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==
-
-yalc@~1.0.0-pre.50:
-  version "1.0.0-pre.53"
-  resolved "https://registry.yarnpkg.com/yalc/-/yalc-1.0.0-pre.53.tgz#c51db2bb924a6908f4cb7e82af78f7e5606810bc"
-  integrity sha512-tpNqBCpTXplnduzw5XC+FF8zNJ9L/UXmvQyyQj7NKrDNavbJtHvzmZplL5ES/RCnjX7JR7W9wz5GVDXVP3dHUQ==
-  dependencies:
-    chalk "^4.1.0"
-    detect-indent "^6.0.0"
-    fs-extra "^8.0.1"
-    glob "^7.1.4"
-    ignore "^5.0.4"
-    ini "^2.0.0"
-    npm-packlist "^2.1.5"
-    yargs "^16.1.1"
-
-yargs-parser@^20.2.2:
-  version "20.2.9"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
-  integrity sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==
-
-yargs@^16.1.1:
-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yn@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
-  integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==
diff --git a/tfjs-master/tfjs-core/.bazelrc b/tfjs-master/tfjs-core/.bazelrc
deleted file mode 100644
index a69cab90b..000000000
--- a/tfjs-master/tfjs-core/.bazelrc
+++ /dev/null
@@ -1 +0,0 @@
-build --symlink_prefix=dist/
diff --git a/tfjs-master/tfjs-core/.npmignore b/tfjs-master/tfjs-core/.npmignore
deleted file mode 100644
index 664d161d9..000000000
--- a/tfjs-master/tfjs-core/.npmignore
+++ /dev/null
@@ -1,31 +0,0 @@
-# Ignore other packages in the mono-repo.
-tfjs-backend-webgpu/
-tfjs-backend-nodegl/
-tfjs-react-native/
-
-.vscode/
-.rpt2_cache/
-src/**/*_test.ts
-integration_tests/
-
-dist/backends/**/*_test.js
-models/
-coverage/
-package/
-**/node_modules/
-karma.conf.js
-*.tgz
-*.log
-.travis.yml
-CONTRIBUTING.md
-tslint.json
-yarn.lock
-DEVELOPMENT.md
-ISSUE_TEMPLATE.md
-PULL_REQUEST_TEMPLATE.md
-rollup.config.js
-tsconfig.json
-.yalc/
-yalc.lock
-tfjs-react-native/
-tfjs-backend-nodegl/
diff --git a/tfjs-master/tfjs-core/.npmrc b/tfjs-master/tfjs-core/.npmrc
deleted file mode 100644
index 43c97e719..000000000
--- a/tfjs-master/tfjs-core/.npmrc
+++ /dev/null
@@ -1 +0,0 @@
-package-lock=false
diff --git a/tfjs-master/tfjs-core/.vscode/launch.json b/tfjs-master/tfjs-core/.vscode/launch.json
deleted file mode 100644
index 9281ba120..000000000
--- a/tfjs-master/tfjs-core/.vscode/launch.json
+++ /dev/null
@@ -1,19 +0,0 @@
-{
-  // Use IntelliSense to learn about possible attributes.
-  // Hover to view descriptions of existing attributes.
-  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-  "version": "0.2.0",
-  "configurations": [
-    {
-      "type": "chrome",
-      "request": "attach",
-      "name": "Attach Karma Chrome",
-      "address": "localhost",
-      "port": 9333,
-      "pathMapping": {
-        "/": "${workspaceRoot}",
-        "/base/": "${workspaceRoot}/"
-      }
-    }
-  ]
-}
diff --git a/tfjs-master/tfjs-core/.vscode/settings.json b/tfjs-master/tfjs-core/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-core/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-core/.vscode/tasks.json b/tfjs-master/tfjs-core/.vscode/tasks.json
deleted file mode 100644
index 594b0eea8..000000000
--- a/tfjs-master/tfjs-core/.vscode/tasks.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
-    "version": "2.0.0",
-    "tasks": [
-      {
-        "command": "yarn",
-        "label": "lint",
-        "type": "shell",
-        "args": [
-          "lint"
-        ],
-        "problemMatcher": {
-          "base": "$tslint5",
-          "owner": "tslint-type-checked",
-          "fileLocation": "absolute"
-        }
-      },
-      {
-        "command": "yarn",
-        "label": "build",
-        "type": "shell",
-        "args": ["build", "--pretty", "false", "--noEmit"],
-        "problemMatcher": [
-          "$tsc"
-        ]
-      }
-    ]
-  }
diff --git a/tfjs-master/tfjs-core/BUILD.bazel b/tfjs-master/tfjs-core/BUILD.bazel
deleted file mode 100644
index ea1b141a9..000000000
--- a/tfjs-master/tfjs-core/BUILD.bazel
+++ /dev/null
@@ -1,184 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//rules:copy_file.bzl", "copy_file")
-load("@build_bazel_rules_nodejs//:index.bzl", "nodejs_test", "pkg_npm")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-# Allow typescript rules in any package to reference this file
-exports_files([
-    "tsconfig.json",
-    "package.json",
-])
-
-tfjs_bundle(
-    name = "tf-core",
-    entry_point = "//tfjs-core/src:index.ts",
-    external = [
-        "crypto",
-    ],
-    leave_as_require = [
-        "crypto",
-        "node-fetch",
-        "util",
-    ],
-    umd_name = "tf",
-    deps = [
-        "//tfjs-core/src:tfjs-core_lib",
-    ],
-)
-
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-    ],
-    root = "src",
-)
-
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-core",
-        ":tf-core.es2017",
-        ":tf-core.es2017.min",
-        ":tf-core.fesm",
-        ":tf-core.fesm.min",
-        ":tf-core.min",
-        ":tf-core.node",
-    ],
-)
-
-copy_ts_library_to_dist(
-    name = "copy_test_snippets",
-    srcs = [
-        "//tfjs-core/scripts/test_snippets:test_snippets_util_lib",
-    ],
-    root = ".",
-)
-
-copy_file(
-    name = "copy_miniprogram",
-    src = ":tf-core.min.js",
-    out = "dist/miniprogram/index.js",
-)
-
-copy_file(
-    name = "copy_miniprogram_map",
-    src = ":tf-core.min.js.map",
-    out = "dist/miniprogram/index.js.map",
-)
-
-pkg_npm(
-    name = "tfjs-core_pkg",
-    package_name = "@tensorflow/tfjs-core",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_miniprogram",
-        ":copy_miniprogram_map",
-        ":copy_src_to_dist",
-        ":copy_test_snippets",
-    ],
-)
-
-tfjs_web_test(
-    name = "tfjs-core_test",
-    srcs = [
-        "//tfjs-core/src:tfjs-core_test_bundle",
-    ],
-    browsers = [
-        "bs_android_10",
-        "bs_chrome_mac",
-        "bs_firefox_mac",
-        "bs_ios_12",
-        "bs_safari_mac",
-        "win_10_chrome",
-    ],
-    static_files = [
-        # Listed here so sourcemaps are served
-        "//tfjs-core/src:tfjs-core_test_bundle",
-    ],
-)
-
-# Defined here because chdir must be a subdirectory of the directory the rule is
-# defined in.
-nodejs_test(
-    name = "test_snippets_test",
-    chdir = "tfjs-core",
-    data = [
-        ":tsconfig.json",
-        "//:tsconfig.json",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/scripts/test_snippets:test_snippets_lib",
-        "//tfjs-core/src:all_srcs",
-    ],
-    entry_point = "//tfjs-core/scripts/test_snippets:test_snippets.ts",
-    link_workspace_root = True,
-    tags = [
-        "ci",
-        # Run the test locally since it makes http requests.
-        "no-remote-exec",
-        "requires-network",
-    ],
-)
-
-nodejs_test(
-    name = "tfjs-core_node_test",
-    data = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/src:setup_test_lib",
-        "//tfjs-core/src:test_node_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-    ],
-    entry_point = "//tfjs-core/src:test_node.ts",
-    link_workspace_root = True,
-    tags = ["ci"],
-)
-
-nodejs_test(
-    name = "tfjs-core_async_backends_test",
-    data = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-    ],
-    entry_point = "//tfjs-core/src:test_async_backends.ts",
-    link_workspace_root = True,
-    tags = ["ci"],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        ":test_snippets_test",
-        ":tfjs-core_async_backends_test",
-        ":tfjs-core_node_test",
-        ":tfjs-core_test",
-        "//tfjs-core/src:worker_node_test",
-        "//tfjs-core/src:worker_test",
-    ],
-)
diff --git a/tfjs-master/tfjs-core/README.md b/tfjs-master/tfjs-core/README.md
deleted file mode 100644
index 0de6436b3..000000000
--- a/tfjs-master/tfjs-core/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# TensorFlow.js Core API
-
-A part of the TensorFlow.js ecosystem, this repo hosts `@tensorflow/tfjs-core`,
-the TensorFlow.js Core API, which provides low-level, hardware-accelerated
-linear algebra operations and an eager API for automatic differentiation.
-
-Check out [js.tensorflow.org](https://js.tensorflow.org) for more
-information about the library, tutorials and API docs.
-
-To keep track of issues we use the [tensorflow/tfjs](https://github.com/tensorflow/tfjs) Github repo.
-
-## Importing
-
-You can install TensorFlow.js via yarn or npm. We recommend using the
-[@tensorflow/tfjs](https://www.npmjs.com/package/@tensorflow/tfjs) npm package,
-which gives you both this Core API and the higher-level
-[Layers API](/tfjs-layers):
-
-```js
-import * as tf from '@tensorflow/tfjs';
-// You have the Core API: tf.matMul(), tf.softmax(), ...
-// You also have Layers API: tf.model(), tf.layers.dense(), ...
-```
-
-On the other hand, if you care about the bundle size and you do not use the
-Layers API, you can import only the Core API:
-
-```js
-import * as tfc from '@tensorflow/tfjs-core';
-// You have the Core API: tfc.matMul(), tfc.softmax(), ...
-// No Layers API.
-```
-
-**Note**: If you are only importing the Core API, you also need to import a
-backend (e.g., [tfjs-backend-cpu](/tfjs-backend-cpu),
-[tfjs-backend-webgl](/tfjs-backend-webgl), [tfjs-backend-wasm](/tfjs-backend-wasm)).
-
-For info about development, check out [DEVELOPMENT.md](/DEVELOPMENT.md).
-
-## For more information
-
-- [TensorFlow.js API documentation](https://js.tensorflow.org/api/latest/)
-- [TensorFlow.js Tutorials](https://js.tensorflow.org/tutorials/)
-
-Thanks <a href="https://www.browserstack.com/">BrowserStack</a> for providing testing support.
diff --git a/tfjs-master/tfjs-core/development/op_modularization.md b/tfjs-master/tfjs-core/development/op_modularization.md
deleted file mode 100644
index e2916561e..000000000
--- a/tfjs-master/tfjs-core/development/op_modularization.md
+++ /dev/null
@@ -1,168 +0,0 @@
- # How to modularize an op
-
-_This document generally describes the world that we are moving towards as we modularise tfjs. Some of its descriptions hold for the current state of the world._ It's primarily a pragmatic workflow guide, you don't have to follow the steps in this exact order, but it can be a helpful starting place/checklist.
-
-Glossary
-
-**Op**: In TensorFlow.js an op is a backend agnostic function that is generally exposed as public API to end users. These are implemented in `tfjs-core`
-
-**Kernel**: In TensorFlow.js a kernel is a backend specific low level implementation of functionality that is used by one or more ops. The kernels and their interfaces that are available in tfjs are defined in `tfjs-core/src/kernel_names.ts`. Kernels should not call other kernels nor call back into the public API of tfjs. Kernels may share code as regular function imports.
-
-**Gradient**: The definition of backward mode operation for a given **Kernel**. These are implemented in tfjs-core and are also backend agnostic (i.e. they call other ops or kernels).
-
-**runKernelFunc**: A function in tfjs-core's engine that executes functions. It can handle both modular and non-modular kernels (non-modular kernels are kernels called through the backend object rather than the kernel registry). Will be replaced with **runKernel** once all kernels are modular across all backends.
-
-## Steps in tfjs-core
-
-Note: We will be modularising **all the ops** before modularizing **any of** the kernels in the various backends.
-
-**Before you start:** Go to [this issue](https://github.com/tensorflow/tfjs/issues/2822) and tell us which op you want to work on by leaving a comment.
-
-- Add necessary kernel names and interfaces to **tfjs-core/src/kernel_names.ts**
-
-    This **must** include an identifier for the kernel and optionally types for the `Inputs` and `Attrs`. Use these identifiers in Op and Kernel definitions. As closely as possible we want to match the interface defined by the [C++ API](https://www.tensorflow.org/api_docs/cc). We cannot always match exactly, so reach out for guidance if you are unsure.
-
-    ```ts
-    export const SquaredDifference = 'SquaredDifference';
-    export type SquaredDifferenceInputs = Pick<NamedTensorInfoMap, 'a'|'b'>;
-    ```
-
-- Create `src/ops/op_name.ts`
-
-    Move op definition into this file. e.g. `tfjs-core/src/ops/squared_difference.ts`
-
-    Note that we still use runKernelFunc to support backends that haven't yet modularized their kernels. This the forward and backward function will be defined here as well as in the modular kernel(s)/gradient(s).
-
-    Generally ops should only do input validation and data transformations to make the parameters **match the kernel interface.** Any other data transformation should be done by kernels. The guiding principle here is that the work of the kernel (as defined by its interface) should not be split between an op and a kernel definition. Note: in some cases when you move responsibility for data manipulation from ops to kernels, _older_ modualized kernels will break (i.e. fail their tests, e.g. kernels in the wasm backend). In these cases you also need to go in and adjust those kernels to match the new input.
-
-    ```ts
-    import {ENGINE, ForwardFunc} from '../engine';
-    import {SquaredDifference, SquaredDifferenceInputs} from '../kernel_names';
-    import {Tensor} from '../tensor';
-    import {NamedTensorMap} from '../tensor_types';
-    import {makeTypesMatch} from '../tensor_util';
-    import {convertToTensor} from '../tensor_util_env';
-    import {TensorLike} from '../types';
-
-    import {assertAndGetBroadcastShape} from './broadcast_util';
-    import {op} from './operation';
-    import {scalar} from './tensor_ops';
-
-
-    function squaredDifference_<T extends Tensor>(
-        a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-      let $a = convertToTensor(a, 'a', 'squaredDifference');
-      let $b = convertToTensor(b, 'b', 'squaredDifference');
-      [$a, $b] = makeTypesMatch($a, $b);
-
-      assertAndGetBroadcastShape($a.shape, $b.shape);
-      // ****************
-      // Modularization note: this gradient definition should be removed from
-      // here once the modular gradient is implemented in the steps below.
-      //*****************
-      const der = (dy: Tensor, saved: Tensor[]) => {
-        const [$a, $b] = saved;
-        const two = scalar(2);
-        const derA = () => dy.mul($a.sub($b).mul(two));
-        const derB = () => dy.mul($b.sub($a).mul(two));
-        return {a: derA, b: derB};
-      };
-      // ****************
-      // END Modularization note
-      //*****************
-
-
-      const forward: ForwardFunc<Tensor> = (backend, save) => {
-        const res = backend.squaredDifference($a, $b);
-        save([$a, $b]);
-        return res;
-      };
-
-      const inputs: SquaredDifferenceInputs = {a: $a, b: $b};
-      const attrs = {};
-
-      const inputsToSave = [$a, $b];
-      const outputToSave: boolean[] = [];
-      return ENGINE.runKernelFunc(
-                 forward, inputs as unknown as NamedTensorMap, der,
-                 SquaredDifference, attrs, inputsToSave, outputToSave) as T;
-    }
-
-    export const squaredDifference = op({squaredDifference_});
-    ```
-
-
-
-- Export modularized op from `src/ops/ops.ts`
-
-    ```ts
-    export {squaredDifference} from './squared_difference';
-    ```
-
-- Make chained op augmentor in `src/public/chained_ops/op_name.ts`
-
-    `src/public/chained_ops/squared_difference.ts`
-
-    ```ts
-
-    import {squaredDifference} from '../../ops/squared_difference';
-    import {Tensor} from '../../tensor';
-    import {Rank, TensorLike} from '../../types';
-
-    declare module '../../tensor' {
-      interface Tensor<R extends Rank = Rank> {
-        squaredDifference<T extends Tensor>(b: Tensor|TensorLike): T;
-      }
-    }
-
-    Tensor.prototype.squaredDifference = function<T extends Tensor>(b: Tensor|
-                                                                    TensorLike): T {
-      this.throwIfDisposed();
-      return squaredDifference(this, b);
-    };
-    ```
-
-- Add augmentor to `src/public/chained_ops/register_all_chained_ops.ts`
-- Add chained op test to `src/public/chained_ops/register_all_chained_ops_test.ts`
-- Remove op from `src/tensor.ts`
-  - Remove it from the `Tensor` class and from the `OpHandler` interface
-
-- Create `src/gradients/kernel_name` for any kernels used that do not already have a modular gradient
-
-    e.g. `src/gradients/SquaredDifference_grad.ts`
-
-    Note that we use **directly imported ops**. Avoid using the chained API
-
-    ```ts
-    import {SquaredDifference} from '../kernel_names';
-    import {GradConfig} from '../kernel_registry';
-    import {mul, sub} from '../ops/binary_ops';
-    import {scalar} from '../ops/tensor_ops';
-    import {Tensor} from '../tensor';
-
-    export const squaredDifferenceGradConfig: GradConfig = {
-      kernelName: SquaredDifference,
-      gradFunc: (dy: Tensor, saved: Tensor[]) => {
-        const [a, b] = saved;
-        const two = scalar(2);
-        const derA = () => mul(dy, mul(two, sub(a, b)));
-        const derB = () => mul(dy, mul(two, sub(b, a)));
-        return {a: derA, b: derB};
-      }
-    };
-    ```
-
-- Add gradient config to `src/register_all_gradients.ts`
-
-    ```ts
-    import {squaredDifferenceGradConfig} from './gradients/SquaredDifference_grad';
-
-    const gradConfigs: GradConfig[] = [
-      // add the gradient config to this list.
-      squaredDifferenceGradConfig,
-    ];
-    ```
-
-## Submit a PR
-
-At this stage you should be able to submit a PR for review. Don't forget to run `yarn test` locally within `tfjs-core` before you do!
diff --git a/tfjs-master/tfjs-core/package.json b/tfjs-master/tfjs-core/package.json
deleted file mode 100644
index 8021b6fda..000000000
--- a/tfjs-master/tfjs-core/package.json
+++ /dev/null
@@ -1,77 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-core",
-  "version": "0.0.0",
-  "description": "Hardware-accelerated JavaScript library for machine intelligence",
-  "private": false,
-  "main": "dist/tf-core.node.js",
-  "jsdelivr": "dist/tf-core.min.js",
-  "unpkg": "dist/tf-core.min.js",
-  "types": "dist/index.d.ts",
-  "jsnext:main": "dist/index.js",
-  "module": "dist/index.js",
-  "miniprogram": "dist/miniprogram",
-  "engines": {
-    "yarn": ">= 1.3.2"
-  },
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-core"
-  },
-  "license": "Apache-2.0",
-  "devDependencies": {
-    "@bazel/bazelisk": "^1.12.0",
-    "@bazel/ibazel": "^0.16.2"
-  },
-  "scripts": {
-    "build": "bazel build :tfjs-core_pkg",
-    "publish-npm": "bazel run :tfjs-core_pkg.publish",
-    "coverage": "bazel coverage :tfjs-core_node_test",
-    "test": "bazel test :tests --test_output=all",
-    "test-dev": "ibazel test :tests --test_output=all",
-    "test-browser": "bazel test :tfjs-core_test --test_output=streamed",
-    "test-browser-debug": "bazel run :tfjs-core_test --test_output=streamed",
-    "test-node": "bazel test :tfjs-core_node_test --test_output=streamed",
-    "test-node-debug": "bazel run :tfjs-core_node_test --config=debug --test_output=streamed",
-    "test-async-backends": "bazel test :tfjs-core_async_backends_test --test_output=streamed",
-    "test-snippets": "bazel test :test_snippets_test --test_output=streamed"
-  },
-  "dependencies": {
-    "@types/long": "^4.0.1",
-    "@types/offscreencanvas": "~2019.7.0",
-    "@types/seedrandom": "^2.4.28",
-    "@webgpu/types": "0.1.38",
-    "long": "4.0.0",
-    "node-fetch": "~2.6.1",
-    "seedrandom": "^3.0.5"
-  },
-  "browser": {
-    "node-fetch": false,
-    "util": false,
-    "crypto": false,
-    "worker_threads": false
-  },
-  "sideEffects": [
-    "./dist/index.js",
-    "./dist/engine.js",
-    "./dist/tensor.js",
-    "./dist/base_side_effects.js",
-    "./dist/flags.js",
-    "./dist/platforms/*.js",
-    "./dist/register_all_gradients.js",
-    "./dist/public/chained_ops/*.js",
-    "./dist/io/*.js",
-    "./src/index.mjs",
-    "./src/engine.mjs",
-    "./src/tensor.mjs",
-    "./src/base_side_effects.mjs",
-    "./src/flags.mjs",
-    "./src/platforms/*.mjs",
-    "./src/register_all_gradients.mjs",
-    "./src/public/chained_ops/*.mjs",
-    "./src/io/*.mjs"
-  ],
-  "resolutions": {
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-core/scripts/build-npm.sh b/tfjs-master/tfjs-core/scripts/build-npm.sh
deleted file mode 100644
index 55dd985ad..000000000
--- a/tfjs-master/tfjs-core/scripts/build-npm.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2017 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn rimraf dist/
-yarn
-
-yarn build
-yarn build-test-snippets
-yarn rollup -c --visualize --npm
-
-# Use minified files for miniprogram
-mkdir dist/miniprogram
-cp dist/tf-core.min.js dist/miniprogram/index.js
-cp dist/tf-core.min.js.map dist/miniprogram/index.js.map
-
-echo "Stored standalone library at dist/tf-core(.min).js"
diff --git a/tfjs-master/tfjs-core/scripts/cloud_funcs/README.md b/tfjs-master/tfjs-core/scripts/cloud_funcs/README.md
deleted file mode 100644
index 2d56e9e01..000000000
--- a/tfjs-master/tfjs-core/scripts/cloud_funcs/README.md
+++ /dev/null
@@ -1,50 +0,0 @@
-This directory contains the following Google Cloud Functions.
-
-### `trigger_nightly`
-Programatically triggers a Cloud Build on master. This function is called by the Cloud Scheduler at 3am EST every day (configurable via the Cloud Scheduler UI).
-You can also trigger the function manually via the Cloud UI.
-
-Command to re-deploy:
-```sh
-gcloud functions deploy nightly \
-  --runtime nodejs8 \
-  --trigger-topic nightly
-```
-
-If a build was triggered by nightly, there is a substitution variable `_NIGHTLY=true`.
-You can forward the substitution as the `NIGHTLY` environment variable so the scripts can use it, by specifying `env: ['NIGHTLY=$_NIGHTLY']` in `cloudbuild.yml`.
-
-### `send_email`
-Sends an email and a chat message with the nightly build status. Every build sends a message to the `cloud-builds` topic with its build information. The `send_email` function is subscribed to that topic and ignores all builds (e.g. builds triggered by pull requests) **except** for the nightly build and sends an email to an internal mailing list with its build status around 3:10am.
-
-Command to re-deploy:
-
-```sh
-gcloud functions deploy send_email \
-  --runtime nodejs8 \
-  --stage-bucket learnjs-174218_cloudbuild \
-  --trigger-topic cloud-builds \
-  --set-env-vars MAILGUN_API_KEY="[API_KEY_HERE]",HANGOUTS_URL="[URL_HERE]"
-```
-
-### `sync_reactnative`
-Makes a request to browserStack to sync the current build of the tfjs-react-native integration app to browserstack. The app itself is stored in a GCP bucket. This needs to be done at least once every 30 days and is triggered via cloud scheduler via the `sync_reactnative` topic.
-Currently set to run weekly on Thursdays at 3AM.
-
-Command to re-deploy:
-
-```sh
-gcloud functions deploy sync_reactnative \
-  --runtime nodejs8 \
-  --trigger-topic sync_reactnative \
-  --set-env-vars HANGOUTS_URL="[URL_HERE]",BOTS_HANGOUTS_URL="[URL_HERE]"
-```
-
-### The pipeline
-
-The pipeline looks like this:
-
-1) At 3am, Cloud Scheduler writes to `nightly` topic
-2) That triggers the `nightly` function, which starts a build programatically
-3) That build runs and writes its status to `cloud-builds` topic
-4) That triggers the `send_email` function, which sends email and chat with the build status.
diff --git a/tfjs-master/tfjs-core/scripts/test-bundle-size.js b/tfjs-master/tfjs-core/scripts/test-bundle-size.js
deleted file mode 100644
index 6c936d863..000000000
--- a/tfjs-master/tfjs-core/scripts/test-bundle-size.js
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-const shell = require('shelljs');
-const {exec} = require('../../scripts/test-util');
-const {showDiff, getFileSizeBytes} = require('../../scripts/bundle-size-util');
-
-// Get the bundle sizes from this change.
-exec(`yarn rollup -c --ci`, {silent: true});
-const minSize = getFileSizeBytes('dist/tf-core.min.js');
-
-// Clone master and get the bundle size from master.
-const dirName = '/tmp/tfjs-core-bundle';
-const coreDirName = 'tfjs-core';
-exec(
-    `git clone --depth=1 --single-branch ` +
-        `https://github.com/tensorflow/tfjs ${dirName}`,
-    {silent: true});
-
-shell.cd(dirName);
-shell.cd(coreDirName);
-exec(`yarn && yarn rollup -c --ci`, {silent: true});
-
-const masterMinSize = getFileSizeBytes('dist/tf-core.min.js');
-
-console.log(`~~~~ minified bundle ~~~~`);
-console.log(`==> post-gzip`)
-showDiff(minSize.gzipFileSizeBytes, masterMinSize.gzipFileSizeBytes);
-console.log();
-console.log(`==> pre-gzip`)
-showDiff(minSize.fileSizeBytes, masterMinSize.fileSizeBytes);
-console.log();
-console.log();
-
-exec(`rm -r ${dirName}`);
diff --git a/tfjs-master/tfjs-core/scripts/test_snippets/BUILD.bazel b/tfjs-master/tfjs-core/scripts/test_snippets/BUILD.bazel
deleted file mode 100644
index 27a10ea3a..000000000
--- a/tfjs-master/tfjs-core/scripts/test_snippets/BUILD.bazel
+++ /dev/null
@@ -1,31 +0,0 @@
-load("//tools:defaults.bzl", "ts_library")
-
-package(default_visibility = ["//visibility:public"])
-
-ts_library(
-    name = "test_snippets_util_lib",
-    srcs = [
-        "util.ts",
-    ],
-    module_name = "@tensorflow/tfjs-core/dist/scripts/test_snippets",
-    deps = [
-        "@npm//@types/node",
-        "@npm//typescript",
-    ],
-)
-
-ts_library(
-    name = "test_snippets_lib",
-    testonly = True,
-    srcs = [
-        "test_snippets.ts",
-    ],
-    module_name = "@tensorflow/tfjs-core/dist/scripts/test_snippets",
-    deps = [
-        ":test_snippets_util_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-    ],
-)
diff --git a/tfjs-master/tfjs-core/scripts/test_snippets/test_snippets.ts b/tfjs-master/tfjs-core/scripts/test_snippets/test_snippets.ts
deleted file mode 100644
index a410d85d3..000000000
--- a/tfjs-master/tfjs-core/scripts/test_snippets/test_snippets.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../src/index';
-import '../../src/public/chained_ops/register_all_chained_ops';
-import '../../src/register_all_gradients';
-import '@tensorflow/tfjs-backend-cpu';
-import {parseAndEvaluateSnippets} from './util';
-
-parseAndEvaluateSnippets(tf);
diff --git a/tfjs-master/tfjs-core/scripts/test_snippets/util.ts b/tfjs-master/tfjs-core/scripts/test_snippets/util.ts
deleted file mode 100644
index 20e5e6bd9..000000000
--- a/tfjs-master/tfjs-core/scripts/test_snippets/util.ts
+++ /dev/null
@@ -1,183 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as fs from 'fs';
-import * as path from 'path';
-import * as ts from 'typescript';
-
-process.on('unhandledRejection', ex => {
-  throw ex;
-});
-
-// Used for logging the number of snippets that have been found.
-let snippetCount = 0;
-// Used for counting the number of errors that have been found.
-let errorCount = 0;
-
-/**
- * Parse and evaluate snippets for the src/index.ts from where this script is
- * run.
- * @param tf The TensorFlow.js module to use when evaluating snippets. If used
- *     outside core, this should be a union of core and the separate package.
- *     This is unused here but is used in eval() of the snippets.
- */
-// tslint:disable-next-line:no-any
-export async function parseAndEvaluateSnippets(tf: any) {
-  const index = path.join(process.cwd(), 'src/index.ts');
-  const tsconfigPath = path.join(process.cwd(), 'tsconfig.json');
-
-  // Use the same compiler options that we use to compile the library
-  // here.
-  const tsconfig = JSON.parse(fs.readFileSync(tsconfigPath, 'utf8'));
-
-  delete tsconfig.compilerOptions.moduleResolution;
-  const program = ts.createProgram([index], tsconfig.compilerOptions);
-
-  const checker = program.getTypeChecker();
-
-  for (const sourceFile of program.getSourceFiles()) {
-    if (!sourceFile.isDeclarationFile) {
-      const children = sourceFile.getChildren();
-      for (let i = 0; i < children.length; i++) {
-        await visit(tf, checker, children[i], sourceFile);
-      }
-    }
-  }
-
-  if (errorCount === 0) {
-    console.log(`Parsed and evaluated ${snippetCount} snippets successfully.`);
-  } else {
-    console.log(
-        `Evaluated ${snippetCount} snippets with ${errorCount} errors.`);
-    process.exit(1);
-  }
-}
-
-async function visit(
-    // tslint:disable-next-line:no-any
-    tf: any, checker: ts.TypeChecker, node: ts.Node,
-    sourceFile: ts.SourceFile) {
-  const children = node.getChildren();
-  for (let i = 0; i < children.length; i++) {
-    await visit(tf, checker, children[i], sourceFile);
-  }
-
-  if (ts.isClassDeclaration(node) || ts.isFunctionDeclaration(node) ||
-      ts.isMethodDeclaration(node) || ts.isInterfaceDeclaration(node)) {
-    const symbol = checker.getSymbolAtLocation(node.name);
-    const jsdoc = getJSDocTag(symbol);
-    if (jsdoc == null) {
-      return;
-    }
-    // Ignore snippets of methods that have been marked with ignoreCI.
-    if (jsdoc['ignoreCI']) {
-      return;
-    }
-
-    const documentation = symbol.getDocumentationComment(checker);
-    if (documentation == null) {
-      return;
-    }
-    for (let i = 0; i < documentation.length; i++) {
-      const doc = documentation[i];
-      const re = /```js.*?```/gs;
-      const matches = re.exec(doc.text);
-      if (matches == null) {
-        return;
-      }
-
-      for (let k = 0; k < matches.length; k++) {
-        snippetCount++;
-
-        const match = matches[k];
-        const lines = match.split('\n');
-        const evalLines: string[] = [];
-        for (let j = 0; j < lines.length; j++) {
-          let line = lines[j];
-          if (line.startsWith('```js')) {
-            line = line.substring('```js'.length);
-          }
-          if (line.endsWith('```')) {
-            line = line.substring(0, line.length - '```'.length);
-          }
-          line = line.trim();
-          if (line.startsWith('*')) {
-            line = line.substring(1).trim();
-          }
-          evalLines.push(line);
-        }
-
-        const srcCode = evalLines.join('\n');
-        const evalString = `(async () => {${srcCode}})()`;
-
-        const oldLog = console.log;
-        const oldWarn = console.warn;
-
-        const reportError = (e: string|Error) => {
-          oldLog();
-          oldLog(`Error executing snippet for ${symbol.name} at ${
-              sourceFile.fileName}`);
-          oldLog();
-          oldLog(`\`\`\`js${srcCode}\`\`\``);
-          oldLog();
-
-          console.error(e);
-          errorCount++;
-        };
-
-        // Overrwrite console.log so we don't spam the console.
-        console.log = (msg: string) => {};
-        console.warn = (msg: string) => {};
-        try {
-          await eval(evalString);
-        } catch (e) {
-          reportError(e);
-        }
-        console.log = oldLog;
-        console.warn = oldWarn;
-      }
-    }
-  }
-}
-
-interface JSDoc {
-  namespace?: string;
-  ignoreCI?: boolean;
-}
-
-function getJSDocTag(symbol: ts.Symbol): JSDoc {
-  const tags = symbol.getJsDocTags();
-  for (let i = 0; i < tags.length; i++) {
-    const jsdocTag = tags[i];
-    if (jsdocTag.name === 'doc' && jsdocTag.text != null) {
-      if (jsdocTag.text.length !== 1) {
-        throw new Error('Expected exactly one jsdoc SymbolDisplayPart but got'
-          + ` ${jsdocTag.text.length} instead: ${jsdocTag.text}`);
-      }
-      const text = jsdocTag.text[0].text.trim();
-      const json = convertDocStringToDocInfoObject(text);
-      return json;
-    }
-  }
-  return null;
-}
-
-function convertDocStringToDocInfoObject(docString: string): JSDoc {
-  const jsonString =
-      docString.replace(/([a-zA-Z0-9]+):/g, '"$1":').replace(/\'/g, '"');
-  return JSON.parse(jsonString);
-}
diff --git a/tfjs-master/tfjs-core/scripts/touch_modular_op_files.ts b/tfjs-master/tfjs-core/scripts/touch_modular_op_files.ts
deleted file mode 100644
index e64c3b330..000000000
--- a/tfjs-master/tfjs-core/scripts/touch_modular_op_files.ts
+++ /dev/null
@@ -1,130 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * A helper script to generate empty files typically used in modularizing an op.
- * params:
- *    --op the name of the op
- *    --chained is this op part of the chained api (optional)
- *    --grad the name of the kernel(s) to create gradient files for
- *
- * It assumes you run it from tfjs_core.
- *
- * Example
- *  npx ts-node -s scripts/touch_modular_op_files.ts --op op_name --grad \
- *    KernelName --chained
- *
- * Generates the following files (they will be empty)
- *    tfjs_core/src/ops/op_name.ts
- *
- *  if --chained is present
- *    tfjs_core/src/public/chained_ops/op_name.ts
- *
- *  if --kernel is present
- *    tfjs_core/src/gradients/KernelName_grad.ts
- *
- * Example 2 (multiple kernels)
- *  npx ts-node -s scripts/touch_modular_op_files.ts --grad Kernel1,Kernel2
- */
-
-import * as argparse from 'argparse';
-import {execSync} from 'child_process';
-import * as fs from 'fs';
-
-const parser = new argparse.ArgumentParser();
-
-parser.addArgument('--op', {help: 'the name of the op'});
-parser.addArgument('--grad', {help: 'the name of the grad.'});
-parser.addArgument('--chained', {
-  action: 'storeTrue',
-  defaultValue: false,
-  help: 'is this op part of the chained api.'
-});
-
-async function main() {
-  const args = parser.parseArgs();
-  console.log('Called touch_modular_op_files with args:', args);
-
-  if (args.op != null) {
-    const ops: string[] = args.op.split(',').filter((o: string) => o != null);
-    ops.forEach(op => {
-      let filePath = `./src/ops/${op}.ts`;
-      let command = `touch ${filePath}`;
-      execSync(command);
-
-      // create a test file
-      filePath = `./src/ops/${op}_test.ts`;
-      command = `touch ${filePath}`;
-      execSync(command);
-
-      if (args.chained) {
-        filePath = `./src/public/chained_ops/${op}.ts`;
-        command = `touch ${filePath}`;
-        execSync(command);
-      }
-    });
-  }
-
-  if (args.grad) {
-    const downcaseFirstChar = (str: string) => {
-      return str.charAt(0).toLowerCase() + str.slice(1);
-    };
-
-    const kernels: string[] =
-        args.grad.split(',').filter((k: string) => k != null);
-
-    kernels.forEach(kernelName => {
-      const gradientFileTemplate = `/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {${kernelName}, ${kernelName}Attrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-
-export const ${downcaseFirstChar(kernelName)}GradConfig: GradConfig = {
-  kernelName: ${kernelName},
-  inputsToSave: [], // UPDATE ME
-  outputsToSave: [], // UPDATE ME
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [] = saved;
-    const {} = attrs as unknown as ${kernelName}Attrs;
-    return {
-    };
-  }
-};
-`;
-      const filePath = `./src/gradients/${kernelName}_grad.ts`;
-      fs.writeFileSync(filePath, gradientFileTemplate, {flag: 'a'});
-    });
-  }
-}
-
-main();
diff --git a/tfjs-master/tfjs-core/scripts/tsconfig.json b/tfjs-master/tfjs-core/scripts/tsconfig.json
deleted file mode 100644
index c5e8c9340..000000000
--- a/tfjs-master/tfjs-core/scripts/tsconfig.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "extends": "../tsconfig.test",
-  "compilerOptions": {
-    "module": "commonjs"
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/BUILD.bazel b/tfjs-master/tfjs-core/src/BUILD.bazel
deleted file mode 100644
index e10b62016..000000000
--- a/tfjs-master/tfjs-core/src/BUILD.bazel
+++ /dev/null
@@ -1,286 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@npm//@bazel/jasmine:index.bzl", "jasmine_node_test")
-load("//tools:defaults.bzl", "esbuild", "ts_library")
-load("//tools:enumerate_tests.bzl", "enumerate_tests")
-load("//tools:make_version_test_file.bzl", "make_version_test_file")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-TEST_SRCS = [
-    "**/*_test.ts",
-    "image_test_util.ts",
-]
-
-TEST_ENTRYPOINTS = [
-    "test_node.ts",
-    "setup_test.ts",
-    "worker_test.ts",
-    "worker_node_test.ts",
-    "platforms/platform_node_test.ts",
-    "ops/from_pixels_worker_test.ts",
-]
-
-# Used for test-snippets
-filegroup(
-    name = "all_srcs",
-    srcs = glob(["**/*.ts"]),
-)
-
-make_version_test_file(
-    name = "version_test",
-    package_json = "//tfjs-core:package.json",
-    version_name = "version_core",
-)
-
-filegroup(
-    name = "shared_test_srcs",
-    srcs = glob(
-        ["**/*_test.ts"],
-        exclude = TEST_ENTRYPOINTS,
-    ) + [
-        ":version_test",
-    ],
-)
-
-# Generates the 'tests.ts' file that imports all test entrypoints.
-enumerate_tests(
-    name = "tests",
-    srcs = [":shared_test_srcs"],
-    root_path = "tfjs-core/src",
-)
-
-# Compiles the majority of tfjs-core using the `@tensorflow/tfjs-core/dist`
-# module name.
-ts_library(
-    name = "tfjs-core_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + TEST_ENTRYPOINTS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-core/dist",
-    deps = [
-        "@npm//@types/jasmine",
-        "@npm//@types/long",
-        "@npm//@types/node",
-        "@npm//@types/offscreencanvas",
-        "@npm//@types/seedrandom",
-        "@npm//@webgpu/types",
-        "@npm//jasmine",
-        "@npm//long",
-        "@npm//node-fetch",
-        "@npm//seedrandom",
-    ],
-)
-
-# Compiles the `index.ts` entrypoint of tfjs-core separately from the rest of
-# the sources in order to use the `@tensorflow/tfjs-core` module name instead
-# of `@tensorflow/tfjs-core/dist`,
-ts_library(
-    name = "tfjs-core_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-core",
-    deps = [
-        ":tfjs-core_src_lib",
-    ],
-)
-
-ts_library(
-    name = "tfjs-core_test_lib",
-    srcs = glob(
-        TEST_SRCS,
-        exclude = TEST_ENTRYPOINTS,
-    ) + [
-        ":tests",
-        ":version_test",
-    ],
-    # TODO(msoulanille): Mark this as testonly once it's no longer needed in the
-    # npm package (for other downstream packages' tests).
-    module_name = "@tensorflow/tfjs-core/dist",
-    deps = [
-        ":tfjs-core_lib",
-        ":tfjs-core_src_lib",
-    ],
-)
-
-ts_library(
-    name = "setup_test_lib",
-    srcs = [
-        "setup_test.ts",
-    ],
-    deps = [
-        ":tfjs-core_src_lib",
-        ":tfjs-core_test_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-    ],
-)
-
-ts_library(
-    name = "test_node_lib",
-    srcs = [
-        "test_node.ts",
-    ],
-    deps = [
-        # test_node.ts depends on setup_test.js as a runtime dependency,
-        # so it is not listed here (it is listed in the nodejs_test, instead).
-        ":tfjs-core_src_lib",
-        ":tfjs-core_test_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-    ],
-)
-
-esbuild(
-    name = "tfjs-core_test_bundle",
-    testonly = True,
-    entry_point = "setup_test.ts",
-    external = [
-        # webworker tests call 'require('@tensorflow/tfjs')', which
-        # is external to the test bundle.
-        # Note: This is not a bazel target. It's just a string.
-        "@tensorflow/tfjs",
-        "worker_threads",
-        "util",
-    ],
-    sources_content = True,
-    deps = [
-        ":setup_test_lib",
-        ":tfjs-core_lib",
-        ":tfjs-core_test_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-    ],
-)
-
-ts_library(
-    name = "worker_node_test_lib",
-    srcs = [
-        "worker_node_test.ts",
-    ],
-    deps = [
-        ":tfjs-core_src_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-    ],
-)
-
-jasmine_node_test(
-    name = "worker_node_test",
-    deps = [
-        ":worker_node_test_lib",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js.map",
-        "//tfjs-core:tf-core.min.js",
-        "//tfjs-core:tf-core.min.js.map",
-    ],
-)
-
-ts_library(
-    name = "platform_node_test_lib",
-    srcs = [
-        "platforms/platform_node_test.ts",
-    ],
-    deps = [
-        ":tfjs-core_lib",
-        ":tfjs-core_src_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "@npm//@types/node",
-    ],
-)
-
-jasmine_node_test(
-    name = "platform_node_test",
-    deps = [
-        ":platform_node_test_lib",
-    ],
-)
-
-ts_library(
-    name = "worker_test_lib",
-    srcs = [
-        "worker_test.ts",
-    ],
-    deps = [
-        ":tfjs-core_lib",
-        ":tfjs-core_src_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-    ],
-)
-
-tfjs_web_test(
-    name = "worker_test",
-    browsers = [
-        "bs_chrome_mac",
-        "bs_firefox_mac",
-        "bs_safari_mac",
-        # Temporarily disabled because BrowserStack does not support loading
-        # absolute paths in iOS, which is required for loading the worker.
-        # https://www.browserstack.com/question/39573
-        # "bs_ios_12",
-        "bs_android_10",
-        "win_10_chrome",
-    ],
-    static_files = [
-        # For the webworker
-        "//tfjs-core:tf-core.min.js",
-        "//tfjs-core:tf-core.min.js.map",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js.map",
-    ],
-    deps = [
-        ":worker_test_lib",
-        "@npm//long:long__umd",
-        "@npm//seedrandom:seedrandom__umd",
-    ],
-)
-
-ts_library(
-    name = "from_pixels_worker_test_lib",
-    srcs = [
-        "ops/from_pixels_worker_test.ts",
-    ],
-    deps = [
-        ":tfjs-core_lib",
-        ":tfjs-core_src_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "@npm//@types/offscreencanvas",
-    ],
-)
-
-tfjs_web_test(
-    name = "from_pixels_worker_test",
-    browsers = [
-        "bs_chrome_mac",
-        # Omit Firefox since it does not support offscreen canvas.
-        "bs_safari_mac",
-        # Temporarily disabled because BrowserStack does not support loading
-        # absolute paths in iOS, which is required for loading the worker.
-        # https://www.browserstack.com/question/39573
-        # "bs_ios_12",
-        "bs_android_10",
-        "win_10_chrome",
-    ],
-    static_files = [
-        # For the webworker
-        "//tfjs-core:tf-core.min.js",
-        "//tfjs-core:tf-core.min.js.map",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js.map",
-    ],
-    deps = [
-        ":from_pixels_worker_test_lib",
-        "@npm//long:long__umd",
-        "@npm//seedrandom:seedrandom__umd",
-    ],
-)
diff --git a/tfjs-master/tfjs-core/src/backends/backend.ts b/tfjs-master/tfjs-core/src/backends/backend.ts
deleted file mode 100644
index 6f23ec0a3..000000000
--- a/tfjs-master/tfjs-core/src/backends/backend.ts
+++ /dev/null
@@ -1,162 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Backend, DataToGPUOptions, GPUData, Tensor} from '../tensor';
-import {DataId} from '../tensor_info';
-import {BackendValues, DataType, WebGLData, WebGPUData} from '../types';
-
-export const EPSILON_FLOAT32 = 1e-7;
-export const EPSILON_FLOAT16 = 1e-4;
-
-// Required information for all backends.
-export interface BackendTimingInfo {
-  kernelMs: number|{error: string};
-  getExtraProfileInfo?(): string;  // a field for additional timing information
-                                   // e.g. packing / unpacking for WebGL backend
-}
-
-export interface TensorStorage {
-  read(dataId: DataId): Promise<BackendValues>;
-  readSync(dataId: DataId): BackendValues;
-  disposeData(dataId: DataId, force?: boolean): boolean;
-  write(values: BackendValues, shape: number[], dtype: DataType): DataId;
-  move(
-      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,
-      refCount: number): void;
-  memory(): {unreliable: boolean;};  // Backend-specific information.
-  /** Returns number of data ids currently in the storage. */
-  numDataIds(): number;
-  refCount(dataId: DataId): number;
-}
-
-/** Convenient class for storing tensor-related data. */
-export class DataStorage<T> {
-  private data = new WeakMap<DataId, T>();
-  private dataIdsCount = 0;
-
-  constructor(private backend: KernelBackend, private dataMover: DataMover) {}
-
-  get(dataId: DataId) {
-    if (!this.data.has(dataId)) {
-      this.dataMover.moveData(this.backend, dataId);
-    }
-    return this.data.get(dataId);
-  }
-
-  set(dataId: DataId, value: T): void {
-    this.dataIdsCount++;
-    this.data.set(dataId, value);
-  }
-
-  has(dataId: DataId): boolean {
-    return this.data.has(dataId);
-  }
-
-  delete(dataId: DataId): boolean {
-    this.dataIdsCount--;
-    return this.data.delete(dataId);
-  }
-
-  numDataIds(): number {
-    return this.dataIdsCount;
-  }
-}
-
-export interface DataMover {
-  /**
-   * To be called by backends whenever they see a dataId that they don't own.
-   * Upon calling this method, the mover will fetch the tensor from another
-   * backend and register it with the current active backend.
-   */
-  moveData(backend: KernelBackend, dataId: DataId): void;
-}
-
-export interface BackendTimer {
-  // check if backend timer is available
-  timerAvailable(): boolean;
-  time(f: () => void): Promise<BackendTimingInfo>;
-}
-
-/**
- * The interface that defines the kernels that should be implemented when
- * adding a new backend. New backends don't need to implement every one of the
- * methods, this can be done gradually (throw an error for unimplemented
- * methods).
- */
-export class KernelBackend implements TensorStorage, Backend, BackendTimer {
-  refCount(dataId: DataId): number {
-    return notYetImplemented('refCount');
-  }
-  incRef(dataId: DataId): void {
-    return notYetImplemented('incRef');
-  }
-  timerAvailable(): boolean {
-    return true;
-  }
-  time(f: () => void): Promise<BackendTimingInfo> {
-    return notYetImplemented('time');
-  }
-  read(dataId: object): Promise<BackendValues> {
-    return notYetImplemented('read');
-  }
-  readSync(dataId: object): BackendValues {
-    return notYetImplemented('readSync');
-  }
-  readToGPU(dataId: object, options?: DataToGPUOptions): GPUData {
-    return notYetImplemented('readToGPU');
-  }
-  numDataIds(): number {
-    return notYetImplemented('numDataIds');
-  }
-  disposeData(dataId: object, force?: boolean): boolean {
-    return notYetImplemented('disposeData');
-  }
-  write(values: BackendValues, shape: number[], dtype: DataType): DataId {
-    return notYetImplemented('write');
-  }
-  move(
-      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,
-      refCount: number): void {
-    return notYetImplemented('move');
-  }
-
-  createTensorFromGPUData(
-      values: WebGLData|WebGPUData, shape: number[], dtype: DataType): Tensor {
-    return notYetImplemented('createTensorFromGPUData');
-  }
-
-  memory(): {unreliable: boolean; reasons?: string[]} {
-    return notYetImplemented('memory');
-  }
-  /** Returns the highest precision for floats in bits (e.g. 16 or 32) */
-  floatPrecision(): 16|32 {
-    return notYetImplemented('floatPrecision');
-  }
-  /** Returns the smallest representable number.  */
-  epsilon(): number {
-    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
-  }
-  dispose(): void {
-    return notYetImplemented('dispose');
-  }
-}
-
-function notYetImplemented(kernelName: string): never {
-  throw new Error(
-      `'${kernelName}' not yet implemented or not found in the registry. ` +
-      `This kernel may not be supported by the tfjs backend you have chosen`);
-}
diff --git a/tfjs-master/tfjs-core/src/backends/backend_test.ts b/tfjs-master/tfjs-core/src/backends/backend_test.ts
deleted file mode 100644
index 550f90fd3..000000000
--- a/tfjs-master/tfjs-core/src/backends/backend_test.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {EPSILON_FLOAT16, EPSILON_FLOAT32} from './backend';
-
-describeWithFlags('epsilon', ALL_ENVS, () => {
-  it('Epsilon is a function of float precision', () => {
-    const epsilonValue = tf.backend().floatPrecision() === 32 ?
-        EPSILON_FLOAT32 :
-        EPSILON_FLOAT16;
-    expect(tf.backend().epsilon()).toBe(epsilonValue);
-  });
-
-  it('abs(epsilon) > 0', async () => {
-    expect(await tf.abs(tf.backend().epsilon()).array()).toBeGreaterThan(0);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/backends/backend_util.ts b/tfjs-master/tfjs-core/src/backends/backend_util.ts
deleted file mode 100644
index 4175b9cc3..000000000
--- a/tfjs-master/tfjs-core/src/backends/backend_util.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {decodeString, encodeString} from '../util';
-
-// Utilities needed by backend consumers of tf-core.
-export * from '../ops/axis_util';
-export * from '../ops/broadcast_util';
-export * from '../ops/concat_util';
-export * from '../ops/conv_util';
-export * from '../ops/fused_util';
-export * from '../ops/fused_types';
-export * from '../ops/ragged_to_dense_util';
-export * from '../ops/reduce_util';
-
-import * as slice_util from '../ops/slice_util';
-export {slice_util};
-
-export {BackendValues, TypedArray, upcastType, PixelData} from '../types';
-export {MemoryInfo, TimingInfo} from '../engine';
-export * from '../ops/rotate_util';
-export * from '../ops/array_ops_util';
-export * from '../ops/gather_nd_util';
-export * from '../ops/scatter_nd_util';
-export * from '../ops/selu_util';
-export * from '../ops/fused_util';
-export * from '../ops/erf_util';
-export * from '../log';
-export * from '../backends/complex_util';
-export * from '../backends/einsum_util';
-export * from '../ops/split_util';
-export * from '../ops/sparse/sparse_fill_empty_rows_util';
-export * from '../ops/sparse/sparse_reshape_util';
-export * from '../ops/sparse/sparse_segment_reduction_util';
-
-import * as segment_util from '../ops/segment_util';
-export {segment_util};
-
-export function fromUint8ToStringArray(vals: Uint8Array[]) {
-  try {
-    // Decode the bytes into string.
-    return vals.map(val => decodeString(val));
-  } catch (err) {
-    throw new Error(
-        `Failed to decode encoded string bytes into utf-8, error: ${err}`);
-  }
-}
-
-export function fromStringArrayToUint8(strings: string[]) {
-  return strings.map(s => encodeString(s));
-}
diff --git a/tfjs-master/tfjs-core/src/backends/complex_util.ts b/tfjs-master/tfjs-core/src/backends/complex_util.ts
deleted file mode 100644
index 28bfdd052..000000000
--- a/tfjs-master/tfjs-core/src/backends/complex_util.ts
+++ /dev/null
@@ -1,153 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TypedArray} from '../types';
-/**
- * Merges real and imaginary Float32Arrays into a single complex Float32Array.
- *
- * The memory layout is interleaved as follows:
- * real: [r0, r1, r2]
- * imag: [i0, i1, i2]
- * complex: [r0, i0, r1, i1, r2, i2]
- *
- * This is the inverse of splitRealAndImagArrays.
- *
- * @param real The real values of the complex tensor values.
- * @param imag The imag values of the complex tensor values.
- * @returns A complex tensor as a Float32Array with merged values.
- */
-export function mergeRealAndImagArrays(
-    real: Float32Array, imag: Float32Array): Float32Array {
-  if (real.length !== imag.length) {
-    throw new Error(
-        `Cannot merge real and imag arrays of different lengths. real:` +
-        `${real.length}, imag: ${imag.length}.`);
-  }
-  const result = new Float32Array(real.length * 2);
-  for (let i = 0; i < result.length; i += 2) {
-    result[i] = real[i / 2];
-    result[i + 1] = imag[i / 2];
-  }
-  return result;
-}
-
-/**
- * Splits a complex Float32Array into real and imag parts.
- *
- * The memory layout is interleaved as follows:
- * complex: [r0, i0, r1, i1, r2, i2]
- * real: [r0, r1, r2]
- * imag: [i0, i1, i2]
- *
- * This is the inverse of mergeRealAndImagArrays.
- *
- * @param complex The complex tensor values.
- * @returns An object with real and imag Float32Array components of the complex
- *     tensor.
- */
-export function splitRealAndImagArrays(complex: Float32Array):
-    {real: Float32Array, imag: Float32Array} {
-  const real = new Float32Array(complex.length / 2);
-  const imag = new Float32Array(complex.length / 2);
-  for (let i = 0; i < complex.length; i += 2) {
-    real[i / 2] = complex[i];
-    imag[i / 2] = complex[i + 1];
-  }
-  return {real, imag};
-}
-
-/**
- * Extracts even indexed complex values in the given array.
- * @param complex The complex tensor values
- */
-export function complexWithEvenIndex(complex: Float32Array):
-    {real: Float32Array, imag: Float32Array} {
-  const len = Math.ceil(complex.length / 4);
-  const real = new Float32Array(len);
-  const imag = new Float32Array(len);
-  for (let i = 0; i < complex.length; i += 4) {
-    real[Math.floor(i / 4)] = complex[i];
-    imag[Math.floor(i / 4)] = complex[i + 1];
-  }
-  return {real, imag};
-}
-
-/**
- * Extracts odd indexed comple values in the given array.
- * @param complex The complex tensor values
- */
-export function complexWithOddIndex(complex: Float32Array):
-    {real: Float32Array, imag: Float32Array} {
-  const len = Math.floor(complex.length / 4);
-  const real = new Float32Array(len);
-  const imag = new Float32Array(len);
-  for (let i = 2; i < complex.length; i += 4) {
-    real[Math.floor(i / 4)] = complex[i];
-    imag[Math.floor(i / 4)] = complex[i + 1];
-  }
-  return {real, imag};
-}
-
-/**
- * Get the map representing a complex value in the given array.
- * @param complex The complex tensor values.
- * @param index An index of the target complex value.
- */
-export function getComplexWithIndex(
-    complex: Float32Array, index: number): {real: number, imag: number} {
-  const real = complex[index * 2];
-  const imag = complex[index * 2 + 1];
-  return {real, imag};
-}
-
-/**
- * Insert a given complex value into the TypedArray.
- * @param data The array in which the complex value is inserted.
- * @param c The complex value to be inserted.
- * @param index An index of the target complex value.
- */
-export function assignToTypedArray(
-    data: TypedArray, real: number, imag: number, index: number) {
-  data[index * 2] = real;
-  data[index * 2 + 1] = imag;
-}
-
-/**
- * Make the list of exponent terms used by FFT.
- */
-export function exponents(
-    n: number, inverse: boolean): {real: Float32Array, imag: Float32Array} {
-  const real = new Float32Array(n / 2);
-  const imag = new Float32Array(n / 2);
-  for (let i = 0; i < Math.ceil(n / 2); i++) {
-    const x = (inverse ? 2 : -2) * Math.PI * (i / n);
-    real[i] = Math.cos(x);
-    imag[i] = Math.sin(x);
-  }
-  return {real, imag};
-}
-
-/**
- * Make the exponent term used by FFT.
- */
-export function exponent(
-    k: number, n: number, inverse: boolean): {real: number, imag: number} {
-  const x = (inverse ? 2 : -2) * Math.PI * (k / n);
-  const real = Math.cos(x);
-  const imag = Math.sin(x);
-  return {real, imag};
-}
diff --git a/tfjs-master/tfjs-core/src/backends/complex_util_test.ts b/tfjs-master/tfjs-core/src/backends/complex_util_test.ts
deleted file mode 100644
index 721bd729a..000000000
--- a/tfjs-master/tfjs-core/src/backends/complex_util_test.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-import * as complex_util from './complex_util';
-
-describe('complex_util', () => {
-  it('mergeRealAndImagArrays', () => {
-    const real = new Float32Array([1, 2, 3]);
-    const imag = new Float32Array([4, 5, 6]);
-    const complex = complex_util.mergeRealAndImagArrays(real, imag);
-    expect(complex).toEqual(new Float32Array([1, 4, 2, 5, 3, 6]));
-  });
-
-  it('splitRealAndImagArrays', () => {
-    const complex = new Float32Array([1, 4, 2, 5, 3, 6]);
-    const result = complex_util.splitRealAndImagArrays(complex);
-    expect(result.real).toEqual(new Float32Array([1, 2, 3]));
-    expect(result.imag).toEqual(new Float32Array([4, 5, 6]));
-  });
-
-  it('complexWithEvenIndex', () => {
-    const complex = new Float32Array([1, 2, 3, 4, 5, 6]);
-    const result = complex_util.complexWithEvenIndex(complex);
-    expect(result.real).toEqual(new Float32Array([1, 5]));
-    expect(result.imag).toEqual(new Float32Array([2, 6]));
-  });
-
-  it('complexWithOddIndex', () => {
-    const complex = new Float32Array([1, 2, 3, 4, 5, 6]);
-    const result = complex_util.complexWithOddIndex(complex);
-    expect(result.real).toEqual(new Float32Array([3]));
-    expect(result.imag).toEqual(new Float32Array([4]));
-  });
-});
-
-describeWithFlags('complex_util exponents', ALL_ENVS, () => {
-  it('exponents inverse=false', () => {
-    const inverse = false;
-    const result = complex_util.exponents(5, inverse);
-    expectArraysClose(result.real, new Float32Array([1, 0.30901700258255005]));
-    expectArraysClose(result.imag, new Float32Array([0, -0.9510565400123596]));
-  });
-  it('exponents inverse=true', () => {
-    const inverse = true;
-    const result = complex_util.exponents(5, inverse);
-    expectArraysClose(result.real, new Float32Array([1, 0.30901700258255005]));
-    expectArraysClose(result.imag, new Float32Array([0, 0.9510565400123596]));
-  });
-});
-
-describeWithFlags('complex_util assignment', ALL_ENVS, () => {
-  it('assign complex value in TypedArray', () => {
-    const t = new Float32Array(4);
-
-    complex_util.assignToTypedArray(t, 1, 2, 0);
-    complex_util.assignToTypedArray(t, 3, 4, 1);
-
-    expectArraysClose(t, new Float32Array([1, 2, 3, 4]));
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/backends/einsum_util.ts b/tfjs-master/tfjs-core/src/backends/einsum_util.ts
deleted file mode 100644
index aef34779f..000000000
--- a/tfjs-master/tfjs-core/src/backends/einsum_util.ts
+++ /dev/null
@@ -1,220 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Utility functions for computing einsum (tensor contraction and summation
- * based on Einstein summation.)
- */
-
-import {Tensor} from '../tensor';
-import {assert} from '../util_base';
-
-const ARROW = '->';
-const ARROW_REGEX = /->/g;
-const COMMA = ',';
-const ELLIPSIS = '...';
-
-/**
- * Parse an equation for einsum.
- *
- * @param equation The einsum equation (e.g., "ij,jk->ik").
- * @param numTensors Number of tensors provided along with `equation`. Used to
- *   check matching number of input tensors.
- * @returns An object consisting of the following fields:
- *   - allDims: all dimension names as strings.
- *   - summedDims: a list of all dimensions being summed over, as indices to
- *     the elements of `allDims`.
- *   - idDims: indices of the dimensions in each input tensor, as indices to
- *     the elements of `allDims.
- */
-export function decodeEinsumEquation(equation: string, numTensors: number): {
-  allDims: string[],
-  summedDims: number[],
-  idDims: number[][],
-} {
-  equation = equation.replace(/\s/g, '');  // Remove witespace in equation.
-  const numArrows =
-      (equation.length - equation.replace(ARROW_REGEX, '').length) /
-      ARROW.length;
-  if (numArrows < 1) {
-    throw new Error('Equations without an arrow are not supported.');
-  } else if (numArrows > 1) {
-    throw new Error(`Equation must contain exactly one arrow ("${ARROW}").`);
-  }
-  const [inputString, outputString] = equation.split(ARROW);
-  assert(
-      inputString.indexOf(ELLIPSIS) === -1,
-      () => `The ellipsis notation ("${ELLIPSIS}") is not supported yet.`);
-  const inputTerms = inputString.split(COMMA);
-  const numInputs = inputTerms.length;
-  if (numTensors !== numInputs) {
-    throw new Error(
-        `Expected ${numInputs} input tensors, received ${numTensors}`);
-  }
-  if (numInputs > 2) {
-    throw new Error(
-        'Support for more than 2 input tensors is not implemented yet.');
-  }
-
-  const allDims: string[] = [];
-  for (let i = 0; i < outputString.length; ++i) {
-    const dimName = outputString[i];
-    if (!inputTerms.some(inputTerm => inputTerm.indexOf(dimName) !== -1)) {
-      throw new Error(
-          `Output subscripts contain the label ${dimName} ` +
-          `not present in the input subscripts.`);
-    }
-    if (allDims.indexOf(dimName) === -1) {
-      allDims.push(dimName);
-    }
-  }
-  for (let i = 0; i < inputString.length; ++i) {
-    const dimName = inputString[i];
-    if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {
-      allDims.push(dimName);
-    }
-  }
-
-  const idDims: number[][] = new Array<number[]>(inputTerms.length);
-  for (let i = 0; i < numInputs; ++i) {
-    if (new Set(inputTerms[i].split('')).size !== inputTerms[i].length) {
-      throw new Error(
-          `Found duplicate axes in input component ${inputTerms[i]}. ` +
-          `Support for duplicate axes in input is not implemented yet.`);
-    }
-    idDims[i] = [];
-    for (let j = 0; j < inputTerms[i].length; ++j) {
-      idDims[i].push(allDims.indexOf(inputTerms[i][j]));
-    }
-  }
-
-  const numDims = allDims.length;          // Number of unique dimensions.
-  const numOutDims = outputString.length;  // Number of output dimensions.
-  const summedDims: number[] = [];         // Dimensions being summed over.
-  for (let i = numOutDims; i < numDims; ++i) {
-    summedDims.push(i);
-  }
-  return {allDims, summedDims, idDims};
-}
-
-/**
- * Get the permutation for a given input tensor.
- *
- * @param nDims Total number of dimension of all tensors involved in the einsum
- *   operation.
- * @param idDims Dimension indices involve in the tensor in question.
- * @returns An object consisting of the following fields:
- *   - permutationIndices: Indices to permute the axes of the tensor with.
- *   - expandDims: Indices to the dimension that need to be expanded from the
- *     tensor after permutation.
- */
-export function getEinsumPermutation(nDims: number, idDims: number[]):
-    {permutationIndices: number[], expandDims: number[]} {
-  let permutationIndices: number[] = new Array<number>(nDims);
-  permutationIndices.fill(-1);
-  for (let i = 0; i < idDims.length; ++i) {
-    permutationIndices[idDims[i]] = i;
-  }
-  const expandDims: number[] = [];
-  for (let i = 0; i < nDims; ++i) {
-    if (permutationIndices[i] === -1) {
-      expandDims.push(i);
-    }
-  }
-  permutationIndices = permutationIndices.filter(d => d !== -1);
-  return {permutationIndices, expandDims};
-}
-
-/**
- * Checks that the dimension sizes from different input tensors match the
- * equation.
- */
-export function checkEinsumDimSizes(
-    nDims: number, idDims: number[][], tensors: Tensor[]) {
-  const dimSizes: number[] = new Array<number>(nDims);
-  for (let i = 0; i < tensors.length; ++i) {
-    const shape: number[] = tensors[i].shape;
-    for (let j = 0; j < idDims[i].length; ++j) {
-      if (dimSizes[idDims[i][j]] === undefined) {
-        dimSizes[idDims[i][j]] = shape[j];
-      } else {
-        assert(
-            dimSizes[idDims[i][j]] === shape[j],
-            () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} ` +
-                `of input shaped ${JSON.stringify(shape)}, ` +
-                `but got dimension ${shape[j]}`);
-      }
-    }
-  }
-}
-
-/**
- * Gets path of computation for einsum.
- *
- * @param summedDims indices to the dimensions being summed over.
- * @param idDims A look up table for the dimensions present in each input
- *     tensor. Each consituent array contains indices for the dimensions in the
- *     corresponding input tensor.
- *
- * @return A map with two fields:
- *   - path: The path of computation, with each element indicating the dimension
- *     being summed over after the element-wise multiplication in that step.
- *   - steps: With the same length as `path`. Each element contains the indices
- *     to the input tensors being used for element-wise multiplication in the
- *     corresponding step.
- */
-export function getEinsumComputePath(summedDims: number[], idDims: number[][]):
-    {path: number[], steps: number[][]} {
-  const path: number[] = summedDims;
-  const steps: number[][] = [];
-  let nSteps = 0;
-  if (summedDims.length === 0) {
-    // Einsum that involes no summing: e.g., transpose and outer product.
-    path.push(-1);
-  }
-  nSteps = summedDims.length + 1;
-  for (let i = 0; i < nSteps; ++i) {
-    steps.push([]);
-  }
-  const computedTermIndices: number[] = [];
-  for (let i = 0; i < path.length; ++i) {
-    const summedDim = path[i];
-    const termIndices = findTermsWithDim(idDims, summedDim);
-    for (const termIndex of termIndices) {
-      if (computedTermIndices.indexOf(termIndex) === -1) {
-        steps[i].push(termIndex);
-        computedTermIndices.push(termIndex);
-      }
-    }
-  }
-  return {path, steps};
-}
-
-/** Determines if an axes permutation is the identity permutation. */
-export function isIdentityPermutation(perm: number[]): boolean {
-  return perm.every((dim: number, index: number) => dim === index);
-}
-
-function findTermsWithDim(idDims: number[][], dim: number): number[] {
-  const termIndices: number[] = [];
-  for (let i = 0; i < idDims.length; ++i) {
-    if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) {
-      termIndices.push(i);
-    }
-  }
-  return termIndices;
-}
diff --git a/tfjs-master/tfjs-core/src/backends/kernel_impls.ts b/tfjs-master/tfjs-core/src/backends/kernel_impls.ts
deleted file mode 100644
index 867abfcef..000000000
--- a/tfjs-master/tfjs-core/src/backends/kernel_impls.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export {nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl} from './non_max_suppression_impl';
-export {whereImpl} from './where_impl';
diff --git a/tfjs-master/tfjs-core/src/backends/non_max_suppression_impl.ts b/tfjs-master/tfjs-core/src/backends/non_max_suppression_impl.ts
deleted file mode 100644
index 51b45a577..000000000
--- a/tfjs-master/tfjs-core/src/backends/non_max_suppression_impl.ts
+++ /dev/null
@@ -1,205 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TypedArray} from '../types';
-import {binaryInsert} from './non_max_suppression_util';
-
-/**
- * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.
- */
-interface Candidate {
-  score: number;
-  boxIndex: number;
-  suppressBeginIndex: number;
-}
-
-interface NonMaxSuppressionResult {
-  selectedIndices: number[];
-  selectedScores?: number[];
-  validOutputs?: number;
-}
-
-export function nonMaxSuppressionV3Impl(
-    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,
-    iouThreshold: number, scoreThreshold: number): NonMaxSuppressionResult {
-  return nonMaxSuppressionImpl_(
-      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,
-      0 /* softNmsSigma */);
-}
-
-export function nonMaxSuppressionV4Impl(
-    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,
-    iouThreshold: number, scoreThreshold: number,
-    padToMaxOutputSize: boolean): NonMaxSuppressionResult {
-  return nonMaxSuppressionImpl_(
-      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,
-      0 /* softNmsSigma */, false /* returnScoresTensor */,
-      padToMaxOutputSize /* padToMaxOutputSize */, true
-      /* returnValidOutputs */);
-}
-
-export function nonMaxSuppressionV5Impl(
-    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,
-    iouThreshold: number, scoreThreshold: number,
-    softNmsSigma: number): NonMaxSuppressionResult {
-  return nonMaxSuppressionImpl_(
-      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma,
-      true /* returnScoresTensor */);
-}
-
-function nonMaxSuppressionImpl_(
-    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,
-    iouThreshold: number, scoreThreshold: number, softNmsSigma: number,
-    returnScoresTensor = false, padToMaxOutputSize = false,
-    returnValidOutputs = false): NonMaxSuppressionResult {
-  // The list is sorted in ascending order, so that we can always pop the
-  // candidate with the largest score in O(1) time.
-  const candidates = [];
-
-  for (let i = 0; i < scores.length; i++) {
-    if (scores[i] > scoreThreshold) {
-      candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});
-    }
-  }
-
-  candidates.sort(ascendingComparator);
-
-  // If softNmsSigma is 0, the outcome of this algorithm is exactly same as
-  // before.
-  const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;
-
-  const selectedIndices: number[] = [];
-  const selectedScores: number[] = [];
-
-  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
-    const candidate = candidates.pop();
-    const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;
-
-    if (originalScore < scoreThreshold) {
-      break;
-    }
-
-    // Overlapping boxes are likely to have similar scores, therefore we
-    // iterate through the previously selected boxes backwards in order to
-    // see if candidate's score should be suppressed. We use
-    // suppressBeginIndex to track and ensure a candidate can be suppressed
-    // by a selected box no more than once. Also, if the overlap exceeds
-    // iouThreshold, we simply ignore the candidate.
-    let ignoreCandidate = false;
-    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
-      const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
-
-      if (iou >= iouThreshold) {
-        ignoreCandidate = true;
-        break;
-      }
-
-      candidate.score =
-          candidate.score * suppressWeight(iouThreshold, scale, iou);
-
-      if (candidate.score <= scoreThreshold) {
-        break;
-      }
-    }
-
-    // At this point, if `candidate.score` has not dropped below
-    // `scoreThreshold`, then we know that we went through all of the
-    // previous selections and can safely update `suppressBeginIndex` to the
-    // end of the selected array. Then we can re-insert the candidate with
-    // the updated score and suppressBeginIndex back in the candidate list.
-    // If on the other hand, `candidate.score` has dropped below the score
-    // threshold, we will not add it back to the candidates list.
-    candidate.suppressBeginIndex = selectedIndices.length;
-
-    if (!ignoreCandidate) {
-      // Candidate has passed all the tests, and is not suppressed, so
-      // select the candidate.
-      if (candidate.score === originalScore) {
-        selectedIndices.push(boxIndex);
-        selectedScores.push(candidate.score);
-      } else if (candidate.score > scoreThreshold) {
-        // Candidate's score is suppressed but is still high enough to be
-        // considered, so add back to the candidates list.
-        binaryInsert(candidates, candidate, ascendingComparator);
-      }
-    }
-  }
-
-  // NonMaxSuppressionV4 feature: padding output to maxOutputSize.
-  const validOutputs = selectedIndices.length;
-  const elemsToPad = maxOutputSize - validOutputs;
-
-  if (padToMaxOutputSize && elemsToPad > 0) {
-    selectedIndices.push(...new Array(elemsToPad).fill(0));
-    selectedScores.push(...new Array(elemsToPad).fill(0.0));
-  }
-
-  const result: NonMaxSuppressionResult = {selectedIndices};
-
-  if (returnScoresTensor) {
-    result['selectedScores'] = selectedScores;
-  }
-
-  if (returnValidOutputs) {
-    result['validOutputs'] = validOutputs;
-  }
-
-  return result;
-}
-
-function intersectionOverUnion(boxes: TypedArray, i: number, j: number) {
-  const iCoord = boxes.subarray(i * 4, i * 4 + 4);
-  const jCoord = boxes.subarray(j * 4, j * 4 + 4);
-  const yminI = Math.min(iCoord[0], iCoord[2]);
-  const xminI = Math.min(iCoord[1], iCoord[3]);
-  const ymaxI = Math.max(iCoord[0], iCoord[2]);
-  const xmaxI = Math.max(iCoord[1], iCoord[3]);
-  const yminJ = Math.min(jCoord[0], jCoord[2]);
-  const xminJ = Math.min(jCoord[1], jCoord[3]);
-  const ymaxJ = Math.max(jCoord[0], jCoord[2]);
-  const xmaxJ = Math.max(jCoord[1], jCoord[3]);
-  const areaI = (ymaxI - yminI) * (xmaxI - xminI);
-  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
-  if (areaI <= 0 || areaJ <= 0) {
-    return 0.0;
-  }
-  const intersectionYmin = Math.max(yminI, yminJ);
-  const intersectionXmin = Math.max(xminI, xminJ);
-  const intersectionYmax = Math.min(ymaxI, ymaxJ);
-  const intersectionXmax = Math.min(xmaxI, xmaxJ);
-  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
-      Math.max(intersectionXmax - intersectionXmin, 0.0);
-  return intersectionArea / (areaI + areaJ - intersectionArea);
-}
-
-// A Gaussian penalty function, this method always returns values in [0, 1].
-// The weight is a function of similarity, the more overlap two boxes are, the
-// smaller the weight is, meaning highly overlapping boxe will be significantly
-// penalized. On the other hand, a non-overlapping box will not be penalized.
-function suppressWeight(iouThreshold: number, scale: number, iou: number) {
-  const weight = Math.exp(scale * iou * iou);
-  return iou <= iouThreshold ? weight : 0.0;
-}
-
-function ascendingComparator(c1: Candidate, c2: Candidate) {
-  // For objects with same scores, we make the object with the larger index go
-  // first. In an array that pops from the end, this means that the object with
-  // the smaller index will be popped first. This ensures the same output as
-  // the TensorFlow python version.
-  return (c1.score - c2.score) ||
-      ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));
-}
diff --git a/tfjs-master/tfjs-core/src/backends/non_max_suppression_util.ts b/tfjs-master/tfjs-core/src/backends/non_max_suppression_util.ts
deleted file mode 100644
index 0a93f4355..000000000
--- a/tfjs-master/tfjs-core/src/backends/non_max_suppression_util.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Inserts a value into a sorted array. This method allows duplicate, meaning it
- * allows inserting duplicate value, in which case, the element will be inserted
- * at the lowest index of the value.
- * @param arr The array to modify.
- * @param element The element to insert.
- * @param comparator Optional. If no comparator is specified, elements are
- * compared using array_util.defaultComparator, which is suitable for Strings
- * and Numbers in ascending arrays. If the array contains multiple instances of
- * the target value, the left-most instance will be returned. To provide a
- * comparator, it should take 2 arguments to compare and return a negative,
- * zero, or a positive number.
- */
-export function binaryInsert<T>(
-    arr: T[], element: T, comparator?: (a: T, b: T) => number) {
-  const index = binarySearch(arr, element, comparator);
-  const insertionPoint = index < 0 ? -(index + 1) : index;
-  arr.splice(insertionPoint, 0, element);
-}
-
-/**
- * Searches the array for the target using binary search, returns the index
- * of the found element, or position to insert if element not found. If no
- * comparator is specified, elements are compared using array_
- * util.defaultComparator, which is suitable for Strings and Numbers in
- * ascending arrays. If the array contains multiple instances of the target
- * value, the left-most instance will be returned.
- * @param arr The array to be searched in.
- * @param target The target to be searched for.
- * @param comparator Should take 2 arguments to compare and return a negative,
- *    zero, or a positive number.
- * @return Lowest index of the target value if found, otherwise the insertion
- *    point where the target should be inserted, in the form of
- *    (-insertionPoint - 1).
- */
-export function binarySearch<T>(
-    arr: T[], target: T, comparator?: (a: T, b: T) => number) {
-  return binarySearch_(arr, target, comparator || defaultComparator);
-}
-
-/**
- * Compares its two arguments for order.
- * @param a The first element to be compared.
- * @param b The second element to be compared.
- * @return A negative number, zero, or a positive number as the first
- *     argument is less than, equal to, or greater than the second.
- */
-function defaultComparator<T>(a: T, b: T): number {
-  return a > b ? 1 : a < b ? -1 : 0;
-}
-
-function binarySearch_<T>(
-    arr: T[], target: T, comparator: (a: T, b: T) => number) {
-  let left = 0;
-  let right = arr.length;
-  let middle = 0;
-  let found = false;
-  while (left < right) {
-    middle = left + ((right - left) >>> 1);
-    const compareResult = comparator(target, arr[middle]);
-    if (compareResult > 0) {
-      left = middle + 1;
-    } else {
-      right = middle;
-      // If compareResult is 0, the value is found. We record it is found,
-      // and then keep looking because there may be duplicate.
-      found = !compareResult;
-    }
-  }
-
-  return found ? left : -left - 1;
-}
diff --git a/tfjs-master/tfjs-core/src/backends/non_max_suppression_util_test.ts b/tfjs-master/tfjs-core/src/backends/non_max_suppression_util_test.ts
deleted file mode 100644
index cec9bb3c5..000000000
--- a/tfjs-master/tfjs-core/src/backends/non_max_suppression_util_test.ts
+++ /dev/null
@@ -1,184 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as non_max_suppression_util from './non_max_suppression_util';
-
-describe('non_max_suppression_util', () => {
-  const insertionPoint = (i: number) => -(i + 1);
-
-  describe('binarySearch', () => {
-    const d = [
-      -897123.9, -321434.58758, -1321.3124, -324, -9, -3, 0, 0, 0, 0.31255, 5,
-      142.88888708, 334, 342, 453, 54254
-    ];
-
-    it('-897123.9 should be found at index 0', () => {
-      const result = non_max_suppression_util.binarySearch(d, -897123.9);
-      expect(result).toBe(0);
-    });
-
-    it(`54254 should be found at index ${d.length - 1}`, () => {
-      const result = non_max_suppression_util.binarySearch(d, 54254);
-      expect(result).toBe(d.length - 1);
-    });
-
-    it('-3 should be found at index 5', () => {
-      const result = non_max_suppression_util.binarySearch(d, -3);
-      expect(result).toBe(5);
-    });
-
-    it('0 should be found at index 6', () => {
-      const result = non_max_suppression_util.binarySearch(d, 0);
-      expect(result).toBe(6);
-    });
-
-    it('-900000 should have an insertion point of 0', () => {
-      const result = non_max_suppression_util.binarySearch(d, -900000);
-      expect(result).toBeLessThan(0);
-      expect(insertionPoint(result)).toBe(0);
-    });
-
-    it(`54255 should have an insertion point of ${d.length}`, () => {
-      const result = non_max_suppression_util.binarySearch(d, 54255);
-      expect(result).toBeLessThan(0);
-      expect(insertionPoint(result)).toEqual(d.length);
-    });
-
-    it('1.1 should have an insertion point of 10', () => {
-      const result = non_max_suppression_util.binarySearch(d, 1.1);
-      expect(result).toBeLessThan(0);
-      expect(insertionPoint(result)).toEqual(10);
-    });
-  });
-
-  describe('binarySearch with custom comparator', () => {
-    const e = [
-      54254,
-      453,
-      342,
-      334,
-      142.88888708,
-      5,
-      0.31255,
-      0,
-      0,
-      0,
-      -3,
-      -9,
-      -324,
-      -1321.3124,
-      -321434.58758,
-      -897123.9,
-    ];
-
-    const revComparator = (a: number, b: number) => (b - a);
-
-    it('54254 should be found at index 0', () => {
-      const result =
-          non_max_suppression_util.binarySearch(e, 54254, revComparator);
-      expect(result).toBe(0);
-    });
-
-    it(`-897123.9 should be found at index ${e.length - 1}`, () => {
-      const result =
-          non_max_suppression_util.binarySearch(e, -897123.9, revComparator);
-      expect(result).toBe(e.length - 1);
-    });
-
-    it('-3 should be found at index 10', () => {
-      const result =
-          non_max_suppression_util.binarySearch(e, -3, revComparator);
-      expect(result).toBe(10);
-    });
-
-    it('0 should be found at index 7', () => {
-      const result = non_max_suppression_util.binarySearch(e, 0, revComparator);
-      expect(result).toBe(7);
-    });
-
-    it('54254.1 should have an insertion point of 0', () => {
-      const result =
-          non_max_suppression_util.binarySearch(e, 54254.1, revComparator);
-      expect(result).toBeLessThan(0);
-      expect(insertionPoint(result)).toBe(0);
-    });
-
-    it(`-897124 should have an insertion point of ${e.length}`, () => {
-      const result =
-          non_max_suppression_util.binarySearch(e, -897124, revComparator);
-      expect(result).toBeLessThan(0);
-      expect(insertionPoint(result)).toBe(e.length);
-    });
-  });
-
-  describe(
-      'binarySearch with custom comparator with single element array', () => {
-        const g = [1];
-
-        const revComparator = (a: number, b: number) => (b - a);
-
-        it('1 should be found at index 0', () => {
-          const result =
-              non_max_suppression_util.binarySearch(g, 1, revComparator);
-          expect(result).toBe(0);
-        });
-
-        it('2 should have an insertion point of 0', () => {
-          const result =
-              non_max_suppression_util.binarySearch(g, 2, revComparator);
-          expect(result).toBeLessThan(0);
-          expect(insertionPoint(result)).toBe(0);
-        });
-
-        it('0 should have an insertion point of 1', () => {
-          const result =
-              non_max_suppression_util.binarySearch(g, 0, revComparator);
-          expect(result).toBeLessThan(0);
-          expect(insertionPoint(result)).toBe(1);
-        });
-      });
-
-  describe('binarySearch test left-most duplicated element', () => {
-    it('should find the index of the first 0', () => {
-      const result = non_max_suppression_util.binarySearch([0, 0, 1], 0);
-      expect(result).toBe(0);
-    });
-
-    it('should find the index of the first 1', () => {
-      const result = non_max_suppression_util.binarySearch([0, 1, 1], 1);
-      expect(result).toBe(1);
-    });
-  });
-
-  describe('binaryInsert', () => {
-    it('inserts correctly', () => {
-      const a: number[] = [];
-
-      non_max_suppression_util.binaryInsert(a, 3);
-      expect(a).toEqual([3]);
-
-      non_max_suppression_util.binaryInsert(a, 3);
-      expect(a).toEqual([3, 3]);
-
-      non_max_suppression_util.binaryInsert(a, 1);
-      expect(a).toEqual([1, 3, 3]);
-
-      non_max_suppression_util.binaryInsert(a, 5);
-      expect(a).toEqual([1, 3, 3, 5]);
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/backends/where_impl.ts b/tfjs-master/tfjs-core/src/backends/where_impl.ts
deleted file mode 100644
index bbfdebae3..000000000
--- a/tfjs-master/tfjs-core/src/backends/where_impl.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/** An implementation of the Where kernel shared between cpu and webgl */
-
-import {buffer} from '../ops/buffer';
-import {Tensor2D} from '../tensor';
-import {TypedArray} from '../types';
-
-export function whereImpl(condShape: number[], condVals: TypedArray): Tensor2D {
-  const indices = [];
-  for (let i = 0; i < condVals.length; i++) {
-    if (condVals[i]) {
-      indices.push(i);
-    }
-  }
-
-  const inBuffer = buffer(condShape, 'int32');
-
-  const out = buffer([indices.length, condShape.length], 'int32');
-  for (let i = 0; i < indices.length; i++) {
-    const loc = inBuffer.indexToLoc(indices[i]);
-    const offset = i * condShape.length;
-    out.values.set(loc, offset);
-  }
-  return out.toTensor() as Tensor2D;
-}
diff --git a/tfjs-master/tfjs-core/src/base.ts b/tfjs-master/tfjs-core/src/base.ts
deleted file mode 100644
index bda9e5657..000000000
--- a/tfjs-master/tfjs-core/src/base.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// base.ts is tfjs-core without auto registration of things like flags,
-// gradients, chained ops or the opHandler. See base_side_effects.ts for parts
-// tfjs core that are required side effects.
-
-/**
- * @fileoverview
- * @suppress {partialAlias} Optimization disabled due to passing the module
- * object into a function below:
- *
- *   import * as ops from './ops/ops';
- *   setOpHandler(ops);
- */
-
-// Serialization.
-import * as io from './io/io';
-import * as math from './math';
-import * as broadcast_util from './ops/broadcast_util';
-import * as browser from './ops/browser';
-import * as gather_util from './ops/gather_nd_util';
-import * as scatter_util from './ops/scatter_nd_util';
-import * as slice_util from './ops/slice_util';
-import * as serialization from './serialization';
-import * as tensor_util from './tensor_util';
-import * as test_util from './test_util';
-import * as util from './util';
-import {version} from './version';
-
-export {InferenceModel, MetaGraph, MetaGraphInfo, ModelPredictConfig, ModelTensorInfo, SavedModelTensorInfo, SignatureDef, SignatureDefEntry, SignatureDefInfo} from './model_types';
-export {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';
-export {AdagradOptimizer} from './optimizers/adagrad_optimizer';
-export {AdamOptimizer} from './optimizers/adam_optimizer';
-export {AdamaxOptimizer} from './optimizers/adamax_optimizer';
-export {MomentumOptimizer} from './optimizers/momentum_optimizer';
-export {Optimizer} from './optimizers/optimizer';
-// Optimizers.
-export {OptimizerConstructors} from './optimizers/optimizer_constructors';
-export {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';
-export {SGDOptimizer} from './optimizers/sgd_optimizer';
-export {DataToGPUOptions, DataToGPUWebGLOption, GPUData, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, TensorBuffer, Variable} from './tensor';
-export {GradSaveFunc, NamedTensorMap, TensorContainer, TensorContainerArray, TensorContainerObject} from './tensor_types';
-export {BackendValues, DataType, DataTypeMap, DataTypeFor, DataValues, NumericDataType, PixelData, Rank, RecursiveArray, ScalarLike, ShapeMap, sumOutType, TensorLike, TypedArray, upcastType, WebGLData, WebGPUData} from './types';
-
-export * from './ops/ops';
-export {Reduction} from './ops/loss_ops_utils';
-
-export * from './train';
-export * from './globals';
-export * from './kernel_registry';
-export {TensorInfo, DataId} from './tensor_info';
-export {customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads} from './gradients';
-
-export {TimingInfo, MemoryInfo, ForwardFunc} from './engine';
-export {Environment, env, ENV} from './environment';
-export {Platform} from './platforms/platform';
-
-export {version as version_core};
-
-// Top-level method exports.
-export {nextFrame} from './browser_util';
-
-// Second level exports.
-import * as backend_util from './backends/backend_util';
-import * as device_util from './device_util';
-export {
-  browser,
-  io,
-  math,
-  serialization,
-  test_util,
-  util,
-  backend_util,
-  broadcast_util,
-  tensor_util,
-  slice_util,
-  gather_util,
-  scatter_util,
-  device_util
-};
-
-import * as kernel_impls from './backends/kernel_impls';
-export {kernel_impls};
-// Backend specific.
-export {KernelBackend, BackendTimingInfo, DataMover, DataStorage} from './backends/backend';
-
-// Export all kernel names / info.
-export * from './kernel_names';
diff --git a/tfjs-master/tfjs-core/src/base_side_effects.ts b/tfjs-master/tfjs-core/src/base_side_effects.ts
deleted file mode 100644
index 6c198966d..000000000
--- a/tfjs-master/tfjs-core/src/base_side_effects.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Required side effectful code for tfjs-core
-
-// Set up Engine and ENV
-import {getOrMakeEngine} from './engine';
-getOrMakeEngine();
-
-// Register backend-agnostic flags.
-import './flags';
-// Register platforms
-import './platforms/platform_browser';
-import './platforms/platform_node';
-
-// Set up OpHandler
-import {buffer} from './ops/buffer';
-import {cast} from './ops/cast';
-import {clone} from './ops/clone';
-import {print} from './ops/print';
-import {OpHandler, setOpHandler} from './tensor';
-const opHandler: OpHandler = {
-  buffer,
-  cast,
-  clone,
-  print
-};
-setOpHandler(opHandler);
diff --git a/tfjs-master/tfjs-core/src/browser_util.ts b/tfjs-master/tfjs-core/src/browser_util.ts
deleted file mode 100644
index e853225a6..000000000
--- a/tfjs-master/tfjs-core/src/browser_util.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const delayCallback: Function = (() => {
-  if (typeof requestAnimationFrame !== 'undefined') {
-    return requestAnimationFrame;
-  } else if (typeof setImmediate !== 'undefined') {
-    return setImmediate;
-  }
-  return (f: Function) => f();  // no delays
-})();
-
-/**
- * Returns a promise that resolves when a requestAnimationFrame has completed.
- *
- * On Node.js this uses setImmediate instead of requestAnimationFrame.
- *
- * This is simply a sugar method so that users can do the following:
- * `await tf.nextFrame();`
- *
- * @doc {heading: 'Performance', subheading: 'Timing'}
- */
-function nextFrame(): Promise<void> {
-  return new Promise<void>(resolve => delayCallback(() => resolve()));
-}
-
-export {nextFrame};
diff --git a/tfjs-master/tfjs-core/src/browser_util_test.ts b/tfjs-master/tfjs-core/src/browser_util_test.ts
deleted file mode 100644
index c49b8ebd1..000000000
--- a/tfjs-master/tfjs-core/src/browser_util_test.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-
-function isFloat(num: number) {
-  return num % 1 !== 0;
-}
-
-describeWithFlags('nextFrame', ALL_ENVS, () => {
-  it('basic usage', async () => {
-    const t0 = tf.util.now();
-    await tf.nextFrame();
-    const t1 = tf.util.now();
-
-    // tf.nextFrame may take no more than 1ms to complete, so this test is
-    // meaningful only if the precision of tf.util.now is better than 1ms.
-    // After version 59, the precision of Firefox's tf.util.now becomes 2ms by
-    // default for security issues, https://caniuse.com/?search=performance.now.
-    // Then, this test is dropped for Firefox, even though it could be
-    // set to better precision through browser setting,
-    // https://github.com/lumen/threading-benchmarks/issues/7.
-    if (isFloat(t0) || isFloat(t1)) {
-      // If t0 or t1 have decimal point, it means the precision is better than
-      // 1ms.
-      expect(t1).toBeGreaterThan(t0);
-    }
-  });
-
-  it('does not block timers', async () => {
-    let flag = false;
-    setTimeout(() => {
-      flag = true;
-    }, 50);
-    const t0 = tf.util.now();
-    expect(flag).toBe(false);
-    while (tf.util.now() - t0 < 1000 && !flag) {
-      await tf.nextFrame();
-    }
-    expect(flag).toBe(true);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/buffer_test.ts b/tfjs-master/tfjs-core/src/buffer_test.ts
deleted file mode 100644
index 41c8effde..000000000
--- a/tfjs-master/tfjs-core/src/buffer_test.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-import {expectArraysClose, expectArraysEqual} from './test_util';
-
-describeWithFlags('tf.buffer', ALL_ENVS, () => {
-  it('float32', async () => {
-    const buff = tf.buffer([1, 2, 3], 'float32');
-    buff.set(1.3, 0, 0, 0);
-    buff.set(2.9, 0, 1, 0);
-    expect(buff.get(0, 0, 0)).toBeCloseTo(1.3);
-    expect(buff.get(0, 0, 1)).toBeCloseTo(0);
-    expect(buff.get(0, 0, 2)).toBeCloseTo(0);
-    expect(buff.get(0, 1, 0)).toBeCloseTo(2.9);
-    expect(buff.get(0, 1, 1)).toBeCloseTo(0);
-    expect(buff.get(0, 1, 2)).toBeCloseTo(0);
-    expectArraysClose(await buff.toTensor().data(), [1.3, 0, 0, 2.9, 0, 0]);
-    expectArraysClose(buff.values, new Float32Array([1.3, 0, 0, 2.9, 0, 0]));
-  });
-
-  it('get() out of range throws', async () => {
-    const t = tf.tensor([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-
-    const buff = await t.buffer();
-    expect(buff.get(0, 0, 0)).toBeCloseTo(1);
-    expect(buff.get(0, 0, 1)).toBeCloseTo(2);
-    expect(() => buff.get(0, 0, 2))
-        .toThrowError(/Requested out of range element/);
-  });
-
-  it('int32', async () => {
-    const buff = tf.buffer([2, 3], 'int32');
-    buff.set(1.3, 0, 0);
-    buff.set(2.1, 1, 1);
-    expect(buff.get(0, 0)).toEqual(1);
-    expect(buff.get(0, 1)).toEqual(0);
-    expect(buff.get(0, 2)).toEqual(0);
-    expect(buff.get(1, 0)).toEqual(0);
-    expect(buff.get(1, 1)).toEqual(2);
-    expect(buff.get(1, 2)).toEqual(0);
-    expectArraysClose(await buff.toTensor().data(), [1, 0, 0, 0, 2, 0]);
-    expectArraysClose(buff.values, new Int32Array([1, 0, 0, 0, 2, 0]));
-  });
-
-  it('bool', async () => {
-    const buff = tf.buffer([4], 'bool');
-    buff.set(true, 1);
-    buff.set(true, 2);
-    expect(buff.get(0)).toBeFalsy();
-    expect(buff.get(1)).toBeTruthy();
-    expect(buff.get(2)).toBeTruthy();
-    expect(buff.get(3)).toBeFalsy();
-    expectArraysClose(await buff.toTensor().data(), [0, 1, 1, 0]);
-    expectArraysClose(buff.values, new Uint8Array([0, 1, 1, 0]));
-  });
-
-  it('string', async () => {
-    const buff = tf.buffer([2, 2], 'string');
-    buff.set('first', 0, 0);
-    buff.set('third', 1, 0);
-    expect(buff.get(0, 0)).toEqual('first');
-    expect(buff.get(0, 1)).toBeFalsy();
-    expect(buff.get(1, 0)).toEqual('third');
-    expect(buff.get(1, 1)).toBeFalsy();
-    expectArraysEqual(
-        await buff.toTensor().data(), ['first', null, 'third', null]);
-  });
-
-  it('throws when passed non-integer shape', () => {
-    const msg = 'Tensor must have a shape comprised of positive ' +
-        'integers but got shape [2,2.2].';
-    expect(() => tf.buffer([2, 2.2])).toThrowError(msg);
-  });
-
-  it('throws when passed negative shape', () => {
-    const msg = 'Tensor must have a shape comprised of positive ' +
-        'integers but got shape [2,-2].';
-    expect(() => tf.buffer([2, -2])).toThrowError(msg);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/debug_mode_test.ts b/tfjs-master/tfjs-core/src/debug_mode_test.ts
deleted file mode 100644
index d32caad0f..000000000
--- a/tfjs-master/tfjs-core/src/debug_mode_test.ts
+++ /dev/null
@@ -1,123 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags, SYNC_BACKEND_ENVS} from './jasmine_util';
-import {convertToTensor} from './tensor_util_env';
-import {expectArraysClose} from './test_util';
-
-describeWithFlags('debug on', SYNC_BACKEND_ENVS, () => {
-  beforeAll(() => {
-    // Silence debug warnings.
-    spyOn(console, 'warn');
-    tf.enableDebugMode();
-  });
-
-  it('debug mode does not error when no nans', async () => {
-    const a = tf.tensor1d([2, -1, 0, 3]);
-    const res = tf.relu(a);
-    expectArraysClose(await res.data(), [2, 0, 0, 3]);
-  });
-
-  it('debug mode errors when nans in tensor construction, float32', () => {
-    const a = () => tf.tensor1d([2, NaN], 'float32');
-    expect(a).toThrowError();
-  });
-
-  it('debug mode errors when nans in tensor construction, int32', () => {
-    const a = () => tf.tensor1d([2, NaN], 'int32');
-    expect(a).toThrowError();
-  });
-
-  it('debug mode errors when Infinity in tensor construction', () => {
-    const a = () => tf.tensor1d([2, Infinity], 'float32');
-    expect(a).toThrowError();
-  });
-
-  it('debug mode errors when nans in tensor created from TypedArray', () => {
-    const a = () => tf.tensor1d(new Float32Array([1, 2, NaN]), 'float32');
-    expect(a).toThrowError();
-  });
-
-  it('debug mode errors when infinities in op output', async () => {
-    const a = tf.tensor1d([1, 2, 3, 4]);
-    const b = tf.tensor1d([2, -1, 0, 3]);
-
-    const c = async () => {
-      const result = a.div(b);
-      // Must await result so we know exception would have happened by the
-      // time we call `expect`.
-      await result.data();
-    };
-
-    await c();
-
-    expect(console.warn).toHaveBeenCalled();
-  });
-
-  it('debug mode errors when nans in op output', async () => {
-    const a = tf.tensor1d([-1, 2]);
-    const b = tf.tensor1d([0.5, 1]);
-
-    const c = async () => {
-      const result = a.pow(b);
-      await result.data();
-    };
-
-    await c();
-
-    expect(console.warn).toHaveBeenCalled();
-  });
-
-  it('debug mode errors when nans in oneHot op (tensorlike), int32', () => {
-    const f = () => tf.oneHot([2, NaN], 3);
-    expect(f).toThrowError();
-  });
-
-  it('debug mode errors when nan in convertToTensor, int32', () => {
-    const a = () => convertToTensor(NaN, 'a', 'test', 'int32');
-    expect(a).toThrowError();
-  });
-
-  it('debug mode errors when nan in convertToTensor array input, int32', () => {
-    const a = () => convertToTensor([NaN], 'a', 'test', 'int32');
-    expect(a).toThrowError();
-  });
-
-  // tslint:disable-next-line: ban
-  xit('A x B', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    const c = tf.matMul(a, b);
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -3, 20]);
-  });
-});
-
-describeWithFlags('debug off', ALL_ENVS, () => {
-  beforeAll(() => {
-    tf.env().set('DEBUG', false);
-  });
-
-  it('no errors where there are nans, and debug mode is disabled', async () => {
-    const a = tf.tensor1d([2, NaN]);
-    const res = tf.relu(a);
-    expectArraysClose(await res.data(), [2, NaN]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/device_util.ts b/tfjs-master/tfjs-core/src/device_util.ts
deleted file mode 100644
index 1ae970298..000000000
--- a/tfjs-master/tfjs-core/src/device_util.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line:no-any
-function _isNavigatorDefined(): boolean {
-  return typeof navigator !== 'undefined' && navigator != null;
-}
-
-let isMobileMockValue: boolean|undefined;
-
-export function mockIsMobile(value: boolean|undefined) {
-  isMobileMockValue = value;
-}
-
-export function isMobile(nav?: Navigator): boolean {
-  if (isMobileMockValue !== undefined) {
-    return isMobileMockValue;
-  }
-  if (nav || _isNavigatorDefined()) {
-    if (!nav) {
-      nav = navigator;
-    }
-    if (nav.product === 'ReactNative') {
-      return true;
-    }
-
-    const a = nav.userAgent || nav.vendor ||
-        // tslint:disable-next-line:no-any
-        (typeof window !== 'undefined' ? (window as any).opera : '');
-    // Use `navigator.userAgentData.mobile` as fallback.
-    if (!a) {
-      // tslint:disable-next-line:no-any
-      const navAny = nav as any;
-      return navAny.userAgentData && navAny.userAgentData.mobile;
-    }
-    // tslint:disable-next-line:max-line-length
-    return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i
-               .test(a) ||
-        // tslint:disable-next-line:max-line-length
-        /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i
-            .test(a.substr(0, 4));
-  }
-  return false;
-}
-
-export function isBrowser(): boolean {
-  return (typeof window !== 'undefined' && window.document != null) ||
-      //@ts-ignore
-      (typeof WorkerGlobalScope !== 'undefined');
-}
diff --git a/tfjs-master/tfjs-core/src/device_util_test.ts b/tfjs-master/tfjs-core/src/device_util_test.ts
deleted file mode 100644
index 15a4812a7..000000000
--- a/tfjs-master/tfjs-core/src/device_util_test.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as device_util from './device_util';
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-
-describeWithFlags('isMobile', ALL_ENVS, () => {
-  it('should not fail when navigator is set', () => {
-    expect(() => device_util.isMobile()).not.toThrow();
-  });
-  it('identifies react native as a mobile device', () => {
-    expect(device_util.isMobile(
-      {product: 'ReactNative'} as Navigator)).toEqual(true);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/engine.ts b/tfjs-master/tfjs-core/src/engine.ts
deleted file mode 100644
index a29b04c24..000000000
--- a/tfjs-master/tfjs-core/src/engine.ts
+++ /dev/null
@@ -1,1315 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BackendTimingInfo, DataMover, KernelBackend} from './backends/backend';
-import {Environment, setEnvironmentGlobal} from './environment';
-import {getGlobalNamespace} from './global_util';
-import {Add, Cast, Identity} from './kernel_names';
-import { getGradient, getKernel, getKernelsForBackend, GradFunc, NamedAttrMap } from './kernel_registry';
-import { TensorInfo } from './tensor_info';
-import * as log from './log';
-import {KernelProfile, Profiler} from './profiler';
-import {backpropagateGradients, getFilteredNodesXToY, TapeNode} from './tape';
-import {DataToGPUOptions, GPUData, setTensorTracker, Tensor, TensorTracker, Variable} from './tensor';
-import {DataId} from './tensor_info';
-import {GradSaveFunc, NamedTensorMap, NamedVariableMap, TensorContainer} from './tensor_types';
-import {getTensorsInContainer} from './tensor_util';
-import {BackendValues, DataType, DataValues} from './types';
-import * as util from './util';
-import {bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape} from './util';
-
-/**
- * A function that computes an output. The save function is for saving tensors
- * computed in the forward pass, that we need in the backward pass.
- */
-export type ForwardFunc<T> = (backend: KernelBackend, save?: GradSaveFunc) => T;
-
-/**
- * @docalias (a: Tensor, b: Tensor,..., save?: Function) => {
- *   value: Tensor,
- *   gradFunc: (dy: Tensor, saved?: NamedTensorMap) => Tensor | Tensor[]
- * }
- */
-export type CustomGradientFunc<T extends Tensor> =
-    (...inputs: Array<Tensor|GradSaveFunc>) => {
-      value: T;
-      gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[];
-    };
-
-export type MemoryInfo = {
-  numTensors: number; numDataBuffers: number; numBytes: number;
-  unreliable?: boolean; reasons: string[];
-};
-
-type KernelInfo = {
-  name: string; bytesAdded: number; totalBytesSnapshot: number;
-  tensorsAdded: number;
-  totalTensorsSnapshot: number;
-  inputShapes: number[][];
-  outputShapes: number[][];
-  kernelTimeMs: number | {error: string} | Promise<number|{error: string}>;
-  extraInfo: string | Promise<string>;
-};
-
-export type ProfileInfo = {
-  newBytes: number; newTensors: number; peakBytes: number;
-  kernels: KernelInfo[];
-  result: TensorContainer;
-  kernelNames: string[];
-};
-
-export interface TimingInfo extends BackendTimingInfo {
-  wallMs: number;
-}
-
-/** @docalias Function */
-export type ScopeFn<T extends TensorContainer> = () => T;
-
-interface ScopeState {
-  track: Tensor[];
-  name: string;
-  id: number;
-}
-
-interface RegisteredKernelInvocation<I extends NamedTensorMap> {
-  kernelName: string;
-  inputs: I;
-  attrs?: NamedAttrMap;
-}
-
-interface CustomGradKernelInvocation<T extends Tensor|Tensor[],
-                                               I extends NamedTensorMap> {
-  forwardFunc: ForwardFunc<T>;
-  backwardsFunc: (dy: T, saved: Tensor[]) => {
-    [P in keyof I]: () => I[P]
-  };
-  inputs: I;
-  attrs?: NamedAttrMap;
-}
-
-function isRegisteredKernelInvocation<T extends Tensor|Tensor[],
-                                                I extends NamedTensorMap>(
-    kernelInvocation: RegisteredKernelInvocation<I>|
-    CustomGradKernelInvocation<T, I>):
-    kernelInvocation is RegisteredKernelInvocation<I> {
-  return (kernelInvocation as RegisteredKernelInvocation<I>).kernelName != null;
-}
-
-class EngineState {
-  // Public since optimizers will use it.
-  registeredVariables: NamedVariableMap = {};
-
-  nextTapeNodeId = 0;
-  numBytes = 0;
-  numTensors = 0;
-  numStringTensors = 0;
-  numDataBuffers = 0;
-
-  activeTape: TapeNode[];
-  // Number of nested tf.grad() statements when computing higher-order
-  // gradients. E.g. `1` for first-order gradients and `2` for second-order
-  // gradients. Used to track if the tape should be removed after a backprop.
-  gradientDepth = 0;
-  // Number of nested kernel calls. When kernel depth is greater than 1, we turn
-  // off the tape.
-  kernelDepth = 0;
-
-  // Keep Tensors that parallel the tapes.
-  activeScope: ScopeState;
-  scopeStack: ScopeState[] = [];
-  /**
-   * Keeps track of the number of data moves during a kernel execution. We
-   * maintain a stack since kernels can call other kernels, recursively.
-   */
-  numDataMovesStack: number[] = [];
-  nextScopeId = 0;
-
-  tensorInfo = new WeakMap<DataId, {
-    backend: KernelBackend,
-    bytes: number,
-    dtype: DataType,
-    shape: number[]
-  }>();
-
-  profiling = false;
-  activeProfile: ProfileInfo = {
-    newBytes: 0,
-    newTensors: 0,
-    peakBytes: 0,
-    kernels: [],
-    result: null,
-    get kernelNames():
-        string[] {
-          return Array.from(new Set(this.kernels.map(k => k.name)));
-        }
-  };
-
-  dispose() {
-    for (const variableName in this.registeredVariables) {
-      this.registeredVariables[variableName].dispose();
-    }
-  }
-}
-
-export class Engine implements TensorTracker, DataMover {
-  state: EngineState;
-  backendName: string;
-  registry: {[id: string]: KernelBackend} = {};
-  registryFactory: {
-    [id: string]: {
-      factory: () => KernelBackend | Promise<KernelBackend>,
-      priority: number
-    }
-  } = {};
-
-  private profiler: Profiler;
-  private backendInstance: KernelBackend;
-  private pendingBackendInit: Promise<boolean>;
-  private pendingBackendInitId = 0;
-
-  constructor(public ENV: Environment) {
-    this.state = new EngineState();
-  }
-
-  async ready(): Promise<void> {
-    if (this.pendingBackendInit != null) {
-      return this.pendingBackendInit.then(() => {});
-    }
-    if (this.backendInstance != null) {
-      return;
-    }
-    const sortedBackends = this.getSortedBackends();
-
-    for (let i = 0; i < sortedBackends.length; i++) {
-      const backendName = sortedBackends[i];
-      const success = await this.initializeBackend(backendName).success;
-      if (success) {
-        await this.setBackend(backendName);
-        return;
-      }
-    }
-
-    throw new Error(
-        `Could not initialize any backends, all backend initializations ` +
-        `failed.`);
-  }
-
-  get backend(): KernelBackend {
-    if (this.pendingBackendInit != null) {
-      throw new Error(
-          `Backend '${this.backendName}' has not yet been initialized. Make ` +
-          `sure to await tf.ready() or await tf.setBackend() before calling ` +
-          `other methods`);
-    }
-    if (this.backendInstance == null) {
-      const {name, asyncInit} = this.initializeBackendsAndReturnBest();
-      if (asyncInit) {
-        throw new Error(
-            `The highest priority backend '${name}' has not yet been ` +
-            `initialized. Make sure to await tf.ready() or ` +
-            `await tf.setBackend() before calling other methods`);
-      }
-      this.setBackend(name);
-    }
-    return this.backendInstance;
-  }
-
-  backendNames(): string[] {
-    return Object.keys(this.registryFactory);
-  }
-
-  findBackend(backendName: string): KernelBackend {
-    if (!(backendName in this.registry)) {
-      // If the backend hasn't been initialized but we have a registry entry for
-      // it, initialize it and return it.
-      if (backendName in this.registryFactory) {
-        const {asyncInit} = this.initializeBackend(backendName);
-        if (asyncInit) {
-          // Backend is not ready yet.
-          return null;
-        }
-      } else {
-        return null;
-      }
-    }
-    return this.registry[backendName];
-  }
-
-  findBackendFactory(backendName: string):
-      () => KernelBackend | Promise<KernelBackend> {
-    if (!(backendName in this.registryFactory)) {
-      return null;
-    }
-    return this.registryFactory[backendName].factory;
-  }
-
-  registerBackend(
-      backendName: string,
-      factory: () => KernelBackend | Promise<KernelBackend>,
-      priority = 1): boolean {
-    if (backendName in this.registryFactory) {
-      log.warn(
-          `${backendName} backend was already registered. ` +
-          `Reusing existing backend factory.`);
-      return false;
-    }
-    this.registryFactory[backendName] = {factory, priority};
-    return true;
-  }
-
-  async setBackend(backendName: string): Promise<boolean> {
-    if (this.registryFactory[backendName] == null) {
-      throw new Error(`Backend name '${backendName}' not found in registry`);
-    }
-    this.backendName = backendName;
-    if (this.registry[backendName] == null) {
-      this.backendInstance = null;
-      const {success, asyncInit} = this.initializeBackend(backendName);
-      const result = asyncInit ? await success : success;
-      if (!result) {
-        return false;
-      }
-    }
-    this.backendInstance = this.registry[backendName];
-    this.setupRegisteredKernels();
-    // Reset the profiler.
-    this.profiler = new Profiler(this.backendInstance);
-
-    return true;
-  }
-
-  private setupRegisteredKernels(): void {
-    const kernels = getKernelsForBackend(this.backendName);
-    kernels.forEach(kernel => {
-      if (kernel.setupFunc != null) {
-        kernel.setupFunc(this.backendInstance);
-      }
-    });
-  }
-
-  private disposeRegisteredKernels(backendName: string): void {
-    const kernels = getKernelsForBackend(backendName);
-    kernels.forEach(kernel => {
-      if (kernel.disposeFunc != null) {
-        kernel.disposeFunc(this.registry[backendName]);
-      }
-    });
-  }
-
-  /**
-   * Initializes a backend by looking up the backend name in the factory
-   * registry and calling the factory method. Returns a boolean representing
-   * whether the initialization of the backend suceeded. Throws an error if
-   * there is no backend in the factory registry.
-   */
-  private initializeBackend(backendName: string):
-      {success: boolean|Promise<boolean>, asyncInit: boolean} {
-    const registryFactoryEntry = this.registryFactory[backendName];
-    if (registryFactoryEntry == null) {
-      throw new Error(
-          `Cannot initialize backend ${backendName}, no registration found.`);
-    }
-
-    try {
-      const backend = registryFactoryEntry.factory();
-      /* Test if the factory returns a promise.
-      Done in a more liberal way than
-      previous 'Promise.resolve(backend)===backend'
-      as we needed to account for custom Promise
-      implementations (e.g. Angular) */
-      if (backend && !(backend instanceof KernelBackend) &&
-          typeof backend.then === 'function') {
-        const promiseId = ++this.pendingBackendInitId;
-        const success =
-            backend
-                .then(backendInstance => {
-                  // Outdated promise. Another backend was set in the meantime.
-                  if (promiseId < this.pendingBackendInitId) {
-                    return false;
-                  }
-                  this.registry[backendName] = backendInstance;
-                  this.pendingBackendInit = null;
-                  return true;
-                })
-                .catch(err => {
-                  // Outdated promise. Another backend was set in the meantime.
-                  if (promiseId < this.pendingBackendInitId) {
-                    return false;
-                  }
-                  this.pendingBackendInit = null;
-                  log.warn(`Initialization of backend ${backendName} failed`);
-                  log.warn(err.stack || err.message);
-                  return false;
-                });
-        this.pendingBackendInit = success;
-        return {success, asyncInit: true};
-      } else {
-        this.registry[backendName] = backend as KernelBackend;
-        return {success: true, asyncInit: false};
-      }
-    } catch (err) {
-      log.warn(`Initialization of backend ${backendName} failed`);
-      log.warn(err.stack || err.message);
-      return {success: false, asyncInit: false};
-    }
-  }
-
-  removeBackend(backendName: string): void {
-    if (!(backendName in this.registryFactory)) {
-      throw new Error(`${backendName} backend not found in registry`);
-    }
-    if (this.backendName === backendName && this.pendingBackendInit != null) {
-      // There is a pending promise of the backend we want to remove. Make it
-      // obsolete.
-      this.pendingBackendInitId++;
-    }
-
-    if (backendName in this.registry) {
-      this.disposeRegisteredKernels(backendName);
-      this.registry[backendName].dispose();
-      delete this.registry[backendName];
-    }
-
-    delete this.registryFactory[backendName];
-
-    // Unset the backend if it is active.
-    if (this.backendName === backendName) {
-      this.pendingBackendInit = null;
-      this.backendName = null;
-      this.backendInstance = null;
-    }
-  }
-
-  private getSortedBackends(): string[] {
-    if (Object.keys(this.registryFactory).length === 0) {
-      throw new Error('No backend found in registry.');
-    }
-    return Object.keys(this.registryFactory).sort((a: string, b: string) => {
-      // Highest priority comes first.
-      return this.registryFactory[b].priority -
-          this.registryFactory[a].priority;
-    });
-  }
-
-  private initializeBackendsAndReturnBest():
-      {name: string, asyncInit: boolean} {
-    const sortedBackends = this.getSortedBackends();
-
-    for (let i = 0; i < sortedBackends.length; i++) {
-      const backendName = sortedBackends[i];
-      const {success, asyncInit} = this.initializeBackend(backendName);
-      if (asyncInit || success) {
-        return {name: backendName, asyncInit};
-      }
-    }
-    throw new Error(
-        `Could not initialize any backends, all backend initializations ` +
-        `failed.`);
-  }
-
-  moveData(backend: KernelBackend, dataId: DataId) {
-    const info = this.state.tensorInfo.get(dataId);
-    const srcBackend = info.backend;
-    const values = this.readSync(dataId);
-    const refCount = srcBackend.refCount(dataId);
-    // Delete the tensor from the old backend and move it to the new
-    // backend.
-    srcBackend.disposeData(dataId, true);
-    info.backend = backend;
-    backend.move(dataId, values, info.shape, info.dtype, refCount);
-    if (this.shouldCheckForMemLeaks()) {
-      // Track the number of moves during a kernel execution to correctly
-      // detect memory leaks.
-      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
-    }
-  }
-
-  tidy<T extends TensorContainer>(nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>):
-      T {
-    let name: string = null;
-    if (fn == null) {
-      // Called with only 1 argument.
-      if (typeof nameOrFn !== 'function') {
-        throw new Error('Please provide a function to tidy()');
-      }
-      fn = nameOrFn;
-    } else {
-      // Called with 2 arguments.
-      if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {
-        throw new Error(
-            'When calling with two arguments, the first argument ' +
-            'to tidy() must be a string');
-      }
-      if (typeof fn !== 'function') {
-        throw new Error(
-            'When calling with two arguments, the 2nd argument ' +
-            'to tidy() must be a function');
-      }
-      name = nameOrFn as string;
-      // TODO(nsthorat,smilkov): Do operation logging and performance
-      // profiling.
-    }
-    let result: T;
-    return this.scopedRun(
-        () => this.startScope(name), () => this.endScope(result), () => {
-          result = fn();
-          if (result instanceof Promise) {
-            console.error('Cannot return a Promise inside of tidy.');
-          }
-          return result;
-        });
-  }
-
-  private scopedRun<T>(start: () => void, end: () => void, f: () => T): T {
-    start();
-    try {
-      const res = f();
-      end();
-      return res;
-    } catch (ex) {
-      end();
-      throw ex;
-    }
-  }
-
-  private static nextTensorId = 0;
-  private nextTensorId(): number {
-    return Engine.nextTensorId++;
-  }
-
-  private static nextVariableId = 0;
-  private nextVariableId(): number {
-    return Engine.nextVariableId++;
-  }
-
-  /**
-   * This method is called instead of the public-facing tensor.clone() when
-   * saving a tensor for backwards pass. It makes sure to add the clone
-   * operation to the tape regardless of being called inside a kernel
-   * execution.
-   */
-  private clone(x: Tensor): Tensor {
-    const y: Tensor = ENGINE.runKernel(Identity,
-                                       {x} as unknown as NamedTensorMap);
-    const inputs = {x};
-    const grad = (dy: Tensor) => ({
-      x: () => {
-        const dtype = 'float32';
-        const gradInputs = {x: dy};
-        const attrs = {dtype};
-
-        return ENGINE.runKernel(
-                   Cast, gradInputs as unknown as NamedTensorMap,
-                   // tslint:disable-next-line: no-unnecessary-type-assertion
-                   attrs as unknown as NamedAttrMap) as Tensor;
-      }
-    });
-    const saved: Tensor[] = [];
-    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});
-    return y;
-  }
-
-  /**
-   * Execute a kernel with the given name and return the output tensor.
-   *
-   * @param kernelName The name of the kernel to execute.
-   * @param inputs A map of input names to tensors.
-   * @param attrs A map of attribute names to their values. An attribute is a
-   *     primitive (non-tensor) input to the kernel.
-   * @param inputsToSave A list of tensors, inputs to save for the backprop
-   *     computation.
-   * @param outputsToSave A list of booleans, specifying which output to save
-   *     for the backprop computation. These are booleans since the output
-   * tensors are not visible to the user.
-   */
-  runKernel<T extends Tensor|Tensor[]>(
-      kernelName: string, inputs: NamedTensorMap, attrs?: NamedAttrMap): T {
-    if (this.backendName == null) {
-      // backend has not been initialized yet (backend initialization is lazy
-      // can be deferred until an op/ kernel is run).
-      // The below getter has side effects that will try to initialize the
-      // backend and set properties like this.backendName
-      // tslint:disable-next-line: no-unused-expression
-      this.backend;
-    }
-    const hasKernel = getKernel(kernelName, this.backendName) != null;
-    if (!hasKernel) {
-      throw new Error(`Kernel '${kernelName}' not registered for backend '${
-          this.backendName}'`);
-    }
-    return this.runKernelFunc({kernelName, inputs, attrs});
-  }
-
-  private shouldCheckForMemLeaks(): boolean {
-    return this.ENV.getBool('IS_TEST');
-  }
-
-  private checkKernelForMemLeak(
-      kernelName: string, numDataIdsBefore: number,
-      outInfos: TensorInfo[]): void {
-    const numDataIdsAfter = this.backend.numDataIds();
-
-    // Count the number of data ids associated with the result of the kernel.
-    let numOutputDataIds = 0;
-    outInfos.forEach(info => {
-      // Complex numbers allocate 3 data ids, one for 'real', one for
-      // 'imaginary', and one for the container that holds the former two.
-      numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);
-    });
-
-    // Account for the number of moves during kernel execution. A "data move"
-    // can happen in the middle of a kernel execution, placing a new (key,value)
-    // pair in the data storage. Since data moves have net zero effect (we
-    // always remove the data from the old backend), we have to cancel them out
-    // when detecting memory leaks.
-    const numMoves =
-        this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
-    const dataIdsLeaked =
-        numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
-    if (dataIdsLeaked > 0) {
-      throw new Error(
-          `Backend '${this.backendName}' has an internal memory leak ` +
-          `(${dataIdsLeaked} data ids) after running '${kernelName}'`);
-    }
-  }
-
-  /**
-   * Internal helper method to execute a kernel Func
-   *
-   * Use `runKernel` to execute kernels from outside of engine.
-   */
-  private runKernelFunc<T extends Tensor|Tensor[], I extends NamedTensorMap>(
-      kernelParams: RegisteredKernelInvocation<I>|
-      CustomGradKernelInvocation<T, I>): T {
-    let outputs: Tensor[];
-    let saved: Tensor[] = [];
-    const isTapeOn = this.isTapeOn();
-
-    const startingBytecount = this.state.numBytes;
-    const startingNumTensors = this.state.numTensors;
-
-    if (this.shouldCheckForMemLeaks()) {
-      this.state.numDataMovesStack.push(0);
-    }
-
-    let kernelFunc: () => Tensor[];
-    if (this.backendName == null) {
-      // backend has not been initialized yet (backend initialization is lazy
-      // can be deferred until an op/ kernel is run).
-      // The below getter has side effects that will try to initialize the
-      // backend and set properties like this.backendName
-      // tslint:disable-next-line: no-unused-expression
-      this.backend;
-    }
-
-    let out: TensorInfo|TensorInfo[];
-
-    const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ?
-        kernelParams.kernelName :
-        this.state.activeScope != null ? this.state.activeScope.name : '';
-
-    // Create the kernelFunc from either a registered kernel OR passed in
-    // forward/backward functions (used by custom grad). In this context a
-    // kernelFunc wraps a kernel implementation with some bookkeeping.
-
-    if (isRegisteredKernelInvocation(kernelParams)) {
-      const {kernelName, inputs, attrs} = kernelParams;
-      if (this.backendName == null) {
-        // backend has not been initialized yet (backend initialization is lazy
-        // can be deferred until an op/ kernel is run).
-        // The below getter has side effects that will try to initialize the
-        // backend and set properties like this.backendName
-        // tslint:disable-next-line: no-unused-expression
-        this.backend;
-      }
-      const kernel = getKernel(kernelName, this.backendName);
-      util.assert(
-          kernel != null,
-          () => `Cannot find registered kernel '${kernelName}' for backend '${
-              this.backendName}'`);
-
-      kernelFunc = () => {
-        const numDataIdsBefore = this.backend.numDataIds();
-        out = kernel.kernelFunc({inputs, attrs, backend: this.backend});
-        const outInfos = Array.isArray(out) ? out : [out];
-        if (this.shouldCheckForMemLeaks()) {
-          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
-        }
-
-        const outTensors = outInfos.map((outInfo: TensorInfo|Tensor) => {
-          // todo (yassogba) remove this option (Tensor) when node backend
-          // methods have been modularized and they all return tensorInfo.
-          // TensorInfos do not have a rank attribute.
-          if ((outInfo as Tensor).rank != null) {
-            return outInfo as Tensor;
-          }
-          return this.makeTensorFromTensorInfo(outInfo);
-        });
-
-        // Save any required inputs and outputs.
-
-        // Do not save unless we are recording to the tape. Otherwise it would
-        // cause a mem leak since there would be no backprop for these tensors
-        // (which would otherwise dispose them).
-        if (isTapeOn) {
-          const tensorsToSave =
-              this.getTensorsForGradient(kernelName, inputs, outTensors);
-          saved = this.saveTensorsForBackwardMode(tensorsToSave);
-        }
-        return outTensors;
-      };
-    } else {
-      const {forwardFunc} = kernelParams;
-      // Running a customGrad op.
-      const saveFunc: GradSaveFunc = (tensors) => {
-        // Do not save unless we are recording to the tape. Otherwise it would
-        // cause a mem leak since we would never run backprop, which disposes
-        // the kept tensors.
-        if (!isTapeOn) {
-          return;
-        }
-        saved = tensors.map(tensor => this.keep(this.clone(tensor)));
-      };
-
-      kernelFunc = () => {
-        const numDataIdsBefore = this.backend.numDataIds();
-        out = this.tidy(() => forwardFunc(this.backend, saveFunc));
-        const outs = (Array.isArray(out) ? out : [out]) as Tensor[];
-        if (this.shouldCheckForMemLeaks()) {
-          // Scope name is used to print a more helpful error message if needed.
-          this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);
-        }
-        return outs;
-      };
-    }
-
-    //
-    // Run the kernelFunc. Optionally profiling it.
-    //
-    const {inputs, attrs} = kernelParams;
-    const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ?
-        null :
-        kernelParams.backwardsFunc;
-
-    let kernelProfile: KernelProfile;
-    this.scopedRun(
-        // Stop recording to a tape when running a kernel.
-        () => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
-          if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {
-            outputs = kernelFunc();
-          } else {
-            kernelProfile = this.profiler.profileKernel(
-                kernelOrScopeName, inputs, () => kernelFunc());
-            if (this.ENV.getBool('DEBUG')) {
-              this.profiler.logKernelProfile(kernelProfile);
-            }
-            outputs = kernelProfile.outputs;
-          }
-        });
-
-    if (isTapeOn) {
-      this.addTapeNode(
-          kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);
-    }
-
-    if (this.state.profiling) {
-      this.state.activeProfile.kernels.push({
-        name: kernelOrScopeName,
-        bytesAdded: this.state.numBytes - startingBytecount,
-        totalBytesSnapshot: this.state.numBytes,
-        tensorsAdded: this.state.numTensors - startingNumTensors,
-        totalTensorsSnapshot: this.state.numTensors,
-        inputShapes: Object.keys(inputs).map(
-            key => inputs[key] != null ? inputs[key].shape : null),
-        outputShapes: outputs.map(item => item.shape),
-        kernelTimeMs: kernelProfile.timeMs,
-        extraInfo: kernelProfile.extraInfo
-      });
-    }
-    return (Array.isArray(out) ? outputs : outputs[0]) as T;
-  }
-
-  /**
-   * Saves tensors used in forward mode for use in backward mode.
-   *
-   * @param tensors the list of tensors to save.
-   */
-  private saveTensorsForBackwardMode(tensors: Tensor[]): Tensor[] {
-    const saved = tensors.map(tensor => this.keep(this.clone(tensor)));
-    return saved;
-  }
-
-  /**
-   * Returns a list of tensors to save for a given gradient calculation.
-   *
-   * @param kernelName name of kernel to look up gradient for.
-   * @param inputs a map of input tensors.
-   * @param outputs an array of output tensors from forward mode of kernel.
-   */
-  private getTensorsForGradient(
-      kernelName: string, inputs: NamedTensorMap,
-      outputs: Tensor[]): Tensor[]|null {
-    const gradConfig = getGradient(kernelName);
-    if (gradConfig != null) {
-      const inputsToSave: string[] = gradConfig.inputsToSave || [];
-      const outputsToSave: boolean[] = gradConfig.outputsToSave || [];
-
-      // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs
-      // specified in inputsToSave will be saved.
-      let inputTensorsToSave: Tensor[];
-      if (gradConfig.saveAllInputs) {
-        util.assert(
-            Array.isArray(inputs),
-            () => 'saveAllInputs is true, expected inputs to be an array.');
-
-        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);
-      } else {
-        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
-      }
-
-      const outputTensorsToSave: Tensor[] =
-          outputs.filter((_, i) => outputsToSave[i]);
-
-      return inputTensorsToSave.concat(outputTensorsToSave);
-    }
-    // We return an empty list rather than throw an error because the kernel we
-    // are looking up may not actually be relevant to backproping through the
-    // overall function
-    //
-    // See 'does not error if irrelevant (pruned) ops are missing grads' test
-    // in gradients_test.ts for an example.
-    return [];
-  }
-
-  /**
-   * Internal method used by public APIs for tensor creation. Makes a new
-   * tensor with the provided shape, dtype and values. It always
-   * creates a new data id and writes the values to the underlying backend.
-   */
-  makeTensor(
-      values: DataValues, shape: number[], dtype: DataType,
-      backend?: KernelBackend): Tensor {
-    if (values == null) {
-      throw new Error('Values passed to engine.makeTensor() are null');
-    }
-    dtype = dtype || 'float32';
-    backend = backend || this.backend;
-    let backendVals = values as BackendValues;
-    if (dtype === 'string' && util.isString(values[0])) {
-      backendVals = (values as string[]).map(d => util.encodeString(d));
-    }
-    const dataId = backend.write(backendVals, shape, dtype);
-    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.trackTensor(t, backend);
-
-    // Count bytes for string tensors.
-    if (dtype === 'string') {
-      const info = this.state.tensorInfo.get(dataId);
-      const newBytes = bytesFromStringArray(backendVals as Uint8Array[]);
-      this.state.numBytes += newBytes - info.bytes;
-      info.bytes = newBytes;
-    }
-    return t;
-  }
-
-  /**
-   * Internal method used by backends. Makes a new tensor
-   * that is a wrapper around an existing data id. It doesn't create
-   * a new data id, only increments the ref count used in memory tracking.
-   * @deprecated
-   */
-  makeTensorFromDataId(
-    dataId: DataId, shape: number[], dtype: DataType,
-    backend?: KernelBackend): Tensor {
-    dtype = dtype || 'float32';
-    const tensorInfo: TensorInfo = {dataId, shape, dtype};
-    return this.makeTensorFromTensorInfo(tensorInfo, backend);
-  }
-
-  /**
-   * Internal method used by backends. Makes a new tensor that is a wrapper
-   * around an existing data id in TensorInfo. It doesn't create a new data id,
-   * only increments the ref count used in memory tracking.
-   */
-  makeTensorFromTensorInfo(tensorInfo: TensorInfo, backend?: KernelBackend):
-      Tensor {
-    const {dataId, shape, dtype} = tensorInfo;
-    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.trackTensor(t, backend);
-    return t;
-  }
-
-  makeVariable(
-      initialValue: Tensor, trainable = true, name?: string,
-      dtype?: DataType): Variable {
-    name = name || this.nextVariableId().toString();
-    if (dtype != null && dtype !== initialValue.dtype) {
-      initialValue = initialValue.cast(dtype);
-    }
-    const v = new Variable(initialValue, trainable, name, this.nextTensorId());
-    if (this.state.registeredVariables[v.name] != null) {
-      throw new Error(`Variable with name ${v.name} was already registered`);
-    }
-    this.state.registeredVariables[v.name] = v;
-    this.incRef(v, this.backend);
-    return v;
-  }
-
-  trackTensor(a: Tensor, backend: KernelBackend): void {
-    this.state.numTensors++;
-    if (a.dtype === 'string') {
-      this.state.numStringTensors++;
-    }
-    // Bytes for complex numbers are counted by their components. Bytes for
-    // string tensors are counted when writing values.
-    let bytes = 0;
-    if (a.dtype !== 'complex64' && a.dtype !== 'string') {
-      bytes = a.size * util.bytesPerElement(a.dtype);
-    }
-    this.state.numBytes += bytes;
-
-    if (!this.state.tensorInfo.has(a.dataId)) {
-      this.state.numDataBuffers++;
-      this.state.tensorInfo.set(a.dataId, {
-        backend: backend || this.backend,
-        dtype: a.dtype,
-        shape: a.shape,
-        bytes
-      });
-    }
-
-    if (!(a instanceof Variable)) {
-      this.track(a);
-    }
-  }
-
-  // Track the tensor by dataId and increase the refCount for the dataId in the
-  // backend.
-  // TODO(pyu10055): This is currently used by makeVariable method, to increase
-  // refCount on the backend for the dataId. It can potentially be replaced with
-  // Identity op indead of calling backend directly.
-  incRef(a: Tensor, backend: KernelBackend): void {
-    this.trackTensor(a, backend);
-    this.backend.incRef(a.dataId);
-  }
-
-  removeDataId(dataId: DataId, backend: KernelBackend) {
-    if (this.state.tensorInfo.has(dataId) &&
-        this.state.tensorInfo.get(dataId).backend === backend) {
-      this.state.tensorInfo.delete(dataId);
-      this.state.numDataBuffers--;
-    }
-  }
-  disposeTensor(a: Tensor): void {
-    if (!this.state.tensorInfo.has(a.dataId)) {
-      return;
-    }
-    const info = this.state.tensorInfo.get(a.dataId);
-
-    this.state.numTensors--;
-    if (a.dtype === 'string') {
-      this.state.numStringTensors--;
-      this.state.numBytes -= info.bytes;
-    }
-    // Don't count bytes for complex numbers as they are counted by their
-    // components.
-    if (a.dtype !== 'complex64' && a.dtype !== 'string') {
-      const bytes = a.size * util.bytesPerElement(a.dtype);
-      this.state.numBytes -= bytes;
-    }
-
-    // Remove the reference to dataId if backend dispose the data successfully
-    if (info.backend.disposeData(a.dataId)) {
-      this.removeDataId(a.dataId, info.backend);
-    }
-
-    // TODO(nsthorat): Construct an error and save the stack trace for
-    // debugging when in debug mode. Creating a stack trace is too expensive
-    // to do unconditionally.
-  }
-
-  disposeVariables(): void {
-    for (const varName in this.state.registeredVariables) {
-      const v = this.state.registeredVariables[varName];
-      this.disposeVariable(v);
-    }
-  }
-
-  disposeVariable(v: Variable): void {
-    this.disposeTensor(v);
-    if (this.state.registeredVariables[v.name] != null) {
-      delete this.state.registeredVariables[v.name];
-    }
-  }
-
-  memory(): MemoryInfo {
-    const info = this.backend.memory() as MemoryInfo;
-    info.numTensors = this.state.numTensors;
-    info.numDataBuffers = this.state.numDataBuffers;
-    info.numBytes = this.state.numBytes;
-    if (this.state.numStringTensors > 0) {
-      info.unreliable = true;
-      if (info.reasons == null) {
-        info.reasons = [];
-      }
-      info.reasons.push(
-          'Memory usage by string tensors is approximate ' +
-          '(2 bytes per character)');
-    }
-    return info;
-  }
-
-  async profile(query: () => (TensorContainer | Promise<TensorContainer>)):
-      Promise<ProfileInfo> {
-    this.state.profiling = true;
-
-    const startBytes = this.state.numBytes;
-    const startNumTensors = this.state.numTensors;
-
-    this.state.activeProfile.kernels = [];
-    this.state.activeProfile.result = await query();
-
-    this.state.profiling = false;
-
-    this.state.activeProfile.peakBytes = Math.max(
-        ...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));
-    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
-    this.state.activeProfile.newTensors =
-        this.state.numTensors - startNumTensors;
-    for (const kernel of this.state.activeProfile.kernels) {
-      kernel.kernelTimeMs = await kernel.kernelTimeMs;
-      kernel.extraInfo = await kernel.extraInfo;
-    }
-    return this.state.activeProfile;
-  }
-
-  isTapeOn(): boolean {
-    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
-  }
-
-  private addTapeNode(
-      kernelName: string, inputs: NamedTensorMap, outputs: Tensor[],
-      gradientsFunc: GradFunc, saved: Tensor[], attrs: NamedAttrMap): void {
-    const tapeNode: TapeNode =
-        {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};
-
-    const gradConfig = getGradient(kernelName);
-    if (gradConfig != null) {
-      gradientsFunc = gradConfig.gradFunc;
-    }
-    if (gradientsFunc != null) {
-      tapeNode.gradient = (dys: Tensor[]) => {
-        // TODO(smilkov): To optimize back-prop, pass dys that are not used in
-        // the backprop graph to the user as null instead of zeros
-        dys = dys.map((dy, i) => {
-          if (dy == null) {
-            const output = outputs[i];
-            const vals = util.makeZerosTypedArray(output.size, output.dtype);
-            return this.makeTensor(vals, output.shape, output.dtype);
-          }
-          return dy;
-        });
-        // Grad functions of ops with single outputs expect a dy, while ops
-        // with multiple outputs expect dys (array of dy).
-        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
-      };
-    }
-    this.state.activeTape.push(tapeNode);
-  }
-
-  keep<T extends Tensor>(result: T): T {
-    result.kept = true;
-    return result;
-  }
-
-  private startTape() {
-    if (this.state.gradientDepth === 0) {
-      this.state.activeTape = [];
-    }
-    this.state.gradientDepth++;
-  }
-
-  private endTape() {
-    this.state.gradientDepth--;
-  }
-
-  /**
-   * Start a scope. Use this with endScope() to achieve the same functionality
-   * as scope() without the need for a function closure.
-   */
-  startScope(name?: string) {
-    const scopeInfo: ScopeState = {
-      track: [],
-      name: 'unnamed scope',
-      id: this.state.nextScopeId++
-    };
-    if (name) {
-      scopeInfo.name = name;
-    }
-    this.state.scopeStack.push(scopeInfo);
-    this.state.activeScope = scopeInfo;
-  }
-
-  /**
-   * End a scope. Use this with startScope() to achieve the same functionality
-   * as scope() without the need for a function closure.
-   */
-  endScope(result?: TensorContainer) {
-    const tensorsToTrackInParent = getTensorsInContainer(result);
-    const tensorsToTrackInParentSet =
-        new Set(tensorsToTrackInParent.map(t => t.id));
-
-    // Dispose the arrays tracked in this scope.
-    for (let i = 0; i < this.state.activeScope.track.length; i++) {
-      const tensor = this.state.activeScope.track[i];
-      if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {
-        tensor.dispose();
-      }
-    }
-
-    const oldScope = this.state.scopeStack.pop();
-    this.state.activeScope = this.state.scopeStack.length === 0 ?
-        null :
-        this.state.scopeStack[this.state.scopeStack.length - 1];
-
-    // Track the current result in the parent scope.
-    tensorsToTrackInParent.forEach(tensor => {
-      // Only track the tensor if was allocated in the inner scope and is not
-      // globally kept.
-      if (!tensor.kept && tensor.scopeId === oldScope.id) {
-        this.track(tensor);
-      }
-    });
-  }
-
-  /**
-   * Returns gradients of `f` with respect to each of the `xs`. The gradients
-   * returned are of the same length as `xs`, but some might be null if `f`
-   * was not a function of that `x`. It also takes optional dy to multiply the
-   * gradient, which defaults to `1`.
-   */
-  gradients<T extends Tensor>(
-      f: () => T, xs: Tensor[], dy?: T,
-      allowNoGradients = false): {value: T, grads: Tensor[]} {
-    util.assert(
-        xs.length > 0, () => 'gradients() received an empty list of xs.');
-    if (dy != null && dy.dtype !== 'float32') {
-      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
-    }
-
-    const y = this.scopedRun(
-        () => this.startTape(), () => this.endTape(),
-        () => this.tidy('forward', f));
-
-    util.assert(
-        y instanceof Tensor,
-        () => 'The result y returned by f() must be a tensor.');
-    // Filter out the nodes that don't connect x => y.
-    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
-    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
-      throw new Error(
-          'Cannot compute gradient of y=f(x) with respect to x. Make sure ' +
-          'that the f you passed encloses all operations that lead from x ' +
-          'to y.');
-    }
-
-    return this.tidy('backward', () => {
-      const accumulatedGradientMap: {[tensorId: number]: Tensor} = {};
-      accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;
-
-      // Backprop gradients through the filtered nodes.
-      backpropagateGradients(
-          accumulatedGradientMap, filteredTape,
-          // Pass the tidy function to avoid circular dep with `tape.ts`.
-          f => this.tidy(f as ScopeFn<Tensor>),
-          // Pass an add function to avoide a circular dep with `tape.ts`.
-          add);
-      const grads = xs.map(x => accumulatedGradientMap[x.id]);
-
-      if (this.state.gradientDepth === 0) {
-        // This means that we are not computing higher-order gradients
-        // and can clean up the tape.
-        this.state.activeTape.forEach(node => {
-          for (const tensor of node.saved) {
-            tensor.dispose();
-          }
-        });
-        this.state.activeTape = null;
-      }
-      return {value: y, grads};
-    });
-  }
-
-  customGrad<T extends Tensor>(f: CustomGradientFunc<T>):
-      (...args: Array<Tensor|GradSaveFunc>) => T {
-    util.assert(
-        util.isFunction(f),
-        () => 'The f passed in customGrad(f) must be a function.');
-    return (...inputs: Tensor[]): T => {
-      util.assert(
-          inputs.every(t => t instanceof Tensor),
-          () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +
-              'tensors');
-
-      let res: {
-        value: T,
-        gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[],
-      };
-      const inputMap: NamedTensorMap = {};
-      inputs.forEach((input, i) => {
-        inputMap[i] = input;
-      });
-
-      const forwardFunc: ForwardFunc<T> = (_, save) => {
-        res = f(...[...inputs, save]);
-        util.assert(
-            res.value instanceof Tensor,
-            () => 'The function f passed in customGrad(f) must return an ' +
-                'object where `obj.value` is a tensor');
-        util.assert(
-            util.isFunction(res.gradFunc),
-            () => 'The function f passed in customGrad(f) must return an ' +
-                'object where `obj.gradFunc` is a function.');
-        return res.value;
-      };
-
-      const backwardsFunc = (dy: T, saved: Tensor[]) => {
-        const gradRes = res.gradFunc(dy, saved);
-        const grads: Tensor[] = Array.isArray(gradRes) ? gradRes : [gradRes];
-        util.assert(
-            grads.length === inputs.length,
-            () => 'The function f passed in customGrad(f) must return an ' +
-                'object where `obj.gradFunc` is a function that returns ' +
-                'the same number of tensors as inputs passed to f(...).');
-        util.assert(
-            grads.every(t => t instanceof Tensor),
-            () => 'The function f passed in customGrad(f) must return an ' +
-                'object where `obj.gradFunc` is a function that returns ' +
-                'a list of only tensors.');
-        const gradMap: {[key: string]: () => Tensor} = {};
-        grads.forEach((grad, i) => {
-          gradMap[i] = () => grad;
-        });
-        return gradMap;
-      };
-
-      return this.runKernelFunc({
-        forwardFunc,
-        backwardsFunc,
-        inputs: inputMap,
-      });
-    };
-  }
-
-  readSync(dataId: DataId): BackendValues {
-    // Route the read to the correct backend.
-    const info = this.state.tensorInfo.get(dataId);
-    return info.backend.readSync(dataId);
-  }
-  read(dataId: DataId): Promise<BackendValues> {
-    // Route the read to the correct backend.
-    const info = this.state.tensorInfo.get(dataId);
-    return info.backend.read(dataId);
-  }
-
-  readToGPU(dataId: DataId, options?: DataToGPUOptions): GPUData {
-    // Route the read to the correct backend.
-    const info = this.state.tensorInfo.get(dataId);
-    return info.backend.readToGPU(dataId, options);
-  }
-
-  async time(query: () => void): Promise<TimingInfo> {
-    const start = now();
-    const timingInfo = await this.backend.time(query) as TimingInfo;
-    timingInfo.wallMs = now() - start;
-    return timingInfo;
-  }
-
-  /**
-   * Tracks a Tensor in the current scope to be automatically cleaned up
-   * when the current scope ends, and returns the value.
-   *
-   * @param result The Tensor to track in the current scope.
-   */
-  private track<T extends Tensor>(result: T): T {
-    if (this.state.activeScope != null) {
-      result.scopeId = this.state.activeScope.id;
-      this.state.activeScope.track.push(result);
-    }
-
-    return result;
-  }
-
-  get registeredVariables(): NamedVariableMap {
-    return this.state.registeredVariables;
-  }
-
-  /**
-   * Resets the engine state. Removes all backends but does not remove
-   * registered backend factories.
-   */
-  reset(): void {
-    // Make any pending promise obsolete.
-    this.pendingBackendInitId++;
-
-    this.state.dispose();
-    this.ENV.reset();
-    this.state = new EngineState();
-
-    for (const backendName in this.registry) {
-      this.disposeRegisteredKernels(backendName);
-      this.registry[backendName].dispose();
-      delete this.registry[backendName];
-    }
-    this.backendName = null;
-    this.backendInstance = null;
-    this.pendingBackendInit = null;
-  }
-}
-
-function ones(shape: number[]): Tensor {
-  const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');
-  return ENGINE.makeTensor(values, shape, 'float32');
-}
-
-export function getOrMakeEngine(): Engine {
-  const ns = getGlobalNamespace() as unknown as {_tfengine: Engine};
-  if (ns._tfengine == null) {
-    const environment = new Environment(ns);
-    ns._tfengine = new Engine(environment);
-  }
-  setEnvironmentGlobal(ns._tfengine.ENV);
-
-  // Tell the current tensor interface that the global engine is responsible
-  // for tracking.
-  setTensorTracker(() => ns._tfengine);
-  return ns._tfengine;
-}
-
-export const ENGINE = getOrMakeEngine();
-
-/**
- * A implementation of the add op for use within engine and tape.
- *
- * This allows us to avoid a circular dependency between add.ts and engine.
- * It is exported to be available in tape tests.
- */
-export function add(a: Tensor, b: Tensor): Tensor {
-  // We duplicate Add here to avoid a circular dependency with add.ts.
-  const inputs = {a, b};
-  return ENGINE.runKernel(Add, inputs as unknown as NamedTensorMap);
-}
diff --git a/tfjs-master/tfjs-core/src/engine_test.ts b/tfjs-master/tfjs-core/src/engine_test.ts
deleted file mode 100644
index 97b7e7871..000000000
--- a/tfjs-master/tfjs-core/src/engine_test.ts
+++ /dev/null
@@ -1,800 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelBackend} from './backends/backend';
-import {ENGINE} from './engine';
-import * as tf from './index';
-import {KernelFunc} from './index';
-import {ALL_ENVS, describeWithFlags, TestKernelBackend} from './jasmine_util';
-import { TensorInfo } from './tensor_info';
-import {Tensor} from './tensor';
-import {expectArraysClose} from './test_util';
-import {BackendValues, DataType} from './types';
-
-describe('Backend registration', () => {
-  beforeAll(() => {
-    // Silences backend registration warnings.
-    spyOn(console, 'warn');
-  });
-
-  let registeredBackends: string[] = [];
-  let registerBackend: typeof tf.registerBackend;
-
-  beforeEach(() => {
-    // Registering a backend changes global state (engine), so we wrap
-    // registration to automatically remove registered backend at the end
-    // of each test.
-    registerBackend = (name, factory, priority) => {
-      registeredBackends.push(name);
-      return tf.registerBackend(name, factory, priority);
-    };
-
-    ENGINE.reset();
-  });
-
-  afterEach(() => {
-    // Remove all registered backends at the end of each test.
-    registeredBackends.forEach(name => {
-      if (tf.findBackendFactory(name) != null) {
-        tf.removeBackend(name);
-      }
-    });
-    registeredBackends = [];
-  });
-
-  it('removeBackend disposes the backend and removes the factory', () => {
-    let backend: KernelBackend;
-    const factory = () => {
-      const newBackend = new TestKernelBackend();
-      if (backend == null) {
-        backend = newBackend;
-        spyOn(backend, 'dispose').and.callThrough();
-      }
-      return newBackend;
-    };
-
-    registerBackend('test-backend', factory);
-
-    expect(tf.findBackend('test-backend') != null).toBe(true);
-    expect(tf.findBackend('test-backend')).toBe(backend);
-    expect(tf.findBackendFactory('test-backend')).toBe(factory);
-
-    tf.removeBackend('test-backend');
-
-    expect(tf.findBackend('test-backend') == null).toBe(true);
-    expect(tf.findBackend('test-backend')).toBe(null);
-    expect((backend.dispose as jasmine.Spy).calls.count()).toBe(1);
-    expect(tf.findBackendFactory('test-backend')).toBe(null);
-  });
-
-  it('findBackend initializes the backend', () => {
-    let backend: KernelBackend;
-    const factory = () => {
-      const newBackend = new TestKernelBackend();
-      if (backend == null) {
-        backend = newBackend;
-      }
-      return newBackend;
-    };
-    registerBackend('custom-cpu', factory);
-
-    expect(tf.findBackend('custom-cpu') != null).toBe(true);
-    expect(tf.findBackend('custom-cpu')).toBe(backend);
-    expect(tf.findBackendFactory('custom-cpu')).toBe(factory);
-  });
-
-  it('custom backend registration', () => {
-    let backend: KernelBackend;
-    const priority = 103;
-    registerBackend('custom-cpu', () => {
-      const newBackend = new TestKernelBackend();
-      if (backend == null) {
-        backend = newBackend;
-      }
-      return newBackend;
-    }, priority);
-
-    expect(tf.backend() != null).toBe(true);
-    expect(tf.backend()).toBe(backend);
-  });
-
-  it('high priority backend registration fails, falls back', () => {
-    let lowPriorityBackend: KernelBackend;
-    const lowPriority = 103;
-    const highPriority = 104;
-    registerBackend('custom-low-priority', () => {
-      lowPriorityBackend = new TestKernelBackend();
-      return lowPriorityBackend;
-    }, lowPriority);
-    registerBackend('custom-high-priority', () => {
-      throw new Error(`High priority backend fails`);
-    }, highPriority);
-
-    expect(tf.backend() != null).toBe(true);
-    expect(tf.backend()).toBe(lowPriorityBackend);
-    expect(tf.getBackend()).toBe('custom-low-priority');
-  });
-
-  it('low priority and high priority backends, setBackend low priority', () => {
-    let lowPriorityBackend: KernelBackend;
-    let highPriorityBackend: KernelBackend;
-    const lowPriority = 103;
-    const highPriority = 104;
-    registerBackend('custom-low-priority', () => {
-      lowPriorityBackend = new TestKernelBackend();
-      return lowPriorityBackend;
-    }, lowPriority);
-    registerBackend('custom-high-priority', () => {
-      highPriorityBackend = new TestKernelBackend();
-      return highPriorityBackend;
-    }, highPriority);
-
-    expect(tf.backend() != null).toBe(true);
-    expect(tf.backend()).toBe(highPriorityBackend);
-    expect(tf.getBackend()).toBe('custom-high-priority');
-
-    tf.setBackend('custom-low-priority');
-
-    expect(tf.backend() != null).toBe(true);
-    expect(tf.backend()).toBe(lowPriorityBackend);
-    expect(tf.getBackend()).toBe('custom-low-priority');
-  });
-
-  it('default custom background null', () => {
-    expect(tf.findBackend('custom')).toBeNull();
-  });
-
-  it('allow custom backend', () => {
-    const backend = new TestKernelBackend();
-    const success = registerBackend('custom', () => backend);
-    expect(success).toBeTruthy();
-    expect(tf.findBackend('custom')).toEqual(backend);
-  });
-
-  it('sync backend with await ready works', async () => {
-    const testBackend = new TestKernelBackend();
-    registerBackend('sync', () => testBackend);
-    tf.setBackend('sync');
-
-    expect(tf.getBackend()).toEqual('sync');
-    await tf.ready();
-    expect(tf.backend()).toEqual(testBackend);
-  });
-
-  it('sync backend without await ready works', async () => {
-    const testBackend = new TestKernelBackend();
-    registerBackend('sync', () => testBackend);
-    tf.setBackend('sync');
-
-    expect(tf.getBackend()).toEqual('sync');
-    expect(tf.backend()).toEqual(testBackend);
-  });
-
-  it('async backend with await ready works', async () => {
-    const testBackend = new TestKernelBackend();
-    registerBackend('async', async () => {
-      await tf.nextFrame();
-      return testBackend;
-    });
-    tf.setBackend('async');
-
-    expect(tf.getBackend()).toEqual('async');
-    await tf.ready();
-    expect(tf.backend()).toEqual(testBackend);
-  });
-
-  it('async backend without await ready does not work', async () => {
-    const testBackend = new TestKernelBackend();
-    registerBackend('async', async () => {
-      await tf.nextFrame();
-      return testBackend;
-    });
-    tf.setBackend('async');
-
-    expect(tf.getBackend()).toEqual('async');
-    expect(() => tf.backend())
-        .toThrowError(/Backend 'async' has not yet been initialized./);
-  });
-
-  it('tf.square() fails if user does not await ready on async backend',
-     async () => {
-       registerBackend('async', async () => {
-         await tf.nextFrame();
-         return new TestKernelBackend();
-       });
-       tf.setBackend('async');
-       expect(() => tf.square(2))
-           .toThrowError(/Backend 'async' has not yet been initialized/);
-     });
-
-  it('tf.square() works when user awaits ready on async backend', async () => {
-    registerBackend('async', async () => {
-      await tf.nextFrame();
-      return new TestKernelBackend();
-    });
-    tf.setBackend('async');
-    await tf.ready();
-    expect(() => tf.square(2)).toThrowError(/'write' not yet implemented/);
-  });
-
-  it('Registering async2 (higher priority) fails, async1 becomes active',
-     async () => {
-       const testBackend = new TestKernelBackend();
-       registerBackend('async1', async () => {
-         await tf.nextFrame();
-         return testBackend;
-       }, 100 /* priority */);
-       registerBackend('async2', async () => {
-         await tf.nextFrame();
-         throw new Error('failed to create async2');
-       }, 101 /* priority */);
-
-       // Await for the library to find the best backend that succesfully
-       // initializes.
-       await tf.ready();
-       expect(tf.backend()).toEqual(testBackend);
-       expect(tf.getBackend()).toBe('async1');
-     });
-
-  it('Registering sync as higher priority and async as lower priority',
-     async () => {
-       const testBackend = new TestKernelBackend();
-       registerBackend('sync', () => testBackend, 101 /* priority */);
-       registerBackend('async', async () => {
-         await tf.nextFrame();
-         return new TestKernelBackend();
-       }, 100 /* priority */);
-
-       // No need to await for ready() since the highest priority one is sync.
-       expect(tf.backend()).toEqual(testBackend);
-       expect(tf.getBackend()).toBe('sync');
-     });
-
-  it('async as higher priority and sync as lower priority with await ready',
-     async () => {
-       const testBackend = new TestKernelBackend();
-       registerBackend('async', async () => {
-         await tf.nextFrame();
-         return testBackend;
-       }, 101 /* priority */);
-       registerBackend(
-           'sync', () => new TestKernelBackend(), 100 /* priority */);
-
-       await tf.ready();
-       expect(tf.backend()).toEqual(testBackend);
-       expect(tf.getBackend()).toBe('async');
-     });
-
-  it('async as higher priority and sync as lower priority w/o await ready',
-     async () => {
-       const testBackend = new TestKernelBackend();
-       registerBackend('async', async () => {
-         await tf.nextFrame();
-         return testBackend;
-       }, 101 /* priority */);
-       registerBackend(
-           'sync', () => new TestKernelBackend(), 100 /* priority */);
-
-       expect(() => tf.backend())
-           .toThrowError(
-               /The highest priority backend 'async' has not yet been/);
-     });
-
-  it('Registering and setting a backend that fails to register', async () => {
-    registerBackend('async', async () => {
-      await tf.nextFrame();
-      throw new Error('failed to create async');
-    });
-    const success = tf.setBackend('async');
-    expect(tf.getBackend()).toBe('async');
-    expect(() => tf.backend())
-        .toThrowError(/Backend 'async' has not yet been initialized/);
-    expect(await success).toBe(false);
-  });
-});
-
-describeWithFlags('memory', ALL_ENVS, () => {
-  it('Sum(float)', async () => {
-    expect(tf.memory().numTensors).toBe(0);
-    expect(tf.memory().numBytes).toBe(0);
-    const sum = tf.tidy(() => {
-      const a = tf.tensor1d([1, 2, 3, 4]);
-      expect(tf.memory().numTensors).toBe(1);
-      expect(tf.memory().numBytes).toBe(4 * 4);
-      return a.sum();
-    });
-    expect(tf.memory().numTensors).toBe(1);
-    expect(tf.memory().numBytes).toBe(4);
-    expectArraysClose(await sum.data(), [1 + 2 + 3 + 4]);
-  });
-
-  it('Sum(bool)', async () => {
-    const sum = tf.tidy(() => {
-      const a = tf.tensor1d([true, true, false, true], 'bool');
-      expect(tf.memory().numTensors).toBe(1);
-      expect(tf.memory().numBytes).toBe(4);
-      return a.sum();
-    });
-    expect(tf.memory().numTensors).toBe(1);
-    expect(tf.memory().numBytes).toBe(4);
-    expect(sum.dtype).toBe('int32');
-    expectArraysClose(await sum.data(), [1 + 1 + 0 + 1]);
-  });
-
-  it('Sum(int32)', async () => {
-    const sum = tf.tidy(() => {
-      const a = tf.tensor1d([1, 1, 0, 1], 'int32');
-      expect(tf.memory().numTensors).toBe(1);
-      expect(tf.memory().numBytes).toBe(4 * 4);
-      return a.sum();
-    });
-    expect(tf.memory().numTensors).toBe(1);
-    expect(tf.memory().numBytes).toBe(4);
-    expect(sum.dtype).toBe('int32');
-    expectArraysClose(await sum.data(), [1 + 1 + 0 + 1]);
-  });
-
-  it('string tensor', () => {
-    const a = tf.tensor([['a', 'bb'], ['c', 'd']]);
-
-    expect(tf.memory().numTensors).toBe(1);
-    expect(tf.memory().numBytes).toBe(5);  // 5 letters, each 1 byte in utf8.
-
-    a.dispose();
-
-    expect(tf.memory().numTensors).toBe(0);
-    expect(tf.memory().numBytes).toBe(0);
-  });
-
-  it('unreliable is true for string tensors', () => {
-    tf.tensor('a');
-    const mem = tf.memory();
-    expect(mem.unreliable).toBe(true);
-    const expectedReason = 'Memory usage by string tensors is approximate ' +
-        '(2 bytes per character)';
-    expect(mem.reasons.indexOf(expectedReason) >= 0).toBe(true);
-  });
-
-  it('makeTensorFromDataId creates a tensor', () => {
-    const tensor = ENGINE.makeTensorFromDataId({}, [3], 'float32');
-    expect(tensor).toBeDefined();
-    expect(tensor.shape).toEqual([3]);
-  });
-});
-
-describeWithFlags('profile', ALL_ENVS, () => {
-  it('squaring', async () => {
-    const profile = await tf.profile(() => {
-      const x = tf.tensor1d([1, 2, 3]);
-      let x2 = x.square();
-      x2.dispose();
-      x2 = x.square();
-      x2.dispose();
-      return x;
-    });
-
-    const result = profile.result as Tensor;
-
-    expect(profile.newBytes).toBe(12);
-    expect(profile.peakBytes).toBe(24);
-    expect(profile.newTensors).toBe(1);
-    expectArraysClose(await result.data(), [1, 2, 3]);
-    expect(profile.kernels.length).toBe(2);
-
-    // Test the types for `kernelTimeMs` and `extraInfo` to confirm the promises
-    // are resolved.
-    expect(profile.kernels[0].kernelTimeMs instanceof Promise).toBe(false);
-    expect(profile.kernels[0].extraInfo instanceof Promise).toBe(false);
-    expect(profile.kernels[1].kernelTimeMs instanceof Promise).toBe(false);
-    expect(profile.kernels[1].extraInfo instanceof Promise).toBe(false);
-
-    // The specific values of `kernelTimeMs` and `extraInfo` are tested in the
-    // tests of Profiler.profileKernel, so their values are not tested here.
-    expect(profile.kernels[0]).toEqual({
-      'name': 'Square',
-      'bytesAdded': 12,
-      'totalBytesSnapshot': 24,
-      'tensorsAdded': 1,
-      'totalTensorsSnapshot': 2,
-      'inputShapes': [[3]],
-      'outputShapes': [[3]],
-      'kernelTimeMs': profile.kernels[0].kernelTimeMs,
-      'extraInfo': profile.kernels[0].extraInfo
-    });
-
-    expect(profile.kernels[1]).toEqual({
-      'name': 'Square',
-      'bytesAdded': 12,
-      'totalBytesSnapshot': 24,
-      'tensorsAdded': 1,
-      'totalTensorsSnapshot': 2,
-      'inputShapes': [[3]],
-      'outputShapes': [[3]],
-      'kernelTimeMs': profile.kernels[1].kernelTimeMs,
-      'extraInfo': profile.kernels[1].extraInfo
-    });
-  });
-
-  it('squaring without disposing', async () => {
-    const profile = await tf.profile(() => {
-      const x = tf.tensor1d([1, 2, 3]);
-      const x2 = x.square();
-      return x2;
-    });
-
-    const result = profile.result as Tensor;
-
-    expect(profile.newBytes).toBe(24);
-    expect(profile.peakBytes).toBe(24);
-    expect(profile.newTensors).toBe(2);
-    expectArraysClose(await result.data(), [1, 4, 9]);
-    expect(profile.kernels.length).toBe(1);
-    expect(profile.kernels[0].kernelTimeMs instanceof Promise).toBe(false);
-    expect(profile.kernels[0].extraInfo instanceof Promise).toBe(false);
-    expect(profile.kernels[0]).toEqual({
-      'name': 'Square',
-      'bytesAdded': 12,
-      'totalBytesSnapshot': 24,
-      'tensorsAdded': 1,
-      'totalTensorsSnapshot': 2,
-      'inputShapes': [[3]],
-      'outputShapes': [[3]],
-      'kernelTimeMs': profile.kernels[0].kernelTimeMs,
-      'extraInfo': profile.kernels[0].extraInfo
-    });
-  });
-
-  it('squaring in async query', async () => {
-    const profile = await tf.profile(async () => {
-      await new Promise(resolve => setTimeout(resolve, 1));
-      const x = tf.tensor1d([1, 2, 3]);
-      const x2 = x.square();
-      x2.dispose();
-      return x;
-    });
-
-    const result = profile.result as Tensor;
-
-    expect(profile.newBytes).toBe(12);
-    expect(profile.peakBytes).toBe(24);
-    expect(profile.newTensors).toBe(1);
-    expectArraysClose(await result.data(), [1, 2, 3]);
-    expect(profile.kernels.length).toBe(1);
-    expect(profile.kernels[0].kernelTimeMs instanceof Promise).toBe(false);
-    expect(profile.kernels[0].extraInfo instanceof Promise).toBe(false);
-    expect(profile.kernels[0]).toEqual({
-      'name': 'Square',
-      'bytesAdded': 12,
-      'totalBytesSnapshot': 24,
-      'tensorsAdded': 1,
-      'totalTensorsSnapshot': 2,
-      'inputShapes': [[3]],
-      'outputShapes': [[3]],
-      'kernelTimeMs': profile.kernels[0].kernelTimeMs,
-      'extraInfo': profile.kernels[0].extraInfo
-    });
-  });
-
-  it('reports correct kernelNames', async () => {
-    const profile = await tf.profile(() => {
-      const x = tf.tensor1d([1, 2, 3]);
-      const x2 = x.square();
-      const x3 = x2.abs();
-      return x3;
-    });
-
-    expect(profile.kernelNames).toEqual(jasmine.arrayWithExactContents([
-      'Square', 'Abs'
-    ]));
-  });
-});
-
-describeWithFlags('disposeVariables', ALL_ENVS, () => {
-  it('reuse same name variable', () => {
-    tf.tensor1d([1, 2, 3]).variable(true, 'v1');
-    tf.tensor1d([1, 2, 3]).variable(true, 'v2');
-    expect(() => {
-      tf.tensor1d([1, 2, 3]).variable(true, 'v1');
-    }).toThrowError();
-    tf.disposeVariables();
-    tf.tensor1d([1, 2, 3]).variable(true, 'v1');
-    tf.tensor1d([1, 2, 3]).variable(true, 'v2');
-  });
-});
-
-/**
- * The following test constraints to the CPU environment because it needs a
- * concrete backend to exist. This test will work for any backend, but currently
- * this is the simplest backend to test against.
- */
-describeWithFlags(
-    'Switching cpu backends',
-    {predicate: testEnv => testEnv.backendName === 'cpu'}, () => {
-      beforeEach(() => {
-        tf.registerBackend('cpu1', tf.findBackendFactory('cpu'));
-        tf.registerBackend('cpu2', tf.findBackendFactory('cpu'));
-      });
-
-      afterEach(() => {
-        tf.removeBackend('cpu1');
-        tf.removeBackend('cpu2');
-      });
-
-      it('Move data from cpu1 to cpu2 backend', async () => {
-        tf.setBackend('cpu1');
-        // This scalar lives in cpu1.
-        const a = tf.scalar(5);
-
-        tf.setBackend('cpu2');
-        // This scalar lives in cpu2.
-        const b = tf.scalar(3);
-
-        expect(tf.memory().numDataBuffers).toBe(2);
-        expect(tf.memory().numTensors).toBe(2);
-        expect(tf.memory().numBytes).toBe(8);
-
-        // Make sure you can read both tensors.
-        expectArraysClose(await a.data(), [5]);
-        expectArraysClose(await b.data(), [3]);
-
-        // Switch back to cpu1.
-        tf.setBackend('cpu1');
-        // Again make sure you can read both tensors.
-        expectArraysClose(await a.data(), [5]);
-        expectArraysClose(await b.data(), [3]);
-
-        tf.dispose([a, b]);
-
-        expect(tf.memory().numDataBuffers).toBe(0);
-        expect(tf.memory().numTensors).toBe(0);
-        expect(tf.memory().numBytes).toBe(0);
-      });
-
-      it('can execute op with data from mixed backends', async () => {
-        const kernelFunc = tf.getKernel('Add', 'cpu').kernelFunc;
-        tf.registerKernel({kernelName: 'Add', backendName: 'cpu1', kernelFunc});
-        tf.registerKernel({kernelName: 'Add', backendName: 'cpu2', kernelFunc});
-
-        tf.setBackend('cpu1');
-        // This scalar lives in cpu1.
-        const a = tf.scalar(5);
-
-        tf.setBackend('cpu2');
-        // This scalar lives in cpu2.
-        const b = tf.scalar(3);
-
-        // Verify that ops can execute with mixed backend data.
-        ENGINE.startScope();
-        tf.setBackend('cpu1');
-        expectArraysClose(await tf.add(a, b).data(), [8]);
-
-        tf.setBackend('cpu2');
-        expectArraysClose(await tf.add(a, b).data(), [8]);
-        ENGINE.endScope();
-
-        tf.dispose([a, b]);
-      });
-    });
-
-/**
- * The following unit test is a special integration-style test that assumes
- * things about CPU & WebGL backends being registered. This tests doesn't live
- * in the backend directory because it is testing engine rather than
- * backend-specific details but needs a real backend to exist. This test will
- * fail if the CPU backends is not registered. This is intentional, we should
- * have coverage for when these backends are enabled and ensure they work with
- * the engine.
- */
-// TODO(#5632): Re-enable these tests
-/*
-describeWithFlags(
-    'Switching WebGL + CPU backends', {
-      predicate: testEnv => testEnv.backendName === 'webgl' &&
-          ENGINE.backendNames().indexOf('webgl') !== -1 &&
-          ENGINE.backendNames().indexOf('cpu') !== -1
-    },
-    () => {
-      beforeEach(() => {
-        tf.registerBackend('webgl1', tf.findBackendFactory('webgl'));
-        tf.registerBackend('webgl2', tf.findBackendFactory('webgl'));
-        tf.registerBackend('cpu1', tf.findBackendFactory('cpu'));
-      });
-
-      afterEach(() => {
-        tf.removeBackend('webgl1');
-        tf.removeBackend('webgl2');
-        tf.removeBackend('cpu1');
-      });
-
-      it('can execute op with data from mixed backends', async () => {
-        tf.setBackend('webgl1');
-        const a = tf.scalar(5);
-
-        tf.setBackend('webgl2');
-        const b = tf.scalar(3);
-
-        tf.setBackend('cpu1');
-        const c = tf.scalar(2);
-
-        // Verify that ops can execute with mixed backend data.
-        ENGINE.startScope();
-        tf.setBackend('webgl1');
-        expectArraysClose(await tf.addN([a, b, c]).data(), [10]);
-
-        tf.setBackend('webgl2');
-        expectArraysClose(await tf.addN([a, b, c]).data(), [10]);
-
-        tf.setBackend('cpu1');
-        expectArraysClose(await tf.addN([a, b, c]).data(), [10]);
-        ENGINE.endScope();
-
-        expect(tf.memory().numTensors).toBe(3);
-        expect(tf.memory().numDataBuffers).toBe(3);
-
-        tf.dispose([a, b, c]);
-
-        expect(tf.memory().numTensors).toBe(0);
-        expect(tf.memory().numDataBuffers).toBe(0);
-      });
-
-      it('fromPixels with mixed backends works', async () => {
-        tf.setBackend('webgl1');
-        const a = tf.browser.fromPixels(
-            new ImageData(new Uint8ClampedArray([1, 2, 3, 4]), 1, 1));
-
-        tf.setBackend('webgl2');
-        const b = tf.browser.fromPixels(
-            new ImageData(new Uint8ClampedArray([5, 6, 7, 8]), 1, 1));
-
-        expectArraysClose(await tf.add(a, b).data(), [6, 8, 10]);
-      });
-
-      it('single tidy multiple backends', () => {
-        const kernelFunc = tf.getKernel('Square', 'webgl').kernelFunc;
-        tf.registerKernel(
-            {kernelName: 'Square', backendName: 'webgl1', kernelFunc});
-        tf.registerKernel(
-            {kernelName: 'Square', backendName: 'webgl2', kernelFunc});
-
-        expect(tf.memory().numTensors).toBe(0);
-
-        tf.tidy(() => {
-          tf.setBackend('webgl1');
-          const a = tf.scalar(1);
-          a.square();  // Uploads to GPU.
-
-          tf.setBackend('webgl2');
-          const b = tf.scalar(1);
-          b.square();  // Uploads to GPU.
-
-          expect(tf.memory().numTensors).toBe(4);
-        });
-        expect(tf.memory().numTensors).toBe(0);
-
-        tf.unregisterKernel('Square', 'webgl1');
-        tf.unregisterKernel('Square', 'webgl2');
-      });
-    });
-*/
-interface TestStorage extends KernelBackend {
-  id: number;
-}
-
-describeWithFlags('Detects memory leaks in kernels', ALL_ENVS, () => {
-  const backendName = 'test-mem';
-  const kernelName = 'MyKernel';
-  const kernelNameComplex = 'Kernel-complex';
-
-  it('Detects memory leak in a kernel', () => {
-    let dataIdsCount = 0;
-    tf.registerBackend(backendName, () => {
-      return {
-        id: 1,
-        dispose: () => null,
-        disposeData: (dataId: {}) => null,
-        numDataIds: () => dataIdsCount
-      } as TestStorage;
-    });
-
-    const kernelWithMemLeak: KernelFunc = () => {
-      dataIdsCount += 2;
-      return {dataId: {}, shape: [], dtype: 'float32'};
-    };
-    tf.registerKernel({kernelName, backendName, kernelFunc: kernelWithMemLeak});
-
-    tf.setBackend(backendName);
-    expect(() => tf.engine().runKernel(kernelName, {}, {}))
-        .toThrowError(
-            /Backend 'test-mem' has an internal memory leak \(1 data ids\)/);
-
-    tf.removeBackend(backendName);
-    tf.unregisterKernel(kernelName, backendName);
-  });
-
-  it('No mem leak in a kernel with multiple outputs', () => {
-    let dataIdsCount = 0;
-    tf.registerBackend(backendName, () => {
-      return {
-        id: 1,
-        dispose: () => null,
-        disposeData: (dataId: {}) => null,
-        numDataIds: () => dataIdsCount
-      } as TestStorage;
-    });
-    tf.setBackend(backendName);
-
-    const kernelWith3Outputs: KernelFunc = () => {
-      dataIdsCount += 3;
-      const t: TensorInfo = {dataId: {}, shape: [], dtype: 'float32'};
-      return [t, t, t];
-    };
-    tf.registerKernel(
-        {kernelName, backendName, kernelFunc: kernelWith3Outputs});
-
-    const res = tf.engine().runKernel(kernelName, {}, {});
-    expect(Array.isArray(res)).toBe(true);
-    expect((res as Array<{}>).length).toBe(3);
-
-    const kernelWithComplexOutputs: KernelFunc = () => {
-      dataIdsCount += 3;
-      return {dataId: {}, shape: [], dtype: 'complex64'};
-    };
-    tf.registerKernel({
-      kernelName: kernelNameComplex,
-      backendName,
-      kernelFunc: kernelWithComplexOutputs
-    });
-
-    const res2 = tf.engine().runKernel(kernelNameComplex, {}, {}) as TensorInfo;
-    expect(res2.shape).toEqual([]);
-    expect(res2.dtype).toEqual('complex64');
-
-    tf.removeBackend(backendName);
-    tf.unregisterKernel(kernelName, backendName);
-    tf.unregisterKernel(kernelNameComplex, backendName);
-  });
-});
-
-// NOTE: This describe is purposefully not a describeWithFlags so that we
-// test tensor allocation where no scopes have been created.
-describe('Memory allocation outside a test scope', () => {
-  it('constructing a tensor works', async () => {
-    const backendName = 'test-backend';
-    tf.registerBackend(backendName, () => {
-      let storedValues: BackendValues = null;
-      return {
-        id: 1,
-        floatPrecision: () => 32,
-        write: (values: BackendValues, shape: number[], dtype: DataType) => {
-          const dataId = {};
-          storedValues = values;
-          return dataId;
-        },
-        read: async (dataId: object) => storedValues,
-        dispose: () => null,
-        disposeData: (dataId: {}) => null
-      } as TestStorage;
-    });
-    tf.setBackend(backendName);
-
-    const a = tf.tensor1d([1, 2, 3]);
-    expectArraysClose(await a.data(), [1, 2, 3]);
-    a.dispose();
-
-    tf.removeBackend(backendName);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/environment.ts b/tfjs-master/tfjs-core/src/environment.ts
deleted file mode 100644
index a3026ee33..000000000
--- a/tfjs-master/tfjs-core/src/environment.ts
+++ /dev/null
@@ -1,219 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Platform} from './platforms/platform';
-import {isPromise} from './util_base';
-
-// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.
-const TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';
-
-type FlagValue = number|boolean|string;
-type FlagEvaluationFn = (() => FlagValue)|(() => Promise<FlagValue>);
-export type Flags = {
-  [featureName: string]: FlagValue
-};
-export type FlagRegistryEntry = {
-  evaluationFn: FlagEvaluationFn;
-  setHook?: (value: FlagValue) => void;
-};
-
-/**
- * The environment contains evaluated flags as well as the registered platform.
- * This is always used as a global singleton and can be retrieved with
- * `tf.env()`.
- *
- * @doc {heading: 'Environment'}
- */
-export class Environment {
-  private flags: Flags = {};
-  private flagRegistry: {[flagName: string]: FlagRegistryEntry} = {};
-
-  private urlFlags: Flags = {};
-
-  platformName: string;
-  platform: Platform;
-
-  // Jasmine spies on this in 'environment_test.ts'
-  getQueryParams = getQueryParams;
-
-  // tslint:disable-next-line: no-any
-  constructor(public global: any) {
-    this.populateURLFlags();
-  }
-
-  setPlatform(platformName: string, platform: Platform) {
-    if (this.platform != null) {
-      if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
-        console.warn(
-            `Platform ${this.platformName} has already been set. ` +
-            `Overwriting the platform with ${platformName}.`);
-      }
-    }
-    this.platformName = platformName;
-    this.platform = platform;
-  }
-
-  registerFlag(
-      flagName: string, evaluationFn: FlagEvaluationFn,
-      setHook?: (value: FlagValue) => void) {
-    this.flagRegistry[flagName] = {evaluationFn, setHook};
-
-    // Override the flag value from the URL. This has to happen here because
-    // the environment is initialized before flags get registered.
-    if (this.urlFlags[flagName] != null) {
-      const flagValue = this.urlFlags[flagName];
-      if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
-        console.warn(
-            `Setting feature override from URL ${flagName}: ${flagValue}.`);
-      }
-      this.set(flagName, flagValue);
-    }
-  }
-
-  async getAsync(flagName: string): Promise<FlagValue> {
-    if (flagName in this.flags) {
-      return this.flags[flagName];
-    }
-
-    this.flags[flagName] = await this.evaluateFlag(flagName);
-    return this.flags[flagName];
-  }
-
-  get(flagName: string): FlagValue {
-    if (flagName in this.flags) {
-      return this.flags[flagName];
-    }
-
-    const flagValue = this.evaluateFlag(flagName);
-    if (isPromise(flagValue)) {
-      throw new Error(
-          `Flag ${flagName} cannot be synchronously evaluated. ` +
-          `Please use getAsync() instead.`);
-    }
-
-    this.flags[flagName] = flagValue;
-    return this.flags[flagName];
-  }
-
-  getNumber(flagName: string): number {
-    return this.get(flagName) as number;
-  }
-
-  getBool(flagName: string): boolean {
-    return this.get(flagName) as boolean;
-  }
-
-  getString(flagName: string): string {
-    return this.get(flagName) as string;
-  }
-
-  getFlags(): Flags {
-    return this.flags;
-  }
-  // For backwards compatibility.
-  get features(): Flags {
-    return this.flags;
-  }
-
-  set(flagName: string, value: FlagValue): void {
-    if (this.flagRegistry[flagName] == null) {
-      throw new Error(
-          `Cannot set flag ${flagName} as it has not been registered.`);
-    }
-    this.flags[flagName] = value;
-    if (this.flagRegistry[flagName].setHook != null) {
-      this.flagRegistry[flagName].setHook(value);
-    }
-  }
-
-  private evaluateFlag(flagName: string): FlagValue|Promise<FlagValue> {
-    if (this.flagRegistry[flagName] == null) {
-      throw new Error(
-          `Cannot evaluate flag '${flagName}': no evaluation function found.`);
-    }
-    return this.flagRegistry[flagName].evaluationFn();
-  }
-
-  setFlags(flags: Flags) {
-    this.flags = Object.assign({}, flags);
-  }
-
-  reset() {
-    this.flags = {};
-    this.urlFlags = {};
-    this.populateURLFlags();
-  }
-
-  private populateURLFlags(): void {
-    if (typeof this.global === 'undefined' ||
-        typeof this.global.location === 'undefined' ||
-        typeof this.global.location.search === 'undefined') {
-      return;
-    }
-
-    const urlParams = this.getQueryParams(this.global.location.search);
-    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
-      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');
-      keyValues.forEach(keyValue => {
-        const [key, value] = keyValue.split(':') as [string, string];
-        this.urlFlags[key] = parseValue(key, value);
-      });
-    }
-  }
-}
-
-export function getQueryParams(queryString: string): {[key: string]: string} {
-  const params = {};
-  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {
-    decodeParam(params, t[0], t[1]);
-    return t.join('=');
-  });
-  return params;
-}
-
-function decodeParam(
-    params: {[key: string]: string}, name: string, value?: string) {
-  params[decodeURIComponent(name)] = decodeURIComponent(value || '');
-}
-
-function parseValue(flagName: string, value: string): FlagValue {
-  const lowerCaseValue = value.toLowerCase();
-  if (lowerCaseValue === 'true' || lowerCaseValue === 'false') {
-    return lowerCaseValue === 'true';
-  } else if (`${+ lowerCaseValue}` === lowerCaseValue) {
-    return +lowerCaseValue;
-  } else {
-    return value;
-  }
-}
-
-/**
- * Returns the current environment (a global singleton).
- *
- * The environment object contains the evaluated feature values as well as the
- * active platform.
- *
- * @doc {heading: 'Environment'}
- */
-export function env() {
-  return ENV;
-}
-
-export let ENV: Environment = null;
-export function setEnvironmentGlobal(environment: Environment) {
-  ENV = environment;
-}
diff --git a/tfjs-master/tfjs-core/src/environment_test.ts b/tfjs-master/tfjs-core/src/environment_test.ts
deleted file mode 100644
index 433e2043a..000000000
--- a/tfjs-master/tfjs-core/src/environment_test.ts
+++ /dev/null
@@ -1,181 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as environment from './environment';
-import {Environment} from './environment';
-
-describe('initializes flags from the url', () => {
-  // Silence console.warns for these tests.
-  beforeAll(() => spyOn(console, 'warn').and.returnValue(null));
-
-  it('no overrides one registered flag', () => {
-    const global = {location: {search: ''}};
-    const env = new Environment(global);
-    spyOn(env, 'getQueryParams').and.returnValue({});
-
-    env.registerFlag('FLAG1', () => false);
-    expect(env.get('FLAG1')).toBe(false);
-  });
-
-  it('one unregistered flag', () => {
-    const global = {location: {search: ''}};
-    const env = new Environment(global);
-    spyOn(env, 'getQueryParams').and.returnValue({'tfjsflags': 'FLAG1:true'});
-    expect(env.features).toEqual({});
-  });
-
-  it('one registered flag true', () => {
-    const global = {location: {search: '?tfjsflags=FLAG1:true'}};
-    const env = new Environment(global);
-    env.registerFlag('FLAG1', () => false);
-
-    expect(env.get('FLAG1')).toBe(true);
-  });
-
-  it('one registered flag false', () => {
-    const global = {location: {search: '?tfjsflags=FLAG1:false'}};
-    const env = new Environment(global);
-    env.registerFlag('FLAG1', () => true);
-
-    expect(env.get('FLAG1')).toBe(false);
-  });
-
-  it('two registered flags', () => {
-    const global = {location: {search: '?tfjsflags=FLAG1:true,FLAG2:200'}};
-    const env = new Environment(global);
-    env.registerFlag('FLAG1', () => false);
-    env.registerFlag('FLAG2', () => 100);
-
-    expect(env.get('FLAG1')).toBe(true);
-    expect(env.get('FLAG2')).toBe(200);
-  });
-
-  it('one registered flag string', () => {
-    const global = {location: {search: '?tfjsflags=FLAG1:FlagString'}};
-    const env = new Environment(global);
-    env.registerFlag('FLAG1', () => 'FlagString');
-
-    expect(env.get('FLAG1')).toBe('FlagString');
-    expect(env.get('FLAG1')).not.toBe('flagstring');
-  });
-
-  it('one registered flag empty string', () => {
-    const global = {location: {search: '?tfjsflags=FLAG1:'}};
-    const env = new Environment(global);
-    env.registerFlag('FLAG1', () => 'FlagString');
-
-    expect(env.get('FLAG1')).toBe('');
-  });
-});
-
-describe('flag registration and evaluation', () => {
-  it('one flag registered', () => {
-    const env = new Environment({});
-
-    const evalObject = {eval: () => true};
-    const spy = spyOn(evalObject, 'eval').and.callThrough();
-
-    env.registerFlag('FLAG1', () => evalObject.eval());
-
-    expect(env.get('FLAG1')).toBe(true);
-    expect(spy.calls.count()).toBe(1);
-
-    // Multiple calls to get do not call the evaluation function again.
-    expect(env.get('FLAG1')).toBe(true);
-    expect(spy.calls.count()).toBe(1);
-  });
-
-  it('one string flag registered', () => {
-    const env = new Environment({});
-
-    env.registerFlag('FLAG1', () => '');
-
-    // Set to a non empty string, this is case sensitive.
-    env.set('FLAG1', 'FlagString');
-    expect(env.get('FLAG1')).toBe('FlagString');
-    expect(env.get('FLAG1')).not.toBe('flagString');
-  });
-
-  it('multiple flags registered', () => {
-    const env = new Environment({});
-
-    const evalObject = {eval1: () => true, eval2: () => 100};
-    const spy1 = spyOn(evalObject, 'eval1').and.callThrough();
-    const spy2 = spyOn(evalObject, 'eval2').and.callThrough();
-
-    env.registerFlag('FLAG1', () => evalObject.eval1());
-    env.registerFlag('FLAG2', () => evalObject.eval2());
-
-    expect(env.get('FLAG1')).toBe(true);
-    expect(spy1.calls.count()).toBe(1);
-    expect(spy2.calls.count()).toBe(0);
-    expect(env.get('FLAG2')).toBe(100);
-    expect(spy1.calls.count()).toBe(1);
-    expect(spy2.calls.count()).toBe(1);
-
-    // Multiple calls to get do not call the evaluation function again.
-    expect(env.get('FLAG1')).toBe(true);
-    expect(env.get('FLAG2')).toBe(100);
-    expect(spy1.calls.count()).toBe(1);
-    expect(spy2.calls.count()).toBe(1);
-  });
-
-  it('setting overrides value', () => {
-    const env = new Environment({});
-
-    const evalObject = {eval: () => true};
-    const spy = spyOn(evalObject, 'eval').and.callThrough();
-
-    env.registerFlag('FLAG1', () => evalObject.eval());
-
-    expect(env.get('FLAG1')).toBe(true);
-    expect(spy.calls.count()).toBe(1);
-
-    env.set('FLAG1', false);
-
-    expect(env.get('FLAG1')).toBe(false);
-    expect(spy.calls.count()).toBe(1);
-  });
-
-  it('set hook is called', () => {
-    const env = new Environment({});
-
-    const evalObject = {eval: () => true, setHook: () => true};
-    const evalSpy = spyOn(evalObject, 'eval').and.callThrough();
-    const setHookSpy = spyOn(evalObject, 'setHook').and.callThrough();
-
-    env.registerFlag(
-        'FLAG1', () => evalObject.eval(), () => evalObject.setHook());
-
-    expect(env.get('FLAG1')).toBe(true);
-    expect(evalSpy.calls.count()).toBe(1);
-    expect(setHookSpy.calls.count()).toBe(0);
-
-    env.set('FLAG1', false);
-
-    expect(env.get('FLAG1')).toBe(false);
-    expect(evalSpy.calls.count()).toBe(1);
-    expect(setHookSpy.calls.count()).toBe(1);
-  });
-});
-
-describe('environment.getQueryParams', () => {
-  it('basic', () => {
-    expect(environment.getQueryParams('?a=1&b=hi&f=animal'))
-        .toEqual({'a': '1', 'b': 'hi', 'f': 'animal'});
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/flags.ts b/tfjs-master/tfjs-core/src/flags.ts
deleted file mode 100644
index e6086f6ab..000000000
--- a/tfjs-master/tfjs-core/src/flags.ts
+++ /dev/null
@@ -1,90 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import './engine';
-
-import * as device_util from './device_util';
-import {env} from './environment';
-
-const ENV = env();
-
-/**
- * This file contains environment-related flag registrations.
- */
-
-/** Whether to enable debug mode. */
-ENV.registerFlag('DEBUG', () => false, debugValue => {
-  if (debugValue) {
-    console.warn(
-        'Debugging mode is ON. The output of every math call will ' +
-        'be downloaded to CPU and checked for NaNs. ' +
-        'This significantly impacts performance.');
-  }
-});
-
-/** Whether we are in a browser (as versus, say, node.js) environment. */
-ENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());
-
-/** Whether we are in a browser (as versus, say, node.js) environment. */
-ENV.registerFlag(
-    'IS_NODE',
-    () => (typeof process !== 'undefined') &&
-        (typeof process.versions !== 'undefined') &&
-        (typeof process.versions.node !== 'undefined'));
-
-/** Whether this browser is Chrome. */
-ENV.registerFlag(
-    'IS_CHROME',
-    () => typeof navigator !== 'undefined' && navigator != null &&
-        navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&
-        /Google Inc/.test(navigator.vendor));
-
-/** Whether this browser is Safari. */
-ENV.registerFlag(
-    'IS_SAFARI',
-    () => typeof navigator !== 'undefined' && navigator != null &&
-        navigator.userAgent != null && /Safari/.test(navigator.userAgent) &&
-        /Apple/.test(navigator.vendor));
-/**
- * True when the environment is "production" where we disable safety checks
- * to gain performance.
- */
-ENV.registerFlag('PROD', () => false);
-
-/**
- * Whether to do sanity checks when inferring a shape from user-provided
- * values, used when creating a new tensor.
- */
-ENV.registerFlag(
-    'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));
-
-/** Whether deprecation warnings are enabled. */
-ENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);
-
-/** True if running unit tests. */
-ENV.registerFlag('IS_TEST', () => false);
-
-/** Whether to check computation result for errors. */
-ENV.registerFlag('CHECK_COMPUTATION_FOR_ERRORS', () => ENV.getBool('DEBUG'));
-
-/** Whether the backend needs to wrap input to imageBitmap. */
-ENV.registerFlag('WRAP_TO_IMAGEBITMAP', () => false);
-
-/** Whether to enable canvas2d willReadFrequently for GPU backends */
-ENV.registerFlag('CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU', () => false);
-
-/** Whether to use setTimeoutCustom */
-ENV.registerFlag('USE_SETTIMEOUTCUSTOM', () => false);
diff --git a/tfjs-master/tfjs-core/src/flags_test.ts b/tfjs-master/tfjs-core/src/flags_test.ts
deleted file mode 100644
index 4677804f6..000000000
--- a/tfjs-master/tfjs-core/src/flags_test.ts
+++ /dev/null
@@ -1,125 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as device_util from './device_util';
-import * as tf from './index';
-
-describe('DEBUG', () => {
-  beforeEach(() => {
-    tf.env().reset();
-    spyOn(console, 'warn').and.callFake((msg: string) => {});
-  });
-  afterAll(() => tf.env().reset());
-
-  it('disabled by default', () => {
-    expect(tf.env().getBool('DEBUG')).toBe(false);
-  });
-
-  it('warns when enabled', () => {
-    const consoleWarnSpy = console.warn as jasmine.Spy;
-    tf.env().set('DEBUG', true);
-    expect(consoleWarnSpy.calls.count()).toBe(1);
-    expect((consoleWarnSpy.calls.first().args[0] as string)
-               .startsWith('Debugging mode is ON. '))
-        .toBe(true);
-
-    expect(tf.env().getBool('DEBUG')).toBe(true);
-    expect(consoleWarnSpy.calls.count()).toBe(1);
-  });
-});
-
-// TODO (yassogba) figure out why this spy is not working / fix this test.
-describe('IS_BROWSER', () => {
-  let isBrowser: boolean;
-  beforeEach(() => {
-    tf.env().reset();
-    spyOn(device_util, 'isBrowser').and.callFake(() => isBrowser);
-  });
-  afterAll(() => tf.env().reset());
-
-  // tslint:disable-next-line: ban
-  xit('isBrowser: true', () => {
-    isBrowser = true;
-    expect(tf.env().getBool('IS_BROWSER')).toBe(true);
-  });
-
-  // tslint:disable-next-line: ban
-  xit('isBrowser: false', () => {
-    isBrowser = false;
-    expect(tf.env().getBool('IS_BROWSER')).toBe(false);
-  });
-});
-
-describe('PROD', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('disabled by default', () => {
-    expect(tf.env().getBool('PROD')).toBe(false);
-  });
-});
-
-describe('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('disabled when debug is disabled', () => {
-    tf.env().set('DEBUG', false);
-    expect(tf.env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')).toBe(false);
-  });
-
-  it('enabled when debug is enabled', () => {
-    // Silence debug warnings.
-    spyOn(console, 'warn');
-    tf.enableDebugMode();
-    expect(tf.env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')).toBe(true);
-  });
-});
-
-describe('DEPRECATION_WARNINGS_ENABLED', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('enabled by default', () => {
-    expect(tf.env().getBool('DEPRECATION_WARNINGS_ENABLED')).toBe(true);
-  });
-});
-
-describe('IS_TEST', () => {
-  beforeEach(() => tf.env().reset());
-  afterAll(() => tf.env().reset());
-
-  it('disabled by default', () => {
-    expect(tf.env().getBool('IS_TEST')).toBe(false);
-  });
-});
-
-describe('async flags test', () => {
-  const asyncFlagName = 'ASYNC_FLAG';
-  beforeEach(() => tf.env().registerFlag(asyncFlagName, async () => true));
-
-  afterEach(() => tf.env().reset());
-
-  it('evaluating async flag works', async () => {
-    const flagVal = await tf.env().getAsync(asyncFlagName);
-    expect(flagVal).toBe(true);
-  });
-
-  it('evaluating async flag synchronously fails', async () => {
-    expect(() => tf.env().get(asyncFlagName)).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/global_util.ts b/tfjs-master/tfjs-core/src/global_util.ts
deleted file mode 100644
index dc869bd8f..000000000
--- a/tfjs-master/tfjs-core/src/global_util.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Note that the identifier globalNameSpace is scoped to this module, but will
-// always resolve to the same global object regardless of how the module is
-// resolved.
-// tslint:disable-next-line:no-any
-let globalNameSpace: {_tfGlobals: Map<string, any>};
-// tslint:disable-next-line:no-any
-export function getGlobalNamespace(): {_tfGlobals: Map<string, any>} {
-  if (globalNameSpace == null) {
-    // tslint:disable-next-line:no-any
-    let ns: any;
-    if (typeof (window) !== 'undefined') {
-      ns = window;
-    } else if (typeof (global) !== 'undefined') {
-      ns = global;
-    } else if (typeof (process) !== 'undefined') {
-      ns = process;
-    } else if (typeof (self) !== 'undefined') {
-      ns = self;
-    } else {
-      throw new Error('Could not find a global object');
-    }
-    globalNameSpace = ns;
-  }
-  return globalNameSpace;
-}
-
-// tslint:disable-next-line:no-any
-function getGlobalMap(): Map<string, any> {
-  const ns = getGlobalNamespace();
-  if (ns._tfGlobals == null) {
-    ns._tfGlobals = new Map();
-  }
-  return ns._tfGlobals;
-}
-
-/**
- * Returns a globally accessible 'singleton' object.
- *
- * @param key the name of the object
- * @param init a function to initialize to initialize this object
- *             the first time it is fetched.
- */
-export function getGlobal<T>(key: string, init: () => T): T {
-  const globalMap = getGlobalMap();
-  if (globalMap.has(key)) {
-    return globalMap.get(key);
-  } else {
-    const singleton = init();
-    globalMap.set(key, singleton);
-    return globalMap.get(key);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/globals.ts b/tfjs-master/tfjs-core/src/globals.ts
deleted file mode 100644
index 9bc658f5f..000000000
--- a/tfjs-master/tfjs-core/src/globals.ts
+++ /dev/null
@@ -1,384 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelBackend} from './backends/backend';
-import {ENGINE, Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo} from './engine';
-import {env} from './environment';
-
-import {Platform} from './platforms/platform';
-import {setDeprecationWarningFn, Tensor} from './tensor';
-import {TensorContainer} from './tensor_types';
-import {getTensorsInContainer} from './tensor_util';
-
-/**
- * Enables production mode which disables correctness checks in favor of
- * performance.
- *
- * @doc {heading: 'Environment'}
- */
-export function enableProdMode(): void {
-  env().set('PROD', true);
-}
-
-/**
- * Enables debug mode which will log information about all executed kernels:
- * the elapsed time of the kernel execution, as well as the rank, shape, and
- * size of the output tensor.
- *
- * Debug mode will significantly slow down your application as it will
- * download the result of every operation to the CPU. This should not be used in
- * production. Debug mode does not affect the timing information of the kernel
- * execution as we do not measure download time in the kernel execution time.
- *
- * See also: `tf.profile`, `tf.memory`.
- *
- * @doc {heading: 'Environment'}
- */
-export function enableDebugMode(): void {
-  env().set('DEBUG', true);
-}
-
-/** Globally disables deprecation warnings */
-export function disableDeprecationWarnings(): void {
-  env().set('DEPRECATION_WARNINGS_ENABLED', false);
-  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
-}
-
-/** Warn users about deprecated functionality. */
-export function deprecationWarn(msg: string) {
-  if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {
-    console.warn(
-        msg + ' You can disable deprecation warnings with ' +
-        'tf.disableDeprecationWarnings().');
-  }
-}
-setDeprecationWarningFn(deprecationWarn);
-
-/**
- * Dispose all variables kept in backend engine.
- *
- * @doc {heading: 'Environment'}
- */
-export function disposeVariables(): void {
-  ENGINE.disposeVariables();
-}
-
-/**
- * It returns the global engine that keeps track of all tensors and backends.
- *
- * @doc {heading: 'Environment'}
- */
-export function engine(): Engine {
-  return ENGINE;
-}
-
-/**
- * Returns memory info at the current time in the program. The result is an
- * object with the following properties:
- *
- * - `numBytes`: Number of bytes allocated (undisposed) at this time.
- * - `numTensors`: Number of unique tensors allocated.
- * - `numDataBuffers`: Number of unique data buffers allocated
- *   (undisposed) at this time, which is ≤ the number of tensors
- *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same
- *   data buffer with `a`).
- * - `unreliable`: True if the memory usage is unreliable. See `reasons` when
- *    `unreliable` is true.
- * - `reasons`: `string[]`, reasons why the memory is unreliable, present if
- *    `unreliable` is true.
- *
- * WebGL Properties:
- * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at
- *     this time.
- *
- * @doc {heading: 'Performance', subheading: 'Memory'}
- */
-export function memory(): MemoryInfo {
-  return ENGINE.memory();
-}
-
-/**
- * Executes the provided function `f()` and returns a promise that resolves
- * with information about the function's memory use:
- * - `newBytes`: the number of new bytes allocated
- * - `newTensors`: the number of new tensors created
- * - `peakBytes`: the peak number of bytes allocated
- * - `kernels`: an array of objects for each kernel involved that reports
- * their input and output shapes, number of bytes used, and number of new
- * tensors created.
- * - `kernelNames`: an array of unique strings with just the names of the
- * kernels in the `kernels` array.
- *
- * ```js
- * const profile = await tf.profile(() => {
- *   const x = tf.tensor1d([1, 2, 3]);
- *   let x2 = x.square();
- *   x2.dispose();
- *   x2 = x.square();
- *   x2.dispose();
- *   return x;
- * });
- *
- * console.log(`newBytes: ${profile.newBytes}`);
- * console.log(`newTensors: ${profile.newTensors}`);
- * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>
- * k.totalBytesSnapshot)}`);
- * ```
- *
- *
- * @doc {heading: 'Performance', subheading: 'Profile'}
- */
-export function profile(f: () => (TensorContainer | Promise<TensorContainer>)):
-    Promise<ProfileInfo> {
-  return ENGINE.profile(f);
-}
-
-/**
- * Executes the provided function `fn` and after it is executed, cleans up all
- * intermediate tensors allocated by `fn` except those returned by `fn`.
- * `fn` must not return a Promise (async functions not allowed). The returned
- * result can be a complex object.
- *
- * Using this method helps avoid memory leaks. In general, wrap calls to
- * operations in `tf.tidy` for automatic memory cleanup.
- *
- * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to
- * dispose variables, please use `tf.disposeVariables` or call dispose()
- * directly on variables.
- *
- * ```js
- * // y = 2 ^ 2 + 1
- * const y = tf.tidy(() => {
- *   // a, b, and one will be cleaned up when the tidy ends.
- *   const one = tf.scalar(1);
- *   const a = tf.scalar(2);
- *   const b = a.square();
- *
- *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
- *
- *   // The value returned inside the tidy function will return
- *   // through the tidy, in this case to the variable y.
- *   return b.add(one);
- * });
- *
- * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
- * y.print();
- * ```
- *
- * @param nameOrFn The name of the closure, or the function to execute.
- *     If a name is provided, the 2nd argument should be the function.
- *     If debug mode is on, the timing and the memory usage of the function
- *     will be tracked and displayed on the console using the provided name.
- * @param fn The function to execute.
- *
- * @doc {heading: 'Performance', subheading: 'Memory'}
- */
-export function tidy<T extends TensorContainer>(
-    nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>): T {
-  return ENGINE.tidy(nameOrFn, fn);
-}
-
-/**
- * Disposes any `tf.Tensor`s found within the provided object.
- *
- * @param container an object that may be a `tf.Tensor` or may directly
- *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If
- *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing
- *     happens. In general it is safe to pass any object here, except that
- *     `Promise`s are not supported.
- *
- * @doc {heading: 'Performance', subheading: 'Memory'}
- */
-export function dispose(container: TensorContainer) {
-  const tensors = getTensorsInContainer(container);
-  tensors.forEach(tensor => tensor.dispose());
-}
-
-/**
- * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed
- * automatically.
- *
- * ```js
- * let b;
- * const y = tf.tidy(() => {
- *   const one = tf.scalar(1);
- *   const a = tf.scalar(2);
- *
- *   // b will not be cleaned up by the tidy. a and one will be cleaned up
- *   // when the tidy ends.
- *   b = tf.keep(a.square());
- *
- *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
- *
- *   // The value returned inside the tidy function will return
- *   // through the tidy, in this case to the variable y.
- *   return b.add(one);
- * });
- *
- * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
- * console.log('y:');
- * y.print();
- * console.log('b:');
- * b.print();
- * ```
- *
- * @param result The tensor to keep from being disposed.
- *
- * @doc {heading: 'Performance', subheading: 'Memory'}
- */
-export function keep<T extends Tensor>(result: T): T {
-  return ENGINE.keep(result);
-}
-
-/**
- * Executes `f()` and returns a promise that resolves with timing
- * information.
- *
- * The result is an object with the following properties:
- *
- * - `wallMs`: Wall execution time.
- * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the
- * WebGL backend and the query timer extension is not available, this will
- * return an error object.
- * - On `WebGL` The following additional properties exist:
- *   - `uploadWaitMs`: CPU blocking time on texture uploads.
- *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).
- *
- * ```js
- * const x = tf.randomNormal([20, 20]);
- * const time = await tf.time(() => x.matMul(x));
- *
- * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);
- * ```
- *
- * @param f The function to execute and time.
- *
- * @doc {heading: 'Performance', subheading: 'Timing'}
- */
-export function time(f: () => void): Promise<TimingInfo> {
-  return ENGINE.time(f);
-}
-
-/**
- * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and
- * executing operations on those tensors. Returns a promise that resolves
- * to a boolean if the backend initialization was successful.
- *
- * Note this disposes the current backend, if any, as well as any tensors
- * associated with it. A new backend is initialized, even if it is of the
- * same type as the previous one.
- *
- * @param backendName The name of the backend. Currently supports
- *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js
- *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).
- *
- * @doc {heading: 'Backends'}
- */
-export function setBackend(backendName: string): Promise<boolean> {
-  return ENGINE.setBackend(backendName);
-}
-
-/**
- * Returns a promise that resolves when the currently selected backend (or the
- * highest priority one) has initialized. Await this promise when you are using
- * a backend that has async initialization.
- *
- * @doc {heading: 'Backends'}
- */
-export function ready(): Promise<void> {
-  return ENGINE.ready();
-}
-
-/**
- * Returns the current backend name (cpu, webgl, etc). The backend is
- * responsible for creating tensors and executing operations on those tensors.
- *
- * @doc {heading: 'Backends'}
- */
-export function getBackend(): string {
-  return ENGINE.backendName;
-}
-
-/**
- * Removes a backend and the registered factory.
- *
- * @doc {heading: 'Backends'}
- */
-export function removeBackend(name: string): void {
-  ENGINE.removeBackend(name);
-}
-
-/**
- * Finds the backend registered under the provided name. Returns null if the
- * name is not in the registry, or the registration hasn't finished yet.
- */
-export function findBackend(name: string): KernelBackend {
-  return ENGINE.findBackend(name);
-}
-
-/**
- * Finds the backend factory registered under the provided name. Returns a
- * function that produces a new backend when called. Returns null if the name
- * is not in the registry.
- */
-export function findBackendFactory(name: string): () =>
-    KernelBackend | Promise<KernelBackend> {
-  return ENGINE.findBackendFactory(name);
-}
-
-/**
- * Registers a global backend. The registration should happen when importing
- * a module file (e.g. when importing `backend_webgl.ts`), and is used for
- * modular builds (e.g. custom tfjs bundle with only webgl support).
- *
- * @param factory The backend factory function. When called, it should
- * return a backend instance, or a promise of an instance.
- * @param priority The priority of the backend (higher = more important).
- *     In case multiple backends are registered, the priority is used to find
- *     the best backend. Defaults to 1.
- * @return False if there is already a registered backend under this name, true
- *     if not.
- *
- * @doc {heading: 'Backends'}
- */
-export function registerBackend(
-    name: string, factory: () => KernelBackend | Promise<KernelBackend>,
-    priority = 1): boolean {
-  return ENGINE.registerBackend(name, factory, priority);
-}
-
-/**
- * Gets the current backend. If no backends have been initialized, this will
- * attempt to initialize the best backend. Will throw an error if the highest
- * priority backend has async initialization, in which case you should call
- * 'await tf.ready()' before running other code.
- *
- * @doc {heading: 'Backends'}
- */
-export function backend(): KernelBackend {
-  return ENGINE.backend;
-}
-
-/**
- * Sets the global platform.
- *
- * @param platformName The name of this platform.
- * @param platform A platform implementation.
- */
-export function setPlatform(platformName: string, platform: Platform) {
-  env().setPlatform(platformName, platform);
-}
diff --git a/tfjs-master/tfjs-core/src/globals_test.ts b/tfjs-master/tfjs-core/src/globals_test.ts
deleted file mode 100644
index 88a2ab40d..000000000
--- a/tfjs-master/tfjs-core/src/globals_test.ts
+++ /dev/null
@@ -1,297 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags, NODE_ENVS} from './jasmine_util';
-import {expectArraysClose} from './test_util';
-
-describe('deprecation warnings', () => {
-  beforeEach(() => {
-    spyOn(console, 'warn').and.callFake((msg: string): void => null);
-  });
-
-  it('deprecationWarn warns', () => {
-    // flags_test.ts verifies deprecation warnings are on by default.
-    const deprecationVal = tf.env().get('DEPRECATION_WARNINGS_ENABLED');
-    tf.env().set('DEPRECATION_WARNINGS_ENABLED', true);
-    tf.deprecationWarn('xyz is deprecated.');
-    tf.env().set('DEPRECATION_WARNINGS_ENABLED', deprecationVal);
-    expect(console.warn).toHaveBeenCalledTimes(1);
-    expect(console.warn)
-        .toHaveBeenCalledWith(
-            'xyz is deprecated. You can disable deprecation warnings with ' +
-            'tf.disableDeprecationWarnings().');
-  });
-
-  it('disableDeprecationWarnings called, deprecationWarn doesnt warn', () => {
-    tf.disableDeprecationWarnings();
-    expect(console.warn).toHaveBeenCalledTimes(1);
-    expect(console.warn)
-        .toHaveBeenCalledWith(
-            'TensorFlow.js deprecation warnings have been disabled.');
-
-    // deprecationWarn no longer warns.
-    tf.deprecationWarn('xyz is deprecated.');
-    expect(console.warn).toHaveBeenCalledTimes(1);
-  });
-});
-
-describe('Flag flipping methods', () => {
-  beforeEach(() => {
-    tf.env().reset();
-  });
-
-  afterEach(() => {
-    tf.env().reset();
-  });
-
-  it('tf.enableProdMode', () => {
-    tf.enableProdMode();
-    expect(tf.env().getBool('PROD')).toBe(true);
-  });
-
-  it('tf.enableDebugMode', () => {
-    // Silence debug warnings.
-    spyOn(console, 'warn');
-    tf.enableDebugMode();
-    expect(tf.env().getBool('DEBUG')).toBe(true);
-  });
-});
-
-describeWithFlags('time cpu', NODE_ENVS, () => {
-  it('simple upload', async () => {
-    const a = tf.zeros([10, 10]);
-    const time = await tf.time(() => a.square());
-    expect(time.kernelMs).toBeGreaterThan(0);
-    expect(time.kernelMs).toBeLessThanOrEqual(time.wallMs);
-  });
-});
-
-describeWithFlags('tidy', ALL_ENVS, () => {
-  it('returns Tensor', async () => {
-    tf.tidy(() => {
-      const a = tf.tensor1d([1, 2, 3]);
-      let b = tf.tensor1d([0, 0, 0]);
-
-      expect(tf.memory().numTensors).toBe(2);
-      tf.tidy(() => {
-        const result = tf.tidy(() => {
-          b = tf.add(a, b);
-          b = tf.add(a, b);
-          b = tf.add(a, b);
-          return tf.add(a, b);
-        });
-
-        // result is new. All intermediates should be disposed.
-        expect(tf.memory().numTensors).toBe(2 + 1);
-        expect(result.shape).toEqual([3]);
-        expect(result.isDisposed).toBe(false);
-      });
-
-      // a, b are still here, result should be disposed.
-      expect(tf.memory().numTensors).toBe(2);
-    });
-
-    expect(tf.memory().numTensors).toBe(0);
-  });
-
-  it('multiple disposes does not affect num arrays', () => {
-    expect(tf.memory().numTensors).toBe(0);
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([1, 2, 3]);
-    expect(tf.memory().numTensors).toBe(2);
-    a.dispose();
-    a.dispose();
-    expect(tf.memory().numTensors).toBe(1);
-    b.dispose();
-    expect(tf.memory().numTensors).toBe(0);
-  });
-
-  it('allows primitive types', () => {
-    const a = tf.tidy(() => 5);
-    expect(a).toBe(5);
-
-    const b = tf.tidy(() => 'hello');
-    expect(b).toBe('hello');
-  });
-
-  it('allows complex types', async () => {
-    const res = tf.tidy(() => {
-      return {a: tf.scalar(1), b: 'hello', c: [tf.scalar(2), 'world']};
-    });
-    expectArraysClose(await res.a.data(), [1]);
-    expectArraysClose(await (res.c[0] as tf.Tensor).data(), [2]);
-  });
-
-  it('returns Tensor[]', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([0, -1, 1]);
-    expect(tf.memory().numTensors).toBe(2);
-
-    tf.tidy(() => {
-      const result = tf.tidy(() => {
-        tf.add(a, b);
-        return [tf.add(a, b), tf.sub(a, b)];
-      });
-
-      // the 2 results are new. All intermediates should be disposed.
-      expect(tf.memory().numTensors).toBe(4);
-      expect(result[0].isDisposed).toBe(false);
-      expect(result[0].shape).toEqual([3]);
-      expect(result[1].isDisposed).toBe(false);
-      expect(result[1].shape).toEqual([3]);
-      expect(tf.memory().numTensors).toBe(4);
-    });
-
-    // the 2 results should be disposed.
-    expect(tf.memory().numTensors).toBe(2);
-    a.dispose();
-    b.dispose();
-    expect(tf.memory().numTensors).toBe(0);
-  });
-
-  it('basic usage without return', () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    let b = tf.tensor1d([0, 0, 0]);
-
-    expect(tf.memory().numTensors).toBe(2);
-
-    tf.tidy(() => {
-      b = tf.add(a, b);
-      b = tf.add(a, b);
-      b = tf.add(a, b);
-      tf.add(a, b);
-    });
-
-    // all intermediates should be disposed.
-    expect(tf.memory().numTensors).toBe(2);
-  });
-
-  it('nested usage', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    let b = tf.tensor1d([0, 0, 0]);
-
-    expect(tf.memory().numTensors).toBe(2);
-
-    tf.tidy(() => {
-      const result = tf.tidy(() => {
-        b = tf.add(a, b);
-        b = tf.tidy(() => {
-          b = tf.tidy(() => {
-            return tf.add(a, b);
-          });
-          // original a, b, and two intermediates.
-          expect(tf.memory().numTensors).toBe(4);
-
-          tf.tidy(() => {
-            tf.add(a, b);
-          });
-          // All the intermediates should be cleaned up.
-          expect(tf.memory().numTensors).toBe(4);
-
-          return tf.add(a, b);
-        });
-        expect(tf.memory().numTensors).toBe(4);
-
-        return tf.add(a, b);
-      });
-
-      expect(tf.memory().numTensors).toBe(3);
-      expect(result.isDisposed).toBe(false);
-      expect(result.shape).toEqual([3]);
-    });
-    expect(tf.memory().numTensors).toBe(2);
-  });
-
-  it('nested usage returns tensor created from outside scope', () => {
-    const x = tf.scalar(1);
-
-    tf.tidy(() => {
-      tf.tidy(() => {
-        return x;
-      });
-    });
-
-    expect(x.isDisposed).toBe(false);
-  });
-
-  it('nested usage with keep works', () => {
-    let b: tf.Tensor;
-    tf.tidy(() => {
-      const a = tf.scalar(1);
-      tf.tidy(() => {
-        b = tf.keep(a);
-      });
-    });
-
-    expect(b.isDisposed).toBe(false);
-    b.dispose();
-  });
-
-  it('single argument', () => {
-    let hasRan = false;
-    tf.tidy(() => {
-      hasRan = true;
-    });
-    expect(hasRan).toBe(true);
-  });
-
-  it('single argument, but not a function throws error', () => {
-    expect(() => {
-      tf.tidy('asdf');
-    }).toThrowError();
-  });
-
-  it('2 arguments, first is string', () => {
-    let hasRan = false;
-    tf.tidy('name', () => {
-      hasRan = true;
-    });
-    expect(hasRan).toBe(true);
-  });
-
-  it('2 arguments, but first is not string throws error', () => {
-    expect(() => {
-      // tslint:disable-next-line:no-any
-      tf.tidy(4 as any, () => {});
-    }).toThrowError();
-  });
-
-  it('2 arguments, but second is not a function throws error', () => {
-    expect(() => {
-      // tslint:disable-next-line:no-any
-      tf.tidy('name', 'another name' as any);
-    }).toThrowError();
-  });
-
-  it('works with arbitrary depth of result', async () => {
-    tf.tidy(() => {
-      const res = tf.tidy(() => {
-        return [tf.scalar(1), [[tf.scalar(2)]], {list: [tf.scalar(3)]}];
-      });
-      expect((res[0] as tf.Tensor).isDisposed).toBe(false);
-      // tslint:disable-next-line:no-any
-      expect((res[1] as any)[0][0].isDisposed).toBe(false);
-      // tslint:disable-next-line:no-any
-      expect((res[2] as any).list[0].isDisposed).toBe(false);
-      expect(tf.memory().numTensors).toBe(3);
-      return res[0];
-    });
-    // Everything but scalar(1) got disposed.
-    expect(tf.memory().numTensors).toBe(1);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/gradients.ts b/tfjs-master/tfjs-core/src/gradients.ts
deleted file mode 100644
index 9ea6c6022..000000000
--- a/tfjs-master/tfjs-core/src/gradients.ts
+++ /dev/null
@@ -1,395 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {CustomGradientFunc, ENGINE} from './engine';
-import {Scalar, Tensor, Variable} from './tensor';
-import {NamedTensorMap} from './tensor_types';
-import {convertToTensor, convertToTensorArray} from './tensor_util_env';
-import {TensorLike} from './types';
-import * as util from './util';
-
-/**
- * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the
- * gradient of `f(x)` with respect to `x`.
- *
- * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to
- * `x` is computed instead. `f(x)` must take a single tensor `x` and return a
- * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.
- *
- * ```js
- * // f(x) = x ^ 2
- * const f = x => x.square();
- * // f'(x) = 2x
- * const g = tf.grad(f);
- *
- * const x = tf.tensor1d([2, 3]);
- * g(x).print();
- * ```
- *
- * ```js
- * // f(x) = x ^ 3
- * const f = x => x.pow(tf.scalar(3, 'int32'));
- * // f'(x) = 3x ^ 2
- * const g = tf.grad(f);
- * // f''(x) = 6x
- * const gg = tf.grad(g);
- *
- * const x = tf.tensor1d([2, 3]);
- * gg(x).print();
- * ```
- *
- * @param f The function f(x), to compute gradient for.
- *
- * @doc {heading: 'Training', subheading: 'Gradients'}
- */
-function grad(f: (x: Tensor) => Tensor): (
-    x: TensorLike|Tensor, dy?: TensorLike|Tensor) => Tensor {
-  util.assert(
-      util.isFunction(f), () => 'The f passed in grad(f) must be a function');
-  return (x: TensorLike|Tensor, dy?: TensorLike|Tensor): Tensor => {
-    // x can be of any dtype, thus null as the last argument.
-    const $x = convertToTensor(x, 'x', 'tf.grad', 'string_or_numeric');
-    const $dy: Tensor =
-        (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;
-    return ENGINE.tidy(() => {
-      const {value, grads} = ENGINE.gradients(() => f($x), [$x], $dy);
-      if ($dy != null) {
-        util.assertShapesMatch(
-            value.shape, $dy.shape,
-            'The shape of dy passed in grad(f)(x, dy) must match the shape ' +
-                'returned by f(x)');
-      }
-      checkGrads(grads);
-      return grads[0];
-    });
-  };
-}
-
-/**
- * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,
- * which gives an array of gradients of `f()` with respect to each input
- * [`x1`,`x2`,...].
- *
- * If `dy` is passed when calling `g()`, the gradient of
- * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.
- * The provided `f` must take one or more tensors and return a single tensor
- * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.
- *
- * ```js
- * // f(a, b) = a * b
- * const f = (a, b) => a.mul(b);
- * // df / da = b, df / db = a
- * const g = tf.grads(f);
- *
- * const a = tf.tensor1d([2, 3]);
- * const b = tf.tensor1d([-2, -3]);
- * const [da, db] = g([a, b]);
- * console.log('da');
- * da.print();
- * console.log('db');
- * db.print();
- * ```
- *
- * @param f The function `f(x1, x2,...)` to compute gradients for.
- *
- * @doc {heading: 'Training', subheading: 'Gradients'}
- */
-function grads(f: (...args: Tensor[]) => Tensor): (
-    args: Array<Tensor|TensorLike>, dy?: Tensor|TensorLike) => Tensor[] {
-  util.assert(
-      util.isFunction(f), () => 'The f passed in grads(f) must be a function');
-  return (args: Array<Tensor|TensorLike>, dy?: Tensor|TensorLike): Tensor[] => {
-    util.assert(
-        Array.isArray(args),
-        () => 'The args passed in grads(f)(args) must be an array ' +
-            'of `Tensor`s or `TensorLike`s');
-    // args can be of any dtype, thus null as the last argument.
-    const $args =
-        convertToTensorArray(args, 'args', 'tf.grads', 'string_or_numeric');
-    const $dy: Tensor =
-        (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;
-    return ENGINE.tidy(() => {
-      const {value, grads} = ENGINE.gradients(() => f(...$args), $args, $dy);
-      if ($dy != null) {
-        util.assertShapesMatch(
-            value.shape, $dy.shape,
-            'The shape of dy passed in grads(f)([x1,...], dy) must ' +
-                'match the shape returned by f([x1,...])');
-      }
-      checkGrads(grads);
-      return grads;
-    });
-  };
-}
-
-/**
- * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`
- * returns a metric you want to show.
- *
- * The result is a rich object with the following properties:
- * - grad: The gradient of `f(x)` w.r.t. `x` (result of `tf.grad`).
- * - value: The value returned by `f(x)`.
- *
- * ```js
- * // f(x) = x ^ 2
- * const f = x => x.square();
- * // f'(x) = 2x
- * const g = tf.valueAndGrad(f);
- *
- * const x = tf.tensor1d([2, 3]);
- * const {value, grad} = g(x);
- *
- * console.log('value');
- * value.print();
- * console.log('grad');
- * grad.print();
- * ```
- *
- * @doc {heading: 'Training', subheading: 'Gradients'}
- */
-function valueAndGrad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (
-    x: I, dy?: O) => {
-  value: O;
-  grad: I;
-} {
-  util.assert(
-      util.isFunction(f),
-      () => 'The f passed in valueAndGrad(f) must be a function');
-  return (x: I, dy?: O) => {
-    util.assert(
-        x instanceof Tensor,
-        () => 'The x passed in valueAndGrad(f)(x) must be a tensor');
-    util.assert(
-        dy == null || dy instanceof Tensor,
-        () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');
-    const {grads, value} = ENGINE.gradients(() => f(x), [x], dy);
-    checkGrads(grads);
-    return {grad: grads[0] as I, value};
-  };
-}
-
-/**
- * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`
- * returns a metric you want to show.
- *
- * The result is a rich object with the following properties:
- * - grads: The gradients of `f()` w.r.t. each input (result of `tf.grads`).
- * - value: The value returned by `f(x)`.
- *
- * ```js
- * // f(a, b) = a * b
- * const f = (a, b) => a.mul(b);
- * // df/da = b, df/db = a
- * const g = tf.valueAndGrads(f);
- *
- * const a = tf.tensor1d([2, 3]);
- * const b = tf.tensor1d([-2, -3]);
- * const {value, grads} = g([a, b]);
- *
- * const [da, db] = grads;
- *
- * console.log('value');
- * value.print();
- *
- * console.log('da');
- * da.print();
- * console.log('db');
- * db.print();
- * ```
- *
- * @doc {heading: 'Training', subheading: 'Gradients'}
- */
-function valueAndGrads<O extends Tensor>(f: (...args: Tensor[]) => O): (
-    args: Tensor[], dy?: O) => {
-  grads: Tensor[];
-  value: O;
-} {
-  util.assert(
-      util.isFunction(f),
-      () => 'The f passed in valueAndGrads(f) must be a function');
-  return (args: Tensor[], dy?: O) => {
-    util.assert(
-        Array.isArray(args) && args.every(arg => arg instanceof Tensor),
-        () => 'The args passed in valueAndGrads(f)(args) must be array of ' +
-            'tensors');
-    util.assert(
-        dy == null || dy instanceof Tensor,
-        () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');
-    const res = ENGINE.gradients(() => f(...args), args, dy);
-    if (dy != null) {
-      util.assertShapesMatch(
-          res.value.shape, dy.shape,
-          'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +
-              'match the shape returned by f([x1,...])');
-    }
-    checkGrads(res.grads);
-    return res;
-  };
-}
-
-/**
- * Computes and returns the gradient of f(x) with respect to the list of
- * trainable variables provided by `varList`. If no list is provided, it
- * defaults to all trainable variables.
- *
- * ```js
- * const a = tf.variable(tf.tensor1d([3, 4]));
- * const b = tf.variable(tf.tensor1d([5, 6]));
- * const x = tf.tensor1d([1, 2]);
- *
- * // f(a, b) = a * x ^ 2 + b * x
- * const f = () => a.mul(x.square()).add(b.mul(x)).sum();
- * // df/da = x ^ 2, df/db = x
- * const {value, grads} = tf.variableGrads(f);
- *
- * Object.keys(grads).forEach(varName => grads[varName].print());
- * ```
- *
- * @param f The function to execute. f() should return a scalar.
- * @param varList The list of variables to compute the gradients with respect
- *     to. Defaults to all trainable variables.
- * @returns An object with the following keys and values:
- *   - `value`: The value of the function `f`.
- *   - `grads`: A map from the names of the variables to the gradients.
- *     If the `varList` argument is provided explicitly and contains a subset of
- *     non-trainable variables, this map in the return value will contain keys
- *     that map the names of the non-trainable variables to `null`.
- *
- * @doc {heading: 'Training', subheading: 'Gradients'}
- */
-function variableGrads(f: () => Scalar, varList?: Variable[]):
-    {value: Scalar, grads: NamedTensorMap} {
-  util.assert(
-      util.isFunction(f),
-      () => 'The f passed in variableGrads(f) must be a function');
-  util.assert(
-      varList == null ||
-          Array.isArray(varList) && varList.every(v => v instanceof Variable),
-      () =>
-          'The varList passed in variableGrads(f, varList) must be an array ' +
-          'of variables');
-
-  const specifiedVarList = varList != null;
-  if (!specifiedVarList) {
-    // Get all of the trainable variables.
-    varList = [];
-    for (const varName in ENGINE.registeredVariables) {
-      varList.push(ENGINE.registeredVariables[varName]);
-    }
-  }
-
-  const specifiedNonTrainable: Variable[] =
-      specifiedVarList ? varList.filter(variable => !variable.trainable) : null;
-
-  // Prune non-trainable variables.
-  const originalVarCount = varList.length;
-  varList = varList.filter(variable => variable.trainable);
-  util.assert(
-      varList.length > 0,
-      () => `variableGrads() expects at least one of the input variables to ` +
-          `be trainable, but none of the ${originalVarCount} variables is ` +
-          `trainable.`);
-
-  const allowNoGradients = true;
-  const {value, grads} = ENGINE.gradients(f, varList, null, allowNoGradients);
-
-  util.assert(
-      grads.some(g => g != null),
-      () => 'Cannot find a connection between any variable and the result of ' +
-          'the loss function y=f(x). Please make sure the operations that ' +
-          'use variables are inside the function f passed to minimize().');
-  util.assert(
-      value.rank === 0,
-      () => `The f passed in variableGrads(f) must return a scalar, but it ` +
-          `returned a rank-${value.rank} tensor`);
-
-  const namedGrads: NamedTensorMap = {};
-  varList.forEach((v, i) => {
-    if (grads[i] != null) {
-      namedGrads[v.name] = grads[i];
-    }
-  });
-  if (specifiedNonTrainable != null) {
-    // If varList is explicitly provided and contains non-trainable values,
-    // add them to the returned gradients with `null` values.
-    specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);
-  }
-  return {value, grads: namedGrads};
-}
-
-/**
- * Overrides the gradient computation of a function `f`.
- *
- * Takes a function
- * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`
- * and returns another function `g(...inputs)` which takes the same inputs as
- * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients
- * with respect to each input of `f` are computed using `f().gradFunc`.
- *
- * The `save` function passed to `f` should be used for saving tensors needed
- * in the gradient. And the `saved` passed to the `gradFunc` is a
- * `NamedTensorMap`, which contains those saved tensors.
- *
- * ```js
- * const customOp = tf.customGrad((x, save) => {
- *   // Save x to make sure it's available later for the gradient.
- *   save([x]);
- *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);
- *   return {
- *     value: x.square(),
- *     // Note `saved.x` which points to the `x` we saved earlier.
- *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]
- *   };
- * });
- *
- * const x = tf.tensor1d([-1, -2, 3]);
- * const dx = tf.grad(x => customOp(x));
- *
- * console.log(`f(x):`);
- * customOp(x).print();
- * console.log(`f'(x):`);
- * dx(x).print();
- * ```
- *
- * @param f The function to evaluate in forward mode, which should return
- *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`
- *     returns the custom gradients of `f` with respect to its inputs.
- *
- * @doc {heading: 'Training', subheading: 'Gradients'}
- */
-function customGrad<T extends Tensor>(f: CustomGradientFunc<T>):
-    (...args: Tensor[]) => T {
-  return ENGINE.customGrad(f);
-}
-
-function checkGrads(grads: Tensor[]) {
-  const numNullGradients = grads.filter(g => g == null).length;
-  if (numNullGradients > 0) {
-    throw new Error(
-        `Cannot compute gradient of y=f(x) with respect to x. Make sure that
-    the f you passed encloses all operations that lead from x to y.`);
-  }
-}
-
-export {
-  customGrad,
-  variableGrads,
-  valueAndGrad,
-  valueAndGrads,
-  grad,
-  grads,
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Abs_grad.ts b/tfjs-master/tfjs-core/src/gradients/Abs_grad.ts
deleted file mode 100644
index a0709ce6d..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Abs_grad.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Abs} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {mul} from '../ops/mul';
-import {step} from '../ops/step';
-import {Tensor} from '../tensor';
-
-export const absGradConfig: GradConfig = {
-  kernelName: Abs,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => mul(dy, step(cast(x, 'float32'), -1))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Acos_grad.ts b/tfjs-master/tfjs-core/src/gradients/Acos_grad.ts
deleted file mode 100644
index 718865319..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Acos_grad.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acos} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {neg} from '../ops/neg';
-import {scalar} from '../ops/scalar';
-import {sqrt} from '../ops/sqrt';
-import {square} from '../ops/square';
-import {sub} from '../ops/sub';
-import {Tensor} from '../tensor';
-
-export const acosGradConfig: GradConfig = {
-  kernelName: Acos,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {
-      x: () => {
-        const a = square(cast(x, 'float32'));
-        const b = sqrt(sub(scalar(1), a));
-        return neg(div(dy, b));
-      }
-
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Acosh_grad.ts b/tfjs-master/tfjs-core/src/gradients/Acosh_grad.ts
deleted file mode 100644
index 5fd891611..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Acosh_grad.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acosh} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {sqrt} from '../ops/sqrt';
-import {square} from '../ops/square';
-import {sub} from '../ops/sub';
-import {Tensor} from '../tensor';
-
-export const acoshGradConfig: GradConfig = {
-  kernelName: Acosh,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {
-      x: () => {
-        const a = sqrt(sub(square(cast(x, 'float32')), 1));
-        return div(dy, a);
-      }
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/AddN_grad.ts b/tfjs-master/tfjs-core/src/gradients/AddN_grad.ts
deleted file mode 100644
index 2969e14c3..000000000
--- a/tfjs-master/tfjs-core/src/gradients/AddN_grad.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AddN} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {Tensor} from '../tensor';
-
-export const addNGradConfig: GradConfig = {
-  kernelName: AddN,
-  saveAllInputs: true,
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const ders: {[key: string]: () => Tensor} = {};
-    saved.forEach((_, i) => {
-      ders[i] = () => dy.clone();
-    });
-    return ders;
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Add_grad.ts b/tfjs-master/tfjs-core/src/gradients/Add_grad.ts
deleted file mode 100644
index 2618ac6d1..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Add_grad.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Add} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import * as broadcast_util from '../ops/broadcast_util';
-import {reshape} from '../ops/reshape';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const addGradConfig: GradConfig = {
-  kernelName: Add,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const outShape =
-        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);
-
-    const derA = () => {
-      let res = dy;
-      const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      let res = dy;
-      const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum(res, reduceAxes);
-      }
-      return reshape(res, b.shape);
-    };
-
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/ArgMax_grad.ts b/tfjs-master/tfjs-core/src/gradients/ArgMax_grad.ts
deleted file mode 100644
index 109523c41..000000000
--- a/tfjs-master/tfjs-core/src/gradients/ArgMax_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMax} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const argMaxGradConfig: GradConfig = {
-  kernelName: ArgMax,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => zerosLike(x)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/ArgMin_grad.ts b/tfjs-master/tfjs-core/src/gradients/ArgMin_grad.ts
deleted file mode 100644
index 063b6e631..000000000
--- a/tfjs-master/tfjs-core/src/gradients/ArgMin_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMin} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const argMinGradConfig: GradConfig = {
-  kernelName: ArgMin,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => zerosLike(x)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Asin_grad.ts b/tfjs-master/tfjs-core/src/gradients/Asin_grad.ts
deleted file mode 100644
index 09851e347..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Asin_grad.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asin} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {scalar} from '../ops/scalar';
-import {sqrt} from '../ops/sqrt';
-import {square} from '../ops/square';
-import {sub} from '../ops/sub';
-import {Tensor} from '../tensor';
-
-export const asinGradConfig: GradConfig = {
-  kernelName: Asin,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32')))))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Asinh_grad.ts b/tfjs-master/tfjs-core/src/gradients/Asinh_grad.ts
deleted file mode 100644
index 4ee93ba40..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Asinh_grad.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asinh} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {add} from '../ops/add';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {scalar} from '../ops/scalar';
-import {sqrt} from '../ops/sqrt';
-import {square} from '../ops/square';
-import {Tensor} from '../tensor';
-
-export const asinhGradConfig: GradConfig = {
-  kernelName: Asinh,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {
-      x: () => {
-        const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));
-        return div(dy, a);
-      }
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Atan2_grad.ts b/tfjs-master/tfjs-core/src/gradients/Atan2_grad.ts
deleted file mode 100644
index b94a282be..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Atan2_grad.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan2} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {add} from '../ops/add';
-import {assertAndGetBroadcastShape, getReductionAxes} from '../ops/broadcast_util';
-import {div} from '../ops/div';
-import {mul} from '../ops/mul';
-import {neg} from '../ops/neg';
-import {reshape} from '../ops/reshape';
-import {square} from '../ops/square';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const atan2GradConfig: GradConfig = {
-  kernelName: Atan2,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-
-    const derA = () => {
-      const d = add(square(a), square(b));
-      let res = mul(dy, div(b, d));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      const d = add(square(a), square(b));
-      let res = neg(mul(dy, div(a, d)));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum(res, reduceAxes);
-      }
-      return reshape(res, b.shape);
-    };
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Atan_grad.ts b/tfjs-master/tfjs-core/src/gradients/Atan_grad.ts
deleted file mode 100644
index cb1b2bf68..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Atan_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {add} from '../ops/add';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {square} from '../ops/square';
-import {Tensor} from '../tensor';
-
-export const atanGradConfig: GradConfig = {
-  kernelName: Atan,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {x: () => div(dy, add(square(cast(x, 'float32')), 1))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Atanh_grad.ts b/tfjs-master/tfjs-core/src/gradients/Atanh_grad.ts
deleted file mode 100644
index c1dcd5865..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Atanh_grad.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atanh} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {square} from '../ops/square';
-import {sub} from '../ops/sub';
-import {scalar} from '../ops/scalar';
-import {Tensor} from '../tensor';
-
-export const atanhGradConfig: GradConfig = {
-  kernelName: Atanh,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {x: () => div(dy, sub(scalar(1), square(cast(x, 'float32'))))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/AvgPool3D_grad.ts b/tfjs-master/tfjs-core/src/gradients/AvgPool3D_grad.ts
deleted file mode 100644
index e66a6c2fb..000000000
--- a/tfjs-master/tfjs-core/src/gradients/AvgPool3D_grad.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool3D, AvgPool3DAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {avgPool3dGrad} from '../ops/avg_pool_3d_grad';
-import {Tensor, Tensor5D} from '../tensor';
-
-export const avgPool3DGradConfig: GradConfig = {
-  kernelName: AvgPool3D,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved as [Tensor5D];
-    const {filterSize, strides, pad, dimRoundingMode} =
-        attrs as unknown as AvgPool3DAttrs;
-
-    return {
-      x: () => avgPool3dGrad(
-          dy as Tensor5D, x, filterSize, strides, pad, dimRoundingMode)
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/AvgPool_grad.ts b/tfjs-master/tfjs-core/src/gradients/AvgPool_grad.ts
deleted file mode 100644
index c46065802..000000000
--- a/tfjs-master/tfjs-core/src/gradients/AvgPool_grad.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool, AvgPoolAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {avgPoolGrad} from '../ops/avg_pool_grad';
-import {Tensor, Tensor4D} from '../tensor';
-
-export const avgPoolGradConfig: GradConfig = {
-  kernelName: AvgPool,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved as [Tensor4D];
-    const {filterSize, strides, pad} = attrs as unknown as AvgPoolAttrs;
-    return {x: () => avgPoolGrad(dy as Tensor4D, x, filterSize, strides, pad)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/BatchMatMul_grad.ts b/tfjs-master/tfjs-core/src/gradients/BatchMatMul_grad.ts
deleted file mode 100644
index 71d109dde..000000000
--- a/tfjs-master/tfjs-core/src/gradients/BatchMatMul_grad.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BatchMatMul, BatchMatMulAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {matMul} from '../ops/mat_mul';
-import {Tensor} from '../tensor';
-
-export const batchMatMulGradConfig: GradConfig = {
-  kernelName: BatchMatMul,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [a, b] = saved;
-
-    const {transposeA, transposeB} = attrs as unknown as BatchMatMulAttrs;
-
-    if (!transposeA && !transposeB) {
-      return {
-        a: () => matMul(dy, b, false, true),
-        b: () => matMul(a, dy, true, false)
-      };
-    } else if (!transposeA && transposeB) {
-      return {
-        a: () => matMul(dy, b, false, false),
-        b: () => matMul(dy, a, true, false)
-      };
-    } else if (transposeA && !transposeB) {
-      return {
-        a: () => matMul(b, dy, false, true),
-        b: () => matMul(a, dy, false, false)
-      };
-    } else {
-      return {
-        a: () => matMul(b, dy, true, true),
-        b: () => matMul(dy, a, true, true)
-      };
-    }
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/BatchToSpaceND_grad.ts b/tfjs-master/tfjs-core/src/gradients/BatchToSpaceND_grad.ts
deleted file mode 100644
index 5f8fce512..000000000
--- a/tfjs-master/tfjs-core/src/gradients/BatchToSpaceND_grad.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BatchToSpaceND, BatchToSpaceNDAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {spaceToBatchND} from '../ops/space_to_batch_nd';
-import {Tensor} from '../tensor';
-
-export const batchToSpaceNDGradConfig: GradConfig = {
-  kernelName: BatchToSpaceND,
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const {blockShape, crops} = attrs as unknown as BatchToSpaceNDAttrs;
-    return {x: () => spaceToBatchND(dy, blockShape, crops)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/BroadcastTo_grad.ts b/tfjs-master/tfjs-core/src/gradients/BroadcastTo_grad.ts
deleted file mode 100644
index 91bdf63d7..000000000
--- a/tfjs-master/tfjs-core/src/gradients/BroadcastTo_grad.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BroadcastTo, BroadCastToAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const broadcastToGradConfig: GradConfig = {
-  kernelName: BroadcastTo,
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const broadCastToAttrs: BroadCastToAttrs =
-        attrs as unknown as BroadCastToAttrs;
-
-    const inputShape = broadCastToAttrs.inputShape;
-    const outputShape = broadCastToAttrs.shape;
-
-    const reps: number[] = Array.from(outputShape);
-    for (let i = inputShape.length - 1; i >= 0; i--) {
-      if (inputShape[i] === outputShape[i]) {
-        reps[i] = 1;
-      } else if (inputShape[i] !== 1) {
-        throw new Error(`broadcastTo(): [${
-            inputShape}] cannot be broadcast to [${outputShape}].`);
-      }
-    }
-    const axes: number[] = [];
-    for (let i = 0; i < reps.length; i++) {
-      if (reps[i] > 1) {
-        axes.push(i);
-      }
-    }
-
-    return {x: () => sum(dy, axes, true /* keepDims */)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Cast_grad.ts b/tfjs-master/tfjs-core/src/gradients/Cast_grad.ts
deleted file mode 100644
index 1efb1ff26..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Cast_grad.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cast} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {Tensor} from '../tensor';
-
-export const castGradConfig: GradConfig = {
-  kernelName: Cast,
-  gradFunc: (dy: Tensor) => {
-    return {x: () => dy.clone()};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Ceil_grad.ts b/tfjs-master/tfjs-core/src/gradients/Ceil_grad.ts
deleted file mode 100644
index 3b3ebabbc..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Ceil_grad.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Ceil} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const ceilGradConfig: GradConfig = {
-  kernelName: Ceil,
-  gradFunc: (dy: Tensor) => {
-    // TODO(manrajgrover): Return null for gradients when backprop supports it.
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/ClipByValue_grad.ts b/tfjs-master/tfjs-core/src/gradients/ClipByValue_grad.ts
deleted file mode 100644
index d054f2be7..000000000
--- a/tfjs-master/tfjs-core/src/gradients/ClipByValue_grad.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ClipByValue, ClipByValueAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {greaterEqual} from '../ops/greater_equal';
-import {lessEqual} from '../ops/less_equal';
-import {logicalAnd} from '../ops/logical_and';
-import {where} from '../ops/where';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const clipByValueGradConfig: GradConfig = {
-  kernelName: ClipByValue,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved;
-    const {clipValueMin, clipValueMax} = attrs as unknown as ClipByValueAttrs;
-    return {
-      x: () => where(
-          logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)),
-          dy, zerosLike(dy)),
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/ComplexAbs_grad.ts b/tfjs-master/tfjs-core/src/gradients/ComplexAbs_grad.ts
deleted file mode 100644
index 6e39616f6..000000000
--- a/tfjs-master/tfjs-core/src/gradients/ComplexAbs_grad.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ComplexAbs} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {absGradConfig} from './Abs_grad';
-
-export const complexAbsGradConfig: GradConfig = {
-  kernelName: ComplexAbs,
-  inputsToSave: ['x'],
-  gradFunc: absGradConfig.gradFunc,
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Concat_grad.ts b/tfjs-master/tfjs-core/src/gradients/Concat_grad.ts
deleted file mode 100644
index cfe617246..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Concat_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Concat, ConcatAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {split} from '../ops/split';
-import {Tensor} from '../tensor';
-import {parseAxisParam} from '../util';
-
-export const concatGradConfig: GradConfig = {
-  kernelName: Concat,
-  saveAllInputs: true,
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const shapes = saved.map(t => t.shape);
-    const {axis} = attrs as unknown as ConcatAttrs;
-    const $axis = parseAxisParam(axis, saved[0].shape)[0];
-    const sizeSplits = shapes.map(s => s[$axis]);
-    const derTensors = split(dy, sizeSplits, $axis);
-    return derTensors.map(t => () => t) as {};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Conv2DBackpropInput_grad.ts b/tfjs-master/tfjs-core/src/gradients/Conv2DBackpropInput_grad.ts
deleted file mode 100644
index fdd911a32..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Conv2DBackpropInput_grad.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Conv2DBackpropInput, Conv2DBackpropInputAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {conv2d} from '../ops/conv2d';
-import {conv2DBackpropFilter} from '../ops/conv2d_backprop_filter';
-import {Tensor, Tensor4D} from '../tensor';
-
-export const conv2DBackpropInputGradConfig: GradConfig = {
-  kernelName: Conv2DBackpropInput,
-  inputsToSave: ['dy', 'filter'],
-  gradFunc: (ddx: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [dy, filter] = saved as [Tensor4D, Tensor4D];
-
-    const {strides, pad, dataFormat, dimRoundingMode} =
-        attrs as unknown as Conv2DBackpropInputAttrs;
-
-    return {
-      dy: () => conv2d(
-          ddx, filter, strides, pad, dataFormat, 1 /* dilations */,
-          dimRoundingMode),
-      filter: () => conv2DBackpropFilter(
-          ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Conv2D_grad.ts b/tfjs-master/tfjs-core/src/gradients/Conv2D_grad.ts
deleted file mode 100644
index 1c4231c0a..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Conv2D_grad.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Conv2D, Conv2DAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {conv2DBackpropFilter} from '../ops/conv2d_backprop_filter';
-import {conv2DBackpropInput} from '../ops/conv2d_backprop_input';
-import * as conv_util from '../ops/conv_util';
-import {Tensor, Tensor4D} from '../tensor';
-import * as util from '../util';
-
-export const conv2DGradConfig: GradConfig = {
-  kernelName: Conv2D,
-  inputsToSave: ['x', 'filter'],
-  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x4D, $filter] = saved as [Tensor4D, Tensor4D];
-    const {dilations, strides, pad, dataFormat} =
-        attrs as unknown as Conv2DAttrs;
-
-    util.assert(
-        conv_util.tupleValuesAreOne(dilations),
-        () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +
-            `are not yet supported in gradients. Got dilations '${dilations}'`);
-
-    return {
-      x: () =>
-          conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),
-      filter: () =>
-          conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Conv3D_grad.ts b/tfjs-master/tfjs-core/src/gradients/Conv3D_grad.ts
deleted file mode 100644
index 9fa5fe97d..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Conv3D_grad.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Conv3D, Conv3DAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {conv3DBackpropFilter} from '../ops/conv3d_backprop_filter';
-import {conv3DBackpropInput} from '../ops/conv3d_backprop_input';
-import {tupleValuesAreOne} from '../ops/conv_util';
-import {Tensor, Tensor5D} from '../tensor';
-import * as util from '../util';
-
-export const conv3DGradConfig: GradConfig = {
-  kernelName: Conv3D,
-  inputsToSave: ['x', 'filter'],
-  gradFunc: (dy: Tensor5D, saved: Tensor[], attrs: NamedAttrMap) => {
-    const {dilations, strides, pad} = attrs as unknown as Conv3DAttrs;
-    util.assert(
-        tupleValuesAreOne(dilations),
-        () =>
-            'Error in gradient of conv3D: dilation rates greater than 1 are ' +
-            `not yet supported in gradients. Got dilations '${dilations}'`);
-
-    const [x5D, $filter] = saved;
-
-    return {
-      x: () => conv3DBackpropInput(
-          (x5D as Tensor5D).shape, dy, $filter as Tensor5D, strides, pad),
-      filter: () => conv3DBackpropFilter(
-          x5D as Tensor5D, dy, ($filter as Tensor5D).shape, strides, pad)
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Cos_grad.ts b/tfjs-master/tfjs-core/src/gradients/Cos_grad.ts
deleted file mode 100644
index bde878ce4..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Cos_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cos} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {mul} from '../ops/mul';
-import {neg} from '../ops/neg';
-import {sin} from '../ops/sin';
-import {Tensor} from '../tensor';
-
-export const cosGradConfig: GradConfig = {
-  kernelName: Cos,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {x: () => mul(neg(sin(cast(x, 'float32'))), dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Cosh_grad.ts b/tfjs-master/tfjs-core/src/gradients/Cosh_grad.ts
deleted file mode 100644
index ea6f9ec66..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Cosh_grad.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cosh} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {mul} from '../ops/mul';
-import {sinh} from '../ops/sinh';
-import {Tensor} from '../tensor';
-
-export const coshGradConfig: GradConfig = {
-  kernelName: Cosh,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {x: () => mul(sinh(cast(x, 'float32')), dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Cumsum_grad.ts b/tfjs-master/tfjs-core/src/gradients/Cumsum_grad.ts
deleted file mode 100644
index bcb386e03..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Cumsum_grad.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cumsum, CumsumAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {getAxesPermutation} from '../ops/axis_util';
-import {cumsum} from '../ops/cumsum';
-import {transpose} from '../ops/transpose';
-import {Tensor} from '../tensor';
-
-export const cumsumGradConfig: GradConfig = {
-  kernelName: Cumsum,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved;
-    const {axis, exclusive, reverse}: CumsumAttrs =
-        attrs as unknown as CumsumAttrs;
-
-    return {
-      x: () => {
-        const permutation = getAxesPermutation([axis], x.rank);
-
-        let out = cumsum(dy, axis, exclusive, !reverse);
-
-        if (permutation != null) {
-          out = transpose(out, permutation);
-        }
-
-        return out;
-      }
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/DepthwiseConv2dNative_grad.ts b/tfjs-master/tfjs-core/src/gradients/DepthwiseConv2dNative_grad.ts
deleted file mode 100644
index 957690663..000000000
--- a/tfjs-master/tfjs-core/src/gradients/DepthwiseConv2dNative_grad.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import * as conv_util from '../ops/conv_util';
-import {depthwiseConv2dNativeBackpropFilter} from '../ops/depthwise_conv2d_native_backprop_filter';
-import {depthwiseConv2dNativeBackpropInput} from '../ops/depthwise_conv2d_native_backprop_input';
-import {Tensor, Tensor4D} from '../tensor';
-import * as util from '../util';
-
-export const depthwiseConv2dNativeGradConfig: GradConfig = {
-  kernelName: DepthwiseConv2dNative,
-  inputsToSave: ['x', 'filter'],
-  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {
-    const {dilations, strides, pad, dimRoundingMode} =
-        attrs as unknown as DepthwiseConv2dNativeAttrs;
-    const $dilations = dilations == null ? [1, 1] as[number,number] : dilations;
-
-    util.assert(
-        conv_util.tupleValuesAreOne($dilations),
-        () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +
-            `greater than 1 are not yet supported. Got dilations ` +
-            `'${$dilations}'`);
-
-    const [x, filter] = saved as [Tensor4D, Tensor4D];
-
-    util.assert(
-        x.rank === 4,
-        () => `Error in gradient of depthwiseConv2dNative: input must be ` +
-            `rank 4, but got rank ${x.rank}.`);
-    util.assert(
-        filter.rank === 4,
-        () => `Error in gradient of depthwiseConv2dNative: filter must be ` +
-            `rank 4, but got rank ${filter.rank}.`);
-    util.assert(
-        x.shape[3] === filter.shape[2],
-        () => `Error in gradient of depthwiseConv2d: number of input ` +
-            `channels (${x.shape[3]}) must match the inChannels dimension ` +
-            `in filter ${filter.shape[2]}.`);
-
-    util.assert(
-        conv_util.eitherStridesOrDilationsAreOne(strides, $dilations),
-        () => 'Error in gradient of depthwiseConv2d: Either strides or ' +
-            `dilations must be  1. Got strides ${strides} and dilations ` +
-            `'${$dilations}'.`);
-
-    conv_util.checkPadOnDimRoundingMode(
-        'depthwiseConv2d', pad, dimRoundingMode);
-
-    return {
-      x: () => depthwiseConv2dNativeBackpropInput(
-          x.shape, dy, filter, strides, pad, $dilations, dimRoundingMode),
-      filter: () => depthwiseConv2dNativeBackpropFilter(
-          x, dy, filter.shape, strides, pad, $dilations, dimRoundingMode),
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Dilation2D_grad.ts b/tfjs-master/tfjs-core/src/gradients/Dilation2D_grad.ts
deleted file mode 100644
index 9352a8577..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Dilation2D_grad.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropFilterInputs, Dilation2DBackpropInput, Dilation2DBackpropInputInputs} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor, Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-
-export const dilation2dGradConfig: GradConfig = {
-  kernelName: Dilation2D,
-  inputsToSave: ['x', 'filter'],
-  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x, filter] = saved as [Tensor4D, Tensor3D];
-
-    const inputInputs: Dilation2DBackpropInputInputs = {x, filter, dy};
-    const filterInputs: Dilation2DBackpropFilterInputs = {x, filter, dy};
-
-    return {
-      x: () => ENGINE.runKernel(
-          Dilation2DBackpropInput,
-          inputInputs as unknown as NamedTensorMap, attrs),
-      filter: () => ENGINE.runKernel(
-          Dilation2DBackpropFilter,
-          filterInputs as unknown as NamedTensorMap, attrs)
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Elu_grad.ts b/tfjs-master/tfjs-core/src/gradients/Elu_grad.ts
deleted file mode 100644
index 159684a36..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Elu_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Elu, EluGrad, EluGradInputs} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-
-export const eluGradConfig: GradConfig = {
-  kernelName: Elu,
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [y] = saved;
-
-    const inputs: EluGradInputs = {dy, y};
-
-    return {x: () => ENGINE.runKernel(EluGrad,
-                                      inputs as unknown as NamedTensorMap)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Erf_grad.ts b/tfjs-master/tfjs-core/src/gradients/Erf_grad.ts
deleted file mode 100644
index dcb11ecf5..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Erf_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Erf} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {exp} from '../ops/exp';
-import {mul} from '../ops/mul';
-import {neg} from '../ops/neg';
-import {square} from '../ops/square';
-import {Tensor} from '../tensor';
-
-export const erfGradConfig: GradConfig = {
-  kernelName: Erf,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));
-    return {x: () => mul(dy, a)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Exp_grad.ts b/tfjs-master/tfjs-core/src/gradients/Exp_grad.ts
deleted file mode 100644
index 44c1acf66..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Exp_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Exp} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {mul} from '../ops/mul';
-import {Tensor} from '../tensor';
-
-export const expGradConfig: GradConfig = {
-  kernelName: Exp,
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [y] = saved;
-    return {x: () => mul(dy, y)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/ExpandDims_grad.ts b/tfjs-master/tfjs-core/src/gradients/ExpandDims_grad.ts
deleted file mode 100644
index 5dd7ca9e3..000000000
--- a/tfjs-master/tfjs-core/src/gradients/ExpandDims_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ExpandDims} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {reshape} from '../ops/reshape';
-import {Tensor} from '../tensor';
-
-export const expandDimsGradConfig: GradConfig = {
-  kernelName: ExpandDims,
-  inputsToSave: ['input'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [input] = saved;
-    return {input: () => reshape(dy, input.shape)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Expm1_grad.ts b/tfjs-master/tfjs-core/src/gradients/Expm1_grad.ts
deleted file mode 100644
index 3bf6e91f6..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Expm1_grad.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Expm1} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {exp} from '../ops/exp';
-import {mul} from '../ops/mul';
-import {Tensor} from '../tensor';
-
-export const expm1GradConfig: GradConfig = {
-  kernelName: Expm1,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => mul(dy, exp(x))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/FloorDiv_grad.ts b/tfjs-master/tfjs-core/src/gradients/FloorDiv_grad.ts
deleted file mode 100644
index 0b4670882..000000000
--- a/tfjs-master/tfjs-core/src/gradients/FloorDiv_grad.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FloorDiv} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {assertAndGetBroadcastShape, getReductionAxes} from '../ops/broadcast_util';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {mul} from '../ops/mul';
-import {neg} from '../ops/neg';
-import {reshape} from '../ops/reshape';
-import {square} from '../ops/square';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const floorDivGradConfig: GradConfig = {
-  kernelName: FloorDiv,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-
-    const derA = () => {
-      const res = div(dy, cast(b, 'float32'));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      let res = mul(dy, cast(a, 'float32'));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = reshape(sum(res, reduceAxes), b.shape);
-      }
-      const tmp = square(b);
-      return neg(div(res, cast(tmp, 'float32')));
-    };
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Floor_grad.ts b/tfjs-master/tfjs-core/src/gradients/Floor_grad.ts
deleted file mode 100644
index 454a8967c..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Floor_grad.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Floor} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const floorGradConfig: GradConfig = {
-  kernelName: Floor,
-  gradFunc: (dy: Tensor) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/FusedBatchNorm_grad.ts b/tfjs-master/tfjs-core/src/gradients/FusedBatchNorm_grad.ts
deleted file mode 100644
index a58f535a4..000000000
--- a/tfjs-master/tfjs-core/src/gradients/FusedBatchNorm_grad.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {FusedBatchNorm, FusedBatchNormAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {add} from '../ops/add';
-import {getReductionAxes} from '../ops/broadcast_util';
-import {mul} from '../ops/mul';
-import {reshape} from '../ops/reshape';
-import {rsqrt} from '../ops/rsqrt';
-import {scalar} from '../ops/scalar';
-import {sub} from '../ops/sub';
-import {sum} from '../ops/sum';
-import {tile} from '../ops/tile';
-import {Tensor} from '../tensor';
-import {Rank, ShapeMap} from '../types';
-
-export const fusedBatchNormGradConfig: GradConfig = {
-  kernelName: FusedBatchNorm,
-  inputsToSave: ['x', 'mean', 'variance', 'scale'],
-  gradFunc: <R extends Rank>(
-      dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const {varianceEpsilon} = attrs as unknown as FusedBatchNormAttrs;
-    const [x, mean, variance, scale] = saved;
-
-    const scaleValue = scale == null ? scalar(1) : scale;
-    const reductionAxes = getReductionAxes(mean.shape, x.shape);
-    const tileShape: number[] = [];
-    if (mean.rank === 1) {
-      for (let i = 0; i < x.shape.length - 1; ++i) {
-        tileShape.push(x.shape[i]);
-      }
-      tileShape.push(1);
-    }
-
-    const xMinusMean = sub(x, mean);
-    const dyTimesScaleValue = mul(dy, scaleValue);
-    const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));
-    const minusHalfRCube = mul(
-        mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance),
-        scalar(-0.5));
-
-    const derX = () => {
-      if (mean.rank === 1) {
-        return reshape(
-            mul(mul(dy,
-                    tile(
-                        reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]),
-                        tileShape)),
-                scaleValue),
-            x.shape);
-      } else {
-        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
-      }
-    };
-    const derMean = () => {
-      let meanDer =
-          mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
-      if (mean.rank === 1) {
-        meanDer = sum(meanDer, reductionAxes);
-      }
-      return reshape(meanDer, mean.shape as ShapeMap[R]);
-    };
-    const derVariance = () => {
-      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
-
-      if (mean.rank === 1) {
-        varianceDer = sum(varianceDer, reductionAxes);
-      }
-      return reshape(varianceDer, mean.shape as ShapeMap[R]);
-    };
-    const derScale = () => {
-      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
-
-      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
-      if (mean.rank === 1) {
-        scaleDer = sum(scaleDer, reductionAxes);
-      }
-      return reshape(scaleDer, mean.shape as ShapeMap[R]);
-    };
-    const derOffset = () => {
-      let offsetDer = dy;
-      if (mean.rank === 1) {
-        offsetDer = sum(offsetDer, reductionAxes);
-      }
-      return reshape(offsetDer, mean.shape as ShapeMap[R]);
-    };
-
-    return {
-      x: derX,
-      mean: derMean,
-      variance: derVariance,
-      scale: derScale,
-      offset: derOffset
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/GatherV2_grad.ts b/tfjs-master/tfjs-core/src/gradients/GatherV2_grad.ts
deleted file mode 100644
index 35d7e19f8..000000000
--- a/tfjs-master/tfjs-core/src/gradients/GatherV2_grad.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GatherV2, GatherV2Attrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {getUndoAxesPermutation} from '../ops/axis_util';
-import {reshape} from '../ops/reshape';
-import {stack} from '../ops/stack';
-import {transpose} from '../ops/transpose';
-import {unsortedSegmentSum} from '../ops/unsorted_segment_sum';
-import {Tensor, Tensor1D} from '../tensor';
-import {parseAxisParam} from '../util';
-
-export const gatherGradConfig: GradConfig = {
-  kernelName: GatherV2,
-  inputsToSave: ['x', 'indices'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x, indices] = saved;
-    const {axis, batchDims} = attrs as unknown as GatherV2Attrs;
-
-    const parsedAxis = parseAxisParam(axis, x.shape)[0];
-
-    const derXBatch = (x: Tensor, indices: Tensor, dy: Tensor) => {
-      return (): Tensor => {
-        const paramsShape = x.shape;
-        const indicesSize = indices.size;
-
-        const outerShape = paramsShape.slice(0, parsedAxis);
-        const outerDims = outerShape.length;
-        const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
-        const innerDims = innerShape.length;
-
-        const outerAxesIndices = arrayRange(0, outerDims);
-        const innerAxesIndices =
-            arrayRange(outerDims + 1, outerDims + 1 + innerDims);
-
-        const valuesShape = arrayConcat([outerShape, [indicesSize],
-                                         innerShape]);
-
-        const values = reshape(dy, valuesShape);
-        const reshapedIndices = reshape(indices, [indicesSize]);
-
-        const transposeDims =
-            arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
-        const valuesTranspose = transpose(values, transposeDims);
-        let paramsGrad = unsortedSegmentSum(
-            valuesTranspose, reshapedIndices as Tensor1D, x.shape[parsedAxis]);
-        const invertTransposeDims = getUndoAxesPermutation(transposeDims);
-        paramsGrad = transpose(paramsGrad, invertTransposeDims);
-        return paramsGrad;
-      };
-    };
-
-    if (batchDims === 1) {
-      const batchSize = x.shape[0];
-      const xBatch = x.split(batchSize, 0);
-      const derXBatched = () => {
-        const stacked = stack(
-          xBatch.map((x, i) => {
-            return derXBatch(x, indices.slice(i,1), dy.slice(i,1))();
-          }));
-        return stacked.reshape(x.shape);
-      };
-      return {x: derXBatched, indices: () => indices};
-    } else {
-      return {x: derXBatch(x, indices, dy), indices: () => indices};
-    }
-  }
-};
-
-function arrayRange(start: number, stop: number): number[] {
-  const result = [];
-  for (let i = start; i < stop; ++i) {
-    result.push(i);
-  }
-  return result;
-}
-
-function arrayConcat(arrays: number[][]): number[] {
-  const result = [];
-  for (let i = 0; i < arrays.length; ++i) {
-    for (let j = 0; j < arrays[i].length; ++j) {
-      result.push(arrays[i][j]);
-    }
-  }
-  return result;
-}
diff --git a/tfjs-master/tfjs-core/src/gradients/GreaterEqual_grad.ts b/tfjs-master/tfjs-core/src/gradients/GreaterEqual_grad.ts
deleted file mode 100644
index abb512efd..000000000
--- a/tfjs-master/tfjs-core/src/gradients/GreaterEqual_grad.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {GreaterEqual} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const greaterEqualGradConfig: GradConfig = {
-  kernelName: GreaterEqual,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    return {a: () => zerosLike(a), b: () => zerosLike(b)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Identity_grad.ts b/tfjs-master/tfjs-core/src/gradients/Identity_grad.ts
deleted file mode 100644
index dc2518921..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Identity_grad.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Identity} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {Tensor} from '../tensor';
-
-export const identityGradConfig: GradConfig = {
-  kernelName: Identity,
-  gradFunc: (dy: Tensor) => {
-    return {x: () => cast(dy, 'float32')};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/IsFinite_grad.ts b/tfjs-master/tfjs-core/src/gradients/IsFinite_grad.ts
deleted file mode 100644
index fa7a40416..000000000
--- a/tfjs-master/tfjs-core/src/gradients/IsFinite_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsFinite} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const isFiniteGradConfig: GradConfig = {
-  kernelName: IsFinite,
-  gradFunc: (dy: Tensor) => {
-    // TODO(nsthorat): Let gradients be null for cases where we want to stop
-    // backpropgation.
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/IsInf_grad.ts b/tfjs-master/tfjs-core/src/gradients/IsInf_grad.ts
deleted file mode 100644
index e56b750ef..000000000
--- a/tfjs-master/tfjs-core/src/gradients/IsInf_grad.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsInf} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const isInfGradConfig: GradConfig = {
-  kernelName: IsInf,
-  gradFunc: (dy: Tensor) => {
-    // TODO(nsthorat): Let gradients be null for cases where we want to stop
-    // backpropgation.
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/IsNan_grad.ts b/tfjs-master/tfjs-core/src/gradients/IsNan_grad.ts
deleted file mode 100644
index c5c5bb9e1..000000000
--- a/tfjs-master/tfjs-core/src/gradients/IsNan_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsNan} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const isNanGradConfig: GradConfig = {
-  kernelName: IsNan,
-  gradFunc: (dy: Tensor) => {
-    // TODO(nsthorat): Let gradients be null for cases where we want to stop
-    // backpropgation.
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/LRN_grad.ts b/tfjs-master/tfjs-core/src/gradients/LRN_grad.ts
deleted file mode 100644
index bbb106cf8..000000000
--- a/tfjs-master/tfjs-core/src/gradients/LRN_grad.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {LRN, LRNAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {localResponseNormalizationBackprop} from '../ops/local_response_normalization_backprop';
-import {Tensor, Tensor4D} from '../tensor';
-
-export const lrnGradConfig: GradConfig = {
-  kernelName: LRN,
-  inputsToSave: ['x'],
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x, y] = saved as [Tensor4D, Tensor4D];
-    const {depthRadius, bias, alpha, beta} = attrs as unknown as LRNAttrs;
-
-    return {
-      x: () => localResponseNormalizationBackprop(
-          x, y, dy, depthRadius, bias, alpha, beta)
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/LeakyRelu_grad.ts b/tfjs-master/tfjs-core/src/gradients/LeakyRelu_grad.ts
deleted file mode 100644
index 99e6170e6..000000000
--- a/tfjs-master/tfjs-core/src/gradients/LeakyRelu_grad.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {LeakyRelu, LeakyReluAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {greater} from '../ops/greater';
-import {mul} from '../ops/mul';
-import {where} from '../ops/where';
-import {Tensor} from '../tensor';
-
-export const leakyReluGradConfig: GradConfig = {
-  kernelName: LeakyRelu,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved;
-    const {alpha} = attrs as unknown as LeakyReluAttrs;
-    const mask = greater(x, 0);
-
-    // Returns `gradients * (features > 0) + alpha * gradients * (features <=
-    // 0)`.
-    return {x: () => where(mask, dy, mul(dy, alpha))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Log1p_grad.ts b/tfjs-master/tfjs-core/src/gradients/Log1p_grad.ts
deleted file mode 100644
index 323b6a04a..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Log1p_grad.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Log1p} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {add} from '../ops/add';
-import {div} from '../ops/div';
-import {Tensor} from '../tensor';
-
-export const log1pGradConfig: GradConfig = {
-  kernelName: Log1p,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => div(dy, add(x, 1))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/LogSoftmax_grad.ts b/tfjs-master/tfjs-core/src/gradients/LogSoftmax_grad.ts
deleted file mode 100644
index 070f1af3b..000000000
--- a/tfjs-master/tfjs-core/src/gradients/LogSoftmax_grad.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {LogSoftmax, LogSoftmaxAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {exp} from '../ops/exp';
-import {mul} from '../ops/mul';
-import {sub} from '../ops/sub';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const logSoftmaxGradConfig: GradConfig = {
-  kernelName: LogSoftmax,
-  inputsToSave: [],
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [value] = saved;
-    const {axis} = attrs as unknown as LogSoftmaxAttrs;
-    return {
-      logits: () => {
-        const keepDims = true;
-        const softmax = exp(value);
-        return sub(dy, mul(sum(dy, axis, keepDims), softmax));
-      }
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Log_grad.ts b/tfjs-master/tfjs-core/src/gradients/Log_grad.ts
deleted file mode 100644
index 090acb3ee..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Log_grad.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Log} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {Tensor} from '../tensor';
-
-export const logGradConfig: GradConfig = {
-  kernelName: Log,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => div(dy, cast(x, 'float32'))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/MaxPool3D_grad.ts b/tfjs-master/tfjs-core/src/gradients/MaxPool3D_grad.ts
deleted file mode 100644
index 8cd9b49e3..000000000
--- a/tfjs-master/tfjs-core/src/gradients/MaxPool3D_grad.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {MaxPool3D, MaxPool3DAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {maxPool3dGrad} from '../ops/max_pool_3d_grad';
-import {Tensor, Tensor5D} from '../tensor';
-
-export const maxPool3DGradConfig: GradConfig = {
-  kernelName: MaxPool3D,
-  inputsToSave: ['x'],
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x, y] = saved as [Tensor5D, Tensor5D];
-    const {filterSize, strides, pad, dimRoundingMode} =
-        attrs as unknown as MaxPool3DAttrs;
-
-    return {
-      x: () => maxPool3dGrad(
-          dy as Tensor5D, x, y, filterSize, strides, pad, dimRoundingMode)
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/MaxPool_grad.ts b/tfjs-master/tfjs-core/src/gradients/MaxPool_grad.ts
deleted file mode 100644
index b391055fc..000000000
--- a/tfjs-master/tfjs-core/src/gradients/MaxPool_grad.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {MaxPool, MaxPoolAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {maxPoolGrad} from '../ops/max_pool_grad';
-import {Tensor, Tensor4D} from '../tensor';
-
-export const maxPoolGradConfig: GradConfig = {
-  kernelName: MaxPool,
-  inputsToSave: ['x'],
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x, y] = saved as [Tensor4D, Tensor4D];
-    const {filterSize, strides, pad} = attrs as unknown as MaxPoolAttrs;
-
-    return {
-      x: () => maxPoolGrad(dy as Tensor4D, x, y, filterSize, strides, pad)
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Max_grad.ts b/tfjs-master/tfjs-core/src/gradients/Max_grad.ts
deleted file mode 100644
index 78db7f94b..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Max_grad.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Max, MaxAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import * as util from '../util';
-
-import {gradForMinAndMax} from './min_max_grad_util';
-
-export const maxGradConfig: GradConfig = {
-  kernelName: Max,
-  inputsToSave: ['x'],
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const maxAttrs: MaxAttrs = attrs as unknown as MaxAttrs;
-    const {reductionIndices} = maxAttrs;
-    const x = saved[0];
-    const y = saved[1];
-    const origAxes = util.parseAxisParam(reductionIndices, x.shape);
-    const maxGrad = gradForMinAndMax(dy, y, x, origAxes);
-    return {
-      x: () => {
-        return maxGrad['x']();
-      }
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Maximum_grad.ts b/tfjs-master/tfjs-core/src/gradients/Maximum_grad.ts
deleted file mode 100644
index bac9c34ae..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Maximum_grad.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Maximum} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {greaterEqual} from '../ops/greater_equal';
-import {less} from '../ops/less';
-import {mul} from '../ops/mul';
-import {Tensor} from '../tensor';
-
-export const maximumGradConfig: GradConfig = {
-  kernelName: Maximum,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));
-    const derB = () => mul(dy, cast(less(a, b), 'float32'));
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Mean_grad.ts b/tfjs-master/tfjs-core/src/gradients/Mean_grad.ts
deleted file mode 100644
index 108afa6af..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Mean_grad.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Mean, MeanAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {computeOutAndReduceShapes} from '../ops/axis_util';
-import {div} from '../ops/div';
-import {mul} from '../ops/mul';
-import {ones} from '../ops/ones';
-import {reshape} from '../ops/reshape';
-import {Tensor} from '../tensor';
-import * as util from '../util';
-
-export const meanGradConfig: GradConfig = {
-  kernelName: Mean,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved;
-    const {axis} = attrs as unknown as MeanAttrs;
-    const axes = util.parseAxisParam(axis, x.shape);
-    const shapes = computeOutAndReduceShapes(x.shape, axes);
-    const reduceShape = shapes[1];
-    const reduceSize = util.sizeFromShape(reduceShape);
-
-    const derX = () => {
-      const expandedDyShape = x.shape.slice();
-      axes.forEach(axis => {
-        expandedDyShape[axis] = 1;
-      });
-      const expandedDy = reshape(dy, expandedDyShape);
-      const res = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);
-      return res;
-    };
-
-    return {x: derX};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Min_grad.ts b/tfjs-master/tfjs-core/src/gradients/Min_grad.ts
deleted file mode 100644
index 200268ab2..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Min_grad.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Min, MinAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import * as util from '../util';
-
-import {gradForMinAndMax} from './min_max_grad_util';
-
-export const minGradConfig: GradConfig = {
-  kernelName: Min,
-  inputsToSave: ['x'],
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const minAttrs: MinAttrs = attrs as unknown as MinAttrs;
-    const {axis} = minAttrs;
-    const [x, y] = saved;
-    const origAxes = util.parseAxisParam(axis, x.shape);
-    const minGrad = gradForMinAndMax(dy, y, x, origAxes);
-    return {
-      x: () => {
-        return minGrad['x']();
-      }
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Minimum_grad.ts b/tfjs-master/tfjs-core/src/gradients/Minimum_grad.ts
deleted file mode 100644
index 3449a1a7e..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Minimum_grad.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Minimum} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {greater} from '../ops/greater';
-import {lessEqual} from '../ops/less_equal';
-import {mul} from '../ops/mul';
-import {Tensor} from '../tensor';
-
-export const minimumGradConfig: GradConfig = {
-  kernelName: Minimum,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));
-    const derB = () => mul(dy, cast(greater(a, b), 'float32'));
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/MirrorPad_grad.ts b/tfjs-master/tfjs-core/src/gradients/MirrorPad_grad.ts
deleted file mode 100644
index 305bf33ed..000000000
--- a/tfjs-master/tfjs-core/src/gradients/MirrorPad_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {MirrorPad, MirrorPadAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {slice} from '../ops/slice';
-import {Tensor} from '../tensor';
-
-export const mirrorPadGradConfig: GradConfig = {
-  kernelName: MirrorPad,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    // Pad introduces values around the original tensor, so the gradient
-    // slices the original shape out of the gradient.
-    const x = saved[0];
-    const {paddings} = attrs as unknown as MirrorPadAttrs;
-    const begin = paddings.map(p => p[0]);
-    return {x: () => slice(dy, begin, x.shape)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Mod_grad.ts b/tfjs-master/tfjs-core/src/gradients/Mod_grad.ts
deleted file mode 100644
index 7b50e7752..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Mod_grad.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Mod} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {assertAndGetBroadcastShape, getReductionAxes} from '../ops/broadcast_util';
-import {div} from '../ops/div';
-import {floor} from '../ops/floor';
-import {mul} from '../ops/mul';
-import {neg} from '../ops/neg';
-import {reshape} from '../ops/reshape';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const modGradConfig: GradConfig = {
-  kernelName: Mod,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-
-    const derA = () => {
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum(dy, reduceAxes), a.shape);
-      }
-      return dy;
-    };
-    const derB = () => {
-      const res = mul(dy, neg(floor(div(a, b))));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum(res, reduceAxes), b.shape);
-      }
-      return res;
-    };
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Multiply_grad.ts b/tfjs-master/tfjs-core/src/gradients/Multiply_grad.ts
deleted file mode 100644
index 1fcc662b2..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Multiply_grad.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Multiply} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {assertAndGetBroadcastShape, getReductionAxes} from '../ops/broadcast_util';
-import {cast} from '../ops/cast';
-import {mul} from '../ops/mul';
-import {reshape} from '../ops/reshape';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const multiplyGradConfig: GradConfig = {
-  kernelName: Multiply,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
-
-    const derA = () => {
-      const res = mul(dy, cast(b, 'float32'));
-      const reduceAxes = getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      const res = mul(dy, cast(a, 'float32'));
-      const reduceAxes = getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum(res, reduceAxes), b.shape);
-      }
-      return res;
-    };
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Neg_grad.ts b/tfjs-master/tfjs-core/src/gradients/Neg_grad.ts
deleted file mode 100644
index 7ae3a9b39..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Neg_grad.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Neg} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {neg} from '../ops/neg';
-import {Tensor} from '../tensor';
-
-export const negGradConfig: GradConfig = {
-  kernelName: Neg,
-  gradFunc: (dy: Tensor) => {
-    return {x: () => neg(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/OneHot_grad.ts b/tfjs-master/tfjs-core/src/gradients/OneHot_grad.ts
deleted file mode 100644
index 4db5271e8..000000000
--- a/tfjs-master/tfjs-core/src/gradients/OneHot_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {OneHot} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zeros} from '../ops/zeros';
-import {Tensor} from '../tensor';
-
-export const oneHotGradConfig: GradConfig = {
-  kernelName: OneHot,
-  inputsToSave: ['indices'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const indices = saved[0];
-    return {indices: () => zeros(indices.shape, 'float32')};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/OnesLike_grad.ts b/tfjs-master/tfjs-core/src/gradients/OnesLike_grad.ts
deleted file mode 100644
index 96bfcfceb..000000000
--- a/tfjs-master/tfjs-core/src/gradients/OnesLike_grad.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {OnesLike} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const onesLikeGradConfig: GradConfig = {
-  kernelName: OnesLike,
-  gradFunc: (dy: Tensor) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Pack_grad.ts b/tfjs-master/tfjs-core/src/gradients/Pack_grad.ts
deleted file mode 100644
index a5e225c76..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Pack_grad.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Pack, PackAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {unstack} from '../ops/unstack';
-import {Tensor} from '../tensor';
-
-export const packGradConfig: GradConfig = {
-  kernelName: Pack,
-  saveAllInputs: true,
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const {axis} = attrs as unknown as PackAttrs;
-    const derTensors = unstack(dy, axis);
-    return derTensors.map(t => () => t) as {};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/PadV2_grad.ts b/tfjs-master/tfjs-core/src/gradients/PadV2_grad.ts
deleted file mode 100644
index 0547c72a0..000000000
--- a/tfjs-master/tfjs-core/src/gradients/PadV2_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {PadV2, PadV2Attrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {slice} from '../ops/slice';
-import {Tensor} from '../tensor';
-
-export const padV2GradConfig: GradConfig = {
-  kernelName: PadV2,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    // Pad introduces values around the original tensor, so the gradient
-    // slices the original shape out of the gradient.
-    const x = saved[0];
-    const {paddings} = attrs as unknown as PadV2Attrs;
-    const begin = paddings.map(p => p[0]);
-    return {x: () => slice(dy, begin, x.shape)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Pow_grad.ts b/tfjs-master/tfjs-core/src/gradients/Pow_grad.ts
deleted file mode 100644
index 90fddcd5c..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Pow_grad.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Pow} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import * as broadcast_util from '../ops/broadcast_util';
-import {cast} from '../ops/cast';
-import {greater} from '../ops/greater';
-import {log} from '../ops/log';
-import {mul} from '../ops/mul';
-import {pow} from '../ops/pow';
-import {reshape} from '../ops/reshape';
-import {scalar} from '../ops/scalar';
-import {sub} from '../ops/sub';
-import {sum} from '../ops/sum';
-import {where} from '../ops/where';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const powGradConfig: GradConfig = {
-  kernelName: Pow,
-  inputsToSave: ['a', 'b'],
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b, y] = saved;
-    const base = a;
-    const exp = b;
-    const outShape =
-        broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);
-
-    const derBase = () => {
-      const expFloat = cast(exp, 'float32');
-      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));
-      const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum(res, reduceAxes);
-      }
-      return reshape(res, base.shape);
-    };
-    const derExp = () => {
-      const condition = greater(base, 0);
-      const logBase = where(condition, log(base), zerosLike(base));
-      let res = mul(dy, mul(y, logBase));
-      const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum(res, reduceAxes);
-      }
-      return reshape(res, exp.shape);
-    };
-    return {a: derBase, b: derExp};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Prelu_grad.ts b/tfjs-master/tfjs-core/src/gradients/Prelu_grad.ts
deleted file mode 100644
index 2eb18ce53..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Prelu_grad.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Prelu} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {getReductionAxes} from '../ops/broadcast_util';
-import {greater} from '../ops/greater';
-import {mul} from '../ops/mul';
-import {reshape} from '../ops/reshape';
-import {sum} from '../ops/sum';
-import {where} from '../ops/where';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const preluGradConfig: GradConfig = {
-  kernelName: Prelu,
-  inputsToSave: ['x', 'alpha'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x, alpha] = saved;
-    const mask = greater(x, 0);
-
-    return {
-      x: () => where(mask, dy, mul(dy, alpha)),
-      alpha: () => {
-        let res = where(mask, zerosLike(dy), mul(dy, x));
-        const reduceAxes = getReductionAxes(alpha.shape, dy.shape);
-        if (reduceAxes.length > 0) {
-          res = sum(res, reduceAxes);
-        }
-        return reshape(res, alpha.shape);
-      }
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Prod_grad.ts b/tfjs-master/tfjs-core/src/gradients/Prod_grad.ts
deleted file mode 100644
index a3fc4a3eb..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Prod_grad.ts
+++ /dev/null
@@ -1,105 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '../base';
-import {Prod, ProdAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {cumprod} from '../ops/cumprod';
-import {mul} from '../ops/mul';
-import {reshape} from '../ops/reshape';
-import {transpose} from '../ops/transpose';
-import {Tensor} from '../tensor';
-
-// Gradient for product operation on a single axis.
-function prodGradFn_(x: Tensor, dy: Tensor, axis: number): Tensor {
-  // The gradient tensor (dy) has a set of axes removed, so we create re-shaped
-  // versions (of size 1) for the removed axis; this supports broadcasting over
-  // those dimensions.
-  const expandedYShape = x.shape.slice();
-  expandedYShape[axis] = 1;
-
-  // The actual gradient computation.
-  const expandedDy = reshape(dy, expandedYShape);
-  const xCumProd = cumprod(x, axis, true, false);
-  const xCumRevProd = cumprod(x, axis, true, true);
-  const dx = mul(xCumProd, xCumRevProd);
-  return mul(expandedDy, dx);
-}
-
-// Support gradients when the product is done on many axes at once.
-// This done py pushing all the axes on which the product is applied into a
-// single axis.
-function prodsGradFn_(x: Tensor, dy: Tensor, axis: number[]): Tensor {
-  // Move all axes for doing prod over to the end of the tensor.
-  const xRank = x.shape.length;
-  const finalProdAxis = xRank - axis.length;
-  const xPermutation = backend_util.getAxesPermutation(axis, xRank);
-  let permutedX = x;
-  if (xPermutation != null) {
-    permutedX = transpose(x, xPermutation);
-  }
-
-  // Reshape all the prod dimensions into a single one, and do compute prod
-  // gradients on that.
-  const newShape = permutedX.shape.slice();
-  const removedShape = newShape.splice(xRank - axis.length, axis.length);
-  const endPartShape = removedShape.reduce((p, c) => p * c, 1);
-  newShape.push(endPartShape);
-  const reshapedPermutedX = permutedX.reshape(newShape);
-  let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis);
-
-  // Undo the re-shaping now we have the dx vector, and permute back to
-  // original axes order.
-  prodGrad = prodGrad.reshape(permutedX.shape);
-  if (xPermutation != null) {
-    const undoPermutation = backend_util.getUndoAxesPermutation(xPermutation);
-    prodGrad = transpose(prodGrad, undoPermutation);
-  }
-  return prodGrad;
-}
-
-// Running example:
-// [
-//   [
-//     [3.0, 4.0],
-//     [5.0, 6.0],
-//     [7.0, 8.0]
-//   ],
-//   [
-//     [3.0, 5.0],
-//     [0.0, 6.0],
-//     [5.0, 6.0]
-//   ]
-// ]
-//
-export const prodGradConfig: GradConfig = {
-  kernelName: Prod,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor|Tensor[], saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved;
-    const {axis} = (attrs as {}) as ProdAttrs;
-    let axisArr = [] as number[];
-    if (axis === undefined || axis === null) {
-      axisArr = x.shape.map((_, i) => i);
-    } else if (typeof axis === 'number') {
-      axisArr = [axis];
-    } else {
-      axisArr = axis;
-    }
-    return {x: () => prodsGradFn_(x, dy as Tensor, axisArr)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/RealDiv_grad.ts b/tfjs-master/tfjs-core/src/gradients/RealDiv_grad.ts
deleted file mode 100644
index 56e517115..000000000
--- a/tfjs-master/tfjs-core/src/gradients/RealDiv_grad.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {RealDiv} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import * as broadcast_util from '../ops/broadcast_util';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {mul} from '../ops/mul';
-import {neg} from '../ops/neg';
-import {reshape} from '../ops/reshape';
-import {square} from '../ops/square';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const divGradConfig: GradConfig = {
-  kernelName: RealDiv,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const outShape =
-        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);
-    const derA = () => {
-      const res = div(dy, cast(b, 'float32'));
-      const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        return reshape(sum(res, reduceAxes), a.shape);
-      }
-      return res;
-    };
-    const derB = () => {
-      let res = mul(dy, cast(a, 'float32'));
-      const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = reshape(sum(res, reduceAxes), b.shape);
-      }
-      const tmp = square(b);
-      return neg(div(res, cast(tmp, 'float32')));
-    };
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Reciprocal_grad.ts b/tfjs-master/tfjs-core/src/gradients/Reciprocal_grad.ts
deleted file mode 100644
index c4bfa6c3d..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Reciprocal_grad.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Reciprocal} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {div} from '../ops/div';
-import {neg} from '../ops/neg';
-import {square} from '../ops/square';
-import {Tensor} from '../tensor';
-
-export const reciprocalGradConfig: GradConfig = {
-  kernelName: Reciprocal,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => div(dy, neg(square(x)))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Relu6_grad.ts b/tfjs-master/tfjs-core/src/gradients/Relu6_grad.ts
deleted file mode 100644
index ebf2ca7a4..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Relu6_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Relu6} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {lessEqual} from '../ops/less_equal';
-import {mul} from '../ops/mul';
-import {step} from '../ops/step';
-import {Tensor} from '../tensor';
-
-export const relu6GradConfig: GradConfig = {
-  kernelName: Relu6,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    const mask = mul(lessEqual(x, 6), step(x));
-
-    return {x: () => mul(dy, cast(mask, 'float32'))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Relu_grad.ts b/tfjs-master/tfjs-core/src/gradients/Relu_grad.ts
deleted file mode 100644
index c96bdcc30..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Relu_grad.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Relu} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {mul} from '../ops/mul';
-import {step} from '../ops/step';
-import {Tensor} from '../tensor';
-
-export const reluGradConfig: GradConfig = {
-  kernelName: Relu,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => mul(dy, cast(step(x), 'float32'))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Reshape_grad.ts b/tfjs-master/tfjs-core/src/gradients/Reshape_grad.ts
deleted file mode 100644
index e6ddb4551..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Reshape_grad.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Reshape} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {reshape} from '../ops/reshape';
-import {Tensor} from '../tensor';
-
-export const reshapeGradConfig: GradConfig = {
-  kernelName: Reshape,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => reshape(dy, x.shape)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/ResizeBilinear_grad.ts b/tfjs-master/tfjs-core/src/gradients/ResizeBilinear_grad.ts
deleted file mode 100644
index 8b10ebc19..000000000
--- a/tfjs-master/tfjs-core/src/gradients/ResizeBilinear_grad.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {ResizeBilinear, ResizeBilinearGrad, ResizeBilinearGradInputs} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-
-export const resizeBilinearGradConfig: GradConfig = {
-  kernelName: ResizeBilinear,
-  inputsToSave: ['images'],
-  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [images] = saved;
-
-    const inputs: ResizeBilinearGradInputs = {dy, images};
-    const imagesDer = () =>
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        ENGINE.runKernel(
-            ResizeBilinearGrad, inputs as unknown as NamedTensorMap, attrs) as
-        Tensor4D;
-
-    return {images: imagesDer};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/ResizeNearestNeighbor_grad.ts b/tfjs-master/tfjs-core/src/gradients/ResizeNearestNeighbor_grad.ts
deleted file mode 100644
index 41733273c..000000000
--- a/tfjs-master/tfjs-core/src/gradients/ResizeNearestNeighbor_grad.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {ResizeNearestNeighbor, ResizeNearestNeighborGrad, ResizeNearestNeighborGradInputs} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-
-export const resizeNearestNeighborGradConfig: GradConfig = {
-  kernelName: ResizeNearestNeighbor,
-  inputsToSave: ['images'],
-  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [images] = saved;
-
-    const inputs: ResizeNearestNeighborGradInputs = {dy, images};
-    const imagesDer = () =>
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        ENGINE.runKernel(
-            ResizeNearestNeighborGrad, inputs as unknown as NamedTensorMap,
-            attrs) as Tensor4D;
-
-    return {images: imagesDer};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Reverse_grad.ts b/tfjs-master/tfjs-core/src/gradients/Reverse_grad.ts
deleted file mode 100644
index 6e7b8a159..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Reverse_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Reverse, ReverseAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {reverse} from '../ops/reverse';
-import {Tensor} from '../tensor';
-import {parseAxisParam} from '../util';
-
-export const reverseGradConfig: GradConfig = {
-  kernelName: Reverse,
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const {dims} = attrs as unknown as ReverseAttrs;
-    const axes = parseAxisParam(dims, dy.shape);
-    return {x: () => reverse(dy, axes)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Round_grad.ts b/tfjs-master/tfjs-core/src/gradients/Round_grad.ts
deleted file mode 100644
index 8ddcb155c..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Round_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Round} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const roundGradConfig: GradConfig = {
-  kernelName: Round,
-  gradFunc: (dy: Tensor) => {
-    // TODO(nsthorat): Let gradients be null for cases where we want to stop
-    // backpropgation.
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Rsqrt_grad.ts b/tfjs-master/tfjs-core/src/gradients/Rsqrt_grad.ts
deleted file mode 100644
index fa77d7792..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Rsqrt_grad.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Rsqrt} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {div} from '../ops/div';
-import {mul} from '../ops/mul';
-import {neg} from '../ops/neg';
-import {pow} from '../ops/pow';
-import {Tensor} from '../tensor';
-
-export const rsqrtGradConfig: GradConfig = {
-  kernelName: Rsqrt,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => neg(div(dy, mul(pow(x, 1.5), 2)))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Select_grad.ts b/tfjs-master/tfjs-core/src/gradients/Select_grad.ts
deleted file mode 100644
index b23ed03e1..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Select_grad.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Select} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {logicalNot} from '../ops/logical_not';
-import {mul} from '../ops/mul';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const selectGradConfig: GradConfig = {
-  kernelName: Select,
-  inputsToSave: ['condition'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [condition] = saved;
-    return {
-      // TODO(julianoks): Return null for condition gradient
-      // when backprop supports it.
-      condition: () => cast(zerosLike(condition), 'float32'),
-      t: () => mul(dy, cast(condition, dy.dtype)),
-      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Selu_grad.ts b/tfjs-master/tfjs-core/src/gradients/Selu_grad.ts
deleted file mode 100644
index 38f6ea5fa..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Selu_grad.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Selu} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {exp} from '../ops/exp';
-import {greater} from '../ops/greater';
-import {mul} from '../ops/mul';
-import {scalar} from '../ops/scalar';
-import {SELU_SCALE, SELU_SCALEALPHA} from '../ops/selu_util';
-import {where} from '../ops/where';
-import {Tensor} from '../tensor';
-
-export const seluGradConfig: GradConfig = {
-  kernelName: Selu,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {
-      x: () => {
-        const mask = greater(x, scalar(0));
-
-        const scaleAlpha = scalar(SELU_SCALEALPHA);
-        const scale = scalar(SELU_SCALE);
-
-        const greaterThanZeroDer = mul(dy, scale);
-        const lessEqualZeroDer =
-            mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));
-
-        return where(mask, greaterThanZeroDer, lessEqualZeroDer);
-      }
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Sigmoid_grad.ts b/tfjs-master/tfjs-core/src/gradients/Sigmoid_grad.ts
deleted file mode 100644
index 5a595406e..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Sigmoid_grad.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Sigmoid} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {mul} from '../ops/mul';
-import {scalar} from '../ops/scalar';
-import {sub} from '../ops/sub';
-import {Tensor} from '../tensor';
-
-export const sigmoidGradConfig: GradConfig = {
-  kernelName: Sigmoid,
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [y] = saved;
-
-    return {x: () => mul(dy, mul(y, sub(scalar(1), y)))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Sign_grad.ts b/tfjs-master/tfjs-core/src/gradients/Sign_grad.ts
deleted file mode 100644
index 794007624..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Sign_grad.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Sign} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const signGradConfig: GradConfig = {
-  kernelName: Sign,
-  gradFunc: (dy: Tensor) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Sin_grad.ts b/tfjs-master/tfjs-core/src/gradients/Sin_grad.ts
deleted file mode 100644
index 2a23be35e..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Sin_grad.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Sin} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {cos} from '../ops/cos';
-import {mul} from '../ops/mul';
-import {Tensor} from '../tensor';
-
-export const sinGradConfig: GradConfig = {
-  kernelName: Sin,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => mul(cos(cast(x, 'float32')), dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Sinh_grad.ts b/tfjs-master/tfjs-core/src/gradients/Sinh_grad.ts
deleted file mode 100644
index 916bfee09..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Sinh_grad.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Sinh} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {cosh} from '../ops/cosh';
-import {mul} from '../ops/mul';
-import {Tensor} from '../tensor';
-
-export const sinhGradConfig: GradConfig = {
-  kernelName: Sinh,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {x: () => mul(cosh(cast(x, 'float32')), dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Slice_grad.ts b/tfjs-master/tfjs-core/src/gradients/Slice_grad.ts
deleted file mode 100644
index 5018f529f..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Slice_grad.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Slice, SliceAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {pad} from '../ops/pad';
-import {parseSliceParams} from '../ops/slice_util';
-import {Tensor} from '../tensor';
-
-export const sliceGradConfig: GradConfig = {
-  kernelName: Slice,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved;
-    const {begin, size} = attrs as unknown as SliceAttrs;
-
-    const inputShape = x.shape;
-    const [begin_, size_] = parseSliceParams(x, begin, size);
-
-    // Create an Nx2 padding where the first column represents how many
-    // zeros are prepended (at start) for each dimension, and the second
-    // column indicates how many zeros are appended (at end).
-
-    // The number of zeros to append is the shape of the input
-    // elementwise-subtracted by both the begin vector and sizes vector.
-    const paddings: Array<[number, number]> = [];
-    for (let i = 0; i < dy.rank; i++) {
-      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
-    }
-    return {x: () => pad(dy, paddings)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Softmax_grad.ts b/tfjs-master/tfjs-core/src/gradients/Softmax_grad.ts
deleted file mode 100644
index e414644d8..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Softmax_grad.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Softmax, SoftmaxAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {mul} from '../ops/mul';
-import {sub} from '../ops/sub';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const softmaxGradConfig: GradConfig = {
-  kernelName: Softmax,
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [y] = saved;
-    const {dim} = attrs as unknown as SoftmaxAttrs;
-    const keepDims = true;
-
-    const dyTimesY = mul(dy, y);
-    return {
-      logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))
-    };
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Softplus_grad.ts b/tfjs-master/tfjs-core/src/gradients/Softplus_grad.ts
deleted file mode 100644
index 257ff601c..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Softplus_grad.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Softplus} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {mul} from '../ops/mul';
-import {sigmoid} from '../ops/sigmoid';
-import {Tensor} from '../tensor';
-
-export const softplusGradConfig: GradConfig = {
-  kernelName: Softplus,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {x: () => mul(dy, sigmoid(x))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/SpaceToBatchND_grad.ts b/tfjs-master/tfjs-core/src/gradients/SpaceToBatchND_grad.ts
deleted file mode 100644
index 4fddd55b2..000000000
--- a/tfjs-master/tfjs-core/src/gradients/SpaceToBatchND_grad.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {SpaceToBatchND, SpaceToBatchNDAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {batchToSpaceND} from '../ops/batch_to_space_nd';
-import {Tensor} from '../tensor';
-
-export const spaceToBatchNDGradConfig: GradConfig = {
-  kernelName: SpaceToBatchND,
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const {blockShape, paddings} = attrs as unknown as SpaceToBatchNDAttrs;
-    return {x: () => batchToSpaceND(dy, blockShape, paddings)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/SplitV_grad.ts b/tfjs-master/tfjs-core/src/gradients/SplitV_grad.ts
deleted file mode 100644
index 01109f4f3..000000000
--- a/tfjs-master/tfjs-core/src/gradients/SplitV_grad.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {SplitV, SplitVAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {concat} from '../ops/concat';
-import {Tensor} from '../tensor';
-
-export const splitVGradConfig: GradConfig = {
-  kernelName: SplitV,
-  gradFunc: (dy: Tensor[], saved: Tensor[], attrs: NamedAttrMap) => {
-    const {axis} = attrs as unknown as SplitVAttrs;
-
-    return {x: () => concat(dy, axis)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Sqrt_grad.ts b/tfjs-master/tfjs-core/src/gradients/Sqrt_grad.ts
deleted file mode 100644
index 71012f49b..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Sqrt_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Sqrt} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {div} from '../ops/div';
-import {mul} from '../ops/mul';
-import {sqrt} from '../ops/sqrt';
-import {Tensor} from '../tensor';
-
-export const sqrtGradConfig: GradConfig = {
-  kernelName: Sqrt,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Square_grad.ts b/tfjs-master/tfjs-core/src/gradients/Square_grad.ts
deleted file mode 100644
index dabc8705a..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Square_grad.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Square} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cast} from '../ops/cast';
-import {mul} from '../ops/mul';
-import {Tensor} from '../tensor';
-
-export const squareGradConfig: GradConfig = {
-  kernelName: Square,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-    return {x: () => mul(dy, mul(cast(x, 'float32'), 2))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/SquaredDifference_grad.ts b/tfjs-master/tfjs-core/src/gradients/SquaredDifference_grad.ts
deleted file mode 100644
index 8e0cccd60..000000000
--- a/tfjs-master/tfjs-core/src/gradients/SquaredDifference_grad.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {SquaredDifference} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {mul} from '../ops/mul';
-import {scalar} from '../ops/scalar';
-import {sub} from '../ops/sub';
-import {Tensor} from '../tensor';
-
-export const squaredDifferenceGradConfig: GradConfig = {
-  kernelName: SquaredDifference,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const two = scalar(2);
-    const derA = () => mul(dy, mul(two, sub(a, b)));
-    const derB = () => mul(dy, mul(two, sub(b, a)));
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Step_grad.ts b/tfjs-master/tfjs-core/src/gradients/Step_grad.ts
deleted file mode 100644
index a24535136..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Step_grad.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Step} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const stepGradConfig: GradConfig = {
-  kernelName: Step,
-  gradFunc: (dy: Tensor) => {
-    // TODO(manrajgrover): Return null for gradients when backprop supports
-    // it.
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Sub_grad.ts b/tfjs-master/tfjs-core/src/gradients/Sub_grad.ts
deleted file mode 100644
index c0ea0258e..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Sub_grad.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Sub} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import * as broadcast_util from '../ops/broadcast_util';
-import {neg} from '../ops/neg';
-import {reshape} from '../ops/reshape';
-import {sum} from '../ops/sum';
-import {Tensor} from '../tensor';
-
-export const subGradConfig: GradConfig = {
-  kernelName: Sub,
-  inputsToSave: ['a', 'b'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [a, b] = saved;
-    const outShape =
-        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);
-
-    const derA = () => {
-      let res = dy;
-      const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum(res, reduceAxes);
-      }
-      return reshape(res, a.shape);
-    };
-    const derB = () => {
-      let res = dy;
-      const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = sum(res, reduceAxes);
-      }
-      return reshape(neg(res), b.shape);
-    };
-
-    return {a: derA, b: derB};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Sum_grad.ts b/tfjs-master/tfjs-core/src/gradients/Sum_grad.ts
deleted file mode 100644
index 7cc942b1d..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Sum_grad.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Sum, SumAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {mul} from '../ops/mul';
-import {ones} from '../ops/ones';
-import {reshape} from '../ops/reshape';
-import {Tensor} from '../tensor';
-import {parseAxisParam} from '../util';
-
-export const sumGradConfig: GradConfig = {
-  kernelName: Sum,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved;
-    const expandedDyShape = x.shape.slice();
-    const {axis} = attrs as unknown as SumAttrs;
-
-    const axes = parseAxisParam(axis, x.shape);
-    axes.forEach(axis => {
-      expandedDyShape[axis] = 1;
-    });
-    const expandedDy = reshape(dy, expandedDyShape);
-    const derX = mul(expandedDy, ones(x.shape, 'float32'));
-
-    return {x: () => derX};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Tan_grad.ts b/tfjs-master/tfjs-core/src/gradients/Tan_grad.ts
deleted file mode 100644
index 858285b1e..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Tan_grad.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tan} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {cos} from '../ops/cos';
-import {div} from '../ops/div';
-import {square} from '../ops/square';
-import {Tensor} from '../tensor';
-
-export const tanGradConfig: GradConfig = {
-  kernelName: Tan,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [x] = saved;
-
-    return {x: () => div(dy, square(cos(x)))};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Tanh_grad.ts b/tfjs-master/tfjs-core/src/gradients/Tanh_grad.ts
deleted file mode 100644
index ccae78f2f..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Tanh_grad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tanh} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {mul} from '../ops/mul';
-import {scalar} from '../ops/scalar';
-import {square} from '../ops/square';
-import {sub} from '../ops/sub';
-import {Tensor} from '../tensor';
-
-export const tanhGradConfig: GradConfig = {
-  kernelName: Tanh,
-  outputsToSave: [true],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [y] = saved;
-
-    return {x: () => mul(sub(scalar(1), square(y)), dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Tile_grad.ts b/tfjs-master/tfjs-core/src/gradients/Tile_grad.ts
deleted file mode 100644
index 4f090be21..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Tile_grad.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tile, TileAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {add} from '../ops/add';
-import {slice} from '../ops/slice';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const tileGradConfig: GradConfig = {
-  kernelName: Tile,
-  inputsToSave: ['x'],
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const [x] = saved;
-    const {reps} = attrs as unknown as TileAttrs;
-
-    const derX = () => {
-      let xGrad = zerosLike(x);
-      // TODO(cais): Maybe reduce memory footprint by avoiding repeated
-      // slicing.
-      if (x.rank === 1) {
-        for (let i = 0; i < reps[0]; ++i) {
-          xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));
-        }
-      } else if (x.rank === 2) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [
-                          x.shape[0], x.shape[1]
-                        ]));
-          }
-        }
-      } else if (x.rank === 3) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            for (let k = 0; k < reps[2]; ++k) {
-              xGrad =
-                  add(xGrad,
-                      slice(
-                          dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]],
-                          [x.shape[0], x.shape[1], x.shape[2]]));
-            }
-          }
-        }
-      } else if (x.rank === 4) {
-        for (let i = 0; i < reps[0]; ++i) {
-          for (let j = 0; j < reps[1]; ++j) {
-            for (let k = 0; k < reps[2]; ++k) {
-              for (let l = 0; l < reps[3]; ++l) {
-                xGrad =
-                    add(xGrad,
-                        slice(
-                            dy,
-                            [
-                              i * x.shape[0], j * x.shape[1], k * x.shape[2],
-                              l * x.shape[3]
-                            ],
-                            [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
-              }
-            }
-          }
-        }
-      } else {
-        throw new Error(
-            `Gradient for tile operation is not implemented for rank-` +
-            `${x.rank} tensors yet.`);
-      }
-      return xGrad;
-    };
-    return {x: derX};
-  },
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Transpose_grad.ts b/tfjs-master/tfjs-core/src/gradients/Transpose_grad.ts
deleted file mode 100644
index b7375c1e6..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Transpose_grad.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Transpose, TransposeAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import * as axis_util from '../ops/axis_util';
-import {transpose} from '../ops/transpose';
-import {Tensor} from '../tensor';
-
-export const transposeGradConfig: GradConfig = {
-  kernelName: Transpose,
-  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {
-    const transposeAttrs: TransposeAttrs = attrs as unknown as TransposeAttrs;
-    const {perm} = transposeAttrs;
-    const undoPerm = axis_util.getUndoAxesPermutation(perm);
-    return {x: () => transpose(dy, undoPerm)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/Unpack_grad.ts b/tfjs-master/tfjs-core/src/gradients/Unpack_grad.ts
deleted file mode 100644
index 1244a55e5..000000000
--- a/tfjs-master/tfjs-core/src/gradients/Unpack_grad.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Unpack, UnpackAttrs} from '../kernel_names';
-import {GradConfig, NamedAttrMap} from '../kernel_registry';
-import {stack} from '../ops/stack';
-import {Tensor} from '../tensor';
-
-export const unpackGradConfig: GradConfig = {
-  kernelName: Unpack,
-  gradFunc: (dy: Tensor[], saved: Tensor[], attrs: NamedAttrMap) => {
-    const unpackAttrs: UnpackAttrs = attrs as unknown as UnpackAttrs;
-    const {axis} = unpackAttrs;
-    return {value: () => stack(dy, axis)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/UnsortedSegmentSum_grad.ts b/tfjs-master/tfjs-core/src/gradients/UnsortedSegmentSum_grad.ts
deleted file mode 100644
index a6bff2ed2..000000000
--- a/tfjs-master/tfjs-core/src/gradients/UnsortedSegmentSum_grad.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {UnsortedSegmentSum} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {expandDims} from '../ops/expand_dims';
-import {gather} from '../ops/gather';
-import {greaterEqual} from '../ops/greater_equal';
-import {logicalAnd} from '../ops/logical_and';
-import {maximum} from '../ops/maximum';
-import {ones} from '../ops/ones';
-import {scalar} from '../ops/scalar';
-import {where} from '../ops/where';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor, Tensor1D} from '../tensor';
-
-export const unsortedSegmentSumGradConfig: GradConfig = {
-  kernelName: UnsortedSegmentSum,
-  inputsToSave: ['segmentIds'],
-  gradFunc: (dy: Tensor, saved: Tensor[]) => {
-    const [segmentIds] = saved;
-
-    const derX = () => {
-      return gatherDropNegatives(dy, segmentIds as Tensor1D);
-    };
-    return {x: derX};
-  }
-};
-
-function gatherDropNegatives<T extends Tensor>(x: T, indices: Tensor1D) {
-  // Helper function for unsorted segment ops. Gathers params for
-  // positive segment ids and gathers 0 for inputs with negative segment id.
-  // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py
-  const zeroClippedIndices = maximum(indices, zerosLike(indices));
-  const gathered = gather(x, zeroClippedIndices as Tensor1D);
-  let isPositive = greaterEqual(indices, scalar(0, 'int32'));
-  const numIters = gathered.rank - isPositive.rank;
-  for (let i = 0; i < numIters; ++i) {
-    isPositive = expandDims(isPositive, i + 1);
-  }
-  isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));
-  const zeroSlice = zerosLike(gathered);
-  return where(isPositive, gathered, zeroSlice);
-}
diff --git a/tfjs-master/tfjs-core/src/gradients/ZerosLike_grad.ts b/tfjs-master/tfjs-core/src/gradients/ZerosLike_grad.ts
deleted file mode 100644
index 688ad14bc..000000000
--- a/tfjs-master/tfjs-core/src/gradients/ZerosLike_grad.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ZerosLike} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
-import {zerosLike} from '../ops/zeros_like';
-import {Tensor} from '../tensor';
-
-export const zerosLikeGradConfig: GradConfig = {
-  kernelName: ZerosLike,
-  gradFunc: (dy: Tensor) => {
-    return {x: () => zerosLike(dy)};
-  }
-};
diff --git a/tfjs-master/tfjs-core/src/gradients/min_max_grad_util.ts b/tfjs-master/tfjs-core/src/gradients/min_max_grad_util.ts
deleted file mode 100644
index cc58a49fe..000000000
--- a/tfjs-master/tfjs-core/src/gradients/min_max_grad_util.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as axis_util from '../ops/axis_util';
-import {cast} from '../ops/cast';
-import {equal} from '../ops/equal';
-import {mul} from '../ops/mul';
-import {reshape} from '../ops/reshape';
-import {Tensor} from '../tensor';
-
-/**
- * Gradient helper function for the min and max operations.
- */
-export function gradForMinAndMax<T extends Tensor>(
-    dy: T, y: T, xOrig: Tensor, origAxes: number[]) {
-  if (y.rank < xOrig.rank) {
-    y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes)) as T;
-  }
-  if (dy.rank < xOrig.rank) {
-    dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes)) as T;
-  }
-  return {
-    x: () => {
-      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));
-      return dx;
-    }
-  };
-}
diff --git a/tfjs-master/tfjs-core/src/gradients_test.ts b/tfjs-master/tfjs-core/src/gradients_test.ts
deleted file mode 100644
index c3c355646..000000000
--- a/tfjs-master/tfjs-core/src/gradients_test.ts
+++ /dev/null
@@ -1,376 +0,0 @@
-
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from './engine';
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-import {Tensor} from './tensor';
-import {expectArraysClose} from './test_util';
-
-describeWithFlags('gradients', ALL_ENVS, () => {
-  it('matmul + relu', async () => {
-    const a = tf.tensor2d([-1, 2, -3, 10, -20, 30], [2, 3]);
-    const b = tf.tensor2d([2, -3, 4, -1, 2, -3], [3, 2]);
-
-    const [da, db] = tf.grads((a: tf.Tensor2D, b: tf.Tensor2D) => {
-      // m = dot(a, b)
-      // y = relu(m)
-      // e = sum(y)
-      const m = tf.matMul(a, b);
-      const y = tf.relu(m);
-      return tf.sum(y);
-    })([a, b]);
-
-    // de/dy = 1
-    // dy/dm = step(m)
-    // de/dm = de/dy * dy/dm = step(m)
-    const dedm = tf.step(tf.matMul(a, b));
-
-    // de/da = dot(de/dy, bT)
-    expect(da.shape).toEqual(a.shape);
-    let transposeA = false;
-    let transposeB = true;
-    expectArraysClose(
-        await da.data(),
-        await tf.matMul(dedm, b, transposeA, transposeB).data());
-
-    // de/db = dot(aT, de/dy)
-    expect(db.shape).toEqual(b.shape);
-    transposeA = true;
-    transposeB = false;
-    expectArraysClose(
-        await db.data(),
-        await tf.matMul(a, dedm, transposeA, transposeB).data());
-  });
-
-  it('grad(f)', async () => {
-    const grad = tf.grad(x => x.square());
-    const result = grad(tf.tensor1d([.1, .2]));
-    expectArraysClose(await result.data(), [.2, .4]);
-  });
-
-  it('calling grad(f) twice works', async () => {
-    const grad = tf.grad(x => x.square());
-
-    const result = grad(tf.tensor1d([.1, .2]));
-    const result2 = grad(tf.tensor1d([.1, .4]));
-    expectArraysClose(await result.data(), [.2, .4]);
-    expectArraysClose(await result2.data(), [.2, .8]);
-  });
-
-  it('grad(f): throwing an error during forward pass', () => {
-    const grad = tf.grad(x => {
-      throw new Error('failed forward pass');
-    });
-    expect(() => grad(tf.zeros([]))).toThrowError();
-    expect(ENGINE.isTapeOn()).toBe(false);
-  });
-
-  it('grad(f): throwing an error during backwards pass', () => {
-    const customOp = tf.customGrad((x: tf.Tensor) => {
-      return {
-        value: x,
-        gradFunc: () => {
-          throw new Error('failed backward pass');
-        }
-      };
-    });
-    const grad = tf.grad(x => customOp(x));
-    expect(() => grad(tf.zeros([]))).toThrowError();
-    expect(ENGINE.isTapeOn()).toBe(false);
-  });
-
-  it('grads(f)', async () => {
-    const grads = tf.grads(x => x.square());
-    const result = grads([tf.tensor1d([.1, .2])]);
-    expectArraysClose(await result[0].data(), [.2, .4]);
-  });
-
-  it('calling grads(f) twice works', async () => {
-    const grads = tf.grads(x => x.square());
-
-    const result = grads([tf.tensor1d([.1, .2])]);
-    const result2 = grads([tf.tensor1d([.1, .4])]);
-    expectArraysClose(await result[0].data(), [.2, .4]);
-    expectArraysClose(await result2[0].data(), [.2, .8]);
-  });
-
-  it('works with reshape', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const exponent = tf.tensor1d([2, 2, 2, 2], 'int32');
-
-    const da = tf.grad(a => {
-      const b = a.flatten();
-      const m = tf.pow(b, exponent);
-      return tf.sum(m);
-    })(a);
-
-    expect(da.shape).toEqual([2, 2]);
-    expectArraysClose(await da.data(), [2, 4, 6, 8]);
-  });
-
-  it('reshape outside tf.grads() throws error', () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const b = a.flatten();
-    const exponent = tf.tensor1d([2, 2, 2, 2], 'int32');
-
-    const f = () => {
-      tf.grads((a, b) => {
-        const m = tf.pow(b, exponent);
-        return tf.sum(m);
-      })([a, b]);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('does not error if irrelevant (pruned) ops are missing grads',
-     async () => {
-       const a = tf.tensor1d([true, true], 'bool');
-       const b = tf.tensor1d([false, true], 'bool');
-       const da = tf.grad(a => {
-         // Logical has no gradients, but it is irrelevant.
-         a.logicalAnd(b);
-         return a.sum();
-       })(a);
-       expectArraysClose(await da.data(), [1, 1]);
-     });
-
-  it('errors if relevant ops are missing grads', () => {
-    const a = tf.tensor1d([true, true], 'bool');
-    const b = tf.tensor1d([false, true], 'bool');
-    const dfda = tf.grad(a => {
-      // Logical has no gradients, but it's relevant to the output.
-      return a.logicalAnd(b);
-    });
-    expect(() => dfda(a)).toThrowError();
-  });
-
-  it('works with asType', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
-    const exponent = tf.tensor2d([2, 2, 2, 2], [2, 2], 'int32');
-
-    const da = tf.grad(a => {
-      const b = a.toFloat();
-      const m = tf.pow(b, exponent);
-      return tf.sum(m);
-    })(a);
-
-    expect(da.shape).toEqual([2, 2]);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [2, 4, 6, 8]);
-  });
-
-  it('asType outside of tf.grads() throws error', () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
-    const b = a.toFloat();
-    const exponent = tf.tensor2d([2, 2, 2, 2], [2, 2], 'int32');
-
-    const f = () => {
-      tf.grad(a => {
-        const m = tf.pow(b, exponent);
-        return tf.sum(m);
-      })(a);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('saves tensors from the forward pass as expected', () => {
-    const x = tf.scalar(1).variable();
-    const optimizer = tf.train.sgd(0.1);
-    optimizer.minimize(() => {
-      const y = x.square();
-      const z = y.square();
-      y.dispose();
-      return z;
-    });
-  });
-
-  it('custom ops do not leak', () => {
-    const before = tf.memory().numTensors;
-    const x = tf.softmax([1, 2, 3, 4]);
-    x.dispose();
-    const now = tf.memory().numTensors;
-    expect(now).toBe(before);
-  });
-});
-
-describeWithFlags('valueAndGradients', ALL_ENVS, () => {
-  it('matmul + relu', async () => {
-    const a = tf.tensor2d([-1, 2, -3, 10, -20, 30], [2, 3]);
-    const b = tf.tensor2d([2, -3, 4, -1, 2, -3], [3, 2]);
-
-    const {value, grads} =
-        tf.valueAndGrads((a: tf.Tensor2D, b: tf.Tensor2D) => {
-          // m = dot(a, b)
-          // y = relu(m)
-          // e = sum(y)
-          const m = tf.matMul(a, b);
-          const y = tf.relu(m);
-          return tf.sum(y);
-        })([a, b]);
-
-    expectArraysClose(await value.data(), 10);
-
-    // de/dy = 1
-    // dy/dm = step(m)
-    // de/dm = de/dy * dy/dm = step(m)
-    const dedm = tf.step(tf.matMul(a, b));
-
-    const [da, db] = grads;
-    // de/da = dot(de/dy, bT)
-    let transposeA = false;
-    let transposeB = true;
-    expectArraysClose(
-        await da.data(),
-        await tf.matMul(dedm, b, transposeA, transposeB).data());
-
-    // de/db = dot(aT, de/dy)
-    transposeA = true;
-    transposeB = false;
-    expectArraysClose(
-        await db.data(),
-        await tf.matMul(a, dedm, transposeA, transposeB).data());
-  });
-
-  it('matmul + relu + inner tidy', async () => {
-    const a = tf.tensor2d([-1, 2, -3, 10, -20, 30], [2, 3]);
-    const b = tf.tensor2d([2, -3, 4, -1, 2, -3], [3, 2]);
-
-    const {value, grads} =
-        tf.valueAndGrads((a: tf.Tensor2D, b: tf.Tensor2D) => {
-          // m = dot(a, b)
-          // y = relu(m)
-          // e = sum(y)
-          const m = tf.matMul(a, b);
-          return tf.tidy(() => {
-            const y = tf.relu(m);
-            return tf.sum(y);
-          });
-        })([a, b]);
-
-    expectArraysClose(await value.data(), 10);
-
-    // de/dy = 1
-    // dy/dm = step(m)
-    // de/dm = de/dy * dy/dm = step(m)
-    const dedm = tf.step(tf.matMul(a, b));
-
-    const [da, db] = grads;
-    // de/da = dot(de/dy, bT)
-    let transposeA = false;
-    let transposeB = true;
-    expectArraysClose(
-        await da.data(),
-        await tf.matMul(dedm, b, transposeA, transposeB).data());
-
-    // de/db = dot(aT, de/dy)
-    transposeA = true;
-    transposeB = false;
-    expectArraysClose(
-        await db.data(),
-        await tf.matMul(a, dedm, transposeA, transposeB).data());
-  });
-});
-
-describeWithFlags('higher-order gradients', ALL_ENVS, () => {
-  it('grad(grad(f))', async () => {
-    const x = tf.tensor1d([.1, .2]);
-    const before = tf.memory().numTensors;
-    const gradgrad = tf.grad(tf.grad(x => x.mul(x).mul(x)));
-    const result = gradgrad(x);
-    expect(tf.memory().numTensors).toBe(before + 1);
-    expectArraysClose(await result.data(), [.6, 1.2]);
-  });
-
-  it('grad(grad(x^2))', async () => {
-    const x = tf.scalar(3);
-    const gradgrad = tf.grad(tf.grad(x => x.square()));
-    const result = gradgrad(x);
-    // grad(grad(x^2)) = grad(2x) = 2
-    expectArraysClose(await result.data(), [2]);
-  });
-
-  it('grads(grads(f))', async () => {
-    const grads = tf.grads(x => x.mul(x).mul(x));
-    const gradsgrads = tf.grads(x => grads([x])[0]);
-    const result = gradsgrads([tf.tensor1d([.1, .2])]);
-    expectArraysClose(await result[0].data(), [.6, 1.2]);
-  });
-});
-
-describeWithFlags('customGradient', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.scalar(3);
-    const b = tf.scalar(2, 'int32');
-    const dy = tf.scalar(4);
-
-    const customPow = tf.customGrad((a: tf.Tensor) => {
-      const value = tf.pow(a, b);
-      const gradFunc = (dy: tf.Tensor) => dy.mul(tf.scalar(0.1));
-      return {value, gradFunc};
-    });
-
-    const {value, grad} = tf.valueAndGrad(a => customPow(a))(a, dy);
-    expect(value.shape).toEqual(a.shape);
-    expectArraysClose(await value.data(), [9]);
-    expect(grad.shape).toEqual(a.shape);
-    expectArraysClose(await grad.data(), [.4]);
-  });
-
-  it('second order derivative through customGradient', async () => {
-    const a = tf.scalar(3);
-    const b = tf.scalar(2, 'int32');
-
-    const dy = tf.scalar(5);
-
-    const customPow = tf.customGrad((a: tf.Tensor, save: tf.GradSaveFunc) => {
-      const value = tf.pow(a, b);
-      save([a]);
-      const gradFunc = (dy: tf.Tensor, saved: Tensor[]) => {
-        const [a] = saved;
-        return dy.mul(a);
-      };
-      return {value, gradFunc};
-    });
-
-    const dda = tf.grad(tf.grad(a => customPow(a)))(a, dy);
-    expect(dda.shape).toEqual(a.shape);
-
-    // First order: dy * a. Second order: dy.
-    expectArraysClose(await dda.data(), await dy.data());
-  });
-
-  it('calling gradient of custom op twice works', async () => {
-    const customOp = tf.customGrad((x: tf.Tensor, save: tf.GradSaveFunc) => {
-      // Override gradient of our custom x ^ 2 op to be dy * abs(x);
-      save([x]);
-      return {
-        value: x.square(),
-        gradFunc: (dy, saved: Tensor[]) => {
-          const [x] = saved;
-          return dy.mul(x.abs());
-        }
-      };
-    });
-    const x = tf.tensor1d([-1, -2, 3]);
-    const grad = tf.grad(x => customOp(x));
-
-    expectArraysClose(await grad(x).data(), [1, 2, 3]);
-    expectArraysClose(await grad(x).data(), [1, 2, 3]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/hash_util.ts b/tfjs-master/tfjs-core/src/hash_util.ts
deleted file mode 100644
index adc7c878a..000000000
--- a/tfjs-master/tfjs-core/src/hash_util.ts
+++ /dev/null
@@ -1,204 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// Workaround for allowing cjs module to be included in bundle created by
-// rollup.
-import * as LongExports from 'long';
-// tslint:disable-next-line
-const Long: LongExports.LongConstructor =
-    // tslint:disable-next-line
-    (LongExports as any).default || LongExports;
-
-export function hexToLong(hex: string): Long {
-  return Long.fromString(hex, true, 16);
-}
-
-// Some primes between 2^63 and 2^64 for various uses.
-// Hex 0xc3a5c85c97cb3127
-const k0: Long = hexToLong('c3a5c85c97cb3127');
-// Hex 0xb492b66fbe98f273
-const k1: Long = hexToLong('b492b66fbe98f273');
-// Hex 0x9ae16a3b2f90404f
-const k2: Long = hexToLong('9ae16a3b2f90404f');
-
-function shiftMix(val: Long): Long {
-  return val.xor(val.shru(47));
-}
-
-function fetch(s: Uint8Array, offset: number, numBytes: number): Long {
-  const bytes = s.slice(offset, offset + numBytes);
-  return Long.fromBytes(Array.from(bytes), true, true);
-}
-
-function fetch64(s: Uint8Array, offset: number): Long {
-  return fetch(s, offset, 8);
-}
-
-function fetch32(s: Uint8Array, offset: number): Long {
-  return fetch(s, offset, 4);
-}
-
-function rotate64(val: Long, shift: number): Long {
-  // Avoid shifting by 64: doing so yields an undefined result.
-  return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));
-}
-
-function hashLen16(u: Long, v: Long, mul = hexToLong('9ddfea08eb382d69')) {
-  // Murmur-inspired hashing.
-  let a = u.xor(v).mul(mul);
-  a = a.xor(a.shru(47));
-  let b = v.xor(a).mul(mul);
-  b = b.xor(b.shru(47));
-  b = b.mul(mul);
-  return b;
-}
-
-// Return a 16-byte hash for 48 bytes.  Quick and dirty.
-// Callers do best to use "random-looking" values for a and b.
-function weakHashLen32WithSeeds(
-    w: Long, x: Long, y: Long, z: Long, a: Long, b: Long) {
-  a = a.add(w);
-  b = rotate64(b.add(a).add(z), 21);
-  const c = a;
-  a = a.add(x);
-  a = a.add(y);
-  b = b.add(rotate64(a, 44));
-  return [a.add(z), b.add(c)];
-}
-
-function weakHashLen32WithSeedsStr(
-    s: Uint8Array, offset: number, a: Long, b: Long) {
-  return weakHashLen32WithSeeds(
-      fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16),
-      fetch64(s, offset + 24), a, b);
-}
-
-function hashLen0to16(s: Uint8Array, len = s.length): Long {
-  if (len >= 8) {
-    const mul = k2.add(len * 2);
-    const a = fetch64(s, 0).add(k2);
-    const b = fetch64(s, len - 8);
-    const c = rotate64(b, 37).mul(mul).add(a);
-    const d = rotate64(a, 25).add(b).mul(mul);
-    return hashLen16(c, d, mul);
-  }
-  if (len >= 4) {
-    const mul = k2.add(len * 2);
-    const a = fetch32(s, 0);
-    return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul);
-  }
-  if (len > 0) {
-    const a = s[0];
-    const b = s[len >> 1];
-    const c = s[len - 1];
-    const y = a + (b << 8);
-    const z = len + (c << 2);
-    return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);
-  }
-  return k2;
-}
-
-function hashLen17to32(s: Uint8Array, len = s.length): Long {
-  const mul = k2.add(len * 2);
-  const a = fetch64(s, 0).mul(k1);
-  const b = fetch64(s, 8);
-  const c = fetch64(s, len - 8).mul(mul);
-  const d = fetch64(s, len - 16).mul(k2);
-  return hashLen16(
-      rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d),
-      a.add(rotate64(b.add(k2), 18)).add(c), mul);
-}
-
-function hashLen33to64(s: Uint8Array, len = s.length): Long {
-  const mul = k2.add(len * 2);
-  const a = fetch64(s, 0).mul(k2);
-  const b = fetch64(s, 8);
-  const c = fetch64(s, len - 8).mul(mul);
-  const d = fetch64(s, len - 16).mul(k2);
-  const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);
-  const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul);
-  const e = fetch64(s, 16).mul(mul);
-  const f = fetch64(s, 24);
-  const g = y.add(fetch64(s, len - 32)).mul(mul);
-  const h = z.add(fetch64(s, len - 24)).mul(mul);
-  return hashLen16(
-      rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h),
-      e.add(rotate64(f.add(a), 18)).add(g), mul);
-}
-
-export function fingerPrint64(s: Uint8Array, len = s.length): Long {
-  const seed: Long = Long.fromNumber(81, true);
-  if (len <= 32) {
-    if (len <= 16) {
-      return hashLen0to16(s, len);
-    } else {
-      return hashLen17to32(s, len);
-    }
-  } else if (len <= 64) {
-    return hashLen33to64(s, len);
-  }
-
-  // For strings over 64 bytes we loop.  Internal state consists of
-  // 56 bytes: v, w, x, y, and z.
-  let x = seed;
-  let y = seed.mul(k1).add(113);
-
-  let z = shiftMix(y.mul(k2).add(113)).mul(k2);
-  let v = [Long.UZERO, Long.UZERO];
-  let w = [Long.UZERO, Long.UZERO];
-  x = x.mul(k2).add(fetch64(s, 0));
-
-  let offset = 0;
-  // Set end so that after the loop we have 1 to 64 bytes left to process.
-  const end = ((len - 1) >> 6) * 64;
-  const last64 = end + ((len - 1) & 63) - 63;
-
-  do {
-    x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);
-    y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);
-    x = x.xor(w[1]);
-    y = y.add(v[0]).add(fetch64(s, offset + 40));
-    z = rotate64(z.add(w[0]), 33).mul(k1);
-    v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));
-    w = weakHashLen32WithSeedsStr(
-        s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
-
-    [z, x] = [x, z];
-    offset += 64;
-  } while (offset !== end);
-  const mul = k1.add(z.and(0xff).shl(1));
-  // Point to the last 64 bytes of input.
-  offset = last64;
-
-  w[0] = w[0].add((len - 1) & 63);
-  v[0] = v[0].add(w[0]);
-  w[0] = w[0].add(v[0]);
-
-  x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul);
-  y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul);
-  x = x.xor(w[1].mul(9));
-  y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));
-  z = rotate64(z.add(w[0]), 33).mul(mul);
-  v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul), x.add(w[0]));
-  w = weakHashLen32WithSeedsStr(
-      s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
-
-  [z, x] = [x, z];
-
-  return hashLen16(
-      hashLen16(v[0], w[0], mul).add(shiftMix(y).mul(k0)).add(z),
-      hashLen16(v[1], w[1], mul).add(x), mul);
-}
diff --git a/tfjs-master/tfjs-core/src/hash_util_test.ts b/tfjs-master/tfjs-core/src/hash_util_test.ts
deleted file mode 100644
index 56e504bf0..000000000
--- a/tfjs-master/tfjs-core/src/hash_util_test.ts
+++ /dev/null
@@ -1,170 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {fingerPrint64, hexToLong} from './hash_util';
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-
-/**
- * The ACMRandom generator is for situations where extreme statistical quality
- * is not important. ACMRandom is useful for testing since it is seeded allowing
- * for reproducible results as well as low overhead so using it will
- * not affect test speed.
- */
-class ACMRandom {
-  static readonly MAX_INT32 = 2147483647;
-  private seed: number;
-
-  constructor(seed: number) {
-    seed = seed & 0x7fffffff;
-    if (seed === 0 || seed === ACMRandom.MAX_INT32) {
-      seed = 1;
-    }
-    this.seed = seed;
-  }
-
-  private next() {
-    const A = hexToLong('41A7');  // bits 14, 8, 7, 5, 2, 1, 0
-    const MAX_INT32 = ACMRandom.MAX_INT32;
-    // We are computing
-    //       seed = (seed * A) % MAX_INT32,    where MAX_INT32 = 2^31-1
-    //
-    // seed must not be zero or MAX_INT32, or else all subsequent computed
-    // values will be zero or MAX_INT32 respectively.  For all other values,
-    // seed will end up cycling through every number in [1,MAX_INT32-1]
-    const product = A.mul(this.seed);
-
-    // Compute (product % MAX_INT32) using the fact that
-    // ((x << 31) % MAX_INT32) == x.
-    this.seed = product.shru(31).add(product.and(MAX_INT32)).getLowBits();
-    // The first reduction may overflow by 1 bit, so we may need to repeat.
-    // mod == MAX_INT32 is not possible; using > allows for the faster
-    // sign-bit-based test.
-    if (this.seed > MAX_INT32) {
-      this.seed -= MAX_INT32;
-    }
-    return this.seed;
-  }
-
-  public rand8() {
-    return (this.next() >> 1) & 0x000000ff;
-  }
-}
-
-describeWithFlags('hash_util', ALL_ENVS, () => {
-  it('check incremental hashes', () => {
-    const buf = new Uint8Array(1000);
-    const r = new ACMRandom(10);
-    for (let i = 0; i < buf.length; ++i) {
-      buf[i] = r.rand8();
-    }
-
-    const expectIteration = (length: number, expectedHash: string) =>
-        expect(fingerPrint64(buf, length)).toEqual(hexToLong(expectedHash));
-
-    expectIteration(0, '9ae16a3b2f90404f');
-    expectIteration(1, '49d8a5e3fa93c327');
-    expectIteration(2, 'fd259abb0ff2bf12');
-    expectIteration(3, '781f1c6437096ac2');
-    expectIteration(4, '0f1369d6c0b45716');
-    expectIteration(5, '02d8cec6394de09a');
-    expectIteration(7, '1faf6c6d43626c48');
-    expectIteration(9, 'c93efd6dbe139be8');
-    expectIteration(12, '65abbc967c87d515');
-    expectIteration(16, '3f61450a03cff5af');
-    expectIteration(20, '5d2fe297e45fed1a');
-    expectIteration(26, 'eb665983aeb9ab94');
-    expectIteration(33, '3a5f3890b124b4a3');
-    expectIteration(42, 'f0c1cd66d7d9f246');
-    expectIteration(53, 'cf7e3e4b1efeba6d');
-    expectIteration(67, '0ced753b45740875');
-    expectIteration(84, 'a585e0be01846ff4');
-    expectIteration(105, 'fb6496deb356cdda');
-    expectIteration(132, 'f2e4c5b6db6c154a');
-    expectIteration(166, '4498451c3bca85a0');
-    expectIteration(208, 'd604355fa4d0b14e');
-    expectIteration(261, 'bb165e6b84ba9cdf');
-    expectIteration(327, '9ebfab4519b1348c');
-    expectIteration(409, '5921974ba2e9a5c2');
-    expectIteration(512, 'a86a96e7a44282e3');
-    expectIteration(640, 'dd731dfee500aa3c');
-    expectIteration(800, 'a69f3400e6c98357');
-    expectIteration(1000, '5c63b66443990bec');
-  });
-
-  // This is more thorough, but if something is wrong the output will be even
-  // less illuminating, because it just checks one integer at the end.
-  it('check many different strings', () => {
-    const iters = 800;
-    const s = new Uint8Array(4 * iters);
-    let len = 0;
-    let h = hexToLong('0');
-
-    // Helper that replaces h with a hash of itself and return a
-    // char that is also a hash of h.  Neither hash needs to be particularly
-    // good.
-    const remix = (): number => {
-      h = h.xor(h.shru(41));
-      h = h.mul(949921979);
-      return 'a'.charCodeAt(0) + h.and(0xfffff).mod(26).getLowBits();
-    };
-
-    for (let i = 0; i < iters; i++) {
-      h = h.xor(fingerPrint64(s, i));
-      s[len++] = remix();
-      h = h.xor(fingerPrint64(s, i * i % len));
-      s[len++] = remix();
-      h = h.xor(fingerPrint64(s, i * i * i % len));
-      s[len++] = remix();
-      h = h.xor(fingerPrint64(s, len));
-      s[len++] = remix();
-      const x0 = s[len - 1];
-      const x1 = s[len - 2];
-      const x2 = s[len - 3];
-      const x3 = s[len >> 1];
-      s[((x0 << 16) + (x1 << 8) + x2) % len] ^= x3;
-      s[((x1 << 16) + (x2 << 8) + x3) % len] ^= i % 256;
-    }
-
-    expect(h).toEqual(hexToLong('7a1d67c50ec7e167'));
-  });
-
-  it('check string hash', () => {
-    const fingerPrintHash = (hash: Long) => {
-      const mul = hexToLong('9ddfea08eb382d69');
-      let b = hash.mul(mul);
-      b = b.xor(b.shru(44));
-      b = b.mul(mul);
-      b = b.xor(b.shru(41));
-      b = b.mul(mul);
-      return b;
-    };
-
-    const getString = (length: number) => Uint8Array.from(
-        'x'.repeat(length).split('').map(char => char.charCodeAt(0)));
-
-    expect(fingerPrintHash(fingerPrint64(getString(40))))
-        .toEqual(hexToLong('2117170c4aebaffe'));
-    expect(fingerPrintHash(fingerPrint64(getString(60))))
-        .toEqual(hexToLong('e252963f3fd7a3af'));
-    expect(fingerPrintHash(fingerPrint64(getString(70))))
-        .toEqual(hexToLong('b0a8cf4a56c570fa'));
-    expect(fingerPrintHash(fingerPrint64(getString(80))))
-        .toEqual(hexToLong('d6ddaa49ddef5839'));
-    expect(fingerPrintHash(fingerPrint64(getString(90))))
-        .toEqual(hexToLong('168f3a694b4dce29'));
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/image_test_util.ts b/tfjs-master/tfjs-core/src/image_test_util.ts
deleted file mode 100644
index 16f646cb0..000000000
--- a/tfjs-master/tfjs-core/src/image_test_util.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-
-/**
- * Returns an image used in various image related tests as a 4d tensor.
- *
- * The image is 8x8 and looks like this:
- * https://drive.google.com/file/d/1Y0AsFZ2w9HsWgJfm8f2uDOGY7A4IHjcK/view?usp=sharing
- *
- */
-export function getTestImageAsTensor4d() {
-  return tf.tensor4d(
-      [
-        156, 100, 111, 255, 171, 120, 117, 255, 183, 138, 109, 255, 200, 155,
-        98,  255, 212, 157, 75,  255, 224, 156, 55,  255, 241, 153, 43,  255,
-        230, 133, 18,  255, 168, 129, 130, 255, 186, 152, 140, 255, 202, 174,
-        137, 255, 220, 190, 128, 255, 230, 193, 104, 255, 241, 188, 82,  255,
-        250, 177, 64,  255, 233, 148, 31,  255, 179, 176, 159, 255, 199, 198,
-        168, 255, 211, 216, 162, 255, 225, 228, 151, 255, 235, 227, 128, 255,
-        243, 220, 106, 255, 247, 201, 81,  255, 222, 164, 41,  255, 163, 208,
-        187, 255, 179, 226, 194, 255, 184, 234, 181, 255, 191, 239, 165, 255,
-        201, 236, 142, 255, 213, 230, 126, 255, 218, 210, 103, 255, 191, 170,
-        61,  255, 108, 214, 202, 255, 119, 226, 206, 255, 121, 231, 192, 255,
-        130, 235, 179, 255, 141, 232, 162, 255, 155, 226, 146, 255, 162, 206,
-        127, 255, 135, 166, 86,  255, 55,  207, 212, 255, 62,  217, 213, 255,
-        64,  219, 201, 255, 76,  225, 193, 255, 87,  220, 175, 255, 94,  206,
-        156, 255, 98,  181, 135, 255, 71,  143, 97,  255, 18,  200, 224, 255,
-        19,  204, 222, 255, 19,  203, 211, 255, 30,  209, 205, 255, 35,  200,
-        186, 255, 37,  177, 164, 255, 39,  150, 141, 255, 15,  115, 105, 255,
-        0,   193, 228, 255, 0,   192, 221, 255, 0,   189, 209, 255, 0,   194,
-        204, 255, 4,   182, 186, 255, 3,   158, 162, 255, 6,   133, 140, 255,
-        0,   102, 113, 255
-      ],
-      [1, 8, 8, 4]);
-}
diff --git a/tfjs-master/tfjs-core/src/index.ts b/tfjs-master/tfjs-core/src/index.ts
deleted file mode 100644
index 6859860a4..000000000
--- a/tfjs-master/tfjs-core/src/index.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Required side effectful code.
-import './base_side_effects';
-
-// TODO(mattSoulanille): Move this to base_side_effects.ts
-// It is here for now because custom bundles need to avoid calling it, and they
-// only replace the index.js file, not the base_side_effects file.
-import {registerOptimizers} from './optimizers/register_optimizers';
-registerOptimizers();
-
-// All exports from this package should be in base.
-export * from './base';
diff --git a/tfjs-master/tfjs-core/src/io/browser_files.ts b/tfjs-master/tfjs-core/src/io/browser_files.ts
deleted file mode 100644
index 816e00a08..000000000
--- a/tfjs-master/tfjs-core/src/io/browser_files.ts
+++ /dev/null
@@ -1,345 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * IOHandlers related to files, such as browser-triggered file downloads,
- * user-selected files in browser.
- */
-
-import '../flags';
-import {env} from '../environment';
-
-import {basename, getModelArtifactsForJSON, getModelArtifactsInfoForJSON, getModelJSONForModelArtifacts} from './io_utils';
-import {IORouter, IORouterRegistry} from './router_registry';
-import {IOHandler, ModelArtifacts, ModelJSON, SaveResult, WeightData, WeightsManifestConfig, WeightsManifestEntry} from './types';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-
-const DEFAULT_FILE_NAME_PREFIX = 'model';
-const DEFAULT_JSON_EXTENSION_NAME = '.json';
-const DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';
-
-function defer<T>(f: () => T): Promise<T> {
-  return new Promise(resolve => setTimeout(resolve)).then(f);
-}
-
-export class BrowserDownloads implements IOHandler {
-  private readonly modelJsonFileName: string;
-  private readonly weightDataFileName: string;
-  private readonly modelJsonAnchor: HTMLAnchorElement;
-  private readonly weightDataAnchor: HTMLAnchorElement;
-
-  static readonly URL_SCHEME = 'downloads://';
-
-  constructor(fileNamePrefix?: string) {
-    if (!env().getBool('IS_BROWSER')) {
-      // TODO(cais): Provide info on what IOHandlers are available under the
-      //   current environment.
-      throw new Error(
-          'browserDownloads() cannot proceed because the current environment ' +
-          'is not a browser.');
-    }
-
-    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
-      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
-    }
-    if (fileNamePrefix == null || fileNamePrefix.length === 0) {
-      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
-    }
-
-    this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
-    this.weightDataFileName =
-        fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
-  }
-
-  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {
-    if (typeof (document) === 'undefined') {
-      throw new Error(
-          'Browser downloads are not supported in ' +
-          'this environment since `document` is not present');
-    }
-
-    // TODO(mattsoulanille): Support saving models over 2GB that exceed
-    // Chrome's ArrayBuffer size limit.
-    const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData);
-
-    const weightsURL = window.URL.createObjectURL(new Blob(
-        [weightBuffer], {type: 'application/octet-stream'}));
-
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error(
-          'BrowserDownloads.save() does not support saving model topology ' +
-          'in binary formats yet.');
-    } else {
-      const weightsManifest: WeightsManifestConfig = [{
-        paths: ['./' + this.weightDataFileName],
-        weights: modelArtifacts.weightSpecs
-      }];
-      const modelJSON: ModelJSON =
-          getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
-
-      const modelJsonURL = window.URL.createObjectURL(
-          new Blob([JSON.stringify(modelJSON)], {type: 'application/json'}));
-
-      // If anchor elements are not provided, create them without attaching them
-      // to parents, so that the downloaded file names can be controlled.
-      const jsonAnchor = this.modelJsonAnchor == null ?
-          document.createElement('a') :
-          this.modelJsonAnchor;
-      jsonAnchor.download = this.modelJsonFileName;
-      jsonAnchor.href = modelJsonURL;
-      // Trigger downloads by evoking a click event on the download anchors.
-      // When multiple downloads are started synchronously, Firefox will only
-      // save the last one.
-      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));
-
-      if (modelArtifacts.weightData != null) {
-        const weightDataAnchor = this.weightDataAnchor == null ?
-            document.createElement('a') :
-            this.weightDataAnchor;
-        weightDataAnchor.download = this.weightDataFileName;
-        weightDataAnchor.href = weightsURL;
-        await defer(
-            () => weightDataAnchor.dispatchEvent(new MouseEvent('click')));
-      }
-
-      return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};
-    }
-  }
-}
-
-class BrowserFiles implements IOHandler {
-  private readonly jsonFile: File;
-  private readonly weightsFiles: File[];
-
-  constructor(files: File[]) {
-    if (files == null || files.length < 1) {
-      throw new Error(
-          `When calling browserFiles, at least 1 file is required, ` +
-          `but received ${files}`);
-    }
-    this.jsonFile = files[0];
-    this.weightsFiles = files.slice(1);
-  }
-
-  async load(): Promise<ModelArtifacts> {
-    return new Promise((resolve, reject) => {
-      const jsonReader = new FileReader();
-      jsonReader.onload = (event: Event) => {
-        // tslint:disable-next-line:no-any
-        const modelJSON = JSON.parse((event.target as any).result) as ModelJSON;
-
-        const modelTopology = modelJSON.modelTopology;
-        if (modelTopology == null) {
-          reject(new Error(`modelTopology field is missing from file ${
-              this.jsonFile.name}`));
-          return;
-        }
-
-        const weightsManifest = modelJSON.weightsManifest;
-        if (weightsManifest == null) {
-          reject(new Error(`weightManifest field is missing from file ${
-              this.jsonFile.name}`));
-          return;
-        }
-
-        if (this.weightsFiles.length === 0) {
-          resolve({modelTopology});
-          return;
-        }
-
-        const modelArtifactsPromise = getModelArtifactsForJSON(
-            modelJSON, (weightsManifest) => this.loadWeights(weightsManifest));
-        resolve(modelArtifactsPromise);
-      };
-
-      jsonReader.onerror = error => reject(
-          `Failed to read model topology and weights manifest JSON ` +
-          `from file '${this.jsonFile.name}'. BrowserFiles supports loading ` +
-          `Keras-style tf.Model artifacts only.`);
-      jsonReader.readAsText(this.jsonFile);
-    });
-  }
-
-  private loadWeights(weightsManifest: WeightsManifestConfig): Promise<[
-    /* weightSpecs */ WeightsManifestEntry[], WeightData,
-  ]> {
-    const weightSpecs: WeightsManifestEntry[] = [];
-    const paths: string[] = [];
-    for (const entry of weightsManifest) {
-      weightSpecs.push(...entry.weights);
-      paths.push(...entry.paths);
-    }
-
-    const pathToFile: {[path: string]: File} =
-        this.checkManifestAndWeightFiles(weightsManifest);
-
-    const promises: Array<Promise<ArrayBuffer>> =
-        paths.map(path => this.loadWeightsFile(path, pathToFile[path]));
-
-    return Promise.all(promises).then(
-        buffers => [weightSpecs, buffers]);
-  }
-
-  private loadWeightsFile(path: string, file: File): Promise<ArrayBuffer> {
-    return new Promise((resolve, reject) => {
-      const weightFileReader = new FileReader();
-      weightFileReader.onload = (event: Event) => {
-        // tslint:disable-next-line:no-any
-        const weightData = (event.target as any).result as ArrayBuffer;
-        resolve(weightData);
-      };
-      weightFileReader.onerror = error =>
-          reject(`Failed to weights data from file of path '${path}'.`);
-      weightFileReader.readAsArrayBuffer(file);
-    });
-  }
-
-  /**
-   * Check the compatibility between weights manifest and weight files.
-   */
-  private checkManifestAndWeightFiles(manifest: WeightsManifestConfig):
-      {[path: string]: File} {
-    const basenames: string[] = [];
-    const fileNames = this.weightsFiles.map(file => basename(file.name));
-    const pathToFile: {[path: string]: File} = {};
-    for (const group of manifest) {
-      group.paths.forEach(path => {
-        const pathBasename = basename(path);
-        if (basenames.indexOf(pathBasename) !== -1) {
-          throw new Error(
-              `Duplicate file basename found in weights manifest: ` +
-              `'${pathBasename}'`);
-        }
-        basenames.push(pathBasename);
-        if (fileNames.indexOf(pathBasename) === -1) {
-          throw new Error(
-              `Weight file with basename '${pathBasename}' is not provided.`);
-        } else {
-          pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];
-        }
-      });
-    }
-
-    if (basenames.length !== this.weightsFiles.length) {
-      throw new Error(
-          `Mismatch in the number of files in weights manifest ` +
-          `(${basenames.length}) and the number of weight files provided ` +
-          `(${this.weightsFiles.length}).`);
-    }
-    return pathToFile;
-  }
-}
-
-export const browserDownloadsRouter: IORouter = (url: string|string[]) => {
-  if (!env().getBool('IS_BROWSER')) {
-    return null;
-  } else {
-    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
-      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
-    } else {
-      return null;
-    }
-  }
-};
-IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
-
-/**
- * Creates an IOHandler that triggers file downloads from the browser.
- *
- * The returned `IOHandler` instance can be used as model exporting methods such
- * as `tf.Model.save` and supports only saving.
- *
- * ```js
- * const model = tf.sequential();
- * model.add(tf.layers.dense(
- *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
- * const saveResult = await model.save('downloads://mymodel');
- * // This will trigger downloading of two files:
- * //   'mymodel.json' and 'mymodel.weights.bin'.
- * console.log(saveResult);
- * ```
- *
- * @param fileNamePrefix Prefix name of the files to be downloaded. For use with
- *   `tf.Model`, `fileNamePrefix` should follow either of the following two
- *   formats:
- *   1. `null` or `undefined`, in which case the default file
- *      names will be used:
- *      - 'model.json' for the JSON file containing the model topology and
- *        weights manifest.
- *      - 'model.weights.bin' for the binary file containing the binary weight
- *        values.
- *   2. A single string or an Array of a single string, as the file name prefix.
- *      For example, if `'foo'` is provided, the downloaded JSON
- *      file and binary weights file will be named 'foo.json' and
- *      'foo.weights.bin', respectively.
- * @param config Additional configuration for triggering downloads.
- * @returns An instance of `BrowserDownloads` `IOHandler`.
- *
- * @doc {
- *   heading: 'Models',
- *   subheading: 'Loading',
- *   namespace: 'io',
- *   ignoreCI: true
- * }
- */
-export function browserDownloads(fileNamePrefix = 'model'): IOHandler {
-  return new BrowserDownloads(fileNamePrefix);
-}
-
-/**
- * Creates an IOHandler that loads model artifacts from user-selected files.
- *
- * This method can be used for loading from files such as user-selected files
- * in the browser.
- * When used in conjunction with `tf.loadLayersModel`, an instance of
- * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
- *
- * ```js
- * // Note: This code snippet won't run properly without the actual file input
- * //   elements in the HTML DOM.
- *
- * // Suppose there are two HTML file input (`<input type="file" ...>`)
- * // elements.
- * const uploadJSONInput = document.getElementById('upload-json');
- * const uploadWeightsInput = document.getElementById('upload-weights');
- * const model = await tf.loadLayersModel(tf.io.browserFiles(
- *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
- * ```
- *
- * @param files `File`s to load from. Currently, this function supports only
- *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
- *   which an `Array` of `File`s is expected (in that order):
- *   - A JSON file containing the model topology and weight manifest.
- *   - Optionally, one or more binary files containing the binary weights.
- *     These files must have names that match the paths in the `weightsManifest`
- *     contained by the aforementioned JSON file, or errors will be thrown
- *     during loading. These weights files have the same format as the ones
- *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
- *     Python PIP package. If no weights files are provided, only the model
- *     topology will be loaded from the JSON file above.
- * @returns An instance of `Files` `IOHandler`.
- *
- * @doc {
- *   heading: 'Models',
- *   subheading: 'Loading',
- *   namespace: 'io',
- *   ignoreCI: true
- * }
- */
-export function browserFiles(files: File[]): IOHandler {
-  return new BrowserFiles(files);
-}
diff --git a/tfjs-master/tfjs-core/src/io/browser_files_test.ts b/tfjs-master/tfjs-core/src/io/browser_files_test.ts
deleted file mode 100644
index 349cc1f17..000000000
--- a/tfjs-master/tfjs-core/src/io/browser_files_test.ts
+++ /dev/null
@@ -1,680 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit tests for file-related IOHandlers.
- */
-
-import * as tf from '../index';
-import {BROWSER_ENVS, describeWithFlags} from '../jasmine_util';
-import {browserDownloads, BrowserDownloads, browserDownloadsRouter} from './browser_files';
-import {WeightsManifestConfig, WeightsManifestEntry} from './types';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-
-const modelTopology1: {} = {
-  'class_name': 'Sequential',
-  'keras_version': '2.1.4',
-  'config': [{
-    'class_name': 'Dense',
-    'config': {
-      'kernel_initializer': {
-        'class_name': 'VarianceScaling',
-        'config': {
-          'distribution': 'uniform',
-          'scale': 1.0,
-          'seed': null,
-          'mode': 'fan_avg'
-        }
-      },
-      'name': 'dense',
-      'kernel_constraint': null,
-      'bias_regularizer': null,
-      'bias_constraint': null,
-      'dtype': 'float32',
-      'activation': 'linear',
-      'trainable': true,
-      'kernel_regularizer': null,
-      'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-      'units': 1,
-      'batch_input_shape': [null, 3],
-      'use_bias': true,
-      'activity_regularizer': null
-    }
-  }],
-  'backend': 'tensorflow'
-};
-const weightSpecs1: tf.io.WeightsManifestEntry[] = [
-  {
-    name: 'dense/kernel',
-    shape: [3, 1],
-    dtype: 'float32',
-  },
-  {
-    name: 'dense/bias',
-    shape: [1],
-    dtype: 'float32',
-  }
-];
-const weightData1 = new ArrayBuffer(16);
-const trainingConfig1: tf.io.TrainingConfig = {
-  loss: 'categorical_crossentropy',
-  metrics: ['accuracy'],
-  optimizer_config: {class_name: 'SGD', config: {learningRate: 0.1}}
-};
-
-const artifacts1: tf.io.ModelArtifacts = {
-  modelTopology: modelTopology1,
-  weightSpecs: weightSpecs1,
-  weightData: weightData1,
-  format: 'layers-model',
-  generatedBy: 'TensorFlow.js v0.0.0',
-  convertedBy: null,
-  modelInitializer: {},
-  trainingConfig: trainingConfig1,
-};
-
-describeWithFlags('browserDownloads', BROWSER_ENVS, () => {
-  class FakeHTMLAnchorElement {
-    download: string;
-    href: string;
-    clicked: number;
-
-    constructor() {
-      this.clicked = 0;
-    }
-
-    dispatchEvent() {
-      this.clicked++;
-    }
-  }
-
-  let fakeAnchors: FakeHTMLAnchorElement[] = [];
-  let fakeAnchorCount = 0;
-
-  beforeEach(() => {
-    fakeAnchorCount = 0;
-    fakeAnchors = [new FakeHTMLAnchorElement(), new FakeHTMLAnchorElement()];
-    spyOn(document, 'createElement').and.callFake((tag: string) => {
-      return fakeAnchors[fakeAnchorCount++] as unknown as HTMLElement;
-    });
-  });
-
-  it('Explicit file name prefix, with existing anchors', async () => {
-    const testStartDate = new Date();
-    const downloadTrigger = tf.io.getSaveHandlers('downloads://test-model')[0];
-    const saveResult = await downloadTrigger.save(artifacts1);
-    expect(saveResult.errors).toEqual(undefined);
-    const artifactsInfo = saveResult.modelArtifactsInfo;
-    expect(artifactsInfo.dateSaved.getTime())
-        .toBeGreaterThanOrEqual(testStartDate.getTime());
-    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-        .toEqual(JSON.stringify(modelTopology1).length);
-    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
-        .toEqual(JSON.stringify(weightSpecs1).length);
-    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
-
-    const jsonAnchor = fakeAnchors[0];
-    const weightDataAnchor = fakeAnchors[1];
-    expect(jsonAnchor.download).toEqual('test-model.json');
-    expect(weightDataAnchor.download).toEqual('test-model.weights.bin');
-
-    // Verify the content of the JSON file.
-    const jsonContent = await fetch(jsonAnchor.href);
-    const modelJSON = JSON.parse(await jsonContent.text()) as tf.io.ModelJSON;
-    expect(modelJSON.modelTopology).toEqual(modelTopology1);
-    expect(modelJSON.format).toEqual('layers-model');
-    expect(modelJSON.generatedBy).toEqual('TensorFlow.js v0.0.0');
-    expect(modelJSON.convertedBy).toEqual(null);
-    expect(modelJSON.modelInitializer).toEqual({});
-    expect(modelJSON.trainingConfig).toEqual(trainingConfig1);
-
-    const weightsManifest = modelJSON.weightsManifest;
-    expect(weightsManifest.length).toEqual(1);
-    expect(weightsManifest[0].paths).toEqual(['./test-model.weights.bin']);
-    expect(weightsManifest[0].weights).toEqual(weightSpecs1);
-
-    // Verify the content of the binary weights file.
-    const response = await fetch(weightDataAnchor.href);
-    const buffer = await response.arrayBuffer();
-    expect(buffer).toEqual(weightData1);
-
-    // Verify that the downloads are triggered through clicks.
-    expect(jsonAnchor.clicked).toEqual(1);
-    expect(weightDataAnchor.clicked).toEqual(1);
-  });
-
-  it('URL scheme in explicit name gets stripped', async () => {
-    const testStartDate = new Date();
-    const downloadTrigger = browserDownloads('downloads://test-model');
-    const saveResult = await downloadTrigger.save(artifacts1);
-    expect(saveResult.errors).toEqual(undefined);
-    const artifactsInfo = saveResult.modelArtifactsInfo;
-    expect(artifactsInfo.dateSaved.getTime())
-        .toBeGreaterThanOrEqual(testStartDate.getTime());
-    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-        .toEqual(JSON.stringify(modelTopology1).length);
-    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
-        .toEqual(JSON.stringify(weightSpecs1).length);
-    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
-
-    const jsonAnchor = fakeAnchors[0];
-    const weightDataAnchor = fakeAnchors[1];
-    expect(jsonAnchor.download).toEqual('test-model.json');
-    expect(weightDataAnchor.download).toEqual('test-model.weights.bin');
-
-    // Verify the content of the JSON file.
-    const jsonContent = await fetch(jsonAnchor.href);
-    const modelTopologyAndWeightsManifest =
-        JSON.parse(await jsonContent.text());
-    expect(modelTopologyAndWeightsManifest.modelTopology)
-        .toEqual(modelTopology1);
-    const weightsManifest = modelTopologyAndWeightsManifest.weightsManifest as
-        WeightsManifestConfig;
-    expect(weightsManifest.length).toEqual(1);
-    expect(weightsManifest[0].paths).toEqual(['./test-model.weights.bin']);
-    expect(weightsManifest[0].weights).toEqual(weightSpecs1);
-
-    // Verify the content of the binary weights file.
-    const response = await fetch(weightDataAnchor.href);
-    const buffer = await response.arrayBuffer();
-    expect(buffer).toEqual(weightData1);
-
-    // Verify that the downloads are triggered through clicks.
-    expect(jsonAnchor.clicked).toEqual(1);
-    expect(weightDataAnchor.clicked).toEqual(1);
-  });
-
-  it('No file name provided, with existing anchors', async () => {
-    const testStartDate = new Date();
-    const downloadTrigger = browserDownloads();
-    const saveResult = await downloadTrigger.save(artifacts1);
-    expect(saveResult.errors).toEqual(undefined);
-    const artifactsInfo = saveResult.modelArtifactsInfo;
-    expect(artifactsInfo.dateSaved.getTime())
-        .toBeGreaterThanOrEqual(testStartDate.getTime());
-    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-        .toEqual(JSON.stringify(modelTopology1).length);
-    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
-        .toEqual(JSON.stringify(weightSpecs1).length);
-    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
-
-    const jsonAnchor = fakeAnchors[0];
-    const weightDataAnchor = fakeAnchors[1];
-
-    // Verify that the default file names are used.
-    expect(jsonAnchor.download).toEqual('model.json');
-    expect(weightDataAnchor.download).toEqual('model.weights.bin');
-
-    // Verify the content of the JSON file.
-    const jsonContent = await fetch(jsonAnchor.href);
-    const modelTopologyAndWeightsManifest =
-        JSON.parse(await jsonContent.text());
-    expect(modelTopologyAndWeightsManifest.modelTopology)
-        .toEqual(modelTopology1);
-    const weightsManifest = modelTopologyAndWeightsManifest.weightsManifest as
-        WeightsManifestConfig;
-    expect(weightsManifest.length).toEqual(1);
-    expect(weightsManifest[0].paths).toEqual(['./model.weights.bin']);
-    expect(weightsManifest[0].weights).toEqual(weightSpecs1);
-
-    // Verify the content of the binary weights file.
-    const response = await fetch(weightDataAnchor.href);
-    const buffer = await response.arrayBuffer();
-    expect(buffer).toEqual(weightData1);
-  });
-
-  it('Download only model topology', async () => {
-    const testStartDate = new Date();
-    const downloadTrigger = browserDownloads();
-    const modelTopologyOnlyArtifacts: tf.io.ModelArtifacts = {
-      modelTopology: modelTopology1,
-    };
-    const saveResult = await downloadTrigger.save(modelTopologyOnlyArtifacts);
-    expect(saveResult.errors).toEqual(undefined);
-    const artifactsInfo = saveResult.modelArtifactsInfo;
-    expect(artifactsInfo.dateSaved.getTime())
-        .toBeGreaterThanOrEqual(testStartDate.getTime());
-    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-        .toEqual(JSON.stringify(modelTopology1).length);
-    expect(saveResult.modelArtifactsInfo.weightSpecsBytes).toEqual(0);
-    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(0);
-
-    const jsonAnchor = fakeAnchors[0];
-    const weightDataAnchor = fakeAnchors[1];
-
-    // Verify that the default file names are used.
-    expect(jsonAnchor.download).toEqual('model.json');
-    expect(jsonAnchor.clicked).toEqual(1);
-    // The weight file should not have been downoaded.
-    expect(weightDataAnchor.download).toEqual(undefined);
-    expect(weightDataAnchor.clicked).toEqual(0);
-
-    // Verify the content of the JSON file.
-    const jsonContent = await fetch(jsonAnchor.href);
-    const modelTopologyAndWeightsManifest =
-        JSON.parse(await jsonContent.text());
-    expect(modelTopologyAndWeightsManifest.modelTopology)
-        .toEqual(modelTopology1);
-  });
-
-  it('browserDownloadsRouter', () => {
-    expect(
-        browserDownloadsRouter('downloads://foo') instanceof BrowserDownloads)
-        .toEqual(true);
-    expect(browserDownloadsRouter('invaliddownloads://foo')).toBeNull();
-    expect(browserDownloadsRouter('foo')).toBeNull();
-  });
-});
-
-describeWithFlags('browserFiles', BROWSER_ENVS, () => {
-  const weightsFile = new File(
-      [weightData1], 'model.weights.bin', {type: 'application/octet-stream'});
-
-  it('One group, one path', async () => {
-    const weightsManifest: WeightsManifestConfig = [{
-      paths: ['./model.weights.bin'],
-      weights: weightSpecs1,
-    }];
-    const modelJSON: tf.io.ModelJSON = {
-      modelTopology: modelTopology1,
-      weightsManifest,
-      format: 'layers-model',
-      generatedBy: 'TensorFlow.js v0.0.0',
-      convertedBy: '1.13.1',
-      modelInitializer: {},
-      trainingConfig: trainingConfig1,
-    };
-    const jsonFile = new File(
-        [JSON.stringify(modelJSON)], 'model.json', {type: 'application/json'});
-
-    const filesHandler = tf.io.browserFiles([jsonFile, weightsFile]);
-    const modelArtifacts = await filesHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
-    expect(modelArtifacts.format).toEqual('layers-model');
-    expect(modelArtifacts.generatedBy).toEqual('TensorFlow.js v0.0.0');
-    expect(modelArtifacts.convertedBy).toEqual('1.13.1');
-    expect(modelArtifacts.modelInitializer).toEqual({});
-    expect(modelArtifacts.trainingConfig).toEqual(trainingConfig1);
-
-    expect(new Uint8Array(CompositeArrayBuffer.join(modelArtifacts.weightData)))
-        .toEqual(new Uint8Array(weightData1));
-  });
-
-  it(`One group, two paths`, async () => {
-    const weightSpecs: WeightsManifestEntry[] = [
-      {
-        name: 'foo',
-        shape: [1, 1],
-        dtype: 'float32',
-      },
-      {
-        name: 'bar',
-        shape: [1, 1],
-        dtype: 'float32',
-      }
-    ];
-    const weightsManifest: WeightsManifestConfig = [{
-      paths: ['./dir1/model.weights.1.bin', './dir2/model.weights.2.bin'],
-      weights: weightSpecs,
-    }];
-    const weightsTopologyAndManifest = {
-      modelTopology: modelTopology1,
-      weightsManifest,
-    };
-    const weightsFile1 = new File(
-        [new Uint8Array([1, 2, 3, 4]).buffer], 'model.weights.1.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile2 = new File(
-        [new Uint8Array([10, 20, 30, 40]).buffer], 'model.weights.2.bin',
-        {type: 'application/octet-stream'});
-
-    const jsonFile = new File(
-        [JSON.stringify(weightsTopologyAndManifest)], 'model.json',
-        {type: 'application/json'});
-
-    const filesHandler =
-        tf.io.browserFiles([jsonFile, weightsFile1, weightsFile2]);
-    const modelArtifacts = await filesHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs);
-    expect(new Uint8Array(CompositeArrayBuffer.join(modelArtifacts.weightData)))
-        .toEqual(new Uint8Array([
-            1, 2, 3, 4, 10, 20, 30, 40
-        ]));
-  });
-
-  it(`Two groups, four paths, reverseOrder=false`, async () => {
-    const weightSpecs1: WeightsManifestEntry[] = [
-      {
-        name: 'foo',
-        shape: [1, 1],
-        dtype: 'float32',
-      },
-      {
-        name: 'bar',
-        shape: [1, 1],
-        dtype: 'float32',
-      }
-    ];
-    const weightSpecs2: WeightsManifestEntry[] = [
-      {
-        name: 'baz',
-        shape: [1, 1],
-        dtype: 'float32',
-      },
-      {
-        name: 'qux',
-        shape: [1, 1],
-        dtype: 'float32',
-      }
-    ];
-    const weightsManifest: WeightsManifestConfig = [
-      {
-        paths: ['./model.weights.1.bin', './model.weights.2.bin'],
-        weights: weightSpecs1,
-      },
-      {
-        paths: ['./model.weights.3.bin', './model.weights.4.bin'],
-        weights: weightSpecs2,
-      }
-    ];
-    const weightsTopologyAndManifest = {
-      modelTopology: modelTopology1,
-      weightsManifest,
-    };
-    const weightsFile1 = new File(
-        [new Uint8Array([1, 3, 5, 7]).buffer], 'model.weights.1.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile2 = new File(
-        [new Uint8Array([10, 30, 50, 70]).buffer], 'model.weights.2.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile3 = new File(
-        [new Uint8Array([2, 4, 6, 8]).buffer], 'model.weights.3.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile4 = new File(
-        [new Uint8Array([20, 40, 60, 80]).buffer], 'model.weights.4.bin',
-        {type: 'application/octet-stream'});
-
-    const jsonFile = new File(
-        [JSON.stringify(weightsTopologyAndManifest)], 'model.json',
-        {type: 'application/json'});
-
-    const filesHandler = tf.io.browserFiles(
-        [jsonFile, weightsFile1, weightsFile2, weightsFile3, weightsFile4]);
-    const modelArtifacts = await filesHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs)
-        .toEqual(weightSpecs1.concat(weightSpecs2));
-    expect(new Uint8Array(CompositeArrayBuffer.join(modelArtifacts.weightData)))
-        .toEqual(new Uint8Array([
-            1, 3, 5, 7, 10, 30, 50, 70, 2, 4, 6, 8, 20, 40, 60, 80
-        ]));
-  });
-
-  it(`Two groups, four paths, reverseOrder=true`, async () => {
-    const weightSpecs1: WeightsManifestEntry[] = [
-      {
-        name: 'foo',
-        shape: [1, 1],
-        dtype: 'float32',
-      },
-      {
-        name: 'bar',
-        shape: [1, 1],
-        dtype: 'float32',
-      }
-    ];
-    const weightSpecs2: WeightsManifestEntry[] = [
-      {
-        name: 'baz',
-        shape: [1, 1],
-        dtype: 'float32',
-      },
-      {
-        name: 'qux',
-        shape: [1, 1],
-        dtype: 'float32',
-      }
-    ];
-    const weightsManifest: WeightsManifestConfig = [
-      {
-        paths: ['./model.weights.1.bin', './model.weights.2.bin'],
-        weights: weightSpecs1,
-      },
-      {
-        paths: ['./model.weights.3.bin', './model.weights.4.bin'],
-        weights: weightSpecs2,
-      }
-    ];
-    const weightsTopologyAndManifest = {
-      modelTopology: modelTopology1,
-      weightsManifest,
-    };
-    const weightsFile1 = new File(
-        [new Uint8Array([1, 3, 5, 7]).buffer], 'model.weights.1.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile2 = new File(
-        [new Uint8Array([10, 30, 50, 70]).buffer], 'model.weights.2.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile3 = new File(
-        [new Uint8Array([2, 4, 6, 8]).buffer], 'model.weights.3.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile4 = new File(
-        [new Uint8Array([20, 40, 60, 80]).buffer], 'model.weights.4.bin',
-        {type: 'application/octet-stream'});
-
-    const jsonFile = new File(
-        [JSON.stringify(weightsTopologyAndManifest)], 'model.json',
-        {type: 'application/json'});
-
-    const filesHandler = tf.io.browserFiles(
-        [jsonFile, weightsFile4, weightsFile3, weightsFile2, weightsFile1]);
-    const modelArtifacts = await filesHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs)
-        .toEqual(weightSpecs1.concat(weightSpecs2));
-    expect(new Uint8Array(CompositeArrayBuffer.join(modelArtifacts.weightData)))
-        .toEqual(new Uint8Array([
-            1, 3, 5, 7, 10, 30, 50, 70, 2, 4, 6, 8, 20, 40, 60, 80
-        ]));
-  });
-
-  it('Upload model topology only', async () => {
-    const weightsManifest: WeightsManifestConfig = [{
-      paths: ['./model.weights.bin'],
-      weights: weightSpecs1,
-    }];
-    const weightsTopologyAndManifest = {
-      modelTopology: modelTopology1,
-      weightsManifest,
-    };
-    const jsonFile = new File(
-        [JSON.stringify(weightsTopologyAndManifest)], 'model.json',
-        {type: 'application/json'});
-
-    // Select only a JSON file.
-    const filesHandler = tf.io.browserFiles([jsonFile]);
-    const modelArtifacts = await filesHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(undefined);
-  });
-
-  it('Mismatch in number of paths and number of files', async () => {
-    const weightsManifest: WeightsManifestConfig = [{
-      paths: ['./model.weights.1.bin'],
-      weights: weightSpecs1,
-    }];
-    const weightsTopologyAndManifest = {
-      modelTopology: weightSpecs1,
-      weightsManifest,
-    };
-    const weightsFile1 = new File(
-        [new Uint8Array([1, 2, 3, 4]).buffer], 'model.weights.1.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile2 = new File(
-        [new Uint8Array([10, 20, 30, 40]).buffer], 'model.weights.2.bin',
-        {type: 'application/octet-stream'});
-
-    const jsonFile = new File(
-        [JSON.stringify(weightsTopologyAndManifest)], 'model.json',
-        {type: 'application/json'});
-
-    // Supply two weights files while the manifest has only one path. This is
-    // expected to fail.
-    const filesHandler =
-        tf.io.browserFiles([jsonFile, weightsFile2, weightsFile1]);
-    try {
-      await filesHandler.load();
-      fail(
-          'Loading with mismatch in number of paths and number of files ' +
-          'succeeded unexpectedly.');
-    } catch (err) {
-      expect(err.message)
-          .toEqual(
-              'Mismatch in the number of files in weights manifest (1) ' +
-              'and the number of weight files provided (2).');
-    }
-  });
-
-  it('Mismatch in manifest paths and file names', async () => {
-    const weightSpecs: WeightsManifestEntry[] = [
-      {
-        name: 'foo',
-        shape: [1, 1],
-        dtype: 'float32',
-      },
-      {
-        name: 'bar',
-        shape: [1, 1],
-        dtype: 'float32',
-      }
-    ];
-    const weightsManifest: WeightsManifestConfig = [{
-      paths: ['./model.weights.1.bin', './model.weights.2.bin'],
-      weights: weightSpecs,
-    }];
-    const weightsTopologyAndManifest = {
-      modelTopology: modelTopology1,
-      weightsManifest,
-    };
-    const weightsFile1 = new File(
-        [new Uint8Array([1, 2, 3, 4]).buffer], 'model.weights.1.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile2 = new File(
-        [new Uint8Array([10, 20, 30, 40]).buffer], 'model.weights.3.bin',
-        {type: 'application/octet-stream'});
-    // Notice the wrong file name here. It is expected to cause load() to
-    // fail.
-
-    const jsonFile = new File(
-        [JSON.stringify(weightsTopologyAndManifest)], 'model.json',
-        {type: 'application/json'});
-
-    const filesHandler =
-        tf.io.browserFiles([jsonFile, weightsFile1, weightsFile2]);
-    try {
-      await filesHandler.load();
-      fail(
-          'Loading with mismatching paths and file names ' +
-          'succeeded unexpectedly.');
-    } catch (err) {
-      expect(err.message)
-          .toEqual(
-              'Weight file with basename \'model.weights.2.bin\' is not ' +
-              'provided.');
-    }
-  });
-
-  it('Duplicate basenames in paths fails', async () => {
-    const weightSpecs: WeightsManifestEntry[] = [
-      {
-        name: 'foo',
-        shape: [1, 1],
-        dtype: 'float32',
-      },
-      {
-        name: 'bar',
-        shape: [1, 1],
-        dtype: 'float32',
-      }
-    ];
-    // Notice the duplicate basenames here (albeit distinct full paths). This
-    // is expected to cause load() to fail.
-    const weightsManifest: WeightsManifestConfig = [{
-      paths: ['./dir1/model.weights.1.bin', './dir2/model.weights.1.bin'],
-      weights: weightSpecs,
-    }];
-    const weightsTopologyAndManifest = {
-      modelTopology: modelTopology1,
-      weightsManifest,
-    };
-    const weightsFile1 = new File(
-        [new Uint8Array([1, 2, 3, 4]).buffer], 'model.weights.1.bin',
-        {type: 'application/octet-stream'});
-    const weightsFile2 = new File(
-        [new Uint8Array([10, 20, 30, 40]).buffer], 'model.weights.2.bin',
-        {type: 'application/octet-stream'});
-    // Notice the wrong file name here. It is expected to cause load() to
-    // fail.
-
-    const jsonFile = new File(
-        [JSON.stringify(weightsTopologyAndManifest)], 'model.json',
-        {type: 'application/json'});
-
-    const filesHandler =
-        tf.io.browserFiles([jsonFile, weightsFile1, weightsFile2]);
-    try {
-      await filesHandler.load();
-      fail('Loading with duplicate basenames in paths succeeded unexpectedly.');
-    } catch (err) {
-      expect(err.message)
-          .toEqual(
-              'Duplicate file basename found in weights manifest: ' +
-              '\'model.weights.1.bin\'');
-    }
-  });
-
-  it('Missing modelTopology from JSON leads to Error', async () => {
-    const weightsManifest: WeightsManifestConfig = [{
-      paths: ['./model.weights.bin'],
-      weights: weightSpecs1,
-    }];
-    const weightsTopologyAndManifest = {
-      weightsManifest,
-    };
-    const jsonFile = new File(
-        [JSON.stringify(weightsTopologyAndManifest)], 'model.json',
-        {type: 'application/json'});
-
-    const filesHandler = tf.io.browserFiles([jsonFile, weightsFile]);
-    try {
-      await filesHandler.load();
-      fail(
-          'Loading with Files IOHandler with missing modelTopology ' +
-          'succeeded unexpectedly.');
-    } catch (err) {
-      expect(err.message)
-          .toMatch(/modelTopology field is missing from file model\.json/);
-    }
-  });
-
-  it('Incorrect number of files leads to Error', () => {
-    expect(() => tf.io.browserFiles(null)).toThrowError(/at least 1 file/);
-    expect(() => tf.io.browserFiles([])).toThrowError(/at least 1 file/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/io/composite_array_buffer.ts b/tfjs-master/tfjs-core/src/io/composite_array_buffer.ts
deleted file mode 100644
index 411fb0740..000000000
--- a/tfjs-master/tfjs-core/src/io/composite_array_buffer.ts
+++ /dev/null
@@ -1,226 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {TypedArray} from '../types';
-import * as util from '../util';
-
-type BufferShard = {
-  start: number,
-  end: number,
-  buffer: ArrayBuffer,
-};
-
-/**
- * Wraps a list of ArrayBuffers into a `slice()`-able object without allocating
- * a large ArrayBuffer.
- *
- * Allocating large ArrayBuffers (~2GB) can be unstable on Chrome. TFJS loads
- * its weights as a list of (usually) 4MB ArrayBuffers and then slices the
- * weight tensors out of them. For small models, it's safe to concatenate all
- * the weight buffers into a single ArrayBuffer and then slice the weight
- * tensors out of it, but for large models, a different approach is needed.
- */
-
-export class CompositeArrayBuffer {
-  private shards: BufferShard[] = [];
-  private previousShardIndex = 0;
-  private bufferUniformSize?: number;
-  public readonly byteLength: number;
-
-  /**
-   * Concatenate a number of ArrayBuffers into one.
-   *
-   * @param buffers An array of ArrayBuffers to concatenate, or a single
-   *     ArrayBuffer.
-   * @returns Result of concatenating `buffers` in order.
-   */
-  static join(buffers?: ArrayBuffer[] | ArrayBuffer) {
-    return new CompositeArrayBuffer(buffers).slice();
-  }
-
-  constructor(buffers?: ArrayBuffer | ArrayBuffer[] | TypedArray |
-    TypedArray[]) {
-    if (buffers == null) {
-      return;
-    }
-    // Normalize the `buffers` input to be `ArrayBuffer[]`.
-    if (!(buffers instanceof Array)) {
-      buffers = [buffers];
-    }
-    buffers = buffers.map((bufferOrTypedArray) => {
-      if (util.isTypedArray(bufferOrTypedArray)) {
-        return bufferOrTypedArray.buffer;
-      }
-      return bufferOrTypedArray;
-    });
-
-    // Skip setting up shards if there are no buffers.
-    if (buffers.length === 0) {
-      return;
-    }
-
-    this.bufferUniformSize = buffers[0].byteLength;
-    let start = 0;
-
-    for (let i = 0; i < buffers.length; i++) {
-      const buffer = buffers[i];
-      // Check that all buffers except the last one have the same length.
-      if (i !== buffers.length - 1 &&
-        buffer.byteLength !== this.bufferUniformSize) {
-        // Unset the buffer uniform size, since the buffer sizes are not
-        // uniform.
-        this.bufferUniformSize = undefined;
-      }
-
-      // Create the shards, including their start and end points.
-      const end = start + buffer.byteLength;
-      this.shards.push({ buffer, start, end });
-      start = end;
-    }
-
-    // Set the byteLenghth
-    if (this.shards.length === 0) {
-      this.byteLength = 0;
-    }
-    this.byteLength = this.shards[this.shards.length - 1].end;
-  }
-
-  slice(start = 0, end = this.byteLength): ArrayBuffer {
-    // If there are no shards, then the CompositeArrayBuffer was initialized
-    // with no data.
-    if (this.shards.length === 0) {
-      return new ArrayBuffer(0);
-    }
-
-    // NaN is treated as zero for slicing. This matches ArrayBuffer's behavior.
-    start = isNaN(Number(start)) ? 0 : start;
-    end = isNaN(Number(end)) ? 0 : end;
-
-    // Fix the bounds to within the array.
-    start = Math.max(0, start);
-    end = Math.min(this.byteLength, end);
-    if (end <= start) {
-      return new ArrayBuffer(0);
-    }
-
-    const startShardIndex = this.findShardForByte(start);
-    if (startShardIndex === -1) {
-      // This should not happen since the start and end indices are always
-      // within 0 and the composite array's length.
-      throw new Error(`Could not find start shard for byte ${start}`);
-    }
-
-    const size = end - start;
-    const outputBuffer = new ArrayBuffer(size);
-    const outputArray = new Uint8Array(outputBuffer);
-    let sliced = 0;
-    for (let i = startShardIndex; i < this.shards.length; i++) {
-      const shard = this.shards[i];
-
-      const globalStart = start + sliced;
-      const localStart = globalStart - shard.start;
-      const outputStart = sliced;
-
-      const globalEnd = Math.min(end, shard.end);
-      const localEnd = globalEnd - shard.start;
-
-      const outputSlice = new Uint8Array(shard.buffer, localStart,
-                                         localEnd - localStart);
-      outputArray.set(outputSlice, outputStart);
-      sliced += outputSlice.length;
-
-      if (end < shard.end) {
-        break;
-      }
-    }
-    return outputBuffer;
-  }
-
-  /**
-   * Get the index of the shard that contains the byte at `byteIndex`.
-   */
-  private findShardForByte(byteIndex: number): number {
-    if (this.shards.length === 0 || byteIndex < 0 ||
-      byteIndex >= this.byteLength) {
-      return -1;
-    }
-
-    // If the buffers have a uniform size, compute the shard directly.
-    if (this.bufferUniformSize != null) {
-      this.previousShardIndex = Math.floor(byteIndex / this.bufferUniformSize);
-      return this.previousShardIndex;
-    }
-
-    // If the buffers don't have a uniform size, we need to search for the
-    // shard. That means we need a function to check where the byteIndex lies
-    // relative to a given shard.
-    function check(shard: BufferShard) {
-      if (byteIndex < shard.start) {
-        return -1;
-      }
-      if (byteIndex >= shard.end) {
-        return 1;
-      }
-      return 0;
-    }
-
-    // For efficiency, try the previous shard first.
-    if (check(this.shards[this.previousShardIndex]) === 0) {
-      return this.previousShardIndex;
-    }
-
-    // Otherwise, use a generic search function.
-    // This should almost never end up being used in practice since the weight
-    // entries should always be in order.
-    const index = search(this.shards, check);
-    if (index === -1) {
-      return -1;
-    }
-
-    this.previousShardIndex = index;
-    return this.previousShardIndex;
-  }
-}
-
-/**
- * Search for an element of a sorted array.
- *
- * @param sortedArray The sorted array to search
- * @param compare A function to compare the current value against the searched
- *     value. Return 0 on a match, negative if the searched value is less than
- *     the value passed to the function, and positive if the searched value is
- *     greater than the value passed to the function.
- * @returns The index of the element, or -1 if it's not in the array.
- */
-export function search<T>(sortedArray: T[], compare: (t: T) => number): number {
-  // Binary search
-  let min = 0;
-  let max = sortedArray.length;
-
-  while (min <= max) {
-    const middle = Math.floor((max - min) / 2) + min;
-    const side = compare(sortedArray[middle]);
-
-    if (side === 0) {
-      return middle;
-    } else if (side < 0) {
-      max = middle;
-    } else {
-      min = middle + 1;
-    }
-  }
-  return -1;
-}
diff --git a/tfjs-master/tfjs-core/src/io/composite_array_buffer_test.ts b/tfjs-master/tfjs-core/src/io/composite_array_buffer_test.ts
deleted file mode 100644
index 0c88bc4ad..000000000
--- a/tfjs-master/tfjs-core/src/io/composite_array_buffer_test.ts
+++ /dev/null
@@ -1,126 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {expectArraysEqual} from '../test_util';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-
-describe('CompositeArrayBuffer', () => {
-  const uniformBuffers = [
-    new Uint8Array([0, 1, 2, 3]).buffer,
-    new Uint8Array([4, 5, 6, 7]).buffer,
-    new Uint8Array([8, 9, 10, 11]).buffer,
-    new Uint8Array([12, 13, 14, 15]).buffer,
-    new Uint8Array([16]).buffer,
-  ];
-
-  const nonUniformBuffers = [
-    new Uint8Array([0, 1, 2]).buffer,
-    new Uint8Array([3, 4, 5, 6, 7]).buffer,
-    new Uint8Array([8, 9, 10, 11]).buffer,
-    new Uint8Array([12, 13, 14, 15, 16]).buffer,
-  ];
-
-  const bufferTestCases = [
-    ['uniform', uniformBuffers],
-    ['non-uniform', nonUniformBuffers]
-  ] as const;
-
-  for (const [buffersType, buffers] of bufferTestCases) {
-    let composite: CompositeArrayBuffer;
-    beforeEach(() => {
-      composite = new CompositeArrayBuffer(buffers);
-    });
-
-    it(`${buffersType}: slices across multiple buffers`, () => {
-      expectArraysEqual(new Uint8Array(composite.slice(1, 13)),
-                        [1,2,3,4,5,6,7,8,9,10,11,12]);
-    });
-
-    it(`${buffersType}: slices to the end of the array when \'end\' is not ` +
-      'specified', () => {
-        expectArraysEqual(new Uint8Array(composite.slice(5)),
-                          [5,6,7,8,9,10,11,12,13,14,15,16]);
-      });
-
-    it(`${buffersType}: makes a copy when slice() is called with no arguments`,
-       () => {
-         expectArraysEqual(new Uint8Array(composite.slice()),
-                           [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]);
-       });
-
-    it(`${buffersType}: slices from zero when start is negative`, () => {
-      expectArraysEqual(new Uint8Array(composite.slice(-4, 5)),
-                        [0,1,2,3,4]);
-    });
-
-    it(`${buffersType}: slices to the end when end is greater than length`,
-       () => {
-         expectArraysEqual(new Uint8Array(composite.slice(7, 1000)),
-                           [7,8,9,10,11,12,13,14,15,16]);
-       });
-
-    it(`${buffersType}: slices multiple ranges out of order`, () => {
-      expectArraysEqual(new Uint8Array(composite.slice(13, 15)), [13, 14]);
-      expectArraysEqual(new Uint8Array(composite.slice(0, 2)), [0, 1]);
-      expectArraysEqual(new Uint8Array(composite.slice(9, 13)),
-                        [9, 10, 11, 12]);
-    });
-  }
-
-  it('can be created from an empty arraybuffer', () => {
-    const array = new Uint8Array([]);
-    const singleComposite = new CompositeArrayBuffer(array.buffer);
-    expectArraysEqual(new Uint8Array(singleComposite.slice()), []);
-  });
-
-  it('can be created from a single array', () => {
-    const array = new Uint8Array([1,2,3]);
-    const singleComposite = new CompositeArrayBuffer(array.buffer);
-    expectArraysEqual(new Uint8Array(singleComposite.slice()), array);
-  });
-
-  it('can be created from zero arrays', () => {
-    const singleComposite = new CompositeArrayBuffer([]);
-    expectArraysEqual(new Uint8Array(singleComposite.slice()),
-                      new Uint8Array());
-  });
-
-  it('can be created from undefined input', () => {
-    const singleComposite = new CompositeArrayBuffer();
-    expectArraysEqual(new Uint8Array(singleComposite.slice()),
-                      new Uint8Array());
-  });
-
-  it('treats NaN as zero when passed as the start of slice', () => {
-    const array = new Uint8Array([1,2,3]);
-    const composite = new CompositeArrayBuffer(array.buffer);
-    expectArraysEqual(new Uint8Array(composite.slice(NaN, 2)), [1,2]);
-  });
-
-  it('treats NaN as zero when passed as the end of slice', () => {
-    const array = new Uint8Array([1,2,3]);
-    const composite = new CompositeArrayBuffer(array.buffer);
-    expectArraysEqual(new Uint8Array(composite.slice(0, NaN)), []);
-  });
-
-  it('supports TypedArray input', () => {
-    // This support is necessary for some tests in tfjs-converter. Maybe those
-    // tests are misconfigured?
-    const array = new Uint8Array([1,2,3]);
-    const composite = new CompositeArrayBuffer(array);
-    expectArraysEqual(new Uint8Array(composite.slice(0, 2)), [1,2]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/io/http.ts b/tfjs-master/tfjs-core/src/io/http.ts
deleted file mode 100644
index c30ce501d..000000000
--- a/tfjs-master/tfjs-core/src/io/http.ts
+++ /dev/null
@@ -1,352 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * IOHandler implementations based on HTTP requests in the web browser.
- *
- * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).
- */
-
-import {env} from '../environment';
-
-import {assert} from '../util';
-import {getModelArtifactsForJSON, getModelArtifactsInfoForJSON, getModelJSONForModelArtifacts, getWeightSpecs} from './io_utils';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-import {IORouter, IORouterRegistry} from './router_registry';
-import {IOHandler, LoadOptions, ModelArtifacts, ModelJSON, OnProgressCallback, SaveResult, WeightData, WeightsManifestConfig, WeightsManifestEntry} from './types';
-import {loadWeightsAsArrayBuffer} from './weights_loader';
-
-const OCTET_STREAM_MIME_TYPE = 'application/octet-stream';
-const JSON_TYPE = 'application/json';
-export class HTTPRequest implements IOHandler {
-  protected readonly path: string;
-  protected readonly requestInit: RequestInit;
-
-  private readonly fetch: Function;
-  private readonly weightUrlConverter: (weightName: string) => Promise<string>;
-
-  readonly DEFAULT_METHOD = 'POST';
-
-  static readonly URL_SCHEME_REGEX = /^https?:\/\//;
-
-  private readonly weightPathPrefix: string;
-  private readonly onProgress: OnProgressCallback;
-
-  constructor(path: string, loadOptions?: LoadOptions) {
-    if (loadOptions == null) {
-      loadOptions = {};
-    }
-    this.weightPathPrefix = loadOptions.weightPathPrefix;
-    this.onProgress = loadOptions.onProgress;
-    this.weightUrlConverter = loadOptions.weightUrlConverter;
-
-    if (loadOptions.fetchFunc != null) {
-      assert(
-          typeof loadOptions.fetchFunc === 'function',
-          () => 'Must pass a function that matches the signature of ' +
-              '`fetch` (see ' +
-              'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');
-      this.fetch = loadOptions.fetchFunc;
-    } else {
-      this.fetch = env().platform.fetch;
-    }
-
-    assert(
-        path != null && path.length > 0,
-        () => 'URL path for http must not be null, undefined or ' +
-            'empty.');
-
-    if (Array.isArray(path)) {
-      assert(
-          path.length === 2,
-          () => 'URL paths for http must have a length of 2, ' +
-              `(actual length is ${path.length}).`);
-    }
-    this.path = path;
-
-    if (loadOptions.requestInit != null &&
-        loadOptions.requestInit.body != null) {
-      throw new Error(
-          'requestInit is expected to have no pre-existing body, but has one.');
-    }
-    this.requestInit = loadOptions.requestInit || {};
-  }
-
-  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error(
-          'BrowserHTTPRequest.save() does not support saving model topology ' +
-          'in binary formats yet.');
-    }
-
-    const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);
-    init.body = new FormData();
-
-    const weightsManifest: WeightsManifestConfig = [{
-      paths: ['./model.weights.bin'],
-      weights: modelArtifacts.weightSpecs,
-    }];
-    const modelTopologyAndWeightManifest: ModelJSON =
-        getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
-
-    init.body.append(
-        'model.json',
-        new Blob(
-            [JSON.stringify(modelTopologyAndWeightManifest)],
-            {type: JSON_TYPE}),
-        'model.json');
-
-    if (modelArtifacts.weightData != null) {
-      // TODO(mattsoulanille): Support saving models over 2GB that exceed
-      // Chrome's ArrayBuffer size limit.
-      const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData);
-
-      init.body.append(
-          'model.weights.bin',
-          new Blob([weightBuffer], {type: OCTET_STREAM_MIME_TYPE}),
-          'model.weights.bin');
-    }
-
-    const response = await this.fetch(this.path, init);
-
-    if (response.ok) {
-      return {
-        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
-        responses: [response],
-      };
-    } else {
-      throw new Error(
-          `BrowserHTTPRequest.save() failed due to HTTP response status ` +
-          `${response.status}.`);
-    }
-  }
-
-  /**
-   * Load model artifacts via HTTP request(s).
-   *
-   * See the documentation to `tf.io.http` for details on the saved
-   * artifacts.
-   *
-   * @returns The loaded model artifacts (if loading succeeds).
-   */
-  async load(): Promise<ModelArtifacts> {
-    const modelConfigRequest = await this.fetch(this.path, this.requestInit);
-
-    if (!modelConfigRequest.ok) {
-      throw new Error(
-          `Request to ${this.path} failed with status code ` +
-          `${modelConfigRequest.status}. Please verify this URL points to ` +
-          `the model JSON of the model to load.`);
-    }
-    let modelJSON: ModelJSON;
-    try {
-      modelJSON = await modelConfigRequest.json();
-    } catch (e) {
-      let message = `Failed to parse model JSON of response from ${this.path}.`;
-      // TODO(nsthorat): Remove this after some time when we're comfortable that
-      // .pb files are mostly gone.
-      if (this.path.endsWith('.pb')) {
-        message += ' Your path contains a .pb file extension. ' +
-            'Support for .pb models have been removed in TensorFlow.js 1.0 ' +
-            'in favor of .json models. You can re-convert your Python ' +
-            'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +
-            'or you can convert your.pb models with the \'pb2json\'' +
-            'NPM script in the tensorflow/tfjs-converter repository.';
-      } else {
-        message += ' Please make sure the server is serving valid ' +
-            'JSON for this request.';
-      }
-      throw new Error(message);
-    }
-
-    // We do not allow both modelTopology and weightsManifest to be missing.
-    const modelTopology = modelJSON.modelTopology;
-    const weightsManifest = modelJSON.weightsManifest;
-    if (modelTopology == null && weightsManifest == null) {
-      throw new Error(
-          `The JSON from HTTP path ${this.path} contains neither model ` +
-          `topology or manifest for weights.`);
-    }
-
-    return getModelArtifactsForJSON(
-        modelJSON, (weightsManifest) => this.loadWeights(weightsManifest));
-  }
-
-  private async loadWeights(weightsManifest: WeightsManifestConfig):
-    Promise<[WeightsManifestEntry[], WeightData]> {
-    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
-    const [prefix, suffix] = parseUrl(weightPath);
-    const pathPrefix = this.weightPathPrefix || prefix;
-
-    const weightSpecs = getWeightSpecs(weightsManifest);
-
-    const fetchURLs: string[] = [];
-    const urlPromises: Array<Promise<string>> = [];
-    for (const weightsGroup of weightsManifest) {
-      for (const path of weightsGroup.paths) {
-        if (this.weightUrlConverter != null) {
-          urlPromises.push(this.weightUrlConverter(path));
-        } else {
-          fetchURLs.push(pathPrefix + path + suffix);
-        }
-      }
-    }
-
-    if (this.weightUrlConverter) {
-      fetchURLs.push(...await Promise.all(urlPromises));
-    }
-
-    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
-      requestInit: this.requestInit,
-      fetchFunc: this.fetch,
-      onProgress: this.onProgress
-    });
-    return [weightSpecs, buffers];
-  }
-}
-
-/**
- * Extract the prefix and suffix of the url, where the prefix is the path before
- * the last file, and suffix is the search params after the last file.
- * ```
- * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'
- * [prefix, suffix] = parseUrl(url)
- * // prefix = 'http://tfhub.dev/model/1/'
- * // suffix = '?tfjs-format=file'
- * ```
- * @param url the model url to be parsed.
- */
-export function parseUrl(url: string): [string, string] {
-  const lastSlash = url.lastIndexOf('/');
-  const lastSearchParam = url.lastIndexOf('?');
-  const prefix = url.substring(0, lastSlash);
-  const suffix =
-      lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';
-  return [prefix + '/', suffix];
-}
-
-export function isHTTPScheme(url: string): boolean {
-  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
-}
-
-export const httpRouter: IORouter =
-    (url: string, loadOptions?: LoadOptions) => {
-      if (typeof fetch === 'undefined' &&
-          (loadOptions == null || loadOptions.fetchFunc == null)) {
-        // `http` uses `fetch` or `node-fetch`, if one wants to use it in
-        // an environment that is not the browser or node they have to setup a
-        // global fetch polyfill.
-        return null;
-      } else {
-        let isHTTP = true;
-        if (Array.isArray(url)) {
-          isHTTP = url.every(urlItem => isHTTPScheme(urlItem));
-        } else {
-          isHTTP = isHTTPScheme(url);
-        }
-        if (isHTTP) {
-          return http(url, loadOptions);
-        }
-      }
-      return null;
-    };
-IORouterRegistry.registerSaveRouter(httpRouter);
-IORouterRegistry.registerLoadRouter(httpRouter);
-
-/**
- * Creates an IOHandler subtype that sends model artifacts to HTTP server.
- *
- * An HTTP request of the `multipart/form-data` mime type will be sent to the
- * `path` URL. The form data includes artifacts that represent the topology
- * and/or weights of the model. In the case of Keras-style `tf.Model`, two
- * blobs (files) exist in form-data:
- *   - A JSON file consisting of `modelTopology` and `weightsManifest`.
- *   - A binary weights file consisting of the concatenated weight values.
- * These files are in the same format as the one generated by
- * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
- *
- * The following code snippet exemplifies the client-side code that uses this
- * function:
- *
- * ```js
- * const model = tf.sequential();
- * model.add(
- *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
- *
- * const saveResult = await model.save(tf.io.http(
- *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
- * console.log(saveResult);
- * ```
- *
- * If the default `POST` method is to be used, without any custom parameters
- * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
- *
- * ```js
- * const saveResult = await model.save('http://model-server:5000/upload');
- * ```
- *
- * The following GitHub Gist
- * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
- * implements a server based on [flask](https://github.com/pallets/flask) that
- * can receive the request. Upon receiving the model artifacts via the requst,
- * this particular server reconstitutes instances of [Keras
- * Models](https://keras.io/models/model/) in memory.
- *
- *
- * @param path A URL path to the model.
- *   Can be an absolute HTTP path (e.g.,
- *   'http://localhost:8000/model-upload)') or a relative path (e.g.,
- *   './model-upload').
- * @param requestInit Request configurations to be used when sending
- *    HTTP request to server using `fetch`. It can contain fields such as
- *    `method`, `credentials`, `headers`, `mode`, etc. See
- *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
- *    for more information. `requestInit` must not have a body, because the
- * body will be set by TensorFlow.js. File blobs representing the model
- * topology (filename: 'model.json') and the weights of the model (filename:
- * 'model.weights.bin') will be appended to the body. If `requestInit` has a
- * `body`, an Error will be thrown.
- * @param loadOptions Optional configuration for the loading. It includes the
- *   following fields:
- *   - weightPathPrefix Optional, this specifies the path prefix for weight
- *     files, by default this is calculated from the path param.
- *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
- *     the `fetch` from node-fetch can be used here.
- *   - onProgress Optional, progress callback function, fired periodically
- *     before the load is completed.
- * @returns An instance of `IOHandler`.
- *
- * @doc {
- *   heading: 'Models',
- *   subheading: 'Loading',
- *   namespace: 'io',
- *   ignoreCI: true
- * }
- */
-export function http(path: string, loadOptions?: LoadOptions): IOHandler {
-  return new HTTPRequest(path, loadOptions);
-}
-
-/**
- * Deprecated. Use `tf.io.http`.
- * @param path
- * @param loadOptions
- */
-export function browserHTTPRequest(
-    path: string, loadOptions?: LoadOptions): IOHandler {
-  return http(path, loadOptions);
-}
diff --git a/tfjs-master/tfjs-core/src/io/http_test.ts b/tfjs-master/tfjs-core/src/io/http_test.ts
deleted file mode 100644
index 18b868940..000000000
--- a/tfjs-master/tfjs-core/src/io/http_test.ts
+++ /dev/null
@@ -1,920 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {BROWSER_ENVS, CHROME_ENVS, describeWithFlags, NODE_ENVS} from '../jasmine_util';
-import {HTTPRequest, httpRouter, parseUrl} from './http';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-
-// Test data.
-const modelTopology1: {} = {
-  'class_name': 'Sequential',
-  'keras_version': '2.1.4',
-  'config': [{
-    'class_name': 'Dense',
-    'config': {
-      'kernel_initializer': {
-        'class_name': 'VarianceScaling',
-        'config': {
-          'distribution': 'uniform',
-          'scale': 1.0,
-          'seed': null,
-          'mode': 'fan_avg'
-        }
-      },
-      'name': 'dense',
-      'kernel_constraint': null,
-      'bias_regularizer': null,
-      'bias_constraint': null,
-      'dtype': 'float32',
-      'activation': 'linear',
-      'trainable': true,
-      'kernel_regularizer': null,
-      'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-      'units': 1,
-      'batch_input_shape': [null, 3],
-      'use_bias': true,
-      'activity_regularizer': null
-    }
-  }],
-  'backend': 'tensorflow'
-};
-const trainingConfig1: tf.io.TrainingConfig = {
-  loss: 'categorical_crossentropy',
-  metrics: ['accuracy'],
-  optimizer_config: {class_name: 'SGD', config: {learningRate: 0.1}}
-};
-
-let fetchSpy: jasmine.Spy;
-
-type TypedArrays = Float32Array|Int32Array|Uint8Array|Uint16Array;
-const fakeResponse =
-    (body: string|TypedArrays|ArrayBuffer, contentType: string, path: string) =>
-        ({
-          ok: true,
-          json() {
-            return Promise.resolve(JSON.parse(body as string));
-          },
-          arrayBuffer() {
-            const buf: ArrayBuffer = (body as TypedArrays).buffer ?
-                (body as TypedArrays).buffer :
-                body as ArrayBuffer;
-            return Promise.resolve(buf);
-          },
-          headers: {get: (key: string) => contentType},
-          url: path
-        }) as unknown as Response;
-
-const setupFakeWeightFiles =
-    (fileBufferMap: {
-      [filename: string]: {
-        data: string|Float32Array|Int32Array|ArrayBuffer|Uint8Array|Uint16Array,
-        contentType: string
-      }
-    },
-     requestInits: {[key: string]: RequestInit}) => {
-      fetchSpy = spyOn(tf.env().platform, 'fetch')
-                     .and.callFake((path: string, init: RequestInit) => {
-                       if (fileBufferMap[path]) {
-                         requestInits[path] = init;
-                         return Promise.resolve(fakeResponse(
-                             fileBufferMap[path].data,
-                             fileBufferMap[path].contentType, path));
-                       } else {
-                         return Promise.reject('path not found');
-                       }
-                     });
-    };
-
-describeWithFlags('http-load fetch', NODE_ENVS, () => {
-  let requestInits: {[key: string]: {headers: {[key: string]: string}}};
-  // tslint:disable-next-line:no-any
-  let originalFetch: any;
-  // simulate a fetch polyfill, this needs to be non-null for spyOn to work
-  beforeEach(() => {
-    // tslint:disable-next-line:no-any
-    originalFetch = (global as any).fetch;
-    // tslint:disable-next-line:no-any
-    (global as any).fetch = () => {};
-    requestInits = {};
-  });
-
-  afterAll(() => {
-    // tslint:disable-next-line:no-any
-    (global as any).fetch = originalFetch;
-  });
-
-  it('1 group, 2 weights, 1 path', async () => {
-    const weightManifest1: tf.io.WeightsManifestConfig = [{
-      paths: ['weightfile0'],
-      weights: [
-        {
-          name: 'dense/kernel',
-          shape: [3, 1],
-          dtype: 'float32',
-        },
-        {
-          name: 'dense/bias',
-          shape: [2],
-          dtype: 'float32',
-        }
-      ]
-    }];
-    const floatData = new Float32Array([1, 3, 3, 7, 4]);
-    setupFakeWeightFiles(
-        {
-          './model.json': {
-            data: JSON.stringify({
-              modelTopology: modelTopology1,
-              weightsManifest: weightManifest1,
-              format: 'tfjs-layers',
-              generatedBy: '1.15',
-              convertedBy: '1.3.1',
-              signature: null,
-              userDefinedMetadata: {}
-            }),
-            contentType: 'application/json'
-          },
-          './weightfile0':
-              {data: floatData, contentType: 'application/octet-stream'},
-        },
-        requestInits);
-
-    const handler = tf.io.http('./model.json');
-    const modelArtifacts = await handler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
-    expect(modelArtifacts.format).toEqual('tfjs-layers');
-    expect(modelArtifacts.generatedBy).toEqual('1.15');
-    expect(modelArtifacts.convertedBy).toEqual('1.3.1');
-    expect(modelArtifacts.userDefinedMetadata).toEqual({});
-    expect(new Float32Array(CompositeArrayBuffer.join(
-        modelArtifacts.weightData))).toEqual(floatData);
-  });
-
-  it('throw exception if no fetch polyfill', () => {
-    // tslint:disable-next-line:no-any
-    delete (global as any).fetch;
-    try {
-      tf.io.http('./model.json');
-    } catch (err) {
-      expect(err.message).toMatch(/Unable to find fetch polyfill./);
-    }
-  });
-});
-
-// Turned off for other browsers due to:
-// https://github.com/tensorflow/tfjs/issues/426
-describeWithFlags('http-save', CHROME_ENVS, () => {
-  // Test data.
-  const weightSpecs1: tf.io.WeightsManifestEntry[] = [
-    {
-      name: 'dense/kernel',
-      shape: [3, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense/bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-  const weightData1 = new ArrayBuffer(16);
-  const artifacts1: tf.io.ModelArtifacts = {
-    modelTopology: modelTopology1,
-    weightSpecs: weightSpecs1,
-    weightData: weightData1,
-    format: 'layers-model',
-    generatedBy: 'TensorFlow.js v0.0.0',
-    convertedBy: null,
-    signature: null,
-    userDefinedMetadata: {},
-    modelInitializer: {},
-    trainingConfig: trainingConfig1
-  };
-
-  let requestInits: RequestInit[] = [];
-
-  beforeEach(() => {
-    requestInits = [];
-    spyOn(tf.env().platform, 'fetch')
-        .and.callFake((path: string, init: RequestInit) => {
-          if (path === 'model-upload-test' ||
-              path === 'http://model-upload-test') {
-            requestInits.push(init);
-            return Promise.resolve(new Response(null, {status: 200}));
-          } else {
-            return Promise.reject(new Response(null, {status: 404}));
-          }
-        });
-  });
-
-  it('Save topology and weights, default POST method', (done) => {
-    const testStartDate = new Date();
-    const handler = tf.io.getSaveHandlers('http://model-upload-test')[0];
-    handler.save(artifacts1)
-        .then(saveResult => {
-          expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
-              .toBeGreaterThanOrEqual(testStartDate.getTime());
-          // Note: The following two assertions work only because there is no
-          //   non-ASCII characters in `modelTopology1` and `weightSpecs1`.
-          expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-              .toEqual(JSON.stringify(modelTopology1).length);
-          expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
-              .toEqual(JSON.stringify(weightSpecs1).length);
-          expect(saveResult.modelArtifactsInfo.weightDataBytes)
-              .toEqual(weightData1.byteLength);
-
-          expect(requestInits.length).toEqual(1);
-          const init = requestInits[0];
-          expect(init.method).toEqual('POST');
-          const body = init.body as FormData;
-          const jsonFile = body.get('model.json') as File;
-          const jsonFileReader = new FileReader();
-          jsonFileReader.onload = (event: Event) => {
-            const modelJSON =
-                // tslint:disable-next-line:no-any
-                JSON.parse((event.target as any).result) as tf.io.ModelJSON;
-            expect(modelJSON.modelTopology).toEqual(modelTopology1);
-            expect(modelJSON.weightsManifest.length).toEqual(1);
-            expect(modelJSON.weightsManifest[0].weights).toEqual(weightSpecs1);
-            expect(modelJSON.trainingConfig).toEqual(trainingConfig1);
-
-            const weightsFile = body.get('model.weights.bin') as File;
-            const weightsFileReader = new FileReader();
-            weightsFileReader.onload = (event: Event) => {
-              // tslint:disable-next-line:no-any
-              const weightData = (event.target as any).result as ArrayBuffer;
-              expect(new Uint8Array(weightData))
-                  .toEqual(new Uint8Array(weightData1));
-              done();
-            };
-            weightsFileReader.onerror = ev => {
-              done.fail(weightsFileReader.error.message);
-            };
-            weightsFileReader.readAsArrayBuffer(weightsFile);
-          };
-          jsonFileReader.onerror = ev => {
-            done.fail(jsonFileReader.error.message);
-          };
-          jsonFileReader.readAsText(jsonFile);
-        })
-        .catch(err => {
-          done.fail(err.stack);
-        });
-  });
-
-  it('Save topology only, default POST method', (done) => {
-    const testStartDate = new Date();
-    const handler = tf.io.getSaveHandlers('http://model-upload-test')[0];
-    const topologyOnlyArtifacts = {modelTopology: modelTopology1};
-    handler.save(topologyOnlyArtifacts)
-        .then(saveResult => {
-          expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
-              .toBeGreaterThanOrEqual(testStartDate.getTime());
-          // Note: The following two assertions work only because there is no
-          //   non-ASCII characters in `modelTopology1` and `weightSpecs1`.
-          expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-              .toEqual(JSON.stringify(modelTopology1).length);
-          expect(saveResult.modelArtifactsInfo.weightSpecsBytes).toEqual(0);
-          expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(0);
-
-          expect(requestInits.length).toEqual(1);
-          const init = requestInits[0];
-          expect(init.method).toEqual('POST');
-          const body = init.body as FormData;
-          const jsonFile = body.get('model.json') as File;
-          const jsonFileReader = new FileReader();
-          jsonFileReader.onload = (event: Event) => {
-            // tslint:disable-next-line:no-any
-            const modelJSON = JSON.parse((event.target as any).result);
-            expect(modelJSON.modelTopology).toEqual(modelTopology1);
-            // No weights should have been sent to the server.
-            expect(body.get('model.weights.bin')).toEqual(null);
-            done();
-          };
-          jsonFileReader.onerror = event => {
-            done.fail(jsonFileReader.error.message);
-          };
-          jsonFileReader.readAsText(jsonFile);
-        })
-        .catch(err => {
-          done.fail(err.stack);
-        });
-  });
-
-  it('Save topology and weights, PUT method, extra headers', (done) => {
-    const testStartDate = new Date();
-    const handler = tf.io.http('model-upload-test', {
-      requestInit: {
-        method: 'PUT',
-        headers:
-            {'header_key_1': 'header_value_1', 'header_key_2': 'header_value_2'}
-      }
-    });
-    handler.save(artifacts1)
-        .then(saveResult => {
-          expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
-              .toBeGreaterThanOrEqual(testStartDate.getTime());
-          // Note: The following two assertions work only because there is no
-          //   non-ASCII characters in `modelTopology1` and `weightSpecs1`.
-          expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-              .toEqual(JSON.stringify(modelTopology1).length);
-          expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
-              .toEqual(JSON.stringify(weightSpecs1).length);
-          expect(saveResult.modelArtifactsInfo.weightDataBytes)
-              .toEqual(weightData1.byteLength);
-
-          expect(requestInits.length).toEqual(1);
-          const init = requestInits[0];
-          expect(init.method).toEqual('PUT');
-
-          // Check headers.
-          expect(init.headers).toEqual({
-            'header_key_1': 'header_value_1',
-            'header_key_2': 'header_value_2'
-          });
-
-          const body = init.body as FormData;
-          const jsonFile = body.get('model.json') as File;
-          const jsonFileReader = new FileReader();
-          jsonFileReader.onload = (event: Event) => {
-            const modelJSON =
-                // tslint:disable-next-line:no-any
-                JSON.parse((event.target as any).result) as tf.io.ModelJSON;
-            expect(modelJSON.format).toEqual('layers-model');
-            expect(modelJSON.generatedBy).toEqual('TensorFlow.js v0.0.0');
-            expect(modelJSON.convertedBy).toEqual(null);
-            expect(modelJSON.modelTopology).toEqual(modelTopology1);
-            expect(modelJSON.modelInitializer).toEqual({});
-            expect(modelJSON.weightsManifest.length).toEqual(1);
-            expect(modelJSON.weightsManifest[0].weights).toEqual(weightSpecs1);
-            expect(modelJSON.trainingConfig).toEqual(trainingConfig1);
-
-            const weightsFile = body.get('model.weights.bin') as File;
-            const weightsFileReader = new FileReader();
-            weightsFileReader.onload = (event: Event) => {
-              // tslint:disable-next-line:no-any
-              const weightData = (event.target as any).result as ArrayBuffer;
-              expect(new Uint8Array(weightData))
-                  .toEqual(new Uint8Array(weightData1));
-              done();
-            };
-            weightsFileReader.onerror = event => {
-              done.fail(weightsFileReader.error.message);
-            };
-            weightsFileReader.readAsArrayBuffer(weightsFile);
-          };
-          jsonFileReader.onerror = event => {
-            done.fail(jsonFileReader.error.message);
-          };
-          jsonFileReader.readAsText(jsonFile);
-        })
-        .catch(err => {
-          done.fail(err.stack);
-        });
-  });
-
-  it('404 response causes Error', (done) => {
-    const handler = tf.io.getSaveHandlers('http://invalid/path')[0];
-    handler.save(artifacts1)
-        .then(saveResult => {
-          done.fail(
-              'Calling http at invalid URL succeeded ' +
-              'unexpectedly');
-        })
-        .catch(err => {
-          expect().nothing();
-          done();
-        });
-  });
-
-  it('getLoadHandlers with one URL string', () => {
-    const handlers = tf.io.getLoadHandlers('http://foo/model.json');
-    expect(handlers.length).toEqual(1);
-    expect(handlers[0] instanceof HTTPRequest).toEqual(true);
-  });
-
-  it('Existing body leads to Error', () => {
-    expect(() => tf.io.http('model-upload-test', {
-      requestInit: {body: 'existing body'}
-    })).toThrowError(/requestInit is expected to have no pre-existing body/);
-  });
-
-  it('Empty, null or undefined URL paths lead to Error', () => {
-    expect(() => tf.io.http(null))
-        .toThrowError(/must not be null, undefined or empty/);
-    expect(() => tf.io.http(undefined))
-        .toThrowError(/must not be null, undefined or empty/);
-    expect(() => tf.io.http(''))
-        .toThrowError(/must not be null, undefined or empty/);
-  });
-
-  it('router', () => {
-    expect(httpRouter('http://bar/foo') instanceof HTTPRequest).toEqual(true);
-    expect(httpRouter('https://localhost:5000/upload') instanceof HTTPRequest)
-        .toEqual(true);
-    expect(httpRouter('localhost://foo')).toBeNull();
-    expect(httpRouter('foo:5000/bar')).toBeNull();
-  });
-});
-
-describeWithFlags('parseUrl', BROWSER_ENVS, () => {
-  it('should parse url with no suffix', () => {
-    const url = 'http://google.com/file';
-    const [prefix, suffix] = parseUrl(url);
-    expect(prefix).toEqual('http://google.com/');
-    expect(suffix).toEqual('');
-  });
-  it('should parse url with suffix', () => {
-    const url = 'http://google.com/file?param=1';
-    const [prefix, suffix] = parseUrl(url);
-    expect(prefix).toEqual('http://google.com/');
-    expect(suffix).toEqual('?param=1');
-  });
-  it('should parse url with multiple serach params', () => {
-    const url = 'http://google.com/a?x=1/file?param=1';
-    const [prefix, suffix] = parseUrl(url);
-    expect(prefix).toEqual('http://google.com/a?x=1/');
-    expect(suffix).toEqual('?param=1');
-  });
-});
-
-describeWithFlags('http-load', BROWSER_ENVS, () => {
-  describe('JSON model', () => {
-    let requestInits: {[key: string]: {headers: {[key: string]: string}}};
-
-    beforeEach(() => {
-      requestInits = {};
-    });
-
-    it('1 group, 2 weights, 1 path', async () => {
-      const weightManifest1: tf.io.WeightsManifestConfig = [{
-        paths: ['weightfile0'],
-        weights: [
-          {
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          },
-          {
-            name: 'dense/bias',
-            shape: [2],
-            dtype: 'float32',
-          }
-        ]
-      }];
-      const floatData = new Float32Array([1, 3, 3, 7, 4]);
-      setupFakeWeightFiles(
-          {
-            './model.json': {
-              data: JSON.stringify({
-                modelTopology: modelTopology1,
-                weightsManifest: weightManifest1,
-                format: 'tfjs-graph-model',
-                generatedBy: '1.15',
-                convertedBy: '1.3.1',
-                signature: null,
-                userDefinedMetadata: {},
-                modelInitializer: {}
-              }),
-              contentType: 'application/json'
-            },
-            './weightfile0':
-                {data: floatData, contentType: 'application/octet-stream'},
-          },
-          requestInits);
-
-      const handler = tf.io.http('./model.json');
-      const modelArtifacts = await handler.load();
-      expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-      expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
-      expect(modelArtifacts.format).toEqual('tfjs-graph-model');
-      expect(modelArtifacts.generatedBy).toEqual('1.15');
-      expect(modelArtifacts.convertedBy).toEqual('1.3.1');
-      expect(modelArtifacts.userDefinedMetadata).toEqual({});
-      expect(modelArtifacts.modelInitializer).toEqual({});
-
-      expect(new Float32Array(CompositeArrayBuffer.join(modelArtifacts
-          .weightData))).toEqual(floatData);
-      expect(Object.keys(requestInits).length).toEqual(2);
-      // Assert that fetch is invoked with `window` as the context.
-      expect(fetchSpy.calls.mostRecent().object).toEqual(window);
-    });
-
-    it('1 group, 2 weights, 1 path, with requestInit', async () => {
-      const weightManifest1: tf.io.WeightsManifestConfig = [{
-        paths: ['weightfile0'],
-        weights: [
-          {
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          },
-          {
-            name: 'dense/bias',
-            shape: [2],
-            dtype: 'float32',
-          }
-        ]
-      }];
-      const floatData = new Float32Array([1, 3, 3, 7, 4]);
-      setupFakeWeightFiles(
-          {
-            './model.json': {
-              data: JSON.stringify({
-                modelTopology: modelTopology1,
-                weightsManifest: weightManifest1
-              }),
-              contentType: 'application/json'
-            },
-            './weightfile0':
-                {data: floatData, contentType: 'application/octet-stream'},
-          },
-          requestInits);
-
-      const handler = tf.io.http(
-          './model.json',
-          {requestInit: {headers: {'header_key_1': 'header_value_1'}}});
-      const modelArtifacts = await handler.load();
-      expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-      expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
-      expect(new Float32Array(CompositeArrayBuffer.join(modelArtifacts
-          .weightData))).toEqual(floatData);
-      expect(Object.keys(requestInits).length).toEqual(2);
-      expect(Object.keys(requestInits).length).toEqual(2);
-      expect(requestInits['./model.json'].headers['header_key_1'])
-          .toEqual('header_value_1');
-      expect(requestInits['./weightfile0'].headers['header_key_1'])
-          .toEqual('header_value_1');
-
-      expect(fetchSpy.calls.mostRecent().object).toEqual(window);
-    });
-
-    it('1 group, 2 weight, 2 paths', async () => {
-      const weightManifest1: tf.io.WeightsManifestConfig = [{
-        paths: ['weightfile0', 'weightfile1'],
-        weights: [
-          {
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          },
-          {
-            name: 'dense/bias',
-            shape: [2],
-            dtype: 'float32',
-          }
-        ]
-      }];
-      const floatData1 = new Float32Array([1, 3, 3]);
-      const floatData2 = new Float32Array([7, 4]);
-      setupFakeWeightFiles(
-          {
-            './model.json': {
-              data: JSON.stringify({
-                modelTopology: modelTopology1,
-                weightsManifest: weightManifest1
-              }),
-              contentType: 'application/json'
-            },
-            './weightfile0':
-                {data: floatData1, contentType: 'application/octet-stream'},
-            './weightfile1':
-                {data: floatData2, contentType: 'application/octet-stream'}
-          },
-          requestInits);
-
-      const handler = tf.io.http('./model.json');
-      const modelArtifacts = await handler.load();
-      expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-      expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
-      expect(new Float32Array(CompositeArrayBuffer.join(modelArtifacts
-        .weightData))).toEqual(new Float32Array([1, 3, 3, 7, 4]));
-    });
-
-    it('2 groups, 2 weight, 2 paths', async () => {
-      const weightsManifest: tf.io.WeightsManifestConfig = [
-        {
-          paths: ['weightfile0'],
-          weights: [{
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          }]
-        },
-        {
-          paths: ['weightfile1'],
-          weights: [{
-            name: 'dense/bias',
-            shape: [2],
-            dtype: 'float32',
-          }],
-        }
-      ];
-      const floatData1 = new Float32Array([1, 3, 3]);
-      const floatData2 = new Float32Array([7, 4]);
-      setupFakeWeightFiles(
-          {
-            './model.json': {
-              data: JSON.stringify(
-                  {modelTopology: modelTopology1, weightsManifest}),
-              contentType: 'application/json'
-            },
-            './weightfile0':
-                {data: floatData1, contentType: 'application/octet-stream'},
-            './weightfile1':
-                {data: floatData2, contentType: 'application/octet-stream'}
-          },
-          requestInits);
-
-      const handler = tf.io.http('./model.json');
-      const modelArtifacts = await handler.load();
-      expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-      expect(modelArtifacts.weightSpecs)
-          .toEqual(
-              weightsManifest[0].weights.concat(weightsManifest[1].weights));
-      expect(new Float32Array(CompositeArrayBuffer.join(
-          modelArtifacts.weightData)))
-              .toEqual(new Float32Array([1, 3, 3, 7, 4]));
-    });
-
-    it('2 groups, 2 weight, 2 paths, Int32 and Uint8 Data', async () => {
-      const weightsManifest: tf.io.WeightsManifestConfig = [
-        {
-          paths: ['weightfile0'],
-          weights: [{
-            name: 'fooWeight',
-            shape: [3, 1],
-            dtype: 'int32',
-          }]
-        },
-        {
-          paths: ['weightfile1'],
-          weights: [{
-            name: 'barWeight',
-            shape: [2],
-            dtype: 'bool',
-          }],
-        }
-      ];
-      const floatData1 = new Int32Array([1, 3, 3]);
-      const floatData2 = new Uint8Array([7, 4]);
-      setupFakeWeightFiles(
-          {
-            'path1/model.json': {
-              data: JSON.stringify(
-                  {modelTopology: modelTopology1, weightsManifest}),
-              contentType: 'application/json'
-            },
-            'path1/weightfile0':
-                {data: floatData1, contentType: 'application/octet-stream'},
-            'path1/weightfile1':
-                {data: floatData2, contentType: 'application/octet-stream'}
-          },
-          requestInits);
-
-      const handler = tf.io.http('path1/model.json');
-      const modelArtifacts = await handler.load();
-      expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-      expect(modelArtifacts.weightSpecs)
-          .toEqual(
-              weightsManifest[0].weights.concat(weightsManifest[1].weights));
-      expect(new Int32Array(CompositeArrayBuffer.join(modelArtifacts.weightData)
-        .slice(0, 12))).toEqual(new Int32Array([1, 3, 3]));
-      expect(new Uint8Array(CompositeArrayBuffer.join(modelArtifacts.weightData)
-        .slice(12, 14))).toEqual(new Uint8Array([7, 4]));
-    });
-
-    it('topology only', async () => {
-      setupFakeWeightFiles(
-          {
-            './model.json': {
-              data: JSON.stringify({modelTopology: modelTopology1}),
-              contentType: 'application/json'
-            },
-          },
-          requestInits);
-
-      const handler = tf.io.http('./model.json');
-      const modelArtifacts = await handler.load();
-      expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-      expect(modelArtifacts.weightSpecs).toBeUndefined();
-      expect(modelArtifacts.weightData).toBeUndefined();
-    });
-
-    it('weights only', async () => {
-      const weightsManifest: tf.io.WeightsManifestConfig = [
-        {
-          paths: ['weightfile0'],
-          weights: [{
-            name: 'fooWeight',
-            shape: [3, 1],
-            dtype: 'int32',
-          }]
-        },
-        {
-          paths: ['weightfile1'],
-          weights: [{
-            name: 'barWeight',
-            shape: [2],
-            dtype: 'float32',
-          }],
-        }
-      ];
-      const floatData1 = new Int32Array([1, 3, 3]);
-      const floatData2 = new Float32Array([-7, -4]);
-      setupFakeWeightFiles(
-          {
-            'path1/model.json': {
-              data: JSON.stringify({weightsManifest}),
-              contentType: 'application/json'
-            },
-            'path1/weightfile0':
-                {data: floatData1, contentType: 'application/octet-stream'},
-            'path1/weightfile1':
-                {data: floatData2, contentType: 'application/octet-stream'}
-          },
-          requestInits);
-
-      const handler = tf.io.http('path1/model.json');
-      const modelArtifacts = await handler.load();
-      expect(modelArtifacts.modelTopology).toBeUndefined();
-      expect(modelArtifacts.weightSpecs)
-          .toEqual(
-              weightsManifest[0].weights.concat(weightsManifest[1].weights));
-      expect(new Int32Array(CompositeArrayBuffer.join(modelArtifacts.weightData)
-          .slice(0, 12))).toEqual(new Int32Array([1, 3, 3]));
-      expect(new Float32Array(CompositeArrayBuffer
-          .join(modelArtifacts.weightData)
-          .slice(12, 20))).toEqual(new Float32Array([-7, -4]));
-    });
-
-    it('Missing modelTopology and weightsManifest leads to error', async () => {
-      setupFakeWeightFiles(
-          {
-            'path1/model.json':
-                {data: JSON.stringify({}), contentType: 'application/json'}
-          },
-          requestInits);
-      const handler = tf.io.http('path1/model.json');
-      handler.load()
-          .then(modelTopology1 => {
-            fail(
-                'Loading from missing modelTopology and weightsManifest ' +
-                'succeeded unexpectedly.');
-          })
-          .catch(err => {
-            expect(err.message)
-                .toMatch(/contains neither model topology or manifest/);
-          });
-      expect().nothing();
-    });
-
-    it('with fetch rejection leads to error', async () => {
-      setupFakeWeightFiles(
-          {
-            'path1/model.json':
-                {data: JSON.stringify({}), contentType: 'text/html'}
-          },
-          requestInits);
-      const handler = tf.io.http('path2/model.json');
-      try {
-        const data = await handler.load();
-        expect(data).toBeDefined();
-        fail('Loading with fetch rejection succeeded unexpectedly.');
-      } catch (err) {
-        // This error is mocked in beforeEach
-        expect(err).toEqual('path not found');
-      }
-    });
-    it('Provide WeightFileTranslateFunc', async () => {
-      const weightManifest1: tf.io.WeightsManifestConfig = [{
-        paths: ['weightfile0'],
-        weights: [
-          {
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          },
-          {
-            name: 'dense/bias',
-            shape: [2],
-            dtype: 'float32',
-          }
-        ]
-      }];
-      const floatData = new Float32Array([1, 3, 3, 7, 4]);
-      setupFakeWeightFiles(
-          {
-            './model.json': {
-              data: JSON.stringify({
-                modelTopology: modelTopology1,
-                weightsManifest: weightManifest1
-              }),
-              contentType: 'application/json'
-            },
-            'auth_weightfile0':
-                {data: floatData, contentType: 'application/octet-stream'},
-          },
-          requestInits);
-      async function prefixWeightUrlConverter(weightFile: string):
-          Promise<string> {
-        // Add 'auth_' prefix to the weight file url.
-        return new Promise(
-            resolve => setTimeout(resolve, 1, 'auth_' + weightFile));
-      }
-
-      const handler = tf.io.http('./model.json', {
-        requestInit: {headers: {'header_key_1': 'header_value_1'}},
-        weightUrlConverter: prefixWeightUrlConverter
-      });
-      const modelArtifacts = await handler.load();
-      expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-      expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
-      expect(new Float32Array(CompositeArrayBuffer.join(
-          modelArtifacts.weightData))).toEqual(floatData);
-      expect(Object.keys(requestInits).length).toEqual(2);
-      expect(Object.keys(requestInits).length).toEqual(2);
-      expect(requestInits['./model.json'].headers['header_key_1'])
-          .toEqual('header_value_1');
-      expect(requestInits['auth_weightfile0'].headers['header_key_1'])
-          .toEqual('header_value_1');
-
-      expect(fetchSpy.calls.mostRecent().object).toEqual(window);
-    });
-  });
-
-  it('Overriding BrowserHTTPRequest fetchFunc', async () => {
-    const weightManifest1: tf.io.WeightsManifestConfig = [{
-      paths: ['weightfile0'],
-      weights: [
-        {
-          name: 'dense/kernel',
-          shape: [3, 1],
-          dtype: 'float32',
-        },
-        {
-          name: 'dense/bias',
-          shape: [2],
-          dtype: 'float32',
-        }
-      ]
-    }];
-    const floatData = new Float32Array([1, 3, 3, 7, 4]);
-
-    const fetchInputs: RequestInfo[] = [];
-    const fetchInits: RequestInit[] = [];
-    async function customFetch(
-        input: RequestInfo, init?: RequestInit): Promise<Response> {
-      fetchInputs.push(input);
-      fetchInits.push(init);
-
-      if (input === './model.json') {
-        return new Response(
-            JSON.stringify({
-              modelTopology: modelTopology1,
-              weightsManifest: weightManifest1,
-              trainingConfig: trainingConfig1
-            }),
-            {status: 200, headers: {'content-type': 'application/json'}});
-      } else if (input === './weightfile0') {
-        return new Response(floatData, {
-          status: 200,
-          headers: {'content-type': 'application/octet-stream'}
-        });
-      } else {
-        return new Response(null, {status: 404});
-      }
-    }
-
-    const handler = tf.io.http(
-        './model.json',
-        {requestInit: {credentials: 'include'}, fetchFunc: customFetch});
-    const modelArtifacts = await handler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.trainingConfig).toEqual(trainingConfig1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
-    expect(new Float32Array(CompositeArrayBuffer
-        .join(modelArtifacts.weightData))).toEqual(floatData);
-
-    expect(fetchInputs).toEqual(['./model.json', './weightfile0']);
-    expect(fetchInits.length).toEqual(2);
-    expect(fetchInits[0].credentials).toEqual('include');
-    expect(fetchInits[1].credentials).toEqual('include');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/io/indexed_db.ts b/tfjs-master/tfjs-core/src/io/indexed_db.ts
deleted file mode 100644
index 42de50941..000000000
--- a/tfjs-master/tfjs-core/src/io/indexed_db.ts
+++ /dev/null
@@ -1,373 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '../flags';
-
-import {env} from '../environment';
-
-import {getModelArtifactsInfoForJSON} from './io_utils';
-import {IORouter, IORouterRegistry} from './router_registry';
-import {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-
-const DATABASE_NAME = 'tensorflowjs';
-const DATABASE_VERSION = 1;
-
-// Model data and ModelArtifactsInfo (metadata) are stored in two separate
-// stores for efficient access of the list of stored models and their metadata.
-// 1. The object store for model data: topology, weights and weight manifests.
-const MODEL_STORE_NAME = 'models_store';
-// 2. The object store for ModelArtifactsInfo, including meta-information such
-//    as the type of topology (JSON vs binary), byte size of the topology, byte
-//    size of the weights, etc.
-const INFO_STORE_NAME = 'model_info_store';
-
-/**
- * Delete the entire database for tensorflow.js, including the models store.
- */
-export async function deleteDatabase(): Promise<void> {
-  const idbFactory = getIndexedDBFactory();
-
-  return new Promise<void>((resolve, reject) => {
-    const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);
-    deleteRequest.onsuccess = () => resolve();
-    deleteRequest.onerror = error => reject(error);
-  });
-}
-
-function getIndexedDBFactory(): IDBFactory {
-  if (!env().getBool('IS_BROWSER')) {
-    // TODO(cais): Add more info about what IOHandler subtypes are available.
-    //   Maybe point to a doc page on the web and/or automatically determine
-    //   the available IOHandlers and print them in the error message.
-    throw new Error(
-        'Failed to obtain IndexedDB factory because the current environment' +
-        'is not a web browser.');
-  }
-  // tslint:disable-next-line:no-any
-  const theWindow: any = typeof window === 'undefined' ? self : window;
-  const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||
-      theWindow.webkitIndexedDB || theWindow.msIndexedDB ||
-      theWindow.shimIndexedDB;
-  if (factory == null) {
-    throw new Error(
-        'The current browser does not appear to support IndexedDB.');
-  }
-  return factory;
-}
-
-function setUpDatabase(openRequest: IDBRequest) {
-  const db = openRequest.result as IDBDatabase;
-  db.createObjectStore(MODEL_STORE_NAME, {keyPath: 'modelPath'});
-  db.createObjectStore(INFO_STORE_NAME, {keyPath: 'modelPath'});
-}
-
-/**
- * IOHandler subclass: Browser IndexedDB.
- *
- * See the doc string of `browserIndexedDB` for more details.
- */
-export class BrowserIndexedDB implements IOHandler {
-  protected readonly indexedDB: IDBFactory;
-  protected readonly modelPath: string;
-
-  static readonly URL_SCHEME = 'indexeddb://';
-
-  constructor(modelPath: string) {
-    this.indexedDB = getIndexedDBFactory();
-
-    if (modelPath == null || !modelPath) {
-      throw new Error(
-          'For IndexedDB, modelPath must not be null, undefined or empty.');
-    }
-    this.modelPath = modelPath;
-  }
-
-  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {
-    // TODO(cais): Support saving GraphDef models.
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error(
-          'BrowserLocalStorage.save() does not support saving model topology ' +
-          'in binary formats yet.');
-    }
-
-    return this.databaseAction(this.modelPath, modelArtifacts) as
-        Promise<SaveResult>;
-  }
-
-  async load(): Promise<ModelArtifacts> {
-    return this.databaseAction(this.modelPath) as Promise<ModelArtifacts>;
-  }
-
-  /**
-   * Perform database action to put model artifacts into or read model artifacts
-   * from IndexedDB object store.
-   *
-   * Whether the action is put or get depends on whether `modelArtifacts` is
-   * specified. If it is specified, the action will be put; otherwise the action
-   * will be get.
-   *
-   * @param modelPath A unique string path for the model.
-   * @param modelArtifacts If specified, it will be the model artifacts to be
-   *   stored in IndexedDB.
-   * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`
-   *   of `ModelArtifacts`, if the action is get.
-   */
-  private databaseAction(modelPath: string, modelArtifacts?: ModelArtifacts):
-      Promise<ModelArtifacts|SaveResult> {
-    return new Promise<ModelArtifacts|SaveResult>((resolve, reject) => {
-      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
-      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
-
-      openRequest.onsuccess = () => {
-        const db = openRequest.result;
-
-        if (modelArtifacts == null) {
-          // Read model out from object store.
-          const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');
-          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
-          const getRequest = modelStore.get(this.modelPath);
-          getRequest.onsuccess = () => {
-            if (getRequest.result == null) {
-              db.close();
-              return reject(new Error(
-                  `Cannot find model with path '${this.modelPath}' ` +
-                  `in IndexedDB.`));
-            } else {
-              resolve(getRequest.result.modelArtifacts);
-            }
-          };
-          getRequest.onerror = error => {
-            db.close();
-            return reject(getRequest.error);
-          };
-          modelTx.oncomplete = () => db.close();
-        } else {
-          // Put model into object store.
-
-          // Concatenate all the model weights into a single ArrayBuffer. Large
-          // models (~1GB) have problems saving if they are not concatenated.
-          // TODO(mattSoulanille): Save large models to multiple indexeddb
-          // records.
-          modelArtifacts.weightData = CompositeArrayBuffer.join(
-              modelArtifacts.weightData);
-          const modelArtifactsInfo: ModelArtifactsInfo =
-              getModelArtifactsInfoForJSON(modelArtifacts);
-          // First, put ModelArtifactsInfo into info store.
-          const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');
-          let infoStore = infoTx.objectStore(INFO_STORE_NAME);
-          let putInfoRequest: IDBRequest<IDBValidKey>;
-          try {
-            putInfoRequest =
-              infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});
-          } catch (error) {
-            return reject(error);
-          }
-          let modelTx: IDBTransaction;
-          putInfoRequest.onsuccess = () => {
-            // Second, put model data into model store.
-            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');
-            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
-            let putModelRequest: IDBRequest<IDBValidKey>;
-            try {
-              putModelRequest = modelStore.put({
-                modelPath: this.modelPath,
-                modelArtifacts,
-                modelArtifactsInfo
-              });
-            } catch (error) {
-              // Sometimes, the serialized value is too large to store.
-              return reject(error);
-            }
-            putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});
-            putModelRequest.onerror = error => {
-              // If the put-model request fails, roll back the info entry as
-              // well.
-              infoStore = infoTx.objectStore(INFO_STORE_NAME);
-              const deleteInfoRequest = infoStore.delete(this.modelPath);
-              deleteInfoRequest.onsuccess = () => {
-                db.close();
-                return reject(putModelRequest.error);
-              };
-              deleteInfoRequest.onerror = error => {
-                db.close();
-                return reject(putModelRequest.error);
-              };
-            };
-          };
-          putInfoRequest.onerror = error => {
-            db.close();
-            return reject(putInfoRequest.error);
-          };
-          infoTx.oncomplete = () => {
-            if (modelTx == null) {
-              db.close();
-            } else {
-              modelTx.oncomplete = () => db.close();
-            }
-          };
-        }
-      };
-      openRequest.onerror = error => reject(openRequest.error);
-    });
-  }
-}
-
-export const indexedDBRouter: IORouter = (url: string|string[]) => {
-  if (!env().getBool('IS_BROWSER')) {
-    return null;
-  } else {
-    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {
-      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
-    } else {
-      return null;
-    }
-  }
-};
-IORouterRegistry.registerSaveRouter(indexedDBRouter);
-IORouterRegistry.registerLoadRouter(indexedDBRouter);
-
-/**
- * Creates a browser IndexedDB IOHandler for saving and loading models.
- *
- * ```js
- * const model = tf.sequential();
- * model.add(
- *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
- *
- * const saveResult = await model.save('indexeddb://MyModel'));
- * console.log(saveResult);
- * ```
- *
- * @param modelPath A unique identifier for the model to be saved. Must be a
- *   non-empty string.
- * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),
- *   which can be used with, e.g., `tf.Model.save`.
- */
-export function browserIndexedDB(modelPath: string): IOHandler {
-  return new BrowserIndexedDB(modelPath);
-}
-
-function maybeStripScheme(key: string) {
-  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?
-      key.slice(BrowserIndexedDB.URL_SCHEME.length) :
-      key;
-}
-
-export class BrowserIndexedDBManager implements ModelStoreManager {
-  private indexedDB: IDBFactory;
-
-  constructor() {
-    this.indexedDB = getIndexedDBFactory();
-  }
-
-  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {
-    return new Promise<{[path: string]: ModelArtifactsInfo}>(
-        (resolve, reject) => {
-          const openRequest =
-              this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
-          openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
-
-          openRequest.onsuccess = () => {
-            const db = openRequest.result;
-            const tx = db.transaction(INFO_STORE_NAME, 'readonly');
-            const store = tx.objectStore(INFO_STORE_NAME);
-            // tslint:disable:max-line-length
-            // Need to cast `store` as `any` here because TypeScript's DOM
-            // library does not have the `getAll()` method even though the
-            // method is supported in the latest version of most mainstream
-            // browsers:
-            // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll
-            // tslint:enable:max-line-length
-            // tslint:disable-next-line:no-any
-            const getAllInfoRequest = (store as any).getAll() as IDBRequest;
-            getAllInfoRequest.onsuccess = () => {
-              const out: {[path: string]: ModelArtifactsInfo} = {};
-              for (const item of getAllInfoRequest.result) {
-                out[item.modelPath] = item.modelArtifactsInfo;
-              }
-              resolve(out);
-            };
-            getAllInfoRequest.onerror = error => {
-              db.close();
-              return reject(getAllInfoRequest.error);
-            };
-            tx.oncomplete = () => db.close();
-          };
-          openRequest.onerror = error => reject(openRequest.error);
-        });
-  }
-
-  async removeModel(path: string): Promise<ModelArtifactsInfo> {
-    path = maybeStripScheme(path);
-    return new Promise<ModelArtifactsInfo>((resolve, reject) => {
-      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
-      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
-
-      openRequest.onsuccess = () => {
-        const db = openRequest.result;
-        const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');
-        const infoStore = infoTx.objectStore(INFO_STORE_NAME);
-
-        const getInfoRequest = infoStore.get(path);
-        let modelTx: IDBTransaction;
-        getInfoRequest.onsuccess = () => {
-          if (getInfoRequest.result == null) {
-            db.close();
-            return reject(new Error(
-                `Cannot find model with path '${path}' ` +
-                `in IndexedDB.`));
-          } else {
-            // First, delete the entry in the info store.
-            const deleteInfoRequest = infoStore.delete(path);
-            const deleteModelData = () => {
-              // Second, delete the entry in the model store.
-              modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');
-              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
-              const deleteModelRequest = modelStore.delete(path);
-              deleteModelRequest.onsuccess = () =>
-                  resolve(getInfoRequest.result.modelArtifactsInfo);
-              deleteModelRequest.onerror = error =>
-                  reject(getInfoRequest.error);
-            };
-            // Proceed with deleting model data regardless of whether deletion
-            // of info data succeeds or not.
-            deleteInfoRequest.onsuccess = deleteModelData;
-            deleteInfoRequest.onerror = error => {
-              deleteModelData();
-              db.close();
-              return reject(getInfoRequest.error);
-            };
-          }
-        };
-        getInfoRequest.onerror = error => {
-          db.close();
-          return reject(getInfoRequest.error);
-        };
-
-        infoTx.oncomplete = () => {
-          if (modelTx == null) {
-            db.close();
-          } else {
-            modelTx.oncomplete = () => db.close();
-          }
-        };
-      };
-      openRequest.onerror = error => reject(openRequest.error);
-    });
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/io/indexed_db_test.ts b/tfjs-master/tfjs-core/src/io/indexed_db_test.ts
deleted file mode 100644
index 2aaff05e2..000000000
--- a/tfjs-master/tfjs-core/src/io/indexed_db_test.ts
+++ /dev/null
@@ -1,307 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit tests for indexed_db.ts.
- */
-
-import * as tf from '../index';
-import {BROWSER_ENVS, describeWithFlags, runWithLock} from '../jasmine_util';
-import {expectArrayBuffersEqual} from '../test_util';
-import {browserIndexedDB, BrowserIndexedDB, BrowserIndexedDBManager, deleteDatabase, indexedDBRouter} from './indexed_db';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-
-describeWithFlags('IndexedDB', BROWSER_ENVS, () => {
-  // Test data.
-  const modelTopology1: {} = {
-    'class_name': 'Sequential',
-    'keras_version': '2.1.4',
-    'config': [{
-      'class_name': 'Dense',
-      'config': {
-        'kernel_initializer': {
-          'class_name': 'VarianceScaling',
-          'config': {
-            'distribution': 'uniform',
-            'scale': 1.0,
-            'seed': null,
-            'mode': 'fan_avg'
-          }
-        },
-        'name': 'dense',
-        'kernel_constraint': null,
-        'bias_regularizer': null,
-        'bias_constraint': null,
-        'dtype': 'float32',
-        'activation': 'linear',
-        'trainable': true,
-        'kernel_regularizer': null,
-        'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-        'units': 1,
-        'batch_input_shape': [null, 3],
-        'use_bias': true,
-        'activity_regularizer': null
-      }
-    }],
-    'backend': 'tensorflow'
-  };
-  const weightSpecs1: tf.io.WeightsManifestEntry[] = [
-    {
-      name: 'dense/kernel',
-      shape: [3, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense/bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-  const weightData1 = new ArrayBuffer(16);
-  const artifacts1: tf.io.ModelArtifacts = {
-    modelTopology: modelTopology1,
-    weightSpecs: weightSpecs1,
-    weightData: weightData1,
-    format: 'layers-model',
-    generatedBy: 'TensorFlow.js v0.0.0',
-    convertedBy: null,
-    modelInitializer: {}
-  };
-
-  const weightSpecs2: tf.io.WeightsManifestEntry[] = [
-    {
-      name: 'dense/new_kernel',
-      shape: [5, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense/new_bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-
-  beforeEach(deleteDatabase);
-
-  afterEach(deleteDatabase);
-
-  it('Save-load round trip', runWithLock(async () => {
-       const testStartDate = new Date();
-       const handler = tf.io.getSaveHandlers('indexeddb://FooModel')[0];
-
-       const saveResult = await handler.save(artifacts1);
-       expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
-           .toBeGreaterThanOrEqual(testStartDate.getTime());
-       // Note: The following two assertions work only because there is no
-       //   non-ASCII characters in `modelTopology1` and `weightSpecs1`.
-       expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-           .toEqual(JSON.stringify(modelTopology1).length);
-       expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
-           .toEqual(JSON.stringify(weightSpecs1).length);
-       expect(saveResult.modelArtifactsInfo.weightDataBytes)
-           .toEqual(weightData1.byteLength);
-
-       const loadedArtifacts = await handler.load();
-       expect(loadedArtifacts.modelTopology).toEqual(modelTopology1);
-       expect(loadedArtifacts.weightSpecs).toEqual(weightSpecs1);
-       expect(loadedArtifacts.format).toEqual('layers-model');
-       expect(loadedArtifacts.generatedBy).toEqual('TensorFlow.js v0.0.0');
-       expect(loadedArtifacts.convertedBy).toEqual(null);
-       expect(loadedArtifacts.modelInitializer).toEqual({});
-       expectArrayBuffersEqual(CompositeArrayBuffer.join(
-           loadedArtifacts.weightData), weightData1);
-  }));
-
-  it('Save two models and load one', runWithLock(async () => {
-       const weightData2 = new ArrayBuffer(24);
-       const artifacts2: tf.io.ModelArtifacts = {
-         modelTopology: modelTopology1,
-         weightSpecs: weightSpecs2,
-         weightData: weightData2,
-       };
-       const handler1 = tf.io.getSaveHandlers('indexeddb://Model/1')[0];
-       const saveResult1 = await handler1.save(artifacts1);
-       // Note: The following two assertions work only because there is no
-       // non-ASCII characters in `modelTopology1` and `weightSpecs1`.
-       expect(saveResult1.modelArtifactsInfo.modelTopologyBytes)
-           .toEqual(JSON.stringify(modelTopology1).length);
-       expect(saveResult1.modelArtifactsInfo.weightSpecsBytes)
-           .toEqual(JSON.stringify(weightSpecs1).length);
-       expect(saveResult1.modelArtifactsInfo.weightDataBytes)
-           .toEqual(weightData1.byteLength);
-
-       const handler2 = tf.io.getSaveHandlers('indexeddb://Model/2')[0];
-       const saveResult2 = await handler2.save(artifacts2);
-       expect(saveResult2.modelArtifactsInfo.dateSaved.getTime())
-           .toBeGreaterThanOrEqual(
-               saveResult1.modelArtifactsInfo.dateSaved.getTime());
-       // Note: The following two assertions work only because there is
-       // no non-ASCII characters in `modelTopology1` and
-       // `weightSpecs1`.
-       expect(saveResult2.modelArtifactsInfo.modelTopologyBytes)
-           .toEqual(JSON.stringify(modelTopology1).length);
-       expect(saveResult2.modelArtifactsInfo.weightSpecsBytes)
-           .toEqual(JSON.stringify(weightSpecs2).length);
-       expect(saveResult2.modelArtifactsInfo.weightDataBytes)
-           .toEqual(weightData2.byteLength);
-
-       const loadedArtifacts = await handler1.load();
-       expect(loadedArtifacts.modelTopology).toEqual(modelTopology1);
-       expect(loadedArtifacts.weightSpecs).toEqual(weightSpecs1);
-       expect(loadedArtifacts.weightData).toBeDefined();
-       expectArrayBuffersEqual(CompositeArrayBuffer.join(
-           loadedArtifacts.weightData), weightData1);
-     }));
-
-  it('Loading nonexistent model fails', runWithLock(async () => {
-       const handler = tf.io.getSaveHandlers('indexeddb://NonexistentModel')[0];
-
-       try {
-         await handler.load();
-         fail('Loading nonexistent model from IndexedDB succeeded unexpectly');
-       } catch (err) {
-         expect(err.message)
-             .toEqual(
-                 'Cannot find model ' +
-                 'with path \'NonexistentModel\' in IndexedDB.');
-       }
-     }));
-
-  it('Null, undefined or empty modelPath throws Error', () => {
-    expect(() => browserIndexedDB(null))
-        .toThrowError(
-            /IndexedDB, modelPath must not be null, undefined or empty/);
-    expect(() => browserIndexedDB(undefined))
-        .toThrowError(
-            /IndexedDB, modelPath must not be null, undefined or empty/);
-    expect(() => browserIndexedDB(''))
-        .toThrowError(
-            /IndexedDB, modelPath must not be null, undefined or empty./);
-  });
-
-  it('router', () => {
-    expect(indexedDBRouter('indexeddb://bar') instanceof BrowserIndexedDB)
-        .toEqual(true);
-    expect(indexedDBRouter('localstorage://bar')).toBeNull();
-    expect(indexedDBRouter('qux')).toBeNull();
-  });
-
-  it('Manager: List models: 0 result', runWithLock(async () => {
-       // Before any model is saved, listModels should return empty result.
-       const models = await new BrowserIndexedDBManager().listModels();
-       expect(models).toEqual({});
-     }));
-
-  it('Manager: List models: 1 result', runWithLock(async () => {
-       const handler = tf.io.getSaveHandlers('indexeddb://baz/QuxModel')[0];
-       const saveResult = await handler.save(artifacts1);
-
-       // After successful saving, there should be one model.
-       const models = await new BrowserIndexedDBManager().listModels();
-       expect(Object.keys(models).length).toEqual(1);
-       expect(models['baz/QuxModel'].modelTopologyType)
-           .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
-       expect(models['baz/QuxModel'].modelTopologyBytes)
-           .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
-       expect(models['baz/QuxModel'].weightSpecsBytes)
-           .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
-       expect(models['baz/QuxModel'].weightDataBytes)
-           .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
-     }));
-
-  it('Manager: List models: 2 results', runWithLock(async () => {
-       // First, save a model.
-       const handler1 = tf.io.getSaveHandlers('indexeddb://QuxModel')[0];
-       const saveResult1 = await handler1.save(artifacts1);
-
-       // Then, save the model under another path.
-       const handler2 = tf.io.getSaveHandlers('indexeddb://repeat/QuxModel')[0];
-       const saveResult2 = await handler2.save(artifacts1);
-
-       // After successful saving, there should be two models.
-       const models = await new BrowserIndexedDBManager().listModels();
-       expect(Object.keys(models).length).toEqual(2);
-       expect(models['QuxModel'].modelTopologyType)
-           .toEqual(saveResult1.modelArtifactsInfo.modelTopologyType);
-       expect(models['QuxModel'].modelTopologyBytes)
-           .toEqual(saveResult1.modelArtifactsInfo.modelTopologyBytes);
-       expect(models['QuxModel'].weightSpecsBytes)
-           .toEqual(saveResult1.modelArtifactsInfo.weightSpecsBytes);
-       expect(models['QuxModel'].weightDataBytes)
-           .toEqual(saveResult1.modelArtifactsInfo.weightDataBytes);
-       expect(models['repeat/QuxModel'].modelTopologyType)
-           .toEqual(saveResult2.modelArtifactsInfo.modelTopologyType);
-       expect(models['repeat/QuxModel'].modelTopologyBytes)
-           .toEqual(saveResult2.modelArtifactsInfo.modelTopologyBytes);
-       expect(models['repeat/QuxModel'].weightSpecsBytes)
-           .toEqual(saveResult2.modelArtifactsInfo.weightSpecsBytes);
-       expect(models['repeat/QuxModel'].weightDataBytes)
-           .toEqual(saveResult2.modelArtifactsInfo.weightDataBytes);
-     }));
-
-  it('Manager: Successful removeModel', runWithLock(async () => {
-       // First, save a model.
-       const handler1 = tf.io.getSaveHandlers('indexeddb://QuxModel')[0];
-       await handler1.save(artifacts1);
-
-       // Then, save the model under another path.
-       const handler2 = tf.io.getSaveHandlers('indexeddb://repeat/QuxModel')[0];
-       await handler2.save(artifacts1);
-
-       // After successful saving, delete the first save, and then
-       // `listModel` should give only one result.
-       const manager = new BrowserIndexedDBManager();
-       await manager.removeModel('QuxModel');
-
-       const models = await manager.listModels();
-       expect(Object.keys(models)).toEqual(['repeat/QuxModel']);
-     }));
-
-  it('Manager: Successful removeModel with URL scheme',
-     runWithLock(async () => {
-       // First, save a model.
-       const handler1 = tf.io.getSaveHandlers('indexeddb://QuxModel')[0];
-       await handler1.save(artifacts1);
-
-       // Then, save the model under another path.
-       const handler2 = tf.io.getSaveHandlers('indexeddb://repeat/QuxModel')[0];
-       await handler2.save(artifacts1);
-
-       // After successful saving, delete the first save, and then
-       // `listModel` should give only one result.
-       const manager = new BrowserIndexedDBManager();
-
-       // Delete a model specified with a path that includes the
-       // indexeddb:// scheme prefix should work.
-       manager.removeModel('indexeddb://QuxModel');
-
-       const models = await manager.listModels();
-       expect(Object.keys(models)).toEqual(['repeat/QuxModel']);
-     }));
-
-  it('Manager: Failed removeModel', runWithLock(async () => {
-       try {
-         // Attempt to delete a nonexistent model is expected to fail.
-         await new BrowserIndexedDBManager().removeModel('nonexistent');
-         fail('Deleting nonexistent model succeeded unexpectedly.');
-       } catch (err) {
-         expect(err.message)
-             .toEqual(
-                 'Cannot find model with path \'nonexistent\' in IndexedDB.');
-       }
-     }));
-});
diff --git a/tfjs-master/tfjs-core/src/io/io.ts b/tfjs-master/tfjs-core/src/io/io.ts
deleted file mode 100644
index 49e9a1e2e..000000000
--- a/tfjs-master/tfjs-core/src/io/io.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Importing local_storage and indexed_db is necessary for the routers to be
-// registered.
-import './indexed_db';
-import './local_storage';
-
-import {browserFiles} from './browser_files';
-import {browserHTTPRequest, http, isHTTPScheme} from './http';
-import {concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsForJSON, getModelArtifactsForJSONSync, getModelArtifactsInfoForJSON, getWeightSpecs} from './io_utils';
-import {fromMemory, fromMemorySync, withSaveHandler, withSaveHandlerSync} from './passthrough';
-import {getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter} from './router_registry';
-import {IOHandler, IOHandlerSync, LoadHandler, LoadOptions, ModelArtifacts, ModelArtifactsInfo, ModelJSON, ModelStoreManager, OnProgressCallback, RequestDetails, SaveConfig, SaveHandler, SaveResult, TrainingConfig, WeightGroup, WeightsManifestConfig, WeightsManifestEntry, WeightData} from './types';
-import {loadWeights, weightsLoaderFactory} from './weights_loader';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-
-export {copyModel, listModels, moveModel, removeModel} from './model_management';
-export {
-  browserFiles,
-  browserHTTPRequest,
-  CompositeArrayBuffer,
-  concatenateArrayBuffers,
-  decodeWeights,
-  encodeWeights,
-  fromMemory,
-  fromMemorySync,
-  getLoadHandlers,
-  getModelArtifactsForJSON,
-  getModelArtifactsForJSONSync,
-  getModelArtifactsInfoForJSON,
-  getSaveHandlers,
-  getWeightSpecs,
-  http,
-  IOHandler,
-  IOHandlerSync,
-  isHTTPScheme,
-  LoadHandler,
-  LoadOptions,
-  loadWeights,
-  ModelArtifacts,
-  ModelArtifactsInfo,
-  ModelJSON,
-  ModelStoreManager,
-  OnProgressCallback,
-  registerLoadRouter,
-  registerSaveRouter,
-  RequestDetails,
-  SaveConfig,
-  SaveHandler,
-  SaveResult,
-  TrainingConfig,
-  WeightData,
-  WeightGroup,
-  weightsLoaderFactory,
-  WeightsManifestConfig,
-  WeightsManifestEntry,
-  withSaveHandler,
-  withSaveHandlerSync,
-};
diff --git a/tfjs-master/tfjs-core/src/io/io_utils.ts b/tfjs-master/tfjs-core/src/io/io_utils.ts
deleted file mode 100644
index fa9005a9b..000000000
--- a/tfjs-master/tfjs-core/src/io/io_utils.ts
+++ /dev/null
@@ -1,622 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {complex} from '../ops/complex';
-import {tensor} from '../ops/tensor';
-import {NamedTensor, NamedTensorMap} from '../tensor_types';
-import {TypedArray} from '../types';
-import {sizeFromShape} from '../util';
-
-import {DTYPE_VALUE_SIZE_MAP, ModelArtifacts, ModelArtifactsInfo, ModelJSON, WeightData, WeightGroup, WeightsManifestConfig, WeightsManifestEntry} from './types';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-
-/** Number of bytes reserved for the length of the string. (32bit integer). */
-const NUM_BYTES_STRING_LENGTH = 4;
-
-/**
- * Encode a map from names to weight values as an ArrayBuffer, along with an
- * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
- *
- * This function does not perform sharding.
- *
- * This function is the reverse of `decodeWeights`.
- *
- * @param tensors A map ("dict") from names to tensors.
- * @param group Group to which the weights belong (optional).
- * @returns A `Promise` of
- *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
- *     concatenated.
- *   - An `Array` of `WeightManifestEntry`s, carrying information including
- *     tensor names, `dtype`s and shapes.
- * @throws Error: on unsupported tensor `dtype`.
- */
-export async function encodeWeights(
-    tensors: NamedTensorMap|NamedTensor[], group?: WeightGroup):
-    Promise<{data: ArrayBuffer, specs: WeightsManifestEntry[]}> {
-  // TODO(adarob, cais): Support quantization.
-  const specs: WeightsManifestEntry[] = [];
-  const dataPromises: Array<Promise<TypedArray>> = [];
-
-  const names: string[] = Array.isArray(tensors) ?
-      tensors.map(tensor => tensor.name) :
-      Object.keys(tensors);
-
-  for (let i = 0; i < names.length; ++i) {
-    const name = names[i];
-    const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
-    if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&
-        t.dtype !== 'string' && t.dtype !== 'complex64') {
-      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);
-    }
-    const spec: WeightsManifestEntry = {name, shape: t.shape, dtype: t.dtype};
-    if (t.dtype === 'string') {
-      const utf8bytes = new Promise<TypedArray>(async resolve => {
-        const vals = await t.bytes() as Uint8Array[];
-        const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +
-            NUM_BYTES_STRING_LENGTH * vals.length;
-        const bytes = new Uint8Array(totalNumBytes);
-        let offset = 0;
-        for (let i = 0; i < vals.length; i++) {
-          const val = vals[i];
-          const bytesOfLength =
-              new Uint8Array(new Uint32Array([val.length]).buffer);
-          bytes.set(bytesOfLength, offset);
-          offset += NUM_BYTES_STRING_LENGTH;
-          bytes.set(val, offset);
-          offset += val.length;
-        }
-        resolve(bytes);
-      });
-      dataPromises.push(utf8bytes);
-    } else {
-      dataPromises.push(t.data());
-    }
-    if (group != null) {
-      spec.group = group;
-    }
-    specs.push(spec);
-  }
-
-  const tensorValues = await Promise.all(dataPromises);
-  return {data: concatenateTypedArrays(tensorValues), specs};
-}
-
-/**
- * Decode flat ArrayBuffer as weights.
- *
- * This function does not handle sharding.
- *
- * This function is the reverse of `encodeWeights`.
- *
- * @param weightData A flat ArrayBuffer or an array of ArrayBuffers carrying the
- *   binary values of the tensors concatenated in the order specified in
- *   `specs`.
- * @param specs Specifications of the names, dtypes and shapes of the tensors
- *   whose value are encoded by `buffer`.
- * @return A map from tensor name to tensor value, with the names corresponding
- *   to names in `specs`.
- * @throws Error, if any of the tensors has unsupported dtype.
- */
-export function decodeWeights(
-    weightData: WeightData,
-    specs: WeightsManifestEntry[]): NamedTensorMap {
-  // TODO(adarob, cais): Support quantization.
-  const compositeBuffer = new CompositeArrayBuffer(weightData);
-  const out: NamedTensorMap = {};
-  let float16Decode: (buffer: Uint16Array) => Float32Array | undefined;
-  let offset = 0;
-  for (const spec of specs) {
-    const name = spec.name;
-    const dtype = spec.dtype;
-    const shape = spec.shape;
-    const size = sizeFromShape(shape);
-    let values: TypedArray|string[]|Uint8Array[];
-
-    if ('quantization' in spec) {
-      const quantization = spec.quantization;
-      if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {
-        if (!('min' in quantization && 'scale' in quantization)) {
-          throw new Error(
-              `Weight ${spec.name} with quantization ${quantization.dtype} ` +
-              `doesn't have corresponding metadata min and scale.`);
-        }
-      } else if (quantization.dtype === 'float16') {
-        if (dtype !== 'float32') {
-          throw new Error(
-              `Weight ${spec.name} is quantized with ${quantization.dtype} ` +
-              `which only supports weights of type float32 not ${dtype}.`);
-        }
-      } else {
-        throw new Error(
-            `Weight ${spec.name} has unknown ` +
-            `quantization dtype ${quantization.dtype}. ` +
-            `Supported quantization dtypes are: ` +
-            `'uint8', 'uint16', and 'float16'.`);
-      }
-      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
-      const byteBuffer =
-          compositeBuffer.slice(offset, offset + size * quantizationSizeFactor);
-      const quantizedArray = (quantization.dtype === 'uint8') ?
-          new Uint8Array(byteBuffer) :
-          new Uint16Array(byteBuffer);
-      if (dtype === 'float32') {
-        if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {
-          values = new Float32Array(quantizedArray.length);
-          for (let i = 0; i < quantizedArray.length; i++) {
-            const v = quantizedArray[i];
-            values[i] = v * quantization.scale + quantization.min;
-          }
-        } else if (quantization.dtype === 'float16') {
-          if (float16Decode === undefined) {
-            float16Decode = getFloat16Decoder();
-          }
-          values = float16Decode(quantizedArray as Uint16Array);
-        } else {
-          throw new Error(
-              `Unsupported quantization type ${quantization.dtype} ` +
-              `for weight type float32.`);
-        }
-      } else if (dtype === 'int32') {
-        if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {
-          throw new Error(
-              `Unsupported quantization type ${quantization.dtype} ` +
-              `for weight type int32.`);
-        }
-        values = new Int32Array(quantizedArray.length);
-        for (let i = 0; i < quantizedArray.length; i++) {
-          const v = quantizedArray[i];
-          values[i] = Math.round(v * quantization.scale + quantization.min);
-        }
-      } else {
-        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-      }
-      offset += size * quantizationSizeFactor;
-    } else if (dtype === 'string') {
-      const size = sizeFromShape(spec.shape);
-      values = [];
-      for (let i = 0; i < size; i++) {
-        const byteLength = new Uint32Array(
-            compositeBuffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
-        offset += NUM_BYTES_STRING_LENGTH;
-        const bytes = new Uint8Array(
-          compositeBuffer.slice(offset, offset + byteLength));
-        (values as Uint8Array[]).push(bytes);
-        offset += byteLength;
-      }
-    } else {
-      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
-      const byteBuffer = compositeBuffer.slice(offset,
-                                               offset + size * dtypeFactor);
-
-      if (dtype === 'float32') {
-        values = new Float32Array(byteBuffer);
-      } else if (dtype === 'int32') {
-        values = new Int32Array(byteBuffer);
-      } else if (dtype === 'bool') {
-        values = new Uint8Array(byteBuffer);
-      } else if (dtype === 'complex64') {
-        values = new Float32Array(byteBuffer);
-        const real = new Float32Array(values.length / 2);
-        const image = new Float32Array(values.length / 2);
-        for (let i = 0; i < real.length; i++) {
-          real[i] = values[i * 2];
-          image[i] = values[i * 2 + 1];
-        }
-        const realTensor = tensor(real, shape, 'float32');
-        const imageTensor = tensor(image, shape, 'float32');
-        out[name] = complex(realTensor, imageTensor);
-        realTensor.dispose();
-        imageTensor.dispose();
-      } else {
-        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
-      }
-      offset += size * dtypeFactor;
-    }
-    if (dtype !== 'complex64') {
-      out[name] = tensor(values, shape, dtype);
-    }
-  }
-  return out;
-}
-
-/**
- * Concatenate TypedArrays into an ArrayBuffer.
- */
-export function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer {
-  // TODO(adarob, cais): Support quantization.
-  if (xs === null) {
-    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
-  }
-
-  let totalByteLength = 0;
-
-  // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'
-  // can have a different byte length from that of the `TypedArray` itself,
-  // for example, when the `TypedArray` is created from an offset in an
-  // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match
-  // the `TypedArray` in byte length. If an element of `xs` does not show
-  // this property, a new `TypedArray` that satisfy this property will be
-  // constructed and pushed into `normalizedXs`.
-  const normalizedXs: TypedArray[] = [];
-  xs.forEach((x: TypedArray) => {
-    totalByteLength += x.byteLength;
-    // tslint:disable:no-any
-    normalizedXs.push(
-        x.byteLength === x.buffer.byteLength ? x :
-                                               new (x.constructor as any)(x));
-    if (!(x as any instanceof Float32Array || x as any instanceof Int32Array ||
-          x as any instanceof Uint8Array)) {
-      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
-    }
-    // tslint:enable:no-any
-  });
-
-  const y = new Uint8Array(totalByteLength);
-  let offset = 0;
-  normalizedXs.forEach((x: TypedArray) => {
-    y.set(new Uint8Array(x.buffer), offset);
-    offset += x.byteLength;
-  });
-
-  return y.buffer;
-}
-
-// Use Buffer on Node.js instead of Blob/atob/btoa
-const useNodeBuffer = typeof Buffer !== 'undefined' &&
-    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||
-     typeof btoa === 'undefined');
-
-/**
- * Calculate the byte length of a JavaScript string.
- *
- * Note that a JavaScript string can contain wide characters, therefore the
- * length of the string is not necessarily equal to the byte length.
- *
- * @param str Input string.
- * @returns Byte length.
- */
-export function stringByteLength(str: string): number {
-  if (useNodeBuffer) {
-    return Buffer.byteLength(str, 'utf8');
-  }
-  return new Blob([str]).size;
-}
-
-/**
- * Encode an ArrayBuffer as a base64 encoded string.
- *
- * @param buffer `ArrayBuffer` to be converted.
- * @returns A string that base64-encodes `buffer`.
- */
-export function arrayBufferToBase64String(buffer: ArrayBuffer): string {
-  if (useNodeBuffer) {
-    return Buffer.from(buffer).toString('base64');
-  }
-  const buf = new Uint8Array(buffer);
-  let s = '';
-  for (let i = 0, l = buf.length; i < l; i++) {
-    s += String.fromCharCode(buf[i]);
-  }
-  return btoa(s);
-}
-
-/**
- * Decode a base64 string as an ArrayBuffer.
- *
- * @param str Base64 string.
- * @returns Decoded `ArrayBuffer`.
- */
-export function base64StringToArrayBuffer(str: string): ArrayBuffer {
-  if (useNodeBuffer) {
-    const buf = Buffer.from(str, 'base64');
-    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
-  }
-  const s = atob(str);
-  const buffer = new Uint8Array(s.length);
-  for (let i = 0; i < s.length; ++i) {
-    buffer.set([s.charCodeAt(i)], i);
-  }
-  return buffer.buffer;
-}
-
-/**
- * Concatenate a number of ArrayBuffers into one.
- *
- * @param buffers An array of ArrayBuffers to concatenate, or a single
- *     ArrayBuffer.
- * @returns Result of concatenating `buffers` in order.
- *
- * @deprecated Use tf.io.CompositeArrayBuffer.join() instead.
- */
-export function concatenateArrayBuffers(buffers: ArrayBuffer[]
-      | ArrayBuffer): ArrayBuffer {
-  return CompositeArrayBuffer.join(buffers);
-}
-
-/**
- * Get the basename of a path.
- *
- * Behaves in a way analogous to Linux's basename command.
- *
- * @param path
- */
-export function basename(path: string): string {
-  const SEPARATOR = '/';
-  path = path.trim();
-  while (path.endsWith(SEPARATOR)) {
-    path = path.slice(0, path.length - 1);
-  }
-  const items = path.split(SEPARATOR);
-  return items[items.length - 1];
-}
-
-/**
- * Create `ModelJSON` from `ModelArtifacts`.
- *
- * @param artifacts Model artifacts, describing the model and its weights.
- * @param manifest Weight manifest, describing where the weights of the
- *     `ModelArtifacts` are stored, and some metadata about them.
- * @returns Object representing the `model.json` file describing the model
- *     artifacts and weights
- */
-export function getModelJSONForModelArtifacts(
-    artifacts: ModelArtifacts, manifest: WeightsManifestConfig): ModelJSON {
-  const result: ModelJSON = {
-    modelTopology: artifacts.modelTopology,
-    format: artifacts.format,
-    generatedBy: artifacts.generatedBy,
-    convertedBy: artifacts.convertedBy,
-    weightsManifest: manifest
-  };
-  if (artifacts.signature != null) {
-    result.signature = artifacts.signature;
-  }
-  if (artifacts.userDefinedMetadata != null) {
-    result.userDefinedMetadata = artifacts.userDefinedMetadata;
-  }
-  if (artifacts.modelInitializer != null) {
-    result.modelInitializer = artifacts.modelInitializer;
-  }
-  if (artifacts.initializerSignature != null) {
-    result.initializerSignature = artifacts.initializerSignature;
-  }
-  if (artifacts.trainingConfig != null) {
-    result.trainingConfig = artifacts.trainingConfig;
-  }
-  return result;
-}
-
-/**
- * Create `ModelArtifacts` from a JSON file and weights.
- *
- * @param modelJSON Object containing the parsed JSON of `model.json`
- * @param weightSpecs The list of WeightsManifestEntry for the model. Must be
- *     passed if the modelJSON has a weightsManifest.
- * @param weightData An ArrayBuffer or array of ArrayBuffers of weight data for
- *     the model corresponding to the weights in weightSpecs. Must be passed if
- *     the modelJSON has a weightsManifest.
- * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
- */
-export function getModelArtifactsForJSONSync(
-    modelJSON: ModelJSON, weightSpecs?: WeightsManifestEntry[],
-    weightData?: WeightData): ModelArtifacts {
-
-  const modelArtifacts: ModelArtifacts = {
-    modelTopology: modelJSON.modelTopology,
-    format: modelJSON.format,
-    generatedBy: modelJSON.generatedBy,
-    convertedBy: modelJSON.convertedBy
-  };
-
-  if (modelJSON.trainingConfig != null) {
-    modelArtifacts.trainingConfig = modelJSON.trainingConfig;
-  }
-  if (modelJSON.weightsManifest != null) {
-    if (!weightSpecs) {
-      throw new Error('modelJSON has weightsManifest but weightSpecs is null');
-    }
-    if (!weightData) {
-      throw new Error('modelJSON has weightsManifest but weightData is null');
-    }
-    modelArtifacts.weightSpecs = weightSpecs;
-    modelArtifacts.weightData = weightData;
-  }
-  if (modelJSON.signature != null) {
-    modelArtifacts.signature = modelJSON.signature;
-  }
-  if (modelJSON.userDefinedMetadata != null) {
-    modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;
-  }
-  if (modelJSON.modelInitializer != null) {
-    modelArtifacts.modelInitializer = modelJSON.modelInitializer;
-  }
-  if (modelJSON.initializerSignature != null) {
-    modelArtifacts.initializerSignature = modelJSON.initializerSignature;
-  }
-
-  return modelArtifacts;
-}
-
-/**
- * Create `ModelArtifacts` from a JSON file.
- *
- * @param modelJSON Object containing the parsed JSON of `model.json`
- * @param loadWeights Function that takes the JSON file's weights manifest,
- *     reads weights from the listed path(s), and returns a Promise of the
- *     weight manifest entries along with the weights data.
- * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
- */
-export async function getModelArtifactsForJSON(
-    modelJSON: ModelJSON,
-    loadWeights: (weightsManifest: WeightsManifestConfig) => Promise<[
-      /* weightSpecs */ WeightsManifestEntry[], WeightData,
-    ]>): Promise<ModelArtifacts> {
-  let weightSpecs: WeightsManifestEntry[] | undefined;
-  let weightData: WeightData | undefined;
-
-  if (modelJSON.weightsManifest != null) {
-    [weightSpecs, weightData] = await loadWeights(modelJSON.weightsManifest);
-  }
-
-  return getModelArtifactsForJSONSync(modelJSON, weightSpecs, weightData);
-}
-
-/**
- * Populate ModelArtifactsInfo fields for a model with JSON topology.
- * @param modelArtifacts
- * @returns A ModelArtifactsInfo object.
- */
-export function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts):
-    ModelArtifactsInfo {
-  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-    throw new Error('Expected JSON model topology, received ArrayBuffer.');
-  }
-
-  return {
-    dateSaved: new Date(),
-    modelTopologyType: 'JSON',
-    modelTopologyBytes: modelArtifacts.modelTopology == null ?
-        0 :
-        stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
-    weightSpecsBytes: modelArtifacts.weightSpecs == null ?
-        0 :
-        stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
-    weightDataBytes: modelArtifacts.weightData == null ?
-        0 :
-        new CompositeArrayBuffer(modelArtifacts.weightData).byteLength,
-  };
-}
-
-/**
- * Concatenate the weights stored in a WeightsManifestConfig into a list of
- * WeightsManifestEntry
- *
- * @param weightsManifest The WeightsManifestConfig to extract weights from.
- * @returns A list of WeightsManifestEntry of the weights in the weightsManifest
- */
-export function getWeightSpecs(weightsManifest: WeightsManifestConfig):
-    WeightsManifestEntry[] {
-  const weightSpecs: WeightsManifestEntry[] = [];
-  for (const entry of weightsManifest) {
-    weightSpecs.push(...entry.weights);
-  }
-  return weightSpecs;
-}
-
-/**
- * Computes mantisa table for casting Float16 to Float32
- * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
- *
- * @returns Uint32Array, 2048 mantissa lookup values.
- */
-function computeFloat16MantisaTable(): Uint32Array {
-  const convertMantissa = (i: number): number => {
-    let m = i << 13;
-    let e = 0;
-
-    while ((m & 0x00800000) === 0) {
-      e -= 0x00800000;
-      m <<= 1;
-    }
-    m &= ~0x00800000;
-    e += 0x38800000;
-
-    return m | e;
-  };
-
-  const mantisaTable = new Uint32Array(2048);
-
-  mantisaTable[0] = 0;
-  for (let i = 1; i < 1024; i++) {
-    mantisaTable[i] = convertMantissa(i);
-  }
-  for (let i = 1024; i < 2048; i++) {
-    mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);
-  }
-
-  return mantisaTable;
-}
-
-/**
- * Computes exponent table for casting Float16 to Float32
- * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
- *
- * @returns Uint32Array, 64 exponent lookup values.
- */
-function computeFloat16ExponentTable(): Uint32Array {
-  const exponentTable = new Uint32Array(64);
-
-  exponentTable[0] = 0;
-  exponentTable[31] = 0x47800000;
-  exponentTable[32] = 0x80000000;
-  exponentTable[63] = 0xc7800000;
-  for (let i = 1; i < 31; i++) {
-    exponentTable[i] = i << 23;
-  }
-  for (let i = 33; i < 63; i++) {
-    exponentTable[i] = 0x80000000 + ((i - 32) << 23);
-  }
-
-  return exponentTable;
-}
-
-/**
- * Computes offset table for casting Float16 to Float32
- * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
- *
- * @returns Uint32Array, 6d offset values.
- */
-function computeFloat16OffsetTable(): Uint32Array {
-  const offsetTable = new Uint32Array(64);
-
-  for (let i = 0; i < 64; i++) {
-    offsetTable[i] = 1024;
-  }
-  offsetTable[0] = offsetTable[32] = 0;
-
-  return offsetTable;
-}
-
-/**
- * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values
- * to a Float32Array.
- *
- * @returns Function (buffer: Uint16Array) => Float32Array which decodes
- *          the Uint16Array of Float16 bytes to a Float32Array.
- */
-export function getFloat16Decoder(): (buffer: Uint16Array) => Float32Array {
-  // Algorithm is based off of
-  // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
-
-  // Cache lookup tables
-  const mantisaTable = computeFloat16MantisaTable();
-  const exponentTable = computeFloat16ExponentTable();
-  const offsetTable = computeFloat16OffsetTable();
-
-  return (quantizedArray: Uint16Array) => {
-    const buffer = new ArrayBuffer(4 * quantizedArray.length);
-    const bufferUint32View = new Uint32Array(buffer);
-    for (let index = 0; index < quantizedArray.length; index++) {
-      const float16Bits = quantizedArray[index];
-      const float32Bits =
-          mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +
-          exponentTable[float16Bits >> 10];
-      bufferUint32View[index] = float32Bits;
-    }
-    return new Float32Array(buffer);
-  };
-}
diff --git a/tfjs-master/tfjs-core/src/io/io_utils_test.ts b/tfjs-master/tfjs-core/src/io/io_utils_test.ts
deleted file mode 100644
index 01e497c07..000000000
--- a/tfjs-master/tfjs-core/src/io/io_utils_test.ts
+++ /dev/null
@@ -1,733 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, BROWSER_ENVS, describeWithFlags} from '../jasmine_util';
-import {scalar, tensor1d, tensor2d} from '../ops/ops';
-import {NamedTensor, NamedTensorMap} from '../tensor_types';
-import {expectArraysEqual} from '../test_util';
-import {expectArraysClose} from '../test_util';
-import {encodeString} from '../util';
-
-import {arrayBufferToBase64String, base64StringToArrayBuffer, basename, concatenateArrayBuffers, concatenateTypedArrays, stringByteLength, getFloat16Decoder} from './io_utils';
-import {WeightsManifestEntry} from './types';
-
-describe('concatenateTypedArrays', () => {
-  it('Single float arrays', () => {
-    const x = new Float32Array([1.1, 2.2, 3.3]);
-    const buffer = concatenateTypedArrays([x]);
-    expect(buffer.byteLength).toEqual(12);
-    expect(new Float32Array(buffer, 0, 3)).toEqual(x);
-  });
-
-  it('Float arrays', () => {
-    const x = new Float32Array([1.1, 2.2, 3.3]);
-    const y = new Float32Array([-1.1, -2.2, -3.3]);
-    const buffer = concatenateTypedArrays([x, y]);
-    expect(buffer.byteLength).toEqual(24);
-    expect(new Float32Array(buffer, 0, 3)).toEqual(x);
-    expect(new Float32Array(buffer, 12, 3)).toEqual(y);
-  });
-  it('Single int32 arrays', () => {
-    const x = new Int32Array([11, 22, 33]);
-    const buffer = concatenateTypedArrays([x]);
-    expect(buffer.byteLength).toEqual(12);
-    expect(new Int32Array(buffer, 0, 3)).toEqual(x);
-  });
-
-  it('Int32 arrays', () => {
-    const x = new Int32Array([11, 22, 33]);
-    const y = new Int32Array([-11, -22, -33]);
-    const buffer = concatenateTypedArrays([x, y]);
-    expect(buffer.byteLength).toEqual(24);
-    expect(new Int32Array(buffer, 0, 3)).toEqual(x);
-    expect(new Int32Array(buffer, 12, 3)).toEqual(y);
-  });
-
-  it('Single uint8 arrays', () => {
-    const x = new Uint8Array([11, 22, 33]);
-    const buffer = concatenateTypedArrays([x]);
-    expect(buffer.byteLength).toEqual(3);
-    expect(new Uint8Array(buffer, 0, 3)).toEqual(x);
-  });
-
-  it('Uint8 arrays', () => {
-    const x = new Uint8Array([11, 22, 33]);
-    const y = new Uint8Array([111, 122, 133]);
-    const buffer = concatenateTypedArrays([x, y]);
-    expect(buffer.byteLength).toEqual(6);
-    expect(new Uint8Array(buffer, 0, 3)).toEqual(x);
-    expect(new Uint8Array(buffer, 3, 3)).toEqual(y);
-  });
-
-  it('Mixed Uint8, Int32 and Float32 arrays', () => {
-    const x = new Uint8Array([0, 1, 1, 0]);
-    const y = new Int32Array([10, 20, 30, 40]);
-    const z = new Float32Array([-1.1, -2.2, -3.3, -4.4]);
-    const buffer = concatenateTypedArrays([x, y, z]);
-    expect(buffer.byteLength).toEqual(1 * 4 + 4 * 4 + 4 * 4);
-    expect(new Uint8Array(buffer, 0, 4)).toEqual(x);
-    expect(new Int32Array(buffer, 4, 4)).toEqual(y);
-    expect(new Float32Array(buffer, 20, 4)).toEqual(z);
-  });
-
-  it('Concatenate Float32Arrays from SubArrays', () => {
-    const x1 = new Float32Array([1.1, 2.2, 3.3]);
-    const x2 = new Float32Array([-1.1, -2.2, -3.3]);
-    const xConcatenated = concatenateTypedArrays([x1, x2]);
-    const y1 = new Float32Array(xConcatenated, 0, 3);
-    const y2 = new Float32Array(xConcatenated, 3 * 4, 3);
-    // At this point, the buffer of y1 is longer than than the actual byte
-    // length of y1, because of the way y1 is constructed. The same is true for
-    // y2.
-    expect(y1.buffer.byteLength).toEqual(6 * 4);
-    expect(y2.buffer.byteLength).toEqual(6 * 4);
-
-    const yConcatenated = concatenateTypedArrays([y1, y2]);
-    expect(yConcatenated.byteLength).toEqual(6 * 4);
-    expect(new Float32Array(yConcatenated, 0, 3)).toEqual(x1);
-    expect(new Float32Array(yConcatenated, 3 * 4, 3)).toEqual(x2);
-  });
-
-  it('Concatenate Int32Array from SubArrays', () => {
-    const x1 = new Int32Array([11, 22, 33]);
-    const x2 = new Int32Array([-11, -22, -33]);
-    const xConcatenated = concatenateTypedArrays([x1, x2]);
-    const y1 = new Int32Array(xConcatenated, 0, 3);
-    const y2 = new Int32Array(xConcatenated, 3 * 4, 3);
-    // At this point, the buffer of y1 is longer than than the actual byte
-    // length of y1, because of the way y1 is constructed. The same is true for
-    // y2.
-    expect(y1.buffer.byteLength).toEqual(6 * 4);
-    expect(y2.buffer.byteLength).toEqual(6 * 4);
-
-    const yConcatenated = concatenateTypedArrays([y1, y2]);
-    expect(yConcatenated.byteLength).toEqual(6 * 4);
-    expect(new Int32Array(yConcatenated, 0, 3)).toEqual(x1);
-    expect(new Int32Array(yConcatenated, 3 * 4, 3)).toEqual(x2);
-  });
-
-  it('Concatenate Uint8Array from SubArrays', () => {
-    const x1 = new Uint8Array([11, 22, 33]);
-    const x2 = new Uint8Array([44, 55, 66]);
-    const xConcatenated = concatenateTypedArrays([x1, x2]);
-    const y1 = new Uint8Array(xConcatenated, 0, 3);
-    const y2 = new Uint8Array(xConcatenated, 3, 3);
-    // At this point, the buffer of y1 is longer than than the actual byte
-    // length of y1, because of the way y1 is constructed. The same is true for
-    // y2.
-    expect(y1.buffer.byteLength).toEqual(6);
-    expect(y2.buffer.byteLength).toEqual(6);
-
-    const yConcatenated = concatenateTypedArrays([y1, y2]);
-    expect(yConcatenated.byteLength).toEqual(6);
-    expect(new Uint8Array(yConcatenated, 0, 3)).toEqual(x1);
-    expect(new Uint8Array(yConcatenated, 3, 3)).toEqual(x2);
-  });
-
-  it('Concatenate mixed TypedArrays from SubArrays', () => {
-    const x1 = new Uint8Array([11, 22, 33, 44]);
-    const x2 = new Int32Array([-44, -55, -66]);
-    const x3 = new Float32Array([1.1, 2.2, 3.3]);
-    const xConcatenated = concatenateTypedArrays([x1, x2, x3]);
-    const y1 = new Uint8Array(xConcatenated, 0, 4);
-    const y2 = new Int32Array(xConcatenated, 4, 3);
-    const y3 = new Float32Array(xConcatenated, 4 + 3 * 4, 3);
-    // At this point, the buffer of y1 is longer than than the actual byte
-    // length of y1, because of the way y1 is constructed. The same is true for
-    // y2 and y3.
-    expect(y1.buffer.byteLength).toEqual(4 + 3 * 4 + 3 * 4);
-    expect(y2.buffer.byteLength).toEqual(4 + 3 * 4 + 3 * 4);
-    expect(y3.buffer.byteLength).toEqual(4 + 3 * 4 + 3 * 4);
-
-    const yConcatenated = concatenateTypedArrays([y1, y2, y3]);
-    expect(yConcatenated.byteLength).toEqual(4 + 3 * 4 + 3 * 4);
-    expect(new Uint8Array(yConcatenated, 0, 4)).toEqual(x1);
-    expect(new Int32Array(yConcatenated, 4, 3)).toEqual(x2);
-    expect(new Float32Array(yConcatenated, 4 + 3 * 4, 3)).toEqual(x3);
-  });
-
-  it('null and undefined inputs', () => {
-    expect(() => concatenateTypedArrays(null)).toThrow();
-    expect(() => concatenateTypedArrays(undefined)).toThrow();
-  });
-
-  it('empty input array', () => {
-    expect(concatenateTypedArrays([]).byteLength).toEqual(0);
-  });
-
-  it('Unsupported dtype', () => {
-    const x = new Int16Array([0, 1, 1, 0]);
-    // tslint:disable-next-line:no-any
-    expect(() => concatenateTypedArrays([x as any]))
-        .toThrowError(/Unsupported TypedArray subtype: Int16Array/);
-  });
-});
-
-describeWithFlags('encodeWeights', ALL_ENVS, () => {
-  it('Float32 tensors as NamedTensorMap', async () => {
-    const tensors: NamedTensorMap = {
-      x1: tensor2d([[10, 20], [30, 40]]),
-      x2: scalar(42),
-      x3: tensor1d([-1.3, -3.7, 1.3, 3.7]),
-    };
-    const dataAndSpecs = await tf.io.encodeWeights(tensors);
-    const data = dataAndSpecs.data;
-    const specs = dataAndSpecs.specs;
-    expect(data.byteLength).toEqual(4 * (4 + 1 + 4));
-    expect(new Float32Array(data, 0, 4)).toEqual(new Float32Array([
-      10, 20, 30, 40
-    ]));
-    expect(new Float32Array(data, 16, 1)).toEqual(new Float32Array([42]));
-    expect(new Float32Array(data, 20, 4)).toEqual(new Float32Array([
-      -1.3, -3.7, 1.3, 3.7
-    ]));
-    expect(specs).toEqual([
-      {
-        name: 'x1',
-        dtype: 'float32',
-        shape: [2, 2],
-      },
-      {
-        name: 'x2',
-        dtype: 'float32',
-        shape: [],
-      },
-      {
-        name: 'x3',
-        dtype: 'float32',
-        shape: [4],
-      }
-    ]);
-  });
-
-  it('Float32 tensors as NamedTensor array', async () => {
-    const tensors: NamedTensor[] = [
-      {name: 'x1234', tensor: tensor2d([[10, 20], [30, 40]])}, {
-        name: 'a42',
-        tensor: scalar(42),
-      },
-      {name: 'b41', tensor: tensor1d([-1.3, -3.7, 1.3, 3.7])}
-    ];
-    const dataAndSpecs = await tf.io.encodeWeights(tensors);
-    const data = dataAndSpecs.data;
-    const specs = dataAndSpecs.specs;
-    expect(data.byteLength).toEqual(4 * (4 + 1 + 4));
-    expect(new Float32Array(data, 0, 4)).toEqual(new Float32Array([
-      10, 20, 30, 40
-    ]));
-    expect(new Float32Array(data, 16, 1)).toEqual(new Float32Array([42]));
-    expect(new Float32Array(data, 20, 4)).toEqual(new Float32Array([
-      -1.3, -3.7, 1.3, 3.7
-    ]));
-    expect(specs).toEqual([
-      {
-        name: 'x1234',
-        dtype: 'float32',
-        shape: [2, 2],
-      },
-      {
-        name: 'a42',
-        dtype: 'float32',
-        shape: [],
-      },
-      {
-        name: 'b41',
-        dtype: 'float32',
-        shape: [4],
-      }
-    ]);
-  });
-
-  it('Empty NamedTensor array', async () => {
-    const tensors: NamedTensor[] = [];
-    const dataAndSpecs = await tf.io.encodeWeights(tensors);
-    const data = dataAndSpecs.data;
-    const specs = dataAndSpecs.specs;
-    expect(data.byteLength).toEqual(0);
-    expect(specs).toEqual([]);
-  });
-
-  it('Int32 tensors', async () => {
-    const tensors: NamedTensorMap = {
-      x1: tensor2d([[10, 20], [30, 40]], [2, 2], 'int32'),
-      x2: scalar(42, 'int32'),
-      x3: tensor1d([-1, -3, -3, -7], 'int32'),
-    };
-    const dataAndSpecs = await tf.io.encodeWeights(tensors);
-    const data = dataAndSpecs.data;
-    const specs = dataAndSpecs.specs;
-    expect(data.byteLength).toEqual(4 * (4 + 1 + 4));
-    expect(new Int32Array(data, 0, 4)).toEqual(new Int32Array([
-      10, 20, 30, 40
-    ]));
-    expect(new Int32Array(data, 16, 1)).toEqual(new Int32Array([42]));
-    expect(new Int32Array(data, 20, 4)).toEqual(new Int32Array([
-      -1, -3, -3, -7
-    ]));
-    expect(specs).toEqual([
-      {
-        name: 'x1',
-        dtype: 'int32',
-        shape: [2, 2],
-      },
-      {
-        name: 'x2',
-        dtype: 'int32',
-        shape: [],
-      },
-      {
-        name: 'x3',
-        dtype: 'int32',
-        shape: [4],
-      }
-    ]);
-  });
-
-  it('Bool tensors', async () => {
-    const tensors: NamedTensorMap = {
-      x1: tensor2d([[true, false], [false, true]], [2, 2], 'bool'),
-      x2: scalar(false, 'bool'),
-      x3: tensor1d([false, true, true, false], 'bool'),
-    };
-    const dataAndSpecs = await tf.io.encodeWeights(tensors);
-    const data = dataAndSpecs.data;
-    const specs = dataAndSpecs.specs;
-    expect(data.byteLength).toEqual(4 + 1 + 4);
-    expect(new Uint8Array(data, 0, 4)).toEqual(new Uint8Array([1, 0, 0, 1]));
-    expect(new Uint8Array(data, 4, 1)).toEqual(new Uint8Array([0]));
-    expect(new Uint8Array(data, 5, 4)).toEqual(new Uint8Array([0, 1, 1, 0]));
-    expect(specs).toEqual([
-      {
-        name: 'x1',
-        dtype: 'bool',
-        shape: [2, 2],
-      },
-      {
-        name: 'x2',
-        dtype: 'bool',
-        shape: [],
-      },
-      {
-        name: 'x3',
-        dtype: 'bool',
-        shape: [4],
-      }
-    ]);
-  });
-
-  it('Complex64 tensors', async () => {
-    const tensors: NamedTensorMap = {
-      x1: tf.complex([1, 2], [1, 2]),
-      x2: tf.complex(1, 2),
-      x3: tf.complex([[1]], [[2]]),
-    };
-    const dataAndSpecs = await tf.io.encodeWeights(tensors);
-    const data = dataAndSpecs.data;
-    const specs = dataAndSpecs.specs;
-    expect(data.byteLength).toEqual(8 * 4);
-    expect(new Float32Array(data, 0, 4)).toEqual(new Float32Array([
-      1, 1, 2, 2
-    ]));
-    expect(new Float32Array(data, 16, 2)).toEqual(new Float32Array([1, 2]));
-    expect(new Float32Array(data, 24, 2)).toEqual(new Float32Array([1, 2]));
-    expect(specs).toEqual([
-      {
-        name: 'x1',
-        dtype: 'complex64',
-        shape: [2],
-      },
-      {
-        name: 'x2',
-        dtype: 'complex64',
-        shape: [],
-      },
-      {
-        name: 'x3',
-        dtype: 'complex64',
-        shape: [1, 1],
-      }
-    ]);
-  });
-  it('String tensors', async () => {
-    const tensors: NamedTensorMap = {
-      x1: tensor2d([['a', 'bc'], ['def', 'g']], [2, 2]),
-      x2: scalar(''),                       // Empty string.
-      x3: tensor1d(['здраво', 'поздрав']),  // Cyrillic.
-      x4: scalar('正常'),                   // East Asian.
-      x5: scalar('hello')                   // Single string.
-    };
-    const dataAndSpecs = await tf.io.encodeWeights(tensors);
-    const data = dataAndSpecs.data;
-    const specs = dataAndSpecs.specs;
-    const x1ByteLength = 7 + 4 * 4;       // 7 ascii chars + 4 ints.
-    const x2ByteLength = 4;               // No chars + 1 int.
-    const x3ByteLength = 13 * 2 + 2 * 4;  // 13 cyrillic letters + 2 ints.
-    const x4ByteLength = 6 + 1 * 4;       // 2 east asian letters + 1 int.
-    const x5ByteLength = 5 + 1 * 4;       // 5 ascii chars + 1 int.
-    expect(data.byteLength)
-        .toEqual(
-            x1ByteLength + x2ByteLength + x3ByteLength + x4ByteLength +
-            x5ByteLength);
-    // x1 'a'.
-    expect(new Uint32Array(data, 0, 1)[0]).toBe(1);
-    expect(new Uint8Array(data, 4, 1)).toEqual(encodeString('a'));
-    // x1 'bc'.
-    expect(new Uint32Array(data.slice(5, 9))[0]).toBe(2);
-    expect(new Uint8Array(data, 9, 2)).toEqual(encodeString('bc'));
-    // x1 'def'.
-    expect(new Uint32Array(data.slice(11, 15))[0]).toBe(3);
-    expect(new Uint8Array(data, 15, 3)).toEqual(encodeString('def'));
-    // x1 'g'.
-    expect(new Uint32Array(data.slice(18, 22))[0]).toBe(1);
-    expect(new Uint8Array(data, 22, 1)).toEqual(encodeString('g'));
-
-    // x2 is empty string.
-    expect(new Uint32Array(data.slice(23, 27))[0]).toBe(0);
-
-    // x3 'здраво'.
-    expect(new Uint32Array(data.slice(27, 31))[0]).toBe(12);
-    expect(new Uint8Array(data, 31, 12)).toEqual(encodeString('здраво'));
-
-    // x3 'поздрав'.
-    expect(new Uint32Array(data.slice(43, 47))[0]).toBe(14);
-    expect(new Uint8Array(data, 47, 14)).toEqual(encodeString('поздрав'));
-
-    // x4 '正常'.
-    expect(new Uint32Array(data.slice(61, 65))[0]).toBe(6);
-    expect(new Uint8Array(data, 65, 6)).toEqual(encodeString('正常'));
-
-    // x5 'hello'.
-    expect(new Uint32Array(data.slice(71, 75))[0]).toBe(5);
-    expect(new Uint8Array(data, 75, 5)).toEqual(encodeString('hello'));
-
-    expect(specs).toEqual([
-      {name: 'x1', dtype: 'string', shape: [2, 2]},
-      {name: 'x2', dtype: 'string', shape: []},
-      {name: 'x3', dtype: 'string', shape: [2]},
-      {name: 'x4', dtype: 'string', shape: []},
-      {name: 'x5', dtype: 'string', shape: []}
-    ]);
-  });
-
-  it('Mixed dtype tensors', async () => {
-    const tensors: NamedTensorMap = {
-      x1: tensor2d([[10, 20], [30, 40]], [2, 2], 'int32'),
-      x2: scalar(13.37, 'float32'),
-      x3: tensor1d([true, false, false, true], 'bool'),
-      x4: tf.complex([1, 1], [2, 2])
-    };
-    const dataAndSpecs = await tf.io.encodeWeights(tensors);
-    const data = dataAndSpecs.data;
-    const specs = dataAndSpecs.specs;
-    expect(data.byteLength).toEqual(4 * 4 + 4 * 1 + 1 * 4 + 4 * 4);
-    expect(new Int32Array(data, 0, 4)).toEqual(new Int32Array([
-      10, 20, 30, 40
-    ]));
-    expect(new Float32Array(data, 16, 1)).toEqual(new Float32Array([13.37]));
-    expect(new Uint8Array(data, 20, 4)).toEqual(new Uint8Array([1, 0, 0, 1]));
-    expect(new Float32Array(data, 24, 4)).toEqual(new Float32Array([
-      1, 2, 1, 2
-    ]));
-    expect(specs).toEqual([
-      {
-        name: 'x1',
-        dtype: 'int32',
-        shape: [2, 2],
-      },
-      {
-        name: 'x2',
-        dtype: 'float32',
-        shape: [],
-      },
-      {
-        name: 'x3',
-        dtype: 'bool',
-        shape: [4],
-      },
-      {
-        name: 'x4',
-        dtype: 'complex64',
-        shape: [2],
-      }
-    ]);
-  });
-});
-
-describeWithFlags('decodeWeights', {}, () => {
-  it('Mixed dtype tensors', async () => {
-    const tensors: NamedTensorMap = {
-      x1: tensor2d([[10, 20], [30, 40]], [2, 2], 'int32'),
-      x2: scalar(13.37, 'float32'),
-      x3: tensor1d([true, false, false], 'bool'),
-      x4: tensor2d([['здраво', 'a'], ['b', 'c']], [2, 2], 'string'),
-      x5: tensor1d([''], 'string'),  // Empty string.
-      x6: scalar('hello'),           // Single string.
-      y1: tensor2d([-10, -20, -30], [3, 1], 'float32'),
-      y2: tf.complex([1, 1], [2, 2])
-    };
-    const dataAndSpecs = await tf.io.encodeWeights(tensors);
-    const data = dataAndSpecs.data;
-    const specs = dataAndSpecs.specs;
-    const decoded = tf.io.decodeWeights(data, specs);
-    expect(Object.keys(decoded).length).toEqual(8);
-    expectArraysEqual(await decoded['x1'].data(), await tensors['x1'].data());
-    expectArraysEqual(await decoded['x2'].data(), await tensors['x2'].data());
-    expectArraysEqual(await decoded['x3'].data(), await tensors['x3'].data());
-    expectArraysEqual(await decoded['x4'].data(), await tensors['x4'].data());
-    expectArraysEqual(await decoded['x5'].data(), await tensors['x5'].data());
-    expectArraysEqual(await decoded['x6'].data(), await tensors['x6'].data());
-    expectArraysEqual(await decoded['y1'].data(), await tensors['y1'].data());
-    expectArraysEqual(await decoded['y2'].data(), await tensors['y2'].data());
-  });
-
-  it('Unsupported dtype raises Error', () => {
-    const buffer = new ArrayBuffer(4);
-    // tslint:disable-next-line:no-any
-    const specs: any = [
-      {
-        name: 'x',
-        dtype: 'int16',
-        shape: [],
-      },
-      {name: 'y', dtype: 'int16', shape: []}
-    ];
-    expect(() => tf.io.decodeWeights(buffer, specs))
-        .toThrowError(/Unsupported dtype in weight \'x\': int16/);
-  });
-
-  it('support quantization uint8 weights', async () => {
-    const manifestSpecs: WeightsManifestEntry[] = [
-      {
-        'name': 'weight0',
-        'dtype': 'float32',
-        'shape': [3],
-        'quantization': {'min': -1, 'scale': 0.1, 'dtype': 'uint8'}
-      },
-      {
-        'name': 'weight1',
-        'dtype': 'int32',
-        'shape': [3],
-        'quantization': {'min': -1, 'scale': 0.1, 'dtype': 'uint8'}
-      }
-    ];
-    const data = new Uint8Array([0, 48, 255, 0, 48, 255]);
-    const decoded = tf.io.decodeWeights(data.buffer, manifestSpecs);
-    const weight0 = decoded['weight0'];
-    expectArraysClose(await weight0.data(), [-1, 3.8, 24.5]);
-    expect(weight0.shape).toEqual([3]);
-    expect(weight0.dtype).toEqual('float32');
-
-    const weight1 = decoded['weight1'];
-    expectArraysEqual(await weight1.data(), [-1, 4, 25]);
-    expect(weight1.shape).toEqual([3]);
-    expect(weight1.dtype).toEqual('int32');
-  });
-
-  it('support quantization uint16 weights', async () => {
-    const manifestSpecs: WeightsManifestEntry[] = [
-      {
-        'name': 'weight0',
-        'dtype': 'float32',
-        'shape': [3],
-        'quantization': {'min': -1, 'scale': 0.1, 'dtype': 'uint16'}
-      },
-      {
-        'name': 'weight1',
-        'dtype': 'int32',
-        'shape': [3],
-        'quantization': {'min': -1, 'scale': 0.1, 'dtype': 'uint16'}
-      }
-    ];
-    const data = new Uint16Array([0, 48, 255, 0, 48, 255]);
-    const decoded = tf.io.decodeWeights(data.buffer, manifestSpecs);
-    const weight0 = decoded['weight0'];
-    expectArraysClose(await weight0.data(), [-1, 3.8, 24.5]);
-    expect(weight0.shape).toEqual([3]);
-    expect(weight0.dtype).toEqual('float32');
-
-    const weight1 = decoded['weight1'];
-    expectArraysEqual(await weight1.data(), [-1, 4, 25]);
-    expect(weight1.shape).toEqual([3]);
-    expect(weight1.dtype).toEqual('int32');
-  });
-  it('support quantization float16 weights', async () => {
-    const manifestSpecs: WeightsManifestEntry[] = [
-      {
-        name: 'weight0',
-        dtype: 'float32',
-        shape: [3],
-        quantization: { dtype: 'float16' },
-      },
-    ];
-    const data = new Uint16Array([13312, 14336, 14848]);
-    const decoded = tf.io.decodeWeights(data.buffer, manifestSpecs);
-    const weight0 = decoded['weight0'];
-    expectArraysClose(await weight0.data(), [0.25, 0.5, 0.75]);
-    expect(weight0.shape).toEqual([3]);
-    expect(weight0.dtype).toEqual('float32');
-  });
-});
-
-describe('stringByteLength', () => {
-  it('ASCII only', () => {
-    const str = '_Lorem ipsum 1337!';
-    expect(stringByteLength(str)).toEqual(str.length);
-  });
-
-  it('Mixed narrow and wide chars', () => {
-    const str = 'aЖ文1';
-    expect(stringByteLength(str.slice(0, 1))).toEqual(1);
-    expect(stringByteLength(str.slice(0, 2))).toEqual(3);
-    expect(stringByteLength(str.slice(0, 3))).toEqual(6);
-    expect(stringByteLength(str.slice(0, 4))).toEqual(7);
-  });
-});
-
-describeWithFlags(
-    'arrayBufferToBase64String-base64StringToArrayBuffer', BROWSER_ENVS, () => {
-      it('Round trip', () => {
-        // Generate some semi-random binary data.
-        const x = [];
-        for (let k = 0; k < 2; ++k) {
-          for (let i = 0; i < 254; ++i) {
-            x.push(i + k);
-          }
-          for (let i = 254; i >= 0; --i) {
-            x.push(i + k);
-          }
-        }
-        const buffer = Uint8Array.from(x).buffer;
-        const base64Str = arrayBufferToBase64String(buffer);
-        const decoded =
-            Array.from(new Uint8Array(base64StringToArrayBuffer(base64Str)));
-        expect(decoded).toEqual(x);
-      });
-    });
-
-describe('concatenateArrayBuffers', () => {
-  // TODO(mattSoulanille): Move these tests to CompositeArrayBuffer.join when
-  // concatenateArrayBuffers is removed.
-  it('Concatenate 3 non-empty ArrayBuffers', () => {
-    const buffer1 = new Uint8Array([1, 2, 3]);
-    const buffer2 = new Uint8Array([11, 22, 33, 44]);
-    const buffer3 = new Uint8Array([111, 222, 100]);
-    const out = concatenateArrayBuffers(
-        [buffer1.buffer, buffer2.buffer, buffer3.buffer]);
-    expect(new Uint8Array(out)).toEqual(new Uint8Array([
-      1, 2, 3, 11, 22, 33, 44, 111, 222, 100
-    ]));
-  });
-
-  it('Concatenate non-empty and empty ArrayBuffers', () => {
-    const buffer1 = new Uint8Array([1, 2, 3]);
-    const buffer2 = new Uint8Array([11, 22, 33, 44]);
-    const buffer3 = new Uint8Array([]);
-    const buffer4 = new Uint8Array([150, 100, 50]);
-    const out = concatenateArrayBuffers(
-        [buffer1.buffer, buffer2.buffer, buffer3.buffer, buffer4.buffer]);
-    expect(new Uint8Array(out)).toEqual(new Uint8Array([
-      1, 2, 3, 11, 22, 33, 44, 150, 100, 50
-    ]));
-  });
-
-  it('A single ArrayBuffer', () => {
-    const buffer1 = new Uint8Array([1, 3, 3, 7]);
-    const out = concatenateArrayBuffers([buffer1.buffer]);
-    expect(new Uint8Array(out)).toEqual(buffer1);
-  });
-
-  it('Zero ArrayBuffers', () => {
-    expect(new Uint8Array(concatenateArrayBuffers([])))
-        .toEqual(new Uint8Array([]));
-  });
-});
-
-describe('basename', () => {
-  it('Paths without slashes', () => {
-    expect(basename('foo.txt')).toEqual('foo.txt');
-    expect(basename('bar')).toEqual('bar');
-  });
-
-  it('Paths with slashes', () => {
-    expect(basename('qux/foo.txt')).toEqual('foo.txt');
-    expect(basename('qux/My Model.json')).toEqual('My Model.json');
-    expect(basename('foo/bar/baz')).toEqual('baz');
-    expect(basename('/foo/bar/baz')).toEqual('baz');
-    expect(basename('foo/bar/baz/')).toEqual('baz');
-    expect(basename('foo/bar/baz//')).toEqual('baz');
-  });
-});
-
-describe('float16', () => {
-  it('decodes NaN to float32 NaN', () => {
-    const decoder = getFloat16Decoder();
-    const float16NaN = 0x00007e00;
-    const buffer = new Uint16Array([float16NaN]);
-    const f32 = decoder(buffer);
-    expect(f32).toEqual(new Float32Array([NaN]));
-  });
-
-  it('decodes ±Infinity to float32 ±Infinity', () => {
-    const decoder = getFloat16Decoder();
-    const positiveInfinity = 0x00007c00;
-    const negativeInfinity = 0xfffffc00;
-    const buffer = new Uint16Array([positiveInfinity, negativeInfinity]);
-    const f32 = decoder(buffer);
-    expect(f32).toEqual(new Float32Array([Infinity, -Infinity]));
-  });
-
-  it('decodes ±0 to float32 ±0', () => {
-    const decoder = getFloat16Decoder();
-    const positiveZero = 0x00000000;
-    const negativeZero = 0xffff8000;
-    const buffer = new Uint16Array([positiveZero, negativeZero]);
-    const f32 = decoder(buffer);
-    expect(f32).toEqual(new Float32Array([0.0, -0.0]));
-  });
-
-  it('decodes -Infinity on underflow', () => {
-    const decoder = getFloat16Decoder();
-    const minVal = 0xfffffbff;
-    const buffer = new Uint16Array([minVal + 1]);
-    const f32 = decoder(buffer);
-    expect(f32).toEqual(new Float32Array([-Infinity]));
-  });
-
-  it('decodes +Infinity on overflow', () => {
-    const decoder = getFloat16Decoder();
-    const maxVal = 0x00007bff;
-    const buffer = new Uint16Array([maxVal + 1]);
-    const f32 = decoder(buffer);
-    expect(f32).toEqual(new Float32Array([Infinity]));
-  });
-
-  it('decodes interpretable float16 to float32', () => {
-    const decoder = getFloat16Decoder();
-    const buffer = new Uint16Array([
-      0x00003400,
-      0x00003800,
-      0x00003A00,
-      0x00003555
-    ]);
-    const f32 = decoder(buffer);
-    expect(f32[0]).toBeCloseTo(0.25);
-    expect(f32[1]).toBeCloseTo(0.5);
-    expect(f32[2]).toBeCloseTo(0.75);
-    expect(f32[3]).toBeCloseTo(0.333);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/io/local_storage.ts b/tfjs-master/tfjs-core/src/io/local_storage.ts
deleted file mode 100644
index f20206a92..000000000
--- a/tfjs-master/tfjs-core/src/io/local_storage.ts
+++ /dev/null
@@ -1,390 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '../flags';
-import {env} from '../environment';
-
-import {assert} from '../util';
-import {arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON} from './io_utils';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-import {IORouter, IORouterRegistry} from './router_registry';
-import {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelJSON, ModelStoreManager, SaveResult} from './types';
-
-const PATH_SEPARATOR = '/';
-const PATH_PREFIX = 'tensorflowjs_models';
-const INFO_SUFFIX = 'info';
-const MODEL_TOPOLOGY_SUFFIX = 'model_topology';
-const WEIGHT_SPECS_SUFFIX = 'weight_specs';
-const WEIGHT_DATA_SUFFIX = 'weight_data';
-const MODEL_METADATA_SUFFIX = 'model_metadata';
-
-/**
- * Purge all tensorflow.js-saved model artifacts from local storage.
- *
- * @returns Paths of the models purged.
- */
-export function purgeLocalStorageArtifacts(): string[] {
-  if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||
-      typeof window.localStorage === 'undefined') {
-    throw new Error(
-        'purgeLocalStorageModels() cannot proceed because local storage is ' +
-        'unavailable in the current environment.');
-  }
-  const LS = window.localStorage;
-  const purgedModelPaths: string[] = [];
-  for (let i = 0; i < LS.length; ++i) {
-    const key = LS.key(i);
-    const prefix = PATH_PREFIX + PATH_SEPARATOR;
-    if (key.startsWith(prefix) && key.length > prefix.length) {
-      LS.removeItem(key);
-      const modelName = getModelPathFromKey(key);
-      if (purgedModelPaths.indexOf(modelName) === -1) {
-        purgedModelPaths.push(modelName);
-      }
-    }
-  }
-  return purgedModelPaths;
-}
-
-type LocalStorageKeys = {
-  /** Key of the localStorage entry storing `ModelArtifactsInfo`. */
-  info: string,
-  /**
-   * Key of the localStorage entry storing the 'modelTopology' key of
-   * `model.json`
-   */
-  topology: string,
-  /**
-   * Key of the localStorage entry storing the `weightsManifest.weights` entries
-   * of `model.json`
-   */
-  weightSpecs: string,
-  /** Key of the localStorage entry storing the weight data in Base64 */
-  weightData: string,
-  /**
-   * Key of the localStorage entry storing the remaining fields of `model.json`
-   * @see {@link ModelMetadata}
-   */
-  modelMetadata: string,
-};
-
-type ModelMetadata = Omit<ModelJSON, 'modelTopology'|'weightsManifest'>;
-
-function getModelKeys(path: string): LocalStorageKeys {
-  return {
-    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
-    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),
-    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),
-    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),
-    modelMetadata:
-        [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)
-  };
-}
-
-function removeItems(keys: LocalStorageKeys): void {
-  for (const key of Object.values(keys)) {
-    window.localStorage.removeItem(key);
-  }
-}
-
-/**
- * Get model path from a local-storage key.
- *
- * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'
- *
- * @param key
- */
-function getModelPathFromKey(key: string) {
-  const items = key.split(PATH_SEPARATOR);
-  if (items.length < 3) {
-    throw new Error(`Invalid key format: ${key}`);
-  }
-  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
-}
-
-function maybeStripScheme(key: string) {
-  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?
-      key.slice(BrowserLocalStorage.URL_SCHEME.length) :
-      key;
-}
-
-/**
- * IOHandler subclass: Browser Local Storage.
- *
- * See the doc string to `browserLocalStorage` for more details.
- */
-export class BrowserLocalStorage implements IOHandler {
-  protected readonly LS: Storage;
-  protected readonly modelPath: string;
-  protected readonly keys: LocalStorageKeys;
-
-  static readonly URL_SCHEME = 'localstorage://';
-
-  constructor(modelPath: string) {
-    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||
-        typeof window.localStorage === 'undefined') {
-      // TODO(cais): Add more info about what IOHandler subtypes are
-      // available.
-      //   Maybe point to a doc page on the web and/or automatically determine
-      //   the available IOHandlers and print them in the error message.
-      throw new Error(
-          'The current environment does not support local storage.');
-    }
-    this.LS = window.localStorage;
-
-    if (modelPath == null || !modelPath) {
-      throw new Error(
-          'For local storage, modelPath must not be null, undefined or empty.');
-    }
-    this.modelPath = modelPath;
-    this.keys = getModelKeys(this.modelPath);
-  }
-
-  /**
-   * Save model artifacts to browser local storage.
-   *
-   * See the documentation to `browserLocalStorage` for details on the saved
-   * artifacts.
-   *
-   * @param modelArtifacts The model artifacts to be stored.
-   * @returns An instance of SaveResult.
-   */
-  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error(
-          'BrowserLocalStorage.save() does not support saving model topology ' +
-          'in binary formats yet.');
-    } else {
-      const topology = JSON.stringify(modelArtifacts.modelTopology);
-      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);
-
-      const modelArtifactsInfo: ModelArtifactsInfo =
-          getModelArtifactsInfoForJSON(modelArtifacts);
-
-      // TODO(mattsoulanille): Support saving models over 2GB that exceed
-      // Chrome's ArrayBuffer size limit.
-      const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData);
-
-      try {
-        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));
-        this.LS.setItem(this.keys.topology, topology);
-        this.LS.setItem(this.keys.weightSpecs, weightSpecs);
-        this.LS.setItem(
-            this.keys.weightData,
-            arrayBufferToBase64String(weightBuffer));
-
-        // Note that JSON.stringify doesn't write out keys that have undefined
-        // values, so for some keys, we set undefined instead of a null-ish
-        // value.
-        const metadata: Required<ModelMetadata> = {
-          format: modelArtifacts.format,
-          generatedBy: modelArtifacts.generatedBy,
-          convertedBy: modelArtifacts.convertedBy,
-          signature: modelArtifacts.signature != null ?
-              modelArtifacts.signature :
-              undefined,
-          userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ?
-              modelArtifacts.userDefinedMetadata :
-              undefined,
-          modelInitializer: modelArtifacts.modelInitializer != null ?
-              modelArtifacts.modelInitializer :
-              undefined,
-          initializerSignature: modelArtifacts.initializerSignature != null ?
-              modelArtifacts.initializerSignature :
-              undefined,
-          trainingConfig: modelArtifacts.trainingConfig != null ?
-              modelArtifacts.trainingConfig :
-              undefined
-        };
-        this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));
-
-        return {modelArtifactsInfo};
-      } catch (err) {
-        // If saving failed, clean up all items saved so far.
-        removeItems(this.keys);
-
-        throw new Error(
-            `Failed to save model '${this.modelPath}' to local storage: ` +
-            `size quota being exceeded is a possible cause of this failure: ` +
-            `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +
-            `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +
-            `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);
-      }
-    }
-  }
-
-  /**
-   * Load a model from local storage.
-   *
-   * See the documentation to `browserLocalStorage` for details on the saved
-   * artifacts.
-   *
-   * @returns The loaded model (if loading succeeds).
-   */
-  async load(): Promise<ModelArtifacts> {
-    const info =
-        JSON.parse(this.LS.getItem(this.keys.info)) as ModelArtifactsInfo;
-    if (info == null) {
-      throw new Error(
-          `In local storage, there is no model with name '${this.modelPath}'`);
-    }
-
-    if (info.modelTopologyType !== 'JSON') {
-      throw new Error(
-          'BrowserLocalStorage does not support loading non-JSON model ' +
-          'topology yet.');
-    }
-
-    const out: ModelArtifacts = {};
-
-    // Load topology.
-    const topology = JSON.parse(this.LS.getItem(this.keys.topology));
-    if (topology == null) {
-      throw new Error(
-          `In local storage, the topology of model '${this.modelPath}' ` +
-          `is missing.`);
-    }
-    out.modelTopology = topology;
-
-    // Load weight specs.
-    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
-    if (weightSpecs == null) {
-      throw new Error(
-          `In local storage, the weight specs of model '${this.modelPath}' ` +
-          `are missing.`);
-    }
-    out.weightSpecs = weightSpecs;
-
-    // Load meta-data fields.
-    const metadataString = this.LS.getItem(this.keys.modelMetadata);
-    if (metadataString != null) {
-      const metadata = JSON.parse(metadataString) as ModelMetadata;
-      out.format = metadata.format;
-      out.generatedBy = metadata.generatedBy;
-      out.convertedBy = metadata.convertedBy;
-      if (metadata.signature != null) {
-        out.signature = metadata.signature;
-      }
-      if (metadata.userDefinedMetadata != null) {
-        out.userDefinedMetadata = metadata.userDefinedMetadata;
-      }
-      if (metadata.modelInitializer != null) {
-        out.modelInitializer = metadata.modelInitializer;
-      }
-      if (metadata.initializerSignature != null) {
-        out.initializerSignature = metadata.initializerSignature;
-      }
-      if (metadata.trainingConfig != null) {
-        out.trainingConfig = metadata.trainingConfig;
-      }
-    }
-
-    // Load weight data.
-    const weightDataBase64 = this.LS.getItem(this.keys.weightData);
-    if (weightDataBase64 == null) {
-      throw new Error(
-          `In local storage, the binary weight values of model ` +
-          `'${this.modelPath}' are missing.`);
-    }
-    out.weightData = base64StringToArrayBuffer(weightDataBase64);
-
-    return out;
-  }
-}
-
-export const localStorageRouter: IORouter = (url: string|string[]) => {
-  if (!env().getBool('IS_BROWSER')) {
-    return null;
-  } else {
-    if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {
-      return browserLocalStorage(
-          url.slice(BrowserLocalStorage.URL_SCHEME.length));
-    } else {
-      return null;
-    }
-  }
-};
-IORouterRegistry.registerSaveRouter(localStorageRouter);
-IORouterRegistry.registerLoadRouter(localStorageRouter);
-
-/**
- * Factory function for local storage IOHandler.
- *
- * This `IOHandler` supports both `save` and `load`.
- *
- * For each model's saved artifacts, four items are saved to local storage.
- *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the
- *     model, such as date saved, type of the topology, size in bytes, etc.
- *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-
- *     style models, this is a stringized JSON.
- *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the
- *     model, can be used to decode the saved binary weight values (see
- *     item below).
- *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary
- *     weight values, stored as a base64-encoded string.
- *
- * Saving may throw an `Error` if the total size of the artifacts exceed the
- * browser-specific quota.
- *
- * @param modelPath A unique identifier for the model to be saved. Must be a
- *   non-empty string.
- * @returns An instance of `IOHandler`, which can be used with, e.g.,
- *   `tf.Model.save`.
- */
-export function browserLocalStorage(modelPath: string): IOHandler {
-  return new BrowserLocalStorage(modelPath);
-}
-
-export class BrowserLocalStorageManager implements ModelStoreManager {
-  private readonly LS: Storage;
-
-  constructor() {
-    assert(
-        env().getBool('IS_BROWSER'),
-        () => 'Current environment is not a web browser');
-    assert(
-        typeof window === 'undefined' ||
-            typeof window.localStorage !== 'undefined',
-        () => 'Current browser does not appear to support localStorage');
-    this.LS = window.localStorage;
-  }
-
-  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {
-    const out: {[path: string]: ModelArtifactsInfo} = {};
-    const prefix = PATH_PREFIX + PATH_SEPARATOR;
-    const suffix = PATH_SEPARATOR + INFO_SUFFIX;
-    for (let i = 0; i < this.LS.length; ++i) {
-      const key = this.LS.key(i);
-      if (key.startsWith(prefix) && key.endsWith(suffix)) {
-        const modelPath = getModelPathFromKey(key);
-        out[modelPath] = JSON.parse(this.LS.getItem(key)) as ModelArtifactsInfo;
-      }
-    }
-    return out;
-  }
-
-  async removeModel(path: string): Promise<ModelArtifactsInfo> {
-    path = maybeStripScheme(path);
-    const keys = getModelKeys(path);
-    if (this.LS.getItem(keys.info) == null) {
-      throw new Error(`Cannot find model at path '${path}'`);
-    }
-    const info = JSON.parse(this.LS.getItem(keys.info)) as ModelArtifactsInfo;
-    removeItems(keys);
-    return info;
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/io/local_storage_test.ts b/tfjs-master/tfjs-core/src/io/local_storage_test.ts
deleted file mode 100644
index 7d2a351d9..000000000
--- a/tfjs-master/tfjs-core/src/io/local_storage_test.ts
+++ /dev/null
@@ -1,404 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {BROWSER_ENVS, describeWithFlags, runWithLock} from '../jasmine_util';
-import {arrayBufferToBase64String, base64StringToArrayBuffer} from './io_utils';
-import {browserLocalStorage, BrowserLocalStorage, BrowserLocalStorageManager, localStorageRouter, purgeLocalStorageArtifacts} from './local_storage';
-
-describeWithFlags('LocalStorage', BROWSER_ENVS, () => {
-  // Test data.
-  const modelTopology1: {} = {
-    'class_name': 'Sequential',
-    'keras_version': '2.1.4',
-    'config': [{
-      'class_name': 'Dense',
-      'config': {
-        'kernel_initializer': {
-          'class_name': 'VarianceScaling',
-          'config': {
-            'distribution': 'uniform',
-            'scale': 1.0,
-            'seed': null,
-            'mode': 'fan_avg'
-          }
-        },
-        'name': 'dense',
-        'kernel_constraint': null,
-        'bias_regularizer': null,
-        'bias_constraint': null,
-        'dtype': 'float32',
-        'activation': 'linear',
-        'trainable': true,
-        'kernel_regularizer': null,
-        'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-        'units': 1,
-        'batch_input_shape': [null, 3],
-        'use_bias': true,
-        'activity_regularizer': null
-      }
-    }],
-    'backend': 'tensorflow'
-  };
-  const weightSpecs1: tf.io.WeightsManifestEntry[] = [
-    {
-      name: 'dense/kernel',
-      shape: [3, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense/bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-  const weightData1 = new ArrayBuffer(16);
-  const trainingConfig1: tf.io.TrainingConfig = {
-    loss: 'categorical_crossentropy',
-    metrics: ['accuracy'],
-    optimizer_config: {class_name: 'SGD', config: {learningRate: 0.1}}
-  };
-
-  const artifacts1: tf.io.ModelArtifacts = {
-    modelTopology: modelTopology1,
-    weightSpecs: weightSpecs1,
-    weightData: weightData1,
-    format: 'layers-model',
-    generatedBy: 'TensorFlow.js v0.0.0',
-    convertedBy: '1.13.1',
-    signature: null,
-    userDefinedMetadata: {},
-    modelInitializer: {},
-    initializerSignature: null,
-    trainingConfig: trainingConfig1,
-  };
-
-  const artifactsV0: tf.io.ModelArtifacts = {
-    modelTopology: modelTopology1,
-    weightSpecs: weightSpecs1,
-    weightData: weightData1
-  };
-
-  function findOverflowingByteSize(): number {
-    const LS = window.localStorage;
-    const probeKey =
-        `tfjs_test_probe_values_${new Date().getTime()}_${Math.random()}`;
-    const minKilobytes = 200;
-    const stepKilobytes = 200;
-    const maxKilobytes = 40000;
-    for (let kilobytes = minKilobytes; kilobytes < maxKilobytes;
-         kilobytes += stepKilobytes) {
-      const bytes = kilobytes * 1024;
-      const data = new ArrayBuffer(bytes);
-      try {
-        const encoded = arrayBufferToBase64String(data);
-        LS.setItem(probeKey, encoded);
-      } catch (err) {
-        return bytes;
-      }
-      LS.removeItem(probeKey);
-    }
-    throw new Error(
-        `Unable to determined overflowing byte size up to ${maxKilobytes} kB.`);
-  }
-
-  beforeEach(() => {
-    purgeLocalStorageArtifacts();
-  });
-
-  afterEach(() => {
-    purgeLocalStorageArtifacts();
-  });
-
-  it('Save artifacts succeeds', runWithLock(async () => {
-       const testStartDate = new Date();
-       const handler = tf.io.getSaveHandlers('localstorage://foo/FooModel')[0];
-       const saveResult = await handler.save(artifacts1);
-
-       expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
-           .toBeGreaterThanOrEqual(testStartDate.getTime());
-       // Note: The following two assertions work only because there is no
-       //   non-ASCII characters in `modelTopology1` and `weightSpecs1`.
-       expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-           .toEqual(JSON.stringify(modelTopology1).length);
-       expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
-           .toEqual(JSON.stringify(weightSpecs1).length);
-       expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
-
-       // Check the content of the saved items in local storage.
-       const LS = window.localStorage;
-       const info =
-           JSON.parse(LS.getItem('tensorflowjs_models/foo/FooModel/info'));
-       expect(Date.parse(info.dateSaved))
-           .toEqual(saveResult.modelArtifactsInfo.dateSaved.getTime());
-       expect(info.modelTopologyBytes)
-           .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
-       expect(info.weightSpecsBytes)
-           .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
-       expect(info.weightDataBytes)
-           .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
-
-       const topologyString =
-           LS.getItem('tensorflowjs_models/foo/FooModel/model_topology');
-       expect(JSON.stringify(modelTopology1)).toEqual(topologyString);
-
-       const weightSpecsString =
-           LS.getItem('tensorflowjs_models/foo/FooModel/weight_specs');
-       expect(JSON.stringify(weightSpecs1)).toEqual(weightSpecsString);
-
-       const weightDataBase64String =
-           LS.getItem('tensorflowjs_models/foo/FooModel/weight_data');
-       expect(base64StringToArrayBuffer(weightDataBase64String))
-           .toEqual(weightData1);
-     }));
-
-  it('Save-load round trip succeeds', runWithLock(async () => {
-       const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
-
-       await handler1.save(artifacts1);
-       const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
-       const loaded = await handler2.load();
-       expect(loaded.modelTopology).toEqual(modelTopology1);
-       expect(loaded.weightSpecs).toEqual(weightSpecs1);
-       expect(loaded.weightData).toEqual(weightData1);
-       expect(loaded.format).toEqual('layers-model');
-       expect(loaded.generatedBy).toEqual('TensorFlow.js v0.0.0');
-       expect(loaded.convertedBy).toEqual('1.13.1');
-       expect(loaded.userDefinedMetadata).toEqual({});
-       expect(loaded.modelInitializer).toEqual({});
-       expect(loaded.initializerSignature).toBeUndefined();
-       expect(loaded.trainingConfig).toEqual(trainingConfig1);
-     }));
-
-  it('Save-load round trip succeeds: v0 format', runWithLock(async () => {
-       const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
-
-       await handler1.save(artifactsV0);
-       const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
-       const loaded = await handler2.load();
-       expect(loaded.modelTopology).toEqual(modelTopology1);
-       expect(loaded.weightSpecs).toEqual(weightSpecs1);
-       expect(loaded.weightData).toEqual(weightData1);
-       expect(loaded.format).toBeUndefined();
-       expect(loaded.generatedBy).toBeUndefined();
-       expect(loaded.convertedBy).toBeUndefined();
-       expect(loaded.userDefinedMetadata).toBeUndefined();
-       expect(loaded.trainingConfig).toBeUndefined();
-       expect(loaded.modelInitializer).toBeUndefined();
-       expect(loaded.initializerSignature).toBeUndefined();
-       expect(loaded.trainingConfig).toBeUndefined();
-     }));
-
-  it('Loading nonexistent model fails.', runWithLock(async () => {
-       const handler =
-           tf.io.getSaveHandlers('localstorage://NonexistentModel')[0];
-       try {
-         await handler.load();
-       } catch (err) {
-         expect(err.message)
-             .toEqual(
-                 'In local storage, there is no model with name ' +
-                 '\'NonexistentModel\'');
-         return;  // Success
-       }
-       fail('Loading nonexistent model succeeded unexpectedly.');
-     }));
-
-  it('Loading model with missing topology fails.', runWithLock(async () => {
-       const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
-       await handler1.save(artifacts1);
-       // Manually remove the topology item from local storage.
-       window.localStorage.removeItem(
-           'tensorflowjs_models/FooModel/model_topology');
-
-       const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
-       try {
-         await handler2.load();
-       } catch (err) {
-         expect(err.message)
-             .toEqual(
-                 'In local storage, the topology of model ' +
-                 '\'FooModel\' is missing.');
-         return;  // Success
-       }
-       fail('Loading of model with missing topology succeeded unexpectedly.');
-     }));
-
-  it('Loading model with missing weight specs fails.', runWithLock(async () => {
-       const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
-       await handler1.save(artifacts1);
-       // Manually remove the weight specs item from local storage.
-       window.localStorage.removeItem(
-           'tensorflowjs_models/FooModel/weight_specs');
-
-       const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
-       try {
-         await handler2.load();
-       } catch (err) {
-         expect(err.message)
-             .toEqual(
-                 'In local storage, the weight specs of model ' +
-                 '\'FooModel\' are missing.');
-         return;  // Success
-       }
-       fail(
-           'Loading of model with missing weight specs ' +
-           'succeeded unexpectedly.');
-     }));
-
-  it('Loading model with missing weight data fails.', runWithLock(async () => {
-       const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
-       await handler1.save(artifacts1);
-
-       // Manually remove the weight data item from local storage.
-       window.localStorage.removeItem(
-           'tensorflowjs_models/FooModel/weight_data');
-
-       const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
-       try {
-         await handler2.load();
-         fail(
-             'Loading of model with missing weight data ' +
-             'succeeded unexpectedly.');
-       } catch (err) {
-         expect(err.message)
-             .toEqual(
-                 'In local storage, the binary weight values of model ' +
-                 '\'FooModel\' are missing.');
-       }
-     }));
-
-  it('Data size too large leads to error thrown', runWithLock(async () => {
-       const overflowByteSize = findOverflowingByteSize();
-       const overflowArtifacts: tf.io.ModelArtifacts = {
-         modelTopology: modelTopology1,
-         weightSpecs: weightSpecs1,
-         weightData: new ArrayBuffer(overflowByteSize),
-       };
-       const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
-       try {
-         await handler1.save(overflowArtifacts);
-         fail(
-             'Saving of model of overflowing-size weight data succeeded ' +
-             'unexpectedly.');
-       } catch (err) {
-         expect(
-             (err.message as string)
-                 .indexOf('Failed to save model \'FooModel\' to local storage'))
-             .toEqual(0);
-       }
-     }));
-
-  it('Null, undefined or empty modelPath throws Error', () => {
-    expect(() => browserLocalStorage(null))
-        .toThrowError(
-            /local storage, modelPath must not be null, undefined or empty/);
-    expect(() => browserLocalStorage(undefined))
-        .toThrowError(
-            /local storage, modelPath must not be null, undefined or empty/);
-    expect(() => browserLocalStorage(''))
-        .toThrowError(
-            /local storage, modelPath must not be null, undefined or empty./);
-  });
-
-  it('router', () => {
-    expect(
-        localStorageRouter('localstorage://bar') instanceof BrowserLocalStorage)
-        .toEqual(true);
-    expect(localStorageRouter('indexeddb://bar')).toBeNull();
-    expect(localStorageRouter('qux')).toBeNull();
-  });
-
-  it('Manager: List models: 0 result', runWithLock(async () => {
-       // Before any model is saved, listModels should return empty result.
-       const out = await new BrowserLocalStorageManager().listModels();
-       expect(out).toEqual({});
-     }));
-
-  it('Manager: List models: 1 result', runWithLock(async () => {
-       const handler = tf.io.getSaveHandlers('localstorage://baz/QuxModel')[0];
-       const saveResult = await handler.save(artifacts1);
-
-       // After successful saving, there should be one model.
-       const out = await new BrowserLocalStorageManager().listModels();
-       if (Object.keys(out).length !== 1) {
-         console.log(JSON.stringify(out, null, 2));
-       }
-
-       expect(Object.keys(out).length).toEqual(1);
-       expect(out['baz/QuxModel'].modelTopologyType)
-           .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
-       expect(out['baz/QuxModel'].modelTopologyBytes)
-           .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
-       expect(out['baz/QuxModel'].weightSpecsBytes)
-           .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
-       expect(out['baz/QuxModel'].weightDataBytes)
-           .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
-     }));
-
-  it('Manager: List models: 2 results', runWithLock(async () => {
-       // First, save a model.
-       const handler1 = tf.io.getSaveHandlers('localstorage://QuxModel')[0];
-       const saveResult1 = await handler1.save(artifacts1);
-
-       // Then, save the model under another path.
-       const handler2 =
-           tf.io.getSaveHandlers('localstorage://repeat/QuxModel')[0];
-       const saveResult2 = await handler2.save(artifacts1);
-
-       // After successful saving, there should be two models.
-       const out = await new BrowserLocalStorageManager().listModels();
-       if (Object.keys(out).length !== 2) {
-         console.log(JSON.stringify(out, null, 2));
-       }
-       expect(Object.keys(out).length).toEqual(2);
-       expect(out['QuxModel'].modelTopologyType)
-           .toEqual(saveResult1.modelArtifactsInfo.modelTopologyType);
-       expect(out['QuxModel'].modelTopologyBytes)
-           .toEqual(saveResult1.modelArtifactsInfo.modelTopologyBytes);
-       expect(out['QuxModel'].weightSpecsBytes)
-           .toEqual(saveResult1.modelArtifactsInfo.weightSpecsBytes);
-       expect(out['QuxModel'].weightDataBytes)
-           .toEqual(saveResult1.modelArtifactsInfo.weightDataBytes);
-       expect(out['repeat/QuxModel'].modelTopologyType)
-           .toEqual(saveResult2.modelArtifactsInfo.modelTopologyType);
-       expect(out['repeat/QuxModel'].modelTopologyBytes)
-           .toEqual(saveResult2.modelArtifactsInfo.modelTopologyBytes);
-       expect(out['repeat/QuxModel'].weightSpecsBytes)
-           .toEqual(saveResult2.modelArtifactsInfo.weightSpecsBytes);
-       expect(out['repeat/QuxModel'].weightDataBytes)
-           .toEqual(saveResult2.modelArtifactsInfo.weightDataBytes);
-     }));
-
-  it('Manager: Successful deleteModel', runWithLock(async () => {
-       // First, save a model.
-       const handler1 = tf.io.getSaveHandlers('localstorage://QuxModel')[0];
-       await handler1.save(artifacts1);
-
-       // Then, save the model under another path.
-       const handler2 =
-           tf.io.getSaveHandlers('localstorage://repeat/QuxModel')[0];
-       await handler2.save(artifacts1);
-
-       // After successful saving, delete the first save, and then
-       // `listModel` should give only one result.
-       const manager = new BrowserLocalStorageManager();
-       await manager.removeModel('QuxModel');
-       const out = await manager.listModels();
-       expect(Object.keys(out)).toEqual(['repeat/QuxModel']);
-     }));
-});
diff --git a/tfjs-master/tfjs-core/src/io/model_management.ts b/tfjs-master/tfjs-core/src/io/model_management.ts
deleted file mode 100644
index 38aaae0ed..000000000
--- a/tfjs-master/tfjs-core/src/io/model_management.ts
+++ /dev/null
@@ -1,356 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Classes and functions for model management across multiple storage mediums.
- *
- * Supported client actions:
- * - Listing models on all registered storage mediums.
- * - Remove model by URL from any registered storage mediums, by using URL
- *   string.
- * - Moving or copying model from one path to another in the same medium or from
- *   one medium to another, by using URL strings.
- */
-
-import {assert} from '../util';
-
-import {IORouterRegistry} from './router_registry';
-import {ModelArtifactsInfo, ModelStoreManager} from './types';
-
-const URL_SCHEME_SUFFIX = '://';
-
-export class ModelStoreManagerRegistry {
-  // Singleton instance.
-  private static instance: ModelStoreManagerRegistry;
-
-  private managers: {[scheme: string]: ModelStoreManager};
-
-  private constructor() {
-    this.managers = {};
-  }
-
-  private static getInstance(): ModelStoreManagerRegistry {
-    if (ModelStoreManagerRegistry.instance == null) {
-      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
-    }
-    return ModelStoreManagerRegistry.instance;
-  }
-
-  /**
-   * Register a save-handler router.
-   *
-   * @param saveRouter A function that maps a URL-like string onto an instance
-   * of `IOHandler` with the `save` method defined or `null`.
-   */
-  static registerManager(scheme: string, manager: ModelStoreManager) {
-    assert(scheme != null, () => 'scheme must not be undefined or null.');
-    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
-      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
-    }
-    assert(scheme.length > 0, () => 'scheme must not be an empty string.');
-    const registry = ModelStoreManagerRegistry.getInstance();
-    assert(
-        registry.managers[scheme] == null,
-        () => `A model store manager is already registered for scheme '${
-            scheme}'.`);
-    registry.managers[scheme] = manager;
-  }
-
-  static getManager(scheme: string): ModelStoreManager {
-    const manager = ModelStoreManagerRegistry.getInstance().managers[scheme];
-    if (manager == null) {
-      throw new Error(`Cannot find model manager for scheme '${scheme}'`);
-    }
-    return manager;
-  }
-
-  static getSchemes(): string[] {
-    return Object.keys(ModelStoreManagerRegistry.getInstance().managers);
-  }
-}
-
-/**
- * Helper method for parsing a URL string into a scheme and a path.
- *
- * @param url E.g., 'localstorage://my-model'
- * @returns A dictionary with two fields: scheme and path.
- *   Scheme: e.g., 'localstorage' in the example above.
- *   Path: e.g., 'my-model' in the example above.
- */
-function parseURL(url: string): {scheme: string, path: string} {
-  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
-    throw new Error(
-        `The url string provided does not contain a scheme. ` +
-        `Supported schemes are: ` +
-        `${ModelStoreManagerRegistry.getSchemes().join(',')}`);
-  }
-  return {
-    scheme: url.split(URL_SCHEME_SUFFIX)[0],
-    path: url.split(URL_SCHEME_SUFFIX)[1],
-  };
-}
-
-async function cloneModelInternal(
-    sourceURL: string, destURL: string,
-    deleteSource = false): Promise<ModelArtifactsInfo> {
-  assert(
-      sourceURL !== destURL,
-      () => `Old path and new path are the same: '${sourceURL}'`);
-
-  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
-  assert(
-      loadHandlers.length > 0,
-      () => `Copying failed because no load handler is found for source URL ${
-          sourceURL}.`);
-  assert(
-      loadHandlers.length < 2,
-      () => `Copying failed because more than one (${loadHandlers.length}) ` +
-          `load handlers for source URL ${sourceURL}.`);
-  const loadHandler = loadHandlers[0];
-
-  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
-  assert(
-      saveHandlers.length > 0,
-      () => `Copying failed because no save handler is found for destination ` +
-          `URL ${destURL}.`);
-  assert(
-      saveHandlers.length < 2,
-      () => `Copying failed because more than one (${loadHandlers.length}) ` +
-          `save handlers for destination URL ${destURL}.`);
-  const saveHandler = saveHandlers[0];
-
-  const sourceScheme = parseURL(sourceURL).scheme;
-  const sourcePath = parseURL(sourceURL).path;
-  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
-
-  const modelArtifacts = await loadHandler.load();
-
-  // If moving within the same storage medium, remove the old model as soon as
-  // the loading is done. Without doing this, it is possible that the combined
-  // size of the two models will cause the cloning to fail.
-  if (deleteSource && sameMedium) {
-    await ModelStoreManagerRegistry.getManager(sourceScheme)
-        .removeModel(sourcePath);
-  }
-
-  const saveResult = await saveHandler.save(modelArtifacts);
-
-  // If moving between mediums, the deletion is done after the save succeeds.
-  // This guards against the case in which saving to the destination medium
-  // fails.
-  if (deleteSource && !sameMedium) {
-    await ModelStoreManagerRegistry.getManager(sourceScheme)
-        .removeModel(sourcePath);
-  }
-
-  return saveResult.modelArtifactsInfo;
-}
-
-/**
- * List all models stored in registered storage mediums.
- *
- * For a web browser environment, the registered mediums are Local Storage and
- * IndexedDB.
- *
- * ```js
- * // First create and save a model.
- * const model = tf.sequential();
- * model.add(tf.layers.dense(
- *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
- * await model.save('localstorage://demo/management/model1');
- *
- * // Then list existing models.
- * console.log(JSON.stringify(await tf.io.listModels()));
- *
- * // Delete the model.
- * await tf.io.removeModel('localstorage://demo/management/model1');
- *
- * // List models again.
- * console.log(JSON.stringify(await tf.io.listModels()));
- * ```
- *
- * @returns A `Promise` of a dictionary mapping URLs of existing models to
- * their model artifacts info. URLs include medium-specific schemes, e.g.,
- *   'indexeddb://my/model/1'. Model artifacts info include type of the
- * model's topology, byte sizes of the topology, weights, etc.
- *
- * @doc {
- *   heading: 'Models',
- *   subheading: 'Management',
- *   namespace: 'io',
- *   ignoreCI: true
- * }
- */
-async function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {
-  const schemes = ModelStoreManagerRegistry.getSchemes();
-  const out: {[url: string]: ModelArtifactsInfo} = {};
-  for (const scheme of schemes) {
-    const schemeOut =
-        await ModelStoreManagerRegistry.getManager(scheme).listModels();
-    for (const path in schemeOut) {
-      const url = scheme + URL_SCHEME_SUFFIX + path;
-      out[url] = schemeOut[path];
-    }
-  }
-  return out;
-}
-
-/**
- * Remove a model specified by URL from a registered storage medium.
- *
- * ```js
- * // First create and save a model.
- * const model = tf.sequential();
- * model.add(tf.layers.dense(
- *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
- * await model.save('localstorage://demo/management/model1');
- *
- * // Then list existing models.
- * console.log(JSON.stringify(await tf.io.listModels()));
- *
- * // Delete the model.
- * await tf.io.removeModel('localstorage://demo/management/model1');
- *
- * // List models again.
- * console.log(JSON.stringify(await tf.io.listModels()));
- * ```
- *
- * @param url A URL to a stored model, with a scheme prefix, e.g.,
- *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
- * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
- *   is successful).
- * @throws Error if deletion fails, e.g., if no model exists at `path`.
- *
- * @doc {
- *   heading: 'Models',
- *   subheading: 'Management',
- *   namespace: 'io',
- *   ignoreCI: true
- * }
- */
-async function removeModel(url: string): Promise<ModelArtifactsInfo> {
-  const schemeAndPath = parseURL(url);
-  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
-  return manager.removeModel(schemeAndPath.path);
-}
-
-/**
- * Copy a model from one URL to another.
- *
- * This function supports:
- *
- * 1. Copying within a storage medium, e.g.,
- *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
- * 2. Copying between two storage mediums, e.g.,
- *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
- *
- * ```js
- * // First create and save a model.
- * const model = tf.sequential();
- * model.add(tf.layers.dense(
- *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
- * await model.save('localstorage://demo/management/model1');
- *
- * // Then list existing models.
- * console.log(JSON.stringify(await tf.io.listModels()));
- *
- * // Copy the model, from Local Storage to IndexedDB.
- * await tf.io.copyModel(
- *     'localstorage://demo/management/model1',
- *     'indexeddb://demo/management/model1');
- *
- * // List models again.
- * console.log(JSON.stringify(await tf.io.listModels()));
- *
- * // Remove both models.
- * await tf.io.removeModel('localstorage://demo/management/model1');
- * await tf.io.removeModel('indexeddb://demo/management/model1');
- * ```
- *
- * @param sourceURL Source URL of copying.
- * @param destURL Destination URL of copying.
- * @returns ModelArtifactsInfo of the copied model (if and only if copying
- *   is successful).
- * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
- *   if `oldPath` and `newPath` are identical.
- *
- * @doc {
- *   heading: 'Models',
- *   subheading: 'Management',
- *   namespace: 'io',
- *   ignoreCI: true
- * }
- */
-async function copyModel(
-    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {
-  const deleteSource = false;
-  return cloneModelInternal(sourceURL, destURL, deleteSource);
-}
-
-/**
- * Move a model from one URL to another.
- *
- * This function supports:
- *
- * 1. Moving within a storage medium, e.g.,
- *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
- * 2. Moving between two storage mediums, e.g.,
- *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
- *
- * ```js
- * // First create and save a model.
- * const model = tf.sequential();
- * model.add(tf.layers.dense(
- *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
- * await model.save('localstorage://demo/management/model1');
- *
- * // Then list existing models.
- * console.log(JSON.stringify(await tf.io.listModels()));
- *
- * // Move the model, from Local Storage to IndexedDB.
- * await tf.io.moveModel(
- *     'localstorage://demo/management/model1',
- *     'indexeddb://demo/management/model1');
- *
- * // List models again.
- * console.log(JSON.stringify(await tf.io.listModels()));
- *
- * // Remove the moved model.
- * await tf.io.removeModel('indexeddb://demo/management/model1');
- * ```
- *
- * @param sourceURL Source URL of moving.
- * @param destURL Destination URL of moving.
- * @returns ModelArtifactsInfo of the copied model (if and only if copying
- *   is successful).
- * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
- *   if `oldPath` and `newPath` are identical.
- *
- * @doc {
- *   heading: 'Models',
- *   subheading: 'Management',
- *   namespace: 'io',
- *   ignoreCI: true
- * }
- */
-async function moveModel(
-    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {
-  const deleteSource = true;
-  return cloneModelInternal(sourceURL, destURL, deleteSource);
-}
-
-export {moveModel, copyModel, removeModel, listModels};
diff --git a/tfjs-master/tfjs-core/src/io/model_management_test.ts b/tfjs-master/tfjs-core/src/io/model_management_test.ts
deleted file mode 100644
index 0f8a958ec..000000000
--- a/tfjs-master/tfjs-core/src/io/model_management_test.ts
+++ /dev/null
@@ -1,491 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {CHROME_ENVS, describeWithFlags, runWithLock} from '../jasmine_util';
-import {deleteDatabase} from './indexed_db';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-import {purgeLocalStorageArtifacts} from './local_storage';
-
-// Disabled for non-Chrome browsers due to:
-// https://github.com/tensorflow/tfjs/issues/427
-describeWithFlags('ModelManagement', CHROME_ENVS, () => {
-  // Test data.
-  const modelTopology1: {} = {
-    'class_name': 'Sequential',
-    'keras_version': '2.1.4',
-    'config': [{
-      'class_name': 'Dense',
-      'config': {
-        'kernel_initializer': {
-          'class_name': 'VarianceScaling',
-          'config': {
-            'distribution': 'uniform',
-            'scale': 1.0,
-            'seed': null,
-            'mode': 'fan_avg'
-          }
-        },
-        'name': 'dense',
-        'kernel_constraint': null,
-        'bias_regularizer': null,
-        'bias_constraint': null,
-        'dtype': 'float32',
-        'activation': 'linear',
-        'trainable': true,
-        'kernel_regularizer': null,
-        'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-        'units': 1,
-        'batch_input_shape': [null, 3],
-        'use_bias': true,
-        'activity_regularizer': null
-      }
-    }],
-    'backend': 'tensorflow'
-  };
-  const weightSpecs1: tf.io.WeightsManifestEntry[] = [
-    {
-      name: 'dense/kernel',
-      shape: [3, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense/bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-  const weightData1 = new ArrayBuffer(16);
-  const artifacts1: tf.io.ModelArtifacts = {
-    modelTopology: modelTopology1,
-    weightSpecs: weightSpecs1,
-    weightData: weightData1,
-  };
-
-  beforeEach(done => {
-    purgeLocalStorageArtifacts();
-    deleteDatabase().then(() => {
-      done();
-    });
-  });
-
-  afterEach(done => {
-    purgeLocalStorageArtifacts();
-    deleteDatabase().then(() => {
-      done();
-    });
-  });
-
-  // TODO(cais): Reenable this test once we fix
-  // https://github.com/tensorflow/tfjs/issues/1198
-  // tslint:disable-next-line:ban
-  xit('List models: 0 result', done => {
-    // Before any model is saved, listModels should return empty result.
-    tf.io.listModels()
-        .then(out => {
-          expect(out).toEqual({});
-          done();
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  // TODO(cais): Reenable this test once we fix
-  // https://github.com/tensorflow/tfjs/issues/1198
-  // tslint:disable-next-line:ban
-  xit('List models: 1 result', done => {
-    const url = 'localstorage://baz/QuxModel';
-    const handler = tf.io.getSaveHandlers(url)[0];
-    handler.save(artifacts1)
-        .then(saveResult => {
-          // After successful saving, there should be one model.
-          tf.io.listModels()
-              .then(out => {
-                expect(Object.keys(out).length).toEqual(1);
-                expect(out[url].modelTopologyType)
-                    .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
-                expect(out[url].modelTopologyBytes)
-                    .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
-                expect(out[url].weightSpecsBytes)
-                    .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
-                expect(out[url].weightDataBytes)
-                    .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
-                done();
-              })
-              .catch(err => done.fail(err.stack));
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  // TODO(cais): Reenable this test once we fix
-  // https://github.com/tensorflow/tfjs/issues/1198
-  // tslint:disable-next-line:ban
-  xit('Manager: List models: 2 results in 2 mediums', done => {
-    const url1 = 'localstorage://QuxModel';
-    const url2 = 'indexeddb://QuxModel';
-
-    // First, save a model in Local Storage.
-    const handler1 = tf.io.getSaveHandlers(url1)[0];
-    handler1.save(artifacts1)
-        .then(saveResult1 => {
-          // Then, save the model in IndexedDB.
-          const handler2 = tf.io.getSaveHandlers(url2)[0];
-          handler2.save(artifacts1)
-              .then(saveResult2 => {
-                // After successful saving, there should be two models.
-                tf.io.listModels()
-                    .then(out => {
-                      expect(Object.keys(out).length).toEqual(2);
-                      expect(out[url1].modelTopologyType)
-                          .toEqual(
-                              saveResult1.modelArtifactsInfo.modelTopologyType);
-                      expect(out[url1].modelTopologyBytes)
-                          .toEqual(saveResult1.modelArtifactsInfo
-                                       .modelTopologyBytes);
-                      expect(out[url1].weightSpecsBytes)
-                          .toEqual(
-                              saveResult1.modelArtifactsInfo.weightSpecsBytes);
-                      expect(out[url1].weightDataBytes)
-                          .toEqual(
-                              saveResult1.modelArtifactsInfo.weightDataBytes);
-                      expect(out[url2].modelTopologyType)
-                          .toEqual(
-                              saveResult2.modelArtifactsInfo.modelTopologyType);
-                      expect(out[url2].modelTopologyBytes)
-                          .toEqual(saveResult2.modelArtifactsInfo
-                                       .modelTopologyBytes);
-                      expect(out[url2].weightSpecsBytes)
-                          .toEqual(
-                              saveResult2.modelArtifactsInfo.weightSpecsBytes);
-                      expect(out[url2].weightDataBytes)
-                          .toEqual(
-                              saveResult2.modelArtifactsInfo.weightDataBytes);
-                      done();
-                    })
-                    .catch(err => done.fail(err.stack));
-              })
-              .catch(err => done.fail(err.stack));
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  // TODO(cais): Reenable this test once we fix
-  // https://github.com/tensorflow/tfjs/issues/1198
-  // tslint:disable-next-line:ban
-  xit('Successful removeModel', done => {
-    // First, save a model.
-    const handler1 = tf.io.getSaveHandlers('localstorage://QuxModel')[0];
-    handler1.save(artifacts1)
-        .then(saveResult1 => {
-          // Then, save the model under another path.
-          const handler2 =
-              tf.io.getSaveHandlers('indexeddb://repeat/QuxModel')[0];
-          handler2.save(artifacts1)
-              .then(saveResult2 => {
-                // After successful saving, delete the first save, and then
-                // `listModel` should give only one result.
-
-                // Delete a model specified with a path that includes the
-                // indexeddb:// scheme prefix should work.
-                tf.io.removeModel('indexeddb://repeat/QuxModel')
-                    .then(deletedInfo => {
-                      tf.io.listModels()
-                          .then(out => {
-                            expect(Object.keys(out)).toEqual([
-                              'localstorage://QuxModel'
-                            ]);
-
-                            tf.io.removeModel('localstorage://QuxModel')
-                                .then(out => {
-                                  // The delete the remaining model.
-                                  tf.io.listModels()
-                                      .then(out => {
-                                        expect(Object.keys(out)).toEqual([]);
-                                        done();
-                                      })
-                                      .catch(err => done.fail(err));
-                                })
-                                .catch(err => done.fail(err));
-                          })
-                          .catch(err => done.fail(err));
-                    })
-                    .catch(err => done.fail(err.stack));
-              })
-              .catch(err => done.fail(err.stack));
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  // TODO(cais): Reenable this test once we fix
-  // https://github.com/tensorflow/tfjs/issues/1198
-  // tslint:disable-next-line:ban
-  xit('Successful copyModel between mediums', done => {
-    const url1 = 'localstorage://a1/FooModel';
-    const url2 = 'indexeddb://a1/FooModel';
-    // First, save a model.
-    const handler1 = tf.io.getSaveHandlers(url1)[0];
-    handler1.save(artifacts1)
-        .then(saveResult => {
-          // Once model is saved, copy the model to another path.
-          tf.io.copyModel(url1, url2)
-              .then(modelInfo => {
-                tf.io.listModels().then(out => {
-                  expect(Object.keys(out).length).toEqual(2);
-                  expect(out[url1].modelTopologyType)
-                      .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
-                  expect(out[url1].modelTopologyBytes)
-                      .toEqual(
-                          saveResult.modelArtifactsInfo.modelTopologyBytes);
-                  expect(out[url1].weightSpecsBytes)
-                      .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
-                  expect(out[url1].weightDataBytes)
-                      .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
-                  expect(out[url2].modelTopologyType)
-                      .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
-                  expect(out[url2].modelTopologyBytes)
-                      .toEqual(
-                          saveResult.modelArtifactsInfo.modelTopologyBytes);
-                  expect(out[url2].weightSpecsBytes)
-                      .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
-                  expect(out[url2].weightDataBytes)
-                      .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
-
-                  // Load the copy and verify the content.
-                  const handler2 = tf.io.getLoadHandlers(url2)[0];
-                  handler2.load()
-                      .then(loaded => {
-                        expect(loaded.modelTopology).toEqual(modelTopology1);
-                        expect(loaded.weightSpecs).toEqual(weightSpecs1);
-                        expect(loaded.weightData).toBeDefined();
-                        expect(new Uint8Array(
-                          CompositeArrayBuffer.join(loaded.weightData)))
-                            .toEqual(new Uint8Array(weightData1));
-                        done();
-                      })
-                      .catch(err => done.fail(err.stack));
-                });
-              })
-              .catch(err => done.fail(err.stack));
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  // TODO(cais): Reenable this test once we fix
-  // https://github.com/tensorflow/tfjs/issues/1198
-  // tslint:disable-next-line:ban
-  xit('Successful moveModel between mediums', done => {
-    const url1 = 'localstorage://a1/FooModel';
-    const url2 = 'indexeddb://a1/FooModel';
-    // First, save a model.
-    const handler1 = tf.io.getSaveHandlers(url1)[0];
-    handler1.save(artifacts1)
-        .then(saveResult => {
-          // Once model is saved, move the model to another path.
-          tf.io.moveModel(url1, url2)
-              .then(modelInfo => {
-                tf.io.listModels().then(out => {
-                  expect(Object.keys(out)).toEqual([url2]);
-                  expect(out[url2].modelTopologyType)
-                      .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
-                  expect(out[url2].modelTopologyBytes)
-                      .toEqual(
-                          saveResult.modelArtifactsInfo.modelTopologyBytes);
-                  expect(out[url2].weightSpecsBytes)
-                      .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
-                  expect(out[url2].weightDataBytes)
-                      .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
-
-                  // Load the copy and verify the content.
-                  const handler2 = tf.io.getLoadHandlers(url2)[0];
-                  handler2.load()
-                      .then(loaded => {
-                        expect(loaded.modelTopology).toEqual(modelTopology1);
-                        expect(loaded.weightSpecs).toEqual(weightSpecs1);
-                        expect(new Uint8Array(
-                          CompositeArrayBuffer.join(loaded.weightData)))
-                            .toEqual(new Uint8Array(weightData1));
-                        done();
-                      })
-                      .catch(err => {
-                        done.fail(err.stack);
-                      });
-                });
-              })
-              .catch(err => done.fail(err.stack));
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  it('Failed copyModel to invalid source URL', runWithLock(done => {
-       const url1 = 'invalidurl';
-       const url2 = 'localstorage://a1/FooModel';
-       tf.io.copyModel(url1, url2)
-           .then(out => {
-             done.fail('Copying from invalid URL succeeded unexpectedly.');
-           })
-           .catch(err => {
-             expect(err.message)
-                 .toEqual(
-                     'Copying failed because no load handler is found for ' +
-                     'source URL invalidurl.');
-             done();
-           });
-     }));
-
-  it('Failed copyModel to invalid destination URL', runWithLock(done => {
-       const url1 = 'localstorage://a1/FooModel';
-       const url2 = 'invalidurl';
-       // First, save a model.
-       const handler1 = tf.io.getSaveHandlers(url1)[0];
-       handler1.save(artifacts1)
-           .then(saveResult => {
-             // Once model is saved, copy the model to another path.
-             tf.io.copyModel(url1, url2)
-                 .then(out => {
-                   done.fail('Copying to invalid URL succeeded unexpectedly.');
-                 })
-                 .catch(err => {
-                   expect(err.message)
-                       .toEqual(
-                           'Copying failed because no save handler is found ' +
-                           'for destination URL invalidurl.');
-                   done();
-                 });
-           })
-           .catch(err => done.fail(err.stack));
-     }));
-
-  it('Failed moveModel to invalid destination URL', runWithLock(done => {
-       const url1 = 'localstorage://a1/FooModel';
-       const url2 = 'invalidurl';
-       // First, save a model.
-       const handler1 = tf.io.getSaveHandlers(url1)[0];
-       handler1.save(artifacts1)
-           .then(saveResult => {
-             // Once model is saved, copy the model to an invalid path, which
-             // should fail.
-             tf.io.moveModel(url1, url2)
-                 .then(out => {
-                   done.fail('Copying to invalid URL succeeded unexpectedly.');
-                 })
-                 .catch(err => {
-                   expect(err.message)
-                       .toEqual(
-                           'Copying failed because no save handler is found ' +
-                           'for destination URL invalidurl.');
-
-                   // Verify that the source has not been removed.
-                   tf.io.listModels()
-                       .then(out => {
-                         expect(Object.keys(out)).toEqual([url1]);
-                         done();
-                       })
-                       .catch(err => done.fail(err.stack));
-                 });
-           })
-           .catch(err => done.fail(err.stack));
-     }));
-
-  it('Failed deletedModel: Absent scheme', runWithLock(done => {
-       // Attempt to delete a nonexistent model is expected to fail.
-       tf.io.removeModel('foo')
-           .then(out => {
-             done.fail(
-                 'Removing model with missing scheme succeeded unexpectedly.');
-           })
-           .catch(err => {
-             expect(err.message)
-                 .toMatch(/The url string provided does not contain a scheme/);
-             expect(err.message.indexOf('localstorage')).toBeGreaterThan(0);
-             expect(err.message.indexOf('indexeddb')).toBeGreaterThan(0);
-             done();
-           });
-     }));
-
-  it('Failed deletedModel: Invalid scheme', runWithLock(done => {
-       // Attempt to delete a nonexistent model is expected to fail.
-       tf.io.removeModel('invalidscheme://foo')
-           .then(out => {
-             done.fail('Removing nonexistent model succeeded unexpectedly.');
-           })
-           .catch(err => {
-             expect(err.message)
-                 .toEqual(
-                     'Cannot find model manager for scheme \'invalidscheme\'');
-             done();
-           });
-     }));
-
-  it('Failed deletedModel: Nonexistent model', runWithLock(done => {
-       // Attempt to delete a nonexistent model is expected to fail.
-       tf.io.removeModel('indexeddb://nonexistent')
-           .then(out => {
-             done.fail('Removing nonexistent model succeeded unexpectedly.');
-           })
-           .catch(err => {
-             expect(err.message)
-                 .toEqual(
-                     'Cannot find model ' +
-                     'with path \'nonexistent\' in IndexedDB.');
-             done();
-           });
-     }));
-
-  it('Failed copyModel', runWithLock(done => {
-       // Attempt to copy a nonexistent model should fail.
-       tf.io.copyModel('indexeddb://nonexistent', 'indexeddb://destination')
-           .then(out => {
-             done.fail('Copying nonexistent model succeeded unexpectedly.');
-           })
-           .catch(err => {
-             expect(err.message)
-                 .toEqual(
-                     'Cannot find model ' +
-                     'with path \'nonexistent\' in IndexedDB.');
-             done();
-           });
-     }));
-
-  it('copyModel: Identical oldPath and newPath leads to Error',
-     runWithLock(done => {
-       tf.io.copyModel('a/1', 'a/1')
-           .then(out => {
-             done.fail(
-                 'Copying with identical ' +
-                 'old & new paths succeeded unexpectedly.');
-           })
-           .catch(err => {
-             expect(err.message)
-                 .toEqual('Old path and new path are the same: \'a/1\'');
-             done();
-           });
-     }));
-
-  it('moveModel: Identical oldPath and newPath leads to Error',
-     runWithLock(done => {
-       tf.io.moveModel('a/1', 'a/1')
-           .then(out => {
-             done.fail(
-                 'Copying with identical ' +
-                 'old & new paths succeeded unexpectedly.');
-           })
-           .catch(err => {
-             expect(err.message)
-                 .toEqual('Old path and new path are the same: \'a/1\'');
-             done();
-           });
-     }));
-});
diff --git a/tfjs-master/tfjs-core/src/io/passthrough.ts b/tfjs-master/tfjs-core/src/io/passthrough.ts
deleted file mode 100644
index e5416002d..000000000
--- a/tfjs-master/tfjs-core/src/io/passthrough.ts
+++ /dev/null
@@ -1,181 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * IOHandlers that pass through the in-memory ModelArtifacts format.
- */
-
-import {IOHandler, IOHandlerSync, LoadHandler, ModelArtifacts, SaveHandler, SaveResult, TrainingConfig, WeightData, WeightsManifestEntry} from './types';
-
-class PassthroughLoader implements IOHandlerSync {
-  constructor(private readonly modelArtifacts?: ModelArtifacts) {}
-
-  load(): ModelArtifacts {
-    return this.modelArtifacts;
-  }
-}
-
-class PassthroughSaver<R extends SaveResult | Promise<SaveResult>> {
-  constructor(
-    private readonly saveHandler: (artifacts: ModelArtifacts) => R) {}
-
-  save(modelArtifacts: ModelArtifacts): R {
-    return this.saveHandler(modelArtifacts);
-  }
-}
-
-class PassthroughAsync implements IOHandler {
-  load?: LoadHandler;
-  save?: SaveHandler;
-
-  constructor(handler: IOHandlerSync) {
-    if (handler.load) {
-      this.load = () => Promise.resolve(handler.load());
-    }
-    if (handler.save) {
-      this.save = (modelArtifacts: ModelArtifacts) =>
-        Promise.resolve(handler.save(modelArtifacts));
-    }
-  }
-}
-
-/**
- * Creates an IOHandler that loads model artifacts from memory.
- *
- * When used in conjunction with `tf.loadLayersModel`, an instance of
- * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
- *
- * ```js
- * const model = await tf.loadLayersModel(tf.io.fromMemory(
- *     modelTopology, weightSpecs, weightData));
- * ```
- *
- * @param modelArtifacts a object containing model topology (i.e., parsed from
- *   the JSON format).
- * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
- *   names, shapes, types, and quantization of the weight data. Optional.
- * @param weightData A single `ArrayBuffer` containing the weight data,
- *   concatenated in the order described by the weightSpecs. Optional.
- * @param trainingConfig Model training configuration. Optional.
- *
- * @returns A passthrough `IOHandler` that simply loads the provided data.
- */
-export function fromMemory(
-    modelArtifacts: {}|ModelArtifacts, weightSpecs?: WeightsManifestEntry[],
-    weightData?: WeightData, trainingConfig?: TrainingConfig): IOHandler {
-
-  const args = arguments as unknown as Parameters<typeof fromMemory>;
-  return new PassthroughAsync(fromMemorySync(...args));
-}
-
-/**
- * Creates an IOHandler that loads model artifacts from memory.
- *
- * When used in conjunction with `tf.loadLayersModel`, an instance of
- * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
- *
- * ```js
- * const model = await tf.loadLayersModel(tf.io.fromMemory(
- *     modelTopology, weightSpecs, weightData));
- * ```
- *
- * @param modelArtifacts a object containing model topology (i.e., parsed from
- *   the JSON format).
- * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
- *   names, shapes, types, and quantization of the weight data. Optional.
- * @param weightData A single `ArrayBuffer` containing the weight data,
- *   concatenated in the order described by the weightSpecs. Optional.
- * @param trainingConfig Model training configuration. Optional.
- *
- * @returns A passthrough `IOHandlerSync` that simply loads the provided data.
- */
-export function fromMemorySync(
-    modelArtifacts: {}|ModelArtifacts, weightSpecs?: WeightsManifestEntry[],
-    weightData?: WeightData, trainingConfig?: TrainingConfig): IOHandlerSync {
-  if (arguments.length === 1) {
-    const isModelArtifacts =
-        (modelArtifacts as ModelArtifacts).modelTopology != null ||
-        (modelArtifacts as ModelArtifacts).weightSpecs != null;
-    if (isModelArtifacts) {
-      return new PassthroughLoader(modelArtifacts as ModelArtifacts);
-    } else {
-      // Legacy support: with only modelTopology.
-      // TODO(cais): Remove this deprecated API.
-      console.warn(
-          'Please call tf.io.fromMemory() with only one argument. ' +
-          'The argument should be of type ModelArtifacts. ' +
-          'The multi-argument signature of tf.io.fromMemory() has been ' +
-          'deprecated and will be removed in a future release.');
-      return new PassthroughLoader({modelTopology: modelArtifacts as {}});
-    }
-  } else {
-    // Legacy support.
-    // TODO(cais): Remove this deprecated API.
-    console.warn(
-        'Please call tf.io.fromMemory() with only one argument. ' +
-        'The argument should be of type ModelArtifacts. ' +
-        'The multi-argument signature of tf.io.fromMemory() has been ' +
-        'deprecated and will be removed in a future release.');
-    return new PassthroughLoader({
-      modelTopology: modelArtifacts as {},
-      weightSpecs,
-      weightData,
-      trainingConfig
-    });
-  }
-}
-
-/**
- * Creates an IOHandler that passes saved model artifacts to a callback.
- *
- * ```js
- * function handleSave(artifacts) {
- *   // ... do something with the artifacts ...
- *   return {modelArtifactsInfo: {...}, ...};
- * }
- *
- * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
- * ```
- *
- * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
- *     promise that resolves to a `SaveResult`.
- */
-export function withSaveHandler(
-    saveHandler: (artifacts: ModelArtifacts) =>
-        Promise<SaveResult>): IOHandler {
-  return new PassthroughSaver(saveHandler);
-}
-
-/**
- * Creates an IOHandlerSync that passes saved model artifacts to a callback.
- *
- * ```js
- * function handleSave(artifacts) {
- *   // ... do something with the artifacts ...
- *   return {modelArtifactsInfo: {...}, ...};
- * }
- *
- * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
- * ```
- *
- * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
- *     `SaveResult`.
- */
-export function withSaveHandlerSync(
-    saveHandler: (artifacts: ModelArtifacts) => SaveResult): IOHandlerSync {
-  return new PassthroughSaver<SaveResult>(saveHandler);
-}
diff --git a/tfjs-master/tfjs-core/src/io/passthrough_test.ts b/tfjs-master/tfjs-core/src/io/passthrough_test.ts
deleted file mode 100644
index 08bbec4ac..000000000
--- a/tfjs-master/tfjs-core/src/io/passthrough_test.ts
+++ /dev/null
@@ -1,270 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit tests for passthrough IOHandlers.
- */
-
-import * as tf from '../index';
-import {BROWSER_ENVS, describeWithFlags} from '../jasmine_util';
-
-const modelTopology1: {} = {
-  'class_name': 'Sequential',
-  'keras_version': '2.1.4',
-  'config': [{
-    'class_name': 'Dense',
-    'config': {
-      'kernel_initializer': {
-        'class_name': 'VarianceScaling',
-        'config': {
-          'distribution': 'uniform',
-          'scale': 1.0,
-          'seed': null,
-          'mode': 'fan_avg'
-        }
-      },
-      'name': 'dense',
-      'kernel_constraint': null,
-      'bias_regularizer': null,
-      'bias_constraint': null,
-      'dtype': 'float32',
-      'activation': 'linear',
-      'trainable': true,
-      'kernel_regularizer': null,
-      'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-      'units': 1,
-      'batch_input_shape': [null, 3],
-      'use_bias': true,
-      'activity_regularizer': null
-    }
-  }],
-  'backend': 'tensorflow'
-};
-
-const weightSpecs1: tf.io.WeightsManifestEntry[] = [
-  {
-    name: 'dense/kernel',
-    shape: [3, 1],
-    dtype: 'float32',
-  },
-  {
-    name: 'dense/bias',
-    shape: [1],
-    dtype: 'float32',
-  }
-];
-
-const weightData1 = new ArrayBuffer(16);
-const artifacts1: tf.io.ModelArtifacts = {
-  modelTopology: modelTopology1,
-  weightSpecs: weightSpecs1,
-  weightData: weightData1,
-};
-
-describeWithFlags('Passthrough Saver', BROWSER_ENVS, () => {
-  it('passes provided arguments through on save', async () => {
-    const testStartDate = new Date();
-    let savedArtifacts: tf.io.ModelArtifacts = null;
-
-    async function saveHandler(artifacts: tf.io.ModelArtifacts):
-        Promise<tf.io.SaveResult> {
-      savedArtifacts = artifacts;
-      return {
-        modelArtifactsInfo: {
-          dateSaved: testStartDate,
-          modelTopologyType: 'JSON',
-          modelTopologyBytes: JSON.stringify(modelTopology1).length,
-          weightSpecsBytes: JSON.stringify(weightSpecs1).length,
-          weightDataBytes: weightData1.byteLength,
-        }
-      };
-    }
-
-    const saveTrigger = tf.io.withSaveHandler(saveHandler);
-    const saveResult = await saveTrigger.save(artifacts1);
-
-    expect(saveResult.errors).toEqual(undefined);
-    const artifactsInfo = saveResult.modelArtifactsInfo;
-    expect(artifactsInfo.dateSaved.getTime())
-        .toBeGreaterThanOrEqual(testStartDate.getTime());
-    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-        .toEqual(JSON.stringify(modelTopology1).length);
-    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
-        .toEqual(JSON.stringify(weightSpecs1).length);
-    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
-
-    expect(savedArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(savedArtifacts.weightSpecs).toEqual(weightSpecs1);
-    expect(savedArtifacts.weightData).toEqual(weightData1);
-  });
-});
-
-describeWithFlags('Passthrough Saver Sync', BROWSER_ENVS, () => {
-  it('passes provided arguments through on save', () => {
-    const testStartDate = new Date();
-    let savedArtifacts: tf.io.ModelArtifacts = null;
-
-    function saveHandler(artifacts: tf.io.ModelArtifacts):
-        tf.io.SaveResult {
-      savedArtifacts = artifacts;
-      return {
-        modelArtifactsInfo: {
-          dateSaved: testStartDate,
-          modelTopologyType: 'JSON',
-          modelTopologyBytes: JSON.stringify(modelTopology1).length,
-          weightSpecsBytes: JSON.stringify(weightSpecs1).length,
-          weightDataBytes: weightData1.byteLength,
-        }
-      };
-    }
-
-    const saveTrigger = tf.io.withSaveHandlerSync(saveHandler);
-    const saveResult = saveTrigger.save(artifacts1);
-
-    expect(saveResult.errors).toEqual(undefined);
-    const artifactsInfo = saveResult.modelArtifactsInfo;
-    expect(artifactsInfo.dateSaved.getTime())
-        .toBeGreaterThanOrEqual(testStartDate.getTime());
-    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
-        .toEqual(JSON.stringify(modelTopology1).length);
-    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
-        .toEqual(JSON.stringify(weightSpecs1).length);
-    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
-
-    expect(savedArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(savedArtifacts.weightSpecs).toEqual(weightSpecs1);
-    expect(savedArtifacts.weightData).toEqual(weightData1);
-  });
-});
-
-describeWithFlags('Passthrough Loader', BROWSER_ENVS, () => {
-  it('load topology and weights: legacy signature', async () => {
-    const passthroughHandler =
-        tf.io.fromMemory(modelTopology1, weightSpecs1, weightData1);
-    const modelArtifacts = await passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
-    expect(modelArtifacts.weightData).toEqual(weightData1);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
-  });
-
-  it('load topology and weights', async () => {
-    const passthroughHandler = tf.io.fromMemory({
-      modelTopology: modelTopology1,
-      weightSpecs: weightSpecs1,
-      weightData: weightData1
-    });
-    const modelArtifacts = await passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
-    expect(modelArtifacts.weightData).toEqual(weightData1);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
-  });
-
-  it('load model topology only: legacy signature', async () => {
-    const passthroughHandler = tf.io.fromMemory(modelTopology1);
-    const modelArtifacts = await passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(undefined);
-    expect(modelArtifacts.weightData).toEqual(undefined);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
-  });
-
-  it('load model topology only', async () => {
-    const passthroughHandler =
-        tf.io.fromMemory({modelTopology: modelTopology1});
-    const modelArtifacts = await passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(undefined);
-    expect(modelArtifacts.weightData).toEqual(undefined);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
-  });
-
-  it('load topology, weights, and user-defined metadata', async () => {
-    const userDefinedMetadata: {} = {'fooField': 'fooValue'};
-    const passthroughHandler = tf.io.fromMemory({
-      modelTopology: modelTopology1,
-      weightSpecs: weightSpecs1,
-      weightData: weightData1,
-      userDefinedMetadata
-    });
-    const modelArtifacts = await passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
-    expect(modelArtifacts.weightData).toEqual(weightData1);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(userDefinedMetadata);
-  });
-});
-
-describeWithFlags('Passthrough Loader Sync', BROWSER_ENVS, () => {
-  it('load topology and weights: legacy signature', () => {
-    const passthroughHandler =
-        tf.io.fromMemorySync(modelTopology1, weightSpecs1, weightData1);
-    const modelArtifacts = passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
-    expect(modelArtifacts.weightData).toEqual(weightData1);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
-  });
-
-  it('load topology and weights', () => {
-    const passthroughHandler = tf.io.fromMemorySync({
-      modelTopology: modelTopology1,
-      weightSpecs: weightSpecs1,
-      weightData: weightData1
-    });
-    const modelArtifacts = passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
-    expect(modelArtifacts.weightData).toEqual(weightData1);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
-  });
-
-  it('load model topology only: legacy signature', () => {
-    const passthroughHandler = tf.io.fromMemorySync(modelTopology1);
-    const modelArtifacts = passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(undefined);
-    expect(modelArtifacts.weightData).toEqual(undefined);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
-  });
-
-  it('load model topology only', () => {
-    const passthroughHandler =
-        tf.io.fromMemorySync({modelTopology: modelTopology1});
-    const modelArtifacts = passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(undefined);
-    expect(modelArtifacts.weightData).toEqual(undefined);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
-  });
-
-  it('load topology, weights, and user-defined metadata', () => {
-    const userDefinedMetadata: {} = {'fooField': 'fooValue'};
-    const passthroughHandler = tf.io.fromMemorySync({
-      modelTopology: modelTopology1,
-      weightSpecs: weightSpecs1,
-      weightData: weightData1,
-      userDefinedMetadata
-    });
-    const modelArtifacts = passthroughHandler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
-    expect(modelArtifacts.weightData).toEqual(weightData1);
-    expect(modelArtifacts.userDefinedMetadata).toEqual(userDefinedMetadata);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/io/progress.ts b/tfjs-master/tfjs-core/src/io/progress.ts
deleted file mode 100644
index 8d6b3d7fa..000000000
--- a/tfjs-master/tfjs-core/src/io/progress.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {assert} from '../util';
-
-import {OnProgressCallback} from './types';
-
-/**
- * Monitor Promise.all progress, fire onProgress callback function.
- *
- * @param promises Promise list going to be monitored
- * @param onProgress Callback function. Fired when a promise resolved.
- * @param startFraction Optional fraction start. Default to 0.
- * @param endFraction Optional fraction end. Default to 1.
- */
-export function monitorPromisesProgress(
-    promises: Array<Promise<{}|void>>, onProgress: OnProgressCallback,
-    startFraction?: number, endFraction?: number) {
-  checkPromises(promises);
-  startFraction = startFraction == null ? 0 : startFraction;
-  endFraction = endFraction == null ? 1 : endFraction;
-  checkFraction(startFraction, endFraction);
-  let resolvedPromise = 0;
-
-  const registerMonitor = (promise: Promise<{}>) => {
-    promise.then(value => {
-      const fraction = startFraction +
-          ++resolvedPromise / promises.length * (endFraction - startFraction);
-      // pass fraction as parameter to callback function.
-      onProgress(fraction);
-      return value;
-    });
-    return promise;
-  };
-
-  function checkPromises(promises: Array<Promise<{}|void>>): void {
-    assert(
-        promises != null && Array.isArray(promises) && promises.length > 0,
-        () => 'promises must be a none empty array');
-  }
-
-  function checkFraction(startFraction: number, endFraction: number): void {
-    assert(
-        startFraction >= 0 && startFraction <= 1,
-        () => `Progress fraction must be in range [0, 1], but ` +
-            `got startFraction ${startFraction}`);
-    assert(
-        endFraction >= 0 && endFraction <= 1,
-        () => `Progress fraction must be in range [0, 1], but ` +
-            `got endFraction ${endFraction}`);
-    assert(
-        endFraction >= startFraction,
-        () => `startFraction must be no more than endFraction, but ` +
-            `got startFraction ${startFraction} and endFraction ` +
-            `${endFraction}`);
-  }
-
-  return Promise.all(promises.map(registerMonitor));
-}
diff --git a/tfjs-master/tfjs-core/src/io/progress_test.ts b/tfjs-master/tfjs-core/src/io/progress_test.ts
deleted file mode 100644
index 7f0692ed9..000000000
--- a/tfjs-master/tfjs-core/src/io/progress_test.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {monitorPromisesProgress} from './progress';
-
-describe('util.monitorPromisesProgress', () => {
-  it('Default progress from 0 to 1', (done) => {
-    const expectFractions: number[] = [0.25, 0.50, 0.75, 1.00];
-    const fractionList: number[] = [];
-    const tasks = Array(4).fill(0).map(() => {
-      return Promise.resolve();
-    });
-    monitorPromisesProgress(tasks, (progress: number) => {
-      fractionList.push(parseFloat(progress.toFixed(2)));
-    }).then(() => {
-      expect(fractionList).toEqual(expectFractions);
-      done();
-    });
-  });
-
-  it('Progress with pre-defined range', (done) => {
-    const startFraction = 0.2;
-    const endFraction = 0.8;
-    const expectFractions: number[] = [0.35, 0.50, 0.65, 0.80];
-    const fractionList: number[] = [];
-    const tasks = Array(4).fill(0).map(() => {
-      return Promise.resolve();
-    });
-    monitorPromisesProgress(tasks, (progress: number) => {
-      fractionList.push(parseFloat(progress.toFixed(2)));
-    }, startFraction, endFraction).then(() => {
-      expect(fractionList).toEqual(expectFractions);
-      done();
-    });
-  });
-
-  it('throws error when progress fraction is out of range', () => {
-    expect(() => {
-      const startFraction = -1;
-      const endFraction = 1;
-      const tasks = Array(4).fill(0).map(() => {
-        return Promise.resolve();
-      });
-      monitorPromisesProgress(
-          tasks, (progress: number) => {}, startFraction, endFraction);
-    }).toThrowError();
-  });
-
-  it('throws error when startFraction more than endFraction', () => {
-    expect(() => {
-      const startFraction = 0.8;
-      const endFraction = 0.2;
-      const tasks = Array(4).fill(0).map(() => {
-        return Promise.resolve();
-      });
-      monitorPromisesProgress(
-          tasks, (progress: number) => {}, startFraction, endFraction);
-    }).toThrowError();
-  });
-
-  it('throws error when promises is null', () => {
-    expect(() => {
-      monitorPromisesProgress(null, (progress: number) => {});
-    }).toThrowError();
-  });
-
-  it('throws error when promises is empty array', () => {
-    expect(() => {
-      monitorPromisesProgress([], (progress: number) => {});
-    }).toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/io/router_registry.ts b/tfjs-master/tfjs-core/src/io/router_registry.ts
deleted file mode 100644
index 7094f9243..000000000
--- a/tfjs-master/tfjs-core/src/io/router_registry.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IOHandler, LoadOptions} from './types';
-
-export type IORouter = (url: string|string[], loadOptions?: LoadOptions) =>
-    IOHandler;
-
-export class IORouterRegistry {
-  // Singleton instance.
-  private static instance: IORouterRegistry;
-
-  private saveRouters: IORouter[];
-  private loadRouters: IORouter[];
-
-  private constructor() {
-    this.saveRouters = [];
-    this.loadRouters = [];
-  }
-
-  private static getInstance(): IORouterRegistry {
-    if (IORouterRegistry.instance == null) {
-      IORouterRegistry.instance = new IORouterRegistry();
-    }
-    return IORouterRegistry.instance;
-  }
-
-  /**
-   * Register a save-handler router.
-   *
-   * @param saveRouter A function that maps a URL-like string onto an instance
-   * of `IOHandler` with the `save` method defined or `null`.
-   */
-  static registerSaveRouter(saveRouter: IORouter) {
-    IORouterRegistry.getInstance().saveRouters.push(saveRouter);
-  }
-
-  /**
-   * Register a load-handler router.
-   *
-   * @param loadRouter A function that maps a URL-like string onto an instance
-   * of `IOHandler` with the `load` method defined or `null`.
-   */
-  static registerLoadRouter(loadRouter: IORouter) {
-    IORouterRegistry.getInstance().loadRouters.push(loadRouter);
-  }
-
-  /**
-   * Look up IOHandler for saving, given a URL-like string.
-   *
-   * @param url
-   * @returns If only one match is found, an instance of IOHandler with the
-   * `save` method defined. If no match is found, `null`.
-   * @throws Error, if more than one match is found.
-   */
-  static getSaveHandlers(url: string|string[]): IOHandler[] {
-    return IORouterRegistry.getHandlers(url, 'save');
-  }
-
-  /**
-   * Look up IOHandler for loading, given a URL-like string.
-   *
-   * @param url
-   * @param loadOptions Optional, custom load options.
-   * @returns All valid handlers for `url`, given the currently registered
-   *   handler routers.
-   */
-  static getLoadHandlers(url: string|string[], loadOptions?: LoadOptions):
-      IOHandler[] {
-    return IORouterRegistry.getHandlers(url, 'load', loadOptions);
-  }
-
-  private static getHandlers(
-      url: string|string[], handlerType: 'save'|'load',
-      loadOptions?: LoadOptions): IOHandler[] {
-    const validHandlers: IOHandler[] = [];
-    const routers = handlerType === 'load' ?
-        IORouterRegistry.getInstance().loadRouters :
-        IORouterRegistry.getInstance().saveRouters;
-    routers.forEach(router => {
-      const handler = router(url, loadOptions);
-      if (handler !== null) {
-        validHandlers.push(handler);
-      }
-    });
-    return validHandlers;
-  }
-}
-
-export const registerSaveRouter = (loudRouter: IORouter) =>
-    IORouterRegistry.registerSaveRouter(loudRouter);
-export const registerLoadRouter = (loudRouter: IORouter) =>
-    IORouterRegistry.registerLoadRouter(loudRouter);
-export const getSaveHandlers = (url: string|string[]) =>
-    IORouterRegistry.getSaveHandlers(url);
-export const getLoadHandlers =
-    (url: string|string[], loadOptions?: LoadOptions) =>
-        IORouterRegistry.getLoadHandlers(url, loadOptions);
diff --git a/tfjs-master/tfjs-core/src/io/router_registry_test.ts b/tfjs-master/tfjs-core/src/io/router_registry_test.ts
deleted file mode 100644
index 834e8e3d1..000000000
--- a/tfjs-master/tfjs-core/src/io/router_registry_test.ts
+++ /dev/null
@@ -1,148 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {BROWSER_ENVS, describeWithFlags} from '../jasmine_util';
-
-import {BrowserIndexedDB, browserIndexedDB} from './indexed_db';
-import {BrowserLocalStorage, browserLocalStorage} from './local_storage';
-import {IORouterRegistry} from './router_registry';
-import {IOHandler, LoadHandler, LoadOptions, SaveHandler} from './types';
-
-describeWithFlags('IORouterRegistry', BROWSER_ENVS, () => {
-  const localStorageRouter = (url: string) => {
-    const scheme = 'localstorage://';
-    if (url.startsWith(scheme)) {
-      return browserLocalStorage(url.slice(scheme.length));
-    } else {
-      return null;
-    }
-  };
-
-  const indexedDBRouter = (url: string) => {
-    const scheme = 'indexeddb://';
-    if (url.startsWith(scheme)) {
-      return browserIndexedDB(url.slice(scheme.length));
-    } else {
-      return null;
-    }
-  };
-
-  class FakeIOHandler implements IOHandler {
-    constructor(url1: string, url2: string) {}
-  }
-
-  const fakeMultiStringRouter = (url: string|string[]) => {
-    const scheme = 'foo://';
-    if (Array.isArray(url) && url.length === 2) {
-      if (url[0].startsWith(scheme) && url[1].startsWith(scheme)) {
-        return new FakeIOHandler(url[0], url[1]);
-      } else {
-        return null;
-      }
-    } else {
-      return null;
-    }
-  };
-
-  let tempRegistryInstance: IORouterRegistry = null;
-  beforeEach(() => {
-    // Force reset registry for testing.
-    // tslint:disable:no-any
-    tempRegistryInstance = (IORouterRegistry as any).instance;
-    (IORouterRegistry as any).instance = null;
-    // tslint:enable:no-any
-  });
-
-  afterEach(() => {
-    // tslint:disable-next-line:no-any
-    (IORouterRegistry as any).instance = tempRegistryInstance;
-  });
-
-  it('getSaveHandler succeeds', () => {
-    IORouterRegistry.registerSaveRouter(localStorageRouter);
-    IORouterRegistry.registerSaveRouter(indexedDBRouter);
-
-    const out1 = tf.io.getSaveHandlers('localstorage://foo-model');
-    expect(out1.length).toEqual(1);
-    expect(out1[0] instanceof BrowserLocalStorage).toEqual(true);
-    const out2 = tf.io.getSaveHandlers('indexeddb://foo-model');
-    expect(out2.length).toEqual(1);
-    expect(out2[0] instanceof BrowserIndexedDB).toEqual(true);
-  });
-
-  it('getLoadHandler succeeds', () => {
-    IORouterRegistry.registerLoadRouter(localStorageRouter);
-    IORouterRegistry.registerLoadRouter(indexedDBRouter);
-
-    const out1 = tf.io.getLoadHandlers('localstorage://foo-model');
-    expect(out1.length).toEqual(1);
-    expect(out1[0] instanceof BrowserLocalStorage).toEqual(true);
-    const out2 = tf.io.getLoadHandlers('indexeddb://foo-model');
-    expect(out2.length).toEqual(1);
-    expect(out2[0] instanceof BrowserIndexedDB).toEqual(true);
-  });
-
-  it('getLoadHandler with string array argument succeeds', () => {
-    IORouterRegistry.registerLoadRouter(fakeMultiStringRouter);
-    const loadHandler =
-        IORouterRegistry.getLoadHandlers(['foo:///123', 'foo:///456']);
-    expect(loadHandler[0] instanceof FakeIOHandler).toEqual(true);
-
-    expect(IORouterRegistry.getLoadHandlers([
-      'foo:///123', 'bar:///456'
-    ])).toEqual([]);
-    expect(IORouterRegistry.getLoadHandlers(['foo:///123'])).toEqual([]);
-    expect(IORouterRegistry.getLoadHandlers('foo:///123')).toEqual([]);
-  });
-
-  it('getSaveHandler fails', () => {
-    IORouterRegistry.registerSaveRouter(localStorageRouter);
-
-    expect(tf.io.getSaveHandlers('invalidscheme://foo-model')).toEqual([]);
-    // Check there is no crosstalk between save and load handlers.
-    expect(tf.io.getLoadHandlers('localstorage://foo-model')).toEqual([]);
-  });
-
-  const fakeLoadOptionsRouter = (url: string, loadOptions?: LoadOptions) => {
-    return new FakeLoadOptionsHandler(url, loadOptions);
-  };
-
-  class FakeLoadOptionsHandler implements IOHandler {
-    save?: SaveHandler;
-    load?: LoadHandler;
-    constructor(url: string, private readonly loadOptions?: LoadOptions) {}
-    get loadOptionsData() {
-      return this.loadOptions;
-    }
-  }
-
-  it('getLoadHandler loadOptions', () => {
-    IORouterRegistry.registerLoadRouter(fakeLoadOptionsRouter);
-
-    const loadOptions: LoadOptions = {
-      onProgress: (fraction: number) => {},
-      fetchFunc: () => {}
-    };
-    const loadHandler = tf.io.getLoadHandlers('foo:///123', loadOptions);
-    expect(loadHandler.length).toEqual(1);
-    expect(loadHandler[0] instanceof FakeLoadOptionsHandler).toEqual(true);
-    // Check callback function passed to IOHandler
-    expect((loadHandler[0] as FakeLoadOptionsHandler).loadOptionsData)
-        .toBe(loadOptions);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/io/types.ts b/tfjs-master/tfjs-core/src/io/types.ts
deleted file mode 100644
index 2dc0893a8..000000000
--- a/tfjs-master/tfjs-core/src/io/types.ts
+++ /dev/null
@@ -1,545 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/* Type definitions for exporting and importing of models. */
-
-/**
- * A map from Tensor dtype to number of bytes per element of the Tensor.
- */
-export const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {
-  'float32': 4,
-  'float16': 2,
-  'int32': 4,
-  'uint16': 2,
-  'uint8': 1,
-  'bool': 1,
-  'complex64': 8
-};
-
-/**
- * A weight manifest.
- *
- * The weight manifest consists of an ordered list of weight-manifest groups.
- * Each weight-manifest group ("group" for short hereafter) consists of a
- * number of weight values stored in a number of paths.
- * See the documentation of `WeightManifestGroupConfig` below for more details.
- */
-export declare type WeightsManifestConfig = WeightsManifestGroupConfig[];
-
-/**
- * A weight-manifest group.
- *
- * Consists of an ordered list of weight values encoded in binary format,
- * stored in an ordered list of paths.
- */
-export declare interface WeightsManifestGroupConfig {
-  /**
-   * An ordered list of paths.
-   *
-   * Paths are intentionally abstract in order to be general. For example, they
-   * can be relative URL paths or relative paths on the file system.
-   */
-  paths: string[];
-
-  /**
-   * Specifications of the weights stored in the paths.
-   */
-  weights: WeightsManifestEntry[];
-}
-
-/**
- * Group to which the weight belongs.
- *
- * - 'optimizer': Weight from a stateful optimizer.
- */
-export type WeightGroup = 'model'|'optimizer';
-
-/**
- * An entry in the weight manifest.
- *
- * The entry contains specification of a weight.
- */
-export declare interface WeightsManifestEntry {
-  /**
-   * Name of the weight, e.g., 'Dense_1/bias'
-   */
-  name: string;
-
-  /**
-   * Shape of the weight.
-   */
-  shape: number[];
-
-  /**
-   * Data type of the weight.
-   */
-  dtype: 'float32'|'int32'|'bool'|'string'|'complex64';
-
-  /**
-   * Type of the weight.
-   *
-   * Optional.
-   *
-   * The value 'optimizer' indicates the weight belongs to an optimizer
-   * (i.e., used only during model training and not during inference).
-   */
-  group?: WeightGroup;
-
-  /**
-   * Information for dequantization of the weight.
-   */
-  quantization?: {
-    scale?: number,  // The scaling constant to multiply by.
-    min?: number,    // The (possibly nudged) minimum weight to add.
-       dtype: 'uint16'|'uint8'|'float16'  // The dtype of the quantized weights.
-  };
-}
-
-/**
- * Options for saving a model.
- * @innamespace io
- */
-export interface SaveConfig {
-  /**
-   * Whether to save only the trainable weights of the model, ignoring the
-   * non-trainable ones.
-   */
-  trainableOnly?: boolean;
-
-  /**
-   * Whether the optimizer will be saved (if exists).
-   *
-   * Default: `false`.
-   */
-  includeOptimizer?: boolean;
-}
-
-/**
- * Result of a saving operation.
- */
-export interface SaveResult {
-  /**
-   * Information about the model artifacts saved.
-   */
-  modelArtifactsInfo: ModelArtifactsInfo;
-
-  /**
-   * HTTP responses from the server that handled the model-saving request (if
-   * any). This is applicable only to server-based saving routes.
-   */
-  responses?: Response[];
-
-  /**
-   * Error messages and related data (if any).
-   */
-  errors?: Array<{}|string>;
-}
-
-export declare interface ModelArtifactsInfo {
-  /**
-   * Timestamp for when the model is saved.
-   */
-  dateSaved: Date;
-
-  /**
-   * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now
-   * come in a JSON format and none of our IOHandlers support a non json
-   * format. We could conder replacing this with 'Binary' if we want to
-   * allow future handlers to save to non json formats (though they will
-   * probably want more information than 'Binary').
-   * Type of the model topology
-   *
-   * Type of the model topology
-   *
-   * Possible values:
-   *   - JSON: JSON config (human-readable, e.g., Keras JSON).
-   *   - GraphDef: TensorFlow
-   *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)
-   *     protocol buffer (binary).
-   */
-  modelTopologyType: 'JSON'|'GraphDef';
-
-  /**
-   * Size of model topology (Keras JSON or GraphDef), in bytes.
-   */
-  modelTopologyBytes?: number;
-
-  /**
-   * Size of weight specification or manifest, in bytes.
-   */
-  weightSpecsBytes?: number;
-
-  /**
-   * Size of weight value data, in bytes.
-   */
-  weightDataBytes?: number;
-}
-
-/** Model training configuration. */
-export declare interface TrainingConfig {
-  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.
-  // See
-  // tslint:disable-next-line:max-line-length
-  // https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/training_config.ts
-  /** Optimizer used for the model training. */
-  optimizer_config: {};
-
-  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.
-  /** Loss function(s) for the model's output(s). */
-  loss: string|string[]|{[key: string]: string};
-
-  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.
-  /** Metric function(s) for the model's output(s). */
-  metrics?: string[]|{[key: string]: string};
-
-  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.
-  weighted_metrics?: string[];
-
-  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.
-  sample_weight_mode?: string;
-
-  loss_weights?: number[]|{[key: string]: number};
-}
-
-export type WeightData = ArrayBuffer | ArrayBuffer[];
-
-/**
- * The serialized artifacts of a model, including topology and weights.
- *
- * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields
- * of this interface are optional, in order to support topology- or weights-only
- * saving and loading.
- *
- * Note this interface is used internally in IOHandlers.  For the file format
- * written to disk as `model.json`, see `ModelJSON`.
- */
-export declare interface ModelArtifacts {
-  /**
-   * Model topology.
-   *
-   * For Keras-style `tf.Model`s, this is a JSON object.
-   * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
-   * encoding of the `GraphDef` protocol buffer.
-   */
-  modelTopology?: {}|ArrayBuffer;
-
-  /**
-   * Serialized configuration for the model's training.
-   */
-  trainingConfig?: TrainingConfig;
-
-  /**
-   * Weight specifications.
-   *
-   * This corresponds to the weightsData below.
-   */
-  weightSpecs?: WeightsManifestEntry[];
-
-  /**
-   * Binary buffer(s) for all weight values in the order specified by
-   * `weightSpecs`. This may be a single ArrayBuffer of all the weights
-   * concatenated together or an Array of ArrayBuffers containing the weights
-   * (weights may be sharded across multiple ArrayBuffers).
-   */
-  weightData?: WeightData;
-
-  /**
-   * Hard-coded format name for models saved from TensorFlow.js or converted
-   * by TensorFlow.js Converter.
-   */
-  format?: string;
-
-  /**
-   * What library is responsible for originally generating this artifact.
-   *
-   * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
-   */
-  generatedBy?: string;
-
-  /**
-   * What library or tool is responsible for converting the original model
-   * to this format, applicable only if the model is output by a converter.
-   *
-   * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
-   *
-   * A value of `null` means the model artifacts are generated without any
-   * conversion process (e.g., saved directly from a TensorFlow.js
-   * `tf.LayersModel` instance.)
-   */
-  convertedBy?: string|null;
-
-  /**
-   * Inputs and outputs signature for saved model.
-   */
-  signature?: {};
-
-  /**
-   * User-defined metadata about the model.
-   */
-  userDefinedMetadata?: {[key: string]: {}};
-
-  /**
-   * Initializer for the model.
-   */
-  modelInitializer?: {};
-
-  /**
-   * Inputs and outputs signature for model initializer.
-   */
-  initializerSignature?: {};
-}
-
-/**
- * The on-disk format of the `model.json` file.
- *
- * TF.js 1.0 always populates the optional fields when writing model.json.
- * Prior versions did not provide those fields.
- */
-export declare interface ModelJSON {
-  /**
-   * Model topology.
-   *
-   * For Keras-style `tf.Model`s, this is a JSON object.
-   * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
-   * encoding of the `GraphDef` protocol buffer.
-   */
-  modelTopology: {};
-
-  /** Model training configuration. */
-  trainingConfig?: TrainingConfig;
-
-  /**
-   * Weights manifest.
-   *
-   * The weights manifest consists of an ordered list of weight-manifest
-   * groups. Each weight-manifest group consists of a number of weight values
-   * stored in a number of paths. See the documentation of
-   * `WeightsManifestConfig` for more details.
-   */
-  weightsManifest: WeightsManifestConfig;
-
-  /**
-   * Hard-coded format name for models saved from TensorFlow.js or converted
-   * by TensorFlow.js Converter.
-   */
-  format?: string;
-
-  /**
-   * What library is responsible for originally generating this artifact.
-   *
-   * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
-   */
-  generatedBy?: string;
-
-  /**
-   * What library or tool is responsible for converting the original model
-   * to this format, applicable only if the model is output by a converter.
-   *
-   * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
-   *
-   * A value of `null` means the model artifacts are generated without any
-   * conversion process (e.g., saved directly from a TensorFlow.js
-   * `tf.LayersModel` instance.)
-   */
-  convertedBy?: string|null;
-
-  /**
-   * Inputs and outputs signature for saved model.
-   */
-  signature?: {};
-
-  /**
-   * User-defined metadata about the model.
-   */
-  userDefinedMetadata?: {[key: string]: {}};
-
-  /**
-   * Initializer for the model.
-   */
-  modelInitializer?: {};
-
-  /**
-   * Inputs and outputs signature for model initializer.
-   */
-  initializerSignature?: {};
-}
-
-/**
- * Type definition for handlers of loading operations.
- */
-export type LoadHandler = () => Promise<ModelArtifacts>;
-
-/**
- * Type definition for handlers of saving operations.
- */
-export type SaveHandler = (modelArtifact: ModelArtifacts) =>
-    Promise<SaveResult>;
-
-/**
- * Interface for a model import/export handler.
- *
- * The `save` and `load` handlers are both optional, in order to allow handlers
- * that support only saving or loading.
- */
-// tslint:disable-next-line:interface-name
-export interface IOHandler {
-  save?: SaveHandler;
-  load?: LoadHandler;
-}
-
-/**
- * Type definition for handlers of synchronous loading operations.
- */
-export type LoadHandlerSync = () => ModelArtifacts;
-
-/**
- * Type definition for handlers of synchronous saving operations.
- */
-export type SaveHandlerSync = (modelArtifact: ModelArtifacts) => SaveResult;
-
-/**
- * Interface for a synchronous model import/export handler.
- *
- * The `save` and `load` handlers are both optional, in order to allow handlers
- * that support only saving or loading.
- */
-// tslint:disable-next-line:interface-name
-export type IOHandlerSync = {
-  save?: SaveHandlerSync;
-  load?: LoadHandlerSync;
-};
-
-/**
- * An interface for the manager of a model store.
- *
- * A model store is defined as a storage medium on which multiple models can
- * be stored. Each stored model has a unique `path` as its identifier.
- * A `ModelStoreManager` for the store allows actions including
- *
- * - Listing the models stored in the store.
- * - Deleting a model from the store.
- */
-export interface ModelStoreManager {
-  /**
-   * List all models in the model store.
-   *
-   * @returns A dictionary mapping paths of existing models to their
-   *   model artifacts info. Model artifacts info include type of the model's
-   *   topology, byte sizes of the topology, weights, etc.
-   */
-  listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;
-
-  /**
-   * Remove a model specified by `path`.
-   *
-   * @param path
-   * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
-   *   is successful).
-   * @throws Error if deletion fails, e.g., if no model exists at `path`.
-   */
-  removeModel(path: string): Promise<ModelArtifactsInfo>;
-}
-
-/**
- * Callback for the progress of a long-running action such as an HTTP
- * request for a large binary object.
- *
- * `fraction` should be a number in the [0, 1] interval, indicating how
- * much of the action has completed.
- */
-export type OnProgressCallback = (fraction: number) => void;
-
-/** @innamespace io */
-export interface LoadOptions {
-  /**
-   * RequestInit (options) for HTTP requests.
-   *
-   * For detailed information on the supported fields, see
-   * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](
-   *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)
-   */
-  requestInit?: RequestInit;
-
-  /**
-   * Progress callback.
-   */
-  onProgress?: OnProgressCallback;
-
-  /**
-   * A function used to override the `window.fetch` function.
-   */
-  fetchFunc?: Function;
-
-  /**
-   * Strict loading model: whether extraneous weights or missing
-   * weights should trigger an `Error`.
-   *
-   * If `true`, require that the provided weights exactly match those
-   * required by the layers. `false` means that both extra weights
-   * and missing weights will be silently ignored.
-   *
-   * Default: `true`.
-   */
-  strict?: boolean;
-
-  /**
-   * Path prefix for weight files, by default this is calculated from the
-   * path of the model JSON file.
-   *
-   * For instance, if the path to the model JSON file is
-   * `http://localhost/foo/model.json`, then the default path prefix will be
-   * `http://localhost/foo/`. If a weight file has the path value
-   * `group1-shard1of2` in the weight manifest, then the weight file will be
-   * loaded from `http://localhost/foo/group1-shard1of2` by default. However,
-   * if you provide a `weightPathPrefix` value of
-   * `http://localhost/foo/alt-weights`, then the weight file will be loaded
-   * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.
-   */
-  weightPathPrefix?: string;
-
-  /**
-   * Whether the module or model is to be loaded from TF Hub.
-   *
-   * Setting this to `true` allows passing a TF-Hub module URL, omitting the
-   * standard model file name and the query parameters.
-   *
-   * Default: `false`.
-   */
-  fromTFHub?: boolean;
-
-  /**
-   * An async function to convert weight file name to URL. The weight file
-   * names are stored in model.json's weightsManifest.paths field. By default we
-   * consider weight files are colocated with the model.json file. For example:
-   *     model.json URL: https://www.google.com/models/1/model.json
-   *     group1-shard1of1.bin url:
-   *        https://www.google.com/models/1/group1-shard1of1.bin
-   *
-   * With this func you can convert the weight file name to any URL.
-   */
-  weightUrlConverter?: (weightFileName: string) => Promise<string>;
-}
-
-/**
- * Additional options for Platform.fetch
- */
-export interface RequestDetails {
-  /**
-   * Is this request for a binary file (as opposed to a json file)
-   */
-  isBinary?: boolean;
-}
diff --git a/tfjs-master/tfjs-core/src/io/weights_loader.ts b/tfjs-master/tfjs-core/src/io/weights_loader.ts
deleted file mode 100644
index 8ad0ef2f8..000000000
--- a/tfjs-master/tfjs-core/src/io/weights_loader.ts
+++ /dev/null
@@ -1,237 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '../environment';
-
-import {NamedTensorMap} from '../tensor_types';
-import * as util from '../util';
-import {CompositeArrayBuffer} from './composite_array_buffer';
-import {decodeWeights} from './io_utils';
-import {monitorPromisesProgress} from './progress';
-import {DTYPE_VALUE_SIZE_MAP, LoadOptions, WeightsManifestConfig, WeightsManifestEntry} from './types';
-
-/**
- * Reads binary weights data from a number of URLs.
- *
- * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.
- * @param requestOptions RequestInit (options) for the HTTP requests.
- * @param fetchFunc Optional overriding value for the `window.fetch` function.
- * @param onProgress Optional, progress callback function, fired periodically
- *   before the load is completed.
- * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same
- *   length as `fetchURLs`.
- */
-export async function loadWeightsAsArrayBuffer(
-  fetchURLs: string[], loadOptions?: LoadOptions): Promise<ArrayBuffer[]> {
-  if (loadOptions == null) {
-    loadOptions = {};
-  }
-
-  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :
-    loadOptions.fetchFunc;
-
-  // Create the requests for all of the weights in parallel.
-  const requests = fetchURLs.map(
-    fetchURL =>
-      fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));
-
-  const fetchStartFraction = 0;
-  const fetchEndFraction = 0.5;
-
-  const responses = loadOptions.onProgress == null ?
-    await Promise.all(requests) :
-    await monitorPromisesProgress(
-      requests, loadOptions.onProgress, fetchStartFraction,
-      fetchEndFraction);
-
-  const bufferPromises = responses.map(response => response.arrayBuffer());
-
-  const bufferStartFraction = 0.5;
-  const bufferEndFraction = 1;
-
-  const buffers = loadOptions.onProgress == null ?
-    await Promise.all(bufferPromises) :
-    await monitorPromisesProgress(
-      bufferPromises, loadOptions.onProgress, bufferStartFraction,
-      bufferEndFraction);
-  return buffers;
-}
-
-/**
- * Reads a weights manifest JSON configuration, fetches the weights and
- * returns them as `Tensor`s.
- *
- * @param manifest The weights manifest JSON.
- * @param filePathPrefix The path prefix for filenames given in the manifest.
- *     Defaults to the empty string.
- * @param weightNames The names of the weights to be fetched.
- */
-export async function loadWeights(
-  manifest: WeightsManifestConfig, filePathPrefix = '',
-  weightNames?: string[],
-  requestInit?: RequestInit): Promise<NamedTensorMap> {
-  // TODO(nsthorat): Groups are currently fetched atomically. If you need a
-  // single weight from a group, the whole group will be fetched. At a future
-  // date, we should support fetching only the individual shards within a
-  // group that are needed to reconstruct the requested weight.
-  // TODO(cais): Use `decodeWeights` for implementation.
-
-  const fetchWeights = (fetchUrls: string[]) =>
-    loadWeightsAsArrayBuffer(fetchUrls, { requestInit });
-  const loadWeights = weightsLoaderFactory(fetchWeights);
-
-  return loadWeights(manifest, filePathPrefix, weightNames);
-}
-
-/**
- * Creates a function, which reads a weights manifest JSON configuration,
- * fetches the weight files using the specified function and returns them as
- * `Tensor`s.
- *
- * ```js
- * // example for creating a nodejs weight loader, which reads the weight files
- * // from disk using fs.readFileSync
- *
- * import * as fs from 'fs'
- *
- * const fetchWeightsFromDisk = (filePaths: string[]) =>
- *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
- *
- * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
- *
- * const manifest = JSON.parse(
- *   fs.readFileSync('./my_model-weights_manifest').toString()
- * )
- * const weightMap = await loadWeights(manifest, './')
- * ```
- * @param fetchWeightsFunction The function used for fetching the weight files.
- * @returns Weight loading function.
- */
-export function weightsLoaderFactory(
-  fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>):
-  (manifest: WeightsManifestConfig, filePathPrefix?: string,
-    weightNames?: string[]) => Promise<NamedTensorMap> {
-  return async (
-    manifest: WeightsManifestConfig, filePathPrefix = '',
-    weightNames?: string[]): Promise<NamedTensorMap> => {
-    // Collect all the groups, weights, and their relative offsets to be
-    // fetched.
-    const groupIndicesToFetchMap = manifest.map(() => false);
-    const groupWeightsToFetch: {
-      [group: number]: Array<{
-        manifestEntry: WeightsManifestEntry; groupOffset: number;
-        sizeBytes: number;
-      }>
-    } = {};
-    const weightsFound =
-      weightNames != null ? weightNames.map(() => false) : [];
-    const allManifestWeightNames: string[] = [];
-    manifest.forEach((manifestGroupConfig, groupIndex) => {
-      let groupOffset = 0;
-      manifestGroupConfig.weights.forEach(weightsEntry => {
-        const rawDtype = ('quantization' in weightsEntry) ?
-          weightsEntry.quantization.dtype :
-          weightsEntry.dtype;
-
-        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *
-          util.sizeFromShape(weightsEntry.shape);
-
-        const enqueueWeightsForFetchingFn = () => {
-          groupIndicesToFetchMap[groupIndex] = true;
-          if (groupWeightsToFetch[groupIndex] == null) {
-            groupWeightsToFetch[groupIndex] = [];
-          }
-
-          groupWeightsToFetch[groupIndex].push({
-            manifestEntry: weightsEntry,
-            groupOffset,
-            sizeBytes: weightsBytes
-          });
-        };
-
-        if (weightNames != null) {
-          weightNames.forEach((weightName, weightIndex) => {
-            if (weightName === weightsEntry.name) {
-              enqueueWeightsForFetchingFn();
-              weightsFound[weightIndex] = true;
-            }
-          });
-        } else {
-          enqueueWeightsForFetchingFn();
-        }
-
-        allManifestWeightNames.push(weightsEntry.name);
-        groupOffset += weightsBytes;
-      });
-    });
-
-    if (!weightsFound.every(found => found)) {
-      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);
-      throw new Error(
-        `Could not find weights in manifest with names: ` +
-        `${weightsNotFound.join(', ')}. \n` +
-        `Manifest JSON has weights with names: ` +
-        `${allManifestWeightNames.join(', ')}.`);
-    }
-
-    // Convert the one-hot boolean groupId => shouldFetch map to a list of group
-    // IDs.
-    const groupIndicesToFetch =
-      groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {
-        if (shouldFetch) {
-          accumulator.push(i);
-        }
-        return accumulator;
-      }, []);
-
-    const fetchUrls: string[] = [];
-    groupIndicesToFetch.forEach(i => {
-      manifest[i].paths.forEach(filepath => {
-        const fetchUrl = filePathPrefix +
-          (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;
-        fetchUrls.push(fetchUrl);
-      });
-    });
-    const buffers = await fetchWeightsFunction(fetchUrls);
-
-    const weightsTensorMap: NamedTensorMap = {};
-    let bufferIndexOffset = 0;
-    groupIndicesToFetch.forEach(i => {
-      const numBuffers = manifest[i].paths.length;
-
-      const weightsBuffer = new CompositeArrayBuffer(
-        buffers.slice(bufferIndexOffset, bufferIndexOffset + numBuffers));
-
-      const weightsEntries = groupWeightsToFetch[i];
-
-      weightsEntries.forEach(weightsEntry => {
-        const byteBuffer = weightsBuffer.slice(
-          weightsEntry.groupOffset,
-          weightsEntry.groupOffset + weightsEntry.sizeBytes);
-        const nameToTensorMap =
-          decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
-        for (const name in nameToTensorMap) {
-          weightsTensorMap[name] = nameToTensorMap[name];
-        }
-      });
-
-      bufferIndexOffset += numBuffers;
-    });
-
-    return weightsTensorMap;
-  };
-}
diff --git a/tfjs-master/tfjs-core/src/io/weights_loader_test.ts b/tfjs-master/tfjs-core/src/io/weights_loader_test.ts
deleted file mode 100644
index 69a00607d..000000000
--- a/tfjs-master/tfjs-core/src/io/weights_loader_test.ts
+++ /dev/null
@@ -1,543 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {BROWSER_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-import {WeightsManifestConfig} from './types';
-
-describeWithFlags('loadWeights', BROWSER_ENVS, () => {
-  const setupFakeWeightFiles = (fileBufferMap: {
-    [filename: string]: Float32Array|Int32Array|ArrayBuffer|Uint8Array|
-    Uint16Array
-  }) => {
-    spyOn(tf.env().platform, 'fetch').and.callFake(async (path: string) => {
-      return new Response(
-          fileBufferMap[path],
-          {headers: {'Content-type': 'application/octet-stream'}});
-    });
-  };
-
-  it('1 group, 1 weight, 1 requested weight', async () => {
-    setupFakeWeightFiles({'./weightfile0': new Float32Array([1, 2, 3])});
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0'],
-      'weights': [{'name': 'weight0', 'dtype': 'float32', 'shape': [3]}]
-    }];
-
-    const weightsNamesToFetch = ['weight0'];
-    const weights =
-        await tf.io.loadWeights(manifest, './', weightsNamesToFetch);
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(1);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(weightsNamesToFetch.length);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [1, 2, 3]);
-    expect(weight0.shape).toEqual([3]);
-    expect(weight0.dtype).toEqual('float32');
-  });
-
-  it('1 group, 2 weights, fetch 1st weight', async () => {
-    setupFakeWeightFiles({'./weightfile0': new Float32Array([1, 2, 3, 4, 5])});
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0'],
-      'weights': [
-        {'name': 'weight0', 'dtype': 'float32', 'shape': [2]},
-        {'name': 'weight1', 'dtype': 'float32', 'shape': [3]}
-      ]
-    }];
-
-    // Load the first weight.
-    const weights = await tf.io.loadWeights(manifest, './', ['weight0']);
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(1);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(1);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [1, 2]);
-    expect(weight0.shape).toEqual([2]);
-    expect(weight0.dtype).toEqual('float32');
-  });
-
-  it('1 group, 2 weights, fetch 2nd weight', async () => {
-    setupFakeWeightFiles({'./weightfile0': new Float32Array([1, 2, 3, 4, 5])});
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0'],
-      'weights': [
-        {'name': 'weight0', 'dtype': 'float32', 'shape': [2]},
-        {'name': 'weight1', 'dtype': 'float32', 'shape': [3]}
-      ]
-    }];
-
-    // Load the second weight.
-    const weights = await tf.io.loadWeights(manifest, './', ['weight1']);
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(1);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(1);
-
-    const weight1 = weights['weight1'];
-    expectArraysClose(await weight1.data(), [3, 4, 5]);
-    expect(weight1.shape).toEqual([3]);
-    expect(weight1.dtype).toEqual('float32');
-  });
-
-  it('1 group, 2 weights, fetch all weights', async () => {
-    setupFakeWeightFiles({'./weightfile0': new Float32Array([1, 2, 3, 4, 5])});
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0'],
-      'weights': [
-        {'name': 'weight0', 'dtype': 'float32', 'shape': [2]},
-        {'name': 'weight1', 'dtype': 'float32', 'shape': [3]}
-      ]
-    }];
-
-    // Load all weights.
-    const weights =
-        await tf.io.loadWeights(manifest, './', ['weight0', 'weight1']);
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(1);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(2);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [1, 2]);
-    expect(weight0.shape).toEqual([2]);
-    expect(weight0.dtype).toEqual('float32');
-
-    const weight1 = weights['weight1'];
-    expectArraysClose(await weight1.data(), [3, 4, 5]);
-    expect(weight1.shape).toEqual([3]);
-    expect(weight1.dtype).toEqual('float32');
-  });
-
-  it('1 group, multiple weights, different dtypes', async () => {
-    const buffer = new ArrayBuffer(5 * 4 + 1);
-    const view = new DataView(buffer);
-    view.setInt32(0, 1, true);
-    view.setInt32(4, 2, true);
-    view.setUint8(8, 1);
-    view.setFloat32(9, 3., true);
-    view.setFloat32(13, 4., true);
-    view.setFloat32(17, 5., true);
-    setupFakeWeightFiles({'./weightfile0': buffer});
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0'],
-      'weights': [
-        {'name': 'weight0', 'dtype': 'int32', 'shape': [2]},
-        {'name': 'weight1', 'dtype': 'bool', 'shape': []},
-        {'name': 'weight2', 'dtype': 'float32', 'shape': [3]},
-      ]
-    }];
-
-    // Load all weights.
-    const weights = await tf.io.loadWeights(
-        manifest, './', ['weight0', 'weight1', 'weight2']);
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(1);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(3);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [1, 2]);
-    expect(weight0.shape).toEqual([2]);
-    expect(weight0.dtype).toEqual('int32');
-
-    const weight1 = weights['weight1'];
-    expectArraysClose(await weight1.data(), [1]);
-    expect(weight1.shape).toEqual([]);
-    expect(weight1.dtype).toEqual('bool');
-
-    const weight2 = weights['weight2'];
-    expectArraysClose(await weight2.data(), [3, 4, 5]);
-    expect(weight2.shape).toEqual([3]);
-    expect(weight2.dtype).toEqual('float32');
-  });
-
-  it('1 group, sharded 1 weight across multiple files', async () => {
-    const shard0 = new Float32Array([1, 2, 3, 4, 5]);
-    const shard1 = new Float32Array([1.1, 2.2]);
-    const shard2 = new Float32Array([10, 20, 30]);
-
-    setupFakeWeightFiles({
-      './weightfile0': shard0,
-      './weightsfile1': shard1,
-      './weightsfile2': shard2
-    });
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0', 'weightsfile1', 'weightsfile2'],
-      'weights': [{'name': 'weight0', 'dtype': 'float32', 'shape': [5, 2]}]
-    }];
-
-    const weights = await tf.io.loadWeights(manifest, './', ['weight0']);
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(3);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(1);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(
-        await weight0.data(), [1, 2, 3, 4, 5, 1.1, 2.2, 10, 20, 30]);
-    expect(weight0.shape).toEqual([5, 2]);
-    expect(weight0.dtype).toEqual('float32');
-  });
-
-  it('1 group, sharded 2 weights across multiple files', async () => {
-    const shard0 = new Int32Array([1, 2, 3, 4, 5]);
-
-    // shard1 contains part of the first weight and part of the second.
-    const shard1 = new ArrayBuffer(5 * 4);
-    const intBuffer = new Int32Array(shard1, 0, 2);
-    intBuffer.set([10, 20]);
-    const floatBuffer = new Float32Array(shard1, intBuffer.byteLength, 3);
-    floatBuffer.set([3.0, 4.0, 5.0]);
-
-    const shard2 = new Float32Array([10, 20, 30]);
-
-    setupFakeWeightFiles({
-      './weightfile0': shard0,
-      './weightsfile1': shard1,
-      './weightsfile2': shard2
-    });
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0', 'weightsfile1', 'weightsfile2'],
-      'weights': [
-        {'name': 'weight0', 'dtype': 'int32', 'shape': [7, 1]},
-        {'name': 'weight1', 'dtype': 'float32', 'shape': [3, 2]}
-      ]
-    }];
-
-    const weights =
-        await tf.io.loadWeights(manifest, './', ['weight0', 'weight1']);
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(3);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(2);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [1, 2, 3, 4, 5, 10, 20]);
-    expect(weight0.shape).toEqual([7, 1]);
-    expect(weight0.dtype).toEqual('int32');
-
-    const weight1 = weights['weight1'];
-    expectArraysClose(await weight1.data(), [3.0, 4.0, 5.0, 10, 20, 30]);
-    expect(weight1.shape).toEqual([3, 2]);
-    expect(weight1.dtype).toEqual('float32');
-  });
-
-  it('2 group, 4 weights, fetches one group', async () => {
-    setupFakeWeightFiles({
-      './weightfile0': new Float32Array([1, 2, 3, 4, 5]),
-      './weightfile1': new Float32Array([6, 7, 8, 9])
-    });
-
-    const manifest: WeightsManifestConfig = [
-      {
-        'paths': ['weightfile0'],
-        'weights': [
-          {'name': 'weight0', 'dtype': 'float32', 'shape': [2]},
-          {'name': 'weight1', 'dtype': 'float32', 'shape': [3]}
-        ]
-      },
-      {
-        'paths': ['weightfile1'],
-        'weights': [
-          {'name': 'weight2', 'dtype': 'float32', 'shape': [3, 1]},
-          {'name': 'weight3', 'dtype': 'float32', 'shape': []}
-        ]
-      }
-    ];
-
-    const weights =
-        await tf.io.loadWeights(manifest, './', ['weight0', 'weight1']);
-    // Only the first group should be fetched.
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(1);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(2);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [1, 2]);
-    expect(weight0.shape).toEqual([2]);
-    expect(weight0.dtype).toEqual('float32');
-
-    const weight1 = weights['weight1'];
-    expectArraysClose(await weight1.data(), [3, 4, 5]);
-    expect(weight1.shape).toEqual([3]);
-    expect(weight1.dtype).toEqual('float32');
-  });
-
-  it('2 group, 4 weights, one weight from each group', async () => {
-    setupFakeWeightFiles({
-      './weightfile0': new Float32Array([1, 2, 3, 4, 5]),
-      './weightfile1': new Float32Array([6, 7, 8, 9])
-    });
-
-    const manifest: WeightsManifestConfig = [
-      {
-        'paths': ['weightfile0'],
-        'weights': [
-          {'name': 'weight0', 'dtype': 'float32', 'shape': [2]},
-          {'name': 'weight1', 'dtype': 'float32', 'shape': [3]}
-        ]
-      },
-      {
-        'paths': ['weightfile1'],
-        'weights': [
-          {'name': 'weight2', 'dtype': 'float32', 'shape': [3, 1]},
-          {'name': 'weight3', 'dtype': 'float32', 'shape': []}
-        ]
-      }
-    ];
-
-    const weights =
-        await tf.io.loadWeights(manifest, './', ['weight0', 'weight2']);
-    // Both groups need to be fetched.
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(2);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(2);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [1, 2]);
-    expect(weight0.shape).toEqual([2]);
-    expect(weight0.dtype).toEqual('float32');
-
-    const weight2 = weights['weight2'];
-    expectArraysClose(await weight2.data(), [6, 7, 8]);
-    expect(weight2.shape).toEqual([3, 1]);
-    expect(weight2.dtype).toEqual('float32');
-  });
-
-  it('2 group, 4 weights, dont specify weights fetchs all', async () => {
-    setupFakeWeightFiles({
-      './weightfile0': new Float32Array([1, 2, 3, 4, 5]),
-      './weightfile1': new Float32Array([6, 7, 8, 9])
-    });
-
-    const manifest: WeightsManifestConfig = [
-      {
-        'paths': ['weightfile0'],
-        'weights': [
-          {'name': 'weight0', 'dtype': 'float32', 'shape': [2]},
-          {'name': 'weight1', 'dtype': 'float32', 'shape': [3]}
-        ]
-      },
-      {
-        'paths': ['weightfile1'],
-        'weights': [
-          {'name': 'weight2', 'dtype': 'float32', 'shape': [3, 1]},
-          {'name': 'weight3', 'dtype': 'float32', 'shape': []}
-        ]
-      }
-    ];
-
-    // Don't pass a third argument to loadWeights to load all weights.
-    const weights = await tf.io.loadWeights(manifest, './');
-    // Both groups need to be fetched.
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(2);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(4);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [1, 2]);
-    expect(weight0.shape).toEqual([2]);
-    expect(weight0.dtype).toEqual('float32');
-
-    const weight1 = weights['weight1'];
-    expectArraysClose(await weight1.data(), [3, 4, 5]);
-    expect(weight1.shape).toEqual([3]);
-    expect(weight1.dtype).toEqual('float32');
-
-    const weight2 = weights['weight2'];
-    expectArraysClose(await weight2.data(), [6, 7, 8]);
-    expect(weight2.shape).toEqual([3, 1]);
-    expect(weight2.dtype).toEqual('float32');
-
-    const weight3 = weights['weight3'];
-    expectArraysClose(await weight3.data(), [9]);
-    expect(weight3.shape).toEqual([]);
-    expect(weight3.dtype).toEqual('float32');
-  });
-
-  it('throws if requested weight not found', async () => {
-    setupFakeWeightFiles({'./weightfile0': new Float32Array([1, 2, 3])});
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0'],
-      'weights': [{'name': 'weight0', 'dtype': 'float32', 'shape': [3]}]
-    }];
-
-    const weightsNamesToFetch = ['doesntexist'];
-    try {
-      await tf.io.loadWeights(manifest, './', weightsNamesToFetch);
-      fail();
-    } catch (e) {
-      expect(e.message).toContain('Could not find weights');
-    }
-  });
-
-  it('throws if requested weight has unknown dtype', async () => {
-    setupFakeWeightFiles({'./weightfile0': new Float32Array([1, 2, 3])});
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0'],
-      'weights': [{
-        'name': 'weight0',
-        // tslint:disable-next-line:no-any
-        'dtype': 'null' as any,
-        'shape': [3]
-      }]
-    }];
-
-    const weightsNamesToFetch = ['weight0'];
-    try {
-      await tf.io.loadWeights(manifest, './', weightsNamesToFetch);
-      fail();
-    } catch (e) {
-      expect(e.message).toContain('Unsupported dtype');
-    }
-  });
-
-  it('should use request option', async () => {
-    setupFakeWeightFiles({'./weightfile0': new Float32Array([1, 2, 3])});
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0'],
-      'weights': [{'name': 'weight0', 'dtype': 'float32', 'shape': [3]}]
-    }];
-
-    const weightsNamesToFetch = ['weight0'];
-    await tf.io.loadWeights(
-        manifest, './', weightsNamesToFetch, {credentials: 'include'});
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(1);
-    expect(tf.env().platform.fetch)
-        .toHaveBeenCalledWith(
-            './weightfile0', {credentials: 'include'}, {isBinary: true});
-  });
-
-  const quantizationTest = async (quantizationDtype: 'uint8'|'uint16') => {
-    const arrayType = quantizationDtype === 'uint8' ? Uint8Array : Uint16Array;
-    setupFakeWeightFiles(
-        {'./weightfile0': new arrayType([0, 48, 255, 0, 48, 255])});
-
-    const manifest: WeightsManifestConfig = [{
-      'paths': ['weightfile0'],
-      'weights': [
-        {
-          'name': 'weight0',
-          'dtype': 'float32',
-          'shape': [3],
-          'quantization': {'min': -1, 'scale': 0.1, 'dtype': quantizationDtype}
-        },
-        {
-          'name': 'weight1',
-          'dtype': 'int32',
-          'shape': [3],
-          'quantization': {'min': -1, 'scale': 0.1, 'dtype': quantizationDtype}
-        }
-      ]
-    }];
-
-    const weightsNamesToFetch = ['weight0', 'weight1'];
-    const weights =
-        await tf.io.loadWeights(manifest, './', weightsNamesToFetch);
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(1);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(weightsNamesToFetch.length);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [-1, 3.8, 24.5]);
-    expect(weight0.shape).toEqual([3]);
-    expect(weight0.dtype).toEqual('float32');
-
-    const weight1 = weights['weight1'];
-    expectArraysEqual(await weight1.data(), [-1, 4, 25]);
-    expect(weight1.shape).toEqual([3]);
-    expect(weight1.dtype).toEqual('int32');
-  };
-
-  it('quantized weights (uint8)', async () => {
-    await quantizationTest('uint8');
-  });
-
-  it('quantized weights (uint16)', async () => {
-    await quantizationTest('uint16');
-  });
-
-  it('2 groups, 1 quantized, 1 unquantized', async () => {
-    setupFakeWeightFiles({
-      './weightfile0': new Uint8Array([0, 48, 255, 0, 48, 255]),
-      './weightfile1': new Float32Array([6, 7, 8, 9])
-    });
-
-    const manifest: WeightsManifestConfig = [
-      {
-        'paths': ['weightfile0'],
-        'weights': [
-          {
-            'name': 'weight0',
-            'dtype': 'float32',
-            'shape': [3],
-            'quantization': {'min': -1, 'scale': 0.1, 'dtype': 'uint8'}
-          },
-          {
-            'name': 'weight1',
-            'dtype': 'int32',
-            'shape': [3],
-            'quantization': {'min': -1, 'scale': 0.1, 'dtype': 'uint8'}
-          }
-        ]
-      },
-      {
-        'paths': ['weightfile1'],
-        'weights': [
-          {'name': 'weight2', 'dtype': 'float32', 'shape': [3, 1]},
-          {'name': 'weight3', 'dtype': 'float32', 'shape': []}
-        ]
-      }
-    ];
-
-    const weights =
-        await tf.io.loadWeights(manifest, './', ['weight0', 'weight2']);
-    // Both groups need to be fetched.
-    expect((tf.env().platform.fetch as jasmine.Spy).calls.count()).toBe(2);
-
-    const weightNames = Object.keys(weights);
-    expect(weightNames.length).toEqual(2);
-
-    const weight0 = weights['weight0'];
-    expectArraysClose(await weight0.data(), [-1, 3.8, 24.5]);
-    expect(weight0.shape).toEqual([3]);
-    expect(weight0.dtype).toEqual('float32');
-
-    const weight2 = weights['weight2'];
-    expectArraysClose(await weight2.data(), [6, 7, 8]);
-    expect(weight2.shape).toEqual([3, 1]);
-    expect(weight2.dtype).toEqual('float32');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/jasmine_util.ts b/tfjs-master/tfjs-core/src/jasmine_util.ts
deleted file mode 100644
index 19f554d18..000000000
--- a/tfjs-master/tfjs-core/src/jasmine_util.ts
+++ /dev/null
@@ -1,326 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// We use the pattern below (as opposed to require('jasmine') to create the
-// jasmine module in order to avoid loading node specific modules which may
-// be ignored in browser environments but cannot be ignored in react-native
-// due to the pre-bundling of dependencies that it must do.
-// tslint:disable-next-line:no-require-imports
-const jasmineRequire = require('jasmine-core/lib/jasmine-core/jasmine.js');
-const jasmineCore = jasmineRequire.core(jasmineRequire);
-import {KernelBackend} from './backends/backend';
-import {ENGINE} from './engine';
-import {env, Environment, Flags} from './environment';
-import {purgeLocalStorageArtifacts} from './io/local_storage';
-import {isPromise} from './util_base';
-
-Error.stackTraceLimit = Infinity;
-jasmineCore.DEFAULT_TIMEOUT_INTERVAL = 20000;
-
-export type Constraints = {
-  flags?: Flags,
-  predicate?: (testEnv: TestEnv) => boolean,
-};
-
-export const NODE_ENVS: Constraints = {
-  predicate: () => env().platformName === 'node'
-};
-export const CHROME_ENVS: Constraints = {
-  flags: {'IS_CHROME': true}
-};
-export const BROWSER_ENVS: Constraints = {
-  predicate: () => env().platformName === 'browser'
-};
-
-export const SYNC_BACKEND_ENVS: Constraints = {
-  predicate: (testEnv: TestEnv) => testEnv.isDataSync === true
-};
-
-export const HAS_WORKER = {
-  predicate: () => typeof (Worker) !== 'undefined' &&
-      typeof (Blob) !== 'undefined' && typeof (URL) !== 'undefined'
-};
-
-export const HAS_NODE_WORKER = {
-  predicate: () => {
-    let hasWorker = true;
-    try {
-      require.resolve('worker_threads');
-    } catch {
-      hasWorker = false;
-    }
-    return typeof (process) !== 'undefined' && hasWorker;
-  }
-};
-
-export const ALL_ENVS: Constraints = {};
-
-// Tests whether the current environment satisfies the set of constraints.
-export function envSatisfiesConstraints(
-    env: Environment, testEnv: TestEnv, constraints: Constraints): boolean {
-  if (constraints == null) {
-    return true;
-  }
-
-  if (constraints.flags != null) {
-    for (const flagName in constraints.flags) {
-      const flagValue = constraints.flags[flagName];
-      if (env.get(flagName) !== flagValue) {
-        return false;
-      }
-    }
-  }
-  if (constraints.predicate != null && !constraints.predicate(testEnv)) {
-    return false;
-  }
-  return true;
-}
-
-export interface TestFilter {
-  include?: string;
-  startsWith?: string;
-  excludes?: string[];
-}
-
-/**
- * Add test filtering logic to Jasmine's specFilter hook.
- *
- * @param testFilters Used for include a test suite, with the ability
- *     to selectively exclude some of the tests.
- *     Either `include` or `startsWith` must exist for a `TestFilter`.
- *     Tests that have the substrings specified by the include or startsWith
- *     will be included in the test run, unless one of the substrings specified
- *     by `excludes` appears in the name.
- * @param customInclude Function to programatically include a test.
- *     If this function returns true, a test will immediately run. Otherwise,
- *     `testFilters` is used for fine-grained filtering.
- *
- * If a test is not handled by `testFilters` or `customInclude`, the test will
- * be excluded in the test run.
- */
-export function setupTestFilters(
-    testFilters: TestFilter[], customInclude: (name: string) => boolean) {
-  const env = jasmine.getEnv();
-
-  // Account for --grep flag passed to karma by saving the existing specFilter.
-  const config = env.configuration();
-  const grepFilter = config.specFilter;
-
-  /**
-   * Filter method that returns boolean, if a given test should run or be
-   * ignored based on its name. The exclude list has priority over the
-   * include list. Thus, if a test matches both the exclude and the include
-   * list, it will be exluded.
-   */
-  // tslint:disable-next-line: no-any
-  const specFilter = (spec: any) => {
-    // Filter out tests if the --grep flag is passed.
-    if (!grepFilter(spec)) {
-      return false;
-    }
-
-    const name = spec.getFullName();
-
-    if (customInclude(name)) {
-      return true;
-    }
-
-    // Include tests of a test suite unless tests are in excludes list.
-    for (let i = 0; i < testFilters.length; ++i) {
-      const testFilter = testFilters[i];
-      if ((testFilter.include != null &&
-           name.indexOf(testFilter.include) > -1) ||
-          (testFilter.startsWith != null &&
-           name.startsWith(testFilter.startsWith))) {
-        if (testFilter.excludes != null) {
-          for (let j = 0; j < testFilter.excludes.length; j++) {
-            if (name.indexOf(testFilter.excludes[j]) > -1) {
-              return false;
-            }
-          }
-        }
-        return true;
-      }
-    }
-
-    // Otherwise ignore the test.
-    return false;
-  };
-
-  env.configure({...config, specFilter});
-}
-
-export function parseTestEnvFromKarmaFlags(
-    args: string[], registeredTestEnvs: TestEnv[]): TestEnv {
-  let flags: Flags;
-  let testEnvName: string;
-
-  args.forEach((arg, i) => {
-    if (arg === '--flags') {
-      flags = JSON.parse(args[i + 1]);
-    } else if (arg === '--testEnv') {
-      testEnvName = args[i + 1];
-    }
-  });
-
-  const testEnvNames = registeredTestEnvs.map(env => env.name).join(', ');
-  if (flags != null && testEnvName == null) {
-    throw new Error(
-        '--testEnv flag is required when --flags is present. ' +
-        `Available values are [${testEnvNames}].`);
-  }
-  if (testEnvName == null) {
-    return null;
-  }
-
-  let testEnv: TestEnv;
-  registeredTestEnvs.forEach(env => {
-    if (env.name === testEnvName) {
-      testEnv = env;
-    }
-  });
-  if (testEnv == null) {
-    throw new Error(
-        `Test environment with name ${testEnvName} not ` +
-        `found. Available test environment names are ` +
-        `${testEnvNames}`);
-  }
-  if (flags != null) {
-    testEnv.flags = flags;
-  }
-
-  return testEnv;
-}
-
-export function describeWithFlags(
-    name: string, constraints: Constraints, tests: (env: TestEnv) => void) {
-  if (TEST_ENVS.length === 0) {
-    throw new Error(
-        `Found no test environments. This is likely due to test environment ` +
-        `registries never being imported or test environment registries ` +
-        `being registered too late.`);
-  }
-
-  TEST_ENVS.forEach(testEnv => {
-    env().setFlags(testEnv.flags);
-    env().set('IS_TEST', true);
-    if (envSatisfiesConstraints(env(), testEnv, constraints)) {
-      const testName =
-          name + ' ' + testEnv.name + ' ' + JSON.stringify(testEnv.flags || {});
-      executeTests(testName, tests, testEnv);
-    }
-  });
-}
-
-export interface TestEnv {
-  name: string;
-  backendName: string;
-  flags?: Flags;
-  isDataSync?: boolean;
-}
-
-export const TEST_ENVS: TestEnv[] = [];
-
-// Whether a call to setTestEnvs has been called so we turn off
-// registration. This allows command line overriding or programmatic
-// overriding of the default registrations.
-let testEnvSet = false;
-export function setTestEnvs(testEnvs: TestEnv[]) {
-  testEnvSet = true;
-  TEST_ENVS.length = 0;
-  TEST_ENVS.push(...testEnvs);
-}
-
-export function registerTestEnv(testEnv: TestEnv) {
-  // When using an explicit call to setTestEnvs, turn off registration of
-  // test environments because the explicit call will set the test
-  // environments.
-  if (testEnvSet) {
-    return;
-  }
-  TEST_ENVS.push(testEnv);
-}
-
-function executeTests(
-    testName: string, tests: (env: TestEnv) => void, testEnv: TestEnv) {
-  describe(testName, () => {
-    beforeAll(async () => {
-      ENGINE.reset();
-      if (testEnv.flags != null) {
-        env().setFlags(testEnv.flags);
-      }
-      env().set('IS_TEST', true);
-      // Await setting the new backend since it can have async init.
-      await ENGINE.setBackend(testEnv.backendName);
-    });
-
-    beforeEach(() => {
-      ENGINE.startScope();
-    });
-
-    afterEach(() => {
-      ENGINE.endScope();
-      ENGINE.disposeVariables();
-    });
-
-    afterAll(() => {
-      ENGINE.reset();
-    });
-
-    tests(testEnv);
-  });
-}
-
-export class TestKernelBackend extends KernelBackend {
-  override dispose(): void {}
-}
-
-let lock = Promise.resolve();
-
-/**
- * Wraps a Jasmine spec's test function so it is run exclusively to others that
- * use runWithLock.
- *
- * @param spec The function that runs the spec. Must return a promise or call
- *     `done()`.
- *
- */
-export function runWithLock(spec: (done?: DoneFn) => Promise<void>| void) {
-  return () => {
-    lock = lock.then(async () => {
-      let done: DoneFn;
-      const donePromise = new Promise<void>((resolve, reject) => {
-        done = (() => {
-                 resolve();
-               }) as DoneFn;
-        done.fail = (message?) => {
-          reject(message);
-        };
-      });
-
-      purgeLocalStorageArtifacts();
-      const result = spec(done);
-
-      if (isPromise(result)) {
-        await result;
-      } else {
-        await donePromise;
-      }
-    });
-    return lock;
-  };
-}
diff --git a/tfjs-master/tfjs-core/src/jasmine_util_test.ts b/tfjs-master/tfjs-core/src/jasmine_util_test.ts
deleted file mode 100644
index fe051fc41..000000000
--- a/tfjs-master/tfjs-core/src/jasmine_util_test.ts
+++ /dev/null
@@ -1,148 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Environment} from './environment';
-import {envSatisfiesConstraints, parseTestEnvFromKarmaFlags, TestEnv} from './jasmine_util';
-
-describe('jasmine_util.envSatisfiesConstraints', () => {
-  it('ENV satisfies empty constraints', () => {
-    const env = new Environment({});
-    env.setFlags({});
-
-    const constraints = {};
-
-    const backendName = 'test-backend';
-
-    expect(
-        envSatisfiesConstraints(env, {name: 'test', backendName}, constraints))
-        .toBe(true);
-  });
-
-  it('ENV satisfies matching flag constraints no predicate', () => {
-    const env = new Environment({});
-    env.setFlags({'TEST-FLAG': true});
-
-    const constraints = {flags: {'TEST-FLAG': true}};
-
-    const backendName = 'test-backend';
-
-    expect(
-        envSatisfiesConstraints(env, {name: 'test', backendName}, constraints))
-        .toBe(true);
-  });
-
-  it('ENV satisfies matching flag and predicate is true', () => {
-    const env = new Environment({});
-    env.setFlags({'TEST-FLAG': true});
-
-    const constraints = {flags: {'TEST-FLAG': true}, predicate: () => true};
-
-    const backendName = 'test-backend';
-
-    expect(
-        envSatisfiesConstraints(env, {name: 'test', backendName}, constraints))
-        .toBe(true);
-  });
-
-  it('ENV doesnt satisfy flags and predicate is true', () => {
-    const env = new Environment({});
-    env.setFlags({'TEST-FLAG': true});
-
-    const constraints = {flags: {'TEST-FLAG': false}, predicate: () => true};
-
-    const backendName = 'test-backend';
-
-    expect(
-        envSatisfiesConstraints(env, {name: 'test', backendName}, constraints))
-        .toBe(false);
-  });
-
-  it('ENV satisfies flags and predicate is false', () => {
-    const env = new Environment({});
-    env.setFlags({'TEST-FLAG': true});
-
-    const constraints = {flags: {'TEST-FLAG': true}, predicate: () => false};
-
-    const backendName = 'test-backend';
-
-    expect(
-        envSatisfiesConstraints(env, {name: 'test', backendName}, constraints))
-        .toBe(false);
-  });
-
-  it('ENV doesnt satiisfy flags and predicate is false', () => {
-    const env = new Environment({});
-    env.setFlags({'TEST-FLAG': true});
-
-    const constraints = {flags: {'TEST-FLAG': false}, predicate: () => false};
-
-    const backendName = 'test-backend';
-
-    expect(
-        envSatisfiesConstraints(env, {name: 'test', backendName}, constraints))
-        .toBe(false);
-  });
-});
-
-describe('jasmine_util.parseKarmaFlags', () => {
-  const registeredTestEnvs: TestEnv[] = [
-    {name: 'test-env', backendName: 'test-backend', isDataSync: true, flags: {}}
-  ];
-
-  it('parse empty args', () => {
-    const res = parseTestEnvFromKarmaFlags([], registeredTestEnvs);
-    expect(res).toBeNull();
-  });
-
-  it('--testEnv test-env --flags {"IS_NODE": true}', () => {
-    const res = parseTestEnvFromKarmaFlags(
-        ['--testEnv', 'test-env', '--flags', '{"IS_NODE": true}'],
-        registeredTestEnvs);
-    expect(res.name).toBe('test-env');
-    expect(res.backendName).toBe('test-backend');
-    expect(res.flags).toEqual({IS_NODE: true});
-  });
-
-  it('"--testEnv unknown" throws error', () => {
-    expect(
-        () => parseTestEnvFromKarmaFlags(
-            ['--testEnv', 'unknown'], registeredTestEnvs))
-        .toThrowError();
-  });
-
-  it('"--flags {}" throws error since --testEnv is missing', () => {
-    expect(
-        () => parseTestEnvFromKarmaFlags(['--flags', '{}'], registeredTestEnvs))
-        .toThrowError();
-  });
-
-  it('"--testEnv cpu --flags" throws error since features value is missing',
-     () => {
-       expect(
-           () => parseTestEnvFromKarmaFlags(
-               ['--testEnv', 'test-env', '--flags'], registeredTestEnvs))
-           .toThrowError();
-     });
-
-  it('"--backend cpu --flags notJson" throws error', () => {
-    expect(
-        () => parseTestEnvFromKarmaFlags(
-            ['--testEnv', 'test-env', '--flags', 'notJson'],
-            registeredTestEnvs))
-        .toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/kernel_names.ts b/tfjs-master/tfjs-core/src/kernel_names.ts
deleted file mode 100644
index 10e7d7d1a..000000000
--- a/tfjs-master/tfjs-core/src/kernel_names.ts
+++ /dev/null
@@ -1,1048 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// Allow UpperCamelCase variable names
-// tslint:disable: variable-name
-// Unfortunately just enabling PascalCase per file (tslint:enable:
-// allow-pascal-case) doesn't work.
-import {NamedTensorInfoMap} from './kernel_registry';
-import {ExplicitPadding} from './ops/conv_util';
-import {Activation} from './ops/fused_types';
-import {TensorInfo} from './tensor_info';
-import {DataType, DrawOptions, PixelData} from './types';
-
-export const Abs = 'Abs';
-export type AbsInputs = UnaryInputs;
-
-export const Acos = 'Acos';
-export type AcosInputs = UnaryInputs;
-
-export const Acosh = 'Acosh';
-export type AcoshInputs = UnaryInputs;
-
-export const Add = 'Add';
-export type AddInputs = BinaryInputs;
-
-export const AddN = 'AddN';
-export type AddNInputs = TensorInfo[];
-
-export const All = 'All';
-export type AllInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface AllAttrs {
-  axis: number|number[];
-  keepDims: boolean;
-}
-
-export const Any = 'Any';
-export type AnyInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface AnyAttrs {
-  axis: number|number[];
-  keepDims: boolean;
-}
-
-export const ArgMax = 'ArgMax';
-export type ArgMaxInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface ArgMaxAttrs {
-  axis: number;
-}
-
-export const ArgMin = 'ArgMin';
-export type ArgMinInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface ArgMinAttrs {
-  axis: number;
-}
-
-export const Asin = 'Asin';
-export type AsinInputs = UnaryInputs;
-
-export const Asinh = 'Asinh';
-export type AsinhInputs = UnaryInputs;
-
-export const Atan = 'Atan';
-export type AtanInputs = UnaryInputs;
-
-export const Atanh = 'Atanh';
-export type AtanhInputs = UnaryInputs;
-
-export const Atan2 = 'Atan2';
-export type Atan2Inputs = BinaryInputs;
-
-export const AvgPool = 'AvgPool';
-export type AvgPoolInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface AvgPoolAttrs {
-  filterSize: [number, number]|number;
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-}
-
-export const AvgPoolGrad = 'AvgPoolGrad';
-export type AvgPoolGradInputs = Pick<NamedTensorInfoMap, 'dy'|'input'>;
-export interface AvgPoolGradAttrs {
-  filterSize: [number, number]|number;
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-}
-
-export const AvgPool3D = 'AvgPool3D';
-export type AvgPool3DInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface AvgPool3DAttrs {
-  filterSize: [number, number, number]|number;
-  strides: [number, number, number]|number;
-  pad: 'valid'|'same'|number;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-  dataFormat: 'NDHWC'|'NCDHW';
-}
-
-export const AvgPool3DGrad = 'AvgPool3DGrad';
-export type AvgPool3DGradInputs = Pick<NamedTensorInfoMap, 'dy'|'input'>;
-export interface AvgPool3DGradAttrs {
-  filterSize: [number, number, number]|number;
-  strides: [number, number, number]|number;
-  pad: 'valid'|'same'|number;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-}
-
-export const BatchMatMul = 'BatchMatMul';
-export type BatchMatMulInputs = Pick<NamedTensorInfoMap, 'a'|'b'>;
-export interface BatchMatMulAttrs {
-  transposeA: boolean;
-  transposeB: boolean;
-}
-
-export const BatchToSpaceND = 'BatchToSpaceND';
-export type BatchToSpaceNDInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface BatchToSpaceNDAttrs {
-  blockShape: number[];
-  crops: number[][];
-}
-
-export type BinaryInputs = Pick<NamedTensorInfoMap, 'a'|'b'>;
-
-export const Bincount = 'Bincount';
-export type BincountInputs = Pick<NamedTensorInfoMap, 'x'|'weights'>;
-export interface BincountAttrs {
-  size: number;
-}
-
-export const BitwiseAnd = 'BitwiseAnd';
-export type BitwiseAndInputs = BinaryInputs;
-
-export const BroadcastTo = 'BroadcastTo';
-export type BroadcastToInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface BroadCastToAttrs {
-  shape: number[];
-  inputShape: number[];  // for gradient
-}
-
-export const BroadcastArgs = 'BroadcastArgs';
-export type BroadcastArgsInputs = Pick<NamedTensorInfoMap, 's0'|'s1'>;
-
-export const Cast = 'Cast';
-export type CastInputs = UnaryInputs;
-export interface CastAttrs {
-  dtype: DataType;
-}
-
-export const Ceil = 'Ceil';
-export type CeilInputs = UnaryInputs;
-
-export const ClipByValue = 'ClipByValue';
-export type ClipByValueInputs = UnaryInputs;
-export interface ClipByValueAttrs {
-  clipValueMin: number;
-  clipValueMax: number;
-}
-
-export const Complex = 'Complex';
-export type ComplexInputs = Pick<NamedTensorInfoMap, 'real'|'imag'>;
-
-export const ComplexAbs = 'ComplexAbs';
-export type ComplexAbsInputs = UnaryInputs;
-
-export const Concat = 'Concat';
-export type ConcatInputs = TensorInfo[];
-export interface ConcatAttrs {
-  axis: number;
-}
-
-export const Conv2D = 'Conv2D';
-export type Conv2DInputs = Pick<NamedTensorInfoMap, 'x'|'filter'>;
-export interface Conv2DAttrs {
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dataFormat: 'NHWC'|'NCHW';
-  dilations: [number, number]|number;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-}
-
-export const Conv2DBackpropFilter = 'Conv2DBackpropFilter';
-export type Conv2DBackpropFilterInputs = Pick<NamedTensorInfoMap, 'x'|'dy'>;
-export interface Conv2DBackpropFilterAttrs {
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dataFormat: 'NHWC'|'NCHW';
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-  filterShape: [number, number, number, number];
-}
-
-export const Conv2DBackpropInput = 'Conv2DBackpropInput';
-export type Conv2DBackpropInputInputs = Pick<NamedTensorInfoMap, 'dy'|'filter'>;
-export interface Conv2DBackpropInputAttrs {
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dataFormat: 'NHWC'|'NCHW';
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-  inputShape: [number, number, number, number];
-}
-
-export const Conv3D = 'Conv3D';
-export type Conv3DInputs = Pick<NamedTensorInfoMap, 'x'|'filter'>;
-export interface Conv3DAttrs {
-  strides: [number, number, number]|number;
-  pad: 'valid'|'same';
-  dataFormat: 'NDHWC'|'NCDHW';
-  dilations: [number, number, number]|number;
-}
-
-export const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';
-export type Conv3DBackpropFilterV2Inputs = Pick<NamedTensorInfoMap, 'x'|'dy'>;
-
-export interface Conv3DBackpropFilterV2Attrs {
-  strides: [number, number, number]|number;
-  pad: 'valid'|'same';
-  filterShape: [number, number, number, number, number];
-}
-
-export const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';
-export type Conv3DBackpropInputV2Inputs =
-    Pick<NamedTensorInfoMap, 'dy'|'filter'>;
-export interface Conv3DBackpropInputV2Attrs {
-  strides: [number, number, number]|number;
-  pad: 'valid'|'same';
-  inputShape: [number, number, number, number, number];
-}
-
-export const Cos = 'Cos';
-export type CosInputs = UnaryInputs;
-
-export const Cosh = 'Cosh';
-export type CoshInputs = UnaryInputs;
-
-export const Cumprod = 'Cumprod';
-export type CumprodInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface CumprodAttrs {
-  axis: number;
-  exclusive: boolean;
-  reverse: boolean;
-}
-
-export const Cumsum = 'Cumsum';
-export type CumsumInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface CumsumAttrs {
-  axis: number;
-  exclusive: boolean;
-  reverse: boolean;
-}
-
-export const CropAndResize = 'CropAndResize';
-export type CropAndResizeInputs =
-    Pick<NamedTensorInfoMap, 'image'|'boxes'|'boxInd'>;
-export interface CropAndResizeAttrs {
-  cropSize: [number, number];
-  method: 'bilinear'|'nearest';
-  extrapolationValue: number;
-}
-
-export const DenseBincount = 'DenseBincount';
-export type DenseBincountInputs = Pick<NamedTensorInfoMap, 'x'|'weights'>;
-export interface DenseBincountAttrs {
-  size: number;
-  binaryOutput?: boolean;
-}
-
-export const DepthToSpace = 'DepthToSpace';
-export type DepthToSpaceInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface DepthToSpaceAttrs {
-  blockSize: number;
-  dataFormat: 'NHWC'|'NCHW';
-}
-
-export const DepthwiseConv2dNative = 'DepthwiseConv2dNative';
-export type DepthwiseConv2dNativeInputs =
-    Pick<NamedTensorInfoMap, 'x'|'filter'>;
-export interface DepthwiseConv2dNativeAttrs {
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dataFormat: 'NHWC'|'NCHW';
-  dilations: [number, number]|number;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-}
-
-export const DepthwiseConv2dNativeBackpropFilter =
-    'DepthwiseConv2dNativeBackpropFilter';
-export type DepthwiseConv2dNativeBackpropFilterInputs =
-    Pick<NamedTensorInfoMap, 'x'|'dy'>;
-export interface DepthwiseConv2dNativeBackpropFilterAttrs {
-  strides: [number, number]|number;
-  dilations: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-  filterShape: [number, number, number, number];
-}
-
-export const DepthwiseConv2dNativeBackpropInput =
-    'DepthwiseConv2dNativeBackpropInput';
-export type DepthwiseConv2dNativeBackpropInputInputs =
-    Pick<NamedTensorInfoMap, 'dy'|'filter'>;
-export interface DepthwiseConv2dNativeBackpropInputAttrs {
-  strides: [number, number]|number;
-  dilations: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-  inputShape: [number, number, number, number];
-}
-
-export const Diag = 'Diag';
-export type DiagInputs = Pick<NamedTensorInfoMap, 'x'>;
-
-export const Dilation2D = 'Dilation2D';
-export type Dilation2DInputs = Pick<NamedTensorInfoMap, 'x'|'filter'>;
-export interface Dilation2DAttrs {
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number;
-  dilations: [number, number]|number;
-}
-
-export const Dilation2DBackpropInput = 'Dilation2DBackpropInput';
-export type Dilation2DBackpropInputInputs =
-    Pick<NamedTensorInfoMap, 'x'|'filter'|'dy'>;
-
-export const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';
-export type Dilation2DBackpropFilterInputs =
-    Pick<NamedTensorInfoMap, 'x'|'filter'|'dy'>;
-
-export const Draw = 'Draw';
-export type DrawInputs = Pick<NamedTensorInfoMap, 'image'>;
-export interface DrawAttrs {
-  canvas: HTMLCanvasElement;
-  options?: DrawOptions;
-}
-
-export const RealDiv = 'RealDiv';
-export type RealDivInputs = BinaryInputs;
-
-export const Einsum = 'Einsum';
-export type EinsumInputs = TensorInfo[];
-export interface EinsumAttrs {
-  equation: string;
-}
-
-export const Elu = 'Elu';
-export type EluInputs = Pick<NamedTensorInfoMap, 'x'>;
-
-export const EluGrad = 'EluGrad';
-export type EluGradInputs = Pick<NamedTensorInfoMap, 'dy'|'y'>;
-
-export const Erf = 'Erf';
-export type ErfInputs = UnaryInputs;
-
-export const Equal = 'Equal';
-export type EqualInputs = BinaryInputs;
-
-export const Exp = 'Exp';
-export type ExpInputs = UnaryInputs;
-
-export const ExpandDims = 'ExpandDims';
-export type ExpandDimsInputs = Pick<NamedTensorInfoMap, 'input'>;
-export interface ExpandDimsAttrs {
-  dim: number;
-}
-
-export const Expm1 = 'Expm1';
-export type Expm1Inputs = UnaryInputs;
-
-export const FFT = 'FFT';
-export type FFTInputs = Pick<NamedTensorInfoMap, 'input'>;
-
-export const Fill = 'Fill';
-export interface FillAttrs {
-  shape: number[];
-  value: number|string;
-  dtype: DataType;
-}
-
-export const FlipLeftRight = 'FlipLeftRight';
-export type FlipLeftRightInputs = Pick<NamedTensorInfoMap, 'image'>;
-
-export const Floor = 'Floor';
-export type FloorInputs = UnaryInputs;
-
-export const FloorDiv = 'FloorDiv';
-export type FloorDivInputs = BinaryInputs;
-
-export const FusedBatchNorm = 'FusedBatchNorm';
-export type FusedBatchNormInputs =
-    Pick<NamedTensorInfoMap, 'x'|'scale'|'offset'|'mean'|'variance'>;
-export interface FusedBatchNormAttrs {
-  varianceEpsilon: number;
-}
-
-export const GatherV2 = 'GatherV2';
-export type GatherV2Inputs = Pick<NamedTensorInfoMap, 'x'|'indices'>;
-export interface GatherV2Attrs {
-  axis: number;
-  batchDims: number;
-}
-
-export const GatherNd = 'GatherNd';
-export type GatherNdInputs = Pick<NamedTensorInfoMap, 'params'|'indices'>;
-
-export const Greater = 'Greater';
-export type GreaterInputs = BinaryInputs;
-
-export const GreaterEqual = 'GreaterEqual';
-export type GreaterEqualInputs = BinaryInputs;
-
-export const Identity = 'Identity';
-export type IdentityInputs = Pick<NamedTensorInfoMap, 'x'>;
-
-export const IFFT = 'IFFT';
-export type IFFTInputs = Pick<NamedTensorInfoMap, 'input'>;
-
-export const Imag = 'Imag';
-export type ImagInputs = Pick<NamedTensorInfoMap, 'input'>;
-
-export const IsFinite = 'IsFinite';
-export type IsFiniteInputs = UnaryInputs;
-
-export const IsInf = 'IsInf';
-export type IsInfInputs = UnaryInputs;
-
-export const IsNan = 'IsNan';
-export type IsNanInputs = UnaryInputs;
-
-export const LeakyRelu = 'LeakyRelu';
-export type LeakyReluInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface LeakyReluAttrs {
-  alpha: number;
-}
-
-export const Less = 'Less';
-export type LessInputs = BinaryInputs;
-
-export const LessEqual = 'LessEqual';
-export type LessEqualInputs = BinaryInputs;
-
-export const LinSpace = 'LinSpace';
-export interface LinSpaceAttrs {
-  start: number;
-  stop: number;
-  num: number;
-}
-export const Log = 'Log';
-export type LogInputs = UnaryInputs;
-
-export const Log1p = 'Log1p';
-export type Log1pInputs = UnaryInputs;
-
-export const LogicalAnd = 'LogicalAnd';
-export type LogicalAndInputs = BinaryInputs;
-
-export const LogicalNot = 'LogicalNot';
-export type LogicalNotInputs = Pick<NamedTensorInfoMap, 'x'>;
-
-export const LogicalOr = 'LogicalOr';
-export type LogicalOrInputs = BinaryInputs;
-
-export const LogicalXor = 'LogicalXor';
-export type LogicalXorInputs = BinaryInputs;
-
-export const LogSoftmax = 'LogSoftmax';
-export type LogSoftmaxInputs = Pick<NamedTensorInfoMap, 'logits'>;
-export interface LogSoftmaxAttrs {
-  axis: number;
-}
-
-export const LowerBound = 'LowerBound';
-export type LowerBoundInputs =
-    Pick<NamedTensorInfoMap, 'sortedSequence'|'values'>;
-
-export const LRN = 'LRN';
-export type LRNInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface LRNAttrs {
-  depthRadius: number;
-  bias: number;
-  alpha: number;
-  beta: number;
-}
-
-export const LRNGrad = 'LRNGrad';
-export type LRNGradInputs = Pick<NamedTensorInfoMap, 'x'|'y'|'dy'>;
-export interface LRNGradAttrs {
-  depthRadius: number;
-  bias: number;
-  alpha: number;
-  beta: number;
-}
-
-export const MatrixBandPart = 'MatrixBandPart';
-export type MatrixBandPartInputs =
-    Pick<NamedTensorInfoMap, 'input'|'numLower'|'numUpper'>;
-export interface MatrixBandPartAttrs {}
-
-export const Max = 'Max';
-export type MaxInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface MaxAttrs {
-  reductionIndices: number|number[];
-  keepDims: boolean;
-}
-
-export const Maximum = 'Maximum';
-export type MaximumInputs = BinaryInputs;
-
-export const MaxPool = 'MaxPool';
-export type MaxPoolInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface MaxPoolAttrs {
-  filterSize: [number, number]|number;
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-}
-
-export const MaxPoolGrad = 'MaxPoolGrad';
-export type MaxPoolGradInputs = Pick<NamedTensorInfoMap, 'dy'|'input'|'output'>;
-export interface MaxPoolGradAttrs {
-  filterSize: [number, number]|number;
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-}
-
-export const MaxPool3D = 'MaxPool3D';
-export type MaxPool3DInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface MaxPool3DAttrs {
-  filterSize: [number, number, number]|number;
-  strides: [number, number, number]|number;
-  pad: 'valid'|'same'|number;
-  dataFormat: 'NDHWC'|'NCDHW';
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-}
-
-export const MaxPool3DGrad = 'MaxPool3DGrad';
-export type MaxPool3DGradInputs =
-    Pick<NamedTensorInfoMap, 'dy'|'input'|'output'>;
-export interface MaxPool3DGradAttrs {
-  filterSize: [number, number, number]|number;
-  strides: [number, number, number]|number;
-  pad: 'valid'|'same'|number;
-  dimRoundingMode?: 'floor'|'round'|'ceil';
-}
-
-export const MaxPoolWithArgmax = 'MaxPoolWithArgmax';
-export type MaxPoolWithArgmaxInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface MaxPoolWithArgmaxAttrs {
-  filterSize: [number, number]|number;
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number;
-  includeBatchInIndex: boolean;
-}
-
-export const Mean = 'Mean';
-export type MeanInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface MeanAttrs {
-  axis: number|number[];
-  keepDims: boolean;
-}
-
-export const Min = 'Min';
-export type MinInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface MinAttrs {
-  axis: number|number[];
-  keepDims: boolean;
-}
-
-export const Minimum = 'Minimum';
-export type MinimumInputs = BinaryInputs;
-
-export const MirrorPad = 'MirrorPad';
-export type MirrorPadInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface MirrorPadAttrs {
-  paddings: Array<[number, number]>;
-  mode: 'reflect'|'symmetric';
-}
-
-export const Mod = 'Mod';
-export type ModInputs = BinaryInputs;
-
-export const Multinomial = 'Multinomial';
-export type MultinomialInputs = Pick<NamedTensorInfoMap, 'logits'>;
-export interface MultinomialAttrs {
-  numSamples: number;
-  seed: number;
-  normalized: boolean;
-}
-
-export const Multiply = 'Multiply';
-export type MultiplyInputs = BinaryInputs;
-
-export const Neg = 'Neg';
-export type NegInputs = UnaryInputs;
-
-export const NotEqual = 'NotEqual';
-export type NotEqualInputs = BinaryInputs;
-
-export const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';
-export type NonMaxSuppressionV3Inputs =
-    Pick<NamedTensorInfoMap, 'boxes'|'scores'>;
-export interface NonMaxSuppressionV3Attrs {
-  maxOutputSize: number;
-  iouThreshold: number;
-  scoreThreshold: number;
-}
-
-export const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';
-export type NonMaxSuppressionV4Inputs =
-    Pick<NamedTensorInfoMap, 'boxes'|'scores'>;
-export interface NonMaxSuppressionV4Attrs {
-  maxOutputSize: number;
-  iouThreshold: number;
-  scoreThreshold: number;
-  padToMaxOutputSize: boolean;
-}
-
-export const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';
-export type NonMaxSuppressionV5Inputs =
-    Pick<NamedTensorInfoMap, 'boxes'|'scores'>;
-export interface NonMaxSuppressionV5Attrs {
-  maxOutputSize: number;
-  iouThreshold: number;
-  scoreThreshold: number;
-  softNmsSigma: number;
-}
-
-export const OnesLike = 'OnesLike';
-export type OnesLikeInputs = UnaryInputs;
-
-export const OneHot = 'OneHot';
-export type OneHotInputs = Pick<NamedTensorInfoMap, 'indices'>;
-export interface OneHotAttrs {
-  depth: number;
-  onValue: number;
-  offValue: number;
-  dtype: DataType;
-}
-
-export const Pack = 'Pack';
-export type PackInputs = TensorInfo[];
-export interface PackAttrs {
-  axis: number;
-}
-
-export const PadV2 = 'PadV2';
-export type PadV2Inputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface PadV2Attrs {
-  paddings: Array<[number, number]>;
-  constantValue: number;
-}
-
-export const Pool = 'Pool';
-export type PoolInputs = Pick<NamedTensorInfoMap, 'input'>;
-
-export const Pow = 'Pow';
-export type PowInputs = BinaryInputs;
-
-export const Prelu = 'Prelu';
-export type PreluInputs = Pick<NamedTensorInfoMap, 'x'|'alpha'>;
-
-export const Prod = 'Prod';
-export type ProdInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface ProdAttrs {
-  axis: number|number[];
-  keepDims: boolean;
-}
-
-export const RaggedGather = 'RaggedGather';
-export type RaggedGatherInputs = {
-  paramsNestedSplits: TensorInfo[]
-}&Pick<NamedTensorInfoMap, 'paramsDenseValues'|'indices'>;
-export interface RaggedGatherAttrs {
-  outputRaggedRank: number;
-}
-
-export const RaggedRange = 'RaggedRange';
-export type RaggedRangeInputs =
-    Pick<NamedTensorInfoMap, 'starts'|'limits'|'deltas'>;
-
-export const RaggedTensorToTensor = 'RaggedTensorToTensor';
-export type RaggedTensorToTensorInputs =
-    Pick<NamedTensorInfoMap, 'shape'|'values'|'defaultValue'>&
-    {rowPartitionTensors: TensorInfo[]};
-export interface RaggedTensorToTensorAttrs {
-  rowPartitionTypes: string[];
-}
-
-export const Range = 'Range';
-export interface RangeAttrs {
-  start: number;
-  stop: number;
-  step: number;
-  dtype: 'float32'|'int32';
-}
-
-export const Real = 'Real';
-export type RealInputs = Pick<NamedTensorInfoMap, 'input'>;
-
-export const Reciprocal = 'Reciprocal';
-export type ReciprocalInputs = UnaryInputs;
-
-export const Relu = 'Relu';
-export type ReluInputs = Pick<NamedTensorInfoMap, 'x'>;
-
-export const Reshape = 'Reshape';
-export type ReshapeInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface ReshapeAttrs {
-  shape: number[];
-}
-
-export const ResizeNearestNeighbor = 'ResizeNearestNeighbor';
-export type ResizeNearestNeighborInputs = Pick<NamedTensorInfoMap, 'images'>;
-export interface ResizeNearestNeighborAttrs {
-  alignCorners: boolean;
-  halfPixelCenters: boolean;
-  size: [number, number];
-}
-
-export const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';
-export type ResizeNearestNeighborGradInputs =
-    Pick<NamedTensorInfoMap, 'images'|'dy'>;
-export type ResizeNearestNeighborGradAttrs = ResizeNearestNeighborAttrs;
-
-export const ResizeBilinear = 'ResizeBilinear';
-export type ResizeBilinearInputs = Pick<NamedTensorInfoMap, 'images'>;
-export interface ResizeBilinearAttrs {
-  alignCorners: boolean;
-  halfPixelCenters: boolean;
-  size: [number, number];
-}
-
-export const ResizeBilinearGrad = 'ResizeBilinearGrad';
-export type ResizeBilinearGradInputs = Pick<NamedTensorInfoMap, 'images'|'dy'>;
-export type ResizeBilinearGradAttrs = ResizeBilinearAttrs;
-
-export const Relu6 = 'Relu6';
-export type Relu6Inputs = Pick<NamedTensorInfoMap, 'x'>;
-
-export const Reverse = 'Reverse';
-export type ReverseInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface ReverseAttrs {
-  dims: number|number[];
-}
-
-export const Round = 'Round';
-export type RoundInputs = UnaryInputs;
-
-export const Rsqrt = 'Rsqrt';
-export type RsqrtInputs = UnaryInputs;
-
-export const ScatterNd = 'ScatterNd';
-export type ScatterNdInputs = Pick<NamedTensorInfoMap, 'indices'|'updates'>;
-export interface ScatterNdAttrs {
-  shape: number[];
-}
-
-export const TensorScatterUpdate = 'TensorScatterUpdate';
-export type TensorScatterUpdateInputs =
-    Pick<NamedTensorInfoMap, 'tensor'|'indices'|'updates'>;
-export interface TensorScatterUpdateAttrs {}
-
-export const SearchSorted = 'SearchSorted';
-export type SearchSortedInputs =
-    Pick<NamedTensorInfoMap, 'sortedSequence'|'values'>;
-export interface SearchSortedAttrs {
-  side: 'left'|'right';
-}
-
-export const Select = 'Select';
-export type SelectInputs = Pick<NamedTensorInfoMap, 'condition'|'t'|'e'>;
-
-export const Selu = 'Selu';
-export type SeluInputs = Pick<NamedTensorInfoMap, 'x'>;
-
-export const Slice = 'Slice';
-export type SliceInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface SliceAttrs {
-  begin: number|number[];
-  size: number|number[];
-}
-export const Sin = 'Sin';
-export type SinInputs = UnaryInputs;
-
-export const Sinh = 'Sinh';
-export type SinhInputs = UnaryInputs;
-
-export const Sign = 'Sign';
-export type SignInputs = UnaryInputs;
-
-export const Sigmoid = 'Sigmoid';
-export type SigmoidInputs = UnaryInputs;
-
-export const Softplus = 'Softplus';
-export type SoftplusInputs = UnaryInputs;
-
-export const Sqrt = 'Sqrt';
-export type SqrtInputs = UnaryInputs;
-
-export const Sum = 'Sum';
-export type SumInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface SumAttrs {
-  axis: number|number[];
-  keepDims: boolean;
-}
-
-export const SpaceToBatchND = 'SpaceToBatchND';
-export type SpaceToBatchNDInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface SpaceToBatchNDAttrs {
-  blockShape: number[];
-  paddings: number[][];
-}
-
-export const SplitV = 'SplitV';
-export type SplitVInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface SplitVAttrs {
-  numOrSizeSplits: number[]|number;
-  axis: number;
-}
-
-export const Softmax = 'Softmax';
-export type SoftmaxInputs = Pick<NamedTensorInfoMap, 'logits'>;
-export interface SoftmaxAttrs {
-  dim: number;
-}
-
-export const SparseFillEmptyRows = 'SparseFillEmptyRows';
-export type SparseFillEmptyRowsInputs =
-    Pick<NamedTensorInfoMap, 'indices'|'values'|'denseShape'|'defaultValue'>;
-
-export const SparseReshape = 'SparseReshape';
-export type SparseReshapeInputs =
-    Pick<NamedTensorInfoMap, 'inputIndices'|'inputShape'|'newShape'>;
-
-export const SparseSegmentMean = 'SparseSegmentMean';
-export type SparseSegmentMeanInputs =
-    Pick<NamedTensorInfoMap, 'data'|'indices'|'segmentIds'>;
-
-export const SparseSegmentSum = 'SparseSegmentSum';
-export type SparseSegmentSumInputs =
-    Pick<NamedTensorInfoMap, 'data'|'indices'|'segmentIds'>;
-
-export const SparseToDense = 'SparseToDense';
-export type SparseToDenseInputs =
-    Pick<NamedTensorInfoMap, 'sparseIndices'|'sparseValues'|'defaultValue'>;
-export interface SparseToDenseAttrs {
-  outputShape: number[];
-}
-
-export const SquaredDifference = 'SquaredDifference';
-export type SquaredDifferenceInputs = BinaryInputs;
-
-export const Square = 'Square';
-export type SquareInputs = Pick<NamedTensorInfoMap, 'x'>;
-
-export const StaticRegexReplace = 'StaticRegexReplace';
-export type StaticRegexReplaceInputs = UnaryInputs;
-export interface StaticRegexReplaceAttrs {
-  pattern: string;
-  rewrite: string;
-  replaceGlobal: boolean;
-}
-
-export const StridedSlice = 'StridedSlice';
-export type StridedSliceInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface StridedSliceAttrs {
-  begin: number[];
-  end: number[];
-  strides: number[];
-  beginMask: number;
-  endMask: number;
-  ellipsisMask: number;
-  newAxisMask: number;
-  shrinkAxisMask: number;
-}
-
-export const StringNGrams = 'StringNGrams';
-export type StringNGramsInputs = Pick<NamedTensorInfoMap, 'data'|'dataSplits'>;
-export interface StringNGramsAttrs {
-  separator: string;
-  nGramWidths: number[];
-  leftPad: string;
-  rightPad: string;
-  padWidth: number;
-  preserveShortSequences: boolean;
-}
-
-export const StringSplit = 'StringSplit';
-export type StringSplitInputs = Pick<NamedTensorInfoMap, 'input'|'delimiter'>;
-export interface StringSplitAttrs {
-  skipEmpty: boolean;
-}
-
-export const StringToHashBucketFast = 'StringToHashBucketFast';
-export type StringToHashBucketFastInputs = Pick<NamedTensorInfoMap, 'input'>;
-export interface StringToHashBucketFastAttrs {
-  numBuckets: number;
-}
-
-export const Sub = 'Sub';
-export type SubInputs = BinaryInputs;
-
-export const Tan = 'Tan';
-export type TanInputs = UnaryInputs;
-
-export const Tanh = 'Tanh';
-export type TanhInputs = UnaryInputs;
-
-export const Tile = 'Tile';
-export type TileInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface TileAttrs {
-  reps: number[];
-}
-
-export const TopK = 'TopK';
-export type TopKInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface TopKAttrs {
-  k: number;
-  sorted: boolean;
-}
-
-export const Transform = 'Transform';
-export type TransformInputs = Pick<NamedTensorInfoMap, 'image'|'transforms'>;
-export interface TransformAttrs {
-  interpolation: 'nearest'|'bilinear';
-  fillMode: 'constant'|'reflect'|'wrap'|'nearest';
-  fillValue: number;
-  outputShape?: [number, number];
-}
-
-export const Transpose = 'Transpose';
-export type TransposeInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface TransposeAttrs {
-  perm: number[];
-}
-
-export const Unique = 'Unique';
-export type UniqueInputs = Pick<NamedTensorInfoMap, 'x'>;
-export interface UniqueAttrs {
-  axis: number;
-}
-
-export type UnaryInputs = Pick<NamedTensorInfoMap, 'x'>;
-
-export const Unpack = 'Unpack';
-export type UnpackInputs = Pick<NamedTensorInfoMap, 'value'>;
-export interface UnpackAttrs {
-  axis: number;
-}
-
-export const UnsortedSegmentSum = 'UnsortedSegmentSum';
-export type UnsortedSegmentSumInputs =
-    Pick<NamedTensorInfoMap, 'x'|'segmentIds'>;
-export interface UnsortedSegmentSumAttrs {
-  numSegments: number;
-}
-
-export const UpperBound = 'UpperBound';
-export type UpperBoundInputs =
-    Pick<NamedTensorInfoMap, 'sortedSequence'|'values'>;
-
-export const ZerosLike = 'ZerosLike';
-export type ZerosLikeInputs = UnaryInputs;
-
-/**
- * TensorFlow.js-only kernels
- */
-export const Step = 'Step';
-export type StepInputs = UnaryInputs;
-export interface StepAttrs {
-  alpha: number;
-}
-
-export const FromPixels = 'FromPixels';
-export interface FromPixelsInputs {
-  pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|
-      HTMLVideoElement|ImageBitmap;
-}
-export interface FromPixelsAttrs {
-  numChannels: number;
-}
-
-export const RotateWithOffset = 'RotateWithOffset';
-export type RotateWithOffsetInputs = Pick<NamedTensorInfoMap, 'image'>;
-export interface RotateWithOffsetAttrs {
-  radians: number;
-  fillValue: number|[number, number, number];
-  center: number|[number, number];
-}
-
-export const _FusedMatMul = '_FusedMatMul';
-// tslint:disable-next-line: class-name
-export interface _FusedMatMulInputs extends NamedTensorInfoMap {
-  a: TensorInfo;
-  b: TensorInfo;
-  bias?: TensorInfo;
-  preluActivationWeights?: TensorInfo;
-}
-// tslint:disable-next-line: class-name
-export interface _FusedMatMulAttrs {
-  transposeA: boolean;
-  transposeB: boolean;
-  activation: Activation;
-  leakyreluAlpha?: number;
-}
-
-export const FusedConv2D = 'FusedConv2D';
-export interface FusedConv2DInputs extends NamedTensorInfoMap {
-  x: TensorInfo;
-  filter: TensorInfo;
-  bias?: TensorInfo;
-  preluActivationWeights?: TensorInfo;
-}
-export interface FusedConv2DAttrs {
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dataFormat: 'NHWC'|'NCHW';
-  dilations: [number, number]|number;
-  dimRoundingMode: 'floor'|'round'|'ceil';
-  activation: Activation;
-  leakyreluAlpha?: number;
-}
-
-export const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';
-export interface FusedDepthwiseConv2DInputs extends NamedTensorInfoMap {
-  x: TensorInfo;
-  filter: TensorInfo;
-  bias?: TensorInfo;
-  preluActivationWeights?: TensorInfo;
-}
-export interface FusedDepthwiseConv2DAttrs {
-  strides: [number, number]|number;
-  pad: 'valid'|'same'|number|ExplicitPadding;
-  dataFormat: 'NHWC'|'NCHW';
-  dilations: [number, number]|number;
-  dimRoundingMode: 'floor'|'round'|'ceil';
-  activation: Activation;
-  leakyreluAlpha?: number;
-}
diff --git a/tfjs-master/tfjs-core/src/kernel_registry.ts b/tfjs-master/tfjs-core/src/kernel_registry.ts
deleted file mode 100644
index c7284a1ce..000000000
--- a/tfjs-master/tfjs-core/src/kernel_registry.ts
+++ /dev/null
@@ -1,209 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {env} from './environment';
-import {getGlobal} from './global_util';
-import * as log from './log';
-import {NamedGradientMap} from './tape';
-import {Tensor} from './tensor';
-import {TensorInfo} from './tensor_info';
-import {RecursiveArray} from './types';
-
-const kernelRegistry =
-  getGlobal('kernelRegistry', () => new Map<`${string}_${string}`,
-    KernelConfig>());
-const gradRegistry =
-  getGlobal('gradRegistry', () => new Map<string, GradConfig>());
-
-type AttributeValue =
-  number | number[] | boolean | boolean[] | string | string[] | NamedAttrMap;
-
-/** These are extra non-tensor/primitive params passed to kernel functions. */
-export type Attribute = AttributeValue | RecursiveArray<AttributeValue>;
-
-/** Specifies the code to run when executing a kernel. */
-export type KernelFunc = (params: {
-  inputs: NamedTensorInfoMap,
-  backend: {},
-  attrs?: NamedAttrMap,
-}) => TensorInfo | TensorInfo[];
-
-/** The function to run when computing a gradient during backprop. */
-export type GradFunc =
-  (dy: Tensor | Tensor[], saved: Tensor[], attrs: NamedAttrMap) =>
-    NamedGradientMap;
-
-/** Function that gets called after the backend initializes. */
-export type KernelSetupFunc = (backend: {}) => void;
-/** Function that gets called right before the backend is disposed. */
-export type KernelDisposeFunc = KernelSetupFunc;
-
-/** Config object for registering a kernel in the global registry. */
-export interface KernelConfig {
-  kernelName: string;
-  backendName: string;
-  kernelFunc: KernelFunc;
-  setupFunc?: KernelSetupFunc;
-  disposeFunc?: KernelDisposeFunc;
-}
-
-/** Config object for registering a gradient in the global registry. */
-export interface GradConfig {
-  kernelName: string;
-  inputsToSave?: string[];
-  // When saveAllInputs is true, all inputs will be saved. Only use this flag
-  // if inputs is an array of Tensors.
-  saveAllInputs?: boolean;
-  outputsToSave?: boolean[];
-  gradFunc: GradFunc;
-}
-
-export interface NamedTensorInfoMap {
-  [name: string]: TensorInfo|undefined;
-}
-
-export interface NamedAttrMap {
-  [name: string]: Attribute;
-}
-
-/**
- * Returns the kernel function (code) associated with the provided names.
- *
- * @param kernelName The official name of the kernel.
- * @param backendName The official name of the backend.
- */
-export function getKernel(
-    kernelName: string, backendName: string): KernelConfig {
-  const key = makeKey(kernelName, backendName);
-  return kernelRegistry.get(key);
-}
-
-/**
- * Returns the registered gradient info associated with the provided kernel.
- * @param kernelName The official TF kernel name.
- */
-export function getGradient(kernelName: string): GradConfig {
-  return gradRegistry.get(kernelName);
-}
-
-export function getKernelsForBackend(backendName: string): KernelConfig[] {
-  const it = kernelRegistry.entries();
-  const result: KernelConfig[] = [];
-
-  while (true) {
-    const {done, value} = it.next();
-    if (done) {
-      break;
-    }
-    const [key, config] = value;
-    const [backend, ] = key.split('_');
-    if (backend === backendName) {
-      result.push(config);
-    }
-  }
-  return result;
-}
-
-/**
- * Registers the function (forward pass) for the kernel in a global registry.
- *
- * @param config A config object with the following properties:
- * - `kernelName` The official name of the kernel.
- * - `backendName` The official name of the backend.
- * - `kernelFunc` The function to run during the forward pass of the kernel.
- * - `setupFunc` Optional. Gets called once, after the backend initializes.
- * - `disposeFunc` Optional. Gets called once, right before the backend is
- * disposed.
- */
-export function registerKernel(config: KernelConfig) {
-  const {kernelName, backendName} = config;
-  const key = makeKey(kernelName, backendName);
-  if (kernelRegistry.has(key)) {
-    log.warn(
-        `The kernel '${kernelName}' for backend ` +
-        `'${backendName}' is already registered`);
-  }
-  kernelRegistry.set(key, config);
-}
-
-/**
- * Registers a gradient function for a given kernel in the global registry,
- * to be used during the back-propagation of that kernel.
- *
- * @param config An object with the following properties:
- * - `kernelName` The name of the kernel that the gradient function is for.
- * - `gradFunc` The function to run during back-propagation.
- */
-export function registerGradient(config: GradConfig) {
-  const {kernelName} = config;
-
-  if (gradRegistry.has(kernelName)) {
-    // TODO (yassogba) after 3.0 assess whether we need to keep this gated
-    // to debug mode.
-    if (env().getBool('DEBUG')) {
-      log.warn(`Overriding the gradient for '${kernelName}'`);
-    }
-  }
-  gradRegistry.set(kernelName, config);
-}
-
-/**
- * Removes the kernel function from the registry.
- *
- * @param kernelName The official name of the kernel.
- * @param backendName The official name of the backend.
- *
- */
-export function unregisterKernel(
-    kernelName: string, backendName: string): void {
-  const key = makeKey(kernelName, backendName);
-  if (!kernelRegistry.has(key)) {
-    throw new Error(
-        `The kernel '${kernelName}' for backend ` +
-        `'${backendName}' is not registered`);
-  }
-  kernelRegistry.delete(key);
-}
-
-/** Removes the registered gradient from the global registry. */
-export function unregisterGradient(kernelName: string): void {
-  if (!gradRegistry.has(kernelName)) {
-    throw new Error(
-        `The gradient '${kernelName}' for backend is not registered`);
-  }
-  gradRegistry.delete(kernelName);
-}
-
-/**
- * Finds kernels that have already been registered to a backend and re-registers
- * them for a new backend. Useful for registering custom backends.
- * @param registeredBackendName Already registered backend.
- * @param newBackendName New backend.
- */
-export function copyRegisteredKernels(
-    registeredBackendName: string, newBackendName: string): void {
-  const kernels = getKernelsForBackend(registeredBackendName);
-  kernels.forEach(kernelConfig => {
-    const newKernelConfig =
-        Object.assign({}, kernelConfig, {backendName: newBackendName});
-    registerKernel(newKernelConfig);
-  });
-}
-
-function makeKey(kernelName: string,
-                 backendName: string): `${string}_${string}` {
-  return `${backendName}_${kernelName}`;
-}
diff --git a/tfjs-master/tfjs-core/src/kernel_registry_test.ts b/tfjs-master/tfjs-core/src/kernel_registry_test.ts
deleted file mode 100644
index 1f7194ae1..000000000
--- a/tfjs-master/tfjs-core/src/kernel_registry_test.ts
+++ /dev/null
@@ -1,411 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-import {KernelBackend} from './index';
-import {ALL_ENVS, describeWithFlags, TestEnv} from './jasmine_util';
-import { KernelFunc } from './kernel_registry';
-import { TensorInfo } from './tensor_info';
-import {expectArraysClose} from './test_util';
-
-describeWithFlags('kernel_registry', ALL_ENVS, (testEnv: TestEnv) => {
-  afterEach(async () => {
-    // Revert backend mutation.
-    await tf.setBackend(testEnv.backendName);
-  });
-
-  it('register a kernel and call it', () => {
-    // Make sure the backend is loaded. Perhaps tf.getBackend
-    // should call tf.backend to make sure the backend is loaded?
-    expect(tf.backend()).toBeDefined();
-    let called = false;
-    tf.registerKernel({
-      kernelName: 'MyKernel',
-      backendName: tf.getBackend(),
-      kernelFunc: ({inputs, attrs}) => {
-        expect(attrs.a).toBe(5);
-        expect(inputs.x.shape).toEqual([2, 2]);
-        expect(inputs.x.dtype).toBe('float32');
-        called = true;
-        return {dtype: 'float32', shape: [3, 3], dataId: {}};
-      }
-    });
-
-    const inputs = {x: tf.zeros([2, 2])};
-    const attrs = {a: 5};
-    const res = tf.engine().runKernel('MyKernel', inputs, attrs) as TensorInfo;
-
-    expect(called).toBe(true);
-    expect(res.dtype).toBe('float32');
-    expect(res.shape).toEqual([3, 3]);
-
-    tf.unregisterKernel('MyKernel', tf.getBackend());
-  });
-
-  it('errors when running non-existent kernel', () => {
-    const inputs = {};
-    const attrs = {};
-    expect(() => tf.engine().runKernel('DoesNotExist', inputs, attrs))
-        .toThrowError();
-  });
-
-  // TODO (yassogba) double registration happens now because a backend might be
-  // imported more than once (e.g. by a top level package and a dependent
-  // package). We may want to remove this test long-term but skip it for
-  // now.
-  // tslint:disable-next-line: ban
-  xit('errors when registering the same kernel twice', () => {
-    interface TestBackend extends KernelBackend {
-      id: number;
-    }
-    tf.registerBackend('backend1', () => {
-      return {
-        id: 1,
-        dispose: () => null,
-        disposeData: (dataId: {}) => null,
-        numDataIds: () => 0
-      } as TestBackend;
-    });
-
-    tf.registerKernel({
-      kernelName: 'MyKernel',
-      backendName: 'backend1',
-      kernelFunc: () => {
-        return null;
-      }
-    });
-    expect(() => tf.registerKernel({
-      kernelName: 'MyKernel',
-      backendName: 'backend1',
-      kernelFunc: () => {
-        return null;
-      }
-    })).toThrowError();
-
-    tf.unregisterKernel('MyKernel', 'backend1');
-    tf.removeBackend('backend1');
-  });
-
-  it('register same kernel on two different backends', async () => {
-    interface TestBackend extends KernelBackend {
-      id: number;
-    }
-    tf.registerBackend('backend1', () => {
-      return {
-        id: 1,
-        dispose: () => null,
-        disposeData: (dataId: {}) => true,
-        numDataIds: () => 0
-      } as TestBackend;
-    });
-    tf.registerBackend('backend2', () => {
-      return {
-        id: 2,
-        dispose: () => null,
-        disposeData: (dataId: {}) => null,
-        numDataIds: () => 0
-      } as TestBackend;
-    });
-
-    let lastStorageId = -1;
-    const kernelFunc: KernelFunc = ({backend}) => {
-      lastStorageId = (backend as TestBackend).id;
-      return {dataId: {}, shape: [], dtype: 'float32'};
-    };
-    tf.registerKernel(
-        {kernelName: 'MyKernel', backendName: 'backend1', kernelFunc});
-    tf.registerKernel(
-        {kernelName: 'MyKernel', backendName: 'backend2', kernelFunc});
-
-    // No kernel has been executed yet.
-    expect(lastStorageId).toBe(-1);
-
-    // Kernel was executed on the first backend.
-    await tf.setBackend('backend1');
-    tf.engine().runKernel('MyKernel', {}, {});
-    expect(lastStorageId).toBe(1);
-
-    // Kernel was executed on the second backend.
-    await tf.setBackend('backend2');
-    tf.engine().runKernel('MyKernel', {}, {});
-    expect(lastStorageId).toBe(2);
-
-    tf.removeBackend('backend1');
-    tf.removeBackend('backend2');
-    tf.unregisterKernel('MyKernel', 'backend1');
-    tf.unregisterKernel('MyKernel', 'backend2');
-  });
-
-  it('register kernel with setup and dispose functions', async () => {
-    const backendName = 'custom-backend';
-    const kernelName = 'MyKernel';
-    interface TestBackend extends KernelBackend {}
-    const customBackend = {
-      dispose: () => null,
-      disposeData: (dataId: {}) => true,
-      numDataIds: () => 0
-    } as TestBackend;
-    tf.registerBackend(backendName, () => customBackend);
-
-    const kernelFunc: KernelFunc = () => {
-      return {dataId: {}, shape: [], dtype: 'float32'};
-    };
-    let setupCalled = false;
-    const setupFunc = (backend: KernelBackend) => {
-      expect(backend).toBe(customBackend);
-      setupCalled = true;
-    };
-    let disposeCalled = false;
-    const disposeFunc = (backend: KernelBackend) => {
-      expect(backend).toBe(customBackend);
-      disposeCalled = true;
-    };
-    tf.registerKernel(
-        {kernelName, backendName, kernelFunc, setupFunc, disposeFunc});
-
-    expect(setupCalled).toBe(false);
-    expect(disposeCalled).toBe(false);
-
-    await tf.setBackend(backendName);
-    expect(setupCalled).toBe(true);
-    expect(disposeCalled).toBe(false);
-
-    // Kernel was executed on the first backend.
-    tf.engine().runKernel(kernelName, {}, {});
-
-    tf.removeBackend(backendName);
-    expect(setupCalled).toBe(true);
-    expect(disposeCalled).toBe(true);
-
-    tf.unregisterKernel(kernelName, backendName);
-  });
-});
-
-describeWithFlags('gradient registry', ALL_ENVS, () => {
-  it('register a kernel with gradient and call it', async () => {
-    let kernelWasCalled = false;
-    let gradientWasCalled = false;
-    const kernelName = 'MyKernel';
-    const x = tf.zeros([2, 2]);
-
-    tf.registerKernel({
-      kernelName,
-      backendName: tf.getBackend(),
-      kernelFunc: () => {
-        kernelWasCalled = true;
-        return {dtype: 'float32', shape: [3, 3], dataId: {}};
-      }
-    });
-
-    tf.registerGradient({
-      kernelName,
-      inputsToSave: ['x'],
-      gradFunc: (dy: tf.Tensor, saved) => {
-        // Make sure saved input (x) was passed to the gradient function.
-        expect(saved[0].dataId).toEqual(x.dataId);
-        // Make sure dy matches the shape of the output.
-        expect(dy.shape).toEqual([3, 3]);
-        gradientWasCalled = true;
-        return {x: () => tf.fill([2, 2], 3)};
-      },
-    });
-
-    const gradFunc =
-        tf.grad(x => tf.engine().runKernel(kernelName, {x}, {} /* attrs */));
-    const dx = gradFunc(x);
-    expect(kernelWasCalled).toBe(true);
-    expect(gradientWasCalled).toBe(true);
-    expect(dx.dtype).toBe('float32');
-    expect(dx.shape).toEqual([2, 2]);
-    expectArraysClose(await dx.data(), [3, 3, 3, 3]);
-    tf.unregisterKernel(kernelName, tf.getBackend());
-    tf.unregisterGradient(kernelName);
-  });
-
-  it('register a kernel with gradient that specifies outputsToSave and call it',
-     async () => {
-       let kernelWasCalled = false;
-       let gradientWasCalled = false;
-       const kernelName = 'MyKernel';
-
-       const tensor = tf.zeros([3, 3], 'float32');
-       const forwardReturnDataId = tensor.dataId;
-       tf.registerKernel({
-         kernelName,
-         backendName: tf.getBackend(),
-         kernelFunc: () => {
-           kernelWasCalled = true;
-           return {
-             dtype: tensor.dtype,
-             shape: tensor.shape,
-             dataId: forwardReturnDataId
-           };
-         }
-       });
-
-       tf.registerGradient({
-         kernelName,
-         outputsToSave: [true],
-         gradFunc: (dy: tf.Tensor, saved) => {
-           // Make sure saved output was passed to the gradient function.
-           expect(saved[0].dataId).toEqual(forwardReturnDataId);
-           // Make sure dy matches the shape of the output.
-           expect(dy.shape).toEqual([3, 3]);
-           gradientWasCalled = true;
-           return {x: () => tf.fill([2, 2], 3)};
-         },
-       });
-
-       const gradFunc = tf.grad(
-           x => tf.engine().runKernel(
-               kernelName, {x}, {} /* attrs */
-               ));
-       const x = tf.zeros([2, 2]);
-       const dx = gradFunc(x);
-       expect(kernelWasCalled).toBe(true);
-       expect(gradientWasCalled).toBe(true);
-       expect(dx.dtype).toBe('float32');
-       expect(dx.shape).toEqual([2, 2]);
-       tf.unregisterKernel(kernelName, tf.getBackend());
-       tf.unregisterGradient(kernelName);
-     });
-
-  it('register a kernel with array inputs and saveAllInputs true', async () => {
-    let kernelWasCalled = false;
-    let gradientWasCalled = false;
-    const kernelName = 'MyKernel';
-    const x = [tf.zeros([2, 2]), tf.zeros([2, 2])];
-
-    const forwardReturnDataId = {};
-    tf.registerKernel({
-      kernelName,
-      backendName: tf.getBackend(),
-      kernelFunc: () => {
-        kernelWasCalled = true;
-        return {dtype: 'float32', shape: [3, 3], dataId: forwardReturnDataId};
-      }
-    });
-
-    tf.registerGradient({
-      kernelName,
-      saveAllInputs: true,
-      gradFunc: (dy: tf.Tensor, saved) => {
-        // Make sure saved input (x) was passed to the gradient function.
-        const [$x0, $x1] = x;
-        expect(saved.length).toEqual(x.length);
-        expect($x0.dataId).toEqual(x[0].dataId);
-        expect($x1.dataId).toEqual(x[1].dataId);
-        gradientWasCalled = true;
-        return {0: () => tf.fill([2, 2], 3), 1: () => tf.fill([2, 2], 3)};
-      }
-    });
-
-    // Inputs as array.
-    const z = (...x: tf.Tensor[]) =>
-        // tslint:disable-next-line: no-unnecessary-type-assertion
-        tf.engine().runKernel(
-            kernelName, x as unknown as tf.NamedTensorMap, {} /* attrs */) as
-        tf.Tensor;
-    const gradFunc = tf.grads(z);
-    const dx = gradFunc(x);
-    expect(kernelWasCalled).toBe(true);
-    expect(gradientWasCalled).toBe(true);
-    expect(dx.length).toEqual(2);
-    expect(dx[0].dtype).toBe('float32');
-    expect(dx[0].shape).toEqual([2, 2]);
-    expect(dx[1].dtype).toBe('float32');
-    expect(dx[1].shape).toEqual([2, 2]);
-    expectArraysClose(await dx[0].data(), [3, 3, 3, 3]);
-    expectArraysClose(await dx[1].data(), [3, 3, 3, 3]);
-    tf.unregisterKernel(kernelName, tf.getBackend());
-    tf.unregisterGradient(kernelName);
-  });
-
-  it('register a kernel with map inputs and saveAllInputs true should throw ' +
-         'error',
-     async () => {
-       const kernelName = 'MyKernel';
-       const x0 = tf.zeros([2, 2]);
-       const x1 = tf.zeros([2, 2]);
-
-       const forwardReturnDataId = {};
-       tf.registerKernel({
-         kernelName,
-         backendName: tf.getBackend(),
-         kernelFunc: () => {
-           return {
-             dtype: 'float32',
-             shape: [3, 3],
-             dataId: forwardReturnDataId
-           };
-         }
-       });
-
-       tf.registerGradient({
-         kernelName,
-         saveAllInputs: true,
-         gradFunc: (dy: tf.Tensor, saved) => {
-           // Make sure saved input (x) was passed to the gradient function.
-           const [$x0, $x1] = saved;
-           expect($x0.dataId).toEqual(x0.dataId);
-           expect($x1.dataId).toEqual(x1.dataId);
-           return {x0: () => tf.fill([2, 2], 3), x1: () => tf.fill([2, 2], 3)};
-         }
-       });
-
-       // Inputs as map.
-       const z = (x0: tf.Tensor, x1: tf.Tensor) =>
-           // tslint:disable-next-line: no-unnecessary-type-assertion
-           tf.engine().runKernel(kernelName, {x0, x1}, {} /* attrs */) as
-           tf.Tensor;
-       const gradFunc = tf.grads(z);
-       expect(() => gradFunc([x0, x1]))
-           .toThrowError(
-               /saveAllInputs is true, expected inputs to be an array/);
-       tf.unregisterKernel(kernelName, tf.getBackend());
-       tf.unregisterGradient(kernelName);
-     });
-
-  it('errors when running non-existent gradient', () => {
-    const kernelName = 'MyKernel';
-    const x = tf.zeros([2, 2]);
-
-    tf.registerKernel({
-      kernelName,
-      backendName: tf.getBackend(),
-      kernelFunc: () => ({dtype: 'float32', shape: [3, 3], dataId: {}})
-    });
-
-    const gradFunc =
-        tf.grad(x => tf.engine().runKernel(kernelName, {x}, {} /* attrs */));
-    expect(() => gradFunc(x))
-        .toThrowError(/gradient function not found for MyKernel/);
-
-    tf.unregisterKernel(kernelName, tf.getBackend());
-  });
-
-  // tslint:disable-next-line: ban
-  xit('warning when registering the same gradient twice', () => {
-    const kernelName = 'MyKernel';
-    tf.registerGradient({kernelName, gradFunc: () => null});
-    spyOn(console, 'warn').and.callFake((msg: string) => {
-      expect(msg).toBe('Overriding the gradient for \'MyKernel\'');
-    });
-    tf.registerGradient({kernelName, gradFunc: () => null});
-    tf.unregisterGradient(kernelName);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/log.ts b/tfjs-master/tfjs-core/src/log.ts
deleted file mode 100644
index 5a03b379c..000000000
--- a/tfjs-master/tfjs-core/src/log.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from './environment';
-
-export function warn(...msg: Array<{}>): void {
-  if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
-    console.warn(...msg);
-  }
-}
-
-export function log(...msg: Array<{}>): void {
-  if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
-    console.log(...msg);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/math.ts b/tfjs-master/tfjs-core/src/math.ts
deleted file mode 100644
index b3f44d537..000000000
--- a/tfjs-master/tfjs-core/src/math.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Exports under the tf.math.* namespace.
- */
-
-import {confusionMatrix} from './ops/confusion_matrix';
-
-export {confusionMatrix};
diff --git a/tfjs-master/tfjs-core/src/model_types.ts b/tfjs-master/tfjs-core/src/model_types.ts
deleted file mode 100644
index 9484e978a..000000000
--- a/tfjs-master/tfjs-core/src/model_types.ts
+++ /dev/null
@@ -1,156 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from './tensor';
-import {NamedTensorMap} from './tensor_types';
-import {DataType} from './types';
-
-export interface ModelPredictConfig {
-  /**
-   * Optional. Batch size (Integer). If unspecified, it will default to 32.
-   */
-  batchSize?: number;
-
-  /**
-   * Optional. Verbosity mode. Defaults to false.
-   */
-  verbose?: boolean;
-}
-
-/**
- * Interface for model input/output tensor info.
- */
-export interface ModelTensorInfo {
-  // Name of the tensor.
-  name: string;
-  // Tensor shape information, Optional.
-  shape?: number[];
-  // Data type of the tensor.
-  dtype: DataType;
-  // TensorFlow native Data type of the tensor.
-  tfDtype?: string;
-}
-
-/**
- * Common interface for a machine learning model that can do inference.
- */
-export interface InferenceModel {
-  /**
-   * Return the array of input tensor info.
-   */
-  readonly inputs: ModelTensorInfo[];
-
-  /**
-   * Return the array of output tensor info.
-   */
-  readonly outputs: ModelTensorInfo[];
-
-  /**
-   * Execute the inference for the input tensors.
-   *
-   * @param input The input tensors, when there is single input for the model,
-   * inputs param should be a Tensor. For models with multiple inputs, inputs
-   * params should be in either Tensor[] if the input order is fixed, or
-   * otherwise NamedTensorMap format.
-   * For batch inference execution, the tensors for each input need to be
-   * concatenated together. For example with mobilenet, the required input shape
-   * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
-   * If we are provide a batched data of 100 images, the input tensor should be
-   * in the shape of [100, 244, 244, 3].
-   *
-   * @param config Prediction configuration for specifying the batch size.
-   *
-   * @returns Inference result tensors. The output would be single Tensor if
-   * model has single output node, otherwise Tensor[] or NamedTensorMap[] will
-   * be returned for model with multiple outputs.
-   */
-  predict(inputs: Tensor|Tensor[]|NamedTensorMap, config: ModelPredictConfig):
-      Tensor|Tensor[]|NamedTensorMap;
-
-  /**
-   * Single Execute the inference for the input tensors and return activation
-   * values for specified output node names without batching.
-   *
-   * @param input The input tensors, when there is single input for the model,
-   * inputs param should be a Tensor. For models with multiple inputs, inputs
-   * params should be in either Tensor[] if the input order is fixed, or
-   * otherwise NamedTensorMap format.
-   *
-   * @param outputs string|string[]. List of output node names to retrieve
-   * activation from.
-   *
-   * @returns Activation values for the output nodes result tensors. The return
-   * type matches specified parameter outputs type. The output would be single
-   * Tensor if single output is specified, otherwise Tensor[] for multiple
-   * outputs.
-   */
-  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs: string|string[]):
-      Tensor|Tensor[];
-}
-
-/**
- * @deprecated Deprecated interface for SavedModel/GraphModel MetaGraph info.
- *     User MetaGraph instead.
- */
-export interface MetaGraphInfo {
-  tags: string[];
-  signatureDefs: SignatureDefInfo;
-}
-
-/**
- * @deprecated Deprecated interface for SavedModel/GraphModel SignatureDef info.
- *     User SignatureDef instead.
- */
-export interface SignatureDefInfo {
-  [key: string]: {
-    inputs: {[key: string]: SavedModelTensorInfo};
-    outputs: {[key: string]: SavedModelTensorInfo};
-  };
-}
-
-/**
- * @deprecated Deprecated interface for SavedModel/GraphModel signature
- *     input/output Tensor info. User ModelTensorInfo instead.
- */
-export interface SavedModelTensorInfo {
-  dtype: string;
-  shape: number[];
-  name: string;
-}
-
-/**
- * Interface for SavedModel/GraphModel MetaGraph info.
- */
-export interface MetaGraph {
-  tags: string[];
-  signatureDefs: SignatureDef;
-}
-
-/**
- * Interface for SavedModel/GraphModel SignatureDef entry.
- */
-export interface SignatureDefEntry {
-  inputs: {[key: string]: ModelTensorInfo};
-  outputs: {[key: string]: ModelTensorInfo};
-}
-
-/**
- * Interface for SavedModel/GraphModel SignatureDef info.
- */
-export interface SignatureDef {
-  [key: string]: SignatureDefEntry;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/abs.ts b/tfjs-master/tfjs-core/src/ops/abs.ts
deleted file mode 100644
index 969b0c805..000000000
--- a/tfjs-master/tfjs-core/src/ops/abs.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Abs, AbsInputs, ComplexAbs, ComplexAbsInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes absolute value element-wise: `abs(x)`
- *
- * ```js
- * const x = tf.tensor1d([-1, 2, -3, 4]);
- *
- * x.abs().print();  // or tf.abs(x)
- * ```
- * @param x The input `tf.Tensor`.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function abs_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'abs');
-
-  if ($x.dtype === 'complex64') {
-    const inputs: ComplexAbsInputs = {x: $x};
-    return ENGINE.runKernel(ComplexAbs, inputs as unknown as NamedTensorMap);
-  } else {
-    const inputs: AbsInputs = {x: $x};
-    return ENGINE.runKernel(Abs, inputs as unknown as NamedTensorMap);
-  }
-}
-
-export const abs = /* @__PURE__ */ op({abs_});
diff --git a/tfjs-master/tfjs-core/src/ops/abs_test.ts b/tfjs-master/tfjs-core/src/ops/abs_test.ts
deleted file mode 100644
index 81ae7219b..000000000
--- a/tfjs-master/tfjs-core/src/ops/abs_test.ts
+++ /dev/null
@@ -1,178 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {backend} from '../index';
-
-describeWithFlags('abs', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([1, -2, 0, 3, -0.1]);
-    const result = tf.abs(a);
-    expectArraysClose(await result.data(), [1, 2, 0, 3, 0.1]);
-  });
-
-  it('5D', async () => {
-    const a = tf.tensor5d([1, -2, 0, -3], [1, 2, 2, 1, 1]);
-    const result = tf.abs(a);
-    expectArraysClose(await result.data(), [1, 2, 0, 3]);
-  });
-
-  it('6D', async () => {
-    const a = tf.tensor6d([1, -2, 5, -3, -1, 4, 7, 8], [1, 2, 2, 2, 1, 1]);
-    const result = tf.abs(a);
-    expectArraysClose(await result.data(), [1, 2, 5, 3, 1, 4, 7, 8]);
-  });
-
-  it('int32', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const a = tf.tensor1d([10, 12345678, -12345678], 'int32');
-      const result = tf.abs(a);
-      expect(result.dtype).toEqual('int32');
-      expectArraysClose(await result.data(), [10, 12345678, 12345678]);
-    }
-  });
-
-  it('complex64 rank-1', async () => {
-    const a = tf.complex([-2, -1, 0, 1, 2], [1, 2, 3, 0, -1]);
-    const result = tf.abs(a);
-    expectArraysClose(await result.data(), [
-      Math.sqrt(-2 * -2 + 1 * 1), Math.sqrt(-1 * -1 + 2 * 2),
-      Math.sqrt(0 * 0 + 3 * 3), Math.sqrt(1 * 1 + 0 * 0),
-      Math.sqrt(2 * 2 + -1 * -1)
-    ]);
-    expect(result.shape).toEqual([5]);
-  });
-
-  it('complex64 rank-2', async () => {
-    const a = tf.complex([[-3, -2, -1], [0, 1, 2]], [[4, 1, 2], [3, 0, -1]]);
-    const result = tf.abs(a);
-    expectArraysClose(await result.data(), [
-      Math.sqrt(-3 * -3 + 4 * 4), Math.sqrt(-2 * -2 + 1 * 1),
-      Math.sqrt(-1 * -1 + 2 * 2), Math.sqrt(0 * 0 + 3 * 3),
-      Math.sqrt(1 * 1 + 0 * 0), Math.sqrt(2 * 2 + -1 * -1)
-    ]);
-    expect(result.shape).toEqual([2, 3]);
-  });
-
-  it('complex64 rank-3', async () => {
-    const a = tf.complex(
-        [[[-3, -2], [-1, 0]], [[1, 2], [3, 4]]],
-        [[[4, 1], [2, 3]], [[0, -1], [-3, -4]]]);
-    const result = tf.abs(a);
-    expectArraysClose(await result.data(), [
-      Math.sqrt(-3 * -3 + 4 * 4), Math.sqrt(-2 * -2 + 1 * 1),
-      Math.sqrt(-1 * -1 + 2 * 2), Math.sqrt(0 * 0 + 3 * 3),
-      Math.sqrt(1 * 1 + 0 * 0), Math.sqrt(2 * 2 + -1 * -1),
-      Math.sqrt(3 * 3 + -3 * -3), Math.sqrt(4 * 4 + -4 * -4)
-    ]);
-    expect(result.shape).toEqual([2, 2, 2]);
-  });
-
-  it('is underflow-safe for complex64', async () => {
-    const floatBits = tf.backend().floatPrecision();
-    let small;
-    switch (floatBits) {
-      case 32:
-        small = 1e-30;
-        break;
-      case 16:
-        small = 1e-4;
-        break;
-      default:
-        throw new Error(`Test not implemented for ENV.engine.floatPrecision()=${
-            floatBits}.`);
-    }
-
-    const a = tf.complex([small, 0, small, 0], [small, small, 0, 0]);
-    const result = tf.abs(a);
-    expectArraysClose(
-        await result.data(),
-        [
-          Math.hypot(small, small), Math.hypot(0, small), Math.hypot(small, 0),
-          Math.hypot(0, 0)
-        ],
-        /*tolerance=*/ small / 100);
-    expect(result.shape).toEqual([4]);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([1, -2, 0, 3, -0.1, NaN]);
-    const result = tf.abs(a);
-    expectArraysClose(await result.data(), [1, 2, 0, 3, 0.1, NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(4);
-    const dy = tf.scalar(8);
-
-    const da = tf.grad(a => tf.abs(a))(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [8 * 1]);
-  });
-
-  it('gradient with clones', () => {
-    const a = tf.scalar(4);
-    const dy = tf.scalar(8);
-
-    const da = tf.grad(a => a.clone().abs().clone())(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, -3, 5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const da = tf.grad(a => tf.abs(a))(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1 * 1, 2 * 1, 3 * -1, 4 * 1]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([3, -1, -2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const da = tf.grad(a => tf.abs(a))(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1 * 1, 2 * -1, 3 * -1, 4 * 1]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.abs({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'abs' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.abs([1, -2, 0, 3, -0.1]);
-    expectArraysClose(await result.data(), [1, 2, 0, 3, 0.1]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.abs('q'))
-        .toThrowError(/Argument 'x' passed to 'abs' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/acos.ts b/tfjs-master/tfjs-core/src/ops/acos.ts
deleted file mode 100644
index e54d3abe4..000000000
--- a/tfjs-master/tfjs-core/src/ops/acos.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Acos, AcosInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, 1, -1, .7]);
- *
- * x.acos().print();  // or tf.acos(x)
- * ```
- * @param x The input tensor.
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function acos_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'acos');
-  const inputs: AcosInputs = {x: $x};
-
-  return ENGINE.runKernel(Acos, inputs as unknown as NamedTensorMap);
-}
-export const acos = /* @__PURE__ */ op({acos_});
diff --git a/tfjs-master/tfjs-core/src/ops/acos_test.ts b/tfjs-master/tfjs-core/src/ops/acos_test.ts
deleted file mode 100644
index a78e6bf07..000000000
--- a/tfjs-master/tfjs-core/src/ops/acos_test.ts
+++ /dev/null
@@ -1,123 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('acos', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [.1, -3, 2, 7, -4];
-    const a = tf.tensor1d(values);
-    const result = tf.acos(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.acos(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.acos(a);
-    expectArraysClose(await res.data(), [Math.acos(4), NaN, Math.acos(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.acos(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [(-1 * 8) / Math.sqrt(1 - (0.5 * 0.5))]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.acos(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [(-1 * 8) / Math.sqrt(1 - (0.5 * 0.5))]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-0.1, 0.2, 0.3, -0.5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.acos(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] =
-          (-1 * dyValues[i]) / Math.sqrt(1 - (aValues[i] * aValues[i]));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-0.3, 0.1, 0.2, 0.3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.acos(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] =
-          (-1 * dyValues[i]) / Math.sqrt(1 - (aValues[i] * aValues[i]));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.acos({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'acos' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [.1, -3, 2, 7, -4];
-    const result = tf.acos(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.acos(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.acos('q'))
-        .toThrowError(/Argument 'x' passed to 'acos' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/acosh.ts b/tfjs-master/tfjs-core/src/ops/acosh.ts
deleted file mode 100644
index f9c5e583c..000000000
--- a/tfjs-master/tfjs-core/src/ops/acosh.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Acosh, AcoshInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:
- * `acosh(x)`
- *
- * ```js
- * const x = tf.tensor1d([10, 1, 3, 5.7]);
- *
- * x.acosh().print();  // or tf.acosh(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function acosh_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'acosh');
-  const inputs: AcoshInputs = {x: $x};
-
-  return ENGINE.runKernel(Acosh, inputs as unknown as NamedTensorMap);
-}
-export const acosh = /* @__PURE__ */ op({acosh_});
diff --git a/tfjs-master/tfjs-core/src/ops/acosh_test.ts b/tfjs-master/tfjs-core/src/ops/acosh_test.ts
deleted file mode 100644
index 400eb39f2..000000000
--- a/tfjs-master/tfjs-core/src/ops/acosh_test.ts
+++ /dev/null
@@ -1,148 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('acosh', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [2, 3, 4, 5, 6];
-    const a = tf.tensor1d(values);
-    const result = tf.acosh(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.acosh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('scalar', async () => {
-    const value = 2;
-    const a = tf.scalar(value);
-    const result = tf.acosh(a);
-
-    const expected = [Math.acosh(value)];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('tensor2d', async () => {
-    const values = [2, 3, 4, 5];
-    const a = tf.tensor2d(values, [2, 2]);
-    const result = tf.acosh(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.acosh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 2]);
-    const res = tf.acosh(a);
-    expectArraysClose(await res.data(), [Math.acosh(4), NaN, Math.acosh(2)]);
-  });
-
-  it('NaN outside function domain', async () => {
-    const a = tf.tensor1d([4, -1, 2]);
-    const res = tf.acosh(a);
-    expectArraysClose(await res.data(), [Math.acosh(4), NaN, Math.acosh(2)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(1.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.acosh(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [8.0 / Math.sqrt(1.5 * 1.5 - 1.0)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(1.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.acosh(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [8.0 / Math.sqrt(1.5 * 1.5 - 1.0)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [2, 3, 5, 10];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.acosh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / Math.sqrt(Math.pow(aValues[i], 2) - 1.0);
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [2, 3, 5, 7];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.acosh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / Math.sqrt(Math.pow(aValues[i], 2) - 1.0);
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.acosh({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'acosh' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [2, 3, 4, 5, 6];
-    const result = tf.acosh(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.acosh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.acosh('q'))
-        .toThrowError(/Argument 'x' passed to 'acosh' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/add.ts b/tfjs-master/tfjs-core/src/ops/add.ts
deleted file mode 100644
index a89132eae..000000000
--- a/tfjs-master/tfjs-core/src/ops/add.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Add, AddInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
- *
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3, 4]);
- * const b = tf.tensor1d([10, 20, 30, 40]);
- *
- * a.add(b).print();  // or tf.add(a, b)
- * ```
- *
- * ```js
- * // Broadcast add a with b.
- * const a = tf.scalar(5);
- * const b = tf.tensor1d([10, 20, 30, 40]);
- *
- * a.add(b).print();  // or tf.add(a, b)
- * ```
- * @param a The first `tf.Tensor` to add.
- * @param b The second `tf.Tensor` to add. Must have the same type as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'add');
-  let $b = convertToTensor(b, 'b', 'add');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  const inputs: AddInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Add, inputs as unknown as NamedTensorMap);
-}
-
-export const add = /* @__PURE__ */ op({add_});
diff --git a/tfjs-master/tfjs-core/src/ops/add_n.ts b/tfjs-master/tfjs-core/src/ops/add_n.ts
deleted file mode 100644
index b4116e677..000000000
--- a/tfjs-master/tfjs-core/src/ops/add_n.ts
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {AddN, AddNInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
- *
- * ```js
- * const a = tf.tensor1d([1, 2]);
- * const b = tf.tensor1d([3, 4]);
- * const c = tf.tensor1d([5, 6]);
- *
- * tf.addN([a, b, c]).print();
- * ```
- * @param tensors A list of tensors with the same shape and dtype.
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function addN_<T extends Tensor>(tensors: Array<T|TensorLike>): T {
-  util.assert(
-      Array.isArray(tensors),
-      () => 'The argument passed to tf.addN() must be a list of tensors');
-  util.assert(
-      tensors.length >= 1,
-      () => `Must pass at least one tensor to tf.addN(), but got ` +
-          `${tensors.length}`);
-
-  const $tensors =
-      tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));
-
-  const firstTensor = $tensors[0];
-  $tensors.forEach(t => {
-    if (t.dtype !== firstTensor.dtype) {
-      throw new Error(
-          'All tensors passed to tf.addN() must have the same dtype');
-    }
-  });
-
-  $tensors.forEach(t => {
-    if (!util.arraysEqual(t.shape, firstTensor.shape)) {
-      throw new Error(
-          'All tensors passed to tf.addN() must have the same shape');
-    }
-  });
-
-  const inputs: AddNInputs = $tensors;
-
-  return ENGINE.runKernel(AddN, inputs as unknown as NamedTensorMap);
-}
-
-export const addN = /* @__PURE__ */ op({addN_});
diff --git a/tfjs-master/tfjs-core/src/ops/add_n_test.ts b/tfjs-master/tfjs-core/src/ops/add_n_test.ts
deleted file mode 100644
index 68cb7bbcc..000000000
--- a/tfjs-master/tfjs-core/src/ops/add_n_test.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('addN', ALL_ENVS, () => {
-  it('a single tensor', async () => {
-    const res = tf.addN([tf.tensor1d([1, 2, 3])]);
-    expectArraysClose(await res.data(), [1, 2, 3]);
-  });
-
-  it('two tensors, int32', async () => {
-    const res = tf.addN([
-      tf.tensor1d([1, 2, -1], 'int32'),
-      tf.tensor1d([5, 3, 2], 'int32'),
-    ]);
-    expectArraysClose(await res.data(), [6, 5, 1]);
-    expect(res.dtype).toBe('int32');
-    expect(res.shape).toEqual([3]);
-  });
-
-  it('three tensors', async () => {
-    const res = tf.addN([
-      tf.tensor1d([1, 2]),
-      tf.tensor1d([5, 3]),
-      tf.tensor1d([-5, -2]),
-    ]);
-    expectArraysClose(await res.data(), [1, 3]);
-    expect(res.dtype).toBe('float32');
-    expect(res.shape).toEqual([2]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const res = tf.addN([[1, 2], [3, 4]]);
-    expectArraysClose(await res.data(), [4, 6]);
-    expect(res.dtype).toBe('float32');
-    expect(res.shape).toEqual([2]);
-  });
-
-  it('list of numbers gets treated as a list of scalars', async () => {
-    const res = tf.addN([1, 2, 3, 4]);
-    expectArraysClose(await res.data(), [10]);
-    expect(res.dtype).toBe('float32');
-    expect(res.shape).toEqual([]);
-  });
-
-  it('errors if list is empty', () => {
-    expect(() => tf.addN([]))
-        .toThrowError(
-            /Must pass at least one tensor to tf.addN\(\), but got 0/);
-  });
-
-  it('errors if argument is not an array', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tf.addN(tf.scalar(3) as any))
-        .toThrowError(
-            /The argument passed to tf.addN\(\) must be a list of tensors/);
-  });
-
-  it('errors if arguments not of same dtype', () => {
-    expect(() => tf.addN([tf.scalar(1, 'int32'), tf.scalar(2, 'float32')]))
-        .toThrowError(
-            /All tensors passed to tf.addN\(\) must have the same dtype/);
-  });
-
-  it('errors if arguments not of same shape', () => {
-    expect(() => tf.addN([tf.scalar(1), tf.tensor1d([2])]))
-        .toThrowError(
-            /All tensors passed to tf.addN\(\) must have the same shape/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/add_test.ts b/tfjs-master/tfjs-core/src/ops/add_test.ts
deleted file mode 100644
index c0145aaa2..000000000
--- a/tfjs-master/tfjs-core/src/ops/add_test.ts
+++ /dev/null
@@ -1,325 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('add', ALL_ENVS, () => {
-  it('c + A', async () => {
-    const c = tf.scalar(5);
-    const a = tf.tensor1d([1, 2, 3]);
-
-    const result = tf.add(c, a);
-
-    expectArraysClose(await result.data(), [6, 7, 8]);
-  });
-
-  it('c + A propagates NaNs', async () => {
-    const c = tf.scalar(NaN);
-    const a = tf.tensor1d([1, 2, 3]);
-
-    const res = tf.add(c, a);
-
-    expectArraysEqual(await res.data(), [NaN, NaN, NaN]);
-  });
-
-  it('A + B broadcasting same rank Tensors different shape', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor2d([2, 3], [2, 1]);
-
-    const result = tf.add(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [3, 4, 0, -1];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('A + B broadcast 2D + 1D', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor1d([1, 2]);
-
-    const result = tf.add(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [2, 4, -2, -2];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('A + B', async () => {
-    const a = tf.tensor1d([2, 5, 1]);
-    const b = tf.tensor1d([4, 2, -1]);
-
-    const result = tf.add(a, b);
-
-    const expected = [6, 7, 0];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('TensorLike', async () => {
-    const a = [2, 5, 1];
-    const b = [4, 2, -1];
-
-    const result = tf.add(a, b);
-
-    const expected = [6, 7, 0];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([2, 5, 1]);
-    const b = [4, 2, -1];
-
-    const result = a.add(b);
-
-    const expected = [6, 7, 0];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('A + B propagates NaNs', async () => {
-    const a = tf.tensor1d([2, 5, NaN]);
-    const b = tf.tensor1d([4, 2, -1]);
-
-    const res = tf.add(a, b);
-    expectArraysClose(await res.data(), [6, 7, NaN]);
-  });
-
-  it('A + B throws when passed tensors with different shape', () => {
-    const a = tf.tensor1d([2, 5, 1, 5]);
-    const b = tf.tensor1d([4, 2, -1]);
-
-    expect(() => tf.add(a, b)).toThrowError();
-    expect(() => tf.add(b, a)).toThrowError();
-  });
-
-  it('2D+scalar broadcast', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.scalar(2);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);
-  });
-
-  it('scalar+1D broadcast', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([1, 2, 3, 4, 5, 6]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([6]);
-    expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);
-  });
-
-  it('2D+2D broadcast each with 1 dim', async () => {
-    const a = tf.tensor2d([1, 2, 5], [1, 3]);
-    const b = tf.tensor2d([7, 3], [2, 1]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [8, 9, 12, 4, 5, 8]);
-  });
-
-  it('2D+2D broadcast inner dim of b', async () => {
-    const a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([7, 3], [2, 1]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [8, 9, 12, 7, 8, 9]);
-  });
-
-  it('3D+scalar', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
-    const b = tf.scalar(-1);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 3, 1]);
-    expectArraysClose(await res.data(), [0, 1, 2, 3, 4, 5]);
-  });
-
-  it('6D+scalar', async () => {
-    const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
-    const b = tf.scalar(-1);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
-    const expectedResult = [
-      -1, 0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-      15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
-      31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
-      47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62
-    ];
-    expectArraysClose(await res.data(), expectedResult);
-  });
-
-  it('6D+2D', async () => {
-    const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
-    const b = tf.tensor2d([11, 13, 17, 19], [2, 2]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
-    const expectedResult = [
-      11, 14, 19, 22, 15, 18, 23, 26, 19, 22, 27, 30, 23, 26, 31, 34,
-      27, 30, 35, 38, 31, 34, 39, 42, 35, 38, 43, 46, 39, 42, 47, 50,
-      43, 46, 51, 54, 47, 50, 55, 58, 51, 54, 59, 62, 55, 58, 63, 66,
-      59, 62, 67, 70, 63, 66, 71, 74, 67, 70, 75, 78, 71, 74, 79, 82
-    ];
-    expectArraysClose(await res.data(), expectedResult);
-  });
-
-  it('add tensors with 0 in shape', async () => {
-    const a = tf.tensor1d([1]);
-    const b = tf.tensor3d([], [0, 0, 5]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([0, 0, 5]);
-    expectArraysEqual(await res.data(), []);
-  });
-
-  it('gradient: scalar + 1D broadcast', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([7, 8, 9]);
-
-    const grads = tf.grads((a, b) => tf.add(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [7 + 8 + 9]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [7, 8, 9]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([7, 8, 9]);
-
-    const grads = tf.grads((a, b) => tf.add(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [7 + 8 + 9]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [7, 8, 9]);
-  });
-
-  it('gradient: 2D + 2D broadcast', async () => {
-    const a = tf.tensor2d([2, 3], [2, 1]);
-    const b = tf.tensor2d([4, 5, 6, 7], [2, 2]);
-    const dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.add(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [5 + 4, 3 + 2]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [5, 4, 3, 2]);
-  });
-
-  it('complex number addition', async () => {
-    const real1 = tf.tensor1d([1]);
-    const imag1 = tf.tensor1d([2]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([3]);
-    const imag2 = tf.tensor1d([4]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const result = complex1.add(complex2);
-
-    expect(result.dtype).toBe('complex64');
-    expect(result.shape).toEqual([1]);
-    expectArraysClose(await result.data(), [4, 6]);
-  });
-
-  it('complex number reshape and then addition', async () => {
-    const real1 = tf.tensor1d([1]);
-    const imag1 = tf.tensor1d([2]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([3]);
-    const imag2 = tf.tensor1d([4]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const complex1Reshaped = complex1.reshape([1, 1, 1]);
-    const complex2Reshaped = complex2.reshape([1, 1, 1]);
-
-    const result = complex1Reshaped.add(complex2Reshaped);
-
-    expect(result.dtype).toBe('complex64');
-    expect(result.shape).toEqual([1, 1, 1]);
-    expectArraysClose(await result.data(), [4, 6]);
-  });
-
-  it('complex number broadcasting addition', async () => {
-    const real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([4]);
-    const imag2 = tf.tensor1d([5]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const result = tf.add(complex1, complex2);
-
-    expect(result.dtype).toEqual('complex64');
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(
-        await result.data(),
-        [1 + 4, 10 + 5, 2 + 4, 20 + 5, -3 + 4, -30 + 5, -4 + 4, -40 + 5]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.add({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'add' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.add(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'add' must be a Tensor/);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    let res = tf.add(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [2]);
-
-    res = tf.add(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [2]);
-
-    res = tf.add(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [1]);
-
-    res = tf.add(tf.complex(4, 7), tf.scalar(1, 'float32'));
-    expect(res.dtype).toBe('complex64');
-    expectArraysClose(await res.data(), [5, 7]);
-
-    res = tf.add(tf.complex(4, 7), tf.scalar(1, 'int32'));
-    expect(res.dtype).toBe('complex64');
-    expectArraysClose(await res.data(), [5, 7]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.add(5, [1, 2, 3]);
-    expectArraysClose(await result.data(), [6, 7, 8]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/all.ts b/tfjs-master/tfjs-core/src/ops/all.ts
deleted file mode 100644
index 2d8bd4886..000000000
--- a/tfjs-master/tfjs-core/src/ops/all.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {All, AllAttrs, AllInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the logical and of elements across dimensions of a `tf.Tensor`.
- *
- * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
- * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
- * `axes`. If `keepDims` is true, the reduced dimensions are retained with
- * length 1. If `axes` has no entries, all dimensions are reduced, and a
- * `tf.Tensor` with a single element is returned.
- *
- * ```js
- * const x = tf.tensor1d([1, 1, 1], 'bool');
- *
- * x.all().print();  // or tf.all(x)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
- *
- * const axis = 1;
- * x.all(axis).print();  // or tf.all(x, axis)
- * ```
- *
- * @param x The input tensor. Must be of dtype bool.
- * @param axis The dimension(s) to reduce. By default it reduces
- *     all dimensions.
- * @param keepDims If true, retains reduced dimensions with size 1.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function all_<T extends Tensor>(
-    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {
-  const $x = convertToTensor(x, 'x', 'all', 'bool');
-
-  const inputs: AllInputs = {x: $x};
-  const attrs: AllAttrs = {axis, keepDims};
-
-  return ENGINE.runKernel(
-      All, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const all = /* @__PURE__ */ op({all_});
diff --git a/tfjs-master/tfjs-core/src/ops/all_test.ts b/tfjs-master/tfjs-core/src/ops/all_test.ts
deleted file mode 100644
index 87945a59b..000000000
--- a/tfjs-master/tfjs-core/src/ops/all_test.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('all', ALL_ENVS, () => {
-  it('Tensor1D', async () => {
-    let a = tf.tensor1d([0, 0, 0], 'bool');
-    expectArraysClose(await tf.all(a).data(), 0);
-
-    a = tf.tensor1d([1, 0, 1], 'bool');
-    expectArraysClose(await tf.all(a).data(), 0);
-
-    a = tf.tensor1d([1, 1, 1], 'bool');
-    expectArraysClose(await tf.all(a).data(), 1);
-  });
-
-  it('ignores NaNs', async () => {
-    const a = tf.tensor1d([1, NaN, 1], 'bool');
-    expectArraysEqual(await tf.all(a).data(), 1);
-  });
-
-  it('2D', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
-    expectArraysClose(await tf.all(a).data(), 0);
-  });
-
-  it('2D axis=[0,1]', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    expectArraysClose(await tf.all(a, [0, 1]).data(), 0);
-  });
-
-  it('2D, axis=0', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
-    let r = tf.all(a, 0);
-
-    expect(r.shape).toEqual([2]);
-    expectArraysClose(await r.data(), [0, 0]);
-
-    r = tf.all(a, 1);
-
-    expect(r.shape).toEqual([2]);
-    expectArraysClose(await r.data(), [1, 0]);
-  });
-
-  it('2D, axis=0, keepDims', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    const r = a.all(0, true /* keepDims */);
-
-    expect(r.shape).toEqual([1, 3]);
-    expectArraysClose(await r.data(), [0, 1, 0]);
-  });
-
-  it('2D, axis=1 provided as a number', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    const r = tf.all(a, 1);
-    expectArraysClose(await r.data(), [0, 0]);
-  });
-
-  it('2D, axis = -1 provided as a number', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    const r = tf.all(a, -1);
-    expectArraysClose(await r.data(), [0, 0]);
-  });
-
-  it('2D, axis=[1]', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    const r = tf.all(a, [1]);
-    expectArraysClose(await r.data(), [0, 0]);
-  });
-
-  it('throws when dtype is not boolean', () => {
-    const a = tf.tensor2d([1, 1, 0, 0], [2, 2]);
-    expect(() => tf.all(a))
-        .toThrowError(
-            /Argument 'x' passed to 'all' must be bool tensor, but got float/);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.all({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'all' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [0, 0, 0];
-    expectArraysClose(await tf.all(a).data(), 0);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.all(['a']))
-        .toThrowError(
-            /Argument 'x' passed to 'all' must be bool tensor, but got string/);
-  });
-
-  it('returns a boolean tensor', () => {
-    const a = tf.tensor1d([1, 0], 'bool');
-    const r = tf.all(a);
-    expect(r.dtype).toEqual('bool');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/any.ts b/tfjs-master/tfjs-core/src/ops/any.ts
deleted file mode 100644
index c1a6e9723..000000000
--- a/tfjs-master/tfjs-core/src/ops/any.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Any, AnyAttrs, AnyInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the logical or of elements across dimensions of a `tf.Tensor`.
- *
- * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
- * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
- * `axes`. If `keepDims` is true, the reduced dimensions are retained with
- * length 1. If `axes` has no entries, all dimensions are reduced, and a
- * `tf.Tensor` with a single element is returned.
- *
- * ```js
- * const x = tf.tensor1d([1, 1, 1], 'bool');
- *
- * x.any().print();  // or tf.any(x)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
- *
- * const axis = 1;
- * x.any(axis).print();  // or tf.any(x, axis)
- * ```
- *
- * @param x The input tensor. Must be of dtype bool.
- * @param axis The dimension(s) to reduce. By default it reduces
- *     all dimensions.
- * @param keepDims If true, retains reduced dimensions with size 1.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function any_<T extends Tensor>(
-    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {
-  const $x = convertToTensor(x, 'x', 'any', 'bool');
-
-  const inputs: AnyInputs = {x: $x};
-  const attrs: AnyAttrs = {axis, keepDims};
-
-  return ENGINE.runKernel(
-      Any, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-// tslint:disable-next-line:variable-name
-export const any = /* @__PURE__ */ op({any_});
diff --git a/tfjs-master/tfjs-core/src/ops/any_test.ts b/tfjs-master/tfjs-core/src/ops/any_test.ts
deleted file mode 100644
index 0353cca98..000000000
--- a/tfjs-master/tfjs-core/src/ops/any_test.ts
+++ /dev/null
@@ -1,115 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('any', ALL_ENVS, () => {
-  it('Tensor1D', async () => {
-    let a = tf.tensor1d([0, 0, 0], 'bool');
-    expectArraysClose(await tf.any(a).data(), 0);
-
-    a = tf.tensor1d([1, 0, 1], 'bool');
-    expectArraysClose(await tf.any(a).data(), 1);
-
-    a = tf.tensor1d([1, 1, 1], 'bool');
-    expectArraysClose(await tf.any(a).data(), 1);
-  });
-
-  it('ignores NaNs', async () => {
-    const a = tf.tensor1d([1, NaN, 0], 'bool');
-    expectArraysEqual(await tf.any(a).data(), 1);
-  });
-
-  it('2D', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
-    expectArraysClose(await tf.any(a).data(), 1);
-  });
-
-  it('2D axis=[0,1]', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    expectArraysClose(await tf.any(a, [0, 1]).data(), 1);
-  });
-
-  it('2D, axis=0', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
-    let r = tf.any(a, 0);
-
-    expect(r.shape).toEqual([2]);
-    expectArraysClose(await r.data(), [1, 1]);
-
-    r = tf.any(a, 1);
-
-    expect(r.shape).toEqual([2]);
-    expectArraysClose(await r.data(), [1, 0]);
-  });
-
-  it('2D, axis=0, keepDims', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    const r = a.any(0, true /* keepDims */);
-
-    expect(r.shape).toEqual([1, 3]);
-    expectArraysClose(await r.data(), [1, 1, 0]);
-  });
-
-  it('2D, axis=1 provided as a number', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    const r = tf.any(a, 1);
-    expectArraysClose(await r.data(), [1, 1]);
-  });
-
-  it('2D, axis = -1 provided as a number', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    const r = tf.any(a, -1);
-    expectArraysClose(await r.data(), [1, 1]);
-  });
-
-  it('2D, axis=[1]', async () => {
-    const a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
-    const r = tf.any(a, [1]);
-    expectArraysClose(await r.data(), [1, 1]);
-  });
-
-  it('throws when dtype is not boolean', () => {
-    const a = tf.tensor2d([1, 1, 0, 0], [2, 2]);
-    expect(() => tf.any(a))
-        .toThrowError(
-            /Argument 'x' passed to 'any' must be bool tensor, but got float/);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.any({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'any' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [0, 0, 0];
-    expectArraysClose(await tf.any(a).data(), 0);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.any(['a']))
-        .toThrowError(/Argument 'x' passed to 'any' must be bool tensor/);
-  });
-
-  it('returns a boolean tensor', () => {
-    const a = tf.tensor1d([1, 0], 'bool');
-    const r = tf.any(a);
-    expect(r.dtype).toEqual('bool');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/arg_max.ts b/tfjs-master/tfjs-core/src/ops/arg_max.ts
deleted file mode 100644
index d1c5deb8f..000000000
--- a/tfjs-master/tfjs-core/src/ops/arg_max.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {ArgMax, ArgMaxAttrs, ArgMaxInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Returns the indices of the maximum values along an `axis`.
- *
- * The result has the same shape as `input` with the dimension along `axis`
- * removed.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3]);
- *
- * x.argMax().print();  // or tf.argMax(x)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
- *
- * const axis = 1;
- * x.argMax(axis).print();  // or tf.argMax(x, axis)
- * ```
- *
- * @param x The input tensor.
- * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function argMax_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {
-  const $x = convertToTensor(x, 'x', 'argMax');
-
-  const inputs: ArgMaxInputs = {x: $x};
-  const attrs: ArgMaxAttrs = {axis};
-
-  return ENGINE.runKernel(
-      ArgMax, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const argMax = /* @__PURE__ */ op({argMax_});
diff --git a/tfjs-master/tfjs-core/src/ops/arg_max_test.ts b/tfjs-master/tfjs-core/src/ops/arg_max_test.ts
deleted file mode 100644
index d7c4cd9ec..000000000
--- a/tfjs-master/tfjs-core/src/ops/arg_max_test.ts
+++ /dev/null
@@ -1,178 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-import * as reduce_util from './reduce_util';
-
-describeWithFlags('argmax', ALL_ENVS, () => {
-  it('Tensor1D', async () => {
-    const a = tf.tensor1d([1, 0, 3, 2]);
-    const result = tf.argMax(a);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), 2);
-  });
-
-  it('one value', async () => {
-    const a = tf.tensor1d([10]);
-    const result = tf.argMax(a);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), 0);
-  });
-
-  it('N > than parallelization threshold', async () => {
-    const n = reduce_util.PARALLELIZE_THRESHOLD * 2;
-    const values = new Float32Array(n);
-    for (let i = 0; i < n; i++) {
-      values[i] = i;
-    }
-    const a = tf.tensor1d(values);
-    const result = tf.argMax(a);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), n - 1);
-  });
-
-  it('3D, N > than parallelization threshold', async () => {
-    const n = reduce_util.PARALLELIZE_THRESHOLD * 2;
-    const values = new Float32Array(n);
-    for (let i = 0; i < n; i++) {
-      values[i] = i;
-    }
-    const a = tf.tensor3d(values, [1, 1, n]);
-    const result = tf.argMax(a, -1);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), n - 1);
-  });
-
-  it('max index corresponds to start of a non-initial window', async () => {
-    const n = reduce_util.PARALLELIZE_THRESHOLD * 2;
-    const windowSize = reduce_util.computeOptimalWindowSize(n);
-    const values = new Float32Array(n);
-    const index = windowSize * 2;
-    values[index] = 1;
-    const a = tf.tensor1d(values);
-    const result = tf.argMax(a);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), index);
-  });
-
-  it('5D, max index corresponds to start of a non-initial window', async () => {
-    const n = reduce_util.PARALLELIZE_THRESHOLD * 2;
-    const windowSize = reduce_util.computeOptimalWindowSize(n);
-    const values = new Float32Array(n);
-    const index = windowSize * 2;
-    values[index] = 1;
-    const a = tf.tensor5d(values, [1, 1, 1, 1, n]);
-    const result = tf.argMax(a, -1);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), index);
-  });
-
-  it('ignores NaNs', async () => {
-    const a = tf.tensor1d([0, 3, 5, NaN, 3]);
-    const res = tf.argMax(a);
-    expect(res.dtype).toBe('int32');
-    expectArraysEqual(await res.data(), 2);
-  });
-
-  it('2D, no axis specified', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    expectArraysEqual(await tf.argMax(a).data(), [1, 0, 1]);
-  });
-
-  it('4D, no axis specified', async () => {
-    const a = tf.tensor4d([3, -1, 0, 100, -7, 2], [2, 1, 1, 3]);
-    expectArraysEqual(await tf.argMax(a).data(), [1, 0, 1]);
-  });
-
-  it('2D, axis=0', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    const r = tf.argMax(a, 0);
-
-    expect(r.shape).toEqual([3]);
-    expect(r.dtype).toBe('int32');
-    expectArraysEqual(await r.data(), [1, 0, 1]);
-  });
-
-  it('6D, axis=0', async () => {
-    const a = tf.tensor6d([3, -1, 0, 100, -7, 2], [2, 1, 1, 1, 1, 3]);
-    const r = tf.argMax(a, 0);
-
-    expect(r.shape).toEqual([1, 1, 1, 1, 3]);
-    expect(r.dtype).toBe('int32');
-    expectArraysEqual(await r.data(), [1, 0, 1]);
-  });
-
-  it('2D, axis=1', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const r = tf.argMax(a, 1);
-    expect(r.dtype).toBe('int32');
-    expectArraysEqual(await r.data(), [2, 0]);
-  });
-
-  it('2D, axis = -1', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const r = tf.argMax(a, -1);
-    expect(r.dtype).toBe('int32');
-    expectArraysEqual(await r.data(), [2, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.argMax({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'argMax' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.argMax([1, 0, 3, 2]);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), 2);
-  });
-
-  it('accepts tensor with bool values', async () => {
-    const t = tf.tensor1d([0, 1], 'bool');
-    const result = tf.argMax(t);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), 1);
-  });
-
-  it('has gradient', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const dy = tf.ones([3], 'float32');
-    const da = tf.grad((x: tf.Tensor2D) => tf.argMax(x))(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([2, 3]);
-    expectArraysClose(await da.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const dy = tf.ones([3], 'float32');
-    const da = tf.grad((x: tf.Tensor2D) => tf.argMax(x.clone()).clone())(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([2, 3]);
-    expectArraysClose(await da.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.argMax(['a']))
-        .toThrowError(/Argument 'x' passed to 'argMax' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/arg_min.ts b/tfjs-master/tfjs-core/src/ops/arg_min.ts
deleted file mode 100644
index b55e81185..000000000
--- a/tfjs-master/tfjs-core/src/ops/arg_min.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {ArgMin, ArgMinAttrs, ArgMinInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Returns the indices of the minimum values along an `axis`.
- *
- * The result has the same shape as `input` with the dimension along `axis`
- * removed.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3]);
- *
- * x.argMin().print();  // or tf.argMin(x)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
- *
- * const axis = 1;
- * x.argMin(axis).print();  // or tf.argMin(x, axis)
- * ```
- *
- * @param x The input tensor.
- * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function argMin_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {
-  const $x = convertToTensor(x, 'x', 'argMin');
-
-  const inputs: ArgMinInputs = {x: $x};
-  const attrs: ArgMinAttrs = {axis};
-
-  return ENGINE.runKernel(
-      ArgMin, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const argMin = /* @__PURE__ */ op({argMin_});
diff --git a/tfjs-master/tfjs-core/src/ops/arg_min_test.ts b/tfjs-master/tfjs-core/src/ops/arg_min_test.ts
deleted file mode 100644
index 21c1b5c3f..000000000
--- a/tfjs-master/tfjs-core/src/ops/arg_min_test.ts
+++ /dev/null
@@ -1,152 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-import * as reduce_util from './reduce_util';
-
-describeWithFlags('argmin', ALL_ENVS, () => {
-  it('Tensor1D', async () => {
-    const a = tf.tensor1d([1, 0, 3, 2]);
-    const result = tf.argMin(a);
-    expectArraysEqual(await result.data(), 1);
-  });
-
-  it('one value', async () => {
-    const a = tf.tensor1d([10]);
-    const result = tf.argMin(a);
-    expectArraysEqual(await result.data(), 0);
-  });
-
-  it('N > than parallelization threshold', async () => {
-    const n = reduce_util.PARALLELIZE_THRESHOLD * 2;
-    const values = new Float32Array(n);
-    for (let i = 0; i < n; i++) {
-      values[i] = n - i;
-    }
-    const a = tf.tensor1d(values);
-    const result = tf.argMin(a);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), n - 1);
-  });
-
-  it('4D, N > than parallelization threshold', async () => {
-    const n = reduce_util.PARALLELIZE_THRESHOLD * 2;
-    const values = new Float32Array(n);
-    for (let i = 0; i < n; i++) {
-      values[i] = n - i;
-    }
-    const a = tf.tensor4d(values, [1, 1, 1, n]);
-    const result = tf.argMin(a, -1);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), n - 1);
-  });
-
-  it('min index corresponds to start of a non-initial window', async () => {
-    const n = reduce_util.PARALLELIZE_THRESHOLD * 2;
-    const windowSize = reduce_util.computeOptimalWindowSize(n);
-    const values = new Float32Array(n);
-    const index = windowSize * 2;
-    values[index] = -1;
-    const a = tf.tensor1d(values);
-    const result = tf.argMin(a);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), index);
-  });
-
-  it('ignores NaNs', async () => {
-    const a = tf.tensor1d([5, 0, NaN, -1, 3]);
-    const res = tf.argMin(a);
-    expectArraysEqual(await res.data(), 3);
-  });
-
-  it('3D, ignores NaNs', async () => {
-    const a = tf.tensor3d([5, 0, NaN, -1, 3], [1, 1, 5]);
-    const res = tf.argMin(a, -1);
-    expectArraysEqual(await res.data(), 3);
-  });
-
-  it('2D, no axis specified', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    expectArraysEqual(await tf.argMin(a).data(), [0, 1, 0]);
-  });
-
-  it('2D, axis=0', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    const r = tf.argMin(a, 0);
-
-    expect(r.shape).toEqual([3]);
-    expect(r.dtype).toBe('int32');
-    expectArraysEqual(await r.data(), [0, 1, 0]);
-  });
-
-  it('2D, axis=1', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, -8], [2, 3]);
-    const r = tf.argMin(a, 1);
-    expectArraysEqual(await r.data(), [1, 2]);
-  });
-
-  it('2D, axis = -1', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, -8], [2, 3]);
-    const r = tf.argMin(a, -1);
-    expectArraysEqual(await r.data(), [1, 2]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.argMin({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'argMin' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.argMin([1, 0, 3, 2]);
-    expectArraysEqual(await result.data(), 1);
-  });
-
-  it('accepts tensor with bool values', async () => {
-    const t = tf.tensor1d([0, 1], 'bool');
-    const result = tf.argMin(t);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), 0);
-  });
-
-  it('has gradient', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const dy = tf.ones([3], 'float32');
-    const da = tf.grad((x: tf.Tensor2D) => tf.argMin(x))(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([2, 3]);
-    expectArraysClose(await da.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const dy = tf.ones([3], 'float32');
-    const da = tf.grad((x: tf.Tensor2D) => tf.argMin(x.clone()).clone())(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([2, 3]);
-    expectArraysClose(await da.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.argMin(['a']))
-        .toThrowError(/Argument 'x' passed to 'argMin' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/arithmetic_test.ts b/tfjs-master/tfjs-core/src/ops/arithmetic_test.ts
deleted file mode 100644
index e681c0270..000000000
--- a/tfjs-master/tfjs-core/src/ops/arithmetic_test.ts
+++ /dev/null
@@ -1,964 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('div', ALL_ENVS, () => {
-  it('same shape', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const c = tf.tensor2d([1, 2, 3, 4, 2, 5], [2, 3]);
-
-    const r = tf.div(a, c);
-
-    expectArraysClose(await r.data(), [1, 1, 1, 1, 2.5, 6 / 5]);
-  });
-
-  it('vec4 same shape', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
-    const expected = [0.2, 0.666, -0.75, 0.571];
-    const result = tf.div(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('TensorLike', async () => {
-    const a = [0, 1, -2, -4, 4, -4];
-    const b = [0.15, 0.2, 0.25, 0.5, 0.7, 1.2];
-    const result = tf.div(a, b);
-
-    expect(result.shape).toEqual([6]);
-    expectArraysClose(
-        await result.data(),
-        [0, 5.0, -8.0, -8.0, 5.714285850524902, -3.3333332538604736]);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([0, 1, -2, -4, 4, -4]);
-    const b = [0.15, 0.2, 0.25, 0.5, 0.7, 1.2];
-    const result = a.div(b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(
-        await result.data(),
-        [0, 5.0, -8.0, -8.0, 5.714285850524902, -3.3333332538604736]);
-  });
-
-  it('division by zero results in infinity', async () => {
-    const r = tf.div(1, 0);
-    const rData = await r.data();
-
-    expect(Array.from(rData)).toEqual([Infinity]);
-  });
-
-  it('integer division implements floor divide', async () => {
-    const a = tf.tensor1d([-6, -6, -5, -4, -3, -3, 3, 3, 2], 'int32');
-    const c = tf.tensor1d([-2, 2, 3, 2, -3, 3, 2, 3, 2], 'int32');
-
-    const r = tf.div(a, c);
-
-    expect(r.dtype).toEqual('int32');
-    expectArraysClose(await r.data(), [3, -3, -2, -2, 1, -1, 1, 1, 1]);
-  });
-
-  it('integer division broadcasts', async () => {
-    const a = tf.tensor1d([-5, -4, 3, 2], 'int32');
-    const c = tf.scalar(2, 'int32');
-
-    const r = tf.div(a, c);
-
-    expect(r.dtype).toEqual('int32');
-    expectArraysClose(await r.data(), [-3, -2, 1, 1]);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor2d([1, 2], [2, 1]);
-    const c = tf.tensor2d([3, NaN], [2, 1]);
-
-    const r = tf.div(a, c);
-
-    expectArraysClose(await r.data(), [1 / 3, NaN]);
-  });
-
-  it('broadcasting same rank Tensors different shape', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor2d([2, 3], [2, 1]);
-
-    const result = tf.div(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [1 / 2, 1, -1, -4 / 3];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('broadcast scalar', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const b = [2];
-
-    const result = tf.div(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [0.5, 1, 1.5, 2];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('broadcast 2D + 1D', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor1d([1, 2]);
-
-    const result = tf.div(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [1, 1, -3, -2];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    let res = tf.div(tf.scalar(6, 'int32'), tf.scalar(3, 'float32'));
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [2]);
-
-    res = tf.div(tf.scalar(6, 'int32'), tf.scalar(true, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [6]);
-  });
-
-  it('throws when passed tensors of different shapes', () => {
-    const a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
-
-    expect(() => tf.div(a, b)).toThrowError();
-    expect(() => tf.div(b, a)).toThrowError();
-  });
-
-  it('scalar divided by array', async () => {
-    const c = tf.scalar(2);
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-
-    const r = tf.div(c, a);
-
-    expectArraysClose(
-        await r.data(), [2 / 1, 2 / 2, 2 / 3, 2 / 4, 2 / 5, 2 / 6]);
-  });
-
-  it('scalar divided by array propagates NaNs', async () => {
-    const c = tf.scalar(NaN);
-    const a = tf.tensor2d([1, 2, 3], [1, 3]);
-
-    const r = tf.div(c, a);
-
-    expectArraysEqual(await r.data(), [NaN, NaN, NaN]);
-  });
-
-  it('array divided by scalar', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const c = tf.scalar(2);
-
-    const r = tf.div(a, c);
-
-    expectArraysClose(
-        await r.data(), [1 / 2, 2 / 2, 3 / 2, 4 / 2, 5 / 2, 6 / 2]);
-  });
-
-  it('array divided by scalar propagates NaNs', async () => {
-    const a = tf.tensor2d([1, 2, NaN], [1, 3]);
-    const c = tf.scalar(2);
-
-    const r = tf.div(a, c);
-    expectArraysClose(await r.data(), [1 / 2, 2 / 2, NaN]);
-  });
-
-  it('gradient: Scalar', async () => {
-    const a = tf.scalar(5);
-    const b = tf.scalar(2);
-    const dy = tf.scalar(4);
-
-    const before = tf.memory().numTensors;
-    const grads = tf.grads((a, b) => tf.div(a, b));
-    const [da, db] = grads([a, b], dy);
-    const now = tf.memory().numTensors;
-    expect(now).toBe(before + 2);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [4 / 2]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-4 * 5 / (2 * 2)]);
-  });
-
-  it('gradient with clones', async () => {
-    const grads = tf.grads((a, b) => tf.div(a.clone(), b.clone()).clone());
-    const [da, db] = grads([5, 2]);
-    expect(da.shape).toEqual([]);
-    expect(db.shape).toEqual([]);
-    expectArraysClose(await da.data(), [1 / 2]);
-    expectArraysClose(await db.data(), [-5 / 4]);
-  });
-
-  it('gradient: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([1, 10, 20]);
-
-    const grads = tf.grads((a, b) => tf.div(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1 / 3, 10 / 4, 20 / 5]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(), [-1 * 1 / 9, -10 * 2 / 16, -20 * 3 / 25]);
-  });
-
-  it('gradient: Tensor1D with int32', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'int32');
-    const b = tf.tensor1d([3, 4, 5], 'int32');
-    const dy = tf.tensor1d([1, 10, 20]);
-
-    const grads = tf.grads((a, b) => tf.div(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1 / 3, 10 / 4, 20 / 5]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(), [-1 * 1 / 9, -10 * 2 / 16, -20 * 3 / 25]);
-  });
-
-  it('gradient: 1d<int32> with 1d<bool> ', async () => {
-    const a = tf.tensor1d([true, false, true], 'bool');
-    const b = tf.tensor1d([1, 2, 3], 'int32');
-    const dy = tf.tensor1d([1, 19, 20]);
-
-    const grads = tf.grads((a, b) => tf.div(a.toInt(), b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1, 19 / 2, 20 / 3]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-1 / 1, 0, -20 / 9]);
-  });
-
-  it('gradient: Tensor2D', async () => {
-    const a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
-    const b = tf.tensor2d([1, 3, 4, 5], [2, 2]);
-    const dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.div(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1 / 1, 10 / 3, 15 / 4, 20 / 5]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(), [-1 * 3 / 1, -10 * 1 / 9, -15 * 2 / 16, -20 * 3 / 25]);
-  });
-
-  it('gradient: scalar / Tensor1D', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([6, 7, 8]);
-
-    const grads = tf.grads((a, b) => tf.div(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [6 / 3 + 7 / 4 + 8 / 5]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-6 * 2 / 9, -7 * 2 / 16, -8 * 2 / 25]);
-  });
-
-  it('gradient: Tensor2D / scalar', async () => {
-    const a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
-    const b = tf.scalar(2);
-    const dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.div(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [6 / 2, 7 / 2, 8 / 2, 9 / 2]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(), [-6 * 2 / 4 + -7 * 3 / 4 + -8 * 4 / 4 + -9 * 5 / 4]);
-  });
-
-  it('gradient: Tensor2D / Tensor2D w/ broadcast', async () => {
-    const a = tf.tensor2d([3, 4], [2, 1]);
-    const b = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
-    const dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.div(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [6 / 2 + 7 / 3, 8 / 4 + 9 / 5]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(), [-6 * 3 / 4, -7 * 3 / 9, -8 * 4 / 16, -9 * 4 / 25]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.div({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'div' must be a Tensor/);
-  });
-
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.div(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'div' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.div([[1, 2, 3], [4, 5, 6]], 2);
-    expect(r.shape).toEqual([2, 3]);
-    expectArraysClose(
-        await r.data(), [1 / 2, 2 / 2, 3 / 2, 4 / 2, 5 / 2, 6 / 2]);
-  });
-});
-
-describeWithFlags('mul', ALL_ENVS, () => {
-  it('same-shaped tensors', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
-    const expected = [5, 6, -12, 28];
-    const result = tf.mul(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('TensorLike', async () => {
-    const a = [[1, 2], [-3, -4]];
-    const b = [[5, 3], [4, -7]];
-    const expected = [5, 6, -12, 28];
-    const result = tf.mul(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = [[5, 3], [4, -7]];
-    const expected = [5, 6, -12, 28];
-    const result = a.mul(b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('broadcasting tensors', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.scalar(2);
-    const expected = [2, 4, -6, -8];
-    const result = tf.mul(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('broadcasting same rank Tensors different shape', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor2d([2, 3], [2, 1]);
-
-    const result = tf.mul(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [2, 4, -9, -12];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('broadcast 2D + 1D', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor1d([1, 2]);
-
-    const result = tf.mul(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [1, 4, -3, -8];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('broadcast 5D + 2D', async () => {
-    const a = tf.range(1, 33).reshape([2, 2, 2, 2, 2]);
-    const b = tf.tensor([2, 3], [2, 1]);
-    const result = tf.mul(a, b);
-    expect(result.shape).toEqual([2, 2, 2, 2, 2]);
-    const expected = [
-      2,  4,  9,  12, 10, 12, 21, 24, 18, 20, 33, 36, 26, 28, 45, 48,
-      34, 36, 57, 60, 42, 44, 69, 72, 50, 52, 81, 84, 58, 60, 93, 96
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('broadcast 6D + 2D', async () => {
-    const a = tf.range(1, 65).reshape([2, 2, 2, 2, 2, 2]);
-    const b = tf.tensor([2, 3], [2, 1]);
-    const result = tf.mul(a, b);
-    expect(result.shape).toEqual([2, 2, 2, 2, 2, 2]);
-    const expected = [
-      2,   4,   9,   12,  10,  12,  21,  24,  18,  20,  33,  36,  26,
-      28,  45,  48,  34,  36,  57,  60,  42,  44,  69,  72,  50,  52,
-      81,  84,  58,  60,  93,  96,  66,  68,  105, 108, 74,  76,  117,
-      120, 82,  84,  129, 132, 90,  92,  141, 144, 98,  100, 153, 156,
-      106, 108, 165, 168, 114, 116, 177, 180, 122, 124, 189, 192
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('gradient: Scalar', async () => {
-    const a = tf.scalar(5);
-    const b = tf.scalar(2);
-    const dy = tf.scalar(4);
-
-    const grads = tf.grads((a, b) => tf.mul(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), await b.mul(dy).data());
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), await a.mul(dy).data());
-  });
-
-  it('gradient with clones', async () => {
-    const grads = tf.grads((a, b) => tf.mul(a.clone(), b.clone()).clone());
-    const [da, db] = grads([4, 2]);
-
-    expect(da.shape).toEqual([]);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), 2);
-
-    expect(db.shape).toEqual([]);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), 4);
-  });
-
-  it('gradient: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([1, 10, 20]);
-
-    const grads = tf.grads((a, b) => tf.mul(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [3 * 1, 4 * 10, 5 * 20]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [1 * 1, 2 * 10, 3 * 20]);
-  });
-
-  it('gradient: Tensor1D with dtype int32', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'int32');
-    const b = tf.tensor1d([3, 4, 5], 'int32');
-    const dy = tf.tensor1d([1, 10, 20]);
-
-    const grads = tf.grads((a, b) => tf.mul(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [3 * 1, 4 * 10, 5 * 20]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [1 * 1, 2 * 10, 3 * 20]);
-  });
-
-  it('gradient: Tensor2D', async () => {
-    const a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
-    const b = tf.tensor2d([1, 3, 4, 5], [2, 2]);
-    const dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.mul(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1 * 1, 3 * 10, 4 * 15, 5 * 20]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [3 * 1, 1 * 10, 2 * 15, 3 * 20]);
-  });
-
-  it('gradient: scalar * Tensor1D', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([6, 7, 8]);
-
-    const grads = tf.grads((a, b) => tf.mul(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [3 * 6 + 4 * 7 + 5 * 8]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [2 * 6, 2 * 7, 2 * 8]);
-  });
-
-  it('gradient: Tensor2D * scalar', async () => {
-    const a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
-    const b = tf.scalar(2);
-    const dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.mul(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [2 * 6, 2 * 7, 2 * 8, 2 * 9]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [2 * 6 + 3 * 7 + 4 * 8 + 5 * 9]);
-  });
-
-  it('gradient: Tensor2D * Tensor2D w/ broadcast', async () => {
-    const a = tf.tensor2d([3, 4], [2, 1]);
-    const b = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
-    const dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.mul(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [2 * 6 + 3 * 7, 4 * 8 + 5 * 9]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [6 * 3, 7 * 3, 8 * 4, 9 * 4]);
-  });
-
-  it('complex number multiplication', async () => {
-    const real1 = tf.tensor1d([2]);
-    const imag1 = tf.tensor1d([3]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([4]);
-    const imag2 = tf.tensor1d([5]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const result = complex1.mul(complex2);
-
-    expect(result.dtype).toBe('complex64');
-    expect(result.shape).toEqual([1]);
-    expectArraysClose(await result.data(), [2 * 4 - 3 * 5, 2 * 5 + 3 * 4]);
-  });
-
-  it('complex number broadcasting multiplication', async () => {
-    const real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([4]);
-    const imag2 = tf.tensor1d([5]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const result = tf.mul(complex1, complex2);
-
-    expect(result.dtype).toEqual('complex64');
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), [
-      1 * 4 - 10 * 5, 1 * 5 + 10 * 4, 2 * 4 - 20 * 5, 2 * 5 + 20 * 4,
-      -3 * 4 + 30 * 5, -3 * 5 + -30 * 4, -4 * 4 + 40 * 5, -4 * 5 + -40 * 4
-    ]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.mul({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'mul' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.mul(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'mul' must be a Tensor/);
-  });
-  it('upcasts when dtypes dont match', async () => {
-    let res = tf.mul(tf.scalar(2, 'int32'), tf.scalar(3, 'float32'));
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [6]);
-
-    res = tf.mul(tf.scalar(2, 'int32'), tf.scalar(true, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [2]);
-
-    res = tf.mul(tf.scalar(2, 'int32'), tf.scalar(false, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [0]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.mul([[1, 2], [-3, -4]], 2);
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), [2, 4, -6, -8]);
-  });
-});
-
-describeWithFlags('pow', ALL_ENVS, () => {
-  it('same-shaped tensors', async () => {
-    const a = tf.tensor2d([1, -2, -3, 0, 7, 1], [2, 3]);
-    const b = tf.tensor2d([5, 3, 4, 5, 2, -3], [2, 3], 'int32');
-    const expected = [1, -8, 81, 0, 49, 1];
-    const result = tf.pow(a, b);
-
-    expect(result.shape).toEqual([2, 3]);
-    expectArraysClose(await result.data(), expected, 0.01);
-  });
-
-  it('TensorLike', async () => {
-    const a = [1, 2, 3];
-    const exp = 2;
-
-    const result = tf.pow(a, exp);
-
-    expect(result.shape).toEqual([3]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [1, 4, 9]);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const exp = 2;
-
-    const result = a.pow(exp);
-
-    expect(result.shape).toEqual([3]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [1, 4, 9]);
-  });
-
-  it('int32^int32 returns int32', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'int32');
-    const exp = tf.scalar(2, 'int32');
-
-    const result = tf.pow(a, exp);
-
-    expect(result.shape).toEqual([3]);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), [1, 4, 9]);
-  });
-
-  it('different-shaped tensors', async () => {
-    const a = tf.tensor2d([1, -2, -3, 0, 7, 1], [2, 3]);
-    const b = tf.scalar(2, 'int32');
-    const expected = [1, 4, 9, 0, 49, 1];
-    const result = tf.pow(a, b);
-
-    expect(result.shape).toEqual([2, 3]);
-    expectArraysClose(await result.data(), expected, 0.05);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor2d([NaN, 3, NaN, 0], [2, 2]);
-    const b = tf.tensor2d([1, 3, 2, 3], [2, 2], 'int32');
-
-    const result = tf.pow(a, b);
-    expectArraysClose(await result.data(), [NaN, 27, NaN, 0], 0.05);
-  });
-
-  it('exponent of 0 returns 1', async () => {
-    const a = tf.tensor1d([-2, -1, 0, 1, 2]);
-    const b = tf.scalar(0);
-
-    const result = tf.pow(a, b);
-    expectArraysClose(await result.data(), [1, 1, 1, 1, 1]);
-  });
-
-  it('handles non int32 exponent param', async () => {
-    const a = tf.tensor1d([2, 4]);
-    const b = tf.tensor1d([.5, 1.2]);
-
-    const result = tf.pow(a, b);
-    const expected = [Math.pow(2, 0.5), Math.pow(4, 1.2)];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('broadcasting same rank Tensors different shape', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor2d([2, 1], [2, 1], 'int32');
-
-    const result = tf.pow(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [1, 4, -3, -4];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('broadcast 2D + 1D', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor1d([1, 2], 'int32');
-
-    const result = tf.pow(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [1, 4, -3, 16];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('gradients: Scalar ^ Scalar', async () => {
-    const a = tf.scalar(5);
-    const b = tf.scalar(2, 'int32');
-    const dy = tf.scalar(3);
-
-    const grads = tf.grads((a, b) => tf.pow(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [2 * 5 * 3]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [3 * Math.pow(5, 2) * Math.log(5)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5);
-    const b = tf.scalar(2, 'int32');
-
-    const grads = tf.grads((a, b) => tf.pow(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b]);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [2 * 5]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [Math.pow(5, 2) * Math.log(5)]);
-  });
-
-  it('gradients: x ^ 2 where x = 0', async () => {
-    const f = (x: tf.Scalar) => x.pow(tf.scalar(2)).asScalar();
-    const g = tf.grad(f)(tf.scalar(0));
-
-    expectArraysClose(await g.data(), [0]);
-  });
-
-  it('gradients: Scalar ^ Scalar fractional exponent', async () => {
-    const a = tf.scalar(4.0);
-    const b = tf.scalar(1.5);
-    const dy = tf.scalar(3.0);
-
-    const grads = tf.grads((a, b) => tf.pow(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1.5 * Math.pow(4, 0.5) * 3]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(), [3.0 * Math.pow(4, 1.5) * Math.log(4.0)]);
-  });
-
-  it('gradients: Tensor ^ Tensor', async () => {
-    const a = tf.tensor1d([-1, .5, 2]);
-    const b = tf.tensor1d([3, 2, -1], 'int32');
-    const dy = tf.tensor1d([1, 5, 10]);
-
-    const grads = tf.grads((a, b) => tf.pow(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(
-        await da.data(),
-        [
-          3 * Math.pow(-1, 2) * 1, 2 * Math.pow(.5, 1) * 5,
-          -1 * Math.pow(2, -2) * 10
-        ],
-        1e-1);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [
-      0, 5 * Math.pow(.5, 2) * Math.log(.5), 10 * Math.pow(2, -1) * Math.log(2)
-    ]);
-  });
-
-  it('gradient wrt exponent with negative base', async () => {
-    const a = tf.tensor1d([-1, -.5, -2.7]);
-    const b = tf.tensor1d([3, 2, -1], 'int32');
-    const dy = tf.tensor1d([1, 1, 1]);
-
-    const grads = tf.grads((a, b) => tf.pow(a, b));
-    const [, db] = grads([a, b], dy);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [0, 0, 0]);
-  });
-
-  it('gradient: scalar / Tensor1D', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([6, 7, 8]);
-
-    const grads = tf.grads((a, b) => tf.pow(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [
-      6 * 3 * Math.pow(2, 2) + 7 * 4 * Math.pow(2, 3) + 8 * 5 * Math.pow(2, 4)
-    ]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [
-      6 * Math.pow(2, 3) * Math.log(2), 7 * Math.pow(2, 4) * Math.log(2),
-      8 * Math.pow(2, 5) * Math.log(2)
-    ]);
-  });
-
-  it('gradient: Tensor2D / scalar', async () => {
-    const a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
-    const b = tf.scalar(2);
-    const dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.pow(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [
-      6 * 2 * Math.pow(2, 1), 7 * 2 * Math.pow(3, 1), 8 * 2 * Math.pow(4, 1),
-      9 * 2 * Math.pow(5, 1)
-    ]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(),
-        [6 * Math.pow(2, 2) * Math.log(2) + 7 * Math.pow(3, 2) * Math.log(3) +
-         8 * Math.pow(4, 2) * Math.log(4) + 9 * Math.pow(5, 2) * Math.log(5)]);
-  });
-
-  it('gradient: Tensor2D / Tensor2D w/ broadcast', async () => {
-    const a = tf.tensor2d([3, 4], [2, 1]);
-    const b = tf.tensor2d([[2, 3], [.4, .5]], [2, 2]);
-    const dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.pow(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [
-      6 * 2 * Math.pow(3, 1) + 7 * 3 * Math.pow(3, 2),
-      8 * .4 * Math.pow(4, .4 - 1) + 9 * .5 * Math.pow(4, .5 - 1)
-    ]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [
-      6 * Math.pow(3, 2) * Math.log(3), 7 * Math.pow(3, 3) * Math.log(3),
-      8 * Math.pow(4, .4) * Math.log(4), 9 * Math.pow(4, .5) * Math.log(4)
-    ]);
-  });
-
-  it('throws when passed base as a non-tensor', () => {
-    expect(() => tf.pow({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'base' passed to 'pow' must be a Tensor/);
-  });
-  it('throws when passed exp as a non-tensor', () => {
-    expect(() => tf.pow(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'exp' passed to 'pow' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.pow([1, 2, 3], 2);
-
-    expect(result.shape).toEqual([3]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [1, 4, 9]);
-  });
-
-  it('negative base and whole exponent not NaN', async () => {
-    const a = tf.tensor1d([-2, -3, -4], 'float32');
-    const b = tf.tensor1d([2, -3, 4], 'float32');
-
-    const expected = [Math.pow(-2, 2), Math.pow(-3, -3), Math.pow(-4, 4)];
-    const result = tf.pow(a, b);
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('negative base and whole exponent not NaN - vec4', async () => {
-    const a = tf.tensor1d([-2, -3, -4, -5], 'float32');
-    const b = tf.tensor1d([2, -3, 4, -5], 'float32');
-
-    const expected =
-        [Math.pow(-2, 2), Math.pow(-3, -3), Math.pow(-4, 4), Math.pow(-5, -5)];
-    const result = tf.pow(a, b);
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('negative base and fract exponent NaN', async () => {
-    const a = tf.tensor1d([-2, -3, -4], 'float32');
-    const b = tf.tensor1d([2.1, -3.01, 4.1], 'float32');
-
-    const expected = [NaN, NaN, NaN];
-    const result = tf.pow(a, b);
-
-    expectArraysClose(await result.data(), expected);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/array_ops_util.ts b/tfjs-master/tfjs-core/src/ops/array_ops_util.ts
deleted file mode 100644
index e567388a3..000000000
--- a/tfjs-master/tfjs-core/src/ops/array_ops_util.ts
+++ /dev/null
@@ -1,153 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Gets the new shape of the input Tensor after it's been reshaped
- * to:
- * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),
- * inputShape[1], ..., inputShape[N-1]]
- *
- * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
- */
-export function getReshaped(
-    inputShape: number[], blockShape: number[], prod: number,
-    batchToSpace = true): number[] {
-  let reshaped: number[] = [];
-  if (batchToSpace) {
-    reshaped = reshaped.concat(blockShape.slice(0));
-    reshaped.push(inputShape[0] / prod);
-    reshaped = reshaped.concat(inputShape.slice(1));
-  } else {
-    reshaped = reshaped.concat(inputShape[0]);
-    const spatialLength = blockShape.length;
-    for (let i = 0; i < spatialLength; ++i) {
-      reshaped =
-          reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
-    }
-    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
-  }
-  return reshaped;
-}
-
-/**
- * Gets the permutation that will transpose the dimensions of the
- * reshaped tensor to shape:
- *
- * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,
- * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
- *
- * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
- */
-export function getPermuted(
-    reshapedRank: number, blockShapeRank: number,
-    batchToSpace = true): number[] {
-  const permuted = [];
-  if (batchToSpace) {
-    permuted.push(blockShapeRank);
-    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
-      if (i <= 2 * blockShapeRank) {
-        permuted.push(i);
-        permuted.push(i - (blockShapeRank + 1));
-      } else {
-        permuted.push(i);
-      }
-    }
-  } else {
-    const permutedBeforeBatch = [];
-    const permutedAfterBatch = [];
-    for (let i = 1; i < reshapedRank; ++i) {
-      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
-        permutedAfterBatch.push(i);
-      } else {
-        permutedBeforeBatch.push(i);
-      }
-    }
-    permuted.push(...permutedBeforeBatch);
-    permuted.push(0);
-    permuted.push(...permutedAfterBatch);
-  }
-  return permuted;
-}
-
-/**
- * Gets the shape of the reshaped and permuted input Tensor before any cropping
- * is applied.  The new shape will be:
- *
- * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,
- * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
- *
- * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
- */
-export function getReshapedPermuted(
-    inputShape: number[], blockShape: number[], prod: number,
-    batchToSpace = true): number[] {
-  const reshapedPermuted = [];
-
-  if (batchToSpace) {
-    reshapedPermuted.push(inputShape[0] / prod);
-  } else {
-    reshapedPermuted.push(inputShape[0] * prod);
-  }
-
-  for (let i = 1; i < inputShape.length; ++i) {
-    if (i <= blockShape.length) {
-      if (batchToSpace) {
-        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
-      } else {
-        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
-      }
-    } else {
-      reshapedPermuted.push(inputShape[i]);
-    }
-  }
-
-  return reshapedPermuted;
-}
-
-/**
- * Converts the crops argument into the beginning coordinates of a slice
- * operation.
- */
-export function getSliceBeginCoords(
-    crops: number[][], blockShape: number): number[] {
-  const sliceBeginCoords = [0];
-  for (let i = 0; i < blockShape; ++i) {
-    sliceBeginCoords.push(crops[i][0]);
-  }
-  return sliceBeginCoords;
-}
-
-/**
- * Converts the crops argument into the size of a slice operation.  When
- * combined with getSliceBeginCoords this function allows the reshaped and
- * permuted Tensor to be cropped to its final output shape of:
- *
- * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,
- * inputShape[M] * blockShape[M-1] -crops[M-1,0] -
- * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]
- *
- * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
- */
-export function getSliceSize(
-    uncroppedShape: number[], crops: number[][], blockShape: number): number[] {
-  const sliceSize = uncroppedShape.slice(0, 1);
-  for (let i = 0; i < blockShape; ++i) {
-    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
-  }
-
-  return sliceSize;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/asin.ts b/tfjs-master/tfjs-core/src/ops/asin.ts
deleted file mode 100644
index 64d8459cf..000000000
--- a/tfjs-master/tfjs-core/src/ops/asin.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Asin, AsinInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, 1, -1, .7]);
- *
- * x.asin().print();  // or tf.asin(x)
- * ```
- * @param x The input tensor.
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function asin_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'asin');
-  const inputs: AsinInputs = {x: $x};
-
-  return ENGINE.runKernel(Asin, inputs as unknown as NamedTensorMap);
-}
-export const asin = /* @__PURE__ */ op({asin_});
diff --git a/tfjs-master/tfjs-core/src/ops/asin_test.ts b/tfjs-master/tfjs-core/src/ops/asin_test.ts
deleted file mode 100644
index ff75ed18f..000000000
--- a/tfjs-master/tfjs-core/src/ops/asin_test.ts
+++ /dev/null
@@ -1,119 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('asin', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [.1, -3, 2, 7, -4];
-    const a = tf.tensor1d(values);
-    const result = tf.asin(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.asin(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.asin(a);
-    expectArraysClose(await res.data(), [Math.asin(4), NaN, Math.asin(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.asin(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 / Math.sqrt(1 - (0.5 * 0.5))]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.asin(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 / Math.sqrt(1 - (0.5 * 0.5))]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-0.1, 0.2, 0.3, -0.5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.asin(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / Math.sqrt(1 - (aValues[i] * aValues[i]));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-0.3, 0.1, 0.2, 0.3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.asin(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / Math.sqrt(1 - (aValues[i] * aValues[i]));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.asin({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'asin' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [.1, -3, 2, 7, -4];
-    const result = tf.asin(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.asin(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.asin('q'))
-        .toThrowError(/Argument 'x' passed to 'asin' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/asinh.ts b/tfjs-master/tfjs-core/src/ops/asinh.ts
deleted file mode 100644
index 3b7314c94..000000000
--- a/tfjs-master/tfjs-core/src/ops/asinh.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Asinh, AsinhInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:
- * `asinh(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, 1, -1, .7]);
- *
- * x.asinh().print();  // or tf.asinh(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function asinh_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'asinh');
-
-  const inputs: AsinhInputs = {x: $x};
-
-  return ENGINE.runKernel(Asinh, inputs as unknown as NamedTensorMap);
-}
-export const asinh = /* @__PURE__ */ op({asinh_});
diff --git a/tfjs-master/tfjs-core/src/ops/asinh_test.ts b/tfjs-master/tfjs-core/src/ops/asinh_test.ts
deleted file mode 100644
index 83251b180..000000000
--- a/tfjs-master/tfjs-core/src/ops/asinh_test.ts
+++ /dev/null
@@ -1,139 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('asinh', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const a = tf.tensor1d(values);
-    const result = tf.asinh(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.asinh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('scalar', async () => {
-    const a = tf.scalar(1);
-    const result = tf.asinh(a);
-
-    const expected = [Math.asinh(1)];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('tensor2D', async () => {
-    const values = [1, -3, 2, 7];
-    const a = tf.tensor2d(values, [2, 2]);
-    const result = tf.asinh(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.asinh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.asinh(a);
-    expectArraysClose(await res.data(), [Math.asinh(4), NaN, Math.asinh(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.asinh(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 / Math.sqrt(1.0 + 0.5 * 0.5)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.asinh(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 / Math.sqrt(1.0 + 0.5 * 0.5)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-1, 2, 3, -5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.asinh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / Math.sqrt(1 + aValues[i] * aValues[i]);
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-3, 1, 2, 3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.asinh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / Math.sqrt(1 + aValues[i] * aValues[i]);
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.asinh({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'asinh' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const result = tf.asinh(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.asinh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.asinh('q'))
-        .toThrowError(/Argument 'x' passed to 'asinh' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/atan.ts b/tfjs-master/tfjs-core/src/ops/atan.ts
deleted file mode 100644
index b56e20283..000000000
--- a/tfjs-master/tfjs-core/src/ops/atan.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Atan, AtanInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, 1, -1, .7]);
- *
- * x.atan().print();  // or tf.atan(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function atan_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'atan');
-
-  const inputs: AtanInputs = {x: $x};
-
-  return ENGINE.runKernel(Atan, inputs as unknown as NamedTensorMap);
-}
-export const atan = /* @__PURE__ */ op({atan_});
diff --git a/tfjs-master/tfjs-core/src/ops/atan2.ts b/tfjs-master/tfjs-core/src/ops/atan2.ts
deleted file mode 100644
index 1150a88f7..000000000
--- a/tfjs-master/tfjs-core/src/ops/atan2.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Atan2, Atan2Inputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.
- * Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);
- * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);
- *
- * tf.atan2(a, b).print()
- * ```
- *
- * @param a The first tensor.
- * @param b The second tensor. Must have the same dtype as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function atan2_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'atan2');
-  let $b = convertToTensor(b, 'b', 'atan2');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  const inputs: Atan2Inputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Atan2, inputs as unknown as NamedTensorMap);
-}
-
-export const atan2 = /* @__PURE__ */ op({atan2_});
diff --git a/tfjs-master/tfjs-core/src/ops/atan_test.ts b/tfjs-master/tfjs-core/src/ops/atan_test.ts
deleted file mode 100644
index 79b7b8579..000000000
--- a/tfjs-master/tfjs-core/src/ops/atan_test.ts
+++ /dev/null
@@ -1,130 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('atan', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const a = tf.tensor1d(values);
-    const result = tf.atan(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.atan(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('6D atan', async () => {
-    const a = tf.range(1, 65).reshape([2, 2, 2, 2, 2, 2]);
-    const result = tf.atan(a);
-
-    const expected = [];
-    for (let i = 1; i < 65; ++i) {
-      expected[i - 1] = Math.atan(i);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.atan(a);
-    expectArraysClose(await res.data(), [Math.atan(4), NaN, Math.atan(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.atan(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 / (1 + (0.5 * 0.5))]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.atan(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 / (1 + (0.5 * 0.5))]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-0.1, 0.2, 0.3, -0.5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.atan(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / (1 + (aValues[i] * aValues[i]));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-0.3, 0.1, 0.2, 0.3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.atan(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / (1 + (aValues[i] * aValues[i]));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.atan({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'atan' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const result = tf.atan(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.atan(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.atan('q'))
-        .toThrowError(/Argument 'x' passed to 'atan' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/atanh.ts b/tfjs-master/tfjs-core/src/ops/atanh.ts
deleted file mode 100644
index 417cc5282..000000000
--- a/tfjs-master/tfjs-core/src/ops/atanh.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Atanh, AtanhInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:
- * `atanh(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, .1, -.1, .7]);
- *
- * x.atanh().print();  // or tf.atanh(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function atanh_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'atanh');
-
-  const inputs: AtanhInputs = {x: $x};
-
-  return ENGINE.runKernel(Atanh, inputs as unknown as NamedTensorMap);
-}
-export const atanh = /* @__PURE__ */ op({atanh_});
diff --git a/tfjs-master/tfjs-core/src/ops/atanh_test.ts b/tfjs-master/tfjs-core/src/ops/atanh_test.ts
deleted file mode 100644
index 26e3b4e89..000000000
--- a/tfjs-master/tfjs-core/src/ops/atanh_test.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('atanh', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [-0.25, 0.25, 0.5, .75, -0.4];
-    const a = tf.tensor1d(values);
-    const result = tf.atanh(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.atanh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('scalar', async () => {
-    const value = 0.2;
-    const a = tf.scalar(value);
-    const result = tf.atanh(a);
-
-    const expected = [Math.atanh(value)];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('tensor2d', async () => {
-    const values = [0.2, 0.3, 0.4, 0.5];
-    const a = tf.tensor2d(values, [2, 2]);
-    const result = tf.atanh(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.atanh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([0.5, NaN, 0]);
-    const res = tf.atanh(a);
-    expectArraysClose(await res.data(), [Math.atanh(0.5), NaN, Math.atanh(0)]);
-  });
-
-  it('NaN outside function domain', async () => {
-    const a = tf.tensor1d([-2, 0, 2]);
-    const res = tf.atanh(a);
-    expectArraysClose(await res.data(), [NaN, Math.atanh(0), NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.atanh(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 / (1 - 0.5 * 0.5)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.atanh(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 / (1 - 0.5 * 0.5)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-0.1, 0.2, 0.3, -0.5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.atanh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / (1 - Math.pow(aValues[i], 2));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-0.3, 0.1, 0.2, 0.3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.atanh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / (1 - Math.pow(aValues[i], 2));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.atanh({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'atanh' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.atanh(0.2);
-    expectArraysClose(await result.data(), [Math.atanh(0.2)]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.atanh('q'))
-        .toThrowError(/Argument 'x' passed to 'atanh' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/avg_pool.ts b/tfjs-master/tfjs-core/src/ops/avg_pool.ts
deleted file mode 100644
index 8e2ce5288..000000000
--- a/tfjs-master/tfjs-core/src/ops/avg_pool.ts
+++ /dev/null
@@ -1,95 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {AvgPool, AvgPoolAttrs, AvgPoolInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {cast} from './cast';
-import * as conv_util from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the 2D average pooling of an image.
- *
- * @param x The input tensor, of rank 4 or rank 3 of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
- *     `filterSize` is a single number, then `filterHeight == filterWidth`.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
- *     `strides` is a single number, then `strideHeight == strideWidth`.
- * @param pad The type of padding algorithm:
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *         https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function avgPool_<T extends Tensor3D|Tensor4D>(
-    x: T|TensorLike, filterSize: [number, number]|number,
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  const $x = convertToTensor(x, 'x', 'avgPool', 'float32');
-  const dilations = 1;
-
-  util.assert(
-      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in avgPool: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-
-  util.assert(
-      x4D.rank === 4,
-      () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
-  conv_util.checkPadOnDimRoundingMode('avgPool', pad, dimRoundingMode);
-  const inputs: AvgPoolInputs = {x: x4D};
-  const attrs: AvgPoolAttrs = {filterSize, strides, pad, dimRoundingMode};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  let res = ENGINE.runKernel(
-                AvgPool, inputs as unknown as NamedTensorMap,
-                attrs as unknown as NamedAttrMap) as T;
-
-  res = cast(res, $x.dtype);
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-
-  return res;
-}
-
-export const avgPool = /* @__PURE__ */ op({avgPool_});
diff --git a/tfjs-master/tfjs-core/src/ops/avg_pool_3d.ts b/tfjs-master/tfjs-core/src/ops/avg_pool_3d.ts
deleted file mode 100644
index 334d5afbc..000000000
--- a/tfjs-master/tfjs-core/src/ops/avg_pool_3d.ts
+++ /dev/null
@@ -1,115 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D, Tensor5D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {cast} from './cast';
-import {checkPadOnDimRoundingMode} from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the 3D average pooling.
- *
- * ```js
- * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
- * const result = tf.avgPool3d(x, 2, 1, 'valid');
- * result.print();
- * ```
- *
- * @param x The input tensor, of rank 5 or rank 4 of shape
- *     `[batch, depth, height, width, inChannels]`.
- * @param filterSize The filter size:
- *     `[filterDepth, filterHeight, filterWidth]`.
- *     If `filterSize` is a single number,
- *     then `filterDepth == filterHeight == filterWidth`.
- * @param strides The strides of the pooling:
- *     `[strideDepth, strideHeight, strideWidth]`.
- *     If `strides` is a single number,
- *     then `strideDepth == strideHeight == strideWidth`.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1*1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
- *     "NDHWC". Specify the data format of the input and output data. With the
- *     default format "NDHWC", the data is stored in the order of: [batch,
- *     depth, height, width, channels]. Only "NDHWC" is currently supported.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function avgPool3d_<T extends Tensor4D|Tensor5D>(
-    x: T|TensorLike, filterSize: [number, number, number]|number,
-    strides: [number, number, number]|number, pad: 'valid'|'same'|number,
-    dimRoundingMode?: 'floor'|'round'|'ceil',
-    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): T {
-  const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');
-
-  let x5D = $x as Tensor5D;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-
-  util.assert(
-      x5D.rank === 5,
-      () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-  util.assert(
-      dataFormat === 'NDHWC',
-      () => `Error in avgPool3d: Only NDHWC is currently supported, ` +
-          `but got dataFormat of ${dataFormat}`);
-  util.assert(
-      (typeof strides === 'number' && strides > 0) ||
-          (Array.isArray(strides) && strides[0] > 0 && strides[1] > 0 &&
-           strides[2] > 0),
-      () => `Error in avgPool3d: Stride must be > 0, but got '${strides}'`);
-  checkPadOnDimRoundingMode('avgPool3d', pad, dimRoundingMode);
-  const inputs: AvgPool3DInputs = {x: x5D};
-  const attrs:
-      AvgPool3DAttrs = {filterSize, strides, pad, dimRoundingMode, dataFormat};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  let res = ENGINE.runKernel(
-                AvgPool3D, inputs as unknown as NamedTensorMap,
-                attrs as unknown as NamedAttrMap) as T;
-
-  res = cast(res, x5D.dtype);
-
-  if (reshapedTo5D) {
-    return reshape(
-               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as
-        T;
-  }
-
-  return res;
-}
-
-export const avgPool3d = /* @__PURE__ */ op({avgPool3d_});
diff --git a/tfjs-master/tfjs-core/src/ops/avg_pool_3d_grad.ts b/tfjs-master/tfjs-core/src/ops/avg_pool_3d_grad.ts
deleted file mode 100644
index 73729d23c..000000000
--- a/tfjs-master/tfjs-core/src/ops/avg_pool_3d_grad.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {AvgPool3DGrad, AvgPool3DGradAttrs, AvgPool3DGradInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D, Tensor5D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {checkPadOnDimRoundingMode} from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the backprop of a 3d avg pool.
- *
- * @param dy The dy error, of rank 5 of shape
- *     [batchSize, depth, height, width, channels].
- * assumed.
- * @param input The original input image, of rank 5 or rank4 of shape
- *     [batchSize, depth, height, width, channels].
- * @param filterSize The filter size:
- *     `[filterDepth, filterHeight, filterWidth]`.
- *     `filterSize` is a single number,
- *     then `filterDepth == filterHeight == filterWidth`.
- * @param strides The strides of the pooling:
- *     `[strideDepth, strideHeight, strideWidth]`. If
- *     `strides` is a single number, then `strideHeight == strideWidth`.
- * @param pad A string from: 'same', 'valid'. The type of padding algorithm
- *     used in the forward prop of the op.
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- */
-function avgPool3dGrad_<T extends Tensor4D|Tensor5D>(
-    dy: T|TensorLike, input: T|TensorLike,
-    filterSize: [number, number, number]|number,
-    strides: [number, number, number]|number, pad: 'valid'|'same'|number,
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  const $dy = convertToTensor(dy, 'dy', 'avgPool3dGrad');
-  const $input = convertToTensor(input, 'input', 'avgPool3dGrad');
-
-  let dy5D = $dy as Tensor5D;
-  let input5D = $input as Tensor5D;
-  let reshapedTo5D = false;
-
-  if ($input.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape(
-        $dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-    input5D = reshape($input, [
-      1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]
-    ]);
-  }
-
-  util.assert(
-      dy5D.rank === 5,
-      () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ` +
-          `${dy5D.rank}.`);
-  util.assert(
-      input5D.rank === 5,
-      () => `Error in avgPool3dGrad: input must be rank 5 but got rank ` +
-          `${input5D.rank}.`);
-  checkPadOnDimRoundingMode('avgPool3dGrad', pad, dimRoundingMode);
-  const inputs: AvgPool3DGradInputs = {dy: dy5D, input: input5D};
-  const attrs: AvgPool3DGradAttrs = {filterSize, strides, pad, dimRoundingMode};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  AvgPool3DGrad, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo5D) {
-    return reshape(
-               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as
-        T;
-  }
-
-  return res;
-}
-
-export const avgPool3dGrad = /* @__PURE__ */ op({avgPool3dGrad_});
diff --git a/tfjs-master/tfjs-core/src/ops/avg_pool_3d_test.ts b/tfjs-master/tfjs-core/src/ops/avg_pool_3d_test.ts
deleted file mode 100644
index 7c2c12cbe..000000000
--- a/tfjs-master/tfjs-core/src/ops/avg_pool_3d_test.ts
+++ /dev/null
@@ -1,363 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('avgPool3d', ALL_ENVS, () => {
-  it('x=[2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-
-    const result = tf.avgPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [4.5]);
-  });
-
-  it('x=[2,2,2,1] f=[1,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-
-    const result = tf.avgPool3d(x, [1, 2, 2], 1, 'valid');
-
-    expect(result.shape).toEqual([2, 1, 1, 1]);
-    expectArraysClose(await result.data(), [2.5, 6.5]);
-  });
-
-  it('x=[1,1,1,1,1] f=[1,1,1] s=1 [0] => [0]', async () => {
-    const x = tf.tensor5d([0], [1, 1, 1, 1, 1]);
-
-    const result = tf.avgPool3d(x, 1, 1, 0);
-
-    expect(result.shape).toEqual([1, 1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [0]);
-  });
-
-  it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-
-    const result = tf.avgPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [4.5]);
-  });
-
-  it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=same', async () => {
-    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const expected = [4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8];
-    const result = tf.avgPool3d(x, 2, 1, 'same');
-
-    expect(result.shape).toEqual([1, 2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27
-        ],
-        [1, 3, 3, 3, 1]);
-    const expected = [7.5, 8.5, 10.5, 11.5, 16.5, 17.5, 19.5, 20.5];
-    const result = tf.avgPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=same', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27
-        ],
-        [1, 3, 3, 3, 1]);
-    const expected = [
-      7.5,  8.5,  9,    10.5, 11.5, 12,   12,   13,   13.5,
-      16.5, 17.5, 18,   19.5, 20.5, 21,   21,   22,   22.5,
-      21,   22,   22.5, 24,   25,   25.5, 25.5, 26.5, 27
-    ];
-    const result = tf.avgPool3d(x, 2, 1, 'same');
-
-    expect(result.shape).toEqual([1, 3, 3, 3, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=valid, propagates NaNs', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,   NaN, 10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, NaN, 23,  24, 25, 26, 27
-        ],
-        [1, 3, 3, 3, 1]);
-    const expected = [7.5, 8.5, 10.5, NaN, NaN, 17.5, NaN, 20.5];
-    const result = tf.avgPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[2,3,3,3,1] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
-          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
-          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54
-        ],
-        [2, 3, 3, 3, 1]);
-    const expected = [
-      7.5, 8.5, 10.5, 11.5, 16.5, 17.5, 19.5, 20.5, 34.5, 35.5, 37.5, 38.5,
-      43.5, 44.5, 46.5, 47.5
-    ];
-    const result = tf.avgPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([2, 2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,3,3,3,2] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
-          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
-          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54
-        ],
-        [1, 3, 3, 3, 2]);
-    const expected =
-        [14, 15, 16, 17, 20, 21, 22, 23, 32, 33, 34, 35, 38, 39, 40, 41];
-    const result = tf.avgPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 2, 2, 2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=1 roundingMode=floor', async () => {
-    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const expected = [
-      1, 1.5, 2,   2, 2.5, 3,   3, 3.5, 4,   3, 3.5, 4,   4, 4.5,
-      5, 5,   5.5, 6, 5,   5.5, 6, 6,   6.5, 7, 7,   7.5, 8
-    ];
-    const result = tf.avgPool3d(x, 2, 1, 1, 'floor');
-
-    expect(result.shape).toEqual([1, 3, 3, 3, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,1,1,1,1] f=[1,1,3] s=1 p=valid', async () => {
-    // Output tensor would have a dimension of zero, if a certain filter's
-    // dimension is larger than the input's.
-    const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
-    const expected: number[] = [];
-    const result = tf.avgPool3d(x, [1, 1, 3], 1, 'valid');
-
-    expect(result.shape).toEqual([1, 1, 1, 0, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,1,1,4,1] f=[1,1,1] s=[1,1,2] p=0', async () => {
-    // Works if the padding is a number.
-    const x = tf.ones([1, 1, 1, 4, 1]) as tf.Tensor5D;
-    const expected = [1, 1];
-    const result = tf.avgPool3d(x, [1, 1, 1], [1, 1, 2], 0);
-
-    expect(result.shape).toEqual([1, 1, 1, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,1,1,1,1] f=[2,2,2] s=1 p=2', async () => {
-    // Works if the padding is larger than filter size.
-    const x = tf.ones([1, 1, 1, 1, 1]) as tf.Tensor5D;
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
-      1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-    ];
-    const result = tf.avgPool3d(x, [2, 2, 2], 1, 2);
-
-    expect(result.shape).toEqual([1, 4, 4, 4, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws when x is not rank 5', async () => {
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor1d([1]);
-
-    expect(() => tf.avgPool3d(x as tf.Tensor5D, 2, 1, 'valid')).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is same', async () => {
-    const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
-    const pad = 'same';
-    const dimRoundingMode = 'round';
-
-    expect(() => tf.avgPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is valid', async () => {
-    const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
-    const pad = 'valid';
-    const dimRoundingMode = 'round';
-
-    expect(() => tf.avgPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is a non-integer number',
-     async () => {
-       const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
-       const pad = 1.2;
-       const dimRoundingMode = 'round';
-
-       expect(() => tf.avgPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-     });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.avgPool3d({} as tf.Tensor5D, 2, 1, 'valid')).toThrowError();
-  });
-
-  it('throws when input dtype is not float32', () => {
-    const a = tf.tensor5d([1], [1, 1, 1, 1, 1], 'int32');
-    expect(() => tf.avgPool3d(a, 2, 1, 0)).toThrowError();
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[[[[0]]]]];  // 1x1x1x1x1
-    const result = tf.avgPool3d(x, 1, 1, 0);
-    expect(result.shape).toEqual([1, 1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [0]);
-  });
-});
-
-describeWithFlags('avgPool3dBackprop', ALL_ENVS, () => {
-  it('gradient x=[2,2,2,1] f=[1,1,1] s=1', async () => {
-    const dy = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-    const x: tf.Tensor4D = tf.ones([2, 2, 2, 1]);
-    const expected = [1, 2, 3, 4, 5, 6, 7, 8];
-
-    const dx = tf.grad((x: tf.Tensor4D) => tf.avgPool3d(x, 1, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,2,2,2,1] f=[1,1,1] s=1', async () => {
-    const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const x: tf.Tensor5D = tf.ones([1, 2, 2, 2, 1]);
-    const expected = [1, 2, 3, 4, 5, 6, 7, 8];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 1, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,2,2,2,1] f=[2,2,2] s=2', async () => {
-    const dy = tf.tensor5d([8], [1, 1, 1, 1, 1]);
-    const x: tf.Tensor5D = tf.ones([1, 2, 2, 2, 1]);
-    const expected = [1, 1, 1, 1, 1, 1, 1, 1];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient with clone x=[1,2,2,2,1] f=[2,2,2] s=1', async () => {
-    const dy = tf.tensor5d([8], [1, 1, 1, 1, 1]);
-    const x: tf.Tensor5D = tf.ones([1, 2, 2, 2, 1]);
-    const expected = [1, 1, 1, 1, 1, 1, 1, 1];
-
-    const dx = tf.grad(
-        (x: tf.Tensor5D) => tf.avgPool3d(x.clone(), 2, 1, 0).clone())(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,3,3,3,1] f=[2,2,2] s=1', async () => {
-    const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const x: tf.Tensor5D = tf.ones([1, 3, 3, 3, 1]);
-    const expected = [
-      0.125, 0.375, 0.25, 0.5, 1.25, 0.75, 0.375, 0.875, 0.5,
-      0.75,  1.75,  1,    2,   4.5,  2.5,  1.25,  2.75,  1.5,
-      0.625, 1.375, 0.75, 1.5, 3.25, 1.75, 0.875, 1.875, 1
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,4,4,4,1] f=[2,2,2] s=2', async () => {
-    const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const x: tf.Tensor5D = tf.ones([1, 4, 4, 4, 1]);
-    const expected = [
-      0.125, 0.125, 0.25,  0.25,  0.125, 0.125, 0.25,  0.25,  0.375, 0.375,
-      0.5,   0.5,   0.375, 0.375, 0.5,   0.5,   0.125, 0.125, 0.25,  0.25,
-      0.125, 0.125, 0.25,  0.25,  0.375, 0.375, 0.5,   0.5,   0.375, 0.375,
-      0.5,   0.5,   0.625, 0.625, 0.75,  0.75,  0.625, 0.625, 0.75,  0.75,
-      0.875, 0.875, 1,     1,     0.875, 0.875, 1,     1,     0.625, 0.625,
-      0.75,  0.75,  0.625, 0.625, 0.75,  0.75,  0.875, 0.875, 1,     1,
-      0.875, 0.875, 1,     1
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 2, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,3,3,3,2] f=[2,2,2] s=1', async () => {
-    const dy = tf.tensor5d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-        [1, 2, 2, 2, 2]);
-    const x: tf.Tensor5D = tf.ones([1, 3, 3, 3, 2]);
-    const expected = [
-      0.125, 0.25,  0.5,  0.75,  0.375, 0.5,   0.75, 1,     2,     2.5,  1.25,
-      1.5,   0.625, 0.75, 1.5,   1.75,  0.875, 1,    1.25,  1.5,   3,    3.5,
-      1.75,  2,     3.5,  4,     8,     9,     4.5,  5,     2.25,  2.5,  5,
-      5.5,   2.75,  3,    1.125, 1.25,  2.5,   2.75, 1.375, 1.5,   2.75, 3,
-      6,     6.5,   3.25, 3.5,   1.625, 1.75,  3.5,  3.75,  1.875, 2
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[2,3,3,3,1] f=[2,2,2] s=1', async () => {
-    const dy = tf.tensor5d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-        [2, 2, 2, 2, 1]);
-    const x: tf.Tensor5D = tf.ones([2, 3, 3, 3, 1]);
-    const expected = [
-      0.125, 0.375, 0.25,  0.5,   1.25, 0.75,  0.375, 0.875, 0.5,   0.75, 1.75,
-      1,     2,     4.5,   2.5,   1.25, 2.75,  1.5,   0.625, 1.375, 0.75, 1.5,
-      3.25,  1.75,  0.875, 1.875, 1,    1.125, 2.375, 1.25,  2.5,   5.25, 2.75,
-      1.375, 2.875, 1.5,   2.75,  5.75, 3,     6,     12.5,  6.5,   3.25, 6.75,
-      3.5,   1.625, 3.375, 1.75,  3.5,  7.25,  3.75,  1.875, 3.875, 2
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.avgPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/avg_pool_grad.ts b/tfjs-master/tfjs-core/src/ops/avg_pool_grad.ts
deleted file mode 100644
index 8f87f1a13..000000000
--- a/tfjs-master/tfjs-core/src/ops/avg_pool_grad.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {AvgPoolGrad, AvgPoolGradAttrs, AvgPoolGradInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {ExplicitPadding} from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the backprop of an 2D avg pool.
- *
- * @param dy The dy error, of rank 4 or rank 3 of shape
- *     [batchSize, height, width, channels]. If rank 3, batch of 1 is
- * assumed.
- * @param input The input image, of rank 4 or rank 3 of shape
- *     [batchSize, height, width, channels]. If rank 3, batch of 1 is
- * assumed.
- * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
- *     `filterSize` is a single number, then `filterHeight == filterWidth`.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
- *     `strides` is a single number, then `strideHeight == strideWidth`.
- * @param pad The type of padding algorithm used in the forward prop of the op.
- *     'same', 'valid', for more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *         https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- */
-function avgPoolGrad_<T extends Tensor3D|Tensor4D>(
-    dy: T|TensorLike, input: T|TensorLike, filterSize: [number, number]|number,
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|ExplicitPadding): T {
-  const $dy = convertToTensor(dy, 'dy', 'avgPoolGrad');
-  const $input = convertToTensor(input, 'input', 'avgPoolGrad');
-
-  util.assert(
-      $input.rank === $dy.rank,
-      () => `Rank of input (${$input.rank}) does not match rank of dy (${
-          $dy.rank})`);
-
-  let input4D = $input as Tensor4D;
-  let dy4D = $dy as Tensor4D;
-  let reshapedTo4D = false;
-
-  if ($input.rank === 3) {
-    reshapedTo4D = true;
-    input4D =
-        reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
-    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
-  }
-
-  util.assert(
-      dy4D.rank === 4,
-      () => `Error in avgPoolGrad: dy must be rank 4 but got rank ` +
-          `${dy4D.rank}.`);
-  util.assert(
-      input4D.rank === 4,
-      () => `Error in avgPoolGrad: input must be rank 4 but got rank ` +
-          `${input4D.rank}.`);
-
-  const inputs: AvgPoolGradInputs = {dy: dy4D, input: input4D};
-
-  const attrs: AvgPoolGradAttrs = {filterSize, strides, pad};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  AvgPoolGrad, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-  return res;
-}
-
-export const avgPoolGrad = /* @__PURE__ */ op({avgPoolGrad_});
diff --git a/tfjs-master/tfjs-core/src/ops/avg_pool_test.ts b/tfjs-master/tfjs-core/src/ops/avg_pool_test.ts
deleted file mode 100644
index 2f9a19181..000000000
--- a/tfjs-master/tfjs-core/src/ops/avg_pool_test.ts
+++ /dev/null
@@ -1,277 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {identityPoolTest} from './identity_pool_test';
-
-describeWithFlags('avgPool', ALL_ENVS, () => {
-  it('x=[1,1,1] f=[1,1] s=1 [0] => [0]', async () => {
-    const a = tf.tensor3d([0], [1, 1, 1]);
-    const result = tf.avgPool(a, 1, 1, 0);
-    expectArraysClose(await result.data(), [0]);
-  });
-
-  it('x=[3,3,1] f=[2,2] s=1', async () => {
-    // Feed forward.
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
-    const result = tf.avgPool(a, 2, 1, 0);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [3, 4, 6.25, 7]);
-  });
-
-  it('input int32 throws error', () => {
-    // Feed forward.
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1], 'int32');
-    expect(() => tf.avgPool(a, 2, 1, 0)).toThrowError();
-  });
-
-  it('x=[2,3,3,1] f=[2,2], s=1', async () => {
-    // Feed forward.
-    const a = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
-    const result = tf.avgPool(a, 2, 1, 0);
-
-    expect(result.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await result.data(), [3, 4, 6.25, 7, 3, 4, 6, 7]);
-  });
-
-  it('x=[3,3,1] f=[2,2] s=1 propagates NaNs', async () => {
-    // Feed forward.
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, NaN, 8], [3, 3, 1]);
-    const result = tf.avgPool(a, 2, 1, 0);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [3, 4, NaN, NaN]);
-  });
-
-  it('x=[3,3,2] f=[2,2] s=1', async () => {
-    // Feed forward.
-    const a = tf.tensor3d(
-        [1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11],
-        [3, 3, 2]);
-    const result = tf.avgPool(a, 2, 1, 0);
-
-    expect(result.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await result.data(), [3, 77, 4, 66, 6.25, 44, 7, 33]);
-  });
-
-  it('x=[4,4,1] f=[2,2] s=2', async () => {
-    // Feed forward.
-    const a = tf.tensor3d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
-    const result = tf.avgPool(a, 2, 2, 0);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [2.5, 4.5, 10.5, 12.5]);
-  });
-
-  it('x=[2,2,1] f=[2,2] s=1 p=same', async () => {
-    // Feed forward.
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const fSize = 2;
-    const strides = 1;
-    const result = tf.avgPool(a, fSize, strides, 'same');
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [2.5, 3, 3.5, 4]);
-  });
-
-  it('x=[2,2,3] f=[2,2] s=1 p=valid', async () => {
-    // Feed forward.
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const fSize = 2;
-    const strides = 1;
-    const result = tf.avgPool(a, fSize, strides, 'valid');
-
-    expect(result.shape).toEqual([1, 1, 3]);
-    expectArraysClose(await result.data(), [5.5, 6.5, 7.5]);
-  });
-
-  it('x=[3,3,1] f=[3,3] s=1 p=explicit', async () => {
-    // Feed forward.
-    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-    const padding =
-        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const result = tf.avgPool(x, 3, 1, padding);
-
-    expect(result.shape).toEqual([4, 2, 1]);
-    expectArraysClose(await result.data(), [2.5, 3, 4, 4.5, 5.5, 6, 7, 7.5]);
-  });
-
-  it('x=[2,2,3] f=[2,2] s=3 p=1 default dimRoundingMode', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.avgPool(x, 2, 3, 1);
-
-    expect(result.shape).toEqual([1, 1, 3]);
-  });
-
-  it('x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.avgPool(x, 1, 2, 1, 'floor');
-
-    expect(result.shape).toEqual([2, 2, 3]);
-  });
-
-  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=floor', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.avgPool(x, 2, 3, 1, 'floor');
-
-    expect(result.shape).toEqual([1, 1, 3]);
-  });
-
-  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=round', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.avgPool(x, 2, 3, 1, 'round');
-
-    expect(result.shape).toEqual([2, 2, 3]);
-  });
-
-  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=ceil', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.avgPool(x, 2, 3, 1, 'ceil');
-
-    expect(result.shape).toEqual([2, 2, 3]);
-  });
-
-  it('gradient x=[1,1,1] f=[1,1] s=1 [0] => [0]', async () => {
-    const x = tf.tensor3d([0], [1, 1, 1]);
-    const dy = tf.tensor3d([0], [1, 1, 1]);
-    const dx = tf.grad((x: tf.Tensor3D) => x.avgPool(1, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [0]);
-  });
-
-  it('gradient with clones', async () => {
-    const x = tf.tensor3d([0], [1, 1, 1]);
-    const dy = tf.tensor3d([0], [1, 1, 1]);
-    const dx = tf.grad(
-        (x: tf.Tensor3D) => tf.avgPool(x.clone(), 1, 1, 0).clone())(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [0]);
-  });
-
-  it('gradient x=[3,3,1] f=[2,2] s=1', async () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const avgMultiplier = 1 / (2 * 2);
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.avgPool(2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [
-      1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,
-      4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,
-      3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier
-    ]);
-  });
-
-  it('gradient x=[3,3,1] f=[3,3] s=1 p=explicit', async () => {
-    // Feed forward.
-    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-    const padding =
-        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const dy = tf.tensor3d([0, 3, 0, 6, 0, 9, 0, 12], [4, 2, 1]);
-    const dx = tf.grad((x: tf.Tensor3D) => x.avgPool(3, 1, padding))(x, dy);
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [0, 1, 1, 0, 2, 2, 0, 3, 3]);
-  });
-
-  it('gradient x=[2,3,3,1] f=[2,2], s=1', async () => {
-    // Feed forward.
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
-    const dy = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2, 1]);
-    const avgMultiplier = 1 / (2 * 2);
-
-    const dx = tf.grad((x: tf.Tensor4D) => x.avgPool(2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [
-      1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,
-      4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,
-      3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier,
-      1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,
-      4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,
-      3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier
-    ]);
-  });
-
-  it('throws when dimRoundingMode is set and pad is same', () => {
-    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-    const pad = 'same';
-    const dimRoundingMode = 'round';
-
-    expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is valid', () => {
-    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-    const pad = 'valid';
-    const dimRoundingMode = 'round';
-
-    expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is a non-integer number',
-     () => {
-       const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-       const pad = 1.2;
-       const dimRoundingMode = 'round';
-
-       expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-     });
-
-  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
-         'number',
-     () => {
-       const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as
-           tf.backend_util.ExplicitPadding;
-       const dimRoundingMode = 'round';
-
-       expect(() => tf.avgPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-     });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.avgPool({} as tf.Tensor3D, 2, 1, 'valid'))
-        .toThrowError(/Argument 'x' passed to 'avgPool' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[[0]]];  // 1x1x1
-    const result = tf.avgPool(a, 1, 1, 0);
-    expectArraysClose(await result.data(), [0]);
-  });
-
-  identityPoolTest(tf.avgPool);
-});
diff --git a/tfjs-master/tfjs-core/src/ops/axis_util.ts b/tfjs-master/tfjs-core/src/ops/axis_util.ts
deleted file mode 100644
index d63c783fc..000000000
--- a/tfjs-master/tfjs-core/src/ops/axis_util.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as util from '../util';
-
-/**
- * Returns true if the axis specifies the inner most dimensions of the
- * array.
- */
-export function axesAreInnerMostDims(axes: number[], rank: number): boolean {
-  for (let i = 0; i < axes.length; ++i) {
-    if (axes[axes.length - i - 1] !== rank - 1 - i) {
-      return false;
-    }
-  }
-  return true;
-}
-
-export function combineLocations(
-    outputLoc: number[], reduceLoc: number[], axes: number[]): number[] {
-  const rank = outputLoc.length + reduceLoc.length;
-  const loc = [];
-  let outIdx = 0;
-  let reduceIdx = 0;
-    for (let dim = 0; dim < rank; dim++) {
-    if (axes.indexOf(dim) === -1) {
-      loc.push(outputLoc[outIdx++]);
-    } else {
-      loc.push(reduceLoc[reduceIdx++]);
-    }
-  }
-  return loc;
-}
-
-export function computeOutAndReduceShapes(
-    aShape: number[], axes: number[]): [number[], number[]] {
-  const outShape = [];
-  const rank = aShape.length;
-  for (let dim = 0; dim < rank; dim++) {
-    if (axes.indexOf(dim) === -1) {
-      outShape.push(aShape[dim]);
-    }
-  }
-  const reduceShape = axes.map(dim => aShape[dim]);
-  return [outShape, reduceShape];
-}
-
-export function expandShapeToKeepDim(
-    shape: number[], axes: number[]): number[] {
-  const reduceSubShape = axes.map(x => 1);
-  return combineLocations(shape, reduceSubShape, axes);
-}
-
-export function assertAxesAreInnerMostDims(
-    msg: string, axes: number[], rank: number): void {
-  util.assert(
-      axesAreInnerMostDims(axes, rank),
-      () => `${msg} supports only inner-most axes for now. ` +
-          `Got axes ${axes} and rank-${rank} input.`);
-}
-
-/**
- * Returns the axes permutation to be used with `tf.transpose`, if such
- * permutation is necessary. Otherwise it returns null. This method is used by
- * operations that operate only on inner-most axes.
- */
-export function getAxesPermutation(axes: number[], rank: number): number[]|
-    null {
-  if (axesAreInnerMostDims(axes, rank)) {
-    return null;
-  }
-  const result: number[] = [];
-  for (let i = 0; i < rank; ++i) {
-    if (axes.indexOf(i) === -1) {
-      result.push(i);
-    }
-  }
-  axes.forEach(axis => result.push(axis));
-  return result;
-}
-
-/** Returns the axes permutation that undoes the original permutation. */
-export function getUndoAxesPermutation(axes: number[]): number[] {
-  return axes.map((axis, i) => [i, axis])
-      .sort((a, b) => a[1] - b[1])
-      .map(x => x[0]);
-}
-
-export function getInnerMostAxes(numAxes: number, rank: number): number[] {
-  const res: number[] = [];
-  for (let i = rank - numAxes; i < rank; ++i) {
-    res.push(i);
-  }
-  return res;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/axis_util_test.ts b/tfjs-master/tfjs-core/src/ops/axis_util_test.ts
deleted file mode 100644
index 60ed27447..000000000
--- a/tfjs-master/tfjs-core/src/ops/axis_util_test.ts
+++ /dev/null
@@ -1,216 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-import * as axis_util from './axis_util';
-
-describe('axis_util combineLocations', () => {
-  it('rank 4, reduce last 2 dims', () => {
-    const loc = axis_util.combineLocations([4, 1], [3, 7], [2, 3]);
-    expect(loc).toEqual([4, 1, 3, 7]);
-  });
-
-  it('rank 4, reduce first two dims', () => {
-    const loc = axis_util.combineLocations([4, 1], [3, 7], [0, 1]);
-    expect(loc).toEqual([3, 7, 4, 1]);
-  });
-
-  it('rank 4, reduce 1st and 3rd dims', () => {
-    const loc = axis_util.combineLocations([4, 1], [3, 7], [0, 2]);
-    expect(loc).toEqual([3, 4, 7, 1]);
-  });
-
-  it('rank 4, reduce 1st and 4th dims', () => {
-    const loc = axis_util.combineLocations([4, 1], [3, 7], [0, 3]);
-    expect(loc).toEqual([3, 4, 1, 7]);
-  });
-
-  it('rank 3, reduce all dims', () => {
-    const loc = axis_util.combineLocations([], [3, 7, 1], [0, 1, 2]);
-    expect(loc).toEqual([3, 7, 1]);
-  });
-
-  it('rank 2, reduce last dim', () => {
-    const loc = axis_util.combineLocations([3], [5], [1]);
-    expect(loc).toEqual([3, 5]);
-  });
-
-  it('rank 2, reduce first dim', () => {
-    const loc = axis_util.combineLocations([3], [5], [0]);
-    expect(loc).toEqual([5, 3]);
-  });
-});
-
-describe('axis_util computeOutAndReduceShapes', () => {
-  it('rank 4, reduce all dims', () => {
-    const [out, red] =
-        axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [0, 1, 2, 3]);
-    expect(out).toEqual([]);
-    expect(red).toEqual([3, 7, 2, 4]);
-  });
-
-  it('rank 4, reduce last 2 dims', () => {
-    const [out, red] =
-        axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [2, 3]);
-    expect(out).toEqual([3, 7]);
-    expect(red).toEqual([2, 4]);
-  });
-
-  it('rank 4, reduce first 2 dims', () => {
-    const [out, red] =
-        axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [0, 1]);
-    expect(out).toEqual([2, 4]);
-    expect(red).toEqual([3, 7]);
-  });
-
-  it('rank 4, reduce last 3 dims', () => {
-    const [out, red] =
-        axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [1, 2, 3]);
-    expect(out).toEqual([3]);
-    expect(red).toEqual([7, 2, 4]);
-  });
-
-  it('rank 4, reduce 1st and 3rd dims', () => {
-    const [out, red] =
-        axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [0, 2]);
-    expect(out).toEqual([7, 4]);
-    expect(red).toEqual([3, 2]);
-  });
-
-  it('rank 3, reduce all dims', () => {
-    const [out, red] =
-        axis_util.computeOutAndReduceShapes([3, 7, 2], [0, 1, 2]);
-    expect(out).toEqual([]);
-    expect(red).toEqual([3, 7, 2]);
-  });
-});
-
-describe('axis_util axesAreInnerMostDims', () => {
-  it('rank 4, reduce last dim', () => {
-    const res = axis_util.axesAreInnerMostDims([3], 4);
-    expect(res).toBe(true);
-  });
-
-  it('rank 4, reduce last 2 dims', () => {
-    const res = axis_util.axesAreInnerMostDims([2, 3], 4);
-    expect(res).toBe(true);
-  });
-
-  it('rank 4, reduce last 3 dims', () => {
-    const res = axis_util.axesAreInnerMostDims([1, 2, 3], 4);
-    expect(res).toBe(true);
-  });
-
-  it('rank 4, reduce all dims', () => {
-    const res = axis_util.axesAreInnerMostDims([0, 1, 2, 3], 4);
-    expect(res).toBe(true);
-  });
-
-  it('rank 4, reduce all but 2nd', () => {
-    const res = axis_util.axesAreInnerMostDims([0, 2, 3], 4);
-    expect(res).toBe(false);
-  });
-
-  it('rank 4, reduce all but 3rd', () => {
-    const res = axis_util.axesAreInnerMostDims([0, 1, 3], 4);
-    expect(res).toBe(false);
-  });
-
-  it('rank 4, reduce all but last', () => {
-    const res = axis_util.axesAreInnerMostDims([0, 1, 2], 4);
-    expect(res).toBe(false);
-  });
-});
-
-describe('axis_util expandShapeToKeepDim', () => {
-  it('2d -> 1d axis=0', () => {
-    const shape = axis_util.expandShapeToKeepDim([2], [0]);
-    expect(shape).toEqual([1, 2]);
-  });
-
-  it('2d -> 1d axis=1', () => {
-    const shape = axis_util.expandShapeToKeepDim([4], [1]);
-    expect(shape).toEqual([4, 1]);
-  });
-
-  it('3d -> 1d axis=1,2', () => {
-    const shape = axis_util.expandShapeToKeepDim([7], [1, 2]);
-    expect(shape).toEqual([7, 1, 1]);
-  });
-
-  it('3d -> 2d axis=1', () => {
-    const shape = axis_util.expandShapeToKeepDim([7, 3], [1]);
-    expect(shape).toEqual([7, 1, 3]);
-  });
-});
-
-describe('axis_util getPermAxes', () => {
-  it('all axes, no perm is needed', () => {
-    const perm = axis_util.getAxesPermutation([0, 1, 2], 3);
-    expect(perm).toBeNull();
-  });
-
-  it('no axes, no perm is needed', () => {
-    const perm = axis_util.getAxesPermutation([], 3);
-    expect(perm).toBeNull();
-  });
-
-  it('inner most 2 axes, no perm is needed', () => {
-    const perm = axis_util.getAxesPermutation([2, 3], 4);
-    expect(perm).toBeNull();
-  });
-
-  it('outer most axis, perm is needed', () => {
-    const perm = axis_util.getAxesPermutation([0], 4);
-    expect(perm).toEqual([1, 2, 3, 0]);
-  });
-
-  it('2 outer most axes, perm is needed', () => {
-    const perm = axis_util.getAxesPermutation([0, 1], 4);
-    expect(perm).toEqual([2, 3, 0, 1]);
-  });
-});
-
-describeWithFlags('axis_util getUndoAxesPermutation', ALL_ENVS, () => {
-  it('4d axes', () => {
-    const axes = [2, 0, 1, 3];
-    expect(axis_util.getUndoAxesPermutation(axes)).toEqual([1, 2, 0, 3]);
-  });
-
-  it('3d axes, no perm', () => {
-    const axes = [0, 1, 2];
-    expect(axis_util.getUndoAxesPermutation(axes)).toEqual([0, 1, 2]);
-  });
-
-  it('3d axes, complete flip', () => {
-    const axes = [2, 1, 0];
-    expect(axis_util.getUndoAxesPermutation(axes)).toEqual([2, 1, 0]);
-  });
-
-  it('4d array with values', async () => {
-    const axes = [2, 0, 1, 3];
-    const undoPermutation = axis_util.getUndoAxesPermutation(axes);
-
-    const a = tf.randomNormal([2, 3, 4, 5]);
-    const aT = tf.transpose(a, axes);
-    const aTT = tf.transpose(aT, undoPermutation);
-    expectArraysClose(await a.data(), await aTT.data());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/basic_lstm_cell.ts b/tfjs-master/tfjs-core/src/ops/basic_lstm_cell.ts
deleted file mode 100644
index d9f1e252f..000000000
--- a/tfjs-master/tfjs-core/src/ops/basic_lstm_cell.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Scalar, Tensor1D, Tensor2D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {add} from './add';
-import {concat} from './concat';
-import {matMul} from './mat_mul';
-import {mul} from './mul';
-import {op} from './operation';
-import {sigmoid} from './sigmoid';
-import {slice} from './slice';
-import {tanh} from './tanh';
-
-/**
- * Computes the next state and output of a BasicLSTMCell.
- *
- * Returns `[newC, newH]`.
- *
- * Derived from tf.contrib.rnn.BasicLSTMCell.
- *
- * @param forgetBias Forget bias for the cell.
- * @param lstmKernel The weights for the cell.
- * @param lstmBias The bias for the cell.
- * @param data The input to the cell.
- * @param c Previous cell state.
- * @param h Previous cell output.
- *
- * @doc {heading: 'Operations', subheading: 'RNN'}
- */
-function basicLSTMCell_(
-    forgetBias: Scalar|TensorLike, lstmKernel: Tensor2D|TensorLike,
-    lstmBias: Tensor1D|TensorLike, data: Tensor2D|TensorLike,
-    c: Tensor2D|TensorLike, h: Tensor2D|TensorLike): [Tensor2D, Tensor2D] {
-  const $forgetBias =
-      convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');
-  const $lstmKernel =
-      convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');
-  const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');
-  const $data = convertToTensor(data, 'data', 'basicLSTMCell');
-  const $c = convertToTensor(c, 'c', 'basicLSTMCell');
-  const $h = convertToTensor(h, 'h', 'basicLSTMCell');
-
-  const combined = concat([$data, $h], 1);
-  const weighted = matMul(combined, $lstmKernel);
-  const res: Tensor2D = add(weighted, $lstmBias);
-
-  // i = input_gate, j = new_input, f = forget_gate, o = output_gate
-  const batchSize = res.shape[0];
-  const sliceCols = res.shape[1] / 4;
-  const sliceSize: [number, number] = [batchSize, sliceCols];
-  const i = slice(res, [0, 0], sliceSize);
-  const j = slice(res, [0, sliceCols], sliceSize);
-  const f = slice(res, [0, sliceCols * 2], sliceSize);
-  const o = slice(res, [0, sliceCols * 3], sliceSize);
-
-  const newC: Tensor2D =
-      add(mul(sigmoid(i), tanh(j)),
-          mul($c, sigmoid(add($forgetBias, f)) as Tensor2D));
-  const newH: Tensor2D = mul(tanh(newC), sigmoid(o));
-  return [newC, newH];
-}
-
-export const basicLSTMCell = /* @__PURE__ */ op({basicLSTMCell_});
diff --git a/tfjs-master/tfjs-core/src/ops/basic_lstm_cell_test.ts b/tfjs-master/tfjs-core/src/ops/basic_lstm_cell_test.ts
deleted file mode 100644
index 663b68a4c..000000000
--- a/tfjs-master/tfjs-core/src/ops/basic_lstm_cell_test.ts
+++ /dev/null
@@ -1,161 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {Rank} from '../types';
-
-describeWithFlags('basicLSTMCell', ALL_ENVS, () => {
-  it('basicLSTMCell with batch=2', async () => {
-    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);
-    const lstmBias = tf.randomNormal<Rank.R1>([4]);
-    const forgetBias = tf.scalar(1.0);
-
-    const data = tf.randomNormal<Rank.R2>([1, 2]);
-    const batchedData = tf.concat2d([data, data], 0);  // 2x2
-    const c = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedC = tf.concat2d([c, c], 0);  // 2x1
-    const h = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedH = tf.concat2d([h, h], 0);  // 2x1
-    const [newC, newH] = tf.basicLSTMCell(
-        forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH);
-    const newCVals = await newC.array();
-    const newHVals = await newH.array();
-    expect(newCVals[0][0]).toEqual(newCVals[1][0]);
-    expect(newHVals[0][0]).toEqual(newHVals[1][0]);
-  });
-
-  it('basicLSTMCell accepts a tensor-like object', async () => {
-    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);
-    const lstmBias = [0, 0, 0, 0];
-    const forgetBias = 1;
-
-    const data = [[0, 0]];                             // 1x2
-    const batchedData = tf.concat2d([data, data], 0);  // 2x2
-    const c = [[0]];                                   // 1x1
-    const batchedC = tf.concat2d([c, c], 0);           // 2x1
-    const h = [[0]];                                   // 1x1
-    const batchedH = tf.concat2d([h, h], 0);           // 2x1
-    const [newC, newH] = tf.basicLSTMCell(
-        forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH);
-    const newCVals = await newC.array();
-    const newHVals = await newH.array();
-    expect(newCVals[0][0]).toEqual(newCVals[1][0]);
-    expect(newHVals[0][0]).toEqual(newHVals[1][0]);
-  });
-});
-describeWithFlags('basicLSTMCell throws with non-tensor', ALL_ENVS, () => {
-  it('input: forgetBias', () => {
-    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);
-    const lstmBias = tf.randomNormal<Rank.R1>([4]);
-
-    const data = tf.randomNormal<Rank.R2>([1, 2]);
-    const batchedData = tf.concat2d([data, data], 0);  // 2x2
-    const c = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedC = tf.concat2d([c, c], 0);  // 2x1
-    const h = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedH = tf.concat2d([h, h], 0);  // 2x1
-    expect(
-        () => tf.basicLSTMCell(
-            {} as tf.Scalar, lstmKernel, lstmBias, batchedData, batchedC,
-            batchedH))
-        .toThrowError(
-            /Argument 'forgetBias' passed to 'basicLSTMCell' must be a Tensor/);
-  });
-  it('input: lstmKernel', () => {
-    const lstmBias = tf.randomNormal<Rank.R1>([4]);
-    const forgetBias = tf.scalar(1.0);
-
-    const data = tf.randomNormal<Rank.R2>([1, 2]);
-    const batchedData = tf.concat2d([data, data], 0);  // 2x2
-    const c = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedC = tf.concat2d([c, c], 0);  // 2x1
-    const h = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedH = tf.concat2d([h, h], 0);  // 2x1
-    expect(
-        () => tf.basicLSTMCell(
-            forgetBias, {} as tf.Tensor2D, lstmBias, batchedData, batchedC,
-            batchedH))
-        .toThrowError(
-            /Argument 'lstmKernel' passed to 'basicLSTMCell' must be a Tensor/);
-  });
-  it('input: lstmBias', () => {
-    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);
-    const forgetBias = tf.scalar(1.0);
-
-    const data = tf.randomNormal<Rank.R2>([1, 2]);
-    const batchedData = tf.concat2d([data, data], 0);  // 2x2
-    const c = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedC = tf.concat2d([c, c], 0);  // 2x1
-    const h = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedH = tf.concat2d([h, h], 0);  // 2x1
-    expect(
-        () => tf.basicLSTMCell(
-            forgetBias, lstmKernel, {} as tf.Tensor1D, batchedData, batchedC,
-            batchedH))
-        .toThrowError(
-            /Argument 'lstmBias' passed to 'basicLSTMCell' must be a Tensor/);
-  });
-  it('input: data', () => {
-    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);
-    const lstmBias = tf.randomNormal<Rank.R1>([4]);
-    const forgetBias = tf.scalar(1.0);
-
-    const c = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedC = tf.concat2d([c, c], 0);  // 2x1
-    const h = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedH = tf.concat2d([h, h], 0);  // 2x1
-    expect(
-        () => tf.basicLSTMCell(
-            forgetBias, lstmKernel, lstmBias, {} as tf.Tensor2D, batchedC,
-            batchedH))
-        .toThrowError(
-            /Argument 'data' passed to 'basicLSTMCell' must be a Tensor/);
-  });
-  it('input: c', () => {
-    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);
-    const lstmBias = tf.randomNormal<Rank.R1>([4]);
-    const forgetBias = tf.scalar(1.0);
-
-    const data = tf.randomNormal<Rank.R2>([1, 2]);
-    const batchedData = tf.concat2d([data, data], 0);  // 2x2
-    const h = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedH = tf.concat2d([h, h], 0);  // 2x1
-    expect(
-        () => tf.basicLSTMCell(
-            forgetBias, lstmKernel, lstmBias, batchedData, {} as tf.Tensor2D,
-            batchedH))
-        .toThrowError(
-            /Argument 'c' passed to 'basicLSTMCell' must be a Tensor/);
-  });
-  it('input: h', () => {
-    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);
-    const lstmBias = tf.randomNormal<Rank.R1>([4]);
-    const forgetBias = tf.scalar(1.0);
-
-    const data = tf.randomNormal<Rank.R2>([1, 2]);
-    const batchedData = tf.concat2d([data, data], 0);  // 2x2
-    const c = tf.randomNormal<Rank.R2>([1, 1]);
-    const batchedC = tf.concat2d([c, c], 0);  // 2x1
-    expect(
-        () => tf.basicLSTMCell(
-            forgetBias, lstmKernel, lstmBias, batchedData, batchedC,
-            {} as tf.Tensor2D))
-        .toThrowError(
-            /Argument 'h' passed to 'basicLSTMCell' must be a Tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/batch_to_space_nd.ts b/tfjs-master/tfjs-core/src/ops/batch_to_space_nd.ts
deleted file mode 100644
index 7deeea9d8..000000000
--- a/tfjs-master/tfjs-core/src/ops/batch_to_space_nd.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of
- * shape `blockShape + [batch]`, interleaves these blocks back into the grid
- * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with
- * the same rank as the input. The spatial dimensions of this intermediate
- * result are then optionally cropped according to `crops` to produce the
- * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise
- * description.
- *
- * ```js
- * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
- * const blockShape = [2, 2];
- * const crops = [[0, 0], [0, 0]];
- *
- * x.batchToSpaceND(blockShape, crops).print();
- * ```
- *
- * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +
- * remainingShape`, where spatialShape has `M` dimensions.
- * @param blockShape A 1-D array. Must have shape `[M]`, all values must
- * be >= 1.
- * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.
- * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input
- * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required
- * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`
- *
- * This operation is equivalent to the following steps:
- *
- * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,
- * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,
- * x.shape[N-1]]`
- *
- * 2. Permute dimensions of `reshaped` to produce `permuted` of shape `[batch /
- * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],
- * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
- *
- * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /
- * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *
- * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
- *
- * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`
- * according to `crops` to produce the output of shape: `[batch /
- * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],
- * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -
- * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function batchToSpaceND_<T extends Tensor>(
-    x: T|TensorLike, blockShape: number[], crops: number[][]): T {
-  const $x = convertToTensor(x, 'x', 'batchToSpaceND');
-  const prod = blockShape.reduce((a, b) => a * b);
-
-  util.assert(
-      $x.rank >= 1 + blockShape.length,
-      () => `input rank is ${$x.rank} but should be > than blockShape.length ${
-          blockShape.length}`);
-
-  util.assert(
-      crops.length === blockShape.length,
-      () => `crops.length is ${
-          crops.length} but should be equal to blockShape.length  ${
-          blockShape.length}`);
-
-  util.assert(
-      $x.shape[0] % prod === 0,
-      () => `input tensor batch is ${
-                $x.shape[0]} but is not divisible by the product of ` +
-          `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);
-
-  const inputs: BatchToSpaceNDInputs = {x: $x};
-  const attrs: BatchToSpaceNDAttrs = {blockShape, crops};
-
-  return ENGINE.runKernel(
-      BatchToSpaceND, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const batchToSpaceND = /* @__PURE__ */ op({batchToSpaceND_});
diff --git a/tfjs-master/tfjs-core/src/ops/batch_to_space_nd_test.ts b/tfjs-master/tfjs-core/src/ops/batch_to_space_nd_test.ts
deleted file mode 100644
index d08e537fc..000000000
--- a/tfjs-master/tfjs-core/src/ops/batch_to_space_nd_test.ts
+++ /dev/null
@@ -1,214 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('batchToSpaceND', ALL_ENVS, () => {
-  it('tensor4d, input shape=[4, 1, 1, 1], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
-    const blockShape = [2, 2];
-    const crops = [[0, 0], [0, 0]];
-
-    const res = tf.batchToSpaceND(t, blockShape, crops);
-    expect(res.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4]);
-  });
-
-  it('tensor4d, input shape=[4, 1, 1, 3], blockShape=[2, 2]', async () => {
-    const t =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [4, 1, 1, 3]);
-    const blockShape = [2, 2];
-    const crops = [[0, 0], [0, 0]];
-
-    const res = tf.batchToSpaceND(t, blockShape, crops);
-    expect(res.shape).toEqual([1, 2, 2, 3]);
-    expectArraysClose(
-        await res.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
-  });
-
-  it('tensor4d, input shape=[4, 2, 2, 1], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d(
-        [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16], [4, 2, 2, 1]);
-    const blockShape = [2, 2];
-    const crops = [[0, 0], [0, 0]];
-
-    const res = tf.batchToSpaceND(t, blockShape, crops);
-    expect(res.shape).toEqual([1, 4, 4, 1]);
-    expectArraysClose(
-        await res.data(),
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);
-  });
-
-  it('tensor4d, input shape=[8, 1, 3, 1], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d(
-        [
-          0, 1, 3, 0, 9,  11, 0, 2, 4, 0, 10, 12,
-          0, 5, 7, 0, 13, 15, 0, 6, 8, 0, 14, 16
-        ],
-        [8, 1, 3, 1]);
-    const blockShape = [2, 2];
-    const crops = [[0, 0], [2, 0]];
-
-    const res = tf.batchToSpaceND(t, blockShape, crops);
-    expect(res.shape).toEqual([2, 2, 4, 1]);
-    expectArraysClose(
-        await res.data(),
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);
-  });
-
-  it('tensor2d, blockShape [1]', async () => {
-    const t = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const blockShape = [2];
-    const crops = [[0, 0]];
-
-    const res = tf.batchToSpaceND(t, blockShape, crops);
-    expect(res.shape).toEqual([1, 4]);
-    expectArraysClose(await res.data(), [1, 3, 2, 4]);
-  });
-
-  it('tensor3d,  blockSHape [1]', async () => {
-    const t = tf.tensor(
-        [
-          -61, 37,  -68, 72,  31,  62, 0,   -13, 28,  54, 96,
-          44,  -55, -64, -88, -94, 65, -32, -96, -73, -2, -77,
-          -14, 47,  33,  15,  70,  20, 75,  28,  84,  -13
-        ],
-        [8, 2, 2]);
-    const blockShape = [2];
-    const crops = [[0, 2]];
-
-    const res = tf.batchToSpaceND(t, blockShape, crops);
-    expect(res.shape).toEqual([4, 2, 2]);
-    expectArraysClose(
-        await res.data(),
-        [-61, 37, 65, -32, 31, 62, -2, -77, 28, 54, 33, 15, -55, -64, 75, 28]);
-  });
-
-  it('tensor3d, blockShape [2]', async () => {
-    const t = tf.tensor(
-        [
-          -61, 37,  -68, 72,  31,  62, 0,   -13, 28,  54, 96,
-          44,  -55, -64, -88, -94, 65, -32, -96, -73, -2, -77,
-          -14, 47,  33,  15,  70,  20, 75,  28,  84,  -13
-        ],
-        [8, 2, 2]);
-    const blockShape = [2, 2];
-    const crops = [[2, 0], [2, 0]];
-
-    const res = tf.batchToSpaceND(t, blockShape, crops);
-    expect(res.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await res.data(), [72, 44, -73, 20, -13, -94, 47, -13]);
-  });
-
-  it('throws when blockShape equal to input rank', () => {
-    const t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
-    const blockShape = [2, 2, 2, 2];
-    const crops = [[0, 0], [0, 0], [0, 0], [0, 0]];
-
-    expect(() => tf.batchToSpaceND(t, blockShape, crops))
-        .toThrowError(
-            `input rank is ${t.rank} but should be > than blockShape.length ${
-                blockShape.length}`);
-  });
-
-  it('throws when crops row dimension not equal to blockshape', () => {
-    const t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
-    const blockShape = [2, 2];
-    const crops = [[0, 0]];
-
-    expect(() => tf.batchToSpaceND(t, blockShape, crops))
-        .toThrowError(`crops.length is ${
-            crops.length} but should be equal to blockShape.length  ${
-            blockShape.length}`);
-  });
-
-  it('throws when input tensor batch not divisible by prod(blockShape)', () => {
-    const t = tf.tensor4d([1, 2, 3, 4, 5], [5, 1, 1, 1]);
-    const blockShape = [2, 2];
-    const crops = [[0, 0], [0, 0]];
-    const prod = blockShape.reduce((a, b) => a * b);
-
-    expect(() => tf.batchToSpaceND(t, blockShape, crops))
-        .toThrowError(
-            `input tensor batch is ${t.shape[0]} but is not divisible by the ` +
-            `product of the elements of blockShape ${
-                blockShape.join(' * ')} === ${prod}`);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const t = [[[[1]]], [[[2]]], [[[3]]], [[[4]]]];
-    const blockShape = [2, 2];
-    const crops = [[0, 0], [0, 0]];
-
-    const res = tf.batchToSpaceND(t, blockShape, crops);
-    expect(res.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4]);
-  });
-
-  it('gradients,  input shape=[4, 2, 2], block shape=[2]', async () => {
-    const t = tf.tensor(
-        [-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94],
-        [4, 2, 2]);
-    const blockShape = [2];
-    const crops = [[0, 2]];
-    const dy = tf.tensor([.01, .02, .03, .04, .05, .06, .07, .08], [2, 2, 2]);
-
-    const gradient =
-        tf.grad(t => tf.batchToSpaceND(t, blockShape, crops))(t, dy);
-    expect(gradient.shape).toEqual([4, 2, 2]);
-    expectArraysClose(await gradient.data(), [
-      0.01, 0.02, 0, 0, 0.05, 0.06, 0, 0, 0.03, 0.04, 0, 0, 0.07, 0.08, 0, 0
-    ]);
-  });
-
-  it('gradients, input shape=[4, 2, 2, 1], block shape=[2, 2]', async () => {
-    const t = tf.tensor4d(
-        [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16], [4, 2, 2, 1]);
-    const blockShape = [2, 2];
-    const crops = [[0, 0], [0, 0]];
-    const dy = tf.tensor(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4, 1]);
-
-    const gradient =
-        tf.grad(t => tf.batchToSpaceND(t, blockShape, crops))(t, dy);
-    expect(gradient.shape).toEqual([4, 2, 2, 1]);
-    expectArraysClose(
-        await gradient.data(),
-        [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16]);
-  });
-
-  it('gradient with clones, input=[4, 2, 2, 1], block shape=[2, 2]',
-     async () => {
-       const t = tf.tensor4d(
-           [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16],
-           [4, 2, 2, 1]);
-       const blockShape = [2, 2];
-       const crops = [[0, 0], [0, 0]];
-       const dy = tf.tensor(
-           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-           [1, 4, 4, 1]);
-
-       const gradient = tf.grad(
-           t => tf.batchToSpaceND(t.clone(), blockShape, crops).clone())(t, dy);
-       expect(gradient.shape).toEqual([4, 2, 2, 1]);
-       expectArraysClose(
-           await gradient.data(),
-           [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16]);
-     });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/batchnorm.ts b/tfjs-master/tfjs-core/src/ops/batchnorm.ts
deleted file mode 100644
index 5b3f1db27..000000000
--- a/tfjs-master/tfjs-core/src/ops/batchnorm.ts
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor, Tensor1D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {Rank, TensorLike} from '../types';
-import * as util from '../util';
-
-import {xAs4D} from './batchnorm_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Batch normalization.
- *
- * As described in
- * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).
- *
- * Mean, variance, scale, and offset can be of two shapes:
- *   - The same shape as the input.
- *   - In the common case, the depth dimension is the last dimension of x, so
- *     the values would be a `tf.Tensor1D` of shape [depth].
- *
- * Also available are stricter rank-specific methods with the same signature
- * as this method that assert that parameters passed are of given rank
- *   - `tf.batchNorm2d`
- *   - `tf.batchNorm3d`
- *   - `tf.batchNorm4d`
- *
- * @param x The input Tensor.
- * @param mean A mean Tensor.
- * @param variance A variance Tensor.
- * @param offset An offset Tensor.
- * @param scale A scale Tensor.
- * @param varianceEpsilon A small float number to avoid dividing by 0.
- *
- * @doc {heading: 'Operations', subheading: 'Normalization'}
- */
-function batchNorm_<R extends Rank>(
-    x: Tensor<R>|TensorLike, mean: Tensor<R>|Tensor1D|TensorLike,
-    variance: Tensor<R>|Tensor1D|TensorLike,
-    offset?: Tensor<R>|Tensor1D|TensorLike,
-    scale?: Tensor<R>|Tensor1D|TensorLike,
-    varianceEpsilon?: number): Tensor<R> {
-  if (varianceEpsilon == null) {
-    varianceEpsilon = 0.001;
-  }
-  const $x = convertToTensor(x, 'x', 'batchNorm');
-  const $mean = convertToTensor(mean, 'mean', 'batchNorm');
-  const $variance = convertToTensor(variance, 'variance', 'batchNorm');
-  let $scale: Tensor<R>|Tensor1D;
-  if (scale != null) {
-    $scale = convertToTensor(scale, 'scale', 'batchNorm');
-  }
-  let $offset: Tensor<R>|Tensor1D;
-  if (offset != null) {
-    $offset = convertToTensor(offset, 'offset', 'batchNorm');
-  }
-
-  util.assert(
-      $mean.rank === $variance.rank,
-      () => 'Batch normalization gradient requires mean and variance to have ' +
-          'equal ranks.');
-  util.assert(
-      $offset == null || $mean.rank === $offset.rank,
-      () => 'Batch normalization gradient requires mean and offset to have ' +
-          'equal ranks.');
-  util.assert(
-      $scale == null || $mean.rank === $scale.rank,
-      () => 'Batch normalization gradient requires mean and scale to have ' +
-          'equal ranks.');
-
-  const x4D: Tensor4D = xAs4D($x);
-
-  const inputs: FusedBatchNormInputs = {
-    x: x4D,
-    scale: $scale,
-    offset: $offset,
-    mean: $mean,
-    variance: $variance
-  };
-
-  const attrs: FusedBatchNormAttrs = {varianceEpsilon};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  FusedBatchNorm, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as Tensor<R>;
-
-  return reshape(res, $x.shape);
-}
-
-export const batchNorm = /* @__PURE__ */ op({batchNorm_});
diff --git a/tfjs-master/tfjs-core/src/ops/batchnorm2d.ts b/tfjs-master/tfjs-core/src/ops/batchnorm2d.ts
deleted file mode 100644
index 0e1bca920..000000000
--- a/tfjs-master/tfjs-core/src/ops/batchnorm2d.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor1D, Tensor2D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {batchNorm} from './batchnorm';
-import {op} from './operation';
-
-/**
- * Batch normalization, strictly for 2D. For the more relaxed version, see
- * `tf.batchNorm`.
- *
- * @param x The input Tensor.
- * @param mean A mean Tensor.
- * @param variance A variance Tensor.
- * @param offset An offset Tensor.
- * @param scale A scale Tensor.
- * @param varianceEpsilon A small float number to avoid dividing by 0.
- */
-function batchNorm2d_(
-    x: Tensor2D|TensorLike, mean: Tensor2D|Tensor1D|TensorLike,
-    variance: Tensor2D|Tensor1D|TensorLike,
-    offset?: Tensor2D|Tensor1D|TensorLike, scale?: Tensor2D|Tensor1D|TensorLike,
-    varianceEpsilon?: number): Tensor2D {
-  const $x = convertToTensor(x, 'x', 'batchNorm');
-  const $mean = convertToTensor(mean, 'mean', 'batchNorm');
-  const $variance = convertToTensor(variance, 'variance', 'batchNorm');
-  let $scale: Tensor2D|Tensor1D;
-  if (scale != null) {
-    $scale = convertToTensor(scale, 'scale', 'batchNorm');
-  }
-  let $offset: Tensor2D|Tensor1D;
-  if (offset != null) {
-    $offset = convertToTensor(offset, 'offset', 'batchNorm');
-  }
-  util.assert(
-      $x.rank === 2,
-      () => `Error in batchNorm2D: x must be rank 2 but got rank ` +
-          `${$x.rank}.`);
-  util.assert(
-      $mean.rank === 2 || $mean.rank === 1,
-      () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +
-          `got rank ${$mean.rank}.`);
-  util.assert(
-      $variance.rank === 2 || $variance.rank === 1,
-      () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +
-          `but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    util.assert(
-        $scale.rank === 2 || $scale.rank === 1,
-        () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +
-            `but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    util.assert(
-        $offset.rank === 2 || $offset.rank === 1,
-        () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +
-            `but got rank ${$offset.rank}.`);
-  }
-
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-
-export const batchNorm2d = /* @__PURE__ */ op({batchNorm2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/batchnorm3d.ts b/tfjs-master/tfjs-core/src/ops/batchnorm3d.ts
deleted file mode 100644
index 69c493b26..000000000
--- a/tfjs-master/tfjs-core/src/ops/batchnorm3d.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor1D, Tensor3D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {batchNorm} from './batchnorm';
-import {op} from './operation';
-
-/**
- * Batch normalization, strictly for 3D. For the more relaxed version, see
- * `tf.batchNorm`.
- *
- * @param x The input Tensor.
- * @param mean A mean Tensor.
- * @param variance A variance Tensor.
- * @param offset An offset Tensor.
- * @param scale A scale Tensor.
- * @param varianceEpsilon A small float number to avoid dividing by 0.
- */
-function batchNorm3d_(
-    x: Tensor3D|TensorLike, mean: Tensor3D|Tensor1D|TensorLike,
-    variance: Tensor3D|Tensor1D|TensorLike,
-    offset?: Tensor3D|Tensor1D|TensorLike, scale?: Tensor3D|Tensor1D|TensorLike,
-    varianceEpsilon?: number): Tensor3D {
-  const $x = convertToTensor(x, 'x', 'batchNorm');
-  const $mean = convertToTensor(mean, 'mean', 'batchNorm');
-  const $variance = convertToTensor(variance, 'variance', 'batchNorm');
-  let $scale: Tensor3D|Tensor1D;
-  if (scale != null) {
-    $scale = convertToTensor(scale, 'scale', 'batchNorm');
-  }
-  let $offset: Tensor3D|Tensor1D;
-  if (offset != null) {
-    $offset = convertToTensor(offset, 'offset', 'batchNorm');
-  }
-  util.assert(
-      $x.rank === 3,
-      () => `Error in batchNorm3D: x must be rank 3 but got rank ` +
-          `${$x.rank}.`);
-  util.assert(
-      $mean.rank === 3 || $mean.rank === 1,
-      () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +
-          `got rank ${$mean.rank}.`);
-  util.assert(
-      $variance.rank === 3 || $variance.rank === 1,
-      () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +
-          `but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    util.assert(
-        $scale.rank === 3 || $scale.rank === 1,
-        () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +
-            `but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    util.assert(
-        $offset.rank === 3 || $offset.rank === 1,
-        () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +
-            `but got rank ${$offset.rank}.`);
-  }
-
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-
-export const batchNorm3d = /* @__PURE__ */ op({batchNorm3d_});
diff --git a/tfjs-master/tfjs-core/src/ops/batchnorm4d.ts b/tfjs-master/tfjs-core/src/ops/batchnorm4d.ts
deleted file mode 100644
index 4dc6aa6d8..000000000
--- a/tfjs-master/tfjs-core/src/ops/batchnorm4d.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor1D, Tensor4D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {batchNorm} from './batchnorm';
-import {op} from './operation';
-
-/**
- * Batch normalization, strictly for 4D. For the more relaxed version, see
- * `tf.batchNorm`.
- *
- * @param x The input Tensor.
- * @param mean A mean Tensor.
- * @param variance A variance Tensor.
- * @param offset An offset Tensor.
- * @param scale A scale Tensor.
- * @param varianceEpsilon A small float number to avoid dividing by 0.
- */
-function batchNorm4d_(
-    x: Tensor4D|TensorLike, mean: Tensor4D|Tensor1D|TensorLike,
-    variance: Tensor4D|Tensor1D|TensorLike,
-    offset?: Tensor4D|Tensor1D|TensorLike, scale?: Tensor4D|Tensor1D|TensorLike,
-    varianceEpsilon?: number): Tensor4D {
-  const $x = convertToTensor(x, 'x', 'batchNorm');
-  const $mean = convertToTensor(mean, 'mean', 'batchNorm');
-  const $variance = convertToTensor(variance, 'variance', 'batchNorm');
-  let $scale: Tensor4D|Tensor1D;
-  if (scale != null) {
-    $scale = convertToTensor(scale, 'scale', 'batchNorm');
-  }
-  let $offset: Tensor4D|Tensor1D;
-  if (offset != null) {
-    $offset = convertToTensor(offset, 'offset', 'batchNorm');
-  }
-  util.assert(
-      $x.rank === 4,
-      () => `Error in batchNorm4D: x must be rank 4 but got rank ` +
-          `${$x.rank}.`);
-  util.assert(
-      $mean.rank === 4 || $mean.rank === 1,
-      () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +
-          `got rank ${$mean.rank}.`);
-  util.assert(
-      $variance.rank === 4 || $variance.rank === 1,
-      () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +
-          `but got rank ${$variance.rank}.`);
-  if ($scale != null) {
-    util.assert(
-        $scale.rank === 4 || $scale.rank === 1,
-        () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +
-            `but got rank ${$scale.rank}.`);
-  }
-  if ($offset != null) {
-    util.assert(
-        $offset.rank === 4 || $offset.rank === 1,
-        () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +
-            `but got rank ${$offset.rank}.`);
-  }
-  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
-}
-
-export const batchNorm4d = /* @__PURE__ */ op({batchNorm4d_});
diff --git a/tfjs-master/tfjs-core/src/ops/batchnorm_test.ts b/tfjs-master/tfjs-core/src/ops/batchnorm_test.ts
deleted file mode 100644
index 97da7f4d5..000000000
--- a/tfjs-master/tfjs-core/src/ops/batchnorm_test.ts
+++ /dev/null
@@ -1,905 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('batchNorm4D', ALL_ENVS, () => {
-  it('simple batchnorm4D, no offset or scale, 2x1x1x2', async () => {
-    const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const varianceEpsilon = .001;
-
-    const result = tf.batchNorm4d(
-        xT, meanT, varianceT, undefined, undefined, varianceEpsilon);
-
-    const x = await xT.array();
-    const mean = await meanT.array();
-    const variance = await varianceT.array();
-    expectArraysClose(await result.data(), [
-      (x[0][0][0][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),
-      (x[0][0][0][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon),
-      (x[1][0][0][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),
-      (x[1][0][0][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm4D, no offset, 2x1x1x2', async () => {
-    const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const scaleT = tf.tensor1d([4, 5]);
-    const varianceEpsilon = .001;
-
-    const result = tf.batchNorm4d(
-        xT, meanT, varianceT, undefined, scaleT, varianceEpsilon);
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    const scale = await scaleT.buffer();
-
-    expectArraysClose(await result.data(), [
-      (x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /
-          Math.sqrt(variance.get(1) + varianceEpsilon),
-      (x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /
-          Math.sqrt(variance.get(1) + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm4D, no scale, 2x1x1x2', async () => {
-    const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const offsetT = tf.tensor1d([4, 5]);
-
-    const varianceEpsilon = .001;
-
-    const result = tf.batchNorm4d(
-        xT, meanT, varianceT, offsetT, undefined, varianceEpsilon);
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    const offset = await offsetT.buffer();
-
-    expectArraysClose(await result.data(), [
-      offset.get(0) +
-          (x.get(0, 0, 0, 0) - mean.get(0)) * 1 /
-              Math.sqrt(variance.get(0) + varianceEpsilon),
-      offset.get(1) +
-          (x.get(0, 0, 0, 1) - mean.get(1)) * 1 /
-              Math.sqrt(variance.get(1) + varianceEpsilon),
-      offset.get(0) +
-          (x.get(1, 0, 0, 0) - mean.get(0)) * 1 /
-              Math.sqrt(variance.get(0) + varianceEpsilon),
-      offset.get(1) +
-          (x.get(1, 0, 0, 1) - mean.get(1)) * 1 /
-              Math.sqrt(variance.get(1) + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm4D, 2x1x1x2', async () => {
-    const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const offsetT = tf.tensor1d([3, 4]);
-    const scaleT = tf.tensor1d([4, 5]);
-
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm4d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    const scale = await scaleT.buffer();
-    const offset = await offsetT.buffer();
-
-    expectArraysClose(await result.data(), [
-      offset.get(0) +
-          (x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /
-              Math.sqrt(variance.get(0) + varianceEpsilon),
-      offset.get(1) +
-          (x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /
-              Math.sqrt(variance.get(1) + varianceEpsilon),
-      offset.get(0) +
-          (x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /
-              Math.sqrt(variance.get(0) + varianceEpsilon),
-      offset.get(1) +
-          (x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /
-              Math.sqrt(variance.get(1) + varianceEpsilon)
-    ]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[[[2, 4]]], [[[9, 23]]]];  // 2x1x1x2
-    const mean = [1, 2];
-    const variance = [2, 3];
-    const offset = [3, 4];
-    const scale = [4, 5];
-
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon);
-
-    expectArraysClose(await result.data(), [
-      offset[0] +
-          (x[0][0][0][0] - mean[0]) * scale[0] /
-              Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[0][0][0][1] - mean[1]) * scale[1] /
-              Math.sqrt(variance[1] + varianceEpsilon),
-      offset[0] +
-          (x[1][0][0][0] - mean[0]) * scale[0] /
-              Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[1][0][0][1] - mean[1]) * scale[1] /
-              Math.sqrt(variance[1] + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm4D gradients, 2x1x1x2', async () => {
-    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const mean = tf.tensor1d([1, 2]);
-    const variance = tf.tensor1d([2, 3]);
-    const offset = tf.tensor1d([3, 4]);
-    const scale = tf.tensor1d([2, 5]);
-
-    const varianceEpsilon = .001;
-
-    const dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);
-    const gradX = tf.grad(
-        (x: tf.Tensor4D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
-    expectArraysClose(await gradX.data(), [-1.414, -2.887, -1.414, -2.887]);
-    expect(gradX.shape).toEqual([2, 1, 1, 2]);
-    const gradMean = tf.grad(
-        (mean: tf.Tensor1D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
-    expectArraysClose(await gradMean.data(), [2.828, 5.773]);
-    expect(gradMean.shape).toEqual([2]);
-    const gradVariance = tf.grad(
-        (variance: tf.Tensor1D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
-    expectArraysClose(await gradVariance.data(), [3.180, 11.060]);
-    expect(gradVariance.shape).toEqual([2]);
-    const gradOffset = tf.grad(
-        (offset: tf.Tensor1D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
-    expectArraysClose(await gradOffset.data(), await dy.sum([0, 1, 2]).data());
-    expect(gradOffset.shape).toEqual([2]);
-    const gradScale = tf.grad(
-        (scale: tf.Tensor1D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
-    expectArraysClose(await gradScale.data(), [-6.362, -13.277]);
-    expect(gradScale.shape).toEqual([2]);
-  });
-
-  it('batchnorm4D gradients, same shapes in x, mean and variance', async () => {
-    const x = tf.tensor4d([10, 20, 30, 40], [2, 1, 1, 2]);
-    const mean = tf.tensor4d([0, 5, 10, 15], [2, 1, 1, 2]);
-    const variance = tf.tensor4d([2, 4, 6, 8], [2, 1, 1, 2]);
-    const scale = tf.tensor4d([2, 5, 2, 5], [2, 1, 1, 2]);
-    const offset = tf.tensor4d([0, 0, 0, 0], [2, 1, 1, 2]);
-
-    const varianceEpsilon = .001;
-
-    const dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);
-    const gradX = tf.grad(
-        (x: tf.Tensor4D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
-    expectArraysClose(await gradX.data(), [-1.414, -2.500, -0.816, -1.768]);
-    expect(gradX.shape).toEqual([2, 1, 1, 2]);
-    const gradMean = tf.grad(
-        (mean: tf.Tensor4D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
-    expectArraysClose(await gradMean.data(), [1.414, 2.500, 0.816, 1.768]);
-    expect(gradMean.shape).toEqual([2, 1, 1, 2]);
-    const gradVariance = tf.grad(
-        (variance: tf.Tensor4D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
-    expectArraysClose(await gradVariance.data(), [3.533, 4.686, 1.360, 2.762]);
-    expect(gradVariance.shape).toEqual([2, 1, 1, 2]);
-    const gradOffset = tf.grad(
-        (offset: tf.Tensor4D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
-    expectArraysClose(await gradOffset.data(), await dy.data());
-    expect(gradOffset.shape).toEqual([2, 1, 1, 2]);
-    const gradScale = tf.grad(
-        (scale: tf.Tensor4D) => tf.batchNorm4d(
-            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
-    expectArraysClose(await gradScale.data(), [-7.069, -7.499, -8.164, -8.838]);
-    expect(gradScale.shape).toEqual([2, 1, 1, 2]);
-  });
-});
-
-describeWithFlags('batchNorm3D', ALL_ENVS, () => {
-  it('simple batchnorm3D, no offset or scale, 2x1x2', async () => {
-    const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const varianceEpsilon = .001;
-
-    const result = tf.batchNorm3d(
-        xT, meanT, varianceT, undefined, undefined, varianceEpsilon);
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    expectArraysClose(await result.data(), [
-      (x.get(0, 0, 0) - mean.get(0)) * 1 /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(0, 0, 1) - mean.get(1)) * 1 /
-          Math.sqrt(variance.get(1) + varianceEpsilon),
-      (x.get(1, 0, 0) - mean.get(0)) * 1 /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(1, 0, 1) - mean.get(1)) * 1 /
-          Math.sqrt(variance.get(1) + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm3D, no offset, 2x1x2', async () => {
-    const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const scaleT = tf.tensor1d([4, 5]);
-    const varianceEpsilon = .001;
-
-    const result = tf.batchNorm3d(
-        xT, meanT, varianceT, undefined, scaleT, varianceEpsilon);
-
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    const scale = await scaleT.buffer();
-    expectArraysClose(await result.data(), [
-      (x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /
-          Math.sqrt(variance.get(1) + varianceEpsilon),
-      (x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /
-          Math.sqrt(variance.get(1) + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm3D, no scale, 2x1x2', async () => {
-    const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const offsetT = tf.tensor1d([4, 5]);
-
-    const varianceEpsilon = .001;
-
-    const result = tf.batchNorm3d(
-        xT, meanT, varianceT, offsetT, undefined, varianceEpsilon);
-
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    const offset = await offsetT.buffer();
-    expectArraysClose(await result.data(), [
-      offset.get(0) +
-          (x.get(0, 0, 0) - mean.get(0)) * 1 /
-              Math.sqrt(variance.get(0) + varianceEpsilon),
-      offset.get(1) +
-          (x.get(0, 0, 1) - mean.get(1)) * 1 /
-              Math.sqrt(variance.get(1) + varianceEpsilon),
-      offset.get(0) +
-          (x.get(1, 0, 0) - mean.get(0)) * 1 /
-              Math.sqrt(variance.get(0) + varianceEpsilon),
-      offset.get(1) +
-          (x.get(1, 0, 1) - mean.get(1)) * 1 /
-              Math.sqrt(variance.get(1) + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm3D, 2x1x2', async () => {
-    const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const offsetT = tf.tensor1d([3, 4]);
-    const scaleT = tf.tensor1d([4, 5]);
-
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm3d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    const offset = await offsetT.buffer();
-    const scale = await scaleT.buffer();
-
-    expectArraysClose(await result.data(), [
-      offset.get(0) +
-          (x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /
-              Math.sqrt(variance.get(0) + varianceEpsilon),
-      offset.get(1) +
-          (x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /
-              Math.sqrt(variance.get(1) + varianceEpsilon),
-      offset.get(0) +
-          (x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /
-              Math.sqrt(variance.get(0) + varianceEpsilon),
-      offset.get(1) +
-          (x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /
-              Math.sqrt(variance.get(1) + varianceEpsilon)
-    ]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[[2, 4]], [[9, 23]]];  // 2x1x2
-    const mean = [1, 2];
-    const variance = [2, 3];
-    const offset = [3, 4];
-    const scale = [4, 5];
-
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon);
-
-    expectArraysClose(await result.data(), [
-      offset[0] +
-          (x[0][0][0] - mean[0]) * scale[0] /
-              Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[0][0][1] - mean[1]) * scale[1] /
-              Math.sqrt(variance[1] + varianceEpsilon),
-      offset[0] +
-          (x[1][0][0] - mean[0]) * scale[0] /
-              Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[1][0][1] - mean[1]) * scale[1] /
-              Math.sqrt(variance[1] + varianceEpsilon)
-    ]);
-  });
-
-  it('batchnorm3D, x,mean,var,offset,scale are all 3D', async () => {
-    const shape: [number, number, number] = [2, 1, 2];
-    const xT = tf.tensor3d([2, 4, 9, 23], shape);
-    const meanT = tf.tensor3d([1, 2, 3, 4], shape);
-    const varianceT = tf.tensor3d([2, 3, 4, 5], shape);
-    const offsetT = tf.tensor3d([3, 4, 5, 6], shape);
-    const scaleT = tf.tensor3d([4, 5, 6, 7], shape);
-
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm3d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);
-
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    const offset = await offsetT.buffer();
-    const scale = await scaleT.buffer();
-    expectArraysClose(await result.data(), [
-      offset.get(0, 0, 0) +
-          (x.get(0, 0, 0) - mean.get(0, 0, 0)) * scale.get(0, 0, 0) /
-              Math.sqrt(variance.get(0, 0, 0) + varianceEpsilon),
-      offset.get(0, 0, 1) +
-          (x.get(0, 0, 1) - mean.get(0, 0, 1)) * scale.get(0, 0, 1) /
-              Math.sqrt(variance.get(0, 0, 1) + varianceEpsilon),
-      offset.get(1, 0, 0) +
-          (x.get(1, 0, 0) - mean.get(1, 0, 0)) * scale.get(1, 0, 0) /
-              Math.sqrt(variance.get(1, 0, 0) + varianceEpsilon),
-      offset.get(1, 0, 1) +
-          (x.get(1, 0, 1) - mean.get(1, 0, 1)) * scale.get(1, 0, 1) /
-              Math.sqrt(variance.get(1, 0, 1) + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm3D gradients, 2x1x2', async () => {
-    const x = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
-    const mean = tf.tensor1d([1, 2]);
-    const variance = tf.tensor1d([2, 3]);
-    const offset = tf.tensor1d([3, 4]);
-    const scale = tf.tensor1d([2, 5]);
-
-    const varianceEpsilon = .001;
-
-    const dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);
-    const gradX = tf.grad(
-        (x: tf.Tensor3D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
-    expectArraysClose(await gradX.data(), [1.414, 2.887, 1.414, 2.887]);
-    expect(gradX.shape).toEqual([2, 1, 2]);
-    const gradMean = tf.grad(
-        (mean: tf.Tensor1D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
-    expectArraysClose(await gradMean.data(), [-2.828, -5.773]);
-    expect(gradMean.shape).toEqual([2]);
-    const gradVariance = tf.grad(
-        (variance: tf.Tensor1D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
-    expectArraysClose(await gradVariance.data(), [-3.180, -11.060]);
-    expect(gradVariance.shape).toEqual([2]);
-    const gradOffset = tf.grad(
-        (offset: tf.Tensor1D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
-    expectArraysClose(await gradOffset.data(), [2, 2]);
-    expect(gradOffset.shape).toEqual([2]);
-    const gradScale = tf.grad(
-        (scale: tf.Tensor1D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
-    expectArraysClose(await gradScale.data(), [6.362, 13.277]);
-    expect(gradScale.shape).toEqual([2]);
-  });
-
-  it('batchnorm3D gradients, same shapes in x, mean and variance', async () => {
-    const x = tf.tensor3d([10, 20, 30, 40], [2, 1, 2]);
-    const mean = tf.tensor3d([0, 5, 10, 15], [2, 1, 2]);
-    const variance = tf.tensor3d([2, 4, 6, 8], [2, 1, 2]);
-    const scale = tf.tensor3d([2, 5, 2, 5], [2, 1, 2]);
-    const offset = tf.tensor3d([0, 0, 0, 0], [2, 1, 2]);
-
-    const varianceEpsilon = .001;
-
-    const dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);
-    const gradX = tf.grad(
-        (x: tf.Tensor3D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
-    expectArraysClose(await gradX.data(), [1.414, 2.500, 0.816, 1.768]);
-    expect(gradX.shape).toEqual([2, 1, 2]);
-    const gradMean = tf.grad(
-        (mean: tf.Tensor3D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
-    expectArraysClose(await gradMean.data(), [-1.414, -2.500, -0.816, -1.768]);
-    expect(gradMean.shape).toEqual([2, 1, 2]);
-    const gradVariance = tf.grad(
-        (variance: tf.Tensor3D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
-    expectArraysClose(
-        await gradVariance.data(), [-3.533, -4.686, -1.360, -2.762]);
-    expect(gradVariance.shape).toEqual([2, 1, 2]);
-    const gradOffset = tf.grad(
-        (offset: tf.Tensor3D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
-    expectArraysClose(await gradOffset.data(), [1, 1, 1, 1]);
-    expect(gradOffset.shape).toEqual([2, 1, 2]);
-    const gradScale = tf.grad(
-        (scale: tf.Tensor3D) => tf.batchNorm3d(
-            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
-    expectArraysClose(await gradScale.data(), [7.069, 7.499, 8.164, 8.838]);
-    expect(gradScale.shape).toEqual([2, 1, 2]);
-  });
-
-  it('batchnorm matches tensorflow, 2x3x3', async () => {
-    const x = tf.tensor3d(
-        [
-          0.49955603, 0.04158615, -1.09440524, 2.03854165, -0.61578344,
-          2.87533573, 1.18105987, 0.807462, 1.87888837, 2.26563962, -0.37040935,
-          1.35848753, -0.75347094, 0.15683117, 0.91925946, 0.34121279,
-          0.92717143, 1.89683965
-        ],
-        [2, 3, 3]);
-    const mean = tf.tensor1d([0.39745062, -0.48062894, 0.4847822]);
-    const variance = tf.tensor1d([0.32375343, 0.67117643, 1.08334653]);
-    const offset = tf.tensor1d([0.69398749, -1.29056387, 0.9429723]);
-    const scale = tf.tensor1d([-0.5607271, 0.9878457, 0.25181573]);
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon);
-
-    expectArraysClose(await result.data(), [
-      0.59352049, -0.66135202, 0.5610874, -0.92077015, -1.45341019, 1.52106473,
-      -0.07704776, 0.26144429, 1.28010017, -1.14422404, -1.15776136, 1.15425493,
-      1.82644104, -0.52249442, 1.04803919, 0.74932291, 0.40568101, 1.2844412
-    ]);
-  });
-});
-
-describeWithFlags('batchNorm2D', ALL_ENVS, () => {
-  it('simple batchnorm2D, no offset or scale, 2x2', async () => {
-    const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const varianceEpsilon = .001;
-
-    const result = tf.batchNorm2d(
-        xT, meanT, varianceT, undefined, undefined, varianceEpsilon);
-
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    expectArraysClose(await result.data(), [
-      (x.get(0, 0) - mean.get(0)) * 1 /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(0, 1) - mean.get(1)) * 1 /
-          Math.sqrt(variance.get(1) + varianceEpsilon),
-      (x.get(1, 0) - mean.get(0)) * 1 /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(1, 1) - mean.get(1)) * 1 /
-          Math.sqrt(variance.get(1) + varianceEpsilon)
-    ]);
-  });
-  it('simple batchnorm2D, no offset, 2x2', async () => {
-    const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const scaleT = tf.tensor1d([4, 5]);
-    const varianceEpsilon = .001;
-
-    const result = tf.batchNorm2d(
-        xT, meanT, varianceT, undefined, scaleT, varianceEpsilon);
-
-    const x = await xT.buffer();
-    const mean = await meanT.buffer();
-    const variance = await varianceT.buffer();
-    const scale = await scaleT.buffer();
-    expectArraysClose(await result.data(), [
-      (x.get(0, 0) - mean.get(0)) * scale.get(0) /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(0, 1) - mean.get(1)) * scale.get(1) /
-          Math.sqrt(variance.get(1) + varianceEpsilon),
-      (x.get(1, 0) - mean.get(0)) * scale.get(0) /
-          Math.sqrt(variance.get(0) + varianceEpsilon),
-      (x.get(1, 1) - mean.get(1)) * scale.get(1) /
-          Math.sqrt(variance.get(1) + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm2D, no scale, 2x2', async () => {
-    const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const offsetT = tf.tensor1d([4, 5]);
-
-    const varianceEpsilon = .001;
-
-    const result = tf.batchNorm2d(
-        xT, meanT, varianceT, offsetT, undefined, varianceEpsilon);
-
-    const offset = await offsetT.array();
-    const mean = await meanT.array();
-    const variance = await varianceT.array();
-    const x = await xT.array();
-
-    expectArraysClose(await result.data(), [
-      offset[0] +
-          (x[0][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[0][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon),
-      offset[0] +
-          (x[1][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[1][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm2D, 2x2', async () => {
-    const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);
-    const meanT = tf.tensor1d([1, 2]);
-    const varianceT = tf.tensor1d([2, 3]);
-    const offsetT = tf.tensor1d([3, 4]);
-    const scaleT = tf.tensor1d([4, 5]);
-
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm2d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);
-
-    const offset = await offsetT.array();
-    const mean = await meanT.array();
-    const variance = await varianceT.array();
-    const scale = await scaleT.array();
-    const x = await xT.array();
-
-    expectArraysClose(await result.data(), [
-      offset[0] +
-          (x[0][0] - mean[0]) * scale[0] /
-              Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[0][1] - mean[1]) * scale[1] /
-              Math.sqrt(variance[1] + varianceEpsilon),
-      offset[0] +
-          (x[1][0] - mean[0]) * scale[0] /
-              Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[1][1] - mean[1]) * scale[1] /
-              Math.sqrt(variance[1] + varianceEpsilon)
-    ]);
-  });
-
-  it('simple batchnorm2D gradients, 2x2', async () => {
-    const x = tf.tensor2d([2, 4, 9, 23], [2, 2]);
-    const mean = tf.tensor1d([1, 2]);
-    const variance = tf.tensor1d([2, 3]);
-    const offset = tf.tensor1d([3, 4]);
-    const scale = tf.tensor1d([2, 5]);
-
-    const varianceEpsilon = .001;
-
-    const dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
-    const [gradX, gradMean, gradVariance, gradOffset, gradScale] = tf.grads(
-        (x: tf.Tensor2D, mean: tf.Tensor1D, variance: tf.Tensor1D,
-         offset: tf.Tensor1D, scale: tf.Tensor1D) =>
-            tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon))(
-        [x, mean, variance, offset, scale], dy);
-    expectArraysClose(await gradX.data(), [1.414, 2.887, 1.414, 2.887]);
-    expect(gradX.shape).toEqual([2, 2]);
-    expectArraysClose(await gradMean.data(), [-2.828, -5.773]);
-    expect(gradMean.shape).toEqual([2]);
-    expectArraysClose(await gradVariance.data(), [-3.180, -11.060]);
-    expect(gradVariance.shape).toEqual([2]);
-    expectArraysClose(await gradOffset.data(), [2, 2]);
-    expect(gradOffset.shape).toEqual([2]);
-    expectArraysClose(await gradScale.data(), [6.362, 13.277]);
-    expect(gradScale.shape).toEqual([2]);
-  });
-
-  it('gradient with clones batchnorm2D', async () => {
-    const x = tf.tensor2d([2, 4, 9, 23], [2, 2]);
-    const mean = tf.tensor1d([1, 2]);
-    const variance = tf.tensor1d([2, 3]);
-    const offset = tf.tensor1d([3, 4]);
-    const scale = tf.tensor1d([2, 5]);
-
-    const varianceEpsilon = .001;
-
-    const dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
-    const [gradX, gradMean, gradVariance, gradOffset, gradScale] = tf.grads(
-        (x: tf.Tensor2D, mean: tf.Tensor1D, variance: tf.Tensor1D,
-         offset: tf.Tensor1D, scale: tf.Tensor1D) =>
-            tf.batchNorm2d(
-                  x.clone(), mean.clone(), variance.clone(), offset.clone(),
-                  scale.clone(), varianceEpsilon)
-                .clone())([x, mean, variance, offset, scale], dy);
-    expectArraysClose(await gradX.data(), [1.414, 2.887, 1.414, 2.887]);
-    expect(gradX.shape).toEqual([2, 2]);
-    expectArraysClose(await gradMean.data(), [-2.828, -5.773]);
-    expect(gradMean.shape).toEqual([2]);
-    expectArraysClose(await gradVariance.data(), [-3.180, -11.060]);
-    expect(gradVariance.shape).toEqual([2]);
-    expectArraysClose(await gradOffset.data(), [2, 2]);
-    expect(gradOffset.shape).toEqual([2]);
-    expectArraysClose(await gradScale.data(), [6.362, 13.277]);
-    expect(gradScale.shape).toEqual([2]);
-  });
-
-  it('batchnorm2D gradients, same shapes in x, mean and variance', async () => {
-    const x = tf.tensor2d([10, 20, 30, 40], [2, 2]);
-    const mean = tf.tensor2d([0, 5, 10, 15], [2, 2]);
-    const variance = tf.tensor2d([2, 4, 6, 8], [2, 2]);
-    const scale = tf.tensor2d([2, 5, 2, 5], [2, 2]);
-    const offset = tf.tensor2d([0, 0, 0, 0], [2, 2]);
-
-    const varianceEpsilon = .001;
-
-    const dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
-    const gradX = tf.grad(
-        (x: tf.Tensor2D) => tf.batchNorm2d(
-            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
-    expectArraysClose(await gradX.data(), [1.414, 2.500, 0.816, 1.768]);
-    expect(gradX.shape).toEqual([2, 2]);
-    const gradMean = tf.grad(
-        (mean: tf.Tensor2D) => tf.batchNorm2d(
-            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
-    expectArraysClose(await gradMean.data(), [-1.414, -2.500, -0.816, -1.768]);
-    expect(gradMean.shape).toEqual([2, 2]);
-    const gradVariance = tf.grad(
-        (variance: tf.Tensor2D) => tf.batchNorm2d(
-            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
-    expectArraysClose(
-        await gradVariance.data(), [-3.533, -4.686, -1.360, -2.762]);
-    expect(gradVariance.shape).toEqual([2, 2]);
-    const gradOffset = tf.grad(
-        (offset: tf.Tensor2D) => tf.batchNorm2d(
-            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
-    expectArraysClose(await gradOffset.data(), [1, 1, 1, 1]);
-    expect(gradOffset.shape).toEqual([2, 2]);
-    const gradScale = tf.grad(
-        (scale: tf.Tensor2D) => tf.batchNorm2d(
-            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
-    expectArraysClose(await gradScale.data(), [7.069, 7.499, 8.164, 8.838]);
-    expect(gradScale.shape).toEqual([2, 2]);
-  });
-
-  it('gradient with clones', () => {
-    const x = tf.zeros([2, 2]);
-    const mean = tf.zeros([2, 2]);
-    const variance = tf.zeros([2, 2]);
-    const scale = tf.zeros([2, 2]);
-    const offset = tf.zeros([2, 2]);
-
-    const varianceEpsilon = .001;
-
-    const gradF = tf.grads(
-        (x: tf.Tensor2D, mean: tf.Tensor2D, variance: tf.Tensor2D,
-         offset: tf.Tensor2D, scale: tf.Tensor2D) =>
-            tf.batchNorm2d(
-                  x.clone(), mean.clone(), variance.clone(), offset.clone(),
-                  scale.clone(), varianceEpsilon)
-                .clone());
-    const [gradX, gradMean, gradVariance, gradOffset, gradScale] =
-        gradF([x, mean, variance, offset, scale]);
-    expect(gradX.shape).toEqual(x.shape);
-    expect(gradMean.shape).toEqual(mean.shape);
-    expect(gradVariance.shape).toEqual(variance.shape);
-    expect(gradOffset.shape).toEqual(offset.shape);
-    expect(gradScale.shape).toEqual(scale.shape);
-  });
-
-  it('batchnorm2D matches tensorflow, 3x3', async () => {
-    const x = tf.tensor2d(
-        [
-          0.3136892, 0.92389025, 0.594782, 0.05021042, 0.67545404, 0.93910035,
-          0.13277993, 0.96474269, 0.88608916
-        ],
-        [3, 3]);
-    const mean = tf.tensor1d([0.19526312, 0.74857256, 0.45166398]);
-    const variance = tf.tensor1d([0.22963001, 0.61521992, 0.46623685]);
-    const offset = tf.tensor1d([0.43098484, 0.77712237, 0.47916298]);
-    const scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon);
-
-    expectArraysClose(await result.data(), [
-      0.58433646, 0.96846228, 0.51936529, 0.24315402, 0.69732157, 0.61608542,
-      0.35007446, 1.01304821, 0.60119441
-    ]);
-  });
-
-  it('throws when passed x as a non-tensor', () => {
-    const mean = tf.tensor1d([1, 2]);
-    const variance = tf.tensor1d([2, 3]);
-
-    expect(() => tf.batchNorm({} as tf.Tensor, mean, variance))
-        .toThrowError(/Argument 'x' passed to 'batchNorm' must be a Tensor/);
-  });
-  it('throws when passed mean as a non-tensor', () => {
-    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const variance = tf.tensor1d([2, 3]);
-
-    expect(() => tf.batchNorm(x, {} as tf.Tensor, variance))
-        .toThrowError(/Argument 'mean' passed to 'batchNorm' must be a Tensor/);
-  });
-  it('throws when passed variance as a non-tensor', () => {
-    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const mean = tf.tensor1d([1, 2]);
-
-    const e = /Argument 'variance' passed to 'batchNorm' must be a Tensor/;
-    expect(() => tf.batchNorm(x, mean, {} as tf.Tensor)).toThrowError(e);
-  });
-  it('throws when passed scale as a non-tensor', () => {
-    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const mean = tf.tensor1d([1, 2]);
-    const variance = tf.tensor1d([2, 3]);
-    const epsilon = .001;
-
-    expect(() => tf.batchNorm(x, mean, variance, epsilon, {} as tf.Tensor))
-        .toThrowError(
-            /Argument 'scale' passed to 'batchNorm' must be a Tensor/);
-  });
-  it('throws when passed offset as a non-tensor', () => {
-    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
-    const mean = tf.tensor1d([1, 2]);
-    const variance = tf.tensor1d([2, 3]);
-    const epsilon = .001;
-    const scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);
-
-    const e = /Argument 'offset' passed to 'batchNorm' must be a Tensor/;
-    expect(
-        () => tf.batchNorm(x, mean, variance, {} as tf.Tensor, scale, epsilon))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[2, 4], [9, 23]];
-    const mean = [1, 2];
-    const variance = [2, 3];
-    const offset = [3, 4];
-    const scale = [4, 5];
-
-    const varianceEpsilon = .001;
-
-    const result =
-        tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon);
-
-    expectArraysClose(await result.data(), [
-      offset[0] +
-          (x[0][0] - mean[0]) * scale[0] /
-              Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[0][1] - mean[1]) * scale[1] /
-              Math.sqrt(variance[1] + varianceEpsilon),
-      offset[0] +
-          (x[1][0] - mean[0]) * scale[0] /
-              Math.sqrt(variance[0] + varianceEpsilon),
-      offset[1] +
-          (x[1][1] - mean[1]) * scale[1] /
-              Math.sqrt(variance[1] + varianceEpsilon)
-    ]);
-  });
-
-  it('throws error when x is a string tensor', () => {
-    const mean = [1, 2];
-    const variance = [2, 3];
-    const offset = [3, 4];
-    const scale = [4, 5];
-
-    const varianceEpsilon = .001;
-
-    const f = () => tf.batchNorm2d(
-        [['a', 'b'], ['c', 'd']], mean, variance, offset, scale,
-        varianceEpsilon);
-    expect(f).toThrowError(
-        /Argument 'x' passed to 'batchNorm' must be numeric/);
-  });
-
-  it('throws error when mean is a string tensor', () => {
-    const x = [[2, 4], [9, 23]];
-    const variance = [2, 3];
-    const offset = [3, 4];
-    const scale = [4, 5];
-
-    const varianceEpsilon = .001;
-
-    const f = () =>
-        tf.batchNorm2d(x, ['a', 'b'], variance, offset, scale, varianceEpsilon);
-    expect(f).toThrowError(
-        /Argument 'mean' passed to 'batchNorm' must be numeric/);
-  });
-
-  it('throws error when variance is a string tensor', () => {
-    const x = [[2, 4], [9, 23]];
-    const mean = [1, 2];
-    const offset = [3, 4];
-    const scale = [4, 5];
-
-    const varianceEpsilon = .001;
-
-    const f = () =>
-        tf.batchNorm2d(x, mean, ['a', 'b'], offset, scale, varianceEpsilon);
-    expect(f).toThrowError(/'variance' passed to 'batchNorm' must be numeric/);
-  });
-
-  it('throws error when scale is a string tensor', () => {
-    const x = [[2, 4], [9, 23]];
-    const mean = [1, 2];
-    const variance = [2, 3];
-    const offset = [3, 4];
-
-    const varianceEpsilon = .001;
-
-    const f = () =>
-        tf.batchNorm2d(x, mean, variance, offset, ['a', 'b'], varianceEpsilon);
-    expect(f).toThrowError(/'scale' passed to 'batchNorm' must be numeric/);
-  });
-
-  it('throws error when offset is a string tensor', () => {
-    const x = [[2, 4], [9, 23]];
-    const mean = [1, 2];
-    const variance = [2, 3];
-    const scale = [4, 5];
-
-    const varianceEpsilon = .001;
-
-    const f = () =>
-        tf.batchNorm2d(x, mean, variance, ['a', 'b'], scale, varianceEpsilon);
-    expect(f).toThrowError(/'offset' passed to 'batchNorm' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/batchnorm_util.ts b/tfjs-master/tfjs-core/src/ops/batchnorm_util.ts
deleted file mode 100644
index f85b4c097..000000000
--- a/tfjs-master/tfjs-core/src/ops/batchnorm_util.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor, Tensor4D} from '../tensor';
-import {Rank} from '../types';
-import {reshape} from './reshape';
-
-export function xAs4D<R extends Rank>(x: Tensor<R>) {
-  let x4D: Tensor4D;
-  if (x.rank === 0 || x.rank === 1) {
-    x4D = reshape(x, [1, 1, 1, x.size]);
-  } else if (x.rank === 2) {
-    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
-  } else if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  } else {
-    x4D = x as Tensor4D;
-  }
-
-  return x4D;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/binary_ops_test.ts b/tfjs-master/tfjs-core/src/ops/binary_ops_test.ts
deleted file mode 100644
index 56f1de170..000000000
--- a/tfjs-master/tfjs-core/src/ops/binary_ops_test.ts
+++ /dev/null
@@ -1,1305 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('prelu', ALL_ENVS, () => {
-  it('basic', async () => {
-    const x = tf.tensor1d([0, 1, -2, -4]);
-    const a = tf.tensor1d([0.15, 0.2, 0.25, 0.15]);
-    const result = tf.prelu(x, a);
-
-    expect(result.shape).toEqual(x.shape);
-    expectArraysClose(await result.data(), [0, 1, -0.5, -0.6]);
-  });
-
-  it('basic TensorLike', async () => {
-    const x = [0, 1, -2, -4];
-    const a = [0.15, 0.2, 0.25, 0.15];
-    const result = tf.prelu(x, a);
-
-    expect(result.shape).toEqual([4]);
-    expectArraysClose(await result.data(), [0, 1, -0.5, -0.6]);
-  });
-
-  it('basic TensorLike chained', async () => {
-    const x = tf.tensor1d([0, 1, -2, -4]);
-    const a = [0.15, 0.2, 0.25, 0.15];
-    const result = x.prelu(a);
-
-    expect(result.shape).toEqual(x.shape);
-    expectArraysClose(await result.data(), [0, 1, -0.5, -0.6]);
-  });
-
-  it('basic int32', async () => {
-    const x = tf.tensor1d([0, 1, -2, -4], 'int32');
-    const a = [0.15, 0.2, 0.25, 0.15];
-    const result = tf.prelu(x, a);
-
-    expect(result.shape).toEqual([4]);
-    expect(result.dtype).toEqual('float32');
-    expectArraysClose(await result.data(), [0, 1, -0.5, -0.6]);
-  });
-
-  it('derivative', async () => {
-    const x = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const a = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
-    const dy = tf.tensor1d([1, 1, 1, 1]);
-
-    const dx = tf.grad(x => tf.prelu(x, a))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expect(dx.dtype).toEqual('float32');
-    expectArraysClose(await dx.data(), [1, 1, 0.25, 0.15]);
-  });
-
-  it('gradient with clones', async () => {
-    const x = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const a = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
-    const dx = tf.grad(x => tf.prelu(x.clone(), a).clone())(x);
-
-    expect(dx.shape).toEqual(x.shape);
-    expect(dx.dtype).toEqual('float32');
-    expectArraysClose(await dx.data(), [1, 1, 0.25, 0.15]);
-  });
-
-  it('derivative where alpha got broadcasted', async () => {
-    const x = tf.tensor2d([[0.5, 3, -0.1, -4]]);
-    const a = tf.tensor2d([[0.2]]);
-    const dy = tf.tensor2d([[1, 1, 1, 1]]);
-
-    const da = tf.grad(a => tf.prelu(x, a))(a, dy);
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [-4.1]);
-  });
-
-  it('throws when passed x as a non-tensor', () => {
-    expect(() => tf.prelu({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'x' passed to 'prelu' must be a Tensor/);
-  });
-  it('throws when passed alpha as a non-tensor', () => {
-    expect(() => tf.prelu(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'alpha' passed to 'prelu' must be a Tensor/);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.prelu(['a'], 0.1))
-        .toThrowError(/Argument 'x' passed to 'prelu' must be numeric tensor/);
-  });
-});
-
-describeWithFlags('maximum', ALL_ENVS, () => {
-  it('float32 and float32', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.5, 3, 0.25, 0.15]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [0.5, 3, -0.1, -4];
-    const b = [0.2, 0.4, 0.25, 0.15];
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual([4]);
-    expectArraysClose(await result.data(), [0.5, 3, 0.25, 0.15]);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = [0.2, 0.4, 0.25, 0.15];
-    const result = a.maximum(b);
-
-    expect(result.shape).toEqual([4]);
-    expectArraysClose(await result.data(), [0.5, 3, 0.25, 0.15]);
-  });
-
-  it('int32 and int32', async () => {
-    const a = tf.tensor1d([1, 5, 2, 3], 'int32');
-    const b = tf.tensor1d([2, 3, 1, 4], 'int32');
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), [2, 5, 2, 4]);
-  });
-
-  it('bool and bool', async () => {
-    const a = tf.tensor1d([true, false, false, true], 'bool');
-    const b = tf.tensor1d([false, false, true, true], 'bool');
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), [1, 0, 1, 1]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const a = tf.tensor1d([1, 0, 0, 1], 'float32');
-    const b = tf.tensor1d([0, 0, 1, 1], 'int32');
-    const res = tf.maximum(a, b);
-    expect(res.shape).toEqual(a.shape);
-    expect(res.dtype).toBe('float32');
-    expectArraysEqual(await res.data(), [1, 0, 1, 1]);
-  });
-
-  it('propagates NaN', async () => {
-    const a = tf.tensor1d([0.5, -0.1, NaN]);
-    const b = tf.tensor1d([0.2, 0.3, 0.25]);
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.5, 0.3, NaN]);
-  });
-
-  it('broadcasts Tensor1D and scalar', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = tf.scalar(0.6);
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.6, 3, 0.6, 0.6]);
-  });
-
-  it('broadcasts scalar and Tensor1D', async () => {
-    const a = tf.scalar(0.6);
-    const b = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [0.6, 3, 0.6, 0.6]);
-  });
-
-  it('broadcasts Tensor1D and Tensor2D', async () => {
-    const a = tf.tensor1d([0.5, 0.3]);
-    const b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [0.5, 0.4, 0.6, 0.3]);
-  });
-
-  it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', async () => {
-    const a = tf.tensor2d([0.5, 0.3], [2, 1]);
-    const b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [0.5, 0.5, 0.6, 0.3]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5.2);
-    const b = tf.scalar(0.6);
-    const dy = tf.scalar(3);
-
-    const grads = tf.grads((a, b) => tf.maximum(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [3 * 1]);
-    expectArraysClose(await db.data(), [3 * 0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5.2);
-    const b = tf.scalar(0.6);
-    const dy = tf.scalar(3);
-
-    const grads = tf.grads((a, b) => tf.maximum(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [3 * 1]);
-    expectArraysClose(await db.data(), [3 * 0]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
-    const b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const grads = tf.grads((a, b) => tf.maximum(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [1 * 1, 2 * 0, 3 * 1, 4 * 1]);
-    expectArraysClose(await db.data(), [1 * 0, 2 * 1, 3 * 0, 4 * 0]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
-    const b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.maximum(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [1 * 1, 2 * 0, 3 * 1, 4 * 1]);
-    expectArraysClose(await db.data(), [1 * 0, 2 * 1, 3 * 0, 4 * 0]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.maximum({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'maximum' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.maximum(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'maximum' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[0.5, 3], [-0.1, -4]];
-    const b = [[0.2, 0.4], [0.25, 0.15]];
-    const result = tf.maximum(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), [0.5, 3, 0.25, 0.15]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.maximum('q', 3))
-        .toThrowError(
-            /Argument 'a' passed to 'maximum' must be numeric tensor/);
-
-    expect(() => tf.maximum(3, 'q'))
-        .toThrowError(
-            /Argument 'b' passed to 'maximum' must be numeric tensor/);
-  });
-});
-
-describeWithFlags('squaredDifference', ALL_ENVS, () => {
-  it('float32 and float32', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
-    const result = tf.squaredDifference(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [
-      Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
-      Math.pow(-4 - 0.15, 2)
-    ]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [0.5, 3, -0.1, -4];
-    const b = [0.2, 0.4, 0.25, 0.15];
-    const result = tf.squaredDifference(a, b);
-
-    expect(result.shape).toEqual([4]);
-    expectArraysClose(await result.data(), [
-      Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
-      Math.pow(-4 - 0.15, 2)
-    ]);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = [0.2, 0.4, 0.25, 0.15];
-    const result = a.squaredDifference(b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [
-      Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
-      Math.pow(-4 - 0.15, 2)
-    ]);
-  });
-
-  it('int32 and int32', async () => {
-    const a = tf.tensor1d([1, 5, 2, 3], 'int32');
-    const b = tf.tensor1d([2, 3, 1, 4], 'int32');
-    const result = tf.squaredDifference(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), [
-      Math.pow(1 - 2, 2), Math.pow(5 - 3, 2), Math.pow(2 - 1, 2),
-      Math.pow(3 - 4, 2)
-    ]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    let res =
-        tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(2, 'float32'));
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [9]);
-
-    res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(true, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [16]);
-
-    res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(false, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [25]);
-  });
-
-  it('propagates NaN', async () => {
-    const a = tf.tensor1d([0.5, -0.1, NaN]);
-    const b = tf.tensor1d([0.2, 0.3, 0.25]);
-    const result = tf.squaredDifference(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(
-        await result.data(),
-        [Math.pow(0.5 - 0.2, 2), Math.pow(-0.1 - 0.3, 2), NaN]);
-  });
-
-  it('broadcasts Tensor1D and scalar', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = tf.scalar(0.6);
-    const result = tf.squaredDifference(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [
-      Math.pow(0.5 - 0.6, 2), Math.pow(3 - 0.6, 2), Math.pow(-0.1 - 0.6, 2),
-      Math.pow(-4 - 0.6, 2)
-    ]);
-  });
-
-  it('broadcasts scalar and Tensor1D', async () => {
-    const a = tf.scalar(0.6);
-    const b = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const result = tf.squaredDifference(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [
-      Math.pow(0.6 - 0.5, 2), Math.pow(0.6 - 3, 2), Math.pow(0.6 - (-0.1), 2),
-      Math.pow(0.6 - (-4), 2)
-    ]);
-  });
-
-  it('broadcasts Tensor1D and Tensor2D', async () => {
-    const a = tf.tensor1d([0.5, 0.3]);
-    const b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
-    const result = tf.squaredDifference(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [
-      Math.pow(0.5 - 0.2, 2), Math.pow(0.3 - 0.4, 2), Math.pow(0.5 - 0.6, 2),
-      Math.pow(0.3 - 0.15, 2)
-    ]);
-  });
-
-  it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', async () => {
-    const a = tf.tensor2d([0.5, 0.3], [2, 1]);
-    const b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
-    const result = tf.squaredDifference(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [
-      Math.pow(0.5 - 0.2, 2), Math.pow(0.5 - 0.4, 2), Math.pow(0.3 - 0.6, 2),
-      Math.pow(0.3 - 0.15, 2)
-    ]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5.2);
-    const b = tf.scalar(0.6);
-    const dy = tf.scalar(3);
-
-    const grads = tf.grads((a, b) => tf.squaredDifference(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [3 * 2 * (5.2 - 0.6)]);
-    expectArraysClose(await db.data(), [3 * 2 * (0.6 - 5.2)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5.2);
-    const b = tf.scalar(0.6);
-    const dy = tf.scalar(3);
-
-    const grads =
-        tf.grads((a, b) => tf.squaredDifference(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [3 * 2 * (5.2 - 0.6)]);
-    expectArraysClose(await db.data(), [3 * 2 * (0.6 - 5.2)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
-    const b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
-    const dy = tf.tensor1d([1, 2, 3, 1]);
-
-    const grads = tf.grads((a, b) => tf.squaredDifference(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [
-      1 * 2 * (1.1 - 1.0), 2 * 2 * (2.6 - 2.7), 3 * 2 * (3 - 3),
-      1 * 2 * (5.9 - 5.8)
-    ]);
-    expectArraysClose(await db.data(), [
-      1 * 2 * (1.0 - 1.1), 2 * 2 * (2.7 - 2.6), 3 * 2 * (3 - 3),
-      1 * 2 * (5.8 - 5.9)
-    ]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
-    const b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.squaredDifference(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [
-      1 * 2 * (0.5 - 0.2), 2 * 2 * (0.3 - 0.4), 3 * 2 * (0.7 - 0.7),
-      4 * 2 * (0.9 - 0.15)
-    ]);
-    expectArraysClose(await db.data(), [
-      1 * 2 * (0.2 - 0.5), 2 * 2 * (0.4 - 0.3), 3 * 2 * (0.7 - 0.7),
-      4 * 2 * (0.15 - 0.9)
-    ]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.squaredDifference({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(
-            /Argument 'a' passed to 'squaredDifference' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.squaredDifference(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(
-            /Argument 'b' passed to 'squaredDifference' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[0.5, 3], [-0.1, -4]];
-    const b = 0.6;
-    const result = tf.squaredDifference(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), [
-      Math.pow(0.5 - 0.6, 2), Math.pow(3 - 0.6, 2), Math.pow(-0.1 - 0.6, 2),
-      Math.pow(-4 - 0.6, 2)
-    ]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.squaredDifference('q', 3))
-        .toThrowError(
-            /Argument 'a' passed to 'squaredDifference' must be numeric/);
-
-    expect(() => tf.squaredDifference(3, 'q'))
-        .toThrowError(
-            /Argument 'b' passed to 'squaredDifference' must be numeric/);
-  });
-});
-
-describeWithFlags('minimum', ALL_ENVS, () => {
-  it('float32 and float32', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.2, 0.4, -0.1, -4]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [0.5, 3, -0.1, -4];
-    const b = [0.2, 0.4, 0.25, 0.15];
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual([4]);
-    expectArraysClose(await result.data(), [0.2, 0.4, -0.1, -4]);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = [0.2, 0.4, 0.25, 0.15];
-    const result = a.minimum(b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.2, 0.4, -0.1, -4]);
-  });
-
-  it('int32 and int32', async () => {
-    const a = tf.tensor1d([1, 5, 2, 3], 'int32');
-    const b = tf.tensor1d([2, 3, 1, 4], 'int32');
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), [1, 3, 1, 3]);
-  });
-
-  it('bool and bool', async () => {
-    const a = tf.tensor1d([true, false, false, true], 'bool');
-    const b = tf.tensor1d([false, false, true, true], 'bool');
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), [0, 0, 0, 1]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const a = tf.tensor1d([1, 0, 0, 1], 'float32');
-    const b = tf.tensor1d([0, 0, 1, 1], 'int32');
-    const res = tf.minimum(a, b);
-    expect(res.shape).toEqual(a.shape);
-    expect(res.dtype).toBe('float32');
-    expectArraysEqual(await res.data(), [0, 0, 0, 1]);
-  });
-
-  it('propagates NaN', async () => {
-    const a = tf.tensor1d([0.5, -0.1, NaN]);
-    const b = tf.tensor1d([0.2, 0.3, 0.25]);
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.2, -0.1, NaN]);
-  });
-
-  it('broadcasts Tensor1D and scalar', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = tf.scalar(0.6);
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.5, 0.6, -0.1, -4]);
-  });
-
-  it('broadcasts scalar and Tensor1D', async () => {
-    const a = tf.scalar(0.6);
-    const b = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [0.5, 0.6, -0.1, -4]);
-  });
-
-  it('broadcasts Tensor1D and Tensor2D', async () => {
-    const a = tf.tensor1d([0.5, 0.3]);
-    const b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [0.2, 0.3, 0.5, 0.15]);
-  });
-
-  it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', async () => {
-    const a = tf.tensor2d([0.5, 0.3], [2, 1]);
-    const b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [0.2, 0.4, 0.3, 0.15]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5.2);
-    const b = tf.scalar(0.6);
-    const dy = tf.scalar(3);
-
-    const grads = tf.grads((a, b) => tf.minimum(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [3 * 0]);
-    expectArraysClose(await db.data(), [3 * 1]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5.2);
-    const b = tf.scalar(0.6);
-    const dy = tf.scalar(3);
-
-    const grads = tf.grads((a, b) => tf.minimum(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [3 * 0]);
-    expectArraysClose(await db.data(), [3 * 1]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
-    const b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const grads = tf.grads((a, b) => tf.minimum(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [1 * 0, 2 * 1, 3 * 1, 4 * 0]);
-    expectArraysClose(await db.data(), [1 * 1, 2 * 0, 3 * 0, 4 * 1]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
-    const b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.minimum(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [1 * 0, 2 * 1, 3 * 1, 4 * 0]);
-    expectArraysClose(await db.data(), [1 * 1, 2 * 0, 3 * 0, 4 * 1]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.minimum({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'minimum' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.minimum(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'minimum' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[0.5, 3], [-0.1, -4]];
-    const b = [[0.2, 0.4], [0.25, 0.15]];
-    const result = tf.minimum(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), [0.2, 0.4, -0.1, -4]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.minimum('q', 3))
-        .toThrowError(/Argument 'a' passed to 'minimum' must be numeric/);
-
-    expect(() => tf.minimum(3, 'q'))
-        .toThrowError(/Argument 'b' passed to 'minimum' must be numeric/);
-  });
-});
-
-describeWithFlags('mod', ALL_ENVS, () => {
-  it('float32 and float32', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
-    const result = tf.mod(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.1, 0.2, 0.15, 0.05]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [0.5, 3, -0.1, -4];
-    const b = [0.2, 0.4, 0.25, 0.15];
-    const result = tf.mod(a, b);
-
-    expect(result.shape).toEqual([4]);
-    expectArraysClose(await result.data(), [0.1, 0.2, 0.15, 0.05]);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([0.5, 3, -0.1, -4]);
-    const b = [0.2, 0.4, 0.25, 0.15];
-    const result = a.mod(b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.1, 0.2, 0.15, 0.05]);
-  });
-
-  it('int32 and int32', async () => {
-    const a = tf.tensor1d([1, 5, 2, 3], 'int32');
-    const b = tf.tensor1d([2, 3, 1, 4], 'int32');
-    const result = tf.mod(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expect(result.dtype).toBe('int32');
-    expectArraysEqual(await result.data(), [1, 2, 0, 3]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    let res = tf.mod(tf.scalar(5, 'int32'), tf.scalar(2, 'float32'));
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [1]);
-
-    res = tf.mod(tf.scalar(5, 'int32'), tf.scalar(true, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [0]);
-  });
-
-  it('propagates NaN', async () => {
-    const a = tf.tensor1d([5, -1, NaN]);
-    const b = tf.tensor1d([2, 3, 0.25]);
-    const result = tf.mod(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [1, 2, NaN]);
-  });
-
-  it('broadcasts Tensor1D and scalar', async () => {
-    const a = tf.tensor1d([0.5, 2.5, -0.1, -4], 'float32');
-    const b = tf.scalar(0.6);
-    const result = tf.mod(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0.5, 0.1, 0.5, 0.2]);
-  });
-
-  it('broadcasts scalar and Tensor1D', async () => {
-    // TODO(manraj): Fix for case fmod(0.6, -0.1)
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 3, -1, -4]);
-    const result = tf.mod(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [2, 2, 0, -2]);
-  });
-
-  it('broadcasts Tensor1D and Tensor2D', async () => {
-    const a = tf.tensor1d([0.5, 0.3]);
-    const b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
-    const result = tf.mod(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [0.1, 0.3, 0.5, 0.0]);
-  });
-
-  it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', async () => {
-    const a = tf.tensor2d([0.5, 0.3], [2, 1]);
-    const b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
-    const result = tf.mod(a, b);
-
-    expect(result.shape).toEqual(b.shape);
-    expectArraysClose(await result.data(), [0.1, 0.1, 0.3, 0.0]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5.2);
-    const b = tf.scalar(0.6);
-    const dy = tf.scalar(3);
-
-    const grads = tf.grads((a, b) => tf.mod(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [3]);
-    expectArraysClose(await db.data(), [3 * -1 * Math.floor(5.2 / 0.6)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5.2);
-    const b = tf.scalar(0.6);
-    const dy = tf.scalar(3);
-
-    const grads = tf.grads((a, b) => tf.mod(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [3]);
-    expectArraysClose(await db.data(), [3 * -1 * Math.floor(5.2 / 0.6)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
-    const b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const grads = tf.grads((a, b) => tf.mod(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [1 * 1, 2 * 1, 3 * 1, 4 * 1]);
-    expectArraysClose(await db.data(), [
-      1 * -1 * Math.floor(1.1 / 1.0), 2 * -1 * Math.floor(2.6 / 2.7),
-      3 * -1 * Math.floor(3 / 3), 4 * -1 * Math.floor(5.9 / 5.8)
-    ]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([0.5, 0.3, 0.7, 0.91], [2, 2]);
-    const b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.mod(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [1 * 1, 2 * 1, 3 * 1, 4 * 1]);
-    expectArraysClose(await db.data(), [
-      1 * -1 * Math.floor(0.5 / 0.2), 2 * -1 * Math.floor(0.3 / 0.4),
-      3 * -1 * Math.floor(0.7 / 0.7), 4 * -1 * Math.floor(0.91 / 0.15)
-    ]);
-  });
-
-  it('gradients: broadcasts scalar and Tensor1D', async () => {
-    const a = tf.scalar(0.7);
-    const b = tf.tensor1d([0.2, 0.3, 0.4, 0.5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const grads = tf.grads((a, b) => tf.mod(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [1 + 2 + 3 + 4]);
-    expectArraysClose(await db.data(), [
-      1 * -1 * Math.floor(0.7 / 0.2), 2 * -1 * Math.floor(0.7 / 0.3),
-      3 * -1 * Math.floor(0.7 / 0.4), 4 * -1 * Math.floor(0.7 / 0.5)
-    ]);
-  });
-
-  it('broadcasts Tensor1D and Tensor2D', async () => {
-    const a = tf.tensor1d([0.5, 0.3]);
-    const b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.mod(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-    expect(da.dtype).toEqual('float32');
-    expect(db.dtype).toEqual('float32');
-
-    expectArraysClose(await da.data(), [1 * 1 + 3 * 1, 2 * 1 + 4 * 1]);
-    expectArraysClose(await db.data(), [
-      1 * -1 * Math.floor(0.5 / 0.2), 2 * -1 * Math.floor(0.3 / 0.4),
-      3 * -1 * Math.floor(0.5 / 0.7), 4 * -1 * Math.floor(0.3 / 0.15)
-    ]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.mod({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'mod' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.mod(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'mod' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[0.5, 3], [-0.1, -4]];
-    const b = [[0.2, 0.4], [0.25, 0.15]];
-    const result = tf.mod(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), [0.1, 0.2, 0.15, 0.05]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.mod('q', 3))
-        .toThrowError(/Argument 'a' passed to 'mod' must be numeric/);
-
-    expect(() => tf.mod(3, 'q'))
-        .toThrowError(/Argument 'b' passed to 'mod' must be numeric/);
-  });
-});
-
-describeWithFlags('atan2', ALL_ENVS, () => {
-  it('same shape', async () => {
-    const aValues = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
-    const bValues = [1.0, 2.5, 3.5, 4.5, 2.0, 5.0];
-
-    const a = tf.tensor2d(aValues, [2, 3]);
-    const c = tf.tensor2d(bValues, [2, 3]);
-
-    const r = tf.atan2(a, c);
-    const expected = [];
-
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.atan2(aValues[i], bValues[i]);
-    }
-    expectArraysClose(await r.data(), expected);
-  });
-
-  it('uses chaining', async () => {
-    const aValues = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
-    const bValues = [1.0, 2.5, 3.5, 4.5, 2.0, 5.0];
-
-    const a = tf.tensor2d(aValues, [2, 3]);
-    const b = tf.tensor2d(bValues, [2, 3]);
-
-    const r = a.atan2(b);
-    const expected = [];
-
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.atan2(aValues[i], bValues[i]);
-    }
-    expectArraysClose(await r.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor2d([1.0, 2.0], [2, 1]);
-    const c = tf.tensor2d([3.0, NaN], [2, 1]);
-
-    const r = tf.atan2(a, c);
-
-    expectArraysClose(await r.data(), [Math.atan2(1.0, 3.0), NaN]);
-  });
-
-  it('broadcasting same rank Tensors different shape', async () => {
-    const aValues = [1.0, 2.0, -3.0, -4.0];
-    const bValues = [2.0, 3.0];
-
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const b = tf.tensor2d(bValues, [2, 1]);
-
-    const result = tf.atan2(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [
-      Math.atan2(1.0, 2.0), Math.atan2(2.0, 2.0), Math.atan2(-3.0, 3.0),
-      Math.atan2(-4.0, 3.0)
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws when passed tensors of different shapes', () => {
-    const a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
-
-    expect(() => tf.atan2(a, b)).toThrowError();
-    expect(() => tf.atan2(b, a)).toThrowError();
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const aValues = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
-    const bValues = [1, 2, 3, 4, 2, 5];
-
-    const a = tf.tensor2d(aValues, [2, 3], 'float32');
-    const c = tf.tensor2d(bValues, [2, 3], 'int32');
-
-    const r = tf.atan2(a, c);
-    const expected = [];
-
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.atan2(aValues[i], bValues[i]);
-    }
-    expect(r.shape).toEqual([2, 3]);
-    expect(r.dtype).toBe('float32');
-    expectArraysClose(await r.data(), expected);
-  });
-
-  it('atan2 of scalar and array propagates NaNs', async () => {
-    const c = tf.scalar(NaN);
-    const a = tf.tensor2d([1, 2, 3], [1, 3]);
-
-    const r = tf.atan2(c, a);
-
-    expectArraysEqual(await r.data(), [NaN, NaN, NaN]);
-  });
-
-  it('atan2 of scalar and array', async () => {
-    const aValues = [1, 2, 3, 4, 5, 6];
-
-    const a = tf.tensor2d(aValues, [2, 3]);
-    const c = tf.scalar(2);
-
-    const r = tf.atan2(a, c);
-    const expected = [];
-
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.atan2(aValues[i], 2);
-    }
-    expectArraysClose(await r.data(), expected);
-  });
-
-  it('atan2 vec4 NaNs', async () => {
-    const aValues = [1.0, 2.0, 3.0, 4.0];
-    const cValues = [3.0, NaN, 3.0, 4.0];
-    const a = tf.tensor2d(aValues, [4, 1]);
-    const c = tf.tensor2d(cValues, [4, 1]);
-
-    const r = tf.atan2(a, c);
-    const expected = [];
-
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.atan2(aValues[i], cValues[i]);
-    }
-    expectArraysClose(await r.data(), expected);
-  });
-
-  it('atan2 vec4 all NaNs', async () => {
-    const aValues = [NaN, 2.0, NaN, NaN];
-    const cValues = [3.0, NaN, 3.0, 4.0];
-    const a = tf.tensor2d(aValues, [4, 1]);
-    const c = tf.tensor2d(cValues, [4, 1]);
-
-    const r = tf.atan2(a, c);
-    const expected = [];
-
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.atan2(aValues[i], cValues[i]);
-    }
-    expectArraysClose(await r.data(), expected);
-  });
-
-  it('gradient: Scalar', async () => {
-    const a = tf.scalar(5);
-    const b = tf.scalar(2);
-    const dy = tf.scalar(4);
-
-    const grads = tf.grads((a, b) => tf.atan2(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [4 * 2 / 29]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [4 * -5 / 29]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5);
-    const b = tf.scalar(2);
-    const dy = tf.scalar(4);
-
-    const grads = tf.grads((a, b) => tf.atan2(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [4 * 2 / 29]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [4 * -5 / 29]);
-  });
-
-  it('gradient: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([1, 10, 20]);
-
-    const grads = tf.grads((a, b) => tf.atan2(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1 * 3 / 10, 10 * 4 / 20, 20 * 5 / 34]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(), [-1 * 1 / 10, -10 * 2 / 20, -20 * 3 / 34]);
-  });
-
-  it('gradient: Tensor2D', async () => {
-    const a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
-    const b = tf.tensor2d([1, 3, 4, 5], [2, 2]);
-    const dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.atan2(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(
-        await da.data(), [1 * 1 / 10, 10 * 3 / 10, 15 * 4 / 20, 20 * 5 / 34]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(),
-        [-1 * 3 / 10, -10 * 1 / 10, -15 * 2 / 20, -20 * 3 / 34]);
-  });
-
-  it('gradient: scalar / Tensor1D', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([6, 7, 8]);
-
-    const grads = tf.grads((a, b) => tf.atan2(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [6 * 3 / 13 + 7 * 4 / 20 + 8 * 5 / 29]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-6 * 2 / 13, -7 * 2 / 20, -8 * 2 / 29]);
-  });
-
-  it('gradient: Tensor2D / scalar', async () => {
-    const a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
-    const b = tf.scalar(2);
-    const dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.atan2(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(
-        await da.data(), [6 * 2 / 8, 7 * 2 / 13, 8 * 2 / 20, 9 * 2 / 29]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(),
-        [-6 * 2 / 8 + -7 * 3 / 13 + -8 * 4 / 20 + -9 * 5 / 29]);
-  });
-
-  it('gradient: Tensor2D / Tensor2D w/ broadcast', async () => {
-    const a = tf.tensor2d([3, 4], [2, 1]);
-    const b = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
-    const dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.atan2(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(
-        await da.data(), [6 * 2 / 13 + 7 * 3 / 18, 8 * 4 / 32 + 9 * 5 / 41]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(
-        await db.data(), [-6 * 3 / 13, -7 * 3 / 18, -8 * 4 / 32, -9 * 4 / 41]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.atan2({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'atan2' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.atan2(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'atan2' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[1, 2, 3], [4, 5, 6]];
-    const c = 2;
-
-    const r = tf.atan2(a, c);
-    const expected = [];
-
-    for (let i = 0; i < 6; i++) {
-      expected[i] = Math.atan2(i + 1, 2);
-    }
-    expectArraysClose(await r.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.atan2('q', 3))
-        .toThrowError(/Argument 'a' passed to 'atan2' must be numeric/);
-
-    expect(() => tf.atan2(3, 'q'))
-        .toThrowError(/Argument 'b' passed to 'atan2' must be numeric/);
-  });
-});
-
-describeWithFlags('div', ALL_ENVS, () => {
-  it('divNoNan divide 0', async () => {
-    // Broadcast div a with b.
-    const a = tf.tensor1d([2, 4, 6, 8]);
-    const b = tf.tensor1d([0, 0, 0, 0]);
-
-    const c = a.divNoNan(b);
-    expect(c.shape).toEqual(a.shape);
-    expectArraysClose(await c.data(), [0, 0, 0, 0]);
-  });
-
-  it('divNoNan divide 0 and non-0', async () => {
-    // Broadcast div a with b.
-    const a = tf.tensor1d([2, 4, 6, 8]);
-    const b = tf.tensor1d([2, 2, 0, 4]);
-
-    const c = a.divNoNan(b);
-    expect(c.shape).toEqual(a.shape);
-    expectArraysClose(await c.data(), [1, 2, 0, 2]);
-  });
-
-  it('divNoNan divide 0 broadcast', async () => {
-    // Broadcast div a with b.
-    const a = tf.tensor1d([2, 4, 6, 8]);
-    const b = tf.scalar(0);
-
-    const c = a.divNoNan(b);
-    expect(c.shape).toEqual(a.shape);
-    expectArraysClose(await c.data(), [0, 0, 0, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/bincount.ts b/tfjs-master/tfjs-core/src/ops/bincount.ts
deleted file mode 100644
index 0c7d952c4..000000000
--- a/tfjs-master/tfjs-core/src/ops/bincount.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Bincount, BincountAttrs, BincountInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor1D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Outputs a vector with length `size` and the same dtype as `weights`.
- *
- * If `weights` are empty, then index `i` stores the number of times the value
- * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the
- * sum of the value in `weights` at each index where the corresponding value in
- * `x` is `i`.
- *
- * Values in `x` outside of the range [0, size) are ignored.
- *
- * @param x The input int tensor, rank 1.
- * @param weights The weights tensor, must have the same shape as x, or a
- *     length-0 Tensor, in which case it acts as all weights equal to 1.
- * @param size Non-negative integer.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function bincount_<T extends Tensor1D>(
-    x: T|TensorLike, weights: T|TensorLike, size: number): T {
-  const $x = convertToTensor(x, 'x', 'bincount');
-  const $weights = convertToTensor(weights, 'weights', 'bincount');
-
-  util.assert(
-      $x.dtype === 'int32',
-      () => `Error in bincount: input ` +
-          `dtype must be int32, but got ${$x.dtype}`);
-  util.assert(size >= 0, () => `size must be non-negative, but got ${size}.`);
-  util.assert(
-      $weights.size === $x.size || $weights.size === 0,
-      () => `Error in bincount: weights must have the same size as input or` +
-          `0-length, but got input shape: ${$x.shape}, weights shape: ` +
-          `${$weights.shape}.`);
-
-  const inputs: BincountInputs = {x: $x, weights: $weights};
-  const attrs: BincountAttrs = {size};
-
-  return ENGINE.runKernel(
-      Bincount, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const bincount = /* @__PURE__ */ op({bincount_});
diff --git a/tfjs-master/tfjs-core/src/ops/bincount_test.ts b/tfjs-master/tfjs-core/src/ops/bincount_test.ts
deleted file mode 100644
index 46b091f44..000000000
--- a/tfjs-master/tfjs-core/src/ops/bincount_test.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('bincount', ALL_ENVS, () => {
-  it('with 0-length weights.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([]);
-    const size = 3;
-
-    const result = tf.bincount(x, weights, size);
-
-    expect(result.shape).toEqual([3]);
-    expectArraysClose(await result.data(), [0, 3, 1]);
-  });
-
-  it('with number out of range.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([]);
-    const size = 2;
-
-    const result = tf.bincount(x, weights, size);
-
-    expect(result.shape).toEqual([2]);
-    expectArraysClose(await result.data(), [0, 3]);
-  });
-
-  it('with 1d float weights.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([0.5, 0.3, 0.3, 0.1]);
-    const size = 3;
-
-    const result = tf.bincount(x, weights, size);
-
-    expect(result.shape).toEqual([3]);
-    expectArraysClose(await result.data(), [0, 1.1, 0.1]);
-  });
-
-  it('with 1d float weights and number out of range.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([0.5, 0.3, 0.3, 0.1]);
-    const size = 2;
-
-    const result = tf.bincount(x, weights, size);
-
-    expect(result.shape).toEqual([2]);
-    expectArraysClose(await result.data(), [0, 1.1]);
-  });
-
-  it('throws error for non int x tensor.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'float32');
-    const weights = tf.tensor1d([]);
-    const size = 3;
-
-    expect(() => tf.bincount(x, weights, size)).toThrowError();
-  });
-
-  it('throws error if size is negative.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([]);
-    const size = -1;
-
-    expect(() => tf.bincount(x, weights, size)).toThrowError();
-  });
-
-  it('throws error when shape is different.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([0.5, 0.3]);
-    const size = 2;
-
-    expect(() => tf.bincount(x, weights, size)).toThrowError();
-  });
-
-  it('hands output from other ops.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([]);
-    const size = 4;
-    const added = tf.add<tf.Tensor1D>(x, tf.tensor1d([1], 'int32'));
-    const result = tf.bincount(added, weights, size);
-
-    expect(result.shape).toEqual([4]);
-    expectArraysClose(await result.data(), [0, 0, 3, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/bitwise_and.ts b/tfjs-master/tfjs-core/src/ops/bitwise_and.ts
deleted file mode 100644
index a8c1e7da1..000000000
--- a/tfjs-master/tfjs-core/src/ops/bitwise_and.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {BitwiseAnd, BitwiseAndInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {Rank} from '../types';
-import {arraysEqual} from '../util_base';
-
-import {op} from './operation';
-
-/**
- * Bitwise `AND` operation for input tensors.
- *
- * Given two input tensors, returns a new tensor
- * with the `AND` calculated values.
- *
- * The method supports int32 values
- *
- *
- * ```js
- * const x = tf.tensor1d([0, 5, 3, 14], 'int32');
- * const y = tf.tensor1d([5, 0, 7, 11], 'int32');
- * tf.bitwiseAnd(x, y).print();
- * ```
- *
- * @param x The input tensor to be calculated.
- * @param y The input tensor to be calculated.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function bitwiseAnd_<R extends Rank>(x: Tensor, y: Tensor): Tensor<R> {
-  const $x = convertToTensor(x, 'x', 'bitwiseAnd');
-  const $y = convertToTensor(y, 'y', 'bitwiseAnd');
-
-  if (!arraysEqual($x.shape, $y.shape)) {
-    throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${
-        $x.shape}, y: ${$y.shape}`);
-  }
-  if ($x.dtype !== 'int32' || $y.dtype !== 'int32') {
-    throw new Error(
-        `BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${
-            $x.dtype} and type of y: ${$y.dtype}`);
-  }
-
-  const inputs: BitwiseAndInputs = {a: $x, b: $y};
-  return ENGINE.runKernel(BitwiseAnd, inputs as unknown as NamedTensorMap);
-}
-export const bitwiseAnd = /* @__PURE__ */ op({bitwiseAnd_});
diff --git a/tfjs-master/tfjs-core/src/ops/bitwise_and_test.ts b/tfjs-master/tfjs-core/src/ops/bitwise_and_test.ts
deleted file mode 100644
index 0a1a21873..000000000
--- a/tfjs-master/tfjs-core/src/ops/bitwise_and_test.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {bitwiseAnd} from './bitwise_and';
-
-describeWithFlags('bitwiseAnd', ALL_ENVS, () => {
-  it('a bitwiseAnd b', async () => {
-    const a = tf.tensor1d([0, 5, 3, 14], 'int32');
-    const b = tf.tensor1d([5, 0, 7, 11], 'int32');
-
-    const res = bitwiseAnd(a, b);
-    expectArraysClose(await res.data(), [0, 0, 3, 10]);
-  });
-
-  it('different shape', () => {
-    const a = tf.tensor1d([0, 5, 3, 14]);
-    const b = tf.tensor1d([5, 0, 7]);
-
-    expect(() => bitwiseAnd(a, b))
-        .toThrowError(/BitwiseAnd: Tensors must have the same shape/);
-  });
-
-  it('wrong type', () => {
-    const a = tf.tensor1d([0, 1, 3, 14], 'float32');
-    const b = tf.tensor1d([5, 0, 7, 12], 'float32');
-
-    expect(() => bitwiseAnd(a, b))
-        .toThrowError(/BitwiseAnd: Only supports 'int32' values in tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/boolean_mask.ts b/tfjs-master/tfjs-core/src/ops/boolean_mask.ts
deleted file mode 100644
index 7c70f788f..000000000
--- a/tfjs-master/tfjs-core/src/ops/boolean_mask.ts
+++ /dev/null
@@ -1,90 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {gather} from './gather';
-import {reshape} from './reshape';
-import {squeeze} from './squeeze';
-import {whereAsync} from './where_async';
-
-/**
- * Apply boolean mask to tensor.
- *
- * ```js
- * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
- * const mask = tf.tensor1d([1, 0, 1], 'bool');
- * const result = await tf.booleanMaskAsync(tensor, mask);
- * result.print();
- * ```
- *
- * @param tensor N-D tensor.
- * @param mask K-D boolean tensor, K <= N and K must be known statically.
- * @param axis A 0-D int Tensor representing the axis in tensor to mask from.
- *     By default, axis is 0 which will mask from the first dimension.
- *     Otherwise K + axis <= N.
- *
- * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
- */
-async function booleanMaskAsync_(
-    tensor: Tensor|TensorLike, mask: Tensor|TensorLike,
-    axis?: number): Promise<Tensor> {
-  const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');
-  const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');
-
-  const axisFrom = axis == null ? 0 : axis;
-  const maskDim = $mask.rank;
-  const tensorShape = $tensor.shape;
-
-  util.assert(maskDim > 0, () => 'mask cannot be scalar');
-  util.assertShapesMatch(
-      tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape,
-      `mask's shape must match the first K dimensions of tensor's shape,`);
-
-  let leadingSize = 1;
-  for (let i = axisFrom; i < axisFrom + maskDim; i++) {
-    leadingSize *= tensorShape[i];
-  }
-  const targetTensorShape =
-      tensorShape.slice(0, axisFrom)
-          .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
-  const reshapedTensor = reshape($tensor, targetTensorShape);
-  const reshapedMask = reshape($mask, [-1]);
-  const positivePositions = await whereAsync(reshapedMask);
-  const indices = squeeze(positivePositions, [1]);
-
-  const res = gather(reshapedTensor, indices, axisFrom);
-
-  // Ensure no memory leak.
-  if (tensor !== $tensor) {
-    $tensor.dispose();
-  }
-  if (mask !== $mask) {
-    $mask.dispose();
-  }
-  indices.dispose();
-  reshapedTensor.dispose();
-  reshapedMask.dispose();
-  positivePositions.dispose();
-
-  return res;
-}
-
-export const booleanMaskAsync = booleanMaskAsync_;
diff --git a/tfjs-master/tfjs-core/src/ops/boolean_mask_test.ts b/tfjs-master/tfjs-core/src/ops/boolean_mask_test.ts
deleted file mode 100644
index a21c05768..000000000
--- a/tfjs-master/tfjs-core/src/ops/boolean_mask_test.ts
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('booleanMaskAsync', ALL_ENVS, () => {
-  it('1d array, 1d mask, default axis', async () => {
-    const array = tf.tensor1d([1, 2, 3]);
-    const mask = tf.tensor1d([1, 0, 1], 'bool');
-    const result = await tf.booleanMaskAsync(array, mask);
-    expect(result.shape).toEqual([2]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [1, 3]);
-  });
-
-  it('2d array, 1d mask, default axis', async () => {
-    const array = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const mask = tf.tensor1d([1, 0, 1], 'bool');
-    const result = await tf.booleanMaskAsync(array, mask);
-    expect(result.shape).toEqual([2, 2]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [1, 2, 5, 6]);
-  });
-
-  it('2d array, 2d mask, default axis', async () => {
-    const array = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const mask = tf.tensor2d([1, 0, 1, 0, 1, 0], [3, 2], 'bool');
-    const result = await tf.booleanMaskAsync(array, mask);
-    expect(result.shape).toEqual([3]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [1, 3, 5]);
-  });
-
-  it('2d array, 1d mask, axis=1', async () => {
-    const array = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const mask = tf.tensor1d([0, 1], 'bool');
-    const axis = 1;
-    const result = await tf.booleanMaskAsync(array, mask, axis);
-    expect(result.shape).toEqual([3, 1]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [2, 4, 6]);
-  });
-
-  it('accepts tensor-like object as array or mask', async () => {
-    const array = [[1, 2], [3, 4], [5, 6]];
-    const mask = [1, 0, 1];
-    const result = await tf.booleanMaskAsync(array, mask);
-    expect(result.shape).toEqual([2, 2]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [1, 2, 5, 6]);
-  });
-
-  it('ensure no memory leak', async () => {
-    const numTensorsBefore = tf.memory().numTensors;
-
-    const array = tf.tensor1d([1, 2, 3]);
-    const mask = tf.tensor1d([1, 0, 1], 'bool');
-
-    const result = await tf.booleanMaskAsync(array, mask);
-    expect(result.shape).toEqual([2]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [1, 3]);
-    array.dispose();
-    mask.dispose();
-    result.dispose();
-
-    const numTensorsAfter = tf.memory().numTensors;
-    expect(numTensorsAfter).toBe(numTensorsBefore);
-  });
-
-  it('should throw if mask is scalar', async () => {
-    const array = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const mask = tf.scalar(1, 'bool');
-    let errorMessage = 'No error thrown.';
-    try {
-      await tf.booleanMaskAsync(array, mask);
-    } catch (error) {
-      errorMessage = error.message;
-    }
-    expect(errorMessage).toBe('mask cannot be scalar');
-  });
-
-  it('should throw if array and mask shape miss match', async () => {
-    const array = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const mask = tf.tensor2d([1, 0], [1, 2], 'bool');
-    let errorMessage = 'No error thrown.';
-    try {
-      await tf.booleanMaskAsync(array, mask);
-    } catch (error) {
-      errorMessage = error.message;
-    }
-    expect(errorMessage)
-        .toBe(
-            `mask's shape must match the first K ` +
-            `dimensions of tensor's shape, Shapes 3,2 and 1,2 must match`);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/broadcast_args.ts b/tfjs-master/tfjs-core/src/ops/broadcast_args.ts
deleted file mode 100644
index dbe5cf174..000000000
--- a/tfjs-master/tfjs-core/src/ops/broadcast_args.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { NamedTensorMap } from '../tensor_types';
-import { ENGINE } from '../engine';
-import { BroadcastArgs, BroadcastArgsInputs } from '../kernel_names';
-import { Tensor } from '../tensor';
-import { convertToTensor } from '../tensor_util_env';
-import { Rank, TensorLike } from '../types';
-
-import { op } from './operation';
-
-/**
- * Return the shape of s0 op s1 with broadcast.
- *
- * compute r0, the broadcasted shape as a tensor.
- * s0, s1 and r0 are all integer vectors.
- *
- * This function returns the shape of the result of an operation between
- * two tensors of size s0 and s1 performed with broadcast.
- *
- * @param s0 A tensor representing a shape
- * @param s1 A tensor representing a shape
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function broadcastArgs_<R extends Rank>(
-  s0: Tensor | TensorLike, s1: Tensor | TensorLike): Tensor<R> {
-  const shape1Input = convertToTensor(s0, 's0', 'broadcastArgs', 'int32');
-  const shape2Input = convertToTensor(s1, 's1', 'broadcastArgs', 'int32');
-
-  if (shape1Input.rank !== 1) {
-    throw new Error(
-      'broadcastArgs(): first input must be a vector (rank=1). ' +
-      `Has rank ${shape1Input.rank}`);
-  }
-
-  if (shape2Input.rank !== 1) {
-    throw new Error(
-      'broadcastArgs(): second input must be a vector (rank=1). ' +
-      `Has rank ${shape2Input.rank}`);
-  }
-
-  const inputs: BroadcastArgsInputs = { s0: shape1Input, s1: shape2Input };
-  return ENGINE.runKernel(BroadcastArgs, inputs as unknown as NamedTensorMap);
-}
-
-export const broadcastArgs = /* @__PURE__ */ op({ broadcastArgs_ });
diff --git a/tfjs-master/tfjs-core/src/ops/broadcast_args_test.ts b/tfjs-master/tfjs-core/src/ops/broadcast_args_test.ts
deleted file mode 100644
index 11793a831..000000000
--- a/tfjs-master/tfjs-core/src/ops/broadcast_args_test.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysEqual} from '../test_util';
-
-describeWithFlags('broadcastArgs', ALL_ENVS, () => {
-  it('([1,1], [1,1]) -> [1,1]', async () => {
-    const s1 = tf.tensor1d([1, 1], 'int32');
-    const s2 = tf.tensor1d([1, 1], 'int32');
-    const expected = [1, 1];
-
-    expectArraysEqual(expected, await tf.broadcastArgs(s1, s2).array());
-  });
-
-  it('([1,1], [4,2]) -> [4,2]', async () => {
-    const s1 = tf.tensor1d([1, 1], 'int32');
-    const s2 = tf.tensor1d([4, 2], 'int32');
-    const expected = [4, 2];
-
-    expectArraysEqual(expected, await tf.broadcastArgs(s1, s2).array());
-  });
-
-  it('([1,6], [3,1]) -> [3,6]', async () => {
-    const s1 = tf.tensor1d([1, 6], 'int32');
-    const s2 = tf.tensor1d([3, 1], 'int32');
-    const expected = [3, 6];
-
-    expectArraysEqual(expected, await tf.broadcastArgs(s1, s2).array());
-  });
-
-  it('([1,6], [3,1,1,1]) -> [3,1,1,6]', async () => {
-    const s1 = tf.tensor1d([1, 6], 'int32');
-    const s2 = tf.tensor1d([3, 1, 1, 1], 'int32');
-    const expected = [3, 1, 1, 6];
-
-    expectArraysEqual(expected, await tf.broadcastArgs(s1, s2).array());
-  });
-
-  it('([1,6,-1], [3,1,1,1]) -> [3,1,6,-1]', async () => {
-    const s1 = tf.tensor1d([1, 6, -1], 'int32');
-    const s2 = tf.tensor1d([3, 1, 1, 1], 'int32');
-    const expected = [3, 1, 6, -1];
-
-    expectArraysEqual(expected, await tf.broadcastArgs(s1, s2).array());
-  });
-
-  it('([1,2], [1,3]) -> error', async () => {
-    const s1 = tf.tensor1d([1, 2], 'int32');
-    const s2 = tf.tensor1d([1, 3], 'int32');
-
-    expect(() => tf.broadcastArgs(s1, s2).arraySync()).toThrowError();
-  });
-
-  it('([[1,1],[1,1]], [[1,1],[1,1]]) -> error', async () => {
-    const s1 = tf.tensor2d([[1, 1], [1, 1]], [2, 2], 'int32');
-    const s2 = tf.tensor2d([[1, 1], [1, 1]], [2, 2], 'int32');
-
-    expect(() => tf.broadcastArgs(s1, s2).arraySync()).toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/broadcast_to.ts b/tfjs-master/tfjs-core/src/ops/broadcast_to.ts
deleted file mode 100644
index 9e5782b5d..000000000
--- a/tfjs-master/tfjs-core/src/ops/broadcast_to.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tile, TileAttrs, TileInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {Rank, ShapeMap, TensorLike} from '../types';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-
-import {clone} from './clone';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Broadcast an array to a compatible shape NumPy-style.
- *
- * The tensor's shape is compared to the broadcast shape from end to beginning.
- * Ones are prepended to the tensor's shape until it has the same length as
- * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is
- * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then
- * the input tensor is tiled N times along that axis (using tf.tile).
- *
- * @param input The tensor that is to be broadcasted.
- * @param shape The input is to be broadcast to this shape.
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function broadcastTo_<R extends Rank>(
-    x: Tensor|TensorLike, shape: ShapeMap[R]): Tensor<R> {
-  let input = convertToTensor(x, 'broadcastTo', 'x');
-  const xShape = input.shape;
-
-  assertNonNegativeIntegerDimensions(shape);
-
-  if (shape.length < input.rank) {
-    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${
-        input.rank}.`);
-  }
-
-  if (shape.length > input.rank) {
-    const newShape = input.shape.slice();
-    while (newShape.length < shape.length) {
-      newShape.unshift(1);
-    }
-    input = reshape(input, newShape);
-  }
-
-  const inputShape = input.shape;
-  const reps: number[] = Array.from(shape);
-  for (let i = shape.length - 1; i >= 0; i--) {
-    if (inputShape[i] === shape[i]) {
-      reps[i] = 1;
-    } else if (input.shape[i] !== 1) {
-      throw new Error(
-          `broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
-    }
-  }
-  const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);
-
-  if (axes.length === 0) {
-    return clone(input) as Tensor<R>;
-  }
-
-  // TODO call broadcastTo kernel directly once backends implement broadcstTo
-  const inputs: TileInputs = {x: input};
-  const attrs: TileAttrs = {reps};
-  return ENGINE.runKernel(
-      Tile, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const broadcastTo = /* @__PURE__ */ op({broadcastTo_});
diff --git a/tfjs-master/tfjs-core/src/ops/broadcast_to_test.ts b/tfjs-master/tfjs-core/src/ops/broadcast_to_test.ts
deleted file mode 100644
index 26c54c04d..000000000
--- a/tfjs-master/tfjs-core/src/ops/broadcast_to_test.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {Tensor} from '../tensor';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('broadcastTo', ALL_ENVS, () => {
-  it('[] -> [3,2]', async () => {
-    const a = tf.scalar(4.2);
-    const A = tf.tensor2d([[4.2, 4.2], [4.2, 4.2], [4.2, 4.2]]);
-
-    expectArraysClose(
-        await A.array(), await tf.broadcastTo(a, A.shape).array());
-
-    // test gradients
-    const w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]),
-          f = (a: Tensor) =>
-              tf.broadcastTo(a, A.shape).mul(w).mean().asScalar(),
-          h = (a: Tensor) => a.mul(w).mean().asScalar();
-
-    const df = tf.grad(f), dh = tf.grad(h);
-
-    expectArraysClose(await df(a).array(), await dh(a).array());
-  });
-
-  it('[2] -> [3,2]', async () => {
-    const a = tf.tensor1d([1, 2]);
-    const A = tf.tensor2d([[1, 2], [1, 2], [1, 2]]);
-    expectArraysClose(
-        await A.array(), await tf.broadcastTo(a, A.shape).array());
-
-    // test gradients
-    const w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]),
-          f = (a: Tensor) =>
-              tf.broadcastTo(a, A.shape).mul(w).mean().asScalar(),
-          h = (a: Tensor) => a.mul(w).mean().asScalar();
-
-    const df = tf.grad(f), dh = tf.grad(h);
-
-    expectArraysClose(await df(a).array(), await dh(a).array());
-  });
-
-  it('[3,1] -> [3,2]', async () => {
-    const a = tf.tensor2d([[1], [2], [3]]);
-    const A = tf.tensor2d([[1, 1], [2, 2], [3, 3]]);
-
-    expectArraysClose(
-        await A.array(), await tf.broadcastTo(a, A.shape).array());
-
-    // test gradients
-    const w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]),
-          f = (a: Tensor) =>
-              tf.broadcastTo(a, A.shape).mul(w).mean().asScalar(),
-          h = (a: Tensor) => a.mul(w).mean().asScalar();
-
-    const df = tf.grad(f), dh = tf.grad(h);
-
-    expectArraysClose(await df(a).array(), await dh(a).array());
-  });
-
-  it('should throw error when shape is not integer', () => {
-    const a = tf.scalar(4.2);
-    expect(() => tf.broadcastTo(a, [2, 2.22, 3.33])).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/broadcast_util.ts b/tfjs-master/tfjs-core/src/ops/broadcast_util.ts
deleted file mode 100644
index 43efde440..000000000
--- a/tfjs-master/tfjs-core/src/ops/broadcast_util.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Returns the dimensions in the input shape that are broadcasted to
- * produce the provided output shape.
- *
- * The returned dimensions are 0-indexed and sorted. An example:
- * inShape = [4, 1, 3]
- * outShape = [5, 4, 3, 3]
- * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.
- */
-export function getBroadcastDims(
-    inShape: number[], outShape: number[]): number[] {
-  const inRank = inShape.length;
-  const dims: number[] = [];
-  for (let i = 0; i < inRank; i++) {
-    const dim = inRank - 1 - i;
-    const a = inShape[dim] || 1;
-    const b = outShape[outShape.length - 1 - i] || 1;
-    if (b > 1 && a === 1) {
-      dims.unshift(dim);
-    }
-  }
-  return dims;
-}
-
-/**
- * Returns the axes in the output space that should be reduced to produce
- * the input space.
- */
-export function getReductionAxes(
-    inShape: number[], outShape: number[]): number[] {
-  const result: number[] = [];
-  for (let i = 0; i < outShape.length; i++) {
-    const inDim = inShape[inShape.length - i - 1];
-    const outAxis = outShape.length - i - 1;
-    const outDim = outShape[outAxis];
-    if (inDim == null || (inDim === 1 && outDim > 1)) {
-      result.unshift(outAxis);
-    }
-  }
-  return result;
-}
-
-export function assertAndGetBroadcastShape(
-    shapeA: number[], shapeB: number[]): number[] {
-  const l = Math.max(shapeA.length, shapeB.length);
-  const result = new Array(l);
-
-  for (let i = 0; i < l; i++) {
-    let a = shapeA[shapeA.length - i - 1];
-    if (a == null) {
-      a = 1;
-    }
-    let b = shapeB[shapeB.length - i - 1];
-    if (b == null) {
-      b = 1;
-    }
-    if (a === 1) {
-      result[l - i - 1] = b;
-    } else if (b === 1) {
-      result[l - i - 1] = a;
-    } else if (a !== b) {
-      const errMsg = `Operands could not be broadcast together with shapes ` +
-          `${shapeA} and ${shapeB}.`;
-      throw Error(errMsg);
-    } else {
-      result[l - i - 1] = a;
-    }
-  }
-  return result;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/broadcast_util_test.ts b/tfjs-master/tfjs-core/src/ops/broadcast_util_test.ts
deleted file mode 100644
index 8a4230590..000000000
--- a/tfjs-master/tfjs-core/src/ops/broadcast_util_test.ts
+++ /dev/null
@@ -1,174 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as broadcast_util from './broadcast_util';
-
-describe('broadcast_util.getBroadcastShape', () => {
-  it('two scalars', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([], []);
-    expect(res).toEqual([]);
-  });
-
-  it('scalar and 1d', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([6], []);
-    expect(res).toEqual([6]);
-  });
-
-  it('scalar and 2d', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([2, 6], []);
-    expect(res).toEqual([2, 6]);
-  });
-
-  it('1d and 2d', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([6], [2, 6]);
-    expect(res).toEqual([2, 6]);
-  });
-
-  it('2d and 3d', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([2, 6], [7, 2, 6]);
-    expect(res).toEqual([7, 2, 6]);
-  });
-
-  it('3d and 3d', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([1, 1, 6], [7, 2, 6]);
-    expect(res).toEqual([7, 2, 6]);
-  });
-
-  it('incompatible inner shape', () => {
-    const f = () =>
-        broadcast_util.assertAndGetBroadcastShape([7, 2, 5], [7, 2, 6]);
-    expect(f).toThrowError();
-  });
-
-  it('incompatible middle shape', () => {
-    const f = () =>
-        broadcast_util.assertAndGetBroadcastShape([7, 3, 6], [7, 2, 6]);
-    expect(f).toThrowError();
-  });
-
-  it('compatible with broadcasting support', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([7, 1, 1], [7, 1, 1]);
-    expect(res).toEqual([7, 1, 1]);
-  });
-
-  it('3d and 3d, each gets broadcasted', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([4, 1, 7], [1, 3, 1]);
-    expect(res).toEqual([4, 3, 7]);
-  });
-
-  it('[0] and [1] = [0]', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([0], [1]);
-    expect(res).toEqual([0]);
-
-    const res2 = broadcast_util.assertAndGetBroadcastShape([1], [0]);
-    expect(res2).toEqual([0]);
-  });
-
-  it('[0] and [0] = [0]', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([0], [0]);
-    expect(res).toEqual([0]);
-  });
-
-  it('[0, 1] and [1, 3] = [0, 3]', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([0, 1], [1, 3]);
-    expect(res).toEqual([0, 3]);
-  });
-
-  it('[5, 0, 3] and [5, 1, 1] = [5, 0, 3]', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([5, 0, 3], [5, 1, 1]);
-    expect(res).toEqual([5, 0, 3]);
-  });
-
-  it('[1] and [0, 0, 4] = [0, 0, 4]', () => {
-    const res = broadcast_util.assertAndGetBroadcastShape([1], [0, 0, 4]);
-    expect(res).toEqual([0, 0, 4]);
-  });
-});
-
-describe('broadcast_util.getBroadcastDims', () => {
-  it('[] => []', () => {
-    const dims = broadcast_util.getBroadcastDims([], []);
-    expect(dims.length).toBe(0);
-  });
-
-  it('[] => [5, 4]', () => {
-    const dims = broadcast_util.getBroadcastDims([], [5, 4]);
-    expect(dims.length).toBe(0);
-  });
-
-  it('[1] => [5]', () => {
-    const dims = broadcast_util.getBroadcastDims([1], [5]);
-    expect(dims).toEqual([0]);
-  });
-
-  it('[5, 1] => [5, 3]', () => {
-    const dims = broadcast_util.getBroadcastDims([5, 1], [5, 3]);
-    expect(dims).toEqual([1]);
-  });
-
-  it('[1, 3] => [5, 3]', () => {
-    const dims = broadcast_util.getBroadcastDims([1, 3], [5, 3]);
-    expect(dims).toEqual([0]);
-  });
-
-  it('[1, 1] => [5, 3]', () => {
-    const dims = broadcast_util.getBroadcastDims([1, 1], [5, 3]);
-    expect(dims).toEqual([0, 1]);
-  });
-
-  it('[4, 1, 3] => [4, 5, 3]', () => {
-    const dims = broadcast_util.getBroadcastDims([4, 1, 3], [4, 5, 3]);
-    expect(dims).toEqual([1]);
-  });
-});
-
-describe('broadcast_util.getReductionAxes', () => {
-  it('[] => []', () => {
-    const axes = broadcast_util.getReductionAxes([], []);
-    expect(axes).toEqual([]);
-  });
-
-  it('[] => [5, 4]', () => {
-    const axes = broadcast_util.getReductionAxes([], [5, 4]);
-    expect(axes).toEqual([0, 1]);
-  });
-
-  it('[1] => [5]', () => {
-    const axes = broadcast_util.getReductionAxes([1], [5]);
-    expect(axes).toEqual([0]);
-  });
-
-  it('[5, 1] => [5, 3]', () => {
-    const axes = broadcast_util.getReductionAxes([5, 1], [5, 3]);
-    expect(axes).toEqual([1]);
-  });
-
-  it('[1, 3] => [5, 3]', () => {
-    const axes = broadcast_util.getReductionAxes([1, 3], [5, 3]);
-    expect(axes).toEqual([0]);
-  });
-
-  it('[1, 1] => [5, 3]', () => {
-    const axes = broadcast_util.getReductionAxes([1, 1], [5, 3]);
-    expect(axes).toEqual([0, 1]);
-  });
-
-  it('[4, 1, 3] => [4, 5, 3]', () => {
-    const axes = broadcast_util.getReductionAxes([4, 1, 3], [4, 5, 3]);
-    expect(axes).toEqual([1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/browser.ts b/tfjs-master/tfjs-core/src/ops/browser.ts
deleted file mode 100644
index 91b4b0491..000000000
--- a/tfjs-master/tfjs-core/src/ops/browser.ts
+++ /dev/null
@@ -1,436 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {env} from '../environment';
-import {Draw, DrawAttrs, DrawInputs, FromPixels, FromPixelsAttrs, FromPixelsInputs} from '../kernel_names';
-import {getKernel, NamedAttrMap} from '../kernel_registry';
-import {Tensor, Tensor2D, Tensor3D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {DrawOptions, ImageOptions, PixelData, TensorLike} from '../types';
-
-import {cast} from './cast';
-import {op} from './operation';
-import {tensor3d} from './tensor3d';
-
-let fromPixels2DContext: CanvasRenderingContext2D;
-let hasToPixelsWarned = false;
-
-/**
- * Creates a `tf.Tensor` from an image.
- *
- * ```js
- * const image = new ImageData(1, 1);
- * image.data[0] = 100;
- * image.data[1] = 150;
- * image.data[2] = 200;
- * image.data[3] = 255;
- *
- * tf.browser.fromPixels(image).print();
- * ```
- *
- * @param pixels The input image to construct the tensor from. The
- * supported image types are all 4-channel. You can also pass in an image
- * object with following attributes:
- * `{data: Uint8Array; width: number; height: number}`
- * @param numChannels The number of channels of the output tensor. A
- * numChannels value less than 4 allows you to ignore channels. Defaults to
- * 3 (ignores alpha channel of input image).
- *
- * @returns A Tensor3D with the shape `[height, width, numChannels]`.
- *
- * Note: fromPixels can be lossy in some cases, same image may result in
- * slightly different tensor values, if rendered by different rendering
- * engines. This means that results from different browsers, or even same
- * browser with CPU and GPU rendering engines can be different. See discussion
- * in details:
- * https://github.com/tensorflow/tfjs/issues/5482
- *
- * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}
- */
-function fromPixels_(
-    pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|
-    HTMLVideoElement|ImageBitmap,
-    numChannels = 3): Tensor3D {
-  // Sanity checks.
-  if (numChannels > 4) {
-    throw new Error(
-        'Cannot construct Tensor with more than 4 channels from pixels.');
-  }
-  if (pixels == null) {
-    throw new Error('pixels passed to tf.browser.fromPixels() can not be null');
-  }
-  let isPixelData = false;
-  let isImageData = false;
-  let isVideo = false;
-  let isImage = false;
-  let isCanvasLike = false;
-  let isImageBitmap = false;
-  if ((pixels as PixelData).data instanceof Uint8Array) {
-    isPixelData = true;
-  } else if (
-      typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {
-    isImageData = true;
-  } else if (
-      typeof (HTMLVideoElement) !== 'undefined' &&
-      pixels instanceof HTMLVideoElement) {
-    isVideo = true;
-  } else if (
-      typeof (HTMLImageElement) !== 'undefined' &&
-      pixels instanceof HTMLImageElement) {
-    isImage = true;
-    // tslint:disable-next-line: no-any
-  } else if ((pixels as any).getContext != null) {
-    isCanvasLike = true;
-  } else if (
-      typeof (ImageBitmap) !== 'undefined' && pixels instanceof ImageBitmap) {
-    isImageBitmap = true;
-  } else {
-    throw new Error(
-        'pixels passed to tf.browser.fromPixels() must be either an ' +
-        `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +
-        `in browser, or OffscreenCanvas, ImageData in webworker` +
-        ` or {data: Uint32Array, width: number, height: number}, ` +
-        `but was ${(pixels as {}).constructor.name}`);
-  }
-  // If the current backend has 'FromPixels' registered, it has a more
-  // efficient way of handling pixel uploads, so we call that.
-  const kernel = getKernel(FromPixels, ENGINE.backendName);
-  if (kernel != null) {
-    const inputs: FromPixelsInputs = {pixels};
-    const attrs: FromPixelsAttrs = {numChannels};
-    return ENGINE.runKernel(
-        FromPixels, inputs as unknown as NamedTensorMap,
-        attrs as unknown as NamedAttrMap);
-  }
-
-  const [width, height] = isVideo ?
-      [
-        (pixels as HTMLVideoElement).videoWidth,
-        (pixels as HTMLVideoElement).videoHeight
-      ] :
-      [pixels.width, pixels.height];
-  let vals: Uint8ClampedArray|Uint8Array;
-
-  if (isCanvasLike) {
-    vals =
-        // tslint:disable-next-line:no-any
-        (pixels as any).getContext('2d').getImageData(0, 0, width, height).data;
-  } else if (isImageData || isPixelData) {
-    vals = (pixels as PixelData | ImageData).data;
-  } else if (isImage || isVideo || isImageBitmap) {
-    if (fromPixels2DContext == null) {
-      if (typeof document === 'undefined') {
-        if (typeof OffscreenCanvas !== 'undefined' &&
-            typeof OffscreenCanvasRenderingContext2D !== 'undefined') {
-          // @ts-ignore
-          fromPixels2DContext = new OffscreenCanvas(1, 1).getContext('2d');
-        } else {
-          throw new Error(
-              'Cannot parse input in current context. ' +
-              'Reason: OffscreenCanvas Context2D rendering is not supported.');
-        }
-      } else {
-        fromPixels2DContext = document.createElement('canvas').getContext(
-            '2d', {willReadFrequently: true});
-      }
-    }
-    fromPixels2DContext.canvas.width = width;
-    fromPixels2DContext.canvas.height = height;
-    fromPixels2DContext.drawImage(
-        pixels as HTMLVideoElement, 0, 0, width, height);
-    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
-  }
-  let values: Int32Array;
-  if (numChannels === 4) {
-    values = new Int32Array(vals);
-  } else {
-    const numPixels = width * height;
-    values = new Int32Array(numPixels * numChannels);
-    for (let i = 0; i < numPixels; i++) {
-      for (let channel = 0; channel < numChannels; ++channel) {
-        values[i * numChannels + channel] = vals[i * 4 + channel];
-      }
-    }
-  }
-  const outShape: [number, number, number] = [height, width, numChannels];
-  return tensor3d(values, outShape, 'int32');
-}
-
-// Helper functions for |fromPixelsAsync| to check whether the input can
-// be wrapped into imageBitmap.
-function isPixelData(pixels: PixelData|ImageData|HTMLImageElement|
-                     HTMLCanvasElement|HTMLVideoElement|
-                     ImageBitmap): pixels is PixelData {
-  return (pixels != null) && ((pixels as PixelData).data instanceof Uint8Array);
-}
-
-function isImageBitmapFullySupported() {
-  return typeof window !== 'undefined' &&
-      typeof (ImageBitmap) !== 'undefined' &&
-      window.hasOwnProperty('createImageBitmap');
-}
-
-function isNonEmptyPixels(pixels: PixelData|ImageData|HTMLImageElement|
-                          HTMLCanvasElement|HTMLVideoElement|ImageBitmap) {
-  return pixels != null && pixels.width !== 0 && pixels.height !== 0;
-}
-
-function canWrapPixelsToImageBitmap(pixels: PixelData|ImageData|
-                                    HTMLImageElement|HTMLCanvasElement|
-                                    HTMLVideoElement|ImageBitmap) {
-  return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) &&
-      isNonEmptyPixels(pixels) && !isPixelData(pixels);
-}
-
-/**
- * Creates a `tf.Tensor` from an image in async way.
- *
- * ```js
- * const image = new ImageData(1, 1);
- * image.data[0] = 100;
- * image.data[1] = 150;
- * image.data[2] = 200;
- * image.data[3] = 255;
- *
- * (await tf.browser.fromPixelsAsync(image)).print();
- * ```
- * This API is the async version of fromPixels. The API will first
- * check |WRAP_TO_IMAGEBITMAP| flag, and try to wrap the input to
- * imageBitmap if the flag is set to true.
- *
- * @param pixels The input image to construct the tensor from. The
- * supported image types are all 4-channel. You can also pass in an image
- * object with following attributes:
- * `{data: Uint8Array; width: number; height: number}`
- * @param numChannels The number of channels of the output tensor. A
- * numChannels value less than 4 allows you to ignore channels. Defaults to
- * 3 (ignores alpha channel of input image).
- *
- * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}
- */
-export async function fromPixelsAsync(
-    pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|
-    HTMLVideoElement|ImageBitmap,
-    numChannels = 3) {
-  let inputs: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|
-      HTMLVideoElement|ImageBitmap = null;
-
-  // Check whether the backend needs to wrap |pixels| to imageBitmap and
-  // whether |pixels| can be wrapped to imageBitmap.
-  if (env().getBool('WRAP_TO_IMAGEBITMAP') &&
-      canWrapPixelsToImageBitmap(pixels)) {
-    // Force the imageBitmap creation to not do any premultiply alpha
-    // ops.
-    let imageBitmap;
-
-    try {
-      // wrap in try-catch block, because createImageBitmap may not work
-      // properly in some browsers, e.g.
-      // https://bugzilla.mozilla.org/show_bug.cgi?id=1335594
-      // tslint:disable-next-line: no-any
-      imageBitmap = await (createImageBitmap as any)(
-          pixels as ImageBitmapSource, {premultiplyAlpha: 'none'});
-    } catch (e) {
-      imageBitmap = null;
-    }
-
-    // createImageBitmap will clip the source size.
-    // In some cases, the input will have larger size than its content.
-    // E.g. new Image(10, 10) but with 1 x 1 content. Using
-    // createImageBitmap will clip the size from 10 x 10 to 1 x 1, which
-    // is not correct. We should avoid wrapping such resouce to
-    // imageBitmap.
-    if (imageBitmap != null && imageBitmap.width === pixels.width &&
-        imageBitmap.height === pixels.height) {
-      inputs = imageBitmap;
-    } else {
-      inputs = pixels;
-    }
-  } else {
-    inputs = pixels;
-  }
-
-  return fromPixels_(inputs, numChannels);
-}
-
-function validateImgTensor(img: Tensor2D|Tensor3D) {
-  if (img.rank !== 2 && img.rank !== 3) {
-    throw new Error(
-        `toPixels only supports rank 2 or 3 tensors, got rank ${img.rank}.`);
-  }
-  const depth = img.rank === 2 ? 1 : img.shape[2];
-
-  if (depth > 4 || depth === 2) {
-    throw new Error(
-        `toPixels only supports depth of size ` +
-        `1, 3 or 4 but got ${depth}`);
-  }
-
-  if (img.dtype !== 'float32' && img.dtype !== 'int32') {
-    throw new Error(
-        `Unsupported type for toPixels: ${img.dtype}.` +
-        ` Please use float32 or int32 tensors.`);
-  }
-}
-
-function validateImageOptions(imageOptions: ImageOptions) {
-  const alpha = imageOptions ?.alpha || 1;
-  if (alpha > 1 || alpha < 0) {
-    throw new Error(`Alpha value ${alpha} is suppoed to be in range [0 - 1].`);
-  }
-}
-
-/**
- * Draws a `tf.Tensor` of pixel values to a byte array or optionally a
- * canvas.
- *
- * When the dtype of the input is 'float32', we assume values in the range
- * [0-1]. Otherwise, when input is 'int32', we assume values in the range
- * [0-255].
- *
- * Returns a promise that resolves when the canvas has been drawn to.
- *
- * @param img A rank-2 tensor with shape `[height, width]`, or a rank-3 tensor
- * of shape `[height, width, numChannels]`. If rank-2, draws grayscale. If
- * rank-3, must have depth of 1, 3 or 4. When depth of 1, draws
- * grayscale. When depth of 3, we draw with the first three components of
- * the depth dimension corresponding to r, g, b and alpha = 1. When depth of
- * 4, all four components of the depth dimension correspond to r, g, b, a.
- * @param canvas The canvas to draw to.
- *
- * @doc {heading: 'Browser', namespace: 'browser'}
- */
-export async function toPixels(
-    img: Tensor2D|Tensor3D|TensorLike,
-    canvas?: HTMLCanvasElement): Promise<Uint8ClampedArray> {
-  let $img = convertToTensor(img, 'img', 'toPixels');
-  if (!(img instanceof Tensor)) {
-    // Assume int32 if user passed a native array.
-    const originalImgTensor = $img;
-    $img = cast(originalImgTensor, 'int32');
-    originalImgTensor.dispose();
-  }
-  validateImgTensor($img);
-
-  const [height, width] = $img.shape.slice(0, 2);
-  const depth = $img.rank === 2 ? 1 : $img.shape[2];
-  const data = await $img.data();
-  const multiplier = $img.dtype === 'float32' ? 255 : 1;
-  const bytes = new Uint8ClampedArray(width * height * 4);
-
-  for (let i = 0; i < height * width; ++i) {
-    const rgba = [0, 0, 0, 255];
-
-    for (let d = 0; d < depth; d++) {
-      const value = data[i * depth + d];
-
-      if ($img.dtype === 'float32') {
-        if (value < 0 || value > 1) {
-          throw new Error(
-              `Tensor values for a float32 Tensor must be in the ` +
-              `range [0 - 1] but encountered ${value}.`);
-        }
-      } else if ($img.dtype === 'int32') {
-        if (value < 0 || value > 255) {
-          throw new Error(
-              `Tensor values for a int32 Tensor must be in the ` +
-              `range [0 - 255] but encountered ${value}.`);
-        }
-      }
-
-      if (depth === 1) {
-        rgba[0] = value * multiplier;
-        rgba[1] = value * multiplier;
-        rgba[2] = value * multiplier;
-      } else {
-        rgba[d] = value * multiplier;
-      }
-    }
-
-    const j = i * 4;
-    bytes[j + 0] = Math.round(rgba[0]);
-    bytes[j + 1] = Math.round(rgba[1]);
-    bytes[j + 2] = Math.round(rgba[2]);
-    bytes[j + 3] = Math.round(rgba[3]);
-  }
-
-  if (canvas != null) {
-    if (!hasToPixelsWarned) {
-      const kernel = getKernel(Draw, ENGINE.backendName);
-      if (kernel != null) {
-        console.warn(
-            'tf.browser.toPixels is not efficient to draw tensor on canvas. ' +
-            'Please try tf.browser.draw instead.');
-        hasToPixelsWarned = true;
-      }
-    }
-
-    canvas.width = width;
-    canvas.height = height;
-    const ctx = canvas.getContext('2d');
-    const imageData = new ImageData(bytes, width, height);
-    ctx.putImageData(imageData, 0, 0);
-  }
-  if ($img !== img) {
-    $img.dispose();
-  }
-  return bytes;
-}
-
-/**
- * Draws a `tf.Tensor` to a canvas.
- *
- * When the dtype of the input is 'float32', we assume values in the range
- * [0-1]. Otherwise, when input is 'int32', we assume values in the range
- * [0-255].
- *
- * @param image The tensor to draw on the canvas. Must match one of
- * these shapes:
- *   - Rank-2 with shape `[height, width`]: Drawn as grayscale.
- *   - Rank-3 with shape `[height, width, 1]`: Drawn as grayscale.
- *   - Rank-3 with shape `[height, width, 3]`: Drawn as RGB with alpha set in
- *     `imageOptions` (defaults to 1, which is opaque).
- *   - Rank-3 with shape `[height, width, 4]`: Drawn as RGBA.
- * @param canvas The canvas to draw to.
- * @param options The configuration arguments for image to be drawn and the
- *     canvas to draw to.
- *
- * @doc {heading: 'Browser', namespace: 'browser'}
- */
-export function draw(
-    image: Tensor2D|Tensor3D|TensorLike, canvas: HTMLCanvasElement,
-    options?: DrawOptions): void {
-  let $img = convertToTensor(image, 'img', 'draw');
-  if (!(image instanceof Tensor)) {
-    // Assume int32 if user passed a native array.
-    const originalImgTensor = $img;
-    $img = cast(originalImgTensor, 'int32');
-    originalImgTensor.dispose();
-  }
-  validateImgTensor($img);
-  validateImageOptions(options?.imageOptions);
-
-  const inputs: DrawInputs = {image: $img};
-  const attrs: DrawAttrs = {canvas, options};
-  ENGINE.runKernel(
-      Draw, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const fromPixels = /* @__PURE__ */ op({fromPixels_});
diff --git a/tfjs-master/tfjs-core/src/ops/buffer.ts b/tfjs-master/tfjs-core/src/ops/buffer.ts
deleted file mode 100644
index 7d8951e6e..000000000
--- a/tfjs-master/tfjs-core/src/ops/buffer.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TensorBuffer} from '../tensor';
-import {DataType, DataTypeMap, Rank, ShapeMap} from '../types';
-import * as util from '../util';
-
-/**
- * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.
- *
- * The values are stored in CPU as `TypedArray`. Fill the buffer using
- * `buffer.set()`, or by modifying directly `buffer.values`.
- *
- * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with
- * those values.
- *
- * ```js
- * // Create a buffer and set values at particular indices.
- * const buffer = tf.buffer([2, 2]);
- * buffer.set(3, 0, 0);
- * buffer.set(5, 1, 0);
- *
- * // Convert the buffer back to a tensor.
- * buffer.toTensor().print();
- * ```
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param dtype The dtype of the buffer. Defaults to 'float32'.
- * @param values The values of the buffer as `TypedArray`. Defaults to
- * zeros.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function buffer<R extends Rank, D extends DataType = 'float32'>(
-    shape: ShapeMap[R], dtype: D = 'float32' as D,
-    values?: DataTypeMap[D]): TensorBuffer<R, D> {
-  dtype = dtype || 'float32' as D;
-  util.assertNonNegativeIntegerDimensions(shape);
-  return new TensorBuffer<R, D>(shape, dtype, values);
-}
diff --git a/tfjs-master/tfjs-core/src/ops/cast.ts b/tfjs-master/tfjs-core/src/ops/cast.ts
deleted file mode 100644
index bee8b2f48..000000000
--- a/tfjs-master/tfjs-core/src/ops/cast.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Cast, CastAttrs, CastInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {DataType, TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Casts a `tf.Tensor` to a new dtype.
- *
- * ```js
- * const x = tf.tensor1d([1.5, 2.5, 3]);
- * tf.cast(x, 'int32').print();
- * ```
- * @param x The input tensor to be casted.
- * @param dtype The dtype to cast the input tensor to.
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function cast_<T extends Tensor>(x: T|TensorLike, dtype: DataType): T {
-  const $x = convertToTensor(x, 'x', 'cast');
-
-  // Sanity checks.
-  if (!util.isValidDtype(dtype)) {
-    throw new Error(`Failed to cast to unknown dtype ${dtype}`);
-  }
-  if (dtype === 'string' && $x.dtype !== 'string' ||
-      dtype !== 'string' && $x.dtype === 'string') {
-    throw new Error('Only strings can be casted to strings');
-  }
-
-  const inputs: CastInputs = {x: $x};
-  const attrs: CastAttrs = {dtype};
-
-  return ENGINE.runKernel(
-      Cast, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const cast = /* @__PURE__ */ op({cast_});
diff --git a/tfjs-master/tfjs-core/src/ops/ceil.ts b/tfjs-master/tfjs-core/src/ops/ceil.ts
deleted file mode 100644
index 1c113343c..000000000
--- a/tfjs-master/tfjs-core/src/ops/ceil.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Ceil, CeilInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`
- *
- * ```js
- * const x = tf.tensor1d([.6, 1.1, -3.3]);
- *
- * x.ceil().print();  // or tf.ceil(x)
- * ```
- * @param x The input Tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function ceil_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'ceil', 'float32');
-
-  const inputs: CeilInputs = {x: $x};
-  return ENGINE.runKernel(Ceil, inputs as unknown as NamedTensorMap);
-}
-export const ceil = /* @__PURE__ */ op({ceil_});
diff --git a/tfjs-master/tfjs-core/src/ops/ceil_test.ts b/tfjs-master/tfjs-core/src/ops/ceil_test.ts
deleted file mode 100644
index daa266011..000000000
--- a/tfjs-master/tfjs-core/src/ops/ceil_test.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('ceil', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([1.5, 2.1, -1.4]);
-    const r = tf.ceil(a);
-    expectArraysClose(await r.data(), [2, 3, -1]);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([1.5, NaN, -1.4]);
-    const r = tf.ceil(a);
-    expectArraysClose(await r.data(), [2, NaN, -1]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5.2);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.ceil(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5.2);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.ceil(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.ceil(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.ceil(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.ceil({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'ceil' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.ceil([1.5, 2.1, -1.4]);
-    expectArraysClose(await r.data(), [2, 3, -1]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.ceil('q'))
-        .toThrowError(/Argument 'x' passed to 'ceil' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.ceil(tf.tensor1d([1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'ceil' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/clip_by_value.ts b/tfjs-master/tfjs-core/src/ops/clip_by_value.ts
deleted file mode 100644
index 642b9d0da..000000000
--- a/tfjs-master/tfjs-core/src/ops/clip_by_value.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {ClipByValue, ClipByValueAttrs, ClipByValueInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-import {fill} from './fill';
-
-import {op} from './operation';
-
-/**
- * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`
- *
- * ```js
- * const x = tf.tensor1d([-1, 2, -3, 4]);
- *
- * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)
- * ```
- * @param x The input tensor.
- * @param clipValueMin Lower bound of range to be clipped to.
- * @param clipValueMax Upper bound of range to be clipped to.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function clipByValue_<T extends Tensor>(
-    x: T|TensorLike, clipValueMin: number, clipValueMax: number): T {
-  const $x = convertToTensor(x, 'x', 'clipByValue');
-  util.assert(
-      (clipValueMin <= clipValueMax),
-      () => `Error in clip: min (${clipValueMin}) must be ` +
-          `less than or equal to max (${clipValueMax}).`);
-
-  if (clipValueMin === clipValueMax) {
-    return fill($x.shape, clipValueMin, $x.dtype) as T;
-  }
-
-  const inputs: ClipByValueInputs = {x: $x};
-  const attrs: ClipByValueAttrs = {clipValueMin, clipValueMax};
-
-  return ENGINE.runKernel(
-      ClipByValue, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const clipByValue = /* @__PURE__ */ op({clipByValue_});
diff --git a/tfjs-master/tfjs-core/src/ops/clip_by_value_test.ts b/tfjs-master/tfjs-core/src/ops/clip_by_value_test.ts
deleted file mode 100644
index 359672f6a..000000000
--- a/tfjs-master/tfjs-core/src/ops/clip_by_value_test.ts
+++ /dev/null
@@ -1,165 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('clipByValue', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
-    const min = -1;
-    const max = 50;
-
-    const result = tf.clipByValue(a, min, max);
-
-    expectArraysClose(await result.data(), [3, -1, 0, 50, -1, 2]);
-  });
-
-  it('basic vec4', async () => {
-    const a = tf.tensor1d([3, -1, 0, 100, -7, 2, 5, NaN]);
-    const min = -1;
-    const max = 50;
-
-    const result = tf.clipByValue(a, min, max);
-
-    expectArraysClose(await result.data(), [3, -1, 0, 50, -1, 2, 5, NaN]);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([3, -1, 0, 100, -7, 2, NaN]);
-    const min = -1;
-    const max = 50;
-
-    const result = tf.clipByValue(a, min, max);
-
-    expectArraysClose(await result.data(), [3, -1, 0, 50, -1, 2, NaN]);
-  });
-
-  it('min greater than max', () => {
-    const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
-    const min = 1;
-    const max = -1;
-
-    const f = () => {
-      tf.clipByValue(a, min, max);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('gradient: 1D tensor', async () => {
-    const min = -1;
-    const max = 2;
-    const x = tf.tensor1d([3, -2, 1]);  // Only 1 is not clipped.
-    const dy = tf.tensor1d([5, 50, 500]);
-    const gradients = tf.grad(x => x.clipByValue(min, max))(x, dy);
-
-    expect(gradients.shape).toEqual(x.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 500]);
-  });
-
-  it('gradient: 1D tensor with max or min value', async () => {
-    const min = -1;
-    const max = 2;
-    const x = tf.tensor1d([-1, 1, 2, 3]);
-    const dy = tf.tensor1d([1, 10, 100, 1000]);
-    const gradients = tf.grad(x => x.clipByValue(min, max))(x, dy);
-
-    expect(gradients.shape).toEqual(x.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [1, 10, 100, 0]);
-  });
-
-  it('gradient: scalar', async () => {
-    const min = -1;
-    const max = 2;
-    const x = tf.scalar(-10);  // Clipped.
-    const dy = tf.scalar(5);
-    const gradients = tf.grad(x => x.clipByValue(min, max))(x, dy);
-
-    expect(gradients.shape).toEqual(x.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradient with clones', async () => {
-    const min = -1;
-    const max = 2;
-    const x = tf.scalar(-10);  // Clipped.
-    const dy = tf.scalar(5);
-    const gradients =
-        tf.grad(x => x.clone().clipByValue(min, max).clone())(x, dy);
-
-    expect(gradients.shape).toEqual(x.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradient with primitive as input', async () => {
-    const min = -1;
-    const max = 2;
-    const x = -10;
-    const dy = tf.scalar(5);
-    const gradients = tf.grad(x => x.clipByValue(min, max))(x, dy);
-    expect(gradients.shape).toEqual([]);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.clipByValue({} as tf.Tensor, 0, 1))
-        .toThrowError(/Argument 'x' passed to 'clipByValue' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const min = -1;
-    const max = 50;
-    const result = tf.clipByValue([3, -1, 0, 100, -7, 2], min, max);
-    expectArraysClose(await result.data(), [3, -1, 0, 50, -1, 2]);
-  });
-
-  it('clip(x, eps, 1-eps) never returns 0 or 1', async () => {
-    const min = tf.backend().epsilon();
-    const max = 0.5;
-    const res = await tf.clipByValue([0, 1], min, max).data();
-    expect(res[0]).toBeGreaterThan(0);
-    expect(res[1]).toBeCloseTo(max);
-  });
-
-  it('clip min = max', async () => {
-    const min = 2;
-    const max = 2;
-    const tensor = tf.tensor([1, 2, 3, 4, 5], [5], 'float32');
-    const result = tf.clipByValue(tensor, min, max);
-    expectArraysClose(await result.data(), [2, 2, 2, 2, 2]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.clipByValue('q', 0, 1))
-        .toThrowError(/Argument 'x' passed to 'clipByValue' must be numeric/);
-  });
-
-  it('clip int32 tensor', async () => {
-    const min = -1;
-    const max = 50;
-    const tensor = tf.tensor([2, 3, 4], [3], 'int32');
-    const result = tf.clipByValue(tensor, min, max);
-    expectArraysClose(await result.data(), [2, 3, 4]);
-    expect(result.dtype).toEqual('int32');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/clone.ts b/tfjs-master/tfjs-core/src/ops/clone.ts
deleted file mode 100644
index 5906b9e76..000000000
--- a/tfjs-master/tfjs-core/src/ops/clone.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Identity, IdentityInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Creates a new tensor with the same values and shape as the specified
- * tensor.
- *
- * ```js
- * const x = tf.tensor([1, 2]);
- *
- * x.clone().print();
- * ```
- *
- * @param x The tensor to clone.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function clone_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric');
-  const inputs: IdentityInputs = {x: $x};
-
-  // Note this op is called tf.identity in python. Hence the kernel name used
-  // here.
-  return ENGINE.runKernel(Identity, inputs as unknown as NamedTensorMap);
-}
-
-export const clone = /* @__PURE__ */ op({clone_});
diff --git a/tfjs-master/tfjs-core/src/ops/clone_test.ts b/tfjs-master/tfjs-core/src/ops/clone_test.ts
deleted file mode 100644
index 85c7bc0e5..000000000
--- a/tfjs-master/tfjs-core/src/ops/clone_test.ts
+++ /dev/null
@@ -1,305 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('clone', ALL_ENVS, () => {
-  it('returns a tensor with the same shape and value', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
-    const aPrime = tf.clone(a);
-    expect(aPrime.shape).toEqual(a.shape);
-    expectArraysClose(await aPrime.data(), await a.data());
-    expect(aPrime.shape).toEqual(a.shape);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const res = tf.clone([[1, 2, 3], [4, 5, 6]]);
-    expect(res.dtype).toBe('float32');
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6]);
-  });
-});
-
-describeWithFlags('clone', ALL_ENVS, () => {
-  it('1D default dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([3]);
-    expectArraysClose(await b.data(), [1, 2, 3]);
-  });
-
-  it('1D float32 dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'float32');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([3]);
-    expectArraysClose(await b.data(), [1, 2, 3]);
-  });
-
-  it('1D int32 dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'int32');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([3]);
-    expectArraysEqual(await b.data(), [1, 2, 3]);
-  });
-
-  it('1D bool dtype', async () => {
-    const a = tf.tensor1d([1, 1, 0], 'bool');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([3]);
-    expectArraysEqual(await b.data(), [1, 1, 0]);
-  });
-
-  it('1D complex64 dtype', async () => {
-    const a = tf.complex([1], [1]);
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([1]);
-    expectArraysEqual(await b.data(), [1, 1]);
-  });
-
-  it('1D string dtype', async () => {
-    const a = tf.tensor1d(['a', 'b', 'c'], 'string');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('string');
-    expect(b.shape).toEqual([3]);
-    expectArraysEqual(await b.data(), ['a', 'b', 'c']);
-  });
-
-  it('2D default dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('2D float32 dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('2D int32 dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysEqual(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('2D bool dtype', async () => {
-    const a = tf.tensor2d([1, 1, 1, 0], [2, 2], 'bool');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 0]);
-  });
-
-  it('2D complex64 dtype', async () => {
-    const a = tf.complex([[1, 3], [5, 7]], [[2, 4], [6, 8]]);
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysEqual(await b.data(), [1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('2D string dtype', async () => {
-    const a = tf.tensor2d(['a', 'b', 'c', 'd'], [2, 2], 'string');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('string');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysEqual(await b.data(), ['a', 'b', 'c', 'd']);
-  });
-
-  it('3D default dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('3D float32 dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('3D int32 dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'int32');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('3D bool dtype', async () => {
-    const a = tf.tensor3d([1, 1, 1, 0], [2, 2, 1], 'bool');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 0]);
-  });
-
-  it('3D complex64 dtype', async () => {
-    const a = tf.complex([[[1], [3]], [[5], [7]]], [[[2], [4]], [[6], [8]]]);
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await b.data(), [1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('3D string dtype', async () => {
-    const a = tf.tensor3d(['a', 'b', 'c', 'd'], [2, 2, 1], 'string');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('string');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await b.data(), ['a', 'b', 'c', 'd']);
-  });
-
-  it('4D default dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('4D float32 dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('4D int32 dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'int32');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('4D bool dtype', async () => {
-    const a = tf.tensor4d([1, 1, 1, 0], [2, 2, 1, 1], 'bool');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 0]);
-  });
-
-  it('4D complex64 dtype', async () => {
-    const a = tf.complex(
-        [[[[1]], [[3]]], [[[5]], [[7]]]], [[[[2]], [[4]]], [[[6]], [[8]]]]);
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('4D string dtype', async () => {
-    const a = tf.tensor4d(['a', 'b', 'c', 'd'], [2, 2, 1, 1], 'string');
-    const b = tf.clone(a);
-    expect(b.dtype).toBe('string');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), ['a', 'b', 'c', 'd']);
-  });
-
-  it('gradient: 1D', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([4, 5, 6]);
-    const da = tf.grad(x => tf.clone(x))(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [4, 5, 6]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([4, 5, 6]);
-    const da = tf.grad(x => tf.clone(x.clone()).clone())(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [4, 5, 6]);
-  });
-
-  it('gradient: 1D string throws error with string dy', () => {
-    const a = tf.tensor1d(['a', 'b', 'c'], 'string');
-    const dy = tf.tensor1d(['d', 'e', 'f']);
-    expect(() => tf.grad(x => tf.clone(x))(a, dy)).toThrowError();
-  });
-
-  it('gradient: 1D string throws error with bool dy', () => {
-    const a = tf.tensor1d(['a', 'b', 'c'], 'string');
-    const dy = tf.tensor1d([false, true, false], 'bool');
-    expect(() => tf.grad(x => tf.clone(x))(a, dy)).toThrowError();
-  });
-
-  it('gradient: 1D string throws error with int32 dy', () => {
-    const a = tf.tensor1d(['a', 'b', 'c'], 'string');
-    const dy = tf.tensor1d([4, 5, 6], 'int32');
-    expect(() => tf.grad(x => tf.clone(x))(a, dy)).toThrowError();
-  });
-
-  it('gradient: 1D string works with float32 dy', async () => {
-    const a = tf.tensor1d(['a', 'b', 'c'], 'string');
-    const dy = tf.tensor1d([4, 5, 6]);
-    const da = tf.grad(x => tf.clone(x))(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [4, 5, 6]);
-  });
-
-  it('gradient: 2D int32', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
-    const dy = tf.tensor2d([5, 6, 7, 8], [2, 2], 'float32');
-    const da = tf.grad(x => tf.clone(x))(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([2, 2]);
-    expectArraysEqual(await da.data(), [5, 6, 7, 8]);
-  });
-
-  it('gradient: 4D bool', async () => {
-    const a = tf.tensor4d([1, 1, 1, 0], [2, 2, 1, 1], 'bool');
-    const dy = tf.tensor4d([5, 6, 7, 8], [2, 2, 1, 1], 'float32');
-    const da = tf.grad(x => tf.clone(x))(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await da.data(), [5, 6, 7, 8]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.clone({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'clone' must be a Tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/complex.ts b/tfjs-master/tfjs-core/src/ops/complex.ts
deleted file mode 100644
index c8105454c..000000000
--- a/tfjs-master/tfjs-core/src/ops/complex.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Complex, ComplexInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Converts two real numbers to a complex number.
- *
- * Given a tensor `real` representing the real part of a complex number, and a
- * tensor `imag` representing the imaginary part of a complex number, this
- * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],
- * where r represents the real part and i represents the imag part.
- *
- * The input tensors real and imag must have the same shape.
- *
- * ```js
- * const real = tf.tensor1d([2.25, 3.25]);
- * const imag = tf.tensor1d([4.75, 5.75]);
- * const complex = tf.complex(real, imag);
- *
- * complex.print();
- * ```
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function complex_<T extends Tensor>(real: T|TensorLike, imag: T|TensorLike): T {
-  const $real = convertToTensor(real, 'real', 'complex');
-  const $imag = convertToTensor(imag, 'imag', 'complex');
-  util.assertShapesMatch(
-      $real.shape, $imag.shape,
-      `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +
-          `must match in call to tf.complex().`);
-
-  const inputs: ComplexInputs = {real: $real, imag: $imag};
-  return ENGINE.runKernel(Complex, inputs as unknown as NamedTensorMap);
-}
-
-export const complex = /* @__PURE__ */ op({complex_});
diff --git a/tfjs-master/tfjs-core/src/ops/complex_ops_test.ts b/tfjs-master/tfjs-core/src/ops/complex_ops_test.ts
deleted file mode 100644
index 2c7539ad3..000000000
--- a/tfjs-master/tfjs-core/src/ops/complex_ops_test.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('complex64', ALL_ENVS, () => {
-  it('tf.complex', async () => {
-    const real = tf.tensor1d([3, 30]);
-    const imag = tf.tensor1d([4, 40]);
-    const complex = tf.complex(real, imag);
-
-    expect(complex.dtype).toBe('complex64');
-    expect(complex.shape).toEqual(real.shape);
-    expectArraysClose(await complex.data(), [3, 4, 30, 40]);
-  });
-
-  it('tf.real', async () => {
-    const complex = tf.complex([3, 30], [4, 40]);
-    const real = tf.real(complex);
-
-    expect(real.dtype).toBe('float32');
-    expect(real.shape).toEqual([2]);
-    expectArraysClose(await real.data(), [3, 30]);
-  });
-
-  it('tf.imag', async () => {
-    const complex = tf.complex([3, 30], [4, 40]);
-    const imag = tf.imag(complex);
-
-    expect(imag.dtype).toBe('float32');
-    expect(imag.shape).toEqual([2]);
-    expectArraysClose(await imag.data(), [4, 40]);
-  });
-
-  it('throws when shapes dont match', () => {
-    const real = tf.tensor1d([3, 30]);
-    const imag = tf.tensor1d([4, 40, 50]);
-
-    const re =
-        /real and imag shapes, 2 and 3, must match in call to tf.complex\(\)/;
-    expect(() => tf.complex(real, imag)).toThrowError(re);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/concat.ts b/tfjs-master/tfjs-core/src/ops/concat.ts
deleted file mode 100644
index afd58226c..000000000
--- a/tfjs-master/tfjs-core/src/ops/concat.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Concat, ConcatAttrs, ConcatInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensorArray} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {assert} from '../util';
-
-import {clone} from './clone';
-import {op} from './operation';
-
-/**
- * Concatenates a list of `tf.Tensor`s along a given axis.
- *
- * The tensors ranks and types must match, and their sizes must match in all
- * dimensions except `axis`.
- *
- * Also available are stricter rank-specific methods that assert that
- * `tensors` are of the given rank:
- *   - `tf.concat1d`
- *   - `tf.concat2d`
- *   - `tf.concat3d`
- *   - `tf.concat4d`
- *
- * Except `tf.concat1d` (which does not have axis param), all methods have
- * same signature as this method.
- *
- * ```js
- * const a = tf.tensor1d([1, 2]);
- * const b = tf.tensor1d([3, 4]);
- * a.concat(b).print();  // or a.concat(b)
- * ```
- *
- * ```js
- * const a = tf.tensor1d([1, 2]);
- * const b = tf.tensor1d([3, 4]);
- * const c = tf.tensor1d([5, 6]);
- * tf.concat([a, b, c]).print();
- * ```
- *
- * ```js
- * const a = tf.tensor2d([[1, 2], [10, 20]]);
- * const b = tf.tensor2d([[3, 4], [30, 40]]);
- * const axis = 1;
- * tf.concat([a, b], axis).print();
- * ```
- * @param tensors A list of tensors to concatenate.
- * @param axis The axis to concatenate along. Defaults to 0 (the first dim).
- *
- * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
- */
-function concat_<T extends Tensor>(tensors: Array<T|TensorLike>, axis = 0): T {
-  assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');
-
-  const $tensors =
-      convertToTensorArray(tensors, 'tensors', 'concat', 'string_or_numeric');
-
-  if ($tensors[0].dtype === 'complex64') {
-    $tensors.forEach(tensor => {
-      if (tensor.dtype !== 'complex64') {
-        throw new Error(`Cannot concatenate complex64 tensors with a tensor
-          with dtype ${tensor.dtype}. `);
-      }
-    });
-  }
-
-  if ($tensors.length === 1) {
-    return clone($tensors[0]);
-  }
-
-  const inputs: ConcatInputs = $tensors;
-  const attr: ConcatAttrs = {axis};
-
-  return ENGINE.runKernel(
-      Concat, inputs as unknown as NamedTensorMap,
-      attr as unknown as NamedAttrMap);
-}
-
-export const concat = /* @__PURE__ */ op({concat_});
diff --git a/tfjs-master/tfjs-core/src/ops/concat_1d.ts b/tfjs-master/tfjs-core/src/ops/concat_1d.ts
deleted file mode 100644
index 84bcc7fc9..000000000
--- a/tfjs-master/tfjs-core/src/ops/concat_1d.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor1D} from '../tensor';
-import {TensorLike} from '../types';
-
-import {concat} from './concat';
-import {op} from './operation';
-
-/**
- * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.
- *
- * For example, if:
- * A: shape(3) = |r1, g1, b1|
- * B: shape(2) = |r2, g2|
- * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|
- *
- * @param tensors A list of`tf.Tensor`s to concatenate.
- * @return The concatenated array.
- */
-function concat1d_(tensors: Array<Tensor1D|TensorLike>): Tensor1D {
-  return concat(tensors, 0 /* axis */);
-}
-
-export const concat1d = /* @__PURE__ */ op({concat1d_});
diff --git a/tfjs-master/tfjs-core/src/ops/concat_2d.ts b/tfjs-master/tfjs-core/src/ops/concat_2d.ts
deleted file mode 100644
index 206fbefd9..000000000
--- a/tfjs-master/tfjs-core/src/ops/concat_2d.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor2D} from '../tensor';
-import {TensorLike} from '../types';
-
-import {concat} from './concat';
-import {op} from './operation';
-
-/**
- * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.
- *
- * For example, if:
- * A: shape(2, 3) = | r1, g1, b1 |
- *                  | r2, g2, b2 |
- *
- * B: shape(2, 3) = | r3, g3, b3 |
- *                  | r4, g4, b4 |
- *
- * C = tf.concat2d([A, B], axis)
- *
- * if axis = 0:
- * C: shape(4, 3) = | r1, g1, b1 |
- *                  | r2, g2, b2 |
- *                  | r3, g3, b3 |
- *                  | r4, g4, b4 |
- *
- * if axis = 1:
- * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |
- *                   | r2, g2, b2, r4, g4, b4 |
- *
- *
- * @param tensors A list of `tf.Tensor`s to concatenate.
- * @param axis The axis to concatenate along.
- * @return The concatenated array.
- */
-function concat2d_(
-    tensors: Array<Tensor2D|TensorLike>, axis: number): Tensor2D {
-  return concat(tensors, axis);
-}
-
-export const concat2d = /* @__PURE__ */ op({concat2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/concat_3d.ts b/tfjs-master/tfjs-core/src/ops/concat_3d.ts
deleted file mode 100644
index c6534b28c..000000000
--- a/tfjs-master/tfjs-core/src/ops/concat_3d.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor3D} from '../tensor';
-import {TensorLike} from '../types';
-
-import {concat} from './concat';
-import {op} from './operation';
-
-/**
- * Concatenates a list of `tf.Tensor3D`s along an axis.
- * See `concat` for details.
- *
- * For example, if:
- * A: shape(2, 1, 3) = | r1, g1, b1 |
- *                     | r2, g2, b2 |
- *
- * B: shape(2, 1, 3) = | r3, g3, b3 |
- *                     | r4, g4, b4 |
- *
- * C = tf.concat3d([A, B], axis)
- *
- * if axis = 0:
- * C: shape(4, 1, 3) = | r1, g1, b1 |
- *                     | r2, g2, b2 |
- *                     | r3, g3, b3 |
- *                     | r4, g4, b4 |
- *
- * if axis = 1:
- * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |
- *                     | r2, g2, b2, r4, g4, b4 |
- *
- * if axis = 2:
- * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |
- *                      | r2, g2, b2, r4, g4, b4 |
- *
- * @param tensors A list of`tf.Tensor`s to concatenate.
- * @param axis The axis to concate along.
- * @return The concatenated array.
- */
-function concat3d_(
-    tensors: Array<Tensor3D|TensorLike>, axis: number): Tensor3D {
-  return concat(tensors, axis);
-}
-
-export const concat3d = /* @__PURE__ */ op({concat3d_});
diff --git a/tfjs-master/tfjs-core/src/ops/concat_4d.ts b/tfjs-master/tfjs-core/src/ops/concat_4d.ts
deleted file mode 100644
index 8c63779d3..000000000
--- a/tfjs-master/tfjs-core/src/ops/concat_4d.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor4D} from '../tensor';
-import {TensorLike} from '../types';
-
-import {concat} from './concat';
-import {op} from './operation';
-
-/**
- * Concatenates a list of `tf.Tensor4D`s along an axis.
- * See `concat` for details.
- *
- * @param tensors A list of `tf.Tensor`s to concatenate.
- * @param axis The axis to concate along.
- * @return The concatenated array.
- */
-function concat4d_(
-    tensors: Array<Tensor4D|TensorLike>, axis: number): Tensor4D {
-  return concat(tensors, axis);
-}
-
-export const concat4d = /* @__PURE__ */ op({concat4d_});
diff --git a/tfjs-master/tfjs-core/src/ops/concat_test.ts b/tfjs-master/tfjs-core/src/ops/concat_test.ts
deleted file mode 100644
index c6ba752c6..000000000
--- a/tfjs-master/tfjs-core/src/ops/concat_test.ts
+++ /dev/null
@@ -1,634 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('concat1d', ALL_ENVS, () => {
-  it('3 + 5', async () => {
-    const a = tf.tensor1d([3]);
-    const b = tf.tensor1d([5]);
-
-    const result = tf.concat1d([a, b]);
-    const expected = [3, 5];
-    expectArraysClose(await result.data(), expected);
-  });
-  it('TensorLike 3 + 5', async () => {
-    const a = [3];
-    const b = [5];
-
-    const result = tf.concat1d([a, b]);
-    const expected = [3, 5];
-    expectArraysClose(await result.data(), expected);
-  });
-  it('TensorLike Chained 3 + 5', async () => {
-    const a = tf.tensor1d([3]);
-    const b = [5];
-
-    const result = a.concat([b]);
-    const expected = [3, 5];
-    expectArraysClose(await result.data(), expected);
-  });
-  it('3 + [5,7]', async () => {
-    const a = tf.tensor1d([3]);
-    const b = tf.tensor1d([5, 7]);
-
-    const result = tf.concat1d([a, b]);
-    const expected = [3, 5, 7];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('[3,5] + 7', async () => {
-    const a = tf.tensor1d([3, 5]);
-    const b = tf.tensor1d([7]);
-
-    const result = tf.concat1d([a, b]);
-    const expected = [3, 5, 7];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('3 + 5 + 7 + 9', async () => {
-    const a = tf.tensor1d([3]);
-    const b = tf.tensor1d([5]);
-    const c = tf.tensor1d([7]);
-    const d = tf.tensor1d([9]);
-
-    const result = tf.concat1d([a, b, c, d]);
-    expectArraysClose(await result.data(), [3, 5, 7, 9]);
-  });
-
-  it('single tensor', async () => {
-    const a = tf.tensor1d([3]);
-
-    const result = tf.concat1d([a]);
-    expectArraysClose(await result.data(), [3]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [3];
-    const b = [5];
-
-    const result = tf.concat1d([a, b]);
-    const expected = [3, 5];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('concat complex input', async () => {
-    // [1+1j, 2+2j]
-    const c1 = tf.complex([1, 2], [1, 2]);
-    // [3+3j, 4+4j]
-    const c2 = tf.complex([3, 4], [3, 4]);
-
-    const axis = 0;
-    const result = tf.concat([c1, c2], axis);
-    const expected = [1, 1, 2, 2, 3, 3, 4, 4];
-    expect(result.dtype).toEqual('complex64');
-    expectArraysClose(await result.data(), expected);
-  });
-});
-
-describeWithFlags('concat2d', ALL_ENVS, () => {
-  it('[[3]] + [[5]], axis=0', async () => {
-    const axis = 0;
-    const a = tf.tensor2d([3], [1, 1]);
-    const b = tf.tensor2d([5], [1, 1]);
-
-    const result = tf.concat2d([a, b], axis);
-    const expected = [3, 5];
-
-    expect(result.shape).toEqual([2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-  it('TensorLike [[3]] + [[5]], axis=0', async () => {
-    const axis = 0;
-    const a = [[3]];
-    const b = [[5]];
-
-    const result = tf.concat2d([a, b], axis);
-    const expected = [3, 5];
-
-    expect(result.shape).toEqual([2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-  it('TensorLike Chained [[3]] + [[5]], axis=0', async () => {
-    const axis = 0;
-    const a = tf.tensor2d([3], [1, 1]);
-    const b = [[5]];
-
-    const result = a.concat([b], axis);
-    const expected = [3, 5];
-
-    expect(result.shape).toEqual([2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('[[3]] + [[5]], axis=1', async () => {
-    const axis = 1;
-    const a = tf.tensor2d([3], [1, 1]);
-    const b = tf.tensor2d([5], [1, 1]);
-
-    const result = tf.concat2d([a, b], axis);
-    const expected = [3, 5];
-
-    expect(result.shape).toEqual([1, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('[[1, 2], [3, 4]] + [[5, 6]], axis=0', async () => {
-    const axis = 0;
-    const a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const b = tf.tensor2d([[5, 6]], [1, 2]);
-
-    const result = tf.concat2d([a, b], axis);
-    const expected = [1, 2, 3, 4, 5, 6];
-
-    expect(result.shape).toEqual([3, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('[[1, 2],[3, 4]] + [[5, 6]] + [[7, 8]], axis=0', async () => {
-    const axis = 0;
-    const a = tf.tensor2d([[1, 2], [3, 4]]);
-    const b = tf.tensor2d([[5, 6]]);
-    const c = tf.tensor2d([[7, 8]]);
-
-    const result = tf.concat2d([a, b, c], axis);
-    const expected = [1, 2, 3, 4, 5, 6, 7, 8];
-
-    expect(result.shape).toEqual([4, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('[[1, 2], [3, 4]] + [[5, 6]], axis=1 throws error', () => {
-    const axis = 1;
-    const a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const b = tf.tensor2d([[5, 6]], [1, 2]);
-
-    expect(() => tf.concat2d([a, b], axis)).toThrowError();
-  });
-
-  it('[[1, 2], [3, 4]] + [[5, 6], [7, 8]], axis=1', async () => {
-    const axis = 1;
-    const a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const b = tf.tensor2d([[5, 6], [7, 8]], [2, 2]);
-
-    const result = tf.concat2d([a, b], axis);
-    const expected = [1, 2, 5, 6, 3, 4, 7, 8];
-
-    expect(result.shape).toEqual([2, 4]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('[[1, 2],[3, 4]] + [[5, 6],[7, 8]] + [[9, 10],[11, 12]], axis=1',
-     async () => {
-       const axis = 1;
-       const a = tf.tensor2d([[1, 2], [3, 4]]);
-       const b = tf.tensor2d([[5, 6], [7, 8]]);
-       const c = tf.tensor2d([[9, 10], [11, 12]]);
-
-       const result = tf.concat2d([a, b, c], axis);
-       const expected = [1, 2, 5, 6, 9, 10, 3, 4, 7, 8, 11, 12];
-
-       expect(result.shape).toEqual([2, 6]);
-       expectArraysClose(await result.data(), expected);
-     });
-
-  it('accepts a tensor-like object', async () => {
-    const axis = 0;
-    const a = [[3]];
-    const b = [[5]];
-
-    const result = tf.concat2d([a, b], axis);
-    const expected = [3, 5];
-
-    expect(result.shape).toEqual([2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('concat zero-sized tensors', async () => {
-    const a = tf.tensor2d([], [0, 5]);
-    const b = tf.tensor2d([], [0, 5]);
-    const c = tf.tensor2d([], [0, 5]);
-
-    const res = tf.concat([a, b, c], /* axis */ 0);
-    expect(res.shape).toEqual([0, 5]);
-    expectArraysEqual(await res.data(), []);
-
-    const res2 = tf.concat([a, b, c], /* axis */ 1);
-    expect(res2.shape).toEqual([0, 15]);
-    expectArraysEqual(await res2.data(), []);
-  });
-
-  it('concat complex input axis=0', async () => {
-    // [[1+1j, 2+2j], [3+3j, 4+4j]]
-    const c1 = tf.complex([[1, 2], [3, 4]], [[1, 2], [3, 4]]);
-    // [[5+5j, 6+6j], [7+7j, 8+8j]]
-    const c2 = tf.complex([[5, 6], [7, 8]], [[5, 6], [7, 8]]);
-
-    const axis = 0;
-    const result = tf.concat([c1, c2], axis);
-    const expected = [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8];
-    expect(result.dtype).toEqual('complex64');
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('concat complex input axis=1', async () => {
-    // [[1+1j, 2+2j], [3+3j, 4+4j]]
-    const c1 = tf.complex([[1, 2], [3, 4]], [[1, 2], [3, 4]]);
-    // [[5+5j, 6+6j], [7+7j, 8+8j]]
-    const c2 = tf.complex([[5, 6], [7, 8]], [[5, 6], [7, 8]]);
-
-    const axis = 1;
-    const result = tf.concat([c1, c2], axis);
-    const expected = [1, 1, 2, 2, 5, 5, 6, 6, 3, 3, 4, 4, 7, 7, 8, 8];
-    expect(result.dtype).toEqual('complex64');
-    expectArraysClose(await result.data(), expected);
-  });
-});
-
-describeWithFlags('concat3d', ALL_ENVS, () => {
-  beforeAll(() => {
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-  });
-
-  it('shapes correct concat axis=-1', async () => {
-    const tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
-    const tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
-    const values = tf.concat3d([tensor1, tensor2], -1);
-    expect(values.shape).toEqual([1, 1, 6]);
-    expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('shapes correct concat axis=0', async () => {
-    const tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
-    const tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
-    const values = tf.concat3d([tensor1, tensor2], 0);
-    expect(values.shape).toEqual([2, 1, 3]);
-    expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('concat axis=0', async () => {
-    const tensor1 = tf.tensor3d([1, 11, 111, 2, 22, 222], [1, 2, 3]);
-    const tensor2 = tf.tensor3d(
-        [5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
-    const values = tf.concat3d([tensor1, tensor2], 0);
-    expect(values.shape).toEqual([3, 2, 3]);
-    expectArraysClose(await values.data(), [
-      1, 11, 111, 2, 22, 222, 5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888
-    ]);
-  });
-
-  it('TensorLike concat axis=0', async () => {
-    const tensor1 = [[[1, 11, 111], [2, 22, 222]]];
-    const tensor2 =
-        [[[5, 55, 555], [6, 66, 666]], [[7, 77, 777], [8, 88, 888]]];
-    const values = tf.concat3d([tensor1, tensor2], 0);
-    expect(values.shape).toEqual([3, 2, 3]);
-    expectArraysClose(await values.data(), [
-      1, 11, 111, 2, 22, 222, 5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888
-    ]);
-  });
-
-  it('Accepts string tensor.', async () => {
-    const tensor1 = tf.tensor3d(['one', 'two', 'three'], [1, 1, 3], 'string');
-    const tensor2 = tf.tensor3d(['four', 'five', 'six'], [1, 1, 3], 'string');
-    const values = tf.concat3d([tensor1, tensor2], 0);
-    expect(values.shape).toEqual([2, 1, 3]);
-    expectArraysClose(
-        await values.data(), ['one', 'two', 'three', 'four', 'five', 'six']);
-  });
-
-  it('TensorLike Chained concat axis=0', async () => {
-    const tensor1 = tf.tensor3d([1, 11, 111, 2, 22, 222], [1, 2, 3]);
-    const tensor2 =
-        [[[5, 55, 555], [6, 66, 666]], [[7, 77, 777], [8, 88, 888]]];
-    const values = tensor1.concat([tensor2], 0);
-    expect(values.shape).toEqual([3, 2, 3]);
-    expectArraysClose(await values.data(), [
-      1, 11, 111, 2, 22, 222, 5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888
-    ]);
-  });
-
-  it('shapes correct concat axis=1', async () => {
-    const tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
-    const tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
-    const values = tf.concat3d([tensor1, tensor2], 1);
-    expect(values.shape).toEqual([1, 2, 3]);
-    expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('concat axis=1', async () => {
-    const tensor1 = tf.tensor3d([1, 11, 111, 3, 33, 333], [2, 1, 3]);
-    const tensor2 = tf.tensor3d(
-        [5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
-    const values = tf.concat3d([tensor1, tensor2], 1);
-    expect(values.shape).toEqual([2, 3, 3]);
-    expectArraysClose(await values.data(), [
-      1, 11, 111, 5, 55, 555, 6, 66, 666, 3, 33, 333, 7, 77, 777, 8, 88, 888
-    ]);
-  });
-
-  it('shapes correct concat axis=2', async () => {
-    const tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
-    const tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
-    const values = tf.concat3d([tensor1, tensor2], 2);
-    expect(values.shape).toEqual([1, 1, 6]);
-    expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('concat a large number of tensors, axis=0', async () => {
-    const tensors = [];
-    const expected = [];
-    for (let i = 0; i < 100; i++) {
-      tensors.push(tf.tensor([i], [1]));
-      expected.push(i);
-    }
-    const axis = 0;
-    const res = tf.concat(tensors, axis);
-    expect(res.shape).toEqual([100]);
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), expected);
-  });
-
-  it('concat a large number of tensors, axis=1', async () => {
-    const tensors = [];
-    const expected = [];
-    for (let i = 0; i < 100; i++) {
-      tensors.push(tf.tensor([i], [1, 1]));
-      expected.push(i);
-    }
-    const axis = 1;
-    const res = tf.concat(tensors, axis);
-    expect(res.shape).toEqual([1, 100]);
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), expected);
-  });
-
-  it('concat axis=2', async () => {
-    const tensor1 = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
-    const tensor2 = tf.tensor3d(
-        [5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
-    const values = tf.concat3d([tensor1, tensor2], 2);
-    expect(values.shape).toEqual([2, 2, 5]);
-    expectArraysClose(await values.data(), [
-      1, 11, 5, 55, 555, 2, 22, 6, 66, 666,
-      3, 33, 7, 77, 777, 4, 44, 8, 88, 888
-    ]);
-  });
-
-  it('concat throws when invalid non-axis shapes, axis=0', () => {
-    const axis = 0;
-    const x1 = tf.tensor3d([1, 11, 111], [1, 1, 3]);
-    const x2 = tf.tensor3d(
-        [5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
-    expect(() => tf.concat3d([x1, x2], axis)).toThrowError();
-  });
-
-  it('concat throws when invalid non-axis shapes, axis=1', () => {
-    const axis = 1;
-    const x1 = tf.tensor3d([1, 11, 111], [1, 1, 3]);
-    const x2 = tf.tensor3d(
-        [5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
-    expect(() => tf.concat3d([x1, x2], axis)).toThrowError();
-  });
-
-  it('concat throws when invalid non-axis shapes and zero size, axis=1', () => {
-    const axis = 1;
-    const x1 = tf.tensor3d([1, 11, 111], [1, 1, 3]);
-    const x2 = tf.tensor3d([], [1, 0, 4]);
-    expect(() => tf.concat3d([x1, x2], axis)).toThrowError();
-  });
-
-  it('concat throws when invalid non-axis shapes, axis=2', () => {
-    const axis = 2;
-    const x1 = tf.tensor3d([1, 11, 2, 22], [1, 2, 2]);
-    const x2 = tf.tensor3d(
-        [5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
-    expect(() => tf.concat3d([x1, x2], axis)).toThrowError();
-  });
-
-  it('gradient concat axis=0', async () => {
-    const x1 = tf.tensor3d([1, 11, 2, 22], [1, 2, 2]);
-    const x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
-    const dy =
-        tf.tensor3d([66, 6, 55, 5, 44, 4, 33, 3, 22, 2, 11, 1], [3, 2, 2]);
-    const axis = 0;
-
-    const grads = tf.grads(
-        (x1: tf.Tensor3D, x2: tf.Tensor3D) => tf.concat3d([x1, x2], axis));
-    const [dx1, dx2] = grads([x1, x2], dy);
-
-    expect(dx1.shape).toEqual(x1.shape);
-    expectArraysClose(await dx1.data(), [66, 6, 55, 5]);
-
-    expect(dx2.shape).toEqual(x2.shape);
-    expectArraysClose(await dx2.data(), [44, 4, 33, 3, 22, 2, 11, 1]);
-  });
-
-  it('gradient with clones', async () => {
-    const x1 = tf.tensor3d([1, 11, 2, 22], [1, 2, 2]);
-    const x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
-    const dy =
-        tf.tensor3d([66, 6, 55, 5, 44, 4, 33, 3, 22, 2, 11, 1], [3, 2, 2]);
-    const axis = 0;
-
-    const grads = tf.grads(
-        (x1: tf.Tensor3D, x2: tf.Tensor3D) =>
-            tf.concat3d([x1.clone(), x2.clone()], axis).clone());
-    const [dx1, dx2] = grads([x1, x2], dy);
-
-    expect(dx1.shape).toEqual(x1.shape);
-    expectArraysClose(await dx1.data(), [66, 6, 55, 5]);
-
-    expect(dx2.shape).toEqual(x2.shape);
-    expectArraysClose(await dx2.data(), [44, 4, 33, 3, 22, 2, 11, 1]);
-  });
-
-  it('gradient concat axis=1', async () => {
-    const x1 = tf.tensor3d([1, 11, 2, 22], [2, 1, 2]);
-    const x2 = tf.tensor3d([3, 33, 4, 44, 5, 55, 6, 66], [2, 2, 2]);
-    const dy =
-        tf.tensor3d([66, 6, 55, 5, 44, 4, 33, 3, 22, 2, 11, 1], [2, 3, 2]);
-    const axis = 1;
-
-    const grads = tf.grads(
-        (x1: tf.Tensor3D, x2: tf.Tensor3D) => tf.concat3d([x1, x2], axis));
-    const [dx1, dx2] = grads([x1, x2], dy);
-
-    expect(dx1.shape).toEqual(x1.shape);
-    expectArraysClose(await dx1.data(), [66, 6, 33, 3]);
-
-    expect(dx2.shape).toEqual(x2.shape);
-    expectArraysClose(await dx2.data(), [55, 5, 44, 4, 22, 2, 11, 1]);
-  });
-
-  it('gradient concat axis=2', async () => {
-    const x1 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
-    const dy = tf.tensor3d(
-        [4, 40, 400, 3, 30, 300, 2, 20, 200, 1, 10, 100], [2, 2, 3]);
-    const axis = 2;
-
-    const grads = tf.grads(
-        (x1: tf.Tensor3D, x2: tf.Tensor3D) => tf.concat3d([x1, x2], axis));
-    const [dx1, dx2] = grads([x1, x2], dy);
-
-    expect(dx1.shape).toEqual(x1.shape);
-    expectArraysClose(await dx1.data(), [4, 3, 2, 1]);
-
-    expect(dx2.shape).toEqual(x2.shape);
-    expectArraysClose(await dx2.data(), [40, 400, 30, 300, 20, 200, 10, 100]);
-  });
-
-  it('gradient concat axis=-1', async () => {
-    const x1 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
-    const dy = tf.tensor3d(
-        [4, 40, 400, 3, 30, 300, 2, 20, 200, 1, 10, 100], [2, 2, 3]);
-    const axis = -1;
-
-    const grads = tf.grads(
-        (x1: tf.Tensor3D, x2: tf.Tensor3D) => tf.concat3d([x1, x2], axis));
-    const [dx1, dx2] = grads([x1, x2], dy);
-
-    expect(dx1.shape).toEqual(x1.shape);
-    expectArraysClose(await dx1.data(), [4, 3, 2, 1]);
-
-    expect(dx2.shape).toEqual(x2.shape);
-    expectArraysClose(await dx2.data(), [40, 400, 30, 300, 20, 200, 10, 100]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const tensor1 = [[[1, 2, 3]]];  // 1x1x3
-    const tensor2 = [[[4, 5, 6]]];  // 1x1x3
-    const values = tf.concat3d([tensor1, tensor2], 0);
-    expect(values.shape).toEqual([2, 1, 3]);
-    expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('concat tensors with 0 in their shape', async () => {
-    const tensor1 = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
-    const tensor2 = tf.tensor3d([], [0, 3, 1]);
-    const values = tf.concat3d([tensor1, tensor2], 0);
-    expect(values.shape).toEqual([2, 3, 1]);
-    expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('concat complex input axis=0', async () => {
-    // [[[1+1j, 2+2j], [3+3j, 4+4j], [5+5j, 6+6j]]]
-    const c1 =
-        tf.complex([[[1, 2], [3, 4], [5, 6]]], [[[1, 2], [3, 4], [5, 6]]]);
-    // [[[7+7j, 8+8j], [9+9j, 10+10j], [11+11j, 12+12j]]]
-    const c2 = tf.complex(
-        [[[7, 8], [9, 10], [11, 12]]], [[[7, 8], [9, 10], [11, 12]]]);
-
-    const axis = 0;
-    const result = tf.concat([c1, c2], axis);
-    const expected = [
-      1, 1, 2, 2, 3, 3, 4,  4,  5,  5,  6,  6,
-      7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12
-    ];
-    expect(result.dtype).toEqual('complex64');
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('concat complex input axis=1', async () => {
-    // [[[1+1j, 2+2j], [3+3j, 4+4j], [5+5j, 6+6j]]]
-    const c1 =
-        tf.complex([[[1, 2], [3, 4], [5, 6]]], [[[1, 2], [3, 4], [5, 6]]]);
-    // [[[7+7j, 8+8j], [9+9j, 10+10j], [11+11j, 12+12j]]]
-    const c2 = tf.complex(
-        [[[7, 8], [9, 10], [11, 12]]], [[[7, 8], [9, 10], [11, 12]]]);
-
-    const axis = 1;
-    const result = tf.concat([c1, c2], axis);
-    const expected = [
-      1, 1, 2, 2, 3, 3, 4,  4,  5,  5,  6,  6,
-      7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12
-    ];
-    expect(result.dtype).toEqual('complex64');
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('concat complex input axis=1', async () => {
-    // [[[1+1j, 2+2j], [3+3j, 4+4j], [5+5j, 6+6j]]]
-    const c1 =
-        tf.complex([[[1, 2], [3, 4], [5, 6]]], [[[1, 2], [3, 4], [5, 6]]]);
-    // [[[7+7j, 8+8j], [9+9j, 10+10j], [11+11j, 12+12j]]]
-    const c2 = tf.complex(
-        [[[7, 8], [9, 10], [11, 12]]], [[[7, 8], [9, 10], [11, 12]]]);
-
-    const axis = 2;
-    const result = tf.concat([c1, c2], axis);
-    const expected = [
-      1, 1, 2,  2,  7, 7, 8, 8, 3,  3,  4,  4,
-      9, 9, 10, 10, 5, 5, 6, 6, 11, 11, 12, 12
-    ];
-    expect(result.dtype).toEqual('complex64');
-    expectArraysClose(await result.data(), expected);
-  });
-});
-
-describeWithFlags('concat throws for non-tensors', ALL_ENVS, () => {
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.concat([{} as tf.Tensor1D]))
-        .toThrowError(
-            /Argument 'tensors\[0\]' passed to 'concat' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const tensor1 = [[[1, 2, 3, 4]]];  // 1x1x4
-    const tensor2 = [[[4, 5, 6, 7]]];  // 1x1x4
-    const values = tf.concat([tensor1, tensor2], 0);
-    expect(values.shape).toEqual([2, 1, 4]);
-    expectArraysClose(await values.data(), [1, 2, 3, 4, 4, 5, 6, 7]);
-  });
-});
-
-describeWithFlags('memory test', ALL_ENVS, () => {
-  it('returns a new tensor when op is effectively a no-op.', async () => {
-    const a = tf.tensor1d([]);
-    const b = tf.tensor1d([3]);
-
-    const result = tf.concat([a, b]);
-
-    a.dispose();
-    b.dispose();
-
-    expectArraysClose(await result.data(), [3]);
-  });
-
-  it('ensure no memory leak', async () => {
-    const numTensorsBefore = tf.memory().numTensors;
-    const numDataIdBefore = tf.engine().backend.numDataIds();
-
-    const a = tf.tensor1d([]);
-    const b = tf.tensor1d([3]);
-
-    const result = tf.concat([a, b]);
-
-    a.dispose();
-    b.dispose();
-    result.dispose();
-
-    const numTensorsAfter = tf.memory().numTensors;
-    const numDataIdAfter = tf.engine().backend.numDataIds();
-    expect(numTensorsAfter).toBe(numTensorsBefore);
-    expect(numDataIdAfter).toBe(numDataIdBefore);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/concat_util.ts b/tfjs-master/tfjs-core/src/ops/concat_util.ts
deleted file mode 100644
index 608b585d2..000000000
--- a/tfjs-master/tfjs-core/src/ops/concat_util.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as util from '../util';
-
-export function assertParamsConsistent(shapes: number[][], axis: number) {
-  const rank = shapes[0].length;
-  shapes.forEach((shape, i) => {
-    util.assert(
-        shape.length === rank,
-        () =>
-            `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +
-            `as the rank of the rest (${rank})`);
-  });
-
-  util.assert(
-      axis >= 0 && axis < rank,
-      () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
-
-  const firstShape = shapes[0];
-  shapes.forEach((shape, i) => {
-    for (let r = 0; r < rank; r++) {
-      util.assert(
-          (r === axis) || (shape[r] === firstShape[r]),
-          () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +
-              `does not match the shape of the rest (${firstShape}) ` +
-              `along the non-concatenated axis ${i}.`);
-    }
-  });
-}
-
-export function computeOutShape(shapes: number[][], axis: number): number[] {
-  const outputShape = shapes[0].slice();
-  for (let i = 1; i < shapes.length; i++) {
-    outputShape[axis] += shapes[i][axis];
-  }
-  return outputShape;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/concat_util_test.ts b/tfjs-master/tfjs-core/src/ops/concat_util_test.ts
deleted file mode 100644
index 2823c47fb..000000000
--- a/tfjs-master/tfjs-core/src/ops/concat_util_test.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as concat_util from './concat_util';
-
-describe('concat_util.assertConcatShapesMatch rank=3D', () => {
-  it('Non-3D tensor x1', () => {
-    const assertFn = () => {
-      concat_util.assertParamsConsistent([[1], [1, 2, 3]], 1);
-    };
-
-    expect(assertFn).toThrow();
-  });
-
-  it('Non-3D tensor x2', () => {
-    const assertFn = () => {
-      concat_util.assertParamsConsistent([[1, 2, 3], [2, 3]], 1);
-    };
-
-    expect(assertFn).toThrow();
-  });
-
-  it('axis out of bound', () => {
-    const assertFn = () => {
-      concat_util.assertParamsConsistent([[1, 2, 3], [1, 2, 3]], 4);
-    };
-
-    expect(assertFn).toThrow();
-  });
-
-  it('non-axis shape mismatch', () => {
-    const assertFn = () => {
-      concat_util.assertParamsConsistent([[2, 3, 3], [2, 2, 4]], 2);
-    };
-
-    expect(assertFn).toThrow();
-  });
-
-  it('shapes line up', () => {
-    const assertFn = () => {
-      concat_util.assertParamsConsistent([[2, 3, 3], [2, 3, 4]], 2);
-    };
-
-    expect(assertFn).not.toThrow();
-  });
-
-  it('3 shapes, all line up', () => {
-    const assertFn = () => {
-      concat_util.assertParamsConsistent([[2, 3, 3], [2, 3, 4], [2, 3, 8]], 2);
-    };
-    expect(assertFn).not.toThrow();
-  });
-
-  it('3 shapes, 3rd shape does not line up', () => {
-    const assertFn = () => {
-      concat_util.assertParamsConsistent([[2, 5, 3], [2, 1, 3], [2, 1, 5]], 1);
-    };
-    expect(assertFn).toThrow();
-  });
-});
-
-describe('concat_util.computeConcatOutputShape', () => {
-  it('compute output shape, axis=0', () => {
-    expect(concat_util.computeOutShape([[2, 2, 3], [1, 2, 3]], 0)).toEqual([
-      3, 2, 3
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/confusion_matrix.ts b/tfjs-master/tfjs-core/src/ops/confusion_matrix.ts
deleted file mode 100644
index b4625987d..000000000
--- a/tfjs-master/tfjs-core/src/ops/confusion_matrix.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor1D, Tensor2D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {cast} from './cast';
-import {matMul} from './mat_mul';
-import {oneHot} from './one_hot';
-import {op} from './operation';
-import {transpose} from './transpose';
-
-/**
- * Computes the confusion matrix from true labels and predicted labels.
- *
- * ```js
- * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');
- * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');
- * const numClasses = 3;
- * const out = tf.math.confusionMatrix(labels, predictions, numClasses);
- * out.print();
- * // Expected output matrix:
- * // [[2, 0, 0],
- * //  [0, 1, 1],
- * //  [0, 0, 1]]
- * ```
- *
- * @param labels The target labels, assumed to be 0-based integers
- *   for the classes. The shape is `[numExamples]`, where
- *   `numExamples` is the number of examples included.
- * @param predictions The predicted classes, assumed to be
- *   0-based integers for the classes. Must have the same shape as `labels`.
- * @param numClasses Number of all classes, as an integer.
- *   Its value must be larger than the largest element in `labels` and
- *   `predictions`.
- * @returns The confusion matrix as a int32-type 2D tensor. The value at
- *   row `r` and column `c` is the number of times examples of actual class
- *   `r` were predicted as class `c`.
- *
- * @doc {heading: 'Operations', subheading: 'Evaluation'}
- */
-export function confusionMatrix_(
-    labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,
-    numClasses: number): Tensor2D {
-  const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');
-  const $predictions =
-      convertToTensor(predictions, 'predictions', 'confusionMatrix');
-
-  util.assert(
-      numClasses == null || numClasses > 0 && Number.isInteger(numClasses),
-      () => `If provided, numClasses must be a positive integer, ` +
-          `but got ${numClasses}`);
-  util.assert(
-      $labels.rank === 1,
-      () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);
-  util.assert(
-      $predictions.rank === 1,
-      () => `Expected the rank of predictions to be 1, ` +
-          `but got ${$predictions.rank}`);
-  util.assert(
-      $labels.shape[0] === $predictions.shape[0],
-      () => `Mismatch in the number of examples: ` +
-          `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +
-          `Labels and predictions should have the same number of elements.`);
-  util.assert(
-      numClasses > 0 && Number.isInteger(numClasses),
-      () => `numClasses is required to be a positive integer, but got ` +
-          `${numClasses}`);
-  // TODO(cais): In the future, if oneHot supports tensors inputs for
-  //   `numClasses`, `confusionMatrix` can make `numClasses` optional.
-
-  const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses) as Tensor2D;
-  const oneHotPredictions =
-      oneHot(cast($predictions, 'int32'), numClasses) as Tensor2D;
-  const oneHotLabelsT: Tensor2D = transpose(oneHotLabels);
-  const product: Tensor2D = matMul(oneHotLabelsT, oneHotPredictions);
-  return cast(product, 'int32');
-}
-
-export const confusionMatrix = /* @__PURE__ */ op({confusionMatrix_});
diff --git a/tfjs-master/tfjs-core/src/ops/confusion_matrix_test.ts b/tfjs-master/tfjs-core/src/ops/confusion_matrix_test.ts
deleted file mode 100644
index fa0f24e34..000000000
--- a/tfjs-master/tfjs-core/src/ops/confusion_matrix_test.ts
+++ /dev/null
@@ -1,174 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysEqual} from '../test_util';
-
-/**
- * Unit tests for confusionMatrix().
- */
-
-describeWithFlags('confusionMatrix', ALL_ENVS, () => {
-  // Reference (Python) TensorFlow code:
-  //
-  // ```py
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // labels = tf.constant([0, 1, 2, 1, 0])
-  // predictions = tf.constant([0, 2, 2, 1, 0])
-  // out = tf.confusion_matrix(labels, predictions, 3)
-  //
-  // print(out)
-  // ```
-  it('3x3 all cases present in both labels and predictions', async () => {
-    const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');
-    const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');
-    const numClasses = 3;
-    const out = tf.math.confusionMatrix(labels, predictions, numClasses);
-    expectArraysEqual(await out.data(), [2, 0, 0, 0, 1, 1, 0, 0, 1]);
-    expect(out.dtype).toBe('int32');
-    expect(out.shape).toEqual([3, 3]);
-  });
-
-  it('float32 arguments are accepted', async () => {
-    const labels = tf.tensor1d([0, 1, 2, 1, 0], 'float32');
-    const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'float32');
-    const numClasses = 3;
-    const out = tf.math.confusionMatrix(labels, predictions, numClasses);
-    expectArraysEqual(await out.data(), [2, 0, 0, 0, 1, 1, 0, 0, 1]);
-    expect(out.dtype).toBe('int32');
-    expect(out.shape).toEqual([3, 3]);
-  });
-
-  // Reference (Python) TensorFlow code:
-  //
-  // ```py
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // labels = tf.constant([3, 3, 2, 2, 1, 1, 0, 0])
-  // predictions = tf.constant([2, 2, 2, 2, 0, 0, 0, 0])
-  // out = tf.confusion_matrix(labels, predictions, 4)
-  //
-  // print(out)
-  // ```
-  it('4x4 all cases present in labels, but not predictions', async () => {
-    const labels = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
-    const predictions = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
-    const numClasses = 4;
-    const out = tf.math.confusionMatrix(labels, predictions, numClasses);
-    expectArraysEqual(
-        await out.data(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]);
-    expect(out.dtype).toBe('int32');
-    expect(out.shape).toEqual([4, 4]);
-  });
-
-  it('4x4 all cases present in predictions, but not labels', async () => {
-    const labels = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
-    const predictions = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
-    const numClasses = 4;
-    const out = tf.math.confusionMatrix(labels, predictions, numClasses);
-    expectArraysEqual(
-        await out.data(), [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0]);
-    expect(out.dtype).toBe('int32');
-    expect(out.shape).toEqual([4, 4]);
-  });
-
-  it('Plain arrays as inputs', async () => {
-    const labels: number[] = [3, 3, 2, 2, 1, 1, 0, 0];
-    const predictions: number[] = [2, 2, 2, 2, 0, 0, 0, 0];
-    const numClasses = 4;
-    const out = tf.math.confusionMatrix(labels, predictions, numClasses);
-    expectArraysEqual(
-        await out.data(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]);
-    expect(out.dtype).toBe('int32');
-    expect(out.shape).toEqual([4, 4]);
-  });
-
-  it('Int32Arrays as inputs', async () => {
-    const labels = new Int32Array([3, 3, 2, 2, 1, 1, 0, 0]);
-    const predictions = new Int32Array([2, 2, 2, 2, 0, 0, 0, 0]);
-    const numClasses = 4;
-    const out = tf.math.confusionMatrix(labels, predictions, numClasses);
-    expectArraysEqual(
-        await out.data(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]);
-    expect(out.dtype).toBe('int32');
-    expect(out.shape).toEqual([4, 4]);
-  });
-
-  // Reference (Python) TensorFlow code:
-  //
-  // ```py
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // labels = tf.constant([0, 4])
-  // predictions = tf.constant([4, 0])
-  // out = tf.confusion_matrix(labels, predictions, 5)
-  //
-  // print(out)
-  // ```
-  it('5x5 predictions and labels both missing some cases', async () => {
-    const labels = tf.tensor1d([0, 4], 'int32');
-    const predictions = tf.tensor1d([4, 0], 'int32');
-    const numClasses = 5;
-    const out = tf.math.confusionMatrix(labels, predictions, numClasses);
-    expectArraysEqual(await out.data(), [
-      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
-      0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
-    ]);
-    expect(out.dtype).toBe('int32');
-    expect(out.shape).toEqual([5, 5]);
-  });
-
-  it('Invalid numClasses leads to Error', () => {
-    expect(
-        () => tf.math.confusionMatrix(
-            tf.tensor1d([0, 1]), tf.tensor1d([1, 0]), 2.5))
-        .toThrowError(/numClasses .* positive integer.* got 2\.5/);
-  });
-
-  it('Incorrect tensor rank leads to Error', () => {
-    expect(
-        () => tf.math.confusionMatrix(
-            // tslint:disable-next-line:no-any
-            tf.scalar(0) as any, tf.scalar(0) as any, 1))
-        .toThrowError(/rank .* 1.*got 0/);
-    expect(
-        () =>
-            // tslint:disable-next-line:no-any
-        tf.math.confusionMatrix(tf.zeros([3, 3]) as any, tf.zeros([9]), 2))
-        .toThrowError(/rank .* 1.*got 2/);
-    expect(
-        () =>
-            // tslint:disable-next-line:no-any
-        tf.math.confusionMatrix(tf.zeros([9]), tf.zeros([3, 3]) as any, 2))
-        .toThrowError(/rank .* 1.*got 2/);
-  });
-
-  it('Mismatch in lengths leads to Error', () => {
-    expect(
-        // tslint:disable-next-line:no-any
-        () => tf.math.confusionMatrix(tf.zeros([3]) as any, tf.zeros([9]), 2))
-        .toThrowError(/Mismatch .* 3 vs.* 9/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/conv1d.ts b/tfjs-master/tfjs-core/src/ops/conv1d.ts
deleted file mode 100644
index 90ca41ae1..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv1d.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor2D, Tensor3D, Tensor4D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {conv2d} from './conv2d';
-import * as conv_util from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes a 1D convolution over the input x.
- *
- * @param x The input tensor, of rank 3 or rank 2, of shape
- *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.
- * @param filter The filter, rank 3, of shape
- *     `[filterWidth, inDepth, outDepth]`.
- * @param stride The number of entries by which the filter is moved right at
- *     each step.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *   - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dataFormat An optional string from "NWC", "NCW". Defaults to "NWC",
- *     the data is stored in the order of [batch, in_width, in_channels]. Only
- *     "NWC" is currently supported.
- * @param dilation The dilation rate in which we sample input values in
- *     atrous convolution. Defaults to `1`. If it is greater than 1, then
- *     stride must be `1`.
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function conv1d_<T extends Tensor2D|Tensor3D>(
-    x: T|TensorLike, filter: Tensor3D|TensorLike, stride: number,
-    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-    dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  const $x = convertToTensor(x, 'x', 'conv1d');
-  const $filter = convertToTensor(filter, 'filter', 'conv1d');
-
-  let x3D = $x as Tensor3D;
-  let reshapedTo3D = false;
-  if ($x.rank === 2) {
-    reshapedTo3D = true;
-    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
-  }
-
-  util.assert(
-      x3D.rank === 3,
-      () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
-  util.assert(
-      $filter.rank === 3,
-      () => `Error in conv1d: filter must be rank 3, but got rank ` +
-          `${$filter.rank}.`);
-  conv_util.checkPadOnDimRoundingMode('conv1d', pad, dimRoundingMode);
-  util.assert(
-      x3D.shape[2] === $filter.shape[1],
-      () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +
-          `input depth for filter ${$filter.shape[1]}.`);
-  util.assert(
-      conv_util.eitherStridesOrDilationsAreOne(stride, dilation),
-      () => 'Error in conv1D: Either stride or dilation must be 1. ' +
-          `Got stride ${stride} and dilation '${dilation}'`);
-  util.assert(
-      conv_util.stridesOrDilationsArePositive(dilation),
-      () => 'Error in conv1D: Dilated rates should be larger than 0.');
-  util.assert(
-      conv_util.stridesOrDilationsArePositive(stride),
-      () => 'Error in conv1D: Stride should be larger than 0.');
-  util.assert(
-      dataFormat === 'NWC',
-      () => `Error in conv1d: got dataFormat of ${
-          dataFormat} but only NWC is currently supported.`);
-
-  const filter4D = reshape(
-      $filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
-  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
-  const strides: [number, number] = [1, stride];
-  const dilations: [number, number] = [1, dilation];
-
-  const conv2dDataFormat = 'NHWC';
-
-  const res = conv2d(
-      (input4D as Tensor4D), (filter4D as Tensor4D), strides, pad,
-      conv2dDataFormat, dilations, dimRoundingMode);
-
-  if (reshapedTo3D) {
-    return reshape(res, [res.shape[2], res.shape[3]]) as T;
-  }
-
-  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]) as T;
-}
-
-export const conv1d = /* @__PURE__ */ op({conv1d_});
diff --git a/tfjs-master/tfjs-core/src/ops/conv1d_test.ts b/tfjs-master/tfjs-core/src/ops/conv1d_test.ts
deleted file mode 100644
index d0205d792..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv1d_test.ts
+++ /dev/null
@@ -1,462 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {Rank} from '../types';
-
-describeWithFlags('conv1d', ALL_ENVS, () => {
-  it('conv1d input=2x2x1,d2=1,f=1,s=1,d=1,p=explicit', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad =
-        [[0, 0], [0, 0], [0, 0], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [3, 6, 9, 12]);
-  });
-
-  it('conv1d input=2x2x1,d2=1,f=1,s=1,d=1,p=same', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [3, 6, 9, 12]);
-  });
-
-  it('conv1d input=4x1,d2=1,f=2x1x1,s=1,d=1,p=valid', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number] = [4, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    const x = tf.tensor2d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([2, 1], [fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
-
-    expect(result.shape).toEqual([3, 1]);
-    expectArraysClose(await result.data(), [4, 7, 10]);
-  });
-
-  it('conv1d input=4x1,d2=1,f=2x1x1,s=1,d=2,p=valid', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number] = [4, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const fSizeDilated = 3;
-    const pad = 'valid';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 2;
-    const dilationWEffective = 1;
-
-    const x = tf.tensor2d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([2, 1], [fSize, inputDepth, outputDepth]);
-    // adding a dilation rate is equivalent to using a filter
-    // with 0s for the dilation rate
-    const wDilated =
-        tf.tensor3d([2, 0, 1], [fSizeDilated, inputDepth, outputDepth]);
-
-    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
-    const expectedResult =
-        tf.conv1d(x, wDilated, stride, pad, dataFormat, dilationWEffective);
-
-    expect(result.shape).toEqual(expectedResult.shape);
-    expectArraysClose(await result.data(), await expectedResult.data());
-  });
-
-  it('conv1d input=14x1,d2=1,f=3x1x1,s=1,d=3,p=valid', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number] = [14, inputDepth];
-    const outputDepth = 1;
-    const fSize = 3;
-    const fSizeDilated = 7;
-    const pad = 'valid';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 3;
-    const dilationWEffective = 1;
-
-    const x = tf.tensor2d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inputShape);
-    const w = tf.tensor3d([3, 2, 1], [fSize, inputDepth, outputDepth]);
-    // adding a dilation rate is equivalent to using a filter
-    // with 0s for the dilation rate
-    const wDilated = tf.tensor3d(
-        [3, 0, 0, 2, 0, 0, 1], [fSizeDilated, inputDepth, outputDepth]);
-
-    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
-    const expectedResult =
-        tf.conv1d(x, wDilated, stride, pad, dataFormat, dilationWEffective);
-
-    expect(result.shape).toEqual(expectedResult.shape);
-    expectArraysClose(await result.data(), await expectedResult.data());
-  });
-
-  it('throws when dimRoundingMode is set and pad is same', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-    const dimRoundingMode = 'round';
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-    expect(
-        () =>
-            tf.conv1d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
-        .toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is valid', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 'valid';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-    const dimRoundingMode = 'round';
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-    expect(
-        () =>
-            tf.conv1d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
-        .toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is a non-integer number',
-     () => {
-       const inputDepth = 1;
-       const inputShape: [number, number, number] = [2, 2, inputDepth];
-       const outputDepth = 1;
-       const fSize = 1;
-       const pad = 1.2;
-       const stride = 1;
-       const dataFormat = 'NWC';
-       const dilation = 1;
-       const dimRoundingMode = 'round';
-
-       const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-       const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-       expect(
-           () => tf.conv1d(
-               x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
-           .toThrowError();
-     });
-
-  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
-         'number',
-     () => {
-       const inputDepth = 1;
-       const inputShape: [number, number, number] = [2, 2, inputDepth];
-       const outputDepth = 1;
-       const fSize = 1;
-       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as
-           tf.backend_util.ExplicitPadding;
-       const stride = 1;
-       const dataFormat = 'NWC';
-       const dilation = 1;
-       const dimRoundingMode = 'round';
-
-       const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-       const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-       expect(
-           () => tf.conv1d(
-               x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
-           .toThrowError();
-     });
-
-  it('TensorLike', async () => {
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    const x = [[[1], [2]], [[3], [4]]];
-    const w = [[[3]]];
-
-    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [3, 6, 9, 12]);
-  });
-  it('TensorLike Chained', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = [[[3]]];
-
-    const result = x.conv1d(w, stride, pad, dataFormat, dilation);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [3, 6, 9, 12]);
-  });
-
-  it('throws when x is not rank 3', () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const w = tf.tensor3d([3, 1], [fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
-        .toThrowError();
-  });
-
-  it('throws when weights is not rank 3', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    // tslint:disable-next-line:no-any
-    const w: any = tf.tensor4d([3, 1, 5, 0], [2, 2, 1, 1]);
-
-    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
-        .toThrowError();
-  });
-
-  it('throws when x depth does not match weight depth', () => {
-    const inputDepth = 1;
-    const wrongInputDepth = 5;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.randomNormal<Rank.R3>([fSize, wrongInputDepth, outputDepth]);
-
-    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
-        .toThrowError();
-  });
-
-  it('throws when stride is less than or equal to 0', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad =
-        [[0, 0], [0, 0], [0, 0], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const stride = 0;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
-        .toThrowError();
-  });
-
-  it('throws when dilation is less than or equal to 0', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad =
-        [[0, 0], [0, 0], [0, 0], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 0;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
-        .toThrowError();
-  });
-
-  it('throws when both stride and dilation are greater than 1', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 2;
-    const dataFormat = 'NWC';
-    const dilation = 2;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
-        .toThrowError();
-  });
-
-  it('throws when passed x as a non-tensor', () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 2;
-    const dataFormat = 'NWC';
-    const dilation = 2;
-
-    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
-
-    expect(
-        () =>
-            tf.conv1d({} as tf.Tensor3D, w, stride, pad, dataFormat, dilation))
-        .toThrowError(/Argument 'x' passed to 'conv1d' must be a Tensor/);
-  });
-
-  it('throws when passed filter as a non-tensor', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const pad = 'same';
-    const stride = 2;
-    const dataFormat = 'NWC';
-    const dilation = 2;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-
-    expect(
-        () =>
-            tf.conv1d(x, {} as tf.Tensor3D, stride, pad, dataFormat, dilation))
-        .toThrowError(/Argument 'filter' passed to 'conv1d' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-    const x = [[[1], [2]], [[3], [4]]];  // 2x2x1
-    const w = [[[3]]];                   // 1x1x1
-
-    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [3, 6, 9, 12]);
-  });
-
-  it('gradient with clones, input=2x2x1,d2=1,f=1,s=1,d=1,p=same', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const filterShape: [number, number, number] =
-        [fSize, inputDepth, outputDepth];
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor3d([3], filterShape);
-
-    const dy = tf.tensor3d([3, 2, 1, 0], inputShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor3D, w: tf.Tensor3D) =>
-            tf.conv1d(x.clone(), w.clone(), stride, pad, dataFormat, dilation)
-                .clone());
-    const [dx, dw] = grads([x, w], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [9, 6, 3, 0]);
-
-    expect(dw.shape).toEqual(w.shape);
-    expectArraysClose(await dw.data(), [10]);
-  });
-
-  it('conv1d gradients input=14x1,d2=1,f=3x1x1,s=1,p=valid', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number] = [14, inputDepth];
-
-    const outputDepth = 1;
-    const fSize = 3;
-    const pad = 'valid';
-    const stride = 1;
-    const dataFormat = 'NWC';
-
-    const x = tf.tensor2d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inputShape);
-    const w = tf.tensor3d([3, 2, 1], [fSize, inputDepth, outputDepth]);
-
-    const dy =
-        tf.tensor2d([3, 2, 1, 0, 3, 2, 1, 0, 3, 2, 1, 0], [12, inputDepth]);
-
-    const grads = tf.grads(
-        (x: tf.Tensor2D, w: tf.Tensor3D) =>
-            tf.conv1d(x, w, stride, pad, dataFormat));
-    const [dx, dw] = grads([x, w], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(
-        await dx.data(), [9, 12, 10, 4, 10, 12, 10, 4, 10, 12, 10, 4, 1, 0]);
-
-    expect(dw.shape).toEqual(w.shape);
-    expectArraysClose(await dw.data(), [102, 120, 138]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/conv2d.ts b/tfjs-master/tfjs-core/src/ops/conv2d.ts
deleted file mode 100644
index 033d92648..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv2d.ts
+++ /dev/null
@@ -1,119 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Conv2D, Conv2DAttrs, Conv2DInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import * as conv_util from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes a 2D convolution over the input x.
- *
- * @param x The input tensor, of rank 4 or rank 3, of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
- * assumed.
- * @param filter The filter, rank 4, of shape
- *     `[filterHeight, filterWidth, inDepth, outDepth]`.
- * @param strides The strides of the convolution: `[strideHeight,
- * strideWidth]`.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *   - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
- *     "NHWC". Specify the data format of the input and output data. With the
- *     default format "NHWC", the data is stored in the order of: [batch,
- *     height, width, channels].
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
- *     in which we sample input values across the height and width dimensions
- *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
- *     number, then `dilationHeight == dilationWidth`. If it is greater than
- *     1, then all values of `strides` must be 1.
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function conv2d_<T extends Tensor3D|Tensor4D>(
-    x: T|TensorLike, filter: Tensor4D|TensorLike,
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-    dataFormat: 'NHWC'|'NCHW' = 'NHWC',
-    dilations: [number, number]|number = [1, 1],
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  const $x = convertToTensor(x, 'x', 'conv2d', 'float32');
-  const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');
-
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-
-  util.assert(
-      x4D.rank === 4,
-      () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
-  util.assert(
-      $filter.rank === 4,
-      () => `Error in conv2d: filter must be rank 4, but got rank ` +
-          `${$filter.rank}.`);
-  conv_util.checkPadOnDimRoundingMode('conv2d', pad, dimRoundingMode);
-  const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
-  util.assert(
-      inDepth === $filter.shape[2],
-      () => `Error in conv2d: depth of input (${inDepth}) must match ` +
-          `input depth for filter ${$filter.shape[2]}.`);
-  util.assert(
-      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in conv2D: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-  util.assert(
-      conv_util.stridesOrDilationsArePositive(dilations),
-      () => 'Error in conv2D: Dilated rates should be larger than 0.');
-  util.assert(
-      conv_util.stridesOrDilationsArePositive(strides),
-      () => 'Error in conv2D: Strides should be larger than 0.');
-
-  const inputs: Conv2DInputs = {x: x4D, filter: $filter};
-  const attrs:
-      Conv2DAttrs = {strides, pad, dataFormat, dilations, dimRoundingMode};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  Conv2D, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-  return res;
-}
-
-export const conv2d = /* @__PURE__ */ op({conv2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/conv2d_backprop_filter.ts b/tfjs-master/tfjs-core/src/ops/conv2d_backprop_filter.ts
deleted file mode 100644
index 353e3fe3c..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv2d_backprop_filter.ts
+++ /dev/null
@@ -1,95 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import * as util from '../util';
-
-import * as conv_util from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the derivative of the filter of a 2D convolution.
- *
- * @param x The input tensor, of rank 4 or rank 3 of shape
- *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
- * @param dy The dy image, of rank 4 or rank 3, of shape
- *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.
- * @param filterShape The shape of the filter, length 4,
- *     [filterHeight, filterWidth, inDepth, outDepth].
- * @param strides The strides of the convolution: [strideHeight,
- * strideWidth].
- * @param pad A string from: 'same', 'valid'. The type of padding algorithm
- *     used in the forward prop of the op.
- * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
- *     "NHWC". Specify the data format of the input and output data. With the
- *     default format "NHWC", the data is stored in the order of: [batch,
- *     height, width, channels].
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- */
-function conv2DBackpropFilter_<T extends Tensor3D|Tensor4D>(
-    x: T, dy: T, filterShape: [number, number, number, number],
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-    dataFormat: 'NHWC'|'NCHW' = 'NHWC',
-    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {
-  let x4D = x as Tensor4D;
-  if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  let dy4D = dy as Tensor4D;
-  if (dy4D.rank === 3) {
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-  util.assert(
-      x4D.rank === 4,
-      () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +
-          `${x4D.shape}.`);
-  util.assert(
-      dy4D.rank === 4,
-      () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +
-          `${dy4D.shape}.`);
-  util.assert(
-      filterShape.length === 4,
-      () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +
-          `${filterShape}.`);
-  const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
-  const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];
-  util.assert(
-      inDepth === filterShape[2],
-      () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +
-          `match input depth in filter (${filterShape[2]}.`);
-  util.assert(
-      outDepth === filterShape[3],
-      () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +
-          `match output depth for filter (${filterShape[3]}).`);
-  conv_util.checkPadOnDimRoundingMode('conv2dDerFilter', pad, dimRoundingMode);
-  const inputs: Conv2DBackpropFilterInputs = {x: x4D, dy: dy4D};
-  const attrs: Conv2DBackpropFilterAttrs =
-      {strides, pad, dataFormat, dimRoundingMode, filterShape};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  return ENGINE.runKernel(
-             Conv2DBackpropFilter, inputs as unknown as NamedTensorMap,
-             attrs as unknown as NamedAttrMap) as Tensor4D;
-}
-
-export const conv2DBackpropFilter = /* @__PURE__ */ op({conv2DBackpropFilter_});
diff --git a/tfjs-master/tfjs-core/src/ops/conv2d_backprop_input.ts b/tfjs-master/tfjs-core/src/ops/conv2d_backprop_input.ts
deleted file mode 100644
index aa92b52cb..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv2d_backprop_input.ts
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import * as util from '../util';
-
-import * as conv_util from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the derivative of the input of a 2D convolution.
- *
- * @param xShape The shape of the input: [batch, height, width, inDepth].
- * If length of 3, batch of 1 is assumed.
- * @param dy The derivative of the output, of rank 4 or rank 3 of shape
- *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is
- * assumed.
- * @param filter The filter, rank 4, of shape
- *     `[filterHeight, filterWidth, inDepth, outDepth]`.
- * @param strides The strides of the convolution: `[strideHeight,
- * strideWidth]`.
- * @param pad The type of padding algorithm used:
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
- *     "NHWC". Specify the data format of the input and output data. With the
- *     default format "NHWC", the data is stored in the order of: [batch,
- *     height, width, channels].
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- */
-function conv2DBackpropInput_<T extends Tensor3D|Tensor4D>(
-    xShape: [number, number, number, number]|[number, number, number], dy: T,
-    filter: Tensor4D, strides: [number, number]|number,
-    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-    dataFormat: 'NHWC'|'NCHW' = 'NHWC',
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  util.assert(
-      xShape.length === dy.rank,
-      () => `Length of inShape ` +
-          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);
-
-  let xShape4D = xShape as [number, number, number, number];
-  let dy4D = dy as Tensor4D;
-  let reshapedTo4D = false;
-  if (dy.rank === 3) {
-    reshapedTo4D = true;
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-    xShape4D = [1, xShape[0], xShape[1], xShape[2]];
-  }
-
-  util.assert(
-      xShape4D.length === 4,
-      () =>
-          `Error in conv2dDerInput: inShape must be length 4, but got length ` +
-          `${xShape4D.length}.`);
-  util.assert(
-      dy4D.rank === 4,
-      () => `Error in conv2dDerInput: dy must be rank 4, but got ` +
-          `rank ${dy4D.rank}`);
-  util.assert(
-      filter.rank === 4,
-      () => `Error in conv2dDerInput: filter must be rank 4, but got ` +
-          `rank ${filter.rank}`);
-  const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];
-  const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];
-  util.assert(
-      inDepth === filter.shape[2],
-      () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +
-          `match input depth for filter ${filter.shape[2]}.`);
-  util.assert(
-      outDepth === filter.shape[3],
-      () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +
-          `match output depth for filter ${filter.shape[3]}.`);
-  conv_util.checkPadOnDimRoundingMode('conv2dDerInput', pad, dimRoundingMode);
-  const inputs: Conv2DBackpropInputInputs = {dy: dy4D, filter};
-  const attrs: Conv2DBackpropInputAttrs =
-      {strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  Conv2DBackpropInput, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-  return res;
-}
-
-export const conv2DBackpropInput = /* @__PURE__ */ op({conv2DBackpropInput_});
diff --git a/tfjs-master/tfjs-core/src/ops/conv2d_separable_test.ts b/tfjs-master/tfjs-core/src/ops/conv2d_separable_test.ts
deleted file mode 100644
index b3db2a41d..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv2d_separable_test.ts
+++ /dev/null
@@ -1,526 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('separableConv2d', ALL_ENVS, () => {
-  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-    const outDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const depthwiseFilter = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-    const pointwiseFilter =
-        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
-
-    const result =
-        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
-
-    expectArraysClose(await result.data(), [
-      0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
-      0.07788632, -0.15577264
-    ]);
-    expect(result.shape).toEqual([1, 2, 2, outDepth]);
-  });
-
-  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2 in tensor',
-     async () => {
-       const fSize = 2;
-       const pad = 'valid';
-       const stride = 1;
-       const chMul = 1;
-       const inDepth = 1;
-       const outDepth = 2;
-
-       const x = tf.tensor4d(
-           [
-             0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-             0.0741907, 0.409265, 0.351377
-           ],
-           [1, 3, 3, inDepth]);
-       const depthwiseFilter = tf.tensor4d(
-           [0.303873, 0.229223, 0.144333, 0.803373],
-           [fSize, fSize, inDepth, chMul],
-       );
-       const pointwiseFilter =
-           tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
-
-       const result =
-           x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);
-
-       expectArraysClose(await result.data(), [
-         0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
-         -0.13408193, 0.07788632, -0.15577264
-       ]);
-       expect(result.shape).toEqual([1, 2, 2, outDepth]);
-     });
-
-  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=2,outDepth=2', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 2;
-    const inDepth = 1;
-    const outDepth = 3;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const depthwiseFilter = tf.tensor4d(
-        [
-          0.303873, 0.229223, 0.144333, 0.803373, -0.303873, -0.229223,
-          -0.144333, -0.803373
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-    const pointwiseFilter = tf.tensor4d(
-        [0.1, -0.2, -0.1, 0.2, 0.15, 0.15], [1, 1, inDepth * chMul, outDepth]);
-
-    const result =
-        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
-
-    expectArraysClose(await result.data(), [
-      0.00305368, 0.0140969, 0.00980358, -0.10853045, -0.06339455, -0.0699412,
-      0.11010849, 0.0347524, 0.05214475, 0.10307151, 0.02221644, 0.04224815
-    ]);
-    expect(result.shape).toEqual([1, 2, 2, outDepth]);
-  });
-
-  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2,3D input',
-     async () => {
-       const fSize = 2;
-       const pad = 'valid';
-       const stride = 1;
-       const chMul = 1;
-       const inDepth = 1;
-       const outDepth = 2;
-
-       const x = tf.tensor3d(
-           [
-             0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-             0.0741907, 0.409265, 0.351377
-           ],
-           [3, 3, inDepth]);
-       const depthwiseFilter = tf.tensor4d(
-           [0.303873, 0.229223, 0.144333, 0.803373],
-           [fSize, fSize, inDepth, chMul],
-       );
-       const pointwiseFilter =
-           tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
-
-       const result =
-           tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
-
-       expectArraysClose(await result.data(), [
-         0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
-         -0.13408193, 0.07788632, -0.15577264
-       ]);
-       expect(result.shape).toEqual([2, 2, outDepth]);
-     });
-
-  it('input=1x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride: [number, number] = [2, 2];
-    const chMul = 1;
-    const inDepth = 1;
-    const outDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
-          0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
-          0.047613, 0.554795
-        ],
-        [1, 4, 4, inDepth]);
-    const depthwiseFilter = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-    const pointwiseFilter =
-        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
-
-    const result =
-        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
-
-    expectArraysClose(await result.data(), [
-      0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544, -0.19803089,
-      0.05555845, -0.11111691
-    ]);
-    expect(result.shape).toEqual([1, 2, 2, outDepth]);
-  });
-
-  it('input=2x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride: [number, number] = [2, 2];
-    const chMul = 1;
-    const inDepth = 1;
-    const outDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.675707,  0.758567,  0.413529,  0.963967,  0.217291,  0.101335,
-          0.804231,  0.329673,  0.924503,  0.728742,  0.180217,  0.210459,
-          0.133869,  0.650827,  0.047613,  0.554795,  -0.675707, -0.758567,
-          -0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,
-          -0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,
-          -0.047613, -0.554795
-        ],
-        [2, 4, 4, inDepth]);
-    const depthwiseFilter = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-    const pointwiseFilter =
-        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
-
-    const result =
-        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
-
-    expectArraysClose(await result.data(), [
-      0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544, -0.19803089,
-      0.05555845, -0.11111691, -0.04919822, 0.09839644, -0.07275512, 0.14551024,
-      -0.09901544, 0.19803089, -0.05555845, 0.11111691
-    ]);
-    expect(result.shape).toEqual([2, 2, 2, outDepth]);
-  });
-
-  it('input=1x4x4x2,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride: [number, number] = [2, 2];
-    const chMul = 1;
-    const inDepth = 2;
-    const outDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.675707,  0.758567,  0.413529,  0.963967,  0.217291,  0.101335,
-          0.804231,  0.329673,  0.924503,  0.728742,  0.180217,  0.210459,
-          0.133869,  0.650827,  0.047613,  0.554795,  -0.675707, -0.758567,
-          -0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,
-          -0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,
-          -0.047613, -0.554795
-        ],
-        [1, 4, 4, inDepth]);
-    const depthwiseFilter = tf.tensor4d(
-        [
-          0.303873, 0.229223, 0.144333, 0.803373, 0.98976838, 0.56597068,
-          0.42654137, 0.66445535
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-    const pointwiseFilter = tf.tensor4d(
-        [0.1, -0.2, 0.05, -0.05], [1, 1, inDepth * chMul, outDepth]);
-
-    const result =
-        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
-
-    expectArraysClose(await result.data(), [
-      0.20072255, -0.32641545, 0.08474462, -0.11823604, -0.20072255, 0.32641545,
-      -0.08474462, 0.11823604
-    ]);
-    expect(result.shape).toEqual([1, 2, 2, outDepth]);
-  });
-
-  it('input=1x4x4x1,f=2,s=1,d=2,p=valid,chMul=1,outDepth=2', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-    const outDepth = 2;
-    const dilationRate = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
-          0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
-          0.047613, 0.554795
-        ],
-        [1, 4, 4, inDepth]);
-    const depthwiseFilter = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-    const pointwiseFilter =
-        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
-
-    const result = tf.separableConv2d(
-        x, depthwiseFilter, pointwiseFilter, stride, pad, dilationRate);
-
-    expectArraysClose(await result.data(), [
-      0.05783373, -0.11566745, 0.07257301, -0.14514601, 0.03079498, -0.06158997,
-      0.06460048, -0.12920095
-    ]);
-    expect(result.shape).toEqual([1, 2, 2, outDepth]);
-  });
-
-  it('input=1x4x4x1,f=2,s=1,d=1,p=same,chMul=1,outDepth=2', async () => {
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-    const outDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
-          0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
-          0.047613, 0.554795
-        ],
-        [1, 4, 4, inDepth]);
-    const depthwiseFilter = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-    const pointwiseFilter =
-        tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
-
-    const result =
-        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
-
-    expectArraysClose(await result.data(), [
-      0.04919822, -0.09839644, 0.09860218, -0.19720435, 0.07275512, -0.14551024,
-      0.03405062, -0.06810125, 0.08081452, -0.16162904, 0.04651042, -0.09302084,
-      0.05150411, -0.10300821, 0.01305549, -0.02611098, 0.09901544, -0.19803089,
-      0.03949417, -0.07898834, 0.05555845, -0.11111691, 0.0144028,  -0.02880561,
-      0.01898637, -0.03797274, 0.02086828, -0.04173655, 0.01416401, -0.02832802,
-      0.01685872, -0.03371745
-    ]);
-    expect(result.shape).toEqual([1, 4, 4, outDepth]);
-  });
-
-  it('TensorLike', async () => {
-    const pad = 'valid';
-    const stride = 1;
-    const outDepth = 2;
-
-    const x = [[
-      [[0.230664], [0.987388], [0.0685208]],
-      [[0.419224], [0.887861], [0.731641]],
-      [[0.0741907], [0.409265], [0.351377]]
-    ]];
-    const depthwiseFilter =
-        [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
-    const pointwiseFilter = [[[[0.1, -0.2]]]];
-
-    const result =
-        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
-
-    expectArraysClose(await result.data(), [
-      0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
-      0.07788632, -0.15577264
-    ]);
-    expect(result.shape).toEqual([1, 2, 2, outDepth]);
-  });
-
-  it('TensorLike Chained', async () => {
-    const pad = 'valid';
-    const stride = 1;
-    const outDepth = 2;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const depthwiseFilter =
-        [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
-    const pointwiseFilter = [[[[0.1, -0.2]]]];
-
-    const result =
-        x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);
-
-    expectArraysClose(await result.data(), [
-      0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
-      0.07788632, -0.15577264
-    ]);
-    expect(result.shape).toEqual([1, 2, 2, outDepth]);
-  });
-
-  it('Incorrect input rank raises error', () => {
-    // tslint:disable-next-line:no-any
-    const x = tf.zeros([4, 4]) as any;
-    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);
-    const pointwiseFilter: tf.Tensor4D = tf.zeros([1, 1, 2, 4]);
-    expect(
-        () =>
-            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
-        .toThrowError(/rank 4/);
-  });
-
-  it('Incorrect depthwise filter rank raises error', () => {
-    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);
-    // tslint:disable-next-line:no-any
-    const depthwiseFilter = tf.zeros([2, 2, 1]) as any;
-    const pointwiseFilter: tf.Tensor4D = tf.zeros([1, 1, 2, 4]);
-    expect(
-        () =>
-            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
-        .toThrowError(/rank 4/);
-  });
-
-  it('Incorrect depthwise filter rank raises error', () => {
-    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);
-    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);
-    // tslint:disable-next-line:no-any
-    const pointwiseFilter = tf.zeros([1, 1, 2]) as any;
-    expect(
-        () =>
-            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
-        .toThrowError(/rank 4/);
-  });
-
-  it('Incorrect point filter 1st dimension raises error', () => {
-    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);
-    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);
-    const pointwiseFilter: tf.Tensor4D = tf.zeros([2, 1, 3, 6]);
-    expect(
-        () =>
-            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
-        .toThrowError(/must be 1, but got 2/);
-  });
-
-  it('Incorrect point filter 2nd dimension raises error', () => {
-    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);
-    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);
-    const pointwiseFilter: tf.Tensor4D = tf.zeros([1, 5, 3, 6]);
-    expect(
-        () =>
-            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
-        .toThrowError(/must be 1, but got 5/);
-  });
-
-  it('Incorrect pointwise filter 3rd dimension raises error', () => {
-    const x: tf.Tensor4D = tf.zeros([1, 4, 4, 1]);
-    const depthwiseFilter: tf.Tensor4D = tf.zeros([2, 2, 1, 3]);
-    const pointwiseFilter: tf.Tensor4D = tf.zeros([1, 1, 4, 6]);
-    expect(
-        () =>
-            tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid'))
-        .toThrowError(/must be 3, but got 4/);
-  });
-
-  it('throws when passed x as a non-tensor', () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-    const outDepth = 2;
-
-    const depthwiseFilter: tf.Tensor4D =
-        tf.zeros([fSize, fSize, inDepth, chMul]);
-    const pointwiseFilter: tf.Tensor4D =
-        tf.zeros([1, 1, inDepth * chMul, outDepth]);
-
-    const e = /Argument 'x' passed to 'separableConv2d' must be a Tensor/;
-    expect(
-        () => tf.separableConv2d(
-            {} as tf.Tensor3D, depthwiseFilter, pointwiseFilter, stride, pad))
-        .toThrowError(e);
-  });
-
-  it('throws when passed depthwiseFilter as a non-tensor', () => {
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-    const outDepth = 2;
-
-    const x: tf.Tensor4D = tf.zeros([1, 3, 3, inDepth]);
-    const pointwiseFilter: tf.Tensor4D =
-        tf.zeros([1, 1, inDepth * chMul, outDepth]);
-
-    const e = new RegExp(
-        'Argument \'depthwiseFilter\' passed to \'separableConv2d\'' +
-        ' must be a Tensor');
-    expect(
-        () => tf.separableConv2d(
-            x, {} as tf.Tensor4D, pointwiseFilter, stride, pad))
-        .toThrowError(e);
-  });
-
-  it('throws when passed pointwiseFilter as a non-tensor', () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x: tf.Tensor4D = tf.zeros([1, 3, 3, inDepth]);
-    const depthwiseFilter: tf.Tensor4D =
-        tf.zeros([fSize, fSize, inDepth, chMul]);
-
-    const e = new RegExp(
-        'Argument \'pointwiseFilter\' passed to \'separableConv2d\'' +
-        ' must be a Tensor');
-    expect(
-        () => tf.separableConv2d(
-            x, depthwiseFilter, {} as tf.Tensor4D, stride, pad))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const pad = 'valid';
-    const stride = 1;
-    const outDepth = 2;
-
-    // 3x3x1
-    const x = [
-      [[0.230664], [0.987388], [0.0685208]],
-      [[0.419224], [0.887861], [0.731641]],
-      [[0.0741907], [0.409265], [0.351377]]
-    ];
-    // 2x2x1x1
-    const depthwiseFilter =
-        [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
-    // 1x1x1x2
-    const pointwiseFilter = [[[[0.1, -0.2]]]];
-
-    const result =
-        tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
-
-    expectArraysClose(await result.data(), [
-      0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
-      0.07788632, -0.15577264
-    ]);
-    expect(result.shape).toEqual([2, 2, outDepth]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/conv2d_test.ts b/tfjs-master/tfjs-core/src/ops/conv2d_test.ts
deleted file mode 100644
index 86a18ae8e..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv2d_test.ts
+++ /dev/null
@@ -1,983 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {Rank} from '../types';
-
-function generateCaseInputs(totalSizeTensor: number, totalSizeFilter: number) {
-  const inp = new Array(totalSizeTensor);
-  const filt = new Array(totalSizeFilter);
-
-  for (let i = 0; i < totalSizeTensor; i++) {
-    inp[i] = i + 1;
-  }
-  for (let i = 0; i < totalSizeFilter; i++) {
-    filt[i] = i + 1;
-  }
-
-  return {input: inp, filter: filt};
-}
-
-describeWithFlags('conv2d', ALL_ENVS, () => {
-  it('x=[1,4,4,1] f=[1,1,1,3] s=2 d=1 p=same', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 3;
-    const fSize = 1;
-    const pad = 'same';
-    const stride: [number, number] = [2, 2];
-
-    const x = tf.tensor3d(
-        [
-          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
-        ],
-        inputShape);
-    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-
-    expectArraysClose(
-        await result.data(),
-        [10, 5, 10, 50, 25, 50, -10, -5, -10, -50, -25, -50]);
-  });
-
-  it('x=[2,2,2,2] f=[1,1,2,2] s=1 d=1 p=0', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    const expected =
-        [-5, 2, -11, 5, -17, 8, -23, 11, -29, 14, -35, 17, -41, 20, -47, 23];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[2,2,1] f=[1,1,1,2] s=1 d=1 p=0', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-
-    expectArraysClose(await result.data(), [2, 4, 6, 8]);
-  });
-
-  it('x=[3,3,2] f=[2,2,2,1] s=1 d=1 p=valid', async () => {
-    const pad = 'valid';
-    const stride = 1;
-
-    const x = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90],
-        [3, 3, 2]);
-    const w = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8], [2, 2, 2, 1]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(resultData, new Float32Array([25.6, 53.5, 157.0, 220.9]));
-  });
-
-  it('x=[2,2,2,1] f=[1,1,1,1] s=1 d=1 p=0', async () => {
-    const inputDepth = 1;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([2, 2, 2, 1]);
-    const expected = [2, 4, 6, 8, 10, 12, 14, 16];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[2,1,2,2] f=[1,1,1,1] s=1 d=1 p=0 NCHW', async () => {
-    const inputDepth = 1;
-    const inShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NCHW';
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat);
-    expect(result.shape).toEqual([2, 1, 2, 2]);
-    const expected = [2, 4, 6, 8, 10, 12, 14, 16];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[4,2,1] f=[4,2,1,1] s=1 d=1 p=same', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2, inputDepth]);
-    const w =
-        tf.tensor4d([3, 1, 5, 0, 2, 7, 8, 9], [4, 2, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([4, 2, 1]);
-    expectArraysClose(resultData, [133, 66, 200, 102, 108, 58, 56, 58]);
-  });
-
-  it('x=[4,2,1] f=[4,2,1,1] s=1 d=1 p=explicit', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const pad =
-        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2, inputDepth]);
-    const w =
-        tf.tensor4d([3, 1, 5, 0, 2, 7, 8, 9], [4, 2, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([4, 2, 1]);
-    expectArraysClose(resultData, [133, 66, 200, 102, 108, 58, 56, 58]);
-  });
-
-  it('x=[2,2,1] f=[2,2,1,1] s=1 d=1 p=same', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(resultData, new Float32Array([20, 26, 13, 12]));
-  });
-
-  it('x=[1,2,2] f=[2,2,1,1] s=1 d=1 p=same NCHW', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [inputDepth, 2, 2];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([1, 2, 2]);
-    expectArraysClose(resultData, [20, 26, 13, 12]);
-  });
-
-  it('x=[4,2,2] f=[1,1,4,4] s=1 d=1 p=same NCHW', async () => {
-    // Skip tensorflow backend due to NCHW not supported.
-    if (tf.getBackend() === 'tensorflow') {
-      return;
-    }
-    const inputDepth = 4;
-    const inputShape: [number, number, number] = [inputDepth, 2, 2];
-    const outputDepth = 4;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor3d(
-        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d(
-        [3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0],
-        [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([4, 2, 2]);
-    expectArraysClose(
-        resultData,
-        [9, 18, 27, 36, 9, 18, 27, 36, 9, 18, 27, 36, 9, 18, 27, 36]);
-  });
-
-  it('x=[3,2,2] f=[1,1,3,4] s=1 d=1 p=same NCHW', async () => {
-    // Skip tensorflow backend due to NCHW not supported.
-    if (tf.getBackend() === 'tensorflow') {
-      return;
-    }
-    const inputDepth = 3;
-    const inputShape: [number, number, number] = [inputDepth, 2, 2];
-    const outputDepth = 4;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d(
-        [3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5],
-        [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([4, 2, 2]);
-    expectArraysClose(
-        resultData,
-        [9, 18, 27, 36, 9, 18, 27, 36, 9, 18, 27, 36, 9, 18, 27, 36]);
-  });
-
-  it('x=[2,2,2,2] f=[1,1,4,4] s=1 d=1 p=same NCHW', async () => {
-    // Skip tensorflow backend due to NCHW not supported.
-    if (tf.getBackend() === 'tensorflow') {
-      return;
-    }
-    const inputDepth = 2;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 3, 5, 7, 2, 4, 6, 8, 9, 11, 13, 15, 10, 12, 14, 16], inputShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(
-        resultData,
-        [-5, -11, -17, -23, 2, 5, 8, 11, -29, -35, -41, -47, 14, 17, 20, 23]);
-  });
-
-  it('x=[4,2,2] f=[2,2,4,4] s=1 d=1 p=same NCHW', async () => {
-    // Skip tensorflow backend due to NCHW not supported.
-    if (tf.getBackend() === 'tensorflow') {
-      return;
-    }
-    const inputDepth = 4;
-    const inputShape: [number, number, number] = [inputDepth, 2, 2];
-    const outputDepth = 4;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor3d(
-        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d(
-        [
-          3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1,
-          1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5,
-          0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0,
-        ],
-        [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([4, 2, 2]);
-    expectArraysClose(
-        resultData,
-        [90, 54, 63, 36, 90, 54, 63, 36, 90, 54, 63, 36, 90, 54, 63, 36]);
-  });
-
-  it('x=[1,2,2] f=[2,2,1,1] s=1 d=1 p=explicit NCHW', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [inputDepth, 2, 2];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad =
-        [[0, 0], [0, 0], [0, 1], [0, 1]] as tf.backend_util.ExplicitPadding;
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([1, 2, 2]);
-    expectArraysClose(resultData, [20, 26, 13, 12]);
-  });
-
-  it('x=[2,2,2] f=[2,2,2,1] s=1 d=1 p=same NCHW', async () => {
-    const inputDepth = 2;
-    const inputShape: [number, number, number] = [inputDepth, 2, 2];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], inputShape);
-    const w = tf.tensor4d(
-        [3, 1, 5, 0, 0, 5, 1, 3], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([1, 2, 2]);
-    expectArraysClose(resultData, [81, 52, 36, 20]);
-  });
-
-  it('x=[2,1,2,2] f=[2,2,1,1] s=1 d=1 p=same NCHW', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([2, 1, 2, 2]);
-    expectArraysClose(resultData, [20, 26, 13, 12, 56, 58, 29, 24]);
-  });
-
-  it('x=[2,2,1] f=[2,2,1,1] s=1 d=1 p=0', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-    expectArraysClose(await result.data(), [20]);
-  });
-
-  it('x=[4,4,1] f=[2,2,1,1] s=1 d=2 p=0', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const fSizeDilated = 3;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 2;
-    const noDilation = 1;
-
-    const x = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 2], [fSize, fSize, inputDepth, outputDepth]);
-    // adding a dilation rate is equivalent to using a filter
-    // with 0s for the dilation rate
-    const wDilated = tf.tensor4d(
-        [3, 0, 1, 0, 0, 0, 5, 0, 2],
-        [fSizeDilated, fSizeDilated, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
-    const expectedResult =
-        tf.conv2d(x, wDilated, stride, pad, dataFormat, noDilation);
-
-    expect(result.shape).toEqual(expectedResult.shape);
-    expectArraysClose(await result.data(), await expectedResult.data());
-    expect(result.shape).toEqual(expectedResult.shape);
-    expect(result.dtype).toBe(expectedResult.dtype);
-  });
-
-  it('x=[1,3,6,1] f=[2,2,1,1] s=[1,2] d=1 p=valid', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number, number] = [1, 3, 6, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'valid';
-    const stride: [number, number] = [1, 2];
-
-    const inputs = generateCaseInputs(1 * 3 * 6 * inputDepth, fSize * fSize);
-    const x = tf.tensor4d(inputs.input, inputShape);
-    const w =
-        tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    expectArraysClose(
-        await result.data(), [58.0, 78.0, 98.0, 118.0, 138.0, 158.0]);
-  });
-
-  it('x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=1 p=same', async () => {
-    const inputDepth = 16;
-    const xSize = 8;
-    const inputShape: [number, number, number, number] =
-        [1, xSize, xSize, inputDepth];
-    const outputDepth = 1;
-    const fSize = 3;
-    const pad = 'same';
-    const stride: [number, number] = [2, 2];
-
-    // TODO(annxingyuan): Make this test work with large inputs using
-    // generateCaseInputs https://github.com/tensorflow/tfjs/issues/3143
-    const inputData = [];
-    for (let i = 0; i < xSize * xSize * inputDepth; i++) {
-      inputData.push(i % 5);
-    }
-
-    const wData = [];
-    for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
-      wData.push(i % 5);
-    }
-
-    const x = tf.tensor4d(inputData, inputShape);
-    const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
-    const result = tf.conv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 4, 4, 1]);
-    expectArraysClose(await result.data(), new Float32Array([
-                        854, 431, 568, 382, 580, 427, 854, 288, 431, 568, 580,
-                        289, 285, 570, 285, 258
-                      ]));
-  });
-
-  it('x=[1,8,8,3] f=[3,3,3,4] s=[2,2] d=1 p=same', async () => {
-    const inputDepth = 3;
-    const xSize = 8;
-    const inputShape: [number, number, number, number] =
-        [1, xSize, xSize, inputDepth];
-    const outputDepth = 4;
-    const fSize = 3;
-    const pad = 'same';
-    const stride: [number, number] = [2, 2];
-
-    // TODO(annxingyuan): Make this test work with large inputs using
-    // generateCaseInputs https://github.com/tensorflow/tfjs/issues/3143
-    const inputData = [];
-    for (let i = 0; i < xSize * xSize * inputDepth; i++) {
-      inputData.push(i % 5);
-    }
-
-    const wData = [];
-    for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
-      wData.push(i % 5);
-    }
-
-    const x = tf.tensor4d(inputData, inputShape);
-    const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 4, 4, 4]);
-    expectArraysClose(
-        await result.data(), new Float32Array([
-          104, 125, 126, 102, 133, 126, 104, 57,  137, 102, 57,  112, 64,
-          40,  76,  92,  116, 53,  110, 142, 50,  104, 133, 137, 104, 125,
-          126, 102, 83,  88,  78,  33,  133, 126, 104, 57,  137, 102, 57,
-          112, 116, 53,  110, 142, 37,  76,  100, 99,  33,  68,  83,  88,
-          70,  83,  76,  64,  92,  88,  64,  40,  51,  44,  27,  50
-        ]));
-  });
-
-  it('x=[1,8,8,3] f=[3,3,3,4] s=[2,2] d=1 p=valid', async () => {
-    const inputDepth = 3;
-    const xSize = 8;
-    const inputShape: [number, number, number, number] =
-        [1, xSize, xSize, inputDepth];
-    const outputDepth = 4;
-    const fSize = 3;
-    const pad = 'valid';
-    const stride: [number, number] = [2, 2];
-
-    const inputData = [];
-    for (let i = 0; i < xSize * xSize * inputDepth; i++) {
-      inputData.push(i % 5);
-    }
-
-    const wData = [];
-    for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
-      wData.push(i % 5);
-    }
-
-    const x = tf.tensor4d(inputData, inputShape);
-    const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.conv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 3, 3, 4]);
-    expectArraysClose(
-        await result.data(), new Float32Array([
-          104, 125, 126, 102, 133, 126, 104, 57,  137, 102, 57,  112,
-          116, 53,  110, 142, 50,  104, 133, 137, 104, 125, 126, 102,
-          133, 126, 104, 57,  137, 102, 57,  112, 116, 53,  110, 142
-        ]));
-  });
-
-  it('x=[1,2,2,3] f=[1,1] s=2 p=1 fractional outputs default rounding',
-     async () => {
-       const inputDepth = 3;
-       const inShape: [number, number, number, number] = [1, 2, 2, inputDepth];
-       const outputDepth = 1;
-       const fSize = 1;
-
-       const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], inShape);
-       const w =
-           tf.tensor4d([2, 2, 1], [fSize, fSize, inputDepth, outputDepth]);
-       const pad =
-           [[0, 0], [1, 1], [1, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-       const strides = 2;
-
-       const result = tf.conv2d(x, w, strides, pad);
-
-       expect(result.shape).toEqual([1, 2, 2, 1]);
-       expectArraysClose(await result.data(), [0, 0, 0, 54]);
-     });
-
-  it('throws when x is not rank 3', () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 0;
-    const stride = 1;
-
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv2d(x, w, stride, pad)).toThrowError();
-  });
-
-  it('throws when weights is not rank 4', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    // tslint:disable-next-line:no-any
-    const w: any = tf.tensor3d([3, 1, 5, 0], [2, 2, 1]);
-
-    expect(() => tf.conv2d(x, w, stride, pad)).toThrowError();
-  });
-
-  it('throws when x depth does not match weight depth', () => {
-    const inputDepth = 1;
-    const wrongInputDepth = 5;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.randomNormal<Rank.R4>([fSize, fSize, wrongInputDepth, outputDepth]);
-
-    expect(() => tf.conv2d(x, w, stride, pad)).toThrowError();
-  });
-
-  it('throws when x depth does not match weight depth NCHW', () => {
-    const inputDepth = 1;
-    const wrongInputDepth = 5;
-    const inputShape: [number, number, number] = [inputDepth, 2, 2];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NCHW';
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.randomNormal<Rank.R4>([fSize, fSize, wrongInputDepth, outputDepth]);
-
-    expect(() => tf.conv2d(x, w, stride, pad, dataFormat)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is same', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-    const dimRoundingMode = 'round';
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.randomNormal<Rank.R4>([fSize, fSize, inputDepth, outputDepth]);
-
-    expect(
-        () =>
-            tf.conv2d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
-        .toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is valid', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-    const dimRoundingMode = 'round';
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.randomNormal<Rank.R4>([fSize, fSize, inputDepth, outputDepth]);
-
-    expect(
-        () =>
-            tf.conv2d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
-        .toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is a non-integer number',
-     () => {
-       const inputDepth = 1;
-       const inputShape: [number, number, number] = [2, 2, inputDepth];
-       const outputDepth = 1;
-       const fSize = 2;
-       const pad = 1.2;
-       const stride = 1;
-       const dataFormat = 'NHWC';
-       const dilation = 1;
-       const dimRoundingMode = 'round';
-
-       const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-       const w =
-           tf.randomNormal<Rank.R4>([fSize, fSize, inputDepth, outputDepth]);
-
-       expect(
-           () => tf.conv2d(
-               x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
-           .toThrowError();
-     });
-
-  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
-         'number',
-     () => {
-       const inputDepth = 1;
-       const inputShape: [number, number, number] = [2, 2, inputDepth];
-       const outputDepth = 1;
-       const fSize = 2;
-       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as
-           tf.backend_util.ExplicitPadding;
-       const stride = 1;
-       const dataFormat = 'NHWC';
-       const dilation = 1;
-       const dimRoundingMode = 'round';
-
-       const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-       const w =
-           tf.randomNormal<Rank.R4>([fSize, fSize, inputDepth, outputDepth]);
-
-       expect(
-           () => tf.conv2d(
-               x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
-           .toThrowError();
-     });
-
-  it('throws when stride is less than or equal to 0', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 0;
-    const stride: [number, number] = [1, 0];
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv2d(x, w, stride, pad)).toThrowError();
-  });
-
-  it('throws when dilation is less than or equal to 0', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation: [number, number] = [1, 0];
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv2d(x, w, stride, pad, dataFormat, dilation))
-        .toThrowError();
-  });
-
-  it('throws when both stride and dilation are greater than 1', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const outputDepth = 1;
-    const fSize = 2;
-    const pad = 0;
-    const stride: [number, number] = [2, 1];
-    const dataFormat = 'NHWC';
-    const dilation: [number, number] = [1, 2];
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-    const w =
-        tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv2d(x, w, stride, pad, dataFormat, dilation))
-        .toThrowError();
-  });
-
-  it('gradient with clones input=[3,3,1] f=[2,2,1,1] s=1 p=0', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number] = [3, 3, inputDepth];
-    const filterSize = 2;
-    const stride = 1;
-    const pad = 0;
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.ones<Rank.R4>(filterShape);
-
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor3d([3, 1, 2, 0], [2, 2, 1]);
-
-    const grads = tf.grads(
-        (x: tf.Tensor3D, filter: tf.Tensor4D) =>
-            x.clone().conv2d(filter.clone(), stride, pad).clone());
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [3, 4, 1, 5, 6, 1, 2, 2, 0]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [13, 19, 31, 37]);
-  });
-
-  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];
-    const filterSize = 2;
-    const stride = 1;
-    const pad = 0;
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.ones<Rank.R4>(filterShape);
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) => x.conv2d(filter, stride, pad));
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(
-        await dx.data(),
-        [3, 4, 1, 5, 6, 1, 2, 2, 0, 3, 4, 1, 5, 6, 1, 2, 2, 0]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [13 * 2, 19 * 2, 31 * 2, 37 * 2]);
-  });
-
-  it('gradient x=[1,1,3,3] f=[2,2,1,1] s=1 p=0 NCHW', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [1, inputDepth, 3, 3];
-    const filterSize = 2;
-    const stride = 1;
-    const pad = 0;
-    const dataFormat = 'NCHW';
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.ones<Rank.R4>(filterShape);
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor4d([3, 1, 2, 0], [1, 1, 2, 2]);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-            x.conv2d(filter, stride, pad, dataFormat));
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [3, 4, 1, 5, 6, 1, 2, 2, 0]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [13, 19, 31, 37]);
-  });
-
-  it('gradient x=[2,1,3,3] f=[2,2,1,1] s=1 p=0 NCHW', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 3, 3];
-    const filterSize = 2;
-    const stride = 1;
-    const pad = 0;
-    const dataFormat = 'NCHW';
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.ones<Rank.R4>(filterShape);
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 1, 2, 2]);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-            x.conv2d(filter, stride, pad, dataFormat));
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(
-        await dx.data(),
-        [3, 4, 1, 5, 6, 1, 2, 2, 0, 3, 4, 1, 5, 6, 1, 2, 2, 0]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [26, 38, 62, 74]);
-  });
-
-  it('throws when passed x as a non-tensor', () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv2d({} as tf.Tensor3D, w, stride, pad))
-        .toThrowError(/Argument 'x' passed to 'conv2d' must be a Tensor/);
-  });
-
-  it('throws when passed filter as a non-tensor', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-
-    expect(() => tf.conv2d(x, {} as tf.Tensor4D, stride, pad))
-        .toThrowError(/Argument 'filter' passed to 'conv2d' must be a Tensor/);
-  });
-
-  it('throws when input is int32', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape,
-        'int32');
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv2d(x, w, stride, pad))
-        .toThrowError(/Argument 'x' passed to 'conv2d' must be float32/);
-  });
-
-  it('throws when filter is int32', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const w = tf.tensor4d(
-        [-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth], 'int32');
-
-    expect(() => tf.conv2d(x, w, stride, pad))
-        .toThrowError(/Argument 'filter' passed to 'conv2d' must be float32/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const pad = 0;
-    const stride = 1;
-    const x = [[[1], [2]], [[3], [4]]];  // 2x2x1
-    const w = [[[[2]]]];                 // 1x1x1x1
-
-    const result = tf.conv2d(x, w, stride, pad);
-    expectArraysClose(await result.data(), [2, 4, 6, 8]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/conv2d_transpose.ts b/tfjs-master/tfjs-core/src/ops/conv2d_transpose.ts
deleted file mode 100644
index 073425aae..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv2d_transpose.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor3D, Tensor4D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {conv2DBackpropInput} from './conv2d_backprop_input';
-import {ExplicitPadding} from './conv_util';
-import {op} from './operation';
-
-/**
- * Computes the transposed 2D convolution of an image, also known as a
- * deconvolution.
- *
- * @param x The input image, of rank 4 or rank 3, of shape
- *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.
- * @param filter The filter, rank 4, of shape
- *     `[filterHeight, filterWidth, outDepth, inDepth]`.
- *     `inDepth` must match `inDepth` in `x`.
- * @param outputShape Output shape, of rank 4 or rank 3:
- *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.
- * @param strides The strides of the original convolution:
- *     `[strideHeight, strideWidth]`.
- * @param pad  The type of padding algorithm used in the non-transpose version
- *    of the op.
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function conv2dTranspose_<T extends Tensor3D|Tensor4D>(
-    x: T|TensorLike, filter: Tensor4D|TensorLike,
-    outputShape: [number, number, number, number]|[number, number, number],
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|ExplicitPadding,
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  const $x = convertToTensor(x, 'x', 'conv2dTranspose');
-  const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');
-
-  return conv2DBackpropInput(
-      outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);
-}
-
-export const conv2dTranspose = /* @__PURE__ */ op({conv2dTranspose_});
diff --git a/tfjs-master/tfjs-core/src/ops/conv2d_transpose_test.ts b/tfjs-master/tfjs-core/src/ops/conv2d_transpose_test.ts
deleted file mode 100644
index 7ae3a94c0..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv2d_transpose_test.ts
+++ /dev/null
@@ -1,709 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {Rank} from '../types';
-
-describeWithFlags('conv2dTranspose', ALL_ENVS, () => {
-  it('input=2x2x1,d2=1,f=2,s=1,p=0', async () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 1;
-    const inputShape: [number, number, number] = [1, 1, origOutputDepth];
-    const fSize = 2;
-    const origPad = 0;
-    const origStride = 1;
-
-    const x = tf.tensor3d([2], inputShape);
-    const w = tf.tensor4d(
-        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
-
-    const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
-    const expected = [6, 2, 10, 0];
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=3x3x1,d2=1,f=2,s=2,p=same', async () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 4;
-    const inputShape: [number, number, number, number] =
-        [1, 2, 2, origOutputDepth];
-    const fSize = 2;
-    const origPad = 'same';
-    const origStride = 2;
-
-    const x = tf.tensor4d(
-        [
-          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
-          0., 0.06, 0.14, 0.24
-        ],
-        inputShape);
-    const w = tf.tensor4d(
-        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],
-        [fSize, fSize, origInputDepth, origOutputDepth]);
-
-    const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);
-    const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];
-
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=3x3x1,d2=1,f=2,s=2,p=explicit', async () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 4;
-    const inputShape: [number, number, number, number] =
-        [1, 2, 2, origOutputDepth];
-    const fSize = 2;
-    const origPad =
-        [[0, 0], [0, 1], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const origStride = 2;
-
-    const x = tf.tensor4d(
-        [
-          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
-          0., 0.06, 0.14, 0.24
-        ],
-        inputShape);
-    const w = tf.tensor4d(
-        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],
-        [fSize, fSize, origInputDepth, origOutputDepth]);
-
-    const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);
-    const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];
-
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', async () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 1;
-    const inputShape: [number, number, number, number] =
-        [2, 1, 1, origOutputDepth];
-    const fSize = 2;
-    const origPad = 0;
-    const origStride = 1;
-
-    const x = tf.tensor4d([2, 3], inputShape);
-    const w = tf.tensor4d(
-        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
-
-    const result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);
-    const expected = [6, 2, 10, 0, 9, 3, 15, 0];
-
-    expect(result.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=2x2x2,output=3x3x2,f=2,s=2,inDepth=2,p=same', async () => {
-    const origInputDepth = 2;
-    const origOutputDepth = 2;
-    const inputShape: [number, number, number, number] =
-        [1, 2, 2, origOutputDepth];
-    const fSize = 2;
-    const origPad = 'same';
-    const origStride = 2;
-
-    const x = tf.tensor4d([0., 1., 2., 3., 4., 5., 6., 7.], inputShape);
-    const w = tf.tensor4d(
-        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],
-        [fSize, fSize, origInputDepth, origOutputDepth]);
-
-    const result = tf.conv2dTranspose(
-        x, w, [1, 3, 3, origInputDepth], origStride, origPad);
-    const expected =
-        [1, 3, 5, 7, 3, 13, 9, 11, 13, 15, 43, 53, 5, 23, 41, 59, 7, 33.];
-
-    expect(result.shape).toEqual([1, 3, 3, origInputDepth]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws when dimRoundingMode is set and pad is same', async () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 4;
-    const inputShape: [number, number, number, number] =
-        [1, 2, 2, origOutputDepth];
-    const fSize = 2;
-    const origPad = 'same';
-    const origStride = 2;
-    const dimRoundingMode = 'round';
-
-    const x = tf.tensor4d(
-        [
-          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
-          0., 0.06, 0.14, 0.24
-        ],
-        inputShape);
-    const w = tf.tensor4d(
-        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],
-        [fSize, fSize, origInputDepth, origOutputDepth]);
-
-    expect(
-        () => tf.conv2dTranspose(
-            x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
-        .toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is valid', async () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 4;
-    const inputShape: [number, number, number, number] =
-        [1, 2, 2, origOutputDepth];
-    const fSize = 2;
-    const origPad = 'valid';
-    const origStride = 2;
-    const dimRoundingMode = 'round';
-
-    const x = tf.tensor4d(
-        [
-          1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,
-          0., 0.06, 0.14, 0.24
-        ],
-        inputShape);
-    const w = tf.tensor4d(
-        [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],
-        [fSize, fSize, origInputDepth, origOutputDepth]);
-
-    expect(
-        () => tf.conv2dTranspose(
-            x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
-        .toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is a non-integer number',
-     async () => {
-       const origInputDepth = 1;
-       const origOutputDepth = 4;
-       const inputShape: [number, number, number, number] =
-           [1, 2, 2, origOutputDepth];
-       const fSize = 2;
-       const origPad = 1.2;
-       const origStride = 2;
-       const dimRoundingMode = 'round';
-
-       const x = tf.tensor4d(
-           [
-             1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,
-             0.28, 0., 0.06, 0.14, 0.24
-           ],
-           inputShape);
-       const w = tf.tensor4d(
-           [
-             0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,
-             15.
-           ],
-           [fSize, fSize, origInputDepth, origOutputDepth]);
-
-       expect(
-           () => tf.conv2dTranspose(
-               x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
-           .toThrowError();
-     });
-
-  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
-         'number',
-     async () => {
-       const origInputDepth = 1;
-       const origOutputDepth = 4;
-       const inputShape: [number, number, number, number] =
-           [1, 2, 2, origOutputDepth];
-       const fSize = 2;
-       const origPad = [[0, 0], [0, 1.1], [0, 1], [0, 0]] as
-           tf.backend_util.ExplicitPadding;
-       const origStride = 2;
-       const dimRoundingMode = 'round';
-
-       const x = tf.tensor4d(
-           [
-             1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34,
-             0.28, 0., 0.06, 0.14, 0.24
-           ],
-           inputShape);
-       const w = tf.tensor4d(
-           [
-             0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,
-             15.
-           ],
-           [fSize, fSize, origInputDepth, origOutputDepth]);
-
-       expect(
-           () => tf.conv2dTranspose(
-               x, w, [1, 3, 3, 1], origStride, origPad, dimRoundingMode))
-           .toThrowError();
-     });
-
-  // Reference (Python) TensorFlow code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // x = tf.constant(np.array([[
-  //     [[-0.14656299], [0.32942239], [-1.90302866]],
-  //     [[-0.06487813], [-2.02637842], [-1.83669377]],
-  //     [[0.82650784], [-0.89249092], [0.01207666]]
-  // ]]).astype(np.float32))
-  // filt = tf.constant(np.array([
-  //     [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
-  //     [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
-  // ]).astype(np.float32))
-  //
-  // with tf.GradientTape() as g:
-  //   g.watch(x)
-  //   g.watch(filt)
-  //   y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2])
-  //   print(y)
-  // (x_grad, filt_grad) = g.gradient(y, [x, filt])
-  //
-  // print("x_grad = %s" % x_grad)
-  // print("filt_grad = %s" % filt_grad)
-  // ```
-  it('gradient with clones input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid',
-     async () => {
-       const inputDepth = 1;
-       const outputDepth = 2;
-       const inputShape: [number, number, number, number] =
-           [1, 3, 3, inputDepth];
-       const filterSize = 2;
-       const stride = 1;
-       const pad = 'valid';
-
-       const filterShape: [number, number, number, number] =
-           [filterSize, filterSize, outputDepth, inputDepth];
-
-       const x = tf.tensor4d(
-           [[
-             [[-0.14656299], [0.32942239], [-1.90302866]],
-             [[-0.06487813], [-2.02637842], [-1.83669377]],
-             [[0.82650784], [-0.89249092], [0.01207666]]
-           ]],
-           inputShape);
-       const filt = tf.tensor4d(
-           [
-             [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
-             [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
-           ],
-           filterShape);
-
-       const grads = tf.grads(
-           (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-               tf.conv2dTranspose(
-                     x.clone(), filter.clone(), [1, 4, 4, outputDepth], stride,
-                     pad)
-                   .clone());
-       const dy = tf.ones([1, 4, 4, outputDepth]);
-       const [xGrad, filtGrad] = grads([x, filt], dy);
-
-       const expectedXGrad = tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947));
-       expectArraysClose(await xGrad.data(), await expectedXGrad.data());
-       const expectedFiltGrad =
-           tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599));
-       expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());
-     });
-
-  // Reference (Python) TensorFlow code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // x = tf.constant(np.array([
-  //     [[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]
-  // ]).astype(np.float32))
-  // filt = tf.constant(np.array([
-  //     [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
-  //     [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
-  // ]).astype(np.float32))
-  //
-  // with tf.GradientTape() as g:
-  //   g.watch(x)
-  //   g.watch(filt)
-  //   y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2], strides=(2,
-  //   2)) print(y)
-  // (x_grad, filt_grad) = g.gradient(y, [x, filt])
-  //
-  // print("x_grad = %s" % -x_grad)
-  // print("filt_grad = %s" % -filt_grad)
-  // ```
-  it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', async () => {
-    const inputDepth = 1;
-    const outputDepth = 2;
-    const inputShape: [number, number, number, number] = [1, 2, 2, inputDepth];
-    const filterSize = 2;
-    const stride: [number, number] = [2, 2];
-    const pad = 'valid';
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, outputDepth, inputDepth];
-
-    const x = tf.tensor4d(
-        [[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]],
-        inputShape);
-    const filt = tf.tensor4d(
-        [
-          [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
-          [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
-        ],
-        filterShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-            tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad));
-    const dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));
-    const [xGrad, filtGrad] = grads([x, filt], dy);
-
-    const expectedXGrad = tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196));
-    expectArraysClose(await xGrad.data(), await expectedXGrad.data());
-    expect(xGrad.shape).toEqual([1, 2, 2, 1]);
-
-    const expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618));
-    expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());
-    expect(filtGrad.shape).toEqual([2, 2, 2, 1]);
-  });
-
-  // Reference (Python) TensorFlow code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // x = tf.constant(np.array([[
-  //     [[1.52433065], [-0.77053435], [-0.64562341]],
-  //     [[0.77962889], [1.58413887], [-0.25581856]],
-  //     [[-0.58966221], [0.05411662], [0.70749138]]
-  // ]]).astype(np.float32))
-  // filt = tf.constant(np.array([
-  //     [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
-  //     [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
-  // ]).astype(np.float32))
-  //
-  // with tf.GradientTape() as g:
-  //   g.watch(x)
-  //   g.watch(filt)
-  //   y = tf.keras.backend.conv2d_transpose(
-  //       x, filt, [1, 3, 3, 2], strides=(1, 1), padding='same')
-  // (x_grad, filt_grad) = g.gradient(y, [x, filt])
-  //
-  // print("x_grad = %s" % x_grad)
-  // print("filt_grad = %s" % filt_grad)
-  // ```
-  it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', async () => {
-    const inputDepth = 1;
-    const outputDepth = 2;
-    const inputShape: [number, number, number, number] = [1, 3, 3, inputDepth];
-    const filterSize = 2;
-    const stride: [number, number] = [1, 1];
-    const pad = 'same';
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, outputDepth, inputDepth];
-
-    const x = tf.tensor4d(
-        [[
-          [[1.52433065], [-0.77053435], [-0.64562341]],
-          [[0.77962889], [1.58413887], [-0.25581856]],
-          [[-0.58966221], [0.05411662], [0.70749138]]
-        ]],
-        inputShape);
-    const filt = tf.tensor4d(
-        [
-          [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
-          [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
-        ],
-        filterShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-            tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));
-    const dy = tf.ones([1, 3, 3, outputDepth]);
-    const [xGrad, filtGrad] = grads([x, filt], dy);
-
-    expectArraysClose(await xGrad.array(), [[
-                        [[1.30709858], [1.30709858], [-0.92814366]],
-                        [[1.30709858], [1.30709858], [-0.92814366]],
-                        [[1.19666437], [1.19666437], [-0.85476589]]
-                      ]]);
-    expectArraysClose(await filtGrad.array(), [
-      [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
-      [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
-    ]);
-  });
-
-  it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] p=explicit', async () => {
-    const inputDepth = 1;
-    const outputDepth = 2;
-    const inputShape: [number, number, number, number] = [1, 3, 3, inputDepth];
-    const filterSize = 2;
-    const stride: [number, number] = [1, 1];
-    const pad =
-        [[0, 0], [0, 1], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, outputDepth, inputDepth];
-
-    const x = tf.tensor4d(
-        [[
-          [[1.52433065], [-0.77053435], [-0.64562341]],
-          [[0.77962889], [1.58413887], [-0.25581856]],
-          [[-0.58966221], [0.05411662], [0.70749138]]
-        ]],
-        inputShape);
-    const filt = tf.tensor4d(
-        [
-          [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
-          [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
-        ],
-        filterShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-            tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));
-    const dy = tf.ones([1, 3, 3, outputDepth]);
-    const [xGrad, filtGrad] = grads([x, filt], dy);
-
-    expectArraysClose(await xGrad.array(), [[
-                        [[1.30709858], [1.30709858], [-0.92814366]],
-                        [[1.30709858], [1.30709858], [-0.92814366]],
-                        [[1.19666437], [1.19666437], [-0.85476589]]
-                      ]]);
-    expectArraysClose(await filtGrad.array(), [
-      [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
-      [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
-    ]);
-  });
-
-  // Reference (Python) TensorFlow code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // x = tf.constant(np.array([[
-  //     [[1.52433065], [-0.77053435]], [[0.77962889], [1.58413887]],
-  // ]]).astype(np.float32))
-  // filt = tf.constant(np.array([
-  //     [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
-  //     [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
-  // ]).astype(np.float32))
-  //
-  // with tf.GradientTape() as g:
-  //   g.watch(x)
-  //   g.watch(filt)
-  //   y = tf.keras.backend.conv2d_transpose(
-  //       x, filt, [1, 3, 3, 2], strides=(2, 2), padding='same')
-  //   print(y.shape)
-  // (x_grad, filt_grad) = g.gradient(y, [x, filt])
-  //
-  // print("x_grad = %s" % x_grad)
-  // print("filt_grad = %s" % filt_grad)
-  // ```
-  it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', async () => {
-    const inputDepth = 2;
-    const outputDepth = 2;
-    const inputShape: [number, number, number, number] = [1, 2, 2, inputDepth];
-    const filterSize = 2;
-    const stride: [number, number] = [2, 2];
-    const pad = 'same';
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, outputDepth, inputDepth];
-
-    const x = tf.tensor4d(
-        [[
-          [[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],
-          [[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]
-        ]],
-        inputShape);
-    const filt = tf.tensor4d(
-        [
-          [
-            [[0.19529686, -0.79594708], [0.70314057, -0.06081263]],
-            [[0.28724744, 0.88522715], [-0.51824096, -0.97120989]]
-          ],
-          [
-            [[0.51872197, -1.17569193], [1.28316791, -0.81225092]],
-            [[-0.44221532, 0.70058174], [-0.4849217, 0.03806348]]
-          ]
-        ],
-        filterShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-            tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));
-    const dy = tf.ones([1, 3, 3, outputDepth]);
-    const [xGrad, filtGrad] = grads([x, filt], dy);
-
-    expectArraysClose(await xGrad.data(), [
-      1.54219678, -2.19204008, 2.70032732, -2.84470257, 0.66744391, -0.94274245,
-      0.89843743, -0.85675972
-    ]);
-    expect(xGrad.shape).toEqual([1, 2, 2, 2]);
-    expectArraysClose(await filtGrad.data(), [
-      -1.00204261, 0.27276259, -1.00204261, 0.27276259, -2.99976385, 0.66119574,
-      -2.99976385, 0.66119574, -1.86705711, 1.27211472, -1.86705711, 1.27211472,
-      -1.81506593, 1.00900095, -1.81506593, 1.00900095
-    ]);
-    expect(filtGrad.shape).toEqual([2, 2, 2, 2]);
-  });
-
-  it('throws when x is not rank 3', () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 1;
-    const fSize = 2;
-    const origPad = 0;
-    const origStride = 1;
-
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor2d([2, 2], [2, 1]);
-    const w = tf.tensor4d(
-        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
-
-    expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))
-        .toThrowError();
-  });
-
-  it('throws when weights is not rank 4', () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 1;
-    const inputShape: [number, number, number] = [1, 1, origOutputDepth];
-    const fSize = 2;
-    const origPad = 0;
-    const origStride = 1;
-
-    const x = tf.tensor3d([2], inputShape);
-    // tslint:disable-next-line:no-any
-    const w: any = tf.tensor3d([3, 1, 5, 0], [fSize, fSize, origInputDepth]);
-
-    expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))
-        .toThrowError();
-  });
-
-  it('throws when x depth does not match weights original output depth', () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 2;
-    const wrongOrigOutputDepth = 3;
-    const inputShape: [number, number, number] = [1, 1, origOutputDepth];
-    const fSize = 2;
-    const origPad = 0;
-    const origStride = 1;
-
-    const x = tf.tensor3d([2, 2], inputShape);
-    const w = tf.randomNormal<Rank.R4>(
-        [fSize, fSize, origInputDepth, wrongOrigOutputDepth]);
-
-    expect(() => tf.conv2dTranspose(x, w, [2, 2, 2], origStride, origPad))
-        .toThrowError();
-  });
-
-  it('throws when passed x as a non-tensor', () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 1;
-    const fSize = 2;
-    const origPad = 0;
-    const origStride = 1;
-
-    const w = tf.tensor4d(
-        [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
-
-    expect(
-        () => tf.conv2dTranspose(
-            {} as tf.Tensor3D, w, [2, 2, 1], origStride, origPad))
-        .toThrowError(
-            /Argument 'x' passed to 'conv2dTranspose' must be a Tensor/);
-  });
-
-  it('throws when passed filter as a non-tensor', () => {
-    const origOutputDepth = 1;
-    const inputShape: [number, number, number] = [1, 1, origOutputDepth];
-    const origPad = 0;
-    const origStride = 1;
-
-    const x = tf.tensor3d([2], inputShape);
-
-    expect(
-        () => tf.conv2dTranspose(
-            x, {} as tf.Tensor4D, [2, 2, 1], origStride, origPad))
-        .toThrowError(
-            /Argument 'filter' passed to 'conv2dTranspose' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const origPad = 0;
-    const origStride = 1;
-
-    const x = [[[2]]];                           // 1x1x1
-    const w = [[[[3]], [[1]]], [[[5]], [[0]]]];  // 2x2x1x1
-
-    const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
-    const expected = [6, 2, 10, 0];
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=8x8x8,output=4x4x8,f=8,s=1,inDepth=8,p=same vec4', async () => {
-    const origInputDepth = 8;
-    const origOutputDepth = 8;
-    const inputShape: [number, number, number, number] =
-        [1, 8, 8, origOutputDepth];
-    const fSize = 8;
-    const origPad = 'same';
-    const origStride: [number, number] = [1, 1];
-    const wShape: [number, number, number, number] =
-        [fSize, fSize, origInputDepth, origOutputDepth];
-
-    const inputData = [];
-    for (let i = 0; i < fSize * fSize * origInputDepth; i++) {
-      inputData.push(i % 5);
-    }
-    const wData = [];
-    for (let i = 0; i < fSize * fSize * origInputDepth * origOutputDepth; i++) {
-      wData.push(i % 5);
-    }
-
-    const x = tf.tensor4d(inputData, inputShape);
-    const w = tf.tensor4d(wData, wShape);
-    const result = tf.conv2dTranspose(
-        x, w, [1, 4, 4, origInputDepth], origStride, origPad);
-    expect(result.shape).toEqual([1, 4, 4, 8]);
-
-    const expected = [
-      512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506,
-      512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506,
-      512, 533, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533,
-      469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533,
-      469, 550, 506, 512, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550,
-      506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550,
-      506, 512, 533, 469, 550, 506, 469, 550, 506, 512, 533, 469, 550, 506, 512,
-      533, 469, 550, 506, 512, 533, 469, 550, 506, 512, 533, 469, 550, 506, 512,
-      533, 469, 550, 506, 512, 533, 469, 550
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/conv3d.ts b/tfjs-master/tfjs-core/src/ops/conv3d.ts
deleted file mode 100644
index 7947f42d3..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv3d.ts
+++ /dev/null
@@ -1,120 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Conv3D, Conv3DAttrs, Conv3DInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D, Tensor5D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {eitherStridesOrDilationsAreOne, stridesOrDilationsArePositive} from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes a 3D convolution over the input x.
- *
- * @param x The input tensor, of rank 5 or rank 4, of shape
- *     `[batch, depth, height, width, channels]`. If rank 4,
- * batch of 1 is assumed.
- * @param filter The filter, rank 5, of shape
- *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.
- *      inChannels must match between input and filter.
- * @param strides The strides of the convolution: `[strideDepth, strideHeight,
- * strideWidth]`.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *   - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dataFormat: An optional string from: "NDHWC", "NCDHW". Defaults to
- *     "NDHWC". Specify the data format of the input and output data. With the
- *     default format "NDHWC", the data is stored in the order of: [batch,
- *     depth, height, width, channels]. Only "NDHWC" is currently supported.
- * @param dilations The dilation rates: `[dilationDepth, dilationHeight,
- *     dilationWidth]` in which we sample input values across the height
- *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.
- *     If `dilations` is a single number, then
- *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater
- *     than 1, then all values of `strides` must be 1.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function conv3d_<T extends Tensor4D|Tensor5D>(
-    x: T|TensorLike, filter: Tensor5D|TensorLike,
-    strides: [number, number, number]|number, pad: 'valid'|'same',
-    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC',
-    dilations: [number, number, number]|number = [1, 1, 1]): T {
-  const $x = convertToTensor(x, 'x', 'conv3d');
-  const $filter = convertToTensor(filter, 'filter', 'conv3d');
-
-  let x5D = $x as Tensor5D;
-  let reshapedTo5D = false;
-
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-  util.assert(
-      x5D.rank === 5,
-      () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
-  util.assert(
-      $filter.rank === 5,
-      () => `Error in conv3d: filter must be rank 5, but got rank ` +
-          `${$filter.rank}.`);
-  util.assert(
-      x5D.shape[4] === $filter.shape[3],
-      () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +
-          `input depth for filter ${$filter.shape[3]}.`);
-  util.assert(
-      eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in conv3D: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-  util.assert(
-      dataFormat === 'NDHWC',
-      () => `Error in conv3d: got dataFormat of ${
-          dataFormat} but only NDHWC is currently supported.`);
-  util.assert(
-      stridesOrDilationsArePositive(dilations),
-      () => 'Error in conv3D: Dilated rates should be larger than 0.');
-  util.assert(
-      stridesOrDilationsArePositive(strides),
-      () => 'Error in conv3D: Strides should be larger than 0.');
-
-  const inputs: Conv3DInputs = {x: x5D, filter: $filter};
-
-  const attrs: Conv3DAttrs = {strides, pad, dataFormat, dilations};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  Conv3D, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo5D) {
-    return reshape(
-               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as
-        T;
-  }
-  return res;
-}
-
-export const conv3d = /* @__PURE__ */ op({conv3d_});
diff --git a/tfjs-master/tfjs-core/src/ops/conv3d_backprop_filter.ts b/tfjs-master/tfjs-core/src/ops/conv3d_backprop_filter.ts
deleted file mode 100644
index 22b0eba3e..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv3d_backprop_filter.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Conv3DBackpropFilterV2, Conv3DBackpropFilterV2Attrs, Conv3DBackpropFilterV2Inputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D, Tensor5D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import * as util from '../util';
-
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the derivative of the filter of a 3D convolution.
- *
- * @param x The input tensor, of rank 5 or rank 4 of shape
- *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is
- *     assumed.
- * @param dy The dy image, of rank 5 or rank 4, of shape
- *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is
- *     assumed.
- * @param filterShape The shape of the filter, length 5,
- *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].
- * @param strides The strides of the convolution: [strideDepth, strideHeight,
- * strideWidth].
- * @param pad A string from: 'same', 'valid'. The type of padding algorithm
- *     used in the forward prop of the op.
- */
-function conv3DBackpropFilter_<T extends Tensor4D|Tensor5D>(
-    x: T, dy: T, filterShape: [number, number, number, number, number],
-    strides: [number, number, number]|number, pad: 'valid'|'same'): Tensor5D {
-  let x5D = x as Tensor5D;
-  if (x.rank === 4) {
-    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);
-  }
-  let dy5D = dy as Tensor5D;
-  if (dy5D.rank === 4) {
-    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-  }
-  util.assert(
-      x5D.rank === 5,
-      () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +
-          `${x5D.shape}.`);
-  util.assert(
-      dy5D.rank === 5,
-      () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +
-          `${dy5D.shape}.`);
-  util.assert(
-      filterShape.length === 5,
-      () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +
-          `${filterShape}.`);
-  util.assert(
-      x5D.shape[4] === filterShape[3],
-      () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +
-          `match input depth in filter (${filterShape[3]}.`);
-  util.assert(
-      dy5D.shape[4] === filterShape[4],
-      () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +
-          `match output depth for filter (${filterShape[4]}).`);
-
-  const inputs: Conv3DBackpropFilterV2Inputs = {x: x5D, dy: dy5D};
-
-  const attrs: Conv3DBackpropFilterV2Attrs = {strides, pad, filterShape};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  return ENGINE.runKernel(
-             Conv3DBackpropFilterV2, inputs as unknown as NamedTensorMap,
-             attrs as unknown as NamedAttrMap) as Tensor5D;
-}
-
-export const conv3DBackpropFilter = /* @__PURE__ */ op({conv3DBackpropFilter_});
diff --git a/tfjs-master/tfjs-core/src/ops/conv3d_backprop_input.ts b/tfjs-master/tfjs-core/src/ops/conv3d_backprop_input.ts
deleted file mode 100644
index 976c30ebb..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv3d_backprop_input.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D, Tensor5D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import * as util from '../util';
-
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the derivative of the input of a 3D convolution.
- *
- * @param xShape The shape of the input: [batch, depth, height, width,
- * in_channels]. If length of 4, batch of 1 is assumed.
- * @param dy The derivative of the output, of rank 5 or rank 4 of shape
- *   `[batch, outDepth, outHeight, outWidth, in_channels]`.
- * If rank 4, batch of 1 is assumed.
- * @param filter The filter, rank 5, of shape
- *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.
- * @param strides The strides of the convolution: `[strideDepth, strideHeight,
- * strideWidth]`.
- * @param pad The type of padding algorithm used:
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- */
-function conv3DBackpropInput_<T extends Tensor4D|Tensor5D>(
-    xShape:
-        [number, number, number, number,
-         number]|[number, number, number, number],
-    dy: T, filter: Tensor5D, strides: [number, number, number]|number,
-    pad: 'valid'|'same'): T {
-  util.assert(
-      xShape.length === dy.rank,
-      () => `Length of inShape ` +
-          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);
-
-  let xShape5D = xShape as [number, number, number, number, number];
-  let dy5D = dy as Tensor5D;
-  let reshapedTo5D = false;
-  if (dy.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
-    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
-  }
-
-  const inDepth = xShape5D[4];
-  const outDepth = dy5D.shape[4];
-  util.assert(
-      xShape5D.length === 5,
-      () =>
-          `Error in conv3dDerInput: inShape must be length 5, but got length ` +
-          `${xShape5D.length}.`);
-  util.assert(
-      dy5D.rank === 5,
-      () => `Error in conv3dDerInput: dy must be rank 5, but got ` +
-          `rank ${dy5D.rank}`);
-  util.assert(
-      filter.rank === 5,
-      () => `Error in conv3dDerInput: filter must be rank 5, but got ` +
-          `rank ${filter.rank}`);
-  util.assert(
-      inDepth === filter.shape[3],
-      () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +
-          `match input depth for filter ${filter.shape[3]}.`);
-  util.assert(
-      outDepth === filter.shape[4],
-      () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +
-          `match output depth for filter ${filter.shape[4]}.`);
-
-  const inputs: Conv3DBackpropInputV2Inputs = {dy: dy5D, filter};
-
-  const attrs:
-      Conv3DBackpropInputV2Attrs = {pad, strides, inputShape: xShape5D};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  Conv3DBackpropInputV2, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo5D) {
-    return reshape(
-               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as
-        T;
-  }
-  return res;
-}
-
-export const conv3DBackpropInput = /* @__PURE__ */ op({conv3DBackpropInput_});
diff --git a/tfjs-master/tfjs-core/src/ops/conv3d_test.ts b/tfjs-master/tfjs-core/src/ops/conv3d_test.ts
deleted file mode 100644
index 2681796d1..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv3d_test.ts
+++ /dev/null
@@ -1,518 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {Tensor5D} from '../tensor';
-import {expectArraysClose} from '../test_util';
-import {sizeFromShape} from '../util';
-
-// Generates small floating point inputs to avoid overflows
-function generateCaseInputs(totalSizeTensor: number, totalSizeFilter: number) {
-  const inp = new Array(totalSizeTensor);
-  const filt = new Array(totalSizeFilter);
-
-  for (let i = 0; i < totalSizeTensor; i++) {
-    inp[i] = (i + 1) / totalSizeTensor;
-  }
-  for (let i = 0; i < totalSizeFilter; i++) {
-    filt[i] = (i + 1) / totalSizeFilter;
-  }
-
-  return {input: inp, filter: filt};
-}
-
-function generateGradientCaseInputs(
-    totalSizeTensor: number, totalSizeFilter: number) {
-  const inp = new Array(totalSizeTensor);
-  const filt = new Array(totalSizeFilter);
-
-  for (let i = 0; i < totalSizeTensor; i++) {
-    inp[i] = i + 1;
-  }
-  for (let i = 0; i < totalSizeFilter; i++) {
-    filt[i] = i + 1;
-  }
-
-  return {input: inp, filter: filt};
-}
-
-function runConv3DTestCase(
-    batch: number, inDepth: number, inHeight: number, inWidth: number,
-    inChannels: number, outChannels: number, fDepth: number, fHeight: number,
-    fWidth: number, pad: 'valid'|'same',
-    stride: [number, number, number]|number) {
-  const inputShape: [number, number, number, number, number] =
-      [batch, inDepth, inHeight, inWidth, inChannels];
-  const filterShape: [number, number, number, number, number] =
-      [fDepth, fHeight, fWidth, inChannels, outChannels];
-
-  const totalSizeTensor = sizeFromShape(inputShape);
-  const totalSizeFilter = sizeFromShape(filterShape);
-  const inputs = generateCaseInputs(totalSizeTensor, totalSizeFilter);
-
-  const x = tf.tensor5d(inputs.input, inputShape);
-  const w = tf.tensor5d(inputs.filter, filterShape);
-
-  const result = tf.conv3d(x, w, stride, pad);
-  return result;
-}
-
-function runGradientConv3DTestCase(
-    batch: number, inDepth: number, inHeight: number, inWidth: number,
-    inChannels: number, outChannels: number, fDepth: number, fHeight: number,
-    fWidth: number, pad: 'valid'|'same',
-    stride: [number, number, number]|number) {
-  const inputShape: [number, number, number, number, number] =
-      [batch, inDepth, inHeight, inWidth, inChannels];
-  const filterShape: [number, number, number, number, number] =
-      [fDepth, fHeight, fWidth, inChannels, outChannels];
-
-  const totalSizeTensor = sizeFromShape(inputShape);
-  const totalSizeFilter = sizeFromShape(filterShape);
-  const inputs = generateGradientCaseInputs(totalSizeTensor, totalSizeFilter);
-
-  const x = tf.tensor5d(inputs.input, inputShape);
-  const w = tf.tensor5d(inputs.filter, filterShape);
-
-  const grads = tf.grads(
-      (x: Tensor5D, filter: Tensor5D) =>
-          tf.conv3d(x.clone(), filter.clone(), stride, pad).clone());
-  const [dx, dfilter] = grads([x, w]);
-
-  expect(dx.shape).toEqual(x.shape);
-  expect(dfilter.shape).toEqual(w.shape);
-
-  return [dx, dfilter];
-}
-
-describeWithFlags('conv3d', ALL_ENVS, () => {
-  it('x=[1, 2, 3, 1, 3] f=[1, 1, 1, 3, 3] s=1 d=1 p=valid', async () => {
-    const batch = 1;
-    const inDepth = 2;
-    const inHeight = 3;
-    const inWidth = 1;
-    const inChannels = 3;
-    const outChannels = 3;
-    const fSize = 1;
-    const pad = 'valid';
-    const stride = 1;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      0.18518519, 0.22222222, 0.25925926, 0.40740741, 0.5, 0.59259259,
-      0.62962963, 0.77777778, 0.92592593, 0.85185185, 1.05555556, 1.25925926,
-      1.07407407, 1.33333333, 1.59259259, 1.2962963, 1.61111111, 1.92592593
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-  it('x=[1, 2, 1, 3, 3] f=[1, 1, 1, 3, 3] s=1 d=1 p=valid', async () => {
-    const batch = 1;
-    const inDepth = 2;
-    const inHeight = 1;
-    const inWidth = 3;
-    const inChannels = 3;
-    const outChannels = 3;
-    const fSize = 1;
-    const pad = 'valid';
-    const stride = 1;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      0.18518519, 0.22222222, 0.25925926, 0.40740741, 0.5, 0.59259259,
-      0.62962963, 0.77777778, 0.92592593, 0.85185185, 1.05555556, 1.25925926,
-      1.07407407, 1.33333333, 1.59259259, 1.2962963, 1.61111111, 1.92592593
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 1, 2, 3, 3] f=[1, 1, 1, 3, 3] s=1 d=1 p=valid', async () => {
-    const batch = 1;
-    const inDepth = 1;
-    const inHeight = 2;
-    const inWidth = 3;
-    const inChannels = 3;
-    const outChannels = 3;
-    const fSize = 1;
-    const pad = 'valid';
-    const stride = 1;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      0.18518519, 0.22222222, 0.25925926, 0.40740741, 0.5, 0.59259259,
-      0.62962963, 0.77777778, 0.92592593, 0.85185185, 1.05555556, 1.25925926,
-      1.07407407, 1.33333333, 1.59259259, 1.2962963, 1.61111111, 1.92592593
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 4, 2, 3, 3] f=[2, 2, 2, 3, 3] s=1 d=1 p=valid', async () => {
-    const batch = 1;
-    const inDepth = 4;
-    const inHeight = 2;
-    const inWidth = 3;
-    const inChannels = 3;
-    const outChannels = 3;
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      3.77199074, 3.85069444, 3.92939815, 4.2650463, 4.35763889, 4.45023148,
-      6.73032407, 6.89236111, 7.05439815, 7.22337963, 7.39930556, 7.57523148,
-      9.68865741, 9.93402778, 10.17939815, 10.18171296, 10.44097222, 10.70023148
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 5, 8, 7, 1] f=[1, 2, 3, 1, 1] s=[2, 3, 1] d=1 p=same', async () => {
-    const batch = 1;
-    const inDepth = 5;
-    const inHeight = 8;
-    const inWidth = 7;
-    const inChannels = 1;
-    const outChannels = 1;
-    const fDepth = 1;
-    const fHeight = 2;
-    const fWidth = 3;
-    const pad = 'same';
-    const stride: [number, number, number] = [2, 3, 1];
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth,
-        fHeight, fWidth, pad, stride);
-
-    const expectedOutput = [
-      0.06071429, 0.08988095, 0.10238095, 0.11488095, 0.12738095, 0.13988095,
-      0.08452381, 0.26071429, 0.35238095, 0.36488095, 0.37738095, 0.38988095,
-      0.40238095, 0.23452381, 0.46071429, 0.61488095, 0.62738095, 0.63988095,
-      0.65238095, 0.66488095, 0.38452381, 1.12738095, 1.48988095, 1.50238095,
-      1.51488095, 1.52738095, 1.53988095, 0.88452381, 1.32738095, 1.75238095,
-      1.76488095, 1.77738095, 1.78988095, 1.80238095, 1.03452381, 1.52738095,
-      2.01488095, 2.02738095, 2.03988095, 2.05238095, 2.06488095, 1.18452381,
-      2.19404762, 2.88988095, 2.90238095, 2.91488095, 2.92738095, 2.93988095,
-      1.68452381, 2.39404762, 3.15238095, 3.16488095, 3.17738095, 3.18988095,
-      3.20238095, 1.83452381, 2.59404762, 3.41488095, 3.42738095, 3.43988095,
-      3.45238095, 3.46488095, 1.98452381
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 4, 2, 3, 3] f=[2, 2, 2, 3, 3] s=2 d=1 p=valid', async () => {
-    const batch = 1;
-    const inDepth = 4;
-    const inHeight = 2;
-    const inWidth = 3;
-    const inChannels = 3;
-    const outChannels = 3;
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 2;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      3.77199074, 3.85069444, 3.92939815, 9.68865741, 9.93402778, 10.17939815
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 6, 7, 8, 2] f=[3, 2, 1, 2, 3] s=3 d=1 p=valid', async () => {
-    const batch = 1;
-    const inDepth = 6;
-    const inHeight = 7;
-    const inWidth = 8;
-    const inChannels = 2;
-    const outChannels = 3;
-    const fDepth = 3;
-    const fHeight = 2;
-    const fWidth = 1;
-    const pad = 'valid';
-    const stride = 3;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth,
-        fHeight, fWidth, pad, stride);
-
-    const expectedOutput = [
-      1.51140873, 1.57167659, 1.63194444, 1.56349206, 1.62673611, 1.68998016,
-      1.6155754,  1.68179563, 1.74801587, 1.9280754,  2.01215278, 2.09623016,
-      1.98015873, 2.0672123,  2.15426587, 2.03224206, 2.12227183, 2.21230159,
-      4.4280754,  4.65500992, 4.88194444, 4.48015873, 4.71006944, 4.93998016,
-      4.53224206, 4.76512897, 4.99801587, 4.84474206, 5.09548611, 5.34623016,
-      4.8968254,  5.15054563, 5.40426587, 4.94890873, 5.20560516, 5.46230159
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 4, 2, 3, 3] f=[2, 2, 2, 3, 3] s=2 d=1 p=same', async () => {
-    const batch = 1;
-    const inDepth = 4;
-    const inHeight = 2;
-    const inWidth = 3;
-    const inChannels = 3;
-    const outChannels = 3;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 2;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      3.77199074, 3.85069444, 3.92939815, 2.0162037, 2.06597222, 2.11574074,
-      9.68865741, 9.93402778, 10.17939815, 4.59953704, 4.73263889, 4.86574074
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 3, 3, 3, 1] f=[1, 1, 1, 1, 1] s=2 d=1 p=same', async () => {
-    const batch = 1;
-    const inDepth = 3;
-    const inHeight = 3;
-    const inWidth = 3;
-    const inChannels = 1;
-    const outChannels = 1;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 2;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      0.03703704, 0.11111111, 0.25925926, 0.33333333, 0.7037037, 0.77777778,
-      0.92592593, 1.
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 3, 3, 3, 1] f=[1, 1, 1, 1, 1] s=2 d=1 p=valid', async () => {
-    const batch = 1;
-    const inDepth = 3;
-    const inHeight = 3;
-    const inWidth = 3;
-    const inChannels = 1;
-    const outChannels = 1;
-    const fSize = 1;
-    const pad = 'valid';
-    const stride = 2;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      0.03703704, 0.11111111, 0.25925926, 0.33333333, 0.7037037, 0.77777778,
-      0.92592593, 1.
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 7, 7, 7, 1] f=[2, 2, 2, 1, 1] s=3 d=1 p=same', async () => {
-    const batch = 1;
-    const inDepth = 7;
-    const inHeight = 7;
-    const inWidth = 7;
-    const inChannels = 1;
-    const outChannels = 1;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 3;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      0.54081633, 0.58017493, 0.28061224, 0.81632653, 0.85568513, 0.40306122,
-      0.41873178, 0.4340379,  0.19642857, 2.46938776, 2.50874636, 1.1377551,
-      2.74489796, 2.78425656, 1.26020408, 1.16873178, 1.1840379,  0.51785714,
-      1.09511662, 1.10604956, 0.44642857, 1.17164723, 1.18258017, 0.47704082,
-      0.3691691,  0.37244898, 0.125
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 7, 7, 7, 1] f=[2, 2, 2, 1, 1] s=3 d=1 p=valid', async () => {
-    const batch = 1;
-    const inDepth = 7;
-    const inHeight = 7;
-    const inWidth = 7;
-    const inChannels = 1;
-    const outChannels = 1;
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 3;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize,
-        fSize, fSize, pad, stride);
-
-    const expectedOutput = [
-      0.540816, 0.580175, 0.816327, 0.855685, 2.469388, 2.508746, 2.744898,
-      2.784257
-    ];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('x=[1, 2, 1, 2, 1] f=[2, 1, 2, 1, 2] s=1 d=1 p=valid', async () => {
-    const batch = 1;
-    const inDepth = 2;
-    const inHeight = 1;
-    const inWidth = 2;
-    const inChannels = 1;
-    const outChannels = 2;
-    const fDepth = 2;
-    const fHeight = 1;
-    const fWidth = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const result = runConv3DTestCase(
-        batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth,
-        fHeight, fWidth, pad, stride);
-
-    const expectedOutput = [1.5625, 1.875];
-
-    expectArraysClose(await result.data(), expectedOutput);
-  });
-
-  it('gradient with clones, x=[1,3,6,1,1] filter=[2,2,1,1,1] s=1 d=1 p=valid',
-     async () => {
-       const batch = 1;
-       const inDepth = 3;
-       const inHeight = 6;
-       const inWidth = 1;
-       const inChannels = 1;
-       const outChannels = 1;
-       const fDepth = 2;
-       const fHeight = 2;
-       const fWidth = 1;
-       const pad = 'valid';
-       const stride = 1;
-       const [dx, dfilter] = runGradientConv3DTestCase(
-           batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth,
-           fHeight, fWidth, pad, stride);
-
-       const expectedFilterOutput = [60.0, 70.0, 120.0, 130.0];
-       const expectedOutput = [
-         1.0, 3.0, 3.0, 3.0, 3.0, 2.0, 4.0, 10.0, 10.0, 10.0, 10.0, 6.0, 3.0,
-         7.0, 7.0, 7.0, 7.0, 4.0
-       ];
-       expectArraysClose(await dx.data(), expectedOutput);
-       expectArraysClose(await dfilter.data(), expectedFilterOutput);
-     });
-
-  it('throws when passed x as a non-tensor', () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 'valid';
-    const stride = 1;
-
-    const w = tf.tensor5d([2], [fSize, fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv3d({} as tf.Tensor4D, w, stride, pad))
-        .toThrowError(/Argument 'x' passed to 'conv3d' must be a Tensor/);
-  });
-
-  it('throws when passed filter as a non-tensor', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 2, 1, inputDepth];
-    const pad = 'valid';
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4], inputShape);
-
-    expect(() => tf.conv3d(x, {} as Tensor5D, stride, pad))
-        .toThrowError(/Argument 'filter' passed to 'conv3d' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const pad = 'valid';
-    const stride = 1;
-    const x = [[[[1], [2]], [[3], [4]]]];  // 2x2x1x1
-    const w = [[[[[2]]]]];                 // 1x1x1x1x1
-
-    const result = tf.conv3d(x, w, stride, pad);
-    expectArraysClose(await result.data(), [2, 4, 6, 8]);
-  });
-
-  it('throws when data format not NDHWC', () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 2, 1, inputDepth];
-    const pad = 'valid';
-    const fSize = 1;
-    const stride = 1;
-    const dataFormat = 'NCDHW';
-
-    const x = tf.tensor4d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor5d([2], [fSize, fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv3d(x, w, stride, pad, dataFormat)).toThrowError();
-  });
-
-  it('throws when stride is less than or equal to 0', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 2, 1, inputDepth];
-    const pad = 'valid';
-    const fSize = 1;
-    const stride = 0;
-    const dataFormat = 'NDHWC';
-
-    const x = tf.tensor4d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor5d([2], [fSize, fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv3d(x, w, stride, pad, dataFormat)).toThrowError();
-  });
-
-  it('throws when dilation is less than or equal to 0', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 2, 1, inputDepth];
-    const pad = 'valid';
-    const fSize = 1;
-    const stride = 0;
-    const dataFormat = 'NDHWC';
-    const dilation: [number, number, number] = [1, 1, 0];
-
-    const x = tf.tensor4d([1, 2, 3, 4], inputShape);
-    const w = tf.tensor5d([2], [fSize, fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.conv3d(x, w, stride, pad, dataFormat, dilation))
-        .toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/conv3d_transpose.ts b/tfjs-master/tfjs-core/src/ops/conv3d_transpose.ts
deleted file mode 100644
index 09e474cde..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv3d_transpose.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor4D, Tensor5D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {conv3DBackpropInput} from './conv3d_backprop_input';
-import {op} from './operation';
-
-/**
- * Computes the transposed 3D convolution of a volume, also known as a
- * deconvolution.
- *
- * @param x The input image, of rank 5 or rank 4, of shape
- *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.
- * @param filter The filter, rank 4, of shape
- *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.
- *     `inDepth` must match `inDepth` in `x`.
- * @param outputShape Output shape, of rank 5 or rank 4:
- *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is
- *    assumed.
- * @param strides The strides of the original convolution:
- *     `[strideDepth, strideHeight, strideWidth]`.
- * @param pad  The type of padding algorithm used in the non-transpose version
- *    of the op.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function conv3dTranspose_<T extends Tensor4D|Tensor5D>(
-    x: T|TensorLike, filter: Tensor5D|TensorLike,
-    outputShape:
-        [number, number, number, number,
-         number]|[number, number, number, number],
-    strides: [number, number, number]|number, pad: 'valid'|'same'): T {
-  const $x = convertToTensor(x, 'x', 'conv3dTranspose');
-  const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');
-
-  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);
-}
-
-export const conv3dTranspose = /* @__PURE__ */ op({conv3dTranspose_});
diff --git a/tfjs-master/tfjs-core/src/ops/conv3d_transpose_test.ts b/tfjs-master/tfjs-core/src/ops/conv3d_transpose_test.ts
deleted file mode 100644
index c2064e713..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv3d_transpose_test.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('conv3dTranspose', ALL_ENVS, () => {
-  // Reference Python TensorFlow code
-  // ```python
-  // import numpy as np
-  // import tensorflow as tf
-  // tf.enable_eager_execution()
-  // x = np.array([2], dtype = np.float32).reshape(1, 1, 1, 1, 1)
-  // w = np.array([5, 4, 8, 7, 1, 2, 6, 3], dtype = np.float32).reshape(2, 2, 2,
-  //   1, 1)
-  // tf.nn.conv3d_transpose(x, w, output_shape=[1, 2, 2, 2, 1], padding='VALID')
-  // ```
-  it('input=2x2x2x1,d2=1,f=2,s=1,p=valid', async () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 1;
-    const inputShape: [number, number, number, number] =
-        [1, 1, 1, origOutputDepth];
-    const fSize = 2;
-    const origPad = 'valid';
-    const origStride = 1;
-
-    const x = tf.tensor4d([2], inputShape);
-    const w = tf.tensor5d(
-        [5, 4, 8, 7, 1, 2, 6, 3],
-        [fSize, fSize, fSize, origInputDepth, origOutputDepth]);
-
-    const result = tf.conv3dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);
-    const expected = [10, 8, 16, 14, 2, 4, 12, 6];
-
-    expect(result.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  // Reference Python TensorFlow code
-  // ```python
-  // import numpy as np
-  // import tensorflow as tf
-  // tf.enable_eager_execution()
-  // x = np.array([2, 3], dtype = np.float32).reshape(2, 1, 1, 1, 1, 1)
-  // w = np.array([5, 4, 8, 7, 1, 2, 6, 3], dtype = np.float32).reshape(2,
-  //   2, 2, 1, 1)
-  // tf.nn.conv3d_transpose(x, w, output_shape=[2, 2, 2, 2, 1], padding='VALID')
-  // ```
-  it('input=2x2x2x1,d2=1,f=2,s=1,p=valid, batch=2', async () => {
-    const origInputDepth = 1;
-    const origOutputDepth = 1;
-    const inputShape: [number, number, number, number, number] =
-        [2, 1, 1, 1, origOutputDepth];
-    const fSize = 2;
-    const origPad = 'valid';
-    const origStride = 1;
-
-    const x = tf.tensor5d([2, 3], inputShape);
-    const w = tf.tensor5d(
-        [5, 4, 8, 7, 1, 2, 6, 3],
-        [fSize, fSize, fSize, origInputDepth, origOutputDepth]);
-
-    const result =
-        tf.conv3dTranspose(x, w, [2, 2, 2, 2, 1], origStride, origPad);
-    const expected = [10, 8, 16, 14, 2, 4, 12, 6, 15, 12, 24, 21, 3, 6, 18, 9];
-
-    expect(result.shape).toEqual([2, 2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/conv_util.ts b/tfjs-master/tfjs-core/src/ops/conv_util.ts
deleted file mode 100644
index 93ef7efcc..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv_util.ts
+++ /dev/null
@@ -1,640 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as util from '../util';
-
-type PadType = 'SAME'|'VALID'|'NUMBER'|'EXPLICIT';
-
-// For NHWC should be in the following form:
-//  [[0, 0], [pad_top,pad_bottom], [pad_left, pad_right], [0, 0]]
-// For NCHW should be in the following form:
-//  [[0, 0], [0, 0], [pad_top,pad_bottom], [pad_left, pad_right]]
-// Reference: https://www.tensorflow.org/api_docs/python/tf/nn/conv2d
-export type ExplicitPadding =
-    [[number, number], [number, number], [number, number], [number, number]];
-
-export type PadInfo = {
-  top: number,
-  left: number,
-  right: number,
-  bottom: number,
-  type: PadType
-};
-
-export type PadInfo3D = {
-  top: number,
-  left: number,
-  right: number,
-  bottom: number,
-  front: number,
-  back: number,
-  type: PadType
-};
-
-/**
- * Information about the forward pass of a convolution/pooling operation.
- * It includes input and output shape, strides, filter size and padding
- * information.
- */
-export type Conv2DInfo = {
-  batchSize: number,
-  inHeight: number,
-  inWidth: number,
-  inChannels: number,
-  outHeight: number,
-  outWidth: number,
-  outChannels: number,
-  dataFormat: 'channelsFirst'|'channelsLast',
-  strideHeight: number,
-  strideWidth: number,
-  dilationHeight: number,
-  dilationWidth: number,
-  filterHeight: number,
-  filterWidth: number,
-  effectiveFilterHeight: number,
-  effectiveFilterWidth: number,
-  padInfo: PadInfo,
-  inShape: [number, number, number, number],
-  outShape: [number, number, number, number],
-  filterShape: [number, number, number, number]
-};
-
-/**
- *
- * @param inputShape Input tensor shape is of the following dimensions:
- *     `[batch, height, width, inChannels]`.
- * @param filterShape The filter shape is of the following dimensions:
- *     `[filterHeight, filterWidth, depth]`.
- * @param strides The strides of the sliding window for each dimension of the
- *     input tensor: `[strideHeight, strideWidth]`.
- *     If `strides` is a single number,
- *     then `strideHeight == strideWidth`.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1*1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dataFormat The data format of the input and output data.
- *     Defaults to 'NHWC'.
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.
- *     Defaults to `[1, 1]`. If `dilations` is a single number, then
- *     `dilationHeight == dilationWidth`.
- */
-export function computeDilation2DInfo(
-    inputShape: [number, number, number, number],
-    filterShape: [number, number, number], strides: number|[number, number],
-    pad: 'same'|'valid'|number, dataFormat: 'NHWC' = 'NHWC',
-    dilations: number|[number, number]) {
-  // `computerConv2DInfo` require filterShape to be in the dimension of:
-  // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have
-  // outDepth, it should have the same depth as the input.
-  // Input shape: [batch, height, width, inChannels]
-  const inputChannels = inputShape[3];
-  const $filterShape =
-      [...filterShape, inputChannels] as [number, number, number, number];
-  const $dataFormat = convertConv2DDataFormat(dataFormat);
-
-  return computeConv2DInfo(
-      inputShape, $filterShape, strides, dilations, pad,
-      null /* roundingMode */, null /* depthWise */, $dataFormat);
-}
-
-export function computePool2DInfo(
-    inShape: [number, number, number, number],
-    filterSize: [number, number]|number, strides: number|[number, number],
-    dilations: number|[number, number],
-    pad: 'same'|'valid'|number|ExplicitPadding,
-    roundingMode?: 'floor'|'round'|'ceil',
-    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {
-  const [filterHeight, filterWidth] = parseTupleParam(filterSize);
-
-  let filterShape: [number, number, number, number];
-  if (dataFormat === 'channelsLast') {
-    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
-  } else if (dataFormat === 'channelsFirst') {
-    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-
-  return computeConv2DInfo(
-      inShape, filterShape, strides, dilations, pad, roundingMode, false,
-      dataFormat);
-}
-
-/**
- * Computes the information for a forward pass of a pooling3D operation.
- */
-export function computePool3DInfo(
-    inShape: [number, number, number, number, number],
-    filterSize: number|[number, number, number],
-    strides: number|[number, number, number],
-    dilations: number|[number, number, number], pad: 'same'|'valid'|number,
-    roundingMode?: 'floor'|'round'|'ceil',
-    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): Conv3DInfo {
-  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
-
-  let filterShape: [number, number, number, number, number];
-  let $dataFormat: 'channelsFirst'|'channelsLast';
-  if (dataFormat === 'NDHWC') {
-    $dataFormat = 'channelsLast';
-    filterShape =
-        [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
-  } else if (dataFormat === 'NCDHW') {
-    $dataFormat = 'channelsFirst';
-    filterShape =
-        [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-
-  return computeConv3DInfo(
-      inShape, filterShape, strides, dilations, pad, false, $dataFormat,
-      roundingMode);
-}
-
-/**
- * Computes the information for a forward pass of a convolution/pooling
- * operation.
- */
-export function computeConv2DInfo(
-    inShape: [number, number, number, number],
-    filterShape: [number, number, number, number],
-    strides: number|[number, number], dilations: number|[number, number],
-    pad: 'same'|'valid'|number|ExplicitPadding,
-    roundingMode?: 'floor'|'round'|'ceil', depthwise = false,
-    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {
-  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];
-  if (dataFormat === 'channelsLast') {
-    [batchSize, inHeight, inWidth, inChannels] = inShape;
-  } else if (dataFormat === 'channelsFirst') {
-    [batchSize, inChannels, inHeight, inWidth] = inShape;
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-
-  const [filterHeight, filterWidth, , filterChannels] = filterShape;
-  const [strideHeight, strideWidth] = parseTupleParam(strides);
-  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
-
-  const effectiveFilterHeight =
-      getEffectiveFilterSize(filterHeight, dilationHeight);
-  const effectiveFilterWidth =
-      getEffectiveFilterSize(filterWidth, dilationWidth);
-  const {padInfo, outHeight, outWidth} = getPadAndOutInfo(
-      pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight,
-      effectiveFilterWidth, roundingMode, dataFormat);
-
-  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-
-  let outShape: [number, number, number, number];
-  if (dataFormat === 'channelsFirst') {
-    outShape = [batchSize, outChannels, outHeight, outWidth];
-  } else if (dataFormat === 'channelsLast') {
-    outShape = [batchSize, outHeight, outWidth, outChannels];
-  }
-
-  return {
-    batchSize,
-    dataFormat,
-    inHeight,
-    inWidth,
-    inChannels,
-    outHeight,
-    outWidth,
-    outChannels,
-    padInfo,
-    strideHeight,
-    strideWidth,
-    filterHeight,
-    filterWidth,
-    effectiveFilterHeight,
-    effectiveFilterWidth,
-    dilationHeight,
-    dilationWidth,
-    inShape,
-    outShape,
-    filterShape
-  };
-}
-
-/**
- * Information about the forward pass of a 3D convolution/pooling operation.
- * It includes input and output shape, strides, filter size and padding
- * information.
- */
-export type Conv3DInfo = {
-  batchSize: number,
-  inDepth: number,
-  inHeight: number,
-  inWidth: number,
-  inChannels: number,
-  outDepth: number,
-  outHeight: number,
-  outWidth: number,
-  outChannels: number,
-  dataFormat: 'channelsFirst'|'channelsLast',
-  strideDepth: number,
-  strideHeight: number,
-  strideWidth: number,
-  dilationDepth: number,
-  dilationHeight: number,
-  dilationWidth: number,
-  filterDepth: number,
-  filterHeight: number,
-  filterWidth: number,
-  effectiveFilterDepth: number,
-  effectiveFilterHeight: number,
-  effectiveFilterWidth: number,
-  padInfo: PadInfo3D,
-  inShape: [number, number, number, number, number],
-  outShape: [number, number, number, number, number],
-  filterShape: [number, number, number, number, number]
-};
-
-/**
- * Computes the information for a forward pass of a 3D convolution/pooling
- * operation.
- */
-export function computeConv3DInfo(
-    inShape: [number, number, number, number, number],
-    filterShape: [number, number, number, number, number],
-    strides: number|[number, number, number],
-    dilations: number|[number, number, number], pad: 'same'|'valid'|number,
-    depthwise = false,
-    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast',
-    roundingMode?: 'floor'|'round'|'ceil'): Conv3DInfo {
-  let [batchSize, inDepth, inHeight, inWidth, inChannels] =
-      [-1, -1, -1, -1, -1];
-  if (dataFormat === 'channelsLast') {
-    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
-  } else if (dataFormat === 'channelsFirst') {
-    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-
-  const [filterDepth, filterHeight, filterWidth, , filterChannels] =
-      filterShape;
-  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);
-  const [dilationDepth, dilationHeight, dilationWidth] =
-      parse3TupleParam(dilations);
-
-  const effectiveFilterDepth =
-      getEffectiveFilterSize(filterDepth, dilationDepth);
-  const effectiveFilterHeight =
-      getEffectiveFilterSize(filterHeight, dilationHeight);
-  const effectiveFilterWidth =
-      getEffectiveFilterSize(filterWidth, dilationWidth);
-  const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(
-      pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth,
-      effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth,
-      roundingMode);
-
-  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
-
-  let outShape: [number, number, number, number, number];
-  if (dataFormat === 'channelsFirst') {
-    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
-  } else if (dataFormat === 'channelsLast') {
-    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
-  }
-
-  return {
-    batchSize,
-    dataFormat,
-    inDepth,
-    inHeight,
-    inWidth,
-    inChannels,
-    outDepth,
-    outHeight,
-    outWidth,
-    outChannels,
-    padInfo,
-    strideDepth,
-    strideHeight,
-    strideWidth,
-    filterDepth,
-    filterHeight,
-    filterWidth,
-    effectiveFilterDepth,
-    effectiveFilterHeight,
-    effectiveFilterWidth,
-    dilationDepth,
-    dilationHeight,
-    dilationWidth,
-    inShape,
-    outShape,
-    filterShape
-  };
-}
-
-function computeOutputShape2D(
-    inShape: [number, number], fieldSize: number, stride: number,
-    zeroPad?: number, roundingMode?: 'floor'|'round'|'ceil'): [number, number] {
-  if (zeroPad == null) {
-    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
-  }
-  const inputRows = inShape[0];
-  const inputCols = inShape[1];
-
-  const outputRows =
-      round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-  const outputCols =
-      round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
-
-  return [outputRows, outputCols];
-}
-
-function computeOutputShape4D(
-    inShape: [number, number, number, number],
-    filterShape: [number, number, number], outChannels: number,
-    strides: [number, number, number], zeroPad?: number,
-    roundingMode?: 'floor'|'round'|'ceil'): [number, number, number, number] {
-  if (zeroPad == null) {
-    zeroPad = computeDefaultPad(inShape, filterShape[0], strides[0]);
-  }
-  const outShape: [number, number, number, number] = [0, 0, 0, outChannels];
-  for (let index = 0; index < 3; index++) {
-    if (inShape[index] + 2 * zeroPad >= filterShape[index]) {
-      outShape[index] = round(
-          (inShape[index] - filterShape[index] + 2 * zeroPad) / strides[index] +
-              1,
-          roundingMode);
-    }
-  }
-  return outShape;
-}
-
-export function computeDefaultPad(
-    inputShape: [number, number]|[number, number, number, number],
-    fieldSize: number, stride: number, dilation = 1): number {
-  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
-  return Math.floor(
-      (inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
-}
-
-function parseTupleParam(param: number|number[]): [number, number, number] {
-  if (typeof param === 'number') {
-    return [param, param, param];
-  }
-  if (param.length === 2) {
-    return [param[0], param[1], 1];
-  }
-  return param as [number, number, number];
-}
-
-function parse3TupleParam(param: number|[number, number, number]):
-    [number, number, number] {
-  return typeof param === 'number' ? [param, param, param] : param;
-}
-
-/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d
- * Atrous convolution is equivalent to standard convolution with upsampled
- * filters with effective_filter_height =
- * filter_height + (filter_height - 1) * (dilation - 1)
- * and effective_filter_width =
- * filter_width + (filter_width - 1) * (dilation - 1),
- * produced by inserting dilation - 1 zeros along consecutive elements across
- * the filters' spatial dimensions.
- * When there is a dilation, this converts a filter dimension to the
- * effective filter dimension, so it can be used in a standard convolution.
- */
-function getEffectiveFilterSize(filterSize: number, dilation: number) {
-  if (dilation <= 1) {
-    return filterSize;
-  }
-
-  return filterSize + (filterSize - 1) * (dilation - 1);
-}
-
-function getPadAndOutInfo(
-    pad: 'same'|'valid'|number|ExplicitPadding, inHeight: number,
-    inWidth: number, strideHeight: number, strideWidth: number,
-    filterHeight: number, filterWidth: number,
-    roundingMode: 'floor'|'round'|'ceil',
-    dataFormat: 'channelsFirst'|
-    'channelsLast'): {padInfo: PadInfo, outHeight: number, outWidth: number} {
-  let padInfo: PadInfo;
-  let outHeight: number;
-  let outWidth: number;
-
-  if (typeof pad === 'number') {
-    const padType = (pad === 0) ? 'VALID' : 'NUMBER';
-    padInfo = {top: pad, bottom: pad, left: pad, right: pad, type: padType};
-    const outShape = computeOutputShape2D(
-        [inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);
-    outHeight = outShape[0];
-    outWidth = outShape[1];
-  } else if (pad === 'same') {
-    outHeight = Math.ceil(inHeight / strideHeight);
-    outWidth = Math.ceil(inWidth / strideWidth);
-    const padAlongHeight =
-        Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
-    const padAlongWidth =
-        Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
-    const top = Math.floor(padAlongHeight / 2);
-    const bottom = padAlongHeight - top;
-    const left = Math.floor(padAlongWidth / 2);
-    const right = padAlongWidth - left;
-    padInfo = {top, bottom, left, right, type: 'SAME'};
-  } else if (pad === 'valid') {
-    padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: 'VALID'};
-    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
-    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
-  } else if (typeof pad === 'object') {
-    const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];
-    const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];
-    const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];
-    const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];
-    const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?
-        'VALID' :
-        'EXPLICIT';
-    padInfo = {top, bottom, left, right, type: padType};
-    outHeight = round(
-        (inHeight - filterHeight + top + bottom) / strideHeight + 1,
-        roundingMode);
-    outWidth = round(
-        (inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
-  } else {
-    throw Error(`Unknown padding parameter: ${pad}`);
-  }
-  return {padInfo, outHeight, outWidth};
-}
-
-function get3DPadAndOutInfo(
-    pad: 'same'|'valid'|number, inDepth: number, inHeight: number,
-    inWidth: number, strideDepth: number, strideHeight: number,
-    strideWidth: number, filterDepth: number, filterHeight: number,
-    filterWidth: number, roundingMode?: 'floor'|'round'|'ceil'): {
-  padInfo: PadInfo3D,
-  outDepth: number,
-  outHeight: number,
-  outWidth: number
-} {
-  let padInfo: PadInfo3D;
-  let outDepth: number;
-  let outHeight: number;
-  let outWidth: number;
-
-  if (pad === 'valid') {
-    pad = 0;
-  }
-
-  if (typeof pad === 'number') {
-    const padType = (pad === 0) ? 'VALID' : 'NUMBER';
-    padInfo = {
-      top: pad,
-      bottom: pad,
-      left: pad,
-      right: pad,
-      front: pad,
-      back: pad,
-      type: padType
-    };
-    const outShape = computeOutputShape4D(
-        [inDepth, inHeight, inWidth, 1],
-        [filterDepth, filterHeight, filterWidth], 1,
-        [strideDepth, strideHeight, strideWidth], pad, roundingMode);
-    outDepth = outShape[0];
-    outHeight = outShape[1];
-    outWidth = outShape[2];
-  } else if (pad === 'same') {
-    outDepth = Math.ceil(inDepth / strideDepth);
-    outHeight = Math.ceil(inHeight / strideHeight);
-    outWidth = Math.ceil(inWidth / strideWidth);
-    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
-    const padAlongHeight =
-        (outHeight - 1) * strideHeight + filterHeight - inHeight;
-    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
-    const front = Math.floor(padAlongDepth / 2);
-    const back = padAlongDepth - front;
-    const top = Math.floor(padAlongHeight / 2);
-    const bottom = padAlongHeight - top;
-    const left = Math.floor(padAlongWidth / 2);
-    const right = padAlongWidth - left;
-
-    padInfo = {top, bottom, left, right, front, back, type: 'SAME'};
-  } else {
-    throw Error(`Unknown padding parameter: ${pad}`);
-  }
-  return {padInfo, outDepth, outHeight, outWidth};
-}
-
-/**
- * Rounds a value depending on the rounding mode
- * @param value
- * @param roundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- */
-function round(value: number, roundingMode?: 'floor'|'round'|'ceil') {
-  if (!roundingMode) {
-    return Math.trunc(value);
-  }
-  switch (roundingMode) {
-    case 'round':
-      // used for Caffe Conv
-      return Math.round(value);
-    case 'ceil':
-      // used for Caffe Pool
-      return Math.ceil(value);
-    case 'floor':
-      return Math.floor(value);
-    default:
-      throw new Error(`Unknown roundingMode ${roundingMode}`);
-  }
-}
-
-export function tupleValuesAreOne(param: number|number[]): boolean {
-  const [dimA, dimB, dimC] = parseTupleParam(param);
-  return dimA === 1 && dimB === 1 && dimC === 1;
-}
-
-export function eitherStridesOrDilationsAreOne(
-    strides: number|number[], dilations: number|number[]): boolean {
-  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
-}
-
-export function stridesOrDilationsArePositive(values: number|
-                                              number[]): boolean {
-  return parseTupleParam(values).every(value => value > 0);
-}
-
-/**
- * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to
- *    'channelsLast'|'channelsFirst'
- * @param dataFormat in 'NHWC'|'NCHW' mode
- * @return dataFormat in 'channelsLast'|'channelsFirst' mode
- * @throws unknown dataFormat
- */
-export function convertConv2DDataFormat(dataFormat: 'NHWC'|'NCHW'):
-    'channelsLast'|'channelsFirst' {
-  if (dataFormat === 'NHWC') {
-    return 'channelsLast';
-  } else if (dataFormat === 'NCHW') {
-    return 'channelsFirst';
-  } else {
-    throw new Error(`Unknown dataFormat ${dataFormat}`);
-  }
-}
-
-/**
- * Check validity of pad when using dimRoundingMode.
- * @param opDesc A string of op description
- * @param pad The type of padding algorithm.
- *   - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *   - `valid` output will be smaller than input if filter is larger
- *       than 1x1.
- *   - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- * @throws unknown padding parameter
- */
-export function checkPadOnDimRoundingMode(
-    opDesc: string, pad: 'valid'|'same'|number|ExplicitPadding,
-    dimRoundingMode?: 'floor'|'round'|'ceil') {
-  if (dimRoundingMode != null) {
-    if (typeof pad === 'string') {
-      throw Error(
-          `Error in ${opDesc}: pad must be an integer when using ` +
-          `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
-    } else if (typeof pad === 'number') {
-      util.assert(
-          util.isInt(pad),
-          () => `Error in ${opDesc}: pad must be an integer when using ` +
-              `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
-    } else if (typeof pad === 'object') {
-      (pad as ExplicitPadding).forEach(p => {
-        p.forEach(v => {
-          util.assert(
-              util.isInt(v),
-              () => `Error in ${opDesc}: pad must be an integer when using ` +
-                  `dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);
-        });
-      });
-    } else {
-      throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad}`);
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/ops/conv_util_test.ts b/tfjs-master/tfjs-core/src/ops/conv_util_test.ts
deleted file mode 100644
index 99f6ba169..000000000
--- a/tfjs-master/tfjs-core/src/ops/conv_util_test.ts
+++ /dev/null
@@ -1,1000 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as conv_util from './conv_util';
-
-describe('conv_util computeConv2DInfo', () => {
-  it('1x1 conv over 1x1 array with same pad', () => {
-    const inShape: [number, number, number, number] = [1, 1, 1, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [1, 1, 1, 1], stride, dilation, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(1);
-    expect(convInfo.effectiveFilterHeight).toEqual(1);
-  });
-
-  it('2x2 conv over 3x3 array with same pad', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 2, 1, 1], stride, dilation, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-    // Should produce non-even padding with extra pixel at the right/bottom.
-    expect(convInfo.padInfo.left).toBe(0);
-    expect(convInfo.padInfo.right).toBe(1);
-    expect(convInfo.padInfo.top).toBe(0);
-    expect(convInfo.padInfo.bottom).toBe(1);
-  });
-
-  it('2x2 conv over 3x3 array with same pad', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 2, 1, 1], stride, dilation, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-  });
-
-  it('2x2 conv over 3x3 array with valid pad', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 2, 1, 1], stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-  });
-
-  it('3x3 conv over 5x5 array with same pad with stride 2', () => {
-    const inShape: [number, number, number, number] = [1, 5, 5, 1];
-    const stride = 2;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [3, 3, 1, 1], stride, dilation, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(3);
-    expect(convInfo.effectiveFilterHeight).toEqual(3);
-
-    expect(convInfo.padInfo.left).toBe(1);
-    expect(convInfo.padInfo.right).toBe(1);
-    expect(convInfo.padInfo.top).toBe(1);
-    expect(convInfo.padInfo.bottom).toBe(1);
-  });
-
-  it('2x2 conv over 3x3 array with valid pad with stride 2', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 2;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 2, 1, 1], stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-  });
-
-  it('2x1 conv over 3x3 array with valid pad with stride 1', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 1, 1, 1], stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(1);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-  });
-
-  it('2x1 conv over 3x3 array with valid pad with strides h=2, w=1', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const strides: [number, number] = [2, 1];
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 1, 1, 1], strides, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(1);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-  });
-
-  it('1x2 conv over 3x3 array with valid pad with stride 1', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [1, 2, 1, 1], stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(1);
-  });
-
-  it('1x2 conv over 3x3 array with valid pad with stride 1, batch=5', () => {
-    const inShape: [number, number, number, number] = [5, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [1, 2, 1, 1], stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(5);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(1);
-  });
-
-  it('2x2 conv over 3x3 array with same pad with dilations 2', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 2, 1, 1], stride, dilations, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    // pad evenly on all sides
-    expect(convInfo.padInfo.left).toBe(1);
-    expect(convInfo.padInfo.right).toBe(1);
-    expect(convInfo.padInfo.top).toBe(1);
-    expect(convInfo.padInfo.bottom).toBe(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(3);
-    expect(convInfo.effectiveFilterHeight).toEqual(3);
-  });
-
-  it('2x1 conv over 3x3 array with same pad with dilations 2', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 1, 1, 1], stride, dilations, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    // pad top and bottom
-    expect(convInfo.padInfo.left).toBe(0);
-    expect(convInfo.padInfo.right).toBe(0);
-    expect(convInfo.padInfo.top).toBe(1);
-    expect(convInfo.padInfo.bottom).toBe(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(1);
-    expect(convInfo.effectiveFilterHeight).toEqual(3);
-  });
-
-  it('3x4 conv over 8x8 array with same pad with dilations h=4 w=3', () => {
-    const inShape: [number, number, number, number] = [1, 8, 8, 1];
-    const stride = 1;
-    const dilations: [number, number] = [4, 3];
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [3, 4, 1, 1], stride, dilations, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(8);
-    expect(convInfo.outWidth).toEqual(8);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(10);
-    expect(convInfo.effectiveFilterHeight).toEqual(9);
-
-    expect(convInfo.padInfo.left).toBe(4);
-    expect(convInfo.padInfo.right).toBe(5);
-    expect(convInfo.padInfo.top).toBe(4);
-    expect(convInfo.padInfo.bottom).toBe(4);
-  });
-
-  it('2x1 conv over 3x3 array with valid pad with dilations 2', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 1, 1, 1], stride, dilations, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(1);
-    expect(convInfo.effectiveFilterHeight).toEqual(3);
-  });
-
-  it('2x2 conv over 3x3 array with valid pad with dilations 2', () => {
-    const inShape: [number, number, number, number] = [1, 3, 3, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 2, 1, 1], stride, dilations, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(3);
-    expect(convInfo.effectiveFilterHeight).toEqual(3);
-  });
-
-  it('2x2 conv over 4x4 array with valid pad with dilations 2', () => {
-    const inShape: [number, number, number, number] = [1, 4, 4, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 2, 1, 1], stride, dilations, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(3);
-    expect(convInfo.effectiveFilterHeight).toEqual(3);
-  });
-});
-
-describe('conv_util computeConv3DInfo', () => {
-  it('1x1x1 conv over 1x1x1 array with same pad', () => {
-    const inShape: [number, number, number, number, number] = [1, 1, 1, 1, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [1, 1, 1, 1, 1], stride, dilation, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-
-  it('2x2x2 conv over 3x3x3 array with same pad', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 2, 2, 1, 1], stride, dilation, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    // Should produce non-even padding with extra pixel at the back/right/bottom
-    expect(convInfo.padInfo.front).toBe(0);
-    expect(convInfo.padInfo.back).toBe(1);
-    expect(convInfo.padInfo.left).toBe(0);
-    expect(convInfo.padInfo.right).toBe(1);
-    expect(convInfo.padInfo.top).toBe(0);
-    expect(convInfo.padInfo.bottom).toBe(1);
-  });
-
-  it('2x2x2 conv over 3x3x3 array with same pad', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 2, 2, 1, 1], stride, dilation, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-
-  it('2x2x2 conv over 3x3x3 array with valid pad', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 2, 2, 1, 1], stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(2);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-
-  it('3x3x3 conv over 5x5x5 array with same pad with stride 2', () => {
-    const inShape: [number, number, number, number, number] = [1, 5, 5, 5, 1];
-    const stride = 2;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [3, 3, 3, 1, 1], stride, dilation, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-
-    expect(convInfo.padInfo.front).toBe(1);
-    expect(convInfo.padInfo.back).toBe(1);
-    expect(convInfo.padInfo.left).toBe(1);
-    expect(convInfo.padInfo.right).toBe(1);
-    expect(convInfo.padInfo.top).toBe(1);
-    expect(convInfo.padInfo.bottom).toBe(1);
-  });
-
-  it('2x2x2 conv over 3x3x3 array with valid pad with stride 2', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 2;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 2, 2, 1, 1], stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-
-  it('2x1x1 conv over 3x3x3 array with valid pad with stride 1', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 1, 1, 1, 1], stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(2);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-
-  it('2x1x1 conv over 3x3x3 array with valid pad with strides d=2, h=1, w=1',
-     () => {
-       const inShape: [number, number, number, number, number] =
-           [1, 3, 3, 3, 1];
-       const strides: [number, number, number] = [2, 1, 1];
-       const dilation = 1;
-       const convInfo = conv_util.computeConv3DInfo(
-           inShape, [2, 1, 1, 1, 1], strides, dilation, 'valid');
-       expect(convInfo.batchSize).toEqual(1);
-       expect(convInfo.outDepth).toEqual(1);
-       expect(convInfo.outHeight).toEqual(3);
-       expect(convInfo.outWidth).toEqual(3);
-       expect(convInfo.outChannels).toEqual(1);
-     });
-
-  it('1x2x2 conv over 3x3x3 array with valid pad with stride 1', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [1, 2, 2, 1, 1], stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-
-  it('1x2x2 conv over 3x3x3 array with valid pad with stride 1, batch=5',
-     () => {
-       const inShape: [number, number, number, number, number] =
-           [5, 3, 3, 3, 1];
-       const stride = 1;
-       const dilation = 1;
-       const convInfo = conv_util.computeConv3DInfo(
-           inShape, [1, 2, 2, 1, 1], stride, dilation, 'valid');
-       expect(convInfo.batchSize).toEqual(5);
-       expect(convInfo.outDepth).toEqual(3);
-       expect(convInfo.outHeight).toEqual(2);
-       expect(convInfo.outWidth).toEqual(2);
-       expect(convInfo.outChannels).toEqual(1);
-     });
-
-  it('2x2x2 conv over 3x3x3 array with same pad with dilations 2', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 2, 2, 1, 1], stride, dilations, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    // pad evenly on all sides
-    expect(convInfo.padInfo.front).toBe(1);
-    expect(convInfo.padInfo.back).toBe(1);
-    expect(convInfo.padInfo.left).toBe(1);
-    expect(convInfo.padInfo.right).toBe(1);
-    expect(convInfo.padInfo.top).toBe(1);
-    expect(convInfo.padInfo.bottom).toBe(1);
-  });
-
-  it('2x1x1 conv over 3x3x3 array with same pad with dilations 2', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 1, 1, 1, 1], stride, dilations, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    // pad top and bottom
-    expect(convInfo.padInfo.front).toBe(1);
-    expect(convInfo.padInfo.back).toBe(1);
-    expect(convInfo.padInfo.left).toBe(0);
-    expect(convInfo.padInfo.right).toBe(0);
-    expect(convInfo.padInfo.top).toBe(0);
-    expect(convInfo.padInfo.bottom).toBe(0);
-  });
-
-  it('3x4x4 conv over 8x8 array with same pad with dilations d=4 h=3 w=3',
-     () => {
-       const inShape: [number, number, number, number, number] =
-           [1, 8, 8, 8, 1];
-       const stride = 1;
-       const dilations: [number, number, number] = [4, 3, 3];
-       const convInfo = conv_util.computeConv3DInfo(
-           inShape, [3, 4, 4, 1, 1], stride, dilations, 'same');
-       expect(convInfo.batchSize).toEqual(1);
-       expect(convInfo.outDepth).toEqual(8);
-       expect(convInfo.outHeight).toEqual(8);
-       expect(convInfo.outWidth).toEqual(8);
-       expect(convInfo.outChannels).toEqual(1);
-
-       expect(convInfo.padInfo.front).toBe(4);
-       expect(convInfo.padInfo.back).toBe(4);
-       expect(convInfo.padInfo.left).toBe(4);
-       expect(convInfo.padInfo.right).toBe(5);
-       expect(convInfo.padInfo.top).toBe(4);
-       expect(convInfo.padInfo.bottom).toBe(5);
-     });
-
-  it('2x1x1 conv over 3x3x3 array with valid pad with dilations 2', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 1, 1, 1, 1], stride, dilations, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(1);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-
-  it('2x2x2 conv over 3x3x3 array with valid pad with dilations 2', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 2, 2, 1, 1], stride, dilations, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-
-  it('2x2x2 conv over 4x4x4 array with valid pad with dilations 2', () => {
-    const inShape: [number, number, number, number, number] = [1, 4, 4, 4, 1];
-    const stride = 1;
-    const dilations = 2;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 2, 2, 1, 1], stride, dilations, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(2);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-});
-
-describe('conv_util computeConv2DInfo with depthwise=true', () => {
-  it('1x1 filter over 1x1 array with same pad', () => {
-    const inChannels = 1;
-    const inShape: [number, number, number, number] = [1, 1, 1, inChannels];
-    const fSize = 1;
-    const chMul = 1;
-    const stride = 1;
-    const dilation = 1;
-    const pad = 'same';
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad, null,
-        true);
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(1);
-    expect(convInfo.effectiveFilterHeight).toEqual(1);
-  });
-
-  it('2x2 filter over 3x3 array with same pad, chMul=3, depth=2', () => {
-    const inChannels = 2;
-    const batchSize = 1;
-    const inSize = 3;
-    const inShape: [number, number, number, number] =
-        [batchSize, inSize, inSize, inChannels];
-    const fSize = 2;
-    const chMul = 3;
-    const stride = 1;
-    const dilation = 1;
-    const pad = 'same';
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad, null,
-        true);
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(6);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-  });
-
-  it('2x2 filter over 3x3 array with valid pad, chMul=3, depth=2', () => {
-    const inChannels = 2;
-    const batchSize = 1;
-    const inSize = 3;
-    const inShape: [number, number, number, number] =
-        [batchSize, inSize, inSize, inChannels];
-    const fSize = 2;
-    const chMul = 3;
-    const stride = 1;
-    const dilation = 1;
-    const pad = 'valid';
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad, null,
-        true);
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(6);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-  });
-});
-
-describe('conv_util computeConv3DInfo with depthwise=true', () => {
-  it('1x1x1 filter over 1x1x1 array with same pad', () => {
-    const inChannels = 1;
-    const inShape: [number, number, number, number, number] =
-        [1, 1, 1, 1, inChannels];
-    const fSize = 1;
-    const chMul = 1;
-    const stride = 1;
-    const dilation = 1;
-    const pad = 'same';
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [fSize, fSize, fSize, inChannels, chMul], stride, dilation,
-        pad, true);
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-  });
-
-  it('2x2x2 filter over 3x3x3 array with same pad, chMul=3, depth=2', () => {
-    const inChannels = 2;
-    const batchSize = 1;
-    const inSize = 3;
-    const inShape: [number, number, number, number, number] =
-        [batchSize, inSize, inSize, inSize, inChannels];
-    const fSize = 2;
-    const chMul = 3;
-    const stride = 1;
-    const dilation = 1;
-    const pad = 'same';
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [fSize, fSize, fSize, inChannels, chMul], stride, dilation,
-        pad, true);
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(6);
-  });
-
-  it('2x2x2 filter over 3x3x3 array with valid pad, chMul=3, depth=2', () => {
-    const inChannels = 2;
-    const batchSize = 1;
-    const inSize = 3;
-    const inShape: [number, number, number, number, number] =
-        [batchSize, inSize, inSize, inSize, inChannels];
-    const fSize = 2;
-    const chMul = 3;
-    const stride = 1;
-    const dilation = 1;
-    const pad = 'valid';
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [fSize, fSize, fSize, inChannels, chMul], stride, dilation,
-        pad, true);
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(2);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(6);
-  });
-});
-
-describe('conv_util computeConv2DInfo channelsFirst', () => {
-  it('2x2 conv over 3x3 array with same pad', () => {
-    const inDepth = 2;
-    const outDepth = 4;
-    const inShape: [number, number, number, number] = [1, inDepth, 3, 3];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 2, inDepth, outDepth], stride, dilation, 'same', null,
-        false, 'channelsFirst');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(4);
-    expect(convInfo.outShape).toEqual([1, 4, 3, 3]);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-    // Should produce non-even padding with extra pixel at the right/bottom.
-    expect(convInfo.padInfo.left).toBe(0);
-    expect(convInfo.padInfo.right).toBe(1);
-    expect(convInfo.padInfo.top).toBe(0);
-    expect(convInfo.padInfo.bottom).toBe(1);
-  });
-
-  it('2x2 conv over 3x3 array with valid pad', () => {
-    const inDepth = 6;
-    const outDepth = 16;
-    const inShape: [number, number, number, number] = [1, inDepth, 3, 3];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [2, 2, inDepth, outDepth], stride, dilation, 'valid', null,
-        false, 'channelsFirst');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(16);
-    expect(convInfo.outShape).toEqual([1, 16, 2, 2]);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-    // Should produce no padding.
-    expect(convInfo.padInfo.left).toBe(0);
-    expect(convInfo.padInfo.right).toBe(0);
-    expect(convInfo.padInfo.top).toBe(0);
-    expect(convInfo.padInfo.bottom).toBe(0);
-  });
-});
-
-describe('conv_util computeConv3DInfo channelsFirst', () => {
-  it('2x2x2 conv over 3x3x3 array with same pad', () => {
-    const inDepth = 2;
-    const outDepth = 4;
-    const inShape: [number, number, number, number, number] =
-        [1, inDepth, 3, 3, 3];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 2, 2, inDepth, outDepth], stride, dilation, 'same', false,
-        'channelsFirst');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(4);
-    expect(convInfo.outShape).toEqual([1, 4, 3, 3, 3]);
-    // Should produce non-even padding with extra pixel at the back/right/bottom
-    expect(convInfo.padInfo.front).toBe(0);
-    expect(convInfo.padInfo.back).toBe(1);
-    expect(convInfo.padInfo.left).toBe(0);
-    expect(convInfo.padInfo.right).toBe(1);
-    expect(convInfo.padInfo.top).toBe(0);
-    expect(convInfo.padInfo.bottom).toBe(1);
-  });
-
-  it('2x2x2 conv over 3x3x3 array with valid pad', () => {
-    const inDepth = 6;
-    const outDepth = 16;
-    const inShape: [number, number, number, number, number] =
-        [1, inDepth, 3, 3, 3];
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computeConv3DInfo(
-        inShape, [2, 2, 2, inDepth, outDepth], stride, dilation, 'valid', false,
-        'channelsFirst');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(2);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(16);
-    expect(convInfo.outShape).toEqual([1, 16, 2, 2, 2]);
-    // Should produce no padding.
-    expect(convInfo.padInfo.front).toBe(0);
-    expect(convInfo.padInfo.back).toBe(0);
-    expect(convInfo.padInfo.left).toBe(0);
-    expect(convInfo.padInfo.right).toBe(0);
-    expect(convInfo.padInfo.top).toBe(0);
-    expect(convInfo.padInfo.bottom).toBe(0);
-  });
-});
-
-describe('conv_util computeConv2DInfo roundingMode', () => {
-  const inChannels = 6;
-  const batchSize = 1;
-  const inSize = 5;
-  const inShape: [number, number, number, number] =
-      [batchSize, inSize, inSize, inChannels];
-  const fSize = 2;
-  const chMul = 12;
-  const stride = 2;
-  const dilation = 1;
-  const pad = 1;
-
-  it('Default truncate the output dimension of Conv Layer', () => {
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad);
-
-    expect(convInfo.outShape).toEqual([batchSize, 3, 3, chMul]);
-  });
-
-  it('Floor the output dimension of Conv Layer', () => {
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad,
-        'floor');
-
-    expect(convInfo.outShape).toEqual([batchSize, 3, 3, chMul]);
-  });
-
-  it('Round the output dimension of Conv Layer', () => {
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad,
-        'round');
-
-    expect(convInfo.outShape).toEqual([batchSize, 4, 4, chMul]);
-  });
-
-  it('Ceil the output dimension of Conv Layer', () => {
-    const convInfo = conv_util.computeConv2DInfo(
-        inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad,
-        'ceil');
-
-    expect(convInfo.outShape).toEqual([batchSize, 4, 4, chMul]);
-  });
-});
-
-describe('conv_util computePoolInfo roundingMode', () => {
-  const inChannels = 6;
-  const batchSize = 1;
-  const inSize = 5;
-  const inShape: [number, number, number, number] =
-      [batchSize, inSize, inSize, inChannels];
-  const fSize = 2;
-  const stride = 2;
-  const dilation = 1;
-  const pad = 1;
-
-  it('Default truncate the output dimension of Pool Layer', () => {
-    const poolInfo = conv_util.computePool2DInfo(
-        inShape, [fSize, fSize], stride, pad, dilation, 'floor');
-
-    expect(poolInfo.outShape).toEqual([batchSize, 3, 3, inChannels]);
-  });
-
-  it('Floor the output dimension of Pool Layer', () => {
-    const poolInfo = conv_util.computePool2DInfo(
-        inShape, [fSize, fSize], stride, pad, dilation, 'floor');
-
-    expect(poolInfo.outShape).toEqual([batchSize, 3, 3, inChannels]);
-  });
-
-  it('Round the output dimension of Pool Layer', () => {
-    const poolInfo = conv_util.computePool2DInfo(
-        inShape, [fSize, fSize], stride, pad, dilation, 'round');
-
-    expect(poolInfo.outShape).toEqual([batchSize, 4, 4, inChannels]);
-  });
-
-  it('Ceil the output dimension of Pool Layer', () => {
-    const poolInfo = conv_util.computePool2DInfo(
-        inShape, [fSize, fSize], stride, pad, dilation, 'ceil');
-
-    expect(poolInfo.outShape).toEqual([batchSize, 4, 4, inChannels]);
-  });
-});
-
-describe('conv_util computePool3dInfo', () => {
-  it('1x1x1 pool over 1x1x1 array with valid pad', () => {
-    const inShape: [number, number, number, number, number] = [1, 1, 1, 1, 1];
-    const filterSize = 1;
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computePool3DInfo(
-        inShape, filterSize, stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterDepth).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(1);
-    expect(convInfo.effectiveFilterHeight).toEqual(1);
-  });
-
-  it('1x1x1 pool over 3x3x3 array with valid pad', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const filterSize = 1;
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computePool3DInfo(
-        inShape, filterSize, stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterDepth).toEqual(1);
-    expect(convInfo.effectiveFilterWidth).toEqual(1);
-    expect(convInfo.effectiveFilterHeight).toEqual(1);
-  });
-
-  it('2x2x2 pool over 3x3x3 array with same pad', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const filterSize = 2;
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computePool3DInfo(
-        inShape, filterSize, stride, dilation, 'same');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(3);
-    expect(convInfo.outHeight).toEqual(3);
-    expect(convInfo.outWidth).toEqual(3);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterDepth).toEqual(2);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-    expect(convInfo.padInfo.top).toEqual(0);
-    expect(convInfo.padInfo.bottom).toEqual(1);
-    expect(convInfo.padInfo.left).toEqual(0);
-    expect(convInfo.padInfo.right).toEqual(1);
-    expect(convInfo.padInfo.front).toEqual(0);
-    expect(convInfo.padInfo.back).toEqual(1);
-    expect(convInfo.padInfo.type).toEqual('SAME');
-  });
-
-  it('2x2x2 pool over 3x3x3 array with valid pad', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const filterSize = 2;
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computePool3DInfo(
-        inShape, filterSize, stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(2);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterDepth).toEqual(2);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-  });
-
-  it('2x2x2 pool over 4x4x4 array with valid pad, stride 2', () => {
-    const inShape: [number, number, number, number, number] = [1, 4, 4, 4, 1];
-    const filterSize = 2;
-    const stride = 2;
-    const dilation = 1;
-    const convInfo = conv_util.computePool3DInfo(
-        inShape, filterSize, stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(2);
-    expect(convInfo.outHeight).toEqual(2);
-    expect(convInfo.outWidth).toEqual(2);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterDepth).toEqual(2);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-  });
-
-  it('2x2x2 pool over 3x3x3 array with valid pad, dilation 2', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const filterSize = 2;
-    const stride = 1;
-    const dilation = 2;
-    const convInfo = conv_util.computePool3DInfo(
-        inShape, filterSize, stride, dilation, 'valid');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(1);
-    expect(convInfo.outHeight).toEqual(1);
-    expect(convInfo.outWidth).toEqual(1);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterDepth).toEqual(3);
-    expect(convInfo.effectiveFilterWidth).toEqual(3);
-    expect(convInfo.effectiveFilterHeight).toEqual(3);
-  });
-
-  it('2x2x2 pool over 3x3x3 array with pad 1, roundingMode floor', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const filterSize = 2;
-    const stride = 1;
-    const dilation = 1;
-    const convInfo = conv_util.computePool3DInfo(
-        inShape, filterSize, stride, dilation, 1, 'floor');
-    expect(convInfo.batchSize).toEqual(1);
-    expect(convInfo.outDepth).toEqual(4);
-    expect(convInfo.outHeight).toEqual(4);
-    expect(convInfo.outWidth).toEqual(4);
-    expect(convInfo.outChannels).toEqual(1);
-    expect(convInfo.effectiveFilterDepth).toEqual(2);
-    expect(convInfo.effectiveFilterWidth).toEqual(2);
-    expect(convInfo.effectiveFilterHeight).toEqual(2);
-    expect(convInfo.padInfo.top).toEqual(1);
-    expect(convInfo.padInfo.bottom).toEqual(1);
-    expect(convInfo.padInfo.left).toEqual(1);
-    expect(convInfo.padInfo.right).toEqual(1);
-    expect(convInfo.padInfo.front).toEqual(1);
-    expect(convInfo.padInfo.back).toEqual(1);
-    expect(convInfo.padInfo.type).toEqual('NUMBER');
-  });
-
-  it('throws unknown dataFormat', () => {
-    const inShape: [number, number, number, number, number] = [1, 3, 3, 3, 1];
-    const filterSize = 2;
-    const stride = 1;
-    const dilation = 1;
-    const fakeDataFormat = 'fakeFormat' as 'NDHWC' | 'NCDHW';
-    expect(
-        () => conv_util.computePool3DInfo(
-            inShape, filterSize, stride, dilation, 1, 'floor', fakeDataFormat))
-        .toThrowError();
-  });
-});
-
-describe('conv_util convertConv2DDataFormat', () => {
-  it('convert NHWC to channelsLast', () => {
-    const dataFormat: 'NHWC'|'NCHW' = 'NHWC';
-    const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);
-    expect($dataFormat).toEqual('channelsLast');
-  });
-
-  it('convert NCHW to channelsFirst', () => {
-    const dataFormat: 'NHWC'|'NCHW' = 'NCHW';
-    const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);
-    expect($dataFormat).toEqual('channelsFirst');
-  });
-
-  it('throws unknown dataFormat', () => {
-    const dataFormat = 'FakeFormat';
-    expect(
-        () => conv_util.convertConv2DDataFormat(dataFormat as 'NHWC' | 'NCHW'))
-        .toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/cos.ts b/tfjs-master/tfjs-core/src/ops/cos.ts
deleted file mode 100644
index 6f3b266d4..000000000
--- a/tfjs-master/tfjs-core/src/ops/cos.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Cos, CosInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
- *
- * x.cos().print();  // or tf.cos(x)
- * ```
- * @param x The input tensor. Must be float32 type.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function cos_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'cos', 'float32');
-
-  const inputs: CosInputs = {x: $x};
-
-  return ENGINE.runKernel(Cos, inputs as unknown as NamedTensorMap);
-}
-export const cos = /* @__PURE__ */ op({cos_});
diff --git a/tfjs-master/tfjs-core/src/ops/cos_test.ts b/tfjs-master/tfjs-core/src/ops/cos_test.ts
deleted file mode 100644
index 557aeb173..000000000
--- a/tfjs-master/tfjs-core/src/ops/cos_test.ts
+++ /dev/null
@@ -1,123 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('cos', ALL_ENVS, () => {
-  it('basic', async () => {
-    // Covers every 1/4pi range from -4pi to 4pi.
-    const values = [1, 3, 4, 6, 7, 9, 10, 12, -1, -3, -4, -6, -7, -9, -10, -12];
-    const a = tf.tensor1d(values);
-    const result = tf.cos(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.cos(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.cos(a);
-    expectArraysClose(await res.data(), [Math.cos(4), NaN, Math.cos(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.cos(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * Math.sin(5) * -1]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.cos(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * Math.sin(5) * -1]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1, 2, 3, -5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.cos(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [
-          1 * Math.sin(-1) * -1, 2 * Math.sin(2) * -1, 3 * Math.sin(3) * -1,
-          4 * Math.sin(-5) * -1
-        ],
-        1e-1);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.cos(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [
-          1 * Math.sin(-3) * -1, 2 * Math.sin(1) * -1, 3 * Math.sin(2) * -1,
-          4 * Math.sin(3) * -1
-        ],
-        1e-1);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.cos({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'cos' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const result = tf.cos(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.cos(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.cos('q'))
-        .toThrowError(/Argument 'x' passed to 'cos' must be float32/);
-  });
-
-  it('throws for int32 tensor', async () => {
-    expect(() => tf.cos(tf.tensor([10], [1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'cos' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/cosh.ts b/tfjs-master/tfjs-core/src/ops/cosh.ts
deleted file mode 100644
index 1bfb9f3e2..000000000
--- a/tfjs-master/tfjs-core/src/ops/cosh.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Cosh, CoshInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, 1, -1, .7]);
- *
- * x.cosh().print();  // or tf.cosh(x)
- * ```
- * @param x The input tensor. Must be float32 type.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function cosh_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'cosh', 'float32');
-  const inputs: CoshInputs = {x: $x};
-
-  return ENGINE.runKernel(Cosh, inputs as unknown as NamedTensorMap);
-}
-export const cosh = /* @__PURE__ */ op({cosh_});
diff --git a/tfjs-master/tfjs-core/src/ops/cosh_test.ts b/tfjs-master/tfjs-core/src/ops/cosh_test.ts
deleted file mode 100644
index ad1da9600..000000000
--- a/tfjs-master/tfjs-core/src/ops/cosh_test.ts
+++ /dev/null
@@ -1,126 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('cosh', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [1, -3, 2, -1, -4];
-    const a = tf.tensor1d(values);
-    const result = tf.cosh(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.cosh(values[i]);
-    }
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.cosh(a);
-    expectArraysClose(await res.data(), [Math.cosh(4), NaN, Math.cosh(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.cosh(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * Math.sinh(0.5)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.cosh(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * Math.sinh(0.5)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-1, 2, 3, -5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.cosh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] * Math.sinh(aValues[i]);
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-3, 1, 2, 3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.cosh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] * Math.sinh(aValues[i]);
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.cosh({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'cosh' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [1, -3, 2, -1, -4];
-    const result = tf.cosh(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.cosh(values[i]);
-    }
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.cosh('q'))
-        .toThrowError(/Argument 'x' passed to 'cosh' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.cosh(tf.tensor([10], [1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'cosh' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/cumprod.ts b/tfjs-master/tfjs-core/src/ops/cumprod.ts
deleted file mode 100644
index e88cd9f5b..000000000
--- a/tfjs-master/tfjs-core/src/ops/cumprod.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { ENGINE } from '../engine';
-import { Cumprod, CumprodAttrs, CumprodInputs } from '../kernel_names';
-import { NamedAttrMap } from '../kernel_registry';
-import { Tensor } from '../tensor';
-import { NamedTensorMap } from '../tensor_types';
-import { convertToTensor } from '../tensor_util_env';
-import { TensorLike } from '../types';
-
-import { op } from './operation';
-
-/**
- * Computes the cumulative product of a `tf.Tensor` along `axis`.
- *
- * ```js
- * const x = tf.tensor([1, 2, 3, 4]);
- * x.cumprod().print();
- * ```
- * ```js
- * const x = tf.tensor([[1, 2], [3, 4]]);
- * x.cumprod().print();
- * ```
- *
- * @param x The input tensor to cumulatively multiply.
- * @param axis The axis along which to multiply. Optional. Defaults to 0.
- * @param exclusive Whether to perform exclusive cumulative product. Optional.
- *     Defaults to false. If set to true then the product of each tensor entry
- *     does not include its own value, but only the values previous to it
- *     along the specified axis.
- * @param reverse Whether to multiply in the opposite direction. Optional.
- *     Defaults to false.
- *
- * @doc {heading: 'Operations', subheading: 'Scan'}
- */
-function cumprod_<T extends Tensor>(
-  x: Tensor | TensorLike,
-  axis = 0,
-  exclusive = false,
-  reverse = false
-): T {
-  const $x = convertToTensor(x, 'x', 'cumprod');
-
-  const inputs: CumprodInputs = { x: $x };
-  const attrs: CumprodAttrs = { axis, exclusive, reverse };
-
-  return ENGINE.runKernel(
-    Cumprod,
-    inputs as unknown as NamedTensorMap,
-    attrs as unknown as NamedAttrMap
-  );
-}
-
-export const cumprod = /* @__PURE__ */ op({ cumprod_ });
diff --git a/tfjs-master/tfjs-core/src/ops/cumprod_test.ts b/tfjs-master/tfjs-core/src/ops/cumprod_test.ts
deleted file mode 100644
index 4e9d2a54a..000000000
--- a/tfjs-master/tfjs-core/src/ops/cumprod_test.ts
+++ /dev/null
@@ -1,167 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
-import { expectArraysClose } from '../test_util';
-
-describeWithFlags('cumprod', ALL_ENVS, () => {
-  it('1D standard', async () => {
-    const res = tf.tensor1d([1, 2, 3, 4]).cumprod();
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [1, 2, 6, 24]);
-  });
-
-  it('1D reverse', async () => {
-    const reverse = true;
-    const exclusive = false;
-    const res = tf.tensor1d([1, 2, 3, 4]).cumprod(0, exclusive, reverse);
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [24, 24, 12, 4]);
-  });
-
-  it('1D exclusive', async () => {
-    const exclusive = true;
-    const res = tf.tensor1d([1, 2, 3, 4]).cumprod(0, exclusive);
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [1, 1, 2, 6]);
-  });
-
-  it('1D exclusive reverse', async () => {
-    const reverse = true;
-    const exclusive = true;
-    const res = tf.tensor1d([1, 2, 3, 4]).cumprod(0, exclusive, reverse);
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [24, 12, 4, 1]);
-  });
-
-  // TODO: once gradients are implemented, create tests something like this.
-  // it('gradient: 1D', async () => {
-  //   const a = tf.tensor1d([1, 2, 3]);
-  //   const dy = tf.tensor1d([4, 5, 6]);
-  //   const da = tf.grad((x) => tf.cumprod(x))(a, dy);
-
-  //   expect(da.shape).toEqual([3]);
-  //   expectArraysClose(await da.data(), [15, 11, 6]);
-  // });
-
-  // it('gradient with clones', async () => {
-  //   const a = tf.tensor1d([1, 2, 3]);
-  //   const dy = tf.tensor1d([4, 5, 6]);
-  //   const da = tf.grad((x) => tf.cumprod(x.clone()).clone())(a, dy);
-
-  //   expect(da.shape).toEqual([3]);
-  //   expectArraysClose(await da.data(), [15, 11, 6]);
-  // });
-
-  it('2D standard', async () => {
-    const res = tf
-      .tensor2d([
-        [1, 2],
-        [3, 4],
-      ])
-      .cumprod(1);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.array(), [[1, 2], [3, 12]]);
-  });
-
-  it('2D reverse exclusive', async () => {
-    const reverse = true;
-    const exclusive = true;
-    const res = tf
-      .tensor2d([
-        [1, 2],
-        [3, 4],
-      ])
-      .cumprod(1, exclusive, reverse);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.array(), [[2, 1], [4, 1]]);
-  });
-
-  it('2D axis=0', async () => {
-    const res = tf
-      .tensor2d([
-        [1, 2],
-        [3, 4],
-      ])
-      .cumprod();
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.array(), [[1, 2], [3, 8]]);
-  });
-
-  it('3D standard', async () => {
-    const res = tf
-      .tensor3d([
-        [
-          [0, 1],
-          [2, 3],
-        ],
-        [
-          [4, 5],
-          [6, 7],
-        ],
-      ])
-      .cumprod(2);
-    expect(res.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await res.array(), [
-      [
-        [0, 0 * 1],
-        [2, 2 * 3]
-      ],
-      [
-        [4, 4 * 5],
-        [6, 6 * 7]
-      ]
-    ]);
-  });
-
-  it('4d axis=2', async () => {
-    const input = tf.add(tf.ones([1, 32, 8, 4]), tf.ones([1, 32, 8, 4]));
-    const res = tf.cumprod(input, 2, false, false);
-
-    expect(res.shape).toEqual([1, 32, 8, 4]);
-
-    const earlySlice = tf.slice(res, [0, 0, 0, 0], [1, 1, 8, 1]);
-    const lateSlice = tf.slice(res, [0, 31, 0, 0], [1, 1, 8, 1]);
-    const expectedDataInEachSlice = [2, 4, 8, 16, 32, 64, 128, 256];
-    expectArraysClose(await earlySlice.data(), expectedDataInEachSlice);
-    expectArraysClose(await lateSlice.data(), expectedDataInEachSlice);
-  });
-
-  it('handle permutation properly', async () => {
-    const res = tf.ones([1, 240, 1, 10]).cumprod(1);
-    expect(res.shape).toEqual([1, 240, 1, 10]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.cumprod({} as tf.Tensor)).toThrowError(
-      /Argument 'x' passed to 'cumprod' must be a Tensor/
-    );
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const res = tf.cumprod([1, 2, 3, 4]);
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [1, 2, 6, 24]);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.cumprod(['a', 'b', 'c'])).toThrowError(
-      /Argument 'x' passed to 'cumprod' must be numeric tensor/
-    );
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/cumsum.ts b/tfjs-master/tfjs-core/src/ops/cumsum.ts
deleted file mode 100644
index f6c781efa..000000000
--- a/tfjs-master/tfjs-core/src/ops/cumsum.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Cumsum, CumsumAttrs, CumsumInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the cumulative sum of a `tf.Tensor` along `axis`.
- *
- * ```js
- * const x = tf.tensor([1, 2, 3, 4]);
- * x.cumsum().print();
- * ```
- * ```js
- * const x = tf.tensor([[1, 2], [3, 4]]);
- * x.cumsum().print();
- * ```
- *
- * @param x The input tensor to be summed.
- * @param axis The axis along which to sum. Optional. Defaults to 0.
- * @param exclusive Whether to perform exclusive cumulative sum. Optional.
- *     Defaults to false. If set to true then the sum of each tensor entry
- *     does not include its own value, but only the values previous to it
- *     along the specified axis.
- * @param reverse Whether to sum in the opposite direction. Optional.
- *     Defaults to false.
- *
- * @doc {heading: 'Operations', subheading: 'Scan'}
- */
-function cumsum_<T extends Tensor>(
-    x: Tensor|TensorLike, axis = 0, exclusive = false, reverse = false): T {
-  const $x = convertToTensor(x, 'x', 'cumsum');
-
-  const inputs: CumsumInputs = {x: $x};
-  const attrs: CumsumAttrs = {axis, exclusive, reverse};
-
-  return ENGINE.runKernel(
-      Cumsum, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const cumsum = /* @__PURE__ */ op({cumsum_});
diff --git a/tfjs-master/tfjs-core/src/ops/cumsum_test.ts b/tfjs-master/tfjs-core/src/ops/cumsum_test.ts
deleted file mode 100644
index e37361cf5..000000000
--- a/tfjs-master/tfjs-core/src/ops/cumsum_test.ts
+++ /dev/null
@@ -1,134 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('cumsum', ALL_ENVS, () => {
-  it('1D standard', async () => {
-    const res = tf.tensor1d([1, 2, 3, 4]).cumsum();
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [1, 3, 6, 10]);
-  });
-
-  it('1D reverse', async () => {
-    const reverse = true;
-    const exclusive = false;
-    const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive, reverse);
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [10, 9, 7, 4]);
-  });
-
-  it('1D exclusive', async () => {
-    const exclusive = true;
-    const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive);
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [0, 1, 3, 6]);
-  });
-
-  it('1D exclusive reverse', async () => {
-    const reverse = true;
-    const exclusive = true;
-    const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive, reverse);
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [9, 7, 4, 0]);
-  });
-
-  it('gradient: 1D', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([4, 5, 6]);
-    const da = tf.grad(x => tf.cumsum(x))(a, dy);
-
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [15, 11, 6]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([4, 5, 6]);
-    const da = tf.grad(x => tf.cumsum(x.clone()).clone())(a, dy);
-
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [15, 11, 6]);
-  });
-
-  it('2D standard', async () => {
-    const res = tf.tensor2d([[1, 2], [3, 4]]).cumsum(1);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [1, 3, 3, 7]);
-  });
-
-  it('2D reverse exclusive', async () => {
-    const reverse = true;
-    const exclusive = true;
-    const res = tf.tensor2d([[1, 2], [3, 4]]).cumsum(1, exclusive, reverse);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [2, 0, 4, 0]);
-  });
-
-  it('2D axis=0', async () => {
-    const res = tf.tensor2d([[1, 2], [3, 4]]).cumsum();
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [1, 2, 4, 6]);
-  });
-
-  it('3D standard', async () => {
-    const res = tf.tensor3d([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]).cumsum(2);
-    expect(res.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await res.data(), [0, 1, 2, 5, 4, 9, 6, 13]);
-  });
-
-  it('4d axis=2', async () => {
-    const input = tf.ones([1, 32, 46, 4]);
-    const res = tf.cumsum(input, 2, false, false);
-
-    expect(res.shape).toEqual([1, 32, 46, 4]);
-
-    const earlySlice = tf.slice(res, [0, 0, 0, 0], [1, 1, 46, 1]);
-    const lateSlice = tf.slice(res, [0, 31, 0, 0], [1, 1, 46, 1]);
-    const expectedDataInEachSlice = [
-      1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
-      17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
-      33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46
-    ];
-    expectArraysClose(await earlySlice.data(), expectedDataInEachSlice);
-    expectArraysClose(await lateSlice.data(), expectedDataInEachSlice);
-  });
-
-  it('handle permutation properly', async () => {
-    const res = tf.ones([1, 240, 1, 10]).cumsum(1);
-    expect(res.shape).toEqual([1, 240, 1, 10]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.cumsum({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'cumsum' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const res = tf.cumsum([1, 2, 3, 4]);
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [1, 3, 6, 10]);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.cumsum([
-      'a', 'b', 'c'
-    ])).toThrowError(/Argument 'x' passed to 'cumsum' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/dense_bincount.ts b/tfjs-master/tfjs-core/src/ops/dense_bincount.ts
deleted file mode 100644
index cab19e466..000000000
--- a/tfjs-master/tfjs-core/src/ops/dense_bincount.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {DenseBincount, DenseBincountAttrs, DenseBincountInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor1D, Tensor2D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Outputs a vector with length `size` and the same dtype as `weights`.
- *
- * If `weights` are empty, then index `i` stores the number of times the value
- * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the
- * sum of the value in `weights` at each index where the corresponding value in
- * `x` is `i`.
- *
- * Values in `x` outside of the range [0, size) are ignored.
- *
- * @param x The input int tensor, rank 1 or rank 2.
- * @param weights The weights tensor, must have the same shape as x, or a
- *     length-0 Tensor, in which case it acts as all weights equal to 1.
- * @param size Non-negative integer.
- * @param binaryOutput Optional. Whether the kernel should count the appearance
- *     or number of occurrences. Defaults to False.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function denseBincount_<T extends Tensor1D|Tensor2D>(
-    x: T|TensorLike, weights: T|TensorLike, size: number,
-    binaryOutput = false): T {
-  const $x = convertToTensor(x, 'x', 'denseBincount');
-  const $weights = convertToTensor(weights, 'weights', 'denseBincount');
-
-  util.assert(
-      $x.dtype === 'int32',
-      () => `Error in denseBincount: input ` +
-          `dtype must be int32, but got ${$x.dtype}`);
-  util.assert(
-      $x.rank <= 2,
-      () => `Error in denseBincount: input must be at most rank 2, but got ` +
-          `rank ${$x.rank}.`);
-  util.assert(size >= 0, () => `size must be non-negative, but got ${size}.`);
-  util.assert(
-      $weights.size === $x.size || $weights.size === 0,
-      () =>
-          `Error in denseBincount: weights must have the same shape as x or ` +
-          `0-length, but got x shape: ${$x.shape}, weights shape: ` +
-          `${$weights.shape}.`);
-
-  const inputs: DenseBincountInputs = {x: $x, weights: $weights};
-  const attrs: DenseBincountAttrs = {size, binaryOutput};
-
-  return ENGINE.runKernel(
-      DenseBincount, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const denseBincount = /* @__PURE__ */ op({denseBincount_});
diff --git a/tfjs-master/tfjs-core/src/ops/dense_bincount_test.ts b/tfjs-master/tfjs-core/src/ops/dense_bincount_test.ts
deleted file mode 100644
index c7a623b04..000000000
--- a/tfjs-master/tfjs-core/src/ops/dense_bincount_test.ts
+++ /dev/null
@@ -1,144 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('denseBincount', ALL_ENVS, () => {
-  it('with 0-length weights.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([]);
-    const size = 3;
-
-    const result = tf.denseBincount(x, weights, size);
-
-    expect(result.shape).toEqual([3]);
-    expectArraysClose(await result.data(), [0, 3, 1]);
-  });
-
-  it('with number out of range.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([]);
-    const size = 2;
-
-    const result = tf.denseBincount(x, weights, size);
-
-    expect(result.shape).toEqual([2]);
-    expectArraysClose(await result.data(), [0, 3]);
-  });
-
-  it('with 1d float weights.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([0.5, 0.3, 0.3, 0.1]);
-    const size = 3;
-
-    const result = tf.denseBincount(x, weights, size);
-
-    expect(result.shape).toEqual([3]);
-    expectArraysClose(await result.data(), [0, 1.1, 0.1]);
-  });
-
-  it('with 1d float weights and number out of range.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([0.5, 0.3, 0.3, 0.1]);
-    const size = 2;
-
-    const result = tf.denseBincount(x, weights, size);
-
-    expect(result.shape).toEqual([2]);
-    expectArraysClose(await result.data(), [0, 1.1]);
-  });
-
-  it('with 2d inputs and 0-length weights.', async () => {
-    const x = tf.tensor2d([[1, 1], [1, 2]], [2, 2], 'int32');
-    const weights = tf.tensor2d([], [0, 0]);
-    const size = 3;
-
-    const result = tf.denseBincount(x, weights, size);
-
-    expect(result.shape).toEqual([2, 3]);
-    expectArraysClose(await result.data(), [0, 2, 0, 0, 1, 1]);
-  });
-
-  it('with 2d inputs and 0-length weights and number out of range.',
-     async () => {
-       const x = tf.tensor2d([[1, 1], [1, 2]], [2, 2], 'int32');
-       const weights = tf.tensor2d([], [0, 0]);
-       const size = 2;
-
-       const result = tf.denseBincount(x, weights, size);
-
-       expect(result.shape).toEqual([2, 2]);
-       expectArraysClose(await result.data(), [0, 2, 0, 1]);
-     });
-
-  it('with 2d inputs and 2d weights.', async () => {
-    const x = tf.tensor2d([[1, 1], [1, 2]], [2, 2], 'int32');
-    const weights = tf.tensor2d([[0.5, 0.3], [0.3, 0.1]]);
-    const size = 3;
-
-    const result = tf.denseBincount(x, weights, size);
-
-    expect(result.shape).toEqual([2, 3]);
-    expectArraysClose(await result.data(), [0, 0.8, 0, 0, 0.3, 0.1]);
-  });
-
-  it('throws error for non int x tensor.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'float32');
-    const weights = tf.tensor1d([]);
-    const size = 3;
-
-    expect(() => tf.denseBincount(x, weights, size)).toThrowError();
-  });
-
-  it('throws error if size is negative.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([]);
-    const size = -1;
-
-    expect(() => tf.denseBincount(x, weights, size)).toThrowError();
-  });
-
-  it('throws error when shape is different for 1d.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([0.5, 0.3]);
-    const size = 2;
-
-    expect(() => tf.denseBincount(x, weights, size)).toThrowError();
-  });
-
-  it('throws error when shape is different for 2d.', async () => {
-    const x = tf.tensor2d([[1, 1], [1, 2]], [2, 2], 'int32');
-    const weights = tf.tensor2d([[0.5], [0.3]]);
-    const size = 3;
-
-    expect(() => tf.denseBincount(x, weights, size)).toThrowError();
-  });
-
-  it('handle output from other ops.', async () => {
-    const x = tf.tensor1d([1, 1, 1, 2], 'int32');
-    const weights = tf.tensor1d([]);
-    const size = 4;
-
-    const result = tf.denseBincount(
-        tf.add<tf.Tensor1D>(x, tf.scalar(1, 'int32')), weights, size);
-
-    expect(result.shape).toEqual([4]);
-    expectArraysClose(await result.data(), [0, 0, 3, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/depth_to_space.ts b/tfjs-master/tfjs-core/src/ops/depth_to_space.ts
deleted file mode 100644
index b48d5eb5a..000000000
--- a/tfjs-master/tfjs-core/src/ops/depth_to_space.ts
+++ /dev/null
@@ -1,105 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike4D} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Rearranges data from depth into blocks of spatial data. More specifically,
- * this op outputs a copy of the input tensor where values from the `depth`
- * dimension are moved in spatial blocks to the `height` and `width` dimensions.
- * The attr `blockSize` indicates the input block size and how the data is
- * moved.
- *
- *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged
- * into non-overlapping blocks of size `blockSize x blockSize`
- *
- *  - The width the output tensor is `inputWidth * blockSize`, whereas the
- * height is `inputHeight * blockSize`
- *
- *  - The Y, X coordinates within each block of the output image are determined
- * by the high order component of the input channel index
- *
- *  - The depth of the input tensor must be divisible by `blockSize *
- * blockSize`
- *
- * The `dataFormat` attr specifies the layout of the input and output tensors
- * with the following options: "NHWC": [ `batch, height, width, channels` ]
- * "NCHW": [ `batch, channels, height, width` ]
- *
- * ```js
- * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
- * const blockSize = 2;
- * const dataFormat = "NHWC";
- *
- * tf.depthToSpace(x, blockSize, dataFormat).print();
- * ```
- *
- * @param x The input tensor of rank 4
- * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block
- * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to "NHWC"
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function depthToSpace_(
-    x: Tensor4D|TensorLike4D, blockSize: number,
-    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): Tensor4D {
-  const $x = convertToTensor(x, 'x', 'depthToSpace', 'float32') as Tensor4D;
-
-  const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];
-  const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];
-  const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];
-
-  util.assert(
-      blockSize > 1,
-      () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
-
-  util.assert(
-      inputHeight * blockSize >= 0,
-      () => `Negative dimension size caused by overflow when multiplying
-    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
-    ${$x.shape}`);
-
-  util.assert(
-      inputWidth * blockSize >= 0,
-      () => `Negative dimension size caused by overflow when multiplying
-    ${inputWidth} and ${blockSize} for depthToSpace with input shape
-        ${$x.shape}`);
-
-  util.assert(
-      (inputDepth % (blockSize * blockSize) === 0),
-      () => `Dimension size must be evenly divisible by ${
-          blockSize * blockSize} but is ${
-          inputDepth} for depthToSpace with input shape ${$x.shape}`);
-
-  const inputs: DepthToSpaceInputs = {x: $x};
-  const attrs: DepthToSpaceAttrs = {blockSize, dataFormat};
-
-  return ENGINE.runKernel(
-      DepthToSpace, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const depthToSpace = /* @__PURE__ */ op({depthToSpace_});
diff --git a/tfjs-master/tfjs-core/src/ops/depth_to_space_test.ts b/tfjs-master/tfjs-core/src/ops/depth_to_space_test.ts
deleted file mode 100644
index 7811387f2..000000000
--- a/tfjs-master/tfjs-core/src/ops/depth_to_space_test.ts
+++ /dev/null
@@ -1,90 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, BROWSER_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('depthToSpace', ALL_ENVS, () => {
-  it('tensor4d, input shape=[1, 1, 1, 4], blockSize=2, format=NHWC',
-     async () => {
-       const t = tf.tensor4d([[[[1, 2, 3, 4]]]]);
-       const blockSize = 2;
-       const dataFormat = 'NHWC';
-
-       const res = tf.depthToSpace(t, blockSize, dataFormat);
-       expect(res.shape).toEqual([1, 2, 2, 1]);
-       expectArraysClose(await res.data(), [1, 2, 3, 4]);
-     });
-
-  it('tensor4d, input shape=[1, 1, 1, 12], blockSize=2, format=NHWC',
-     async () => {
-       const t = tf.tensor4d([[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]);
-       const blockSize = 2;
-       const dataFormat = 'NHWC';
-
-       const res = tf.depthToSpace(t, blockSize, dataFormat);
-       expect(res.shape).toEqual([1, 2, 2, 3]);
-       expectArraysClose(
-           await res.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
-     });
-
-  it('tensor4d, input shape=[1, 2, 2, 4], blockSize=2, format=NHWC',
-     async () => {
-       const t = tf.tensor4d([
-         [[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]]]
-       ]);
-       const blockSize = 2;
-       const dataFormat = 'NHWC';
-
-       const res = tf.depthToSpace(t, blockSize, dataFormat);
-       expect(res.shape).toEqual([1, 4, 4, 1]);
-       expectArraysClose(
-           await res.data(),
-           [1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 13, 14, 11, 12, 15, 16]);
-     });
-
-  it('throws when depth not divisible by blockSize * blockSize', () => {
-    const t = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
-    const blockSize = 3;
-
-    expect(() => tf.depthToSpace(t, blockSize))
-        .toThrowError(`Dimension size must be evenly divisible by ${
-            blockSize * blockSize} but is ${
-            t.shape[3]} for depthToSpace with input shape ${t.shape}`);
-  });
-
-  it('throws for int32 input', async () => {
-    const t = tf.tensor4d([[[[1, 2, 3, 4]]]], [1, 1, 1, 4], 'int32');
-    const blockSize = 2;
-    const dataFormat = 'NHWC';
-
-    expect(() => tf.depthToSpace(t, blockSize, dataFormat))
-        .toThrowError(/Argument 'x' passed to 'depthToSpace' must be float32/);
-  });
-});
-
-describeWithFlags('depthToSpace', BROWSER_ENVS, () => {
-  it('throws when blocksize < 2', () => {
-    const t = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
-    const blockSize = 1;
-
-    expect(() => tf.depthToSpace(t, blockSize))
-        .toThrowError(
-            `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/depthwise_conv2d.ts b/tfjs-master/tfjs-core/src/ops/depthwise_conv2d.ts
deleted file mode 100644
index 67d8c0eee..000000000
--- a/tfjs-master/tfjs-core/src/ops/depthwise_conv2d.ts
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import * as conv_util from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Depthwise 2D convolution.
- *
- * Given a 4D `input` array and a `filter` array of shape
- * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
- * `inChannels` convolutional filters of depth 1, this op applies a
- * different filter to each input channel (expanding from 1 channel to
- * `channelMultiplier` channels for each), then concatenates the results
- * together. The output has `inChannels * channelMultiplier` channels.
- *
- * See
- * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
- *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
- * for more details.
- *
- * @param x The input tensor, of rank 4 or rank 3, of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
- * assumed.
- * @param filter The filter tensor, rank 4, of shape
- *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
- * @param strides The strides of the convolution: `[strideHeight,
- * strideWidth]`. If strides is a single number, then `strideHeight ==
- * strideWidth`.
- * @param pad The type of padding algorithm.
- *   - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *   - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *   - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
- *     in which we sample input values across the height and width dimensions
- *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
- *     number, then `dilationHeight == dilationWidth`. If it is greater than
- *     1, then all values of `strides` must be 1.
- * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
- *     "NHWC". Specify the data format of the input and output data. With the
- *     default format "NHWC", the data is stored in the order of: [batch,
- *     height, width, channels]. Only "NHWC" is currently supported.
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function depthwiseConv2d_<T extends Tensor3D|Tensor4D>(
-    x: T|TensorLike, filter: Tensor4D|TensorLike,
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-    dataFormat: 'NHWC'|'NCHW' = 'NHWC',
-    dilations: [number, number]|number = [1, 1],
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');
-  const $filter =
-      convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');
-
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  util.assert(
-      x4D.rank === 4,
-      () => `Error in depthwiseConv2d: input must be rank 4, but got ` +
-          `rank ${x4D.rank}.`);
-  util.assert(
-      $filter.rank === 4,
-      () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +
-          `${$filter.rank}.`);
-  const inChannels = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
-  util.assert(
-      inChannels === $filter.shape[2],
-      () => `Error in depthwiseConv2d: number of input channels ` +
-          `(${inChannels}) must match the inChannels dimension in ` +
-          `filter ${$filter.shape[2]}.`);
-  conv_util.checkPadOnDimRoundingMode('depthwiseConv2d', pad, dimRoundingMode);
-  const inputs: DepthwiseConv2dNativeInputs = {x: x4D, filter: $filter};
-  const attrs: DepthwiseConv2dNativeAttrs =
-      {strides, pad, dataFormat, dilations, dimRoundingMode};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  DepthwiseConv2dNative, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-  return res;
-}
-
-export const depthwiseConv2d = /* @__PURE__ */ op({depthwiseConv2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/depthwise_conv2d_native_backprop_filter.ts b/tfjs-master/tfjs-core/src/ops/depthwise_conv2d_native_backprop_filter.ts
deleted file mode 100644
index 235dfbbba..000000000
--- a/tfjs-master/tfjs-core/src/ops/depthwise_conv2d_native_backprop_filter.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropFilterAttrs, DepthwiseConv2dNativeBackpropFilterInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-
-import {ExplicitPadding} from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-function depthwiseConv2dNativeBackpropFilter_<T extends Tensor3D|Tensor4D>(
-    x: T, dy: T, filterShape: [number, number, number, number],
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|ExplicitPadding,
-    dilations: [number, number]|number = [1, 1],
-    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {
-  let x4D = x as Tensor4D;
-  if (x.rank === 3) {
-    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
-  }
-  let dy4D = dy as Tensor4D;
-  if (dy4D.rank === 3) {
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-
-  const inputs: DepthwiseConv2dNativeBackpropFilterInputs = {x: x4D, dy: dy4D};
-  const attrs: DepthwiseConv2dNativeBackpropFilterAttrs =
-      {strides, pad, dimRoundingMode, dilations, filterShape};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  return ENGINE.runKernel(
-             DepthwiseConv2dNativeBackpropFilter,
-             inputs as unknown as NamedTensorMap,
-             attrs as unknown as NamedAttrMap) as Tensor4D;
-}
-
-export const depthwiseConv2dNativeBackpropFilter =
-    op({depthwiseConv2dNativeBackpropFilter_});
diff --git a/tfjs-master/tfjs-core/src/ops/depthwise_conv2d_native_backprop_input.ts b/tfjs-master/tfjs-core/src/ops/depthwise_conv2d_native_backprop_input.ts
deleted file mode 100644
index f90037968..000000000
--- a/tfjs-master/tfjs-core/src/ops/depthwise_conv2d_native_backprop_input.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {DepthwiseConv2dNativeBackpropInput, DepthwiseConv2dNativeBackpropInputAttrs, DepthwiseConv2dNativeBackpropInputInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-
-import {ExplicitPadding} from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-function depthwiseConv2dNativeBackpropInput_<T extends Tensor3D|Tensor4D>(
-    xShape: [number, number, number, number], dy: T, filter: Tensor4D,
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|ExplicitPadding,
-    dilations: [number, number]|number = [1, 1],
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  let dy4D = dy as Tensor4D;
-  let reshapedTo4D = false;
-  if (dy.rank === 3) {
-    reshapedTo4D = true;
-    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
-  }
-
-  const inputs: DepthwiseConv2dNativeBackpropInputInputs = {dy: dy4D, filter};
-  const attrs: DepthwiseConv2dNativeBackpropInputAttrs =
-      {strides, pad, dimRoundingMode, dilations, inputShape: xShape};
-
-  const res =
-      // tslint:disable-next-line: no-unnecessary-type-assertion
-      ENGINE.runKernel(
-          DepthwiseConv2dNativeBackpropInput,
-          inputs as unknown as NamedTensorMap,
-          attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-  return res;
-}
-
-export const depthwiseConv2dNativeBackpropInput =
-    op({depthwiseConv2dNativeBackpropInput_});
diff --git a/tfjs-master/tfjs-core/src/ops/depthwise_conv2d_test.ts b/tfjs-master/tfjs-core/src/ops/depthwise_conv2d_test.ts
deleted file mode 100644
index e6ccc7b0e..000000000
--- a/tfjs-master/tfjs-core/src/ops/depthwise_conv2d_test.ts
+++ /dev/null
@@ -1,1368 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {Rank} from '../types';
-
-describeWithFlags('depthwiseConv2D', ALL_ENVS, () => {
-  it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    const expected = [1.07022, 1.03167, 0.67041, 0.778863];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x3x3x1,f=2,s=1,d=1,p=explicit,chMul=1', async () => {
-    const fSize = 2;
-    const pad =
-        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 5, 3, 1]);
-    const expected = [
-      0.826533, 0.197560, 0.0098898, 1.070216, 1.031675, 0.126422, 0.6704096,
-      0.778863, 0.273041, 0.116357, 0.204908, 0.106774, 0, 0, 0
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x5x5x1,f=3,s=1,d=1,p=valid,chMul=1', async () => {
-    const fSize = 3;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
-          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,
-          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,
-          0.741935, 0.974474, 0.621102, 0.171411
-        ],
-        [1, 5, 5, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.125386, 0.975199, 0.640437, 0.281895, 0.990968, 0.347208, 0.889702,
-          0.180695, 0.691992
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    const expected = [
-      2.540022, 2.505885, 2.454062, 2.351701, 2.459601, 3.076421, 3.29848,
-      3.437421, 2.93419
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x3x3x1,f=2,s=1,d=2,p=valid,chMul=1', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const dilation = 2;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-    // adding a dilation rate is equivalent to using a filter
-    // with 0s for the dilation rate
-    const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
-    const wDilated = tf.tensor4d(
-        [0.303873, 0, 0.229223, 0, 0, 0, 0.144333, 0, 0.803373],
-        [fSizeDilated, fSizeDilated, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
-
-    const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
-
-    expect(result.shape).toEqual(expectedResult.shape);
-    expectArraysClose(await result.data(), await expectedResult.data());
-  });
-
-  it('input=1x5x5x1,f=3,s=1,d=2,p=valid,chMul=1', async () => {
-    const fSize = 3;
-    const pad = 'valid';
-    const stride = 1;
-    const dilation = 2;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
-          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,
-          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,
-          0.741935, 0.974474, 0.621102, 0.171411
-        ],
-        [1, 5, 5, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.125386, 0.975199, 0.640437, 0.281895, 0.990968, 0.347208, 0.889702,
-          0.180695, 0.691992
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-    // adding a dilation rate is equivalent to using a filter
-    // with 0s for the dilation rate
-    const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
-    const wDilated = tf.tensor4d(
-        [
-          0.125386, 0, 0.975199, 0, 0.640437, 0, 0, 0, 0, 0,
-          0.281895, 0, 0.990968, 0, 0.347208, 0, 0, 0, 0, 0,
-          0.889702, 0, 0.180695, 0, 0.691992
-        ],
-        [fSizeDilated, fSizeDilated, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
-
-    const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
-
-    expect(result.shape).toEqual(expectedResult.shape);
-    expectArraysClose(await result.data(), await expectedResult.data());
-  });
-
-  it('input=1x5x5x1,f=2,s=1,d=4,p=valid,chMul=1', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const dilation = 4;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
-          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,
-          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,
-          0.741935, 0.974474, 0.621102, 0.171411
-        ],
-        [1, 5, 5, inDepth]);
-    const w = tf.tensor4d(
-        [0.125386, 0.975199, 0.640437, 0.281895],
-        [fSize, fSize, inDepth, chMul],
-    );
-    // adding a dilation rate is equivalent to using a filter
-    // with 0s for the dilation rate
-    const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
-    const wDilated = tf.tensor4d(
-        [
-          0.125386, 0, 0, 0, 0.975199, 0, 0, 0,        0, 0, 0, 0,       0,
-          0,        0, 0, 0, 0,        0, 0, 0.640437, 0, 0, 0, 0.281895
-        ],
-        [fSizeDilated, fSizeDilated, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
-
-    const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
-
-    expect(result.shape).toEqual(expectedResult.shape);
-    expectArraysClose(await result.data(), await expectedResult.data());
-  });
-
-  it('input=1x3x3x2,f=2,s=1,d=1,p=same,chMul=1', async () => {
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,
-          0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,
-          0.0506303, 0.789682, 0.177473, 0.793569
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.614293, 0.0648011, 0.101113, 0.452887, 0.0582746, 0.426481,
-          0.872743, 0.765767
-        ],
-        [fSize, fSize, inDepth, chMul]);
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 3, 3, 2]);
-
-    const expected = [
-      0.485445, 0.995389, 0.95166, 0.927856, 0.636516, 0.253547, 0.378414,
-      1.10771, 0.430373, 1.23126, 0.290885, 0.372855, 0.3962, 0.379995,
-      0.0490466, 0.410569, 0.10902, 0.0514242
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x5x5x1,f=3,s=1,d=1,p=same,chMul=1', async () => {
-    const fSize = 3;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
-          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,
-          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,
-          0.741935, 0.974474, 0.621102, 0.171411
-        ],
-        [1, 5, 5, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.125386, 0.975199, 0.640437, 0.281895, 0.990968, 0.347208, 0.889702,
-          0.180695, 0.691992
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 5, 5, 1]);
-    const expected = [
-      0.684796, 1.179251, 1.680593, 0.885615, 1.152995, 1.52291,  2.540022,
-      2.505885, 2.454062, 1.871258, 2.371015, 2.351701, 2.459601, 3.076421,
-      1.323994, 1.985572, 3.29848,  3.437421, 2.93419,  1.823238, 1.410545,
-      2.352186, 2.19622,  1.348218, 0.774635
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x3x3x2,f=2,s=1,d=2,p=same,chMul=1', async () => {
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dilation = 2;
-    const inDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,
-          0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,
-          0.0506303, 0.789682, 0.177473, 0.793569
-        ],
-        [1, 3, 3, inDepth]);
-
-    const w: tf.Tensor4D =
-        tf.stack(
-              [
-                tf.tensor2d(
-                    [0.614293, 0.0648011, 0.101113, 0.452887], [fSize, fSize]),
-                tf.tensor2d(
-                    [0.0582746, 0.426481, 0.872743, 0.765767], [fSize, fSize])
-              ],
-              2)
-            .expandDims(3);
-
-    // adding a dilation rate is equivalent to using a filter
-    // with 0s for the dilation rate
-    const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
-    const wDilated: tf.Tensor4D =
-        tf.stack(
-              [
-                tf.tensor2d(
-                    [0.614293, 0, 0.0648011, 0, 0, 0, 0.101113, 0, 0.452887],
-                    [fSizeDilated, fSizeDilated]),
-                tf.tensor2d(
-                    [0.0582746, 0, 0.426481, 0, 0, 0, 0.872743, 0, 0.765767],
-                    [fSizeDilated, fSizeDilated])
-              ],
-              2)
-            .expandDims(3);
-
-    expect(wDilated.shape).toEqual([fSizeDilated, fSizeDilated, inDepth, 1]);
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
-
-    const expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
-
-    expect(result.shape).toEqual(expectedResult.shape);
-    expectArraysClose(await result.data(), await expectedResult.data());
-  });
-
-  it('input=1x5x5x1,f=3,s=1,d=2,p=valid,chMul=1', async () => {
-    const fSize = 3;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
-          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,
-          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,
-          0.741935, 0.974474, 0.621102, 0.171411
-        ],
-        [1, 5, 5, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.125386, 0.975199, 0.640437, 0.281895, 0.990968, 0.347208, 0.889702,
-          0.180695, 0.691992
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    const expected = [
-      2.540022, 2.505885, 2.454062, 2.351701, 2.459601, 3.076421, 3.29848,
-      3.437421, 2.93419
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x5x5x4,f=3,s=1,d=1,p=same,chMul=1', async () => {
-    const fSize = 3;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 4;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891,  0.083324,  0.537043,  0.644331,
-          0.563037, 0.211859, 0.633501,  0.186427,  0.777034,  0.50001,
-          0.607341, 0.95303,  0.696479,  0.050387,  0.62045,   0.728049,
-          0.028043, 0.437009, 0.712881,  0.741935,  0.974474,  0.621102,
-          0.171411, 0.675707, 0.758567,  0.413529,  0.963967,  0.217291,
-          0.101335, 0.804231, 0.329673,  0.924503,  0.728742,  0.180217,
-          0.210459, 0.133869, 0.650827,  0.047613,  0.554795,  0.653365,
-          0.442196, 0.261945, 0.0528113, 0.656698,  0.127345,  0.610039,
-          0.169131, 0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,
-          0.274711, 0.359377, 0.512113,  0.689682,  0.941571,  0.31961,
-          0.743826, 0.858147, 0.984766,  0.926973,  0.579597,  0.444104,
-          0.505969, 0.241437, 0.937999,  0.0957074, 0.773611,  0.46023,
-          0.469379, 0.363789, 0.269745,  0.486136,  0.894215,  0.794299,
-          0.724615, 0.261945, 0.0528113, 0.656698,  0.127345,  0.610039,
-          0.169131, 0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,
-          0.274711, 0.359377, 0.512113,  0.689682,  0.941571,  0.31961,
-          0.743826, 0.858147, 0.984766,  0.926973
-        ],
-        [1, 5, 5, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.6511372, 0.8699447, 0.6511372, 0.8699447, 0.267792,  0.9981787,
-          0.267792,  0.9981787, 0.4913572, 0.3321196, 0.4913572, 0.3321196,
-          0.5286497, 0.4241803, 0.5286497, 0.4241803, 0.0175446, 0.8365464,
-          0.0175446, 0.8365464, 0.1768399, 0.2874831, 0.1768399, 0.2874831,
-          0.0933998, 0.5764548, 0.0933998, 0.5764548, 0.0661623, 0.8850273,
-          0.0661623, 0.8850273, 0.8700929, 0.205422,  0.8700929, 0.205422
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 5, 5, 4]);
-    const expected = [
-      0.29389750957489014, 1.055132269859314,   0.8355544209480286,
-      0.7652503848075867,  1.116986632347107,   1.7007107734680176,
-      0.7228718996047974,  1.2455471754074097,  0.7690584063529968,
-      1.4749835729599,     1.1460752487182617,  1.5098011493682861,
-      0.7502411007881165,  2.056602716445923,   1.0519171953201294,
-      1.012758731842041,   0.37667199969291687, 1.6647151708602905,
-      0.4798099994659424,  0.532977283000946,   0.4293096363544464,
-      1.8309053182601929,  0.7433272004127502,  1.1491419076919556,
-      1.3050479888916016,  2.7769954204559326,  1.6411027908325195,
-      2.1799824237823486,  1.0364032983779907,  2.7503039836883545,
-      1.7060394287109375,  2.880652904510498,   1.8967751264572144,
-      3.3914175033569336,  1.734355092048645,   2.076633930206299,
-      0.7774094939231873,  3.1432321071624756,  0.9456352591514587,
-      1.0863502025604248,  0.8477171659469604,  2.5510711669921875,
-      1.169355869293213,   2.0218098163604736,  2.23183274269104,
-      3.257829189300537,   1.939490556716919,   2.96195650100708,
-      1.0946838855743408,  2.4252827167510986,  1.329919695854187,
-      3.0390005111694336,  1.8967963457107544,  2.775693416595459,
-      1.5250799655914307,  2.4470155239105225,  0.40530526638031006,
-      2.775503158569336,   0.8836789727210999,  1.1361782550811768,
-      0.4407186806201935,  2.3912413120269775,  0.38215696811676025,
-      2.047299861907959,   1.080580234527588,   3.09224534034729,
-      1.2943278551101685,  3.1656715869903564,  0.9704407453536987,
-      2.8066811561584473,  1.419780969619751,   3.1822099685668945,
-      1.720312237739563,   3.279745578765869,   2.0871992111206055,
-      2.6629819869995117,  0.5254714488983154,  3.3779194355010986,
-      0.73943030834198,    2.0616414546966553,  0.5148154497146606,
-      1.6852912902832031,  0.5320349931716919,  1.7935365438461304,
-      1.1387810707092285,  2.119696617126465,   1.2744661569595337,
-      2.3705403804779053,  1.0399315357208252,  1.6817822456359863,
-      0.8927359580993652,  1.6332063674926758,  1.3386595249176025,
-      1.8818190097808838,  1.267898440361023,   1.6589205265045166,
-      0.8288722038269043,  2.119757890701294,   0.8847255706787109,
-      1.5954076051712036
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x5x5x4,f=5,s=2,d=1,p=same,chMul=1', async () => {
-    const fSize = 5;
-    const pad = 'same';
-    const stride = 2;
-    const chMul = 1;
-    const inDepth = 4;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891,  0.083324,  0.537043,  0.644331,
-          0.563037, 0.211859, 0.633501,  0.186427,  0.777034,  0.50001,
-          0.607341, 0.95303,  0.696479,  0.050387,  0.62045,   0.728049,
-          0.028043, 0.437009, 0.712881,  0.741935,  0.974474,  0.621102,
-          0.171411, 0.675707, 0.758567,  0.413529,  0.963967,  0.217291,
-          0.101335, 0.804231, 0.329673,  0.924503,  0.728742,  0.180217,
-          0.210459, 0.133869, 0.650827,  0.047613,  0.554795,  0.653365,
-          0.442196, 0.261945, 0.0528113, 0.656698,  0.127345,  0.610039,
-          0.169131, 0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,
-          0.274711, 0.359377, 0.512113,  0.689682,  0.941571,  0.31961,
-          0.743826, 0.858147, 0.984766,  0.926973,  0.579597,  0.444104,
-          0.505969, 0.241437, 0.937999,  0.0957074, 0.773611,  0.46023,
-          0.469379, 0.363789, 0.269745,  0.486136,  0.894215,  0.794299,
-          0.724615, 0.261945, 0.0528113, 0.656698,  0.127345,  0.610039,
-          0.169131, 0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,
-          0.274711, 0.359377, 0.512113,  0.689682,  0.941571,  0.31961,
-          0.743826, 0.858147, 0.984766,  0.926973
-        ],
-        [1, 5, 5, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.6511372, 0.8699447, 0.6511372, 0.8699447, 0.267792,  0.9981787,
-          0.267792,  0.9981787, 0.4913572, 0.3321196, 0.4913572, 0.3321196,
-          0.5286497, 0.4241803, 0.5286497, 0.4241803, 0.0175446, 0.8365464,
-          0.0175446, 0.8365464, 0.1768399, 0.2874831, 0.1768399, 0.2874831,
-          0.0933998, 0.5764548, 0.0933998, 0.5764548, 0.0661623, 0.8850273,
-          0.0661623, 0.8850273, 0.8700929, 0.205422,  0.8700929, 0.205422,
-          0.149194,  0.089009,  0.654891,  0.083324,  0.537043,  0.644331,
-          0.563037,  0.211859,  0.633501,  0.186427,  0.777034,  0.50001,
-          0.607341,  0.95303,   0.696479,  0.050387,  0.62045,   0.728049,
-          0.028043,  0.437009,  0.712881,  0.741935,  0.974474,  0.621102,
-          0.171411,  0.125386,  0.975199,  0.640437,  0.281895,  0.990968,
-          0.347208,  0.889702,  0.180695,  0.691992,  0.347154,  0.386692,
-          0.327191,  0.483784,  0.591807,  0.24263,   0.95182,   0.174353,
-          0.592136,  0.623469,  0.988244,  0.660731,  0.946534,  0.0801365,
-          0.864889,  0.874602,  0.240347,  0.906352,  0.478657,  0.825918,
-          0.380769,  0.184705,  0.238241,  0.201907,  0.294087,  0.181165,
-          0.191303,  0.7225,    0.430064,  0.900622
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 3, 3, 4]);
-    const expected = [
-      2.2883458137512207, 2.5740344524383545, 2.3246560096740723,
-      2.27826189994812,   3.0600292682647705, 5.021538734436035,
-      4.432307720184326,  2.6976213455200195, 1.8467353582382202,
-      3.617821216583252,  2.0940940380096436, 1.3091316223144531,
-      2.4892354011535645, 4.767732620239258,  3.126866579055786,
-      3.4326541423797607, 4.181705474853516,  8.082467079162598,
-      6.922453880310059,  5.922790050506592,  2.819075345993042,
-      5.9510369300842285, 3.7211103439331055, 2.7263708114624023,
-      1.164026141166687,  3.3068809509277344, 1.6575196981430054,
-      2.738445997238159,  2.288442850112915,  5.463253021240234,
-      2.840029239654541,  3.8579823970794678, 1.440760612487793,
-      3.862100839614868,  2.3826799392700195, 2.2323575019836426
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x5x5x1,f=3,s=1,d=2,p=explicit,chMul=1', async () => {
-    const fSize = 3;
-    const pad =
-        [[0, 0], [0, 0], [0, 1], [0, 1]] as tf.backend_util.ExplicitPadding;
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
-          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,
-          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,
-          0.741935, 0.974474, 0.621102, 0.171411
-        ],
-        [1, 5, 5, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.125386, 0.975199, 0.640437, 0.281895, 0.990968, 0.347208, 0.889702,
-          0.180695, 0.691992
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 3, 4, 1]);
-    const expected = [
-      2.540022, 2.505885, 2.454062, 1.871258, 2.35170, 2.459601, 3.076421,
-      1.32399, 3.298480, 3.437421, 2.93419, 1.823238
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x3x3x4,f=3,s=1,d=2,p=same,chMul=1', async () => {
-    const fSize = 3;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 4;
-    const dilation = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.5227615, 0.3477598, 0.5227615, 0.3477598, 0.4690094, 0.408161,
-          0.4690094, 0.408161,  0.3239015, 0.2372907, 0.3239015, 0.2372907,
-          0.6136674, 0.7918105, 0.6136674, 0.7918105, 0.9145211, 0.218611,
-          0.9145211, 0.218611,  0.3778793, 0.2392365, 0.3778793, 0.2392365,
-          0.2340134, 0.1251984, 0.2340134, 0.1251984, 0.6222534, 0.1327361,
-          0.6222534, 0.1327361, 0.7697753, 0.1216059, 0.7697753, 0.1216059
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.6511372, 0.8699447, 0.6511372, 0.8699447, 0.267792,  0.9981787,
-          0.267792,  0.9981787, 0.4913572, 0.3321196, 0.4913572, 0.3321196,
-          0.5286497, 0.4241803, 0.5286497, 0.4241803, 0.0175446, 0.8365464,
-          0.0175446, 0.8365464, 0.1768399, 0.2874831, 0.1768399, 0.2874831,
-          0.0933998, 0.5764548, 0.0933998, 0.5764548, 0.0661623, 0.8850273,
-          0.0661623, 0.8850273, 0.8700929, 0.205422,  0.8700929, 0.205422
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
-    expect(result.shape).toEqual([1, 3, 3, 4]);
-    const expected = [
-      0.7517092227935791,  0.4949187934398651,  0.7517092227935791,
-      0.4949187934398651,  0.04939830303192139, 0.4589206874370575,
-      0.04939830303192139, 0.4589206874370575,  0.3548273742198944,
-      0.5258132815361023,  0.3548273742198944,  0.5258132815361023,
-      0.0775906890630722,  0.7311626672744751,  0.0775906890630722,
-      0.7311626672744751,  0.01604490540921688, 0.1828782558441162,
-      0.01604490540921688, 0.1828782558441162,  0.3310448229312897,
-      0.5360028743743896,  0.3310448229312897,  0.5360028743743896,
-      0.4393753409385681,  0.565629243850708,   0.4393753409385681,
-      0.565629243850708,   0.13651414215564728, 0.5184575319290161,
-      0.13651414215564728, 0.5184575319290161,  0.5643441677093506,
-      0.6942259669303894,  0.5643441677093506,  0.6942259669303894
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=1x3x3x2,f=2,s=1,p=same,chMul=2', async () => {
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 2;
-    const inDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
-          0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
-          0.047613, 0.554795, 0.653365, 0.442196
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.347154, 0.386692, 0.327191, 0.483784, 0.591807, 0.24263, 0.95182,
-          0.174353, 0.592136, 0.623469, 0.988244, 0.660731, 0.946534, 0.0801365,
-          0.864889, 0.874602
-        ],
-        [fSize, fSize, inDepth, chMul]);
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 3, 3, 4]);
-
-    const expected = [
-      1.83059,   0.937125,  2.1218,   1.39024,  0.990167, 0.803472,
-      1.31405,   1.14959,   0.182147, 0.196385, 0.241141, 0.188081,
-      0.950656,  0.622581,  1.92451,  1.20179,  1.07422,  0.483268,
-      1.36948,   1.14256,   0.449444, 0.477042, 0.505857, 0.393989,
-      0.0746509, 0.0633184, 0.74101,  0.41159,  0.403195, 0.176938,
-      0.602415,  0.345499,  0.226819, 0.252651, 0.144682, 0.213927
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=2x3x3x2,f=2,s=1,p=same,chMul=2', async () => {
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 2;
-    const inDepth = 2;
-
-    const x = tf.tensor4d(
-        [
-          0.261945, 0.0528113, 0.656698,  0.127345,  0.610039, 0.169131,
-          0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,  0.274711,
-          0.359377, 0.512113,  0.689682,  0.941571,  0.31961,  0.743826,
-          0.858147, 0.984766,  0.926973,  0.579597,  0.444104, 0.505969,
-          0.241437, 0.937999,  0.0957074, 0.773611,  0.46023,  0.469379,
-          0.363789, 0.269745,  0.486136,  0.894215,  0.794299, 0.724615
-        ],
-        [2, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [
-          0.240347, 0.906352, 0.478657, 0.825918, 0.380769, 0.184705, 0.238241,
-          0.201907, 0.294087, 0.181165, 0.191303, 0.7225, 0.430064, 0.900622,
-          0.670338, 0.33478
-        ],
-        [fSize, fSize, inDepth, chMul]);
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([2, 3, 3, 4]);
-
-    const expected = [
-      0.863379, 1.3119,   0.102795, 0.154853, 1.02704,   1.62173,  0.293466,
-      0.261764, 0.387876, 0.701529, 0.133508, 0.338167,  0.880395, 1.28039,
-      0.786492, 0.775361, 0.884845, 1.43995,  0.764374,  1.0196,   0.291162,
-      0.801428, 0.273788, 0.764303, 0.348985, 0.45311,   0.469447, 0.613073,
-      0.287461, 0.684128, 0.627899, 0.927844, 0.0768174, 0.28968,  0.356037,
-      0.614339, 0.67138,  1.07894,  1.30747,  1.86705,   0.617971, 1.35402,
-      0.860607, 1.29693,  0.242087, 0.485892, 0.331979,  0.757015, 0.410527,
-      0.740235, 1.28431,  1.42516,  0.68281,  0.975185,  1.13892,  1.62237,
-      0.344208, 0.561029, 0.363292, 0.911203, 0.272541,  0.419513, 0.342154,
-      0.403335, 0.419286, 0.587321, 0.600655, 0.884853,  0.190907, 0.719914,
-      0.346842, 0.598472
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('input=2x3x3x2,f=2,s=1,d=2,p=same,chMul=2',
-     async () => {
-       const fSize = 2;
-       const pad = 'same';
-       const stride = 1;
-       const inDepth = 2;
-       const dilation = 2;
-       const noDilation = 1;
-
-       const x = tf.tensor4d(
-           [
-             0.261945, 0.0528113, 0.656698,  0.127345,  0.610039, 0.169131,
-             0.458647, 0.0988288, 0.966109,  0.0421747, 0.82035,  0.274711,
-             0.359377, 0.512113,  0.689682,  0.941571,  0.31961,  0.743826,
-             0.858147, 0.984766,  0.926973,  0.579597,  0.444104, 0.505969,
-             0.241437, 0.937999,  0.0957074, 0.773611,  0.46023,  0.469379,
-             0.363789, 0.269745,  0.486136,  0.894215,  0.794299, 0.724615
-           ],
-           [2, 3, 3, inDepth]);
-
-       const w = tf.stack(
-                     [
-                       tf.stack(
-                           [
-                             tf.tensor2d(
-                                 [0.240347, 0.906352, 0.478657, 0.825918],
-                                 [fSize, fSize]),
-                             tf.tensor2d(
-                                 [0.380769, 0.184705, 0.238241, 0.201907],
-                                 [fSize, fSize])
-                           ],
-                           2),
-                       tf.stack(
-                           [
-                             tf.tensor2d(
-                                 [0.294087, 0.181165, 0.191303, 0.7225],
-                                 [fSize, fSize]),
-                             tf.tensor2d(
-                                 [0.430064, 0.900622, 0.670338, 0.33478],
-                                 [fSize, fSize])
-                           ],
-                           2)
-                     ],
-                     3) as tf.Tensor4D;
-
-       const fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
-       const wDilated = tf.stack([
-      tf.stack(
-          [
-            tf.tensor2d(
-              [0.240347, 0, 0.906352, 0, 0, 0, 0.478657, 0, 0.825918],
-              [fSizeDilated, fSizeDilated]),
-            tf.tensor2d(
-              [0.380769, 0, 0.184705, 0, 0, 0, 0.238241, 0, 0.201907],
-              [fSizeDilated, fSizeDilated])
-          ],
-          2),
-      tf.stack(
-          [
-            tf.tensor2d([0.294087, 0, 0.181165, 0, 0, 0, 0.191303, 0, 0.7225],
-              [fSizeDilated, fSizeDilated]),
-            tf.tensor2d(
-              [0.430064, 0, 0.900622, 0, 0, 0, 0.670338, 0, 0.33478],
-              [fSizeDilated, fSizeDilated])
-          ],
-          2)
-    ], 3) as tf.Tensor4D;
-
-       const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
-
-       const expectedResult =
-           tf.depthwiseConv2d(x, wDilated, stride, pad, 'NHWC', noDilation);
-
-       expect(result.shape).toEqual(expectedResult.shape);
-       expectArraysClose(await result.data(), await expectedResult.data());
-     });
-
-  it('input=2x3x3x2,f=3,s=1,d=2,p=same,chMul=2', async () => {
-    const fSize = 3;
-    const pad = 'same';
-    const stride = 1;
-    const inDepth = 2;
-    const dilation = 2;
-
-    const x = tf.tensor4d(
-        [[
-          [
-            [0.52276146, 0.34775984], [0.4690094, 0.40816104],
-            [0.32390153, 0.23729074], [0.61366737, 0.7918105],
-            [0.9145211, 0.218611], [0.37787926, 0.23923647],
-            [0.23401344, 0.12519836]
-          ],
-
-          [
-            [0.6222534, 0.13273609], [0.7697753, 0.12160587],
-            [0.0448128, 0.94806635], [0.4199953, 0.7140714],
-            [0.01420832, 0.47453713], [0.02061439, 0.37226152],
-            [0.62741446, 0.23167181]
-          ],
-
-          [
-            [0.7257557, 0.14352751], [0.3011638, 0.3869065],
-            [0.09286129, 0.25151742], [0.7566397, 0.13099921],
-            [0.65324724, 0.38959372], [0.65826, 0.7505318],
-            [0.35919082, 0.85470796]
-          ],
-
-          [
-            [0.24827361, 0.2826661], [0.24717247, 0.27446854],
-            [0.27112448, 0.68068564], [0.11082292, 0.7948675],
-            [0.41535318, 0.659986], [0.22165525, 0.18149579],
-            [0.42273378, 0.9558281]
-          ],
-
-          [
-            [0.943074, 0.6799041], [0.78851473, 0.07249606],
-            [0.771909, 0.7925967], [0.9551083, 0.03087568],
-            [0.82589805, 0.94797385], [0.5895462, 0.5045923],
-            [0.9667754, 0.24292922]
-          ],
-
-          [
-            [0.67123663, 0.109761], [0.04002762, 0.51942277],
-            [0.37868536, 0.8467603], [0.77171385, 0.51604605],
-            [0.8192849, 0.38843668], [0.19607484, 0.5591624],
-            [0.45990825, 0.35768318]
-          ],
-
-          [
-            [0.67443585, 0.6256168], [0.9373623, 0.6498393],
-            [0.7623085, 0.13218105], [0.9349631, 0.7660191],
-            [0.50054944, 0.7738123], [0.30201948, 0.525643],
-            [0.30896342, 0.21111596]
-          ]
-        ]],
-        [1, 7, 7, inDepth]);
-
-    const w = tf.tensor4d(
-        [
-          [
-            [[0.65113723], [0.8699447]],
-
-            [[0.267792], [0.9981787]],
-
-            [[0.4913572], [0.33211958]]
-          ],
-          [
-            [[0.5286497], [0.42418027]],
-
-            [[0.01754463], [0.8365464]],
-
-            [[0.17683995], [0.2874831]]
-          ],
-          [
-            [[0.09339976], [0.57645476]],
-
-            [[0.06616235], [0.8850273]],
-
-            [[0.87009287], [0.20542204]]
-          ]
-        ],
-        [fSize, fSize, inDepth, 1],
-    );
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
-
-    expect(result.shape).toEqual([1, 7, 7, 2]);
-    expectArraysClose(await result.data(), [
-      0.19526604, 0.5378273, 0.795022,   0.9384107,  1.0860794,  0.7942326,
-      0.9764694,  1.3974442, 0.5930813,  0.9848901,  0.44526684, 1.275759,
-      0.572345,   1.1784878, 0.27117175, 0.773588,   0.20055711, 0.71320784,
-      0.73477566, 1.8867722, 0.64123434, 1.6549369,  0.55551285, 2.0385633,
-      0.24740812, 1.233143,  0.08528192, 1.6214795,  1.062326,   1.3828603,
-      1.4494176,  1.1022222, 2.2350664,  2.283423,   1.5940895,  1.8871424,
-      1.6627852,  2.4903212, 1.0405337,  2.0754304,  1.1508893,  1.9568737,
-      0.6148571,  1.1505995, 1.1105528,  1.3823687,  1.4342139,  2.9909487,
-      1.0210396,  2.6467443, 1.0563798,  3.3963797,  0.42652097, 2.274134,
-      0.51121074, 2.264094,  1.1009313,  1.6042703,  1.510688,   1.2317145,
-      2.025515,   2.3658662, 1.6722159,  2.0787857,  1.3785586,  2.895031,
-      1.2915218,  2.2051222, 1.0423074,  2.4303207,  0.27844793, 0.84346974,
-      0.25781655, 1.1208354, 0.9447272,  2.0111258,  0.3689065,  1.9052455,
-      0.79137695, 2.355344,  0.5429248,  1.5593178,  0.8248403,  1.9922242,
-      0.77847,    1.5032601, 0.8622418,  0.84645665, 1.6850245,  2.2958806,
-      1.6242284,  1.329045,  1.6652328,  2.480535,   1.2793491,  1.2951884,
-      1.0667037,  1.5720158
-    ]);
-  });
-
-  it('input=1x8x8x2,f=3,s=1,d=3,p=valid,chMul=1', async () => {
-    const fSize = 3;
-    const pad = 'valid';
-    const stride = 1;
-    const inDepth = 2;
-    const dilation = 3;
-
-    const x = tf.tensor4d(
-        [
-          0.09941668063402176,  0.05248984694480896, 0.4567521810531616,
-          0.8002573847770691,   0.810535192489624,   0.7010623216629028,
-          0.5898630023002625,   0.05883334204554558, 0.2314797043800354,
-          0.45427876710891724,  0.10960108041763306, 0.9710874557495117,
-          0.18139968812465668,  0.8959258794784546,  0.35156702995300293,
-          0.6495933532714844,   0.5185067653656006,  0.3260101079940796,
-          0.7837356925010681,   0.9170011281967163,  0.465780109167099,
-          0.0857422724366188,   0.38354963064193726, 0.8134718537330627,
-          0.8768209218978882,   0.38151195645332336, 0.5045309066772461,
-          0.8152258396148682,   0.2782581150531769,  0.545160174369812,
-          0.1587309092283249,   0.5507456064224243,  0.2704062759876251,
-          0.7736618518829346,   0.9871141314506531,  0.29300180077552795,
-          0.3038032352924347,   0.36257433891296387, 0.967268168926239,
-          0.7251133918762207,   0.6244085431098938,  0.8398842215538025,
-          0.42696574330329895,  0.25569799542427063, 0.5784937143325806,
-          0.22755105793476105,  0.8869972229003906,  0.05128923058509827,
-          0.6748542785644531,   0.97468101978302,    0.5549167394638062,
-          0.5639380812644958,   0.821204662322998,   0.5207878947257996,
-          0.8831672668457031,   0.6721863746643066,  0.23375047743320465,
-          0.040671784430742264, 0.24522553384304047, 0.6293181777000427,
-          0.6886807680130005,   0.29527169466018677, 0.48199158906936646,
-          0.5751473307609558,   0.817806601524353,   0.38846832513809204,
-          0.5553714036941528,   0.1839468777179718,  0.5287416577339172,
-          0.4813096523284912,   0.477756530046463,   0.641162633895874,
-          0.03040425479412079,  0.20608118176460266, 0.7930338978767395,
-          0.727353572845459,    0.42868077754974365, 0.6136374473571777,
-          0.06312728673219681,  0.4346885681152344,  0.004786544945091009,
-          0.4951920807361603,   0.588252604007721,   0.724294126033783,
-          0.07830118387937546,  0.07353833317756653, 0.7818689346313477,
-          0.8137099742889404,   0.6505773067474365,  0.5716961026191711,
-          0.5416423678398132,   0.855529248714447,   0.8958709239959717,
-          0.3598312437534332,   0.31329575181007385, 0.5971285104751587,
-          0.034069616347551346, 0.6229354739189148,  0.24074052274227142,
-          0.3356363773345947,   0.1049640029668808,  0.2543765604496002,
-          0.1635538637638092,   0.8082090616226196,  0.9097364544868469,
-          0.6435819268226624,   0.6100808382034302,  0.29750677943229675,
-          0.0738643929362297,   0.8887753486633301,  0.7692861557006836,
-          0.6412256360054016,   0.16205888986587524, 0.9414404034614563,
-          0.5698712468147278,   0.6834514737129211,  0.41202589869499207,
-          0.9096908569335938,   0.8094117045402527,  0.42103442549705505,
-          0.8905773162841797,   0.069722980260849,   0.014392468146979809,
-          0.22018849849700928,  0.30076053738594055, 0.8472294211387634,
-          0.852762758731842,    0.5004454851150513
-        ],
-        [1, 8, 8, inDepth]);
-
-    const w = tf.tensor4d(
-        [
-          0.5785998106002808, 0.7439202666282654, 0.2178175300359726,
-          0.8782838582992554, 0.6579487919807434, 0.6556791067123413,
-          0.7341834306716919, 0.3332836329936981, 0.037182893604040146,
-          0.7394348382949829, 0.04031887650489807, 0.19104436039924622,
-          0.7014378309249878, 0.5309979319572449, 0.8485966920852661,
-          0.6609954237937927, 0.021728534251451492, 0.9289031624794006
-        ],
-        [fSize, fSize, inDepth, 1],
-    );
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    expectArraysClose(await result.data(), [
-      1.0257229804992676, 3.247040033340454, 1.9391249418258667,
-      2.9474055767059326, 2.0091731548309326, 3.600433826446533,
-      2.334312677383423, 2.548961877822876
-    ]);
-  });
-
-  it('Tensor3D is allowed', async () => {
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 3;
-    const inDepth = 2;
-
-    const x = tf.zeros<Rank.R3>([3, 3, inDepth]);
-    const w = tf.zeros<Rank.R4>([fSize, fSize, inDepth, chMul]);
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([3, 3, inDepth * chMul]);
-  });
-
-  it('Pass null for dilations, which defaults to [1, 1]', () => {
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 3;
-    const inDepth = 2;
-    const dilations: [number, number] = null;
-
-    const x = tf.zeros<Rank.R3>([3, 3, inDepth]);
-    const w = tf.zeros<Rank.R4>([fSize, fSize, inDepth, chMul]);
-    const result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilations);
-    expect(result.shape).toEqual([3, 3, inDepth * chMul]);
-  });
-
-  it('TensorLike', async () => {
-    const pad = 'valid';
-    const stride = 1;
-
-    const x = [[
-      [[0.230664], [0.987388], [0.0685208]],
-      [[0.419224], [0.887861], [0.731641]],
-      [[0.0741907], [0.409265], [0.351377]]
-    ]];
-    const w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
-
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-
-    const expected = [1.07022, 1.03167, 0.67041, 0.778863];
-    expectArraysClose(await result.data(), expected);
-  });
-  it('TensorLike Chained', async () => {
-    const pad = 'valid';
-    const stride = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
-
-    const result = x.depthwiseConv2d(w, stride, pad);
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-
-    const expected = [1.07022, 1.03167, 0.67041, 0.778863];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws when passed x as a non-tensor', () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 2;
-    const dataFormat = 'NHWC';
-    const dilation = 2;
-
-    const w = tf.tensor4d([3], [fSize, fSize, inputDepth, outputDepth]);
-
-    const e = /Argument 'x' passed to 'depthwiseConv2d' must be a Tensor/;
-    expect(
-        () => tf.depthwiseConv2d(
-            {} as tf.Tensor3D, w, stride, pad, dataFormat, dilation))
-        .toThrowError(e);
-  });
-
-  it('throws when passed filter as a non-tensor', () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [2, 2, inputDepth];
-    const pad = 'same';
-    const stride = 2;
-    const dataFormat = 'NHWC';
-    const dilation = 2;
-
-    const x = tf.tensor3d([1, 2, 3, 4], inputShape);
-
-    const e = /Argument 'filter' passed to 'depthwiseConv2d' must be a Tensor/;
-    expect(
-        () => tf.depthwiseConv2d(
-            x, {} as tf.Tensor4D, stride, pad, dataFormat, dilation))
-        .toThrowError(e);
-  });
-
-  it('throws when input is int32', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, inDepth], 'int32');
-    const w = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const errRegex = /Argument 'x' passed to 'depthwiseConv2d' must be float32/;
-    expect(() => tf.depthwiseConv2d(x, w, stride, pad)).toThrowError(errRegex);
-  });
-
-  it('throws when filter is int32', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4],
-        [fSize, fSize, inDepth, chMul],
-        'int32',
-    );
-
-    const errRegex =
-        /Argument 'filter' passed to 'depthwiseConv2d' must be float32/;
-    expect(() => tf.depthwiseConv2d(x, w, stride, pad)).toThrowError(errRegex);
-  });
-
-  it('throws when dimRoundingMode is set and pad is same', () => {
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-    const dimRoundingMode = 'round';
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-    expect(
-        () => tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', 1, dimRoundingMode))
-        .toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is valid', () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-    const dimRoundingMode = 'round';
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-    expect(
-        () => tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', 1, dimRoundingMode))
-        .toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is a non-integer number',
-     () => {
-       const fSize = 2;
-       const pad = 1.2;
-       const stride = 1;
-       const chMul = 1;
-       const inDepth = 1;
-       const dimRoundingMode = 'round';
-
-       const x = tf.tensor4d(
-           [
-             0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-             0.0741907, 0.409265, 0.351377
-           ],
-           [1, 3, 3, inDepth]);
-       const w = tf.tensor4d(
-           [0.303873, 0.229223, 0.144333, 0.803373],
-           [fSize, fSize, inDepth, chMul],
-       );
-       expect(
-           () => tf.depthwiseConv2d(
-               x, w, stride, pad, 'NHWC', 1, dimRoundingMode))
-           .toThrowError();
-     });
-
-  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
-         'number',
-     () => {
-       const fSize = 2;
-       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as
-           tf.backend_util.ExplicitPadding;
-       const stride = 1;
-       const chMul = 1;
-       const inDepth = 1;
-       const dimRoundingMode = 'round';
-
-       const x = tf.tensor4d(
-           [
-             0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-             0.0741907, 0.409265, 0.351377
-           ],
-           [1, 3, 3, inDepth]);
-       const w = tf.tensor4d(
-           [0.303873, 0.229223, 0.144333, 0.803373],
-           [fSize, fSize, inDepth, chMul],
-       );
-       expect(
-           () => tf.depthwiseConv2d(
-               x, w, stride, pad, 'NHWC', 1, dimRoundingMode))
-           .toThrowError();
-     });
-
-  it('accepts a tensor-like object', async () => {
-    const pad = 'valid';
-    const stride = 1;
-    // 1x3x3x1
-    const x = [[
-      [[0.230664], [0.987388], [0.0685208]],
-      [[0.419224], [0.887861], [0.731641]],
-      [[0.0741907], [0.409265], [0.351377]]
-    ]];
-    // 2x2x1x1
-    const w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
-    const result = tf.depthwiseConv2d(x, w, stride, pad);
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-
-    const expected = [1.07022, 1.03167, 0.67041, 0.778863];
-    expectArraysClose(await result.data(), expected);
-  });
-});
-
-describeWithFlags('depthwiseConv2d gradients', ALL_ENVS, () => {
-  let images: tf.Tensor4D;
-  let filter: tf.Tensor4D;
-  let result: tf.Tensor4D;
-  const stride = 1;
-  const pad = 'same';
-
-  beforeEach(() => {
-    // two 2x2 RGB images => 2x2x2x3
-    images = tf.tensor4d([
-      [[[2, 3, 1], [3, 0, 2]], [[0, 4, 1], [3, 1, 3]]],
-      [[[2, 1, 0], [0, 3, 3]], [[4, 0, 1], [1, 4, 1]]]
-    ]);
-    // 2x2 filters, chMul = 2 => 2x2x3x2
-    filter = tf.tensor4d([
-      [[[1, 1], [1, 1], [0, 0]], [[0, 1], [1, 1], [1, 1]]],
-      [[[1, 0], [1, 1], [0, 0]], [[0, 1], [1, 0], [0, 0]]]
-    ]);
-    // result of convolution operatoin
-    result = tf.tensor4d([
-      [
-        [[2, 8, 8, 7, 2, 2], [6, 3, 1, 1, 0, 0]],
-        [[0, 3, 5, 5, 3, 3], [3, 3, 1, 1, 0, 0]]
-      ],
-      [
-        [[6, 3, 8, 4, 3, 3], [1, 0, 7, 7, 0, 0]],
-        [[4, 5, 4, 4, 1, 1], [1, 1, 4, 4, 0, 0]]
-      ]
-    ]);
-  });
-
-  it('wrt input', async () => {
-    const {value, grad} = tf.valueAndGrad(
-        (x: tf.Tensor4D) => tf.depthwiseConv2d(x, filter, stride, pad))(images);
-
-    expectArraysClose(await value.data(), await result.data());
-
-    const expectedGrad = tf.tensor4d([
-      [[[2., 2., 0.], [3., 4., 2.]], [[3., 4., 0.], [5., 7., 2.]]],
-      [[[2., 2., 0.], [3., 4., 2.]], [[3., 4., 0.], [5., 7., 2.]]]
-    ]);
-
-    expectArraysClose(await grad.data(), await expectedGrad.data());
-  });
-
-  // The gradients of normal and depthwise 2D convolutions are actually the same
-  // in the special case that dy = 1, so we also test the gradient of a function
-  // of the output to disambiguate the two methods.
-  it('wrt input, squared output', async () => {
-    const grad = tf.grad(
-        (x: tf.Tensor4D) =>
-            tf.square(tf.depthwiseConv2d(x, filter, stride, pad)))(images);
-
-    const expectedGrad = tf.tensor4d([
-      [[[20., 30., 0.], [34., 34., 8.]], [[10., 50., 0.], [46., 44., 12.]]],
-      [[[18., 24., 0.], [8., 52., 12.]], [[30., 40., 0.], [22., 76., 4.]]]
-    ]);
-
-    expectArraysClose(await grad.data(), await expectedGrad.data());
-  });
-
-  it('wrt filter', async () => {
-    const {value, grad} = tf.valueAndGrad(
-        (f: tf.Tensor4D) => tf.depthwiseConv2d(images, f, stride, pad))(filter);
-
-    expectArraysClose(await value.data(), await result.data());
-
-    const expectedGrad = tf.tensor4d([
-      [[[15., 15.], [16., 16.], [12., 12.]], [[7., 7.], [8., 8.], [9., 9.]]],
-      [[[8., 8.], [9., 9.], [6., 6.]], [[4., 4.], [5., 5.], [4., 4.]]]
-    ]);
-
-    expectArraysClose(await grad.data(), await expectedGrad.data());
-  });
-
-  it('gradient with clones', async () => {
-    const [dx, dFilter] = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-            tf.depthwiseConv2d(x.clone(), filter.clone(), stride, pad).clone())(
-        [images, filter]);
-    expect(dx.shape).toEqual(images.shape);
-    expect(dFilter.shape).toEqual(filter.shape);
-  });
-
-  // Also disambiguate regular vs. depthwise filter gradients
-  it('wrt filter, squared output', async () => {
-    const grad = tf.grad(
-        (f: tf.Tensor4D) =>
-            tf.square(tf.depthwiseConv2d(images, f, stride, pad)))(filter);
-
-    const expectedGrad = tf.tensor4d([
-      [
-        [[120., 122.], [180., 166.], [12., 12.]],
-        [[20., 76.], [90., 66.], [46., 46.]]
-      ],
-      [
-        [[86., 42.], [122., 114.], [10., 10.]],
-        [[24., 54.], [80., 46.], [18., 18.]]
-      ]
-    ]);
-
-    expectArraysClose(await grad.data(), await expectedGrad.data());
-  });
-
-  it('throws error on dilations > 1', () => {
-    const grad = tf.grad(
-        (x: tf.Tensor4D) =>
-            tf.depthwiseConv2d(x, filter, stride, pad, 'NHWC', 2));
-
-    expect(() => grad(images))
-        .toThrowError(/dilation rates greater than 1 are not yet supported/);
-  });
-
-  it('wrt input, stride=2, pad=valid', async () => {
-    const dx = tf.grad(
-        (x: tf.Tensor4D) => tf.depthwiseConv2d(x, filter, 2, 'valid'))(images);
-
-    expectArraysClose(await dx.data(), [
-      2., 2., 0., 1., 2., 2., 1., 2., 0., 1., 1., 0.,
-      2., 2., 0., 1., 2., 2., 1., 2., 0., 1., 1., 0.
-    ]);
-    expect(dx.shape).toEqual([2, 2, 2, 3]);
-  });
-
-  it('wrt filter, stride=2, pad=valid', async () => {
-    const df = tf.grad(
-        (f: tf.Tensor4D) => tf.depthwiseConv2d(images, f, 2, 'valid'))(filter);
-
-    expectArraysClose(await df.data(), [
-      4., 4., 4., 4., 1., 1., 3., 3., 3., 3., 5., 5.,
-      4., 4., 4., 4., 2., 2., 4., 4., 5., 5., 4., 4.
-    ]);
-    expect(df.shape).toEqual([2, 2, 3, 2]);
-  });
-
-  it('gradient with clones', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const stride = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-
-    const f = tf.tensor4d(
-        [0.303873, 0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const [dx, df] = tf.grads(
-        (x: tf.Tensor4D, f: tf.Tensor4D) =>
-            tf.depthwiseConv2d(x.clone(), f.clone(), stride, pad).clone())(
-        [x, f]);
-
-    expectArraysClose(await dx.data(), [
-      0.303873, 0.533096, 0.229223, 0.448206, 1.480802, 1.032596, 0.144333,
-      0.947706, 0.803373
-    ]);
-    expect(dx.shape).toEqual([1, 3, 3, 1]);
-
-    expectArraysClose(
-        await df.data(), [2.525137, 2.6754108, 1.7905407, 2.380144]);
-    expect(df.shape).toEqual([2, 2, 1, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/diag.ts b/tfjs-master/tfjs-core/src/ops/diag.ts
deleted file mode 100644
index 20216db0e..000000000
--- a/tfjs-master/tfjs-core/src/ops/diag.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Diag, DiagInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-
-import {op} from './operation';
-
-/**
- * Returns a diagonal tensor with given diagonal values.
- *
- * Given a diagonal, this operation returns a tensor with the diagonal and
- * everything else padded with zeros.
- *
- * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor
- * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- *
- * tf.diag(x).print()
- * ```
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2])
- *
- * tf.diag(x).print()
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function diag_(x: Tensor): Tensor {
-  const $x = convertToTensor(x, 'x', 'diag');
-
-  const inputs: DiagInputs = {x: $x};
-
-  return ENGINE.runKernel(Diag, inputs as unknown as NamedTensorMap);
-}
-
-export const diag = /* @__PURE__ */ op({diag_});
diff --git a/tfjs-master/tfjs-core/src/ops/diag_test.ts b/tfjs-master/tfjs-core/src/ops/diag_test.ts
deleted file mode 100644
index 588c49104..000000000
--- a/tfjs-master/tfjs-core/src/ops/diag_test.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('diag', ALL_ENVS, () => {
-  it('1d', async () => {
-    const m = tf.tensor1d([5]);
-    const diag = tf.diag(m);
-    expect(diag.shape).toEqual([1, 1]);
-    expectArraysClose(await diag.data(), [5]);
-  });
-  it('2d', async () => {
-    const m = tf.tensor2d([8, 2, 3, 4, 5, 1], [3, 2]);
-    const diag = tf.diag(m);
-    expect(diag.shape).toEqual([3, 2, 3, 2]);
-    expectArraysClose(await diag.data(), [
-      8, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0,
-      0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1
-    ]);
-  });
-  it('3d', async () => {
-    const m = tf.tensor3d([8, 5, 5, 7, 9, 10, 15, 1, 2, 14, 12, 3], [2, 2, 3]);
-    const diag = tf.diag(m);
-    expect(diag.shape).toEqual([2, 2, 3, 2, 2, 3]);
-    expectArraysClose(await diag.data(), [
-      8, 0, 0,  0, 0,  0, 0, 0, 0, 0, 0, 0, 0,  5, 0, 0,  0, 0, 0, 0, 0,
-      0, 0, 0,  0, 0,  5, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0,  0, 0, 7, 0, 0,
-      0, 0, 0,  0, 0,  0, 0, 0, 0, 0, 9, 0, 0,  0, 0, 0,  0, 0, 0, 0, 0,
-      0, 0, 10, 0, 0,  0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 15, 0, 0, 0, 0, 0,
-      0, 0, 0,  0, 0,  0, 0, 1, 0, 0, 0, 0, 0,  0, 0, 0,  0, 0, 0, 0, 2,
-      0, 0, 0,  0, 0,  0, 0, 0, 0, 0, 0, 0, 14, 0, 0, 0,  0, 0, 0, 0, 0,
-      0, 0, 0,  0, 12, 0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0,  0, 3,
-    ]);
-  });
-  it('4d', async () => {
-    const m = tf.tensor4d(
-        [
-          8, 5, 5, 7, 9,  10, 15, 1, 2, 14, 12, 3,
-          9, 6, 6, 8, 10, 11, 16, 2, 3, 15, 13, 4
-        ],
-        [2, 2, 3, 2]);
-    const diag = tf.diag(m);
-    expect(diag.shape).toEqual([2, 2, 3, 2, 2, 2, 3, 2]);
-    expectArraysClose(await diag.data(), [
-      8,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 5, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  5, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  7, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 9, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 10, 0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 15, 0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 1,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 2, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  14, 0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  12, 0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  3, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 9, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 6, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  6, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  8, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 10, 0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 11, 0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 16, 0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 2, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  3, 0, 0,  0,  0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  15, 0, 0,
-      0,  0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      13, 0, 0, 0, 0,  0,  0, 0,  0,  0, 0,  0, 0, 0, 0, 0,  0, 0, 0,  0,  0, 0,
-      0,  0, 0, 4
-    ]);
-  });
-  it('int32', async () => {
-    const m = tf.tensor1d([5, 3], 'int32');
-    const diag = tf.diag(m);
-    expect(diag.shape).toEqual([2, 2]);
-    expect(diag.dtype).toBe('int32');
-    expectArraysEqual(await diag.data(), [5, 0, 0, 3]);
-  });
-  it('bool', async () => {
-    const m = tf.tensor1d([5, 3], 'bool');
-    const diag = tf.diag(m);
-    expect(diag.shape).toEqual([2, 2]);
-    expect(diag.dtype).toBe('bool');
-    expectArraysEqual(await diag.data(), [1, 0, 0, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/dilation2d.ts b/tfjs-master/tfjs-core/src/ops/dilation2d.ts
deleted file mode 100644
index 51c27da39..000000000
--- a/tfjs-master/tfjs-core/src/ops/dilation2d.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Dilation2D, Dilation2DAttrs, Dilation2DInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the grayscale dilation over the input `x`.
- *
- * @param x The input tensor, rank 3 or rank 4 of shape
- *     `[batch, height, width, depth]`. If rank 3, batch of 1 is assumed.
- * @param filter The filter tensor, rank 3, of shape
- *     `[filterHeight, filterWidth, depth]`.
- * @param strides The strides of the sliding window for each dimension of the
- *     input tensor: `[strideHeight, strideWidth]`.
- *     If `strides` is a single number,
- *     then `strideHeight == strideWidth`.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1*1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dataFormat Specify the data format of the input and output data.
- *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the
- *      default format "NHWC", the data is stored in the order of: [batch,
- *      height, width, channels].
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
- *     in which we sample input values across the height and width dimensions
- *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`
- *     is a single number, then `dilationHeight == dilationWidth`. If it is
- *     greater than 1, then all values of `strides` must be 1.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function dilation2d_<T extends Tensor3D|Tensor4D>(
-    x: T|TensorLike, filter: Tensor3D|TensorLike,
-    strides: [number, number]|number, pad: 'valid'|'same',
-    dilations: [number, number]|number = [1, 1],
-    dataFormat: 'NHWC' = 'NHWC'): T {
-  const $x = convertToTensor(x, 'x', 'dilation2d');
-  const $filter = convertToTensor(filter, 'filter', 'dilation2d');
-
-  util.assert(
-      $x.rank === 3 || $x.rank === 4,
-      () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +
-          `${$x.rank}.`);
-  util.assert(
-      $filter.rank === 3,
-      () => `Error in dilation2d: filter must be rank 3, but got rank ` +
-          `${$filter.rank}.`);
-  util.assert(
-      dataFormat === 'NHWC',
-      () => `Error in dilation2d: Only NHWC is currently supported, ` +
-          `but got dataFormat of ${dataFormat}`);
-
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-
-  if ($x.rank === 3) {
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-    reshapedTo4D = true;
-  }
-
-  util.assert(
-      x4D.shape[3] === $filter.shape[2],
-      () => `Error in dilation2d:  input and filter must have the same depth: ${
-          x4D.shape[3]} vs ${$filter.shape[2]}`);
-
-  const inputs: Dilation2DInputs = {x: x4D, filter: $filter};
-  const attrs: Dilation2DAttrs = {strides, pad, dilations};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  Dilation2D, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-
-  return res;
-}
-
-export const dilation2d = /* @__PURE__ */ op({dilation2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/dilation2d_test.ts b/tfjs-master/tfjs-core/src/ops/dilation2d_test.ts
deleted file mode 100644
index 05e0a8933..000000000
--- a/tfjs-master/tfjs-core/src/ops/dilation2d_test.ts
+++ /dev/null
@@ -1,280 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('dilation2d', ALL_ENVS, () => {
-  it('valid padding.', async () => {
-    const inputShape: [number, number, number, number] = [1, 2, 2, 1];
-    const filterShape: [number, number, number] = [2, 2, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
-    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
-
-    const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');
-
-    expect(result.shape).toEqual([1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [.5]);
-  });
-
-  it('same padding.', async () => {
-    const inputShape: [number, number, number, number] = [1, 2, 2, 1];
-    const filterShape: [number, number, number] = [2, 2, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
-    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
-
-    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
-
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await result.data(), [.5, .6, .7, .8]);
-  });
-
-  it('same padding depth 3.', async () => {
-    const inputShape: [number, number, number, number] = [1, 2, 2, 3];
-    const filterShape: [number, number, number] = [2, 2, 3];
-    const x = tf.tensor4d(
-        [.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);
-    const filter = tf.tensor3d(
-        [.4, .5, .3, .3, .4, .2, .1, .2, .0, .0, .1, -.1], filterShape);
-
-    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
-
-    expect(result.shape).toEqual([1, 2, 2, 3]);
-    expectArraysClose(
-        await result.data(), [.5, .7, .3, .6, .8, .4, .7, .9, .5, .8, 1., .6]);
-  });
-
-  it('same padding batch 2.', async () => {
-    const inputShape: [number, number, number, number] = [2, 2, 2, 1];
-    const filterShape: [number, number, number] = [2, 2, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4, .2, .3, .4, .5], inputShape);
-    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
-
-    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
-
-    expect(result.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await result.data(), [.5, .6, .7, .8, .6, .7, .8, .9]);
-  });
-
-  it('same padding filter 2.', async () => {
-    const inputShape: [number, number, number, number] = [1, 3, 3, 1];
-    const filterShape: [number, number, number] = [2, 2, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
-    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
-
-    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
-
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    expectArraysClose(
-        await result.data(), [.7, .8, .7, 1, 1.1, 1, 1.1, 1.2, 1.3]);
-  });
-
-  it('valid padding non-square-window.', async () => {
-    const inputShape: [number, number, number, number] = [1, 2, 2, 1];
-    const filterShape: [number, number, number] = [1, 2, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
-    const filter = tf.tensor3d([.4, .3], filterShape);
-
-    const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');
-
-    expect(result.shape).toEqual([1, 2, 1, 1]);
-    expectArraysClose(await result.data(), [.5, .7]);
-  });
-
-  it('same padding dilations 2.', async () => {
-    const inputShape: [number, number, number, number] = [1, 3, 3, 1];
-    const filterShape: [number, number, number] = [2, 2, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
-    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
-
-    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same', 2);
-
-    // Because dilations = 2, the effective filter is [3, 3, 1]:
-    // filter_eff = [[[.4], [.0], [.3]],
-    //               [[.0], [.0], [.0]],
-    //               [[.1], [.0], [.2]]]
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    expectArraysClose(
-        await result.data(), [.7, .8, .6, 1., 1.1, .9, .8, .9, .9]);
-  });
-
-  it('valid padding uneven stride.', async () => {
-    const inputShape: [number, number, number, number] = [1, 3, 4, 1];
-    const filterShape: [number, number, number] = [2, 2, 1];
-    const x = tf.tensor4d(
-        [.1, .2, .3, .4, .5, .6, .7, .8, .9, 1., 1.1, 1.2], inputShape);
-    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
-
-    const result = tf.dilation2d(x, filter, [1, 2] /* strides */, 'valid');
-
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await result.data(), [.8, 1., 1.2, 1.4]);
-  });
-
-  it('throws when input rank is not 3 or 4.', async () => {
-    const filterShape: [number, number, number] = [1, 1, 1];
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor1d([.5]);
-    const filter = tf.tensor3d([.4], filterShape);
-
-    expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();
-  });
-
-  it('thorws when filter is not rank 3.', async () => {
-    const inputShape: [number, number, number, number] = [1, 2, 2, 1];
-    const filterShape: [number, number] = [2, 2];
-    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
-    // tslint:disable-next-line:no-any
-    const filter: any = tf.tensor2d([.4, .3, .1, .0], filterShape);
-
-    expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();
-  });
-
-  it('throws when data format is not NHWC.', async () => {
-    const inputShape: [number, number, number, number] = [1, 2, 2, 1];
-    const filterShape: [number, number, number] = [2, 2, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
-    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
-    // tslint:disable-next-line:no-any
-    const dataFormat: any = 'NCHW';
-
-    expect(
-        () => tf.dilation2d(x, filter, 1 /* strides */, 'valid', 1, dataFormat))
-        .toThrowError();
-  });
-
-  it('dilation gradient valid padding.', async () => {
-    const inputShape: [number, number, number, number] = [1, 3, 3, 1];
-    const filterShape: [number, number, number] = [1, 1, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
-    const filter = tf.tensor3d([.5], filterShape);
-    const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor3D) =>
-            x.dilation2d(filter, 1, 'valid'));
-
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [3.1]);
-  });
-
-  it('dilation gradient same padding.', async () => {
-    const inputShape: [number, number, number, number] = [1, 3, 3, 1];
-    const filterShape: [number, number, number] = [1, 1, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
-    const filter = tf.tensor3d([.5], filterShape);
-    const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor3D) =>
-            x.dilation2d(filter, 1, 'same'));
-
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [3.1]);
-  });
-
-  it('dilation gradient same padding depth 2.', async () => {
-    const inputShape: [number, number, number, number] = [1, 2, 2, 3];
-    const filterShape: [number, number, number] = [1, 1, 3];
-    const x = tf.tensor4d(
-        [.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);
-    const filter = tf.tensor3d([.4, .5, .6], filterShape);
-    const dy = tf.tensor4d(
-        [.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1], inputShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor3D) =>
-            x.dilation2d(filter, 1, 'same'));
-
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(
-        await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [1.4, .6, 1.9]);
-  });
-
-  it('dilation gradient valid padding filter 2.', async () => {
-    const inputShape: [number, number, number, number] = [1, 3, 3, 1];
-    const filterShape: [number, number, number] = [2, 2, 1];
-    const dyShape: [number, number, number, number] = [1, 2, 2, 1];
-    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
-    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
-    const dy = tf.tensor4d([.2, .3, .4, .2], dyShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor3D) =>
-            x.dilation2d(filter, 1, 'valid'));
-
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [0, 0, 0, 0, .2, .3, 0, .4, .2]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [0, 0, 0, 1.1]);
-  });
-
-  it('dilation gradient same padding filter 2 depth 3.', async () => {
-    const inputShape: [number, number, number, number] = [1, 3, 3, 3];
-    const filterShape: [number, number, number] = [2, 2, 3];
-    const x = tf.tensor4d(
-        [
-          .1, .2, .3, .4, .5, .6, .7, .8, .9, .3, .2, .3, .4, .5,
-          .1, .9, .6, .3, .4, .5, .6, .2, .3, .5, .1, .2, .3
-        ],
-        inputShape);
-    const filter = tf.tensor3d(
-        [.4, .3, .1, .2, .2, .1, .7, .3, .8, .4, .9, .1], filterShape);
-    const dy = tf.tensor4d(
-        [
-          .2, .3, .4, .2, .1, .5, 0,  .8, .7, .1, .2, .1, .2, .3,
-          .4, .5, .6, .6, .6, .7, .8, .3, .2, .1, .2, .4, .2
-        ],
-        inputShape);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor3D) =>
-            x.dilation2d(filter, 1, 'same'));
-
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [
-      0, 0,  0,  0,  0,  0,  0,  .8, .5, .2, 0,  .4, 0, .3,
-      0, .9, .7, .7, .7, .7, .9, .3, .4, .5, .2, .7, .8
-    ]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(
-        await dfilter.data(),
-        [1.6, 2.7, 1.1, .2, 0, .5, .3, 0, 2.2, .2, .9, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/div.ts b/tfjs-master/tfjs-core/src/ops/div.ts
deleted file mode 100644
index 7448306c0..000000000
--- a/tfjs-master/tfjs-core/src/ops/div.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {RealDiv, RealDivInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {floorDiv} from './floorDiv';
-import {op} from './operation';
-
-/**
- * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([1, 4, 9, 16]);
- * const b = tf.tensor1d([1, 2, 3, 4]);
- *
- * a.div(b).print();  // or tf.div(a, b)
- * ```
- *
- * ```js
- * // Broadcast div a with b.
- * const a = tf.tensor1d([2, 4, 6, 8]);
- * const b = tf.scalar(2);
- *
- * a.div(b).print();  // or tf.div(a, b)
- * ```
- *
- * @param a The first tensor as the numerator.
- * @param b The second tensor as the denominator. Must have the same dtype as
- * `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function div_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'div');
-  let $b = convertToTensor(b, 'b', 'div');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  if ($a.dtype === 'int32' && $b.dtype === 'int32') {
-    return floorDiv($a, $b);
-  }
-
-  const inputs: RealDivInputs = {a: $a, b: $b};
-  const attrs = {};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  return ENGINE.runKernel(RealDiv,
-                          inputs as unknown as NamedTensorMap, attrs) as T;
-}
-
-export const div = /* @__PURE__ */ op({div_});
diff --git a/tfjs-master/tfjs-core/src/ops/div_no_nan.ts b/tfjs-master/tfjs-core/src/ops/div_no_nan.ts
deleted file mode 100644
index 65840ba35..000000000
--- a/tfjs-master/tfjs-core/src/ops/div_no_nan.ts
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {div} from './div';
-import {equal} from './equal';
-import {op} from './operation';
-import {where} from './where';
-import {zerosLike} from './zeros_like';
-
-/**
- * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0
- * if denominator is 0.
- *
- *
- * ```js
- * const a = tf.tensor1d([1, 4, 9, 16]);
- * const b = tf.tensor1d([1, 2, 3, 4]);
- * const c = tf.tensor1d([0, 0, 0, 0]);
- *
- * a.divNoNan(b).print();  // or tf.divNoNan(a, b)
- * a.divNoNan(c).print();  // or tf.divNoNan(a, c)
- * ```
- *
- * ```js
- * // Broadcast div a with b.
- * const a = tf.tensor1d([2, 4, 6, 8]);
- * const b = tf.scalar(2);
- * const c = tf.scalar(0);
- *
- * a.divNoNan(b).print();  // or tf.divNoNan(a, b)
- * a.divNoNan(c).print();  // or tf.divNoNan(a, c)
- * ```
- *
- * @param a The first tensor as the numerator.
- * @param b The second tensor as the denominator. Must have the same dtype as
- * `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function divNoNan_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  // TODO: Make this into its own kernel.
-  let $a = convertToTensor(a, 'a', 'div');
-  let $b = convertToTensor(b, 'b', 'div');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  const divResult = div($a, $b);
-  const zeros = zerosLike(divResult);
-  const bEqualsZero = equal($b, zeros);
-  return where(bEqualsZero, zeros, divResult) as T;
-}
-
-export const divNoNan = /* @__PURE__ */ op({divNoNan_});
diff --git a/tfjs-master/tfjs-core/src/ops/dot.ts b/tfjs-master/tfjs-core/src/ops/dot.ts
deleted file mode 100644
index 48667d57b..000000000
--- a/tfjs-master/tfjs-core/src/ops/dot.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor,} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {matMul} from './mat_mul';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.
- *
- * ```js
- * const a = tf.tensor1d([1, 2]);
- * const b = tf.tensor2d([[1, 2], [3, 4]]);
- * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
- *
- * a.dot(b).print();  // or tf.dot(a, b)
- * b.dot(a).print();
- * b.dot(c).print();
- * ```
- * @param t1 The first tensor in the dot operation.
- * @param t2 The second tensor in the dot operation.
- *
- * @doc {heading: 'Operations', subheading: 'Matrices'}
- */
-function dot_(t1: Tensor|TensorLike, t2: Tensor|TensorLike): Tensor {
-  const $t1 = convertToTensor(t1, 't1', 'dot');
-  const $t2 = convertToTensor(t2, 't2', 'dot');
-
-  util.assert(
-      ($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2),
-      () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +
-          `${$t1.rank} and ${$t2.rank}.`);
-
-  const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);
-  const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);
-
-  util.assert(
-      t1Inner === t2Inner,
-      () => `Error in dot: inner dimensions of inputs must match, but got ` +
-          `${t1Inner} and ${t2Inner}.`);
-
-  if ($t1.rank === 1 && $t2.rank === 1) {
-    const t12D = reshape($t1, [1, -1]);
-    const t22D = reshape($t2, [-1, 1]);
-    const t1t2 = matMul(t12D, t22D);
-    return reshape(t1t2, []);
-  } else if ($t1.rank === 1 && $t2.rank === 2) {
-    const t12D = reshape($t1, [1, -1]);
-    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-    const t1t2 = matMul(t12D, t22D);
-    return reshape(t1t2, [t1t2.size]);
-  } else if ($t1.rank === 2 && $t2.rank === 1) {
-    const t22D = reshape($t2, [-1, 1]);
-    const t1t2 = matMul($t1, t22D);
-    return reshape(t1t2, [t1t2.size]);
-  } else {
-    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
-    const t1t2 = matMul($t1, t22D);
-    return t1t2;
-  }
-}
-
-export const dot = /* @__PURE__ */ op({dot_});
diff --git a/tfjs-master/tfjs-core/src/ops/draw_test.ts b/tfjs-master/tfjs-core/src/ops/draw_test.ts
deleted file mode 100644
index e38427d9a..000000000
--- a/tfjs-master/tfjs-core/src/ops/draw_test.ts
+++ /dev/null
@@ -1,215 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {BROWSER_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-function readPixelsFromCanvas(
-    canvas: HTMLCanvasElement, contextType: string, width: number,
-    height: number) {
-  let actualData;
-  if (contextType === '2d') {
-    const ctx = canvas.getContext(contextType);
-    actualData = ctx.getImageData(0, 0, width, height).data;
-  } else {
-    const tmpCanvas = document.createElement('canvas');
-    tmpCanvas.width = width;
-    tmpCanvas.height = height;
-    const ctx = tmpCanvas.getContext('2d');
-
-    ctx.drawImage(canvas, 0, 0);
-    actualData = new Uint8ClampedArray(
-        ctx.getImageData(0, 0, ctx.canvas.width, ctx.canvas.height).data);
-  }
-  return actualData;
-}
-
-function convertToRGBA(
-    data: number[], shape: number[], dtype: string, alpha = 1) {
-  const [height, width] = shape.slice(0, 2);
-  const depth = shape.length === 2 ? 1 : shape[2];
-  const multiplier = dtype === 'float32' ? 255 : 1;
-  const bytes = new Uint8ClampedArray(width * height * 4);
-
-  for (let i = 0; i < height * width; ++i) {
-    const rgba = [0, 0, 0, 255 * alpha];
-
-    for (let d = 0; d < depth; d++) {
-      const value = data[i * depth + d];
-
-      if (dtype === 'float32') {
-        if (value < 0 || value > 1) {
-          throw new Error(
-              `Tensor values for a float32 Tensor must be in the ` +
-              `range [0 - 1] but encountered ${value}.`);
-        }
-      } else if (dtype === 'int32') {
-        if (value < 0 || value > 255) {
-          throw new Error(
-              `Tensor values for a int32 Tensor must be in the ` +
-              `range [0 - 255] but encountered ${value}.`);
-        }
-      }
-
-      if (depth === 1) {
-        rgba[0] = value * multiplier;
-        rgba[1] = value * multiplier;
-        rgba[2] = value * multiplier;
-      } else {
-        rgba[d] = value * multiplier;
-      }
-    }
-
-    const j = i * 4;
-    bytes[j + 0] = Math.round(rgba[0]);
-    bytes[j + 1] = Math.round(rgba[1]);
-    bytes[j + 2] = Math.round(rgba[2]);
-    bytes[j + 3] = Math.round(rgba[3]);
-  }
-  return bytes;
-}
-
-function drawAndReadback(
-    contextType: string, data: number[], shape: number[], dtype: string,
-    alpha = 1, canvasUsedAs2d = false) {
-  const [height, width] = shape.slice(0, 2);
-  let img;
-  if (shape.length === 3) {
-    img = tf.tensor3d(
-        data, shape as [number, number, number], dtype as keyof tf.DataTypeMap);
-  } else {
-    img = tf.tensor2d(
-        data, shape as [number, number], dtype as keyof tf.DataTypeMap);
-  }
-  const canvas = document.createElement('canvas');
-  canvas.width = width;
-  canvas.height = height;
-  if (canvasUsedAs2d) {
-    canvas.getContext('2d');
-  }
-  const drawOptions = {contextOptions: {contextType}, imageOptions: {alpha}};
-  // tslint:disable-next-line:no-any
-  tf.browser.draw(img, canvas as any, drawOptions);
-  const actualData = readPixelsFromCanvas(canvas, contextType, width, height);
-  const expectedData = convertToRGBA(data, shape, dtype, alpha);
-  img.dispose();
-  return [actualData, expectedData];
-}
-
-// CPU and GPU handle pixel value differently. The epsilon may possibly grow
-// after each draw and read back.
-const DRAW_EPSILON = 6.0;
-
-describeWithFlags('draw on canvas context', BROWSER_ENVS, (env) => {
-  let contextType: string;
-  beforeAll(async () => {
-    await tf.setBackend(env.name);
-    contextType = env.name === 'cpu' ? '2d' : env.name;
-  });
-
-  it('draw image with 4 channels and int values', async () => {
-    const data =
-        [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160];
-    const shape = [2, 2, 4];
-    const dtype = 'int32';
-    const startNumTensors = tf.memory().numTensors;
-    const [actualData, expectedData] =
-        drawAndReadback(contextType, data, shape, dtype);
-    expect(tf.memory().numTensors).toEqual(startNumTensors);
-    expectArraysClose(actualData, expectedData, DRAW_EPSILON);
-  });
-
-  it('draw image with 4 channels and int values, alpha=0.5', async () => {
-    const data =
-        [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160];
-    const shape = [2, 2, 4];
-    const dtype = 'int32';
-    const startNumTensors = tf.memory().numTensors;
-    const [actualData, expectedData] =
-        drawAndReadback(contextType, data, shape, dtype, 0.5);
-    expect(tf.memory().numTensors).toEqual(startNumTensors);
-    expectArraysClose(actualData, expectedData, DRAW_EPSILON);
-  });
-
-  it('draw image with 4 channels and float values', async () => {
-    const data =
-        [.1, .2, .3, .4, .5, .6, .7, .8, .09, .1, .11, .12, .13, .14, .15, .16];
-    const shape = [2, 2, 4];
-    const dtype = 'float32';
-    const [actualData, expectedData] =
-        drawAndReadback(contextType, data, shape, dtype);
-    expectArraysClose(actualData, expectedData, DRAW_EPSILON);
-  });
-
-  it('draw image with 3 channels and int values', async () => {
-    const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
-    const shape = [2, 2, 3];
-    const dtype = 'int32';
-    const [actualData, expectedData] =
-        drawAndReadback(contextType, data, shape, dtype);
-    expectArraysEqual(actualData, expectedData);
-  });
-
-  it('draw image with 3 channels and int values, alpha=0.5', async () => {
-    const data = [101, 32, 113, 14, 35, 76, 17, 38, 59, 70, 81, 92];
-    const shape = [2, 2, 3];
-    const dtype = 'int32';
-    const alpha = 0.5;
-    const [actualData, expectedData] =
-        drawAndReadback(contextType, data, shape, dtype, alpha);
-    expectArraysClose(actualData, expectedData, DRAW_EPSILON);
-  });
-
-  it('draw image with 3 channels and float values', async () => {
-    const data = [.1, .2, .3, .4, .5, .6, .7, .8, .9, .1, .11, .12];
-    const shape = [2, 2, 3];
-    const dtype = 'float32';
-    const [actualData, expectedData] =
-        drawAndReadback(contextType, data, shape, dtype);
-    expectArraysClose(actualData, expectedData, DRAW_EPSILON);
-  });
-
-  it('draw 2D image in grayscale', async () => {
-    const data = [100, 12, 90, 64];
-    const shape = [2, 2];
-    const dtype = 'int32';
-    const [actualData, expectedData] =
-        drawAndReadback(contextType, data, shape, dtype);
-    expectArraysEqual(actualData, expectedData);
-  });
-
-  it('draw image with alpha=0.5', async () => {
-    const data = [101, 212, 113, 14, 35, 76, 17, 38, 59, 70, 81, 92];
-    const shape = [6, 2, 1];
-    const dtype = 'int32';
-    const alpha = 0.5;
-    const [actualData, expectedData] =
-        drawAndReadback(contextType, data, shape, dtype, alpha);
-    expectArraysClose(actualData, expectedData, DRAW_EPSILON);
-  });
-
-  it('draw image works when canvas has been used for 2d', async () => {
-    const data = [101, 212, 113, 14, 35, 76, 17, 38, 59, 70, 81, 92];
-    const shape = [6, 2, 1];
-    const dtype = 'int32';
-    // Set canvasUsedAs2d to true so the canvas will be first used for 2d.
-    const [actualData, expectedData] =
-        drawAndReadback(contextType, data, shape, dtype, 1, true);
-    expectArraysClose(actualData, expectedData, DRAW_EPSILON);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/dropout.ts b/tfjs-master/tfjs-core/src/ops/dropout.ts
deleted file mode 100644
index ee9fbdba2..000000000
--- a/tfjs-master/tfjs-core/src/ops/dropout.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {add} from './add';
-import {div} from './div';
-import {getNoiseShape} from './dropout_util';
-import {floor} from './floor';
-import {mul} from './mul';
-import {op} from './operation';
-import {randomUniform} from './random_uniform';
-
-/**
- * Computes dropout.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 2, 1]);
- * const rate = 0.75;
- * const output = tf.dropout(x, rate);
- * output.print();
- * ```
- *
- * @param x A floating point Tensor or TensorLike.
- * @param rate A float in the range [0, 1). The probability that each element
- *   of x is discarded.
- * @param noiseShape An array of numbers of type int32, representing the
- * shape for randomly generated keep/drop flags. If the noiseShape has null
- * value, it will be automatically replaced with the x's relative dimension
- * size. Optional.
- * @param seed Used to create random seeds. Optional.
- * @returns A Tensor of the same shape of x.
- *
- * @doc {heading: 'Operations', subheading: 'Dropout'}
- */
-function dropout_(
-    x: Tensor|TensorLike, rate: number, noiseShape?: number[],
-    seed?: number|string): Tensor {
-  const $x = convertToTensor(x, 'x', 'dropout');
-
-  util.assert(
-      $x.dtype === 'float32',
-      () => `x has to be a floating point tensor since it's going to be ` +
-          `scaled, but got a ${$x.dtype} tensor instead.`);
-  util.assert(
-      rate >= 0 && rate < 1,
-      () => `rate must be a float in the range [0, 1), but got ${rate}.`);
-
-  if (rate === 0) {
-    return x instanceof Tensor ? $x.clone() : $x;
-  }
-
-  const $noiseShape = getNoiseShape($x, noiseShape);
-  const keepProb = 1 - rate;
-  const multiplier = div(
-      floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)),
-      keepProb);
-
-  return mul($x, multiplier);
-}
-
-export const dropout = /* @__PURE__ */ op({dropout_});
diff --git a/tfjs-master/tfjs-core/src/ops/dropout_test.ts b/tfjs-master/tfjs-core/src/ops/dropout_test.ts
deleted file mode 100644
index 75e8d4f36..000000000
--- a/tfjs-master/tfjs-core/src/ops/dropout_test.ts
+++ /dev/null
@@ -1,174 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-import {tensor1d, tensor2d} from './ops';
-
-describeWithFlags('dropout', ALL_ENVS, () => {
-  it('x 1d array, rate 0', async () => {
-    const x = tensor1d([1, 2, 2, 1]);
-    const rate = 0;
-    const output = tf.dropout(x, rate);
-    expect(output.dtype).toEqual(x.dtype);
-    expect(output.shape).toEqual(x.shape);
-    expectArraysClose(await x.data(), await output.data());
-  });
-
-  it('x 1d array, rate 0.75', async () => {
-    const x = tensor1d([1, 2, 2, 1]);
-    const rate = 0.75;
-    const output = tf.dropout(x, rate);
-    expect(output.dtype).toEqual(x.dtype);
-    expect(output.shape).toEqual(x.shape);
-    const xValues = await x.data();
-    const outputValues = await output.data();
-    for (let i = 0; i < xValues.length; i++) {
-      if (outputValues[i] !== 0) {
-        expect(outputValues[i]).toBeCloseTo(1 / (1 - rate) * xValues[i]);
-      }
-    }
-  });
-
-  it('x 2d array, rate 0', async () => {
-    const x = tensor2d([1, 5, 2, 4, 3, 6], [2, 3]);
-    const rate = 0;
-    const output = tf.dropout(x, rate);
-    expect(output.dtype).toEqual(x.dtype);
-    expect(output.shape).toEqual(x.shape);
-    expectArraysClose(await x.data(), await output.data());
-  });
-
-  it('x 2d array, rate 0.75', async () => {
-    const x = tensor2d([1, 5, 2, 4, 3, 6], [2, 3]);
-    const rate = 0.75;
-    const output = tf.dropout(x, rate);
-    expect(output.dtype).toEqual(x.dtype);
-    expect(output.shape).toEqual(x.shape);
-    const xValues = await x.data();
-    const outputValues = await output.data();
-    for (let i = 0; i < xValues.length; i++) {
-      if (outputValues[i] !== 0) {
-        expect(outputValues[i]).toBeCloseTo(1 / (1 - rate) * xValues[i]);
-      }
-    }
-  });
-
-  it('x 1d array, rate 0.75, with noise shape length = 1', async () => {
-    const x = tensor1d([1, 2, 2, 1]);
-    const rate = 0.75;
-    const noiseShape = [1];
-    const output = tf.dropout(x, rate, noiseShape);
-    expect(output.dtype).toEqual(x.dtype);
-    expect(output.shape).toEqual(x.shape);
-    const xValues = await x.data();
-    const outputValues = await output.data();
-    const maskedOutput = outputValues[0];
-    for (let i = 0; i < xValues.length; i++) {
-      if (maskedOutput === 0) {
-        expect(outputValues[i]).toBe(maskedOutput);
-      }
-      if (outputValues[i] !== 0) {
-        expect(outputValues[i]).toBeCloseTo(1 / (1 - rate) * xValues[i]);
-      }
-    }
-  });
-
-  it('x 2d array, rate 0.75, with noise shape length = 2', async () => {
-    const x = tensor2d([1, 5, 2, 4, 3, 6], [2, 3]);
-    const rate = 0.75;
-    const noiseShape = [2, 1];
-    const output = tf.dropout(x, rate, noiseShape);
-    expect(output.dtype).toEqual(x.dtype);
-    expect(output.shape).toEqual(x.shape);
-    const xValues = await x.data();
-    const outputValues = await output.data();
-    for (let i = 0; i < x.shape[0]; i++) {
-      const maskedOutput = outputValues[i * x.shape[1]];
-      if (maskedOutput !== 0) {
-        expect(maskedOutput)
-            .toBeCloseTo(1 / (1 - rate) * xValues[i * x.shape[1]]);
-      } else {
-        for (let j = 0; j < x.shape[1]; j++) {
-          expect(outputValues[i * x.shape[1] + j]).toBe(maskedOutput);
-        }
-      }
-    }
-  });
-
-  it('broadcast noise shape', async () => {
-    const x = tensor2d([1, 5, 2, 4, 3, 6], [2, 3]);
-    const rate = 0.75;
-    // broadcast noise shape, same output as using noiseShape [2, 1]
-    const noiseShape = [1];
-    const output = tf.dropout(x, rate, noiseShape);
-    expect(output.dtype).toEqual(x.dtype);
-    expect(output.shape).toEqual(x.shape);
-    const xValues = await x.data();
-    const outputValues = await output.data();
-    for (let i = 0; i < x.shape[0]; i++) {
-      const maskedOutput = outputValues[i * x.shape[1]];
-      if (maskedOutput !== 0) {
-        expect(maskedOutput)
-            .toBeCloseTo(1 / (1 - rate) * xValues[i * x.shape[1]]);
-      } else {
-        for (let j = 0; j < x.shape[1]; j++) {
-          expect(outputValues[i * x.shape[1] + j]).toBe(maskedOutput);
-        }
-      }
-    }
-  });
-
-  it('x 1d array, rate 0.75, with seed', async () => {
-    const x = tensor1d([1, 2, 2, 1]);
-    const rate = 0.75;
-    const seed = 23;
-    const output = tf.dropout(x, rate, null, seed);
-    expect(output.dtype).toEqual(x.dtype);
-    expect(output.shape).toEqual(x.shape);
-    const xValues = await x.data();
-    const outputValues = await output.data();
-    for (let i = 0; i < xValues.length; i++) {
-      if (outputValues[i] !== 0) {
-        expect(outputValues[i]).toBeCloseTo(1 / (1 - rate) * xValues[i]);
-      }
-    }
-  });
-
-  it('x TensorLike object', async () => {
-    const x = [1.0, 2.0, 2.0, 1.0];
-    const rate = 0;
-    const output = tf.dropout(x, rate);
-    expect(output.dtype).toEqual('float32');
-    expect(output.shape).toEqual([4]);
-    expectArraysClose(await output.data(), x);
-  });
-
-  it('throws when x.dtype != float32', async () => {
-    const x = tensor1d([1, 2, 2, 1], 'int32');
-    const rate = 0.75;
-    expect(() => tf.dropout(x, rate)).toThrowError();
-  });
-
-  it('throws when rate is not in the range [0, 1)', async () => {
-    const x = tensor1d([1, 2, 2, 1]);
-    const rate = 1.5;
-    expect(() => tf.dropout(x, rate)).toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/dropout_util.ts b/tfjs-master/tfjs-core/src/ops/dropout_util.ts
deleted file mode 100644
index 160585e18..000000000
--- a/tfjs-master/tfjs-core/src/ops/dropout_util.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import * as util from '../util';
-
-/**
- * Normalize noise shape based on provided tensor and noise shape.
- *
- * @param x Tensor.
- * @param noiseShape The shape for the randomly generated keep/drop flags, as
- *   an array of numbers. Optional.
- * @returns Normalized noise shape.
- */
-export function getNoiseShape(x: Tensor, noiseShape?: number[]): number[] {
-  if (noiseShape == null) {
-    return x.shape.slice();
-  }
-  if (util.arraysEqual(x.shape, noiseShape)) {
-    return noiseShape;
-  }
-  if (x.shape.length === noiseShape.length) {
-    const newDimension: number[] = [];
-    for (let i = 0; i < x.shape.length; i++) {
-      if (noiseShape[i] == null && x.shape[i] != null) {
-        newDimension.push(x.shape[i]);
-      } else {
-        newDimension.push(noiseShape[i]);
-      }
-    }
-    return newDimension;
-  }
-
-  return noiseShape;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/dropout_util_test.ts b/tfjs-master/tfjs-core/src/ops/dropout_util_test.ts
deleted file mode 100644
index b5c388d72..000000000
--- a/tfjs-master/tfjs-core/src/ops/dropout_util_test.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {getNoiseShape} from './dropout_util';
-
-describeWithFlags('getNoiseShape', ALL_ENVS, () => {
-  it('x.shape == noiseShape', async () => {
-    const x = tf.ones([2, 3]);
-    const noiseShape = [2, 3];
-    const shape = getNoiseShape(x, noiseShape);
-    expect(shape).toEqual([2, 3]);
-  });
-
-  it('x.shape and noiseShape have same length, different value', async () => {
-    const x = tf.ones([2, 3]);
-    const noiseShape = [2, 1];
-    const shape = getNoiseShape(x, noiseShape);
-    expect(shape).toEqual([2, 1]);
-  });
-
-  it('noiseShape has null value', async () => {
-    const x = tf.ones([2, 3]);
-    const noiseShape = [2, null];
-    const shape = getNoiseShape(x, noiseShape);
-    expect(shape).toEqual([2, 3]);
-  });
-
-  it('x.shape and noiseShape has different length', async () => {
-    const x = tf.ones([2, 3, 4]);
-    const noiseShape = [2, 3];
-    const shape = getNoiseShape(x, noiseShape);
-    expect(shape).toEqual([2, 3]);
-  });
-
-  it('noiseShape is null', async () => {
-    const x = tf.ones([2, 3]);
-    const shape = getNoiseShape(x, null);
-    expect(shape).toEqual([2, 3]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/einsum.ts b/tfjs-master/tfjs-core/src/ops/einsum.ts
deleted file mode 100644
index 1fde865fa..000000000
--- a/tfjs-master/tfjs-core/src/ops/einsum.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Einsum, EinsumAttrs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-
-import {op} from './operation';
-
-/**
- * Tensor contraction over specified indices and outer product.
- *
- * `einsum` allows defining Tensors by defining their element-wise computation.
- * This computation is based on
- * [Einstein summation](https://en.wikipedia.org/wiki/Einstein_notation).
- *
- * Some special cases include:
- *
- * Matrix multiplication:
- * ```js
- * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
- * const y = tf.tensor2d([[0, 1], [2, 3], [4, 5]]);
- * x.print();
- * y.print();
- * tf.einsum('ij,jk->ik', x, y).print();
- * ```
- *
- * Dot product:
- * ```js
- * const x = tf.tensor1d([1, 2, 3]);
- * const y = tf.tensor1d([0, 1, 2]);
- * x.print();
- * y.print();
- * tf.einsum('i,i->', x, y).print();
- * ```
- *
- * Batch dot product:
- * ```js
- * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
- * const y = tf.tensor2d([[0, 1, 2], [3, 4, 5]]);
- * x.print();
- * y.print();
- * tf.einsum('bi,bi->b', x, y).print();
- * ```
- *
- * Outer prouduct:
- * ```js
- * const x = tf.tensor1d([1, 3, 5]);
- * const y = tf.tensor1d([2, 4, 6]);
- * x.print();
- * y.print();
- * tf.einsum('i,j->ij', x, y).print();
- * ```
- *
- * Matrix transpose:
- * ```js
- * const x = tf.tensor2d([[1, 2], [3, 4]]);
- * x.print();
- * tf.einsum('ij->ji', x).print();
- * ```
- *
- * Batch matrix transpose:
- * ```js
- * const x = tf.tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]);
- * x.print();
- * tf.einsum('bij->bji', x).print();
- * ```
- *
- * Limitations:
- *
- * This implementation of einsum has the following limitations:
- *
- * - Does not support >2 input tensors.
- * - Does not support duplicate axes for any given input tensor. E.g., equation
- *   'ii->' is not supported.
- * - The `...` notation is not supported.
- *
- * @param equation a string describing the contraction, in the same format as
- * [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html).
- * @param tensors the input(s) to contract (each one a Tensor), whose shapes
- *     should be consistent with equation.
- * @returns The output tensor.
- *
- * @doc {heading: 'Tensors', subheading: 'Matrices'}
- */
-export function einsum_(equation: string, ...tensors: Tensor[]): Tensor {
-  const $tensors =
-      tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'einsum'));
-  const attrs: EinsumAttrs = {equation};
-  return ENGINE.runKernel(
-      Einsum, $tensors as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const einsum = /* @__PURE__ */ op({einsum_});
diff --git a/tfjs-master/tfjs-core/src/ops/einsum_test.ts b/tfjs-master/tfjs-core/src/ops/einsum_test.ts
deleted file mode 100644
index 14a701203..000000000
--- a/tfjs-master/tfjs-core/src/ops/einsum_test.ts
+++ /dev/null
@@ -1,265 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-import {tensor1d, tensor2d, tensor3d} from './ops';
-
-describeWithFlags('einsum', ALL_ENVS, () => {
-  it('two scalars', async () => {
-    const x = tf.scalar(2);
-    const y = tf.scalar(3);
-    const out = tf.einsum(',->', x, y);
-    expectArraysClose(await out.data(), 6);
-  });
-
-  it('1D tensor and scalars: reduce', async () => {
-    const x = tensor1d([2, 3]);
-    const y = tf.scalar(4);
-    const out = tf.einsum('i,->', x, y);
-    expectArraysClose(await out.data(), 20);
-  });
-
-  it('1D tensor and scalars: multiply', async () => {
-    const x = tensor1d([2, 3]);
-    const y = tf.scalar(4);
-    const out = tf.einsum('i,->i', x, y);
-    expectArraysClose(await out.data(), [8, 12]);
-  });
-
-  it('1d reduce sum', async () => {
-    const x = tensor1d([2, 4, 6]);
-    const out = tf.einsum('i->', x);
-    expectArraysClose(await out.data(), 12);
-  });
-
-  it('2d matrix reduce sum', async () => {
-    const x = tensor2d([[1, 2], [3, 4]]);
-    const out = tf.einsum('ij->', x);
-    expectArraysClose(await out.data(), 10);
-  });
-
-  it('2d matrices multiply and reduce summing', async () => {
-    const x = tensor2d([[1, 2], [3, 4]]);
-    const y = tensor2d([[4, 3], [2, 1]]);
-    const out = tf.einsum('ij,ji->', x, y);
-    expectArraysClose(await out.data(), 21);
-  });
-
-  it('2d matrix times scalar and reduce summing', async () => {
-    const x = tensor2d([[1, 2], [3, 4]]);
-    const y = tf.scalar(5);
-    const out = tf.einsum('ij,->', x, y);
-    expectArraysClose(await out.data(), 50);
-  });
-
-  it('two 1d tensors dot', async () => {
-    const x = tensor1d([1, 3, 5]);
-    const y = tensor1d([2, 4, 6]);
-    const out = tf.einsum('i,i->', x, y);
-    expectArraysClose(await out.data(), 44);
-  });
-
-  it('two 1d tensors outer', async () => {
-    const x = tensor1d([1, 3, 5]);
-    const y = tensor1d([2, 4, 6]);
-    const out = tf.einsum('i,j->ij', x, y);
-    expectArraysClose(await out.data(), [[2, 4, 6], [6, 12, 18], [10, 20, 30]]);
-  });
-
-  it('2d matrix calculate trace: duplicate axes not implemented yet', () => {
-    const x = tensor2d([[1, 2], [3, 4]]);
-    expect(() => tf.einsum('ii->', x)).toThrowError(/not implemented yet/);
-  });
-
-  it('2d and 1d matrix & vector multiply', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const y = tensor1d([2, 4, 6]);
-    const out = tf.einsum('ij,j->i', x, y);
-    expectArraysClose(await out.data(), [28, 64]);
-  });
-
-  it('2d matrix sum along columns', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const out = tf.einsum('ij->j', x);
-    expectArraysClose(await out.data(), [5, 7, 9]);
-  });
-
-  it('2d matrix sum along rows', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const out = tf.einsum('ij->i', x);
-    expectArraysClose(await out.data(), [6, 15]);
-  });
-
-  it('2d matrix transposing', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const out = tf.einsum('ij->ji', x);
-    expectArraysClose(await out.data(), [[1, 4], [2, 5], [3, 6]]);
-  });
-
-  it('2d matrix multiply', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const y = tensor2d([[0, 1], [2, 3], [4, 5]]);
-    const out = tf.einsum('ij,jk->ik', x, y);
-    expectArraysClose(await out.data(), [[16, 22], [34, 49]]);
-  });
-
-  it('2d matrix multiply and transposing', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const y = tensor2d([[0, 1], [2, 3], [4, 5]]);
-    const out = tf.einsum('ij,jk->ki', x, y);
-    expectArraysClose(await out.data(), [[16, 34], [22, 49]]);
-  });
-
-  it('two 2d matrices batch dot', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const y = tensor2d([[0, 1, 2], [3, 4, 5]]);
-    const out = tf.einsum('bi,bi->b', x, y);
-    expectArraysClose(await out.data(), [8, 62]);
-  });
-
-  it('two 2d matrices batch outer', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const y = tensor2d([[0, 1, 2], [3, 4, 5]]);
-    const out = tf.einsum('bi,bj->bij', x, y);
-    expectArraysClose(await out.data(), [
-      [[0, 1, 2], [0, 2, 4], [0, 3, 6]],
-      [[12, 16, 20], [15, 20, 25], [18, 24, 30]]
-    ]);
-  });
-
-  it('two 3d tensors batch matmul', async () => {
-    const x = tf.reshape(tf.range(1, 13), [2, 2, 3]);
-    const y = tf.reshape(tf.range(1, 19), [2, 3, 3]);
-    const out = tf.einsum('bij,bjk->bik', x, y);
-    expectArraysClose(
-        await out.data(),
-        [[[30, 36, 42], [66, 81, 96]], [[318, 342, 366], [435, 468, 501]]]);
-  });
-
-  it('two 3d tensors A', async () => {
-    const x = tf.reshape(tf.range(1, 9), [2, 2, 2]);
-    const y = tf.reshape(tf.range(1, 13), [2, 3, 2]);
-    const out = tf.einsum('adc,abc->abd', x, y);
-    expectArraysClose(
-        await out.data(),
-        [[[5, 11], [11, 25], [17, 39]], [[83, 113], [105, 143], [127, 173]]]);
-  });
-
-  it('two 3d tensors B', async () => {
-    const x = tf.reshape(tf.range(1, 9), [2, 2, 2]);
-    const y = tf.reshape(tf.range(1, 13), [2, 3, 2]);
-    const out = tf.einsum('adc,abc->adb', x, y);
-    expectArraysClose(
-        await out.data(),
-        [[[5, 11, 17], [11, 25, 39]], [[83, 105, 127], [113, 143, 173]]]);
-  });
-
-  it('one 3d tensor: batch matrix transposing', async () => {
-    const x = tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]);
-    const out = tf.einsum('bij->bji', x);
-    expectArraysClose(
-        await out.data(), [[[1, 3], [2, 4]], [[-1, -3], [-2, -4]]]);
-  });
-
-  it('4d tensor and 3d tensor, contracting two dimensions', async () => {
-    const x = tf.reshape(tf.range(1, 33), [2, 4, 2, 2]);
-    const y = tf.reshape(tf.range(1, 9), [2, 2, 2]);
-    const out = tf.einsum('abcd,cde->abe', x, y);
-    expectArraysClose(await out.data(), [
-      [[50, 60], [114, 140], [178, 220], [242, 300]],
-      [[306, 380], [370, 460], [434, 540], [498, 620]]
-    ]);
-  });
-
-  it('two 4d tensors, contracting one dimension', async () => {
-    const x = tf.reshape(tf.range(1, 33), [2, 4, 2, 2]);
-    const y = tf.reshape(tf.range(1, 25), [2, 3, 2, 2]);
-    const out = tf.einsum('aecd,abcd->acbe', x, y);
-    expectArraysClose(await out.data(), [
-      [
-        [[5, 17, 29, 41], [17, 61, 105, 149], [29, 105, 181, 257]],
-        [[25, 53, 81, 109], [53, 113, 173, 233], [81, 173, 265, 357]]
-      ],
-      [
-        [[473, 581, 689, 797], [613, 753, 893, 1033], [753, 925, 1097, 1269]],
-        [[605, 729, 853, 977], [761, 917, 1073, 1229], [917, 1105, 1293, 1481]]
-      ]
-    ]);
-  });
-
-  it('two 4d tensors, contracting two dimensions', async () => {
-    const x = tf.reshape(tf.range(1, 33), [2, 4, 2, 2]);
-    const y = tf.reshape(tf.range(1, 25), [2, 3, 2, 2]);
-    const out = tf.einsum('aecd,abcd->abe', x, y);
-    expectArraysClose(await out.data(), [
-      [[30, 70, 110, 150], [70, 174, 278, 382], [110, 278, 446, 614]],
-      [
-        [1078, 1310, 1542, 1774], [1374, 1670, 1966, 2262],
-        [1670, 2030, 2390, 2750]
-      ]
-    ]);
-  });
-
-  it('mismatched dimensions throws error', () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const y = tensor2d([[0, 1], [2, 3]]);
-    expect(() => tf.einsum('ij,jk->ik', x, y))
-        .toThrowError(/dimension 3 at axis 0 of (the )?input shaped \[2,2\]/);
-  });
-
-  it('incorrect equation throws error', () => {
-    const x = tensor2d([[1, 2], [3, 4]]);
-    const y = tensor2d([[0, 1], [2, 3]]);
-    expect(() => tf.einsum('', x, y))
-        .toThrowError(/Equations without an arrow|Expecting exactly one/);
-    expect(() => tf.einsum('ij,jk>ik', x, y))
-        .toThrowError(/Equations without an arrow|Expecting exactly one/);
-  });
-
-  it('incorrect number of tensors throws error', () => {
-    const x = tensor2d([[1, 2], [3, 4]]);
-    const y = tensor2d([[0, 1], [2, 3]]);
-    expect(() => tf.einsum('ij->ji', x, y))
-        .toThrowError(/Expected 1 inputs? (tensors, received)?(but got:)? 2/);
-  });
-
-  it('more than two input tensors throws error', async () => {
-    const x = tensor2d([[1, 2], [3, 4]]);
-    const y = tensor2d([[0, 1], [2, 3]]);
-    const z = tensor2d([[-1, 0], [1, 2]]);
-    expect(() => tf.einsum('ij,jk,kl->il', x, y, z))
-        .toThrowError(/(more than 2 input tensors)|(Expecting 1 or 2 input)/);
-  });
-
-  it('nonexistent dimension throws error', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const y = tensor2d([[0, 1], [2, 3], [4, 5]]);
-    expect(() => tf.einsum('ij,jk->in', x, y))
-        .toThrowError(
-            'Output subscripts contain the label n not present in ' +
-            'the input subscripts.');
-  });
-
-  it('two arrows in equation throws error', async () => {
-    const x = tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const y = tensor2d([[0, 1], [2, 3], [4, 5]]);
-    expect(() => tf.einsum('ij,jk->ik->i', x, y)).toThrowError(/exactly one/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/elu.ts b/tfjs-master/tfjs-core/src/ops/elu.ts
deleted file mode 100644
index e1820d808..000000000
--- a/tfjs-master/tfjs-core/src/ops/elu.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Elu, EluInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes exponential linear element-wise: `x > 0 ? x : (e ^ x) - 1`.
- *
- * ```js
- * const x = tf.tensor1d([-1, 1, -3, 2]);
- *
- * x.elu().print();  // or tf.elu(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function elu_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'elu', 'float32');
-
-  const inputs: EluInputs = {x: $x};
-
-  return ENGINE.runKernel(Elu, inputs as unknown as NamedTensorMap);
-}
-
-export const elu = /* @__PURE__ */ op({elu_});
diff --git a/tfjs-master/tfjs-core/src/ops/elu_test.ts b/tfjs-master/tfjs-core/src/ops/elu_test.ts
deleted file mode 100644
index 35ec625bf..000000000
--- a/tfjs-master/tfjs-core/src/ops/elu_test.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('elu', ALL_ENVS, () => {
-  it('calculate elu', async () => {
-    const a = tf.tensor1d([1, -1, 0]);
-    const result = tf.elu(a);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [1, -0.6321, 0]);
-  });
-
-  it('elu propagates NaN', async () => {
-    const a = tf.tensor1d([1, NaN]);
-    const result = tf.elu(a);
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [1, NaN]);
-  });
-
-  it('derivative', async () => {
-    const x = tf.tensor1d([1, 3, -2, 0.4]);
-    const dy = tf.tensor1d([5, 50, 500, 5000]);
-    const gradients = tf.grad(a => tf.elu(a))(x, dy);
-
-    expect(gradients.shape).toEqual(x.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [5, 50, 500 * Math.exp(-2), 5000]);
-  });
-
-  it('gradient with clones', async () => {
-    const x = tf.tensor1d([1, 3, -2, 0.4]);
-    const dy = tf.tensor1d([5, 50, 500, 5000]);
-    const gradients = tf.grad(a => tf.elu(a.clone()).clone())(x, dy);
-
-    expect(gradients.shape).toEqual(x.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [5, 50, 500 * Math.exp(-2), 5000]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.elu({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'elu' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.elu([1, -1, 0]);
-    expect(result.shape).toEqual(result.shape);
-    expectArraysClose(await result.data(), [1, -0.6321, 0]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.elu('q'))
-        .toThrowError(/Argument 'x' passed to 'elu' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.elu(tf.tensor1d([1, 2, 3], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'elu' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/ensure_shape.ts b/tfjs-master/tfjs-core/src/ops/ensure_shape.ts
deleted file mode 100644
index 0e3f3bd65..000000000
--- a/tfjs-master/tfjs-core/src/ops/ensure_shape.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {Rank, ShapeMap} from '../types';
-import {arraysEqualWithNull} from '../util_base';
-
-import {op} from './operation';
-
-/**
- * Checks the input tensor mathes the given shape.
- *
- * Given an input tensor, returns a new tensor with the same values as the
- * input tensor with shape `shape`.
- *
- * The method supports the null value in tensor. It will still check the shapes,
- * and null is a placeholder.
- *
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- * const y = tf.tensor1d([1, null, 3, 4]);
- * const z = tf.tensor2d([1, 2, 3, 4], [2,2]);
- * tf.ensureShape(x, [4]).print();
- * tf.ensureShape(y, [4]).print();
- * tf.ensureShape(z, [null, 2]).print();
- * ```
- *
- * @param x The input tensor to be ensured.
- * @param shape A TensorShape representing the shape of this tensor, an array
- *     or null.
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function ensureShape_<R extends Rank>(x: Tensor, shape: ShapeMap[R]): Tensor {
-  const $x = convertToTensor(x, 'x', 'ensureShape', 'string_or_numeric');
-  if (!arraysEqualWithNull($x.shape, shape)) {
-    throw new Error(`EnsureShape: Shape of tensor ${
-        $x.shape} is not compatible with expected shape ${shape}`);
-  }
-
-  return x;
-}
-export const ensureShape = /* @__PURE__ */ op({ensureShape_});
diff --git a/tfjs-master/tfjs-core/src/ops/ensure_shape_test.ts b/tfjs-master/tfjs-core/src/ops/ensure_shape_test.ts
deleted file mode 100644
index 4a6146345..000000000
--- a/tfjs-master/tfjs-core/src/ops/ensure_shape_test.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {ensureShape} from './ensure_shape';
-
-describeWithFlags('ensureShape', ALL_ENVS, () => {
-  it('basic', () => {
-    const x = tf.ones([2, 3]);
-    expect(ensureShape(x, [2, 3])).toEqual(x);
-  });
-
-  it('different shape', () => {
-    const x = tf.ones([2, 3]);
-    expect(() => ensureShape(x, [5, 3])).toThrowError(/EnsureShape:/);
-  });
-
-  it('different length', () => {
-    const x = tf.tensor1d([1, 2, 3, 4]);
-    expect(() => ensureShape(x, [1, 3])).toThrowError(/EnsureShape:/);
-  });
-
-  it('null shape', () => {
-    const x = tf.tensor2d([1, null, 3, 4], [2, 2]);
-    expect(ensureShape(x, [2, 2])).toEqual(x);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/equal.ts b/tfjs-master/tfjs-core/src/ops/equal.ts
deleted file mode 100644
index 210e4fc61..000000000
--- a/tfjs-master/tfjs-core/src/ops/equal.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Equal, EqualInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns the truth value of (a == b) element-wise. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3]);
- * const b = tf.tensor1d([2, 2, 2]);
- *
- * a.equal(b).print();
- * ```
- *
- * @param a The first input tensor.
- * @param b The second input tensor. Must have the same dtype as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function equal_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'equal', 'string_or_numeric');
-  let $b = convertToTensor(b, 'b', 'equal', 'string_or_numeric');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: EqualInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Equal, inputs as unknown as NamedTensorMap);
-}
-
-export const equal = /* @__PURE__ */ op({equal_});
diff --git a/tfjs-master/tfjs-core/src/ops/equal_test.ts b/tfjs-master/tfjs-core/src/ops/equal_test.ts
deleted file mode 100644
index 35b18d982..000000000
--- a/tfjs-master/tfjs-core/src/ops/equal_test.ts
+++ /dev/null
@@ -1,294 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('equal', ALL_ENVS, () => {
-  it('Tensor1D - int32', async () => {
-    let a = tf.tensor1d([1, 4, 5], 'int32');
-    let b = tf.tensor1d([2, 3, 5], 'int32');
-
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);
-
-    a = tf.tensor1d([2, 2, 2], 'int32');
-    b = tf.tensor1d([2, 2, 2], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1]);
-
-    a = tf.tensor1d([0, 0], 'int32');
-    b = tf.tensor1d([3, 3], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0]);
-  });
-  it('Tensor1D - float32', async () => {
-    let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
-
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);
-
-    a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1]);
-
-    a = tf.tensor1d([0.45, 0.123], 'float32');
-    b = tf.tensor1d([3.123, 3.321], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const a = [1.1, 4.1, 5];
-    const b = [2.2, 3.2, 5];
-
-    let res =
-        tf.equal(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [0, 0, 1]);
-
-    res = tf.equal(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [1, 0, 0]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [1.1, 4.1, 5.1];
-    const b = [2.2, 3.2, 5.1];
-
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);
-  });
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    const b = [2.2, 3.2, 5.1];
-
-    expectArraysClose(await a.equal(b).data(), [0, 0, 1]);
-  });
-
-  it('mismatched Tensor1D shapes - int32', () => {
-    const a = tf.tensor1d([1, 2], 'int32');
-    const b = tf.tensor1d([1, 2, 3], 'int32');
-    const f = () => {
-      tf.equal(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('mismatched Tensor1D shapes - float32', () => {
-    const a = tf.tensor1d([1.1, 2.1], 'float32');
-    const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
-    const f = () => {
-      tf.equal(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('NaNs in Tensor1D - float32', async () => {
-    const a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
-    const b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0]);
-  });
-  it('scalar and 1D broadcast', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([1, 2, 3, 4, 5, 2]);
-    const res = tf.equal(a, b);
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([6]);
-    expectArraysEqual(await res.data(), [0, 1, 0, 0, 0, 1]);
-  });
-
-  // Tensor2D:
-  it('Tensor2D - int32', async () => {
-    let a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
-    let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0, 0, 0]);
-
-    a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1]);
-  });
-  it('Tensor2D - float32', async () => {
-    let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
-    let b =
-        tf.tensor2d([[2.1, 4.1, 5.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 1, 1, 0, 0, 0]);
-
-    a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor2D shapes - int32', async () => {
-    const a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
-    const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 1, 0, 1, 0, 0]);
-  });
-  it('broadcasting Tensor2D shapes - float32', async () => {
-    const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
-    const b =
-        tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 1, 0, 1, 0, 0]);
-  });
-  it('NaNs in Tensor2D - float32', async () => {
-    const a = tf.tensor2d([[1.1, NaN], [1.1, NaN]], [2, 2], 'float32');
-    const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1, 0]);
-  });
-  it('broadcasting Tensor2D shapes each with 1 dim', async () => {
-    const a = tf.tensor2d([1, 2, 5], [1, 3]);
-    const b = tf.tensor2d([5, 1], [2, 1]);
-    const res = tf.equal(a, b);
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysEqual(await res.data(), [0, 0, 1, 1, 0, 0]);
-  });
-  it('2D and scalar broadcast', async () => {
-    const a = tf.tensor2d([1, 2, 3, 2, 5, 6], [2, 3]);
-    const b = tf.scalar(2);
-    const res = tf.equal(a, b);
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysEqual(await res.data(), [0, 1, 0, 1, 0, 0]);
-  });
-
-  // Tensor3D:
-  it('Tensor3D - int32', async () => {
-    let a =
-        tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
-    let b =
-        tf.tensor3d([[[2], [3], [6]], [[7], [10], [12]]], [2, 3, 1], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0, 0, 1]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1, 1, 1]);
-  });
-  it('Tensor3D - float32', async () => {
-    let a = tf.tensor3d(
-        [[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
-    let b = tf.tensor3d(
-        [[[2.1], [3.1], [6.1]], [[7.1], [10.1], [12.1]]], [2, 3, 1], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0, 0, 1]);
-
-    a = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
-    b = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor3D shapes - int32', async () => {
-    const a = tf.tensor3d(
-        [[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2],
-        'int32');
-    const b =
-        tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
-    expectArraysClose(
-        await tf.equal(a, b).data(), [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor3D shapes - float32', async () => {
-    const a = tf.tensor3d(
-        [
-          [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
-          [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
-        ],
-        [2, 3, 2], 'float32');
-    const b = tf.tensor3d(
-        [[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
-    expectArraysClose(
-        await tf.equal(a, b).data(), [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0]);
-  });
-  it('NaNs in Tensor3D - float32', async () => {
-    const a = tf.tensor3d(
-        [[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
-    const b = tf.tensor3d(
-        [[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1, 0, 1, 0]);
-  });
-  it('3D and scalar', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, -1], [2, 3, 1]);
-    const b = tf.scalar(-1);
-    const res = tf.equal(a, b);
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([2, 3, 1]);
-    expectArraysEqual(await res.data(), [0, 0, 0, 0, 0, 1]);
-  });
-
-  // Tensor4D:
-  it('Tensor4D - int32', async () => {
-    let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
-    let b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 1]);
-
-    a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1]);
-
-    a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0]);
-  });
-  it('Tensor4D - float32', async () => {
-    let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
-    let b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 1]);
-
-    a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 1, 1, 1]);
-
-    a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor4D shapes - int32', async () => {
-    const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
-    const b = tf.tensor4d(
-        [[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 0, 0, 0, 1, 0, 0, 0]);
-  });
-  it('broadcasting Tensor4D shapes - float32', async () => {
-    const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d(
-        [[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]],
-        [2, 2, 1, 2], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [1, 0, 0, 0, 1, 0, 0, 0]);
-  });
-  it('NaNs in Tensor4D - float32', async () => {
-    const a = tf.tensor4d([1.1, NaN, 1.1, 0.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1, 0]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.equal({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'equal' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.equal(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'equal' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 4, 5];
-    const b = [2, 3, 5];
-    expectArraysClose(await tf.equal(a, b).data(), [0, 0, 1]);
-  });
-
-  it('should support string comparison', async () => {
-    const tensorA = tf.tensor('aa', [], 'string');
-    const tensorB = tf.tensor(['aa', 'ab', 'aaa'], [3], 'string');
-    const result = await tf.equal(tensorA, tensorB);
-
-    expectArraysEqual(result.shape, [3]);
-    expectArraysEqual(await result.data(), [1, 0, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/erf.ts b/tfjs-master/tfjs-core/src/ops/erf.ts
deleted file mode 100644
index 2e949bd7e..000000000
--- a/tfjs-master/tfjs-core/src/ops/erf.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Erf, ErfInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {cast} from './cast';
-import {op} from './operation';
-
-/**
- * Computes Gauss error function of the input `tf.Tensor` element-wise:
- * `erf(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, .1, -.1, .7]);
- *
- * x.erf().print(); // or tf.erf(x);
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function erf_<T extends Tensor>(x: T|TensorLike): T {
-  let $x = convertToTensor(x, 'x', 'erf');
-  util.assert(
-      $x.dtype === 'int32' || $x.dtype === 'float32',
-      () => 'Input dtype must be `int32` or `float32`.');
-
-  if ($x.dtype === 'int32') {
-    $x = cast($x, 'float32');
-  }
-
-  const inputs: ErfInputs = {x: $x};
-  return ENGINE.runKernel(Erf, inputs as unknown as NamedTensorMap);
-}
-export const erf = /* @__PURE__ */ op({erf_});
diff --git a/tfjs-master/tfjs-core/src/ops/erf_test.ts b/tfjs-master/tfjs-core/src/ops/erf_test.ts
deleted file mode 100644
index 0dd9b0374..000000000
--- a/tfjs-master/tfjs-core/src/ops/erf_test.ts
+++ /dev/null
@@ -1,135 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('erf', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [-0.25, 0.25, 0.5, .75, -0.4];
-    const a = tf.tensor1d(values);
-    const result = tf.erf(a);
-    const expected = [-0.2763264, 0.2763264, 0.5204999, 0.7111556, -0.4283924];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('blowup', async () => {
-    const values = [-1.4, -2.5, -3.1, -4.4];
-    const a = tf.tensor1d(values);
-    const result = tf.erf(a);
-    const expected = [-0.9522852, -0.999593, -0.9999883, -1];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('scalar', async () => {
-    const a = tf.scalar(1);
-    const result = tf.erf(a);
-    const expected = [0.8427008];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('scalar in int32', async () => {
-    const a = tf.scalar(1, 'int32');
-    const result = tf.erf(a);
-    const expected = [0.8427008];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('tensor2d', async () => {
-    const values = [0.2, 0.3, 0.4, 0.5];
-    const a = tf.tensor2d(values, [2, 2]);
-    const result = tf.erf(a);
-    const expected = [0.2227026, 0.32862678, 0.42839235, 0.5204999];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([0.5, NaN, 0]);
-    const res = tf.erf(a);
-    expectArraysClose(await res.data(), [0.5204999, NaN, 0.0]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-    const gradients = tf.grad(a => tf.erf(a))(a, dy);
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [8 * 2 * Math.exp(-0.5 * 0.5) / Math.sqrt(Math.PI)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-    const gradients = tf.grad(a => tf.erf(a.clone()).clone())(a, dy);
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [8 * 2 * Math.exp(-0.5 * 0.5) / Math.sqrt(Math.PI)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-0.1, 0.2, 0.3, -0.5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-    const gradients = tf.grad(a => tf.erf(a))(a, dy);
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] * 2 * Math.exp(-aValues[i] * aValues[i]) /
-          Math.sqrt(Math.PI);
-    }
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-0.3, 0.1, 0.2, 0.3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-    const gradients = tf.grad(a => tf.erf(a))(a, dy);
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] * 2 * Math.exp(-aValues[i] * aValues[i]) /
-          Math.sqrt(Math.PI);
-    }
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.erf({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'erf' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.erf(1);
-    expectArraysClose(await result.data(), [0.8427008]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.erf('q'))
-        .toThrowError(/Argument 'x' passed to 'erf' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/erf_util.ts b/tfjs-master/tfjs-core/src/ops/erf_util.ts
deleted file mode 100644
index a974c457f..000000000
--- a/tfjs-master/tfjs-core/src/ops/erf_util.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export const ERF_P = 0.3275911;
-export const ERF_A1 = 0.254829592;
-export const ERF_A2 = -0.284496736;
-export const ERF_A3 = 1.421413741;
-export const ERF_A4 = -1.453152027;
-export const ERF_A5 = 1.061405429;
diff --git a/tfjs-master/tfjs-core/src/ops/euclidean_norm.ts b/tfjs-master/tfjs-core/src/ops/euclidean_norm.ts
deleted file mode 100644
index cd8bbb473..000000000
--- a/tfjs-master/tfjs-core/src/ops/euclidean_norm.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {TensorLike} from '../types';
-
-import {norm} from './norm';
-import {op} from './operation';
-
-/**
- * Computes the Euclidean norm of scalar, vectors, and matrices.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- *
- * x.euclideanNorm().print();  // or tf.euclideanNorm(x)
- * ```
- *
- * @param x The input array.
- * @param axis Optional. If axis is null (the default), the input is
- * considered a vector and a single vector norm is computed over the entire
- * set of values in the Tensor, i.e. euclideanNorm(x) is equivalent
- * to euclideanNorm(x.reshape([-1])). If axis is an integer, the input
- * is considered a batch of vectors, and axis determines the axis in x
- * over which to compute vector norms. If axis is a 2-tuple of integer it is
- * considered a batch of matrices and axis determines the axes in NDArray
- * over which to compute a matrix norm.
- * @param keepDims Optional. If true, the norm has the same dimensionality
- * as the input.
- *
- * @doc {heading: 'Operations', subheading: 'Matrices'}
- */
-function euclideanNorm_(
-    x: Tensor|TensorLike, axis: number|number[] = null,
-    keepDims = false): Tensor {
-  return norm(x, 'euclidean', axis, keepDims);
-}
-
-export const euclideanNorm = /* @__PURE__ */ op({euclideanNorm_});
diff --git a/tfjs-master/tfjs-core/src/ops/euclidean_norm_test.ts b/tfjs-master/tfjs-core/src/ops/euclidean_norm_test.ts
deleted file mode 100644
index f8cf83f94..000000000
--- a/tfjs-master/tfjs-core/src/ops/euclidean_norm_test.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-// Because euclidean is the default norm option, the norm_test covers
-// the tests for euclideanNorm. Here we basically just test the API
-// can be called correctly.
-describeWithFlags('euclideanNorm', ALL_ENVS, () => {
-  it('vector euclidean norm', async () => {
-    const a = tf.tensor1d([1, -2, 3, -4]);
-    const result = tf.euclideanNorm(a);
-
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), 5.4772);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/exp.ts b/tfjs-master/tfjs-core/src/ops/exp.ts
deleted file mode 100644
index fe06482d0..000000000
--- a/tfjs-master/tfjs-core/src/ops/exp.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Exp, ExpInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, -3]);
- *
- * x.exp().print();  // or tf.exp(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function exp_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'exp');
-
-  const inputs: ExpInputs = {x: $x};
-  return ENGINE.runKernel(Exp, inputs as unknown as NamedTensorMap);
-}
-export const exp = /* @__PURE__ */ op({exp_});
diff --git a/tfjs-master/tfjs-core/src/ops/exp_test.ts b/tfjs-master/tfjs-core/src/ops/exp_test.ts
deleted file mode 100644
index a29e0dea4..000000000
--- a/tfjs-master/tfjs-core/src/ops/exp_test.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {backend} from '../index';
-
-describeWithFlags('exp', ALL_ENVS, () => {
-  it('exp', async () => {
-    const a = tf.tensor1d([1, 2, 0]);
-    const r = tf.exp(a);
-
-    expectArraysClose(await r.data(), [Math.exp(1), Math.exp(2), 1]);
-  });
-
-  it('int32', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const a = tf.tensor1d([10], 'int32');
-      const r = tf.exp(a);
-
-      expect(r.dtype).toEqual('float32');
-      expectArraysClose(await r.data(), [Math.exp(10)]);
-    }
-  });
-
-  it('exp propagates NaNs', async () => {
-    const a = tf.tensor1d([1, NaN, 0]);
-    const r = tf.exp(a);
-    expectArraysClose(await r.data(), [Math.exp(1), NaN, 1]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.exp(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [3 * Math.exp(0.5)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.exp(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [3 * Math.exp(0.5)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1, 2, 3, -5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.exp(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [1 * Math.exp(-1), 2 * Math.exp(2), 3 * Math.exp(3), 4 * Math.exp(-5)],
-        1e-1);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.exp(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [1 * Math.exp(-3), 2 * Math.exp(1), 3 * Math.exp(2), 4 * Math.exp(3)],
-        1e-1);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.exp({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'exp' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.exp([1, 2, 0]);
-    expectArraysClose(await r.data(), [Math.exp(1), Math.exp(2), 1]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.exp('q'))
-        .toThrowError(/Argument 'x' passed to 'exp' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/expand_dims.ts b/tfjs-master/tfjs-core/src/ops/expand_dims.ts
deleted file mode 100644
index 583bde048..000000000
--- a/tfjs-master/tfjs-core/src/ops/expand_dims.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {ExpandDims, ExpandDimsAttrs, ExpandDimsInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension
- * into the tensor's shape.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- * const axis = 1;
- * x.expandDims(axis).print();
- * ```
- *
- * @param x The input tensor whose dimensions are to be expanded.
- * @param axis The dimension index at which to insert shape of `1`. Defaults
- *     to 0 (the first dimension).
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function expandDims_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {
-  const $x = convertToTensor(x, 'x', 'expandDims', 'string_or_numeric');
-
-  util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');
-
-  const inputs: ExpandDimsInputs = {input: $x};
-  const attrs: ExpandDimsAttrs = {dim: axis};
-
-  return ENGINE.runKernel(
-      ExpandDims, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const expandDims = /* @__PURE__ */ op({expandDims_});
diff --git a/tfjs-master/tfjs-core/src/ops/expand_dims_test.ts b/tfjs-master/tfjs-core/src/ops/expand_dims_test.ts
deleted file mode 100644
index 387f083b2..000000000
--- a/tfjs-master/tfjs-core/src/ops/expand_dims_test.ts
+++ /dev/null
@@ -1,168 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('expandDims', ALL_ENVS, () => {
-  it('scalar, default axis is 0', async () => {
-    const res = tf.scalar(1).expandDims();
-    expect(res.shape).toEqual([1]);
-    expectArraysClose(await res.data(), [1]);
-  });
-
-  it('scalar, axis is out of bounds throws error', () => {
-    const f = () => tf.scalar(1).expandDims(1);
-    expect(f).toThrowError();
-  });
-
-  it('1d, axis=-3', () => {
-    expect(() => {
-      tf.tensor1d([1, 2, 3]).expandDims(-3);
-    }).toThrowError();
-  });
-
-  it('1d, axis=-2', async () => {
-    const res = tf.tensor1d([1, 2, 3]).expandDims(-2 /* axis */);
-    expect(res.shape).toEqual([1, 3]);
-    expectArraysClose(await res.data(), [1, 2, 3]);
-  });
-
-  it('1d, axis=-1', async () => {
-    const res = tf.tensor1d([1, 2, 3]).expandDims(-1 /* axis */);
-    expect(res.shape).toEqual([3, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3]);
-  });
-
-  it('1d, axis=0', async () => {
-    const res = tf.tensor1d([1, 2, 3]).expandDims(0 /* axis */);
-    expect(res.shape).toEqual([1, 3]);
-    expectArraysClose(await res.data(), [1, 2, 3]);
-  });
-
-  it('1d, axis=1', async () => {
-    const res = tf.tensor1d([1, 2, 3]).expandDims(1 /* axis */);
-    expect(res.shape).toEqual([3, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3]);
-  });
-
-  it('2d, axis=-4', () => {
-    expect(() => {
-      tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(-4 /* axis */);
-    }).toThrowError();
-  });
-
-  it('2d, axis=-3', async () => {
-    const res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(-3 /* axis */);
-    expect(res.shape).toEqual([1, 3, 2]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('2d, axis=-2', async () => {
-    const res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(-2 /* axis */);
-    expect(res.shape).toEqual([3, 1, 2]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('2d, axis=-1', async () => {
-    const res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(-1 /* axis */);
-    expect(res.shape).toEqual([3, 2, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('2d, axis=0', async () => {
-    const res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(0 /* axis */);
-    expect(res.shape).toEqual([1, 3, 2]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('2d, axis=1', async () => {
-    const res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(1 /* axis */);
-    expect(res.shape).toEqual([3, 1, 2]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('2d, axis=2', async () => {
-    const res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(2 /* axis */);
-    expect(res.shape).toEqual([3, 2, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('4d, axis=0', async () => {
-    const res = tf.tensor4d([[[[4]]]]).expandDims();
-    expect(res.shape).toEqual([1, 1, 1, 1, 1]);
-    expectArraysClose(await res.data(), [4]);
-  });
-
-  it('1d string tensor', async () => {
-    const t = tf.tensor(['hello', 'world']);
-    const res = t.expandDims();
-    expect(res.shape).toEqual([1, 2]);
-    expectArraysClose(await res.data(), ['hello', 'world']);
-  });
-
-  it('2d string tensor, axis=1', async () => {
-    const t = tf.tensor([['a', 'b'], ['c', 'd']]);
-    const res = t.expandDims(1);
-    expect(res.shape).toEqual([2, 1, 2]);
-    expectArraysClose(await res.data(), ['a', 'b', 'c', 'd']);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.expandDims({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'expandDims' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const res = tf.expandDims(7);
-    expect(res.shape).toEqual([1]);
-    expectArraysClose(await res.data(), [7]);
-  });
-
-  it('works with 0 in shape', async () => {
-    const a = tf.tensor2d([], [0, 3]);
-    const res = a.expandDims();
-    expect(res.shape).toEqual([1, 0, 3]);
-    expectArraysClose(await res.data(), []);
-
-    const res2 = a.expandDims(1);
-    expect(res2.shape).toEqual([0, 1, 3]);
-    expectArraysClose(await res2.data(), []);
-
-    const res3 = a.expandDims(2);
-    expect(res3.shape).toEqual([0, 3, 1]);
-    expectArraysClose(await res3.data(), []);
-  });
-  it('ensure no memory leak', async () => {
-    const numTensorsBefore = tf.memory().numTensors;
-    const numDataIdBefore = tf.engine().backend.numDataIds();
-
-    const t = tf.scalar(1);
-    const res = t.expandDims();
-    expect(res.shape).toEqual([1]);
-    expectArraysClose(await res.data(), [1]);
-
-    res.dispose();
-    t.dispose();
-
-    const numTensorsAfter = tf.memory().numTensors;
-    const numDataIdAfter = tf.engine().backend.numDataIds();
-    expect(numTensorsAfter).toBe(numTensorsBefore);
-    expect(numDataIdAfter).toBe(numDataIdBefore);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/expm1.ts b/tfjs-master/tfjs-core/src/ops/expm1.ts
deleted file mode 100644
index 5fc08d438..000000000
--- a/tfjs-master/tfjs-core/src/ops/expm1.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Expm1, Expm1Inputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes exponential of the input `tf.Tensor` minus one element-wise.
- * `e ^ x - 1`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, -3]);
- *
- * x.expm1().print();  // or tf.expm1(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function expm1_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'expm1');
-
-  const inputs: Expm1Inputs = {x: $x};
-  return ENGINE.runKernel(Expm1, inputs as unknown as NamedTensorMap);
-}
-export const expm1 = /* @__PURE__ */ op({expm1_});
diff --git a/tfjs-master/tfjs-core/src/ops/expm1_test.ts b/tfjs-master/tfjs-core/src/ops/expm1_test.ts
deleted file mode 100644
index 6b1908fec..000000000
--- a/tfjs-master/tfjs-core/src/ops/expm1_test.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('expm1', ALL_ENVS, () => {
-  it('expm1', async () => {
-    const a = tf.tensor1d([1, 2, 0]);
-    const r = tf.expm1(a);
-
-    expectArraysClose(
-        await r.data(), [Math.expm1(1), Math.expm1(2), Math.expm1(0)]);
-  });
-
-  it('expm1 propagates NaNs', async () => {
-    const a = tf.tensor1d([1, NaN, 0]);
-    const r = tf.expm1(a);
-    expectArraysClose(await r.data(), [Math.expm1(1), NaN, Math.expm1(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.expm1(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [3 * Math.exp(0.5)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.expm1(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [3 * Math.exp(0.5)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1, 2, 3, -5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.expm1(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [1 * Math.exp(-1), 2 * Math.exp(2), 3 * Math.exp(3), 4 * Math.exp(-5)],
-        1e-1);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.expm1(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [1 * Math.exp(-3), 2 * Math.exp(1), 3 * Math.exp(2), 4 * Math.exp(3)],
-        1e-1);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.expm1({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'expm1' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.expm1([1, 2, 0]);
-
-    expectArraysClose(
-        await r.data(), [Math.expm1(1), Math.expm1(2), Math.expm1(0)]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.expm1('q'))
-        .toThrowError(/Argument 'x' passed to 'expm1' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/eye.ts b/tfjs-master/tfjs-core/src/ops/eye.ts
deleted file mode 100644
index 312848b81..000000000
--- a/tfjs-master/tfjs-core/src/ops/eye.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor2D} from '../tensor';
-import {DataType} from '../types';
-
-import {buffer} from './buffer';
-import {expandDims} from './expand_dims';
-import {op} from './operation';
-import {reshape} from './reshape';
-import {tile} from './tile';
-
-/**
- * Create an identity matrix.
- *
- * @param numRows Number of rows.
- * @param numColumns Number of columns. Defaults to `numRows`.
- * @param batchShape If provided, will add the batch shape to the beginning
- *   of the shape of the returned `tf.Tensor` by repeating the identity
- *   matrix.
- * @param dtype Data type.
- * @returns Identity matrix of the specified size and data type, possibly
- *   with batch repetition if `batchShape` is specified.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function eye_(
-    numRows: number, numColumns?: number,
-    batchShape?:
-        [
-          number
-        ]|[number,
-           number]|[number, number, number]|[number, number, number, number],
-    dtype: DataType = 'float32'): Tensor2D {
-  if (numColumns == null) {
-    numColumns = numRows;
-  }
-  const buff = buffer([numRows, numColumns], dtype);
-  const n = numRows <= numColumns ? numRows : numColumns;
-  for (let i = 0; i < n; ++i) {
-    buff.set(1, i, i);
-  }
-  const out: Tensor2D = reshape(buff.toTensor(), [numRows, numColumns]);
-  if (batchShape == null) {
-    return out;
-  } else {
-    if (batchShape.length === 1) {
-      return tile(expandDims(out, 0), [batchShape[0], 1, 1]) as Tensor2D;
-    } else if (batchShape.length === 2) {
-      // tslint:disable-next-line:no-unnecessary-type-assertion
-      return tile(
-                 expandDims(expandDims(out, 0), 0),
-                 [batchShape[0], batchShape[1], 1, 1]) as Tensor2D;
-    } else if (batchShape.length === 3) {
-      // tslint:disable-next-line:no-unnecessary-type-assertion
-      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
-               batchShape[0], batchShape[1], batchShape[2], 1, 1
-             ]) as Tensor2D;
-    } else {
-      throw new Error(
-          `eye() currently supports only 1D and 2D ` +
-          // tslint:disable-next-line:no-any
-          `batchShapes, but received ${(batchShape as any).length}D.`);
-    }
-  }
-}
-
-export const eye = /* @__PURE__ */ op({eye_});
diff --git a/tfjs-master/tfjs-core/src/ops/eye_test.ts b/tfjs-master/tfjs-core/src/ops/eye_test.ts
deleted file mode 100644
index 4cdf67160..000000000
--- a/tfjs-master/tfjs-core/src/ops/eye_test.ts
+++ /dev/null
@@ -1,92 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('eye', ALL_ENVS, () => {
-  it('1x1', async () => {
-    const r = tf.eye(1);
-    expectArraysClose(await r.data(), [1]);
-    expect(r.shape).toEqual([1, 1]);
-    expect(r.dtype).toBe('float32');
-  });
-
-  it('2x2', async () => {
-    const r = tf.eye(2);
-    expect(r.shape).toEqual([2, 2]);
-    expect(r.dtype).toBe('float32');
-    expectArraysClose(await r.data(), [1, 0, 0, 1]);
-  });
-
-  it('3x3', async () => {
-    const r = tf.eye(3);
-    expect(r.shape).toEqual([3, 3]);
-    expect(r.dtype).toBe('float32');
-    expectArraysClose(await r.data(), [1, 0, 0, 0, 1, 0, 0, 0, 1]);
-  });
-
-  it('3x4', async () => {
-    const r = tf.eye(3, 4);
-    expect(r.shape).toEqual([3, 4]);
-    expect(r.dtype).toBe('float32');
-    expectArraysClose(await r.data(), [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0]);
-  });
-
-  it('4x3', async () => {
-    const r = tf.eye(4, 3);
-    expect(r.shape).toEqual([4, 3]);
-    expectArraysClose(await r.data(), [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]);
-  });
-
-  it('with 1D batchShape', async () => {
-    const r = tf.eye(2, 2, [3]);
-    expect(r.shape).toEqual([3, 2, 2]);
-    expectArraysClose(await r.data(), [1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1]);
-  });
-
-  it('with 2D batchShape', async () => {
-    const r = tf.eye(2, 2, [2, 3]);
-    expect(r.shape).toEqual([2, 3, 2, 2]);
-    expectArraysClose(await r.data(), [
-      1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1
-    ]);
-  });
-
-  it('with 3D batchShape', async () => {
-    const r = tf.eye(2, 2, [2, 2, 3]);
-    expect(r.shape).toEqual([2, 2, 3, 2, 2]);
-    expectArraysClose(await r.data(), [
-      1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,
-      1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1
-    ]);
-  });
-
-  it('3x3, int32', async () => {
-    const r = tf.eye(3, 3, null, 'int32');
-    expect(r.dtype).toBe('int32');
-    expect(r.shape).toEqual([3, 3]);
-    expectArraysClose(await r.data(), [1, 0, 0, 0, 1, 0, 0, 0, 1]);
-  });
-
-  it('3x3, bool', async () => {
-    const r = tf.eye(3, 3, null, 'bool');
-    expect(r.dtype).toBe('bool');
-    expect(r.shape).toEqual([3, 3]);
-    expectArraysClose(await r.data(), [1, 0, 0, 0, 1, 0, 0, 0, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/fill.ts b/tfjs-master/tfjs-core/src/ops/fill.ts
deleted file mode 100644
index 70f2a18eb..000000000
--- a/tfjs-master/tfjs-core/src/ops/fill.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Fill, FillAttrs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {DataType, Rank, ShapeMap} from '../types';
-import {inferDtype} from '../util';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-
-/**
- * Creates a `tf.Tensor` filled with a scalar value.
- *
- * ```js
- * tf.fill([2, 2], 4).print();
- * ```
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param value The scalar value to fill the tensor with.
- * @param dtype The type of an element in the resulting tensor. Defaults to
- *     'float32' if the given param value is a number, otherwise 'string'.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function fill<R extends Rank>(
-    shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor<R> {
-  assertNonNegativeIntegerDimensions(shape);
-
-  dtype = dtype || inferDtype(value);
-  const attrs: FillAttrs = {shape, value, dtype};
-
-  return ENGINE.runKernel(Fill, {}, attrs as unknown as NamedAttrMap);
-}
-
-export {fill};
diff --git a/tfjs-master/tfjs-core/src/ops/fill_test.ts b/tfjs-master/tfjs-core/src/ops/fill_test.ts
deleted file mode 100644
index 2ba6d9221..000000000
--- a/tfjs-master/tfjs-core/src/ops/fill_test.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('fill', ALL_ENVS, () => {
-  it('1D fill', async () => {
-    const a = tf.fill([3], 2);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [2, 2, 2]);
-  });
-
-  it('1D fill with inf', async () => {
-    const a = tf.fill([3], Number.POSITIVE_INFINITY);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [
-      Number.POSITIVE_INFINITY, Number.POSITIVE_INFINITY,
-      Number.POSITIVE_INFINITY
-    ]);
-  });
-
-  it('1D fill string', async () => {
-    const a = tf.fill([3], 'aa');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), ['aa', 'aa', 'aa']);
-  });
-
-  it('2D fill', async () => {
-    const a = tf.fill([3, 2], 2);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysClose(await a.data(), [2, 2, 2, 2, 2, 2]);
-  });
-
-  it('2D fill string', async () => {
-    const a = tf.fill([3, 2], 'a');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysEqual(await a.data(), ['a', 'a', 'a', 'a', 'a', 'a']);
-  });
-
-  it('3D fill', async () => {
-    const a = tf.fill([3, 2, 1], 2);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2, 1]);
-    expectArraysClose(await a.data(), [2, 2, 2, 2, 2, 2]);
-  });
-
-  it('4D fill', async () => {
-    const a = tf.fill([3, 2, 1, 2], 2);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2, 1, 2]);
-    expectArraysClose(await a.data(), [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]);
-  });
-
-  it('5D fill', async () => {
-    const a = tf.fill([2, 1, 2, 1, 2], 2);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 1, 2, 1, 2]);
-    expectArraysClose(await a.data(), [2, 2, 2, 2, 2, 2, 2, 2]);
-  });
-
-  it('should throw error when shape is not integer', () => {
-    expect(() => tf.fill([2, 2.22, 3.33], 1)).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/floor.ts b/tfjs-master/tfjs-core/src/ops/floor.ts
deleted file mode 100644
index 8405647af..000000000
--- a/tfjs-master/tfjs-core/src/ops/floor.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Floor, FloorInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.
- *
- * ```js
- * const x = tf.tensor1d([.6, 1.1, -3.3]);
- *
- * x.floor().print();  // or tf.floor(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function floor_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'floor', 'float32');
-
-  const inputs: FloorInputs = {x: $x};
-  return ENGINE.runKernel(Floor, inputs as unknown as NamedTensorMap);
-}
-export const floor = /* @__PURE__ */ op({floor_});
diff --git a/tfjs-master/tfjs-core/src/ops/floorDiv.ts b/tfjs-master/tfjs-core/src/ops/floorDiv.ts
deleted file mode 100644
index 4d0a09400..000000000
--- a/tfjs-master/tfjs-core/src/ops/floorDiv.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {FloorDiv, FloorDivInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
- * The result is rounded with floor function.
- *
- *
- * ```js
- * const a = tf.tensor1d([1, 4, 9, 16]);
- * const b = tf.tensor1d([1, 2, 3, 4]);
- *
- * a.floorDiv(b).print();  // or tf.div(a, b)
- * ```
- *
- * ```js
- * // Broadcast div a with b.
- * const a = tf.tensor1d([2, 4, 6, 8]);
- * const b = tf.scalar(2);
- *
- * a.floorDiv(b).print();  // or tf.floorDiv(a, b)
- * ```
- *
- * @param a The first tensor as the numerator.
- * @param b The second tensor as the denominator. Must have the same dtype as
- * `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function floorDiv_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'floorDiv');
-  let $b = convertToTensor(b, 'b', 'floorDiv');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  const inputs: FloorDivInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(FloorDiv, inputs as unknown as NamedTensorMap);
-}
-
-export const floorDiv = /* @__PURE__ */ op({floorDiv_});
diff --git a/tfjs-master/tfjs-core/src/ops/floor_test.ts b/tfjs-master/tfjs-core/src/ops/floor_test.ts
deleted file mode 100644
index d7d5c5614..000000000
--- a/tfjs-master/tfjs-core/src/ops/floor_test.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('floor', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([1.5, 2.1, -1.4]);
-    const r = tf.floor(a);
-
-    expectArraysClose(await r.data(), [1, 2, -2]);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([1.5, NaN, -1.4]);
-    const r = tf.floor(a);
-    expectArraysClose(await r.data(), [1, NaN, -2]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5.2);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.floor(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5.2);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.floor(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.floor(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.floor(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.floor({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'floor' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.floor([1.5, 2.1, -1.4]);
-    expectArraysClose(await r.data(), [1, 2, -2]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.floor('q'))
-        .toThrowError(/Argument 'x' passed to 'floor' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.floor(tf.tensor1d([1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'floor' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/floordiv_test.ts b/tfjs-master/tfjs-core/src/ops/floordiv_test.ts
deleted file mode 100644
index 46fe81c8b..000000000
--- a/tfjs-master/tfjs-core/src/ops/floordiv_test.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('floorDiv', ALL_ENVS, () => {
-  it('floorDiv', async () => {
-    const a = tf.tensor1d([10, 20, -20, -40], 'int32');
-    const b = tf.tensor1d([10, 12, 8, 5], 'int32');
-    const result = tf.floorDiv(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [1, 1, -3, -8]);
-  });
-
-  it('floorDiv vec4', async () => {
-    const a = tf.tensor1d([10, 20, -20, -40], 'int32');
-    const b = tf.tensor1d([10, 12, 8, 5], 'int32');
-    const result = tf.floorDiv(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [1, 1, -3, -8]);
-  });
-
-  it('floorDiv float32', async () => {
-    const a = tf.tensor1d([0.0, -6.0, 5.9, -5.9], 'float32');
-    const b = tf.tensor1d([3.0, 3.0, -3.1, -3.1], 'float32');
-    const result = tf.floorDiv(a, b);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0, -2, -2, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/from_pixels_test.ts b/tfjs-master/tfjs-core/src/ops/from_pixels_test.ts
deleted file mode 100644
index 7cff8ed0a..000000000
--- a/tfjs-master/tfjs-core/src/ops/from_pixels_test.ts
+++ /dev/null
@@ -1,329 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {BROWSER_ENVS, describeWithFlags, NODE_ENVS} from '../jasmine_util';
-import {createVideoElement, expectArraysClose, expectArraysEqual, play} from '../test_util';
-
-class MockContext {
-  getImageData(x: number, y: number, width: number, height: number) {
-    const data = new Uint8ClampedArray(width * height * 4);
-    for (let i = 0; i < data.length; ++i) {
-      data[i] = i + 1;
-    }
-    return {data};
-  }
-}
-
-class MockCanvas {
-  constructor(public width: number, public height: number) {}
-  getContext(type: '2d'): MockContext {
-    return new MockContext();
-  }
-}
-
-describeWithFlags('fromPixels, mock canvas', NODE_ENVS, () => {
-  it('accepts a canvas-like element', async () => {
-    const c = new MockCanvas(2, 2);
-    // tslint:disable-next-line:no-any
-    const t = tf.browser.fromPixels(c as any);
-    expect(t.dtype).toBe('int32');
-    expect(t.shape).toEqual([2, 2, 3]);
-    expectArraysEqual(
-        await t.data(), [1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15]);
-  });
-
-  it('accepts a canvas-like element, numChannels=4', async () => {
-    const c = new MockCanvas(2, 2);
-    // tslint:disable-next-line:no-any
-    const t = tf.browser.fromPixels(c as any, 4);
-    expect(t.dtype).toBe('int32');
-    expect(t.shape).toEqual([2, 2, 4]);
-    expectArraysEqual(
-        await t.data(),
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);
-  });
-
-  it('errors when passed a non-canvas object', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tf.browser.fromPixels(5 as any)).toThrowError();
-  });
-});
-
-describeWithFlags('fromPixels', BROWSER_ENVS, () => {
-  it('ImageData 1x1x3', async () => {
-    const pixels = new ImageData(1, 1);
-    pixels.data[0] = 0;
-    pixels.data[1] = 80;
-    pixels.data[2] = 160;
-    pixels.data[3] = 240;
-
-    const array = tf.browser.fromPixels(pixels, 3);
-
-    expectArraysEqual(await array.data(), [0, 80, 160]);
-  });
-
-  it('ImageData 1x1x4', async () => {
-    const pixels = new ImageData(1, 1);
-    pixels.data[0] = 0;
-    pixels.data[1] = 80;
-    pixels.data[2] = 160;
-    pixels.data[3] = 240;
-
-    const array = tf.browser.fromPixels(pixels, 4);
-
-    expectArraysEqual(await array.data(), [0, 80, 160, 240]);
-  });
-
-  it('ImageData 2x2x3', async () => {
-    const pixels = new ImageData(2, 2);
-
-    for (let i = 0; i < 8; i++) {
-      pixels.data[i] = i * 2;
-    }
-    for (let i = 8; i < 16; i++) {
-      pixels.data[i] = i * 2;
-    }
-
-    const array = tf.browser.fromPixels(pixels, 3);
-
-    expectArraysEqual(
-        await array.data(), [0, 2, 4, 8, 10, 12, 16, 18, 20, 24, 26, 28]);
-  });
-
-  it('ImageData 2x2x4', async () => {
-    const pixels = new ImageData(2, 2);
-    for (let i = 0; i < 8; i++) {
-      pixels.data[i] = i * 2;
-    }
-    for (let i = 8; i < 16; i++) {
-      pixels.data[i] = i * 2;
-    }
-
-    const array = tf.browser.fromPixels(pixels, 4);
-
-    expectArraysClose(
-        await array.data(),
-        new Int32Array(
-            [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]));
-  });
-
-  it('fromPixels, 3 channels', async () => {
-    const pixels = new ImageData(1, 2);
-    pixels.data[0] = 2;
-    pixels.data[1] = 3;
-    pixels.data[2] = 4;
-    pixels.data[3] = 255;  // Not used.
-    pixels.data[4] = 5;
-    pixels.data[5] = 6;
-    pixels.data[6] = 7;
-    pixels.data[7] = 255;  // Not used.
-    const res = tf.browser.fromPixels(pixels, 3);
-    expect(res.shape).toEqual([2, 1, 3]);
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [2, 3, 4, 5, 6, 7]);
-  });
-
-  it('fromPixels, reshape, then do tf.add()', async () => {
-    const pixels = new ImageData(1, 1);
-    pixels.data[0] = 2;
-    pixels.data[1] = 3;
-    pixels.data[2] = 4;
-    pixels.data[3] = 255;  // Not used.
-    const a = tf.browser.fromPixels(pixels, 3).reshape([1, 1, 1, 3]);
-    const res = a.add(tf.scalar(2, 'int32'));
-    expect(res.shape).toEqual([1, 1, 1, 3]);
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [4, 5, 6]);
-  });
-
-  it('fromPixels + fromPixels', async () => {
-    const pixelsA = new ImageData(1, 1);
-    pixelsA.data[0] = 255;
-    pixelsA.data[1] = 3;
-    pixelsA.data[2] = 4;
-    pixelsA.data[3] = 255;  // Not used.
-    const pixelsB = new ImageData(1, 1);
-    pixelsB.data[0] = 5;
-    pixelsB.data[1] = 6;
-    pixelsB.data[2] = 7;
-    pixelsB.data[3] = 255;  // Not used.
-    const a = tf.browser.fromPixels(pixelsA, 3).toFloat();
-    const b = tf.browser.fromPixels(pixelsB, 3).toFloat();
-    const res = a.add(b);
-    expect(res.shape).toEqual([1, 1, 3]);
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [260, 9, 11]);
-  });
-  it('fromPixels for PixelData type', async () => {
-    const dataA = new Uint8Array([255, 3, 4, 255]);
-    const pixelsA = {width: 1, height: 1, data: dataA};
-
-    const dataB = new Uint8Array([5, 6, 7, 255]);
-    const pixelsB = {width: 1, height: 1, data: dataB};
-    const a = tf.browser.fromPixels(pixelsA, 3).toFloat();
-    const b = tf.browser.fromPixels(pixelsB, 3).toFloat();
-    const res = a.add(b);
-    expect(res.shape).toEqual([1, 1, 3]);
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [260, 9, 11]);
-  });
-
-  it('fromPixels for HTMLCanvasElement', async () => {
-    const canvas = document.createElement('canvas');
-    canvas.width = 1;
-    canvas.height = 1;
-    const ctx = canvas.getContext('2d');
-    const pixels = new ImageData(1, 1);
-    pixels.data[0] = 0;
-    pixels.data[1] = 80;
-    pixels.data[2] = 160;
-    pixels.data[3] = 240;
-    ctx.putImageData(pixels, 1, 1);
-    const res = tf.browser.fromPixels(canvas);
-    expect(res.shape).toEqual([1, 1, 3]);
-    const data = await res.data();
-    expect(data.length).toEqual(1 * 1 * 3);
-  });
-  it('fromPixels for HTMLImageElement', async () => {
-    const img = new Image(10, 10);
-    img.src = 'data:image/gif;base64' +
-        ',R0lGODlhAQABAIAAAP///wAAACH5BAEAAAAALAAAAAABAAEAAAICRAEAOw==';
-    const res = tf.browser.fromPixels(img);
-    expect(res.shape).toEqual([10, 10, 3]);
-    const data = await res.data();
-    expect(data.length).toEqual(10 * 10 * 3);
-  });
-  it('fromPixels for HTMLVideoElement', async () => {
-    const source = document.createElement('source');
-    source.src =
-        // tslint:disable-next-line:max-line-length
-        'data:video/mp4;base64,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';
-    source.type = 'video/mp4';
-
-    const video = await createVideoElement(source);
-    document.body.appendChild(video);
-    await play(video);
-
-    const res = tf.browser.fromPixels(video);
-    expect(res.shape).toEqual([90, 160, 3]);
-    const data = await res.data();
-    expect(data.length).toEqual(90 * 160 * 3);
-    document.body.removeChild(video);
-  }, 30_000 /* 30 seconds */);
-
-  it('throws when passed a primitive number', () => {
-    const msg = /pixels passed to tf.browser.fromPixels\(\) must be either/;
-    // tslint:disable-next-line:no-any
-    expect(() => tf.browser.fromPixels(3 as any)).toThrowError(msg);
-  });
-
-  it('throws when passed a string', () => {
-    const msg = /pixels passed to tf.browser.fromPixels\(\) must be either/;
-    // tslint:disable-next-line:no-any
-    expect(() => tf.browser.fromPixels('test' as any)).toThrowError(msg);
-  });
-
-  it('canvas and image match', async () => {
-    const img = new Image();
-    const size = 80;
-    img.src =
-        // tslint:disable-next-line:max-line-length
-        'data:image/jpeg;base64,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';
-
-    await new Promise(resolve => {
-      img.onload = () => resolve(img);
-    });
-
-    img.width = size;
-    img.height = size;
-
-    const pixels = await tf.browser.fromPixels(img, 4);
-
-    const canvas = document.createElement('canvas');
-    canvas.width = size;
-    canvas.height = size;
-    const ctx = canvas.getContext('2d');
-    ctx.drawImage(img, 0, 0, size, size);
-    const actual = ctx.getImageData(0, 0, size, size).data;
-    const actualInt32 = Int32Array.from(actual);
-    const pixelsData = await pixels.data();
-
-    expectArraysClose(pixelsData, actualInt32, 10);
-  });
-
-  if (tf.env().getBool('IS_CHROME')) {
-    it('fromPixels for ImageBitmap', async () => {
-      const imageDataWidth = 1;
-      const imageDataHeight = 2;
-      const numChannel = 3;
-      const pixels = new ImageData(imageDataWidth, imageDataHeight);
-      for (let i = 0; i < imageDataWidth * imageDataHeight * 4; ++i) {
-        if (i % 4 === 3) {
-          pixels.data[i] = 255;
-        } else {
-          pixels.data[i] = i;
-        }
-      }
-
-      const imageBitmap = await createImageBitmap(pixels);
-      const res = tf.browser.fromPixels(imageBitmap, numChannel);
-      imageBitmap.close();
-      expect(res.shape).toEqual([imageDataHeight, imageDataWidth, numChannel]);
-      const data = await res.data();
-      expect(data.length)
-          .toEqual(imageDataHeight * imageDataWidth * numChannel);
-      expectArraysEqual(await res.data(), [0, 1, 2, 4, 5, 6]);
-    });
-
-    it('fromPixels for ImageBitmap outShape changes', async () => {
-      const imageDataWidth = 2;
-      const imageDataHeight = 2;
-      let numChannel = 3;
-      const pixels = new ImageData(imageDataWidth, imageDataHeight);
-      for (let i = 0; i < imageDataWidth * imageDataHeight * 4; ++i) {
-        if (i % 4 === 3) {
-          pixels.data[i] = 255;
-        } else {
-          pixels.data[i] = i;
-        }
-      }
-
-      const imageBitmap = await createImageBitmap(pixels);
-      const res = tf.browser.fromPixels(imageBitmap, numChannel);
-      expect(res.shape).toEqual([imageDataHeight, imageDataWidth, numChannel]);
-      const data = await res.data();
-      expect(data.length)
-          .toEqual(imageDataHeight * imageDataWidth * numChannel);
-      expectArraysEqual(
-          await res.data(), [0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14]);
-
-      // Change output shapes
-      numChannel = 4;
-      const resShapeChange = tf.browser.fromPixels(imageBitmap, numChannel);
-      expect(resShapeChange.shape).toEqual([
-        imageDataHeight, imageDataWidth, numChannel
-      ]);
-      const data2 = await resShapeChange.data();
-      expect(data2.length)
-          .toEqual(imageDataHeight * imageDataWidth * numChannel);
-      expectArraysEqual(
-          await resShapeChange.data(),
-          [0, 1, 2, 255, 4, 5, 6, 255, 8, 9, 10, 255, 12, 13, 14, 255]);
-    });
-  }
-});
diff --git a/tfjs-master/tfjs-core/src/ops/from_pixels_worker_test.ts b/tfjs-master/tfjs-core/src/ops/from_pixels_worker_test.ts
deleted file mode 100644
index 5d6d0bf57..000000000
--- a/tfjs-master/tfjs-core/src/ops/from_pixels_worker_test.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import {expectArraysEqual} from '../test_util';
-
-describe('fromPixels worker', () => {
-  it('fromPixels for ImageBitmap, worker', (done) => {
-    // Necessary preconditions
-    if (typeof (createImageBitmap) === 'undefined' ||
-        typeof (Worker) === 'undefined') {
-      done();
-      return;
-    }
-
-    // Test-only preconditions
-    if (typeof (ImageData) === 'undefined' || typeof (Blob) === 'undefined' ||
-        typeof (URL) === 'undefined') {
-      pending('Test-only js APIs are not supported in this context');
-      done();
-      return;
-    }
-
-    const str2workerURL = (str: string): string => {
-      const blob = new Blob([str], {type: 'application/javascript'});
-      return URL.createObjectURL(blob);
-    };
-
-    // The source code of a web worker.
-    const workerTest = `
-    importScripts(location.origin + '/base/tfjs/tfjs-core/tf-core.min.js');
-    importScripts(location.origin
-      + '/base/tfjs/tfjs-backend-cpu/tf-backend-cpu.min.js');
-
-    self.onmessage = (msg) => {
-      const bitmap = msg.data;
-      const tensor = tf.browser.fromPixels(bitmap, 4);
-      tensor.data().then((data) => {
-        const thinTensor = {shape: tensor.shape, dtype: tensor.dtype, data: data};
-        self.postMessage(thinTensor);
-      });
-    };
-    `;
-
-    const worker = new Worker(str2workerURL(workerTest));
-
-    worker.onmessage = (msg) => {
-      const thinTensor = msg.data;
-      expect(thinTensor.shape).toEqual([1, 1, 4]);
-      expect(thinTensor.dtype).toBe('int32');
-      expectArraysEqual(thinTensor.data, [1, 2, 3, 255]);
-      done();
-      worker.terminate();
-    };
-
-    worker.onerror = (e) => {
-      if(typeof OffscreenCanvas === 'undefined' ||
-         typeof OffscreenCanvasRenderingContext2D === 'undefined') {
-        expect(e.message).toEqual(
-            'Cannot parse input in current context. ' +
-            'Reason: OffscreenCanvas Context2D rendering is not supported.'
-        );
-      } else {
-        throw e;
-      }
-      done();
-      worker.terminate();
-    };
-
-    const imData = new ImageData(new Uint8ClampedArray([1, 2, 3, 255]), 1, 1);
-    createImageBitmap(imData).then((bitmap) => {
-      worker.postMessage(bitmap, [bitmap]);
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/fused/conv2d.ts b/tfjs-master/tfjs-core/src/ops/fused/conv2d.ts
deleted file mode 100644
index 1fc1dff74..000000000
--- a/tfjs-master/tfjs-core/src/ops/fused/conv2d.ts
+++ /dev/null
@@ -1,332 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {customGrad} from '../../gradients';
-import {FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor, Tensor3D, Tensor4D} from '../../tensor';
-import {GradSaveFunc, NamedTensorMap} from '../../tensor_types';
-import {makeTypesMatch} from '../../tensor_util';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-import {add} from '../add';
-import * as broadcast_util from '../broadcast_util';
-import {conv2d as unfusedConv2d} from '../conv2d';
-import {conv2DBackpropFilter} from '../conv2d_backprop_filter';
-import {conv2DBackpropInput} from '../conv2d_backprop_input';
-import * as conv_util from '../conv_util';
-import {Activation} from '../fused_types';
-import {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';
-import {op} from '../operation';
-import {reshape} from '../reshape';
-
-/**
- * Computes a 2D convolution over the input x, optionally fused with adding a
- * bias and applying an activation.
- *
- * ```js
- * const inputDepth = 2;
- * const inShape = [2, 2, 2, inputDepth];
- * const outputDepth = 2;
- * const fSize = 1;
- * const pad = 0;
- * const strides = 1;
- *
- * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
- * 16], inShape);
- * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,
- * outputDepth]);
- *
- * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',
- * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();
- * ```
- *
- * @param obj An object with the following properties:
- * @param x The input tensor, of rank 4 or rank 3, of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
- * assumed.
- * @param filter The filter, rank 4, of shape
- *     `[filterHeight, filterWidth, inDepth, outDepth]`.
- * @param strides The strides of the convolution: `[strideHeight,
- * strideWidth]`.
- * @param pad The type of padding algorithm.
- *   - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *   - `valid` output will be smaller than input if filter is larger
- *       than 1x1.
- *   - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to
- *     "NHWC". Specify the data format of the input and output data. With the
- *     default format "NHWC", the data is stored in the order of: [batch,
- *     height, width, channels]. Only "NHWC" is currently supported.
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
- *     in which we sample input values across the height and width dimensions
- *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
- *     number, then `dilationHeight == dilationWidth`. If it is greater than
- *     1, then all values of `strides` must be 1.
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- * @param bias Tensor to be added to the result.
- * @param activation Name of activation kernel (defaults to `linear`) to be
- *     applied
- *      after biasAdd.
- * @param preluActivationWeights Tensor of prelu weights to be applied as part
- *     of a `prelu` activation, typically the same shape as `x`.
- * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
- *     activation.
- */
-function fusedConv2d_<T extends Tensor3D|Tensor4D>({
-  x,
-  filter,
-  strides,
-  pad,
-  dataFormat = 'NHWC',
-  dilations = [1, 1],
-  dimRoundingMode,
-  bias,
-  activation = 'linear',
-  preluActivationWeights,
-  leakyreluAlpha
-}: {
-  x: T|TensorLike,
-  filter: Tensor4D|TensorLike,
-  strides: [number, number]|number,
-  pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-  dataFormat?: 'NHWC'|'NCHW',
-  dilations?: [number, number]|number,
-  dimRoundingMode?: 'floor'|'round'|'ceil',
-  bias?: Tensor|TensorLike,
-  activation?: Activation,
-  preluActivationWeights?: Tensor,
-  leakyreluAlpha?: number
-}): T {
-  activation = activation || 'linear';
-
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    // TODO: Transpose bias and preluActivationWeights properly for NCHW
-    // format before computation.
-    util.assert(
-        dataFormat === 'NHWC',
-        () => `Error in fused conv2d: got dataFormat of ${dataFormat} but ` +
-            `only NHWC is currently supported for the case of gradient depth ` +
-            `is 0 and the activation is not linear.`);
-
-    let result = unfusedConv2d(
-        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
-    if (bias != null) {
-      result = add(result, bias);
-    }
-
-    return applyActivation(
-               result, activation, preluActivationWeights, leakyreluAlpha) as T;
-  }
-
-  const $x = convertToTensor(x, 'x', 'conv2d', 'float32');
-  const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');
-
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  util.assert(
-      x4D.rank === 4,
-      () => `Error in fused conv2d: input must be rank 4, but got rank ` +
-          `${x4D.rank}.`);
-  util.assert(
-      $filter.rank === 4,
-      () => `Error in fused conv2d: filter must be rank 4, but got rank ` +
-          `${$filter.rank}.`);
-  conv_util.checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode);
-  const inputChannels = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
-  util.assert(
-      $filter.shape[2] === inputChannels,
-      () => `Error in conv2d: depth of input (${inputChannels}) must match ` +
-          `input depth for filter ${$filter.shape[2]}.`);
-  util.assert(
-      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in conv2D: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-
-  const convInfo = conv_util.computeConv2DInfo(
-      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
-
-  let $bias: Tensor;
-  if (bias != null) {
-    $bias = convertToTensor(bias, 'bias', 'fused conv2d');
-    [$bias] = makeTypesMatch($bias, $x);
-
-    // According to TensorFlow, the bias is supposed be a 1-D tensor or a
-    // scalar.
-    //
-    // 3-D or 4-D bias is not disabled for NHWC format, because they are
-    // currently being used in some cases. For examplem in our code base,
-    // https://github.com/tensorflow/tfjs/blob/b53bd47e880367ae57493f0ea628abaf08db2d5d/tfjs-core/src/ops/fused/fused_conv2d_test.ts#L1972.
-    if (dataFormat === 'NHWC') {
-      broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-    } else {
-      util.assert(
-          $bias.shape.length <= 1,
-          () => `Error in fused conv2d: only supports scalar or 1-D Tensor ` +
-              `bias for NCHW format but got the bias of ` +
-              `rank-${$bias.shape.length}.`);
-
-      util.assert(
-          $bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels ||
-              $bias.shape[0] === 1,
-          () => `Error in fused conv2d: bias shape (${$bias.shape}) is not ` +
-              `compatible with the number of output channels ` +
-              `(${convInfo.outChannels})`);
-    }
-  }
-
-  let $preluActivationWeights: Tensor;
-  if (preluActivationWeights != null) {
-    // PReLU's activation weights could be a scalar, a 1-D tensor or a 3-D
-    // tensor.
-    const alphaShape = preluActivationWeights.shape;
-    util.assert(
-        alphaShape.length <= 1 || alphaShape.length === 3,
-        () => `Error in fused conv2d: only supports scalar, 1-D Tensor or ` +
-            `3-D Tensor PReLU activation weights but got a tensor of ` +
-            `rank-${alphaShape.length}.`);
-
-    if (alphaShape.length === 1) {
-      // Whether the data format is NCHW or NHWC, the 1-D PReLU activation
-      // weights tensor should be aligned with the output channels of conv2d
-      // result.
-      util.assert(
-          alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels,
-          () => `Error in fused conv2d: PReLU activation weights ` +
-              `(${alphaShape}) is not compatible with the number of output ` +
-              `channels (${convInfo.outChannels}).`);
-    } else if (alphaShape.length === 3) {
-      // Whether the data format is NCHW or NHWC, the PReLU activation weights
-      // tensor should has the compatible shape with the result of conv2d.
-      try {
-        broadcast_util.assertAndGetBroadcastShape(
-            alphaShape, convInfo.outShape);
-      } catch (e) {
-        const errMsg =
-            `Error in fused conv2d: PReLU activation weights (${alphaShape}) ` +
-            `is not compatible with the output shape of the conv2d ` +
-            `(${convInfo.outShape}).`;
-        throw Error(errMsg);
-      }
-    }
-
-    $preluActivationWeights = convertToTensor(
-        preluActivationWeights, 'prelu weights', 'fused conv2d');
-  }
-
-  const grad = (dy: Tensor4D, saved: Tensor[]) => {
-    util.assert(
-        dataFormat === 'NHWC',
-        () => `Error in gradient of fused conv2D: got dataFormat of ${
-            dataFormat} but only NHWC is currently supported.`);
-
-    const [$filter, x4D, y, $bias] =
-        saved as [Tensor4D, Tensor4D, Tensor4D, Tensor];
-
-    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;
-
-    util.assert(
-        conv_util.tupleValuesAreOne(dilations),
-        () => 'Error in gradient of fused conv2D: ' +
-            `dilation rates greater than 1 ` +
-            `are not yet supported in gradients. Got dilations '${dilations}'`);
-
-    const xDer =
-        conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);
-    const filterDer =
-        conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);
-    const der: Tensor[] = [xDer, filterDer];
-
-    if ($bias != null) {
-      const biasDer = getFusedBiasGradient($bias, dyActivation);
-      der.push(biasDer);
-    }
-    return der;
-  };
-
-  const inputs: FusedConv2DInputs = {
-    x: x4D,
-    filter: $filter,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-
-  const attrs: FusedConv2DAttrs = {
-    strides,
-    pad,
-    dataFormat,
-    dilations,
-    dimRoundingMode,
-    activation,
-    leakyreluAlpha
-  };
-
-  // Depending on the the params passed in we will have different number of
-  // inputs and thus a a different number of elements in the gradient.
-  if (bias == null) {
-    const customOp =
-        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {
-          let res: Tensor4D|Tensor3D =
-              // tslint:disable-next-line: no-unnecessary-type-assertion
-              ENGINE.runKernel(
-                  FusedConv2D, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap);
-
-          save([filter, x4D, res]);
-
-          if (reshapedTo4D) {
-            // tslint:disable-next-line: no-unnecessary-type-assertion
-            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as
-                Tensor3D;
-          }
-
-          return {value: res, gradFunc: grad};
-        });
-    return customOp(x4D, $filter) as T;
-  } else {
-    const customOpWithBias = customGrad(
-        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {
-          let res: Tensor4D|Tensor3D = ENGINE.runKernel(
-              FusedConv2D, inputs as unknown as NamedTensorMap,
-              attrs as unknown as NamedAttrMap);
-
-          save([filter, x4D, res, bias]);
-
-          if (reshapedTo4D) {
-            // tslint:disable-next-line: no-unnecessary-type-assertion
-            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as
-                Tensor3D;
-          }
-
-          return {value: res, gradFunc: grad};
-        });
-
-    return customOpWithBias(x4D, $filter, $bias) as T;
-  }
-}
-export const conv2d = /* @__PURE__ */ op({fusedConv2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/fused/depthwise_conv2d.ts b/tfjs-master/tfjs-core/src/ops/fused/depthwise_conv2d.ts
deleted file mode 100644
index 8df3f7cab..000000000
--- a/tfjs-master/tfjs-core/src/ops/fused/depthwise_conv2d.ts
+++ /dev/null
@@ -1,260 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {customGrad} from '../../gradients';
-import {FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor, Tensor3D, Tensor4D} from '../../tensor';
-import {GradSaveFunc, NamedTensorMap} from '../../tensor_types';
-import {makeTypesMatch} from '../../tensor_util';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-import {add} from '../add';
-import * as broadcast_util from '../broadcast_util';
-import * as conv_util from '../conv_util';
-import {depthwiseConv2d as unfusedDepthwiseConv2d} from '../depthwise_conv2d';
-import {depthwiseConv2dNativeBackpropFilter} from '../depthwise_conv2d_native_backprop_filter';
-import {depthwiseConv2dNativeBackpropInput} from '../depthwise_conv2d_native_backprop_input';
-import {Activation} from '../fused_types';
-import {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';
-import {op} from '../operation';
-import {reshape} from '../reshape';
-
-/**
- * Computes depthwise 2D convolution, optionally fused with adding a
- * bias and applying an activation.
- *
- * Given a 4D `input` array and a `filter` array of shape
- * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
- * `inChannels` convolutional filters of depth 1, this op applies a
- * different filter to each input channel (expanding from 1 channel to
- * `channelMultiplier` channels for each), then concatenates the results
- * together. The output has `inChannels * channelMultiplier` channels.
- *
- * See
- * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
- *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
- * for more details.
- *
- * @param obj An object with the following properties:
- * @param x The input tensor, of rank 4 or rank 3, of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
- * assumed.
- * @param filter The filter tensor, rank 4, of shape
- *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
- * @param strides The strides of the convolution: `[strideHeight,
- * strideWidth]`. If strides is a single number, then `strideHeight ==
- * strideWidth`.
- * @param pad The type of padding algorithm.
- *   - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *   - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *   - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
- *     in which we sample input values across the height and width dimensions
- *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
- *     number, then `dilationHeight == dilationWidth`. If it is greater than
- *     1, then all values of `strides` must be 1.
- * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
- *     "NHWC". Specify the data format of the input and output data. With the
- *     default format "NHWC", the data is stored in the order of: [batch,
- *     height, width, channels]. Only "NHWC" is currently supported.
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- * @param bias Tensor to be added to the result.
- * @param activation Name of activation kernel (defaults to `linear`).
- * @param preluActivationWeights Tensor of prelu weights to be applied as part
- *     of a `prelu` activation, typically the same shape as `x`.
- * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
- *     activation.
- */
-function fusedDepthwiseConv2d_<T extends Tensor3D|Tensor4D>({
-  x,
-  filter,
-  strides,
-  pad,
-  dataFormat = 'NHWC',
-  dilations = [1, 1],
-  dimRoundingMode,
-  bias,
-  activation = 'linear',
-  preluActivationWeights,
-  leakyreluAlpha
-}: {
-  x: T|TensorLike,
-  filter: Tensor4D|TensorLike,
-  strides: [number, number]|number,
-  pad: 'valid'|'same'|number,
-  dataFormat?: 'NHWC'|'NCHW',
-  dilations?: [number, number]|number,
-  dimRoundingMode?: 'floor'|'round'|'ceil',
-  bias?: Tensor|TensorLike,
-  activation?: Activation,
-  preluActivationWeights?: Tensor,
-  leakyreluAlpha?: number
-}): T {
-  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-    let result = unfusedDepthwiseConv2d(
-        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
-    if (bias != null) {
-      result = add(result, bias);
-    }
-
-    return applyActivation(
-               result, activation, preluActivationWeights, leakyreluAlpha) as T;
-  }
-
-  const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');
-  const $filter =
-      convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');
-
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-  util.assert(
-      x4D.rank === 4,
-      () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +
-          `rank ${x4D.rank}.`);
-  util.assert(
-      $filter.rank === 4,
-      () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +
-          `but got rank ${$filter.rank}.`);
-  util.assert(
-      x4D.shape[3] === $filter.shape[2],
-      () => `Error in fused depthwiseConv2d: number of input channels ` +
-          `(${x4D.shape[3]}) must match the inChannels dimension in ` +
-          `filter ${$filter.shape[2]}.`);
-  if (dilations == null) {
-    dilations = [1, 1];
-  }
-  util.assert(
-      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () =>
-          'Error in fused depthwiseConv2d: Either strides or dilations must ' +
-          `be 1. Got strides ${strides} and dilations '${dilations}'`);
-  conv_util.checkPadOnDimRoundingMode(
-      'fused depthwiseConv2d', pad, dimRoundingMode);
-  const convInfo = conv_util.computeConv2DInfo(
-      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,
-      true /* depthwise */);
-
-  let $bias: Tensor;
-  if (bias != null) {
-    $bias = convertToTensor(bias, 'bias', 'fused conv2d');
-    [$bias] = makeTypesMatch($bias, $x);
-
-    broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
-  }
-
-  let $preluActivationWeights: Tensor;
-  if (preluActivationWeights != null) {
-    $preluActivationWeights = convertToTensor(
-        preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');
-  }
-
-  const grad = (dy: Tensor4D, saved: Tensor[]) => {
-    util.assert(
-        conv_util.tupleValuesAreOne(dilations),
-        () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +
-            `greater than 1 are not yet supported. Got dilations ` +
-            `'${dilations}'`);
-    const [$filter, x4D, y, bias] = saved;
-
-    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;
-
-    const xDer = depthwiseConv2dNativeBackpropInput(
-        (x4D as Tensor4D).shape, dyActivation, $filter as Tensor4D, strides,
-        pad, dilations, dimRoundingMode);
-    const filterDer = depthwiseConv2dNativeBackpropFilter(
-        x4D as Tensor4D, dyActivation, ($filter as Tensor4D).shape, strides,
-        pad, dilations, dimRoundingMode);
-
-    if (bias != null) {
-      const biasDer = getFusedBiasGradient($bias, dyActivation);
-      return [xDer, filterDer, biasDer];
-    }
-    return [xDer, filterDer];
-  };
-
-  const inputs: FusedDepthwiseConv2DInputs = {
-    x: x4D,
-    filter: $filter,
-    bias: $bias,
-    preluActivationWeights: $preluActivationWeights
-  };
-  const attrs: FusedDepthwiseConv2DAttrs = {
-    strides,
-    pad,
-    dataFormat,
-    dilations,
-    dimRoundingMode,
-    activation,
-    leakyreluAlpha
-  };
-
-  // Depending on the the params passed in we will have different number of
-  // inputs and thus a a different number of elements in the gradient.
-  if (bias == null) {
-    const customOp =
-        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {
-          // tslint:disable-next-line: no-unnecessary-type-assertion
-          let res: Tensor4D|Tensor3D = ENGINE.runKernel(
-              FusedDepthwiseConv2D, inputs as unknown as NamedTensorMap,
-              attrs as unknown as NamedAttrMap);
-
-          save([filter, x4D, res]);
-
-          if (reshapedTo4D) {
-            // tslint:disable-next-line: no-unnecessary-type-assertion
-            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as
-                Tensor3D;
-          }
-
-          return {value: res, gradFunc: grad};
-        });
-    return customOp(x4D, $filter) as T;
-  } else {
-    const customOpWithBias = customGrad(
-        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {
-          // tslint:disable-next-line: no-unnecessary-type-assertion
-          let res: Tensor4D|Tensor3D = ENGINE.runKernel(
-              FusedDepthwiseConv2D, inputs as unknown as NamedTensorMap,
-              attrs as unknown as NamedAttrMap);
-
-          save([filter, x4D, res, bias]);
-
-          if (reshapedTo4D) {
-            // tslint:disable-next-line: no-unnecessary-type-assertion
-            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as
-                Tensor3D;
-          }
-
-          return {value: res, gradFunc: grad};
-        });
-
-    return customOpWithBias(x4D, $filter, $bias) as T;
-  }
-}
-export const depthwiseConv2d = /* @__PURE__ */ op({fusedDepthwiseConv2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/fused/fused_conv2d_test.ts b/tfjs-master/tfjs-core/src/ops/fused/fused_conv2d_test.ts
deleted file mode 100644
index e58714795..000000000
--- a/tfjs-master/tfjs-core/src/ops/fused/fused_conv2d_test.ts
+++ /dev/null
@@ -1,2125 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-function generateCaseInputs(totalSizeTensor: number, totalSizeFilter: number) {
-  const inp = new Array(totalSizeTensor);
-  const filt = new Array(totalSizeFilter);
-
-  for (let i = 0; i < totalSizeTensor; i++) {
-    inp[i] = i * 0.001 - totalSizeTensor * 0.001 / 2;
-  }
-  for (let i = 0; i < totalSizeFilter; i++) {
-    const sign = i % 2 === 0 ? -1 : 1;
-    filt[i] = i * 0.001 * sign;
-  }
-
-  return {input: inp, filter: filt};
-}
-
-describeWithFlags('fused conv2d', ALL_ENVS, () => {
-  it('basic', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({x, filter: w, strides: stride, pad});
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    const expected =
-        [-5, 2, -11, 5, -17, 8, -23, 11, -29, 14, -35, 17, -41, 20, -47, 23];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic with relu', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'relu'
-    });
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    const expected = [0, 2, 0, 5, 0, 8, 0, 11, 0, 14, 0, 17, 0, 20, 0, 23];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('relu with stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=1 p=same',
-     async () => {
-       const inputDepth = 16;
-       const xSize = 8;
-       const inputShape: [number, number, number, number] =
-           [1, xSize, xSize, inputDepth];
-       const outputDepth = 1;
-       const fSize = 3;
-       const pad = 'same';
-       const stride: [number, number] = [2, 2];
-
-       // TODO(annxingyuan): Make this test work with large inputs
-       // https://github.com/tensorflow/tfjs/issues/3143
-       const inputData = [];
-       for (let i = 0; i < xSize * xSize * inputDepth; i++) {
-         inputData.push(i % 5);
-       }
-
-       const wData = [];
-       for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
-         wData.push(i % 5);
-       }
-
-       const x = tf.tensor4d(inputData, inputShape);
-       const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
-
-       const result = tf.fused.conv2d({
-         x,
-         filter: w,
-         strides: stride,
-         pad,
-         dataFormat: 'NHWC',
-         dilations: [1, 1],
-         activation: 'relu'
-       });
-       expect(result.shape).toEqual([1, 4, 4, 1]);
-       expectArraysClose(await result.data(), new Float32Array([
-                           854, 431, 568, 382, 580, 427, 854, 288, 431, 568,
-                           580, 289, 285, 570, 285, 258
-                         ]));
-     });
-
-  it('relu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same',
-     async () => {
-       const inputDepth = 16;
-       const xSize = 8;
-       const inputShape: [number, number, number, number] =
-           [1, xSize, xSize, inputDepth];
-       const outputDepth = 8;
-       const fSize = 3;
-       const pad = 'same';
-       const stride: [number, number] = [2, 2];
-
-       const inputs = generateCaseInputs(
-           1 * xSize * xSize * inputDepth,
-           fSize * fSize * inputDepth * outputDepth);
-       const x = tf.tensor4d(inputs.input, inputShape);
-       const w =
-           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
-       const result = tf.fused.conv2d({
-         x,
-         filter: w,
-         strides: stride,
-         pad,
-         dataFormat: 'NHWC',
-         dilations: [1, 1],
-         activation: 'relu',
-         bias
-       });
-       expect(result.shape).toEqual([1, 4, 4, 8]);
-       expectArraysClose(await result.data(), new Float32Array([
-                           25.75398063659668,
-                           0,
-                           26.857805252075195,
-                           0,
-                           33.961631774902344,
-                           0,
-                           30.065458297729492,
-                           0,
-                           23.118206024169922,
-                           0,
-                           24.212820053100586,
-                           0,
-                           31.307422637939453,
-                           0,
-                           27.402034759521484,
-                           0,
-                           20.482431411743164,
-                           0,
-                           21.567821502685547,
-                           0,
-                           28.653217315673828,
-                           0,
-                           24.73861312866211,
-                           0,
-                           11.078080177307129,
-                           0,
-                           12.130399703979492,
-                           0,
-                           19.182720184326172,
-                           0,
-                           15.235037803649902,
-                           0,
-                           4.6677775382995605,
-                           0.31717729568481445,
-                           5.697869777679443,
-                           0,
-                           12.727968215942383,
-                           2.2569849491119385,
-                           8.758066177368164,
-                           4.226885795593262,
-                           2.0319995880126953,
-                           2.9575586318969727,
-                           3.052880048751831,
-                           1.9366796016693115,
-                           10.073760032653809,
-                           4.915799617767334,
-                           6.094639778137207,
-                           6.89492130279541,
-                           0,
-                           5.5979437828063965,
-                           0.4078875780105591,
-                           4.586280822753906,
-                           7.419551849365234,
-                           7.5746169090271,
-                           3.43121600151062,
-                           9.562952041625977,
-                           0,
-                           6.404943943023682,
-                           0,
-                           5.401776313781738,
-                           6.5998077392578125,
-                           8.398608207702637,
-                           2.602976083755493,
-                           10.395440101623535,
-                           0,
-                           21.440250396728516,
-                           0,
-                           20.483882904052734,
-                           0,
-                           23.527509689331055,
-                           0,
-                           25.571144104003906,
-                           0,
-                           24.080629348754883,
-                           0,
-                           23.133480072021484,
-                           0,
-                           26.186328887939453,
-                           0,
-                           28.239177703857422,
-                           0,
-                           26.721012115478516,
-                           0,
-                           25.783079147338867,
-                           0,
-                           28.84514808654785,
-                           0,
-                           30.907209396362305,
-                           0,
-                           18.914127349853516,
-                           0,
-                           17.960111618041992,
-                           0,
-                           21.006093978881836,
-                           0,
-                           23.052082061767578,
-                           0,
-                           17.89089584350586,
-                           0,
-                           16.95684814453125,
-                           0,
-                           20.022798538208008,
-                           0,
-                           22.088754653930664,
-                           0,
-                           19.06132698059082,
-                           0,
-                           18.133424758911133,
-                           0,
-                           21.205520629882812,
-                           0,
-                           23.27761459350586,
-                           0,
-                           20.23175811767578,
-                           0,
-                           19.309999465942383,
-                           0,
-                           22.388240814208984,
-                           0,
-                           24.46647834777832,
-                           0,
-                           13.584352493286133,
-                           0,
-                           12.6395845413208,
-                           0,
-                           15.694815635681152,
-                           0,
-                           17.750045776367188
-                         ]));
-     });
-
-  it('prelu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same',
-     async () => {
-       const inputDepth = 16;
-       const xSize = 8;
-       const inputShape: [number, number, number, number] =
-           [1, xSize, xSize, inputDepth];
-       const outputDepth = 8;
-       const fSize = 3;
-       const pad = 'same';
-       const stride: [number, number] = [2, 2];
-
-       const inputs = generateCaseInputs(
-           1 * xSize * xSize * inputDepth,
-           fSize * fSize * inputDepth * outputDepth);
-       const x = tf.tensor4d(inputs.input, inputShape);
-       const w =
-           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
-       const preluActivationWeights = tf.tensor1d([1, 2, 3, 4, 5, 6, 7, 8]);
-
-       const result = tf.fused.conv2d({
-         x,
-         filter: w,
-         strides: stride,
-         pad,
-         dataFormat: 'NHWC',
-         dilations: [1, 1],
-         activation: 'prelu',
-         preluActivationWeights,
-         bias
-       });
-       expect(result.shape).toEqual([1, 4, 4, 8]);
-       expectArraysClose(
-           await result.data(), new Float32Array([
-             25.75398063659668,   -41.61178970336914,  26.857805252075195,
-             -87.63885498046875,  33.961631774902344,  -114.0812759399414,
-             30.065458297729492,  -136.93893432617188, 23.118206024169922,
-             -36.33102035522461,  24.212820053100586,  -77.04048156738281,
-             31.307422637939453,  -98.12835693359375,  27.402034759521484,
-             -115.5947265625,     20.482431411743164,  -31.050262451171875,
-             21.567821502685547,  -66.44209289550781,  28.653217315673828,
-             -82.17544555664062,  24.73861312866211,   -94.25041198730469,
-             11.078080177307129,  -12.208478927612305, 12.130399703979492,
-             -28.626232147216797, 19.182720184326172,  -25.253299713134766,
-             15.235037803649902,  -18.08960723876953,  4.6677775382995605,
-             0.31717729568481445, 5.697869777679443,   -2.8516759872436523,
-             12.727968215942383,  2.2569849491119385,  8.758066177368164,
-             4.226885795593262,   2.0319995880126953,  2.9575586318969727,
-             3.052880048751831,   1.9366796016693115,  10.073760032653809,
-             4.915799617767334,   6.094639778137207,   6.89492130279541,
-             -0.6037763357162476, 5.5979437828063965,  0.4078875780105591,
-             4.586280822753906,   7.419551849365234,   7.5746169090271,
-             3.43121600151062,    9.562952041625977,   -1.4065279960632324,
-             6.404943943023682,   -1.2100803852081299, 5.401776313781738,
-             6.5998077392578125,  8.398608207702637,   2.602976083755493,
-             10.395440101623535,  -16.418434143066406, 21.440250396728516,
-             -46.38618850708008,  20.483882904052734,  -42.52848815917969,
-             23.527509689331055,  -87.84530639648438,  25.571144104003906,
-             -19.054208755493164, 24.080629348754883,  -54.32115936279297,
-             23.133480072021484,  -55.79951477050781,  26.186328887939453,
-             -106.48924255371094, 28.239177703857422,  -21.689987182617188,
-             26.721012115478516,  -62.25614929199219,  25.783079147338867,
-             -69.070556640625,    28.84514808654785,   -125.13325500488281,
-             30.907209396362305,  -13.891133308410645, 18.914127349853516,
-             -38.81135940551758,  17.960111618041992,  -29.915504455566406,
-             21.006093978881836,  -70.20361328125,     23.052082061767578,
-             -12.857919692993164, 17.89089584350586,   -35.771610260009766,
-             16.95684814453125,   -24.949115753173828, 20.022798538208008,
-             -63.39042282104492,  22.088754653930664,  -14.02528190612793,
-             19.06132698059082,   -39.2921257019043,   18.133424758911133,
-             -30.847349166870117, 21.205520629882812,  -71.69097137451172,
-             23.27761459350586,   -15.192638397216797, 20.23175811767578,
-             -42.8126335144043,   19.309999465942383,  -36.74560546875,
-             22.388240814208984,  -79.99152374267578,  24.46647834777832,
-             -8.556736946105957,  13.584352493286133,  -22.835901260375977,
-             12.6395845413208,    -3.336000442504883,  15.694815635681152,
-             -33.0570182800293,   17.750045776367188
-           ]));
-     });
-
-  it('relu6 bias stride 2 x=[1,8,8,16] f=[3,3,16,8] s=[2,2] d=8 p=same',
-     async () => {
-       const inputDepth = 16;
-       const xSize = 8;
-       const inputShape: [number, number, number, number] =
-           [1, xSize, xSize, inputDepth];
-       const outputDepth = 8;
-       const fSize = 3;
-       const pad = 'same';
-       const stride: [number, number] = [2, 2];
-
-       const inputs = generateCaseInputs(
-           1 * xSize * xSize * inputDepth,
-           fSize * fSize * inputDepth * outputDepth);
-       const x = tf.tensor4d(inputs.input, inputShape);
-       const w =
-           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
-
-       const result = tf.fused.conv2d({
-         x,
-         filter: w,
-         strides: stride,
-         pad,
-         dataFormat: 'NHWC',
-         dilations: [1, 1],
-         activation: 'relu6',
-         bias
-       });
-       expect(result.shape).toEqual([1, 4, 4, 8]);
-       const resultData = await result.data();
-       expectArraysClose(resultData, new Float32Array([
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           4.6677775382995605,
-                           0.31717729568481445,
-                           5.697869777679443,
-                           0,
-                           6,
-                           2.2569849491119385,
-                           6,
-                           4.226885795593262,
-                           2.0319995880126953,
-                           2.9575586318969727,
-                           3.052880048751831,
-                           1.9366796016693115,
-                           6,
-                           4.915799617767334,
-                           6,
-                           6,
-                           0,
-                           5.5979437828063965,
-                           0.4078875780105591,
-                           4.586280822753906,
-                           6,
-                           6,
-                           3.43121600151062,
-                           6,
-                           0,
-                           6,
-                           0,
-                           5.401776313781738,
-                           6,
-                           6,
-                           2.602976083755493,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6,
-                           0,
-                           6
-                         ]));
-     });
-
-  it('leakyrelu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same',
-     async () => {
-       const inputDepth = 16;
-       const xSize = 8;
-       const inputShape: [number, number, number, number] =
-           [1, xSize, xSize, inputDepth];
-       const outputDepth = 8;
-       const fSize = 3;
-       const pad = 'same';
-       const stride: [number, number] = [2, 2];
-
-       const inputs = generateCaseInputs(
-           1 * xSize * xSize * inputDepth,
-           fSize * fSize * inputDepth * outputDepth);
-       const x = tf.tensor4d(inputs.input, inputShape);
-       const w =
-           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
-       const leakyreluAlpha = 0.3;
-
-       const result = tf.fused.conv2d({
-         x,
-         filter: w,
-         strides: stride,
-         pad,
-         dataFormat: 'NHWC',
-         dilations: [1, 1],
-         activation: 'leakyrelu',
-         leakyreluAlpha,
-         bias
-       });
-       expect(result.shape).toEqual([1, 4, 4, 8]);
-       expectArraysClose(
-           await result.data(), new Float32Array([
-             25.75398063659668,    -6.241768836975098,   26.857805252075195,
-             -6.5729146003723145,  33.961631774902344,   -5.704063892364502,
-             30.065458297729492,   -5.135210037231445,   23.118206024169922,
-             -5.449653148651123,   24.212820053100586,   -5.778036117553711,
-             31.307422637939453,   -4.906418323516846,   27.402034759521484,
-             -4.334802627563477,   20.482431411743164,   -4.657539367675781,
-             21.567821502685547,   -4.983157157897949,   28.653217315673828,
-             -4.108772277832031,   24.73861312866211,    -3.534390687942505,
-             11.078080177307129,   -1.8312718868255615,  12.130399703979492,
-             -2.1469674110412598,  19.182720184326172,   -1.262665033340454,
-             15.235037803649902,   -0.6783602833747864,  4.6677775382995605,
-             0.31717729568481445,  5.697869777679443,    -0.21387571096420288,
-             12.727968215942383,   2.2569849491119385,   8.758066177368164,
-             4.226885795593262,    2.0319995880126953,   2.9575586318969727,
-             3.052880048751831,    1.9366796016693115,   10.073760032653809,
-             4.915799617767334,    6.094639778137207,    6.89492130279541,
-             -0.18113291263580322, 5.5979437828063965,   0.4078875780105591,
-             4.586280822753906,    7.419551849365234,    7.5746169090271,
-             3.43121600151062,     9.562952041625977,    -0.42195841670036316,
-             6.404943943023682,    -0.12100804597139359, 5.401776313781738,
-             6.5998077392578125,   8.398608207702637,    2.602976083755493,
-             10.395440101623535,   -4.925530433654785,   21.440250396728516,
-             -4.6386189460754395,  20.483882904052734,   -2.5517091751098633,
-             23.527509689331055,   -3.764799118041992,   25.571144104003906,
-             -5.7162628173828125,  24.080629348754883,   -5.432116508483887,
-             23.133480072021484,   -3.347970962524414,   26.186328887939453,
-             -4.5638251304626465,  28.239177703857422,   -6.5069966316223145,
-             26.721012115478516,   -6.225615501403809,   25.783079147338867,
-             -4.144233703613281,   28.84514808654785,    -5.36285400390625,
-             30.907209396362305,   -4.167340278625488,   18.914127349853516,
-             -3.881135940551758,   17.960111618041992,   -1.794930338859558,
-             21.006093978881836,   -3.0087265968322754,  23.052082061767578,
-             -3.8573760986328125,  17.89089584350586,    -3.5771610736846924,
-             16.95684814453125,    -1.4969470500946045,  20.022798538208008,
-             -2.7167325019836426,  22.088754653930664,   -4.207584857940674,
-             19.06132698059082,    -3.9292125701904297,  18.133424758911133,
-             -1.8508410453796387,  21.205520629882812,   -3.0724704265594482,
-             23.27761459350586,    -4.557791709899902,   20.23175811767578,
-             -4.28126335144043,    19.309999465942383,   -2.2047364711761475,
-             22.388240814208984,   -3.428208351135254,   24.46647834777832,
-             -2.567021131515503,   13.584352493286133,   -2.283590316772461,
-             12.6395845413208,     -0.20016004145145416, 15.694815635681152,
-             -1.41672945022583,    17.750045776367188
-           ]));
-     });
-
-  it('throws when dimRoundingMode is set and pad is same', () => {
-    const inputDepth = 16;
-    const xSize = 8;
-    const inputShape: [number, number, number, number] =
-        [1, xSize, xSize, inputDepth];
-    const outputDepth = 8;
-    const fSize = 3;
-    const pad = 'same';
-    const stride: [number, number] = [2, 2];
-
-    const inputs = generateCaseInputs(
-        1 * xSize * xSize * inputDepth,
-        fSize * fSize * inputDepth * outputDepth);
-    const x = tf.tensor4d(inputs.input, inputShape);
-    const w =
-        tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-    const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
-    const leakyreluAlpha = 0.3;
-
-    expect(() => tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'leakyrelu',
-      leakyreluAlpha,
-      bias,
-      dimRoundingMode: 'round'
-    })).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is valid', () => {
-    const inputDepth = 16;
-    const xSize = 8;
-    const inputShape: [number, number, number, number] =
-        [1, xSize, xSize, inputDepth];
-    const outputDepth = 8;
-    const fSize = 3;
-    const pad = 'valid';
-    const stride: [number, number] = [2, 2];
-
-    const inputs = generateCaseInputs(
-        1 * xSize * xSize * inputDepth,
-        fSize * fSize * inputDepth * outputDepth);
-    const x = tf.tensor4d(inputs.input, inputShape);
-    const w =
-        tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-    const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
-    const leakyreluAlpha = 0.3;
-
-    expect(() => tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'leakyrelu',
-      leakyreluAlpha,
-      bias,
-      dimRoundingMode: 'round'
-    })).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is a non-integer number',
-     () => {
-       const inputDepth = 16;
-       const xSize = 8;
-       const inputShape: [number, number, number, number] =
-           [1, xSize, xSize, inputDepth];
-       const outputDepth = 8;
-       const fSize = 3;
-       const pad = 1.2;
-       const stride: [number, number] = [2, 2];
-
-       const inputs = generateCaseInputs(
-           1 * xSize * xSize * inputDepth,
-           fSize * fSize * inputDepth * outputDepth);
-       const x = tf.tensor4d(inputs.input, inputShape);
-       const w =
-           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
-       const leakyreluAlpha = 0.3;
-
-       expect(() => tf.fused.conv2d({
-         x,
-         filter: w,
-         strides: stride,
-         pad,
-         dataFormat: 'NHWC',
-         dilations: [1, 1],
-         activation: 'leakyrelu',
-         leakyreluAlpha,
-         bias,
-         dimRoundingMode: 'round'
-       })).toThrowError();
-     });
-
-  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
-         'number',
-     () => {
-       const inputDepth = 16;
-       const xSize = 8;
-       const inputShape: [number, number, number, number] =
-           [1, xSize, xSize, inputDepth];
-       const outputDepth = 8;
-       const fSize = 3;
-       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as
-           tf.backend_util.ExplicitPadding;
-       const stride: [number, number] = [2, 2];
-
-       const inputs = generateCaseInputs(
-           1 * xSize * xSize * inputDepth,
-           fSize * fSize * inputDepth * outputDepth);
-       const x = tf.tensor4d(inputs.input, inputShape);
-       const w =
-           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
-       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
-       const leakyreluAlpha = 0.3;
-
-       expect(() => tf.fused.conv2d({
-         x,
-         filter: w,
-         strides: stride,
-         pad,
-         dataFormat: 'NHWC',
-         dilations: [1, 1],
-         activation: 'leakyrelu',
-         leakyreluAlpha,
-         bias,
-         dimRoundingMode: 'round'
-       })).toThrowError();
-     });
-
-  it('basic with bias', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      bias: tf.tensor1d([5, 6])
-    });
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    const expected =
-        [0, 8, -6, 11, -12, 14, -18, 17, -24, 20, -30, 23, -36, 26, -42, 29];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic with explicit padding', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const pad =
-        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const stride = 1;
-    const dataFormat = 'NHWC';
-    const dilation = 1;
-
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2, inputDepth]);
-    const w =
-        tf.tensor4d([3, 1, 5, 0, 2, 7, 8, 9], [4, 2, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d(
-        {x, filter: w, strides: stride, pad, dataFormat, dilations: dilation});
-
-    const resultData = await result.data();
-    expect(result.shape).toEqual([4, 2, 1]);
-    expectArraysClose(resultData, [133, 66, 200, 102, 108, 58, 56, 58]);
-  });
-
-  it('basic with elu', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'elu'
-    });
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    const expected =
-        [-0.99326, 2, -1, 5, -1, 8, -1, 11, -1, 14, -1, 17, -1, 20, -1, 23];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic with prelu', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const alpha = tf.tensor3d([0.25, 0.75], [1, 1, 2]);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    const expected = [
-      -1.25, 2, -2.75, 5, -4.25, 8, -5.75, 11, -7.25, 14, -8.75, 17, -10.25, 20,
-      -11.75, 23
-    ];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic with leakyrelu', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const alpha = 0.3;
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'leakyrelu',
-      leakyreluAlpha: alpha
-    });
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    const expected = [
-      -1.5, 2, -3.3000001907348633, 5, -5.100000381469727, 8,
-      -6.900000095367432, 11, -8.700000762939453, 14, -10.5, 17,
-      -12.300000190734863, 20, -14.100000381469727, 23
-    ];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic with sigmoid', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const alpha = 0.3;
-    const w = tf.tensor4d(
-        [-0.1, 0.1, -0.2, 0.05], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'sigmoid',
-      leakyreluAlpha: alpha
-    });
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    const expected = [
-      0.3775407, 0.549834, 0.24973989, 0.6224593, 0.15446526, 0.6899744,
-      0.09112296, 0.7502601, 0.0521535, 0.80218387, 0.02931219, 0.84553474,
-      0.0163025, 0.8807971, 0.0090133, 0.908877
-    ];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic with broadcasted bias and relu', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      bias: tf.scalar(5),
-      activation: 'relu'
-    });
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    const expected = [0, 7, 0, 10, 0, 13, 0, 16, 0, 19, 0, 22, 0, 25, 0, 28];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic in NCHW', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 3, 5, 7, 2, 4, 6, 8], inShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d(
-        {x, filter: w, strides: stride, pad, dataFormat: 'NCHW'});
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [-5, -11, -17, -23, 2, 5, 8, 11];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic in NCHW with scalar bias', async () => {
-    const inputDepth = 4;
-    const inputShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 4;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d(
-        [3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const bias = tf.scalar(1);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      bias
-    });
-
-    expect(result.shape).toEqual([1, 4, 2, 2]);
-    const expected =
-        [10, 19, 28, 37, 10, 19, 28, 37, 10, 19, 28, 37, 10, 19, 28, 37];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic in NCHW with 1-D bias', async () => {
-    const inputDepth = 4;
-    const inputShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 4;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d(
-        [3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const bias = tf.tensor1d([1, 2, 1, 2]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      bias
-    });
-
-    expect(result.shape).toEqual([1, 4, 2, 2]);
-    const expected =
-        [10, 19, 28, 37, 11, 20, 29, 38, 10, 19, 28, 37, 11, 20, 29, 38];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic in NCHW with bias and multiple batches', async () => {
-    const inputDepth = 4;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 4;
-    const fSize = 1;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [
-          1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
-          1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4
-        ],
-        inputShape);
-    const w = tf.tensor4d(
-        [3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const bias = tf.tensor1d([1, 2, 1, 2]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      bias
-    });
-
-    expect(result.shape).toEqual([2, 4, 2, 2]);
-    const expected = [
-      10, 19, 28, 37, 11, 20, 29, 38, 10, 19, 28, 37, 11, 20, 29, 38,
-      10, 19, 28, 37, 11, 20, 29, 38, 10, 19, 28, 37, 11, 20, 29, 38
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic in NCHW with scalar PReLU actiavation', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.scalar(10);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [-110, -140, -170, -200, 3.5, 5, 6.5, 8];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic in NCHW with 1-D PReLU actiavation', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor1d([10, 100]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [-110, -140, -170, -200, 3.5, 5, 6.5, 8];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic in NCHW with 3-D PReLU actiavation', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w =
-        tf.tensor4d([-1, -1, -2, -2], [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor3d([1, 10], [2, 1, 1]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [-11, -14, -17, -20, -110, -140, -170, -200];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic in NCHW with full 3-D PReLU actiavation', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w =
-        tf.tensor4d([-1, -1, -2, -2], [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [-11, -28, -51, -80, -55, -84, -119, -160];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('im2row', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 3;
-    const fSize = 1;
-    const pad = 'same';
-    const strides: [number, number] = [2, 2];
-
-    const x = tf.tensor3d(
-        [
-          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
-        ],
-        inputShape);
-    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({x, filter: w, strides, pad});
-
-    expectArraysClose(
-        await result.data(),
-        [10, 5, 10, 50, 25, 50, -10, -5, -10, -50, -25, -50]);
-  });
-
-  it('im2row with relu', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 3;
-    const fSize = 1;
-    const pad = 'same';
-    const strides: [number, number] = [2, 2];
-
-    const x = tf.tensor3d(
-        [
-          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
-        ],
-        inputShape);
-    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'relu'
-    });
-
-    expectArraysClose(
-        await result.data(), [10, 5, 10, 50, 25, 50, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('im2row with prelu', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 3;
-    const fSize = 1;
-    const pad = 'same';
-    const strides: [number, number] = [2, 2];
-
-    const x = tf.tensor3d(
-        [
-          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
-        ],
-        inputShape);
-    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor3d([0.5], [1, 1, inputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expectArraysClose(
-        await result.data(),
-        [10, 5, 10, 50, 25, 50, -5, -2.5, -5, -25, -12.5, -25]);
-  });
-
-  it('im2row with leakyrelu', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 3;
-    const fSize = 1;
-    const pad = 'same';
-    const strides: [number, number] = [2, 2];
-
-    const x = tf.tensor3d(
-        [
-          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
-        ],
-        inputShape);
-    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = 0.3;
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'leakyrelu',
-      leakyreluAlpha: alpha
-    });
-
-    expectArraysClose(await result.data(), [
-      10, 5, 10, 50, 25, 50, -3, -1.5, -3, -15.000000953674316,
-      -7.500000476837158, -15.000000953674316
-    ]);
-  });
-
-  it('pointwise with prelu', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 3;
-    const fSize = 1;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-
-    const x = tf.tensor3d(
-        [
-          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
-        ],
-        inputShape);
-    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor3d([0.5], [1, 1, inputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expectArraysClose(await result.data(), [
-      10,  5,    10,  30,  15,   30,  50,  25,    50,  70,  35,    70,
-      20,  10,   20,  40,  20,   40,  60,  30,    60,  80,  40,    80,
-      -5,  -2.5, -5,  -15, -7.5, -15, -25, -12.5, -25, -35, -17.5, -35,
-      -10, -5,   -10, -20, -10,  -20, -30, -15,   -30, -40, -20,   -40
-    ]);
-  });
-
-  it('pointwise with leakyrelu', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 3;
-    const fSize = 1;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-
-    const x = tf.tensor3d(
-        [
-          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
-        ],
-        inputShape);
-    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = 0.3;
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      activation: 'leakyrelu',
-      leakyreluAlpha: alpha
-    });
-
-    expectArraysClose(await result.data(), [
-      10,
-      5,
-      10,
-      30,
-      15,
-      30,
-      50,
-      25,
-      50,
-      70,
-      35,
-      70,
-      20,
-      10,
-      20,
-      40,
-      20,
-      40,
-      60,
-      30,
-      60,
-      80,
-      40,
-      80,
-      -3,
-      -1.5,
-      -3,
-      -9,
-      -4.5,
-      -9,
-      -15.000000953674316,
-      -7.500000476837158,
-      -15.000000953674316,
-      -21,
-      -10.5,
-      -21,
-      -6,
-      -3,
-      -6,
-      -12,
-      -6,
-      -12,
-      -18,
-      -9,
-      -18,
-      -24,
-      -12,
-      -24
-    ]);
-  });
-
-  it('im2row with broadcasted bias and relu', async () => {
-    const inputDepth = 1;
-    const inputShape: [number, number, number] = [4, 4, inputDepth];
-    const outputDepth = 3;
-    const fSize = 1;
-    const pad = 'same';
-    const strides: [number, number] = [2, 2];
-
-    const x = tf.tensor3d(
-        [
-          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
-        ],
-        inputShape);
-    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat: 'NHWC',
-      dilations: [1, 1],
-      bias: tf.scalar(5),
-      activation: 'relu'
-    });
-
-    expectArraysClose(
-        await result.data(), [15, 10, 15, 55, 30, 55, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('im2row in NCHW', async () => {
-    const inputDepth = 2;
-    const inputShape: [number, number, number] = [inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], inputShape);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],
-        [fSize, fSize, inputDepth, outputDepth]);
-
-    const result =
-        tf.fused.conv2d({x, filter: w, strides, pad, dataFormat: 'NCHW'});
-
-    expectArraysClose(
-        await result.data(), [-32, -8, 100, 28, -40, -12, 122, 40]);
-  });
-
-  it('im2row in NCHW with scalar bias', async () => {
-    const inputDepth = 4;
-    const inputShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 4;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d(
-        [
-          3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1,
-          1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5,
-          0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0,
-        ],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const bias = tf.scalar(1);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      bias
-    });
-
-    expect(result.shape).toEqual([1, 4, 2, 2]);
-    const expected =
-        [91, 55, 64, 37, 91, 55, 64, 37, 91, 55, 64, 37, 91, 55, 64, 37];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('im2row in NCHW with 1-D bias', async () => {
-    const inputDepth = 4;
-    const inputShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 4;
-    const fSize = 2;
-    const pad = 'same';
-    const stride = 1;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
-    const w = tf.tensor4d(
-        [
-          3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1,
-          1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5,
-          0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0,
-        ],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const bias = tf.tensor1d([1, 2, 1, 2]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      bias
-    });
-
-    expect(result.shape).toEqual([1, 4, 2, 2]);
-    const expected =
-        [91, 55, 64, 37, 92, 56, 65, 38, 91, 55, 64, 37, 92, 56, 65, 38];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('im2row in NCHW with scalar PReLU actiavation weights', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-    const pad = 'same';
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w = tf.tensor4d(
-        [-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.scalar(10);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [-120, -320, 2, -120, -120, -320, 2, -120];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('im2row in NCHW with 1-D PReLU actiavation weights', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-    const pad = 'same';
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w = tf.tensor4d(
-        [-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor1d([1, 10]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [-12, -32, 2, -12, -120, -320, 2, -120];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('im2row in NCHW with 3-D PReLU actiavation weights', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-    const pad = 'same';
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w = tf.tensor4d(
-        [-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor3d([1, 10], [2, 1, 1]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [-12, -32, 2, -12, -120, -320, 2, -120];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('im2row in NCHW with full 3-D PReLU actiavation weights', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-    const pad = 'same';
-    const stride = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
-    const w = tf.tensor4d(
-        [-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides: stride,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    const expected = [-12, -64, 2, -48, -60, -192, 2, -96];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('batch in NCHW', async () => {
-    const inputDepth = 2;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],
-        [fSize, fSize, inputDepth, outputDepth]);
-
-    const result = tf.fused.conv2d(
-        {x, filter: w, strides, pad, dataFormat, dilations: dilation});
-
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(await result.data(), [
-      -32, -8, 100, 28, -40, -12, 122, 40, -32, -8, 228, 60, -40, -12, 282, 88
-    ]);
-  });
-
-  it('batch in NCHW with scalar bias', async () => {
-    const inputDepth = 2;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const bias = tf.scalar(1);
-
-    const result = tf.fused.conv2d(
-        {x, filter: w, strides, pad, dataFormat, dilations: dilation, bias});
-
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(await result.data(), [
-      -31, -7, 101, 29, -39, -11, 123, 41, -31, -7, 229, 61, -39, -11, 283, 89
-    ]);
-  });
-
-  it('batch in NCHW with 1-D bias', async () => {
-    const inputDepth = 2;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const bias = tf.tensor1d([1, 2]);
-
-    const result = tf.fused.conv2d(
-        {x, filter: w, strides, pad, dataFormat, dilations: dilation, bias});
-
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(await result.data(), [
-      -31, -7, 101, 29, -38, -10, 124, 42, -31, -7, 229, 61, -38, -10, 284, 90
-    ]);
-  });
-
-  it('batch in NCHW with scalar PReLU actiavation weights', async () => {
-    const inputDepth = 2;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.scalar(10);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(await result.data(), [
-      -320, -80, 100, 28, -400, -120, 122, 40, -320, -80, 228, 60, -400, -120,
-      282, 88
-    ]);
-  });
-
-  it('batch in NCHW with 1-D PReLU actiavation weights', async () => {
-    const inputDepth = 2;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor1d([1, 10]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(await result.data(), [
-      -32, -8, 100, 28, -400, -120, 122, 40, -32, -8, 228, 60, -400, -120, 282,
-      88
-    ]);
-  });
-
-  it('batch in NCHW with 3-D PReLU actiavation weights', async () => {
-    const inputDepth = 2;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor3d([1, 10], [2, 1, 1]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(await result.data(), [
-      -32, -8, 100, 28, -400, -120, 122, 40, -32, -8, 228, 60, -400, -120, 282,
-      88
-    ]);
-  });
-
-  it('batch in NCHW with full 3-D PReLU actiavation weights', async () => {
-    const inputDepth = 2;
-    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];
-    const outputDepth = 2;
-    const fSize = 2;
-    const pad = 'same';
-    const strides: [number, number] = [1, 1];
-    const dataFormat = 'NCHW';
-    const dilation = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],
-        [fSize, fSize, inputDepth, outputDepth]);
-    const alpha = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-
-    const result = tf.fused.conv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      dataFormat,
-      dilations: dilation,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(result.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(await result.data(), [
-      -32, -16, 100, 28, -200, -72, 122, 40, -32, -16, 228, 60, -200, -72, 282,
-      88
-    ]);
-  });
-
-  it('backProp input x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];
-    const filterSize = 2;
-    const strides = 1;
-    const pad = 0;
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D) => tf.fused.conv2d({x, filter, strides, pad}));
-    const [dx] = grads([x], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(
-        await dx.data(),
-        [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]);
-  });
-
-  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];
-    const filterSize = 2;
-    const strides = 1;
-    const pad = 0;
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-            tf.fused.conv2d({x, filter, strides, pad}));
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(
-        await dx.data(),
-        [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [26, 38, 62, 74]);
-  });
-
-  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];
-    const filterSize = 2;
-    const strides = 1;
-    const pad = 0;
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
-    const bias = tf.ones([2, 2, 2, 1]);
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
-
-    const fusedGrads =
-        tf.grads((x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.conv2d({
-          x,
-          filter: w,
-          strides,
-          pad,
-          dataFormat: 'NHWC',
-          dilations: [1, 1],
-          bias: b
-        }));
-    const [dxFused, dfilterFused, dbiasFused] =
-        fusedGrads([x, filter, bias], dy);
-
-    const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {
-      const conv = tf.conv2d(x, filter, strides, pad);
-      const sum = tf.add(conv, bias);
-      return sum;
-    });
-    const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
-
-    expectArraysClose(await dxFused.array(), await dx.array());
-    expectArraysClose(await dfilterFused.array(), await dfilter.array());
-    expectArraysClose(await dbiasFused.array(), await dbias.array());
-  });
-
-  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and relu',
-     async () => {
-       const inputDepth = 1;
-       const outputDepth = 1;
-       const inputShape: [number, number, number, number] =
-           [2, 3, 3, inputDepth];
-       const filterSize = 2;
-       const strides = 1;
-       const pad = 0;
-
-       const filterShape: [number, number, number, number] =
-           [filterSize, filterSize, inputDepth, outputDepth];
-       const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
-       const bias = tf.ones([2, 2, 2, 1]);
-
-       const x = tf.tensor4d(
-           [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-       const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
-
-       const fusedGrads =
-           tf.grads((x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.conv2d({
-             x,
-             filter: w,
-             strides,
-             pad,
-             dataFormat: 'NHWC',
-             dilations: [1, 1],
-             bias: b,
-             activation: 'relu'
-           }));
-       const [dxFused, dfilterFused, dbiasFused] =
-           fusedGrads([x, filter, bias], dy);
-
-       const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {
-         const conv = tf.conv2d(x, filter, strides, pad);
-         const sum = tf.add(conv, bias);
-         return tf.relu(sum);
-       });
-       const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
-
-       expectArraysClose(await dxFused.array(), await dx.array());
-       expectArraysClose(await dfilterFused.array(), await dfilter.array());
-       expectArraysClose(await dbiasFused.array(), await dbias.array());
-     });
-
-  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and elu', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];
-    const filterSize = 2;
-    const strides = 1;
-    const pad = 0;
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
-    const bias = tf.ones([2, 2, 2, 1]);
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
-
-    const fusedGrads =
-        tf.grads((x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.conv2d({
-          x,
-          filter: w,
-          strides,
-          pad,
-          dataFormat: 'NHWC',
-          dilations: [1, 1],
-          bias: b,
-          activation: 'elu'
-        }));
-    const [dxFused, dfilterFused, dbiasFused] =
-        fusedGrads([x, filter, bias], dy);
-
-    const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {
-      const conv = tf.conv2d(x, filter, strides, pad);
-      const sum = tf.add(conv, bias);
-      return tf.elu(sum);
-    });
-    const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
-
-    expectArraysClose(await dxFused.array(), await dx.array());
-    expectArraysClose(await dfilterFused.array(), await dfilter.array());
-    expectArraysClose(await dbiasFused.array(), await dbias.array());
-  });
-
-  it('throws when input is int32', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape,
-        'int32');
-    const w =
-        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
-
-    expect(() => tf.fused.conv2d({x, filter: w, strides: stride, pad}))
-        .toThrowError(/Argument 'x' passed to 'conv2d' must be float32/);
-  });
-
-  it('throws when filter is int32', async () => {
-    const inputDepth = 2;
-    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];
-    const outputDepth = 2;
-    const fSize = 1;
-    const pad = 0;
-    const stride = 1;
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
-    const w = tf.tensor4d(
-        [-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth], 'int32');
-
-    expect(() => tf.fused.conv2d({x, filter: w, strides: stride, pad}))
-        .toThrowError(/Argument 'filter' passed to 'conv2d' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/fused/fused_depthwise_conv2d_test.ts b/tfjs-master/tfjs-core/src/ops/fused/fused_depthwise_conv2d_test.ts
deleted file mode 100644
index 4ec897952..000000000
--- a/tfjs-master/tfjs-core/src/ops/fused/fused_depthwise_conv2d_test.ts
+++ /dev/null
@@ -1,442 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('fused depthwiseConv2D', ALL_ENVS, () => {
-  it('basic', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const strides = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [-0.303873, -0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.fused.depthwiseConv2d({x, filter: w, strides, pad});
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    const expected = [0.47737, 0.40018, 0.00859, -0.09615];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic with relu', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const strides = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [-0.303873, -0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.fused.depthwiseConv2d(
-        {x, filter: w, strides, pad, activation: 'relu'});
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    const expected = [0.47737, 0.40018, 0.00859, 0];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic with channel-wise broadcasted bias and relu', async () => {
-    const strides = 1;
-    const pad = 'same';
-    const x = tf.tensor4d(
-        [
-          0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8,
-          0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8
-        ],
-        [1, 3, 3, 4]);
-    const w = tf.tensor4d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [2, 2, 4, 1]);
-    const bias = tf.tensor1d([0, 1, 2, 3]);
-
-    const result = tf.fused.depthwiseConv2d({x, filter: w, strides, pad, bias});
-    expect(result.shape).toEqual([1, 3, 3, 4]);
-    const expected = [
-      124, 167, 92,  142, 112, 117, 76,  124, 16, 28, 44, 64,
-      88,  134, 134, 88,  76,  120, 154, 205, 40, 58, 80, 106,
-      4,   18,  36,  31,  20,  33,  50,  71,  0,  7,  16, 27
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('basic with broadcasted bias and relu', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const strides = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
-          0.0741907, 0.409265, 0.351377
-        ],
-        [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [-0.303873, -0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.fused.depthwiseConv2d(
-        {x, filter: w, strides, pad, bias: tf.scalar(1), activation: 'relu'});
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    const expected = [1.47737, 1.40018, 1.00859, 0.90385];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  // For WebGPU DepthwiseConv2D3x3Program.
-  it('basic with channel-wise broadcasted bias and relu filter 3x3',
-     async () => {
-       const fSize = 3;
-       const pad = 'same';
-       const strides = 1;
-       const chMul = 1;
-       const inDepth = 4;
-
-       const x = tf.tensor4d(
-           [
-             0.230664,  0.987388, 0.0685208, 0.419224,  0.887861, 0.731641,
-             0.0741907, 0.409265, 0.351377,  0.230664,  0.987388, 0.0685208,
-             0.419224,  0.887861, 0.731641,  0.0741907, 0.409265, 0.351377,
-             0.230664,  0.987388, 0.0685208, 0.419224,  0.887861, 0.731641,
-             0.0741907, 0.409265, 0.351377,  0.230664,  0.987388, 0.0685208,
-             0.419224,  0.887861, 0.731641,  0.0741907, 0.409265, 0.351377
-           ],
-           [1, 3, 3, inDepth]);
-       const w = tf.tensor4d(
-           [
-             -0.303873, -0.229223, 0.144333,  0.803373,  -0.303873, -0.229223,
-             0.144333,  0.803373,  -0.303873, -0.229223, 0.144333,  0.803373,
-             -0.303873, -0.229223, 0.144333,  0.803373,  -0.303873, -0.229223,
-             0.144333,  0.803373,  -0.303873, -0.229223, 0.144333,  0.803373,
-             -0.303873, -0.229223, 0.144333,  0.803373,  -0.303873, -0.229223,
-             0.144333,  0.803373,  -0.303873, -0.229223, 0.144333,  0.803373
-           ],
-           [fSize, fSize, inDepth, chMul],
-       );
-       const bias = tf.tensor1d([0, 1, 2, 3]);
-       const result =
-           tf.fused.depthwiseConv2d({x, filter: w, strides, pad, bias});
-       expect(result.shape).toEqual([1, 3, 3, 4]);
-       const expected = [
-         -0.5916450023651123, 0.32189714908599854, 2.1594903469085693,
-         4.518429279327393,   -0.7192406058311462, 0.1729278564453125,
-         2.4301507472991943,  5.161257743835449,   -0.521757185459137,
-         0.6027780771255493,  2.3146610260009766,  4.764861583709717,
-         -0.9142301082611084, 0.212377667427063,   2.2707135677337646,
-         5.417022228240967,   -1.264151692390442,  0.046402156352996826,
-         2.6004443168640137,  6.342137336730957,   -1.044123649597168,
-         0.5700653791427612,  2.434239149093628,   5.760432243347168,
-         -0.5743405818939209, 0.6064186692237854,  2.250115394592285,
-         4.751436233520508,   -0.8174881339073181, 0.4933167099952698,
-         2.437333583831787,   5.621503829956055,   -0.6675527095794678,
-         0.7906477451324463,  2.2810182571411133,  5.376591682434082
-       ];
-       expectArraysClose(await result.data(), expected);
-     });
-
-  it('prelu', async () => {
-    const fSize = 3;
-    const pad = 'valid';
-    const strides = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
-          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,
-          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,
-          0.741935, 0.974474, 0.621102, 0.171411
-        ],
-        [1, 5, 5, inDepth]);
-    const alpha = tf.tensor4d(
-        [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], [1, 3, 3, 1]);
-    const w = tf.tensor4d(
-        [
-          -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,
-          -0.889702, -0.180695, -0.691992
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.fused.depthwiseConv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    const expected = [
-      -0.25400, -0.50118, -0.73622, -0.94068, -1.2298, -1.84585, -2.3089,
-      -2.7499, -2.64077
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('leakyrelu', async () => {
-    const fSize = 3;
-    const pad = 'valid';
-    const strides = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
-          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,
-          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,
-          0.741935, 0.974474, 0.621102, 0.171411
-        ],
-        [1, 5, 5, inDepth]);
-    const alpha = 0.3;
-    const w = tf.tensor4d(
-        [
-          -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,
-          -0.889702, -0.180695, -0.691992
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.fused.depthwiseConv2d({
-      x,
-      filter: w,
-      strides,
-      pad,
-      activation: 'leakyrelu',
-      leakyreluAlpha: alpha
-    });
-
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    const expected = [
-      -0.7620067596435547, -0.7517655491828918, -0.7362186312675476,
-      -0.7055101990699768, -0.7378802299499512, -0.9229262471199036,
-      -0.9895440340042114, -1.031226396560669, -0.8802568912506104
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('sigmoid', async () => {
-    const fSize = 3;
-    const pad = 'valid';
-    const strides = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d(
-        [
-          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
-          0.211859, 0.633501, 0.186427, 0.777034, 0.50001,  0.607341, 0.95303,
-          0.696479, 0.050387, 0.62045,  0.728049, 0.028043, 0.437009, 0.712881,
-          0.741935, 0.974474, 0.621102, 0.171411
-        ],
-        [1, 5, 5, inDepth]);
-    const w = tf.tensor4d(
-        [
-          -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,
-          -0.889702, -0.180695, -0.691992
-        ],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    const result = tf.fused.depthwiseConv2d(
-        {x, filter: w, strides, pad, activation: 'sigmoid'});
-
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    const expected = [
-      0.07309964, 0.07544667, 0.07914197, 0.08693069, 0.07873929, 0.04409045,
-      0.03562334, 0.0311462, 0.05048907
-    ];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];
-    const filterSize = 2;
-    const strides = 1;
-    const pad = 0;
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
-
-    const grads = tf.grads(
-        (x: tf.Tensor4D, filter: tf.Tensor4D) =>
-            tf.fused.depthwiseConv2d({x, filter, strides, pad}));
-    const [dx, dfilter] = grads([x, filter], dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(
-        await dx.data(),
-        [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]);
-
-    expect(dfilter.shape).toEqual(filterShape);
-    expectArraysClose(await dfilter.data(), [26, 38, 62, 74]);
-  });
-
-  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias', async () => {
-    const inputDepth = 1;
-    const outputDepth = 1;
-    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];
-    const filterSize = 2;
-    const strides = 1;
-    const pad = 0;
-
-    const filterShape: [number, number, number, number] =
-        [filterSize, filterSize, inputDepth, outputDepth];
-    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
-    const bias = tf.ones([2, 2, 2, 1]);
-
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
-
-    const fusedGrads = tf.grads(
-        (x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.depthwiseConv2d({
-          x,
-          filter: w,
-          strides,
-          pad,
-          dataFormat: 'NHWC',
-          dilations: [1, 1],
-          bias: b
-        }));
-    const [dxFused, dfilterFused, dbiasFused] =
-        fusedGrads([x, filter, bias], dy);
-
-    const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {
-      const conv = tf.depthwiseConv2d(x, filter, strides, pad);
-      const sum = tf.add(conv, bias);
-      return sum;
-    });
-    const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
-
-    expectArraysClose(await dxFused.array(), await dx.array());
-    expectArraysClose(await dfilterFused.array(), await dfilter.array());
-    expectArraysClose(await dbiasFused.array(), await dbias.array());
-  });
-
-  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and activation',
-     async () => {
-       const inputDepth = 1;
-       const outputDepth = 1;
-       const inputShape: [number, number, number, number] =
-           [2, 3, 3, inputDepth];
-       const filterSize = 2;
-       const strides = 1;
-       const pad = 0;
-
-       const filterShape: [number, number, number, number] =
-           [filterSize, filterSize, inputDepth, outputDepth];
-       const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
-       const bias = tf.ones([2, 2, 2, 1]);
-
-       const x = tf.tensor4d(
-           [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
-       const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
-
-       const fusedGrads = tf.grads(
-           (x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.depthwiseConv2d({
-             x,
-             filter: w,
-             strides,
-             pad,
-             dataFormat: 'NHWC',
-             dilations: [1, 1],
-             bias: b,
-             activation: 'relu'
-           }));
-       const [dxFused, dfilterFused, dbiasFused] =
-           fusedGrads([x, filter, bias], dy);
-
-       const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {
-         const conv = tf.depthwiseConv2d(x, filter, strides, pad);
-         const sum = tf.add(conv, bias);
-         return tf.relu(sum);
-       });
-       const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
-
-       expectArraysClose(await dxFused.array(), await dx.array());
-       expectArraysClose(await dfilterFused.array(), await dfilter.array());
-       expectArraysClose(await dbiasFused.array(), await dbias.array());
-     });
-
-  it('throws when input is int32', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const strides = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, inDepth], 'int32');
-    const w = tf.tensor4d(
-        [-0.303873, -0.229223, 0.144333, 0.803373],
-        [fSize, fSize, inDepth, chMul],
-    );
-
-    expect(() => tf.fused.depthwiseConv2d({x, filter: w, strides, pad}))
-        .toThrowError(
-            /Argument 'x' passed to 'depthwiseConv2d' must be float32/);
-  });
-
-  it('throws when filter is int32', async () => {
-    const fSize = 2;
-    const pad = 'valid';
-    const strides = 1;
-    const chMul = 1;
-    const inDepth = 1;
-
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, inDepth]);
-    const w = tf.tensor4d(
-        [1, 2, 3, 4],
-        [fSize, fSize, inDepth, chMul],
-        'int32',
-    );
-
-    expect(() => tf.fused.depthwiseConv2d({x, filter: w, strides, pad}))
-        .toThrowError(
-            /Argument 'filter' passed to 'depthwiseConv2d' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/fused/fused_mat_mul_test.ts b/tfjs-master/tfjs-core/src/ops/fused/fused_mat_mul_test.ts
deleted file mode 100644
index be8385c55..000000000
--- a/tfjs-master/tfjs-core/src/ops/fused/fused_mat_mul_test.ts
+++ /dev/null
@@ -1,402 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('fused matmul', ALL_ENVS, () => {
-  it('fused A x B', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    const c = tf.fused.matMul({a, b});
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -3, 20]);
-  });
-
-  it('fused A x B with relu', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const c = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: null, activation: 'relu'});
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, 0, 20]);
-  });
-
-  it('fused A x B with elu', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const c = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: null, activation: 'elu'});
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -0.9502, 20]);
-  });
-
-  it('fused A x B with relu6', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const c = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: null, activation: 'relu6'});
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 6, 0, 6]);
-  });
-
-  it('fused A x B with prelu', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const alpha = tf.tensor2d([0.5, 0.5], [1, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const c = tf.fused.matMul({
-      a,
-      b,
-      transposeA,
-      transposeB,
-      bias: null,
-      activation: 'prelu',
-      preluActivationWeights: alpha
-    });
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -1.5, 20]);
-  });
-
-  it('fused A x B with leakyrelu', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const alpha = 0.3;
-    const transposeA = false;
-    const transposeB = false;
-
-    const c = tf.fused.matMul({
-      a,
-      b,
-      transposeA,
-      transposeB,
-      bias: null,
-      activation: 'leakyrelu',
-      leakyreluAlpha: alpha
-    });
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -0.9000000357627869, 20]);
-  });
-
-  it('fused A x B with leakyrelu not provided.', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const c = tf.fused.matMul({
-      a,
-      b,
-      transposeA,
-      transposeB,
-      bias: null,
-      activation: 'leakyrelu'
-    });
-
-    expect(c.shape).toEqual([2, 2]);
-    // leakyRelu should use default alpha=0.2.
-    expectArraysClose(await c.data(), [0, 8, -0.6000000238418579, 20]);
-  });
-
-  it('fused A x B with sigmoid', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const c = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: null, activation: 'sigmoid'});
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0.5, 0.99966455, 0.04742587, 1]);
-  });
-
-  it('fused A x B with relu transpose', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [2, 3]);
-    const transposeA = false;
-    const transposeB = true;
-
-    const c = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: null, activation: 'relu'});
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 9, 0, 24]);
-  });
-
-  it('fused A x B with 2d bias and relu', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const d = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: c, activation: 'relu'});
-
-    expect(d.shape).toEqual([2, 2]);
-    expectArraysClose(await d.data(), [1, 9, 0, 21]);
-  });
-
-  it('fused A x B with relu and broadcasted bias', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const c = tf.tensor1d([1, 1]);
-    const act: tf.fused.Activation = 'relu';
-    const transposeA = false;
-    const transposeB = false;
-
-    const d = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: c, activation: act});
-
-    expect(d.shape).toEqual([2, 2]);
-    expectArraysClose(await d.data(), [1, 9, 0, 21]);
-  });
-
-  it('fused A x B with elu and broadcasted bias', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const c = tf.tensor1d([1, 1]);
-    const act: tf.fused.Activation = 'elu';
-    const transposeA = false;
-    const transposeB = false;
-
-    const d = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: c, activation: act});
-
-    expect(d.shape).toEqual([2, 2]);
-    expectArraysClose(await d.data(), [1, 9, -0.8647, 21]);
-  });
-
-  it('fused A x B with elu and broadcasted shape', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const c = tf.tensor1d([1, 1]);
-    const act: tf.fused.Activation = 'elu';
-    const transposeA = false;
-    const transposeB = false;
-
-    const d = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: c, activation: act});
-
-    expect(d.shape).toEqual([1, 2, 2]);
-    expectArraysClose(await d.data(), [1, 9, -0.8647, 21]);
-  });
-
-  it('fused A x B with relu and broadcasted bias different rank', async () => {
-    const a = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [2, 2, 3]);
-    const b = tf.tensor3d([0, 1, -3, 2, 2, 1, 0, 1, -3, 2, 2, 1], [2, 3, 2]);
-    const c = tf.tensor2d([1, 2], [1, 2]);
-    const act: tf.fused.Activation = 'relu';
-    const transposeA = false;
-    const transposeB = false;
-
-    const d = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: c, activation: act});
-
-    expect(d.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await d.data(), [2, 6, 0, 18, 0, 30, 0, 42]);
-  });
-
-  it('fused A x B with 2d bias only', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-    const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const d = tf.fused.matMul(
-        {a, b, transposeA, transposeB, bias: c, activation: 'linear'});
-
-    expect(d.shape).toEqual([2, 2]);
-    expectArraysClose(await d.data(), [1, 9, -2, 21]);
-  });
-
-  it('fused A x B with relu gradient', async () => {
-    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
-    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
-    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const grads = tf.grads((a, b) => {
-      const prod = tf.matMul(a, b, transposeA, transposeB);
-      return tf.relu(prod);
-    });
-
-    const fusedGrads = tf.grads((a, b) => {
-      return tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'relu'});
-    });
-
-    const [da, db] = grads([a, b], dy);
-    const [fusedDa, fusedDb] = fusedGrads([a, b], dy);
-    expectArraysClose(await da.array(), await fusedDa.array());
-    expectArraysClose(await db.data(), await fusedDb.array());
-  });
-
-  it('gradient with clones A x B with relu', () => {
-    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
-    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
-    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const fusedGrads = tf.grads((a, b) => {
-      return tf.fused
-          .matMul({
-            a: a.clone(),
-            b: b.clone(),
-            transposeA,
-            transposeB,
-            bias: null,
-            activation: 'relu'
-          })
-          .clone();
-    });
-
-    const [fusedDa, fusedDb] = fusedGrads([a, b], dy);
-    expect(fusedDa.shape).toEqual(a.shape);
-    expect(fusedDb.shape).toEqual(b.shape);
-  });
-
-  it('fused A x B with relu bias gradient', async () => {
-    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
-    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
-    const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
-
-    const grads = tf.grads((a, b, c) => {
-      const prod = tf.matMul(a, b, transposeA, transposeB);
-      const sum = tf.add(prod, c);
-      return tf.relu(sum);
-    });
-
-    const fusedGrads = tf.grads((a, b, c) => {
-      return tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: 'relu'});
-    });
-
-    const [da, db, dc] = grads([a, b, c], dy);
-    const [fusedDa, fusedDb, fusedDc] = fusedGrads([a, b, c], dy);
-
-    expectArraysClose(await da.array(), await fusedDa.array());
-    expectArraysClose(await db.array(), await fusedDb.array());
-    expectArraysClose(await dc.array(), await fusedDc.array());
-  });
-
-  it('fused A x B with relu bias gradient transpose', async () => {
-    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [3, 2]);
-    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
-    const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
-    const transposeA = true;
-    const transposeB = false;
-
-    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
-
-    const grads = tf.grads((a, b, c) => {
-      const prod = tf.matMul(a, b, transposeA, transposeB);
-      const sum = tf.add(prod, c);
-      return tf.relu(sum);
-    });
-
-    const fusedGrads = tf.grads((a, b, c) => {
-      return tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: 'relu'});
-    });
-
-    const [da, db, dc] = grads([a, b, c], dy);
-    const [fusedDa, fusedDb, fusedDc] = fusedGrads([a, b, c], dy);
-
-    expectArraysClose(await da.array(), await fusedDa.array());
-    expectArraysClose(await db.array(), await fusedDb.array());
-    expectArraysClose(await dc.array(), await fusedDc.array());
-  });
-
-  it('fused A x B with relu and broadcasted bias gradient', async () => {
-    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
-    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
-    const c = tf.tensor2d([[1]]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
-
-    const grads = tf.grads((a, b, c) => {
-      const prod = tf.matMul(a, b, transposeA, transposeB);
-      const sum = tf.add(prod, c);
-      return tf.relu(sum);
-    });
-
-    const fusedGrads = tf.grads((a, b, c) => {
-      return tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: c, activation: 'relu'});
-    });
-
-    const [da, db, dc] = grads([a, b, c], dy);
-    const [fusedDa, fusedDb, fusedDc] = fusedGrads([a, b, c], dy);
-
-    expectArraysClose(await da.array(), await fusedDa.array());
-    expectArraysClose(await db.array(), await fusedDb.array());
-    expectArraysClose(await dc.array(), await fusedDc.array());
-  });
-
-  it('fused matmul with relu6 and gradients', async () => {
-    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
-    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
-    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
-    const transposeA = false;
-    const transposeB = false;
-
-    const fusedGrads = tf.grads((a, b) => {
-      return tf.fused.matMul(
-          {a, b, transposeA, transposeB, bias: null, activation: 'relu6'});
-    });
-    const [fusedDa, fusedDb] = fusedGrads([a, b], dy);
-
-    const grads = tf.grads((a, b) => {
-      const prod = tf.matMul(a, b, transposeA, transposeB);
-      return tf.relu6(prod);
-    });
-    const [da, db] = grads([a, b], dy);
-
-    expectArraysClose(await da.array(), await fusedDa.array());
-    expectArraysClose(await db.data(), await fusedDb.array());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/fused/mat_mul.ts b/tfjs-master/tfjs-core/src/ops/fused/mat_mul.ts
deleted file mode 100644
index b1ba4fd19..000000000
--- a/tfjs-master/tfjs-core/src/ops/fused/mat_mul.ts
+++ /dev/null
@@ -1,213 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {customGrad} from '../../gradients';
-import {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor, Tensor3D} from '../../tensor';
-import {GradSaveFunc, NamedTensorMap} from '../../tensor_types';
-import {makeTypesMatch} from '../../tensor_util';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-
-import {add} from '../add';
-import * as broadcast_util from '../broadcast_util';
-import {Activation} from '../fused_types';
-import {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';
-import {matMul as unfusedMatMul} from '../mat_mul';
-import {op} from '../operation';
-import {reshape} from '../reshape';
-
-/**
- * Computes the dot product of two matrices with optional activation and bias.
- *
- * ```js
- * const a = tf.tensor2d([-1, -2], [1, 2]);
- * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- * const bias = tf.tensor2d([1, 2], [1, 2]);
- *
- * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();
- * ```
- *
- * @param obj An object with the following properties:
- * - `a` First matrix in dot product operation.
- * - `b` Second matrix in dot product operation.
- * - `transposeA` If true, `a` is transposed before multiplication.
- * - `transposeB` If true, `b` is transposed before multiplication.
- * - `bias` Matrix to be added to the result.
- * - `activation` Name of activation kernel (defaults to `linear`).
- * - `preluActivationWeights` Tensor of prelu weights.
- * - `leakyreluAlpha` Alpha of leakyrelu.
- */
-function fusedMatMul_({
-  a,
-  b,
-  transposeA = false,
-  transposeB = false,
-  bias,
-  activation = 'linear',
-  preluActivationWeights,
-  leakyreluAlpha = 0.2,
-}: {
-  a: Tensor|TensorLike,
-  b: Tensor|TensorLike,
-  transposeA?: boolean,
-  transposeB?: boolean,
-  bias?: Tensor|TensorLike,
-  activation?: Activation,
-  preluActivationWeights?: Tensor
-  leakyreluAlpha?: number
-}): Tensor {
-    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
-      let result = unfusedMatMul(a, b, transposeA, transposeB);
-      if (bias != null) {
-        result = add(result, bias);
-      }
-
-      return applyActivation(
-                 result, activation, preluActivationWeights, leakyreluAlpha);
-    }
-
-    let $a = convertToTensor(a, 'a', 'fused matMul');
-    let $b = convertToTensor(b, 'b', 'fused matMul');
-    [$a, $b] = makeTypesMatch($a, $b);
-
-    const innerShapeA =
-        transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
-    const innerShapeB =
-        transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
-
-    const outerShapeA =
-        transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
-    const outerShapeB =
-        transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
-
-    const outerDimsA = $a.shape.slice(0, -2);
-    const outerDimsB = $b.shape.slice(0, -2);
-    const batchDimA = util.sizeFromShape(outerDimsA);
-    const batchDimB = util.sizeFromShape(outerDimsB);
-
-    util.assert(
-        innerShapeA === innerShapeB,
-        () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +
-            `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +
-            `${$b.shape} and transposeA=${transposeA}` +
-            ` and transposeB=${transposeB} must match.`);
-
-    const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape(
-        $a.shape.slice(0, -2), $b.shape.slice(0, -2));
-    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
-
-    const a3D: Tensor3D = transposeA ?
-        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :
-        reshape($a, [batchDimA, outerShapeA, innerShapeA]);
-    const b3D: Tensor3D = transposeB ?
-        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :
-        reshape($b, [batchDimB, innerShapeB, outerShapeB]);
-
-    let $bias: Tensor;
-    if (bias != null) {
-      $bias = convertToTensor(bias, 'bias', 'fused matMul');
-      [$bias] = makeTypesMatch($bias, $a);
-
-      broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);
-    }
-
-    let $preluActivationWeights: Tensor;
-    if (preluActivationWeights != null) {
-      $preluActivationWeights = convertToTensor(
-          preluActivationWeights, 'prelu weights', 'fused matMul');
-    }
-
-    const grad = (dy: Tensor3D, saved: Tensor[]) => {
-      const [a3D, b3D, y, $bias] = saved;
-      // we reshape dy because the result of the forward is not
-      // necessarily going to be a 3d tensor due to a reshape done at the end of
-      // the customOp.
-      const dyActivation =
-          getFusedDyActivation(reshape(dy, y.shape), y, activation);
-      let aDer: Tensor;
-      let bDer: Tensor;
-
-      if (!transposeA && !transposeB) {
-        aDer = unfusedMatMul(dyActivation, b3D, false, true);
-        bDer = unfusedMatMul(a3D, dyActivation, true, false);
-      } else if (!transposeA && transposeB) {
-        aDer = unfusedMatMul(dyActivation, b3D, false, false);
-        bDer = unfusedMatMul(dyActivation, a3D, true, false);
-      } else if (transposeA && !transposeB) {
-        aDer = unfusedMatMul(b3D, dyActivation, false, true);
-        bDer = unfusedMatMul(a3D, dyActivation, false, false);
-      } else {
-        aDer = unfusedMatMul(b3D, dyActivation, true, true);
-        bDer = unfusedMatMul(dyActivation, a3D, true, true);
-      }
-
-      if (bias != null) {
-        const biasDer = getFusedBiasGradient($bias, dyActivation);
-        return [aDer, bDer, biasDer];
-      } else {
-        return [aDer, bDer];
-      }
-    };
-
-    const inputs: _FusedMatMulInputs = {
-      a: a3D,
-      b: b3D,
-      bias: $bias,
-      preluActivationWeights: $preluActivationWeights
-    };
-    const attrs: _FusedMatMulAttrs =
-        {transposeA, transposeB, activation, leakyreluAlpha};
-
-    // Depending on the the params passed in we will have different number of
-    // inputs and thus a a different number of elements in the gradient.
-    if (bias == null) {
-      const customOp =
-          customGrad((a3D: Tensor3D, b3D: Tensor3D, save: GradSaveFunc) => {
-            const res =
-                // tslint:disable-next-line: no-unnecessary-type-assertion
-                ENGINE.runKernel(
-                    _FusedMatMul, inputs as unknown as NamedTensorMap,
-                    attrs as unknown as NamedAttrMap) as Tensor;
-
-            save([a3D, b3D, res]);
-
-            return {value: reshape(res, outShape), gradFunc: grad};
-          });
-      return customOp(a3D, b3D);
-    } else {
-      const customOpWithBias = customGrad(
-          (a3D: Tensor3D, b3D: Tensor3D, $bias: Tensor, save: GradSaveFunc) => {
-            const res =
-                // tslint:disable-next-line: no-unnecessary-type-assertion
-                ENGINE.runKernel(
-                    _FusedMatMul, inputs as unknown as NamedTensorMap,
-                    attrs as unknown as NamedAttrMap) as Tensor;
-
-            save([a3D, b3D, res, $bias]);
-
-            return {value: reshape(res, outShape), gradFunc: grad};
-          });
-
-      return customOpWithBias(a3D, b3D, $bias);
-    }
-  }
-
-  export const matMul = /* @__PURE__ */ op({fusedMatMul_});
diff --git a/tfjs-master/tfjs-core/src/ops/fused_ops.ts b/tfjs-master/tfjs-core/src/ops/fused_ops.ts
deleted file mode 100644
index 13ebb8cbf..000000000
--- a/tfjs-master/tfjs-core/src/ops/fused_ops.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {conv2d} from './fused/conv2d';
-import {depthwiseConv2d} from './fused/depthwise_conv2d';
-import {matMul} from './fused/mat_mul';
-import {Activation} from './fused_types';
-
-export {Activation, conv2d, depthwiseConv2d, matMul};
diff --git a/tfjs-master/tfjs-core/src/ops/fused_types.ts b/tfjs-master/tfjs-core/src/ops/fused_types.ts
deleted file mode 100644
index 086e26d0c..000000000
--- a/tfjs-master/tfjs-core/src/ops/fused_types.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor3D, Tensor4D} from '../tensor';
-import {Conv2DInfo} from './conv_util';
-
-export type FusedConv2DConfig = {
-  input: Tensor4D,
-  filter: Tensor4D,
-  convInfo: Conv2DInfo,
-  bias?: Tensor,
-  activation?: Activation,
-  preluActivationWeights?: Tensor,
-  leakyreluAlpha?: number
-};
-
-export type FusedBatchMatMulConfig = {
-  a: Tensor3D,
-  b: Tensor3D,
-  transposeA: boolean,
-  transposeB: boolean,
-  bias?: Tensor,
-  activation?: Activation,
-  preluActivationWeights?: Tensor,
-  leakyreluAlpha?: number
-};
-
-export type Activation =
-    'linear'|'relu'|'prelu'|'elu'|'relu6'|'leakyrelu'|'sigmoid';
diff --git a/tfjs-master/tfjs-core/src/ops/fused_util.ts b/tfjs-master/tfjs-core/src/ops/fused_util.ts
deleted file mode 100644
index 5afa311c5..000000000
--- a/tfjs-master/tfjs-core/src/ops/fused_util.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-
-import * as broadcast_util from './broadcast_util';
-import {elu} from './elu';
-import {Activation} from './fused_types';
-import {leakyRelu} from './leaky_relu';
-import {mul} from './mul';
-import {prelu} from './prelu';
-import {relu} from './relu';
-import {relu6} from './relu6';
-import {reshape} from './reshape';
-import {sigmoid} from './sigmoid';
-import {step} from './step';
-import {sum} from './sum';
-
-// Returns gradient for fused activation.
-export function getFusedDyActivation(
-    dy: Tensor, y: Tensor, activation: Activation): Tensor {
-  if (activation == null || activation === 'linear') {
-    return dy;
-  }
-  if (activation === 'relu') {
-    return mul(dy, step(y));
-  }
-  throw new Error(
-      `Cannot compute gradient for fused activation ${activation}.`);
-}
-
-// Returns gradient for fused bias.
-export function getFusedBiasGradient(
-    bias: Tensor, dyActivation: Tensor): Tensor {
-  let res = dyActivation;
-  const reduceAxes =
-      broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);
-  if (reduceAxes.length > 0) {
-    res = sum(res, reduceAxes);
-  }
-  return reshape(res, bias.shape);
-}
-
-export function applyActivation(
-    x: Tensor, activation: Activation, preluActivationWeights?: Tensor,
-    leakyreluAlpha?: number): Tensor {
-  if (activation === 'linear') {
-    return x;
-  } else if (activation === 'relu') {
-    return relu(x);
-  } else if (activation === 'elu') {
-    return elu(x);
-  } else if (activation === 'relu6') {
-    return relu6(x);
-  } else if (activation === 'prelu') {
-    return prelu(x, preluActivationWeights);
-  } else if (activation === 'leakyrelu') {
-    return leakyRelu(x, leakyreluAlpha);
-  } else if (activation === 'sigmoid') {
-    return sigmoid(x);
-  }
-  throw new Error(`Unknown fused activation ${activation}.`);
-}
-
-// Whether we should call fused ops.
-export const shouldFuse = (gradientDepth: number, activation: Activation) => {
-  const gradientMode = gradientDepth > 0;
-  return !gradientMode || activation === 'linear';
-};
diff --git a/tfjs-master/tfjs-core/src/ops/gather.ts b/tfjs-master/tfjs-core/src/ops/gather.ts
deleted file mode 100644
index 7ca40b3a4..000000000
--- a/tfjs-master/tfjs-core/src/ops/gather.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {GatherV2, GatherV2Attrs, GatherV2Inputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Gather slices from tensor `x`'s axis `axis` according to `indices`.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- * const indices = tf.tensor1d([1, 3, 3], 'int32');
- *
- * x.gather(indices).print();
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- * const indices = tf.tensor1d([1, 1, 0], 'int32');
- *
- * x.gather(indices).print();
- * ```
- * @param x The input tensor whose slices are to be gathered.
- * @param indices The indices of the values to extract.
- * @param axis The axis over which to select values. Defaults to 0.
- * @param batchDims Optional. The number of batch dimensions. It must be less
- *     than or equal to rank(indices). Defaults to 0.
- *     The output tensor will have shape of
- *     `x.shape[:axis] + indices.shape[batchDims:] + x.shape[axis + 1:]`
- *
- * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
- */
-function gather_<T extends Tensor>(
-    x: T|TensorLike, indices: Tensor|TensorLike, axis = 0, batchDims = 0): T {
-  const $x = convertToTensor(x, 'x', 'gather');
-  const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');
-
-  const inputs: GatherV2Inputs = {x: $x, indices: $indices};
-  const attrs: GatherV2Attrs = {axis, batchDims};
-
-  return ENGINE.runKernel(
-      GatherV2, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const gather = /* @__PURE__ */ op({gather_});
diff --git a/tfjs-master/tfjs-core/src/ops/gather_nd.ts b/tfjs-master/tfjs-core/src/ops/gather_nd.ts
deleted file mode 100644
index 305c539fb..000000000
--- a/tfjs-master/tfjs-core/src/ops/gather_nd.ts
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {GatherNd, GatherNdInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {op} from './operation';
-
-/**
- * Gather slices from input tensor into a Tensor with shape specified by
- * `indices`.
- *
- * `indices` is a K-dimensional integer tensor, best thought of as a
- * (K-1)-dimensional tensor of indices into input, where each element defines a
- * slice of input:
- * output[\\(i_0, ..., i_{K-2}\\)] = input[indices[\\(i_0, ..., i_{K-2}\\)]]
- *
- * Whereas in `tf.gather`, `indices` defines slices into the first dimension of
- * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions
- * of input, where N = indices.shape[-1].
- *
- * The last dimension of indices can be at most the rank of input:
- * indices.shape[-1] <= input.rank
- *
- * The last dimension of `indices` corresponds to elements
- * (if indices.shape[-1] == input.rank) or slices
- * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of
- * input.
- * The output tensor has shape
- * indices.shape[:-1] + input.shape[indices.shape[-1]:]
- *
- * Note that on CPU, if an out of bound index is found, an error is returned. On
- * GPU, if an out of bound index is found, a 0 is stored in the corresponding
- * output value.
- *
- * ```js
- * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');
- * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);
- * tf.gatherND(input, indices).print() // [10, 11]
- * ```
- *
- * @param x The tensor from which to gather values.
- * @param indices Index tensor, must be of type int32.
- *
- * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
- */
-function gatherND_(x: Tensor|TensorLike, indices: Tensor|TensorLike): Tensor {
-  const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');
-  const $x = convertToTensor(x, 'x', 'gatherND', 'string_or_numeric');
-
-  const inputs: GatherNdInputs = {params: $x, indices: $indices};
-
-  return ENGINE.runKernel(GatherNd, inputs as unknown as NamedTensorMap);
-}
-
-export const gatherND = /* @__PURE__ */ op({gatherND_});
diff --git a/tfjs-master/tfjs-core/src/ops/gather_nd_test.ts b/tfjs-master/tfjs-core/src/ops/gather_nd_test.ts
deleted file mode 100644
index 87b01dc01..000000000
--- a/tfjs-master/tfjs-core/src/ops/gather_nd_test.ts
+++ /dev/null
@@ -1,142 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-import {gatherND} from './gather_nd';
-import {scalar, tensor1d, tensor2d, tensor3d} from './ops';
-
-describeWithFlags('gatherND', ALL_ENVS, () => {
-  it('should work for simple slice', async () => {
-    const indices = tensor2d([0, 4, 8], [3, 1], 'int32');
-    const input =
-        tensor1d([100, 101, 102, 777, 778, 779, 1000, 1001, 1002], 'int32');
-    const shape = [3];
-    const result = gatherND(input, indices);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(input.dtype);
-    expectArraysClose(await result.data(), [100, 778, 1002]);
-  });
-
-  it('should work for indexing 2d', async () => {
-    const indices = tensor2d([0, 2], [2, 1], 'int32');
-    const input = tensor2d(
-        [
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
-        ],
-        [8, 4], 'float32');
-    const shape = [2, 4];
-    const result = gatherND(input, indices);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(input.dtype);
-    expectArraysClose(await result.data(), [5, 5, 5, 5, 7, 7, 7, 7]);
-  });
-
-  it('should work for indexing 3d', async () => {
-    const indices = tensor2d([0, 2, 1, 1], [2, 2], 'int32');
-    const input = tensor3d(
-        [
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
-        ],
-        [2, 4, 4], 'float32');
-    const shape = [2, 4];
-    const result = gatherND(input, indices);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(input.dtype);
-    expectArraysClose(await result.data(), [7, 7, 7, 7, 6, 6, 6, 6]);
-  });
-
-  it('should work for batch slice', async () => {
-    const indices = tensor3d([0, 4, 2], [3, 1, 1], 'int32');
-    const input =
-        tensor1d([100, 101, 102, 777, 778, 779, 10000, 10001, 10002], 'int32');
-    const shape = [3, 1];
-    const result = gatherND(input, indices);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(input.dtype);
-    expectArraysClose(await result.data(), [100, 778, 102]);
-  });
-
-  it('should work for batch indexing 2d', async () => {
-    const indices = tensor3d([0, 2], [2, 1, 1], 'int32');
-    const input = tensor2d(
-        [
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
-        ],
-        [8, 4], 'float32');
-    const shape = [2, 1, 4];
-    const result = gatherND(input, indices);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(input.dtype);
-    expectArraysClose(await result.data(), [5, 5, 5, 5, 7, 7, 7, 7]);
-  });
-
-  it('should work for batch indexing 3d', async () => {
-    const indices = tensor3d([0, 2, 1, 1], [2, 1, 2], 'int32');
-    const input = tensor3d(
-        [
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
-        ],
-        [2, 4, 4], 'float32');
-    const shape = [2, 1, 4];
-    const result = gatherND(input, indices);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(input.dtype);
-    expectArraysClose(await result.data(), [7, 7, 7, 7, 6, 6, 6, 6]);
-  });
-
-  it('should work for TensorLike inputs', async () => {
-    const indices = [[0], [4], [8]];
-    const input = [100, 101, 102, 777, 778, 779, 1000, 1001, 1002];
-    const shape = [3];
-    const result = gatherND(input, indices);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual('float32');
-    expectArraysClose(await result.data(), [100, 778, 1002]);
-  });
-
-  it('should throw error when indices are not int32', () => {
-    const indices = tensor1d([1], 'float32');
-    const input = tensor2d(
-        [100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004],
-        [3, 4], 'float32');
-    expect(() => gatherND(input, indices)).toThrow();
-  });
-  it('should throw error when indices are scalar', () => {
-    const indices = scalar(1, 'int32');
-    const input = tensor2d(
-        [100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004],
-        [3, 4], 'float32');
-    expect(() => gatherND(input, indices)).toThrow();
-  });
-  it('should throw error when x is scalar', () => {
-    const indices = tensor2d([0, 4, 2], [3, 1], 'int32');
-    const input = scalar(1.0, 'float32');
-    expect(() => gatherND(input, indices)).toThrow();
-  });
-  it('should throw error when indices inner dim > x shape length', () => {
-    const indices = tensor2d([0, 4, 2], [1, 3], 'int32');
-    const input =
-        tensor2d([100, 101, 102, 10000, 10001, 10002], [3, 2], 'float32');
-    expect(() => gatherND(input, indices)).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/gather_nd_util.ts b/tfjs-master/tfjs-core/src/ops/gather_nd_util.ts
deleted file mode 100644
index 6e9887e24..000000000
--- a/tfjs-master/tfjs-core/src/ops/gather_nd_util.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import { TensorInfo } from '../tensor_info';
-import {computeStrides, sizeFromShape} from '../util';
-
-/**
- * Validate gather nd inputs.
- *
- * @param tensor The tensor contains the source values.
- * @param indices The tensor contains the indices to slice the source.
- *
- * @returns [resultShape, numUpdates, sliceSize, strides]
- */
-export function prepareAndValidate(tensor: TensorInfo, indices: TensorInfo):
-    [number[], number, number, number[]] {
-  const tensorRank = tensor.shape.length;
-  const indicesRank = indices.shape.length;
-  if (tensorRank < 1) {
-    throw new Error(
-        'tf.gatherND() expects the input to be rank 1 or higher,' +
-        ` but the rank was ${tensorRank}.`);
-  }
-  if (indicesRank < 1) {
-    throw new Error(
-        'tf.gatherND() expects the indices to be rank 1 or higher,' +
-        ` but the rank was ${indicesRank}.`);
-  }
-  if (indices.dtype !== 'int32') {
-    throw new Error(
-        'tf.gatherND() expects the indices to be int32 type,' +
-        ` but the dtype was ${indices.dtype}.`);
-  }
-  if (indices.shape[indicesRank - 1] > tensorRank) {
-    throw new Error(
-        'index innermost dimension length must be <= tensor rank; saw: ' +
-        `${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);
-  }
-
-  if (sizeFromShape(tensor.shape) === 0) {
-    throw new Error(
-        'Requested more than 0 entries, but input is empty.' +
-        ` Input shape: ${tensor.shape}.`);
-  }
-
-  const indicesShape = indices.shape;
-  const sliceRank = indicesShape[indicesShape.length - 1];
-
-  // The result shape is
-  //   indices.shape[:-1] + params.shape[indices.shape[-1]:]
-  let nResult = 1;
-  for (let i = 0; i < indicesShape.length - 1; ++i) {
-    nResult *= indicesShape[i];
-  }
-
-  const inputShape = tensor.shape;
-
-  const resultShape = indicesShape.slice();
-  resultShape.pop();
-
-  let sliceSize = 1;
-  for (let i = sliceRank; i < tensorRank; ++i) {
-    sliceSize *= inputShape[i];
-    resultShape.push(inputShape[i]);
-  }
-
-  const strides =
-      [...computeStrides(tensor.shape).map(stride => stride / sliceSize),
-       1].slice(0, sliceRank);
-
-  return [resultShape, nResult, sliceSize, strides];
-}
diff --git a/tfjs-master/tfjs-core/src/ops/gather_test.ts b/tfjs-master/tfjs-core/src/ops/gather_test.ts
deleted file mode 100644
index 14bb00eac..000000000
--- a/tfjs-master/tfjs-core/src/ops/gather_test.ts
+++ /dev/null
@@ -1,613 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('gather', ALL_ENVS, (env) => {
-  it('1D (gather), scalar indices', async () => {
-    const t = tf.tensor1d([1, 2, 3]);
-
-    const t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);
-
-    expect(t2.shape).toEqual([]);
-    expectArraysClose(await t2.data(), [2]);
-  });
-
-  it('1D (gather), 1D indices', async () => {
-    const t = tf.tensor1d([1, 2, 3]);
-
-    const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
-
-    expect(t2.shape).toEqual([4]);
-    expectArraysClose(await t2.data(), [1, 3, 1, 2]);
-  });
-
-  it('1D (gather), 2D indices', async () => {
-    const t = tf.tensor1d([1, 2, 3]);
-
-    const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [1, 4], 'int32'), 0);
-
-    expect(t2.shape).toEqual([1, 4]);
-    expectArraysClose(await t2.data(), [1, 3, 1, 2]);
-  });
-
-  it('2D (gather), scalar indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    let t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);
-    expect(t2.shape).toEqual([2]);
-    expectArraysClose(await t2.data(), [2, 22]);
-
-    t2 = tf.gather(t, tf.scalar(1, 'int32'), 1);
-    expect(t2.shape).toEqual([2]);
-    expectArraysClose(await t2.data(), [11, 22]);
-  });
-
-  it('2D (gather), 1D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    let t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 0);
-    expect(t2.shape).toEqual([4, 2]);
-    expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);
-
-    t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 1);
-    expect(t2.shape).toEqual([2, 4]);
-    expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);
-  });
-
-  it('2D (gather), 2D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    let t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 0);
-    expect(t2.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);
-
-    t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1);
-    expect(t2.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);
-  });
-
-  it('2D (gather), 2D indices, non-zero batchDims', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, 1);
-    expect(t2.shape).toEqual([2, 2]);
-    expectArraysClose(await t2.data(), [11, 1, 2, 22]);
-  });
-
-  it('2D (gather), 2D indices, negative batchDims', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, -1);
-    expect(t2.shape).toEqual([2, 2]);
-    expectArraysClose(await t2.data(), [11, 1, 2, 22]);
-  });
-
-  it('3D (gather), 1D indices', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-
-    const t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 2);
-
-    expect(t2.shape).toEqual([2, 2, 4]);
-    expectArraysClose(
-        await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);
-  });
-
-  it('3D (gather), 2D indices', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-
-    const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 2);
-
-    expect(t2.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(
-        await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);
-  });
-
-  it('3D (gather), 2D indices, non-zero batchDims', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4], [1, 2, 2]);
-
-    const t2 = tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1);
-
-    expect(t2.shape).toEqual([1, 2, 3]);
-    expectArraysClose(await t2.data(), [2, 1, 2, 4, 3, 4]);
-  });
-
-  it('throws when batch dims greater than axis', () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-
-    expect(() => tf.gather(t, tf.tensor3d([1, 0, 1], [1, 1, 3], 'int32'), 2, 3))
-        .toThrowError(/must be less than or equal to axis/);
-  });
-
-  it('throws when batch dims greater than indices rank', () => {
-    const t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2]);
-
-    expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 3))
-        .toThrowError(/Expect batchDims in the range of /);
-  });
-
-  it('throws when batch dims do not match', () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-
-    expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1))
-        .toThrowError(/should be equal to indices.shape/);
-  });
-
-  it('bool (gather), 1D indices', async () => {
-    const t = tf.tensor1d([true, false, true], 'bool');
-
-    const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
-
-    expect(t2.shape).toEqual([4]);
-    expect(t2.dtype).toBe('bool');
-    expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));
-  });
-
-  it('bool (gather), 2D indices', async () => {
-    const t = tf.tensor1d([true, false, true], 'bool');
-
-    const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);
-
-    expect(t2.shape).toEqual([2, 2]);
-    expect(t2.dtype).toBe('bool');
-    expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));
-  });
-
-  it('int32 (gather), 1D indices', async () => {
-    const t = tf.tensor1d([1, 2, 5], 'int32');
-
-    const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
-
-    expect(t2.shape).toEqual([4]);
-    expect(t2.dtype).toBe('int32');
-    expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));
-  });
-
-  it('int32 (gather), 2D indices', async () => {
-    const t = tf.tensor1d([1, 2, 5], 'int32');
-
-    const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);
-
-    expect(t2.shape).toEqual([2, 2]);
-    expect(t2.dtype).toBe('int32');
-    expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));
-  });
-
-  it('propagates NaNs', async () => {
-    const t = tf.tensor1d([1, 2, NaN]);
-
-    const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
-
-    expect(t2.shape).toEqual([4]);
-    expectArraysClose(await t2.data(), [1, NaN, 1, 2]);
-  });
-
-  it('chaining, axis=1', () => {
-    const x = tf.zeros([2, 4, 6]);
-    // [0, 2, 4]
-    const indices = tf.range(0, 6, 2, 'int32');
-    const axis = 2;
-    expect(x.gather(indices, axis).shape).toEqual([2, 4, 3]);
-  });
-
-  it('indices not int32 throws error', () => {
-    const x = tf.zeros([2, 4, 6]);
-    // [0, 2, 4]
-    const indices = tf.range(0, 6, 2);
-    const axis = 2;
-    expect(() => x.gather(indices, axis)).toThrowError();
-  });
-
-  it('throws when passed x as a non-tensor', () => {
-    expect(() => tf.gather({} as tf.Tensor, tf.tensor1d([1])))
-        .toThrowError(/Argument 'x' passed to 'gather' must be a Tensor/);
-  });
-
-  it('throws when passed indices as a non-tensor', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tf.gather(tf.tensor1d([1]), {} as any))
-        .toThrowError(/Argument 'indices' passed to 'gather' must be a Tensor/);
-  });
-
-  it('throws when index is out of bound', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    expect(() => tf.gather(t, tf.tensor1d([100], 'int32')))
-        .toThrowError(/GatherV2: the index value 100 is not in \[0, 1\]/);
-    expect(() => tf.gather(t, tf.tensor1d([-1], 'int32')))
-        .toThrowError(/GatherV2: the index value -1 is not in \[0, 1\]/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const res = tf.gather([1, 2, 3], [0, 2, 0, 1], 0);
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [1, 3, 1, 2]);
-  });
-
-  it('gradient 1D (gather), 1D indices', async () => {
-    const t = tf.tensor1d([1, 2, 3]);
-    const indices = tf.tensor1d([0, 2, 0, 1], 'int32');
-    const dy = tf.tensor([3, 4, 5, 6]);
-
-    const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [8, 6, 4]);
-  });
-
-  it('gradient with clones', () => {
-    const t = tf.tensor1d([1, 2, 3]);
-    const indices = tf.tensor1d([0, 2, 0, 1], 'int32');
-    const gradF = tf.grad(t => tf.gather(t.clone(), indices.clone()).clone());
-    const dt = gradF(t);
-    expect(dt.shape).toEqual(t.shape);
-  });
-
-  it('gradient 1D (gather), 2D indices', async () => {
-    const t = tf.tensor1d([1, 2, 3]);
-    const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');
-    const dy = tf.tensor2d([3, 4, 5, 6], [2, 2]);
-
-    const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [8, 6, 4]);
-  });
-
-  it('gradient 2D (gather) axis=0 shape=[2, 2] 1D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
-    const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [4, 2]);
-    const axis = 0;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [12, 14, 12, 14]);
-  });
-
-  it('gradient 2D (gather) axis=0 shape=[2, 2] 2D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
-    const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);
-    const axis = 0;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [12, 14, 12, 14]);
-  });
-
-  it('gradient 2D (gather) axis=0 shape=[4, 1] 1D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
-    const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
-    const dy = tf.tensor([23, 7, 19, 13], [4, 1]);
-    const axis = 0;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [26, 36, 0, 0]);
-  });
-
-  it('gradient 2D (gather) axis=0 shape=[4, 1] 2D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
-    const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
-    const dy = tf.tensor([23, 7, 19, 13], [2, 2, 1]);
-    const axis = 0;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [26, 36, 0, 0]);
-  });
-
-  it('gradient 2D (gather) axis=1 shape=[4, 2] 1D indices batchDims 1',
-     async () => {
-       const t = tf.variable(tf.tensor([[0, 1],
-                                        [1, 2],
-                                        [2, 3],
-                                        [3, 4]]));
-       const indices = tf.tensor([0, 1, 0, 1], [4, 1], 'int32');
-       const dy = tf.tensor([1, 1, 1, 1], [4, 1]);
-       const axis = 1;
-
-       const gradients = tf.grad(t => tf.gather(t, indices, axis, 1))(t, dy);
-
-       expect(gradients.shape).toEqual(t.shape);
-       expectArraysClose(await gradients.data(), [1, 0, 0, 1, 1, 0, 0, 1]);
-     });
-
-  it('gradient 2D (gather) axis=1 shape=[2, 2] 1D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
-    const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 4]);
-    const axis = 1;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [9, 9, 17, 17]);
-  });
-
-  it('gradient 2D (gather) axis=1 shape=[2, 2] 2D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
-    const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);
-    const axis = 1;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [9, 9, 17, 17]);
-  });
-
-  it('gradient 2D (gather) axis=1 shape=[4, 1] 1D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
-    const indices = tf.tensor1d([0, 0, 0, 0], 'int32');
-    const dy = tf.tensor(
-        [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 4]);
-    const axis = 1;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [18, 34, 50, 66]);
-  });
-
-  it('gradient 2D (gather) axis=1 shape=[4, 1] 2D indices', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
-    const indices = tf.tensor2d([0, 0, 0, 0], [2, 2], 'int32');
-    const dy = tf.tensor(
-        [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 2, 2]);
-    const axis = 1;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(await gradients.data(), [18, 34, 50, 66]);
-  });
-
-  it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 1D indices', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
-    const dy = tf.tensor(
-        [
-          2, -3, 4,  15, 6,  0.7, 1, 18, 0.01, 0,  12, 13,
-          4, 15, 12, -7, 18, 19,  2, 21, 6,    23, 24, 25
-        ],
-        [4, 3, 2]);
-    const axis = 0;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);
-  });
-
-  it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 2D indices', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
-    const dy = tf.tensor(
-        [
-          2, -3, 4,  15, 6,  0.7, 1, 18, 0.01, 0,  12, 13,
-          4, 15, 12, -7, 18, 19,  2, 21, 6,    23, 24, 25
-        ],
-        [2, 2, 3, 2]);
-    const axis = 0;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);
-  });
-
-  it('gradient 3D (gather) axis=0 shape=[1, 4, 4]', async () => {
-    const t = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
-    const indices = tf.tensor1d([0, 0], 'int32');
-    const dy = tf.tensor(
-        [
-          2,  -3, 4, 15, 6, 0.7, 1,  18, 0.01, 0,  12, 13, 4, 15, 12, -7,
-          18, 19, 2, 21, 6, 23,  24, 25, 101,  31, 34, 54, 1, 0,  -3, -4
-        ],
-        [2, 4, 4]);
-    const axis = 0;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);
-  });
-
-  it('gradient 3D (gather) axis=0 shape=[1, 4, 4] 1D indices', async () => {
-    const t = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
-    const indices = tf.tensor1d([0, 0], 'int32');
-    const dy = tf.tensor(
-        [
-          2,  -3, 4, 15, 6, 0.7, 1,  18, 0.01, 0,  12, 13, 4, 15, 12, -7,
-          18, 19, 2, 21, 6, 23,  24, 25, 101,  31, 34, 54, 1, 0,  -3, -4
-        ],
-        [2, 4, 4]);
-    const axis = 0;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);
-  });
-
-  it('gradient 3D (gather) axis=1 shape=[2, 3, 2] 2D indices', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');
-    const dy = tf.tensor(
-        [2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7],
-        [2, 2, 2, 2]);
-    const axis = 1;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [0, 0, 3, 15, 10, 15.7, 0, 0, 12.01, -7, 16, 28]);
-  });
-
-  it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 1D indices', async () => {
-    const t = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
-    const indices = tf.tensor1d([1, 2, 2, 1], 'int32');
-    const dy = tf.tensor(
-        [2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7],
-        [1, 4, 4]);
-    const axis = 1;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);
-  });
-
-  it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 2D indices', async () => {
-    const t = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
-    const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');
-    const dy = tf.tensor(
-        [2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7],
-        [1, 2, 2, 4]);
-    const axis = 1;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);
-  });
-
-  it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 1D indices', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const indices = tf.tensor1d([1, 0, 1, 0], 'int32');
-    const dy = tf.tensor(
-        [
-          2, -3, 4,  15, 6,  0.7, 1, 18, 0.01, 0,  12, 13,
-          4, 15, 12, -7, 18, 19,  2, 21, 6,    23, 24, 25
-        ],
-        [2, 3, 4]);
-    const axis = 2;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);
-  });
-
-  it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 2D indices', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const indices = tf.tensor2d([1, 0, 1, 0], [2, 2], 'int32');
-    const dy = tf.tensor(
-        [
-          2, -3, 4,  15, 6,  0.7, 1, 18, 0.01, 0,  12, 13,
-          4, 15, 12, -7, 18, 19,  2, 21, 6,    23, 24, 25
-        ],
-        [2, 3, 2, 2]);
-    const axis = 2;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);
-  });
-
-  it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 1D indices', async () => {
-    const t = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);
-    const indices = tf.tensor1d([1, 3, 1], 'int32');
-    const dy =
-        tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 3]);
-    const axis = 2;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);
-  });
-
-  it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 2D indices', async () => {
-    const t = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);
-    const indices = tf.tensor2d([1, 3, 1], [1, 3], 'int32');
-    const dy =
-        tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 1, 3]);
-    const axis = 2;
-
-    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
-
-    expect(gradients.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradients.data(),
-        [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);
-  });
-
-  it('ensure no memory leak', async () => {
-    const numTensorsBefore = tf.memory().numTensors;
-    const numDataIdBefore = tf.engine().backend.numDataIds();
-    const t = tf.tensor1d([1, 2, 3]);
-    const t1 = tf.scalar(1, 'int32');
-    const t2 = tf.gather(t, t1, 0);
-
-    expect(t2.shape).toEqual([]);
-    expectArraysClose(await t2.data(), [2]);
-
-    t.dispose();
-    t1.dispose();
-    t2.dispose();
-
-    const numTensorsAfter = tf.memory().numTensors;
-    const numDataIdAfter = tf.engine().backend.numDataIds();
-    expect(numTensorsAfter).toBe(numTensorsBefore);
-    expect(numDataIdAfter).toBe(numDataIdBefore);
-  });
-
-  it('fills with zero when index is out of bound', async () => {
-    if (env.backendName === 'webgl' || env.backendName === 'webgpu') {
-      const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-      const tInt = tf.tensor2d([1, 11, 2, 22], [2, 2], 'int32');
-
-      const index = tf.tensor1d([0, 1, 100, -1, 2, -4], 'int32');
-      const res = tf.gather(t, index);
-      const resInt = tf.gather(tInt, index);
-
-      const expected = [1, 11, 2, 22, 0, 0, 0, 0, 0, 0, 0, 0];
-      expectArraysClose(await res.data(), expected);
-      expectArraysClose(await resInt.data(), expected);
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/greater.ts b/tfjs-master/tfjs-core/src/ops/greater.ts
deleted file mode 100644
index 570d31c53..000000000
--- a/tfjs-master/tfjs-core/src/ops/greater.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Greater, GreaterInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns the truth value of (a > b) element-wise. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3]);
- * const b = tf.tensor1d([2, 2, 2]);
- *
- * a.greater(b).print();
- * ```
- *
- * @param a The first input tensor.
- * @param b The second input tensor. Must have the same dtype as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function greater_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'greater', 'string_or_numeric');
-  let $b = convertToTensor(b, 'b', 'greater', 'string_or_numeric');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: GreaterInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Greater, inputs as unknown as NamedTensorMap);
-}
-
-export const greater = /* @__PURE__ */ op({greater_});
diff --git a/tfjs-master/tfjs-core/src/ops/greater_equal.ts b/tfjs-master/tfjs-core/src/ops/greater_equal.ts
deleted file mode 100644
index 2a4a727af..000000000
--- a/tfjs-master/tfjs-core/src/ops/greater_equal.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {GreaterEqual, GreaterEqualInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns the truth value of (a >= b) element-wise. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3]);
- * const b = tf.tensor1d([2, 2, 2]);
- *
- * a.greaterEqual(b).print();
- * ```
- *
- * @param a The first input tensor.
- * @param b The second input tensor. Must have the same dtype as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function greaterEqual_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'greaterEqual', 'string_or_numeric');
-  let $b = convertToTensor(b, 'b', 'greaterEqual', 'string_or_numeric');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: GreaterEqualInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(GreaterEqual, inputs as unknown as NamedTensorMap);
-}
-
-export const greaterEqual = /* @__PURE__ */ op({greaterEqual_});
diff --git a/tfjs-master/tfjs-core/src/ops/greater_equal_test.ts b/tfjs-master/tfjs-core/src/ops/greater_equal_test.ts
deleted file mode 100644
index 1b6642048..000000000
--- a/tfjs-master/tfjs-core/src/ops/greater_equal_test.ts
+++ /dev/null
@@ -1,365 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('greaterEqual', ALL_ENVS, () => {
-  // Tensor1D:
-  it('Tensor1D - int32', async () => {
-    let a = tf.tensor1d([1, 4, 5], 'int32');
-    let b = tf.tensor1d([2, 3, 5], 'int32');
-    let res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 1]);
-
-    a = tf.tensor1d([2, 2, 2], 'int32');
-    b = tf.tensor1d([2, 2, 2], 'int32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1]);
-
-    a = tf.tensor1d([0, 0], 'int32');
-    b = tf.tensor1d([3, 3], 'int32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0]);
-  });
-  it('Tensor1D - float32', async () => {
-    let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
-    let res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 1]);
-
-    a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1]);
-
-    a = tf.tensor1d([0.45, 0.123], 'float32');
-    b = tf.tensor1d([3.123, 3.321], 'float32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const a = [1.1, 4.1, 5];
-    const b = [2.2, 3.2, 5];
-
-    let res = tf.greaterEqual(
-        tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [0, 1, 1]);
-
-    res =
-        tf.greaterEqual(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [1, 1, 1]);
-  });
-
-  it('mismatched Tensor1D shapes - int32', () => {
-    const a = tf.tensor1d([1, 2], 'int32');
-    const b = tf.tensor1d([1, 2, 3], 'int32');
-    const f = () => {
-      tf.greaterEqual(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('mismatched Tensor1D shapes - float32', () => {
-    const a = tf.tensor1d([1.1, 2.1], 'float32');
-    const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
-    const f = () => {
-      tf.greaterEqual(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('NaNs in Tensor1D - float32', async () => {
-    const a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
-    const b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0]);
-  });
-
-  // Tensor2D:
-  it('Tensor2D - int32', async () => {
-    let a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
-    let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
-    let res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 1]);
-
-    a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('Tensor2D - float32', async () => {
-    let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
-    let b =
-        tf.tensor2d([[2.1, 3.1, 6.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
-    let res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 1]);
-
-    a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor2D shapes - int32', async () => {
-    const a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
-    const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 0, 1, 0, 0]);
-  });
-  it('broadcasting Tensor2D shapes - float32', async () => {
-    const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
-    const b =
-        tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 0, 1, 0, 0]);
-  });
-  it('NaNs in Tensor2D - float32', async () => {
-    const a = tf.tensor2d([[1.1, NaN], [0.1, NaN]], [2, 2], 'float32');
-    const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0, 0]);
-  });
-
-  // Tensor3D:
-  it('Tensor3D - int32', async () => {
-    let a =
-        tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
-    let b =
-        tf.tensor3d([[[2], [3], [6]], [[7], [10], [11]]], [2, 3, 1], 'int32');
-    let res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 1]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1, 1, 1]);
-  });
-  it('Tensor3D - float32', async () => {
-    let a = tf.tensor3d(
-        [[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
-    let b = tf.tensor3d(
-        [[[2.1], [3.1], [6.1]], [[7.1], [10.1], [11.1]]], [2, 3, 1], 'float32');
-    let res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 1]);
-
-    a = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.2]]], [2, 3, 1], 'float32');
-    b = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor3D shapes - int32', async () => {
-    const a = tf.tensor3d(
-        [[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2],
-        'int32');
-    const b =
-        tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1]);
-  });
-  it('broadcasting Tensor3D shapes - float32', async () => {
-    const a = tf.tensor3d(
-        [
-          [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
-          [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
-        ],
-        [2, 3, 2], 'float32');
-    const b = tf.tensor3d(
-        [[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1]);
-  });
-  it('NaNs in Tensor3D - float32', async () => {
-    const a = tf.tensor3d(
-        [[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
-    const b = tf.tensor3d(
-        [[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
-  });
-
-  // Tensor4D:
-  it('Tensor4D - int32', async () => {
-    let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
-    let b = tf.tensor4d([2, 3, 6, 7], [2, 2, 1, 1], 'int32');
-    let res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1]);
-
-    a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-
-    a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-  });
-  it('Tensor4D - float32', async () => {
-    let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
-    let b = tf.tensor4d([2.1, 3.1, 6.1, 7.1], [2, 2, 1, 1], 'float32');
-    let res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1]);
-
-    a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-
-    a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
-    res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor4D shapes - int32', async () => {
-    const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
-    const b = tf.tensor4d(
-        [[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0, 0, 1, 0, 1, 1]);
-  });
-  it('broadcasting Tensor4D shapes - float32', async () => {
-    const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d(
-        [[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]],
-        [2, 2, 1, 2], 'float32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0, 0, 1, 0, 1, 1]);
-  });
-  it('NaNs in Tensor4D - float32', async () => {
-    const a = tf.tensor4d([1.1, NaN, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0, 0]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.greaterEqual({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'greaterEqual' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.greaterEqual(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'greaterEqual' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 4, 5];
-    const b = [2, 3, 5];
-    const res = tf.greaterEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 1]);
-  });
-
-  it('has gradient', async () => {
-    const a = tf.tensor1d([3, 2, 5]);
-    const b = tf.tensor1d([4, 1, 5]);
-    const dy = tf.ones([3], 'float32');
-    const da = tf.grad((a: tf.Tensor1D) => tf.greaterEqual(a, b))(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [0, 0, 0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([3, 2, 5]);
-    const b = tf.tensor1d([4, 1, 5]);
-    const dy = tf.ones([3], 'float32');
-    const da = tf.grad(
-        (a: tf.Tensor1D) => tf.greaterEqual(a.clone(), b.clone()).clone())(
-        a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [0, 0, 0]);
-  });
-
-  it('should support string comparison', async () => {
-    const tensorA = tf.tensor('a', [], 'string');
-    const tensorB = tf.tensor(['a', 'b', ''], [3], 'string');
-    const result = await tf.greaterEqual(tensorA, tensorB);
-
-    expectArraysEqual(result.shape, [3]);
-    expectArraysEqual(await result.data(), [1, 0, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/greater_test.ts b/tfjs-master/tfjs-core/src/ops/greater_test.ts
deleted file mode 100644
index ff00acfa0..000000000
--- a/tfjs-master/tfjs-core/src/ops/greater_test.ts
+++ /dev/null
@@ -1,364 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('greater', ALL_ENVS, () => {
-  it('Tensor1D - int32', async () => {
-    let a = tf.tensor1d([1, 4, 5], 'int32');
-    let b = tf.tensor1d([2, 3, 5], 'int32');
-    let res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0]);
-
-    a = tf.tensor1d([2, 2, 2], 'int32');
-    b = tf.tensor1d([2, 2, 2], 'int32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0]);
-
-    a = tf.tensor1d([3, 3], 'int32');
-    b = tf.tensor1d([0, 0], 'int32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1]);
-  });
-  it('Tensor1D - float32', async () => {
-    let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
-    let res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0]);
-
-    a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0]);
-
-    a = tf.tensor1d([3.123, 3.321], 'float32');
-    b = tf.tensor1d([0.45, 0.123], 'float32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const a = [1.1, 4.1, 5.2];
-    const b = [2.2, 3.2, 5.1];
-
-    let res =
-        tf.greater(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [0, 1, 1]);
-
-    res = tf.greater(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [0, 1, 1]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [1.1, 4.1, 5.1];
-    const b = [2.2, 3.2, 5.1];
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0]);
-  });
-  it('TensorLike Chained', async () => {
-    const a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    const b = [2.2, 3.2, 5.1];
-    const res = a.greater(b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0]);
-  });
-  it('mismatched Tensor1D shapes - int32', () => {
-    const a = tf.tensor1d([1, 2], 'int32');
-    const b = tf.tensor1d([1, 2, 3], 'int32');
-    const f = () => {
-      tf.greater(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('mismatched Tensor1D shapes - float32', () => {
-    const a = tf.tensor1d([1.1, 2.1], 'float32');
-    const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
-    const f = () => {
-      tf.greater(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('NaNs in Tensor1D - float32', async () => {
-    const a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
-    const b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0]);
-  });
-
-  // Tensor2D:
-  it('Tensor2D - int32', async () => {
-    let a = tf.tensor2d([[1, 4, 5], [8, 9, 11]], [2, 3], 'int32');
-    let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
-    let res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 0]);
-
-    a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-  });
-  it('Tensor2D - float32', async () => {
-    let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 11.1]], [2, 3], 'float32');
-    let b =
-        tf.tensor2d([[2.1, 3.1, 6.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
-    let res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 0]);
-
-    a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor2D shapes - int32', async () => {
-    const a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
-    const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor2D shapes - float32', async () => {
-    const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
-    const b =
-        tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0, 0, 0, 0]);
-  });
-  it('NaNs in Tensor2D - float32', async () => {
-    const a = tf.tensor2d([[1.1, NaN], [0.1, NaN]], [2, 2], 'float32');
-    const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0, 0]);
-  });
-
-  // Tensor3D:
-  it('Tensor3D - int32', async () => {
-    let a =
-        tf.tensor3d([[[1], [4], [5]], [[8], [9], [11]]], [2, 3, 1], 'int32');
-    let b =
-        tf.tensor3d([[[2], [3], [6]], [[7], [10], [11]]], [2, 3, 1], 'int32');
-    let res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 0]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0, 0, 0]);
-  });
-  it('Tensor3D - float32', async () => {
-    let a = tf.tensor3d(
-        [[[1.1], [4.1], [5.1]], [[8.1], [9.1], [11.1]]], [2, 3, 1], 'float32');
-    let b = tf.tensor3d(
-        [[[2.1], [3.1], [6.1]], [[7.1], [10.1], [11.1]]], [2, 3, 1], 'float32');
-    let res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 0]);
-
-    a = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.2]]], [2, 3, 1], 'float32');
-    b = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0, 0, 1]);
-  });
-  it('broadcasting Tensor3D shapes - int32', async () => {
-    const a = tf.tensor3d(
-        [[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2],
-        'int32');
-    const b =
-        tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1]);
-  });
-  it('broadcasting Tensor3D shapes - float32', async () => {
-    const a = tf.tensor3d(
-        [
-          [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
-          [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
-        ],
-        [2, 3, 2], 'float32');
-    const b = tf.tensor3d(
-        [[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1]);
-  });
-  it('NaNs in Tensor3D - float32', async () => {
-    const a = tf.tensor3d(
-        [[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
-    const b = tf.tensor3d(
-        [[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0, 0, 0, 0]);
-  });
-
-  // Tensor4D:
-  it('Tensor4D - int32', async () => {
-    let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
-    let b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32');
-    let res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 0]);
-
-    a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-
-    a = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('Tensor4D - float32', async () => {
-    let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
-    let b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32');
-    let res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 0]);
-
-    a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-
-    a = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor4D shapes - int32', async () => {
-    const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
-    const b = tf.tensor4d(
-        [[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0, 0, 0, 1, 1]);
-  });
-  it('broadcasting Tensor4D shapes - float32', async () => {
-    const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d(
-        [[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]],
-        [2, 2, 1, 2], 'float32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0, 0, 0, 1, 1]);
-  });
-  it('NaNs in Tensor4D - float32', async () => {
-    const a = tf.tensor4d([1.1, NaN, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
-    const res = tf.greater(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0, 0]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.greater({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'greater' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.greater(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'greater' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 4, 5];
-    const b = [2, 3, 5];
-
-    const res = tf.greater(a, b);
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0]);
-  });
-
-  it('works with 0 sized tensors', async () => {
-    const a = tf.tensor2d([], [0, 5]);
-    const b = tf.tensor1d([1, 2, 3, 4, 5]);
-    const res = tf.greater(a, b);
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([0, 5]);
-    expectArraysClose(await res.data(), []);
-  });
-
-  it('should support string comparison', async () => {
-    const tensorA = tf.tensor('a', [], 'string');
-    const tensorB = tf.tensor(['a', 'b', ''], [3], 'string');
-    const result = await tf.greater(tensorA, tensorB);
-
-    expectArraysEqual(result.shape, [3]);
-    expectArraysEqual(await result.data(), [0, 0, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/identity_pool_test.ts b/tfjs-master/tfjs-core/src/ops/identity_pool_test.ts
deleted file mode 100644
index 8611e8bc0..000000000
--- a/tfjs-master/tfjs-core/src/ops/identity_pool_test.ts
+++ /dev/null
@@ -1,91 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {expectArraysClose} from '../test_util';
-
-/**
- * Test utility for testing AvgPool, MaxPool, etc where kernel size is 1x1,
- * effectively making them act as the identity function except where strides
- * affect the output.
- */
-export function identityPoolTest(pool: typeof tf.avgPool) {
-  it('1x1 pool size (identity)', async () => {
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const a = tf.range(0, 10).reshape([1, 1, 1, 10]) as tf.Tensor4D;
-    const result = pool(a, [1, 1], [1, 1], 'valid');
-    expectArraysClose(await result.data(), await a.data());
-  });
-
-  it('1x1 pool size with strides', async () => {
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const a = tf.range(0, 150).reshape([1, 10, 15, 1]) as tf.Tensor4D;
-    const result = pool(a, [1, 1], [3, 4], 'valid');
-    expectArraysClose(await result.data(), [
-      0, 4, 8, 12,
-      45, 49, 53, 57,
-      90, 94, 98, 102,
-      135, 139, 143, 147,
-    ]);
-  });
-
-  it('1x1 pool size batched', async () => {
-    // 7 batches of 3 x 4
-    const shape = [7, 3, 4, 1];
-    const size = shape.reduce((a, b) => a * b, 1);
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const a = tf.range(0, size).reshape(shape) as tf.Tensor4D;
-    const result = pool(a, [1, 1], [1, 1], 'valid');
-    expectArraysClose(await result.data(), await a.data());
-  });
-
-  it('1x1 pool size batched with strides', async () => {
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const a = tf.range(0, 300).reshape([2, 10, 15, 1]) as tf.Tensor4D;
-    const result = pool(a, [1, 1], [3, 4], 'valid');
-    expectArraysClose(await result.data(), [
-      // Batch 0
-      0, 4, 8, 12,
-      45, 49, 53, 57,
-      90, 94, 98, 102,
-      135, 139, 143, 147,
-      // Batch 1
-      150, 154, 158, 162,
-      195, 199, 203, 207,
-      240, 244, 248, 252,
-      285, 289, 293, 297,
-    ]);
-  });
-
-  it('1x1 pool size batched with strides and channels', async () => {
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const a = tf.range(0, 900).reshape([2, 10, 15, 3]) as tf.Tensor4D;
-    const result = pool(a, [1, 1], [3, 4], 'valid');
-    expectArraysClose(await result.data(), [
-      // Batch 0
-      0, 1, 2, 12, 13, 14, 24, 25, 26, 36, 37, 38,
-      135, 136, 137, 147, 148, 149, 159, 160, 161, 171, 172, 173,
-      270, 271, 272, 282, 283, 284, 294, 295, 296, 306, 307, 308,
-      405, 406, 407, 417, 418, 419, 429, 430, 431, 441, 442, 443,
-      // Batch 1
-      450, 451, 452, 462, 463, 464, 474, 475, 476, 486, 487, 488,
-      585, 586, 587, 597, 598, 599, 609, 610, 611, 621, 622, 623,
-      720, 721, 722, 732, 733, 734, 744, 745, 746, 756, 757, 758,
-      855, 856, 857, 867, 868, 869, 879, 880, 881, 891, 892, 893,
-    ]);
-  });
-}
diff --git a/tfjs-master/tfjs-core/src/ops/ifft_test.ts b/tfjs-master/tfjs-core/src/ops/ifft_test.ts
deleted file mode 100644
index 1218d3c9d..000000000
--- a/tfjs-master/tfjs-core/src/ops/ifft_test.ts
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('1D IFFT', ALL_ENVS, () => {
-  it('should return the same value with TensorFlow (2 elements)', async () => {
-    const t1Real = tf.tensor1d([1, 2]);
-    const t1Imag = tf.tensor1d([1, 1]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(await tf.spectral.ifft(t1).data(), [1.5, 1, -0.5, 0]);
-  });
-
-  it('should calculate FFT from Tensor directly', async () => {
-    const t1Real = tf.tensor1d([1, 2]);
-    const t1Imag = tf.tensor1d([1, 1]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(await t1.ifft().data(), [1.5, 1, -0.5, 0]);
-  });
-
-  it('should return the same value as TensorFlow (3 elements)', async () => {
-    const t1Real = tf.tensor1d([1, 2, 3]);
-    const t1Imag = tf.tensor1d([0, 0, 0]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(await tf.spectral.ifft(t1).data(), [
-      2, -3.9736431e-08, -0.49999997, -.28867507, -0.49999994, 2.8867519e-01
-    ]);
-  });
-
-  it('should return the same value as TensorFlow with imaginary (3 elements)',
-     async () => {
-       const t1Real = tf.tensor1d([1, 2, 3]);
-       const t1Imag = tf.tensor1d([1, 2, 3]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.ifft(t1).data(),
-           [2, 1.9999999, -0.21132492, -0.78867507, -0.7886752, -0.2113249]);
-     });
-
-  it('should return the same value as TensorFlow (negative 3 elements)',
-     async () => {
-       const t1Real = tf.tensor1d([-1, -2, -3]);
-       const t1Imag = tf.tensor1d([-1, -2, -3]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.ifft(t1).data(),
-           [-2, -1.9999999, 0.21132492, 0.78867507, 0.7886752, 0.2113249]);
-     });
-
-  it('should return the same value with TensorFlow (4 elements)', async () => {
-    const t1Real = tf.tensor1d([1, 2, 3, 4]);
-    const t1Imag = tf.tensor1d([0, 0, 0, 0]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(
-        await tf.spectral.ifft(t1).data(),
-        [2.5, 0, -0.5, -0.5, -0.5, 0, -0.5, 0.5]);
-  });
-
-  it('should return the same value as TensorFlow with imaginary (4 elements)',
-     async () => {
-       const t1Real = tf.tensor1d([1, 2, 3, 4]);
-       const t1Imag = tf.tensor1d([1, 2, 3, 4]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.ifft(t1).data(),
-           [2.5, 2.5, 0, -1, -0.5, -0.5, -1, 0]);
-     });
-});
-
-describeWithFlags('2D IFFT', ALL_ENVS, () => {
-  it('2D: should return the same value as TensorFlow', async () => {
-    const t1Real = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const t1Imag = tf.tensor2d([5, 6, 7, 8], [2, 2]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    const y = tf.spectral.ifft(t1);
-    expectArraysClose(
-        await y.data(), [1.5, 5.5, -0.5, -0.5, 3.5, 7.5, -0.5, -0.5]);
-    expect(y.shape).toEqual(t1Real.shape);
-  });
-
-  it('3D: should return the same value as TensorFlow', async () => {
-    const t1Real = tf.tensor3d([1, 2, 3, 4, -1, -2, -3, -4], [2, 2, 2]);
-    const t1Imag = tf.tensor3d([5, 6, 7, 8, -5, -6, -7, -8], [2, 2, 2]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    const y = tf.spectral.ifft(t1);
-    expectArraysClose(await y.data(), [
-      1.5, 5.5, -0.5, -0.5, 3.5, 7.5, -0.5, -0.5, -1.5, -5.5, 0.5, 0.5, -3.5,
-      -7.5, 0.5, 0.5
-    ]);
-    expect(y.shape).toEqual(t1Real.shape);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/imag.ts b/tfjs-master/tfjs-core/src/ops/imag.ts
deleted file mode 100644
index 4c372ad24..000000000
--- a/tfjs-master/tfjs-core/src/ops/imag.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Imag, ImagInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {op} from './operation';
-/**
- * Returns the imaginary part of a complex (or real) tensor.
- *
- * Given a tensor input, this operation returns a tensor of type float that is
- * the imaginary part of each element in input considered as a complex number.
- * If input is real, a tensor of all zeros is returned.
- *
- * ```js
- * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);
- * tf.imag(x).print();
- * ```
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function imag_<T extends Tensor>(input: T|TensorLike): T {
-  const $input = convertToTensor(input, 'input', 'imag');
-
-  const inputs: ImagInputs = {input: $input};
-  return ENGINE.runKernel(Imag, inputs as unknown as NamedTensorMap);
-}
-
-export const imag = /* @__PURE__ */ op({imag_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/crop_and_resize.ts b/tfjs-master/tfjs-core/src/ops/image/crop_and_resize.ts
deleted file mode 100644
index b637cb8bb..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/crop_and_resize.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor1D, Tensor2D, Tensor4D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-
-import {op} from '../operation';
-
-/**
- * Extracts crops from the input image tensor and resizes them using bilinear
- * sampling or nearest neighbor sampling (possibly with aspect ratio change)
- * to a common output size specified by cropSize.
- *
- * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,
- *     where imageHeight and imageWidth must be positive, specifying the
- *     batch of images from which to take crops
- * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is
- *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized
- *     coordinates of the box in the `boxInd[i]`th image in the batch
- * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range
- *     `[0, batch)` that specifies the image that the `i`-th box refers to.
- * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`
- *     specifying the size to which all crops are resized to.
- * @param method Optional string from `'bilinear' | 'nearest'`,
- *     defaults to bilinear, which specifies the sampling method for resizing
- * @param extrapolationValue A threshold for deciding when to remove boxes based
- *     on score. Defaults to 0.
- * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function cropAndResize_(
-    image: Tensor4D|TensorLike,
-    boxes: Tensor2D|TensorLike,
-    boxInd: Tensor1D|TensorLike,
-    cropSize: [number, number],
-    method: 'bilinear'|'nearest' = 'bilinear',
-    extrapolationValue = 0,
-    ): Tensor4D {
-  const $image = convertToTensor(image, 'image', 'cropAndResize');
-  const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');
-  const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');
-
-  const numBoxes = $boxes.shape[0];
-
-  util.assert(
-      $image.rank === 4,
-      () => 'Error in cropAndResize: image must be rank 4,' +
-          `but got rank ${$image.rank}.`);
-  util.assert(
-      $boxes.rank === 2 && $boxes.shape[1] === 4,
-      () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +
-          `but had shape ${$boxes.shape}.`);
-  util.assert(
-      $boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,
-      () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +
-          `but had shape ${$boxes.shape}.`);
-  util.assert(
-      cropSize.length === 2,
-      () => `Error in cropAndResize: cropSize must be of length 2, but got ` +
-          `length ${cropSize.length}.`);
-  util.assert(
-      cropSize[0] >= 1 && cropSize[1] >= 1,
-      () => `cropSize must be atleast [1,1], but was ${cropSize}`);
-  util.assert(
-      method === 'bilinear' || method === 'nearest',
-      () => `method must be bilinear or nearest, but was ${method}`);
-
-  const inputs:
-      CropAndResizeInputs = {image: $image, boxes: $boxes, boxInd: $boxInd};
-  const attrs: CropAndResizeAttrs = {method, extrapolationValue, cropSize};
-  const res = ENGINE.runKernel(
-      CropAndResize, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-  return res as Tensor4D;
-}
-
-export const cropAndResize = /* @__PURE__ */ op({cropAndResize_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/crop_and_resize_test.ts b/tfjs-master/tfjs-core/src/ops/image/crop_and_resize_test.ts
deleted file mode 100644
index e785b48ea..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/crop_and_resize_test.ts
+++ /dev/null
@@ -1,327 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('cropAndResize', ALL_ENVS, () => {
-  it('1x1-bilinear', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [1, 1], 'bilinear', 0);
-
-    expect(output.shape).toEqual([1, 1, 1, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [2.5]);
-  });
-
-  it('5x5-bilinear, no change in shape', async () => {
-    const image: tf.Tensor4D = tf.ones([1, 5, 5, 3]);
-    const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [5, 5], 'bilinear', 0);
-
-    expect(output.shape).toEqual([1, 5, 5, 3]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), await image.data());
-  });
-
-  it('5x5-bilinear, no arguments passed in for method or extrapolation',
-     async () => {
-       const image: tf.Tensor4D = tf.ones([1, 5, 5, 3]);
-       const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1], [1, 4]);
-       const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-       const output = tf.image.cropAndResize(image, boxes, boxInd, [5, 5]);
-
-       expect(output.shape).toEqual([1, 5, 5, 3]);
-       expect(output.dtype).toBe('float32');
-       expectArraysClose(await output.data(), await image.data());
-     });
-
-  it('5x5-bilinear, just a crop, no resize', async () => {
-    const image: tf.Tensor4D = tf.ones([1, 6, 6, 3]);
-    const boxes: tf.Tensor2D = tf.tensor2d([0.5, 0.5, 1, 1], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
-
-    expect(output.shape).toEqual([1, 3, 3, 3]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), await tf.ones([1, 3, 3, 3]).data());
-  });
-
-  it('1x1-nearest', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [1, 1], 'nearest', 0);
-
-    expect(output.shape).toEqual([1, 1, 1, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [4.0]);
-  });
-  it('1x1Flipped-bilinear', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([1, 1, 0, 0], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [1, 1], 'bilinear', 0);
-
-    expect(output.shape).toEqual([1, 1, 1, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [2.5]);
-  });
-  it('1x1Flipped-nearest', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([1, 1, 0, 0], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [1, 1], 'nearest', 0);
-
-    expect(output.shape).toEqual([1, 1, 1, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [4.0]);
-  });
-  it('3x3-bilinear', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
-
-    expect(output.shape).toEqual([1, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [1, 1.5, 2, 2, 2.5, 3, 3, 3.5, 4]);
-  });
-  it('3x3-nearest', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'nearest', 0);
-
-    expect(output.shape).toEqual([1, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [1, 2, 2, 3, 4, 4, 3, 4, 4]);
-  });
-  it('3x3Flipped-bilinear', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([1, 1, 0, 0], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
-
-    expect(output.shape).toEqual([1, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [4, 3.5, 3, 3, 2.5, 2, 2, 1.5, 1]);
-  });
-  it('3x3Flipped-nearest', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([1, 1, 0, 0], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'nearest', 0);
-
-    expect(output.shape).toEqual([1, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [4, 4, 3, 4, 4, 3, 2, 2, 1]);
-  });
-  it('3x3to2x2-bilinear', async () => {
-    const image: tf.Tensor4D =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, 1]);
-    const boxes: tf.Tensor2D =
-        tf.tensor2d([0, 0, 1, 1, 0, 0, 0.5, 0.5], [2, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0, 0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [2, 2], 'bilinear', 0);
-
-    expect(output.shape).toEqual([2, 2, 2, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [1, 3, 7, 9, 1, 2, 4, 5]);
-  });
-  it('3x3to2x2-nearest', async () => {
-    const image: tf.Tensor4D =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, 1]);
-    const boxes: tf.Tensor2D =
-        tf.tensor2d([0, 0, 1, 1, 0, 0, 0.5, 0.5], [2, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0, 0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [2, 2], 'nearest', 0);
-
-    expect(output.shape).toEqual([2, 2, 2, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [1, 3, 7, 9, 1, 2, 4, 5]);
-  });
-  it('3x3to2x2Flipped-bilinear', async () => {
-    const image: tf.Tensor4D =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, 1]);
-    const boxes: tf.Tensor2D =
-        tf.tensor2d([1, 1, 0, 0, 0.5, 0.5, 0, 0], [2, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0, 0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [2, 2], 'bilinear', 0);
-
-    expect(output.shape).toEqual([2, 2, 2, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [9, 7, 3, 1, 5, 4, 2, 1]);
-  });
-  it('3x3to2x2Flipped-nearest', async () => {
-    const image: tf.Tensor4D =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, 1]);
-    const boxes: tf.Tensor2D =
-        tf.tensor2d([1, 1, 0, 0, 0.5, 0.5, 0, 0], [2, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0, 0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [2, 2], 'nearest', 0);
-
-    expect(output.shape).toEqual([2, 2, 2, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [9, 7, 3, 1, 5, 4, 2, 1]);
-  });
-  it('3x3-BoxisRectangular', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1.5], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
-
-    expect(output.shape).toEqual([1, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(
-        await output.data(), [1, 1.75, 0, 2, 2.75, 0, 3, 3.75, 0]);
-  });
-  it('3x3-BoxisRectangular-nearest', async () => {
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1.5], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'nearest', 0);
-
-    expect(output.shape).toEqual([1, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), [1, 2, 0, 3, 4, 0, 3, 4, 0]);
-  });
-  it('2x2to3x3-Extrapolated', async () => {
-    const val = -1;
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([-1, -1, 1, 1], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', val);
-
-    expect(output.shape).toEqual([1, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(
-        await output.data(), [val, val, val, val, 1, 2, val, 3, 4]);
-  });
-  it('2x2to3x3-Extrapolated-Float', async () => {
-    const val = -1.5;
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([-1, -1, 1, 1], [1, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', val);
-
-    expect(output.shape).toEqual([1, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(
-        await output.data(), [val, val, val, val, 1, 2, val, 3, 4]);
-  });
-  it('2x2to3x3-NoCrop', async () => {
-    const val = -1.0;
-    const image: tf.Tensor4D = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([], [0, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', val);
-
-    expect(output.shape).toEqual([0, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), []);
-  });
-  it('MultipleBoxes-DifferentBoxes', async () => {
-    const image: tf.Tensor4D =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-    const boxes: tf.Tensor2D =
-        tf.tensor2d([0, 0, 1, 1.5, 0, 0, 1.5, 1], [2, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0, 1], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
-
-    expect(output.shape).toEqual([2, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(
-        await output.data(),
-        [1, 1.75, 0, 2, 2.75, 0, 3, 3.75, 0, 5, 5.5, 6, 6.5, 7, 7.5, 0, 0, 0]);
-  });
-  it('MultipleBoxes-DifferentBoxes-Nearest', async () => {
-    const image: tf.Tensor4D =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-    const boxes: tf.Tensor2D = tf.tensor2d([0, 0, 1, 1.5, 0, 0, 2, 1], [2, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0, 1], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'nearest', 0);
-
-    expect(output.shape).toEqual([2, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(
-        await output.data(),
-        [1, 2, 0, 3, 4, 0, 3, 4, 0, 5, 6, 6, 7, 8, 8, 0, 0, 0]);
-  });
-  it('int32 image returns float output', async () => {
-    const image: tf.Tensor4D =
-        tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1], 'int32');
-    const boxes: tf.Tensor2D =
-        tf.tensor2d([0, 0, 1, 1.5, 0, 0, 1.5, 1], [2, 4]);
-    const boxInd: tf.Tensor1D = tf.tensor1d([0, 1], 'int32');
-
-    const output =
-        tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
-
-    expect(output.shape).toEqual([2, 3, 3, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(
-        await output.data(),
-        [1, 1.75, 0, 2, 2.75, 0, 3, 3.75, 0, 5, 5.5, 6, 6.5, 7, 7.5, 0, 0, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/flip_left_right.ts b/tfjs-master/tfjs-core/src/ops/image/flip_left_right.ts
deleted file mode 100644
index ad3c83a9f..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/flip_left_right.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {FlipLeftRight, FlipLeftRightInputs} from '../../kernel_names';
-import {Tensor4D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-import {op} from '../operation';
-
-/**
- * Flips the image left to right. Currently available in the CPU, WebGL, and
- * WASM backends.
- *
- * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
- */
-/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
-function flipLeftRight_(image: Tensor4D|TensorLike): Tensor4D {
-  const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');
-
-  util.assert(
-      $image.rank === 4,
-      () => 'Error in flipLeftRight: image must be rank 4,' +
-          `but got rank ${$image.rank}.`);
-
-  const inputs: FlipLeftRightInputs = {image: $image};
-  const res =
-      ENGINE.runKernel(FlipLeftRight, inputs as unknown as NamedTensorMap, {});
-  return res as Tensor4D;
-}
-
-export const flipLeftRight = /* @__PURE__ */ op({flipLeftRight_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/flip_left_right_test.ts b/tfjs-master/tfjs-core/src/ops/image/flip_left_right_test.ts
deleted file mode 100644
index b53fe57ea..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/flip_left_right_test.ts
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {getTestImageAsTensor4d} from '../../image_test_util';
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('flipLeftRight', ALL_ENVS, () => {
-  it('should flip (1x2x2x4)', async () => {
-    const image: tf.Tensor4D = tf.tensor([
-      // batch 1
-      [
-        // row 1
-        [
-          [1, 2, 3, 255],
-          [4, 5, 6, 255],
-        ],
-        // row 2
-        [
-          [7, 8, 9, 255],
-          [10, 11, 12, 255],
-        ],
-      ],
-    ]);
-    const flipped = tf.image.flipLeftRight(image);
-    expectArraysClose(
-        [4, 5, 6, 255, 1, 2, 3, 255, 10, 11, 12, 255, 7, 8, 9, 255],
-        await flipped.data());
-  });
-
-  it('should flip (2x2x2x4)', async () => {
-    const image: tf.Tensor4D = tf.tensor([
-      // batch 1
-      [
-        // row 1
-        [
-          [1, 2, 3, 255],
-          [4, 5, 6, 255],
-        ],
-        // row 2
-        [
-          [7, 8, 9, 255],
-          [10, 11, 12, 255],
-        ],
-      ],
-      // batch 2
-      [
-        // row 1
-        [
-          [101, 102, 103, 255],
-          [104, 105, 106, 255],
-        ],
-        // row 2
-        [
-          [107, 108, 109, 255],
-          [110, 111, 112, 255],
-        ],
-      ],
-    ]);
-    const flipped = tf.image.flipLeftRight(image);
-    expectArraysClose(
-        [
-          // batch 1
-          ...[4, 5, 6, 255, 1, 2, 3, 255, 10, 11, 12, 255, 7, 8, 9, 255],
-          // batch 2
-          ...[104, 105, 106, 255, 101, 102, 103, 255, 110, 111, 112, 255, 107,
-              108, 109, 255],
-        ],
-        await flipped.data());
-  });
-
-  it('should flip (from image)', async () => {
-    const flippedPixels =
-        tf.image.flipLeftRight(getTestImageAsTensor4d()).toInt();
-    const flippedPixelsData = await flippedPixels.data();
-
-    const expected = [
-      230, 133, 18,  255, 241, 153, 43,  255, 224, 156, 55,  255, 212, 157, 75,
-      255, 200, 155, 98,  255, 183, 138, 109, 255, 171, 120, 117, 255, 156, 100,
-      111, 255, 233, 148, 31,  255, 250, 177, 64,  255, 241, 188, 82,  255, 230,
-      193, 104, 255, 220, 190, 128, 255, 202, 174, 137, 255, 186, 152, 140, 255,
-      168, 129, 130, 255, 222, 164, 41,  255, 247, 201, 81,  255, 243, 220, 106,
-      255, 235, 227, 128, 255, 225, 228, 151, 255, 211, 216, 162, 255, 199, 198,
-      168, 255, 179, 176, 159, 255, 191, 170, 61,  255, 218, 210, 103, 255, 213,
-      230, 126, 255, 201, 236, 142, 255, 191, 239, 165, 255, 184, 234, 181, 255,
-      179, 226, 194, 255, 163, 208, 187, 255, 135, 166, 86,  255, 162, 206, 127,
-      255, 155, 226, 146, 255, 141, 232, 162, 255, 130, 235, 179, 255, 121, 231,
-      192, 255, 119, 226, 206, 255, 108, 214, 202, 255, 71,  143, 97,  255, 98,
-      181, 135, 255, 94,  206, 156, 255, 87,  220, 175, 255, 76,  225, 193, 255,
-      64,  219, 201, 255, 62,  217, 213, 255, 55,  207, 212, 255, 15,  115, 105,
-      255, 39,  150, 141, 255, 37,  177, 164, 255, 35,  200, 186, 255, 30,  209,
-      205, 255, 19,  203, 211, 255, 19,  204, 222, 255, 18,  200, 224, 255, 0,
-      102, 113, 255, 6,   133, 140, 255, 3,   158, 162, 255, 4,   182, 186, 255,
-      0,   194, 204, 255, 0,   189, 209, 255, 0,   192, 221, 255, 0,   193, 228,
-      255
-    ];
-
-    expectArraysClose(expected, flippedPixelsData);
-  });
-
-  it('throws when input is int32', async () => {
-    const image: tf.Tensor4D = tf.tensor(
-        [[[[1, 2, 3, 255]], [[7, 8, 9, 255]]]], [1, 2, 1, 4], 'int32');
-    expect(() => tf.image.flipLeftRight(image))
-        .toThrowError(
-            /Argument 'image' passed to 'flipLeftRight' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/grayscale_to_rgb.ts b/tfjs-master/tfjs-core/src/ops/image/grayscale_to_rgb.ts
deleted file mode 100644
index a50343e50..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/grayscale_to_rgb.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor2D, Tensor3D, Tensor4D, Tensor5D, Tensor6D} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-
-import {op} from '../operation';
-import {tile} from '../tile';
-
-/**
- * Converts images from grayscale to RGB format.
- *
- * @param image A grayscale tensor to convert. The `image`'s last dimension must
- *     be size 1 with at least a two-dimensional shape.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function grayscaleToRGB_<T extends Tensor2D|Tensor3D|Tensor4D|Tensor5D|
-                         Tensor6D>(image: T|TensorLike): T {
-  const $image = convertToTensor(image, 'image', 'grayscaleToRGB');
-
-  const lastDimsIdx = $image.rank - 1;
-  const lastDims = $image.shape[lastDimsIdx];
-
-  util.assert(
-      $image.rank >= 2,
-      () => 'Error in grayscaleToRGB: images must be at least rank 2, ' +
-          `but got rank ${$image.rank}.`);
-
-  util.assert(
-      lastDims === 1,
-      () => 'Error in grayscaleToRGB: last dimension of a grayscale image ' +
-          `should be size 1, but got size ${lastDims}.`);
-
-  const reps = new Array($image.rank);
-
-  reps.fill(1, 0, lastDimsIdx);
-  reps[lastDimsIdx] = 3;
-
-  return tile($image, reps);
-}
-
-export const grayscaleToRGB = /* @__PURE__ */ op({grayscaleToRGB_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/grayscale_to_rgb_test.ts b/tfjs-master/tfjs-core/src/ops/image/grayscale_to_rgb_test.ts
deleted file mode 100644
index c79457325..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/grayscale_to_rgb_test.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {Tensor2D} from '../../tensor';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('grayscaleToRGB', ALL_ENVS, () => {
-  it('should convert (1,1,3,1) images into (1,1,3,3)', async () => {
-    const grayscale = tf.tensor4d([1.0, 2.0, 3.0], [1, 1, 3, 1]);
-
-    const rgb = tf.image.grayscaleToRGB(grayscale);
-    const rgbData = await rgb.data();
-
-    const expected = [1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0];
-
-    expect(rgb.shape).toEqual([1, 1, 3, 3]);
-    expectArraysClose(rgbData, expected);
-  });
-
-  it('should convert (1,2,1) images into (1,2,3)', async () => {
-    const grayscale = tf.tensor3d([1.6, 2.4], [1, 2, 1]);
-
-    const rgb = tf.image.grayscaleToRGB(grayscale);
-    const rgbData = await rgb.data();
-
-    const expected = [1.6, 1.6, 1.6, 2.4, 2.4, 2.4];
-
-    expect(rgb.shape).toEqual([1, 2, 3]);
-    expectArraysClose(rgbData, expected);
-  });
-
-  it('should convert (2,1) images into (2,3)', async () => {
-    const grayscale = tf.tensor2d([16, 24], [2, 1]);
-
-    const rgb = tf.image.grayscaleToRGB(grayscale);
-    const rgbData = await rgb.data();
-
-    const expected = [16, 16, 16, 24, 24, 24];
-
-    expect(rgb.shape).toEqual([2, 3]);
-    expectArraysClose(rgbData, expected);
-  });
-
-  it('should convert [[[191], [3]]] array into (1,2,3) images', async () => {
-    const grayscale = [[[191], [3]]];
-
-    const rgb = tf.image.grayscaleToRGB(grayscale);
-    const rgbData = await rgb.data();
-
-    const expected = [191, 191, 191, 3, 3, 3];
-
-    expect(rgb.shape).toEqual([1, 2, 3]);
-    expectArraysClose(rgbData, expected);
-  });
-
-  it('should throw an error because of input last dim is not 1', () => {
-    const grayscale = tf.tensor4d([1.0, 1.0, 2.0, 2.0, 3.0, 3.0], [1, 1, 3, 2]);
-
-    expect(() => tf.image.grayscaleToRGB(grayscale))
-        .toThrowError(/last dimension of a grayscale image should be size 1/);
-  });
-
-  it('should throw an error because of image\'s rank is less than 2', () => {
-    const grayscale = tf.tensor1d([1, 2, 3]) as unknown as Tensor2D;
-
-    expect(() => tf.image.grayscaleToRGB(grayscale))
-        .toThrowError(/images must be at least rank 2/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression.ts b/tfjs-master/tfjs-core/src/ops/image/non_max_suppression.ts
deleted file mode 100644
index 5aea5272c..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {NonMaxSuppressionV3} from '../../kernel_names';
-import {Tensor1D, Tensor2D} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {nonMaxSuppSanityCheck} from '../nonmax_util';
-import {op} from '../operation';
-
-/**
- * Performs non maximum suppression of bounding boxes based on
- * iou (intersection over union).
- *
- * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
- *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
- *     the bounding box.
- * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
- * @param maxOutputSize The maximum number of boxes to be selected.
- * @param iouThreshold A float representing the threshold for deciding whether
- *     boxes overlap too much with respect to IOU. Must be between [0, 1].
- *     Defaults to 0.5 (50% box overlap).
- * @param scoreThreshold A threshold for deciding when to remove boxes based
- *     on score. Defaults to -inf, which means any score is accepted.
- * @return A 1D tensor with the selected box indices.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function nonMaxSuppression_(
-    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
-    maxOutputSize: number, iouThreshold = 0.5,
-    scoreThreshold = Number.NEGATIVE_INFINITY): Tensor1D {
-  const $boxes =
-      convertToTensor(boxes, 'boxes', 'nonMaxSuppression', 'float32');
-  const $scores =
-      convertToTensor(scores, 'scores', 'nonMaxSuppression', 'float32');
-
-  const inputs = nonMaxSuppSanityCheck(
-      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-  maxOutputSize = inputs.maxOutputSize;
-  iouThreshold = inputs.iouThreshold;
-  scoreThreshold = inputs.scoreThreshold;
-
-  const attrs = {maxOutputSize, iouThreshold, scoreThreshold};
-  return ENGINE.runKernel(
-      NonMaxSuppressionV3, {boxes: $boxes, scores: $scores}, attrs);
-}
-
-export const nonMaxSuppression = /* @__PURE__ */ op({nonMaxSuppression_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_async.ts b/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_async.ts
deleted file mode 100644
index 44713f590..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_async.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {nonMaxSuppressionV3Impl} from '../../backends/non_max_suppression_impl';
-import {Tensor1D, Tensor2D} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {nonMaxSuppSanityCheck} from '../nonmax_util';
-import {tensor1d} from '../tensor1d';
-
-/**
- * Performs non maximum suppression of bounding boxes based on
- * iou (intersection over union).
- *
- * This is the async version of `nonMaxSuppression`
- *
- * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
- *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
- *     the bounding box.
- * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
- * @param maxOutputSize The maximum number of boxes to be selected.
- * @param iouThreshold A float representing the threshold for deciding whether
- *     boxes overlap too much with respect to IOU. Must be between [0, 1].
- *     Defaults to 0.5 (50% box overlap).
- * @param scoreThreshold A threshold for deciding when to remove boxes based
- *     on score. Defaults to -inf, which means any score is accepted.
- * @return A 1D tensor with the selected box indices.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-async function nonMaxSuppressionAsync_(
-    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
-    maxOutputSize: number, iouThreshold = 0.5,
-    scoreThreshold = Number.NEGATIVE_INFINITY): Promise<Tensor1D> {
-  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
-  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
-
-  const inputs = nonMaxSuppSanityCheck(
-      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
-  maxOutputSize = inputs.maxOutputSize;
-  iouThreshold = inputs.iouThreshold;
-  scoreThreshold = inputs.scoreThreshold;
-
-  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-  const boxesVals = boxesAndScores[0];
-  const scoresVals = boxesAndScores[1];
-
-  // We call a cpu based impl directly with the typedarray data  here rather
-  // than a kernel because all kernels are synchronous (and thus cannot await
-  // .data()).
-  const {selectedIndices} = nonMaxSuppressionV3Impl(
-      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-
-  return tensor1d(selectedIndices, 'int32');
-}
-
-export const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
diff --git a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_async_test.ts b/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_async_test.ts
deleted file mode 100644
index f1c2fff07..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_async_test.ts
+++ /dev/null
@@ -1,114 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../../test_util';
-
-describeWithFlags('nonMaxSuppressionAsync', ALL_ENVS, () => {
-  describe('NonMaxSuppressionAsync basic', () => {
-    it('select from three clusters', async () => {
-      const boxes = tf.tensor2d(
-          [
-            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-      const maxOutputSize = 3;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-      const indices = await tf.image.nonMaxSuppressionAsync(
-          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
-
-      expect(indices.shape).toEqual([3]);
-      expectArraysEqual(await indices.data(), [3, 0, 5]);
-    });
-
-    it('accepts a tensor-like object', async () => {
-      const boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];
-      const scores = [1, 2];
-      const indices = await tf.image.nonMaxSuppressionAsync(boxes, scores, 10);
-      expect(indices.shape).toEqual([2]);
-      expect(indices.dtype).toEqual('int32');
-      expectArraysEqual(await indices.data(), [1, 0]);
-    });
-  });
-
-  describe('NonMaxSuppressionWithScoreAsync', () => {
-    it('select from three clusters with SoftNMS', async () => {
-      const boxes = tf.tensor2d(
-          [
-            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-      const maxOutputSize = 6;
-      const iouThreshold = 1.0;
-      const scoreThreshold = 0;
-      const softNmsSigma = 0.5;
-
-      const numTensorsBefore = tf.memory().numTensors;
-
-      const {selectedIndices, selectedScores} =
-          await tf.image.nonMaxSuppressionWithScoreAsync(
-              boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,
-              softNmsSigma);
-
-      const numTensorsAfter = tf.memory().numTensors;
-
-      expectArraysEqual(await selectedIndices.data(), [3, 0, 1, 5, 4, 2]);
-
-      expectArraysClose(
-          await selectedScores.data(), [0.95, 0.9, 0.384, 0.3, 0.256, 0.197]);
-
-      // The number of tensors should increase by the number of tensors
-      // returned (i.e. selectedIndices and selectedScores).
-      expect(numTensorsAfter).toEqual(numTensorsBefore + 2);
-    });
-  });
-
-  describe('NonMaxSuppressionPaddedAsync', () => {
-    it('select from three clusters with pad five.', async () => {
-      const boxes = tf.tensor2d(
-          [
-            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-      const maxOutputSize = 5;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0.0;
-
-      const before = tf.memory().numTensors;
-
-      const {selectedIndices, validOutputs} =
-          await tf.image.nonMaxSuppressionPaddedAsync(
-              boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, true);
-
-      const after = tf.memory().numTensors;
-
-      expectArraysEqual(await selectedIndices.data(), [3, 0, 5, 0, 0]);
-      expectArraysEqual(await validOutputs.data(), 3);
-
-      // The number of tensors should increase by the number of tensors
-      // returned (i.e. selectedIndices and selectedScores).
-      expect(after).toEqual(before + 2);
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_padded.ts b/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_padded.ts
deleted file mode 100644
index 58839e666..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_padded.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor, Tensor1D, Tensor2D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-
-import {nonMaxSuppSanityCheck} from '../nonmax_util';
-import {op} from '../operation';
-
-/**
- * Asynchronously performs non maximum suppression of bounding boxes based on
- * iou (intersection over union), with an option to pad results.
- *
- * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
- *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
- *     the bounding box.
- * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
- * @param maxOutputSize The maximum number of boxes to be selected.
- * @param iouThreshold A float representing the threshold for deciding whether
- *     boxes overlap too much with respect to IOU. Must be between [0, 1].
- *     Defaults to 0.5 (50% box overlap).
- * @param scoreThreshold A threshold for deciding when to remove boxes based
- *     on score. Defaults to -inf, which means any score is accepted.
- * @param padToMaxOutputSize Defaults to false. If true, size of output
- *     `selectedIndices` is padded to maxOutputSize.
- * @return A map with the following properties:
- *     - selectedIndices: A 1D tensor with the selected box indices.
- *     - validOutputs: A scalar denoting how many elements in `selectedIndices`
- *       are valid. Valid elements occur first, then padding.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function nonMaxSuppressionPadded_(
-    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
-    maxOutputSize: number, iouThreshold = 0.5,
-    scoreThreshold = Number.NEGATIVE_INFINITY,
-    padToMaxOutputSize = false): NamedTensorMap {
-  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
-  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
-
-  const params = nonMaxSuppSanityCheck(
-      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,
-      null /* softNmsSigma */);
-  const $maxOutputSize = params.maxOutputSize;
-  const $iouThreshold = params.iouThreshold;
-  const $scoreThreshold = params.scoreThreshold;
-
-  const inputs: NonMaxSuppressionV4Inputs = {boxes: $boxes, scores: $scores};
-  const attrs: NonMaxSuppressionV4Attrs = {
-    maxOutputSize: $maxOutputSize,
-    iouThreshold: $iouThreshold,
-    scoreThreshold: $scoreThreshold,
-    padToMaxOutputSize
-  };
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const result = ENGINE.runKernel(
-                     NonMaxSuppressionV4, inputs as unknown as NamedTensorMap,
-                     attrs as unknown as NamedAttrMap) as Tensor[];
-
-  return {selectedIndices: result[0], validOutputs: result[1]};
-}
-
-export const nonMaxSuppressionPadded = /* @__PURE__ */ op({nonMaxSuppressionPadded_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_padded_async.ts b/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_padded_async.ts
deleted file mode 100644
index 5222dbc0b..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_padded_async.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {nonMaxSuppressionV4Impl} from '../../backends/non_max_suppression_impl';
-import {Tensor1D, Tensor2D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {nonMaxSuppSanityCheck} from '../nonmax_util';
-import {scalar} from '../scalar';
-import {tensor1d} from '../tensor1d';
-
-/**
- * Asynchronously performs non maximum suppression of bounding boxes based on
- * iou (intersection over union), with an option to pad results.
- *
- * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
- *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
- *     the bounding box.
- * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
- * @param maxOutputSize The maximum number of boxes to be selected.
- * @param iouThreshold A float representing the threshold for deciding whether
- *     boxes overlap too much with respect to IOU. Must be between [0, 1].
- *     Defaults to 0.5 (50% box overlap).
- * @param scoreThreshold A threshold for deciding when to remove boxes based
- *     on score. Defaults to -inf, which means any score is accepted.
- * @param padToMaxOutputSize Defaults to false. If true, size of output
- *     `selectedIndices` is padded to maxOutputSize.
- * @return A map with the following properties:
- *     - selectedIndices: A 1D tensor with the selected box indices.
- *     - validOutputs: A scalar denoting how many elements in `selectedIndices`
- *       are valid. Valid elements occur first, then padding.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-async function nonMaxSuppressionPaddedAsync_(
-    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
-    maxOutputSize: number, iouThreshold = 0.5,
-    scoreThreshold = Number.NEGATIVE_INFINITY,
-    padToMaxOutputSize = false): Promise<NamedTensorMap> {
-  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
-  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
-
-  const params = nonMaxSuppSanityCheck(
-      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,
-      null /* softNmsSigma */);
-  const $maxOutputSize = params.maxOutputSize;
-  const $iouThreshold = params.iouThreshold;
-  const $scoreThreshold = params.scoreThreshold;
-
-  const [boxesVals, scoresVals] =
-      await Promise.all([$boxes.data(), $scores.data()]);
-
-  // We call a cpu based impl directly with the typedarray data here rather
-  // than a kernel because all kernels are synchronous (and thus cannot await
-  // .data()).
-  const {selectedIndices, validOutputs} = nonMaxSuppressionV4Impl(
-      boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold,
-      padToMaxOutputSize);
-
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-
-  return {
-    selectedIndices: tensor1d(selectedIndices, 'int32'),
-    validOutputs: scalar(validOutputs, 'int32')
-  };
-}
-
-export const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
diff --git a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_test.ts b/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_test.ts
deleted file mode 100644
index 247d74ab2..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_test.ts
+++ /dev/null
@@ -1,316 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../../test_util';
-
-describeWithFlags('nonMaxSuppression', ALL_ENVS, () => {
-  describe('NonMaxSuppression Basic', () => {
-    it('select from three clusters', async () => {
-      const boxes = tf.tensor2d(
-          [
-            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-      const maxOutputSize = 3;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-      const indices = tf.image.nonMaxSuppression(
-          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
-
-      expect(indices.shape).toEqual([3]);
-      expectArraysEqual(await indices.data(), [3, 0, 5]);
-    });
-
-    it('select from three clusters flipped coordinates', async () => {
-      const boxes = tf.tensor2d(
-          [
-            1, 1,  0, 0,  0, 0.1,  1, 1.1,  0, .9,  1, -0.1,
-            0, 10, 1, 11, 1, 10.1, 0, 11.1, 1, 101, 0, 100
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-      const maxOutputSize = 3;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-      const indices = tf.image.nonMaxSuppression(
-          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
-
-      expect(indices.shape).toEqual([3]);
-      expectArraysEqual(await indices.data(), [3, 0, 5]);
-    });
-
-    it('select at most two boxes from three clusters', async () => {
-      const boxes = tf.tensor2d(
-          [
-            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-      const maxOutputSize = 2;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-      const indices = tf.image.nonMaxSuppression(
-          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
-
-      expect(indices.shape).toEqual([2]);
-      expectArraysEqual(await indices.data(), [3, 0]);
-    });
-
-    it('select at most thirty boxes from three clusters', async () => {
-      const boxes = tf.tensor2d(
-          [
-            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-      const maxOutputSize = 30;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-      const indices = tf.image.nonMaxSuppression(
-          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
-
-      expect(indices.shape).toEqual([3]);
-      expectArraysEqual(await indices.data(), [3, 0, 5]);
-    });
-
-    it('select single box', async () => {
-      const boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
-      const scores = tf.tensor1d([0.9]);
-      const maxOutputSize = 3;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-      const indices = tf.image.nonMaxSuppression(
-          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
-
-      expect(indices.shape).toEqual([1]);
-      expectArraysEqual(await indices.data(), [0]);
-    });
-
-    it('select from ten identical boxes', async () => {
-      const numBoxes = 10;
-      const corners = new Array(numBoxes)
-                          .fill(0)
-                          .map(_ => [0, 0, 1, 1])
-                          .reduce((arr, curr) => arr.concat(curr));
-      const boxes = tf.tensor2d(corners, [numBoxes, 4]);
-      const scores = tf.tensor1d(Array(numBoxes).fill(0.9));
-      const maxOutputSize = 3;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-      const indices = tf.image.nonMaxSuppression(
-          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
-
-      expect(indices.shape).toEqual([1]);
-      expectArraysEqual(await indices.data(), [0]);
-    });
-
-    it('inconsistent box and score shapes', () => {
-      const boxes = tf.tensor2d(
-          [
-            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5]);
-      const maxOutputSize = 30;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-      expect(
-          () => tf.image.nonMaxSuppression(
-              boxes, scores, maxOutputSize, iouThreshold, scoreThreshold))
-          .toThrowError(/scores has incompatible shape with boxes/);
-    });
-
-    it('invalid iou threshold', () => {
-      const boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
-      const scores = tf.tensor1d([0.9]);
-      const maxOutputSize = 3;
-      const iouThreshold = 1.2;
-      const scoreThreshold = 0;
-      expect(
-          () => tf.image.nonMaxSuppression(
-              boxes, scores, maxOutputSize, iouThreshold, scoreThreshold))
-          .toThrowError(/iouThreshold must be in \[0, 1\]/);
-    });
-
-    it('empty input', async () => {
-      const boxes = tf.tensor2d([], [0, 4]);
-      const scores = tf.tensor1d([]);
-      const maxOutputSize = 3;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-      const indices = tf.image.nonMaxSuppression(
-          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
-
-      expect(indices.shape).toEqual([0]);
-      expectArraysEqual(await indices.data(), []);
-    });
-
-    it('accepts a tensor-like object', async () => {
-      const boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];
-      const scores = [1, 2];
-      const indices = tf.image.nonMaxSuppression(boxes, scores, 10);
-      expect(indices.shape).toEqual([2]);
-      expect(indices.dtype).toEqual('int32');
-      expectArraysEqual(await indices.data(), [1, 0]);
-    });
-
-    it('throws when boxes is int32', async () => {
-      const boxes = tf.tensor2d([[0, 0, 1, 1], [0, 1, 1, 2]], [2, 4], 'int32');
-      const scores = [1, 2];
-      expect(() => tf.image.nonMaxSuppression(boxes, scores, 10))
-          .toThrowError(
-              /Argument 'boxes' passed to 'nonMaxSuppression' must be float32/);
-    });
-
-    it('throws when scores is int32', async () => {
-      const boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];
-      const scores = tf.tensor1d([1, 2], 'int32');
-      const errRegex =
-          /Argument 'scores' passed to 'nonMaxSuppression' must be float32/;
-      expect(() => tf.image.nonMaxSuppression(boxes, scores, 10))
-          .toThrowError(errRegex);
-    });
-
-    it('works when inputs are not explicitly initialized on the CPU',
-       async () => {
-         // This test ensures that asynchronous backends work with NMS, which
-         // requires inputs to reside on the CPU.
-         const boxes = tf.tensor2d(
-             [
-               0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-               0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-             ],
-             [6, 4]);
-         const a = tf.tensor1d([0, 1, -2, -4, 4, -4]);
-         const b = tf.tensor1d([0.15, 0.2, 0.25, 0.5, 0.7, 1.2]);
-         const scores = a.div(b);
-         const maxOutputSize = 2;
-         const iouThreshold = 0.5;
-         const scoreThreshold = 0;
-         await scores.data();
-         const indices = tf.image.nonMaxSuppression(
-             boxes, scores as tf.Tensor1D, maxOutputSize, iouThreshold,
-             scoreThreshold);
-
-         expect(indices.shape).toEqual([2]);
-         expectArraysEqual(await indices.data(), [4, 1]);
-       });
-  });
-
-  describe('NonMaxSuppressionWithScore', () => {
-    it('select from three clusters with SoftNMS', async () => {
-      const boxes = tf.tensor2d(
-          [
-            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-      const maxOutputSize = 6;
-      const iouThreshold = 1.0;
-      const scoreThreshold = 0;
-      const softNmsSigma = 0.5;
-
-      const {selectedIndices, selectedScores} =
-          tf.image.nonMaxSuppressionWithScore(
-              boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,
-              softNmsSigma);
-
-      expectArraysEqual(await selectedIndices.data(), [3, 0, 1, 5, 4, 2]);
-
-      expectArraysClose(
-          await selectedScores.data(), [0.95, 0.9, 0.384, 0.3, 0.256, 0.197]);
-    });
-  });
-
-  describe('NonMaxSuppressionPadded', () => {
-    it('select from three clusters with pad five.', async () => {
-      const boxes = tf.tensor2d(
-          [
-            0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-          ],
-          [6, 4]);
-      const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-      const maxOutputSize = 5;
-      const iouThreshold = 0.5;
-      const scoreThreshold = 0;
-
-      const before = tf.memory().numTensors;
-      const {selectedIndices, validOutputs} = tf.image.nonMaxSuppressionPadded(
-          boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, true);
-      const after = tf.memory().numTensors;
-
-      expectArraysEqual(await selectedIndices.data(), [3, 0, 5, 0, 0]);
-      expectArraysEqual(await validOutputs.data(), 3);
-      expect(after).toEqual(before + 2);
-    });
-
-    it('select from three clusters with pad five and score threshold.',
-       async () => {
-         const boxes = tf.tensor2d(
-             [
-               0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-               0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-             ],
-             [6, 4]);
-         const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-         const maxOutputSize = 6;
-         const iouThreshold = 0.5;
-         const scoreThreshold = 0.4;
-
-         const before = tf.memory().numTensors;
-         const {selectedIndices, validOutputs} =
-             tf.image.nonMaxSuppressionPadded(
-                 boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,
-                 true);
-         const after = tf.memory().numTensors;
-
-         expectArraysEqual(await selectedIndices.data(), [3, 0, 0, 0, 0, 0]);
-         expectArraysEqual(await validOutputs.data(), 2);
-         expect(after).toEqual(before + 2);
-       });
-
-    it('select from three clusters with no padding when pad option is false.',
-       async () => {
-         const boxes = tf.tensor2d(
-             [
-               0, 0,  1, 1,  0, 0.1,  1, 1.1,  0, -0.1, 1, 0.9,
-               0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100,  1, 101
-             ],
-             [6, 4]);
-         const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
-         const maxOutputSize = 5;
-         const iouThreshold = 0.5;
-         const scoreThreshold = 0.0;
-
-         const {selectedIndices, validOutputs} =
-             tf.image.nonMaxSuppressionPadded(
-                 boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,
-                 false);
-
-         expectArraysEqual(await selectedIndices.data(), [3, 0, 5]);
-         expectArraysEqual(await validOutputs.data(), 3);
-       });
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_with_score.ts b/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_with_score.ts
deleted file mode 100644
index 060c61356..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_with_score.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {NonMaxSuppressionV5, NonMaxSuppressionV5Attrs, NonMaxSuppressionV5Inputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor, Tensor1D, Tensor2D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-
-import {nonMaxSuppSanityCheck} from '../nonmax_util';
-import {op} from '../operation';
-
-/**
- * Performs non maximum suppression of bounding boxes based on
- * iou (intersection over union).
- *
- * This op also supports a Soft-NMS mode (cf.
- * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
- * of other overlapping boxes, therefore favoring different regions of the image
- * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
- * parameter to be larger than 0.
- *
- * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
- *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
- *     the bounding box.
- * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
- * @param maxOutputSize The maximum number of boxes to be selected.
- * @param iouThreshold A float representing the threshold for deciding whether
- *     boxes overlap too much with respect to IOU. Must be between [0, 1].
- *     Defaults to 0.5 (50% box overlap).
- * @param scoreThreshold A threshold for deciding when to remove boxes based
- *     on score. Defaults to -inf, which means any score is accepted.
- * @param softNmsSigma A float representing the sigma parameter for Soft NMS.
- *     When sigma is 0, it falls back to nonMaxSuppression.
- * @return A map with the following properties:
- *     - selectedIndices: A 1D tensor with the selected box indices.
- *     - selectedScores: A 1D tensor with the corresponding scores for each
- *       selected box.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function nonMaxSuppressionWithScore_(
-    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
-    maxOutputSize: number, iouThreshold = 0.5,
-    scoreThreshold = Number.NEGATIVE_INFINITY,
-    softNmsSigma = 0.0): NamedTensorMap {
-  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
-  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
-
-  const params = nonMaxSuppSanityCheck(
-      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,
-      softNmsSigma);
-  maxOutputSize = params.maxOutputSize;
-  iouThreshold = params.iouThreshold;
-  scoreThreshold = params.scoreThreshold;
-  softNmsSigma = params.softNmsSigma;
-
-  const inputs: NonMaxSuppressionV5Inputs = {boxes: $boxes, scores: $scores};
-  const attrs: NonMaxSuppressionV5Attrs =
-      {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const result = ENGINE.runKernel(
-                     NonMaxSuppressionV5, inputs as unknown as NamedTensorMap,
-                     attrs as unknown as NamedAttrMap) as Tensor[];
-
-  return {selectedIndices: result[0], selectedScores: result[1]};
-}
-
-export const nonMaxSuppressionWithScore = /* @__PURE__ */ op({nonMaxSuppressionWithScore_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_with_score_async.ts b/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_with_score_async.ts
deleted file mode 100644
index 2f76f6df5..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/non_max_suppression_with_score_async.ts
+++ /dev/null
@@ -1,94 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {nonMaxSuppressionV5Impl} from '../../backends/non_max_suppression_impl';
-import {Tensor1D, Tensor2D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {nonMaxSuppSanityCheck} from '../nonmax_util';
-import {tensor1d} from '../tensor1d';
-
-/**
- * Asynchronously performs non maximum suppression of bounding boxes based on
- * iou (intersection over union).
- *
- * This op also supports a Soft-NMS mode (cf.
- * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
- * of other overlapping boxes, therefore favoring different regions of the image
- * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
- * parameter to be larger than 0.
- *
- * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
- *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
- *     the bounding box.
- * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
- * @param maxOutputSize The maximum number of boxes to be selected.
- * @param iouThreshold A float representing the threshold for deciding whether
- *     boxes overlap too much with respect to IOU. Must be between [0, 1].
- *     Defaults to 0.5 (50% box overlap).
- * @param scoreThreshold A threshold for deciding when to remove boxes based
- *     on score. Defaults to -inf, which means any score is accepted.
- * @param softNmsSigma A float representing the sigma parameter for Soft NMS.
- *     When sigma is 0, it falls back to nonMaxSuppression.
- * @return A map with the following properties:
- *     - selectedIndices: A 1D tensor with the selected box indices.
- *     - selectedScores: A 1D tensor with the corresponding scores for each
- *       selected box.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-async function nonMaxSuppressionWithScoreAsync_(
-    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,
-    maxOutputSize: number, iouThreshold = 0.5,
-    scoreThreshold = Number.NEGATIVE_INFINITY,
-    softNmsSigma = 0.0): Promise<NamedTensorMap> {
-  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
-  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
-
-  const params = nonMaxSuppSanityCheck(
-      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,
-      softNmsSigma);
-  maxOutputSize = params.maxOutputSize;
-  iouThreshold = params.iouThreshold;
-  scoreThreshold = params.scoreThreshold;
-  softNmsSigma = params.softNmsSigma;
-
-  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
-  const boxesVals = boxesAndScores[0];
-  const scoresVals = boxesAndScores[1];
-
-  // We call a cpu based impl directly with the typedarray data  here rather
-  // than a kernel because all kernels are synchronous (and thus cannot await
-  // .data()).
-  const {selectedIndices, selectedScores} = nonMaxSuppressionV5Impl(
-      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold,
-      softNmsSigma);
-
-  if ($boxes !== boxes) {
-    $boxes.dispose();
-  }
-  if ($scores !== scores) {
-    $scores.dispose();
-  }
-
-  return {
-    selectedIndices: tensor1d(selectedIndices, 'int32'),
-    selectedScores: tensor1d(selectedScores)
-  };
-}
-
-export const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
diff --git a/tfjs-master/tfjs-core/src/ops/image/resize_bilinear.ts b/tfjs-master/tfjs-core/src/ops/image/resize_bilinear.ts
deleted file mode 100644
index a43206043..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/resize_bilinear.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor3D, Tensor4D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-
-import {op} from '../operation';
-import {reshape} from '../reshape';
-
-/**
- * Bilinear resize a single 3D image or a batch of 3D images to a new shape.
- *
- * @param images The images, of rank 4 or rank 3, of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param size The new shape `[newHeight, newWidth]` to resize the
- *     images to. Each channel is resized individually.
- * @param alignCorners Defaults to `false`. If true, rescale
- *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
- *     corners of images and resized images. If false, rescale by
- *     `new_height / height`. Treat similarly the width dimension.
- * @param halfPixelCenters Defaults to `false`. Whether to assume pixel centers
- *     are at 0.5, which would make the floating point coordinates of the top
- *     left pixel 0.5, 0.5.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function resizeBilinear_<T extends Tensor3D|Tensor4D>(
-    images: T|TensorLike, size: [number, number], alignCorners = false,
-    halfPixelCenters = false): T {
-  const $images = convertToTensor(images, 'images', 'resizeBilinear');
-
-  util.assert(
-      $images.rank === 3 || $images.rank === 4,
-      () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +
-          `rank ${$images.rank}.`);
-  util.assert(
-      size.length === 2,
-      () => `Error in resizeBilinear: new shape must 2D, but got shape ` +
-          `${size}.`);
-  util.assert(
-      halfPixelCenters === false || alignCorners === false,
-      () => `Error in resizeBilinear: If halfPixelCenters is true, ` +
-          `alignCorners must be false.`);
-
-  let batchImages = $images as Tensor4D;
-  let reshapedTo4D = false;
-  if ($images.rank === 3) {
-    reshapedTo4D = true;
-    batchImages = reshape(
-        $images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-  }
-
-  const [] = size;
-
-  const inputs: ResizeBilinearInputs = {images: batchImages};
-  const attrs: ResizeBilinearAttrs = {alignCorners, halfPixelCenters, size};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  ResizeBilinear, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-  return res;
-}
-
-export const resizeBilinear = /* @__PURE__ */ op({resizeBilinear_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/resize_bilinear_test.ts b/tfjs-master/tfjs-core/src/ops/image/resize_bilinear_test.ts
deleted file mode 100644
index 4c7ace516..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/resize_bilinear_test.ts
+++ /dev/null
@@ -1,979 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('resizeBilinear', ALL_ENVS, () => {
-  it('simple alignCorners=false', async () => {
-    const input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
-    const output = input.resizeBilinear([3, 3], false);
-
-    expectArraysClose(
-        await output.data(), [2, 2, 2, 10 / 3, 10 / 3, 10 / 3, 4, 4, 4]);
-  });
-
-  it('5x5-bilinear, no change in shape', async () => {
-    const image: tf.Tensor4D = tf.ones([1, 5, 5, 3]);
-
-    const alignCorners = false;
-    const output = tf.image.resizeBilinear(image, [5, 5], alignCorners);
-
-    expect(output.shape).toEqual([1, 5, 5, 3]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(await output.data(), await image.data());
-  });
-
-  it('simple alignCorners=true', async () => {
-    const input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
-    const output = input.resizeBilinear([3, 3], true);
-
-    expectArraysClose(await output.data(), [2, 2, 2, 3, 3, 3, 4, 4, 4]);
-  });
-
-  it('works when rows are copied', async () => {
-    const input = tf.tensor3d(
-        [
-          1.56324531, 2.13817752, 1.44398421, 1.07632684, 0.59306785,
-          -0.36970865, 1.62451879, 1.8367334, 1.13944798, 2.01993218,
-          2.01919952, 2.67524054
-        ],
-        [2, 3, 2]);
-    const output = input.resizeBilinear([4, 3], false);
-
-    expectArraysClose(await output.data(), [
-      1.5632453,  2.13817763, 1.44398415, 1.07632685, 0.59306782, -0.36970866,
-      1.59388208, 1.98745549, 1.2917161,  1.54812956, 1.30613375, 1.15276587,
-      1.62451875, 1.83673334, 1.13944793, 2.01993227, 2.01919961, 2.67524052,
-      1.62451875, 1.83673334, 1.13944793, 2.01993227, 2.01919961, 2.67524052
-    ]);
-  });
-
-  it('works for ints', async () => {
-    const input = tf.tensor3d([1, 2, 3, 4, 5], [1, 5, 1], 'int32');
-    const output = input.resizeBilinear([1, 10], false);
-
-    expect(output.shape).toEqual([1, 10, 1]);
-    expect(output.dtype).toBe('float32');
-    expectArraysClose(
-        await output.data(), [1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5]);
-  });
-
-  it('matches tensorflow w/ random numbers alignCorners=false, ' +
-         'halfPixelCenters=true',
-     async () => {
-       const input = tf.tensor3d(
-           [
-             1.19074044, 0.91373104, 2.01611669, -0.52270832, 0.38725395,
-             1.30809779, 0.61835143, 3.49600659, 2.09230986, 0.56473997,
-             0.03823943, 1.19864896
-           ],
-           [2, 3, 2]);
-       const output = input.resizeBilinear([4, 5], false, true);
-
-       expectArraysClose(await output.data(), [
-         1.1907405, 0.913731,  1.520891,  0.3391553,  2.0161166, -0.5227083,
-         1.0387988, 0.5757756, 0.3872539, 1.3080978,  1.0476432, 1.5592999,
-         1.442652,  0.8352414, 2.0351648, -0.2508462, 0.9940659, 0.6681031,
-         0.3000003, 1.2807356, 0.7614487, 2.8504376,  1.2861738, 1.8274136,
-         2.0732617, 0.2928779, 0.9046002, 0.8527579,  0.125493,  1.2260112,
-         0.6183515, 3.4960065, 1.2079349, 2.3234997,  2.09231,   0.5647399,
-         0.8598673, 0.9450854, 0.0382394, 1.1986489
-       ]);
-     });
-
-  it('batch of 2, simple, alignCorners=false, ' +
-         'halfPixelCenters=true',
-     async () => {
-       const input = tf.tensor4d([2, 2, 4, 4, 3, 3, 5, 5], [2, 2, 2, 1]);
-       const output =
-           input.resizeBilinear([3, 3], false /* alignCorners */, true);
-
-       expectArraysClose(
-           await output.data(),
-           [2, 2, 2, 3, 3, 3, 4, 4, 4, 3, 3, 3, 4, 4, 4, 5, 5, 5]);
-     });
-
-  it('target width = 1, alignCorners=false, ' +
-         'halfPixelCenters=true',
-     async () => {
-       const input = tf.tensor3d([
-         [
-           [120.68856811523438, 134.51638793945312, 83.03671264648438],
-           [121.58008575439453, 113.28836059570312, 136.3172149658203],
-           [79.38370513916016, 101.87127685546875, 104.54979705810547],
-           [96.31678771972656, 111.77168273925781, 83.73509979248047]
-         ],
-         [
-           [119.45088195800781, 88.98846435546875, 97.47553253173828],
-           [117.5562973022461, 108.26356506347656, 99.62212371826172],
-           [136.62701416015625, 94.10433197021484, 80.97366333007812],
-           [83.61205291748047, 90.60148620605469, 81.82512664794922]
-         ],
-         [
-           [103.0362777709961, 123.1098403930664, 125.62944030761719],
-           [92.2915267944336, 103.15729522705078, 119.18060302734375],
-           [102.93293762207031, 117.821044921875, 99.40152740478516],
-           [96.32952117919922, 105.80963134765625, 104.8491439819336]
-         ],
-         [
-           [104.87507629394531, 134.0189208984375, 111.02627563476562],
-           [85.4534683227539, 107.68426513671875, 103.03722381591797],
-           [89.70533752441406, 98.25298309326172, 78.42916870117188],
-           [113.6744613647461, 95.8189697265625, 122.75005340576172]
-         ]
-       ]);
-
-       const output = input.resizeBilinear([3, 1], false, true);
-
-       const expected = [
-         104.917, 106.514, 115.411, 112.352, 105.837, 99.7945, 89.2515, 104.222,
-         93.8262
-       ];
-       expectArraysClose(await output.data(), expected);
-       expect(output.shape).toEqual([3, 1, 3]);
-     });
-
-  it('matches tensorflow w/ random numbers alignCorners=false', async () => {
-    const input = tf.tensor3d(
-        [
-          1.19074044, 0.91373104, 2.01611669, -0.52270832, 0.38725395,
-          1.30809779, 0.61835143, 3.49600659, 2.09230986, 0.56473997,
-          0.03823943, 1.19864896
-        ],
-        [2, 3, 2]);
-    const output = input.resizeBilinear([4, 5], false);
-
-    expectArraysClose(await output.data(), [
-      1.19074047, 0.91373104, 1.68596613, 0.05186744, 1.69034398, -0.15654698,
-      0.7130264,  0.94193673, 0.38725394, 1.30809784, 0.9045459,  2.20486879,
-      1.59434628, 0.89455694, 1.68591988, 0.26748738, 0.58103991, 1.00690198,
-      0.21274668, 1.25337338, 0.6183514,  3.49600649, 1.50272655, 1.73724651,
-      1.68149579, 0.69152176, 0.44905344, 1.07186723, 0.03823943, 1.19864893,
-      0.6183514,  3.49600649, 1.50272655, 1.73724651, 1.68149579, 0.69152176,
-      0.44905344, 1.07186723, 0.03823943, 1.19864893
-    ]);
-  });
-
-  it('matches tensorflow w/ random numbers alignCorners=true', async () => {
-    const input = tf.tensor3d(
-        [
-          1.56324531, 2.13817752, 1.44398421, 1.07632684, 0.59306785,
-          -0.36970865, 1.62451879, 1.8367334, 1.13944798, 2.01993218,
-          2.01919952, 2.67524054
-        ],
-        [2, 3, 2]);
-    const output = input.resizeBilinear([4, 5], true);
-
-    expectArraysClose(await output.data(), [
-      1.5632453,  2.13817763, 1.50361478, 1.60725224,  1.44398427, 1.07632685,
-      1.01852608, 0.35330909, 0.59306782, -0.36970866, 1.58366978, 2.03769612,
-      1.46307099, 1.71427906, 1.3424722,  1.39086199,  1.20545864, 1.01806819,
-      1.06844509, 0.6452744,  1.60409427, 1.93721485,  1.42252707, 1.82130599,
-      1.24096,    1.70539713, 1.3923912,  1.68282723,  1.54382229, 1.66025746,
-      1.62451875, 1.83673346, 1.38198328, 1.92833281,  1.13944793, 2.01993227,
-      1.57932377, 2.34758639, 2.01919961, 2.67524052
-    ]);
-  });
-
-  it('batch of 2, simple, alignCorners=true', async () => {
-    const input = tf.tensor4d([2, 2, 4, 4, 3, 3, 5, 5], [2, 2, 2, 1]);
-    const output = input.resizeBilinear([3, 3], true /* alignCorners */);
-
-    expectArraysClose(
-        await output.data(),
-        [2, 2, 2, 3, 3, 3, 4, 4, 4, 3, 3, 3, 4, 4, 4, 5, 5, 5]);
-  });
-
-  it('target width = 1, alignCorners=true', async () => {
-    const input = tf.tensor3d([
-      [
-        [120.68856811523438, 134.51638793945312, 83.03671264648438],
-        [121.58008575439453, 113.28836059570312, 136.3172149658203],
-        [79.38370513916016, 101.87127685546875, 104.54979705810547],
-        [96.31678771972656, 111.77168273925781, 83.73509979248047]
-      ],
-      [
-        [119.45088195800781, 88.98846435546875, 97.47553253173828],
-        [117.5562973022461, 108.26356506347656, 99.62212371826172],
-        [136.62701416015625, 94.10433197021484, 80.97366333007812],
-        [83.61205291748047, 90.60148620605469, 81.82512664794922]
-      ],
-      [
-        [103.0362777709961, 123.1098403930664, 125.62944030761719],
-        [92.2915267944336, 103.15729522705078, 119.18060302734375],
-        [102.93293762207031, 117.821044921875, 99.40152740478516],
-        [96.32952117919922, 105.80963134765625, 104.8491439819336]
-      ],
-      [
-        [104.87507629394531, 134.0189208984375, 111.02627563476562],
-        [85.4534683227539, 107.68426513671875, 103.03722381591797],
-        [89.70533752441406, 98.25298309326172, 78.42916870117188],
-        [113.6744613647461, 95.8189697265625, 122.75005340576172]
-      ]
-    ]);
-
-    const output = input.resizeBilinear([3, 1], true);
-
-    const expected = [
-      120.68857, 134.51639, 83.03671, 111.243576, 106.04915, 111.55249,
-      104.87508, 134.01892, 111.026276
-    ];
-    expectArraysClose(await output.data(), expected);
-    expect(output.shape).toEqual([3, 1, 3]);
-  });
-
-  it('target height = 1, alignCorners=true', async () => {
-    const input = tf.tensor3d([
-      [
-        [120.68856811523438, 134.51638793945312, 83.03671264648438],
-        [121.58008575439453, 113.28836059570312, 136.3172149658203],
-        [79.38370513916016, 101.87127685546875, 104.54979705810547],
-        [96.31678771972656, 111.77168273925781, 83.73509979248047]
-      ],
-      [
-        [119.45088195800781, 88.98846435546875, 97.47553253173828],
-        [117.5562973022461, 108.26356506347656, 99.62212371826172],
-        [136.62701416015625, 94.10433197021484, 80.97366333007812],
-        [83.61205291748047, 90.60148620605469, 81.82512664794922]
-      ],
-      [
-        [103.0362777709961, 123.1098403930664, 125.62944030761719],
-        [92.2915267944336, 103.15729522705078, 119.18060302734375],
-        [102.93293762207031, 117.821044921875, 99.40152740478516],
-        [96.32952117919922, 105.80963134765625, 104.8491439819336]
-      ],
-      [
-        [104.87507629394531, 134.0189208984375, 111.02627563476562],
-        [85.4534683227539, 107.68426513671875, 103.03722381591797],
-        [89.70533752441406, 98.25298309326172, 78.42916870117188],
-        [113.6744613647461, 95.8189697265625, 122.75005340576172]
-      ]
-    ]);
-
-    const output = input.resizeBilinear([1, 3], true);
-
-    const expected = [
-      120.68857, 134.51639, 83.03671, 100.481895, 107.57982, 120.4335, 96.31679,
-      111.77168, 83.7351
-    ];
-    expectArraysClose(await output.data(), expected);
-    expect(output.shape).toEqual([1, 3, 3]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    const e = /Argument 'images' passed to 'resizeBilinear' must be a Tensor/;
-    expect(() => tf.image.resizeBilinear({} as tf.Tensor3D, [
-      1, 1
-    ])).toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const input = [[[2], [2]], [[4], [4]]];  // 2x2x1
-    const output = tf.image.resizeBilinear(input, [3, 3], false);
-    expectArraysClose(
-        await output.data(), [2, 2, 2, 10 / 3, 10 / 3, 10 / 3, 4, 4, 4]);
-  });
-});
-
-describeWithFlags('resizeBilinear gradients', ALL_ENVS, () => {
-  it('greyscale: upscale, same aspect ratio', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
-    ]);
-
-    const size: [number, number] = [4, 4];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [6.0, 17.0, 38.0, 75.0];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('with clones, greyscale: upscale, same aspect ratio', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
-    ]);
-
-    const size: [number, number] = [4, 4];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeBilinear(i.clone(), size, alignCorners).clone());
-
-    const output = g(input, dy);
-    const expected = [6.0, 17.0, 38.0, 75.0];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: upscale, same aspect ratio, align corners', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
-    ]);
-
-    const size: [number, number] = [4, 4];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected =
-        [17.333330154418945, 23.999998092651367, 44.0, 50.66666793823242];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: upscale, taller than wider', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]],
-      [[17.0], [18.0], [19.0], [20.0]], [[21.0], [22.0], [23.0], [24.0]],
-      [[25.0], [26.0], [27.0], [28.0]], [[29.0], [30.0], [31.0], [32.0]],
-      [[33.0], [34.0], [35.0], [36.0]]
-    ]);
-
-    const size: [number, number] = [9, 4];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      25.55555534362793, 55.5555534362793, 208.44444274902344, 376.4444274902344
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: upscale, taller than wider, align corners', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]],
-      [[17.0], [18.0], [19.0], [20.0]], [[21.0], [22.0], [23.0], [24.0]],
-      [[25.0], [26.0], [27.0], [28.0]], [[29.0], [30.0], [31.0], [32.0]],
-      [[33.0], [34.0], [35.0], [36.0]]
-    ]);
-
-    const size: [number, number] = [9, 4];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [99.0, 114.0, 219.00001525878906, 233.99998474121094];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: upscale, wider than taller', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0]],
-      [[8.0], [9.0], [10.0], [11.0], [12.0], [13.0], [14.0]],
-      [[15.0], [16.0], [17.0], [18.0], [19.0], [20.0], [21.0]],
-      [[22.0], [23.0], [24.0], [25.0], [26.0], [27.0], [28.0]]
-    ]);
-
-    const size: [number, number] = [4, 7];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      14.428570747375488, 52.07142639160156, 98.71427917480469,
-      240.78573608398438
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: upscale, wider than taller, align corners', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0]],
-      [[8.0], [9.0], [10.0], [11.0], [12.0], [13.0], [14.0]],
-      [[15.0], [16.0], [17.0], [18.0], [19.0], [20.0], [21.0]],
-      [[22.0], [23.0], [24.0], [25.0], [26.0], [27.0], [28.0]]
-    ]);
-
-    const size: [number, number] = [4, 7];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [51.33332824707031, 70.0, 133.0, 151.66668701171875];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  // Downscale
-
-  it('greyscale: downscale, same aspect ratio', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0,
-      0.0
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: downscale, same aspect ratio, align corners', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0,
-      4.0
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: downscale, taller than wider', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]], [[5.0], [6.0]]]);
-
-    const size: [number, number] = [3, 2];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1.0, 0.0, 2.0, 0.0, 1.9999998807907104, 0.0, 2.6666665077209473, 0.0,
-      2.6666665077209473, 0.0, 3.3333330154418945, 0.0, 3.333333730697632, 0.0,
-      4.000000476837158, 0.0
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: downscale, taller than wider, align corners', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]], [[5.0], [6.0]]]);
-
-    const size: [number, number] = [3, 2];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1.0, 0.0, 0.0, 2.0, 1.5, 0.0, 0.0, 2.0, 1.5, 0.0, 0.0, 2.0, 5.0, 0.0, 0.0,
-      6.0
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: downscale, wider than taller', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-
-    const dy = tf.tensor3d([[[1.0], [2.0], [3.0]], [[4.0], [5.0], [6.0]]]);
-
-    const size: [number, number] = [2, 3];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1.0, 1.3333332538604736, 1.6666665077209473, 2.000000238418579, 0.0, 0.0,
-      0.0, 0.0, 4.0, 3.3333330154418945, 3.6666665077209473, 4.000000476837158,
-      0.0, 0.0, 0.0, 0.0
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: downscale, wider than taller, align corners', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-
-    const dy = tf.tensor3d([[[1.0], [2.0], [3.0]], [[4.0], [5.0], [6.0]]]);
-
-    const size: [number, number] = [2, 3];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1.0, 1.0, 1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 2.5, 2.5,
-      6.0
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  // No Op
-
-  it('greyscale: same size', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [1.0, 2.0, 3.0, 4.0];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('greyscale: same size, align corners', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [1.0, 2.0, 3.0, 4.0];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  // 3 channel upscale
-  it('color: upscale, wider than taller', async () => {
-    const input = tf.tensor3d([
-      [
-        [115.11029815673828, 111.90936279296875, 66.87433624267578],
-        [72.03849029541016, 81.86637878417969, 119.53585815429688]
-      ],
-      [
-        [68.555419921875, 97.49642181396484, 116.90741729736328],
-        [128.69467163085938, 86.78314208984375, 104.3116683959961]
-      ]
-    ]);
-
-    const dy = tf.tensor3d([
-      [
-        [1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0],
-        [13.0, 14.0, 15.0]
-      ],
-      [
-        [16.0, 17.0, 18.0], [19.0, 20.0, 21.0], [22.0, 23.0, 24.0],
-        [25.0, 26.0, 27.0], [28.0, 29.0, 30.0]
-      ],
-      [
-        [31.0, 32.0, 33.0], [34.0, 35.0, 36.0], [37.0, 38.0, 39.0],
-        [40.0, 41.0, 42.0], [43.0, 44.0, 45.0]
-      ]
-    ]);
-
-    const size: [number, number] = [3, 5];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      15.399999618530273, 17.799999237060547, 20.19999885559082,
-      56.26666259765625, 60.533329010009766, 64.79999542236328,
-      80.00000762939453, 83.0, 86.0, 178.33334350585938, 183.66668701171875,
-      189.00001525878906
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('color: upscale, wider than taller, align corners', async () => {
-    const input = tf.tensor3d([
-      [
-        [115.11029815673828, 111.90936279296875, 66.87433624267578],
-        [72.03849029541016, 81.86637878417969, 119.53585815429688]
-      ],
-      [
-        [68.555419921875, 97.49642181396484, 116.90741729736328],
-        [128.69467163085938, 86.78314208984375, 104.3116683959961]
-      ]
-    ]);
-
-    const dy = tf.tensor3d([
-      [
-        [1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0],
-        [13.0, 14.0, 15.0]
-      ],
-      [
-        [16.0, 17.0, 18.0], [19.0, 20.0, 21.0], [22.0, 23.0, 24.0],
-        [25.0, 26.0, 27.0], [28.0, 29.0, 30.0]
-      ],
-      [
-        [31.0, 32.0, 33.0], [34.0, 35.0, 36.0], [37.0, 38.0, 39.0],
-        [40.0, 41.0, 42.0], [43.0, 44.0, 45.0]
-      ]
-    ]);
-
-    const size: [number, number] = [3, 5];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      33.75, 37.5, 41.25, 56.25, 60.0, 63.75, 108.75, 112.5, 116.25, 131.25,
-      135.0, 138.75
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  // 3 channel downscale
-
-  it('color: downscale, taller than wider', async () => {
-    const input = tf.tensor3d([
-      [
-        [120.68856811523438, 134.51638793945312, 83.03671264648438],
-        [121.58008575439453, 113.28836059570312, 136.3172149658203],
-        [79.38370513916016, 101.87127685546875, 104.54979705810547],
-        [96.31678771972656, 111.77168273925781, 83.73509979248047]
-      ],
-      [
-        [119.45088195800781, 88.98846435546875, 97.47553253173828],
-        [117.5562973022461, 108.26356506347656, 99.62212371826172],
-        [136.62701416015625, 94.10433197021484, 80.97366333007812],
-        [83.61205291748047, 90.60148620605469, 81.82512664794922]
-      ],
-      [
-        [103.0362777709961, 123.1098403930664, 125.62944030761719],
-        [92.2915267944336, 103.15729522705078, 119.18060302734375],
-        [102.93293762207031, 117.821044921875, 99.40152740478516],
-        [96.32952117919922, 105.80963134765625, 104.8491439819336]
-      ],
-      [
-        [104.87507629394531, 134.0189208984375, 111.02627563476562],
-        [85.4534683227539, 107.68426513671875, 103.03722381591797],
-        [89.70533752441406, 98.25298309326172, 78.42916870117188],
-        [113.6744613647461, 95.8189697265625, 122.75005340576172]
-      ]
-    ]);
-
-    const dy =
-        tf.tensor3d([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0]]]);
-
-    const size: [number, number] = [3, 1];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1.0,
-      2.0,
-      3.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      2.6666665077209473,
-      3.3333330154418945,
-      3.999999761581421,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      3.666666269302368,
-      4.3333330154418945,
-      4.999999523162842,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      4.6666669845581055,
-      5.333333969116211,
-      6.000000953674316,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0,
-      0.0
-    ];
-
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('color: downscale, width = 1, align corners', async () => {
-    const input = tf.tensor3d([
-      [
-        [120.68856811523438, 134.51638793945312, 83.03671264648438],
-        [121.58008575439453, 113.28836059570312, 136.3172149658203],
-        [79.38370513916016, 101.87127685546875, 104.54979705810547],
-        [96.31678771972656, 111.77168273925781, 83.73509979248047]
-      ],
-      [
-        [119.45088195800781, 88.98846435546875, 97.47553253173828],
-        [117.5562973022461, 108.26356506347656, 99.62212371826172],
-        [136.62701416015625, 94.10433197021484, 80.97366333007812],
-        [83.61205291748047, 90.60148620605469, 81.82512664794922]
-      ],
-      [
-        [103.0362777709961, 123.1098403930664, 125.62944030761719],
-        [92.2915267944336, 103.15729522705078, 119.18060302734375],
-        [102.93293762207031, 117.821044921875, 99.40152740478516],
-        [96.32952117919922, 105.80963134765625, 104.8491439819336]
-      ],
-      [
-        [104.87507629394531, 134.0189208984375, 111.02627563476562],
-        [85.4534683227539, 107.68426513671875, 103.03722381591797],
-        [89.70533752441406, 98.25298309326172, 78.42916870117188],
-        [113.6744613647461, 95.8189697265625, 122.75005340576172]
-      ]
-    ]);
-
-    const dy =
-        tf.tensor3d([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0]]]);
-
-    const size: [number, number] = [3, 1];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1.0, 2.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-      2.0, 2.5, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-      2.0, 2.5, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-      7.0, 8.0, 9.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
-    ];
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('color: downscale, height = 1, align corners', async () => {
-    const input = tf.tensor3d([
-      [
-        [120.68856811523438, 134.51638793945312, 83.03671264648438],
-        [121.58008575439453, 113.28836059570312, 136.3172149658203],
-        [79.38370513916016, 101.87127685546875, 104.54979705810547],
-        [96.31678771972656, 111.77168273925781, 83.73509979248047]
-      ],
-      [
-        [119.45088195800781, 88.98846435546875, 97.47553253173828],
-        [117.5562973022461, 108.26356506347656, 99.62212371826172],
-        [136.62701416015625, 94.10433197021484, 80.97366333007812],
-        [83.61205291748047, 90.60148620605469, 81.82512664794922]
-      ],
-      [
-        [103.0362777709961, 123.1098403930664, 125.62944030761719],
-        [92.2915267944336, 103.15729522705078, 119.18060302734375],
-        [102.93293762207031, 117.821044921875, 99.40152740478516],
-        [96.32952117919922, 105.80963134765625, 104.8491439819336]
-      ],
-      [
-        [104.87507629394531, 134.0189208984375, 111.02627563476562],
-        [85.4534683227539, 107.68426513671875, 103.03722381591797],
-        [89.70533752441406, 98.25298309326172, 78.42916870117188],
-        [113.6744613647461, 95.8189697265625, 122.75005340576172]
-      ]
-    ]);
-
-    const dy = tf.tensor3d([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]);
-
-    const size: [number, number] = [1, 3];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1., 2., 3., 2., 2.5, 3., 2., 2.5, 3., 7., 8., 9., 0., 0., 0., 0.,
-      0., 0., 0., 0., 0.,  0., 0., 0.,  0., 0., 0., 0., 0., 0., 0., 0.,
-      0., 0., 0., 0., 0.,  0., 0., 0.,  0., 0., 0., 0., 0., 0., 0., 0.
-    ];
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('color: downscale, taller than wider, align corners', async () => {
-    const input = tf.tensor3d([
-      [
-        [120.68856811523438, 134.51638793945312, 83.03671264648438],
-        [121.58008575439453, 113.28836059570312, 136.3172149658203],
-        [79.38370513916016, 101.87127685546875, 104.54979705810547],
-        [96.31678771972656, 111.77168273925781, 83.73509979248047]
-      ],
-      [
-        [119.45088195800781, 88.98846435546875, 97.47553253173828],
-        [117.5562973022461, 108.26356506347656, 99.62212371826172],
-        [136.62701416015625, 94.10433197021484, 80.97366333007812],
-        [83.61205291748047, 90.60148620605469, 81.82512664794922]
-      ],
-      [
-        [103.0362777709961, 123.1098403930664, 125.62944030761719],
-        [92.2915267944336, 103.15729522705078, 119.18060302734375],
-        [102.93293762207031, 117.821044921875, 99.40152740478516],
-        [96.32952117919922, 105.80963134765625, 104.8491439819336]
-      ],
-      [
-        [104.87507629394531, 134.0189208984375, 111.02627563476562],
-        [85.4534683227539, 107.68426513671875, 103.03722381591797],
-        [89.70533752441406, 98.25298309326172, 78.42916870117188],
-        [113.6744613647461, 95.8189697265625, 122.75005340576172]
-      ]
-    ]);
-
-    const dy = tf.tensor3d([
-      [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],
-      [[13.0, 14.0, 15.0], [16.0, 17.0, 18.0]]
-    ]);
-
-    const size: [number, number] = [3, 2];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = [
-      1.0,  2.0,  3.0,  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0,  5.0,  6.0,
-      3.5,  4.0,  4.5,  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0,  5.5,  6.0,
-      3.5,  4.0,  4.5,  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0,  5.5,  6.0,
-      13.0, 14.0, 15.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 16.0, 17.0, 18.0
-    ];
-    expectArraysClose(await output.data(), expected);
-  });
-
-  // 3 channel no-op
-
-  it('color: same size', async () => {
-    const input = tf.tensor3d([
-      [
-        [115.11029815673828, 111.90936279296875, 66.87433624267578],
-        [72.03849029541016, 81.86637878417969, 119.53585815429688]
-      ],
-      [
-        [68.555419921875, 97.49642181396484, 116.90741729736328],
-        [128.69467163085938, 86.78314208984375, 104.3116683959961]
-      ]
-    ]);
-
-    const dy = tf.tensor3d([
-      [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
-    ]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected =
-        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0];
-    expectArraysClose(await output.data(), expected);
-  });
-
-  it('color: same size, align corners', async () => {
-    const input = tf.tensor3d([
-      [
-        [115.11029815673828, 111.90936279296875, 66.87433624267578],
-        [72.03849029541016, 81.86637878417969, 119.53585815429688]
-      ],
-      [
-        [68.555419921875, 97.49642181396484, 116.90741729736328],
-        [128.69467163085938, 86.78314208984375, 104.3116683959961]
-      ]
-    ]);
-
-    const dy = tf.tensor3d([
-      [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
-    ]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) => tf.image.resizeBilinear(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected =
-        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0];
-    expectArraysClose(await output.data(), expected);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/resize_nearest_neighbor.ts b/tfjs-master/tfjs-core/src/ops/image/resize_nearest_neighbor.ts
deleted file mode 100644
index 730e49b3c..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/resize_nearest_neighbor.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor3D, Tensor4D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-
-import {op} from '../operation';
-import {reshape} from '../reshape';
-
-/**
- * NearestNeighbor resize a batch of 3D images to a new shape.
- *
- * @param images The images, of rank 4 or rank 3, of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param size The new shape `[newHeight, newWidth]` to resize the
- *     images to. Each channel is resized individually.
- * @param alignCorners Defaults to False. If true, rescale
- *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
- *     corners of images and resized images. If false, rescale by
- *     `new_height / height`. Treat similarly the width dimension.
- * @param halfPixelCenters Defaults to `false`. Whether to assume pixels are of
- *      half the actual dimensions, and yield more accurate resizes. This flag
- *      would also make the floating point coordinates of the top left pixel
- *      0.5, 0.5.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function resizeNearestNeighbor_<T extends Tensor3D|Tensor4D>(
-    images: T|TensorLike, size: [number, number], alignCorners = false,
-    halfPixelCenters = false): T {
-  const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');
-
-  util.assert(
-      $images.rank === 3 || $images.rank === 4,
-      () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +
-          `rank ${$images.rank}.`);
-  util.assert(
-      size.length === 2,
-      () =>
-          `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +
-          `${size}.`);
-  util.assert(
-      $images.dtype === 'float32' || $images.dtype === 'int32',
-      () => '`images` must have `int32` or `float32` as dtype');
-  util.assert(
-      halfPixelCenters === false || alignCorners === false,
-      () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, ` +
-          `alignCorners must be false.`);
-  let batchImages = $images as Tensor4D;
-  let reshapedTo4D = false;
-  if ($images.rank === 3) {
-    reshapedTo4D = true;
-    batchImages = reshape(
-        $images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
-  }
-  const [] = size;
-
-  const inputs: ResizeNearestNeighborInputs = {images: batchImages};
-  const attrs:
-      ResizeNearestNeighborAttrs = {alignCorners, halfPixelCenters, size};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  ResizeNearestNeighbor, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-  return res;
-}
-
-export const resizeNearestNeighbor = /* @__PURE__ */ op({resizeNearestNeighbor_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/resize_nearest_neighbor_test.ts b/tfjs-master/tfjs-core/src/ops/image/resize_nearest_neighbor_test.ts
deleted file mode 100644
index 8bbeb72cf..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/resize_nearest_neighbor_test.ts
+++ /dev/null
@@ -1,734 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('resizeNearestNeighbor', ALL_ENVS, () => {
-  it('simple alignCorners=false', async () => {
-    const input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
-    const output = input.resizeNearestNeighbor([3, 3], false);
-
-    expectArraysClose(await output.data(), [2, 2, 2, 2, 2, 2, 4, 4, 4]);
-  });
-
-  it('simple alignCorners=true', async () => {
-    const input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
-    const output = input.resizeNearestNeighbor([3, 3], true);
-
-    expectArraysClose(await output.data(), [2, 2, 2, 4, 4, 4, 4, 4, 4]);
-  });
-
-  it('5x2To2x2 alignCorners=false halfPixelCenters=true', async () => {
-    const input = tf.tensor4d([1, 2, 3, 4, 5, 1, 2, 3, 4, 5], [1, 2, 5, 1]);
-    const output = input.resizeNearestNeighbor([2, 2], false, true);
-
-    expectArraysClose(await output.data(), [2, 4, 2, 4]);
-  });
-
-  it('2x2To1x1 alignCorners=false halfPixelCenters=true', async () => {
-    const input = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const output = input.resizeNearestNeighbor([1, 1], false, true);
-
-    expectArraysClose(await output.data(), [4]);
-  });
-
-  it('2x2To3x3 alignCorners=false halfPixelCenters=true', async () => {
-    const input = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const output = input.resizeNearestNeighbor([3, 3], false, true);
-
-    expectArraysClose(await output.data(), [1, 2, 2, 3, 4, 4, 3, 4, 4]);
-  });
-
-  it('3x3To2x2 alignCorners=false halfPixelCenters=true', async () => {
-    const input = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, 1]);
-    const output = input.resizeNearestNeighbor([2, 2], false, true);
-
-    expectArraysClose(await output.data(), [1, 3, 7, 9]);
-  });
-
-  it('2x2To2x5 alignCorners=false halfPixelCenters=true', async () => {
-    const input = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const output = input.resizeNearestNeighbor([2, 5], false, true);
-
-    expectArraysClose(await output.data(), [1, 1, 2, 2, 2, 3, 3, 4, 4, 4]);
-  });
-
-  it('4x4To3x3 alignCorners=false halfPixelCenters=true', async () => {
-    const input = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4, 1]);
-    const output = input.resizeNearestNeighbor([3, 3], false, true);
-
-    expectArraysClose(await output.data(), [1, 3, 4, 9, 11, 12, 13, 15, 16]);
-  });
-
-  it('2x2To5x2 alignCorners=false halfPixelCenters=true', async () => {
-    const input = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const output = input.resizeNearestNeighbor([5, 2], false, true);
-
-    expectArraysClose(await output.data(), [1, 2, 1, 2, 3, 4, 3, 4, 3, 4]);
-  });
-
-  it('2x2To4x4 alignCorners=false halfPixelCenters=true', async () => {
-    const input = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const output = input.resizeNearestNeighbor([4, 4], false, true);
-
-    expectArraysClose(
-        await output.data(), [1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4]);
-  });
-
-  it('matches tensorflow w/ random numbers alignCorners=false', async () => {
-    const input = tf.tensor3d(
-        [
-          1.19074044, 0.91373104, 2.01611669, -0.52270832, 0.38725395,
-          1.30809779, 0.61835143, 3.49600659, 2.09230986, 0.56473997,
-          0.03823943, 1.19864896
-        ],
-        [2, 3, 2]);
-    const output = input.resizeNearestNeighbor([4, 5], false);
-
-    expectArraysClose(await output.data(), [
-      1.19074047,   0.913731039, 1.19074047,   0.913731039,  2.01611662,
-      -0.522708297, 2.01611662,  -0.522708297, 0.38725394,   1.30809784,
-      1.19074047,   0.913731039, 1.19074047,   0.913731039,  2.01611662,
-      -0.522708297, 2.01611662,  -0.522708297, 0.38725394,   1.30809784,
-      0.61835146,   3.49600649,  0.61835146,   3.49600649,   2.09230995,
-      0.564739943,  2.09230995,  0.564739943,  0.0382394306, 1.19864893,
-      0.61835146,   3.49600649,  0.61835146,   3.49600649,   2.09230995,
-      0.564739943,  2.09230995,  0.564739943,  0.0382394306, 1.19864893
-    ]);
-  });
-
-  it('matches tensorflow w/ random numbers alignCorners=true', async () => {
-    const input = tf.tensor3d(
-        [
-          1.19074044, 0.91373104, 2.01611669, -0.52270832, 0.38725395,
-          1.30809779, 0.61835143, 3.49600659, 2.09230986, 0.56473997,
-          0.03823943, 1.19864896
-        ],
-        [2, 3, 2]);
-    const output = input.resizeNearestNeighbor([4, 5], true);
-
-    expectArraysClose(await output.data(), [
-      1.19074044, 0.91373104,  2.01611669, -0.52270832, 2.01611669, -0.52270832,
-      0.38725395, 1.30809779,  0.38725395, 1.30809779,  1.19074044, 0.91373104,
-      2.01611669, -0.52270832, 2.01611669, -0.52270832, 0.38725395, 1.30809779,
-      0.38725395, 1.30809779,  0.61835143, 3.49600659,  2.09230986, 0.56473997,
-      2.09230986, 0.56473997,  0.03823943, 1.19864896,  0.03823943, 1.19864896,
-      0.61835143, 3.49600659,  2.09230986, 0.56473997,  2.09230986, 0.56473997,
-      0.03823943, 1.19864896,  0.03823943, 1.19864896
-    ]);
-  });
-
-  it('batch of 2, simple, alignCorners=true', async () => {
-    const input = tf.tensor4d([2, 2, 4, 4, 3, 3, 5, 5], [2, 2, 2, 1]);
-    const output = input.resizeNearestNeighbor([3, 3], true /* alignCorners */);
-
-    expectArraysClose(
-        await output.data(),
-        [2, 2, 2, 4, 4, 4, 4, 4, 4, 3, 3, 3, 5, 5, 5, 5, 5, 5]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    const e =
-        /Argument 'images' passed to 'resizeNearestNeighbor' must be a Tensor/;
-    expect(() => tf.image.resizeNearestNeighbor({} as tf.Tensor3D, [
-      1, 1
-    ])).toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const input = [[[2], [2]], [[4], [4]]];  // 2x2x1
-    const output = tf.image.resizeNearestNeighbor(input, [3, 3], false);
-
-    expectArraysClose(await output.data(), [2, 2, 2, 2, 2, 2, 4, 4, 4]);
-  });
-
-  it('does not throw when some output dim is 1 and alignCorners=true', () => {
-    const input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
-    expect(() => input.resizeNearestNeighbor([1, 3], true)).not.toThrow();
-  });
-});
-
-describeWithFlags('resizeNearestNeighbor gradients', ALL_ENVS, () => {
-  it('greyscale: upscale, same aspect ratio', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
-    ]);
-
-    const size: [number, number] = [4, 4];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([[[14.0], [22.0]], [[46.0], [54.0]]]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('with clones, greyscale: upscale, same aspect ratio', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
-    ]);
-
-    const size: [number, number] = [4, 4];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i.clone(), size, alignCorners)
-                .clone());
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([[[14.0], [22.0]], [[46.0], [54.0]]]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: upscale, same aspect ratio, align corners', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
-    ]);
-
-    const size: [number, number] = [4, 4];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([[[14.0], [22.0]], [[46.0], [54.0]]]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: upscale, taller than wider', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]],
-      [[17.0], [18.0], [19.0], [20.0]], [[21.0], [22.0], [23.0], [24.0]],
-      [[25.0], [26.0], [27.0], [28.0]], [[29.0], [30.0], [31.0], [32.0]],
-      [[33.0], [34.0], [35.0], [36.0]]
-    ]);
-
-    const size: [number, number] = [9, 4];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([[[95.0], [115.0]], [[220.0], [236.0]]]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: upscale, taller than wider, align corners', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
-      [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]],
-      [[17.0], [18.0], [19.0], [20.0]], [[21.0], [22.0], [23.0], [24.0]],
-      [[25.0], [26.0], [27.0], [28.0]], [[29.0], [30.0], [31.0], [32.0]],
-      [[33.0], [34.0], [35.0], [36.0]]
-    ]);
-
-    const size: [number, number] = [9, 4];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([[[60.0], [76.0]], [[255.0], [275.0]]]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: upscale, wider than taller', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0]],
-      [[8.0], [9.0], [10.0], [11.0], [12.0], [13.0], [14.0]],
-      [[15.0], [16.0], [17.0], [18.0], [19.0], [20.0], [21.0]],
-      [[22.0], [23.0], [24.0], [25.0], [26.0], [27.0], [28.0]]
-    ]);
-
-    const size: [number, number] = [4, 7];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([[[48.0], [57.0]], [[160.0], [141.0]]]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: upscale, wider than taller, align corners', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0]],
-      [[8.0], [9.0], [10.0], [11.0], [12.0], [13.0], [14.0]],
-      [[15.0], [16.0], [17.0], [18.0], [19.0], [20.0], [21.0]],
-      [[22.0], [23.0], [24.0], [25.0], [26.0], [27.0], [28.0]]
-    ]);
-
-    const size: [number, number] = [4, 7];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([[[33.0], [72.0]], [[117.0], [184.0]]]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  //
-  // Downscaling
-  //
-
-  it('greyscale: downscale, same aspect ratio', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[1.0], [0.0], [2.0], [0.0]], [[0.0], [0.0], [0.0], [0.0]],
-      [[3.0], [0.0], [4.0], [0.0]], [[0.0], [0.0], [0.0], [0.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: downscale, same aspect ratio, align corners', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[1.0], [0.0], [0.0], [2.0]], [[0.0], [0.0], [0.0], [0.0]],
-      [[0.0], [0.0], [0.0], [0.0]], [[3.0], [0.0], [0.0], [4.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: downscale, taller than wider', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]], [[5.0], [6.0]]]);
-
-    const size: [number, number] = [3, 2];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[1.0], [0.0], [2.0], [0.0]], [[3.0], [0.0], [4.0], [0.0]],
-      [[5.0], [0.0], [6.0], [0.0]], [[0.0], [0.0], [0.0], [0.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: downscale, taller than wider, align corners', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]], [[5.0], [6.0]]]);
-
-    const size: [number, number] = [3, 2];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[1.0], [0.0], [0.0], [2.0]], [[0.0], [0.0], [0.0], [0.0]],
-      [[3.0], [0.0], [0.0], [4.0]], [[5.0], [0.0], [0.0], [6.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: downscale, taller than wider', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-    const dy = tf.tensor3d([[[1.0], [2.0], [3.0]], [[4.0], [5.0], [6.0]]]);
-
-    const size: [number, number] = [2, 3];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[1.0], [2.0], [3.0], [0.0]], [[0.0], [0.0], [0.0], [0.0]],
-      [[4.0], [5.0], [6.0], [0.0]], [[0.0], [0.0], [0.0], [0.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: downscale, taller than wider, align corners', async () => {
-    const input = tf.tensor3d([
-      [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
-      [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
-    ]);
-    const dy = tf.tensor3d([[[1.0], [2.0], [3.0]], [[4.0], [5.0], [6.0]]]);
-
-    const size: [number, number] = [2, 3];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[1.0], [0.0], [2.0], [3.0]], [[0.0], [0.0], [0.0], [0.0]],
-      [[0.0], [0.0], [0.0], [0.0]], [[4.0], [0.0], [5.0], [6.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: downscale, same size', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('greyscale: downscale, same size, align corners', async () => {
-    const input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
-    const dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  //
-  // 3 channel images
-  //
-
-  it('color: upscale, wider than taller', async () => {
-    const input = tf.tensor3d([
-      [
-        [100.26818084716797, 74.61857604980469, 81.62117767333984],
-        [127.86964416503906, 85.0583267211914, 102.95439147949219]
-      ],
-      [
-        [104.3798828125, 96.70733642578125, 92.60601043701172],
-        [77.63021850585938, 68.55794525146484, 96.17212677001953]
-      ]
-    ]);
-
-    const dy = tf.tensor3d([
-      [
-        [1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0],
-        [13.0, 14.0, 15.0]
-      ],
-      [
-        [16.0, 17.0, 18.0], [19.0, 20.0, 21.0], [22.0, 23.0, 24.0],
-        [25.0, 26.0, 27.0], [28.0, 29.0, 30.0]
-      ],
-      [
-        [31.0, 32.0, 33.0], [34.0, 35.0, 36.0], [37.0, 38.0, 39.0],
-        [40.0, 41.0, 42.0], [43.0, 44.0, 45.0]
-      ]
-    ]);
-
-    const size: [number, number] = [3, 5];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[69.0, 75.0, 81.0], [76.0, 80.0, 84.0]],
-      [[102.0, 105.0, 108.0], [83.0, 85.0, 87.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('color: upscale, wider than taller, align corners', async () => {
-    const input = tf.tensor3d([
-      [
-        [100.26818084716797, 74.61857604980469, 81.62117767333984],
-        [127.86964416503906, 85.0583267211914, 102.95439147949219]
-      ],
-      [
-        [104.3798828125, 96.70733642578125, 92.60601043701172],
-        [77.63021850585938, 68.55794525146484, 96.17212677001953]
-      ]
-    ]);
-
-    const dy = tf.tensor3d([
-      [
-        [1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0],
-        [13.0, 14.0, 15.0]
-      ],
-      [
-        [16.0, 17.0, 18.0], [19.0, 20.0, 21.0], [22.0, 23.0, 24.0],
-        [25.0, 26.0, 27.0], [28.0, 29.0, 30.0]
-      ],
-      [
-        [31.0, 32.0, 33.0], [34.0, 35.0, 36.0], [37.0, 38.0, 39.0],
-        [40.0, 41.0, 42.0], [43.0, 44.0, 45.0]
-      ]
-    ]);
-
-    const size: [number, number] = [3, 5];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[5.0, 7.0, 9.0], [30.0, 33.0, 36.0]],
-      [[100.0, 104.0, 108.0], [195.0, 201.0, 207.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('color: downscale, taller than wider', async () => {
-    const input = tf.tensor3d([
-      [
-        [97.98934936523438, 77.24969482421875, 113.70111846923828],
-        [111.34081268310547, 113.15758514404297, 157.90521240234375],
-        [105.77980041503906, 85.75989532470703, 69.62374114990234],
-        [125.94231414794922, 73.11385345458984, 87.03099822998047]
-      ],
-      [
-        [62.25117111206055, 90.23927307128906, 119.1966552734375],
-        [93.55166625976562, 95.9106674194336, 115.56237030029297],
-        [102.98121643066406, 98.1983413696289, 97.55982971191406],
-        [86.47753143310547, 97.04051208496094, 121.50492095947266]
-      ],
-      [
-        [92.4140853881836, 118.45619201660156, 108.0341796875],
-        [126.43061065673828, 123.28077697753906, 121.03379821777344],
-        [128.6694793701172, 98.47042846679688, 114.47464752197266],
-        [93.31566619873047, 95.2713623046875, 102.51188659667969]
-      ],
-      [
-        [101.55884552001953, 83.31947326660156, 119.08016204833984],
-        [128.28546142578125, 92.56212615966797, 74.85054779052734],
-        [88.9786148071289, 119.43685913085938, 73.06110382080078],
-        [98.17908477783203, 105.54570007324219, 93.45832061767578]
-      ]
-    ]);
-
-    const dy =
-        tf.tensor3d([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0]]]);
-
-    const size: [number, number] = [3, 1];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[1.0, 2.0, 3.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
-      [[4.0, 5.0, 6.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
-      [[7.0, 8.0, 9.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
-      [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('color: downscale, taller than wider, align corners', async () => {
-    const input = tf.tensor3d([
-      [
-        [97.98934936523438, 77.24969482421875, 113.70111846923828],
-        [111.34081268310547, 113.15758514404297, 157.90521240234375],
-        [105.77980041503906, 85.75989532470703, 69.62374114990234],
-        [125.94231414794922, 73.11385345458984, 87.03099822998047]
-      ],
-      [
-        [62.25117111206055, 90.23927307128906, 119.1966552734375],
-        [93.55166625976562, 95.9106674194336, 115.56237030029297],
-        [102.98121643066406, 98.1983413696289, 97.55982971191406],
-        [86.47753143310547, 97.04051208496094, 121.50492095947266]
-      ],
-      [
-        [92.4140853881836, 118.45619201660156, 108.0341796875],
-        [126.43061065673828, 123.28077697753906, 121.03379821777344],
-        [128.6694793701172, 98.47042846679688, 114.47464752197266],
-        [93.31566619873047, 95.2713623046875, 102.51188659667969]
-      ],
-      [
-        [101.55884552001953, 83.31947326660156, 119.08016204833984],
-        [128.28546142578125, 92.56212615966797, 74.85054779052734],
-        [88.9786148071289, 119.43685913085938, 73.06110382080078],
-        [98.17908477783203, 105.54570007324219, 93.45832061767578]
-      ]
-    ]);
-
-    const dy =
-        tf.tensor3d([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0]]]);
-
-    const size: [number, number] = [3, 1];
-    const alignCorners = true;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[1.0, 2.0, 3.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
-      [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
-      [[4.0, 5.0, 6.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
-      [[7.0, 8.0, 9.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-
-  it('color: same size', async () => {
-    const input = tf.tensor3d([
-      [
-        [100.26818084716797, 74.61857604980469, 81.62117767333984],
-        [127.86964416503906, 85.0583267211914, 102.95439147949219]
-      ],
-      [
-        [104.3798828125, 96.70733642578125, 92.60601043701172],
-        [77.63021850585938, 68.55794525146484, 96.17212677001953]
-      ]
-    ]);
-
-    const dy = tf.tensor3d([
-      [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
-    ]);
-
-    const size: [number, number] = [2, 2];
-    const alignCorners = false;
-    const g = tf.grad(
-        (i: tf.Tensor3D) =>
-            tf.image.resizeNearestNeighbor(i, size, alignCorners));
-
-    const output = g(input, dy);
-    const expected = tf.tensor3d([
-      [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
-    ]);
-
-    expectArraysClose(await output.data(), await expected.data());
-    expect(output.shape).toEqual(expected.shape);
-    expect(output.dtype).toBe(expected.dtype);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/rgb_to_grayscale.ts b/tfjs-master/tfjs-core/src/ops/image/rgb_to_grayscale.ts
deleted file mode 100644
index 43a3cda96..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/rgb_to_grayscale.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor2D, Tensor3D, Tensor4D, Tensor5D, Tensor6D} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-import {cast} from '../cast';
-import {einsum} from '../einsum';
-import {expandDims} from '../expand_dims';
-import {op} from '../operation';
-import {tensor1d} from '../tensor1d';
-
-/**
- * Converts images from RGB format to grayscale.
- *
- * @param image A RGB tensor to convert. The `image`'s last dimension must
- *     be size 3 with at least a two-dimensional shape.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function rgbToGrayscale_<T extends Tensor2D|Tensor3D|Tensor4D|Tensor5D|
-                         Tensor6D>(image: T|TensorLike): T {
-  const $image = convertToTensor(image, 'image', 'RGBToGrayscale');
-
-  const lastDimsIdx = $image.rank - 1;
-  const lastDims = $image.shape[lastDimsIdx];
-
-  util.assert(
-      $image.rank >= 2,
-      () => 'Error in RGBToGrayscale: images must be at least rank 2, ' +
-          `but got rank ${$image.rank}.`);
-
-  util.assert(
-      lastDims === 3,
-      () => 'Error in RGBToGrayscale: last dimension of an RGB image ' +
-          `should be size 3, but got size ${lastDims}.`);
-
-  // Remember original dtype so we can convert back if needed
-  const origDtype = $image.dtype;
-  const fltImage = cast($image, 'float32');
-
-  const rgbWeights = tensor1d([0.2989, 0.5870, 0.1140]);
-
-  let grayFloat;
-  switch ($image.rank) {
-    case 2:
-      grayFloat = einsum('ij,j->i', fltImage, rgbWeights);
-      break;
-    case 3:
-      grayFloat = einsum('ijk,k->ij', fltImage, rgbWeights);
-      break;
-    case 4:
-      grayFloat = einsum('ijkl,l->ijk', fltImage, rgbWeights);
-      break;
-    case 5:
-      grayFloat = einsum('ijklm,m->ijkl', fltImage, rgbWeights);
-      break;
-    case 6:
-      grayFloat = einsum('ijklmn,n->ijklm', fltImage, rgbWeights);
-      break;
-    default:
-      throw new Error('Not a valid tensor rank.');
-  }
-  grayFloat = expandDims(grayFloat, -1);
-
-  return cast(grayFloat, origDtype) as T;
-}
-
-export const rgbToGrayscale = /* @__PURE__ */ op({rgbToGrayscale_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/rgb_to_grayscale_test.ts b/tfjs-master/tfjs-core/src/ops/image/rgb_to_grayscale_test.ts
deleted file mode 100644
index bbeb37cbc..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/rgb_to_grayscale_test.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-import {Tensor2D} from '../../tensor';
-
-describeWithFlags('RGBToGrayscale', ALL_ENVS, () => {
-  it('should return int32 dtype tensor for int32 dtype input', async () => {
-    const rgb = tf.tensor2d([16,24,56,1,2,9], [2,3], 'int32');
-    const grayscale = tf.image.rgbToGrayscale(rgb);
-
-    const expected = [[25],[2]];
-    const grayscaleData = await grayscale.data();
-
-    expect(grayscale.shape).toEqual([2,1]);
-    expectArraysClose(grayscaleData, expected);
-  });
-
-  it('basic 3 rank array conversion', async () => {
-    const rgb = [[[1.0, 2.0, 3.0]]];
-    const grayscale = tf.image.rgbToGrayscale(rgb);
-
-    const expected = [[[1.8149]]];
-    const grayscaleData = await grayscale.data();
-
-    expect(grayscale.shape).toEqual([1, 1, 1]);
-    expectArraysClose(grayscaleData, expected);
-  });
-
-  it('basic 4 rank array conversion', async () => {
-    const rgb = [[[[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]]];
-    const grayscale = tf.image.rgbToGrayscale(rgb);
-
-    const expected = [[[[1.8149],[2.8148]]]];
-    const grayscaleData = await grayscale.data();
-
-    expect(grayscale.shape).toEqual([1, 1, 2, 1]);
-    expectArraysClose(grayscaleData, expected);
-  });
-
-  it('should throw an error because of input last dim is not 3', () => {
-    const grayscale = tf.tensor4d([1.0, 1.0, 2.0, 2.0, 3.0, 3.0], [1, 1, 3, 2]);
-
-    expect(() => tf.image.rgbToGrayscale(grayscale))
-        .toThrowError(/last dimension of an RGB image should be size 3/);
-  });
-
-  it('should throw an error because of image\'s rank is less than 2', () => {
-    const grayscale = tf.tensor1d([1, 2, 3]) as unknown as Tensor2D;
-
-    expect(() => tf.image.rgbToGrayscale(grayscale))
-        .toThrowError(/images must be at least rank 2/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/rotate_with_offset.ts b/tfjs-master/tfjs-core/src/ops/image/rotate_with_offset.ts
deleted file mode 100644
index 71a183a9a..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/rotate_with_offset.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor4D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-
-import {op} from '../operation';
-
-/**
- * Rotates the input image tensor counter-clockwise with an optional offset
- * center of rotation. Currently available in the CPU, WebGL, and WASM backends.
- *
- * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
- * @param radians The amount of rotation.
- * @param fillValue The value to fill in the empty space leftover
- *     after rotation. Can be either a single grayscale value (0-255), or an
- *     array of three numbers `[red, green, blue]` specifying the red, green,
- *     and blue channels. Defaults to `0` (black).
- * @param center The center of rotation. Can be either a single value (0-1), or
- *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates
- *     the image around its center).
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function rotateWithOffset_(
-    image: Tensor4D|TensorLike, radians: number,
-    fillValue: number|[number, number, number] = 0,
-    center: number|[number, number] = 0.5): Tensor4D {
-  const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');
-
-  util.assert(
-      $image.rank === 4,
-      () => 'Error in rotateWithOffset: image must be rank 4,' +
-          `but got rank ${$image.rank}.`);
-
-  const inputs: RotateWithOffsetInputs = {image: $image};
-  const attrs: RotateWithOffsetAttrs = {radians, fillValue, center};
-  const res = ENGINE.runKernel(
-      RotateWithOffset, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-  return res as Tensor4D;
-}
-
-export const rotateWithOffset = /* @__PURE__ */ op({rotateWithOffset_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/rotate_with_offset_test.ts b/tfjs-master/tfjs-core/src/ops/image/rotate_with_offset_test.ts
deleted file mode 100644
index 88242004a..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/rotate_with_offset_test.ts
+++ /dev/null
@@ -1,155 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {getTestImageAsTensor4d} from '../../image_test_util';
-import * as tf from '../../index';
-import {BROWSER_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('rotateWithOffset', BROWSER_ENVS, () => {
-  it('should rotate counterclockwise 90 degrees', async () => {
-    const rotatedPixels =
-        tf.image.rotateWithOffset(getTestImageAsTensor4d(), 90 * Math.PI / 180)
-            .toInt();
-    const rotatedPixelsData = await rotatedPixels.data();
-
-    const expected = [
-      0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
-      0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
-      0,   0,   230, 133, 18,  255, 233, 148, 31,  255, 222, 164, 41,  255, 191,
-      170, 61,  255, 135, 166, 86,  255, 71,  143, 97,  255, 15,  115, 105, 255,
-      0,   102, 113, 255, 241, 153, 43,  255, 250, 177, 64,  255, 247, 201, 81,
-      255, 218, 210, 103, 255, 162, 206, 127, 255, 98,  181, 135, 255, 39,  150,
-      141, 255, 6,   133, 140, 255, 224, 156, 55,  255, 241, 188, 82,  255, 243,
-      220, 106, 255, 213, 230, 126, 255, 155, 226, 146, 255, 94,  206, 156, 255,
-      37,  177, 164, 255, 3,   158, 162, 255, 212, 157, 75,  255, 230, 193, 104,
-      255, 235, 227, 128, 255, 201, 236, 142, 255, 141, 232, 162, 255, 87,  220,
-      175, 255, 35,  200, 186, 255, 4,   182, 186, 255, 200, 155, 98,  255, 220,
-      190, 128, 255, 225, 228, 151, 255, 191, 239, 165, 255, 130, 235, 179, 255,
-      76,  225, 193, 255, 30,  209, 205, 255, 0,   194, 204, 255, 183, 138, 109,
-      255, 202, 174, 137, 255, 211, 216, 162, 255, 184, 234, 181, 255, 121, 231,
-      192, 255, 64,  219, 201, 255, 19,  203, 211, 255, 0,   189, 209, 255, 171,
-      120, 117, 255, 186, 152, 140, 255, 199, 198, 168, 255, 179, 226, 194, 255,
-      119, 226, 206, 255, 62,  217, 213, 255, 19,  204, 222, 255, 0,   192, 221,
-      255
-    ];
-
-    expectArraysClose(expected, rotatedPixelsData);
-  });
-
-  it('should rotate clockwise 90 degrees', async () => {
-    const rotatedPixels =
-        tf.image.rotateWithOffset(getTestImageAsTensor4d(), -90 * Math.PI / 180)
-            .toInt();
-    const rotatedPixelsData = await rotatedPixels.data();
-
-    const expected = [
-      0,   0,   0,   0,   0,   193, 228, 255, 18,  200, 224, 255, 55,  207, 212,
-      255, 108, 214, 202, 255, 163, 208, 187, 255, 179, 176, 159, 255, 168, 129,
-      130, 255, 0,   0,   0,   0,   0,   192, 221, 255, 19,  204, 222, 255, 62,
-      217, 213, 255, 119, 226, 206, 255, 179, 226, 194, 255, 199, 198, 168, 255,
-      186, 152, 140, 255, 0,   0,   0,   0,   0,   189, 209, 255, 19,  203, 211,
-      255, 64,  219, 201, 255, 121, 231, 192, 255, 184, 234, 181, 255, 211, 216,
-      162, 255, 202, 174, 137, 255, 0,   0,   0,   0,   0,   194, 204, 255, 30,
-      209, 205, 255, 76,  225, 193, 255, 130, 235, 179, 255, 191, 239, 165, 255,
-      225, 228, 151, 255, 220, 190, 128, 255, 0,   0,   0,   0,   4,   182, 186,
-      255, 35,  200, 186, 255, 87,  220, 175, 255, 141, 232, 162, 255, 201, 236,
-      142, 255, 235, 227, 128, 255, 230, 193, 104, 255, 0,   0,   0,   0,   3,
-      158, 162, 255, 37,  177, 164, 255, 94,  206, 156, 255, 155, 226, 146, 255,
-      213, 230, 126, 255, 243, 220, 106, 255, 241, 188, 82,  255, 0,   0,   0,
-      0,   6,   133, 140, 255, 39,  150, 141, 255, 98,  181, 135, 255, 162, 206,
-      127, 255, 218, 210, 103, 255, 247, 201, 81,  255, 250, 177, 64,  255, 0,
-      0,   0,   0,   0,   102, 113, 255, 15,  115, 105, 255, 71,  143, 97,  255,
-      135, 166, 86,  255, 191, 170, 61,  255, 222, 164, 41,  255, 233, 148, 31,
-      255
-    ];
-
-    expectArraysClose(expected, rotatedPixelsData);
-  });
-
-  it('offset center of rotation', async () => {
-    const rotatedPixels =
-        tf.image
-            .rotateWithOffset(
-                getTestImageAsTensor4d(), 45 * Math.PI / 180, 0, [0.25, 0.75])
-            .toInt();
-    const rotatedPixelsData = await rotatedPixels.data();
-
-    const expected = [
-      224, 156, 55,  255, 250, 177, 64,  255, 247, 201, 81,  255, 222, 164, 41,
-      255, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
-      0,   0,   230, 193, 104, 255, 243, 220, 106, 255, 247, 201, 81,  255, 218,
-      210, 103, 255, 135, 166, 86,  255, 0,   0,   0,   0,   0,   0,   0,   0,
-      0,   0,   0,   0,   225, 228, 151, 255, 235, 227, 128, 255, 213, 230, 126,
-      255, 162, 206, 127, 255, 98,  181, 135, 255, 71,  143, 97,  255, 0,   0,
-      0,   0,   0,   0,   0,   0,   225, 228, 151, 255, 191, 239, 165, 255, 141,
-      232, 162, 255, 94,  206, 156, 255, 98,  181, 135, 255, 39,  150, 141, 255,
-      0,   102, 113, 255, 0,   0,   0,   0,   184, 234, 181, 255, 130, 235, 179,
-      255, 76,  225, 193, 255, 87,  220, 175, 255, 37,  177, 164, 255, 6,   133,
-      140, 255, 6,   133, 140, 255, 0,   0,   0,   0,   119, 226, 206, 255, 64,
-      219, 201, 255, 76,  225, 193, 255, 30,  209, 205, 255, 4,   182, 186, 255,
-      3,   158, 162, 255, 0,   0,   0,   0,   0,   0,   0,   0,   62,  217, 213,
-      255, 62,  217, 213, 255, 19,  203, 211, 255, 0,   194, 204, 255, 0,   194,
-      204, 255, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   55,
-      207, 212, 255, 19,  204, 222, 255, 0,   192, 221, 255, 0,   189, 209, 255,
-      0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
-      0
-    ];
-
-    expectArraysClose(expected, rotatedPixelsData);
-  });
-
-  it('offset center of rotation with white fill', async () => {
-    const rotatedPixels =
-        tf.image
-            .rotateWithOffset(
-                getTestImageAsTensor4d(), 45 * Math.PI / 180, 255, [0.25, 0.75])
-            .toInt();
-    const rotatedPixelsData = await rotatedPixels.data();
-
-    const expected = [
-      224, 156, 55,  255, 250, 177, 64,  255, 247, 201, 81,  255, 222, 164, 41,
-      255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-      255, 255, 230, 193, 104, 255, 243, 220, 106, 255, 247, 201, 81,  255, 218,
-      210, 103, 255, 135, 166, 86,  255, 255, 255, 255, 255, 255, 255, 255, 255,
-      255, 255, 255, 255, 225, 228, 151, 255, 235, 227, 128, 255, 213, 230, 126,
-      255, 162, 206, 127, 255, 98,  181, 135, 255, 71,  143, 97,  255, 255, 255,
-      255, 255, 255, 255, 255, 255, 225, 228, 151, 255, 191, 239, 165, 255, 141,
-      232, 162, 255, 94,  206, 156, 255, 98,  181, 135, 255, 39,  150, 141, 255,
-      0,   102, 113, 255, 255, 255, 255, 255, 184, 234, 181, 255, 130, 235, 179,
-      255, 76,  225, 193, 255, 87,  220, 175, 255, 37,  177, 164, 255, 6,   133,
-      140, 255, 6,   133, 140, 255, 255, 255, 255, 255, 119, 226, 206, 255, 64,
-      219, 201, 255, 76,  225, 193, 255, 30,  209, 205, 255, 4,   182, 186, 255,
-      3,   158, 162, 255, 255, 255, 255, 255, 255, 255, 255, 255, 62,  217, 213,
-      255, 62,  217, 213, 255, 19,  203, 211, 255, 0,   194, 204, 255, 0,   194,
-      204, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 55,
-      207, 212, 255, 19,  204, 222, 255, 0,   192, 221, 255, 0,   189, 209, 255,
-      255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-      255
-    ];
-
-    expectArraysClose(expected, rotatedPixelsData);
-  });
-
-  it('throws when input is int32', async () => {
-    expect(
-        () => tf.image.rotateWithOffset(
-            tf.tensor4d([1, 2, 3, 255], [1, 1, 1, 4], 'int32'),
-            90 * Math.PI / 180))
-        .toThrowError(
-            /Argument 'image' passed to 'rotateWithOffset' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/threshold.ts b/tfjs-master/tfjs-core/src/ops/image/threshold.ts
deleted file mode 100644
index 6efa58004..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/threshold.ts
+++ /dev/null
@@ -1,163 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * https://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { Tensor1D, Tensor3D } from '../../tensor';
-import { tensor1d } from '../tensor1d';
-import { TensorLike } from '../../types';
-import { op } from '../operation';
-import { cast } from '../cast';
-import { split } from '../split';
-import { bincount } from '../bincount';
-import { lessEqual } from '../less_equal';
-import { greater } from '../greater';
-import { sum } from '../sum';
-import { add } from '../add';
-import { mul } from '../mul';
-import { div } from '../div';
-import { sub } from '../sub';
-import { round } from '../round';
-import { where } from '../where';
-import { fill } from '../fill';
-import {slice} from '../slice';
-import { range } from '../range';
-import { tensor } from '../tensor';
-import * as util from '../../util';
-import { convertToTensor } from '../../tensor_util_env';
-
-/**
- * Performs image binarization with corresponding threshold
- * (depends on the method)value, which creates a binary image from a grayscale.
- * @param image 3d tensor of shape [imageHeight,imageWidth, depth],
- * where imageHeight and imageWidth must be positive.The image color
- * range should be [0, 255].
- * @param method Optional string from `'binary' | 'otsu'`
- * which specifies the method for thresholding. Defaults to 'binary'.
- * @param inverted Optional boolean whichspecifies
- * if colours should be inverted. Defaults to false.
- * @param threshValue Optional number which defines threshold value from 0 to 1.
- * Defaults to 0.5.
- * @return A 3d tensor of shape [imageHeight,imageWidth, depth], which
- * contains binarized image.
- */
-
-function threshold_(
-    image: Tensor3D | TensorLike,
-    method = 'binary',
-    inverted = false,
-    threshValue = 0.5
-): Tensor3D {
-    const $image = convertToTensor(image, 'image', 'threshold');
-
-    /* 0.2989, 0.5870, 0.1140 are represent luma coefficients in CCIR601.
-	Reference for converting between RGB and grayscale: https://en.wikipedia.org/wiki/Luma_%28video%29  */
-
-    const RED_INTENCITY_COEF = 0.2989;
-    const GREEN_INTENCITY_COEF = 0.5870;
-    const BLUE_INTENCITY_COEF = 0.1140;
-    const totalPixelsInImage = $image.shape[0] * $image.shape[1];
-
-    let $threshold = mul(tensor1d([threshValue]), 255);
-    let r, g, b, grayscale;
-
-    util.assert(
-        $image.rank === 3,
-        () => 'Error in threshold: image must be rank 3,' +
-            `but got rank ${$image.rank}.`);
-
-    util.assert(
-        $image.shape[2] === 3 || $image.shape[2]=== 1,
-        () => 'Error in threshold: ' +
-            'image color channel must be equal to 3 or 1' +
-            `but got ${$image.shape[2]}.`);
-
-    util.assert(
-      $image.dtype === 'int32' || $image.dtype === 'float32',
-      () => 'Error in dtype: image dtype must be int32 or float32,' +
-          `but got dtype ${$image.dtype}.`);
-
-    util.assert(
-      method === 'otsu' || method === 'binary',
-      () => `Method must be binary or otsu, but was ${method}`);
-
-    if ($image.shape[2] === 3) {
-        [r, g, b] = split($image, [1, 1, 1], -1);
-        const $r = mul(r,RED_INTENCITY_COEF);
-        const $g = mul(g,GREEN_INTENCITY_COEF);
-        const $b = mul(b,BLUE_INTENCITY_COEF);
-        grayscale = add(add($r, $g), $b);
-    } else {
-        grayscale = image;
-    }
-
-    if (method === 'otsu') {
-        const $histogram = bincount(cast(round(grayscale), 'int32') as Tensor1D,
-            tensor([]),
-            256);
-        $threshold = otsu($histogram, totalPixelsInImage);
-    }
-
-    const invCondition = inverted ?
-        lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);
-
-    const result = cast(mul(invCondition,255), 'int32');
-
-    return result as Tensor3D;
-}
-
-function otsu(histogram: Tensor1D, total: number):Tensor1D {
-
-    let bestThresh = tensor1d([-1]);
-    let bestInBetVar = tensor1d([0]);
-    let cInBetVar = tensor1d([0]);
-    let classFirst, classSecond, meanFirst,
-        meanSec, weightForeground, weightBack;
-
-    for (let index = 0; index < histogram.size-1; index++) {
-
-        classFirst = slice(histogram, 0, index + 1);
-
-        classSecond = slice(histogram,index + 1);
-
-        weightForeground = div(sum(classFirst),total);
-
-        weightBack = div(sum(classSecond),total);
-
-        const meanFirstDivA = sum(mul(classFirst, range(0, classFirst.size)));
-
-        meanFirst = div(meanFirstDivA, sum(classFirst) );
-
-        const meanSecFill = fill(classSecond.shape, classFirst.size);
-        const meanSecAdd = add(range(0,classSecond.size),meanSecFill);
-        const meanSecMul = mul(classSecond, (meanSecAdd));
-        meanSec = div(sum(meanSecMul), sum(classSecond));
-
-        const cInBetVarSubA = sub(meanFirst, meanSec);
-        const cInBetVarSubB = sub(meanFirst, meanSec);
-        const cInBetVarMul = mul(weightForeground, weightBack);
-        cInBetVar = mul(mul(cInBetVarMul,cInBetVarSubA), cInBetVarSubB);
-
-        const condition = greater(cInBetVar, bestInBetVar);
-
-        bestInBetVar = where(condition, cInBetVar, bestInBetVar);
-
-        bestThresh = where(condition, tensor1d([index]), bestThresh);
-
-    }
-    return bestThresh;
-}
-
-export const threshold = /* @__PURE__ */ op({ threshold_ });
diff --git a/tfjs-master/tfjs-core/src/ops/image/threshold_test.ts b/tfjs-master/tfjs-core/src/ops/image/threshold_test.ts
deleted file mode 100644
index ba5c9dafb..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/threshold_test.ts
+++ /dev/null
@@ -1,183 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysEqual} from '../../test_util';
-
-describeWithFlags('threshold', ALL_ENVS, () => {
-  let originalTimeout: number;
-  beforeEach(() => {
-    originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 80_000;
-  });
-  afterAll(() => {
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout;
-  });
-
-  it('default method binary, no arguments passed but input image', async () => {
-    const image: tf.Tensor3D = tf.tensor3d(
-        [144, 255, 51, 13, 32, 59, 222, 100, 51, 69, 71, 222], [2, 2, 3]);
-
-    const thresholded = tf.image.threshold(image);
-
-    expect(thresholded.shape).toEqual([2, 2, 1]);
-    expect(thresholded.dtype).toBe('int32');
-    expectArraysEqual(await thresholded.data(), [255, 0, 255, 0]);
-  });
-
-  it('default method binary, inverted: false, threshValue = 0.7', async () => {
-    const image: tf.Tensor3D = tf.tensor3d(
-        [
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 254, 253, 252, 235,
-          195, 252, 234, 192, 255, 254, 253, 255, 255, 255, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 253, 245, 237, 247, 198, 107,
-          239, 156, 43,  236, 139, 68,  246, 201, 139, 255, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 252, 239, 235, 241,
-          171, 122, 233, 125, 44,  250, 221, 183, 252, 240, 229, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-          255, 251, 233, 227, 229, 105, 42,  249, 221, 190, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-          255, 255, 255, 254, 247, 245, 243, 184, 144, 255, 254, 254, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 253, 244, 241, 249,
-          217, 206, 251, 231, 224, 251, 230, 223, 250, 225, 217, 250, 224, 215,
-          250, 228, 220, 247, 235, 231, 235, 234, 234, 255, 255, 255, 255, 253,
-          253, 252, 235, 230, 253, 243, 240, 251, 233, 226, 253, 242, 238, 254,
-          247, 244, 252, 235, 230, 242, 232, 229, 240, 240, 240, 255, 255, 255
-        ],
-        [7, 10, 3]);
-
-    const method = 'binary';
-    const inverted = false;
-    const threshValue = 0.7;
-    const output = tf.image.threshold(image, method, inverted, threshValue);
-
-    expect(output.shape).toEqual([7, 10, 1]);
-    expect(output.dtype).toBe('int32');
-    expectArraysEqual(await output.data(), [
-      255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-      0,   0,   255, 255, 255, 255, 255, 255, 255, 255, 0,   255, 255, 255,
-      255, 255, 255, 255, 255, 255, 0,   255, 255, 255, 255, 255, 255, 255,
-      255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-      255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255
-    ]);
-  });
-
-  it('default method binary, inverted: true, threshValue = 0.7', async () => {
-    const image: tf.Tensor3D = tf.tensor3d(
-        [
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 254, 253, 252, 235,
-          195, 252, 234, 192, 255, 254, 253, 255, 255, 255, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 253, 245, 237, 247, 198, 107,
-          239, 156, 43,  236, 139, 68,  246, 201, 139, 255, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 252, 239, 235, 241,
-          171, 122, 233, 125, 44,  250, 221, 183, 252, 240, 229, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-          255, 251, 233, 227, 229, 105, 42,  249, 221, 190, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-          255, 255, 255, 254, 247, 245, 243, 184, 144, 255, 254, 254, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 253, 244, 241, 249,
-          217, 206, 251, 231, 224, 251, 230, 223, 250, 225, 217, 250, 224, 215,
-          250, 228, 220, 247, 235, 231, 235, 234, 234, 255, 255, 255, 255, 253,
-          253, 252, 235, 230, 253, 243, 240, 251, 233, 226, 253, 242, 238, 254,
-          247, 244, 252, 235, 230, 242, 232, 229, 240, 240, 240, 255, 255, 255
-        ],
-        [7, 10, 3]);
-
-    const threshValue = 0.7;
-    const method = 'binary';
-    const inverted = true;
-    const output = tf.image.threshold(image, method, inverted, threshValue);
-
-    expect(output.shape).toEqual([7, 10, 1]);
-    expect(output.dtype).toBe('int32');
-    expectArraysEqual(await output.data(), [
-      0, 0, 0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 0, 0, 255, 255, 0,   0,
-      0, 0, 0, 0, 0, 0, 255, 0, 0, 0, 0, 0, 0, 0, 0,   0,   255, 0,
-      0, 0, 0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 0, 0, 0,   0,   0,   0,
-      0, 0, 0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 0, 0, 0,   0
-    ]);
-  });
-
-  it('method otsu', async () => {
-    const image: tf.Tensor3D = tf.tensor3d(
-        [
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 254, 253, 252, 235,
-          195, 252, 234, 192, 255, 254, 253, 255, 255, 255, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 253, 245, 237, 247, 198, 107,
-          239, 156, 43,  236, 139, 68,  246, 201, 139, 255, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 252, 239, 235, 241,
-          171, 122, 233, 125, 44,  250, 221, 183, 252, 240, 229, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-          255, 251, 233, 227, 229, 105, 42,  249, 221, 190, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-          255, 255, 255, 254, 247, 245, 243, 184, 144, 255, 254, 254, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 253, 244, 241, 249,
-          217, 206, 251, 231, 224, 251, 230, 223, 250, 225, 217, 250, 224, 215,
-          250, 228, 220, 247, 235, 231, 235, 234, 234, 255, 255, 255, 255, 253,
-          253, 252, 235, 230, 253, 243, 240, 251, 233, 226, 253, 242, 238, 254,
-          247, 244, 252, 235, 230, 242, 232, 229, 240, 240, 240, 255, 255, 255
-        ],
-        [7, 10, 3]);
-
-    const method = 'otsu';
-    const output = tf.image.threshold(image, method);
-
-    expect(output.shape).toEqual([7, 10, 1]);
-    expect(output.dtype).toBe('int32');
-    expectArraysEqual(await output.data(), [
-      255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0,
-      0,   0,   255, 255, 255, 255, 255, 255, 255, 0,   0,   255, 255, 255,
-      255, 255, 255, 255, 255, 255, 0,   255, 255, 255, 255, 255, 255, 255,
-      255, 255, 0,   255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-      255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255
-    ]);
-  });
-
-  it('method otsu, inverted = true', async () => {
-    const image: tf.Tensor3D = tf.tensor3d(
-        [
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 254, 253, 252, 235,
-          195, 252, 234, 192, 255, 254, 253, 255, 255, 255, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 253, 245, 237, 247, 198, 107,
-          239, 156, 43,  236, 139, 68,  246, 201, 139, 255, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 252, 239, 235, 241,
-          171, 122, 233, 125, 44,  250, 221, 183, 252, 240, 229, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-          255, 251, 233, 227, 229, 105, 42,  249, 221, 190, 255, 255, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
-          255, 255, 255, 254, 247, 245, 243, 184, 144, 255, 254, 254, 255, 255,
-          255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 253, 244, 241, 249,
-          217, 206, 251, 231, 224, 251, 230, 223, 250, 225, 217, 250, 224, 215,
-          250, 228, 220, 247, 235, 231, 235, 234, 234, 255, 255, 255, 255, 253,
-          253, 252, 235, 230, 253, 243, 240, 251, 233, 226, 253, 242, 238, 254,
-          247, 244, 252, 235, 230, 242, 232, 229, 240, 240, 240, 255, 255, 255
-        ],
-        [7, 10, 3]);
-    const method = 'otsu';
-    const inverted = true;
-    const output = tf.image.threshold(image, method, inverted);
-
-    expect(output.shape).toEqual([7, 10, 1]);
-    expect(output.dtype).toBe('int32');
-    expectArraysEqual(await output.data(), [
-      0, 0, 0, 0, 0, 0,   0,   0, 0,   0, 0, 0, 0, 255, 255, 255, 0,   0,
-      0, 0, 0, 0, 0, 255, 255, 0, 0,   0, 0, 0, 0, 0,   0,   0,   255, 0,
-      0, 0, 0, 0, 0, 0,   0,   0, 255, 0, 0, 0, 0, 0,   0,   0,   0,   0,
-      0, 0, 0, 0, 0, 0,   0,   0, 0,   0, 0, 0, 0, 0,   0,   0
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/image/transform.ts b/tfjs-master/tfjs-core/src/ops/image/transform.ts
deleted file mode 100644
index f0d44a89b..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/transform.ts
+++ /dev/null
@@ -1,94 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../../engine';
-import {Transform, TransformAttrs, TransformInputs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor2D, Tensor4D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import * as util from '../../util';
-
-import {op} from '../operation';
-
-/**
- * Applies the given transform(s) to the image(s).
- *
- * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
- * @param transforms Projective transform matrix/matrices. A tensor1d of length
- *     8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0,
- *     b1, b2, c0, c1], then it maps the output point (x, y) to a transformed
- *     input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k),
- *     where k = c0 x + c1 y + 1. The transforms are inverted compared to the
- *     transform mapping input points to output points.
- * @param interpolation Interpolation mode.
- *     Supported values: 'nearest', 'bilinear'. Default to 'nearest'.
- * @param fillMode Points outside the boundaries of the input are filled
- *     according to the given mode, one of 'constant', 'reflect', 'wrap',
- *     'nearest'. Default to 'constant'.
- *     'reflect': (d c b a | a b c d | d c b a ) The input is extended by
- *     reflecting about the edge of the last pixel.
- *     'constant': (k k k k | a b c d | k k k k) The input is extended by
- *     filling all values beyond the edge with the same constant value k.
- *     'wrap': (a b c d | a b c d | a b c d) The input is extended by
- *     wrapping around to the opposite edge.
- *     'nearest': (a a a a | a b c d | d d d d) The input is extended by
- *     the nearest pixel.
- * @param fillValue A float represents the value to be filled outside the
- *     boundaries when fillMode is 'constant'.
- * @param Output dimension after the transform, [height, width]. If undefined,
- *     output is the same size as input image.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
- */
-function transform_(
-    image: Tensor4D|TensorLike, transforms: Tensor2D|TensorLike,
-    interpolation: 'nearest'|'bilinear' = 'nearest',
-    fillMode: 'constant'|'reflect'|'wrap'|'nearest' = 'constant', fillValue = 0,
-    outputShape?: [number, number]): Tensor4D {
-  const $image = convertToTensor(image, 'image', 'transform', 'float32');
-  const $transforms =
-      convertToTensor(transforms, 'transforms', 'transform', 'float32');
-
-  util.assert(
-      $image.rank === 4,
-      () => 'Error in transform: image must be rank 4,' +
-          `but got rank ${$image.rank}.`);
-
-  util.assert(
-      $transforms.rank === 2 &&
-          ($transforms.shape[0] === $image.shape[0] ||
-           $transforms.shape[0] === 1) &&
-          $transforms.shape[1] === 8,
-      () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);
-
-  util.assert(
-      outputShape == null || outputShape.length === 2,
-      () =>
-          'Error in transform: outputShape must be [height, width] or null, ' +
-          `but got ${outputShape}.`);
-
-  const inputs: TransformInputs = {image: $image, transforms: $transforms};
-  const attrs:
-      TransformAttrs = {interpolation, fillMode, fillValue, outputShape};
-
-  return ENGINE.runKernel(
-      Transform, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const transform = /* @__PURE__ */ op({transform_});
diff --git a/tfjs-master/tfjs-core/src/ops/image/transform_test.ts b/tfjs-master/tfjs-core/src/ops/image/transform_test.ts
deleted file mode 100644
index 15a6524a3..000000000
--- a/tfjs-master/tfjs-core/src/ops/image/transform_test.ts
+++ /dev/null
@@ -1,128 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('image.transform', ALL_ENVS, () => {
-  it('extreme projective transform.', async () => {
-    const images = tf.tensor4d(
-        [1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1], [1, 4, 4, 1]);
-    const transform = tf.tensor2d([1, 0, 0, 0, 1, 0, -1, 0], [1, 8]);
-    const transformedImages =
-        tf.image.transform(images, transform, 'nearest', 'constant', 0).toInt();
-    const transformedImagesData = await transformedImages.data();
-
-    const expected = [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0];
-
-    expectArraysClose(transformedImagesData, expected);
-  });
-
-  it('static output shape.', async () => {
-    const images = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const transform = tf.randomUniform([1, 8], -1, 1);
-    const transformedImages = tf.image.transform(
-        images, transform as tf.Tensor2D, 'nearest', 'constant', 0, [3, 5]);
-
-    expectArraysClose(transformedImages.shape, [1, 3, 5, 1]);
-  });
-
-  it('fill=constant, interpolation=nearest.', async () => {
-    const images = tf.tensor4d(
-        [1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0], [1, 4, 4, 1]);
-    const transform = tf.tensor2d([0, 0.5, 1, -1, 2, 3, 0, 0], [1, 8]);
-    const transformedImages = tf.image.transform(images, transform);
-    const transformedImagesData = await transformedImages.data();
-    const expected = [1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0];
-
-    expectArraysClose(transformedImagesData, expected);
-  });
-
-  it('fill=constant, interpolation=bilinear.', async () => {
-    const images = tf.tensor4d(
-        [1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0], [1, 4, 4, 1]);
-    const transform = tf.tensor2d([0, 0.5, 1, -1, 2, 3, 0, 0], [1, 8]);
-    const transformedImages = tf.image.transform(images, transform, 'bilinear');
-    const transformedImagesData = await transformedImages.data();
-    const expected = [1, 0, 1, 1, 0, 0, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0];
-
-    expectArraysClose(transformedImagesData, expected);
-  });
-
-  it('fill=reflect, interpolation=bilinear.', async () => {
-    const images = tf.tensor4d(
-        [1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0], [1, 4, 4, 1]);
-    const transform = tf.tensor2d([0, 0.5, 1, -1, 2, 3, 0, 0], [1, 8]);
-    const transformedImages =
-        tf.image.transform(images, transform, 'bilinear', 'reflect');
-    const transformedImagesData = await transformedImages.data();
-
-    const expected =
-        [1, 0, 1, 1, 0.5, 0.5, 0.5, 0.5, 1, 0, 1, 0, 0, 0.5, 0.5, 0];
-
-    expectArraysClose(transformedImagesData, expected);
-  });
-
-  it('fill=wrap, interpolation=bilinear.', async () => {
-    const images = tf.tensor4d(
-        [1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0], [1, 4, 4, 1]);
-    const transform = tf.tensor2d([0, 0.5, 1, -1, 2, 3, 0, 0], [1, 8]);
-    const transformedImages =
-        tf.image.transform(images, transform, 'bilinear', 'wrap');
-    const transformedImagesData = await transformedImages.data();
-
-    const expected =
-        [1, 0, 1, 1, 0.5, 1, 0.5, 0.5, 1, 1, 0, 1, 0.5, 0.5, 0.5, 0.5];
-
-    expectArraysClose(transformedImagesData, expected);
-  });
-
-  it('fill=nearest, interpolation=bilinear.', async () => {
-    const images = tf.tensor4d(
-        [1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0], [1, 4, 4, 1]);
-    const transform = tf.tensor2d([0, 0.5, 1, -1, 2, 3, 0, 0], [1, 8]);
-    const transformedImages =
-        tf.image.transform(images, transform, 'bilinear', 'nearest');
-    const transformedImagesData = await transformedImages.data();
-
-    const expected = [1, 0, 1, 1, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0];
-
-    expectArraysClose(transformedImagesData, expected);
-  });
-
-  it('throws when input is int32.', async () => {
-    const images = tf.tensor4d(
-        [1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1], [1, 4, 4, 1],
-        'int32');
-    const transform = tf.tensor2d([1, 0, 0, 0, 1, 0, -1, 0], [1, 8]);
-    expect(
-        () => tf.image.transform(images, transform, 'nearest', 'constant', 0))
-        .toThrowError(/Argument 'image' passed to 'transform' must be float32/);
-  });
-
-  it('throws when transforms is int32.', async () => {
-    const images = tf.tensor4d(
-        [1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1],
-        [1, 4, 4, 1],
-    );
-    const transform = tf.tensor2d([1, 0, 0, 0, 1, 0, -1, 0], [1, 8], 'int32');
-    expect(
-        () => tf.image.transform(images, transform, 'nearest', 'constant', 0))
-        .toThrowError(
-            /Argument 'transforms' passed to 'transform' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/in_top_k.ts b/tfjs-master/tfjs-core/src/ops/in_top_k.ts
deleted file mode 100644
index dfb9df57f..000000000
--- a/tfjs-master/tfjs-core/src/ops/in_top_k.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {assert, assertShapesMatch, getTypedArrayFromDType} from '../util';
-import {tensor} from './tensor';
-
-/**
- * Returns whether the targets are in the top K predictions.
- *
- * ```js
- * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);
- * const targets = tf.tensor1d([2, 0]);
- * const precision = await tf.inTopKAsync(predictions, targets);
- * precision.print();
- * ```
- * @param predictions 2-D or higher `tf.Tensor` with last dimension being
- *     at least `k`.
- * @param targets 1-D or higher `tf.Tensor`.
- * @param k Optional Number of top elements to look at for computing precision,
- *     default to 1.
- *
- * @doc {heading: 'Operations', subheading: 'Evaluation'}
- */
-async function inTopKAsync_<T extends Tensor, U extends Tensor>(
-    predictions: T|TensorLike, targets: U|TensorLike, k = 1): Promise<U> {
-  const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');
-  const $targets = convertToTensor(targets, 'targets', 'inTopK');
-
-  assert(
-      $predictions.rank > 1,
-      () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +
-          `but got ${$predictions.rank}`);
-  assert(
-      $predictions.rank - 1 === $targets.rank,
-      () => `predictions rank should be 1 larger than ` +
-          `targets rank, but got predictions rank ` +
-          `${$predictions.rank} and targets rank ${$targets.rank}`);
-  assertShapesMatch(
-      $predictions.shape.slice(0, $predictions.shape.length - 1),
-      $targets.shape,
-      `predictions's shape should be align with the targets' shape, ` +
-          'except the last dimension.');
-  const lastDim = $predictions.shape[$predictions.shape.length - 1];
-  assert(
-      k > 0 && k <= lastDim,
-      () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +
-          `dimension (${lastDim}), but got ${k}`);
-
-  const predictionsVals = await $predictions.data();
-  const targetsVals = await $targets.data();
-
-  // Reshape predictionsVals into a 2d tensor [batch, lastDim]
-  // and look up topK along lastDim.
-  const [batch, size] = [predictionsVals.length / lastDim, lastDim];
-  const precision = getTypedArrayFromDType('bool', batch);
-
-  for (let b = 0; b < batch; b++) {
-    const offset = b * size;
-    const vals = predictionsVals.subarray(offset, offset + size);
-    const valAndInd: Array<{value: number, index: number}> = [];
-    for (let i = 0; i < vals.length; i++) {
-      valAndInd.push({value: vals[i], index: i});
-    }
-    valAndInd.sort((a, b) => b.value - a.value);
-
-    precision[b] = 0;
-    for (let i = 0; i < k; i++) {
-      if (valAndInd[i].index === targetsVals[b]) {
-        precision[b] = 1;
-        break;
-      }
-    }
-  }
-
-  if (predictions !== $predictions) {
-    $predictions.dispose();
-  }
-  if (targets !== $targets) {
-    $targets.dispose();
-  }
-
-  // Output precision has the same shape as targets.
-  return tensor(precision, $targets.shape, 'bool') as U;
-}
-
-export const inTopKAsync = inTopKAsync_;
diff --git a/tfjs-master/tfjs-core/src/ops/in_top_k_test.ts b/tfjs-master/tfjs-core/src/ops/in_top_k_test.ts
deleted file mode 100644
index b23f338be..000000000
--- a/tfjs-master/tfjs-core/src/ops/in_top_k_test.ts
+++ /dev/null
@@ -1,152 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-import {tensor1d, tensor2d, tensor3d} from './ops';
-
-describeWithFlags('inTopKAsync', ALL_ENVS, async () => {
-  it('predictions 2d array, targets 1d array, with default k', async () => {
-    const predictions = tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);
-    const targets = tensor1d([2, 0]);
-    const precision = await tf.inTopKAsync(predictions, targets);
-    expect(precision.shape).toEqual([2]);
-    expect(precision.dtype).toBe('bool');
-    expectArraysClose(await precision.data(), [1, 0]);
-  });
-
-  it('predictions 2d array, targets 1d array, with k=2', async () => {
-    const predictions = tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);
-    const targets = tensor1d([2, 0]);
-    const k = 2;
-    const precision = await tf.inTopKAsync(predictions, targets, k);
-    expect(precision.shape).toEqual([2]);
-    expect(precision.dtype).toBe('bool');
-    expectArraysClose(await precision.data(), [1, 1]);
-  });
-
-  it('predictions 3d array, targets 2d array, with default k', async () => {
-    const predictions =
-        tensor3d([[[1, 5, 2], [4, 3, 6]], [[3, 2, 1], [1, 2, 3]]]);
-    const targets = tensor2d([[1, 2], [0, 1]]);
-    const precision = await tf.inTopKAsync(predictions, targets);
-    expect(precision.shape).toEqual([2, 2]);
-    expect(precision.dtype).toBe('bool');
-    expectArraysClose(await precision.data(), [1, 1, 1, 0]);
-  });
-
-  it('predictions 3d array, targets 2d array, with k=2', async () => {
-    const predictions =
-        tensor3d([[[1, 5, 2], [4, 3, 6]], [[3, 2, 1], [1, 2, 3]]]);
-    const targets = tensor2d([[1, 2], [0, 1]]);
-    const k = 2;
-    const precision = await tf.inTopKAsync(predictions, targets, k);
-    expect(precision.shape).toEqual([2, 2]);
-    expect(precision.dtype).toBe('bool');
-    expectArraysClose(await precision.data(), [1, 1, 1, 1]);
-  });
-
-  it('lower-index element count first, with default k', async () => {
-    const predictions = tensor2d([[1, 2, 2, 1]]);
-
-    const targets1 = tensor1d([1]);
-    const precision1 = await tf.inTopKAsync(predictions, targets1);
-    expect(precision1.shape).toEqual([1]);
-    expect(precision1.dtype).toBe('bool');
-    expectArraysClose(await precision1.data(), [1]);
-
-    const targets2 = tensor1d([2]);
-    const precision2 = await tf.inTopKAsync(predictions, targets2);
-    expect(precision2.shape).toEqual([1]);
-    expect(precision2.dtype).toBe('bool');
-    expectArraysClose(await precision2.data(), [0]);
-  });
-
-  it('accept tensor-like object, with default k', async () => {
-    const predictions = [[20, 10, 40, 30], [30, 50, -20, 10]];
-    const targets = [2, 0];
-    const precision = await tf.inTopKAsync(predictions, targets);
-    expect(precision.shape).toEqual([2]);
-    expect(precision.dtype).toBe('bool');
-    expectArraysClose(await precision.data(), [1, 0]);
-  });
-
-  it('doesnt leak tensors with tensor-like objects', async () => {
-    const numTensors = tf.memory().numTensors;
-
-    const predictions = [[20, 10, 40, 30], [30, 50, -20, 10]];
-    const targets = [2, 0];
-    const precision = await tf.inTopKAsync(predictions, targets);
-    precision.dispose();
-
-    expect(tf.memory().numTensors).toBe(numTensors);
-  });
-
-  it('throws when predictions_rank <2', async () => {
-    const predictions = tensor1d([20, 10, 40, 30]);
-    const targets = [2];
-
-    // expect(...).toThrowError() does not support async functions.
-    // See https://github.com/jasmine/jasmine/issues/1410
-    try {
-      await tf.inTopKAsync(predictions, targets);
-      throw new Error('The line above should have thrown an error');
-    } catch (ex) {
-      expect(ex.message)
-          .toEqual(
-              'inTopK() expects the predictions to ' +
-              'be of rank 2 or higher, but got 1');
-    }
-  });
-
-  it('throws when prediction.rank != targets.rank + 1', async () => {
-    const predictions = tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);
-    const targets = tensor2d([[0], [0]]);
-
-    // expect(...).toThrowError() does not support async functions.
-    // See https://github.com/jasmine/jasmine/issues/1410
-    try {
-      await tf.inTopKAsync(predictions, targets);
-      throw new Error('The line above should have thrown an error');
-    } catch (ex) {
-      expect(ex.message)
-          .toEqual(
-              'predictions rank should be 1 larger than targets rank,' +
-              ' but got predictions rank 2 and targets rank 2');
-    }
-  });
-
-  it('throws when k > size of last dimension of predictions', async () => {
-    const predictions = tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);
-    const targets = tensor1d([2, 0]);
-    const k = 5;
-
-    // expect(...).toThrowError() does not support async functions.
-    // See https://github.com/jasmine/jasmine/issues/1410
-    try {
-      await tf.inTopKAsync(predictions, targets, k);
-      throw new Error('The line above should have thrown an error');
-    } catch (ex) {
-      expect(ex.message)
-          .toEqual(
-              '\'k\' passed to inTopK() must be > 0 && <= the predictions ' +
-              'last dimension (4), but got 5');
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/is_finite.ts b/tfjs-master/tfjs-core/src/ops/is_finite.ts
deleted file mode 100644
index 9cfd66561..000000000
--- a/tfjs-master/tfjs-core/src/ops/is_finite.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {IsFinite, IsFiniteInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Returns which elements of x are finite.
- *
- * ```js
- * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
- *
- * x.isFinite().print();  // or tf.isNaN(x)
- * ```
- * @param x The input Tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function isFinite_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'isFinite');
-
-  const inputs: IsFiniteInputs = {x: $x};
-
-  return ENGINE.runKernel(IsFinite, inputs as unknown as NamedTensorMap);
-}
-export const isFinite = /* @__PURE__ */ op({isFinite_});
diff --git a/tfjs-master/tfjs-core/src/ops/is_finite_test.ts b/tfjs-master/tfjs-core/src/ops/is_finite_test.ts
deleted file mode 100644
index 8d09349ad..000000000
--- a/tfjs-master/tfjs-core/src/ops/is_finite_test.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('isFinite', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
-    const r = tf.isFinite(a);
-    expect(r.dtype).toEqual('bool');
-    expectArraysClose(await r.data(), [0, 0, 0, 1, 1]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(NaN);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.isFinite(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
-    const dy = tf.tensor1d([1, 1, 1, 1, 1]);
-
-    const gradients = tf.grad(a => tf.isFinite(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0, 0]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([NaN, Infinity, -Infinity, 0], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.isFinite(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.isFinite({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'isFinite' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.isFinite([NaN, Infinity, -Infinity, 0, 1]);
-    expectArraysClose(await r.data(), [0, 0, 0, 1, 1]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.isFinite('q'))
-        .toThrowError(/Argument 'x' passed to 'isFinite' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/is_inf.ts b/tfjs-master/tfjs-core/src/ops/is_inf.ts
deleted file mode 100644
index 21230c07a..000000000
--- a/tfjs-master/tfjs-core/src/ops/is_inf.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {IsInf, IsInfInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Returns which elements of x are Infinity or -Infinity.
- *
- * ```js
- * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
- *
- * x.isInf().print();  // or tf.isNaN(x)
- * ```
- * @param x The input Tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function isInf_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'isInf');
-
-  const inputs: IsInfInputs = {x: $x};
-
-  return ENGINE.runKernel(IsInf, inputs as unknown as NamedTensorMap);
-}
-export const isInf = /* @__PURE__ */ op({isInf_});
diff --git a/tfjs-master/tfjs-core/src/ops/is_inf_test.ts b/tfjs-master/tfjs-core/src/ops/is_inf_test.ts
deleted file mode 100644
index 07e948ab7..000000000
--- a/tfjs-master/tfjs-core/src/ops/is_inf_test.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('isInf', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
-    const r = tf.isInf(a);
-    expect(r.dtype).toEqual('bool');
-    expectArraysClose(await r.data(), [0, 1, 1, 0, 0]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(NaN);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.isInf(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
-    const dy = tf.tensor1d([1, 1, 1, 1, 1]);
-
-    const gradients = tf.grad(a => tf.isInf(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0, 0]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([NaN, Infinity, -Infinity, 0], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.isInf(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.isInf({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'isInf' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.isInf([NaN, Infinity, -Infinity, 0, 1]);
-    expectArraysClose(await r.data(), [0, 1, 1, 0, 0]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.isInf('q'))
-        .toThrowError(/Argument 'x' passed to 'isInf' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/is_nan.ts b/tfjs-master/tfjs-core/src/ops/is_nan.ts
deleted file mode 100644
index 42f83db4b..000000000
--- a/tfjs-master/tfjs-core/src/ops/is_nan.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {IsNan, IsNanInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Returns which elements of x are NaN.
- *
- * ```js
- * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
- *
- * x.isNaN().print();  // or tf.isNaN(x)
- * ```
- * @param x The input Tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function isNaN_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'isNaN');
-  const inputs: IsNanInputs = {x: $x};
-
-  return ENGINE.runKernel(IsNan, inputs as unknown as NamedTensorMap);
-}
-export const isNaN = /* @__PURE__ */ op({isNaN_});
diff --git a/tfjs-master/tfjs-core/src/ops/is_nan_test.ts b/tfjs-master/tfjs-core/src/ops/is_nan_test.ts
deleted file mode 100644
index db6144ab0..000000000
--- a/tfjs-master/tfjs-core/src/ops/is_nan_test.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('isNaN', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
-    const r = tf.isNaN(a);
-    expect(r.dtype).toEqual('bool');
-    expectArraysClose(await r.data(), [1, 0, 0, 0, 0]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(NaN);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.isNaN(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
-    const dy = tf.tensor1d([1, 1, 1, 1, 1]);
-
-    const gradients = tf.grad(a => tf.isNaN(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0, 0]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([NaN, Infinity, -Infinity, 0], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.isNaN(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.isNaN({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'isNaN' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.isNaN([NaN, Infinity, -Infinity, 0, 1]);
-    expectArraysClose(await r.data(), [1, 0, 0, 0, 0]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.isNaN('q'))
-        .toThrowError(/Argument 'x' passed to 'isNaN' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/leaky_relu.ts b/tfjs-master/tfjs-core/src/ops/leaky_relu.ts
deleted file mode 100644
index ce0a13219..000000000
--- a/tfjs-master/tfjs-core/src/ops/leaky_relu.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {LeakyRelu, LeakyReluAttrs, LeakyReluInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes leaky rectified linear element-wise.
- *
- * See
- * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](
- *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)
- *
- * ```js
- * const x = tf.tensor1d([-1, 2, -3, 4]);
- *
- * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)
- * ```
- * @param x The input tensor.
- * @param alpha The scaling factor for negative values, defaults to 0.2.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function leakyRelu_<T extends Tensor>(x: T|TensorLike, alpha = 0.2): T {
-  const $x = convertToTensor(x, 'x', 'leakyRelu');
-
-  const inputs: LeakyReluInputs = {x: $x};
-  const attrs: LeakyReluAttrs = {alpha};
-
-  return ENGINE.runKernel(
-      LeakyRelu, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const leakyRelu = /* @__PURE__ */ op({leakyRelu_});
diff --git a/tfjs-master/tfjs-core/src/ops/leaky_relu_test.ts b/tfjs-master/tfjs-core/src/ops/leaky_relu_test.ts
deleted file mode 100644
index 36018b9b7..000000000
--- a/tfjs-master/tfjs-core/src/ops/leaky_relu_test.ts
+++ /dev/null
@@ -1,125 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {backend} from '../index';
-
-describeWithFlags('leakyrelu', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([0, 1, -2]);
-    const result = tf.leakyRelu(a);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0, 1, -0.4]);
-  });
-
-  it('int32', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const a = tf.tensor1d([0, 1, -2], 'int32');
-      const result = tf.leakyRelu(a);
-
-      expect(result.shape).toEqual(a.shape);
-      expect(result.dtype).toEqual('float32');
-      expectArraysClose(await result.data(), [0, 1, -0.4]);
-    }
-  });
-
-  it('propagates NaN', async () => {
-    const a = tf.tensor1d([0, 1, NaN]);
-    const result = tf.leakyRelu(a);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [0, 1, NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(-4);
-    const dy = tf.scalar(8);
-    const alpha = 0.1;
-
-    const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * alpha]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(-4);
-    const dy = tf.scalar(8);
-    const alpha = 0.1;
-
-    const gradients =
-        tf.grad((a) => tf.leakyRelu(a.clone(), alpha).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * alpha]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [1, -1, 0.1];
-    const dyValues = [1, 2, 3];
-    const alpha = 0.1;
-
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-
-    expectArraysClose(await gradients.data(), [1, 2 * alpha, 3]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [1, -1, 0.1, 0.5];
-    const dyValues = [1, 2, 3, 4];
-    const alpha = 0.1;
-
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-
-    expectArraysClose(await gradients.data(), [1, 2 * alpha, 3, 4]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.leakyRelu({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'leakyRelu' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.leakyRelu([0, 1, -2]);
-
-    expect(result.shape).toEqual([3]);
-    expectArraysClose(await result.data(), [0, 1, -0.4]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.leakyRelu('q'))
-        .toThrowError(/Argument 'x' passed to 'leakyRelu' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/less.ts b/tfjs-master/tfjs-core/src/ops/less.ts
deleted file mode 100644
index 5910b69e9..000000000
--- a/tfjs-master/tfjs-core/src/ops/less.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Less, LessInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns the truth value of (a < b) element-wise. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3]);
- * const b = tf.tensor1d([2, 2, 2]);
- *
- * a.less(b).print();
- * ```
- * @param a The first input tensor.
- * @param b The second input tensor. Must have the same dtype as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function less_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'less', 'string_or_numeric');
-  let $b = convertToTensor(b, 'b', 'less', 'string_or_numeric');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: LessInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Less, inputs as unknown as NamedTensorMap);
-}
-
-export const less = /* @__PURE__ */ op({less_});
diff --git a/tfjs-master/tfjs-core/src/ops/less_equal.ts b/tfjs-master/tfjs-core/src/ops/less_equal.ts
deleted file mode 100644
index 423e3f9ab..000000000
--- a/tfjs-master/tfjs-core/src/ops/less_equal.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {LessEqual, LessEqualInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns the truth value of (a <= b) element-wise. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3]);
- * const b = tf.tensor1d([2, 2, 2]);
- *
- * a.lessEqual(b).print();
- * ```
- *
- * @param a The first input tensor.
- * @param b The second input tensor. Must have the same dtype as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function lessEqual_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'lessEqual', 'string_or_numeric');
-  let $b = convertToTensor(b, 'b', 'lessEqual', 'string_or_numeric');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: LessEqualInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(LessEqual, inputs as unknown as NamedTensorMap);
-}
-
-export const lessEqual = /* @__PURE__ */ op({lessEqual_});
diff --git a/tfjs-master/tfjs-core/src/ops/less_equal_test.ts b/tfjs-master/tfjs-core/src/ops/less_equal_test.ts
deleted file mode 100644
index fba5e5038..000000000
--- a/tfjs-master/tfjs-core/src/ops/less_equal_test.ts
+++ /dev/null
@@ -1,354 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('lessEqual', ALL_ENVS, () => {
-  it('Tensor1D - int32', async () => {
-    let a = tf.tensor1d([1, 4, 5], 'int32');
-    let b = tf.tensor1d([2, 3, 5], 'int32');
-    let res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1]);
-
-    a = tf.tensor1d([2, 2, 2], 'int32');
-    b = tf.tensor1d([2, 2, 2], 'int32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1]);
-
-    a = tf.tensor1d([0, 0], 'int32');
-    b = tf.tensor1d([3, 3], 'int32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1]);
-  });
-  it('Tensor1D - float32', async () => {
-    let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
-    let res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1]);
-
-    a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1]);
-
-    a = tf.tensor1d([0.45, 0.123], 'float32');
-    b = tf.tensor1d([3.123, 3.321], 'float32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const a = [1.1, 4.1, 5];
-    const b = [2.2, 3.2, 5];
-
-    let res =
-        tf.lessEqual(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [1, 0, 1]);
-
-    res = tf.lessEqual(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [1, 0, 0]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [1.1, 4.1, 5.1];
-    const b = [2.2, 3.2, 5.1];
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1]);
-  });
-  it('TensorLike Chained', async () => {
-    const a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    const b = [2.2, 3.2, 5.1];
-    const res = a.lessEqual(b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1]);
-  });
-  it('mismatched Tensor1D shapes - int32', () => {
-    const a = tf.tensor1d([1, 2], 'int32');
-    const b = tf.tensor1d([1, 2, 3], 'int32');
-    const f = () => {
-      tf.lessEqual(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('mismatched Tensor1D shapes - float32', () => {
-    const a = tf.tensor1d([1.1, 2.1], 'float32');
-    const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
-    const f = () => {
-      tf.lessEqual(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('NaNs in Tensor1D - float32', async () => {
-    const a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
-    const b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0]);
-  });
-
-  // Tensor2D:
-  it('Tensor2D - int32', async () => {
-    let a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
-    let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
-    let res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('Tensor2D - float32', async () => {
-    let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
-    let b =
-        tf.tensor2d([[2.1, 3.1, 6.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
-    let res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor2D shapes - int32', async () => {
-    const a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
-    const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor2D shapes - float32', async () => {
-    const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
-    const b =
-        tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 1, 1, 1, 1]);
-  });
-  it('NaNs in Tensor2D - float32', async () => {
-    const a = tf.tensor2d([[1.1, NaN], [0.1, NaN]], [2, 2], 'float32');
-    const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 1, 0]);
-  });
-
-  // Tensor3D:
-  it('Tensor3D - int32', async () => {
-    let a =
-        tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
-    let b =
-        tf.tensor3d([[[2], [3], [6]], [[7], [10], [11]]], [2, 3, 1], 'int32');
-    let res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1, 1, 1]);
-  });
-  it('Tensor3D - float32', async () => {
-    let a = tf.tensor3d(
-        [[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
-    let b = tf.tensor3d(
-        [[[2.1], [3.1], [6.1]], [[7.1], [10.1], [11.1]]], [2, 3, 1], 'float32');
-    let res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.2]]], [2, 3, 1], 'float32');
-    b = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1, 1, 0]);
-  });
-  it('broadcasting Tensor3D shapes - int32', async () => {
-    const a = tf.tensor3d(
-        [[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2],
-        'int32');
-    const b =
-        tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0]);
-  });
-  it('broadcasting Tensor3D float32', async () => {
-    const a = tf.tensor3d(
-        [
-          [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
-          [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
-        ],
-        [2, 3, 2], 'float32');
-    const b = tf.tensor3d(
-        [[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0]);
-  });
-  it('NaNs in Tensor3D - float32', async () => {
-    const a = tf.tensor3d(
-        [[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
-    const b = tf.tensor3d(
-        [[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 1, 1, 1, 0]);
-  });
-
-  // Tensor4D:
-  it('Tensor4D - int32', async () => {
-    let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
-    let b = tf.tensor4d([2, 3, 6, 7], [2, 2, 1, 1], 'int32');
-    let res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0]);
-
-    a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-
-    a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('Tensor4D - float32', async () => {
-    let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
-    let b = tf.tensor4d([2.1, 3.1, 6.1, 7.1], [2, 2, 1, 1], 'float32');
-    let res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0]);
-
-    a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-
-    a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
-    res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor4D shapes - int32', async () => {
-    const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
-    const b = tf.tensor4d(
-        [[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1, 1, 1, 0, 0]);
-  });
-  it('broadcasting Tensor4D shapes - float32', async () => {
-    const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d(
-        [[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]],
-        [2, 2, 1, 2], 'float32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1, 1, 1, 0, 0]);
-  });
-  it('NaNs in Tensor4D - float32', async () => {
-    const a = tf.tensor4d([1.1, NaN, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 1, 0]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.lessEqual({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'lessEqual' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.lessEqual(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'lessEqual' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 4, 5];
-    const b = [2, 3, 5];
-    const res = tf.lessEqual(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1]);
-  });
-
-  it('should support string comparison', async () => {
-    const tensorA = tf.tensor('a', [], 'string');
-    const tensorB = tf.tensor(['a', 'b', ''], [3], 'string');
-    const result = await tf.lessEqual(tensorA, tensorB);
-
-    expectArraysEqual(result.shape, [3]);
-    expectArraysEqual(await result.data(), [1, 1, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/less_test.ts b/tfjs-master/tfjs-core/src/ops/less_test.ts
deleted file mode 100644
index a67f72e0a..000000000
--- a/tfjs-master/tfjs-core/src/ops/less_test.ts
+++ /dev/null
@@ -1,353 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('less', ALL_ENVS, () => {
-  it('Tensor1D - int32', async () => {
-    let a = tf.tensor1d([1, 4, 5], 'int32');
-    let b = tf.tensor1d([2, 3, 5], 'int32');
-    let res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0]);
-
-    a = tf.tensor1d([2, 2, 2], 'int32');
-    b = tf.tensor1d([2, 2, 2], 'int32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0]);
-
-    a = tf.tensor1d([0, 0], 'int32');
-    b = tf.tensor1d([3, 3], 'int32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1]);
-  });
-  it('Tensor1D - float32', async () => {
-    let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
-    let res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0]);
-
-    a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0]);
-
-    a = tf.tensor1d([0.45, 0.123], 'float32');
-    b = tf.tensor1d([3.123, 3.321], 'float32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1]);
-  });
-  it('TensorLike', async () => {
-    const a = [1.1, 4.1, 5.1];
-    const b = [2.2, 3.2, 5.1];
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0]);
-  });
-  it('TensorLike Chained', async () => {
-    const a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    const b = [2.2, 3.2, 5.1];
-    const res = a.less(b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const a = [1.1, 4.1, 5.2];
-    const b = [2.2, 3.2, 5.1];
-
-    let res = tf.less(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [1, 0, 0]);
-
-    res = tf.less(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [0, 0, 0]);
-  });
-
-  it('mismatched Tensor1D shapes - int32', () => {
-    const a = tf.tensor1d([1, 2], 'int32');
-    const b = tf.tensor1d([1, 2, 3], 'int32');
-    const f = () => {
-      tf.less(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('mismatched Tensor1D shapes - float32', () => {
-    const a = tf.tensor1d([1.1, 2.1], 'float32');
-    const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
-    const f = () => {
-      tf.less(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('NaNs in Tensor1D - float32', async () => {
-    const a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
-    const b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0]);
-  });
-
-  // Tensor2D:
-  it('Tensor2D - int32', async () => {
-    let a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
-    let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
-    let res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-  });
-  it('Tensor2D - float32', async () => {
-    let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
-    let b =
-        tf.tensor2d([[2.1, 3.1, 6.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
-    let res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor2D shapes - int32', async () => {
-    const a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
-    const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 1, 0, 1, 1]);
-  });
-  it('broadcasting Tensor2D shapes - float32', async () => {
-    const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
-    const b =
-        tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 1, 0, 1, 1]);
-  });
-  it('NaNs in Tensor2D - float32', async () => {
-    const a = tf.tensor2d([[1.1, NaN], [0.1, NaN]], [2, 2], 'float32');
-    const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 1, 0]);
-  });
-
-  // Tensor3D:
-  it('Tensor3D - int32', async () => {
-    let a =
-        tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
-    let b =
-        tf.tensor3d([[[2], [3], [6]], [[7], [10], [11]]], [2, 3, 1], 'int32');
-    let res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0, 0, 0]);
-  });
-  it('Tensor3D - float32', async () => {
-    let a = tf.tensor3d(
-        [[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
-    let b = tf.tensor3d(
-        [[[2.1], [3.1], [6.1]], [[7.1], [10.1], [11.1]]], [2, 3, 1], 'float32');
-    let res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.0]]], [2, 3, 1], 'float32');
-    b = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0, 0, 1]);
-  });
-  it('broadcasting Tensor3D shapes - int32', async () => {
-    const a = tf.tensor3d(
-        [[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2],
-        'int32');
-    const b =
-        tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0]);
-  });
-  it('broadcasting Tensor3D float32', async () => {
-    const a = tf.tensor3d(
-        [
-          [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
-          [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
-        ],
-        [2, 3, 2], 'float32');
-    const b = tf.tensor3d(
-        [[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0]);
-  });
-  it('NaNs in Tensor3D - float32', async () => {
-    const a = tf.tensor3d(
-        [[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
-    const b = tf.tensor3d(
-        [[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 1, 0, 0]);
-  });
-
-  // Tensor4D:
-  it('Tensor4D - int32', async () => {
-    let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
-    let b = tf.tensor4d([2, 3, 6, 7], [2, 2, 1, 1], 'int32');
-    let res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0]);
-
-    a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-
-    a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('Tensor4D - float32', async () => {
-    let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
-    let b = tf.tensor4d([2.1, 3.1, 6.1, 7.1], [2, 2, 1, 1], 'float32');
-    let res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 1, 0]);
-
-    a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 0, 0]);
-
-    a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
-    res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor4D shapes - int32', async () => {
-    const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
-    const b = tf.tensor4d(
-        [[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 1, 1, 0, 1, 0, 0]);
-  });
-  it('broadcasting Tensor4D shapes - float32', async () => {
-    const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d(
-        [[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]],
-        [2, 2, 1, 2], 'float32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 1, 1, 1, 0, 1, 0, 0]);
-  });
-  it('NaNs in Tensor4D - float32', async () => {
-    const a = tf.tensor4d([1.1, NaN, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
-    const res = tf.less(a, b);
-
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [0, 0, 1, 0]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.less({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'less' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.less(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'less' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 4, 5];
-    const b = [2, 3, 5];
-
-    const res = tf.less(a, b);
-    expect(res.dtype).toBe('bool');
-    expectArraysClose(await res.data(), [1, 0, 0]);
-  });
-
-  it('should support string comparison', async () => {
-    const tensorA = tf.tensor('a', [], 'string');
-    const tensorB = tf.tensor(['a', 'b', ''], [3], 'string');
-    const result = await tf.less(tensorA, tensorB);
-
-    expectArraysEqual(result.shape, [3]);
-    expectArraysEqual(await result.data(), [0, 1, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/linalg/band_part.ts b/tfjs-master/tfjs-core/src/ops/linalg/band_part.ts
deleted file mode 100644
index f58ea2b49..000000000
--- a/tfjs-master/tfjs-core/src/ops/linalg/band_part.ts
+++ /dev/null
@@ -1,141 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Scalar, Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {assert} from '../../util';
-
-import {greaterEqual} from '../greater_equal';
-import {less} from '../less';
-import {lessEqual} from '../less_equal';
-import {logicalAnd} from '../logical_and';
-import {minimum} from '../minimum';
-import {neg} from '../neg';
-import {op} from '../operation';
-import {range} from '../range';
-import {reshape} from '../reshape';
-import {stack} from '../stack';
-import {sub} from '../sub';
-import {unstack} from '../unstack';
-import {where} from '../where';
-import {zeros} from '../zeros';
-
-/**
- * Copy a tensor setting everything outside a central band in each innermost
- * matrix to zero.
- *
- * The band part is computed as follows: Assume input has `k` dimensions
- * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where
- * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
- * The indicator function
- * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)`
- * `&& (num_upper < 0 || (n-m) <= num_upper)`
- *
- * ```js
- * const x = tf.tensor2d([[ 0,  1,  2, 3],
- *                        [-1,  0,  1, 2],
- *                        [-2, -1,  0, 1],
- *                        [-3, -2, -1, 0]]);
- * let y = tf.linalg.bandPart(x, 1, -1);
- * y.print(); // [[ 0,  1,  2, 3],
- *            //  [-1,  0,  1, 2],
- *            //  [ 0, -1,  0, 1],
- *            //  [ 0, 0 , -1, 0]]
- * let z = tf.linalg.bandPart(x, 2, 1);
- * z.print(); // [[ 0,  1,  0, 0],
- *            //  [-1,  0,  1, 0],
- *            //  [-2, -1,  0, 1],
- *            //  [ 0, -2, -1, 0]]
- * ```
- *
- * @param x Rank `k` tensor
- * @param numLower Number of subdiagonals to keep.
- *   If negative, keep entire lower triangle.
- * @param numUpper Number of subdiagonals to keep.
- *   If negative, keep entire upper triangle.
- * @returns Rank `k` tensor of the same shape as input.
- *   The extracted banded tensor.
- *
- * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}
- */
-function bandPart_<T extends Tensor>(
-    a: T|TensorLike, numLower: number|Scalar, numUpper: number|Scalar): T {
-  const $a = convertToTensor(a, 'a', 'bandPart');
-  assert(
-      $a.rank >= 2,
-      () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
-
-  const shape = $a.shape;
-  const [M, N] = $a.shape.slice(-2);
-
-  let $numLower: Scalar;
-  let $numUpper: Scalar;
-  if (typeof numLower === 'number') {
-    assert(
-        numLower % 1 === 0,
-        () => `bandPart(): numLower must be an integer, got ${numLower}.`);
-    assert(
-        numLower <= M,
-        () => `bandPart(): numLower (${numLower})` +
-            ` must not be greater than the number of rows (${M}).`);
-    $numLower =
-        convertToTensor(numLower < 0 ? M : numLower, 'numLower', 'bandPart') as
-        Scalar;
-  } else {
-    assert(
-        numLower.dtype === 'int32',
-        () => `bandPart(): numLower's dtype must be an int32.`);
-    // If numLower is a Scalar, checking `numLower <= M` could hurt performance,
-    // but minimum(numLower, M) could avoid unexpected results.
-    $numLower = where(less(numLower, 0), M, minimum(numLower, M)) as Scalar;
-  }
-
-  if (typeof numUpper === 'number') {
-    assert(
-        numUpper % 1 === 0,
-        () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
-    assert(
-        numUpper <= N,
-        () => `bandPart(): numUpper (${numUpper})` +
-            ` must not be greater than the number of columns (${N}).`);
-    $numUpper =
-        convertToTensor(numUpper < 0 ? N : numUpper, 'numUpper', 'bandPart') as
-        Scalar;
-  } else {
-    assert(
-        numUpper.dtype === 'int32',
-        () => `bandPart(): numUpper's dtype must be an int32.`);
-    $numUpper = where(less(numUpper, 0), N, minimum(numUpper, N)) as Scalar;
-  }
-
-  const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);
-  const j = range(0, N, 1, 'int32');
-  const ij = sub(i, j);
-
-  const inBand =
-      logicalAnd(lessEqual(ij, $numLower), greaterEqual(ij, neg($numUpper)));
-
-  const zero = zeros([M, N], $a.dtype);
-
-  return reshape(
-             stack(unstack(reshape($a, [-1, M, N]))
-                       .map(mat => where(inBand, mat, zero))),
-             shape) as T;
-}
-
-export const bandPart = /* @__PURE__ */ op({bandPart_});
diff --git a/tfjs-master/tfjs-core/src/ops/linalg/band_part_test.ts b/tfjs-master/tfjs-core/src/ops/linalg/band_part_test.ts
deleted file mode 100644
index fc0021453..000000000
--- a/tfjs-master/tfjs-core/src/ops/linalg/band_part_test.ts
+++ /dev/null
@@ -1,190 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {Tensor2D, Tensor3D} from '../../tensor';
-import {expectArraysClose} from '../../test_util';
-
-import {scalar, tensor1d, tensor2d, tensor3d} from '../ops';
-
-describeWithFlags('bandPart', ALL_ENVS, () => {
-  it('keeps tensor unchanged', async () => {
-    const x: Tensor2D = tensor2d([1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, -1, -1).array(),
-        [[1, 1, 1], [1, 1, 1], [1, 1, 1]]);
-  });
-
-  it('upper triangular matrix', async () => {
-    const x: Tensor2D = tensor2d([1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, 0, -1).array(),
-        [[1, 1, 1], [0, 1, 1], [0, 0, 1]]);
-  });
-
-  it('lower triangular matrix', async () => {
-    const x: Tensor2D = tensor2d([1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, -1, 0).array(),
-        [[1, 0, 0], [1, 1, 0], [1, 1, 1]]);
-  });
-
-  it('diagonal elements', async () => {
-    const x: Tensor2D = tensor2d([1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, 0, 0).array(),
-        [[1, 0, 0], [0, 1, 0], [0, 0, 1]]);
-  });
-
-  it('lower triangular elements', async () => {
-    const x: Tensor2D = tensor2d([1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, 1, 0).array(),
-        [[1, 0, 0], [1, 1, 0], [0, 1, 1]]);
-  });
-
-  it('upper triangular elements', async () => {
-    const x: Tensor2D = tensor2d([1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, 0, 1).array(),
-        [[1, 1, 0], [0, 1, 1], [0, 0, 1]]);
-  });
-
-  it('4X4 matrix - tensorflow python examples', async () => {
-    const x: Tensor2D = tensor2d(
-        [[0, 1, 2, 3], [-1, 0, 1, 2], [-2, -1, 0, 1], [-3, -2, -1, 0]]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, 1, -1).array(),
-        [[0, 1, 2, 3], [-1, 0, 1, 2], [0, -1, 0, 1], [0, 0, -1, 0]]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, 2, 1).array(),
-        [[0, 1, 0, 0], [-1, 0, 1, 0], [-2, -1, 0, 1], [0, -2, -1, 0]]);
-  });
-
-  it('3 dimensional matrix', async () => {
-    const x: Tensor3D = tensor3d([[[1, 1], [1, 1]], [[1, 1], [1, 1]]]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, 0, 0).array(),
-        [[[1, 0], [0, 1]], [[1, 0], [0, 1]]]);
-  });
-
-  it('2X3X3 tensor', async () => {
-    const x: Tensor3D = tensor3d(
-        [[[1, 1, 1], [1, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1], [1, 1, 1]]]);
-    expectArraysClose(
-        await tf.linalg.bandPart(x, 1, 2).array(),
-        [[[1, 1, 1], [1, 1, 1], [0, 1, 1]], [[1, 1, 1], [1, 1, 1], [0, 1, 1]]]);
-  });
-
-  const la = tf.linalg;
-
-  it('fails for scalar', async () => {
-    const x = scalar(1);
-    expect(() => la.bandPart(x, 1, 2)).toThrowError(/bandPart.*rank/i);
-  });
-
-  it('fails for 1D tensor', async () => {
-    const x = tensor1d([1, 2, 3, 4, 5]);
-    expect(() => la.bandPart(x, 1, 2)).toThrowError(/bandPart.*rank/i);
-  });
-
-  it('fails if numLower or numUpper too large', async () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
-
-    for (const numLower of [3, 5, 8, 13]) {
-      for (const numUpper of [-1, 0, 1, 2]) {
-        expect(() => tf.linalg.bandPart(a, numLower, numUpper))
-            .toThrowError(/bandPart.*numLower/i);
-      }
-    }
-
-    for (const numLower of [-1, 0, 1]) {
-      for (const numUpper of [4, 5, 9]) {
-        expect(() => tf.linalg.bandPart(a, numLower, numUpper))
-            .toThrowError(/bandPart.*numUpper/i);
-      }
-    }
-
-    for (const numLower of [3, 5, 8, 13]) {
-      for (const numUpper of [4, 5, 9]) {
-        expect(() => tf.linalg.bandPart(a, numLower, numUpper))
-            .toThrowError(/bandPart.*(numLower|numUpper)/i);
-      }
-    }
-  });
-
-  it('works for 3x4 example', async () => {
-    const a = tf.tensor2d([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]);
-
-    expectArraysClose(
-        await la.bandPart(a, 0, 0).array(),
-        [[1, 0, 0, 0], [0, 6, 0, 0], [0, 0, 11, 0]]);
-    expectArraysClose(
-        await la.bandPart(a, 0, 1).array(),
-        [[1, 2, 0, 0], [0, 6, 7, 0], [0, 0, 11, 12]]);
-    expectArraysClose(
-        await la.bandPart(a, 0, 2).array(),
-        [[1, 2, 3, 0], [0, 6, 7, 8], [0, 0, 11, 12]]);
-    for (const numUpper of [3, 4, -1, -2]) {
-      expectArraysClose(
-          await la.bandPart(a, 0, numUpper).array(),
-          [[1, 2, 3, 4], [0, 6, 7, 8], [0, 0, 11, 12]]);
-    }
-
-    expectArraysClose(
-        await la.bandPart(a, 1, 0).array(),
-        [[1, 0, 0, 0], [5, 6, 0, 0], [0, 10, 11, 0]]);
-    expectArraysClose(
-        await la.bandPart(a, 1, 1).array(),
-        [[1, 2, 0, 0], [5, 6, 7, 0], [0, 10, 11, 12]]);
-    expectArraysClose(
-        await la.bandPart(a, 1, 2).array(),
-        [[1, 2, 3, 0], [5, 6, 7, 8], [0, 10, 11, 12]]);
-    for (const numUpper of [3, 4, -1, -2]) {
-      expectArraysClose(
-          await la.bandPart(a, 1, numUpper).array(),
-          [[1, 2, 3, 4], [5, 6, 7, 8], [0, 10, 11, 12]]);
-    }
-
-    for (const numLower of [2, 3, -1, -2]) {
-      expectArraysClose(
-          await la.bandPart(a, numLower, 0).array(),
-          [[1, 0, 0, 0], [5, 6, 0, 0], [9, 10, 11, 0]]);
-      expectArraysClose(
-          await la.bandPart(a, numLower, 1).array(),
-          [[1, 2, 0, 0], [5, 6, 7, 0], [9, 10, 11, 12]]);
-      expectArraysClose(
-          await la.bandPart(a, numLower, 2).array(),
-          [[1, 2, 3, 0], [5, 6, 7, 8], [9, 10, 11, 12]]);
-      for (const numUpper of [3, 4, -1, -2]) {
-        expectArraysClose(
-            await la.bandPart(a, numLower, numUpper).array(),
-            [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]);
-      }
-    }
-  });
-
-  it('works for tensor numLower and tensor numUpper', async () => {
-    const x: Tensor2D = tensor2d([1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3]);
-    expectArraysClose(
-        await tf.linalg
-            .bandPart(x, tf.scalar(-1, 'int32'), tf.scalar(0, 'int32'))
-            .array(),
-        [[1, 0, 0], [1, 1, 0], [1, 1, 1]]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/linalg/gram_schmidt.ts b/tfjs-master/tfjs-core/src/ops/linalg/gram_schmidt.ts
deleted file mode 100644
index b2be6eae9..000000000
--- a/tfjs-master/tfjs-core/src/ops/linalg/gram_schmidt.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {Tensor1D, Tensor2D} from '../../tensor';
-import {assert} from '../../util';
-
-import {div} from '../div';
-import {mul} from '../mul';
-import {norm} from '../norm';
-import {op} from '../operation';
-import {split} from '../split';
-import {squeeze} from '../squeeze';
-import {stack} from '../stack';
-import {sub} from '../sub';
-import {sum} from '../sum';
-
-/**
- * Gram-Schmidt orthogonalization.
- *
- * ```js
- * const x = tf.tensor2d([[1, 2], [3, 4]]);
- * let y = tf.linalg.gramSchmidt(x);
- * y.print();
- * console.log('Orthogonalized:');
- * y.dot(y.transpose()).print();  // should be nearly the identity matrix.
- * console.log('First row direction maintained:');
- * const data = await y.array();
- * console.log(data[0][1] / data[0][0]);  // should be nearly 2.
- * ```
- *
- * @param xs The vectors to be orthogonalized, in one of the two following
- *   formats:
- *   - An Array of `tf.Tensor1D`.
- *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows
- *     of `xs`.
- *   In each case, all the vectors must have the same length and the length
- *   must be greater than or equal to the number of vectors.
- * @returns The orthogonalized and normalized vectors or matrix.
- *   Orthogonalization means that the vectors or the rows of the matrix
- *   are orthogonal (zero inner products). Normalization means that each
- *   vector or each row of the matrix has an L2 norm that equals `1`.
- *
- * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}
- */
-function gramSchmidt_(xs: Tensor1D[]|Tensor2D): Tensor1D[]|Tensor2D {
-  let inputIsTensor2D: boolean;
-  if (Array.isArray(xs)) {
-    inputIsTensor2D = false;
-    assert(
-        xs != null && xs.length > 0,
-        () => 'Gram-Schmidt process: input must not be null, undefined, or ' +
-            'empty');
-    const dim = xs[0].shape[0];
-    for (let i = 1; i < xs.length; ++i) {
-      assert(
-          xs[i].shape[0] === dim,
-          () =>
-              'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +
-              `(${(xs as Tensor1D[])[i].shape[0]} vs. ${dim})`);
-    }
-  } else {
-    inputIsTensor2D = true;
-    xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));
-  }
-
-  assert(
-      xs.length <= xs[0].shape[0],
-      () => `Gram-Schmidt: Number of vectors (${
-                (xs as Tensor1D[]).length}) exceeds ` +
-          `number of dimensions (${(xs as Tensor1D[])[0].shape[0]}).`);
-
-  const ys: Tensor1D[] = [];
-  const xs1d = xs;
-  for (let i = 0; i < xs.length; ++i) {
-    ys.push(ENGINE.tidy(() => {
-      let x = xs1d[i];
-      if (i > 0) {
-        for (let j = 0; j < i; ++j) {
-          const proj = mul(sum(mul(ys[j], x)), ys[j]);
-          x = sub(x, proj);
-        }
-      }
-      return div(x, norm(x, 'euclidean'));
-    }));
-  }
-
-  if (inputIsTensor2D) {
-    return stack(ys, 0) as Tensor2D;
-  } else {
-    return ys;
-  }
-}
-
-export const gramSchmidt = /* @__PURE__ */ op({gramSchmidt_});
diff --git a/tfjs-master/tfjs-core/src/ops/linalg/gram_schmidt_test.ts b/tfjs-master/tfjs-core/src/ops/linalg/gram_schmidt_test.ts
deleted file mode 100644
index 89b87c7b6..000000000
--- a/tfjs-master/tfjs-core/src/ops/linalg/gram_schmidt_test.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {Tensor1D, Tensor2D} from '../../tensor';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('gramSchmidt-tiny', ALL_ENVS, () => {
-  it('2x2, Array of Tensor1D', async () => {
-    const xs: Tensor1D[] = [
-      tf.randomNormal([2], 0, 1, 'float32', 1),
-      tf.randomNormal([2], 0, 1, 'float32', 2)
-    ];
-    const ys = tf.linalg.gramSchmidt(xs) as Tensor1D[];
-    const y = tf.stack(ys) as Tensor2D;
-    // Test that the results are orthogonalized and normalized.
-    expectArraysClose(
-        await y.transpose().matMul(y).array(), await tf.eye(2).array());
-    // Test angle between xs[0] and ys[0] is zero, i.e., the orientation of the
-    // first vector is kept.
-    expectArraysClose(
-        await tf.sum(xs[0].mul(ys[0])).array(),
-        await tf.norm(xs[0]).mul(tf.norm(ys[0])).array());
-  });
-
-  it('3x3, Array of Tensor1D', async () => {
-    const xs: Tensor1D[] = [
-      tf.randomNormal([3], 0, 1, 'float32', 1),
-      tf.randomNormal([3], 0, 1, 'float32', 2),
-      tf.randomNormal([3], 0, 1, 'float32', 3)
-    ];
-    const ys = tf.linalg.gramSchmidt(xs) as Tensor1D[];
-    const y = tf.stack(ys) as Tensor2D;
-    expectArraysClose(
-        await y.transpose().matMul(y).array(), await tf.eye(3).array());
-    expectArraysClose(
-        await tf.sum(xs[0].mul(ys[0])).array(),
-        await tf.norm(xs[0]).mul(tf.norm(ys[0])).array());
-  });
-
-  it('3x3, Matrix', async () => {
-    const xs: Tensor2D = tf.randomNormal([3, 3], 0, 1, 'float32', 1);
-    const y = tf.linalg.gramSchmidt(xs) as Tensor2D;
-    expectArraysClose(
-        await y.transpose().matMul(y).array(), await tf.eye(3).array());
-  });
-
-  it('2x3, Matrix', async () => {
-    const xs: Tensor2D = tf.randomNormal([2, 3], 0, 1, 'float32', 1);
-    const y = tf.linalg.gramSchmidt(xs) as Tensor2D;
-    const yT: Tensor2D = y.transpose();
-    expectArraysClose(await y.matMul(yT).array(), await tf.eye(2).array());
-  });
-
-  it('3x2 Matrix throws Error', () => {
-    const xs = tf.tensor2d([[1, 2], [3, -1], [5, 1]]);
-    expect(() => tf.linalg.gramSchmidt(xs))
-        .toThrowError(
-            /Number of vectors \(3\) exceeds number of dimensions \(2\)/);
-  });
-
-  it('Mismatching dimensions input throws Error', () => {
-    const xs: Tensor1D[] =
-        [tf.tensor1d([1, 2, 3]), tf.tensor1d([-1, 5, 1]), tf.tensor1d([0, 0])];
-
-    expect(() => tf.linalg.gramSchmidt(xs)).toThrowError(/Non-unique/);
-  });
-
-  it('Empty input throws Error', () => {
-    expect(() => tf.linalg.gramSchmidt([])).toThrowError(/empty/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/linalg/qr.ts b/tfjs-master/tfjs-core/src/ops/linalg/qr.ts
deleted file mode 100644
index b78e8c260..000000000
--- a/tfjs-master/tfjs-core/src/ops/linalg/qr.ts
+++ /dev/null
@@ -1,201 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../../engine';
-import {dispose} from '../../globals';
-import {Tensor, Tensor2D} from '../../tensor';
-import {assert} from '../../util';
-
-import {clone} from '../clone';
-import {concat} from '../concat';
-import {div} from '../div';
-import {eye} from '../eye';
-import {greater} from '../greater';
-import {matMul} from '../mat_mul';
-import {mul} from '../mul';
-import {neg} from '../neg';
-import {norm} from '../norm';
-import {op} from '../operation';
-import {reshape} from '../reshape';
-import {slice} from '../slice';
-import {stack} from '../stack';
-import {sub} from '../sub';
-import {tensor2d} from '../tensor2d';
-import {transpose} from '../transpose';
-import {unstack} from '../unstack';
-import {where} from '../where';
-
-/**
- * Compute QR decomposition of m-by-n matrix using Householder transformation.
- *
- * Implementation based on
- *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]
- * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)
- *
- * ```js
- * const a = tf.tensor2d([[1, 2], [3, 4]]);
- * let [q, r] = tf.linalg.qr(a);
- * console.log('Q');
- * q.print();
- * console.log('R');
- * r.print();
- * console.log('Orthogonalized');
- * q.dot(q.transpose()).print()  // should be nearly the identity matrix.
- * console.log('Reconstructed');
- * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];
- * ```
- *
- * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose
- *   it has the shape `[..., M, N]`.
- * @param fullMatrices An optional boolean parameter. Defaults to `false`.
- *   If `true`, compute full-sized `Q`. If `false` (the default),
- *   compute only the leading N columns of `Q` and `R`.
- * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,
- *   i.e., its columns all have unit norm and are mutually orthogonal.
- *   If `M >= N`,
- *     If `fullMatrices` is `false` (default),
- *       - `Q` has a shape of `[..., M, N]`,
- *       - `R` has a shape of `[..., N, N]`.
- *     If `fullMatrices` is `true` (default),
- *       - `Q` has a shape of `[..., M, M]`,
- *       - `R` has a shape of `[..., M, N]`.
- *   If `M < N`,
- *     - `Q` has a shape of `[..., M, M]`,
- *     - `R` has a shape of `[..., M, N]`.
- * @throws If the rank of `x` is less than 2.
- *
- * @doc {heading:'Operations',
- *       subheading:'Linear Algebra',
- *       namespace:'linalg'}
- */
-function qr_(x: Tensor, fullMatrices = false): [Tensor, Tensor] {
-  assert(
-      x.rank >= 2,
-      () => `qr() requires input tensor to have a rank >= 2, but got rank ${
-          x.rank}`);
-
-  if (x.rank === 2) {
-    return qr2d(x as Tensor2D, fullMatrices);
-  } else {
-    // Rank > 2.
-    // TODO(cais): Below we split the input into individual 2D tensors,
-    //   perform QR decomposition on them and then stack the results back
-    //   together. We should explore whether this can be parallelized.
-    const outerDimsProd = x.shape.slice(0, x.shape.length - 2)
-                              .reduce((value, prev) => value * prev);
-    const x2ds = unstack(
-        reshape(
-            x,
-            [
-              outerDimsProd, x.shape[x.shape.length - 2],
-              x.shape[x.shape.length - 1]
-            ]),
-        0);
-    const q2ds: Tensor2D[] = [];
-    const r2ds: Tensor2D[] = [];
-    x2ds.forEach(x2d => {
-      const [q2d, r2d] = qr2d(x2d as Tensor2D, fullMatrices);
-      q2ds.push(q2d);
-      r2ds.push(r2d);
-    });
-    const q = reshape(stack(q2ds, 0), x.shape);
-    const r = reshape(stack(r2ds, 0), x.shape);
-    return [q, r];
-  }
-}
-
-function qr2d(x: Tensor2D, fullMatrices = false): [Tensor2D, Tensor2D] {
-  return ENGINE.tidy(() => {
-    assert(
-        x.shape.length === 2,
-        () => `qr2d() requires a 2D Tensor, but got a ${
-            x.shape.length}D Tensor.`);
-
-    const m = x.shape[0];
-    const n = x.shape[1];
-
-    let q = eye(m);    // Orthogonal transform so far.
-    let r = clone(x);  // Transformed matrix so far.
-
-    const one2D = tensor2d([[1]], [1, 1]);
-    let w: Tensor2D = clone(one2D);
-
-    const iters = m >= n ? n : m;
-    for (let j = 0; j < iters; ++j) {
-      // This tidy within the for-loop ensures we clean up temporary
-      // tensors as soon as they are no longer needed.
-      const rTemp = r;
-      const wTemp = w;
-      const qTemp = q;
-      [w, r, q] = ENGINE.tidy((): [Tensor2D, Tensor2D, Tensor2D] => {
-        // Find H = I - tau * w * w', to put zeros below R(j, j).
-        const rjEnd1 = slice(r, [j, j], [m - j, 1]);
-        const normX = norm(rjEnd1);
-        const rjj = slice(r, [j, j], [1, 1]);
-
-        // The sign() function returns 0 on 0, which causes division by zero.
-        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
-
-        const u1 = sub(rjj, mul(s, normX));
-        const wPre = div(rjEnd1, u1);
-        if (wPre.shape[0] === 1) {
-          w = clone(one2D);
-        } else {
-          w = concat(
-              [
-                one2D,
-                slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) as
-                    Tensor2D
-              ],
-              0);
-        }
-        const tau = neg(div(matMul(s, u1), normX)) as Tensor2D;
-
-        // -- R := HR, Q := QH.
-        const rjEndAll = slice(r, [j, 0], [m - j, n]);
-        const tauTimesW: Tensor2D = mul(tau, w);
-        const wT: Tensor2D = transpose(w);
-        if (j === 0) {
-          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-        } else {
-          const rTimesTau: Tensor2D =
-              sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
-        }
-        const tawTimesWT: Tensor2D = transpose(tauTimesW);
-        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
-        if (j === 0) {
-          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-        } else {
-          const qTimesTau: Tensor2D =
-              sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
-          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
-        }
-        return [w, r, q];
-      });
-      dispose([rTemp, wTemp, qTemp]);
-    }
-
-    if (!fullMatrices && m > n) {
-      q = slice(q, [0, 0], [m, n]);
-      r = slice(r, [0, 0], [n, n]);
-    }
-
-    return [q, r];
-  }) as [Tensor2D, Tensor2D];
-}
-
-export const qr = /* @__PURE__ */ op({qr_});
diff --git a/tfjs-master/tfjs-core/src/ops/linalg/qr_test.ts b/tfjs-master/tfjs-core/src/ops/linalg/qr_test.ts
deleted file mode 100644
index 6361ff486..000000000
--- a/tfjs-master/tfjs-core/src/ops/linalg/qr_test.ts
+++ /dev/null
@@ -1,144 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-import {scalar, tensor1d, tensor2d, tensor3d, tensor4d} from '../ops';
-
-describeWithFlags('qr', ALL_ENVS, () => {
-  it('1x1', async () => {
-    const x = tensor2d([[10]], [1, 1]);
-    const [q, r] = tf.linalg.qr(x);
-    expectArraysClose(await q.array(), [[-1]]);
-    expectArraysClose(await r.array(), [[-10]]);
-  });
-
-  it('2x2', async () => {
-    const x = tensor2d([[1, 3], [-2, -4]], [2, 2]);
-    const [q, r] = tf.linalg.qr(x);
-    expectArraysClose(await q.array(), [[-0.4472, -0.8944], [0.8944, -0.4472]]);
-    expectArraysClose(await r.array(), [[-2.2361, -4.9193], [0, -0.8944]]);
-  });
-
-  it('2x2x2', async () => {
-    const x = tensor3d([[[-1, -3], [2, 4]], [[1, 3], [-2, -4]]], [2, 2, 2]);
-    const [q, r] = tf.linalg.qr(x);
-    expectArraysClose(await q.array(), [
-      [[-0.4472, -0.8944], [0.8944, -0.4472]],
-      [[-0.4472, -0.8944], [0.8944, -0.4472]]
-    ]);
-    expectArraysClose(
-        await r.array(),
-        [[[2.2361, 4.9193], [0, 0.8944]], [[-2.2361, -4.9193], [0, -0.8944]]]);
-  });
-
-  it('2x1x2x2', async () => {
-    const x =
-        tensor4d([[[[-1, -3], [2, 4]]], [[[1, 3], [-2, -4]]]], [2, 1, 2, 2]);
-    const [q, r] = tf.linalg.qr(x);
-    expectArraysClose(await q.array(), [
-      [[[-0.4472, -0.8944], [0.8944, -0.4472]]],
-      [[[-0.4472, -0.8944], [0.8944, -0.4472]]],
-    ]);
-    expectArraysClose(await r.array(), [
-      [[[2.2361, 4.9193], [0, 0.8944]]], [[[-2.2361, -4.9193], [0, -0.8944]]]
-    ]);
-  });
-
-  it('3x3', async () => {
-    const x = tensor2d([[1, 3, 2], [-2, 0, 7], [8, -9, 4]], [3, 3]);
-    const [q, r] = tf.linalg.qr(x);
-    expectArraysClose(await q.array(), [
-      [-0.1204, 0.8729, 0.4729], [0.2408, -0.4364, 0.8669],
-      [-0.9631, -0.2182, 0.1576]
-    ]);
-    expectArraysClose(
-        await r.array(),
-        [[-8.3066, 8.3066, -2.4077], [0, 4.5826, -2.1822], [0, 0, 7.6447]]);
-  });
-
-  it('3x3, zero on diagonal', async () => {
-    const x = tensor2d([[0, 2, 2], [1, 1, 1], [0, 1, 2]], [3, 3]);
-    const [q, r] = tf.linalg.qr(x);
-    expectArraysClose(await q.data(), [
-      [0., -0.89442719, 0.4472136], [1., 0., 0.], [0., -0.4472136, -0.89442719]
-    ]);
-    expectArraysClose(
-        await r.data(),
-        [[1., 1., 1.], [0., -2.23606798, -2.68328157], [0., 0., -0.89442719]]);
-  });
-
-  it('3x2, fullMatrices = default false', async () => {
-    const x = tensor2d([[1, 2], [3, -3], [-2, 1]], [3, 2]);
-    const [q, r] = tf.linalg.qr(x);
-    expectArraysClose(
-        await q.array(),
-        [[-0.2673, 0.9221], [-0.8018, -0.3738], [0.5345, -0.0997]]);
-    expectArraysClose(await r.array(), [[-3.7417, 2.4054], [0, 2.8661]]);
-  });
-
-  it('3x2, fullMatrices = true', async () => {
-    const x = tensor2d([[1, 2], [3, -3], [-2, 1]], [3, 2]);
-    const [q, r] = tf.linalg.qr(x, true);
-    expectArraysClose(await q.array(), [
-      [-0.2673, 0.9221, 0.2798], [-0.8018, -0.3738, 0.4663],
-      [0.5345, -0.0997, 0.8393]
-    ]);
-    expectArraysClose(
-        await r.array(), [[-3.7417, 2.4054], [0, 2.8661], [0, 0]]);
-  });
-
-  it('2x3, fullMatrices = default false', async () => {
-    const x = tensor2d([[1, 2, 3], [-3, -2, 1]], [2, 3]);
-    const [q, r] = tf.linalg.qr(x);
-    expectArraysClose(
-        await q.array(), [[-0.3162278, -0.9486833], [0.9486833, -0.31622773]]);
-    expectArraysClose(
-        await r.array(),
-        [[-3.162, -2.5298, -2.3842e-07], [0, -1.2649, -3.162]]);
-  });
-
-  it('2x3, fullMatrices = true', async () => {
-    const x = tensor2d([[1, 2, 3], [-3, -2, 1]], [2, 3]);
-    const [q, r] = tf.linalg.qr(x, true);
-    expectArraysClose(
-        await q.array(), [[-0.3162278, -0.9486833], [0.9486833, -0.31622773]]);
-    expectArraysClose(
-        await r.array(),
-        [[-3.162, -2.5298, -2.3842e-07], [0, -1.2649, -3.162]]);
-  });
-
-  it('Does not leak memory', () => {
-    const x = tensor2d([[1, 3], [-2, -4]], [2, 2]);
-    // The first call to qr creates and keeps internal singleton tensors.
-    // Subsequent calls should always create exactly two tensors.
-    tf.linalg.qr(x);
-    // Count before real call.
-    const numTensors = tf.memory().numTensors;
-    tf.linalg.qr(x);
-    expect(tf.memory().numTensors).toEqual(numTensors + 2);
-  });
-
-  it('Insuffient input tensor rank leads to error', () => {
-    const x1 = scalar(12);
-    expect(() => tf.linalg.qr(x1)).toThrowError(/rank >= 2.*got rank 0/);
-    const x2 = tensor1d([12]);
-    expect(() => tf.linalg.qr(x2)).toThrowError(/rank >= 2.*got rank 1/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/linspace.ts b/tfjs-master/tfjs-core/src/ops/linspace.ts
deleted file mode 100644
index 3cc500eac..000000000
--- a/tfjs-master/tfjs-core/src/ops/linspace.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {LinSpace, LinSpaceAttrs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor1D} from '../tensor';
-
-/**
- * Return an evenly spaced sequence of numbers over the given interval.
- *
- * ```js
- * tf.linspace(0, 9, 10).print();
- * ```
- * @param start The start value of the sequence.
- * @param stop The end value of the sequence.
- * @param num The number of values to generate.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function linspace(start: number, stop: number, num: number): Tensor1D {
-  if (num <= 0) {
-    throw new Error('The number of values should be positive.');
-  }
-
-  const attrs: LinSpaceAttrs = {start, stop, num};
-  return ENGINE.runKernel(LinSpace, {}, attrs as unknown as NamedAttrMap);
-}
diff --git a/tfjs-master/tfjs-core/src/ops/linspace_test.ts b/tfjs-master/tfjs-core/src/ops/linspace_test.ts
deleted file mode 100644
index af2502060..000000000
--- a/tfjs-master/tfjs-core/src/ops/linspace_test.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('linspace', ALL_ENVS, () => {
-  it('start stop', async () => {
-    const a = tf.linspace(1, 10, 10);
-    expectArraysEqual(
-        await a.data(), [1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]);
-    expect(a.shape).toEqual([10]);
-
-    const b = tf.linspace(12, 17, 8);
-    expectArraysClose(await b.data(), [
-      12., 12.71428571, 13.42857143, 14.14285714, 14.85714286, 15.57142857,
-      16.28571429, 17.
-    ]);
-    expect(b.shape).toEqual([8]);
-
-    const c = tf.linspace(9, 0, 6);
-    expectArraysClose(await c.data(), [9., 7.2, 5.4, 3.6, 1.8, 0.]);
-    expect(c.shape).toEqual([6]);
-  });
-
-  it('negative start stop', async () => {
-    const a = tf.linspace(-4, 5, 6);
-    expectArraysClose(await a.data(), [-4., -2.2, -0.4, 1.4, 3.2, 5.]);
-    expect(a.shape).toEqual([6]);
-  });
-
-  it('start negative stop', async () => {
-    const a = tf.linspace(4, -5, 6);
-    expectArraysClose(await a.data(), [4., 2.2, 0.4, -1.4, -3.2, -5.]);
-    expect(a.shape).toEqual([6]);
-  });
-
-  it('negative start negative stop', async () => {
-    const a = tf.linspace(-4, -5, 6);
-    expectArraysClose(await a.data(), [-4., -4.2, -4.4, -4.6, -4.8, -5.]);
-    expect(a.shape).toEqual([6]);
-
-    const b = tf.linspace(-9, -4, 5);
-    expectArraysClose(await b.data(), [-9., -7.75, -6.5, -5.25, -4.]);
-    expect(b.shape).toEqual([5]);
-  });
-
-  it('should throw with no samples', () => {
-    expect(() => tf.linspace(2, 10, 0)).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/local_response_normalization.ts b/tfjs-master/tfjs-core/src/ops/local_response_normalization.ts
deleted file mode 100644
index 5d08cbc1d..000000000
--- a/tfjs-master/tfjs-core/src/ops/local_response_normalization.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {LRN, LRNAttrs, LRNInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Normalizes the activation of a local neighborhood across or within
- * channels.
- *
- * @param x The input tensor. The 4-D input tensor is treated as a 3-D array
- *     of 1D vectors (along the last dimension), and each vector is
- *     normalized independently.
- * @param depthRadius The number of adjacent channels in the 1D normalization
- *     window.
- * @param bias A constant bias term for the basis.
- * @param alpha A scale factor, usually positive.
- * @param beta An exponent.
- *
- * @doc {heading: 'Operations', subheading: 'Normalization'}
- */
-function localResponseNormalization_<T extends Tensor3D|Tensor4D>(
-    x: T|TensorLike, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T {
-  const $x = convertToTensor(x, 'x', 'localResponseNormalization');
-  util.assert(
-      $x.rank === 4 || $x.rank === 3,
-      () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
-               rank ${$x.rank}.`);
-  util.assert(
-      util.isInt(depthRadius),
-      () => `Error in localResponseNormalization: depthRadius must be an ` +
-          `integer but got depthRadius ${depthRadius}.`);
-
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-
-  const inputs: LRNInputs = {x: x4D};
-
-  const attrs: LRNAttrs = {depthRadius, bias, alpha, beta};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  LRN, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  } else {
-    return res;
-  }
-}
-
-export const localResponseNormalization = /* @__PURE__ */ op({localResponseNormalization_});
diff --git a/tfjs-master/tfjs-core/src/ops/local_response_normalization_backprop.ts b/tfjs-master/tfjs-core/src/ops/local_response_normalization_backprop.ts
deleted file mode 100644
index bd6d1a784..000000000
--- a/tfjs-master/tfjs-core/src/ops/local_response_normalization_backprop.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {LRNGrad, LRNGradAttrs, LRNGradInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-
-import {op} from './operation';
-
-function localResponseNormalizationBackprop_<T extends Tensor4D>(
-    x: T, y: T, dy: T, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T {
-  const inputs: LRNGradInputs = {x, y, dy};
-
-  const attrs: LRNGradAttrs = {depthRadius, bias, alpha, beta};
-
-  return ENGINE.runKernel(
-      LRNGrad, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const localResponseNormalizationBackprop =
-    op({localResponseNormalizationBackprop_});
diff --git a/tfjs-master/tfjs-core/src/ops/local_response_normalization_test.ts b/tfjs-master/tfjs-core/src/ops/local_response_normalization_test.ts
deleted file mode 100644
index 0f8ce5bb7..000000000
--- a/tfjs-master/tfjs-core/src/ops/local_response_normalization_test.ts
+++ /dev/null
@@ -1,1069 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {Tensor4D} from '../tensor';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-const sqArr = (arr: number[]) => arr.map(d => d * d);
-const sumArr = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);
-
-// tslint:disable-next-line:no-any
-const flatten = (arr: any): number[] => {
-  // tslint:disable-next-line:no-any
-  return arr.reduce((prev: any, curr: any) => {
-    return prev.concat(Array.isArray(curr) ? flatten(curr) : curr);
-  }, []);
-};
-
-describeWithFlags('localResponseNormalization with Tensor3D', ALL_ENVS, () => {
-  it('throws error with invalid input', () => {
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor2d([1, 20, 300, 4], [1, 4]);
-    const radius = 3;
-
-    expect(() => x.localResponseNormalization(radius)).toThrowError();
-  });
-
-  it('throws error with invalid radius', () => {
-    const x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
-    const radius = 0.5;
-
-    expect(() => x.localResponseNormalization(radius)).toThrowError();
-  });
-
-  it('computes simple normalization across channels', async () => {
-    const xT = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
-    const radius = 1;
-    const bias = 1;
-    const alpha = 1;
-    const beta = 0.5;
-
-    const result = xT.localResponseNormalization(radius, bias, alpha, beta);
-
-    const f = (...vals: number[]) =>
-        Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
-
-    const x = await xT.array();
-    expectArraysClose(await result.data(), [
-      x[0][0][0] * f(x[0][0][0], x[0][0][1]),
-      x[0][0][1] * f(x[0][0][0], x[0][0][1], x[0][0][2]),
-      x[0][0][2] * f(x[0][0][1], x[0][0][2], x[0][0][3]),
-      x[0][0][3] * f(x[0][0][2], x[0][0][3]),
-    ]);
-  });
-
-  it('uses beta = 1.0 to test GPU optimization', async () => {
-    const xT = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
-    const radius = 1;
-    const bias = 1;
-    const alpha = 1;
-    const beta = 1.0;
-
-    const result = xT.localResponseNormalization(radius, bias, alpha, beta);
-
-    const f = (...vals: number[]) =>
-        Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
-
-    const x = await xT.array();
-    expectArraysClose(await result.data(), [
-      x[0][0][0] * f(x[0][0][0], x[0][0][1]),
-      x[0][0][1] * f(x[0][0][0], x[0][0][1], x[0][0][2]),
-      x[0][0][2] * f(x[0][0][1], x[0][0][2], x[0][0][3]),
-      x[0][0][3] * f(x[0][0][2], x[0][0][3]),
-    ]);
-  });
-
-  it('uses beta = 0.75 to test GPU optimization', async () => {
-    const xT = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
-    const radius = 1;
-    const bias = 1;
-    const alpha = 1;
-    const beta = 0.75;
-
-    const result = xT.localResponseNormalization(radius, bias, alpha, beta);
-
-    const f = (...vals: number[]) =>
-        Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
-
-    const x = await xT.array();
-    expectArraysClose(await result.data(), [
-      x[0][0][0] * f(x[0][0][0], x[0][0][1]),
-      x[0][0][1] * f(x[0][0][0], x[0][0][1], x[0][0][2]),
-      x[0][0][2] * f(x[0][0][1], x[0][0][2], x[0][0][3]),
-      x[0][0][3] * f(x[0][0][2], x[0][0][3]),
-    ]);
-  });
-
-  it('computes complex normalization across channels', async () => {
-    const xT = tf.tensor3d(
-        [1, 20, 300, 4, 5, 15, 24, 200, 1, 20, 300, 4, 5, 15, 24, 200],
-        [2, 2, 4]);
-    const radius = 1;
-    const bias = 1;
-    const alpha = 1;
-    const beta = 0.5;
-    const result = xT.localResponseNormalization(radius, bias, alpha, beta);
-
-    const f = (...vals: number[]) =>
-        Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
-
-    // 1       | 2       | 3       | 4
-    // ------- | ------- | ------- | -------
-    // o x . . | x o x . | . x o x | . . x o
-
-    const x = await xT.array();
-    expectArraysClose(await result.data(), [
-      // 1 - 4
-      x[0][0][0] * f(x[0][0][0], x[0][0][1]),
-      x[0][0][1] * f(x[0][0][0], x[0][0][1], x[0][0][2]),
-      x[0][0][2] * f(x[0][0][1], x[0][0][2], x[0][0][3]),
-      x[0][0][3] * f(x[0][0][2], x[0][0][3]),
-
-      // 1 - 4
-      x[0][1][0] * f(x[0][1][0], x[0][1][1]),
-      x[0][1][1] * f(x[0][1][0], x[0][1][1], x[0][1][2]),
-      x[0][1][2] * f(x[0][1][1], x[0][1][2], x[0][1][3]),
-      x[0][1][3] * f(x[0][1][2], x[0][1][3]),
-
-      // 1 - 4
-      x[1][0][0] * f(x[1][0][0], x[1][0][1]),
-      x[1][0][1] * f(x[1][0][0], x[1][0][1], x[1][0][2]),
-      x[1][0][2] * f(x[1][0][1], x[1][0][2], x[1][0][3]),
-      x[1][0][3] * f(x[1][0][2], x[1][0][3]),
-
-      // 1 - 4
-      x[1][1][0] * f(x[1][1][0], x[1][1][1]),
-      x[1][1][1] * f(x[1][1][0], x[1][1][1], x[1][1][2]),
-      x[1][1][2] * f(x[1][1][1], x[1][1][2], x[1][1][3]),
-      x[1][1][3] * f(x[1][1][2], x[1][1][3]),
-    ]);
-  });
-
-  it('yields same result as tensorflow', async () => {
-    // t = tf.random_uniform([1, 3, 3, 8])
-    // l = tf.nn.lrn(t, depth_radius=2)
-    // print(tf.Session().run([t, l]))
-
-    const input = [
-      [
-        [
-          0.95782757, 0.12892687, 0.63624668, 0.70160735, 0.77376258,
-          0.54166114, 0.71172535, 0.65087497
-        ],
-        [
-          0.91872108, 0.38846886, 0.37847793, 0.50477624, 0.42154622,
-          0.43310916, 0.36253822, 0.07576156
-        ],
-        [
-          0.48662257, 0.4154036, 0.81704032, 0.91660416, 0.87671542, 0.64215934,
-          0.29933751, 0.90671134
-        ]
-      ],
-
-      [
-        [
-          0.6208992, 0.60847163, 0.41475761, 0.2127713, 0.65306914, 0.13923979,
-          0.32003641, 0.28183973
-        ],
-        [
-          0.04751575, 0.26870155, 0.45150304, 0.58678186, 0.99118924,
-          0.58878231, 0.30913198, 0.18836617
-        ],
-        [
-          0.16166461, 0.56322742, 0.67908955, 0.2269547, 0.38491273, 0.97113752,
-          0.51210916, 0.69430435
-        ]
-      ],
-
-      [
-        [
-          0.06625497, 0.13011181, 0.59202921, 0.88871598, 0.6366322, 0.47911358,
-          0.96530843, 0.74259472
-        ],
-        [
-          0.62660718, 0.0445286, 0.18430257, 0.76863647, 0.87511849, 0.53588808,
-          0.27980685, 0.30281997
-        ],
-        [
-          0.73987067, 0.91034842, 0.26241004, 0.72832751, 0.78974342,
-          0.50751543, 0.05434644, 0.8231523
-        ]
-      ]
-    ];
-
-    const expected = [
-      [
-        [
-          0.62630326, 0.07662392, 0.34354961, 0.41885775, 0.42621866,
-          0.29751951, 0.42365381, 0.4364861
-        ],
-        [
-          0.62828875, 0.251122, 0.23605582, 0.36483878, 0.30624411, 0.32672295,
-          0.29576892, 0.06582346
-        ],
-        [
-          0.3376624, 0.24321821, 0.42558169, 0.46646208, 0.45103404, 0.32380751,
-          0.17021206, 0.59476018
-        ]
-      ],
-
-      [
-        [
-          0.44719055, 0.43318295, 0.26775005, 0.14921051, 0.49148726,
-          0.10764983, 0.25084552, 0.25714993
-        ],
-        [
-          0.04202608, 0.21094096, 0.27973703, 0.34166718, 0.57487047,
-          0.35158369, 0.19708875, 0.15495601
-        ],
-        [
-          0.12034657, 0.41341963, 0.47968671, 0.13278878, 0.22735766,
-          0.57154536, 0.30411762, 0.42352781
-        ]
-      ],
-
-      [
-        [
-          0.05656794, 0.08849642, 0.36951816, 0.53186077, 0.33065733,
-          0.24236222, 0.54666328, 0.45085984
-        ],
-        [
-          0.52425432, 0.03133496, 0.11043368, 0.46954039, 0.5271349, 0.31946796,
-          0.1876673, 0.25085902
-        ],
-        [
-          0.47316891, 0.5277527, 0.13831842, 0.40036613, 0.50113004, 0.28860986,
-          0.03395459, 0.59127772
-        ]
-      ]
-    ];
-
-    const x = tf.tensor3d(flatten(input), [3, 3, 8]);
-    const radius = 2;
-    const bias = 1;
-    const alpha = 1;
-    const beta = 0.5;
-
-    const result = x.localResponseNormalization(radius, bias, alpha, beta);
-
-    expectArraysClose(await result.data(), flatten(expected));
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[[1, 20, 300, 4]]];  // 1x1x4
-    const radius = 1;
-    const bias = 1;
-    const alpha = 1;
-    const beta = 0.5;
-
-    const result = tf.localResponseNormalization(x, radius, bias, alpha, beta);
-
-    const f = (...vals: number[]) =>
-        Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
-
-    expectArraysClose(await result.data(), [
-      x[0][0][0] * f(x[0][0][0], x[0][0][1]),
-      x[0][0][1] * f(x[0][0][0], x[0][0][1], x[0][0][2]),
-      x[0][0][2] * f(x[0][0][1], x[0][0][2], x[0][0][3]),
-      x[0][0][3] * f(x[0][0][2], x[0][0][3]),
-    ]);
-  });
-});
-
-describeWithFlags('localResponseNormalization with Tensor4D', ALL_ENVS, () => {
-  it('throws error with invalid input', () => {
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor2d([1, 20, 300, 4], [1, 4]);
-    const radius = 3;
-
-    expect(() => x.localResponseNormalization(radius)).toThrowError();
-  });
-
-  it('throws error with invalid radius', () => {
-    const x = tf.tensor4d([1, 20, 300, 4], [1, 1, 1, 4]);
-    const radius = 0.5;
-
-    expect(() => x.localResponseNormalization(radius)).toThrowError();
-  });
-
-  it('computes simple normalization across channels', async () => {
-    const xT = tf.tensor4d([1, 20, 300, 4, 1, 20, 300, 4], [2, 1, 1, 4]);
-    const radius = 1;
-    const bias = 1;
-    const alpha = 1;
-    const beta = 0.5;
-
-    const result = xT.localResponseNormalization(radius, bias, alpha, beta);
-
-    const f = (...vals: number[]) =>
-        Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
-
-    // Easier to read using these vars
-    const b0 = 0;
-    const b1 = 1;
-    const x = await xT.array();
-    expectArraysClose(await result.data(), [
-      x[b0][0][0][0] * f(x[b0][0][0][0], x[b0][0][0][1]),
-      x[b0][0][0][1] * f(x[b0][0][0][0], x[b0][0][0][1], x[b0][0][0][2]),
-      x[b0][0][0][2] * f(x[b0][0][0][1], x[b0][0][0][2], x[b0][0][0][3]),
-      x[b0][0][0][3] * f(x[b0][0][0][2], x[b0][0][0][3]),
-
-      x[b1][0][0][0] * f(x[b1][0][0][0], x[b1][0][0][1]),
-      x[b1][0][0][1] * f(x[b1][0][0][0], x[b1][0][0][1], x[b1][0][0][2]),
-      x[b1][0][0][2] * f(x[b1][0][0][1], x[b1][0][0][2], x[b1][0][0][3]),
-      x[b1][0][0][3] * f(x[b1][0][0][2], x[b1][0][0][3]),
-    ]);
-  });
-
-  it('yields same result as tensorflow', async () => {
-    // t = tf.random_uniform([2, 3, 3, 8])
-    // l = tf.nn.lrn(t, depth_radius=2)
-    // print(tf.Session().run([t, l]))
-
-    const input = [
-      [
-        [
-          [
-            0.5659827, 0.57000327, 0.75555623, 0.89843333, 0.55120194,
-            0.53531718, 0.56402838, 0.95481384
-          ],
-          [
-            0.57334661, 0.65172958, 0.75794137, 0.80764937, 0.376616,
-            0.92726362, 0.36422753, 0.60535395
-          ],
-          [
-            0.82404268, 0.01054764, 0.4649173, 0.91637003, 0.82287347, 0.043468,
-            0.44953859, 0.92056584
-          ]
-        ],
-
-        [
-          [
-            0.68583369, 0.52534163, 0.53325927, 0.39608097, 0.9337523,
-            0.37397444, 0.81212556, 0.5697
-          ],
-          [
-            0.34278774, 0.57656682, 0.2356832, 0.02636456, 0.49111438,
-            0.17981696, 0.65398049, 0.70132935
-          ],
-          [
-            0.14241767, 0.68376505, 0.65419888, 0.69369483, 0.21489143,
-            0.46235347, 0.0559243, 0.60612857
-          ]
-        ],
-
-        [
-          [
-            0.59678483, 0.09368539, 0.3017447, 0.36870825, 0.68145788,
-            0.52048779, 0.46136606, 0.94114387
-          ],
-          [
-            0.3156569, 0.75275254, 0.31970251, 0.3154043, 0.61088014,
-            0.13359487, 0.99048364, 0.33625424
-          ],
-          [
-            0.82103574, 0.52066624, 0.63629258, 0.42294252, 0.93214262,
-            0.57041013, 0.66087878, 0.7019999
-          ]
-        ]
-      ],
-
-      [
-        [
-          [
-            0.21894431, 0.43085241, 0.79883206, 0.19462204, 0.68623316,
-            0.08703053, 0.82380795, 0.85634673
-          ],
-          [
-            0.45011401, 0.70312083, 0.86319792, 0.83205295, 0.67109787,
-            0.82081223, 0.46556532, 0.46408331
-          ],
-          [
-            0.07028461, 0.0038743, 0.44619524, 0.0611403, 0.96373355,
-            0.80561554, 0.42428243, 0.46897113
-          ]
-        ],
-
-        [
-          [
-            0.21006894, 0.48764861, 0.36842632, 0.23030031, 0.69685507,
-            0.31707478, 0.68662715, 0.0639503
-          ],
-          [
-            0.53940296, 0.50777435, 0.12625301, 0.12324154, 0.89205229,
-            0.69380629, 0.33191144, 0.81000078
-          ],
-          [
-            0.52650976, 0.71220326, 0.07246161, 0.08874547, 0.42528927,
-            0.36320579, 0.54055619, 0.79342318
-          ]
-        ],
-
-        [
-          [
-            0.75916636, 0.74499428, 0.76877356, 0.87210917, 0.93040991,
-            0.49491942, 0.70801985, 0.14901721
-          ],
-          [
-            0.27037835, 0.89302075, 0.69147241, 0.23044991, 0.98916364,
-            0.60161841, 0.63691151, 0.56759977
-          ],
-          [
-            0.56307781, 0.92782414, 0.25880754, 0.98518133, 0.04097319,
-            0.24640906, 0.54566145, 0.99261606
-          ]
-        ]
-      ]
-    ];
-
-    const expected = [
-      [
-        [
-          [
-            0.38019636, 0.32782161, 0.414222, 0.49507114, 0.3040463, 0.28107059,
-            0.33586296, 0.60191077
-          ],
-          [
-            0.37577698, 0.37752095, 0.42895618, 0.4225589, 0.2054275,
-            0.52219951, 0.23032214, 0.39414096
-          ],
-          [
-            0.59856331, 0.00637784, 0.25168711, 0.5541048, 0.48015645,
-            0.02301128, 0.27214608, 0.6427291
-          ]
-        ],
-
-        [
-          [
-            0.48127589, 0.35518789, 0.30486941, 0.23976389, 0.52926594,
-            0.21061926, 0.46920502, 0.39090639
-          ],
-          [
-            0.27937523, 0.46979892, 0.17829391, 0.02044933, 0.37045884,
-            0.12140442, 0.44160855, 0.50198948
-          ],
-          [
-            0.10289387, 0.44164398, 0.41853485, 0.42720893, 0.14580171,
-            0.31817055, 0.043797, 0.48155668
-          ]
-        ],
-
-        [
-          [
-            0.49458414, 0.07425242, 0.21042404, 0.26262277, 0.46205613,
-            0.30202535, 0.27406475, 0.61140078
-          ],
-          [
-            0.23736385, 0.55076694, 0.2135559, 0.21463785, 0.38077739,
-            0.08309806, 0.62830603, 0.23137885
-          ],
-          [
-            0.5355776, 0.32740855, 0.3451882, 0.24221195, 0.51988536,
-            0.31387195, 0.37391993, 0.46748781
-          ]
-        ]
-      ],
-
-      [
-        [
-          [
-            0.16003507, 0.31178808, 0.51775187, 0.12722474, 0.40769571,
-            0.05085804, 0.48455271, 0.5505302
-          ],
-          [
-            0.2880325, 0.39714804, 0.45591024, 0.4131493, 0.34525412, 0.4554069,
-            0.29119283, 0.31980222
-          ],
-          [
-            0.0640529, 0.00352532, 0.3052578, 0.03666528, 0.56009793,
-            0.46656418, 0.24587312, 0.32762629
-          ]
-        ],
-
-        [
-          [
-            0.17643087, 0.40210918, 0.2634095, 0.16233148, 0.4649446,
-            0.21803913, 0.47819966, 0.05093931
-          ],
-          [
-            0.43121469, 0.403974, 0.08191212, 0.07693455, 0.57362044,
-            0.39671475, 0.19025819, 0.54028469
-          ],
-          [
-            0.39356521, 0.53120333, 0.05151648, 0.06554616, 0.33433318,
-            0.2425479, 0.36161765, 0.5536595
-          ]
-        ],
-
-        [
-          [
-            0.46011236, 0.39919043, 0.36865807, 0.43511948, 0.46734285,
-            0.26861796, 0.43624333, 0.11205748
-          ],
-          [
-            0.17642327, 0.57622254, 0.37609601, 0.12030836, 0.54640025,
-            0.34052721, 0.36361033, 0.3926385
-          ],
-          [
-            0.37581176, 0.51741964, 0.14429154, 0.57254595, 0.02646073,
-            0.13531584, 0.35629693, 0.64837402
-          ]
-        ]
-      ]
-    ];
-
-    const x = tf.tensor4d(flatten(input), [2, 3, 3, 8]);
-    const radius = 2;
-
-    const result = x.localResponseNormalization(radius);
-
-    expectArraysClose(await result.data(), flatten(expected));
-  });
-
-  it('yields same result as tensorflow with inner most dims of odd shape',
-     async () => {
-       // t = tf.random_uniform([1, 5, 5, 3])
-       // l = tf.nn.lrn(t, depth_radius=2)
-       // print(tf.Session().run([t, l]))
-
-       const input = [[
-         [
-           [0.08576167, 0.5713569, 0.10008252],
-           [0.9822943, 0.11068773, 0.5733849],
-           [0.52175903, 0.7347398, 0.760726], [0.7118578, 0.3927865, 0.7521831],
-           [0.849753, 0.43948555, 0.42316127]
-         ],
-
-         [
-           [0.5843748, 0.27483034, 0.45537806],
-           [0.91386235, 0.56130767, 0.2968701],
-           [0.37907827, 0.11928034, 0.32693362],
-           [0.8294349, 0.9177762, 0.01197743],
-           [0.44460166, 0.22238493, 0.93720853]
-         ],
-
-         [
-           [0.12325168, 0.62378526, 0.6220398],
-           [0.9955342, 0.8281578, 0.9977399],
-           [0.78915524, 0.48492992, 0.70430815],
-           [0.856709, 0.91682327, 0.53920233],
-           [0.9217057, 0.32411182, 0.16391528]
-         ],
-
-         [
-           [0.3235209, 0.43057775, 0.5644517],
-           [0.93911314, 0.09265935, 0.05458856],
-           [0.06284857, 0.6895604, 0.88354754],
-           [0.32220483, 0.72595966, 0.3620714],
-           [0.15844965, 0.931878, 0.8501971]
-         ],
-
-         [
-           [0.07301581, 0.7518866, 0.40925968],
-           [0.82419384, 0.40474093, 0.53465044],
-           [0.34532738, 0.21671772, 0.50855494],
-           [0.04778886, 0.7952956, 0.64908195],
-           [0.8807392, 0.09571135, 0.7910882]
-         ]
-       ]];
-
-       const expected = [[
-         [
-           [0.07398141, 0.4928751, 0.08633514],
-           [0.6468731, 0.07289152, 0.37759283],
-           [0.33744285, 0.4751862, 0.49199253],
-           [0.4770374, 0.2632181, 0.50406057],
-           [0.58718365, 0.30368677, 0.2924066]
-         ],
-
-         [
-           [0.45850667, 0.21563481, 0.35729447],
-           [0.6108259, 0.37517825, 0.19842808],
-           [0.3370665, 0.10606097, 0.29070085],
-           [0.52141804, 0.5769532, 0.00752952],
-           [0.30495936, 0.15253738, 0.6428463]
-         ],
-
-         [
-           [0.09209093, 0.46607855, 0.4647744],
-           [0.51949346, 0.43215245, 0.5206444],
-           [0.5143535, 0.31606635, 0.45905212],
-           [0.50611794, 0.54163164, 0.31854454],
-           [0.65478665, 0.23025146, 0.11644664]
-         ],
-
-         [
-           [0.25507566, 0.3394832, 0.4450343],
-           [0.68247277, 0.06733745, 0.03967063],
-           [0.04180532, 0.45867857, 0.587714],
-           [0.24273802, 0.546913, 0.27277213], [0.097959, 0.5761189, 0.525621]
-         ],
-
-         [
-           [0.05538246, 0.57030565, 0.31042328],
-           [0.56486595, 0.27739152, 0.36642575],
-           [0.2892991, 0.18155594, 0.42604348],
-           [0.03332775, 0.55463576, 0.452667],
-           [0.56725365, 0.06164437, 0.50951254]
-         ]
-       ]];
-
-       const x = tf.tensor4d(input, [1, 5, 5, 3]);
-       const radius = 2;
-
-       const result = x.localResponseNormalization(radius);
-
-       expectArraysClose(await result.data(), flatten(expected));
-     });
-
-  it('throws when passed a non-tensor', () => {
-    const e =
-        /Argument 'x' passed to 'localResponseNormalization' must be a Tensor/;
-    expect(() => tf.localResponseNormalization({} as tf.Tensor3D))
-        .toThrowError(e);
-  });
-
-  it('gradient with 3D input', async () => {
-    const input = [
-      [
-        [
-          0.95782757, 0.12892687, 0.63624668, 0.70160735, 0.77376258,
-          0.54166114, 0.71172535, 0.65087497
-        ],
-        [
-          0.91872108, 0.38846886, 0.37847793, 0.50477624, 0.42154622,
-          0.43310916, 0.36253822, 0.07576156
-        ],
-        [
-          0.48662257, 0.4154036, 0.81704032, 0.91660416, 0.87671542, 0.64215934,
-          0.29933751, 0.90671134
-        ]
-      ],
-
-      [
-        [
-          0.6208992, 0.60847163, 0.41475761, 0.2127713, 0.65306914, 0.13923979,
-          0.32003641, 0.28183973
-        ],
-        [
-          0.04751575, 0.26870155, 0.45150304, 0.58678186, 0.99118924,
-          0.58878231, 0.30913198, 0.18836617
-        ],
-        [
-          0.16166461, 0.56322742, 0.67908955, 0.2269547, 0.38491273, 0.97113752,
-          0.51210916, 0.69430435
-        ]
-      ],
-
-      [
-        [
-          0.06625497, 0.13011181, 0.59202921, 0.88871598, 0.6366322, 0.47911358,
-          0.96530843, 0.74259472
-        ],
-        [
-          0.62660718, 0.0445286, 0.18430257, 0.76863647, 0.87511849, 0.53588808,
-          0.27980685, 0.30281997
-        ],
-        [
-          0.73987067, 0.91034842, 0.26241004, 0.72832751, 0.78974342,
-          0.50751543, 0.05434644, 0.8231523
-        ]
-      ]
-    ];
-
-    const expected = [[
-      [
-        [
-          0.27552658, 0.52414668, 0.11137494, 0.24928074, 0.07215497,
-          0.16210511, 0.19277242, 0.38672262
-        ],
-        [
-          0.23314378, 0.38181645, 0.30470729, 0.35180706, 0.37793165,
-          0.41450983, 0.60044503, 0.83605933
-        ],
-        [
-          0.51801264, 0.38517883, 0.02934788, 0.03102355, 0.08222333,
-          0.09746625, 0.4151727, 0.29936206
-        ]
-      ],
-
-      [
-        [
-          0.37059873, 0.32463685, 0.26611608, 0.54228389, 0.30733055,
-          0.66392428, 0.55629295, 0.79049641
-        ],
-        [
-          0.87162501, 0.68129337, 0.35793597, 0.18797961, -0.03660985,
-          0.23235559, 0.48184156, 0.76417446
-        ],
-        [
-          0.65893668, 0.41059417, 0.26254228, 0.40696776, 0.3330358, 0.01789692,
-          0.3162199, 0.28867012
-        ]
-      ],
-
-      [
-        [
-          0.83880937, 0.62594998, 0.324698, 0.13046435, 0.09858654, 0.17851587,
-          0.09067203, 0.30748016
-        ],
-        [
-          0.57213897, 0.67710453, 0.45385274, 0.19951296, 0.07371041,
-          0.20141563, 0.51362634, 0.7163325
-        ],
-        [
-          0.33668244, 0.09696329, 0.33500126, 0.08948036, 0.26512182,
-          0.19593786, 0.59144169, 0.379444
-        ]
-      ]
-    ]];
-    const radius = 2.0;
-    const bias = 1.0;
-    const alpha = 1.0;
-    const beta = 0.5;
-
-    const t = tf.tensor3d(input);
-    const dy = tf.onesLike(t);
-
-    const gradients = tf.grad(
-        (t: tf.Tensor3D) =>
-            tf.localResponseNormalization(t, radius, bias, alpha, beta))(t, dy);
-
-    expectArraysEqual(gradients.shape, t.shape);
-    expectArraysClose(await gradients.data(), flatten(expected));
-  });
-
-  it('gradient with clones', () => {
-    const t = tf.zeros([3, 3, 8]);
-    const radius = 2.0;
-    const bias = 1.0;
-    const alpha = 1.0;
-    const beta = 0.5;
-    const dt = tf.grad(
-        (t: tf.Tensor3D) =>
-            tf.localResponseNormalization(t.clone(), radius, bias, alpha, beta)
-                .clone())(t);
-
-    expectArraysEqual(dt.shape, t.shape);
-  });
-
-  it('gradient with 4D input', async () => {
-    const input = [
-      [
-        [
-          [
-            0.5659827, 0.57000327, 0.75555623, 0.89843333, 0.55120194,
-            0.53531718, 0.56402838, 0.95481384
-          ],
-          [
-            0.57334661, 0.65172958, 0.75794137, 0.80764937, 0.376616,
-            0.92726362, 0.36422753, 0.60535395
-          ],
-          [
-            0.82404268, 0.01054764, 0.4649173, 0.91637003, 0.82287347, 0.043468,
-            0.44953859, 0.92056584
-          ]
-        ],
-
-        [
-          [
-            0.68583369, 0.52534163, 0.53325927, 0.39608097, 0.9337523,
-            0.37397444, 0.81212556, 0.5697
-          ],
-          [
-            0.34278774, 0.57656682, 0.2356832, 0.02636456, 0.49111438,
-            0.17981696, 0.65398049, 0.70132935
-          ],
-          [
-            0.14241767, 0.68376505, 0.65419888, 0.69369483, 0.21489143,
-            0.46235347, 0.0559243, 0.60612857
-          ]
-        ],
-
-        [
-          [
-            0.59678483, 0.09368539, 0.3017447, 0.36870825, 0.68145788,
-            0.52048779, 0.46136606, 0.94114387
-          ],
-          [
-            0.3156569, 0.75275254, 0.31970251, 0.3154043, 0.61088014,
-            0.13359487, 0.99048364, 0.33625424
-          ],
-          [
-            0.82103574, 0.52066624, 0.63629258, 0.42294252, 0.93214262,
-            0.57041013, 0.66087878, 0.7019999
-          ]
-        ]
-      ],
-
-      [
-        [
-          [
-            0.21894431, 0.43085241, 0.79883206, 0.19462204, 0.68623316,
-            0.08703053, 0.82380795, 0.85634673
-          ],
-          [
-            0.45011401, 0.70312083, 0.86319792, 0.83205295, 0.67109787,
-            0.82081223, 0.46556532, 0.46408331
-          ],
-          [
-            0.07028461, 0.0038743, 0.44619524, 0.0611403, 0.96373355,
-            0.80561554, 0.42428243, 0.46897113
-          ]
-        ],
-
-        [
-          [
-            0.21006894, 0.48764861, 0.36842632, 0.23030031, 0.69685507,
-            0.31707478, 0.68662715, 0.0639503
-          ],
-          [
-            0.53940296, 0.50777435, 0.12625301, 0.12324154, 0.89205229,
-            0.69380629, 0.33191144, 0.81000078
-          ],
-          [
-            0.52650976, 0.71220326, 0.07246161, 0.08874547, 0.42528927,
-            0.36320579, 0.54055619, 0.79342318
-          ]
-        ],
-
-        [
-          [
-            0.75916636, 0.74499428, 0.76877356, 0.87210917, 0.93040991,
-            0.49491942, 0.70801985, 0.14901721
-          ],
-          [
-            0.27037835, 0.89302075, 0.69147241, 0.23044991, 0.98916364,
-            0.60161841, 0.63691151, 0.56759977
-          ],
-          [
-            0.56307781, 0.92782414, 0.25880754, 0.98518133, 0.04097319,
-            0.24640906, 0.54566145, 0.99261606
-          ]
-        ]
-      ]
-    ];
-
-    const dyVals = [
-      [
-        [
-          [
-            1.40394282, -1.68962789, -0.21134049, 1.15015793, 1.51244378,
-            0.42844626, -2.70123291, 0.06449971
-          ],
-          [
-            -0.29038581, 0.67567694, 0.95617437, -1.07383668, 0.20920482,
-            0.39050213, -0.81124371, 2.42158198
-          ],
-          [
-            -1.01235235, -0.63514435, -1.49017262, -0.01205151, 0.78492945,
-            -0.20330679, -2.31419802, -0.31220308
-          ]
-        ],
-
-        [
-          [
-            0.07061944, -0.46716127, 0.91232526, -1.30444264, -0.07080109,
-            0.13207501, 0.26701283, -0.48946589
-          ],
-          [
-            -0.74995744, -0.79466617, -1.03790498, -0.32234526, 1.33345711,
-            0.11863081, 1.93010819, 0.47857195
-          ],
-          [
-            0.37702683, -0.7804451, 0.45868117, 1.06967258, -0.65336537,
-            0.3594887, 0.62512684, 0.77009726
-          ]
-        ],
-
-        [
-          [
-            0.76865023, 1.00893021, -0.24408816, -0.3943336, 0.47094285,
-            -2.61926222, 1.52929449, 0.7862013
-          ],
-          [
-            -1.20878386, -0.26222935, -0.9076528, 0.03079577, -0.01467486,
-            -0.06949636, 0.05466342, 1.44880533
-          ],
-          [
-            0.05611863, 0.15142779, 0.7802065, -1.2623471, 0.09119794,
-            -0.20110528, 0.17715968, -0.48476508
-          ]
-        ]
-      ],
-
-      [
-        [
-          [
-            0.1549256, 0.94472402, -0.70033115, -1.05752802, -0.63035947,
-            -1.35643113, -0.27211693, 2.33576941
-          ],
-          [
-            0.81070906, -0.58353454, -0.3253817, 2.53953528, -1.40062141,
-            1.7728076, -0.59849483, 1.49650824
-          ],
-          [
-            -0.00610052, -2.29434419, -1.77995121, -0.66354084, -0.70676774,
-            -0.81570011, -1.30821037, 0.40997007
-          ]
-        ],
-
-        [
-          [
-            -1.02013469, -0.74198806, -0.82677251, -0.00890179, -1.62196338,
-            -0.5095427, 1.26501179, 0.12931485
-          ],
-          [
-            -1.14763546, 0.11011696, -0.23312508, 0.29730096, -0.49138394,
-            -0.27012363, -0.15987533, -1.84277928
-          ],
-          [
-            -0.03816459, -0.73517877, -2.00476885, 0.47192496, -0.27395752,
-            0.99806124, 1.54439747, -1.02016675
-          ]
-        ],
-
-        [
-          [
-            -1.27831209, -0.6961385, -0.73713994, -1.97954738, 0.39108652,
-            -0.46152538, 1.8255372, 2.18119025
-          ],
-          [
-            0.56322283, -1.59858179, 1.54127491, -0.57665956, -1.0098567,
-            0.93239671, 0.25231698, -0.7346009
-          ],
-          [
-            0.41614994, -1.20103085, 0.4330301, -1.23348403, -0.46117213,
-            -0.3780126, 0.35449561, -0.60129249
-          ]
-        ]
-      ]
-    ];
-
-    const depthRadius = 1;
-    const bias = 1;
-    const alpha = 1;
-    const beta = 0.75;
-
-    const expected = [
-      [
-        [
-          [
-            0.88732064, -0.98597342, -0.00569269, 0.09561057, 0.42255375,
-            0.30286378, -1.17104781, 0.44769961
-          ],
-          [
-            -0.22329885, 0.19271846, 0.41454071, -0.50674957, 0.14660946,
-            0.1591837, -0.83707076, 1.19177234
-          ],
-          [
-            -0.26728818, -0.3847312, -0.72818488, 0.09040837, 0.24023688,
-            -0.11545581, -1.09341288, 0.33930668
-          ]
-        ],
-
-        [
-          [
-            0.10079086, -0.38184536, 0.60918945, -0.7267822, 0.13867335,
-            0.03526202, 0.17270499, -0.2705338
-          ],
-          [
-            -0.38344458, -0.15589149, -0.68160093, -0.27644777, 0.79392856,
-            -0.14384332, 0.67121017, -0.23130262
-          ],
-          [
-            0.31069142, -0.39895257, 0.11755499, 0.39481708, -0.5234766,
-            0.2511853, 0.40955079, 0.3492966
-          ]
-        ],
-
-        [
-          [
-            0.32660595, 0.7240563, -0.18117335, -0.2649861, 0.67781603,
-            -1.46250272, 0.8465963, -0.05466701
-          ],
-          [
-            -0.71582067, 0.20831716, -0.50778204, 0.07256755, -0.00893679,
-            -0.03798783, -0.18604305, 0.75747406
-          ],
-          [
-            -0.00540833, -0.07677216, 0.41930205, -0.69235319, 0.20631291,
-            -0.11946303, 0.19601521, -0.21237698
-          ]
-        ]
-      ],
-
-      [
-        [
-          [
-            0.0800111, 0.60922205, -0.31155977, -0.46448132, -0.15912701,
-            -0.72455585, -0.5727275, 0.71780092
-          ],
-          [
-            0.50568235, -0.31544152, -0.40618286, 0.97909468, -1.15286613,
-            0.8145386, -0.77758539, 0.93794745
-          ],
-          [
-            -0.00535599, -1.99269259, -1.15343952, -0.31053686, 0.01680636,
-            0.10109296, -0.66026396, 0.35474917
-          ]
-        ],
-
-        [
-          [
-            -0.74113333, -0.20625943, -0.4339568, 0.21517368, -0.5734458,
-            -0.23481363, 0.53855389, 0.05860626
-          ],
-          [
-            -0.61435795, 0.29290834, -0.19639145, 0.20930134, -0.08880179,
-            0.02209887, 0.21427482, -0.51696646
-          ],
-          [
-            0.13036536, -0.19079237, -1.43941545, 0.42789665, -0.29732707,
-            0.52354813, 0.78893, -0.59992862
-          ]
-        ],
-
-        [
-          [
-            -0.328383, 0.15830949, 0.13110149, -0.492423, 0.46827313,
-            -0.58950633, 0.56422544, 1.44929576
-          ],
-          [
-            0.46141064, -0.80682266, 0.92562175, -0.28897452, -0.30567497,
-            0.50646484, 0.16439518, -0.38878182
-          ],
-          [
-            0.41004074, -0.38593128, 0.42881966, -0.22443436, -0.24573228,
-            -0.2941249, 0.31119603, -0.17903978
-          ]
-        ]
-      ]
-    ];
-
-    const t = tf.tensor(input);
-    const dy = tf.tensor(dyVals);
-
-    const gradients = tf.grad(
-        t => tf.localResponseNormalization(
-            t as Tensor4D, depthRadius, bias, alpha, beta))(t, dy as Tensor4D);
-
-    expectArraysClose(await gradients.data(), flatten(expected));
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/log.ts b/tfjs-master/tfjs-core/src/ops/log.ts
deleted file mode 100644
index de499454a..000000000
--- a/tfjs-master/tfjs-core/src/ops/log.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Log, LogInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, Math.E]);
- *
- * x.log().print();  // or tf.log(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function log_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'log', 'float32');
-
-  const inputs: LogInputs = {x: $x};
-  return ENGINE.runKernel(Log, inputs as unknown as NamedTensorMap);
-}
-export const log = /* @__PURE__ */ op({log_});
diff --git a/tfjs-master/tfjs-core/src/ops/log1p.ts b/tfjs-master/tfjs-core/src/ops/log1p.ts
deleted file mode 100644
index 0b085f201..000000000
--- a/tfjs-master/tfjs-core/src/ops/log1p.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Log1p, Log1pInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes natural logarithm of the input `tf.Tensor` plus one
- * element-wise: `ln(1 + x)`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, Math.E - 1]);
- *
- * x.log1p().print();  // or tf.log1p(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function log1p_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'log1p');
-
-  const inputs: Log1pInputs = {x: $x};
-  return ENGINE.runKernel(Log1p, inputs as unknown as NamedTensorMap);
-}
-export const log1p = /* @__PURE__ */ op({log1p_});
diff --git a/tfjs-master/tfjs-core/src/ops/log1p_test.ts b/tfjs-master/tfjs-core/src/ops/log1p_test.ts
deleted file mode 100644
index 5befbfaff..000000000
--- a/tfjs-master/tfjs-core/src/ops/log1p_test.ts
+++ /dev/null
@@ -1,94 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('log1p', ALL_ENVS, () => {
-  it('log1p', async () => {
-    const a = tf.tensor1d([1, 2]);
-    const r = tf.log1p(a);
-    expectArraysClose(await r.data(), [Math.log1p(1), Math.log1p(2)]);
-  });
-
-  it('log1p propagates NaNs', async () => {
-    const a = tf.tensor1d([1, NaN]);
-    const r = tf.log1p(a);
-    expectArraysClose(await r.data(), [Math.log1p(1), NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.log1p(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [3 / (1 + 5)]);
-  });
-
-  it('gradient with clones', () => {
-    const a = tf.scalar(5);
-    const gradients = tf.grad(a => a.clone().log1p().clone())(a);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1, 2, 3, -5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.log1p(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [Infinity, 2 / (1 + 2), 3 / (1 + 3), 4 / (1 + -5)]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.log1p(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [1 / (1 + -3), 2 / (1 + 1), 3 / (1 + 2), 4 / (1 + 3)]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.log1p({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'log1p' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.log1p([1, 2]);
-    expectArraysClose(await r.data(), [Math.log1p(1), Math.log1p(2)]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.log1p('q'))
-        .toThrowError(/Argument 'x' passed to 'log1p' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/log_sigmoid.ts b/tfjs-master/tfjs-core/src/ops/log_sigmoid.ts
deleted file mode 100644
index 9a6574ad1..000000000
--- a/tfjs-master/tfjs-core/src/ops/log_sigmoid.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {customGrad} from '../gradients';
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {mul} from './mul';
-import {neg} from './neg';
-import {op} from './operation';
-import {sigmoid} from './sigmoid';
-import {softplus} from './softplus';
-
-/**
- * Computes log sigmoid of the input `tf.Tensor` element-wise:
- * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.
- *
- * ```js
- * const x = tf.tensor1d([0, 1, -1, .7]);
- *
- * x.logSigmoid().print();  // or tf.logSigmoid(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function logSigmoid_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'logSigmoid');
-
-  // Use a custom gradient to maintain previous implementation.
-  // There is no LogSigmoid kernel in TF so we can't use engine.runKernel
-  // directly
-  const customOp = customGrad((x: Tensor) => {
-    // TODO(yassogba) we can remove the chained softplus call here only
-    // after backends have modualrized softplus at which point we can call
-    // engine runKernel(..., Sotfplus, ...) directly.
-    const value = neg(softplus(neg(x)));
-
-    const gradFunc = (dy: T) => {
-      const derX = mul(dy, sigmoid(neg(x)));
-      return derX;
-    };
-    return {value, gradFunc};
-  });
-
-  return customOp($x) as T;
-}
-export const logSigmoid = /* @__PURE__ */ op({logSigmoid_});
diff --git a/tfjs-master/tfjs-core/src/ops/log_sigmoid_test.ts b/tfjs-master/tfjs-core/src/ops/log_sigmoid_test.ts
deleted file mode 100644
index 8e7ee974d..000000000
--- a/tfjs-master/tfjs-core/src/ops/log_sigmoid_test.ts
+++ /dev/null
@@ -1,162 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('logSigmoid', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const a = tf.tensor1d(values);
-
-    const result = tf.logSigmoid(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.log(1 / (1 + Math.exp(-values[i])));
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('scalar', async () => {
-    const a = tf.scalar(-2);
-
-    const result = tf.logSigmoid(a);
-
-    const expected = [Math.log(1 / (1 + Math.exp(2)))];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('tensor2D', async () => {
-    const values = [1, 2, -3, 5];
-    const a = tf.tensor2d(values, [2, 2]);
-
-    const result = tf.logSigmoid(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.log(1 / (1 + Math.exp(-values[i])));
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('larger magnitude negative inputs', async () => {
-    const values = [-100, -200, -3000];
-    const a = tf.tensor1d(values);
-
-    const result = tf.logSigmoid(a);
-
-    const expected = [-100, -200, -3000];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('larger magnitude positive inputs', async () => {
-    const values = [100, 200, 3000, 50000];
-    const a = tf.tensor1d(values);
-
-    const result = tf.logSigmoid(a);
-
-    const expected = [0, 0, 0, 0];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([3, NaN]);
-    const res = tf.logSigmoid(a);
-    expectArraysClose(
-        await res.data(), [Math.log(1 / (1 + Math.exp(-3))), NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(3);
-    const dy = tf.scalar(4);
-    const dyVal = await dy.array();
-
-    const da = tf.grad(a => tf.logSigmoid(a))(a, dy);
-    const aVal = await a.array();
-    const y = 1 / (1 + Math.exp(aVal));
-    expectArraysClose(await da.data(), [dyVal * y]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, -3, 5]);
-    const aVals = await a.array();
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-    const dyVals = await dy.array();
-    const da = tf.grad(a => tf.logSigmoid(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      const y = 1 / (1 + Math.exp(aVals[i]));
-      expected[i] = dyVals[i] * y;
-    }
-
-    expectArraysClose(await da.data(), expected);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, -3, 5]);
-    const aVals = await a.array();
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-    const dyVals = await dy.array();
-    const da = tf.grad(a => tf.logSigmoid(a.clone()).clone())(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      const y = 1 / (1 + Math.exp(aVals[i]));
-      expected[i] = dyVals[i] * y;
-    }
-
-    expectArraysClose(await da.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([1, 2, -3, 5], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const da = tf.grad(a => tf.logSigmoid(a))(a, dy);
-
-    const expected = [];
-    const aVals = await a.data();
-    const dyVals = await dy.data();
-    for (let i = 0; i < a.size; i++) {
-      const y = 1 / (1 + Math.exp(aVals[i]));
-      expected[i] = dyVals[i] * y;
-    }
-
-    expectArraysClose(await da.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.logSigmoid({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'logSigmoid' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.logSigmoid(-2);
-    const expected = [Math.log(1 / (1 + Math.exp(2)))];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.logSigmoid('q'))
-        .toThrowError(/Argument 'x' passed to 'logSigmoid' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/log_softmax.ts b/tfjs-master/tfjs-core/src/ops/log_softmax.ts
deleted file mode 100644
index e1f975e00..000000000
--- a/tfjs-master/tfjs-core/src/ops/log_softmax.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {customGrad} from '../gradients';
-
-import {Tensor} from '../tensor';
-import {GradSaveFunc} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {cast} from './cast';
-import {exp} from './exp';
-import {log} from './log';
-import {max} from './max';
-import {mul} from './mul';
-import {op} from './operation';
-import {sub} from './sub';
-import {sum} from './sum';
-
-/**
- * Computes the log softmax.
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3]);
- *
- * a.logSoftmax().print();  // or tf.logSoftmax(a)
- * ```
- *
- * ```js
- * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
- *
- * a.logSoftmax().print();  // or tf.logSoftmax(a)
- * ```
- *
- * @param logits The logits array.
- * @param axis The dimension softmax would be performed on. Defaults to `-1`
- *     which indicates the last dimension.
- *
- * @doc {heading: 'Operations', subheading: 'Normalization'}
- */
-function logSoftmax_<T extends Tensor>(logits: T|TensorLike, axis = -1): T {
-  const $logits = convertToTensor(logits, 'logits', 'logSoftmax');
-
-  if (axis === -1) {
-    axis = $logits.rank - 1;
-  }
-  if (axis !== $logits.rank - 1) {
-    throw Error(
-        'Log Softmax along a non-last dimension is not yet supported. ' +
-        `Logits was rank ${$logits.rank} and axis was ${axis}`);
-  }
-
-  // const forward: ForwardFunc<Tensor> = (backend, save) => {
-  //   const keepDims = true;
-  //   const xMax = max(logits, axis, true);
-  //   const shifted = sub(logits, xMax);
-  //   const value =
-  //       sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis,
-  //       keepDims)));
-  //   save([value]);
-  //   return value;
-  // };
-
-  // Use a custom gradient for numerical stability.
-  const customOp = customGrad((logits: Tensor, save: GradSaveFunc) => {
-    const keepDims = true;
-    const xMax = max(logits, axis, true);
-    const shifted = sub(logits, xMax);
-    const value =
-        sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));
-    save([value]);
-
-    const gradFunc = (dy: Tensor, saved: Tensor[]) => {
-      const [value] = saved;
-      const keepDims = true;
-      const softmax = exp(value);
-      return sub(dy, mul(sum(dy, axis, keepDims), softmax));
-    };
-    return {value, gradFunc};
-  });
-
-  return customOp($logits) as T;
-
-  // TODO Use Engine.runKernel when CPU/WebGL/WASM backends implement this.
-  // const inputs: LogSoftmaxInputs = {logits: $logits};
-  // const attrs: LogSoftmaxAttrs = {axis};
-  // return ENGINE.runKernel(
-  //            LogSoftmax, inputs as unknown as NamedTensorMap,
-  //            attrs as unknown as NamedAttrMap);
-}
-
-export const logSoftmax = /* @__PURE__ */ op({logSoftmax_});
diff --git a/tfjs-master/tfjs-core/src/ops/log_softmax_test.ts b/tfjs-master/tfjs-core/src/ops/log_softmax_test.ts
deleted file mode 100644
index 56c9b442e..000000000
--- a/tfjs-master/tfjs-core/src/ops/log_softmax_test.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('logSoftmax', ALL_ENVS, () => {
-  it('regular test', async () => {
-    const y = tf.logSoftmax(tf.tensor1d([2, 1, 3]));
-
-    expectArraysClose(await y.data(), [-1.407606, -2.4076061, -0.407606]);
-  });
-
-  it('Huge difference', async () => {
-    const y = tf.logSoftmax(tf.tensor1d([-1000, +1000]));
-
-    expectArraysClose(await y.data(), [-2000, 0]);
-  });
-
-  it('Propagates NaNs', async () => {
-    const a = tf.tensor1d([2, 1, NaN]);
-    const y = tf.logSoftmax(a);
-    expectArraysClose(await y.data(), [NaN, NaN, NaN]);
-  });
-
-  it('2D, axis=1', async () => {
-    const y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);
-    const expected =
-        [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];
-    expect(y.rank).toBe(2);
-    expectArraysClose(await y.data(), expected);
-  });
-
-  it('2D, implicit axis=1', async () => {
-    const y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));
-    const expected =
-        [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];
-    expect(y.rank).toBe(2);
-    expectArraysClose(await y.data(), expected);
-  });
-
-  it('1D gradient', async () => {
-    const x = tf.tensor1d([1, 2, 10]);
-    const dy = tf.tensor1d([1, 2, 3]);
-    const dx = tf.grad((x) => x.logSoftmax())(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [0.9992599, 1.9979881, -2.9972477]);
-  });
-
-  it('2D, axis=0 throws error', () => {
-    const f = () => {
-      tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.logSoftmax({} as tf.Tensor))
-        .toThrowError(
-            /Argument 'logits' passed to 'logSoftmax' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const y = tf.logSoftmax([2, 1, 3]);
-
-    expectArraysClose(await y.data(), [-1.407606, -2.4076061, -0.407606]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/log_sum_exp.ts b/tfjs-master/tfjs-core/src/ops/log_sum_exp.ts
deleted file mode 100644
index cb9662dae..000000000
--- a/tfjs-master/tfjs-core/src/ops/log_sum_exp.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {parseAxisParam} from '../util';
-
-import {add} from './add';
-import {expandShapeToKeepDim} from './axis_util';
-import {exp} from './exp';
-import {log} from './log';
-import {max} from './max';
-import {op} from './operation';
-import {reshape} from './reshape';
-import {sub} from './sub';
-import {sum} from './sum';
-
-/**
- * Computes the log(sum(exp(elements across the reduction dimensions))).
- *
- * Reduces the input along the dimensions given in `axis`. Unless `keepDims`
- * is true, the rank of the array is reduced by 1 for each entry in `axis`.
- * If `keepDims` is true, the reduced dimensions are retained with length 1.
- * If `axis` has no entries, all dimensions are reduced, and an array with a
- * single element is returned.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3]);
- *
- * x.logSumExp().print();  // or tf.logSumExp(x)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * const axis = 1;
- * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)
- * ```
- * @param x The input tensor.
- * @param axis The dimension(s) to reduce. If null (the default),
- *     reduces all dimensions.
- * @param keepDims If true, retains reduced dimensions with length
- *     of 1. Defaults to false.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function logSumExp_<T extends Tensor>(
-    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {
-  const $x = convertToTensor(x, 'x', 'logSumExp');
-
-  const axes = parseAxisParam(axis, $x.shape);
-  const xMax = max($x, axes, true /* keepDims */);
-  const a = sub($x, xMax);
-  const b = exp(a);
-  const c = sum(b, axes);
-  const d = log(c);
-  const res = add(reshape(xMax, d.shape), d);
-
-  if (keepDims) {
-    const newShape = expandShapeToKeepDim(res.shape, axes);
-    return reshape(res, newShape) as T;
-  }
-  return res as T;
-}
-
-export const logSumExp = /* @__PURE__ */ op({logSumExp_});
diff --git a/tfjs-master/tfjs-core/src/ops/log_sum_exp_test.ts b/tfjs-master/tfjs-core/src/ops/log_sum_exp_test.ts
deleted file mode 100644
index 0c64ff008..000000000
--- a/tfjs-master/tfjs-core/src/ops/log_sum_exp_test.ts
+++ /dev/null
@@ -1,134 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('logSumExp', ALL_ENVS, () => {
-  it('0', async () => {
-    const a = tf.scalar(0);
-    const result = tf.logSumExp(a);
-    expectArraysClose(await result.data(), 0);
-  });
-
-  it('basic', async () => {
-    const a = tf.tensor1d([1, 2, -3]);
-    const result = tf.logSumExp(a);
-
-    expectArraysClose(
-        await result.data(),
-        Math.log(Math.exp(1) + Math.exp(2) + Math.exp(-3)));
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([1, 2, NaN]);
-    const result = tf.logSumExp(a);
-    expectArraysEqual(await result.data(), NaN);
-  });
-
-  it('axes=0 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const r = tf.logSumExp(a, [0]);
-
-    expect(r.shape).toEqual([2]);
-    const expected = [
-      Math.log(Math.exp(1) + Math.exp(3) + Math.exp(0)),
-      Math.log(Math.exp(2) + Math.exp(0) + Math.exp(1))
-    ];
-    expectArraysClose(await r.data(), expected);
-  });
-
-  it('axes=0 in 2D array, keepDims', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const r = tf.logSumExp(a, [0], true /* keepDims */);
-
-    expect(r.shape).toEqual([1, 2]);
-    const expected = [
-      Math.log(Math.exp(1) + Math.exp(3) + Math.exp(0)),
-      Math.log(Math.exp(2) + Math.exp(0) + Math.exp(1))
-    ];
-    expectArraysClose(await r.data(), expected);
-  });
-
-  it('axes=1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.logSumExp(a, [1]);
-
-    expect(res.shape).toEqual([3]);
-    const expected = [
-      Math.log(Math.exp(1) + Math.exp(2)),
-      Math.log(Math.exp(3) + Math.exp(0)),
-      Math.log(Math.exp(0) + Math.exp(1)),
-    ];
-    expectArraysClose(await res.data(), expected);
-  });
-
-  it('axes = -1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.logSumExp(a, -1);
-
-    expect(res.shape).toEqual([3]);
-    const expected = [
-      Math.log(Math.exp(1) + Math.exp(2)),
-      Math.log(Math.exp(3) + Math.exp(0)),
-      Math.log(Math.exp(0) + Math.exp(1)),
-    ];
-    expectArraysClose(await res.data(), expected);
-  });
-
-  it('2D, axes=1 provided as a single digit', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
-    const res = tf.logSumExp(a, 1);
-
-    expect(res.shape).toEqual([2]);
-    const expected = [
-      Math.log(Math.exp(1) + Math.exp(2) + Math.exp(3)),
-      Math.log(Math.exp(0) + Math.exp(0) + Math.exp(1))
-    ];
-    expectArraysClose(await res.data(), expected);
-  });
-
-  it('axes=0,1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.logSumExp(a, [0, 1]);
-
-    expect(res.shape).toEqual([]);
-    const expected = [Math.log(
-        Math.exp(1) + Math.exp(2) + Math.exp(3) + Math.exp(0) + Math.exp(0) +
-        Math.exp(1))];
-    expectArraysClose(await res.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.logSumExp({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'logSumExp' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.logSumExp([1, 2, -3]);
-    expectArraysClose(
-        await result.data(),
-        Math.log(Math.exp(1) + Math.exp(2) + Math.exp(-3)));
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.logSumExp(['a']))
-        .toThrowError(
-            /Argument 'x' passed to 'logSumExp' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/log_test.ts b/tfjs-master/tfjs-core/src/ops/log_test.ts
deleted file mode 100644
index fa1b505ae..000000000
--- a/tfjs-master/tfjs-core/src/ops/log_test.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('log', ALL_ENVS, () => {
-  it('log', async () => {
-    const a = tf.tensor1d([1, 2]);
-    const r = tf.log(a);
-    expectArraysClose(await r.data(), [Math.log(1), Math.log(2)]);
-  });
-
-  it('log 6D', async () => {
-    const a = tf.range(1, 65).reshape([2, 2, 2, 2, 2, 2]);
-    const r = tf.log(a);
-
-    const expectedResult = [];
-    for (let i = 1; i < 65; i++) {
-      expectedResult[i - 1] = Math.log(i);
-    }
-
-    expectArraysClose(await r.data(), expectedResult);
-  });
-
-  it('log propagates NaNs', async () => {
-    const a = tf.tensor1d([1, NaN]);
-    const r = tf.log(a);
-    expectArraysClose(await r.data(), [Math.log(1), NaN]);
-  });
-
-  it('negtive values', async () => {
-    const a = tf.tensor1d([1, -1]);
-    const r = tf.log(a);
-    expectArraysClose(await r.data(), [Math.log(1), NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.log(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [3 / 5]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.log(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [3 / 5]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1, 2, 3, -5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.log(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [1 / -1, 2 / 2, 3 / 3, 4 / -5]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.log(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [1 / -3, 2 / 1, 3 / 2, 4 / 3]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.log({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'log' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.log([1, 2]);
-    expectArraysClose(await r.data(), [Math.log(1), Math.log(2)]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.log('q'))
-        .toThrowError(/Argument 'x' passed to 'log' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.log(tf.tensor1d([1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'log' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/logical_and.ts b/tfjs-master/tfjs-core/src/ops/logical_and.ts
deleted file mode 100644
index 07ba68643..000000000
--- a/tfjs-master/tfjs-core/src/ops/logical_and.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {LogicalAnd, LogicalAndInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns the truth value of `a AND b` element-wise. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([false, false, true, true], 'bool');
- * const b = tf.tensor1d([false, true, false, true], 'bool');
- *
- * a.logicalAnd(b).print();
- * ```
- *
- * @param a The first input tensor. Must be of dtype bool.
- * @param b The second input tensor. Must be of dtype bool.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function logicalAnd_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');
-  const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: LogicalAndInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(LogicalAnd, inputs as unknown as NamedTensorMap);
-}
-
-export const logicalAnd = /* @__PURE__ */ op({logicalAnd_});
diff --git a/tfjs-master/tfjs-core/src/ops/logical_and_test.ts b/tfjs-master/tfjs-core/src/ops/logical_and_test.ts
deleted file mode 100644
index bf44b4d44..000000000
--- a/tfjs-master/tfjs-core/src/ops/logical_and_test.ts
+++ /dev/null
@@ -1,126 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('logicalAnd', ALL_ENVS, () => {
-  it('Tensor1D.', async () => {
-    let a = tf.tensor1d([1, 0, 0], 'bool');
-    let b = tf.tensor1d([0, 1, 0], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0]);
-
-    a = tf.tensor1d([0, 0, 0], 'bool');
-    b = tf.tensor1d([0, 0, 0], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0]);
-
-    a = tf.tensor1d([1, 1], 'bool');
-    b = tf.tensor1d([1, 1], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [1, 1]);
-  });
-  it('mismatched Tensor1D shapes', () => {
-    const a = tf.tensor1d([1, 0], 'bool');
-    const b = tf.tensor1d([0, 1, 0], 'bool');
-    const f = () => {
-      tf.logicalAnd(a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Tensor2D', async () => {
-    let a = tf.tensor2d([[1, 0, 1], [0, 0, 0]], [2, 3], 'bool');
-    let b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 0, 0, 0]);
-
-    a = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
-    b = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 1, 1, 1]);
-  });
-  it('broadcasting Tensor2D shapes', async () => {
-    const a = tf.tensor2d([[1], [0]], [2, 1], 'bool');
-    const b = tf.tensor2d([[0, 1, 0], [0, 1, 0]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 1, 0, 0, 0, 0]);
-  });
-
-  it('Tensor3D', async () => {
-    let a = tf.tensor3d([[[1], [0], [1]], [[0], [0], [1]]], [2, 3, 1], 'bool');
-    let b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 1, 0, 0, 0]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
-    b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 1, 1, 1]);
-  });
-  it('broadcasting Tensor3D shapes', async () => {
-    const a = tf.tensor3d(
-        [[[1, 0], [0, 0], [1, 1]], [[0, 0], [0, 1], [0, 0]]], [2, 3, 2],
-        'bool');
-    const b =
-        tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
-    expectArraysClose(
-        await tf.logicalAnd(a, b).data(), [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('Tensor4D', async () => {
-    let a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
-    let b = tf.tensor4d([0, 1, 1, 0], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 1, 0]);
-
-    a = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
-    b = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 0]);
-
-    a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
-    b = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor4D shapes', async () => {
-    const a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
-    const b = tf.tensor4d(
-        [[[[1, 0]], [[0, 0]]], [[[0, 0]], [[1, 1]]]], [2, 2, 1, 2], 'bool');
-    expectArraysClose(
-        await tf.logicalAnd(a, b).data(), [1, 0, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [true, false, false];
-    const b = [false, true, false];
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0]);
-  });
-
-  it('TensorLike Chained', async () => {
-    const a = tf.tensor1d([1, 0, 0], 'bool');
-    const b = [false, true, false];
-    expectArraysClose(await a.logicalAnd(b).data(), [0, 0, 0]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.logicalAnd({} as tf.Tensor, tf.scalar(1, 'bool')))
-        .toThrowError(/Argument 'a' passed to 'logicalAnd' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.logicalAnd(tf.scalar(1, 'bool'), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'logicalAnd' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 0, 0, 1];
-    const b = [0, 1, 0, 1];
-    expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/logical_not.ts b/tfjs-master/tfjs-core/src/ops/logical_not.ts
deleted file mode 100644
index 739097948..000000000
--- a/tfjs-master/tfjs-core/src/ops/logical_not.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {LogicalNot, LogicalNotInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {op} from './operation';
-
-/**
- * Returns the truth value of `NOT x` element-wise.
- *
- * ```js
- * const a = tf.tensor1d([false, true], 'bool');
- *
- * a.logicalNot().print();
- * ```
- *
- * @param x The input tensor. Must be of dtype 'bool'.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function logicalNot_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');
-  const inputs: LogicalNotInputs = {x: $x};
-  return ENGINE.runKernel(LogicalNot, inputs as unknown as NamedTensorMap);
-}
-
-export const logicalNot = /* @__PURE__ */ op({logicalNot_});
diff --git a/tfjs-master/tfjs-core/src/ops/logical_not_test.ts b/tfjs-master/tfjs-core/src/ops/logical_not_test.ts
deleted file mode 100644
index 09ce959d1..000000000
--- a/tfjs-master/tfjs-core/src/ops/logical_not_test.ts
+++ /dev/null
@@ -1,94 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('logicalNot', ALL_ENVS, () => {
-  it('Tensor1D.', async () => {
-    let a = tf.tensor1d([1, 0, 0], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [0, 1, 1]);
-
-    a = tf.tensor1d([0, 0, 0], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [1, 1, 1]);
-
-    a = tf.tensor1d([1, 1], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [0, 0]);
-  });
-  it('Tests chaining in Tensor1D', async () => {
-    let a = tf.tensor1d([1, 0, 0], 'bool');
-    expectArraysClose(await a.logicalNot().data(), [0, 1, 1]);
-
-    a = tf.tensor1d([0, 0, 0], 'bool');
-    expectArraysClose(await a.logicalNot().data(), [1, 1, 1]);
-
-    a = tf.tensor1d([1, 1], 'bool');
-    expectArraysClose(await a.logicalNot().data(), [0, 0]);
-  });
-
-  it('Tensor2D', async () => {
-    let a = tf.tensor2d([[1, 0, 1], [0, 0, 0]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [0, 1, 0, 1, 1, 1]);
-
-    a = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [1, 1, 1, 0, 0, 0]);
-  });
-
-  it('Tensor3D', async () => {
-    let a = tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [0, 1, 0, 1, 1, 1]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [1, 1, 1, 0, 0, 0]);
-  });
-
-  it('Tensor4D', async () => {
-    let a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [0, 1, 0, 1]);
-
-    a = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [1, 1, 1, 1]);
-
-    a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [0, 0, 0, 0]);
-  });
-
-  it('Tensor6D', async () => {
-    let a = tf.tensor6d([1, 0, 1, 0], [2, 2, 1, 1, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalNot(a).data(), [0, 1, 0, 1]);
-
-    a = tf.zeros([2, 2, 2, 2, 2, 2]).cast('bool');
-    let expectedResult = new Uint8Array(64).fill(1);
-    expectedResult = expectedResult.fill(1);
-    expectArraysClose(await tf.logicalNot(a).data(), expectedResult);
-
-    a = tf.ones([2, 2, 2, 2, 2, 2]).cast('bool');
-    expectedResult = expectedResult.fill(0);
-    expectArraysClose(await tf.logicalNot(a).data(), expectedResult);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.logicalNot({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'logicalNot' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 0, 0];
-    expectArraysClose(await tf.logicalNot(a).data(), [0, 1, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/logical_or.ts b/tfjs-master/tfjs-core/src/ops/logical_or.ts
deleted file mode 100644
index b339878df..000000000
--- a/tfjs-master/tfjs-core/src/ops/logical_or.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {LogicalOr, LogicalOrInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns the truth value of `a OR b` element-wise. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([false, false, true, true], 'bool');
- * const b = tf.tensor1d([false, true, false, true], 'bool');
- *
- * a.logicalOr(b).print();
- * ```
- * @param a The first input tensor. Must be of dtype bool.
- * @param b The second input tensor. Must be of dtype bool.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function logicalOr_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');
-  const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: LogicalOrInputs = {a: $a, b: $b};
-  return ENGINE.runKernel(LogicalOr, inputs as unknown as NamedTensorMap);
-}
-export const logicalOr = /* @__PURE__ */ op({logicalOr_});
diff --git a/tfjs-master/tfjs-core/src/ops/logical_or_test.ts b/tfjs-master/tfjs-core/src/ops/logical_or_test.ts
deleted file mode 100644
index 56069dd55..000000000
--- a/tfjs-master/tfjs-core/src/ops/logical_or_test.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('logicalOr', ALL_ENVS, () => {
-  it('Tensor1D.', async () => {
-    let a = tf.tensor1d([1, 0, 0], 'bool');
-    let b = tf.tensor1d([0, 1, 0], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [1, 1, 0]);
-
-    a = tf.tensor1d([0, 0, 0], 'bool');
-    b = tf.tensor1d([0, 0, 0], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [0, 0, 0]);
-
-    a = tf.tensor1d([1, 1], 'bool');
-    b = tf.tensor1d([1, 1], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [1, 1]);
-  });
-  it('mismatched Tensor1D shapes', () => {
-    const a = tf.tensor1d([1, 0], 'bool');
-    const b = tf.tensor1d([0, 1, 0], 'bool');
-    const f = () => {
-      tf.logicalOr(a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Tensor2D', async () => {
-    let a = tf.tensor2d([[1, 0, 1], [0, 0, 0]], [2, 3], 'bool');
-    let b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
-    b = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [0, 0, 0, 1, 1, 1]);
-  });
-  it('broadcasting Tensor2D shapes', async () => {
-    const a = tf.tensor2d([[1], [0]], [2, 1], 'bool');
-    const b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [1, 1, 1, 0, 1, 0]);
-  });
-
-  it('Tensor3D', async () => {
-    let a = tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
-    let b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [1, 0, 1, 1, 0, 0]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
-    b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [0, 0, 0, 1, 1, 1]);
-  });
-  it('broadcasting Tensor3D shapes', async () => {
-    const a = tf.tensor3d(
-        [[[1, 0], [0, 0], [1, 1]], [[0, 0], [0, 1], [0, 0]]], [2, 3, 2],
-        'bool');
-    const b =
-        tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
-    expectArraysClose(
-        await tf.logicalOr(a, b).data(), [1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0]);
-  });
-
-  it('Tensor4D', async () => {
-    let a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
-    let b = tf.tensor4d([0, 1, 0, 0], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [1, 1, 1, 0]);
-
-    a = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
-    b = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [0, 0, 0, 0]);
-
-    a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
-    b = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalOr(a, b).data(), [1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor4D shapes', async () => {
-    const a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
-    const b = tf.tensor4d(
-        [[[[1, 0]], [[0, 0]]], [[[0, 0]], [[1, 1]]]], [2, 2, 1, 2], 'bool');
-    expectArraysClose(
-        await tf.logicalOr(a, b).data(), [1, 1, 0, 0, 1, 1, 1, 1]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [true, false, false];
-    const b = [false, true, false];
-    expectArraysClose(await tf.logicalOr(a, b).data(), [1, 1, 0]);
-  });
-
-  it('TensorLike Chained', async () => {
-    const a = tf.tensor1d([1, 0, 0], 'bool');
-    const b = [false, true, false];
-    expectArraysClose(await a.logicalOr(b).data(), [1, 1, 0]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.logicalOr({} as tf.Tensor, tf.scalar(1, 'bool')))
-        .toThrowError(/Argument 'a' passed to 'logicalOr' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.logicalOr(tf.scalar(1, 'bool'), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'logicalOr' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 0, 0, 1];
-    const b = [0, 1, 0, 1];
-    expectArraysClose(await tf.logicalOr(a, b).data(), [1, 1, 0, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/logical_xor.ts b/tfjs-master/tfjs-core/src/ops/logical_xor.ts
deleted file mode 100644
index 0820a58e7..000000000
--- a/tfjs-master/tfjs-core/src/ops/logical_xor.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {logicalAnd} from './logical_and';
-import {logicalNot} from './logical_not';
-import {logicalOr} from './logical_or';
-import {op} from './operation';
-
-/**
- * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([false, false, true, true], 'bool');
- * const b = tf.tensor1d([false, true, false, true], 'bool');
- *
- * a.logicalXor(b).print();
- * ```
- *
- * @param a The first input tensor. Must be of dtype bool.
- * @param b The second input tensor. Must be of dtype bool.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function logicalXor_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');
-  const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  // x ^ y = (x | y) & ~(x & y)
-  return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));
-}
-
-export const logicalXor = /* @__PURE__ */ op({logicalXor_});
diff --git a/tfjs-master/tfjs-core/src/ops/logical_xor_test.ts b/tfjs-master/tfjs-core/src/ops/logical_xor_test.ts
deleted file mode 100644
index 07f1adc3c..000000000
--- a/tfjs-master/tfjs-core/src/ops/logical_xor_test.ts
+++ /dev/null
@@ -1,111 +0,0 @@
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('logicalXor', ALL_ENVS, () => {
-  it('Tensor1D.', async () => {
-    let a = tf.tensor1d([1, 0, 0], 'bool');
-    let b = tf.tensor1d([0, 1, 0], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [1, 1, 0]);
-
-    a = tf.tensor1d([0, 0, 0], 'bool');
-    b = tf.tensor1d([0, 0, 0], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [0, 0, 0]);
-
-    a = tf.tensor1d([1, 1], 'bool');
-    b = tf.tensor1d([1, 1], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [0, 0]);
-  });
-  it('mismatched Tensor1D shapes', () => {
-    const a = tf.tensor1d([1, 0], 'bool');
-    const b = tf.tensor1d([0, 1, 0], 'bool');
-    const f = () => {
-      tf.logicalXor(a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  // Tensor2D:
-  it('Tensor2D', async () => {
-    let a = tf.tensor2d([[1, 0, 1], [0, 0, 0]], [2, 3], 'bool');
-    let b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [1, 0, 1, 0, 1, 0]);
-
-    a = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
-    b = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [0, 0, 0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor2D shapes', async () => {
-    const a = tf.tensor2d([[1], [0]], [2, 1], 'bool');
-    const b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [1, 1, 1, 0, 1, 0]);
-  });
-
-  // Tensor3D:
-  it('Tensor3D', async () => {
-    let a = tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
-    let b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [1, 0, 0, 1, 0, 0]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
-    b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [0, 0, 0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor3D shapes', async () => {
-    const a = tf.tensor3d(
-        [[[1, 0], [0, 0], [1, 1]], [[0, 0], [0, 1], [0, 0]]], [2, 3, 2],
-        'bool');
-    const b =
-        tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
-    expectArraysClose(
-        await tf.logicalXor(a, b).data(), [1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0]);
-  });
-
-  // Tensor4D:
-  it('Tensor4D', async () => {
-    let a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
-    let b = tf.tensor4d([0, 1, 1, 0], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [1, 1, 0, 0]);
-
-    a = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
-    b = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [0, 0, 0, 0]);
-
-    a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
-    b = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
-    expectArraysClose(await tf.logicalXor(a, b).data(), [0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor4D shapes', async () => {
-    const a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
-    const b = tf.tensor4d(
-        [[[[1, 0]], [[0, 0]]], [[[0, 0]], [[1, 1]]]], [2, 2, 1, 2], 'bool');
-    expectArraysClose(
-        await tf.logicalXor(a, b).data(), [0, 1, 0, 0, 1, 1, 1, 1]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [true, false, false];
-    const b = [false, true, false];
-    expectArraysClose(await tf.logicalXor(a, b).data(), [1, 1, 0]);
-  });
-
-  it('TensorLike Chained', async () => {
-    const a = tf.tensor1d([1, 0, 0], 'bool');
-    const b = [false, true, false];
-    expectArraysClose(await a.logicalXor(b).data(), [1, 1, 0]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.logicalXor({} as tf.Tensor, tf.scalar(1, 'bool')))
-        .toThrowError(/Argument 'a' passed to 'logicalXor' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.logicalXor(tf.scalar(1, 'bool'), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'logicalXor' must be a Tensor/);
-  });
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 0, 0, 1];
-    const b = [0, 1, 0, 1];
-    expectArraysClose(await tf.logicalXor(a, b).data(), [1, 1, 0, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/loss_ops_utils.ts b/tfjs-master/tfjs-core/src/ops/loss_ops_utils.ts
deleted file mode 100644
index b8ee1fd49..000000000
--- a/tfjs-master/tfjs-core/src/ops/loss_ops_utils.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export enum Reduction {
-  NONE,
-  MEAN,
-  SUM,
-  SUM_BY_NONZERO_WEIGHTS
-}
diff --git a/tfjs-master/tfjs-core/src/ops/losses/absolute_difference.ts b/tfjs-master/tfjs-core/src/ops/losses/absolute_difference.ts
deleted file mode 100644
index fc4336498..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/absolute_difference.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {assertShapesMatch} from '../../util';
-import {abs} from '../abs';
-import {Reduction} from '../loss_ops_utils';
-import {op} from '../operation';
-import {sub} from '../sub';
-
-import {computeWeightedLoss} from './compute_weighted_loss';
-
-/**
- * Computes the absolute difference loss between two tensors.
- *
- * @param labels The ground truth output tensor, same dimensions as
- *    'predictions'.
- * @param predictions The predicted outputs.
- * @param weights Tensor whose rank is either 0, or the same rank as
- *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
- *    must be either `1`, or the same as the corresponding `losses`
- *    dimension).
- * @param reduction Type of reduction to apply to loss. Should be of type
- *    `Reduction`
- *
- * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
- */
-function absoluteDifference_<T extends Tensor, O extends Tensor>(
-    labels: T|TensorLike, predictions: T|TensorLike,
-    weights?: Tensor|TensorLike,
-    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
-  const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');
-  const $predictions =
-      convertToTensor(predictions, 'predictions', 'absoluteDifference');
-  let $weights: Tensor = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, 'weights', 'absoluteDifference');
-  }
-  assertShapesMatch(
-      $labels.shape, $predictions.shape, 'Error in absoluteDifference: ');
-
-  const losses = abs(sub($labels, $predictions));
-  return computeWeightedLoss(losses, $weights, reduction);
-}
-
-export const absoluteDifference = /* @__PURE__ */ op({absoluteDifference_});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/absolute_difference_test.ts b/tfjs-master/tfjs-core/src/ops/losses/absolute_difference_test.ts
deleted file mode 100644
index 4ebac0b00..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/absolute_difference_test.ts
+++ /dev/null
@@ -1,222 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('absoluteDifference', ALL_ENVS, () => {
-  it('1D', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-
-    const y = tf.losses.absoluteDifference(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (Math.abs(1 - 0.3) + Math.abs(2 - (-0.6)) + Math.abs(3 - (-0.1))) / 3);
-  });
-
-  it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.absoluteDifference(label, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (Math.abs(1 - 0.3) * 0.1 + Math.abs(2 - (-0.6)) * 0.2 +
-         Math.abs(3 - (-0.1)) * 0.3) /
-            3);
-  });
-
-  it('1D - weighted - Reduction.NONE', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.absoluteDifference(
-        label, predictions, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [
-      Math.abs(1 - 0.3) * 0.1, Math.abs(2 - (-0.6)) * 0.2,
-      Math.abs(3 - (-0.1)) * 0.3
-    ]);
-  });
-
-  it('1D - Reduction.MEAN', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-
-    const y = tf.losses.absoluteDifference(
-        label, predictions, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (Math.abs(1 - 0.3) + Math.abs(2 - (-0.6)) + Math.abs(3 - (-0.1))) / 3);
-  });
-
-  it('1D - weighted - Reduction.MEAN', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.absoluteDifference(
-        label, predictions, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((Math.abs(1 - 0.3) * 0.1) + (Math.abs(2 - (-0.6)) * 0.2) +
-         (Math.abs(3 - (-0.1)) * 0.3)) /
-            0.6);
-  });
-
-  it('2D', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-
-    const y = tf.losses.absoluteDifference(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (Math.abs(4 - 1) + Math.abs(8 - 9) + Math.abs(12 - 2) +
-         Math.abs(8 - (-5)) + Math.abs(1 - (-2)) + Math.abs(3 - 6)) /
-            6);
-  });
-
-  it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.absoluteDifference(label, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (Math.abs(4 - 1) * 3 + Math.abs(8 - 9) * 0 + Math.abs(12 - 2) * 5 +
-         Math.abs(8 - (-5)) * 0 + Math.abs(1 - (-2)) * 4 +
-         Math.abs(3 - 6) * 2) /
-            4);
-  });
-
-  it('2D - weighted - Reduction.NONE', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.absoluteDifference(
-        label, predictions, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([2, 3]);
-    expectArraysClose(await y.data(), [
-      Math.abs(4 - 1) * 3, Math.abs(8 - 9) * 6, Math.abs(12 - 2) * 5,
-      Math.abs(8 - (-5)) * 0, Math.abs(1 - (-2)) * 4, Math.abs(3 - 6) * 2
-    ]);
-  });
-
-  it('2D - Reduction.MEAN', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-
-    const y = tf.losses.absoluteDifference(
-        label, predictions, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (Math.abs(4 - 1) + Math.abs(8 - 9) + Math.abs(12 - 2) +
-         Math.abs(8 - (-5)) + Math.abs(1 - (-2)) + Math.abs(3 - 6)) /
-            6);
-  });
-
-  it('2D - weighted - Reduction.MEAN', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.absoluteDifference(
-        label, predictions, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (Math.abs(4 - 1) * 3 + Math.abs(8 - 9) * 6 + Math.abs(12 - 2) * 5 +
-         Math.abs(8 - (-5)) * 0 + Math.abs(1 - (-2)) * 4 +
-         Math.abs(3 - 6) * 2) /
-            20);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e =
-        /Argument 'labels' passed to 'absoluteDifference' must be a Tensor/;
-    expect(
-        () => tf.losses.absoluteDifference(
-            {} as tf.Tensor, predictions, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e = new RegExp(
-        'Argument \'predictions\' passed to \'absoluteDifference\' ' +
-        'must be a Tensor');
-    expect(
-        () => tf.losses.absoluteDifference(
-            label, {} as tf.Tensor, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed weights as a non-tensor', () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-
-    const e =
-        /Argument 'weights' passed to 'absoluteDifference' must be a Tensor/;
-    expect(
-        () => tf.losses.absoluteDifference(
-            label, predictions, {} as tf.Tensor, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const predictions = [1, 2, 3];
-    const label = [0.3, -0.6, -0.1];
-    const weights = [0.1, 0.2, 0.3];
-
-    const y = tf.losses.absoluteDifference(
-        label, predictions, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [
-      Math.abs(1 - 0.3) * 0.1, Math.abs(2 - (-0.6)) * 0.2,
-      Math.abs(3 - (-0.1)) * 0.3
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/compute_weighted_loss.ts b/tfjs-master/tfjs-core/src/ops/losses/compute_weighted_loss.ts
deleted file mode 100644
index ef079a3af..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/compute_weighted_loss.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-
-import {cast} from '../cast';
-import {div} from '../div';
-import {Reduction} from '../loss_ops_utils';
-import {mean} from '../mean';
-import {mul} from '../mul';
-import {notEqual} from '../not_equal';
-import {ones} from '../ones';
-import {op} from '../operation';
-import {scalar} from '../scalar';
-import {sum} from '../sum';
-
-/**
- * Computes the weighted loss between two tensors.
- *
- * @param losses Tensor of shape `[batch_size, d1, ..., dN]`.
- * @param weights Tensor whose rank is either 0, or the same rank as
- *    `losses`, and must be broadcastable to `losses` (i.e., all
- *    dimensions must be either `1`, or the same as the corresponding
- *    `losses` dimension).
- *
- * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
- */
-function computeWeightedLoss_<T extends Tensor, O extends Tensor>(
-    losses: T|TensorLike, weights?: Tensor|TensorLike,
-    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
-  const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');
-  let $weights: Tensor = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');
-  }
-
-  const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);
-
-  if (reduction === Reduction.NONE) {
-    return weightedLoss as O;
-  }
-  if (reduction === Reduction.SUM) {
-    return sum(weightedLoss);
-  }
-  if (reduction === Reduction.MEAN) {
-    if ($weights == null) {
-      return mean(weightedLoss);
-    } else {
-      const broadcastFactor = $losses.size / $weights.size;
-      const result = div(sum(weightedLoss), sum($weights));
-      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :
-                                   result as O;
-    }
-  }
-  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {
-    if ($weights == null) {
-      return div(sum(weightedLoss), scalar($losses.size));
-    } else {
-      const broadcastedWeights = mul($weights, ones($losses.shape));
-
-      const numNonZeros =
-          cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');
-      return div(sum(weightedLoss), numNonZeros);
-    }
-  }
-
-  throw Error(`Unknown reduction: ${reduction}`);
-}
-export const computeWeightedLoss = /* @__PURE__ */ op({computeWeightedLoss_});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/compute_weighted_loss_test.ts b/tfjs-master/tfjs-core/src/ops/losses/compute_weighted_loss_test.ts
deleted file mode 100644
index 29c282b67..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/compute_weighted_loss_test.ts
+++ /dev/null
@@ -1,237 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('computeWeightedLoss', ALL_ENVS, () => {
-  it('1D - no weights', async () => {
-    const losses = tf.tensor1d([1, 2, 3]);
-
-    const y = tf.losses.computeWeightedLoss(losses);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (1 + 2 + 3) / 3);
-  });
-
-  it('1D - no weights - Reduction.NONE', async () => {
-    const losses = tf.tensor1d([1, 2, 3]);
-
-    const y =
-        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [1, 2, 3]);
-  });
-
-  it('1D - no weights - Reduction.MEAN', async () => {
-    const losses = tf.tensor1d([1, 2, 3]);
-
-    const y =
-        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (1 + 2 + 3) / 3);
-  });
-
-  it('1D - no weights - Reduction.SUM', async () => {
-    const losses = tf.tensor1d([1, 2, 3]);
-
-    const y =
-        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (1 + 2 + 3));
-  });
-
-  it('1D - weights', async () => {
-    const losses = tf.tensor1d([1, 2, 3]);
-    const weights = tf.tensor1d([0.1, 0, 0.3]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2);
-  });
-
-  it('2D - weights - broadcast', async () => {
-    const losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.06666667);
-  });
-
-  it('1D - weights - Reduction.NONE', async () => {
-    const losses = tf.tensor1d([1, 2, 3]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [1 * 0.1, 2 * 0.2, 3 * 0.3]);
-  });
-
-  it('1D - weights - Reduction.MEAN', async () => {
-    const losses = tf.tensor1d([1, 2, 3]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3) / 0.6);
-  });
-
-  it('1D - weights - Reduction.SUM', async () => {
-    const losses = tf.tensor1d([1, 2, 3]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3));
-  });
-
-  it('2D - no weights', async () => {
-    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-
-    const y = tf.losses.computeWeightedLoss(losses);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (4 + 8 + 12 + 8 + 1 + 3) / 6);
-  });
-
-  it('2D - weights', async () => {
-    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4);
-  });
-
-  it('2D - no weights - Reduction.MEAN', async () => {
-    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-
-    const y =
-        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (4 + 8 + 12 + 8 + 1 + 3) / 6);
-  });
-
-  it('2D - weights - Reduction.MEAN', async () => {
-    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4);
-  });
-
-  it('2D - weights - broadcast - MEAN', async () => {
-    const losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (0.3 + 0.1 + 0.2) / (3 * 0.6));
-  });
-
-  it('2D - no weights - Reduction.SUM', async () => {
-    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-
-    const y =
-        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (4 + 8 + 12 + 8 + 1 + 3));
-  });
-
-  it('2D - weights - Reduction.SUM', async () => {
-    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(), (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6));
-  });
-
-  it('2D - no weights - Reduction.NONE', async () => {
-    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-
-    const y =
-        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([2, 3]);
-    expectArraysClose(await y.data(), [4, 8, 12, 8, 1, 3]);
-  });
-
-  it('2D - weights - Reduction.NONE', async () => {
-    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
-
-    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([2, 3]);
-    expectArraysClose(
-        await y.data(), [4 * 1, 8 * 0, 12 * 2, (8 * -5), 1 * 0, 3 * 6]);
-  });
-
-  it('throws when passed losses as a non-tensor', () => {
-    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
-
-    const e =
-        /Argument 'losses' passed to 'computeWeightedLoss' must be a Tensor/;
-    expect(
-        () => tf.losses.computeWeightedLoss(
-            {} as tf.Tensor, weights, tf.Reduction.NONE))
-        .toThrowError(e);
-  });
-
-  it('throws when passed weights as a non-tensor', () => {
-    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-
-    const e =
-        /Argument 'weights' passed to 'computeWeightedLoss' must be a Tensor/;
-    expect(
-        () => tf.losses.computeWeightedLoss(
-            losses, {} as tf.Tensor, tf.Reduction.NONE))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const losses = [1, 2, 3];
-    const weights = [0.1, 0, 0.3];
-    const y = tf.losses.computeWeightedLoss(losses, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/cosine_distance.ts b/tfjs-master/tfjs-core/src/ops/losses/cosine_distance.ts
deleted file mode 100644
index 2a6983f21..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/cosine_distance.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {assertShapesMatch} from '../../util';
-import {Reduction} from '../loss_ops_utils';
-import {mul} from '../mul';
-import {op} from '../operation';
-import {scalar} from '../scalar';
-import {sub} from '../sub';
-import {sum} from '../sum';
-
-import {computeWeightedLoss} from './compute_weighted_loss';
-
-/**
- * Computes the cosine distance loss between two tensors.
- *
- * @param labels The ground truth output tensor, same dimensions as
- *    'predictions'.
- * @param predictions The predicted outputs.
- * @param axis The dimension along which the cosine distance is computed.
- * @param weights Tensor whose rank is either 0, or the same rank as
- *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
- *    must be either `1`, or the same as the corresponding `losses`
- *    dimension).
- * @param reduction Type of reduction to apply to loss. Should be of type
- *    `Reduction`
- *
- * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
- */
-function cosineDistance_<T extends Tensor, O extends Tensor>(
-    labels: T|TensorLike, predictions: T|TensorLike, axis: number,
-    weights?: Tensor|TensorLike,
-    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
-  const $labels = convertToTensor(labels, 'labels', 'cosineDistance');
-  const $predictions =
-      convertToTensor(predictions, 'predictions', 'cosineDistance');
-  let $weights: Tensor = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, 'weights', 'cosineDistance');
-  }
-  assertShapesMatch(
-      $labels.shape, $predictions.shape, 'Error in cosineDistance: ');
-
-  const one = scalar(1);
-  const losses = sub(one, sum(mul($labels, $predictions), axis, true));
-  return computeWeightedLoss(losses, $weights, reduction);
-}
-export const cosineDistance = /* @__PURE__ */ op({cosineDistance_});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/cosine_distance_test.ts b/tfjs-master/tfjs-core/src/ops/losses/cosine_distance_test.ts
deleted file mode 100644
index 0b20b549d..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/cosine_distance_test.ts
+++ /dev/null
@@ -1,198 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('cosineDistance', ALL_ENVS, () => {
-  it('1D', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-
-    const y = tf.losses.cosineDistance(label, predictions, 0);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1));
-  });
-
-  it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-    const weights = tf.scalar(0.1);
-
-    const y = tf.losses.cosineDistance(label, predictions, 0, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1);
-  });
-
-  it('1D - weighted - Reduction.NONE', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-    const weights = tf.scalar(0.1);
-
-    const y = tf.losses.cosineDistance(
-        label, predictions, 0, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([1]);
-    expectArraysClose(
-        await y.data(), [(1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]);
-  });
-
-  it('1D - Reduction.MEAN', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-
-    const y = tf.losses.cosineDistance(
-        label, predictions, 0, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)));
-  });
-
-  it('1D - weighted - Reduction.MEAN', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-    const weights = tf.scalar(0.1);
-
-    const y = tf.losses.cosineDistance(
-        label, predictions, 0, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(), ((1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1) / 0.1);
-  });
-
-  it('2D', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-
-    const y = tf.losses.cosineDistance(label, predictions, 1);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((1 - (4 * 1 + 8 * 9 + 12 * 2)) + (1 - (8 * -5 + 1 * -2 + 3 * 6))) / 2);
-  });
-
-  it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 0], [2, 1]);
-
-    const y = tf.losses.cosineDistance(label, predictions, 1, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3 +
-         (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0) /
-            1);
-  });
-
-  it('2D - weighted - Reduction.NONE', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 0], [2, 1]);
-
-    const y = tf.losses.cosineDistance(
-        label, predictions, 1, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([2, 1]);
-    expectArraysClose(await y.data(), [
-      (1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3, (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0
-    ]);
-  });
-
-  it('2D - Reduction.MEAN', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-
-    const y = tf.losses.cosineDistance(
-        label, predictions, 1, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((1 - (4 * 1 + 8 * 9 + 12 * 2)) + (1 - (8 * -5 + 1 * -2 + 3 * 6))) / 2);
-  });
-
-  it('2D - weighted - Reduction.MEAN', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 0], [2, 1]);
-
-    const y = tf.losses.cosineDistance(
-        label, predictions, 1, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3 +
-         (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0) /
-            3);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e = /Argument 'labels' passed to 'cosineDistance' must be a Tensor/;
-    expect(
-        () => tf.losses.cosineDistance(
-            {} as tf.Tensor, predictions, 0, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e = new RegExp(
-        'Argument \'predictions\' passed to \'cosineDistance\' ' +
-        'must be a Tensor');
-    expect(
-        () => tf.losses.cosineDistance(
-            label, {} as tf.Tensor, 0, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed weights as a non-tensor', () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-
-    const e = /Argument 'weights' passed to 'cosineDistance' must be a Tensor/;
-    expect(
-        () => tf.losses.cosineDistance(
-            label, predictions, 0, {} as tf.Tensor, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const predictions = [1, 2, 3];
-    const label = [0.3, -0.6, -0.1];
-    const weights = 0.1;
-
-    const y = tf.losses.cosineDistance(
-        label, predictions, 0, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([1]);
-    expectArraysClose(
-        await y.data(), [(1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/hinge_loss.ts b/tfjs-master/tfjs-core/src/ops/losses/hinge_loss.ts
deleted file mode 100644
index 7bddda916..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/hinge_loss.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {assertShapesMatch} from '../../util';
-import {Reduction} from '../loss_ops_utils';
-import {mul} from '../mul';
-import {op} from '../operation';
-import {relu} from '../relu';
-import {scalar} from '../scalar';
-import {sub} from '../sub';
-
-import {computeWeightedLoss} from './compute_weighted_loss';
-
-/**
- * Computes the Hinge loss between two tensors.
- *
- * @param labels The ground truth output tensor, same dimensions as
- *    'predictions'.
- * @param predictions The predicted outputs.
- * @param weights Tensor whose rank is either 0, or the same rank as
- *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
- *    must be either `1`, or the same as the corresponding `losses`
- *    dimension).
- * @param reduction Type of reduction to apply to loss. Should be of type
- *    `Reduction`
- *
- * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
- */
-function hingeLoss_<T extends Tensor, O extends Tensor>(
-    labels: T|TensorLike, predictions: T|TensorLike,
-    weights?: Tensor|TensorLike,
-    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
-  let $labels = convertToTensor(labels, 'labels', 'hingeLoss');
-  const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');
-  let $weights: Tensor = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, 'weights', 'hingeLoss');
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');
-
-  const one = scalar(1);
-  // Convert binary labels to (-1, 1)
-  $labels = sub(mul(scalar(2), $labels), one);
-  const losses = relu(sub(one, mul($labels, $predictions)));
-  return computeWeightedLoss(losses, $weights, reduction);
-}
-export const hingeLoss = /* @__PURE__ */ op({hingeLoss_});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/hinge_loss_test.ts b/tfjs-master/tfjs-core/src/ops/losses/hinge_loss_test.ts
deleted file mode 100644
index faaec6bc3..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/hinge_loss_test.ts
+++ /dev/null
@@ -1,181 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('hingeLoss', ALL_ENVS, () => {
-  it('1D', async () => {
-    const predictions = tf.tensor1d([0, 0, 1, 1]);
-    const label = tf.tensor1d([0, 1, 0, 1]);
-
-    const y = tf.losses.hingeLoss(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 1.0);
-  });
-
-  it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const predictions = tf.tensor1d([0, 0, 1, 1]);
-    const label = tf.tensor1d([0, 1, 0, 1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);
-
-    const y = tf.losses.hingeLoss(label, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.225);
-  });
-
-  it('1D - weighted - Reduction.NONE', async () => {
-    const predictions = tf.tensor1d([0, 0, 1, 1]);
-    const label = tf.tensor1d([0, 1, 0, 1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);
-
-    const y =
-        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([4]);
-    expectArraysClose(await y.data(), [0.1, 0.2, 0.6, 0.0]);
-  });
-
-  it('1D - Reduction.MEAN', async () => {
-    const predictions = tf.tensor1d([0, 0, 1, 1]);
-    const label = tf.tensor1d([0, 1, 0, 1]);
-
-    const y =
-        tf.losses.hingeLoss(label, predictions, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 1.0);
-  });
-
-  it('1D - weighted - Reduction.MEAN', async () => {
-    const predictions = tf.tensor1d([0, 0, 1, 1]);
-    const label = tf.tensor1d([0, 1, 0, 1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);
-
-    const y =
-        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.9);
-  });
-
-  it('2D', async () => {
-    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
-    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
-
-    const y = tf.losses.hingeLoss(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.8333333);
-  });
-
-  it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
-    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
-    const weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);
-
-    const y = tf.losses.hingeLoss(label, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.26666668);
-  });
-
-  it('2D - weighted - Reduction.NONE', async () => {
-    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
-    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
-    const weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);
-
-    const y =
-        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([2, 3]);
-    expectArraysClose(await y.data(), [0.1, 0.2, 0.3, 0, 1, 0]);
-  });
-
-  it('2D - Reduction.MEAN', async () => {
-    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
-    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
-
-    const y =
-        tf.losses.hingeLoss(label, predictions, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.8333333);
-  });
-
-  it('2D - weighted - Reduction.MEAN', async () => {
-    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
-    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
-    const weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);
-
-    const y =
-        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.76190484);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const predictions = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
-    const weights = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
-
-    const e = /Argument 'labels' passed to 'hingeLoss' must be a Tensor/;
-    expect(
-        () => tf.losses.hingeLoss(
-            {} as tf.Tensor, predictions, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const label = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
-    const weights = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
-
-    const e = new RegExp(
-        'Argument \'predictions\' passed to \'hingeLoss\' ' +
-        'must be a Tensor');
-    expect(
-        () => tf.losses.hingeLoss(
-            label, {} as tf.Tensor, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed weights as a non-tensor', () => {
-    const predictions = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
-    const label = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
-
-    const e = /Argument 'weights' passed to 'hingeLoss' must be a Tensor/;
-    expect(
-        () => tf.losses.hingeLoss(
-            label, predictions, {} as tf.Tensor, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const predictions = [0, 0, 1, 1];
-    const label = [0, 1, 0, 1];
-    const weights = [0.1, 0.2, 0.3, 0.4];
-
-    const y =
-        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([4]);
-    expectArraysClose(await y.data(), [0.1, 0.2, 0.6, 0.0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/huber_loss.ts b/tfjs-master/tfjs-core/src/ops/losses/huber_loss.ts
deleted file mode 100644
index 6db6aac97..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/huber_loss.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {assertShapesMatch} from '../../util';
-import {abs} from '../abs';
-import {add} from '../add';
-import {Reduction} from '../loss_ops_utils';
-import {minimum} from '../minimum';
-import {mul} from '../mul';
-import {op} from '../operation';
-import {scalar} from '../scalar';
-import {square} from '../square';
-import {sub} from '../sub';
-
-import {computeWeightedLoss} from './compute_weighted_loss';
-
-/**
- * Computes the Huber loss between two tensors.
- *
- * @param labels The ground truth output tensor, same dimensions as
- *    'predictions'.
- * @param predictions The predicted outputs.
- * @param weights Tensor whose rank is either 0, or the same rank as
- *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
- *    must be either `1`, or the same as the corresponding `losses`
- *    dimension).
- * @param delta Point where Huber loss changes from quadratic to linear.
- * @param reduction Type of reduction to apply to loss. Should be of type
- *    `Reduction`.
- *
- * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
- */
-function huberLoss_<T extends Tensor, O extends Tensor>(
-    labels: T|TensorLike, predictions: T|TensorLike,
-    weights?: Tensor|TensorLike, delta = 1.0,
-    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
-  const $labels = convertToTensor(labels, 'labels', 'huberLoss');
-  const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');
-  let $weights: Tensor = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, 'weights', 'huberLoss');
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');
-
-  const deltaScalar = scalar(delta);
-  const error = abs(sub($predictions, $labels));
-  const quadratic = minimum(error, deltaScalar);
-  const linear = sub(error, quadratic);
-
-  const losses =
-      add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
-  return computeWeightedLoss(losses, $weights, reduction);
-}
-export const huberLoss = /* @__PURE__ */ op({huberLoss_});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/huber_loss_test.ts b/tfjs-master/tfjs-core/src/ops/losses/huber_loss_test.ts
deleted file mode 100644
index f1ef22079..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/huber_loss_test.ts
+++ /dev/null
@@ -1,191 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('huberLoss', ALL_ENVS, () => {
-  it('1D', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-
-    const y = tf.losses.huberLoss(labels, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 1.1816667);
-  });
-
-  it('1D - delta', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-    const delta = 0.4;
-
-    const y = tf.losses.huberLoss(labels, predictions, undefined, delta);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.58666664);
-  });
-
-  it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.huberLoss(labels, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.30816665);
-  });
-
-  it('1D - weighted - Reduction.NONE', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.huberLoss(
-        labels, predictions, weights, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [0.0245, 0.17999999, 0.72]);
-  });
-
-  it('1D - Reduction.MEAN', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-
-    const y = tf.losses.huberLoss(
-        labels, predictions, undefined, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 1.1816667);
-  });
-
-  it('1D - weighted - Reduction.MEAN', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.huberLoss(
-        labels, predictions, weights, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 1.5408332);
-  });
-
-  it('2D', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-
-    const y = tf.losses.huberLoss(labels, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.01795);
-  });
-
-  it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-    const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.huberLoss(labels, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.040875003);
-  });
-
-  it('2D - weighted - Reduction.NONE', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-    const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.huberLoss(
-        labels, predictions, weights, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([2, 3]);
-    expectArraysClose(await y.data(), [0.135, 0., 0.001, 0., 0.005, 0.0225]);
-  });
-
-  it('2D - Reduction.MEAN', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-
-    const y = tf.losses.huberLoss(
-        labels, predictions, undefined, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.01795);
-  });
-
-  it('2D - weighted - Reduction.MEAN', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-    const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.huberLoss(
-        labels, predictions, weights, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.011678572);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e = /Argument 'labels' passed to 'huberLoss' must be a Tensor/;
-    expect(
-        () => tf.losses.huberLoss(
-            {} as tf.Tensor, predictions, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e = new RegExp(
-        'Argument \'predictions\' passed to \'huberLoss\' ' +
-        'must be a Tensor');
-    expect(
-        () => tf.losses.huberLoss(
-            labels, {} as tf.Tensor, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed weights as a non-tensor', () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-
-    const e = /Argument 'weights' passed to 'huberLoss' must be a Tensor/;
-    expect(
-        () => tf.losses.huberLoss(
-            labels, predictions, {} as tf.Tensor, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const labels = [1, 2, 3];
-    const predictions = [0.3, 0.6, 0.1];
-    const weights = [0.1, 0.2, 0.3];
-
-    const y = tf.losses.huberLoss(
-        labels, predictions, weights, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [0.0245, 0.17999999, 0.72]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/log_loss.ts b/tfjs-master/tfjs-core/src/ops/losses/log_loss.ts
deleted file mode 100644
index 0fae4f326..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/log_loss.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {assertShapesMatch} from '../../util';
-import {add} from '../add';
-import {log} from '../log';
-import {Reduction} from '../loss_ops_utils';
-import {mul} from '../mul';
-import {neg} from '../neg';
-import {op} from '../operation';
-import {scalar} from '../scalar';
-import {sub} from '../sub';
-
-import {computeWeightedLoss} from './compute_weighted_loss';
-
-/**
- * Computes the log loss between two tensors.
- *
- * @param labels The ground truth output tensor, same dimensions as
- *    'predictions'.
- * @param predictions The predicted outputs.
- * @param weights Tensor whose rank is either 0, or the same rank as
- *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
- *    must be either `1`, or the same as the corresponding `losses`
- *    dimension).
- * @param epsilon A small increment to avoid taking log of zero
- * @param reduction Type of reduction to apply to loss. Should be of type
- *    `Reduction`
- *
- * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
- */
-function logLoss_<T extends Tensor, O extends Tensor>(
-    labels: T|TensorLike, predictions: T|TensorLike,
-    weights?: Tensor|TensorLike, epsilon = 1e-7,
-    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
-  const $labels = convertToTensor(labels, 'labels', 'logLoss');
-  const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');
-  let $weights: Tensor = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, 'weights', 'logLoss');
-  }
-  assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');
-
-  const one = scalar(1);
-  const epsilonScalar = scalar(epsilon);
-
-  const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));
-  const l2 =
-      mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));
-  const losses = sub(l1, l2);
-  return computeWeightedLoss(losses, $weights, reduction);
-}
-export const logLoss = /* @__PURE__ */ op({logLoss_});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/log_loss_test.ts b/tfjs-master/tfjs-core/src/ops/losses/log_loss_test.ts
deleted file mode 100644
index 6a10d12db..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/log_loss_test.ts
+++ /dev/null
@@ -1,191 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('logLoss', ALL_ENVS, () => {
-  it('1D', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-
-    const y = tf.losses.logLoss(labels, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 2.668788);
-  });
-
-  it('1D - Check for negative values', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, -0.6, -0.1]);
-
-    const y = tf.losses.logLoss(labels, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), NaN);
-  });
-
-  it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.logLoss(labels, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.7168596);
-  });
-
-  it('1D - weighted - Reduction.NONE', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.logLoss(
-        labels, predictions, weights, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [0.12039725, 0.02107204, 2.0091095]);
-  });
-
-  it('1D - Reduction.MEAN', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-
-    const y = tf.losses.logLoss(
-        labels, predictions, undefined, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 2.668788);
-  });
-
-  it('1D - weighted - Reduction.MEAN', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.logLoss(
-        labels, predictions, weights, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 3.5842977);
-  });
-
-  it('2D', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-
-    const y = tf.losses.logLoss(labels, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.60019904);
-  });
-
-  it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-    const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.logLoss(labels, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 1.8866577);
-  });
-
-  it('2D - weighted - Reduction.NONE', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-    const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.logLoss(
-        labels, predictions, weights, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([2, 3]);
-    expectArraysClose(
-        await y.data(), [2.9527497, 0., 1.8451363, 0., 1.3829476, 1.3657978]);
-  });
-
-  it('2D - Reduction.MEAN', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-
-    const y = tf.losses.logLoss(
-        labels, predictions, undefined, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.60019904);
-  });
-
-  it('2D - weighted - Reduction.MEAN', async () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-    const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.logLoss(
-        labels, predictions, weights, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0.53904504);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e = /Argument 'labels' passed to 'logLoss' must be a Tensor/;
-    expect(
-        () => tf.losses.logLoss(
-            {} as tf.Tensor, predictions, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e = new RegExp(
-        'Argument \'predictions\' passed to \'logLoss\' ' +
-        'must be a Tensor');
-    expect(
-        () => tf.losses.logLoss(
-            labels, {} as tf.Tensor, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed weights as a non-tensor', () => {
-    const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
-    const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
-
-    const e = /Argument 'weights' passed to 'logLoss' must be a Tensor/;
-    expect(
-        () => tf.losses.logLoss(
-            labels, predictions, {} as tf.Tensor, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const labels = [1, 2, 3];
-    const predictions = [0.3, 0.6, 0.1];
-    const weights = [0.1, 0.2, 0.3];
-
-    const y = tf.losses.logLoss(
-        labels, predictions, weights, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [0.12039725, 0.02107204, 2.0091095]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/mean_squared_error.ts b/tfjs-master/tfjs-core/src/ops/losses/mean_squared_error.ts
deleted file mode 100644
index af1a110d0..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/mean_squared_error.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {assertShapesMatch} from '../../util';
-import {Reduction} from '../loss_ops_utils';
-import {op} from '../operation';
-import {squaredDifference} from '../squared_difference';
-
-import {computeWeightedLoss} from './compute_weighted_loss';
-
-/**
- * Computes the mean squared error between two tensors.
- *
- * @param labels The ground truth output tensor, same dimensions as
- *    'predictions'.
- * @param predictions The predicted outputs.
- * @param weights Tensor whose rank is either 0, or the same rank as
- *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
- *    must be either `1`, or the same as the corresponding `losses`
- *    dimension).
- * @param reduction Type of reduction to apply to loss. Should be of type
- *    `Reduction`
- *
- * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
- */
-function meanSquaredError_<T extends Tensor, O extends Tensor>(
-    labels: T|TensorLike, predictions: T|TensorLike,
-    weights?: Tensor|TensorLike,
-    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
-  const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');
-  const $predictions =
-      convertToTensor(predictions, 'predictions', 'meanSquaredError');
-  let $weights: Tensor = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, 'weights', 'meanSquaredError');
-  }
-  assertShapesMatch(
-      $labels.shape, $predictions.shape, 'Error in meanSquaredError: ');
-
-  const losses = squaredDifference($labels, $predictions);
-  return computeWeightedLoss(losses, $weights, reduction);
-}
-export const meanSquaredError = /* @__PURE__ */ op({meanSquaredError_});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/mean_squared_error_test.ts b/tfjs-master/tfjs-core/src/ops/losses/mean_squared_error_test.ts
deleted file mode 100644
index e0f93b908..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/mean_squared_error_test.ts
+++ /dev/null
@@ -1,227 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('meanSquaredError', ALL_ENVS, () => {
-  it('1D', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-
-    const y = tf.losses.meanSquaredError(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((1 - 0.3) * (1 - 0.3) + (2 - (-0.6)) * (2 - (-0.6)) +
-         (3 - (-0.1)) * (3 - (-0.1))) /
-            3);
-  });
-
-  it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.meanSquaredError(label, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((1 - 0.3) * (1 - 0.3) * 0.1 + (2 - (-0.6)) * (2 - (-0.6)) * 0.2 +
-         (3 - (-0.1)) * (3 - (-0.1)) * 0.3) /
-            3);
-  });
-
-  it('1D - weighted - Reduction.NONE', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.meanSquaredError(
-        label, predictions, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [
-      (1 - 0.3) * (1 - 0.3) * 0.1, (2 - (-0.6)) * (2 - (-0.6)) * 0.2,
-      (3 - (-0.1)) * (3 - (-0.1)) * 0.3
-    ]);
-  });
-
-  it('1D - Reduction.MEAN', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-
-    const y = tf.losses.meanSquaredError(
-        label, predictions, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((1 - 0.3) * (1 - 0.3) + (2 - (-0.6)) * (2 - (-0.6)) +
-         (3 - (-0.1)) * (3 - (-0.1))) /
-            3);
-  });
-
-  it('1D - weighted - Reduction.MEAN', async () => {
-    const predictions = tf.tensor1d([1, 2, 3]);
-    const label = tf.tensor1d([0.3, -0.6, -0.1]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.meanSquaredError(
-        label, predictions, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        (((1 - 0.3) * (1 - 0.3) * 0.1) + ((2 - (-0.6)) * (2 - (-0.6)) * 0.2) +
-         ((3 - (-0.1)) * (3 - (-0.1)) * 0.3)) /
-            0.6);
-  });
-
-  it('2D', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-
-    const y = tf.losses.meanSquaredError(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((4 - 1) * (4 - 1) + (8 - 9) * (8 - 9) + (12 - 2) * (12 - 2) +
-         (8 - (-5)) * (8 - (-5)) + (1 - (-2)) * (1 - (-2)) +
-         (3 - 6) * (3 - 6)) /
-            6);
-  });
-
-  it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.meanSquaredError(label, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((4 - 1) * (4 - 1) * 3 + (8 - 9) * (8 - 9) * 0 +
-         (12 - 2) * (12 - 2) * 5 + (8 - (-5)) * (8 - (-5)) * 0 +
-         (1 - (-2)) * (1 - (-2)) * 4 + (3 - 6) * (3 - 6) * 2) /
-            4);
-  });
-
-  it('2D - weighted - Reduction.NONE', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.meanSquaredError(
-        label, predictions, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([2, 3]);
-    expectArraysClose(await y.data(), [
-      (4 - 1) * (4 - 1) * 3, (8 - 9) * (8 - 9) * 6, (12 - 2) * (12 - 2) * 5,
-      (8 - (-5)) * (8 - (-5)) * 0, (1 - (-2)) * (1 - (-2)) * 4,
-      (3 - 6) * (3 - 6) * 2
-    ]);
-  });
-
-  it('2D - Reduction.MEAN', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-
-    const y = tf.losses.meanSquaredError(
-        label, predictions, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((4 - 1) * (4 - 1) + (8 - 9) * (8 - 9) + (12 - 2) * (12 - 2) +
-         (8 - (-5)) * (8 - (-5)) + (1 - (-2)) * (1 - (-2)) +
-         (3 - 6) * (3 - 6)) /
-            6);
-  });
-
-  it('2D - weighted - Reduction.MEAN', async () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const y = tf.losses.meanSquaredError(
-        label, predictions, weights, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        ((4 - 1) * (4 - 1) * 3 + (8 - 9) * (8 - 9) * 6 +
-         (12 - 2) * (12 - 2) * 5 + (8 - (-5)) * (8 - (-5)) * 0 +
-         (1 - (-2)) * (1 - (-2)) * 4 + (3 - 6) * (3 - 6) * 2) /
-            20);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e = /Argument 'labels' passed to 'meanSquaredError' must be a Tensor/;
-    expect(
-        () => tf.losses.meanSquaredError(
-            {} as tf.Tensor, predictions, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed label as a non-tensor', () => {
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
-
-    const e = new RegExp(
-        'Argument \'predictions\' passed to \'meanSquaredError\' ' +
-        'must be a Tensor');
-    expect(
-        () => tf.losses.meanSquaredError(
-            label, {} as tf.Tensor, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed weights as a non-tensor', () => {
-    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
-    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
-
-    const e =
-        /Argument 'weights' passed to 'meanSquaredError' must be a Tensor/;
-    expect(
-        () => tf.losses.meanSquaredError(
-            label, predictions, {} as tf.Tensor, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const predictions = [1, 2, 3];
-    const label = [0.3, -0.6, -0.1];
-    const weights = [0.1, 0.2, 0.3];
-
-    const y = tf.losses.meanSquaredError(
-        label, predictions, weights, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [
-      (1 - 0.3) * (1 - 0.3) * 0.1, (2 - (-0.6)) * (2 - (-0.6)) * 0.2,
-      (3 - (-0.1)) * (3 - (-0.1)) * 0.3
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/sigmoid_cross_entropy.ts b/tfjs-master/tfjs-core/src/ops/losses/sigmoid_cross_entropy.ts
deleted file mode 100644
index f3f59d1b0..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/sigmoid_cross_entropy.ts
+++ /dev/null
@@ -1,121 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {assertShapesMatch} from '../../util';
-import {abs} from '../abs';
-import {add} from '../add';
-import {exp} from '../exp';
-import {log1p} from '../log1p';
-import {Reduction} from '../loss_ops_utils';
-import {mul} from '../mul';
-import {neg} from '../neg';
-import {op} from '../operation';
-import {relu} from '../relu';
-import {scalar} from '../scalar';
-import {sub} from '../sub';
-
-import {computeWeightedLoss} from './compute_weighted_loss';
-
-function sigmoidCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(
-    labels: T|TensorLike, logits: T|TensorLike): O {
-  const $labels =
-      convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');
-  const $logits =
-      convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');
-  assertShapesMatch(
-      $labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');
-
-  /**
-   * Implementation Details:
-   *
-   * For brevity, let `x = logits`, `z = labels`.  The logistic loss is
-   *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
-   *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
-   *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
-   *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
-   *   = (1 - z) * x + log(1 + exp(-x))
-   *   = x - x * z + log(1 + exp(-x))
-   *
-   *   For x < 0, to avoid overflow in exp(-x), we reformulate the above
-   *     x - x * z + log(1 + exp(-x))
-   *   = log(exp(x)) - x * z + log(1 + exp(-x))
-   *   = - x * z + log(1 + exp(x))
-   *
-   * Hence, to ensure stability and avoid overflow, the implementation uses
-   * this equivalent formulation:
-   *     max(x, 0) - x * z + log(1 + exp(-abs(x)))
-   */
-  const maxOutput = relu($logits);
-  const outputXTarget = mul($logits, $labels);
-  const sigmoidOutput = log1p(exp(neg(abs($logits))));
-
-  return add(sub(maxOutput, outputXTarget), sigmoidOutput);
-}
-
-/**
- * Computes the sigmoid cross entropy loss between two tensors.
- *
- * If labelSmoothing is nonzero, smooth the labels towards 1/2:
- *
- *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)
- *                         + 0.5 * labelSmoothing
- *
- * @param multiClassLabels The ground truth output tensor of shape
- * [batch_size, num_classes], same dimensions as 'predictions'.
- * @param logits The predicted outputs.
- * @param weights Tensor whose rank is either 0, or the same rank as
- *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
- *    must be either `1`, or the same as the corresponding `losses`
- *    dimension).
- * @param labelSmoothing If greater than 0, then smooth the labels.
- * @param reduction Type of reduction to apply to loss. Should be of type
- *    `Reduction`
- *
- * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }
- */
-function sigmoidCrossEntropy_<T extends Tensor, O extends Tensor>(
-    multiClassLabels: T|TensorLike, logits: T|TensorLike,
-    weights?: Tensor|TensorLike, labelSmoothing = 0,
-    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
-  let $multiClassLabels = convertToTensor(
-      multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');
-  const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');
-  let $weights: Tensor = null;
-  if (weights != null) {
-    $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');
-  }
-  assertShapesMatch(
-      $multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');
-
-  if (labelSmoothing > 0) {
-    const labelSmoothingScalar = scalar(labelSmoothing);
-    const one = scalar(1);
-    const half = scalar(0.5);
-
-    $multiClassLabels =
-        add(mul($multiClassLabels, sub(one, labelSmoothingScalar)),
-            mul(half, labelSmoothingScalar));
-  }
-  const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
-
-  return computeWeightedLoss(losses, $weights, reduction);
-}
-
-export const sigmoidCrossEntropy = /* @__PURE__ */ op({sigmoidCrossEntropy_});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/sigmoid_cross_entropy_test.ts b/tfjs-master/tfjs-core/src/ops/losses/sigmoid_cross_entropy_test.ts
deleted file mode 100644
index acb2947af..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/sigmoid_cross_entropy_test.ts
+++ /dev/null
@@ -1,175 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('sigmoidCrossEntropy', ALL_ENVS, () => {
-  it('All wrong', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const y = tf.losses.sigmoidCrossEntropy(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 6.6667123);
-  });
-
-  it('All right', async () => {
-    const label = tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const y = tf.losses.sigmoidCrossEntropy(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0);
-  });
-
-  it('Weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-
-    const y = tf.losses.sigmoidCrossEntropy(label, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 1.3333424);
-  });
-
-  it('Weighted - Reduction.NONE', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-
-    const y = tf.losses.sigmoidCrossEntropy(
-        label, predictions, weights, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3, 3]);
-    expectArraysClose(await y.data(), [
-      1.0000046, 9.0797803e-06, 3.0000138e+00, 1.0000046e+00, 2.0000093e+00,
-      1.3619671e-05, 4.5398901e-06, 2.0000093e+00, 3.0000138e+00
-    ]);
-  });
-
-  it('Reduction.MEAN', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const y = tf.losses.sigmoidCrossEntropy(
-        label, predictions, undefined, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 6.6667123);
-  });
-
-  it('Weighted - Reduction.MEAN', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-
-    const y = tf.losses.sigmoidCrossEntropy(
-        label, predictions, weights, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        6.666712284088135,
-    );
-  });
-
-  it('Label Smoothing - Weighted - Reduction.MEAN', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-    const labelSmoothing = 0.3;
-
-    const y = tf.losses.sigmoidCrossEntropy(
-        label, predictions, weights, labelSmoothing, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 6.1667128);
-  });
-
-  it('throws when multiClassLabels and logits are of different shapes', () => {
-    const multiClassLabels =
-        tf.tensor2d([10, 10, 10, 10, 10, 10, 10, 10, 10], [3, 3]);
-    const logits = tf.tensor2d([10, 10, 10, 10, 10, 10], [2, 3]);
-
-    const e = new RegExp(
-        'Error in sigmoidCrossEntropy:  Shapes 3,3 and 2,3 must match');
-    expect(() => tf.losses.sigmoidCrossEntropy(multiClassLabels, logits))
-        .toThrowError(e);
-  });
-
-  it('throws when passed multiClassLabels as a non-tensor', () => {
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-
-    const e = new RegExp(
-        'Argument \'multiClassLabels\' passed to \'sigmoidCrossEntropy\' ' +
-        'must be a Tensor');
-
-    expect(
-        () => tf.losses.sigmoidCrossEntropy(
-            {} as tf.Tensor, predictions, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed logits as a non-tensor', () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-
-    const e = new RegExp(
-        'Argument \'logits\' passed to \'sigmoidCrossEntropy\' ' +
-        'must be a Tensor');
-    expect(
-        () => tf.losses.sigmoidCrossEntropy(
-            label, {} as tf.Tensor, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed weights as a non-tensor', () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const e =
-        /Argument 'weights' passed to 'sigmoidCrossEntropy' must be a Tensor/;
-    expect(
-        () => tf.losses.sigmoidCrossEntropy(
-            label, predictions, {} as tf.Tensor, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/softmax_cross_entropy.ts b/tfjs-master/tfjs-core/src/ops/losses/softmax_cross_entropy.ts
deleted file mode 100644
index 16d21d21a..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/softmax_cross_entropy.ts
+++ /dev/null
@@ -1,156 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {customGrad} from '../../gradients';
-import {Tensor} from '../../tensor';
-import {GradSaveFunc} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {assertShapesMatch} from '../../util';
-import {add} from '../add';
-import {expandShapeToKeepDim} from '../axis_util';
-import {cast} from '../cast';
-import {div} from '../div';
-import {exp} from '../exp';
-import {logSumExp} from '../log_sum_exp';
-import {Reduction} from '../loss_ops_utils';
-import {mul} from '../mul';
-import {neg} from '../neg';
-import {op} from '../operation';
-import {reshape} from '../reshape';
-import {scalar} from '../scalar';
-import {sub} from '../sub';
-import {sum} from '../sum';
-
-import {computeWeightedLoss} from './compute_weighted_loss';
-
-/**
- * Computes softmax cross entropy between logits and labels.
- *
- * Measures the probability error in discrete classification tasks in which
- * the classes are mutually exclusive (each entry is in exactly one class).
- * For example, each CIFAR-10 image is labeled with one and only one label: an
- * image can be a dog or a truck, but not both.
- *
- * `NOTE`: While the classes are mutually exclusive, their probabilities need
- * not be. All that is required is that each row of labels is a valid
- * probability distribution. If they are not, the computation of the gradient
- * will be incorrect.
- *
- * `WARNING`: This op expects unscaled logits, since it performs a softmax on
- * logits internally for efficiency. Do not call this op with the output of
- * softmax, as it will produce incorrect results.
- *
- * logits and labels must have the same shape, e.g. [batch_size, num_classes]
- * and the same dtype.
- * @param labels The labels array.
- * @param logits The logits array.
- * @param dim The dimension softmax would be performed on. Defaults to `-1`
- *     which indicates the last dimension.
- */
-function softmaxCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(
-    labels: T, logits: T, dim = -1): O {
-  if (dim === -1) {
-    dim = logits.rank - 1;
-  }
-
-  if (dim !== logits.rank - 1) {
-    throw Error(
-        `Softmax cross entropy along a non-last dimension is not yet ` +
-        `supported. Labels / logits was rank ${logits.rank} ` +
-        `and dim was ${dim}`);
-  }
-  // Use a custom gradient for numerical stability.
-  const customOp =
-      customGrad((labels: Tensor, logits: Tensor, save: GradSaveFunc) => {
-        // Reference:
-        //   1. http://cs231n.github.io/linear-classify/#softmax
-        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/
-        const keepDims = true;
-        const lse = logSumExp(logits, [dim], keepDims);
-        const logResult = sub(cast(logits, 'float32'), lse);
-        save([labels, logResult]);
-
-        const costVector = neg(mul(logResult, labels));
-        const value: O = sum(costVector, [dim]);
-
-        const gradFunc = (dy: O, saved: Tensor[]) => {
-          const [labels, logResult] = saved;
-          const dyShape = expandShapeToKeepDim(dy.shape, [dim]);
-          return [
-            mul(reshape(dy, dyShape),
-                sub(cast(labels, 'float32'), exp(logResult))),
-            mul(reshape(dy, dyShape),
-                sub(exp(logResult), cast(labels, 'float32'))),
-          ];
-        };
-        return {value, gradFunc};
-      });
-
-  return customOp(labels, logits);
-}
-
-/**
- * Computes the softmax cross entropy loss between two tensors.
- *
- * If labelSmoothing is nonzero, smooth the labels towards 1/2:
- *
- *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)
- *                         + labelSmoothing / numClasses
- *
- * @param onehotLabels One hot encoded labels
- *    [batch_size, num_classes], same dimensions as 'predictions'.
- * @param logits The predicted outputs.
- * @param weights Tensor whose rank is either 0, or 1, and must be
- *    broadcastable to `loss`  of shape [batch_size]
- * @param labelSmoothing If greater than 0, then smooth the labels.
- * @param reduction Type of reduction to apply to loss. Should be of type
- *    `Reduction`
- *
- * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }
- */
-function softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(
-    onehotLabels: T|TensorLike, logits: T|TensorLike,
-    weights?: Tensor|TensorLike, labelSmoothing = 0,
-    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
-  let $onehotLabels =
-      convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');
-  const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');
-  let $weights: Tensor = null;
-
-  if (weights != null) {
-    $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');
-  }
-
-  assertShapesMatch(
-      $onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');
-
-  if (labelSmoothing > 0) {
-    const labelSmoothingScalar = scalar(labelSmoothing);
-    const one = scalar(1);
-    const numClasses = scalar($onehotLabels.shape[1]);
-
-    $onehotLabels =
-        add(mul($onehotLabels, sub(one, labelSmoothingScalar)),
-            div(labelSmoothingScalar, numClasses));
-  }
-
-  const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
-
-  return computeWeightedLoss(losses, $weights, reduction);
-}
-
-export const softmaxCrossEntropy = /* @__PURE__ */ op({softmaxCrossEntropy_});
diff --git a/tfjs-master/tfjs-core/src/ops/losses/softmax_cross_entropy_test.ts b/tfjs-master/tfjs-core/src/ops/losses/softmax_cross_entropy_test.ts
deleted file mode 100644
index a2352e45b..000000000
--- a/tfjs-master/tfjs-core/src/ops/losses/softmax_cross_entropy_test.ts
+++ /dev/null
@@ -1,185 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('softmaxCrossEntropy', ALL_ENVS, () => {
-  it('All wrong', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const y = tf.losses.softmaxCrossEntropy(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 20);
-  });
-
-  it('All right', async () => {
-    const label = tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const y = tf.losses.softmaxCrossEntropy(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 0);
-  });
-
-  it('Weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const weights =
-        tf.tensor2d([[0.1, 0.2, 0.3], [0.1, 0.2, 0.3], [0.1, 0.2, 0.3]]);
-
-    const y = tf.losses.softmaxCrossEntropy(label, predictions, weights);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 4);
-  });
-
-  it('Weighted - Reduction.NONE', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.softmaxCrossEntropy(
-        label, predictions, weights, undefined, tf.Reduction.NONE);
-
-    expect(y.shape).toEqual([3]);
-    expectArraysClose(await y.data(), [2, 4, 6]);
-  });
-
-  it('Reduction.MEAN', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const y = tf.losses.softmaxCrossEntropy(
-        label, predictions, undefined, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 20);
-  });
-
-  it('Weighted - Reduction.MEAN', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-    const weights = tf.tensor1d([0.1, 0.2, 0.3]);
-
-    const y = tf.losses.softmaxCrossEntropy(
-        label, predictions, weights, undefined, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(
-        await y.data(),
-        20,
-    );
-  });
-
-  it('Label Smoothing - Weighted - Reduction.MEAN', async () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-    const labelSmoothing = 0.3;
-
-    const y = tf.losses.softmaxCrossEntropy(
-        label, predictions, weights, labelSmoothing, tf.Reduction.MEAN);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 18);
-  });
-
-  it('throws when multiClassLabels and logits are of different shapes', () => {
-    const multiClassLabels =
-        tf.tensor2d([10, 10, 10, 10, 10, 10, 10, 10, 10], [3, 3]);
-    const logits = tf.tensor2d([10, 10, 10, 10, 10, 10], [2, 3]);
-
-    const e = new RegExp(
-        'Error in softmaxCrossEntropy:  Shapes 3,3 and 2,3 must match');
-    expect(() => tf.losses.softmaxCrossEntropy(multiClassLabels, logits))
-        .toThrowError(e);
-  });
-
-  it('throws when passed multiClassLabels as a non-tensor', () => {
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-
-    const e = new RegExp(
-        'Argument \'onehotLabels\' passed to \'softmaxCrossEntropy\' ' +
-        'must be a Tensor');
-
-    expect(
-        () => tf.losses.softmaxCrossEntropy(
-            {} as tf.Tensor, predictions, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed logits as a non-tensor', () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
-
-    const e = new RegExp(
-        'Argument \'logits\' passed to \'softmaxCrossEntropy\' ' +
-        'must be a Tensor');
-    expect(
-        () => tf.losses.softmaxCrossEntropy(
-            label, {} as tf.Tensor, weights, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('throws when passed weights as a non-tensor', () => {
-    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
-    const predictions = tf.tensor2d(
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],
-        [3, 3]);
-
-    const e =
-        /Argument 'weights' passed to 'softmaxCrossEntropy' must be a Tensor/;
-    expect(
-        () => tf.losses.softmaxCrossEntropy(
-            label, predictions, {} as tf.Tensor, tf.Reduction.MEAN))
-        .toThrowError(e);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const label = [[0, 0, 1], [1, 0, 0], [0, 1, 0]];
-    const predictions =
-        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]];
-
-    const y = tf.losses.softmaxCrossEntropy(label, predictions);
-
-    expect(y.shape).toEqual([]);
-    expectArraysClose(await y.data(), 20);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/lower_bound.ts b/tfjs-master/tfjs-core/src/ops/lower_bound.ts
deleted file mode 100644
index 5be8197de..000000000
--- a/tfjs-master/tfjs-core/src/ops/lower_bound.ts
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {TensorLike} from '../types';
-import {searchSorted} from './search_sorted';
-
-/**
- * Searches for where a value would go in a sorted sequence.
- *
- * This is not a method for checking containment (like javascript in).
- *
- * The typical use case for this operation is "binning", "bucketing", or
- * "discretizing". The values are assigned to bucket-indices based on the edges
- * listed in 'sortedSequence'. This operation returns the bucket-index for each
- * value.
- *
- * The index returned corresponds to the first edge greater than or equal to the
- * value.
- *
- * The axis is not settable for this operation. It always operates on the
- * innermost dimension (axis=-1). The operation will accept any number of outer
- * dimensions.
- *
- * Note: This operation assumes that 'lowerBound' is sorted along the
- * innermost axis, maybe using 'sort(..., axis=-1)'. If the sequence is not
- * sorted no error is raised and the content of the returned tensor is not well
- * defined.
- *
- * ```js
- * const edges = tf.tensor1d([-1, 3.3, 9.1, 10.0]);
- * let values = tf.tensor1d([0.0, 4.1, 12.0]);
- * const result1 = tf.lowerBound(edges, values);
- * result1.print(); // [1, 2, 4]
- *
- * const seq = tf.tensor1d([0, 3, 9, 10, 10]);
- * values = tf.tensor1d([0, 4, 10]);
- * const result2 = tf.lowerBound(seq, values);
- * result2.print(); // [0, 2, 3]
- *
- * const sortedSequence = tf.tensor2d([[0., 3., 8., 9., 10.],
- *                                     [1., 2., 3., 4., 5.]]);
- * values = tf.tensor2d([[9.8, 2.1, 4.3],
- *                       [0.1, 6.6, 4.5, ]]);
- * const result3 = tf.lowerBound(sortedSequence, values);
- * result3.print(); // [[4, 1, 2], [0, 5, 4]]
- * ```
- * @param sortedSequence: N-D. Sorted sequence.
- * @param values: N-D. Search values.
- * @return An N-D int32 tensor the size of values containing the result of
- *     applying lower bound to each value. The result is not a global index to
- *     the entire Tensor, but the index in the last dimension.
- * @doc {heading: 'Operations', subheading: 'Evaluation'}
- */
-export function lowerBound(
-    sortedSequence: Tensor|TensorLike, values: Tensor|TensorLike): Tensor {
-  return searchSorted(sortedSequence, values, 'left');
-}
diff --git a/tfjs-master/tfjs-core/src/ops/lower_bound_test.ts b/tfjs-master/tfjs-core/src/ops/lower_bound_test.ts
deleted file mode 100644
index 0a9083465..000000000
--- a/tfjs-master/tfjs-core/src/ops/lower_bound_test.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('lowerBound', ALL_ENVS, () => {
-  it('test1D', async () => {
-    // Tests against numpy generated data.
-    const NUMPY_DATA = {
-      'float32': [
-        [
-          -945.2247924804688,  -921.8904418945312, -829.9115600585938,
-          -719.2261352539062,  -660.3391723632812, -603.7969970703125,
-          -591.0955200195312,  -373.1516418457031, -165.39039611816406,
-          -161.61097717285156, 117.37965393066406, 340.9350280761719,
-          370.4389953613281,   384.6452331542969,  601.4891357421875,
-          752.7783203125,      756.23486328125,    756.2850341796875,
-          789.2133178710938,   936.5231323242188
-        ],
-        [
-          -165.95599365234375, 440.64898681640625, -999.771240234375,
-          -395.3348693847656, -706.4882202148438, -815.3228149414062,
-          -627.4795532226562, -308.8785400390625, -206.46505737304688,
-          77.63346862792969
-        ],
-        [8, 14, 0, 7, 4, 3, 5, 8, 8, 10]
-      ],
-      'int32': [
-        [
-          -961, -893, -793, -739, -706, -576, -468, -439, -424, -412,
-          -104, -16,  148,  178,  357,  399,  496,  578,  817,  977
-        ],
-        [-803, -157, 915, 66, 383, -368, 373, 669, -963, 500],
-        [2, 10, 19, 12, 15, 10, 15, 18, 0, 17]
-      ],
-    };
-    for (const dtype of ['float32', 'int32'] as const ) {
-      const [sortedSequence, values, npAns] = NUMPY_DATA[dtype];
-
-      const result = tf.lowerBound(sortedSequence, values);
-
-      expectArraysClose(await result.data(), npAns);
-    }
-  });
-
-  it('lowerBound2D', async () => {
-    for (const dtype of ['float32', 'int32'] as const ) {
-      const sortedSequence =
-          tf.tensor2d([[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]], undefined, dtype);
-      const values = tf.tensor2d([[2, 4, 9], [0, 2, 6]], undefined, dtype);
-      const correctAns = [[1, 2, 2], [0, 1, 5]];
-
-      const result = tf.lowerBound(sortedSequence, values);
-
-      expectArraysClose(await result.data(), correctAns);
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/mat_mul.ts b/tfjs-master/tfjs-core/src/ops/mat_mul.ts
deleted file mode 100644
index 9a8c3129d..000000000
--- a/tfjs-master/tfjs-core/src/ops/mat_mul.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the dot product of two matrices, A * B. These must be matrices.
- *
- * ```js
- * const a = tf.tensor2d([1, 2], [1, 2]);
- * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * a.matMul(b).print();  // or tf.matMul(a, b)
- * ```
- * @param a First matrix in dot product operation.
- * @param b Second matrix in dot product operation.
- * @param transposeA If true, `a` is transposed before multiplication.
- * @param transposeB If true, `b` is transposed before multiplication.
- *
- * @doc {heading: 'Operations', subheading: 'Matrices'}
- */
-function matMul_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike, transposeA = false,
-    transposeB = false): T {
-  let $a = convertToTensor(a, 'a', 'matMul');
-  let $b = convertToTensor(b, 'b', 'matMul');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  const inputs: BatchMatMulInputs = {a: $a, b: $b};
-  const attrs: BatchMatMulAttrs = {transposeA, transposeB};
-
-  return ENGINE.runKernel(
-      BatchMatMul, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const matMul = /* @__PURE__ */ op({matMul_});
diff --git a/tfjs-master/tfjs-core/src/ops/mat_mul_test.ts b/tfjs-master/tfjs-core/src/ops/mat_mul_test.ts
deleted file mode 100644
index 636ef14e9..000000000
--- a/tfjs-master/tfjs-core/src/ops/mat_mul_test.ts
+++ /dev/null
@@ -1,1320 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Empirically determined minimal shared dimension in matmul before we forward
-// to a.mul(b).sum() in order to take advantage of GPU parallelism. See
-// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.
-// Copied from webgl backend.
-// TODO(yassogba, annyuan) copy tests over to webgl backend that want to
-// explicitly test this threshold.
-export const MATMUL_SHARED_DIM_THRESHOLD = 1000;
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-import {Rank} from '../types';
-
-describeWithFlags('matmul', ALL_ENVS, () => {
-  it('A x B', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    const c = tf.matMul(a, b);
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -3, 20]);
-  });
-
-  it('[8,4]x[4,8]', async () => {
-    const a = tf.tensor2d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
-          17, 18, 19, 20, 21, 22, 23, 24, 1, 2,  3,  4,  5,  6,  7,  8
-        ],
-        [8, 4]);
-    const b = tf.tensor2d(
-        [
-          0,  1,  -3, 2, 1,  -1, 0, 5,  6, 7, 8, 0, -2, -2, 1, 9,
-          11, 10, 0,  1, -3, 2,  1, -1, 1, 2, 3, 4, 5,  6,  7, 8
-        ],
-        [4, 8]);
-
-    const c = tf.matMul(a, b);
-    const cData = await c.data();
-
-    expect(c.shape).toEqual([8, 8]);
-    expectArraysClose(cData, [
-      49,  53,  25,  21,  8,   25,  33,  52,  121, 133, 57,  49,  12,
-      45,  69,  136, 193, 213, 89,  77,  16,  65,  105, 220, 265, 293,
-      121, 105, 20,  85,  141, 304, 337, 373, 153, 133, 24,  105, 177,
-      388, 409, 453, 185, 161, 28,  125, 213, 472, 49,  53,  25,  21,
-      8,   25,  33,  52,  121, 133, 57,  49,  12,  45,  69,  136
-    ]);
-  });
-
-  it('broadcast with unequal batch dims', async () => {
-    const a = tf.tensor3d(
-        [
-          2, 1, 3, 2, 1,  1,  1, 5, 6, 7, 8, 1,
-          2, 2, 1, 9, 11, 10, 1, 1, 3, 2, 1, 1
-        ],
-        [4, 3, 2]);
-    const b = tf.tensor3d([1, 0.5], [1, 2, 1]);
-    const c = tf.matMul(a, b);
-    expect(c.shape).toEqual([4, 3, 1]);
-    expectArraysClose(
-        await c.data(), [2.5, 4, 1.5, 3.5, 9.5, 8.5, 3, 5.5, 16, 1.5, 4, 1.5]);
-  });
-
-  it('broadcast with unequal ranks', async () => {
-    const a = tf.tensor5d(
-        [
-          2, 1, 3, 2, 1,  1,  1, 5, 6, 7, 8, 1,
-          2, 2, 1, 9, 11, 10, 1, 1, 3, 2, 1, 1
-        ],
-        [1, 2, 2, 3, 2]);
-    const b = tf.tensor2d([1, 0.5], [2, 1]);
-    const c = tf.matMul(a, b);
-    expect(c.shape).toEqual([1, 2, 2, 3, 1]);
-    expectArraysClose(
-        await c.data(), [2.5, 4, 1.5, 3.5, 9.5, 8.5, 3, 5.5, 16, 1.5, 4, 1.5]);
-  });
-
-  it('matmul followed by mul', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-
-    const c = tf.matMul(a, b);
-
-    const f = tf.tensor2d([0, 1, 0.5, 0, 0.25, 2], [2, 3]);
-    const d = tf.mul(c, f);
-
-    const dData = await d.data();
-
-    expect(d.shape).toEqual([2, 3]);
-    expectArraysClose(dData, [0, 12, 7.5, 0, 6.5, 66]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const a = [1, 2, 3, 4, 5, 6];
-    const b = [0, 1, -3, 2, 2, 1];
-
-    let c = tf.matMul(
-        tf.tensor(a, [2, 3], 'float32'), tf.tensor(b, [3, 2], 'int32'));
-
-    expect(c.shape).toEqual([2, 2]);
-    expect(c.dtype).toBe('float32');
-    expectArraysClose(await c.data(), [0, 8, -3, 20]);
-
-    c = tf.matMul(tf.tensor(a, [2, 3], 'int32'), tf.tensor(b, [3, 2], 'bool'));
-
-    expect(c.shape).toEqual([2, 2]);
-    expect(c.dtype).toBe('int32');
-    expectArraysClose(await c.data(), [5, 6, 11, 15]);
-  });
-
-  it('A x B^t', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
-
-    const transposeA = false;
-    const transposeB = true;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-
-    const expected = [7, 10, 16, 31];
-    expectArraysClose(await c.data(), expected);
-  });
-
-  it('A^t x B', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
-
-    const transposeA = true;
-    const transposeB = false;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-
-    const expected = [17, 12, 2, 22, 15, 4, 27, 18, 6];
-    expectArraysClose(await c.data(), expected);
-  });
-
-  it('A^t x B^t', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
-
-    const transposeA = true;
-    const transposeB = true;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-
-    const expected = [11, 13, 14, 20];
-    expectArraysClose(await c.data(), expected);
-  });
-
-  it('A x B^t shapes do not match', () => {
-    const a = tf.zeros<Rank.R2>([2, 3]);
-    const b = tf.zeros<Rank.R2>([3, 2]);
-
-    const f = () => {
-      const transposeA = false;
-      const transposeB = true;
-      tf.matMul(a, b, transposeA, transposeB);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('A^t x B shapes do not match', () => {
-    const a = tf.zeros<Rank.R2>([2, 3]);
-    const b = tf.zeros<Rank.R2>([3, 2]);
-
-    const f = () => {
-      const transposeA = true;
-      const transposeB = false;
-      tf.matMul(a, b, transposeA, transposeB);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('A^t x B^t shapes do not match', () => {
-    const a = tf.zeros<Rank.R2>([3, 2]);
-    const b = tf.zeros<Rank.R2>([3, 2]);
-
-    const f = () => {
-      const transposeA = true;
-      const transposeB = true;
-      tf.matMul(a, b, transposeA, transposeB);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('matmul throws when inner dimensions dont match', () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1, 2, 2], [4, 2]);
-
-    expect(() => tf.matMul(a, b)).toThrowError();
-  });
-
-  it('matmul throws when passed non matrices', () => {
-    // tslint:disable-next-line:no-any
-    const a: any =
-        tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1, 2, 2], [4, 2]);
-
-    expect(() => tf.matMul(a, b)).toThrowError();
-    expect(() => tf.matMul(b, a)).toThrowError();
-  });
-
-  it('matmul throws when passed a vector', () => {
-    // tslint:disable-next-line:no-any
-    const v: any = tf.tensor1d([2, 3]);
-    const matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    expect(() => tf.matMul(matrix, v)).toThrowError();
-  });
-
-  it('Vector times matrix', async () => {
-    const v = tf.tensor1d([2, 3]);
-    const matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const result = tf.dot(v, matrix);
-
-    const expected = [11, 16];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('Vector times matrix with implicit reshape', async () => {
-    const v = tf.tensor1d([2, 3]);
-
-    const matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const result = tf.dot(v, matrix);
-
-    const expected = [11, 16];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('Matrix times vector', async () => {
-    const matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const v = tf.tensor1d([2, 3]);
-    const result = tf.dot(matrix, v);
-
-    const expected = [8, 18];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('batched matmul with the matrices being vectors', async () => {
-    const batch = 3;
-    const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-    const values = new Float32Array(batch * sharedDim);
-    values[10] = 2;
-
-    const a = tf.tensor(values, [batch, 1, sharedDim]);
-    const b = tf.tensor(values, [batch, sharedDim, 1]);
-    const result = tf.matMul(a, b);
-    expect(result.shape).toEqual([batch, 1, 1]);
-    expectArraysClose(await result.data(), [4, 0, 0]);
-  });
-
-  it('batched matmul called twice so memory of output is reused', async () => {
-    const batch = 3;
-    const n = 2;
-    const vals = new Float32Array(batch * n * n);
-    vals[0] = 2;
-    vals[4] = 3;
-    vals[8] = 4;
-
-    const a = tf.tensor(vals, [batch, n, n]);
-    const b = tf.tensor(vals, [batch, n, n]);
-    const result = tf.matMul(a, b);
-    expect(result.shape).toEqual([batch, n, n]);
-    expectArraysClose(
-        await result.data(), [4, 0, 0, 0, 9, 0, 0, 0, 16, 0, 0, 0]);
-    // Dispose the first output, so memory of the second output (which has the
-    // same shape), could be reused.
-    result.dispose();
-
-    const vals2 = new Float32Array(batch * n * n);
-    vals2[3] = 2;
-    vals2[7] = 3;
-    vals2[11] = 4;
-    const a2 = tf.tensor(vals2, [batch, n, n]);
-    const b2 = tf.tensor(vals2, [batch, n, n]);
-    const result2 = tf.matMul(a2, b2);
-    expect(result2.shape).toEqual([batch, n, n]);
-    expectArraysClose(
-        await result2.data(), [0, 0, 0, 4, 0, 0, 0, 9, 0, 0, 0, 16]);
-  });
-
-  it('batched matmul with the matrices being vectors transposedA', async () => {
-    const batch = 3;
-    const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-    const values = new Float32Array(batch * sharedDim);
-    values[10] = 2;
-
-    const a = tf.tensor(values, [batch, sharedDim, 1]);
-    const b = tf.tensor(values, [batch, sharedDim, 1]);
-    const transposeA = true;
-    const transposeB = false;
-    const result = tf.matMul(a, b, transposeA, transposeB);
-    expect(result.shape).toEqual([batch, 1, 1]);
-    expectArraysClose(await result.data(), [4, 0, 0]);
-  });
-
-  it('batched matmul with the matrices being vectors transposedB', async () => {
-    const batch = 3;
-    const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-    const values = new Float32Array(batch * sharedDim);
-    values[10] = 2;
-
-    const a = tf.tensor(values, [batch, 1, sharedDim]);
-    const b = tf.tensor(values, [batch, 1, sharedDim]);
-    const transposeA = false;
-    const transposeB = true;
-    const result = tf.matMul(a, b, transposeA, transposeB);
-    expect(result.shape).toEqual([batch, 1, 1]);
-    expectArraysClose(await result.data(), [4, 0, 0]);
-  });
-
-  it('batched matmul with matrix x vector', async () => {
-    const batch = 3;
-    const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-    const values = new Float32Array(batch * sharedDim);
-    values[10] = 2;
-
-    const a = tf.ones([batch, 2, sharedDim]);
-    const b = tf.tensor(values, [batch, sharedDim, 1]);
-    const result = tf.matMul(a, b);
-    expect(result.shape).toEqual([batch, 2, 1]);
-    expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-  });
-
-  it('batched matmul with matrix x vector transposedA', async () => {
-    const batch = 3;
-    const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-    const values = new Float32Array(batch * sharedDim);
-    values[10] = 2;
-
-    const a = tf.ones([batch, sharedDim, 2]);
-    const b = tf.tensor(values, [batch, sharedDim, 1]);
-    const transposeA = true;
-    const transposeB = false;
-    const result = tf.matMul(a, b, transposeA, transposeB);
-    expect(result.shape).toEqual([batch, 2, 1]);
-    expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-  });
-
-  it('batched matmul with matrix x vector transposedB', async () => {
-    const batch = 3;
-    const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-    const values = new Float32Array(batch * sharedDim);
-    values[10] = 2;
-
-    const a = tf.ones([batch, 2, sharedDim]);
-    const b = tf.tensor(values, [batch, 1, sharedDim]);
-    const transposeA = false;
-    const transposeB = true;
-    const result = tf.matMul(a, b, transposeA, transposeB);
-    expect(result.shape).toEqual([batch, 2, 1]);
-    expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-  });
-
-  it('batched matmul with vector x matrix', async () => {
-    const batch = 3;
-    const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-    const values = new Float32Array(batch * sharedDim);
-    values[10] = 2;
-
-    const a = tf.tensor(values, [batch, 1, sharedDim]);
-    const b = tf.ones([batch, sharedDim, 2]);
-    const result = tf.matMul(a, b);
-    expect(result.shape).toEqual([batch, 1, 2]);
-    expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-  });
-
-  it('batched matmul with vector x matrix transposedA', async () => {
-    const batch = 3;
-    const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-    const values = new Float32Array(batch * sharedDim);
-    values[10] = 2;
-
-    const a = tf.tensor(values, [batch, sharedDim, 1]);
-    const b = tf.ones([batch, sharedDim, 2]);
-    const transposeA = true;
-    const transposeB = false;
-    const result = tf.matMul(a, b, transposeA, transposeB);
-    expect(result.shape).toEqual([batch, 1, 2]);
-    expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-  });
-
-  it('batched matmul with vector x matrix transposedB', async () => {
-    const batch = 3;
-    const sharedDim = MATMUL_SHARED_DIM_THRESHOLD + 1;
-    const values = new Float32Array(batch * sharedDim);
-    values[10] = 2;
-
-    const a = tf.tensor(values, [batch, 1, sharedDim]);
-    const b = tf.ones([batch, 2, sharedDim]);
-    const transposeA = false;
-    const transposeB = true;
-    const result = tf.matMul(a, b, transposeA, transposeB);
-    expect(result.shape).toEqual([batch, 1, 2]);
-    expectArraysClose(await result.data(), [2, 2, 0, 0, 0, 0]);
-  });
-
-  it('Matrix * vector propagates NaNs', async () => {
-    const matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const v = tf.tensor1d([2, NaN]);
-    const result = tf.dot(matrix, v);
-
-    const expected = [NaN, NaN];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('matrix times vector throws when not passed a matrix', () => {
-    const v = tf.tensor1d([2, 3]);
-
-    // tslint:disable-next-line:no-any
-    const matrix: any = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-
-    expect(() => tf.dot(matrix, v)).toThrowError();
-  });
-
-  it('Dot product', async () => {
-    const v1 = tf.tensor1d([2, 3]);
-    const v2 = tf.tensor1d([2, 1]);
-    const result = tf.dot(v1, v2);
-
-    expectArraysClose(await result.data(), [7]);
-  });
-
-  it('Dot product propagates NaNs', async () => {
-    const v1 = tf.tensor1d([2, NaN]);
-    const v2 = tf.tensor1d([2, 1]);
-    const result = tf.dot(v1, v2);
-    expectArraysEqual(await result.data(), [NaN]);
-  });
-
-  it('Dot product throws when vectors are different size', () => {
-    const v1 = tf.tensor1d([2, 3, 3]);
-    const v2 = tf.tensor1d([2, 1]);
-
-    expect(() => tf.dot(v1, v2)).toThrowError();
-    expect(() => tf.dot(v2, v1)).toThrowError();
-  });
-
-  it('Outer product', async () => {
-    const v1 = tf.tensor1d([2, 3]);
-    const v2 = tf.tensor1d([2, 1]);
-    const result = tf.outerProduct(v1, v2);
-
-    const expected = [4, 2, 6, 3];
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('outer product accepts a tensor-like object', async () => {
-    const v1 = [2, 3];
-    const v2 = [2, 1];
-    const result = tf.outerProduct(v1, v2);
-    const expected = [4, 2, 6, 3];
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('gradients: A * B', async () => {
-    const aT = tf.tensor2d([1, 2, 3, 10, 20, 30], [2, 3]);
-    const bT = tf.tensor2d([2, 3, 4, 1, 2, 3], [3, 2]);
-    const dyT = tf.tensor2d([1, 10, 20, 30], [2, 2]);
-
-    const transposeA = false;
-    const transposeB = false;
-    const grads = tf.grads(
-
-        (a: tf.Tensor2D, b: tf.Tensor2D) =>
-            tf.matMul(a, b, transposeA, transposeB));
-    const [da, db] = grads([aT, bT], dyT);
-
-    // da = dy * bT
-    expect(da.shape).toEqual(aT.shape);
-
-    const a = await aT.buffer();
-    const dy = await dyT.buffer();
-    const b = await bT.buffer();
-    expectArraysClose(
-        await da.data(),
-        [
-          dy.get(0, 0) * b.get(0, 0) + dy.get(0, 1) * b.get(0, 1),
-          dy.get(0, 0) * b.get(1, 0) + dy.get(0, 1) * b.get(1, 1),
-          dy.get(0, 0) * b.get(2, 0) + dy.get(0, 1) * b.get(2, 1),
-          dy.get(1, 0) * b.get(0, 0) + dy.get(1, 1) * b.get(0, 1),
-          dy.get(1, 0) * b.get(1, 0) + dy.get(1, 1) * b.get(1, 1),
-          dy.get(1, 0) * b.get(2, 0) + dy.get(1, 1) * b.get(2, 1)
-        ],
-        1e-1);
-
-    // db = aT * dy
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      a.get(0, 0) * dy.get(0, 0) + a.get(1, 0) * dy.get(1, 0),
-      a.get(0, 0) * dy.get(0, 1) + a.get(1, 0) * dy.get(1, 1),
-      a.get(0, 1) * dy.get(0, 0) + a.get(1, 1) * dy.get(1, 0),
-      a.get(0, 1) * dy.get(0, 1) + a.get(1, 1) * dy.get(1, 1),
-      a.get(0, 2) * dy.get(0, 0) + a.get(1, 2) * dy.get(1, 0),
-      a.get(0, 2) * dy.get(0, 1) + a.get(1, 2) * dy.get(1, 1)
-    ]);
-  });
-
-  it('gradient with clones', () => {
-    const a = tf.tensor2d([1, 2, 3, 10, 20, 30], [2, 3]);
-    const b = tf.tensor2d([2, 3, 4, 1, 2, 3], [3, 2]);
-
-    const grads = tf.grads(
-        (a: tf.Tensor2D, b: tf.Tensor2D) =>
-            tf.matMul(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b]);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-  });
-
-  it('gradients: a * bT', async () => {
-    const aT = tf.tensor2d([1, 2, 3, 10, 20, 30], [3, 2]);
-    const bT = tf.tensor2d([2, 3, 4, 1, 2, 3], [3, 2]);
-    const dyT = tf.tensor2d([1, 10, 20, 30, 40, 50, 60, 70, 80], [3, 3]);
-
-    const transposeA = false;
-    const transposeB = true;
-    const grads = tf.grads(
-
-        (a: tf.Tensor2D, b: tf.Tensor2D) =>
-            tf.matMul(a, b, transposeA, transposeB));
-    const [da, db] = grads([aT, bT], dyT);
-
-    // da = dy * b
-    expect(da.shape).toEqual(aT.shape);
-    const a = await aT.buffer();
-    const dy = await dyT.buffer();
-    const b = await bT.buffer();
-    expectArraysClose(await da.data(), [
-      dy.get(0, 0) * b.get(0, 0) + dy.get(0, 1) * b.get(1, 0) +
-          dy.get(0, 2) * b.get(2, 0),
-      dy.get(0, 0) * b.get(0, 1) + dy.get(0, 1) * b.get(1, 1) +
-          dy.get(0, 2) * b.get(2, 1),
-      dy.get(1, 0) * b.get(0, 0) + dy.get(1, 1) * b.get(1, 0) +
-          dy.get(1, 2) * b.get(2, 0),
-      dy.get(1, 0) * b.get(0, 1) + dy.get(1, 1) * b.get(1, 1) +
-          dy.get(1, 2) * b.get(2, 1),
-      dy.get(2, 0) * b.get(0, 0) + dy.get(2, 1) * b.get(1, 0) +
-          dy.get(2, 2) * b.get(2, 0),
-      dy.get(2, 0) * b.get(0, 1) + dy.get(2, 1) * b.get(1, 1) +
-          dy.get(2, 2) * b.get(2, 1)
-    ]);
-
-    // db = dyT * a
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      dy.get(0, 0) * a.get(0, 0) + dy.get(1, 0) * a.get(1, 0) +
-          dy.get(2, 0) * a.get(2, 0),
-      dy.get(0, 0) * a.get(0, 1) + dy.get(1, 0) * a.get(1, 1) +
-          dy.get(2, 0) * a.get(2, 1),
-      dy.get(0, 1) * a.get(0, 0) + dy.get(1, 1) * a.get(1, 0) +
-          dy.get(2, 1) * a.get(2, 0),
-      dy.get(0, 1) * a.get(0, 1) + dy.get(1, 1) * a.get(1, 1) +
-          dy.get(2, 1) * a.get(2, 1),
-      dy.get(0, 2) * a.get(0, 0) + dy.get(1, 2) * a.get(1, 0) +
-          dy.get(2, 2) * a.get(2, 0),
-      dy.get(0, 2) * a.get(0, 1) + dy.get(1, 2) * a.get(1, 1) +
-          dy.get(2, 2) * a.get(2, 1)
-    ]);
-  });
-
-  it('gradients: aT * b', async () => {
-    const aT = tf.tensor2d([1, 2, 3, 10, 20, 30], [3, 2]);
-    const bT = tf.tensor2d([2, 3, 4, 1, 2, 3], [3, 2]);
-    const dyT = tf.tensor2d([1, 10, 20, 30], [2, 2]);
-
-    const transposeA = true;
-    const transposeB = false;
-    const grads = tf.grads(
-
-        (a: tf.Tensor2D, b: tf.Tensor2D) =>
-            tf.matMul(a, b, transposeA, transposeB));
-    const [da, db] = grads([aT, bT], dyT);
-
-    // da = b * dyT
-    expect(da.shape).toEqual(aT.shape);
-    const a = await aT.buffer();
-    const dy = await dyT.buffer();
-    const b = await bT.buffer();
-    expectArraysClose(await da.data(), [
-      dy.get(0, 0) * b.get(0, 0) + dy.get(0, 1) * b.get(0, 1),
-      dy.get(1, 0) * b.get(0, 0) + dy.get(1, 1) * b.get(0, 1),
-      dy.get(0, 0) * b.get(1, 0) + dy.get(0, 1) * b.get(1, 1),
-      dy.get(1, 0) * b.get(1, 0) + dy.get(1, 1) * b.get(1, 1),
-      dy.get(0, 0) * b.get(2, 0) + dy.get(0, 1) * b.get(2, 1),
-      dy.get(1, 0) * b.get(2, 0) + dy.get(1, 1) * b.get(2, 1)
-    ]);
-
-    // db = a * dy
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      dy.get(0, 0) * a.get(0, 0) + dy.get(1, 0) * a.get(0, 1),
-      dy.get(0, 1) * a.get(0, 0) + dy.get(1, 1) * a.get(0, 1),
-      dy.get(0, 0) * a.get(1, 0) + dy.get(1, 0) * a.get(1, 1),
-      dy.get(0, 1) * a.get(1, 0) + dy.get(1, 1) * a.get(1, 1),
-      dy.get(0, 0) * a.get(2, 0) + dy.get(1, 0) * a.get(2, 1),
-      dy.get(0, 1) * a.get(2, 0) + dy.get(1, 1) * a.get(2, 1)
-    ]);
-  });
-
-  it('gradients: aT * bT', async () => {
-    const aT = tf.tensor2d([1, 2, 3, 10, 20, 30], [3, 2]);
-    const bT = tf.tensor2d([2, 3, 4, 1, 2, 3], [2, 3]);
-    const dyT = tf.tensor2d([1, 10, 20, 30], [2, 2]);
-
-    const transposeA = true;
-    const transposeB = true;
-    const grads = tf.grads(
-
-        (a: tf.Tensor2D, b: tf.Tensor2D) =>
-            tf.matMul(a, b, transposeA, transposeB));
-    const [da, db] = grads([aT, bT], dyT);
-
-    // da = bT * dyT
-    expect(da.shape).toEqual(aT.shape);
-    const a = await aT.buffer();
-    const dy = await dyT.buffer();
-    const b = await bT.buffer();
-    expectArraysClose(await da.data(), [
-      dy.get(0, 0) * b.get(0, 0) + dy.get(0, 1) * b.get(1, 0),
-      dy.get(1, 0) * b.get(0, 0) + dy.get(1, 1) * b.get(1, 0),
-      dy.get(0, 0) * b.get(0, 1) + dy.get(0, 1) * b.get(1, 1),
-      dy.get(1, 0) * b.get(0, 1) + dy.get(1, 1) * b.get(1, 1),
-      dy.get(0, 0) * b.get(0, 2) + dy.get(0, 1) * b.get(1, 2),
-      dy.get(1, 0) * b.get(0, 2) + dy.get(1, 1) * b.get(1, 2)
-    ]);
-
-    // db = dyT * aT
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      dy.get(0, 0) * a.get(0, 0) + dy.get(1, 0) * a.get(0, 1),
-      dy.get(0, 0) * a.get(1, 0) + dy.get(1, 0) * a.get(1, 1),
-      dy.get(0, 0) * a.get(2, 0) + dy.get(1, 0) * a.get(2, 1),
-      dy.get(0, 1) * a.get(0, 0) + dy.get(1, 1) * a.get(0, 1),
-      dy.get(0, 1) * a.get(1, 0) + dy.get(1, 1) * a.get(1, 1),
-      dy.get(0, 1) * a.get(2, 0) + dy.get(1, 1) * a.get(2, 1)
-    ]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.matMul({} as tf.Tensor2D, tf.tensor2d([2], [1, 1])))
-        .toThrowError(/Argument 'a' passed to 'matMul' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.matMul(tf.tensor2d([2], [1, 1]), {} as tf.Tensor2D))
-        .toThrowError(/Argument 'b' passed to 'matMul' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[1, 2, 3], [4, 5, 6]];     // 2x3
-    const b = [[0, 1], [-3, 2], [2, 1]];  // 3x2
-    const c = tf.matMul(a, b);
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -3, 20]);
-  });
-
-  it('accepts a tensor-like object chained', async () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3]);  // 2x3
-    const b = [[0, 1], [-3, 2], [2, 1]];                    // 3x2
-    const c = a.matMul(b);
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -3, 20]);
-  });
-
-  it('a * b where a has zero in its shape', async () => {
-    const a = tf.tensor2d([], [0, 3]);
-    const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const c = tf.matMul(a, b);
-    expect(c.shape).toEqual([0, 2]);
-    expect(c.rank).toBe(2);
-    expect(c.size).toBe(0);
-    expectArraysClose(await c.data(), []);
-  });
-
-  it('(a * b) * c where a has zero in its shape, so a*b does also',
-     async () => {
-       const a = tf.tensor2d([], [0, 3]);
-       const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-       const ab = tf.matMul(a, b);
-       expect(ab.shape).toEqual([0, 2]);
-       expectArraysClose(await ab.data(), []);
-       const c = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-       const res = tf.matMul(ab, c);
-       expect(res.shape).toEqual([0, 3]);
-       expectArraysClose(await res.data(), []);
-     });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.matMul([['a']], [['b']]))
-        .toThrowError(/Argument 'a' passed to 'matMul' must be numeric tensor/);
-  });
-});
-
-describeWithFlags('matmulBatch', ALL_ENVS, () => {
-  it('A x B', async () => {
-    const a = tf.tensor3d(
-        [
-          -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-          -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-        ],
-        [5, 2, 3]);
-    const b = tf.tensor3d(
-        [
-          -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-          5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-        ],
-        [5, 3, 2]);
-
-    const c = tf.matMul(a, b);
-    expect(c.shape).toEqual([5, 2, 2]);
-    expectArraysClose(await c.data(), [
-      87, 20, -6,  -32, -24, -50, -36, -5, 24, 98,
-      70, 33, -64, 47,  -42, -28, -71, 24, 37, 5
-    ]);
-  });
-
-  it('A x B in 4D', async () => {
-    const a = tf.tensor4d(
-        [
-          -2, 3,  5,  -5, 3,  9,  -3, -5, 1,   1,  -9, 9,   -6, 6,  -8,
-          -7, -1, 3,  9,  -7, -7, 2,  10, -6,  -8, -6, 9,   -6, 4,  -1,
-          9,  -6, 10, 8,  -9, 5,  -8, -7, 0,   2,  -5, -1,  -9, -4, 3,
-          -2, 6,  -4, 7,  1,  -5, -4, 9,  -8,  -6, -8, 4,   -1, 4,  3,
-          -7, 8,  -7, 5,  -3, -2, -4, 9,  2,   -1, 1,  -10, -3, 5,  -4,
-          6,  -8, -8, 9,  -3, -5, 10, 3,  -3,  -3, 9,  3,   -3, 2,  -8,
-          10, 1,  9,  -2, -2, -3, -4, 6,  -10, -1, 8,  -8,  7,  3,  -2,
-          3,  6,  -2, -2, -4, 1,  -5, -4, 0,   5,  1,  9,   -8, -2, -1
-        ],
-        [4, 5, 2, 3]);
-    const b = tf.tensor4d(
-        [
-          -4, -3, -2, -6, 6,  -1, -4, -1, 7,  -4, 8,  -9,  -9, 0,   -1,
-          -4, -6, -7, -3, -4, -7, 6,  -8, 1,  -2, 1,  -1,  -3, 8,   -5,
-          9,  -2, 5,  9,  -2, 2,  -5, -5, -8, -1, -2, -3,  -2, -10, 6,
-          -3, 0,  1,  6,  7,  1,  2,  -4, -5, 2,  -5, -7,  9,  3,   -6,
-          6,  4,  -4, 6,  10, -3, -2, 8,  10, -8, 10, -1,  -9, -7,  -8,
-          -3, 1,  1,  -2, -9, -7, -6, -1, 0,  7,  -9, -7,  -5, 0,   -4,
-          -4, -7, 2,  4,  6,  6,  -4, -6, -8, 3,  -8, -9,  6,  9,   -4,
-          1,  -1, 0,  8,  9,  0,  -5, 3,  -1, 5,  0,  -10, 7,  -2,  6
-        ],
-        [4, 5, 3, 2]);
-
-    const transposeA = false;
-    const transposeB = false;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-
-    expectArraysClose(await c.data(), [
-      32,  -17, 68,  -12,  -15, 14,  5,   -46, 96,  32,  46,  -17, 78,   -85,
-      -28, 46,  94,  -35,  0,   -13, 31,  -52, 17,  -87, 96,  47,  32,   -2,
-      -6,  105, 40,  -2,   63,  76,  17,  30,  56,  -66, -21, 23,  -144, 41,
-      22,  8,   118, -106, -88, -6,  -17, 2,   2,   -26, 8,   -63, -38,  -108,
-      -84, -30, -35, 49,   16,  -12, -14, -12, 48,  132, 4,   102, 32,   66,
-      -4,  33,  -13, 1,    -40, -25, -3,  61,  -18, -20
-    ]);
-  });
-
-  it('A x B^t', async () => {
-    const a = tf.tensor3d(
-        [
-          -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-          -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-        ],
-        [5, 2, 3]);
-    const b = tf.tensor3d(
-        [
-          -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-          5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-        ],
-        [5, 2, 3]);
-
-    const transposeA = false;
-    const transposeB = true;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-    expect(c.shape).toEqual([5, 2, 2]);
-    expectArraysClose(await c.data(), [
-      66, 35, -48,  14, -45, -33, -12, 7,  -76, 64,
-      3,  66, -119, -9, -64, -60, -76, 48, 33,  -16
-    ]);
-  });
-
-  it('A^t x B', async () => {
-    const a = tf.tensor3d(
-        [
-          -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-          -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-        ],
-        [5, 2, 3]);
-    const b = tf.tensor3d(
-        [
-          -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-          5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-        ],
-        [5, 2, 3]);
-
-    const transposeA = true;
-    const transposeB = false;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-
-    expectArraysClose(await c.data(), [
-      40,  -36, 5,   40,  34, 5,   48,  80, 6,  -6, 21,  -48, -23, -20, -50,
-      -12, -21, -12, -58, 15, -96, 23,  6,  39, 20, 109, 42,  -67, 45,  -40,
-      76,  -52, 40,  -15, 1,  -60, -58, -3, 36, 40, -6,  -24, 51,  -33, -28
-    ]);
-  });
-
-  it('A^t x B in 4D', async () => {
-    const a = tf.tensor4d(
-        [
-          -2, 3,  5,  -5, 3,  9,  -3, -5, 1,   1,  -9, 9,   -6, 6,  -8,
-          -7, -1, 3,  9,  -7, -7, 2,  10, -6,  -8, -6, 9,   -6, 4,  -1,
-          9,  -6, 10, 8,  -9, 5,  -8, -7, 0,   2,  -5, -1,  -9, -4, 3,
-          -2, 6,  -4, 7,  1,  -5, -4, 9,  -8,  -6, -8, 4,   -1, 4,  3,
-          -7, 8,  -7, 5,  -3, -2, -4, 9,  2,   -1, 1,  -10, -3, 5,  -4,
-          6,  -8, -8, 9,  -3, -5, 10, 3,  -3,  -3, 9,  3,   -3, 2,  -8,
-          10, 1,  9,  -2, -2, -3, -4, 6,  -10, -1, 8,  -8,  7,  3,  -2,
-          3,  6,  -2, -2, -4, 1,  -5, -4, 0,   5,  1,  9,   -8, -2, -1
-        ],
-        [4, 5, 2, 3]);
-    const b = tf.tensor4d(
-        [
-          -4, -3, -2, -6, 6,  -1, -4, -1, 7,  -4, 8,  -9,  -9, 0,   -1,
-          -4, -6, -7, -3, -4, -7, 6,  -8, 1,  -2, 1,  -1,  -3, 8,   -5,
-          9,  -2, 5,  9,  -2, 2,  -5, -5, -8, -1, -2, -3,  -2, -10, 6,
-          -3, 0,  1,  6,  7,  1,  2,  -4, -5, 2,  -5, -7,  9,  3,   -6,
-          6,  4,  -4, 6,  10, -3, -2, 8,  10, -8, 10, -1,  -9, -7,  -8,
-          -3, 1,  1,  -2, -9, -7, -6, -1, 0,  7,  -9, -7,  -5, 0,   -4,
-          -4, -7, 2,  4,  6,  6,  -4, -6, -8, 3,  -8, -9,  6,  9,   -4,
-          1,  -1, 0,  8,  9,  0,  -5, 3,  -1, 5,  0,  -10, 7,  -2,  6
-        ],
-        [4, 5, 2, 3]);
-
-    const transposeA = true;
-    const transposeB = false;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-
-    expectArraysClose(await c.data(), [
-      38,  -24, 9,   -30, 9,   -9,  -74,  39,  -19,  8,    11,  -30, 56,  -67,
-      46,  -40, 71,  -74, 82,  42,  55,   -50, 6,    1,    60,  -18, -13, -15,
-      -52, -61, 81,  -52, 59,  -15, 76,   43,  34,   -56,  38,  0,   26,  -14,
-      -15, 1,   -4,  153, -34, 61,  -135, 30,  -48,  135,  -30, 60,  38,  36,
-      58,  40,  45,  71,  1,   2,   3,    24,  90,   -56,  -10, 40,  -18, 6,
-      -30, 14,  34,  65,  27,  24,  -29,  -44, -46,  -3,   35,  -21, 27,  48,
-      20,  52,  32,  35,  -11, -46, -12,  22,  13,   30,   2,   -23, -54, -48,
-      34,  16,  -42, -39, -26, 82,  89,   76,  -84,  30,   9,   27,  30,  -21,
-      -43, -48, 60,  20,  24,  -78, -91,  -63, -12,  24,   21,  28,  48,  35,
-      -6,  27,  33,  53,  -81, -71, 61,   -27, 11,   -48,  -82, 8,   -12, -19,
-      -10, -48, -81, 0,   13,  32,  41,   0,   -100, -120, 16,  124, 152, 45,
-      60,  -28, 24,  21,  -12, -14, -16,  8,   9,    -33,  5,   -12, -48, 4,
-      8,   9,   0,   -31, 16,  -98, -9,   4,   -22,  38,   2,   -96
-    ]);
-  });
-
-  it('A^t x B^t', async () => {
-    const a = tf.tensor3d(
-        [
-          -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-          -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-        ],
-        [5, 3, 2]);
-    const b = tf.tensor3d(
-        [
-          -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-          5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-        ],
-        [5, 2, 3]);
-
-    const transposeA = true;
-    const transposeB = true;
-    const c = tf.matMul(a, b, transposeA, transposeB);
-    expectArraysClose(await c.data(), [
-      66,  42, 16,  -56, -12, 6,   -30, 19,  -1, 102,
-      -94, 14, -56, 32,  100, -56, -47, -11, 5,  -31
-    ]);
-  });
-
-  it('A has more batch dimensions than B', async () => {
-    const a = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [2, 2, 2, 2]);
-    const b = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-    const c = tf.matMul(a, b);
-    expectArraysClose(await c.data(), [5, 11, 39, 53, 29, 35, 95, 109]);
-  });
-
-  it('batch dimensions do not match', () => {
-    const a = tf.tensor3d(
-        [
-          -5, -5, -6, 8,  -2, -8, 4, -7, -6, -9, -1, 3,
-          7,  -2, 5,  -6, 3,  8,  7, -8, 1,  4,  -4, 6
-        ],
-        [4, 3, 2]);
-    const b = tf.tensor3d(
-        [
-          -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-          5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-        ],
-        [5, 2, 3]);
-
-    const f = () => {
-      tf.matMul(a, b, false, false);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('gradients: A x B', async () => {
-    const a = tf.tensor3d(
-        [
-          -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-          -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-        ],
-        [5, 2, 3]);
-    const b = tf.tensor3d(
-        [
-          -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-          5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-        ],
-        [5, 3, 2]);
-    const dy = tf.tensor3d(
-        [8, 2, -3, -2, -8, 4, 5, 7, 4, -4, -4, 5, 8, 10, 1, 0, 6, 6, -4, 7],
-        [5, 2, 2]);
-
-    const grads = tf.grads(
-        (a: tf.Tensor3D, b: tf.Tensor3D) => tf.matMul(a, b, false, false));
-    const [da, db] = grads([a, b], dy);
-
-    // da = dy * bT
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [
-      -72, -8, -56, 32, 3,   21,   -12, -40, 40, 36,  44, 51, -52, -44, -4,
-      61,  49, 13,  -2, -10, -108, -9,  0,   -1, -24, 60, -6, 49,  26,  -40
-    ]);
-
-    // db = aT * dy
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      -64, -26, -34, -6, -24, 4,   -77, -47, 51, -35, 63,  -3,  52,  -58, -20,
-      23,  -12, 20,  60, 70,  -68, -80, 14,  10, 44,  -11, -32, -10, -46, -68
-    ]);
-  });
-
-  it('4d gradients: A x B', async () => {
-    const a = tf.tensor4d(
-        [
-          -2, 3,  5,  -5, 3,  9,  -3, -5, 1,   1,  -9, 9,   -6, 6,  -8,
-          -7, -1, 3,  9,  -7, -7, 2,  10, -6,  -8, -6, 9,   -6, 4,  -1,
-          9,  -6, 10, 8,  -9, 5,  -8, -7, 0,   2,  -5, -1,  -9, -4, 3,
-          -2, 6,  -4, 7,  1,  -5, -4, 9,  -8,  -6, -8, 4,   -1, 4,  3,
-          -7, 8,  -7, 5,  -3, -2, -4, 9,  2,   -1, 1,  -10, -3, 5,  -4,
-          6,  -8, -8, 9,  -3, -5, 10, 3,  -3,  -3, 9,  3,   -3, 2,  -8,
-          10, 1,  9,  -2, -2, -3, -4, 6,  -10, -1, 8,  -8,  7,  3,  -2,
-          3,  6,  -2, -2, -4, 1,  -5, -4, 0,   5,  1,  9,   -8, -2, -1
-        ],
-        [4, 5, 2, 3]);
-    const b = tf.tensor4d(
-        [
-          -4, -3, -2, -6, 6,  -1, -4, -1, 7,  -4, 8,  -9,  -9, 0,   -1,
-          -4, -6, -7, -3, -4, -7, 6,  -8, 1,  -2, 1,  -1,  -3, 8,   -5,
-          9,  -2, 5,  9,  -2, 2,  -5, -5, -8, -1, -2, -3,  -2, -10, 6,
-          -3, 0,  1,  6,  7,  1,  2,  -4, -5, 2,  -5, -7,  9,  3,   -6,
-          6,  4,  -4, 6,  10, -3, -2, 8,  10, -8, 10, -1,  -9, -7,  -8,
-          -3, 1,  1,  -2, -9, -7, -6, -1, 0,  7,  -9, -7,  -5, 0,   -4,
-          -4, -7, 2,  4,  6,  6,  -4, -6, -8, 3,  -8, -9,  6,  9,   -4,
-          1,  -1, 0,  8,  9,  0,  -5, 3,  -1, 5,  0,  -10, 7,  -2,  6
-        ],
-        [4, 5, 3, 2]);
-    const dy = tf.tensor4d(
-        [
-          8,  -7, 0,  -9,  -5, -5, 0,  3,  7,  -4, 6,  -8,  -8, 0,  -1, -8,
-          -9, -7, -4, -9,  2,  3,  5,  8,  -5, -7, 3,  -10, -5, -9, -5, 1,
-          7,  1,  -9, -10, 8,  5,  0,  8,  -6, 4,  0,  -5,  8,  -7, -2, 1,
-          -8, 9,  9,  -7,  1,  7,  -2, 5,  -2, 9,  1,  -5,  7,  5,  -7, -6,
-          6,  7,  -8, 7,   4,  -5, 4,  -5, 3,  -4, -5, 4,   -6, 3,  -8, 10
-        ],
-        [4, 5, 2, 2]);
-
-    const grads = tf.grads(
-        (a: tf.Tensor4D, b: tf.Tensor4D) => tf.matMul(a, b, false, false));
-    const [da, db] = grads([a, b], dy);
-
-    // da = dy * bT
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [
-      -11,  26,  55,  27,  54,  9,   25,  -15, 5,   -3,   -12, -27, -63, 9,
-      -14,  -54, 26,  20,  24,  56,  64,  35,  -41, 0,    11,  30,  -37, -1,
-      31,   13,  12,  37,  2,   29,  97,  6,   60,  47,   31,  35,  -14, 24,
-      100,  -3,  -9,  0,   -33, 1,   49,  9,   -33, -124, -29, 86,  -9,  -11,
-      -6,   -40, 72,  -48, -20, 48,  -72, -20, -30, 15,   -72, 136, 87,  12,
-      -28,  -21, 9,   37,  1,   -32, -51, 2,   -65, -49,  -1,  -41, -16, 2,
-      -95,  -31, -36, 52,  18,  20,  -63, 34,  72,  70,   -38, -78, -66, -27,
-      -111, -10, 85,  1,   -21, -21, -4,  -21, -21, -4,   -12, 20,  13,  -4,
-      -20,  -19, -30, 81,  30,  -40, 150, 76
-    ]);
-
-    // db = aT * dy
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      -16, 59,  24,  -48,  40,   -116, 15,  18,  25,  -2,  -5,  22,  -84, 80,
-      36,  -16, -38, 8,    -74,  -16,  46,  -80, 62,  48,  96,  110, 38,  6,
-      -77, -54, 58,  91,   -57,  -90,  45,  70,  46,  36,  20,  99,  -3,  10,
-      55,  79,  -10, 42,   5,    -31,  85,  47,  -74, -89, 37,  75,  -48, -38,
-      -64, -8,  32,  44,   42,   -53,  -48, 47,  42,  -18, -30, 27,  70,  -62,
-      36,  -24, 78,  -69,  -112, 101,  -40, 20,  -11, 113, -9,  -6,  1,   -50,
-      3,   -12, -16, 71,   -14,  67,   84,  62,  21,  17,  84,  63,  -16, -35,
-      -28, 98,  4,   -126, 40,   -50,  36,  -45, -16, 20,  19,  -12, 8,   0,
-      3,   -4,  34,  -65,  10,   -17,  -46, 17
-    ]);
-  });
-
-  it('gradients: A x B^t', async () => {
-    const a = tf.tensor3d(
-        [
-          -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-          -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-        ],
-        [5, 3, 2]);
-    const b = tf.tensor3d(
-        [
-          -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-          5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-        ],
-        [5, 3, 2]);
-    const dy = tf.tensor3d(
-        [
-          -0, 7,  5, 0,  -9, 5, -7, 6,  -5, -3,  -2, -2, -4, 10, -3,
-          5,  -1, 3, -2, -9, 4, -5, 7,  9,  -10, -8, -8, -5, -0, -1,
-          3,  3,  4, 9,  -7, 6, -2, -9, 5,  1,   -5, -3, -1, 9,  4
-        ],
-        [5, 3, 3]);
-
-    const grads = tf.grads(
-        (a: tf.Tensor3D, b: tf.Tensor3D) => tf.matMul(a, b, false, true));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [
-      -42, 0,  -26,  0,  85,  28,  -19, -29, 51, -16, 6,   37,  94,  -27, 50,
-      71,  24, -202, 46, -25, -31, -22, -87, 10, -7,  -80, -36, -15, 55,  35
-    ]);
-
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      14,  56, 7,    -155, -45, 55, 7,   72,  -67, -79, 7, 50, -69, -46, -52,
-      -88, 49, -126, -68,  106, 31, -30, -27, 60,  -19, 5, 27, 43,  55,  -13
-    ]);
-  });
-
-  it('4d gradients: A x B^t', async () => {
-    const a = tf.tensor4d(
-        [
-          -2, 3,  5,  -5, 3,  9,  -3, -5, 1,   1,  -9, 9,   -6, 6,  -8,
-          -7, -1, 3,  9,  -7, -7, 2,  10, -6,  -8, -6, 9,   -6, 4,  -1,
-          9,  -6, 10, 8,  -9, 5,  -8, -7, 0,   2,  -5, -1,  -9, -4, 3,
-          -2, 6,  -4, 7,  1,  -5, -4, 9,  -8,  -6, -8, 4,   -1, 4,  3,
-          -7, 8,  -7, 5,  -3, -2, -4, 9,  2,   -1, 1,  -10, -3, 5,  -4,
-          6,  -8, -8, 9,  -3, -5, 10, 3,  -3,  -3, 9,  3,   -3, 2,  -8,
-          10, 1,  9,  -2, -2, -3, -4, 6,  -10, -1, 8,  -8,  7,  3,  -2,
-          3,  6,  -2, -2, -4, 1,  -5, -4, 0,   5,  1,  9,   -8, -2, -1
-        ],
-        [4, 5, 3, 2]);
-    const b = tf.tensor4d(
-        [
-          -4, -3, -2, -6, 6,  -1, -4, -1, 7,  -4, 8,  -9,  -9, 0,   -1,
-          -4, -6, -7, -3, -4, -7, 6,  -8, 1,  -2, 1,  -1,  -3, 8,   -5,
-          9,  -2, 5,  9,  -2, 2,  -5, -5, -8, -1, -2, -3,  -2, -10, 6,
-          -3, 0,  1,  6,  7,  1,  2,  -4, -5, 2,  -5, -7,  9,  3,   -6,
-          6,  4,  -4, 6,  10, -3, -2, 8,  10, -8, 10, -1,  -9, -7,  -8,
-          -3, 1,  1,  -2, -9, -7, -6, -1, 0,  7,  -9, -7,  -5, 0,   -4,
-          -4, -7, 2,  4,  6,  6,  -4, -6, -8, 3,  -8, -9,  6,  9,   -4,
-          1,  -1, 0,  8,  9,  0,  -5, 3,  -1, 5,  0,  -10, 7,  -2,  6
-        ],
-        [4, 5, 3, 2]);
-    const dy = tf.tensor4d(
-        [
-          5,  -1, -5, -4, -1,  9,  1,   -2, 10,  7,  -1, 6,   -8, 8,  -3,
-          9,  -4, 2,  -4, -8,  8,  4,   8,  -10, -8, -8, 6,   6,  -5, 9,
-          -1, -7, -5, -3, -3,  2,  -6,  5,  8,   -9, 5,  -8,  -3, 8,  6,
-          2,  8,  5,  9,  7,   6,  2,   -3, 10,  7,  7,  -3,  4,  -3, -6,
-          -8, -8, 9,  0,  -8,  -3, -2,  -2, 8,   2,  3,  -6,  3,  6,  -3,
-          7,  7,  -9, -3, 8,   7,  7,   -1, -6,  5,  2,  -1,  -1, 1,  5,
-          0,  -4, 3,  -4, -10, 1,  -2,  -8, -9,  -6, 4,  4,   -7, -1, -1,
-          -9, 7,  1,  -1, 8,   0,  -2,  -7, 5,   7,  8,  9,   -3, -8, -6,
-          -7, -8, -1, 8,  -4,  7,  5,   -9, 9,   3,  0,  -10, 7,  -9, 4,
-          -7, 5,  -2, -2, 3,   3,  -6,  2,  0,   8,  -5, -10, 3,  -7, 0,
-          -6, 2,  3,  -1, 3,   3,  -10, 1,  3,   -7, -1, 8,   -2, -1, -1,
-          -3, -9, 7,  4,  -6,  3,  0,   -7, -4,  -5, -8, -6,  10, -6, 4
-        ],
-        [4, 5, 3, 3]);
-
-    const grads = tf.grads(
-        (a: tf.Tensor4D, b: tf.Tensor4D) => tf.matMul(a, b, false, true));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [
-      -48, -4,   72,  9,   60,  -1,  13,   -57, 64,  3,   -48, -11, -4,  -24,
-      16,  38,   44,  -10, -55, -45, 92,   -43, 14,  -4,  71,  -61, -51, 16,
-      46,  -57,  48,  78,  104, 57,  -17,  -11, -85, -33, 16,  1,   86,  21,
-      -48, 21,   -8,  34,  14,  -35, 36,   48,  85,  108, -38, -40, 3,   -8,
-      -7,  -1,   6,   -16, 46,  -33, 26,   -79, -70, -29, 92,  -84, -6,  -47,
-      98,  -129, -55, -17, 79,  40,  -118, -64, 68,  75,  71,  111, 5,   -48,
-      98,  -36,  21,  13,  112, -34, 26,   57,  32,  44,  28,  50,  88,  27,
-      44,  -39,  -16, 15,  -21, -6,  -67,  -89, -46, -64, -19, -12, -3,  11,
-      41,  63,   78,  -73, 67,  -92, 102,  -18
-    ]);
-
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      -27,  44,   -9,  -16, 85,  30,  -110, 38,  47,  -23, -39, -15, 0,    -76,
-      -8,   -128, 26,  136, 31,  -26, -26,  39,  136, -85, -45, 93,  37,   -68,
-      -112, -6,   90,  70,  169, -7,  15,   68,  -16, -33, -16, -47, -21,  0,
-      6,    -4,   84,  24,  15,  20,  -41,  -1,  79,  -86, 87,  -23, -26,  -64,
-      18,   9,    52,  64,  34,  -16, 122,  -66, -1,  47,  1,   43,  -11,  -33,
-      -17,  27,   -45, -73, -60, -66, -92,  -42, 32,  -85, -44, -44, -28,  -13,
-      8,    -20,  9,   -9,  -49, 79,  -76,  15,  73,  -7,  7,   -8,  -110, 93,
-      106,  -39,  64,  -84, -29, -19, 13,   14,  63,  2,   -15, 23,  17,   49,
-      -3,   -31,  -65, 30,  -95, 63,  -82,  40
-    ]);
-  });
-
-  it('gradients: A^t x B', async () => {
-    const a = tf.tensor3d(
-        [
-          -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-          -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-        ],
-        [5, 3, 2]);
-    const b = tf.tensor3d(
-        [
-          -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-          5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-        ],
-        [5, 3, 2]);
-    const dy = tf.tensor3d(
-        [8, 2, -3, -2, -8, 4, 5, 7, 4, -4, -4, 5, 8, 10, 1, 0, 6, 6, -4, 7],
-        [5, 2, 2]);
-
-    const grads = tf.grads(
-        (a: tf.Tensor3D, b: tf.Tensor3D) => tf.matMul(a, b, true, false));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [
-      -72, 32, -8, 3,  -56, 21,  -12, 36,   -40, 44,  40, 51, -52, 61, -44,
-      49,  -4, 13, -2, -9,  -10, 0,   -108, -1,  -24, 49, 60, 26,  -6, -40
-    ]);
-
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      -25, 0,   -72, -28, 8,  12, -67, -33, 3,   -87, 23, 17,  36,  -38, 44,
-      -50, -20, 28,  48,  70, 12, 10,  -26, -40, 40,  -4, -34, -89, 20,  -2
-    ]);
-  });
-
-  it('gradients: A^t x B^t', async () => {
-    const a = tf.tensor3d(
-        [
-          -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,  7,  -2, 5,
-          -6, 3,  8,  7, -8, 1,  4, -4, 6,  4,  -4, -9, -5, 2,  -2
-        ],
-        [5, 3, 2]);
-    const b = tf.tensor3d(
-        [
-          -8, -4, -1, 0,  -7, 0, 3,  3,  6,   2,  -1, 8, -4, 9, -6,
-          5,  8,  9,  -9, 7,  0, -1, -1, -10, -7, 3,  4, 6,  3, -4
-        ],
-        [5, 2, 3]);
-    const dy = tf.tensor3d(
-        [8, 2, -3, -2, -8, 4, 5, 7, 4, -4, -4, 5, 8, 10, 1, 0, 6, 6, -4, 7],
-        [5, 2, 2]);
-
-    const grads = tf.grads(
-        (a: tf.Tensor3D, b: tf.Tensor3D) => tf.matMul(a, b, true, true));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [
-      -64, 24,  -46, 26,  -8, 3,  -16, 29,   -28, 8,  -16, 86, -36, 41, 4,
-      4,   -60, 69,  -82, -9, 46, 7,   -100, 0,   -6, 70,  36, 9,   0,  -44
-    ]);
-
-    expect(db.shape).toEqual(b.shape);
-    expectArraysClose(await db.data(), [
-      -25, -72, 8,  0,  -28, 12,  -67, 3,  23,  -33, -87, 17, 36, 44,  -20,
-      -38, -50, 28, 48, 12,  -26, 70,  10, -40, 40,  -34, 20, -4, -89, -2
-    ]);
-  });
-});
-
-describeWithFlags('dot', ALL_ENVS, () => {
-  let a: tf.Tensor1D;
-  let b: tf.Tensor2D;
-  let c: tf.Tensor2D;
-  let d: tf.Tensor3D;
-  let e: tf.Scalar;
-  let f: tf.Tensor3D;
-
-  beforeEach(() => {
-    a = tf.tensor1d([1, 2]);
-    b = tf.tensor2d([[1, 2], [3, 4]]);
-    c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
-    d = tf.tensor3d([1, 2], [1, 1, 2]);
-    e = tf.scalar(1);
-    f = tf.tensor3d([1, 2, 1, 2], [2, 1, 2]);
-  });
-
-  it('vector-vector', async () => {
-    const aa = tf.dot(a, a);
-    expectArraysClose(await aa.data(), [5]);
-    expect(aa.shape).toEqual([]);
-  });
-
-  it('vector-matrix', async () => {
-    const ab = tf.dot(a, b);
-    const ac = tf.dot(a, c);
-    expect(ab.shape).toEqual([2]);
-    expect(ac.shape).toEqual([3]);
-    expectArraysClose(await ab.data(), [7, 10]);
-    expectArraysClose(await ac.data(), [9, 12, 15]);
-  });
-
-  it('matrix-vector', async () => {
-    const ba = b.dot(a);
-    expect(ba.shape).toEqual([2]);
-    expectArraysClose(await ba.data(), [5, 11]);
-  });
-
-  it('matrix-matrix', async () => {
-    const bb = tf.dot(b, b);
-    const bc = tf.dot(b, c);
-    expect(bb.shape).toEqual([2, 2]);
-    expect(bc.shape).toEqual([2, 3]);
-    expectArraysClose(await bb.data(), [7, 10, 15, 22]);
-    expectArraysClose(await bc.data(), [9, 12, 15, 19, 26, 33]);
-  });
-
-  it('matmul A x B asymmetric', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-
-    const c = tf.matMul(a, b);
-    const cData = await c.data();
-
-    expect(c.shape).toEqual([2, 3]);
-    expectArraysClose(cData, [9, 12, 15, 19, 26, 33]);
-  });
-
-  it('broadcast batch shape', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [1, 2, 2]);
-    const b = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-
-    const c = tf.matMul(a, b);
-    const cData = await c.data();
-
-    expect(c.shape).toEqual([1, 2, 3]);
-    expectArraysClose(cData, [9, 12, 15, 19, 26, 33]);
-  });
-
-  it('throws error on incompatible dimensions', () => {
-    expect(() => tf.dot(c, f)).toThrowError();
-  });
-
-  it('throws error when inputs are not rank 1 or 2', () => {
-    expect(() => tf.dot(a, d)).toThrowError();
-    expect(() => tf.dot(a, e)).toThrowError();
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 2, 3];
-    const res = tf.dot(a, a);
-    expectArraysClose(await res.data(), [14]);
-    expect(res.shape).toEqual([]);
-  });
-
-  it('throws error for string tensors', () => {
-    expect(() => tf.dot('a', 'b'))
-        .toThrowError(/Argument 't1' passed to 'dot' must be numeric tensor/);
-  });
-
-  it('ensure no memory leak', async () => {
-    const numTensorsBefore = tf.memory().numTensors;
-    const numDataIdBefore = tf.engine().backend.numDataIds();
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
-
-    const c = tf.matMul(a, b);
-
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [0, 8, -3, 20]);
-
-    a.dispose();
-    b.dispose();
-    c.dispose();
-
-    const numTensorsAfter = tf.memory().numTensors;
-    const numDataIdAfter = tf.engine().backend.numDataIds();
-    expect(numTensorsAfter).toBe(numTensorsBefore);
-    expect(numDataIdAfter).toBe(numDataIdBefore);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/max.ts b/tfjs-master/tfjs-core/src/ops/max.ts
deleted file mode 100644
index 1f0d63077..000000000
--- a/tfjs-master/tfjs-core/src/ops/max.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Max, MaxAttrs, MaxInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the maximum of elements across dimensions of a `tf.Tensor`.
- *
- * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
- * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
- * `axes`. If `keepDims` is true, the reduced dimensions are retained with
- * length 1. If `axes` has no entries, all dimensions are reduced, and a
- * `tf.Tensor` with a single element is returned.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3]);
- *
- * x.max().print();  // or tf.max(x)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * const axis = 1;
- * x.max(axis).print();  // or tf.max(x, axis)
- * ```
- *
- * @param x The input tensor.
- * @param axis The dimension(s) to reduce. By default it reduces
- *     all dimensions.
- * @param keepDims If true, retains reduced dimensions with size 1.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function max_<T extends Tensor>(
-    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {
-  const $x = convertToTensor(x, 'x', 'max');
-
-  const inputs: MaxInputs = {x: $x};
-  const attrs: MaxAttrs = {reductionIndices: axis, keepDims};
-
-  return ENGINE.runKernel(
-      Max, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const max = /* @__PURE__ */ op({max_});
diff --git a/tfjs-master/tfjs-core/src/ops/max_pool.ts b/tfjs-master/tfjs-core/src/ops/max_pool.ts
deleted file mode 100644
index 5d07c5c82..000000000
--- a/tfjs-master/tfjs-core/src/ops/max_pool.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {MaxPool, MaxPoolAttrs, MaxPoolInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor3D, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import * as conv_util from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the 2D max pooling of an image.
- *
- * @param x The input tensor, of rank 4 or rank 3 of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
- *     `filterSize` is a single number, then `filterHeight == filterWidth`.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
- *     `strides` is a single number, then `strideHeight == strideWidth`.
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
- *     in which we sample input values across the height and width dimensions
- *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single
- *     number, then `dilationHeight == dilationWidth`. If it is greater than
- *     1, then all values of `strides` must be 1.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- */
-function maxPool_<T extends Tensor3D|Tensor4D>(
-    x: T|TensorLike, filterSize: [number, number]|number,
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  const $x = convertToTensor(x, 'x', 'maxPool');
-  const dilations = 1;
-
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-
-  util.assert(
-      x4D.rank === 4,
-      () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
-  util.assert(
-      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in maxPool: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-  conv_util.checkPadOnDimRoundingMode('maxPool', pad, dimRoundingMode);
-  const inputs: MaxPoolInputs = {x: x4D};
-  const attrs: MaxPoolAttrs = {filterSize, strides, pad, dimRoundingMode};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  MaxPool, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-  return res;
-}
-
-export const maxPool = /* @__PURE__ */ op({maxPool_});
diff --git a/tfjs-master/tfjs-core/src/ops/max_pool_3d.ts b/tfjs-master/tfjs-core/src/ops/max_pool_3d.ts
deleted file mode 100644
index c306e6637..000000000
--- a/tfjs-master/tfjs-core/src/ops/max_pool_3d.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D, Tensor5D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {checkPadOnDimRoundingMode} from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the 3D max pooling.
- *
- * ```js
- * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
- * const result = tf.maxPool3d(x, 2, 1, 'valid');
- * result.print();
- * ```
- *
- * @param x The input tensor, of rank 5 or rank 4 of shape
- *     `[batch, depth, height, width, inChannels]`.
- * @param filterSize The filter size:
- *     `[filterDepth, filterHeight, filterWidth]`.
- *     If `filterSize` is a single number,
- *     then `filterDepth == filterHeight == filterWidth`.
- * @param strides The strides of the pooling:
- *     `[strideDepth, strideHeight, strideWidth]`.
- *     If `strides` is a single number,
- *     then `strideDepth == strideHeight == strideWidth`.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1*1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
- *     "NDHWC". Specify the data format of the input and output data. With the
- *     default format "NDHWC", the data is stored in the order of: [batch,
- *     depth, height, width, channels]. Only "NDHWC" is currently supported.
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function maxPool3d_<T extends Tensor4D|Tensor5D>(
-    x: T|TensorLike, filterSize: [number, number, number]|number = [1, 1, 1],
-    strides: [number, number, number]|number, pad: 'valid'|'same'|number,
-    dimRoundingMode?: 'floor'|'round'|'ceil',
-    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): T {
-  const $x = convertToTensor(x, 'x', 'maxPool3d');
-
-  let x5D = $x as Tensor5D;
-  let reshapedTo5D = false;
-  if ($x.rank === 4) {
-    reshapedTo5D = true;
-    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
-  }
-
-  util.assert(
-      x5D.rank === 5,
-      () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
-  util.assert(
-      dataFormat === 'NDHWC',
-      () => `Error in maxPool3d: Only NDHWC is currently supported, ` +
-          `but got dataFormat of ${dataFormat}`);
-  checkPadOnDimRoundingMode('maxPool3d', pad, dimRoundingMode);
-  const inputs: MaxPool3DInputs = {x: x5D};
-  const attrs:
-      MaxPool3DAttrs = {filterSize, strides, pad, dimRoundingMode, dataFormat};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  MaxPool3D, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo5D) {
-    return reshape(
-               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as
-        T;
-  }
-
-  return res;
-}
-
-export const maxPool3d = /* @__PURE__ */ op({maxPool3d_});
diff --git a/tfjs-master/tfjs-core/src/ops/max_pool_3d_grad.ts b/tfjs-master/tfjs-core/src/ops/max_pool_3d_grad.ts
deleted file mode 100644
index 3e4f30224..000000000
--- a/tfjs-master/tfjs-core/src/ops/max_pool_3d_grad.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {MaxPool3DGrad, MaxPool3DGradAttrs, MaxPool3DGradInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D, Tensor5D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {checkPadOnDimRoundingMode} from './conv_util';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the backprop of a 3d max pool.
- *
- * @param dy The dy error, of rank 5 of shape
- *     [batchSize, depth, height, width, channels].
- * assumed.
- * @param input The original input image, of rank 5 or rank 4 of shape
- *     [batchSize, depth, height, width, channels].
- * @param output The original output image, of rank 5 of shape
- *     [batchSize, outDepth, outHeight, outWidth, channels].
- * @param filterSize The filter size:
- *     `[filterDepth, filterHeight, filterWidth]`.
- *     `filterSize` is a single number,
- *     then `filterDepth == filterHeight == filterWidth`.
- * @param strides The strides of the pooling:
- *     `[strideDepth, strideHeight, strideWidth]`. If
- *     `strides` is a single number, then `strideHeight == strideWidth`.
- * @param pad A string from: 'same', 'valid'. The type of padding algorithm
- *     used in the forward prop of the op.
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- */
-function maxPool3dGrad_<T extends Tensor4D|Tensor5D>(
-    dy: T|TensorLike, input: T|TensorLike, output: T|TensorLike,
-    filterSize: [number, number, number]|number,
-    strides: [number, number, number]|number, pad: 'valid'|'same'|number,
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  const $dy = convertToTensor(dy, 'dy', 'maxPool3dGrad');
-  const $input = convertToTensor(input, 'input', 'maxPool3dGrad');
-  const $output = convertToTensor(output, 'output', 'maxPool3dGrad');
-
-  let dy5D = $dy as Tensor5D;
-  let input5D = $input as Tensor5D;
-  let output5D = $output as Tensor5D;
-  let reshapedTo5D = false;
-
-  if ($input.rank === 4) {
-    reshapedTo5D = true;
-    dy5D = reshape(
-        $dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
-    input5D = reshape($input, [
-      1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]
-    ]);
-    output5D = reshape($output, [
-      1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]
-    ]);
-  }
-
-  util.assert(
-      dy5D.rank === 5,
-      () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ` +
-          `${dy5D.rank}.`);
-  util.assert(
-      input5D.rank === 5,
-      () => `Error in maxPool3dGrad: input must be rank 5 but got rank ` +
-          `${input5D.rank}.`);
-  util.assert(
-      output5D.rank === 5,
-      () => `Error in maxPool3dGrad: output must be rank 5 but got rank ` +
-          `${output5D.rank}.`);
-  checkPadOnDimRoundingMode('maxPool3dGrad', pad, dimRoundingMode);
-  const inputs:
-      MaxPool3DGradInputs = {dy: dy5D, input: input5D, output: output5D};
-  const attrs: MaxPool3DGradAttrs = {filterSize, strides, pad, dimRoundingMode};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  MaxPool3DGrad, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as T;
-
-  if (reshapedTo5D) {
-    return reshape(
-               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as
-        T;
-  }
-
-  return res;
-}
-
-export const maxPool3dGrad = /* @__PURE__ */ op({maxPool3dGrad_});
diff --git a/tfjs-master/tfjs-core/src/ops/max_pool_3d_test.ts b/tfjs-master/tfjs-core/src/ops/max_pool_3d_test.ts
deleted file mode 100644
index 6fbb04721..000000000
--- a/tfjs-master/tfjs-core/src/ops/max_pool_3d_test.ts
+++ /dev/null
@@ -1,395 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('maxPool3d', ALL_ENVS, () => {
-  it('4D x=[2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-
-    const result = tf.maxPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [8]);
-  });
-
-  it('x=[1,1,1,1,1] f=[1,1,1] s=1 [0] => [0]', async () => {
-    const x = tf.tensor5d([0], [1, 1, 1, 1, 1]);
-
-    const result = tf.maxPool3d(x, 1, 1, 0);
-
-    expect(result.shape).toEqual([1, 1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [0]);
-  });
-
-  it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-
-    const result = tf.maxPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [8]);
-  });
-
-  it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=same', async () => {
-    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const expected = [8, 8, 8, 8, 8, 8, 8, 8];
-    const result = tf.maxPool3d(x, 2, 1, 'same');
-
-    expect(result.shape).toEqual([1, 2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27
-        ],
-        [1, 3, 3, 3, 1]);
-    const expected = [14, 15, 17, 18, 23, 24, 26, 27];
-    const result = tf.maxPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=same', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27
-        ],
-        [1, 3, 3, 3, 1]);
-    const expected = [
-      14, 15, 15, 17, 18, 18, 17, 18, 18, 23, 24, 24, 26, 27,
-      27, 26, 27, 27, 23, 24, 24, 26, 27, 27, 26, 27, 27
-    ];
-    const result = tf.maxPool3d(x, 2, 1, 'same');
-
-    expect(result.shape).toEqual([1, 3, 3, 3, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,3,3,3,1] f=[2,2,2] s=1 p=valid, ignores NaNs', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  NaN, 8,   NaN, 10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21,  NaN, 23,  24, 25, 26, 27
-        ],
-        [1, 3, 3, 3, 1]);
-    const expected = [14, 15, 17, 18, 23, 24, 26, 27];
-    const result = tf.maxPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[2,3,3,3,1] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
-          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
-          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54
-        ],
-        [2, 3, 3, 3, 1]);
-    const expected =
-        [14, 15, 17, 18, 23, 24, 26, 27, 41, 42, 44, 45, 50, 51, 53, 54];
-    const result = tf.maxPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([2, 2, 2, 2, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,3,3,3,2] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
-          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
-          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54
-        ],
-        [1, 3, 3, 3, 2]);
-    const expected =
-        [27, 28, 29, 30, 33, 34, 35, 36, 45, 46, 47, 48, 51, 52, 53, 54];
-    const result = tf.maxPool3d(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([1, 2, 2, 2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('x=[1,2,2,2,1] f=[2,2,2] s=1 p=1 roundingMode=floor', async () => {
-    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const expected = [
-      1, 2, 2, 3, 4, 4, 3, 4, 4, 5, 6, 6, 7, 8,
-      8, 7, 8, 8, 5, 6, 6, 7, 8, 8, 7, 8, 8
-    ];
-    const result = tf.maxPool3d(x, 2, 1, 1, 'floor');
-
-    expect(result.shape).toEqual([1, 3, 3, 3, 1]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws when x is not rank 5', async () => {
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor1d([1]);
-
-    expect(() => tf.maxPool3d(x as tf.Tensor5D, 2, 1, 'valid')).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is same', () => {
-    const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
-    const pad = 'same';
-    const dimRoundingMode = 'round';
-
-    expect(() => tf.maxPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is valid', () => {
-    const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
-    const pad = 'valid';
-    const dimRoundingMode = 'round';
-
-    expect(() => tf.maxPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is a non-integer number',
-     () => {
-       const x = tf.tensor5d([1], [1, 1, 1, 1, 1]);
-       const pad = 1.2;
-       const dimRoundingMode = 'round';
-
-       expect(() => tf.maxPool3d(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-     });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.maxPool3d({} as tf.Tensor5D, 2, 1, 'valid')).toThrowError();
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[[[[0]]]]];  // 1x1x1x1x1
-    const result = tf.maxPool3d(x, 1, 1, 0);
-    expect(result.shape).toEqual([1, 1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [0]);
-  });
-});
-
-describeWithFlags('maxPool3dBackprop', ALL_ENVS, () => {
-  it('gradient x=[2,2,2,1] f=[1,1,1] s=1', async () => {
-    const dy = tf.tensor4d([1, 2, 1, 2, 1, 2, 1, 2], [2, 2, 2, 1]);
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-    const expected = [1, 2, 1, 2, 1, 2, 1, 2];
-
-    const dx = tf.grad((x: tf.Tensor4D) => tf.maxPool3d(x, 1, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,2,2,2,1] f=[1,1,1] s=1', async () => {
-    const dy = tf.tensor5d([1, 2, 1, 2, 1, 2, 1, 2], [1, 2, 2, 2, 1]);
-    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const expected = [1, 2, 1, 2, 1, 2, 1, 2];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.maxPool3d(x, 1, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,2,2,2,1] f=[2,2,2] s=2', async () => {
-    const dy = tf.tensor5d([1], [1, 1, 1, 1, 1]);
-    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const expected = [0, 0, 0, 0, 0, 0, 0, 1];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.maxPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient with clone x=[1,2,2,2,1] f=[2,2,2] s=1', async () => {
-    const dy = tf.tensor5d([1], [1, 1, 1, 1, 1]);
-    const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const expected = [0, 0, 0, 0, 0, 0, 0, 1];
-
-    const dx = tf.grad(
-        (x: tf.Tensor5D) => tf.maxPool3d(x.clone(), 2, 1, 0).clone())(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,3,3,3,1] f=[2,2,2] s=1 no dup max value', async () => {
-    const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27
-        ],
-        [1, 3, 3, 3, 1]);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
-      2, 0, 3, 4, 0, 0, 0, 0, 5, 6, 0, 7, 8
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.maxPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,3,3,3,1] f=[2,2,2] s=1 dup max value', async () => {
-    const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 27,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 14
-        ],
-        [1, 3, 3, 3, 1]);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 36,
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.maxPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,4,4,4,1] f=[2,2,2] s=2', async () => {
-    const dy = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
-          17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
-          33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
-          49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64
-        ],
-        [1, 4, 4, 4, 1]);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
-      0, 2, 0, 0, 0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 6, 0, 0, 0, 0, 0, 7, 0, 8
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.maxPool3d(x, 2, 2, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,3,3,3,2] f=[2,2,2] s=1 no dup max value', async () => {
-    const dy = tf.tensor5d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-        [1, 2, 2, 2, 2]);
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
-          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
-          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54
-        ],
-        [1, 3, 3, 3, 2]);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0,  0,  0, 0, 0,  0,  0,  0,
-      0, 0, 0, 0, 0, 0, 0, 0, 1, 2,  3,  4,  0, 0, 5,  6,  7,  8,
-      0, 0, 0, 0, 0, 0, 0, 0, 9, 10, 11, 12, 0, 0, 13, 14, 15, 16
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.maxPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[1,3,3,3,2] f=[2,2,2] s=1 dup max value', async () => {
-    const dy = tf.tensor5d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-        [1, 2, 2, 2, 2]);
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 53, 54,
-          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
-          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 27, 28
-        ],
-        [1, 3, 3, 3, 2]);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0,  0,  0, 0, 0, 0, 0, 0, 0, 0,
-      0, 0, 0, 0, 0, 0, 0, 0, 64, 72, 0, 0, 0, 0, 0, 0, 0, 0,
-      0, 0, 0, 0, 0, 0, 0, 0, 0,  0,  0, 0, 0, 0, 0, 0, 0, 0
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.maxPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[2,3,3,3,1] f=[2,2,2] s=1 no dup max value', async () => {
-    const dy = tf.tensor5d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-        [2, 2, 2, 2, 1]);
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
-          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
-          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54
-        ],
-        [2, 3, 3, 3, 1]);
-    const expected = [
-      0, 0, 0, 0, 0, 0,  0, 0,  0,  0, 0, 0, 0, 1,  2,  0, 3,  4,
-      0, 0, 0, 0, 5, 6,  0, 7,  8,  0, 0, 0, 0, 0,  0,  0, 0,  0,
-      0, 0, 0, 0, 9, 10, 0, 11, 12, 0, 0, 0, 0, 13, 14, 0, 15, 16
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.maxPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[2,3,3,3,1] f=[2,2,2] s=1 dup max value', async () => {
-    const dy = tf.tensor5d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-        [2, 2, 2, 2, 1]);
-    const x = tf.tensor5d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 27,
-          15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 14, 28,
-          29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 54, 42,
-          43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 41
-        ],
-        [2, 3, 3, 3, 1]);
-    const expected = [
-      0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 0, 0, 0, 36, 0, 0, 0, 0,
-      0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0, 0,
-      0, 0, 0, 0, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0, 0
-    ];
-
-    const dx = tf.grad((x: tf.Tensor5D) => tf.maxPool3d(x, 2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/max_pool_grad.ts b/tfjs-master/tfjs-core/src/ops/max_pool_grad.ts
deleted file mode 100644
index 32ca887e8..000000000
--- a/tfjs-master/tfjs-core/src/ops/max_pool_grad.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {MaxPoolGrad, MaxPoolGradAttrs, MaxPoolGradInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import * as conv_util from './conv_util';
-import {op} from './operation';
-
-/**
- * Computes the backprop of a 2D max pool.
- *
- * @param dy The dy error, of rank 4 or rank 3 of shape
- *     [batchSize, height, width, channels]. If rank 3, batch of 1 is
- * assumed.
- * @param input The original input image, of rank 4, of shape
- *     [batchSize, height, width, channels].
- * @param output The original output image, of rank 4, of shape
- *     [batchSize, outHeight, outWidth, channels].
- * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
- *     `filterSize` is a single number, then `filterHeight == filterWidth`.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
- *     `strides` is a single number, then `strideHeight == strideWidth`.
- * @param pad The type of padding algorithm used in the forward prop of the op.
- *     'same', 'valid', for more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- */
-function maxPoolGrad_(
-    dy: Tensor4D|TensorLike, input: Tensor4D|TensorLike,
-    output: Tensor4D|TensorLike, filterSize: [number, number]|number,
-    strides: [number, number]|number,
-    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {
-  const $dy = convertToTensor(dy, 'dy', 'maxPoolGrad');
-  const $input = convertToTensor(input, 'input', 'maxPoolGrad');
-  const $output = convertToTensor(output, 'output', 'maxPoolGrad');
-
-  util.assert(
-      $input.rank === $dy.rank,
-      () => `Rank of input (${$input.rank}) does not match rank of dy ` +
-          `(${$dy.rank})`);
-
-  util.assert(
-      $dy.rank === 4,
-      () => `Error in maxPoolGrad: dy must be rank 4 but got rank ` +
-          `${$dy.rank}.`);
-  util.assert(
-      $input.rank === 4,
-      () => `Error in maxPoolGrad: input must be rank 4 but got rank ` +
-          `${$input.rank}.`);
-  conv_util.checkPadOnDimRoundingMode('maxPoolGrad', pad, dimRoundingMode);
-  const inputs: MaxPoolGradInputs = {dy: $dy, input: $input, output: $output};
-  const attrs: MaxPoolGradAttrs = {filterSize, strides, pad, dimRoundingMode};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  return ENGINE.runKernel(
-             MaxPoolGrad, inputs as unknown as NamedTensorMap,
-             attrs as unknown as NamedAttrMap) as Tensor4D;
-}
-
-export const maxPoolGrad = /* @__PURE__ */ op({maxPoolGrad_});
diff --git a/tfjs-master/tfjs-core/src/ops/max_pool_test.ts b/tfjs-master/tfjs-core/src/ops/max_pool_test.ts
deleted file mode 100644
index ba9823698..000000000
--- a/tfjs-master/tfjs-core/src/ops/max_pool_test.ts
+++ /dev/null
@@ -1,453 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import { identityPoolTest } from './identity_pool_test';
-
-describeWithFlags('maxPool', ALL_ENVS, () => {
-  it('x=[1,1,1] f=[1,1] s=1 [0] => [0]', async () => {
-    const x = tf.tensor3d([123], [1, 1, 1]);
-
-    const result = tf.maxPool(x, 1, 1, 0);
-
-    expectArraysClose(await result.data(), [123]);
-  });
-
-  it('x=[3,3,1] f=[2,2] s=1, p=0', async () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
-
-    const result = tf.maxPool(x, 2, 1, 0);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 6, 9, 9]);
-  });
-
-  it('x=[3,3,1] f=[2,2] s=1 p=same', async () => {
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
-
-    const result = tf.maxPool(x, 2, 1, 'same');
-    const resultData = await result.data();
-
-    tf.test_util.expectArraysClose(
-        resultData, new Float32Array([5, 6, 6, 9, 9, 8, 9, 9, 8]));
-  });
-
-  it('x=[3,3,1] f=[3,3] s=1 p=explicit', async () => {
-    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-    const padding =
-        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const result = tf.maxPool(x, 3, 1, padding);
-
-    expect(result.shape).toEqual([4, 2, 1]);
-    expectArraysClose(await result.data(), [5, 5, 8, 8, 8, 8, 8, 8]);
-  });
-
-  it('x=[2,3,3,1] f=[2,2] s=1', async () => {
-    // Feed forward.
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
-
-    const result = tf.maxPool(x, 2, 1, 0);
-
-    expect(result.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 6, 9, 9, 5, 6, 8, 9]);
-  });
-
-  it('[x=[3,3,1] f=[2,2] s=1 ignores NaNs', async () => {
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, NaN, 9], [3, 3, 1]);
-
-    const result = tf.maxPool(x, 2, 1, 0);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 6, 7, 9]);
-  });
-
-  it('x=[3,3,2] f=[2,2] s=1', async () => {
-    // Feed forward.
-    const x = tf.tensor3d(
-        [1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11],
-        [3, 3, 2]);
-
-    const result = tf.maxPool(x, 2, 1, 0);
-
-    expect(result.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await result.data(), [5, 99, 6, 88, 9, 66, 9, 55]);
-  });
-
-  it('x=[4,4,1] f=[2,2] s=2', async () => {
-    // Feed forward.
-    const x = tf.tensor3d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
-
-    const result = tf.maxPool(x, 2, 2, 0);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 7, 13, 15]);
-  });
-
-  it('x=[2,2,1] f=[2,2] s=1 p=same', async () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const fSize = 2;
-    const strides = 1;
-    const result = tf.maxPool(x, fSize, strides, 'same');
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [4, 4, 4, 4]);
-  });
-
-  it('x=[2,2,3] f=[2,2] s=3 p=1 default dimRoundingMode', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.maxPool(x, 2, 3, 1);
-
-    expect(result.shape).toEqual([1, 1, 3]);
-  });
-
-  it('x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.maxPool(x, 1, 2, 1, 'floor');
-
-    expect(result.shape).toEqual([2, 2, 3]);
-  });
-
-  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=floor', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.maxPool(x, 2, 3, 1, 'floor');
-
-    expect(result.shape).toEqual([1, 1, 3]);
-  });
-
-  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=round', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.maxPool(x, 2, 3, 1, 'round');
-
-    expect(result.shape).toEqual([2, 2, 3]);
-  });
-
-  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=ceil', () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-    const result = tf.maxPool(x, 2, 3, 1, 'ceil');
-
-    expect(result.shape).toEqual([2, 2, 3]);
-  });
-
-  it('throws when x is not rank 3', () => {
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
-
-    expect(() => tf.maxPool(x, 2, 1, 0)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is same', () => {
-    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-    const pad = 'same';
-    const dimRoundingMode = 'round';
-
-    expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is valid', () => {
-    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-    const pad = 'valid';
-    const dimRoundingMode = 'round';
-
-    expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-  });
-
-  it('throws when dimRoundingMode is set and pad is a non-integer number',
-     () => {
-       const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-       const pad = 1.2;
-       const dimRoundingMode = 'round';
-
-       expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-     });
-
-  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
-         'number',
-     () => {
-       const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as
-           tf.backend_util.ExplicitPadding;
-       const dimRoundingMode = 'round';
-
-       expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
-     });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.maxPool({} as tf.Tensor3D, 2, 1, 'valid'))
-        .toThrowError(/Argument 'x' passed to 'maxPool' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[[0]]];  // 1x1x1
-    const result = tf.maxPool(x, 1, 1, 0);
-    expectArraysClose(await result.data(), [0]);
-  });
-
-  identityPoolTest(tf.maxPool);
-});
-
-describeWithFlags('maxPoolBackprop', ALL_ENVS, () => {
-  it('gradients x=[3,3,1] f=[2,2] s=1 no dup max value, test #1', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
-    const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient with clones x=[3,3,1] f=[2,2] s=1', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
-    const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4];
-
-    const dx = tf.grad(
-        (x: tf.Tensor3D) => tf.maxPool(x.clone(), 2, 1, 0).clone())(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradients x=[3,3,1] f=[2,2] s=1 no dup max value, test #2', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d([9, 5, 6, 6, 8, 4, 9, 5, 10], [3, 3, 1]);
-    const expected = [1, 0, 0, 0, 2, 0, 3, 0, 4];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradients x=[2,3,3,1] f=[2,2] s=1 no duplicate max value', async () => {
-    // This test batches the [3,3,1] tests.
-    const dy = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2, 1]);
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 5, 6, 6, 8, 4, 9, 5, 10], [2, 3, 3, 1]);
-    const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4, 1, 0, 0, 0, 2, 0, 3, 0, 4];
-
-    const dx = tf.grad((x: tf.Tensor4D) => x.maxPool(2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[3,3,1] f=[2,2] s=1 dup max value, test 1', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d([0, 0, 0, 0, 5, 0, 0, 0, 0], [3, 3, 1]);
-    const expected = [0, 0, 0, 0, 10, 0, 0, 0, 0];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[3,3,1] f=[2,2] s=1 dup max value, test 2', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d([1, 3, 2, 1, 2, 1, 1, 1, 5], [3, 3, 1]);
-    const expected = [0, 3, 0, 0, 3, 0, 0, 0, 4];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[2,3,3,1] f=[2,2] s=1 dup max value in 2nd input',
-     async () => {
-       const dy = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-       const x = tf.tensor4d(
-           [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 9, 8],
-           [2, 3, 3, 1]);
-       const expected = new Float32Array(
-           [0, 0, 0, 0, 1, 2, 0, 3, 4, 0, 0, 0, 0, 5, 6, 0, 15, 0]);
-
-       const dx = tf.grad((x: tf.Tensor4D) => x.maxPool(2, 1, 0))(x, dy);
-
-       expect(dx.shape).toEqual(x.shape);
-       expectArraysClose(await dx.data(), expected);
-     });
-
-  it('gradient x=[4,4,1] f=[2,2] s=2 test #1', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
-    const expected = [0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 3, 0, 4];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 2, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[4,4,1] f=[2,2] s=2 test #2', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d(
-        [1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1], [4, 4, 1]);
-    const expected = [0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 2, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[5,5,1] f=[3,3] s=2 no duplicate max value', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d(
-        [
-          0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12,
-          13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
-        ],
-        [5, 5, 1]);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
-      0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4
-    ];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(3, 2, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[5,5,1] f=[3,3] s=2 duplicate max value', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d(
-        [
-          0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 24,
-          13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 12
-        ],
-        [5, 5, 1]);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-    ];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(3, 2, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  // Max pool backprop depth > 1.
-  it('gradient x=[3,3,2] f=[2,2] s=1, no duplicate max value', async () => {
-    // This test combines the first two 3x3x1 tests with no duplicates to
-    // make depth=2,
-    // dy is slightly modified to show the difference.
-    const dy = tf.tensor3d([1, 44, 2, 33, 3, 22, 4, 11], [2, 2, 2]);
-    const x = tf.tensor3d(
-        [1, 99, 2, 55, 3, 66, 4, 66, 5, 88, 6, 44, 7, 99, 8, 55, 9, 100],
-        [3, 3, 2]);
-    const expected = [0, 44, 0, 0, 0, 0, 0, 0, 1, 33, 2, 0, 0, 22, 3, 0, 4, 11];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[3,3,2] f=[2,2] s=1 duplicate max value', async () => {
-    // This test combines the first two 3x3x1 tests with duplicates to
-    // make depth=2,
-    // dy is slightly modified to show the difference.
-    const dy = tf.tensor3d([1, 44, 2, 33, 3, 22, 4, 11], [2, 2, 2]);
-    const x = tf.tensor3d(
-        [0, 1, 0, 3, 0, 2, 0, 1, 5, 2, 0, 1, 0, 1, 0, 1, 0, 5], [3, 3, 2]);
-    const expected = new Float32Array(
-        [0, 0, 0, 77, 0, 0, 0, 0, 10, 22, 0, 0, 0, 0, 0, 0, 0, 11]);
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[4,4,2] f=[2,2] s=1', async () => {
-    // This test combines the first two 4x4x1 tests with duplicates to make
-    // depth=2,
-    // dy is slightly modified to show the difference.
-    const dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
-    const x = tf.tensor3d(
-        [
-          0, 1, 1, 2, 2,  2, 3,  1, 4,  1, 5,  1, 6,  1, 7,  1,
-          8, 1, 9, 1, 10, 1, 11, 1, 12, 1, 13, 2, 14, 2, 15, 1
-        ],
-        [4, 4, 2]);
-    const expected = [
-      0, 0, 0, 11, 0, 22, 0, 0, 0, 0, 1, 0,  0, 0,  2, 0,
-      0, 0, 0, 0,  0, 0,  0, 0, 0, 0, 3, 33, 0, 44, 4, 0
-    ];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 2, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=[3,3,1] f=3 s=1 p=explicit', async () => {
-    const dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [4, 2, 1]);
-    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-    const padding =
-        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const expected = [0, 0, 0, 0, 0, 12, 0, 0, 108];
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(3, 1, padding))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('gradient x=5x5x2, f=3, s=2 no duplicate max value', async () => {
-    // This test combines the first two 5x5x1 tests with duplicates to make
-    // depth=2,
-    // dy is slightly modified to show the difference.
-    const dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
-    const x = tf.tensor3d(
-        [
-          0,  0,  1,  1,  2,  2,  3,  3,  4,  4,  5,  5,  6,  6,  7,  7,  8,
-          8,  9,  9,  10, 10, 11, 11, 12, 24, 13, 13, 14, 14, 15, 15, 16, 16,
-          17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 12
-        ],
-        [5, 5, 2]);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 0, 0, 0,
-      0, 0, 0, 0, 0, 0, 0, 1, 110, 0, 0, 2, 0, 0, 0, 0, 0,
-      0, 0, 0, 0, 0, 0, 0, 0, 0,   0, 3, 0, 0, 0, 4, 0
-    ];
-
-    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(3, 2, 0))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/max_pool_with_argmax.ts b/tfjs-master/tfjs-core/src/ops/max_pool_with_argmax.ts
deleted file mode 100644
index 9dd079f1f..000000000
--- a/tfjs-master/tfjs-core/src/ops/max_pool_with_argmax.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor, Tensor4D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the 2D max pooling of an image with Argmax index.
- * The indices in argmax are flattened, so that a maximum value at position `[b,
- * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if
- * include_batch_in_index is False; `((b * height + y) * width + x) * channels
- * +c` if include_batch_in_index is True.
- *
- * The indices returned are always in `[0, height) x [0, width)` before
- * flattening.
- *
- * @param x The input tensor, of rank 4 or rank 3 of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
- *     `filterSize` is a single number, then `filterHeight == filterWidth`.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
- *     `strides` is a single number, then `strideHeight == strideWidth`.
- * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
- *     "NDHWC". Specify the data format of the input and output data. With the
- *     default format "NDHWC", the data is stored in the order of: [batch,
- *     depth, height, width, channels]. Only "NDHWC" is currently supported.
- * @param pad The type of padding algorithm.
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param includeBatchIndex Defaults to False. Whether to include batch
- *    dimension in flattened index of argmax.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function maxPoolWithArgmax_<T extends Tensor4D>(
-    x: T|TensorLike, filterSize: [number, number]|number,
-    strides: [number, number]|number, pad: 'valid'|'same'|number,
-    includeBatchInIndex = false): NamedTensorMap {
-  const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');
-
-  const inputs: MaxPoolWithArgmaxInputs = {x: $x};
-  const attrs:
-      MaxPoolWithArgmaxAttrs = {filterSize, strides, pad, includeBatchInIndex};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const result = ENGINE.runKernel(
-                     MaxPoolWithArgmax, inputs as unknown as NamedTensorMap,
-                     attrs as unknown as NamedAttrMap) as Tensor[];
-
-  return {result: result[0], indexes: result[1]};
-}
-
-export const maxPoolWithArgmax = /* @__PURE__ */ op({maxPoolWithArgmax_});
diff --git a/tfjs-master/tfjs-core/src/ops/max_pool_with_argmax_test.ts b/tfjs-master/tfjs-core/src/ops/max_pool_with_argmax_test.ts
deleted file mode 100644
index cbdb2621b..000000000
--- a/tfjs-master/tfjs-core/src/ops/max_pool_with_argmax_test.ts
+++ /dev/null
@@ -1,170 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('maxPoolWithArgmax', ALL_ENVS, () => {
-  it('x=[1,1,1] f=[1,1] s=1 d=1 [0] => [0]', async () => {
-    const x = tf.tensor4d([0], [1, 1, 1, 1]);
-
-    const padding = 0;
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, [1, 1], [1, 1], padding);
-    expectArraysClose(await result.data(), [0]);
-    expectArraysClose(await indexes.data(), [0]);
-  });
-
-  it('x=[2,2,2,1] f=[2,2,2] s=1 p=valid', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 'valid');
-
-    expect(result.shape).toEqual([2, 1, 1, 1]);
-    expectArraysClose(await result.data(), [4, 8]);
-    expect(indexes.shape).toEqual([2, 1, 1, 1]);
-    expectArraysClose(await indexes.data(), [3, 3]);
-  });
-
-  it('x=[2,2,2,1] f=[2,2,2] s=1 p=valid includeBatchInIndex=true', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 'valid', true);
-
-    expect(result.shape).toEqual([2, 1, 1, 1]);
-    expectArraysClose(await result.data(), [4, 8]);
-    expect(indexes.shape).toEqual([2, 1, 1, 1]);
-    expectArraysClose(await indexes.data(), [3, 7]);
-  });
-
-  it('x=[1,3,3,1] f=[2,2] s=1, p=0', async () => {
-    // Feed forward.
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8], [1, 3, 3, 1]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0);
-
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 6, 9, 9]);
-    expect(indexes.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await indexes.data(), [4, 5, 7, 7]);
-  });
-
-  it('x=[1,3,3,1] f=[2,2] s=1 p=same', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8], [1, 3, 3, 1]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 'same');
-
-    expect(result.shape).toEqual([1, 3, 3, 1]);
-    tf.test_util.expectArraysClose(
-        await result.data(), new Float32Array([5, 6, 6, 9, 9, 8, 9, 9, 8]));
-    expect(indexes.shape).toEqual([1, 3, 3, 1]);
-    expectArraysClose(await indexes.data(), [4, 5, 5, 7, 7, 8, 7, 7, 8]);
-  });
-
-  it('x=[2,3,3,1] f=[2,2] s=1', async () => {
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0);
-    expect(result.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 6, 9, 9, 5, 6, 8, 9]);
-    expect(indexes.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await indexes.data(), [4, 5, 7, 7, 4, 5, 7, 8]);
-  });
-
-  it('x=[2,3,3,1] f=[2,2] s=1 includeBatchInIndex=true', async () => {
-    const x = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0, true);
-    expect(result.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 6, 9, 9, 5, 6, 8, 9]);
-    expect(indexes.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await indexes.data(), [4, 5, 7, 7, 13, 14, 16, 17]);
-  });
-
-  it('[x=[1,3,3,1] f=[2,2] s=1 ignores NaNs', async () => {
-    const x = tf.tensor4d([NaN, 1, 2, 3, 4, 5, 6, 7, 9], [1, 3, 3, 1]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0);
-
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await result.data(), [4, 5, 7, 9]);
-    expect(indexes.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await indexes.data(), [4, 5, 7, 8]);
-  });
-
-  it('x=[1, 3,3,2] f=[2,2] s=1', async () => {
-    // Feed forward.
-    const x = tf.tensor4d(
-        [1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11],
-        [1, 3, 3, 2]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 0);
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    expectArraysClose(await result.data(), [5, 99, 6, 88, 9, 66, 9, 55]);
-    expect(indexes.shape).toEqual([1, 2, 2, 2]);
-    expectArraysClose(await indexes.data(), [8, 1, 10, 3, 14, 7, 14, 9]);
-  });
-
-  it('x=[1,4,4,1] f=[2,2] s=2', async () => {
-    // Feed forward.
-    const x = tf.tensor4d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [1, 4, 4, 1]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 2, 0);
-
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 7, 13, 15]);
-    expect(indexes.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await indexes.data(), [5, 7, 13, 15]);
-  });
-
-  it('x=[1,2,2,1] f=[2,2] s=1 p=same', async () => {
-    // Feed forward.
-    const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 2, 1, 'same');
-    expect(result.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await result.data(), [4, 4, 4, 4]);
-    expect(indexes.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await indexes.data(), [3, 3, 3, 3]);
-  });
-
-  it('throws when x is not rank 4', () => {
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3]);
-
-    expect(() => tf.maxPoolWithArgmax(x, 2, 1, 0)).toThrowError();
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.maxPoolWithArgmax({} as tf.Tensor4D, 2, 1, 'valid'))
-        .toThrowError(
-            /Argument 'x' passed to 'maxPoolWithArgmax' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[[[0]]]];  // 1x1x1
-    const {result, indexes} = tf.maxPoolWithArgmax(x, 1, 1, 0);
-    expectArraysClose(await result.data(), [0]);
-    expect(indexes.shape).toEqual([1, 1, 1, 1]);
-    expectArraysClose(await indexes.data(), [0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/max_test.ts b/tfjs-master/tfjs-core/src/ops/max_test.ts
deleted file mode 100644
index 790043db4..000000000
--- a/tfjs-master/tfjs-core/src/ops/max_test.ts
+++ /dev/null
@@ -1,272 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('max', ALL_ENVS, () => {
-  it('with one element dominating', async () => {
-    const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
-    const r = tf.max(a);
-    expectArraysClose(await r.data(), 100);
-  });
-
-  it('with all elements being the same', async () => {
-    const a = tf.tensor1d([3, 3, 3]);
-    const r = tf.max(a);
-    expectArraysClose(await r.data(), 3);
-  });
-
-  it('with a large dimension', async () => {
-    const aData = new Float32Array(1000);
-    aData[0] = 1;
-    const a = tf.tensor1d(aData);
-    const r = tf.max(a);
-    expectArraysClose(await r.data(), 1);
-  });
-
-  it('return NaNs', async () => {
-    expectArraysClose(await tf.max([3, NaN, 2]).data(), NaN);
-  });
-
-  it('2D', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    expectArraysClose(await tf.max(a).data(), 100);
-  });
-
-  it('2D axis=[0,1]', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    expectArraysClose(await tf.max(a, [0, 1]).data(), 100);
-  });
-
-  it('2D, axis=0', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    const r = tf.max(a, [0]);
-
-    expect(r.shape).toEqual([3]);
-    expectArraysClose(await r.data(), [100, -1, 2]);
-  });
-
-  it('2D, axis=0, keepDims', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    const r = tf.max(a, [0], true /* keepDims */);
-
-    expect(r.shape).toEqual([1, 3]);
-    expectArraysClose(await r.data(), [100, -1, 2]);
-  });
-
-  it('2D, axis=1 provided as a number', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const r = tf.max(a, 1);
-    expectArraysClose(await r.data(), [5, 100]);
-  });
-
-  it('2D, axis = -1 provided as a number', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const r = tf.max(a, -1);
-    expectArraysClose(await r.data(), [5, 100]);
-  });
-
-  it('2D, axis=[1]', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const r = tf.max(a, [1]);
-    expectArraysClose(await r.data(), [5, 100]);
-  });
-
-  it('6D, axis=[5]', async () => {
-    const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
-    const r = tf.max(a, [5]);
-    const expectedResult = [
-      1,  3,  5,  7,  9,  11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31,
-      33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63
-    ];
-    expectArraysClose(await r.data(), expectedResult);
-  });
-
-  it('axis permutation does not change input', async () => {
-    const input = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    const inputDataBefore = await input.data();
-
-    tf.max(input, [1, 0]);
-
-    const inputDataAfter = await input.data();
-    expectArraysClose(inputDataBefore, inputDataAfter);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.max({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'max' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.max([3, -1, 0, 100, -7, 2]);
-    expectArraysClose(await r.data(), 100);
-  });
-
-  it('accepts int32 tensor', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3], 'int32');
-    expect(a.dtype).toEqual('int32');
-    expectArraysClose(await tf.max(a).data(), 100);
-  });
-
-  it('max gradient: Scalar', async () => {
-    const x = tf.scalar(42);
-    const dy = tf.scalar(-1);
-    const gradients = tf.grad(v => tf.max(v))(x, dy);
-    expectArraysClose(await gradients.data(), [-1]);
-  });
-
-  it('gradient with clones', async () => {
-    const x = tf.scalar(42);
-    const dy = tf.scalar(-1);
-    const gradients = tf.grad(v => tf.max(v.clone()).clone())(x, dy);
-    expectArraysClose(await gradients.data(), [-1]);
-  });
-
-  it('max gradient: 1D, ties', async () => {
-    const x = tf.tensor1d([1, 3, 7, 7]);
-    const dy = tf.scalar(-1);
-    const gradients = tf.grad(v => tf.max(v))(x, dy);
-    expectArraysClose(await gradients.data(), [0, 0, -1, -1]);
-  });
-
-  it('max gradient: 2D, axes=-1, keepDims=false', async () => {
-    const x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = -1;
-    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('max gradient: ties, 2D, axes=-1, keepDims=false', async () => {
-    const x = tf.tensor2d([[0, 20, 20], [-10, -30, -10]]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = -1;
-    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, -1]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('max gradient: 2D, axes=0, keepDims=false', async () => {
-    const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
-    const dy = tf.tensor1d([-1, -1, -1]);
-    const axis = 0;
-    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('max gradient: 2D, axes=-1, keepDims=true', async () => {
-    const x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);
-    const dy = tf.tensor2d([[-1], [-1]]);
-    const axis = -1;
-    const keepDims = true;
-    const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('max gradient: 2D, axes=0, keepDims=true', async () => {
-    const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
-    const dy = tf.tensor2d([[-1, -1, -1]]);
-    const axis = 0;
-    const keepDims = true;
-    const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
-    expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('max gradient: 3D, axis=1 keepDims=false', async () => {
-    const x = tf.ones([2, 1, 250]);
-    const axis = 1;
-    const gradients = tf.grad(v => tf.max(v, axis))(x);
-    expect(gradients.shape).toEqual(x.shape);
-  });
-
-  it('max gradient: 3D, axes=[1, 2], keepDims=false', async () => {
-    const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = [1, 2];
-    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, 0, -1, 0, 0, 0]);
-    expect(gradients.shape).toEqual([2, 2, 2]);
-  });
-
-  it('max gradient: ties, 3D, axes=[1, 2], keepDims=false', async () => {
-    const x = tf.tensor3d([[[0, 20], [20, 20]], [[-10, -30], [-10, -15]]]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = [1, 2];
-    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, -1, -1, -1, 0, -1, 0]);
-    expect(gradients.shape).toEqual([2, 2, 2]);
-  });
-
-  it('max gradient: 3D, axes=2, keepDims=false', async () => {
-    const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
-    const dy = tf.tensor2d([[-1, -1], [-1, -1]]);
-    const axis = 2;
-    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
-    expect(gradients.shape).toEqual([2, 2, 2]);
-  });
-
-  it('max gradient: 3D, axes=2, keepDims=true', async () => {
-    const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
-    const dy = tf.tensor3d([[[-1], [-1]], [[-1], [-1]]]);
-    const axis = 2;
-    const keepDims = true;
-    const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
-    expect(gradients.shape).toEqual([2, 2, 2]);
-  });
-
-  it('max gradient: ties, 4D, axes=[1, 2, 3], keepDims=false', async () => {
-    const x = tf.tensor4d([
-      [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],
-      [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]
-    ]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = [1, 2, 3];
-    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
-    expectArraysClose(
-        await gradients.data(),
-        [0, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1]);
-    expect(gradients.shape).toEqual([2, 2, 2, 2]);
-  });
-
-  it('max gradient: ties, 4D, axes=[2, 3], keepDims=true', async () => {
-    const x = tf.tensor4d([
-      [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],
-      [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]
-    ]);
-    const dy = tf.tensor4d([[[[-1]], [[-2]]], [[[-3]], [[-4]]]]);
-    const axis = [2, 3];
-    const keepDims = true;
-    const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
-    expectArraysClose(
-        await gradients.data(),
-        [0, -1, -1, -1, -2, 0, -2, 0, -3, 0, 0, 0, 0, -4, 0, -4]);
-    expect(gradients.shape).toEqual([2, 2, 2, 2]);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.max(['a']))
-        .toThrowError(/Argument 'x' passed to 'max' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/maximum.ts b/tfjs-master/tfjs-core/src/ops/maximum.ts
deleted file mode 100644
index 1abcb6d5d..000000000
--- a/tfjs-master/tfjs-core/src/ops/maximum.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Maximum, MaximumInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {cast} from './cast';
-import {op} from './operation';
-
-/**
- * Returns the max of a and b (`a > b ? a : b`) element-wise.
- * Supports broadcasting.
- *
- * We also expose `tf.maximumStrict` which has the same signature as this op and
- * asserts that `a` and `b` are the same shape (does not broadcast).
- *
- * ```js
- * const a = tf.tensor1d([1, 4, 3, 16]);
- * const b = tf.tensor1d([1, 2, 9, 4]);
- *
- * a.maximum(b).print();  // or tf.maximum(a, b)
- * ```
- *
- * ```js
- * // Broadcast maximum a with b.
- * const a = tf.tensor1d([2, 4, 6, 8]);
- * const b = tf.scalar(5);
- *
- * a.maximum(b).print();  // or tf.maximum(a, b)
- * ```
- *
- * @param a The first tensor.
- * @param b The second tensor. Must have the same type as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function maximum_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'maximum');
-  let $b = convertToTensor(b, 'b', 'maximum');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  if ($a.dtype === 'bool') {
-    $a = cast($a, 'int32');
-    $b = cast($b, 'int32');
-  }
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: MaximumInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Maximum, inputs as unknown as NamedTensorMap);
-}
-
-export const maximum = /* @__PURE__ */ op({maximum_});
diff --git a/tfjs-master/tfjs-core/src/ops/mean.ts b/tfjs-master/tfjs-core/src/ops/mean.ts
deleted file mode 100644
index db504b150..000000000
--- a/tfjs-master/tfjs-core/src/ops/mean.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Mean, MeanAttrs, MeanInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the mean of elements across dimensions of a `tf.Tensor`.
- *
- * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is
- * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.
- * If `keepDims` is true, the reduced dimensions are retained with length 1.
- * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with
- * a single element is returned.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3]);
- *
- * x.mean().print();  // or tf.mean(a)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * const axis = 1;
- * x.mean(axis).print();  // or tf.mean(x, axis)
- * ```
- *
- * @param x The input tensor.
- * @param axis The dimension(s) to reduce. By default it reduces
- *     all dimensions.
- * @param keepDims If true, retains reduced dimensions with size 1.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function mean_<T extends Tensor>(
-    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {
-  const $x = convertToTensor(x, 'x', 'mean');
-
-  const inputs: MeanInputs = {x: $x};
-  const attrs: MeanAttrs = {axis, keepDims};
-
-  return ENGINE.runKernel(
-      Mean, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const mean = /* @__PURE__ */ op({mean_});
diff --git a/tfjs-master/tfjs-core/src/ops/mean_test.ts b/tfjs-master/tfjs-core/src/ops/mean_test.ts
deleted file mode 100644
index 1df483385..000000000
--- a/tfjs-master/tfjs-core/src/ops/mean_test.ts
+++ /dev/null
@@ -1,172 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('mean', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor2d(
-        [
-          0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
-          16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
-        ],
-        [16, 2]);
-    const r = tf.mean(a);
-
-    expect(r.dtype).toBe('float32');
-    expectArraysClose(await r.data(), 15.5);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
-    const r = tf.mean(a);
-
-    expect(r.dtype).toBe('float32');
-    expectArraysEqual(await r.data(), NaN);
-  });
-
-  it('mean(int32) => float32', async () => {
-    const a = tf.tensor1d([1, 5, 7, 3], 'int32');
-    const r = tf.mean(a);
-
-    expect(r.dtype).toBe('float32');
-    expectArraysClose(await r.data(), 4);
-  });
-
-  it('mean(bool) => float32', async () => {
-    const a = tf.tensor1d([true, false, false, true, true], 'bool');
-    const r = tf.mean(a);
-
-    expect(r.dtype).toBe('float32');
-    expectArraysClose(await r.data(), 3 / 5);
-  });
-
-  it('2D array with keep dim', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.mean(a, null, true /* keepDims */);
-
-    expect(res.shape).toEqual([1, 1]);
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [7 / 6]);
-  });
-
-  it('axis=0 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.mean(a, [0]);
-
-    expect(res.shape).toEqual([2]);
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [4 / 3, 1]);
-  });
-
-  it('axis=0 in 2D array, keepDims', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.mean(a, [0], true /* keepDims */);
-
-    expect(res.shape).toEqual([1, 2]);
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [4 / 3, 1]);
-  });
-
-  it('axis=1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.mean(a, [1]);
-
-    expect(res.dtype).toBe('float32');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [1.5, 1.5, 0.5]);
-  });
-
-  it('axis = -1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.mean(a, [-1]);
-
-    expect(res.dtype).toBe('float32');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [1.5, 1.5, 0.5]);
-  });
-
-  it('2D, axis=1 provided as number', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
-    const res = tf.mean(a, 1);
-
-    expect(res.shape).toEqual([2]);
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [2, 1 / 3]);
-  });
-
-  it('axis=0,1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.mean(a, [0, 1]);
-
-    expect(res.shape).toEqual([]);
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [7 / 6]);
-  });
-
-  it('gradients', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const dy = tf.scalar(1.5);
-
-    const da = tf.grad(a => a.mean())(a, dy);
-    const dyVal = await dy.array();
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [
-      dyVal / a.size, dyVal / a.size, dyVal / a.size, dyVal / a.size,
-      dyVal / a.size, dyVal / a.size
-    ]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const dy = tf.scalar(1.5);
-
-    const da = tf.grad(a => a.clone().mean().clone())(a, dy);
-    const dyVal = await dy.array();
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [
-      dyVal / a.size, dyVal / a.size, dyVal / a.size, dyVal / a.size,
-      dyVal / a.size, dyVal / a.size
-    ]);
-  });
-
-  it('gradients throws for defined axis', () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const dy = tf.scalar(1.5);
-
-    expect(() => tf.grad(a => a.mean(1))(a, dy)).toThrowError();
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.mean({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'mean' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.mean([[1, 2, 3], [0, 0, 1]]);
-
-    expect(r.dtype).toBe('float32');
-    expectArraysClose(await r.data(), 7 / 6);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.mean(['a']))
-        .toThrowError(/Argument 'x' passed to 'mean' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/meshgrid.ts b/tfjs-master/tfjs-core/src/ops/meshgrid.ts
deleted file mode 100644
index 4603527b4..000000000
--- a/tfjs-master/tfjs-core/src/ops/meshgrid.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {matMul} from './mat_mul';
-import {ones} from './ones';
-import {reshape} from './reshape';
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {sizeFromShape} from '../util_base';
-
-/**
- * Broadcasts parameters for evaluation on an N-D grid.
- *
- * Given N one-dimensional coordinate arrays `*args`, returns a list `outputs`
- * of N-D coordinate arrays for evaluating expressions on an N-D grid.
- *
- * Notes:
- * `meshgrid` supports cartesian ('xy') and matrix ('ij') indexing conventions.
- * When the `indexing` argument is set to 'xy' (the default), the broadcasting
- * instructions for the first two dimensions are swapped.
- * Examples:
- * Calling `const [X, Y] = meshgrid(x, y)` with the tensors
- *
- * ```javascript
- * const x = [1, 2, 3];
- * const y = [4, 5, 6];
- * const [X, Y] = tf.meshgrid(x, y);
- * // X = [[1, 2, 3],
- * //      [1, 2, 3],
- * //      [1, 2, 3]]
- * // Y = [[4, 4, 4],
- * //      [5, 5, 5],
- * //      [6, 6, 6]]
- * ```
- *
- * @param x Tensor with rank geq 1.
- * @param y Tensor with rank geq 1.
- * @param indexing
- *
- * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
- */
-export function meshgrid<T extends Tensor>(
-    x?: T|TensorLike, y?: T|TensorLike, {indexing = 'xy'} = {}): T[] {
-  if (indexing !== 'xy' && indexing !== 'ij') {
-    throw new TypeError(
-        `${indexing} is not a valid third argument to meshgrid`);
-  }
-  if (x === undefined) {
-    return [];
-  }
-  let $x = convertToTensor(
-      x, 'x', 'meshgrid', x instanceof Tensor ? x.dtype : 'float32');
-
-  if (y === undefined) {
-    return [$x];
-  }
-  let $y = convertToTensor(
-      y, 'y', 'meshgrid', y instanceof Tensor ? y.dtype : 'float32');
-
-  const w = sizeFromShape($x.shape);
-  const h = sizeFromShape($y.shape);
-
-  if (indexing === 'xy') {
-    $x = reshape($x, [1, -1]) as T;
-    $y = reshape($y, [-1, 1]) as T;
-    return [
-      matMul(ones([h, 1], $x.dtype), $x),
-      matMul($y, ones([1, w], $y.dtype)),
-    ];
-  }
-
-  $x = reshape($x, [-1, 1]) as T;
-  $y = reshape($y, [1, -1]) as T;
-  return [
-    matMul($x, ones([1, h], $x.dtype)),
-    matMul(ones([w, 1], $y.dtype), $y),
-  ];
-}
diff --git a/tfjs-master/tfjs-core/src/ops/meshgrid_test.ts b/tfjs-master/tfjs-core/src/ops/meshgrid_test.ts
deleted file mode 100644
index 50f6b2793..000000000
--- a/tfjs-master/tfjs-core/src/ops/meshgrid_test.ts
+++ /dev/null
@@ -1,121 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF {} KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {Tensor} from '../tensor';
-import {expectArraysEqual, expectArraysClose} from '../test_util';
-
-describeWithFlags('no input', ALL_ENVS, () => {
-  it('Should return an empty tensor ', async () => {
-    expect(tf.meshgrid()).toEqual([]);
-  });
-});
-
-describeWithFlags('single input', ALL_ENVS, () => {
-  it('Should return a tensor with the same data', async () => {
-    const x = [1, 2, 3, 4];
-    const [got] = tf.meshgrid(x);
-
-    expectArraysEqual(await got.data(), x);
-  });
-});
-
-describeWithFlags('simple inputs', ALL_ENVS, () => {
-  it('Should handle the simple 2D case', async () => {
-    const x = [1, 2, 3];
-    const y = [4, 5, 6, 7];
-    const [X, Y] = tf.meshgrid(x, y);
-
-    // 'close' instead of 'equal' because of matmul precision
-    // in certain backends (WebGL).
-    expectArraysClose(
-        await X.data(), [[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]]);
-    expectArraysClose(
-        await Y.data(), [[4, 4, 4], [5, 5, 5], [6, 6, 6], [7, 7, 7]]);
-  });
-
-  it('Should support \'ij\' indexing', async () => {
-    const x = [1, 2, 3];
-    const y = [4, 5, 6, 7];
-    const [X, Y] = tf.meshgrid(x, y, {indexing: 'ij'});
-
-    // 'close' instead of 'equal' because of matmul precision
-    // in certain backends (WebGL).
-    expectArraysClose(
-        await X.data(), [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]]);
-    expectArraysClose(
-        await Y.data(), [[4, 5, 6, 7], [4, 5, 6, 7], [4, 5, 6, 7]]);
-  });
-});
-
-describeWithFlags('higher dimensional input', ALL_ENVS, () => {
-  it('Should flatten higher dimensional', async () => {
-    const x = [1, 2, 3];
-    const a = [[1, 1], [1, 1]];
-
-    const [X, A] = tf.meshgrid(x, a);
-
-    // 'close' instead of 'equal' because of matmul precision
-    // in certain backends (WebGL).
-    expectArraysClose(
-        await X.data(), [[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]]);
-    expectArraysClose(
-        await A.data(), [[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]]);
-  });
-});
-
-describeWithFlags('dtypes', ALL_ENVS, () => {
-  it('Should use float32 for arrays of numbers', async () => {
-    const x = [1, 2];
-    const y = [3, 4];
-    const [X, Y] = tf.meshgrid(x, y);
-
-    expect(X.dtype).toBe('float32');
-    expect(Y.dtype).toBe('float32');
-  });
-
-  it('Should use the input tensor dtype', async () => {
-    const x = tf.tensor1d([1, 2], 'int32');
-    const y = tf.tensor1d([3, 4], 'float32');
-    const [X, Y] = tf.meshgrid(x, y);
-
-    expect(X.dtype).toBe('int32');
-    expect(Y.dtype).toBe('float32');
-  });
-});
-
-describeWithFlags('scalars', ALL_ENVS, () => {
-  it('Should treat them as 1D tensors', async () => {
-    const [X] = tf.meshgrid(0);
-    // 'close' instead of 'equal' because of matmul precision
-    // in certain backends (WebGL).
-    expectArraysClose(await X.data(), [0]);
-
-    const [Y, Z] = tf.meshgrid([0], 1);
-    expectArraysClose(await Y.data(), [[0]]);
-    expectArraysClose(await Z.data(), [[1]]);
-  });
-});
-
-describeWithFlags('invalid arguments', ALL_ENVS, () => {
-  it('Should throw an Error', () => {
-    expect(() => tf.meshgrid((() => {}) as unknown as Tensor)).toThrow();
-    expect(() => tf.meshgrid([1], (() => {}) as unknown as Tensor)).toThrow();
-    expect(() => tf.meshgrid([1], [2], {indexing: 'foobar'})).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/min.ts b/tfjs-master/tfjs-core/src/ops/min.ts
deleted file mode 100644
index 378483f95..000000000
--- a/tfjs-master/tfjs-core/src/ops/min.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Min, MinAttrs, MinInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the minimum value from the input.
- *
- * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
- * is true, the rank of the array is reduced by 1 for each entry in `axes`.
- * If `keepDims` is true, the reduced dimensions are retained with length 1.
- * If `axes` has no entries, all dimensions are reduced, and an array with a
- * single element is returned.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3]);
- *
- * x.min().print();  // or tf.min(x)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * const axis = 1;
- * x.min(axis).print();  // or tf.min(x, axis)
- * ```
- *
- * @param x The input Tensor.
- * @param axis The dimension(s) to reduce. By default it reduces
- *     all dimensions.
- * @param keepDims If true, retains reduced dimensions with size 1.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function min_<T extends Tensor>(
-    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {
-  const $x = convertToTensor(x, 'x', 'min');
-
-  const inputs: MinInputs = {x: $x};
-  const attrs: MinAttrs = {axis, keepDims};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  return ENGINE.runKernel(
-             Min, inputs as unknown as NamedTensorMap,
-             attrs as unknown as NamedAttrMap) as T;
-}
-
-export const min = /* @__PURE__ */ op({min_});
diff --git a/tfjs-master/tfjs-core/src/ops/min_test.ts b/tfjs-master/tfjs-core/src/ops/min_test.ts
deleted file mode 100644
index ae5ae01ab..000000000
--- a/tfjs-master/tfjs-core/src/ops/min_test.ts
+++ /dev/null
@@ -1,251 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {backend} from '../index';
-
-describeWithFlags('min', ALL_ENVS, () => {
-  it('Tensor1D', async () => {
-    const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
-    expectArraysClose(await tf.min(a).data(), -7);
-  });
-
-  it('return NaNs', async () => {
-    const a = tf.tensor1d([3, NaN, 2]);
-    expectArraysClose(await tf.min(a).data(), NaN);
-  });
-
-  it('2D', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    expectArraysClose(await tf.min(a).data(), -7);
-  });
-
-  it('2D axis=[0,1]', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    expectArraysClose(await tf.min(a, [0, 1]).data(), -7);
-  });
-
-  it('2D, axis=0', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    const r = tf.min(a, 0);
-
-    expect(r.shape).toEqual([3]);
-    expectArraysClose(await r.data(), [3, -7, 0]);
-  });
-
-  it('2D, axis=0, keepDims', async () => {
-    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    const r = tf.min(a, 0, true /* keepDims */);
-
-    expect(r.shape).toEqual([1, 3]);
-    expectArraysClose(await r.data(), [3, -7, 0]);
-  });
-
-  it('2D, axis=1 provided as a number', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const r = tf.min(a, 1);
-    expectArraysClose(await r.data(), [2, -7]);
-  });
-
-  it('2D, axis = -1 provided as a number', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const r = tf.min(a, -1);
-    expectArraysClose(await r.data(), [2, -7]);
-  });
-
-  it('2D, axis=[1]', async () => {
-    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
-    const r = tf.min(a, [1]);
-    expectArraysClose(await r.data(), [2, -7]);
-  });
-
-  it('axis permutation does not change input', async () => {
-    const input = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
-    const inputDataBefore = await input.data();
-
-    tf.min(input, [1, 0]);
-
-    const inputDataAfter = await input.data();
-    expectArraysClose(inputDataBefore, inputDataAfter);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.min({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'min' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    expectArraysClose(await tf.min([3, -1, 0, 100, -7, 2]).data(), -7);
-  });
-
-  it('accpets int32 input', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const a = tf.tensor1d([12345678, 12345679], 'int32');
-      expect(a.dtype).toEqual('int32');
-      expectArraysClose(await tf.min(a).data(), 12345678);
-    }
-  });
-
-  it('min gradient: Scalar', async () => {
-    const x = tf.scalar(42);
-    const dy = tf.scalar(-1);
-    const gradients = tf.grad(v => tf.min(v))(x, dy);
-    expectArraysClose(await gradients.data(), -1);
-  });
-
-  it('gradient with clones', async () => {
-    const x = tf.scalar(42);
-    const dy = tf.scalar(-1);
-    const gradients = tf.grad(v => tf.min(v.clone()).clone())(x, dy);
-    expectArraysClose(await gradients.data(), -1);
-  });
-
-  it('min gradient: 1D, ties', async () => {
-    const x = tf.tensor1d([-1, -3, -7, -7]);
-    const dy = tf.scalar(-1);
-    const gradients = tf.grad(v => tf.min(v))(x, dy);
-    expectArraysClose(await gradients.data(), [0, 0, -1, -1]);
-  });
-
-  it('min gradient: 2D, axes=-1, keepDims=false', async () => {
-    const x = tf.tensor2d([[-0, -20, -10], [10, 30, 20]]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = -1;
-    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('min gradient: ties, 2D, axes=-1, keepDims=false', async () => {
-    const x = tf.tensor2d([[0, -20, -20], [10, 30, 10]]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = -1;
-    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, -1]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('min gradient: 2D, axes=0, keepDims=false', async () => {
-    const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
-    const dy = tf.tensor1d([-1, -1, -1]);
-    const axis = 0;
-    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('min gradient: 2D, axes=-1, keepDims=true', async () => {
-    const x = tf.tensor2d([[0, -20, -10], [10, 30, 20]]);
-    const dy = tf.tensor2d([[-1], [-1]]);
-    const axis = -1;
-    const keepDims = true;
-    const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('min gradient: 2D, axes=0, keepDims=true', async () => {
-    const x = tf.tensor2d([[0, -20, -10], [10, 30, -20]]);
-    const dy = tf.tensor2d([[-1, -1, -1]]);
-    const axis = 0;
-    const keepDims = true;
-    const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);
-    expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
-    expect(gradients.shape).toEqual([2, 3]);
-  });
-
-  it('max gradient: 3D, axis=1 keepDims=false', async () => {
-    const x = tf.ones([2, 1, 250]);
-    const axis = 1;
-    const gradients = tf.grad(v => tf.min(v, axis))(x);
-    expect(gradients.shape).toEqual(x.shape);
-  });
-
-  it('min gradient: 3D, axes=[1, 2], keepDims=false', async () => {
-    const x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = [1, 2];
-    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, 0, -1, 0, 0, 0]);
-    expect(gradients.shape).toEqual([2, 2, 2]);
-  });
-
-  it('min gradient: ties, 3D, axes=[1, 2], keepDims=false', async () => {
-    const x = tf.tensor3d([[[0, -20], [-20, -20]], [[10, 30], [10, 15]]]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = [1, 2];
-    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, -1, -1, -1, 0, -1, 0]);
-    expect(gradients.shape).toEqual([2, 2, 2]);
-  });
-
-  it('min gradient: 3D, axes=2, keepDims=false', async () => {
-    const x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);
-    const dy = tf.tensor2d([[-1, -1], [-1, -1]]);
-    const axis = 2;
-    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
-    expect(gradients.shape).toEqual([2, 2, 2]);
-  });
-
-  it('min gradient: 3D, axes=2, keepDims=true', async () => {
-    const x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);
-    const dy = tf.tensor3d([[[-1], [-1]], [[-1], [-1]]]);
-    const axis = 2;
-    const keepDims = true;
-    const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);
-    expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
-    expect(gradients.shape).toEqual([2, 2, 2]);
-  });
-
-  it('min gradient: ties, 4D, axes=[1, 2, 3], keepDims=false', async () => {
-    const x = tf.tensor4d([
-      [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]],
-      [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]]
-    ]);
-    const dy = tf.tensor1d([-1, -1]);
-    const axis = [1, 2, 3];
-    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
-    expectArraysClose(
-        await gradients.data(),
-        [0, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1]);
-    expect(gradients.shape).toEqual([2, 2, 2, 2]);
-  });
-
-  it('min gradient: ties, 4D, axes=[2, 3], keepDims=true', async () => {
-    const x = tf.tensor4d([
-      [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]],
-      [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]]
-    ]);
-    const dy = tf.tensor4d([[[[-1]], [[-2]]], [[[-3]], [[-4]]]]);
-    const axis = [2, 3];
-    const keepDims = true;
-    const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);
-    expectArraysClose(
-        await gradients.data(),
-        [0, -1, -1, -1, -2, 0, -2, 0, -3, 0, 0, 0, 0, -4, 0, -4]);
-    expect(gradients.shape).toEqual([2, 2, 2, 2]);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.min(['a']))
-        .toThrowError(/Argument 'x' passed to 'min' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/minimum.ts b/tfjs-master/tfjs-core/src/ops/minimum.ts
deleted file mode 100644
index 4efc40edf..000000000
--- a/tfjs-master/tfjs-core/src/ops/minimum.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Minimum, MinimumInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {cast} from './cast';
-import {op} from './operation';
-
-/**
- * Returns the min of a and b (`a < b ? a : b`) element-wise.
- * Supports broadcasting.
- *
- * We also expose `minimumStrict` which has the same signature as this op and
- * asserts that `a` and `b` are the same shape (does not broadcast).
- *
- * ```js
- * const a = tf.tensor1d([1, 4, 3, 16]);
- * const b = tf.tensor1d([1, 2, 9, 4]);
- *
- * a.minimum(b).print();  // or tf.minimum(a, b)
- * ```
- *
- * ```js
- * // Broadcast minimum a with b.
- * const a = tf.tensor1d([2, 4, 6, 8]);
- * const b = tf.scalar(5);
- *
- * a.minimum(b).print();  // or tf.minimum(a, b)
- * ```
- *
- * @param a The first tensor.
- * @param b The second tensor. Must have the same type as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function minimum_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'minimum');
-  let $b = convertToTensor(b, 'b', 'minimum');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  if ($a.dtype === 'bool') {
-    $a = cast($a, 'int32');
-    $b = cast($b, 'int32');
-  }
-
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: MinimumInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Minimum, inputs as unknown as NamedTensorMap);
-}
-
-export const minimum = /* @__PURE__ */ op({minimum_});
diff --git a/tfjs-master/tfjs-core/src/ops/mirror_pad.ts b/tfjs-master/tfjs-core/src/ops/mirror_pad.ts
deleted file mode 100644
index 8b4db2b54..000000000
--- a/tfjs-master/tfjs-core/src/ops/mirror_pad.ts
+++ /dev/null
@@ -1,92 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {MirrorPad, MirrorPadAttrs, MirrorPadInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Pads a `tf.Tensor` using mirror padding.
- *
- * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.
- *
- * ```js
- * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);
- * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();
- * ```
- * @param x The tensor to pad.
- * @param paddings An array of length `R` (the rank of the tensor), where
- * each element is a length-2 tuple of ints `[padBefore, padAfter]`,
- * specifying how much to pad along each dimension of the tensor.
- * In "reflect" mode, the padded regions do not include the borders,
- * while in "symmetric" mode the padded regions do include the borders.
- * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,
- * then the output is `[1, 2, 3, 2, 1]` in "reflect" mode, and
- * `[1, 2, 3, 3, 2]` in "symmetric" mode.
- * If `mode` is "reflect" then both `paddings[D, 0]` and `paddings[D, 1]`
- * must be no greater than `x.shape[D] - 1`. If mode is "symmetric"
- * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than
- * `x.shape[D]`
- * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`
- */
-/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
-function mirrorPad_<T extends Tensor>(
-    x: T|TensorLike, paddings: Array<[number, number]>,
-    mode: 'reflect'|'symmetric'): T {
-  util.assert(
-      mode === 'reflect' || mode === 'symmetric',
-      () => `Invalid mode. Mode must be either reflect or symmetric. ` +
-          `Got ${mode}.`);
-
-  const $x = convertToTensor(x, 'x', 'mirrorPad');
-  if ($x.rank === 0) {
-    throw new Error(
-        'mirrorPad(scalar) is not defined. ' +
-        'Pass non-scalar to mirrorPad');
-  }
-  util.assert(
-      paddings.length === $x.rank,
-      () => `Padding doesn't match input. Must be ${$x.rank}. ` +
-          `Got ${paddings.length}.`);
-  const shapeOffset = mode === 'reflect' ? 1 : 0;
-  for (let i = 0; i < $x.rank; i++) {
-    util.assert(
-        paddings[i].length === 2,
-        () => `Invalid number of paddings. Must be length of 2 each.`);
-    util.assert(
-        paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset &&
-            paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset,
-        () => `Padding in dimension ${i} cannot be greater than or equal ` +
-            `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` +
-            `shape ${$x.shape}`);
-  }
-
-  const attrs: MirrorPadAttrs = {paddings, mode};
-  const inputs: MirrorPadInputs = {x: $x};
-  return ENGINE.runKernel(
-      MirrorPad, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const mirrorPad = /* @__PURE__ */ op({mirrorPad_});
diff --git a/tfjs-master/tfjs-core/src/ops/mirror_pad_test.ts b/tfjs-master/tfjs-core/src/ops/mirror_pad_test.ts
deleted file mode 100644
index b0e4ac9c2..000000000
--- a/tfjs-master/tfjs-core/src/ops/mirror_pad_test.ts
+++ /dev/null
@@ -1,195 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('mirrorPad', ALL_ENVS, () => {
-  it('MirrorPad tensor1d', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'int32');
-    let b = tf.mirrorPad(a, [[2, 2]], 'reflect');
-    expectArraysClose(await b.data(), [3, 2, 1, 2, 3, 2, 1]);
-    expect(b.shape).toEqual([7]);
-
-    b = tf.mirrorPad(a, [[2, 2]], 'symmetric');
-    expectArraysClose(await b.data(), [2, 1, 1, 2, 3, 3, 2]);
-    expect(b.shape).toEqual([7]);
-  });
-
-  it('MirrorPad tensor2d', async () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
-    let b = tf.mirrorPad(a, [[1, 1], [1, 1]], 'reflect');
-    // 5, 4, 5, 6, 5
-    // 2, 1, 2, 3, 2
-    // 5, 4, 5, 6, 5
-    // 2, 1, 2, 3, 2
-    expectArraysClose(
-        await b.data(),
-        [5, 4, 5, 6, 5, 2, 1, 2, 3, 2, 5, 4, 5, 6, 5, 2, 1, 2, 3, 2]);
-    expect(b.shape).toEqual([4, 5]);
-
-    b = tf.mirrorPad(a, [[1, 1], [1, 1]], 'symmetric');
-    // 1, 1, 2, 3, 3
-    // 1, 1, 2, 3, 3
-    // 4, 4, 5, 6, 6
-    // 4, 4, 5, 6, 6
-    expectArraysClose(
-        await b.data(),
-        [1, 1, 2, 3, 3, 1, 1, 2, 3, 3, 4, 4, 5, 6, 6, 4, 4, 5, 6, 6]);
-    expect(b.shape).toEqual([4, 5]);
-  });
-
-  it('MirrorPad tensor3d', async () => {
-    const a = tf.tensor3d([[[1, 2]], [[3, 4]]], [2, 1, 2], 'int32');
-    let b = tf.mirrorPad(a, [[1, 1], [0, 0], [1, 1]], 'reflect');
-    // 4, 3, 4, 3
-
-    // 2, 1, 2, 1
-
-    // 4, 3, 4, 3
-
-    // 2, 1, 2, 1
-    expectArraysClose(
-        await b.data(), [4, 3, 4, 3, 2, 1, 2, 1, 4, 3, 4, 3, 2, 1, 2, 1]);
-    expect(b.shape).toEqual([4, 1, 4]);
-
-    b = tf.mirrorPad(a, [[1, 1], [0, 0], [1, 1]], 'symmetric');
-    // 1, 1, 2, 2
-
-    // 1, 1, 2, 2
-
-    // 3, 3, 4, 4
-
-    // 3, 3, 4, 4
-    expectArraysClose(
-        await b.data(), [1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4]);
-    expect(b.shape).toEqual([4, 1, 4]);
-  });
-
-  it('MirrorPad tensor4d', async () => {
-    const a = tf.tensor4d([[[[1, 2, 3, 4]]]], [1, 1, 1, 4], 'int32');
-    let b = tf.mirrorPad(a, [[0, 0], [0, 0], [0, 0], [1, 1]], 'reflect');
-    let expected = tf.tensor4d([[[[2, 1, 2, 3, 4, 3]]]], [1, 1, 1, 6], 'int32');
-    expectArraysClose(await b.data(), await expected.data());
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([1, 1, 1, 6]);
-
-    b = tf.mirrorPad(a, [[0, 0], [0, 0], [0, 0], [1, 1]], 'symmetric');
-    expected = tf.tensor4d([[[[1, 1, 2, 3, 4, 4]]]], [1, 1, 1, 6], 'int32');
-    expectArraysClose(await b.data(), await expected.data());
-    expect(b.shape).toEqual([1, 1, 1, 6]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.mirrorPad({} as tf.Tensor, [[0, 0]], 'reflect'))
-        .toThrowError(/Argument 'x' passed to 'mirrorPad' must be a Tensor/);
-  });
-
-  it('does not leak memory', () => {
-    const a = tf.tensor4d([[[[1, 2, 3, 4]]]], [1, 1, 1, 4], 'int32');
-    // The first call to mirrorPad may create and keeps internal
-    // singleton tensors. Subsequent calls should always create exactly
-    // one new tensor.
-    tf.mirrorPad(a, [[0, 0], [0, 0], [0, 0], [1, 1]], 'reflect');
-    // Count before real call.
-    const numTensors = tf.memory().numTensors;
-    tf.mirrorPad(a, [[0, 0], [0, 0], [0, 0], [1, 1]], 'reflect');
-    expect(tf.memory().numTensors).toEqual(numTensors + 1);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[1, 2, 3], [4, 5, 6]];
-    const res = tf.mirrorPad(x, [[1, 1], [1, 1]], 'reflect');
-    // 5, 4, 5, 6, 5
-    // 2, 1, 2, 3, 2
-    // 5, 4, 5, 6, 5
-    // 2, 1, 2, 3, 2
-    expectArraysClose(
-        await res.data(),
-        [5, 4, 5, 6, 5, 2, 1, 2, 3, 2, 5, 4, 5, 6, 5, 2, 1, 2, 3, 2]);
-    expect(res.shape).toEqual([4, 5]);
-  });
-
-  it('Should handle invalid paddings', () => {
-    const a = tf.tensor1d([1, 2, 3, 4], 'int32');
-    const f = () => {
-      // tslint:disable-next-line:no-any
-      tf.mirrorPad(a, [2, 2, 2] as any, 'reflect');
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Should handle paddings that are out of range', () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
-    let f = () => {
-      // tslint:disable-next-line:no-any
-      tf.mirrorPad(a, [[4, 1], [1, 1]], 'reflect');
-    };
-    expect(f).toThrowError();
-
-    f = () => {
-      // tslint:disable-next-line:no-any
-      tf.mirrorPad(a, [[-1, 1], [1, 1]], 'reflect');
-    };
-    expect(f).toThrowError();
-
-    f = () => {
-      // tslint:disable-next-line:no-any
-      tf.mirrorPad(a, [[2, 1], [1, 1]], 'reflect');
-    };
-    expect(f).toThrowError();
-
-    f = () => {
-      // tslint:disable-next-line:no-any
-      tf.mirrorPad(a, [[3, 1], [1, 1]], 'symmetric');
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Should handle NaNs', async () => {
-    const a = tf.tensor2d([[1, NaN], [1, NaN]], [2, 2]);
-    const b = tf.mirrorPad(a, [[1, 1], [1, 1]], 'reflect');
-    // NaN, 1, NaN, 1
-    // NaN, 1, NaN, 1
-    // NaN, 1, NaN, 1
-    // NaN, 1, NaN, 1
-    expectArraysClose(
-        await b.data(),
-        [NaN, 1, NaN, 1, NaN, 1, NaN, 1, NaN, 1, NaN, 1, NaN, 1, NaN, 1]);
-    expect(b.shape).toEqual([4, 4]);
-  });
-
-  it('grad', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);
-    const da = tf.grad(
-        (a: tf.Tensor1D) => tf.mirrorPad(a, [[2, 1]], 'reflect'))(a, dy);
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [30, 40, 50]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);
-    const da = tf.grad(
-        (a: tf.Tensor1D) =>
-            tf.mirrorPad(a.clone(), [[2, 1]], 'reflect').clone())(a, dy);
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [30, 40, 50]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/mod.ts b/tfjs-master/tfjs-core/src/ops/mod.ts
deleted file mode 100644
index 22acb8d61..000000000
--- a/tfjs-master/tfjs-core/src/ops/mod.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Mod, ModInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Returns the mod of a and b element-wise.
- * `floor(x / y) * y + mod(x, y) = x`
- * Supports broadcasting.
- *
- * We also expose `tf.modStrict` which has the same signature as this op and
- * asserts that `a` and `b` are the same shape (does not broadcast).
- *
- * ```js
- * const a = tf.tensor1d([1, 4, 3, 16]);
- * const b = tf.tensor1d([1, 2, 9, 4]);
- *
- * a.mod(b).print();  // or tf.mod(a, b)
- * ```
- *
- * ```js
- * // Broadcast a mod b.
- * const a = tf.tensor1d([2, 4, 6, 8]);
- * const b = tf.scalar(5);
- *
- * a.mod(b).print();  // or tf.mod(a, b)
- * ```
- *
- * @param a The first tensor.
- * @param b The second tensor. Must have the same type as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function mod_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'mod');
-  let $b = convertToTensor(b, 'b', 'mod');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  const inputs: ModInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Mod, inputs as unknown as NamedTensorMap);
-}
-
-export const mod = /* @__PURE__ */ op({mod_});
diff --git a/tfjs-master/tfjs-core/src/ops/moments.ts b/tfjs-master/tfjs-core/src/ops/moments.ts
deleted file mode 100644
index d022626ab..000000000
--- a/tfjs-master/tfjs-core/src/ops/moments.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {parseAxisParam} from '../util';
-
-import {expandShapeToKeepDim} from './axis_util';
-import {cast} from './cast';
-import {mean} from './mean';
-import {op} from './operation';
-import {reshape} from './reshape';
-import {square} from './square';
-import {sub} from './sub';
-
-/**
- * Calculates the mean and variance of `x`. The mean and variance are
- * calculated by aggregating the contents of `x` across `axes`. If `x` is
- * 1-D and `axes = [0]` this is just the mean and variance of a vector.
- *
- * @param x The input tensor.
- * @param axis The dimension(s) along with to compute mean and
- *     variance. By default it reduces all dimensions.
- * @param keepDims If true, the moments have the same dimensionality as the
- *     input.
- * @return An object with two keys: `mean` and `variance`.
- *
- * @doc {heading: 'Operations', subheading: 'Normalization'}
- */
-function moments_(
-    x: Tensor|TensorLike, axis: number|number[] = null,
-    keepDims = false): {mean: Tensor, variance: Tensor} {
-  x = convertToTensor(x, 'x', 'moments');
-  const axes = parseAxisParam(axis, x.shape);
-  const xMean = mean(x, axes, keepDims);
-  let keepDimsShape = xMean.shape;
-  if (!keepDims) {
-    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
-  }
-  const devSquared =
-      square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));
-  const variance = mean(devSquared, axes, keepDims);
-  return {mean: xMean, variance};
-}
-
-export const moments = /* @__PURE__ */ op({moments_});
diff --git a/tfjs-master/tfjs-core/src/ops/moments_test.ts b/tfjs-master/tfjs-core/src/ops/moments_test.ts
deleted file mode 100644
index 2f6f2e334..000000000
--- a/tfjs-master/tfjs-core/src/ops/moments_test.ts
+++ /dev/null
@@ -1,148 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('moments', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const {mean, variance} = tf.moments(a);
-
-    expect(mean.dtype).toBe('float32');
-    expect(variance.dtype).toBe('float32');
-    expectArraysClose(await mean.data(), 7 / 6);
-    expectArraysClose(await variance.data(), 1.1389);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
-    const {mean, variance} = tf.moments(a);
-
-    expect(mean.dtype).toBe('float32');
-    expect(variance.dtype).toBe('float32');
-    expectArraysEqual(await mean.data(), NaN);
-    expectArraysEqual(await variance.data(), NaN);
-  });
-
-  it('moments(int32) => float32', async () => {
-    const a = tf.tensor1d([1, 5, 7, 3], 'int32');
-    const {mean, variance} = tf.moments(a);
-
-    expect(mean.dtype).toBe('float32');
-    expect(variance.dtype).toBe('float32');
-    expectArraysClose(await mean.data(), 4);
-    expectArraysClose(await variance.data(), 5);
-  });
-
-  it('moments(bool) => float32', async () => {
-    const a = tf.tensor1d([true, false, false, true, true], 'bool');
-    const {mean, variance} = tf.moments(a);
-
-    expect(mean.dtype).toBe('float32');
-    expect(variance.dtype).toBe('float32');
-    expectArraysClose(await mean.data(), 3 / 5);
-    expectArraysClose(await variance.data(), 0.23999998);
-  });
-
-  it('2D array with keep dim', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const {mean, variance} = tf.moments(a, null, true /* keepDims */);
-
-    expect(mean.shape).toEqual([1, 1]);
-    expect(mean.dtype).toBe('float32');
-    expect(variance.shape).toEqual([1, 1]);
-    expect(variance.dtype).toBe('float32');
-    expectArraysClose(await mean.data(), [7 / 6]);
-    expectArraysClose(await variance.data(), [1.138889]);
-  });
-
-  it('axis=0 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const {mean, variance} = tf.moments(a, [0]);
-
-    expect(mean.shape).toEqual([2]);
-    expect(mean.dtype).toBe('float32');
-    expect(variance.shape).toEqual([2]);
-    expect(variance.dtype).toBe('float32');
-    expectArraysClose(await mean.data(), [4 / 3, 1]);
-    expectArraysClose(await variance.data(), [1.556, 2 / 3]);
-  });
-
-  it('axis=1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const {mean, variance} = tf.moments(a, [1]);
-
-    expect(mean.dtype).toBe('float32');
-    expect(mean.shape).toEqual([3]);
-    expect(variance.dtype).toBe('float32');
-    expect(variance.shape).toEqual([3]);
-    expectArraysClose(await mean.data(), [1.5, 1.5, 0.5]);
-    expectArraysClose(await variance.data(), [0.25, 2.25, 0.25]);
-  });
-
-  it('2D, axis=1 provided as number', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
-    const {mean, variance} = tf.moments(a, 1);
-
-    expect(mean.shape).toEqual([2]);
-    expect(mean.dtype).toBe('float32');
-    expect(variance.shape).toEqual([2]);
-    expect(variance.dtype).toBe('float32');
-    expectArraysClose(await mean.data(), [2, 1 / 3]);
-    expectArraysClose(await variance.data(), [2 / 3, 0.222]);
-  });
-
-  it('2D, axis=-1 provided as number', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
-    const {mean, variance} = tf.moments(a, -1);
-
-    expect(mean.shape).toEqual([2]);
-    expect(mean.dtype).toBe('float32');
-    expect(variance.shape).toEqual([2]);
-    expect(variance.dtype).toBe('float32');
-    expectArraysClose(await mean.data(), [2, 1 / 3]);
-    expectArraysClose(await variance.data(), [2 / 3, 0.222]);
-  });
-
-  it('axis=0,1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const {mean, variance} = tf.moments(a, [0, 1]);
-
-    expect(mean.shape).toEqual([]);
-    expect(mean.dtype).toBe('float32');
-    expect(variance.shape).toEqual([]);
-    expect(variance.dtype).toBe('float32');
-    expectArraysClose(await mean.data(), [7 / 6]);
-    expectArraysClose(await variance.data(), [1.1389]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.moments({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'moments' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const {mean, variance} = tf.moments([1, 2, 3, 0, 0, 1]);
-
-    expect(mean.dtype).toBe('float32');
-    expect(variance.dtype).toBe('float32');
-    expectArraysClose(await mean.data(), 7 / 6);
-    expectArraysClose(await variance.data(), 1.1389);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/moving_average.ts b/tfjs-master/tfjs-core/src/ops/moving_average.ts
deleted file mode 100644
index 6852ca460..000000000
--- a/tfjs-master/tfjs-core/src/ops/moving_average.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Scalar, Tensor} from '../tensor';
-import {assertTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {add} from './add';
-import {div} from './div';
-import {mul} from './mul';
-import {op} from './operation';
-import {pow} from './pow';
-import {scalar} from './scalar';
-import {sub} from './sub';
-
-/**
- * Compute the moving average of a variable.
- *
- * Without zeroDebias, the moving average operation is defined by:
- *   `v += delta`
- * where
- *   `delta = (1 - decay) * (x - v)`
- *
- * With zeroDebias (default), the `delta` term is scaled to debias the
- * effect of the (assumed) zero-initialization of `v`.
- *   `delta /= (1 - decay ^ step)`
- *
- * For more details on the zero-debiasing algorithm, see:
- *   https://arxiv.org/abs/1412.6980
- *
- * Note that this function is completely stateless and does not keep track of
- * step count. The step count needs to be maintained by the caller and passed
- * in as `step`.
- *
- * @param v The current moving average value.
- * @param x New input value, must have the same shape and dtype as `v`.
- * @param decay The decay factor. Typical values are 0.95 and 0.99.
- * @param step Step count.
- * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).
- * @returns The new moving average value.
- *
- * @doc {heading: 'Operations', subheading: 'Moving Average'}
- */
-function movingAverage_<T extends Tensor>(
-    v: T|TensorLike, x: T|TensorLike, decay: number|Scalar,
-    step?: number|Scalar, zeroDebias = true): T {
-  const $v = convertToTensor(v, 'v', 'movingAverage');
-  const $x = convertToTensor(x, 'x', 'movingAverage');
-  const $decay = convertToTensor(decay, 'decay', 'movingAverage');
-
-  assertTypesMatch($v, $x);
-  util.assert(
-      util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');
-
-  const one = scalar(1);
-  const oneMinusDecay = sub(one, $decay);
-
-  let update = mul(sub($x, $v), oneMinusDecay);
-  if (zeroDebias) {
-    util.assert(
-        step != null, () => 'When using zeroDebias: true, step is required.');
-    const $step = convertToTensor(step, 'step', 'movingAverage');
-    update = div(update, sub(one, pow($decay, $step)));
-  }
-  return add($v, update);
-}
-
-export const movingAverage = /* @__PURE__ */ op({movingAverage_});
diff --git a/tfjs-master/tfjs-core/src/ops/moving_average_test.ts b/tfjs-master/tfjs-core/src/ops/moving_average_test.ts
deleted file mode 100644
index dc3789f79..000000000
--- a/tfjs-master/tfjs-core/src/ops/moving_average_test.ts
+++ /dev/null
@@ -1,164 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('movingAverage', ALL_ENVS, () => {
-  // Use the following tensorflow to generate reference values for
-  // `zeroDebias` = `true`;
-  //
-  // ```python
-  // import tensorflow as tf
-  // from tensorflow.python.training.moving_averages import
-  // assign_moving_average
-  //
-  // with tf.Session() as sess:
-  //   v = tf.get_variable("v1", shape=[2, 2], dtype=tf.float32,
-  //                       initializer=tf.zeros_initializer)
-  //   x = tf.Variable([[1.0, 2.0], [3.0, 4.0]])
-  //   inc_x = x.assign_add([[10.0, 10.0], [10.0, 10.0]])
-  //   update = assign_moving_average(v, x, 0.6)
-  //
-  //   sess.run(tf.global_variables_initializer())
-  //
-  //   sess.run(update)
-  //   print(sess.run(v))
-  //
-  //   sess.run(inc_x)
-  //   sess.run(update)
-  //   print(sess.run(v))
-  // ```
-
-  it('zeroDebias=true, decay and step are numbers', async () => {
-    const v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
-    const x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const decay = 0.6;
-
-    const v1 = tf.movingAverage(v0, x, decay, 1);
-    expectArraysClose(await v1.array(), [[1, 2], [3, 4]]);
-
-    const y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
-    const v2 = tf.movingAverage(v1, y, decay, 2);
-    expectArraysClose(await v2.array(), [[7.25, 8.25], [9.25, 10.25]]);
-  });
-
-  it('zeroDebias=true, decay and step are scalars', async () => {
-    const v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
-    const x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const decay = tf.scalar(0.6);
-
-    const v1 = tf.movingAverage(v0, x, decay, tf.scalar(1));
-    expectArraysClose(await v1.array(), [[1, 2], [3, 4]]);
-
-    const y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
-    const v2 = tf.movingAverage(v1, y, decay, tf.scalar(2));
-    expectArraysClose(await v2.array(), [[7.25, 8.25], [9.25, 10.25]]);
-  });
-
-  // Use the following tensorflow to generate reference values for
-  // `zeroDebias` = `false`;
-  //
-  // ```python
-  // import tensorflow as tf
-  // from tensorflow.python.training.moving_averages import
-  // assign_moving_average
-  //
-  // with tf.Session() as sess:
-  //   v = tf.get_variable("v1", shape=[2, 2], dtype=tf.float32,
-  //                       initializer=tf.zeros_initializer)
-  //   x = tf.Variable([[1.0, 2.0], [3.0, 4.0]])
-  //   inc_x = x.assign_add([[10.0, 10.0], [10.0, 10.0]])
-  //   update = assign_moving_average(v, x, 0.6, zero_debias=False)
-  //
-  //   sess.run(tf.global_variables_initializer())
-  //
-  //   sess.run(update)
-  //   print(sess.run(v))
-  //
-  //   sess.run(inc_x)
-  //   sess.run(update)
-  //   print(sess.run(v))
-  // ```
-
-  it('zeroDebias=false, decay and step are numbers', async () => {
-    const v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
-    const x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const decay = 0.6;
-
-    const v1 = tf.movingAverage(v0, x, decay, null, false);
-    expectArraysClose(await v1.array(), [[0.4, 0.8], [1.2, 1.6]]);
-
-    const y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
-    const v2 = tf.movingAverage(v1, y, decay, null, false);
-    expectArraysClose(await v2.array(), [[4.64, 5.28], [5.92, 6.56]]);
-  });
-
-  it('zeroDebias=false, decay is scalar', async () => {
-    const v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
-    const x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const decay = tf.scalar(0.6);
-
-    const v1 = tf.movingAverage(v0, x, decay, null, false);
-    expectArraysClose(await v1.array(), [[0.4, 0.8], [1.2, 1.6]]);
-
-    const y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
-    const v2 = tf.movingAverage(v1, y, decay, null, false);
-    expectArraysClose(await v2.array(), [[4.64, 5.28], [5.92, 6.56]]);
-  });
-
-  it('zeroDebias=true, no step throws error', () => {
-    const v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
-    const x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const decay = tf.scalar(0.6);
-
-    expect(() => tf.movingAverage(v0, x, decay, null)).toThrowError();
-  });
-
-  it('shape mismatch in v and x throws error', () => {
-    const v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
-    const x = tf.tensor2d([[1, 2]], [1, 2]);
-    const decay = tf.scalar(0.6);
-
-    expect(() => tf.movingAverage(v0, x, decay, null)).toThrowError();
-  });
-
-  it('throws when passed v as a non-tensor', () => {
-    const x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-
-    expect(() => tf.movingAverage({} as tf.Tensor, x, 1))
-        .toThrowError(
-            /Argument 'v' passed to 'movingAverage' must be a Tensor/);
-  });
-  it('throws when passed v as a non-tensor', () => {
-    const v = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
-
-    expect(() => tf.movingAverage(v, {} as tf.Tensor, 1))
-        .toThrowError(
-            /Argument 'x' passed to 'movingAverage' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const v0 = [[0, 0], [0, 0]];  // 2x2
-    const x = [[1, 2], [3, 4]];   // 2x2
-    const decay = 0.6;
-
-    const v1 = tf.movingAverage(v0, x, decay, 1);
-    expectArraysClose(await v1.array(), [[1, 2], [3, 4]]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/mul.ts b/tfjs-master/tfjs-core/src/ops/mul.ts
deleted file mode 100644
index 18ebb3d46..000000000
--- a/tfjs-master/tfjs-core/src/ops/mul.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Multiply, MultiplyInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.
- *
- * We also expose `tf.mulStrict` which has the same signature as this op and
- * asserts that `a` and `b` are the same shape (does not broadcast).
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3, 4]);
- * const b = tf.tensor1d([2, 3, 4, 5]);
- *
- * a.mul(b).print();  // or tf.mul(a, b)
- * ```
- *
- * ```js
- * // Broadcast mul a with b.
- * const a = tf.tensor1d([1, 2, 3, 4]);
- * const b = tf.scalar(5);
- *
- * a.mul(b).print();  // or tf.mul(a, b)
- * ```
- * @param a The first tensor to multiply.
- * @param b The second tensor to multiply. Must have the same dtype as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function mul_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'mul');
-  let $b = convertToTensor(b, 'b', 'mul');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  const inputs: MultiplyInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Multiply, inputs as unknown as NamedTensorMap);
-}
-export const mul = /* @__PURE__ */ op({mul_});
diff --git a/tfjs-master/tfjs-core/src/ops/multi_rnn_cell.ts b/tfjs-master/tfjs-core/src/ops/multi_rnn_cell.ts
deleted file mode 100644
index 617bcebb0..000000000
--- a/tfjs-master/tfjs-core/src/ops/multi_rnn_cell.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor2D} from '../tensor';
-import {convertToTensor, convertToTensorArray} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {op} from './operation';
-
-/**
- * @docalias (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D]
- */
-export type LSTMCellFunc = {
-  (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D];
-};
-
-/**
- * Computes the next states and outputs of a stack of LSTMCells.
- *
- * Each cell output is used as input to the next cell.
- *
- * Returns `[cellState, cellOutput]`.
- *
- * Derived from tf.contrib.rn.MultiRNNCell.
- *
- * @param lstmCells Array of LSTMCell functions.
- * @param data The input to the cell.
- * @param c Array of previous cell states.
- * @param h Array of previous cell outputs.
- *
- * @doc {heading: 'Operations', subheading: 'RNN'}
- */
-function multiRNNCell_(
-    lstmCells: LSTMCellFunc[], data: Tensor2D|TensorLike,
-    c: Array<Tensor2D|TensorLike>,
-    h: Array<Tensor2D|TensorLike>): [Tensor2D[], Tensor2D[]] {
-  const $data = convertToTensor(data, 'data', 'multiRNNCell');
-  const $c = convertToTensorArray(c, 'c', 'multiRNNCell');
-  const $h = convertToTensorArray(h, 'h', 'multiRNNCell');
-
-  let input = $data;
-  const newStates = [];
-  for (let i = 0; i < lstmCells.length; i++) {
-    const output = lstmCells[i](input, $c[i], $h[i]);
-    newStates.push(output[0]);
-    newStates.push(output[1]);
-    input = output[1];
-  }
-  const newC: Tensor2D[] = [];
-  const newH: Tensor2D[] = [];
-  for (let i = 0; i < newStates.length; i += 2) {
-    newC.push(newStates[i]);
-    newH.push(newStates[i + 1]);
-  }
-  return [newC, newH];
-}
-export const multiRNNCell = /* @__PURE__ */ op({multiRNNCell_});
diff --git a/tfjs-master/tfjs-core/src/ops/multi_rnn_cell_test.ts b/tfjs-master/tfjs-core/src/ops/multi_rnn_cell_test.ts
deleted file mode 100644
index 0ffd99668..000000000
--- a/tfjs-master/tfjs-core/src/ops/multi_rnn_cell_test.ts
+++ /dev/null
@@ -1,142 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {Tensor2D} from '../tensor';
-import {expectArraysClose} from '../test_util';
-import {Rank} from '../types';
-
-describeWithFlags('lstm', ALL_ENVS, () => {
-  it('MultiRNNCell with 2 BasicLSTMCells', async () => {
-    const lstmKernel1 = tf.tensor2d(
-        [
-          0.26242125034332275, -0.8787832260131836, 0.781475305557251,
-          1.337337851524353, 0.6180247068405151, -0.2760246992111206,
-          -0.11299663782119751, -0.46332040429115295, -0.1765323281288147,
-          0.6807947158813477, -0.8326982855796814, 0.6732975244522095
-        ],
-        [3, 4]);
-    const lstmBias1 = tf.tensor1d(
-        [1.090713620185852, -0.8282332420349121, 0, 1.0889357328414917]);
-    const lstmKernel2 = tf.tensor2d(
-        [
-          -1.893059492111206, -1.0185645818710327, -0.6270437240600586,
-          -2.1829540729522705, -0.4583775997161865, -0.5454602241516113,
-          -0.3114445209503174, 0.8450229167938232
-        ],
-        [2, 4]);
-    const lstmBias2 = tf.tensor1d(
-        [0.9906240105628967, 0.6248329877853394, 0, 1.0224634408950806]);
-
-    const forgetBias = tf.scalar(1.0);
-    const lstm1 = (data: Tensor2D, c: Tensor2D, h: Tensor2D) =>
-        tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
-    const lstm2 = (data: Tensor2D, c: Tensor2D, h: Tensor2D) =>
-        tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
-    const c = [
-      tf.zeros<Rank.R2>([1, lstmBias1.shape[0] / 4]),
-      tf.zeros<Rank.R2>([1, lstmBias2.shape[0] / 4])
-    ];
-    const h = [
-      tf.zeros<Rank.R2>([1, lstmBias1.shape[0] / 4]),
-      tf.zeros<Rank.R2>([1, lstmBias2.shape[0] / 4])
-    ];
-
-    const onehot = tf.buffer<Rank.R2>([1, 2], 'float32');
-    onehot.set(1.0, 0, 0);
-
-    const output = tf.multiRNNCell([lstm1, lstm2], onehot.toTensor(), c, h);
-
-    expectArraysClose(await output[0][0].data(), [-0.7440074682235718]);
-    expectArraysClose(await output[0][1].data(), [0.7460772395133972]);
-    expectArraysClose(await output[1][0].data(), [-0.5802832245826721]);
-    expectArraysClose(await output[1][1].data(), [0.5745711922645569]);
-  });
-});
-
-describeWithFlags('multiRNN throws when passed non-tensor', ALL_ENVS, () => {
-  it('input: data', () => {
-    const lstmKernel1: tf.Tensor2D = tf.zeros([3, 4]);
-    const lstmBias1: tf.Tensor1D = tf.zeros([4]);
-    const lstmKernel2: tf.Tensor2D = tf.zeros([2, 4]);
-    const lstmBias2: tf.Tensor1D = tf.zeros([4]);
-
-    const forgetBias = tf.scalar(1.0);
-    const lstm1 = (data: Tensor2D, c: Tensor2D, h: Tensor2D) =>
-        tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
-    const lstm2 = (data: Tensor2D, c: Tensor2D, h: Tensor2D) =>
-        tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
-    const c = [
-      tf.zeros<Rank.R2>([1, lstmBias1.shape[0] / 4]),
-      tf.zeros<Rank.R2>([1, lstmBias2.shape[0] / 4])
-    ];
-    const h = [
-      tf.zeros<Rank.R2>([1, lstmBias1.shape[0] / 4]),
-      tf.zeros<Rank.R2>([1, lstmBias2.shape[0] / 4])
-    ];
-
-    expect(() => tf.multiRNNCell([lstm1, lstm2], {} as tf.Tensor2D, c, h))
-        .toThrowError(
-            /Argument 'data' passed to 'multiRNNCell' must be a Tensor/);
-  });
-
-  it('input: c', () => {
-    const lstmKernel1: tf.Tensor2D = tf.zeros([3, 4]);
-    const lstmBias1: tf.Tensor1D = tf.zeros([4]);
-    const lstmKernel2: tf.Tensor2D = tf.zeros([2, 4]);
-    const lstmBias2: tf.Tensor1D = tf.zeros([4]);
-
-    const forgetBias = tf.scalar(1.0);
-    const lstm1 = (data: Tensor2D, c: Tensor2D, h: Tensor2D) =>
-        tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
-    const lstm2 = (data: Tensor2D, c: Tensor2D, h: Tensor2D) =>
-        tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
-
-    const h = [
-      tf.zeros<Rank.R2>([1, lstmBias1.shape[0] / 4]),
-      tf.zeros<Rank.R2>([1, lstmBias2.shape[0] / 4])
-    ];
-    const data: tf.Tensor2D = tf.zeros([1, 2]);
-
-    expect(() => tf.multiRNNCell([lstm1, lstm2], data, [{} as tf.Tensor2D], h))
-        .toThrowError(
-            /Argument 'c\[0\]' passed to 'multiRNNCell' must be a Tensor/);
-  });
-
-  it('input: h', () => {
-    const lstmKernel1: tf.Tensor2D = tf.zeros([3, 4]);
-    const lstmBias1: tf.Tensor1D = tf.zeros([4]);
-    const lstmKernel2: tf.Tensor2D = tf.zeros([2, 4]);
-    const lstmBias2: tf.Tensor1D = tf.zeros([4]);
-
-    const forgetBias = tf.scalar(1.0);
-    const lstm1 = (data: Tensor2D, c: Tensor2D, h: Tensor2D) =>
-        tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
-    const lstm2 = (data: Tensor2D, c: Tensor2D, h: Tensor2D) =>
-        tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
-    const c = [
-      tf.zeros<Rank.R2>([1, lstmBias1.shape[0] / 4]),
-      tf.zeros<Rank.R2>([1, lstmBias2.shape[0] / 4])
-    ];
-    const data: tf.Tensor2D = tf.zeros([1, 2]);
-
-    expect(() => tf.multiRNNCell([lstm1, lstm2], data, c, [{} as tf.Tensor2D]))
-        .toThrowError(
-            /Argument 'h\[0\]' passed to 'multiRNNCell' must be a Tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/multinomial.ts b/tfjs-master/tfjs-core/src/ops/multinomial.ts
deleted file mode 100644
index 13563ddc8..000000000
--- a/tfjs-master/tfjs-core/src/ops/multinomial.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Multinomial, MultinomialAttrs, MultinomialInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor1D, Tensor2D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Creates a `tf.Tensor` with values drawn from a multinomial distribution.
- *
- * ```js
- * const probs = tf.tensor([.75, .25]);
- * tf.multinomial(probs, 3).print();
- * ```
- *
- * @param logits 1D array with unnormalized log-probabilities, or
- *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`
- *     parameter.
- * @param numSamples Number of samples to draw for each row slice.
- * @param seed The seed number.
- * @param normalized Whether the provided `logits` are normalized true
- *     probabilities (sum to 1). Defaults to false.
- * @return 1D array of shape `[numSamples]`, or 2D array of shape
- *     `[batchSize, numSamples]`, depending on the rank of the input.
- *
- * @doc {heading: 'Tensors', subheading: 'Random'}
- */
-function multinomial_(
-    logits: Tensor1D|Tensor2D|TensorLike, numSamples: number, seed?: number,
-    normalized = false): Tensor1D|Tensor2D {
-  const $logits = convertToTensor(logits, 'logits', 'multinomial');
-  const numOutcomes = $logits.size;
-  const origRank = $logits.rank;
-  if (numOutcomes < 2) {
-    throw new Error(
-        `Error in multinomial: you need at least 2 outcomes, but got ` +
-        `${numOutcomes}.`);
-  }
-  if (origRank > 2) {
-    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
-  }
-  // TODO(lina128): Investigate correct seed behavior. The code seems not allow
-  // setting see to 0.
-  seed = seed || Math.random();
-
-  // The kernel only accepts (and returns) rank 2 tensors.
-  const logits2D: Tensor2D =
-      origRank === 1 ? reshape($logits, [1, -1]) : $logits as Tensor2D;
-
-  const inputs: MultinomialInputs = {logits: logits2D};
-  const attrs: MultinomialAttrs = {numSamples, seed, normalized};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  const res = ENGINE.runKernel(
-                  Multinomial, inputs as unknown as NamedTensorMap,
-                  attrs as unknown as NamedAttrMap) as Tensor2D;
-
-  // tslint:disable-next-line:no-unnecessary-type-assertion
-  return origRank === 1 ? reshape(res, [res.size]) as Tensor1D : res;
-}
-
-export const multinomial = /* @__PURE__ */ op({multinomial_});
diff --git a/tfjs-master/tfjs-core/src/ops/multinomial_test.ts b/tfjs-master/tfjs-core/src/ops/multinomial_test.ts
deleted file mode 100644
index 412253026..000000000
--- a/tfjs-master/tfjs-core/src/ops/multinomial_test.ts
+++ /dev/null
@@ -1,139 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {Tensor1D} from '../tensor';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('multinomial', ALL_ENVS, () => {
-  const NUM_SAMPLES = 1000;
-  // Allowed Variance in probability (in %).
-  const EPSILON = 0.05;
-  const SEED = 3.14;
-
-  it('Flip a fair coin and check bounds', async () => {
-    const probs = tf.tensor1d([1, 1]);
-    const result = tf.multinomial(probs, NUM_SAMPLES, SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual([NUM_SAMPLES]);
-    const outcomeProbs = computeProbs(await result.data(), 2);
-    expectArraysClose(outcomeProbs, [0.5, 0.5], EPSILON);
-  });
-
-  it('Flip a two-sided coin with 100% of heads', async () => {
-    const logits = tf.tensor1d([1, -100]);
-    const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual([NUM_SAMPLES]);
-    const outcomeProbs = computeProbs(await result.data(), 2);
-    expectArraysClose(outcomeProbs, [1, 0], EPSILON);
-  });
-
-  it('Flip a two-sided coin with 100% of tails', async () => {
-    const logits = tf.tensor1d([-100, 1]);
-    const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual([NUM_SAMPLES]);
-    const outcomeProbs = computeProbs(await result.data(), 2);
-    expectArraysClose(outcomeProbs, [0, 1], EPSILON);
-  });
-
-  it('Flip a single-sided coin throws error', () => {
-    const probs = tf.tensor1d([1]);
-    expect(() => tf.multinomial(probs, NUM_SAMPLES, SEED)).toThrowError();
-  });
-
-  it('Flip a ten-sided coin and check bounds', async () => {
-    const numOutcomes = 10;
-    const logits = tf.fill([numOutcomes], 1).as1D();
-    const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual([NUM_SAMPLES]);
-    const outcomeProbs = computeProbs(await result.data(), numOutcomes);
-    expect(outcomeProbs.length).toBeLessThanOrEqual(numOutcomes);
-  });
-
-  it('Flip 3 three-sided coins, each coin is 100% biases', async () => {
-    const numOutcomes = 3;
-    const logits = tf.tensor2d(
-        [[-100, -100, 1], [-100, 1, -100], [1, -100, -100]], [3, numOutcomes]);
-    const result = tf.multinomial(logits, NUM_SAMPLES, SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual([3, NUM_SAMPLES]);
-
-    // First coin always gets last event.
-    let outcomeProbs =
-        computeProbs((await result.data()).slice(0, NUM_SAMPLES), numOutcomes);
-    expectArraysClose(outcomeProbs, [0, 0, 1], EPSILON);
-
-    // Second coin always gets middle event.
-    outcomeProbs = computeProbs(
-        (await result.data()).slice(NUM_SAMPLES, 2 * NUM_SAMPLES), numOutcomes);
-    expectArraysClose(outcomeProbs, [0, 1, 0], EPSILON);
-
-    // Third coin always gets first event
-    outcomeProbs =
-        computeProbs((await result.data()).slice(2 * NUM_SAMPLES), numOutcomes);
-    expectArraysClose(outcomeProbs, [1, 0, 0], EPSILON);
-  });
-
-  it('passing Tensor3D throws error', () => {
-    const probs = tf.zeros([3, 2, 2]);
-    const normalized = true;
-    expect(() => tf.multinomial(probs as Tensor1D, 3, SEED, normalized))
-        .toThrowError();
-  });
-
-  it('throws when passed a non-tensor', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tf.multinomial({} as any, NUM_SAMPLES, SEED))
-        .toThrowError(
-            /Argument 'logits' passed to 'multinomial' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object for logits (biased coin)', async () => {
-    const res = tf.multinomial([-100, 1], NUM_SAMPLES, SEED);
-    expect(res.dtype).toBe('int32');
-    expect(res.shape).toEqual([NUM_SAMPLES]);
-    const outcomeProbs = computeProbs(await res.data(), 2);
-    expectArraysClose(outcomeProbs, [0, 1], EPSILON);
-  });
-
-  it('creates the same data given the same seed', async () => {
-    const res1 = tf.multinomial([1, 2, 3, 4], NUM_SAMPLES, SEED);
-    const res2 = tf.multinomial([1, 2, 3, 4], NUM_SAMPLES, SEED);
-    expectArraysClose(await res1.data(), await res2.data());
-  });
-
-  function computeProbs(
-      events: Float32Array|Uint8Array|Int32Array, numOutcomes: number) {
-    const counts = [];
-    for (let i = 0; i < numOutcomes; ++i) {
-      counts[i] = 0;
-    }
-    const numSamples = events.length;
-    for (let i = 0; i < events.length; ++i) {
-      counts[events[i]]++;
-    }
-    // Normalize counts to be probabilities between [0, 1].
-    for (let i = 0; i < counts.length; i++) {
-      counts[i] /= numSamples;
-    }
-    return counts;
-  }
-});
diff --git a/tfjs-master/tfjs-core/src/ops/neg.ts b/tfjs-master/tfjs-core/src/ops/neg.ts
deleted file mode 100644
index 7df6607a0..000000000
--- a/tfjs-master/tfjs-core/src/ops/neg.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Neg, NegInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes `-1 * x` element-wise.
- *
- * ```js
- * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);
- *
- * x.neg().print();  // or tf.neg(x)
- * ```
- *
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function neg_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'neg');
-
-  const inputs: NegInputs = {x: $x};
-  return ENGINE.runKernel(Neg, inputs as unknown as NamedTensorMap);
-}
-export const neg = /* @__PURE__ */ op({neg_});
diff --git a/tfjs-master/tfjs-core/src/ops/neg_test.ts b/tfjs-master/tfjs-core/src/ops/neg_test.ts
deleted file mode 100644
index 89b96b6e0..000000000
--- a/tfjs-master/tfjs-core/src/ops/neg_test.ts
+++ /dev/null
@@ -1,105 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {backend} from '../index';
-
-describeWithFlags('neg', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([1, -3, 2, 7, -4]);
-    const result = tf.neg(a);
-    expectArraysClose(await result.data(), [-1, 3, -2, -7, 4]);
-  });
-
-  it('int32', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const a = tf.tensor1d([1, -3, 12345678, -12345678], 'int32');
-      const result = tf.neg(a);
-      expect(result.dtype).toEqual('int32');
-      expectArraysClose(await result.data(), [-1, 3, -12345678, 12345678]);
-    }
-  });
-
-  it('propagate NaNs', async () => {
-    const a = tf.tensor1d([1, -3, 2, 7, NaN]);
-    const result = tf.neg(a);
-    const expected = [-1, 3, -2, -7, NaN];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(4);
-    const dy = tf.scalar(8);
-
-    const da = tf.grad(a => tf.neg(a))(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [8 * -1]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(4);
-    const dy = tf.scalar(8);
-
-    const da = tf.grad(a => tf.neg(a.clone()).clone())(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [8 * -1]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, -3, 5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const da = tf.grad(a => tf.neg(a))(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1 * -1, 2 * -1, 3 * -1, 4 * -1]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([3, -1, -2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const da = tf.grad(a => tf.neg(a))(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1 * -1, 2 * -1, 3 * -1, 4 * -1]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.neg({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'neg' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.neg([1, -3, 2, 7, -4]);
-    expectArraysClose(await result.data(), [-1, 3, -2, -7, 4]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.neg('q'))
-        .toThrowError(/Argument 'x' passed to 'neg' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/nonmax_util.ts b/tfjs-master/tfjs-core/src/ops/nonmax_util.ts
deleted file mode 100644
index d85d2fcf3..000000000
--- a/tfjs-master/tfjs-core/src/ops/nonmax_util.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor1D, Tensor2D} from '../tensor';
-import * as util from '../util';
-
-function nonMaxSuppSanityCheck(
-    boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,
-    iouThreshold: number, scoreThreshold: number, softNmsSigma?: number): {
-  maxOutputSize: number,
-  iouThreshold: number,
-  scoreThreshold: number,
-  softNmsSigma: number
-} {
-  if (iouThreshold == null) {
-    iouThreshold = 0.5;
-  }
-  if (scoreThreshold == null) {
-    scoreThreshold = Number.NEGATIVE_INFINITY;
-  }
-  if (softNmsSigma == null) {
-    softNmsSigma = 0.0;
-  }
-
-  const numBoxes = boxes.shape[0];
-  maxOutputSize = Math.min(maxOutputSize, numBoxes);
-
-  util.assert(
-      0 <= iouThreshold && iouThreshold <= 1,
-      () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
-  util.assert(
-      boxes.rank === 2,
-      () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
-  util.assert(
-      boxes.shape[1] === 4,
-      () =>
-          `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
-  util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');
-  util.assert(
-      scores.shape[0] === numBoxes,
-      () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +
-          `but was ${scores.shape[0]}`);
-  util.assert(
-      0 <= softNmsSigma && softNmsSigma <= 1,
-      () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
-  return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
-}
-
-export {nonMaxSuppSanityCheck};
diff --git a/tfjs-master/tfjs-core/src/ops/norm.ts b/tfjs-master/tfjs-core/src/ops/norm.ts
deleted file mode 100644
index 049dec92d..000000000
--- a/tfjs-master/tfjs-core/src/ops/norm.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {parseAxisParam} from '../util';
-
-import {abs} from './abs';
-import * as axis_util from './axis_util';
-import {max} from './max';
-import {min} from './min';
-import {op} from './operation';
-import {pow} from './pow';
-import {reshape} from './reshape';
-import {scalar} from './scalar';
-import {sqrt} from './sqrt';
-import {square} from './square';
-import {sum} from './sum';
-
-/**
- * Computes the norm of scalar, vectors, and matrices.
- * This function can compute several different vector norms (the 1-norm, the
- * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)
- * and matrix norms (Frobenius, 1-norm, and inf-norm).
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- *
- * x.norm().print();  // or tf.norm(x)
- * ```
- *
- * @param x The input array.
- * @param ord Optional. Order of the norm. Supported norm types are
- * following:
- *
- *  | ord        | norm for matrices         | norm for vectors
- *  |------------|---------------------------|---------------------
- *  |'euclidean' |Frobenius norm             |2-norm
- *  |'fro'       |Frobenius norm	           |
- *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))
- *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))
- *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))
- *  |2           |                           |sum(abs(x)^2)^(1/2)
- *
- * @param axis Optional. If axis is null (the default), the input is
- * considered a vector and a single vector norm is computed over the entire
- * set of values in the Tensor, i.e. norm(x, ord) is equivalent
- * to norm(x.reshape([-1]), ord). If axis is an integer, the input
- * is considered a batch of vectors, and axis determines the axis in x
- * over which to compute vector norms. If axis is a 2-tuple of integer it is
- * considered a batch of matrices and axis determines the axes in NDArray
- * over which to compute a matrix norm.
- * @param keepDims Optional. If true, the norm has the same dimensionality
- * as the input.
- *
- * @doc {heading: 'Operations', subheading: 'Matrices'}
- */
-function norm_(
-    x: Tensor|TensorLike, ord: number|'euclidean'|'fro' = 'euclidean',
-    axis: number|number[] = null, keepDims = false): Tensor {
-  x = convertToTensor(x, 'x', 'norm');
-
-  const norm = normImpl(x, ord, axis);
-  let keepDimsShape = norm.shape;
-  if (keepDims) {
-    const axes = parseAxisParam(axis, x.shape);
-    keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);
-  }
-  return reshape(norm, keepDimsShape);
-}
-
-function normImpl(
-    x: Tensor, p: number|string, axis: number|number[] = null): Tensor {
-  if (x.rank === 0) {
-    return abs(x);
-  }
-
-  // consider vector when no axis is specified
-  if (x.rank !== 1 && axis === null) {
-    return normImpl(reshape(x, [-1]), p, axis);
-  }
-
-  // vector
-  if (x.rank === 1 || typeof axis === 'number' ||
-      Array.isArray(axis) && axis.length === 1) {
-    if (p === 1) {
-      return sum(abs(x), axis);
-    }
-    if (p === Infinity) {
-      return max(abs(x), axis);
-    }
-    if (p === -Infinity) {
-      return min(abs(x), axis);
-    }
-    if (p === 'euclidean' || p === 2) {
-      // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2
-      return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));
-    }
-
-    throw new Error(`Error in norm: invalid ord value: ${p}`);
-  }
-
-  // matrix (assumption axis[0] < axis[1])
-  if (Array.isArray(axis) && axis.length === 2) {
-    if (p === 1) {
-      return max(sum(abs(x), axis[0]), axis[1] - 1);
-    }
-    if (p === Infinity) {
-      return max(sum(abs(x), axis[1]), axis[0]);
-    }
-    if (p === -Infinity) {
-      return min(sum(abs(x), axis[1]), axis[0]);
-    }
-    if (p === 'fro' || p === 'euclidean') {
-      // norm(x) = sqrt(sum(pow(x, 2)))
-      return sqrt(sum(square(x), axis));
-    }
-
-    throw new Error(`Error in norm: invalid ord value: ${p}`);
-  }
-
-  throw new Error(`Error in norm: invalid axis: ${axis}`);
-}
-
-export const norm = /* @__PURE__ */ op({norm_});
diff --git a/tfjs-master/tfjs-core/src/ops/norm_test.ts b/tfjs-master/tfjs-core/src/ops/norm_test.ts
deleted file mode 100644
index d4ffaa7e6..000000000
--- a/tfjs-master/tfjs-core/src/ops/norm_test.ts
+++ /dev/null
@@ -1,291 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('norm', ALL_ENVS, () => {
-  it('scalar norm', async () => {
-    const a = tf.scalar(-22.0);
-    const norm = tf.norm(a);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 22);
-  });
-
-  it('vector inf norm', async () => {
-    const a = tf.tensor1d([1, -2, 3, -4]);
-    const norm = tf.norm(a, Infinity);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 4);
-  });
-
-  it('vector -inf norm', async () => {
-    const a = tf.tensor1d([1, -2, 3, -4]);
-    const norm = tf.norm(a, -Infinity);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 1);
-  });
-
-  it('vector 1 norm', async () => {
-    const a = tf.tensor1d([1, -2, 3, -4]);
-    const norm = tf.norm(a, 1);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 10);
-  });
-
-  it('vector euclidean norm', async () => {
-    const a = tf.tensor1d([1, -2, 3, -4]);
-    const norm = tf.norm(a, 'euclidean');
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 5.4772);
-  });
-
-  it('vector 2-norm', async () => {
-    const a = tf.tensor1d([1, -2, 3, -4]);
-    const norm = tf.norm(a, 2);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 5.4772);
-  });
-
-  it('vector >2-norm to throw error', () => {
-    const a = tf.tensor1d([1, -2, 3, -4]);
-    expect(() => tf.norm(a, 3)).toThrowError();
-  });
-
-  it('matrix inf norm', async () => {
-    const a = tf.tensor2d([1, 2, -3, 1, 0, 1], [3, 2]);
-    const norm = tf.norm(a, Infinity, [0, 1]);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 4);
-  });
-
-  it('matrix -inf norm', async () => {
-    const a = tf.tensor2d([1, 2, -3, 1, 0, 1], [3, 2]);
-    const norm = tf.norm(a, -Infinity, [0, 1]);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 1);
-  });
-
-  it('matrix 1 norm', async () => {
-    const a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
-    const norm = tf.norm(a, 1, [0, 1]);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 5);
-  });
-
-  it('matrix euclidean norm', async () => {
-    const a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
-    const norm = tf.norm(a, 'euclidean', [0, 1]);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 4.123);
-  });
-
-  it('matrix fro norm', async () => {
-    const a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
-    const norm = tf.norm(a, 'fro', [0, 1]);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 4.123);
-  });
-
-  it('matrix other norm to throw error', () => {
-    const a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
-    expect(() => tf.norm(a, 2, [0, 1])).toThrowError();
-  });
-
-  it('propagates NaNs for norm', async () => {
-    const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
-    const norm = tf.norm(a);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysEqual(await norm.data(), NaN);
-  });
-
-  it('axis=null in 2D array norm', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const norm = tf.norm(a, Infinity);
-
-    expect(norm.shape).toEqual([]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [3]);
-  });
-
-  it('2D array norm with keep dim', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const norm = tf.norm(a, Infinity, null, true /* keepDims */);
-
-    expect(norm.shape).toEqual([1, 1]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [3]);
-  });
-
-  it('axis=0 in 2D array norm', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const norm = tf.norm(a, Infinity, [0]);
-
-    expect(norm.shape).toEqual([2]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [3, 2]);
-  });
-
-  it('axis=1 in 2D array norm', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const norm = tf.norm(a, Infinity, [1]);
-
-    expect(norm.dtype).toBe('float32');
-    expect(norm.shape).toEqual([3]);
-    expectArraysClose(await norm.data(), [2, 3, 1]);
-  });
-
-  it('axis=1 keepDims in 2D array norm', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const norm = tf.norm(a, Infinity, [1], true);
-
-    expect(norm.dtype).toBe('float32');
-    expect(norm.shape).toEqual([3, 1]);
-    expectArraysClose(await norm.data(), [2, 3, 1]);
-  });
-
-  it('2D norm with axis=1 provided as number', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
-    const norm = tf.norm(a, Infinity, 1);
-
-    expect(norm.shape).toEqual([2]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [3, 1]);
-  });
-
-  it('axis=0,1 in 2D array norm', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const norm = tf.norm(a, Infinity, [0, 1]);
-
-    expect(norm.shape).toEqual([]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [3]);
-  });
-
-  it('axis=0,1 keepDims in 2D array norm', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const norm = tf.norm(a, Infinity, [0, 1], true);
-
-    expect(norm.shape).toEqual([1, 1]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [3]);
-  });
-
-  it('3D norm axis=0,1, matrix inf norm', async () => {
-    const a = tf.tensor3d([1, 2, -3, 1, 0, 1], [3, 2, 1]);
-    const norm = tf.norm(a, Infinity, [0, 1]);
-
-    expect(norm.shape).toEqual([1]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [4]);
-  });
-
-  it('axis=0,1 keepDims in 3D array norm', async () => {
-    const a = tf.tensor3d([1, 2, 3, 0, 0, 1], [3, 2, 1]);
-    const norm = tf.norm(a, Infinity, [0, 1], true);
-
-    expect(norm.shape).toEqual([1, 1, 1]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [3]);
-  });
-
-  it('axis=0,1 keepDims in 3D array norm', async () => {
-    const a = tf.tensor3d([1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1], [3, 2, 2]);
-    const norm = tf.norm(a, Infinity, [0, 1], true);
-
-    expect(norm.shape).toEqual([1, 1, 2]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [4, 3]);
-  });
-
-  it('axis=null in 3D array norm', async () => {
-    const a = tf.tensor3d([1, 2, 3, 0, 0, 1], [3, 2, 1]);
-    const norm = tf.norm(a, Infinity);
-
-    expect(norm.shape).toEqual([]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [3]);
-  });
-
-  it('axis=null in 4D array norm', async () => {
-    const a = tf.tensor4d([1, 2, 3, 0, 0, 1], [3, 2, 1, 1]);
-    const norm = tf.norm(a, Infinity);
-
-    expect(norm.shape).toEqual([]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [3]);
-  });
-
-  it('axis=0,1 in 4D array norm', async () => {
-    const a = tf.tensor4d(
-        [
-          1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1,
-          1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1
-        ],
-        [3, 2, 2, 2]);
-    const norm = tf.norm(a, Infinity, [0, 1]);
-
-    expect(norm.shape).toEqual([2, 2]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [4, 3, 4, 3]);
-  });
-
-  it('axis=0,1 in 4D array norm', async () => {
-    const a = tf.tensor4d(
-        [
-          1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1,
-          1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1
-        ],
-        [3, 2, 2, 2]);
-    const norm = tf.norm(a, Infinity, [0, 1], true);
-
-    expect(norm.shape).toEqual([1, 1, 2, 2]);
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), [4, 3, 4, 3]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.norm({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'norm' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const norm = tf.norm([1, -2, 3, -4], 1);
-
-    expect(norm.dtype).toBe('float32');
-    expectArraysClose(await norm.data(), 10);
-  });
-
-  it('throws error for string tensors', () => {
-    expect(() => tf.norm([
-      'a', 'b'
-    ])).toThrowError(/Argument 'x' passed to 'norm' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/not_equal.ts b/tfjs-master/tfjs-core/src/ops/not_equal.ts
deleted file mode 100644
index 6387fdb67..000000000
--- a/tfjs-master/tfjs-core/src/ops/not_equal.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {NotEqual, NotEqualInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns the truth value of (a != b) element-wise. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3]);
- * const b = tf.tensor1d([0, 2, 3]);
- *
- * a.notEqual(b).print();
- * ```
- * @param a The first input tensor.
- * @param b The second input tensor. Must have the same dtype as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function notEqual_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'notEqual', 'string_or_numeric');
-  let $b = convertToTensor(b, 'b', 'notEqual', 'string_or_numeric');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: NotEqualInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(NotEqual, inputs as unknown as NamedTensorMap);
-}
-
-export const notEqual = /* @__PURE__ */ op({notEqual_});
diff --git a/tfjs-master/tfjs-core/src/ops/not_equal_test.ts b/tfjs-master/tfjs-core/src/ops/not_equal_test.ts
deleted file mode 100644
index af984e035..000000000
--- a/tfjs-master/tfjs-core/src/ops/not_equal_test.ts
+++ /dev/null
@@ -1,301 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('notEqual', ALL_ENVS, () => {
-  it('Tensor1D - int32', async () => {
-    let a = tf.tensor1d([1, 4, 5], 'int32');
-    let b = tf.tensor1d([2, 3, 5], 'int32');
-
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]);
-
-    a = tf.tensor1d([2, 2, 2], 'int32');
-    b = tf.tensor1d([2, 2, 2], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0]);
-
-    a = tf.tensor1d([0, 0], 'int32');
-    b = tf.tensor1d([3, 3], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1]);
-  });
-  it('Tensor1D - float32', async () => {
-    let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
-
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]);
-
-    a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0]);
-
-    a = tf.tensor1d([0.45, 0.123], 'float32');
-    b = tf.tensor1d([3.123, 3.321], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1]);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    const a = [1.1, 4.1, 5];
-    const b = [2.2, 3.2, 5];
-
-    let res =
-        tf.notEqual(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [1, 1, 0]);
-
-    res = tf.notEqual(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [0, 1, 1]);
-  });
-
-  it('TensorLike', async () => {
-    const a = [1.1, 4.1, 5.1];
-    const b = [2.2, 3.2, 5.1];
-
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]);
-  });
-  it('TensorLike Chained', async () => {
-    const a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
-    const b = [2.2, 3.2, 5.1];
-
-    expectArraysClose(await a.notEqual(b).data(), [1, 1, 0]);
-  });
-  it('mismatched Tensor1D shapes - int32', () => {
-    const a = tf.tensor1d([1, 2], 'int32');
-    const b = tf.tensor1d([1, 2, 3], 'int32');
-    const f = () => {
-      tf.notEqual(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('mismatched Tensor1D shapes - float32', () => {
-    const a = tf.tensor1d([1.1, 2.1], 'float32');
-    const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
-    const f = () => {
-      tf.notEqual(a, b);
-    };
-    expect(f).toThrowError();
-  });
-  it('NaNs in Tensor1D - float32', async () => {
-    const a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
-    const b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1]);
-  });
-  it('works with NaNs', async () => {
-    const a = tf.tensor1d([2, 5, NaN]);
-    const b = tf.tensor1d([4, 5, -1]);
-
-    const res = tf.notEqual(a, b);
-    expect(res.dtype).toBe('bool');
-    expectArraysEqual(await res.data(), [1, 0, 1]);
-  });
-  it('scalar and 1D broadcast', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([1, 2, 3, 4, 5, 2]);
-    const res = tf.notEqual(a, b);
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([6]);
-    expectArraysEqual(await res.data(), [1, 0, 1, 1, 1, 0]);
-  });
-
-  // Tensor2D:
-  it('Tensor2D - int32', async () => {
-    let a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
-    let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1, 1, 1]);
-
-    a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]);
-  });
-  it('Tensor2D - float32', async () => {
-    let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
-    let b =
-        tf.tensor2d([[2.1, 4.1, 5.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 0, 0, 1, 1, 1]);
-
-    a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor2D shapes - int32', async () => {
-    const a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
-    const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 0, 1, 0, 1, 1]);
-  });
-  it('broadcasting Tensor2D shapes - float32', async () => {
-    const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
-    const b =
-        tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 0, 1, 0, 1, 1]);
-  });
-  it('NaNs in Tensor2D - float32', async () => {
-    const a = tf.tensor2d([[1.1, NaN], [1.1, NaN]], [2, 2], 'float32');
-    const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0, 1]);
-  });
-  it('2D and scalar broadcast', async () => {
-    const a = tf.tensor2d([1, 2, 3, 2, 5, 6], [2, 3]);
-    const b = tf.scalar(2);
-    const res = tf.notEqual(a, b);
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysEqual(await res.data(), [1, 0, 1, 0, 1, 1]);
-  });
-  it('broadcasting Tensor2D shapes each with 1 dim', async () => {
-    const a = tf.tensor2d([1, 2, 5], [1, 3]);
-    const b = tf.tensor2d([5, 1], [2, 1]);
-    const res = tf.notEqual(a, b);
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysEqual(await res.data(), [1, 1, 0, 0, 1, 1]);
-  });
-
-  // Tensor3D:
-  it('Tensor3D - int32', async () => {
-    let a =
-        tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
-    let b =
-        tf.tensor3d([[[2], [3], [6]], [[7], [10], [12]]], [2, 3, 1], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1, 1, 0]);
-
-    a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0, 0, 0]);
-  });
-  it('Tensor3D - float32', async () => {
-    let a = tf.tensor3d(
-        [[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
-    let b = tf.tensor3d(
-        [[[2.1], [3.1], [6.1]], [[7.1], [10.1], [12.1]]], [2, 3, 1], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1, 1, 0]);
-
-    a = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
-    b = tf.tensor3d(
-        [[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0, 0, 0]);
-  });
-  it('broadcasting Tensor3D shapes - int32', async () => {
-    const a = tf.tensor3d(
-        [[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2],
-        'int32');
-    const b =
-        tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
-    expectArraysClose(
-        await tf.notEqual(a, b).data(), [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor3D shapes - float32', async () => {
-    const a = tf.tensor3d(
-        [
-          [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
-          [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
-        ],
-        [2, 3, 2], 'float32');
-    const b = tf.tensor3d(
-        [[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
-    expectArraysClose(
-        await tf.notEqual(a, b).data(), [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]);
-  });
-  it('NaNs in Tensor3D - float32', async () => {
-    const a = tf.tensor3d(
-        [[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
-    const b = tf.tensor3d(
-        [[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0, 1, 0, 1]);
-  });
-  it('3D and scalar', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, -1], [2, 3, 1]);
-    const b = tf.scalar(-1);
-    const res = tf.notEqual(a, b);
-    expect(res.dtype).toBe('bool');
-    expect(res.shape).toEqual([2, 3, 1]);
-    expectArraysEqual(await res.data(), [1, 1, 1, 1, 1, 0]);
-  });
-
-  // Tensor4D:
-  it('Tensor4D - int32', async () => {
-    let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
-    let b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 0]);
-
-    a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]);
-
-    a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
-    b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1]);
-  });
-  it('Tensor4D - float32', async () => {
-    let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
-    let b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 0]);
-
-    a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 0, 0, 0]);
-
-    a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
-    b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 1, 1]);
-  });
-  it('broadcasting Tensor4D shapes - int32', async () => {
-    const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
-    const b = tf.tensor4d(
-        [[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 1, 1, 1, 0, 1, 1, 1]);
-  });
-  it('broadcasting Tensor4D shapes - float32', async () => {
-    const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d(
-        [[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]],
-        [2, 2, 1, 2], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [0, 1, 1, 1, 0, 1, 1, 1]);
-  });
-  it('NaNs in Tensor4D - float32', async () => {
-    const a = tf.tensor4d([1.1, NaN, 1.1, 0.1], [2, 2, 1, 1], 'float32');
-    const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0, 1]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.notEqual({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'notEqual' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.notEqual(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'notEqual' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = tf.tensor1d([1, 4, 5], 'int32');
-    const b = tf.tensor1d([2, 3, 5], 'int32');
-    expectArraysClose(await tf.notEqual(a, b).data(), [1, 1, 0]);
-  });
-
-  it('should support string comparison', async () => {
-    const tensorA = tf.tensor('', [], 'string');
-    const tensorB = tf.tensor(['a', 'b', ''], [3], 'string');
-    const result = await tf.notEqual(tensorA, tensorB);
-
-    expectArraysEqual(result.shape, [3]);
-    expectArraysEqual(await result.data(), [1, 1, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/one_hot.ts b/tfjs-master/tfjs-core/src/ops/one_hot.ts
deleted file mode 100644
index dfbffc8cd..000000000
--- a/tfjs-master/tfjs-core/src/ops/one_hot.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {OneHot, OneHotAttrs, OneHotInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {DataType, TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take
- * value `onValue` (defaults to 1), while all other locations take value
- * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank
- * `R+1` with the last axis of size `depth`.
- * `indices` used to encode prediction class must start from 0. For example,
- *  if you have 3 classes of data, class 1 should be encoded as 0, class 2
- *  should be 1, and class 3 should be 2.
- *
- * ```js
- * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();
- * ```
- *
- * @param indices `tf.Tensor` of indices with dtype `int32`. Indices must
- * start from 0.
- * @param depth The depth of the one hot dimension.
- * @param onValue A number used to fill in the output when the index matches
- * the location.
- * @param offValue A number used to fill in the output when the index does
- *     not match the location.
- * @param dtype The dtype of the output tensor, default to 'int32'.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function oneHot_(
-    indices: Tensor|TensorLike, depth: number, onValue = 1, offValue = 0,
-    dtype: DataType = 'int32'): Tensor {
-  if (depth < 2) {
-    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
-  }
-  const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');
-
-  const inputs: OneHotInputs = {indices: $indices};
-  const attrs: OneHotAttrs = {dtype, depth, onValue, offValue};
-
-  return ENGINE.runKernel(
-      OneHot, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const oneHot = /* @__PURE__ */ op({oneHot_});
diff --git a/tfjs-master/tfjs-core/src/ops/one_hot_test.ts b/tfjs-master/tfjs-core/src/ops/one_hot_test.ts
deleted file mode 100644
index 4cdfde974..000000000
--- a/tfjs-master/tfjs-core/src/ops/one_hot_test.ts
+++ /dev/null
@@ -1,172 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('oneHot', ALL_ENVS, () => {
-  it('Depth 1 throws error', () => {
-    const indices = tf.tensor1d([0, 0, 0], 'int32');
-    expect(() => tf.oneHot(indices, 1)).toThrowError();
-  });
-
-  it('Depth 2, diagonal', async () => {
-    const indices = tf.tensor1d([0, 1], 'int32');
-    const res = tf.oneHot(indices, 2);
-
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [1, 0, 0, 1]);
-  });
-
-  it('Scalar input as Tensor', async () => {
-    const indices = tf.scalar(2, 'int32');
-    const res = tf.oneHot(indices, 4);
-
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [0, 0, 1, 0]);
-  });
-
-  it('Scalar input as number', async () => {
-    const indices = 2;
-    const res = tf.oneHot(indices, 4);
-
-    expect(res.shape).toEqual([4]);
-    expectArraysClose(await res.data(), [0, 0, 1, 0]);
-  });
-
-  it('oneHot with chaining compiles', () => {
-    const indices = 2;
-    // Asserts that there is no compiler error.
-    tf.oneHot(indices, 4).toFloat();
-  });
-
-  it('Depth 2, transposed diagonal', async () => {
-    const indices = tf.tensor1d([1, 0], 'int32');
-    const res = tf.oneHot(indices, 2);
-
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [0, 1, 1, 0]);
-  });
-
-  it('Depth 3, 4 events', async () => {
-    const indices = tf.tensor1d([2, 1, 2, 0], 'int32');
-    const res = tf.oneHot(indices, 3);
-
-    expect(res.shape).toEqual([4, 3]);
-    expectArraysClose(await res.data(), [0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0]);
-  });
-
-  it('Out of range events do not trigger onValue', async () => {
-    const indices = tf.tensor1d([-1, 5, 12345], 'int32');
-    const res = tf.oneHot(indices, 5);
-    expect(res.shape).toEqual([3, 5]);
-    expectArraysClose(
-        await res.data(), [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('Depth 2 onValue=3, offValue=-2', async () => {
-    const indices = tf.tensor1d([0, 1], 'int32');
-    const res = tf.oneHot(indices, 2, 3, -2);
-
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [3, -2, -2, 3]);
-  });
-
-  it('indices not int32 throws error', () => {
-    const indices = tf.tensor1d([0, 1], 'float32');
-    expect(() => tf.oneHot(indices, 2)).toThrowError();
-  });
-
-  it('check output dtype', () => {
-    const expectedType = 'int32';
-    const indices = tf.tensor1d([0, 1], 'int32');
-    const res = tf.oneHot(indices, 2);
-
-    expect(res.dtype).toEqual(expectedType);
-  });
-
-  it('check specified output dtype', () => {
-    const expectedType = 'float32';
-    const indices = tf.tensor1d([0, 1], 'int32');
-    const res = tf.oneHot(indices, 2, 1, 0, 'float32');
-
-    expect(res.dtype).toEqual(expectedType);
-  });
-
-  it('oneHot accepts a tensor-like object', async () => {
-    const res = tf.oneHot([0, 1], 2);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [1, 0, 0, 1]);
-  });
-
-  it('has gradient', async () => {
-    const a = tf.tensor1d([0, 1, 2], 'int32');
-    const dy = tf.ones([3, 3], 'float32');
-    const da = tf.grad((x: tf.Tensor1D) => tf.oneHot(x, 3))(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [0, 0, 0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([0, 1, 2], 'int32');
-    const dy = tf.ones([3, 3], 'float32');
-    const da =
-        tf.grad((x: tf.Tensor1D) => tf.oneHot(x.clone(), 3).clone())(a, dy);
-
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [0, 0, 0]);
-  });
-
-  it('gradient when indices is 3d', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [1, 2, 2], 'int32');
-    const dy = tf.ones([1, 2, 2, 3], 'float32');
-    const depth = 3;
-    const da = tf.grad(x => tf.oneHot(x, depth))(a, dy);
-    expect(da.dtype).toBe('float32');
-    expect(da.shape).toEqual(a.shape);
-    expectArraysClose(await da.data(), [0, 0, 0, 0]);
-  });
-
-  it('oneHot with indices as 2d', async () => {
-    const indices = tf.tensor2d([[1, 3], [2, 3]], [2, 2], 'int32');
-    const depth = 4;
-    const res = tf.oneHot(indices, depth);
-    expect(res.shape).toEqual([2, 2, depth]);
-    expectArraysClose(
-        await res.data(), [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1]);
-  });
-
-  it('Supports chaining', async () => {
-    const indices =
-        tf.tensor2d([[1, 2, 3], [2, 3, 1], [4, 5, 6]], [3, 3], 'int32');
-    const depth = 6;
-    const onValue = 3;
-    const offValue = 7;
-    const res = indices.oneHot(depth, onValue, offValue);
-
-    expect(res.shape).toEqual([3, 3, 6]);
-    expectArraysClose(await res.data(), [
-      7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7,
-      7, 7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 7, 7,
-      7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/ones.ts b/tfjs-master/tfjs-core/src/ops/ones.ts
deleted file mode 100644
index 0946a9d87..000000000
--- a/tfjs-master/tfjs-core/src/ops/ones.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tensor} from '../tensor';
-import {DataType, Rank, ShapeMap} from '../types';
-import {makeOnesTypedArray, sizeFromShape} from '../util';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-import {complex} from './complex';
-import {zeros} from './zeros';
-
-/**
- * Creates a `tf.Tensor` with all elements set to 1.
- *
- * ```js
- * tf.ones([2, 2]).print();
- * ```
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param dtype The type of an element in the resulting tensor. Defaults to
- *     'float'.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function ones<R extends Rank>(
-    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {
-  assertNonNegativeIntegerDimensions(shape);
-  if (dtype === 'complex64') {
-    const real = ones(shape, 'float32');
-    const imag = zeros(shape, 'float32');
-    return complex(real, imag);
-  }
-  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
-  return ENGINE.makeTensor(values, shape, dtype) as Tensor<R>;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/ones_like.ts b/tfjs-master/tfjs-core/src/ops/ones_like.ts
deleted file mode 100644
index f9e6ba1a8..000000000
--- a/tfjs-master/tfjs-core/src/ops/ones_like.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {OnesLike, OnesLikeInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the
- * given tensor.
- *
- * ```js
- * const x = tf.tensor([1, 2]);
- * tf.onesLike(x).print();
- * ```
- * @param x A tensor.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function onesLike_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'onesLike');
-
-  const inputs: OnesLikeInputs = {x: $x};
-  return ENGINE.runKernel(OnesLike, inputs as unknown as NamedTensorMap);
-}
-
-export const onesLike = /* @__PURE__ */ op({onesLike_});
diff --git a/tfjs-master/tfjs-core/src/ops/ones_like_test.ts b/tfjs-master/tfjs-core/src/ops/ones_like_test.ts
deleted file mode 100644
index 6addedd71..000000000
--- a/tfjs-master/tfjs-core/src/ops/ones_like_test.ts
+++ /dev/null
@@ -1,308 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('onesLike', ALL_ENVS, () => {
-  it('1D default dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([3]);
-    expectArraysClose(await b.data(), [1, 1, 1]);
-  });
-
-  it('chainable 1D default dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = a.onesLike();
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([3]);
-    expectArraysClose(await b.data(), [1, 1, 1]);
-  });
-
-  it('1D float32 dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'float32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([3]);
-    expectArraysClose(await b.data(), [1, 1, 1]);
-  });
-
-  it('1D int32 dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'int32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([3]);
-    expectArraysEqual(await b.data(), [1, 1, 1]);
-  });
-
-  it('1D bool dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'bool');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([3]);
-    expectArraysEqual(await b.data(), [1, 1, 1]);
-  });
-
-  it('1D complex dtype', async () => {
-    const real = tf.tensor1d([1, 2, 3], 'float32');
-    const imag = tf.tensor1d([1, 2, 3], 'float32');
-    const a = tf.complex(real, imag);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([3]);
-    expectArraysEqual(await b.data(), [1, 0, 1, 0, 1, 0]);
-  });
-
-  it('2D default dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('2D float32 dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('2D int32 dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('2D bool dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'bool');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('2D complex dtype', async () => {
-    const real = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
-    const imag = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
-    const a = tf.complex(real, imag);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysEqual(await b.data(), [1, 0, 1, 0, 1, 0, 1, 0]);
-  });
-
-  it('3D default dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('3D float32 dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('3D int32 dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'int32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('3D bool dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'bool');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('3D complex dtype', async () => {
-    const real = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
-    const imag = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
-    const a = tf.complex(real, imag);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await b.data(), [1, 0, 1, 0, 1, 0, 1, 0]);
-  });
-
-  it('4D default dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('4D float32 dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('4D int32 dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'int32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('4D bool dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'bool');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('4D default dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('4D complex dtype', async () => {
-    const real = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
-    const imag = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
-    const a = tf.complex(real, imag);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 0, 1, 0, 1, 0, 1, 0]);
-  });
-
-  it('5D float32 dtype', async () => {
-    const a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'float32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('5D int32 dtype', async () => {
-    const a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'int32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('5D bool dtype', async () => {
-    const a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'bool');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('5D default dtype', async () => {
-    const a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1]);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('5D complex dtype', async () => {
-    const real = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'float32');
-    const imag = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'float32');
-    const a = tf.complex(real, imag);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 0, 1, 0, 1, 0, 1, 0]);
-  });
-
-  it('6D int32 dtype', async () => {
-    const a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'int32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual(a.shape);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('6D bool dtype', async () => {
-    const a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'bool');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual(a.shape);
-    expectArraysEqual(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('6D default dtype', async () => {
-    const a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1]);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual(a.shape);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('6D float32 dtype', async () => {
-    const a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'float32');
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual(a.shape);
-    expectArraysClose(await b.data(), [1, 1, 1, 1]);
-  });
-
-  it('6D complex dtype', async () => {
-    const real = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'float32');
-    const imag = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'float32');
-    const a = tf.complex(real, imag);
-    const b = tf.onesLike(a);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1, 1]);
-    expectArraysEqual(await b.data(), [1, 0, 1, 0, 1, 0, 1, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.onesLike({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'onesLike' must be a Tensor/);
-  });
-
-  it('onesLike gradient', async () => {
-    const x = tf.tensor2d([[0, 1, 2], [4, 5, 6]]);
-    const gradients = tf.grad(x => tf.onesLike(x))(x);
-    expect(gradients.shape).toEqual([2, 3]);
-    expectArraysEqual(await gradients.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const res = tf.onesLike([[1, 2], [3, 4]]);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysEqual(await res.data(), [1, 1, 1, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/ones_test.ts b/tfjs-master/tfjs-core/src/ops/ones_test.ts
deleted file mode 100644
index 2550c561d..000000000
--- a/tfjs-master/tfjs-core/src/ops/ones_test.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('ones', ALL_ENVS, () => {
-  it('1D default dtype', async () => {
-    const a = tf.ones([3]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [1, 1, 1]);
-  });
-
-  it('1D float32 dtype', async () => {
-    const a = tf.ones([3], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [1, 1, 1]);
-  });
-
-  it('1D int32 dtype', async () => {
-    const a = tf.ones([3], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), [1, 1, 1]);
-  });
-
-  it('1D bool dtype', async () => {
-    const a = tf.ones([3], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), [1, 1, 1]);
-  });
-
-  it('2D default dtype', async () => {
-    const a = tf.ones([3, 2]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysClose(await a.data(), [1, 1, 1, 1, 1, 1]);
-  });
-
-  it('2D float32 dtype', async () => {
-    const a = tf.ones([3, 2], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysClose(await a.data(), [1, 1, 1, 1, 1, 1]);
-  });
-
-  it('2D int32 dtype', async () => {
-    const a = tf.ones([3, 2], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysEqual(await a.data(), [1, 1, 1, 1, 1, 1]);
-  });
-
-  it('2D bool dtype', async () => {
-    const a = tf.ones([3, 2], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysEqual(await a.data(), [1, 1, 1, 1, 1, 1]);
-  });
-
-  it('3D default dtype', async () => {
-    const a = tf.ones([2, 2, 2]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await a.data(), [1, 1, 1, 1, 1, 1, 1, 1]);
-  });
-
-  it('3D float32 dtype', async () => {
-    const a = tf.ones([2, 2, 2], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await a.data(), [1, 1, 1, 1, 1, 1, 1, 1]);
-  });
-
-  it('3D int32 dtype', async () => {
-    const a = tf.ones([2, 2, 2], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2, 2]);
-    expectArraysEqual(await a.data(), [1, 1, 1, 1, 1, 1, 1, 1]);
-  });
-
-  it('3D bool dtype', async () => {
-    const a = tf.ones([2, 2, 2], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([2, 2, 2]);
-    expectArraysEqual(await a.data(), [1, 1, 1, 1, 1, 1, 1, 1]);
-  });
-
-  it('4D default dtype', async () => {
-    const a = tf.ones([3, 2, 1, 1]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2, 1, 1]);
-    expectArraysClose(await a.data(), [1, 1, 1, 1, 1, 1]);
-  });
-
-  it('4D float32 dtype', async () => {
-    const a = tf.ones([3, 2, 1, 1], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2, 1, 1]);
-    expectArraysClose(await a.data(), [1, 1, 1, 1, 1, 1]);
-  });
-
-  it('4D int32 dtype', async () => {
-    const a = tf.ones([3, 2, 1, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3, 2, 1, 1]);
-    expectArraysEqual(await a.data(), [1, 1, 1, 1, 1, 1]);
-  });
-
-  it('4D bool dtype', async () => {
-    const a = tf.ones([3, 2, 1, 1], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([3, 2, 1, 1]);
-    expectArraysEqual(await a.data(), [1, 1, 1, 1, 1, 1]);
-  });
-
-  it('should throw error when shape is not integer', () => {
-    expect(() => tf.ones([2, 2.22, 3.33])).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/operation.ts b/tfjs-master/tfjs-core/src/ops/operation.ts
deleted file mode 100644
index 50aa9e8c9..000000000
--- a/tfjs-master/tfjs-core/src/ops/operation.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {isPromise} from '../util';
-
-export const OP_SCOPE_SUFFIX = '__op';
-
-/**
- * Used for wrapping functions that perform math operations on
- * Tensors. The function will be wrapped in a named scope that cleans all
- * memory usage after the function is done.
- */
-export function op<T extends Function>(f: {[name: string]: T}): T {
-  const keys = Object.keys(f);
-  if (keys.length !== 1) {
-    throw new Error(
-        `Please provide an object with a single key ` +
-        `(operation name) mapping to a function. Got an object with ` +
-        `${keys.length} keys.`);
-  }
-
-  let opName = keys[0];
-  const fn = f[opName];
-
-  // Strip the underscore from the end of the function name.
-  if (opName.endsWith('_')) {
-    opName = opName.substring(0, opName.length - 1);
-  }
-
-  // add an __op suffix to distinguish ops from kernels in tf.profile
-  opName = opName + OP_SCOPE_SUFFIX;
-
-  // tslint:disable-next-line:no-any
-  const f2 = (...args: any[]) => {
-    ENGINE.startScope(opName);
-    try {
-      const result = fn(...args);
-      if (isPromise(result)) {
-        console.error('Cannot return a Promise inside of tidy.');
-      }
-      ENGINE.endScope(result);
-      return result;
-    } catch (ex) {
-      ENGINE.endScope(null);
-      throw ex;
-    }
-  };
-  Object.defineProperty(f2, 'name', {value: opName, configurable: true});
-
-  // tslint:disable-next-line:no-any
-  return f2 as any as T;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/operation_test.ts b/tfjs-master/tfjs-core/src/ops/operation_test.ts
deleted file mode 100644
index 80d274102..000000000
--- a/tfjs-master/tfjs-core/src/ops/operation_test.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {op} from './operation';
-
-describeWithFlags('operation', ALL_ENVS, () => {
-  it('executes and preserves function name', () => {
-    const f = () => 2;
-    const opfn = /* @__PURE__ */ op({'opName': f});
-
-    expect(opfn.name).toBe('opName__op');
-    expect(opfn()).toBe(2);
-  });
-
-  it('executes, preserves function name, strips underscore', () => {
-    const f = () => 2;
-    const opfn = /* @__PURE__ */ op({'opName_': f});
-
-    expect(opfn.name).toBe('opName__op');
-    expect(opfn()).toBe(2);
-  });
-
-  it('throws when passing an object with multiple keys', () => {
-    const f = () => 2;
-    expect(() => op({'opName_': f, 'opName2_': f}))
-        .toThrowError(/Please provide an object with a single key/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/ops.ts b/tfjs-master/tfjs-core/src/ops/ops.ts
deleted file mode 100644
index 0fbf5dcee..000000000
--- a/tfjs-master/tfjs-core/src/ops/ops.ts
+++ /dev/null
@@ -1,343 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Modularized ops.
-export {abs} from './abs';
-export {acos} from './acos';
-export {acosh} from './acosh';
-export {add} from './add';
-export {addN} from './add_n';
-export {all} from './all';
-export {any} from './any';
-export {argMax} from './arg_max';
-export {argMin} from './arg_min';
-export {asin} from './asin';
-export {asinh} from './asinh';
-export {atan} from './atan';
-export {atan2} from './atan2';
-export {atanh} from './atanh';
-export {avgPool} from './avg_pool';
-export {avgPool3d} from './avg_pool_3d';
-export {basicLSTMCell} from './basic_lstm_cell';
-export {batchToSpaceND} from './batch_to_space_nd';
-export {batchNorm} from './batchnorm';
-export {batchNorm2d} from './batchnorm2d';
-export {batchNorm3d} from './batchnorm3d';
-export {batchNorm4d} from './batchnorm4d';
-export {bincount} from './bincount';
-export {bitwiseAnd} from './bitwise_and';
-export {broadcastArgs} from './broadcast_args';
-export {broadcastTo} from './broadcast_to';
-export {buffer} from './buffer';
-export {cast} from './cast';
-export {ceil} from './ceil';
-export {clipByValue} from './clip_by_value';
-export {clone} from './clone';
-export {complex} from './complex';
-export {concat} from './concat';
-export {concat1d} from './concat_1d';
-export {concat2d} from './concat_2d';
-export {concat3d} from './concat_3d';
-export {concat4d} from './concat_4d';
-export {conv1d} from './conv1d';
-export {conv2d} from './conv2d';
-export {conv2dTranspose} from './conv2d_transpose';
-export {conv3d} from './conv3d';
-export {conv3dTranspose} from './conv3d_transpose';
-export {cos} from './cos';
-export {cosh} from './cosh';
-export {cumprod} from './cumprod';
-export {cumsum} from './cumsum';
-export {denseBincount} from './dense_bincount';
-export {depthToSpace} from './depth_to_space';
-export {depthwiseConv2d} from './depthwise_conv2d';
-export {diag} from './diag';
-export {dilation2d} from './dilation2d';
-export {div} from './div';
-export {divNoNan} from './div_no_nan';
-export {dot} from './dot';
-export {einsum} from './einsum';
-export {elu} from './elu';
-export {ensureShape} from './ensure_shape';
-export {equal} from './equal';
-export {erf} from './erf';
-export {euclideanNorm} from './euclidean_norm';
-export {exp} from './exp';
-export {expandDims} from './expand_dims';
-export {expm1} from './expm1';
-export {eye} from './eye';
-export {fill} from './fill';
-export {floor} from './floor';
-export {floorDiv} from './floorDiv';
-export {gather} from './gather';
-export {greater} from './greater';
-export {greaterEqual} from './greater_equal';
-export {imag} from './imag';
-export {isFinite} from './is_finite';
-export {isInf} from './is_inf';
-export {isNaN} from './is_nan';
-export {leakyRelu} from './leaky_relu';
-export {less} from './less';
-export {lessEqual} from './less_equal';
-export {linspace} from './linspace';
-export {localResponseNormalization} from './local_response_normalization';
-export {log} from './log';
-export {log1p} from './log1p';
-export {logSigmoid} from './log_sigmoid';
-export {logSoftmax} from './log_softmax';
-export {logSumExp} from './log_sum_exp';
-export {logicalAnd} from './logical_and';
-export {logicalNot} from './logical_not';
-export {logicalOr} from './logical_or';
-export {logicalXor} from './logical_xor';
-export {lowerBound} from './lower_bound';
-export {matMul} from './mat_mul';
-export {max} from './max';
-export {maxPool} from './max_pool';
-export {maxPool3d} from './max_pool_3d';
-export {maxPoolWithArgmax} from './max_pool_with_argmax';
-export {maximum} from './maximum';
-export {mean} from './mean';
-export {meshgrid} from './meshgrid';
-export {min} from './min';
-export {minimum} from './minimum';
-export {mirrorPad} from './mirror_pad';
-export {mod} from './mod';
-export {moments} from './moments';
-export {mul} from './mul';
-export {LSTMCellFunc, multiRNNCell} from './multi_rnn_cell';
-export {multinomial} from './multinomial';
-export {neg} from './neg';
-export {notEqual} from './not_equal';
-export {oneHot} from './one_hot';
-export {ones} from './ones';
-export {onesLike} from './ones_like';
-export {outerProduct} from './outer_product';
-export {pad} from './pad';
-export {pad1d} from './pad1d';
-export {pad2d} from './pad2d';
-export {pad3d} from './pad3d';
-export {pad4d} from './pad4d';
-export {pool} from './pool';
-export {pow} from './pow';
-export {prelu} from './prelu';
-export {print} from './print';
-export {prod} from './prod';
-export {raggedGather} from './ragged_gather';
-export {raggedRange} from './ragged_range';
-export {raggedTensorToTensor} from './ragged_tensor_to_tensor';
-export {rand} from './rand';
-export {randomGamma} from './random_gamma';
-export {randomNormal} from './random_normal';
-export {randomStandardNormal} from './random_standard_normal';
-export {randomUniform} from './random_uniform';
-export {randomUniformInt} from './random_uniform_int';
-export {range} from './range';
-export {real} from './real';
-export {reciprocal} from './reciprocal';
-export {relu} from './relu';
-export {relu6} from './relu6';
-export {reshape} from './reshape';
-export {reverse} from './reverse';
-export {reverse1d} from './reverse_1d';
-export {reverse2d} from './reverse_2d';
-export {reverse3d} from './reverse_3d';
-export {reverse4d} from './reverse_4d';
-export {round} from './round';
-export {rsqrt} from './rsqrt';
-export {scalar} from './scalar';
-export {selu} from './selu';
-export {separableConv2d} from './separable_conv2d';
-export {setdiff1dAsync} from './setdiff1d_async';
-export {sigmoid} from './sigmoid';
-export {sign} from './sign';
-export {sin} from './sin';
-export {sinh} from './sinh';
-export {slice} from './slice';
-export {slice1d} from './slice1d';
-export {slice2d} from './slice2d';
-export {slice3d} from './slice3d';
-export {slice4d} from './slice4d';
-export {softmax} from './softmax';
-export {softplus} from './softplus';
-export {spaceToBatchND} from './space_to_batch_nd';
-export {fft} from './spectral/fft';
-export {ifft} from './spectral/ifft';
-export {irfft} from './spectral/irfft';
-export {rfft} from './spectral/rfft';
-export {split} from './split';
-export {sqrt} from './sqrt';
-export {square} from './square';
-export {squaredDifference} from './squared_difference';
-export {squeeze} from './squeeze';
-export {stack} from './stack';
-export {step} from './step';
-export {stridedSlice} from './strided_slice';
-export {sub} from './sub';
-export {sum} from './sum';
-export {tan} from './tan';
-export {tanh} from './tanh';
-export {tensor} from './tensor';
-export {tensor1d} from './tensor1d';
-export {tensor2d} from './tensor2d';
-export {tensor3d} from './tensor3d';
-export {tensor4d} from './tensor4d';
-export {tensor5d} from './tensor5d';
-export {tensor6d} from './tensor6d';
-export {tensorScatterUpdate} from './tensor_scatter_update';
-export {tile} from './tile';
-export {topk} from './topk';
-export {truncatedNormal} from './truncated_normal';
-export {unique} from './unique';
-export {unsortedSegmentSum} from './unsorted_segment_sum';
-export {unstack} from './unstack';
-export {upperBound} from './upper_bound';
-export {variable} from './variable';
-export {where} from './where';
-export {whereAsync} from './where_async';
-export {zeros} from './zeros';
-export {zerosLike} from './zeros_like';
-
-export * from './boolean_mask';
-export * from './transpose';
-export * from './norm';
-export * from './moving_average';
-export * from './scatter_nd';
-export * from './search_sorted';
-export * from './sparse_to_dense';
-export * from './gather_nd';
-export * from './dropout';
-export * from './signal_ops_util';
-export * from './in_top_k';
-
-export {op, OP_SCOPE_SUFFIX} from './operation';
-
-import {rfft} from './spectral/rfft';
-import {fft} from './spectral/fft';
-import {ifft} from './spectral/ifft';
-import {irfft} from './spectral/irfft';
-const spectral = {
-  fft,
-  ifft,
-  rfft,
-  irfft
-};
-
-import * as fused from './fused_ops';
-
-import {hammingWindow} from './signal/hamming_window';
-import {hannWindow} from './signal/hann_window';
-import {frame} from './signal/frame';
-import {stft} from './signal/stft';
-const signal = {
-  hammingWindow,
-  hannWindow,
-  frame,
-  stft,
-};
-
-// Image Ops namespace
-import {cropAndResize} from './image/crop_and_resize';
-import {flipLeftRight} from './image/flip_left_right';
-import {grayscaleToRGB} from './image/grayscale_to_rgb';
-import {rgbToGrayscale} from './image/rgb_to_grayscale';
-import {rotateWithOffset} from './image/rotate_with_offset';
-import {nonMaxSuppression} from './image/non_max_suppression';
-import {nonMaxSuppressionAsync} from './image/non_max_suppression_async';
-import {nonMaxSuppressionWithScore} from './image/non_max_suppression_with_score';
-import {nonMaxSuppressionWithScoreAsync} from './image/non_max_suppression_with_score_async';
-import {nonMaxSuppressionPadded} from './image/non_max_suppression_padded';
-import {nonMaxSuppressionPaddedAsync} from './image/non_max_suppression_padded_async';
-import {resizeBilinear} from './image/resize_bilinear';
-import {resizeNearestNeighbor} from './image/resize_nearest_neighbor';
-import {threshold} from './image/threshold';
-import {transform} from './image/transform';
-const image = {
-  flipLeftRight,
-  grayscaleToRGB,
-  resizeNearestNeighbor,
-  resizeBilinear,
-  rgbToGrayscale,
-  rotateWithOffset,
-  cropAndResize,
-  nonMaxSuppression,
-  nonMaxSuppressionAsync,
-  nonMaxSuppressionWithScore,
-  nonMaxSuppressionWithScoreAsync,
-  nonMaxSuppressionPadded,
-  nonMaxSuppressionPaddedAsync,
-  threshold,
-  transform
-};
-
-// linalg namespace
-import {bandPart} from './linalg/band_part';
-import {gramSchmidt} from './linalg/gram_schmidt';
-import {qr} from './linalg/qr';
-const linalg = {
-  bandPart,
-  gramSchmidt,
-  qr
-};
-
-// losses namespace;
-import {absoluteDifference} from './losses/absolute_difference';
-import {computeWeightedLoss} from './losses/compute_weighted_loss';
-import {cosineDistance} from './losses/cosine_distance';
-import {hingeLoss} from './losses/hinge_loss';
-import {huberLoss} from './losses/huber_loss';
-import {logLoss} from './losses/log_loss';
-import {meanSquaredError} from './losses/mean_squared_error';
-import {sigmoidCrossEntropy} from './losses/sigmoid_cross_entropy';
-import {softmaxCrossEntropy} from './losses/softmax_cross_entropy';
-const losses = {
-  absoluteDifference,
-  computeWeightedLoss,
-  cosineDistance,
-  hingeLoss,
-  huberLoss,
-  logLoss,
-  meanSquaredError,
-  sigmoidCrossEntropy,
-  softmaxCrossEntropy
-};
-
-import {sparseFillEmptyRows} from './sparse/sparse_fill_empty_rows';
-import {sparseReshape} from './sparse/sparse_reshape';
-import {sparseSegmentMean} from './sparse/sparse_segment_mean';
-import {sparseSegmentSum} from './sparse/sparse_segment_sum';
-const sparse = {
-  sparseFillEmptyRows,
-  sparseReshape,
-  sparseSegmentMean,
-  sparseSegmentSum
-};
-
-import {stringNGrams} from './string/string_n_grams';
-import {stringSplit} from './string/string_split';
-import {stringToHashBucketFast} from './string/string_to_hash_bucket_fast';
-import {staticRegexReplace} from './string/static_regex_replace';
-// tslint:disable-next-line:variable-name
-const string = {
-  stringNGrams,
-  stringSplit,
-  stringToHashBucketFast,
-  staticRegexReplace,
-};
-
-// Second level exports.
-export {image, linalg, losses, spectral, fused, signal, sparse, string};
diff --git a/tfjs-master/tfjs-core/src/ops/ops_for_converter.ts b/tfjs-master/tfjs-core/src/ops/ops_for_converter.ts
deleted file mode 100644
index 3c284da93..000000000
--- a/tfjs-master/tfjs-core/src/ops/ops_for_converter.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * This file exports ops used by the converters executors. By default it
- * re-exports all ops. In a custom build this is aliased to a file that will
- * only exports ops for a given model.json.
- */
-export * from './ops';
diff --git a/tfjs-master/tfjs-core/src/ops/outer_product.ts b/tfjs-master/tfjs-core/src/ops/outer_product.ts
deleted file mode 100644
index c3063867d..000000000
--- a/tfjs-master/tfjs-core/src/ops/outer_product.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor1D, Tensor2D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {matMul} from './mat_mul';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Computes the outer product of two vectors, `v1` and `v2`.
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3]);
- * const b = tf.tensor1d([3, 4, 5]);
- *
- * tf.outerProduct(a, b).print();
- * ```
- * @param v1 The first vector in the outer product operation.
- * @param v2 The second vector in the outer product operation.
- *
- * @doc {heading: 'Operations', subheading: 'Matrices'}
- */
-function outerProduct_(
-    v1: Tensor1D|TensorLike, v2: Tensor1D|TensorLike): Tensor2D {
-  const $v1 = convertToTensor(v1, 'v1', 'outerProduct');
-  const $v2 = convertToTensor(v2, 'v2', 'outerProduct');
-
-  util.assert(
-      $v1.rank === 1 && $v2.rank === 1,
-      () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +
-          `${$v1.rank} and ${$v2.rank}.`);
-
-  const v12D = reshape($v1, [-1, 1]);
-  const v22D = reshape($v2, [1, -1]);
-  return matMul(v12D, v22D);
-}
-
-export const outerProduct = /* @__PURE__ */ op({outerProduct_});
diff --git a/tfjs-master/tfjs-core/src/ops/pad.ts b/tfjs-master/tfjs-core/src/ops/pad.ts
deleted file mode 100644
index eec8c3d1b..000000000
--- a/tfjs-master/tfjs-core/src/ops/pad.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {PadV2, PadV2Attrs, PadV2Inputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Pads a `tf.Tensor` with a given value and paddings.
- *
- * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,
- * refer to `tf.mirrorPad`.
- *
- * Also available are stricter rank-specific methods with the same signature
- * as this method that assert that `paddings` is of given length.
- *   - `tf.pad1d`
- *   - `tf.pad2d`
- *   - `tf.pad3d`
- *   - `tf.pad4d`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- * x.pad([[1, 2]]).print();
- * ```
- * @param x The tensor to pad.
- * @param paddings An array of length `R` (the rank of the tensor), where
- * each element is a length-2 tuple of ints `[padBefore, padAfter]`,
- * specifying how much to pad along each dimension of the tensor.
- * @param constantValue The pad value to use. Defaults to 0.
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function pad_<T extends Tensor>(
-    x: T|TensorLike, paddings: Array<[number, number]>, constantValue = 0): T {
-  const $x = convertToTensor(x, 'x', 'pad');
-  if ($x.rank === 0) {
-    throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');
-  }
-
-  const attrs: PadV2Attrs = {paddings, constantValue};
-  const inputs: PadV2Inputs = {x: $x};
-  return ENGINE.runKernel(
-      PadV2, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const pad = /* @__PURE__ */ op({pad_});
diff --git a/tfjs-master/tfjs-core/src/ops/pad1d.ts b/tfjs-master/tfjs-core/src/ops/pad1d.ts
deleted file mode 100644
index e775fb402..000000000
--- a/tfjs-master/tfjs-core/src/ops/pad1d.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor1D} from '../tensor';
-import {TensorLike} from '../types';
-import {assert} from '../util';
-import {op} from './operation';
-import {pad} from './pad';
-
-/**
- * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.
- */
-function pad1d_(
-    x: Tensor1D|TensorLike, paddings: [number, number],
-    constantValue = 0): Tensor1D {
-  assert(
-      paddings.length === 2,
-      () => 'Invalid number of paddings. Must be length of 2.');
-  return pad(x, [paddings], constantValue);
-}
-
-export const pad1d = /* @__PURE__ */ op({pad1d_});
diff --git a/tfjs-master/tfjs-core/src/ops/pad2d.ts b/tfjs-master/tfjs-core/src/ops/pad2d.ts
deleted file mode 100644
index 5225f2673..000000000
--- a/tfjs-master/tfjs-core/src/ops/pad2d.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor2D} from '../tensor';
-import {TensorLike} from '../types';
-import {assert} from '../util';
-import {op} from './operation';
-import {pad} from './pad';
-
-/**
- * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.
- */
-function pad2d_(
-    x: Tensor2D|TensorLike, paddings: [[number, number], [number, number]],
-    constantValue = 0): Tensor2D {
-  assert(
-      paddings.length === 2 && paddings[0].length === 2 &&
-          paddings[1].length === 2,
-      () => 'Invalid number of paddings. Must be length of 2 each.');
-  return pad(x, paddings, constantValue);
-}
-
-export const pad2d = /* @__PURE__ */ op({pad2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/pad3d.ts b/tfjs-master/tfjs-core/src/ops/pad3d.ts
deleted file mode 100644
index 39201a0bc..000000000
--- a/tfjs-master/tfjs-core/src/ops/pad3d.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor3D} from '../tensor';
-import {TensorLike} from '../types';
-import {assert} from '../util';
-import {op} from './operation';
-import {pad} from './pad';
-
-/**
- * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.
- */
-function pad3d_(
-    x: Tensor3D|TensorLike,
-    paddings: [[number, number], [number, number], [number, number]],
-    constantValue = 0): Tensor3D {
-  assert(
-      paddings.length === 3 && paddings[0].length === 2 &&
-          paddings[1].length === 2 && paddings[2].length === 2,
-      () => 'Invalid number of paddings. Must be length of 2 each.');
-  return pad(x, paddings, constantValue);
-}
-
-export const pad3d = /* @__PURE__ */ op({pad3d_});
diff --git a/tfjs-master/tfjs-core/src/ops/pad4d.ts b/tfjs-master/tfjs-core/src/ops/pad4d.ts
deleted file mode 100644
index f3e7693f7..000000000
--- a/tfjs-master/tfjs-core/src/ops/pad4d.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor4D} from '../tensor';
-import {TensorLike} from '../types';
-import {assert} from '../util';
-import {op} from './operation';
-import {pad} from './pad';
-
-/**
- * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.
- */
-function pad4d_(
-    x: Tensor4D|TensorLike,
-    paddings:
-        [
-          [number, number], [number, number], [number, number], [number, number]
-        ],
-    constantValue = 0): Tensor4D {
-  assert(
-      paddings.length === 4 && paddings[0].length === 2 &&
-          paddings[1].length === 2 && paddings[2].length === 2 &&
-          paddings[3].length === 2,
-      () => 'Invalid number of paddings. Must be length of 2 each.');
-  return pad(x, paddings, constantValue);
-}
-
-export const pad4d = /* @__PURE__ */ op({pad4d_});
diff --git a/tfjs-master/tfjs-core/src/ops/pad_test.ts b/tfjs-master/tfjs-core/src/ops/pad_test.ts
deleted file mode 100644
index a1370281d..000000000
--- a/tfjs-master/tfjs-core/src/ops/pad_test.ts
+++ /dev/null
@@ -1,296 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('pad 1d', ALL_ENVS, () => {
-  it('Should pad 1D arrays', async () => {
-    const a = tf.tensor1d([1, 2, 3, 4, 5, 6], 'int32');
-    const b = tf.pad1d(a, [2, 3]);
-    expectArraysClose(await b.data(), [0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0]);
-  });
-
-  it('Should not pad 1D arrays with 0s', async () => {
-    const a = tf.tensor1d([1, 2, 3, 4], 'int32');
-    const b = tf.pad1d(a, [0, 0]);
-    expectArraysClose(await b.data(), [1, 2, 3, 4]);
-  });
-
-  it('Should handle padding with custom value', async () => {
-    let a = tf.tensor1d([1, 2, 3, 4], 'int32');
-    let b = tf.pad1d(a, [2, 3], 9);
-    expectArraysClose(await b.data(), [9, 9, 1, 2, 3, 4, 9, 9, 9]);
-
-    a = tf.tensor1d([1, 2, 3, 4]);
-    b = tf.pad1d(a, [2, 1], 1.1);
-    expectArraysClose(await b.data(), [1.1, 1.1, 1, 2, 3, 4, 1.1]);
-
-    a = tf.tensor1d([1, 2, 3, 4]);
-    b = tf.pad1d(a, [2, 1], 1);
-    expectArraysClose(await b.data(), [1, 1, 1, 2, 3, 4, 1]);
-
-    a = tf.tensor1d([1, 2, 3, 4]);
-    b = tf.pad1d(a, [2, 1], Number.NEGATIVE_INFINITY);
-    expectArraysClose(await b.data(), [
-      Number.NEGATIVE_INFINITY, Number.NEGATIVE_INFINITY, 1, 2, 3, 4,
-      Number.NEGATIVE_INFINITY
-    ]);
-
-    a = tf.tensor1d([1, 2, 3, 4]);
-    b = tf.pad1d(a, [2, 1], Number.POSITIVE_INFINITY);
-    expectArraysClose(await b.data(), [
-      Number.POSITIVE_INFINITY, Number.POSITIVE_INFINITY, 1, 2, 3, 4,
-      Number.POSITIVE_INFINITY
-    ]);
-  });
-
-  it('Should handle NaNs with 1D arrays', async () => {
-    const a = tf.tensor1d([1, NaN, 2, NaN]);
-    const b = tf.pad1d(a, [1, 1]);
-    expectArraysClose(await b.data(), [0, 1, NaN, 2, NaN, 0]);
-  });
-
-  it('Should handle invalid paddings', () => {
-    const a = tf.tensor1d([1, 2, 3, 4], 'int32');
-    const f = () => {
-      // tslint:disable-next-line:no-any
-      tf.pad1d(a, [2, 2, 2] as any);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Should handle empty tensor with meaningful dims', async () => {
-    const a = tf.tensor([], [0, 1]);
-    const b = tf.pad(a, [[0, 4], [0, 0]], 2);
-
-    expectArraysClose(await b.data(), [2, 2, 2, 2]);
-    expectArraysClose(b.shape, [4, 1]);
-  });
-
-  it('grad', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);
-    const da = tf.grad((a: tf.Tensor1D) => tf.pad1d(a, [2, 1]))(a, dy);
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [30, 40, 50]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);
-    const da =
-        tf.grad((a: tf.Tensor1D) => tf.pad1d(a.clone(), [2, 1]).clone())(a, dy);
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [30, 40, 50]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [1, 2, 3, 4, 5, 6];
-    const b = tf.pad1d(a, [2, 3]);
-    expectArraysClose(await b.data(), [0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0]);
-  });
-});
-
-describeWithFlags('pad 2d', ALL_ENVS, () => {
-  it('Should pad 2D arrays', async () => {
-    let a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
-    let b = tf.pad2d(a, [[1, 1], [1, 1]]);
-    // 0, 0, 0
-    // 0, 1, 0
-    // 0, 2, 0
-    // 0, 0, 0
-    expectArraysClose(await b.data(), [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);
-
-    a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
-    b = tf.pad2d(a, [[2, 2], [1, 1]]);
-    // 0, 0, 0, 0, 0
-    // 0, 0, 0, 0, 0
-    // 0, 1, 2, 3, 0
-    // 0, 4, 5, 6, 0
-    // 0, 0, 0, 0, 0
-    // 0, 0, 0, 0, 0
-    expectArraysClose(await b.data(), [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,
-      0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-    ]);
-  });
-
-  it('Should not pad 2D arrays with 0s', async () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
-    const b = tf.pad2d(a, [[0, 0], [0, 0]]);
-    expectArraysClose(await b.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('Should handle padding with custom value', async () => {
-    let a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
-    let b = tf.pad2d(a, [[1, 1], [1, 1]], 10);
-    expectArraysClose(await b.data(), [
-      10, 10, 10, 10, 10, 10, 1,  2,  3,  10,
-      10, 4,  5,  6,  10, 10, 10, 10, 10, 10
-    ]);
-
-    a = tf.tensor2d([[1], [1]], [2, 1]);
-    b = tf.pad2d(a, [[1, 1], [1, 1]], -2.1);
-    expectArraysClose(
-        await b.data(),
-        [-2.1, -2.1, -2.1, -2.1, 1, -2.1, -2.1, 1, -2.1, -2.1, -2.1, -2.1]);
-
-    a = tf.tensor2d([[1], [1]], [2, 1]);
-    b = tf.pad2d(a, [[1, 1], [1, 1]], -2);
-    expectArraysClose(
-        await b.data(), [-2, -2, -2, -2, 1, -2, -2, 1, -2, -2, -2, -2]);
-  });
-
-  it('Should handle NaNs with 2D arrays', async () => {
-    const a = tf.tensor2d([[1, NaN], [1, NaN]], [2, 2]);
-    const b = tf.pad2d(a, [[1, 1], [1, 1]]);
-    // 0, 0, 0,   0
-    // 0, 1, NaN, 0
-    // 0, 1, NaN, 0
-    // 0, 0, 0,   0
-    expectArraysClose(
-        await b.data(), [0, 0, 0, 0, 0, 1, NaN, 0, 0, 1, NaN, 0, 0, 0, 0, 0]);
-  });
-
-  it('Should handle invalid paddings', () => {
-    const a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
-    const f = () => {
-      // tslint:disable-next-line:no-any
-      tf.pad2d(a, [[2, 2, 2], [1, 1, 1]] as any);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('grad', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 4]]);
-    const dy = tf.tensor2d([[0, 0, 0], [10, 20, 0], [30, 40, 0]], [3, 3]);
-    const da =
-        tf.grad((a: tf.Tensor2D) => tf.pad2d(a, [[1, 0], [0, 1]]))(a, dy);
-    expect(da.shape).toEqual([2, 2]);
-    expectArraysClose(await da.data(), [10, 20, 30, 40]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[1, 2, 3], [4, 5, 6]];  // 2x3
-    const b = tf.pad2d(a, [[0, 0], [0, 0]]);
-    expectArraysClose(await b.data(), [1, 2, 3, 4, 5, 6]);
-  });
-});
-
-describeWithFlags('pad 3d', ALL_ENVS, () => {
-  it('works with 3d tensor, float32', async () => {
-    const a = tf.tensor3d([[[1]], [[2]]], [2, 1, 1], 'float32');
-    const b = tf.pad3d(a, [[1, 1], [1, 1], [1, 1]]);
-    // 0, 0, 0
-    // 0, 0, 0
-    // 0, 0, 0
-
-    // 0, 0, 0
-    // 0, 1, 0
-    // 0, 0, 0
-
-    // 0, 0, 0
-    // 0, 2, 0
-    // 0, 0, 0
-
-    // 0, 0, 0
-    // 0, 0, 0
-    // 0, 0, 0
-    expect(b.shape).toEqual([4, 3, 3]);
-    expectArraysClose(await b.data(), [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
-      0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-    ]);
-  });
-});
-
-describeWithFlags('pad 4d', ALL_ENVS, () => {
-  it('Should pad 4D arrays', async () => {
-    const a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');
-    const b = tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    const expected = tf.tensor4d(
-        [[[[0], [0], [0]], [[0], [9], [0]], [[0], [0], [0]]]], [1, 3, 3, 1],
-        'int32');
-    expectArraysClose(await b.data(), await expected.data());
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([1, 3, 3, 1]);
-  });
-
-  it('does not leak memory', () => {
-    const a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');
-    // The first call to pad may create and keeps internal singleton tensors.
-    // Subsequent calls should always create exactly one new tensor.
-    tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    // Count before real call.
-    const numTensors = tf.memory().numTensors;
-    tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    expect(tf.memory().numTensors).toEqual(numTensors + 1);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[[[9]]]];  // 1x1x1x1
-    const b = tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    const expected = tf.tensor4d(
-        [[[[0], [0], [0]], [[0], [9], [0]], [[0], [0], [0]]]], [1, 3, 3, 1],
-        'float32');
-    expectArraysClose(await b.data(), await expected.data());
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([1, 3, 3, 1]);
-  });
-});
-
-describeWithFlags('pad', ALL_ENVS, () => {
-  it('Pad tensor2d', async () => {
-    let a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
-    let b = tf.pad(a, [[1, 1], [1, 1]]);
-    // 0, 0, 0
-    // 0, 1, 0
-    // 0, 2, 0
-    // 0, 0, 0
-    expectArraysClose(await b.data(), [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);
-
-    a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
-    b = tf.pad(a, [[2, 2], [1, 1]]);
-    // 0, 0, 0, 0, 0
-    // 0, 0, 0, 0, 0
-    // 0, 1, 2, 3, 0
-    // 0, 4, 5, 6, 0
-    // 0, 0, 0, 0, 0
-    // 0, 0, 0, 0, 0
-    expectArraysClose(await b.data(), [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,
-      0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-    ]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.pad({} as tf.Tensor, [[0, 0]]))
-        .toThrowError(/Argument 'x' passed to 'pad' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[1], [2]];
-    const res = tf.pad(x, [[1, 1], [1, 1]]);
-    // 0, 0, 0
-    // 0, 1, 0
-    // 0, 2, 0
-    // 0, 0, 0
-    expectArraysClose(await res.data(), [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/pool.ts b/tfjs-master/tfjs-core/src/ops/pool.ts
deleted file mode 100644
index 84398b419..000000000
--- a/tfjs-master/tfjs-core/src/ops/pool.ts
+++ /dev/null
@@ -1,167 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor3D, Tensor4D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {avgPool} from './avg_pool';
-import {batchToSpaceND} from './batch_to_space_nd';
-import * as conv_util from './conv_util';
-import {maxPool} from './max_pool';
-import {op} from './operation';
-import {reshape} from './reshape';
-import {spaceToBatchND} from './space_to_batch_nd';
-
-/**
- * Performs an N-D pooling operation
- *
- * @param input The input tensor, of rank 4 or rank 3 of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
- * @param windowShape The filter size: `[filterHeight, filterWidth]`. If
- *     `filterSize` is a single number, then `filterHeight == filterWidth`.
- * @param poolingType The type of pooling, either 'max' or 'avg'.
- * @param pad The type of padding algorithm:
- *    - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *    - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *    - For more info, see this guide:
- *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
- *         https://www.tensorflow.org/api_guides/python/nn#Convolution)
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
- *     in which we sample input values across the height and width dimensions
- *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single
- *     number, then `dilationHeight == dilationWidth`. If it is greater than
- *     1, then all values of `strides` must be 1.
- * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
- *     `strides` is a single number, then `strideHeight == strideWidth`.
- * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
- *     provided, it will default to truncate.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function pool_<T extends Tensor3D|Tensor4D>(
-    input: T|TensorLike, windowShape: [number, number]|number,
-    poolingType: 'avg'|'max',
-    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
-    dilations?: [number, number]|number, strides?: [number, number]|number,
-    dimRoundingMode?: 'floor'|'round'|'ceil') {
-  if (dilations == null) {
-    dilations = [1, 1];
-  }
-  if (strides == null) {
-    strides = 1;
-  }
-  if (pad === 0) {
-    pad = 'valid';
-  }
-
-  const $x = convertToTensor(input, 'x', 'maxPool');
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-
-  util.assert(
-      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
-      () => 'Error in pool: Either strides or dilations must be 1. ' +
-          `Got strides ${strides} and dilations '${dilations}'`);
-
-  const convInfo = conv_util.computePool2DInfo(
-      x4D.shape, windowShape, strides, dilations, pad);
-  const dilation: [number, number] =
-      [convInfo.dilationHeight, convInfo.dilationWidth];
-
-  // The following implementation does batchToSpace(pool(spaceToBatch(x)))
-  // whenever dilation > 1 since the TF kernels do not support dilation > 1.
-  // tslint:disable-next-line:max-line-length
-  // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037
-
-  let basePadding: number[][];
-  if (pad === 'same') {
-    basePadding = withSpaceToBatchBasePaddings(
-        [convInfo.filterHeight, convInfo.filterWidth], dilation);
-  } else {
-    basePadding = [[0, 0], [0, 0]];
-  }
-
-  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
-  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings(
-      [convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
-  const convertedPad = isDilationOne ? pad : 'valid';
-  const convertedX =
-      isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
-
-  const forwardOp = poolingType === 'avg' ?
-      () => avgPool(convertedX, windowShape, strides, convertedPad,
-                    dimRoundingMode) :
-      () => maxPool(convertedX, windowShape, strides, convertedPad,
-                    dimRoundingMode);
-  const y = forwardOp();
-
-  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-
-  return res as T;
-}
-
-// Helper function to compute crops and paddings for pool with dilation > 1.
-// tslint:disable-next-line:max-line-length
-// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184
-function requiredSpaceToBatchPaddings(
-    inputShape: [number, number], blockShape: [number, number],
-    basePadding: number[][]) {
-  const padStart = basePadding.map(b => b[0]);
-  const origPadEnd = basePadding.map(b => b[1]);
-  const fullInputShape = inputShape.concat(padStart, origPadEnd);
-  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
-  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
-  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
-  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
-  return [paddings, crops];
-}
-
-// Helper function to compute base paddings for pool with dilation > 1.
-// tslint:disable-next-line:max-line-length
-// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524
-function withSpaceToBatchBasePaddings(
-    filterShape: [number, number], dilation: [number, number]) {
-  // Spatial dimensions of the filters and the upsampled filters in which we
-  // introduce (rate - 1) zeros between consecutive filter values.
-  const dilatedFilterShape = filterShape.map((s, i) => {
-    return s + (s - 1) * (dilation[i] - 1);
-  });
-  const padExtraShape = dilatedFilterShape.map(s => s - 1);
-
-  // When padding is odd, we pad more at end, following the same
-  // convention as conv2d.
-  const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));
-  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
-  return padExtraShape.map((_, i) => {
-    return [padExtraStart[i], padExtraEnd[i]];
-  });
-}
-
-export const pool = /* @__PURE__ */ op({pool_});
diff --git a/tfjs-master/tfjs-core/src/ops/pool_test.ts b/tfjs-master/tfjs-core/src/ops/pool_test.ts
deleted file mode 100644
index 69294dc45..000000000
--- a/tfjs-master/tfjs-core/src/ops/pool_test.ts
+++ /dev/null
@@ -1,536 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('pool', ALL_ENVS, () => {
-  // First test that tf.pool calls are consistent with maxPool/avgPool by
-  // duplicating some maxPool/avgPool tests. The implementation code is the
-  // same, so we don't need the same level of thoroughness here.
-
-  it('max x=[1,1,1] f=[1,1] s=1 d=1 [0] => [0]', async () => {
-    const x = tf.tensor3d([0], [1, 1, 1]);
-
-    const windowShape = 1;
-    const padding = 0;
-
-    const result = tf.pool(x, windowShape, 'max', padding);
-    expectArraysClose(await result.data(), [0]);
-  });
-
-  it('max x=[3,3,1] f=[2,2] s=1 d=1', async () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate: number = undefined;
-    const strides: number = undefined;
-
-    const result =
-        tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 6, 9, 9]);
-  });
-
-  it('max x=[4,4,1] f=[2,2] s=2 d=1', async () => {
-    // Feed forward.
-    const x = tf.tensor3d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate: number = undefined;
-    const strides = 2;
-    const result =
-        tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [5, 7, 13, 15]);
-  });
-
-  it('max x=[2,2,1] f=[2,2] s=1 d=1 p=same', async () => {
-    // Feed forward.
-    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-    const windowShape = 2;
-    const padding = 'same';
-    const dilationRate: number = undefined;
-    const strides = 1;
-
-    const result =
-        tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [4, 4, 4, 4]);
-  });
-
-  it('max x=[3,3,1] f=[3,3] s=1 d=1 p=explicit', async () => {
-    // Feed forward.
-    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-    const windowShape = 3;
-    const padding =
-        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const dilationRate: number = undefined;
-    const strides = 1;
-
-    const result =
-        tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
-    expect(result.shape).toEqual([4, 2, 1]);
-    expectArraysClose(await result.data(), [5, 5, 8, 8, 8, 8, 8, 8]);
-  });
-
-  it('max x=[3,3,1] f=[3,3] s=3 d=1 p=explicit defualt dimRoundingMode',
-    async () => {
-      // Feed forward.
-      const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-      const windowShape = 3;
-      const padding =
-          [[0, 0], [2, 2], [1, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-      const dilationRate: number = undefined;
-      const strides = 3;
-
-      const result =
-          tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
-      expect(result.shape).toEqual([2, 1, 1]);
-  });
-
-  it('max x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=floor',
-    async () => {
-      // Feed forward.
-      const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-      const windowShape = 3;
-      const padding =
-          [[0, 0], [2, 2], [1, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-      const dilationRate: number = undefined;
-      const strides = 3;
-
-      const result = tf.pool(x, windowShape, 'max', padding, dilationRate,
-                             strides, 'floor');
-      expect(result.shape).toEqual([2, 1, 1]);
-  });
-
-  it('max x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=round',
-    async () => {
-      // Feed forward.
-      const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-      const windowShape = 3;
-      const padding =
-          [[0, 0], [2, 2], [1, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-      const dilationRate: number = undefined;
-      const strides = 3;
-
-      const result = tf.pool(x, windowShape, 'max', padding, dilationRate,
-                             strides, 'round');
-      expect(result.shape).toEqual([2, 2, 1]);
-  });
-
-  it('max x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=ceil',
-    async () => {
-      // Feed forward.
-      const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-      const windowShape = 3;
-      const padding =
-          [[0, 0], [2, 2], [1, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-      const dilationRate: number = undefined;
-      const strides = 3;
-
-      const result = tf.pool(x, windowShape, 'max', padding, dilationRate,
-                             strides, 'ceil');
-      expect(result.shape).toEqual([3, 2, 1]);
-  });
-
-  it('max x=[2,2,3] f=[1,1] s=2 p=1 fractional outputs default rounding',
-    async () => {
-      // Feed forward.
-      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-
-      const windowShape = 1;
-      const padding = 1;
-      const dilationRate: number = undefined;
-      const strides = 2;
-
-      const result =
-          tf.pool(a, windowShape, 'max', padding, dilationRate, strides);
-      expect(result.shape).toEqual([2, 2, 3]);
-      expectArraysClose(
-        await result.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
-  });
-
-  it('avg x=[1,1,1] f=[1,1] s=1 d=1 [0] => [0]', async () => {
-    const a = tf.tensor3d([0], [1, 1, 1]);
-
-    const windowShape = 1;
-    const padding = 0;
-
-    const result = tf.pool(a, windowShape, 'avg', padding);
-    expectArraysClose(await result.data(), [0]);
-  });
-
-  it('avg x=[3,3,1] f=[2,2] s=1 d=1', async () => {
-    // Feed forward.
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate: number = undefined;
-    const strides: number = undefined;
-
-    const result =
-        tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [3, 4, 6.25, 7]);
-  });
-
-  it('avg x=[4,4,1] f=[2,2] s=2 d=1', async () => {
-    // Feed forward.
-    const a = tf.tensor3d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate: number = undefined;
-    const strides = 2;
-
-    const result =
-        tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [2.5, 4.5, 10.5, 12.5]);
-  });
-
-  it('avg x=[2,2,1] f=[2,2] s=1 p=same', async () => {
-    // Feed forward.
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-
-    const windowShape = 2;
-    const padding = 'same';
-    const dilationRate: number = undefined;
-    const strides = 1;
-
-    const result =
-        tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [2.5, 3, 3.5, 4]);
-  });
-
-  it('avg x=[3,3,1] f=[3,3] s=1 d=1 p=explicit', async () => {
-    // Feed forward.
-    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-    const windowShape = 3;
-    const padding =
-        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-    const dilationRate: number = undefined;
-    const strides = 1;
-
-    const result =
-        tf.pool(x, windowShape, 'avg', padding, dilationRate, strides);
-    expect(result.shape).toEqual([4, 2, 1]);
-    expectArraysClose(
-        await result.data(), [2.5, 3, 4, 4.5, 5.5, 6, 7, 7.5]);
-  });
-
-  it('avg x=[3,3,1] f=[3,3] s=3 d=1 p=explicit defualt dimRoundingMode',
-    async () => {
-      // Feed forward.
-      const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-      const windowShape = 3;
-      const padding =
-          [[0, 0], [2, 2], [1, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-      const dilationRate: number = undefined;
-      const strides = 3;
-
-      const result =
-          tf.pool(x, windowShape, 'avg', padding, dilationRate, strides);
-      expect(result.shape).toEqual([2, 1, 1]);
-  });
-
-  it('avg x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=floor',
-    async () => {
-      // Feed forward.
-      const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-      const windowShape = 3;
-      const padding =
-          [[0, 0], [2, 2], [1, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-      const dilationRate: number = undefined;
-      const strides = 3;
-
-      const result = tf.pool(x, windowShape, 'avg', padding, dilationRate,
-                             strides, 'floor');
-      expect(result.shape).toEqual([2, 1, 1]);
-  });
-
-  it('avg x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=round',
-    async () => {
-      // Feed forward.
-      const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-      const windowShape = 3;
-      const padding =
-          [[0, 0], [2, 2], [1, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-      const dilationRate: number = undefined;
-      const strides = 3;
-
-      const result = tf.pool(x, windowShape, 'avg', padding, dilationRate,
-                             strides, 'round');
-      expect(result.shape).toEqual([2, 2, 1]);
-  });
-
-  it('avg x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=ceil',
-    async () => {
-      // Feed forward.
-      const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
-
-      const windowShape = 3;
-      const padding =
-          [[0, 0], [2, 2], [1, 1], [0, 0]] as tf.backend_util.ExplicitPadding;
-      const dilationRate: number = undefined;
-      const strides = 3;
-
-      const result = tf.pool(x, windowShape, 'avg', padding, dilationRate,
-                             strides, 'ceil');
-      expect(result.shape).toEqual([3, 2, 1]);
-  });
-
-  it('avg x=[2,2,3] f=[1,1] s=2 p=1 fractional outputs default rounding',
-    async () => {
-      // Feed forward.
-      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
-
-      const windowShape = 1;
-      const padding = 1;
-      const dilationRate: number = undefined;
-      const strides = 2;
-
-      const result =
-          tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
-      expect(result.shape).toEqual([2, 2, 3]);
-      expectArraysClose(
-        await result.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
-  });
-
-  // tf.pool supports dilation, unlike maxPool or avgPool
-  it('max x=[4,3,1] f=[2,2] s=1 d=2', async () => {
-    // Feed forward.
-    const x = tf.tensor3d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 4, 1]);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate = 2;
-    const strides: number = undefined;
-
-    const result =
-        tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await result.data(), [11, 12, 15, 16]);
-  });
-
-  it('max x=[2,4,4,1] f=[2,2] s=1 d=2', async () => {
-    // Feed forward.
-    const x = tf.tensor4d(
-        [
-          1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
-          1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 11, 13, 14, 16, 15
-        ],
-        [2, 4, 4, 1]);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate = 2;
-    const strides: number = undefined;
-
-    const result =
-        tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
-
-    expect(result.shape).toEqual([2, 2, 2, 1]);
-    expectArraysClose(await result.data(), [11, 12, 15, 16, 12, 11, 16, 15]);
-  });
-
-  it('avg x=[4,4,1] f=[2,2] s=1 d=2', async () => {
-    // Feed forward.
-    const a = tf.tensor3d(
-        [1, 3, 2, 4, 6, 5, 8, 7, 9, 10, 12, 11, 16, 15, 14, 13], [4, 4, 1]);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate = 2;
-    const strides: number = undefined;
-
-    const result =
-        tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
-
-    expect(result.shape).toEqual([2, 2, 1]);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [6, 7, 11, 10]);
-  });
-
-  it('max throws when neither s=1 nor d=1', () => {
-    // Feed forward.
-    const x = tf.tensor3d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate = 2;
-    const strides = 2;
-
-    expect(() => tf.pool(x, windowShape, 'max', padding, dilationRate, strides))
-        .toThrowError();
-  });
-
-  it('avg throws when neither s=1 nor d=1', () => {
-    // Feed forward.
-    const x = tf.tensor3d(
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate = 2;
-    const strides = 2;
-
-    expect(() => tf.pool(x, windowShape, 'avg', padding, dilationRate, strides))
-        .toThrowError();
-  });
-});
-
-describeWithFlags('poolBackprop', ALL_ENVS, () => {
-  it('max gradients x=[3,3,1] f=[2,2] s=1 d=1 no dup max value', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
-    const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4];
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate: number = undefined;
-    const strides: number = undefined;
-
-    const dx = tf.grad(
-        (x: tf.Tensor3D) =>
-            x.pool(windowShape, 'max', padding, dilationRate, strides))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('max gradients x=[3,3,1] f=[2,2] s=1 d=2 no dup max value, test #1',
-     async () => {
-       const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-       const x = tf.tensor3d(
-           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 4, 1]);
-       const expected = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4];
-
-       const windowShape = 2;
-       const padding = 0;
-       const dilationRate = 2;
-       const strides: number = undefined;
-
-       const dx = tf.grad(
-           (x: tf.Tensor3D) => x.pool(
-               windowShape, 'max', padding, dilationRate, strides))(x, dy);
-
-       expect(dx.shape).toEqual(x.shape);
-       expectArraysClose(await dx.data(), expected);
-     });
-
-  it('max gradients x=[3,3,1] f=[2,2] s=1 d=2 no dup max value, test #2',
-     async () => {
-       const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-       const x = tf.tensor3d(
-           [9, 5, 8, 6, 3, 1, 2, 4, 7, 3, 6, 4, 11, 15, 10, 16], [4, 4, 1]);
-       const expected = [1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 4];
-
-       const windowShape = 2;
-       const padding = 0;
-       const dilationRate = 2;
-       const strides: number = undefined;
-
-       const dx = tf.grad(
-           (x: tf.Tensor3D) => x.pool(
-               windowShape, 'max', padding, dilationRate, strides))(x, dy);
-
-       expect(dx.shape).toEqual(x.shape);
-       expectArraysClose(await dx.data(), expected);
-     });
-
-  it('max gradient x=[3,3,1] f=[2,2] s=1 d=2 dup max value', async () => {
-    const dy = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
-    const x = tf.tensor3d(
-        [
-          0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1,
-          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-        ],
-        [5, 5, 1]);
-    const expected = [
-      0, 0, 0, 0, 0, 0, 5, 10, 0, 0, 0, 10, 20,
-      0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0, 0
-    ];
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate = 2;
-    const strides: number = undefined;
-
-    const dx = tf.grad(
-        (x: tf.Tensor3D) =>
-            x.pool(windowShape, 'max', padding, dilationRate, strides))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), expected);
-  });
-
-  it('avg gradient x=[4,4,1] f=[2,2] s=1 d=2', async () => {
-    const x = tf.tensor3d(
-        [
-          1,  3,  2,  4,  6,  5,  8,  7,  9,  10, 12, 11, 16,
-          15, 14, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25
-        ],
-        [5, 5, 1]);
-    const dy = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
-    const f = 1 / (2 * 2);
-
-    const windowShape = 2;
-    const padding = 0;
-    const dilationRate = 2;
-    const strides: number = undefined;
-
-    const dx = tf.grad(
-        (x: tf.Tensor3D) =>
-            x.pool(windowShape, 'avg', padding, dilationRate, strides))(x, dy);
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [
-      1 * f, 2 * f, 4 * f,  2 * f,  3 * f,  4 * f,  5 * f, 10 * f, 5 * f,
-      6 * f, 8 * f, 10 * f, 20 * f, 10 * f, 12 * f, 4 * f, 5 * f,  10 * f,
-      5 * f, 6 * f, 7 * f,  8 * f,  16 * f, 8 * f,  9 * f
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/pow.ts b/tfjs-master/tfjs-core/src/ops/pow.ts
deleted file mode 100644
index aabae6236..000000000
--- a/tfjs-master/tfjs-core/src/ops/pow.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Pow, PowInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the power of one `tf.Tensor` to another. Supports broadcasting.
- *
- * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for
- * corresponding elements in x and y. The result's dtype will be the upcasted
- * type of the `base` and `exp` dtypes.
- *
- * ```js
- * const a = tf.tensor([[2, 3], [4, 5]])
- * const b = tf.tensor([[1, 2], [3, 0]]).toInt();
- *
- * a.pow(b).print();  // or tf.pow(a, b)
- * ```
- *
- * ```js
- * const a = tf.tensor([[1, 2], [3, 4]])
- * const b = tf.tensor(2).toInt();
- *
- * a.pow(b).print();  // or tf.pow(a, b)
- * ```
- * We also expose `powStrict` which has the same signature as this op and
- * asserts that `base` and `exp` are the same shape (does not broadcast).
- *
- * @param base The base `tf.Tensor` to pow element-wise.
- * @param exp The exponent `tf.Tensor` to pow element-wise.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function pow_<T extends Tensor>(
-    base: Tensor|TensorLike, exp: Tensor|TensorLike): T {
-  let $base = convertToTensor(base, 'base', 'pow');
-  let $exp = convertToTensor(exp, 'exp', 'pow');
-  [$base, $exp] = makeTypesMatch($base, $exp);
-
-  const inputs: PowInputs = {a: $base, b: $exp};
-
-  return ENGINE.runKernel(Pow, inputs as unknown as NamedTensorMap);
-}
-
-export const pow = /* @__PURE__ */ op({pow_});
diff --git a/tfjs-master/tfjs-core/src/ops/prelu.ts b/tfjs-master/tfjs-core/src/ops/prelu.ts
deleted file mode 100644
index f500f5dc0..000000000
--- a/tfjs-master/tfjs-core/src/ops/prelu.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Prelu, PreluInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes leaky rectified linear element-wise with parametric alphas.
- *
- * `x < 0 ? alpha * x : f(x) = x`
- *
- * ```js
- * const x = tf.tensor1d([-1, 2, -3, 4]);
- * const alpha = tf.scalar(0.1);
- *
- * x.prelu(alpha).print();  // or tf.prelu(x, alpha)
- * ```
- * @param x The input tensor.
- * @param alpha Scaling factor for negative values.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function prelu_<T extends Tensor>(x: T|TensorLike, alpha: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'prelu');
-  const $alpha = convertToTensor(alpha, 'alpha', 'prelu');
-
-  const inputs: PreluInputs = {x: $x, alpha: $alpha};
-  return ENGINE.runKernel(Prelu, inputs as unknown as NamedTensorMap);
-}
-
-export const prelu = /* @__PURE__ */ op({prelu_});
diff --git a/tfjs-master/tfjs-core/src/ops/print.ts b/tfjs-master/tfjs-core/src/ops/print.ts
deleted file mode 100644
index 475136c2b..000000000
--- a/tfjs-master/tfjs-core/src/ops/print.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-
-/**
- * Prints information about the `tf.Tensor` including its data.
- *
- * ```js
- * const verbose = true;
- * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);
- * ```
- * @param x The tensor to be printed.
- * @param verbose Whether to print verbose information about the ` Tensor`,
- * including dtype and size.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function print<T extends Tensor>(x: T, verbose = false): void {
-  console.log(x.toString(verbose));
-}
diff --git a/tfjs-master/tfjs-core/src/ops/prod.ts b/tfjs-master/tfjs-core/src/ops/prod.ts
deleted file mode 100644
index f9e482162..000000000
--- a/tfjs-master/tfjs-core/src/ops/prod.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Prod, ProdAttrs, ProdInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {cast} from './cast';
-import {op} from './operation';
-
-/**
- * Computes the product of elements across dimensions of a `tf.Tensor`.
- *
- * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
- * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
- * `axes`. If `keepDims` is true, the reduced dimensions are retained with
- * length 1. If `axes` has no entries, all dimensions are reduced, and a
- * `tf.Tensor` with a single element is returned.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3]);
- *
- * x.prod().print();  // or tf.prod(x)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * const axis = 1;
- * x.prod(axis).print();  // or tf.prod(x, axis)
- * ```
- *
- * @param x The input tensor to compute the product over. If the dtype is `bool`
- *   it will be converted to `int32` and the output dtype will be `int32`.
- * @param axis The dimension(s) to reduce. By default it reduces
- *     all dimensions.
- * @param keepDims If true, retains reduced dimensions with size 1.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function prod_<T extends Tensor>(
-    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {
-  let $x = convertToTensor(x, 'x', 'prod');
-
-  if ($x.dtype === 'bool') {
-    // bool is not an allowed type for the underlying kernel.
-    $x = cast($x, 'int32');
-  }
-
-  const inputs: ProdInputs = {x: $x};
-  const attrs: ProdAttrs = {axis, keepDims};
-
-  return ENGINE.runKernel(
-      Prod, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const prod = /* @__PURE__ */ op({prod_});
diff --git a/tfjs-master/tfjs-core/src/ops/prod_test.ts b/tfjs-master/tfjs-core/src/ops/prod_test.ts
deleted file mode 100644
index e208bd0a5..000000000
--- a/tfjs-master/tfjs-core/src/ops/prod_test.ts
+++ /dev/null
@@ -1,228 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('prod', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const result = tf.prod(a);
-    expectArraysClose(await result.data(), 0);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
-    expectArraysEqual(await tf.prod(a).data(), NaN);
-  });
-
-  it('prod over dtype int32', async () => {
-    const a = tf.tensor1d([1, 5, 7, 3], 'int32');
-    const prod = tf.prod(a);
-    expectArraysEqual(await prod.data(), 105);
-  });
-
-  it('prod over dtype bool', async () => {
-    const a = tf.tensor1d([true, false, false, true, true], 'bool');
-    const prod = tf.prod(a);
-    expectArraysEqual(await prod.data(), 0);
-  });
-
-  it('prods all values in 2D array with keep dim', async () => {
-    const a = tf.tensor2d([1, 2, 3, 1, 0, 1], [3, 2]);
-    const res = tf.prod(a, null, true /* keepDims */);
-
-    expect(res.shape).toEqual([1, 1]);
-    expectArraysClose(await res.data(), 0);
-  });
-
-  it('prods across axis=0 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 1, 0, 1], [3, 2]);
-    const res = tf.prod(a, [0]);
-
-    expect(res.shape).toEqual([2]);
-    expectArraysClose(await res.data(), [0, 2]);
-  });
-
-  it('prods across axis=0 in 2D array, keepDims', async () => {
-    const a = tf.tensor2d([1, 2, 3, 1, 0, 1], [3, 2]);
-    const res = tf.prod(a, [0], true /* keepDims */);
-
-    expect(res.shape).toEqual([1, 2]);
-    expectArraysClose(await res.data(), [0, 2]);
-  });
-
-  it('prods across axis=1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 1, 1, 1], [3, 2]);
-    const res = tf.prod(a, [1]);
-
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [2, 3, 1]);
-  });
-
-  it('2D, axis=1 provided as number', async () => {
-    const a = tf.tensor2d([1, 2, 3, 1, 1, 1], [2, 3]);
-    const res = tf.prod(a, 1);
-
-    expect(res.shape).toEqual([2]);
-    expectArraysClose(await res.data(), [6, 1]);
-  });
-
-  it('2D, axis = -1 provided as number', async () => {
-    const a = tf.tensor2d([1, 2, 3, 1, 1, 1], [2, 3]);
-    const res = tf.prod(a, -1);
-
-    expect(res.shape).toEqual([2]);
-    expectArraysClose(await res.data(), [6, 1]);
-  });
-
-  it('prods across axis=0,1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 1, 1, 1], [3, 2]);
-    const res = tf.prod(a, [0, 1]);
-
-    expect(res.shape).toEqual([]);
-    expectArraysClose(await res.data(), [6]);
-  });
-
-  it('2D, axis=[-1,-2] in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 1, 1, 1], [3, 2]);
-    const res = tf.prod(a, [-1, -2]);
-
-    expect(res.shape).toEqual([]);
-    expectArraysClose(await res.data(), [6]);
-  });
-
-  // Illustrative example from the C++ implementation for comparison. See:
-  // https://github.com/tensorflow/tensorflow/blob/5dee95f8bafe3f342693d2135da451283b1fbaec/tensorflow/cc/gradients/math_grad.cc
-  it('gradients: prod(multi-dimensional tensor with zeros)', async () => {
-    const a = tf.tensor([
-      [[3.0, 4.0], [5.0, 6.0], [7.0, 8.0]], [[3.0, 5.0], [0.0, 6.0], [5.0, 6.0]]
-    ]);
-    const dy = tf.tensor([[10, 10], [10, 10]]);
-
-    const gradients = tf.grad(a => a.prod(1))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.array(), [
-      [ // (axis 1 values) * (dy)
-        [5 * 7 * 10, 6 * 8 * 10], [3 * 7 * 10, 4 * 8 * 10],
-        [3 * 5 * 10, 4 * 6 * 10]
-      ],
-      [
-        [0 * 5 * 10, 6 * 6 * 10], [3 * 5 * 10, 5 * 6 * 10],
-        [3 * 0 * 10, 5 * 6 * 10]
-      ]
-    ]);
-  });
-
-  it('gradients: prod(reduce on 2 axes)', async () => {
-    const a = tf.tensor([
-      [[3.0, 4.0], [5.0, 6.0], [7.0, 8.0]], [[3.0, 5.0], [0.0, 6.0], [5.0, 6.0]]
-    ]);
-    const dy = tf.tensor([2, 2]);
-
-    const gradients = tf.grad(a => a.prod([0, 1]))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-
-    expectArraysClose(await gradients.array(), [
-      [
-        // (axis 0 values) * (axis 1 values) * (dy)
-        [3 * 0 * 5 * (5 * 7) * 2, 5 * 6 * 6 * (6 * 8) * 2],
-        [3 * 0 * 5 * (3 * 7) * 2, 5 * 6 * 6 * (4 * 8) * 2],
-        [3 * 0 * 5 * (3 * 5) * 2, 5 * 6 * 6 * (4 * 6) * 2]
-      ],
-      [
-        [3 * 5 * 7 * (0 * 5) * 2, 4 * 6 * 8 * (6 * 6) * 2],
-        [3 * 5 * 7 * (3 * 5) * 2, 4 * 6 * 8 * (5 * 6) * 2],
-        [3 * 5 * 7 * (3 * 0) * 2, 4 * 6 * 8 * (5 * 6) * 2]
-      ]
-    ]);
-  });
-
-  it('gradients: prod(tensor with zeros)', async () => {
-    const a = tf.tensor2d([[1, 2, 0], [1, 2, 3]], [2, 3]);
-    const dy = tf.tensor([12, 12]);
-
-    const gradients = tf.grad(a => a.prod(1))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.array(),
-        [[0, 0, 1 * 2 * 12], [6 * 12, 3 * 12, 2 * 12]]);
-  });
-
-  it('gradients: prod(2d) with gradient broadcasting', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const dy = tf.scalar(24);
-
-    const gradients = tf.grad(a => a.prod())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.array(),
-        [[2 * 3 * 4 * 24, 3 * 4 * 24], [4 * 2 * 24, 3 * 2 * 24]]);
-  });
-
-  it('gradients: prod(2d, axis=0) and varied gradient values', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 1], [1, 4]], [3, 2]);
-    const dy = tf.tensor1d([3, 8]);
-    const axis = 0;
-
-    const gradients = tf.grad(a => a.prod(axis))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.array(),
-        [[3 * 3, 4 * 8], [1 * 3, 8 * 8], [3 * 3, 2 * 8]]);
-  });
-
-  it('gradients: prod(2d, axis=1)', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 1], [1, 4]], [3, 2]);
-    const dy = tf.tensor1d([4, 3, 4]);
-    const axis = 1;
-
-    const gradients = tf.grad(a => a.prod(axis))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.array(),
-        [[2 * 4, 1 * 4], [1 * 3, 3 * 3], [4 * 4, 1 * 4]]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.prod({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'prod' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.prod([[1, 2], [3, 1], [1, 1]]);
-    expectArraysClose(await result.data(), 6);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.prod(['a']))
-        .toThrowError(/Argument 'x' passed to 'prod' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/ragged_gather.ts b/tfjs-master/tfjs-core/src/ops/ragged_gather.ts
deleted file mode 100644
index 24a741e36..000000000
--- a/tfjs-master/tfjs-core/src/ops/ragged_gather.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {RaggedGather, RaggedGatherAttrs, RaggedGatherInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {op} from './operation';
-
-/**
- * Gather ragged slices from params axis 0 according to indices.
- *
- * @param paramsNestedSplits: A list of at least 1 Tensor with type 'int32' The
- *     nestedRowSplits tensors that define the row-partitioning for the params
- *     RaggedTensor input.
- * @param paramsDenseValues: A Tensor. The flatValues for the params
- *     RaggedTensor.
- * @param indices: A Tensor. Must be one of type: int32. Indices in the
- *     outermost dimension of params of the values that should be gathered.
- * @param outputRaggedRank: An int that is >= 0. The ragged rank of the output
- *     RaggedTensor. outputNestedSplits will contain this number of rowSplits
- *     tensors. This value should equal indices.shape.ndims + params.raggedRank
- *     - 1.
- * @return A map with the following properties:
- *     - outputNestedSplits: A list of outputRaggedRank Tensor objects with the
- * same type as paramsNestedSplits.
- *     - outputDenseValues: A Tensor. Has the same type as paramsDenseValues.
- * @doc {heading: 'Operations', subheading: 'Ragged'}
- */
-
-interface RaggedGatherMap {
-  outputNestedSplits: Tensor[];
-  outputDenseValues: Tensor;
-}
-
-function raggedGather_(
-    paramsNestedSplits: Tensor[], paramsDenseValues: Tensor|TensorLike,
-    indices: Tensor|TensorLike, outputRaggedRank: number): RaggedGatherMap {
-  const $paramsNestedSplits = paramsNestedSplits.map(
-      (t, i) => convertToTensor(t, `tensors${i}`, 'raggedGather', 'int32'));
-  const $paramsDenseValues =
-      convertToTensor(paramsDenseValues, 'paramsDenseValues', 'raggedGather');
-  const $indices = convertToTensor(indices, 'indices', 'raggedGather', 'int32');
-
-  const inputs: RaggedGatherInputs = {
-    paramsNestedSplits: $paramsNestedSplits,
-    paramsDenseValues: $paramsDenseValues,
-    indices: $indices,
-  };
-  const attrs: RaggedGatherAttrs = {outputRaggedRank};
-
-  const result: Tensor[] =
-      ENGINE.runKernel(RaggedGather, inputs as {}, attrs as {});
-  return {
-    outputNestedSplits: result.slice(0, result.length - 1),
-    outputDenseValues: result[result.length - 1],
-  };
-}
-
-export const raggedGather = /* @__PURE__ */ op({raggedGather_});
diff --git a/tfjs-master/tfjs-core/src/ops/ragged_gather_test.ts b/tfjs-master/tfjs-core/src/ops/ragged_gather_test.ts
deleted file mode 100644
index 954f52859..000000000
--- a/tfjs-master/tfjs-core/src/ops/ragged_gather_test.ts
+++ /dev/null
@@ -1,174 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-async function runRaggedGather(
-    indicesShape: number[], indices: number[], paramsNestedSplits: number[][],
-    paramsDenseValuesShape: number[], paramsDenseValues: number[]) {
-  const paramsRaggedRank = paramsNestedSplits.length;
-  const numSplits = paramsRaggedRank + indicesShape.length - 1;
-
-  const paramsNestedSplitsTensors =
-      paramsNestedSplits.map(values => tf.tensor1d(values, 'int32'));
-  const paramsDenseValuesTensor =
-      tf.tensor(paramsDenseValues, paramsDenseValuesShape);
-  const indicesTensor = tf.tensor(indices, indicesShape, 'int32');
-
-  const output = tf.raggedGather(
-      paramsNestedSplitsTensors, paramsDenseValuesTensor, indicesTensor,
-      numSplits);
-
-  tf.dispose(paramsNestedSplitsTensors);
-  tf.dispose([paramsDenseValuesTensor, indicesTensor]);
-
-  expect(output.outputDenseValues.dtype).toEqual('float32');
-
-  output.outputNestedSplits.forEach(splits => {
-    expect(splits.dtype).toEqual('int32');
-    expect(splits.shape.length).toEqual(1);
-  });
-
-  return {
-    outputDenseValues: await output.outputDenseValues.data(),
-    outputDenseValuesShape: output.outputDenseValues.shape,
-    outputNestedSplits: await Promise.all(
-        output.outputNestedSplits.map(splits => splits.data())),
-    tensors: output.outputNestedSplits.concat([output.outputDenseValues])
-  };
-}
-
-describeWithFlags('raggedGather ', ALL_ENVS, () => {
-  it('RaggedGather', async () => {
-    const result = await runRaggedGather(
-        [4], [2, 1, 0, 3], [[0, 3, 3, 7, 9]], [9],
-        [.1, .2, .3, .4, .5, .6, .7, .8, .9]);
-
-    expect(result.outputNestedSplits.length).toEqual(1);
-    expectArraysClose(result.outputNestedSplits[0], [0, 4, 4, 7, 9]);
-
-    expectArraysClose(
-        result.outputDenseValues, [.4, .5, .6, .7, .1, .2, .3, .8, .9]);
-    expectArraysEqual(result.outputDenseValuesShape, [9]);
-  });
-
-  it('RaggedGather3DParams', async () => {
-    const result = await runRaggedGather(
-        [5], [2, 1, 0, 2, 3], [[0, 1, 3, 3, 5, 6], [0, 0, 2, 3, 5, 8, 9]], [9],
-        [.1, .2, .3, .4, .5, .6, .7, .8, .9]);
-
-    expect(result.outputNestedSplits.length).toEqual(2);
-    expectArraysClose(result.outputNestedSplits[0], [0, 0, 2, 3, 3, 5]);
-    expectArraysClose(result.outputNestedSplits[1], [0, 2, 3, 3, 5, 8]);
-
-    expectArraysClose(
-        result.outputDenseValues, [.1, .2, .3, .4, .5, .6, .7, .8]);
-    expectArraysEqual(result.outputDenseValuesShape, [8]);
-  });
-
-  it('RaggedGather4DParams', async () => {
-    const result = await runRaggedGather(
-        [4], [2, 1, 0, 2], [[0, 1, 3, 3], [0, 0, 3, 4]], [4, 2],
-        [1, 2, 3, 4, 5, 6, 7, 8]);
-
-    expect(result.outputNestedSplits.length).toEqual(2);
-    expectArraysClose(result.outputNestedSplits[0], [0, 0, 2, 3, 3]);
-    expectArraysClose(result.outputNestedSplits[1], [0, 3, 4, 4]);
-
-    expectArraysClose(result.outputDenseValues, [1, 2, 3, 4, 5, 6, 7, 8]);
-    expectArraysEqual(result.outputDenseValuesShape, [4, 2]);
-  });
-
-  it('RaggedGather2DIndices', async () => {
-    const result = await runRaggedGather(
-        [2, 2], [2, 1, 0, 3], [[0, 3, 3, 7, 9]], [9],
-        [.1, .2, .3, .4, .5, .6, .7, .8, .9]);
-
-    expect(result.outputNestedSplits.length).toEqual(2);
-    expectArraysClose(result.outputNestedSplits[0], [0, 2, 4]);
-    expectArraysClose(result.outputNestedSplits[1], [0, 4, 4, 7, 9]);
-
-    expectArraysClose(
-        result.outputDenseValues, [.4, .5, .6, .7, .1, .2, .3, .8, .9]);
-    expectArraysEqual(result.outputDenseValuesShape, [9]);
-  });
-
-  it('RaggedGatherScalarIndices', async () => {
-    const result = await runRaggedGather(
-        [], [2], [[0, 3, 3, 7, 9]], [9], [.1, .2, .3, .4, .5, .6, .7, .8, .9]);
-
-    expect(result.outputNestedSplits.length).toEqual(0);
-
-    expectArraysClose(result.outputDenseValues, [.4, .5, .6, .7]);
-    expectArraysEqual(result.outputDenseValuesShape, [4]);
-  });
-
-  it('OutOfBounds', async () => {
-    await expectAsync(runRaggedGather([2], [2, 10], [[0, 3, 3, 7, 9]], [9], [
-      .1, .2, .3, .4, .5, .6, .7, .8, .9
-    ])).toBeRejectedWithError('indices[1] = 10 is not in [0, 4)');
-  });
-
-  it('InvalidSplitsNotSorted', async () => {
-    await expectAsync(runRaggedGather(
-                          [2], [0, 2], [[0, 3, 5, 2, 9]], [9],
-                          [.1, .2, .3, .4, .5, .6, .7, .8, .9]))
-        .toBeRejectedWithError(
-            'Ragged splits must be sorted in ascending order');
-  });
-
-  it('InvalidSplitsNegative', async () => {
-    await expectAsync(runRaggedGather([2], [0, 2], [[-1, 3, 2, 7, 9]], [9], [
-      .1, .2, .3, .4, .5, .6, .7, .8, .9
-    ])).toBeRejectedWithError('Ragged splits must be non-negative');
-  });
-
-  it('InvalidSplitsEmpty', async () => {
-    await expectAsync(runRaggedGather([0], [], [[]], [0], []))
-        .toBeRejectedWithError('Ragged splits may not be empty');
-  });
-
-  it('InvalidSplitsTooBig', async () => {
-    await expectAsync(runRaggedGather(
-                          [2], [0, 2], [[0, 20, 40, 80, 100]], [9],
-                          [.1, .2, .3, .4, .5, .6, .7, .8, .9]))
-        .toBeRejectedWithError('Ragged splits must not point past values');
-  });
-
-  it('BadValuesShape', async () => {
-    await expectAsync(runRaggedGather([0], [], [[0]], [], [.1]))
-        .toBeRejectedWithError('params.rank must be nonzero');
-  });
-
-  it('does not have memory leak.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const result = await runRaggedGather(
-        [4], [2, 1, 0, 3], [[0, 3, 3, 7, 9]], [9],
-        [.1, .2, .3, .4, .5, .6, .7, .8, .9]);
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 2);
-
-    result.tensors.map(tensor => tensor.dispose());
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/ragged_range.ts b/tfjs-master/tfjs-core/src/ops/ragged_range.ts
deleted file mode 100644
index e2f5f20b1..000000000
--- a/tfjs-master/tfjs-core/src/ops/ragged_range.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {RaggedRange, RaggedRangeInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {op} from './operation';
-
-/**
- * Returns a RaggedTensor result composed from rtDenseValues and rtNestedSplits,
- * such that result[i] = [starts[i], starts[i] + deltas[i], ..., limits[i]]).
- *
- * @param starts: A Tensor. Must be one of the following types:
- *     'float32', 'int32'. The starts of each range.
- * @param limits: A Tensor. Must have the same type as starts. The limits of
- *     each range.
- * @param deltas: A Tensor. Must have the same type as starts. The deltas of
- *     each range.
- * @return A map with the following properties:
- *     - rtNestedSplits: A Tensor of type 'int32'.
- *     - rtDenseValues: A Tensor. Has the same type as starts.
- */
-
-function raggedRange_(
-    starts: Tensor|TensorLike, limits: Tensor|TensorLike,
-    deltas: Tensor|TensorLike): NamedTensorMap {
-  const $starts = convertToTensor(starts, 'starts', 'raggedRange');
-  const $limits =
-      convertToTensor(limits, 'limits', 'raggedRange', $starts.dtype);
-  const $deltas =
-      convertToTensor(deltas, 'deltas', 'raggedRange', $starts.dtype);
-
-  const inputs: RaggedRangeInputs = {
-    starts: $starts,
-    limits: $limits,
-    deltas: $deltas,
-  };
-
-  const result: Tensor[] = ENGINE.runKernel(RaggedRange, inputs as {});
-  return {
-    rtNestedSplits: result[0],
-    rtDenseValues: result[1],
-  };
-}
-
-export const raggedRange = /* @__PURE__ */ op({raggedRange_});
diff --git a/tfjs-master/tfjs-core/src/ops/ragged_range_test.ts b/tfjs-master/tfjs-core/src/ops/ragged_range_test.ts
deleted file mode 100644
index ccd1b32fe..000000000
--- a/tfjs-master/tfjs-core/src/ops/ragged_range_test.ts
+++ /dev/null
@@ -1,187 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-async function runRaggedGather(
-    starts: tf.Tensor, limits: tf.Tensor, deltas: tf.Tensor) {
-  const output = tf.raggedRange(starts, limits, deltas);
-
-  expect(output.rtNestedSplits.dtype).toEqual('int32');
-  expect(output.rtNestedSplits.shape.length).toEqual(1);
-
-  expect(output.rtDenseValues.dtype).toEqual(starts.dtype);
-  expect(output.rtDenseValues.shape.length).toEqual(1);
-
-  return {
-    rtNestedSplits: await output.rtNestedSplits.data(),
-    rtDenseValues: await output.rtDenseValues.data(),
-    tensors: Object.values(output)
-  };
-}
-
-describeWithFlags('raggedRange ', ALL_ENVS, () => {
-  it('IntValues', async () => {
-    const result = await runRaggedGather(
-        tf.tensor1d([0, 5, 8, 5], 'int32'), tf.tensor1d([8, 7, 8, 1], 'int32'),
-        tf.tensor1d([2, 1, 1, -1], 'int32'));
-
-    // Expected: [[0, 2, 4, 6], [5, 6], [], [5, 4, 3, 2]]
-    expectArraysClose(result.rtNestedSplits, [0, 4, 6, 6, 10]);
-    expectArraysClose(result.rtDenseValues, [0, 2, 4, 6, 5, 6, 5, 4, 3, 2]);
-  });
-
-  it('FloatValues', async () => {
-    const result = await runRaggedGather(
-        tf.tensor1d([0, 5, 8, 5], 'float32'),
-        tf.tensor1d([8, 7, 8, 1], 'float32'),
-        tf.tensor1d([2, 1, 1, -1], 'float32'));
-
-    // Expected: [[0, 2, 4, 6], [5, 6], [], [5, 4, 3, 2]]
-    expectArraysClose(result.rtNestedSplits, [0, 4, 6, 6, 10]);
-    expectArraysClose(result.rtDenseValues, [0, 2, 4, 6, 5, 6, 5, 4, 3, 2]);
-  });
-
-  it('RangeSizeOverflow', async () => {
-    await expectAsync(runRaggedGather(
-                          tf.tensor1d([1.1, 0.1], 'float32'),
-                          tf.tensor1d([10, 1e10], 'float32'),
-                          tf.tensor1d([1, 1e-10], 'float32')))
-        .toBeRejectedWithError(
-            'Requires ((limit - start) / delta) <= 2147483647');
-  });
-
-  it('BroadcastDeltas', async () => {
-    const result = await runRaggedGather(
-        tf.tensor1d([0, 5, 8], 'int32'), tf.tensor1d([8, 7, 8], 'int32'),
-        tf.scalar(1, 'int32'));
-
-    // Expected: [[0, 1, 2, 3, 4, 5, 6, 7], [5, 6], []]
-    expectArraysClose(result.rtNestedSplits, [0, 8, 10, 10]);
-    expectArraysClose(result.rtDenseValues, [0, 1, 2, 3, 4, 5, 6, 7, 5, 6]);
-  });
-
-  it('BroadcastLimitsAndDeltas', async () => {
-    const result = await runRaggedGather(
-        tf.scalar(0, 'int32'), tf.tensor1d([3, 0, 2], 'int32'),
-        tf.scalar(1, 'int32'));
-
-    // Expected: [[0, 1, 2], [], [0, 1]]
-    expectArraysClose(result.rtNestedSplits, [0, 3, 3, 5]);
-    expectArraysClose(result.rtDenseValues, [0, 1, 2, 0, 1]);
-  });
-
-  it('BroadcastStartsAndLimits', async () => {
-    const result = await runRaggedGather(
-        tf.scalar(0, 'int32'), tf.scalar(12, 'int32'),
-        tf.tensor1d([3, 4, 5], 'int32'));
-
-    // Expected: [[0, 3, 6, 9], [0, 4, 8], [0, 5, 10]]
-    expectArraysClose(result.rtNestedSplits, [0, 4, 7, 10]);
-    expectArraysClose(result.rtDenseValues, [0, 3, 6, 9, 0, 4, 8, 0, 5, 10]);
-  });
-
-  it('AllScalarInputs', async () => {
-    const result = await runRaggedGather(
-        tf.scalar(0, 'int32'), tf.scalar(5, 'int32'), tf.scalar(1, 'int32'));
-
-    // Expected: [[0, 1, 2, 3, 4]]
-    expectArraysClose(result.rtNestedSplits, [0, 5]);
-    expectArraysClose(result.rtDenseValues, [0, 1, 2, 3, 4]);
-  });
-
-  it('InvalidArgsStarts', async () => {
-    await expectAsync(runRaggedGather(
-                          tf.tensor2d([0, 5, 8, 5], [4, 1], 'int32'),
-                          tf.tensor1d([8, 7, 8, 1], 'int32'),
-                          tf.tensor1d([2, 1, 1, -1], 'int32')))
-        .toBeRejectedWithError('starts must be a scalar or vector');
-  });
-
-  it('InvalidArgsLimits', async () => {
-    await expectAsync(runRaggedGather(
-                          tf.tensor1d([0, 5, 8, 5], 'int32'),
-                          tf.tensor2d([8, 7, 8, 1], [4, 1], 'int32'),
-                          tf.tensor1d([2, 1, 1, -1], 'int32')))
-        .toBeRejectedWithError('limits must be a scalar or vector');
-  });
-
-  it('InvalidArgsDeltas', async () => {
-    await expectAsync(runRaggedGather(
-                          tf.tensor1d([0, 5, 8, 5], 'int32'),
-                          tf.tensor1d([8, 7, 8, 1], 'int32'),
-                          tf.tensor2d([2, 1, 1, -1], [4, 1], 'int32')))
-        .toBeRejectedWithError('deltas must be a scalar or vector');
-  });
-
-  it('InvalidArgsShapeMismatch', async () => {
-    await expectAsync(runRaggedGather(
-                          tf.tensor1d([0, 5, 8, 5], 'int32'),
-                          tf.tensor1d([7, 8, 1], 'int32'),
-                          tf.tensor1d([2, 1, 1, -1], 'int32')))
-        .toBeRejectedWithError(
-            'starts, limits, and deltas must have the same shape');
-  });
-
-  it('InvalidArgsZeroDelta', async () => {
-    await expectAsync(runRaggedGather(
-                          tf.tensor1d([0, 5, 8, 5], 'int32'),
-                          tf.tensor1d([7, 8, 8, 1], 'int32'),
-                          tf.tensor1d([2, 1, 0, -1], 'int32')))
-        .toBeRejectedWithError('Requires delta != 0');
-  });
-
-  it('EmptyRangePositiveDelta', async () => {
-    const result = await runRaggedGather(
-        tf.tensor1d([0, 5], 'int32'), tf.tensor1d([5, 0], 'int32'),
-        tf.scalar(2, 'int32'));
-
-    // Expected: [[0, 2, 4], []]
-    expectArraysClose(result.rtNestedSplits, [0, 3, 3]);
-    expectArraysClose(result.rtDenseValues, [0, 2, 4]);
-  });
-
-  it('EmptyRangeNegativeDelta', async () => {
-    const result = await runRaggedGather(
-        tf.tensor1d([0, 5], 'int32'), tf.tensor1d([5, 0], 'int32'),
-        tf.scalar(-2, 'int32'));
-
-    // Expected: [[], [5, 3, 1]]
-    expectArraysClose(result.rtNestedSplits, [0, 0, 3]);
-    expectArraysClose(result.rtDenseValues, [5, 3, 1]);
-  });
-
-  it('does not have memory leak.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const starts = tf.tensor1d([0, 5, 8, 5], 'int32');
-    const limits = tf.tensor1d([8, 7, 8, 1], 'int32');
-    const deltas = tf.tensor1d([2, 1, 1, -1], 'int32');
-    const result = await runRaggedGather(starts, limits, deltas);
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 5);
-
-    tf.dispose([starts, limits, deltas]);
-    tf.dispose(result.tensors);
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/ragged_tensor_to_tensor.ts b/tfjs-master/tfjs-core/src/ops/ragged_tensor_to_tensor.ts
deleted file mode 100644
index 2485a16a3..000000000
--- a/tfjs-master/tfjs-core/src/ops/ragged_tensor_to_tensor.ts
+++ /dev/null
@@ -1,102 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {RaggedTensorToTensor, RaggedTensorToTensorAttrs, RaggedTensorToTensorInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {op} from './operation';
-
-/**
- * Create a dense tensor from a ragged tensor, possibly altering its shape.
- *
- * The raggedTensorToTensor op creates a dense tensor from am array of row
- * partition tensors, a value vector, and default values. If the shape is
- * unspecified, the minimal shape required to contain all the elements in the
- * ragged tensor (the natural shape) will be used. If some dimensions are left
- * unspecified, then the size of the natural shape is used in that dimension.
- *
- * The defaultValue will be broadcast to the output shape. After that, the
- * values from the ragged tensor overwrite the default values. Note that the
- * defaultValue must have less dimensions than the value.
- *
- * The row partition tensors are in the order of the dimensions. At present, the
- * types can be: "ROW_SPLITS": the row_splits tensor from the ragged tensor.
- *   "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor.
- *   "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it
- * is preceded by "FIRST_DIM_SIZE".
- * ```
- * @param shape: A Tensor. Must be one of the following types: 'int32'. The
- *     desired shape of the output tensor. If left unspecified (empty), the
- *     minimal shape required to contain all the elements in the ragged tensor
- *     (the natural shape) will be used. If some dimensions are left
- *     unspecified, then the size of the natural shape is used in that
- *     dimension.
- *
- *     Note that dense dimensions cannot be modified by the shape argument.
- *     Trying to change the size of a dense dimension will cause the op to fail.
- *     Examples: natural shape: [4, 5, 6] shape: -1 output shape: [4, 5, 6]
- *
- *     natural shape: [4, 5, 6] shape: [3, -1, 2] output shape: [3, 5, 2]
- *
- *     natural shape: [4, 5, 6] shape: [3, 7, 2] output shape: [3, 7, 2]
- * @param values: A Tensor. A 1D tensor representing the values of the ragged
- *     tensor.
- * @param defaultValue: A Tensor. Must have the same type as values. The
- *     defaultValue when the shape is larger than the ragged tensor. The
- *     defaultValue is broadcast until it is the shape of the output tensor,
- *     and then overwritten by values in the ragged tensor. The default value
- *     must be compatible with this broadcast operation, and must have fewer
- *     dimensions than the value tensor.
- * @param rowPartitionTensors: A list of at least 1 Tensor objects with the same
- *     type in: 'int32'.
- * @param rowPartitionTypes: A list of strings. The types of the row partition
- *     tensors. At present, these can be:
- *     "ROW_SPLITS": the row_splits tensor from the ragged tensor.
- *     "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor.
- *     "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then
- *         it is preceeded by "FIRST_DIM_SIZE". The tensors are in the order of
- *         the dimensions.
- * @return A Tensor. Has the same type as values.
- * @doc {heading: 'Operations', subheading: 'Ragged'}
- */
-function raggedTensorToTensor_(
-    shape: Tensor|TensorLike, values: Tensor|TensorLike,
-    defaultValue: Tensor|TensorLike, rowPartitionTensors: Tensor[],
-    rowPartitionTypes: string[]): Tensor {
-  const $shape =
-      convertToTensor(shape, 'shape', 'raggedTensorToTensor', 'int32');
-  const $values = convertToTensor(values, 'values', 'raggedTensorToTensor');
-  const $defaultValue = convertToTensor(
-      defaultValue, 'defaultValue', 'raggedTensorToTensor', $values.dtype);
-  const $rowPartitionTensors = rowPartitionTensors.map(
-      (t, i) =>
-          convertToTensor(t, `tensors${i}`, 'raggedTensorToTensor', 'int32'));
-
-  const inputs: RaggedTensorToTensorInputs = {
-    shape: $shape,
-    values: $values,
-    defaultValue: $defaultValue,
-    rowPartitionTensors: $rowPartitionTensors
-  };
-  const attrs: RaggedTensorToTensorAttrs = {rowPartitionTypes};
-
-  return ENGINE.runKernel(RaggedTensorToTensor, inputs as {}, attrs as {});
-}
-
-export const raggedTensorToTensor = /* @__PURE__ */ op({raggedTensorToTensor_});
diff --git a/tfjs-master/tfjs-core/src/ops/ragged_tensor_to_tensor_test.ts b/tfjs-master/tfjs-core/src/ops/ragged_tensor_to_tensor_test.ts
deleted file mode 100644
index f8cd58142..000000000
--- a/tfjs-master/tfjs-core/src/ops/ragged_tensor_to_tensor_test.ts
+++ /dev/null
@@ -1,301 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('raggedTensorToTensor ', ALL_ENVS, () => {
-  it('RaggedTensorToTensor', async () => {
-    const shape = [4, 4];
-    const values = [.1, .2, .3, .4, .5, .6, .7, .8, .9];
-    const defaultValue = 1.5;
-    const rowPartitionTensors = [
-      tf.scalar(4, 'int32'), tf.tensor1d([0, 0, 0, 2, 2, 2, 2, 3, 3], 'int32')
-    ];
-    const rowPartitionTypes = ['FIRST_DIM_SIZE', 'VALUE_ROWIDS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      .1, .2, .3, 1.5, 1.5, 1.5, 1.5, 1.5, .4, .5, .6, .7, .8, .9, 1.5, 1.5
-    ]);
-  });
-
-  it('RaggedTensorToTensorRowSplits', async () => {
-    const shape = [4, 4];
-    const values = [.1, .2, .3, .4, .5, .6, .7, .8, .9];
-    const defaultValue = 1.5;
-    const rowPartitionTensors = [tf.tensor1d([0, 3, 3, 7, 9], 'int32')];
-    const rowPartitionTypes = ['ROW_SPLITS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      .1, .2, .3, 1.5, 1.5, 1.5, 1.5, 1.5, .4, .5, .6, .7, .8, .9, 1.5, 1.5
-    ]);
-  });
-
-  it('RaggedTensorToTensor3DParams', async () => {
-    const shape = [5, 2, 3];
-    const values = [.1, .2, .3, .4, .5, .6, .7, .8, .9];
-    const defaultValue = 1.5;
-    const rowPartitionTensors = [
-      tf.scalar(5, 'int32'), tf.tensor1d([0, 1, 1, 3, 3, 4], 'int32'),
-      tf.tensor1d([1, 1, 2, 3, 3, 4, 4, 4, 5], 'int32')
-    ];
-    const rowPartitionTypes =
-        ['FIRST_DIM_SIZE', 'VALUE_ROWIDS', 'VALUE_ROWIDS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      1.5, 1.5, 1.5, 1.5, 1.5, 1.5, .1, .2, 1.5, .3, 1.5, 1.5, 1.5, 1.5, 1.5,
-      1.5, 1.5, 1.5, .4,  .5,  1.5, .6, .7, .8,  .9, 1.5, 1.5, 1.5, 1.5, 1.5
-    ]);
-  });
-
-  it('RaggedTensorToTensor3DParamsRowSplits', async () => {
-    const shape = [5, 2, 3];
-    const values = [.1, .2, .3, .4, .5, .6, .7, .8, .9];
-    const defaultValue = 1.5;
-    const rowPartitionTensors = [
-      tf.tensor1d([0, 1, 3, 3, 5, 6], 'int32'),
-      tf.tensor1d([0, 0, 2, 3, 5, 8, 9], 'int32')
-    ];
-    const rowPartitionTypes = ['ROW_SPLITS', 'ROW_SPLITS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      1.5, 1.5, 1.5, 1.5, 1.5, 1.5, .1, .2, 1.5, .3, 1.5, 1.5, 1.5, 1.5, 1.5,
-      1.5, 1.5, 1.5, .4,  .5,  1.5, .6, .7, .8,  .9, 1.5, 1.5, 1.5, 1.5, 1.5
-    ]);
-  });
-
-  it('RaggedTensorToTensor3DParamsRowSplits2', async () => {
-    const shape = [3, 2, 3];
-    const values = [0, 1, 2, 3];
-    const defaultValue = 5;
-    const rowPartitionTensors = [
-      tf.tensor1d([0, 2, 2, 3], 'int32'), tf.tensor1d([0, 3, 3, 4], 'int32')
-    ];
-    const rowPartitionTypes = ['ROW_SPLITS', 'ROW_SPLITS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(
-        await result.data(),
-        [0, 1, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5]);
-  });
-
-  it('RaggedTensorToTensor4DParams', async () => {
-    const shape = [4, 2, 3, 2];
-    const values = [1, 2, 3, 4, 5, 6, 7, 8];
-    const defaultValue = 15;
-    const rowPartitionTensors = [
-      tf.scalar(5, 'int32'), tf.tensor1d([0, 1, 1], 'int32'),
-      tf.tensor1d([1, 1, 1, 2], 'int32'),
-      tf.tensor1d([0, 0, 1, 1, 2, 2, 3, 3], 'int32')
-    ];
-    const rowPartitionTypes =
-        ['FIRST_DIM_SIZE', 'VALUE_ROWIDS', 'VALUE_ROWIDS', 'VALUE_ROWIDS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 1,  2,  3,  4,
-      5,  6,  7,  8,  15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
-      15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15
-    ]);
-  });
-
-  it('RaggedTensorToTensor4DParamsRowSplit', async () => {
-    const shape = [4, 2, 3, 2];
-    const values = [1, 2, 3, 4, 5, 6, 7, 8];
-    const defaultValue = 15;
-    const rowPartitionTensors = [
-      tf.tensor1d([0, 1, 3], 'int32'), tf.tensor1d([0, 0, 3, 4], 'int32'),
-      tf.tensor1d([0, 2, 4, 6, 8], 'int32')
-    ];
-    const rowPartitionTypes = ['ROW_SPLITS', 'ROW_SPLITS', 'ROW_SPLITS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 1,  2,  3,  4,
-      5,  6,  7,  8,  15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
-      15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15
-    ]);
-  });
-
-  it('RaggedTensorToTensorContractExpanded', async () => {
-    const shape = [3, 5];
-    const values = [.1, .2, .3, .4, .5, .6, .7, .8, .9];
-    const defaultValue = 1.5;
-    const rowPartitionTensors = [
-      tf.scalar(4, 'int32'),
-      tf.tensor1d([0, 0, 0, 2, 2, 2, 2, 3, 3], 'int32'),
-    ];
-    const rowPartitionTypes = ['FIRST_DIM_SIZE', 'VALUE_ROWIDS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      .1, .2, .3, 1.5, 1.5,     //
-      1.5, 1.5, 1.5, 1.5, 1.5,  //
-      .4, .5, .6, .7, 1.5
-    ]);
-  });
-
-  it('RaggedTensorToTensorContractExpandedDense', async () => {
-    const shape = [3, 5, 2];
-    const values = tf.tensor2d(
-        [
-          .1, 1.1, .2, 1.2, .3, 1.3, .4, 1.4, .5, 1.5, .6, 1.6, .7, 1.7, .8,
-          1.8, .9, 1.9
-        ],
-        [9, 2]);
-    const defaultValue = 1.5;
-    const rowPartitionTensors = [
-      tf.scalar(4, 'int32'),
-      tf.tensor1d([0, 0, 0, 2, 2, 2, 2, 3, 3], 'int32'),
-    ];
-    const rowPartitionTypes = ['FIRST_DIM_SIZE', 'VALUE_ROWIDS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      .1,  1.1, .2,  1.2, .3,  1.3, 1.5, 1.5, 1.5, 1.5,  //
-      1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,  //
-      .4,  1.4, .5,  1.5, .6,  1.6, .7,  1.7, 1.5, 1.5
-    ]);
-  });
-
-  it('RaggedTensorToTensorConstrained', async () => {
-    const shape = [3, 3];
-    const values = [.1, .2, .3, .4, .5, .6, .7, .8, .9];
-    const defaultValue = 1.5;
-    const rowPartitionTensors = [
-      tf.scalar(4, 'int32'),
-      tf.tensor1d([0, 0, 0, 2, 2, 2, 2, 3, 3], 'int32'),
-    ];
-    const rowPartitionTypes = ['FIRST_DIM_SIZE', 'VALUE_ROWIDS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      .1, .2, .3,     //
-      1.5, 1.5, 1.5,  //
-      .4, .5, .6
-    ]);
-  });
-
-  it('RaggedTensorToTensor3DParamsConstrained', async () => {
-    const shape = [4, 1, 2];
-    const values = [.1, .2, .3, .4, .5, .6, .7, .8, .9];
-    const defaultValue = 1.5;
-    const rowPartitionTensors = [
-      tf.scalar(5, 'int32'),
-      tf.tensor1d([0, 1, 1, 3, 3, 4], 'int32'),
-      tf.tensor1d([1, 1, 2, 3, 3, 4, 4, 4, 5], 'int32'),
-    ];
-    const rowPartitionTypes =
-        ['FIRST_DIM_SIZE', 'VALUE_ROWIDS', 'VALUE_ROWIDS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(
-        await result.data(), [1.5, 1.5, .1, .2, 1.5, 1.5, .4, .5]);
-  });
-
-  it('RaggedTensorToTensor4DParamsConstrained', async () => {
-    const shape = [2, 2, 2, 2];
-    const values = [1, 2, 3, 4, 5, 6, 7, 8];
-    const defaultValue = 15;
-    const rowPartitionTensors = [
-      tf.scalar(5, 'int32'),
-      tf.tensor1d([0, 1, 1], 'int32'),
-      tf.tensor1d([1, 1, 1, 2], 'int32'),
-      tf.tensor1d([0, 0, 1, 1, 2, 2, 3, 3], 'int32'),
-    ];
-    const rowPartitionTypes =
-        ['FIRST_DIM_SIZE', 'VALUE_ROWIDS', 'VALUE_ROWIDS', 'VALUE_ROWIDS'];
-    const result = tf.raggedTensorToTensor(
-        shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes);
-
-    expectArraysEqual(result.shape, shape);
-    expectArraysClose(await result.data(), [
-      15, 15, 15, 15,  //
-      15, 15, 15, 15,  //
-      1, 2, 3, 4,      //
-      7, 8, 15, 15,    //
-    ]);
-  });
-
-  it('shape wrong dimensions', async () => {
-    const shape = [10, 7, 10, 20];
-    const values = [1, 2, 3, 4];
-    const defaultValue = 15;
-    const rowPartitionTensors = [
-      tf.scalar(5, 'int32'),
-      tf.tensor1d([0, 1, 1], 'int32'),
-      tf.tensor1d([1, 1, 1, 2], 'int32'),
-    ];
-    const rowPartitionTypes =
-        ['FIRST_DIM_SIZE', 'VALUE_ROWIDS', 'VALUE_ROWIDS'];
-
-    expect(
-        () => tf.raggedTensorToTensor(
-            shape, values, defaultValue, rowPartitionTensors,
-            rowPartitionTypes))
-        .toThrowError(/are incompatible/);
-  });
-
-  it('does not have memory leak.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const rowPartitionTensors = [
-      tf.scalar(4, 'int32'), tf.tensor1d([0, 0, 0, 2, 2, 2, 2, 3, 3], 'int32')
-    ];
-    const result = tf.raggedTensorToTensor(
-        [4, 4], [.1, .2, .3, .4, .5, .6, .7, .8, .9], 1.5, rowPartitionTensors,
-        ['FIRST_DIM_SIZE', 'VALUE_ROWIDS']);
-
-    await result.data();
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 3);
-
-    result.dispose();
-    rowPartitionTensors.map(tensor => tensor.dispose());
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/ragged_to_dense_util.ts b/tfjs-master/tfjs-core/src/ops/ragged_to_dense_util.ts
deleted file mode 100644
index 4b002e014..000000000
--- a/tfjs-master/tfjs-core/src/ops/ragged_to_dense_util.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export enum RowPartitionType {
-  FIRST_DIM_SIZE,
-  VALUE_ROWIDS,
-  ROW_LENGTHS,
-  ROW_SPLITS,
-  ROW_LIMITS,
-  ROW_STARTS
-}
-
-export function combineRaggedTensorToTensorShapes(
-    raggedRank: number, shape: number[], valueShape: number[]) {
-  // Test for consistency of valueShape and shape specified.
-  // If shape is unspecified and valueShape is specified, then copy
-  // over the size from the valueShape dimension.
-
-  let outputShape: number[] = new Array();
-  if (valueShape == null && shape == null) {
-    return outputShape;
-  }
-
-  if (shape == null) {
-    // Here, value_shape must be of known size.
-    while (outputShape.length < raggedRank + valueShape.length) {
-      outputShape.push(-1);
-    }
-  } else {
-    outputShape = shape.slice();
-  }
-  if (valueShape == null) {
-    return outputShape;
-  }
-  // At this point, valueShape and output_shape have known ranks.
-  if (raggedRank + valueShape.length !== outputShape.length) {
-    throw new Error(
-        `rt input.shape and shape=${shape} are incompatible: rt input.rank = ${
-            raggedRank +
-            valueShape.length}, but shape.rank = ${outputShape.length}`);
-  }
-
-  for (let i = 1; i < valueShape.length; ++i) {
-    const valueDim = valueShape[i];
-    const outputShapeDimIndex =
-        outputShape[outputShape.length - valueShape.length + i];
-    const outputShapeDim = outputShape[outputShapeDimIndex];
-
-    if (valueDim >= 0) {
-      if (outputShapeDim >= 0) {
-        if (outputShapeDim !== valueDim) {
-          throw new Error(`rt input.shape and shape=${
-              shape} are incompatible: rt input.shape[${i + raggedRank}] = ${
-              valueDim} but shape[${i + raggedRank}] = ${outputShapeDim}`);
-        }
-      } else {
-        outputShape[outputShapeDimIndex] = valueDim;
-      }
-    }
-  }
-  return outputShape;
-}
-
-export function getRowPartitionTypesHelper(rowPartitionTypeStrings: string[]) {
-  const stringToType = {
-    'FIRST_DIM_SIZE': RowPartitionType.FIRST_DIM_SIZE,
-    'VALUE_ROWIDS': RowPartitionType.VALUE_ROWIDS,
-    'ROW_LENGTHS': RowPartitionType.ROW_LENGTHS,
-    'ROW_SPLITS': RowPartitionType.ROW_SPLITS,
-    'ROW_LIMITS': RowPartitionType.ROW_LIMITS,
-    'ROW_STARTS': RowPartitionType.ROW_STARTS
-  };
-
-  const result: RowPartitionType[] = [];
-  for (const typeStr of rowPartitionTypeStrings) {
-    if (typeStr in stringToType) {
-      result.push(stringToType[typeStr as keyof typeof stringToType]);
-    } else {
-      break;
-    }
-  }
-
-  return result;
-}
-
-export function getRaggedRank(rowPartitionTypes: RowPartitionType[]) {
-  if (rowPartitionTypes.length === 0) {
-    return 0;
-  }
-  if (rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) {
-    return rowPartitionTypes.length - 1;
-  }
-  return rowPartitionTypes.length;
-}
-
-export function validateDefaultValueShape(
-    defaultValueShape: number[], valueShape: number[]) {
-  if (defaultValueShape == null || valueShape == null) {
-    return;
-  }
-
-  const defaultNDims = defaultValueShape.length;
-  const valuesNDims = valueShape.length;
-  if (defaultNDims >= valuesNDims) {
-    throw new Error(`defaultValue.shape=${
-        defaultValueShape} and ragged tensor flatValues.shape=${
-        valueShape}, are incompatible: defaultValue.rank = ${
-        defaultNDims} must be less than ragged tensor input flatValues.rank = ${
-        valuesNDims})`);
-  }
-  for (let i = 0; i < Math.min(defaultNDims, valuesNDims - 1); ++i) {
-    const defaultDim = defaultValueShape[i];
-    const valueDim = valueShape[i + 1];
-    if (defaultDim >= 0 && valueDim >= 0 && defaultDim !== 1 &&
-        defaultDim !== valueDim) {
-      throw new Error(`defaultValue.shape=${
-          defaultValueShape}, and ragged tensor input flatValues.shape=${
-          valueShape} are incompatible: defaultValue.shape[${
-          i - defaultValueShape.length}] = ${
-          defaultDim} but ragged tensor input.flatValues.shape[${
-          i - defaultValueShape.length}] = ${valueDim}`);
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/ops/rand.ts b/tfjs-master/tfjs-core/src/ops/rand.ts
deleted file mode 100644
index 0245756ed..000000000
--- a/tfjs-master/tfjs-core/src/ops/rand.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tensor} from '../tensor';
-import {DataType, Rank, ShapeMap} from '../types';
-import {sizeFromShape} from '../util';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-
-import {op} from './operation';
-
-/**
- * Creates a `tf.Tensor` with values sampled from a random number generator
- * function defined by the user.
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param randFunction A random number generator function which is called
- * for each element in the output tensor.
- * @param dtype The data type of the output tensor. Defaults to 'float32'.
- *
- * @doc {heading: 'Tensors', subheading: 'Random'}
- */
-function rand_<R extends Rank>(
-    shape: ShapeMap[R], randFunction: () => number,
-    dtype?: DataType): Tensor<R> {
-  assertNonNegativeIntegerDimensions(shape);
-  const size = sizeFromShape(shape);
-  let values = null;
-  if (dtype == null || dtype === 'float32') {
-    values = new Float32Array(size);
-  } else if (dtype === 'int32') {
-    values = new Int32Array(size);
-  } else if (dtype === 'bool') {
-    values = new Uint8Array(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  for (let i = 0; i < size; i++) {
-    values[i] = randFunction();
-  }
-  return ENGINE.makeTensor(values, shape, dtype) as Tensor<R>;
-}
-
-export const rand = /* @__PURE__ */ op({rand_});
diff --git a/tfjs-master/tfjs-core/src/ops/rand_test.ts b/tfjs-master/tfjs-core/src/ops/rand_test.ts
deleted file mode 100644
index 387f736a1..000000000
--- a/tfjs-master/tfjs-core/src/ops/rand_test.ts
+++ /dev/null
@@ -1,294 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {util} from '..';
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectValuesInRange} from '../test_util';
-
-import {MPRandGauss, RandGamma, UniformRandom} from './rand_util';
-import {expectArrayInMeanStdRange, jarqueBeraNormalityTest} from './rand_util';
-
-describeWithFlags('rand', ALL_ENVS, () => {
-  it('should return a random 1D float32 array', async () => {
-    const shape: [number] = [10];
-
-    // Enusre defaults to float32 w/o type:
-    let result = tf.rand(shape, () => util.randUniform(0, 2));
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 2);
-
-    result = tf.rand(shape, () => util.randUniform(0, 1.5));
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 1.5);
-  });
-
-  it('should return a random 1D int32 array', async () => {
-    const shape: [number] = [10];
-    const result = tf.rand(shape, () => util.randUniform(0, 2), 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 1D bool array', async () => {
-    const shape: [number] = [10];
-    const result = tf.rand(shape, () => util.randUniform(0, 1), 'bool');
-    expect(result.dtype).toBe('bool');
-    expectValuesInRange(await result.data(), 0, 1);
-  });
-
-  it('should return a random 2D float32 array', async () => {
-    const shape = [3, 4];
-
-    // Enusre defaults to float32 w/o type:
-    let result = tf.rand(shape, () => util.randUniform(0, 2.5));
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 2.5);
-
-    result = tf.rand(shape, () => util.randUniform(0, 1.5), 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 1.5);
-  });
-
-  it('should return a random 2D int32 array', async () => {
-    const shape = [3, 4];
-    const result = tf.rand(shape, () => util.randUniform(0, 2), 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 2D bool array', async () => {
-    const shape = [3, 4];
-    const result = tf.rand(shape, () => util.randUniform(0, 1), 'bool');
-    expect(result.dtype).toBe('bool');
-    expectValuesInRange(await result.data(), 0, 1);
-  });
-
-  it('should return a random 3D float32 array', async () => {
-    const shape = [3, 4, 5];
-
-    // Enusre defaults to float32 w/o type:
-    let result = tf.rand(shape, () => util.randUniform(0, 2.5));
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 2.5);
-
-    result = tf.rand(shape, () => util.randUniform(0, 1.5), 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 1.5);
-  });
-
-  it('should return a random 3D int32 array', async () => {
-    const shape = [3, 4, 5];
-    const result = tf.rand(shape, () => util.randUniform(0, 2), 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 3D bool array', async () => {
-    const shape = [3, 4, 5];
-    const result = tf.rand(shape, () => util.randUniform(0, 1), 'bool');
-    expect(result.dtype).toBe('bool');
-    expectValuesInRange(await result.data(), 0, 1);
-  });
-
-  it('should return a random 4D float32 array', async () => {
-    const shape = [3, 4, 5, 6];
-
-    // Enusre defaults to float32 w/o type:
-    let result = tf.rand(shape, () => util.randUniform(0, 2.5));
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 2.5);
-
-    result = tf.rand(shape, () => util.randUniform(0, 1.5));
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 1.5);
-  });
-
-  it('should return a random 4D int32 array', async () => {
-    const shape = [3, 4, 5, 6];
-    const result = tf.rand(shape, () => util.randUniform(0, 2), 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 4D bool array', async () => {
-    const shape = [3, 4, 5, 6];
-    const result = tf.rand(shape, () => util.randUniform(0, 1), 'bool');
-    expect(result.dtype).toBe('bool');
-    expectValuesInRange(await result.data(), 0, 1);
-  });
-
-  it('should throw error when shape is not integer', () => {
-    expect(() => tf.rand([2, 2.22, 3.33], () => util.randUniform(0, 2)))
-        .toThrow();
-  });
-});
-
-function isFloat(n: number): boolean {
-  return Number(n) === n && n % 1 !== 0;
-}
-
-describe('MPRandGauss', () => {
-  const EPSILON = 0.05;
-  const SEED = 2002;
-
-  it('should default to float32 numbers', () => {
-    const rand = new MPRandGauss(0, 1.5);
-    expect(isFloat(rand.nextValue())).toBe(true);
-  });
-
-  it('should handle a mean/stdv of float32 numbers', () => {
-    const rand =
-        new MPRandGauss(0, 1.5, 'float32', false /* truncated */, SEED);
-    const values = [];
-    const size = 10000;
-    for (let i = 0; i < size; i++) {
-      values.push(rand.nextValue());
-    }
-    expectArrayInMeanStdRange(values, 0, 1.5, EPSILON);
-    jarqueBeraNormalityTest(values);
-  });
-
-  it('should handle int32 numbers', () => {
-    const rand = new MPRandGauss(0, 1, 'int32');
-    expect(isFloat(rand.nextValue())).toBe(false);
-  });
-
-  it('should handle a mean/stdv of int32 numbers', () => {
-    const rand = new MPRandGauss(0, 2, 'int32', false /* truncated */, SEED);
-    const values = [];
-    const size = 10000;
-    for (let i = 0; i < size; i++) {
-      values.push(rand.nextValue());
-    }
-    expectArrayInMeanStdRange(values, 0, 2, EPSILON);
-    jarqueBeraNormalityTest(values);
-  });
-
-  it('Should not have a more than 2x std-d from mean for truncated values',
-     () => {
-       const stdv = 1.5;
-       const rand = new MPRandGauss(0, stdv, 'float32', true /* truncated */);
-       for (let i = 0; i < 1000; i++) {
-         expect(Math.abs(rand.nextValue())).toBeLessThan(stdv * 2);
-       }
-     });
-});
-
-describe('RandGamma', () => {
-  const SEED = 2002;
-
-  it('should default to float32 numbers', () => {
-    const rand = new RandGamma(2, 2, 'float32');
-    expect(isFloat(rand.nextValue())).toBe(true);
-  });
-
-  it('should handle an alpha/beta of float32 numbers', () => {
-    const rand = new RandGamma(2, 2, 'float32', SEED);
-    const values = [];
-    const size = 10000;
-    for (let i = 0; i < size; i++) {
-      values.push(rand.nextValue());
-    }
-    expectValuesInRange(values, 0, 30);
-  });
-
-  it('should handle int32 numbers', () => {
-    const rand = new RandGamma(2, 2, 'int32');
-    expect(isFloat(rand.nextValue())).toBe(false);
-  });
-
-  it('should handle an alpha/beta of int32 numbers', () => {
-    const rand = new RandGamma(2, 2, 'int32', SEED);
-    const values = [];
-    const size = 10000;
-    for (let i = 0; i < size; i++) {
-      values.push(rand.nextValue());
-    }
-    expectValuesInRange(values, 0, 30);
-  });
-});
-
-describe('UniformRandom', () => {
-  it('float32, no seed', () => {
-    const min = 0.2;
-    const max = 0.24;
-    const dtype = 'float32';
-    const xs: number[] = [];
-    for (let i = 0; i < 10; ++i) {
-      const rand = new UniformRandom(min, max, dtype);
-      const x = rand.nextValue();
-      xs.push(x);
-    }
-    expect(Math.min(...xs)).toBeGreaterThanOrEqual(min);
-    expect(Math.max(...xs)).toBeLessThan(max);
-  });
-
-  it('int32, no seed', () => {
-    const min = 13;
-    const max = 37;
-    const dtype = 'int32';
-    const xs: number[] = [];
-    for (let i = 0; i < 10; ++i) {
-      const rand = new UniformRandom(min, max, dtype);
-      const x = rand.nextValue();
-      expect(Number.isInteger(x)).toEqual(true);
-      xs.push(x);
-    }
-    expect(Math.min(...xs)).toBeGreaterThanOrEqual(min);
-    expect(Math.max(...xs)).toBeLessThanOrEqual(max);
-  });
-
-  it('seed is number', () => {
-    const min = -1.2;
-    const max = -0.4;
-    const dtype = 'float32';
-    const seed = 1337;
-    const xs: number[] = [];
-    for (let i = 0; i < 10; ++i) {
-      const rand = new UniformRandom(min, max, dtype, seed);
-      const x = rand.nextValue();
-      expect(x).toBeGreaterThanOrEqual(min);
-      expect(x).toBeLessThan(max);
-      xs.push(x);
-    }
-    // Assert deterministic results.
-    expect(Math.min(...xs)).toEqual(Math.max(...xs));
-  });
-
-  it('seed === null', () => {
-    const min = 0;
-    const max = 1;
-    const dtype = 'float32';
-    const seed: number = null;
-    const rand = new UniformRandom(min, max, dtype, seed);
-    const x = rand.nextValue();
-    expect(x).toBeGreaterThanOrEqual(0);
-    expect(x).toBeLessThan(1);
-  });
-
-  it('seed === undefined', () => {
-    const min = 0;
-    const max = 1;
-    const dtype = 'float32';
-    const seed: number = undefined;
-    const rand = new UniformRandom(min, max, dtype, seed);
-    const x = rand.nextValue();
-    expect(x).toBeGreaterThanOrEqual(0);
-    expect(x).toBeLessThan(1);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/rand_util.ts b/tfjs-master/tfjs-core/src/ops/rand_util.ts
deleted file mode 100644
index ccd2f9375..000000000
--- a/tfjs-master/tfjs-core/src/ops/rand_util.ts
+++ /dev/null
@@ -1,288 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as seedrandom from 'seedrandom';
-
-import {expectNumbersClose, testEpsilon} from '../test_util';
-import {TypedArray} from '../types';
-
-export interface RandomBase {
-  nextValue(): number;
-}
-
-export interface RandomGamma {
-  nextValue(): number;
-}
-
-export interface RandNormalDataTypes {
-  float32: Float32Array;
-  int32: Int32Array;
-}
-
-export interface RandGammaDataTypes {
-  float32: Float32Array;
-  int32: Int32Array;
-}
-
-// https://en.wikipedia.org/wiki/Marsaglia_polar_method
-export class MPRandGauss implements RandomBase {
-  private mean: number;
-  private stdDev: number;
-  private nextVal: number;
-  private dtype?: keyof RandNormalDataTypes;
-  private truncated?: boolean;
-  private upper?: number;
-  private lower?: number;
-  private random: seedrandom.prng;
-
-  constructor(
-      mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes,
-      truncated?: boolean, seed?: number) {
-    this.mean = mean;
-    this.stdDev = stdDeviation;
-    this.dtype = dtype;
-    this.nextVal = NaN;
-    this.truncated = truncated;
-    if (this.truncated) {
-      this.upper = this.mean + this.stdDev * 2;
-      this.lower = this.mean - this.stdDev * 2;
-    }
-    const seedValue = seed ? seed : Math.random();
-    this.random = seedrandom.alea(seedValue.toString());
-  }
-
-  /** Returns next sample from a Gaussian distribution. */
-  public nextValue(): number {
-    if (!isNaN(this.nextVal)) {
-      const value = this.nextVal;
-      this.nextVal = NaN;
-      return value;
-    }
-
-    let resultX: number, resultY: number;
-    let isValid = false;
-    while (!isValid) {
-      let v1: number, v2: number, s: number;
-      do {
-        v1 = 2 * this.random() - 1;
-        v2 = 2 * this.random() - 1;
-        s = v1 * v1 + v2 * v2;
-      } while (s >= 1 || s === 0);
-
-      const mul = Math.sqrt(-2.0 * Math.log(s) / s);
-      resultX = this.mean + this.stdDev * v1 * mul;
-      resultY = this.mean + this.stdDev * v2 * mul;
-
-      if (!this.truncated || this.isValidTruncated(resultX)) {
-        isValid = true;
-      }
-    }
-
-    if (!this.truncated || this.isValidTruncated(resultY)) {
-      this.nextVal = this.convertValue(resultY);
-    }
-    return this.convertValue(resultX);
-  }
-
-  /** Handles proper rounding for non-floating-point numbers. */
-  private convertValue(value: number): number {
-    if (this.dtype == null || this.dtype === 'float32') {
-      return value;
-    }
-    return Math.round(value);
-  }
-
-  /** Returns true if less than 2-standard-deviations from the mean. */
-  private isValidTruncated(value: number): boolean {
-    return value <= this.upper && value >= this.lower;
-  }
-}
-
-// Marsaglia, George, and Wai Wan Tsang. 2000. "A Simple Method for Generating
-// Gamma Variables."
-export class RandGamma implements RandomGamma {
-  private alpha: number;
-  private beta: number;
-  private d: number;
-  private c: number;
-  private dtype?: keyof RandGammaDataTypes;
-  private randu: seedrandom.prng;
-  private randn: MPRandGauss;
-
-  constructor(
-      alpha: number, beta: number, dtype: keyof RandGammaDataTypes,
-      seed?: number) {
-    this.alpha = alpha;
-    this.beta = 1 / beta;  // convert rate to scale parameter
-    this.dtype = dtype;
-
-    const seedValue = seed ? seed : Math.random();
-    this.randu = seedrandom.alea(seedValue.toString());
-    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
-
-    if (alpha < 1) {
-      this.d = alpha + (2 / 3);
-    } else {
-      this.d = alpha - (1 / 3);
-    }
-    this.c = 1 / Math.sqrt(9 * this.d);
-  }
-
-  /** Returns next sample from a gamma distribution. */
-  public nextValue(): number {
-    let x2: number, v0: number, v1: number, x: number, u: number, v: number;
-    while (true) {
-      do {
-        x = this.randn.nextValue();
-        v = 1 + (this.c * x);
-      } while (v <= 0);
-      v *= v * v;
-      x2 = x * x;
-      v0 = 1 - (0.331 * x2 * x2);
-      v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));
-      u = this.randu();
-      if (u < v0 || Math.log(u) < v1) {
-        break;
-      }
-    }
-    v = (1 / this.beta) * this.d * v;
-    if (this.alpha < 1) {
-      v *= Math.pow(this.randu(), 1 / this.alpha);
-    }
-    return this.convertValue(v);
-  }
-  /** Handles proper rounding for non-floating-point numbers. */
-  private convertValue(value: number): number {
-    if (this.dtype === 'float32') {
-      return value;
-    }
-    return Math.round(value);
-  }
-}
-
-export class UniformRandom implements RandomBase {
-  private min: number;
-  private range: number;
-  private random: seedrandom.prng;
-  private dtype?: keyof RandNormalDataTypes;
-
-  constructor(
-      min = 0, max = 1, dtype?: keyof RandNormalDataTypes,
-      seed?: string|number) {
-    this.min = min;
-    this.range = max - min;
-    this.dtype = dtype;
-    if (seed == null) {
-      seed = Math.random();
-    }
-    if (typeof seed === 'number') {
-      seed = seed.toString();
-    }
-
-    if (!this.canReturnFloat() && this.range <= 1) {
-      throw new Error(
-          `The difference between ${min} - ${max} <= 1 and dtype is not float`);
-    }
-    this.random = seedrandom.alea(seed);
-  }
-
-  /** Handles proper rounding for non floating point numbers. */
-  private canReturnFloat = () =>
-      (this.dtype == null || this.dtype === 'float32');
-
-  private convertValue(value: number): number {
-    if (this.canReturnFloat()) {
-      return value;
-    }
-    return Math.round(value);
-  }
-
-  nextValue() {
-    return this.convertValue(this.min + this.range * this.random());
-  }
-}
-
-export function jarqueBeraNormalityTest(values: TypedArray|number[]) {
-  // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test
-  const n = values.length;
-  const s = skewness(values);
-  const k = kurtosis(values);
-  const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));
-  // JB test requires 2-degress of freedom from Chi-Square @ 0.95:
-  // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm
-  const CHI_SQUARE_2DEG = 5.991;
-  if (jb > CHI_SQUARE_2DEG) {
-    throw new Error(`Invalid p-value for JB: ${jb}`);
-  }
-}
-
-export function expectArrayInMeanStdRange(
-    actual: TypedArray|number[], expectedMean: number, expectedStdDev: number,
-    epsilon?: number) {
-  if (epsilon == null) {
-    epsilon = testEpsilon();
-  }
-  const actualMean = mean(actual);
-  expectNumbersClose(actualMean, expectedMean, epsilon);
-  expectNumbersClose(
-      standardDeviation(actual, actualMean), expectedStdDev, epsilon);
-}
-
-function mean(values: TypedArray|number[]) {
-  let sum = 0;
-  for (let i = 0; i < values.length; i++) {
-    sum += values[i];
-  }
-  return sum / values.length;
-}
-
-function standardDeviation(values: TypedArray|number[], mean: number) {
-  let squareDiffSum = 0;
-  for (let i = 0; i < values.length; i++) {
-    const diff = values[i] - mean;
-    squareDiffSum += diff * diff;
-  }
-  return Math.sqrt(squareDiffSum / values.length);
-}
-
-function kurtosis(values: TypedArray|number[]) {
-  // https://en.wikipedia.org/wiki/Kurtosis
-  const valuesMean = mean(values);
-  const n = values.length;
-  let sum2 = 0;
-  let sum4 = 0;
-  for (let i = 0; i < n; i++) {
-    const v = values[i] - valuesMean;
-    sum2 += Math.pow(v, 2);
-    sum4 += Math.pow(v, 4);
-  }
-  return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);
-}
-
-function skewness(values: TypedArray|number[]) {
-  // https://en.wikipedia.org/wiki/Skewness
-  const valuesMean = mean(values);
-  const n = values.length;
-  let sum2 = 0;
-  let sum3 = 0;
-  for (let i = 0; i < n; i++) {
-    const v = values[i] - valuesMean;
-    sum2 += Math.pow(v, 2);
-    sum3 += Math.pow(v, 3);
-  }
-  return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);
-}
diff --git a/tfjs-master/tfjs-core/src/ops/random_gamma.ts b/tfjs-master/tfjs-core/src/ops/random_gamma.ts
deleted file mode 100644
index 36f537596..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_gamma.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {Rank, ShapeMap} from '../types';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-
-import {buffer} from './buffer';
-import {op} from './operation';
-import {RandGamma} from './rand_util';
-
-/**
- * Creates a `tf.Tensor` with values sampled from a gamma distribution.
- *
- * ```js
- * tf.randomGamma([2, 2], 1).print();
- * ```
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param alpha The shape parameter of the gamma distribution.
- * @param beta The inverse scale parameter of the gamma distribution. Defaults
- *     to 1.
- * @param dtype The data type of the output. Defaults to float32.
- * @param seed The seed for the random number generator.
- *
- * @doc {heading: 'Tensors', subheading: 'Random'}
- */
-function randomGamma_<R extends Rank>(
-    shape: ShapeMap[R], alpha: number, beta = 1,
-    dtype: 'float32'|'int32' = 'float32', seed?: number): Tensor<R> {
-  assertNonNegativeIntegerDimensions(shape);
-  if (beta == null) {
-    beta = 1;
-  }
-  if (dtype == null) {
-    dtype = 'float32';
-  }
-  if (dtype !== 'float32' && dtype !== 'int32') {
-    throw new Error(`Unsupported data type ${dtype}`);
-  }
-  const rgamma = new RandGamma(alpha, beta, dtype, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = rgamma.nextValue();
-  }
-  return res.toTensor();
-}
-
-export const randomGamma = /* @__PURE__ */ op({randomGamma_});
diff --git a/tfjs-master/tfjs-core/src/ops/random_gamma_test.ts b/tfjs-master/tfjs-core/src/ops/random_gamma_test.ts
deleted file mode 100644
index 6d95400cb..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_gamma_test.ts
+++ /dev/null
@@ -1,129 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectValuesInRange} from '../test_util';
-
-const GAMMA_MIN = 0;
-const GAMMA_MAX = 40;
-
-describeWithFlags('randomGamma', ALL_ENVS, () => {
-  it('should return a random 1D float32 array', async () => {
-    const shape: [number] = [10];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomGamma(shape, 2, 2);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-
-    result = tf.randomGamma(shape, 2, 2, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should return a random 1D int32 array', async () => {
-    const shape: [number] = [10];
-    const result = tf.randomGamma(shape, 2, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should return a random 2D float32 array', async () => {
-    const shape: [number, number] = [3, 4];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomGamma(shape, 2, 2);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-
-    result = tf.randomGamma(shape, 2, 2, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should return a random 2D int32 array', async () => {
-    const shape: [number, number] = [3, 4];
-    const result = tf.randomGamma(shape, 2, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should return a random 3D float32 array', async () => {
-    const shape: [number, number, number] = [3, 4, 5];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomGamma(shape, 2, 2);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-
-    result = tf.randomGamma(shape, 2, 2, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should return a random 3D int32 array', async () => {
-    const shape: [number, number, number] = [3, 4, 5];
-    const result = tf.randomGamma(shape, 2, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should return a random 4D float32 array', async () => {
-    const shape: [number, number, number, number] = [3, 4, 5, 6];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomGamma(shape, 2, 2);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-
-    result = tf.randomGamma(shape, 2, 2, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should return a random 4D int32 array', async () => {
-    const shape: [number, number, number, number] = [3, 4, 5, 6];
-    const result = tf.randomGamma(shape, 2, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should return a random 5D float32 array', async () => {
-    const shape: [number, number, number, number, number] = [2, 3, 4, 5, 6];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomGamma(shape, 2, 2);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-
-    result = tf.randomGamma(shape, 2, 2, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should return a random 5D int32 array', async () => {
-    const shape: [number, number, number, number, number] = [2, 3, 4, 5, 6];
-    const result = tf.randomGamma(shape, 2, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), GAMMA_MIN, GAMMA_MAX);
-  });
-
-  it('should throw error when shape is not integer', () => {
-    expect(() => tf.randomGamma([2, 2.22, 3.33], 2, 2)).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/random_normal.ts b/tfjs-master/tfjs-core/src/ops/random_normal.ts
deleted file mode 100644
index 4a14f9476..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_normal.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {DataType, Rank, ShapeMap} from '../types';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-
-import {buffer} from './buffer';
-import {op} from './operation';
-import {MPRandGauss} from './rand_util';
-
-/**
- * Creates a `tf.Tensor` with values sampled from a normal distribution.
- *
- * ```js
- * tf.randomNormal([2, 2]).print();
- * ```
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param mean The mean of the normal distribution.
- * @param stdDev The standard deviation of the normal distribution.
- * @param dtype The data type of the output.
- * @param seed The seed for the random number generator.
- *
- * @doc {heading: 'Tensors', subheading: 'Random'}
- */
-function randomNormal_<R extends Rank>(
-    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',
-    seed?: number): Tensor<R> {
-  assertNonNegativeIntegerDimensions(shape);
-  if (dtype != null && (dtype as DataType) === 'bool') {
-    throw new Error(`Unsupported data type ${dtype}`);
-  }
-  const randGauss =
-      new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = randGauss.nextValue();
-  }
-  return res.toTensor();
-}
-
-export const randomNormal = /* @__PURE__ */ op({randomNormal_});
diff --git a/tfjs-master/tfjs-core/src/ops/random_normal_test.ts b/tfjs-master/tfjs-core/src/ops/random_normal_test.ts
deleted file mode 100644
index 16a1f4f36..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_normal_test.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArrayInMeanStdRange, jarqueBeraNormalityTest} from './rand_util';
-
-describeWithFlags('randomNormal', ALL_ENVS, () => {
-  const SEED = 2002;
-  const EPSILON = 0.05;
-
-  it('should return a float32 1D of random normal values', async () => {
-    const SAMPLES = 10000;
-
-    // Ensure defaults to float32.
-    let result = tf.randomNormal([SAMPLES], 0, 0.5, null, SEED);
-    expect(result.dtype).toBe('float32');
-    expect(result.shape).toEqual([SAMPLES]);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 0.5, EPSILON);
-
-    result = tf.randomNormal([SAMPLES], 0, 1.5, 'float32', SEED);
-    expect(result.dtype).toBe('float32');
-    expect(result.shape).toEqual([SAMPLES]);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 1.5, EPSILON);
-  });
-
-  it('should return a int32 1D of random normal values', async () => {
-    const SAMPLES = 10000;
-    const result = tf.randomNormal([SAMPLES], 0, 2, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual([SAMPLES]);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 2, EPSILON);
-  });
-
-  it('should return a float32 2D of random normal values', async () => {
-    const SAMPLES = 100;
-
-    // Ensure defaults to float32.
-    let result = tf.randomNormal([SAMPLES, SAMPLES], 0, 2.5, null, SEED);
-    expect(result.dtype).toBe('float32');
-    expect(result.shape).toEqual([SAMPLES, SAMPLES]);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 2.5, EPSILON);
-
-    result = tf.randomNormal([SAMPLES, SAMPLES], 0, 3.5, 'float32', SEED);
-    expect(result.dtype).toBe('float32');
-    expect(result.shape).toEqual([SAMPLES, SAMPLES]);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 3.5, EPSILON);
-  });
-
-  it('should return a int32 2D of random normal values', async () => {
-    const SAMPLES = 100;
-    const result = tf.randomNormal([SAMPLES, SAMPLES], 0, 2, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual([SAMPLES, SAMPLES]);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 2, EPSILON);
-  });
-
-  it('should return a float32 3D of random normal values', async () => {
-    const SAMPLES_SHAPE = [20, 20, 20];
-
-    // Ensure defaults to float32.
-    let result = tf.randomNormal(SAMPLES_SHAPE, 0, 0.5, null, SEED);
-    expect(result.dtype).toBe('float32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 0.5, EPSILON);
-
-    result = tf.randomNormal(SAMPLES_SHAPE, 0, 1.5, 'float32', SEED);
-    expect(result.dtype).toBe('float32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 1.5, EPSILON);
-  });
-
-  it('should return a int32 3D of random normal values', async () => {
-    const SAMPLES_SHAPE = [20, 20, 20];
-    const result = tf.randomNormal(SAMPLES_SHAPE, 0, 2, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 2, EPSILON);
-  });
-
-  it('should return a float32 4D of random normal values', async () => {
-    const SAMPLES_SHAPE = [10, 10, 10, 10];
-
-    // Ensure defaults to float32.
-    let result = tf.randomNormal(SAMPLES_SHAPE, 0, 0.5, null, SEED);
-    expect(result.dtype).toBe('float32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 0.5, EPSILON);
-
-    result = tf.randomNormal(SAMPLES_SHAPE, 0, 1.5, 'float32', SEED);
-    expect(result.dtype).toBe('float32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 1.5, EPSILON);
-  });
-
-  it('should return a int32 4D of random normal values', async () => {
-    const SAMPLES_SHAPE = [10, 10, 10, 10];
-
-    const result = tf.randomNormal(SAMPLES_SHAPE, 0, 2, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 2, EPSILON);
-  });
-
-  it('should return a int32 5D of random normal values', async () => {
-    const SAMPLES_SHAPE = [10, 10, 10, 10, 10];
-
-    const result = tf.randomNormal(SAMPLES_SHAPE, 0, 2, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 2, EPSILON);
-  });
-
-  it('should throw error when shape is not integer', () => {
-    expect(() => tf.randomNormal([2, 2.22, 3.33], 0, 2, 'int32', SEED))
-        .toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/random_standard_normal.ts b/tfjs-master/tfjs-core/src/ops/random_standard_normal.ts
deleted file mode 100644
index 417147dc5..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_standard_normal.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {DataType, Rank, ShapeMap} from '../types';
-
-import {op} from './operation';
-import {randomNormal} from './random_normal';
-
-/**
- * Creates a `tf.Tensor` with values sampled from a normal distribution.
- *
- * The generated values will have mean 0 and standard deviation 1.
- *
- * ```js
- * tf.randomStandardNormal([2, 2]).print();
- * ```
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param dtype The data type of the output.
- * @param seed The seed for the random number generator.
- *
- * @doc {heading: 'Tensors', subheading: 'Random'}
- */
-function randomStandardNormal_<R extends Rank>(
-    shape: ShapeMap[R], dtype?: 'float32'|'int32', seed?: number): Tensor<R> {
-  if (dtype != null && (dtype as DataType) === 'bool') {
-    throw new Error(`Unsupported data type ${dtype}`);
-  }
-  return randomNormal(shape, 0, 1, dtype, seed);
-}
-
-export const randomStandardNormal = /* @__PURE__ */ op({randomStandardNormal_});
diff --git a/tfjs-master/tfjs-core/src/ops/random_standard_normal_test.ts b/tfjs-master/tfjs-core/src/ops/random_standard_normal_test.ts
deleted file mode 100644
index 189a783f2..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_standard_normal_test.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-
-import {expectArrayInMeanStdRange, jarqueBeraNormalityTest} from './rand_util';
-
-describeWithFlags('randomStandardNormal', ALL_ENVS, () => {
-  const SEED = 42;
-  const EPSILON = 0.05;
-
-  it('should return a float32 1D of random standard normal values',
-     async () => {
-       const SAMPLES = 10000;
-
-       // Ensure defaults to float32.
-       let result = tf.randomStandardNormal([SAMPLES], null, SEED);
-       expect(result.dtype).toBe('float32');
-       expect(result.shape).toEqual([SAMPLES]);
-       jarqueBeraNormalityTest(await result.data());
-       expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-
-       result = tf.randomStandardNormal([SAMPLES], 'float32', SEED);
-       expect(result.dtype).toBe('float32');
-       expect(result.shape).toEqual([SAMPLES]);
-       jarqueBeraNormalityTest(await result.data());
-       expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-     });
-
-  it('should return a int32 1D of random standard normal values', async () => {
-    const SAMPLES = 10000;
-    const result = tf.randomStandardNormal([SAMPLES], 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual([SAMPLES]);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-  });
-
-  it('should return a float32 2D of random standard normal values',
-     async () => {
-       const SAMPLES = 100;
-
-       // Ensure defaults to float32.
-       let result = tf.randomStandardNormal([SAMPLES, SAMPLES], null, SEED);
-       expect(result.dtype).toBe('float32');
-       expect(result.shape).toEqual([SAMPLES, SAMPLES]);
-       jarqueBeraNormalityTest(await result.data());
-       expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-
-       result = tf.randomStandardNormal([SAMPLES, SAMPLES], 'float32', SEED);
-       expect(result.dtype).toBe('float32');
-       expect(result.shape).toEqual([SAMPLES, SAMPLES]);
-       jarqueBeraNormalityTest(await result.data());
-       expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-     });
-
-  it('should return a int32 2D of random standard normal values', async () => {
-    const SAMPLES = 100;
-    const result = tf.randomStandardNormal([SAMPLES, SAMPLES], 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual([SAMPLES, SAMPLES]);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-  });
-
-  it('should return a float32 3D of random standard normal values',
-     async () => {
-       const SAMPLES_SHAPE = [20, 20, 20];
-
-       // Ensure defaults to float32.
-       let result = tf.randomStandardNormal(SAMPLES_SHAPE, null, SEED);
-       expect(result.dtype).toBe('float32');
-       expect(result.shape).toEqual(SAMPLES_SHAPE);
-       jarqueBeraNormalityTest(await result.data());
-       expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-
-       result = tf.randomStandardNormal(SAMPLES_SHAPE, 'float32', SEED);
-       expect(result.dtype).toBe('float32');
-       expect(result.shape).toEqual(SAMPLES_SHAPE);
-       jarqueBeraNormalityTest(await result.data());
-       expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-     });
-
-  it('should return a int32 3D of random standard normal values', async () => {
-    const SAMPLES_SHAPE = [20, 20, 20];
-    const result = tf.randomStandardNormal(SAMPLES_SHAPE, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-  });
-
-  it('should return a float32 4D of random standard normal values',
-     async () => {
-       const SAMPLES_SHAPE = [10, 10, 10, 10];
-
-       // Ensure defaults to float32.
-       let result = tf.randomStandardNormal(SAMPLES_SHAPE, null, SEED);
-       expect(result.dtype).toBe('float32');
-       expect(result.shape).toEqual(SAMPLES_SHAPE);
-       jarqueBeraNormalityTest(await result.data());
-       expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-
-       result = tf.randomStandardNormal(SAMPLES_SHAPE, 'float32', SEED);
-       expect(result.dtype).toBe('float32');
-       expect(result.shape).toEqual(SAMPLES_SHAPE);
-       jarqueBeraNormalityTest(await result.data());
-       expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-     });
-
-  it('should return a int32 4D of random standard normal values', async () => {
-    const SAMPLES_SHAPE = [10, 10, 10, 10];
-
-    const result = tf.randomStandardNormal(SAMPLES_SHAPE, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-  });
-
-  it('should return a int32 5D of random standard normal values', async () => {
-    const SAMPLES_SHAPE = [10, 10, 10, 10, 10];
-
-    const result = tf.randomStandardNormal(SAMPLES_SHAPE, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    expect(result.shape).toEqual(SAMPLES_SHAPE);
-    jarqueBeraNormalityTest(await result.data());
-    expectArrayInMeanStdRange(await result.data(), 0, 1, EPSILON);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/random_uniform.ts b/tfjs-master/tfjs-core/src/ops/random_uniform.ts
deleted file mode 100644
index 93ace1e41..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_uniform.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {DataType, Rank, ShapeMap} from '../types';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-
-import {buffer} from './buffer';
-import {op} from './operation';
-import {UniformRandom} from './rand_util';
-
-/**
- * Creates a `tf.Tensor` with values sampled from a uniform distribution.
- *
- * The generated values follow a uniform distribution in the range [minval,
- * maxval). The lower bound minval is included in the range, while the upper
- * bound maxval is excluded.
- *
- * ```js
- * tf.randomUniform([2, 2]).print();
- * ```
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param minval The lower bound on the range of random values to generate.
- *   Defaults to 0.
- * @param maxval The upper bound on the range of random values to generate.
- *   Defaults to 1.
- * @param dtype The data type of the output tensor. Defaults to 'float32'.
- * @param seed An optional int. Defaults to 0. If seed is set to be non-zero,
- *   the random number generator is seeded by the given seed. Otherwise, it is
- *   seeded by a random seed.
- *
- * @doc {heading: 'Tensors', subheading: 'Random'}
- */
-function randomUniform_<R extends Rank>(
-    shape: ShapeMap[R], minval = 0, maxval = 1, dtype: DataType = 'float32',
-    seed?: number|string): Tensor<R> {
-  assertNonNegativeIntegerDimensions(shape);
-  const res = buffer(shape, dtype);
-  const random = new UniformRandom(minval, maxval, null, seed);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = random.nextValue();
-  }
-  return res.toTensor();
-}
-
-export const randomUniform = /* @__PURE__ */ op({randomUniform_});
diff --git a/tfjs-master/tfjs-core/src/ops/random_uniform_int.ts b/tfjs-master/tfjs-core/src/ops/random_uniform_int.ts
deleted file mode 100644
index 9d64584df..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_uniform_int.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {Rank, ShapeMap} from '../types';
-import {op} from './operation';
-import {randomUniform} from './random_uniform';
-
-/**
- * Creates a `tf.Tensor` with integers sampled from a uniform distribution.
- *
- * The generated values are uniform integers in the range [minval, maxval). The
- * lower bound minval is included in the range, while the upper bound maxval is
- * excluded.
- *
- * ```js
- * tf.randomUniformInt([2, 2], 0, 10).print();
- * ```
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param minval Inclusive lower bound on the generated integers.
- * @param maxval Exclusive upper bound on the generated integers.
- * @param seed An optional int. Defaults to 0. If seed is set to be non-zero,
- *   the random number generator is seeded by the given seed. Otherwise, it is
- *   seeded by a random seed.
- *
- * @doc {heading: 'Tensors', subheading: 'Random'}
- */
-function randomUniformInt_<R extends Rank>(
-  shape: ShapeMap[R], minval: number, maxval: number,
-    seed?: number|string): Tensor<R> {
-  // TODO(mattsoulanille): Handle optional seed2 input.
-  return randomUniform(shape, minval, maxval, 'int32', seed);
-}
-
-export const randomUniformInt = /* @__PURE__ */ op({randomUniformInt_});
diff --git a/tfjs-master/tfjs-core/src/ops/random_uniform_int_test.ts b/tfjs-master/tfjs-core/src/ops/random_uniform_int_test.ts
deleted file mode 100644
index c3e7de21a..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_uniform_int_test.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import { expectArraysEqual, expectValuesInRange} from '../test_util';
-
-describeWithFlags('randomUniformInt', ALL_ENVS, () => {
-  it('should return a random 1D int32 array', async () => {
-    const shape: [number] = [10];
-    const result = tf.randomUniformInt(shape, 0, 2);
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 2D int32 array', async () => {
-    const shape: [number, number] = [3, 4];
-    const result = tf.randomUniformInt(shape, 0, 2);
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 3D int32 array', async () => {
-    const shape: [number, number, number] = [3, 4, 5];
-    const result = tf.randomUniformInt(shape, 0, 2);
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 4D int32 array', async () => {
-    const shape: [number, number, number, number] = [3, 4, 5, 6];
-    const result = tf.randomUniformInt(shape, 0, 2);
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 5D int32 array', async () => {
-    const shape: [number, number, number, number, number] = [2, 3, 4, 5, 6];
-    const result = tf.randomUniformInt(shape, 0, 2);
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should throw error when shape is not integer', () => {
-    expect(() => tf.randomUniformInt([2, 2.22, 3.33], 0, 1)).toThrow();
-  });
-
-  it('should return the same result when seed is specified', async () => {
-    const seed = 123;
-    const shape = [4, 4];
-    const result1 = tf.randomUniformInt(shape, 0, 1000, seed);
-    const result2 = tf.randomUniformInt(shape, 0, 1000, seed);
-    expectArraysEqual(await result1.data(), await result2.data());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/random_uniform_test.ts b/tfjs-master/tfjs-core/src/ops/random_uniform_test.ts
deleted file mode 100644
index 5da5946ca..000000000
--- a/tfjs-master/tfjs-core/src/ops/random_uniform_test.ts
+++ /dev/null
@@ -1,161 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectValuesInRange} from '../test_util';
-
-describeWithFlags('randomUniform', ALL_ENVS, () => {
-  it('should return a random 1D float32 array', async () => {
-    const shape: [number] = [10];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomUniform(shape, 0, 2.5);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 2.5);
-
-    result = tf.randomUniform(shape, 0, 1.5, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 1.5);
-  });
-
-  it('should return a random 1D int32 array', async () => {
-    const shape: [number] = [10];
-    const result = tf.randomUniform(shape, 0, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 1D bool array', async () => {
-    const shape: [number] = [10];
-    const result = tf.randomUniform(shape, 0, 1, 'bool');
-    expect(result.dtype).toBe('bool');
-    expectValuesInRange(await result.data(), 0, 1);
-  });
-
-  it('should return a random 2D float32 array', async () => {
-    const shape: [number, number] = [3, 4];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomUniform(shape, 0, 2.5);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 2.5);
-
-    result = tf.randomUniform(shape, 0, 1.5, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 1.5);
-  });
-
-  it('should return a random 2D int32 array', async () => {
-    const shape: [number, number] = [3, 4];
-    const result = tf.randomUniform(shape, 0, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 2D bool array', async () => {
-    const shape: [number, number] = [3, 4];
-    const result = tf.randomUniform(shape, 0, 1, 'bool');
-    expect(result.dtype).toBe('bool');
-    expectValuesInRange(await result.data(), 0, 1);
-  });
-
-  it('should return a random 3D float32 array', async () => {
-    const shape: [number, number, number] = [3, 4, 5];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomUniform(shape, 0, 2.5);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 2.5);
-
-    result = tf.randomUniform(shape, 0, 1.5, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 1.5);
-  });
-
-  it('should return a random 3D int32 array', async () => {
-    const shape: [number, number, number] = [3, 4, 5];
-    const result = tf.randomUniform(shape, 0, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 3D bool array', async () => {
-    const shape: [number, number, number] = [3, 4, 5];
-    const result = tf.randomUniform(shape, 0, 1, 'bool');
-    expect(result.dtype).toBe('bool');
-    expectValuesInRange(await result.data(), 0, 1);
-  });
-
-  it('should return a random 4D float32 array', async () => {
-    const shape: [number, number, number, number] = [3, 4, 5, 6];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomUniform(shape, 0, 2.5);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 2.5);
-
-    result = tf.randomUniform(shape, 0, 1.5, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 1.5);
-  });
-
-  it('should return a random 4D int32 array', async () => {
-    const shape: [number, number, number, number] = [3, 4, 5, 6];
-    const result = tf.randomUniform(shape, 0, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 4D bool array', async () => {
-    const shape: [number, number, number, number] = [3, 4, 5, 6];
-    const result = tf.randomUniform(shape, 0, 1, 'bool');
-    expect(result.dtype).toBe('bool');
-    expectValuesInRange(await result.data(), 0, 1);
-  });
-
-  it('should return a random 5D float32 array', async () => {
-    const shape: [number, number, number, number, number] = [2, 3, 4, 5, 6];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.randomUniform(shape, 0, 2.5);
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 2.5);
-
-    result = tf.randomUniform(shape, 0, 1.5, 'float32');
-    expect(result.dtype).toBe('float32');
-    expectValuesInRange(await result.data(), 0, 1.5);
-  });
-
-  it('should return a random 5D int32 array', async () => {
-    const shape: [number, number, number, number, number] = [2, 3, 4, 5, 6];
-    const result = tf.randomUniform(shape, 0, 2, 'int32');
-    expect(result.dtype).toBe('int32');
-    expectValuesInRange(await result.data(), 0, 2);
-  });
-
-  it('should return a random 5D bool array', async () => {
-    const shape: [number, number, number, number, number] = [2, 3, 4, 5, 6];
-    const result = tf.randomUniform(shape, 0, 1, 'bool');
-    expect(result.dtype).toBe('bool');
-    expectValuesInRange(await result.data(), 0, 1);
-  });
-
-  it('should throw error when shape is not integer', () => {
-    expect(() => tf.randomUniform([2, 2.22, 3.33], 0, 1)).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/range.ts b/tfjs-master/tfjs-core/src/ops/range.ts
deleted file mode 100644
index 3ffe2fd96..000000000
--- a/tfjs-master/tfjs-core/src/ops/range.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Range, RangeAttrs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor1D} from '../tensor';
-
-/**
- * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.
- *
- * The tensor is a half-open interval meaning it includes start, but
- * excludes stop. Decrementing ranges and negative step values are also
- * supported.
- *
- *
- * ```js
- * tf.range(0, 9, 2).print();
- * ```
- *
- * @param start An integer start value
- * @param stop An integer stop value
- * @param step An integer increment (will default to 1 or -1)
- * @param dtype The data type of the output tensor. Defaults to 'float32'.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function range(
-    start: number, stop: number, step = 1,
-    dtype: 'float32'|'int32' = 'float32'): Tensor1D {
-  if (step === 0) {
-    throw new Error('Cannot have a step of zero');
-  }
-
-  const attrs: RangeAttrs = {start, stop, step, dtype};
-
-  return ENGINE.runKernel(Range, {} /* inputs */,
-      attrs as unknown as NamedAttrMap);
-}
diff --git a/tfjs-master/tfjs-core/src/ops/range_test.ts b/tfjs-master/tfjs-core/src/ops/range_test.ts
deleted file mode 100644
index 3fed0223c..000000000
--- a/tfjs-master/tfjs-core/src/ops/range_test.ts
+++ /dev/null
@@ -1,160 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysEqual} from '../test_util';
-
-describeWithFlags('range', ALL_ENVS, () => {
-  it('start stop', async () => {
-    const a = tf.range(0, 3);
-    expectArraysEqual(await a.data(), [0, 1, 2]);
-    expect(a.shape).toEqual([3]);
-
-    const b = tf.range(3, 8);
-    expectArraysEqual(await b.data(), [3, 4, 5, 6, 7]);
-    expect(b.shape).toEqual([5]);
-  });
-
-  it('start stop negative', async () => {
-    const a = tf.range(-2, 3);
-    expectArraysEqual(await a.data(), [-2, -1, 0, 1, 2]);
-    expect(a.shape).toEqual([5]);
-
-    const b = tf.range(4, -2);
-    expectArraysEqual(await b.data(), [4, 3, 2, 1, 0, -1]);
-    expect(b.shape).toEqual([6]);
-  });
-
-  it('start stop step', async () => {
-    const a = tf.range(4, 15, 4);
-    expectArraysEqual(await a.data(), [4, 8, 12]);
-    expect(a.shape).toEqual([3]);
-
-    const b = tf.range(4, 11, 4);
-    expectArraysEqual(await b.data(), [4, 8]);
-    expect(b.shape).toEqual([2]);
-
-    const c = tf.range(4, 17, 4);
-    expectArraysEqual(await c.data(), [4, 8, 12, 16]);
-    expect(c.shape).toEqual([4]);
-
-    const d = tf.range(0, 30, 5);
-    expectArraysEqual(await d.data(), [0, 5, 10, 15, 20, 25]);
-    expect(d.shape).toEqual([6]);
-
-    const e = tf.range(-3, 9, 2);
-    expectArraysEqual(await e.data(), [-3, -1, 1, 3, 5, 7]);
-    expect(e.shape).toEqual([6]);
-
-    const f = tf.range(3, 3);
-    expectArraysEqual(await f.data(), new Float32Array(0));
-    expect(f.shape).toEqual([0]);
-
-    const g = tf.range(3, 3, 1);
-    expectArraysEqual(await g.data(), new Float32Array(0));
-    expect(g.shape).toEqual([0]);
-
-    const h = tf.range(3, 3, 4);
-    expectArraysEqual(await h.data(), new Float32Array(0));
-    expect(h.shape).toEqual([0]);
-
-    const i = tf.range(-18, -2, 5);
-    expectArraysEqual(await i.data(), [-18, -13, -8, -3]);
-    expect(i.shape).toEqual([4]);
-  });
-
-  it('start stop large step', async () => {
-    const a = tf.range(3, 10, 150);
-    expectArraysEqual(await a.data(), [3]);
-    expect(a.shape).toEqual([1]);
-
-    const b = tf.range(10, 500, 205);
-    expectArraysEqual(await b.data(), [10, 215, 420]);
-    expect(b.shape).toEqual([3]);
-
-    const c = tf.range(3, -10, -150);
-    expectArraysEqual(await c.data(), [3]);
-    expect(c.shape).toEqual([1]);
-
-    const d = tf.range(-10, -500, -205);
-    expectArraysEqual(await d.data(), [-10, -215, -420]);
-    expect(d.shape).toEqual([3]);
-  });
-
-  it('start stop negative step', async () => {
-    const a = tf.range(0, -10, -1);
-    expectArraysEqual(await a.data(), [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]);
-    expect(a.shape).toEqual([10]);
-
-    const b = tf.range(0, -10);
-    expectArraysEqual(await b.data(), [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]);
-    expect(b.shape).toEqual([10]);
-
-    const c = tf.range(3, -4, -2);
-    expectArraysEqual(await c.data(), [3, 1, -1, -3]);
-    expect(c.shape).toEqual([4]);
-
-    const d = tf.range(-3, -18, -5);
-    expectArraysEqual(await d.data(), [-3, -8, -13]);
-    expect(d.shape).toEqual([3]);
-  });
-
-  it('start stop incompatible step', async () => {
-    const a = tf.range(3, 10, -2);
-    expectArraysEqual(await a.data(), new Float32Array(0));
-    expect(a.shape).toEqual([0]);
-
-    const b = tf.range(40, 3, 2);
-    expectArraysEqual(await b.data(), new Float32Array(0));
-    expect(b.shape).toEqual([0]);
-  });
-
-  it('zero step', () => {
-    expect(() => tf.range(2, 10, 0)).toThrow();
-  });
-
-  it('should have default dtype', async () => {
-    const a = tf.range(1, 4);
-    expectArraysEqual(await a.data(), [1, 2, 3]);
-    expect(a.dtype).toEqual('float32');
-    expect(a.shape).toEqual([3]);
-  });
-
-  it('should have float32 dtype', async () => {
-    const a = tf.range(1, 4, undefined, 'float32');
-    expectArraysEqual(await a.data(), [1, 2, 3]);
-    expect(a.dtype).toEqual('float32');
-    expect(a.shape).toEqual([3]);
-  });
-
-  it('should have int32 dtype', async () => {
-    const a = tf.range(1, 4, undefined, 'int32');
-    expectArraysEqual(await a.data(), [1, 2, 3]);
-    expect(a.dtype).toEqual('int32');
-    expect(a.shape).toEqual([3]);
-  });
-
-  it('should support large number for int32 dtype', async () => {
-    const length = Math.pow(2, 24) + 10;
-    const a = tf.range(1, length, 1, 'int32');
-    const data = await a.data();
-    expect(data[length - 2]).toEqual(length - 1);
-    expect(a.dtype).toEqual('int32');
-    expect(a.shape).toEqual([length - 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/real.ts b/tfjs-master/tfjs-core/src/ops/real.ts
deleted file mode 100644
index b6e2ba8ef..000000000
--- a/tfjs-master/tfjs-core/src/ops/real.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Real, RealInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {op} from './operation';
-
-/**
- * Returns the real part of a complex (or real) tensor.
- *
- * Given a tensor input, this operation returns a tensor of type float that is
- * the real part of each element in input considered as a complex number.
- *
- * If the input is real, it simply makes a clone.
- *
- * ```js
- * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);
- * tf.real(x).print();
- * ```
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function real_<T extends Tensor>(input: T|TensorLike): T {
-  const $input = convertToTensor(input, 'input', 'real');
-
-  const inputs: RealInputs = {input: $input};
-  return ENGINE.runKernel(Real, inputs as unknown as NamedTensorMap);
-}
-
-export const real = /* @__PURE__ */ op({real_});
diff --git a/tfjs-master/tfjs-core/src/ops/reciprocal.ts b/tfjs-master/tfjs-core/src/ops/reciprocal.ts
deleted file mode 100644
index ae57cec7d..000000000
--- a/tfjs-master/tfjs-core/src/ops/reciprocal.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Reciprocal, ReciprocalInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes reciprocal of x element-wise: `1 / x`
- *
- * ```js
- * const x = tf.tensor1d([0, 1, 2]);
- *
- * x.reciprocal().print();  // or tf.reciprocal(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function reciprocal_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'reciprocal');
-
-  const inputs: ReciprocalInputs = {x: $x};
-  return ENGINE.runKernel(Reciprocal, inputs as unknown as NamedTensorMap);
-}
-export const reciprocal = /* @__PURE__ */ op({reciprocal_});
diff --git a/tfjs-master/tfjs-core/src/ops/reciprocal_test.ts b/tfjs-master/tfjs-core/src/ops/reciprocal_test.ts
deleted file mode 100644
index 4d5ed4d2c..000000000
--- a/tfjs-master/tfjs-core/src/ops/reciprocal_test.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('reciprocal', ALL_ENVS, () => {
-  it('1D array', async () => {
-    const a = tf.tensor1d([2, 3, 0, NaN]);
-    const r = tf.reciprocal(a);
-    expectArraysClose(await r.data(), [1 / 2, 1 / 3, Infinity, NaN]);
-  });
-
-  it('2D array', async () => {
-    const a = tf.tensor2d([1, Infinity, 0, NaN], [2, 2]);
-    const r = tf.reciprocal(a);
-    expect(r.shape).toEqual([2, 2]);
-    expectArraysClose(await r.data(), [1 / 1, 0, Infinity, NaN]);
-  });
-
-  it('reciprocal propagates NaNs', async () => {
-    const a = tf.tensor1d([1.5, NaN]);
-    const r = tf.reciprocal(a);
-    expectArraysClose(await r.data(), [1 / 1.5, NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.reciprocal(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [-1 * 8 * (1 / (5 * 5))]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.reciprocal(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [-1 * 8 * (1 / (5 * 5))]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1, 2, 3, -5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.reciprocal(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [
-      -1 * 1 * (1 / (-1 * -1)), -1 * 2 * (1 / (2 * 2)), -1 * 3 * (1 / (3 * 3)),
-      -1 * 4 * (1 / (-5 * -5))
-    ]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-1, 2, 3, -5], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.reciprocal(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [
-      -1 * 1 * (1 / (-1 * -1)), -1 * 2 * (1 / (2 * 2)), -1 * 3 * (1 / (3 * 3)),
-      -1 * 4 * (1 / (-5 * -5))
-    ]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.reciprocal({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'reciprocal' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.reciprocal([2, 3, 0, NaN]);
-    expectArraysClose(await r.data(), [1 / 2, 1 / 3, Infinity, NaN]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.reciprocal('q'))
-        .toThrowError(/Argument 'x' passed to 'reciprocal' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/reduce_util.ts b/tfjs-master/tfjs-core/src/ops/reduce_util.ts
deleted file mode 100644
index 3a0290d68..000000000
--- a/tfjs-master/tfjs-core/src/ops/reduce_util.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Inputs of size above this threshold will be parallelized by calling multiple
- * shader programs.
- */
-import {nearestDivisor} from '../util';
-
-export const PARALLELIZE_THRESHOLD = 30;
-
-export interface ReduceInfo {
-  windowSize: number;
-  batchSize: number;
-  inSize: number;
-  outSize: number;
-}
-
-export function computeOptimalWindowSize(inSize: number): number {
-  if (inSize <= PARALLELIZE_THRESHOLD) {
-    return inSize;
-  }
-  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-}
diff --git a/tfjs-master/tfjs-core/src/ops/relu.ts b/tfjs-master/tfjs-core/src/ops/relu.ts
deleted file mode 100644
index 2630fe572..000000000
--- a/tfjs-master/tfjs-core/src/ops/relu.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Relu, ReluInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes rectified linear element-wise: `max(x, 0)`.
- *
- * ```js
- * const x = tf.tensor1d([-1, 2, -3, 4]);
- *
- * x.relu().print();  // or tf.relu(x)
- * ```
- * @param x The input tensor. If the dtype is `bool`, the output dtype will be
- *     `int32`.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function relu_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'relu');
-
-  const inputs: ReluInputs = {x: $x};
-
-  return ENGINE.runKernel(Relu, inputs as unknown as NamedTensorMap);
-}
-
-export const relu = /* @__PURE__ */ op({relu_});
diff --git a/tfjs-master/tfjs-core/src/ops/relu6.ts b/tfjs-master/tfjs-core/src/ops/relu6.ts
deleted file mode 100644
index 9b84750a1..000000000
--- a/tfjs-master/tfjs-core/src/ops/relu6.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Relu6, Relu6Inputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.
- *
- * ```js
- * const x = tf.tensor1d([-1, 2, -3, 8]);
- *
- * x.relu6().print();  // or tf.relu6(x)
- * ```
- * @param x The input tensor. If the dtype is `bool`, the output dtype will be
- *     `int32`.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function relu6_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'relu6');
-
-  const inputs: Relu6Inputs = {x: $x};
-
-  return ENGINE.runKernel(Relu6, inputs as unknown as NamedTensorMap);
-}
-
-export const relu6 = /* @__PURE__ */ op({relu6_});
diff --git a/tfjs-master/tfjs-core/src/ops/relu6_test.ts b/tfjs-master/tfjs-core/src/ops/relu6_test.ts
deleted file mode 100644
index bcc9542c5..000000000
--- a/tfjs-master/tfjs-core/src/ops/relu6_test.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('relu6', ALL_ENVS, () => {
-  it('basic relu6', async () => {
-    const a = tf.tensor1d([1, -2, 0, 8, -0.1]);
-    const result = tf.relu6(a);
-    expectArraysClose(await result.data(), [1, 0, 0, 6, 0]);
-  });
-
-  it('int32', async () => {
-    const a = tf.tensor1d([12345678, -2, 0, 3, -1], 'int32');
-    const result = tf.relu6(a);
-    expect(result.dtype).toEqual('int32');
-    expectArraysClose(await result.data(), [6, 0, 0, 3, 0]);
-  });
-
-  it('gradients: relu6', async () => {
-    const a = tf.scalar(8);
-    const dy = tf.scalar(5);
-
-    const grad = tf.grad(a => tf.relu6(a));
-    const da = grad(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [0]);
-  });
-
-  it('gradients: relu6 array', async () => {
-    const a = tf.tensor2d([8, -1, 0, .1], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const grad = tf.grad(a => tf.relu6(a));
-    const da = grad(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [0, 0, 0, 4]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/relu_test.ts b/tfjs-master/tfjs-core/src/ops/relu_test.ts
deleted file mode 100644
index 0e995f3e3..000000000
--- a/tfjs-master/tfjs-core/src/ops/relu_test.ts
+++ /dev/null
@@ -1,139 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {backend} from '../index';
-
-describeWithFlags('relu', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([1, -2, 0, 3, -0.1]);
-    const result = tf.relu(a);
-    expectArraysClose(await result.data(), [1, 0, 0, 3, 0]);
-  });
-
-  it('int32', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const a = tf.tensor1d([12345678, -2, 0, 3, -1], 'int32');
-      const result = tf.relu(a);
-      expect(result.dtype).toEqual('int32');
-      expectArraysClose(await result.data(), [12345678, 0, 0, 3, 0]);
-    }
-  });
-
-  it('5D', async () => {
-    const a = tf.tensor5d([1, -2, 5, -3], [1, 2, 2, 1, 1]);
-    const result = tf.relu(a);
-    expectArraysClose(await result.data(), [1, 0, 5, 0]);
-  });
-
-  it('6D', async () => {
-    const a = tf.tensor6d([1, -2, 5, -3, -1, 4, 7, 8], [1, 2, 2, 2, 1, 1]);
-    const result = tf.relu(a);
-    expectArraysClose(await result.data(), [1, 0, 5, 0, 0, 4, 7, 8]);
-  });
-
-  it('does nothing to positive values', async () => {
-    const a = tf.scalar(1);
-    const result = tf.relu(a);
-    expectArraysClose(await result.data(), [1]);
-  });
-
-  it('sets negative values to 0', async () => {
-    const a = tf.scalar(-1);
-    const result = tf.relu(a);
-    expectArraysClose(await result.data(), [0]);
-  });
-
-  it('preserves zero values', async () => {
-    const a = tf.scalar(0);
-    const result = tf.relu(a);
-    expectArraysClose(await result.data(), [0]);
-  });
-
-  it('propagates NaNs, float32', async () => {
-    const a = tf.tensor1d([1, -2, 0, 3, -0.1, NaN]);
-    const result = tf.relu(a);
-    expect(result.dtype).toBe('float32');
-    expectArraysClose(await result.data(), [1, 0, 0, 3, 0, NaN]);
-  });
-
-  it('gradients: positive scalar', async () => {
-    const a = tf.scalar(3);
-    const dy = tf.scalar(5);
-
-    const grad = tf.grad(a => tf.relu(a));
-    const da = grad(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [5]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(3);
-    const dy = tf.scalar(5);
-
-    const grad = tf.grad(a => tf.relu(a.clone()).clone());
-    const da = grad(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [5]);
-  });
-
-  it('gradients: negative scalar', async () => {
-    const a = tf.scalar(-3);
-    const dy = tf.scalar(5);
-
-    const grad = tf.grad(a => tf.relu(a));
-    const da = grad(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [0]);
-  });
-
-  it('gradients: array', async () => {
-    const a = tf.tensor2d([1, -1, 0, .1], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const grad = tf.grad(a => tf.relu(a));
-    const da = grad(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1, 0, 0, 4]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.relu({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'relu' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.relu([1, -2, 0, 3, -0.1]);
-    expectArraysClose(await result.data(), [1, 0, 0, 3, 0]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.relu('q'))
-        .toThrowError(/Argument 'x' passed to 'relu' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/reshape.ts b/tfjs-master/tfjs-core/src/ops/reshape.ts
deleted file mode 100644
index d213719f6..000000000
--- a/tfjs-master/tfjs-core/src/ops/reshape.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Reshape, ReshapeAttrs, ReshapeInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {Rank, ShapeMap, TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Reshapes a `tf.Tensor` to a given shape.
- *
- * Given an input tensor, returns a new tensor with the same values as the
- * input tensor with shape `shape`.
- *
- * If one component of shape is the special value -1, the size of that
- * dimension is computed so that the total size remains constant. In
- * particular, a shape of [-1] flattens into 1-D. At most one component of
- * shape can be -1.
- *
- * If shape is 1-D or higher, then the operation returns a tensor with shape
- * shape filled with the values of tensor. In this case, the number of
- * elements implied by shape must be the same as the number of elements in
- * tensor.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- * x.reshape([2, 2]).print();
- * ```
- *
- * @param x The input tensor to be reshaped.
- * @param shape An array of integers defining the output tensor shape.
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function reshape_<R extends Rank>(
-    x: Tensor|TensorLike, shape: ShapeMap[R]): Tensor<R> {
-  const $x = convertToTensor(x, 'x', 'reshape', 'string_or_numeric');
-
-  const inputs: ReshapeInputs = {x: $x};
-  const attrs: ReshapeAttrs = {shape};
-  return ENGINE.runKernel(
-      Reshape, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-export const reshape = /* @__PURE__ */ op({reshape_});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse.ts b/tfjs-master/tfjs-core/src/ops/reverse.ts
deleted file mode 100644
index caa9f0333..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Reverse, ReverseAttrs, ReverseInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Reverses a `tf.Tensor` along a specified axis.
- *
- * Also available are stricter rank-specific methods that assert that `x` is
- * of the given rank:
- *   - `tf.reverse1d`
- *   - `tf.reverse2d`
- *   - `tf.reverse3d`
- *   - `tf.reverse4d`
- *
- * Except `tf.reverse1d` (which does not have axis param), all methods have
- * same signature as this method.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- *
- * x.reverse().print();
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * const axis = 1;
- * x.reverse(axis).print();
- * ```
- * @param x The input tensor to be reversed.
- * @param axis The set of dimensions to reverse. Must be in the
- *     range [-rank(x), rank(x)). Defaults to all axes.
- *
- * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
- */
-function reverse_<T extends Tensor>(
-    x: T|TensorLike, axis?: number|number[]): T {
-  const $x = convertToTensor(x, 'x', 'reverse');
-
-  const inputs: ReverseInputs = {x: $x};
-  const attrs: ReverseAttrs = {dims: axis};
-
-  return ENGINE.runKernel(
-      Reverse, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const reverse = /* @__PURE__ */ op({reverse_});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse_1d.ts b/tfjs-master/tfjs-core/src/ops/reverse_1d.ts
deleted file mode 100644
index 5dc27f62a..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse_1d.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor1D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-import {op} from './operation';
-import {reverse} from './reverse';
-
-/**
- * Reverses a `tf.Tensor1D`.
- *
- * @param x The input tensor.
- */
-function reverse1d_(x: Tensor1D|TensorLike): Tensor1D {
-  const $x = convertToTensor(x, 'x', 'reverse');
-  util.assert(
-      $x.rank === 1,
-      () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
-  return reverse($x, 0);
-}
-
-export const reverse1d = /* @__PURE__ */ op({reverse1d_});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse_1d_test.ts b/tfjs-master/tfjs-core/src/ops/reverse_1d_test.ts
deleted file mode 100644
index 06b6f8cbb..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse_1d_test.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('reverse1d', ALL_ENVS, () => {
-  it('reverse a 1D array', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const result = tf.reverse1d(input);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [5, 4, 3, 2, 1]);
-  });
-
-  it('reverse a 1D array, even length', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4]);
-    const result = tf.reverse1d(input);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [4, 3, 2, 1]);
-  });
-
-  it('grad', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([10, 20, 30]);
-    const da = tf.grad((a: tf.Tensor1D) => tf.reverse1d(a))(a, dy);
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [30, 20, 10]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([10, 20, 30]);
-    const da =
-        tf.grad((a: tf.Tensor1D) => tf.reverse1d(a.clone()).clone())(a, dy);
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [30, 20, 10]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const input = [1, 2, 3, 4, 5];
-    const result = tf.reverse1d(input);
-    expect(result.shape).toEqual([5]);
-    expectArraysClose(await result.data(), [5, 4, 3, 2, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse_2d.ts b/tfjs-master/tfjs-core/src/ops/reverse_2d.ts
deleted file mode 100644
index 45c869423..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse_2d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor2D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-import {op} from './operation';
-import {reverse} from './reverse';
-
-/**
- * Reverses a `tf.Tensor2D` along a specified axis.
- *
- * @param x The input tensor.
- * @param axis The set of dimensions to reverse. Must be in the
- *     range [-rank(x), rank(x)). Defaults to all axes.
- */
-function reverse2d_(x: Tensor2D|TensorLike, axis?: number|number[]): Tensor2D {
-  const $x = convertToTensor(x, 'x', 'reverse');
-  util.assert(
-      $x.rank === 2,
-      () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-
-export const reverse2d = /* @__PURE__ */ op({reverse2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse_2d_test.ts b/tfjs-master/tfjs-core/src/ops/reverse_2d_test.ts
deleted file mode 100644
index d88053d4d..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse_2d_test.ts
+++ /dev/null
@@ -1,102 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('reverse2d', ALL_ENVS, () => {
-  it('reverse a 2D array at axis [0]', async () => {
-    const axis = [0];
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const result = tf.reverse2d(a, axis);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [4, 5, 6, 1, 2, 3]);
-  });
-
-  it('reverse a 2D array at axis [1]', async () => {
-    const axis = [1];
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const result = tf.reverse2d(a, axis);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [3, 2, 1, 6, 5, 4]);
-  });
-
-  it('reverse a 2D array odd rows and columns at axis [0, 1]', async () => {
-    const axis = [0, 1];
-    const a = tf.tensor2d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [3, 5]);
-    const result = tf.reverse2d(a, axis);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(
-        await result.data(),
-        [15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]);
-  });
-
-  it('throws error with invalid input', () => {
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor1d([1, 20, 300, 4]);
-    expect(() => tf.reverse2d(x, [0])).toThrowError();
-  });
-
-  it('throws error with invalid axis param', () => {
-    const x = tf.tensor2d([1, 20, 300, 4], [1, 4]);
-    expect(() => tf.reverse2d(x, [2])).toThrowError();
-    expect(() => tf.reverse2d(x, [-3])).toThrowError();
-  });
-
-  it('throws error with non integer axis param', () => {
-    const x = tf.tensor2d([1, 20, 300, 4], [1, 4]);
-    expect(() => tf.reverse2d(x, [0.5])).toThrowError();
-  });
-
-  it('grad', async () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const dy = tf.tensor2d([[10, 20, 30], [40, 50, 60]]);
-    const da = tf.grad((a: tf.Tensor2D) => tf.reverse2d(a))(a, dy);
-    expect(da.shape).toEqual([2, 3]);
-    expectArraysClose(await da.data(), [60, 50, 40, 30, 20, 10]);
-  });
-
-  it('grad with reverse(axis=0)', async () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const dy = tf.tensor2d([[10, 20, 30], [40, 50, 60]]);
-    const da = tf.grad((a: tf.Tensor2D) => tf.reverse2d(a, 0))(a, dy);
-    expect(da.shape).toEqual([2, 3]);
-    expectArraysClose(await da.data(), [40, 50, 60, 10, 20, 30]);
-  });
-
-  it('grad with reverse(axis=1)', async () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const dy = tf.tensor2d([[10, 20, 30], [40, 50, 60]]);
-    const da = tf.grad((a: tf.Tensor2D) => tf.reverse2d(a, 1))(a, dy);
-    expect(da.shape).toEqual([2, 3]);
-    expectArraysClose(await da.data(), [30, 20, 10, 60, 50, 40]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const axis = [0];
-    const a = [[1, 2, 3], [4, 5, 6]];  // 2x3
-    const result = tf.reverse2d(a, axis);
-
-    expect(result.shape).toEqual([2, 3]);
-    expectArraysClose(await result.data(), [4, 5, 6, 1, 2, 3]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse_3d.ts b/tfjs-master/tfjs-core/src/ops/reverse_3d.ts
deleted file mode 100644
index a1d6f1f34..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse_3d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor3D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-import {op} from './operation';
-import {reverse} from './reverse';
-
-/**
- * Reverses a `tf.Tensor3D` along a specified axis.
- *
- * @param x The input tensor.
- * @param axis The set of dimensions to reverse. Must be in the
- *     range [-rank(x), rank(x)). Defaults to all axes.
- */
-function reverse3d_(x: Tensor3D|TensorLike, axis?: number|number[]): Tensor3D {
-  const $x = convertToTensor(x, 'x', 'reverse');
-  util.assert(
-      $x.rank === 3,
-      () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-
-export const reverse3d = /* @__PURE__ */ op({reverse3d_});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse_3d_test.ts b/tfjs-master/tfjs-core/src/ops/reverse_3d_test.ts
deleted file mode 100644
index 3fa307a49..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse_3d_test.ts
+++ /dev/null
@@ -1,124 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('reverse3d', ALL_ENVS, () => {
-  // [
-  //   [
-  //     [0,  1,  2,  3],
-  //     [4,  5,  6,  7],
-  //     [8,  9,  10, 11]
-  //   ],
-  //   [
-  //     [12, 13, 14, 15],
-  //     [16, 17, 18, 19],
-  //     [20, 21, 22, 23]
-  //   ]
-  // ]
-  const shape: [number, number, number] = [2, 3, 4];
-  const data = [
-    0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11,
-    12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
-  ];
-
-  it('reverse a 3D array at axis [0]', async () => {
-    const input = tf.tensor3d(data, shape);
-    const result = tf.reverse3d(input, [0]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
-      0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11
-    ]);
-  });
-
-  it('reverse a 3D array at axis [1]', async () => {
-    const input = tf.tensor3d(data, shape);
-    const result = tf.reverse3d(input, [1]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      8,  9,  10, 11, 4,  5,  6,  7,  0,  1,  2,  3,
-      20, 21, 22, 23, 16, 17, 18, 19, 12, 13, 14, 15
-    ]);
-  });
-
-  it('reverse a 3D array at axis [2]', async () => {
-    const input = tf.tensor3d(data, shape);
-    const result = tf.reverse3d(input, [2]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      3,  2,  1,  0,  7,  6,  5,  4,  11, 10, 9,  8,
-      15, 14, 13, 12, 19, 18, 17, 16, 23, 22, 21, 20
-    ]);
-  });
-
-  it('reverse a 3D array at axis [0, 1]', async () => {
-    const input = tf.tensor3d(data, shape);
-    const result = tf.reverse3d(input, [0, 1]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      20, 21, 22, 23, 16, 17, 18, 19, 12, 13, 14, 15,
-      8,  9,  10, 11, 4,  5,  6,  7,  0,  1,  2,  3
-    ]);
-  });
-
-  it('reverse a 3D array at axis [0, 2]', async () => {
-    const input = tf.tensor3d(data, shape);
-    const result = tf.reverse3d(input, [0, 2]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      15, 14, 13, 12, 19, 18, 17, 16, 23, 22, 21, 20,
-      3,  2,  1,  0,  7,  6,  5,  4,  11, 10, 9,  8
-    ]);
-  });
-
-  it('reverse a 3D array at axis [1, 2]', async () => {
-    const input = tf.tensor3d(data, shape);
-    const result = tf.reverse3d(input, [1, 2]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      11, 10, 9,  8,  7,  6,  5,  4,  3,  2,  1,  0,
-      23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12
-    ]);
-  });
-
-  it('throws error with invalid input', () => {
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor2d([1, 20, 300, 4], [1, 4]);
-    expect(() => tf.reverse3d(x, [1])).toThrowError();
-  });
-
-  it('throws error with invalid axis param', () => {
-    const x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
-    expect(() => tf.reverse3d(x, [3])).toThrowError();
-    expect(() => tf.reverse3d(x, [-4])).toThrowError();
-  });
-
-  it('throws error with non integer axis param', () => {
-    const x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
-    expect(() => tf.reverse3d(x, [0.5])).toThrowError();
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const input = [[[1], [2], [3]], [[4], [5], [6]]];  // 2x3x1
-    const result = tf.reverse3d(input, [0]);
-    expect(result.shape).toEqual([2, 3, 1]);
-    expectArraysClose(await result.data(), [4, 5, 6, 1, 2, 3]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse_4d.ts b/tfjs-master/tfjs-core/src/ops/reverse_4d.ts
deleted file mode 100644
index 1f39c953f..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse_4d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor4D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-import {op} from './operation';
-import {reverse} from './reverse';
-
-/**
- * Reverses a `tf.Tensor4D` along a specified axis.
- *
- * @param x The input tensor.
- * @param axis The set of dimensions to reverse. Must be in the
- *     range [-rank(x), rank(x)). Defaults to all axes.
- */
-function reverse4d_(x: Tensor4D|TensorLike, axis?: number|number[]): Tensor4D {
-  const $x = convertToTensor(x, 'x', 'reverse');
-  util.assert(
-      $x.rank === 4,
-      () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
-  return reverse($x, axis);
-}
-
-export const reverse4d = /* @__PURE__ */ op({reverse4d_});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse_4d_test.ts b/tfjs-master/tfjs-core/src/ops/reverse_4d_test.ts
deleted file mode 100644
index fe43426af..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse_4d_test.ts
+++ /dev/null
@@ -1,164 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('reverse4d', ALL_ENVS, () => {
-  // [
-  //   [
-  //     [
-  //       [0,  1,  2,  3],
-  //       [4,  5,  6,  7],
-  //       [8,  9,  10, 11]
-  //     ],
-  //     [
-  //       [12, 13, 14, 15],
-  //       [16, 17, 18, 19],
-  //       [20, 21, 22, 23]
-  //     ]
-  //   ],
-  //   [
-  //     [
-  //       [24, 25, 26, 27],
-  //       [28, 29, 30, 31],
-  //       [32, 33, 34, 35]
-  //     ],
-  //     [
-  //       [36, 37, 38, 39],
-  //       [40, 41, 42, 43],
-  //       [44, 45, 46, 47]
-  //     ]
-  //   ],
-  //   [
-  //     [
-  //       [48, 49, 50, 51],
-  //       [52, 53, 54, 55],
-  //       [56, 57, 58, 59]
-  //     ],
-  //     [
-  //       [60, 61, 62, 63],
-  //       [64, 65, 66, 67],
-  //       [68, 69, 70, 71]
-  //     ]
-  //   ]
-  // ]
-  const shape: [number, number, number, number] = [3, 2, 3, 4];
-  const data = [
-    0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17,
-    18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
-    36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-    54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71
-  ];
-
-  it('reverse a 4D array at axis [0]', async () => {
-    const input = tf.tensor4d(data, shape);
-    const result = tf.reverse4d(input, [0]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,
-      66, 67, 68, 69, 70, 71, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
-      36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 0,  1,  2,  3,  4,  5,
-      6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
-    ]);
-  });
-
-  it('reverse a 4D array at axis [1]', async () => {
-    const input = tf.tensor4d(data, shape);
-    const result = tf.reverse4d(input, [1]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0,  1,  2,  3,  4,  5,
-      6,  7,  8,  9,  10, 11, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
-      24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 60, 61, 62, 63, 64, 65,
-      66, 67, 68, 69, 70, 71, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59
-    ]);
-  });
-
-  it('reverse a 4D array at axis [2]', async () => {
-    const input = tf.tensor4d(data, shape);
-    const result = tf.reverse4d(input, [2]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      8,  9,  10, 11, 4,  5,  6,  7,  0,  1,  2,  3,  20, 21, 22, 23, 16, 17,
-      18, 19, 12, 13, 14, 15, 32, 33, 34, 35, 28, 29, 30, 31, 24, 25, 26, 27,
-      44, 45, 46, 47, 40, 41, 42, 43, 36, 37, 38, 39, 56, 57, 58, 59, 52, 53,
-      54, 55, 48, 49, 50, 51, 68, 69, 70, 71, 64, 65, 66, 67, 60, 61, 62, 63
-    ]);
-  });
-
-  it('reverse a 4D array at axis [3]', async () => {
-    const input = tf.tensor4d(data, shape);
-    const result = tf.reverse4d(input, [3]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      3,  2,  1,  0,  7,  6,  5,  4,  11, 10, 9,  8,  15, 14, 13, 12, 19, 18,
-      17, 16, 23, 22, 21, 20, 27, 26, 25, 24, 31, 30, 29, 28, 35, 34, 33, 32,
-      39, 38, 37, 36, 43, 42, 41, 40, 47, 46, 45, 44, 51, 50, 49, 48, 55, 54,
-      53, 52, 59, 58, 57, 56, 63, 62, 61, 60, 67, 66, 65, 64, 71, 70, 69, 68
-    ]);
-  });
-
-  it('reverse a 4D array at axis [0, 2]', async () => {
-    const input = tf.tensor4d(data, shape);
-    const result = tf.reverse4d(input, [0, 2]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      56, 57, 58, 59, 52, 53, 54, 55, 48, 49, 50, 51, 68, 69, 70, 71, 64, 65,
-      66, 67, 60, 61, 62, 63, 32, 33, 34, 35, 28, 29, 30, 31, 24, 25, 26, 27,
-      44, 45, 46, 47, 40, 41, 42, 43, 36, 37, 38, 39, 8,  9,  10, 11, 4,  5,
-      6,  7,  0,  1,  2,  3,  20, 21, 22, 23, 16, 17, 18, 19, 12, 13, 14, 15
-    ]);
-  });
-
-  it('reverse a 4D array at axis [1, 3]', async () => {
-    const input = tf.tensor4d(data, shape);
-    const result = tf.reverse4d(input, [1, 3]);
-    expect(result.shape).toEqual(input.shape);
-    expectArraysClose(await result.data(), [
-      15, 14, 13, 12, 19, 18, 17, 16, 23, 22, 21, 20, 3,  2,  1,  0,  7,  6,
-      5,  4,  11, 10, 9,  8,  39, 38, 37, 36, 43, 42, 41, 40, 47, 46, 45, 44,
-      27, 26, 25, 24, 31, 30, 29, 28, 35, 34, 33, 32, 63, 62, 61, 60, 67, 66,
-      65, 64, 71, 70, 69, 68, 51, 50, 49, 48, 55, 54, 53, 52, 59, 58, 57, 56
-    ]);
-  });
-
-  it('throws error with invalid input', () => {
-    // tslint:disable-next-line:no-any
-    const x: any = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
-    expect(() => tf.reverse4d(x, [1])).toThrowError();
-  });
-
-  it('throws error with invalid axis param', () => {
-    const x = tf.tensor4d([1, 20, 300, 4], [1, 1, 1, 4]);
-    expect(() => tf.reverse4d(x, [4])).toThrowError();
-    expect(() => tf.reverse4d(x, [-5])).toThrowError();
-  });
-
-  it('throws error with non integer axis param', () => {
-    const x = tf.tensor4d([1, 20, 300, 4], [1, 1, 1, 4]);
-    expect(() => tf.reverse4d(x, [0.5])).toThrowError();
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const input = [[[[1]], [[2]], [[3]]], [[[4]], [[5]], [[6]]]];  // 2x3x1x1
-    const result = tf.reverse4d(input, [0]);
-    expect(result.shape).toEqual([2, 3, 1, 1]);
-    expectArraysClose(await result.data(), [4, 5, 6, 1, 2, 3]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/reverse_test.ts b/tfjs-master/tfjs-core/src/ops/reverse_test.ts
deleted file mode 100644
index 5b6fd635a..000000000
--- a/tfjs-master/tfjs-core/src/ops/reverse_test.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {backend} from '../index';
-
-describeWithFlags('reverse', ALL_ENVS, () => {
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.reverse({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'reverse' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const input = [1, 2, 3];
-    const result = tf.reverse(input);
-    expect(result.shape).toEqual([3]);
-    expectArraysClose(await result.data(), [3, 2, 1]);
-  });
-
-  it('works with int32 input', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const input = tf.tensor1d([1, 2, 12345678], 'int32');
-      const result = tf.reverse(input);
-      expect(result.shape).toEqual([3]);
-      expect(result.dtype).toEqual('int32');
-      expectArraysClose(await result.data(), [12345678, 2, 1]);
-    }
-  });
-
-  it('ensure no memory leak', async () => {
-    const numTensorsBefore = tf.memory().numTensors;
-    const numDataIdBefore = tf.engine().backend.numDataIds();
-
-    const input = tf.tensor1d([1, 2, 3]);
-    const result = tf.reverse(input);
-    expect(result.shape).toEqual([3]);
-    expectArraysClose(await result.data(), [3, 2, 1]);
-
-    input.dispose();
-    result.dispose();
-
-    const numTensorsAfter = tf.memory().numTensors;
-    const numDataIdAfter = tf.engine().backend.numDataIds();
-    expect(numTensorsAfter).toBe(numTensorsBefore);
-    expect(numDataIdAfter).toBe(numDataIdBefore);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/rotate_util.ts b/tfjs-master/tfjs-core/src/ops/rotate_util.ts
deleted file mode 100644
index bfc111b67..000000000
--- a/tfjs-master/tfjs-core/src/ops/rotate_util.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Returns the image center in pixels.
-export function getImageCenter(
-    center: number|[number, number], imageHeight: number,
-    imageWidth: number): [number, number] {
-  const centerX =
-      imageWidth * (typeof center === 'number' ? center : center[0]);
-  const centerY =
-      imageHeight * (typeof center === 'number' ? center : center[1]);
-  return [centerX, centerY];
-}
diff --git a/tfjs-master/tfjs-core/src/ops/round.ts b/tfjs-master/tfjs-core/src/ops/round.ts
deleted file mode 100644
index 8cb0daaea..000000000
--- a/tfjs-master/tfjs-core/src/ops/round.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Round, RoundInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes round of input `tf.Tensor` element-wise: `round(x)`.
- * It implements banker's rounding.
- *
- * ```js
- * const x = tf.tensor1d([.6, 1.1, -3.3]);
- *
- * x.round().print();  // or tf.round(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function round_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'round');
-  const inputs: RoundInputs = {x: $x};
-
-  return ENGINE.runKernel(Round, inputs as unknown as NamedTensorMap);
-}
-
-export const round = /* @__PURE__ */ op({round_});
diff --git a/tfjs-master/tfjs-core/src/ops/round_test.ts b/tfjs-master/tfjs-core/src/ops/round_test.ts
deleted file mode 100644
index 69ba9ac9e..000000000
--- a/tfjs-master/tfjs-core/src/ops/round_test.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {backend} from '../index';
-
-describeWithFlags('round', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([0.9, 2.5, 2.3, 1.5, -4.5]);
-    const r = a.round();
-
-    expectArraysClose(await r.data(), [1, 2, 2, 2, -4]);
-  });
-
-  it('int32', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const a = tf.tensor1d([-12345678, 10, 12345678], 'int32');
-      const r = a.round();
-
-      expect(r.dtype).toEqual('int32');
-      expectArraysClose(await r.data(), [-12345678, 10, 12345678]);
-    }
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([1.5, NaN, -1.4]);
-    const r = tf.round(a);
-    expectArraysClose(await r.data(), [2, NaN, -1]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5.2);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.round(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5.2);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.round(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.round(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.round(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.round({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'round' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.round([0.9, 2.5, 2.3, 1.5, -4.5]);
-    expectArraysClose(await r.data(), [1, 2, 2, 2, -4]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.round('q'))
-        .toThrowError(/Argument 'x' passed to 'round' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/rsqrt.ts b/tfjs-master/tfjs-core/src/ops/rsqrt.ts
deleted file mode 100644
index 8a90db92a..000000000
--- a/tfjs-master/tfjs-core/src/ops/rsqrt.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Rsqrt, RsqrtInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes reciprocal of square root of the input `tf.Tensor` element-wise:
- * `y = 1 / sqrt(x)`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 4, -1]);
- *
- * x.rsqrt().print();  // or tf.rsqrt(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function rsqrt_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'rsqrt', 'float32');
-
-  const inputs: RsqrtInputs = {x: $x};
-
-  return ENGINE.runKernel(Rsqrt, inputs as unknown as NamedTensorMap);
-}
-export const rsqrt = /* @__PURE__ */ op({rsqrt_});
diff --git a/tfjs-master/tfjs-core/src/ops/rsqrt_test.ts b/tfjs-master/tfjs-core/src/ops/rsqrt_test.ts
deleted file mode 100644
index 333fb46ae..000000000
--- a/tfjs-master/tfjs-core/src/ops/rsqrt_test.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('rsqrt', ALL_ENVS, () => {
-  it('rsqrt', async () => {
-    const a = tf.tensor1d([2, 4]);
-    const r = tf.rsqrt(a);
-    expectArraysClose(await r.data(), [1 / Math.sqrt(2), 1 / Math.sqrt(4)]);
-  });
-
-  it('rsqrt propagates NaNs', async () => {
-    const a = tf.tensor1d([1, NaN]);
-    const r = tf.rsqrt(a);
-    expectArraysClose(await r.data(), [1 / Math.sqrt(1), NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(4);
-    const dy = tf.scalar(8);
-
-    const da = tf.grad(a => tf.rsqrt(a))(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [(-1 * 8) / (2 * Math.pow(4, 1.5))]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(4);
-    const dy = tf.scalar(8);
-
-    const da = tf.grad(a => tf.rsqrt(a.clone()).clone())(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [(-1 * 8) / (2 * Math.pow(4, 1.5))]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, 3, 5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.rsqrt(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [
-          -1 * 1 / (2 * Math.pow(1, 1.5)), -1 * 2 / (2 * Math.pow(2, 1.5)),
-          -1 * 3 / (2 * Math.pow(3, 1.5)), -1 * 4 / (2 * Math.pow(5, 1.5))
-        ],
-        1e-1);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.rsqrt(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [
-          -1 * 1 / (2 * Math.pow(3, 1.5)), -1 * 2 / (2 * Math.pow(1, 1.5)),
-          -1 * 3 / (2 * Math.pow(2, 1.5)), -1 * 4 / (2 * Math.pow(3, 1.5))
-        ],
-        1e-1);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.rsqrt({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'rsqrt' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.rsqrt([2, 4]);
-    expectArraysClose(await r.data(), [1 / Math.sqrt(2), 1 / Math.sqrt(4)]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.rsqrt('q'))
-        .toThrowError(/Argument 'x' passed to 'rsqrt' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.rsqrt(tf.tensor1d([1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'rsqrt' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/scalar.ts b/tfjs-master/tfjs-core/src/ops/scalar.ts
deleted file mode 100644
index 107a3d076..000000000
--- a/tfjs-master/tfjs-core/src/ops/scalar.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Scalar} from '../tensor';
-import {DataType} from '../types';
-import {isTypedArray} from '../util';
-import {makeTensor} from './tensor_ops_util';
-
-/**
- * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.
- *
- * The same functionality can be achieved with `tf.tensor`, but in general
- * we recommend using `tf.scalar` as it makes the code more readable.
- *
- * ```js
- * tf.scalar(3.14).print();
- * ```
- *
- * @param value The value of the scalar.
- * @param dtype The data type.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function scalar(
-    value: number|boolean|string|Uint8Array, dtype?: DataType): Scalar {
-  if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&
-      dtype !== 'complex64') {
-    throw new Error(
-        'Error creating a new Scalar: value must be a primitive ' +
-        '(number|boolean|string)');
-  }
-  if (dtype === 'string' && isTypedArray(value) &&
-      !(value instanceof Uint8Array)) {
-    throw new Error(
-        'When making a scalar from encoded string, ' +
-        'the value must be `Uint8Array`.');
-  }
-  const shape: number[] = [];
-  const inferredShape: number[] = [];
-  return makeTensor(value, shape, inferredShape, dtype) as Scalar;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/scatter_nd.ts b/tfjs-master/tfjs-core/src/ops/scatter_nd.ts
deleted file mode 100644
index 6dfd19ace..000000000
--- a/tfjs-master/tfjs-core/src/ops/scatter_nd.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {ScatterNd, ScatterNdAttrs, ScatterNdInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {Rank, ShapeMap, TensorLike} from '../types';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-
-import {op} from './operation';
-import * as scatter_nd_util from './scatter_nd_util';
-
-/**
- * Creates a new tensor by applying sparse updates to individual
- * values or slices within a zero tensor of the given shape tensor according to
- * indices. This operator is the inverse of the `tf.gatherND` operator which
- * extracts values or slices from a given tensor.
- *
- * ```js
- * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');
- * const updates = tf.tensor1d([9, 10, 11, 12]);
- * const shape = [8];
- * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]
- * ```
- *
- * @param indices The tensor contains the indices into the output tensor.
- * @param updates The tensor contains the value for the indices.
- * @param shape: The shape of the output tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
- */
-function scatterND_<R extends Rank>(
-    indices: Tensor|TensorLike, updates: Tensor|TensorLike,
-    shape: ShapeMap[R]): Tensor<R> {
-  assertNonNegativeIntegerDimensions(shape);
-  const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');
-  const $updates = convertToTensor(updates, 'updates', 'scatterND');
-  scatter_nd_util.validateInput($updates, $indices, shape);
-
-  const inputs: ScatterNdInputs = {indices: $indices, updates: $updates};
-  const attrs: ScatterNdAttrs = {shape};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  return ENGINE.runKernel(
-             ScatterNd, inputs as unknown as NamedTensorMap,
-             attrs as unknown as NamedAttrMap) as Tensor<R>;
-}
-
-export const scatterND = /* @__PURE__ */ op({scatterND_});
diff --git a/tfjs-master/tfjs-core/src/ops/scatter_nd_test.ts b/tfjs-master/tfjs-core/src/ops/scatter_nd_test.ts
deleted file mode 100644
index 9c081f01b..000000000
--- a/tfjs-master/tfjs-core/src/ops/scatter_nd_test.ts
+++ /dev/null
@@ -1,159 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('scatterND', ALL_ENVS, () => {
-  it('should work for 2d', async () => {
-    const indices = tf.tensor1d([0, 4, 2], 'int32');
-    const updates = tf.tensor2d(
-        [100, 101, 102, 777, 778, 779, 1000, 1001, 1002], [3, 3], 'int32');
-    const shape = [5, 3];
-    const result = tf.scatterND(indices, updates, shape);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(
-        await result.data(),
-        [100, 101, 102, 0, 0, 0, 1000, 1001, 1002, 0, 0, 0, 777, 778, 779]);
-  });
-
-  it('should work for simple 1d', async () => {
-    const indices = tf.tensor1d([3], 'int32');
-    const updates = tf.tensor1d([101], 'float32');
-    const shape = [5];
-    const result = tf.scatterND(indices, updates, shape);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(await result.data(), [0, 0, 0, 101, 0]);
-  });
-
-  it('should work for multiple 1d', async () => {
-    const indices = tf.tensor1d([0, 4, 2], 'int32');
-    const updates = tf.tensor1d([100, 101, 102], 'float32');
-    const shape = [5];
-    const result = tf.scatterND(indices, updates, shape);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(await result.data(), [100, 0, 102, 0, 101]);
-  });
-
-  it('should work for high rank updates', async () => {
-    const indices = tf.tensor2d([0, 2], [2, 1], 'int32');
-    const updates = tf.tensor3d(
-        [
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
-        ],
-        [2, 4, 4], 'float32');
-    const shape = [4, 4, 4];
-    const result = tf.scatterND(indices, updates, shape);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(await result.data(), [
-      5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0,
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,
-      8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-    ]);
-  });
-
-  it('should work for high rank indices', async () => {
-    const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');
-    const updates = tf.tensor1d([10, 20], 'float32');
-    const shape = [3, 3];
-    const result = tf.scatterND(indices, updates, shape);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(await result.data(), [0, 20, 10, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('should work for high rank indices and update', () => {
-    const indices = tf.tensor2d([1, 0, 0, 1, 0, 1], [3, 2], 'int32');
-    const updates = tf.ones([3, 256], 'float32');
-    const shape = [2, 2, 256];
-    const result = tf.scatterND(indices, updates, shape);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-  });
-
-  it('should sum the duplicated indices', async () => {
-    const indices = tf.tensor1d([0, 4, 2, 1, 3, 0], 'int32');
-    const updates = tf.tensor1d([10, 20, 30, 40, 50, 60], 'float32');
-    const shape = [8];
-    const result = tf.scatterND(indices, updates, shape);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(await result.data(), [70, 40, 30, 50, 20, 0, 0, 0]);
-  });
-
-  it('should work for tensorLike input', async () => {
-    const indices = [0, 4, 2];
-    const updates = [100, 101, 102];
-    const shape = [5];
-    const result = tf.scatterND(indices, updates, shape);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual('float32');
-    expectArraysClose(await result.data(), [100, 0, 102, 0, 101]);
-  });
-
-  it('should throw error when indices type is not int32', () => {
-    const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'float32');
-    const updates = tf.tensor1d([10, 20], 'float32');
-    const shape = [3, 3];
-    expect(() => tf.scatterND(indices, updates, shape)).toThrow();
-  });
-
-  it('should throw error when indices and update mismatch', () => {
-    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
-    const updates = tf.tensor2d(
-        [100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004],
-        [3, 4], 'float32');
-    const shape = [5, 3];
-    expect(() => tf.scatterND(indices, updates, shape)).toThrow();
-  });
-
-  it('should throw error when indices and update count mismatch', () => {
-    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
-    const updates =
-        tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');
-    const shape = [5, 3];
-    expect(() => tf.scatterND(indices, updates, shape)).toThrow();
-  });
-
-  it('should throw error when indices are scalar', () => {
-    const indices = tf.scalar(1, 'int32');
-    const updates =
-        tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');
-    const shape = [5, 3];
-    expect(() => tf.scatterND(indices, updates, shape)).toThrow();
-  });
-
-  it('should throw error when update is scalar', () => {
-    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
-    const updates = tf.scalar(1, 'float32');
-    const shape = [5, 3];
-    expect(() => tf.scatterND(indices, updates, shape)).toThrow();
-  });
-
-  it('should throw error when shape is not integer', () => {
-    const indices = [0, 4, 2];
-    const updates = [100, 101, 102];
-    const shape = [5.55, 2.22];
-    expect(() => tf.scatterND(indices, updates, shape)).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/scatter_nd_util.ts b/tfjs-master/tfjs-core/src/ops/scatter_nd_util.ts
deleted file mode 100644
index e52b2934e..000000000
--- a/tfjs-master/tfjs-core/src/ops/scatter_nd_util.ts
+++ /dev/null
@@ -1,149 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import { TensorInfo } from '../tensor_info';
-import {Tensor} from '../tensor';
-import {computeStrides, sizeFromShape} from '../util';
-
-/**
- * Check whether updates.shape = indices.shape[:batchDim] +
- * shape[sliceDim:]
- *
- * @param x The input tensor.
- */
-export function validateUpdateShape(
-    shape: number[], indices: Tensor, updates: Tensor) {
-  const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;
-  const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;
-
-  const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +
-      `shape[sliceDim:], got updates.shape: ${updates.shape}` +
-      `, indices.shape: ${indices.shape}, shape: ${shape}` +
-      `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
-
-  if (updates.rank < batchDim) {
-    throw new Error(shapeError + ` update.rank < ${batchDim}. `);
-  }
-  if (shape.length < sliceDim + (updates.rank - batchDim)) {
-    throw new Error(
-        shapeError +
-        ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
-  }
-  if (updates.rank !== batchDim + shape.length - sliceDim) {
-    throw new Error(
-        shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
-  }
-  for (let d = 0; d < batchDim; ++d) {
-    if (updates.shape[d] !== indices.shape[d]) {
-      throw new Error(
-          shapeError +
-          ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${
-              indices.shape[d]}).`);
-    }
-  }
-  for (let d = 0; d < updates.rank - batchDim; ++d) {
-    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
-      throw new Error(
-          shapeError +
-          ` updates.shape[${d + batchDim}] (${
-              updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${
-              shape[d + batchDim]})`);
-    }
-  }
-}
-
-export interface ScatterShapeInfo {
-  sliceRank: number;
-  numUpdates: number;
-  sliceSize: number;
-  strides: number[];
-  outputSize: number;
-}
-/**
- * Validate scatter nd inputs.
- *
- * @param update The tensor contains the update values.
- * @param indices The tensor contains the indices for the update values.
- * @param shape The shape of the output tensor.
- */
-export function validateInput(
-    updates: Tensor, indices: Tensor, shape: number[]) {
-  if (indices.rank < 1) {
-    throw new Error(
-        'tf.scatterND() expects the indices to be rank 1 or higher,' +
-        ` but the rank was ${indices.rank}.`);
-  }
-  if (updates.rank < 1) {
-    throw new Error(
-        'tf.scatterND() expects the updates to be rank 1 or higher,' +
-        ` but the rank was ${updates.rank}.`);
-  }
-  if (indices.dtype !== 'int32') {
-    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${
-        indices.dtype}`);
-  }
-  if (shape.length < 1) {
-    throw new Error(
-        `Output rank must be greater or equal to 1, but got shape: ${shape}`);
-  }
-
-  if (shape.length === 0) {
-    if (indices.size === 0) {
-      throw new Error(`Indices specified for empty output. indices shape: ${
-          indices.shape}`);
-    }
-    if (updates.size === 0) {
-      throw new Error(`Updates specified for empty output. updates shape: ${
-          updates.shape}`);
-    }
-  }
-
-  validateUpdateShape(shape, indices, updates);
-}
-
-/**
- * Calculate the shape information for the output.
- *
- * @param update The tensor contains the update values.
- * @param indices The tensor contains the indices for the update values.
- * @param shape The shape of the output tensor.
- *
- * @returns ScatterShapeInfo
- */
-export function calculateShapes(
-    updates: TensorInfo, indices: TensorInfo,
-    shape: number[]): ScatterShapeInfo {
-  // Calculate the number of dimensions in indices
-  const indicesRank = indices.shape.length;
-  const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;
-
-  // Calculate the number of elements that make up each slice of our updated
-  // tensor. This allows us to work with flattened tensors and copy over whole
-  // slices at a time.
-  const totalNd = shape.length;
-
-  let sliceSize = 1;
-  for (let i = sliceRank; i < totalNd; ++i) {
-    sliceSize *= shape[i];
-  }
-
-  const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;
-  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
-
-  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
-  const outputSize = sizeFromShape(shape);
-  return {sliceRank, numUpdates, sliceSize, strides, outputSize};
-}
diff --git a/tfjs-master/tfjs-core/src/ops/search_sorted.ts b/tfjs-master/tfjs-core/src/ops/search_sorted.ts
deleted file mode 100644
index 9be9022c2..000000000
--- a/tfjs-master/tfjs-core/src/ops/search_sorted.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {SearchSorted, SearchSortedAttrs, SearchSortedInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {sizeFromShape} from '../util_base';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-const INT32_MAX = 2147483648;
-/**
- * Searches for where a value would go in a sorted sequence.
- *
- * This is not a method for checking containment (like javascript in).
- *
- * The typical use case for this operation is "binning", "bucketing", or
- * "discretizing". The values are assigned to bucket-indices based on the edges
- * listed in 'sortedSequence'. This operation returns the bucket-index for each
- * value.
- *
- * The side argument controls which index is returned if a value lands exactly
- * on an edge.
- *
- * The axis is not settable for this operation. It always operates on the
- * innermost dimension (axis=-1). The operation will accept any number of outer
- * dimensions.
- *
- * Note: This operation assumes that 'sortedSequence' is sorted along the
- * innermost axis, maybe using 'sort(..., axis=-1)'. If the sequence is not
- * sorted no error is raised and the content of the returned tensor is not well
- * defined.
- *
- * ```js
- * const edges = tf.tensor1d([-1, 3.3, 9.1, 10.0]);
- * let values = tf.tensor1d([0.0, 4.1, 12.0]);
- * const result1 = tf.searchSorted(edges, values, 'left');
- * result1.print(); // [1, 2, 4]
- *
- * const seq = tf.tensor1d([0, 3, 9, 10, 10]);
- * values = tf.tensor1d([0, 4, 10]);
- * const result2 = tf.searchSorted(seq, values, 'left');
- * result2.print(); // [0, 2, 3]
- * const result3 = tf.searchSorted(seq, values, 'right');
- * result3.print(); // [1, 2, 5]
- *
- * const sortedSequence = tf.tensor2d([[0., 3., 8., 9., 10.],
- *                                     [1., 2., 3., 4., 5.]]);
- * values = tf.tensor2d([[9.8, 2.1, 4.3],
- *                       [0.1, 6.6, 4.5, ]]);
- * const result4 = tf.searchSorted(sortedSequence, values, 'left');
- * result4.print(); // [[4, 1, 2], [0, 5, 4]]
- * ```
- * @param sortedSequence: N-D. Sorted sequence.
- * @param values: N-D. Search values.
- * @param side: 'left'|'right'. Defaults to 'left'. 'left' corresponds to lower
- *     bound and 'right' to upper bound.
- * @return An N-D int32 tensor the size of values containing the result of
- *     applying either lower bound or upper bound (depending on side) to each
- *     value. The result is not a global index to the entire Tensor, but the
- *     index in the last dimension.
- * @doc {heading: 'Operations', subheading: 'Evaluation'}
- */
-function searchSorted_(
-    sortedSequence: Tensor|TensorLike, values: Tensor|TensorLike,
-    side: 'left'|'right' = 'left'): Tensor {
-  const $sortedSequence =
-      convertToTensor(sortedSequence, 'sortedSequence', 'searchSorted');
-  const $values = convertToTensor(values, 'values', 'searchSorted');
-
-  const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1];
-  const valuesSize = $values.shape[$values.shape.length - 1];
-  const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]);
-  const $values2D = reshape($values, [-1, valuesSize]);
-
-  if ($sortedSequence2D.rank < 2) {
-    throw new Error(`Sorted input argument must be at least 2-dimensional`);
-  }
-  if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) {
-    throw new Error(
-        `Leading dimension of 'sortedSequence' and 'values' must match.`);
-  }
-  if (sizeFromShape($values2D.shape) >= INT32_MAX) {
-    throw new Error(`values tensor size must less than ${INT32_MAX}`);
-  }
-  if ($sortedSequence2D.shape[1] >= INT32_MAX) {
-    throw new Error(`trailing dim_size must less than ${
-        INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`);
-  }
-
-  const inputs: SearchSortedInputs = {
-    sortedSequence: $sortedSequence2D,
-    values: $values2D,
-  };
-  const attrs: SearchSortedAttrs = {side};
-
-  return ENGINE.runKernel(SearchSorted, inputs as {}, attrs as {});
-}
-
-export const searchSorted = /* @__PURE__ */ op({searchSorted_});
diff --git a/tfjs-master/tfjs-core/src/ops/search_sorted_test.ts b/tfjs-master/tfjs-core/src/ops/search_sorted_test.ts
deleted file mode 100644
index d9ecf2f5d..000000000
--- a/tfjs-master/tfjs-core/src/ops/search_sorted_test.ts
+++ /dev/null
@@ -1,117 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('searchSorted', ALL_ENVS, () => {
-  it('test1D', async () => {
-    // Tests against numpy generated data.
-    const NUMPY_DATA = {
-      'left-float32': [
-        [
-          -945.2247924804688,  -921.8904418945312, -829.9115600585938,
-          -719.2261352539062,  -660.3391723632812, -603.7969970703125,
-          -591.0955200195312,  -373.1516418457031, -165.39039611816406,
-          -161.61097717285156, 117.37965393066406, 340.9350280761719,
-          370.4389953613281,   384.6452331542969,  601.4891357421875,
-          752.7783203125,      756.23486328125,    756.2850341796875,
-          789.2133178710938,   936.5231323242188
-        ],
-        [
-          -165.95599365234375, 440.64898681640625, -999.771240234375,
-          -395.3348693847656, -706.4882202148438, -815.3228149414062,
-          -627.4795532226562, -308.8785400390625, -206.46505737304688,
-          77.63346862792969
-        ],
-        [8, 14, 0, 7, 4, 3, 5, 8, 8, 10]
-      ],
-      'left-int32': [
-        [
-          -961, -893, -793, -739, -706, -576, -468, -439, -424, -412,
-          -104, -16,  148,  178,  357,  399,  496,  578,  817,  977
-        ],
-        [-803, -157, 915, 66, 383, -368, 373, 669, -963, 500],
-        [2, 10, 19, 12, 15, 10, 15, 18, 0, 17]
-      ],
-      'right-float32': [
-        [
-          -725.0505981445312,  -721.4473266601562, -669.2916259765625,
-          -460.14422607421875, -304.4682922363281, -302.20330810546875,
-          -204.64633178710938, -143.817626953125,  243.3914337158203,
-          247.34442138671875,  326.88299560546875, 451.9959716796875,
-          501.62420654296875,  501.8848571777344,  614.7825927734375,
-          766.6121826171875,   791.7724609375,     806.8038330078125,
-          855.0171508789062,   929.6801147460938
-        ],
-        [
-          -795.3311157226562, -171.88803100585938, 388.8003234863281,
-          -171.64146423339844, -900.0930786132812, 71.79280853271484,
-          327.58929443359375, 29.77822494506836, 889.1895141601562,
-          173.11007690429688
-        ],
-        [0, 7, 11, 7, 0, 8, 11, 8, 19, 8]
-      ],
-      'right-int32': [
-        [
-          -968, -867, -751, -725, -655, -346, -285, 54,  246, 381,
-          393,  423,  507,  510,  771,  817,  846,  858, 865, 994
-        ],
-        [-770, 898, -100, 156, -183, -525, 806, 147, -994, 234],
-        [2, 19, 7, 8, 7, 5, 15, 8, 0, 8]
-      ]
-    };
-    for (const side of ['left', 'right'] as const ) {
-      for (const dtype of ['float32', 'int32'] as const ) {
-        // tslint:disable-next-line:no-unnecessary-type-assertion
-        const key = `${side}-${dtype}` as keyof typeof NUMPY_DATA;
-        const [sortedSequence, values, npAns] = NUMPY_DATA[key];
-
-        const result = tf.searchSorted(sortedSequence, values, side);
-
-        expectArraysClose(await result.data(), npAns);
-      }
-    }
-  });
-
-  it('lowerBound2D', async () => {
-    for (const dtype of ['float32', 'int32'] as const ) {
-      const sortedSequence =
-          tf.tensor2d([[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]], undefined, dtype);
-      const values = tf.tensor2d([[2, 4, 9], [0, 2, 6]], undefined, dtype);
-      const correctAns = [[1, 2, 2], [0, 1, 5]];
-
-      const result = tf.searchSorted(sortedSequence, values, 'left');
-
-      expectArraysClose(await result.data(), correctAns);
-    }
-  });
-
-  it('upperBound2D', async () => {
-    for (const dtype of ['float32', 'int32'] as const ) {
-      const sortedSequence =
-          tf.tensor2d([[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]], undefined, dtype);
-      const values = tf.tensor2d([[2, 4, 9], [0, 2, 6]], undefined, dtype);
-      const correctAns = [[1, 2, 4], [0, 2, 5]];
-
-      const result = tf.searchSorted(sortedSequence, values, 'right');
-
-      expectArraysClose(await result.data(), correctAns);
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/segment_util.ts b/tfjs-master/tfjs-core/src/ops/segment_util.ts
deleted file mode 100644
index 928655bf6..000000000
--- a/tfjs-master/tfjs-core/src/ops/segment_util.ts
+++ /dev/null
@@ -1,135 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { TensorInfo } from '../tensor_info';
-import {nearestDivisor} from '../util';
-
-import {PARALLELIZE_THRESHOLD} from './reduce_util';
-
-export interface SegOpInfo {
-  windowSize: number;
-  batchSize: number;
-  inSize: number;
-  numSegments: number;
-}
-
-export function segOpComputeOptimalWindowSize(
-    inSize: number, numSegments: number): number {
-  let done = false;
-  let res;
-
-  if (inSize <= PARALLELIZE_THRESHOLD) {
-    res = inSize;
-    done = true;
-  } else {
-    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
-  }
-
-  while (!done) {
-    if (res > numSegments || res === inSize) {
-      done = true;
-    } else {
-      res = nearestDivisor(inSize, res + 1);
-    }
-  }
-  return res;
-}
-
-export function computeOutShape(
-    aShape: number[], axis: number, numSegments: number): number[] {
-  const outShape = [];
-  const rank = aShape.length;
-  for (let dim = 0; dim < rank; dim++) {
-    if (dim !== axis) {
-      outShape.push(aShape[dim]);
-    } else {
-      outShape.push(numSegments);
-    }
-  }
-  return outShape;
-}
-
-export interface GatherOpShapeInfo {
-  batchSize: number;
-  sliceSize: number;
-  outerSize: number;
-  dimSize: number;
-  outputShape: number[];
-}
-
-export function collectGatherOpShapeInfo(
-    x: TensorInfo, indices: TensorInfo, axis: number,
-    batchDims: number): GatherOpShapeInfo {
-  const indicesRank = indices.shape.length;
-  const xRank = x.shape.length;
-
-  if (batchDims !== 0) {
-    if (batchDims < -indicesRank || batchDims > indicesRank) {
-      throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${
-          indicesRank}], but got ${batchDims}`);
-    }
-  }
-
-  if (batchDims < 0) {
-    batchDims += indicesRank;
-  }
-
-  if (batchDims > xRank) {
-    throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
-    ${xRank}).`);
-  }
-
-  if (axis < batchDims) {
-    throw new Error(`batchDims (${
-        batchDims}) must be less than or equal to axis (${axis}).`);
-  }
-
-  for (let i = 0; i < batchDims; ++i) {
-    if (x.shape[i] !== indices.shape[i]) {
-      throw new Error(
-          `x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${
-              i}]: ${indices.shape[i]}.`);
-    }
-  }
-  const dimSize = x.shape[axis];
-
-  const outputShape: number[] = [];
-  let batchSize = 1;
-  let outerSize = 1;
-  let sliceSize = 1;
-
-  for (let i = 0; i < batchDims; ++i) {
-    outputShape.push(x.shape[i]);
-    batchSize *= x.shape[i];
-  }
-
-  for (let i = batchDims; i < axis; i++) {
-    outputShape.push(x.shape[i]);
-    outerSize *= x.shape[i];
-  }
-
-  for (let i = batchDims; i < indicesRank; i++) {
-    outputShape.push(indices.shape[i]);
-  }
-
-  for (let i = axis + 1; i < xRank; i++) {
-    outputShape.push(x.shape[i]);
-    sliceSize *= x.shape[i];
-  }
-
-  return {batchSize, sliceSize, outerSize, dimSize, outputShape};
-}
diff --git a/tfjs-master/tfjs-core/src/ops/selu.ts b/tfjs-master/tfjs-core/src/ops/selu.ts
deleted file mode 100644
index 47f61405e..000000000
--- a/tfjs-master/tfjs-core/src/ops/selu.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Selu, SeluInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes scaled exponential linear element-wise.
- *
- * `x < 0 ? scale * alpha * (exp(x) - 1) : scale * x`
- *
- * ```js
- * const x = tf.tensor1d([-1, 2, -3, 4]);
- *
- * x.selu().print();  // or tf.selu(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function selu_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'selu');
-
-  const inputs: SeluInputs = {x: $x};
-
-  return ENGINE.runKernel(Selu, inputs as unknown as NamedTensorMap);
-}
-
-export const selu = /* @__PURE__ */ op({selu_});
diff --git a/tfjs-master/tfjs-core/src/ops/selu_test.ts b/tfjs-master/tfjs-core/src/ops/selu_test.ts
deleted file mode 100644
index 1803b5dfc..000000000
--- a/tfjs-master/tfjs-core/src/ops/selu_test.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-import * as selu_util from './selu_util';
-
-describeWithFlags('selu', ALL_ENVS, () => {
-  const scaleAlpha = selu_util.SELU_SCALEALPHA;
-  const scale = selu_util.SELU_SCALE;
-
-  it('calculate selu', async () => {
-    const a = tf.tensor1d([1, -1, 0]);
-    const result = tf.selu(a);
-
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [1.0507, -1.1113, 0]);
-  });
-
-  it('selu propagates NaN', async () => {
-    const a = tf.tensor1d([1, NaN]);
-    const result = tf.selu(a);
-    expect(result.shape).toEqual(a.shape);
-    expectArraysClose(await result.data(), [1.0507, NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    let aValue = 1;
-    let dyValue = 1;
-    let a = tf.scalar(aValue);
-    let dy = tf.scalar(dyValue);
-
-    let gradients = tf.grad(a => tf.selu(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [dyValue * scale]);
-
-    aValue = -1;
-    dyValue = 2;
-    a = tf.scalar(aValue);
-    dy = tf.scalar(dyValue);
-
-    gradients = tf.grad(a => tf.selu(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [dyValue * scaleAlpha * Math.exp(aValue)]);
-  });
-
-  it('gradient with clones', async () => {
-    const aValue = 1;
-    const dyValue = 1;
-    const a = tf.scalar(aValue);
-    const dy = tf.scalar(dyValue);
-
-    const gradients = tf.grad(a => tf.selu(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [dyValue * scale]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [1, -1, 0];
-    const dyValues = [1, 2, 3];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.selu(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      if (aValues[i] > 0) {
-        expected[i] = dyValues[i] * scale;
-      } else {
-        expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);
-      }
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [1, -1, 0, 0.5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.selu(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      if (aValues[i] > 0) {
-        expected[i] = dyValues[i] * scale;
-      } else {
-        expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);
-      }
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.selu({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'selu' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.selu([1, -1, 0]);
-    expect(result.shape).toEqual([3]);
-    expectArraysClose(await result.data(), [1.0507, -1.1113, 0]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.selu('q'))
-        .toThrowError(/Argument 'x' passed to 'selu' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/selu_util.ts b/tfjs-master/tfjs-core/src/ops/selu_util.ts
deleted file mode 100644
index 1263c0089..000000000
--- a/tfjs-master/tfjs-core/src/ops/selu_util.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export const SELU_SCALEALPHA = 1.7580993408473768599402175208123;
-export const SELU_SCALE = 1.0507009873554804934193349852946;
diff --git a/tfjs-master/tfjs-core/src/ops/separable_conv2d.ts b/tfjs-master/tfjs-core/src/ops/separable_conv2d.ts
deleted file mode 100644
index 58b5b67ee..000000000
--- a/tfjs-master/tfjs-core/src/ops/separable_conv2d.ts
+++ /dev/null
@@ -1,139 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor3D, Tensor4D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {conv2d} from './conv2d';
-import {depthwiseConv2d} from './depthwise_conv2d';
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * 2-D convolution with separable filters.
- *
- * Performs a depthwise convolution that acts separately on channels followed
- * by a pointwise convolution that mixes channels. Note that this is
- * separability between dimensions [1, 2] and 3, not spatial separability
- * between dimensions 1 and 2.
- *
- * See
- * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](
- *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)
- * for more details.
- *
- * @param x The input tensor, of rank 4 or rank 3, of shape
- *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
- * assumed.
- * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape
- *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is
- *     the filter used in the first step.
- * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape
- *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is
- *     the filter used in the second step.
- * @param strides The strides of the convolution: `[strideHeight,
- * strideWidth]`. If strides is a single number, then `strideHeight ==
- * strideWidth`.
- * @param pad The type of padding algorithm.
- *   - `same` and stride 1: output will be of same size as input,
- *       regardless of filter size.
- *   - `valid`: output will be smaller than input if filter is larger
- *       than 1x1.
- *   - For more info, see this guide:
- *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
- *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
- * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
- *     in which we sample input values across the height and width dimensions
- *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
- *     number, then `dilationHeight == dilationWidth`. If it is greater than
- *     1, then all values of `strides` must be 1.
- * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
- *     "NHWC". Specify the data format of the input and output data. With the
- *     default format "NHWC", the data is stored in the order of: [batch,
- *     height, width, channels]. Only "NHWC" is currently supported.
- *
- * @doc {heading: 'Operations', subheading: 'Convolution'}
- */
-function separableConv2d_<T extends Tensor3D|Tensor4D>(
-    x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike,
-    pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,
-    pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],
-    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {
-  const $x = convertToTensor(x, 'x', 'separableConv2d');
-  const $depthwiseFilter =
-      convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');
-  const $pointwiseFilter =
-      convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');
-
-  let x4D = $x as Tensor4D;
-  let reshapedTo4D = false;
-  if ($x.rank === 3) {
-    reshapedTo4D = true;
-    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
-  }
-
-  if (dataFormat === 'NCHW') {
-    throw new Error(
-        'separableConv2d currently does not support dataFormat NCHW; only ' +
-        'NHWC is supported');
-  }
-
-  util.assert(
-      x4D.rank === 4,
-      () => `Error in separableConv2d: input must be rank 4, but got ` +
-          `rank ${x4D.rank}.`);
-  util.assert(
-      $depthwiseFilter.rank === 4,
-      () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +
-          `got rank ${$depthwiseFilter.rank}.`);
-  util.assert(
-      $pointwiseFilter.rank === 4,
-      () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +
-          `got rank ${$depthwiseFilter.rank}.`);
-  util.assert(
-      $pointwiseFilter.shape[0] === 1,
-      () =>
-          `Error in separableConv2d: the first dimension of pointwise filter ` +
-          ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);
-  util.assert(
-      $pointwiseFilter.shape[1] === 1,
-      () => `Error in separableConv2d: the second dimension of pointwise ` +
-          `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
-
-  const inChannels = $depthwiseFilter.shape[2];
-  const channelMultiplier = $depthwiseFilter.shape[3];
-  util.assert(
-      $pointwiseFilter.shape[2] === inChannels * channelMultiplier,
-      () =>
-          `Error in separableConv2d: the third dimension of pointwise filter ` +
-          `must be ${inChannels * channelMultiplier}, ` +
-          `but got ${$pointwiseFilter.shape[2]}.`);
-
-  const depthwise = depthwiseConv2d(
-      x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
-  const pointwiseStride = 1;
-  const res =
-      conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);
-
-  if (reshapedTo4D) {
-    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
-  }
-  return res as T;
-}
-
-export const separableConv2d = /* @__PURE__ */ op({separableConv2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/setdiff1d_async.ts b/tfjs-master/tfjs-core/src/ops/setdiff1d_async.ts
deleted file mode 100644
index d69bc6b8e..000000000
--- a/tfjs-master/tfjs-core/src/ops/setdiff1d_async.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor, TensorBuffer} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-/**
- * Computes the difference between two lists of numbers.
- *
- * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`
- * that represents all values that are in `x` but not in `y`. The returned
- * Tensor `out` is sorted in the same order that the numbers appear in `x`
- * (duplicates are preserved). This operation also returns a Tensor indices that
- * represents the position of each out element in `x`. In other words:
- *
- * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`
- *
- * ```js
- * const x = [1, 2, 3, 4, 5, 6];
- * const y = [1, 3, 5];
- *
- * const [out, indices] = await tf.setdiff1dAsync(x, y);
- * out.print(); // [2, 4, 6]
- * indices.print(); // [1, 3, 5]
- * ```
- *
- * @param x 1-D Tensor. Values to keep.
- * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the
- *     output.
- * @returns Promise of Tensor tuple [out, indices].
- *  out: Tensor with the same type as x.
- *  indices: A Tensor of type int32.
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-async function setdiff1dAsync_(
-    x: Tensor|TensorLike, y: Tensor|TensorLike): Promise<[Tensor, Tensor]> {
-  const $x = convertToTensor(x, 'x', 'setdiff1d');
-  const $y = convertToTensor(y, 'y', 'setdiff1d');
-
-  util.assert(
-      $x.dtype === $y.dtype,
-      () => `x and y should have the same dtype, but got x (${
-          $x.dtype}) and y (${$y.dtype}).`);
-
-  util.assert(
-      $x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
-
-  util.assert(
-      $y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
-
-  const xVals = await $x.data();
-  const yVals = await $y.data();
-  const ySet = new Set(yVals);
-
-  let outputSize = 0;
-  for (let i = 0; i < xVals.length; i++) {
-    if (!ySet.has(xVals[i])) {
-      outputSize++;
-    }
-  }
-
-  const buffer = new TensorBuffer([outputSize], $x.dtype);
-  const indices = new TensorBuffer([outputSize], 'int32');
-  for (let i = 0, p = 0; i < xVals.length; i++) {
-    if (!ySet.has(xVals[i])) {
-      buffer.values[p] = xVals[i];
-      indices.values[p] = i;
-      p++;
-    }
-  }
-  return [buffer.toTensor(), indices.toTensor()];
-}
-export const setdiff1dAsync = setdiff1dAsync_;
diff --git a/tfjs-master/tfjs-core/src/ops/setdiff1d_async_test.ts b/tfjs-master/tfjs-core/src/ops/setdiff1d_async_test.ts
deleted file mode 100644
index fda13ebb2..000000000
--- a/tfjs-master/tfjs-core/src/ops/setdiff1d_async_test.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('setdiff1dAsync', ALL_ENVS, () => {
-  it('1d int32 tensor', async () => {
-    const x = tf.tensor1d([1, 2, 3, 4], 'int32');
-    const y = tf.tensor1d([1, 2], 'int32');
-    const [out, indices] = await tf.setdiff1dAsync(x, y);
-    expect(out.dtype).toBe('int32');
-    expect(indices.dtype).toBe('int32');
-    expect(out.shape).toEqual([2]);
-    expect(indices.shape).toEqual([2]);
-    expectArraysClose(await out.data(), [3, 4]);
-    expectArraysClose(await indices.data(), [2, 3]);
-  });
-
-  it('1d float32 tensor', async () => {
-    const x = tf.tensor1d([1, 2, 3, 4], 'float32');
-    const y = tf.tensor1d([1, 3], 'float32');
-    const [out, indices] = await tf.setdiff1dAsync(x, y);
-    expect(out.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expect(out.shape).toEqual([2]);
-    expect(indices.shape).toEqual([2]);
-    expectArraysClose(await out.data(), [2, 4]);
-    expectArraysClose(await indices.data(), [1, 3]);
-  });
-
-  it('empty output', async () => {
-    const x = tf.tensor1d([1, 2, 3, 4], 'float32');
-    const y = tf.tensor1d([1, 2, 3, 4], 'float32');
-    const [out, indices] = await tf.setdiff1dAsync(x, y);
-    expect(out.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expect(out.shape).toEqual([0]);
-    expect(indices.shape).toEqual([0]);
-    expectArraysClose(await out.data(), []);
-    expectArraysClose(await indices.data(), []);
-  });
-
-  it('tensor like', async () => {
-    const x = [1, 2, 3, 4];
-    const y = [1, 3];
-    const [out, indices] = await tf.setdiff1dAsync(x, y);
-    expect(out.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expect(out.shape).toEqual([2]);
-    expect(indices.shape).toEqual([2]);
-    expectArraysClose(await out.data(), [2, 4]);
-    expectArraysClose(await indices.data(), [1, 3]);
-  });
-
-  it('should throw if x is not 1d', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4], [4, 1], 'float32');
-    const y = tf.tensor1d([1, 2, 3, 4], 'float32');
-    try {
-      await tf.setdiff1dAsync(x, y);
-      throw new Error('The line above should have thrown an error');
-    } catch (ex) {
-      expect(ex.message).toBe('x should be 1D tensor, but got x (4,1).');
-    }
-  });
-
-  it('should throw if y is not 1d', async () => {
-    const x = tf.tensor1d([1, 2, 3, 4], 'float32');
-    const y = tf.tensor2d([1, 2, 3, 4], [4, 1], 'float32');
-    try {
-      await tf.setdiff1dAsync(x, y);
-      throw new Error('The line above should have thrown an error');
-    } catch (ex) {
-      expect(ex.message).toBe('y should be 1D tensor, but got y (4,1).');
-    }
-  });
-
-  it('should throw if x and y dtype mismatch', async () => {
-    const x = tf.tensor1d([1, 2, 3, 4], 'float32');
-    const y = tf.tensor1d([1, 2, 3, 4], 'int32');
-    try {
-      await tf.setdiff1dAsync(x, y);
-      throw new Error('The line above should have thrown an error');
-    } catch (ex) {
-      expect(ex.message)
-          .toBe(
-              'x and y should have the same dtype,' +
-              ' but got x (float32) and y (int32).');
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sigmoid.ts b/tfjs-master/tfjs-core/src/ops/sigmoid.ts
deleted file mode 100644
index f1c550cd4..000000000
--- a/tfjs-master/tfjs-core/src/ops/sigmoid.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Sigmoid, SigmoidInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes sigmoid element-wise, `1 / (1 + exp(-x))`
- *
- * ```js
- * const x = tf.tensor1d([0, -1, 2, -3]);
- *
- * x.sigmoid().print();  // or tf.sigmoid(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function sigmoid_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'sigmoid', 'float32');
-
-  const inputs: SigmoidInputs = {x: $x};
-
-  return ENGINE.runKernel(Sigmoid, inputs as unknown as NamedTensorMap);
-}
-export const sigmoid = /* @__PURE__ */ op({sigmoid_});
diff --git a/tfjs-master/tfjs-core/src/ops/sigmoid_test.ts b/tfjs-master/tfjs-core/src/ops/sigmoid_test.ts
deleted file mode 100644
index d5a643c13..000000000
--- a/tfjs-master/tfjs-core/src/ops/sigmoid_test.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('sigmoid', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const a = tf.tensor1d(values);
-
-    const result = tf.sigmoid(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = 1 / (1 + Math.exp(-values[i]));
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('6D', async () => {
-    const a = tf.ones([2, 2, 2, 2, 2, 2]);
-    const result = tf.sigmoid(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = 1 / (1 + Math.exp(-1.0));
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([3, NaN]);
-    const res = tf.sigmoid(a);
-    expectArraysClose(await res.data(), [1 / (1 + Math.exp(-3)), NaN]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, -3, 5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const da = tf.grad(a => tf.sigmoid(a))(a, dy);
-
-    const aVals = await a.array();
-    const dyVals = await dy.array();
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      const y = 1 / (1 + Math.exp(-aVals[i]));
-      expected[i] = dyVals[i] * y * (1 - y);
-    }
-
-    expectArraysClose(await da.data(), expected);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, -3, 5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const da = tf.grad(a => tf.sigmoid(a.clone()).clone())(a, dy);
-
-    const aVals = await a.array();
-    const dyVals = await dy.array();
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      const y = 1 / (1 + Math.exp(-aVals[i]));
-      expected[i] = dyVals[i] * y * (1 - y);
-    }
-
-    expectArraysClose(await da.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.sigmoid({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'sigmoid' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const result = tf.sigmoid(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = 1 / (1 + Math.exp(-values[i]));
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.sigmoid('q'))
-        .toThrowError(/Argument 'x' passed to 'sigmoid' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.sigmoid(tf.tensor1d([1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'sigmoid' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sign.ts b/tfjs-master/tfjs-core/src/ops/sign.ts
deleted file mode 100644
index 0543b160c..000000000
--- a/tfjs-master/tfjs-core/src/ops/sign.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Sign, SignInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Returns an element-wise indication of the sign of a number.
- *
- * ```js
- * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);
- *
- * x.sign().print();  // or tf.sign(x)
- * ```
- * @param x The input Tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function sign_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'sign');
-  const inputs: SignInputs = {x: $x};
-  return ENGINE.runKernel(Sign, inputs as unknown as NamedTensorMap);
-}
-export const sign = /* @__PURE__ */ op({sign_});
diff --git a/tfjs-master/tfjs-core/src/ops/sign_test.ts b/tfjs-master/tfjs-core/src/ops/sign_test.ts
deleted file mode 100644
index 40410b3f9..000000000
--- a/tfjs-master/tfjs-core/src/ops/sign_test.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('sign', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor1d([1.5, 0, NaN, -1.4]);
-    const r = tf.sign(a);
-    expectArraysClose(await r.data(), [1, 0, 0, -1]);
-  });
-
-  it('does not propagate NaNs', async () => {
-    const a = tf.tensor1d([1.5, NaN, -1.4]);
-    const r = tf.sign(a);
-    expectArraysClose(await r.data(), [1, 0, -1]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5.2);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.sign(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5.2);
-    const dy = tf.scalar(3);
-
-    const gradients = tf.grad(a => tf.sign(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);
-    const dy = tf.tensor1d([-1, 1, 1, -1]);
-
-    const gradients = tf.grad(a => tf.sign(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.sign(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.sign({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'sign' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.sign([1.5, 0, NaN, -1.4]);
-    expectArraysClose(await r.data(), [1, 0, 0, -1]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.sign('q'))
-        .toThrowError(/Argument 'x' passed to 'sign' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/signal/frame.ts b/tfjs-master/tfjs-core/src/ops/signal/frame.ts
deleted file mode 100644
index 56fed2e68..000000000
--- a/tfjs-master/tfjs-core/src/ops/signal/frame.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor1D} from '../../tensor';
-import {concat} from '../concat';
-import {fill} from '../fill';
-import {op} from '../operation';
-import {reshape} from '../reshape';
-import {slice} from '../slice';
-import {tensor2d} from '../tensor2d';
-
-/**
- * Expands input into frames of frameLength.
- * Slides a window size with frameStep.
- *
- * ```js
- * tf.signal.frame([1, 2, 3], 2, 1).print();
- * ```
- * @param signal The input tensor to be expanded
- * @param frameLength Length of each frame
- * @param frameStep The frame hop size in samples.
- * @param padEnd Whether to pad the end of signal with padValue.
- * @param padValue A number to use where the input signal does
- *     not exist when padEnd is True.
- *
- * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
- */
-function frame_(
-    signal: Tensor1D, frameLength: number, frameStep: number, padEnd = false,
-    padValue = 0): Tensor {
-  let start = 0;
-  const output: Tensor[] = [];
-  while (start + frameLength <= signal.size) {
-    output.push(slice(signal, start, frameLength));
-    start += frameStep;
-  }
-
-  if (padEnd) {
-    while (start < signal.size) {
-      const padLen = (start + frameLength) - signal.size;
-      const pad = concat([
-        slice(signal, start, frameLength - padLen), fill([padLen], padValue)
-      ]);
-      output.push(pad);
-      start += frameStep;
-    }
-  }
-
-  if (output.length === 0) {
-    return tensor2d([], [0, frameLength]);
-  }
-
-  return reshape(concat(output), [output.length, frameLength]);
-}
-export const frame = /* @__PURE__ */ op({frame_});
diff --git a/tfjs-master/tfjs-core/src/ops/signal/frame_test.ts b/tfjs-master/tfjs-core/src/ops/signal/frame_test.ts
deleted file mode 100644
index a4912356a..000000000
--- a/tfjs-master/tfjs-core/src/ops/signal/frame_test.ts
+++ /dev/null
@@ -1,124 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('frame', ALL_ENVS, () => {
-  it('3 length frames', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 3;
-    const frameStep = 1;
-    const output = tf.signal.frame(input, frameLength, frameStep);
-    expect(output.shape).toEqual([3, 3]);
-    expectArraysClose(await output.data(), [1, 2, 3, 2, 3, 4, 3, 4, 5]);
-  });
-
-  it('3 length frames with step 2', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 3;
-    const frameStep = 2;
-    const output = tf.signal.frame(input, frameLength, frameStep);
-    expect(output.shape).toEqual([2, 3]);
-    expectArraysClose(await output.data(), [1, 2, 3, 3, 4, 5]);
-  });
-
-  it('3 length frames with step 5', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 3;
-    const frameStep = 5;
-    const output = tf.signal.frame(input, frameLength, frameStep);
-    expect(output.shape).toEqual([1, 3]);
-    expectArraysClose(await output.data(), [1, 2, 3]);
-  });
-
-  it('Exceeding frame length', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 6;
-    const frameStep = 1;
-    const output = tf.signal.frame(input, frameLength, frameStep);
-    expect(output.shape).toEqual([0, 6]);
-    expectArraysClose(await output.data(), []);
-  });
-
-  it('Zero frame step', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 6;
-    const frameStep = 0;
-    const output = tf.signal.frame(input, frameLength, frameStep);
-    expect(output.shape).toEqual([0, 6]);
-    expectArraysClose(await output.data(), []);
-  });
-
-  it('Padding with default value', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 3;
-    const frameStep = 3;
-    const padEnd = true;
-    const output = tf.signal.frame(input, frameLength, frameStep, padEnd);
-    expect(output.shape).toEqual([2, 3]);
-    expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 0]);
-  });
-
-  it('Padding with the given value', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 3;
-    const frameStep = 3;
-    const padEnd = true;
-    const padValue = 100;
-    const output =
-        tf.signal.frame(input, frameLength, frameStep, padEnd, padValue);
-    expect(output.shape).toEqual([2, 3]);
-    expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 100]);
-  });
-
-  it('Padding all remaining frames with step=1', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 4;
-    const frameStep = 1;
-    const padEnd = true;
-    const output = tf.signal.frame(input, frameLength, frameStep, padEnd);
-    expect(output.shape).toEqual([5, 4]);
-    expectArraysClose(
-        await output.data(),
-        [1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 5, 0, 4, 5, 0, 0, 5, 0, 0, 0]);
-  });
-
-  it('Padding all remaining frames with step=1 and given pad-value',
-     async () => {
-       const input = tf.tensor1d([1, 2, 3, 4, 5]);
-       const frameLength = 4;
-       const frameStep = 1;
-       const padEnd = true;
-       const padValue = 42;
-       const output =
-           tf.signal.frame(input, frameLength, frameStep, padEnd, padValue);
-       expect(output.shape).toEqual([5, 4]);
-       expectArraysClose(
-           await output.data(),
-           [1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 5, 42, 4, 5, 42, 42, 5, 42, 42, 42]);
-     });
-
-  it('Padding all remaining frames with step=2', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const output = tf.signal.frame(input, 4, 2, true);
-    expect(output.shape).toEqual([3, 4]);
-    expectArraysClose(
-        await output.data(), [1, 2, 3, 4, 3, 4, 5, 0, 5, 0, 0, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/signal/hamming_window.ts b/tfjs-master/tfjs-core/src/ops/signal/hamming_window.ts
deleted file mode 100644
index d0044b6fc..000000000
--- a/tfjs-master/tfjs-core/src/ops/signal/hamming_window.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor1D} from '../../tensor';
-import {op} from '../operation';
-import {cosineWindow} from '../signal_ops_util';
-
-/**
- * Generate a hamming window.
- *
- * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
- *
- * ```js
- * tf.signal.hammingWindow(10).print();
- * ```
- * @param The length of window
- *
- * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
- */
-function hammingWindow_(windowLength: number): Tensor1D {
-  return cosineWindow(windowLength, 0.54, 0.46);
-}
-export const hammingWindow = /* @__PURE__ */ op({hammingWindow_});
diff --git a/tfjs-master/tfjs-core/src/ops/signal/hamming_window_test.ts b/tfjs-master/tfjs-core/src/ops/signal/hamming_window_test.ts
deleted file mode 100644
index 2d2f1aa30..000000000
--- a/tfjs-master/tfjs-core/src/ops/signal/hamming_window_test.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('hammingWindow', ALL_ENVS, () => {
-  it('length=3', async () => {
-    const ret = tf.signal.hammingWindow(3);
-    expectArraysClose(await ret.data(), [0.08, 1, 0.08]);
-  });
-
-  it('length=6', async () => {
-    const ret = tf.signal.hammingWindow(6);
-    expectArraysClose(await ret.data(), [0.08, 0.31, 0.77, 1., 0.77, 0.31]);
-  });
-
-  it('length=7', async () => {
-    const ret = tf.signal.hammingWindow(7);
-    expectArraysClose(
-        await ret.data(), [0.08, 0.31, 0.77, 1, 0.77, 0.31, 0.08]);
-  });
-
-  it('length=20', async () => {
-    const ret = tf.signal.hammingWindow(20);
-    expectArraysClose(await ret.data(), [
-      0.08000001, 0.10251403, 0.16785222, 0.2696188,  0.3978522,
-      0.54,       0.68214786, 0.8103813,  0.9121479,  0.977486,
-      1.,         0.977486,   0.9121478,  0.8103812,  0.6821477,
-      0.54,       0.39785212, 0.2696187,  0.16785222, 0.102514
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/signal/hann_window.ts b/tfjs-master/tfjs-core/src/ops/signal/hann_window.ts
deleted file mode 100644
index 561de6237..000000000
--- a/tfjs-master/tfjs-core/src/ops/signal/hann_window.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor1D} from '../../tensor';
-import {op} from '../operation';
-import {cosineWindow} from '../signal_ops_util';
-
-/**
- * Generate a Hann window.
- *
- * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
- *
- * ```js
- * tf.signal.hannWindow(10).print();
- * ```
- * @param The length of window
- *
- * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
- */
-function hannWindow_(windowLength: number): Tensor1D {
-  return cosineWindow(windowLength, 0.5, 0.5);
-}
-
-export const hannWindow = /* @__PURE__ */ op({hannWindow_});
diff --git a/tfjs-master/tfjs-core/src/ops/signal/hann_window_test.ts b/tfjs-master/tfjs-core/src/ops/signal/hann_window_test.ts
deleted file mode 100644
index 5eb50694c..000000000
--- a/tfjs-master/tfjs-core/src/ops/signal/hann_window_test.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('hannWindow', ALL_ENVS, () => {
-  it('length=3', async () => {
-    const ret = tf.signal.hannWindow(3);
-    expectArraysClose(await ret.data(), [0, 1, 0]);
-  });
-
-  it('length=7', async () => {
-    const ret = tf.signal.hannWindow(7);
-    expectArraysClose(await ret.data(), [0, 0.25, 0.75, 1, 0.75, 0.25, 0]);
-  });
-
-  it('length=6', async () => {
-    const ret = tf.signal.hannWindow(6);
-    expectArraysClose(await ret.data(), [0., 0.25, 0.75, 1., 0.75, 0.25]);
-  });
-
-  it('length=20', async () => {
-    const ret = tf.signal.hannWindow(20);
-    expectArraysClose(await ret.data(), [
-      0.,  0.02447176, 0.09549153, 0.20610738, 0.34549153,
-      0.5, 0.65450853, 0.79389274, 0.9045085,  0.97552824,
-      1.,  0.97552824, 0.9045085,  0.7938925,  0.65450835,
-      0.5, 0.34549144, 0.20610726, 0.09549153, 0.02447173
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/signal/stft.ts b/tfjs-master/tfjs-core/src/ops/signal/stft.ts
deleted file mode 100644
index cf581a842..000000000
--- a/tfjs-master/tfjs-core/src/ops/signal/stft.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor1D} from '../../tensor';
-import {mul} from '../mul';
-import {op} from '../operation';
-import {enclosingPowerOfTwo} from '../signal_ops_util';
-import {rfft} from '../spectral/rfft';
-
-import {frame} from './frame';
-import {hannWindow} from './hann_window';
-
-/**
- * Computes the Short-time Fourier Transform of signals
- * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
- *
- * ```js
- * const input = tf.tensor1d([1, 1, 1, 1, 1])
- * tf.signal.stft(input, 3, 1).print();
- * ```
- * @param signal 1-dimensional real value tensor.
- * @param frameLength The window length of samples.
- * @param frameStep The number of samples to step.
- * @param fftLength The size of the FFT to apply.
- * @param windowFn A callable that takes a window length and returns 1-d tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
- */
-function stft_(
-    signal: Tensor1D, frameLength: number, frameStep: number,
-    fftLength?: number,
-    windowFn: (length: number) => Tensor1D = hannWindow): Tensor {
-  if (fftLength == null) {
-    fftLength = enclosingPowerOfTwo(frameLength);
-  }
-  const framedSignal = frame(signal, frameLength, frameStep);
-  const windowedSignal = mul(framedSignal, windowFn(frameLength));
-  return rfft(windowedSignal, fftLength);
-}
-export const stft = /* @__PURE__ */ op({stft_});
diff --git a/tfjs-master/tfjs-core/src/ops/signal/stft_test.ts b/tfjs-master/tfjs-core/src/ops/signal/stft_test.ts
deleted file mode 100644
index cde17fb8f..000000000
--- a/tfjs-master/tfjs-core/src/ops/signal/stft_test.ts
+++ /dev/null
@@ -1,192 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('stft', ALL_ENVS, () => {
-  it('3 length with hann window', async () => {
-    const input = tf.tensor1d([1, 1, 1, 1, 1]);
-    const frameLength = 3;
-    const frameStep = 1;
-    const output = tf.signal.stft(input, frameLength, frameStep);
-    expect(output.shape).toEqual([3, 3]);
-    expectArraysClose(await output.data(), [
-      1.0,
-      0.0,
-      0.0,
-      -1.0,
-      -1.0,
-      0.0,
-      1.0,
-      0.0,
-      0.0,
-      -1.0,
-      -1.0,
-      0.0,
-      1.0,
-      0.0,
-      0.0,
-      -1.0,
-      -1.0,
-      0.0,
-    ]);
-  });
-
-  it('3 length with hann window (sequencial number)', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 3;
-    const frameStep = 1;
-    const output = tf.signal.stft(input, frameLength, frameStep);
-    expect(output.shape).toEqual([3, 3]);
-    expectArraysClose(await output.data(), [
-      2.0, 0.0, 0.0, -2.0, -2.0, 0.0, 3.0, 0.0, 0.0, -3.0, -3.0, 0.0, 4.0, 0.0,
-      0.0, -4.0, -4.0, 0.0
-    ]);
-  });
-
-  it('3 length, 2 step with hann window', async () => {
-    const input = tf.tensor1d([1, 1, 1, 1, 1]);
-    const frameLength = 3;
-    const frameStep = 2;
-    const output = tf.signal.stft(input, frameLength, frameStep);
-    expect(output.shape).toEqual([2, 3]);
-    expectArraysClose(
-        await output.data(),
-        [1.0, 0.0, 0.0, -1.0, -1.0, 0.0, 1.0, 0.0, 0.0, -1.0, -1.0, 0.0]);
-  });
-
-  it('3 fftLength, 5 frameLength, 2 step', async () => {
-    const input = tf.tensor1d([1, 1, 1, 1, 1, 1]);
-    const frameLength = 5;
-    const frameStep = 1;
-    const fftLength = 3;
-    const output = tf.signal.stft(input, frameLength, frameStep, fftLength);
-    expect(output.shape[0]).toEqual(2);
-    expectArraysClose(
-        await output.data(),
-        [1.5, 0.0, -0.749999, 0.433, 1.5, 0.0, -0.749999, 0.433]);
-  });
-
-  it('5 length with hann window', async () => {
-    const input = tf.tensor1d([1, 1, 1, 1, 1]);
-    const frameLength = 5;
-    const frameStep = 1;
-    const output = tf.signal.stft(input, frameLength, frameStep);
-    expect(output.shape).toEqual([1, 5]);
-    expectArraysClose(
-        await output.data(),
-        [2.0, 0.0, 0.0, -1.7071068, -1.0, 0.0, 0.0, 0.29289323, 0.0, 0.0]);
-  });
-
-  it('5 length with hann window (sequential)', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 5;
-    const frameStep = 1;
-    const output = tf.signal.stft(input, frameLength, frameStep);
-    expect(output.shape).toEqual([1, 5]);
-    expectArraysClose(await output.data(), [
-      6.0, 0.0, -0.70710677, -5.1213202, -3.0, 1.0, 0.70710677, 0.87867975, 0.0,
-      0.0
-    ]);
-  });
-
-  it('3 length with hamming window', async () => {
-    const input = tf.tensor1d([1, 1, 1, 1, 1]);
-    const frameLength = 3;
-    const frameStep = 1;
-    const fftLength = 3;
-    const output = tf.signal.stft(
-        input, frameLength, frameStep, fftLength,
-        (length) => tf.signal.hammingWindow(length));
-    expect(output.shape).toEqual([3, 2]);
-    expectArraysClose(await output.data(), [
-      1.16, 0.0, -0.46, -0.79674333, 1.16, 0.0, -0.46, -0.79674333, 1.16, 0.0,
-      -0.46, -0.79674333
-    ]);
-  });
-
-  it('3 length, 2 step with hamming window', async () => {
-    const input = tf.tensor1d([1, 1, 1, 1, 1]);
-    const frameLength = 3;
-    const frameStep = 2;
-    const fftLength = 3;
-    const output = tf.signal.stft(
-        input, frameLength, frameStep, fftLength,
-        (length) => tf.signal.hammingWindow(length));
-    expect(output.shape).toEqual([2, 2]);
-    expectArraysClose(
-        await output.data(),
-        [1.16, 0.0, -0.46, -0.79674333, 1.16, 0.0, -0.46, -0.79674333]);
-  });
-
-  it('3 fftLength, 5 frameLength, 2 step with hamming window', async () => {
-    const input = tf.tensor1d([1, 1, 1, 1, 1, 1]);
-    const frameLength = 5;
-    const frameStep = 1;
-    const fftLength = 3;
-    const output = tf.signal.stft(
-        input, frameLength, frameStep, fftLength,
-        (length) => tf.signal.hammingWindow(length));
-    expect(output.shape).toEqual([2, 2]);
-    expectArraysClose(
-        await output.data(),
-        [1.619999, 0.0, -0.69, 0.39837, 1.619999, 0.0, -0.69, 0.39837]);
-  });
-
-  it('5 length with hann window (sequential)', async () => {
-    const input = tf.tensor1d([1, 2, 3, 4, 5]);
-    const frameLength = 5;
-    const frameStep = 1;
-    const fftLength = 5;
-    const output = tf.signal.stft(
-        input, frameLength, frameStep, fftLength,
-        (length) => tf.signal.hammingWindow(length));
-    expect(output.shape).toEqual([1, 3]);
-    expectArraysClose(
-        await output.data(),
-        [6.72, 0.0, -3.6371822, -1.1404576, 0.4771822, 0.39919350]);
-  });
-
-  it('3 length without window function', async () => {
-    const input = tf.tensor1d([1, 1, 1, 1, 1]);
-    const frameLength = 3;
-    const frameStep = 1;
-    const fftLength = 3;
-    const ident = (length: number) => tf.ones([length]).as1D();
-    const output =
-        tf.signal.stft(input, frameLength, frameStep, fftLength, ident);
-    expect(output.shape).toEqual([3, 2]);
-    expectArraysClose(
-        await output.data(),
-        [3.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0]);
-  });
-
-  it('3 length, 2 step without window function', async () => {
-    const input = tf.tensor1d([1, 1, 1, 1, 1]);
-    const frameLength = 3;
-    const frameStep = 2;
-    const fftLength = 3;
-    const ident = (length: number) => tf.ones([length]).as1D();
-    const output =
-        tf.signal.stft(input, frameLength, frameStep, fftLength, ident);
-    expect(output.shape).toEqual([2, 2]);
-    expectArraysClose(
-        await output.data(), [3.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/signal_ops_util.ts b/tfjs-master/tfjs-core/src/ops/signal_ops_util.ts
deleted file mode 100644
index 15f5a2783..000000000
--- a/tfjs-master/tfjs-core/src/ops/signal_ops_util.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor1D} from '../tensor';
-import {tensor1d} from './tensor1d';
-
-export function enclosingPowerOfTwo(value: number) {
-  // Return 2**N for integer N such that 2**N >= value.
-  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));
-}
-
-export function cosineWindow(
-    windowLength: number, a: number, b: number): Tensor1D {
-  const even = 1 - windowLength % 2;
-  const newValues = new Float32Array(windowLength);
-  for (let i = 0; i < windowLength; ++i) {
-    const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);
-    newValues[i] = a - b * Math.cos(cosArg);
-  }
-  return tensor1d(newValues, 'float32');
-}
diff --git a/tfjs-master/tfjs-core/src/ops/sin.ts b/tfjs-master/tfjs-core/src/ops/sin.ts
deleted file mode 100644
index 1b4e9cf10..000000000
--- a/tfjs-master/tfjs-core/src/ops/sin.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Sin, SinInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes sin of the input Tensor element-wise: `sin(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
- *
- * x.sin().print();  // or tf.sin(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function sin_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'sin', 'float32');
-
-  const inputs: SinInputs = {x: $x};
-
-  return ENGINE.runKernel(Sin, inputs as unknown as NamedTensorMap);
-}
-export const sin = /* @__PURE__ */ op({sin_});
diff --git a/tfjs-master/tfjs-core/src/ops/sin_test.ts b/tfjs-master/tfjs-core/src/ops/sin_test.ts
deleted file mode 100644
index 3a96e910c..000000000
--- a/tfjs-master/tfjs-core/src/ops/sin_test.ts
+++ /dev/null
@@ -1,117 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('sin', ALL_ENVS, () => {
-  it('basic', async () => {
-    // Covers every 1/4pi range from -4pi to 4pi.
-    const values = [1, 3, 4, 6, 7, 9, 10, 12, -1, -3, -4, -6, -7, -9, -10, -12];
-    const a = tf.tensor1d(values);
-    const result = tf.sin(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.sin(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.sin(a);
-    expectArraysClose(await res.data(), [Math.sin(4), NaN, Math.sin(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.sin(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * Math.cos(5)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.sin(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * Math.cos(5)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1, 2, 3, -5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.sin(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [1 * Math.cos(-1), 2 * Math.cos(2), 3 * Math.cos(3), 4 * Math.cos(-5)],
-        1e-1);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.sin(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [1 * Math.cos(-3), 2 * Math.cos(1), 3 * Math.cos(2), 4 * Math.cos(3)],
-        1e-1);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.sin({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'sin' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const result = tf.sin(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.sin(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.sin('q'))
-        .toThrowError(/Argument 'x' passed to 'sin' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.sin(tf.tensor1d([1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'sin' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sinh.ts b/tfjs-master/tfjs-core/src/ops/sinh.ts
deleted file mode 100644
index ef9f0b7d8..000000000
--- a/tfjs-master/tfjs-core/src/ops/sinh.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Sinh, SinhInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, 1, -1, .7]);
- *
- * x.sinh().print();  // or tf.sinh(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function sinh_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'sinh');
-  const inputs: SinhInputs = {x: $x};
-
-  return ENGINE.runKernel(Sinh, inputs as unknown as NamedTensorMap);
-}
-export const sinh = /* @__PURE__ */ op({sinh_});
diff --git a/tfjs-master/tfjs-core/src/ops/sinh_test.ts b/tfjs-master/tfjs-core/src/ops/sinh_test.ts
deleted file mode 100644
index efd6e86b9..000000000
--- a/tfjs-master/tfjs-core/src/ops/sinh_test.ts
+++ /dev/null
@@ -1,119 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('sinh', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [1, -3, 2, -1, -4];
-    const a = tf.tensor1d(values);
-    const result = tf.sinh(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.sinh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.sinh(a);
-    expectArraysClose(await res.data(), [Math.sinh(4), NaN, Math.sinh(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.sinh(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * Math.cosh(0.5)]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.sinh(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [8 * Math.cosh(0.5)]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-1, 2, 3, -5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.sinh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] * Math.cosh(aValues[i]);
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-3, 1, 2, 3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.sinh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] * Math.cosh(aValues[i]);
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.sinh({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'sinh' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [1, -3, 2, -1, -4];
-    const result = tf.sinh(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.sinh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.sinh('q'))
-        .toThrowError(/Argument 'x' passed to 'sinh' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/slice.ts b/tfjs-master/tfjs-core/src/ops/slice.ts
deleted file mode 100644
index cf5e4d3b5..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Slice, SliceAttrs, SliceInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {Rank, TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`
- * and is of size `size`.
- *
- * Also available are stricter rank-specific methods with the same signature
- * as this method that assert that `x` is of the given rank:
- *   - `tf.slice1d`
- *   - `tf.slice2d`
- *   - `tf.slice3d`
- *   - `tf.slice4d`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- *
- * x.slice([1], [2]).print();
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * x.slice([1, 0], [1, 2]).print();
- * ```
- * @param x The input `tf.Tensor` to slice from.
- * @param begin The coordinates to start the slice from. The length can be
- *     less than the rank of x - the rest of the axes will have implicit 0 as
- *     start. Can also be a single number, in which case it specifies the
- *     first axis.
- * @param size The size of the slice. The length can be less than the rank of
- *     x - the rest of the axes will have implicit -1. A value of -1 requests
- *     the rest of the dimensions in the axis. Can also be a single number,
- *     in which case it specifies the size of the first axis.
- *
- * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
- */
-function slice_<R extends Rank, T extends Tensor<R>>(
-    x: T|TensorLike, begin: number|number[], size?: number|number[]): T {
-  const $x = convertToTensor(x, 'x', 'slice', 'string_or_numeric');
-
-  if ($x.rank === 0) {
-    throw new Error('Slicing scalar is not possible');
-  }
-
-  const inputs: SliceInputs = {x: $x};
-  const attrs: SliceAttrs = {begin, size};
-
-  return ENGINE.runKernel(
-      Slice, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const slice = /* @__PURE__ */ op({slice_});
diff --git a/tfjs-master/tfjs-core/src/ops/slice1d.ts b/tfjs-master/tfjs-core/src/ops/slice1d.ts
deleted file mode 100644
index 358489e36..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice1d.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor1D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-import {slice} from './slice';
-
-/**
- * Extracts a 1D slice from 1D array starting at coordinates `begin` and is
- * of length `size`. See `slice` for details.
- */
-function slice1d_(
-    x: Tensor1D|TensorLike, begin: number, size: number): Tensor1D {
-  const $x = convertToTensor(x, 'x', 'slice1d');
-  util.assert(
-      $x.rank === 1,
-      () =>
-          `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, [begin], [size]);
-}
-export const slice1d = /* @__PURE__ */ op({slice1d_});
diff --git a/tfjs-master/tfjs-core/src/ops/slice1d_test.ts b/tfjs-master/tfjs-core/src/ops/slice1d_test.ts
deleted file mode 100644
index 156d41b1a..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice1d_test.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('slice1d', ALL_ENVS, () => {
-  it('slices 1x1 into 1x1 (effectively a copy)', async () => {
-    const a = tf.tensor1d([5]);
-    const result = tf.slice1d(a, 0, 1);
-
-    expect(result.shape).toEqual([1]);
-    expectArraysClose(await result.data(), 5);
-  });
-
-  it('slices 5x1 into shape 2x1 starting at 3', async () => {
-    const a = tf.tensor1d([1, 2, 3, 4, 5]);
-    const result = tf.slice1d(a, 3, 2);
-
-    expect(result.shape).toEqual([2]);
-    expectArraysClose(await result.data(), [4, 5]);
-  });
-
-  it('slices 5x1 into shape 3x1 starting at 1', async () => {
-    const a = tf.tensor1d([1, 2, 3, 4, 5]);
-    const result = tf.slice1d(a, 1, 3);
-
-    expect(result.shape).toEqual([3]);
-    expectArraysClose(await result.data(), [2, 3, 4]);
-  });
-
-  it('grad', async () => {
-    const a = tf.tensor1d([1, 2, 3, 4, 5]);
-    const dy = tf.tensor1d([10, 100]);
-    const da = tf.grad((a: tf.Tensor1D) => tf.slice1d(a, 1, 2))(a, dy);
-    expect(da.shape).toEqual([5]);
-    expectArraysClose(await da.data(), [0, 10, 100, 0, 0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, 3, 4, 5]);
-    const dy = tf.tensor1d([10, 100]);
-    const da =
-        tf.grad((a: tf.Tensor1D) => tf.slice1d(a.clone(), 1, 2).clone())(a, dy);
-    expect(da.shape).toEqual([5]);
-    expectArraysClose(await da.data(), [0, 10, 100, 0, 0]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [5];
-    const result = tf.slice1d(a, 0, 1);
-    expect(result.shape).toEqual([1]);
-    expectArraysClose(await result.data(), 5);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/slice2d.ts b/tfjs-master/tfjs-core/src/ops/slice2d.ts
deleted file mode 100644
index bb3d2369a..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice2d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor2D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-import {slice} from './slice';
-
-/**
- * Extracts a 2D slice from a 2D array starting at coordinates `begin` and
- * is of size `size`. See `slice` for details.
- */
-function slice2d_(
-    x: Tensor2D|TensorLike, begin: [number, number],
-    size: [number, number]): Tensor2D {
-  const $x = convertToTensor(x, 'x', 'slice2d');
-  util.assert(
-      $x.rank === 2,
-      () =>
-          `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-export const slice2d = /* @__PURE__ */ op({slice2d_});
diff --git a/tfjs-master/tfjs-core/src/ops/slice2d_test.ts b/tfjs-master/tfjs-core/src/ops/slice2d_test.ts
deleted file mode 100644
index 4f963645d..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice2d_test.ts
+++ /dev/null
@@ -1,227 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {Rank} from '../types';
-
-describeWithFlags('slice2d', ALL_ENVS, () => {
-  it('slicing a 1x1 from a 1x1 returns a 1x1', () => {
-    const a = tf.tensor2d([0], [1, 1]);
-    const b = tf.slice2d(a, [0, 0], [1, 1]);
-    expect(b.shape).toEqual([1, 1]);
-  });
-
-  it('returns a tensor of slice size', () => {
-    const a = tf.zeros<Rank.R2>([100, 100]);
-    const b = tf.slice2d(a, [0, 0], [12, 34]);
-    expect(b.shape).toEqual([12, 34]);
-  });
-
-  it('returns the upper-left submatrix when begin is [0, 0]', async () => {
-    const a = tf.randomUniform<Rank.R2>([10, 10], -1, 1);
-    const b = tf.slice2d(a, [0, 0], [2, 2]);
-    const aValues = await a.data();
-
-    expectArraysClose(
-        await b.data(), [aValues[0], aValues[1], aValues[10], aValues[11]]);
-  });
-
-  it('returns the rectangle specified', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [4, 3]);
-    const b = tf.slice2d(a, [1, 1], [3, 2]);
-
-    expectArraysClose(await b.data(), [5, 6, 8, 9, 11, 12]);
-  });
-
-  it('throws when requesting out of bounds slice', () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [4, 3]);
-    expect(() => tf.slice2d(a, [1, 1], [10, 10])).toThrowError();
-  });
-
-  it('grad', async () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
-    const dy = tf.tensor2d([[20], [50]]);
-    const da =
-        tf.grad((x: tf.Tensor2D) => tf.slice2d(a, [0, 1], [2, 1]))(a, dy);
-    expect(da.shape).toEqual([2, 3]);
-    expectArraysClose(await da.data(), [0, 20, 0, 0, 50, 0]);
-  });
-
-  it('accepts a tensor-like object', () => {
-    const a = [[0]];  // 1x1
-    const b = tf.slice2d(a, [0, 0], [1, 1]);
-    expect(b.shape).toEqual([1, 1]);
-  });
-
-  it('slice an already sliced tensor, first was not continous', async () => {
-    const a = [
-      [1, 2, 3, 4],
-      [5, 6, 7, 8],
-      [9, 10, 11, 12],
-    ];  // 3x4.
-    const b = tf.slice(a, [0, 1]);
-    const c = tf.slice(b, [1, 1], [1, 1]);
-    expect(c.shape).toEqual([1, 1]);
-    expectArraysClose(await c.data(), [7]);
-  });
-
-  it('slice an already sliced tensor, first was continous', async () => {
-    const a = [
-      [1, 2, 3, 4],
-      [5, 6, 7, 8],
-      [9, 10, 11, 12],
-    ];  // 3x4.
-    const b = tf.slice(a, [1, 0]);
-    const c = tf.slice(b, [1, 0]);
-    expect(c.shape).toEqual([1, 4]);
-    expectArraysClose(await c.data(), [9, 10, 11, 12]);
-  });
-
-  it('slice an already sliced tensor and do async read', async () => {
-    const a = [
-      [1, 2, 3, 4],
-      [5, 6, 7, 8],
-      [9, 10, 11, 12],
-    ];  // 3x4.
-    const b = tf.slice(a, [0, 1]);
-    const c = tf.slice(b, [1, 1], [1, 1]);
-    expect(c.shape).toEqual([1, 1]);
-    expectArraysClose(await c.data(), new Float32Array([7]));
-  });
-
-  it('square a sliced texture, followed by non-sliced texture of same shape',
-     async () => {  // Tests collisions in the shader cache.
-       // Make a 2x3 tensor, upload to gpu and reshape to 3x2.
-       const input = tf.tensor([[1, 2, 3], [4, 5, 6]]).abs().as2D(3, 2);
-       const slicedInput = tf.slice(input, [0, 0], [3, 2]);
-       // First square program takes the sliced input.
-       const a = slicedInput.square();
-       expectArraysClose(await a.data(), [1, 4, 9, 16, 25, 36]);
-       // Second square program takes the non-sliced input.
-       const b = tf.square(input);
-       expectArraysClose(await b.data(), [1, 4, 9, 16, 25, 36]);
-     });
-
-  it('square a non-sliced texture, followed by a sliced texture of same shape',
-     async () => {  // Tests collisions in the shader cache.
-       // Make a 2x3 tensor, upload to gpu and reshape to 3x2.
-       const input = tf.tensor([[1, 2, 3], [4, 5, 6]]).abs().as2D(3, 2);
-       // Make a sliced version of the same tensor with the same shape.
-       const slicedInput = tf.slice(input, [0, 0], [3, 2]);
-       // First square program takes the non-sliced input.
-       const a = input.square();
-       expectArraysClose(await a.data(), [1, 4, 9, 16, 25, 36]);
-       // Second square program takes the sliced input.
-       const b = tf.square(slicedInput);
-       expectArraysClose(await b.data(), [1, 4, 9, 16, 25, 36]);
-     });
-
-  it('slice a tensor and do async read', async () => {
-    const a = [
-      [1, 2, 3, 4],
-      [5, 6, 7, 8],
-      [9, 10, 11, 12],
-    ];  // 3x4.
-    const b = tf.slice(a, [0, 1], [3, 2]);
-    expect(b.shape).toEqual([3, 2]);
-    const vals = await b.data();
-    expectArraysClose(vals, new Float32Array([2, 3, 6, 7, 10, 11]));
-  });
-
-  it('flatten a sliced tensor that was continuous in memory', async () => {
-    const a = [
-      [1, 2, 3, 4],
-      [5, 6, 7, 8],
-      [9, 10, 11, 12],
-    ];  // 3x4.
-    const b = tf.slice(a, [1, 0]).flatten();
-    expect(b.shape).toEqual([8]);
-    expectArraysClose(await b.data(), [5, 6, 7, 8, 9, 10, 11, 12]);
-  });
-
-  it('slice a tensor that was not continuous in memory', async () => {
-    const a = [
-      [1, 2, 3, 4],
-      [5, 6, 7, 8],
-      [9, 10, 11, 12],
-    ];  // 3x4.
-    const b = tf.slice(a, [0, 1]);
-    expect(b.shape).toEqual([3, 3]);
-    expectArraysClose(await b.data(), [2, 3, 4, 6, 7, 8, 10, 11, 12]);
-  });
-
-  it('flatten a sliced tensor that was not continuous in memory', async () => {
-    const a = [
-      [1, 2, 3, 4],
-      [5, 6, 7, 8],
-      [9, 10, 11, 12],
-    ];  // 3x4.
-    const b = tf.slice(a, [0, 1]).flatten();
-    expect(b.shape).toEqual([9]);
-    expectArraysClose(await b.data(), [2, 3, 4, 6, 7, 8, 10, 11, 12]);
-  });
-
-  it('flatten a sliced tensor not continuous in memory and run program',
-     async () => {
-       const a = [
-         [1, 2, 3, 4],
-         [5, 6, 7, 8],
-         [9, 10, 11, 12],
-       ];  // 3x4.
-       const b = tf.slice(a, [0, 1]).flatten();
-       const c = tf.square(b);
-       expectArraysClose(await c.data(), [4, 9, 16, 36, 49, 64, 100, 121, 144]);
-     });
-
-  it('reshape a sliced 1d into a 2d tensor', async () => {
-    const a = [1, 2, 3, 4, 5];
-    const b = tf.slice(a, 1).as2D(2, 2);
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await b.data(), [2, 3, 4, 5]);
-  });
-
-  it('reshape a sliced 1d into a 2d tensor and run program', async () => {
-    const a = [1, 2, 3, 4, 5];
-    const b = tf.slice(a, 1).as2D(2, 2).square();
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await b.data(), [4, 9, 16, 25]);
-  });
-
-  it('broadcast the original with the sliced tensor', async () => {
-    const a = [[1, 2], [3, 4]];
-    const b = tf.slice(a, [0, 1]);
-    const c = tf.add(a, b);
-    expect(c.shape).toEqual([2, 2]);
-    expectArraysClose(await c.data(), [3, 4, 7, 8]);
-  });
-
-  it('zero-sized slice out of a non-zero sized tensor', async () => {
-    const a = tf.zeros([4, 2]);
-    const res = tf.slice(a, [0, 0], [0, 2]);
-    expect(res.shape).toEqual([0, 2]);
-    expectArraysClose(await res.data(), []);
-  });
-
-  it('zero-sized slice out of a zero-sized tensor', async () => {
-    const a = tf.zeros([0, 4]);
-    const res = tf.slice(a, [0, 1], [0, 3]);
-    expect(res.shape).toEqual([0, 3]);
-    expectArraysClose(await res.data(), []);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/slice3d.ts b/tfjs-master/tfjs-core/src/ops/slice3d.ts
deleted file mode 100644
index 73c39acc7..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice3d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor3D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-import {slice} from './slice';
-
-/**
- * Extracts a 3D slice from a 3D array starting at coordinates `begin` and
- * is of size `size`. See `slice` for details.
- */
-function slice3d_(
-    x: Tensor3D|TensorLike, begin: [number, number, number],
-    size: [number, number, number]): Tensor3D {
-  const $x = convertToTensor(x, 'x', 'slice3d');
-  util.assert(
-      $x.rank === 3,
-      () =>
-          `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-export const slice3d = /* @__PURE__ */ op({slice3d_});
diff --git a/tfjs-master/tfjs-core/src/ops/slice3d_test.ts b/tfjs-master/tfjs-core/src/ops/slice3d_test.ts
deleted file mode 100644
index ca38dccb6..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice3d_test.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('slice3d', ALL_ENVS, () => {
-  it('slices 1x1x1 into shape 1x1x1 (effectively a copy)', async () => {
-    const a = tf.tensor3d([[[5]]], [1, 1, 1]);
-    const result = tf.slice3d(a, [0, 0, 0], [1, 1, 1]);
-
-    expect(result.shape).toEqual([1, 1, 1]);
-    expectArraysClose(await result.data(), [5]);
-  });
-
-  it('slices 2x2x2 array into 1x2x2 starting at [1, 0, 0]', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-    const result = tf.slice3d(a, [1, 0, 0], [1, 2, 2]);
-
-    expect(result.shape).toEqual([1, 2, 2]);
-    expectArraysClose(await result.data(), [5, 6, 7, 8]);
-  });
-
-  it('slices 2x2x2 array into 2x1x1 starting at [0, 1, 1]', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-    const result = tf.slice3d(a, [0, 1, 1], [2, 1, 1]);
-
-    expect(result.shape).toEqual([2, 1, 1]);
-    expectArraysClose(await result.data(), [4, 8]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[[5]]];  // 1x1x1
-    const result = tf.slice3d(a, [0, 0, 0], [1, 1, 1]);
-
-    expect(result.shape).toEqual([1, 1, 1]);
-    expectArraysClose(await result.data(), [5]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/slice4d.ts b/tfjs-master/tfjs-core/src/ops/slice4d.ts
deleted file mode 100644
index cb50be36e..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice4d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor4D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-import {slice} from './slice';
-
-/**
- * Extracts a 4D slice from a 4D array starting at coordinates `begin` and
- * is of size `size`. See `slice` for details.
- */
-function slice4d_(
-    x: Tensor4D|TensorLike, begin: [number, number, number, number],
-    size: [number, number, number, number]): Tensor4D {
-  const $x = convertToTensor(x, 'x', 'slice4d');
-  util.assert(
-      $x.rank === 4,
-      () =>
-          `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
-  return slice($x, begin, size);
-}
-export const slice4d = /* @__PURE__ */ op({slice4d_});
diff --git a/tfjs-master/tfjs-core/src/ops/slice4d_test.ts b/tfjs-master/tfjs-core/src/ops/slice4d_test.ts
deleted file mode 100644
index be36a7c3c..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice4d_test.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('slice4d', ALL_ENVS, () => {
-  it('slices 1x1x1x1 into shape 1x1x1x1 (effectively a copy)', async () => {
-    const a = tf.tensor4d([[[[5]]]], [1, 1, 1, 1]);
-    const result = tf.slice4d(a, [0, 0, 0, 0], [1, 1, 1, 1]);
-
-    expect(result.shape).toEqual([1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [5]);
-  });
-
-  it('slices 2x2x2x2 array into 1x2x2x2 starting at [1, 0, 0, 0]', async () => {
-    const a = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 11, 22, 33, 44, 55, 66, 77, 88],
-        [2, 2, 2, 2],
-    );
-    const result = tf.slice4d(a, [1, 0, 0, 0], [1, 2, 2, 2]);
-
-    expect(result.shape).toEqual([1, 2, 2, 2]);
-    expectArraysClose(await result.data(), [11, 22, 33, 44, 55, 66, 77, 88]);
-  });
-
-  it('slices 2x2x2x2 array into 2x1x1x1 starting at [0, 1, 1, 1]', async () => {
-    const a = tf.tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 11, 22, 33, 44, 55, 66, 77, 88], [2, 2, 2, 2]);
-    const result = tf.slice4d(a, [0, 1, 1, 1], [2, 1, 1, 1]);
-
-    expect(result.shape).toEqual([2, 1, 1, 1]);
-    expectArraysClose(await result.data(), [8, 88]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[[[5]]]];  // 1x1x1x1
-    const result = tf.slice4d(a, [0, 0, 0, 0], [1, 1, 1, 1]);
-
-    expect(result.shape).toEqual([1, 1, 1, 1]);
-    expectArraysClose(await result.data(), [5]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/slice_test.ts b/tfjs-master/tfjs-core/src/ops/slice_test.ts
deleted file mode 100644
index a98ec45ea..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice_test.ts
+++ /dev/null
@@ -1,267 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags, SYNC_BACKEND_ENVS} from '../jasmine_util';
-import {encodeStrings, expectArraysClose} from '../test_util';
-import {TensorLike1D} from '../types';
-
-describeWithFlags('slice ', ALL_ENVS, () => {
-  describeWithFlags('ergonomics', ALL_ENVS, () => {
-    it('slices 2x2x2 array into 2x1x1 no size', async () => {
-      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-      const result = a.slice([0, 1, 1]);
-      expect(result.shape).toEqual([2, 1, 1]);
-      expectArraysClose(await result.data(), [4, 8]);
-    });
-
-    it('slices 2x2x2 array into 1x2x2 with scalar begin no size', async () => {
-      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-      const result = a.slice(1);
-      expect(result.shape).toEqual([1, 2, 2]);
-      expectArraysClose(await result.data(), [5, 6, 7, 8]);
-    });
-
-    it('slices 2x2x2 array using 2d size and 2d size', async () => {
-      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-      const result = a.slice([0, 1]);
-      expect(result.shape).toEqual([2, 1, 2]);
-      expectArraysClose(await result.data(), [3, 4, 7, 8]);
-    });
-
-    it('slices 2x2x2 array using negative size', async () => {
-      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-      const result = a.slice([0, 1], [-1, 1]);
-      expect(result.shape).toEqual([2, 1, 2]);
-      expectArraysClose(await result.data(), [3, 4, 7, 8]);
-    });
-
-    it('slices 2x2x2 array using 1d size', async () => {
-      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-      const result = a.slice(0, 1);
-      expect(result.shape).toEqual([1, 2, 2]);
-      expectArraysClose(await result.data(), [1, 2, 3, 4]);
-    });
-
-    it('throws when passed a non-tensor', () => {
-      expect(() => tf.slice({} as tf.Tensor, 0, 0))
-          .toThrowError(/Argument 'x' passed to 'slice' must be a Tensor/);
-    });
-
-    it('accepts a tensor-like object', async () => {
-      const a = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]];  // 2x2x2
-      const result = tf.slice(a, [0, 1, 1]);
-      expect(result.shape).toEqual([2, 1, 1]);
-      expectArraysClose(await result.data(), [4, 8]);
-    });
-
-    it('should match source tensor dtype', () => {
-      const a = tf.tensor1d([1, 2, 3, 4, 5], 'int32');
-      const b = a.asType('float32');
-
-      expect(tf.slice(b, 0).dtype).toEqual('float32');
-    });
-
-    it('throws when begin is negative', async () => {
-      const a = [[1, 2], [3, 4]];  // 2x2
-      expect(() => tf.slice(a, [-1, 1], [
-        1, 1
-      ])).toThrowError(/slice\(\) does not support negative begin indexing./);
-    });
-  });
-
-  describeWithFlags('shallow slicing', ALL_ENVS, () => {
-    it('shallow slice an input that was cast', async () => {
-      const a = tf.tensor([[1, 2], [3, 4]], [2, 2], 'int32');
-      const b = a.toFloat();
-      const c = b.slice(1, 1);
-      expect(c.dtype).toBe('float32');
-      expect(c.shape).toEqual([1, 2]);
-      expectArraysClose(await c.data(), [3, 4]);
-    });
-
-    it('delayed async read of sliced tensor has no mem leak', async () => {
-      const a = tf.zeros([10]);
-      const b = tf.slice(a, 0, 1);
-      const nBefore = tf.memory().numTensors;
-      expect(nBefore).toBe(2);
-      await b.data();
-      const nAfter = tf.memory().numTensors;
-      expect(nAfter).toBe(2);
-      tf.dispose([a, b]);
-      expect(tf.memory().numTensors).toBe(0);
-    });
-  });
-
-  describeWithFlags('shallow slicing', SYNC_BACKEND_ENVS, () => {
-    it('delayed sync read of sliced tensor has no mem leak', () => {
-      const a = tf.zeros([10]);
-      const b = tf.slice(a, 0, 1);
-      const nBefore = tf.memory().numTensors;
-      expect(nBefore).toBe(2);
-      b.dataSync();
-      const nAfter = tf.memory().numTensors;
-      expect(nAfter).toBe(2);
-      tf.dispose([a, b]);
-      expect(tf.memory().numTensors).toBe(0);
-    });
-  });
-
-  describeWithFlags('slice5d', ALL_ENVS, () => {
-    it('slices 1x1x1x1x1 into shape 1x1x1x1x1 (effectively a copy)',
-       async () => {
-         const a = tf.tensor5d([[[[[5]]]]], [1, 1, 1, 1, 1]);
-         const result = tf.slice(a, [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]);
-
-         expect(result.shape).toEqual([1, 1, 1, 1, 1]);
-         expectArraysClose(await result.data(), [5]);
-       });
-
-    it('slices 2x2x2x2x2 array into 1x2x2x2x2 starting at [1,0,0,0,0]',
-       async () => {
-         const a = tf.tensor5d(
-             [
-               1,  2,  3,   4,   5,   6,   7,   8,   9,   10, 11,
-               12, 13, 14,  15,  16,  11,  22,  33,  44,  55, 66,
-               77, 88, 111, 222, 333, 444, 555, 666, 777, 888
-             ],
-             [2, 2, 2, 2, 2]);
-         const result = tf.slice(a, [1, 0, 0, 0, 0], [1, 2, 2, 2, 2]);
-
-         expect(result.shape).toEqual([1, 2, 2, 2, 2]);
-         expectArraysClose(await result.data(), [
-           11, 22, 33, 44, 55, 66, 77, 88, 111, 222, 333, 444, 555, 666, 777,
-           888
-         ]);
-       });
-
-    it('slices 2x2x2x2x2 array into 2x1x1x1x1 starting at [0,1,1,1,1]',
-       async () => {
-         const a = tf.tensor5d(
-             [
-               1,  2,  3,   4,   5,   6,   7,   8,   9,   10, 11,
-               12, 13, 14,  15,  16,  11,  22,  33,  44,  55, 66,
-               77, 88, 111, 222, 333, 444, 555, 666, 777, 888
-             ],
-             [2, 2, 2, 2, 2]);
-         const result = tf.slice(a, [0, 1, 1, 1, 1], [2, 1, 1, 1, 1]);
-
-         expect(result.shape).toEqual([2, 1, 1, 1, 1]);
-         expectArraysClose(await result.data(), [16, 888]);
-       });
-
-    it('accepts a tensor-like object', async () => {
-      const a = [[[[[5]]]]];  // 1x1x1x1x1
-      const result = tf.slice(a, [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]);
-
-      expect(result.shape).toEqual([1, 1, 1, 1, 1]);
-      expectArraysClose(await result.data(), [5]);
-    });
-  });
-
-  describeWithFlags('slice6d', ALL_ENVS, () => {
-    it('slices 1x1x1x1x1x1 into shape 1x1x1x1x1x1 (effectively a copy)',
-       async () => {
-         const a = tf.tensor6d([[[[[[5]]]]]], [1, 1, 1, 1, 1, 1]);
-         const result = tf.slice(a, [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]);
-
-         expect(result.shape).toEqual([1, 1, 1, 1, 1, 1]);
-         expectArraysClose(await result.data(), [5]);
-       });
-
-    it('slices 2x2x2x2x2x2 array into 1x2x2x2x2x2 starting at [1,0,0,0,0,0]',
-       async () => {
-         const a = tf.tensor6d(
-             [
-               31,  32,  33,   34,   35,   36,   37,   38,   39,   310,  311,
-               312, 313, 314,  315,  316,  311,  322,  333,  344,  355,  366,
-               377, 388, 3111, 3222, 3333, 3444, 3555, 3666, 3777, 3888,
-
-               1,   2,   3,    4,    5,    6,    7,    8,    9,    10,   11,
-               12,  13,  14,   15,   16,   11,   22,   33,   44,   55,   66,
-               77,  88,  111,  222,  333,  444,  555,  666,  777,  888
-             ],
-             [2, 2, 2, 2, 2, 2]);
-         const result = tf.slice(a, [1, 0, 0, 0, 0, 0], [1, 2, 2, 2, 2, 2]);
-
-         expect(result.shape).toEqual([1, 2, 2, 2, 2, 2]);
-         expectArraysClose(await result.data(), [
-           1,  2,  3,   4,   5,   6,   7,   8,   9,   10, 11,
-           12, 13, 14,  15,  16,  11,  22,  33,  44,  55, 66,
-           77, 88, 111, 222, 333, 444, 555, 666, 777, 888
-         ]);
-       });
-
-    it('slices 2x2x2x2x2x2 array into 2x1x1x1x1x1 starting at [0,1,1,1,1,1]',
-       async () => {
-         const a = tf.tensor6d(
-             [
-               31,  32,  33,   34,   35,   36,   37,   38,   39,   310,  311,
-               312, 313, 314,  315,  316,  311,  322,  333,  344,  355,  366,
-               377, 388, 3111, 3222, 3333, 3444, 3555, 3666, 3777, 3888,
-
-               1,   2,   3,    4,    5,    6,    7,    8,    9,    10,   11,
-               12,  13,  14,   15,   16,   11,   22,   33,   44,   55,   66,
-               77,  88,  111,  222,  333,  444,  555,  666,  777,  888
-             ],
-             [2, 2, 2, 2, 2, 2]);
-         const result = tf.slice(a, [0, 1, 1, 1, 1, 1], [2, 1, 1, 1, 1, 1]);
-
-         expect(result.shape).toEqual([2, 1, 1, 1, 1, 1]);
-         expectArraysClose(await result.data(), [3888, 888]);
-       });
-
-    it('accepts a tensor-like object', async () => {
-      const a = [[[[[[5]]]]]];  // 1x1x1x1x1x1
-      const result = tf.slice(a, [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]);
-
-      expect(result.shape).toEqual([1, 1, 1, 1, 1, 1]);
-      expectArraysClose(await result.data(), [5]);
-    });
-  });
-
-  describeWithFlags('accepts string', ALL_ENVS, () => {
-    it('slices 2x2x2 array into 2x1x1 no size.', async () => {
-      const a = tf.tensor3d(
-          ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'],
-          [2, 2, 2], 'string');
-      const result = a.slice([0, 1, 1]);
-      expect(result.shape).toEqual([2, 1, 1]);
-      expectArraysClose(await result.data(), ['four', 'eight']);
-    });
-
-    it('slices 2x2x2 array into 1x2x2 with scalar begin no size.', async () => {
-      const a = tf.tensor3d(
-          ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'],
-          [2, 2, 2]);
-      const result = a.slice(1);
-      expect(result.shape).toEqual([1, 2, 2]);
-      expectArraysClose(await result.data(), ['five', 'six', 'seven', 'eight']);
-    });
-
-    it('slice encoded string.', async () => {
-      const bytes =
-          encodeStrings([
-            'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'
-          ]) as TensorLike1D;
-      const a = tf.tensor3d(bytes, [2, 2, 2], 'string');
-      const result = a.slice([0, 1, 1]);
-      expect(result.shape).toEqual([2, 1, 1]);
-      expectArraysClose(await result.data(), ['four', 'eight']);
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/slice_util.ts b/tfjs-master/tfjs-core/src/ops/slice_util.ts
deleted file mode 100644
index d50c79992..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice_util.ts
+++ /dev/null
@@ -1,720 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { TensorInfo } from '../tensor_info';
-import * as util from '../util';
-
-const NEW_AXIS = -2;
-const SHRINK_AXIS = -1;
-
-// Sparse slicing specification
-// if one does foo[3:5, ..., -3], the begin, end and strides will have length
-// of 3.
-interface StridedSliceSparseSpec {
-  dims: number;
-  numAddAxisAfterEllipsis: number;
-  begin: number[];
-  end: number[];
-  strides: number[];
-  beginMask: number;
-  endMask: number;
-  ellipsisMask: number;
-  newAxisMask: number;
-  shrinkAxisMask: number;
-}
-
-// Dense slicing specification
-// all ellipses and newaxis are expanded out. So if foo[3:5, ..., -3] where foo
-// is 10 dimensional, each array of begin, end, strides will have 10 entries
-// where as the sparse can have length less than the rank of foo.
-interface StridedSliceDenseSpec {
-  dims: number;
-  beginMask?: number;
-  endMask?: number;
-  beginValid: boolean;
-  endValid: boolean;
-  begin?: number[];
-  end?: number[];
-  strides?: number[];
-  // This array helps construct the final shape of the slice.
-  // The final tensor is reduced in rank whenever a single index e.g. foo[3]
-  // is called for. The final tensor increases in rank with newAxis entries.
-  // If an index in this array is positive, the size of the dimension is
-  // obtained from canonical end-begin.  Otherwise, if it is a NEW_AXIS, it will
-  // be 1. A shrunk dimension is skipped.
-  finalShapeGatherIndices?: number[];
-  // This array has the same size as finalShapeGatherIndices, but it remembers
-  // the sparse index that a dimension comes from, instead of dense index.
-  // A -1 in this vector means the index is not from the sparse input.
-  finalShapeGatherIndicesSparse?: number[];
-  inputShapeGatherIndicesSparse?: number[];
-  // The dense indexed shrink mask is which processing dimensions should be
-  // shrunk. For example, if foo.shape = [10, 10, 10, 10], foo[3, ..., 5] has
-  // sparseShrinkAxisMask of 5 (0101) and denseShrinkAxisMask of 9 (1001),
-  // yielding a final shape [10, 10].
-  shrinkAxisMask?: number;
-}
-
-export type SliceInfo = {
-  finalShapeSparse: number[],
-  finalShape: number[],
-  isIdentity: boolean,
-  sliceDim0: boolean,
-  isSimpleSlice: boolean,
-  begin: number[],
-  end: number[],
-  strides: number[]
-};
-
-export function assertParamsValid(
-    input: TensorInfo, begin: number[], size: number[]): void {
-  const inputRank = input.shape.length;
-  util.assert(
-      inputRank === begin.length,
-      () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +
-          `match the rank of the array (${inputRank}).`);
-  util.assert(
-      inputRank === size.length,
-      () => `Error in slice${inputRank}D: Length of size ${size} must ` +
-          `match the rank of the array (${inputRank}).`);
-
-  for (let i = 0; i < inputRank; ++i) {
-    util.assert(
-        begin[i] + size[i] <= input.shape[i],
-        () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +
-            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${
-                  input.shape[i]})`);
-  }
-}
-
-/** Converts a binary mask to an array of axes. Used in stridedSlice(). */
-export function maskToAxes(mask: number): number[] {
-  const axes = [];
-  let axis = 0;
-  while (mask > 0) {
-    if (mask & 1) {
-      axes.push(axis);
-    }
-    mask /= 2;
-    axis++;
-  }
-  return axes;
-}
-
-/** Computes the output shape given the strided slice params. */
-export function computeOutShape(
-    begin: number[], end: number[], strides: number[]): number[] {
-  const size = [];
-  for (let axis = 0; axis < begin.length; axis++) {
-    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
-  }
-  return size;
-}
-
-// Creates full selection at the elided dimensions. If the dimension matches
-// the ellipsis mask, override the current stride value. Otherwise, insert.
-export function stridesWithElidedDims(
-    strides: number[], ellipsisInsertionIndex: number, numElidedAxes: number,
-    inputShape: number[]): number[] {
-  const newStrides = [...strides];
-  for (let i = newStrides.length; i < inputShape.length; i++) {
-    newStrides.push(1);
-  }
-  for (let i = 0; i < numElidedAxes; i++) {
-    if (i === 0) {
-      newStrides[ellipsisInsertionIndex] = 1;
-    } else {
-      newStrides.splice(
-          ellipsisInsertionIndex, 0 /* num elements to delete */,
-          1 /* element to add */);
-      newStrides.pop();
-    }
-  }
-  return newStrides;
-}
-
-function unnormalizeAxis(
-    ellipsisInsertionIndex: number, numElidedAxes: number,
-    normalizedAxis: number): number {
-  if (normalizedAxis <= ellipsisInsertionIndex) {
-    return normalizedAxis;
-  }
-
-  return normalizedAxis - (numElidedAxes - 1);
-}
-
-function getElidedAxes(numElidedAxes: number, ellipsisInsertionIndex: number) {
-  const elidedAxes = [];
-  for (let i = 0; i < numElidedAxes; i++) {
-    elidedAxes.push(ellipsisInsertionIndex + i);
-  }
-  return elidedAxes;
-}
-
-// Normalize the start, end and strides.
-export function getNormalizedAxes(
-    inputShape: number[], ellipsisAxes: number[], numInterpolatedAxes: number,
-    begin: number[], end: number[], strides: number[], beginMask: number,
-    endMask: number,
-    ellipsisMask: number): {begin: number[], end: number[], strides: number[]} {
-  const inputRank = inputShape.length;
-  let normalizedBegin = new Array(inputRank),
-      normalizedEnd = new Array(inputRank),
-      normalizedStrides = new Array(inputRank);
-  if (ellipsisAxes.length && numInterpolatedAxes > 0) {
-    const fullIndex = ellipsisAxes[0];
-
-    // The ellipsis applies to the masked index as well as any dimensions
-    // that are interpolated.
-    const numElidedAxes = numInterpolatedAxes + 1;
-    normalizedBegin = startIndicesWithElidedDims(
-        beginMask, fullIndex, numElidedAxes, begin, inputShape);
-    normalizedEnd = stopIndicesWithElidedDims(
-        endMask, fullIndex, numElidedAxes, end, inputShape);
-    normalizedStrides =
-        stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
-  } else {
-    for (let axis = 0; axis < inputRank; axis++) {
-      normalizedBegin[axis] = startForAxis(
-          beginMask, begin, strides, inputShape, axis, ellipsisMask);
-      normalizedEnd[axis] =
-          stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
-      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
-    }
-  }
-
-  return {
-    begin: normalizedBegin,
-    end: normalizedEnd,
-    strides: normalizedStrides
-  };
-}
-
-// Creates full selection at the elided dimensions. If the dimension matches
-// the ellipsis mask, override the current start value. Otherwise, insert.
-export function startIndicesWithElidedDims(
-    beginMask: number, ellipsisInsertionIndex: number, numElidedAxes: number,
-    originalBegin: number[], inputShape: number[]): number[] {
-  const newIndices = [...inputShape];
-  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-
-  for (let axis = 0; axis < newIndices.length; axis++) {
-    if (elidedAxes.indexOf(axis) > -1) {
-      newIndices[axis] = 0;
-    } else {
-      const originalAxis =
-          unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-      let originalValue = originalBegin[originalAxis];
-      if (beginMask & 1 << originalAxis) {
-        originalValue = 0;
-      }
-
-      newIndices[axis] = originalValue;
-    }
-  }
-  return newIndices;
-}
-
-// Creates full selection at the elided dimensions. If the dimension matches
-// the ellipsis mask, override the current stop value. Otherwise, insert.
-export function stopIndicesWithElidedDims(
-    endMask: number, ellipsisInsertionIndex: number, numElidedAxes: number,
-    originalEnd: number[], inputShape: number[]): number[] {
-  const newIndices = [...inputShape];
-  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
-
-  for (let axis = 0; axis < newIndices.length; axis++) {
-    if (elidedAxes.indexOf(axis) > -1) {
-      newIndices[axis] = Number.MAX_SAFE_INTEGER;
-    } else {
-      const originalAxis =
-          unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
-      let originalValue = originalEnd[originalAxis];
-      if (endMask & 1 << originalAxis) {
-        originalValue = Number.MAX_SAFE_INTEGER;
-      }
-      newIndices[axis] = originalValue;
-    }
-  }
-
-  for (let i = 0; i < newIndices.length; i++) {
-    // Handle negative indices
-    const axisSize = inputShape[i];
-    if (newIndices[i] < 0) {
-      newIndices[i] += axisSize;
-    }
-    newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);
-  }
-  return newIndices;
-}
-
-export function stridesForAxis(
-    strides: number[], axis: number, ellipsisMask: number): number {
-  let stride = strides[axis];
-  if (ellipsisMask & (1 << axis) || stride == null) {
-    stride = 1;
-  }
-
-  return stride;
-}
-
-export function startForAxis(
-    beginMask: number, startIndices: number[], strides: number[],
-    inputShape: number[], axis: number, ellipsisMask: number): number {
-  // Begin with the specified index
-  let start = startIndices[axis];
-  const stride = strides[axis] || 1;
-
-  // Check the axis bit from right of masked axes, or the begin index is not set
-  // for the axis.
-  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
-    if (stride > 0) {
-      // Forward iteration - use the first element. These values will get
-      // clamped below (Note: We could have set them to 0 and axis_size-1, but
-      // use lowest() and max() to maintain symmetry with StopForAxis())
-      start = Number.MIN_SAFE_INTEGER;
-    } else {
-      // Backward iteration - use the last element.
-      start = Number.MAX_SAFE_INTEGER;
-    }
-  }
-
-  // Handle negative indices
-  const axisSize = inputShape[axis];
-  if (start < 0) {
-    start += axisSize;
-  }
-
-  // Clamping
-  start = util.clamp(0, start, axisSize - 1);
-
-  return start;
-}
-
-export function stopForAxis(
-    endMask: number, stopIndices: number[], strides: number[],
-    inputShape: number[], axis: number, ellipsisMask: number): number {
-  // Begin with the specified index
-  let stop = stopIndices[axis];
-  const stride = strides[axis] || 1;
-
-  // Check the axis bit from right of masked axes, or if the stop index is not
-  // set for this axis.
-  if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {
-    if (stride > 0) {
-      // Forward iteration - use the last element. These values will get
-      // clamped below
-      stop = Number.MAX_SAFE_INTEGER;
-    } else {
-      // Backward iteration - use the first element.
-      stop = Number.MIN_SAFE_INTEGER;
-    }
-  }
-
-  // Handle negative indices
-  const axisSize = inputShape[axis];
-  if (stop < 0) {
-    stop += axisSize;
-  }
-
-  // Clamping
-  // Because the end index points one past the last element, we need slightly
-  // different clamping ranges depending on the direction.
-  if (stride > 0) {
-    // Forward iteration
-    stop = util.clamp(0, stop, axisSize);
-  } else {
-    // Backward iteration
-    stop = util.clamp(-1, stop, axisSize - 1);
-  }
-
-  return stop;
-}
-
-/**
- * Returns true if the slice occupies a continous set of elements in the
- * 'flat' space.
- */
-export function isSliceContinous(
-    shape: number[], begin: number[], size: number[]) {
-  // Index of the first axis that has size > 1.
-  let firstNonOneAxis = size.length;
-  for (let i = 0; i < size.length; i++) {
-    if (size[i] > 1) {
-      firstNonOneAxis = i;
-      break;
-    }
-  }
-
-  for (let i = firstNonOneAxis + 1; i < size.length; i++) {
-    if (begin[i] > 0 || size[i] !== shape[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-
-export function computeFlatOffset(begin: number[], strides: number[]): number {
-  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
-  for (let i = 0; i < begin.length - 1; i++) {
-    flatOffset += begin[i] * strides[i];
-  }
-  return flatOffset;
-}
-
-export function parseSliceParams(
-    x: TensorInfo, begin: number|number[], size?: number|number[]) {
-  // The following logic allows for more ergonomic calls.
-  let begin_: number[];
-  const xRank = x.shape.length;
-  if (typeof begin === 'number') {
-    begin_ = [begin, ...new Array(xRank - 1).fill(0)];
-  } else if (begin.length < xRank) {
-    begin_ = begin.concat(new Array(xRank - begin.length).fill(0));
-  } else {
-    begin_ = begin.slice();
-  }
-  begin_.forEach(d => {
-    util.assert(
-        d !== -1, () => 'slice() does not support negative begin indexing.');
-  });
-  let size_: number[];
-  if (size == null) {
-    size_ = new Array(xRank).fill(-1);
-  } else if (typeof size === 'number') {
-    size_ = [size, ...new Array(xRank - 1).fill(-1)];
-  } else if (size.length < xRank) {
-    size_ = size.concat(new Array(xRank - size.length).fill(-1));
-  } else {
-    size_ = size;
-  }
-  size_ = size_.map((d, i) => {
-    if (d >= 0) {
-      return d;
-    } else {
-      util.assert(
-          d === -1,
-          () => `Negative size values should be exactly -1 but got ` +
-              `${d} for the slice() size at index ${i}.`);
-      return x.shape[i] - begin_[i];
-    }
-  });
-  return [begin_, size_];
-}
-
-// Convert the slicing specification from a sparse representation to a dense
-// representation. This means that all ellipses and newaxis are expanded out.
-export function sliceInfo(
-    xShape: number[], begin: number[], end: number[], strides: number[],
-    beginMask: number, endMask: number, ellipsisMask: number,
-    newAxisMask: number, shrinkAxisMask: number): SliceInfo {
-  let stridesNonNull;
-  if (strides == null) {
-    stridesNonNull = new Array(begin.length);
-    stridesNonNull.fill(1);
-  } else {
-    stridesNonNull = strides;
-  }
-
-  // Only one non-zero bit is allowed in ellipsisMask, which means ellipsisMask
-  // is a power of 2. Use bit compares to ensure ellipsisMask is 0 or a power
-  // of 2. When i is a power of 2, i & (i - 1) is always 0.
-  // Also ref:
-  // https://stackoverflow.com/questions/600293/how-to-check-if-a-number-is-a-power-of-2
-  if (ellipsisMask != null && (ellipsisMask & (ellipsisMask - 1)) !== 0) {
-    throw new Error('Multiple ellipses in slice is not allowed.');
-  }
-
-  // Step 1: Account for ellipsis and new axis.
-  // Check for ellipsis and count how many non-newaxis there are after.
-  let ellipsisSeen = false;
-
-  const sparseSpec: StridedSliceSparseSpec = {
-    dims: stridesNonNull.length,
-    numAddAxisAfterEllipsis: 0,
-    begin: begin.slice(),
-    end: end.slice(),
-    strides: stridesNonNull.slice(),
-    beginMask,
-    endMask,
-    ellipsisMask,
-    newAxisMask,
-    shrinkAxisMask
-  };
-
-  for (let i = 0; i < sparseSpec.dims; i++) {
-    if (ellipsisSeen && ((1 << i) & newAxisMask) !== 0) {
-      sparseSpec.numAddAxisAfterEllipsis++;
-    }
-    if ((1 << i) & ellipsisMask) {
-      ellipsisSeen = true;
-    }
-  }
-  // If no ellipsis insert one at the end.
-  if (!ellipsisSeen) {
-    sparseSpec.ellipsisMask |= (1 << sparseSpec.dims);
-    sparseSpec.dims++;  // this effects loop iteration below
-  }
-
-  // Step 2: Make a sparse spec into a full index spec.
-  //
-  // The sparse spec deos not correspond to the number of dimensions.
-  // Make a dense spec that cooresponds to the number of dimensions.
-  //
-  // For example suppose foo[...,3:] on foo.shape = [2, 2, 3] then we need to
-  // produce the missing beginMask for the first two dimensions i.e. from
-  // beginMaskSpec = 0, endMaskSpec = 2, we achieve beginMask = 6 (110),
-  // endMask = 7 (111).
-  const denseSpec: StridedSliceDenseSpec = {
-    dims: xShape.length,
-    beginMask: 0,
-    endMask: 0,
-    beginValid: false,
-    endValid: false
-  };
-
-  buildDenseSpec(sparseSpec, denseSpec);
-
-  // Step 3: Make implicit ranges (non-zero beginMasks and endMasks) explicit
-  // and bounds check.
-  let isIdentity = true;
-  let sliceDim0 = true;
-  let isSimpleSlice = true;
-  const processingShape = [];
-  const finalShape = [];
-
-  for (let i = 0; i < xShape.length; ++i) {
-    if (denseSpec.strides[i] === 0) {
-      throw Error(`strides[${i}] must be non-zero`);
-    }
-    const shrinkI = !!(denseSpec.shrinkAxisMask & (1 << i));
-    const dimI = xShape[i];
-    if (dimI === -1) {
-      processingShape.push(shrinkI ? 1 : -1);
-      continue;
-    }
-
-    const masks =
-        [denseSpec.beginMask & (1 << i), denseSpec.endMask & (1 << i)];
-    const validRange = [
-      denseSpec.strides[i] > 0 ? 0 : -1,
-      denseSpec.strides[i] > 0 ? dimI : dimI - 1
-    ];
-
-    if (shrinkI && denseSpec.strides[i] <= 0) {
-      throw Error('only stride 1 allowed on non-range indexing.');
-    }
-
-    isSimpleSlice = isSimpleSlice && (denseSpec.strides[i] === 1);
-
-    const beginAndEndMasked =
-        !!((denseSpec.beginMask & (1 << i)) && (denseSpec.endMask & (1 << i)));
-
-    if (denseSpec.beginValid && denseSpec.endValid) {
-      if (shrinkI) {
-        // If we are shrinking, the end index is now possibly incorrect. In
-        // particular foo[-1] produces sparseBegin = -1, sparseEnd = 0.
-        // and canonical puts these to n-1 and 0, which implies a degenerate
-        // interval. Fortunately, it is now safe to re-create end as begin + 1.
-        const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] :
-                                              denseSpec.begin[i];
-        denseSpec.begin[i] = xFwd;
-        denseSpec.end[i] = denseSpec.begin[i] + 1;
-        if (xFwd < 0 || xFwd >= dimI) {
-          throw Error(`slice index ${denseSpec.begin[i]} of dimension ${
-              i} out of bounds.`);
-        }
-      } else {
-        denseSpec.begin[i] = canonical(
-            denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks,
-            validRange);
-        denseSpec.end[i] = canonical(
-            denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);
-      }
-      // Update optimization values
-      const takeAllInDimension = denseSpec.strides[i] === 1 &&
-          denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;
-      isIdentity = isIdentity && takeAllInDimension;
-      sliceDim0 = sliceDim0 &&
-          ((i === 0 && denseSpec.strides[i] === 1) || takeAllInDimension);
-    } else {
-      isIdentity =
-          isIdentity && ((denseSpec.strides[i] === 1) && beginAndEndMasked);
-      sliceDim0 = sliceDim0 &&
-          ((i === 0 && denseSpec.strides[i] === 1) || beginAndEndMasked);
-    }
-    // Compute the processing shape (the intermediate Eigen will produce)
-    let intervalLength;
-    let knownInterval = false;
-    if (denseSpec.beginValid && denseSpec.endValid) {
-      intervalLength = denseSpec.end[i] - denseSpec.begin[i];
-      knownInterval = true;
-    } else if (shrinkI) {
-      // The dimension is still known as 1 for the processingShape, but will be
-      // discarded for the final shape.
-      intervalLength = 1;
-      knownInterval = true;
-    } else if (beginAndEndMasked) {
-      // Even if we don't have values for begin or end, we do know that this
-      // dimension covers the whole interval. If we have shape information for
-      // this dimension, that tells us the interval length.
-      if (dimI >= 0) {
-        if (denseSpec.strides[i] < 0) {
-          intervalLength = -dimI;
-        } else {
-          intervalLength = dimI;
-        }
-        knownInterval = true;
-      }
-    }
-    if (knownInterval) {
-      let sizeI;
-      // Hold zero if the interval is degenerate, otherwise account for
-      // remainder
-      if (intervalLength === 0 ||
-          ((intervalLength < 0) !== (denseSpec.strides[i] < 0))) {
-        sizeI = 0;
-      } else {
-        sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) +
-            (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);
-      }
-      processingShape.push(sizeI);
-    } else {
-      processingShape.push(-1);
-    }
-  }
-
-  // Step 4: Compute the final shape
-  //
-  // newAxis will increase dimension by 1 (with a one-size dimension)
-  // slices like foo[3, ...] will reduce dimension by 1.
-  // This cannot be done earlier, because it depends on Step 3.
-  for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length;
-       ++denseDim) {
-    const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];
-    if (gatherIndex >= 0) {
-      finalShape.push(processingShape[gatherIndex]);
-    } else if (gatherIndex === NEW_AXIS) {
-      finalShape.push(1);
-    }
-  }
-
-  const finalShapeSparse = finalShape.filter(
-      (dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);
-
-  return {
-    finalShapeSparse,
-    finalShape,
-    isIdentity,
-    sliceDim0,
-    isSimpleSlice,
-    begin: denseSpec.begin,
-    end: denseSpec.end,
-    strides: denseSpec.strides
-  };
-}
-
-function buildDenseSpec(
-    sparse: StridedSliceSparseSpec, dense: StridedSliceDenseSpec) {
-  dense.beginMask = 0;
-  dense.endMask = 0;
-  dense.shrinkAxisMask = 0;
-
-  let fullIndex = 0;
-  dense.beginValid = sparse.begin != null;
-  dense.endValid = sparse.end != null;
-
-  dense.begin = new Array(dense.dims);
-  dense.end = new Array(dense.dims);
-  dense.strides = new Array(dense.dims);
-  dense.finalShapeGatherIndices = [];
-  dense.finalShapeGatherIndicesSparse = [];
-  dense.inputShapeGatherIndicesSparse = new Array(dense.dims);
-
-  for (let i = 0; i < sparse.dims; i++) {
-    if ((1 << i) & sparse.ellipsisMask) {
-      // Only the bit that has ellipsis will fall in this condition.
-      // Expand the ellipsis into the appropriate indices
-      // Note: this only works because we guaranteed one ellipsis.
-      const nextIndex = Math.min(
-          dense.dims - (sparse.dims - i) + 1 + sparse.numAddAxisAfterEllipsis,
-          dense.dims);
-      for (; fullIndex < nextIndex; fullIndex++) {
-        // newAxis aren't real axis so you have to skip.
-        dense.begin[fullIndex] = 0;
-        dense.end[fullIndex] = 0;
-        dense.strides[fullIndex] = 1;
-        dense.beginMask |= (1 << fullIndex);
-        dense.endMask |= (1 << fullIndex);
-        dense.finalShapeGatherIndices.push(fullIndex);
-        dense.finalShapeGatherIndicesSparse.push(-1);
-        dense.inputShapeGatherIndicesSparse[fullIndex] = i;
-      }
-    } else if ((1 << i) & sparse.newAxisMask) {
-      // Only the bit that has newAxis will fall in this condition.
-      dense.finalShapeGatherIndices.push(NEW_AXIS);
-      dense.finalShapeGatherIndicesSparse.push(-1);
-    } else {
-      if (fullIndex === dense.begin.length) {
-        throw Error(
-            `Index out of range using input dim ${fullIndex}; input ` +
-            `has only ${dense.dims} dims, ${dense.begin.length}.`);
-      }
-
-      // Gather slicing spec into appropriate index.
-      if (sparse.begin != null) {
-        dense.begin[fullIndex] = sparse.begin[i];
-      }
-      if (sparse.end != null) {
-        dense.end[fullIndex] = sparse.end[i];
-      }
-      dense.strides[fullIndex] = sparse.strides[i];
-      if (sparse.beginMask & (1 << i)) {
-        dense.beginMask |= (1 << fullIndex);
-      }
-      if (sparse.endMask & (1 << i)) {
-        dense.endMask |= (1 << fullIndex);
-      }
-      // If shrink, record where to get the dimensionality from (i.e. newAxis)
-      // creates a fake 1 size dimension. Also remember shrink axis (now in
-      // dense form) so we can ignore dense.end below.
-      if (sparse.shrinkAxisMask & (1 << i)) {
-        dense.finalShapeGatherIndices.push(SHRINK_AXIS);
-        dense.finalShapeGatherIndicesSparse.push(-1);
-        dense.shrinkAxisMask |= (1 << fullIndex);
-      } else {
-        dense.finalShapeGatherIndices.push(fullIndex);
-        // Remember that where in the sparse shape the dense dim comes from.
-        dense.finalShapeGatherIndicesSparse.push(i);
-      }
-      dense.inputShapeGatherIndicesSparse[fullIndex] = i;
-      fullIndex++;
-    }
-  }
-}
-
-function canonical(
-    x: number, c: number, strideI: number, dimI: number, masks: number[],
-    validRange: number[]) {
-  if (masks[c]) {
-    return strideI > 0 ? validRange[c] : validRange[(c + 1) & 1];
-  } else {
-    const xFwd = x < 0 ? dimI + x : x;  // make negative indices positive
-    return xFwd < validRange[0] ? validRange[0] :
-                                  xFwd > validRange[1] ? validRange[1] : xFwd;
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/ops/slice_util_test.ts b/tfjs-master/tfjs-core/src/ops/slice_util_test.ts
deleted file mode 100644
index ae6207866..000000000
--- a/tfjs-master/tfjs-core/src/ops/slice_util_test.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-
-import {isSliceContinous} from './slice_util';
-
-describeWithFlags('isSliceContinous', ALL_ENVS, () => {
-  it('[] => []', () => {
-    const shape: number[] = [];
-    const size: number[] = [];
-    const begin: number[] = [];
-    expect(isSliceContinous(shape, begin, size)).toBeTruthy();
-  });
-
-  it('[5] sliced to [3]', () => {
-    const shape = [5];
-    const size = [3];
-    const begin = [1];
-    expect(isSliceContinous(shape, begin, size)).toBeTruthy();
-  });
-
-  it('[5, 3] sliced to [2, 3] skipping a row', () => {
-    const shape = [5, 3];
-    const size = [2, 3];
-    const begin = [1, 0];
-    expect(isSliceContinous(shape, begin, size)).toBeTruthy();
-  });
-
-  it('[5, 3] sliced to [5, 2] skipping a column', () => {
-    const shape = [5, 3];
-    const size = [5, 2];
-    const begin = [0, 1];
-    expect(isSliceContinous(shape, begin, size)).toBeFalsy();
-  });
-
-  it('[5, 3] sliced to [1, 2] skipping a row and column', () => {
-    const shape = [5, 3];
-    const size = [1, 2];
-    const begin = [2, 1];
-    expect(isSliceContinous(shape, begin, size)).toBeTruthy();
-  });
-
-  it('[1, 5, 3] sliced to [1, 2, 3], skipping middle axis', () => {
-    const shape = [1, 5, 3];
-    const size = [1, 2, 3];
-    const begin = [0, 2, 0];
-    expect(isSliceContinous(shape, begin, size)).toBeTruthy();
-  });
-
-  it('[2, 5, 3] sliced to [2, 2, 3], skipping middle axis', () => {
-    const shape = [2, 5, 3];
-    const size = [2, 2, 3];
-    const begin = [0, 2, 0];
-    expect(isSliceContinous(shape, begin, size)).toBeFalsy();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/softmax.ts b/tfjs-master/tfjs-core/src/ops/softmax.ts
deleted file mode 100644
index 55c646a01..000000000
--- a/tfjs-master/tfjs-core/src/ops/softmax.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Softmax, SoftmaxAttrs, SoftmaxInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes the softmax normalized vector given the logits.
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3]);
- *
- * a.softmax().print();  // or tf.softmax(a)
- * ```
- *
- * ```js
- * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
- *
- * a.softmax().print();  // or tf.softmax(a)
- * ```
- *
- * @param logits The logits array.
- * @param dim The dimension softmax would be performed on. Defaults to `-1`
- *     which indicates the last dimension.
- *
- * @doc {heading: 'Operations', subheading: 'Normalization'}
- */
-function softmax_<T extends Tensor>(logits: T|TensorLike, dim = -1): T {
-  const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');
-
-  if (dim === -1) {
-    dim = $logits.rank - 1;
-  }
-  if (dim !== $logits.rank - 1) {
-    throw Error(
-        'Softmax along a non-last dimension is not yet supported. ' +
-        `Logits was rank ${$logits.rank} and dim was ${dim}`);
-  }
-
-  const inputs: SoftmaxInputs = {logits: $logits};
-  const attrs: SoftmaxAttrs = {dim};
-
-  return ENGINE.runKernel(
-      Softmax, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const softmax = /* @__PURE__ */ op({softmax_});
diff --git a/tfjs-master/tfjs-core/src/ops/softmax_test.ts b/tfjs-master/tfjs-core/src/ops/softmax_test.ts
deleted file mode 100644
index e6dcf10f4..000000000
--- a/tfjs-master/tfjs-core/src/ops/softmax_test.ts
+++ /dev/null
@@ -1,151 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('softmax', ALL_ENVS, () => {
-  it('regular test', async () => {
-    const y = tf.softmax(tf.tensor1d([2, 1, 3]));
-
-    expectArraysClose(await y.data(), [0.24472847, 0.09003057, 0.66524095]);
-    expectArraysClose(await y.sum().data(), 1);
-  });
-
-  it('overflow', async () => {
-    const y = tf.softmax(tf.tensor1d([100, 100]));
-
-    expectArraysClose(await y.data(), [0.5, 0.5]);
-  });
-
-  it('underflow', async () => {
-    const y = tf.softmax(tf.tensor1d([-100, -100]));
-
-    expectArraysClose(await y.data(), [0.5, 0.5]);
-  });
-
-  it('odd number of inputs', async () => {
-    const y = tf.softmax(tf.tensor1d([-400, -400, 0, -400, -400, -400, -400]));
-
-    expectArraysClose(await y.data(), [0, 0, 1, 0, 0, 0, 0]);
-  });
-
-  it('Huge difference between probabilities', async () => {
-    const y = tf.softmax(tf.tensor1d([-1000, +1000]));
-
-    expectArraysClose(await y.data(), [0, 1]);
-  });
-
-  it('Propagates NaNs', async () => {
-    const a = tf.tensor1d([2, 1, NaN]);
-    const y = tf.softmax(a);
-    expectArraysClose(await y.data(), [NaN, NaN, NaN]);
-  });
-
-  it('2D, dim=1', async () => {
-    const y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);
-    const expected = [
-      0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847
-    ];
-    expect(y.rank).toBe(2);
-    expectArraysClose(await y.data(), expected);
-  });
-
-  it('2D, implicit dim=1', async () => {
-    const y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));
-    const expected = [
-      0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847
-    ];
-    expect(y.rank).toBe(2);
-    expectArraysClose(await y.data(), expected);
-  });
-
-  it('2D, dim=0 throws error', () => {
-    const f = () => {
-      tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('1D gradient', async () => {
-    const x = tf.tensor1d([10, 0, -1]);
-    const y = tf.softmax(x);
-    const dy = tf.tensor1d([1, 2, 3]);
-    const dx = tf.grad((x) => x.softmax())(x, dy);
-
-    const totalSum: tf.Scalar = tf.sum(tf.mul(dy, y));
-
-    const dyVals = await dy.array();
-    const sumVals = await totalSum.array();
-    const yVals = await y.array();
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [
-      (dyVals[0] - sumVals) * yVals[0],
-      (dyVals[1] - sumVals) * yVals[1],
-      (dyVals[2] - sumVals) * yVals[2],
-    ]);
-  });
-
-  it('gradient with clones', () => {
-    const x = tf.tensor1d([10, 0, -1]);
-    const dx = tf.grad((x) => x.clone().softmax().clone())(x);
-    expect(dx.shape).toEqual(x.shape);
-    expect(dx.dtype).toBe('float32');
-  });
-
-  it('2D gradient', async () => {
-    const x = tf.tensor2d([10, 0, -1, 5, 4, 3], [2, 3]);
-    const y = tf.softmax(x);
-    const dy = tf.tensor2d([3, 2, 1, 1, 2, 3], [2, 3]);
-    const dx = tf.grad((x) => x.softmax())(x, dy);
-
-    const axis = -1;
-    const totalSum: tf.Tensor1D = tf.sum(tf.mul(dy, y), axis);
-
-    const dyVals = await dy.array();
-    const sumVals = await totalSum.array();
-    const yVals = await y.array();
-
-    expect(dx.shape).toEqual(x.shape);
-    expectArraysClose(await dx.data(), [
-      (dyVals[0][0] - sumVals[0]) * yVals[0][0],
-      (dyVals[0][1] - sumVals[0]) * yVals[0][1],
-      (dyVals[0][2] - sumVals[0]) * yVals[0][2],
-      (dyVals[1][0] - sumVals[1]) * yVals[1][0],
-      (dyVals[1][1] - sumVals[1]) * yVals[1][1],
-      (dyVals[1][2] - sumVals[1]) * yVals[1][2]
-    ]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.softmax({} as tf.Tensor))
-        .toThrowError(/Argument 'logits' passed to 'softmax' must be a Tensor/);
-  });
-
-  it('throws when passed an int32 tensor', async () => {
-    expect(() => tf.softmax(tf.tensor1d([2, 1, 3], 'int32')))
-        .toThrowError(/Argument 'logits' passed to 'softmax' must be float32/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const y = tf.softmax([2, 1, 3]);
-
-    expectArraysClose(await y.data(), [0.24472847, 0.09003057, 0.66524095]);
-    expectArraysClose(await y.sum().data(), 1);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/softplus.ts b/tfjs-master/tfjs-core/src/ops/softplus.ts
deleted file mode 100644
index e0604b5c3..000000000
--- a/tfjs-master/tfjs-core/src/ops/softplus.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Softplus, SoftplusInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`
- *
- * ```js
- * const x = tf.tensor1d([0, 1, -1, .7]);
- *
- * x.softplus().print();  // or tf.softplus(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function softplus_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'softplus');
-
-  const inputs: SoftplusInputs = {x: $x};
-  return ENGINE.runKernel(Softplus, inputs as unknown as NamedTensorMap);
-}
-export const softplus = /* @__PURE__ */ op({softplus_});
diff --git a/tfjs-master/tfjs-core/src/ops/softplus_test.ts b/tfjs-master/tfjs-core/src/ops/softplus_test.ts
deleted file mode 100644
index a4b3a1d01..000000000
--- a/tfjs-master/tfjs-core/src/ops/softplus_test.ts
+++ /dev/null
@@ -1,159 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('softplus', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const a = tf.tensor1d(values);
-
-    const result = tf.softplus(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.log((1 + Math.exp(values[i])));
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('scalar', async () => {
-    const a = tf.scalar(-2);
-
-    const result = tf.softplus(a);
-
-    const expected = [Math.log((1 + Math.exp(-2)))];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('tensor2D', async () => {
-    const values = [1, 2, -3, 5];
-    const a = tf.tensor2d(values, [2, 2]);
-
-    const result = tf.softplus(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.log((1 + Math.exp(values[i])));
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('larger magnitude negative inputs', async () => {
-    const values = [-100, -200, -3000, -50000];
-    const a = tf.tensor1d(values);
-
-    const result = tf.softplus(a);
-
-    const expected = [0, 0, 0, 0];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('larger magnitude positive inputs', async () => {
-    const values = [100, 200, 3000];
-    const a = tf.tensor1d(values);
-
-    const result = tf.softplus(a);
-
-    const expected = [100, 200, 3000];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([3, NaN]);
-    const res = tf.softplus(a);
-    expectArraysClose(await res.data(), [Math.log((1 + Math.exp(3))), NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(3);
-    const dy = tf.scalar(4);
-    const aVal = await a.array();
-    const dyVal = await dy.array();
-
-    const da = tf.grad(a => tf.softplus(a))(a, dy);
-    const y = 1 / (1 + Math.exp(-aVal));
-
-    expectArraysClose(await da.data(), [dyVal * y]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(3);
-    const dy = tf.scalar(4);
-    const aVal = await a.array();
-    const dyVal = await dy.array();
-
-    const da = tf.grad(a => tf.softplus(a.clone()).clone())(a, dy);
-    const y = 1 / (1 + Math.exp(-aVal));
-
-    expectArraysClose(await da.data(), [dyVal * y]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, -3, 5]);
-    const aVals = await a.array();
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-    const dyVals = await dy.array();
-    const da = tf.grad(a => tf.softplus(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      const y = 1 / (1 + Math.exp(-aVals[i]));
-      expected[i] = dyVals[i] * y;
-    }
-
-    expectArraysClose(await da.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([1, 2, -3, 5], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const da = tf.grad(a => tf.softplus(a))(a, dy);
-
-    const expected = [];
-    const aVals = await a.data();
-    const dyVals = await dy.data();
-
-    for (let i = 0; i < a.size; i++) {
-      const y = 1 / (1 + Math.exp(-aVals[i]));
-      expected[i] = dyVals[i] * y;
-    }
-
-    expectArraysClose(await da.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.softplus({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'softplus' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.softplus(-2);
-    const expected = [Math.log((1 + Math.exp(-2)))];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.softplus('q'))
-        .toThrowError(/Argument 'x' passed to 'softplus' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/space_to_batch_nd.ts b/tfjs-master/tfjs-core/src/ops/space_to_batch_nd.ts
deleted file mode 100644
index 9b7b44ffe..000000000
--- a/tfjs-master/tfjs-core/src/ops/space_to_batch_nd.ts
+++ /dev/null
@@ -1,115 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * This operation divides "spatial" dimensions `[1, ..., M]` of the input into
- * a grid of blocks of shape `blockShape`, and interleaves these blocks with
- * the "batch" dimension (0) such that in the output, the spatial
- * dimensions `[1, ..., M]` correspond to the position within the grid,
- * and the batch dimension combines both the position within a spatial block
- * and the original batch position. Prior to division into blocks,
- * the spatial dimensions of the input are optionally zero padded
- * according to `paddings`. See below for a precise description.
- *
- * ```js
- * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
- * const blockShape = [2, 2];
- * const paddings = [[0, 0], [0, 0]];
- *
- * x.spaceToBatchND(blockShape, paddings).print();
- * ```
- *
- * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +
- * remainingShape`, where spatialShape has `M` dimensions.
- * @param blockShape A 1-D array. Must have shape `[M]`, all values must
- * be >= 1.
- * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=
- *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad
- * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It
- * is required that
- * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`
- *
- * This operation is equivalent to the following steps:
- *
- * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input
- * according to `paddings` to produce `padded` of shape paddedShape.
- *
- * 2. Reshape `padded` to `reshapedPadded` of shape:
- * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,
- * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`
- *
- * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`
- * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,
- * paddedShape[M] / blockShape[M-1]] + remainingShape`
- *
- * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the
- * batch dimension, producing an output tensor of shape:
- * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,
- * paddedShape[M] / blockShape[M-1]] + remainingShape`
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function spaceToBatchND_<T extends Tensor>(
-    x: T|TensorLike, blockShape: number[], paddings: number[][]): T {
-  const $x = convertToTensor(x, 'x', 'spaceToBatchND');
-
-  util.assert(
-      $x.rank >= 1 + blockShape.length,
-      () => `input rank ${$x.rank} should be > than [blockShape] ${
-          blockShape.length}`);
-
-  util.assert(
-      paddings.length === blockShape.length,
-      () => `paddings.shape[0] ${
-          paddings.length} must be equal to [blockShape] ${blockShape.length}`);
-
-  util.assert(
-      $x.shape.reduce(
-          (a, b, i) => {
-            if (i > 0 && i <= blockShape.length) {
-              return a &&
-                  ((b + paddings[i - 1][0] + paddings[i - 1][1]) %
-                       blockShape[i - 1] ===
-                   0);
-            }
-            return a;
-          },
-          true),
-      () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${
-          paddings.toString()} must be divisible by blockShapes ${
-          blockShape.toString()}`);
-
-  const inputs: SpaceToBatchNDInputs = {x: $x};
-  const attrs: SpaceToBatchNDAttrs = {blockShape, paddings};
-
-  return ENGINE.runKernel(
-      SpaceToBatchND, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const spaceToBatchND = /* @__PURE__ */ op({spaceToBatchND_});
diff --git a/tfjs-master/tfjs-core/src/ops/space_to_batch_nd_test.ts b/tfjs-master/tfjs-core/src/ops/space_to_batch_nd_test.ts
deleted file mode 100644
index 11da95ac1..000000000
--- a/tfjs-master/tfjs-core/src/ops/space_to_batch_nd_test.ts
+++ /dev/null
@@ -1,268 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('spaceToBatchND', ALL_ENVS, () => {
-  it('tensor4d, input shape=[1, 2, 2, 1], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d([[[[1], [2]], [[3], [4]]]], [1, 2, 2, 1]);
-    const blockShape = [2, 2];
-    const paddings = [[0, 0], [0, 0]];
-
-    const res = tf.spaceToBatchND(t, blockShape, paddings);
-    expect(res.shape).toEqual([4, 1, 1, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4]);
-  });
-
-  it('tensor4d, input shape=[1, 2, 2, 3], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d(
-        [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]], [1, 2, 2, 3]);
-    const blockShape = [2, 2];
-    const paddings = [[0, 0], [0, 0]];
-
-    const res = tf.spaceToBatchND(t, blockShape, paddings);
-    expect(res.shape).toEqual([4, 1, 1, 3]);
-    expectArraysClose(
-        await res.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
-  });
-
-  it('tensor4d, input shape=[1, 4, 4, 1], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d(
-        [[
-          [[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]],
-          [[13], [14], [15], [16]]
-        ]],
-        [1, 4, 4, 1]);
-    const blockShape = [2, 2];
-    const paddings = [[0, 0], [0, 0]];
-
-    const res = tf.spaceToBatchND(t, blockShape, paddings);
-    expect(res.shape).toEqual([4, 2, 2, 1]);
-    expectArraysClose(
-        await res.data(),
-        [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16]);
-  });
-
-  it('tensor4d, input shape=[2, 6, 6, 1], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
-          16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
-          31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
-          46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
-          61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
-        ],
-        [2, 6, 6, 1]);
-    const blockShape = [2, 2];
-    const paddings = [[0, 0], [0, 0]];
-
-    const res = tf.spaceToBatchND(t, blockShape, paddings);
-    expect(res.shape).toEqual([8, 3, 3, 1]);
-    expectArraysClose(await res.data(), [
-      1, 3,  5,  13, 15, 17, 25, 27, 29, 37, 39, 41, 49, 51, 53, 61, 63, 65,
-      2, 4,  6,  14, 16, 18, 26, 28, 30, 38, 40, 42, 50, 52, 54, 62, 64, 66,
-      7, 9,  11, 19, 21, 23, 31, 33, 35, 43, 45, 47, 55, 57, 59, 67, 69, 71,
-      8, 10, 12, 20, 22, 24, 32, 34, 36, 44, 46, 48, 56, 58, 60, 68, 70, 72
-    ]);
-  });
-
-  it('tensor4d, input shape=[2, 2, 4, 1], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d(
-        [
-          [[[1], [2], [3], [4]], [[5], [6], [7], [8]]],
-          [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]
-        ],
-        [2, 2, 4, 1]);
-    const blockShape = [2, 2];
-    const paddings = [[0, 0], [2, 0]];
-
-    const res = tf.spaceToBatchND(t, blockShape, paddings);
-    expect(res.shape).toEqual([8, 1, 3, 1]);
-    expectArraysClose(await res.data(), [
-      0, 1, 3, 0, 9,  11, 0, 2, 4, 0, 10, 12,
-      0, 5, 7, 0, 13, 15, 0, 6, 8, 0, 14, 16
-    ]);
-  });
-
-  it('tensor2d, blockShape [2]', async () => {
-    const t = tf.tensor2d([1, 3, 2, 4], [1, 4]);
-    const blockShape = [2];
-    const paddings = [[0, 0]];
-
-    const res = tf.spaceToBatchND(t, blockShape, paddings);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4]);
-  });
-
-  it('throws when blockShape equal to input rank', () => {
-    const t = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const blockShape = [2, 2, 2, 2];
-    const paddings = [[0, 0], [0, 0], [0, 0], [0, 0]];
-
-    expect(() => tf.spaceToBatchND(t, blockShape, paddings))
-        .toThrowError('input rank 4 should be > than [blockShape] 4');
-  });
-
-  it('throws when paddings row dimension not equal to blockshape', () => {
-    const t = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const blockShape = [2, 2];
-    const paddings = [[0, 0]];
-
-    expect(() => tf.spaceToBatchND(t, blockShape, paddings))
-        .toThrowError('paddings.shape[0] 1 must be equal to [blockShape] 2');
-  });
-
-  it('throws when input tensor spatial dimension not divisible by blockshapes',
-     () => {
-       const t = tf.tensor4d([1, 2, 3, 4, 5, 6], [1, 2, 3, 1]);
-       const blockShape = [2, 2];
-       const paddings = [[0, 0], [0, 0]];
-
-       expect(() => tf.spaceToBatchND(t, blockShape, paddings))
-           .toThrowError(
-               'input spatial dimensions 2,3,1 with paddings 0,0,0,0 must be ' +
-               'divisible by blockShapes 2,2');
-     });
-
-  it('accepts a tensor-like object', async () => {
-    const t = [[[[1], [2]], [[3], [4]]]];
-    const blockShape = [2, 2];
-    const paddings = [[0, 0], [0, 0]];
-
-    const res = tf.spaceToBatchND(t, blockShape, paddings);
-    expect(res.shape).toEqual([4, 1, 1, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4]);
-  });
-});
-
-describeWithFlags('batchToSpaceND X spaceToBatchND', ALL_ENVS, () => {
-  it('tensor4d, input shape=[4, 1, 1, 1], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
-    const blockShape = [2, 2];
-    const crops = [[0, 0], [0, 0]];
-    const paddings = [[0, 0], [0, 0]];
-
-    const b2s = tf.batchToSpaceND(t, blockShape, crops);
-    expect(b2s.shape).toEqual([1, 2, 2, 1]);
-    expectArraysClose(await b2s.data(), [1, 2, 3, 4]);
-
-    const s2b = tf.spaceToBatchND(b2s, blockShape, paddings);
-    expect(s2b.shape).toEqual([4, 1, 1, 1]);
-    expectArraysClose(await s2b.data(), [1, 2, 3, 4]);
-  });
-
-  it('tensor4d, input shape=[2, 6, 6, 1], blockShape=[2, 2]', async () => {
-    const t = tf.tensor4d(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
-          16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
-          31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
-          46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
-          61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
-        ],
-        [2, 6, 6, 1]);
-    const blockShape = [2, 2];
-    const crops = [[0, 0], [0, 0]];
-    const paddings = [[0, 0], [0, 0]];
-
-    const s2b = tf.spaceToBatchND(t, blockShape, paddings);
-    expect(s2b.shape).toEqual([8, 3, 3, 1]);
-    expectArraysClose(await s2b.data(), [
-      1, 3,  5,  13, 15, 17, 25, 27, 29, 37, 39, 41, 49, 51, 53, 61, 63, 65,
-      2, 4,  6,  14, 16, 18, 26, 28, 30, 38, 40, 42, 50, 52, 54, 62, 64, 66,
-      7, 9,  11, 19, 21, 23, 31, 33, 35, 43, 45, 47, 55, 57, 59, 67, 69, 71,
-      8, 10, 12, 20, 22, 24, 32, 34, 36, 44, 46, 48, 56, 58, 60, 68, 70, 72
-    ]);
-
-    const b2s = tf.batchToSpaceND(s2b, blockShape, crops);
-    expect(b2s.shape).toEqual([2, 6, 6, 1]);
-    expectArraysClose(await b2s.data(), [
-      1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18,
-      19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
-      37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-      55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
-    ]);
-  });
-
-  it('gradients,  input shape=[4, 2, 2], block shape=[2]', async () => {
-    const t = tf.tensor(
-        [-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94],
-        [4, 2, 2]);
-    const blockShape = [2];
-    const paddings = [[0, 2]];
-    const dy = tf.tensor(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
-          17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
-        ],
-        [8, 2, 2]);
-
-    const gradient =
-        tf.grad(t => tf.spaceToBatchND(t, blockShape, paddings))(t, dy);
-    expect(gradient.shape).toEqual([4, 2, 2]);
-    expectArraysClose(
-        await gradient.data(),
-        [1, 2, 17, 18, 5, 6, 21, 22, 9, 10, 25, 26, 13, 14, 29, 30]);
-  });
-
-  it('gradient with clones input=[4, 2, 2], block shape=[2]', async () => {
-    const t = tf.tensor(
-        [-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94],
-        [4, 2, 2]);
-    const blockShape = [2];
-    const paddings = [[0, 2]];
-    const dy = tf.tensor(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
-          17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
-        ],
-        [8, 2, 2]);
-
-    const gradient = tf.grad(
-        t => tf.spaceToBatchND(t.clone(), blockShape, paddings).clone())(t, dy);
-    expect(gradient.shape).toEqual([4, 2, 2]);
-    expectArraysClose(
-        await gradient.data(),
-        [1, 2, 17, 18, 5, 6, 21, 22, 9, 10, 25, 26, 13, 14, 29, 30]);
-  });
-
-  it('gradients, input shape=[2, 2, 4, 1], block shape=[2, 2]', async () => {
-    const t = tf.tensor4d(
-        [
-          [[[1], [2], [3], [4]], [[5], [6], [7], [8]]],
-          [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]
-        ],
-        [2, 2, 4, 1]);
-    const blockShape = [2, 2];
-    const paddings = [[0, 0], [2, 0]];
-    const dy = tf.tensor(
-        [
-          1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12,
-          13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
-        ],
-        [8, 1, 3, 1]);
-
-    const gradient =
-        tf.grad(t => tf.spaceToBatchND(t, blockShape, paddings))(t, dy);
-    expect(gradient.shape).toEqual([2, 2, 4, 1]);
-    expectArraysClose(
-        await gradient.data(),
-        [2, 8, 3, 9, 14, 20, 15, 21, 5, 11, 6, 12, 17, 23, 18, 24]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_fill_empty_rows.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_fill_empty_rows.ts
deleted file mode 100644
index db258c3c8..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_fill_empty_rows.ts
+++ /dev/null
@@ -1,127 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {SparseFillEmptyRows, SparseFillEmptyRowsInputs} from '../../kernel_names';
-import {Scalar, Tensor, Tensor1D, Tensor2D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {ScalarLike, TensorLike} from '../../types';
-import {op} from '../operation';
-
-/**
- * The input SparseTensor is represented via the map of inputs {`indices`,
- * `values`, `denseShape`}. The output SparseTensor has the same `denseShape`
- * but with indices `outputIndices` and values `outputValues`. This op inserts a
- * single entry for every row that doesn't have any values. The index is created
- * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`.
- *
- * For example, suppose `spInput` has shape [5, 6] and non-empty values:
- * [0, 1]: a
- * [0, 3]: b
- * [2, 0]: c
- * [3, 1]: d
- *
- * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:
- * [0, 1]: a
- * [0, 3]: b
- * [1, 0]: `defaultValue`
- * [2, 0]: c
- * [3, 1]: d
- * [4, 0]: `defaultValue`
- *
- * The output SparseTensor will be in row-major order and will have the same
- * shape as the input.
- *
- * This op also returns an indicator vector shaped [dense_shape[0]] such that
- * emptyRowIndicator[i] = True iff row i was an empty row.
- *
- * And a reverse index map vector shaped [indices.shape[0]] that is used during
- * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] ==
- * outputIndices[outi, j] for all j
- *
- * ```js
- * const result = tf.sparse.sparseFillEmptyRows(
- *   [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]],
- *   [0, 10, 13, 14, 32, 33], [5, 6], -1);
- * console.log(result);
- * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4],
- *                                  //  [2, 0], [3, 2], [3, 3], [4, 0]]
- * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1]
- * result['emptyRowIndicator'].print(); // [false, false, true, false, true]
- * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6]
- * ```
- * @param indices: 2-D. The indices of the sparse tensor.
- * @param values: 1-D. The values of the sparse tensor.
- * @param denseShape: 1-D. The shape of the sparse tensor.
- * @param defaultValue: 0-D. Default value to insert into location [row, 0, ...,
- *     0] for rows missing from the input sparse tensor.
- * @return A map with the following properties:
- *     - outputIndices
- *     - outputValues: 1-D. The values of the filled sparse tensor.
- *     - emptyRowIndicator: 1-D. Whether the dense row was missing in the input
- * sparse tensor.
- *     - reverseIndexMap: 1-D. A map from the input indices to the output
- * indices.
- * @doc {heading: 'Operations', subheading: 'Sparse'}
- */
-function sparseFillEmptyRows_(
-    indices: Tensor2D|TensorLike, values: Tensor1D|TensorLike,
-    denseShape: Tensor1D|TensorLike,
-    defaultValue: Scalar|ScalarLike): NamedTensorMap {
-  const $indices =
-      convertToTensor(indices, 'indices', 'sparseFillEmptyRows', 'int32');
-  const $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');
-  const $denseShape =
-      convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32');
-  const $defaultValue = convertToTensor(
-      defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype);
-
-  if ($indices.rank !== 2) {
-    throw new Error(`Indices should be Tensor2D but received shape
-        ${$indices.shape}`);
-  }
-  if ($values.rank !== 1) {
-    throw new Error(
-        `Values should be Tensor1D but received shape ${$values.shape}`);
-  }
-  if ($denseShape.rank !== 1) {
-    throw new Error(`Dense shape should be Tensor1D but received shape ${
-        $denseShape.shape}`);
-  }
-  if ($defaultValue.rank !== 0) {
-    throw new Error(`Default value should be a scalar but received shape ${
-        $defaultValue.shape}`);
-  }
-
-  const inputs: SparseFillEmptyRowsInputs = {
-    indices: $indices,
-    values: $values,
-    denseShape: $denseShape,
-    defaultValue: $defaultValue
-  };
-
-  const result: Tensor[] = ENGINE.runKernel(SparseFillEmptyRows, inputs as {});
-  return {
-    outputIndices: result[0],
-    outputValues: result[1],
-    emptyRowIndicator: result[2],
-    reverseIndexMap: result[3]
-  };
-}
-
-export const sparseFillEmptyRows = /* @__PURE__ */ op({sparseFillEmptyRows_});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_fill_empty_rows_test.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_fill_empty_rows_test.ts
deleted file mode 100644
index 78cae3f23..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_fill_empty_rows_test.ts
+++ /dev/null
@@ -1,202 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('sparseFillEmptyRows', ALL_ENVS, () => {
-  it('fill number', async () => {
-    const sparseTensor = {
-      ind: tf.tensor2d(
-          [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]], [6, 2], 'int32'),
-      val: [0, 10, 13, 14, 32, 33],
-      shape: [5, 6],
-    };
-    const result = tf.sparse.sparseFillEmptyRows(
-        sparseTensor.ind, sparseTensor.val, sparseTensor.shape, -1);
-    expectArraysClose(
-        await result.outputIndices.data(),
-        [[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]]);
-    expectArraysClose(
-        await result.outputValues.data(), [0, 10, 13, 14, -1, 32, 33, -1]);
-    expectArraysClose(await result.emptyRowIndicator.data(), [0, 0, 1, 0, 1]);
-    expectArraysClose(await result.reverseIndexMap.data(), [0, 1, 2, 3, 5, 6]);
-  });
-
-  it('fill float', async () => {
-    const sparseTensor = {
-      ind: tf.tensor2d(
-          [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]], [6, 2], 'int32'),
-      val: tf.tensor1d([0.0, 10.0, 13.0, 14.0, 32.0, 33.0], 'float32'),
-      shape: [5, 6],
-    };
-    const result = tf.sparse.sparseFillEmptyRows(
-        sparseTensor.ind, sparseTensor.val, sparseTensor.shape, -1);
-    expectArraysClose(
-        await result.outputIndices.data(),
-        [[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]]);
-    expectArraysClose(
-        await result.outputValues.data(), [0, 10, 13, 14, -1, 32, 33, -1]);
-    expectArraysClose(await result.emptyRowIndicator.data(), [0, 0, 1, 0, 1]);
-    expectArraysClose(await result.reverseIndexMap.data(), [0, 1, 2, 3, 5, 6]);
-  });
-
-  it('no empty rows', async () => {
-    const sparseTensor = {
-      ind: tf.tensor2d([[0, 0], [1, 0], [1, 3], [1, 4]], [4, 2], 'int32'),
-      val: [0, 10, 13, 14],
-      shape: [2, 6],
-    };
-    const result = tf.sparse.sparseFillEmptyRows(
-        sparseTensor.ind, sparseTensor.val, sparseTensor.shape, -1);
-    expectArraysClose(
-        await result.outputIndices.data(), [[0, 0], [1, 0], [1, 3], [1, 4]]);
-    expectArraysClose(await result.outputValues.data(), [0, 10, 13, 14]);
-    expectArraysClose(await result.emptyRowIndicator.data(), [0, 0]);
-    expectArraysClose(await result.reverseIndexMap.data(), [0, 1, 2, 3]);
-  });
-
-  it('no empty rows and unordered', async () => {
-    const sparseTensor = {
-      ind: tf.tensor2d([[1, 2], [1, 3], [0, 1], [0, 3]], [4, 2], 'int32'),
-      val: [1, 3, 2, 4],
-      shape: [2, 5],
-    };
-    const result = tf.sparse.sparseFillEmptyRows(
-        sparseTensor.ind, sparseTensor.val, sparseTensor.shape, -1);
-    expectArraysClose(
-        await result.outputIndices.data(), [[0, 1], [0, 3], [1, 2], [1, 3]]);
-    expectArraysClose(await result.outputValues.data(), [2, 4, 1, 3]);
-    expectArraysClose(await result.emptyRowIndicator.data(), [0, 0]);
-    expectArraysClose(await result.reverseIndexMap.data(), [2, 3, 0, 1]);
-  });
-
-  it('no rows', async () => {
-    const sparseTensor = {
-      ind: tf.tensor2d([], [0, 2], 'int32'),
-      val: [] as number[],
-      shape: [0, 5],
-    };
-    const result = tf.sparse.sparseFillEmptyRows(
-        sparseTensor.ind, sparseTensor.val, sparseTensor.shape, -1);
-    expectArraysClose(await result.outputIndices.data(), []);
-    expectArraysClose(await result.outputValues.data(), []);
-    expectArraysClose(await result.emptyRowIndicator.data(), []);
-    expectArraysClose(await result.reverseIndexMap.data(), []);
-  });
-
-  it('check output metadata', async () => {
-    const sparseTensor = {
-      ind: tf.tensor2d(
-          [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]], [6, 2], 'int32'),
-      val: tf.tensor1d([0, 10, 13, 14, 32, 33], 'float32'),
-      shape: [5, 6],
-    };
-    const result = tf.sparse.sparseFillEmptyRows(
-        sparseTensor.ind, sparseTensor.val, sparseTensor.shape, -1);
-
-    expectArraysClose(
-        await result.outputIndices.data(),
-        [[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]]);
-    expectArraysClose(
-        await result.outputValues.data(), [0, 10, 13, 14, -1, 32, 33, -1]);
-    expectArraysClose(await result.emptyRowIndicator.data(), [0, 0, 1, 0, 1]);
-    expectArraysClose(await result.reverseIndexMap.data(), [0, 1, 2, 3, 5, 6]);
-
-    expect(result.outputIndices.shape).toEqual([8, 2]);
-    expect(result.outputValues.shape).toEqual([8]);
-    expect(result.emptyRowIndicator.shape).toEqual([5]);
-    expect(result.reverseIndexMap.shape).toEqual([6]);
-
-    expect(result.outputIndices.dtype).toEqual(sparseTensor.ind.dtype);
-    expect(result.outputValues.dtype).toEqual(sparseTensor.val.dtype);
-    expect(result.emptyRowIndicator.dtype).toEqual('bool');
-    expect(result.reverseIndexMap.dtype).toEqual(sparseTensor.ind.dtype);
-  });
-
-  it('does not have memory leak.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const sparseTensor = {
-      ind: tf.tensor2d(
-          [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]], [6, 2], 'int32'),
-      val: [0, 10, 13, 14, 32, 33],
-      shape: [5, 6],
-    };
-    const indices = sparseTensor.ind;
-    const values = tf.tensor1d(sparseTensor.val, 'float32');
-    const denseShape = tf.tensor1d(sparseTensor.shape, 'int32');
-    const result =
-        tf.sparse.sparseFillEmptyRows(indices, values, denseShape, -1);
-
-    await result.outputIndices.data();
-    await result.outputValues.data();
-    await result.emptyRowIndicator.data();
-    await result.reverseIndexMap.data();
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 7);
-
-    indices.dispose();
-    values.dispose();
-    denseShape.dispose();
-    result.outputIndices.dispose();
-    result.outputValues.dispose();
-    result.emptyRowIndicator.dispose();
-    result.reverseIndexMap.dispose();
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-
-  it('throw error if dense rows is empty and indices is not', async () => {
-    const sparseTensor = {
-      ind: tf.tensor2d([[0, 0]], [1, 2], 'int32'),
-      val: [1],
-      shape: [0, 5],
-    };
-    expect(
-        () => tf.sparse.sparseFillEmptyRows(
-            sparseTensor.ind, sparseTensor.val, sparseTensor.shape, -1))
-        .toThrowError(/indices\.shape\[0\] = 1/);
-  });
-
-  it('throw error if negative row', async () => {
-    const sparseTensor = {
-      ind: tf.tensor2d([[-1, 0]], [1, 2], 'int32'),
-      val: [1],
-      shape: [5, 5],
-    };
-    expect(
-        () => tf.sparse.sparseFillEmptyRows(
-            sparseTensor.ind, sparseTensor.val, sparseTensor.shape, -1))
-        .toThrowError('indices(0, 0) is invalid: -1 < 0');
-  });
-
-  it('throw error if row exceeds number of dense rows', async () => {
-    const sparseTensor = {
-      ind: tf.tensor2d([[5, 0]], [1, 2], 'int32'),
-      val: [1],
-      shape: [5, 5],
-    };
-    expect(
-        () => tf.sparse.sparseFillEmptyRows(
-            sparseTensor.ind, sparseTensor.val, sparseTensor.shape, -1))
-        .toThrowError('indices(0, 0) is invalid: 5 >= 5');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_fill_empty_rows_util.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_fill_empty_rows_util.ts
deleted file mode 100644
index 6b4e8c218..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_fill_empty_rows_util.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Generates sparse fill empty rows indices, dense shape mismatch error message.
- *
- * @param indicesLength The first dimension of indices.
- */
-export function getSparseFillEmptyRowsIndicesDenseShapeMismatch(
-    indicesLength: number) {
-  return `Received SparseTensor with denseShape[0] = 0 but
-  indices.shape[0] = ${indicesLength}`;
-}
-
-/**
- * Generates sparse fill empty rows negative index error message.
- *
- * @param index The index with a negative value.
- * @param value The negative value.
- */
-export function getSparseFillEmptyRowsNegativeIndexErrorMessage(
-    index: number, value: number) {
-  return `indices(${index}, 0) is invalid: ${value} < 0`;
-}
-
-/**
- * Generates sparse fill empty rows out of range index error message.
- *
- * @param index The index with an out of range value.
- * @param value The out of range value.
- * @param limit The upper limit for indices.
- */
-export function getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(
-    index: number, value: number, limit: number) {
-  return `indices(${index}, 0) is invalid: ${value} >= ${limit}`;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_reshape.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_reshape.ts
deleted file mode 100644
index 5bd750c7e..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_reshape.ts
+++ /dev/null
@@ -1,92 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {SparseReshape, SparseReshapeInputs} from '../../kernel_names';
-import {Tensor, Tensor1D, Tensor2D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {op} from '../operation';
-
-/**
- * This operation has the same semantics as reshape on the represented dense
- * tensor. The `inputIndices` are recomputed based on the requested `newShape`.
- * If one component of `newShape` is the special value -1, the size of that
- * dimension is computed so that the total dense size remains constant. At most
- * one component of `newShape` can be -1. The number of dense elements implied
- * by `newShape` must be the same as the number of dense elements originally
- * implied by `inputShape`. Reshaping does not affect the order of values in the
- * SparseTensor. If the input tensor has rank R_in and N non-empty values, and
- * `newShape` has length R_out, then `inputIndices` has shape [N, R_in],
- * `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and
- * `outputShape` has length R_out.
- *
- * ```js
- * const result = tf.sparse.sparseReshape(
- *   [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],
- *   [2, 3, 6], [9, -1]);
- * console.log(result);
- * result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]]
- * result['outputShape'].print(); // [9, 4]
- * ```
- * @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty
- * values in a SparseTensor.
- * @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense
- * shape.
- * @param newShape: 1-D. R_out Tensor1D with the requested new dense shape.
- * @return A map with the following properties:
- *     - outputIndices: 2-D. N x R_out matrix with the updated indices of
- *       non-empty values in the output SparseTensor.
- *     - outputShape: 1-D. R_out vector with the full dense shape of the output
- *       SparseTensor. This is the same as newShape but with any -1 dimensions
- *        filled in.
- * @doc {heading: 'Operations', subheading: 'Sparse'}
- */
-function sparseReshape_(
-    inputIndices: Tensor2D|TensorLike, inputShape: Tensor1D|TensorLike,
-    newShape: Tensor1D|TensorLike): NamedTensorMap {
-  const $inputIndices =
-      convertToTensor(inputIndices, 'inputIndices', 'sparseReshape', 'int32');
-  const $inputShape =
-      convertToTensor(inputShape, 'inputShape', 'sparseReshape', 'int32');
-  const $newShape =
-      convertToTensor(newShape, 'newShape', 'sparseReshape', 'int32');
-
-  if ($inputIndices.rank !== 2) {
-    throw new Error(`Input indices should be Tensor2D but received shape
-        ${$inputIndices.shape}`);
-  }
-  if ($inputShape.rank !== 1) {
-    throw new Error(`Input shape should be Tensor1D but received shape ${
-        $inputShape.shape}`);
-  }
-  if ($newShape.rank !== 1) {
-    throw new Error(
-        `New shape should be Tensor1D but received shape ${$newShape.shape}`);
-  }
-
-  const inputs: SparseReshapeInputs = {
-    inputIndices: $inputIndices,
-    inputShape: $inputShape,
-    newShape: $newShape
-  };
-  const result: Tensor[] = ENGINE.runKernel(SparseReshape, inputs as {});
-  return {outputIndices: result[0], outputShape: result[1]};
-}
-
-export const sparseReshape = /* @__PURE__ */ op({sparseReshape_});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_reshape_test.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_reshape_test.ts
deleted file mode 100644
index 9e33cc215..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_reshape_test.ts
+++ /dev/null
@@ -1,188 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-function sparseTensorValue5x6() {
-  const ind = tf.tensor2d(
-      [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]], [6, 2], 'int32');
-  const val = [0, 10, 13, 14, 32, 33];
-  const shape = [5, 6];
-  return {ind, val, shape};
-}
-
-function sparseTensorValue2x3x4() {
-  const ind = tf.tensor2d(
-      [
-        [0, 0, 1], [0, 1, 0], [0, 1, 2], [1, 0, 3], [1, 1, 1], [1, 1, 3],
-        [1, 2, 2]
-      ],
-      [7, 3], 'int32');
-  const val = [1, 10, 12, 103, 111, 113, 122];
-  const shape = [2, 3, 4];
-  return {ind, val, shape};
-}
-describeWithFlags('sparseReshape', ALL_ENVS, () => {
-  it('preserve static shape info', async () => {
-    const sparseTensor = sparseTensorValue5x6();
-    const result = tf.sparse.sparseReshape(
-        sparseTensor.ind, sparseTensor.shape, [1, 5, 2, 3]);
-    expectArraysClose(await result.outputShape.data(), [1, 5, 2, 3]);
-  });
-
-  it('preserve shape info with inferred dim', async () => {
-    const sparseTensor = sparseTensorValue2x3x4();
-    const result =
-        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [2, -1]);
-    expectArraysClose(await result.outputShape.data(), [2, 3 * 4]);
-  });
-
-  it('does not have memory leak.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const sparseTensor = sparseTensorValue5x6();
-    const indices = sparseTensor.ind;
-    const shape = tf.tensor1d(sparseTensor.shape, 'int32');
-    const newShape = tf.tensor1d([1, 5, 2, 3], 'int32');
-    const result = tf.sparse.sparseReshape(indices, shape, newShape);
-
-    await result.outputIndices.data();
-    await result.outputShape.data();
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 5);
-
-    indices.dispose();
-    shape.dispose();
-    newShape.dispose();
-    result.outputIndices.dispose();
-    result.outputShape.dispose();
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-
-  it('throw error if more than one inferred dim', async () => {
-    const sparseTensor = sparseTensorValue2x3x4();
-    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
-      -1, 2, -1
-    ])).toThrowError(/only one output dimension may be -1/);
-  });
-
-  it('throw error if impossible new shape', async () => {
-    const sparseTensor = sparseTensorValue2x3x4();
-    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
-      -1, 7
-    ])).toThrowError(/multiple of 7/);
-  });
-  it('throw error if negative output dim', async () => {
-    const sparseTensor = sparseTensorValue2x3x4();
-    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
-      1, -7
-    ])).toThrowError('size 1 must be non-negative, not -7');
-  });
-  it('throw error if negative output dim', async () => {
-    const sparseTensor = sparseTensorValue2x3x4();
-    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
-      -1, 0
-    ])).toThrowError(/unless all specified input sizes are non-zero/);
-  });
-  it('same shape', async () => {
-    const sparseTensor = sparseTensorValue5x6();
-    const result =
-        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [5, 6]);
-    expectArraysClose(
-        await result.outputIndices.data(), await sparseTensor.ind.data());
-    expectArraysClose(await result.outputShape.data(), sparseTensor.shape);
-  });
-
-  it('same shape with inferred dim', async () => {
-    const sparseTensor = sparseTensorValue5x6();
-    const result =
-        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [-1, 6]);
-    expectArraysClose(
-        await result.outputIndices.data(), await sparseTensor.ind.data());
-    expectArraysClose(await result.outputShape.data(), sparseTensor.shape);
-  });
-
-  it('new shape with same rank', async () => {
-    const sparseTensor = sparseTensorValue5x6();
-    const result =
-        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [3, 10]);
-    expectArraysClose(
-        await result.outputIndices.data(),
-        [[0, 0], [0, 6], [0, 9], [1, 0], [2, 0], [2, 1]]);
-    expectArraysClose(await result.outputShape.data(), [3, 10]);
-  });
-
-  it('new shape with same rank with inferred dim', async () => {
-    const sparseTensor = sparseTensorValue5x6();
-    const result =
-        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [3, -1]);
-    expectArraysClose(
-        await result.outputIndices.data(),
-        [[0, 0], [0, 6], [0, 9], [1, 0], [2, 0], [2, 1]]);
-    expectArraysClose(await result.outputShape.data(), [3, 10]);
-  });
-  it('up rank', async () => {
-    const sparseTensor = sparseTensorValue5x6();
-    const result = tf.sparse.sparseReshape(
-        sparseTensor.ind, sparseTensor.shape, [2, 3, 5]);
-    expectArraysClose(
-        await result.outputIndices.data(),
-        [[0, 0, 0], [0, 1, 1], [0, 1, 4], [0, 2, 0], [1, 1, 0], [1, 1, 1]]);
-    expectArraysClose(await result.outputShape.data(), [2, 3, 5]);
-  });
-  it('up rank with inferred dim', async () => {
-    const sparseTensor = sparseTensorValue5x6();
-    const result = tf.sparse.sparseReshape(
-        sparseTensor.ind, sparseTensor.shape, [2, -1, 5]);
-    expectArraysClose(
-        await result.outputIndices.data(),
-        [[0, 0, 0], [0, 1, 1], [0, 1, 4], [0, 2, 0], [1, 1, 0], [1, 1, 1]]);
-    expectArraysClose(await result.outputShape.data(), [2, 3, 5]);
-  });
-
-  it('down rank', async () => {
-    const sparseTensor = sparseTensorValue2x3x4();
-    const result =
-        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [6, 4]);
-    expectArraysClose(
-        await result.outputIndices.data(),
-        [[0, 1], [1, 0], [1, 2], [3, 3], [4, 1], [4, 3], [5, 2]]);
-    expectArraysClose(await result.outputShape.data(), [6, 4]);
-  });
-
-  it('down rank with inferred dim', async () => {
-    const sparseTensor = sparseTensorValue2x3x4();
-    const result =
-        tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [6, -1]);
-    expectArraysClose(
-        await result.outputIndices.data(),
-        [[0, 1], [1, 0], [1, 2], [3, 3], [4, 1], [4, 3], [5, 2]]);
-    expectArraysClose(await result.outputShape.data(), [6, 4]);
-  });
-
-  it('throw error if mismatch size', async () => {
-    const sparseTensor = sparseTensorValue5x6();
-    expect(() => tf.sparse.sparseReshape(sparseTensor.ind, sparseTensor.shape, [
-      4, 7
-    ])).toThrowError(/Input to reshape is a tensor with 30 dense values/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_reshape_util.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_reshape_util.ts
deleted file mode 100644
index 037d1b39c..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_reshape_util.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {sizeFromShape} from '../../util';
-
-/**
- * Generates sparse reshape multiple negative 1 output dimension error message.
- *
- * @param dim1 The first dimension with a negative 1 value.
- * @param dim2 The second dimension with a negative 1 value.
- */
-export function getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(
-    dim1: number, dim2: number) {
-  return `only one output dimension may be -1, not both ${dim1} and ${dim2}`;
-}
-
-/**
- * Generates sparse reshape negative output dimension error message.
- *
- * @param dim The dimension with a negative value.
- * @param value The negative value.
- */
-export function getSparseReshapeNegativeOutputDimErrorMessage(
-    dim: number, value: number) {
-  return `size ${dim} must be non-negative, not ${value}`;
-}
-
-/**
- * Generates sparse reshape empty tensor zero output dimension error message.
- *
- */
-export function getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {
-  return 'reshape cannot infer the missing input size for an empty tensor ' +
-      'unless all specified input sizes are non-zero';
-}
-
-/**
- * Generates sparse reshape input output multiple mismatch error message.
- *
- * @param inputShape the input shape.
- * @param outputShape the requested output shape.
- */
-export function getSparseReshapeInputOutputMultipleErrorMessage(
-    inputShape: number[], outputShape: number[]) {
-  const inputSize = sizeFromShape(inputShape);
-  const outputSize = sizeFromShape(outputShape);
-  return `Input to reshape is a SparseTensor with ${inputSize}
-  dense values, but the requested shape requires a multiple of ${
-      outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`;
-}
-
-/**
- * Generates sparse reshape input output inequality error message.
- *
- * @param inputShape the input shape.
- * @param outputShape the requested output shape.
- */
-export function getSparseReshapeInputOutputMismatchErrorMessage(
-    inputShape: number[], outputShape: number[]) {
-  const inputSize = sizeFromShape(inputShape);
-  const outputSize = sizeFromShape(outputShape);
-  return `Input to reshape is a tensor with ${
-      inputSize} dense values, but the requested shape has ${
-      outputSize}. inputShape=${inputShape} outputShape=${outputShape}`;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_mean.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_mean.ts
deleted file mode 100644
index df5bedc8b..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_mean.ts
+++ /dev/null
@@ -1,90 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {SparseSegmentMean, SparseSegmentMeanInputs} from '../../kernel_names';
-import {Tensor, Tensor1D} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {op} from '../operation';
-
-/**
- * Computes the mean along sparse segments of a tensor.
- *
- * ```js
- * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [6,7,8,9]]);
- * // Select two rows, one segment.
- * const result1 = tf.sparse.sparseSegmentMean(c,
- *                                           tf.tensor1d([0, 1], 'int32'),
- *                                           tf.tensor1d([0, 0], 'int32'));
- * result1.print(); // [[0, 0, 0, 0]]
- *
- * // Select two rows, two segments.
- * const result2 = tf.sparse.sparseSegmentMean(c,
- *                                             tf.tensor1d([0, 1], 'int32'),
- *                                             tf.tensor1d([0, 1], 'int32'));
- * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]
- *
- * // Select all rows, two segments.
- * const result3 = tf.sparse.sparseSegmentMean(c,
- *                                             tf.tensor1d([0, 1, 2], 'int32'),
- *                                             tf.tensor1d([0, 1, 1], 'int32'));
- * result3.print(); // [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]]
- * ```
- * @param data: A Tensor of at least one dimension with data that will be
- *     assembled in the output.
- * @param indices: A 1-D Tensor with indices into data. Has same rank as
- *     segmentIds.
- * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values
- *     should be sorted and can be repeated.
- * @return Has same shape as data, except for dimension 0 which has equal to
- *         the number of segments.
- *
- * @doc {heading: 'Operations', subheading: 'Sparse'}
- */
-function sparseSegmentMean_(
-    data: Tensor|TensorLike, indices: Tensor1D|TensorLike,
-    segmentIds: Tensor1D|TensorLike): Tensor {
-  const $data = convertToTensor(data, 'data', 'sparseSegmentMean');
-  const $indices =
-      convertToTensor(indices, 'indices', 'sparseSegmentMean', 'int32');
-  const $segmentIds =
-      convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentMean', 'int32');
-
-  if ($data.rank < 1) {
-    throw new Error(
-        `Data should be at least 1 dimensional but received scalar`);
-  }
-  if ($indices.rank !== 1) {
-    throw new Error(`Indices should be Tensor1D but received shape
-          ${$indices.shape}`);
-  }
-  if ($segmentIds.rank !== 1) {
-    throw new Error(`Segment ids should be Tensor1D but received shape
-          ${$segmentIds.shape}`);
-  }
-
-  const inputs: SparseSegmentMeanInputs = {
-    data: $data,
-    indices: $indices,
-    segmentIds: $segmentIds
-  };
-
-  return ENGINE.runKernel(SparseSegmentMean, inputs as {});
-}
-
-export const sparseSegmentMean = /* @__PURE__ */ op({sparseSegmentMean_});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_mean_test.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_mean_test.ts
deleted file mode 100644
index ac15345b8..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_mean_test.ts
+++ /dev/null
@@ -1,196 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-function TensorValue3x4Float() {
-  return tf.tensor2d([[1, 2, 3, 4], [-1, -2, -3, -4], [6, 7, 8, 9]]);
-}
-
-function TensorValue3x4Integer() {
-  return tf.tensor2d(
-      [[1, 2, 3, 4], [-1, -2, -3, -4], [6, 7, 8, 9]], [3, 4], 'int32');
-}
-
-function TensorValue10() {
-  return tf.tensor1d(Array.from(Array(10), (_, i) => i + 1));
-}
-
-function TensorValue10x4() {
-  return tf.tensor2d(Array.from(Array(40), (_, i) => i + 1), [10, 4]);
-}
-
-function TensorValue10x2x4() {
-  return tf.tensor3d(Array.from(Array(80), (_, i) => i + 1), [10, 2, 4]);
-}
-
-describeWithFlags('sparseSegmentMean', ALL_ENVS, () => {
-  it('two rows one segment', async () => {
-    const result =
-        tf.sparse.sparseSegmentMean(TensorValue3x4Float(), [0, 1], [0, 0]);
-    expectArraysClose(await result.data(), [[0, 0, 0, 0]]);
-  });
-
-  it('two rows two segments', async () => {
-    const result =
-        tf.sparse.sparseSegmentMean(TensorValue3x4Float(), [0, 1], [0, 1]);
-    expectArraysClose(await result.data(), [[1, 2, 3, 4], [-1, -2, -3, -4]]);
-  });
-
-  it('all rows two segments', async () => {
-    const result = tf.sparse.sparseSegmentMean(
-        TensorValue3x4Float(), [0, 1, 2], [0, 1, 1]);
-    expectArraysClose(
-        await result.data(), [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]]);
-  });
-
-  it('integer data', async () => {
-    const result = tf.sparse.sparseSegmentMean(
-        TensorValue3x4Integer(), [0, 1, 2], [0, 1, 1]);
-    expectArraysClose(await result.data(), [[1, 2, 3, 4], [2, 2, 2, 2]]);
-  });
-
-  it('0 dimensional input invalid', async () => {
-    expect(() => tf.sparse.sparseSegmentMean(tf.scalar(1), [], []))
-        .toThrowError(/should be at least 1 dimensional/);
-  });
-
-  it('1 dimensional input', async () => {
-    const result = tf.sparse.sparseSegmentMean(
-        TensorValue10(), [8, 3, 0, 9], [0, 1, 2, 2]);
-    expectArraysClose(await result.data(), [9, 4, 5.5]);
-  });
-
-  it('3 dimensional input', async () => {
-    const result = tf.sparse.sparseSegmentMean(
-        TensorValue10x2x4(), [8, 3, 0, 9], [0, 1, 2, 2]);
-    expectArraysClose(await result.data(), [
-      [[65, 66, 67, 68], [69, 70, 71, 72]],
-      [[25, 26, 27, 28], [29, 30, 31, 32]], [[37, 38, 39, 40], [41, 42, 43, 44]]
-    ]);
-  });
-
-  it('segment ids hole', async () => {
-    const result = tf.sparse.sparseSegmentMean(
-        TensorValue10x4(), [8, 3, 0, 9], [0, 3, 3, 3]);
-    expectArraysClose(
-        await result.data(),
-        [[33, 34, 35, 36], [0, 0, 0, 0], [0, 0, 0, 0], [17, 18, 19, 20]]);
-  });
-
-  it('segment ids > zero', async () => {
-    const result = tf.sparse.sparseSegmentMean(
-        TensorValue10x4(), [8, 3, 0, 9], [2, 3, 3, 3]);
-    expectArraysClose(
-        await result.data(),
-        [[0, 0, 0, 0], [0, 0, 0, 0], [33, 34, 35, 36], [17, 18, 19, 20]]);
-  });
-
-  it('baseline valid', async () => {
-    // Baseline for the *invalid* tests below.
-    const result = tf.sparse.sparseSegmentMean(
-        TensorValue10x4(), [8, 3, 0, 9], [0, 1, 2, 2]);
-    expectArraysClose(
-        await result.data(),
-        [[33, 34, 35, 36], [13, 14, 15, 16], [19, 20, 21, 22]]);
-  });
-
-  it('does not have memory leak.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const data = TensorValue3x4Float();
-    const indices = tf.tensor1d([0, 1], 'int32');
-    const segmentIds = tf.tensor1d([0, 0], 'int32');
-    const result = tf.sparse.sparseSegmentMean(data, indices, segmentIds);
-
-    await result.data();
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 4);
-
-    data.dispose();
-    indices.dispose();
-    segmentIds.dispose();
-    result.dispose();
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-
-  it('throw error if indices < 0', async () => {
-    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, -1, 0, 9], [
-      0, 1, 2, 2
-    ])).toThrowError(/indices\[1\] == -1 out of range \[0, 10\)/);
-  });
-
-  it('throw error if indices >= max rows', async () => {
-    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 10], [
-      0, 1, 2, 2
-    ])).toThrowError(/indices\[3\] == 10 out of range \[0, 10\)/);
-  });
-
-  it('throw error if segment ids are not increasing', async () => {
-    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [
-      0, 1, 0, 1
-    ])).toThrowError('segment ids are not increasing');
-  });
-
-  it('throw error if segment id is out of range', async () => {
-    expect(
-        () => tf.sparse.sparseSegmentMean(
-            TensorValue10x4(), [8, 3, 0, 9], [0, 1, 2, 0]))
-        .toThrowError(
-            'Segment id 1 out of range [0, 1), possibly because segmentIds ' +
-            'input is not sorted.');
-  });
-
-  it('throw error if segment id is out of range and negative', async () => {
-    expect(
-        () => tf.sparse.sparseSegmentMean(
-            TensorValue10x4(), [8, 3, 0, 9], [-1, 0, 1, 1]))
-        .toThrowError(
-            'Segment id -1 out of range [0, 2), possibly because segmentIds ' +
-            'input is not sorted.');
-  });
-
-  it('throw error if segment id is negative', async () => {
-    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [
-      0, 0, 0, -1
-    ])).toThrowError('segment ids must be >= 0');
-  });
-
-  it('throw error if segment id is negative and there is a hole', async () => {
-    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [
-      0, 0, 0, -2
-    ])).toThrowError('segment ids must be >= 0');
-  });
-
-  it('throw error if indices has invalid rank', async () => {
-    expect(
-        () => tf.sparse.sparseSegmentMean(
-            TensorValue10x4(), [[8, 3, 0, 9]], [0, 0, 0, -2]))
-        .toThrowError(/should be Tensor1D/);
-  });
-
-  it('throw error if segments has invalid rank', async () => {
-    expect(() => tf.sparse.sparseSegmentMean(TensorValue10x4(), [8, 3, 0, 9], [
-      [0, 0, 0, -2]
-    ])).toThrowError(/should be Tensor1D/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_reduction_util.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_reduction_util.ts
deleted file mode 100644
index c645112cc..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_reduction_util.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Generates sparse segment reduction negative segment ids error message.
- *
- */
-export function getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {
-  return `segment ids must be >= 0`;
-}
-
-/**
- * Generates sparse segment reduction non increasing segment ids error message.
- *
- */
-export function getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {
-  return `segment ids are not increasing`;
-}
-
-/**
- * Generates sparse segment reduction segment id out of range error message.
- *
- * @param segmentId The segment id index that is out of range.
- * @param outputRows Upper bound of valid segment id values.
- */
-export function getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(
-    segmentId: number, outputRows: number) {
-  return `Segment id ${segmentId} out of range [0, ${
-      outputRows}), possibly because segmentIds input is not sorted.`;
-}
-
-/**
- * Generates sparse segment reduction input indice out of range error message.
- *
- * @param index The index that holds the out of range value.
- * @param indexValue The value that is out of range.
- * @param inputRows Upper bound of valid index values.
- */
-export function getSparseSegmentReductionIndicesOutOfRangeErrorMessage(
-    index: number, indexValue: number, inputRows: number) {
-  return `Bad: indices[${index}] == ${indexValue} out of range [0, ${
-      inputRows})`;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_sum.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_sum.ts
deleted file mode 100644
index 23d64f1e5..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_sum.ts
+++ /dev/null
@@ -1,90 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {SparseSegmentSum, SparseSegmentSumInputs} from '../../kernel_names';
-import {Tensor, Tensor1D} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {op} from '../operation';
-
-/**
- * Computes the sum along sparse segments of a tensor.
- *
- * ```js
- * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]);
- * // Select two rows, one segment.
- * const result1 = tf.sparse.sparseSegmentSum(c,
- *                                           tf.tensor1d([0, 1], 'int32'),
- *                                           tf.tensor1d([0, 0], 'int32'));
- * result1.print(); // [[0, 0, 0, 0]]
- *
- * // Select two rows, two segments.
- * const result2 = tf.sparse.sparseSegmentSum(c,
- *                                           tf.tensor1d([0, 1], 'int32'),
- *                                           tf.tensor1d([0, 1], 'int32'));
- * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]
- *
- * // Select all rows, two segments.
- * const result3 = tf.sparse.sparseSegmentSum(c,
- *                                           tf.tensor1d([0, 1, 2], 'int32'),
- *                                           tf.tensor1d([0, 0, 1], 'int32'));
- * result3.print(); // [[0, 0, 0, 0], [5, 6, 7, 8]]
- * ```
- * @param data: A Tensor of at least one dimension with data that will be
- *     assembled in the output.
- * @param indices: A 1-D Tensor with indices into data. Has same rank as
- *     segmentIds.
- * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values
- *     should be sorted and can be repeated.
- * @return Has same shape as data, except for dimension 0 which has equal to
- *         the number of segments.
- *
- * @doc {heading: 'Operations', subheading: 'Sparse'}
- */
-function sparseSegmentSum_(
-    data: Tensor|TensorLike, indices: Tensor1D|TensorLike,
-    segmentIds: Tensor1D|TensorLike): Tensor {
-  const $data = convertToTensor(data, 'data', 'sparseSegmentSum');
-  const $indices =
-      convertToTensor(indices, 'indices', 'sparseSegmentSum', 'int32');
-  const $segmentIds =
-      convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentSum', 'int32');
-
-  if ($data.rank < 1) {
-    throw new Error(
-        `Data should be at least 1 dimensional but received scalar`);
-  }
-  if ($indices.rank !== 1) {
-    throw new Error(`Indices should be Tensor1D but received shape
-         ${$indices.shape}`);
-  }
-  if ($segmentIds.rank !== 1) {
-    throw new Error(`Segment ids should be Tensor1D but received shape
-         ${$segmentIds.shape}`);
-  }
-
-  const inputs: SparseSegmentSumInputs = {
-    data: $data,
-    indices: $indices,
-    segmentIds: $segmentIds
-  };
-
-  return ENGINE.runKernel(SparseSegmentSum, inputs as {});
-}
-
-export const sparseSegmentSum = /* @__PURE__ */ op({sparseSegmentSum_});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_sum_test.ts b/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_sum_test.ts
deleted file mode 100644
index a0c7fdcf0..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse/sparse_segment_sum_test.ts
+++ /dev/null
@@ -1,181 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-function TensorValue3x4() {
-  return tf.tensor2d([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]]);
-}
-
-function TensorValue10() {
-  return tf.tensor1d(Array.from(Array(10), (_, i) => i + 1));
-}
-
-function TensorValue10x4() {
-  return tf.tensor2d(Array.from(Array(40), (_, i) => i + 1), [10, 4]);
-}
-
-function TensorValue10x2x4() {
-  return tf.tensor3d(Array.from(Array(80), (_, i) => i + 1), [10, 2, 4]);
-}
-
-describeWithFlags('sparseSegmentSum', ALL_ENVS, () => {
-  it('two rows one segment', async () => {
-    const result = tf.sparse.sparseSegmentSum(TensorValue3x4(), [0, 1], [0, 0]);
-    expectArraysClose(await result.data(), [[0, 0, 0, 0]]);
-  });
-
-  it('two rows two segments', async () => {
-    const result = tf.sparse.sparseSegmentSum(TensorValue3x4(), [0, 1], [0, 1]);
-    expectArraysClose(await result.data(), [[1, 2, 3, 4], [-1, -2, -3, -4]]);
-  });
-
-  it('all rows one segment', async () => {
-    const result =
-        tf.sparse.sparseSegmentSum(TensorValue3x4(), [0, 1, 2], [0, 0, 1]);
-    expectArraysClose(await result.data(), [[0, 0, 0, 0], [5, 6, 7, 8]]);
-  });
-
-  it('0 dimensional input invalid', async () => {
-    expect(() => tf.sparse.sparseSegmentSum(tf.scalar(1), [], []))
-        .toThrowError(/should be at least 1 dimensional/);
-  });
-
-  it('1 dimensional input', async () => {
-    const result =
-        tf.sparse.sparseSegmentSum(TensorValue10(), [8, 3, 0, 9], [0, 1, 2, 2]);
-    expectArraysClose(await result.data(), [9, 4, 11]);
-  });
-
-  it('3 dimensional input', async () => {
-    const result = tf.sparse.sparseSegmentSum(
-        TensorValue10x2x4(), [8, 3, 0, 9], [0, 1, 2, 2]);
-    expectArraysClose(await result.data(), [
-      [[65, 66, 67, 68], [69, 70, 71, 72]],
-      [[25, 26, 27, 28], [29, 30, 31, 32]], [[74, 76, 78, 80], [82, 84, 86, 88]]
-    ]);
-  });
-
-  it('segment ids hole', async () => {
-    const result = tf.sparse.sparseSegmentSum(
-        TensorValue10x4(), [8, 3, 0, 9], [0, 3, 3, 3]);
-    expectArraysClose(
-        await result.data(),
-        [[33, 34, 35, 36], [0, 0, 0, 0], [0, 0, 0, 0], [51, 54, 57, 60]]);
-  });
-
-  it('segment ids > zero', async () => {
-    const result = tf.sparse.sparseSegmentSum(
-        TensorValue10x4(), [8, 3, 0, 9], [2, 3, 3, 3]);
-    expectArraysClose(
-        await result.data(),
-        [[0, 0, 0, 0], [0, 0, 0, 0], [33, 34, 35, 36], [51, 54, 57, 60]]);
-  });
-
-  it('baseline valid', async () => {
-    // Baseline for the *invalid* tests below.
-    const result = tf.sparse.sparseSegmentSum(
-        TensorValue10x4(), [8, 3, 0, 9], [0, 1, 2, 2]);
-    expectArraysClose(
-        await result.data(),
-        [[33, 34, 35, 36], [13, 14, 15, 16], [38, 40, 42, 44]]);
-  });
-
-  it('does not have memory leak.', async () => {
-    const beforeDataIds = tf.engine().backend.numDataIds();
-
-    const data = TensorValue3x4();
-    const indices = tf.tensor1d([0, 1], 'int32');
-    const segmentIds = tf.tensor1d([0, 0], 'int32');
-    const result = tf.sparse.sparseSegmentSum(data, indices, segmentIds);
-
-    await result.data();
-
-    const afterResDataIds = tf.engine().backend.numDataIds();
-    expect(afterResDataIds).toEqual(beforeDataIds + 4);
-
-    data.dispose();
-    indices.dispose();
-    segmentIds.dispose();
-    result.dispose();
-
-    const afterDisposeDataIds = tf.engine().backend.numDataIds();
-    expect(afterDisposeDataIds).toEqual(beforeDataIds);
-  });
-
-  it('indices invalid 1', async () => {
-    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, -1, 0, 9], [
-      0, 1, 2, 2
-    ])).toThrowError(/indices\[1\] == -1 out of range \[0, 10\)/);
-  });
-
-  it('indices invalid 2', async () => {
-    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 10], [
-      0, 1, 2, 2
-    ])).toThrowError(/indices\[3\] == 10 out of range \[0, 10\)/);
-  });
-
-  it('segments invalid 2', async () => {
-    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [
-      0, 1, 0, 1
-    ])).toThrowError('segment ids are not increasing');
-  });
-
-  it('segments invalid 3', async () => {
-    expect(
-        () => tf.sparse.sparseSegmentSum(
-            TensorValue10x4(), [8, 3, 0, 9], [0, 1, 2, 0]))
-        .toThrowError(
-            'Segment id 1 out of range [0, 1), possibly because segmentIds ' +
-            'input is not sorted.');
-  });
-
-  it('segments invalid 4', async () => {
-    expect(
-        () => tf.sparse.sparseSegmentSum(
-            TensorValue10x4(), [8, 3, 0, 9], [-1, 0, 1, 1]))
-        .toThrowError(
-            'Segment id -1 out of range [0, 2), possibly because segmentIds ' +
-            'input is not sorted.');
-  });
-
-  it('segments invalid 6', async () => {
-    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [
-      0, 0, 0, -1
-    ])).toThrowError('segment ids must be >= 0');
-  });
-
-  it('segments invalid 7', async () => {
-    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [
-      0, 0, 0, -2
-    ])).toThrowError('segment ids must be >= 0');
-  });
-
-  it('indices invalid rank', async () => {
-    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [[8, 3, 0, 9]], [
-      0, 0, 0, -2
-    ])).toThrowError(/should be Tensor1D/);
-  });
-
-  it('segments invalid rank', async () => {
-    expect(() => tf.sparse.sparseSegmentSum(TensorValue10x4(), [8, 3, 0, 9], [
-      [0, 0, 0, -2]
-    ])).toThrowError(/should be Tensor1D/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse_to_dense.ts b/tfjs-master/tfjs-core/src/ops/sparse_to_dense.ts
deleted file mode 100644
index e7c22898d..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse_to_dense.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {SparseToDense, SparseToDenseAttrs, SparseToDenseInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import * as sparse_to_dense from '../ops/sparse_to_dense_util';
-import {Scalar, Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {Rank, ScalarLike, ShapeMap, TensorLike} from '../types';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-
-import {op} from './operation';
-
-/**
- * Converts a sparse representation into a dense tensor.
- *
- * Builds an array dense with shape outputShape such that:
- *
- * // If sparseIndices is scalar
- * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)
- *
- * // If sparseIndices is a vector, then for each i
- * dense[sparseIndices[i]] = sparseValues[i]
- *
- * // If sparseIndices is an n by d matrix, then for each i in [0, n)
- * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]
- * All other values in dense are set to defaultValue. If sparseValues is a
- * scalar, all sparse indices are set to this single value.
- *
- * If indices are repeated the final value is summed over all values for those
- * indices.
- *
- * ```js
- * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');
- * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');
- * const shape = [8];
- * tf.sparseToDense(indices, values, shape).print();
- * ```
- *
- * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
- * sparseIndices[i] contains the complete index where sparseValues[i] will be
- * placed.
- * @param sparseValues A 0-D or 1-D Tensor. Values
- * corresponding to each row of sparseIndices, or a scalar value to be used for
- * all sparse indices.
- * @param outputShape Shape of the dense output tensor. The type is inferred.
- * @param defaultValue Scalar. Value to set for indices not specified in
- * sparseIndices. Defaults to zero.
- *
- * @doc {heading: 'Operations', subheading: 'Normalization'}
- */
-function sparseToDense_<R extends Rank>(
-    sparseIndices: Tensor|TensorLike, sparseValues: Tensor|TensorLike,
-    outputShape: ShapeMap[R], defaultValue: Scalar|ScalarLike = 0): Tensor<R> {
-  assertNonNegativeIntegerDimensions(outputShape);
-
-  const $sparseIndices =
-      convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');
-  const $sparseValues = convertToTensor(
-      sparseValues, 'sparseValues', 'sparseToDense', 'string_or_numeric');
-  const $defaultValue = convertToTensor(
-      defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);
-
-  sparse_to_dense.validateInput(
-      $sparseIndices, $sparseValues, outputShape, $defaultValue);
-
-  const inputs: SparseToDenseInputs = {
-    sparseIndices: $sparseIndices,
-    sparseValues: $sparseValues,
-    defaultValue: $defaultValue
-  };
-
-  const attrs: SparseToDenseAttrs = {outputShape};
-
-  return ENGINE.runKernel(
-      SparseToDense, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const sparseToDense = /* @__PURE__ */ op({sparseToDense_});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse_to_dense_test.ts b/tfjs-master/tfjs-core/src/ops/sparse_to_dense_test.ts
deleted file mode 100644
index acb6195ab..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse_to_dense_test.ts
+++ /dev/null
@@ -1,160 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-let defaultValue: tf.Scalar;
-describeWithFlags('sparseToDense', ALL_ENVS, () => {
-  beforeEach(() => defaultValue = tf.scalar(0, 'int32'));
-  it('should work for scalar indices', async () => {
-    const indices = tf.scalar(2, 'int32');
-    const values = tf.scalar(100, 'int32');
-    const shape = [6];
-    const result = tf.sparseToDense(indices, values, shape, defaultValue);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(values.dtype);
-    expectArraysClose(await result.data(), [0, 0, 100, 0, 0, 0]);
-  });
-  it('should work for vector', async () => {
-    const indices = tf.tensor1d([0, 2, 4], 'int32');
-    const values = tf.tensor1d([100, 101, 102], 'int32');
-    const shape = [6];
-    const result = tf.sparseToDense(indices, values, shape, defaultValue);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(values.dtype);
-    expectArraysClose(await result.data(), [100, 0, 101, 0, 102, 0]);
-  });
-  it('should work for scalar value', async () => {
-    const indices = tf.tensor1d([0, 2, 4], 'int32');
-    const values = tf.scalar(10, 'int32');
-    const shape = [6];
-    const result = tf.sparseToDense(indices, values, shape, defaultValue);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(values.dtype);
-    expectArraysClose(await result.data(), [10, 0, 10, 0, 10, 0]);
-  });
-  it('should work for matrix', async () => {
-    const indices = tf.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
-    const values = tf.tensor1d([5, 6], 'float32');
-    const shape = [2, 2];
-    const result =
-        tf.sparseToDense(indices, values, shape, defaultValue.toFloat());
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(values.dtype);
-    expectArraysClose(await result.data(), [0, 5, 0, 6]);
-  });
-  it('should work for string', async () => {
-    const indices = tf.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
-    const values = tf.tensor1d(['a', 'b'], 'string');
-    const shape = [2, 2];
-    const result =
-        tf.sparseToDense(indices, values, shape, tf.scalar('c', 'string'));
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(values.dtype);
-    expectArraysEqual(await result.data(), ['c', 'a', 'c', 'b']);
-  });
-
-  it('should throw exception if default value does not match dtype', () => {
-    const indices = tf.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
-    const values = tf.tensor1d([5, 6], 'float32');
-    const shape = [2, 2];
-    expect(
-        () => tf.sparseToDense(indices, values, shape, tf.scalar(1, 'int32')))
-        .toThrowError();
-  });
-
-  it('should allow setting default value', async () => {
-    const indices = tf.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
-    const values = tf.tensor1d([5, 6], 'float32');
-    const shape = [2, 2];
-    const result = tf.sparseToDense(indices, values, shape, tf.scalar(1));
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(values.dtype);
-    expectArraysClose(await result.data(), [1, 5, 1, 6]);
-  });
-
-  it('no default value passed', async () => {
-    const indices = tf.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
-    const values = tf.tensor1d([5, 6], 'float32');
-    const shape = [2, 2];
-    const result = tf.sparseToDense(indices, values, shape);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(values.dtype);
-    expectArraysClose(await result.data(), [0, 5, 0, 6]);
-  });
-
-  it('should support TensorLike inputs', async () => {
-    const indices = [[0, 1], [1, 1]];
-    const values = [5, 6];
-    const shape = [2, 2];
-    const result =
-        tf.sparseToDense(indices, values, shape, defaultValue.toFloat());
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual('float32');
-    expectArraysClose(await result.data(), [0, 5, 0, 6]);
-  });
-
-  it('should work with 0-sized tensors', async () => {
-    const indices = tf.zeros([0], 'int32');
-    const values = tf.zeros([0]);
-    const defaultValue = tf.scalar(5);
-    const result = tf.sparseToDense(indices, values, [3], defaultValue);
-    expectArraysClose(await result.data(), [5, 5, 5]);
-  });
-
-  it('should throw error when indices are not int32', () => {
-    const indices = tf.scalar(2, 'float32');
-    const values = tf.scalar(100, 'int32');
-    const shape = [6];
-    expect(() => tf.sparseToDense(indices, values, shape, defaultValue))
-        .toThrow();
-  });
-
-  it('should throw error when indices rank > 2', () => {
-    const indices = tf.tensor3d([1], [1, 1, 1], 'int32');
-    const values = tf.tensor1d([100], 'float32');
-    const shape = [6];
-    expect(() => tf.sparseToDense(indices, values, shape, defaultValue))
-        .toThrow();
-  });
-
-  it('should throw error when values has rank > 1', () => {
-    const indices = tf.tensor1d([0, 4, 2], 'int32');
-    const values = tf.tensor2d([1.0, 2.0, 3.0], [3, 1], 'float32');
-    const shape = [6];
-    expect(() => tf.sparseToDense(indices, values, shape, defaultValue))
-        .toThrow();
-  });
-
-  it('should throw error when values has wrong size', () => {
-    const indices = tf.tensor1d([0, 4, 2], 'int32');
-    const values = tf.tensor1d([1.0, 2.0, 3.0, 4.0], 'float32');
-    const shape = [6];
-    expect(() => tf.sparseToDense(indices, values, shape, defaultValue))
-        .toThrow();
-  });
-
-  it('should throw error when shape is not integer', () => {
-    const indices = [[0, 1], [1, 1]];
-    const values = [5, 6];
-    const shape = [2.22, 2.22];
-    expect(() => tf.sparseToDense(indices, values, shape, tf.scalar(1)))
-        .toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sparse_to_dense_util.ts b/tfjs-master/tfjs-core/src/ops/sparse_to_dense_util.ts
deleted file mode 100644
index 88e326ff8..000000000
--- a/tfjs-master/tfjs-core/src/ops/sparse_to_dense_util.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Tensor} from '../tensor';
-
-/**
- * Validate sparseToDense inputs.
- *
- * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
- * sparseIndices[i] contains the complete index where sparseValues[i] will be
- * placed.
- * @param sparseValues A 0-D or 1-D Tensor. Values
- * corresponding to each row of sparseIndices, or a scalar value to be used for
- * all sparse indices.
- * @param outputShape number[]. Shape of the dense output tensor.
- * @param validateIndices boolean. indice validation is not supported, error
- * will be thrown if it is set.
- */
-export function validateInput(
-    sparseIndices: Tensor, sparseValues: Tensor, outputShape: number[],
-    defaultValues: Tensor) {
-  if (sparseIndices.dtype !== 'int32') {
-    throw new Error(
-        'tf.sparseToDense() expects the indices to be int32 type,' +
-        ` but the dtype was ${sparseIndices.dtype}.`);
-  }
-  if (sparseIndices.rank > 2) {
-    throw new Error(
-        'sparseIndices should be a scalar, vector, or matrix,' +
-        ` but got shape ${sparseIndices.shape}.`);
-  }
-
-  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
-  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
-
-  if (outputShape.length !== numDims) {
-    throw new Error(
-        'outputShape has incorrect number of elements:,' +
-        ` ${outputShape.length}, should be: ${numDims}.`);
-  }
-
-  const numValues = sparseValues.size;
-  if (!(sparseValues.rank === 0 ||
-        sparseValues.rank === 1 && numValues === numElems)) {
-    throw new Error(
-        'sparseValues has incorrect shape ' +
-        `${sparseValues.shape}, should be [] or [${numElems}]`);
-  }
-
-  if (sparseValues.dtype !== defaultValues.dtype) {
-    throw new Error('sparseValues.dtype must match defaultValues.dtype');
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/ops/spectral/fft.ts b/tfjs-master/tfjs-core/src/ops/spectral/fft.ts
deleted file mode 100644
index 287be8801..000000000
--- a/tfjs-master/tfjs-core/src/ops/spectral/fft.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {FFT, FFTInputs} from '../../kernel_names';
-import {Tensor} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {assert} from '../../util';
-import {op} from '../operation';
-
-/**
- * Fast Fourier transform.
- *
- * Computes the 1-dimensional discrete Fourier transform over the inner-most
- * dimension of input.
- *
- * ```js
- * const real = tf.tensor1d([1, 2, 3]);
- * const imag = tf.tensor1d([1, 2, 3]);
- * const x = tf.complex(real, imag);
- *
- * x.fft().print();  // tf.spectral.fft(x).print();
- * ```
- * @param input The complex input to compute an fft over.
- *
- * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
- */
-function fft_(input: Tensor): Tensor {
-  assert(
-      input.dtype === 'complex64',
-      () => `The dtype for tf.spectral.fft() must be complex64 ` +
-          `but got ${input.dtype}.`);
-
-  const inputs: FFTInputs = {input};
-
-  return ENGINE.runKernel(FFT, inputs as unknown as NamedTensorMap);
-}
-
-export const fft = /* @__PURE__ */ op({fft_});
diff --git a/tfjs-master/tfjs-core/src/ops/spectral/fft_test.ts b/tfjs-master/tfjs-core/src/ops/spectral/fft_test.ts
deleted file mode 100644
index 4d48d0154..000000000
--- a/tfjs-master/tfjs-core/src/ops/spectral/fft_test.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('1D FFT', ALL_ENVS, () => {
-  it('should return the same value with TensorFlow (2 elements)', async () => {
-    const t1Real = tf.tensor1d([1, 2]);
-    const t1Imag = tf.tensor1d([1, 1]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(await tf.spectral.fft(t1).data(), [3, 2, -1, 0]);
-  });
-
-  it('should calculate FFT from Tensor directly', async () => {
-    const t1Real = tf.tensor1d([1, 2]);
-    const t1Imag = tf.tensor1d([1, 1]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(await t1.fft().data(), [3, 2, -1, 0]);
-  });
-
-  it('should return the same value as TensorFlow (3 elements)', async () => {
-    const t1Real = tf.tensor1d([1, 2, 3]);
-    const t1Imag = tf.tensor1d([0, 0, 0]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(
-        await tf.spectral.fft(t1).data(),
-        [6, 0, -1.5, 0.866025, -1.5, -0.866025]);
-  });
-
-  it('should return the same value as TensorFlow with imaginary (3 elements)',
-     async () => {
-       const t1Real = tf.tensor1d([1, 2, 3]);
-       const t1Imag = tf.tensor1d([1, 2, 3]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.fft(t1).data(),
-           [6, 6, -2.3660252, -0.63397473, -0.6339747, -2.3660254]);
-     });
-
-  it('should return the same value as TensorFlow (negative 3 elements)',
-     async () => {
-       const t1Real = tf.tensor1d([-1, -2, -3]);
-       const t1Imag = tf.tensor1d([-1, -2, -3]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.fft(t1).data(),
-           [-5.9999995, -6, 2.3660252, 0.63397473, 0.6339747, 2.3660254]);
-     });
-
-  it('should return the same value with TensorFlow (4 elements)', async () => {
-    const t1Real = tf.tensor1d([1, 2, 3, 4]);
-    const t1Imag = tf.tensor1d([0, 0, 0, 0]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(
-        await tf.spectral.fft(t1).data(), [10, 0, -2, 2, -2, 0, -2, -2]);
-  });
-
-  it('should return the same value as TensorFlow with imaginary (4 elements)',
-     async () => {
-       const t1Real = tf.tensor1d([1, 2, 3, 4]);
-       const t1Imag = tf.tensor1d([1, 2, 3, 4]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.fft(t1).data(), [10, 10, -4, 0, -2, -2, 0, -4]);
-     });
-});
-
-describeWithFlags('2D FFT', ALL_ENVS, () => {
-  it('2D: should return the same value as TensorFlow', async () => {
-    const t1Real = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const t1Imag = tf.tensor2d([5, 6, 7, 8], [2, 2]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    const y = tf.spectral.fft(t1);
-    expectArraysClose(await y.data(), [3, 11, -1, -1, 7, 15, -1, -1]);
-    expect(y.shape).toEqual(t1Real.shape);
-  });
-
-  it('3D: should return the same value as TensorFlow', async () => {
-    const t1Real = tf.tensor3d([1, 2, 3, 4, -1, -2, -3, -4], [2, 2, 2]);
-    const t1Imag = tf.tensor3d([5, 6, 7, 8, -5, -6, -7, -8], [2, 2, 2]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    const y = tf.spectral.fft(t1);
-    expectArraysClose(
-        await y.data(),
-        [3, 11, -1, -1, 7, 15, -1, -1, -3, -11, 1, 1, -7, -15, 1, 1]);
-    expect(y.shape).toEqual(t1Real.shape);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/spectral/ifft.ts b/tfjs-master/tfjs-core/src/ops/spectral/ifft.ts
deleted file mode 100644
index 541c7c42e..000000000
--- a/tfjs-master/tfjs-core/src/ops/spectral/ifft.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {IFFT, IFFTInputs} from '../../kernel_names';
-import {Tensor} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {assert} from '../../util';
-import {op} from '../operation';
-
-/**
- * Inverse fast Fourier transform.
- *
- * Computes the inverse 1-dimensional discrete Fourier transform over the
- * inner-most dimension of input.
- *
- * ```js
- * const real = tf.tensor1d([1, 2, 3]);
- * const imag = tf.tensor1d([1, 2, 3]);
- * const x = tf.complex(real, imag);
- *
- * x.ifft().print();  // tf.spectral.ifft(x).print();
- * ```
- * @param input The complex input to compute an ifft over.
- *
- * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
- */
-function ifft_(input: Tensor): Tensor {
-  assert(
-      input.dtype === 'complex64',
-      () => `The dtype for tf.spectral.ifft() must be complex64 ` +
-          `but got ${input.dtype}.`);
-
-  const inputs: IFFTInputs = {input};
-
-  return ENGINE.runKernel(IFFT, inputs as unknown as NamedTensorMap);
-}
-
-export const ifft = /* @__PURE__ */ op({ifft_});
diff --git a/tfjs-master/tfjs-core/src/ops/spectral/irfft.ts b/tfjs-master/tfjs-core/src/ops/spectral/irfft.ts
deleted file mode 100644
index 6f457b61f..000000000
--- a/tfjs-master/tfjs-core/src/ops/spectral/irfft.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor2D} from '../../tensor';
-import {complex} from '../complex';
-import {concat} from '../concat';
-import {imag} from '../imag';
-import {mul} from '../mul';
-import {op} from '../operation';
-import {real} from '../real';
-import {reshape} from '../reshape';
-import {reverse} from '../reverse';
-import {scalar} from '../scalar';
-import {slice} from '../slice';
-
-import {ifft} from './ifft';
-
-/**
- * Inversed real value input fast Fourier transform.
- *
- * Computes the 1-dimensional inversed discrete Fourier transform over the
- * inner-most dimension of the real input.
- *
- * ```js
- * const real = tf.tensor1d([1, 2, 3]);
- * const imag = tf.tensor1d([0, 0, 0]);
- * const x = tf.complex(real, imag);
- *
- * x.irfft().print();
- * ```
- * @param input The real value input to compute an irfft over.
- *
- * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
- */
-function irfft_(input: Tensor): Tensor {
-  const innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = input.size / innerDimensionSize;
-  let ret: Tensor;
-  if (innerDimensionSize <= 2) {
-    const complexInput = reshape(input, [batch, innerDimensionSize]);
-    ret = ifft(complexInput);
-  } else {
-    // The length of unique components of the DFT of a real-valued signal
-    // is 2 * (input_len - 1)
-    const outputShape = [batch, 2 * (innerDimensionSize - 1)];
-    const realInput = reshape(real(input), [batch, innerDimensionSize]);
-    const imagInput = reshape(imag(input), [batch, innerDimensionSize]);
-
-    const realConjugate =
-        reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
-    const imagConjugate: Tensor2D = mul(
-        reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1),
-        scalar(-1));
-
-    const r = concat([realInput, realConjugate], 1);
-    const i = concat([imagInput, imagConjugate], 1);
-    const complexInput =
-        reshape(complex(r, i), [outputShape[0], outputShape[1]]);
-    ret = ifft(complexInput);
-  }
-  ret = real(ret);
-  // reshape the result if the input is 3D tensor.
-  if (input.rank === 3 && input.shape[0] !== 0) {
-    const temp = ret;
-    const batch = input.shape[0];
-    ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);
-    temp.dispose();
-  }
-  return ret;
-}
-
-export const irfft = /* @__PURE__ */ op({irfft_});
diff --git a/tfjs-master/tfjs-core/src/ops/spectral/irfft_test.ts b/tfjs-master/tfjs-core/src/ops/spectral/irfft_test.ts
deleted file mode 100644
index 600b67950..000000000
--- a/tfjs-master/tfjs-core/src/ops/spectral/irfft_test.ts
+++ /dev/null
@@ -1,127 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('1D IRFFT', ALL_ENVS, () => {
-  it('should return the same value with TensorFlow (2 elements)', async () => {
-    const t1Real = tf.tensor1d([1, 2]);
-    const t1Imag = tf.tensor1d([0, 0]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(await tf.spectral.irfft(t1).data(), [1.5, -0.5]);
-  });
-
-  it('should calculate from the tensor directly', async () => {
-    const t1Real = tf.tensor1d([1, 2]);
-    const t1Imag = tf.tensor1d([0, 0]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(await t1.irfft().data(), [1.5, -0.5]);
-  });
-
-  it('should return the same value with TensorFlow (5 elements)', async () => {
-    const t1Real = tf.tensor1d([1, 2, 3, 4, 5]);
-    const t1Imag = tf.tensor1d([0, 0, 0, 0, 0]);
-    const t1 = tf.complex(t1Real, t1Imag);
-    expectArraysClose(
-        await tf.spectral.irfft(t1).data(),
-        [3, -0.8535534, 0, -0.14644662, 0, -0.14644662, 0, -0.8535534]);
-  });
-
-  it('should return the same value with TensorFlow (5 elements) with imag',
-     async () => {
-       const t1Real = tf.tensor1d([1, 2, 3, 4, 5]);
-       const t1Imag = tf.tensor1d([1, 2, 3, 4, 5]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.irfft(t1).data(),
-           [3, -2.6642137, 0.5, -0.45710677, 0, 0.16421354, -0.5, 0.95710677]);
-     });
-});
-
-describeWithFlags('2D IRFFT', ALL_ENVS, () => {
-  it('should return the same value with TensorFlow (2x2 elements)',
-     async () => {
-       const t1Real = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-       const t1Imag = tf.tensor2d([0, 0, 0, 0], [2, 2]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.irfft(t1).data(), [1.5, -0.5, 3.5, -0.5]);
-     });
-
-  it('should return the same value with TensorFlow (2x3 elements)',
-     async () => {
-       const t1Real = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-       const t1Imag = tf.tensor2d([0, 0, 0, 0, 0, 0], [2, 3]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.irfft(t1).data(),
-           [2, -0.5, 0, -0.5, 5, -0.5, 0, -0.5]);
-     });
-
-  it('should return the same value with TensorFlow (2x3 elements) with imag',
-     async () => {
-       const t1Real = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-       const t1Imag = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       expectArraysClose(
-           await tf.spectral.irfft(t1).data(), [2, -1.5, 0, 0.5, 5, -3, 0, 2]);
-     });
-});
-
-describeWithFlags('3D IRFFT', ALL_ENVS, () => {
-  it('should return the same value with TensorFlow (2x2x2 elements)',
-     async () => {
-       const t1Real = tf.tensor3d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2]);
-       const t1Imag = tf.tensor3d([0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       const result = tf.spectral.irfft(t1);
-       expect(result.shape).toEqual([2, 2, 2]);
-
-       expectArraysClose(
-           await result.data(), [1.5, -0.5, 3.5, -0.5, 1.5, -0.5, 3.5, -0.5]);
-     });
-
-  it('should return the same value with TensorFlow (2x2x3 elements)',
-     async () => {
-       const t1Real =
-           tf.tensor3d([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [2, 2, 3]);
-       const t1Imag =
-           tf.tensor3d([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 3]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       const result = tf.spectral.irfft(t1);
-       expect(result.shape).toEqual([2, 2, 4]);
-       expectArraysClose(await result.data(), [
-         2, -0.5, 0, -0.5, 5, -0.5, 0, -0.5, 2, -0.5, 0, -0.5, 5, -0.5, 0, -0.5
-       ]);
-     });
-
-  it('should return the same value with TensorFlow (2x2x3 elements) with imag',
-     async () => {
-       const t1Real =
-           tf.tensor3d([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [2, 2, 3]);
-       const t1Imag =
-           tf.tensor3d([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [2, 2, 3]);
-       const t1 = tf.complex(t1Real, t1Imag);
-       const result = tf.spectral.irfft(t1);
-       expect(result.shape).toEqual([2, 2, 4]);
-       expectArraysClose(
-           await result.data(),
-           [2, -1.5, 0, 0.5, 5, -3, 0, 2, 2, -1.5, 0, 0.5, 5, -3, 0, 2]);
-     });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/spectral/rfft.ts b/tfjs-master/tfjs-core/src/ops/spectral/rfft.ts
deleted file mode 100644
index 6b9706910..000000000
--- a/tfjs-master/tfjs-core/src/ops/spectral/rfft.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../../tensor';
-import {assert} from '../../util';
-import {complex} from '../complex';
-import {concat} from '../concat';
-import {imag} from '../imag';
-import {op} from '../operation';
-import {real} from '../real';
-import {reshape} from '../reshape';
-import {slice} from '../slice';
-import {split} from '../split';
-import {zeros} from '../zeros';
-import {zerosLike} from '../zeros_like';
-
-import {fft} from './fft';
-
-/**
- * Real value input fast Fourier transform.
- *
- * Computes the 1-dimensional discrete Fourier transform over the
- * inner-most dimension of the real input.
- *
- * ```js
- * const real = tf.tensor1d([1, 2, 3]);
- *
- * real.rfft().print();
- * ```
- * @param input The real value input to compute an rfft over.
- *
- * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
- */
-function rfft_(input: Tensor, fftLength?: number): Tensor {
-  assert(
-      input.dtype === 'float32',
-      () => `The dtype for rfft() must be real value but got ${input.dtype}`);
-
-  let innerDimensionSize = input.shape[input.shape.length - 1];
-  const batch = input.size / innerDimensionSize;
-
-  let adjustedInput: Tensor;
-  if (fftLength != null && fftLength < innerDimensionSize) {
-    // Need to crop
-    const begin = input.shape.map(v => 0);
-    const size = input.shape.map(v => v);
-    size[input.shape.length - 1] = fftLength;
-    adjustedInput = slice(input, begin, size);
-    innerDimensionSize = fftLength;
-  } else if (fftLength != null && fftLength > innerDimensionSize) {
-    // Need to pad with zeros
-    const zerosShape = input.shape.map(v => v);
-    zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;
-    adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);
-    innerDimensionSize = fftLength;
-  } else {
-    adjustedInput = input;
-  }
-
-  // Complement the input with zero imaginary numbers.
-  const zerosInput = zerosLike(adjustedInput);
-  const complexInput =
-      reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
-
-  const ret = fft(complexInput);
-
-  // Exclude complex conjugations. These conjugations are put symmetrically.
-  const half = Math.floor(innerDimensionSize / 2) + 1;
-  const realValues = real(ret);
-  const imagValues = imag(ret);
-  const realComplexConjugate = split(
-      realValues, [half, innerDimensionSize - half],
-      realValues.shape.length - 1);
-  const imagComplexConjugate = split(
-      imagValues, [half, innerDimensionSize - half],
-      imagValues.shape.length - 1);
-
-  const outputShape = adjustedInput.shape.slice();
-  outputShape[adjustedInput.shape.length - 1] = half;
-
-  return reshape(
-      complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
-}
-
-export const rfft = /* @__PURE__ */ op({rfft_});
diff --git a/tfjs-master/tfjs-core/src/ops/spectral/rfft_test.ts b/tfjs-master/tfjs-core/src/ops/spectral/rfft_test.ts
deleted file mode 100644
index 41e132283..000000000
--- a/tfjs-master/tfjs-core/src/ops/spectral/rfft_test.ts
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('1D RFFT', ALL_ENVS, () => {
-  it('should return the same value with TensorFlow (3 elements)', async () => {
-    const t1Real = tf.tensor1d([1, 2, 3]);
-    expectArraysClose(
-        await tf.spectral.rfft(t1Real).data(),
-        [6, 1.1920929e-07, -1.4999999, 8.6602521e-01]);
-  });
-
-  it('should calculate from tensor directly', async () => {
-    const t1Real = tf.tensor1d([1, 2, 3]);
-    expectArraysClose(
-        await t1Real.rfft().data(),
-        [6, 1.1920929e-07, -1.4999999, 8.6602521e-01]);
-  });
-
-  it('should return the same value with TensorFlow (6 elements)', async () => {
-    const t1Real = tf.tensor1d([-3, -2, -1, 1, 2, 3]);
-    expectArraysClose(await tf.spectral.rfft(t1Real).data(), [
-      -5.8859587e-07, 1.1920929e-07, -3.9999995, 6.9282026e+00, -2.9999998,
-      1.7320497, -4.0000000, -2.3841858e-07
-    ]);
-  });
-
-  it('should return the same value without any fftLength', async () => {
-    const t1Real = tf.tensor1d([-3, -2, -1, 1, 2, 3]);
-    const fftLength = 6;
-    expectArraysClose(await tf.spectral.rfft(t1Real, fftLength).data(), [
-      -5.8859587e-07, 1.1920929e-07, -3.9999995, 6.9282026e+00, -2.9999998,
-      1.7320497, -4.0000000, -2.3841858e-07
-    ]);
-  });
-
-  it('should return the value with cropped input', async () => {
-    const t1Real = tf.tensor1d([-3, -2, -1, 1, 2, 3]);
-    const fftLength = 3;
-    expectArraysClose(
-        await tf.spectral.rfft(t1Real, fftLength).data(),
-        [-6, 0.0, -1.5000002, 0.866]);
-  });
-
-  it('should return the value with padded input', async () => {
-    const t1Real = tf.tensor1d([-3, -2, -1]);
-    const fftLength = 4;
-    expectArraysClose(
-        await tf.spectral.rfft(t1Real, fftLength).data(),
-        [-6, 0, -2, 2, -2, 0]);
-  });
-});
-
-describeWithFlags('2D RFFT', ALL_ENVS, () => {
-  it('should return the same value with TensorFlow (2x2 elements)',
-     async () => {
-       const t1Real = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-       expectArraysClose(
-           await tf.spectral.rfft(t1Real).data(), [3, 0, -1, 0, 7, 0, -1, 0]);
-     });
-
-  it('should return the same value with TensorFlow (2x3 elements)',
-     async () => {
-       const t1Real = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-       expectArraysClose(await tf.spectral.rfft(t1Real).data(), [
-         6, 1.1920929e-07, -1.4999999, 8.6602521e-01, 15, -5.9604645e-08,
-         -1.4999998, 8.6602545e-01
-       ]);
-     });
-
-  it('should return the same value with TensorFlow (2x2x2 elements)',
-     async () => {
-       const t1Real = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-       expectArraysClose(
-           await tf.spectral.rfft(t1Real).data(),
-           [3, 0, -1, 0, 7, 0, -1, 0, 11, 0, -1, 0, 15, 0, -1, 0]);
-     });
-
-  it('should return the value with cropping', async () => {
-    const t1Real = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const fftLength = 2;
-    expectArraysClose(
-        await tf.spectral.rfft(t1Real, fftLength).data(),
-        [3, 0, -1, 0, 9, 0, -1, 0]);
-  });
-
-  it('should return the value with padding', async () => {
-    const t1Real = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const fftLength = 4;
-    expectArraysClose(
-        await tf.spectral.rfft(t1Real, fftLength).data(),
-        [6, 0, -2, -2, 2, 0, 15, 0, -2, -5, 5, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/split.ts b/tfjs-master/tfjs-core/src/ops/split.ts
deleted file mode 100644
index e67d829dc..000000000
--- a/tfjs-master/tfjs-core/src/ops/split.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {SplitV, SplitVAttrs, SplitVInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Splits a `tf.Tensor` into sub tensors.
- *
- * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`
- * into `numOrSizeSplits` smaller tensors.
- * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.
- *
- * If `numOrSizeSplits` is a number array, splits `x` into
- * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the
- * same size as `x` except along dimension `axis` where the size is
- * `numOrSizeSplits[i]`.
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
- * const [a, b] = tf.split(x, 2, 1);
- * a.print();
- * b.print();
- *
- * const [c, d, e] = tf.split(x, [1, 2, 1], 1);
- * c.print();
- * d.print();
- * e.print();
- * ```
- *
- * @param x The input tensor to split.
- * @param numOrSizeSplits Either an integer indicating the number of
- * splits along the axis or an array of integers containing the sizes of
- * each output tensor along the axis. If a number then it must evenly divide
- * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.
- * Can contain one -1 indicating that dimension is to be inferred.
- * @param axis The dimension along which to split. Defaults to 0 (the first
- * dim).
- *
- * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
- */
-function split_<T extends Tensor>(
-    x: Tensor|TensorLike, numOrSizeSplits: number[]|number, axis = 0): T[] {
-  const $x = convertToTensor(x, 'x', 'split');
-
-  const inputs: SplitVInputs = {x: $x};
-  const attr: SplitVAttrs = {numOrSizeSplits, axis};
-
-  return ENGINE.runKernel(
-             SplitV, inputs as unknown as NamedTensorMap,
-             attr as unknown as NamedAttrMap) as unknown as T[];
-}
-
-export const split = /* @__PURE__ */ op({split_});
diff --git a/tfjs-master/tfjs-core/src/ops/split_test.ts b/tfjs-master/tfjs-core/src/ops/split_test.ts
deleted file mode 100644
index 95e51e6c1..000000000
--- a/tfjs-master/tfjs-core/src/ops/split_test.ts
+++ /dev/null
@@ -1,169 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('split', ALL_ENVS, () => {
-  it('split by number', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const res = tf.split(x, 2, 1);
-    expect(res.length).toEqual(2);
-    expect(res[0].shape).toEqual([2, 2]);
-    expectArraysClose(await res[0].data(), [1, 2, 5, 6]);
-    expect(res[1].shape).toEqual([2, 2]);
-    expectArraysClose(await res[1].data(), [3, 4, 7, 8]);
-  });
-
-  it('split by sizes', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const res = tf.split(x, [1, 2, 1], 1);
-    expect(res.length).toEqual(3);
-    expect(res[0].shape).toEqual([2, 1]);
-    expectArraysClose(await res[0].data(), [1, 5]);
-    expect(res[1].shape).toEqual([2, 2]);
-    expectArraysClose(await res[1].data(), [2, 3, 6, 7]);
-    expect(res[2].shape).toEqual([2, 1]);
-    expectArraysClose(await res[2].data(), [4, 8]);
-  });
-
-  it('chainable split by sizes', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const res = x.split([1, 2, 1], 1);
-
-    expect(res.length).toEqual(3);
-    expect(res[0].shape).toEqual([2, 1]);
-    expectArraysClose(await res[0].data(), [1, 5]);
-    expect(res[1].shape).toEqual([2, 2]);
-    expectArraysClose(await res[1].data(), [2, 3, 6, 7]);
-    expect(res[2].shape).toEqual([2, 1]);
-    expectArraysClose(await res[2].data(), [4, 8]);
-  });
-  it('should support -1 split', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const res = x.split([1, 1, -1], 1);
-
-    expect(res.length).toEqual(3);
-    expect(res[0].shape).toEqual([2, 1]);
-    expectArraysClose(await res[0].data(), [1, 5]);
-    expect(res[1].shape).toEqual([2, 1]);
-    expectArraysClose(await res[1].data(), [2, 6]);
-    expect(res[2].shape).toEqual([2, 2]);
-    expectArraysClose(await res[2].data(), [3, 4, 7, 8]);
-  });
-
-  it('multiple negative number throws error', () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const f = () => tf.split(x, [1, -1, -1], 1);
-    expect(f).toThrowError();
-  });
-  it('sizes to not sum to axis size throws error', () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const f = () => tf.split(x, [1, 2], 1);
-    expect(f).toThrowError();
-  });
-
-  it('number of splits does not evenly divide axis', () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const f = () => tf.split(x, 3, 1);
-    expect(f).toThrowError();
-  });
-
-  it('can split, axis=-2', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-    const numSplits = 2;
-    const axis = -2;
-    const res = tf.split(a, numSplits, axis);
-    expect(res.length).toBe(2);
-    expect(res[0].shape).toEqual([2, 1, 2]);
-    expect(res[1].shape).toEqual([2, 1, 2]);
-    expectArraysClose(await res[0].data(), [1, 2, 5, 6]);
-    expectArraysClose(await res[1].data(), [3, 4, 7, 8]);
-  });
-
-  it('can split a zero-sized tensor, axis=0', async () => {
-    const a = tf.zeros([4, 0]);
-    const numSplits = 4;
-    const axis = 0;
-    const res = tf.split(a, numSplits, axis);
-    expect(res.length).toBe(4);
-    expect(res[0].shape).toEqual([1, 0]);
-    expect(res[1].shape).toEqual([1, 0]);
-    expect(res[2].shape).toEqual([1, 0]);
-    expect(res[3].shape).toEqual([1, 0]);
-    expectArraysClose(await res[0].data(), []);
-    expectArraysClose(await res[1].data(), []);
-    expectArraysClose(await res[2].data(), []);
-    expectArraysClose(await res[3].data(), []);
-  });
-
-  it('can split a zero-sized tensor, axis=1', async () => {
-    const a = tf.zeros([0, 4]);
-    const numSplits = 4;
-    const axis = 1;
-    const res = tf.split(a, numSplits, axis);
-    expect(res.length).toBe(4);
-    expect(res[0].shape).toEqual([0, 1]);
-    expect(res[1].shape).toEqual([0, 1]);
-    expect(res[2].shape).toEqual([0, 1]);
-    expect(res[3].shape).toEqual([0, 1]);
-    expectArraysClose(await res[0].data(), []);
-    expectArraysClose(await res[1].data(), []);
-    expectArraysClose(await res[2].data(), []);
-    expectArraysClose(await res[3].data(), []);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.split({} as tf.Tensor, 1))
-        .toThrowError(/Argument 'x' passed to 'split' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[1, 2, 3, 4], [5, 6, 7, 8]];
-    const res = tf.split(x, 2, 1);
-    expect(res.length).toEqual(2);
-    expect(res[0].shape).toEqual([2, 2]);
-    expectArraysClose(await res[0].data(), [1, 2, 5, 6]);
-    expect(res[1].shape).toEqual([2, 2]);
-    expectArraysClose(await res[1].data(), [3, 4, 7, 8]);
-  });
-
-  it('gradient of 1st output', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const da = tf.grad(x => tf.split(x, [1, 2])[0])(a);
-
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [1, 0, 0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const da = tf.grad(x => tf.split(x.clone(), [1, 2])[0].clone())(a);
-
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [1, 0, 0]);
-  });
-
-  it('gradient of 2nd output', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const da = tf.grad(x => tf.split(x, [1, 2])[1])(a);
-
-    expect(da.shape).toEqual([3]);
-    expectArraysClose(await da.data(), [0, 1, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/split_util.ts b/tfjs-master/tfjs-core/src/ops/split_util.ts
deleted file mode 100644
index 87cb5f2be..000000000
--- a/tfjs-master/tfjs-core/src/ops/split_util.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import { TensorInfo } from '../tensor_info';
-import {Tensor} from '../tensor';
-import {assert} from '../util';
-
-/**
- * Prepare the split size array. When the input is a number, the axis is evenly
- * divided among the split size. When the input contains the negative value, the
- * rest of the axis is allocated toward that.
- */
-export function prepareSplitSize(
-    x: Tensor|TensorInfo, numOrSizeSplits: number[]|number,
-    axis = 0): number[] {
-  let splitSizes = [];
-  if (typeof (numOrSizeSplits) === 'number') {
-    assert(
-        x.shape[axis] % numOrSizeSplits === 0,
-        () => 'Number of splits must evenly divide the axis.');
-    splitSizes =
-        new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
-  } else {
-    const numOfNegs = numOrSizeSplits.reduce((count, value) => {
-      if (value === -1) {
-        count += 1;
-      }
-      return count;
-    }, 0);
-    assert(
-        numOfNegs <= 1,
-        () => 'There should be only one negative value in split array.');
-    const negIndex = numOrSizeSplits.indexOf(-1);
-    // Allow the number of split array to be -1, which indicates the rest
-    // of dimension is allocated to that split.
-    if (negIndex !== -1) {
-      const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);
-      numOrSizeSplits[negIndex] = x.shape[axis] - total;
-    }
-    assert(
-        x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b),
-        () => 'The sum of sizes must match the size of the axis dimension.');
-    splitSizes = numOrSizeSplits;
-  }
-
-  return splitSizes;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/sqrt.ts b/tfjs-master/tfjs-core/src/ops/sqrt.ts
deleted file mode 100644
index f37c6925e..000000000
--- a/tfjs-master/tfjs-core/src/ops/sqrt.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Sqrt, SqrtInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 4, -1]);
- *
- * x.sqrt().print();  // or tf.sqrt(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function sqrt_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'sqrt', 'float32');
-
-  const inputs: SqrtInputs = {x: $x};
-
-  return ENGINE.runKernel(Sqrt, inputs as unknown as NamedTensorMap);
-}
-export const sqrt = /* @__PURE__ */ op({sqrt_});
diff --git a/tfjs-master/tfjs-core/src/ops/sqrt_test.ts b/tfjs-master/tfjs-core/src/ops/sqrt_test.ts
deleted file mode 100644
index e890eeaa9..000000000
--- a/tfjs-master/tfjs-core/src/ops/sqrt_test.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('sqrt', ALL_ENVS, () => {
-  it('sqrt', async () => {
-    const a = tf.tensor1d([2, 4]);
-    const r = tf.sqrt(a);
-    expectArraysClose(await r.data(), [Math.sqrt(2), Math.sqrt(4)]);
-  });
-
-  it('sqrt propagates NaNs', async () => {
-    const a = tf.tensor1d([1, NaN]);
-    const r = tf.sqrt(a);
-    expectArraysClose(await r.data(), [Math.sqrt(1), NaN]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(4);
-    const dy = tf.scalar(8);
-
-    const da = tf.grad(a => tf.sqrt(a))(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [8 / (2 * Math.sqrt(4))]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(4);
-    const dy = tf.scalar(8);
-
-    const da = tf.grad(a => tf.sqrt(a.clone()).clone())(a, dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [8 / (2 * Math.sqrt(4))]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, 3, 5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.sqrt(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [
-          1 / (2 * Math.sqrt(1)), 2 / (2 * Math.sqrt(2)),
-          3 / (2 * Math.sqrt(3)), 4 / (2 * Math.sqrt(5))
-        ],
-        1e-1);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.sqrt(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [
-          1 / (2 * Math.sqrt(3)), 2 / (2 * Math.sqrt(1)),
-          3 / (2 * Math.sqrt(2)), 4 / (2 * Math.sqrt(3))
-        ],
-        1e-1);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.sqrt({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'sqrt' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.sqrt([2, 4]);
-    expectArraysClose(await r.data(), [Math.sqrt(2), Math.sqrt(4)]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.sqrt('q'))
-        .toThrowError(/Argument 'x' passed to 'sqrt' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.sqrt(tf.tensor1d([1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'sqrt' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/square.ts b/tfjs-master/tfjs-core/src/ops/square.ts
deleted file mode 100644
index 8b275e1dc..000000000
--- a/tfjs-master/tfjs-core/src/ops/square.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {op} from './operation';
-
-/**
- * Computes square of `x` element-wise: `x ^ 2`
- *
- * ```js
- * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);
- *
- * x.square().print();  // or tf.square(x)
- * ```
- * @param x The input Tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function square_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'square');
-  const attrs = {};
-  return ENGINE.runKernel('Square', {x: $x}, attrs);
-}
-
-export const square = /* @__PURE__ */ op({square_});
diff --git a/tfjs-master/tfjs-core/src/ops/square_test.ts b/tfjs-master/tfjs-core/src/ops/square_test.ts
deleted file mode 100644
index 9c5b16c35..000000000
--- a/tfjs-master/tfjs-core/src/ops/square_test.ts
+++ /dev/null
@@ -1,157 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectNumbersClose} from '../test_util';
-import { backend } from '../index';
-
-describeWithFlags('square', ALL_ENVS, () => {
-  it('1D array', async () => {
-    const a = tf.tensor1d([2, 4, Math.sqrt(2)]);
-    const r = tf.square(a);
-    expectArraysClose(await r.data(), [4, 16, 2]);
-  });
-
-  it('2D array', async () => {
-    const a = tf.tensor2d([1, 2, Math.sqrt(2), Math.sqrt(3)], [2, 2]);
-    const r = tf.square(a);
-    expect(r.shape).toEqual([2, 2]);
-    expectArraysClose(await r.data(), [1, 4, 2, 3]);
-  });
-
-  it('5D array', async () => {
-    const a = tf.tensor5d([1, 2, Math.sqrt(2), Math.sqrt(3)], [1, 1, 2, 2, 1]);
-    const r = tf.square(a);
-    expect(r.shape).toEqual([1, 1, 2, 2, 1]);
-    expectArraysClose(await r.data(), [1, 4, 2, 3]);
-  });
-
-  it('6D array', async () => {
-    const a = tf.tensor6d(
-        [1, 2, Math.sqrt(2), Math.sqrt(3), 3, 4, Math.sqrt(7), Math.sqrt(13)],
-        [1, 1, 2, 2, 2, 1]);
-    const r = tf.square(a);
-    expect(r.shape).toEqual(a.shape);
-    expectArraysClose(await r.data(), [1, 4, 2, 3, 9, 16, 7, 13]);
-  });
-
-  it('square propagates NaNs', async () => {
-    const a = tf.tensor1d([1.5, NaN]);
-    const r = tf.square(a);
-    expectArraysClose(await r.data(), [2.25, NaN]);
-  });
-
-  it('int32', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const a = tf.tensor1d([2, 4, 40000], 'int32');
-      const r = tf.square(a);
-      expect(r.dtype).toEqual('int32');
-      const data = await r.data();
-      expectNumbersClose(data[0], 4);
-      expectNumbersClose(data[1], 16);
-      // Epsilon must be larger here for webgl1
-      // TODO: Use expectArraysClose when it supports epsilons scaled by the
-      // numbers being compared.
-      expectNumbersClose(data[2], 1_600_000_000, 1_000 /* epsilon */);
-    }
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.square(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [2 * 5 * 8]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.square(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [2 * 5 * 8]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([-1, 2, 3, -5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.square(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [-2, 4 * 2, 6 * 3, -10 * 4]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.square(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [-6 * 1, 2 * 2, 4 * 3, 6 * 4]);
-  });
-
-  it('gradients: Tensor5D', async () => {
-    const a = tf.tensor5d([-3, 1, 2, 3], [1, 1, 1, 2, 2]);
-    const dy = tf.tensor5d([1, 2, 3, 4], [1, 1, 1, 2, 2]);
-
-    const gradients = tf.grad(a => tf.square(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [-6 * 1, 2 * 2, 4 * 3, 6 * 4]);
-  });
-
-  it('gradients: Tensor6D', async () => {
-    const a = tf.tensor6d([-3, 1, 2, 3, -4, 5, 12, 3], [1, 1, 1, 2, 2, 2]);
-    const dy = tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 1, 2, 2, 2]);
-
-    const gradients = tf.grad(a => tf.square(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(),
-        [-6 * 1, 2 * 2, 4 * 3, 6 * 4, -8 * 5, 10 * 6, 24 * 7, 6 * 8]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.square({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'square' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const r = tf.square([2, 4, Math.sqrt(2)]);
-    expectArraysClose(await r.data(), [4, 16, 2]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.square('q'))
-        .toThrowError(/Argument 'x' passed to 'square' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/squared_difference.ts b/tfjs-master/tfjs-core/src/ops/squared_difference.ts
deleted file mode 100644
index 1c145801e..000000000
--- a/tfjs-master/tfjs-core/src/ops/squared_difference.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {SquaredDifference, SquaredDifferenceInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns (a - b) * (a - b) element-wise.
- * Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([1, 4, 3, 16]);
- * const b = tf.tensor1d([1, 2, 9, 4]);
- *
- * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
- * ```
- *
- * ```js
- * // Broadcast squared difference  a with b.
- * const a = tf.tensor1d([2, 4, 6, 8]);
- * const b = tf.scalar(5);
- *
- * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
- * ```
- *
- * @param a The first tensor.
- * @param b The second tensor. Must have the same type as `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function squaredDifference_<T extends Tensor>(
-    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'squaredDifference');
-  let $b = convertToTensor(b, 'b', 'squaredDifference');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const inputs: SquaredDifferenceInputs = {a: $a, b: $b};
-  const attrs = {};
-
-  return ENGINE.runKernel(
-      SquaredDifference, inputs as unknown as NamedTensorMap, attrs);
-}
-
-export const squaredDifference = /* @__PURE__ */ op({squaredDifference_});
diff --git a/tfjs-master/tfjs-core/src/ops/squeeze.ts b/tfjs-master/tfjs-core/src/ops/squeeze.ts
deleted file mode 100644
index 009a5d91c..000000000
--- a/tfjs-master/tfjs-core/src/ops/squeeze.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {squeezeShape} from '../util';
-
-import {op} from './operation';
-import {reshape} from './reshape';
-
-/**
- * Removes dimensions of size 1 from the shape of a `tf.Tensor`.
- *
- * ```js
- * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);
- * x.squeeze().print();
- * ```
- *
- * @param x The input tensor to be squeezed.
- * @param axis An optional list of numbers. If specified, only
- *     squeezes the dimensions listed. The dimension index starts at 0. It
- * is an error to squeeze a dimension that is not 1.
- *
- * @doc {heading: 'Tensors', subheading: 'Transformations'}
- */
-function squeeze_<T extends Tensor>(x: Tensor|TensorLike, axis?: number[]): T {
-  const $x = convertToTensor(x, 'x', 'squeeze', 'string_or_numeric');
-  return reshape($x, squeezeShape($x.shape, axis).newShape) as T;
-}
-
-export const squeeze = /* @__PURE__ */ op({squeeze_});
diff --git a/tfjs-master/tfjs-core/src/ops/squeeze_test.ts b/tfjs-master/tfjs-core/src/ops/squeeze_test.ts
deleted file mode 100644
index b7f3f1d20..000000000
--- a/tfjs-master/tfjs-core/src/ops/squeeze_test.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysEqual} from '../test_util';
-
-async function expectTensor(
-    tensor: tf.Tensor, shape: number[], value: string|number) {
-  const length = shape.length === 0 ? 1 : shape.reduce((a, b) => a * b);
-  expect(tensor.shape).toEqual(shape);
-  expectArraysEqual(await tensor.data(), new Array(length).fill(value));
-}
-
-describeWithFlags('squeeze', ALL_ENVS, () => {
-  it('default', async () => {
-    const assertType = async (dtype: 'string'|'float32') => {
-      const value = dtype === 'string' ? 'test' : 0.0;
-      // Nothing to squeeze.
-      await expectTensor(tf.squeeze(tf.fill([2], value)), [2], value);
-
-      // Squeeze the middle element away.
-      await expectTensor(tf.squeeze(tf.fill([2, 1, 2], value)), [2, 2], value);
-
-      // Squeeze on both ends.
-      await expectTensor(
-          tf.squeeze(tf.fill([1, 2, 1, 3, 1], value)), [2, 3], value);
-    };
-
-    await assertType('string');
-    await assertType('float32');
-  });
-
-  it('specific dimension', async () => {
-    const assertType = async (dtype: 'string'|'float32') => {
-      const value = dtype === 'string' ? 'test' : 0.0;
-      const shape = [1, 2, 1, 3, 1];
-      // Positive squeeze dim index.
-      await expectTensor(
-          tf.squeeze(tf.fill(shape, value), [0]), [2, 1, 3, 1], value);
-      await expectTensor(
-          tf.squeeze(tf.fill(shape, value), [2, 4]), [1, 2, 3], value);
-      await expectTensor(
-          tf.squeeze(tf.fill(shape, value), [0, 4, 2]), [2, 3], value);
-
-      // Negative squeeze dim index.
-      await expectTensor(
-          tf.squeeze(tf.fill(shape, value), [-1]), [1, 2, 1, 3], value);
-      await expectTensor(
-          tf.squeeze(tf.fill(shape, value), [-3, -5]), [2, 3, 1], value);
-      await expectTensor(
-          tf.squeeze(tf.fill(shape, value), [-3, -5, -1]), [2, 3], value);
-    };
-
-    await assertType('string');
-    await assertType('float32');
-  });
-
-  it('all ones', async () => {
-    const assertType = async (dtype: 'string'|'float32') => {
-      const value = dtype === 'string' ? 'test' : 0.0;
-      await expectTensor(tf.squeeze(tf.fill([1, 1, 1], value)), [], value);
-    };
-
-    await assertType('string');
-    await assertType('float32');
-  });
-
-  it('squeeze only ones', async () => {
-    const assertType = async (dtype: 'string'|'float32') => {
-      const value = dtype === 'string' ? 'test' : 0.0;
-      const shape = [1, 1, 3];
-      await expectTensor(tf.squeeze(tf.fill(shape, value)), [3], value);
-      await expectTensor(tf.squeeze(tf.fill(shape, value), [0]), [1, 3], value);
-      await expectTensor(tf.squeeze(tf.fill(shape, value), [1]), [1, 3], value);
-      expect(() => tf.squeeze(tf.fill(shape, value), [2])).toThrowError();
-    };
-
-    await assertType('string');
-    await assertType('float32');
-  });
-
-  it('squeeze errors', async () => {
-    const assertType = async (dtype: 'string'|'float32') => {
-      const value = dtype === 'string' ? 'test' : 0.0;
-      const shape = [1, 2, 1];
-      expect(() => tf.squeeze(tf.fill(shape, value), [-4])).toThrowError();
-      expect(() => tf.squeeze(tf.fill(shape, value), [0, -4])).toThrowError();
-      expect(() => tf.squeeze(tf.fill(shape, value), [3])).toThrowError();
-      expect(() => tf.squeeze(tf.fill(shape, value), [2, 3])).toThrowError();
-    };
-
-    await assertType('string');
-    await assertType('float32');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/stack.ts b/tfjs-master/tfjs-core/src/ops/stack.ts
deleted file mode 100644
index 9cc1a44b3..000000000
--- a/tfjs-master/tfjs-core/src/ops/stack.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Pack, PackAttrs, PackInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensorArray} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.
- *
- * ```js
- * const a = tf.tensor1d([1, 2]);
- * const b = tf.tensor1d([3, 4]);
- * const c = tf.tensor1d([5, 6]);
- * tf.stack([a, b, c]).print();
- * ```
- *
- * @param tensors A list of tensor objects with the same shape and dtype.
- * @param axis The axis to stack along. Defaults to 0 (the first dim).
- *
- * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
- */
-function stack_<T extends Tensor>(
-    tensors: Array<T|TensorLike>, axis = 0): Tensor {
-  const $tensors =
-      convertToTensorArray(tensors, 'tensors', 'stack', 'string_or_numeric');
-
-  util.assert(
-      $tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');
-
-  if ($tensors.length > 0) {
-    util.assert(
-        axis <= $tensors[0].rank, () => 'Axis must be <= rank of the tensor');
-  }
-
-  const inputs: PackInputs = $tensors;
-  const attrs: PackAttrs = {axis};
-
-  return ENGINE.runKernel(
-      Pack, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const stack = /* @__PURE__ */ op({stack_});
diff --git a/tfjs-master/tfjs-core/src/ops/stack_test.ts b/tfjs-master/tfjs-core/src/ops/stack_test.ts
deleted file mode 100644
index ac85c0fd4..000000000
--- a/tfjs-master/tfjs-core/src/ops/stack_test.ts
+++ /dev/null
@@ -1,132 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('stack', ALL_ENVS, () => {
-  it('scalars 3, 5 and 7', async () => {
-    const a = tf.scalar(3);
-    const b = tf.scalar(5);
-    const c = tf.scalar(7);
-    const res = tf.stack([a, b, c]);
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [3, 5, 7]);
-  });
-
-  it('scalars 3, 5 and 7 along axis=1 throws error', () => {
-    const a = tf.scalar(3);
-    const b = tf.scalar(5);
-    const c = tf.scalar(7);
-    const f = () => tf.stack([a, b, c], 1);
-    expect(f).toThrowError();
-  });
-
-  it('non matching shapes throws error', () => {
-    const a = tf.scalar(3);
-    const b = tf.tensor1d([5]);
-    const f = () => tf.stack([a, b]);
-    expect(f).toThrowError();
-  });
-
-  it('non matching dtypes throws error', () => {
-    const a = tf.scalar(3);
-    const b = tf.scalar(5, 'bool');
-    const f = () => tf.stack([a, b]);
-    expect(f).toThrowError();
-  });
-
-  it('2d but axis=3 throws error', () => {
-    const a = tf.zeros([2, 2]);
-    const b = tf.zeros([2, 2]);
-    const f = () => tf.stack([a, b], 3 /* axis */);
-    expect(f).toThrowError();
-  });
-
-  it('[1,2], [3,4] and [5,6], axis=0', async () => {
-    const a = tf.tensor1d([1, 2]);
-    const b = tf.tensor1d([3, 4]);
-    const c = tf.tensor1d([5, 6]);
-    const res = tf.stack([a, b, c], 0 /* axis */);
-    expect(res.shape).toEqual([3, 2]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('[1,2], [3,4] and [5,6], axis=1', async () => {
-    const a = tf.tensor1d([1, 2]);
-    const b = tf.tensor1d([3, 4]);
-    const c = tf.tensor1d([5, 6]);
-    const res = tf.stack([a, b, c], 1 /* axis */);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [1, 3, 5, 2, 4, 6]);
-  });
-
-  it('[[1,2],[3,4]] and [[5, 6], [7, 8]], axis=0', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 4]]);
-    const b = tf.tensor2d([[5, 6], [7, 8]]);
-    const res = tf.stack([a, b], 0 /* axis */);
-    expect(res.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('[[1,2],[3,4]] and [[5, 6], [7, 8]], axis=2', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 4]]);
-    const b = tf.tensor2d([[5, 6], [7, 8]]);
-    const c = tf.tensor2d([[9, 10], [11, 12]]);
-    const res = tf.stack([a, b, c], 2 /* axis */);
-    expect(res.shape).toEqual([2, 2, 3]);
-    expectArraysClose(
-        await res.data(), [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]);
-  });
-
-  it('single tensor', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 4]]);
-    const res = tf.stack([a], 2 /* axis */);
-    expect(res.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.stack([{} as tf.Tensor]))
-        .toThrowError(
-            /Argument 'tensors\[0\]' passed to 'stack' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = [[1, 2], [3, 4]];
-    const res = tf.stack([a], 2 /* axis */);
-    expect(res.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4]);
-  });
-
-  it('accepts string.', async () => {
-    const a = tf.scalar('three', 'string');
-    const b = tf.scalar('five', 'string');
-    const c = tf.scalar('seven', 'string');
-    const res = tf.stack([a, b, c]);
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), ['three', 'five', 'seven']);
-  });
-
-  it('chain api', async () => {
-    const a = tf.tensor([1, 2]);
-    const res = a.stack(tf.tensor([3, 4]));
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/step.ts b/tfjs-master/tfjs-core/src/ops/step.ts
deleted file mode 100644
index 2b30da708..000000000
--- a/tfjs-master/tfjs-core/src/ops/step.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Step, StepAttrs, StepInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha`
- *
- * ```js
- * const x = tf.tensor1d([0, 2, -1, -3]);
- *
- * x.step(.5).print();  // or tf.step(x, .5)
- * ```
- * @param x The input tensor.
- * @param alpha The gradient when input is negative. Defaults to 0.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function step_<T extends Tensor>(x: T|TensorLike, alpha = 0.0): T {
-  const $x = convertToTensor(x, 'x', 'step');
-
-  const inputs: StepInputs = {x: $x};
-  const attrs: StepAttrs = {alpha};
-
-  return ENGINE.runKernel(
-      Step, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-export const step = /* @__PURE__ */ op({step_});
diff --git a/tfjs-master/tfjs-core/src/ops/step_test.ts b/tfjs-master/tfjs-core/src/ops/step_test.ts
deleted file mode 100644
index 09cef370f..000000000
--- a/tfjs-master/tfjs-core/src/ops/step_test.ts
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('step kernel', ALL_ENVS, () => {
-  it('with 1d tensor', async () => {
-    const a = tf.tensor1d([1, -2, -.01, 3, -0.1]);
-    const result = tf.step(a);
-    expectArraysClose(await result.data(), [1, 0, 0, 1, 0]);
-  });
-
-  it('with 1d tensor and alpha', async () => {
-    const a = tf.tensor1d([1, -2, -.01, 3, NaN]);
-    const result = tf.step(a, 0.1);
-    expectArraysClose(await result.data(), [1, 0.1, 0.1, 1, NaN]);
-  });
-
-  it('with 2d tensor', async () => {
-    const a = tf.tensor2d([1, -5, -3, 4], [2, 2]);
-    const result = tf.step(a);
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), [1, 0, 0, 1]);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([1, -2, -.01, 3, NaN]);
-    const result = tf.step(a);
-    expectArraysClose(await result.data(), [1, 0, 0, 1, NaN]);
-  });
-
-  it('with int32 tensor', async () => {
-    const a = tf.tensor1d([1, -2, 12345678, -12345678], 'int32');
-    const result = tf.step(a);
-    expect(result.dtype).toEqual('int32');
-    expectArraysClose(await result.data(), [1, 0, 1, 0]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(-4);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.step(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(-4);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.step(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const a = tf.tensor1d([1, 2, -3, 5]);
-    const dy = tf.tensor1d([1, 2, 3, 4]);
-
-    const gradients = tf.grad(a => tf.step(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const a = tf.tensor2d([3, -1, -2, 3], [2, 2]);
-    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-
-    const gradients = tf.grad(a => tf.step(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.step({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'step' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.step([1, -2, -.01, 3, -0.1]);
-    expectArraysClose(await result.data(), [1, 0, 0, 1, 0]);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.step('q'))
-        .toThrowError(/Argument 'x' passed to 'step' must be numeric/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/strided_slice.ts b/tfjs-master/tfjs-core/src/ops/strided_slice.ts
deleted file mode 100644
index 22165786e..000000000
--- a/tfjs-master/tfjs-core/src/ops/strided_slice.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {StridedSlice, StridedSliceAttrs, StridedSliceInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Extracts a strided slice of a tensor.
- *
- * Roughly speaking, this op extracts a slice of size (end-begin)/stride from
- * the given input tensor (x). Starting at the location specified by begin the
- * slice continues by adding stride to the index until all dimensions are not
- * less than end. Note that a stride can be negative, which causes a reverse
- * slice.
- *
- * ```js
- * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
- *    [3, 2, 3]);
- * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]
- * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],
- *                                                     // [4, 4, 4]]]
- * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],
- *                                                     // [3, 3, 3]]]
- * ```
- *
- * @param x The tensor to stride slice.
- * @param begin The coordinates to start the slice from.
- * @param end: The coordinates to end the slice at.
- * @param strides: The size of the slice.
- * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored
- *      and the fullest possible range in that dimension is used instead.
- * @param endMask: If the ith bit of endMask is set, end[i] is ignored
- *      and the fullest possible range in that dimension is used instead.
- * @param shrinkAxisMask: a bitmask where bit i implies that
- * the ith specification should shrink the dimensionality. begin and end must
- * imply a slice of size 1 in the dimension.
- *
- * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
- */
-function stridedSlice_(
-    x: Tensor|TensorLike, begin: number[], end: number[], strides?: number[],
-    beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,
-    shrinkAxisMask = 0): Tensor {
-  const $x = convertToTensor(x, 'x', 'stridedSlice', 'string_or_numeric');
-
-  const inputs: StridedSliceInputs = {x: $x};
-  const attrs: StridedSliceAttrs = {
-    begin,
-    end,
-    strides,
-    beginMask,
-    endMask,
-    ellipsisMask,
-    newAxisMask,
-    shrinkAxisMask
-  };
-
-  return ENGINE.runKernel(
-      StridedSlice, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const stridedSlice = /* @__PURE__ */ op({stridedSlice_});
diff --git a/tfjs-master/tfjs-core/src/ops/strided_slice_test.ts b/tfjs-master/tfjs-core/src/ops/strided_slice_test.ts
deleted file mode 100644
index 3c40aa628..000000000
--- a/tfjs-master/tfjs-core/src/ops/strided_slice_test.ts
+++ /dev/null
@@ -1,491 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {backend} from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('stridedSlice', ALL_ENVS, () => {
-  it('with ellipsisMask=1', async () => {
-    const t = tf.tensor2d([
-      [1, 2, 3, 4, 5],
-      [2, 3, 4, 5, 6],
-      [3, 4, 5, 6, 7],
-      [4, 5, 6, 7, 8],
-      [5, 6, 7, 8, 9],
-      [6, 7, 8, 9, 10],
-      [7, 8, 9, 10, 11],
-      [8, 8, 9, 10, 11],
-      [9, 8, 9, 10, 11],
-      [10, 8, 9, 10, 11],
-    ]);
-    const begin = [0, 4];
-    const end = [0, 5];
-    const strides = [1, 1];
-    const beginMask = 0;
-    const endMask = 0;
-    const ellipsisMask = 1;
-    const output =
-        t.stridedSlice(begin, end, strides, beginMask, endMask, ellipsisMask);
-    expect(output.shape).toEqual([10, 1]);
-    expectArraysClose(await output.data(), [5, 6, 7, 8, 9, 10, 11, 11, 11, 11]);
-  });
-
-  it('with ellipsisMask=1, begin / end masks and start / end normalization',
-     async () => {
-       const t = tf.randomNormal([1, 6, 2006, 4]);
-       const output =
-           tf.stridedSlice(t, [0, 0, 0], [0, 2004, 0], [1, 1, 1], 6, 4, 1);
-       expect(output.shape).toEqual([1, 6, 2004, 4]);
-     });
-
-  it('with ellipsisMask=1 and start / end normalization', async () => {
-    const t = tf.tensor3d([
-      [[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]
-    ]);
-    const begin = [1, 0];
-    const end = [2, 1];
-    const strides = [1, 1];
-    const beginMask = 0;
-    const endMask = 0;
-    const ellipsisMask = 1;
-
-    const output = tf.stridedSlice(
-        t, begin, end, strides, beginMask, endMask, ellipsisMask);
-    expect(output.shape).toEqual([3, 2, 1]);
-    expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('with ellipsisMask=2', async () => {
-    const t = tf.tensor3d([
-      [[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]
-    ]);
-    const begin = [1, 0, 0];
-    const end = [2, 1, 3];
-    const strides = [1, 1, 1];
-    const beginMask = 0;
-    const endMask = 0;
-    const ellipsisMask = 2;
-    const output = tf.stridedSlice(
-        t, begin, end, strides, beginMask, endMask, ellipsisMask);
-    expect(output.shape).toEqual([1, 2, 3]);
-    expectArraysClose(await output.data(), [3, 3, 3, 4, 4, 4]);
-  });
-
-  it('with ellipsisMask=2 and start / end normalization', async () => {
-    const t = tf.tensor4d([
-      [[[1, 1], [1, 1], [1, 1]], [[2, 2], [2, 2], [2, 2]]],
-
-      [[[3, 3], [3, 3], [3, 3]], [[4, 4], [4, 4], [4, 4]]],
-
-      [[[5, 5], [5, 5], [5, 5]], [[6, 6], [6, 6], [6, 6]]]
-    ]);
-
-    const begin = [1, 0, 0];
-    const end = [2, 1, 1];
-    const strides = [1, 1, 1];
-    const beginMask = 0;
-    const endMask = 0;
-    const ellipsisMask = 2;
-    const output = tf.stridedSlice(
-        t, begin, end, strides, beginMask, endMask, ellipsisMask);
-    expect(output.shape).toEqual([1, 2, 3, 1]);
-    expectArraysClose(await output.data(), [3, 3, 3, 4, 4, 4]);
-  });
-
-  it('both ellipsis mask and newAxisMask are set', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const result = tf.stridedSlice(tensor, [0], [3], [2], 0, 0, 1, 1);
-    expectArraysClose(await result.data(), [0, 1, 2, 3]);
-  });
-
-  it('both ellipsis mask and shrinkAxisMask are set', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const result = tf.stridedSlice(tensor, [0], [3], [2], 0, 0, 1, 0, 1);
-    expectArraysClose(await result.data(), [0, 1, 2, 3]);
-  });
-
-  it('stridedSlice with first axis being new', async () => {
-    // Python slice code: t[tf.newaxis,0:3]
-    const t = tf.tensor1d([0, 1, 2, 3]);
-    const begin = [0, 0];
-    const end = [1, 3];
-    const strides = [1, 2];
-    const beginMask = 0;
-    const endMask = 0;
-    const ellipsisMask = 0;
-    const newAxisMask = 1;
-
-    const output = tf.stridedSlice(
-        t, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask);
-    expect(output.shape).toEqual([1, 2]);
-    expectArraysClose(await output.data(), [0, 2]);
-  });
-
-  it('strided slice with several new axes', async () => {
-    // Python slice code: t[1:2,tf.newaxis,0:3,tf.newaxis,2:5]
-    const t = tf.zeros([2, 3, 4, 5]);
-    const begin = [1, 0, 0, 0, 2];
-    const end = [2, 1, 3, 1, 5];
-    const strides: number[] = [1, 1, 1, 1, 1];
-    const beginMask = 0;
-    const endMask = 0;
-    const ellipsisMask = 0;
-    const newAxisMask = 0b1010;
-    const output = tf.stridedSlice(
-        t, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask);
-    expect(output.shape).toEqual([1, 1, 3, 1, 2, 5]);
-    expectArraysClose(await output.data(), new Array(30).fill(0));
-  });
-
-  it('strided slice with new axes and shrink axes', () => {
-    // Python slice code: t[1:2,tf.newaxis,1,tf.newaxis,2,2:5]
-    const t = tf.zeros([2, 3, 4, 5]);
-    const begin = [1, 0, 1, 0, 2, 2];
-    const end = [2, 1, 2, 1, 3, 5];
-    const strides: number[] = [1, 1, 1, 1, 1, 1];
-    const beginMask = 0;
-    const endMask = 0;
-    const ellipsisMask = 0;
-    const newAxisMask = 0b1010;
-    const shrinkAxisMask = 0b10100;
-    const output = tf.stridedSlice(
-        t, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask,
-        shrinkAxisMask);
-    expect(output.shape).toEqual([1, 1, 1, 3]);
-  });
-
-  it('stridedSlice should support 1d tensor', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [0], [3], [2]);
-    expect(output.shape).toEqual([2]);
-    expectArraysClose(await output.data(), [0, 2]);
-  });
-
-  it('stridedSlice should support 1d tensor', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [0], [3], [2]);
-    expect(output.shape).toEqual([2]);
-    expectArraysClose(await output.data(), [0, 2]);
-  });
-
-  it('stridedSlice with 1d tensor should be used by tensor directly',
-     async () => {
-       const t = tf.tensor1d([0, 1, 2, 3]);
-       const output = t.stridedSlice([0], [3], [2]);
-       expect(output.shape).toEqual([2]);
-       expectArraysClose(await output.data(), [0, 2]);
-     });
-
-  it('stridedSlice should support 1d tensor empty result', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [10], [3], [2]);
-    expect(output.shape).toEqual([0]);
-    expectArraysClose(await output.data(), []);
-  });
-
-  it('stridedSlice should support 1d tensor negative begin', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [-3], [3], [1]);
-    expect(output.shape).toEqual([2]);
-    expectArraysClose(await output.data(), [1, 2]);
-  });
-
-  it('stridedSlice should support 1d tensor out of range begin', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [-5], [3], [1]);
-    expect(output.shape).toEqual([3]);
-    expectArraysClose(await output.data(), [0, 1, 2]);
-  });
-
-  it('stridedSlice should support 1d tensor negative end', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [1], [-2], [1]);
-    expect(output.shape).toEqual([1]);
-    expectArraysClose(await output.data(), [1]);
-  });
-
-  it('stridedSlice should support 1d tensor out of range end', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [-3], [5], [1]);
-    expect(output.shape).toEqual([3]);
-    expectArraysClose(await output.data(), [1, 2, 3]);
-  });
-
-  it('stridedSlice should support 1d tensor begin mask', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [1], [3], [1], 1);
-    expect(output.shape).toEqual([3]);
-    expectArraysClose(await output.data(), [0, 1, 2]);
-  });
-
-  it('stridedSlice should support 1d tensor nagtive begin and stride',
-     async () => {
-       const tensor = tf.tensor1d([0, 1, 2, 3]);
-       const output = tf.stridedSlice(tensor, [-2], [-3], [-1]);
-       expect(output.shape).toEqual([1]);
-       expectArraysClose(await output.data(), [2]);
-     });
-
-  it('stridedSlice should support 1d tensor' +
-         ' out of range begin and negative stride',
-     async () => {
-       const tensor = tf.tensor1d([0, 1, 2, 3]);
-       const output = tf.stridedSlice(tensor, [5], [-2], [-1]);
-       expect(output.shape).toEqual([1]);
-       expectArraysClose(await output.data(), [3]);
-     });
-
-  it('stridedSlice should support 1d tensor nagtive end and stride',
-     async () => {
-       const tensor = tf.tensor1d([0, 1, 2, 3]);
-       const output = tf.stridedSlice(tensor, [2], [-4], [-1]);
-       expect(output.shape).toEqual([2]);
-       expectArraysClose(await output.data(), [2, 1]);
-     });
-
-  it('stridedSlice should support 1d tensor' +
-         ' out of range end and negative stride',
-     async () => {
-       const tensor = tf.tensor1d([0, 1, 2, 3]);
-       const output = tf.stridedSlice(tensor, [-3], [-5], [-1]);
-       expect(output.shape).toEqual([2]);
-       expectArraysClose(await output.data(), [1, 0]);
-     });
-
-  it('stridedSlice should support 1d tensor end mask', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [1], [3], [1], 0, 1);
-    expect(output.shape).toEqual([3]);
-    expectArraysClose(await output.data(), [1, 2, 3]);
-  });
-
-  it('stridedSlice should support 1d tensor shrink axis mask', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [1], [3], [1], 0, 0, 0, 0, 1);
-    expect(output.shape).toEqual([]);
-    expectArraysClose(await output.data(), [1]);
-  });
-
-  it('stridedSlice should support 1d tensor negative stride', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [-1], [-4], [-1]);
-    expect(output.shape).toEqual([3]);
-    expectArraysClose(await output.data(), [3, 2, 1]);
-  });
-
-  it('stridedSlice should support 1d tensor even length stride', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [0], [2], [2]);
-    expect(output.shape).toEqual([1]);
-    expectArraysClose(await output.data(), [0]);
-  });
-
-  it('stridedSlice should support 1d tensor odd length stride', async () => {
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [0], [3], [2]);
-    expect(output.shape).toEqual([2]);
-    expectArraysClose(await output.data(), [0, 2]);
-  });
-
-  it('stridedSlice should support 2d tensor identity', async () => {
-    const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const output = tf.stridedSlice(tensor, [0, 0], [2, 3], [1, 1]);
-    expect(output.shape).toEqual([2, 3]);
-    expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('stridedSlice should support 2d tensor', async () => {
-    const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1]);
-    expect(output.shape).toEqual([1, 2]);
-    expectArraysClose(await output.data(), [4, 5]);
-  });
-
-  it('stridedSlice should support 2d tensor strides', async () => {
-    const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const output = tf.stridedSlice(tensor, [0, 0], [2, 3], [2, 2]);
-    expect(output.shape).toEqual([1, 2]);
-    expectArraysClose(await output.data(), [1, 3]);
-  });
-
-  it('stridedSlice with 2d tensor should be used by tensor directly',
-     async () => {
-       const t = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-       const output = t.stridedSlice([1, 0], [2, 2], [1, 1]);
-       expect(output.shape).toEqual([1, 2]);
-       expectArraysClose(await output.data(), [4, 5]);
-     });
-
-  it('stridedSlice should support 2d tensor negative strides', async () => {
-    const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const output = tf.stridedSlice(tensor, [1, -1], [2, -4], [2, -1]);
-    expect(output.shape).toEqual([1, 3]);
-    expectArraysClose(await output.data(), [6, 5, 4]);
-  });
-
-  it('stridedSlice should support 2d tensor begin mask', async () => {
-    const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1], 1);
-    expect(output.shape).toEqual([2, 2]);
-    expectArraysClose(await output.data(), [1, 2, 4, 5]);
-  });
-
-  it('stridedSlice should support 2d tensor shrink mask', async () => {
-    const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const output =
-        tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1], 0, 0, 0, 0, 1);
-    expect(output.shape).toEqual([2]);
-    expectArraysClose(await output.data(), [4, 5]);
-  });
-
-  it('stridedSlice should support 2d tensor end mask', async () => {
-    const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1], 0, 2);
-    expect(output.shape).toEqual([1, 3]);
-    expectArraysClose(await output.data(), [4, 5, 6]);
-  });
-
-  it('stridedSlice should support 2d tensor' +
-         ' negative strides and begin mask',
-     async () => {
-       const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-       const output = tf.stridedSlice(tensor, [1, -2], [2, -4], [1, -1], 2);
-       expect(output.shape).toEqual([1, 3]);
-       expectArraysClose(await output.data(), [6, 5, 4]);
-     });
-
-  it('stridedSlice should support 2d tensor' +
-         ' negative strides and end mask',
-     async () => {
-       const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-       const output = tf.stridedSlice(tensor, [1, -2], [2, -3], [1, -1], 0, 2);
-       expect(output.shape).toEqual([1, 2]);
-       expectArraysClose(await output.data(), [5, 4]);
-     });
-
-  it('stridedSlice should support 3d tensor identity', async () => {
-    const tensor =
-        tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const output = tf.stridedSlice(tensor, [0, 0, 0], [2, 3, 2], [1, 1, 1]);
-    expect(output.shape).toEqual([2, 3, 2]);
-    expectArraysClose(
-        await output.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
-  });
-
-  it('stridedSlice should support 3d tensor negative stride', async () => {
-    const tensor =
-        tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const output =
-        tf.stridedSlice(tensor, [-1, -1, -1], [-3, -4, -3], [-1, -1, -1]);
-    expect(output.shape).toEqual([2, 3, 2]);
-    expectArraysClose(
-        await output.data(), [12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]);
-  });
-
-  it('stridedSlice should support 3d tensor strided 2', async () => {
-    const tensor =
-        tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const output = tf.stridedSlice(tensor, [0, 0, 0], [2, 3, 2], [2, 2, 2]);
-    expect(output.shape).toEqual([1, 2, 1]);
-    expectArraysClose(await output.data(), [1, 5]);
-  });
-
-  it('stridedSlice should support 3d tensor shrink mask', async () => {
-    const tensor =
-        tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
-    const output =
-        tf.stridedSlice(tensor, [0, 0, 0], [2, 3, 2], [1, 1, 1], 0, 0, 0, 0, 1);
-    expect(output.shape).toEqual([3, 2]);
-    expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('stridedSlice should support 3d with smaller length of begin array',
-     async () => {
-       const tensor =
-           tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 1, 2]);
-       const output =
-           tf.stridedSlice(tensor, [1, 0], [2, 3], [1, 1], 0, 0, 0, 0, 0);
-       expect(output.shape).toEqual([1, 3, 1, 2]);
-       expectArraysClose(await output.data(), [7, 8, 9, 10, 11, 12]);
-     });
-
-  it('stridedSlice should support 3d with smaller length of end array',
-     async () => {
-       const tensor =
-           tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 1, 2]);
-       const output =
-           tf.stridedSlice(tensor, [1, 0], [2, 3], [1, 1], 0, 0, 0, 0, 0);
-       expect(output.shape).toEqual([1, 3, 1, 2]);
-       expectArraysClose(await output.data(), [7, 8, 9, 10, 11, 12]);
-     });
-
-  it('stridedSlice should throw when passed a non-tensor', () => {
-    expect(() => tf.stridedSlice({} as tf.Tensor, [0], [0], [1]))
-        .toThrowError(/Argument 'x' passed to 'stridedSlice' must be a Tensor/);
-  });
-
-  it('stridedSlice should handle negative end with ellipsisMask', () => {
-    const a = tf.ones([1, 240, 1, 10]);
-    const output =
-        tf.stridedSlice(a, [0, 0, 0], [0, -1, 0], [1, 1, 1], 3, 1, 4);
-    expect(output.shape).toEqual([1, 239, 1, 10]);
-  });
-
-  it('stridedSlice should handle negative begin with ellipsis_mask', () => {
-    const a = tf.ones([1, 36, 17, 3]);
-    const output = tf.stridedSlice(a, [0, -1], [0, 0], [1, 1], 0, 2, 1, 0, 0);
-    expect(output.shape).toEqual([1, 36, 17, 1]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const tensor = [0, 1, 2, 3];
-    const output = tf.stridedSlice(tensor, [0], [3], [2]);
-    expect(output.shape).toEqual([2]);
-    expectArraysClose(await output.data(), [0, 2]);
-  });
-
-  it('accepts int32 tensor', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const tensor = tf.tensor2d([1, 2, 3, 4, 12345678, 6], [2, 3], 'int32');
-      const output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1]);
-      expect(output.shape).toEqual([1, 2]);
-      expect(output.dtype).toEqual('int32');
-      expectArraysClose(await output.data(), [4, 12345678]);
-    }
-  });
-
-  it('ensure no memory leak', async () => {
-    const numTensorsBefore = tf.memory().numTensors;
-    const numDataIdBefore = tf.engine().backend.numDataIds();
-
-    const tensor = tf.tensor1d([0, 1, 2, 3]);
-    const output = tf.stridedSlice(tensor, [0], [3], [2]);
-    expect(output.shape).toEqual([2]);
-    expectArraysClose(await output.data(), [0, 2]);
-
-    tensor.dispose();
-    output.dispose();
-
-    const numTensorsAfter = tf.memory().numTensors;
-    const numDataIdAfter = tf.engine().backend.numDataIds();
-    expect(numTensorsAfter).toBe(numTensorsBefore);
-    expect(numDataIdAfter).toBe(numDataIdBefore);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/string/static_regex_replace.ts b/tfjs-master/tfjs-core/src/ops/string/static_regex_replace.ts
deleted file mode 100644
index 8656a2429..000000000
--- a/tfjs-master/tfjs-core/src/ops/string/static_regex_replace.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {StaticRegexReplace, StaticRegexReplaceAttrs} from '../../kernel_names';
-import {NamedAttrMap} from '../../kernel_registry';
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {op} from '../operation';
-
-/**
- * Replace the match of a `pattern` in `input` with `rewrite`.
- *
- * ```js
- * const result = tf.string.staticRegexReplace(
- *     ['format       this   spacing      better'], ' +', ' ');
- * result.print(); // ['format this spacing better']
- * ```
- * @param input: A Tensor of type string. The text to be processed.
- * @param pattern: A string. The regular expression to match the input.
- * @param rewrite: A string. The rewrite to be applied to the matched
- *     expression.
- * @param replaceGlobal: An optional bool. Defaults to True. If True, the
- *     replacement is global, otherwise the replacement is done only on the
- *     first match.
- * @return A Tensor of type string.
- *
- * @doc {heading: 'Operations', subheading: 'String'}
- */
-function staticRegexReplace_(
-  input: Tensor | TensorLike, pattern: string, rewrite: string,
-  replaceGlobal=true): Tensor {
-
-  const $input = convertToTensor(input, 'input', 'staticRegexReplace',
-                                 'string');
-  const attrs: StaticRegexReplaceAttrs = {pattern, rewrite, replaceGlobal};
-  return ENGINE.runKernel(StaticRegexReplace, {x: $input},
-                          attrs as unknown as NamedAttrMap);
-}
-
-export const staticRegexReplace = /* @__PURE__ */ op({staticRegexReplace_});
diff --git a/tfjs-master/tfjs-core/src/ops/string/static_regex_replace_test.ts b/tfjs-master/tfjs-core/src/ops/string/static_regex_replace_test.ts
deleted file mode 100644
index a10ef1e45..000000000
--- a/tfjs-master/tfjs-core/src/ops/string/static_regex_replace_test.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import { DataTypeFor } from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-
-describeWithFlags('staticRegexReplace', ALL_ENVS, () => {
-  it('replaces the first instance of a string', async () => {
-    const result = tf.string.staticRegexReplace(
-      ['this', 'is', 'a', 'test test'], 'test', 'result', false);
-
-    expect(await result.data<DataTypeFor<string>>())
-      .toEqual(['this', 'is', 'a', 'result test']);
-  });
-
-  it('replaces a string globally by default', async () => {
-    const result = tf.string.staticRegexReplace(
-      ['this', 'is', 'a', 'test test'], 'test', 'result');
-
-    expect(await result.data<DataTypeFor<string>>())
-      .toEqual(['this', 'is', 'a', 'result result']);
-  });
-
-  it('matches using regex', async () => {
-    const result = tf.string.staticRegexReplace(
-      ['This     will  have normal    whitespace'], ' +', ' ');
-
-    expect(await result.data<DataTypeFor<string>>())
-      .toEqual(['This will have normal whitespace']);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/string/string_n_grams.ts b/tfjs-master/tfjs-core/src/ops/string/string_n_grams.ts
deleted file mode 100644
index b17df03b3..000000000
--- a/tfjs-master/tfjs-core/src/ops/string/string_n_grams.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {StringNGrams, StringNGramsAttrs, StringNGramsInputs} from '../../kernel_names';
-import {Tensor, Tensor1D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {op} from '../operation';
-
-/**
- * Creates ngrams from ragged string data.
- *
- * This op accepts a ragged tensor with 1 ragged dimension containing only
- * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams
- * of that string, joined along the innermost axis.
- *
- * ```js
- * const result = tf.string.stringNGrams(
- *   ['a', 'b', 'c', 'd'], tf.tensor1d([0, 2, 4], 'int32'),
- *   '|', [1, 2], 'LP', 'RP', -1, false);
- * result['nGrams'].print(); // ['a', 'b', 'LP|a', 'a|b', 'b|RP',
- *                           //  'c', 'd', 'LP|c', 'c|d', 'd|RP']
- * result['nGramsSplits'].print(); // [0, 5, 10]
- * ```
- * @param data: The values tensor of the ragged string tensor to make ngrams out
- *     of. Must be a 1D string tensor.
- * @param dataSplits: The splits tensor of the ragged string tensor to make
- *     ngrams out of.
- * @param separator: The string to append between elements of the token. Use ""
- *     for no separator.
- * @param nGramWidths: The sizes of the ngrams to create.
- * @param leftPad: The string to use to pad the left side of the ngram sequence.
- *     Only used if pad_width !== 0.
- * @param rightPad: The string to use to pad the right side of the ngram
- *     sequence. Only used if pad_width !== 0.
- * @param padWidth: The number of padding elements to add to each side of each
- *     sequence. Note that padding will never be greater than `nGramWidths`-1
- *     regardless of this value. If `padWidth`=-1, then add max(`nGramWidths`)-1
- *     elements.
- * @param preserveShortSequences: If true, then ensure that at least one ngram
- *     is generated for each input sequence. In particular, if an input sequence
- *     is shorter than min(ngramWidth) + 2*padWidth, then generate a single
- *     ngram containing the entire sequence. If false, then no ngrams are
- *     generated for these short input sequences.
- * @return A map with the following properties:
- *     - nGrams: The values tensor of the output ngrams ragged tensor.
- *     - nGramsSplits: The splits tensor of the output ngrams ragged tensor.
- *
- * @doc {heading: 'Operations', subheading: 'String'}
- */
-function stringNGrams_(
-    data: Tensor1D|TensorLike, dataSplits: Tensor|TensorLike, separator: string,
-    nGramWidths: number[], leftPad: string, rightPad: string, padWidth: number,
-    preserveShortSequences: boolean): NamedTensorMap {
-  const $data = convertToTensor(data, 'data', 'stringNGrams', 'string');
-  if ($data.dtype !== 'string') {
-    throw new Error('Data must be of datatype string');
-  }
-  if ($data.shape.length !== 1) {
-    throw new Error(`Data must be a vector, saw: ${$data.shape}`);
-  }
-
-  const $dataSplits = convertToTensor(dataSplits, 'dataSplits', 'stringNGrams');
-  if ($dataSplits.dtype !== 'int32') {
-    throw new Error('Data splits must be of datatype int32');
-  }
-
-  const attrs: StringNGramsAttrs = {
-    separator,
-    nGramWidths,
-    leftPad,
-    rightPad,
-    padWidth,
-    preserveShortSequences
-  };
-
-  const inputs: StringNGramsInputs = {data: $data, dataSplits: $dataSplits};
-  const result: Tensor[] =
-      ENGINE.runKernel(StringNGrams, inputs as {}, attrs as {});
-  return {nGrams: result[0], nGramsSplits: result[1]};
-}
-
-export const stringNGrams = /* @__PURE__ */ op({stringNGrams_});
diff --git a/tfjs-master/tfjs-core/src/ops/string/string_n_grams_test.ts b/tfjs-master/tfjs-core/src/ops/string/string_n_grams_test.ts
deleted file mode 100644
index 88c5c3d7e..000000000
--- a/tfjs-master/tfjs-core/src/ops/string/string_n_grams_test.ts
+++ /dev/null
@@ -1,465 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysEqual} from '../../test_util';
-
-async function expectResult(
-    result: tf.NamedTensorMap, nGrams: string[], nGramsSplits: number[]) {
-  expectArraysEqual(await result.nGrams.data(), nGrams);
-  expectArraysEqual(await result.nGramsSplits.data(), nGramsSplits);
-
-  expect(result.nGrams.shape).toEqual([nGrams.length]);
-  expect(result.nGramsSplits.shape).toEqual([nGramsSplits.length]);
-
-  expect(result.nGrams.dtype).toEqual('string');
-  expect(result.nGramsSplits.dtype).toEqual('int32');
-}
-
-describeWithFlags('stringNGrams', ALL_ENVS, () => {
-  it('padded trigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [3], 'LP', 'RP', -1, false);
-    const nGrams = [
-      'LP|LP|a', 'LP|a|b', 'a|b|c', 'b|c|d', 'c|d|RP', 'd|RP|RP',  // 0
-      'LP|LP|e', 'LP|e|f', 'e|f|RP', 'f|RP|RP'                     // 1
-    ];
-    const nGramsSplits = [0, 6, 10];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('padded bigrams and trigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [2, 3], 'LP', 'RP', -1, false);
-    const nGrams = [
-      'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP', 'LP|LP|a', 'LP|a|b', 'a|b|c',
-      'b|c|d', 'c|d|RP', 'd|RP|RP',                                    // 0
-      'LP|e', 'e|f', 'f|RP', 'LP|LP|e', 'LP|e|f', 'e|f|RP', 'f|RP|RP'  // 1
-    ];
-    const nGramsSplits = [0, 11, 18];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('padded bigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [2], 'LP', 'RP', -1, false);
-    const nGrams = [
-      'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP',  // 0
-      'LP|e', 'e|f', 'f|RP'                 // 1
-    ];
-    const nGramsSplits = [0, 5, 8];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('padding is at most nGramSize - 1', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [2], 'LP', 'RP', 4, false);
-    const nGrams = [
-      'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP',  // 0
-      'LP|e', 'e|f', 'f|RP'                 // 1
-    ];
-    const nGramsSplits = [0, 5, 8];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('padded unigram and bigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [1, 2], 'LP', 'RP', -1, false);
-    const nGrams = [
-      'a', 'b', 'c', 'd', 'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP',  // 0
-      'e', 'f', 'LP|e', 'e|f', 'f|RP'                           // 1
-    ];
-    const nGramsSplits = [0, 9, 14];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('overlapping padded nGrams', async () => {
-    // This test validates that n-grams with both left and right padding in a
-    // single ngram token are created correctly.
-
-    // Batch items are:
-    // 0: "a"
-    // 1: "b", "c", "d"
-    // 2: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|',
-        [3], 'LP', 'RP', -1, false);
-    const nGrams = [
-      'LP|LP|a', 'LP|a|RP', 'a|RP|RP',                    // 0
-      'LP|LP|b', 'LP|b|c', 'b|c|d', 'c|d|RP', 'd|RP|RP',  // 1
-      'LP|LP|e', 'LP|e|f', 'e|f|RP', 'f|RP|RP'            // 2
-    ];
-    const nGramsSplits = [0, 3, 8, 12];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('overlapping padded multi char nGrams', async () => {
-    // Batch items are:
-    // 0: "a"
-    // 1: "b", "c", "d"
-    // 2: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['aa', 'bb', 'cc', 'dd', 'ee', 'ff'],
-        tf.tensor1d([0, 1, 4, 6], 'int32'), '|', [3], 'LP', 'RP', -1, false);
-    const nGrams = [
-      'LP|LP|aa', 'LP|aa|RP', 'aa|RP|RP',                          // 0
-      'LP|LP|bb', 'LP|bb|cc', 'bb|cc|dd', 'cc|dd|RP', 'dd|RP|RP',  // 1
-      'LP|LP|ee', 'LP|ee|ff', 'ee|ff|RP', 'ff|RP|RP'               // 2
-    ];
-    const nGramsSplits = [0, 3, 8, 12];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('multi overlapping padded nGrams', async () => {
-    // This test validates that n-grams with more than 1 padding value on each
-    // side are created correctly.
-
-    // Batch items are:
-    // 0: "a"
-    const result = tf.string.stringNGrams(
-        ['a'], tf.tensor1d([0, 1], 'int32'), '|', [5], 'LP', 'RP', -1, false);
-    const nGrams = [
-      'LP|LP|LP|LP|a', 'LP|LP|LP|a|RP', 'LP|LP|a|RP|RP', 'LP|a|RP|RP|RP',
-      'a|RP|RP|RP|RP'
-    ];
-    const nGramsSplits = [0, 5];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('unpadded trigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [3], '', '', 0, false);
-    const nGrams = ['a|b|c', 'b|c|d'];
-    const nGramsSplits = [0, 2, 2];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('unpadded trigrams with empty sequence', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 4, 6], 'int32'), '|',
-        [3], '', '', 0, false);
-    const nGrams = ['a|b|c', 'b|c|d'];
-    const nGramsSplits = [0, 2, 2, 2];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('unpadded trigrams with preserve short', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [3], '', '', 0, true);
-    const nGrams = ['a|b|c', 'b|c|d', 'e|f'];
-    const nGramsSplits = [0, 2, 3];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('unpadded trigrams with preserve short and empty sequence', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 4, 6], 'int32'), '|',
-        [3], '', '', 0, true);
-    const nGrams = ['a|b|c', 'b|c|d', 'e|f'];
-    const nGramsSplits = [0, 2, 2, 3];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('unpadded trigrams and quad grams with preserve short', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [4, 3], '', '', 0, true);
-    const nGrams = ['a|b|c|d', 'a|b|c', 'b|c|d', 'e|f'];
-    const nGramsSplits = [0, 3, 4];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('unpadded bigrams and trigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [2, 3], '', '', 0, false);
-    const nGrams = ['a|b', 'b|c', 'c|d', 'a|b|c', 'b|c|d', 'e|f'];
-    const nGramsSplits = [0, 5, 6];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('unpadded bigrams and trigrams with preserve short', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [2, 3], '', '', 0, true);
-    // Note that in this case, because the bigram 'e|f' was already generated,
-    // the op will not generate a special preserveShort bigram.
-    const nGrams = ['a|b', 'b|c', 'c|d', 'a|b|c', 'b|c|d', 'e|f'];
-    const nGramsSplits = [0, 5, 6];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('unpadded trigrams and bigrams with preserve short', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [3, 2], '', '', 0, true);
-    // Note that in this case, because the bigram 'e|f' was already generated,
-    // the op will not generate a special preserveShort bigram.
-    const nGrams = ['a|b|c', 'b|c|d', 'a|b', 'b|c', 'c|d', 'e|f'];
-    const nGramsSplits = [0, 5, 6];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('unpadded bigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [2], '', '', 0, false);
-    const nGrams = ['a|b', 'b|c', 'c|d', 'e|f'];
-    const nGramsSplits = [0, 3, 4];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('overlapping unpadded nGrams', async () => {
-    // Batch items are:
-    // 0: "a"
-    // 1: "b", "c", "d"
-    // 2: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|',
-        [3], '', '', 0, false);
-    const nGrams = ['b|c|d'];
-    const nGramsSplits = [0, 0, 1, 1];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('overlapping unpadded nGrams no output', async () => {
-    // Batch items are:
-    // 0: "a"
-    // 1: "b", "c", "d"
-    // 2: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|',
-        [5], '', '', 0, false);
-    const nGrams: string[] = [];
-    const nGramsSplits = [0, 0, 0, 0];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('singly padded trigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [3], 'LP', 'RP', 1, false);
-    const nGrams = [
-      'LP|a|b', 'a|b|c', 'b|c|d', 'c|d|RP',  // 0
-      'LP|e|f', 'e|f|RP'
-    ];
-    const nGramsSplits = [0, 4, 6];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('singly padded bigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [2], 'LP', 'RP', 1, false);
-    const nGrams = [
-      'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP',  // 0
-      'LP|e', 'e|f', 'f|RP'
-    ];
-    const nGramsSplits = [0, 5, 8];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('singly padded bigrams and 5grams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [2, 5], 'LP', 'RP', 1, false);
-    const nGrams = [
-      'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP', 'LP|a|b|c|d', 'a|b|c|d|RP',  // 0
-      'LP|e', 'e|f', 'f|RP'
-    ];
-    const nGramsSplits = [0, 7, 10];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('singly padded 5grams with preserve short', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [5], 'LP', 'RP', 1, true);
-    const nGrams = [
-      'LP|a|b|c|d', 'a|b|c|d|RP',  // 0
-      'LP|e|f|RP'
-    ];
-    const nGramsSplits = [0, 2, 3];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('overlapping singly padded nGrams', async () => {
-    // Batch items are:
-    // 0: "a"
-    // 1: "b", "c", "d"
-    // 2: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|',
-        [3], 'LP', 'RP', 1, false);
-    const nGrams = [
-      'LP|a|RP',                    // 0
-      'LP|b|c', 'b|c|d', 'c|d|RP',  // 1
-      'LP|e|f', 'e|f|RP'
-    ];
-    const nGramsSplits = [0, 1, 4, 6];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('overlapping singly padded nGrams no output', async () => {
-    // Batch items are:
-    // 0: "a"
-    // 1: "b", "c", "d"
-    // 2: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|',
-        [5], 'LP', 'RP', 1, false);
-    const nGrams = ['LP|b|c|d|RP'];
-    const nGramsSplits = [0, 0, 1, 1];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('singly padded unigrams', async () => {
-    // Batch items are:
-    // 0: "a", "b", "c", "d"
-    // 1: "e", "f"
-    const result = tf.string.stringNGrams(
-        ['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|',
-        [1], 'LP', 'RP', 1, false);
-    const nGrams = ['a', 'b', 'c', 'd', 'e', 'f'];
-    const nGramsSplits = [0, 4, 6];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('empty input', async () => {
-    const result = tf.string.stringNGrams(
-        tf.tensor1d([], 'string'), tf.tensor1d([], 'int32'), '|', [1], 'LP',
-        'RP', 3, false);
-    const nGrams: string[] = [];
-    const nGramsSplits: number[] = [];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('no tokens', async () => {
-    // Batch items are:
-    // 0:
-    // 1: "a"
-    const result = tf.string.stringNGrams(
-        ['a'], tf.tensor1d([0, 0, 1], 'int32'), '|', [3], 'L', 'R', -1, false);
-    const nGrams = [
-      'L|L|R', 'L|R|R',          // no input in first split
-      'L|L|a', 'L|a|R', 'a|R|R'  // second split
-    ];
-    const nGramsSplits = [0, 2, 5];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('no tokens no pad', async () => {
-    // Batch items are:
-    // 0:
-    // 1: "a"
-    const result = tf.string.stringNGrams(
-        ['a'], tf.tensor1d([0, 0, 1], 'int32'), '|', [3], '', '', 0, false);
-    const nGrams: string[] = [];
-    const nGramsSplits = [0, 0, 0];
-    await expectResult(result, nGrams, nGramsSplits);
-  });
-
-  it('throw error if first partition index is not 0', async () => {
-    expect(
-        () => tf.string.stringNGrams(
-            ['a'], tf.tensor1d([1, 1, 1], 'int32'), '|', [3], '', '', 0, false))
-        .toThrowError(/First split value must be 0/);
-  });
-
-  it('throw error if partition indices are decreasing', async () => {
-    expect(
-        () => tf.string.stringNGrams(
-            ['a'], tf.tensor1d([0, 1, 0], 'int32'), '|', [3], '', '', 0, false))
-        .toThrowError(/must be in \[1, 1\]/);
-  });
-
-  it('throw error if partition index is >= input size', async () => {
-    expect(
-        () => tf.string.stringNGrams(
-            ['a'], tf.tensor1d([0, 2, 1], 'int32'), '|', [3], '', '', 0, false))
-        .toThrowError(/must be in \[0, 1\]/);
-  });
-
-  it('throw error if last partition index is !== input size', async () => {
-    expect(
-        () => tf.string.stringNGrams(
-            ['a'], tf.tensor1d([0, 0], 'int32'), '|', [3], '', '', 0, false))
-        .toThrowError(/Last split value must be data size/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/string/string_split.ts b/tfjs-master/tfjs-core/src/ops/string/string_split.ts
deleted file mode 100644
index 7a4ef9d9d..000000000
--- a/tfjs-master/tfjs-core/src/ops/string/string_split.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {StringSplit, StringSplitAttrs, StringSplitInputs} from '../../kernel_names';
-import {Scalar, Tensor, Tensor1D} from '../../tensor';
-import {NamedTensorMap} from '../../tensor_types';
-import {convertToTensor} from '../../tensor_util_env';
-import {ScalarLike, TensorLike} from '../../types';
-import {op} from '../operation';
-
-/**
- * Split elements of `input` based on `delimiter` into a SparseTensor .
- *
- * Let N be the size of source (typically N will be the batch size). Split each
- * element of `input` based on `delimiter` and return a SparseTensor containing
- * the splitted tokens. Empty tokens are ignored if `skipEmpty` is set to True.
- *
- * `delimiter` can be empty, or a string of split characters. If `delimiter` is
- * an empty string, each element of `input` is split into individual
- * character strings. Otherwise every character of `delimiter` is a potential
- * split point.
- *
- * ```js
- * const result = tf.string.stringSplit(['hello world',  'a b c'], ' ');
- * result['indices'].print(); // [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]]
- * result['values'].print(); // ['hello', 'world', 'a', 'b', 'c']
- * result['shape'].print(); // [2, 3]
- * ```
- * @param input: 1-D. Strings to split.
- * @param delimiter: 0-D. Delimiter characters, or empty string.
- * @param skipEmpty: Optional. If true, skip the empty strings from the result.
- *     Defaults to true.
- * @return A map with the following properties:
- *     - indices: A dense matrix of int32 representing the indices of the sparse
- *       tensor.
- *     - values: A vector of strings corresponding to the splited values.
- *     - shape: a length-2 vector of int32 representing the shape of the sparse
- * tensor, where the first value is N and the second value is the maximum number
- * of tokens in a single input entry.
- *
- * @doc {heading: 'Operations', subheading: 'String'}
- */
-function stringSplit_(
-    input: Tensor1D|TensorLike, delimiter: Scalar|ScalarLike,
-    skipEmpty = true): NamedTensorMap {
-  const $input = convertToTensor(input, 'input', 'stringSplit', 'string');
-  const $delimiter =
-      convertToTensor(delimiter, 'delimiter', 'stringSplit', 'string');
-
-  if ($input.rank !== 1) {
-    throw new Error(
-        `Input should be Tensor1D but received shape ${$input.shape}`);
-  }
-  if ($delimiter.rank !== 0) {
-    throw new Error(
-        `Delimiter should be a scalar but received shape ${$delimiter.shape}`);
-  }
-
-  const attrs: StringSplitAttrs = {skipEmpty};
-  const inputs: StringSplitInputs = {input: $input, delimiter: $delimiter};
-  const result: Tensor[] =
-      ENGINE.runKernel(StringSplit, inputs as {}, attrs as {});
-  return {indices: result[0], values: result[1], shape: result[2]};
-}
-
-export const stringSplit = /* @__PURE__ */ op({stringSplit_});
diff --git a/tfjs-master/tfjs-core/src/ops/string/string_split_test.ts b/tfjs-master/tfjs-core/src/ops/string/string_split_test.ts
deleted file mode 100644
index 9067f8164..000000000
--- a/tfjs-master/tfjs-core/src/ops/string/string_split_test.ts
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysEqual} from '../../test_util';
-
-async function expectResult(
-    result: tf.NamedTensorMap, indices: number[][], values: string[],
-    shape: [number, number]) {
-  expectArraysEqual(await result.indices.data(), indices);
-  expectArraysEqual(await result.values.data(), values);
-  expectArraysEqual(await result.shape.data(), shape);
-
-  expect(result.indices.shape).toEqual([indices.length, 2]);
-  expect(result.values.shape).toEqual([values.length]);
-  expect(result.shape.shape).toEqual([2]);
-
-  expect(result.indices.dtype).toEqual('int32');
-  expect(result.values.dtype).toEqual('string');
-  expect(result.shape.dtype).toEqual('int32');
-}
-
-describeWithFlags('stringSplit', ALL_ENVS, () => {
-  it('white space delimiter', async () => {
-    const result = tf.string.stringSplit(['pigs on the wing', 'animals'], ' ');
-    await expectResult(
-        result, [[0, 0], [0, 1], [0, 2], [0, 3], [1, 0]],
-        ['pigs', 'on', 'the', 'wing', 'animals'], [2, 4]);
-  });
-
-  it('empty delimiter', async () => {
-    const result = tf.string.stringSplit(['hello', 'hola', 'hi'], '');
-    await expectResult(
-        result,
-        [
-          [0, 0], [0, 1], [0, 2], [0, 3], [0, 4], [1, 0], [1, 1], [1, 2],
-          [1, 3], [2, 0], [2, 1]
-        ],
-        ['h', 'e', 'l', 'l', 'o', 'h', 'o', 'l', 'a', 'h', 'i'], [3, 5]);
-  });
-
-  it('empty token', async () => {
-    const result = tf.string.stringSplit(
-        ['', ' a', 'b ', ' c', ' ', ' d ', '  e', 'f  ', '  g  ', '  '], ' ');
-    await expectResult(
-        result, [[1, 0], [2, 0], [3, 0], [5, 0], [6, 0], [7, 0], [8, 0]],
-        ['a', 'b', 'c', 'd', 'e', 'f', 'g'], [10, 1]);
-  });
-
-  it('set empty token', async () => {
-    const result = tf.string.stringSplit(
-        ['', ' a', 'b ', ' c', ' ', ' d ', '. e', 'f .', ' .g. ', ' .'], ' .');
-    await expectResult(
-        result, [[1, 0], [2, 0], [3, 0], [5, 0], [6, 0], [7, 0], [8, 0]],
-        ['a', 'b', 'c', 'd', 'e', 'f', 'g'], [10, 1]);
-  });
-
-  it('with delimiter', async () => {
-    const input = ['hello|world', 'hello world'];
-    let result = tf.string.stringSplit(input, '|');
-    await expectResult(
-        result, [[0, 0], [0, 1], [1, 0]], ['hello', 'world', 'hello world'],
-        [2, 2]);
-    result = tf.string.stringSplit(input, '| ');
-    await expectResult(
-        result, [[0, 0], [0, 1], [1, 0], [1, 1]],
-        ['hello', 'world', 'hello', 'world'], [2, 2]);
-    result =
-        tf.string.stringSplit(['hello.cruel,world', 'hello cruel world'], '.,');
-    await expectResult(
-        result, [[0, 0], [0, 1], [0, 2], [1, 0]],
-        ['hello', 'cruel', 'world', 'hello cruel world'], [2, 3]);
-  });
-
-  it('no skip empty', async () => {
-    const input = ['#a', 'b#', '#c#'];
-    let result = tf.string.stringSplit(input, '#', false);
-    await expectResult(
-        result, [[0, 0], [0, 1], [1, 0], [1, 1], [2, 0], [2, 1], [2, 2]],
-        ['', 'a', 'b', '', '', 'c', ''], [3, 3]);
-    result = tf.string.stringSplit(input, '#');
-    await expectResult(
-        result, [[0, 0], [1, 0], [2, 0]], ['a', 'b', 'c'], [3, 1]);
-  });
-
-  it('large input does not cause an argument overflow', async () => {
-    const input = 'a'.repeat(200000);
-    const result = tf.string.stringSplit([input], '');
-    await expectResult(
-        result, Array(input.length).fill(0).map((_, i) => [0, i]),
-        input.split(''), [1, input.length]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/string/string_to_hash_bucket_fast.ts b/tfjs-master/tfjs-core/src/ops/string/string_to_hash_bucket_fast.ts
deleted file mode 100644
index acac976c3..000000000
--- a/tfjs-master/tfjs-core/src/ops/string/string_to_hash_bucket_fast.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../../engine';
-import {StringToHashBucketFast, StringToHashBucketFastAttrs, StringToHashBucketFastInputs} from '../../kernel_names';
-import {Tensor} from '../../tensor';
-import {convertToTensor} from '../../tensor_util_env';
-import {TensorLike} from '../../types';
-import {op} from '../operation';
-
-/**
- * Converts each string in the input Tensor to its hash mod by a number of
- * buckets.
- *
- * The hash function is deterministic on the content of the string within the
- * process and will never change. However, it is not suitable for cryptography.
- * This function may be used when CPU time is scarce and inputs are trusted or
- * unimportant. There is a risk of adversaries constructing inputs that all hash
- * to the same bucket.
- *
- * ```js
- * const result = tf.string.stringToHashBucketFast(
- *   ['Hello', 'TensorFlow', '2.x'], 3);
- * result.print(); // [0, 2, 2]
- * ```
- * @param input: The strings to assign a hash bucket.
- * @param numBuckets: The number of buckets.
- * @return A Tensor of the same shape as the input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'String'}
- */
-function stringToHashBucketFast_(
-    input: Tensor|TensorLike, numBuckets: number): Tensor {
-  const $input =
-      convertToTensor(input, 'input', 'stringToHashBucketFast', 'string');
-  const attrs: StringToHashBucketFastAttrs = {numBuckets};
-
-  if (numBuckets <= 0) {
-    throw new Error(`Number of buckets must be at least 1`);
-  }
-
-  const inputs: StringToHashBucketFastInputs = {input: $input};
-  return ENGINE.runKernel(StringToHashBucketFast, inputs as {}, attrs as {});
-}
-
-export const stringToHashBucketFast = /* @__PURE__ */ op({stringToHashBucketFast_});
diff --git a/tfjs-master/tfjs-core/src/ops/string/string_to_hash_bucket_fast_test.ts b/tfjs-master/tfjs-core/src/ops/string/string_to_hash_bucket_fast_test.ts
deleted file mode 100644
index 7e91b8f65..000000000
--- a/tfjs-master/tfjs-core/src/ops/string/string_to_hash_bucket_fast_test.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-import {expectArraysClose} from '../../test_util';
-
-describeWithFlags('stringToHashBucketFast', ALL_ENVS, () => {
-  it('throw error if negative buckets', async () => {
-    expect(() => tf.string.stringToHashBucketFast(['a', 'b', 'c'], -1))
-        .toThrowError(/must be at least 1/);
-  });
-
-  it('throw error if zero buckets', async () => {
-    expect(() => tf.string.stringToHashBucketFast(['a', 'b', 'c'], 0))
-        .toThrowError(/must be at least 1/);
-  });
-
-  it('one bucket maps values to zero', async () => {
-    const result = tf.string.stringToHashBucketFast(['a', 'b', 'c'], 1);
-    expectArraysClose(await result.data(), [0, 0, 0]);
-  });
-
-  it('multiple buckets', async () => {
-    const result = tf.string.stringToHashBucketFast(['a', 'b', 'c', 'd'], 10);
-    // fingerPrint64('a') -> 12917804110809363939 -> mod 10 -> 9
-    // fingerPrint64('b') -> 11795596070477164822 -> mod 10 -> 2
-    // fingerPrint64('c') -> 11430444447143000872 -> mod 10 -> 2
-    // fingerPrint64('d') -> 4470636696479570465 -> mod 10 -> 5
-    expectArraysClose(await result.data(), [9, 2, 2, 5]);
-  });
-
-  it('empty input', async () => {
-    const result =
-        tf.string.stringToHashBucketFast(tf.tensor1d([], 'string'), 2147483648);
-    expectArraysClose(await result.data(), []);
-  });
-
-  it('preserve size', async () => {
-    const result = tf.string.stringToHashBucketFast(
-        [[['a'], ['b']], [['c'], ['d']], [['a'], ['b']]], 10);
-    expectArraysClose(await result.data(), [9, 2, 2, 5, 9, 2]);
-    expect(result.shape).toEqual([3, 2, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sub.ts b/tfjs-master/tfjs-core/src/ops/sub.ts
deleted file mode 100644
index 57902c704..000000000
--- a/tfjs-master/tfjs-core/src/ops/sub.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Sub, SubInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {makeTypesMatch} from '../tensor_util';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.
- *
- * ```js
- * const a = tf.tensor1d([10, 20, 30, 40]);
- * const b = tf.tensor1d([1, 2, 3, 4]);
- *
- * a.sub(b).print();  // or tf.sub(a, b)
- * ```
- *
- * ```js
- * // Broadcast subtract a with b.
- * const a = tf.tensor1d([10, 20, 30, 40]);
- * const b = tf.scalar(5);
- *
- * a.sub(b).print();  // or tf.sub(a, b)
- * ```
- * @param a The first `tf.Tensor` to subtract from.
- * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as
- * `a`.
- *
- * @doc {heading: 'Operations', subheading: 'Arithmetic'}
- */
-function sub_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'sub');
-  let $b = convertToTensor(b, 'b', 'sub');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  const inputs: SubInputs = {a: $a, b: $b};
-
-  return ENGINE.runKernel(Sub, inputs as unknown as NamedTensorMap);
-}
-
-export const sub = /* @__PURE__ */ op({sub_});
diff --git a/tfjs-master/tfjs-core/src/ops/sub_test.ts b/tfjs-master/tfjs-core/src/ops/sub_test.ts
deleted file mode 100644
index ce1476c53..000000000
--- a/tfjs-master/tfjs-core/src/ops/sub_test.ts
+++ /dev/null
@@ -1,340 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('sub', ALL_ENVS, () => {
-  it('c - A', async () => {
-    const c = tf.scalar(5);
-    const a = tf.tensor1d([7, 2, 3]);
-
-    const result = tf.sub(c, a);
-
-    expectArraysClose(await result.data(), [-2, 3, 2]);
-  });
-
-  it('A - c', async () => {
-    const a = tf.tensor1d([1, 2, -3]);
-    const c = tf.scalar(5);
-
-    const result = tf.sub(a, c);
-
-    expectArraysClose(await result.data(), [-4, -3, -8]);
-  });
-
-  it('A - c propagates NaNs', async () => {
-    const a = tf.tensor1d([1, NaN, 3]);
-    const c = tf.scalar(5);
-
-    const res = tf.sub(a, c);
-
-    expectArraysClose(await res.data(), [-4, NaN, -2]);
-  });
-
-  it('A - B', async () => {
-    const a = tf.tensor1d([2, 5, 1]);
-    const b = tf.tensor1d([4, 2, -1]);
-
-    const result = tf.sub(a, b);
-
-    const expected = [-2, 3, 2];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('TensorLike', async () => {
-    const a = [2, 5, 1];
-    const b = [4, 2, -1];
-
-    const result = tf.sub(a, b);
-
-    const expected = [-2, 3, 2];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([2, 5, 1]);
-    const b = [4, 2, -1];
-
-    const result = a.sub(b);
-
-    const expected = [-2, 3, 2];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('A - B propagates NaNs', async () => {
-    const a = tf.tensor1d([2, 5, 1]);
-    const b = tf.tensor1d([4, NaN, -1]);
-
-    const res = tf.sub(a, b);
-
-    expectArraysClose(await res.data(), [-2, NaN, 2]);
-  });
-
-  it('A - B throws when passed tensors with different shape', () => {
-    const a = tf.tensor1d([2, 5, 1, 5]);
-    const b = tf.tensor1d([4, 2, -1]);
-
-    expect(() => tf.sub(a, b)).toThrowError();
-    expect(() => tf.sub(b, a)).toThrowError();
-  });
-
-  it('A - B broadcasting same rank Tensors different shape', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor2d([2, 3], [2, 1]);
-
-    const result = tf.sub(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [-1, 0, -6, -7];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('A - B broadcast 2D + 1D', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor1d([1, 2]);
-
-    const result = tf.sub(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [0, 0, -4, -6];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('2D-scalar broadcast', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.scalar(2);
-    const res = tf.sub(a, b);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [-1, 0, 1, 2, 3, 4]);
-  });
-
-  it('scalar-1D broadcast', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([1, 2, 3, 4, 5, 6]);
-    const res = tf.sub(a, b);
-    expect(res.shape).toEqual([6]);
-    expectArraysClose(await res.data(), [1, 0, -1, -2, -3, -4]);
-  });
-
-  it('2D-2D broadcast each with 1 dim', async () => {
-    const a = tf.tensor2d([1, 2, 5], [1, 3]);
-    const b = tf.tensor2d([7, 3], [2, 1]);
-    const res = tf.sub(a, b);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [-6, -5, -2, -2, -1, 2]);
-  });
-
-  it('2D-2D broadcast inner dim of b', async () => {
-    const a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([7, 3], [2, 1]);
-    const res = tf.sub(a, b);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [-6, -5, -2, 1, 2, 3]);
-  });
-
-  it('3D-scalar', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
-    const b = tf.scalar(-1);
-    const res = tf.sub(a, b);
-    expect(res.shape).toEqual([2, 3, 1]);
-    expectArraysClose(await res.data(), [2, 3, 4, 5, 6, 7]);
-  });
-
-  it('vec4 same shape', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
-    const expected = [-4, -1, -7, 3];
-    const result = tf.sub(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('gradients: basic 1D arrays', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([3, 2, 1]);
-    const dy = tf.tensor1d([1, 10, 20]);
-
-    const grads = tf.grads((a, b) => tf.sub(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1, 10, 20]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-1, -10, -20]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([3, 2, 1]);
-    const dy = tf.tensor1d([1, 10, 20]);
-
-    const grads = tf.grads((a, b) => tf.sub(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1, 10, 20]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-1, -10, -20]);
-  });
-
-  it('gradients: basic 2D arrays', async () => {
-    const a = tf.tensor2d([0, 1, 2, 3], [2, 2]);
-    const b = tf.tensor2d([3, 2, 1, 0], [2, 2]);
-    const dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.sub(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [1, 10, 15, 20]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-1, -10, -15, -20]);
-  });
-
-  it('gradient: 1D - scalar broadcast', async () => {
-    const a = tf.tensor1d([3, 4, 5]);
-    const b = tf.scalar(2);
-    const dy = tf.tensor1d([7, 8, 9]);
-
-    const grads = tf.grads((a, b) => tf.sub(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [7, 8, 9]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-7 - 8 - 9]);
-  });
-
-  it('gradient: scalar - 1D broadcast', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([7, 8, 9]);
-
-    const grads = tf.grads((a, b) => tf.sub(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [7 + 8 + 9]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-7, -8, -9]);
-  });
-
-  it('gradient: 2D - 2D broadcast', async () => {
-    const a = tf.tensor2d([4, 5, 6, 7], [2, 2]);
-    const b = tf.tensor2d([2, 3], [2, 1]);
-    const dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.sub(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [5, 4, 3, 2]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [-5 - 4, -3 - 2]);
-  });
-
-  it('complex number subtraction', async () => {
-    const real1 = tf.tensor1d([3]);
-    const imag1 = tf.tensor1d([5]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([1]);
-    const imag2 = tf.tensor1d([0]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const result = complex1.sub(complex2);
-
-    expect(result.dtype).toBe('complex64');
-    expect(result.shape).toEqual([1]);
-    expectArraysClose(await result.data(), [2, 5]);
-  });
-
-  it('complex number broadcasting subtraction', async () => {
-    const real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([4]);
-    const imag2 = tf.tensor1d([5]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const result = tf.sub(complex1, complex2);
-
-    expect(result.dtype).toEqual('complex64');
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(
-        await result.data(),
-        [1 - 4, 10 - 5, 2 - 4, 20 - 5, -3 - 4, -30 - 5, -4 - 4, -40 - 5]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.sub({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'sub' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.sub(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'sub' must be a Tensor/);
-  });
-  it('upcasts when dtypes dont match', async () => {
-    let res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [0]);
-
-    res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [0]);
-
-    res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [1]);
-
-    res = tf.sub(tf.complex(4, 7), tf.scalar(1, 'float32'));
-    expect(res.dtype).toBe('complex64');
-    expectArraysClose(await res.data(), [3, 7]);
-
-    res = tf.sub(tf.complex(4, 7), tf.scalar(1, 'int32'));
-    expect(res.dtype).toBe('complex64');
-    expectArraysClose(await res.data(), [3, 7]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.sub(5, [7, 2, 3]);
-    expectArraysClose(await result.data(), [-2, 3, 2]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/sum.ts b/tfjs-master/tfjs-core/src/ops/sum.ts
deleted file mode 100644
index 4d65163a9..000000000
--- a/tfjs-master/tfjs-core/src/ops/sum.ts
+++ /dev/null
@@ -1,73 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ENGINE} from '../engine';
-import {Sum, SumAttrs, SumInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {cast} from './cast';
-import {op} from './operation';
-
-/**
- * Computes the sum of elements across dimensions of a `tf.Tensor`.
- *
- * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
- * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
- * `axes`. If `keepDims` is true, the reduced dimensions are retained with
- * length 1. If axes has no entries, all dimensions are reduced, and a
- * `tf.Tensor` with a single element is returned.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3]);
- *
- * x.sum().print();  // or tf.sum(x)
- * ```
- *
- * ```js
- * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * const axis = 1;
- * x.sum(axis).print();  // or tf.sum(x, axis)
- * ```
- *
- * @param x The input tensor to compute the sum over. If the dtype is `bool`
- *   it will be converted to `int32` and the output dtype will be `int32`.
- * @param axis The dimension(s) to reduce. By default it reduces
- *     all dimensions.
- * @param keepDims If true, retains reduced dimensions with size 1.
- *
- * @doc {heading: 'Operations', subheading: 'Reduction'}
- */
-function sum_<T extends Tensor>(
-    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {
-  let $x = convertToTensor(x, 'x', 'sum');
-  if ($x.dtype === 'bool') {
-    $x = cast($x, 'int32');
-  }
-
-  const inputs: SumInputs = {x: $x};
-  const attrs: SumAttrs = {axis, keepDims};
-
-  return ENGINE.runKernel(
-      Sum, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const sum = /* @__PURE__ */ op({sum_});
diff --git a/tfjs-master/tfjs-core/src/ops/sum_test.ts b/tfjs-master/tfjs-core/src/ops/sum_test.ts
deleted file mode 100644
index bceb9f595..000000000
--- a/tfjs-master/tfjs-core/src/ops/sum_test.ts
+++ /dev/null
@@ -1,178 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('sum', ALL_ENVS, () => {
-  it('basic', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const result = tf.sum(a);
-    expectArraysClose(await result.data(), 7);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
-    expectArraysEqual(await tf.sum(a).data(), NaN);
-  });
-
-  it('sum over dtype int32', async () => {
-    const a = tf.tensor1d([1, 5, 7, 3], 'int32');
-    const sum = tf.sum(a);
-    expectArraysEqual(await sum.data(), 16);
-  });
-
-  it('sum over dtype bool', async () => {
-    const a = tf.tensor1d([true, false, false, true, true], 'bool');
-    const sum = tf.sum(a);
-    expectArraysEqual(await sum.data(), 3);
-  });
-
-  it('sums all values in 2D array with keep dim', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.sum(a, null, true /* keepDims */);
-
-    expect(res.shape).toEqual([1, 1]);
-    expectArraysClose(await res.data(), [7]);
-  });
-
-  it('sums across axis=0 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.sum(a, [0]);
-
-    expect(res.shape).toEqual([2]);
-    expectArraysClose(await res.data(), [4, 3]);
-  });
-
-  it('sums across axis=0 in 2D array, keepDims', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.sum(a, [0], true /* keepDims */);
-
-    expect(res.shape).toEqual([1, 2]);
-    expectArraysClose(await res.data(), [4, 3]);
-  });
-
-  it('sums across axis=1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.sum(a, [1]);
-
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [3, 3, 1]);
-  });
-
-  it('2D, axis=1 provided as number', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
-    const res = tf.sum(a, 1);
-
-    expect(res.shape).toEqual([2]);
-    expectArraysClose(await res.data(), [6, 1]);
-  });
-
-  it('2D, axis = -1 provided as number', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
-    const res = tf.sum(a, -1);
-
-    expect(res.shape).toEqual([2]);
-    expectArraysClose(await res.data(), [6, 1]);
-  });
-
-  it('sums across axis=0,1 in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.sum(a, [0, 1]);
-
-    expect(res.shape).toEqual([]);
-    expectArraysClose(await res.data(), [7]);
-  });
-
-  it('2D, axis=[-1,-2] in 2D array', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const res = tf.sum(a, [-1, -2]);
-
-    expect(res.shape).toEqual([]);
-    expectArraysClose(await res.data(), [7]);
-  });
-
-  it('4D, axis=[2, 2, 1], need permutation.', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
-    const res = tf.sum(a, 0);
-
-    expect(res.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4]);
-  });
-
-  it('gradients: sum(2d)', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const dy = tf.scalar(10);
-
-    const gradients = tf.grad(a => a.sum())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [10, 10, 10, 10, 10, 10]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
-    const dy = tf.scalar(10);
-
-    const gradients = tf.grad(a => a.clone().sum().clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [10, 10, 10, 10, 10, 10]);
-  });
-
-  it('gradients: sum(2d, axis=0)', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 0], [0, 1]], [3, 2]);
-    const dy = tf.tensor1d([10, 20]);
-    const axis = 0;
-
-    const gradients = tf.grad(a => a.sum(axis))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [10, 20, 10, 20, 10, 20]);
-  });
-
-  it('gradients: sum(2d, axis=1)', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 0], [0, 1]], [3, 2]);
-    const dy = tf.tensor1d([10, 20, 30]);
-    const axis = 1;
-
-    const gradients = tf.grad(a => a.sum(axis))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), [10, 10, 20, 20, 30, 30]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.sum({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'sum' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.sum([[1, 2], [3, 0], [0, 1]]);
-    expectArraysClose(await result.data(), 7);
-  });
-
-  it('throws error for string tensor', () => {
-    expect(() => tf.sum(['a']))
-        .toThrowError(/Argument 'x' passed to 'sum' must be numeric tensor/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/tan.ts b/tfjs-master/tfjs-core/src/ops/tan.ts
deleted file mode 100644
index 187ce1838..000000000
--- a/tfjs-master/tfjs-core/src/ops/tan.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tan, TanInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
- *
- * x.tan().print();  // or tf.tan(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function tan_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'tan', 'float32');
-
-  const inputs: TanInputs = {x: $x};
-
-  return ENGINE.runKernel(Tan, inputs as unknown as NamedTensorMap);
-}
-export const tan = /* @__PURE__ */ op({tan_});
diff --git a/tfjs-master/tfjs-core/src/ops/tan_test.ts b/tfjs-master/tfjs-core/src/ops/tan_test.ts
deleted file mode 100644
index 33126d170..000000000
--- a/tfjs-master/tfjs-core/src/ops/tan_test.ts
+++ /dev/null
@@ -1,144 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, TEST_EPSILON_FLOAT16} from '../test_util';
-
-describeWithFlags('tan', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const a = tf.tensor1d(values);
-    const result = tf.tan(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = Math.tan(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('numbers exceed float32 precision', async () => {
-    const values = [
-      -608065414.8781943,
-      781902002.7943993,
-      -470910673.97399473,
-      1786759246.171617,
-      1873777868.5510726,
-      -1015107953.8969269,
-      830023227.6215034,
-    ];
-    const a = tf.tensor1d(values, 'float32');
-    const result = tf.tan(a);
-
-    const expected = [...new Float32Array(values).map((v) => Math.tan(v))];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.tan(a);
-    expectArraysClose(await res.data(), [Math.tan(4), NaN, Math.tan(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.tan(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [8 / (Math.cos(0.5) * Math.cos(0.5))]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.tan(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [8 / (Math.cos(0.5) * Math.cos(0.5))]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-1, 2, 3, -5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.tan(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / (Math.cos(aValues[i]) * Math.cos(aValues[i]));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    // The grad(tan(x)) which relies on 1/cos(x) is less precise on Windows.
-    expectArraysClose(await gradients.data(), expected, TEST_EPSILON_FLOAT16);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-3, 1, 2, 3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.tan(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = dyValues[i] / (Math.cos(aValues[i]) * Math.cos(aValues[i]));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.tan({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'tan' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const result = tf.tan(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = Math.tan(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.tan('q'))
-        .toThrowError(/Argument 'x' passed to 'tan' must be float32/);
-  });
-
-  it('throws for int32 tensor', () => {
-    expect(() => tf.tan(tf.tensor1d([1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'tan' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/tanh.ts b/tfjs-master/tfjs-core/src/ops/tanh.ts
deleted file mode 100644
index 499240151..000000000
--- a/tfjs-master/tfjs-core/src/ops/tanh.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tanh, TanhInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`
- *
- * ```js
- * const x = tf.tensor1d([0, 1, -1, 70]);
- *
- * x.tanh().print();  // or tf.tanh(x)
- * ```
- * @param x The input tensor.
- *
- * @doc {heading: 'Operations', subheading: 'Basic math'}
- */
-function tanh_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'tanh', 'float32');
-
-  const inputs: TanhInputs = {x: $x};
-
-  return ENGINE.runKernel(Tanh, inputs as unknown as NamedTensorMap);
-}
-export const tanh = /* @__PURE__ */ op({tanh_});
diff --git a/tfjs-master/tfjs-core/src/ops/tanh_test.ts b/tfjs-master/tfjs-core/src/ops/tanh_test.ts
deleted file mode 100644
index 61f566915..000000000
--- a/tfjs-master/tfjs-core/src/ops/tanh_test.ts
+++ /dev/null
@@ -1,129 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import * as util from '../util';
-
-describeWithFlags('tanh', ALL_ENVS, () => {
-  it('basic', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const a = tf.tensor1d(values);
-    const result = tf.tanh(a);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] = util.tanh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('propagates NaNs', async () => {
-    const a = tf.tensor1d([4, NaN, 0]);
-    const res = tf.tanh(a);
-    expectArraysClose(await res.data(), [util.tanh(4), NaN, util.tanh(0)]);
-  });
-
-  it('gradients: Scalar', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.tanh(a))(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [8 * (1 - (Math.tanh(0.5) * Math.tanh(0.5)))]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(0.5);
-    const dy = tf.scalar(8);
-
-    const gradients = tf.grad(a => tf.tanh(a.clone()).clone())(a, dy);
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(
-        await gradients.data(), [8 * (1 - (Math.tanh(0.5) * Math.tanh(0.5)))]);
-  });
-
-  it('gradients: Tensor1D', async () => {
-    const aValues = [-1, 2, 3, -5];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor1d(aValues);
-    const dy = tf.tensor1d(dyValues);
-
-    const gradients = tf.grad(a => tf.tanh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] =
-          dyValues[i] * (1 - (Math.tanh(aValues[i]) * Math.tanh(aValues[i])));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('gradients: Tensor2D', async () => {
-    const aValues = [-3, 1, 2, 3];
-    const dyValues = [1, 2, 3, 4];
-    const a = tf.tensor2d(aValues, [2, 2]);
-    const dy = tf.tensor2d(dyValues, [2, 2]);
-
-    const gradients = tf.grad(a => tf.tanh(a))(a, dy);
-
-    const expected = [];
-    for (let i = 0; i < a.size; i++) {
-      expected[i] =
-          dyValues[i] * (1 - (Math.tanh(aValues[i]) * Math.tanh(aValues[i])));
-    }
-
-    expect(gradients.shape).toEqual(a.shape);
-    expect(gradients.dtype).toEqual('float32');
-    expectArraysClose(await gradients.data(), expected);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.tanh({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'tanh' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const values = [1, -3, 2, 7, -4];
-    const result = tf.tanh(values);
-
-    const expected = [];
-    for (let i = 0; i < values.length; i++) {
-      expected[i] = util.tanh(values[i]);
-    }
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.tanh('q'))
-        .toThrowError(/Argument 'x' passed to 'tanh' must be float32/);
-  });
-
-  it('throws for string tensor', () => {
-    expect(() => tf.tanh(tf.tensor1d([1], 'int32')))
-        .toThrowError(/Argument 'x' passed to 'tanh' must be float32/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/tensor.ts b/tfjs-master/tfjs-core/src/ops/tensor.ts
deleted file mode 100644
index 9b664e07a..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor.ts
+++ /dev/null
@@ -1,208 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {inferShape} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {DataType, Rank, ShapeMap, WebGLData, WebGPUData} from '../types';
-
-import {makeTensor} from './tensor_ops_util';
-
-/**
- * Creates a `tf.Tensor` with the provided values, shape and dtype.
- *
- * ```js
- * // Pass an array of values to create a vector.
- * tf.tensor([1, 2, 3, 4]).print();
- * ```
- *
- * ```js
- * // Pass a nested array of values to make a matrix or a higher
- * // dimensional tensor.
- * tf.tensor([[1, 2], [3, 4]]).print();
- * ```
- *
- * ```js
- * // Pass a flat array and specify a shape yourself.
- * tf.tensor([1, 2, 3, 4], [2, 2]).print();
- * ```
- *
- * ```js
- * // Pass a `WebGLData` object and specify a shape yourself.
- *
- * // This makes it possible for TF.js applications to avoid GPU / CPU sync.
- * // For example, if your application includes a preprocessing step on the GPU,
- * // you could upload the GPU output directly to TF.js, rather than first
- * // downloading the values.
- *
- * // Example for WebGL2:
- * if (tf.findBackend('custom-webgl') == null) {
- *   const customCanvas = document.createElement('canvas');
- *   const customBackend = new tf.MathBackendWebGL(customCanvas);
- *   tf.registerBackend('custom-webgl', () => customBackend);
- * }
- * const savedBackend = tf.getBackend();
- * await tf.setBackend('custom-webgl');
- * const gl = tf.backend().gpgpu.gl;
- * const texture = gl.createTexture();
- * const tex2d = gl.TEXTURE_2D;
- * const width = 2;
- * const height = 2;
- *
- * gl.bindTexture(tex2d, texture);
- * gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
- * gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
- * gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
- * gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
- * gl.texImage2D(
- *   tex2d, 0, gl.RGBA32F, // internalFormat
- *   width, height, 0,
- *   gl.RGBA, // textureFormat
- *   gl.FLOAT, // textureType
- *   new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
- * );
- *
- * // Currently, the `texture` has 4 pixels:
- * // Pixel0 is {R:0, G:1, B:2, A:3}
- * // Pixel1 is {R:4, G:5, B:6, A:7}
- * // Pixel2 is {R:8, G:9, B:10, A:11}
- * // Pixel3 is {R:12, G:13, B:14, A:15}
- *
- * const logicalShape = [height * width * 2];
- * const a = tf.tensor({texture, height, width, channels: 'BR'}, logicalShape);
- * a.print();
- * // Tensor value will be [2, 0, 6, 4, 10, 8, 14, 12], since [2, 0] is the
- * // values of 'B' and 'R' channels of Pixel0, [6, 4] is the values of 'B' and
- * 'R'
- * // channels of Pixel1...
- *
- * // For postprocessing on the GPU, it's possible to retrieve the texture
- * // backing any tensor by calling the tensor's `dataToGPU` method like
- * // so:
- *
- * const tex = a.dataToGPU();
- * await tf.setBackend(savedBackend);
- * ```
- *
- * ```js
- * // Pass a `WebGPUData` object and specify a shape yourself.
- *
- * // This makes it possible for TF.js applications to avoid GPU / CPU sync.
- * // For example, if your application includes a preprocessing step on the GPU,
- * // you could upload the GPU output directly to TF.js, rather than first
- * // downloading the values. Unlike WebGL, this optionally supports zero copy
- * // by WebGPUData.zeroCopy. When zeroCopy is false or undefined(default), this
- * // passing GPUBuffer can be destroyed after tensor is created. When zeroCopy
- * // is true, this GPUBuffer is bound directly by the tensor, so do not destroy
- * // this GPUBuffer until all access is done.
- *
- * // Example for WebGPU:
- * function createGPUBufferFromData(device, data, dtype) {
- *   const bytesPerElement = 4;
- *   const sizeInBytes = data.length * bytesPerElement;
- *
- *   const gpuWriteBuffer = device.createBuffer({
- *     mappedAtCreation: true,
- *     size: sizeInBytes,
- *     usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC
- *   });
- *   const arrayBuffer = gpuWriteBuffer.getMappedRange();
- *   if (dtype === 'float32') {
- *     new Float32Array(arrayBuffer).set(data);
- *   } else if (dtype === 'int32') {
- *     new Int32Array(arrayBuffer).set(data);
- *   } else {
- *     throw new Error(
- *         `Creating tensor from GPUBuffer only supports` +
- *         `'float32'|'int32' dtype, while the dtype is ${dtype}.`);
- *   }
- *   gpuWriteBuffer.unmap();
- *
- *   const gpuReadBuffer = device.createBuffer({
- *     mappedAtCreation: false,
- *     size: sizeInBytes,
- *     usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.STORAGE |
- *         GPUBufferUsage.COPY_SRC
- *   });
- *
- *   const copyEncoder = device.createCommandEncoder();
- *   copyEncoder.copyBufferToBuffer(
- *       gpuWriteBuffer, 0, gpuReadBuffer, 0, sizeInBytes);
- *   const copyCommands = copyEncoder.finish();
- *   device.queue.submit([copyCommands]);
- *   gpuWriteBuffer.destroy();
- *   return gpuReadBuffer;
- * }
- *
- * const savedBackend = tf.getBackend();
- * await tf.setBackend('webgpu').catch(
- *     () => {throw new Error(
- *         'Failed to use WebGPU backend. Please use Chrome Canary to run.')});
- * const dtype = 'float32';
- * const device = tf.backend().device;
- * const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
- * const bData = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4];
- * const expected = [2, 4, 6, 8, 6, 8, 10, 12, 10, 12, 14, 16, 14, 16, 18, 20];
- * const aBuffer = createGPUBufferFromData(device, aData, dtype);
- * const shape = [aData.length];
- * // To use zeroCopy, use {buffer: aBuffer, zeroCopy: true} instead and destroy
- * // aBuffer untill all access is done.
- * const a = tf.tensor({buffer: aBuffer}, shape, dtype);
- * const b = tf.tensor(bData, shape, dtype);
- * const result = tf.add(a, b);
- * result.print();
- * a.dispose();
- * b.dispose();
- * result.dispose();
- * aBuffer.destroy();
- * await tf.setBackend(savedBackend);
- * ```
- * @param values The values of the tensor. Can be nested array of numbers,
- *     or a flat array, or a `TypedArray`, or a `WebGLData` object, or a
- * `WebGPUData` object. If the values are strings, they will be encoded as utf-8
- * and kept as `Uint8Array[]`. If the values is a `WebGLData` object, the dtype
- * could only be 'float32' or 'int32' and the object has to have: 1. texture, a
- * `WebGLTexture`, the texture must share the same `WebGLRenderingContext` with
- * TFJS's WebGL backend (you could create a custom WebGL backend from your
- * texture's canvas) and the internal texture format for the input texture must
- * be floating point or normalized integer; 2. height, the height of the
- * texture; 3. width, the width of the texture; 4. channels, a non-empty subset
- * of 'RGBA', indicating the values of which channels will be passed to the
- * tensor, such as 'R' or 'BR' (The order of the channels affect the order of
- * tensor values. ). (If the values passed from texture is less than the tensor
- * size, zeros will be padded at the rear.). If the values is a `WebGPUData`
- * object, the dtype could only be 'float32' or 'int32 and the object has to
- * have: buffer, a `GPUBuffer`. The buffer must: 1. share the same `GPUDevice`
- * with TFJS's WebGPU backend; 2. buffer.usage should at least support
- * GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC; 3. buffer.size should not
- * be smaller than the byte size of tensor shape. WebGPUData optionally supports
- * zero copy by flag zeroCopy. When zeroCopy is false or undefined(default),
- * this passing GPUBuffer can be destroyed after tensor is created. When
- * zeroCopy is true, this GPUBuffer is bound directly by the tensor, so do not
- * destroy this GPUBuffer until all access is done.
- * @param shape The shape of the tensor. Optional. If not provided,
- *   it is inferred from `values`.
- * @param dtype The data type.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function tensor<R extends Rank>(
-    values: TensorLike|WebGLData|WebGPUData, shape?: ShapeMap[R],
-    dtype?: DataType): Tensor<R> {
-  const inferredShape = inferShape(values, dtype);
-  return makeTensor(values, shape, inferredShape, dtype) as Tensor<R>;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/tensor1d.ts b/tfjs-master/tfjs-core/src/ops/tensor1d.ts
deleted file mode 100644
index 4ae545ac0..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor1d.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor1D} from '../tensor';
-import {inferShape} from '../tensor_util_env';
-import {TensorLike1D} from '../types';
-import {DataType} from '../types';
-import {assertNonNull} from '../util';
-import {makeTensor} from './tensor_ops_util';
-
-/**
- * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.
- *
- * The same functionality can be achieved with `tf.tensor`, but in general
- * we recommend using `tf.tensor1d` as it makes the code more readable.
- *
- * ```js
- * tf.tensor1d([1, 2, 3]).print();
- * ```
- *
- * @param values The values of the tensor. Can be array of numbers,
- *     or a `TypedArray`.
- * @param dtype The data type.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function tensor1d(values: TensorLike1D, dtype?: DataType): Tensor1D {
-  assertNonNull(values);
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 1) {
-    throw new Error('tensor1d() requires values to be a flat/TypedArray');
-  }
-  const shape: number[] = null;
-  return makeTensor(values, shape, inferredShape, dtype) as Tensor1D;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/tensor2d.ts b/tfjs-master/tfjs-core/src/ops/tensor2d.ts
deleted file mode 100644
index 0d4571549..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor2d.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor2D} from '../tensor';
-import {inferShape} from '../tensor_util_env';
-import {TensorLike2D} from '../types';
-import {DataType} from '../types';
-import {assertNonNull} from '../util';
-import {makeTensor} from './tensor_ops_util';
-
-/**
- * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.
- *
- * The same functionality can be achieved with `tf.tensor`, but in general
- * we recommend using `tf.tensor2d` as it makes the code more readable.
- *
- *  ```js
- * // Pass a nested array.
- * tf.tensor2d([[1, 2], [3, 4]]).print();
- * ```
- * ```js
- * // Pass a flat array and specify a shape.
- * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();
- * ```
- *
- * @param values The values of the tensor. Can be nested array of numbers,
- *     or a flat array, or a `TypedArray`.
- * @param shape The shape of the tensor. If not provided, it is inferred from
- *     `values`.
- * @param dtype The data type.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function tensor2d(
-    values: TensorLike2D, shape?: [number, number],
-    dtype?: DataType): Tensor2D {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 2) {
-    throw new Error('tensor2d() requires shape to have two numbers');
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 2 && inferredShape.length !== 1) {
-    throw new Error(
-        'tensor2d() requires values to be number[][] or flat/TypedArray');
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error(
-        'tensor2d() requires shape to be provided when `values` ' +
-        'are a flat/TypedArray');
-  }
-  return makeTensor(values, shape, inferredShape, dtype) as Tensor2D;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/tensor3d.ts b/tfjs-master/tfjs-core/src/ops/tensor3d.ts
deleted file mode 100644
index e15e60825..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor3d.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor3D} from '../tensor';
-import {inferShape} from '../tensor_util_env';
-import {TensorLike3D} from '../types';
-import {DataType} from '../types';
-import {assertNonNull} from '../util';
-import {makeTensor} from './tensor_ops_util';
-
-/**
- * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.
- *
- * The same functionality can be achieved with `tf.tensor`, but in general
- * we recommend using `tf.tensor3d` as it makes the code more readable.
- *
- *  ```js
- * // Pass a nested array.
- * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();
- * ```
- * ```js
- * // Pass a flat array and specify a shape.
- * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();
- * ```
- *
- * @param values The values of the tensor. Can be nested array of numbers,
- *     or a flat array, or a `TypedArray`.
- * @param shape The shape of the tensor. If not provided,  it is inferred from
- *     `values`.
- * @param dtype The data type.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function tensor3d(
-    values: TensorLike3D, shape?: [number, number, number],
-    dtype?: DataType): Tensor3D {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 3) {
-    throw new Error('tensor3d() requires shape to have three numbers');
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 3 && inferredShape.length !== 1) {
-    throw new Error(
-        'tensor3d() requires values to be number[][][] or flat/TypedArray');
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error(
-        'tensor3d() requires shape to be provided when `values` ' +
-        'are a flat array');
-  }
-  return makeTensor(values, shape, inferredShape, dtype) as Tensor3D;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/tensor4d.ts b/tfjs-master/tfjs-core/src/ops/tensor4d.ts
deleted file mode 100644
index d4969d8bb..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor4d.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor4D} from '../tensor';
-import {inferShape} from '../tensor_util_env';
-import {TensorLike4D} from '../types';
-import {DataType} from '../types';
-import {assertNonNull} from '../util';
-import {makeTensor} from './tensor_ops_util';
-
-/**
- * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.
- *
- * The same functionality can be achieved with `tf.tensor`, but in general
- * we recommend using `tf.tensor4d` as it makes the code more readable.
- *
- *  ```js
- * // Pass a nested array.
- * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();
- * ```
- * ```js
- * // Pass a flat array and specify a shape.
- * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();
- * ```
- *
- * @param values The values of the tensor. Can be nested array of numbers,
- *     or a flat array, or a `TypedArray`.
- * @param shape The shape of the tensor. Optional. If not provided,
- *   it is inferred from `values`.
- * @param dtype The data type.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function tensor4d(
-    values: TensorLike4D, shape?: [number, number, number, number],
-    dtype?: DataType): Tensor4D {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 4) {
-    throw new Error('tensor4d() requires shape to have four numbers');
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 4 && inferredShape.length !== 1) {
-    throw new Error(
-        'tensor4d() requires values to be number[][][][] or flat/TypedArray');
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error(
-        'tensor4d() requires shape to be provided when `values` ' +
-        'are a flat array');
-  }
-  return makeTensor(values, shape, inferredShape, dtype) as Tensor4D;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/tensor5d.ts b/tfjs-master/tfjs-core/src/ops/tensor5d.ts
deleted file mode 100644
index f2e53e6be..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor5d.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor5D} from '../tensor';
-import {inferShape} from '../tensor_util_env';
-import {TensorLike5D} from '../types';
-import {DataType} from '../types';
-import {assertNonNull} from '../util';
-import {makeTensor} from './tensor_ops_util';
-
-/**
- * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.
- *
- * The same functionality can be achieved with `tf.tensor`, but in general
- * we recommend using `tf.tensor5d` as it makes the code more readable.
- *
- *  ```js
- * // Pass a nested array.
- * tf.tensor5d([[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]).print();
- * ```
- * ```js
- * // Pass a flat array and specify a shape.
- * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();
- * ```
- *
- * @param values The values of the tensor. Can be nested array of numbers,
- *     or a flat array, or a `TypedArray`.
- * @param shape The shape of the tensor. Optional. If not provided,
- *   it is inferred from `values`.
- * @param dtype The data type.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function tensor5d(
-    values: TensorLike5D, shape?: [number, number, number, number, number],
-    dtype?: DataType): Tensor5D {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 5) {
-    throw new Error('tensor5d() requires shape to have five numbers');
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 5 && inferredShape.length !== 1) {
-    throw new Error(
-        'tensor5d() requires values to be ' +
-        'number[][][][][] or flat/TypedArray');
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error(
-        'tensor5d() requires shape to be provided when `values` ' +
-        'are a flat array');
-  }
-  return makeTensor(values, shape, inferredShape, dtype) as Tensor5D;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/tensor6d.ts b/tfjs-master/tfjs-core/src/ops/tensor6d.ts
deleted file mode 100644
index 63f5feea2..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor6d.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor6D} from '../tensor';
-import {inferShape} from '../tensor_util_env';
-import {TensorLike6D} from '../types';
-import {DataType} from '../types';
-import {assertNonNull} from '../util';
-import {makeTensor} from './tensor_ops_util';
-
-/**
- * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.
- *
- * The same functionality can be achieved with `tf.tensor`, but in general
- * we recommend using `tf.tensor6d` as it makes the code more readable.
- *
- *  ```js
- * // Pass a nested array.
- * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();
- * ```
- * ```js
- * // Pass a flat array and specify a shape.
- * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();
- * ```
- *
- * @param values The values of the tensor. Can be nested array of numbers,
- *     or a flat array, or a `TypedArray`.
- * @param shape The shape of the tensor. Optional. If not provided,
- *   it is inferred from `values`.
- * @param dtype The data type.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function tensor6d(
-    values: TensorLike6D,
-    shape?: [number, number, number, number, number, number],
-    dtype?: DataType): Tensor6D {
-  assertNonNull(values);
-  if (shape != null && shape.length !== 6) {
-    throw new Error('tensor6d() requires shape to have six numbers');
-  }
-  const inferredShape = inferShape(values, dtype);
-  if (inferredShape.length !== 6 && inferredShape.length !== 1) {
-    throw new Error(
-        'tensor6d() requires values to be number[][][][][][] or ' +
-        'flat/TypedArray');
-  }
-  if (inferredShape.length === 1 && shape == null) {
-    throw new Error(
-        'tensor6d() requires shape to be provided when `values` ' +
-        'are a flat array');
-  }
-  shape = shape ||
-      inferredShape as [number, number, number, number, number, number];
-  return makeTensor(values, shape, inferredShape, dtype) as Tensor6D;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/tensor_ops_util.ts b/tfjs-master/tfjs-core/src/ops/tensor_ops_util.ts
deleted file mode 100644
index c38783e25..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor_ops_util.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tensor} from '../tensor';
-import {isWebGLData, isWebGPUData, TensorLike, TypedArray, WebGLData, WebGPUData} from '../types';
-import {DataType} from '../types';
-import {assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray} from '../util';
-
-/** This is shared code across all tensor creation methods. */
-export function makeTensor(
-    values: TensorLike|WebGLData|WebGPUData, shape: number[],
-    inferredShape: number[], dtype?: DataType): Tensor {
-  if (dtype == null) {
-    dtype = inferDtype(values);
-  } else if (dtype === 'complex64') {
-    throw new Error(
-        `Cannot construct a complex64 tensor directly. ` +
-        `Please use tf.complex(real, imag).`);
-  }
-
-  if (isWebGPUData(values) || isWebGLData(values)) {
-    if (dtype !== 'float32' && dtype !== 'int32') {
-      throw new Error(
-          `Creating tensor from GPU data only supports ` +
-          `'float32'|'int32' dtype, while the dtype is ${dtype}.`);
-    }
-    return ENGINE.backend.createTensorFromGPUData(
-        values, shape || inferredShape, dtype);
-  }
-
-  if (!isTypedArray(values) && !Array.isArray(values) &&
-      typeof values !== 'number' && typeof values !== 'boolean' &&
-      typeof values !== 'string') {
-    throw new Error(
-        'values passed to tensor(values) must be a number/boolean/string or ' +
-        'an array of numbers/booleans/strings, or a TypedArray');
-  }
-  // Verify that the shape matches the inferred shape.
-  if (shape != null) {
-    assertNonNegativeIntegerDimensions(shape);
-
-    const providedSize = sizeFromShape(shape);
-    const inferredSize = sizeFromShape(inferredShape);
-    assert(
-        providedSize === inferredSize,
-        () =>
-            `Based on the provided shape, [${shape}], the tensor should have ` +
-            `${providedSize} values but has ${inferredSize}`);
-
-    for (let i = 0; i < inferredShape.length; ++i) {
-      const inferred = inferredShape[i];
-      const flatDimsDontMatch = i === inferredShape.length - 1 ?
-          inferred !== sizeFromShape(shape.slice(i)) :
-          true;
-      assert(
-          inferredShape[i] === shape[i] || !flatDimsDontMatch,
-          () => `Error creating a new Tensor. Inferred shape ` +
-              `(${inferredShape}) does not match the provided ` +
-              `shape (${shape}). `);
-    }
-  }
-
-  if (!isTypedArray(values) && !Array.isArray(values)) {
-    values = [values] as number[];
-  }
-
-  shape = shape || inferredShape;
-  values = dtype !== 'string' ?
-      toTypedArray(values, dtype) :
-      flatten(values as string[], [], true) as string[];
-  return ENGINE.makeTensor(values as TypedArray, shape, dtype);
-}
diff --git a/tfjs-master/tfjs-core/src/ops/tensor_scatter_update.ts b/tfjs-master/tfjs-core/src/ops/tensor_scatter_update.ts
deleted file mode 100644
index 6589ca970..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor_scatter_update.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {TensorScatterUpdate, TensorScatterUpdateAttrs, TensorScatterUpdateInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {Rank, TensorLike} from '../types';
-
-import {op} from './operation';
-import * as scatter_nd_util from './scatter_nd_util';
-
-/**
- * Creates a new tensor by applying sparse updates to individual
- * values or slices to the passed in tensor according to
- * indices. This operator is the similar to scatterNd op, except that the
- * udpates are scattered on an existing tensor (as opposed to a zero-tensor).
- *
- * If indices contains duplicates, then we pick the last update for the index.
- *
- * If an out of bound index is found on CPU, an error is returned.
- *
- * Warning: There are some GPU specific semantics for this operation.
- *  - If an out of bound index is found, the index is ignored.
- *  - The order in which updates are applied is nondeterministic, so the output
- * will be nondeterministic if indices contains duplicates.
- * ```js
- * const shape = [8];
- * const tensor = tf.ones(shape);
- * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');
- * const updates = tf.tensor1d([9, 10, 11, 12]);
- *
- * tf.tensorScatterUpdate(tensor, indices, updates).print();
- *    //[1, 11, 1, 10, 9, 1, 1, 12]
- * ```
- *
- * @param tensor A Tensor. Tensor to copy/update.
- * @param indices The tensor contains the indices into the output tensor, must
- *     have at least 2 axes: (num_updates, index_depth).
- * @param updates The tensor contains the value for the indices.
- *
- * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
- */
-function tensorScatterUpdate_<R extends Rank>(
-    tensor: Tensor<R>|TensorLike, indices: Tensor|TensorLike,
-    updates: Tensor|TensorLike): Tensor<R> {
-  const $tensor = convertToTensor(tensor, 'tensor', 'tensorScatterupdate');
-  const $indices =
-      convertToTensor(indices, 'indices', 'tensorScatterupdate', 'int32');
-  const $updates = convertToTensor(updates, 'updates', 'tensorScatterupdate');
-  scatter_nd_util.validateInput($updates, $indices, $tensor.shape);
-  if ($tensor.dtype !== $updates.dtype) {
-    throw new Error(
-        `tensor and updates must have the same dtype, instead they are ${
-            $tensor.dtype} and ${$updates.dtype}.`);
-  }
-
-  const inputs: TensorScatterUpdateInputs = {
-    tensor: $tensor,
-    indices: $indices,
-    updates: $updates
-  };
-  const attrs: TensorScatterUpdateAttrs = {};
-
-  // tslint:disable-next-line: no-unnecessary-type-assertion
-  return ENGINE.runKernel(
-             TensorScatterUpdate, inputs as unknown as NamedTensorMap,
-             attrs as unknown as NamedAttrMap) as Tensor<R>;
-}
-
-export const tensorScatterUpdate = op({tensorScatterUpdate_});
diff --git a/tfjs-master/tfjs-core/src/ops/tensor_scatter_update_test.ts b/tfjs-master/tfjs-core/src/ops/tensor_scatter_update_test.ts
deleted file mode 100644
index 439b66335..000000000
--- a/tfjs-master/tfjs-core/src/ops/tensor_scatter_update_test.ts
+++ /dev/null
@@ -1,162 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('tensorScatterUpdate', ALL_ENVS, () => {
-  it('should work for 2d', async () => {
-    const tensor = tf.ones([5, 3], 'int32');
-    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
-    const updates = tf.tensor2d(
-        [100, 101, 102, 777, 778, 779, 1000, 1001, 1002], [3, 3], 'int32');
-    const result = tf.tensorScatterUpdate(tensor, indices, updates);
-    expect(result.shape).toEqual(tensor.shape);
-    expect(result.dtype).toEqual(tensor.dtype);
-    expectArraysClose(
-        await result.data(),
-        [100, 101, 102, 1, 1, 1, 1000, 1001, 1002, 1, 1, 1, 777, 778, 779]);
-  });
-
-  it('should work for simple 1d', async () => {
-    const shape = [5];
-    const tensor = tf.ones(shape);
-    const indices = tf.tensor2d([3], [1, 1], 'int32');
-    const updates = tf.tensor1d([101], 'float32');
-    const result = tf.tensorScatterUpdate(tensor, indices, updates);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(await result.data(), [1, 1, 1, 101, 1]);
-  });
-
-  it('should work for multiple 1d', async () => {
-    const shape = [5];
-    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
-    const updates = tf.tensor1d([100, 101, 102], 'float32');
-    const tensor = tf.ones(shape);
-
-    const result = tf.tensorScatterUpdate(tensor, indices, updates);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(await result.data(), [100, 1, 102, 1, 101]);
-  });
-
-  it('should work for high rank updates', async () => {
-    const indices = tf.tensor2d([0, 2], [2, 1], 'int32');
-    const updates = tf.tensor3d(
-        [
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
-          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
-        ],
-        [2, 4, 4], 'float32');
-    const shape = [4, 4, 4];
-    const tensor = tf.ones(shape);
-    const result = tf.tensorScatterUpdate(tensor, indices, updates);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(await result.data(), [
-      5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1,
-      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,
-      8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
-    ]);
-  });
-
-  it('should work for high rank indices', async () => {
-    const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');
-    const updates = tf.tensor1d([10, 20], 'float32');
-    const shape = [3, 3];
-    const tensor = tf.ones(shape);
-    const result = tf.tensorScatterUpdate(tensor, indices, updates);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-    expectArraysClose(await result.data(), [1, 20, 10, 1, 1, 1, 1, 1, 1]);
-  });
-
-  it('should work for high rank indices and update', () => {
-    const indices = tf.tensor2d([1, 1, 1, 1, 1, 1], [3, 2], 'int32');
-    const updates = tf.ones([3, 256], 'float32');
-    const shape = [2, 2, 256];
-    const tensor = tf.ones(shape);
-    const result = tf.tensorScatterUpdate(tensor, indices, updates);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual(updates.dtype);
-  });
-
-  it('should work for tensorLike input', async () => {
-    const indices = [[0], [4], [2]];
-    const updates = [100, 101, 102];
-    const shape = [5];
-    const tensor = tf.ones(shape);
-    const result = tf.tensorScatterUpdate(tensor, indices, updates);
-    expect(result.shape).toEqual(shape);
-    expect(result.dtype).toEqual('float32');
-    expectArraysClose(await result.data(), [100, 1, 102, 1, 101]);
-  });
-
-  it('should throw error when indices type is not int32', () => {
-    const indices = tf.tensor2d([0, 2, 1, 1], [2, 2], 'float32');
-    const updates = tf.tensor1d([10, 20], 'float32');
-    const shape = [3, 3];
-    const tensor = tf.ones(shape);
-    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
-  });
-
-  it('should throw error when tensor and updates type does not match', () => {
-    const indices = tf.tensor2d([0, 2, 1, 1], [2, 2], 'float32');
-    const updates = tf.tensor1d([10, 20], 'int32');
-    const shape = [3, 3];
-    const tensor = tf.ones(shape, 'float32');
-    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
-  });
-
-  it('should throw error when indices and update mismatch', () => {
-    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
-    const updates = tf.tensor2d(
-        [100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004],
-        [3, 4], 'float32');
-    const shape = [5, 3];
-    const tensor = tf.ones(shape);
-    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
-  });
-
-  it('should throw error when indices and update count mismatch', () => {
-    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
-    const updates =
-        tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');
-    const shape = [5, 3];
-    const tensor = tf.ones(shape);
-    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
-  });
-
-  it('should throw error when indices are scalar', () => {
-    const indices = tf.scalar(1, 'int32');
-    const updates =
-        tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');
-    const shape = [5, 3];
-    const tensor = tf.ones(shape);
-    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
-  });
-
-  it('should throw error when update is scalar', () => {
-    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
-    const updates = tf.scalar(1, 'float32');
-    const shape = [5, 3];
-    const tensor = tf.ones(shape);
-    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/tile.ts b/tfjs-master/tfjs-core/src/ops/tile.ts
deleted file mode 100644
index a468a61ff..000000000
--- a/tfjs-master/tfjs-core/src/ops/tile.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tile, TileAttrs, TileInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Construct a tensor by repeating it the number of times given by reps.
- *
- * This operation creates a new tensor by replicating `input` `reps`
- * times. The output tensor's `i`th dimension has `input.shape[i] *
- * reps[i]` elements, and the values of `input` are replicated
- * `reps[i]` times along the `i`th dimension. For example, tiling
- * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.
- *
- * ```js
- * const a = tf.tensor1d([1, 2]);
- *
- * a.tile([2]).print();    // or tf.tile(a, [2])
- * ```
- *
- * ```js
- * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * a.tile([1, 2]).print();  // or tf.tile(a, [1,2])
- * ```
- * @param x The tensor to tile.
- * @param reps Determines the number of replications per dimension.
- *
- * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
- */
-function tile_<T extends Tensor>(x: T|TensorLike, reps: number[]): T {
-  const $x = convertToTensor(x, 'x', 'tile', 'string_or_numeric');
-  util.assert(
-      $x.rank === reps.length,
-      () => `Error in transpose: rank of input ${$x.rank} ` +
-          `must match length of reps ${reps}.`);
-
-  const inputs: TileInputs = {x: $x};
-  const attrs: TileAttrs = {reps};
-
-  return ENGINE.runKernel(
-      Tile, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const tile = /* @__PURE__ */ op({tile_});
diff --git a/tfjs-master/tfjs-core/src/ops/tile_test.ts b/tfjs-master/tfjs-core/src/ops/tile_test.ts
deleted file mode 100644
index 205531e53..000000000
--- a/tfjs-master/tfjs-core/src/ops/tile_test.ts
+++ /dev/null
@@ -1,271 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('tile', ALL_ENVS, () => {
-  it('1D (tile)', async () => {
-    const t = tf.tensor1d([1, 2, 3]);
-    const t2 = tf.tile(t, [2]);
-
-    expect(t2.shape).toEqual([6]);
-    expectArraysClose(await t2.data(), [1, 2, 3, 1, 2, 3]);
-  });
-
-  it('2D (tile)', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
-    let t2 = tf.tile(t, [1, 2]);
-
-    expect(t2.shape).toEqual([2, 4]);
-    expectArraysClose(await t2.data(), [1, 11, 1, 11, 2, 22, 2, 22]);
-
-    t2 = tf.tile(t, [2, 1]);
-    expect(t2.shape).toEqual([4, 2]);
-    expectArraysClose(await t2.data(), [1, 11, 2, 22, 1, 11, 2, 22]);
-
-    t2 = tf.tile(t, [2, 2]);
-    expect(t2.shape).toEqual([4, 4]);
-    expectArraysClose(
-        await t2.data(),
-        [1, 11, 1, 11, 2, 22, 2, 22, 1, 11, 1, 11, 2, 22, 2, 22]);
-  });
-
-  it('3D (tile)', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-    const t2 = tf.tile(t, [1, 2, 1]);
-
-    expect(t2.shape).toEqual([2, 4, 2]);
-    expectArraysClose(
-        await t2.data(), [1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 7, 8, 5, 6, 7, 8]);
-  });
-
-  it('4D (tile)', async () => {
-    const t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2]);
-    const t2 = tf.tile(t, [1, 2, 1, 1]);
-
-    expect(t2.shape).toEqual([1, 4, 2, 2]);
-    expectArraysClose(
-        await t2.data(), [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('5D (tile)', async () => {
-    const t = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2]);
-    const t2 = tf.tile(t, [1, 2, 1, 1, 1]);
-
-    expect(t2.shape).toEqual([1, 2, 2, 2, 2]);
-    expectArraysClose(
-        await t2.data(), [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('6D (tile)', async () => {
-    const t = tf.tensor6d(
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-        [1, 1, 2, 2, 2, 2]);
-    const t2 = tf.tile(t, [1, 2, 1, 1, 1, 1]);
-
-    expect(t2.shape).toEqual([1, 2, 2, 2, 2, 2]);
-    expectArraysClose(await t2.data(), [
-      1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
-      1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
-    ]);
-  });
-
-  it('1d string tensor', async () => {
-    const a = tf.tensor(['a', 'b', 'c']);
-    const res = tf.tile(a, [2]);
-    expect(res.shape).toEqual([6]);
-    expectArraysEqual(await res.data(), ['a', 'b', 'c', 'a', 'b', 'c']);
-  });
-
-  it('2d string tensor', async () => {
-    const a = tf.tensor([['a', 'b'], ['c', 'd']]);
-    const res = tf.tile(a, [2, 3]);
-    expect(res.shape).toEqual([4, 6]);
-    expectArraysEqual(await res.data(), [
-      'a', 'b', 'a', 'b', 'a', 'b', 'c', 'd', 'c', 'd', 'c', 'd',
-      'a', 'b', 'a', 'b', 'a', 'b', 'c', 'd', 'c', 'd', 'c', 'd'
-    ]);
-  });
-
-  it('6D string tensor', async () => {
-    const t = tf.tensor6d(
-        [
-          'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight',
-          'nine', 'ten', 'eleven', 'twelve', 'thirteen', 'fourteen', 'fifteen',
-          'sixteen'
-        ],
-        [1, 1, 2, 2, 2, 2]);
-    const t2 = tf.tile(t, [1, 2, 1, 1, 1, 1]);
-
-    expect(t2.shape).toEqual([1, 2, 2, 2, 2, 2]);
-    expectArraysClose(await t2.data(), [
-      'one',      'two',      'three',   'four',    'five',     'six',
-      'seven',    'eight',    'nine',    'ten',     'eleven',   'twelve',
-      'thirteen', 'fourteen', 'fifteen', 'sixteen', 'one',      'two',
-      'three',    'four',     'five',    'six',     'seven',    'eight',
-      'nine',     'ten',      'eleven',  'twelve',  'thirteen', 'fourteen',
-      'fifteen',  'sixteen'
-    ]);
-  });
-
-  it('propagates NaNs', async () => {
-    const t = tf.tensor1d([1, 2, NaN]);
-
-    const t2 = tf.tile(t, [2]);
-
-    expect(t2.shape).toEqual([6]);
-    expectArraysClose(await t2.data(), [1, 2, NaN, 1, 2, NaN]);
-  });
-
-  it('1D bool (tile)', async () => {
-    const t = tf.tensor1d([true, false, true], 'bool');
-    const t2 = tf.tile(t, [2]);
-
-    expect(t2.shape).toEqual([6]);
-    expect(t2.dtype).toBe('bool');
-    expectArraysEqual(await t2.data(), [1, 0, 1, 1, 0, 1]);
-  });
-
-  it('2D bool (tile)', async () => {
-    const t = tf.tensor2d([true, false, true, true], [2, 2], 'bool');
-    let t2 = tf.tile(t, [1, 2]);
-
-    expect(t2.shape).toEqual([2, 4]);
-    expect(t2.dtype).toBe('bool');
-    expectArraysEqual(await t2.data(), [1, 0, 1, 0, 1, 1, 1, 1]);
-
-    t2 = tf.tile(t, [2, 1]);
-    expect(t2.shape).toEqual([4, 2]);
-    expect(t2.dtype).toBe('bool');
-    expectArraysEqual(await t2.data(), [1, 0, 1, 1, 1, 0, 1, 1]);
-
-    t2 = tf.tile(t, [2, 2]);
-    expect(t2.shape).toEqual([4, 4]);
-    expect(t2.dtype).toBe('bool');
-    expectArraysEqual(
-        await t2.data(), [1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1]);
-  });
-
-  it('3D bool (tile)', async () => {
-    const t = tf.tensor3d(
-        [true, false, true, false, true, false, true, false], [2, 2, 2],
-        'bool');
-    const t2 = tf.tile(t, [1, 2, 1]);
-
-    expect(t2.shape).toEqual([2, 4, 2]);
-    expect(t2.dtype).toBe('bool');
-    expectArraysEqual(
-        await t2.data(), [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0]);
-  });
-
-  it('1D int32 (tile)', async () => {
-    const t = tf.tensor1d([1, 2, 5], 'int32');
-    const t2 = tf.tile(t, [2]);
-
-    expect(t2.shape).toEqual([6]);
-    expect(t2.dtype).toBe('int32');
-    expectArraysEqual(await t2.data(), [1, 2, 5, 1, 2, 5]);
-  });
-
-  it('2D int32 (tile)', async () => {
-    const t = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
-    let t2 = tf.tile(t, [1, 2]);
-
-    expect(t2.shape).toEqual([2, 4]);
-    expect(t2.dtype).toBe('int32');
-    expectArraysEqual(await t2.data(), [1, 2, 1, 2, 3, 4, 3, 4]);
-
-    t2 = tf.tile(t, [2, 1]);
-    expect(t2.shape).toEqual([4, 2]);
-    expect(t2.dtype).toBe('int32');
-    expectArraysEqual(await t2.data(), [1, 2, 3, 4, 1, 2, 3, 4]);
-
-    t2 = tf.tile(t, [2, 2]);
-    expect(t2.shape).toEqual([4, 4]);
-    expect(t2.dtype).toBe('int32');
-    expectArraysEqual(
-        await t2.data(), [1, 2, 1, 2, 3, 4, 3, 4, 1, 2, 1, 2, 3, 4, 3, 4]);
-  });
-
-  it('3D int32 (tile)', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2], 'int32');
-    const t2 = tf.tile(t, [1, 2, 1]);
-
-    expect(t2.shape).toEqual([2, 4, 2]);
-    expect(t2.dtype).toBe('int32');
-    expectArraysEqual(
-        await t2.data(), [1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 7, 8, 5, 6, 7, 8]);
-  });
-
-  it('1D (tile) gradient', async () => {
-    const x = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([0.1, 0.2, 0.3, 1, 2, 3, 10, 20, 30]);
-    const gradients = tf.grad(x => tf.tile(x, [3]))(x, dy);
-    expectArraysClose(await gradients.data(), [11.1, 22.2, 33.3]);
-    expect(gradients.shape).toEqual([3]);
-  });
-
-  it('gradient with clones', async () => {
-    const x = tf.tensor1d([1, 2, 3]);
-    const dy = tf.tensor1d([0.1, 0.2, 0.3, 1, 2, 3, 10, 20, 30]);
-    const gradients = tf.grad(x => tf.tile(x.clone(), [3]).clone())(x, dy);
-    expectArraysClose(await gradients.data(), [11.1, 22.2, 33.3]);
-    expect(gradients.shape).toEqual([3]);
-  });
-
-  it('2D (tile) gradient', async () => {
-    const x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const dy = tf.tensor2d([[1, 2, 10, 20], [3, 4, 30, 40]], [2, 4]);
-    const gradients = tf.grad(x => tf.tile(x, [1, 2]))(x, dy);
-    expectArraysClose(await gradients.data(), [11, 22, 33, 44]);
-    expect(gradients.shape).toEqual([2, 2]);
-  });
-
-  it('3D (tile) gradient', async () => {
-    const x = tf.tensor3d([[[1], [2]], [[3], [4]]], [2, 2, 1]);
-    const dy = tf.tensor3d([[[1, 10], [2, 20]], [[3, 30], [4, 40]]], [2, 2, 2]);
-    const gradients = tf.grad(x => tf.tile(x, [1, 1, 2]))(x, dy);
-    expectArraysClose(await gradients.data(), [11, 22, 33, 44]);
-    expect(gradients.shape).toEqual([2, 2, 1]);
-  });
-
-  it('4D (tile) gradient', async () => {
-    const x = tf.tensor4d([[[[1]], [[2]]], [[[3]], [[4]]]], [2, 2, 1, 1]);
-    const dy = tf.tensor4d(
-        [
-          [[[.01, .1], [1, 10]], [[.02, .2], [2, 20]]],
-          [[[.03, .3], [3, 30]], [[.04, .4], [4, 40]]]
-        ],
-        [2, 2, 2, 2]);
-    const gradients = tf.grad(x => tf.tile(x, [1, 1, 2, 2]))(x, dy);
-    expectArraysClose(await gradients.data(), [11.11, 22.22, 33.33, 44.44]);
-    expect(gradients.shape).toEqual([2, 2, 1, 1]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.tile({} as tf.Tensor, [1]))
-        .toThrowError(/Argument 'x' passed to 'tile' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const res = tf.tile([1, 2, 3], [2]);
-    expect(res.shape).toEqual([6]);
-    expectArraysClose(await res.data(), [1, 2, 3, 1, 2, 3]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/to_pixels_test.ts b/tfjs-master/tfjs-core/src/ops/to_pixels_test.ts
deleted file mode 100644
index 4c33a66d4..000000000
--- a/tfjs-master/tfjs-core/src/ops/to_pixels_test.ts
+++ /dev/null
@@ -1,168 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectPromiseToFail} from '../test_util';
-
-describeWithFlags('toPixels no canvas', ALL_ENVS, () => {
-  it('draws a rank-2 float32 tensor', async () => {
-    const x = tf.tensor2d([.15, .2], [2, 1], 'float32');
-
-    const data = await tf.browser.toPixels(x);
-    const expected = new Uint8ClampedArray([
-      Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255), 255,
-      Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255), 255
-    ]);
-    expect(data).toEqual(expected);
-  });
-
-  it('draws a rank-2 int32 tensor', async () => {
-    const x = tf.tensor2d([10, 20], [2, 1], 'int32');
-    const data = await tf.browser.toPixels(x);
-    const expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
-    expect(data).toEqual(expected);
-  });
-
-  it('draws a rank-3 float32 tensor, 1 channel', async () => {
-    const x = tf.tensor3d([.15, .2], [2, 1, 1], 'float32');
-
-    const data = await tf.browser.toPixels(x);
-    const expected = new Uint8ClampedArray([
-      Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255), 255,
-      Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255), 255
-    ]);
-    expect(data).toEqual(expected);
-  });
-
-  it('draws a rank-3 int32 tensor, 1 channel', async () => {
-    const x = tf.tensor3d([10, 20], [2, 1, 1], 'int32');
-
-    const data = await tf.browser.toPixels(x);
-    const expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
-    expect(data).toEqual(expected);
-  });
-
-  it('draws a rank-3 float32 tensor, 3 channel', async () => {
-    // 0.1 and 0.3 are changed to 0.1001 and 0.3001 to avoid boundary conditions
-    // such as Math.round(~25.5) which on Mobile Safari gives 25 and Desktop
-    // gives 26.
-    const x =
-        tf.tensor3d([.05, .1001, .15, .2, .25, .3001], [2, 1, 3], 'float32');
-
-    const data = await tf.browser.toPixels(x);
-    const expected = new Uint8ClampedArray([
-      Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),
-      255, Math.round(.2 * 255), Math.round(.25 * 255), Math.round(.3001 * 255),
-      255
-    ]);
-    expect(data).toEqual(expected);
-  });
-
-  it('draws a rank-3 int32 tensor, 3 channel', async () => {
-    const x = tf.tensor3d([10, 20, 30, 40, 50, 60], [2, 1, 3], 'int32');
-
-    const data = await tf.browser.toPixels(x);
-    const expected = new Uint8ClampedArray([10, 20, 30, 255, 40, 50, 60, 255]);
-    expect(data).toEqual(expected);
-  });
-
-  it('draws a rank-3 float32 tensor, 4 channel', async () => {
-    const x = tf.tensor3d(
-        [.05, .1001, .15, .2, .25, .3001, .35, .4], [2, 1, 4], 'float32');
-
-    const data = await tf.browser.toPixels(x);
-    const expected = new Uint8ClampedArray([
-      Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),
-      Math.round(.20 * 255), Math.round(.25 * 255), Math.round(.3001 * 255),
-      Math.round(.35 * 255), Math.round(.4 * 255)
-    ]);
-    expect(data).toEqual(expected);
-  });
-
-  it('draws a rank-3 int32 tensor, 4 channel', async () => {
-    const x = tf.tensor3d([10, 20, 30, 40, 50, 60, 70, 80], [2, 1, 4], 'int32');
-
-    const data = await tf.browser.toPixels(x);
-    const expected = new Uint8ClampedArray([10, 20, 30, 40, 50, 60, 70, 80]);
-    expect(data).toEqual(expected);
-  });
-
-  it('throws for scalars', done => {
-    // tslint:disable-next-line:no-any
-    expectPromiseToFail(() => tf.browser.toPixels(tf.scalar(1) as any), done);
-  });
-
-  it('throws for rank-1 tensors', done => {
-    expectPromiseToFail(
-        // tslint:disable-next-line:no-any
-        () => tf.browser.toPixels(tf.tensor1d([1]) as any), done);
-  });
-  it('throws for rank-4 tensors', done => {
-    expectPromiseToFail(
-        // tslint:disable-next-line:no-any
-        () => tf.browser.toPixels(tf.tensor4d([1], [1, 1, 1, 1]) as any), done);
-  });
-  it('throws for bool dtype', done => {
-    expectPromiseToFail(
-        () => tf.browser.toPixels(tf.tensor2d([1], [1, 1], 'bool')), done);
-  });
-  it('throws for rank-3 depth = 2', done => {
-    expectPromiseToFail(
-        () => tf.browser.toPixels(tf.tensor3d([1, 2], [1, 1, 2])), done);
-  });
-  it('throws for rank-3 depth = 5', done => {
-    expectPromiseToFail(
-        () => tf.browser.toPixels(tf.tensor3d([1, 2, 3, 4, 5], [1, 1, 5])),
-        done);
-  });
-  it('throws for float32 tensor with values not in [0 - 1]', done => {
-    expectPromiseToFail(
-        () => tf.browser.toPixels(tf.tensor2d([-1, .5], [1, 2])), done);
-  });
-  it('throws for int32 tensor with values not in [0 - 255]', done => {
-    expectPromiseToFail(
-        () => tf.browser.toPixels(tf.tensor2d([-1, 100], [1, 2], 'int32')),
-        done);
-  });
-  it('throws when passed a non-tensor', done => {
-    // tslint:disable-next-line:no-any
-    expectPromiseToFail(() => tf.browser.toPixels({} as any), done);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[10], [20]];  // 2x1;
-    const data = await tf.browser.toPixels(x);
-
-    const expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
-    expect(data).toEqual(expected);
-  });
-
-  it('does not leak memory', async () => {
-    const x = tf.tensor2d([[.1], [.2]], [2, 1]);
-    const startNumTensors = tf.memory().numTensors;
-    await tf.browser.toPixels(x);
-    expect(tf.memory().numTensors).toEqual(startNumTensors);
-  });
-
-  it('does not leak memory given a tensor-like object', async () => {
-    const x = [[10], [20]];  // 2x1;
-    const startNumTensors = tf.memory().numTensors;
-    await tf.browser.toPixels(x);
-    expect(tf.memory().numTensors).toEqual(startNumTensors);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/topk.ts b/tfjs-master/tfjs-core/src/ops/topk.ts
deleted file mode 100644
index f184dcb4b..000000000
--- a/tfjs-master/tfjs-core/src/ops/topk.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {TopK, TopKAttrs, TopKInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Finds the values and indices of the `k` largest entries along the last
- * dimension.
- *
- * If the input is a vector (rank=1), finds the k largest entries in the vector
- * and outputs their values and indices as vectors. Thus values[j] is the j-th
- * largest entry in input, and its index is indices[j].
- * For higher rank inputs, computes the top k entries along the last dimension.
- *
- * If two elements are equal, the lower-index element appears first.
- *
- * ```js
- * const a = tf.tensor2d([[1, 5], [4, 3]]);
- * const {values, indices} = tf.topk(a);
- * values.print();
- * indices.print();
- * ```
- * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.
- * @param k Number of top elements to look for along the last dimension.
- * @param sorted If true, the resulting `k` elements will be sorted by the
- *     values in descending order.
- *
- * @doc {heading: 'Operations', subheading: 'Evaluation'}
- */
-function topk_<T extends Tensor>(
-    x: T|TensorLike, k = 1, sorted = true): {values: T, indices: T} {
-  const $x = convertToTensor(x, 'x', 'topk');
-  if ($x.rank === 0) {
-    throw new Error('topk() expects the input to be of rank 1 or higher');
-  }
-  const lastDim = $x.shape[$x.shape.length - 1];
-
-  if (k < 0) {
-    throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);
-  }
-
-  if (k > lastDim) {
-    throw new Error(
-        `'k' passed to topk() must be <= the last dimension (${lastDim}) ` +
-        `but got ${k}`);
-  }
-
-  const inputs: TopKInputs = {x: $x};
-  const attrs: TopKAttrs = {k, sorted};
-
-  const [values, indices] = ENGINE.runKernel(
-      TopK, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap) as [T, T];
-
-  return {values, indices};
-}
-
-export const topk = /* @__PURE__ */ op({topk_});
diff --git a/tfjs-master/tfjs-core/src/ops/topk_test.ts b/tfjs-master/tfjs-core/src/ops/topk_test.ts
deleted file mode 100644
index 20253a817..000000000
--- a/tfjs-master/tfjs-core/src/ops/topk_test.ts
+++ /dev/null
@@ -1,289 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-import {scalar} from './scalar';
-import {tensor1d} from './tensor1d';
-import {tensor2d} from './tensor2d';
-import {tensor3d} from './tensor3d';
-
-describeWithFlags('topk', ALL_ENVS, () => {
-  beforeAll(() => {
-    // Ensure WebGL environment uses GPU
-    if (tf.getBackend() === 'webgl') {
-      tf.env().set('TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD', 0);
-      tf.env().set('TOPK_K_CPU_HANDOFF_THRESHOLD', 1024);
-    }
-  });
-
-  it('1d array with k = 0', async () => {
-    const a = tensor1d([20, 10, 40, 30]);
-    const {values, indices} = tf.topk(a, 0);
-
-    expect(values.shape).toEqual([0]);
-    expect(indices.shape).toEqual([0]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), []);
-    expectArraysClose(await indices.data(), []);
-  });
-
-  it('1d array with length 1', async () => {
-    const a = tensor1d([20]);
-    const {values, indices} = tf.topk(a, 1);
-
-    expect(values.shape).toEqual([1]);
-    expect(indices.shape).toEqual([1]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [20]);
-    expectArraysClose(await indices.data(), [0]);
-  });
-
-  it('1d array with default k', async () => {
-    const a = tensor1d([20, 10, 40, 30]);
-    const {values, indices} = tf.topk(a);
-
-    expect(values.shape).toEqual([1]);
-    expect(indices.shape).toEqual([1]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [40]);
-    expectArraysClose(await indices.data(), [2]);
-  });
-
-  it('1d array with default k from tensor.topk', async () => {
-    const a = tensor1d([20, 10, 40, 30]);
-    const {values, indices} = a.topk();
-
-    expect(values.shape).toEqual([1]);
-    expect(indices.shape).toEqual([1]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [40]);
-    expectArraysClose(await indices.data(), [2]);
-  });
-
-  it('2d array with default k', async () => {
-    const a = tensor2d([[10, 50], [40, 30]]);
-    const {values, indices} = tf.topk(a);
-
-    expect(values.shape).toEqual([2, 1]);
-    expect(indices.shape).toEqual([2, 1]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [50, 40]);
-    expectArraysClose(await indices.data(), [1, 0]);
-  });
-
-  it('2d array with k=2', async () => {
-    const a = tensor2d([
-      [1, 5, 2],
-      [4, 3, 6],
-      [3, 2, 1],
-      [1, 2, 3],
-    ]);
-    const k = 2;
-    const {values, indices} = tf.topk(a, k);
-
-    expect(values.shape).toEqual([4, 2]);
-    expect(indices.shape).toEqual([4, 2]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [5, 2, 6, 4, 3, 2, 3, 2]);
-    expectArraysClose(await indices.data(), [1, 2, 2, 0, 0, 1, 2, 1]);
-  });
-
-  it('2d array with k=2 from tensor.topk', async () => {
-    const a = tensor2d([
-      [1, 5, 2],
-      [4, 3, 6],
-      [3, 2, 1],
-      [1, 2, 3],
-    ]);
-    const k = 2;
-    const {values, indices} = a.topk(k);
-
-    expect(values.shape).toEqual([4, 2]);
-    expect(indices.shape).toEqual([4, 2]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [5, 2, 6, 4, 3, 2, 3, 2]);
-    expectArraysClose(await indices.data(), [1, 2, 2, 0, 0, 1, 2, 1]);
-  });
-
-  it('3d array with k=3', async () => {
-    const a = tensor3d([
-      [[1, 5, 2], [4, 3, 6]],
-      [[3, 2, 1], [1, 2, 3]],
-    ]);  // 2x2x3.
-    const k = 3;
-    const {values, indices} = tf.topk(a, k);
-
-    expect(values.shape).toEqual([2, 2, 3]);
-    expect(indices.shape).toEqual([2, 2, 3]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(
-        await values.data(), [5, 2, 1, 6, 4, 3, 3, 2, 1, 3, 2, 1]);
-    expectArraysClose(
-        await indices.data(), [1, 2, 0, 2, 0, 1, 0, 1, 2, 2, 1, 0]);
-  });
-
-  it('topk(int32) propagates int32 dtype', async () => {
-    const a = tensor1d([2, 3, 1, 4], 'int32');
-    const {values, indices} = tf.topk(a);
-
-    expect(values.shape).toEqual([1]);
-    expect(indices.shape).toEqual([1]);
-    expect(values.dtype).toBe('int32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [4]);
-    expectArraysClose(await indices.data(), [3]);
-  });
-
-  it('lower-index element appears first, k=4', async () => {
-    const a = tensor1d([1, 2, 2, 1], 'int32');
-    const k = 4;
-    const {values, indices} = tf.topk(a, k);
-
-    expect(values.shape).toEqual([4]);
-    expect(indices.shape).toEqual([4]);
-    expect(values.dtype).toBe('int32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [2, 2, 1, 1]);
-    expectArraysClose(await indices.data(), [1, 2, 0, 3]);
-  });
-
-  it('lower-index element appears first, k=65', async () => {
-    const a = [
-      1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2,
-      1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2,
-      1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1
-    ];
-    const k = a.length;
-    const {values, indices} = tf.topk(a, k);
-
-    expectArraysClose(await values.data(), [
-      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-      2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
-    ]);
-    expectArraysClose(await indices.data(), [
-      2,  4,  8,  9,  12, 15, 18, 21, 23, 27, 30, 33, 38, 40, 41, 42, 43,
-      45, 47, 48, 49, 51, 52, 54, 55, 57, 61, 63, 0,  1,  3,  5,  6,  7,
-      10, 11, 13, 14, 16, 17, 19, 20, 22, 24, 25, 26, 28, 29, 31, 32, 34,
-      35, 36, 37, 39, 44, 46, 50, 53, 56, 58, 59, 60, 62, 64
-    ]);
-  });
-
-  it('lower-index element appears first, sorted=false', async () => {
-    const a = [
-      1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2,
-      1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2,
-      1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1
-    ];
-    const k = a.length;
-    const {values, indices} = tf.topk(a, k, false);
-
-    expect(values.shape).toEqual([k]);
-    expect(indices.shape).toEqual([k]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-
-    const valuesData = await values.data();
-    valuesData.sort();
-    expectArraysClose(valuesData, [
-      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,
-      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
-    ]);
-
-    const indicesData = await indices.data();
-    const onesIndices = indicesData.filter((index: number) => a[index] === 1);
-    const twosIndices = indicesData.filter((index: number) => a[index] === 2);
-    expectArraysClose(onesIndices, [
-      0,  1,  3,  5,  6,  7,  10, 11, 13, 14, 16, 17, 19,
-      20, 22, 24, 25, 26, 28, 29, 31, 32, 34, 35, 36, 37,
-      39, 44, 46, 50, 53, 56, 58, 59, 60, 62, 64
-    ]);
-    expectArraysClose(twosIndices, [
-      2,  4,  8,  9,  12, 15, 18, 21, 23, 27, 30, 33, 38, 40,
-      41, 42, 43, 45, 47, 48, 49, 51, 52, 54, 55, 57, 61, 63
-    ]);
-  });
-
-  it('throws when k < 0', () => {
-    const a = tensor2d([[10, 50], [40, 30]]);
-    expect(() => tf.topk(a, -1))
-        .toThrowError(/'k' passed to topk\(\) must be >= 0/);
-  });
-
-  it('throws when k > size of array', () => {
-    const a = tensor2d([[10, 50], [40, 30]]);
-    expect(() => tf.topk(a, 3))
-        .toThrowError(/'k' passed to topk\(\) must be <= the last dimension/);
-  });
-
-  it('throws when passed a scalar', () => {
-    const a = scalar(2);
-    expect(() => tf.topk(a))
-        .toThrowError(/topk\(\) expects the input to be of rank 1 or higher/);
-  });
-
-  it('negative infinity input', async () => {
-    const a = [-Infinity, -Infinity, -Infinity, -Infinity, -Infinity];
-    const k = a.length;
-    const {values, indices} = tf.topk(a, k);
-
-    expect(values.shape).toEqual([k]);
-    expect(indices.shape).toEqual([k]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), a);
-    expectArraysClose(await indices.data(), [0, 1, 2, 3, 4]);
-  });
-
-  it('accepts a tensor-like object, k=2', async () => {
-    const a = [20, 10, 40, 30];
-    const k = 2;
-    const {values, indices} = tf.topk(a, k);
-
-    expect(values.shape).toEqual([2]);
-    expect(indices.shape).toEqual([2]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [40, 30]);
-    expectArraysClose(await indices.data(), [2, 3]);
-  });
-
-  it('handles output tensors from other ops', async () => {
-    const a = tensor1d([20, 10, 40, 30]);
-    const b = scalar(2);
-    const {values, indices} = tf.topk(tf.add(a, b));
-
-    expect(values.shape).toEqual([1]);
-    expect(indices.shape).toEqual([1]);
-    expect(values.dtype).toBe('float32');
-    expect(indices.dtype).toBe('int32');
-    expectArraysClose(await values.data(), [42]);
-    expectArraysClose(await indices.data(), [2]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/transpose.ts b/tfjs-master/tfjs-core/src/ops/transpose.ts
deleted file mode 100644
index 1bf5d1bce..000000000
--- a/tfjs-master/tfjs-core/src/ops/transpose.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {tidy} from '../globals';
-import {Transpose, TransposeAttrs, TransposeInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-import {complex} from './complex';
-import {imag} from './imag';
-import {neg} from './neg';
-import {op} from './operation';
-import {real} from './real';
-
-/**
- * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.
- *
- * The returned `tf.Tensor`'s dimension `i` will correspond to the input
- * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,
- * where `n` is the rank of the input `tf.Tensor`. Hence by default, this
- * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.
- *
- * ```js
- * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
- *
- * a.transpose().print();  // or tf.transpose(a)
- * ```
- *
- * @param x The tensor to transpose.
- * @param perm The permutation of the dimensions of a.
- * @param conjugate Will conjugate complex input if true.
- *
- * @doc {heading: 'Operations', subheading: 'Matrices'}
- */
-function transpose_<T extends Tensor>(
-    x: T|TensorLike, perm?: number[], conjugate?: boolean): T {
-  const $x = convertToTensor(x, 'x', 'transpose');
-
-  if (perm == null) {
-    perm = $x.shape.map((s, i) => i).reverse();
-  }
-  util.assert(
-      $x.rank === perm.length,
-      () => `Error in transpose: rank of input ${$x.rank} ` +
-          `must match length of perm ${perm}.`);
-  perm.forEach(axis => {
-    util.assert(
-        axis >= 0 && axis < $x.rank,
-        () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +
-            ` but got ${perm}`);
-  });
-
-  if ($x.rank <= 1) {
-    return $x.clone();
-  }
-
-  const inputs: TransposeInputs = {x: $x};
-  const attrs: TransposeAttrs = {perm};
-
-  if ($x.dtype === 'complex64') {
-    return tidy(() => {
-      let $real = real($x);
-      let $imag = imag($x);
-      $real = ENGINE.runKernel(
-          Transpose, {x: $real} as unknown as NamedTensorMap,
-          attrs as unknown as NamedAttrMap);
-      $imag = ENGINE.runKernel(
-          Transpose, {x: $imag} as unknown as NamedTensorMap,
-          attrs as unknown as NamedAttrMap);
-      if (conjugate) {
-        $imag = neg($imag);
-      }
-      return complex($real, $imag);
-    });
-  }
-
-  return ENGINE.runKernel(
-      Transpose, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const transpose = /* @__PURE__ */ op({transpose_});
diff --git a/tfjs-master/tfjs-core/src/ops/transpose_test.ts b/tfjs-master/tfjs-core/src/ops/transpose_test.ts
deleted file mode 100644
index d81ba908e..000000000
--- a/tfjs-master/tfjs-core/src/ops/transpose_test.ts
+++ /dev/null
@@ -1,263 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('transpose', ALL_ENVS, () => {
-  it('of scalar is no-op', async () => {
-    const a = tf.scalar(3);
-    expectArraysClose(await tf.transpose(a).data(), [3]);
-  });
-
-  it('of 1D is no-op', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    expectArraysClose(await tf.transpose(a).data(), [1, 2, 3]);
-  });
-
-  it('of scalar with perm of incorrect rank throws error', () => {
-    const a = tf.scalar(3);
-    const perm = [0];  // Should be empty array.
-    expect(() => tf.transpose(a, perm)).toThrowError();
-  });
-
-  it('of 1d with perm out of bounds throws error', () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const perm = [1];
-    expect(() => tf.transpose(a, perm)).toThrowError();
-  });
-
-  it('of 1d with perm incorrect rank throws error', () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const perm = [0, 0];  // Should be of length 1.
-    expect(() => tf.transpose(a, perm)).toThrowError();
-  });
-
-  it('2D (no change)', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22, 3, 33, 4, 44], [2, 4]);
-    const t2 = tf.transpose(t, [0, 1]);
-
-    expect(t2.shape).toEqual(t.shape);
-    expectArraysClose(await t2.array(), await t.array());
-  });
-
-  it('2D (transpose)', async () => {
-    const t = tf.tensor2d([1, 11, 2, 22, 3, 33, 4, 44], [2, 4]);
-    const t2 = tf.transpose(t, [1, 0]);
-
-    expect(t2.shape).toEqual([4, 2]);
-    expectArraysClose(await t2.data(), [1, 3, 11, 33, 2, 4, 22, 44]);
-  });
-
-  it('2D, shape has ones', async () => {
-    const t = tf.tensor2d([1, 2, 3, 4], [1, 4]);
-    const t2 = tf.transpose(t, [1, 0]);
-
-    expect(t2.shape).toEqual([4, 1]);
-    expectArraysClose(await t2.data(), [1, 2, 3, 4]);
-  });
-
-  it('3D [r, c, d] => [d, r, c]', async () => {
-    const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
-    const t2 = tf.transpose(t, [2, 0, 1]);
-
-    expect(t2.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await t2.data(), [1, 2, 3, 4, 11, 22, 33, 44]);
-  });
-
-  it('3D [r, c, d] => [d, c, r]', async () => {
-    const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
-    const t2 = tf.transpose(t, [2, 1, 0]);
-
-    expect(t2.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await t2.data(), [1, 3, 2, 4, 11, 33, 22, 44]);
-  });
-
-  it('3D [r, c, d] => [d, r, c], shape has ones', async () => {
-    const perm = [2, 0, 1];
-
-    const t = tf.tensor3d([1, 2, 3, 4], [2, 1, 2]);
-    const tt = tf.transpose(t, perm);
-    expect(tt.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await tt.data(), [1, 3, 2, 4]);
-
-    const t2 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const tt2 = tf.transpose(t2, perm);
-    expect(tt2.shape).toEqual([1, 2, 2]);
-    expectArraysClose(await tt2.data(), [1, 2, 3, 4]);
-
-    const t3 = tf.tensor3d([1, 2, 3, 4], [1, 2, 2]);
-    const tt3 = tf.transpose(t3, perm);
-    expect(tt3.shape).toEqual([2, 1, 2]);
-    expectArraysClose(await tt3.data(), [1, 3, 2, 4]);
-  });
-
-  it('3D [r, c, d] => [r, d, c]', async () => {
-    const perm = [0, 2, 1];
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-    const tt = tf.transpose(t, perm);
-    expect(tt.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await tt.data(), [1, 3, 2, 4, 5, 7, 6, 8]);
-  });
-
-  it('5D [r, c, d, e, f] => [r, c, d, f, e]', async () => {
-    const t = tf.tensor5d(
-        new Array(32).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2]);
-    const t2 = tf.transpose(t, [0, 1, 2, 4, 3]);
-
-    expect(t2.shape).toEqual([2, 2, 2, 2, 2]);
-    expectArraysClose(await t2.data(), [
-      1,  3,  2,  4,  5,  7,  6,  8,  9,  11, 10, 12, 13, 15, 14, 16,
-      17, 19, 18, 20, 21, 23, 22, 24, 25, 27, 26, 28, 29, 31, 30, 32
-    ]);
-  });
-
-  it('4D [r, c, d, e] => [c, r, d, e]', async () => {
-    const t =
-        tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);
-    const t2 = tf.transpose(t, [1, 0, 2, 3]);
-
-    expect(t2.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(
-        await t2.data(),
-        [1, 2, 3, 4, 9, 10, 11, 12, 5, 6, 7, 8, 13, 14, 15, 16]);
-  });
-
-  it('4D [r, c, d, e] => [c, r, e, d]', async () => {
-    const t =
-        tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);
-    const t2 = tf.transpose(t, [1, 0, 3, 2]);
-
-    expect(t2.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(
-        await t2.data(),
-        [1, 3, 2, 4, 9, 11, 10, 12, 5, 7, 6, 8, 13, 15, 14, 16]);
-  });
-
-  it('4D [r, c, d, e] => [e, r, c, d]', async () => {
-    const t =
-        tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);
-    const t2 = tf.transpose(t, [3, 0, 1, 2]);
-
-    expect(t2.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(
-        await t2.data(),
-        [1, 3, 5, 7, 9, 11, 13, 15, 2, 4, 6, 8, 10, 12, 14, 16]);
-  });
-
-  it('4D [r, c, d, e] => [d, c, e, r]', async () => {
-    const t =
-        tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);
-    const t2 = tf.transpose(t, [2, 1, 3, 0]);
-
-    expect(t2.shape).toEqual([2, 2, 2, 2]);
-    expectArraysClose(
-        await t2.data(),
-        [1, 9, 2, 10, 5, 13, 6, 14, 3, 11, 4, 12, 7, 15, 8, 16]);
-  });
-
-  it('5D [r, c, d, e, f] => [c, r, d, e, f]', async () => {
-    const t = tf.tensor5d(
-        new Array(32).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2]);
-    const t2 = tf.transpose(t, [1, 0, 2, 3, 4]);
-
-    expect(t2.shape).toEqual([2, 2, 2, 2, 2]);
-    expectArraysClose(await t2.data(), [
-      1, 2,  3,  4,  5,  6,  7,  8,  17, 18, 19, 20, 21, 22, 23, 24,
-      9, 10, 11, 12, 13, 14, 15, 16, 25, 26, 27, 28, 29, 30, 31, 32
-    ]);
-  });
-
-  it('6D [r, c, d, e, f] => [r, c, d, f, e]', async () => {
-    const t = tf.tensor6d(
-        new Array(64).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2, 2]);
-    const t2 = tf.transpose(t, [0, 1, 2, 3, 5, 4]);
-    expect(t2.shape).toEqual([2, 2, 2, 2, 2, 2]);
-    expectArraysClose(await t2.data(), [
-      1,  3,  2,  4,  5,  7,  6,  8,  9,  11, 10, 12, 13, 15, 14, 16,
-      17, 19, 18, 20, 21, 23, 22, 24, 25, 27, 26, 28, 29, 31, 30, 32,
-      33, 35, 34, 36, 37, 39, 38, 40, 41, 43, 42, 44, 45, 47, 46, 48,
-      49, 51, 50, 52, 53, 55, 54, 56, 57, 59, 58, 60, 61, 63, 62, 64
-    ]);
-  });
-
-  it('6D [r, c, d, e, f, g] => [c, r, d, e, f, g]', async () => {
-    const t = tf.tensor6d(
-        new Array(64).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2, 2]);
-    const t2 = tf.transpose(t, [1, 0, 2, 3, 4, 5]);
-    expect(t2.shape).toEqual([2, 2, 2, 2, 2, 2]);
-    expectArraysClose(await t2.data(), [
-      1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
-      33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
-      17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
-      49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64
-    ]);
-  });
-
-  it('gradient 3D [r, c, d] => [d, c, r]', async () => {
-    const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
-    const perm = [2, 1, 0];
-    const dy = tf.tensor3d([111, 211, 121, 221, 112, 212, 122, 222], [2, 2, 2]);
-    const dt = tf.grad(t => t.transpose(perm))(t, dy);
-    expect(dt.shape).toEqual(t.shape);
-    expect(dt.dtype).toEqual('float32');
-    expectArraysClose(
-        await dt.data(), [111, 112, 121, 122, 211, 212, 221, 222]);
-  });
-
-  it('gradient with clones', async () => {
-    const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
-    const perm = [2, 1, 0];
-    const dy = tf.tensor3d([111, 211, 121, 221, 112, 212, 122, 222], [2, 2, 2]);
-    const dt = tf.grad(t => t.clone().transpose(perm).clone())(t, dy);
-    expect(dt.shape).toEqual(t.shape);
-    expect(dt.dtype).toEqual('float32');
-    expectArraysClose(
-        await dt.data(), [111, 112, 121, 122, 211, 212, 221, 222]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.transpose({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'transpose' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const t = [[1, 11, 2, 22], [3, 33, 4, 44]];
-    const res = tf.transpose(t, [1, 0]);
-
-    expect(res.shape).toEqual([4, 2]);
-    expectArraysClose(await res.data(), [1, 3, 11, 33, 2, 4, 22, 44]);
-  });
-
-  it('accepts complex64 input', async () => {
-    const real = tf.tensor2d([[1, 2], [3, 4]]);
-    const imag = tf.tensor2d([[-4, 5], [6, 7]]);
-
-    let res = tf.transpose(tf.complex(real, imag));
-
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose([[1, 3], [2, 4]], await tf.real(res).data());
-    expectArraysClose([[-4, 6], [5, 7]], await tf.imag(res).data());
-
-    // Test taking conjugate tranpose.
-    res = tf.transpose(tf.complex(real, imag), [1, 0], true);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose([[1, 3], [2, 4]], await tf.real(res).data());
-    expectArraysClose([[4, -6], [-5, -7]], await tf.imag(res).data());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/truncated_normal.ts b/tfjs-master/tfjs-core/src/ops/truncated_normal.ts
deleted file mode 100644
index 22541b9fa..000000000
--- a/tfjs-master/tfjs-core/src/ops/truncated_normal.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {DataType, Rank, ShapeMap} from '../types';
-import {assertNonNegativeIntegerDimensions} from '../util_base';
-
-import {buffer} from './buffer';
-import {op} from './operation';
-import {MPRandGauss} from './rand_util';
-
-/**
- * Creates a `tf.Tensor` with values sampled from a truncated normal
- * distribution.
- *
- * ```js
- * tf.truncatedNormal([2, 2]).print();
- * ```
- *
- * The generated values follow a normal distribution with specified mean and
- * standard deviation, except that values whose magnitude is more than 2
- * standard deviations from the mean are dropped and re-picked.
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param mean The mean of the normal distribution.
- * @param stdDev The standard deviation of the normal distribution.
- * @param dtype The data type of the output tensor.
- * @param seed The seed for the random number generator.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function truncatedNormal_<R extends Rank>(
-    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',
-    seed?: number): Tensor<R> {
-  assertNonNegativeIntegerDimensions(shape);
-  if (dtype != null && (dtype as DataType) === 'bool') {
-    throw new Error(`Unsupported data type $ { dtype }`);
-  }
-  const randGauss =
-      new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);
-  const res = buffer(shape, dtype);
-  for (let i = 0; i < res.values.length; i++) {
-    res.values[i] = randGauss.nextValue();
-  }
-  return res.toTensor();
-}
-
-export const truncatedNormal = /* @__PURE__ */ op({truncatedNormal_});
diff --git a/tfjs-master/tfjs-core/src/ops/truncated_normal_test.ts b/tfjs-master/tfjs-core/src/ops/truncated_normal_test.ts
deleted file mode 100644
index e6471a191..000000000
--- a/tfjs-master/tfjs-core/src/ops/truncated_normal_test.ts
+++ /dev/null
@@ -1,132 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {TypedArray} from '../types';
-import {expectArrayInMeanStdRange} from './rand_util';
-
-describeWithFlags('truncatedNormal', ALL_ENVS, () => {
-  // Expect slightly higher variances for truncated values.
-  const EPSILON = 0.60;
-  const SEED = 2002;
-
-  function assertTruncatedValues(
-      values: TypedArray, mean: number, stdv: number) {
-    const bounds = mean + stdv * 2;
-    for (let i = 0; i < values.length; i++) {
-      expect(Math.abs(values[i])).toBeLessThanOrEqual(bounds);
-    }
-  }
-
-  it('should return a random 1D float32 array', async () => {
-    const shape: [number] = [1000];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.truncatedNormal(shape, 0, 3.5, null, SEED);
-    expect(result.dtype).toBe('float32');
-    assertTruncatedValues(await result.data(), 0, 3.5);
-    expectArrayInMeanStdRange(await result.data(), 0, 3.5, EPSILON);
-
-    result = tf.truncatedNormal(shape, 0, 4.5, 'float32', SEED);
-    expect(result.dtype).toBe('float32');
-    assertTruncatedValues(await result.data(), 0, 4.5);
-    expectArrayInMeanStdRange(await result.data(), 0, 4.5, EPSILON);
-  });
-
-  it('should return a randon 1D int32 array', async () => {
-    const shape: [number] = [1000];
-    const result = tf.truncatedNormal(shape, 0, 5, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    assertTruncatedValues(await result.data(), 0, 5);
-    expectArrayInMeanStdRange(await result.data(), 0, 5, EPSILON);
-  });
-
-  it('should return a 2D float32 array', async () => {
-    const shape: [number, number] = [50, 50];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.truncatedNormal(shape, 0, 3.5, null, SEED);
-    expect(result.dtype).toBe('float32');
-    assertTruncatedValues(await result.data(), 0, 3.5);
-    expectArrayInMeanStdRange(await result.data(), 0, 3.5, EPSILON);
-
-    result = tf.truncatedNormal(shape, 0, 4.5, 'float32', SEED);
-    expect(result.dtype).toBe('float32');
-    assertTruncatedValues(await result.data(), 0, 4.5);
-    expectArrayInMeanStdRange(await result.data(), 0, 4.5, EPSILON);
-  });
-
-  it('should return a 2D int32 array', async () => {
-    const shape: [number, number] = [50, 50];
-    const result = tf.truncatedNormal(shape, 0, 5, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    assertTruncatedValues(await result.data(), 0, 5);
-    expectArrayInMeanStdRange(await result.data(), 0, 5, EPSILON);
-  });
-
-  it('should return a 3D float32 array', async () => {
-    const shape: [number, number, number] = [10, 10, 10];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.truncatedNormal(shape, 0, 3.5, null, SEED);
-    expect(result.dtype).toBe('float32');
-    assertTruncatedValues(await result.data(), 0, 3.5);
-    expectArrayInMeanStdRange(await result.data(), 0, 3.5, EPSILON);
-
-    result = tf.truncatedNormal(shape, 0, 4.5, 'float32', SEED);
-    expect(result.dtype).toBe('float32');
-    assertTruncatedValues(await result.data(), 0, 4.5);
-    expectArrayInMeanStdRange(await result.data(), 0, 4.5, EPSILON);
-  });
-
-  it('should return a 3D int32 array', async () => {
-    const shape: [number, number, number] = [10, 10, 10];
-    const result = tf.truncatedNormal(shape, 0, 5, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    assertTruncatedValues(await result.data(), 0, 5);
-    expectArrayInMeanStdRange(await result.data(), 0, 5, EPSILON);
-  });
-
-  it('should return a 4D float32 array', async () => {
-    const shape: [number, number, number, number] = [5, 5, 5, 5];
-
-    // Ensure defaults to float32 w/o type:
-    let result = tf.truncatedNormal(shape, 0, 3.5, null, SEED);
-    expect(result.dtype).toBe('float32');
-    assertTruncatedValues(await result.data(), 0, 3.5);
-    expectArrayInMeanStdRange(await result.data(), 0, 3.5, EPSILON);
-
-    result = tf.truncatedNormal(shape, 0, 4.5, 'float32', SEED);
-    expect(result.dtype).toBe('float32');
-    assertTruncatedValues(await result.data(), 0, 4.5);
-    expectArrayInMeanStdRange(await result.data(), 0, 4.5, EPSILON);
-  });
-
-  it('should return a 4D int32 array', async () => {
-    const shape: [number, number, number, number] = [5, 5, 5, 5];
-    const result = tf.truncatedNormal(shape, 0, 5, 'int32', SEED);
-    expect(result.dtype).toBe('int32');
-    assertTruncatedValues(await result.data(), 0, 5);
-    expectArrayInMeanStdRange(await result.data(), 0, 5, EPSILON);
-  });
-
-  it('should throw error when shape is not integer', () => {
-    expect(() => tf.truncatedNormal([5.55, 3.33, 2.22], 0, 5, 'int32', SEED))
-        .toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/unique.ts b/tfjs-master/tfjs-core/src/ops/unique.ts
deleted file mode 100644
index 6ea1cf91a..000000000
--- a/tfjs-master/tfjs-core/src/ops/unique.ts
+++ /dev/null
@@ -1,91 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Unique, UniqueAttrs, UniqueInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor, Tensor1D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {assert} from '../util';
-
-import {op} from './operation';
-
-/**
- * Finds unique elements along an axis of a tensor.
- *
- * It returns a tensor `values` containing all of the unique elements along the
- * `axis` of the given tensor `x` in the same order that they occur along the
- * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor
- * `indices` the same size as the number of the elements in `x` along the `axis`
- * dimension. It contains the index in the unique output `values`.
- *
- * ```js
- * // A 1-D tensor
- * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);
- * const {values, indices} = tf.unique(a);
- * values.print();   // [1, 2, 4, 7, 8,]
- * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]
- * ```
- *
- * ```js
- * // A 2-D tensor with axis=0
- * //
- * // 'a' is: [[1, 0, 0],
- * //          [1, 0, 0],
- * //          [2, 0, 0]]
- * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);
- * const {values, indices} = tf.unique(a, 0)
- * values.print();   // [[1, 0, 0],
- *                   //  [2, 0, 0]]
- * indices.print();  // [0, 0, 1]
- * ```
- *
- * ```js
- * // A 2-D tensor with axis=1
- * //
- * // 'a' is: [[1, 0, 0],
- * //          [1, 0, 0],
- * //          [2, 0, 0]]
- * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);
- * const {values, indices} = tf.unique(a, 1)
- * values.print();   // [[1, 0],
- *                   //  [1, 0],
- *                   //  [2, 0]]
- * indices.print();  // [0, 1, 1]
- * ```
- * @param x A tensor (int32, string, bool).
- * @param axis The axis of the tensor to find the unique elements.
- * @returns [uniqueElements, indices] (see above for details)
- *
- * @doc {heading: 'Operations', subheading: 'Evaluation'}
- */
-function unique_<T extends Tensor>(
-    x: T|TensorLike, axis = 0): {values: T, indices: Tensor1D} {
-  const $x = convertToTensor(x, 'x', 'unique', 'string_or_numeric');
-  assert($x.rank > 0, () => 'The input tensor must be at least 1D');
-
-  const inputs: UniqueInputs = {x: $x};
-  const attrs: UniqueAttrs = {axis};
-  const [values, indices] = ENGINE.runKernel(
-      Unique, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap) as [T, Tensor1D];
-  return {values, indices};
-}
-
-export const unique = /* @__PURE__ */ op({unique_});
diff --git a/tfjs-master/tfjs-core/src/ops/unique_test.ts b/tfjs-master/tfjs-core/src/ops/unique_test.ts
deleted file mode 100644
index 127413b29..000000000
--- a/tfjs-master/tfjs-core/src/ops/unique_test.ts
+++ /dev/null
@@ -1,167 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysEqual} from '../test_util';
-
-import {tensor1d} from './tensor1d';
-
-describeWithFlags('unique', ALL_ENVS, () => {
-  it('1d tensor with int32', async () => {
-    const x = tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);
-    const {values, indices} = tf.unique(x);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual(x.shape);
-    expect(values.shape).toEqual([5]);
-    expectArraysEqual(await values.data(), [1, 2, 4, 7, 8]);
-    expectArraysEqual(await indices.data(), [0, 0, 1, 2, 2, 2, 3, 4, 4]);
-  });
-
-  it('1d tensor with string', async () => {
-    const x = tensor1d(['a', 'b', 'b', 'c', 'c']);
-    const {values, indices} = tf.unique(x);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual(x.shape);
-    expect(values.dtype).toEqual('string');
-    expect(values.shape).toEqual([3]);
-    expectArraysEqual(await values.data(), ['a', 'b', 'c']);
-    expectArraysEqual(await indices.data(), [0, 1, 1, 2, 2]);
-  });
-
-  it('1d tensor with bool', async () => {
-    const x = tensor1d([true, true, false]);
-    const {values, indices} = tf.unique(x);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual(x.shape);
-    expect(values.dtype).toEqual('bool');
-    expect(values.shape).toEqual([2]);
-    expectArraysEqual(await values.data(), [true, false]);
-    expectArraysEqual(await indices.data(), [0, 0, 1]);
-  });
-
-  it('1d tensor with NaN and Infinity', async () => {
-    const x = tensor1d([NaN, Infinity, NaN, Infinity]);
-    const {values, indices} = tf.unique(x);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual(x.shape);
-    expect(values.shape).toEqual([2]);
-    expectArraysEqual(await values.data(), [NaN, Infinity]);
-    expectArraysEqual(await indices.data(), [0, 1, 0, 1]);
-  });
-
-  it('2d tensor with axis=0', async () => {
-    const x = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);
-    const {values, indices} = tf.unique(x, 0);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual([x.shape[0]]);
-    expect(values.shape).toEqual([2, 3]);
-    expectArraysEqual(await values.data(), [1, 0, 0, 2, 0, 0]);
-    expectArraysEqual(await indices.data(), [0, 0, 1]);
-  });
-
-  it('2d tensor with axis=1', async () => {
-    const x = tf.tensor2d([[1, 0, 0, 1], [1, 0, 0, 1], [2, 0, 0, 2]]);
-    const {values, indices} = tf.unique(x, 1);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual([x.shape[1]]);
-    expect(values.shape).toEqual([3, 2]);
-    expectArraysEqual(await values.data(), [[1, 0], [1, 0], [2, 0]]);
-    expectArraysEqual(await indices.data(), [0, 1, 1, 0]);
-  });
-
-  it('2d tensor with string', async () => {
-    const x = tf.tensor2d([['a', 'b', 'b'], ['a', 'b', 'b'], ['c', 'b', 'b']]);
-    const {values, indices} = tf.unique(x, 0);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual([x.shape[0]]);
-    expect(values.dtype).toEqual('string');
-    expect(values.shape).toEqual([2, 3]);
-    expectArraysEqual(await values.data(), ['a', 'b', 'b', 'c', 'b', 'b']);
-    expectArraysEqual(await indices.data(), [0, 0, 1]);
-  });
-
-  it('2d tensor with strings that have comma', async () => {
-    const x = tf.tensor2d([['a', 'b,c', 'd'], ['a', 'b', 'c,d']]);
-    const {values, indices} = tf.unique(x, 0);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual([x.shape[0]]);
-    expect(values.dtype).toEqual('string');
-    expect(values.shape).toEqual([2, 3]);
-    expectArraysEqual(await values.data(), ['a', 'b,c', 'd', 'a', 'b', 'c,d']);
-    expectArraysEqual(await indices.data(), [0, 1]);
-  });
-
-  it('3d tensor with axis=0', async () => {
-    const x =
-        tf.tensor3d([[[1, 0], [1, 0]], [[1, 0], [1, 0]], [[1, 1], [1, 1]]]);
-    const {values, indices} = tf.unique(x, 0);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual([x.shape[0]]);
-    expect(values.shape).toEqual([2, 2, 2]);
-    expectArraysEqual(await values.data(), [1, 0, 1, 0, 1, 1, 1, 1]);
-    expectArraysEqual(await indices.data(), [0, 0, 1]);
-  });
-
-  it('3d tensor with axis=1', async () => {
-    const x =
-        tf.tensor3d([[[1, 0], [1, 0]], [[1, 0], [1, 0]], [[1, 1], [1, 1]]]);
-    const {values, indices} = tf.unique(x, 1);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual([x.shape[1]]);
-    expect(values.shape).toEqual([3, 1, 2]);
-    expectArraysEqual(await values.data(), [[[1, 0]], [[1, 0]], [[1, 1]]]);
-    expectArraysEqual(await indices.data(), [0, 0]);
-  });
-
-  it('3d tensor with axis=2', async () => {
-    const x = tf.tensor3d([[[1, 0, 1]], [[1, 0, 1]]]);
-    const {values, indices} = tf.unique(x, 2);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual([x.shape[2]]);
-    expect(values.shape).toEqual([2, 1, 2]);
-    expectArraysEqual(await values.data(), [1, 0, 1, 0]);
-    expectArraysEqual(await indices.data(), [0, 1, 0]);
-  });
-
-  it('3d tensor with string', async () => {
-    const x = tf.tensor3d([
-      [['a', 'b'], ['a', 'b']], [['a', 'b'], ['a', 'b']],
-      [['a', 'a'], ['a', 'a']]
-    ]);
-    const {values, indices} = tf.unique(x, 0);
-
-    expect(indices.dtype).toBe('int32');
-    expect(indices.shape).toEqual([x.shape[0]]);
-    expect(values.dtype).toEqual('string');
-    expect(values.shape).toEqual([2, 2, 2]);
-    expectArraysEqual(
-        await values.data(), ['a', 'b', 'a', 'b', 'a', 'a', 'a', 'a']);
-    expectArraysEqual(await indices.data(), [0, 0, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/unsorted_segment_sum.ts b/tfjs-master/tfjs-core/src/ops/unsorted_segment_sum.ts
deleted file mode 100644
index 8e25c7737..000000000
--- a/tfjs-master/tfjs-core/src/ops/unsorted_segment_sum.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {UnsortedSegmentSum, UnsortedSegmentSumAttrs, UnsortedSegmentSumInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor, Tensor1D} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import {assert, isInt} from '../util';
-
-import {op} from './operation';
-
-/**
- * Computes the sum along segments of a `tf.Tensor`.
- *
- * ```js
- * const x = tf.tensor1d([1, 2, 3, 4]);
- * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');
- * const numSegments = 3;
- *
- * x.unsortedSegmentSum(segmentIds, numSegments).print()
- * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)
- * ```
- * @param x The `tf.Tensor` that will be summed along its segments.
- * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s
- * dimension along the `axis`.  Maps each element of `x` to a segment.
- * @param numSegments The number of distinct `segmentIds`.
- *
- * @doc {heading: 'Operations', subheading: 'Segment'}
- */
-function unsortedSegmentSum_<T extends Tensor>(
-    x: T|TensorLike, segmentIds: Tensor1D|TensorLike, numSegments: number): T {
-  const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');
-  const $segmentIds =
-      convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');
-  assert(isInt(numSegments), () => 'numSegments must be of dtype int');
-
-  const inputs: UnsortedSegmentSumInputs = {x: $x, segmentIds: $segmentIds};
-  const attrs: UnsortedSegmentSumAttrs = {numSegments};
-
-  return ENGINE.runKernel(
-      UnsortedSegmentSum, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const unsortedSegmentSum = /* @__PURE__ */ op({unsortedSegmentSum_});
diff --git a/tfjs-master/tfjs-core/src/ops/unsorted_segment_sum_test.ts b/tfjs-master/tfjs-core/src/ops/unsorted_segment_sum_test.ts
deleted file mode 100644
index 5df33d087..000000000
--- a/tfjs-master/tfjs-core/src/ops/unsorted_segment_sum_test.ts
+++ /dev/null
@@ -1,171 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {PARALLELIZE_THRESHOLD} from './reduce_util';
-
-describeWithFlags('unsortedSegmentSum', ALL_ENVS, () => {
-  it('tensor1D', async () => {
-    const t = tf.tensor1d([1, 2, 3, 4]);
-    const segmentIds = tf.tensor1d([0, 2, 0, 1], 'int32');
-    const numSegments = 3;
-    const res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
-
-    expect(res.shape).toEqual([numSegments]);
-    expectArraysClose(await res.data(), [4, 4, 2]);
-  });
-
-  it('tensor2D', async () => {
-    const t = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const segmentIds = tf.tensor1d([0, 0], 'int32');
-    const numSegments = 2;
-    const res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
-
-    expect(res.shape).toEqual([numSegments, 2]);
-    expectArraysClose(await res.data(), [4, 6, 0, 0]);
-  });
-
-  it('tensor3D', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 2, 2]);
-    const segmentIds = tf.tensor1d([2, 1, 2], 'int32');
-    const numSegments = 3;
-    const res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
-
-    expect(res.shape).toEqual([numSegments, 2, 2]);
-    expectArraysClose(
-        await res.data(), [0, 0, 0, 0, 5, 6, 7, 8, 10, 12, 14, 16]);
-  });
-
-  it('N > than parallelization threshold, tensor1D', async () => {
-    const n = PARALLELIZE_THRESHOLD * 2;
-    const values = new Float32Array(n);
-    const numSegments = 5;
-    const segmentIdValues = new Float32Array(n);
-    const vals = new Float32Array(numSegments);
-    for (let i = 0; i < n; i++) {
-      values[i] = i;
-      segmentIdValues[i] = i % numSegments;
-      vals[i % numSegments] += i;
-    }
-    const t = tf.tensor1d(values);
-    const segmentIds = tf.tensor1d(segmentIdValues, 'int32');
-    const res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
-
-    expect(res.shape).toEqual([numSegments]);
-    expectArraysClose(await res.data(), vals);
-  });
-
-  it('ignores negative segmentIds', async () => {
-    const t = tf.tensor1d([1, 2, 3, 4]);
-    const segmentIds = tf.tensor1d([0, 2, -1, 1], 'int32');
-    const numSegments = 3;
-
-    const res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
-
-    expect(res.shape).toEqual([numSegments]);
-    expectArraysClose(await res.data(), [1, 4, 2]);
-  });
-
-  it('gradient ignores negative segmentIds', async () => {
-    const t = tf.tensor1d([1, 2, 3, 4]);
-    const segmentIds = tf.tensor1d([0, 2, -1, 1], 'int32');
-    const numSegments = 3;
-
-    const dy = tf.tensor1d([11, 2, 7]);
-    const gradient =
-        tf.grad(a => tf.unsortedSegmentSum(a, segmentIds, numSegments))(t, dy);
-
-    expect(gradient.shape).toEqual(t.shape);
-    expectArraysClose(await gradient.data(), [11, 7, 0, 2]);
-  });
-
-  it('tensor1D gradient', async () => {
-    const t = tf.tensor1d([1, 2, 3, 4]);
-    const segmentIds = tf.tensor1d([0, 2, 0, 1], 'int32');
-    const numSegments = 3;
-
-    const dy = tf.tensor1d([11, 2, 7]);
-    const gradient =
-        tf.grad(a => tf.unsortedSegmentSum(a, segmentIds, numSegments))(t, dy);
-
-    expect(gradient.shape).toEqual(t.shape);
-    expectArraysClose(await gradient.data(), [11, 7, 11, 2]);
-  });
-
-  it('gradient with clones', async () => {
-    const t = tf.tensor1d([1, 2, 3, 4]);
-    const segmentIds = tf.tensor1d([0, 2, 0, 1], 'int32');
-    const numSegments = 3;
-
-    const dy = tf.tensor1d([11, 2, 7]);
-    const gradient = tf.grad(
-        a => tf.unsortedSegmentSum(a.clone(), segmentIds.clone(), numSegments)
-                 .clone())(t, dy);
-
-    expect(gradient.shape).toEqual(t.shape);
-    expectArraysClose(await gradient.data(), [11, 7, 11, 2]);
-  });
-
-  it('tensor2D gradient', async () => {
-    const t = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const segmentIds = tf.tensor1d([0, 0], 'int32');
-    const numSegments = 2;
-
-    const dy = tf.tensor2d([11, 2, 4, 5], [2, 2]);
-    const gradient =
-        tf.grad(a => tf.unsortedSegmentSum(a, segmentIds, numSegments))(t, dy);
-
-    expect(gradient.shape).toEqual(t.shape);
-    expectArraysClose(await gradient.data(), [11, 2, 11, 2]);
-  });
-
-  it('tensor3D gradient', async () => {
-    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 2, 2]);
-    const segmentIds = tf.tensor1d([2, 1, 2], 'int32');
-    const numSegments = 3;
-
-    const dy =
-        tf.tensor3d([11, 2, 4, 5, 17, 31, 1, 0, -1, 14, 3, 28], [3, 2, 2]);
-    const gradient =
-        tf.grad(a => tf.unsortedSegmentSum(a, segmentIds, numSegments))(t, dy);
-
-    expect(gradient.shape).toEqual(t.shape);
-    expectArraysClose(
-        await gradient.data(), [-1, 14, 3, 28, 17, 31, 1, 0, -1, 14, 3, 28]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [1, 2, 3, 4];
-    const segmentIds = [0, 2, 0, 1];
-    const numSegments = 3;
-    const res = tf.unsortedSegmentSum(x, segmentIds, numSegments);
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [4, 4, 2]);
-  });
-
-  it('accepts a tensor-like object chained', async () => {
-    const x = tf.tensor1d([1, 2, 3, 4]);
-    const segmentIds = [0, 2, 0, 1];
-    const numSegments = 3;
-    const res = x.unsortedSegmentSum(segmentIds, numSegments);
-
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [4, 4, 2]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/unstack.ts b/tfjs-master/tfjs-core/src/ops/unstack.ts
deleted file mode 100644
index c578e0001..000000000
--- a/tfjs-master/tfjs-core/src/ops/unstack.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Unpack, UnpackAttrs, UnpackInputs} from '../kernel_names';
-import {NamedAttrMap} from '../kernel_registry';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-import * as util from '../util';
-
-import {op} from './operation';
-
-/**
- * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.
- *
- * ```js
- * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- *
- * tf.unstack(a).forEach(tensor => tensor.print());
- * ```
- *
- * @param x A tensor object.
- * @param axis The axis to unstack along. Defaults to 0 (the first dim).
- *
- * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
- */
-function unstack_(x: Tensor|TensorLike, axis = 0): Tensor[] {
-  const $x = convertToTensor(x, 'x', 'unstack', 'string_or_numeric');
-  util.assert(
-      axis >= -$x.shape.length && axis < $x.shape.length,
-      () =>
-          `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
-
-  const inputs: UnpackInputs = {value: $x};
-  const attrs: UnpackAttrs = {axis};
-
-  return ENGINE.runKernel(
-      Unpack, inputs as unknown as NamedTensorMap,
-      attrs as unknown as NamedAttrMap);
-}
-
-export const unstack = /* @__PURE__ */ op({unstack_});
diff --git a/tfjs-master/tfjs-core/src/ops/unstack_test.ts b/tfjs-master/tfjs-core/src/ops/unstack_test.ts
deleted file mode 100644
index b1d8179e8..000000000
--- a/tfjs-master/tfjs-core/src/ops/unstack_test.ts
+++ /dev/null
@@ -1,278 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('unstack', ALL_ENVS, () => {
-  it('unstack by default', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const res = tf.unstack(x);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(1);
-    expect(res[0].shape).toEqual([4]);
-    expectArraysClose(await res[0].data(), [1, 2, 3, 4]);
-    expect(res[1].rank).toEqual(1);
-    expect(res[1].shape).toEqual([4]);
-    expectArraysClose(await res[1].data(), [5, 6, 7, 8]);
-  });
-
-  it('chain api', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const res = x.unstack();
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(1);
-    expect(res[0].shape).toEqual([4]);
-    expectArraysClose(await res[0].data(), [1, 2, 3, 4]);
-    expect(res[1].rank).toEqual(1);
-    expect(res[1].shape).toEqual([4]);
-    expectArraysClose(await res[1].data(), [5, 6, 7, 8]);
-  });
-
-  it('unstack with negative integer axis', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-
-    let res = tf.unstack(x, -1);
-    expect(res.length).toEqual(4);
-    expect(res[0].rank).toEqual(1);
-    expect(res[0].shape).toEqual([2]);
-    expectArraysClose(await res[0].data(), [1, 5]);
-    expect(res[1].rank).toEqual(1);
-    expect(res[1].shape).toEqual([2]);
-    expectArraysClose(await res[1].data(), [2, 6]);
-    expect(res[2].rank).toEqual(1);
-    expect(res[2].shape).toEqual([2]);
-    expectArraysClose(await res[2].data(), [3, 7]);
-    expect(res[3].rank).toEqual(1);
-    expect(res[3].shape).toEqual([2]);
-    expectArraysClose(await res[3].data(), [4, 8]);
-
-    res = tf.unstack(x, -2);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(1);
-    expect(res[0].shape).toEqual([4]);
-    expectArraysClose(await res[0].data(), [1, 2, 3, 4]);
-    expect(res[1].rank).toEqual(1);
-    expect(res[1].shape).toEqual([4]);
-    expectArraysClose(await res[1].data(), [5, 6, 7, 8]);
-  });
-
-  it('unstack into 3 tensors', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const res = tf.unstack(x, 0);
-    expect(res.length).toEqual(3);
-    expect(res[0].rank).toEqual(1);
-    expect(res[0].shape).toEqual([2]);
-    expectArraysClose(await res[0].data(), [1, 2]);
-    expect(res[1].rank).toEqual(1);
-    expect(res[1].shape).toEqual([2]);
-    expectArraysClose(await res[1].data(), [3, 4]);
-    expect(res[2].rank).toEqual(1);
-    expect(res[2].shape).toEqual([2]);
-    expectArraysClose(await res[2].data(), [5, 6]);
-  });
-
-  it('unstack by axis=1', async () => {
-    const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-    const res = tf.unstack(x, 1);
-    expect(res.length).toEqual(4);
-    expect(res[0].rank).toEqual(1);
-    expect(res[0].shape).toEqual([2]);
-    expectArraysClose(await res[0].data(), [1, 5]);
-    expect(res[1].rank).toEqual(1);
-    expect(res[1].shape).toEqual([2]);
-    expectArraysClose(await res[1].data(), [2, 6]);
-    expect(res[2].rank).toEqual(1);
-    expect(res[2].shape).toEqual([2]);
-    expectArraysClose(await res[2].data(), [3, 7]);
-    expect(res[3].rank).toEqual(1);
-    expect(res[3].shape).toEqual([2]);
-    expectArraysClose(await res[3].data(), [4, 8]);
-  });
-
-  it('unstack rank 3 tensor', async () => {
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-    const res = tf.unstack(x);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(2);
-    expect(res[0].shape).toEqual([2, 2]);
-    expectArraysClose(await res[0].data(), [1, 2, 3, 4]);
-    expect(res[1].rank).toEqual(2);
-    expect(res[1].shape).toEqual([2, 2]);
-    expectArraysClose(await res[1].data(), [5, 6, 7, 8]);
-  });
-
-  it('unstack rank 3 tensor with axis=1', async () => {
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-    const res = tf.unstack(x, 1);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(2);
-    expect(res[0].shape).toEqual([2, 2]);
-    expectArraysClose(await res[0].data(), [1, 2, 5, 6]);
-    expect(res[1].rank).toEqual(2);
-    expect(res[1].shape).toEqual([2, 2]);
-    expectArraysClose(await res[1].data(), [3, 4, 7, 8]);
-  });
-
-  it('unstack rank 3 tensor with axis=2', async () => {
-    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-    const res = tf.unstack(x, 2);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(2);
-    expect(res[0].shape).toEqual([2, 2]);
-    expectArraysClose(await res[0].data(), [1, 3, 5, 7]);
-    expect(res[1].rank).toEqual(2);
-    expect(res[1].shape).toEqual([2, 2]);
-    expectArraysClose(await res[1].data(), [2, 4, 6, 8]);
-  });
-
-  it('unstack rank 4 tensor', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-    const res = tf.unstack(x);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(3);
-    expect(res[0].shape).toEqual([2, 2, 1]);
-    expectArraysClose(await res[0].data(), [1, 2, 3, 4]);
-    expect(res[1].rank).toEqual(3);
-    expect(res[1].shape).toEqual([2, 2, 1]);
-    expectArraysClose(await res[1].data(), [5, 6, 7, 8]);
-  });
-
-  it('unstack rank 4 tensor with axis=1', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-    const res = tf.unstack(x, 1);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(3);
-    expect(res[0].shape).toEqual([2, 2, 1]);
-    expectArraysClose(await res[0].data(), [1, 2, 5, 6]);
-    expect(res[1].rank).toEqual(3);
-    expect(res[1].shape).toEqual([2, 2, 1]);
-    expectArraysClose(await res[1].data(), [3, 4, 7, 8]);
-  });
-
-  it('unstack rank 4 tensor with axis=2', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-    const res = tf.unstack(x, 2);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(3);
-    expect(res[0].shape).toEqual([2, 2, 1]);
-    expectArraysClose(await res[0].data(), [1, 3, 5, 7]);
-    expect(res[1].rank).toEqual(3);
-    expect(res[1].shape).toEqual([2, 2, 1]);
-    expectArraysClose(await res[1].data(), [2, 4, 6, 8]);
-  });
-
-  it('unstack rank 4 tensor with axis=3', async () => {
-    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-    const res = tf.unstack(x, 3);
-    expect(res.length).toEqual(1);
-    expect(res[0].rank).toEqual(3);
-    expect(res[0].shape).toEqual([2, 2, 2]);
-    expectArraysClose(await res[0].data(), [1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.unstack({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'unstack' must be a Tensor/);
-  });
-
-  it('throws when passed an invalid axis', () => {
-    expect(() => {
-      const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
-      tf.unstack(x, 3);
-    }).toThrowError('Axis = 3 is not in [-2, 2)');
-    expect(() => {
-      const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
-      tf.unstack(x, 3);
-    }).toThrowError('Axis = 3 is not in [-3, 3)');
-    expect(() => {
-      const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-      tf.unstack(x, 5);
-    }).toThrowError('Axis = 5 is not in [-4, 4)');
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const x = [[1, 2, 3, 4], [5, 6, 7, 8]];
-    const res = tf.unstack(x);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(1);
-    expect(res[0].shape).toEqual([4]);
-    expectArraysClose(await res[0].data(), [1, 2, 3, 4]);
-    expect(res[1].rank).toEqual(1);
-    expect(res[1].shape).toEqual([4]);
-    expectArraysClose(await res[1].data(), [5, 6, 7, 8]);
-  });
-
-  it('accepts string', async () => {
-    const x =
-        [['one', 'two', 'three', 'four'], ['five', 'six', 'seven', 'eight']];
-    const res = tf.unstack(x);
-    expect(res.length).toEqual(2);
-    expect(res[0].rank).toEqual(1);
-    expect(res[0].shape).toEqual([4]);
-    expectArraysClose(await res[0].data(), ['one', 'two', 'three', 'four']);
-    expect(res[1].rank).toEqual(1);
-    expect(res[1].shape).toEqual([4]);
-    expectArraysClose(await res[1].data(), ['five', 'six', 'seven', 'eight']);
-  });
-
-  it('grad of unstack axis=0', async () => {
-    const x = tf.tensor([[1, 2, 3], [4, 5, 6]]);
-    const dx1 = tf.grad(x => tf.unstack(x)[0])(x);
-    expect(dx1.shape).toEqual([2, 3]);
-    expect(dx1.dtype).toBe('float32');
-    expectArraysClose(await dx1.data(), [1, 1, 1, 0, 0, 0]);
-
-    const dx2 = tf.grad(x => tf.unstack(x)[1])(x);
-    expect(dx2.shape).toEqual([2, 3]);
-    expect(dx2.dtype).toBe('float32');
-    expectArraysClose(await dx2.data(), [0, 0, 0, 1, 1, 1]);
-  });
-
-  it('gradient with clones', async () => {
-    const x = tf.tensor([[1, 2, 3], [4, 5, 6]]);
-    const dx1 = tf.grad(x => tf.unstack(x.clone())[0].clone())(x);
-    expect(dx1.shape).toEqual([2, 3]);
-    expect(dx1.dtype).toBe('float32');
-    expectArraysClose(await dx1.data(), [1, 1, 1, 0, 0, 0]);
-
-    const dx2 = tf.grad(x => tf.unstack(x.clone())[1].clone())(x);
-    expect(dx2.shape).toEqual([2, 3]);
-    expect(dx2.dtype).toBe('float32');
-    expectArraysClose(await dx2.data(), [0, 0, 0, 1, 1, 1]);
-  });
-
-  it('grad of unstack axis=1', async () => {
-    const x = tf.tensor([[1, 2, 3], [4, 5, 6]]);
-    const axis = 1;
-    const dx1 = tf.grad(x => tf.unstack(x, axis)[0])(x);
-    expect(dx1.shape).toEqual([2, 3]);
-    expect(dx1.dtype).toBe('float32');
-    expectArraysClose(await dx1.data(), [1, 0, 0, 1, 0, 0]);
-
-    const dx2 = tf.grad(x => tf.unstack(x, axis)[1])(x);
-    expect(dx2.shape).toEqual([2, 3]);
-    expect(dx2.dtype).toBe('float32');
-    expectArraysClose(await dx2.data(), [0, 1, 0, 0, 1, 0]);
-
-    const dx3 = tf.grad(x => tf.unstack(x, axis)[2])(x);
-    expect(dx3.shape).toEqual([2, 3]);
-    expect(dx3.dtype).toBe('float32');
-    expectArraysClose(await dx3.data(), [0, 0, 1, 0, 0, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/upper_bound.ts b/tfjs-master/tfjs-core/src/ops/upper_bound.ts
deleted file mode 100644
index 9636fba5a..000000000
--- a/tfjs-master/tfjs-core/src/ops/upper_bound.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '../tensor';
-import {TensorLike} from '../types';
-import {searchSorted} from './search_sorted';
-
-/**
- * Searches for where a value would go in a sorted sequence.
- *
- * This is not a method for checking containment (like javascript in).
- *
- * The typical use case for this operation is "binning", "bucketing", or
- * "discretizing". The values are assigned to bucket-indices based on the edges
- * listed in 'sortedSequence'. This operation returns the bucket-index for each
- * value.
- *
- * The index returned corresponds to the first edge greater than the value.
- *
- * The axis is not settable for this operation. It always operates on the
- * innermost dimension (axis=-1). The operation will accept any number of outer
- * dimensions.
- *
- * Note: This operation assumes that 'upperBound' is sorted along the
- * innermost axis, maybe using 'sort(..., axis=-1)'. If the sequence is not
- * sorted no error is raised and the content of the returned tensor is not well
- * defined.
- *
- * ```js
- * const seq = tf.tensor1d([0, 3, 9, 10, 10]);
- * const values = tf.tensor1d([0, 4, 10]);
- * const result = tf.upperBound(seq, values);
- * result.print(); // [1, 2, 5]
- * ```
- * @param sortedSequence: N-D. Sorted sequence.
- * @param values: N-D. Search values.
- * @return An N-D int32 tensor the size of values containing the result of
- *     applying upper bound to each value. The result is not a global index to
- *     the entire Tensor, but the index in the last dimension.
- * @doc {heading: 'Operations', subheading: 'Evaluation'}
- */
-export function upperBound(
-    sortedSequence: Tensor|TensorLike, values: Tensor|TensorLike): Tensor {
-  return searchSorted(sortedSequence, values, 'right');
-}
diff --git a/tfjs-master/tfjs-core/src/ops/upper_bound_test.ts b/tfjs-master/tfjs-core/src/ops/upper_bound_test.ts
deleted file mode 100644
index 80676ebfb..000000000
--- a/tfjs-master/tfjs-core/src/ops/upper_bound_test.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('upperBound', ALL_ENVS, () => {
-  it('test1D', async () => {
-    // Tests against numpy generated data.
-    const NUMPY_DATA = {
-      'float32': [
-        [
-          -725.0505981445312,  -721.4473266601562, -669.2916259765625,
-          -460.14422607421875, -304.4682922363281, -302.20330810546875,
-          -204.64633178710938, -143.817626953125,  243.3914337158203,
-          247.34442138671875,  326.88299560546875, 451.9959716796875,
-          501.62420654296875,  501.8848571777344,  614.7825927734375,
-          766.6121826171875,   791.7724609375,     806.8038330078125,
-          855.0171508789062,   929.6801147460938
-        ],
-        [
-          -795.3311157226562, -171.88803100585938, 388.8003234863281,
-          -171.64146423339844, -900.0930786132812, 71.79280853271484,
-          327.58929443359375, 29.77822494506836, 889.1895141601562,
-          173.11007690429688
-        ],
-        [0, 7, 11, 7, 0, 8, 11, 8, 19, 8]
-      ],
-      'int32': [
-        [
-          -968, -867, -751, -725, -655, -346, -285, 54,  246, 381,
-          393,  423,  507,  510,  771,  817,  846,  858, 865, 994
-        ],
-        [-770, 898, -100, 156, -183, -525, 806, 147, -994, 234],
-        [2, 19, 7, 8, 7, 5, 15, 8, 0, 8]
-      ]
-    };
-    for (const dtype of ['float32', 'int32'] as const ) {
-      const [sortedSequence, values, npAns] = NUMPY_DATA[dtype];
-
-      const result = tf.upperBound(sortedSequence, values);
-
-      expectArraysClose(await result.data(), npAns);
-    }
-  });
-
-  it('upperBound2D', async () => {
-    for (const dtype of ['float32', 'int32'] as const ) {
-      const sortedSequence =
-          tf.tensor2d([[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]], undefined, dtype);
-      const values = tf.tensor2d([[2, 4, 9], [0, 2, 6]], undefined, dtype);
-      const correctAns = [[1, 2, 4], [0, 2, 5]];
-
-      const result = tf.upperBound(sortedSequence, values);
-
-      expectArraysClose(await result.data(), correctAns);
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/variable.ts b/tfjs-master/tfjs-core/src/ops/variable.ts
deleted file mode 100644
index e1e8d767a..000000000
--- a/tfjs-master/tfjs-core/src/ops/variable.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tensor, Variable} from '../tensor';
-import {DataType, Rank} from '../types';
-
-/**
- * Creates a new variable with the provided initial value.
- * ```js
- * const x = tf.variable(tf.tensor([1, 2, 3]));
- * x.assign(tf.tensor([4, 5, 6]));
- *
- * x.print();
- * ```
- *
- * @param initialValue Initial value for the tensor.
- * @param trainable If true, optimizers are allowed to update it.
- * @param name Name of the variable. Defaults to a unique id.
- * @param dtype If set, initialValue will be converted to the given type.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function variable<R extends Rank>(
-    initialValue: Tensor<R>, trainable = true, name?: string,
-    dtype?: DataType): Variable<R> {
-  return ENGINE.makeVariable(initialValue, trainable, name, dtype) as
-      Variable<R>;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/where.ts b/tfjs-master/tfjs-core/src/ops/where.ts
deleted file mode 100644
index 2d5eea32a..000000000
--- a/tfjs-master/tfjs-core/src/ops/where.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Select, SelectInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {broadcastTo} from './broadcast_to';
-import {assertAndGetBroadcastShape} from './broadcast_util';
-import {op} from './operation';
-
-/**
- * Returns the elements, either `a` or `b` depending on the `condition`.
- *
- * If the condition is true, select from `a`, otherwise select from `b`.
- *
- * ```js
- * const cond = tf.tensor1d([false, false, true], 'bool');
- * const a = tf.tensor1d([1 , 2, 3]);
- * const b = tf.tensor1d([-1, -2, -3]);
- *
- * a.where(cond, b).print();
- * ```
- *
- * @param condition The input condition. Must be of dtype bool.
- * @param a If `condition` is rank 1, `a` may have a higher rank but
- *     its first dimension must match the size of `condition`.
- * @param b A tensor with the same dtype as `a` and with shape that is
- *     compatible with `a`.
- * @return A tensor with same dtype as `a` and `b`, and shape that is
- *     broadcastable from `a` and `b`.
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-function where_<T extends Tensor>(
-    condition: Tensor|TensorLike, a: T|TensorLike, b: T|TensorLike): T {
-  const $a = convertToTensor(a, 'a', 'where');
-  const $b = convertToTensor(b, 'b', 'where');
-  const $condition = convertToTensor(condition, 'condition', 'where', 'bool');
-  // TODO: move this logic to forward function when the broadcastTo op is
-  // implemented in WASM.
-  // Find the broadcastable shape for $condition, $a, and $b.
-  const broadcastShape = assertAndGetBroadcastShape(
-      assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);
-  const $broadcastedCondition = broadcastTo($condition, broadcastShape);
-  const $broadcastedA = broadcastTo($a, broadcastShape);
-  const $broadcastedB = broadcastTo($b, broadcastShape);
-
-  const inputs: SelectInputs = {
-    condition: $broadcastedCondition,
-    t: $broadcastedA,
-    e: $broadcastedB
-  };
-  return ENGINE.runKernel(Select, inputs as unknown as NamedTensorMap);
-}
-
-export const where = /* @__PURE__ */ op({where_});
diff --git a/tfjs-master/tfjs-core/src/ops/where_async.ts b/tfjs-master/tfjs-core/src/ops/where_async.ts
deleted file mode 100644
index 1455d9500..000000000
--- a/tfjs-master/tfjs-core/src/ops/where_async.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {whereImpl} from '../backends/where_impl';
-import {Tensor, Tensor2D} from '../tensor';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-/**
- * Returns the coordinates of true elements of condition.
- *
- * The coordinates are returned in a 2-D tensor where the first dimension (rows)
- * represents the number of true elements, and the second dimension (columns)
- * represents the coordinates of the true elements. Keep in mind, the shape of
- * the output tensor can vary depending on how many true values there are in
- * input. Indices are output in row-major order. The resulting tensor has the
- * shape `[numTrueElems, condition.rank]`.
- *
- * This is analogous to calling the python `tf.where(cond)` without an x or y.
- *
- * ```js
- * const cond = tf.tensor1d([false, false, true], 'bool');
- * const result = await tf.whereAsync(cond);
- * result.print();
- * ```
- *
- * @doc {heading: 'Operations', subheading: 'Logical'}
- */
-async function whereAsync_(condition: Tensor|TensorLike): Promise<Tensor2D> {
-  const $condition =
-      convertToTensor(condition, 'condition', 'whereAsync', 'bool');
-  const vals = await $condition.data();
-  const res = whereImpl($condition.shape, vals);
-  if (condition !== $condition) {
-    $condition.dispose();
-  }
-  return res;
-}
-
-export const whereAsync = whereAsync_;
diff --git a/tfjs-master/tfjs-core/src/ops/where_async_test.ts b/tfjs-master/tfjs-core/src/ops/where_async_test.ts
deleted file mode 100644
index 11259ea9e..000000000
--- a/tfjs-master/tfjs-core/src/ops/where_async_test.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('whereAsync', ALL_ENVS, () => {
-  it('1d tensor', async () => {
-    const condition = tf.tensor1d([true, false, true, true], 'bool');
-    const res = await tf.whereAsync(condition);
-    expect(res.dtype).toBe('int32');
-    expect(res.shape).toEqual([3, 1]);
-    expectArraysClose(await res.data(), [0, 2, 3]);
-  });
-
-  it('2d tensor', async () => {
-    const condition = tf.tensor2d(
-        [[true, false, false], [false, true, true]], [2, 3], 'bool');
-    const res = await tf.whereAsync(condition);
-    expect(res.dtype).toBe('int32');
-    expect(res.shape).toEqual([3, 2]);
-    expectArraysClose(await res.data(), [0, 0, 1, 1, 1, 2]);
-  });
-
-  it('3d tensor', async () => {
-    const condition = tf.tensor3d(
-        [
-          [[true, false, false], [false, true, true]],
-          [[false, false, false], [true, true, false]]
-        ],
-        [2, 2, 3], 'bool');
-    const res = await tf.whereAsync(condition);
-    expect(res.dtype).toBe('int32');
-    expect(res.shape).toEqual([5, 3]);
-    expectArraysClose(
-        await res.data(), [0, 0, 0, 0, 1, 1, 0, 1, 2, 1, 1, 0, 1, 1, 1]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const condition = [true, false, true];
-    const res = await tf.whereAsync(condition);
-    expect(res.dtype).toBe('int32');
-    expect(res.shape).toEqual([2, 1]);
-    expectArraysClose(await res.data(), [0, 2]);
-  });
-
-  it('throws error if condition is not of type bool', async () => {
-    const condition = tf.tensor1d([1, 0, 1]);
-    // expect(...).toThrowError() does not support async functions.
-    // See https://github.com/jasmine/jasmine/issues/1410
-    try {
-      await tf.whereAsync(condition);
-      throw new Error('The line above should have thrown an error');
-    } catch (ex) {
-      expect(ex.message)
-          .toMatch(/Argument 'condition' passed to 'whereAsync' must be bool/);
-    }
-  });
-
-  it('returns tensor with 0 in shape when no values are true', async () => {
-    const condition = [[[false]], [[false]], [[false]]];
-    const res = await tf.whereAsync(condition);
-    expect(res.dtype).toBe('int32');
-    expect(res.shape).toEqual([0, 3]);
-    expectArraysClose(await res.data(), []);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/where_test.ts b/tfjs-master/tfjs-core/src/ops/where_test.ts
deleted file mode 100644
index a3e6826e0..000000000
--- a/tfjs-master/tfjs-core/src/ops/where_test.ts
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-import {backend} from '../index';
-
-describeWithFlags('where', ALL_ENVS, () => {
-  it('Scalars.', async () => {
-    const a = tf.scalar(10);
-    const b = tf.scalar(20);
-    const c = tf.scalar(1, 'bool');
-
-    expectArraysClose(await tf.where(c, a, b).data(), [10]);
-  });
-
-  it('Invalid condition type', () => {
-    const c = tf.tensor1d([1, 0, 1, 0], 'int32');
-    const a = tf.tensor1d([10, 10, 10, 10], 'bool');
-    const b = tf.tensor1d([20, 20, 20, 20], 'bool');
-    const f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Tensor1D', async () => {
-    const c = tf.tensor1d([1, 0, 1, 0], 'bool');
-    const a = tf.tensor1d([10, 10, 10, 10]);
-    const b = tf.tensor1d([20, 20, 20, 20]);
-    expectArraysClose(await tf.where(c, a, b).data(), [10, 20, 10, 20]);
-  });
-
-  it('Tensor1D different a/b shapes', () => {
-    let c = tf.tensor1d([1, 0, 1, 0], 'bool');
-    let a = tf.tensor1d([10, 10, 10]);
-    let b = tf.tensor1d([20, 20, 20, 20]);
-    let f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-
-    c = tf.tensor1d([1, 0, 1, 0], 'bool');
-    a = tf.tensor1d([10, 10, 10, 10]);
-    b = tf.tensor1d([20, 20, 20]);
-    f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Tensor1D different condition/a shapes', () => {
-    const c = tf.tensor1d([1, 0, 1, 0], 'bool');
-    const a = tf.tensor1d([10, 10, 10]);
-    const b = tf.tensor1d([20, 20, 20]);
-    const f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Tensor2D', async () => {
-    const c = tf.tensor2d([[1, 0], [0, 1]], [2, 2], 'bool');
-    const a = tf.tensor2d([[10, 10], [10, 10]], [2, 2]);
-    const b = tf.tensor2d([[5, 5], [5, 5]], [2, 2]);
-    expectArraysClose(await tf.where(c, a, b).data(), [10, 5, 5, 10]);
-  });
-
-  it('Tensor2D different a/b shapes', () => {
-    let c = tf.tensor2d([[1, 1], [0, 0]], [2, 2], 'bool');
-    let a = tf.tensor2d([[5, 5, 5], [5, 5, 5]], [2, 3]);
-    let b = tf.tensor2d([[4, 4], [4, 4]], [2, 2]);
-    let f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-
-    c = tf.tensor2d([[1, 1], [0, 0]], [2, 2], 'bool');
-    a = tf.tensor2d([[5, 5], [5, 5]], [2, 2]);
-    b = tf.tensor2d([[4, 4, 4], [4, 4, 4]], [2, 3]);
-    f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Tensor2D different condition/a shapes', () => {
-    const c = tf.tensor2d([[1, 0], [0, 1]], [2, 2], 'bool');
-    const a = tf.tensor2d([[10, 10, 10], [10, 10, 10]], [2, 3]);
-    const b = tf.tensor2d([[5, 5, 5], [5, 5, 5]], [2, 3]);
-    const f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('broadcasting Tensor2D shapes', async () => {
-    const c = tf.tensor1d([1, 0], 'bool');
-    let a = tf.tensor2d([[10], [10]], [2, 1]);
-    let b = tf.tensor2d([[5], [5]], [2, 1]);
-    let res = tf.where(c, a, b);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysClose(await res.data(), [10, 5, 10, 5]);
-
-    a = tf.tensor2d([[10, 10], [10, 10], [10, 10]], [3, 2]);
-    b = tf.tensor2d([[5], [5], [5]], [3, 1]);
-    res = tf.where(c, a, b);
-    expect(res.shape).toEqual([3, 2]);
-    expectArraysClose(await res.data(), [10, 5, 10, 5, 10, 5]);
-  });
-
-  it('Tensor3D', async () => {
-    const c =
-        tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
-    const a = tf.tensor3d([[[5], [5], [5]], [[5], [5], [5]]], [2, 3, 1]);
-    const b = tf.tensor3d([[[3], [3], [3]], [[3], [3], [3]]], [2, 3, 1]);
-    expectArraysClose(await tf.where(c, a, b).data(), [5, 3, 5, 3, 3, 3]);
-  });
-
-  it('Tensor3D with scalar condition', async () => {
-    const a = tf.ones([1, 3, 3]);
-    const b = tf.zeros([1, 3, 3]);
-
-    expectArraysClose(
-        await tf.where(tf.ones([1], 'bool'), a, b).data(),
-        [1, 1, 1, 1, 1, 1, 1, 1, 1]);
-    expectArraysClose(
-        await tf.where(tf.zeros([1], 'bool'), a, b).data(),
-        [0, 0, 0, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('Tensor3D different a/b shapes', () => {
-    const c =
-        tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
-    let a = tf.tensor3d([[[5], [5]], [[5], [5]]], [2, 2, 1]);
-    let b = tf.tensor3d([[[3], [3], [3]], [[3], [3], [3]]], [2, 3, 1]);
-    let f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-
-    a = tf.tensor3d([[[5], [5], [5]], [[5], [5], [5]]], [2, 3, 1]);
-    b = tf.tensor3d([[[3], [3]], [[3], [3]]], [2, 2, 1]);
-    f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Tensor3D different condition/a shapes', () => {
-    const c = tf.tensor3d([[[1], [0]], [[0], [0]]], [2, 2, 1], 'bool');
-    const a = tf.tensor3d([[[5], [5], [5]], [[5], [5], [5]]], [2, 3, 1]);
-    const b = tf.tensor3d([[[3], [3], [3]], [[3], [3], [3]]], [2, 3, 1]);
-    const f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('broadcasting Tensor3D shapes', async () => {
-    const c = tf.tensor1d([1, 0], 'bool');
-    let a: tf.Tensor = tf.tensor3d([[[9]], [[9]], [[9]], [[9]]], [4, 1, 1]);
-    let b = tf.tensor3d([[[8]], [[8]], [[8]], [[8]]], [4, 1, 1]);
-    let res = tf.where(c, a, b);
-    expect(res.shape).toEqual([4, 1, 2]);
-    expectArraysClose(await res.data(), [9, 8, 9, 8, 9, 8, 9, 8]);
-
-    a = tf.tensor2d([[9], [9]], [2, 1]);
-    b = tf.tensor3d([[[8]], [[8]], [[8]], [[8]]], [4, 1, 1]);
-    res = tf.where(c, a, b);
-    expect(res.shape).toEqual([4, 2, 2]);
-    expectArraysClose(
-        await res.data(), [9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8]);
-  });
-
-  it('Tensor4D', async () => {
-    const c = tf.tensor4d([1, 0, 1, 1], [2, 2, 1, 1], 'bool');
-    const a = tf.tensor4d([7, 7, 7, 7], [2, 2, 1, 1]);
-    const b = tf.tensor4d([3, 3, 3, 3], [2, 2, 1, 1]);
-    expectArraysClose(await tf.where(c, a, b).data(), [7, 3, 7, 7]);
-  });
-
-  it('Tensor4D different a/b shapes', () => {
-    const c = tf.tensor4d([1, 0, 1, 1], [2, 2, 1, 1], 'bool');
-    const a = tf.tensor4d([7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [2, 3, 2, 1]);
-    const b = tf.tensor4d([3, 3, 3, 3], [2, 2, 1, 1]);
-    const f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('Tensor4D different condition/a shapes', () => {
-    const c =
-        tf.tensor4d([0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1], [2, 3, 2, 1], 'bool');
-    const a = tf.tensor4d([7, 7, 7, 7], [2, 2, 1, 1]);
-    const b = tf.tensor4d([3, 3, 3, 3], [2, 2, 1, 1]);
-    const f = () => {
-      tf.where(c, a, b);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('TensorLike', async () => {
-    expectArraysClose(await tf.where(true, 10, 20).data(), [10]);
-  });
-
-  it('TensorLike Chained', async () => {
-    const a = tf.scalar(10);
-    expectArraysClose(await a.where(true, 20).data(), [10]);
-  });
-
-  it('int32', async () => {
-    if (backend() && backend().floatPrecision() === 32) {
-      // TODO: Use skip() instead when it is implemented
-      const c = tf.tensor1d([1, 0, 0], 'bool');
-      const a = tf.tensor1d([12345678, 10, 10], 'int32');
-      const b = tf.tensor1d([20, 20, -12345678], 'int32');
-      const res = tf.where(c, a, b);
-      expect(res.dtype).toEqual('int32');
-      expectArraysClose(await res.data(), [12345678, 20, -12345678]);
-    }
-  });
-
-  it('throws when passed condition as a non-tensor', () => {
-    expect(
-        () => tf.where(
-            {} as tf.Tensor, tf.scalar(1, 'bool'), tf.scalar(1, 'bool')))
-        .toThrowError(
-            /Argument 'condition' passed to 'where' must be a Tensor/);
-  });
-  it('throws when passed a as a non-tensor', () => {
-    expect(
-        () => tf.where(
-            tf.scalar(1, 'bool'), {} as tf.Tensor, tf.scalar(1, 'bool')))
-        .toThrowError(/Argument 'a' passed to 'where' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(
-        () => tf.where(
-            tf.scalar(1, 'bool'), tf.scalar(1, 'bool'), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'where' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const a = 10;
-    const b = 20;
-    const c = 1;
-    expectArraysClose(await tf.where(c, a, b).data(), [10]);
-  });
-
-  it('1D gradient', async () => {
-    const c = tf.tensor1d([1, 0, 1], 'bool');
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([4, 5, 6]);
-    const dy = tf.tensor1d([1, 2, 3]);
-    const grads = tf.grads((c, a, b) => tf.where(c, a, b));
-    const [dc, da, db] = grads([c, a, b], dy);
-    expectArraysClose(await dc.data(), [0, 0, 0]);
-    expectArraysClose(await da.data(), [1, 0, 3]);
-    expectArraysClose(await db.data(), [0, 2, 0]);
-    expect(dc.shape).toEqual(c.shape);
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-  });
-
-  it('gradient with clones', async () => {
-    const c = tf.tensor1d([1, 0, 1], 'bool');
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([4, 5, 6]);
-    const dy = tf.tensor1d([1, 2, 3]);
-    const grads = tf.grads(
-        (c, a, b) => tf.where(c.clone(), a.clone(), b.clone()).clone());
-    const [dc, da, db] = grads([c, a, b], dy);
-    expectArraysClose(await dc.data(), [0, 0, 0]);
-    expectArraysClose(await da.data(), [1, 0, 3]);
-    expectArraysClose(await db.data(), [0, 2, 0]);
-    expect(dc.shape).toEqual(c.shape);
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-  });
-
-  it('2D gradient', async () => {
-    const c = tf.tensor2d([1, 0, 1, 1, 1, 0], [2, 3], 'bool');
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([7, 8, 9, 10, 11, 12], [2, 3]);
-    const dy = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const grads = tf.grads((c, a, b) => tf.where(c, a, b));
-    const [dc, da, db] = grads([c, a, b], dy);
-    expectArraysClose(await dc.data(), [0, 0, 0, 0, 0, 0]);
-    expectArraysClose(await da.data(), [1, 0, 3, 4, 5, 0]);
-    expectArraysClose(await db.data(), [0, 2, 0, 0, 0, 6]);
-    expect(dc.shape).toEqual(c.shape);
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-  });
-  it('3D gradient', async () => {
-    const c = tf.tensor3d([1, 1, 0, 1, 1, 0], [2, 3, 1], 'bool');
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
-    const b = tf.tensor3d([7, 8, 9, 10, 11, 12], [2, 3, 1]);
-    const dy = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
-    const grads = tf.grads((c, a, b) => tf.where(c, a, b));
-    const [dc, da, db] = grads([c, a, b], dy);
-    expectArraysClose(await dc.data(), [0, 0, 0, 0, 0, 0]);
-    expectArraysClose(await da.data(), [1, 2, 0, 4, 5, 0]);
-    expectArraysClose(await db.data(), [0, 0, 3, 0, 0, 6]);
-    expect(dc.shape).toEqual(c.shape);
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-  });
-  it('4D gradient', async () => {
-    const c = tf.tensor4d([1, 1, 0, 1], [2, 2, 1, 1], 'bool');
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
-    const b = tf.tensor4d([5, 6, 7, 8], [2, 2, 1, 1]);
-    const dy = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
-    const grads = tf.grads((c, a, b) => tf.where(c, a, b));
-    const [dc, da, db] = grads([c, a, b], dy);
-    expectArraysClose(await dc.data(), [0, 0, 0, 0]);
-    expectArraysClose(await da.data(), [1, 2, 0, 4]);
-    expectArraysClose(await db.data(), [0, 0, 3, 0]);
-    expect(dc.shape).toEqual(c.shape);
-    expect(da.shape).toEqual(a.shape);
-    expect(db.shape).toEqual(b.shape);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/zeros.ts b/tfjs-master/tfjs-core/src/ops/zeros.ts
deleted file mode 100644
index d7831a353..000000000
--- a/tfjs-master/tfjs-core/src/ops/zeros.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {Tensor} from '../tensor';
-import {DataType, Rank, ShapeMap} from '../types';
-import {assertNonNegativeIntegerDimensions, makeZerosTypedArray, sizeFromShape} from '../util';
-
-import {complex} from './complex';
-
-/**
- * Creates a `tf.Tensor` with all elements set to 0.
- *
- * ```js
- * tf.zeros([2, 2]).print();
- * ```
- *
- * @param shape An array of integers defining the output tensor shape.
- * @param dtype The type of an element in the resulting tensor. Can
- *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-export function zeros<R extends Rank>(
-    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {
-  assertNonNegativeIntegerDimensions(shape);
-  if (dtype === 'complex64') {
-    const real = zeros(shape, 'float32');
-    const imag = zeros(shape, 'float32');
-    return complex(real, imag);
-  }
-  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
-  return ENGINE.makeTensor(values, shape, dtype) as Tensor<R>;
-}
diff --git a/tfjs-master/tfjs-core/src/ops/zeros_like.ts b/tfjs-master/tfjs-core/src/ops/zeros_like.ts
deleted file mode 100644
index cb77117ec..000000000
--- a/tfjs-master/tfjs-core/src/ops/zeros_like.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {ZerosLike, ZerosLikeInputs} from '../kernel_names';
-import {Tensor} from '../tensor';
-import {NamedTensorMap} from '../tensor_types';
-import {convertToTensor} from '../tensor_util_env';
-import {TensorLike} from '../types';
-
-import {op} from './operation';
-
-/**
- * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the
- * given tensor.
- *
- * ```js
- * const x = tf.tensor([1, 2]);
- * tf.zerosLike(x).print();
- * ```
- *
- * @param x The tensor of required shape.
- *
- * @doc {heading: 'Tensors', subheading: 'Creation'}
- */
-function zerosLike_<T extends Tensor>(x: T|TensorLike): T {
-  const $x = convertToTensor(x, 'x', 'zerosLike');
-  const inputs: ZerosLikeInputs = {x: $x};
-  return ENGINE.runKernel(ZerosLike, inputs as unknown as NamedTensorMap);
-}
-export const zerosLike = /* @__PURE__ */ op({zerosLike_});
diff --git a/tfjs-master/tfjs-core/src/ops/zeros_like_test.ts b/tfjs-master/tfjs-core/src/ops/zeros_like_test.ts
deleted file mode 100644
index 124ef88e0..000000000
--- a/tfjs-master/tfjs-core/src/ops/zeros_like_test.ts
+++ /dev/null
@@ -1,248 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('zerosLike', ALL_ENVS, () => {
-  it('1D default dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([3]);
-    expectArraysClose(await b.data(), [0, 0, 0]);
-  });
-
-  it('chainable 1D default dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = a.zerosLike();
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([3]);
-    expectArraysClose(await b.data(), [0, 0, 0]);
-  });
-
-  it('1D float32 dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'float32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([3]);
-    expectArraysClose(await b.data(), [0, 0, 0]);
-  });
-
-  it('1D int32 dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'int32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([3]);
-    expectArraysEqual(await b.data(), [0, 0, 0]);
-  });
-
-  it('1D bool dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'bool');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([3]);
-    expectArraysEqual(await b.data(), [0, 0, 0]);
-  });
-
-  it('2D default dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('2D float32 dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('2D int32 dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('2D bool dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'bool');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('3D default dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('3D float32 dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('3D int32 dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'int32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('3D bool dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'bool');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('4D default dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('4D float32 dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('4D int32 dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'int32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('4D bool dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'bool');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('4D default dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('5D float32 dtype', async () => {
-    const a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'float32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('5D int32 dtype', async () => {
-    const a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'int32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('5D bool dtype', async () => {
-    const a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'bool');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1]);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('5D default dtype', async () => {
-    const a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1]);
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('6D float32 dtype', async () => {
-    const a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'float32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([1, 2, 2, 1, 1, 1]);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('6D int32 dtype', async () => {
-    const a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'int32');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual(a.shape);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('6D bool dtype', async () => {
-    const a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'bool');
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual(a.shape);
-    expectArraysEqual(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('6D default dtype', async () => {
-    const a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1]);
-    const b = tf.zerosLike(a);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual(a.shape);
-    expectArraysClose(await b.data(), [0, 0, 0, 0]);
-  });
-
-  it('zerosLike gradient', async () => {
-    const x = tf.tensor2d([[0, 1, 2], [4, 5, 6]]);
-    const gradients = tf.grad(x => tf.zerosLike(x))(x);
-    expect(gradients.shape).toEqual([2, 3]);
-    expectArraysEqual(await gradients.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('throws when passed a non-tensor', () => {
-    expect(() => tf.zerosLike({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'zerosLike' must be a Tensor/);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const res = tf.zerosLike([[1, 2], [3, 4]]);
-    expect(res.shape).toEqual([2, 2]);
-    expectArraysEqual(await res.data(), [0, 0, 0, 0]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/ops/zeros_test.ts b/tfjs-master/tfjs-core/src/ops/zeros_test.ts
deleted file mode 100644
index 7656dd9df..000000000
--- a/tfjs-master/tfjs-core/src/ops/zeros_test.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose, expectArraysEqual} from '../test_util';
-
-describeWithFlags('zeros', ALL_ENVS, () => {
-  it('1D default dtype', async () => {
-    const a: tf.Tensor1D = tf.zeros([3]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [0, 0, 0]);
-  });
-
-  it('1D float32 dtype', async () => {
-    const a = tf.zeros([3], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [0, 0, 0]);
-  });
-
-  it('1D int32 dtype', async () => {
-    const a = tf.zeros([3], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), [0, 0, 0]);
-  });
-
-  it('1D bool dtype', async () => {
-    const a = tf.zeros([3], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), [0, 0, 0]);
-  });
-
-  it('2D default dtype', async () => {
-    const a = tf.zeros([3, 2]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysClose(await a.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('2D float32 dtype', async () => {
-    const a = tf.zeros([3, 2], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysClose(await a.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('2D int32 dtype', async () => {
-    const a = tf.zeros([3, 2], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysEqual(await a.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('2D bool dtype', async () => {
-    const a = tf.zeros([3, 2], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([3, 2]);
-    expectArraysEqual(await a.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('3D default dtype', async () => {
-    const a = tf.zeros([2, 2, 2]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await a.data(), [0, 0, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('3D float32 dtype', async () => {
-    const a = tf.zeros([2, 2, 2], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2, 2]);
-    expectArraysClose(await a.data(), [0, 0, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('3D int32 dtype', async () => {
-    const a = tf.zeros([2, 2, 2], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2, 2]);
-    expectArraysEqual(await a.data(), [0, 0, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('3D bool dtype', async () => {
-    const a = tf.zeros([2, 2, 2], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([2, 2, 2]);
-    expectArraysEqual(await a.data(), [0, 0, 0, 0, 0, 0, 0, 0]);
-  });
-
-  it('4D default dtype', async () => {
-    const a = tf.zeros([3, 2, 1, 1]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2, 1, 1]);
-    expectArraysClose(await a.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('4D float32 dtype', async () => {
-    const a = tf.zeros([3, 2, 1, 1], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3, 2, 1, 1]);
-    expectArraysClose(await a.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('4D int32 dtype', async () => {
-    const a = tf.zeros([3, 2, 1, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3, 2, 1, 1]);
-    expectArraysEqual(await a.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('4D bool dtype', async () => {
-    const a = tf.zeros([3, 2, 1, 1], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([3, 2, 1, 1]);
-    expectArraysEqual(await a.data(), [0, 0, 0, 0, 0, 0]);
-  });
-
-  it('should throw error when shape is not integer', () => {
-    expect(() => tf.zeros([2, 2.22, 3.33])).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/optimizers/adadelta_optimizer.ts b/tfjs-master/tfjs-core/src/optimizers/adadelta_optimizer.ts
deleted file mode 100644
index eb4c5e423..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/adadelta_optimizer.ts
+++ /dev/null
@@ -1,154 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {dispose, tidy} from '../globals';
-import {add} from '../ops/add';
-import {div} from '../ops/div';
-import {mul} from '../ops/mul';
-import {sqrt} from '../ops/ops';
-import {square} from '../ops/square';
-import {zerosLike} from '../ops/zeros_like';
-import {ConfigDict, Serializable, SerializableConstructor} from '../serialization';
-import {NamedTensor, NamedVariableMap} from '../tensor_types';
-
-import {Optimizer, OptimizerVariable} from './optimizer';
-
-/** @doclink Optimizer */
-export class AdadeltaOptimizer extends Optimizer {
-  /** @nocollapse */
-  static get className() {
-    // Name matters for Python compatibility.
-    // This is a getter instead of a property because when it's a property, it
-    // prevents the entire class from being tree-shaken.
-    return 'Adadelta';
-  }
-  private accumulatedGrads: OptimizerVariable[] = [];
-  private accumulatedUpdates: OptimizerVariable[] = [];
-
-  constructor(
-      protected learningRate: number, protected rho: number,
-      protected epsilon: number = null) {
-    super();
-
-    if (epsilon == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-
-  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {
-    const variableNames = Array.isArray(variableGradients) ?
-        variableGradients.map(item => item.name) :
-        Object.keys(variableGradients);
-
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      const trainable = false;
-      if (this.accumulatedGrads[i] == null) {
-        this.accumulatedGrads[i] = {
-          originalName: `${name}/accum_grad`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedUpdates[i] == null) {
-        this.accumulatedUpdates[i] = {
-          originalName: `${name}/accum_var`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-
-      const gradient = Array.isArray(variableGradients) ?
-          variableGradients[i].tensor :
-          variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-
-      const accumulatedGrad = this.accumulatedGrads[i].variable;
-      const accumulatedUpdate = this.accumulatedUpdates[i].variable;
-
-      tidy(() => {
-        const newAccumulatedGrad =
-            add(mul(accumulatedGrad, this.rho),
-                mul(square(gradient), 1 - this.rho));
-
-        const updates =
-            mul(div(sqrt(add(accumulatedUpdate, this.epsilon)),
-                    sqrt(add(accumulatedGrad, this.epsilon))),
-                gradient);
-
-        const newAccumulatedUpdate =
-            add(mul(accumulatedUpdate, this.rho),
-                mul(square(updates), 1 - this.rho));
-
-        accumulatedGrad.assign(newAccumulatedGrad);
-        accumulatedUpdate.assign(newAccumulatedUpdate);
-
-        const newValue = add(mul(updates, -this.learningRate), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-
-  override dispose(): void {
-    if (this.accumulatedUpdates != null) {
-      dispose(this.accumulatedGrads.map(v => v.variable));
-      dispose(this.accumulatedUpdates.map(v => v.variable));
-    }
-  }
-
-  override async getWeights(): Promise<NamedTensor[]> {
-    // Order matters for Python compatibility.
-    const variables: OptimizerVariable[] =
-        [...this.accumulatedGrads, ...this.accumulatedUpdates];
-    return [await this.saveIterations()].concat(
-        variables.map(v => ({name: v.originalName, tensor: v.variable})));
-  }
-
-  override async setWeights(weightValues: NamedTensor[]): Promise<void> {
-    weightValues = await this.extractIterations(weightValues);
-    const variableCount = weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedGrads =
-        weightValues.slice(0, variableCount).map(v => ({
-                                                   originalName: v.name,
-                                                   variable: v.tensor.variable(
-                                                       trainable)
-                                                 }));
-    this.accumulatedUpdates =
-        weightValues.slice(variableCount, variableCount * 2)
-            .map(v => ({
-                   originalName: v.name,
-                   variable: v.tensor.variable(trainable)
-                 }));
-  }
-
-  getConfig(): ConfigDict {
-    return {
-      'learningRate': this.learningRate,
-      'rho': this.rho,
-      'epsilon': this.epsilon
-    };
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends Serializable>(
-      cls: SerializableConstructor<T>, config: ConfigDict): T {
-    return new cls(config['learningRate'], config['rho'], config['epsilon']);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/optimizers/adadelta_optimizer_test.ts b/tfjs-master/tfjs-core/src/optimizers/adadelta_optimizer_test.ts
deleted file mode 100644
index d66c09607..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/adadelta_optimizer_test.ts
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('AdadeltaOptimizer', ALL_ENVS, () => {
-  it('basic', async () => {
-    const initialTensors = tf.memory().numTensors;
-    const learningRate = .1;
-    const rho = .95;
-    const optimizer = tf.train.adadelta(learningRate, rho);
-
-    const x = tf.tensor1d([1, 2]).variable();
-
-    const f: () => tf.Scalar = () => x.square().sum();
-
-    let numTensors = tf.memory().numTensors;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true);
-
-    // Cost & 2 accumulators should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 3);
-
-    // epsilon = 1-e8
-    // newAccumulatedGrad = rho * accumulatedGrad + (1 - rho) * grad ^ 2
-    // updates = -grad * sqrt(accumulatedUpdate + epsilon) /
-    //     sqrt(accumulatedGrad + epsilon)
-    // newAccumulatedUpdate = rho * accumulatedUpdate + (1 - rho) * updates ^ 2
-    // x += learningRate * updates
-    //
-    // de/dx = [2, 4]
-    // accumulatedGrad = [0, 0]
-    // newAccumulatedGrad = [.2, .8]
-    // updates = [-2, -4]
-    // newAccumulatedUpdate = [.2, .8]
-    // x = [0.8, 1.6]
-    expectArraysClose(await x.data(), [0.8, 1.6]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(f, /* returnCost */ false);
-
-    // de/dx = [1.6, 3.2]
-    // accumulatedGrad = [.2, .8]
-    // accumulatedUpdate = [.2, .8]
-    // newAccumulatedGrad = [0.318, 1.272]
-    // updates = [-1.6, -3.2]
-    // x = [0.64, 1.28]
-    expectArraysClose(await x.data(), [0.64, 1.28]);
-
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    expect(cost).toBe(null);
-
-    x.dispose();
-    optimizer.dispose();
-
-    // The only additional tensor remaining is the argument to variable().
-    expect(tf.memory().numTensors).toBe(initialTensors + 1);
-  });
-
-  it('Save, load weights and continue training', async () => {
-    const learningRate = .1;
-    const rho = .95;
-    const optimizer1 = tf.train.adadelta(learningRate, rho);
-
-    const x = tf.tensor1d([1, 2]).variable();
-    const f: () => tf.Scalar = () => x.square().sum();
-
-    let cost = optimizer1.minimize(f, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 5);
-    expectArraysClose(await x.data(), [0.8, 1.6]);
-
-    const weights = await optimizer1.getWeights();
-    expect(weights.length).toEqual(3);
-    expect(weights[0].name).toEqual('iter');
-
-    const optimizer2 = tf.train.adadelta(learningRate, rho);
-    await optimizer2.setWeights(weights);
-
-    cost = optimizer2.minimize(f, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 3.2);
-    expectArraysClose(await x.data(), [0.64, 1.28]);
-    expect(optimizer2.iterations).toEqual(2);
-  });
-
-  it('serialization round-trip', () => {
-    const originalOpt = tf.train.adadelta(0.1, 0.2, 2e-8);
-    const reserialized = tf.AdadeltaOptimizer.fromConfig(
-        tf.AdadeltaOptimizer, originalOpt.getConfig());
-    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/optimizers/adagrad_optimizer.ts b/tfjs-master/tfjs-core/src/optimizers/adagrad_optimizer.ts
deleted file mode 100644
index 393f51143..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/adagrad_optimizer.ts
+++ /dev/null
@@ -1,120 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {dispose, tidy} from '../globals';
-import {add} from '../ops/add';
-import {div} from '../ops/div';
-import {fill} from '../ops/fill';
-import {mul} from '../ops/mul';
-import {sqrt} from '../ops/sqrt';
-import {square} from '../ops/square';
-import {ConfigDict, Serializable, SerializableConstructor} from '../serialization';
-import {NamedTensor, NamedVariableMap} from '../tensor_types';
-
-import {Optimizer, OptimizerVariable} from './optimizer';
-
-/** @doclink Optimizer */
-export class AdagradOptimizer extends Optimizer {
-  /** @nocollapse */
-  static get className() {
-    // Name matters for Python compatibility.
-    // This is a getter instead of a property because when it's a property, it
-    // prevents the entire class from being tree-shaken.
-    return 'Adagrad';
-  }
-
-  private accumulatedGrads: OptimizerVariable[] = [];
-
-  constructor(
-      protected learningRate: number, private initialAccumulatorValue = 0.1) {
-    super();
-  }
-
-  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {
-    const variableNames = Array.isArray(variableGradients) ?
-        variableGradients.map(item => item.name) :
-        Object.keys(variableGradients);
-
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      if (this.accumulatedGrads[i] == null) {
-        const trainable = false;
-        this.accumulatedGrads[i] = {
-          originalName: `${name}/accumulator`,
-          variable: tidy(
-              () => fill(value.shape, this.initialAccumulatorValue)
-                        .variable(trainable))
-        };
-      }
-
-      const gradient = Array.isArray(variableGradients) ?
-          variableGradients[i].tensor :
-          variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-
-      const accumulatedGrad = this.accumulatedGrads[i].variable;
-
-      tidy(() => {
-        const newAccumulatedGrad = add(accumulatedGrad, square(gradient));
-        accumulatedGrad.assign(newAccumulatedGrad);
-
-        const newValue = add(
-            mul(div(gradient,
-                    sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))),
-                -this.learningRate),
-            value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-
-  override dispose(): void {
-    if (this.accumulatedGrads != null) {
-      dispose(this.accumulatedGrads.map(v => v.variable));
-    }
-  }
-
-  override async getWeights(): Promise<NamedTensor[]> {
-    // Order matters for Python compatibility.
-    return [await this.saveIterations()].concat(this.accumulatedGrads.map(
-        v => ({name: v.originalName, tensor: v.variable})));
-  }
-
-  override async setWeights(weightValues: NamedTensor[]): Promise<void> {
-    weightValues = await this.extractIterations(weightValues);
-    const trainable = false;
-    this.accumulatedGrads = weightValues.map(
-        v => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-  }
-
-  getConfig(): ConfigDict {
-    return {
-      'learningRate': this.learningRate,
-      'initialAccumulatorValue': this.initialAccumulatorValue,
-    };
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends Serializable>(
-      cls: SerializableConstructor<T>, config: ConfigDict): T {
-    return new cls(config['learningRate'], config['initialAccumulatorValue']);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/optimizers/adagrad_optimizer_test.ts b/tfjs-master/tfjs-core/src/optimizers/adagrad_optimizer_test.ts
deleted file mode 100644
index fbe213a28..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/adagrad_optimizer_test.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('AdagradOptimizer', ALL_ENVS, () => {
-  it('basic', async () => {
-    const initialTensors = tf.memory().numTensors;
-    const learningRate = .1;
-    const initialAccumulatorValue = .1;
-    const optimizer = tf.train.adagrad(learningRate, initialAccumulatorValue);
-
-    const x = tf.tensor1d([1, 2]).variable();
-
-    const f: () => tf.Scalar = () => x.square().sum();
-
-    let numTensors = tf.memory().numTensors;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true);
-
-    // Cost & accumulator should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-
-    // epsilon = 1-e8
-    // newAccumulatedGrad = accumulatedGrad + grad^2
-    // x -= (learningRate * grad) / sqrt(newAccumulatedGrad + eps)
-    // de/dx = [2, 4]
-    // accumulatedGrad = [0.1, 0.1]
-    // newAccumulatedGrad = [4.1, 16.1]
-    // x = [0.9012270405, 1.900311042]
-    expectArraysClose(await x.data(), [0.9012270405, 1.9003110428]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(f, /* returnCost */ false);
-
-    // de/dx = [1.802454081, 3.9501555214]
-    // accumulatedGrad = [4.1, 16.1]
-    // newAccumulatedGrad = [7.3488407141, 31.7037286432]
-    // x = [0.8347372764, 1.83015597828]
-
-    // TODO: Fix numerical precision.
-    expectArraysClose(await x.data(), [0.8347372764, 1.83015597828], 1e-2);
-
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    expect(cost).toBe(null);
-
-    x.dispose();
-    optimizer.dispose();
-
-    // The only additional tensor remaining is the argument to variable().
-    expect(tf.memory().numTensors).toBe(initialTensors + 1);
-  });
-
-  it('Continue training after loading weights', async () => {
-    const learningRate = .1;
-    const initialAccumulatorValue = .1;
-    const optimizer1 = tf.train.adagrad(learningRate, initialAccumulatorValue);
-
-    const x = tf.tensor1d([2, 4]).variable();
-    const f: () => tf.Scalar = () => x.square().sum();
-    let cost = optimizer1.minimize(f, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 20);
-
-    const weights = await optimizer1.getWeights();
-    expect(weights.length).toEqual(2);
-    expect(weights[0].name).toEqual('iter');
-    expect(weights[1].name).toEqual(`${x.name}/accumulator`);
-
-    const optimizer2 = tf.train.adam(learningRate, initialAccumulatorValue);
-    await optimizer2.setWeights(weights);
-
-    cost = optimizer2.minimize(f, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 18.82179);
-    expect(optimizer2.iterations).toEqual(2);
-  });
-
-  it('serialization round-trip', () => {
-    const originalOpt = tf.train.adagrad(0.1, 0.2);
-    const reserialized = tf.AdagradOptimizer.fromConfig(
-        tf.AdagradOptimizer, originalOpt.getConfig());
-    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/optimizers/adam_optimizer.ts b/tfjs-master/tfjs-core/src/optimizers/adam_optimizer.ts
deleted file mode 100644
index ad43b432f..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/adam_optimizer.ts
+++ /dev/null
@@ -1,184 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {dispose, tidy} from '../globals';
-import {add} from '../ops/add';
-import {div} from '../ops/div';
-import {mul} from '../ops/mul';
-import {pow} from '../ops/pow';
-import {scalar} from '../ops/scalar';
-import {sqrt} from '../ops/sqrt';
-import {square} from '../ops/square';
-import {sub} from '../ops/sub';
-import {zerosLike} from '../ops/zeros_like';
-import {ConfigDict, Serializable, SerializableConstructor} from '../serialization';
-import {Variable} from '../tensor';
-import {NamedTensor, NamedVariableMap} from '../tensor_types';
-
-import {Optimizer, OptimizerVariable} from './optimizer';
-
-export class AdamOptimizer extends Optimizer {
-  /** @nocollapse */
-  static get className() {
-    // Name matters for Python compatibility.
-    // This is a getter instead of a property because when it's a property, it
-    // prevents the entire class from being tree-shaken.
-    return 'Adam';
-  }
-  private accBeta1: Variable;
-  private accBeta2: Variable;
-
-  private accumulatedFirstMoment: OptimizerVariable[] = [];
-  private accumulatedSecondMoment: OptimizerVariable[] = [];
-
-  constructor(
-      protected learningRate: number, protected beta1: number,
-      protected beta2: number, protected epsilon: number = null) {
-    super();
-    tidy(() => {
-      // accB* will be updated by batch.
-      this.accBeta1 = scalar(beta1).variable();
-      this.accBeta2 = scalar(beta2).variable();
-    });
-
-    if (epsilon == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-
-  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {
-    const varNames = Array.isArray(variableGradients) ?
-        variableGradients.map(v => v.name) :
-        Object.keys(variableGradients);
-    tidy(() => {
-      const oneMinusAccBeta1 = sub(1, this.accBeta1);
-      const oneMinusAccBeta2 = sub(1, this.accBeta2);
-
-      varNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedFirstMoment[i] == null) {
-          this.accumulatedFirstMoment[i] = {
-            originalName: `${name}/m`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-        if (this.accumulatedSecondMoment[i] == null) {
-          this.accumulatedSecondMoment[i] = {
-            originalName: `${name}/v`,
-            variable: tidy(() => zerosLike(value).variable(trainable))
-          };
-        }
-
-        const gradient = Array.isArray(variableGradients) ?
-            variableGradients[i].tensor :
-            variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-
-        const firstMoment = this.accumulatedFirstMoment[i].variable;
-        const secondMoment = this.accumulatedSecondMoment[i].variable;
-
-        const newFirstMoment =
-            add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-        const newSecondMoment =
-            add(mul(secondMoment, this.beta2),
-                mul(square(gradient), 1 - this.beta2));
-
-        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
-        const biasCorrectedSecondMoment =
-            div(newSecondMoment, oneMinusAccBeta2);
-
-        firstMoment.assign(newFirstMoment);
-        secondMoment.assign(newSecondMoment);
-
-        const newValue =
-            add(mul(div(biasCorrectedFirstMoment,
-                        add(sqrt(biasCorrectedSecondMoment), this.epsilon)),
-                    -this.learningRate),
-                value);
-        value.assign(newValue);
-      });
-
-      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-      this.accBeta2.assign(mul(this.accBeta2, this.beta2));
-    });
-    this.incrementIterations();
-  }
-
-  override dispose(): void {
-    this.accBeta1.dispose();
-    this.accBeta2.dispose();
-
-    if (this.accumulatedFirstMoment != null) {
-      dispose(this.accumulatedFirstMoment.map(v => v.variable));
-    }
-    if (this.accumulatedSecondMoment != null) {
-      dispose(this.accumulatedSecondMoment.map(v => v.variable));
-    }
-  }
-
-  override async getWeights(): Promise<NamedTensor[]> {
-    // Order matters for Python compatibility.
-    const variables: OptimizerVariable[] =
-        [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
-    return [await this.saveIterations()].concat(
-        variables.map(v => ({name: v.originalName, tensor: v.variable})));
-  }
-
-  override async setWeights(weightValues: NamedTensor[]): Promise<void> {
-    weightValues = await this.extractIterations(weightValues);
-    tidy(() => {
-      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
-      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
-    });
-
-    const variableCount = weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedFirstMoment =
-        weightValues.slice(0, variableCount).map(v => ({
-                                                   originalName: v.name,
-                                                   variable: v.tensor.variable(
-                                                       trainable)
-                                                 }));
-    this.accumulatedSecondMoment =
-        weightValues.slice(variableCount, variableCount * 2)
-            .map(v => ({
-                   originalName: v.name,
-                   variable: v.tensor.variable(trainable)
-                 }));
-  }
-
-  getConfig(): ConfigDict {
-    return {
-      'learningRate': this.learningRate,
-      'beta1': this.beta1,
-      'beta2': this.beta2,
-      'epsilon': this.epsilon,
-    };
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends Serializable>(
-      cls: SerializableConstructor<T>, config: ConfigDict): T {
-    return new cls(
-        config['learningRate'], config['beta1'], config['beta2'],
-        config['epsilon']);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/optimizers/adam_optimizer_test.ts b/tfjs-master/tfjs-core/src/optimizers/adam_optimizer_test.ts
deleted file mode 100644
index 89f6252e9..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/adam_optimizer_test.ts
+++ /dev/null
@@ -1,124 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('AdamOptimizer', ALL_ENVS, () => {
-  it('basic', async () => {
-    const initialTensors = tf.memory().numTensors;
-    const learningRate = .1;
-    const beta1 = .8;
-    const beta2 = .9;
-    const optimizer = tf.train.adam(learningRate, beta1, beta2);
-
-    const x = tf.tensor1d([2, 4]).variable();
-
-    const f: () => tf.Scalar = () => x.square().sum();
-
-    let numTensors = tf.memory().numTensors;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true);
-
-    // Cost & 2 accumulators should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 3);
-    // new_first_m = [
-    //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
-    //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2
-    // ] = [.8, 1.6]
-    // new_second_m = [
-    //    beta2 * old_second_m_w1 + (1-beta2) * grad_w1**2,
-    //    beta2 * old_second_m_w2 + (1-beta2) * grad_w2**2
-    // ] = [1.6, 6.4]
-    // m = [new_first_m/(1-acc_beta1)] = [4, 8]
-    // v = [new_second_m/(1-acc_beta2)] = [16, 64]
-    // x = [x - lr * m / sqrt(v)] = [1.9, 3.9]
-    //
-    expectArraysClose(await x.data(), [1.9, 3.9]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(f, /* returnCost */ false);
-
-    // new_first_m = [
-    //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
-    //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2
-    // ] = [1.4, 2.84]
-    // new_second_m = [
-    //    beta2 * old_second_m_w1 + (1-beta2) * grad_w1**2,
-    //    beta2 * old_second_m_w2 + (1-beta2) * grad_w2**2
-    // ] = [2.884, 11.884]
-    // m = [new_first_m/(1-acc_beta1)] = [3.888888, 7.88889]
-    // v = [new_second_m/(1-acc_beta2)] = [15.1789, 62.5473]
-    // x = [x - lr * m / sqrt(v)] = [1.8000001, 3.8002]
-    //
-    expectArraysClose(await x.data(), [1.8000001, 3.8002]);
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    expect(cost).toBe(null);
-
-    x.dispose();
-    optimizer.dispose();
-
-    // The only additional tensor remaining should be the argument to
-    // variable().
-    expect(tf.memory().numTensors).toBe(initialTensors + 1);
-  });
-
-  it('Continue training after loading weights', async () => {
-    const initialTensors = tf.memory().numTensors;
-    const learningRate = .1;
-    const beta1 = .8;
-    const beta2 = .9;
-    const optimizer1 = tf.train.adam(learningRate, beta1, beta2);
-
-    const x = tf.tensor1d([2, 4]).variable();
-    const f: () => tf.Scalar = () => x.square().sum();
-    let cost = optimizer1.minimize(f, /* returnCost */ true);
-    expect(optimizer1.iterations).toEqual(1);
-    expectArraysClose(await cost.data(), 20);
-
-    const weights = await optimizer1.getWeights();
-    expect(weights.length).toEqual(3);
-    expect(weights[0].name).toEqual('iter');
-    expect(weights[1].name).toEqual(`${x.name}/m`);
-    expect(weights[2].name).toEqual(`${x.name}/v`);
-
-    const optimizer2 = tf.train.adam(learningRate, beta1, beta2);
-    await optimizer2.setWeights(weights);
-
-    cost = optimizer2.minimize(f, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 18.82);
-    expect(optimizer2.iterations).toEqual(2);
-
-    const optimizer3 = tf.train.adam(learningRate, beta1, beta2);
-    await optimizer3.setWeights(await optimizer2.getWeights());
-    cost = optimizer2.minimize(f, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 17.681284);
-    expect(optimizer3.iterations).toEqual(initialTensors + 2);
-  });
-
-  it('serialization round-trip', () => {
-    const originalOpt = tf.train.adam(0.1, 0.2, 0.3, 2e-8);
-    const reserialized =
-        tf.AdamOptimizer.fromConfig(tf.AdamOptimizer, originalOpt.getConfig());
-    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/optimizers/adamax_optimizer.ts b/tfjs-master/tfjs-core/src/optimizers/adamax_optimizer.ts
deleted file mode 100644
index 0b9a80ae8..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/adamax_optimizer.ts
+++ /dev/null
@@ -1,162 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {dispose, tidy} from '../globals';
-import {abs} from '../ops/abs';
-import {add} from '../ops/add';
-import {div} from '../ops/div';
-import {maximum} from '../ops/maximum';
-import {mul} from '../ops/mul';
-import {scalar} from '../ops/scalar';
-import {sub} from '../ops/sub';
-import {zerosLike} from '../ops/zeros_like';
-import {ConfigDict, Serializable, SerializableConstructor} from '../serialization';
-import {Variable} from '../tensor';
-import {NamedTensor, NamedVariableMap} from '../tensor_types';
-
-import {Optimizer, OptimizerVariable} from './optimizer';
-
-export class AdamaxOptimizer extends Optimizer {
-  /** @nocollapse */
-  static get className() {
-    // Name matters for Python compatibility.
-    // This is a getter instead of a property because when it's a property, it
-    // prevents the entire class from being tree-shaken.
-    return 'Adamax';
-  }
-  private accBeta1: Variable;
-  private iteration: Variable;
-
-  private accumulatedFirstMoment: OptimizerVariable[] = [];
-  private accumulatedWeightedInfNorm: OptimizerVariable[] = [];
-
-  constructor(
-      protected learningRate: number, protected beta1: number,
-      protected beta2: number, protected epsilon: number = null,
-      protected decay = 0.0) {
-    super();
-
-    tidy(() => {
-      this.iteration = scalar(0).variable();
-      this.accBeta1 = scalar(beta1).variable();
-    });
-
-    if (epsilon == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-  }
-
-  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {
-    const variableNames = Array.isArray(variableGradients) ?
-        variableGradients.map(item => item.name) :
-        Object.keys(variableGradients);
-
-    tidy(() => {
-      const oneMinusAccBeta1 = sub(1, this.accBeta1);
-      const lr =
-          div(-this.learningRate, add(mul(this.iteration, this.decay), 1));
-
-      variableNames.forEach((name, i) => {
-        const value = ENGINE.registeredVariables[name];
-        const trainable = false;
-        if (this.accumulatedFirstMoment[i] == null) {
-          this.accumulatedFirstMoment[i] = {
-            originalName: `${name}/m`,
-            variable: zerosLike(value).variable(trainable)
-          };
-        }
-        if (this.accumulatedWeightedInfNorm[i] == null) {
-          this.accumulatedWeightedInfNorm[i] = {
-            originalName: `${name}/v`,
-            variable: zerosLike(value).variable(trainable)
-          };
-        }
-
-        const gradient = Array.isArray(variableGradients) ?
-            variableGradients[i].tensor :
-            variableGradients[name];
-        if (gradient == null) {
-          return;
-        }
-
-        const firstMoment = this.accumulatedFirstMoment[i].variable;
-        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
-
-        const newFirstMoment =
-            add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
-
-        const ut0 = mul(weightedInfNorm, this.beta2);
-        const ut1 = abs(gradient);
-
-        const newWeightedInfNorm = maximum(ut0, ut1);
-
-        firstMoment.assign(newFirstMoment);
-        weightedInfNorm.assign(newWeightedInfNorm);
-
-        const newValue =
-            add(mul(div(lr, oneMinusAccBeta1),
-                    div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))),
-                value);
-
-        value.assign(newValue);
-      });
-
-      this.iteration.assign(add(this.iteration, 1));
-      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
-    });
-    this.incrementIterations();
-  }
-
-  override dispose(): void {
-    this.accBeta1.dispose();
-    this.iteration.dispose();
-
-    if (this.accumulatedFirstMoment != null) {
-      dispose(this.accumulatedFirstMoment.map(v => v.variable));
-    }
-    if (this.accumulatedWeightedInfNorm != null) {
-      dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));
-    }
-  }
-
-  override async getWeights(): Promise<NamedTensor[]> {
-    throw new Error('getWeights() is not implemented for Adamax yet.');
-  }
-
-  override async setWeights(weightValues: NamedTensor[]): Promise<void> {
-    throw new Error('setWeights() is not implemented for Adamax yet.');
-  }
-
-  getConfig(): ConfigDict {
-    return {
-      'learningRate': this.learningRate,
-      'beta1': this.beta1,
-      'beta2': this.beta2,
-      'epsilon': this.epsilon,
-      'decay': this.decay
-    };
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends Serializable>(
-      cls: SerializableConstructor<T>, config: ConfigDict): T {
-    return new cls(
-        config['learningRate'], config['beta1'], config['beta2'],
-        config['epsilon'], config['decay']);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/optimizers/adamax_optimizer_test.ts b/tfjs-master/tfjs-core/src/optimizers/adamax_optimizer_test.ts
deleted file mode 100644
index 182a7a64e..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/adamax_optimizer_test.ts
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('AdamaxOptimizer', ALL_ENVS, () => {
-  it('basic', async () => {
-    const initialTensors = tf.memory().numTensors;
-    const learningRate = 0.1;
-    const beta1 = 0.8;
-    const beta2 = 0.9;
-    const decay = 0.1;
-    const optimizer =
-        tf.train.adamax(learningRate, beta1, beta2, undefined, decay);
-
-    const x = tf.tensor1d([2, 4]).variable();
-
-    const f: () => tf.Scalar = () => x.square().sum();
-
-    let numTensors = tf.memory().numTensors;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true);
-
-    // Cost & 2 accumulators should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 3);
-    // new_first_m = [
-    //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
-    //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2
-    // ] = [.8, 1.6]
-    //
-    // ut_0 = beta2 * old_weighted_inf_norm = [0, 0]
-    // u1_1 = [
-    //    abs(grad_w1),
-    //    abs(grad_w2)
-    // ] = [4, 8]
-    // new_weighted_inf_norm = max(ut_0, ut_1) = [4, 8]
-    //
-    // coefficient = alpha / (1-beta1) = 0.5
-    // updates = coefficient * [
-    //    new_first_m1 / new_weighted_inf_norm1,
-    //    new_first_m2 / new_weighted_inf_norm2
-    // ] = [0.1, 0.1]
-    // w = [
-    //    w1_old - updates_1,
-    //    w2_old - updates_2
-    // ] = [1.9, 3.9]
-    //
-    expectArraysClose(await x.data(), [1.9, 3.9]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(f, /* returnCost */ false);
-
-    // gradient = [3.8, 7.8]
-    // new_first_m = [
-    //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
-    //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2
-    // ] = [
-    //    0.8 * 0.8 + 0.2 * 3.8,
-    //    0.8 * 1.6 + 0.2 * 7.8
-    // ] = [1.4, 2.84]
-    //
-    // ut_0 = beta2 * old_weighted_inf_norm = [
-    //    0.9 * 4,
-    //    0.9 * 8
-    // ] = [3.6, 7.2]
-    // u1_1 = [
-    //    abs(grad_w1),
-    //    abs(grad_w2)
-    // ] = [3.8, 7.8]
-    // new_weighted_inf_norm = max(ut_0, ut_1) = [3.8, 7.8]
-    //
-    // alpha = 0.1 / (1 + 0.1 * 1) = 0.0909090909
-    //
-    // coefficient = alpha / (1 - beta1*beta1) = 0.25252525
-    // updates = coefficient * [
-    //    new_first_m1 / new_weighted_inf_norm1,
-    //    new_first_m2 / new_weighted_inf_norm2
-    // ] = [0.09303, 0.09194]
-    // w = [
-    //    w1_old - updates_1,
-    //    w2_old - updates_2
-    // ] = [1.80697, 3.8086]
-    //
-    expectArraysClose(await x.data(), [1.80697, 3.8086]);
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    expect(cost).toBe(null);
-
-    x.dispose();
-    optimizer.dispose();
-
-    // The only additional tensor remaining should be the argument to
-    // variable().
-    expect(tf.memory().numTensors).toBe(initialTensors + 1);
-  });
-
-  it('serialization round-trip', () => {
-    const originalOpt = tf.train.adamax(0.1, 0.2, 0.3, 2e-8, 0.1);
-    const reserialized = tf.AdamaxOptimizer.fromConfig(
-        tf.AdamaxOptimizer, originalOpt.getConfig());
-    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/optimizers/momentum_optimizer.ts b/tfjs-master/tfjs-core/src/optimizers/momentum_optimizer.ts
deleted file mode 100644
index 4ed161575..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/momentum_optimizer.ts
+++ /dev/null
@@ -1,133 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {dispose, tidy} from '../globals';
-import {add} from '../ops/add';
-import {mul} from '../ops/mul';
-import {scalar} from '../ops/scalar';
-import {zerosLike} from '../ops/zeros_like';
-import {ConfigDict, Serializable, SerializableConstructor} from '../serialization';
-import {Scalar, Tensor} from '../tensor';
-import {NamedTensor, NamedVariableMap} from '../tensor_types';
-
-import {OptimizerVariable} from './optimizer';
-import {SGDOptimizer} from './sgd_optimizer';
-
-/** @doclink Optimizer */
-export class MomentumOptimizer extends SGDOptimizer {
-  /** @nocollapse */
-  // Name matters for Python compatibility.
-  static override get className() {
-    // Name matters for Python compatibility.
-    // This is a getter instead of a property because when it's a property, it
-    // prevents the entire class from being tree-shaken.
-    return 'Momentum';
-  }
-  private m: Scalar;
-  private accumulations: OptimizerVariable[] = [];
-
-  constructor(
-      protected override learningRate: number, private momentum: number,
-      private useNesterov = false) {
-    super(learningRate);
-    this.m = scalar(this.momentum);
-  }
-
-  override applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {
-    const variableNames = Array.isArray(variableGradients) ?
-        variableGradients.map(item => item.name) :
-        Object.keys(variableGradients);
-
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      if (this.accumulations[i] == null) {
-        const trainable = false;
-        this.accumulations[i] = {
-          originalName: `${name}/momentum`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-
-      const accumulation = this.accumulations[i].variable;
-      const gradient = Array.isArray(variableGradients) ?
-          variableGradients[i].tensor :
-          variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-
-      tidy(() => {
-        let newValue: Tensor;
-        const newAccumulation = add(mul(this.m, accumulation), gradient);
-        if (this.useNesterov) {
-          newValue = add(
-              mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);
-        } else {
-          newValue = add(mul(this.c, newAccumulation), value);
-        }
-        accumulation.assign(newAccumulation);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-
-  override dispose(): void {
-    this.m.dispose();
-    if (this.accumulations != null) {
-      dispose(this.accumulations.map(v => v.variable));
-    }
-  }
-
-  /**
-   * Sets the momentum of the optimizer.
-   *
-   * @param momentum
-   */
-  setMomentum(momentum: number) {
-    this.momentum = momentum;
-  }
-
-  override async getWeights(): Promise<NamedTensor[]> {
-    // Order matters for Python compatibility.
-    return [await this.saveIterations()].concat(this.accumulations.map(
-        v => ({name: v.originalName, tensor: v.variable})));
-  }
-
-  override async setWeights(weightValues: NamedTensor[]): Promise<void> {
-    weightValues = await this.extractIterations(weightValues);
-    const trainable = false;
-    this.accumulations = weightValues.map(
-        v => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
-  }
-
-  override getConfig(): ConfigDict {
-    return {
-      'learningRate': this.learningRate,
-      'momentum': this.momentum,
-      'useNesterov': this.useNesterov
-    };
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends Serializable>(
-      cls: SerializableConstructor<T>, config: ConfigDict): T {
-    return new cls(
-        config['learningRate'], config['momentum'], config['useNesterov']);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/optimizers/momentum_optimizer_test.ts b/tfjs-master/tfjs-core/src/optimizers/momentum_optimizer_test.ts
deleted file mode 100644
index 164751810..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/momentum_optimizer_test.ts
+++ /dev/null
@@ -1,151 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('MomentumOptimizer', ALL_ENVS, () => {
-  it('basic', async () => {
-    const learningRate = .1;
-    const momentum = .5;
-    const optimizer = tf.train.momentum(learningRate, momentum);
-
-    const x = tf.tensor1d([1, 2]).variable();
-
-    const f: () => tf.Scalar = () => x.square().sum();
-
-    let numTensors = tf.memory().numTensors;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true);
-
-    // Cost & velocity should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-
-    // newAccumulation = momentum * accumulation + gradient
-    // newVariable += -learningRate * newAccumulation + variable
-    //
-    // de/dx = [2, 4]
-    // newAccumulation = [2, 4]
-    // x = [.8, 1.6]
-    expectArraysClose(await x.data(), [.8, 1.6]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(f, /* returnCost */ false);
-
-    // de/dx = [1.6, 3.2]
-    // accumulation = [2, 4]
-    // newAccumulation = [2.6, 5.2]
-    // x = [0.54, 1.08]
-    expectArraysClose(await x.data(), [0.54, 1.08]);
-
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    expect(cost).toBe(null);
-
-    x.dispose();
-    numTensors = tf.memory().numTensors;
-    optimizer.dispose();
-
-    // The optimizer.dispose() call should have disposed the m variable and the
-    // momentum variable for x.
-    expect(tf.memory().numTensors).toBe(numTensors - 2);
-  });
-
-  it('basic - with Nesterov', async () => {
-    const learningRate = .1;
-    const momentum = .5;
-    const useNesterov = true;
-    const optimizer = tf.train.momentum(learningRate, momentum, useNesterov);
-
-    const x = tf.tensor1d([1, 2]).variable();
-
-    const f: () => tf.Scalar = () => x.square().sum();
-
-    let numTensors = tf.memory().numTensors;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true);
-
-    // Cost and velocity should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-
-    // newAccumulation = momentum * accumulation + gradient
-    // newVariable = -learningRate * (newAccumulation * momentum + gradient) +
-    //                variable
-    //
-    // de/dx = [2, 4]
-    // newAccumulation = [2, 4]
-    // newVariable = -0.1 * ([2, 4] * 0.5 + [2, 4]) + [1, 2]
-    // x = [.7, 1.4]
-    expectArraysClose(await x.data(), [.7, 1.4]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(f, /* returnCost */ false);
-
-    // de/dx = [1.4, 2.8]
-    // accumulation = [2, 4]
-    // newAccumulation = [0.5 * 2 + 1.4, 0.5 * 4 + 2.8] = [2.4, 4.8]
-    // newVariable = -0.1 * ([2.4, 4.8] * 0.5 + [1.4, 2.8]) + [0.7, 1.4]
-    // x = [0.44, 0.88]
-    expectArraysClose(await x.data(), [0.44, 0.88]);
-
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    expect(cost).toBe(null);
-
-    x.dispose();
-    numTensors = tf.memory().numTensors;
-    optimizer.dispose();
-
-    // The optimizer.dispose() call should have disposed the m variable and the
-    // momentum variable for x.
-    expect(tf.memory().numTensors).toBe(numTensors - 2);
-  });
-
-  it('Save, load weights and conntinue training', async () => {
-    const learningRate = .1;
-    const momentum = .5;
-    const useNesterov = true;
-    const optimizer1 = tf.train.momentum(learningRate, momentum, useNesterov);
-
-    const x = tf.tensor1d([1, 2]).variable();
-    const f: () => tf.Scalar = () => x.square().sum();
-
-    let cost = optimizer1.minimize(f, /* returnCost */ true);
-
-    // The iterations counter and the accumulation for the variable x.
-    const optimizer2 = tf.train.momentum(learningRate, momentum, useNesterov);
-    await optimizer2.setWeights(await optimizer1.getWeights());
-    cost = optimizer2.minimize(f, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 2.45);
-    expectArraysClose(await x.data(), [0.44, 0.88]);
-    expect(optimizer2.iterations).toEqual(2);
-  });
-
-  it('serialization round-trip', () => {
-    const originalOpt = tf.train.momentum(0.1, 0.2, true);
-    const reserialized = tf.MomentumOptimizer.fromConfig(
-        tf.MomentumOptimizer, originalOpt.getConfig());
-    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/optimizers/optimizer.ts b/tfjs-master/tfjs-core/src/optimizers/optimizer.ts
deleted file mode 100644
index dca3e1380..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/optimizer.ts
+++ /dev/null
@@ -1,173 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {dispose} from '../globals';
-import {variableGrads} from '../gradients';
-import {scalar} from '../ops/ops';
-import {Serializable} from '../serialization';
-import {Scalar, Variable} from '../tensor';
-import {NamedTensor, NamedTensorMap} from '../tensor_types';
-
-/**
- * A variable that belongs to an optimizer.
- *
- * The `originalName` field is required for keeping track of the canonical
- * name of the variable, which is usually the name of the model weight that
- * the variable is related to plus a suffix, e.g., 'dense1/kernel/momentum'.
- * The name of the `Variable` object itself cannot be used directly due to
- * possible deduplication: Every `Variable` must have a unique name but more
- * than one optimizer objects of the same type may be created for the same model
- * or the same `Variable`.
- */
-export interface OptimizerVariable {
-  originalName: string;
-  variable: Variable;
-}
-
-/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */
-export abstract class Optimizer extends Serializable {
-  protected iterations_: number;
-
-  /**
-   * Executes `f()` and minimizes the scalar output of `f()` by computing
-   * gradients of y with respect to the list of trainable variables provided by
-   * `varList`. If no list is provided, it defaults to all trainable variables.
-   *
-   * @param f The function to execute and whose output to minimize.
-   * @param returnCost Whether to return the scalar cost value produced by
-   * executing `f()`.
-   * @param varList An optional list of variables to update. If specified, only
-   * the trainable variables in varList will be updated by minimize. Defaults to
-   * all trainable variables.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers'}
-   */
-  minimize(f: () => Scalar, returnCost = false, varList?: Variable[]): Scalar
-      |null {
-    const {value, grads} = this.computeGradients(f, varList);
-
-    if (varList != null) {
-      const gradArray: NamedTensor[] =
-          varList.map(v => ({name: v.name, tensor: grads[v.name]}));
-      this.applyGradients(gradArray);
-    } else {
-      this.applyGradients(grads);
-    }
-
-    // Dispose gradients.
-    dispose(grads);
-
-    if (returnCost) {
-      return value;
-    } else {
-      value.dispose();
-      return null;
-    }
-  }
-
-  /**
-   * The number of iterations that this optimizer instance has been invoked for.
-   */
-  get iterations(): number {
-    if (this.iterations_ == null) {
-      this.iterations_ = 0;
-    }
-    return this.iterations_;
-  }
-
-  protected incrementIterations() {
-    this.iterations_ = this.iterations + 1;
-  }
-
-  /**
-   * Executes f() and computes the gradient of the scalar output of f() with
-   * respect to the list of trainable variables provided by `varList`. If no
-   * list is provided, it defaults to all trainable variables.
-   *
-   * @param f The function to execute and whose output to use for computing
-   * gradients with respect to variables.
-   * @param varList An optional list of variables to compute gradients with
-   * respect to. If specified, only the trainable variables in varList will have
-   * gradients computed with respect to. Defaults to all trainable variables.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers'}
-   */
-  computeGradients(f: () => Scalar, varList?: Variable[]):
-      {value: Scalar, grads: NamedTensorMap} {
-    return variableGrads(f, varList);
-  }
-
-  /**
-   * Updates variables by using the computed gradients.
-   *
-   * @param variableGradients A mapping of variable name to its gradient value.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers'}
-   */
-  abstract applyGradients(variableGradients: NamedTensorMap|
-                          NamedTensor[]): void;
-
-  /**
-   * Dispose the variables (if any) owned by this optimizer instance.
-   */
-  dispose(): void {
-    if (this.iterations_ != null) {
-      dispose(this.iterations_);
-    }
-  }
-
-  async saveIterations(): Promise<NamedTensor> {
-    if (this.iterations_ == null) {
-      this.iterations_ = 0;
-    }
-    return {
-      name: 'iter',  // Named for Python compatibility.
-      // TODO(cais): Use 'int64' type when available.
-      tensor: scalar(this.iterations_, 'int32')
-    };
-  }
-
-  async getWeights(): Promise<NamedTensor[]> {
-    throw new Error('getWeights() is not implemented for this optimizer yet.');
-  }
-
-  async setWeights(weightValues: NamedTensor[]): Promise<void> {
-    throw new Error(
-        `setWeights() is not implemented for this optimizer class ` +
-        `${this.getClassName()}`);
-  }
-
-  /**
-   * Extract the first element of the weight values and set it
-   * as the iterations counter variable of this instance of optimizer.
-   *
-   * @param weightValues
-   * @returns Weight values with the first element consumed and excluded.
-   */
-  protected async extractIterations(weightValues: NamedTensor[]):
-      Promise<NamedTensor[]> {
-    this.iterations_ = (await weightValues[0].tensor.data())[0];
-    return weightValues.slice(1);
-  }
-}
-
-Object.defineProperty(Optimizer, Symbol.hasInstance, {
-  value: (instance: Optimizer) => {
-    return instance.minimize != null && instance.computeGradients != null &&
-        instance.applyGradients != null;
-  }
-});
diff --git a/tfjs-master/tfjs-core/src/optimizers/optimizer_constructors.ts b/tfjs-master/tfjs-core/src/optimizers/optimizer_constructors.ts
deleted file mode 100644
index 66966329a..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/optimizer_constructors.ts
+++ /dev/null
@@ -1,189 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AdadeltaOptimizer} from './adadelta_optimizer';
-import {AdagradOptimizer} from './adagrad_optimizer';
-import {AdamOptimizer} from './adam_optimizer';
-import {AdamaxOptimizer} from './adamax_optimizer';
-import {MomentumOptimizer} from './momentum_optimizer';
-import {RMSPropOptimizer} from './rmsprop_optimizer';
-import {SGDOptimizer} from './sgd_optimizer';
-
-export class OptimizerConstructors {
-  /**
-   * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.
-   *
-   * ```js
-   * // Fit a quadratic function by learning the coefficients a, b, c.
-   * const xs = tf.tensor1d([0, 1, 2, 3]);
-   * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);
-   *
-   * const a = tf.scalar(Math.random()).variable();
-   * const b = tf.scalar(Math.random()).variable();
-   * const c = tf.scalar(Math.random()).variable();
-   *
-   * // y = a * x^2 + b * x + c.
-   * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);
-   * const loss = (pred, label) => pred.sub(label).square().mean();
-   *
-   * const learningRate = 0.01;
-   * const optimizer = tf.train.sgd(learningRate);
-   *
-   * // Train the model.
-   * for (let i = 0; i < 10; i++) {
-   *   optimizer.minimize(() => loss(f(xs), ys));
-   * }
-   *
-   * // Make predictions.
-   * console.log(
-   *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);
-   * const preds = f(xs).dataSync();
-   * preds.forEach((pred, i) => {
-   *   console.log(`x: ${i}, pred: ${pred}`);
-   * });
-   * ```
-   *
-   * @param learningRate The learning rate to use for the SGD algorithm.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
-   */
-  static sgd(learningRate: number): SGDOptimizer {
-    return new SGDOptimizer(learningRate);
-  }
-
-  /**
-   * Constructs a `tf.MomentumOptimizer` that uses momentum gradient
-   * descent.
-   *
-   * See
-   * [http://proceedings.mlr.press/v28/sutskever13.pdf](
-   * http://proceedings.mlr.press/v28/sutskever13.pdf)
-   *
-   * @param learningRate The learning rate to use for the Momentum gradient
-   * descent algorithm.
-   * @param momentum The momentum to use for the momentum gradient descent
-   * algorithm.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
-   */
-  static momentum(learningRate: number, momentum: number, useNesterov = false):
-      MomentumOptimizer {
-    return new MomentumOptimizer(learningRate, momentum, useNesterov);
-  }
-
-  /**
-   * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient
-   * descent. This implementation uses plain momentum and is not centered
-   * version of RMSProp.
-   *
-   * See
-   * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](
-   * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
-   *
-   * @param learningRate The learning rate to use for the RMSProp gradient
-   * descent algorithm.
-   * @param decay The discounting factor for the history/coming gradient.
-   * @param momentum The momentum to use for the RMSProp gradient descent
-   * algorithm.
-   * @param epsilon Small value to avoid zero denominator.
-   * @param centered If true, gradients are normalized by the estimated
-   * variance of the gradient.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
-   */
-  static rmsprop(
-      learningRate: number, decay = .9, momentum = 0.0, epsilon: number = null,
-      centered = false): RMSPropOptimizer {
-    return new RMSPropOptimizer(
-        learningRate, decay, momentum, epsilon, centered);
-  }
-
-  /**
-   * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.
-   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
-   *
-   * @param learningRate The learning rate to use for the Adam gradient
-   * descent algorithm.
-   * @param beta1 The exponential decay rate for the 1st moment estimates.
-   * @param beta2 The exponential decay rate for the 2nd moment estimates.
-   * @param epsilon A small constant for numerical stability.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
-   */
-  static adam(
-      learningRate = 0.001, beta1 = 0.9, beta2 = 0.999,
-      epsilon: number = null): AdamOptimizer {
-    return new AdamOptimizer(learningRate, beta1, beta2, epsilon);
-  }
-
-  /**
-   * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.
-   * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)
-   *
-   * @param learningRate The learning rate to use for the Adadelta gradient
-   * descent algorithm.
-   * @param rho The learning rate decay over each update.
-   * @param epsilon A constant epsilon used to better condition the grad
-   * update.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
-   */
-  static adadelta(learningRate = .001, rho = .95, epsilon: number = null):
-      AdadeltaOptimizer {
-    return new AdadeltaOptimizer(learningRate, rho, epsilon);
-  }
-
-  /**
-   * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.
-   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
-   *
-   * @param learningRate The learning rate to use for the Adamax gradient
-   * descent algorithm.
-   * @param beta1 The exponential decay rate for the 1st moment estimates.
-   * @param beta2 The exponential decay rate for the 2nd moment estimates.
-   * @param epsilon A small constant for numerical stability.
-   * @param decay The learning rate decay over each update.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
-   */
-  static adamax(
-      learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon: number = null,
-      decay = 0.0): AdamaxOptimizer {
-    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);
-  }
-
-  /**
-   * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.
-   * See
-   * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](
-   * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
-   * or
-   * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](
-   * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)
-   *
-   * @param learningRate The learning rate to use for the Adagrad gradient
-   * descent algorithm.
-   * @param initialAccumulatorValue Starting value for the accumulators, must be
-   * positive.
-   *
-   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
-   */
-  static adagrad(learningRate: number, initialAccumulatorValue = 0.1):
-      AdagradOptimizer {
-    return new AdagradOptimizer(learningRate, initialAccumulatorValue);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/optimizers/optimizer_test.ts b/tfjs-master/tfjs-core/src/optimizers/optimizer_test.ts
deleted file mode 100644
index d09e9ecef..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/optimizer_test.ts
+++ /dev/null
@@ -1,234 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {Variable} from '../tensor';
-import {expectArraysClose} from '../test_util';
-
-import {Optimizer} from './optimizer';
-import {SGDOptimizer} from './sgd_optimizer';
-
-describeWithFlags('optimizer', ALL_ENVS, () => {
-  it('basic', async () => {
-    const initialTensors = tf.memory().numTensors;
-    const learningRate = .1;
-    const optimizer = tf.train.sgd(learningRate);
-
-    const x = tf.scalar(4).variable();
-    const bias = tf.scalar(1).variable();
-    const strayVariable = tf.scalar(-1).variable();
-
-    let numTensors = tf.memory().numTensors;
-
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const f = () => x.square().add(bias) as tf.Scalar;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true);
-
-    // Cost should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-
-    // de/dx = 2x
-    const expectedX1 = -2 * 4 * learningRate + 4;
-    // de/db = 1
-    const expectedBias1 = -1 * learningRate + 1;
-    expectArraysClose(await x.data(), [expectedX1]);
-    expectArraysClose(await bias.data(), [expectedBias1]);
-    expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);
-    // The stray variable should remain unchanged.
-    expectArraysClose(await strayVariable.data(), [-1]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(f, /* returnCost */ false);
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    const expectedX2 = -2 * expectedX1 * learningRate + expectedX1;
-    const expectedBias2 = -learningRate + expectedBias1;
-    expectArraysClose(await x.data(), [expectedX2]);
-    expectArraysClose(await bias.data(), [expectedBias2]);
-    expect(cost).toBe(null);
-    // The stray variable should remain unchanged.
-    expectArraysClose(await strayVariable.data(), [-1]);
-
-    optimizer.dispose();
-    x.dispose();
-    bias.dispose();
-    strayVariable.dispose();
-    // The only additional tensors remaining are the arguments to variable().
-    expect(tf.memory().numTensors).toBe(initialTensors + 3);
-  });
-
-  it('varList array of all variables', async () => {
-    const learningRate = .1;
-    const optimizer = new SGDOptimizer(learningRate);
-
-    const x = tf.scalar(4).variable();
-    const bias = tf.scalar(1).variable();
-    const strayVariable = tf.scalar(-1).variable();
-    const varList = [x, bias];
-
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const f = () => x.square().add(bias) as tf.Scalar;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true, varList);
-
-    // de/dx = 2x
-    const expectedX1 = -2 * 4 * learningRate + 4;
-    // de/db = 1
-    const expectedBias1 = -1 * learningRate + 1;
-    expectArraysClose(await x.data(), [expectedX1]);
-    expectArraysClose(await bias.data(), [expectedBias1]);
-    expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);
-    // The stray variable should remain unchanged.
-    expectArraysClose(await strayVariable.data(), [-1]);
-
-    cost = optimizer.minimize(f, /* returnCost */ false, varList);
-
-    const expectedX2 = -2 * expectedX1 * learningRate + expectedX1;
-    const expectedBias2 = -learningRate + expectedBias1;
-    expectArraysClose(await x.data(), [expectedX2]);
-    expectArraysClose(await bias.data(), [expectedBias2]);
-    // The stray variable should remain unchanged.
-    expectArraysClose(await strayVariable.data(), [-1]);
-    expect(cost).toBe(null);
-  });
-
-  it('varList empty array of variables throws error', () => {
-    const learningRate = .1;
-    const optimizer = new SGDOptimizer(learningRate);
-
-    const x = tf.scalar(4).variable();
-    const bias = tf.scalar(1).variable();
-    // Stray variable.
-    tf.scalar(-1).variable();
-    const varList: Variable[] = [];
-
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const f = () => x.square().add(bias) as tf.Scalar;
-
-    expect(() => optimizer.minimize(f, /* returnCost */ true, varList))
-        .toThrowError();
-  });
-
-  it('varList subset of variables update', async () => {
-    const learningRate = .1;
-    const optimizer = new SGDOptimizer(learningRate);
-
-    const x = tf.scalar(4).variable();
-    const bias = tf.scalar(1).variable();
-    const strayVariable = tf.scalar(-1).variable();
-    const varList = [x];
-
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const f = () => x.square().add(bias) as tf.Scalar;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true, varList);
-
-    // de/dx = 2x
-    const expectedValue1 = -2 * 4 * learningRate + 4;
-    expectArraysClose(await x.data(), [expectedValue1]);
-    // bias should remain unchanged.
-    expectArraysClose(await bias.data(), [1]);
-    expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);
-    // The stray variable should remain unchanged.
-    expectArraysClose(await strayVariable.data(), [-1]);
-
-    cost = optimizer.minimize(f, /* returnCost */ false, varList);
-
-    const expectedValue2 = -2 * expectedValue1 * learningRate + expectedValue1;
-    expectArraysClose(await x.data(), [expectedValue2]);
-    // Bias still should remain unchanged.
-    expectArraysClose(await bias.data(), [1]);
-    expect(cost).toBe(null);
-    // The stray variable should remain unchanged.
-    expectArraysClose(await strayVariable.data(), [-1]);
-  });
-
-  it('only bias trainable', async () => {
-    const learningRate = .1;
-    const optimizer = new SGDOptimizer(learningRate);
-
-    const trainable = false;
-    const x = tf.scalar(4).variable(trainable);
-    const bias = tf.scalar(1).variable();
-    const strayVariable = tf.scalar(-1).variable();
-
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const f = () => x.square().add(bias) as tf.Scalar;
-
-    let cost = optimizer.minimize(f, /* returnCost */ true);
-
-    // x should not have been updated.
-    expectArraysClose(await x.data(), [4]);
-    // de/db = 1
-    const expectedBias1 = -1 * learningRate + 1;
-    expectArraysClose(await bias.data(), [expectedBias1]);
-    expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);
-    // The stray variable should remain unchanged.
-    expectArraysClose(await strayVariable.data(), [-1]);
-
-    cost = optimizer.minimize(f, /* returnCost */ false);
-
-    // x should not have been updated.
-    expectArraysClose(await x.data(), [4]);
-    const expectedBias2 = -learningRate + expectedBias1;
-    expectArraysClose(await bias.data(), [expectedBias2]);
-    expect(cost).toBe(null);
-    // The stray variable should remain unchanged.
-    expectArraysClose(await strayVariable.data(), [-1]);
-  });
-
-  it('only bias trainable, only x in varList throws error', () => {
-    const learningRate = .1;
-    const optimizer = new SGDOptimizer(learningRate);
-
-    const trainable = false;
-    const x = tf.scalar(4).variable(trainable);
-    const bias = tf.scalar(1).variable();
-    // stray variable.
-    tf.scalar(-1).variable();
-    const varList = [x];
-
-    // tslint:disable-next-line: no-unnecessary-type-assertion
-    const f = () => x.square().add(bias) as tf.Scalar;
-
-    expect(() => optimizer.minimize(f, /* returnCost */ true, varList))
-        .toThrowError();
-  });
-
-  it('instanceof Optimizer', () => {
-    const learningRate = .1;
-    const optimizer = new SGDOptimizer(learningRate);
-
-    expect(optimizer instanceof Optimizer).toBe(true);
-  });
-
-  it('throws error when f returns a non-scalar', () => {
-    const learningRate = .1;
-    const optimizer = new SGDOptimizer(learningRate);
-
-    const x = tf.tensor1d([1, 2]).variable();
-    const f = () => x.square();
-
-    // tslint:disable-next-line:no-any
-    expect(() => optimizer.minimize(f as any)).toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/optimizers/register_optimizers.ts b/tfjs-master/tfjs-core/src/optimizers/register_optimizers.ts
deleted file mode 100644
index 263cb1d94..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/register_optimizers.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AdadeltaOptimizer} from './adadelta_optimizer';
-import {AdagradOptimizer} from './adagrad_optimizer';
-import {AdamOptimizer} from './adam_optimizer';
-import {AdamaxOptimizer} from './adamax_optimizer';
-import {MomentumOptimizer} from './momentum_optimizer';
-import {RMSPropOptimizer} from './rmsprop_optimizer';
-import {SGDOptimizer} from './sgd_optimizer';
-import {registerClass} from '../serialization';
-
-const OPTIMIZERS = [
-  AdadeltaOptimizer,
-  AdagradOptimizer,
-  AdamOptimizer,
-  AdamaxOptimizer,
-  MomentumOptimizer,
-  RMSPropOptimizer,
-  SGDOptimizer,
-];
-
-export function registerOptimizers() {
-  for (const optimizer of OPTIMIZERS) {
-    registerClass(optimizer);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/optimizers/rmsprop_optimizer.ts b/tfjs-master/tfjs-core/src/optimizers/rmsprop_optimizer.ts
deleted file mode 100644
index 5b8dd0b70..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/rmsprop_optimizer.ts
+++ /dev/null
@@ -1,214 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {dispose, tidy} from '../globals';
-import {add} from '../ops/add';
-import {div} from '../ops/div';
-import {mul} from '../ops/mul';
-import {sqrt} from '../ops/sqrt';
-import {square} from '../ops/square';
-import {sub} from '../ops/sub';
-import {zerosLike} from '../ops/zeros_like';
-import {ConfigDict, Serializable, SerializableConstructor} from '../serialization';
-import {NamedTensor, NamedTensorMap} from '../tensor_types';
-
-import {Optimizer, OptimizerVariable} from './optimizer';
-
-/** @doclink Optimizer */
-export class RMSPropOptimizer extends Optimizer {
-  /** @nocollapse */
-  static get className() {
-    // Name matters for Python compatibility.
-    // This is a getter instead of a property because when it's a property, it
-    // prevents the entire class from being tree-shaken.
-    return 'RMSProp';
-  }
-  private centered: boolean;
-
-  private accumulatedMeanSquares: OptimizerVariable[] = [];
-  private accumulatedMoments: OptimizerVariable[] = [];
-  private accumulatedMeanGrads: OptimizerVariable[] = [];
-
-  constructor(
-      protected learningRate: number, protected decay = 0.9,
-      protected momentum = 0.0, protected epsilon: number = null,
-      centered = false) {
-    super();
-
-    this.centered = centered;
-
-    if (epsilon == null) {
-      this.epsilon = ENGINE.backend.epsilon();
-    }
-    if (learningRate == null) {
-      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
-    }
-  }
-
-  applyGradients(variableGradients: NamedTensorMap|NamedTensor[]) {
-    const variableNames = Array.isArray(variableGradients) ?
-        variableGradients.map(item => item.name) :
-        Object.keys(variableGradients);
-
-    variableNames.forEach((name, i) => {
-      const value = ENGINE.registeredVariables[name];
-      const trainable = false;
-      if (this.accumulatedMeanSquares[i] == null) {
-        this.accumulatedMeanSquares[i] = {
-          originalName: `${name}/rms`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedMoments[i] == null) {
-        this.accumulatedMoments[i] = {
-          originalName: `${name}/momentum`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-      if (this.accumulatedMeanGrads[i] == null && this.centered) {
-        this.accumulatedMeanGrads[i] = {
-          originalName: `${name}/mg`,
-          variable: tidy(() => zerosLike(value).variable(trainable))
-        };
-      }
-
-      const gradient = Array.isArray(variableGradients) ?
-          variableGradients[i].tensor :
-          variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-
-      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
-      const accumulatedMoments = this.accumulatedMoments[i].variable;
-      tidy(() => {
-        const newAccumulatedMeanSquare =
-            add(mul(accumulatedMeanSquare, this.decay),
-                mul(square(gradient), 1 - this.decay));
-
-        if (this.centered) {
-          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
-          // Centered gradient
-          const newAccumulatedMeanGrad =
-              add(mul(accumulatedMeanGrad, this.decay),
-                  mul(gradient, 1 - this.decay));
-
-          const gradContribution =
-              div(mul(gradient, this.learningRate),
-                  sqrt(
-                      sub(newAccumulatedMeanSquare,
-                          add(square(newAccumulatedMeanGrad), this.epsilon))));
-          const newAccumulatedMoments =
-              add(mul(accumulatedMoments, this.momentum), gradContribution);
-
-          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
-          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
-          accumulatedMoments.assign(newAccumulatedMoments);
-
-          const newValue = sub(value, newAccumulatedMoments);
-          value.assign(newValue);
-        } else {
-          // Plain gradient
-          const newAccumulatedMeanSquare =
-              add(mul(accumulatedMeanSquare, this.decay),
-                  mul(square(gradient), 1 - this.decay));
-
-          const newAccumulatedMoments =
-              add(mul(accumulatedMoments, this.momentum),
-                  div(mul(gradient, this.learningRate),
-                      sqrt(add(newAccumulatedMeanSquare, this.epsilon))));
-
-          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
-          accumulatedMoments.assign(newAccumulatedMoments);
-
-          const newValue = sub(value, newAccumulatedMoments);
-          value.assign(newValue);
-        }
-      });
-    });
-    this.incrementIterations();
-  }
-
-  override dispose(): void {
-    if (this.accumulatedMeanSquares != null) {
-      dispose(this.accumulatedMeanSquares.map(v => v.variable));
-    }
-    if (this.accumulatedMeanGrads != null && this.centered) {
-      dispose(this.accumulatedMeanGrads.map(v => v.variable));
-    }
-    if (this.accumulatedMoments != null) {
-      dispose(this.accumulatedMoments.map(v => v.variable));
-    }
-  }
-
-  override async getWeights(): Promise<NamedTensor[]> {
-    // Order matters for Python compatibility.
-    const variables: OptimizerVariable[] =
-        [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
-    if (this.centered) {
-      variables.push(...this.accumulatedMeanGrads);
-    }
-    return [await this.saveIterations()].concat(
-        variables.map(v => ({name: v.originalName, tensor: v.variable})));
-  }
-
-  override async setWeights(weightValues: NamedTensor[]): Promise<void> {
-    weightValues = await this.extractIterations(weightValues);
-    const variableCount =
-        this.centered ? weightValues.length / 3 : weightValues.length / 2;
-    const trainable = false;
-    this.accumulatedMeanSquares =
-        weightValues.slice(0, variableCount).map(v => ({
-                                                   originalName: v.name,
-                                                   variable: v.tensor.variable(
-                                                       trainable)
-                                                 }));
-    this.accumulatedMoments =
-        weightValues.slice(variableCount, variableCount * 2)
-            .map(v => ({
-                   originalName: v.name,
-                   variable: v.tensor.variable(trainable)
-                 }));
-    if (this.centered) {
-      this.accumulatedMeanGrads =
-          weightValues.slice(variableCount * 2, variableCount * 3)
-              .map(v => ({
-                     originalName: v.name,
-                     variable: v.tensor.variable(trainable)
-                   }));
-    }
-  }
-
-  getConfig(): ConfigDict {
-    return {
-      'learningRate': this.learningRate,
-      'decay': this.decay,
-      'momentum': this.momentum,
-      'epsilon': this.epsilon,
-      'centered': this.centered
-    };
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends Serializable>(
-      cls: SerializableConstructor<T>, config: ConfigDict): T {
-    return new cls(
-        config['learningRate'], config['decay'], config['momentum'],
-        config['epsilon'], config['centered']);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/optimizers/rmsprop_optimizer_test.ts b/tfjs-master/tfjs-core/src/optimizers/rmsprop_optimizer_test.ts
deleted file mode 100644
index 642d65a5b..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/rmsprop_optimizer_test.ts
+++ /dev/null
@@ -1,228 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('RMSPropOptimizer', ALL_ENVS, () => {
-  it('basic', async () => {
-    const initialTensors = tf.memory().numTensors;
-    const learningRate = 0.1;
-    const moment = 0.1;
-    const rho = 0.95;
-    const optimizer = tf.train.rmsprop(learningRate, rho, moment);
-
-    const x = tf.tensor1d([1, 2]).variable();
-
-    const f = () => x.square().sum();
-
-    let numTensors = tf.memory().numTensors;
-
-    let cost = optimizer.minimize(f as () => tf.Scalar, /* returnCost */ true);
-
-    // Cost & 2 accumulators should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 3);
-
-    // epsilon = 1e-8
-    // newAccumulatedMeanSquare =
-    //          rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = (0.2)
-    // newAccumulatedMoments = momentum * accumulatedMoments +
-    //          learning_rate * gradient / sqrt(newAccumulatedMeanSquare +
-    //          epsilon) = 0.1 * 0 + ((0.1 * 2) / sqrt(0.2 + 1e-8)) = 0.44721
-    // x -= learningRate * newAccumulatedMoments
-    //
-    // de/dx = [2, 4]
-    // accumulatedMeanSquare = [0, 0]
-    // newAccumulatedMeanSquare = [.2, .8]
-    // accumulatedMoments = [0, 0]
-    // newAccumulatedMoments = [0.44721, 0.44721]
-    // x = [0.55279, 1.55279]
-    expectArraysClose(await x.data(), [0.55279, 1.55279]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(f as () => tf.Scalar, /* returnCost */ false);
-
-    // x = [0.55279, 1.55279]
-    // de/dx = [1.10558, 3.10558]
-    // accumulatedMeanSquare = [0.2, 0.8]
-    // newAccumulatedMeanSquare = [0.25105125, 1.242231]
-    // accumulatedMoments = [0.44721, 0.44721]
-    // newAccumulatedMoments = [0.26534, 0.32336]
-    // x = [0.28745, 1.22943]
-
-    // TODO: Fix numerical precision.
-    expectArraysClose(await x.data(), [0.28745, 1.222943], 1e-2);
-
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    expect(cost).toBe(null);
-
-    x.dispose();
-    optimizer.dispose();
-    // The only additional tensor remaining is the argument to variable().
-    expect(tf.memory().numTensors).toBe(initialTensors + 1);
-  });
-
-  it('gradient with centered momentum', async () => {
-    const initialTensors = tf.memory().numTensors;
-    const learningRate = 0.1;
-    const moment = 0.1;
-    const rho = 0.95;
-    const eps = 1e-8;
-    const optimizer = tf.train.rmsprop(learningRate, rho, moment, eps, true);
-
-    const x = tf.tensor1d([1, 2]).variable();
-
-    const f = () => x.square().sum();
-
-    let numTensors = tf.memory().numTensors;
-
-    let cost = optimizer.minimize(f as () => tf.Scalar, /* returnCost */ true);
-
-    // Cost & 3 accumulators should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 4);
-
-    // epsilon = 1e-8
-    // newAccumulatedMeanSquare =
-    //          rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = [.2, .8]
-    // newAccumulatedMeanGrad =
-    //          rho * accumulatedMeanGrad + (1 - rho) * grad = [0.1, 0.2]
-    // newAccumulatedMoments = momentum * accumulatedMoments +
-    //          learning_rate * gradient / sqrt(newAccumulatedMeanSquare
-    //            - newAccumulatedMeanGrad * 2 +
-    //              epsilon) = 0.1 * 0 + ((0.1 * 2)
-    //                / sqrt(0.2 - 0.01 + 1e-8)) = 0.458831
-    // x -= learningRate * newAccumulatedMoments
-    //
-    // de/dx = [2, 4]
-    // accumulatedMeanSquare = [0, 0]
-    // newAccumulatedMeanSquare = [.2, .8]
-    // newAccumulatedMeanGrad = [.1, .2]
-    // accumulatedMoments = [0, 0]
-    // newAccumulatedMoments = [0.45883, 0.458831]
-    // x = [0.54117, 1.541169]
-    expectArraysClose(await x.data(), [0.54117, 1.541169]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(f as () => tf.Scalar, /* returnCost */ false);
-
-    // x = [0.54117, 1.541169]
-    // de/dx = [1.08234, 3.082338]
-    // accumulatedMeanSquare = [0.2, 0.8]
-    // accumulatedMeanGrad = [.1, .2]
-    // newAccumulatedMeanSquare = [0.248572, 1.235040]
-    // newAccumulatedMeanGrad = [0.149117, 0.3441169]
-    // accumulatedMoments = [0.45883, 0.458831]
-    // newAccumulatedMoments = [0.273385, 0.3375766]
-    // x = [0.267785, 1.2035924]
-
-    // TODO: Fix numerical precision.
-    expectArraysClose(await x.data(), [0.267785, 1.2035924], 1e-2);
-
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    expect(cost).toBe(null);
-
-    x.dispose();
-    optimizer.dispose();
-    // The only additional tensor remaining is the argument to variable().
-    expect(tf.memory().numTensors).toBe(initialTensors + 1);
-  });
-
-  it('Save and load weights: centered = false', async () => {
-    const learningRate = 0.1;
-    const moment = 0.1;
-    const rho = 0.95;
-    const optimizer1 = tf.train.rmsprop(learningRate, rho, moment);
-
-    const x = tf.tensor1d([1, 2]).variable();
-    const f: () => tf.Scalar = () => x.square().sum();
-
-    let cost = optimizer1.minimize(f, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 5);
-    expectArraysClose(await x.data(), [0.5527865, 1.5527864]);
-
-    const weights = await optimizer1.getWeights();
-    // An iteration variable and two optimizer state variables.
-    expect(weights.length).toEqual(3);
-
-    const optimizer2 = tf.train.rmsprop(learningRate, rho, moment);
-    await optimizer2.setWeights(weights);
-
-    cost = optimizer2.minimize(f, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 2.7167187);
-    expectArraysClose(await x.data(), [0.2874418, 1.2294267]);
-    expect(optimizer2.iterations).toEqual(2);
-  });
-
-  it('Save, load weights and continue training: centered = true', async () => {
-    const learningRate = 0.1;
-    const moment = 0.1;
-    const rho = 0.95;
-    const epsilon: number = undefined;
-    const centered = true;
-    const optimizer1 =
-        tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);
-
-    const x = tf.tensor1d([1, 2]).variable();
-    const f = () => x.square().sum();
-
-    let cost = optimizer1.minimize(f as () => tf.Scalar, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 5);
-    expectArraysClose(await x.data(), [0.5411684, 1.5411685]);
-
-    const weights = await optimizer1.getWeights();
-    // An iteration variable and three optimizer state variables.
-    expect(weights.length).toEqual(4);
-
-    const optimizer2 =
-        tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);
-    await optimizer2.setWeights(weights);
-
-    cost = optimizer2.minimize(f as () => tf.Scalar, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 2.668063);
-    expectArraysClose(await x.data(), [0.2677834, 1.2035918]);
-    expect(optimizer2.iterations).toEqual(2);
-
-    const optimizer3 =
-        tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);
-    await optimizer3.setWeights(await optimizer2.getWeights());
-    cost = optimizer3.minimize(f as () => tf.Scalar, /* returnCost */ true);
-    expectArraysClose(await cost.data(), 1.520341);
-    expect(optimizer3.iterations).toEqual(3);
-  });
-
-  it('serialization round-trip', () => {
-    const originalOpt = tf.train.rmsprop(0.1, 0.5, 0.1, 1e-7, true);
-    const reserialized = tf.RMSPropOptimizer.fromConfig(
-        tf.RMSPropOptimizer, originalOpt.getConfig());
-    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
-  });
-
-  it('must define learning rate', () => {
-    const learningRate: number = undefined;
-    expect(() => tf.train.rmsprop(learningRate))
-        .toThrowError(/learningRate for RMSPropOptimizer must be defined./);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/optimizers/sgd_optimizer.ts b/tfjs-master/tfjs-core/src/optimizers/sgd_optimizer.ts
deleted file mode 100644
index 0935c973b..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/sgd_optimizer.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from '../engine';
-import {keep, tidy} from '../globals';
-import {add} from '../ops/add';
-import {mul} from '../ops/mul';
-import {scalar} from '../ops/scalar';
-import {ConfigDict, Serializable, SerializableConstructor} from '../serialization';
-import {Scalar} from '../tensor';
-import {NamedTensor, NamedTensorMap} from '../tensor_types';
-
-import {Optimizer} from './optimizer';
-
-/** @doclink Optimizer */
-export class SGDOptimizer extends Optimizer {
-  /** @nocollapse */
-  static get className() {
-    // Name matters for Python compatibility.
-    // This is a getter instead of a property because when it's a property, it
-    // prevents the entire class from being tree-shaken.
-    return 'SGD';
-  }
-  protected c: Scalar;
-
-  constructor(protected learningRate: number) {
-    super();
-    this.setLearningRate(learningRate);
-  }
-
-  applyGradients(variableGradients: NamedTensorMap|NamedTensor[]) {
-    const varNames = Array.isArray(variableGradients) ?
-        variableGradients.map(v => v.name) :
-        Object.keys(variableGradients);
-    varNames.forEach((name, i) => {
-      const gradient = Array.isArray(variableGradients) ?
-          variableGradients[i].tensor :
-          variableGradients[name];
-      if (gradient == null) {
-        return;
-      }
-      const value = ENGINE.registeredVariables[name];
-      tidy(() => {
-        const newValue = add(mul(this.c, gradient), value);
-        value.assign(newValue);
-      });
-    });
-    this.incrementIterations();
-  }
-
-  /**
-   * Sets the learning rate of the optimizer.
-   */
-  setLearningRate(learningRate: number) {
-    this.learningRate = learningRate;
-    if (this.c != null) {
-      this.c.dispose();
-    }
-    this.c = keep(scalar(-learningRate));
-  }
-
-  override dispose() {
-    this.c.dispose();
-  }
-
-  override async getWeights(): Promise<NamedTensor[]> {
-    return [await this.saveIterations()];
-  }
-
-  override async setWeights(weightValues: NamedTensor[]): Promise<void> {
-    weightValues = await this.extractIterations(weightValues);
-    if (weightValues.length !== 0) {
-      throw new Error('SGD optimizer does not have settable weights.');
-    }
-  }
-
-  getConfig(): ConfigDict {
-    return {'learningRate': this.learningRate};
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends Serializable>(
-      cls: SerializableConstructor<T>, config: ConfigDict): T {
-    return new cls(config['learningRate']);
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/optimizers/sgd_optimizer_test.ts b/tfjs-master/tfjs-core/src/optimizers/sgd_optimizer_test.ts
deleted file mode 100644
index 0a07ea7da..000000000
--- a/tfjs-master/tfjs-core/src/optimizers/sgd_optimizer_test.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import {ALL_ENVS, describeWithFlags} from '../jasmine_util';
-import {expectArraysClose} from '../test_util';
-
-describeWithFlags('SGDOptimizer', ALL_ENVS, () => {
-  it('basic', async () => {
-    const initialTensors = tf.memory().numTensors;
-    const learningRate = .1;
-    const optimizer = tf.train.sgd(learningRate);
-
-    const x = tf.scalar(4).variable();
-
-    let numTensors = tf.memory().numTensors;
-
-    let cost = optimizer.minimize(() => x.square(), /* returnCost */ true);
-
-    // Cost should be the only additional arrays.
-    expect(tf.memory().numTensors).toBe(numTensors + 1);
-
-    // de/dx = 2x
-    const expectedValue1 = -2 * 4 * learningRate + 4;
-    expectArraysClose(await x.data(), [expectedValue1]);
-    expectArraysClose(await cost.data(), [Math.pow(4, 2)]);
-
-    cost.dispose();
-    numTensors = tf.memory().numTensors;
-
-    cost = optimizer.minimize(() => x.square(), /* returnCost */ false);
-    // There should be no new additional Tensors.
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    const expectedValue2 = -2 * expectedValue1 * learningRate + expectedValue1;
-    expectArraysClose(await x.data(), [expectedValue2]);
-    expect(cost).toBe(null);
-
-    optimizer.dispose();
-    x.dispose();
-    // The only additional tensor remaining is the argument to variable().
-    expect(tf.memory().numTensors).toBe(initialTensors + 1);
-  });
-
-  it('Set and get weights: empty', async () => {
-    const x = tf.scalar(4).variable();
-
-    const learningRate = .1;
-    const optimizer1 = tf.train.sgd(learningRate);
-
-    let weights = await optimizer1.getWeights();
-    expect(optimizer1.iterations).toEqual(0);
-
-    optimizer1.minimize(() => x.square());
-
-    weights = await optimizer1.getWeights();
-    expect(optimizer1.iterations).toEqual(1);
-    expect(weights.length).toEqual(1);
-    expect(weights[0].name).toEqual('iter');
-    expectArraysClose(await weights[0].tensor.data(), 1);
-
-    const optimizer2 = tf.train.sgd(learningRate);
-    await optimizer2.setWeights(weights);
-    optimizer2.minimize(() => x.square());
-    expectArraysClose(await x.data(), 2.56);
-    expect(optimizer2.iterations).toEqual(2);
-
-    const optimizer3 = tf.train.sgd(learningRate);
-    await optimizer3.setWeights(await optimizer2.getWeights());
-    optimizer3.minimize(() => x.square());
-    expectArraysClose(await x.data(), 2.048);
-    expect(optimizer3.iterations).toEqual(3);
-  });
-
-  it('serialization round-trip', () => {
-    const learningRate = .1;
-    const originalOpt = tf.train.sgd(learningRate);
-    const reserialized =
-        tf.SGDOptimizer.fromConfig(tf.SGDOptimizer, originalOpt.getConfig());
-    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/platforms/is_typed_array_browser.ts b/tfjs-master/tfjs-core/src/platforms/is_typed_array_browser.ts
deleted file mode 100644
index 346908408..000000000
--- a/tfjs-master/tfjs-core/src/platforms/is_typed_array_browser.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export function isTypedArrayBrowser(a: unknown): a is Uint8Array
-  | Float32Array | Int32Array | Uint8ClampedArray {
-  return a instanceof Float32Array || a instanceof Int32Array ||
-    a instanceof Uint8Array || a instanceof Uint8ClampedArray;
-}
diff --git a/tfjs-master/tfjs-core/src/platforms/platform.ts b/tfjs-master/tfjs-core/src/platforms/platform.ts
deleted file mode 100644
index 60934f39d..000000000
--- a/tfjs-master/tfjs-core/src/platforms/platform.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {RequestDetails} from '../io/types';
-
-/**
- * At any given time a single platform is active and represents and
- * implementation of this interface. In practice, a platform is an environment
- * where TensorFlow.js can be executed, e.g. the browser or Node.js.
- */
-export interface Platform {
-  /**
-   * Makes an HTTP request.
-   * @param path The URL path to make a request to
-   * @param init The request init. See init here:
-   *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
-   */
-  fetch(path: string, requestInits?: RequestInit, options?: RequestDetails):
-      Promise<Response>;
-
-  /**
-   * Returns the current high-resolution time in milliseconds relative to an
-   * arbitrary time in the past. It works across different platforms (node.js,
-   * browsers).
-   */
-  now(): number;
-
-  /**
-   * Encode the provided string into an array of bytes using the provided
-   * encoding.
-   */
-  encode(text: string, encoding: string): Uint8Array;
-  /** Decode the provided bytes into a string using the provided encoding. */
-  decode(bytes: Uint8Array, encoding: string): string;
-
-  setTimeoutCustom?(functionRef: Function, delay: number): void;
-
-  isTypedArray(a: unknown): a is Float32Array|Int32Array|Uint8Array|
-    Uint8ClampedArray;
-}
diff --git a/tfjs-master/tfjs-core/src/platforms/platform_browser.ts b/tfjs-master/tfjs-core/src/platforms/platform_browser.ts
deleted file mode 100644
index d4b16ed22..000000000
--- a/tfjs-master/tfjs-core/src/platforms/platform_browser.ts
+++ /dev/null
@@ -1,117 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '../flags';
-
-import {env} from '../environment';
-import {BrowserIndexedDB, BrowserIndexedDBManager} from '../io/indexed_db';
-import {BrowserLocalStorage, BrowserLocalStorageManager} from '../io/local_storage';
-import {ModelStoreManagerRegistry} from '../io/model_management';
-
-import {Platform} from './platform';
-import {isTypedArrayBrowser} from './is_typed_array_browser';
-
-export class PlatformBrowser implements Platform {
-  // According to the spec, the built-in encoder can do only UTF-8 encoding.
-  // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder
-  private textEncoder: TextEncoder;
-
-  // For setTimeoutCustom
-  private readonly messageName = 'setTimeoutCustom';
-  private functionRefs: Function[] = [];
-  private handledMessageCount = 0;
-  private hasEventListener = false;
-
-  fetch(path: string, init?: RequestInit): Promise<Response> {
-    return fetch(path, init);
-  }
-
-  now(): number {
-    return performance.now();
-  }
-
-  encode(text: string, encoding: string): Uint8Array {
-    if (encoding !== 'utf-8' && encoding !== 'utf8') {
-      throw new Error(
-          `Browser's encoder only supports utf-8, but got ${encoding}`);
-    }
-    if (this.textEncoder == null) {
-      this.textEncoder = new TextEncoder();
-    }
-    return this.textEncoder.encode(text);
-  }
-  decode(bytes: Uint8Array, encoding: string): string {
-    return new TextDecoder(encoding).decode(bytes);
-  }
-
-  // If the setTimeout nesting level is greater than 5 and timeout is less
-  // than 4ms, timeout will be clamped to 4ms, which hurts the perf.
-  // Interleaving window.postMessage and setTimeout will trick the browser and
-  // avoid the clamp.
-  setTimeoutCustom(functionRef: Function, delay: number): void {
-    if (typeof window === 'undefined' ||
-        !env().getBool('USE_SETTIMEOUTCUSTOM')) {
-      setTimeout(functionRef, delay);
-      return;
-    }
-
-    this.functionRefs.push(functionRef);
-    setTimeout(() => {
-      window.postMessage(
-          {name: this.messageName, index: this.functionRefs.length - 1}, '*');
-    }, delay);
-
-    if (!this.hasEventListener) {
-      this.hasEventListener = true;
-      window.addEventListener('message', (event: MessageEvent) => {
-        if (event.source === window && event.data.name === this.messageName) {
-          event.stopPropagation();
-          const functionRef = this.functionRefs[event.data.index];
-          functionRef();
-          this.handledMessageCount++;
-          if (this.handledMessageCount === this.functionRefs.length) {
-            this.functionRefs = [];
-            this.handledMessageCount = 0;
-          }
-        }
-      }, true);
-    }
-  }
-
-  isTypedArray(a: unknown): a is Uint8Array | Float32Array | Int32Array
-    | Uint8ClampedArray {
-    return isTypedArrayBrowser(a);
-  }
-}
-
-if (env().get('IS_BROWSER')) {
-  env().setPlatform('browser', new PlatformBrowser());
-
-  // Register LocalStorage IOHandler
-  try {
-    ModelStoreManagerRegistry.registerManager(
-        BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());
-  } catch (err) {
-  }
-
-  // Register IndexedDB IOHandler
-  try {
-    ModelStoreManagerRegistry.registerManager(
-        BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());
-  } catch (err) {
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/platforms/platform_browser_test.ts b/tfjs-master/tfjs-core/src/platforms/platform_browser_test.ts
deleted file mode 100644
index 6b7661a3d..000000000
--- a/tfjs-master/tfjs-core/src/platforms/platform_browser_test.ts
+++ /dev/null
@@ -1,165 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '../environment';
-import {BROWSER_ENVS, describeWithFlags} from '../jasmine_util';
-
-import {PlatformBrowser} from './platform_browser';
-
-describeWithFlags('PlatformBrowser', BROWSER_ENVS, async () => {
-  it('fetch calls window.fetch', async () => {
-    const response = new Response();
-    spyOn(self, 'fetch').and.returnValue(Promise.resolve(response));
-    const platform = new PlatformBrowser();
-
-    await platform.fetch('test/url', {method: 'GET'});
-
-    expect(self.fetch).toHaveBeenCalledWith('test/url', {method: 'GET'});
-  });
-
-  it('now should use performance.now', async () => {
-    const platform = new PlatformBrowser();
-
-    const ms = 1234567;
-    spyOn(performance, 'now').and.returnValue(ms);
-    expect(platform.now()).toEqual(ms);
-  });
-
-  it('encodeUTF8 single string', () => {
-    const platform = new PlatformBrowser();
-    const bytes = platform.encode('hello', 'utf-8');
-    expect(bytes.length).toBe(5);
-    expect(bytes).toEqual(new Uint8Array([104, 101, 108, 108, 111]));
-  });
-
-  it('encodeUTF8 two strings delimited', () => {
-    const platform = new PlatformBrowser();
-    const bytes = platform.encode('hello\x00world', 'utf-8');
-    expect(bytes.length).toBe(11);
-    expect(bytes).toEqual(
-        new Uint8Array([104, 101, 108, 108, 111, 0, 119, 111, 114, 108, 100]));
-  });
-
-  it('encodeUTF8 cyrillic', () => {
-    const platform = new PlatformBrowser();
-    const bytes = platform.encode('Здраво', 'utf-8');
-    expect(bytes.length).toBe(12);
-    expect(bytes).toEqual(new Uint8Array(
-        [208, 151, 208, 180, 209, 128, 208, 176, 208, 178, 208, 190]));
-  });
-
-  it('decode single string', () => {
-    const platform = new PlatformBrowser();
-    const s =
-        platform.decode(new Uint8Array([104, 101, 108, 108, 111]), 'utf-8');
-    expect(s.length).toBe(5);
-    expect(s).toEqual('hello');
-  });
-
-  it('decode two strings delimited', () => {
-    const platform = new PlatformBrowser();
-    const s = platform.decode(
-        new Uint8Array([104, 101, 108, 108, 111, 0, 119, 111, 114, 108, 100]),
-        'utf-8');
-    expect(s.length).toBe(11);
-    expect(s).toEqual('hello\x00world');
-  });
-
-  it('decode cyrillic', () => {
-    const platform = new PlatformBrowser();
-    const s = platform.decode(
-        new Uint8Array(
-            [208, 151, 208, 180, 209, 128, 208, 176, 208, 178, 208, 190]),
-        'utf-8');
-    expect(s.length).toBe(6);
-    expect(s).toEqual('Здраво');
-  });
-});
-
-describeWithFlags('setTimeout', BROWSER_ENVS, () => {
-  const totalCount = 100;
-  // Skip the first few samples because the browser does not clamp the timeout
-  const skipCount = 5;
-
-  it('setTimeout', (done) => {
-    let count = 0;
-    let startTime = performance.now();
-    let totalTime = 0;
-    setTimeout(_testSetTimeout, 0);
-
-    function _testSetTimeout() {
-      const endTime = performance.now();
-      count++;
-      if (count > skipCount) {
-        totalTime += endTime - startTime;
-      }
-      if (count === totalCount) {
-        const averageTime = totalTime / (totalCount - skipCount);
-        console.log(`averageTime of setTimeout is ${averageTime} ms`);
-        expect(averageTime).toBeGreaterThan(4);
-        done();
-        return;
-      }
-      startTime = performance.now();
-      setTimeout(_testSetTimeout, 0);
-    }
-  });
-
-  it('setTimeoutCustom', (done) => {
-    let count = 0;
-    let startTime = performance.now();
-    let totalTime = 0;
-    let originUseSettimeoutcustom: boolean;
-
-    originUseSettimeoutcustom = env().getBool('USE_SETTIMEOUTCUSTOM');
-    env().set('USE_SETTIMEOUTCUSTOM', true);
-    env().platform.setTimeoutCustom(_testSetTimeoutCustom, 0);
-
-    function _testSetTimeoutCustom() {
-      const endTime = performance.now();
-      count++;
-      if (count > skipCount) {
-        totalTime += endTime - startTime;
-      }
-      if (count === totalCount) {
-        const averageTime = totalTime / (totalCount - skipCount);
-        console.log(`averageTime of setTimeoutCustom is ${averageTime} ms`);
-        expect(averageTime).toBeLessThan(4);
-        done();
-        env().set('USE_SETTIMEOUTCUSTOM', originUseSettimeoutcustom);
-        return;
-      }
-      startTime = performance.now();
-      env().platform.setTimeoutCustom(_testSetTimeoutCustom, 0);
-    }
-  });
-
-  it('isTypedArray returns false if not a typed array', () => {
-    const platform = new PlatformBrowser();
-    expect(platform.isTypedArray([1, 2, 3])).toBeFalse();
-  });
-
-  for (const typedArrayConstructor of [Float32Array, Int32Array, Uint8Array,
-      Uint8ClampedArray]) {
-    it(`isTypedArray returns true if it is a ${typedArrayConstructor.name}`,
-       () => {
-         const platform = new PlatformBrowser();
-         const array = new typedArrayConstructor([1,2,3]);
-         expect(platform.isTypedArray(array)).toBeTrue();
-       });
-  }
-});
diff --git a/tfjs-master/tfjs-core/src/platforms/platform_node.ts b/tfjs-master/tfjs-core/src/platforms/platform_node.ts
deleted file mode 100644
index 361eca12f..000000000
--- a/tfjs-master/tfjs-core/src/platforms/platform_node.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {env} from '../environment';
-import {Platform} from './platform';
-
-// We are wrapping this within an object so it can be stubbed by Jasmine.
-export const getNodeFetch = {
-  // tslint:disable-next-line:no-require-imports
-  importFetch: () => require('node-fetch')
-};
-
-type FetchFn = (url: string, init?: RequestInit) => Promise<Response>;
-let systemFetch: FetchFn;
-// These getters and setters are for testing so we don't export a mutable
-// variable.
-export function resetSystemFetch() {
-  systemFetch = null;
-}
-export function setSystemFetch(fetchFn: FetchFn) {
-  systemFetch = fetchFn;
-}
-export function getSystemFetch(): FetchFn {
-  return systemFetch;
-}
-
-export class PlatformNode implements Platform {
-  private textEncoder: TextEncoder;
-  // tslint:disable-next-line:no-any
-  util: any;
-
-  constructor() {
-    // tslint:disable-next-line:no-require-imports
-    this.util = require('util');
-    // According to the spec, the built-in encoder can do only UTF-8 encoding.
-    // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder
-    this.textEncoder = new this.util.TextEncoder();
-  }
-
-  fetch(path: string, requestInits?: RequestInit): Promise<Response> {
-    if (env().global.fetch != null) {
-      return env().global.fetch(path, requestInits);
-    }
-
-    if (systemFetch == null) {
-      systemFetch = getNodeFetch.importFetch();
-    }
-    return systemFetch(path, requestInits);
-  }
-
-  now(): number {
-    const time = process.hrtime();
-    return time[0] * 1000 + time[1] / 1000000;
-  }
-
-  encode(text: string, encoding: string): Uint8Array {
-    if (encoding !== 'utf-8' && encoding !== 'utf8') {
-      throw new Error(
-          `Node built-in encoder only supports utf-8, but got ${encoding}`);
-    }
-    return this.textEncoder.encode(text);
-  }
-  decode(bytes: Uint8Array, encoding: string): string {
-    if (bytes.length === 0) {
-      return '';
-    }
-    return new this.util.TextDecoder(encoding).decode(bytes);
-  }
-  isTypedArray(a: unknown): a is Float32Array | Int32Array | Uint8Array
-    | Uint8ClampedArray {
-    return this.util.types.isFloat32Array(a)
-      || this.util.types.isInt32Array(a)
-      || this.util.types.isUint8Array(a)
-      || this.util.types.isUint8ClampedArray(a);
-  }
-}
-
-if (env().get('IS_NODE') && !env().get('IS_BROWSER')) {
-  env().setPlatform('node', new PlatformNode());
-}
diff --git a/tfjs-master/tfjs-core/src/platforms/platform_node_test.ts b/tfjs-master/tfjs-core/src/platforms/platform_node_test.ts
deleted file mode 100644
index 8e7d5808f..000000000
--- a/tfjs-master/tfjs-core/src/platforms/platform_node_test.ts
+++ /dev/null
@@ -1,157 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '../index';
-import * as platform_node from './platform_node';
-import {PlatformNode} from './platform_node';
-import * as vm from 'node:vm';
-
-describe('PlatformNode', () => {
-  it('fetch should use global.fetch if defined', async () => {
-    const globalFetch = tf.env().global.fetch;
-
-    spyOn(tf.env().global, 'fetch').and.returnValue(() => {});
-
-    const platform = new PlatformNode();
-
-    await platform.fetch('test/url', {method: 'GET'});
-
-    expect(tf.env().global.fetch).toHaveBeenCalledWith('test/url', {
-      method: 'GET'
-    });
-
-    tf.env().global.fetch = globalFetch;
-  });
-
-  it('fetch should use node-fetch with tf.env().global.fetch is null',
-     async () => {
-       const globalFetch = tf.env().global.fetch;
-       tf.env().global.fetch = null;
-
-       const platform = new PlatformNode();
-
-       const savedFetch = platform_node.getSystemFetch();
-
-       // Null out the system fetch so we force it to require node-fetch.
-       platform_node.resetSystemFetch();
-
-       const testFetch = {fetch: (url: string, init: RequestInit) => () => {}};
-
-       // Mock the actual fetch call.
-       spyOn(testFetch, 'fetch').and.returnValue(() => {});
-       // Mock the import to override the real require of node-fetch.
-       spyOn(platform_node.getNodeFetch, 'importFetch')
-           .and.callFake(
-               () => (url: string, init: RequestInit) =>
-                   testFetch.fetch(url, init));
-
-       await platform.fetch('test/url', {method: 'GET'});
-
-       expect(platform_node.getNodeFetch.importFetch).toHaveBeenCalled();
-       expect(testFetch.fetch).toHaveBeenCalledWith('test/url', {
-         method: 'GET'
-       });
-
-       platform_node.setSystemFetch(savedFetch);
-       tf.env().global.fetch = globalFetch;
-     });
-
-  it('now should use process.hrtime', async () => {
-    const time: [number, number] = [100, 200];
-    spyOn(process, 'hrtime').and.returnValue(time);
-    expect(tf.env().platform.now()).toEqual(time[0] * 1000 + time[1] / 1000000);
-  });
-
-  it('encodeUTF8 single string', () => {
-    const platform = new PlatformNode();
-    const bytes = platform.encode('hello', 'utf-8');
-    expect(bytes.length).toBe(5);
-    expect(bytes).toEqual(new Uint8Array([104, 101, 108, 108, 111]));
-  });
-
-  it('encodeUTF8 two strings delimited', () => {
-    const platform = new PlatformNode();
-    const bytes = platform.encode('hello\x00world', 'utf-8');
-    expect(bytes.length).toBe(11);
-    expect(bytes).toEqual(
-        new Uint8Array([104, 101, 108, 108, 111, 0, 119, 111, 114, 108, 100]));
-  });
-
-  it('encodeUTF8 cyrillic', () => {
-    const platform = new PlatformNode();
-    const bytes = platform.encode('Здраво', 'utf-8');
-    expect(bytes.length).toBe(12);
-    expect(bytes).toEqual(new Uint8Array(
-        [208, 151, 208, 180, 209, 128, 208, 176, 208, 178, 208, 190]));
-  });
-
-  it('decode single string', () => {
-    const platform = new PlatformNode();
-    const s =
-        platform.decode(new Uint8Array([104, 101, 108, 108, 111]), 'utf8');
-    expect(s.length).toBe(5);
-    expect(s).toEqual('hello');
-  });
-
-  it('decode two strings delimited', () => {
-    const platform = new PlatformNode();
-    const s = platform.decode(
-        new Uint8Array([104, 101, 108, 108, 111, 0, 119, 111, 114, 108, 100]),
-        'utf8');
-    expect(s.length).toBe(11);
-    expect(s).toEqual('hello\x00world');
-  });
-
-  it('decode cyrillic', () => {
-    const platform = new PlatformNode();
-    const s = platform.decode(
-        new Uint8Array(
-            [208, 151, 208, 180, 209, 128, 208, 176, 208, 178, 208, 190]),
-        'utf8');
-    expect(s.length).toBe(6);
-    expect(s).toEqual('Здраво');
-  });
-
-  describe('isTypedArray', () => {
-    let platform: PlatformNode;
-    beforeEach(() => {
-      platform = new PlatformNode();
-    });
-
-    it('returns false if not a typed array', () => {
-      expect(platform.isTypedArray([1, 2, 3])).toBeFalse();
-    });
-
-    for (const typedArrayConstructor of [Float32Array, Int32Array, Uint8Array,
-                                         Uint8ClampedArray]) {
-      it(`returns true if it is a ${typedArrayConstructor.name}`,
-         () => {
-           const array = new typedArrayConstructor([1,2,3]);
-           expect(platform.isTypedArray(array)).toBeTrue();
-         });
-    }
-
-    it('works on values created in a new node context', async () => {
-      const array = await new Promise((resolve) => {
-        const code = `resolve(new Uint8Array([1, 2, 3]));`;
-        vm.runInNewContext(code, {resolve});
-      });
-
-      expect(platform.isTypedArray(array)).toBeTrue();
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/profiler.ts b/tfjs-master/tfjs-core/src/profiler.ts
deleted file mode 100644
index 8e315bd52..000000000
--- a/tfjs-master/tfjs-core/src/profiler.ts
+++ /dev/null
@@ -1,142 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BackendTimer, BackendTimingInfo} from './backends/backend';
-import {env} from './environment';
-import {Tensor} from './tensor';
-import {NamedTensorMap} from './tensor_types';
-import {DataType, DataTypeMap, TypedArray} from './types';
-import * as util from './util';
-
-export type KernelProfile = {
-  kernelName: string,
-  outputs: Tensor[],
-  inputs: NamedTensorMap,
-  timeMs: Promise<number|{error: string}>,
-  extraInfo: Promise<string>
-};
-
-export class Profiler {
-  constructor(private backendTimer: BackendTimer, private logger?: Logger) {
-    if (logger == null) {
-      this.logger = new Logger();
-    }
-  }
-
-  profileKernel(kernelName: string, inputs: NamedTensorMap, f: () => Tensor[]):
-      KernelProfile {
-    let outputs: Tensor[];
-    const holdResultWrapperFn = () => {
-      outputs = f();
-    };
-    let timer: Promise<BackendTimingInfo>;
-    const start = util.now();
-    if (this.backendTimer.timerAvailable()) {
-      timer = this.backendTimer.time(holdResultWrapperFn);
-    } else {
-      holdResultWrapperFn();
-      for (const output of outputs) {
-        output.dataSync();
-      }
-      timer = Promise.resolve({kernelMs: util.now() - start});
-    }
-    if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) {
-      for (let i = 0; i < outputs.length; i++) {
-        const output = outputs[i];
-        // Dangling promise here because we don't want to propagate up
-        // asynchronicity.
-        output.data().then(tensorVals => {
-          checkComputationForErrors(tensorVals, output.dtype, kernelName);
-        });
-      }
-    }
-
-    const kernelProfile = {
-      kernelName,
-      outputs,
-      inputs,
-      timeMs: timer.then(timing => timing.kernelMs),
-      extraInfo: timer.then(
-          timing => timing.getExtraProfileInfo != null ?
-              timing.getExtraProfileInfo() :
-              '')
-    };
-    return kernelProfile;
-  }
-
-  logKernelProfile(kernelProfile: KernelProfile): void {
-    const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;
-
-    outputs.forEach(result => {
-      Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {
-        this.logger.logKernelProfile(
-            kernelName, result, valueContainer[0], valueContainer[1], inputs,
-            valueContainer[2]);
-      });
-    });
-  }
-}
-
-export function checkComputationForErrors<D extends DataType>(
-    vals: DataTypeMap[D], dtype: D, kernelName: string): boolean {
-  if (dtype !== 'float32') {
-    // Only floating point computations will generate NaN values
-    return false;
-  }
-  for (let i = 0; i < vals.length; i++) {
-    const num = vals[i] as number;
-    if (isNaN(num) || !isFinite(num)) {
-      // Throwing custom exception so behavior is testable.
-      console.warn(`Found ${num} in the result of '${kernelName}'`);
-      return true;
-    }
-  }
-  return false;
-}
-
-export class Logger {
-  logKernelProfile(
-      name: string, result: Tensor, vals: TypedArray,
-      timeMs: number|{error: string}, inputs: NamedTensorMap,
-      extraInfo?: string) {
-    const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :
-                                              timeMs['error'];
-    const paddedName = util.rightPad(name, 25);
-    const rank = result.rank;
-    const size = result.size;
-    const shape = util.rightPad(result.shape.toString(), 14);
-    let inputShapesDescription = '';
-
-    for (const name in inputs) {
-      const input = inputs[name];
-      if (input != null) {
-        // The input might be a non-tensor (e.g HTMLImageElement), in which case
-        // we claim the output shape as input shape.
-        const inputShape = input.shape || result.shape;
-        const inputRank = inputShape.length;
-        inputShapesDescription +=
-            `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;
-      }
-    }
-
-    console.log(
-        `%c${paddedName}\t%c${time}\t%c${rank}D ${shape}\t%c${size}\t%c${
-            inputShapesDescription}\t%c${extraInfo}`,
-        'font-weight:bold', 'color:red', 'color:blue', 'color: orange',
-        'color: green', 'color: steelblue');
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/profiler_test.ts b/tfjs-master/tfjs-core/src/profiler_test.ts
deleted file mode 100644
index e97b643f8..000000000
--- a/tfjs-master/tfjs-core/src/profiler_test.ts
+++ /dev/null
@@ -1,248 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BackendTimer, BackendTimingInfo} from './backends/backend';
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags, SYNC_BACKEND_ENVS} from './jasmine_util';
-import {checkComputationForErrors, KernelProfile, Logger, Profiler} from './profiler';
-import {Tensor} from './tensor';
-import {NamedTensorMap} from './tensor_types';
-import {TypedArray} from './types';
-
-class TestBackendTimer implements BackendTimer {
-  private counter = 1;
-  constructor(
-      private delayMs: number, private queryTimeMs: number,
-      private extraInfo: string) {}
-
-  timerAvailable() {
-    return true;
-  }
-
-  async time(query: () => void): Promise<BackendTimingInfo> {
-    query();
-    const kernelMs = await new Promise<number>(
-        resolve => setTimeout(
-            () => resolve(this.queryTimeMs * this.counter++), this.delayMs));
-    return {kernelMs, getExtraProfileInfo: () => this.extraInfo};
-  }
-}
-
-class TestLogger extends Logger {
-  override logKernelProfile(
-      name: string, result: Tensor, vals: TypedArray, timeMs: number) {}
-}
-
-function promiseCheckWrapper(acturalValPromise: Promise<{}>, truthVal: {}) {
-  return acturalValPromise.then(acturalVal => {
-    expect(acturalVal).toEqual(truthVal);
-  });
-}
-
-function checkKernelProfile(acturalVal: KernelProfile, truthVal: {
-  kernelName: string,
-  outputs: Tensor[],
-  timeMs: number|{error: string},
-  inputs: NamedTensorMap,
-  extraInfo: string
-}) {
-  expect(acturalVal.kernelName).toBe(truthVal.kernelName);
-  expect(acturalVal.inputs).toBe(truthVal.inputs);
-  acturalVal.outputs.forEach((output, index) => {
-    expect(output).toBe(truthVal.outputs[index]);
-  });
-
-  const promiseContainer = [
-    promiseCheckWrapper(acturalVal.timeMs, truthVal.timeMs),
-    promiseCheckWrapper(acturalVal.extraInfo, truthVal.extraInfo),
-  ];
-  return Promise.all(promiseContainer);
-}
-
-describeWithFlags('profiler.Profiler', SYNC_BACKEND_ENVS, () => {
-  it('profiles simple function', doneFn => {
-    const delayMs = 5;
-    const queryTimeMs = 10;
-    const inputs = {'x': tf.tensor1d([1])};
-    const extraInfo = '';
-    const timer = new TestBackendTimer(delayMs, queryTimeMs, extraInfo);
-    const logger = new TestLogger();
-    const profiler = new Profiler(timer, logger);
-
-    spyOn(timer, 'time').and.callThrough();
-    spyOn(logger, 'logKernelProfile').and.callThrough();
-
-    const timeSpy = timer.time as jasmine.Spy;
-
-    let kernelCalled = false;
-    const result = 1;
-    const resultScalar = tf.scalar(result);
-
-    const kernelProfile = profiler.profileKernel('MatMul', inputs, () => {
-      kernelCalled = true;
-      return [resultScalar];
-    });
-    setTimeout(() => {
-      expect(timeSpy.calls.count()).toBe(1);
-      expect(kernelCalled).toBe(true);
-
-      checkKernelProfile(kernelProfile, {
-        kernelName: 'MatMul',
-        outputs: [resultScalar],
-        timeMs: queryTimeMs,
-        inputs,
-        extraInfo,
-      }).then(() => doneFn());
-    }, delayMs * 2);
-  });
-
-  it('profiles nested kernel with optional inputs', doneFn => {
-    const delayMs = 5;
-    const queryTimeMs = 10;
-    const inputs: {'x': tf.Tensor,
-                   'bias': null} = {'x': tf.tensor1d([1]), 'bias': null};
-    const extraInfo = '';
-    const timer = new TestBackendTimer(delayMs, queryTimeMs, extraInfo);
-    const logger = new TestLogger();
-    const profiler = new Profiler(timer, logger);
-
-    spyOn(timer, 'time').and.callThrough();
-    spyOn(logger, 'logKernelProfile').and.callThrough();
-    const timeSpy = timer.time as jasmine.Spy;
-
-    let matmulKernelCalled = false;
-    let maxKernelCalled = false;
-    const result = 1;
-    const resultScalar = tf.scalar(result);
-
-    let innerKernelProfile: KernelProfile;
-    const outerKernelProfile = profiler.profileKernel('MatMul', inputs, () => {
-      innerKernelProfile = profiler.profileKernel('Max', inputs, () => {
-        maxKernelCalled = true;
-        return [resultScalar];
-      });
-      matmulKernelCalled = true;
-      return innerKernelProfile.outputs;
-    });
-
-    setTimeout(() => {
-      expect(timeSpy.calls.count()).toBe(2);
-      expect(matmulKernelCalled).toBe(true);
-      expect(maxKernelCalled).toBe(true);
-
-      const checkInnerKernelProfile = checkKernelProfile(innerKernelProfile, {
-        kernelName: 'Max',
-        outputs: [resultScalar],
-        timeMs: queryTimeMs,
-        inputs,
-        extraInfo
-      });
-      const checkOuterKernelProfile = checkKernelProfile(outerKernelProfile, {
-        kernelName: 'MatMul',
-        outputs: [resultScalar],
-        timeMs: queryTimeMs * 2,
-        inputs,
-        extraInfo
-      });
-      Promise.all([checkInnerKernelProfile, checkOuterKernelProfile])
-          .then(() => doneFn());
-    }, delayMs * 2);
-  });
-
-  it('log kernelProfile', doneFn => {
-    const delayMs = 5;
-    const queryTimeMs = 10;
-    const inputs = {'x': tf.tensor1d([1])};
-    const extraInfo = '';
-    const timer = new TestBackendTimer(delayMs, queryTimeMs, extraInfo);
-    const logger = new TestLogger();
-    const profiler = new Profiler(timer, logger);
-
-    spyOn(logger, 'logKernelProfile').and.callThrough();
-    const logKernelProfileSpy = logger.logKernelProfile as jasmine.Spy;
-
-    const result = 1;
-    const resultScalar = tf.scalar(result);
-
-    const kernelProfiles = profiler.profileKernel('MatMul', inputs, () => {
-      return [resultScalar];
-    });
-    profiler.logKernelProfile(kernelProfiles);
-
-    setTimeout(() => {
-      expect(logKernelProfileSpy.calls.count()).toBe(1);
-
-      expect(logKernelProfileSpy.calls.first().args).toEqual([
-        'MatMul', resultScalar, new Float32Array([result]), queryTimeMs, inputs,
-        extraInfo
-      ]);
-      doneFn();
-    }, delayMs * 2);
-  });
-});
-
-describe('profiler.checkComputationForErrors', () => {
-  beforeAll(() => {
-    // Silence warnings.
-    spyOn(console, 'warn');
-  });
-
-  it('Float32Array has NaN', () => {
-    expect(checkComputationForErrors(
-               new Float32Array([1, 2, 3, NaN, 4, 255]), 'float32', 'test'))
-        .toBe(true);
-  });
-
-  it('Float32Array has Infinity', () => {
-    expect(
-        checkComputationForErrors(
-            new Float32Array([1, 2, 3, Infinity, 4, 255]), 'float32', 'test'))
-        .toBe(true);
-  });
-
-  it('Float32Array no NaN', () => {
-    // Int32 and Bool NaNs should not trigger an error.
-    expect(checkComputationForErrors(
-               new Float32Array([1, 2, 3, -1, 4, 255]), 'float32', 'test'))
-        .toBe(false);
-  });
-});
-
-describeWithFlags('profiler.Logger', ALL_ENVS, () => {
-  it('skips logging for undefined input node in input tensor map', () => {
-    const kernelName = 'FusedConv2D';
-    const vals = new Float32Array(1);
-    const outputs = tf.tensor1d([1]);
-    const timeMs = 10;
-    const inputs: NamedTensorMap = {
-      'x': tf.tensor1d([1]),
-      'filter': tf.tensor1d([1]),
-      'bias': tf.tensor1d([1]),
-      'preluActivationWeights': undefined
-    };
-    const extraInfo = '';
-    const logger = new Logger();
-    spyOn(console, 'log');
-    const consoleLogSpy = console.log as jasmine.Spy;
-
-    logger.logKernelProfile(
-        kernelName, outputs, vals, timeMs, inputs, extraInfo);
-
-    expect(consoleLogSpy.calls.first().args)
-        .not.toContain('preluActivationWeights');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/abs.ts b/tfjs-master/tfjs-core/src/public/chained_ops/abs.ts
deleted file mode 100644
index 0fcdeb5ea..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/abs.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {abs} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    abs<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.abs = function<T extends Tensor>(this: T) {
-  this.throwIfDisposed();
-  return abs(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/acos.ts b/tfjs-master/tfjs-core/src/public/chained_ops/acos.ts
deleted file mode 100644
index 85df1a43a..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/acos.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {acos} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    acos<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.acos = function<T extends Tensor>(this: T) {
-  this.throwIfDisposed();
-  return acos(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/acosh.ts b/tfjs-master/tfjs-core/src/public/chained_ops/acosh.ts
deleted file mode 100644
index 7d11985fa..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/acosh.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {acosh} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    acosh<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.acosh = function<T extends Tensor>(this: T) {
-  this.throwIfDisposed();
-  return acosh(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/add.ts b/tfjs-master/tfjs-core/src/public/chained_ops/add.ts
deleted file mode 100644
index 71b14e459..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/add.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {add} from '../../ops/add';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    add<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.add = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return add(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/all.ts b/tfjs-master/tfjs-core/src/public/chained_ops/all.ts
deleted file mode 100644
index 946a7a244..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/all.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {all} from '../../ops/all';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    all<T extends Tensor>(this: T, axis?: number|number[], keepDims?: boolean):
-        T;
-  }
-}
-
-getGlobalTensorClass().prototype.all = function<T extends Tensor>(
-    this: T, axis?: number|number[], keepDims?: boolean): T {
-  this.throwIfDisposed();
-  return all(this, axis, keepDims);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/any.ts b/tfjs-master/tfjs-core/src/public/chained_ops/any.ts
deleted file mode 100644
index 52b54c0a9..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/any.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {any} from '../../ops/any';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    any<T extends Tensor>(this: T, axis?: number|number[], keepDims?: boolean):
-        T;
-  }
-}
-
-getGlobalTensorClass().prototype.any = function<T extends Tensor>(
-    this: T, axis?: number|number[], keepDims?: boolean): T {
-  this.throwIfDisposed();
-  return any(this, axis, keepDims);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/arg_max.ts b/tfjs-master/tfjs-core/src/public/chained_ops/arg_max.ts
deleted file mode 100644
index 71140c0fd..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/arg_max.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {argMax} from '../../ops/arg_max';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    argMax<T extends Tensor>(axis?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.argMax = function<T extends Tensor>(
-    axis?: number): T {
-  this.throwIfDisposed();
-  return argMax(this, axis);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/arg_min.ts b/tfjs-master/tfjs-core/src/public/chained_ops/arg_min.ts
deleted file mode 100644
index ef8c6c75d..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/arg_min.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {argMin} from '../../ops/arg_min';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    argMin<T extends Tensor>(axis?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.argMin = function<T extends Tensor>(
-    axis: number): T {
-  this.throwIfDisposed();
-  return argMin(this, axis);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/as1d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/as1d.ts
deleted file mode 100644
index a8fdca330..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/as1d.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {reshape} from '../../ops/reshape';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    as1D<T extends Tensor>(): Tensor1D;
-  }
-}
-
-/**
- * Converts a `tf.Tensor` to a `tf.Tensor1D`.
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.as1D = function<T extends Tensor>(): T {
-  this.throwIfDisposed();
-  return reshape(this, [this.size]) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/as2d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/as2d.ts
deleted file mode 100644
index 2ad763b92..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/as2d.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {reshape} from '../../ops/reshape';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    as2D<T extends Tensor>(rows: number, columns: number): Tensor2D;
-  }
-}
-
-/**
- * Converts a `tf.Tensor` to a `tf.Tensor2D`.
- *
- * @param rows Number of rows in `tf.Tensor2D`.
- * @param columns Number of columns in `tf.Tensor2D`.
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.as2D = function<T extends Tensor>(
-    rows: number, columns: number): T {
-  this.throwIfDisposed();
-  return reshape(this, [rows, columns]) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/as3d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/as3d.ts
deleted file mode 100644
index 8193f81bf..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/as3d.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {reshape} from '../../ops/reshape';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    as3D<T extends Tensor>(rows: number, columns: number, depth: number):
-        Tensor3D;
-  }
-}
-
-/**
- * Converts a `tf.Tensor` to a `tf.Tensor3D`.
- *
- * @param rows Number of rows in `tf.Tensor3D`.
- * @param columns Number of columns in `tf.Tensor3D`.
- * @param depth Depth of `tf.Tensor3D`.
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.as3D = function<T extends Tensor>(
-    rows: number, columns: number, depth: number): T {
-  this.throwIfDisposed();
-  return reshape(this, [rows, columns, depth]) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/as4d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/as4d.ts
deleted file mode 100644
index 877902eca..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/as4d.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {reshape} from '../../ops/reshape';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    as4D<T extends Tensor>(
-        rows: number, columns: number, depth: number, depth2: number): Tensor4D;
-  }
-}
-
-/**
- * Converts a `tf.Tensor` to a `tf.Tensor4D`.
- *
- * @param rows Number of rows in `tf.Tensor4D`.
- * @param columns Number of columns in `tf.Tensor4D`.
- * @param depth Depth of `tf.Tensor4D`.
- * @param depth2 4th dimension of `tf.Tensor4D`.
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.as4D = function<T extends Tensor>(
-    rows: number, columns: number, depth: number, depth2: number): T {
-  this.throwIfDisposed();
-  return reshape(this, [rows, columns, depth, depth2]) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/as5d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/as5d.ts
deleted file mode 100644
index 0ba0bd2f1..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/as5d.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {reshape} from '../../ops/reshape';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    as5D<T extends Tensor>(
-        rows: number, columns: number, depth: number, depth2: number,
-        depth3: number): Tensor5D;
-  }
-}
-
-/**
- * Converts a `tf.Tensor` to a `tf.Tensor5D`.
- *
- * @param rows Number of rows in `tf.Tensor5D`.
- * @param columns Number of columns in `tf.Tensor5D`.
- * @param depth Depth of `tf.Tensor5D`.
- * @param depth2 4th dimension of `tf.Tensor5D`.
- * @param depth3 5th dimension of 'tf.Tensor5D'
- *
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.as5D = function<T extends Tensor>(
-    rows: number, columns: number, depth: number, depth2: number,
-    depth3: number): T {
-  this.throwIfDisposed();
-  return reshape(this, [rows, columns, depth, depth2, depth3]) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/as_scalar.ts b/tfjs-master/tfjs-core/src/public/chained_ops/as_scalar.ts
deleted file mode 100644
index 1786cb3f2..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/as_scalar.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {reshape} from '../../ops/reshape';
-import {getGlobalTensorClass, Scalar, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-import {assert} from '../../util';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    asScalar<T extends Tensor>(): Scalar;
-  }
-}
-
-/**
- * Converts a size-1 `tf.Tensor` to a `tf.Scalar`.
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.asScalar = function<T extends Tensor>(this: T):
-    Scalar {
-  this.throwIfDisposed();
-  assert(this.size === 1, () => 'The array must have only 1 element.');
-  return reshape(this, []);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/as_type.ts b/tfjs-master/tfjs-core/src/public/chained_ops/as_type.ts
deleted file mode 100644
index 0de1b04e1..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/as_type.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {cast} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {DataType, Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    asType<T extends Tensor>(this: T, dtype: DataType): T;
-  }
-}
-
-/**
- * Casts a `tf.Tensor` to a specified dtype.
- *
- * @param dtype Data-type to cast the tensor to.
- *
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.asType = function<T extends Tensor>(
-    this: T, dtype: DataType): T {
-  this.throwIfDisposed();
-  return cast<T>(this, dtype);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/asin.ts b/tfjs-master/tfjs-core/src/public/chained_ops/asin.ts
deleted file mode 100644
index 5e46de55f..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/asin.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {asin} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    asin<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.asin = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return asin(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/asinh.ts b/tfjs-master/tfjs-core/src/public/chained_ops/asinh.ts
deleted file mode 100644
index 962b442fc..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/asinh.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {asinh} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    asinh<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.asinh = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return asinh(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/atan.ts b/tfjs-master/tfjs-core/src/public/chained_ops/atan.ts
deleted file mode 100644
index bbe4ac6a5..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/atan.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {atan} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    atan<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.atan = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return atan(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/atan2.ts b/tfjs-master/tfjs-core/src/public/chained_ops/atan2.ts
deleted file mode 100644
index a979a9f2e..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/atan2.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {atan2} from '../../ops/atan2';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    atan2<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.atan2 = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return atan2(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/atanh.ts b/tfjs-master/tfjs-core/src/public/chained_ops/atanh.ts
deleted file mode 100644
index a52cf0d1f..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/atanh.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {atanh} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    atanh<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.atanh = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return atanh(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/avg_pool.ts b/tfjs-master/tfjs-core/src/public/chained_ops/avg_pool.ts
deleted file mode 100644
index d928e300d..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/avg_pool.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ExplicitPadding} from '../../ops/conv_util';
-import {avgPool} from '../../ops/avg_pool';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    avgPool<T extends Tensor3D|Tensor4D>(
-        filterSize: [number, number]|number, strides: [number, number]|number,
-        pad: 'valid'|'same'|number|ExplicitPadding,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.avgPool =
-    function<T extends Tensor3D|Tensor4D>(
-        this: T, filterSize: [number, number]|number,
-        strides: [number, number]|number,
-        pad: 'valid'|'same'|number|ExplicitPadding,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  this.throwIfDisposed();
-  return avgPool(this, filterSize, strides, pad, dimRoundingMode);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/batch_to_space_nd.ts b/tfjs-master/tfjs-core/src/public/chained_ops/batch_to_space_nd.ts
deleted file mode 100644
index b23460a06..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/batch_to_space_nd.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {batchToSpaceND} from '../../ops/batch_to_space_nd';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    batchToSpaceND<R extends Rank>(blockShape: number[], crops: number[][]):
-        Tensor<R>;
-  }
-}
-
-getGlobalTensorClass().prototype.batchToSpaceND = function<R extends Rank>(
-    blockShape: number[], crops: number[][]): Tensor<R> {
-  this.throwIfDisposed();
-  return batchToSpaceND(this, blockShape, crops);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/batchnorm.ts b/tfjs-master/tfjs-core/src/public/chained_ops/batchnorm.ts
deleted file mode 100644
index 0a006632f..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/batchnorm.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {batchNorm} from '../../ops/batchnorm';
-import {getGlobalTensorClass, Tensor, Tensor1D} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    batchNorm<T extends Tensor>(
-        mean: Tensor<R>|Tensor1D|TensorLike,
-        variance: Tensor<R>|Tensor1D|TensorLike,
-        offset?: Tensor<R>|Tensor1D|TensorLike,
-        scale?: Tensor<R>|Tensor1D|TensorLike,
-        varianceEpsilon?: number): Tensor<R>;
-  }
-}
-
-getGlobalTensorClass().prototype.batchNorm = function<R extends Rank>(
-    mean: Tensor<R>|Tensor1D|TensorLike,
-    variance: Tensor<R>|Tensor1D|TensorLike,
-    offset?: Tensor<R>|Tensor1D|TensorLike,
-    scale?: Tensor<R>|Tensor1D|TensorLike,
-    varianceEpsilon?: number): Tensor<R> {
-  this.throwIfDisposed();
-  return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/broadcast_to.ts b/tfjs-master/tfjs-core/src/public/chained_ops/broadcast_to.ts
deleted file mode 100644
index 90afc691b..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/broadcast_to.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {broadcastTo} from '../../ops/broadcast_to';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, ShapeMap} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    broadcastTo<R extends Rank>(shape: ShapeMap[R]): Tensor<R>;
-  }
-}
-
-getGlobalTensorClass().prototype.broadcastTo = function<R extends Rank>(
-    shape: ShapeMap[R]): Tensor<R> {
-  this.throwIfDisposed();
-  return broadcastTo(this, shape);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/cast.ts b/tfjs-master/tfjs-core/src/public/chained_ops/cast.ts
deleted file mode 100644
index 1b9988384..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/cast.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {cast} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {DataType, Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    cast<T extends Tensor>(dtype: DataType): T;
-  }
-}
-
-getGlobalTensorClass().prototype.cast = function<T extends Tensor>(
-    dtype: DataType): T {
-  this.throwIfDisposed();
-  return cast(this, dtype) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/ceil.ts b/tfjs-master/tfjs-core/src/public/chained_ops/ceil.ts
deleted file mode 100644
index 80e3feed5..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/ceil.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {ceil} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    ceil<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.ceil = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return ceil(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/clip_by_value.ts b/tfjs-master/tfjs-core/src/public/chained_ops/clip_by_value.ts
deleted file mode 100644
index e51fb4ba1..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/clip_by_value.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {clipByValue} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    clipByValue<T extends Tensor>(min: number, max: number): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.clipByValue = function<T extends Tensor>(
-    min: number, max: number): T {
-  this.throwIfDisposed();
-  return clipByValue(this, min, max) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/concat.ts b/tfjs-master/tfjs-core/src/public/chained_ops/concat.ts
deleted file mode 100644
index 7975053ad..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/concat.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {concat} from '../../ops/concat';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    concat<T extends Tensor>(tensors: T|Array<T|TensorLike>, axis?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.concat = function<T extends Tensor>(
-    x: T|Array<T|TensorLike>, axis?: number): T {
-  this.throwIfDisposed();
-  if (x instanceof Tensor) {
-    x = [x];
-  }
-  return concat([this, ...x], axis) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/conv1d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/conv1d.ts
deleted file mode 100644
index 09755748a..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/conv1d.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {conv1d} from '../../ops/conv1d';
-import {ExplicitPadding} from '../../ops/conv_util';
-import {getGlobalTensorClass, Tensor2D, Tensor3D} from '../../tensor';
-import {Rank, TensorLike3D} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    conv1d<T extends Tensor2D|Tensor3D>(
-        filter: Tensor3D|TensorLike3D, stride: number,
-        pad: 'valid'|'same'|number|ExplicitPadding, dataFormat?: 'NWC'|'NCW',
-        dilation?: number, dimRoundingMode?: 'floor'|'round'|'ceil'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.conv1d = function<T extends Tensor2D|Tensor3D>(
-    filter: Tensor3D|TensorLike3D, stride: number,
-    pad: 'valid'|'same'|number|ExplicitPadding, dataFormat?: 'NWC'|'NCW',
-    dilation?: number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  this.throwIfDisposed();
-  return conv1d(
-             this, filter, stride, pad, dataFormat, dilation,
-             dimRoundingMode) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/conv2d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/conv2d.ts
deleted file mode 100644
index 6e3aa1b65..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/conv2d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {conv2d} from '../../ops/conv2d';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank, TensorLike4D} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    conv2d<T extends Tensor3D|Tensor4D>(
-        filter: Tensor4D|TensorLike4D, strides: [number, number]|number,
-        pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',
-        dilations?: [number, number]|number,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.conv2d = function<T extends Tensor3D|Tensor4D>(
-    filter: Tensor4D|TensorLike4D, strides: [number, number]|number,
-    pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',
-    dilations?: [number, number]|number,
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  this.throwIfDisposed();
-  return conv2d(
-             this, filter, strides, pad, dataFormat, dilations,
-             dimRoundingMode) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/conv2d_transpose.ts b/tfjs-master/tfjs-core/src/public/chained_ops/conv2d_transpose.ts
deleted file mode 100644
index 92a994002..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/conv2d_transpose.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {conv2dTranspose} from '../../ops/conv2d_transpose';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank, TensorLike4D} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    conv2dTranspose<T extends Tensor3D|Tensor4D>(
-        filter: Tensor4D|TensorLike4D,
-        outputShape: [number, number, number, number]|[number, number, number],
-        strides: [number, number]|number, pad: 'valid'|'same'|number,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.conv2dTranspose =
-    function<T extends Tensor3D|Tensor4D>(
-        filter: Tensor4D|TensorLike4D,
-        outputShape: [number, number, number, number]|[number, number, number],
-        strides: [number, number]|number, pad: 'valid'|'same'|number,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  this.throwIfDisposed();
-  return conv2dTranspose(
-             this, filter, outputShape, strides, pad, dimRoundingMode) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/cos.ts b/tfjs-master/tfjs-core/src/public/chained_ops/cos.ts
deleted file mode 100644
index cfaf703ab..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/cos.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {cos} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    cos<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.cos = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return cos(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/cosh.ts b/tfjs-master/tfjs-core/src/public/chained_ops/cosh.ts
deleted file mode 100644
index f625051f4..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/cosh.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {cosh} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    cosh<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.cosh = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return cosh(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/cumprod.ts b/tfjs-master/tfjs-core/src/public/chained_ops/cumprod.ts
deleted file mode 100644
index 2f7b7a6ae..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/cumprod.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import { cumprod } from '../../ops/cumprod';
-import { getGlobalTensorClass, Tensor } from '../../tensor';
-import { Rank } from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    cumprod<R extends Rank>(
-      axis?: number,
-      exclusive?: boolean,
-      reverse?: boolean
-    ): Tensor<R>;
-  }
-}
-
-getGlobalTensorClass().prototype.cumprod = function <R extends Rank>(
-  axis?: number,
-  exclusive?: boolean,
-  reverse?: boolean
-): Tensor<R> {
-  this.throwIfDisposed();
-  return cumprod(this, axis, exclusive, reverse);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/cumsum.ts b/tfjs-master/tfjs-core/src/public/chained_ops/cumsum.ts
deleted file mode 100644
index ae61f2db1..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/cumsum.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {cumsum} from '../../ops/cumsum';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    cumsum<R extends Rank>(
-        axis?: number, exclusive?: boolean, reverse?: boolean): Tensor<R>;
-  }
-}
-
-getGlobalTensorClass().prototype.cumsum = function<R extends Rank>(
-    axis?: number, exclusive?: boolean, reverse?: boolean): Tensor<R> {
-  this.throwIfDisposed();
-  return cumsum(this, axis, exclusive, reverse);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/depth_to_space.ts b/tfjs-master/tfjs-core/src/public/chained_ops/depth_to_space.ts
deleted file mode 100644
index 5d7c07bda..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/depth_to_space.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {depthToSpace} from '../../ops/depth_to_space';
-import {getGlobalTensorClass, Tensor4D} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    depthToSpace<T extends Tensor4D>(
-        blockSize: number, dataFormat: 'NHWC'|'NCHW'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.depthToSpace = function<T extends Tensor4D>(
-    blockSize: number, dataFormat: 'NHWC'|'NCHW'): T {
-  this.throwIfDisposed();
-  return depthToSpace(this, blockSize, dataFormat) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/depthwise_conv2d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/depthwise_conv2d.ts
deleted file mode 100644
index 1fbfa161c..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/depthwise_conv2d.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {depthwiseConv2d} from '../../ops/depthwise_conv2d';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank, TensorLike4D} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    depthwiseConv2d<T extends Tensor3D|Tensor4D>(
-        filter: Tensor4D|TensorLike4D, strides: [number, number]|number,
-        pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',
-        dilations?: [number, number]|number,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.depthwiseConv2d =
-    function<T extends Tensor3D|Tensor4D>(
-        filter: Tensor4D|TensorLike4D, strides: [number, number]|number,
-        pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',
-        dilations?: [number, number]|number,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  this.throwIfDisposed();
-  return depthwiseConv2d(
-             this, filter, strides, pad, dataFormat, dilations,
-             dimRoundingMode) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/dilation2d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/dilation2d.ts
deleted file mode 100644
index cdac3522e..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/dilation2d.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {dilation2d} from '../../ops/dilation2d';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank, TensorLike3D} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    dilation2d<T extends Tensor3D|Tensor4D>(
-        filter: Tensor3D|TensorLike3D, strides: [number, number]|number,
-        pad: 'valid'|'same', dilations?: [number, number]|number,
-        dataFormat?: 'NHWC'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.dilation2d =
-    function<T extends Tensor3D|Tensor4D>(
-        filter: Tensor3D|TensorLike3D, strides: [number, number]|number,
-        pad: 'valid'|'same', dilations?: [number, number]|number,
-        dataFormat?: 'NHWC'): T {
-  this.throwIfDisposed();
-  return dilation2d(this, filter, strides, pad, dilations, dataFormat) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/div.ts b/tfjs-master/tfjs-core/src/public/chained_ops/div.ts
deleted file mode 100644
index c4d6b595a..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/div.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {div} from '../../ops/div';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    div<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.div = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return div(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/div_no_nan.ts b/tfjs-master/tfjs-core/src/public/chained_ops/div_no_nan.ts
deleted file mode 100644
index 2bfb3b71c..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/div_no_nan.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {divNoNan} from '../../ops/div_no_nan';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    divNoNan<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.divNoNan = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return divNoNan(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/dot.ts b/tfjs-master/tfjs-core/src/public/chained_ops/dot.ts
deleted file mode 100644
index 869f9a7d8..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/dot.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {dot} from '../../ops/dot';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    dot<T extends Tensor>(b: Tensor|TensorLike): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.dot = function<T extends Tensor>(
-    b: T|TensorLike): Tensor {
-  this.throwIfDisposed();
-  return dot(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/elu.ts b/tfjs-master/tfjs-core/src/public/chained_ops/elu.ts
deleted file mode 100644
index 244070334..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/elu.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {elu} from '../../ops/elu';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    elu<T extends Tensor>(): T;
-  }
-}
-
-getGlobalTensorClass().prototype.elu = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return elu(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/equal.ts b/tfjs-master/tfjs-core/src/public/chained_ops/equal.ts
deleted file mode 100644
index c2cc13e94..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/equal.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {equal} from '../../ops/equal';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    equal<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.equal = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return equal(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/erf.ts b/tfjs-master/tfjs-core/src/public/chained_ops/erf.ts
deleted file mode 100644
index ac7ad5e97..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/erf.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {erf} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    erf<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.erf = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return erf(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/euclidean_norm.ts b/tfjs-master/tfjs-core/src/public/chained_ops/euclidean_norm.ts
deleted file mode 100644
index 892f05e37..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/euclidean_norm.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {euclideanNorm} from '../../ops/euclidean_norm';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    euclideanNorm<T extends Tensor>(
-        this: T, axis?: number|number[], keepDims?: boolean): T;
-  }
-}
-
-getGlobalTensorClass().prototype.euclideanNorm = function<T extends Tensor>(
-    this: T, axis?: number|number[], keepDims?: boolean): T {
-  this.throwIfDisposed();
-  return euclideanNorm(this, axis, keepDims) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/exp.ts b/tfjs-master/tfjs-core/src/public/chained_ops/exp.ts
deleted file mode 100644
index aa2e04cf4..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/exp.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {exp} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    exp<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.exp = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return exp(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/expand_dims.ts b/tfjs-master/tfjs-core/src/public/chained_ops/expand_dims.ts
deleted file mode 100644
index 2f31f9d03..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/expand_dims.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {expandDims} from '../../ops/expand_dims';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    expandDims<T extends Tensor>(axis?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.expandDims = function<T extends Tensor>(
-    axis?: number): T {
-  this.throwIfDisposed();
-  return expandDims(this, axis);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/expm1.ts b/tfjs-master/tfjs-core/src/public/chained_ops/expm1.ts
deleted file mode 100644
index 59827b452..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/expm1.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {expm1} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    expm1<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.expm1 = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return expm1(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/fft.ts b/tfjs-master/tfjs-core/src/public/chained_ops/fft.ts
deleted file mode 100644
index 643d68123..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/fft.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {fft} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    fft<T extends Tensor>(this: Tensor): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.fft = function<T extends Tensor>(this: Tensor):
-    T {
-  this.throwIfDisposed();
-  return fft(this) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/flatten.ts b/tfjs-master/tfjs-core/src/public/chained_ops/flatten.ts
deleted file mode 100644
index d5c9c447a..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/flatten.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {reshape} from '../../ops/reshape';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    flatten<T extends Tensor>(): Tensor1D;
-  }
-}
-
-/**
- * Flatten a Tensor to a 1D array.
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.flatten = function<T extends Tensor>(): T {
-  this.throwIfDisposed();
-  return reshape(this, [this.size]) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/floor.ts b/tfjs-master/tfjs-core/src/public/chained_ops/floor.ts
deleted file mode 100644
index 7f2d63a26..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/floor.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {floor} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    floor<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.floor = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return floor(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/floorDiv.ts b/tfjs-master/tfjs-core/src/public/chained_ops/floorDiv.ts
deleted file mode 100644
index a003560dc..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/floorDiv.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {floorDiv} from '../../ops/floorDiv';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    floorDiv<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.floorDiv = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return floorDiv(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/gather.ts b/tfjs-master/tfjs-core/src/public/chained_ops/gather.ts
deleted file mode 100644
index 391de4924..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/gather.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {gather} from '../../ops/gather';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    gather<T extends Tensor>(
-        this: T, indices: Tensor|TensorLike, axis?: number,
-        batchDims?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.gather = function<T extends Tensor>(
-    this: T, indices: Tensor|TensorLike, axis?: number, batchDims?: number): T {
-  this.throwIfDisposed();
-  return gather(this, indices, axis, batchDims);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/greater.ts b/tfjs-master/tfjs-core/src/public/chained_ops/greater.ts
deleted file mode 100644
index 05a901d20..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/greater.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {greater} from '../../ops/greater';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    greater<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.greater = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return greater(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/greater_equal.ts b/tfjs-master/tfjs-core/src/public/chained_ops/greater_equal.ts
deleted file mode 100644
index c225b03bf..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/greater_equal.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {greaterEqual} from '../../ops/greater_equal';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    greaterEqual<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.greaterEqual = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return greaterEqual(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/ifft.ts b/tfjs-master/tfjs-core/src/public/chained_ops/ifft.ts
deleted file mode 100644
index 7f737c8fc..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/ifft.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {ifft} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    ifft<T extends Tensor>(this: Tensor): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.ifft = function<T extends Tensor>(
-    this: Tensor): T {
-  this.throwIfDisposed();
-  return ifft(this) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/irfft.ts b/tfjs-master/tfjs-core/src/public/chained_ops/irfft.ts
deleted file mode 100644
index 951ba1ae4..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/irfft.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {irfft} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    irfft<T extends Tensor>(this: Tensor): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.irfft = function<T extends Tensor>(
-    this: Tensor): T {
-  this.throwIfDisposed();
-  return irfft(this) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/is_finite.ts b/tfjs-master/tfjs-core/src/public/chained_ops/is_finite.ts
deleted file mode 100644
index 6c02751f3..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/is_finite.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {isFinite} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    isFinite<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.isFinite = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return isFinite(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/is_inf.ts b/tfjs-master/tfjs-core/src/public/chained_ops/is_inf.ts
deleted file mode 100644
index cc9ef249f..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/is_inf.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {isInf} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    isInf<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.isInf = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return isInf(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/is_nan.ts b/tfjs-master/tfjs-core/src/public/chained_ops/is_nan.ts
deleted file mode 100644
index 094f3c428..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/is_nan.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {isNaN} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    isNaN<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.isNaN = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return isNaN(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/leaky_relu.ts b/tfjs-master/tfjs-core/src/public/chained_ops/leaky_relu.ts
deleted file mode 100644
index 4d7f42650..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/leaky_relu.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {leakyRelu} from '../../ops/leaky_relu';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    leakyRelu<T extends Tensor>(alpha: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.leakyRelu = function<T extends Tensor>(
-    this: T, alpha: number): T {
-  this.throwIfDisposed();
-  return leakyRelu(this, alpha);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/less.ts b/tfjs-master/tfjs-core/src/public/chained_ops/less.ts
deleted file mode 100644
index 76859d9fd..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/less.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {less} from '../../ops/less';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    less<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.less = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return less(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/less_equal.ts b/tfjs-master/tfjs-core/src/public/chained_ops/less_equal.ts
deleted file mode 100644
index b03276541..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/less_equal.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {lessEqual} from '../../ops/less_equal';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    lessEqual<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.lessEqual = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return lessEqual(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/local_response_normalization.ts b/tfjs-master/tfjs-core/src/public/chained_ops/local_response_normalization.ts
deleted file mode 100644
index d641c6f13..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/local_response_normalization.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {localResponseNormalization} from '../../ops/local_response_normalization';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    localResponseNormalization<T extends Tensor>(
-        depthRadius?: number, bias?: number, alpha?: number, beta?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.localResponseNormalization =
-    function<T extends Tensor>(
-        depthRadius?: number, bias?: number, alpha?: number, beta?: number): T {
-  this.throwIfDisposed();
-  return localResponseNormalization(this, depthRadius, bias, alpha, beta) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/log.ts b/tfjs-master/tfjs-core/src/public/chained_ops/log.ts
deleted file mode 100644
index 4fef252d8..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/log.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {log} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    log<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.log = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return log(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/log1p.ts b/tfjs-master/tfjs-core/src/public/chained_ops/log1p.ts
deleted file mode 100644
index 73e47e808..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/log1p.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {log1p} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    log1p<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.log1p = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return log1p(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/log_sigmoid.ts b/tfjs-master/tfjs-core/src/public/chained_ops/log_sigmoid.ts
deleted file mode 100644
index 820c67378..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/log_sigmoid.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {logSigmoid} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    logSigmoid<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.logSigmoid = function<T extends Tensor>(
-    this: T): T {
-  this.throwIfDisposed();
-  return logSigmoid(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/log_softmax.ts b/tfjs-master/tfjs-core/src/public/chained_ops/log_softmax.ts
deleted file mode 100644
index 5d33e04ce..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/log_softmax.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {logSoftmax} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    logSoftmax<T extends Tensor>(this: T, axis?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.logSoftmax = function<T extends Tensor>(
-    this: T, axis?: number): T {
-  this.throwIfDisposed();
-  return logSoftmax(this, axis);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/log_sum_exp.ts b/tfjs-master/tfjs-core/src/public/chained_ops/log_sum_exp.ts
deleted file mode 100644
index 765c8358d..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/log_sum_exp.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {logSumExp} from '../../ops/log_sum_exp';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    logSumExp<T extends Tensor>(
-        this: T, axis?: number|number[], keepDims?: boolean): T;
-  }
-}
-
-getGlobalTensorClass().prototype.logSumExp = function<T extends Tensor>(
-    this: T, axis?: number|number[], keepDims?: boolean): T {
-  this.throwIfDisposed();
-  return logSumExp(this, axis, keepDims);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/logical_and.ts b/tfjs-master/tfjs-core/src/public/chained_ops/logical_and.ts
deleted file mode 100644
index da3c15121..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/logical_and.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {logicalAnd} from '../../ops/logical_and';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    logicalAnd<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.logicalAnd = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return logicalAnd(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/logical_not.ts b/tfjs-master/tfjs-core/src/public/chained_ops/logical_not.ts
deleted file mode 100644
index 3cd14a810..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/logical_not.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {logicalNot} from '../../ops/logical_not';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    logicalNot<T extends Tensor>(): T;
-  }
-}
-
-getGlobalTensorClass().prototype.logicalNot = function<T extends Tensor>(): T {
-  this.throwIfDisposed();
-  return logicalNot(this) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/logical_or.ts b/tfjs-master/tfjs-core/src/public/chained_ops/logical_or.ts
deleted file mode 100644
index 66374ae74..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/logical_or.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {logicalOr} from '../../ops/logical_or';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    logicalOr<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.logicalOr = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return logicalOr(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/logical_xor.ts b/tfjs-master/tfjs-core/src/public/chained_ops/logical_xor.ts
deleted file mode 100644
index c20bc54df..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/logical_xor.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {logicalXor} from '../../ops/logical_xor';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    logicalXor<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.logicalXor = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return logicalXor(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/mat_mul.ts b/tfjs-master/tfjs-core/src/public/chained_ops/mat_mul.ts
deleted file mode 100644
index a56257fd0..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/mat_mul.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {matMul} from '../../ops/mat_mul';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    matMul<T extends Tensor>(
-        b: Tensor|TensorLike, transposeA?: boolean,
-        transposeB?: boolean): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.matMul = function<T extends Tensor>(
-    this: T, b: Tensor|TensorLike, transposeA?: boolean,
-    transposeB?: boolean): Tensor {
-  this.throwIfDisposed();
-  return matMul(this, b, transposeA, transposeB);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/max.ts b/tfjs-master/tfjs-core/src/public/chained_ops/max.ts
deleted file mode 100644
index 0d530ca56..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/max.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {max} from '../../ops/max';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    max<T extends Tensor>(axis?: number|number[], keepDims?: boolean): T;
-  }
-}
-
-getGlobalTensorClass().prototype.max = function<T extends Tensor>(
-    axis?: number|number[], keepDims?: boolean): T {
-  this.throwIfDisposed();
-  return max(this, axis, keepDims);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/max_pool.ts b/tfjs-master/tfjs-core/src/public/chained_ops/max_pool.ts
deleted file mode 100644
index 5bd102c94..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/max_pool.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ExplicitPadding} from '../../ops/conv_util';
-import {maxPool} from '../../ops/max_pool';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    maxPool<T extends Tensor3D|Tensor4D>(
-        filterSize: [number, number]|number, strides: [number, number]|number,
-        pad: 'valid'|'same'|number|ExplicitPadding,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.maxPool =
-    function<T extends Tensor3D|Tensor4D>(
-        this: T, filterSize: [number, number]|number,
-        strides: [number, number]|number,
-        pad: 'valid'|'same'|number|ExplicitPadding,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  this.throwIfDisposed();
-  return maxPool(this, filterSize, strides, pad, dimRoundingMode);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/maximum.ts b/tfjs-master/tfjs-core/src/public/chained_ops/maximum.ts
deleted file mode 100644
index e397003db..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/maximum.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {maximum} from '../../ops/maximum';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    maximum<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.maximum = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return maximum(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/mean.ts b/tfjs-master/tfjs-core/src/public/chained_ops/mean.ts
deleted file mode 100644
index 0a5afec6e..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/mean.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {mean} from '../../ops/mean';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    mean<T extends Tensor>(axis?: number|number[], keepDims?: boolean): T;
-  }
-}
-
-getGlobalTensorClass().prototype.mean = function<T extends Tensor>(
-    axis?: number|number[], keepDims?: boolean): T {
-  this.throwIfDisposed();
-  return mean(this, axis, keepDims);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/min.ts b/tfjs-master/tfjs-core/src/public/chained_ops/min.ts
deleted file mode 100644
index 24fab0bc4..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/min.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {min} from '../../ops/min';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    min<T extends Tensor>(axis?: number|number[], keepDims?: boolean): T;
-  }
-}
-
-getGlobalTensorClass().prototype.min = function<T extends Tensor>(
-    axis?: number|number[], keepDims?: boolean): T {
-  this.throwIfDisposed();
-  return min(this, axis, keepDims);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/minimum.ts b/tfjs-master/tfjs-core/src/public/chained_ops/minimum.ts
deleted file mode 100644
index 5b63edf52..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/minimum.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {minimum} from '../../ops/minimum';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    minimum<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.minimum = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return minimum(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/mirror_pad.ts b/tfjs-master/tfjs-core/src/public/chained_ops/mirror_pad.ts
deleted file mode 100644
index 77e91c1f8..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/mirror_pad.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {mirrorPad} from '../../ops/mirror_pad';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    mirrorPad<T extends Tensor>(
-        paddings: Array<[number, number]>, mode: 'reflect'|'symmetric'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.mirrorPad = function<T extends Tensor>(
-    this: T, paddings: Array<[number, number]>,
-    mode: 'reflect'|'symmetric'): T {
-  this.throwIfDisposed();
-  return mirrorPad(this, paddings, mode);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/mod.ts b/tfjs-master/tfjs-core/src/public/chained_ops/mod.ts
deleted file mode 100644
index 1d48dba97..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/mod.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {mod} from '../../ops/mod';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    mod<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.mod = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return mod(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/mul.ts b/tfjs-master/tfjs-core/src/public/chained_ops/mul.ts
deleted file mode 100644
index 7ba2974bb..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/mul.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {mul} from '../../ops/mul';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    mul<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.mul = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return mul(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/neg.ts b/tfjs-master/tfjs-core/src/public/chained_ops/neg.ts
deleted file mode 100644
index f0ff8d1af..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/neg.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {neg} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    neg<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.neg = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return neg(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/norm.ts b/tfjs-master/tfjs-core/src/public/chained_ops/norm.ts
deleted file mode 100644
index f5db2a7cf..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/norm.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {norm} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    norm<T extends Tensor>(
-        ord?: number|'euclidean'|'fro', axis?: number|number[],
-        keepDims?: boolean): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.norm = function<T extends Tensor>(
-    ord?: number|'euclidean'|'fro', axis?: number|number[],
-    keepDims?: boolean) {
-  this.throwIfDisposed();
-  return norm(this, ord, axis, keepDims) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/not_equal.ts b/tfjs-master/tfjs-core/src/public/chained_ops/not_equal.ts
deleted file mode 100644
index a8eb6b649..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/not_equal.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {notEqual} from '../../ops/not_equal';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    notEqual<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.notEqual = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return notEqual(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/one_hot.ts b/tfjs-master/tfjs-core/src/public/chained_ops/one_hot.ts
deleted file mode 100644
index afeda04fe..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/one_hot.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {oneHot} from '../../ops/one_hot';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    oneHot(depth: number, onValue: number, offValue: number): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.oneHot = function(
-    depth: number, onValue = 1, offValue = 0): Tensor {
-  this.throwIfDisposed();
-  return oneHot(this, depth, onValue, offValue);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/ones_like.ts b/tfjs-master/tfjs-core/src/public/chained_ops/ones_like.ts
deleted file mode 100644
index 31b16a789..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/ones_like.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {onesLike} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    onesLike<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.onesLike = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return onesLike(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/pad.ts b/tfjs-master/tfjs-core/src/public/chained_ops/pad.ts
deleted file mode 100644
index e8c7b1af1..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/pad.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {pad} from '../../ops/pad';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    pad<T extends Tensor>(
-        paddings: Array<[number, number]>, constantValue?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.pad = function<T extends Tensor>(
-    this: T, paddings: Array<[number, number]>, constantValue: number): T {
-  this.throwIfDisposed();
-  return pad(this, paddings, constantValue);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/pool.ts b/tfjs-master/tfjs-core/src/public/chained_ops/pool.ts
deleted file mode 100644
index 0906aceed..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/pool.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {ExplicitPadding} from '../../ops/conv_util';
-import {pool} from '../../ops/pool';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    pool<T extends Tensor3D|Tensor4D>(
-        windowShape: [number, number]|number, poolingType: 'avg'|'max',
-        padding: 'valid'|'same'|number|ExplicitPadding,
-        diationRate?: [number, number]|number,
-        strides?: [number, number]|number,
-        dimRoundingMode?: 'floor'|'round'|'ceil'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.pool = function<T extends Tensor3D|Tensor4D>(
-    this: T, windowShape: [number, number]|number, poolingType: 'max'|'avg',
-    padding: 'valid'|'same'|number|ExplicitPadding,
-    dilationRate?: [number, number]|number,
-    strides?: [number, number]|number,
-    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
-  this.throwIfDisposed();
-  return pool(this, windowShape, poolingType, padding, dilationRate, strides,
-              dimRoundingMode);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/pow.ts b/tfjs-master/tfjs-core/src/public/chained_ops/pow.ts
deleted file mode 100644
index a8c06315e..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/pow.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {pow} from '../../ops/pow';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    pow<T extends Tensor>(exp: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.pow = function<T extends Tensor>(
-    exp: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return pow(this, exp);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/prelu.ts b/tfjs-master/tfjs-core/src/public/chained_ops/prelu.ts
deleted file mode 100644
index 9f762c761..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/prelu.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {prelu} from '../../ops/prelu';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    prelu<T extends Tensor>(alpha: T|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.prelu = function<T extends Tensor>(
-    this: T, alpha: T|TensorLike): T {
-  this.throwIfDisposed();
-  return prelu(this, alpha);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/prod.ts b/tfjs-master/tfjs-core/src/public/chained_ops/prod.ts
deleted file mode 100644
index b642f2939..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/prod.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {prod} from '../../ops/prod';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    prod<T extends Tensor>(this: T, axis?: number|number[], keepDims?: boolean):
-        T;
-  }
-}
-
-getGlobalTensorClass().prototype.prod = function<T extends Tensor>(
-    this: T, axis?: number|number[], keepDims?: boolean): T {
-  this.throwIfDisposed();
-  return prod(this, axis, keepDims);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/reciprocal.ts b/tfjs-master/tfjs-core/src/public/chained_ops/reciprocal.ts
deleted file mode 100644
index e7392385a..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/reciprocal.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {reciprocal} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    reciprocal<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.reciprocal = function<T extends Tensor>(
-    this: T): T {
-  this.throwIfDisposed();
-  return reciprocal(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts b/tfjs-master/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts
deleted file mode 100644
index c7deab89e..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts
+++ /dev/null
@@ -1,154 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import './abs';
-import './acos';
-import './acosh';
-import './add';
-import './all';
-import './any';
-import './arg_max';
-import './arg_min';
-import './as_scalar';
-import './as_type';
-import './as1d';
-import './as2d';
-import './as3d';
-import './as4d';
-import './as5d';
-import './asin';
-import './asinh';
-import './atan';
-import './atan2';
-import './atanh';
-import './avg_pool';
-import './batch_to_space_nd';
-import './batchnorm';
-import './broadcast_to';
-import './cast';
-import './ceil';
-import './clip_by_value';
-import './concat';
-import './conv1d';
-import './conv2d_transpose';
-import './conv2d';
-import './cos';
-import './cosh';
-import './cumprod';
-import './cumsum';
-import './depth_to_space';
-import './depthwise_conv2d';
-import './dilation2d';
-import './div_no_nan';
-import './div';
-import './dot';
-import './elu';
-import './equal';
-import './erf';
-import './euclidean_norm';
-import './exp';
-import './expand_dims';
-import './expm1';
-import './fft';
-import './flatten';
-import './floor';
-import './floorDiv';
-import './gather';
-import './greater_equal';
-import './greater';
-import './ifft';
-import './irfft';
-import './is_finite';
-import './is_inf';
-import './is_nan';
-import './leaky_relu';
-import './less_equal';
-import './less';
-import './local_response_normalization';
-import './log_sigmoid';
-import './log_softmax';
-import './log_sum_exp';
-import './log';
-import './log1p';
-import './logical_and';
-import './logical_not';
-import './logical_or';
-import './logical_xor';
-import './mat_mul';
-import './max_pool';
-import './max';
-import './maximum';
-import './mean';
-import './min';
-import './minimum';
-import './mirror_pad';
-import './mod';
-import './mul';
-import './neg';
-import './norm';
-import './not_equal';
-import './one_hot';
-import './ones_like';
-import './pad';
-import './pool';
-import './pow';
-import './prelu';
-import './prod';
-import './reciprocal';
-import './relu';
-import './relu6';
-import './reshape_as';
-import './reshape';
-import './resize_bilinear';
-import './resize_nearest_neighbor';
-import './reverse';
-import './rfft';
-import './round';
-import './rsqrt';
-import './selu';
-import './separable_conv2d';
-import './sigmoid';
-import './sign';
-import './sin';
-import './sinh';
-import './slice';
-import './softmax';
-import './softplus';
-import './space_to_batch_nd';
-import './split';
-import './sqrt';
-import './square';
-import './squared_difference';
-import './squeeze';
-import './stack';
-import './step';
-import './strided_slice';
-import './sub';
-import './sum';
-import './tan';
-import './tanh';
-import './tile';
-import './to_bool';
-import './to_float';
-import './to_int';
-import './topk';
-import './transpose';
-import './unique';
-import './unsorted_segment_sum';
-import './unstack';
-import './where';
-import './zeros_like';
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts b/tfjs-master/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts
deleted file mode 100644
index 0a5c21079..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts
+++ /dev/null
@@ -1,176 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '../../public/chained_ops/register_all_chained_ops';
-
-import * as tf from '../../index';
-import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
-
-// Testing for presence of chained op in this file will allow us to more easily
-// customize when we want this test to run. Currently it will run be default
-// (And karma will always load the chain augmentor files). But this gives us
-// flexibility to change in future.
-
-const CHAINED_OPS = [
-  'abs',
-  'acos',
-  'acosh',
-  'add',
-  'all',
-  'any',
-  'argMax',
-  'argMin',
-  'as1D',
-  'as2D',
-  'as3D',
-  'as4D',
-  'as5D',
-  'asin',
-  'asinh',
-  'asScalar',
-  'asType',
-  'atan',
-  'atan2',
-  'atanh',
-  'avgPool',
-  'batchNorm',
-  'batchToSpaceND',
-  'broadcastTo',
-  'cast',
-  'ceil',
-  'clipByValue',
-  'concat',
-  'conv1d',
-  'conv2d',
-  'conv2dTranspose',
-  'cos',
-  'cosh',
-  'cumprod',
-  'cumsum',
-  'depthToSpace',
-  'depthwiseConv2d',
-  'dilation2d',
-  'div',
-  'divNoNan',
-  'dot',
-  'elu',
-  'equal',
-  'erf',
-  'exp',
-  'expandDims',
-  'expm1',
-  'fft',
-  'flatten',
-  'floor',
-  'floorDiv',
-  'gather',
-  'greater',
-  'greaterEqual',
-  'ifft',
-  'irfft',
-  'isFinite',
-  'isInf',
-  'isNaN',
-  'leakyRelu',
-  'less',
-  'lessEqual',
-  'localResponseNormalization',
-  'log',
-  'log1p',
-  'logicalAnd',
-  'logicalNot',
-  'logicalOr',
-  'logicalXor',
-  'logSigmoid',
-  'logSoftmax',
-  'logSumExp',
-  'matMul',
-  'max',
-  'maximum',
-  'maxPool',
-  'mean',
-  'min',
-  'minimum',
-  'mirrorPad',
-  'mod',
-  'mul',
-  'neg',
-  'norm',
-  'notEqual',
-  'oneHot',
-  'onesLike',
-  'pad',
-  'pool',
-  'pow',
-  'prelu',
-  'prod',
-  'reciprocal',
-  'relu',
-  'relu6',
-  'reshape',
-  'reshapeAs',
-  'resizeBilinear',
-  'resizeNearestNeighbor',
-  'reverse',
-  'rfft',
-  'round',
-  'rsqrt',
-  'selu',
-  'separableConv2d',
-  'sigmoid',
-  'sign',
-  'sin',
-  'sinh',
-  'slice',
-  'softmax',
-  'softplus',
-  'spaceToBatchND',
-  'split',
-  'sqrt',
-  'square',
-  'square',
-  'squeeze',
-  'stack',
-  'step',
-  'stridedSlice',
-  'sub',
-  'sum',
-  'tan',
-  'tanh',
-  'tile',
-  'toBool',
-  'toFloat',
-  'toInt',
-  'topk',
-  'transpose',
-  'unique',
-  'unsortedSegmentSum',
-  'unstack',
-  'where',
-  'zerosLike'
-];
-
-describeWithFlags('chained ops', ALL_ENVS, () => {
-  it('all chained ops should exist on tensor ', async () => {
-    const tensor = tf.tensor([1, 2, 3]);
-    for (const opName of CHAINED_OPS) {
-      //@ts-ignore
-      expect(typeof tensor[opName])
-          .toBe('function', `${opName} chained op not found`);
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/relu.ts b/tfjs-master/tfjs-core/src/public/chained_ops/relu.ts
deleted file mode 100644
index 119873ed4..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/relu.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {relu} from '../../ops/relu';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    relu<T extends Tensor>(): T;
-  }
-}
-
-getGlobalTensorClass().prototype.relu = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return relu(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/relu6.ts b/tfjs-master/tfjs-core/src/public/chained_ops/relu6.ts
deleted file mode 100644
index 05c602aea..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/relu6.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {relu6} from '../../ops/relu6';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    relu6<T extends Tensor>(): T;
-  }
-}
-
-getGlobalTensorClass().prototype.relu6 = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return relu6(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/reshape.ts b/tfjs-master/tfjs-core/src/public/chained_ops/reshape.ts
deleted file mode 100644
index 75a1265d3..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/reshape.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {reshape} from '../../ops/reshape';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    reshape<T extends Tensor>(shape: number[]): T;
-  }
-}
-
-getGlobalTensorClass().prototype.reshape = function<T extends Tensor>(
-    shape: number[]): T {
-  this.throwIfDisposed();
-  return reshape(this, shape) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/reshape_as.ts b/tfjs-master/tfjs-core/src/public/chained_ops/reshape_as.ts
deleted file mode 100644
index 6a2e5de8f..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/reshape_as.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {reshape} from '../../ops/reshape';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    reshapeAs<T extends Tensor>(x: T): T;
-  }
-}
-
-/**
- * Reshapes the tensor into the shape of the provided tensor.
- *
- * @param x The tensor of required shape.
- *
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.reshapeAs = function<T extends Tensor>(x: T):
-    T {
-  this.throwIfDisposed();
-  return reshape(this, x.shape) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/resize_bilinear.ts b/tfjs-master/tfjs-core/src/public/chained_ops/resize_bilinear.ts
deleted file mode 100644
index 76766cdd8..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/resize_bilinear.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {resizeBilinear} from '../../ops/image/resize_bilinear';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    resizeBilinear<T extends Tensor3D|Tensor4D>(
-        newShape2D: [number, number], alignCorners?: boolean,
-        halfPixelCenters?: boolean): T;
-  }
-}
-
-getGlobalTensorClass().prototype.resizeBilinear =
-    function<T extends Tensor3D|Tensor4D>(
-        this: T, newShape2D: [number, number], alignCorners?: boolean,
-        halfPixelCenters?: boolean): T {
-  this.throwIfDisposed();
-  return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/resize_nearest_neighbor.ts b/tfjs-master/tfjs-core/src/public/chained_ops/resize_nearest_neighbor.ts
deleted file mode 100644
index 4ea740abc..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/resize_nearest_neighbor.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {resizeNearestNeighbor} from '../../ops/image/resize_nearest_neighbor';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    resizeNearestNeighbor<T extends Tensor3D|Tensor4D>(
-        newShape2D: [number, number], alignCorners?: boolean,
-        halfFloatCenters?: boolean): T;
-  }
-}
-
-getGlobalTensorClass().prototype.resizeNearestNeighbor =
-    function<T extends Tensor3D|Tensor4D>(
-        this: T, newShape2D: [number, number], alignCorners?: boolean,
-        halfFloatCenters?: boolean): T {
-  this.throwIfDisposed();
-  return resizeNearestNeighbor(
-      this, newShape2D, alignCorners, halfFloatCenters);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/reverse.ts b/tfjs-master/tfjs-core/src/public/chained_ops/reverse.ts
deleted file mode 100644
index 7a4189a3e..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/reverse.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {reverse} from '../../ops/reverse';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    reverse<T extends Tensor>(this: T, axis?: number|number[]): T;
-  }
-}
-
-getGlobalTensorClass().prototype.reverse = function<T extends Tensor>(
-    this: T, axis?: number|number[]): T {
-  this.throwIfDisposed();
-  return reverse(this, axis);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/rfft.ts b/tfjs-master/tfjs-core/src/public/chained_ops/rfft.ts
deleted file mode 100644
index 121b1aadf..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/rfft.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {rfft} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    rfft<T extends Tensor>(this: Tensor): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.rfft = function<T extends Tensor>(
-    this: Tensor): T {
-  this.throwIfDisposed();
-  return rfft(this) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/round.ts b/tfjs-master/tfjs-core/src/public/chained_ops/round.ts
deleted file mode 100644
index c0d337eb3..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/round.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {round} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    round<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.round = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return round(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/rsqrt.ts b/tfjs-master/tfjs-core/src/public/chained_ops/rsqrt.ts
deleted file mode 100644
index 197f16e57..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/rsqrt.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {rsqrt} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    rsqrt<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.rsqrt = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return rsqrt(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/selu.ts b/tfjs-master/tfjs-core/src/public/chained_ops/selu.ts
deleted file mode 100644
index 527d2b92e..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/selu.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {selu} from '../../ops/selu';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    selu<T extends Tensor>(): T;
-  }
-}
-
-getGlobalTensorClass().prototype.selu = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return selu(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/separable_conv2d.ts b/tfjs-master/tfjs-core/src/public/chained_ops/separable_conv2d.ts
deleted file mode 100644
index 496bc7734..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/separable_conv2d.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {separableConv2d} from '../../ops/separable_conv2d';
-import {getGlobalTensorClass, Tensor3D, Tensor4D} from '../../tensor';
-import {Rank, TensorLike, TensorLike4D} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    separableConv2d<T extends Tensor3D|Tensor4D>(
-        depthwiseFilter: Tensor4D|TensorLike4D,
-        pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,
-        pad: 'valid'|'same', dilation?: [number, number]|number,
-        dataFormat?: 'NHWC'|'NCHW'): T;
-  }
-}
-
-getGlobalTensorClass().prototype.separableConv2d =
-    function<T extends Tensor3D|Tensor4D>(
-        depthwiseFilter: Tensor4D|TensorLike4D,
-        pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,
-        pad: 'valid'|'same', dilation?: [number, number]|number,
-        dataFormat?: 'NHWC'|'NCHW'): T {
-  this.throwIfDisposed();
-  return separableConv2d(
-             this, depthwiseFilter, pointwiseFilter, strides, pad, dilation,
-             dataFormat) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/sigmoid.ts b/tfjs-master/tfjs-core/src/public/chained_ops/sigmoid.ts
deleted file mode 100644
index e962ae995..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/sigmoid.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {sigmoid} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    sigmoid<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.sigmoid = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return sigmoid(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/sign.ts b/tfjs-master/tfjs-core/src/public/chained_ops/sign.ts
deleted file mode 100644
index a05ec15e8..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/sign.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {sign} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    sign<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.sign = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return sign(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/sin.ts b/tfjs-master/tfjs-core/src/public/chained_ops/sin.ts
deleted file mode 100644
index b46cb27e8..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/sin.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {sin} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    sin<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.sin = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return sin(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/sinh.ts b/tfjs-master/tfjs-core/src/public/chained_ops/sinh.ts
deleted file mode 100644
index c699d78e7..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/sinh.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {sinh} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    sinh<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.sinh = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return sinh(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/slice.ts b/tfjs-master/tfjs-core/src/public/chained_ops/slice.ts
deleted file mode 100644
index 369dcd417..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/slice.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {slice} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    slice<T extends Tensor>(
-        this: T, begin: number|number[], size?: number|number[]): T;
-  }
-}
-
-getGlobalTensorClass().prototype.slice = function<T extends Tensor>(
-    this: T, begin: number|number[], size?: number|number[]): T {
-  this.throwIfDisposed();
-  return slice(this, begin, size);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/softmax.ts b/tfjs-master/tfjs-core/src/public/chained_ops/softmax.ts
deleted file mode 100644
index 56d388918..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/softmax.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {softmax} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    softmax<T extends Tensor>(this: T, dim?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.softmax = function<T extends Tensor>(
-    this: T, dim: number): T {
-  this.throwIfDisposed();
-  return softmax(this, dim);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/softplus.ts b/tfjs-master/tfjs-core/src/public/chained_ops/softplus.ts
deleted file mode 100644
index 756b43ec4..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/softplus.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {softplus} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    softplus<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.softplus = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return softplus(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/space_to_batch_nd.ts b/tfjs-master/tfjs-core/src/public/chained_ops/space_to_batch_nd.ts
deleted file mode 100644
index 81e4856b2..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/space_to_batch_nd.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {spaceToBatchND} from '../../ops/space_to_batch_nd';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    spaceToBatchND<R extends Rank>(blockShape: number[], paddings: number[][]):
-        Tensor<R>;
-  }
-}
-
-getGlobalTensorClass().prototype.spaceToBatchND = function<R extends Rank>(
-    blockShape: number[], paddings: number[][]): Tensor<R> {
-  this.throwIfDisposed();
-  return spaceToBatchND(this, blockShape, paddings);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/split.ts b/tfjs-master/tfjs-core/src/public/chained_ops/split.ts
deleted file mode 100644
index 524e7d1ef..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/split.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {split} from '../../ops/split';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    split<T extends Tensor>(numOrSizeSplits: number[]|number, axis?: number):
-        T[];
-  }
-}
-
-getGlobalTensorClass().prototype.split = function<T extends Tensor>(
-    numOrSizeSplits: number[]|number, axis?: number): T[] {
-  this.throwIfDisposed();
-  return split(this, numOrSizeSplits, axis);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/sqrt.ts b/tfjs-master/tfjs-core/src/public/chained_ops/sqrt.ts
deleted file mode 100644
index 8875fda87..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/sqrt.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {sqrt} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    sqrt<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.sqrt = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return sqrt(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/square.ts b/tfjs-master/tfjs-core/src/public/chained_ops/square.ts
deleted file mode 100644
index d66859308..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/square.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {square} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    square<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.square = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return square(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/squared_difference.ts b/tfjs-master/tfjs-core/src/public/chained_ops/squared_difference.ts
deleted file mode 100644
index a74c0563a..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/squared_difference.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {squaredDifference} from '../../ops/squared_difference';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    squaredDifference<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.squaredDifference = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return squaredDifference(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/squeeze.ts b/tfjs-master/tfjs-core/src/public/chained_ops/squeeze.ts
deleted file mode 100644
index 65fd26739..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/squeeze.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {squeeze} from '../../ops/squeeze';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    squeeze<T extends Tensor>(axis?: number[]): T;
-  }
-}
-
-getGlobalTensorClass().prototype.squeeze = function<T extends Tensor>(
-    axis?: number[]): T {
-  this.throwIfDisposed();
-  return squeeze(this, axis);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/stack.ts b/tfjs-master/tfjs-core/src/public/chained_ops/stack.ts
deleted file mode 100644
index dd1874363..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/stack.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {stack} from '../../ops/stack';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    stack<T extends Tensor>(x: Tensor|Tensor[], axis?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.stack = function<T extends Tensor>(
-    x: Tensor|Tensor[], axis?: number): T {
-  this.throwIfDisposed();
-  const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];
-  return stack(tensorsToBeStacked, axis) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/step.ts b/tfjs-master/tfjs-core/src/public/chained_ops/step.ts
deleted file mode 100644
index 10397f68b..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/step.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {step} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    step<T extends Tensor>(this: T, alpha?: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.step = function<T extends Tensor>(
-    this: T, alpha?: number) {
-  this.throwIfDisposed();
-  return step(this, alpha);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/strided_slice.ts b/tfjs-master/tfjs-core/src/public/chained_ops/strided_slice.ts
deleted file mode 100644
index 9170b0d0d..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/strided_slice.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {stridedSlice} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    stridedSlice<T extends Tensor>(
-        this: Tensor, begin: number[], end: number[], strides: number[],
-        beginMask?: number, endMask?: number, ellipsisMask?: number,
-        newAxisMask?: number, shrinkAxisMask?: number): Tensor;
-  }
-}
-
-getGlobalTensorClass().prototype.stridedSlice = function<T extends Tensor>(
-    this: Tensor, begin: number[], end: number[], strides: number[],
-    beginMask?: number, endMask?: number, ellipsisMask?: number,
-    newAxisMask?: number, shrinkAxisMask?: number): T {
-  this.throwIfDisposed();
-  return stridedSlice(
-             this, begin, end, strides, beginMask, endMask, ellipsisMask,
-             newAxisMask, shrinkAxisMask) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/sub.ts b/tfjs-master/tfjs-core/src/public/chained_ops/sub.ts
deleted file mode 100644
index 5e0fa3f77..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/sub.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {sub} from '../../ops/sub';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    sub<T extends Tensor>(b: Tensor|TensorLike): T;
-  }
-}
-
-getGlobalTensorClass().prototype.sub = function<T extends Tensor>(
-    b: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return sub(this, b);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/sum.ts b/tfjs-master/tfjs-core/src/public/chained_ops/sum.ts
deleted file mode 100644
index 01f3a2b74..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/sum.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {sum} from '../../ops/sum';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    sum<T extends Tensor>(axis?: number|number[], keepDims?: boolean): T;
-  }
-}
-
-getGlobalTensorClass().prototype.sum = function<T extends Tensor>(
-    axis?: number|number[], keepDims?: boolean): T {
-  this.throwIfDisposed();
-  return sum(this, axis, keepDims);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/tan.ts b/tfjs-master/tfjs-core/src/public/chained_ops/tan.ts
deleted file mode 100644
index 7717f596e..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/tan.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {tan} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    tan<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.tan = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return tan(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/tanh.ts b/tfjs-master/tfjs-core/src/public/chained_ops/tanh.ts
deleted file mode 100644
index 2d8656955..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/tanh.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {tanh} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    tanh<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.tanh = function<T extends Tensor>(this: T): T {
-  this.throwIfDisposed();
-  return tanh(this);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/tile.ts b/tfjs-master/tfjs-core/src/public/chained_ops/tile.ts
deleted file mode 100644
index 731b0457d..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/tile.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {tile} from '../../ops/tile';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    tile<T extends Tensor>(b: number[]): T;
-  }
-}
-
-getGlobalTensorClass().prototype.tile = function<T extends Tensor>(
-    reps: number[]): T {
-  this.throwIfDisposed();
-  return tile(this, reps) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/to_bool.ts b/tfjs-master/tfjs-core/src/public/chained_ops/to_bool.ts
deleted file mode 100644
index 123f1342d..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/to_bool.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {cast} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    toBool<T extends Tensor>(this: T): T;
-  }
-}
-
-/**
- * Casts the array to type `bool`
- *
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.toBool = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return cast<T>(this, 'bool');
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/to_float.ts b/tfjs-master/tfjs-core/src/public/chained_ops/to_float.ts
deleted file mode 100644
index 59ea64dde..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/to_float.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {cast} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    toFloat<T extends Tensor>(this: T): T;
-  }
-}
-
-/**
- * Casts the array to type `float32`
- *
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.toFloat = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return cast<T>(this, 'float32');
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/to_int.ts b/tfjs-master/tfjs-core/src/public/chained_ops/to_int.ts
deleted file mode 100644
index ef6bd9466..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/to_int.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {cast} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    toInt<T extends Tensor>(this: T): T;
-  }
-}
-
-/**
- * Casts the array to type `int32`
- *
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-getGlobalTensorClass().prototype.toInt = function<T extends Tensor>(this: T):
-    T {
-  this.throwIfDisposed();
-  return cast<T>(this, 'int32');
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/topk.ts b/tfjs-master/tfjs-core/src/public/chained_ops/topk.ts
deleted file mode 100644
index 2ccbf9473..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/topk.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {topk} from '../../ops/topk';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    topk<T extends Tensor>(this: T, k?: number, sorted?: boolean):
-        {values: T, indices: T};
-  }
-}
-
-getGlobalTensorClass().prototype.topk = function<T extends Tensor>(
-    this: T, k?: number, sorted?: boolean): {values: T, indices: T} {
-  this.throwIfDisposed();
-  return topk(this, k, sorted) as {values: T, indices: T};
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/transpose.ts b/tfjs-master/tfjs-core/src/public/chained_ops/transpose.ts
deleted file mode 100644
index dd5fbe48c..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/transpose.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {transpose} from '../../ops/transpose';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    transpose<T extends Tensor>(perm?: number[]): T;
-  }
-}
-
-getGlobalTensorClass().prototype.transpose = function<T extends Tensor>(
-    this: T, perm?: number[]): T {
-  this.throwIfDisposed();
-  return transpose(this, perm);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/unique.ts b/tfjs-master/tfjs-core/src/public/chained_ops/unique.ts
deleted file mode 100644
index 139336bcb..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/unique.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {unique} from '../../ops/unique';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    unique<T extends Tensor>(this: T, axis?: number): {values: T, indices: T};
-  }
-}
-
-getGlobalTensorClass().prototype.unique = function<T extends Tensor>(
-    this: T, axis?: number): {values: T, indices: T} {
-  this.throwIfDisposed();
-  return unique(this, axis) as {values: T, indices: T};
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/unsorted_segment_sum.ts b/tfjs-master/tfjs-core/src/public/chained_ops/unsorted_segment_sum.ts
deleted file mode 100644
index a5fff2bd4..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/unsorted_segment_sum.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {unsortedSegmentSum} from '../../ops/unsorted_segment_sum';
-import {getGlobalTensorClass, Tensor, Tensor1D} from '../../tensor';
-import {Rank, TensorLike1D} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    unsortedSegmentSum<T extends Tensor>(
-        this: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T;
-  }
-}
-
-getGlobalTensorClass().prototype.unsortedSegmentSum =
-    function<T extends Tensor>(
-        this: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T {
-  this.throwIfDisposed();
-  return unsortedSegmentSum(this, segmentIds, numSegments);
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/unstack.ts b/tfjs-master/tfjs-core/src/public/chained_ops/unstack.ts
deleted file mode 100644
index 69ebda651..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/unstack.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {unstack} from '../../ops/unstack';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    unstack<T extends Tensor>(axis?: number): T[];
-  }
-}
-
-getGlobalTensorClass().prototype.unstack = function<T extends Tensor>(
-    axis?: number): T[] {
-  this.throwIfDisposed();
-  return unstack(this, axis) as T[];
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/where.ts b/tfjs-master/tfjs-core/src/public/chained_ops/where.ts
deleted file mode 100644
index c3ed66e24..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/where.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {where} from '../../ops/where';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank, TensorLike} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    where<T extends Tensor>(condition: Tensor|TensorLike, x: Tensor|TensorLike):
-        T;
-  }
-}
-
-getGlobalTensorClass().prototype.where = function<T extends Tensor>(
-    condition: Tensor|TensorLike, x: Tensor|TensorLike): T {
-  this.throwIfDisposed();
-  return where(condition, this, x) as T;
-};
diff --git a/tfjs-master/tfjs-core/src/public/chained_ops/zeros_like.ts b/tfjs-master/tfjs-core/src/public/chained_ops/zeros_like.ts
deleted file mode 100644
index 56104301e..000000000
--- a/tfjs-master/tfjs-core/src/public/chained_ops/zeros_like.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO update import path once op is modularized.
-import {zerosLike} from '../../ops/ops';
-import {getGlobalTensorClass, Tensor} from '../../tensor';
-import {Rank} from '../../types';
-
-declare module '../../tensor' {
-  interface Tensor<R extends Rank = Rank> {
-    zerosLike<T extends Tensor>(this: T): T;
-  }
-}
-
-getGlobalTensorClass().prototype.zerosLike = function<T extends Tensor>(
-    this: T): T {
-  this.throwIfDisposed();
-  return zerosLike(this);
-};
diff --git a/tfjs-master/tfjs-core/src/register_all_gradients.ts b/tfjs-master/tfjs-core/src/register_all_gradients.ts
deleted file mode 100644
index e3e0e281c..000000000
--- a/tfjs-master/tfjs-core/src/register_all_gradients.ts
+++ /dev/null
@@ -1,232 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {absGradConfig} from './gradients/Abs_grad';
-import {acosGradConfig} from './gradients/Acos_grad';
-import {acoshGradConfig} from './gradients/Acosh_grad';
-import {addGradConfig} from './gradients/Add_grad';
-import {addNGradConfig} from './gradients/AddN_grad';
-import {argMaxGradConfig} from './gradients/ArgMax_grad';
-import {argMinGradConfig} from './gradients/ArgMin_grad';
-import {asinGradConfig} from './gradients/Asin_grad';
-import {asinhGradConfig} from './gradients/Asinh_grad';
-import {atan2GradConfig} from './gradients/Atan2_grad';
-import {atanGradConfig} from './gradients/Atan_grad';
-import {atanhGradConfig} from './gradients/Atanh_grad';
-import {avgPool3DGradConfig} from './gradients/AvgPool3D_grad';
-import {avgPoolGradConfig} from './gradients/AvgPool_grad';
-import {batchMatMulGradConfig} from './gradients/BatchMatMul_grad';
-import {batchToSpaceNDGradConfig} from './gradients/BatchToSpaceND_grad';
-import {broadcastToGradConfig} from './gradients/BroadcastTo_grad';
-import {castGradConfig} from './gradients/Cast_grad';
-import {ceilGradConfig} from './gradients/Ceil_grad';
-import {clipByValueGradConfig} from './gradients/ClipByValue_grad';
-import {complexAbsGradConfig} from './gradients/ComplexAbs_grad';
-import {concatGradConfig} from './gradients/Concat_grad';
-import {conv2DGradConfig} from './gradients/Conv2D_grad';
-import {conv2DBackpropInputGradConfig} from './gradients/Conv2DBackpropInput_grad';
-import {conv3DGradConfig} from './gradients/Conv3D_grad';
-import {cosGradConfig} from './gradients/Cos_grad';
-import {coshGradConfig} from './gradients/Cosh_grad';
-import {cumsumGradConfig} from './gradients/Cumsum_grad';
-import {depthwiseConv2dNativeGradConfig} from './gradients/DepthwiseConv2dNative_grad';
-import {dilation2dGradConfig} from './gradients/Dilation2D_grad';
-import {eluGradConfig} from './gradients/Elu_grad';
-import {erfGradConfig} from './gradients/Erf_grad';
-import {expGradConfig} from './gradients/Exp_grad';
-import {expandDimsGradConfig} from './gradients/ExpandDims_grad';
-import {expm1GradConfig} from './gradients/Expm1_grad';
-import {floorGradConfig} from './gradients/Floor_grad';
-import {floorDivGradConfig} from './gradients/FloorDiv_grad';
-import {fusedBatchNormGradConfig} from './gradients/FusedBatchNorm_grad';
-import {gatherGradConfig} from './gradients/GatherV2_grad';
-import {greaterEqualGradConfig} from './gradients/GreaterEqual_grad';
-import {identityGradConfig} from './gradients/Identity_grad';
-import {isFiniteGradConfig} from './gradients/IsFinite_grad';
-import {isInfGradConfig} from './gradients/IsInf_grad';
-import {isNanGradConfig} from './gradients/IsNan_grad';
-import {leakyReluGradConfig} from './gradients/LeakyRelu_grad';
-import {log1pGradConfig} from './gradients/Log1p_grad';
-import {logGradConfig} from './gradients/Log_grad';
-import {logSoftmaxGradConfig} from './gradients/LogSoftmax_grad';
-import {lrnGradConfig} from './gradients/LRN_grad';
-import {maxGradConfig} from './gradients/Max_grad';
-import {maximumGradConfig} from './gradients/Maximum_grad';
-import {maxPool3DGradConfig} from './gradients/MaxPool3D_grad';
-import {maxPoolGradConfig} from './gradients/MaxPool_grad';
-import {meanGradConfig} from './gradients/Mean_grad';
-import {minGradConfig} from './gradients/Min_grad';
-import {minimumGradConfig} from './gradients/Minimum_grad';
-import {mirrorPadGradConfig} from './gradients/MirrorPad_grad';
-import {modGradConfig} from './gradients/Mod_grad';
-import {multiplyGradConfig} from './gradients/Multiply_grad';
-import {negGradConfig} from './gradients/Neg_grad';
-import {oneHotGradConfig} from './gradients/OneHot_grad';
-import {onesLikeGradConfig} from './gradients/OnesLike_grad';
-import {packGradConfig} from './gradients/Pack_grad';
-import {padV2GradConfig} from './gradients/PadV2_grad';
-import {powGradConfig} from './gradients/Pow_grad';
-import {preluGradConfig} from './gradients/Prelu_grad';
-import {prodGradConfig} from './gradients/Prod_grad';
-import {divGradConfig} from './gradients/RealDiv_grad';
-import {reciprocalGradConfig} from './gradients/Reciprocal_grad';
-import {relu6GradConfig} from './gradients/Relu6_grad';
-import {reluGradConfig} from './gradients/Relu_grad';
-import {reshapeGradConfig} from './gradients/Reshape_grad';
-import {resizeBilinearGradConfig} from './gradients/ResizeBilinear_grad';
-import {resizeNearestNeighborGradConfig} from './gradients/ResizeNearestNeighbor_grad';
-import {reverseGradConfig} from './gradients/Reverse_grad';
-import {roundGradConfig} from './gradients/Round_grad';
-import {rsqrtGradConfig} from './gradients/Rsqrt_grad';
-import {selectGradConfig} from './gradients/Select_grad';
-import {seluGradConfig} from './gradients/Selu_grad';
-import {sigmoidGradConfig} from './gradients/Sigmoid_grad';
-import {signGradConfig} from './gradients/Sign_grad';
-import {sinGradConfig} from './gradients/Sin_grad';
-import {sinhGradConfig} from './gradients/Sinh_grad';
-import {sliceGradConfig} from './gradients/Slice_grad';
-import {softmaxGradConfig} from './gradients/Softmax_grad';
-import {softplusGradConfig} from './gradients/Softplus_grad';
-import {spaceToBatchNDGradConfig} from './gradients/SpaceToBatchND_grad';
-import {splitVGradConfig} from './gradients/SplitV_grad';
-import {sqrtGradConfig} from './gradients/Sqrt_grad';
-import {squareGradConfig} from './gradients/Square_grad';
-import {squaredDifferenceGradConfig} from './gradients/SquaredDifference_grad';
-import {stepGradConfig} from './gradients/Step_grad';
-import {subGradConfig} from './gradients/Sub_grad';
-import {sumGradConfig} from './gradients/Sum_grad';
-import {tanGradConfig} from './gradients/Tan_grad';
-import {tanhGradConfig} from './gradients/Tanh_grad';
-import {tileGradConfig} from './gradients/Tile_grad';
-import {transposeGradConfig} from './gradients/Transpose_grad';
-import {unpackGradConfig} from './gradients/Unpack_grad';
-import {unsortedSegmentSumGradConfig} from './gradients/UnsortedSegmentSum_grad';
-import {zerosLikeGradConfig} from './gradients/ZerosLike_grad';
-import {GradConfig} from './kernel_registry';
-import {registerGradient} from './kernel_registry';
-
-// Export all kernel configs here so that the package can auto register them
-const gradConfigs: GradConfig[] = [
-  absGradConfig,
-  acosGradConfig,
-  acoshGradConfig,
-  addGradConfig,
-  addNGradConfig,
-  argMaxGradConfig,
-  argMinGradConfig,
-  asinGradConfig,
-  asinhGradConfig,
-  atan2GradConfig,
-  atanGradConfig,
-  atanhGradConfig,
-  avgPool3DGradConfig,
-  avgPoolGradConfig,
-  batchMatMulGradConfig,
-  batchToSpaceNDGradConfig,
-  broadcastToGradConfig,
-  castGradConfig,
-  ceilGradConfig,
-  clipByValueGradConfig,
-  complexAbsGradConfig,
-  concatGradConfig,
-  conv2DBackpropInputGradConfig,
-  conv2DGradConfig,
-  conv3DGradConfig,
-  cosGradConfig,
-  coshGradConfig,
-  cumsumGradConfig,
-  depthwiseConv2dNativeGradConfig,
-  dilation2dGradConfig,
-  divGradConfig,
-  eluGradConfig,
-  erfGradConfig,
-  expGradConfig,
-  expandDimsGradConfig,
-  expm1GradConfig,
-  floorDivGradConfig,
-  floorGradConfig,
-  fusedBatchNormGradConfig,
-  gatherGradConfig,
-  greaterEqualGradConfig,
-  identityGradConfig,
-  isFiniteGradConfig,
-  isInfGradConfig,
-  isNanGradConfig,
-  leakyReluGradConfig,
-  log1pGradConfig,
-  logGradConfig,
-  logSoftmaxGradConfig,
-  lrnGradConfig,
-  maxGradConfig,
-  maxGradConfig,
-  maximumGradConfig,
-  maxPool3DGradConfig,
-  maxPoolGradConfig,
-  meanGradConfig,
-  minGradConfig,
-  minimumGradConfig,
-  mirrorPadGradConfig,
-  modGradConfig,
-  multiplyGradConfig,
-  negGradConfig,
-  oneHotGradConfig,
-  onesLikeGradConfig,
-  packGradConfig,
-  padV2GradConfig,
-  padV2GradConfig,
-  powGradConfig,
-  preluGradConfig,
-  prodGradConfig,
-  reciprocalGradConfig,
-  relu6GradConfig,
-  reluGradConfig,
-  reshapeGradConfig,
-  resizeBilinearGradConfig,
-  resizeNearestNeighborGradConfig,
-  reverseGradConfig,
-  roundGradConfig,
-  rsqrtGradConfig,
-  selectGradConfig,
-  seluGradConfig,
-  sigmoidGradConfig,
-  signGradConfig,
-  sinGradConfig,
-  sinhGradConfig,
-  sliceGradConfig,
-  softmaxGradConfig,
-  softplusGradConfig,
-  spaceToBatchNDGradConfig,
-  spaceToBatchNDGradConfig,
-  splitVGradConfig,
-  splitVGradConfig,
-  sqrtGradConfig,
-  squaredDifferenceGradConfig,
-  squareGradConfig,
-  stepGradConfig,
-  subGradConfig,
-  sumGradConfig,
-  tanGradConfig,
-  tanhGradConfig,
-  tileGradConfig,
-  transposeGradConfig,
-  unpackGradConfig,
-  unsortedSegmentSumGradConfig,
-  zerosLikeGradConfig
-];
-
-for (const gradientConfig of gradConfigs) {
-  registerGradient(gradientConfig);
-}
diff --git a/tfjs-master/tfjs-core/src/serialization.ts b/tfjs-master/tfjs-core/src/serialization.ts
deleted file mode 100644
index 621a9a639..000000000
--- a/tfjs-master/tfjs-core/src/serialization.ts
+++ /dev/null
@@ -1,282 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {assert} from './util';
-
-/**
- * Types to support JSON-esque data structures internally.
- *
- * Internally ConfigDict's use camelCase keys and values where the
- * values are class names to be instantiated.  On the python side, these
- * will be snake_case.  Internally we allow Enums into the values for better
- * type safety, but these need to be converted to raw primitives (usually
- * strings) for round-tripping with python.
- *
- * toConfig returns the TS-friendly representation. model.toJSON() returns
- * the pythonic version as that's the portable format.  If you need to
- * python-ify a non-model level toConfig output, you'll need to use a
- * convertTsToPythonic from serialization_utils in -Layers.
- *
- */
-export declare type ConfigDictValue =
-    boolean | number | string | null | ConfigDictArray | ConfigDict;
-export declare interface ConfigDict {
-  [key: string]: ConfigDictValue;
-}
-export declare interface ConfigDictArray extends Array<ConfigDictValue> {}
-
-/**
- * Type to represent the class-type of Serializable objects.
- *
- * Ie the class prototype with access to the constructor and any
- * static members/methods. Instance methods are not listed here.
- *
- * Source for this idea: https://stackoverflow.com/a/43607255
- */
-export declare type SerializableConstructor<T extends Serializable> = {
-  // tslint:disable-next-line:no-any
-  new (...args: any[]): T; className: string; fromConfig: FromConfigMethod<T>;
-};
-export declare type FromConfigMethod<T extends Serializable> =
-    (cls: SerializableConstructor<T>, config: ConfigDict) => T;
-
-/**
- * Maps to mapping between the custom object and its name.
- *
- * After registering a custom class, these two maps will add key-value pairs
- * for the class object and the registered name.
- *
- * Therefore we can get the relative registered name by calling
- * getRegisteredName() function.
- *
- * For example:
- * GLOBAL_CUSTOM_OBJECT: {key=registeredName: value=corresponding
- * CustomObjectClass}
- *
- * GLOBAL_CUSTOM_NAMES: {key=CustomObjectClass: value=corresponding
- * registeredName}
- *
- */
-const GLOBAL_CUSTOM_OBJECT =
-    new Map<string, SerializableConstructor<Serializable>>();
-
-const GLOBAL_CUSTOM_NAMES =
-    new Map<SerializableConstructor<Serializable>, string>();
-
-/**
- * Serializable defines the serialization contract.
- *
- * TFJS requires serializable classes to return their className when asked
- * to avoid issues with minification.
- */
-export abstract class Serializable {
-  /**
-   * Return the class name for this class to use in serialization contexts.
-   *
-   * Generally speaking this will be the same thing that constructor.name
-   * would have returned.  However, the class name needs to be robust
-   * against minification for serialization/deserialization to work properly.
-   *
-   * There's also places such as initializers.VarianceScaling, where
-   * implementation details between different languages led to different
-   * class hierarchies and a non-leaf node is used for serialization purposes.
-   */
-  getClassName(): string {
-    return (this.constructor as SerializableConstructor<Serializable>)
-        .className;
-  }
-
-  /**
-   * Return all the non-weight state needed to serialize this object.
-   */
-  abstract getConfig(): ConfigDict;
-
-  /**
-   * Creates an instance of T from a ConfigDict.
-   *
-   * This works for most descendants of serializable.  A few need to
-   * provide special handling.
-   * @param cls A Constructor for the class to instantiate.
-   * @param config The Configuration for the object.
-   */
-  /** @nocollapse */
-  static fromConfig<T extends Serializable>(
-      cls: SerializableConstructor<T>, config: ConfigDict): T {
-    return new cls(config);
-  }
-}
-
-/**
- * Maps string keys to class constructors.
- *
- * Used during (de)serialization from the cross-language JSON format, which
- * requires the class name in the serialization format matches the class
- * names as used in Python, should it exist.
- */
-export class SerializationMap {
-  private static instance: SerializationMap;
-  classNameMap: {
-    [className: string]:
-        [SerializableConstructor<Serializable>, FromConfigMethod<Serializable>]
-  };
-
-  private constructor() {
-    this.classNameMap = {};
-  }
-
-  /**
-   * Returns the singleton instance of the map.
-   */
-  static getMap(): SerializationMap {
-    if (SerializationMap.instance == null) {
-      SerializationMap.instance = new SerializationMap();
-    }
-    return SerializationMap.instance;
-  }
-
-  /**
-   * Registers the class as serializable.
-   */
-  static register<T extends Serializable>(cls: SerializableConstructor<T>) {
-    SerializationMap.getMap().classNameMap[cls.className] =
-        [cls, cls.fromConfig];
-  }
-}
-
-/**
- * Register a class with the serialization map of TensorFlow.js.
- *
- * This is often used for registering custom Layers, so they can be
- * serialized and deserialized.
- *
- * Example 1. Register the class without package name and specified name.
- *
- * ```js
- * class MyCustomLayer extends tf.layers.Layer {
- *   static className = 'MyCustomLayer';
- *
- *   constructor(config) {
- *     super(config);
- *   }
- * }
- * tf.serialization.registerClass(MyCustomLayer);
- * console.log(tf.serialization.GLOBALCUSTOMOBJECT.get("Custom>MyCustomLayer"));
- * console.log(tf.serialization.GLOBALCUSTOMNAMES.get(MyCustomLayer));
- * ```
- *
- * Example 2. Register the class with package name: "Package" and specified
- * name: "MyLayer".
- * ```js
- * class MyCustomLayer extends tf.layers.Layer {
- *   static className = 'MyCustomLayer';
- *
- *   constructor(config) {
- *     super(config);
- *   }
- * }
- * tf.serialization.registerClass(MyCustomLayer, "Package", "MyLayer");
- * console.log(tf.serialization.GLOBALCUSTOMOBJECT.get("Package>MyLayer"));
- * console.log(tf.serialization.GLOBALCUSTOMNAMES.get(MyCustomLayer));
- * ```
- *
- * Example 3. Register the class with specified name: "MyLayer".
- * ```js
- * class MyCustomLayer extends tf.layers.Layer {
- *   static className = 'MyCustomLayer';
- *
- *   constructor(config) {
- *     super(config);
- *   }
- * }
- * tf.serialization.registerClass(MyCustomLayer, undefined, "MyLayer");
- * console.log(tf.serialization.GLOBALCUSTOMOBJECT.get("Custom>MyLayer"));
- * console.log(tf.serialization.GLOBALCUSTOMNAMES.get(MyCustomLayer));
- * ```
- *
- * Example 4. Register the class with specified package name: "Package".
- * ```js
- * class MyCustomLayer extends tf.layers.Layer {
- *   static className = 'MyCustomLayer';
- *
- *   constructor(config) {
- *     super(config);
- *   }
- * }
- * tf.serialization.registerClass(MyCustomLayer, "Package");
- * console.log(tf.serialization.GLOBALCUSTOMOBJECT
- * .get("Package>MyCustomLayer"));
- * console.log(tf.serialization.GLOBALCUSTOMNAMES
- * .get(MyCustomLayer));
- * ```
- *
- * @param cls The class to be registered. It must have a public static member
- *   called `className` defined and the value must be a non-empty string.
- * @param pkg The pakcage name that this class belongs to. This used to define
- *     the key in GlobalCustomObject. If not defined, it defaults to `Custom`.
- * @param name The name that user specified. It defaults to the actual name of
- *     the class as specified by its static `className` property.
- * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}
- */
-export function registerClass<T extends Serializable>(
-    cls: SerializableConstructor<T>, pkg?: string, name?: string) {
-  assert(
-      cls.className != null,
-      () => `Class being registered does not have the static className ` +
-          `property defined.`);
-  assert(
-      typeof cls.className === 'string',
-      () => `className is required to be a string, but got type ` +
-          typeof cls.className);
-  assert(
-      cls.className.length > 0,
-      () => `Class being registered has an empty-string as its className, ` +
-          `which is disallowed.`);
-
-  if (typeof pkg === 'undefined') {
-    pkg = 'Custom';
-  }
-
-  if (typeof name === 'undefined') {
-    name = cls.className;
-  }
-
-  const className = name;
-  const registerName = pkg + '>' + className;
-
-  SerializationMap.register(cls);
-  GLOBAL_CUSTOM_OBJECT.set(registerName, cls);
-  GLOBAL_CUSTOM_NAMES.set(cls, registerName);
-
-  return cls;
-}
-
-/**
- * Get the registered name of a class. If the class has not been registered,
- * return the class name.
- *
- * @param cls The class we want to get register name for. It must have a public
- *     static member called `className` defined.
- * @returns registered name or class name.
- */
-export function getRegisteredName<T extends Serializable>(
-    cls: SerializableConstructor<T>) {
-  if (GLOBAL_CUSTOM_NAMES.has(cls)) {
-    return GLOBAL_CUSTOM_NAMES.get(cls);
-  } else {
-    return cls.className;
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/serialization_test.ts b/tfjs-master/tfjs-core/src/serialization_test.ts
deleted file mode 100644
index 2c135b979..000000000
--- a/tfjs-master/tfjs-core/src/serialization_test.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Optimizer} from './optimizers/optimizer';
-import {ConfigDict, registerClass, SerializationMap} from './serialization';
-import {NamedVariableMap} from './tensor_types';
-
-describe('registerClass', () => {
-  const randomClassName = `OptimizerForTest${Math.random()}`;
-  class OptimizerForTest extends Optimizer {
-    static className = randomClassName;
-    constructor() {
-      super();
-    }
-    applyGradients(variableGradients: NamedVariableMap) {}
-
-    getConfig(): ConfigDict {
-      return {};
-    }
-  }
-  it('registerClass succeeds', () => {
-    registerClass(OptimizerForTest);
-    expect(SerializationMap.getMap().classNameMap[randomClassName] != null)
-        .toEqual(true);
-  });
-
-  class OptimizerWithoutClassName extends Optimizer {
-    constructor() {
-      super();
-    }
-    applyGradients(variableGradients: NamedVariableMap) {}
-
-    getConfig(): ConfigDict {
-      return {};
-    }
-  }
-  it('registerClass fails on missing className', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => registerClass(OptimizerWithoutClassName as any))
-        .toThrowError(/does not have the static className property/);
-  });
-
-  class OptimizerWithEmptyClassName extends Optimizer {
-    static className = '';
-    constructor() {
-      super();
-    }
-    applyGradients(variableGradients: NamedVariableMap) {}
-
-    getConfig(): ConfigDict {
-      return {};
-    }
-  }
-  it('registerClass fails on missing className', () => {
-    expect(() => registerClass(OptimizerWithEmptyClassName))
-        .toThrowError(/has an empty-string as its className/);
-  });
-
-  class OptimizerWithNonStringClassName extends Optimizer {
-    static className = 42;
-    constructor() {
-      super();
-    }
-    applyGradients(variableGradients: NamedVariableMap) {}
-
-    getConfig(): ConfigDict {
-      return {};
-    }
-  }
-  it('registerClass fails on missing className', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => registerClass(OptimizerWithNonStringClassName as any))
-        .toThrowError(/is required to be a string, but got type number/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/setup_test.ts b/tfjs-master/tfjs-core/src/setup_test.ts
deleted file mode 100644
index 96bb5ed65..000000000
--- a/tfjs-master/tfjs-core/src/setup_test.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Register the CPU backend as a default backend for tests.
-import '@tensorflow/tfjs-backend-cpu';
-/**
- * This file is necessary so we register all test environments before we start
- * executing tests.
- */
-import {setTestEnvs, setupTestFilters, TestFilter} from './jasmine_util';
-// Register all chained ops for tests.
-import './public/chained_ops/register_all_chained_ops';
-// Register all gradients for tests
-import './register_all_gradients';
-
-// Set up a CPU test env as the default test env
-setTestEnvs([{name: 'cpu', backendName: 'cpu', isDataSync: true}]);
-
-const TEST_FILTERS: TestFilter[] = [];
-const customInclude = () => true;
-setupTestFilters(TEST_FILTERS, customInclude);
-
-// Import and run all the tests.
-// This import, which registers all tests, must be a require because it must run
-// after the test environment is set up.
-// tslint:disable-next-line:no-require-imports
-require('./tests');
diff --git a/tfjs-master/tfjs-core/src/tape.ts b/tfjs-master/tfjs-core/src/tape.ts
deleted file mode 100644
index 3266a2170..000000000
--- a/tfjs-master/tfjs-core/src/tape.ts
+++ /dev/null
@@ -1,193 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from './tensor';
-import {NamedTensorMap} from './tensor_types';
-import * as util from './util';
-
-export interface TapeNode {
-  id: number;
-  kernelName: string;
-  outputs: Tensor[];
-  inputs: NamedTensorMap;
-  // Optional params, defined only for ops with gradient impl.
-  gradient?: (dys: Tensor[]) => NamedGradientMap;
-  saved?: Tensor[];
-}
-
-export type NamedGradientMap = {
-  [inputName: string]: () => Tensor;
-};
-
-/**
- * Computes a list of TapeNodes that connect x to y, filtering everything else
- * out and preserving the order of the original tape elements.
- *
- * @param tape The tape elements to filter.
- * @param xs The input Tensors.
- * @param y The output Tensor.
- */
-export function getFilteredNodesXToY(
-    tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {
-  // Forward pass to compute all the nodes and Tensors that are transitively a
-  // function of x.
-  const tensorsFromX: {[tensorId: number]: boolean} = {};
-  const nodesFromX: {[nodeId: number]: boolean} = {};
-  for (let i = 0; i < xs.length; i++) {
-    tensorsFromX[xs[i].id] = true;
-  }
-
-  for (let i = 0; i < tape.length; i++) {
-    const node = tape[i];
-    const nodeInputs = node.inputs;
-    for (const inputName in nodeInputs) {
-      const input = nodeInputs[inputName];
-
-      let anyInputFromX = false;
-      for (let j = 0; j < xs.length; j++) {
-        if (tensorsFromX[input.id]) {
-          node.outputs.forEach(output => tensorsFromX[output.id] = true);
-          anyInputFromX = true;
-          nodesFromX[node.id] = true;
-          break;
-        }
-      }
-
-      if (anyInputFromX) {
-        break;
-      }
-    }
-  }
-
-  // Backward pass to find all of the nodes and Tensors that lead to y.
-  const tensorsLeadToY: {[tensorId: number]: boolean} = {};
-  tensorsLeadToY[y.id] = true;
-  const nodesToY: {[nodeId: number]: boolean} = {};
-
-  for (let i = tape.length - 1; i >= 0; i--) {
-    const node = tape[i];
-    const nodeInputs = node.inputs;
-
-    // If any of the outputs lead to y, mark all of the inputs as leading to y.
-    for (let j = 0; j < node.outputs.length; j++) {
-      if (tensorsLeadToY[node.outputs[j].id]) {
-        for (const inputName in nodeInputs) {
-          tensorsLeadToY[nodeInputs[inputName].id] = true;
-          nodesToY[node.id] = true;
-        }
-        break;
-      }
-    }
-  }
-
-  // Return the paths that come from x and lead to y.
-  const filteredTape: TapeNode[] = [];
-  for (let i = 0; i < tape.length; i++) {
-    const node = tape[i];
-
-    if (nodesFromX[node.id] && nodesToY[node.id]) {
-      // Prune the inputs from the node that aren't a function of x.
-      const prunedInputs: {[inputName: string]: Tensor} = {};
-      for (const inputName in node.inputs) {
-        const nodeInput = node.inputs[inputName];
-        if (tensorsFromX[nodeInput.id]) {
-          prunedInputs[inputName] = nodeInput;
-        }
-      }
-
-      // Copy the node and overwrite inputsAndArgs to the pruned version.
-      const prunedNode = Object.assign({}, node);
-      prunedNode.inputs = prunedInputs;
-      prunedNode.outputs = node.outputs;
-
-      filteredTape.push(prunedNode);
-    }
-  }
-
-  return filteredTape;
-}
-
-/**
- * Backpropagate gradients through the filtered TapeNodes.
- *
- * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map
- * is mutated by this method.
- * @param filteredTape The filtered TapeNodes to backprop through.
- */
-export function backpropagateGradients(
-    tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},
-    filteredTape: TapeNode[], tidy: (f: Function) => Tensor,
-    add: (a: Tensor, b: Tensor) => Tensor) {
-  // Walk the tape backward and keep a map of Tensor to its gradient.
-  for (let i = filteredTape.length - 1; i >= 0; i--) {
-    const node = filteredTape[i];
-
-    const dys: Tensor[] = [];
-    node.outputs.forEach(o => {
-      const gradTensor = tensorAccumulatedGradientMap[o.id];
-      if (gradTensor != null) {
-        dys.push(gradTensor);
-      } else {
-        // This particular output is not in the back-propagation subgraph, so it
-        // does not affect the final output, thus we put null for its dy.
-        dys.push(null);
-      }
-    });
-
-    if (node.gradient == null) {
-      throw new Error(
-          `Cannot compute gradient: gradient function not found ` +
-          `for ${node.kernelName}.`);
-    }
-
-    // Backprop dy through this node and accumulate gradients over the inputs.
-    const inputGradients = node.gradient(dys);
-
-    for (const inputName in node.inputs) {
-      if (!(inputName in inputGradients)) {
-        throw new Error(
-            `Cannot backprop through input ${inputName}. ` +
-            `Available gradients found: ${Object.keys(inputGradients)}.`);
-      }
-
-      // Call the gradient function.
-      const dx = tidy(() => inputGradients[inputName]());
-      if (dx.dtype !== 'float32') {
-        throw new Error(
-            `Error in gradient for op ${
-                node.kernelName}. The gradient of input ` +
-            `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
-      }
-      const x = node.inputs[inputName];
-      if (!util.arraysEqual(dx.shape, x.shape)) {
-        throw new Error(
-            `Error in gradient for op ${
-                node.kernelName}. The gradient of input ` +
-            `'${inputName}' has shape '${dx.shape}', which does not match ` +
-            `the shape of the input '${x.shape}'`);
-      }
-
-      if (tensorAccumulatedGradientMap[x.id] == null) {
-        tensorAccumulatedGradientMap[x.id] = dx;
-      } else {
-        const curGradient = tensorAccumulatedGradientMap[x.id];
-        tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);
-        curGradient.dispose();
-      }
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/tape_test.ts b/tfjs-master/tfjs-core/src/tape_test.ts
deleted file mode 100644
index 874c4146d..000000000
--- a/tfjs-master/tfjs-core/src/tape_test.ts
+++ /dev/null
@@ -1,423 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {add, ScopeFn} from './engine';
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-import {zerosLike} from './ops/ops';
-import {backpropagateGradients, getFilteredNodesXToY, TapeNode} from './tape';
-import {expectArraysClose} from './test_util';
-
-describeWithFlags('getFilteredNodesXToY', ALL_ENVS, () => {
-  it('no paths from x to y', () => {
-    const x = tf.scalar(1);
-    const intermediate1 = tf.scalar(0);
-
-    const intermediate2 = tf.scalar(0);
-    const y = tf.scalar(2);
-
-    const tape: TapeNode[] = [
-      {
-        id: 0,
-        kernelName: 'node0',
-        inputs: {x},
-        outputs: [intermediate1],
-        gradient: null
-      },
-      {
-        id: 1,
-        kernelName: 'node1',
-        inputs: {intermediate2},
-        outputs: [y],
-        gradient: null
-      }
-    ];
-
-    const filteredTapeNodes = getFilteredNodesXToY(tape, [x], y);
-
-    expect(filteredTapeNodes.length).toBe(0);
-    expect(filteredTapeNodes).toEqual([]);
-  });
-
-  it('one operation x => y', () => {
-    const x = tf.scalar(1);
-    const y = tf.scalar(2);
-
-    const tape: TapeNode[] = [
-      {id: 0, kernelName: 'node0', inputs: {x}, outputs: [y], gradient: null}
-    ];
-
-    const filteredTapeNodes = getFilteredNodesXToY(tape, [x], y);
-
-    expect(filteredTapeNodes.length).toBe(1);
-    expect(filteredTapeNodes).toEqual(tape);
-  });
-
-  it('1 operation [x0, x1] => y, all input paths', () => {
-    const x0 = tf.scalar(0);
-    const x1 = tf.scalar(1);
-    const y = tf.scalar(2);
-
-    const tape: TapeNode[] = [{
-      id: 0,
-      kernelName: 'node0',
-      inputs: {x0, x1},
-      outputs: [y],
-      gradient: null
-    }];
-
-    const filteredTapeNodes = getFilteredNodesXToY(tape, [x0, x1], y);
-
-    expect(filteredTapeNodes.length).toBe(1);
-    expect(filteredTapeNodes).toEqual(tape);
-  });
-
-  it('one operation [x0, x1] => y, one input paths', () => {
-    const x0 = tf.scalar(0);
-    const x1 = tf.scalar(1);
-    const y = tf.scalar(2);
-
-    const tape: TapeNode[] = [{
-      id: 0,
-      kernelName: 'node0',
-      inputs: {x0, x1},
-      outputs: [y],
-      gradient: null
-    }];
-
-    const filteredTapeNodes = getFilteredNodesXToY(tape, [x0], y);
-
-    expect(filteredTapeNodes.length).toBe(1);
-    // x1 input should be pruned, we don't ask for the gradient of x1.
-    expect(filteredTapeNodes[0]).toEqual({
-      id: 0,
-      kernelName: 'node0',
-      inputs: {x0},
-      outputs: [y],
-      gradient: null
-    });
-  });
-
-  it('two operations x => intermediate => y', () => {
-    const x = tf.scalar(1);
-    const intermediate = tf.scalar(0);
-    const y = tf.scalar(2);
-
-    const tape: TapeNode[] = [
-      {
-        id: 0,
-        kernelName: 'node0',
-        inputs: {x},
-        outputs: [intermediate],
-        gradient: null
-      },
-      {
-        id: 1,
-        kernelName: 'node1',
-        inputs: {intermediate},
-        outputs: [y],
-        gradient: null
-      }
-    ];
-
-    const filteredTapeNodes = getFilteredNodesXToY(tape, [x], y);
-
-    expect(filteredTapeNodes.length).toBe(2);
-    expect(filteredTapeNodes).toEqual(tape);
-  });
-
-  it('two operations [x0, x1], [x2] => ' +
-         'intermediate => y',
-     () => {
-       const x0 = tf.scalar(1);
-       const x1 = tf.scalar(2);
-       const x2 = tf.scalar(3);
-       const intermediate = tf.scalar(4);
-       const y = tf.scalar(2);
-
-       const tape: TapeNode[] = [
-         {
-           id: 0,
-           kernelName: 'node0',
-           inputs: {x0, x1},
-           outputs: [intermediate],
-           gradient: null
-         },
-         {
-           id: 1,
-           kernelName: 'node1',
-           inputs: {x2, intermediate},
-           outputs: [y],
-           gradient: null
-         }
-       ];
-
-       const filteredTapeNodes = getFilteredNodesXToY(tape, [x0, x1, x2], y);
-
-       expect(filteredTapeNodes.length).toBe(2);
-       expect(filteredTapeNodes).toEqual(tape);
-     });
-
-  it('x => y and x => orphan', () => {
-    const x = tf.scalar(1);
-    const orphan = tf.scalar(0);
-    const y = tf.scalar(2);
-
-    const tape: TapeNode[] = [
-      {
-        id: 0,
-        kernelName: 'node0',
-        inputs: {x},
-        outputs: [orphan],
-        gradient: null
-      },
-      {id: 1, kernelName: 'node1', inputs: {x}, outputs: [y], gradient: null}
-    ];
-
-    const filteredTapeNodes = getFilteredNodesXToY(tape, [x], y);
-
-    expect(filteredTapeNodes.length).toBe(1);
-    // The orphan should be removed.
-    expect(filteredTapeNodes[0]).toEqual(tape[1]);
-  });
-
-  it('x => y and orphan => y', () => {
-    const x = tf.scalar(1);
-    const orphan = tf.scalar(0);
-    const y = tf.scalar(2);
-
-    const tape: TapeNode[] = [{
-      id: 0,
-      kernelName: 'node0',
-      inputs: {x, orphan},
-      outputs: [y],
-      gradient: null
-    }];
-
-    const filteredTapeNodes = getFilteredNodesXToY(tape, [x], y);
-
-    expect(filteredTapeNodes.length).toBe(1);
-    // The orphan should be pruned from the node's input.
-    expect(filteredTapeNodes[0]).toEqual({
-      id: 0,
-      kernelName: 'node0',
-      inputs: {x},
-      outputs: [y],
-      gradient: null
-    });
-  });
-
-  it('1 op with 3 outputs x => y1, y2, y3', () => {
-    const x = tf.scalar(1);
-    const y1 = tf.scalar(2);
-    const y2 = tf.scalar(2);
-    const y3 = tf.scalar(2);
-
-    const tape: TapeNode[] = [{
-      id: 0,
-      kernelName: 'node0',
-      inputs: {x},
-      outputs: [y1, y2, y3],
-      gradient: null
-    }];
-
-    const filteredNodes1 = getFilteredNodesXToY(tape, [x], y1);
-    expect(filteredNodes1.length).toBe(1);
-    expect(filteredNodes1).toEqual(tape);
-
-    const filteredNodes2 = getFilteredNodesXToY(tape, [x], y2);
-    expect(filteredNodes2.length).toBe(1);
-    expect(filteredNodes2).toEqual(tape);
-
-    const filteredNodes3 = getFilteredNodesXToY(tape, [x], y3);
-    expect(filteredNodes3.length).toBe(1);
-    expect(filteredNodes3).toEqual(tape);
-  });
-});
-
-describeWithFlags('backpropagateGradients', ALL_ENVS, () => {
-  it('Throws if gradient is not defined', () => {
-    const x = tf.scalar(0);
-    const y = tf.scalar(1);
-
-    const dy = tf.scalar(1);
-
-    const accumulatedGradientsMap: {[tensorId: number]: tf.Tensor} = {};
-    accumulatedGradientsMap[y.id] = dy;
-
-    const tape: TapeNode[] = [
-      {id: 0, kernelName: 'node0', inputs: {x}, outputs: [y], gradient: null}
-    ];
-
-    expect(
-        () => backpropagateGradients(
-            accumulatedGradientsMap, tape,
-            f => tf.tidy(f as ScopeFn<tf.Tensor>), add))
-        .toThrowError();
-  });
-
-  it('basic backprop with 1 node', async () => {
-    const x = tf.scalar(0);
-    const y = tf.scalar(1);
-
-    const dy = tf.scalar(1);
-
-    const accumulatedGradientsMap: {[tensorId: number]: tf.Tensor} = {};
-    accumulatedGradientsMap[y.id] = dy;
-
-    const tape: TapeNode[] = [{
-      id: 0,
-      kernelName: 'node0',
-      inputs: {x},
-      outputs: [y],
-      gradient: (dys: tf.Tensor[]) => {
-        return {x: () => dys[0].add(tf.scalar(1))};
-      }
-    }];
-
-    backpropagateGradients(
-        accumulatedGradientsMap, tape, f => tf.tidy(f as ScopeFn<tf.Tensor>),
-        add);
-
-    expectArraysClose(await accumulatedGradientsMap[x.id].data(), [2]);
-  });
-
-  it('basic backprop with 2 nodes', async () => {
-    const x = tf.scalar(0);
-    const intermediate = tf.scalar(1);
-    const y = tf.scalar(2);
-
-    const dy = tf.scalar(1);
-
-    const accumulatedGradientsMap: {[tensorId: number]: tf.Tensor} = {};
-    accumulatedGradientsMap[y.id] = dy;
-
-    const tape: TapeNode[] = [
-      {
-        id: 0,
-        kernelName: 'node0',
-        inputs: {x},
-        outputs: [intermediate],
-        gradient: (dys: tf.Tensor[]) => {
-          return {x: () => dys[0].add(tf.scalar(1))};
-        }
-      },
-      {
-        id: 1,
-        kernelName: 'node1',
-        inputs: {intermediate},
-        outputs: [y],
-        gradient: (dys: tf.Tensor[]) => {
-          return {intermediate: () => dys[0].add(tf.scalar(1))};
-        }
-      }
-    ];
-
-    backpropagateGradients(
-        accumulatedGradientsMap, tape, f => tf.tidy(f as ScopeFn<tf.Tensor>),
-        add);
-
-    // dx = dy + 1 + 1
-    expectArraysClose(await accumulatedGradientsMap[x.id].data(), [3]);
-  });
-
-  it('basic backprop with a split node accumulates gradients', async () => {
-    const x = tf.scalar(0);
-    const intermediate1 = tf.scalar(1);
-    const intermediate2 = tf.scalar(2);
-    const y = tf.scalar(3);
-
-    const dy = tf.scalar(1);
-
-    const accumulatedGradientsMap: {[tensorId: number]: tf.Tensor} = {};
-    accumulatedGradientsMap[y.id] = dy;
-
-    const tape: TapeNode[] = [
-      {
-        id: 0,
-        kernelName: 'node0',
-        inputs: {x},
-        outputs: [intermediate1],
-        gradient: (dys: tf.Tensor[]) => {
-          return {x: () => dys[0].add(tf.scalar(1))};
-        }
-      },
-      {
-        id: 1,
-        kernelName: 'node1',
-        inputs: {x},
-        outputs: [intermediate2],
-        gradient: (dys: tf.Tensor[]) => {
-          return {x: () => dys[0].add(tf.scalar(1))};
-        }
-      },
-      {
-        id: 2,
-        kernelName: 'node2',
-        inputs: {intermediate1, intermediate2},
-        outputs: [y],
-        gradient: (dys: tf.Tensor[]) => {
-          return {
-            intermediate1: () => dys[0].add(tf.scalar(1)),
-            intermediate2: () => dys[0].add(tf.scalar(1))
-          };
-        }
-      }
-    ];
-
-    backpropagateGradients(
-        accumulatedGradientsMap, tape, f => tf.tidy(f as ScopeFn<tf.Tensor>),
-        add);
-
-    // dx = dy + 1 + 1 + 1 + 1 + 1
-    expectArraysClose(
-        await accumulatedGradientsMap[x.id].data(), [(await dy.data())[0] + 5]);
-  });
-
-  it('backprop over 1 node with 3 outputs, w.r.t to the 2nd output',
-     async () => {
-       const x = tf.tensor1d([1, 1, 1]);
-       const y1 = tf.scalar(1);
-       const y2 = tf.scalar(1);
-       const y3 = tf.scalar(1);
-
-       const accumulatedGradientsMap: {[tensorId: number]: tf.Tensor} = {};
-       // Backproping through the 2nd output.
-       const dy2 = tf.scalar(5);
-       accumulatedGradientsMap[y2.id] = dy2;
-
-       let dys: tf.Scalar[];
-       const tape: TapeNode[] = [{
-         id: 0,
-         kernelName: 'node0',
-         inputs: {x},
-         outputs: [y1, y2, y3],
-         gradient: (dys_: tf.Scalar[]) => {
-           dys = dys_.map(dy => dy || zerosLike(y1));
-           return {x: () => tf.stack(dys)};
-         }
-       }];
-
-       backpropagateGradients(
-           accumulatedGradientsMap, tape, f => tf.tidy(f as ScopeFn<tf.Tensor>),
-           add);
-       expectArraysClose(await accumulatedGradientsMap[x.id].data(), [0, 5, 0]);
-       expectArraysClose(await dys[0].data(), [0]);
-       expectArraysClose(await dys[1].data(), [5]);
-       expectArraysClose(await dys[2].data(), [0]);
-     });
-});
diff --git a/tfjs-master/tfjs-core/src/tensor.ts b/tfjs-master/tfjs-core/src/tensor.ts
deleted file mode 100644
index 137d3b487..000000000
--- a/tfjs-master/tfjs-core/src/tensor.ts
+++ /dev/null
@@ -1,610 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Workaround for: https://github.com/bazelbuild/rules_nodejs/issues/1265
-/// <reference types="@webgpu/types/dist" />
-
-import {getGlobal} from './global_util';
-import {tensorToString} from './tensor_format';
-import {DataId, TensorInfo} from './tensor_info';
-import {ArrayMap, BackendValues, DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TypedArray} from './types';
-import * as util from './util';
-import {computeStrides, toNestedArray} from './util';
-
-export interface TensorData<D extends DataType> {
-  dataId?: DataId;
-  values?: DataTypeMap[D];
-}
-
-// This interface mimics KernelBackend (in backend.ts), which would create a
-// circular dependency if imported.
-export interface Backend {}
-
-/**
- * A mutable object, similar to `tf.Tensor`, that allows users to set values
- * at locations before converting to an immutable `tf.Tensor`.
- *
- * See `tf.buffer` for creating a tensor buffer.
- *
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-export class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
-  size: number;
-  shape: ShapeMap[R];
-  strides: number[];
-  values: DataTypeMap[D];
-
-  constructor(shape: ShapeMap[R], public dtype: D, values?: DataTypeMap[D]) {
-    this.shape = shape.slice() as ShapeMap[R];
-    this.size = util.sizeFromShape(shape);
-
-    if (values != null) {
-      const n = values.length;
-      util.assert(
-          n === this.size,
-          () => `Length of values '${n}' does not match the size ` +
-              `inferred by the shape '${this.size}'.`);
-    }
-    if (dtype === 'complex64') {
-      throw new Error(
-          `complex64 dtype TensorBuffers are not supported. Please create ` +
-          `a TensorBuffer for the real and imaginary parts separately and ` +
-          `call tf.complex(real, imag).`);
-    }
-    this.values = values || util.getArrayFromDType(dtype, this.size);
-    this.strides = computeStrides(shape);
-  }
-
-  /**
-   * Sets a value in the buffer at a given location.
-   *
-   * @param value The value to set.
-   * @param locs  The location indices.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Creation'}
-   */
-  set(value: SingleValueMap[D], ...locs: number[]): void {
-    if (locs.length === 0) {
-      locs = [0];
-    }
-    util.assert(
-        locs.length === this.rank,
-        () => `The number of provided coordinates (${locs.length}) must ` +
-            `match the rank (${this.rank})`);
-
-    const index = this.locToIndex(locs);
-    this.values[index] = value as number;
-  }
-
-  /**
-   * Returns the value in the buffer at the provided location.
-   *
-   * @param locs The location indices.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Creation'}
-   */
-  get(...locs: number[]): SingleValueMap[D] {
-    if (locs.length === 0) {
-      locs = [0];
-    }
-    let i = 0;
-    for (const loc of locs) {
-      if (loc < 0 || loc >= this.shape[i]) {
-        const msg = `Requested out of range element at ${locs}. ` +
-            `  Buffer shape=${this.shape}`;
-        throw new Error(msg);
-      }
-      i++;
-    }
-    let index = locs[locs.length - 1];
-    for (let i = 0; i < locs.length - 1; ++i) {
-      index += this.strides[i] * locs[i];
-    }
-    return this.values[index] as SingleValueMap[D];
-  }
-
-  locToIndex(locs: number[]): number {
-    if (this.rank === 0) {
-      return 0;
-    } else if (this.rank === 1) {
-      return locs[0];
-    }
-    let index = locs[locs.length - 1];
-    for (let i = 0; i < locs.length - 1; ++i) {
-      index += this.strides[i] * locs[i];
-    }
-    return index;
-  }
-
-  indexToLoc(index: number): number[] {
-    if (this.rank === 0) {
-      return [];
-    } else if (this.rank === 1) {
-      return [index];
-    }
-    const locs: number[] = new Array(this.shape.length);
-    for (let i = 0; i < locs.length - 1; ++i) {
-      locs[i] = Math.floor(index / this.strides[i]);
-      index -= locs[i] * this.strides[i];
-    }
-    locs[locs.length - 1] = index;
-    return locs;
-  }
-
-  get rank() {
-    return this.shape.length;
-  }
-
-  /**
-   * Creates an immutable `tf.Tensor` object from the buffer.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Creation'}
-   */
-  toTensor(): Tensor<R> {
-    return trackerFn().makeTensor(this.values, this.shape, this.dtype) as
-        Tensor<R>;
-  }
-}
-
-export interface DataToGPUWebGLOption {
-  customTexShape?: [number, number];
-}
-
-export type DataToGPUOptions = DataToGPUWebGLOption;
-
-export interface GPUData {
-  tensorRef: Tensor;
-  texture?: WebGLTexture;
-  buffer?: GPUBuffer;
-  texShape?: [number, number];
-}
-
-export interface TensorTracker {
-  makeTensor(
-      values: DataValues, shape: number[], dtype: DataType,
-      backend?: Backend): Tensor;
-  makeVariable(
-      initialValue: Tensor, trainable?: boolean, name?: string,
-      dtype?: DataType): Variable;
-  incRef(a: Tensor, backend: Backend): void;
-  disposeTensor(t: Tensor): void;
-  disposeVariable(v: Variable): void;
-  read(dataId: DataId): Promise<BackendValues>;
-  readSync(dataId: DataId): BackendValues;
-  readToGPU(dataId: DataId, options?: DataToGPUOptions): GPUData;
-}
-
-/**
- * The Tensor class calls into this handler to delegate chaining operations.
- */
-export interface OpHandler {
-  cast<T extends Tensor>(x: T, dtype: DataType): T;
-  buffer<R extends Rank, D extends DataType>(
-      shape: ShapeMap[R], dtype: D,
-      values?: DataTypeMap[D]): TensorBuffer<R, D>;
-  print<T extends Tensor>(x: T, verbose: boolean): void;
-  clone<T extends Tensor>(x: T): T;
-  // TODO(yassogba) bring reshape back?
-}
-
-// For tracking tensor creation and disposal.
-let trackerFn: () => TensorTracker = null;
-// Used by chaining methods to call into ops.
-let opHandler: OpHandler = null;
-// Used to warn about deprecated methods.
-let deprecationWarningFn: (msg: string) => void = null;
-// This here so that we can use this method on dev branches and keep the
-// functionality at master.
-// tslint:disable-next-line:no-unused-expression
-[deprecationWarningFn];
-
-/**
- * An external consumer can register itself as the tensor tracker. This way
- * the Tensor class can notify the tracker for every tensor created and
- * disposed.
- */
-export function setTensorTracker(fn: () => TensorTracker) {
-  trackerFn = fn;
-}
-
-/**
- * An external consumer can register itself as the op handler. This way the
- * Tensor class can have chaining methods that call into ops via the op
- * handler.
- */
-export function setOpHandler(handler: OpHandler) {
-  opHandler = handler;
-}
-
-/**
- * Sets the deprecation warning function to be used by this file. This way the
- * Tensor class can be a leaf but still use the environment.
- */
-export function setDeprecationWarningFn(fn: (msg: string) => void) {
-  deprecationWarningFn = fn;
-}
-
-// Declare this namespace to make Tensor class augmentation work in google3.
-export declare namespace Tensor {}
-/**
- * A `tf.Tensor` object represents an immutable, multidimensional array of
- * numbers that has a shape and a data type.
- *
- * For performance reasons, functions that create tensors do not necessarily
- * perform a copy of the data passed to them (e.g. if the data is passed as a
- * `Float32Array`), and changes to the data will change the tensor. This is not
- * a feature and is not supported. To avoid this behavior, use the tensor before
- * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.
- *
- * See `tf.tensor` for details on how to create a `tf.Tensor`.
- *
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-export class Tensor<R extends Rank = Rank> implements TensorInfo {
-  /** Unique id of this tensor. */
-  readonly id: number;
-  /**
-   * Id of the bucket holding the data for this tensor. Multiple arrays can
-   * point to the same bucket (e.g. when calling array.reshape()).
-   */
-  dataId: DataId;
-  /** The shape of the tensor. */
-  readonly shape: ShapeMap[R];
-  /** Number of elements in the tensor. */
-  readonly size: number;
-  /** The data type for the array. */
-  readonly dtype: DataType;
-  /** The rank type for the array (see `Rank` enum). */
-  readonly rankType: R;
-
-  /** Whether this tensor has been globally kept. */
-  kept = false;
-  /** The id of the scope this tensor is being tracked in. */
-  scopeId: number;
-  /** The keras mask that some keras layers attach to the tensor */
-  kerasMask?: Tensor;
-
-  /**
-   * Number of elements to skip in each dimension when indexing. See
-   * https://docs.scipy.org/doc/numpy/reference/generated/\
-   * numpy.ndarray.strides.html
-   */
-  readonly strides: number[];
-
-  constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number) {
-    this.shape = shape.slice() as ShapeMap[R];
-    this.dtype = dtype || 'float32';
-    this.size = util.sizeFromShape(shape);
-    this.strides = computeStrides(shape);
-    this.dataId = dataId;
-    this.id = id;
-    this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;
-  }
-
-  get rank(): number {
-    return this.shape.length;
-  }
-
-  /**
-   * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  async buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>> {
-    const vals = await this.data<D>();
-    return opHandler.buffer(this.shape, this.dtype as D, vals);
-  }
-
-  /**
-   * Returns a `tf.TensorBuffer` that holds the underlying data.
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D> {
-    return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());
-  }
-
-  /**
-   * Returns the tensor data as a nested array. The transfer of data is done
-   * asynchronously.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  async array(): Promise<ArrayMap[R]> {
-    const vals = await this.data();
-    return toNestedArray(this.shape, vals, this.dtype === 'complex64') as
-        ArrayMap[R];
-  }
-
-  /**
-   * Returns the tensor data as a nested array. The transfer of data is done
-   * synchronously.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  arraySync(): ArrayMap[R] {
-    return toNestedArray(
-               this.shape, this.dataSync(), this.dtype === 'complex64') as
-        ArrayMap[R];
-  }
-
-  /**
-   * Asynchronously downloads the values from the `tf.Tensor`. Returns a
-   * promise of `TypedArray` that resolves when the computation has finished.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  async data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]> {
-    this.throwIfDisposed();
-    const data = trackerFn().read(this.dataId);
-    if (this.dtype === 'string') {
-      const bytes = await data as Uint8Array[];
-      try {
-        return bytes.map(b => util.decodeString(b)) as DataTypeMap[D];
-      } catch {
-        throw new Error(
-            'Failed to decode the string bytes into utf-8. ' +
-            'To get the original bytes, call tensor.bytes().');
-      }
-    }
-    return data as Promise<DataTypeMap[D]>;
-  }
-
-  /**
-   * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`
-   * and `data()`, this method prevents data from being downloaded to CPU.
-   *
-   * For WebGL backend, the data will be stored on a densely packed texture.
-   * This means that the texture will use the RGBA channels to store value.
-   *
-   * For WebGPU backend, the data will be stored on a buffer. There is no
-   * parameter, so can not use a user-defined size to create the buffer.
-   *
-   * @param options:
-   *     For WebGL,
-   *         - customTexShape: Optional. If set, will use the user defined
-   *     texture shape to create the texture.
-   *
-   * @returns For WebGL backend, a GPUData contains the new texture and
-   *     its information.
-   *     {
-   *        tensorRef: The tensor that is associated with this texture,
-   *        texture: WebGLTexture,
-   *        texShape: [number, number] // [height, width]
-   *     }
-   *
-   *     For WebGPU backend, a GPUData contains the new buffer.
-   *     {
-   *        tensorRef: The tensor that is associated with this buffer,
-   *        buffer: GPUBuffer,
-   *     }
-   *
-   *     Remember to dispose the GPUData after it is used by
-   *     `res.tensorRef.dispose()`.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  dataToGPU(options?: DataToGPUOptions): GPUData {
-    this.throwIfDisposed();
-    return trackerFn().readToGPU(this.dataId, options);
-  }
-
-  /**
-   * Synchronously downloads the values from the `tf.Tensor`. This blocks the
-   * UI thread until the values are ready, which can cause performance issues.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D] {
-    this.throwIfDisposed();
-    const data = trackerFn().readSync(this.dataId);
-    if (this.dtype === 'string') {
-      try {
-        return (data as Uint8Array[]).map(b => util.decodeString(b)) as
-            DataTypeMap[D];
-      } catch {
-        throw new Error(
-            'Failed to decode the string bytes into utf-8. ' +
-            'To get the original bytes, call tensor.bytes().');
-      }
-    }
-    return data as DataTypeMap[D];
-  }
-
-  /** Returns the underlying bytes of the tensor's data. */
-  async bytes(): Promise<Uint8Array[]|Uint8Array> {
-    this.throwIfDisposed();
-    const data = await trackerFn().read(this.dataId);
-    if (this.dtype === 'string') {
-      return data as Uint8Array[];
-    } else {
-      return new Uint8Array((data as TypedArray).buffer);
-    }
-  }
-
-  /**
-   * Disposes `tf.Tensor` from memory.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  dispose(): void {
-    if (this.isDisposed) {
-      return;
-    }
-    if (this.kerasMask) {
-      this.kerasMask.dispose();
-    }
-    trackerFn().disposeTensor(this);
-    this.isDisposedInternal = true;
-  }
-
-  protected isDisposedInternal = false;
-  get isDisposed(): boolean {
-    return this.isDisposedInternal;
-  }
-
-  throwIfDisposed() {
-    if (this.isDisposed) {
-      throw new Error(`Tensor is disposed.`);
-    }
-  }
-
-  /**
-   * Prints the `tf.Tensor`. See `tf.print` for details.
-   *
-   * @param verbose Whether to print verbose information about the tensor,
-   *    including dtype and size.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  print(verbose = false): void {
-    return opHandler.print(this, verbose);
-  }
-
-  /**
-   * Returns a copy of the tensor. See `tf.clone` for details.
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  clone<T extends Tensor>(this: T): T {
-    this.throwIfDisposed();
-    return opHandler.clone(this);
-  }
-
-  /**
-   * Returns a human-readable description of the tensor. Useful for logging.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  toString(verbose = false): string {
-    const vals = this.dataSync();
-    return tensorToString(vals, this.shape, this.dtype, verbose);
-  }
-
-  cast<T extends this>(dtype: DataType): T {
-    this.throwIfDisposed();
-    return opHandler.cast(this as T, dtype);
-  }
-  variable(trainable = true, name?: string, dtype?: DataType): Variable<R> {
-    this.throwIfDisposed();
-    return trackerFn().makeVariable(this, trainable, name, dtype) as
-        Variable<R>;
-  }
-}
-
-Object.defineProperty(Tensor, Symbol.hasInstance, {
-  value: (instance: Tensor) => {
-    // Implementation note: we should use properties of the object that will be
-    // defined before the constructor body has finished executing (methods).
-    // This is because when this code is transpiled by babel, babel will call
-    // classCallCheck before the constructor body is run.
-    // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.
-    return !!instance && instance.data != null && instance.dataSync != null &&
-        instance.throwIfDisposed != null;
-  }
-});
-
-export function getGlobalTensorClass() {
-  // Use getGlobal so that we can augment the Tensor class across package
-  // boundaries becase the node resolution alg may result in different modules
-  // being returned for this file depending on the path they are loaded from.
-  return getGlobal('Tensor', () => {
-    return Tensor;
-  });
-}
-
-// Global side effect. Cache global reference to Tensor class
-getGlobalTensorClass();
-
-export interface NumericTensor<R extends Rank = Rank> extends Tensor<R> {
-  dtype: NumericDataType;
-  dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
-  data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
-  dataToGPU(options?: DataToGPUOptions): GPUData;
-}
-
-export interface StringTensor<R extends Rank = Rank> extends Tensor<R> {
-  dtype: 'string';
-  dataSync<D extends DataType = 'string'>(): DataTypeMap[D];
-  data<D extends DataType = 'string'>(): Promise<DataTypeMap[D]>;
-}
-
-/** @doclink Tensor */
-export type Scalar = Tensor<Rank.R0>;
-/** @doclink Tensor */
-export type Tensor1D = Tensor<Rank.R1>;
-/** @doclink Tensor */
-export type Tensor2D = Tensor<Rank.R2>;
-/** @doclink Tensor */
-export type Tensor3D = Tensor<Rank.R3>;
-/** @doclink Tensor */
-export type Tensor4D = Tensor<Rank.R4>;
-/** @doclink Tensor */
-export type Tensor5D = Tensor<Rank.R5>;
-/** @doclink Tensor */
-export type Tensor6D = Tensor<Rank.R6>;
-
-/**
- * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
- *
- * @doc {heading: 'Tensors', subheading: 'Classes'}
- */
-export class Variable<R extends Rank = Rank> extends Tensor<R> {
-  name: string;
-
-  constructor(
-      initialValue: Tensor<R>, public trainable: boolean, name: string,
-      tensorId: number) {
-    super(
-        initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
-    this.name = name;
-  }
-
-  /**
-   * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
-   * the same shape and dtype as the old `tf.Tensor`.
-   *
-   * @param newValue New tensor to be assigned to this variable.
-   *
-   * @doc {heading: 'Tensors', subheading: 'Classes'}
-   */
-  assign(newValue: Tensor<R>): void {
-    if (newValue.dtype !== this.dtype) {
-      throw new Error(
-          `dtype of the new value (${newValue.dtype}) and ` +
-          `previous value (${this.dtype}) must match`);
-    }
-    if (!util.arraysEqual(newValue.shape, this.shape)) {
-      throw new Error(
-          `shape of the new value (${newValue.shape}) and ` +
-          `previous value (${this.shape}) must match`);
-    }
-    trackerFn().disposeTensor(this);
-    this.dataId = newValue.dataId;
-    trackerFn().incRef(this, null /* backend */);
-  }
-
-  override dispose(): void {
-    trackerFn().disposeVariable(this);
-    this.isDisposedInternal = true;
-  }
-}
-
-Object.defineProperty(Variable, Symbol.hasInstance, {
-  value: (instance: Variable) => {
-    return instance instanceof Tensor && instance.assign != null &&
-        instance.assign instanceof Function;
-  }
-});
diff --git a/tfjs-master/tfjs-core/src/tensor_format.ts b/tfjs-master/tfjs-core/src/tensor_format.ts
deleted file mode 100644
index 79fc2f657..000000000
--- a/tfjs-master/tfjs-core/src/tensor_format.ts
+++ /dev/null
@@ -1,197 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, TypedArray} from './types';
-import {computeStrides, isString, rightPad, sizeFromShape} from './util';
-
-// Maximum number of values before we decide to show ellipsis.
-const FORMAT_LIMIT_NUM_VALS = 20;
-// Number of first and last values to show when displaying a, b,...,y, z.
-const FORMAT_NUM_FIRST_LAST_VALS = 3;
-// Number of significant digits to show.
-const FORMAT_NUM_SIG_DIGITS = 7;
-
-export function tensorToString(
-    vals: TypedArray|string[], shape: number[], dtype: DataType,
-    verbose: boolean) {
-  const strides = computeStrides(shape);
-  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
-  const rank = shape.length;
-  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
-  const lines = ['Tensor'];
-  if (verbose) {
-    lines.push(`  dtype: ${dtype}`);
-    lines.push(`  rank: ${rank}`);
-    lines.push(`  shape: [${shape}]`);
-    lines.push(`  values:`);
-  }
-  lines.push(valsLines.map(l => '    ' + l).join('\n'));
-  return lines.join('\n');
-}
-
-function computeMaxSizePerColumn(
-    vals: TypedArray|string[], shape: number[], dtype: DataType,
-    strides: number[]): number[] {
-  const n = sizeFromShape(shape);
-  const numCols = strides[strides.length - 1];
-  const padPerCol = new Array(numCols).fill(0);
-  const rank = shape.length;
-  const valuesOrTuples =
-      dtype === 'complex64' ? createComplexTuples(vals) : vals;
-
-  if (rank > 1) {
-    for (let row = 0; row < n / numCols; row++) {
-      const offset = row * numCols;
-      for (let j = 0; j < numCols; j++) {
-        padPerCol[j] = Math.max(
-            padPerCol[j],
-            valToString(valuesOrTuples[offset + j], 0, dtype).length);
-      }
-    }
-  }
-  return padPerCol;
-}
-
-function valToString(
-    val: number|string|[number, number], pad: number, dtype: DataType) {
-  let valStr: string;
-  if (Array.isArray(val)) {
-    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +
-        `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
-  } else if (isString(val)) {
-    valStr = `'${val}'`;
-  } else if (dtype === 'bool') {
-    valStr = boolNumToString(val);
-  } else {
-    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
-  }
-
-  return rightPad(valStr, pad);
-}
-
-function boolNumToString(v: number): string {
-  return v === 0 ? 'false' : 'true';
-}
-
-function subTensorToString(
-    vals: TypedArray|string[], shape: number[], dtype: DataType,
-    strides: number[], padPerCol: number[], isLast = true): string[] {
-  const storagePerElement = dtype === 'complex64' ? 2 : 1;
-
-  const size = shape[0];
-  const rank = shape.length;
-  if (rank === 0) {
-    if (dtype === 'complex64') {
-      const complexTuple = createComplexTuples(vals);
-      return [valToString(complexTuple[0], 0, dtype)];
-    }
-    if (dtype === 'bool') {
-      return [boolNumToString(vals[0] as number)];
-    }
-    return [vals[0].toString()];
-  }
-
-  if (rank === 1) {
-    if (size > FORMAT_LIMIT_NUM_VALS) {
-      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
-
-      let firstVals = Array.from<number|string|[number, number]>(
-          vals.slice(0, firstValsSize));
-      let lastVals = Array.from<number|string|[number, number]>(vals.slice(
-          (size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement,
-          size * storagePerElement));
-      if (dtype === 'complex64') {
-        firstVals = createComplexTuples(firstVals);
-        lastVals = createComplexTuples(lastVals);
-      }
-      return [
-        '[' +
-        firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))
-            .join(', ') +
-        ', ..., ' +
-        lastVals
-            .map(
-                (x, i) => valToString(
-                    x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))
-            .join(', ') +
-        ']'
-      ];
-    }
-    const displayVals: Array<number|string|[number, number]> =
-        dtype === 'complex64' ? createComplexTuples(vals) :
-                                Array.from<number|string>(vals);
-
-    return [
-      '[' +
-      displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))
-          .join(', ') +
-      ']'
-    ];
-  }
-
-  // The array is rank 2 or more.
-  const subshape = shape.slice(1);
-  const substrides = strides.slice(1);
-  const stride = strides[0] * storagePerElement;
-  const lines: string[] = [];
-  if (size > FORMAT_LIMIT_NUM_VALS) {
-    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(
-          vals.slice(start, end), subshape, dtype, substrides, padPerCol,
-          false /* isLast */));
-    }
-    lines.push('...');
-    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(
-          vals.slice(start, end), subshape, dtype, substrides, padPerCol,
-          i === size - 1 /* isLast */));
-    }
-  } else {
-    for (let i = 0; i < size; i++) {
-      const start = i * stride;
-      const end = start + stride;
-      lines.push(...subTensorToString(
-          vals.slice(start, end), subshape, dtype, substrides, padPerCol,
-          i === size - 1 /* isLast */));
-    }
-  }
-  const sep = rank === 2 ? ',' : '';
-  lines[0] = '[' + (size > 0 ? lines[0] + sep : '');
-  for (let i = 1; i < lines.length - 1; i++) {
-    lines[i] = ' ' + lines[i] + sep;
-  }
-  let newLineSep = ',\n';
-  for (let i = 2; i < rank; i++) {
-    newLineSep += '\n';
-  }
-  lines[lines.length - 1] =
-      ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);
-  return lines;
-}
-
-function createComplexTuples(vals: Array<{}>|
-                             TypedArray): Array<[number, number]> {
-  const complexTuples: Array<[number, number]> = [];
-  for (let i = 0; i < vals.length; i += 2) {
-    complexTuples.push([vals[i], vals[i + 1]] as [number, number]);
-  }
-  return complexTuples;
-}
diff --git a/tfjs-master/tfjs-core/src/tensor_info.ts b/tfjs-master/tfjs-core/src/tensor_info.ts
deleted file mode 100644
index 7ec529358..000000000
--- a/tfjs-master/tfjs-core/src/tensor_info.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType} from './types';
-
-/**
- * We wrap data id since we use weak map to avoid memory leaks.
- * Since we have our own memory management, we have a reference counter
- * mapping a tensor to its data, so there is always a pointer (even if that
- * data is otherwise garbage collectable).
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/
- * Global_Objects/WeakMap
- */
-export type DataId = object;  // object instead of {} to force non-primitive.
-
-/** Holds metadata for a given tensor. */
-export interface TensorInfo {
-  dataId: DataId;
-  shape: number[];
-  dtype: DataType;
-}
diff --git a/tfjs-master/tfjs-core/src/tensor_test.ts b/tfjs-master/tfjs-core/src/tensor_test.ts
deleted file mode 100644
index caa75247c..000000000
--- a/tfjs-master/tfjs-core/src/tensor_test.ts
+++ /dev/null
@@ -1,2368 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags, SYNC_BACKEND_ENVS} from './jasmine_util';
-import {tensor5d} from './ops/ops';
-import {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from './tensor';
-import {encodeStrings, expectArraysClose, expectArraysEqual, expectNumbersClose} from './test_util';
-import {Rank, TensorLike1D, TensorLike2D, TensorLike3D, TensorLike4D, TypedArray} from './types';
-import {encodeString} from './util';
-
-describeWithFlags('tensor', ALL_ENVS, () => {
-  it('Tensors of arbitrary size', async () => {
-    // [1, 2, 3]
-    let t: Tensor = tf.tensor1d([1, 2, 3]);
-    expect(t.rank).toBe(1);
-    expect(t.size).toBe(3);
-    expectArraysClose(await t.data(), [1, 2, 3]);
-
-    // [[1, 2, 3]]
-    t = tf.tensor2d([1, 2, 3], [1, 3]);
-    expect(t.rank).toBe(2);
-    expect(t.size).toBe(3);
-    expectArraysClose(await t.data(), [1, 2, 3]);
-
-    // [[1, 2, 3],
-    //  [4, 5, 6]]
-    t = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    expect(t.rank).toBe(2);
-    expect(t.size).toBe(6);
-
-    expectArraysClose(await t.data(), [1, 2, 3, 4, 5, 6]);
-
-    // Shape mismatch with the values.
-    expect(() => tf.tensor2d([1], [1, 2])).toThrowError();
-  });
-
-  it('Tensors of explicit size', async () => {
-    const t = tf.tensor1d([5, 3, 2]);
-    expect(t.rank).toBe(1);
-    expect(t.shape).toEqual([3]);
-
-    // tslint:disable-next-line:no-any
-    expect(() => tf.tensor3d([1, 2], [1, 2, 3, 5] as any)).toThrowError();
-
-    const t4 = tf.tensor4d([1, 2, 3, 4], [1, 2, 1, 2]);
-    expectArraysClose(await t4.data(), [1, 2, 3, 4]);
-
-    // Tensor of ones.
-    const x = tf.ones<Rank.R3>([3, 4, 2]);
-    expect(x.rank).toBe(3);
-    expect(x.size).toBe(24);
-    expectArraysClose(await x.data(), [
-      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
-    ]);
-
-    // Tensor of zeros.
-    const z = tf.zeros<Rank.R3>([3, 4, 2]);
-    expect(z.rank).toBe(3);
-    expect(z.size).toBe(24);
-    expectArraysClose(await z.data(), [
-      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    ]);
-  });
-
-  it('Tensor dataSync CPU --> GPU', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    expectArraysClose(await a.data(), new Float32Array([1, 2, 3, 4, 5, 6]));
-  });
-
-  it('Tensor.data() CPU --> GPU', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    expectArraysClose(await a.data(), new Float32Array([1, 2, 3, 4, 5, 6]));
-  });
-
-  it('Tensor.data() packed CPU --> GPU', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    tf.matMul(a, tf.tensor2d([1, 2], [2, 1]));
-    expectArraysClose(await a.data(), new Float32Array([1, 2, 3, 4, 5, 6]));
-  });
-
-  it('Scalar basic methods', async () => {
-    const a = tf.scalar(5);
-    expectArraysClose(await a.data(), [5]);
-    expect(a.rank).toBe(0);
-    expect(a.size).toBe(1);
-    expect(a.shape).toEqual([]);
-  });
-
-  it('indexToLoc Scalar', async () => {
-    const a = await tf.scalar(0).buffer();
-    expect(a.indexToLoc(0)).toEqual([]);
-
-    const b = await tf.zeros<Rank.R0>([]).buffer();
-    expect(b.indexToLoc(0)).toEqual([]);
-  });
-
-  it('indexToLoc Tensor1D', async () => {
-    const a = await tf.zeros([3]).buffer();
-    expect(a.indexToLoc(0)).toEqual([0]);
-    expect(a.indexToLoc(1)).toEqual([1]);
-    expect(a.indexToLoc(2)).toEqual([2]);
-
-    const b = await tf.zeros<Rank.R1>([3]).buffer();
-    expect(b.indexToLoc(0)).toEqual([0]);
-    expect(b.indexToLoc(1)).toEqual([1]);
-    expect(b.indexToLoc(2)).toEqual([2]);
-  });
-
-  it('indexToLoc Tensor2D', async () => {
-    const a = await tf.zeros([3, 2]).buffer();
-    expect(a.indexToLoc(0)).toEqual([0, 0]);
-    expect(a.indexToLoc(1)).toEqual([0, 1]);
-    expect(a.indexToLoc(2)).toEqual([1, 0]);
-    expect(a.indexToLoc(3)).toEqual([1, 1]);
-    expect(a.indexToLoc(4)).toEqual([2, 0]);
-    expect(a.indexToLoc(5)).toEqual([2, 1]);
-
-    const b = await tf.zeros<Rank.R2>([3, 2]).buffer();
-    expect(b.indexToLoc(0)).toEqual([0, 0]);
-    expect(b.indexToLoc(1)).toEqual([0, 1]);
-    expect(b.indexToLoc(2)).toEqual([1, 0]);
-    expect(b.indexToLoc(3)).toEqual([1, 1]);
-    expect(b.indexToLoc(4)).toEqual([2, 0]);
-    expect(b.indexToLoc(5)).toEqual([2, 1]);
-  });
-
-  it('indexToLoc Tensor3D', async () => {
-    const a = await tf.zeros([3, 2, 2]).buffer();
-    expect(a.indexToLoc(0)).toEqual([0, 0, 0]);
-    expect(a.indexToLoc(1)).toEqual([0, 0, 1]);
-    expect(a.indexToLoc(2)).toEqual([0, 1, 0]);
-    expect(a.indexToLoc(3)).toEqual([0, 1, 1]);
-    expect(a.indexToLoc(4)).toEqual([1, 0, 0]);
-    expect(a.indexToLoc(5)).toEqual([1, 0, 1]);
-    expect(a.indexToLoc(11)).toEqual([2, 1, 1]);
-
-    const b = await tf.zeros<Rank.R3>([3, 2, 2]).buffer();
-    expect(b.indexToLoc(0)).toEqual([0, 0, 0]);
-    expect(b.indexToLoc(1)).toEqual([0, 0, 1]);
-    expect(b.indexToLoc(2)).toEqual([0, 1, 0]);
-    expect(b.indexToLoc(3)).toEqual([0, 1, 1]);
-    expect(b.indexToLoc(4)).toEqual([1, 0, 0]);
-    expect(b.indexToLoc(5)).toEqual([1, 0, 1]);
-    expect(b.indexToLoc(11)).toEqual([2, 1, 1]);
-  });
-
-  it('indexToLoc Tensor 5D', async () => {
-    const values = new Float32Array([1, 2, 3, 4]);
-    const a = await tensor5d(values, [2, 1, 1, 1, 2]).buffer();
-    expect(a.indexToLoc(0)).toEqual([0, 0, 0, 0, 0]);
-    expect(a.indexToLoc(1)).toEqual([0, 0, 0, 0, 1]);
-    expect(a.indexToLoc(2)).toEqual([1, 0, 0, 0, 0]);
-    expect(a.indexToLoc(3)).toEqual([1, 0, 0, 0, 1]);
-  });
-
-  it('locToIndex Scalar', async () => {
-    const a = await tf.scalar(0).buffer();
-    expect(a.locToIndex([])).toEqual(0);
-
-    const b = await tf.zeros<Rank.R0>([]).buffer();
-    expect(b.locToIndex([])).toEqual(0);
-  });
-
-  it('locToIndex Tensor1D', async () => {
-    const a = await tf.zeros<Rank.R1>([3]).buffer();
-    expect(a.locToIndex([0])).toEqual(0);
-    expect(a.locToIndex([1])).toEqual(1);
-    expect(a.locToIndex([2])).toEqual(2);
-
-    const b = await tf.zeros<Rank.R1>([3]).buffer();
-    expect(b.locToIndex([0])).toEqual(0);
-    expect(b.locToIndex([1])).toEqual(1);
-    expect(b.locToIndex([2])).toEqual(2);
-  });
-
-  it('locToIndex Tensor2D', async () => {
-    const a = await tf.zeros<Rank.R2>([3, 2]).buffer();
-    expect(a.locToIndex([0, 0])).toEqual(0);
-    expect(a.locToIndex([0, 1])).toEqual(1);
-    expect(a.locToIndex([1, 0])).toEqual(2);
-    expect(a.locToIndex([1, 1])).toEqual(3);
-    expect(a.locToIndex([2, 0])).toEqual(4);
-    expect(a.locToIndex([2, 1])).toEqual(5);
-
-    const b = await tf.zeros<Rank.R2>([3, 2]).buffer();
-    expect(b.locToIndex([0, 0])).toEqual(0);
-    expect(b.locToIndex([0, 1])).toEqual(1);
-    expect(b.locToIndex([1, 0])).toEqual(2);
-    expect(b.locToIndex([1, 1])).toEqual(3);
-    expect(b.locToIndex([2, 0])).toEqual(4);
-    expect(b.locToIndex([2, 1])).toEqual(5);
-  });
-
-  it('locToIndex Tensor3D', async () => {
-    const a = await tf.zeros<Rank.R3>([3, 2, 2]).buffer();
-    expect(a.locToIndex([0, 0, 0])).toEqual(0);
-    expect(a.locToIndex([0, 0, 1])).toEqual(1);
-    expect(a.locToIndex([0, 1, 0])).toEqual(2);
-    expect(a.locToIndex([0, 1, 1])).toEqual(3);
-    expect(a.locToIndex([1, 0, 0])).toEqual(4);
-    expect(a.locToIndex([1, 0, 1])).toEqual(5);
-    expect(a.locToIndex([2, 1, 1])).toEqual(11);
-
-    const b = await tf.zeros<Rank.R3>([3, 2, 2]).buffer();
-    expect(b.locToIndex([0, 0, 0])).toEqual(0);
-    expect(b.locToIndex([0, 0, 1])).toEqual(1);
-    expect(b.locToIndex([0, 1, 0])).toEqual(2);
-    expect(b.locToIndex([0, 1, 1])).toEqual(3);
-    expect(b.locToIndex([1, 0, 0])).toEqual(4);
-    expect(b.locToIndex([1, 0, 1])).toEqual(5);
-    expect(b.locToIndex([2, 1, 1])).toEqual(11);
-  });
-
-  it('Tensor assignability (asserts compiler)', () => {
-    // This test asserts compilation, not doing any run-time assertion.
-    const a: Tensor<Rank.R0> = null;
-    const b: Scalar = a;
-    expect(b).toBeNull();
-
-    const a1: Tensor<Rank.R1> = null;
-    const b1: Tensor1D = a1;
-    expect(b1).toBeNull();
-
-    const a2: Tensor<Rank.R2> = null;
-    const b2: Tensor2D = a2;
-    expect(b2).toBeNull();
-
-    const a3: Tensor<Rank.R3> = null;
-    const b3: Tensor3D = a3;
-    expect(b3).toBeNull();
-
-    const a4: Tensor<Rank.R4> = null;
-    const b4: Tensor4D = a4;
-    expect(b4).toBeNull();
-  });
-
-  it('tf.tensor1d() from number[]', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    expectArraysClose(await a.data(), [1, 2, 3]);
-  });
-
-  it('tf.tensor1d() throw error with null input value', () => {
-    expect(() => tf.tensor1d(null))
-        .toThrowError(
-            'The input to the tensor constructor ' +
-            'must be a non-null value.');
-  });
-
-  it('tf.tensor1d() from string[]', async () => {
-    const a = tf.tensor1d(['aa', 'bb', 'cc']);
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), ['aa', 'bb', 'cc']);
-  });
-
-  it('tf.tensor1d() from encoded strings', async () => {
-    const bytes = encodeStrings(['aa', 'bb', 'cc']) as TensorLike1D;
-    const a = tf.tensor1d(bytes, 'string');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), ['aa', 'bb', 'cc']);
-  });
-
-  it('tf.tensor1d() from encoded strings without dtype errors', async () => {
-    // We do not want to infer 'string' when the user passes Uint8Array in order
-    // to be forward compatible in the future when we add uint8 dtype.
-    const bytes = encodeStrings(['aa', 'bb', 'cc']) as TensorLike1D;
-    expect(() => tf.tensor1d(bytes)).toThrowError();
-  });
-
-  it('tf.tensor1d() from encoded strings, shape mismatch', () => {
-    const bytes = encodeStrings([['aa'], ['bb'], ['cc']]) as TensorLike1D;
-    expect(() => tf.tensor1d(bytes)).toThrowError();
-  });
-
-  it('tf.tensor1d() from number[][], shape mismatch', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tf.tensor1d([[1], [2], [3]] as any)).toThrowError();
-  });
-
-  it('tf.tensor1d() from string[][], shape mismatch', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tf.tensor1d([['a'], ['b'], ['c']] as any)).toThrowError();
-  });
-
-  it('tf.tensor2d() from number[][]', async () => {
-    const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('tf.tensor2d() from string[][]', async () => {
-    const a = tf.tensor2d([['aa', 'bb'], ['cc', 'dd']]);
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([2, 2]);
-    expectArraysEqual(await a.data(), ['aa', 'bb', 'cc', 'dd']);
-  });
-
-  it('tf.tensor2d() from encoded strings', async () => {
-    const bytes = encodeStrings([['aa', 'bb'], ['cc', 'dd']]) as TensorLike2D;
-    const a = tf.tensor2d(bytes, [2, 2], 'string');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([2, 2]);
-    expectArraysEqual(await a.data(), ['aa', 'bb', 'cc', 'dd']);
-  });
-
-  it('tf.tensor2d() from encoded strings without dtype errors', async () => {
-    // We do not want to infer 'string' when the user passes Uint8Array in order
-    // to be forward compatible in the future when we add uint8 dtype.
-    const bytes = encodeStrings([['aa', 'bb'], ['cc', 'dd']]) as TensorLike2D;
-    expect(() => tf.tensor2d(bytes)).toThrowError();
-  });
-
-  it('tf.tensor2d() from encoded strings, shape mismatch', () => {
-    const bytes = encodeStrings([['aa', 'bb'], ['cc', 'dd']]) as TensorLike2D;
-    expect(() => tf.tensor2d(bytes, [3, 2], 'string')).toThrowError();
-  });
-
-  it('tf.tensor2d() requires shape to be of length 2', () => {
-    // tslint:disable-next-line:no-any
-    const shape: any = [4];
-    expect(() => tf.tensor2d([1, 2, 3, 4], shape)).toThrowError();
-  });
-
-  it('tf.tensor2d() from number[][], but shape does not match', () => {
-    // Actual shape is [2, 3].
-    expect(() => tf.tensor2d([[1, 2, 3], [4, 5, 6]], [3, 2])).toThrowError();
-  });
-
-  it('tf.tensor2d() from string[][], but shape does not match', () => {
-    // Actual shape is [2, 3].
-    const vals = [['a', 'b', 'c'], ['d', 'e', 'f']];
-    expect(() => tf.tensor2d(vals, [3, 2])).toThrowError();
-  });
-
-  it('tf.tensor2d() from number[], but no shape throws error', () => {
-    expect(() => tf.tensor2d([1, 2, 3, 4])).toThrowError();
-  });
-
-  it('tf.tensor2d() from string[], but no shape throws error', () => {
-    expect(() => tf.tensor2d(['a', 'b', 'c', 'd'])).toThrowError();
-  });
-
-  it('tf.tensor2d() throw error with null input value', () => {
-    expect(() => tf.tensor2d(null))
-        .toThrowError(
-            'The input to the tensor constructor ' +
-            'must be a non-null value.');
-  });
-
-  it('tensor3d() from number[][][]', async () => {
-    const a = tf.tensor3d([[[1], [2], [3]], [[4], [5], [6]]], [2, 3, 1]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('tensor3d() from string[][][]', async () => {
-    const vals = [[['a'], ['b'], ['c']], [['d'], ['e'], ['f']]];
-    const a = tf.tensor3d(vals, [2, 3, 1]);
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([2, 3, 1]);
-    expectArraysEqual(await a.data(), ['a', 'b', 'c', 'd', 'e', 'f']);
-  });
-
-  it('tf.tensor3d() from encoded strings', async () => {
-    const bytes = encodeStrings([[['a'], ['b'], ['c']], [['d'], ['e'], ['f']]]);
-    const a = tf.tensor3d(bytes as TensorLike3D, [2, 3, 1], 'string');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([2, 3, 1]);
-    expectArraysEqual(await a.data(), ['a', 'b', 'c', 'd', 'e', 'f']);
-  });
-
-  it('tf.tensor3d() from encoded strings without dtype errors', async () => {
-    // We do not want to infer 'string' when the user passes Uint8Array in order
-    // to be forward compatible in the future when we add uint8 dtype.
-    const bytes = encodeStrings([[['a'], ['b'], ['c']], [['d'], ['e'], ['f']]]);
-    expect(() => tf.tensor3d(bytes as TensorLike3D)).toThrowError();
-  });
-
-  it('tf.tensor3d() from encoded strings, shape mismatch', () => {
-    const bytes = encodeStrings([[['a'], ['b'], ['c']], [['d'], ['e'], ['f']]]);
-    // Actual shape is [2, 3, 1].
-    expect(() => tf.tensor3d(bytes as TensorLike3D, [3, 2, 1], 'string'))
-        .toThrowError();
-  });
-
-  it('tensor3d() from number[][][], but shape does not match', () => {
-    const values = [[[1], [2], [3]], [[4], [5], [6]]];
-    // Actual shape is [2, 3, 1].
-    expect(() => tf.tensor3d(values, [3, 2, 1])).toThrowError();
-  });
-
-  it('tf.tensor3d() from number[], but no shape throws error', () => {
-    expect(() => tf.tensor3d([1, 2, 3, 4])).toThrowError();
-  });
-
-  it('tf.tensor3d() requires shape to be of length 3', () => {
-    // tslint:disable-next-line:no-any
-    const shape: any = [4, 1];
-    expect(() => tf.tensor3d([1, 2, 3, 4], shape)).toThrowError();
-  });
-
-  it('tf.tensor3d() throw error with null input value', () => {
-    expect(() => tf.tensor3d(null))
-        .toThrowError(
-            'The input to the tensor constructor ' +
-            'must be a non-null value.');
-  });
-
-  it('tensor4d() from number[][][][]', async () => {
-    const a = tf.tensor4d([[[[1]], [[2]]], [[[4]], [[5]]]], [2, 2, 1, 1]);
-    expectArraysClose(await a.data(), [1, 2, 4, 5]);
-  });
-
-  it('tensor4d() from string[][][][]', async () => {
-    const vals = [[[['a']], [['b']]], [[['c']], [['d']]]];
-    const a = tf.tensor4d(vals, [2, 2, 1, 1]);
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await a.data(), ['a', 'b', 'c', 'd']);
-  });
-
-  it('tf.tensor4d() from encoded strings', async () => {
-    const bytes = encodeStrings([[[['a']], [['b']]], [[['c']], [['d']]]]);
-    const a = tf.tensor4d(bytes as TensorLike4D, [2, 2, 1, 1], 'string');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await a.data(), ['a', 'b', 'c', 'd']);
-  });
-
-  it('tf.tensor4d() from encoded strings without dtype errors', async () => {
-    // We do not want to infer 'string' when the user passes Uint8Array in order
-    // to be forward compatible in the future when we add uint8 dtype.
-    const bytes = encodeStrings([[[['a']], [['b']]], [[['c']], [['d']]]]);
-    expect(() => tf.tensor4d(bytes as TensorLike4D)).toThrowError();
-  });
-
-  it('tf.tensor4d() from encoded strings, shape mismatch', () => {
-    const bytes = encodeStrings([[[['a']], [['b']]], [[['c']], [['d']]]]);
-    // Actual shape is [2, 2, 1. 1].
-    expect(() => tf.tensor4d(bytes as TensorLike4D, [2, 1, 2, 1], 'string'))
-        .toThrowError();
-  });
-
-  it('tensor4d() from string[][][][] infer shape', async () => {
-    const vals = [[[['a']], [['b']]], [[['c']], [['d']]]];
-    const a = tf.tensor4d(vals);
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await a.data(), ['a', 'b', 'c', 'd']);
-  });
-
-  it('tensor4d() from number[][][][], but shape does not match', () => {
-    const f = () => {
-      // Actual shape is [2, 2, 1, 1].
-      tf.tensor4d([[[[1]], [[2]]], [[[4]], [[5]]]], [2, 1, 2, 1]);
-    };
-    expect(f).toThrowError();
-  });
-
-  it('tf.tensor4d() from number[], but no shape throws error', () => {
-    expect(() => tf.tensor4d([1, 2, 3, 4])).toThrowError();
-  });
-
-  it('tf.tensor4d() requires shape to be of length 4', () => {
-    // tslint:disable-next-line:no-any
-    const shape: any = [4, 1];
-    expect(() => tf.tensor4d([1, 2, 3, 4], shape)).toThrowError();
-  });
-
-  it('tf.tensor4d() throw error with null input value', () => {
-    expect(() => tf.tensor4d(null))
-        .toThrowError(
-            'The input to the tensor constructor ' +
-            'must be a non-null value.');
-  });
-
-  it('tf.tensor5d() throw error with null input value', () => {
-    expect(() => tf.tensor5d(null))
-        .toThrowError(
-            'The input to the tensor constructor ' +
-            'must be a non-null value.');
-  });
-
-  it('tf.tensor6d() throw error with null input value', () => {
-    expect(() => tf.tensor6d(null))
-        .toThrowError(
-            'The input to the tensor constructor ' +
-            'must be a non-null value.');
-  });
-
-  it('default dtype', async () => {
-    const a = tf.scalar(3);
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), 3);
-  });
-
-  it('float32 dtype', async () => {
-    const a = tf.scalar(3, 'float32');
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), 3);
-  });
-
-  it('int32 dtype', async () => {
-    const a = tf.scalar(3, 'int32');
-    expect(a.dtype).toBe('int32');
-    expectArraysEqual(await a.data(), 3);
-  });
-
-  it('int32 dtype, 3.9 => 3, like numpy', async () => {
-    const a = tf.scalar(3.9, 'int32');
-    expect(a.dtype).toBe('int32');
-    expectArraysEqual(await a.data(), 3);
-  });
-
-  it('int32 dtype, -3.9 => -3, like numpy', async () => {
-    const a = tf.scalar(-3.9, 'int32');
-    expect(a.dtype).toBe('int32');
-    expectArraysEqual(await a.data(), -3);
-  });
-
-  it('bool dtype, 3 => true, like numpy', async () => {
-    const a = tf.scalar(3, 'bool');
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), 1);
-  });
-
-  it('bool dtype, -2 => true, like numpy', async () => {
-    const a = tf.scalar(-2, 'bool');
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), 1);
-  });
-
-  it('bool dtype, 0 => false, like numpy', async () => {
-    const a = tf.scalar(0, 'bool');
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), 0);
-  });
-
-  it('bool dtype from boolean', async () => {
-    const a = tf.scalar(false, 'bool');
-    expectArraysEqual(await a.data(), 0);
-    expect(a.dtype).toBe('bool');
-
-    const b = tf.scalar(true, 'bool');
-    expectArraysEqual(await a.data(), 0);
-    expect(b.dtype).toBe('bool');
-  });
-
-  it('int32 dtype from boolean', async () => {
-    const a = tf.scalar(true, 'int32');
-    expectArraysEqual(await a.data(), 1);
-    expect(a.dtype).toBe('int32');
-  });
-
-  it('default dtype from boolean', async () => {
-    const a = tf.scalar(false);
-    expectArraysEqual(await a.data(), 0);
-    expect(a.dtype).toBe('bool');
-  });
-
-  it('default dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [1, 2, 3]);
-  });
-
-  it('float32 dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [1, 2, 3]);
-  });
-
-  it('int32 dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), [1, 2, 3]);
-  });
-
-  it('int32 dtype, non-ints get floored, like numpy', async () => {
-    const a = tf.tensor1d([1.1, 2.5, 3.9], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), [1, 2, 3]);
-  });
-
-  it('int32 dtype, negative non-ints get ceiled, like numpy', async () => {
-    const a = tf.tensor1d([-1.1, -2.5, -3.9], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), [-1, -2, -3]);
-  });
-
-  it('bool dtype, !=0 is truthy, 0 is falsy, like numpy', async () => {
-    const a = tf.tensor1d([1, -2, 0, 3], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([4]);
-    expectArraysEqual(await a.data(), [1, 1, 0, 1]);
-  });
-
-  it('default dtype from boolean[]', async () => {
-    const a = tf.tensor1d([false, false, true]);
-    expect(a.dtype).toBe('bool');
-    expectArraysClose(await a.data(), [0, 0, 1]);
-  });
-
-  it('default dtype from UInt8Array', async () => {
-    const a = tf.tensor1d(new Uint8Array([1, 5, 2]));
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [1, 5, 2]);
-  });
-
-  it('default dtype from Int32Array', async () => {
-    const a = tf.tensor1d(new Int32Array([1, 5, 2]));
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [1, 5, 2]);
-  });
-
-  it('tf.tensor() from Float32Array and number[]', async () => {
-    const a = tf.tensor([
-      new Float32Array([1, 2]), new Float32Array([3, 4]),
-      new Float32Array([5, 6]), [7, 8]
-    ]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([4, 2]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('tf.tensor() from Int32Array and number[]', async () => {
-    const a = tf.tensor([
-      new Int32Array([1, 2]), new Int32Array([3, 4]), new Int32Array([5, 6]),
-      [7, 8]
-    ]);
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([4, 2]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('tf.tensor() from mixed TypedArray', async () => {
-    const a = tf.tensor([
-      new Float32Array([1, 2]), new Int32Array([3, 4]), new Uint8Array([5, 6]),
-      [7, 8]
-    ]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([4, 2]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4, 5, 6, 7, 8]);
-  });
-
-  it('tf.tensor() from TypedArrays which are themselves 3D', () => {
-    // 2 tensors, each with shape 20x20x3, as flat Float32Arrays.
-    const img1 = new Float32Array(20 * 20 * 3);
-    const img2 = new Float32Array(20 * 20 * 3);
-    const t = tf.tensor([img1, img2], [2, 20, 20, 3]);
-    expect(t.dtype).toBe('float32');
-    expect(t.shape).toEqual([2, 20, 20, 3]);
-  });
-
-  it('tf.tensor() from TypedArrays which are themselves 3D, wrong shape',
-     () => {
-       const img1 = new Float32Array(20 * 20 * 3);
-       const img2 = new Float32Array(20 * 20 * 3);
-       expect(() => tf.tensor([img1, img2], [3, 20, 20, 3])).toThrowError();
-     });
-
-  it('default dtype from ascii string', async () => {
-    const a = tf.tensor('hello');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([]);
-    expectArraysEqual(await a.data(), ['hello']);
-  });
-
-  it('default dtype from utf-8 string', async () => {
-    const a = tf.tensor('даниел');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([]);
-    expectArraysEqual(await a.data(), ['даниел']);
-  });
-
-  it('default dtype from empty string', async () => {
-    const a = tf.tensor('');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([]);
-    expectArraysEqual(await a.data(), ['']);
-  });
-
-  it('default dtype from unicode escaped string', async () => {
-    const a = tf.tensor('\u0434\u0430\u043d\u0438\u0435\u043b');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([]);
-    expectArraysEqual(await a.data(), ['даниел']);
-  });
-
-  it('default dtype from string[]', async () => {
-    const a = tf.tensor(['a', 'b']);
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([2]);
-    expectArraysEqual(await a.data(), ['a', 'b']);
-  });
-
-  it('float32 dtype from boolean[]', async () => {
-    const a = tf.tensor1d([false, false, true], 'float32');
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), [0, 0, 1]);
-  });
-
-  it('int32 dtype from boolean[]', async () => {
-    const a = tf.tensor1d([false, false, true], 'int32');
-    expect(a.dtype).toBe('int32');
-    expectArraysEqual(await a.data(), [0, 0, 1]);
-  });
-
-  it('bool dtype from boolean[]', async () => {
-    const a = tf.tensor1d([false, false, true], 'bool');
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), [0, 0, 1]);
-  });
-
-  it('default dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('float32 dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('int32 dtype', async () => {
-    const a = tf.tensor2d([[1, 2], [3, 4]], [2, 2], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2]);
-    expectArraysEqual(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('int32 dtype, non-ints get floored, like numpy', async () => {
-    const a = tf.tensor2d([1.1, 2.5, 3.9, 4.0], [2, 2], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2]);
-    expectArraysEqual(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('int32 dtype, negative non-ints get ceiled, like numpy', async () => {
-    const a = tf.tensor2d([-1.1, -2.5, -3.9, -4.0], [2, 2], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2]);
-    expectArraysEqual(await a.data(), [-1, -2, -3, -4]);
-  });
-
-  it('bool dtype, !=0 is truthy, 0 is falsy, like numpy', async () => {
-    const a = tf.tensor2d([1, -2, 0, 3], [2, 2], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([2, 2]);
-    expectArraysEqual(await a.data(), [1, 1, 0, 1]);
-  });
-
-  it('default dtype from boolean[]', async () => {
-    const a = tf.tensor2d([[false, false], [true, false]], [2, 2]);
-    expect(a.dtype).toBe('bool');
-    expectArraysClose(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('float32 dtype from boolean[]', async () => {
-    const a = tf.tensor2d([[false, false], [true, false]], [2, 2], 'float32');
-    expect(a.dtype).toBe('float32');
-    expectArraysEqual(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('int32 dtype from boolean[]', async () => {
-    const a = tf.tensor2d([[false, false], [true, false]], [2, 2], 'int32');
-    expect(a.dtype).toBe('int32');
-    expectArraysEqual(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('bool dtype from boolean[]', async () => {
-    const a = tf.tensor2d([[false, false], [true, false]], [2, 2], 'bool');
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('default dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('float32 dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2, 1]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('int32 dtype', async () => {
-    const a = tf.tensor3d([[[1], [2]], [[3], [4]]], [2, 2, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('int32 dtype, non-ints get floored, like numpy', async () => {
-    const a = tf.tensor3d([1.1, 2.5, 3.9, 4.0], [2, 2, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('int32 dtype, negative non-ints get ceiled, like numpy', async () => {
-    const a = tf.tensor3d([-1.1, -2.5, -3.9, -4.0], [2, 2, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await a.data(), [-1, -2, -3, -4]);
-  });
-
-  it('bool dtype, !=0 is truthy, 0 is falsy, like numpy', async () => {
-    const a = tf.tensor3d([1, -2, 0, 3], [2, 2, 1], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([2, 2, 1]);
-    expectArraysEqual(await a.data(), [1, 1, 0, 1]);
-  });
-
-  it('default dtype from boolean[]', async () => {
-    const a = tf.tensor3d([[[false], [false]], [[true], [false]]], [2, 2, 1]);
-    expect(a.dtype).toBe('bool');
-    expectArraysClose(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('float32 dtype from boolean[]', async () => {
-    const a = tf.tensor3d(
-        [[[false], [false]], [[true], [false]]], [2, 2, 1], 'float32');
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('int32 dtype from boolean[]', async () => {
-    const a = tf.tensor3d(
-        [[[false], [false]], [[true], [false]]], [2, 2, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expectArraysEqual(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('bool dtype from boolean[]', async () => {
-    const a =
-        tf.tensor3d([[[false], [false]], [[true], [false]]], [2, 2, 1], 'bool');
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('default dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('float32 dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expectArraysClose(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('int32 dtype', async () => {
-    const a =
-        tf.tensor4d([[[[1]], [[2]]], [[[3]], [[4]]]], [2, 2, 1, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('int32 dtype, non-ints get floored, like numpy', async () => {
-    const a = tf.tensor4d([1.1, 2.5, 3.9, 4.0], [2, 2, 1, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('int32 dtype, negative non-ints get ceiled, like numpy', async () => {
-    const a = tf.tensor4d([-1.1, -2.5, -3.9, -4.0], [2, 2, 1, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await a.data(), [-1, -2, -3, -4]);
-  });
-
-  it('bool dtype, !=0 is truthy, 0 is falsy, like numpy', async () => {
-    const a = tf.tensor4d([1, -2, 0, 3], [2, 2, 1, 1], 'bool');
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expectArraysEqual(await a.data(), [1, 1, 0, 1]);
-  });
-
-  it('default dtype from boolean[]', async () => {
-    const a =
-        tf.tensor4d([[[[false], [false]], [[true], [false]]]], [1, 2, 2, 1]);
-    expect(a.dtype).toBe('bool');
-    expectArraysClose(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('float32 dtype from boolean[]', async () => {
-    const a = tf.tensor4d(
-        [[[[false], [false]], [[true], [false]]]], [1, 2, 2, 1], 'float32');
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('int32 dtype from boolean[]', async () => {
-    const a = tf.tensor4d(
-        [[[[false], [false]], [[true], [false]]]], [1, 2, 2, 1], 'int32');
-    expect(a.dtype).toBe('int32');
-    expectArraysEqual(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('bool dtype from boolean[]', async () => {
-    const a = tf.tensor4d(
-        [[[[false], [false]], [[true], [false]]]], [1, 2, 2, 1], 'bool');
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), [0, 0, 1, 0]);
-  });
-
-  it('Scalar default dtype', async () => {
-    const a = tf.scalar(4);
-    const b = a.reshape([1, 1]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([1, 1]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Scalar float32 dtype', () => {
-    const a = tf.scalar(4, 'float32');
-    const b = a.reshape([1, 1]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([1, 1]);
-  });
-
-  it('Scalar string dtype', () => {
-    const a = tf.scalar('test', 'string');
-    const b = a.reshape([1, 1]);
-    expect(b.dtype).toBe('string');
-    expect(b.shape).toEqual([1, 1]);
-  });
-
-  it('scalar from encoded string', async () => {
-    const a = tf.scalar(encodeString('hello'), 'string');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([]);
-    expectArraysEqual(await a.data(), ['hello']);
-  });
-
-  it('scalar from encoded string, but missing dtype', async () => {
-    // We do not want to infer 'string' when the user passes Uint8Array in order
-    // to be forward compatible in the future when we add uint8 dtype.
-    expect(() => tf.scalar(encodeString('hello'))).toThrowError();
-  });
-
-  it('scalar from encoded string, but value is not uint8array', async () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tf.scalar(new Float32Array([1, 2, 3]) as any)).toThrowError();
-  });
-
-  it('Scalar inferred dtype from bool', async () => {
-    const a = tf.scalar(true);
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([]);
-    expectArraysClose(await a.data(), [1]);
-  });
-
-  it('Scalar inferred dtype from string', async () => {
-    const a = tf.scalar('hello');
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([]);
-    expectArraysEqual(await a.data(), ['hello']);
-  });
-
-  it('Scalar int32 dtype', () => {
-    const a = tf.scalar(4, 'int32');
-    const b = a.reshape([1, 1]);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([1, 1]);
-  });
-
-  it('Scalar bool dtype', async () => {
-    const a = tf.scalar(4, 'bool');
-    const b = a.reshape([1, 1, 1]);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([1, 1, 1]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Scalar complex64 dtype', async () => {
-    const a = tf.complex(4, 5);
-    const b = a.reshape([1, 1]);
-    expectArraysClose(await a.data(), [4, 5]);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([1, 1]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor1D default dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3, 4]);
-    const b = a.reshape([2, 2]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor1D inferred dtype from bools', async () => {
-    const a = tf.tensor1d([true, false, false, true]);
-    expect(a.dtype).toBe('bool');
-    expect(a.shape).toEqual([4]);
-    expectArraysClose(await a.data(), [1, 0, 0, 1]);
-  });
-
-  it('Tensor1D inferred dtype from strings', async () => {
-    const a = tf.tensor1d(['a', 'b', 'c']);
-    expect(a.dtype).toBe('string');
-    expect(a.shape).toEqual([3]);
-    expectArraysEqual(await a.data(), ['a', 'b', 'c']);
-  });
-
-  it('Tensor1D float32 dtype', () => {
-    const a = tf.tensor1d([1, 2, 3, 4], 'float32');
-    const b = a.reshape([2, 2]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2]);
-  });
-
-  it('Tensor1D int32 dtype', async () => {
-    const a = tf.tensor1d([1, 2, 3, 4], 'int32');
-    const b = a.reshape([2, 2]);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor1D complex64 dtype', async () => {
-    const a = tf.complex([1, 3, 5, 7], [2, 4, 6, 8]);
-    const b = a.reshape([2, 2]);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([2, 2]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor2D default dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([6]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor2D float32 dtype', () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3], 'float32');
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([6]);
-  });
-
-  it('Tensor2D int32 dtype', () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3], 'int32');
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([6]);
-  });
-
-  it('Tensor2D bool dtype', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3], 'bool');
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([6]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor2D complex64 dtype', async () => {
-    const a = tf.complex([[1, 3, 5], [7, 9, 11]], [[2, 4, 6], [8, 10, 12]]);
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([6]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor3D default dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([6]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor3D float32 dtype', () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1], 'float32');
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([6]);
-  });
-
-  it('Tensor3D int32 dtype', () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1], 'int32');
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([6]);
-  });
-
-  it('Tensor3D bool dtype', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1], 'bool');
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([6]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor3D complex64 dtype', async () => {
-    const a = tf.complex(
-        [[[1], [3], [5]], [[7], [9], [11]]],
-        [[[2], [4], [6]], [[8], [10], [12]]]);
-    const b = a.reshape([6]);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([6]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor4D default dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4, 5, 6], [2, 3, 1, 1]);
-    const b = a.reshape([2, 3]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 3]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor4D float32 dtype', () => {
-    const a = tf.tensor4d([1, 2, 3, 4, 5, 6], [2, 3, 1, 1], 'float32');
-    const b = a.reshape([2, 3]);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 3]);
-  });
-
-  it('Tensor4D int32 dtype', async () => {
-    const a = tf.tensor4d([1, 2, 3, 4, 5, 6], [2, 3, 1, 1], 'int32');
-    const b = a.reshape([3, 2]);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([3, 2]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor4D complex64 dtype', async () => {
-    const a = tf.complex(
-        [[[[1]], [[3]], [[5]]], [[[7]], [[9]], [[11]]]],
-        [[[[2]], [[4]], [[6]]], [[[8]], [[10]], [[12]]]]);
-    const b = a.reshape([3, 2]);
-    expect(b.dtype).toBe('complex64');
-    expect(b.shape).toEqual([3, 2]);
-    expectArraysClose(await a.data(), await b.data());
-  });
-
-  it('Tensor4D bool dtype', () => {
-    const a = tf.tensor4d([1, 2, 3, 4, 5, 6], [2, 3, 1, 1], 'bool');
-    const b = a.reshape([3, 2]);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([3, 2]);
-  });
-
-  it('.data() with casting, string tensor', async () => {
-    const a = tf.tensor(['a', 'b']);
-    const data: string[] = await a.data<'string'>();
-    expect(data).toEqual(['a', 'b']);
-  });
-
-  it('reshape is functional', async () => {
-    const a = tf.scalar(2.4);
-    const b = a.reshape([]);
-    expect(a.id).not.toBe(b.id);
-    b.dispose();
-    expectArraysClose(await a.data(), [2.4]);
-  });
-
-  it('reshape a string tensor', async () => {
-    const a = tf.tensor(['a', 'b']);
-    const b = a.reshape([2, 1, 1]);
-    expect(b.dtype).toBe('string');
-    expect(b.shape).toEqual([2, 1, 1]);
-    expectArraysEqual(await b.data(), ['a', 'b']);
-  });
-
-  it('reshape throws when passed a non-tensor', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tf.reshape({} as any, []))
-        .toThrowError(/Argument 'x' passed to 'reshape' must be a Tensor/);
-  });
-
-  it('reshape accepts a tensor-like object', async () => {
-    const res = tf.reshape([[1, 2, 3], [4, 5, 6]], [3, 2]);
-    expect(res.dtype).toBe('float32');
-    expect(res.shape).toEqual([3, 2]);
-    expectArraysClose(await res.data(), [1, 2, 3, 4, 5, 6]);
-  });
-
-  it('cast bool -> bool', () => {
-    const a = tf.tensor1d([1, 0], 'bool');
-    expect(a.cast('bool').dtype).toEqual('bool');
-  });
-
-  it('cast bool -> int32', () => {
-    const a = tf.tensor1d([1, 0], 'bool');
-    expect(a.cast('int32').dtype).toEqual('int32');
-  });
-
-  it('cast bool -> float32', () => {
-    const a = tf.tensor1d([1, 0], 'bool');
-    expect(a.cast('float32').dtype).toEqual('float32');
-  });
-
-  it('cast int32 -> bool', () => {
-    const a = tf.tensor1d([1, 0], 'int32');
-    expect(a.cast('bool').dtype).toEqual('bool');
-  });
-
-  it('cast int32 -> int32', () => {
-    const a = tf.tensor1d([1, 2], 'int32');
-    expect(a.cast('int32').dtype).toEqual('int32');
-  });
-
-  it('cast int32 -> float32', () => {
-    const a = tf.tensor1d([1, 2], 'int32');
-    expect(a.cast('float32').dtype).toEqual('float32');
-  });
-
-  it('cast float32 -> bool', () => {
-    const a = tf.tensor1d([1.0, 0.0]);
-    expect(a.cast('bool').dtype).toEqual('bool');
-  });
-
-  it('cast float32 -> int32', () => {
-    const a = tf.tensor1d([1.0, 2.0]);
-    expect(a.cast('int32').dtype).toEqual('int32');
-  });
-
-  it('cast float32 -> int32. async download', async () => {
-    const a = tf.tensor1d([1, 2]);
-    const aInt = a.cast('int32');
-    expect(aInt.dtype).toEqual('int32');
-
-    const asyncData = await aInt.data();
-    expect(asyncData instanceof Int32Array).toEqual(true);
-  });
-
-  it('cast float32 -> int32. queued async download', async () => {
-    const a = tf.tensor1d([1, 2]);
-    const aInt = a.cast('int32');
-    expect(aInt.dtype).toEqual('int32');
-
-    const [first, second] = await Promise.all([aInt.data(), aInt.data()]);
-    expect(first instanceof Int32Array).toEqual(true);
-    expect(second instanceof Int32Array).toEqual(true);
-  });
-
-  it('cast float32 -> int32. sync download', async () => {
-    const a = tf.tensor1d([1, 2]).cast('int32');
-    expect(a.dtype).toEqual('int32');
-
-    const data = await a.data();
-    expect(data instanceof Int32Array).toEqual(true);
-  });
-
-  it('cast float32 -> float32', () => {
-    const a = tf.tensor1d([1.0, 2.0]);
-    expect(a.cast('float32').dtype).toEqual('float32');
-  });
-
-  it('cast complex64 -> float32', async () => {
-    const a = tf.complex([1.0, 2.0], [3.0, 4.0]);
-    const result = a.cast('float32');
-
-    expect(result.dtype).toEqual('float32');
-    expectArraysClose(await result.data(), [1.0, 2.0]);
-  });
-
-  it('cast complex64 -> int32', async () => {
-    const a = tf.complex([1.0, 2.0], [3.0, 4.0]);
-    const result = a.cast('int32');
-
-    expect(result.dtype).toEqual('int32');
-    expectArraysEqual(await result.data(), [1, 2]);
-  });
-
-  it('cast complex64 -> bool', async () => {
-    const a = tf.complex([1.0, 0.0], [1.0, 1.0]);
-    const result = a.cast('bool');
-
-    expect(result.dtype).toEqual('bool');
-    expectArraysEqual(await result.data(), [true, false]);
-  });
-
-  it('cast throws when passed a non-tensor', () => {
-    expect(() => tf.cast({} as tf.Tensor, 'float32'))
-        .toThrowError(/Argument 'x' passed to 'cast' must be a Tensor/);
-  });
-
-  it('cast accepts a tensor-like object', async () => {
-    const a = [1.0, 2.0];
-    const res = tf.cast(a, 'int32');
-    expect(res.dtype).toEqual('int32');
-    expectArraysClose(await res.data(), [1, 2]);
-  });
-
-  it('cast string -> !string throws error', () => {
-    const a = ['a', 'b'];
-    expect(() => tf.cast(a, 'int32')).toThrowError();
-    expect(() => tf.cast(a, 'float32')).toThrowError();
-    expect(() => tf.cast(a, 'bool')).toThrowError();
-    expect(() => tf.cast(a, 'complex64')).toThrowError();
-  });
-
-  it('cast !string -> string throws error', () => {
-    expect(() => tf.cast(tf.tensor(1, [], 'float32'), 'string')).toThrowError();
-    expect(() => tf.cast(tf.tensor(1, [], 'int32'), 'string')).toThrowError();
-    expect(() => tf.cast(tf.tensor(1, [], 'bool'), 'string')).toThrowError();
-    expect(() => tf.cast(tf.tensor(1, [], 'complex64'), 'string'))
-        .toThrowError();
-  });
-
-  it('scalar bool -> int32', async () => {
-    const a = tf.scalar(true, 'bool').toInt();
-    expect(a.dtype).toBe('int32');
-    expectArraysEqual(await a.data(), 1);
-  });
-
-  it('Tensor1D float32 -> int32', async () => {
-    const a = tf.tensor1d([1.1, 3.9, -2.9, 0]).toInt();
-    expect(a.dtype).toBe('int32');
-    expectArraysEqual(await a.data(), [1, 3, -2, 0]);
-  });
-
-  it('Tensor2D float32 -> bool', async () => {
-    const a = tf.tensor2d([1.1, 3.9, -2.9, 0], [2, 2]).asType('bool');
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), [1, 1, 1, 0]);
-  });
-
-  it('Tensor2D int32 -> bool', async () => {
-    const a = tf.tensor2d([1, 3, 0, -1], [2, 2], 'int32').toBool();
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), [1, 1, 0, 1]);
-  });
-
-  it('Tensor3D bool -> float32', async () => {
-    const a =
-        tf.tensor3d([true, false, false, true], [2, 2, 1], 'bool').toFloat();
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), [1, 0, 0, 1]);
-  });
-
-  it('bool CPU -> GPU -> CPU', async () => {
-    const a = tf.tensor1d([1, 2, 0, 0, 5], 'bool');
-    expectArraysEqual(await a.data(), [1, 1, 0, 0, 1]);
-  });
-
-  it('int32 CPU -> GPU -> CPU', async () => {
-    const a = tf.tensor1d([1, 2, 0, 0, 5], 'int32');
-    expectArraysEqual(await a.data(), [1, 2, 0, 0, 5]);
-  });
-
-  it('asType is functional', async () => {
-    const a = tf.scalar(2.4, 'float32');
-    const b = a.toFloat();
-    expect(a.id).not.toBe(b.id);
-    b.dispose();
-    expectArraysClose(await a.data(), [2.4]);
-  });
-
-  it('squeeze no axis', () => {
-    const a = tf.tensor2d([4, 2, 1], [3, 1], 'bool');
-    const b = a.squeeze();
-    expect(b.shape).toEqual([3]);
-  });
-
-  it('squeeze with axis', () => {
-    const a = tf.tensor3d([4, 2, 1], [3, 1, 1], 'bool');
-    const b = a.squeeze([1]);
-    expect(b.shape).toEqual([3, 1]);
-  });
-
-  it('squeeze with negative axis', () => {
-    const a = tf.tensor3d([4, 2, 1], [3, 1, 1], 'bool');
-    const b = a.squeeze([-1]);
-    expect(b.shape).toEqual([3, 1]);
-  });
-
-  it('squeeze with multiple negative axis', () => {
-    const a = tf.tensor3d([4, 2, 1], [3, 1, 1], 'bool');
-    const b = a.squeeze([-1, -2]);
-    expect(b.shape).toEqual([3]);
-  });
-
-  it('squeeze wrong axis', () => {
-    const a = tf.tensor3d([4, 2, 1], [3, 1, 1], 'bool');
-    expect(() => a.squeeze([0, 1])).toThrowError();
-  });
-
-  it('squeeze wrong negative axis', () => {
-    const a = tf.tensor3d([4, 2, 1], [3, 1, 1], 'bool');
-    expect(() => a.squeeze([-3, -2])).toThrowError();
-  });
-
-  it('squeeze axis out of range', () => {
-    const a = tf.tensor3d([4, 2, 1], [3, 1, 1], 'bool');
-    expect(() => a.squeeze([10, 11])).toThrowError();
-  });
-
-  it('squeeze negative axis out of range', () => {
-    const a = tf.tensor3d([4, 2, 1], [3, 1, 1], 'bool');
-    expect(() => a.squeeze([-13, -12])).toThrowError();
-  });
-
-  it('squeeze throws when passed a non-tensor', () => {
-    expect(() => tf.squeeze({} as tf.Tensor))
-        .toThrowError(/Argument 'x' passed to 'squeeze' must be a Tensor/);
-  });
-
-  it('squeeze accepts a tensor-like object', async () => {
-    const res = tf.squeeze([[[4]], [[2]], [[1]]] /* shape is [3, 1, 1] */);
-    expect(res.shape).toEqual([3]);
-    expectArraysClose(await res.data(), [4, 2, 1]);
-  });
-
-  it('squeeze a zero-sized tensor', () => {
-    const a = tf.tensor3d([], [0, 1, 0]);
-    const res = tf.squeeze(a);
-    expect(res.shape).toEqual([0, 0]);
-  });
-
-  it('squeeze can take an empty list of axis', () => {
-    const a = tf.zeros([2, 1, 3, 1, 4]);
-    const axes: number[] = [];
-    // Empty axes list means all possible axes.
-    const res = tf.squeeze(a, axes);
-    expect(res.shape).toEqual([2, 3, 4]);
-  });
-
-  it('squeeze a complex64 tensor', async () => {
-    const a = tf.complex([[4], [1], [5]], [[2], [3], [6]]);
-    const b = a.squeeze();
-    expect(b.shape).toEqual([3]);
-    expectArraysClose(await b.data(), [4, 2, 1, 3, 5, 6]);
-  });
-
-  it('scalar -> 2d', () => {
-    const a = tf.scalar(4, 'int32');
-    const b = a.as2D(1, 1);
-    expect(b.dtype).toBe('int32');
-    expect(b.shape).toEqual([1, 1]);
-  });
-
-  it('1d -> 2d', () => {
-    const a = tf.tensor1d([4, 2, 1], 'bool');
-    const b = a.as2D(3, 1);
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([3, 1]);
-  });
-
-  it('2d -> 4d', () => {
-    const a = tf.tensor2d([4, 2, 1, 3], [2, 2]);
-    const b = a.as4D(1, 1, 2, 2);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([1, 1, 2, 2]);
-  });
-
-  it('3d -> 2d', () => {
-    const a = tf.tensor3d([4, 2, 1, 3], [2, 2, 1], 'float32');
-    const b = a.as2D(2, 2);
-    expect(b.dtype).toBe('float32');
-    expect(b.shape).toEqual([2, 2]);
-  });
-
-  it('4d -> 1d', () => {
-    const a = tf.tensor4d([4, 2, 1, 3], [2, 2, 1, 1], 'bool');
-    const b = a.as1D();
-    expect(b.dtype).toBe('bool');
-    expect(b.shape).toEqual([4]);
-  });
-
-  it('throws when passed non-integer shape', () => {
-    const msg = 'Tensor must have a shape comprised of positive ' +
-        'integers but got shape [2,2.2].';
-    expect(() => tf.tensor([1, 2, 3, 4], [2, 2.2])).toThrowError(msg);
-  });
-
-  it('throws when passed negative shape', () => {
-    const msg = 'Tensor must have a shape comprised of positive ' +
-        'integers but got shape [2,-2].';
-    expect(() => tf.tensor([1, 2, 3, 4], [2, -2])).toThrowError(msg);
-  });
-
-  it('ones with complex type', async () => {
-    // Imaginary part should be zero.
-    const a = tf.ones([2, 2], 'complex64');
-    expectArraysClose(await a.data(), [1, 0, 1, 0, 1, 0, 1, 0]);
-  });
-
-  it('can create a tensor where values.size != buffer.size', async () => {
-    const a = new Float32Array([1, 2, 3, 4, 5]);
-    const b = a.subarray(0, 2);
-    const t = tf.tensor1d(b);
-    expect(t.shape).toEqual([2]);
-    expectArraysClose(await t.data(), [1, 2]);
-  });
-});
-
-describeWithFlags('tensor debug mode', ALL_ENVS, () => {
-  beforeAll(() => {
-    // Silence debug warnings.
-    spyOn(console, 'warn');
-    tf.enableDebugMode();
-  });
-
-  it('tf.tensor() from TypedArray + number[] fails due to wrong shape', () => {
-    expect(() => tf.tensor([
-      new Float32Array([1, 2]),
-      new Float32Array([3, 4]),
-      new Float32Array([5, 6]),
-      // Should be of length 4
-      [7, 8, 9, 10],
-    ]))
-        .toThrowError(
-            /Element arr\[3\] should have 2 elements, but has 4 elements/);
-  });
-});
-
-describeWithFlags('tensor dataSync', SYNC_BACKEND_ENVS, () => {
-  it('.dataSync() with casting, string tensor', () => {
-    const a = tf.tensor(['a', 'b']);
-    const data: string[] = a.dataSync<'string'>();
-    expect(data).toEqual(['a', 'b']);
-  });
-});
-
-describeWithFlags('tensor arraySync', SYNC_BACKEND_ENVS, () => {
-  it('.arraySync() with a non-complex tensor', () => {
-    const a = tf.tensor([1, 2, 3, 4, 5, 6], [2, 3]);
-    expect(a.arraySync()).toEqual([[1, 2, 3], [4, 5, 6]]);
-  });
-
-  it('.arraySync() with a complex tensor', () => {
-    const a = tf.complex([[1, 2], [3, 4]], [[11, 12], [13, 14]]);
-    expect(a.arraySync()).toEqual([[1, 11, 2, 12], [3, 13, 4, 14]]);
-  });
-
-  // The other cases should be covered by toNestedArray tests in util_test.ts.
-});
-
-describeWithFlags('tensor.toString', SYNC_BACKEND_ENVS, () => {
-  it('scalar verbose', () => {
-    const verbose = true;
-    const str = tf.scalar(5).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: float32\n' +
-        '  rank: 0\n' +
-        '  shape: []\n' +
-        '  values:\n' +
-        '    5');
-  });
-
-  it('string scalar verbose', () => {
-    const verbose = true;
-    const str = tf.scalar('test').toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: string\n' +
-        '  rank: 0\n' +
-        '  shape: []\n' +
-        '  values:\n' +
-        '    test');
-  });
-
-  it('bool scalar verbose', () => {
-    const verbose = true;
-    const str = tf.scalar(true).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: bool\n' +
-        '  rank: 0\n' +
-        '  shape: []\n' +
-        '  values:\n' +
-        '    true');
-  });
-
-  it('2D 0 shaped tensor verbose', () => {
-    const verbose = true;
-    const str = tf.zeros([0, 1]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: float32\n' +
-        '  rank: 2\n' +
-        '  shape: [0,1]\n' +
-        '  values:\n' +
-        '     []');
-  });
-
-  it('3D 0 shaped tensor verbose', () => {
-    const verbose = true;
-    const str = tf.zeros([1, 0, 1]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: float32\n' +
-        '  rank: 3\n' +
-        '  shape: [1,0,1]\n' +
-        '  values:\n' +
-        '     [ []]');
-  });
-
-  it('1d tensor verbose', () => {
-    const verbose = true;
-    const str = tf.zeros([4]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: float32\n' +
-        '  rank: 1\n' +
-        '  shape: [4]\n' +
-        '  values:\n' +
-        '    [0, 0, 0, 0]');
-  });
-
-  it('1d string tensor verbose', () => {
-    const verbose = true;
-    const str = tf.tensor(['a', 'bb', 'ccc']).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: string\n' +
-        '  rank: 1\n' +
-        '  shape: [3]\n' +
-        '  values:\n' +
-        '    [\'a\', \'bb\', \'ccc\']');
-  });
-
-  it('1d bool tensor verbose', () => {
-    const verbose = true;
-    const str = tf.tensor([true, false, true]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: bool\n' +
-        '  rank: 1\n' +
-        '  shape: [3]\n' +
-        '  values:\n' +
-        '    [true, false, true]');
-  });
-
-  it('2d tensor verbose', () => {
-    const verbose = true;
-    const str = tf.zeros([3, 3]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: float32\n' +
-        '  rank: 2\n' +
-        '  shape: [3,3]\n' +
-        '  values:\n' +
-        '    [[0, 0, 0],\n' +
-        '     [0, 0, 0],\n' +
-        '     [0, 0, 0]]');
-  });
-
-  it('2d string tensor verbose', () => {
-    const verbose = true;
-    const vals = [
-      ['a', 'bb', 'ccc'],
-      ['d', 'e', 'f'],
-      ['g', 'h', 'i'],
-    ];
-    const str = tf.tensor(vals).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: string\n' +
-        '  rank: 2\n' +
-        '  shape: [3,3]\n' +
-        '  values:\n' +
-        '    [[\'a\', \'bb\', \'ccc\'],\n' +
-        '     [\'d\', \'e\' , \'f\'  ],\n' +
-        '     [\'g\', \'h\' , \'i\'  ]]');
-  });
-
-  it('2d bool tensor verbose', () => {
-    const verbose = true;
-    const str = tf.zeros([3, 3], 'bool').toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: bool\n' +
-        '  rank: 2\n' +
-        '  shape: [3,3]\n' +
-        '  values:\n' +
-        '    [[false, false, false],\n' +
-        '     [false, false, false],\n' +
-        '     [false, false, false]]');
-  });
-
-  it('3d tensor verbose', () => {
-    const verbose = true;
-    const str = tf.zeros([3, 3, 2]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: float32\n' +
-        '  rank: 3\n' +
-        '  shape: [3,3,2]\n' +
-        '  values:\n' +
-        '    [[[0, 0],\n' +
-        '      [0, 0],\n' +
-        '      [0, 0]],\n\n' +
-        '     [[0, 0],\n' +
-        '      [0, 0],\n' +
-        '      [0, 0]],\n\n' +
-        '     [[0, 0],\n' +
-        '      [0, 0],\n' +
-        '      [0, 0]]]');
-  });
-
-  it('3d string tensor verbose', () => {
-    const verbose = true;
-    const vals = [
-      [['a', 'bb'], ['ccc', 'dddd']],
-      [['e', 'ff'], ['ggg', 'hhhh']],
-      [['i', 'jj'], ['kkk', 'llll']],
-    ];
-    const str = tf.tensor(vals).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: string\n' +
-        '  rank: 3\n' +
-        '  shape: [3,2,2]\n' +
-        '  values:\n' +
-        '    [[[\'a\'  , \'bb\'  ],\n' +
-        '      [\'ccc\', \'dddd\']],\n\n' +
-        '     [[\'e\'  , \'ff\'  ],\n' +
-        '      [\'ggg\', \'hhhh\']],\n\n' +
-        '     [[\'i\'  , \'jj\'  ],\n' +
-        '      [\'kkk\', \'llll\']]]');
-  });
-
-  it('3d bool tensor verbose', () => {
-    const verbose = true;
-    const str = tf.ones([3, 3, 2], 'bool').toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: bool\n' +
-        '  rank: 3\n' +
-        '  shape: [3,3,2]\n' +
-        '  values:\n' +
-        '    [[[true, true],\n' +
-        '      [true, true],\n' +
-        '      [true, true]],\n\n' +
-        '     [[true, true],\n' +
-        '      [true, true],\n' +
-        '      [true, true]],\n\n' +
-        '     [[true, true],\n' +
-        '      [true, true],\n' +
-        '      [true, true]]]');
-  });
-
-  it('1d long tensor verbose', () => {
-    const verbose = true;
-    const str = tf.zeros([100]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: float32\n' +
-        '  rank: 1\n' +
-        '  shape: [100]\n' +
-        '  values:\n' +
-        '    [0, 0, 0, ..., 0, 0, 0]');
-  });
-
-  it('1d long string tensor verbose', () => {
-    const verbose = true;
-    const str = tf.fill([100], 'hi').toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: string\n' +
-        '  rank: 1\n' +
-        '  shape: [100]\n' +
-        '  values:\n' +
-        '    [\'hi\', \'hi\', \'hi\', ..., \'hi\', \'hi\', \'hi\']');
-  });
-
-  it('2d long tensor verbose', () => {
-    const verbose = true;
-    const str = tf.zeros([100, 100]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: float32\n' +
-        '  rank: 2\n' +
-        '  shape: [100,100]\n' +
-        '  values:\n' +
-        '    [[0, 0, 0, ..., 0, 0, 0],\n' +
-        '     [0, 0, 0, ..., 0, 0, 0],\n' +
-        '     [0, 0, 0, ..., 0, 0, 0],\n' +
-        '     ...,\n' +
-        '     [0, 0, 0, ..., 0, 0, 0],\n' +
-        '     [0, 0, 0, ..., 0, 0, 0],\n' +
-        '     [0, 0, 0, ..., 0, 0, 0]]');
-  });
-
-  it('2d long string tensor verbose', () => {
-    const verbose = true;
-    const str = tf.fill([100, 100], 'a').toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: string\n' +
-        '  rank: 2\n' +
-        '  shape: [100,100]\n' +
-        '  values:\n' +
-        '    [[\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
-        '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
-        '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
-        '     ...,\n' +
-        '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
-        '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
-        '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\']]');
-  });
-
-  it('2d with padding to align columns verbose', () => {
-    const verbose = true;
-    const str = tf.tensor([
-                    [0.8597712, 3, 0.2740789], [0.6696132, 0.4825962, 2.75],
-                    [1.991, 0.0640865, 0.2983858]
-                  ]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: float32\n' +
-        '  rank: 2\n' +
-        '  shape: [3,3]\n' +
-        '  values:\n' +
-        '    [[0.8597712, 3        , 0.2740789],\n' +
-        '     [0.6696132, 0.4825962, 2.75     ],\n' +
-        '     [1.9910001, 0.0640865, 0.2983858]]');
-  });
-
-  it('2d string tensor with padding verbose', () => {
-    const verbose = true;
-    const str = tf.tensor([
-                    ['abcdef', 'a', 'abcdef'],
-                    ['abcdef', 'abcdef', 'abc'],
-                    ['abcd', 'abcdef', 'abcdef'],
-                  ]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: string\n' +
-        '  rank: 2\n' +
-        '  shape: [3,3]\n' +
-        '  values:\n' +
-        '    [[\'abcdef\', \'a\'     , \'abcdef\'],\n' +
-        '     [\'abcdef\', \'abcdef\', \'abc\'   ],\n' +
-        '     [\'abcd\'  , \'abcdef\', \'abcdef\']]');
-  });
-
-  it('scalar', () => {
-    const str = tf.scalar(5).toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    5');
-  });
-
-  it('scalar string', () => {
-    const str = tf.scalar('hello').toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    hello');
-  });
-
-  it('1d tensor', () => {
-    const str = tf.zeros([4]).toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [0, 0, 0, 0]');
-  });
-
-  it('2d tensor', () => {
-    const str = tf.zeros([3, 3]).toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [[0, 0, 0],\n' +
-        '     [0, 0, 0],\n' +
-        '     [0, 0, 0]]');
-  });
-
-  it('3d tensor', () => {
-    const str = tf.zeros([3, 3, 2]).toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [[[0, 0],\n' +
-        '      [0, 0],\n' +
-        '      [0, 0]],\n\n' +
-        '     [[0, 0],\n' +
-        '      [0, 0],\n' +
-        '      [0, 0]],\n\n' +
-        '     [[0, 0],\n' +
-        '      [0, 0],\n' +
-        '      [0, 0]]]');
-  });
-
-  it('1d long tensor', () => {
-    const str = tf.zeros([100]).toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [0, 0, 0, ..., 0, 0, 0]');
-  });
-
-  it('2d long tensor', () => {
-    const str = tf.zeros([100, 100]).toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [[0, 0, 0, ..., 0, 0, 0],\n' +
-        '     [0, 0, 0, ..., 0, 0, 0],\n' +
-        '     [0, 0, 0, ..., 0, 0, 0],\n' +
-        '     ...,\n' +
-        '     [0, 0, 0, ..., 0, 0, 0],\n' +
-        '     [0, 0, 0, ..., 0, 0, 0],\n' +
-        '     [0, 0, 0, ..., 0, 0, 0]]');
-  });
-
-  it('2d with padding to align columns', () => {
-    const str = tf.tensor([
-                    [0.8597712, 3, 0.2740789], [0.6696132, 0.4825962, 2.75],
-                    [1.991, 0.0640865, 0.2983858]
-                  ]).toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [[0.8597712, 3        , 0.2740789],\n' +
-        '     [0.6696132, 0.4825962, 2.75     ],\n' +
-        '     [1.9910001, 0.0640865, 0.2983858]]');
-  });
-
-  it('scalar complex64 verbose', () => {
-    const verbose = true;
-    const str = tf.complex(5, 6).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: complex64\n' +
-        '  rank: 0\n' +
-        '  shape: []\n' +
-        '  values:\n' +
-        '    5 + 6j');
-  });
-
-  it('1d complex64 tensor verbose', () => {
-    const verbose = true;
-    const str = tf.complex([3, 5], [4, 6]).toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: complex64\n' +
-        '  rank: 1\n' +
-        '  shape: [2]\n' +
-        '  values:\n' +
-        '    [3 + 4j, 5 + 6j]');
-  });
-
-  it('2d complex64 tensor verbose', () => {
-    const verbose = true;
-    const str = tf.complex(tf.linspace(0, 8, 9), tf.linspace(8, 0, 9))
-                    .reshape([3, 3])
-                    .toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: complex64\n' +
-        '  rank: 2\n' +
-        '  shape: [3,3]\n' +
-        '  values:\n' +
-        '    [[0 + 8j, 1 + 7j, 2 + 6j],\n' +
-        '     [3 + 5j, 4 + 4j, 5 + 3j],\n' +
-        '     [6 + 2j, 7 + 1j, 8 + 0j]]');
-  });
-
-  it('3d complex64 tensor verbose', () => {
-    const verbose = true;
-    const str = tf.complex(tf.linspace(0, 17, 18), tf.linspace(17, 0, 18))
-                    .reshape([3, 3, 2])
-                    .toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: complex64\n' +
-        '  rank: 3\n' +
-        '  shape: [3,3,2]\n' +
-        '  values:\n' +
-        '    [[[0 + 17j, 1 + 16j],\n' +
-        '      [2 + 15j, 3 + 14j],\n' +
-        '      [4 + 13j, 5 + 12j]],\n\n' +
-        '     [[6 + 11j, 7 + 10j],\n' +
-        '      [8 + 9j , 9 + 8j ],\n' +
-        '      [10 + 7j, 11 + 6j]],\n\n' +
-        '     [[12 + 5j, 13 + 4j],\n' +
-        '      [14 + 3j, 15 + 2j],\n' +
-        '      [16 + 1j, 17 + 0j]]]');
-  });
-
-  it('1d long complex64 tensor verbose', () => {
-    const verbose = true;
-    const str = tf.complex(tf.linspace(0, 99, 100), tf.linspace(99, 0, 100))
-                    .toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: complex64\n' +
-        '  rank: 1\n' +
-        '  shape: [100]\n' +
-        '  values:\n' +
-        '    [0 + 99j, 1 + 98j, 2 + 97j, ..., 97 + 2j, 98 + 1j, 99 + 0j]');
-  });
-
-  it('2d long complex64 tensor verbose', () => {
-    const verbose = true;
-
-    const dim = 100;
-    const str = tf.complex(
-                      tf.linspace(0, dim * dim - 1, dim * dim),
-                      tf.linspace(dim * dim - 1, 0, dim * dim))
-                    .reshape([dim, dim])
-                    .toString(verbose);
-
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: complex64\n' +
-        '  rank: 2\n' +
-        '  shape: [100,100]\n' +
-        '  values:\n' +
-        // tslint:disable:max-line-length
-        '    [[0 + 9999j   , 1 + 9998j   , 2 + 9997j   , ..., 97 + 9902j  , 98 + 9901j  , 99 + 9900j  ],\n' +
-        '     [100 + 9899j , 101 + 9898j , 102 + 9897j , ..., 197 + 9802j , 198 + 9801j , 199 + 9800j ],\n' +
-        '     [200 + 9799j , 201 + 9798j , 202 + 9797j , ..., 297 + 9702j , 298 + 9701j , 299 + 9700j ],\n' +
-        '     ...,\n' +
-        '     [9700 + 299j , 9701 + 298j , 9702 + 297j , ..., 9797 + 202j , 9798 + 201j , 9799 + 200j ],\n' +
-        '     [9800 + 199j , 9801 + 198j , 9802 + 197j , ..., 9897 + 102j , 9898 + 101j , 9899 + 100j ],\n' +
-        '     [9900 + 99j  , 9901 + 98j  , 9902 + 97j  , ..., 9997 + 2j   , 9998 + 1j   , 9999 + 0j   ]]');
-    // tslint:enable:max-line-length
-  });
-
-  it('2d complex64 with padding to align columns verbose', () => {
-    const verbose = true;
-
-    const str = tf.complex(
-                      [
-                        [0.8597712, 3, 0.2740789], [0.6696132, 0.4825962, 2.75],
-                        [1.991, 0.0640865, 0.2983858]
-                      ],
-                      [[1, 1.0102332, 3], [2, 5, 2.34424], [1.23, 2, 0.123]])
-                    .toString(verbose);
-    expect(str).toEqual(
-        'Tensor\n' +
-        '  dtype: complex64\n' +
-        '  rank: 2\n' +
-        '  shape: [3,3]\n' +
-        '  values:\n' +
-        '    [[0.8597712 + 1j   , 3 + 1.0102332j, 0.2740789 + 3j    ],\n' +
-        '     [0.6696132 + 2j   , 0.4825962 + 5j, 2.75 + 2.34424j   ],\n' +
-        '     [1.9910001 + 1.23j, 0.0640865 + 2j, 0.2983858 + 0.123j]]');
-  });
-
-  it('scalar complex64', () => {
-    const str = tf.complex(5, 4).toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    5 + 4j');
-  });
-
-  it('1d complex64 tensor', () => {
-    const str =
-        tf.complex(tf.linspace(0, 3, 4), tf.linspace(3, 0, 4)).toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [0 + 3j, 1 + 2j, 2 + 1j, 3 + 0j]');
-  });
-
-  it('2d complex64 tensor', () => {
-    const str = tf.complex(tf.linspace(0, 8, 9), tf.linspace(8, 0, 9))
-                    .reshape([3, 3])
-                    .toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [[0 + 8j, 1 + 7j, 2 + 6j],\n' +
-        '     [3 + 5j, 4 + 4j, 5 + 3j],\n' +
-        '     [6 + 2j, 7 + 1j, 8 + 0j]]');
-  });
-
-  it('3d complex64 tensor', () => {
-    const str = tf.complex(tf.linspace(0, 17, 18), tf.linspace(17, 0, 18))
-                    .reshape([3, 3, 2])
-                    .toString();
-
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [[[0 + 17j, 1 + 16j],\n' +
-        '      [2 + 15j, 3 + 14j],\n' +
-        '      [4 + 13j, 5 + 12j]],\n\n' +
-        '     [[6 + 11j, 7 + 10j],\n' +
-        '      [8 + 9j , 9 + 8j ],\n' +
-        '      [10 + 7j, 11 + 6j]],\n\n' +
-        '     [[12 + 5j, 13 + 4j],\n' +
-        '      [14 + 3j, 15 + 2j],\n' +
-        '      [16 + 1j, 17 + 0j]]]');
-  });
-
-  it('1d long complex64 tensor', () => {
-    const str =
-        tf.complex(tf.linspace(0, 99, 100), tf.linspace(99, 0, 100)).toString();
-
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [0 + 99j, 1 + 98j, 2 + 97j, ..., 97 + 2j, 98 + 1j, 99 + 0j]');
-  });
-
-  it('2d long complex64 tensor', () => {
-    const dim = 100;
-    const str = tf.complex(
-                      tf.linspace(0, dim * dim - 1, dim * dim),
-                      tf.linspace(dim * dim - 1, 0, dim * dim))
-                    .reshape([dim, dim])
-                    .toString();
-
-    expect(str).toEqual(
-        'Tensor\n' +
-        // tslint:disable:max-line-length
-        '    [[0 + 9999j   , 1 + 9998j   , 2 + 9997j   , ..., 97 + 9902j  , 98 + 9901j  , 99 + 9900j  ],\n' +
-        '     [100 + 9899j , 101 + 9898j , 102 + 9897j , ..., 197 + 9802j , 198 + 9801j , 199 + 9800j ],\n' +
-        '     [200 + 9799j , 201 + 9798j , 202 + 9797j , ..., 297 + 9702j , 298 + 9701j , 299 + 9700j ],\n' +
-        '     ...,\n' +
-        '     [9700 + 299j , 9701 + 298j , 9702 + 297j , ..., 9797 + 202j , 9798 + 201j , 9799 + 200j ],\n' +
-        '     [9800 + 199j , 9801 + 198j , 9802 + 197j , ..., 9897 + 102j , 9898 + 101j , 9899 + 100j ],\n' +
-        '     [9900 + 99j  , 9901 + 98j  , 9902 + 97j  , ..., 9997 + 2j   , 9998 + 1j   , 9999 + 0j   ]]');
-    // tslint:enable:max-line-length
-  });
-
-  it('2d complex64 with padding to align columns', () => {
-    const str = tf.complex(
-                      [
-                        [0.8597712, 3, 0.2740789], [0.6696132, 0.4825962, 2.75],
-                        [1.991, 0.0640865, 0.2983858]
-                      ],
-                      [[1, 1.0102332, 3], [2, 5, 2.34424], [1.23, 2, 0.123]])
-                    .toString();
-    expect(str).toEqual(
-        'Tensor\n' +
-        '    [[0.8597712 + 1j   , 3 + 1.0102332j, 0.2740789 + 3j    ],\n' +
-        '     [0.6696132 + 2j   , 0.4825962 + 5j, 2.75 + 2.34424j   ],\n' +
-        '     [1.9910001 + 1.23j, 0.0640865 + 2j, 0.2983858 + 0.123j]]');
-  });
-});
-
-describeWithFlags('tensor grad', ALL_ENVS, () => {
-  it('grad with second derivative', async () => {
-    // f(x) = x ^ 3
-    const f = (x: Tensor) => x.pow(tf.scalar(3, 'int32'));
-    // f'(x) = 3x ^ 2
-    const g = tf.grad(f);
-    // f''(x) = 6x
-    const gg = tf.grad(g);
-    const x = tf.tensor1d([2, 3]);
-    const data = gg(x);
-    expectArraysClose(await data.data(), [12, 18]);
-  });
-});
-
-describeWithFlags('tensor.data', ALL_ENVS, () => {
-  it('interleaving .data() and .dataSync()', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([4, 5, 6]);
-
-    const ra = a.square();
-    const rb = b.square();
-
-    expectArraysClose(await a.data(), [1, 2, 3]);
-    expectArraysClose(await b.data(), [4, 5, 6]);
-    expectArraysClose(await rb.data(), [16, 25, 36]);
-    expectArraysClose(await ra.data(), [1, 4, 9]);
-  });
-
-  it('.data() postpones disposal of tensor', done => {
-    expect(tf.memory().numTensors).toBe(0);
-    tf.tidy(() => {
-      const a = tf.scalar(5);
-      expect(tf.memory().numTensors).toBe(1);
-      a.square();  // Uploads it on GPU.
-      a.data().then(vals => {
-        // The tidy above should not dispose the scalar since there is
-        // a pending data read.
-        expectNumbersClose(vals[0], 5);
-      });
-    });
-
-    // tidy ends immediately, but should not dispose the scalar.
-
-    setTimeout(() => {
-      // tidy should dispose the tensor.
-      expect(tf.memory().numTensors).toBe(0);
-      done();
-    });
-  });
-
-  it('calling .data() twice works (2 subscribers to a single read)', done => {
-    tf.tidy(() => {
-      const a = tf.scalar(5);
-      a.square();  // Uploads it on GPU.
-      a.data().then(vals => {
-        expectNumbersClose(vals[0], 5);
-      });
-      a.data()
-          .then(vals => {
-            expectNumbersClose(vals[0], 5);
-          })
-          .then(done);
-    });
-    // tidy ends immediately, but should not dispose the scalar since there is
-    // a pending data read.
-  });
-});
-
-describeWithFlags('x instanceof Tensor', ALL_ENVS, () => {
-  it('x: Tensor', () => {
-    const t = tf.scalar(1);
-    expect(t instanceof Tensor).toBe(true);
-  });
-
-  it('x: other object, fails', () => {
-    const t = {something: 'else'};
-    expect(t instanceof Tensor).toBe(false);
-  });
-
-  it('x: undefined or null, fails', () => {
-    // tslint:disable-next-line:no-any
-    expect((undefined as any) instanceof Tensor).toBe(false);
-    // tslint:disable-next-line:no-any
-    expect((null as any) instanceof Tensor).toBe(false);
-  });
-});
-
-describeWithFlags('tensor with 0 in shape', ALL_ENVS, () => {
-  it('1d of shape [0]', async () => {
-    const a = tf.tensor1d([]);
-    expect(a.dtype).toBe('float32');
-    expect(a.rank).toBe(1);
-    expect(a.shape).toEqual([0]);
-    expectArraysEqual(await a.data(), []);
-  });
-
-  it('1d string tensor of shape [0]', async () => {
-    const a = tf.tensor1d([], 'string');
-    expect(a.dtype).toBe('string');
-    expect(a.rank).toBe(1);
-    expect(a.shape).toEqual([0]);
-    expectArraysEqual(await a.data(), []);
-  });
-
-  it('2d of shape [0, 5]', async () => {
-    const a = tf.tensor2d([], [0, 5]);
-    expect(a.dtype).toBe('float32');
-    expect(a.rank).toBe(2);
-    expect(a.shape).toEqual([0, 5]);
-    expectArraysEqual(await a.data(), []);
-  });
-
-  it('2d string tensor of shape [0, 5]', async () => {
-    const a = tf.tensor2d([], [0, 5], 'string');
-    expect(a.dtype).toBe('string');
-    expect(a.rank).toBe(2);
-    expect(a.shape).toEqual([0, 5]);
-    expectArraysEqual(await a.data(), []);
-  });
-
-  it('2d throws when values are not empty', () => {
-    const values = [1, 2, 3, 4];
-    expect(() => tf.tensor2d(values, [0, 5], 'float32'))
-        .toThrowError(
-            'Based on the provided shape, [0,5], the ' +
-            'tensor should have 0 values but has 4');
-  });
-
-  it('3d of shape [0, 3, 0]', async () => {
-    const a = tf.tensor3d([], [0, 3, 0]);
-    expect(a.dtype).toBe('float32');
-    expect(a.rank).toBe(3);
-    expect(a.shape).toEqual([0, 3, 0]);
-    expectArraysEqual(await a.data(), []);
-  });
-
-  it('3d throws when values are not empty', () => {
-    const values = [1, 2, 3];
-    expect(() => tf.tensor3d(values, [0, 3, 0], 'float32'))
-        .toThrowError(
-            'Based on the provided shape, [0,3,0], the ' +
-            'tensor should have 0 values but has 3');
-  });
-
-  it('4d of shape [1, 3, 0, 5]', async () => {
-    const a = tf.tensor4d([], [1, 3, 0, 5]);
-    expect(a.dtype).toBe('float32');
-    expect(a.rank).toBe(4);
-    expect(a.shape).toEqual([1, 3, 0, 5]);
-    expectArraysEqual(await a.data(), []);
-  });
-
-  it('4d throws when values are not empty', () => {
-    const values = [1, 2, 3];
-    expect(() => tf.tensor4d(values, [1, 3, 0, 5], 'float32'))
-        .toThrowError(
-            'Based on the provided shape, [1,3,0,5], the ' +
-            'tensor should have 0 values but has 3');
-  });
-
-  it('complex64 with 0 in shape', async () => {
-    const areal = tf.tensor2d([], [0, 5]);
-    const breal = tf.tensor2d([], [0, 5]);
-    const a = tf.complex(areal, breal);
-    expect(a.dtype).toBe('complex64');
-    expect(a.rank).toBe(2);
-    expect(a.shape).toEqual([0, 5]);
-    expectArraysEqual(await a.data(), []);
-  });
-});
-
-describeWithFlags('tensor.bytes()', ALL_ENVS, () => {
-  /** Helper method to get the bytes from a typed array. */
-  function getBytes(a: TypedArray): Uint8Array {
-    return new Uint8Array(a.buffer);
-  }
-
-  it('float32 tensor', async () => {
-    const a = tf.tensor([1.1, 3.2, 7], [3], 'float32');
-    expect(await a.bytes()).toEqual(getBytes(new Float32Array([1.1, 3.2, 7])));
-  });
-
-  it('int32 tensor', async () => {
-    const a = tf.tensor([1.1, 3.2, 7], [3], 'int32');
-    expect(await a.bytes()).toEqual(getBytes(new Int32Array([1, 3, 7])));
-  });
-
-  it('bool tensor', async () => {
-    const a = tf.tensor([true, true, false], [3], 'bool');
-    expect(await a.bytes()).toEqual(new Uint8Array([1, 1, 0]));
-  });
-
-  it('string tensor from native strings', async () => {
-    const a = tf.tensor(['hello', 'world'], [2], 'string');
-    expect(await a.bytes()).toEqual([
-      encodeString('hello'), encodeString('world')
-    ]);
-  });
-
-  it('string tensor from encoded bytes', async () => {
-    const a = tf.tensor(
-        [encodeString('hello'), encodeString('world')], [2], 'string');
-    expect(await a.bytes()).toEqual([
-      encodeString('hello'), encodeString('world')
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/tensor_types.ts b/tfjs-master/tfjs-core/src/tensor_types.ts
deleted file mode 100644
index f8763f264..000000000
--- a/tfjs-master/tfjs-core/src/tensor_types.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Variable} from './tensor';
-
-/** @docalias {[name: string]: Tensor} */
-export type NamedTensorMap = {
-  [name: string]: Tensor;
-};
-
-export interface NamedTensor {
-  name: string;
-  tensor: Tensor;
-}
-
-export type NamedVariableMap = {
-  [name: string]: Variable;
-};
-
-export type GradSaveFunc = (save: Tensor[]) => void;
-
-/**
- * @docalias void|number|string|TypedArray|Tensor|Tensor[]|{[key:
- * string]:Tensor|number|string}
- */
-export type TensorContainer =
-    void|Tensor|string|number|boolean|TensorContainerObject|
-    TensorContainerArray|Float32Array|Int32Array|Uint8Array;
-export interface TensorContainerObject {
-  [x: string]: TensorContainer;
-}
-export interface TensorContainerArray extends Array<TensorContainer> {}
diff --git a/tfjs-master/tfjs-core/src/tensor_util.ts b/tfjs-master/tfjs-core/src/tensor_util.ts
deleted file mode 100644
index 58db4e962..000000000
--- a/tfjs-master/tfjs-core/src/tensor_util.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from './tensor';
-import {TensorContainer, TensorContainerArray} from './tensor_types';
-import {upcastType} from './types';
-import {assert} from './util';
-
-export function makeTypesMatch<T extends Tensor>(a: T, b: T): [T, T] {
-  if (a.dtype === b.dtype) {
-    return [a, b];
-  }
-  const dtype = upcastType(a.dtype, b.dtype);
-  return [a.cast(dtype), b.cast(dtype)];
-}
-
-export function assertTypesMatch(a: Tensor, b: Tensor): void {
-  assert(
-      a.dtype === b.dtype,
-      () => `The dtypes of the first(${a.dtype}) and` +
-          ` second(${b.dtype}) input must match`);
-}
-
-export function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean {
-  return tensorList.some(x => x.id === tensor.id);
-}
-
-/**
- * Extracts any `Tensor`s found within the provided object.
- *
- * @param container an object that may be a `Tensor` or may directly contain
- *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it
- *   is safe to pass any object here, except that `Promise`s are not
- *   supported.
- * @returns An array of `Tensors` found within the passed object. If the
- *   argument is simply a `Tensor', a list containing that `Tensor` is
- *   returned. If the object is not a `Tensor` or does not
- *   contain `Tensors`, an empty list is returned.
- */
-export function getTensorsInContainer(result: TensorContainer): Tensor[] {
-  const list: Tensor[] = [];
-  const seen = new Set<{}|void>();
-  walkTensorContainer(result, list, seen);
-  return list;
-}
-
-function walkTensorContainer(
-    container: TensorContainer, list: Tensor[], seen: Set<{}|void>): void {
-  if (container == null) {
-    return;
-  }
-  if (container instanceof Tensor) {
-    list.push(container);
-    return;
-  }
-  if (!isIterable(container)) {
-    return;
-  }
-  // Iteration over keys works also for arrays.
-  const iterable = container as TensorContainerArray;
-  for (const k in iterable) {
-    const val = iterable[k];
-    if (!seen.has(val)) {
-      seen.add(val);
-      walkTensorContainer(val, list, seen);
-    }
-  }
-}
-
-// tslint:disable-next-line:no-any
-function isIterable(obj: any): boolean {
-  return Array.isArray(obj) || typeof obj === 'object';
-}
diff --git a/tfjs-master/tfjs-core/src/tensor_util_env.ts b/tfjs-master/tfjs-core/src/tensor_util_env.ts
deleted file mode 100644
index 60fd1272b..000000000
--- a/tfjs-master/tfjs-core/src/tensor_util_env.ts
+++ /dev/null
@@ -1,145 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from './engine';
-import {env} from './environment';
-import {getGlobalTensorClass, Tensor} from './tensor';
-import {DataType, isWebGLData, isWebGPUData, TensorLike, WebGLData, WebGPUData} from './types';
-import {assert, flatten, inferDtype, isTypedArray, toTypedArray} from './util';
-import {bytesPerElement} from './util_base';
-
-export function inferShape(
-    val: TensorLike|WebGLData|WebGPUData, dtype?: DataType): number[] {
-  let firstElem: typeof val = val;
-
-  if (isTypedArray(val)) {
-    return dtype === 'string' ? [] : [val.length];
-  }
-
-  if (isWebGLData(val)) {
-    const usedChannels = val.channels || 'RGBA';
-    return [val.height, val.width * usedChannels.length];
-  } else if (isWebGPUData(val)) {
-    return [val.buffer.size / (dtype == null ? 4 : bytesPerElement(dtype))];
-  }
-  if (!Array.isArray(val)) {
-    return [];  // Scalar.
-  }
-  const shape: number[] = [];
-
-  while (Array.isArray(firstElem) ||
-         isTypedArray(firstElem) && dtype !== 'string') {
-    shape.push(firstElem.length);
-    firstElem = firstElem[0];
-  }
-  if (Array.isArray(val) &&
-      env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {
-    deepAssertShapeConsistency(val, shape, []);
-  }
-
-  return shape;
-}
-
-function deepAssertShapeConsistency(
-    val: TensorLike, shape: number[], indices: number[]) {
-  indices = indices || [];
-  if (!(Array.isArray(val)) && !isTypedArray(val)) {
-    assert(
-        shape.length === 0,
-        () => `Element arr[${indices.join('][')}] is a primitive, ` +
-            `but should be an array/TypedArray of ${shape[0]} elements`);
-    return;
-  }
-  assert(
-      shape.length > 0,
-      () => `Element arr[${indices.join('][')}] should be a primitive, ` +
-          `but is an array of ${val.length} elements`);
-  assert(
-      val.length === shape[0],
-      () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +
-          `elements, but has ${val.length} elements`);
-  const subShape = shape.slice(1);
-  for (let i = 0; i < val.length; ++i) {
-    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
-  }
-}
-
-function assertDtype(
-    expectedDtype: DataType|'numeric'|'string_or_numeric',
-    actualDType: DataType, argName: string, functionName: string) {
-  if (expectedDtype === 'string_or_numeric') {
-    return;
-  }
-  if (expectedDtype == null) {
-    throw new Error(`Expected dtype cannot be null.`);
-  }
-  if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||
-      expectedDtype === 'numeric' && actualDType === 'string') {
-    throw new Error(
-        `Argument '${argName}' passed to '${functionName}' must ` +
-        `be ${expectedDtype} tensor, but got ${actualDType} tensor`);
-  }
-}
-
-export function convertToTensor<T extends Tensor>(
-    x: T|TensorLike, argName: string, functionName: string,
-    parseAsDtype: DataType|'numeric'|'string_or_numeric' = 'numeric'): T {
-  if (x instanceof getGlobalTensorClass()) {
-    assertDtype(parseAsDtype, x.dtype, argName, functionName);
-    return x;
-  }
-  let inferredDtype = inferDtype(x);
-  // If the user expects a bool/int/float, use that info to update the
-  // inferredDtype when it is not a string.
-  if (inferredDtype !== 'string' &&
-      ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {
-    inferredDtype = parseAsDtype as DataType;
-  }
-  assertDtype(parseAsDtype, inferredDtype, argName, functionName);
-
-  if ((x == null) ||
-      (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&
-       typeof x !== 'boolean' && typeof x !== 'string')) {
-    const type = x == null ? 'null' : (x as {}).constructor.name;
-    throw new Error(
-        `Argument '${argName}' passed to '${functionName}' must be a ` +
-        `Tensor or TensorLike, but got '${type}'`);
-  }
-  const inferredShape = inferShape(x, inferredDtype);
-  if (!isTypedArray(x) && !Array.isArray(x)) {
-    x = [x] as number[];
-  }
-  const skipTypedArray = true;
-  const values = inferredDtype !== 'string' ?
-      toTypedArray(x, inferredDtype as DataType) :
-      flatten(x as string[], [], skipTypedArray) as string[];
-  return ENGINE.makeTensor(values, inferredShape, inferredDtype) as T;
-}
-
-export function convertToTensorArray<T extends Tensor>(
-    arg: Array<T|TensorLike>, argName: string, functionName: string,
-    parseAsDtype: DataType|'numeric'|'string_or_numeric' = 'numeric'): T[] {
-  if (!Array.isArray(arg)) {
-    throw new Error(
-        `Argument ${argName} passed to ${functionName} must be a ` +
-        '`Tensor[]` or `TensorLike[]`');
-  }
-  const tensors = arg as T[];
-  return tensors.map(
-      (t, i) =>
-          convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));
-}
diff --git a/tfjs-master/tfjs-core/src/tensor_util_test.ts b/tfjs-master/tfjs-core/src/tensor_util_test.ts
deleted file mode 100644
index ae9043f50..000000000
--- a/tfjs-master/tfjs-core/src/tensor_util_test.ts
+++ /dev/null
@@ -1,230 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-import {Tensor} from './tensor';
-import {getTensorsInContainer, isTensorInList} from './tensor_util';
-import {convertToTensor} from './tensor_util_env';
-import {expectArraysClose, expectArraysEqual} from './test_util';
-
-describeWithFlags('tensor_util.isTensorInList', ALL_ENVS, () => {
-  it('not in list', () => {
-    const a = tf.scalar(1);
-    const list: Tensor[] = [tf.scalar(1), tf.tensor1d([1, 2, 3])];
-
-    expect(isTensorInList(a, list)).toBe(false);
-  });
-
-  it('in list', () => {
-    const a = tf.scalar(1);
-    const list: Tensor[] = [tf.scalar(2), tf.tensor1d([1, 2, 3]), a];
-
-    expect(isTensorInList(a, list)).toBe(true);
-  });
-});
-
-describeWithFlags('getTensorsInContainer', ALL_ENVS, () => {
-  it('null input returns empty tensor', () => {
-    const results = getTensorsInContainer(null);
-
-    expect(results).toEqual([]);
-  });
-
-  it('tensor input returns one element tensor', () => {
-    const x = tf.scalar(1);
-    const results = getTensorsInContainer(x);
-
-    expect(results).toEqual([x]);
-  });
-
-  it('name tensor map returns flattened tensor', () => {
-    const x1 = tf.scalar(1);
-    const x2 = tf.scalar(3);
-    const x3 = tf.scalar(4);
-    const results = getTensorsInContainer({x1, x2, x3});
-
-    expect(results).toEqual([x1, x2, x3]);
-  });
-
-  it('can extract from arbitrary depth', () => {
-    const container = [
-      {x: tf.scalar(1), y: tf.scalar(2)}, [[[tf.scalar(3)]], {z: tf.scalar(4)}]
-    ];
-    const results = getTensorsInContainer(container);
-    expect(results.length).toBe(4);
-  });
-
-  it('works with loops in container', () => {
-    const container = [tf.scalar(1), tf.scalar(2), [tf.scalar(3)]];
-    const innerContainer = [container];
-    // tslint:disable-next-line:no-any
-    container.push(innerContainer as any);
-    const results = getTensorsInContainer(container);
-    expect(results.length).toBe(3);
-  });
-});
-
-describeWithFlags('convertToTensor', ALL_ENVS, () => {
-  it('primitive integer, NaN converts to zero, no error thrown', async () => {
-    const a = () => convertToTensor(NaN, 'a', 'test', 'int32');
-    expect(a).not.toThrowError();
-
-    const b = convertToTensor(NaN, 'b', 'test', 'int32');
-    expect(b.rank).toBe(0);
-    expect(b.dtype).toBe('int32');
-    expectArraysClose(await b.data(), 0);
-  });
-
-  it('primitive number', async () => {
-    const a = convertToTensor(3, 'a', 'test');
-    expect(a.rank).toBe(0);
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), 3);
-  });
-
-  it('primitive integer, NaN converts to zero', async () => {
-    const a = convertToTensor(NaN, 'a', 'test', 'int32');
-    expect(a.rank).toBe(0);
-    expect(a.dtype).toBe('int32');
-    expectArraysClose(await a.data(), 0);
-  });
-
-  it('primitive boolean, parsed as bool tensor', async () => {
-    const a = convertToTensor(true, 'a', 'test');
-    expect(a.rank).toBe(0);
-    expect(a.dtype).toBe('bool');
-    expectArraysClose(await a.data(), 1);
-  });
-
-  it('primitive boolean, forced to be parsed as bool tensor', async () => {
-    const a = convertToTensor(true, 'a', 'test', 'bool');
-    expect(a.rank).toBe(0);
-    expect(a.dtype).toBe('bool');
-    expectArraysEqual(await a.data(), 1);
-  });
-
-  it('array1d', async () => {
-    const a = convertToTensor([1, 2, 3], 'a', 'test');
-    expect(a.rank).toBe(1);
-    expect(a.dtype).toBe('float32');
-    expect(a.shape).toEqual([3]);
-    expectArraysClose(await a.data(), [1, 2, 3]);
-  });
-
-  it('array2d', async () => {
-    const a = convertToTensor([[1], [2], [3]], 'a', 'test');
-    expect(a.rank).toBe(2);
-    expect(a.shape).toEqual([3, 1]);
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), [1, 2, 3]);
-  });
-
-  it('array3d', async () => {
-    const a = convertToTensor([[[1], [2]], [[3], [4]]], 'a', 'test');
-    expect(a.rank).toBe(3);
-    expect(a.shape).toEqual([2, 2, 1]);
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('array4d', async () => {
-    const a = convertToTensor([[[[1]], [[2]]], [[[3]], [[4]]]], 'a', 'test');
-    expect(a.rank).toBe(4);
-    expect(a.shape).toEqual([2, 2, 1, 1]);
-    expect(a.dtype).toBe('float32');
-    expectArraysClose(await a.data(), [1, 2, 3, 4]);
-  });
-
-  it('passing a tensor returns the tensor itself', () => {
-    const s = tf.scalar(3);
-    const res = convertToTensor(s, 'a', 'test');
-    expect(res === s).toBe(true);
-  });
-
-  it('passing a tensor with wrong type errors', () => {
-    const s = tf.scalar(3);
-    expect(() => convertToTensor(s, 'p', 'f', 'bool'))
-        .toThrowError(
-            /Argument 'p' passed to 'f' must be bool tensor, but got float32/);
-  });
-
-  it('fails when passed a string and force numeric is true', () => {
-    const expectedDtype = 'numeric';
-    expect(() => convertToTensor('hello', 'p', 'test', expectedDtype))
-        .toThrowError();
-  });
-
-  it('force numeric is true by default', () => {
-    // Should fail to parse a string tensor since force numeric is true.
-    expect(() => convertToTensor('hello', 'p', 'test')).toThrowError();
-  });
-
-  it('primitive string, do not force numeric', () => {
-    const t = convertToTensor('hello', 'p', 'test', 'string_or_numeric');
-    expect(t.dtype).toBe('string');
-    expect(t.shape).toEqual([]);
-  });
-
-  it('string[], do not force numeric', () => {
-    const t =
-        convertToTensor(['a', 'b', 'c'], 'p', 'test', 'string_or_numeric');
-    expect(t.dtype).toBe('string');
-    expect(t.shape).toEqual([3]);
-  });
-
-  it('string, explicitly parse as bool', () => {
-    expect(() => convertToTensor('a', 'argName', 'func', 'bool'))
-        .toThrowError(
-            'Argument \'argName\' passed to \'func\' must be bool tensor' +
-            ', but got string tensor');
-  });
-
-  it('string, throw error if pass null.', () => {
-    expect(() => convertToTensor('a', 'argName', 'func', null))
-        .toThrowError('Expected dtype cannot be null.');
-  });
-
-  it('fails to convert a dict to tensor', () => {
-    expect(() => convertToTensor({} as number, 'a', 'test'))
-        .toThrowError(
-            'Argument \'a\' passed to \'test\' must be a Tensor ' +
-            'or TensorLike, but got \'Object\'');
-  });
-
-  it('fails to convert a string to tensor', () => {
-    expect(() => convertToTensor('asdf', 'a', 'test'))
-        .toThrowError(
-            'Argument \'a\' passed to \'test\' must be numeric tensor, ' +
-            'but got string tensor');
-  });
-});
-
-describeWithFlags('convertToTensor debug mode', ALL_ENVS, () => {
-  beforeAll(() => {
-    // Silence debug warnings.
-    spyOn(console, 'warn');
-    tf.enableDebugMode();
-  });
-
-  it('fails to convert a non-valid shape array to tensor', () => {
-    const a = [[1, 2], [3], [4, 5, 6]];  // 2nd element has only 1 entry.
-    expect(() => convertToTensor(a, 'a', 'test'))
-        .toThrowError(
-            'Element arr[1] should have 2 elements, but has 1 elements');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/test_async_backends.ts b/tfjs-master/tfjs-core/src/test_async_backends.ts
deleted file mode 100644
index 02111e6e2..000000000
--- a/tfjs-master/tfjs-core/src/test_async_backends.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-/**
- * This file tests that we don't have any dataSyncs in the unconstrainted tests
- * so that we can run backends that have async init and async data reads against
- * our exported test files.
- */
-
-// Use require here to workaround this being a circular dependency.
-// This should only be done in tests.
-// tslint:disable-next-line: no-require-imports
-require('@tensorflow/tfjs-backend-cpu');
-import './index';
-import './public/chained_ops/register_all_chained_ops';
-import './register_all_gradients';
-import {setTestEnvs} from './jasmine_util';
-import {registerBackend, engine} from './globals';
-import {KernelBackend} from './backends/backend';
-import {getKernelsForBackend, registerKernel} from './kernel_registry';
-
-// tslint:disable-next-line:no-require-imports
-const jasmine = require('jasmine');
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-class AsyncCPUBackend extends KernelBackend {}
-const asyncBackend = new AsyncCPUBackend();
-
-// backend is cast as any so that we can access methods through bracket
-// notation.
-const backend: KernelBackend = engine().findBackend('cpu');
-const proxyBackend = new Proxy(asyncBackend, {
-  get(target, name, receiver) {
-    if (name === 'readSync') {
-      throw new Error(
-          `Found dataSync() in a unit test. This is disabled so unit tests ` +
-          `can run in backends that only support async data. Please use ` +
-          `.data() in unit tests or if you truly are testing dataSync(), ` +
-          `constrain your test with SYNC_BACKEND_ENVS`);
-    }
-    //@ts-ignore;
-    const origSymbol = backend[name];
-    if (typeof origSymbol === 'function') {
-      // tslint:disable-next-line:no-any
-      return (...args: any[]) => {
-        return origSymbol.apply(backend, args);
-      };
-    } else {
-      return origSymbol;
-    }
-  }
-});
-
-const proxyBackendName = 'test-async-cpu';
-
-// The registration is async on purpose, so we know our testing infra works
-// with backends that have async init (e.g. WASM and WebGPU).
-registerBackend(proxyBackendName, async () => proxyBackend);
-
-// All the kernels are registered under the 'cpu' name, so we need to
-// register them also under the proxy backend name.
-const kernels = getKernelsForBackend('cpu');
-kernels.forEach(({kernelName, kernelFunc, setupFunc}) => {
-  registerKernel(
-      {kernelName, backendName: proxyBackendName, kernelFunc, setupFunc});
-});
-
-setTestEnvs([{
-  name: proxyBackendName,
-  backendName: proxyBackendName,
-  isDataSync: false,
-}]);
-
-const runner = new jasmine();
-
-runner.loadConfig({spec_files: ['tfjs-core/src/**/**_test.js'], random: false});
-runner.execute();
diff --git a/tfjs-master/tfjs-core/src/test_node.ts b/tfjs-master/tfjs-core/src/test_node.ts
deleted file mode 100644
index 63c1a6785..000000000
--- a/tfjs-master/tfjs-core/src/test_node.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/env node
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {setTestEnvs} from './jasmine_util';
-
-// tslint:disable-next-line:no-require-imports
-const jasmine = require('jasmine');
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-setTestEnvs([{name: 'node', backendName: 'cpu'}]);
-
-const runner = new jasmine();
-runner.loadConfig({spec_files: ['tfjs-core/src/setup_test.js'], random: false});
-runner.execute();
diff --git a/tfjs-master/tfjs-core/src/test_util.ts b/tfjs-master/tfjs-core/src/test_util.ts
deleted file mode 100644
index 3e82c6307..000000000
--- a/tfjs-master/tfjs-core/src/test_util.ts
+++ /dev/null
@@ -1,224 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENGINE} from './engine';
-import {inferShape} from './tensor_util_env';
-import {RecursiveArray, TensorLike, TypedArray} from './types';
-import {arraysEqual, encodeString, flatten, isString, isTypedArray} from './util';
-
-const TEST_EPSILON_FLOAT32 = 1e-3;
-export const TEST_EPSILON_FLOAT16 = 1e-1;
-
-export function expectArraysClose(
-    actual: TypedArray|number|RecursiveArray<number>,
-    expected: TypedArray|number|RecursiveArray<number>, epsilon?: number) {
-  if (epsilon == null) {
-    epsilon = testEpsilon();
-  }
-  return expectArraysPredicate(
-      actual, expected, (a, b) => areClose(a as number, b as number, epsilon));
-}
-
-export function testEpsilon() {
-  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :
-                                                  TEST_EPSILON_FLOAT16;
-}
-
-function expectArraysPredicate(
-    actual: TensorLike, expected: TensorLike,
-    predicate: (a: {}, b: {}) => boolean) {
-  let checkClassType = true;
-  if (isTypedArray(actual) || isTypedArray(expected)) {
-    checkClassType = false;
-  }
-  if (isTypedArray(actual) && isTypedArray(expected)) {
-    checkClassType = true;
-  }
-  if (checkClassType) {
-    const aType = actual.constructor.name;
-    const bType = expected.constructor.name;
-
-    if (aType !== bType) {
-      throw new Error(
-          `Arrays are of different type. Actual: ${aType}. ` +
-          `Expected: ${bType}`);
-    }
-  }
-
-  if (Array.isArray(actual) && Array.isArray(expected)) {
-    const actualShape = inferShape(actual);
-    const expectedShape = inferShape(expected);
-    if (!arraysEqual(actualShape, expectedShape)) {
-      throw new Error(
-          `Arrays have different shapes. ` +
-          `Actual: [${actualShape}]. Expected: [${expectedShape}]`);
-    }
-  }
-
-  const actualFlat =
-      isTypedArray(actual) ? actual : flatten(actual as RecursiveArray<number>);
-  const expectedFlat = isTypedArray(expected) ?
-      expected :
-      flatten(expected as RecursiveArray<number>);
-
-  if (actualFlat.length !== expectedFlat.length) {
-    throw new Error(
-        `Arrays have different lengths actual: ${actualFlat.length} vs ` +
-        `expected: ${expectedFlat.length}.\n` +
-        `Actual:   ${actualFlat}.\n` +
-        `Expected: ${expectedFlat}.`);
-  }
-  for (let i = 0; i < expectedFlat.length; ++i) {
-    const a = actualFlat[i];
-    const e = expectedFlat[i];
-
-    if (!predicate(a, e)) {
-      throw new Error(
-          `Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\n` +
-          `Actual:   ${actualFlat}.\n` +
-          `Expected: ${expectedFlat}.`);
-    }
-  }
-  if (typeof expect !== 'undefined') {
-    expect().nothing();
-  }
-}
-
-export interface DoneFn {
-  (): void;
-  fail: (message?: Error|string) => void;
-}
-
-export function expectPromiseToFail(fn: () => Promise<{}>, done: DoneFn): void {
-  fn().then(() => done.fail(), () => done());
-  if (typeof expect !== 'undefined') {
-    expect().nothing();
-  }
-}
-
-export function expectArraysEqual(actual: TensorLike, expected: TensorLike) {
-  const exp = typeof expected === 'string' || typeof expected === 'number' ||
-          typeof expected === 'boolean' ?
-      [expected] as number[] :
-      expected as number[];
-  if (isString(actual) || isString((actual as string[])[0]) ||
-      isString(expected) || isString((expected as string[])[0])) {
-    // tslint:disable-next-line: triple-equals
-    return expectArraysPredicate(actual, exp, (a, b) => a == b);
-  }
-  return expectArraysPredicate(
-      actual, expected, (a, b) => areClose(a as number, b as number, 0));
-}
-
-export function expectNumbersClose(a: number, e: number, epsilon?: number) {
-  if (epsilon == null) {
-    epsilon = testEpsilon();
-  }
-  if (!areClose(a, e, epsilon)) {
-    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
-  }
-  if (typeof expect !== 'undefined') {
-    expect().nothing();
-  }
-}
-
-function areClose(a: number, e: number, epsilon: number): boolean {
-  if (!isFinite(a) && !isFinite(e)) {
-    return true;
-  }
-  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {
-    return false;
-  }
-  return true;
-}
-
-export function expectValuesInRange(
-    actual: TypedArray|number[], low: number, high: number) {
-  for (let i = 0; i < actual.length; i++) {
-    if (actual[i] < low || actual[i] > high) {
-      throw new Error(
-          `Value out of range:${actual[i]} low: ${low}, high: ${high}`);
-    }
-  }
-}
-
-export function expectArrayBuffersEqual(
-    actual: ArrayBuffer, expected: ArrayBuffer) {
-  // Safari does not like comparing ArrayBuffers directly. Wrapping in
-  // a Float32Array solves this issue.
-  const actualArray = new Float32Array(actual);
-  const expectedArray = new Float32Array(expected);
-  if (actualArray.length !== expectedArray.length) {
-    throw new Error(
-        'Expected ArrayBuffer to be of length ' +
-        `${expectedArray.length}, but it was ${actualArray.length}`);
-  }
-
-  for (let i = 0; i < expectedArray.length; i++) {
-    if (actualArray[i] !== expectedArray[i]) {
-      throw new Error(
-          `Expected ArrayBuffer value at ${i} to be ` +
-          `${expectedArray[i]} but got ${actualArray[i]} instead`);
-    }
-  }
-}
-
-/** Encodes strings into utf-8 bytes. */
-export function encodeStrings(a: RecursiveArray<{}>):
-    RecursiveArray<Uint8Array> {
-  for (let i = 0; i < (a as Array<{}>).length; i++) {
-    const val = a[i];
-    if (Array.isArray(val)) {
-      encodeStrings(val);
-    } else {
-      a[i] = encodeString(val as string);
-    }
-  }
-  return a as RecursiveArray<Uint8Array>;
-}
-
-/** Creates an HTMLVideoElement with autoplay-friendly default settings. */
-export function createVideoElement(source: HTMLSourceElement):
-    Promise<HTMLVideoElement> {
-  const video = document.createElement('video');
-  if ('playsInline' in video) {
-    // tslint:disable-next-line:no-any
-    (video as any).playsInline = true;
-  }
-  video.muted = true;
-  video.loop = true;
-  video.style.position = 'fixed';
-  video.style.left = '0px';
-  video.style.top = '0px';
-
-  video.preload = 'auto';
-  video.appendChild(source);
-  return new Promise(resolve => {
-    video.addEventListener('loadeddata', _ => resolve(video));
-    video.load();
-  });
-}
-
-export async function play(video: HTMLVideoElement) {
-  await video.play();
-  if ('requestVideoFrameCallback' in video) {
-    await new Promise(resolve => {
-      // tslint:disable-next-line:no-any
-      (video as any).requestVideoFrameCallback(resolve);
-    });
-  }
-}
diff --git a/tfjs-master/tfjs-core/src/test_util_test.ts b/tfjs-master/tfjs-core/src/test_util_test.ts
deleted file mode 100644
index 20559be81..000000000
--- a/tfjs-master/tfjs-core/src/test_util_test.ts
+++ /dev/null
@@ -1,148 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-import {expectArraysClose, expectArraysEqual} from './test_util';
-
-describeWithFlags('expectArraysEqual', ALL_ENVS, () => {
-  it('same arrays', () => {
-    expectArraysEqual([1, 2, 3], [1, 2, 3]);
-  });
-
-  it('throws on different arrays', () => {
-    expect(() => expectArraysEqual([1, 2, 3], [3, 2, 1]))
-        .toThrowError(/Arrays differ/);
-  });
-
-  it('same nested arrays', () => {
-    expectArraysEqual([[1, 2], [3, 4]], [[1, 2], [3, 4]]);
-  });
-
-  it('throws on different nested arrays', () => {
-    expect(() => expectArraysEqual([[1, 2], [3, 4]], [[1, 2], [4, 3]]))
-        .toThrowError(/Arrays differ/);
-  });
-
-  it('throws on different nested shapes', () => {
-    expect(() => expectArraysEqual([[1, 2], [3, 4]], [1, 2, 3, 4]))
-        .toThrowError(
-            /Arrays have different shapes. Actual: \[2,2\]. Expected: \[4\]/);
-  });
-
-  it('float32 with regular array', () => {
-    expectArraysEqual(new Float32Array([1, 2, 3]), [1, 2, 3]);
-  });
-
-  it('throws on different values of float32 with regular array', () => {
-    expect(() => expectArraysEqual(new Float32Array([1, 2, 3]), [1, 2, 4]))
-        .toThrowError(/Arrays differ/);
-  });
-
-  it('int32 with regular array', () => {
-    expectArraysEqual(new Int32Array([1, 2, 3]), [1, 2, 3]);
-  });
-
-  it('throws on different values of int32 with regular array', () => {
-    expect(() => expectArraysEqual(new Int32Array([1, 2, 3]), [1, 2, 4]))
-        .toThrowError(/Arrays differ/);
-  });
-
-  it('throws on float32 with int32', () => {
-    expect(
-        () => expectArraysEqual(
-            new Float32Array([1, 2, 3]), new Int32Array([1, 2, 3])))
-        .toThrowError(/Arrays are of different type/);
-  });
-
-  it('throws on int32 with uint8', () => {
-    expect(
-        () => expectArraysEqual(
-            new Int32Array([1, 2, 3]), new Uint8Array([1, 2, 3])))
-        .toThrowError(/Arrays are of different type/);
-  });
-});
-
-describeWithFlags('expectArraysClose', ALL_ENVS, () => {
-  it('same arrays', () => {
-    expectArraysClose([1, 2, 3], [1, 2, 3]);
-  });
-
-  it('throws on different arrays', () => {
-    expect(() => expectArraysClose([1, 2, 3], [3, 2, 1]))
-        .toThrowError(/Arrays differ/);
-  });
-
-  it('same nested arrays', () => {
-    expectArraysClose([[1, 2], [3, 4]], [[1, 2], [3, 4]]);
-  });
-
-  it('throws on different nested arrays', () => {
-    expect(() => expectArraysClose([[1, 2], [3, 4]], [[1, 2], [4, 3]]))
-        .toThrowError(/Arrays differ/);
-  });
-
-  it('throws on different nested shapes', () => {
-    expect(() => expectArraysClose([[1, 2], [3, 4]], [1, 2, 3, 4]))
-        .toThrowError(
-            /Arrays have different shapes. Actual: \[2,2\]. Expected: \[4\]/);
-  });
-
-  it('float32 with regular array', () => {
-    expectArraysClose(new Float32Array([1, 2, 3]), [1, 2, 3]);
-  });
-
-  it('throws on different values of float32 with regular array', () => {
-    expect(() => expectArraysClose(new Float32Array([1, 2, 3]), [1, 2, 4]))
-        .toThrowError(/Arrays differ/);
-  });
-
-  it('int32 with regular array', () => {
-    expectArraysClose(new Int32Array([1, 2, 3]), [1, 2, 3]);
-  });
-
-  it('throws on different values of int32 with regular array', () => {
-    expect(() => expectArraysClose(new Int32Array([1, 2, 3]), [1, 2, 4]))
-        .toThrowError(/Arrays differ/);
-  });
-
-  it('throws on float32 with int32', () => {
-    expect(
-        () => expectArraysClose(
-            new Float32Array([1, 2, 3]), new Int32Array([1, 2, 3])))
-        .toThrowError(/Arrays are of different type/);
-  });
-
-  it('throws on int32 with uint8', () => {
-    expect(
-        () => expectArraysClose(
-            new Int32Array([1, 2, 3]), new Uint8Array([1, 2, 3])))
-        .toThrowError(/Arrays are of different type/);
-  });
-
-  it('similar arrays with good epsilon', () => {
-    const epsilon = 0.1;
-    expectArraysClose(new Float32Array([1, 2, 3.08]), [1, 2, 3], epsilon);
-  });
-
-  it('similar arrays with bad epsilon', () => {
-    const epsilon = 0.01;
-    expect(
-        () => expectArraysClose(
-            new Float32Array([1, 2, 3.08]), [1, 2, 3], epsilon))
-        .toThrowError(/Arrays differ/);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/train.ts b/tfjs-master/tfjs-core/src/train.ts
deleted file mode 100644
index b470ae8a7..000000000
--- a/tfjs-master/tfjs-core/src/train.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {OptimizerConstructors} from './optimizers/optimizer_constructors';
-
-export const train = OptimizerConstructors;
diff --git a/tfjs-master/tfjs-core/src/types.ts b/tfjs-master/tfjs-core/src/types.ts
deleted file mode 100644
index 994e447ba..000000000
--- a/tfjs-master/tfjs-core/src/types.ts
+++ /dev/null
@@ -1,246 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/** @docalias number[] */
-export interface ShapeMap {
-  R0: number[];
-  R1: [number];
-  R2: [number, number];
-  R3: [number, number, number];
-  R4: [number, number, number, number];
-  R5: [number, number, number, number, number];
-  R6: [number, number, number, number, number, number];
-}
-
-/** @docalias number[] */
-export interface ArrayMap {
-  R0: number;
-  R1: number[];
-  R2: number[][];
-  R3: number[][][];
-  R4: number[][][][];
-  R5: number[][][][][];
-  R6: number[][][][][][];
-}
-
-export interface DataTypeMap {
-  float32: Float32Array;
-  int32: Int32Array;
-  bool: Uint8Array;
-  complex64: Float32Array;
-  string: string[];
-}
-
-export interface SingleValueMap {
-  bool: boolean;
-  int32: number;
-  float32: number;
-  complex64: number;
-  string: string;
-}
-
-/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */
-export type DataType = keyof DataTypeMap;
-export type NumericDataType = 'float32'|'int32'|'bool'|'complex64';
-
-export type DataTypeFor<T extends number|string|boolean> =
-    T extends number|boolean ? NumericDataType : T extends string ? 'string' :
-                                                                    never;
-
-export type TypedArray = Float32Array|Int32Array|Uint8Array;
-/** Tensor data used in tensor creation and user-facing API. */
-export type DataValues = DataTypeMap[DataType];
-/** The underlying tensor data that gets stored in a backend. */
-export type BackendValues = Float32Array|Int32Array|Uint8Array|Uint8Array[];
-
-export enum Rank {
-  R0 = 'R0',
-  R1 = 'R1',
-  R2 = 'R2',
-  R3 = 'R3',
-  R4 = 'R4',
-  R5 = 'R5',
-  R6 = 'R6'
-}
-
-export type FlatVector = boolean[]|number[]|TypedArray;
-export type RegularArray<T> =
-    T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];
-
-// tslint:disable-next-line:no-any
-export interface RecursiveArray<T extends any> {
-  [index: number]: T|RecursiveArray<T>;
-}
-
-// Looks for upcasting types. Used, for example, in operations with mixed dtype
-// inputs.
-enum UpcastInt32AndMap {
-  'float32' = 'float32',
-  'int32' = 'int32',
-  'bool' = 'int32',
-  'complex64' = 'complex64'
-}
-
-enum UpcastBoolAndMap {
-  'float32' = 'float32',
-  'int32' = 'int32',
-  'bool' = 'bool',
-  'complex64' = 'complex64'
-}
-
-enum UpcastFloat32AndMap {
-  'float32' = 'float32',
-  'int32' = 'float32',
-  'bool' = 'float32',
-  'complex64' = 'complex64'
-}
-
-enum UpcastComplex64AndMap {
-  'float32' = 'complex64',
-  'int32' = 'complex64',
-  'bool' = 'complex64',
-  'complex64' = 'complex64'
-}
-
-const upcastTypeMap = {
-  'float32': UpcastFloat32AndMap,
-  'int32': UpcastInt32AndMap,
-  'bool': UpcastBoolAndMap,
-  'complex64': UpcastComplex64AndMap
-};
-
-export function upcastType(typeA: DataType, typeB: DataType): DataType {
-  if (typeA === 'string' || typeB === 'string') {
-    if (typeA === 'string' && typeB === 'string') {
-      return 'string';
-    }
-    throw new Error(`Can not upcast ${typeA} with ${typeB}`);
-  }
-  return upcastTypeMap[typeA][typeB];
-}
-
-/** Returns the output type after summation. */
-export function sumOutType(type: DataType): DataType {
-  return upcastType(type, 'int32');
-}
-
-/** @docalias TypedArray|Array */
-export type TensorLike =
-    TypedArray|number|boolean|string|RecursiveArray<number|number[]|TypedArray>|
-    RecursiveArray<boolean>|RecursiveArray<string>|Uint8Array[];
-export type ScalarLike = number|boolean|string|Uint8Array;
-/** @docalias TypedArray|Array */
-export type TensorLike1D = TypedArray|number[]|boolean[]|string[]|Uint8Array[];
-/** @docalias TypedArray|Array */
-export type TensorLike2D = TypedArray|number[]|number[][]|boolean[]|boolean[][]|
-    string[]|string[][]|Uint8Array[]|Uint8Array[][];
-/** @docalias TypedArray|Array */
-export type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|
-    boolean[][][]|string[]|string[][][]|Uint8Array[]|Uint8Array[][][];
-/** @docalias TypedArray|Array */
-export type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|
-    boolean[][][][]|string[]|string[][][][]|Uint8Array[]|Uint8Array[][][][];
-/** @docalias TypedArray|Array */
-export type TensorLike5D =
-    TypedArray|number[]|number[][][][][]|boolean[]|boolean[][][][][]|string[]|
-    string[][][][][]|Uint8Array[]|Uint8Array[][][][][];
-/** @docalias TypedArray|Array */
-export type TensorLike6D =
-    TypedArray|number[]|number[][][][][][]|boolean[]|boolean[][][][][][]|
-    string[]|string[][][][][][]|Uint8Array[]|Uint8Array[][][][][];
-
-/** Type for representing image data in Uint8Array type. */
-export interface PixelData {
-  width: number;
-  height: number;
-  data: Uint8Array;
-}
-
-/**
- * Type for representing all permutations and combinations of 'RGBA' channels.
- */
-export type WebGLChannels = 'A'|'B'|'G'|'R'|'AB'|'AG'|'AR'|'BA'|'BG'|'BR'|'GA'|
-    'GB'|'GR'|'RA'|'RB'|'RG'|'ABG'|'ABR'|'AGB'|'AGR'|'ARB'|'ARG'|'BAG'|'BAR'|
-    'BGA'|'BGR'|'BRA'|'BRG'|'GAB'|'GAR'|'GBA'|'GBR'|'GRA'|'GRB'|'RAB'|'RAG'|
-    'RBA'|'RBG'|'RGA'|'RGB'|'ABGR'|'ABRG'|'AGBR'|'AGRB'|'ARBG'|'ARGB'|'BAGR'|
-    'BARG'|'BGAR'|'BGRA'|'BRAG'|'BRGA'|'GABR'|'GARB'|'GBAR'|'GBRA'|'GRAB'|
-    'GRBA'|'RABG'|'RAGB'|'RBAG'|'RBGA'|'RGAB'|'RGBA';
-
-/** Type for representing a texture data to create a tensor. */
-export interface WebGLData {
-  texture: WebGLTexture;
-  height: number;
-  width: number;
-  channels: WebGLChannels;
-}
-
-/**
- * Type for representing a buffer data to create a tensor. Buffer usage should
- * at least support GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC. When
- * zeroCopy is false or undefined (default), this GPUBuffer will be copied to
- * the tensor's resource buffer. When zeroCopy is true, tensor will use this
- * GPUBuffer as tensor's resource buffer, user should not destroy this GPUBuffer
- * until all access is done. If not specified at creating a tensor, tensor type
- * is float32.
- */
-export interface WebGPUData {
-  buffer: GPUBuffer;
-  zeroCopy?: boolean;
-}
-
-export function isWebGLData(values: unknown): values is WebGLData {
-  return values != null && typeof values === 'object' && 'texture' in values &&
-      values.texture instanceof WebGLTexture;
-}
-export function isWebGPUData(values: unknown): values is WebGPUData {
-  return typeof GPUBuffer !== 'undefined' && values != null &&
-      typeof values === 'object' && 'buffer' in values &&
-      values.buffer instanceof GPUBuffer;
-}
-
-export interface ImageOptions {
-  /**
-   * Optional. A number in range [0-1]. If the image is a 2D tensor or a 3D
-   * tensor with 1 or 3 channels, the alpha channels would set as its value;
-   * otherwise, it would not make effects.
-   */
-  alpha?: number;
-}
-
-export interface ContextOptions {
-  /**
-   * Optional.  If the canvas has created a context, it would not make effects.
-   * If it is not set, it would be variable based on the current backend.
-   */
-  contextType?: string;
-  /**
-   * Optional. A WebGLContextAttributes configuration. If the canvas has created
-   * a context, it would not make effects.
-   */
-  contextAttributes?: WebGLContextAttributes;
-}
-
-export interface DrawOptions {
-  /**
-   * Optional. An object of options to customize the values of image tensor.
-   */
-  imageOptions?: ImageOptions;
-  /**
-   * Optional. An object to configure the context of the canvas to draw to.
-   */
-  contextOptions?: ContextOptions;
-}
diff --git a/tfjs-master/tfjs-core/src/types_test.ts b/tfjs-master/tfjs-core/src/types_test.ts
deleted file mode 100644
index ad9c89683..000000000
--- a/tfjs-master/tfjs-core/src/types_test.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {upcastType} from './types';
-
-describe('upcastType', () => {
-  it('upcasts bool to bool', () => {
-    expect(upcastType('bool', 'bool')).toBe('bool');
-  });
-
-  it('upcasts bool/int32 to int32', () => {
-    expect(upcastType('bool', 'int32')).toBe('int32');
-    expect(upcastType('int32', 'int32')).toBe('int32');
-  });
-
-  it('upcasts bool/int32/float32 to float32', () => {
-    expect(upcastType('bool', 'float32')).toBe('float32');
-    expect(upcastType('int32', 'float32')).toBe('float32');
-    expect(upcastType('float32', 'float32')).toBe('float32');
-  });
-
-  it('upcasts bool/int32/float32/complex64 to complex64', () => {
-    expect(upcastType('bool', 'complex64')).toBe('complex64');
-    expect(upcastType('int32', 'complex64')).toBe('complex64');
-    expect(upcastType('float32', 'complex64')).toBe('complex64');
-    expect(upcastType('complex64', 'complex64')).toBe('complex64');
-  });
-
-  it('fails to upcast anything other than string with string', () => {
-    expect(() => upcastType('bool', 'string')).toThrowError();
-    expect(() => upcastType('int32', 'string')).toThrowError();
-    expect(() => upcastType('float32', 'string')).toThrowError();
-    expect(() => upcastType('complex64', 'string')).toThrowError();
-    // Ok upcasting string to string.
-    expect(upcastType('string', 'string')).toBe('string');
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/util.ts b/tfjs-master/tfjs-core/src/util.ts
deleted file mode 100644
index 644a81196..000000000
--- a/tfjs-master/tfjs-core/src/util.ts
+++ /dev/null
@@ -1,193 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from './environment';
-import {isTypedArrayBrowser} from './platforms/is_typed_array_browser';
-import {BackendValues, DataType, RecursiveArray, TensorLike, TypedArray} from './types';
-import * as base from './util_base';
-export * from './util_base';
-export * from './hash_util';
-
-/**
- * Create typed array for scalar value. Used for storing in `DataStorage`.
- */
-export function createScalarValue(
-    value: DataType, dtype: DataType): BackendValues {
-  if (dtype === 'string') {
-    return encodeString(value);
-  }
-
-  return toTypedArray([value], dtype);
-}
-
-function noConversionNeeded(a: TensorLike, dtype: DataType): boolean {
-  return (a instanceof Float32Array && dtype === 'float32') ||
-      (a instanceof Int32Array && dtype === 'int32') ||
-      (a instanceof Uint8Array && dtype === 'bool');
-}
-
-export function toTypedArray(a: TensorLike, dtype: DataType): TypedArray {
-  if (dtype === 'string') {
-    throw new Error('Cannot convert a string[] to a TypedArray');
-  }
-  if (Array.isArray(a)) {
-    a = flatten(a);
-  }
-
-  if (env().getBool('DEBUG')) {
-    base.checkConversionForErrors(a as number[], dtype);
-  }
-  if (noConversionNeeded(a, dtype)) {
-    return a as TypedArray;
-  }
-  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
-    return new Float32Array(a as number[]);
-  } else if (dtype === 'int32') {
-    return new Int32Array(a as number[]);
-  } else if (dtype === 'bool') {
-    const bool = new Uint8Array((a as number[]).length);
-    for (let i = 0; i < bool.length; ++i) {
-      if (Math.round((a as number[])[i]) !== 0) {
-        bool[i] = 1;
-      }
-    }
-    return bool;
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-
-/**
- * Returns the current high-resolution time in milliseconds relative to an
- * arbitrary time in the past. It works across different platforms (node.js,
- * browsers).
- *
- * ```js
- * console.log(tf.util.now());
- * ```
- *
- * @doc {heading: 'Util', namespace: 'util'}
- */
-export function now(): number {
-  return env().platform.now();
-}
-
-/**
- * Returns a platform-specific implementation of
- * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).
- *
- * If `fetch` is defined on the global object (`window`, `process`, etc.),
- * `tf.util.fetch` returns that function.
- *
- * If not, `tf.util.fetch` returns a platform-specific solution.
- *
- * ```js
- * const resource = await tf.util.fetch('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs');
- * // handle response
- * ```
- *
- * @doc {heading: 'Util'}
- */
-export function fetch(
-    path: string, requestInits?: RequestInit): Promise<Response> {
-  return env().platform.fetch(path, requestInits);
-}
-
-/**
- * Encodes the provided string into bytes using the provided encoding scheme.
- *
- * @param s The string to encode.
- * @param encoding The encoding scheme. Defaults to utf-8.
- *
- * @doc {heading: 'Util'}
- */
-export function encodeString(s: string, encoding = 'utf-8'): Uint8Array {
-  encoding = encoding || 'utf-8';
-  return env().platform.encode(s, encoding);
-}
-
-/**
- * Decodes the provided bytes into a string using the provided encoding scheme.
- * @param bytes The bytes to decode.
- *
- * @param encoding The encoding scheme. Defaults to utf-8.
- *
- * @doc {heading: 'Util'}
- */
-export function decodeString(bytes: Uint8Array, encoding = 'utf-8'): string {
-  encoding = encoding || 'utf-8';
-  return env().platform.decode(bytes, encoding);
-}
-
-export function isTypedArray(a: {}): a is Float32Array|Int32Array|Uint8Array|
-    Uint8ClampedArray {
-  // TODO(mattsoulanille): Remove this fallback in 5.0.0
-  if (env().platform.isTypedArray != null) {
-    return env().platform.isTypedArray(a);
-  } else {
-    return isTypedArrayBrowser(a);
-  }
-}
-
-// NOTE: We explicitly type out what T extends instead of any so that
-// util.flatten on a nested array of number doesn't try to infer T as a
-// number[][], causing us to explicitly type util.flatten<number>().
-/**
- *  Flattens an arbitrarily nested array.
- *
- * ```js
- * const a = [[1, 2], [3, 4], [5, [6, [7]]]];
- * const flat = tf.util.flatten(a);
- * console.log(flat);
- * ```
- *
- *  @param arr The nested array to flatten.
- *  @param result The destination array which holds the elements.
- *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults
- *      to false.
- *
- * @doc {heading: 'Util', namespace: 'util'}
- */
-export function
-flatten<T extends number|boolean|string|Promise<number>|TypedArray>(
-    arr: T|RecursiveArray<T>, result: T[] = [], skipTypedArray = false): T[] {
-  if (result == null) {
-    result = [];
-  }
-  if (typeof arr === 'boolean' || typeof arr === 'number' ||
-    typeof arr === 'string' || base.isPromise(arr) || arr == null ||
-      isTypedArray(arr) && skipTypedArray) {
-    result.push(arr as T);
-  } else if (Array.isArray(arr) || isTypedArray(arr)) {
-    for (let i = 0; i < arr.length; ++i) {
-      flatten(arr[i], result, skipTypedArray);
-    }
-  } else {
-    let maxIndex = -1;
-    for (const key of Object.keys(arr)) {
-      // 0 or positive integer.
-      if (/^([1-9]+[0-9]*|0)$/.test(key)) {
-        maxIndex = Math.max(maxIndex, Number(key));
-      }
-    }
-    for (let i = 0; i <= maxIndex; i++) {
-      // tslint:disable-next-line: no-unnecessary-type-assertion
-      flatten((arr as RecursiveArray<T>)[i], result, skipTypedArray);
-    }
-  }
-  return result;
-}
diff --git a/tfjs-master/tfjs-core/src/util_base.ts b/tfjs-master/tfjs-core/src/util_base.ts
deleted file mode 100644
index cb7498b21..000000000
--- a/tfjs-master/tfjs-core/src/util_base.ts
+++ /dev/null
@@ -1,747 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BackendValues, DataType, DataTypeMap, FlatVector, NumericDataType, TensorLike, TypedArray, WebGLData, WebGPUData} from './types';
-
-/**
- * Shuffles the array in-place using Fisher-Yates algorithm.
- *
- * ```js
- * const a = [1, 2, 3, 4, 5];
- * tf.util.shuffle(a);
- * console.log(a);
- * ```
- *
- * @param array The array to shuffle in-place.
- *
- * @doc {heading: 'Util', namespace: 'util'}
- */
-// tslint:disable-next-line:no-any
-export function shuffle(array: any[]|Uint32Array|Int32Array|
-                        Float32Array): void {
-  let counter = array.length;
-  let index = 0;
-  // While there are elements in the array
-  while (counter > 0) {
-    // Pick a random index
-    index = (Math.random() * counter) | 0;
-    // Decrease counter by 1
-    counter--;
-    // And swap the last element with it
-    swap(array, counter, index);
-  }
-}
-
-/**
- * Shuffles two arrays in-place the same way using Fisher-Yates algorithm.
- *
- * ```js
- * const a = [1,2,3,4,5];
- * const b = [11,22,33,44,55];
- * tf.util.shuffleCombo(a, b);
- * console.log(a, b);
- * ```
- *
- * @param array The first array to shuffle in-place.
- * @param array2 The second array to shuffle in-place with the same permutation
- *     as the first array.
- *
- * @doc {heading: 'Util', namespace: 'util'}
- */
-export function shuffleCombo(
-    // tslint:disable-next-line:no-any
-    array: any[]|Uint32Array|Int32Array|Float32Array,
-    // tslint:disable-next-line:no-any
-    array2: any[]|Uint32Array|Int32Array|Float32Array): void {
-  if (array.length !== array2.length) {
-    throw new Error(
-        `Array sizes must match to be shuffled together ` +
-        `First array length was ${array.length}` +
-        `Second array length was ${array2.length}`);
-  }
-  let counter = array.length;
-  let index = 0;
-  // While there are elements in the array
-  while (counter > 0) {
-    // Pick a random index
-    index = (Math.random() * counter) | 0;
-    // Decrease counter by 1
-    counter--;
-    // And swap the last element of each array with it
-    swap(array, counter, index);
-    swap(array2, counter, index);
-  }
-}
-
-/** Clamps a value to a specified range. */
-export function clamp(min: number, x: number, max: number): number {
-  return Math.max(min, Math.min(x, max));
-}
-
-export function nearestLargerEven(val: number): number {
-  return val % 2 === 0 ? val : val + 1;
-}
-
-export function swap<T>(
-    object: {[index: number]: T}, left: number, right: number) {
-  const temp = object[left];
-  object[left] = object[right];
-  object[right] = temp;
-}
-
-export function sum(arr: number[]): number {
-  let sum = 0;
-  for (let i = 0; i < arr.length; i++) {
-    sum += arr[i];
-  }
-  return sum;
-}
-
-/**
- * Returns a sample from a uniform [a, b) distribution.
- *
- * @param a The minimum support (inclusive).
- * @param b The maximum support (exclusive).
- * @return A pseudorandom number on the half-open interval [a,b).
- */
-export function randUniform(a: number, b: number) {
-  const r = Math.random();
-  return (b * r) + (1 - r) * a;
-}
-
-/** Returns the squared Euclidean distance between two vectors. */
-export function distSquared(a: FlatVector, b: FlatVector): number {
-  let result = 0;
-  for (let i = 0; i < a.length; i++) {
-    const diff = Number(a[i]) - Number(b[i]);
-    result += diff * diff;
-  }
-  return result;
-}
-
-/**
- * Asserts that the expression is true. Otherwise throws an error with the
- * provided message.
- *
- * ```js
- * const x = 2;
- * tf.util.assert(x === 2, 'x is not 2');
- * ```
- *
- * @param expr The expression to assert (as a boolean).
- * @param msg A function that returns the message to report when throwing an
- *     error. We use a function for performance reasons.
- *
- * @doc {heading: 'Util', namespace: 'util'}
- */
-export function assert(expr: boolean, msg: () => string) {
-  if (!expr) {
-    throw new Error(typeof msg === 'string' ? msg : msg());
-  }
-}
-
-export function assertShapesMatch(
-    shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {
-  assert(
-      arraysEqual(shapeA, shapeB),
-      () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-}
-
-export function assertNonNull(a: TensorLike): void {
-  assert(
-      a != null,
-      () => `The input to the tensor constructor must be a non-null value.`);
-}
-
-/**
- * Returns the size (number of elements) of the tensor given its shape.
- *
- * ```js
- * const shape = [3, 4, 2];
- * const size = tf.util.sizeFromShape(shape);
- * console.log(size);
- * ```
- *
- * @doc {heading: 'Util', namespace: 'util'}
- */
-export function sizeFromShape(shape: number[]): number {
-  if (shape.length === 0) {
-    // Scalar.
-    return 1;
-  }
-  let size = shape[0];
-  for (let i = 1; i < shape.length; i++) {
-    size *= shape[i];
-  }
-  return size;
-}
-
-export function isScalarShape(shape: number[]): boolean {
-  return shape.length === 0;
-}
-
-export function arraysEqualWithNull(n1: number[], n2: number[]) {
-  if (n1 === n2) {
-    return true;
-  }
-
-  if (n1 == null || n2 == null) {
-    return false;
-  }
-
-  if (n1.length !== n2.length) {
-    return false;
-  }
-
-  for (let i = 0; i < n1.length; i++) {
-    if (n1[i] !== null && n2[i] !== null && n1[i] !== n2[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-
-export function arraysEqual(n1: FlatVector, n2: FlatVector) {
-  if (n1 === n2) {
-    return true;
-  }
-  if (n1 == null || n2 == null) {
-    return false;
-  }
-
-  if (n1.length !== n2.length) {
-    return false;
-  }
-  for (let i = 0; i < n1.length; i++) {
-    if (n1[i] !== n2[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-
-export function isInt(a: number): boolean {
-  return a % 1 === 0;
-}
-
-export function tanh(x: number): number {
-  // tslint:disable-next-line:no-any
-  if ((Math as any).tanh != null) {
-    // tslint:disable-next-line:no-any
-    return (Math as any).tanh(x);
-  }
-  if (x === Infinity) {
-    return 1;
-  } else if (x === -Infinity) {
-    return -1;
-  } else {
-    const e2x = Math.exp(2 * x);
-    return (e2x - 1) / (e2x + 1);
-  }
-}
-
-export function sizeToSquarishShape(size: number): [number, number] {
-  const width = Math.ceil(Math.sqrt(size));
-  return [width, Math.ceil(size / width)];
-}
-
-/**
- * Creates a new array with randomized indices to a given quantity.
- *
- * ```js
- * const randomTen = tf.util.createShuffledIndices(10);
- * console.log(randomTen);
- * ```
- *
- * @param number Quantity of how many shuffled indices to create.
- *
- * @doc {heading: 'Util', namespace: 'util'}
- */
-export function createShuffledIndices(n: number): Uint32Array {
-  const shuffledIndices = new Uint32Array(n);
-  for (let i = 0; i < n; ++i) {
-    shuffledIndices[i] = i;
-  }
-  shuffle(shuffledIndices);
-  return shuffledIndices;
-}
-
-export function rightPad(a: string, size: number): string {
-  if (size <= a.length) {
-    return a;
-  }
-  return a + ' '.repeat(size - a.length);
-}
-
-export function repeatedTry(
-    checkFn: () => boolean, delayFn = (counter: number) => 0,
-    maxCounter?: number,
-    scheduleFn?: (functionRef: Function, delay: number) =>
-        void): Promise<void> {
-  return new Promise<void>((resolve, reject) => {
-    let tryCount = 0;
-
-    const tryFn = () => {
-      if (checkFn()) {
-        resolve();
-        return;
-      }
-
-      tryCount++;
-
-      const nextBackoff = delayFn(tryCount);
-
-      if (maxCounter != null && tryCount >= maxCounter) {
-        reject();
-        return;
-      }
-
-      if (scheduleFn != null) {
-        scheduleFn(tryFn, nextBackoff);
-      } else {
-        // google3 does not allow assigning another variable to setTimeout.
-        // Don't refactor this so scheduleFn has a default value of setTimeout.
-        setTimeout(tryFn, nextBackoff);
-      }
-    };
-
-    tryFn();
-  });
-}
-
-/**
- * Given the full size of the array and a shape that may contain -1 as the
- * implicit dimension, returns the inferred shape where -1 is replaced.
- * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].
- *
- * @param shape The shape, which may contain -1 in some dimension.
- * @param size The full size (number of elements) of the array.
- * @return The inferred shape where -1 is replaced with the inferred size.
- */
-export function inferFromImplicitShape(
-    shape: number[], size: number): number[] {
-  let shapeProd = 1;
-  let implicitIdx = -1;
-
-  for (let i = 0; i < shape.length; ++i) {
-    if (shape[i] >= 0) {
-      shapeProd *= shape[i];
-    } else if (shape[i] === -1) {
-      if (implicitIdx !== -1) {
-        throw Error(
-            `Shapes can only have 1 implicit size. ` +
-            `Found -1 at dim ${implicitIdx} and dim ${i}`);
-      }
-      implicitIdx = i;
-    } else if (shape[i] < 0) {
-      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
-    }
-  }
-
-  if (implicitIdx === -1) {
-    if (size > 0 && size !== shapeProd) {
-      throw Error(`Size(${size}) must match the product of shape ${shape}`);
-    }
-    return shape;
-  }
-
-  if (shapeProd === 0) {
-    throw Error(
-        `Cannot infer the missing size in [${shape}] when ` +
-        `there are 0 elements`);
-  }
-  if (size % shapeProd !== 0) {
-    throw Error(
-        `The implicit shape can't be a fractional number. ` +
-        `Got ${size} / ${shapeProd}`);
-  }
-
-  const newShape = shape.slice();
-  newShape[implicitIdx] = size / shapeProd;
-  return newShape;
-}
-
-export function parseAxisParam(
-    axis: number|number[], shape: number[]): number[] {
-  const rank = shape.length;
-
-  // Normalize input
-  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
-
-  // Check for valid range
-  assert(
-      axis.every(ax => ax >= -rank && ax < rank),
-      () =>
-          `All values in axis param must be in range [-${rank}, ${rank}) but ` +
-          `got axis ${axis}`);
-
-  // Check for only integers
-  assert(
-      axis.every(ax => isInt(ax)),
-      () => `All values in axis param must be integers but ` +
-          `got axis ${axis}`);
-
-  // Handle negative axis.
-  return axis.map(a => a < 0 ? rank + a : a);
-}
-
-/** Reduces the shape by removing all dimensions of shape 1. */
-export function squeezeShape(shape: number[], axis?: number[]):
-    {newShape: number[], keptDims: number[]} {
-  const newShape: number[] = [];
-  const keptDims: number[] = [];
-  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
-  const axes = (axis == null || isEmptyArray) ?
-      null :
-      parseAxisParam(axis, shape).sort();
-  let j = 0;
-  for (let i = 0; i < shape.length; ++i) {
-    if (axes != null) {
-      if (axes[j] === i && shape[i] !== 1) {
-        throw new Error(
-            `Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
-      }
-      if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
-        newShape.push(shape[i]);
-        keptDims.push(i);
-      }
-      if (axes[j] <= i) {
-        j++;
-      }
-    }
-    if (shape[i] !== 1) {
-      newShape.push(shape[i]);
-      keptDims.push(i);
-    }
-  }
-  return {newShape, keptDims};
-}
-
-export function getTypedArrayFromDType<D extends NumericDataType>(
-    dtype: D, size: number): DataTypeMap[D] {
-  return getArrayFromDType<D>(dtype, size);
-}
-
-export function getArrayFromDType<D extends DataType>(
-    dtype: D, size: number): DataTypeMap[D] {
-  let values = null;
-  if (dtype == null || dtype === 'float32') {
-    values = new Float32Array(size);
-  } else if (dtype === 'int32') {
-    values = new Int32Array(size);
-  } else if (dtype === 'bool') {
-    values = new Uint8Array(size);
-  } else if (dtype === 'string') {
-    values = new Array<string>(size);
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-  return values as DataTypeMap[D];
-}
-
-export function checkConversionForErrors<D extends DataType>(
-    vals: DataTypeMap[D]|number[], dtype: D): void {
-  for (let i = 0; i < vals.length; i++) {
-    const num = vals[i] as number;
-    if (isNaN(num) || !isFinite(num)) {
-      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
-    }
-  }
-}
-
-/** Returns true if the dtype is valid. */
-export function isValidDtype(dtype: DataType): boolean {
-  return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||
-      dtype === 'int32' || dtype === 'string';
-}
-
-/**
- * Returns true if the new type can't encode the old type without loss of
- * precision.
- */
-export function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {
-  if (newType === 'complex64') {
-    return false;
-  }
-  if (newType === 'float32' && oldType !== 'complex64') {
-    return false;
-  }
-  if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {
-    return false;
-  }
-  if (newType === 'bool' && oldType === 'bool') {
-    return false;
-  }
-  return true;
-}
-
-export function bytesPerElement(dtype: DataType): number {
-  if (dtype === 'float32' || dtype === 'int32') {
-    return 4;
-  } else if (dtype === 'complex64') {
-    return 8;
-  } else if (dtype === 'bool') {
-    return 1;
-  } else {
-    throw new Error(`Unknown dtype ${dtype}`);
-  }
-}
-
-/**
- * Returns the approximate number of bytes allocated in the string array - 2
- * bytes per character. Computing the exact bytes for a native string in JS
- * is not possible since it depends on the encoding of the html page that
- * serves the website.
- */
-export function bytesFromStringArray(arr: Uint8Array[]): number {
-  if (arr == null) {
-    return 0;
-  }
-  let bytes = 0;
-  arr.forEach(x => bytes += x.length);
-  return bytes;
-}
-
-/** Returns true if the value is a string. */
-export function isString(value: {}): value is string {
-  return typeof value === 'string' || value instanceof String;
-}
-
-export function isBoolean(value: {}): boolean {
-  return typeof value === 'boolean';
-}
-
-export function isNumber(value: {}): boolean {
-  return typeof value === 'number';
-}
-
-export function inferDtype(values: TensorLike|WebGLData|WebGPUData): DataType {
-  if (Array.isArray(values)) {
-    return inferDtype(values[0]);
-  }
-  if (values instanceof Float32Array) {
-    return 'float32';
-  } else if (
-      values instanceof Int32Array || values instanceof Uint8Array ||
-      values instanceof Uint8ClampedArray) {
-    return 'int32';
-  } else if (isNumber(values)) {
-    return 'float32';
-  } else if (isString(values)) {
-    return 'string';
-  } else if (isBoolean(values)) {
-    return 'bool';
-  }
-  return 'float32';
-}
-
-export function isFunction(f: Function) {
-  return !!(f && f.constructor && f.call && f.apply);
-}
-
-export function nearestDivisor(size: number, start: number): number {
-  for (let i = start; i < size; ++i) {
-    if (size % i === 0) {
-      return i;
-    }
-  }
-  return size;
-}
-
-export function computeStrides(shape: number[]): number[] {
-  const rank = shape.length;
-  if (rank < 2) {
-    return [];
-  }
-
-  // Last dimension has implicit stride of 1, thus having D-1 (instead of D)
-  // strides.
-  const strides = new Array(rank - 1);
-  strides[rank - 2] = shape[rank - 1];
-  for (let i = rank - 3; i >= 0; --i) {
-    strides[i] = strides[i + 1] * shape[i + 1];
-  }
-  return strides;
-}
-
-function createNestedArray(
-    offset: number, shape: number[], a: TypedArray, isComplex = false) {
-  const ret = new Array();
-  if (shape.length === 1) {
-    const d = shape[0] * (isComplex ? 2 : 1);
-    for (let i = 0; i < d; i++) {
-      ret[i] = a[offset + i];
-    }
-  } else {
-    const d = shape[0];
-    const rest = shape.slice(1);
-    const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
-    for (let i = 0; i < d; i++) {
-      ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);
-    }
-  }
-  return ret;
-}
-
-// Provide a nested array of TypedArray in given shape.
-export function toNestedArray(
-    shape: number[], a: TypedArray, isComplex = false) {
-  if (shape.length === 0) {
-    // Scalar type should return a single number.
-    return a[0];
-  }
-  const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
-  if (size === 0) {
-    // A tensor with shape zero should be turned into empty list.
-    return [];
-  }
-  if (size !== a.length) {
-    throw new Error(`[${shape}] does not match the input size ${a.length}${
-        isComplex ? ' for a complex tensor' : ''}.`);
-  }
-
-  return createNestedArray(0, shape, a, isComplex);
-}
-
-export function convertBackendValuesAndArrayBuffer(
-    data: BackendValues|ArrayBuffer, dtype: DataType) {
-  // If is type Uint8Array[], return it directly.
-  if (Array.isArray(data)) {
-    return data;
-  }
-  if (dtype === 'float32') {
-    return data instanceof Float32Array ? data : new Float32Array(data);
-  } else if (dtype === 'int32') {
-    return data instanceof Int32Array ? data : new Int32Array(data);
-  } else if (dtype === 'bool' || dtype === 'string') {
-    return Uint8Array.from(new Int32Array(data));
-  } else {
-    throw new Error(`Unknown dtype ${dtype}`);
-  }
-}
-
-export function makeOnesTypedArray<D extends DataType>(
-    size: number, dtype: D): DataTypeMap[D] {
-  const array = makeZerosTypedArray(size, dtype);
-  for (let i = 0; i < array.length; i++) {
-    array[i] = 1;
-  }
-  return array;
-}
-
-export function makeZerosTypedArray<D extends DataType>(
-    size: number, dtype: D): DataTypeMap[D] {
-  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
-    return new Float32Array(size) as DataTypeMap[D];
-  } else if (dtype === 'int32') {
-    return new Int32Array(size) as DataTypeMap[D];
-  } else if (dtype === 'bool') {
-    return new Uint8Array(size) as DataTypeMap[D];
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-
-/**
- * Make nested `TypedArray` filled with zeros.
- * @param shape The shape information for the nested array.
- * @param dtype dtype of the array element.
- */
-export function makeZerosNestedTypedArray<D extends DataType>(
-    shape: number[], dtype: D) {
-  const size = shape.reduce((prev, curr) => prev * curr, 1);
-  if (dtype == null || dtype === 'float32') {
-    return toNestedArray(shape, new Float32Array(size));
-  } else if (dtype === 'int32') {
-    return toNestedArray(shape, new Int32Array(size));
-  } else if (dtype === 'bool') {
-    return toNestedArray(shape, new Uint8Array(size));
-  } else {
-    throw new Error(`Unknown data type ${dtype}`);
-  }
-}
-
-export function assertNonNegativeIntegerDimensions(shape: number[]) {
-  shape.forEach(dimSize => {
-    assert(
-        Number.isInteger(dimSize) && dimSize >= 0,
-        () =>
-            `Tensor must have a shape comprised of positive integers but got ` +
-            `shape [${shape}].`);
-  });
-}
-
-/**
- * Computes flat index for a given location (multidimentionsal index) in a
- * Tensor/multidimensional array.
- *
- * @param locs Location in the tensor.
- * @param rank Rank of the tensor.
- * @param strides Tensor strides.
- */
-export function locToIndex(
-    locs: number[], rank: number, strides: number[]): number {
-  if (rank === 0) {
-    return 0;
-  } else if (rank === 1) {
-    return locs[0];
-  }
-  let index = locs[locs.length - 1];
-  for (let i = 0; i < locs.length - 1; ++i) {
-    index += strides[i] * locs[i];
-  }
-  return index;
-}
-
-/**
- * Computes the location (multidimensional index) in a
- * tensor/multidimentional array for a given flat index.
- *
- * @param index Index in flat array.
- * @param rank Rank of tensor.
- * @param strides Strides of tensor.
- */
-export function indexToLoc(
-    index: number, rank: number, strides: number[]): number[] {
-  if (rank === 0) {
-    return [];
-  } else if (rank === 1) {
-    return [index];
-  }
-  const locs: number[] = new Array(rank);
-  for (let i = 0; i < locs.length - 1; ++i) {
-    locs[i] = Math.floor(index / strides[i]);
-    index -= locs[i] * strides[i];
-  }
-  locs[locs.length - 1] = index;
-  return locs;
-}
-
-/**
- * This method asserts whether an object is a Promise instance.
- * @param object
- */
-// tslint:disable-next-line: no-any
-export function isPromise(object: any): object is Promise<unknown> {
-  //  We chose to not use 'obj instanceOf Promise' for two reasons:
-  //  1. It only reliably works for es6 Promise, not other Promise
-  //  implementations.
-  //  2. It doesn't work with framework that uses zone.js. zone.js monkey
-  //  patch the async calls, so it is possible the obj (patched) is
-  //  comparing to a pre-patched Promise.
-  return object && object.then && typeof object.then === 'function';
-}
diff --git a/tfjs-master/tfjs-core/src/util_test.ts b/tfjs-master/tfjs-core/src/util_test.ts
deleted file mode 100644
index 7e7ef6fbc..000000000
--- a/tfjs-master/tfjs-core/src/util_test.ts
+++ /dev/null
@@ -1,703 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-import {complex, scalar, tensor2d} from './ops/ops';
-import {inferShape} from './tensor_util_env';
-import * as util from './util';
-import {env} from './environment';
-
-describe('Util', () => {
-  it('Correctly gets size from shape', () => {
-    expect(util.sizeFromShape([1, 2, 3, 4])).toEqual(24);
-  });
-
-  it('Correctly identifies scalars', () => {
-    expect(util.isScalarShape([])).toBe(true);
-    expect(util.isScalarShape([1, 2])).toBe(false);
-    expect(util.isScalarShape([1])).toBe(false);
-  });
-
-  it('Number arrays equal', () => {
-    expect(util.arraysEqual([1, 2, 3, 6], [1, 2, 3, 6])).toBe(true);
-    expect(util.arraysEqual([1, 2], [1, 2, 3])).toBe(false);
-    expect(util.arraysEqual([1, 2, 5], [1, 2])).toBe(false);
-  });
-
-  it('Arrays shuffle randomly', () => {
-    // Create 1000 numbers ordered
-    const a = Array.apply(0, {length: 1000}).map(Number.call, Number).slice(1);
-    const b = [].concat(a);  // copy ES5 style
-    util.shuffle(a);
-    expect(a).not.toEqual(b);
-    expect(a.length).toEqual(b.length);
-  });
-
-  it('Multiple arrays shuffle together', () => {
-    // Create 1000 numbers ordered
-    const a = Array.apply(0, {length: 1000}).map(Number.call, Number).slice(1);
-    const b = [].concat(a);  // copies
-    const c = [].concat(a);
-    util.shuffleCombo(a, b);
-    expect(a).not.toEqual(c);
-    expect(a).toEqual(b);
-    expect(a.length).toEqual(c.length);
-  });
-
-  it('Is integer', () => {
-    expect(util.isInt(0.5)).toBe(false);
-    expect(util.isInt(1)).toBe(true);
-  });
-
-  it('Size to squarish shape (perfect square)', () => {
-    expect(util.sizeToSquarishShape(9)).toEqual([3, 3]);
-  });
-
-  it('Size to squarish shape (prime number)', () => {
-    expect(util.sizeToSquarishShape(11)).toEqual([4, 3]);
-  });
-
-  it('Size to squarish shape (almost square)', () => {
-    expect(util.sizeToSquarishShape(35)).toEqual([6, 6]);
-  });
-
-  it('Size of 1 to squarish shape', () => {
-    expect(util.sizeToSquarishShape(1)).toEqual([1, 1]);
-  });
-
-  it('infer shape single number', () => {
-    expect(inferShape(4)).toEqual([]);
-  });
-
-  it('infer shape 1d array', () => {
-    expect(inferShape([1, 2, 5])).toEqual([3]);
-  });
-
-  it('infer shape 2d array', () => {
-    expect(inferShape([[1, 2, 5], [5, 4, 1]])).toEqual([2, 3]);
-  });
-
-  it('infer shape 3d array', () => {
-    const a = [[[1, 2], [2, 3], [5, 6]], [[5, 6], [4, 5], [1, 2]]];
-    expect(inferShape(a)).toEqual([2, 3, 2]);
-  });
-
-  it('infer shape 4d array', () => {
-    const a = [
-      [[[1], [2]], [[2], [3]], [[5], [6]]], [[[5], [6]], [[4], [5]], [[1], [2]]]
-    ];
-    expect(inferShape(a)).toEqual([2, 3, 2, 1]);
-  });
-
-  it('infer shape of typed array', () => {
-    const a = new Float32Array([1, 2, 3, 4, 5]);
-    expect(inferShape(a)).toEqual([5]);
-  });
-
-  it('infer shape of clamped typed array', () => {
-    const a = new Uint8ClampedArray([1, 2, 3, 4, 5]);
-    expect(inferShape(a)).toEqual([5]);
-  });
-
-  it('infer shape of Uint8Array[], string tensor', () => {
-    const a = [new Uint8Array([1, 2]), new Uint8Array([3, 4])];
-    expect(inferShape(a, 'string')).toEqual([2]);
-  });
-
-  it('infer shape of Uint8Array[][], string tensor', () => {
-    const a = [
-      [new Uint8Array([1]), new Uint8Array([2])],
-      [new Uint8Array([1]), new Uint8Array([2])]
-    ];
-    expect(inferShape(a, 'string')).toEqual([2, 2]);
-  });
-
-  it('infer shape of Uint8Array[][][], string tensor', () => {
-    const a = [
-      [[new Uint8Array([1, 2])], [new Uint8Array([2, 1])]],
-      [[new Uint8Array([1, 2])], [new Uint8Array([2, 1])]]
-    ];
-    expect(inferShape(a, 'string')).toEqual([2, 2, 1]);
-  });
-  describe('isTypedArray', () => {
-    it('checks if a value is a typed array', () => {
-      expect(util.isTypedArray(new Uint8Array([1,2,3]))).toBeTrue();
-      expect(util.isTypedArray([1,2,3])).toBeFalse();
-    });
-    it('uses fallback if platform is missing isTypedArray', () => {
-      const tmpIsTypedArray = env().platform.isTypedArray;
-      try {
-        env().platform.isTypedArray = null;
-        expect(util.isTypedArray(new Uint8Array([1,2,3]))).toBeTrue();
-        expect(util.isTypedArray([1,2,3])).toBeFalse();
-      } finally {
-        env().platform.isTypedArray = tmpIsTypedArray;
-      }
-    });
-  });
-});
-
-describe('util.flatten', () => {
-  it('empty', () => {
-    const data: number[] = [];
-    expect(util.flatten(data)).toEqual([]);
-  });
-
-  it('nested number arrays', () => {
-    expect(util.flatten([[1, 2, 3], [4, 5, 6]])).toEqual([1, 2, 3, 4, 5, 6]);
-    expect(util.flatten([[[1, 2], [3, 4], [5, 6], [7, 8]]])).toEqual([
-      1, 2, 3, 4, 5, 6, 7, 8
-    ]);
-    expect(util.flatten([1, 2, 3, 4, 5, 6])).toEqual([1, 2, 3, 4, 5, 6]);
-  });
-
-  it('nested string arrays', () => {
-    expect(util.flatten([['a', 'b'], ['c', [['d']]]])).toEqual([
-      'a', 'b', 'c', 'd'
-    ]);
-    expect(util.flatten([['a', ['b']], ['c', [['d']], 'e']])).toEqual([
-      'a', 'b', 'c', 'd', 'e'
-    ]);
-  });
-
-  it('mixed TypedArray and number[]', () => {
-    const data =
-        [new Float32Array([1, 2]), 3, [4, 5, new Float32Array([6, 7])]];
-    expect(util.flatten(data)).toEqual([1, 2, 3, 4, 5, 6, 7]);
-  });
-
-  it('nested Uint8Arrays, skipTypedArray=true', () => {
-    const data = [
-      [new Uint8Array([1, 2]), new Uint8Array([3, 4])],
-      [new Uint8Array([5, 6]), new Uint8Array([7, 8])]
-    ];
-    expect(util.flatten(data, [], true)).toEqual([
-      new Uint8Array([1, 2]), new Uint8Array([3, 4]), new Uint8Array([5, 6]),
-      new Uint8Array([7, 8])
-    ]);
-  });
-
-  it('Int8Array', () => {
-    const data = [new Int8Array([1, 2])];
-    expect(util.flatten(data)).toEqual([1, 2]);
-  });
-
-  it('index signature', () => {
-    const data: {[index: number]: number} = {0: 1, 1: 2};
-    // Will be ignored since array iteration ignores negatives.
-    data[-1] = -1;
-    // Will be ignored since non-integer array keys are ignored.
-    data[3.2] = 4;
-    expect(util.flatten(data)).toEqual([1, 2]);
-  });
-});
-
-function encodeStrings(a: string[]): Uint8Array[] {
-  return a.map(s => util.encodeString(s));
-}
-
-describe('util.bytesFromStringArray', () => {
-  it('count bytes after utf8 encoding', () => {
-    expect(util.bytesFromStringArray(encodeStrings(['a', 'bb', 'ccc'])))
-        .toBe(6);
-    expect(util.bytesFromStringArray(encodeStrings(['a', 'bb', 'cccddd'])))
-        .toBe(9);
-    expect(util.bytesFromStringArray(encodeStrings(['даниел']))).toBe(6 * 2);
-  });
-});
-
-describe('util.inferDtype', () => {
-  it('a single string => string', () => {
-    expect(util.inferDtype('hello')).toBe('string');
-  });
-
-  it('a single boolean => bool', () => {
-    expect(util.inferDtype(true)).toBe('bool');
-    expect(util.inferDtype(false)).toBe('bool');
-  });
-
-  it('a single number => float32', () => {
-    expect(util.inferDtype(0)).toBe('float32');
-    expect(util.inferDtype(34)).toBe('float32');
-  });
-
-  it('a list of strings => string', () => {
-    // Flat.
-    expect(util.inferDtype(['a', 'b', 'c'])).toBe('string');
-    // Nested.
-    expect(util.inferDtype([
-      [['a']], [['b']], [['c']], [['d']]
-    ])).toBe('string');
-  });
-
-  it('a list of bools => float32', () => {
-    // Flat.
-    expect(util.inferDtype([false, true, false])).toBe('bool');
-    // Nested.
-    expect(util.inferDtype([
-      [[true]], [[false]], [[true]], [[true]]
-    ])).toBe('bool');
-  });
-
-  it('a list of numbers => float32', () => {
-    // Flat.
-    expect(util.inferDtype([0, 1, 2])).toBe('float32');
-    // Nested.
-    expect(util.inferDtype([[[0]], [[1]], [[2]], [[3]]])).toBe('float32');
-  });
-});
-
-describe('util.repeatedTry', () => {
-  it('resolves', (doneFn) => {
-    let counter = 0;
-    const checkFn = () => {
-      counter++;
-      if (counter === 2) {
-        return true;
-      }
-      return false;
-    };
-
-    util.repeatedTry(checkFn).then(doneFn).catch(() => {
-      throw new Error('Rejected backoff.');
-    });
-  });
-  it('rejects', (doneFn) => {
-    const checkFn = () => false;
-
-    util.repeatedTry(checkFn, () => 0, 5)
-        .then(() => {
-          throw new Error('Backoff resolved');
-        })
-        .catch(doneFn);
-  });
-});
-
-describe('util.inferFromImplicitShape', () => {
-  it('empty shape', () => {
-    const result = util.inferFromImplicitShape([], 0);
-    expect(result).toEqual([]);
-  });
-
-  it('[2, 3, 4] -> [2, 3, 4]', () => {
-    const result = util.inferFromImplicitShape([2, 3, 4], 24);
-    expect(result).toEqual([2, 3, 4]);
-  });
-
-  it('[2, -1, 4] -> [2, 3, 4], size=24', () => {
-    const result = util.inferFromImplicitShape([2, -1, 4], 24);
-    expect(result).toEqual([2, 3, 4]);
-  });
-
-  it('[-1, 3, 4] -> [2, 3, 4], size=24', () => {
-    const result = util.inferFromImplicitShape([-1, 3, 4], 24);
-    expect(result).toEqual([2, 3, 4]);
-  });
-
-  it('[2, 3, -1] -> [2, 3, 4], size=24', () => {
-    const result = util.inferFromImplicitShape([2, 3, -1], 24);
-    expect(result).toEqual([2, 3, 4]);
-  });
-
-  it('[2, -1, -1] throws error', () => {
-    expect(() => util.inferFromImplicitShape([2, -1, -1], 24)).toThrowError();
-  });
-
-  it('[2, 3, -1] size=13 throws error', () => {
-    expect(() => util.inferFromImplicitShape([2, 3, -1], 13)).toThrowError();
-  });
-
-  it('[2, 3, 4] size=25 (should be 24) throws error', () => {
-    expect(() => util.inferFromImplicitShape([2, 3, 4], 25)).toThrowError();
-  });
-});
-
-describe('util parseAxisParam', () => {
-  it('axis=null returns no axes for scalar', () => {
-    const axis: number = null;
-    const shape: number[] = [];
-    expect(util.parseAxisParam(axis, shape)).toEqual([]);
-  });
-
-  it('axis=null returns 0 axis for Tensor1D', () => {
-    const axis: number = null;
-    const shape = [4];
-    expect(util.parseAxisParam(axis, shape)).toEqual([0]);
-  });
-
-  it('axis=null returns all axes for Tensor3D', () => {
-    const axis: number[] = null;
-    const shape = [3, 1, 2];
-    expect(util.parseAxisParam(axis, shape)).toEqual([0, 1, 2]);
-  });
-
-  it('axis as a single number', () => {
-    const axis = 1;
-    const shape = [3, 1, 2];
-    expect(util.parseAxisParam(axis, shape)).toEqual([1]);
-  });
-
-  it('axis as single negative number', () => {
-    const axis = -1;
-    const shape = [3, 1, 2];
-    expect(util.parseAxisParam(axis, shape)).toEqual([2]);
-
-    const axis2 = -2;
-    expect(util.parseAxisParam(axis2, shape)).toEqual([1]);
-
-    const axis3 = -3;
-    expect(util.parseAxisParam(axis3, shape)).toEqual([0]);
-  });
-
-  it('axis as list of negative numbers', () => {
-    const axis = [-1, -3];
-    const shape = [3, 1, 2];
-    expect(util.parseAxisParam(axis, shape)).toEqual([2, 0]);
-  });
-
-  it('axis as list of positive numbers', () => {
-    const axis = [0, 2];
-    const shape = [3, 1, 2];
-    expect(util.parseAxisParam(axis, shape)).toEqual([0, 2]);
-  });
-
-  it('axis as combo of positive and negative numbers', () => {
-    const axis = [0, -1];
-    const shape = [3, 1, 2];
-    expect(util.parseAxisParam(axis, shape)).toEqual([0, 2]);
-  });
-
-  it('axis out of range throws error', () => {
-    const axis = -4;
-    const shape = [3, 1, 2];
-    expect(() => util.parseAxisParam(axis, shape)).toThrowError();
-
-    const axis2 = 4;
-    expect(() => util.parseAxisParam(axis2, shape)).toThrowError();
-  });
-
-  it('axis a list with one number out of range throws error', () => {
-    const axis = [0, 4];
-    const shape = [3, 1, 2];
-    expect(() => util.parseAxisParam(axis, shape)).toThrowError();
-  });
-
-  it('axis with decimal value throws error', () => {
-    const axis = 0.5;
-    const shape = [3, 1, 2];
-    expect(() => util.parseAxisParam(axis, shape)).toThrowError();
-  });
-});
-
-describe('util.squeezeShape', () => {
-  it('scalar', () => {
-    const {newShape, keptDims} = util.squeezeShape([]);
-    expect(newShape).toEqual([]);
-    expect(keptDims).toEqual([]);
-  });
-
-  it('1x1 reduced to scalar', () => {
-    const {newShape, keptDims} = util.squeezeShape([1, 1]);
-    expect(newShape).toEqual([]);
-    expect(keptDims).toEqual([]);
-  });
-
-  it('1x3x1 reduced to [3]', () => {
-    const {newShape, keptDims} = util.squeezeShape([1, 3, 1]);
-    expect(newShape).toEqual([3]);
-    expect(keptDims).toEqual([1]);
-  });
-
-  it('1x1x4 reduced to [4]', () => {
-    const {newShape, keptDims} = util.squeezeShape([1, 1, 4]);
-    expect(newShape).toEqual([4]);
-    expect(keptDims).toEqual([2]);
-  });
-
-  it('2x3x4 not reduction', () => {
-    const {newShape, keptDims} = util.squeezeShape([2, 3, 4]);
-    expect(newShape).toEqual([2, 3, 4]);
-    expect(keptDims).toEqual([0, 1, 2]);
-  });
-
-  describe('with axis', () => {
-    it('should only reduce dimensions specified by axis', () => {
-      const {newShape, keptDims} = util.squeezeShape([1, 1, 1, 1, 4], [1, 2]);
-      expect(newShape).toEqual([1, 1, 4]);
-      expect(keptDims).toEqual([0, 3, 4]);
-    });
-    it('should only reduce dimensions specified by negative axis', () => {
-      const {newShape, keptDims} = util.squeezeShape([1, 1, 1, 1, 4], [-2, -3]);
-      expect(newShape).toEqual([1, 1, 4]);
-      expect(keptDims).toEqual([0, 1, 4]);
-    });
-    it('should only reduce dimensions specified by negative axis', () => {
-      const axis = [-2, -3];
-      util.squeezeShape([1, 1, 1, 1, 4], axis);
-      expect(axis).toEqual([-2, -3]);
-    });
-    it('throws error when specified axis is not squeezable', () => {
-      expect(() => util.squeezeShape([1, 1, 2, 1, 4], [1, 2])).toThrowError();
-    });
-    it('throws error when specified negative axis is not squeezable', () => {
-      expect(() => util.squeezeShape([1, 1, 2, 1, 4], [-1, -2])).toThrowError();
-    });
-    it('throws error when specified axis is out of range', () => {
-      expect(() => util.squeezeShape([1, 1, 2, 1, 4], [11, 22])).toThrowError();
-    });
-    it('throws error when specified negative axis is out of range', () => {
-      expect(() => util.squeezeShape([1, 1, 2, 1, 4], [
-        -11, -22
-      ])).toThrowError();
-    });
-  });
-});
-
-describe('util.checkConversionForErrors', () => {
-  it('Float32Array has NaN', () => {
-    expect(
-        () => util.checkConversionForErrors(
-            new Float32Array([1, 2, 3, NaN, 4, 255]), 'float32'))
-        .toThrowError();
-  });
-
-  it('Float32Array has Infinity', () => {
-    expect(
-        () => util.checkConversionForErrors(
-            new Float32Array([1, 2, 3, Infinity, 4, 255]), 'float32'))
-        .toThrowError();
-  });
-
-  it('Int32Array has NaN', () => {
-    expect(() => util.checkConversionForErrors([1, 2, 3, 4, NaN], 'int32'))
-        .toThrowError();
-  });
-});
-
-describe('util.hasEncodingLoss', () => {
-  it('complex64 to any', () => {
-    expect(util.hasEncodingLoss('complex64', 'complex64')).toBe(false);
-    expect(util.hasEncodingLoss('complex64', 'float32')).toBe(true);
-    expect(util.hasEncodingLoss('complex64', 'int32')).toBe(true);
-    expect(util.hasEncodingLoss('complex64', 'bool')).toBe(true);
-  });
-
-  it('any to complex64', () => {
-    expect(util.hasEncodingLoss('bool', 'complex64')).toBe(false);
-    expect(util.hasEncodingLoss('int32', 'complex64')).toBe(false);
-    expect(util.hasEncodingLoss('float32', 'complex64')).toBe(false);
-    expect(util.hasEncodingLoss('complex64', 'complex64')).toBe(false);
-  });
-
-  it('any to float32', () => {
-    expect(util.hasEncodingLoss('bool', 'float32')).toBe(false);
-    expect(util.hasEncodingLoss('int32', 'float32')).toBe(false);
-    expect(util.hasEncodingLoss('float32', 'float32')).toBe(false);
-    expect(util.hasEncodingLoss('complex64', 'float32')).toBe(true);
-  });
-
-  it('float32 to any', () => {
-    expect(util.hasEncodingLoss('float32', 'float32')).toBe(false);
-    expect(util.hasEncodingLoss('float32', 'int32')).toBe(true);
-    expect(util.hasEncodingLoss('float32', 'bool')).toBe(true);
-    expect(util.hasEncodingLoss('float32', 'complex64')).toBe(false);
-  });
-
-  it('int32 to lower', () => {
-    expect(util.hasEncodingLoss('int32', 'int32')).toBe(false);
-    expect(util.hasEncodingLoss('int32', 'bool')).toBe(true);
-  });
-
-  it('lower to int32', () => {
-    expect(util.hasEncodingLoss('bool', 'int32')).toBe(false);
-  });
-
-  it('bool to bool', () => {
-    expect(util.hasEncodingLoss('bool', 'bool')).toBe(false);
-  });
-});
-
-describeWithFlags('util.toNestedArray', ALL_ENVS, () => {
-  it('2 dimensions', () => {
-    const a = new Float32Array([1, 2, 3, 4, 5, 6]);
-    expect(util.toNestedArray([2, 3], a)).toEqual([[1, 2, 3], [4, 5, 6]]);
-  });
-
-  it('3 dimensions (2x2x3)', () => {
-    const a = new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]);
-    expect(util.toNestedArray([2, 2, 3], a)).toEqual([
-      [[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]]
-    ]);
-  });
-
-  it('3 dimensions (3x2x2)', () => {
-    const a = new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]);
-    expect(util.toNestedArray([3, 2, 2], a)).toEqual([
-      [[0, 1], [2, 3]], [[4, 5], [6, 7]], [[8, 9], [10, 11]]
-    ]);
-  });
-
-  it('invalid dimension', () => {
-    const a = new Float32Array([1, 2, 3]);
-    expect(() => util.toNestedArray([2, 2], a)).toThrowError();
-  });
-
-  it('tensor to nested array', async () => {
-    const x = tensor2d([1, 2, 3, 4], [2, 2]);
-    expect(util.toNestedArray(x.shape, await x.data())).toEqual([
-      [1, 2], [3, 4]
-    ]);
-  });
-
-  it('scalar to nested array', async () => {
-    const x = scalar(1);
-    expect(util.toNestedArray(x.shape, await x.data())).toEqual(1);
-  });
-
-  it('tensor with zero shape', () => {
-    const a = new Float32Array([0, 1]);
-    expect(util.toNestedArray([1, 0, 2], a)).toEqual([]);
-  });
-});
-
-describeWithFlags('util.toNestedArray for a complex tensor', ALL_ENVS, () => {
-  it('2 dimensions', () => {
-    const a = new Float32Array([1, 11, 2, 12, 3, 13, 4, 14, 5, 15, 6, 16]);
-    expect(util.toNestedArray([2, 3], a, true)).toEqual([
-      [1, 11, 2, 12, 3, 13], [4, 14, 5, 15, 6, 16]
-    ]);
-  });
-
-  it('3 dimensions (2x2x3)', () => {
-    const a = new Float32Array([
-      0, 50, 1, 51, 2, 52, 3, 53, 4,  54, 5,  55,
-      6, 56, 7, 57, 8, 58, 9, 59, 10, 60, 11, 61
-    ]);
-    expect(util.toNestedArray([2, 2, 3], a, true)).toEqual([
-      [[0, 50, 1, 51, 2, 52], [3, 53, 4, 54, 5, 55]],
-      [[6, 56, 7, 57, 8, 58], [9, 59, 10, 60, 11, 61]]
-    ]);
-  });
-
-  it('3 dimensions (3x2x2)', () => {
-    const a = new Float32Array([
-      0, 50, 1, 51, 2, 52, 3, 53, 4,  54, 5,  55,
-      6, 56, 7, 57, 8, 58, 9, 59, 10, 60, 11, 61
-    ]);
-    expect(util.toNestedArray([3, 2, 2], a, true)).toEqual([
-      [[0, 50, 1, 51], [2, 52, 3, 53]], [[4, 54, 5, 55], [6, 56, 7, 57]],
-      [[8, 58, 9, 59], [10, 60, 11, 61]]
-    ]);
-  });
-
-  it('invalid dimension', () => {
-    const a = new Float32Array([1, 11, 2, 12, 3, 13]);
-    expect(() => util.toNestedArray([2, 2], a, true)).toThrowError();
-  });
-
-  it('tensor to nested array', async () => {
-    const x = complex([[1, 2], [3, 4]], [[11, 12], [13, 14]]);
-    expect(util.toNestedArray(x.shape, await x.data(), true)).toEqual([
-      [1, 11, 2, 12], [3, 13, 4, 14]
-    ]);
-  });
-});
-
-describe('util.fetch', () => {
-  it('should call the platform fetch', () => {
-    spyOn(tf.env().platform, 'fetch')
-        .and.callFake(async () => ({} as unknown as Response));
-
-    util.fetch('test/path', {method: 'GET'});
-
-    expect(tf.env().platform.fetch).toHaveBeenCalledWith('test/path', {
-      method: 'GET'
-    });
-  });
-});
-
-describe('util.encodeString', () => {
-  it('Encode an empty string, default encoding', () => {
-    const res = util.encodeString('');
-    expect(res).toEqual(new Uint8Array([]));
-  });
-
-  it('Encode an empty string, utf-8 encoding', () => {
-    const res = util.encodeString('', 'utf-8');
-    expect(res).toEqual(new Uint8Array([]));
-  });
-
-  it('Encode an empty string, invalid decoding', () => {
-    expect(() => util.encodeString('', 'foobarbax')).toThrowError();
-  });
-
-  it('Encode cyrillic letters', () => {
-    const res = util.encodeString('Kaкo стe');
-    expect(res).toEqual(
-        new Uint8Array([75, 97, 208, 186, 111, 32, 209, 129, 209, 130, 101]));
-  });
-
-  it('Encode ascii letters', () => {
-    const res = util.encodeString('hello');
-    expect(res).toEqual(new Uint8Array([104, 101, 108, 108, 111]));
-  });
-});
-
-describe('util.decodeString', () => {
-  it('decode an empty string', () => {
-    const s = util.decodeString(new Uint8Array([]));
-    expect(s).toEqual('');
-  });
-
-  it('decode ascii', () => {
-    const s = util.decodeString(new Uint8Array([104, 101, 108, 108, 111]));
-    expect(s).toEqual('hello');
-  });
-
-  it('decode cyrillic', () => {
-    const s = util.decodeString(
-        new Uint8Array([75, 97, 208, 186, 111, 32, 209, 129, 209, 130, 101]));
-    expect(s).toEqual('Kaкo стe');
-  });
-
-  it('decode utf-16', () => {
-    const s = util.decodeString(
-        new Uint8Array([255, 254, 237, 139, 0, 138, 4, 89, 6, 116]), 'utf-16');
-
-    // UTF-16 allows optional presence of byte-order-mark (BOM)
-    // Construct string for '语言处理', with and without BOM
-    const expected = String.fromCodePoint(0x8bed, 0x8a00, 0x5904, 0x7406);
-    const expectedBOM =
-        String.fromCodePoint(0xfeff, 0x8bed, 0x8a00, 0x5904, 0x7406);
-
-    if (s.codePointAt(0) === 0xfeff) {
-      expect(s).toEqual(expectedBOM);
-    } else {
-      expect(s).toEqual(expected);
-    }
-  });
-
-  it('assert promise', () => {
-    const promise = new Promise(() => {});
-    expect(util.isPromise(promise)).toBeTruthy();
-    const promise2 = {then: () => {}};
-    expect(util.isPromise(promise2)).toBeTruthy();
-    const promise3 = {};
-    expect(util.isPromise(promise3)).toBeFalsy();
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/variable_test.ts b/tfjs-master/tfjs-core/src/variable_test.ts
deleted file mode 100644
index 80763bb03..000000000
--- a/tfjs-master/tfjs-core/src/variable_test.ts
+++ /dev/null
@@ -1,232 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-import {ALL_ENVS, describeWithFlags} from './jasmine_util';
-import {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Variable} from './tensor';
-import {expectArraysClose} from './test_util';
-import {Rank} from './types';
-
-describeWithFlags('variable', ALL_ENVS, () => {
-  it('simple assign', async () => {
-    const v = tf.variable(tf.tensor1d([1, 2, 3]));
-    expectArraysClose(await v.data(), [1, 2, 3]);
-
-    v.assign(tf.tensor1d([4, 5, 6]));
-    expectArraysClose(await v.data(), [4, 5, 6]);
-  });
-
-  it('simple chain assign', async () => {
-    const v = tf.tensor1d([1, 2, 3]).variable();
-    expectArraysClose(await v.data(), [1, 2, 3]);
-
-    v.assign(tf.tensor1d([4, 5, 6]));
-    expectArraysClose(await v.data(), [4, 5, 6]);
-  });
-
-  it('default names are unique', () => {
-    const v = tf.variable(tf.tensor1d([1, 2, 3]));
-    expect(v.name).not.toBeNull();
-
-    const v2 = tf.variable(tf.tensor1d([1, 2, 3]));
-    expect(v2.name).not.toBeNull();
-    expect(v.name).not.toBe(v2.name);
-  });
-
-  it('user provided name', () => {
-    const v = tf.variable(tf.tensor1d([1, 2, 3]), true, 'myName');
-    expect(v.name).toBe('myName');
-  });
-
-  it('if name already used, throw error', () => {
-    tf.variable(tf.tensor1d([1, 2, 3]), true, 'myName');
-    expect(() => tf.variable(tf.tensor1d([1, 2, 3]), true, 'myName'))
-        .toThrowError();
-  });
-
-  it('ops can take variables', async () => {
-    const value = tf.tensor1d([1, 2, 3]);
-    const v = tf.variable(value);
-    const res = tf.sum(v);
-    expectArraysClose(await res.data(), [6]);
-  });
-
-  it('chained variables works', async () => {
-    const v = tf.tensor1d([1, 2, 3]).variable();
-    const res = tf.sum(v);
-    expectArraysClose(await res.data(), [6]);
-  });
-
-  it('variables are not affected by tidy', async () => {
-    let v: Variable<Rank.R1>;
-    expect(tf.memory().numTensors).toBe(0);
-
-    tf.tidy(() => {
-      const value = tf.tensor1d([1, 2, 3], 'float32');
-      expect(tf.memory().numTensors).toBe(1);
-
-      v = tf.variable(value);
-      expect(tf.memory().numTensors).toBe(2);
-    });
-
-    expect(tf.memory().numTensors).toBe(1);
-    expectArraysClose(await v.data(), [1, 2, 3]);
-
-    v.dispose();
-    expect(tf.memory().numTensors).toBe(0);
-  });
-
-  it('disposing a named variable allows creating new named variable', () => {
-    const numTensors = tf.memory().numTensors;
-    const t = tf.scalar(1);
-    const varName = 'var';
-    const v = tf.variable(t, true, varName);
-
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-
-    v.dispose();
-    t.dispose();
-
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    // Create another variable with the same name.
-    const t2 = tf.scalar(1);
-    const v2 = tf.variable(t2, true, varName);
-
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-
-    t2.dispose();
-    v2.dispose();
-
-    expect(tf.memory().numTensors).toBe(numTensors);
-  });
-
-  it('double disposing a variable works', () => {
-    const numTensors = tf.memory().numTensors;
-
-    const t = tf.scalar(1);
-    const v = tf.variable(t);
-
-    expect(tf.memory().numTensors).toBe(numTensors + 2);
-
-    t.dispose();
-    v.dispose();
-
-    expect(tf.memory().numTensors).toBe(numTensors);
-
-    // Double dispose the variable.
-    v.dispose();
-    expect(tf.memory().numTensors).toBe(numTensors);
-  });
-
-  it('constructor does not dispose', async () => {
-    const a = tf.scalar(2);
-    const v = tf.variable(a);
-
-    expect(tf.memory().numTensors).toBe(2);
-    expect(tf.memory().numDataBuffers).toBe(1);
-    expectArraysClose(await v.data(), [2]);
-    expectArraysClose(await a.data(), [2]);
-  });
-
-  it('variables are assignable to tensors', () => {
-    // This test asserts compilation, not doing any run-time assertion.
-    const x0: Variable<Rank.R0> = null;
-    const y0: Scalar = x0;
-    expect(y0).toBeNull();
-
-    const x1: Variable<Rank.R1> = null;
-    const y1: Tensor1D = x1;
-    expect(y1).toBeNull();
-
-    const x2: Variable<Rank.R2> = null;
-    const y2: Tensor2D = x2;
-    expect(y2).toBeNull();
-
-    const x3: Variable<Rank.R3> = null;
-    const y3: Tensor3D = x3;
-    expect(y3).toBeNull();
-
-    const x4: Variable<Rank.R4> = null;
-    const y4: Tensor4D = x4;
-    expect(y4).toBeNull();
-
-    const xh: Variable = null;
-    const yh: Tensor = xh;
-    expect(yh).toBeNull();
-  });
-
-  it('assign does not dispose old data', async () => {
-    let v: Variable<Rank.R1>;
-    v = tf.variable(tf.tensor1d([1, 2, 3]));
-
-    expect(tf.memory().numTensors).toBe(2);
-    expect(tf.memory().numDataBuffers).toBe(1);
-
-    expectArraysClose(await v.data(), [1, 2, 3]);
-
-    const secondArray = tf.tensor1d([4, 5, 6]);
-    expect(tf.memory().numTensors).toBe(3);
-    expect(tf.memory().numDataBuffers).toBe(2);
-
-    v.assign(secondArray);
-    expectArraysClose(await v.data(), [4, 5, 6]);
-    // Assign doesn't dispose the 1st array.
-    expect(tf.memory().numTensors).toBe(3);
-    expect(tf.memory().numDataBuffers).toBe(2);
-
-    v.dispose();
-    // Disposing the variable disposes itself. The input to variable and
-    // secondArray are the only remaining tensors.
-    expect(tf.memory().numTensors).toBe(2);
-    expect(tf.memory().numDataBuffers).toBe(2);
-  });
-
-  it('shape must match', () => {
-    const v = tf.variable(tf.tensor1d([1, 2, 3]));
-    expect(() => v.assign(tf.tensor1d([1, 2]))).toThrowError();
-    // tslint:disable-next-line:no-any
-    expect(() => v.assign(tf.tensor2d([3, 4], [1, 2]) as any)).toThrowError();
-  });
-
-  it('dtype must match', () => {
-    const v = tf.variable(tf.tensor1d([1, 2, 3]));
-    // tslint:disable-next-line:no-any
-    expect(() => v.assign(tf.tensor1d([1, 1, 1], 'int32') as any))
-        .toThrowError();
-    // tslint:disable-next-line:no-any
-    expect(() => v.assign(tf.tensor1d([true, false, true], 'bool') as any))
-        .toThrowError();
-  });
-});
-
-describeWithFlags('x instanceof Variable', ALL_ENVS, () => {
-  it('x: Variable', () => {
-    const t = tf.variable(tf.scalar(1));
-    expect(t instanceof Variable).toBe(true);
-  });
-
-  it('x: other object, fails', () => {
-    const t = {something: 'else'};
-    expect(t instanceof Variable).toBe(false);
-  });
-
-  it('x: Tensor, fails', () => {
-    const t = tf.scalar(1);
-    expect(t instanceof Variable).toBe(false);
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/version.ts b/tfjs-master/tfjs-core/src/version.ts
deleted file mode 100644
index 5fa574e7d..000000000
--- a/tfjs-master/tfjs-core/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.0';
-export {version};
diff --git a/tfjs-master/tfjs-core/src/worker_node_test.ts b/tfjs-master/tfjs-core/src/worker_node_test.ts
deleted file mode 100644
index 46d7225ed..000000000
--- a/tfjs-master/tfjs-core/src/worker_node_test.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import {expectArraysClose} from './test_util';
-// tslint:disable:no-require-imports
-
-const workerTestNode = `
-// Web worker scripts in node live relative to the CWD, not to the dir of the
-// file that spawned them.
-const tf = require('@tensorflow/tfjs-core');
-require('@tensorflow/tfjs-backend-cpu');
-const {parentPort} = require('worker_threads');
-
-let a = tf.tensor1d([1, 2, 3]);
-const b = tf.tensor1d([3, 2, 1]);
-a = tf.add(a, b);
-parentPort.postMessage({data: a.dataSync()});
-`;
-
-describe('computation in worker (node env)', () => {
-  // tslint:disable-next-line: ban
-  it('tensor in worker', (done) => {
-    const {Worker} = require('worker_threads');
-    const worker = new Worker(workerTestNode, {eval: true});
-    // tslint:disable-next-line:no-any
-    worker.on('message', (msg: any) => {
-      const data = msg.data;
-      expectArraysClose(data, [4, 4, 4]);
-      done();
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-core/src/worker_test.ts b/tfjs-master/tfjs-core/src/worker_test.ts
deleted file mode 100644
index 7c7ac6f07..000000000
--- a/tfjs-master/tfjs-core/src/worker_test.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import {expectArraysClose} from './test_util';
-
-const str2workerURL = (str: string): string => {
-  const blob =
-      new Blob([str], {type: 'application/javascript'});
-  return URL.createObjectURL(blob);
-};
-
-// The source code of a web worker.
-const workerTest = `
-importScripts('${location.origin}/base/tfjs/tfjs-core/tf-core.min.js');
-importScripts('${location.origin}'
-  + '/base/tfjs/tfjs-backend-cpu/tf-backend-cpu.min.js');
-
-let a = tf.tensor1d([1, 2, 3]);
-const b = tf.tensor1d([3, 2, 1]);
-a = tf.add(a, b);
-self.postMessage({data: a.dataSync()});
-`;
-
-describe('computation in worker', () => {
-  it('tensor in worker', (done) => {
-    const worker = new Worker(str2workerURL(workerTest));
-    worker.onmessage = (msg) => {
-      const data = msg.data.data;
-      expectArraysClose(data, [4, 4, 4]);
-      done();
-    };
-  });
-});
diff --git a/tfjs-master/tfjs-core/test.html b/tfjs-master/tfjs-core/test.html
deleted file mode 100644
index 80627d13f..000000000
--- a/tfjs-master/tfjs-core/test.html
+++ /dev/null
@@ -1,19 +0,0 @@
-
-<script src="dist/tf-core.js"></script>
-
-<img id="imgz" src="https://i.imgur.com/qMms916.jpg" crossorigin="anonymous"/>
-
-<video id="img" crossorigin="anonymous" autoplay="autoplay">
-  <source src="data:video/mp4;base64,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" type="video/mp4"</video>
-<script>
-const vid = document.getElementById('img');
-
-console.log(vid);
-
-vid.addEventListener('loadeddata', () => {
-  const x = tf.browser.fromPixels(vid);
-
-  x.print();
-});
-
-</script>
diff --git a/tfjs-master/tfjs-core/tsconfig.json b/tfjs-master/tfjs-core/tsconfig.json
deleted file mode 100644
index ecb732e21..000000000
--- a/tfjs-master/tfjs-core/tsconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-core/yarn.lock b/tfjs-master/tfjs-core/yarn.lock
deleted file mode 100644
index 7d36af034..000000000
--- a/tfjs-master/tfjs-core/yarn.lock
+++ /dev/null
@@ -1,73 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@bazel/bazelisk@^1.12.0":
-  version "1.12.0"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.12.0.tgz#f08aebbf4afcb12684422450b0845dd6ef5cfe50"
-  integrity sha512-7oQusq1e4AIyFgotxVV7Pc40Et0QyvoVjujL+7/qV5Vrbfh0Nj3CfqSgl63weEyI4r0+K6RlGVsjfRuBi05p5w==
-
-"@bazel/ibazel@^0.16.2":
-  version "0.16.2"
-  resolved "https://registry.yarnpkg.com/@bazel/ibazel/-/ibazel-0.16.2.tgz#05dd7f06659759fda30f87b15534f1e42f1201bb"
-  integrity sha512-KgqAWMH0emL6f3xH6nqyTryoBMqlJ627LBIe9PT1PRRQPz2FtHib3FIHJPukp1slzF3hJYZvdiVwgPnHbaSOOA==
-
-"@types/long@^4.0.1":
-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/@types/long/-/long-4.0.1.tgz#459c65fa1867dafe6a8f322c4c51695663cc55e9"
-  integrity sha512-5tXH6Bx/kNGd3MgffdmP4dy2Z+G4eaXw0SE81Tq3BNadtnMR5/ySMzX4SLEzHJzSmPNn4HIdpQsBvXMUykr58w==
-
-"@types/offscreencanvas@~2019.7.0":
-  version "2019.7.0"
-  resolved "https://registry.yarnpkg.com/@types/offscreencanvas/-/offscreencanvas-2019.7.0.tgz#e4a932069db47bb3eabeb0b305502d01586fa90d"
-  integrity sha512-PGcyveRIpL1XIqK8eBsmRBt76eFgtzuPiSTyKHZxnGemp2yzGzWpjYKAfK3wIMiU7eH+851yEpiuP8JZerTmWg==
-
-"@types/seedrandom@^2.4.28":
-  version "2.4.30"
-  resolved "https://registry.yarnpkg.com/@types/seedrandom/-/seedrandom-2.4.30.tgz#d2efe425869b84163c2d56e779dddadb9372cbfa"
-  integrity sha512-AnxLHewubLVzoF/A4qdxBGHCKifw8cY32iro3DQX9TPcetE95zBeVt3jnsvtvAUf1vwzMfwzp4t/L2yqPlnjkQ==
-
-"@webgpu/types@0.1.38":
-  version "0.1.38"
-  resolved "https://registry.npmjs.org/@webgpu/types/-/types-0.1.38.tgz#6fda4b410edc753d3213c648320ebcf319669020"
-  integrity sha512-7LrhVKz2PRh+DD7+S+PVaFd5HxaWQvoMqBbsV9fNJO1pjUs1P8bM2vQVNfk+3URTqbuTI7gkXi0rfsN0IadoBA==
-
-long@4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/long/-/long-4.0.0.tgz#9a7b71cfb7d361a194ea555241c92f7468d5bf28"
-  integrity sha512-XsP+KhQif4bjX1kbuSiySJFNAehNxgLb6hPRGJ9QsUr8ajHkuXGdrHmFUTUUXhDwVX2R5bY4JNZEwbUiMhV+MA==
-
-minimist@1.2.6:
-  version "1.2.6"
-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.6.tgz#8637a5b759ea0d6e98702cfb3a9283323c93af44"
-  integrity sha512-Jsjnk4bw3YJqYzbdyBiNsPWHPfO++UGG749Cxs6peCu5Xg4nrena6OVxOYxrQTqww0Jmwt+Ref8rggumkTLz9Q==
-
-node-fetch@~2.6.1:
-  version "2.6.7"
-  resolved "https://registry.yarnpkg.com/node-fetch/-/node-fetch-2.6.7.tgz#24de9fba827e3b4ae44dc8b20256a379160052ad"
-  integrity sha512-ZjMPFEfVx5j+y2yF35Kzx5sF7kDzxuDj6ziH4FFbOp87zKDZNx8yExJIb05OGF4Nlt9IHFIMBkRl41VdvcNdbQ==
-  dependencies:
-    whatwg-url "^5.0.0"
-
-seedrandom@^3.0.5:
-  version "3.0.5"
-  resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-3.0.5.tgz#54edc85c95222525b0c7a6f6b3543d8e0b3aa0a7"
-  integrity sha512-8OwmbklUNzwezjGInmZ+2clQmExQPvomqjL7LFqOYqtmuxRgQYqOD3mHaU+MvZn5FLUeVxVfQjwLZW/n/JFuqg==
-
-tr46@~0.0.3:
-  version "0.0.3"
-  resolved "https://registry.yarnpkg.com/tr46/-/tr46-0.0.3.tgz#8184fd347dac9cdc185992f3a6622e14b9d9ab6a"
-  integrity sha1-gYT9NH2snNwYWZLzpmIuFLnZq2o=
-
-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
-
-whatwg-url@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
-  integrity sha1-lmRU6HZUYuN2RNNib2dCzotwll0=
-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
diff --git a/tfjs-master/tfjs-data/.npmignore b/tfjs-master/tfjs-data/.npmignore
deleted file mode 100644
index bdb35b2a9..000000000
--- a/tfjs-master/tfjs-data/.npmignore
+++ /dev/null
@@ -1,27 +0,0 @@
-.rpt2_cache/
-.vscode/
-src/**/*_test.ts
-src/test_node.ts
-demo/
-scripts/
-coverage/
-node_modules/
-karma.conf.js
-dist/**/*_test.js
-dist/**/*_test.d.ts
-dist/test_node.js
-dist/util/test_utils.js
-*.tgz
-cloudbuild.yml
-.npmignore
-CONTRIBUTING.md
-DEVELOPMENT.md
-ISSUE_TEMPLATE.md
-pull_request_template.md
-tslint.json
-rollup.config.js
-WORKSPACE
-**.yalc/
-**yalc.lock
-yarn.lock
-yarn-error.log
diff --git a/tfjs-master/tfjs-data/.nycrc b/tfjs-master/tfjs-data/.nycrc
deleted file mode 100644
index 5b0a53ac7..000000000
--- a/tfjs-master/tfjs-data/.nycrc
+++ /dev/null
@@ -1,19 +0,0 @@
-{
-    "extension": [
-      ".ts"
-    ],
-    "reporter": [
-      "html",
-      "text-summary"
-    ],
-    "include": [
-      "src/**/*"
-    ],
-    "exclude": [
-      "src/test_node.ts",
-      "src/**/*_test.ts"
-    ],
-    "cache": false,
-    "sourceMap": true,
-    "instrument": true
-}
diff --git a/tfjs-master/tfjs-data/.vscode/settings.json b/tfjs-master/tfjs-data/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-data/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-data/BUILD.bazel b/tfjs-master/tfjs-data/BUILD.bazel
deleted file mode 100644
index ea0b80f38..000000000
--- a/tfjs-master/tfjs-data/BUILD.bazel
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//rules:copy_file.bzl", "copy_file")
-load("@build_bazel_rules_nodejs//:index.bzl", "nodejs_test", "pkg_npm")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-# Allow typescript rules in any package to reference this file
-exports_files([
-    "package.json",
-    "README.md",
-])
-
-nodejs_test(
-    name = "tfjs-data_test",
-    data = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-data/src:tfjs-data_test_lib",
-    ],
-    entry_point = "//tfjs-data/src:test_node.ts",
-    tags = ["ci"],
-)
-
-# Defined here because chdir must be a subdirectory of the directory the rule is
-# defined in.
-nodejs_test(
-    name = "test_snippets_test",
-    chdir = "tfjs-data",
-    data = [
-        ":tsconfig.json",
-        "//:tsconfig.json",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-data/scripts:test_snippets_lib",
-        "//tfjs-data/src:all_srcs",
-    ],
-    entry_point = "//tfjs-data/scripts:test_snippets.ts",
-    tags = ["ci"],
-)
-
-tfjs_web_test(
-    name = "tfjs-data_browser_test",
-    srcs = [
-        "//tfjs-data/src:tfjs-data_test_bundle",
-    ],
-    browsers = [
-        "bs_chrome_mac",
-        "win_10_chrome",
-    ],
-    local_browser = "chrome_autoplay",
-    static_files = [
-        # Listed here so sourcemaps are served
-        "//tfjs-data/src:tfjs-data_test_bundle",
-    ],
-)
-
-tfjs_bundle(
-    name = "tf-data",
-    entry_point = "//tfjs-data/src:index.ts",
-    external = [
-        "@tensorflow/tfjs-core",
-    ],
-    globals = {
-        "@tensorflow/tfjs-core": "tf",
-    },
-    umd_name = "tf",
-    deps = [
-        "//tfjs-data/src:tfjs-data_lib",
-        "//tfjs-data/src:tfjs-data_src_lib",
-    ],
-)
-
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-data/src:tfjs-data_lib",
-        "//tfjs-data/src:tfjs-data_src_lib",
-    ],
-    root = "src",
-)
-
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-data",
-        ":tf-data.es2017",
-        ":tf-data.es2017.min",
-        ":tf-data.fesm",
-        ":tf-data.fesm.min",
-        ":tf-data.min",
-        ":tf-data.node",
-    ],
-)
-
-copy_file(
-    name = "copy_miniprogram",
-    src = ":tf-data.min.js",
-    out = "dist/miniprogram/index.js",
-)
-
-copy_file(
-    name = "copy_miniprogram_map",
-    src = ":tf-data.min.js.map",
-    out = "dist/miniprogram/index.js.map",
-)
-
-pkg_npm(
-    name = "tfjs-data_pkg",
-    package_name = "@tensorflow/tfjs-data",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_miniprogram",
-        ":copy_miniprogram_map",
-        ":copy_src_to_dist",
-    ],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        ":tfjs-data_browser_test",
-        ":tfjs-data_test",
-    ],
-)
diff --git a/tfjs-master/tfjs-data/README.md b/tfjs-master/tfjs-data/README.md
deleted file mode 100644
index aadff92d5..000000000
--- a/tfjs-master/tfjs-data/README.md
+++ /dev/null
@@ -1,89 +0,0 @@
-# TensorFlow.js Data
-
-**This repo is under active development and is not production-ready. We are
-actively developing as an open source project.**
-
-TensorFlow.js Data provides simple APIs to load and parse data from disk or over
-the web in a variety of formats, and to prepare that data for use in machine
-learning models (e.g. via operations like filter, map, shuffle, and batch).
-
-This project is the JavaScript analogue of
-[tf.data](https://www.tensorflow.org/guide/data) on the
-Python/C++ side.  TF.js Data will match the tf.data API to the extent possible.
-
-To keep track of issues we use the [tensorflow/tfjs](https://github.com/tensorflow/tfjs/issues?q=is%3Aissue+is%3Aopen+label%3Acomp%3Adata) Github repo with `comp:data` tag.
-
-## Importing
-
-There are two ways to import TensorFlow.js Data
-
-1. You can access TensorFlow.js Data through the union package: [@tensorflow/tfjs](https://www.npmjs.com/package/@tensorflow/tfjs)
-2. You can get TensorFlow.js Data as a module:
-   [@tensorflow/tfjs-data](https://www.npmjs.com/package/@tensorflow/tfjs-data).
-   Note that `tfjs-data` has peer dependency on tfjs-core, so if you import
-   `@tensorflow/tfjs-data`, you also need to import
-   `@tensorflow/tfjs-core`.
-
-## Sample Usage
-
-Reading a CSV file
-
-```js
-import * as tf from '@tensorflow/tfjs';
-
-const csvUrl = 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';
-
-async function run() {
-  // We want to predict the column "medv", which represents a median value of a
-  // home (in $1000s), so we mark it as a label.
-  const csvDataset = tf.data.csv(
-    csvUrl, {
-      columnConfigs: {
-        medv: {
-          isLabel: true
-        }
-      }
-    });
-  // Number of features is the number of column names minus one for the label
-  // column.
-  const numOfFeatures = (await csvDataset.columnNames()).length - 1;
-
-  // Prepare the Dataset for training.
-  const flattenedDataset =
-    csvDataset
-    .map(({xs, ys}) => {
-      // Convert xs(features) and ys(labels) from object form (keyed by column
-      // name) to array form.
-      return {xs: Object.values(xs), ys: Object.values(ys)};
-    })
-    .batch(10);
-
-  // Define the model.
-  const model = tf.sequential();
-  model.add(tf.layers.dense({
-    inputShape: [numOfFeatures],
-    units: 1
-  }));
-  model.compile({
-    optimizer: tf.train.sgd(0.000001),
-    loss: 'meanSquaredError'
-  });
-
-  // Fit the model using the prepared Dataset
-  return model.fitDataset(flattenedDataset, {
-    epochs: 10,
-    callbacks: {
-      onEpochEnd: async (epoch, logs) => {
-        console.log(epoch, logs.loss);
-      }
-    }
-  });
-}
-
-run().then(() => console.log('Done'));
-```
-
-## For more information
-
-- [TensorFlow.js API documentation](https://js.tensorflow.org/api/latest/)
-- [TensorFlow.js Tutorials](https://js.tensorflow.org/tutorials/)
diff --git a/tfjs-master/tfjs-data/ROADMAP.md b/tfjs-master/tfjs-data/ROADMAP.md
deleted file mode 100644
index 4ca466170..000000000
--- a/tfjs-master/tfjs-data/ROADMAP.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# TensorFlow.js Data Roadmap
-
-**This repo is under active development as an open source project.**
-
-Following is a list of major upcoming features. For details please see the [issue list](https://github.com/tensorflow/tfjs/issues).
-
-## v0.2.0
-1. Add/Update API to load local file as stream in Node.js env [tracking issue](https://github.com/tensorflow/tfjs/issues/1008)
-2. Provide microphone data source [tracking issue](https://github.com/tensorflow/tfjs/issues/1004)
-3. Provide webcam data source [tracking issue](https://github.com/tensorflow/tfjs/issues/1005)
-
-## v0.3.0
-1. Support decoding/resizing image data [tracking issue](https://github.com/tensorflow/tfjs/issues/1003)
-2. Statistics API for `Dataset`
-
-## Future
-1. Support HTTP request with headers for `UrlDataSource` [tracking issue](https://github.com/tensorflow/tfjs/issues/1002)
-
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/.parcelrc b/tfjs-master/tfjs-data/demo/boston-housing/.parcelrc
deleted file mode 100644
index becb72f78..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/.parcelrc
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "extends": "@parcel/config-default",
-  "transformers": {
-    "*.{ts,tsx}": ["@parcel/transformer-typescript-tsc"]
-  }
-}
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/README.md b/tfjs-master/tfjs-data/demo/boston-housing/README.md
deleted file mode 100644
index 56c1790ea..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/README.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# TensorFlow.js Example: Multivariate Linear Regression for browser
-
-This example shows you how to perform linear regression with tfjs-data API using [Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html) which is a famous dataset derived from information collected by the U.S. Census Service concerning housing in the area of Boston Mass.
-
-Prepare the environment:
-```sh
-$ npm install
-# Or
-$ yarn
-```
-
-Temporary (before we move to tfjs-examples):
-```sh
-cd ../../
-yarn publish-local
-cd demo/boston-housing
-yarn link-local @tensorflow/tfjs-data
-```
-
-To build and watch the example, run:
-```sh
-$ yarn watch
-```
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/data.ts b/tfjs-master/tfjs-data/demo/boston-housing/data.ts
deleted file mode 100644
index 01ffac66d..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/data.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfd from '@tensorflow/tfjs-data';
-
-// Boston Housing data constants:
-const BASE_URL =
-    'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/';
-
-const TRAIN_FILENAME = 'boston-housing-train.csv';
-const VALIDATION_FILENAME = 'boston-housing-validation.csv';
-const TEST_FILENAME = 'boston-housing-test.csv';
-
-/** Helper class to handle loading training, validation and test data. */
-export class BostonHousingDataset {
-  trainDataset: tfd.Dataset<tfd.DataElement> = null;
-  validationDataset: tfd.Dataset<tfd.DataElement> = null;
-  testDataset: tfd.Dataset<tfd.DataElement> = null;
-  numFeatures: number = null;
-
-  private constructor() {}
-
-  static async create() {
-    const result = new BostonHousingDataset();
-    await result.loadData();
-    return result;
-  }
-
-  /**
-   * Downloads, converts and shuffles the data.
-   */
-  private async loadData() {
-    console.log('* Downloading data *');
-
-    const trainData = await this.prepareDataset(`${BASE_URL}${TRAIN_FILENAME}`);
-    // Sets number of features so it can be used in the model. Need to exclude
-    // the column of label.
-    this.trainDataset = trainData.dataset;
-    this.numFeatures = trainData.numFeatures;
-    this.validationDataset =
-        (await this.prepareDataset(`${BASE_URL}${VALIDATION_FILENAME}`))
-            .dataset;
-    this.testDataset =
-        (await this.prepareDataset(`${BASE_URL}${TEST_FILENAME}`)).dataset;
-  }
-
-  /**
-   * Prepare dataset from provided url.
-   */
-  private async prepareDataset(url: string) {
-    // We want to predict the column "medv", which represents a median value of
-    // a home (in $1000s), so we mark it as a label.
-    const csvDataset = tfd.csv(url, {columnConfigs: {medv: {isLabel: true}}});
-
-    // Reduces the object-type data to an array of numbers.
-    const convertedDataset =
-        csvDataset.map((row: Array<{[key: string]: number}>) => {
-          const [rawFeatures, rawLabel] = row;
-          const convertedFeatures = Object.values(rawFeatures);
-          const convertedLabel = [rawLabel['medv']];
-          return [convertedFeatures, convertedLabel];
-        });
-    return {
-      dataset: convertedDataset.shuffle(100),
-      numFeatures: (await csvDataset.columnNames()).length - 1
-    };
-  }
-}
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/index.html b/tfjs-master/tfjs-data/demo/boston-housing/index.html
deleted file mode 100644
index 4e3bb11ae..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/index.html
+++ /dev/null
@@ -1,56 +0,0 @@
-<!--
-Copyright 2018 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================
--->
-
-<html>
-
-<head>
-  <link rel="stylesheet" href="https://code.getmdl.io/1.3.0/material.cyan-teal.min.css" />
-  <style>
-    textarea {
-      border: none;
-      resize: none;
-      font-size: 20px;
-      text-align: center;
-    }
-    .container {
-      width: 80%;
-      margin-left: auto;
-      margin-right: auto;
-      text-align: center;
-    }
-    #plot {
-      width: auto;
-      height: auto;
-    }
-  </style>
-</head>
-
-<body>
-  <div class="container">
-    <h3>TensorFlow.js: Train Multivariate Regression with the Layers API</h3>
-    <div id="plot"></div>
-    <textarea rows="3" cols="60" readonly="true" id="status">Loading data...</textarea>
-    <textarea rows="3" cols="60" readonly="true" id="baselineStatus">Baseline not computed...</textarea>
-    <div id="buttons">
-      <button id="simple-mlr">Train Linear Regressor</button>
-      <button id="nn-mlr">Train Neural Network Regressor</button>
-    </div>
-  </div>
-  <script type="module" src="index.ts"></script>
-</body>
-
-</html>
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/index.ts b/tfjs-master/tfjs-data/demo/boston-housing/index.ts
deleted file mode 100644
index 2e12b7121..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/index.ts
+++ /dev/null
@@ -1,190 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import {DataElement, Dataset} from '@tensorflow/tfjs-data';
-
-import {BostonHousingDataset} from './data';
-// TODO(kangyi, soergel): Remove this once we have a public statistics API.
-import {computeDatasetStatistics, DatasetStatistics} from './stats';
-import * as ui from './ui';
-
-// Some hyperparameters for model training.
-const NUM_EPOCHS = 250;
-
-const BATCH_SIZE = 40;
-
-const LEARNING_RATE = 0.01;
-
-interface PreparedData {
-  trainData: Dataset<DataElement>;
-  validationData: Dataset<DataElement>;
-  testData: Dataset<DataElement>;
-}
-
-const preparedData: PreparedData = {
-  trainData: null,
-  validationData: null,
-  testData: null
-};
-
-let bostonData: BostonHousingDataset;
-
-// Converts loaded data into tensors and creates normalized versions of the
-// features.
-export async function loadDataAndNormalize() {
-  // TODO(kangyizhang): Statistics should be generated from trainDataset
-  // directly. Update following codes after
-  // https://github.com/tensorflow/tfjs-data/issues/32 is resolved.
-
-  // Gets mean and standard deviation of data.
-  // row[0] is feature data.
-  const featureStats = await computeDatasetStatistics(
-      bostonData.trainDataset.map((row: Array<{key: number}>) => row[0]));
-
-  // Normalizes data.
-  preparedData.trainData =
-      bostonData.trainDataset
-          .map(row => normalizeFeatures(row as number[][], featureStats))
-          .batch(BATCH_SIZE);
-  preparedData.validationData =
-      bostonData.validationDataset
-          .map(row => normalizeFeatures(row as number[][], featureStats))
-          .batch(BATCH_SIZE);
-  preparedData.testData =
-      bostonData.testDataset
-          .map(row => normalizeFeatures(row as number[][], featureStats))
-          .batch(BATCH_SIZE);
-}
-
-/**
- * Normalizes features with statistics and returns a new object.
- */
-// TODO(kangyizhang, bileschi): Replace these with preprocessing layers once
-// they are available.
-function normalizeFeatures(row: number[][], featureStats: DatasetStatistics) {
-  const features = row[0];
-  const normalizedFeatures: number[] = [];
-  features.forEach(
-      (value, index) => normalizedFeatures.push(
-          (value - featureStats[index].mean) / featureStats[index].stddev));
-  return [normalizedFeatures, row[1]];
-}
-
-/**
- * Builds and returns Linear Regression Model.
- *
- * @returns {tf.Sequential} The linear regression model.
- */
-export const linearRegressionModel = (): tf.Sequential => {
-  const model = tf.sequential();
-  model.add(tf.layers.dense({inputShape: [bostonData.numFeatures], units: 1}));
-
-  return model;
-};
-
-/**
- * Builds and returns Multi Layer Perceptron Regression Model
- * with 2 hidden layers, each with 10 units activated by sigmoid.
- *
- * @returns {tf.Sequential} The multi layer perceptron regression model.
- */
-export const multiLayerPerceptronRegressionModel = (): tf.Sequential => {
-  const model = tf.sequential();
-  model.add(tf.layers.dense({
-    inputShape: [bostonData.numFeatures],
-    units: 50,
-    activation: 'sigmoid'
-  }));
-  model.add(tf.layers.dense({units: 50, activation: 'sigmoid'}));
-  model.add(tf.layers.dense({units: 1}));
-
-  return model;
-};
-
-/**
- * Compiles `model` and trains it using the train data and runs model against
- * test data. Issues a callback to update the UI after each epcoh.
- *
- * @param {tf.Sequential} model Model to be trained.
- */
-export const run = async (model: tf.Sequential) => {
-  await ui.updateStatus('Compiling model...');
-  model.compile(
-      {optimizer: tf.train.sgd(LEARNING_RATE), loss: 'meanSquaredError'});
-
-  let trainLoss: number;
-  let valLoss: number;
-
-  await ui.updateStatus('Starting training process...');
-  await model.fitDataset(preparedData.trainData, {
-    epochs: NUM_EPOCHS,
-    validationData: preparedData.validationData,
-    callbacks: {
-      onEpochEnd: async (epoch: number, logs) => {
-        await ui.updateStatus(`Epoch ${epoch + 1} of ${NUM_EPOCHS} completed.`);
-        trainLoss = logs.loss;
-        valLoss = logs.val_loss;
-        await ui.plotData(epoch, trainLoss, valLoss);
-      }
-    }
-  });
-
-  await ui.updateStatus('Running on test data...');
-  const result =
-      (await model.evaluateDataset(preparedData.testData, {})) as tf.Tensor;
-  const testLoss = result.dataSync()[0];
-  await ui.updateStatus(
-      `Final train-set loss: ${trainLoss.toFixed(4)}\n` +
-      `Final validation-set loss: ${valLoss.toFixed(4)}\n` +
-      `Test-set loss: ${testLoss.toFixed(4)}`);
-};
-
-export const computeBaseline = async () => {
-  // TODO(kangyizhang): Remove this once statistics support nested object.
-  // row[1] is target data.
-  const targetStats = await computeDatasetStatistics(
-      bostonData.trainDataset.map((row: Array<{key: number}>) => row[1]));
-  const trainMean = targetStats[0].mean;
-  let testSquareError = 0;
-  let testCount = 0;
-
-  await bostonData.testDataset.forEach((row: number[]) => {
-    testSquareError += Math.pow(row[1] - trainMean, 2);
-    testCount++;
-  });
-
-  if (testCount === 0) {
-    throw new Error('No test data found!');
-  }
-  const baseline = testSquareError / testCount;
-  const baselineMsg =
-      `Baseline loss (meanSquaredError) is ${baseline.toFixed(2)}`;
-  ui.updateBaselineStatus(baselineMsg);
-};
-
-document.addEventListener('DOMContentLoaded', async () => {
-  bostonData = await BostonHousingDataset.create();
-  ui.updateStatus('Data loaded, converting to tensors');
-  await loadDataAndNormalize();
-  ui.updateStatus(
-      'Data is now available as tensors.\n' +
-      'Click a train button to begin.');
-  ui.updateBaselineStatus('Estimating baseline loss');
-  computeBaseline();
-  await ui.setup();
-}, false);
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/normalization.ts b/tfjs-master/tfjs-data/demo/boston-housing/normalization.ts
deleted file mode 100644
index 4b5aa4122..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/normalization.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor1D, Tensor2D} from '@tensorflow/tfjs';
-/**
- * Calculates the mean and standard deviation of each column of a data array.
- *
- * @param {Tensor2d} data Dataset from which to calculate the mean and
- *                        std of each column independently.
- *
- * @returns {Object} Contains the mean and standard deviation of each vector
- *                   column as 1d tensors.
- */
-export function determineMeanAndStddev(data: Tensor2D): {} {
-  const dataMean = data.mean(0);
-  // TODO(bileschi): Simplify when and if tf.var / tf.std added to the API.
-  const diffFromMean = data.sub(dataMean);
-  const squaredDiffFromMean = diffFromMean.square();
-  const variance = squaredDiffFromMean.mean(0);
-  const dataStd = variance.sqrt();
-  return {dataMean, dataStd};
-}
-/**
- * Given expected mean and standard deviation, normalizes a dataset by
- * subtracting the mean and dividing by the standard deviation.
- *
- * @param {Tensor2d} data: Data to normalize. Shape: [batch, numFeatures].
- * @param {Tensor1d} dataMean: Expected mean of the data. Shape [numFeatures].
- * @param {Tensor1d} dataStd: Expected std of the data. Shape [numFeatures]
- *
- * @returns {Tensor2d}: Tensor the same shape as data, but each column
- * normalized to have zero mean and unit standard deviation.
- */
-export function normalizeTensor(
-    data: Tensor2D, dataMean: Tensor1D, dataStd: Tensor1D): Tensor2D {
-  return data.sub(dataMean).div(dataStd);
-}
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/package.json b/tfjs-master/tfjs-data/demo/boston-housing/package.json
deleted file mode 100644
index f12888ec3..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/package.json
+++ /dev/null
@@ -1,36 +0,0 @@
-{
-  "name": "tfjs-data-demo-boston-housing",
-  "version": "0.1.0",
-  "description": "",
-  "main": "index.js",
-  "license": "Apache-2.0",
-  "private": true,
-  "engines": {
-    "node": ">=8.9.0"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs": "0.14.0",
-    "vega-embed": "^6.20.8",
-    "vega-lite": "5.2.0",
-    "vega": "5.23.0"
-  },
-  "scripts": {
-    "link-local": "yalc link",
-    "watch": "cross-env NODE_ENV=development parcel index.html --no-hmr --open"
-  },
-  "devDependencies": {
-    "@parcel/config-default": "^2.3.2",
-    "@parcel/transformer-typescript-tsc": "^2.3.2",
-    "clang-format": "~1.2.2",
-    "cross-env": "^5.1.6",
-    "parcel": "~2.3.2",
-    "ts-node": "~6.0.2",
-    "tslint": "~5.9.1",
-    "typescript": "~2.8.3",
-    "yalc": "^1.0.0-pre.53"
-  },
-  "resolutions": {
-    "node-fetch": "2.6.7",
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/stats.ts b/tfjs-master/tfjs-data/demo/boston-housing/stats.ts
deleted file mode 100644
index 5f1a38b30..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/stats.ts
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {Dataset} from '@tensorflow/tfjs-data';
-
-// TODO(kangyizhang): Remove this file once we have statistics API public.
-
-/**
- * A map from string keys (aka column names) to values for a single element.
- */
-export type TabularRecord = {
-  [key: string]: number
-};
-
-/** An interface representing numeric statistics of a column. */
-export interface NumericColumnStatistics {
-  min: number;
-  max: number;
-  mean: number;
-  variance: number;
-  stddev: number;
-  length: number;
-}
-
-/**
- * An interface representing column level NumericColumnStatistics for a
- * Dataset.
- */
-export interface DatasetStatistics {
-  [key: string]: NumericColumnStatistics;
-}
-
-/**
- * Provides a function that calculates column level statistics, i.e. min, max,
- * variance, stddev.
- *
- * @param dataset The Dataset object whose statistics will be calculated.
- * @return A DatasetStatistics object that contains NumericColumnStatistics of
- *     each column.
- */
-export async function computeDatasetStatistics(
-    dataset: Dataset<TabularRecord>) {
-  const result: DatasetStatistics = {};
-
-  await dataset.forEach(e => {
-    for (const key of Object.keys(e)) {
-      const value = e[key];
-      if (typeof (value) === 'string') {
-        // No statistics for string element.
-      } else {
-        let previousMean = 0;
-        let previousLength = 0;
-        let previousVariance = 0;
-        let columnStats: NumericColumnStatistics = result[key];
-        if (columnStats == null) {
-          columnStats = {
-            min: Number.POSITIVE_INFINITY,
-            max: Number.NEGATIVE_INFINITY,
-            mean: 0,
-            variance: 0,
-            stddev: 0,
-            length: 0
-          };
-          result[key] = columnStats;
-        } else {
-          previousMean = columnStats.mean;
-          previousLength = columnStats.length;
-          previousVariance = columnStats.variance;
-        }
-
-        // Calculate accumulated mean and variance following tf.Transform
-        // implementation
-        const combinedLength = previousLength + 1;
-        const combinedMean =
-            previousMean + (1 / combinedLength) * (value - previousMean);
-        const combinedVariance = previousVariance +
-            (1 / combinedLength) *
-                (((value - combinedMean) * (value - previousMean)) -
-                 previousVariance);
-
-        columnStats.min = Math.min(columnStats.min, value);
-        columnStats.max = Math.max(columnStats.max, value);
-        columnStats.length = combinedLength;
-        columnStats.mean = combinedMean;
-        columnStats.variance = combinedVariance;
-        columnStats.stddev = Math.sqrt(combinedVariance);
-      }
-    }
-  });
-  // Variance and stddev should be NaN for the case of a single element.
-  for (const key in result) {
-    const stat: NumericColumnStatistics = result[key];
-    if (stat.length === 1) {
-      stat.variance = NaN;
-    }
-  }
-  return result;
-}
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/tsconfig.json b/tfjs-master/tfjs-data/demo/boston-housing/tsconfig.json
deleted file mode 100644
index 412e34ae4..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/tsconfig.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "commonjs",
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "removeComments": true,
-    "preserveConstEnums": true,
-    "target": "es6",
-    "lib": [
-      "es2017",
-      "dom"
-    ],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false,
-    "experimentalDecorators": true,
-    "skipLibCheck": true
-  }
-}
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/ui.ts b/tfjs-master/tfjs-data/demo/boston-housing/ui.ts
deleted file mode 100644
index 2d44cb2c3..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/ui.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import renderChart, {Result} from 'vega-embed';
-import {VisualizationSpec} from 'vega-embed';
-
-import {linearRegressionModel, multiLayerPerceptronRegressionModel, run} from './index';
-
-const statusElement = document.getElementById('status') as HTMLTextAreaElement;
-export const updateStatus = (message: string) => {
-  statusElement.value = message;
-};
-
-const baselineStatusElement =
-    document.getElementById('baselineStatus') as HTMLTextAreaElement;
-export const updateBaselineStatus = (message: string) => {
-  baselineStatusElement.value = message;
-};
-
-export const setup = async () => {
-  const trainSimpleLinearRegression = document.getElementById('simple-mlr');
-  const trainNeuralNetworkLinearRegression = document.getElementById('nn-mlr');
-
-  trainSimpleLinearRegression.addEventListener('click', async (e) => {
-    const model = linearRegressionModel();
-    losses = [{}];
-    await run(model);
-  }, false);
-
-  trainNeuralNetworkLinearRegression.addEventListener('click', async (e) => {
-    const model = multiLayerPerceptronRegressionModel();
-    losses = [{}];
-    await run(model);
-  }, false);
-};
-
-let losses = [{}];
-export const plotData = async (
-    epoch: number, trainLoss: number, valLoss: number, result?: Result) => {
-  losses.push({'epoch': epoch, 'loss': trainLoss, 'split': 'Train Loss'});
-  losses.push({'epoch': epoch, 'loss': valLoss, 'split': 'Validation Loss'});
-
-  const spec = {
-    '$schema': 'https://vega.github.io/schema/vega-lite/v2.json',
-    'width': 250,
-    'height': 250,
-    'data': {'values': losses},
-    'mark': 'line',
-    'encoding': {
-      'x': {'field': 'epoch', 'type': 'quantitative'},
-      'y': {'field': 'loss', 'type': 'quantitative'},
-      'color': {'field': 'split', 'type': 'nominal'}
-    }
-  } as VisualizationSpec;
-
-  return renderChart('#plot', spec, {actions: false});
-};
diff --git a/tfjs-master/tfjs-data/demo/boston-housing/yarn.lock b/tfjs-master/tfjs-data/demo/boston-housing/yarn.lock
deleted file mode 100644
index a47b6ca60..000000000
--- a/tfjs-master/tfjs-data/demo/boston-housing/yarn.lock
+++ /dev/null
@@ -1,2962 +0,0 @@
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-# yarn lockfile v1
-
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-  dependencies:
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-  version "4.2.1"
-  resolved "https://registry.yarnpkg.com/css-select/-/css-select-4.2.1.tgz#9e665d6ae4c7f9d65dbe69d0316e3221fb274cdd"
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-    css-what "^5.1.0"
-    domhandler "^4.3.0"
-    domutils "^2.8.0"
-    nth-check "^2.0.1"
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-css-tree@^1.1.2, css-tree@^1.1.3:
-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/css-tree/-/css-tree-1.1.3.tgz#eb4870fb6fd7707327ec95c2ff2ab09b5e8db91d"
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-  dependencies:
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-    source-map "^0.6.1"
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-css-what@^5.1.0:
-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/css-what/-/css-what-5.1.0.tgz#3f7b707aadf633baf62c2ceb8579b545bb40f7fe"
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-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/cssesc/-/cssesc-3.0.0.tgz#37741919903b868565e1c09ea747445cd18983ee"
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-  resolved "https://registry.yarnpkg.com/cssnano-preset-default/-/cssnano-preset-default-5.2.4.tgz#eced79bbc1ab7270337c4038a21891daac2329bc"
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-    postcss-colormin "^*"
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-    postcss-discard-comments "^*"
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-    postcss-discard-overridden "^*"
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-    postcss-minify-font-values "^*"
-    postcss-minify-gradients "^*"
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-    postcss-minify-selectors "^*"
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-    postcss-normalize-display-values "^*"
-    postcss-normalize-positions "^*"
-    postcss-normalize-repeat-style "^*"
-    postcss-normalize-string "^*"
-    postcss-normalize-timing-functions "^*"
-    postcss-normalize-unicode "^*"
-    postcss-normalize-url "^*"
-    postcss-normalize-whitespace "^*"
-    postcss-ordered-values "^*"
-    postcss-reduce-initial "^*"
-    postcss-reduce-transforms "^*"
-    postcss-svgo "^*"
-    postcss-unique-selectors "^*"
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-cssnano-utils@^*, cssnano-utils@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/cssnano-utils/-/cssnano-utils-3.1.0.tgz#95684d08c91511edfc70d2636338ca37ef3a6861"
-  integrity sha512-JQNR19/YZhz4psLX/rQ9M83e3z2Wf/HdJbryzte4a3NSuafyp9w/I4U+hx5C2S9g41qlstH7DEWnZaaj83OuEA==
-
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-  version "5.1.4"
-  resolved "https://registry.yarnpkg.com/cssnano/-/cssnano-5.1.4.tgz#c648192e8e2f1aacb7d839e6aa3706b50cc7f8e4"
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-    yaml "^1.10.2"
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-  version "4.2.0"
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-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-3.2.2.tgz#f8ac4705c5b06914a7e0025bbf8d5f1513f6a86e"
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-    internmap "1 - 2"
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-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-3.0.1.tgz#03316e595955d1fcd39d9f3610ad41bb90194d0a"
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-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-3.1.0.tgz#395b2833dfac71507f12ac2f7af23bf819de24e2"
-  integrity sha512-zg/chbXyeBtMQ1LbD/WSoW2DpC3I0mpmPdW+ynRTj/x2DAWYrIY7qeZIHidozwV24m4iavr15lNwIwLxRmOxhA==
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-  version "6.0.2"
-  resolved "https://registry.yarnpkg.com/d3-delaunay/-/d3-delaunay-6.0.2.tgz#7fd3717ad0eade2fc9939f4260acfb503f984e92"
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-    delaunator "5"
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-"d3-dispatch@1 - 3":
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-dispatch/-/d3-dispatch-3.0.1.tgz#5fc75284e9c2375c36c839411a0cf550cbfc4d5e"
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-
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-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-3.0.1.tgz#c63af978f4d6a0d084a52a673922be2160789b73"
-  integrity sha512-UG6OvdI5afDIFP9w4G0mNq50dSOsXHJaRE8arAS5o9ApWnIElp8GZw1Dun8vP8OyHOZ/QJUKUJwxiiCCnUwm+Q==
-  dependencies:
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-    iconv-lite "0.6"
-    rw "1"
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-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-force/-/d3-force-3.0.0.tgz#3e2ba1a61e70888fe3d9194e30d6d14eece155c4"
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-  dependencies:
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-    d3-quadtree "1 - 3"
-    d3-timer "1 - 3"
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-"d3-format@1 - 3", d3-format@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-3.1.0.tgz#9260e23a28ea5cb109e93b21a06e24e2ebd55641"
-  integrity sha512-YyUI6AEuY/Wpt8KWLgZHsIU86atmikuoOmCfommt0LYHiQSPjvX2AcFc38PX0CBpr2RCyZhjex+NS/LPOv6YqA==
-
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-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-4.0.0.tgz#dc229e5ead78d31869a4e87cf1f45bd2716c48ca"
-  integrity sha512-p0bK60CEzph1iqmnxut7d/1kyTmm3UWtPlwdkM31AU+LW+BXazd5zJdoCn7VFxNCHXRngPHRnsNn5uGjLRGndg==
-  dependencies:
-    commander "7"
-    d3-array "1 - 3"
-    d3-geo "1.12.0 - 3"
-
-"d3-geo@1.12.0 - 3":
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-3.0.1.tgz#4f92362fd8685d93e3b1fae0fd97dc8980b1ed7e"
-  integrity sha512-Wt23xBych5tSy9IYAM1FR2rWIBFWa52B/oF/GYe5zbdHrg08FU8+BuI6X4PvTwPDdqdAdq04fuWJpELtsaEjeA==
-  dependencies:
-    d3-array "2.5.0 - 3"
-
-d3-geo@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-3.1.0.tgz#74fd54e1f4cebd5185ac2039217a98d39b0a4c0e"
-  integrity sha512-JEo5HxXDdDYXCaWdwLRt79y7giK8SbhZJbFWXqbRTolCHFI5jRqteLzCsq51NKbUoX0PjBVSohxrx+NoOUujYA==
-  dependencies:
-    d3-array "2.5.0 - 3"
-
-d3-hierarchy@^3.1.2:
-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/d3-hierarchy/-/d3-hierarchy-3.1.2.tgz#b01cd42c1eed3d46db77a5966cf726f8c09160c6"
-  integrity sha512-FX/9frcub54beBdugHjDCdikxThEqjnR93Qt7PvQTOHxyiNCAlvMrHhclk3cD5VeAaq9fxmfRp+CnWw9rEMBuA==
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-"d3-interpolate@1.2.0 - 3", d3-interpolate@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-3.0.1.tgz#3c47aa5b32c5b3dfb56ef3fd4342078a632b400d"
-  integrity sha512-3bYs1rOD33uo8aqJfKP3JWPAibgw8Zm2+L9vBKEHJ2Rg+viTR7o5Mmv5mZcieN+FRYaAOWX5SJATX6k1PWz72g==
-  dependencies:
-    d3-color "1 - 3"
-
-d3-path@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-3.1.0.tgz#22df939032fb5a71ae8b1800d61ddb7851c42526"
-  integrity sha512-p3KP5HCf/bvjBSSKuXid6Zqijx7wIfNW+J/maPs+iwR35at5JCbLUT0LzF1cnjbCHWhqzQTIN2Jpe8pRebIEFQ==
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-"d3-quadtree@1 - 3":
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-quadtree/-/d3-quadtree-3.0.1.tgz#6dca3e8be2b393c9a9d514dabbd80a92deef1a4f"
-  integrity sha512-04xDrxQTDTCFwP5H6hRhsRcb9xxv2RzkcsygFzmkSIOJy3PeRJP7sNk3VRIbKXcog561P9oU0/rVH6vDROAgUw==
-
-d3-scale@^4.0.2:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-4.0.2.tgz#82b38e8e8ff7080764f8dcec77bd4be393689396"
-  integrity sha512-GZW464g1SH7ag3Y7hXjf8RoUuAFIqklOAq3MRl4OaWabTFJY9PN/E1YklhXLh+OQ3fM9yS2nOkCoS+WLZ6kvxQ==
-  dependencies:
-    d3-array "2.10.0 - 3"
-    d3-format "1 - 3"
-    d3-interpolate "1.2.0 - 3"
-    d3-time "2.1.1 - 3"
-    d3-time-format "2 - 4"
-
-d3-shape@^3.2.0:
-  version "3.2.0"
-  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-3.2.0.tgz#a1a839cbd9ba45f28674c69d7f855bcf91dfc6a5"
-  integrity sha512-SaLBuwGm3MOViRq2ABk3eLoxwZELpH6zhl3FbAoJ7Vm1gofKx6El1Ib5z23NUEhF9AsGl7y+dzLe5Cw2AArGTA==
-  dependencies:
-    d3-path "^3.1.0"
-
-"d3-time-format@2 - 4", d3-time-format@^4.1.0:
-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/d3-time-format/-/d3-time-format-4.1.0.tgz#7ab5257a5041d11ecb4fe70a5c7d16a195bb408a"
-  integrity sha512-dJxPBlzC7NugB2PDLwo9Q8JiTR3M3e4/XANkreKSUxF8vvXKqm1Yfq4Q5dl8budlunRVlUUaDUgFt7eA8D6NLg==
-  dependencies:
-    d3-time "1 - 3"
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-"d3-time@1 - 3", "d3-time@2.1.1 - 3":
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-3.0.0.tgz#65972cb98ae2d4954ef5c932e8704061335d4975"
-  integrity sha512-zmV3lRnlaLI08y9IMRXSDshQb5Nj77smnfpnd2LrBa/2K281Jijactokeak14QacHs/kKq0AQ121nidNYlarbQ==
-  dependencies:
-    d3-array "2 - 3"
-
-d3-time@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-3.1.0.tgz#9310db56e992e3c0175e1ef385e545e48a9bb5c7"
-  integrity sha512-VqKjzBLejbSMT4IgbmVgDjpkYrNWUYJnbCGo874u7MMKIWsILRX+OpX/gTk8MqjpT1A/c6HY2dCA77ZN0lkQ2Q==
-  dependencies:
-    d3-array "2 - 3"
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-"d3-timer@1 - 3", d3-timer@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-timer/-/d3-timer-3.0.1.tgz#6284d2a2708285b1abb7e201eda4380af35e63b0"
-  integrity sha512-ndfJ/JxxMd3nw31uyKoY2naivF+r29V+Lc0svZxe1JvvIRmi8hUsrMvdOwgS1o6uBHmiz91geQ0ylPP0aj1VUA==
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-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/delaunator/-/delaunator-5.0.0.tgz#60f052b28bd91c9b4566850ebf7756efe821d81b"
-  integrity sha512-AyLvtyJdbv/U1GkiS6gUUzclRoAY4Gs75qkMygJJhU75LW4DNuSF2RMzpxs9jw9Oz1BobHjTdkG3zdP55VxAqw==
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-    robust-predicates "^3.0.0"
-
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
-  integrity sha1-3zrhmayt+31ECqrgsp4icrJOxhk=
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-  version "6.1.0"
-  resolved "https://registry.yarnpkg.com/detect-indent/-/detect-indent-6.1.0.tgz#592485ebbbf6b3b1ab2be175c8393d04ca0d57e6"
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-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/detect-libc/-/detect-libc-1.0.3.tgz#fa137c4bd698edf55cd5cd02ac559f91a4c4ba9b"
-  integrity sha1-+hN8S9aY7fVc1c0CrFWfkaTEups=
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-  version "3.5.0"
-  resolved "https://registry.yarnpkg.com/diff/-/diff-3.5.0.tgz#800c0dd1e0a8bfbc95835c202ad220fe317e5a12"
-  integrity sha512-A46qtFgd+g7pDZinpnwiRJtxbC1hpgf0uzP3iG89scHk0AUC7A1TGxf5OiiOUv/JMZR8GOt8hL900hV0bOy5xA==
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-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/dom-serializer/-/dom-serializer-1.3.2.tgz#6206437d32ceefaec7161803230c7a20bc1b4d91"
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-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/domelementtype/-/domelementtype-2.2.0.tgz#9a0b6c2782ed6a1c7323d42267183df9bd8b1d57"
-  integrity sha512-DtBMo82pv1dFtUmHyr48beiuq792Sxohr+8Hm9zoxklYPfa6n0Z3Byjj2IV7bmr2IyqClnqEQhfgHJJ5QF0R5A==
-
-domhandler@^4.2.0, domhandler@^4.2.2, domhandler@^4.3.0:
-  version "4.3.0"
-  resolved "https://registry.yarnpkg.com/domhandler/-/domhandler-4.3.0.tgz#16c658c626cf966967e306f966b431f77d4a5626"
-  integrity sha512-fC0aXNQXqKSFTr2wDNZDhsEYjCiYsDWl3D01kwt25hm1YIPyDGHvvi3rw+PLqHAl/m71MaiF7d5zvBr0p5UB2g==
-  dependencies:
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-
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-  version "2.8.0"
-  resolved "https://registry.yarnpkg.com/domutils/-/domutils-2.8.0.tgz#4437def5db6e2d1f5d6ee859bd95ca7d02048135"
-  integrity sha512-w96Cjofp72M5IIhpjgobBimYEfoPjx1Vx0BSX9P30WBdZW2WIKU0T1Bd0kz2eNZ9ikjKgHbEyKx8BB6H1L3h3A==
-  dependencies:
-    dom-serializer "^1.0.1"
-    domelementtype "^2.2.0"
-    domhandler "^4.2.0"
-
-dotenv-expand@^5.1.0:
-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/dotenv-expand/-/dotenv-expand-5.1.0.tgz#3fbaf020bfd794884072ea26b1e9791d45a629f0"
-  integrity sha512-YXQl1DSa4/PQyRfgrv6aoNjhasp/p4qs9FjJ4q4cQk+8m4r6k4ZSiEyytKG8f8W9gi8WsQtIObNmKd+tMzNTmA==
-
-dotenv@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-7.0.0.tgz#a2be3cd52736673206e8a85fb5210eea29628e7c"
-  integrity sha512-M3NhsLbV1i6HuGzBUH8vXrtxOk+tWmzWKDMbAVSUp3Zsjm7ywFeuwrUXhmhQyRK1q5B5GGy7hcXPbj3bnfZg2g==
-
-electron-to-chromium@^1.4.76:
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-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yargs@~17.2.1:
-  version "17.2.1"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-17.2.1.tgz#e2c95b9796a0e1f7f3bf4427863b42e0418191ea"
-  integrity sha512-XfR8du6ua4K6uLGm5S6fA+FIJom/MdJcFNVY8geLlp2v8GYbOXD4EB1tPNZsRn4vBzKGMgb5DRZMeWuFc2GO8Q==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yn@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/yn/-/yn-2.0.0.tgz#e5adabc8acf408f6385fc76495684c88e6af689a"
-  integrity sha1-5a2ryKz0CPY4X8dklWhMiOavaJo=
diff --git a/tfjs-master/tfjs-data/karma.conf.js b/tfjs-master/tfjs-data/karma.conf.js
deleted file mode 100644
index c35661221..000000000
--- a/tfjs-master/tfjs-data/karma.conf.js
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-module.exports = function(config) {
-  config.set({
-    frameworks: ['jasmine', 'karma-typescript'],
-    files: [{pattern: 'src/**/*.ts'}],
-    exclude: ['src/test_node.ts'],
-    preprocessors: {
-      '**/*.ts': ['karma-typescript'],  // *.tsx for React Jsx
-    },
-    karmaTypescriptConfig:
-        {tsconfig: 'tsconfig.json', compilerOptions: {module: 'commonjs'}},
-    reporters: ['progress', 'karma-typescript'],
-    browsers: ['Chrome'],
-    port: 9200,
-    browserStack: {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_KEY,
-      tunnelIdentifier:
-          `tfjs_data_${Date.now()}_${Math.floor(Math.random() * 1000)}`
-    },
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 30000,
-    customLaunchers: {
-      bs_chrome_mac: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_firefox_mac: {
-        base: 'BrowserStack',
-        browser: 'firefox',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      chrome_with_swift_shader: {
-        base: 'Chrome',
-        flags: ['--blacklist-accelerated-compositing', '--blacklist-webgl']
-      }
-    },
-    client: {jasmine: {random: false}, args: ['--grep', config.grep || '']}
-  });
-};
diff --git a/tfjs-master/tfjs-data/package.json b/tfjs-master/tfjs-data/package.json
deleted file mode 100644
index 12f6b9369..000000000
--- a/tfjs-master/tfjs-data/package.json
+++ /dev/null
@@ -1,49 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-data",
-  "version": "0.0.0",
-  "description": "TensorFlow Data API in JavaScript",
-  "private": false,
-  "main": "dist/tf-data.node.js",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-data"
-  },
-  "jsdelivr": "dist/tf-data.min.js",
-  "unpkg": "dist/tf-data.min.js",
-  "types": "dist/index.d.ts",
-  "jsnext:main": "dist/index.js",
-  "module": "dist/index.js",
-  "miniprogram": "dist/miniprogram",
-  "license": "Apache-2.0",
-  "devDependencies": {
-    "@bazel/bazelisk": "^1.12.0",
-    "@bazel/ibazel": "^0.16.2"
-  },
-  "scripts": {
-    "build": "bazel build :tfjs-data_pkg",
-    "publish-npm": "bazel run :tfjs-data_pkg.publish",
-    "test": "bazel test :tests",
-    "test-dev": "ibazel test :tests",
-    "test-debug": "yarn test-node-debug",
-    "test-browser": "bazel test :tfjs-data_browser_test --test-output=streamed",
-    "test-browser-debug": "bazel run :tfjs-data_browser_test --test-output=streamed",
-    "test-node": "bazel test --test_output=streamed :tfjs-data_test",
-    "test-node-debug": "bazel run --config=debug --test_output=streamed :tfjs-data_test"
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core",
-    "seedrandom": "^3.0.5"
-  },
-  "dependencies": {
-    "@types/node-fetch": "^2.1.2",
-    "node-fetch": "~2.6.1",
-    "string_decoder": "^1.3.0"
-  },
-  "browser": {
-    "fs": false,
-    "node-fetch": false,
-    "string_decoder": false,
-    "crypto": false
-  }
-}
diff --git a/tfjs-master/tfjs-data/scripts/BUILD.bazel b/tfjs-master/tfjs-data/scripts/BUILD.bazel
deleted file mode 100644
index 51a069cc1..000000000
--- a/tfjs-master/tfjs-data/scripts/BUILD.bazel
+++ /dev/null
@@ -1,19 +0,0 @@
-load("//tools:defaults.bzl", "ts_library")
-
-package(default_visibility = ["//visibility:public"])
-
-ts_library(
-    name = "test_snippets_lib",
-    testonly = True,
-    srcs = [
-        "test_snippets.ts",
-    ],
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/scripts/test_snippets:test_snippets_util_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-data/src:tfjs-data_lib",
-        "//tfjs-layers/src:tfjs-layers_lib",
-    ],
-)
diff --git a/tfjs-master/tfjs-data/scripts/build-npm.sh b/tfjs-master/tfjs-data/scripts/build-npm.sh
deleted file mode 100644
index 64aa361da..000000000
--- a/tfjs-master/tfjs-data/scripts/build-npm.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Exit immediately if a command exits with a non-zero status.
-set -e
-
-yarn rimraf dist/
-yarn
-yarn build-ci
-yarn rollup -c --visualize --npm
-
-# Use minified files for miniprogram
-mkdir dist/miniprogram
-cp dist/tf-data.min.js dist/miniprogram/index.js
-cp dist/tf-data.min.js.map dist/miniprogram/index.js.map
-
-echo "Stored standalone library at dist/tf-data(.min).js"
-npm pack
diff --git a/tfjs-master/tfjs-data/scripts/test-ci.sh b/tfjs-master/tfjs-data/scripts/test-ci.sh
deleted file mode 100644
index fa0851578..000000000
--- a/tfjs-master/tfjs-data/scripts/test-ci.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2018 Google LLC
-#
-# Use of this source code is governed by an MIT-style
-# license that can be found in the LICENSE file or at
-# https://opensource.org/licenses/MIT.
-# =============================================================================
-
-set -e
-
-# Regular testing.
-yarn build
-yarn lint
-yarn test
diff --git a/tfjs-master/tfjs-data/scripts/test_snippets.ts b/tfjs-master/tfjs-data/scripts/test_snippets.ts
deleted file mode 100644
index 0b42d3358..000000000
--- a/tfjs-master/tfjs-data/scripts/test_snippets.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import '@tensorflow/tfjs-backend-cpu';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {parseAndEvaluateSnippets} from '@tensorflow/tfjs-core/dist/scripts/test_snippets/util';
-import * as tfl from '@tensorflow/tfjs-layers';
-
-import * as tfd from '../src/index';
-
-const tf = {
-  data: {...tfd},
-  ...tfc,
-  ...tfl
-};
-parseAndEvaluateSnippets(tf);
diff --git a/tfjs-master/tfjs-data/src/BUILD.bazel b/tfjs-master/tfjs-data/src/BUILD.bazel
deleted file mode 100644
index 3504b2c21..000000000
--- a/tfjs-master/tfjs-data/src/BUILD.bazel
+++ /dev/null
@@ -1,120 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("//tools:defaults.bzl", "esbuild", "ts_library")
-load("//tools:enumerate_tests.bzl", "enumerate_tests")
-
-package(default_visibility = ["//visibility:public"])
-
-# Used for test-snippets
-filegroup(
-    name = "all_srcs",
-    srcs = glob(["**/*.ts"]),
-)
-
-TEST_SRCS = [
-    "**/*_test.ts",
-    "setup_tests.ts",
-]
-
-filegroup(
-    name = "all_test_entrypoints",
-    srcs = glob(
-        ["**/*_test.ts"],
-    ),
-)
-
-# Generates the 'tests.ts' file that imports all test entrypoints.
-enumerate_tests(
-    name = "tests",
-    srcs = [":all_test_entrypoints"],
-    root_path = "tfjs-data/src",
-)
-
-# Compiles the majority of tfjs-data using the `@tensorflow/tfjs-data/dist`
-# module name.
-ts_library(
-    name = "tfjs-data_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-data/dist",
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "@npm//@types/seedrandom",
-        "@npm//seedrandom",
-        "@npm//string_decoder",
-    ],
-)
-
-# Compiles the `index.ts` entrypoint of tfjs-data separately from the rest of
-# the sources in order to use the `@tensorflow/tfjs-data` module name instead
-# of `@tensorflow/tfjs-data/dist`,
-ts_library(
-    name = "tfjs-data_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-data",
-    deps = [
-        ":tfjs-data_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-    ],
-)
-
-ts_library(
-    name = "tfjs-data_test_lib",
-    # disable testonly for the issue in the nodejs build target.
-    # https://github.com/bazelbuild/rules_nodejs/pull/2984
-    #testonly = True,
-    srcs = glob(TEST_SRCS) + [":tests"],
-    module_name = "@tensorflow/tfjs-data/dist",
-    deps = [
-        ":tfjs-data_lib",
-        ":tfjs-data_src_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-        "@npm//@types/jasmine",
-        "@npm//jasmine",
-    ],
-)
-
-esbuild(
-    name = "tfjs-data_test_bundle",
-    testonly = True,
-    args = {"keepNames": True},
-    entry_point = "setup_tests.ts",
-    external = [
-        # webworker tests call 'require('@tensorflow/tfjs')', which
-        # is external to the test bundle.
-        # Note: This is not a bazel target. It's just a string.
-        "@tensorflow/tfjs",
-        "buffer",
-        "fs",
-        "worker_threads",
-        "util",
-    ],
-    sources_content = True,
-    deps = [
-        ":tfjs-data_lib",
-        ":tfjs-data_test_lib",
-    ],
-)
diff --git a/tfjs-master/tfjs-data/src/dataset.ts b/tfjs-master/tfjs-data/src/dataset.ts
deleted file mode 100644
index df93df975..000000000
--- a/tfjs-master/tfjs-data/src/dataset.ts
+++ /dev/null
@@ -1,705 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {TensorContainer, TensorLike} from '@tensorflow/tfjs-core';
-import * as seedrandom from 'seedrandom';
-
-import {iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, LazyIterator, ZipMismatchMode} from './iterators/lazy_iterator';
-import {Container} from './types';
-import {canTensorify, deepMapAndAwaitAll, DeepMapResult, isIterable} from './util/deep_map';
-
-/**
- * A nested structure of Datasets, used as the input to zip().
- */
-export type DatasetContainer = Container<Dataset<TensorContainer>>;
-
-// TODO(soergel): consider vectorized operations within the pipeline.
-
-/**
- * Represents a potentially large list of independent data elements (typically
- * 'samples' or 'examples').
- *
- * A 'data example' may be a primitive, an array, a map from string keys to
- * values, or any nested structure of these.
- *
- * A `Dataset` represents an ordered collection of elements, together with a
- * chain of transformations to be performed on those elements. Each
- * transformation is a method of `Dataset` that returns another `Dataset`, so
- * these may be chained, e.g.
- * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.
- *
- * Data loading and transformation is done in a lazy, streaming fashion.  The
- * dataset may be iterated over multiple times; each iteration starts the data
- * loading anew and recapitulates the transformations.
- *
- * A `Dataset` is typically processed as a stream of unbatched examples -- i.e.,
- * its transformations are applied one example at a time. Batching produces a
- * new `Dataset` where each element is a batch. Batching should usually come
- * last in a pipeline, because data transformations are easier to express on a
- * per-example basis than on a per-batch basis.
- *
- * The following code examples are calling `await dataset.forEachAsync(...)` to
- * iterate once over the entire dataset in order to print out the data.
- *
- * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}
- */
-export abstract class Dataset<T extends tf.TensorContainer> {
-  /*
-   * Provide a new stream of elements.  Note this will also start new streams
-   * from any underlying `Dataset`s.
-   *
-   * CAUTION: Any Tensors contained within the elements returned from
-   * this stream *must* be manually disposed to avoid a GPU memory leak.
-   * The tf.tidy() approach cannot be used in an asynchronous context.
-   */
-  abstract iterator(): Promise<LazyIterator<T>>;
-
-  readonly size: number = null;
-
-  // TODO(soergel): Make Datasets report whether repeated iterator() calls
-  // produce the same result (e.g., reading from a file) or different results
-  // (e.g., from the webcam).  Currently we don't make this distinction but it
-  // could be important for the user to know.
-  // abstract isDeterministic(): boolean;
-
-  /**
-   * Groups elements into batches.
-   *
-   * It is assumed that each of the incoming dataset elements has the same
-   * structure -- i.e. the same set of keys at each location in an object
-   * hierarchy.  For each key, the resulting `Dataset` provides a batched
-   * element collecting all of the incoming values for that key.
-   *
-   *  * Incoming primitives are grouped into a 1-D Tensor.
-   *  * Incoming Tensors are grouped into a new Tensor where the 0th axis is
-   *    the batch dimension.
-   *  * Incoming arrays are converted to Tensor and then batched.
-   *  * A nested array is interpreted as an n-D Tensor, so the batched result
-   *    has n+1 dimensions.
-   *  * An array that cannot be converted to Tensor produces an error.
-   *
-   * If an array should not be batched as a unit, it should first be converted
-   * to an object with integer keys.
-   *
-   * Here are a few examples:
-   *
-   * Batch a dataset of numbers:
-   * ```js
-   * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);
-   * await a.forEachAsync(e => e.print());
-   * ```
-   *
-   * Batch a dataset of arrays:
-   * ```js
-   * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);
-   * await b.forEachAsync(e => e.print());
-   * ```
-   *
-   * Batch a dataset of objects:
-   * ```js
-   * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},
-   *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},
-   *   {a: 8, b: 18}]).batch(4);
-   * await c.forEachAsync(e => {
-   *   console.log('{');
-   *   for(var key in e) {
-   *     console.log(key+':');
-   *     e[key].print();
-   *   }
-   *   console.log('}');
-   * })
-   * ```
-   *
-   * @param batchSize The number of elements desired per batch.
-   * @param smallLastBatch Whether to emit the final batch when it has fewer
-   *   than batchSize elements. Default true.
-   * @returns A `Dataset`, from which a stream of batches can be obtained.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  batch(batchSize: number, smallLastBatch = true): Dataset<tf.TensorContainer> {
-    const base = this;
-    tf.util.assert(
-        batchSize > 0, () => `batchSize needs to be positive, but it is
-      ${batchSize}`);
-    let size;
-    if (this.size === Infinity || this.size == null) {
-      // If the size of this dataset is infinity or null, the new size keeps the
-      // same.
-      size = this.size;
-    } else if (smallLastBatch) {
-      // If the size of this dataset is known and include small last batch, the
-      // new size is full batch count plus last batch.
-      size = Math.ceil(this.size / batchSize);
-    } else {
-      // If the size of this dataset is known and not include small last batch,
-      // the new size is full batch count.
-      size = Math.floor(this.size / batchSize);
-    }
-    return datasetFromIteratorFn(async () => {
-      return (await base.iterator())
-          .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);
-    }, size);
-  }
-
-  /**
-   * Concatenates this `Dataset` with another.
-   *
-   * ```js
-   * const a = tf.data.array([1, 2, 3]);
-   * const b = tf.data.array([4, 5, 6]);
-   * const c = a.concatenate(b);
-   * await c.forEachAsync(e => console.log(e));
-   * ```
-   *
-   * @param dataset A `Dataset` to be concatenated onto this one.
-   * @returns A `Dataset`.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  concatenate(dataset: Dataset<T>): Dataset<T> {
-    const base = this;
-    let size;
-    if (this.size === Infinity || dataset.size === Infinity) {
-      // If the size of any of these two dataset is infinity, new size is
-      // infinity.
-      size = Infinity;
-    } else if (this.size != null && dataset.size != null) {
-      // If the size of both datasets are known and not infinity, new size is
-      // sum the size of these two datasets.
-      size = this.size + dataset.size;
-    } else {
-      // If neither of these two datasets has infinite size and any of these two
-      // datasets' size is null, the new size is null.
-      size = null;
-    }
-    return datasetFromIteratorFn(
-        async () =>
-            (await base.iterator()).concatenate(await dataset.iterator()),
-        size);
-  }
-
-  /**
-   * Filters this dataset according to `predicate`.
-   *
-   * ```js
-   * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-   *   .filter(x => x%2 === 0);
-   * await a.forEachAsync(e => console.log(e));
-   * ```
-   *
-   * @param predicate A function mapping a dataset element to a boolean or a
-   * `Promise` for one.
-   *
-   * @returns A `Dataset` of elements for which the predicate was true.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  filter(predicate: (value: T) => boolean): Dataset<T> {
-    const base = this;
-    let size;
-    if (this.size === Infinity) {
-      // If the size of this dataset is infinity, new size is infinity
-      size = Infinity;
-    } else {
-      // If this dataset has limited elements, new size is null because it might
-      // exhausted randomly.
-      size = null;
-    }
-    return datasetFromIteratorFn(async () => {
-      return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));
-    }, size);
-  }
-
-  /**
-   * Apply a function to every element of the dataset.
-   *
-   * After the function is applied to a dataset element, any Tensors contained
-   * within that element are disposed.
-   *
-   * ```js
-   * const a = tf.data.array([1, 2, 3]);
-   * await a.forEachAsync(e => console.log(e));
-   * ```
-   *
-   * @param f A function to apply to each dataset element.
-   * @returns A `Promise` that resolves after all elements have been processed.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  async forEachAsync(f: (input: T) => void): Promise<void> {
-    return (await this.iterator()).forEachAsync(f);
-  }
-
-  /**
-   * Maps this dataset through a 1-to-1 transform.
-   *
-   * ```js
-   * const a = tf.data.array([1, 2, 3]).map(x => x*x);
-   * await a.forEachAsync(e => console.log(e));
-   * ```
-   *
-   * @param transform A function mapping a dataset element to a transformed
-   *   dataset element.
-   *
-   * @returns A `Dataset` of transformed elements.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  map<O extends tf.TensorContainer>(transform: (value: T) => O): Dataset<O> {
-    const base = this;
-    return datasetFromIteratorFn(async () => {
-      return (await base.iterator()).map(x => tf.tidy(() => transform(x)));
-    }, this.size);
-  }
-
-  /**
-   * Maps this dataset through an async 1-to-1 transform.
-   *
-   * ```js
-   * const a =
-   *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){
-   *    setTimeout(() => {
-   *      resolve(x * x);
-   *    }, Math.random()*1000 + 500);
-   *  }));
-   * console.log(await a.toArray());
-   * ```
-   *
-   * @param transform A function mapping a dataset element to a `Promise` for a
-   *   transformed dataset element.  This transform is responsible for disposing
-   *   any intermediate `Tensor`s, i.e. by wrapping its computation in
-   *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous
-   *   `map()` case).
-   *
-   * @returns A `Dataset` of transformed elements.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  mapAsync<O extends tf.TensorContainer>(transform: (value: T) => Promise<O>):
-      Dataset<O> {
-    const base = this;
-    return datasetFromIteratorFn(async () => {
-      return (await base.iterator()).mapAsync(transform);
-    }, this.size);
-  }
-
-  /**
-   *  Creates a `Dataset` that prefetches elements from this dataset.
-   *
-   * @param bufferSize: An integer specifying the number of elements to be
-   *   prefetched.
-   * @returns A `Dataset`.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  prefetch(bufferSize: number): Dataset<T> {
-    if (bufferSize == null) {
-      throw new RangeError(
-          '`Dataset.prefetch()` requires bufferSize to be specified.');
-    }
-
-    const base = this;
-    return datasetFromIteratorFn(
-        async () => (await base.iterator()).prefetch(bufferSize), this.size);
-  }
-
-  /**
-   * Repeats this dataset `count` times.
-   *
-   * NOTE: If this dataset is a function of global state (e.g. a random number
-   * generator), then different repetitions may produce different elements.
-   *
-   * ```js
-   * const a = tf.data.array([1, 2, 3]).repeat(3);
-   * await a.forEachAsync(e => console.log(e));
-   * ```
-   *
-   * @param count: (Optional) An integer, representing the number of times
-   *   the dataset should be repeated. The default behavior (if `count` is
-   *   `undefined` or negative) is for the dataset be repeated indefinitely.
-   * @returns A `Dataset`.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  repeat(count?: number): Dataset<T> {
-    const base = this;
-    let size;
-    if (this.size != null && count > 0) {
-      // If this dataset has size and count is positive, new size is current
-      // size multiply count. This also covers the case that current size is
-      // infinity.
-      size = this.size * count;
-    } else if (count === 0) {
-      // If count is 0, new size is 0.
-      size = 0;
-    } else if (this.size != null && (count === undefined || count < 0)) {
-      // If this dataset has size and count is undefined or negative, the
-      // dataset will be repeated indefinitely and new size is infinity.
-      size = Infinity;
-    } else {
-      // If the size of this dataset is null, the new dataset's size is null.
-      size = null;
-    }
-    return datasetFromIteratorFn(async () => {
-      const iteratorIterator = iteratorFromFunction(
-          async () => ({value: await base.iterator(), done: false}));
-      return iteratorFromConcatenated(iteratorIterator.take(count));
-    }, size);
-  }
-
-  /**
-   * Creates a `Dataset` that skips `count` initial elements from this dataset.
-   *
-   * ```js
-   * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);
-   * await a.forEachAsync(e => console.log(e));
-   * ```
-   *
-   * @param count: The number of elements of this dataset that should be skipped
-   *   to form the new dataset.  If `count` is greater than the size of this
-   *   dataset, the new dataset will contain no elements.  If `count`
-   *   is `undefined` or negative, skips the entire dataset.
-   *
-   * @returns A `Dataset`.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  skip(count: number): Dataset<T> {
-    const base = this;
-    let size;
-    if (this.size != null && count >= 0 && this.size >= count) {
-      // If the size of this dataset is greater than count, the new dataset's
-      // size is current size minus skipped size.This also covers the case that
-      // current size is infinity.
-      size = this.size - count;
-    } else if (
-        this.size != null &&
-        (this.size < count || count === undefined || count < 0)) {
-      // If the size of this dataset is smaller than count, or count is
-      // undefined or negative, skips the entire dataset and the new size is 0.
-      size = 0;
-    } else {
-      // If the size of this dataset is null, the new dataset's size is null.
-      size = null;
-    }
-    return datasetFromIteratorFn(
-        async () => (await base.iterator()).skip(count), size);
-  }
-
-  // TODO(soergel): deep sharded shuffle, where supported
-
-  static readonly MAX_BUFFER_SIZE = 10000;
-
-  /**
-   * Pseudorandomly shuffles the elements of this dataset. This is done in a
-   * streaming manner, by sampling from a given number of prefetched elements.
-   *
-   * ```js
-   * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);
-   * await a.forEachAsync(e => console.log(e));
-   * ```
-   *
-   * @param bufferSize: An integer specifying the number of elements from this
-   *   dataset from which the new dataset will sample.
-   * @param seed: (Optional) An integer specifying the random seed that will
-   *   be used to create the distribution.
-   * @param reshuffleEachIteration: (Optional) A boolean, which if true
-   *   indicates that the dataset should be pseudorandomly reshuffled each time
-   *   it is iterated over. If false, elements will be returned in the same
-   *   shuffled order on each iteration. (Defaults to `true`.)
-   * @returns A `Dataset`.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  shuffle(bufferSize: number, seed?: string, reshuffleEachIteration = true):
-      Dataset<T> {
-    if (bufferSize == null || bufferSize < 0) {
-      if (this.size == null) {
-        throw new RangeError(
-            '`Dataset.shuffle()` requires bufferSize to be specified.');
-      } else {
-        throw new RangeError(
-            '`Dataset.shuffle()` requires bufferSize to be specified.  ' +
-            'If your data fits in main memory (for regular JS objects), ' +
-            'and/or GPU memory (for `tf.Tensor`s), consider setting ' +
-            `bufferSize to the dataset size (${this.size} elements)`);
-      }
-    }
-    const base = this;
-    const random = seedrandom.alea(seed || tf.util.now().toString());
-    return datasetFromIteratorFn(async () => {
-      let seed2 = random.int32();
-      if (reshuffleEachIteration) {
-        seed2 += random.int32();
-      }
-      return (await base.iterator()).shuffle(bufferSize, seed2.toString());
-    }, this.size);
-  }
-
-  /**
-   * Creates a `Dataset` with at most `count` initial elements from this
-   * dataset.
-   *
-   * ```js
-   * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);
-   * await a.forEachAsync(e => console.log(e));
-   * ```
-   *
-   * @param count: The number of elements of this dataset that should be taken
-   *   to form the new dataset.  If `count` is `undefined` or negative, or if
-   *   `count` is greater than the size of this dataset, the new dataset will
-   *   contain all elements of this dataset.
-   * @returns A `Dataset`.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  take(count: number): Dataset<T> {
-    const base = this;
-    let size;
-    if (this.size != null && this.size > count) {
-      // If the size of this dataset is greater than count, the new dataset's
-      // size is count.
-      size = count;
-    } else if (this.size != null && this.size <= count) {
-      // If the size of this dataset is equal or smaller than count, the new
-      // dataset's size is the size of this dataset.
-      size = this.size;
-    } else {
-      // If the size of this dataset is null, the new dataset's size is null.
-      size = null;
-    }
-    return datasetFromIteratorFn(
-        async () => (await base.iterator()).take(count), size);
-  }
-
-  /**
-   * Collect all elements of this dataset into an array.
-   *
-   * Obviously this will succeed only for small datasets that fit in memory.
-   * Useful for testing and generally should be avoided if possible.
-   *
-   * ```js
-   * const a = tf.data.array([1, 2, 3, 4, 5, 6]);
-   * console.log(await a.toArray());
-   * ```
-   *
-   * @returns A Promise for an array of elements, which will resolve
-   *   when a new stream has been obtained and fully consumed.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  async toArray() {
-    if (this.size === Infinity) {
-      throw new Error('Can not convert infinite data stream to array.');
-    }
-    return (await this.iterator()).toArray();
-  }
-
-  /**
-   * Collect all elements of this dataset into an array with prefetching 100
-   * elements. This is useful for testing, because the prefetch changes the
-   * order in which the Promises are resolved along the processing pipeline.
-   * This may help expose bugs where results are dependent on the order of
-   * Promise resolution rather than on the logical order of the stream (i.e.,
-   * due to hidden mutable state).
-   *
-   * @returns A Promise for an array of elements, which will resolve
-   *   when a new stream has been obtained and fully consumed.
-   */
-  async toArrayForTest() {
-    if (this.size === Infinity) {
-      throw new Error('Can not convert infinite data stream to array.');
-    }
-    return (await this.iterator()).toArrayForTest();
-  }
-}
-
-/**
- * Create a `Dataset` defined by a provided iterator() function.
- *
- * ```js
- * let i = -1;
- * const func = () =>
- *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};
- * const iter = tf.data.iteratorFromFunction(func);
- * const ds = tf.data.datasetFromIteratorFn(iter);
- * await ds.forEachAsync(e => console.log(e));
- * ```
- */
-export function datasetFromIteratorFn<T extends tf.TensorContainer>(
-    iteratorFn: () => Promise<LazyIterator<T>>,
-    size: number = null): Dataset<T> {
-  return new class extends Dataset<T> {
-    override size = size;
-
-    /*
-     * Provide a new stream of elements.  Note this will also start new streams
-     * from any underlying `Dataset`s.
-     */
-    async iterator(): Promise<LazyIterator<T>> {
-      return iteratorFn();
-    }
-  }
-  ();
-}
-
-/**
- * Create a `Dataset` from an array of elements.
- *
- * Create a Dataset from an array of objects:
- * ```js
- * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);
- * await a.forEachAsync(e => console.log(e));
- * ```
- *
- * Create a Dataset from an array of numbers:
- * ```js
- * const a = tf.data.array([4, 5, 6]);
- * await a.forEachAsync(e => console.log(e));
- * ```
- * @param items An array of elements that will be parsed as items in a dataset.
- *
- * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}
- */
-export function array<T extends tf.TensorContainer>(items: T[]): Dataset<T> {
-  return datasetFromIteratorFn(
-      async () => iteratorFromItems(items), items.length);
-}
-
-/**
- * Create a `Dataset` by zipping together an array, dict, or nested
- * structure of `Dataset`s (and perhaps additional constants).
- * The underlying datasets must provide elements in a consistent order such that
- * they correspond.
- *
- * The number of elements in the resulting dataset is the same as the size of
- * the smallest dataset in datasets.
- *
- * The nested structure of the `datasets` argument determines the
- * structure of elements in the resulting iterator.
- *
- * Note this means that, given an array of two datasets that produce dict
- * elements, the result is a dataset that produces elements that are arrays
- * of two dicts:
- *
- * Zip an array of datasets:
- * ```js
- * console.log('Zip two datasets of objects:');
- * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);
- * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);
- * const ds3 = tf.data.zip([ds1, ds2]);
- * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));
- *
- * // If the goal is to merge the dicts in order to produce elements like
- * // {a: ..., b: ...}, this requires a second step such as:
- * console.log('Merge the objects:');
- * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});
- * await ds4.forEachAsync(e => console.log(e));
- * ```
- *
- * Zip a dict of datasets:
- * ```js
- * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);
- * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);
- * const c = tf.data.zip({c: a, d: b});
- * await c.forEachAsync(e => console.log(JSON.stringify(e)));
- * ```
- *
- * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}
- */
-export function zip<O extends tf.TensorContainer>(datasets: DatasetContainer):
-    Dataset<O> {
-  // manually type-check the argument for JS users
-  if (!isIterable(datasets)) {
-    throw new Error('The argument to zip() must be an object or array.');
-  }
-  let size;
-  if (Array.isArray(datasets)) {
-    for (let i = 0; i < datasets.length; i++) {
-      size = size == null ? (datasets[i] as Dataset<O>).size :
-                            Math.min(size, (datasets[i] as Dataset<O>).size);
-    }
-  } else if (datasets instanceof Object) {
-    for (const ds in datasets) {
-      size = size == null ? (datasets[ds] as Dataset<O>).size :
-                            Math.min(size, (datasets[ds] as Dataset<O>).size);
-    }
-  }
-  return datasetFromIteratorFn<O>(async () => {
-    const streams = await deepMapAndAwaitAll(datasets, d => {
-      if (d instanceof Dataset) {
-        return {value: d.iterator(), recurse: false};
-      } else if (isIterable(d)) {
-        return {value: null, recurse: true};
-      } else {
-        throw new Error(
-            'Leaves of the structure passed to zip() must be Datasets, ' +
-            'not primitives.');
-      }
-    });
-    return iteratorFromZipped<O>(streams, ZipMismatchMode.SHORTEST);
-  }, size);
-}
-
-/**
- * A zip function for use with deepZip, passed via the columnMajorBatch call.
- *
- * Accepts an array of identically-structured nested elements and either batches
- * them (if they are primitives, numeric arrays, or Tensors) or requests
- * recursion (if not).
- */
-// tslint:disable-next-line:no-any
-function deepBatchConcat(rows: any[]): DeepMapResult {
-  if (rows === null) {
-    return null;
-  }
-
-  // use the first item to decide whether to recurse or batch here.
-  const exampleRow = rows[0];
-
-  if (canTensorify(exampleRow)) {
-    // rows is an array of primitives, Tensors, or arrays.  Batch them.
-    const value = batchConcat(rows);
-    return {value, recurse: false};
-  }
-
-  // the example row is an object, so recurse into it.
-  return {value: null, recurse: true};
-}
-
-/**
- * Assembles a list of same-shaped numbers, number arrays, or Tensors
- * into a single new Tensor where axis 0 is the batch dimension.
- */
-function batchConcat<T extends(TensorLike | tf.Tensor)>(arrays: T[]):
-    tf.Tensor {
-  if (arrays.length === 0) {
-    // We can't return an empty Tensor because we don't know the element shape.
-    throw new Error('Can\'t make a batch of zero elements.');
-  }
-
-  if (arrays[0] instanceof tf.Tensor) {
-    // Input is an array of Tensors
-    return tf.stack(arrays as tf.Tensor[]);
-  } else {
-    // Input is a possibly-nested array of numbers.
-    return tf.tensor(arrays as TensorLike);
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/dataset_test.ts b/tfjs-master/tfjs-data/src/dataset_test.ts
deleted file mode 100644
index df4c9fc3f..000000000
--- a/tfjs-master/tfjs-data/src/dataset_test.ts
+++ /dev/null
@@ -1,992 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {TensorContainerObject} from '@tensorflow/tfjs-core';
-import {array, DatasetContainer} from './dataset';
-import * as tfd from './index';
-import {iteratorFromItems, LazyIterator} from './iterators/lazy_iterator';
-import {describeAllEnvs} from './util/test_utils';
-
-class TestObjectIterator extends LazyIterator<{}> {
-  data = Array.from({length: 100}, (v, k) => k);
-  currentIndex = 0;
-
-  summary() {
-    return `TestObjects`;
-  }
-
-  async next(): Promise<IteratorResult<{}>> {
-    if (this.currentIndex >= 100) {
-      return {value: null, done: true};
-    }
-    const elementNumber = this.data[this.currentIndex];
-    const result = {
-      'number': elementNumber,
-      'numberArray': [elementNumber, elementNumber ** 2, elementNumber ** 3],
-      'Tensor':
-          tf.tensor1d([elementNumber, elementNumber ** 2, elementNumber ** 3]),
-      'Tensor2': tf.tensor2d(
-          [
-            elementNumber, elementNumber ** 2, elementNumber ** 3,
-            elementNumber ** 4
-          ],
-          [2, 2]),
-      'string': `Item ${elementNumber}`
-    };
-    this.currentIndex++;
-    return {value: result, done: false};
-  }
-}
-
-export class TestDataset extends tfd.Dataset<TensorContainerObject> {
-  override readonly size: number;
-  constructor(setSize = false) {
-    super();
-    if (setSize) {
-      this.size = 200;
-    }
-  }
-  async iterator(): Promise<LazyIterator<{}>> {
-    return new TestObjectIterator();
-  }
-}
-
-// tslint:disable-next-line:no-any
-function complexifyExampleAsDict(simple: any): {} {
-  const v = simple['number'];
-  const w = simple['numberArray'];
-  const x = simple['Tensor'];
-  const y = simple['Tensor2'];
-  const z = simple['string'];
-  return {
-    a: {v, w, q: {aa: {x, y, z}, ab: {v, w, x}}},
-    b: {
-      ba: {baa: y, bab: z, bac: v},
-      bb: {bba: w, bbb: x, bbc: y},
-      bc: {bca: z, bcb: v, bcc: w}
-    },
-    c: {ca: {x, y, z}, cb: {v, w, x}, cc: {y, z, v}},
-  };
-}
-
-describeAllEnvs('Dataset', () => {
-  it('can be concatenated', async () => {
-    const a = tfd.array([{'item': 1}, {'item': 2}, {'item': 3}]);
-    const b = tfd.array([{'item': 4}, {'item': 5}, {'item': 6}]);
-    const result = await a.concatenate(b).toArrayForTest();
-    expect(result).toEqual([
-      {'item': 1}, {'item': 2}, {'item': 3}, {'item': 4}, {'item': 5},
-      {'item': 6}
-    ]);
-  });
-
-  it('can be created by concatenating multiple underlying datasets via ' +
-         'reduce',
-     async () => {
-       const a = tfd.array([{'item': 1}, {'item': 2}]);
-       const b = tfd.array([{'item': 3}, {'item': 4}]);
-       const c = tfd.array([{'item': 5}, {'item': 6}]);
-       const concatenated = [a, b, c].reduce((a, b) => a.concatenate(b));
-       const result = await concatenated.toArrayForTest();
-       expect(result).toEqual([
-         {'item': 1}, {'item': 2}, {'item': 3}, {'item': 4}, {'item': 5},
-         {'item': 6}
-       ]);
-     });
-
-  it('can be created by zipping an array of datasets with primitive ' +
-         'elements',
-     async () => {
-       const a = tfd.array([1, 2, 3]);
-       const b = tfd.array([4, 5, 6]);
-       const result = await tfd.zip([a, b]).toArrayForTest();
-       expect(result).toEqual([[1, 4], [2, 5], [3, 6]]);
-     });
-
-  it('can be created by zipping an array of datasets with object elements',
-     async () => {
-       const a = tfd.array([{a: 1}, {a: 2}, {a: 3}]);
-       const b = tfd.array([{b: 4}, {b: 5}, {b: 6}]);
-       const result = await tfd.zip([a, b]).toArrayForTest();
-       expect(result).toEqual(
-           [[{a: 1}, {b: 4}], [{a: 2}, {b: 5}], [{a: 3}, {b: 6}]]);
-     });
-
-  it('can be created by zipping a dict of datasets', async () => {
-    const a = tfd.array([{a: 1}, {a: 2}, {a: 3}]);
-    const b = tfd.array([{b: 4}, {b: 5}, {b: 6}]);
-    const result = await tfd.zip({c: a, d: b}).toArrayForTest();
-    expect(result).toEqual([
-      {c: {a: 1}, d: {b: 4}}, {c: {a: 2}, d: {b: 5}}, {c: {a: 3}, d: {b: 6}}
-    ]);
-  });
-
-  it('can be created by zipping a nested structure of datasets', async () => {
-    const a = tfd.array([1, 2, 3]);
-    const b = tfd.array([4, 5, 6]);
-    const c = tfd.array([7, 8, 9]);
-    const d = tfd.array([10, 11, 12]);
-    const result = await tfd.zip({a, bcd: [b, {c, d}]}).toArrayForTest();
-
-    expect(result).toEqual([
-      {a: 1, bcd: [4, {c: 7, d: 10}]},
-      {a: 2, bcd: [5, {c: 8, d: 11}]},
-      {a: 3, bcd: [6, {c: 9, d: 12}]},
-    ]);
-  });
-
-  it('can be created by zipping datasets of different sizes', async () => {
-    const a = tfd.array([1, 2]);
-    const b = tfd.array([3, 4, 5, 6]);
-    const result = await tfd.zip([a, b]).toArrayForTest();
-    expect(result).toEqual([[1, 3], [2, 4]]);
-  });
-
-  it('zipping a native string throws an error', async () => {
-    try {
-      // tslint:disable-next-line:no-any no-construct
-      await tfd.zip('test' as any);
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual(
-          'The argument to zip() must be an object or array.');
-    }
-  });
-
-  it('zipping a string object throws a meaningful error', async () => {
-    try {
-      // tslint:disable-next-line:no-any no-construct
-      await tfd.zip(new String('test') as any).iterator();
-      fail();
-    } catch (e) {
-      // This error is not specific to the error case arising from
-      //   typeof(new String('test')) === 'object'
-      // Instead this error is thrown because the leaves of the structure
-      // are the letters t, e, s, and t, as well a number for the length. I
-      // think it's a fine error message for this situation anyway.
-      expect(e.message).toEqual(
-          'Leaves of the structure passed to zip() must be Datasets, ' +
-          'not primitives.');
-    }
-  });
-
-  it('zipping a structure with repeated elements works', async () => {
-    const a = tfd.array([1, 2, 3]);
-    const b = tfd.array([4, 5, 6]);
-    const c = tfd.array([7, 8, 9]);
-    const d = tfd.array([10, 11, 12]);
-    const result =
-        await tfd.zip({a, abacd: [a, b, {a, c, d}]}).toArrayForTest();
-
-    expect(result).toEqual([
-      {a: 1, abacd: [1, 4, {a: 1, c: 7, d: 10}]},
-      {a: 2, abacd: [2, 5, {a: 2, c: 8, d: 11}]},
-      {a: 3, abacd: [3, 6, {a: 3, c: 9, d: 12}]},
-    ]);
-  });
-
-  it('zipping a structure with cycles throws an error', async () => {
-    try {
-      // tslint:disable-next-line:no-any
-      const a = tfd.array([1, 2, 3]);
-      const b = tfd.array([4, 5, 6]);
-      const c: DatasetContainer = [tfd.array([7, 8, 9])];
-      const abc: DatasetContainer = [a, b, c];
-      c.push(abc);
-      await tfd.zip({a, abc}).iterator();
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual('Circular references are not supported.');
-    }
-  });
-
-  it('zip propagates errors thrown when iterating constituent datasets',
-     async () => {
-       try {
-         const makeIterator = () => {
-           let count = 0;
-           return {
-             next: () => {
-               if (count > 2) {
-                 throw new Error('propagate me!');
-               }
-               return {value: count++, done: false};
-             }
-           };
-         };
-         const a = tfd.generator(makeIterator);
-         const b = tfd.array([3, 4, 5, 6]);
-         // Using toArray() rather than toArrayForTest().  The prefetch in
-         // the latter, in combination with expecting an exception, causes
-         // unrelated tests to fail (See
-         // https://github.com/tensorflow/tfjs/issues/1330.
-         await (await tfd.zip([a, b]).iterator()).toArray();
-         fail();
-       } catch (e) {
-         expect(e.message).toMatch(/propagate me!/);
-       }
-     });
-
-  it('can be repeated a fixed number of times', async () => {
-    const a = tfd.array([{'item': 1}, {'item': 2}, {'item': 3}]);
-    const result = await a.repeat(4).toArrayForTest();
-    expect(result).toEqual([
-      {'item': 1},
-      {'item': 2},
-      {'item': 3},
-      {'item': 1},
-      {'item': 2},
-      {'item': 3},
-      {'item': 1},
-      {'item': 2},
-      {'item': 3},
-      {'item': 1},
-      {'item': 2},
-      {'item': 3},
-    ]);
-  });
-
-  it('can be repeated indefinitely', async () => {
-    const a = tfd.array([{'item': 1}, {'item': 2}, {'item': 3}]);
-    await a.repeat().take(234).toArrayForTest();
-  });
-
-  it('can be repeated with state in a closure', async () => {
-    // This tests a tricky bug having to do with 'this' being set properly.
-    // See
-    // https://github.com/Microsoft/TypeScript/wiki/%27this%27-in-TypeScript
-
-    class CustomDataset extends tfd.Dataset<{}> {
-      state = {val: 1};
-      async iterator() {
-        const result = iteratorFromItems([
-          {'item': this.state.val++}, {'item': this.state.val++},
-          {'item': this.state.val++}
-        ]);
-        return result;
-      }
-    }
-    const a = new CustomDataset();
-    await a.repeat().take(1234).toArrayForTest();
-  });
-
-  it('can collect all items into memory', async () => {
-    const ds = new TestDataset();
-    const items = await ds.toArrayForTest();
-    expect(items.length).toEqual(100);
-    // The test dataset has 100 elements, each containing 2 Tensors.
-    expect(tf.memory().numTensors).toEqual(200);
-  });
-
-  it('batches entries into column-oriented batches', async () => {
-    const ds = new TestDataset();
-    const bds = ds.batch(8);
-    const batchIterator = await bds.iterator();
-    const result = await batchIterator.toArrayForTest();
-
-    expect(result.length).toEqual(13);
-    result.slice(0, 12).forEach(batch => {
-      const b = batch as TensorContainerObject;
-      expect((b['number'] as tf.Tensor).shape).toEqual([8]);
-      expect((b['numberArray'] as tf.Tensor).shape).toEqual([8, 3]);
-      expect((b['Tensor'] as tf.Tensor).shape).toEqual([8, 3]);
-      expect((b['Tensor2'] as tf.Tensor).shape).toEqual([8, 2, 2]);
-      expect((b['string'] as tf.Tensor).shape).toEqual([8]);
-    });
-    tf.dispose(result);
-    expect(tf.memory().numTensors).toBe(0);
-  });
-
-  it('batches entries without leaking Tensors', async () => {
-    // The prior test confirms this too, but this formulation is more
-    // specific.
-
-    // First show that an unbatched test iterator creates 2 Tensors per
-    // element.
-    const ds = new TestDataset();
-    const iter = await ds.iterator();
-    const element = await iter.next();
-    expect(tf.memory().numTensors).toBe(2);
-    tf.dispose(element.value);
-    expect(tf.memory().numTensors).toBe(0);
-
-    // Now obtain batches, which should contain 4 Tensors each.
-    const bDs = new TestDataset().batch(8);
-    const bIter = await bDs.iterator();
-    const bElement = await bIter.next();
-    // The batch element contains four Tensors, and the 8*2 Tensors in the
-    // original unbatched elements have already been disposed.
-    expect(tf.memory().numTensors).toBe(5);
-    tf.dispose(bElement.value);
-    expect(tf.memory().numTensors).toBe(0);
-  });
-
-  it('batches complex nested objects into column-oriented batches',
-     async () => {
-       // Our "complexified" examples map the simple examples into a deep
-       // nested structure. This test shows that batching complexified
-       // examples produces the same result as complexifying batches of
-       // simple examples.
-
-       const complexThenBatch =
-           new TestDataset().map(complexifyExampleAsDict).batch(8);
-       const batchThenComplex =
-           new TestDataset().batch(8).map(complexifyExampleAsDict);
-
-       const compareDataset = tfd.zip({complexThenBatch, batchThenComplex});
-
-       const result = await (await compareDataset.iterator()).toArrayForTest();
-
-       expect(result.length).toEqual(13);
-       // tslint:disable-next-line:no-any
-       result.slice(0, 12).forEach((compare: any) => {
-         // TODO(soergel): could use deepMap to implement deep compare.
-         // For now, just spot-check a few deep entries.
-         expect(compare.complexThenBatch.a.v.shape)
-             .toEqual(compare.batchThenComplex.a.v.shape);
-         expect(compare.complexThenBatch.a.v.dataSync())
-             .toEqual(compare.batchThenComplex.a.v.dataSync());
-
-         expect(compare.complexThenBatch.a.q.ab.x.shape)
-             .toEqual(compare.batchThenComplex.a.q.ab.x.shape);
-         expect(compare.complexThenBatch.a.q.ab.x.dataSync())
-             .toEqual(compare.batchThenComplex.a.q.ab.x.dataSync());
-
-         expect(compare.complexThenBatch.b.bb.bbb.shape)
-             .toEqual(compare.batchThenComplex.b.bb.bbb.shape);
-         expect(compare.complexThenBatch.b.bb.bbb.dataSync())
-             .toEqual(compare.batchThenComplex.b.bb.bbb.dataSync());
-
-         expect(compare.complexThenBatch.c.ca.x.shape)
-             .toEqual(compare.batchThenComplex.c.ca.x.shape);
-         expect(compare.complexThenBatch.c.ca.x.dataSync())
-             .toEqual(compare.batchThenComplex.c.ca.x.dataSync());
-       });
-       tf.dispose(result);
-       expect(tf.memory().numTensors).toBe(0);
-     });
-
-  it('batches nested numeric arrays into a single Tensor', async () => {
-    const dataset =
-        new TestDataset()
-            .map((e) => {
-              const a = e.number as number;
-              const b = a * 2;
-              const c = b + 5;
-              return {
-                foo: [[[a, b], [c, a], [b, c]], [[b, c], [a, b], [c, a]]]
-              };
-            })
-            .batch(8);
-
-    const result = await (await dataset.iterator()).toArrayForTest();
-    expect(result.length).toEqual(13);
-
-    // tslint:disable-next-line:no-any
-    result.slice(0, 12).forEach((e: any) => {
-      expect(e.foo instanceof tf.Tensor).toBeTruthy();
-      expect(e.foo.shape).toEqual([8, 2, 3, 2]);
-    });
-    // tslint:disable-next-line:no-any
-    expect((result[12] as any).foo.shape).toEqual([4, 2, 3, 2]);
-
-    tf.dispose(result);
-    expect(tf.memory().numTensors).toBe(0);
-  });
-
-  // TODO(soergel, smilkov): Reinstate this once tfjs-core enforces
-  // TensorLike.
-  /*
-  it('throws an error when given an array containing a dict', async done
-  => {
-    const dataset = array([[1, {a: 2, b: 3}], [4, {a: 5, b: 6}]]).batch(2);
-    try {
-      await (await dataset.iterator()).collect();
-      done.fail();
-    } catch (e) {
-      expect(e.message).toEqual('TODO');
-      done();
-    }
-    expect(tf.memory().numTensors).toBe(0);
-  });
-  */
-
-  it('batch creates a small last batch', async () => {
-    const ds = new TestDataset();
-    const bds = ds.batch(8);
-    const batchIterator = await bds.iterator();
-    const result = await batchIterator.toArrayForTest();
-    const lastBatch = result[result.length - 1] as TensorContainerObject;
-    expect((lastBatch['number'] as tf.Tensor).shape).toEqual([4]);
-    expect((lastBatch['numberArray'] as tf.Tensor).shape).toEqual([4, 3]);
-    expect((lastBatch['Tensor'] as tf.Tensor).shape).toEqual([4, 3]);
-    expect((lastBatch['Tensor2'] as tf.Tensor).shape).toEqual([4, 2, 2]);
-    expect((lastBatch['string'] as tf.Tensor).shape).toEqual([4]);
-
-    const expectedNumberLastBatch = tf.tensor1d([96, 97, 98, 99]);
-    tf.test_util.expectArraysClose(
-        await (lastBatch['number'] as tf.Tensor).array(),
-        await expectedNumberLastBatch.array());
-
-    const expectedNumberArrayLastBatch = tf.tensor2d(
-        [
-          [96, 96 ** 2, 96 ** 3], [97, 97 ** 2, 97 ** 3],
-          [98, 98 ** 2, 98 ** 3], [99, 99 ** 2, 99 ** 3]
-        ],
-        [4, 3]);
-    tf.test_util.expectArraysClose(
-        await (lastBatch['numberArray'] as tf.Tensor).array(),
-        await expectedNumberArrayLastBatch.array());
-
-    const expectedTensorLastBatch = tf.tensor2d(
-        [
-          [96, 96 ** 2, 96 ** 3], [97, 97 ** 2, 97 ** 3],
-          [98, 98 ** 2, 98 ** 3], [99, 99 ** 2, 99 ** 3]
-        ],
-        [4, 3]);
-    tf.test_util.expectArraysClose(
-        await (lastBatch['Tensor'] as tf.Tensor).array(),
-        await expectedTensorLastBatch.array());
-
-    const expectedTensor2LastBatch = tf.tensor3d(
-        [
-          [[96, 96 ** 2], [96 ** 3, 96 ** 4]],
-          [[97, 97 ** 2], [97 ** 3, 97 ** 4]],
-          [[98, 98 ** 2], [98 ** 3, 98 ** 4]],
-          [[99, 99 ** 2], [99 ** 3, 99 ** 4]],
-        ],
-        [4, 2, 2]);
-    tf.test_util.expectArraysClose(
-        await (lastBatch['Tensor2'] as tf.Tensor).array(),
-        await expectedTensor2LastBatch.array());
-
-    const expectedStringLastBatch =
-        tf.tensor1d(['Item 96', 'Item 97', 'Item 98', 'Item 99']);
-    tf.test_util.expectArraysEqual(
-        await (lastBatch['string'] as tf.Tensor).array(),
-        await expectedStringLastBatch.array());
-
-    tf.dispose(result);
-    tf.dispose(expectedNumberLastBatch);
-    tf.dispose(expectedNumberArrayLastBatch);
-    tf.dispose(expectedTensorLastBatch);
-    tf.dispose(expectedTensor2LastBatch);
-    tf.dispose(expectedStringLastBatch);
-
-    expect(tf.memory().numTensors).toBe(0);
-  });
-
-  it('skip does not leak Tensors', async () => {
-    const ds = new TestDataset();
-    expect(tf.memory().numTensors).toEqual(0);
-    const result = await ds.skip(15).toArrayForTest();
-    // The test dataset had 100 elements; we skipped 15; 85 remain.
-    expect(result.length).toEqual(85);
-    // Each element of the test dataset contains 2 Tensors;
-    // 85 elements remain, so 2 * 85 = 170 Tensors remain.
-    expect(tf.memory().numTensors).toEqual(170);
-  });
-
-  it('filter does not leak Tensors', async () => {
-    const ds = new TestDataset();
-    expect(tf.memory().numTensors).toEqual(0);
-    await ds.filter(x => ((x['number'] as number) % 2 === 0)).toArrayForTest();
-    // Each element of the test dataset contains 2 Tensors.
-    // There were 100 elements, but we filtered out half of them.
-    // Thus 50 * 2 = 100 Tensors remain.
-    expect(tf.memory().numTensors).toEqual(100);
-  });
-
-  it('shuffle does not leak Tensors', async () => {
-    const ds = new TestDataset();
-    expect(tf.memory().numTensors).toEqual(0);
-    await ds.shuffle(1000).toArrayForTest();
-    // The shuffle operation emitted all of the tensors.
-    expect(tf.memory().numTensors).toEqual(200);
-  });
-
-  it('shuffle throws an error when bufferSize is not specified and ' +
-         'dataset.size is unknown.',
-     async () => {
-       const ds = new TestDataset();
-
-       expect(() => ds.shuffle(undefined))
-           .toThrowError(
-               '`Dataset.shuffle()` requires bufferSize to be specified.');
-     });
-
-  it('shuffle throws an error when bufferSize is not specified and ' +
-         'dataset.size is known.',
-     async () => {
-       const ds = new TestDataset(true);
-
-       expect(() => ds.shuffle(undefined))
-           .toThrowError(
-               '`Dataset.shuffle()` requires bufferSize to be ' +
-               'specified.  If your data fits in main memory (for ' +
-               'regular JS objects), and/or GPU memory (for ' +
-               '`tf.Tensor`s), consider setting bufferSize to the ' +
-               'dataset size (200 elements)');
-     });
-
-  it('prefetch throws an error when bufferSize is not specified.', async () => {
-    const ds = new TestDataset();
-
-    expect(() => ds.prefetch(undefined))
-        .toThrowError(
-            '`Dataset.prefetch()` requires bufferSize to be specified.');
-  });
-
-  it('prefetch does not leak Tensors', async () => {
-    const ds = new TestDataset();
-    expect(tf.memory().numTensors).toEqual(0);
-    await ds.prefetch(1000).toArray();
-    // The prefetch operation emitted all of the tensors.
-    expect(tf.memory().numTensors).toEqual(200);
-  });
-
-  it('map does not leak Tensors when none are returned', async () => {
-    const ds = new TestDataset();
-    expect(tf.memory().numTensors).toEqual(0);
-    await ds.map(x => ({'constant': 1})).toArrayForTest();
-    // The map operation consumed all of the tensors and emitted none.
-    expect(tf.memory().numTensors).toEqual(0);
-  });
-
-  it('map does not lose or leak Tensors when some inputs are passed ' +
-         'through',
-     async () => {
-       const ds = new TestDataset();
-       expect(tf.memory().numTensors).toEqual(0);
-       await ds.map(x => ({'Tensor2': x['Tensor2']})).toArrayForTest();
-       // Each element of the test dataset contains 2 Tensors.
-       // Our map operation retained one of the Tensors and discarded the
-       // other. Thus the mapped data contains 100 elements with 1 Tensor
-       // each.
-       expect(tf.memory().numTensors).toEqual(100);
-     });
-
-  it('map does not leak Tensors when inputs are replaced', async () => {
-    const ds = new TestDataset();
-    expect(tf.memory().numTensors).toEqual(0);
-    await ds.map(x => ({'a': tf.tensor1d([1, 2, 3])})).toArrayForTest();
-    // Each element of the test dataset contains 2 Tensors.
-    // Our map operation discarded both Tensors and created one new one.
-    // Thus the mapped data contains 100 elements with 1 Tensor each.
-    expect(tf.memory().numTensors).toEqual(100);
-  });
-
-  it('forEachAsync does not leak Tensors', async () => {
-    const ds = new TestDataset();
-    let count = 0;
-    await ds.forEachAsync(element => {
-      count++;
-      return {};
-    });
-    // forEachAsync traversed the entire dataset of 100 elements.
-    expect(count).toEqual(100);
-    // forEachAsync consumed all of the input Tensors.
-    expect(tf.memory().numTensors).toEqual(0);
-  });
-
-  it('clone tensors when returning iterator of a dataset generated from ' +
-         'existing tensors',
-     async () => {
-       expect(tf.memory().numTensors).toEqual(0);
-       const a = tf.ones([2, 1]);
-       const b = tf.ones([2, 1]);
-       expect(tf.memory().numTensors).toEqual(2);
-       const ds = tfd.array([a, b]);
-       // Pre-existing tensors are not cloned during dataset creation.
-       expect(tf.memory().numTensors).toEqual(2);
-
-       let count = 0;
-       // ds.forEachAsync() automatically disposes incoming Tensors after
-       // processing them.
-       await ds.forEachAsync(elem => {
-         count++;
-         expect(elem.isDisposed).toBeFalsy();
-       });
-       expect(count).toEqual(2);
-       // Cloned tensors are disposed after traverse, while original tensors
-       // stay.
-       expect(tf.memory().numTensors).toEqual(2);
-
-       await ds.forEachAsync(elem => {
-         count++;
-         expect(elem.isDisposed).toBeFalsy();
-       });
-       expect(count).toEqual(4);
-       expect(tf.memory().numTensors).toEqual(2);
-
-       await ds.forEachAsync(elem => {
-         count++;
-         expect(elem.isDisposed).toBeFalsy();
-       });
-       expect(count).toEqual(6);
-       expect(tf.memory().numTensors).toEqual(2);
-
-       expect(a.isDisposed).toBeFalsy();
-       expect(b.isDisposed).toBeFalsy();
-     });
-
-  it('clone tensors in nested structures when returning iterator of a ' +
-         'dataset generated from existing tensors',
-     async () => {
-       expect(tf.memory().numTensors).toEqual(0);
-       const a = tf.ones([2, 1]);
-       const b = tf.ones([2, 1]);
-       const c = tf.ones([2, 1]);
-       const d = tf.ones([2, 1]);
-       expect(tf.memory().numTensors).toEqual(4);
-       const ds = tfd.array([{foo: a, bar: b}, {foo: c, bar: d}]);
-       // Pre-existing tensors are not cloned during dataset creation.
-       expect(tf.memory().numTensors).toEqual(4);
-
-       let count = 0;
-       // ds.forEachAsync() automatically disposes incoming Tensors after
-       // processing them.
-       await ds.forEachAsync(elem => {
-         count++;
-         expect(elem.foo.isDisposed).toBeFalsy();
-         expect(elem.bar.isDisposed).toBeFalsy();
-       });
-       expect(count).toEqual(2);
-       // Cloned tensors are disposed after traverse, while original tensors
-       // stay.
-       expect(tf.memory().numTensors).toEqual(4);
-
-       await ds.forEachAsync(elem => {
-         count++;
-         expect(elem.foo.isDisposed).toBeFalsy();
-         expect(elem.bar.isDisposed).toBeFalsy();
-       });
-       expect(count).toEqual(4);
-       expect(tf.memory().numTensors).toEqual(4);
-
-       await ds.forEachAsync(elem => {
-         count++;
-         expect(elem.foo.isDisposed).toBeFalsy();
-         expect(elem.bar.isDisposed).toBeFalsy();
-       });
-       expect(count).toEqual(6);
-       expect(tf.memory().numTensors).toEqual(4);
-
-       expect(a.isDisposed).toBeFalsy();
-       expect(b.isDisposed).toBeFalsy();
-     });
-
-  it('traverse dataset from tensors without leaking Tensors', async () => {
-    expect(tf.memory().numTensors).toEqual(0);
-    const a = tf.ones([2, 1]);
-    const b = tf.ones([2, 1]);
-    const c = tf.ones([2, 1]);
-    const d = tf.ones([2, 1]);
-    expect(tf.memory().numTensors).toEqual(4);
-    const ds = tfd.array([a, b, c, d]).take(2);
-    // Pre-existing tensors are not cloned during dataset creation.
-    expect(tf.memory().numTensors).toEqual(4);
-
-    let count = 0;
-    // ds.forEachAsync() automatically disposes incoming Tensors after
-    // processing them.
-    await ds.forEachAsync(elem => {
-      count++;
-      expect(elem.isDisposed).toBeFalsy();
-    });
-    expect(count).toEqual(2);
-    // Cloned tensors are disposed after traverse, while original tensors
-    // stay.
-    expect(tf.memory().numTensors).toEqual(4);
-
-    await ds.forEachAsync(elem => {
-      count++;
-      expect(elem.isDisposed).toBeFalsy();
-    });
-    expect(count).toEqual(4);
-    expect(tf.memory().numTensors).toEqual(4);
-
-    await ds.forEachAsync(elem => {
-      count++;
-      expect(elem.isDisposed).toBeFalsy();
-    });
-    expect(count).toEqual(6);
-    expect(tf.memory().numTensors).toEqual(4);
-
-    expect(a.isDisposed).toBeFalsy();
-    expect(b.isDisposed).toBeFalsy();
-  });
-
-  it('can get correct size of dataset from objects array', async () => {
-    const ds = tfd.array([{'item': 1}, {'item': 2}, {'item': 3}]);
-    expect(ds.size).toEqual(3);
-  });
-
-  it('can get correct size of dataset from number array', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]);
-    expect(ds.size).toEqual(5);
-  });
-
-  it('can get size 0 from empty dataset', async () => {
-    const ds = tfd.array([]);
-    expect(ds.size).toEqual(0);
-  });
-
-  it('size is null if dataset may exhausted randomly', async () => {
-    const makeIterator = () => {
-      let i = -1;
-      return {
-        next: () =>
-            ++i < 7 ? {value: i, done: false} : {value: null, done: true}
-      };
-    };
-    const ds = tfd.generator(makeIterator);
-    expect(ds.size).toBeNull();
-  });
-
-  it('repeat dataset has correct size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).repeat(3);
-    expect(ds.size).toEqual(15);
-  });
-
-  it('repeat dataset forever has infinite size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).repeat();
-    expect(ds.size).toEqual(Infinity);
-  });
-
-  it('repeat unknown size dataset has null size', async () => {
-    const makeIterator = () => {
-      let i = -1;
-      return {
-        next: () =>
-            ++i < 7 ? {value: i, done: false} : {value: null, done: true}
-      };
-    };
-    const ds = tfd.generator(makeIterator).repeat(3);
-    expect(ds.size).toBeNull();
-  });
-
-  it('take dataset has correct size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).take(3);
-    expect(ds.size).toEqual(3);
-  });
-
-  it('take dataset without enough elements has correct size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).take(10);
-    expect(ds.size).toEqual(5);
-  });
-
-  it('take dataset with unknown size has null size', async () => {
-    const makeIterator = () => {
-      let i = -1;
-      return {
-        next: () =>
-            ++i < 7 ? {value: i, done: false} : {value: null, done: true}
-      };
-    };
-    const ds = tfd.generator(makeIterator).take(3);
-    expect(ds.size).toBeNull();
-  });
-
-  it('take dataset with infinite elements has correct size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).repeat().take(10);
-    expect(ds.size).toEqual(10);
-  });
-
-  it('skip dataset has correct size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).skip(2);
-    expect(ds.size).toEqual(3);
-  });
-
-  it('skip dataset without enough elements has correct size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).skip(10);
-    expect(ds.size).toEqual(0);
-  });
-
-  it('skip dataset with unknown size has null size', async () => {
-    const makeIterator = () => {
-      let i = -1;
-      return {
-        next: () =>
-            ++i < 7 ? {value: i, done: false} : {value: null, done: true}
-      };
-    };
-    const ds = tfd.generator(makeIterator).skip(3);
-    expect(ds.size).toBeNull();
-  });
-
-  it('skip dataset with infinite elements has infinity size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).repeat().skip(10);
-    expect(ds.size).toEqual(Infinity);
-  });
-
-  it('batch dataset with small last batch has correct size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5, 6, 7]).batch(2, true);
-    expect(ds.size).toEqual(4);
-  });
-
-  it('batch dataset without small last batch has correct size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5, 6, 7]).batch(2, false);
-    expect(ds.size).toEqual(3);
-  });
-
-  it('batch dataset with unknown size has null size', async () => {
-    const makeIterator = () => {
-      let i = -1;
-      return {
-        next: () =>
-            ++i < 7 ? {value: i, done: false} : {value: null, done: true}
-      };
-    };
-    const ds = tfd.generator(makeIterator).batch(2);
-    expect(ds.size).toBeNull();
-  });
-
-  it('batch dataset with infinite elements has infinity size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).repeat().batch(2);
-    expect(ds.size).toEqual(Infinity);
-  });
-
-  it('map dataset preserves regular size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).map(e => e + 1);
-    expect(ds.size).toEqual(5);
-  });
-
-  it('map dataset preserves infinity size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).repeat().map(e => e + 1);
-    expect(ds.size).toEqual(Infinity);
-  });
-
-  it('map dataset preserves null size', async () => {
-    const makeIterator = () => {
-      let i = -1;
-      return {
-        next: () =>
-            ++i < 7 ? {value: i, done: false} : {value: null, done: true}
-      };
-    };
-    const ds = tfd.generator(makeIterator).map(e => e + 1);
-    expect(ds.size).toBeNull();
-  });
-
-  it('filter dataset preserves infinity size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).repeat().filter(e => e % 2 === 0);
-    expect(ds.size).toEqual(Infinity);
-  });
-
-  it('filter dataset with regular size has null size', async () => {
-    const ds = tfd.array([1, 2, 3, 4, 5]).filter(e => e % 2 === 0);
-    expect(ds.size).toBeNull();
-  });
-
-  it('filter dataset with null size has null size', async () => {
-    const makeIterator = () => {
-      let i = -1;
-      return {
-        next: () =>
-            ++i < 7 ? {value: i, done: false} : {value: null, done: true}
-      };
-    };
-    const ds = tfd.generator(makeIterator).filter(e => e % 2 === 0);
-    expect(ds.size).toBeNull();
-  });
-
-  it('zipping an array of datasets with primitive elements has correct ' +
-         'size',
-     async () => {
-       const a = tfd.array([1, 2, 3]);
-       const b = tfd.array([4, 5, 6, 7, 8]);
-       const result = await tfd.zip([a, b]);
-       expect(result.size).toEqual(3);
-     });
-
-  it('zipping an array of datasets with object elements has correct size',
-     async () => {
-       const a = tfd.array([{a: 1}, {a: 2}, {a: 3}]);
-       const b = tfd.array([{b: 4}, {b: 5}, {b: 6}, {b: 7}, {b: 8}]);
-       const result = await tfd.zip([a, b]);
-       expect(result.size).toEqual(3);
-     });
-
-  it('zipping an object of datasets with primitive elements has correct ' +
-         'size',
-     async () => {
-       const a = tfd.array([1, 2, 3]);
-       const b = tfd.array([4, 5, 6, 7, 8]);
-       const result = await tfd.zip({'a': a, 'b': b});
-       expect(result.size).toEqual(3);
-     });
-
-  it('zipping an object of datasets with object elements has correct size',
-     async () => {
-       const a = tfd.array([{a: 1}, {a: 2}, {a: 3}]);
-       const b = tfd.array([{b: 4}, {b: 5}, {b: 6}, {b: 7}, {b: 8}]);
-       const result = await tfd.zip({'a': a, 'b': b});
-       expect(result.size).toEqual(3);
-     });
-
-  it('converting dataset with infinite elements to array throws error',
-     async () => {
-       try {
-         const ds = tfd.array([1, 2, 3, 4, 5]).repeat();
-         expect(ds.size).toEqual(Infinity);
-         await ds.toArrayForTest();
-         fail();
-       } catch (e) {
-         expect(e.message).toEqual(
-             'Can not convert infinite data stream to array.');
-       }
-     });
-
-  it('ArrayBuffer type data is not converted', async () => {
-    const a = new Float32Array([1, 2, 3]);
-    const ds = tfd.array([a]);
-    const result = await ds.toArrayForTest();
-    const resultArray = result[0];
-    expect(resultArray[0]).toBe(1);
-    expect(resultArray[1]).toBe(2);
-    expect(resultArray[2]).toBe(3);
-    expect(ArrayBuffer.isView(resultArray)).toBe(true);
-    expect(resultArray.constructor === Float32Array).toBe(true);
-  });
-});
-
-describeAllEnvs('Dataset with DEBUG mode', () => {
-  beforeAll(() => {
-    tf.env().set('DEBUG', true);
-  });
-
-  it('throws an error when given an array of inconsistent shape',
-     async () => {
-       const dataset = array([[[1, 2], [3]], [[4, 5], [6]]]).batch(2);
-       try {
-         // Using toArray() rather than toArrayForTest().  The prefetch in
-         // the latter, in combination with expecting an exception, causes
-         // unrelated tests to fail (See
-         // https://github.com/tensorflow/tfjs/issues/1330.
-         await (await dataset.iterator()).toArray();
-         fail();
-       } catch (e) {
-         expect(e.message).toEqual(
-             'Element arr[0][1] should have 2 elements, ' +
-             'but has 1 elements');
-       }
-       expect(tf.memory().numTensors).toBe(0);
-     });
-});
diff --git a/tfjs-master/tfjs-data/src/datasets/csv_dataset.ts b/tfjs-master/tfjs-data/src/datasets/csv_dataset.ts
deleted file mode 100644
index 8cc1276d6..000000000
--- a/tfjs-master/tfjs-data/src/datasets/csv_dataset.ts
+++ /dev/null
@@ -1,396 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {TensorContainer, util} from '@tensorflow/tfjs-core';
-import {Dataset} from '../dataset';
-import {DataSource} from '../datasource';
-import {LazyIterator} from '../iterators/lazy_iterator';
-import {ColumnConfig, CSVConfig} from '../types';
-import {TextLineDataset} from './text_line_dataset';
-
-const CODE_QUOTE = '"';
-const STATE_OUT = Symbol('out');
-const STATE_FIELD = Symbol('field');
-const STATE_QUOTE = Symbol('quote');
-const STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');
-const STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');
-
-/**
- * Represents a potentially large collection of delimited text records.
- *
- * The produced `TensorContainer`s each contain one key-value pair for
- * every column of the table.  When a field is empty in the incoming data, the
- * resulting value is `undefined`, or throw error if it is required.  Values
- * that can be parsed as numbers are emitted as type `number`, other values
- * are parsed as `string`.
- *
- * The results are not batched.
- *
- * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}
- */
-export class CSVDataset extends Dataset<TensorContainer> {
-  base: TextLineDataset;
-  private hasHeader = true;
-  private fullColumnNames: string[] = null;
-  private columnNamesValidated = false;
-  private columnConfigs: {[key: string]: ColumnConfig} = null;
-  private configuredColumnsOnly = false;
-  private delimiter = ',';
-  private delimWhitespace = false;
-
-  /**
-   * Returns column names of the csv dataset. If `configuredColumnsOnly` is
-   * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is
-   * false and `columnNames` is provided, `columnNames`. If
-   * `configuredColumnsOnly` is false and `columnNames` is not provided, return
-   * all column names parsed from the csv file. For example usage please go to
-   * `tf.data.csv`.
-   *
-   * @doc {heading: 'Data', subheading: 'Classes'}
-   */
-  async columnNames() {
-    if (!this.columnNamesValidated) {
-      await this.setColumnNames();
-    }
-    return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :
-                                        this.fullColumnNames;
-  }
-
-  /* 1) If `columnNames` is provided as string[], use this string[] as output
-   * keys in corresponding order. The length must match the number of inferred
-   * columns if `hasHeader` is true .
-   * 2) If `columnNames` is not provided, parse header line as `columnNames` if
-   * hasHeader is true. If `hasHeader` is false, throw an error.
-   * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must
-   * exist in parsed `columnNames`.
-   */
-  private async setColumnNames() {
-    const columnNamesFromFile = await this.maybeReadHeaderLine();
-    if (!this.fullColumnNames && !columnNamesFromFile) {
-      // Throw an error if columnNames is not provided and no header line.
-      throw new Error(
-          'Column names must be provided if there is no header line.');
-    } else if (this.fullColumnNames && columnNamesFromFile) {
-      // Check provided columnNames match header line.
-      util.assert(
-          columnNamesFromFile.length === this.fullColumnNames.length,
-          () => 'The length of provided columnNames (' +
-              this.fullColumnNames.length.toString() +
-              ') does not match the length of the header line read from ' +
-              'file (' + columnNamesFromFile.length.toString() + ').');
-    }
-    if (!this.fullColumnNames) {
-      this.fullColumnNames = columnNamesFromFile;
-    }
-    // Check if there are duplicate column names.
-    const counts: {[key: string]: number} = this.fullColumnNames.reduce(
-        (countAcc: {[key: string]: number}, name) => {
-          countAcc[name] = (countAcc[name] + 1) || 1;
-          return countAcc;
-        },
-        {});
-    const duplicateNames =
-        Object.keys(counts).filter((name) => (counts[name] > 1));
-    util.assert(
-        duplicateNames.length === 0,
-        () => 'Duplicate column names found: ' + duplicateNames.toString());
-    // Check if keys in columnConfigs match columnNames.
-    if (this.columnConfigs) {
-      for (const key of Object.keys(this.columnConfigs)) {
-        const index = this.fullColumnNames.indexOf(key);
-        if (index === -1) {
-          throw new Error(
-              'The key "' + key +
-              '" provided in columnConfigs does not match any of the column ' +
-              'names (' + this.fullColumnNames.toString() + ').');
-        }
-      }
-    }
-    this.columnNamesValidated = true;
-  }
-
-  private async maybeReadHeaderLine() {
-    if (this.hasHeader) {
-      const iter = await this.base.iterator();
-      const firstElement = await iter.next();
-      if (firstElement.done) {
-        throw new Error('No data was found for CSV parsing.');
-      }
-      const firstLine: string = firstElement.value;
-      const headers = this.parseRow(firstLine, false);
-      return headers;
-    } else {
-      return null;
-    }
-  }
-
-  /**
-   * Create a `CSVDataset`.
-   *
-   * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.
-   * @param csvConfig (Optional) A CSVConfig object that contains configurations
-   *     of reading and decoding from CSV file(s).
-   *
-   *     hasHeader: (Optional) A boolean value that indicates whether the first
-   *     row of provided CSV file is a header line with column names, and should
-   *     not be included in the data. Defaults to `true`.
-   *
-   *     columnNames: (Optional) A list of strings that corresponds to
-   *     the CSV column names, in order. If provided, it ignores the column
-   *     names inferred from the header row. If not provided, infers the column
-   *     names from the first row of the records. If hasHeader is false and
-   *     columnNames is not provided, this method throws an error.
-   *
-   *     columnConfigs: (Optional) A dictionary whose key is column names, value
-   *     is an object stating if this column is required, column's data type,
-   *     default value, and if this column is label. If provided, keys must
-   *     correspond to names provided in columnNames or inferred from the file
-   *     header lines. If isLabel is true any column, returns an array of two
-   *     items: the first item is a dict of features key/value pairs, the second
-   *     item is a dict of labels key/value pairs. If no feature is marked as
-   *     label, returns a dict of features only.
-   *
-   *     configuredColumnsOnly (Optional) If true, only columns provided in
-   *     columnConfigs will be parsed and provided during iteration.
-   *
-   *     delimiter (Optional) The string used to parse each line of the input
-   *     file. Defaults to `,`.
-   */
-  constructor(protected readonly input: DataSource, csvConfig?: CSVConfig) {
-    super();
-    this.base = new TextLineDataset(input);
-    if (!csvConfig) {
-      csvConfig = {};
-    }
-    this.hasHeader = csvConfig.hasHeader === false ? false : true;
-    this.fullColumnNames = csvConfig.columnNames;
-    this.columnConfigs = csvConfig.columnConfigs;
-    this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;
-    if (csvConfig.delimWhitespace) {
-      util.assert(
-          csvConfig.delimiter == null,
-          () =>
-              'Delimiter should not be provided when delimWhitespace is true.');
-      this.delimWhitespace = true;
-      this.delimiter = ' ';
-    } else {
-      this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';
-    }
-  }
-
-  async iterator(): Promise<LazyIterator<TensorContainer>> {
-    if (!this.columnNamesValidated) {
-      await this.setColumnNames();
-    }
-    let lines = await this.base.iterator();
-    if (this.hasHeader) {
-      // We previously read the first line to get the columnNames.
-      // Now that we're providing data, skip it.
-      lines = lines.skip(1);
-    }
-    return lines.map(x => this.makeDataElement(x));
-  }
-
-  makeDataElement(line: string): TensorContainer {
-    const values = this.parseRow(line);
-    const features: {[key: string]: TensorContainer} = {};
-    const labels: {[key: string]: TensorContainer} = {};
-
-    for (let i = 0; i < this.fullColumnNames.length; i++) {
-      const key = this.fullColumnNames[i];
-      const config = this.columnConfigs ? this.columnConfigs[key] : null;
-      if (this.configuredColumnsOnly && !config) {
-        // This column is not selected.
-        continue;
-      } else {
-        const value = values[i];
-        let parsedValue = null;
-        if (value === '') {
-          // If default value is provided, use it. If default value is not
-          // provided, set as undefined.
-          if (config && config.default !== undefined) {
-            parsedValue = config.default;
-          } else if (config && (config.required || config.isLabel)) {
-            throw new Error(
-                `Required column ${key} is empty in this line: ${line}`);
-          } else {
-            parsedValue = undefined;
-          }
-        } else {
-          // A value is present, so parse it based on type
-          const valueAsNum = Number(value);
-          if (isNaN(valueAsNum)) {
-            // The value is a string and this column is declared as boolean
-            // in config, parse it as boolean.
-            if (config && config.dtype === 'bool') {
-              parsedValue = this.getBoolean(value);
-            } else {
-              // Set value as string
-              parsedValue = value;
-            }
-          } else if (!config || !config.dtype) {
-            // If this value is a number and no type config is provided, return
-            // it as number.
-            parsedValue = valueAsNum;
-          } else {
-            // If this value is a number and data type is provided, parse it
-            // according to provided data type.
-            switch (config.dtype) {
-              case 'float32':
-                parsedValue = valueAsNum;
-                break;
-              case 'int32':
-                parsedValue = Math.floor(valueAsNum);
-                break;
-              case 'bool':
-                parsedValue = this.getBoolean(value);
-                break;
-              default:
-                parsedValue = valueAsNum;
-            }
-          }
-        }
-        // Check if this column is label.
-        (config && config.isLabel) ? labels[key] = parsedValue :
-                                     features[key] = parsedValue;
-      }
-    }
-    // If label exists, return an object of features and labels as {xs:features,
-    // ys:labels}, otherwise return features only.
-    if (Object.keys(labels).length === 0) {
-      return features;
-
-    } else {
-      return {xs: features, ys: labels};
-    }
-  }
-
-  private getBoolean(value: string): number {
-    if (value === '1' || value.toLowerCase() === 'true') {
-      return 1;
-    } else {
-      return 0;
-    }
-  }
-
-  // adapted from https://beta.observablehq.com/@mbostock/streaming-csv
-  private parseRow(line: string, validateElementCount = true): string[] {
-    const result: string[] = [];
-    let readOffset = 0;
-    const readLength = line.length;
-    let currentState = STATE_OUT;
-    // Goes through the line to parse quote.
-    for (let i = 0; i < readLength; i++) {
-      switch (currentState) {
-        // Before enter a new field
-        case STATE_OUT:
-          switch (line.charAt(i)) {
-            // Enter a quoted field
-            case CODE_QUOTE:
-              readOffset = i + 1;
-              currentState = STATE_QUOTE;
-              break;
-            // Read an empty field
-            case this.delimiter:
-              readOffset = i + 1;
-              // If delimiter is white space and configured to collapse
-              // multiple white spaces, ignore this white space.
-              if (this.delimiter === ' ' && this.delimWhitespace) {
-                break;
-              }
-              result.push('');
-              currentState = STATE_OUT;
-              break;
-            // Enter an unquoted field
-            default:
-              currentState = STATE_FIELD;
-              readOffset = i;
-              break;
-          }
-          break;
-        // In an unquoted field
-        case STATE_FIELD:
-          switch (line.charAt(i)) {
-            // Exit an unquoted field, add it to result
-            case this.delimiter:
-              result.push(line.substring(readOffset, i));
-              currentState = STATE_OUT;
-              readOffset = i + 1;
-              break;
-            default:
-          }
-          break;
-        // In a quoted field
-        case STATE_QUOTE:
-          switch (line.charAt(i)) {
-            // Read a quote after a quote
-            case CODE_QUOTE:
-              currentState = STATE_QUOTE_AFTER_QUOTE;
-              break;
-            default:
-          }
-          break;
-        // This state means it's right after a second quote in a field
-        case STATE_QUOTE_AFTER_QUOTE:
-          switch (line.charAt(i)) {
-            // Finished a quoted field
-            case this.delimiter:
-              result.push(line.substring(readOffset, i - 1));
-              currentState = STATE_OUT;
-              readOffset = i + 1;
-              break;
-            // Finished a quoted part in a quoted field
-            case CODE_QUOTE:
-              currentState = STATE_QUOTE;
-              break;
-            // In a quoted part in a quoted field
-            default:
-              currentState = STATE_WITHIN_QUOTE_IN_QUOTE;
-              break;
-          }
-          break;
-        case STATE_WITHIN_QUOTE_IN_QUOTE:
-          switch (line.charAt(i)) {
-            // Exit a quoted part in a quoted field
-            case CODE_QUOTE:
-              currentState = STATE_QUOTE;
-              break;
-            default:
-          }
-          break;
-        default:
-      }
-    }
-    // Adds last item based on if it is quoted.
-    if (currentState === STATE_QUOTE_AFTER_QUOTE) {
-      result.push(line.substring(readOffset, readLength - 1));
-    } else {
-      result.push(line.substring(readOffset));
-    }
-    // Check if each row has the same number of elements as column names.
-    if (validateElementCount && result.length !== this.fullColumnNames.length) {
-      throw new Error(`Invalid row in csv file. Should have ${
-          this.fullColumnNames.length} elements in a row, but got ${result}`);
-    }
-    return result;
-  }
-}
-
-// TODO(soergel): add more basic datasets for parity with tf.data
-// tf.data.FixedLengthRecordDataset()
-// tf.data.TFRecordDataset()
diff --git a/tfjs-master/tfjs-data/src/datasets/csv_dataset_test.ts b/tfjs-master/tfjs-data/src/datasets/csv_dataset_test.ts
deleted file mode 100644
index fc62b2bb6..000000000
--- a/tfjs-master/tfjs-data/src/datasets/csv_dataset_test.ts
+++ /dev/null
@@ -1,527 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-
-import {array} from '../dataset';
-import {FileDataSource} from '../sources/file_data_source';
-
-import {CSVDataset} from './csv_dataset';
-
-const csvString = `ab,cd,ef
-ghi,,jkl
-,mn,op
-1.4,7.8,12
-qrs,tu,
-v,w,x
-y,z,`;
-
-const csvStringWithHeaders = `foo,bar,baz
-` + csvString;
-
-const csvData =
-    env().get('IS_BROWSER') ? new Blob([csvString]) : Buffer.from(csvString);
-
-const csvDataWithHeaders = env().get('IS_BROWSER') ?
-    new Blob([csvStringWithHeaders]) :
-    Buffer.from(csvStringWithHeaders);
-
-const csvDataExtra = `A,B,C
-1,2,3
-2,2,3
-3,2,3
-4,2,3
-5,2,3
-6,2,3
-7,2,3`;
-
-const csvDataSemicolon = `A;B;C
-1;2;3
-2;2;3
-3;2;3
-4;2;3
-5;2;3
-6;2;3
-7;2;3`;
-
-const csvMixedType = `A,B,C,D
-1,True,3,1
-2,False,2,0
-3,True,1,1
-1,False,3,0
-2,True,2,1
-3,False,1,0
-1,True,3,1
-2,False,2,0`;
-
-const csvWithQuote = `"A",B,"C"
-1,"2",3
-2,2,3
-3,"""2",3
-4,"2,",
-"5",2,3
-6,2,"345"123,456""
-7,"2",3`;
-
-const csvWithMultiWhitespaces = `A B    C
-1 2  3
-2 2 3
-3  2 3
-4 2 3
-5    2    3
-6 2 3
-7 2 3`;
-
-const csvWithSingleWhitespace = `A B C\n` +
-    `1 2 3\n` +
-    `2  \n` +
-    `3 2 3`;
-
-const csvWithMissingElement = `A,B,C
-1,2,3
-2,
-3,2,3`;
-
-const csvWithDOSLineBreaker = `A,B,C\r\n1,2,3\r\nv,\rw,x\r\n3,2,3`;
-
-const csvDataWithHeadersExtra = env().get('IS_BROWSER') ?
-    new Blob([csvDataExtra]) :
-    Buffer.from(csvDataExtra);
-const csvDataWithSemicolon = env().get('IS_BROWSER') ?
-    new Blob([csvDataSemicolon]) :
-    Buffer.from(csvDataSemicolon);
-const csvDataWithMixedType = env().get('IS_BROWSER') ?
-    new Blob([csvMixedType]) :
-    Buffer.from(csvMixedType);
-const csvDataWithQuote = env().get('IS_BROWSER') ? new Blob([csvWithQuote]) :
-                                                   Buffer.from(csvWithQuote);
-const csvDataWithMultiWhitespaces = env().get('IS_BROWSER') ?
-    new Blob([csvWithMultiWhitespaces]) :
-    Buffer.from(csvWithMultiWhitespaces);
-const csvDataWithMissingElement = env().get('IS_BROWSER') ?
-    new Blob([csvWithMissingElement]) :
-    Buffer.from(csvWithMissingElement);
-const csvDataWithSingleWhitespace = env().get('IS_BROWSER') ?
-    new Blob([csvWithSingleWhitespace]) :
-    Buffer.from(csvWithSingleWhitespace);
-const csvDataWithDOSLineBreaker = env().get('IS_BROWSER') ?
-    new Blob([csvWithDOSLineBreaker]) :
-    Buffer.from(csvWithDOSLineBreaker);
-
-describe('CSVDataset', () => {
-  it('produces a stream of dicts containing UTF8-decoded csv data',
-     async () => {
-       const source = new FileDataSource(csvData, {chunkSize: 10});
-       const dataset = new CSVDataset(
-           source, {hasHeader: false, columnNames: ['foo', 'bar', 'baz']});
-
-       expect(await dataset.columnNames()).toEqual(['foo', 'bar', 'baz']);
-
-       const iter = await dataset.iterator();
-       const result = await iter.toArrayForTest();
-
-       expect(result).toEqual([
-         {'foo': 'ab', 'bar': 'cd', 'baz': 'ef'},
-         {'foo': 'ghi', 'bar': undefined, 'baz': 'jkl'},
-         {'foo': undefined, 'bar': 'mn', 'baz': 'op'},
-         {'foo': 1.4, 'bar': 7.8, 'baz': 12},
-         {'foo': 'qrs', 'bar': 'tu', 'baz': undefined},
-         {'foo': 'v', 'bar': 'w', 'baz': 'x'},
-         {'foo': 'y', 'bar': 'z', 'baz': undefined},
-       ]);
-     });
-
-  it('reads CSV column headers when requested', async () => {
-    const source = new FileDataSource(csvDataWithHeaders, {chunkSize: 10});
-    const dataset = new CSVDataset(source);
-
-    expect(await dataset.columnNames()).toEqual(['foo', 'bar', 'baz']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result).toEqual([
-      {'foo': 'ab', 'bar': 'cd', 'baz': 'ef'},
-      {'foo': 'ghi', 'bar': undefined, 'baz': 'jkl'},
-      {'foo': undefined, 'bar': 'mn', 'baz': 'op'},
-      {'foo': 1.4, 'bar': 7.8, 'baz': 12},
-      {'foo': 'qrs', 'bar': 'tu', 'baz': undefined},
-      {'foo': 'v', 'bar': 'w', 'baz': 'x'},
-      {'foo': 'y', 'bar': 'z', 'baz': undefined},
-    ]);
-  });
-
-  it('throw error when column configs mismatch column names', async () => {
-    try {
-      const source = new FileDataSource(csvData, {chunkSize: 10});
-      const dataset = new CSVDataset(source, {
-        hasHeader: false,
-        columnNames: ['foo', 'bar', 'baz'],
-        columnConfigs: {'A': {required: true}}
-      });
-      await dataset.columnNames();
-      fail();
-    } catch (error) {
-      expect(error.message)
-          .toBe(
-              'The key "A" provided in columnConfigs does not ' +
-              'match any of the column names (foo,bar,baz).');
-    }
-  });
-
-  it('throw error when no header line and no column names provided',
-     async () => {
-       try {
-         const source = new FileDataSource(csvData, {chunkSize: 10});
-         const dataset = new CSVDataset(source, {hasHeader: false});
-         await dataset.columnNames();
-         fail();
-       } catch (error) {
-         expect(error.message)
-             .toBe('Column names must be provided if there is no header line.');
-       }
-     });
-
-  it('take first line as columnNames by default', async () => {
-    const source = new FileDataSource(csvDataWithHeaders, {chunkSize: 10});
-    const dataset = new CSVDataset(source);
-    expect(await dataset.columnNames()).toEqual(['foo', 'bar', 'baz']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result).toEqual([
-      {'foo': 'ab', 'bar': 'cd', 'baz': 'ef'},
-      {'foo': 'ghi', 'bar': undefined, 'baz': 'jkl'},
-      {'foo': undefined, 'bar': 'mn', 'baz': 'op'},
-      {'foo': 1.4, 'bar': 7.8, 'baz': 12},
-      {'foo': 'qrs', 'bar': 'tu', 'baz': undefined},
-      {'foo': 'v', 'bar': 'w', 'baz': 'x'},
-      {'foo': 'y', 'bar': 'z', 'baz': undefined},
-    ]);
-  });
-
-  it('emits rows in order despite async requests', async () => {
-    const source = new FileDataSource(csvDataWithHeadersExtra, {chunkSize: 10});
-    const ds = new CSVDataset(source);
-    expect(await ds.columnNames()).toEqual(['A', 'B', 'C']);
-    const csvIterator = await ds.iterator();
-    const promises = [
-      csvIterator.next(), csvIterator.next(), csvIterator.next(),
-      csvIterator.next(), csvIterator.next()
-    ];
-    const elements = await Promise.all(promises);
-    expect(elements[0].value).toEqual({A: 1, B: 2, C: 3});
-    expect(elements[1].value).toEqual({A: 2, B: 2, C: 3});
-    expect(elements[2].value).toEqual({A: 3, B: 2, C: 3});
-    expect(elements[3].value).toEqual({A: 4, B: 2, C: 3});
-    expect(elements[4].value).toEqual({A: 5, B: 2, C: 3});
-  });
-
-  it('throw error when required column is empty', async () => {
-    try {
-      const source = new FileDataSource(csvData, {chunkSize: 10});
-      const dataset = new CSVDataset(source, {
-        hasHeader: false,
-        columnNames: ['foo', 'bar', 'baz'],
-        columnConfigs: {'foo': {required: true}}
-      });
-      expect(await dataset.columnNames()).toEqual(['foo', 'bar', 'baz']);
-      const iter = await dataset.iterator();
-      await iter.toArrayForTest();
-      fail();
-    } catch (error) {
-      expect(error.message)
-          .toBe('Required column foo is empty in this line: ,mn,op');
-    }
-  });
-
-  it('fill default value when provided', async () => {
-    const source = new FileDataSource(csvData, {chunkSize: 10});
-    const dataset = new CSVDataset(source, {
-      hasHeader: false,
-      columnNames: ['foo', 'bar', 'baz'],
-      columnConfigs: {'foo': {default: 'abc'}, 'baz': {default: 123}}
-    });
-
-    expect(await dataset.columnNames()).toEqual(['foo', 'bar', 'baz']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result).toEqual([
-      {'foo': 'ab', 'bar': 'cd', 'baz': 'ef'},
-      {'foo': 'ghi', 'bar': undefined, 'baz': 'jkl'},
-      {'foo': 'abc', 'bar': 'mn', 'baz': 'op'},
-      {'foo': 1.4, 'bar': 7.8, 'baz': 12},
-      {'foo': 'qrs', 'bar': 'tu', 'baz': 123},
-      {'foo': 'v', 'bar': 'w', 'baz': 'x'},
-      {'foo': 'y', 'bar': 'z', 'baz': 123},
-    ]);
-  });
-
-  it('provide delimiter through parameter', async () => {
-    const source = new FileDataSource(csvDataWithSemicolon, {chunkSize: 10});
-    const dataset = new CSVDataset(source, {delimiter: ';'});
-    expect(await dataset.columnNames()).toEqual(['A', 'B', 'C']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result[0]).toEqual({A: 1, B: 2, C: 3});
-    expect(result[1]).toEqual({A: 2, B: 2, C: 3});
-    expect(result[2]).toEqual({A: 3, B: 2, C: 3});
-    expect(result[3]).toEqual({A: 4, B: 2, C: 3});
-    expect(result[4]).toEqual({A: 5, B: 2, C: 3});
-  });
-
-  it('provide datatype through parameter to parse different types',
-     async () => {
-       const source = new FileDataSource(csvDataWithMixedType, {chunkSize: 10});
-       const dataset = new CSVDataset(source, {
-         columnConfigs: {
-           'A': {dtype: 'int32'},
-           'B': {dtype: 'bool'},
-           'C': {dtype: 'int32'},
-           'D': {dtype: 'bool'}
-         }
-       });
-       expect(await dataset.columnNames()).toEqual(['A', 'B', 'C', 'D']);
-       const iter = await dataset.iterator();
-       const result = await iter.toArrayForTest();
-
-       expect(result).toEqual([
-         {'A': 1, 'B': 1, 'C': 3, 'D': 1}, {'A': 2, 'B': 0, 'C': 2, 'D': 0},
-         {'A': 3, 'B': 1, 'C': 1, 'D': 1}, {'A': 1, 'B': 0, 'C': 3, 'D': 0},
-         {'A': 2, 'B': 1, 'C': 2, 'D': 1}, {'A': 3, 'B': 0, 'C': 1, 'D': 0},
-         {'A': 1, 'B': 1, 'C': 3, 'D': 1}, {'A': 2, 'B': 0, 'C': 2, 'D': 0}
-       ]);
-     });
-
-  it('reads CSV with selected column in order', async () => {
-    const source = new FileDataSource(csvDataWithHeaders, {chunkSize: 10});
-    const dataset = new CSVDataset(
-        source,
-        {columnConfigs: {'bar': {}, 'foo': {}}, configuredColumnsOnly: true});
-
-    expect(await dataset.columnNames()).toEqual(['bar', 'foo']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result).toEqual([
-      {'bar': 'cd', 'foo': 'ab'},
-      {'bar': undefined, 'foo': 'ghi'},
-      {'bar': 'mn', 'foo': undefined},
-      {'bar': 7.8, 'foo': 1.4},
-      {'bar': 'tu', 'foo': 'qrs'},
-      {'bar': 'w', 'foo': 'v'},
-      {'bar': 'z', 'foo': 'y'},
-    ]);
-  });
-
-  it('reads CSV with wrong column', async () => {
-    try {
-      const source = new FileDataSource(csvDataWithHeaders, {chunkSize: 10});
-      const dataset =
-          new CSVDataset(source, {columnNames: ['bar', 'foooooooo']});
-      await dataset.columnNames();
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual(
-          'The length of provided columnNames (2) does not match the length ' +
-          'of the header line read from file (3).');
-    }
-  });
-
-  it('reads CSV with column names override header', async () => {
-    const source = new FileDataSource(csvDataWithHeaders, {chunkSize: 10});
-    const dataset = new CSVDataset(source, {columnNames: ['a', 'b', 'c']});
-    expect(await dataset.columnNames()).toEqual(['a', 'b', 'c']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result).toEqual([
-      {'a': 'ab', 'b': 'cd', 'c': 'ef'},
-      {'a': 'ghi', 'b': undefined, 'c': 'jkl'},
-      {'a': undefined, 'b': 'mn', 'c': 'op'},
-      {'a': 1.4, 'b': 7.8, 'c': 12},
-      {'a': 'qrs', 'b': 'tu', 'c': undefined},
-      {'a': 'v', 'b': 'w', 'c': 'x'},
-      {'a': 'y', 'b': 'z', 'c': undefined},
-    ]);
-  });
-
-  it('reads CSV with missing label value', async () => {
-    try {
-      const source = new FileDataSource(csvDataWithHeaders, {chunkSize: 10});
-      const dataset =
-          new CSVDataset(source, {columnConfigs: {'baz': {isLabel: true}}});
-      expect(await dataset.columnNames()).toEqual(['foo', 'bar', 'baz']);
-      const iter = await dataset.iterator();
-      // Using toArray() rather than toArrayForTest().  The prefetch in
-      // the latter, in combination with expecting an exception, causes
-      // unrelated tests to fail (See
-      // https://github.com/tensorflow/tfjs/issues/1330.
-      await iter.toArray();
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual(
-          'Required column baz is empty in this line: qrs,tu,');
-    }
-  });
-
-  it('reads CSV with label column', async () => {
-    const source = new FileDataSource(csvDataWithHeadersExtra, {chunkSize: 10});
-    const dataset =
-        new CSVDataset(source, {columnConfigs: {'C': {isLabel: true}}});
-    expect(await dataset.columnNames()).toEqual(['A', 'B', 'C']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result).toEqual([
-      {xs: {'A': 1, 'B': 2}, ys: {'C': 3}},
-      {xs: {'A': 2, 'B': 2}, ys: {'C': 3}},
-      {xs: {'A': 3, 'B': 2}, ys: {'C': 3}},
-      {xs: {'A': 4, 'B': 2}, ys: {'C': 3}},
-      {xs: {'A': 5, 'B': 2}, ys: {'C': 3}},
-      {xs: {'A': 6, 'B': 2}, ys: {'C': 3}}, {xs: {'A': 7, 'B': 2}, ys: {'C': 3}}
-    ]);
-  });
-
-  it('reads CSV with quote', async () => {
-    const source = new FileDataSource(csvDataWithQuote, {chunkSize: 10});
-    const dataset = new CSVDataset(source);
-    expect(await dataset.columnNames()).toEqual(['A', 'B', 'C']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result).toEqual([
-      {'A': 1, 'B': 2, 'C': 3}, {'A': 2, 'B': 2, 'C': 3},
-      {'A': 3, 'B': '""2', 'C': 3}, {'A': 4, 'B': '2,', 'C': undefined},
-      {'A': 5, 'B': 2, 'C': 3}, {'A': 6, 'B': 2, 'C': '345"123,456"'},
-      {'A': 7, 'B': 2, 'C': 3}
-    ]);
-  });
-
-  it('check duplicate column names', async () => {
-    try {
-      const csvStringWithDuplicateColumnNames = `foo,bar,foo
-    ` + csvString;
-      const csvDataWithDuplicateColumnNames = env().get('IS_BROWSER') ?
-          new Blob([csvStringWithDuplicateColumnNames]) :
-          Buffer.from(csvStringWithDuplicateColumnNames);
-      const source =
-          new FileDataSource(csvDataWithDuplicateColumnNames, {chunkSize: 10});
-      const dataset = new CSVDataset(source);
-      await dataset.columnNames();
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual('Duplicate column names found: foo');
-    }
-  });
-
-  it('throw error with missing elements', async () => {
-    try {
-      const source =
-          new FileDataSource(csvDataWithMissingElement, {chunkSize: 10});
-      const dataset = new CSVDataset(source);
-      expect(await dataset.columnNames()).toEqual(['A', 'B', 'C']);
-      const iter = await dataset.iterator();
-      await iter.toArrayForTest();
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual(
-          'Invalid row in csv file. Should have 3 elements in a row, ' +
-          'but got 2,');
-    }
-  });
-
-  it('collapse continuous white spaces', async () => {
-    const source =
-        new FileDataSource(csvDataWithMultiWhitespaces, {chunkSize: 10});
-    const dataset = new CSVDataset(source, {delimWhitespace: true});
-    expect(await dataset.columnNames()).toEqual(['A', 'B', 'C']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result[0]).toEqual({A: 1, B: 2, C: 3});
-    expect(result[1]).toEqual({A: 2, B: 2, C: 3});
-    expect(result[2]).toEqual({A: 3, B: 2, C: 3});
-    expect(result[3]).toEqual({A: 4, B: 2, C: 3});
-    expect(result[4]).toEqual({A: 5, B: 2, C: 3});
-  });
-
-  it('parse correctly with single white spaces delimiter', async () => {
-    const source =
-        new FileDataSource(csvDataWithSingleWhitespace, {chunkSize: 10});
-    const dataset = new CSVDataset(source, {delimiter: ' '});
-    expect(await dataset.columnNames()).toEqual(['A', 'B', 'C']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result[0]).toEqual({A: 1, B: 2, C: 3});
-    expect(result[1]).toEqual({A: 2, B: undefined, C: undefined});
-    expect(result[2]).toEqual({A: 3, B: 2, C: 3});
-  });
-
-  it('throw error when delimiter is provided and delimWhitespace is true',
-     async () => {
-       try {
-         const source =
-             new FileDataSource(csvDataWithMultiWhitespaces, {chunkSize: 10});
-         const dataset =
-             new CSVDataset(source, {delimiter: ',', delimWhitespace: true});
-         expect(await dataset.columnNames()).toEqual(['A', 'B', 'C']);
-         fail();
-       } catch (e) {
-         expect(e.message).toEqual(
-             'Delimiter should not be provided when delimWhitespace is true.');
-       }
-     });
-
-  it('parse correctly when csv file has extra line breaker', async () => {
-    const source =
-        new FileDataSource(csvDataWithDOSLineBreaker, {chunkSize: 10});
-    const dataset = new CSVDataset(source);
-    expect(await dataset.columnNames()).toEqual(['A', 'B', 'C']);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result[0]).toEqual({A: 1, B: 2, C: 3});
-    expect(result[1]).toEqual({A: 'v', B: '\rw', C: 'x'});
-    expect(result[2]).toEqual({A: 3, B: 2, C: 3});
-  });
-
-  it('can be used with concatenate', async () => {
-    const source = new FileDataSource(csvData, {chunkSize: 10});
-    const dataset = new CSVDataset(
-        source, {hasHeader: false, columnNames: ['foo', 'bar', 'baz']});
-    const datasetAfterAdd =
-        dataset.concatenate(array([{'foo': '1', 'bar': '2', 'baz': '3'}]));
-
-    const iter = await datasetAfterAdd.iterator();
-    const result = await iter.toArrayForTest();
-
-    expect(result).toEqual([
-      {'foo': 'ab', 'bar': 'cd', 'baz': 'ef'},
-      {'foo': 'ghi', 'bar': undefined, 'baz': 'jkl'},
-      {'foo': undefined, 'bar': 'mn', 'baz': 'op'},
-      {'foo': 1.4, 'bar': 7.8, 'baz': 12},
-      {'foo': 'qrs', 'bar': 'tu', 'baz': undefined},
-      {'foo': 'v', 'bar': 'w', 'baz': 'x'},
-      {'foo': 'y', 'bar': 'z', 'baz': undefined},
-      {'foo': '1', 'bar': '2', 'baz': '3'},  // This is the added entry.
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/datasets/text_line_dataset.ts b/tfjs-master/tfjs-data/src/datasets/text_line_dataset.ts
deleted file mode 100644
index 62b5e5396..000000000
--- a/tfjs-master/tfjs-data/src/datasets/text_line_dataset.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {Dataset} from '../dataset';
-import {DataSource} from '../datasource';
-import {LazyIterator} from '../iterators/lazy_iterator';
-
-/**
- * Represents a potentially large collection of text lines.
- *
- * The results are not batched.
- */
-export class TextLineDataset extends Dataset<string> {
-  /**
-   * Create a `TextLineDataset`.
-   *
-   * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.
-   */
-  constructor(protected readonly input: DataSource) {
-    super();
-  }
-
-  async iterator(): Promise<LazyIterator<string>> {
-    const inputIterator = await this.input.iterator();
-    const utf8Iterator = inputIterator.decodeUTF8();
-    const lineIterator = utf8Iterator.split('\n').map(line => {
-      // Windows/DOS format text file has extra line breaker at the end of line.
-      if (line.endsWith('\r')) {
-        line = line.slice(0, -1);
-      }
-      return line;
-    });
-    return lineIterator;
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/datasets/text_line_dataset_test.ts b/tfjs-master/tfjs-data/src/datasets/text_line_dataset_test.ts
deleted file mode 100644
index de95bd6af..000000000
--- a/tfjs-master/tfjs-data/src/datasets/text_line_dataset_test.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-import {FileDataSource} from '../sources/file_data_source';
-import {TextLineDataset} from './text_line_dataset';
-
-const runes = `ᚠᛇᚻ᛫ᛒᛦᚦ᛫ᚠᚱᚩᚠᚢᚱ᛫ᚠᛁᚱᚪ᛫ᚷᛖᚻᚹᛦᛚᚳᚢᛗ
-ᛋᚳᛖᚪᛚ᛫ᚦᛖᚪᚻ᛫ᛗᚪᚾᚾᚪ᛫ᚷᛖᚻᚹᛦᛚᚳ᛫ᛗᛁᚳᛚᚢᚾ᛫ᚻᛦᛏ᛫ᛞᚫᛚᚪᚾ
-ᚷᛁᚠ᛫ᚻᛖ᛫ᚹᛁᛚᛖ᛫ᚠᚩᚱ᛫ᛞᚱᛁᚻᛏᚾᛖ᛫ᛞᚩᛗᛖᛋ᛫ᚻᛚᛇᛏᚪᚾ᛬`;
-
-const textWithDOSLineBreaks = 'abc\rdefg\r\nhijklmn\r\nopqrst';
-
-const testBlob =
-    env().get('IS_BROWSER') ? new Blob([runes]) : Buffer.from(runes);
-
-const textBlobWithDOSLineBreaks = env().get('IS_BROWSER') ?
-    new Blob([textWithDOSLineBreaks]) :
-    Buffer.from(textWithDOSLineBreaks);
-
-describe('TextLineDataset', () => {
-  it('Produces a stream of strings containing UTF8-decoded text lines',
-     async () => {
-       const source = new FileDataSource(testBlob, {chunkSize: 10});
-       const dataset = new TextLineDataset(source);
-       const iter = await dataset.iterator();
-       const result = await iter.toArrayForTest();
-
-       expect(result).toEqual([
-         'ᚠᛇᚻ᛫ᛒᛦᚦ᛫ᚠᚱᚩᚠᚢᚱ᛫ᚠᛁᚱᚪ᛫ᚷᛖᚻᚹᛦᛚᚳᚢᛗ',
-         'ᛋᚳᛖᚪᛚ᛫ᚦᛖᚪᚻ᛫ᛗᚪᚾᚾᚪ᛫ᚷᛖᚻᚹᛦᛚᚳ᛫ᛗᛁᚳᛚᚢᚾ᛫ᚻᛦᛏ᛫ᛞᚫᛚᚪᚾ',
-         'ᚷᛁᚠ᛫ᚻᛖ᛫ᚹᛁᛚᛖ᛫ᚠᚩᚱ᛫ᛞᚱᛁᚻᛏᚾᛖ᛫ᛞᚩᛗᛖᛋ᛫ᚻᛚᛇᛏᚪᚾ᛬',
-       ]);
-     });
-
-  it('Parses lines from windows/DOS text correctly', async () => {
-    const source =
-        new FileDataSource(textBlobWithDOSLineBreaks, {chunkSize: 10});
-    const dataset = new TextLineDataset(source);
-    const iter = await dataset.iterator();
-    const result = await iter.toArrayForTest();
-
-    // \r is retained when not followed by \n
-    expect(result[0]).toEqual('abc\rdefg');
-    expect(result[1]).toEqual('hijklmn');
-    expect(result[2]).toEqual('opqrst');
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/datasource.ts b/tfjs-master/tfjs-data/src/datasource.ts
deleted file mode 100644
index 4ec37b18a..000000000
--- a/tfjs-master/tfjs-data/src/datasource.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {ByteChunkIterator} from './iterators/byte_chunk_iterator';
-
-/**
- * Represents a data source readable as a stream of binary data chunks.
- *
- * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this
- * provides a means to repeatedly create streams from the underlying data
- * sources.
- */
-export abstract class DataSource {
-  /**
-   * Obtain a new stream of binary data chunks.
-   *
-   * Starts the new stream from the beginning of the data source, even if other
-   * streams have been obtained previously.
-   */
-  abstract iterator(): Promise<ByteChunkIterator>;
-
-  // TODO(soergel): consider chainable Dataset construction here
-}
-
-// TODO(soergel): consider convenience factory functions here
-// in combination with chainable source->dataset above, e.g.:
-// tf.data.url(...).asCsvDataset().shuffle().batch()
diff --git a/tfjs-master/tfjs-data/src/index.ts b/tfjs-master/tfjs-data/src/index.ts
deleted file mode 100644
index 540e441fb..000000000
--- a/tfjs-master/tfjs-data/src/index.ts
+++ /dev/null
@@ -1,25 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export {array, Dataset, zip} from './dataset';
-export {CSVDataset} from './datasets/csv_dataset';
-export {TextLineDataset} from './datasets/text_line_dataset';
-export {csv, func, generator, microphone, webcam} from './readers';
-export {FileDataSource} from './sources/file_data_source';
-export {URLDataSource} from './sources/url_data_source';
-export {ColumnConfig, MicrophoneConfig, WebcamConfig} from './types';
-export {version as version_data} from './version';
diff --git a/tfjs-master/tfjs-data/src/iterators/byte_chunk_iterator.ts b/tfjs-master/tfjs-data/src/iterators/byte_chunk_iterator.ts
deleted file mode 100644
index 8de22ed37..000000000
--- a/tfjs-master/tfjs-data/src/iterators/byte_chunk_iterator.ts
+++ /dev/null
@@ -1,126 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-import {LazyIterator, OneToManyIterator} from './lazy_iterator';
-import {StringIterator} from './string_iterator';
-
-export abstract class ByteChunkIterator extends LazyIterator<Uint8Array> {
-  /**
-   * Decode a stream of UTF8-encoded byte arrays to a stream of strings.
-   *
-   * The byte arrays producetd from the ByteChunkIterator on which this is
-   * called will be interpreted as concatenated.  No assumptions are made about
-   * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a
-   * character may span the boundary between chunks.  This naturally happens,
-   * for instance, when reading fixed-size byte arrays from a file.
-   */
-  decodeUTF8(): StringIterator {
-    return new Utf8Iterator(this);
-  }
-}
-
-// ============================================================================
-// The following private classes serve to implement the chainable methods
-// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,
-// due to resulting trouble with circular imports.
-// ============================================================================
-
-// We wanted multiple inheritance, e.g.
-//   class Utf8Iterator extends QueueIterator<string>, StringIterator
-// but the TypeScript mixin approach is a bit hacky, so we take this adapter
-// approach instead.
-
-class Utf8Iterator extends StringIterator {
-  private impl: Utf8IteratorImpl;
-
-  constructor(protected upstream: LazyIterator<Uint8Array>) {
-    super();
-    this.impl = new Utf8IteratorImpl(upstream);
-  }
-
-  summary() {
-    return this.impl.summary();
-  }
-
-  async next() {
-    return this.impl.next();
-  }
-}
-
-/**
- * Decode a stream of UTF8-encoded byte arrays to a stream of strings.
- *
- * This is tricky because the incoming byte array boundaries may disrupt a
- * multi-byte UTF8 character. Thus any incomplete character data at the end of
- * a chunk must be carried over and prepended to the next chunk before
- * decoding. Luckily with native decoder, TextDecoder in browser and
- * string_decoder in node, byte array boundaries are handled automatically.
- *
- * In the context of an input pipeline for machine learning, UTF8 decoding is
- * needed to parse text files containing training examples or prediction
- * requests (e.g., formatted as CSV or JSON). We cannot use the built-in
- * decoding provided by FileReader.readAsText() because here we are in a
- * streaming context, which FileReader does not support.
- *
- * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded
- *   text, which should be interpreted as concatenated.  No assumptions are
- *   made about the boundaries of the incoming chunks, so a multi-byte UTF8
- *   encoding of a character may span the boundary between chunks.  This
- *   naturally happens, for instance, when reading fixed-size byte arrays from a
- *   file.
- */
-class Utf8IteratorImpl extends OneToManyIterator<string> {
-  // `decoder` as `any` here to dynamically assign value based on the
-  // environment.
-  // tslint:disable-next-line:no-any
-  decoder: any;
-
-  constructor(protected readonly upstream: LazyIterator<Uint8Array>) {
-    super();
-    if (env().get('IS_BROWSER')) {
-      this.decoder = new TextDecoder('utf-8');
-    } else {
-      // tslint:disable-next-line:no-require-imports
-      const {StringDecoder} = require('string_decoder');
-      this.decoder = new StringDecoder('utf8');
-    }
-  }
-  summary() {
-    return `${this.upstream.summary()} -> Utf8`;
-  }
-
-  async pump(): Promise<boolean> {
-    const chunkResult = await this.upstream.next();
-    let chunk;
-    if (chunkResult.done) {
-      return false;
-    } else {
-      chunk = chunkResult.value;
-    }
-
-    let text: string;
-    if (env().get('IS_BROWSER')) {
-      text = this.decoder.decode(chunk, {stream: true});
-    } else {
-      text = this.decoder.write(Buffer.from(chunk.buffer));
-    }
-    this.outputQueue.push(text);
-    return true;
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/iterators/byte_chunk_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/byte_chunk_iterator_test.ts
deleted file mode 100644
index 68cbbb99e..000000000
--- a/tfjs-master/tfjs-data/src/iterators/byte_chunk_iterator_test.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-import {FileChunkIterator} from './file_chunk_iterator';
-
-const runes = `ᚠᛇᚻ᛫ᛒᛦᚦ᛫ᚠᚱᚩᚠᚢᚱ᛫ᚠᛁᚱᚪ᛫ᚷᛖᚻᚹᛦᛚᚳᚢᛗ
-ᛋᚳᛖᚪᛚ᛫ᚦᛖᚪᚻ᛫ᛗᚪᚾᚾᚪ᛫ᚷᛖᚻᚹᛦᛚᚳ᛫ᛗᛁᚳᛚᚢᚾ᛫ᚻᛦᛏ᛫ᛞᚫᛚᚪᚾ
-ᚷᛁᚠ᛫ᚻᛖ᛫ᚹᛁᛚᛖ᛫ᚠᚩᚱ᛫ᛞᚱᛁᚻᛏᚾᛖ᛫ᛞᚩᛗᛖᛋ᛫ᚻᛚᛇᛏᚪᚾ᛬`;
-
-const testData =
-    env().get('IS_BROWSER') ? new Blob([runes]) : Buffer.from(runes);
-
-describe('ByteChunkIterator.decodeUTF8()', () => {
-  it('Correctly reassembles split characters', async () => {
-    const byteChunkIterator = new FileChunkIterator(testData, {chunkSize: 50});
-    expect((env().get('IS_BROWSER') ? (testData as Blob).size :
-                                      (testData as Buffer).byteLength))
-        .toEqual(323);
-    const utf8Iterator = byteChunkIterator.decodeUTF8();
-
-    const result = await utf8Iterator.toArrayForTest();
-    // The test string is 109 characters long; its UTF8 encoding is 323
-    // bytes. We read it in chunks of 50 bytes, so there were 7 chunks of
-    // bytes. The UTF decoder slightly adjusted the boundaries between the
-    // chunks to allow decoding, but did not change the number of chunks,
-    // so 7 chunks remain.
-    expect(result.length).toEqual(7);
-    const totalCharacters = result.map(x => x.length).reduce((a, b) => a + b);
-    expect(totalCharacters).toEqual(109);
-    expect(result.join('')).toEqual(runes);
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/iterators/chained_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/chained_iterator_test.ts
deleted file mode 100644
index 42e2a7f09..000000000
--- a/tfjs-master/tfjs-data/src/iterators/chained_iterator_test.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {ChainedIterator} from './lazy_iterator';
-import {iteratorFromItems} from './lazy_iterator';
-import {TestIntegerIterator} from './lazy_iterator_test';
-
-const SHORT_STREAM_LENGTH = 15;
-
-describe('ChainedIterator', () => {
-  it('produces a single underlying stream as expected', async () => {
-    const chainedIterator = new ChainedIterator(
-        iteratorFromItems([new TestIntegerIterator(SHORT_STREAM_LENGTH)]));
-
-    const expectedResult: number[] = [];
-    for (let i = 0; i < 1; i++) {
-      for (let j = 0; j < SHORT_STREAM_LENGTH; j++) {
-        expectedResult[i * SHORT_STREAM_LENGTH + j] = j;
-      }
-    }
-
-    const result = await chainedIterator.toArrayForTest();
-    expect(result).toEqual(expectedResult);
-  });
-  it('produces multiple underlying streams as expected', async () => {
-    const chainedIterator = new ChainedIterator(iteratorFromItems([
-      new TestIntegerIterator(SHORT_STREAM_LENGTH),
-      new TestIntegerIterator(SHORT_STREAM_LENGTH),
-      new TestIntegerIterator(SHORT_STREAM_LENGTH)
-    ]));
-
-    const expectedResult: number[] = [];
-    for (let i = 0; i < 3; i++) {
-      for (let j = 0; j < SHORT_STREAM_LENGTH; j++) {
-        expectedResult[i * SHORT_STREAM_LENGTH + j] = j;
-      }
-    }
-
-    const result = await chainedIterator.toArrayForTest();
-    expect(result).toEqual(expectedResult);
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/iterators/file_chunk_iterator.ts b/tfjs-master/tfjs-data/src/iterators/file_chunk_iterator.ts
deleted file mode 100644
index 865c07217..000000000
--- a/tfjs-master/tfjs-data/src/iterators/file_chunk_iterator.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-// inspired by https://github.com/maxogden/filereader-stream
-import {env, util} from '@tensorflow/tfjs-core';
-import {FileElement} from '../types';
-import {ByteChunkIterator} from './byte_chunk_iterator';
-
-export interface FileChunkIteratorOptions {
-  /** The byte offset at which to begin reading the File or Blob. Default 0. */
-  offset?: number;
-  /** The number of bytes to read at a time. Default 1MB. */
-  chunkSize?: number;
-}
-
-/**
- * Provide a stream of chunks from a File, Blob, or Uint8Array.
- * @param file The source File, Blob or Uint8Array.
- * @param options Optional settings controlling file reading.
- * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the
- *   input File, Blob or Uint8Array.
- */
-export class FileChunkIterator extends ByteChunkIterator {
-  offset: number;
-  chunkSize: number;
-
-  constructor(
-      protected file: FileElement,
-      protected options: FileChunkIteratorOptions = {}) {
-    super();
-    util.assert(
-        (file instanceof Uint8Array) ||
-            (env().get('IS_BROWSER') ?
-                 (file instanceof File || file instanceof Blob) :
-                 false),
-        () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +
-            'right now.');
-    this.offset = options.offset || 0;
-    // default 1MB chunk has tolerable perf on large files
-    this.chunkSize = options.chunkSize || 1024 * 1024;
-  }
-
-  summary() {
-    return `FileChunks ${this.file}`;
-  }
-
-  async next(): Promise<IteratorResult<Uint8Array>> {
-    if (this.offset >= ((this.file instanceof Uint8Array) ?
-                            this.file.byteLength :
-                            this.file.size)) {
-      return {value: null, done: true};
-    }
-    const chunk = new Promise<Uint8Array>((resolve, reject) => {
-      const end = this.offset + this.chunkSize;
-      if (this.file instanceof Uint8Array) {
-        // Note if end > this.uint8Array.byteLength, we just get a small last
-        // chunk.
-        resolve(new Uint8Array(this.file.slice(this.offset, end)));
-      } else {
-        // This branch assumes that this.file type is File or Blob, which
-        // means it is in the browser environment.
-
-        // TODO(soergel): is this a performance issue?
-        const fileReader = new FileReader();
-        fileReader.onload = (event) => {
-          let data: string|ArrayBuffer|Uint8Array = fileReader.result;
-          // Not sure we can trust the return type of
-          // FileReader.readAsArrayBuffer See e.g.
-          // https://github.com/node-file-api/FileReader/issues/2
-          if (data instanceof ArrayBuffer) {
-            data = new Uint8Array(data);
-          }
-          if (!(data instanceof Uint8Array)) {
-            return reject(new TypeError('FileReader returned unknown type.'));
-          }
-          resolve(data);
-        };
-        fileReader.onabort = (event) => {
-          return reject(new Error('Aborted'));
-        };
-        fileReader.onerror = (event) => {
-          return reject(new Error(event.type));
-        };
-        // TODO(soergel): better handle onabort, onerror
-        // Note if end > this.file.size, we just get a small last chunk.
-        const slice = this.file.slice(this.offset, end);
-        // We can't use readAsText here (even if we know the file is text)
-        // because the slice boundary may fall within a multi-byte character.
-        fileReader.readAsArrayBuffer(slice);
-      }
-      this.offset = end;
-    });
-    return {value: (await chunk), done: false};
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/iterators/file_chunk_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/file_chunk_iterator_test.ts
deleted file mode 100644
index 4dc028af6..000000000
--- a/tfjs-master/tfjs-data/src/iterators/file_chunk_iterator_test.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-import {FileChunkIterator} from './file_chunk_iterator';
-
-const range = (start: number, end: number) => {
-  return Array.from({length: (end - start)}, (v, k) => k + start);
-};
-
-const testData = env().get('IS_BROWSER') ?
-    new Blob([new Uint8Array(range(0, 55))]) :
-    new Uint8Array(range(0, 55));
-
-describe('FileChunkIterator', () => {
-  it('Reads the entire file and then closes the stream', async () => {
-    const readIterator = new FileChunkIterator(testData, {chunkSize: 10});
-    const result = await readIterator.toArrayForTest();
-    expect(result.length).toEqual(6);
-    const totalBytes = result.map(x => x.length).reduce((a, b) => a + b);
-    expect(totalBytes).toEqual(55);
-  });
-
-  it('Reads chunks in order', async () => {
-    const readIterator = new FileChunkIterator(testData, {chunkSize: 10});
-    const result = await readIterator.toArrayForTest();
-    expect(result[0][0]).toEqual(0);
-    expect(result[1][0]).toEqual(10);
-    expect(result[2][0]).toEqual(20);
-    expect(result[3][0]).toEqual(30);
-    expect(result[4][0]).toEqual(40);
-    expect(result[5][0]).toEqual(50);
-  });
-
-  it('Reads chunks of expected sizes', async () => {
-    const readIterator = new FileChunkIterator(testData, {chunkSize: 10});
-    const result = await readIterator.toArrayForTest();
-    expect(result[0].length).toEqual(10);
-    expect(result[1].length).toEqual(10);
-    expect(result[2].length).toEqual(10);
-    expect(result[3].length).toEqual(10);
-    expect(result[4].length).toEqual(10);
-    expect(result[5].length).toEqual(5);
-  });
-
-  it('Provides mistyped data throws error', () => {
-    expect(() => {
-      // tslint:disable-next-line:no-unused-expression
-      new FileChunkIterator(null);
-    })
-        .toThrowError(
-            'FileChunkIterator only supports File, Blob and Uint8Array right ' +
-            'now.');
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/iterators/lazy_iterator.ts b/tfjs-master/tfjs-data/src/iterators/lazy_iterator.ts
deleted file mode 100644
index 2025a508f..000000000
--- a/tfjs-master/tfjs-data/src/iterators/lazy_iterator.ts
+++ /dev/null
@@ -1,1190 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import * as seedrandom from 'seedrandom';
-
-import {Container} from '../types';
-import {deepClone} from '../util/deep_clone';
-import {deepMapAndAwaitAll, DeepMapAsyncResult, DeepMapResult, deepZip, zipToList} from '../util/deep_map';
-import {GrowingRingBuffer} from '../util/growing_ring_buffer';
-import {RingBuffer} from '../util/ring_buffer';
-
-/**
- * A nested structure of LazyIterators, used as the input to zip().
- */
-export type IteratorContainer = Container<LazyIterator<tf.TensorContainer>>;
-
-// Here we implement a simple asynchronous iterator.
-// This lets us avoid using either third-party stream libraries or
-// recent TypeScript language support requiring polyfills.
-
-/**
- * Create a `LazyIterator` from an array of items.
- */
-export function iteratorFromItems<T>(items: T[]): LazyIterator<T> {
-  return new ArrayIterator(items);
-}
-
-/**
- * Create a `LazyIterator` of incrementing integers.
- */
-export function iteratorFromIncrementing(start: number): LazyIterator<number> {
-  let i = start;
-  return iteratorFromFunction(() => ({value: i++, done: false}));
-}
-
-/**
- * Create a `LazyIterator` from a function.
- *
- * ```js
- * let i = -1;
- * const func = () =>
- *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};
- * const iter = tf.data.iteratorFromFunction(func);
- * await iter.forEachAsync(e => console.log(e));
- * ```
- *
- * @param func A function that produces data on each call.
- */
-export function iteratorFromFunction<T>(
-    func: () =>
-        IteratorResult<T>| Promise<IteratorResult<T>>): LazyIterator<T> {
-  return new FunctionCallIterator(func);
-}
-
-/**
- * Create a `LazyIterator` by concatenating underlying streams, which are
- * themselves provided as a stream.
- *
- * This can also be thought of as a "stream flatten" operation.
- *
- * @param baseIterators A stream of streams to be concatenated.
- * @param baseErrorHandler An optional function that can intercept `Error`s
- *   raised during a `next()` call on the base stream.  This function can decide
- *   whether the error should be propagated, whether the error should be
- *   ignored, or whether the base stream should be terminated.
- */
-export function iteratorFromConcatenated<T>(
-    baseIterators: LazyIterator<LazyIterator<T>>,
-    baseErrorHandler?: (e: Error) => boolean): LazyIterator<T> {
-  return new ChainedIterator(baseIterators, baseErrorHandler);
-}
-
-/**
- * Create a `LazyIterator` by concatenating streams produced by calling a
- * stream-generating function a given number of times.
- *
- * Since a `LazyIterator` is read-once, it cannot be repeated, but this
- * function can be used to achieve a similar effect:
- *
- *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);
- *
- * @param iteratorFunc: A function that produces a new stream on each call.
- * @param count: The number of times to call the function.
- * @param baseErrorHandler An optional function that can intercept `Error`s
- *   raised during a `next()` call on the base stream.  This function can decide
- *   whether the error should be propagated, whether the error should be
- *   ignored, or whether the base stream should be terminated.
- */
-export function iteratorFromConcatenatedFunction<T>(
-    iteratorFunc: () => IteratorResult<LazyIterator<T>>, count: number,
-    baseErrorHandler?: (e: Error) => boolean): LazyIterator<T> {
-  return iteratorFromConcatenated(
-      iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);
-}
-
-/**
- * Create a `LazyIterator` by zipping together an array, dict, or nested
- * structure of `LazyIterator`s (and perhaps additional constants).
- *
- * The underlying streams must provide elements in a consistent order such
- * that they correspond.
- *
- * Typically, the underlying streams should have the same number of
- * elements. If they do not, the behavior is determined by the
- * `mismatchMode` argument.
- *
- * The nested structure of the `iterators` argument determines the
- * structure of elements in the resulting iterator.
- *
- * @param iterators: An array or object containing LazyIterators at the
- * leaves.
- * @param mismatchMode: Determines what to do when one underlying iterator
- * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)
- * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`
- * causes the zipped iterator to terminate with the furst underlying
- * streams, so elements remaining on the longer streams are ignored.
- * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling
- * in nulls for the exhausted streams, until all streams are exhausted.
- */
-export function iteratorFromZipped<O extends tf.TensorContainer>(
-    iterators: IteratorContainer,
-    mismatchMode: ZipMismatchMode = ZipMismatchMode.FAIL): LazyIterator<O> {
-  return new ZipIterator<O>(iterators, mismatchMode);
-}
-
-/**
- * An asynchronous iterator, providing lazy access to a potentially
- * unbounded stream of elements.
- *
- * Iterator can be obtained from a dataset:
- * `const iter = await dataset.iterator();`
- */
-export abstract class LazyIterator<T> {
-  // This class implements AsyncIterator<T>, but we have not yet set the
-  // TypeScript --downlevelIteration flag to enable that.
-
-  abstract summary(): string;
-
-  /**
-   * Returns a `Promise` for the next element in the stream.
-   *
-   * When an item can be provided successfully, the return value is
-   * `{value:T, done:false}`.
-   *
-   * Calling next() on a closed stream returns `{value:null, done:true}`.
-   */
-  abstract next(): Promise<IteratorResult<T>>;
-
-  /**
-   * Collect all remaining elements of a bounded stream into an array.
-   * Obviously this will succeed only for small streams that fit in memory.
-   * Useful for testing.
-   *
-   * @returns A Promise for an array of stream elements, which will resolve
-   *   when the stream is exhausted.
-   */
-  async toArray(): Promise<T[]> {
-    const result: T[] = [];
-    let x = await this.next();
-    while (!x.done) {
-      result.push(x.value);
-      x = await this.next();
-    }
-    return result;
-  }
-
-  /**
-   * Collect all elements of this dataset into an array with prefetching 100
-   * elements. This is useful for testing, because the prefetch changes the
-   * order in which the Promises are resolved along the processing pipeline.
-   * This may help expose bugs where results are dependent on the order of
-   * Promise resolution rather than on the logical order of the stream (i.e.,
-   * due to hidden mutable state).
-   *
-   * @returns A Promise for an array of stream elements, which will resolve
-   *   when the stream is exhausted.
-   */
-  async toArrayForTest(): Promise<T[]> {
-    const stream = this.prefetch(100);
-    const result: T[] = [];
-    let x = await stream.next();
-    while (!x.done) {
-      result.push(x.value);
-      x = await stream.next();
-    }
-    return result;
-  }
-
-  /**
-   * Draw items from the stream until it is exhausted.
-   *
-   * This can be useful when the stream has side effects but no output.  In
-   * that case, calling this function guarantees that the stream will be
-   * fully processed.
-   */
-  async resolveFully(): Promise<void> {
-    let x = await this.next();
-    while (!x.done) {
-      x = await this.next();
-    }
-  }
-
-  /**
-   * Draw items from the stream until it is exhausted, or a predicate fails.
-   *
-   * This can be useful when the stream has side effects but no output.  In
-   * that case, calling this function guarantees that the stream will be
-   * fully processed.
-   */
-  async resolveWhile(predicate: (r: T) => boolean): Promise<void> {
-    let x = await this.next();
-    let shouldContinue = predicate(x.value);
-    while ((!x.done) && shouldContinue) {
-      x = await this.next();
-      shouldContinue = predicate(x.value);
-    }
-  }
-
-  /**
-   * Handles errors thrown on this stream using a provided handler function.
-   *
-   * @param handler A function that handles any `Error` thrown during a `next()`
-   *   call and returns true if the stream should continue (dropping the failed
-   *   call) or false if the stream should quietly terminate.  If the handler
-   *   itself throws (or rethrows) an `Error`, that will be propagated.
-   *
-   * @returns A `LazyIterator` of elements passed through from upstream,
-   *   possibly filtering or terminating on upstream `next()` calls that
-   *   throw an `Error`.
-   */
-  handleErrors(handler: (error: Error) => boolean): LazyIterator<T> {
-    return new ErrorHandlingLazyIterator(this, handler);
-  }
-
-  // TODO(soergel): Implement reduce() etc.
-
-  /**
-   * Filters this stream according to `predicate`.
-   *
-   * @param predicate A function mapping a stream element to a boolean or a
-   * `Promise` for one.
-   *
-   * @returns A `LazyIterator` of elements for which the predicate was true.
-   */
-  filter(predicate: (value: T) => boolean): LazyIterator<T> {
-    return new FilterIterator(this, predicate);
-  }
-
-  /**
-   * Maps this stream through a 1-to-1 transform.
-   *
-   * @param transform A function mapping a stream element to a transformed
-   *   element.
-   *
-   * @returns A `LazyIterator` of transformed elements.
-   */
-  map<O>(transform: (value: T) => O): LazyIterator<O> {
-    return new MapIterator(this, transform);
-  }
-
-  /**
-   * Maps this stream through an async 1-to-1 transform.
-   *
-   * @param transform A function mapping a stream element to a `Promise` for a
-   *   transformed stream element.
-   *
-   * @returns A `LazyIterator` of transformed elements.
-   */
-  mapAsync<O>(transform: (value: T) => Promise<O>): LazyIterator<O> {
-    return new AsyncMapIterator(this, transform);
-  }
-
-  /**
-   * Maps this stream through a 1-to-1 transform, forcing serial execution.
-   *
-   * @param transform A function mapping a stream element to a transformed
-   *   element.
-   *
-   * @returns A `LazyIterator` of transformed elements.
-   */
-  serialMapAsync<O>(transform: (value: T) => Promise<O>): LazyIterator<O> {
-    return new AsyncMapIterator(this, transform).serial();
-  }
-
-  /**
-   * Maps this stream through a 1-to-many transform.
-   *
-   * @param transform A function mapping a stream element to an array of
-   *   transformed elements.
-   *
-   * @returns A `DataStream` of transformed elements.
-   */
-  flatmap<O>(transform: (value: T) => O[]): LazyIterator<O> {
-    return new FlatmapIterator(this, transform);
-  }
-
-  /**
-   * Apply a function to every element of the stream.
-   *
-   * @param f A function to apply to each stream element.
-   */
-  async forEachAsync(f: (value: T) => void): Promise<void> {
-    return this.map(f).resolveFully();
-  }
-
-  /**
-   * Apply a function to every element of the stream, forcing serial execution.
-   *
-   * @param f A function to apply to each stream element.  Should return 'true'
-   *   to indicate that the stream should continue, or 'false' to cause it to
-   *   terminate.
-   */
-  async serialForEach(f: (value: T) => Promise<boolean>): Promise<void> {
-    return this.serialMapAsync(f).resolveWhile(x => (x === true));
-  }
-
-  /**
-   * Groups elements into batches, represented as arrays of elements.
-   *
-   * We can think of the elements of this iterator as 'rows' (even if they are
-   * nested structures).  By the same token, consecutive values for a given
-   * key within the elements form a 'column'.  This matches the usual sense of
-   * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).
-   *
-   * Thus, "Row-major" means that the resulting batch is simply a collection of
-   * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major
-   * form, which is needed for vectorized computation.
-   *
-   * @param batchSize The number of elements desired per batch.
-   * @param smallLastBatch Whether to emit the final batch when it has fewer
-   *   than batchSize elements. Default true.
-   * @returns A `LazyIterator` of batches of elements, represented as arrays
-   *   of the original element type.
-   */
-  rowMajorBatch(batchSize: number, smallLastBatch = true): LazyIterator<T[]> {
-    return new RowMajorBatchIterator(this, batchSize, smallLastBatch);
-  }
-
-  /**
-   * Groups elements into batches, represented in column-major form.
-   *
-   * We can think of the elements of this iterator as 'rows' (even if they are
-   * nested structures).  By the same token, consecutive values for a given
-   * key within the elements form a 'column'.  This matches the usual sense of
-   * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).
-   *
-   * Thus, "column-major" means that the resulting batch is a (potentially
-   * nested) structure representing the columns.  Each column entry, then,
-   * contains a collection of the values found in that column for a range of
-   * input elements.  This representation allows for vectorized computation, in
-   * contrast to the row-major form.
-   *
-   * The inputs should all have the same nested structure (i.e., of arrays and
-   * dicts).  The result is a single object with the same nested structure,
-   * where the leaves are arrays collecting the values of the inputs at that
-   * location (or, optionally, the result of a custom function applied to those
-   * arrays).
-   *
-   * @param batchSize The number of elements desired per batch.
-   * @param smallLastBatch Whether to emit the final batch when it has fewer
-   *   than batchSize elements. Default true.
-   * @param zipFn: (optional) A function that expects an array of elements at a
-   *   single node of the object tree, and returns a `DeepMapResult`.  The
-   *   `DeepMapResult` either provides a result value for that node (i.e.,
-   *   representing the subtree), or indicates that the node should be processed
-   *   recursively.  The default zipFn recurses as far as possible and places
-   *   arrays at the leaves.
-   * @returns A `LazyIterator` of batches of elements, represented as an object
-   *   with collections at the leaves.
-   */
-  columnMajorBatch(
-      batchSize: number, smallLastBatch = true,
-      // tslint:disable-next-line:no-any
-      zipFn: (xs: any[]) => DeepMapResult = zipToList):
-      LazyIterator<tf.TensorContainer> {
-    // First collect the desired number of input elements as a row-major batch.
-    const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);
-    // Now 'rotate' or 'pivot' the data, collecting all values from each column
-    // in the batch (i.e., for each key within the elements) into an array.
-    return rowBatches.map(x => deepZip(x, zipFn));
-  }
-
-  /**
-   * Concatenate this `LazyIterator` with another.
-   *
-   * @param iterator A `LazyIterator` to be concatenated onto this one.
-   * @param baseErrorHandler An optional function that can intercept `Error`s
-   *   raised during a `next()` call on the base stream.  This function can
-   *   decide whether the error should be propagated, whether the error should
-   *   be ignored, or whether the base stream should be terminated.
-   * @returns A `LazyIterator`.
-   */
-  concatenate(
-      iterator: LazyIterator<T>,
-      baseErrorHandler?: (e: Error) => boolean): LazyIterator<T> {
-    return new ChainedIterator(
-        iteratorFromItems([this, iterator]), baseErrorHandler);
-  }
-
-  /**
-   * Limits this stream to return at most `count` items.
-   *
-   * @param count The maximum number of items to provide from the stream. If
-   * a negative or undefined value is given, the entire stream is returned
-   *   unaltered.
-   */
-  take(count: number): LazyIterator<T> {
-    if (count < 0 || count == null) {
-      return this;
-    }
-    return new TakeIterator(this, count);
-  }
-
-  /**
-   * Skips the first `count` items in this stream.
-   *
-   * @param count The number of items to skip.  If a negative or undefined
-   * value is given, the entire stream is returned unaltered.
-   */
-  skip(count: number): LazyIterator<T> {
-    if (count < 0 || count == null) {
-      return this;
-    }
-    return new SkipIterator(this, count);
-  }
-
-  /**
-   * Prefetch the first `bufferSize` items in this stream.
-   *
-   * Note this prefetches Promises, but makes no guarantees about when those
-   * Promises resolve.
-   *
-   * @param bufferSize: An integer specifying the number of elements to be
-   *   prefetched.
-   */
-  prefetch(bufferSize: number): LazyIterator<T> {
-    return new PrefetchIterator(this, bufferSize);
-  }
-
-  // TODO(soergel): deep sharded shuffle, where supported
-
-  /**
-   * Randomly shuffles the elements of this stream.
-   *
-   * @param bufferSize: An integer specifying the number of elements from
-   * this stream from which the new stream will sample.
-   * @param seed: (Optional.) An integer specifying the random seed that
-   * will be used to create the distribution.
-   */
-  shuffle(windowSize: number, seed?: string): LazyIterator<T> {
-    return new ShuffleIterator(this, windowSize, seed);
-  }
-
-  /**
-   * Force an iterator to execute serially: each next() call will await the
-   * prior one, so that they cannot execute concurrently.
-   */
-  serial(): LazyIterator<T> {
-    return new SerialIterator(this);
-  }
-}
-
-// ============================================================================
-// The following private classes serve to implement the chainable methods
-// on LazyIterator.  Unfortunately they can't be placed in separate files,
-// due to resulting trouble with circular imports.
-// ============================================================================
-
-// Iterators that just extend LazyIterator directly
-// ============================================================================
-
-class ArrayIterator<T> extends LazyIterator<T> {
-  private trav = 0;
-  constructor(protected items: T[]) {
-    super();
-  }
-
-  summary() {
-    return `Array of ${this.items.length} items`;
-  }
-
-  async next(): Promise<IteratorResult<T>> {
-    if (this.trav >= this.items.length) {
-      return {value: null, done: true};
-    }
-    const item = this.items[this.trav];
-    this.trav++;
-    return {value: deepClone(item), done: false};
-  }
-}
-
-class FunctionCallIterator<T> extends LazyIterator<T> {
-  constructor(
-      protected nextFn: () => IteratorResult<T>| Promise<IteratorResult<T>>) {
-    super();
-  }
-
-  summary() {
-    return `Function call`;
-  }
-
-  async next(): Promise<IteratorResult<T>> {
-    try {
-      return this.nextFn();
-    } catch (e) {
-      // Modify the error message but leave the stack trace intact
-      e.message =
-          `Error thrown while iterating through a dataset: ${e.message}`;
-      throw e;
-    }
-  }
-}
-
-class SerialIterator<T> extends LazyIterator<T> {
-  // Strict Promise execution order:
-  // a next() call may not even begin until the previous one completes.
-  private lastRead: Promise<IteratorResult<T>>;
-
-  constructor(protected upstream: LazyIterator<T>) {
-    super();
-    this.lastRead = Promise.resolve({value: null, done: false});
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> Serial`;
-  }
-
-  async next(): Promise<IteratorResult<T>> {
-    // This sets this.lastRead to a new Promise right away, as opposed to
-    // saying `await this.lastRead; this.lastRead = this.serialNext();` which
-    // would not work because this.nextRead would be updated only after the
-    // promise resolves.
-    this.lastRead = this.lastRead.then(() => this.serialNext());
-    return this.lastRead;
-  }
-
-  private async serialNext(): Promise<IteratorResult<T>> {
-    return this.upstream.next();
-  }
-}
-
-class SkipIterator<T> extends LazyIterator<T> {
-  // Strict Promise execution order:
-  // a next() call may not even begin until the previous one completes.
-  private lastRead: Promise<IteratorResult<T>>;
-
-  // Local state that should not be clobbered by out-of-order execution.
-  count = 0;
-
-  constructor(protected upstream: LazyIterator<T>, protected maxCount: number) {
-    super();
-    this.lastRead = Promise.resolve({value: null, done: false});
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> Skip`;
-  }
-
-  async next(): Promise<IteratorResult<T>> {
-    // This sets this.lastRead to a new Promise right away, as opposed to
-    // saying `await this.lastRead; this.lastRead = this.serialNext();` which
-    // would not work because this.nextRead would be updated only after the
-    // promise resolves.
-    this.lastRead = this.lastRead.then(() => this.serialNext());
-    return this.lastRead;
-  }
-
-  private async serialNext(): Promise<IteratorResult<T>> {
-    // TODO(soergel): consider tradeoffs of reading in parallel, eg.
-    // collecting next() promises in an Array and then waiting for
-    // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:
-    // maybe delayed GC.
-    while (this.count++ < this.maxCount) {
-      const skipped = await this.upstream.next();
-      // short-circuit if upstream is already empty
-      if (skipped.done) {
-        return skipped;
-      }
-      tf.dispose(skipped.value as {});
-    }
-    return this.upstream.next();
-  }
-}
-
-class TakeIterator<T> extends LazyIterator<T> {
-  count = 0;
-  constructor(protected upstream: LazyIterator<T>, protected maxCount: number) {
-    super();
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> Take`;
-  }
-
-  async next(): Promise<IteratorResult<T>> {
-    if (this.count++ >= this.maxCount) {
-      return {value: null, done: true};
-    }
-    return this.upstream.next();
-  }
-}
-
-// Note this batch just groups items into row-wise element arrays.
-// Rotating these to a column-wise representation happens only at the dataset
-// level.
-class RowMajorBatchIterator<T> extends LazyIterator<T[]> {
-  // Strict Promise execution order:
-  // a next() call may not even begin until the previous one completes.
-  private lastRead: Promise<IteratorResult<T[]>>;
-
-  constructor(
-      protected upstream: LazyIterator<T>, protected batchSize: number,
-      protected enableSmallLastBatch = true) {
-    super();
-    this.lastRead = Promise.resolve({value: null, done: false});
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> RowMajorBatch`;
-  }
-
-  async next(): Promise<IteratorResult<T[]>> {
-    // This sets this.lastRead to a new Promise right away, as opposed to
-    // saying `await this.lastRead; this.lastRead = this.serialNext();` which
-    // would not work because this.nextRead would be updated only after the
-    // promise resolves.
-    this.lastRead = this.lastRead.then(() => this.serialNext());
-    return this.lastRead;
-  }
-
-  private async serialNext(): Promise<IteratorResult<T[]>> {
-    const batch: T[] = [];
-    while (batch.length < this.batchSize) {
-      const item = await this.upstream.next();
-      if (item.done) {
-        if (this.enableSmallLastBatch && batch.length > 0) {
-          return {value: batch, done: false};
-        }
-        return {value: null, done: true};
-      }
-      batch.push(item.value);
-    }
-    return {value: batch, done: false};
-  }
-}
-
-class FilterIterator<T> extends LazyIterator<T> {
-  // Strict Promise execution order:
-  // a next() call may not even begin until the previous one completes.
-  private lastRead: Promise<IteratorResult<T>>;
-
-  constructor(
-      protected upstream: LazyIterator<T>,
-      protected predicate: (value: T) => boolean) {
-    super();
-    this.lastRead = Promise.resolve({value: null, done: false});
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> Filter`;
-  }
-
-  async next(): Promise<IteratorResult<T>> {
-    // This sets this.lastRead to a new Promise right away, as opposed to
-    // saying `await this.lastRead; this.lastRead = this.serialNext();` which
-    // would not work because this.nextRead would be updated only after the
-    // promise resolves.
-    this.lastRead = this.lastRead.then(() => this.serialNext());
-    return this.lastRead;
-  }
-
-  private async serialNext(): Promise<IteratorResult<T>> {
-    while (true) {
-      const item = await this.upstream.next();
-      if (item.done || this.predicate(item.value)) {
-        return item;
-      }
-      tf.dispose(item.value as {});
-    }
-  }
-}
-
-class MapIterator<I, O> extends LazyIterator<O> {
-  constructor(
-      protected upstream: LazyIterator<I>,
-      protected transform: (value: I) => O) {
-    super();
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> Map`;
-  }
-
-  async next(): Promise<IteratorResult<O>> {
-    const item = await this.upstream.next();
-    if (item.done) {
-      return {value: null, done: true};
-    }
-    const inputTensors = tf.tensor_util.getTensorsInContainer(item.value as {});
-    // Careful: the transform may mutate the item in place.
-    // That's why we have to remember the input Tensors above, and then
-    // below dispose only those that were not passed through to the output.
-    // Note too that the transform function is responsible for tidying
-    // any intermediate Tensors.  Here we are concerned only about the
-    // inputs.
-    const mapped = this.transform(item.value);
-    const outputTensors = tf.tensor_util.getTensorsInContainer(mapped as {});
-
-    // TODO(soergel) faster intersection
-    // TODO(soergel) move to tf.disposeExcept(in, out)?
-    for (const t of inputTensors) {
-      if (!tf.tensor_util.isTensorInList(t, outputTensors)) {
-        t.dispose();
-      }
-    }
-    return {value: mapped, done: false};
-  }
-}
-
-class ErrorHandlingLazyIterator<T> extends LazyIterator<T> {
-  count = 0;
-  constructor(
-      protected upstream: LazyIterator<T>,
-      protected handler: (error: Error) => boolean) {
-    super();
-    this.lastRead = Promise.resolve({value: null, done: false});
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> handleErrors`;
-  }
-
-  // Strict Promise execution order:
-  // a next() call may not even begin until the previous one completes.
-  private lastRead: Promise<IteratorResult<T>>;
-
-  async next(): Promise<IteratorResult<T>> {
-    // This sets this.lastRead to a new Promise right away, as opposed to
-    // saying `await this.lastRead; this.lastRead = this.serialNext();` which
-    // would not work because this.nextRead would be updated only after the
-    // promise resolves.
-    this.lastRead = this.lastRead.then(() => this.serialNext());
-    return this.lastRead;
-  }
-
-  async serialNext(): Promise<IteratorResult<T>> {
-    while (true) {
-      try {
-        return await this.upstream.next();
-      } catch (e) {
-        if (!this.handler(e)) {
-          return {value: null, done: true};
-        }
-        // If the handler returns true, loop and fetch the next upstream item.
-
-        // If the upstream iterator throws an endless stream of errors, and if
-        // the handler says to ignore them, then we loop forever here.  That is
-        // the correct behavior-- it's up to the handler to decide when to stop.
-      }
-    }
-  }
-}
-
-class AsyncMapIterator<I, O> extends LazyIterator<O> {
-  constructor(
-      protected upstream: LazyIterator<I>,
-      protected transform: (value: I) => Promise<O>) {
-    super();
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> AsyncMap`;
-  }
-
-  async next(): Promise<IteratorResult<O>> {
-    const item = await this.upstream.next();
-    if (item.done) {
-      return {value: null, done: true};
-    }
-    const inputTensors = tf.tensor_util.getTensorsInContainer(item.value as {});
-    // Careful: the transform may mutate the item in place.
-    // That's why we have to remember the input Tensors above, and then
-    // below dispose only those that were not passed through to the output.
-    // Note too that the transform function is responsible for tidying
-    // any intermediate Tensors.  Here we are concerned only about the
-    // inputs.
-    const mapped = await this.transform(item.value);
-    const outputTensors = tf.tensor_util.getTensorsInContainer(mapped as {});
-
-    // TODO(soergel) faster intersection
-    // TODO(soergel) move to tf.disposeExcept(in, out)?
-    for (const t of inputTensors) {
-      if (!tf.tensor_util.isTensorInList(t, outputTensors)) {
-        t.dispose();
-      }
-    }
-    return {value: mapped, done: false};
-  }
-}
-
-// Iterators that maintain a queue of pending items
-// ============================================================================
-
-/**
- * A base class for transforming streams that operate by maintaining an
- * output queue of elements that are ready to return via next().  This is
- * commonly required when the transformation is 1-to-many:  A call to next()
- * may trigger a call to the underlying stream, which will produce many
- * mapped elements of this stream-- of which we need to return only one, so
- * we have to queue the rest.
- */
-export abstract class OneToManyIterator<T> extends LazyIterator<T> {
-  // Strict Promise execution order:
-  // a next() call may not even begin until the previous one completes.
-  private lastRead: Promise<IteratorResult<T>>;
-
-  // Local state that should not be clobbered by out-of-order execution.
-  protected outputQueue: RingBuffer<T>;
-
-  constructor() {
-    super();
-    this.outputQueue = new GrowingRingBuffer<T>();
-    this.lastRead = Promise.resolve({value: null, done: false});
-  }
-
-  async next(): Promise<IteratorResult<T>> {
-    // This sets this.lastRead to a new Promise right away, as opposed to
-    // saying `await this.lastRead; this.lastRead = this.serialNext();` which
-    // would not work because this.nextRead would be updated only after the
-    // promise resolves.
-    this.lastRead = this.lastRead.then(() => this.serialNext());
-    return this.lastRead;
-  }
-
-  /**
-   * Read one or more chunks from upstream and process them, possibly
-   * reading or writing a carryover, and adding processed items to the
-   * output queue.  Note it's possible that no items are added to the queue
-   * on a given pump() call, even if the upstream stream is not closed
-   * (e.g., because items are filtered).
-   *
-   * @return `true` if any action was taken, i.e. fetching items from the
-   *   upstream source OR adding items to the output queue.  `false` if the
-   *   upstream source is exhausted AND nothing was added to the queue
-   * (i.e., any remaining carryover).
-   */
-  protected abstract pump(): Promise<boolean>;
-
-  async serialNext(): Promise<IteratorResult<T>> {
-    // Fetch so that the queue contains at least one item if possible.
-    // If the upstream source is exhausted, AND there are no items left in
-    // the output queue, then this stream is also exhausted.
-    while (this.outputQueue.length() === 0) {
-      // TODO(soergel): consider parallel reads.
-      if (!await this.pump()) {
-        return {value: null, done: true};
-      }
-    }
-    return {value: this.outputQueue.shift(), done: false};
-  }
-}
-class FlatmapIterator<I, O> extends OneToManyIterator<O> {
-  constructor(
-      protected upstream: LazyIterator<I>,
-      protected transform: (value: I) => O[]) {
-    super();
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> Flatmap`;
-  }
-
-  async pump(): Promise<boolean> {
-    const item = await this.upstream.next();
-    if (item.done) {
-      return false;
-    }
-    const inputTensors = tf.tensor_util.getTensorsInContainer(item.value as {});
-    // Careful: the transform may mutate the item in place.
-    // that's why we have to remember the input Tensors above, and then
-    // below dispose only those that were not passed through to the output.
-    // Note too that the transform function is responsible for tidying any
-    // intermediate Tensors.  Here we are concerned only about the inputs.
-    const mappedArray = this.transform(item.value);
-    const outputTensors =
-        tf.tensor_util.getTensorsInContainer(mappedArray as {});
-    this.outputQueue.pushAll(mappedArray);
-
-    // TODO(soergel) faster intersection, and deduplicate outputTensors
-    // TODO(soergel) move to tf.disposeExcept(in, out)?
-    for (const t of inputTensors) {
-      if (!tf.tensor_util.isTensorInList(t, outputTensors)) {
-        t.dispose();
-      }
-    }
-
-    return true;
-  }
-}
-
-/**
- * Provides a `LazyIterator` that concatenates a stream of underlying
- * streams.
- *
- * Doing this in a concurrency-safe way requires some trickery.  In
- * particular, we want this stream to return the elements from the
- * underlying streams in the correct order according to when next() was
- * called, even if the resulting Promises resolve in a different order.
- */
-export class ChainedIterator<T> extends LazyIterator<T> {
-  // Strict Promise execution order:
-  // a next() call may not even begin until the previous one completes.
-  private lastRead: Promise<IteratorResult<T>> = null;
-
-  // Local state that should not be clobbered by out-of-order execution.
-  private iterator: LazyIterator<T> = null;
-  private moreIterators: LazyIterator<LazyIterator<T>>;
-
-  constructor(
-      iterators: LazyIterator<LazyIterator<T>>,
-      private readonly baseErrorHandler?: (e: Error) => boolean) {
-    super();
-    this.moreIterators = iterators;
-  }
-
-  summary() {
-    const upstreamSummaries = 'TODO: fill in upstream of chained summaries';
-    return `${upstreamSummaries} -> Chained`;
-  }
-
-  async next(): Promise<IteratorResult<T>> {
-    this.lastRead = this.readFromChain(this.lastRead);
-    return this.lastRead;
-  }
-
-  private async readFromChain(lastRead: Promise<IteratorResult<T>>):
-      Promise<IteratorResult<T>> {
-    // Must await on the previous read since the previous read may have advanced
-    // the stream of streams, from which we need to read.
-    // This is unfortunate since we can't parallelize reads. Which means
-    // prefetching of chained streams is a no-op.
-    // One solution is to prefetch immediately upstream of this.
-    await lastRead;
-    if (this.iterator == null) {
-      const iteratorResult = await this.moreIterators.next();
-      if (iteratorResult.done) {
-        // No more streams to stream from.
-        return {value: null, done: true};
-      }
-      this.iterator = iteratorResult.value;
-      if (this.baseErrorHandler != null) {
-        this.iterator = this.iterator.handleErrors(this.baseErrorHandler);
-      }
-    }
-    const itemResult = await this.iterator.next();
-    if (itemResult.done) {
-      this.iterator = null;
-      return this.readFromChain(lastRead);
-    }
-    return itemResult;
-  }
-}
-
-export enum ZipMismatchMode {
-  FAIL,      // require zipped streams to have the same length
-  SHORTEST,  // terminate zip when the first stream is exhausted
-  LONGEST    // use nulls for exhausted streams; use up the longest stream.
-}
-
-/**
- * Provides a `LazyIterator` that zips together an array, dict, or nested
- * structure of `LazyIterator`s (and perhaps additional constants).
- *
- * The underlying streams must provide elements in a consistent order such
- * that they correspond.
- *
- * Typically, the underlying streams should have the same number of
- * elements. If they do not, the behavior is determined by the
- * `mismatchMode` argument.
- *
- * The nested structure of the `iterators` argument determines the
- * structure of elements in the resulting iterator.
- *
- * Doing this in a concurrency-safe way requires some trickery.  In
- * particular, we want this stream to return the elements from the
- * underlying streams in the correct order according to when next() was
- * called, even if the resulting Promises resolve in a different order.
- *
- * @param iterators: An array or object containing LazyIterators at the
- * leaves.
- * @param mismatchMode: Determines what to do when one underlying iterator
- * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)
- * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`
- * causes the zipped iterator to terminate with the furst underlying
- * streams, so elements remaining on the longer streams are ignored.
- * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling
- * in nulls for the exhausted streams, until all streams are exhausted.
- */
-class ZipIterator<O extends tf.TensorContainer> extends LazyIterator<O> {
-  private count = 0;
-  private currentPromise: Promise<IteratorResult<O>> = null;
-
-  constructor(
-      protected readonly iterators: IteratorContainer,
-      protected readonly mismatchMode: ZipMismatchMode = ZipMismatchMode.FAIL) {
-    super();
-  }
-
-  summary() {
-    const upstreamSummaries = 'TODO: fill in upstream of zip summaries';
-    return `{${upstreamSummaries}} -> Zip`;
-  }
-
-  private async nextState(afterState: Promise<IteratorResult<O>>):
-      Promise<IteratorResult<O>> {
-    // This chaining ensures that the underlying next() are not even called
-    // before the previous ones have resolved.
-    await afterState;
-
-    // Collect underlying iterator "done" signals as a side effect in
-    // getNext()
-    let numIterators = 0;
-    let iteratorsDone = 0;
-
-    function getNext(container: IteratorContainer): DeepMapAsyncResult {
-      if (container instanceof LazyIterator) {
-        const result = container.next();
-        return {
-          value: result.then(x => {
-            numIterators++;
-            if (x.done) {
-              iteratorsDone++;
-            }
-            return x.value;
-          }),
-          recurse: false
-        };
-      } else {
-        return {value: null, recurse: true};
-      }
-    }
-
-    const mapped: O = await deepMapAndAwaitAll(this.iterators, getNext);
-
-    if (numIterators === iteratorsDone) {
-      // The streams have all ended.
-      return {value: null, done: true};
-    }
-    if (iteratorsDone > 0) {
-      switch (this.mismatchMode) {
-        case ZipMismatchMode.FAIL:
-          throw new Error(
-              'Zipped streams should have the same length. ' +
-              `Mismatched at element ${this.count}.`);
-        case ZipMismatchMode.SHORTEST:
-          return {value: null, done: true};
-        case ZipMismatchMode.LONGEST:
-        default:
-          // Continue.  The exhausted streams already produced value: null.
-      }
-    }
-
-    this.count++;
-    return {value: mapped, done: false};
-  }
-
-  async next(): Promise<IteratorResult<O>> {
-    this.currentPromise = this.nextState(this.currentPromise);
-    return this.currentPromise;
-  }
-}
-
-// Iterators that maintain a ring buffer of pending promises
-// ============================================================================
-
-/**
- * A stream that prefetches a given number of items from an upstream source,
- * returning them in FIFO order.
- *
- * Note this prefetches Promises, but makes no guarantees about when those
- * Promises resolve.
- */
-export class PrefetchIterator<T> extends LazyIterator<T> {
-  protected buffer: RingBuffer<Promise<IteratorResult<T>>>;
-
-  constructor(
-      protected upstream: LazyIterator<T>, protected bufferSize: number) {
-    super();
-    this.buffer = new RingBuffer<Promise<IteratorResult<T>>>(bufferSize);
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> Prefetch`;
-  }
-
-  /**
-   * Refill the prefetch buffer.  Returns only after the buffer is full, or
-   * the upstream source is exhausted.
-   */
-  protected refill() {
-    while (!this.buffer.isFull()) {
-      const v = this.upstream.next();
-      this.buffer.push(v);
-    }
-  }
-
-  next(): Promise<IteratorResult<T>> {
-    this.refill();
-    // This shift will never throw an error because the buffer is always
-    // full after a refill. If the stream is exhausted, the buffer will be
-    // full of Promises that will resolve to the end-of-stream signal.
-    return this.buffer.shift();
-  }
-}
-
-/**
- * A stream that performs a sliding-window random shuffle on an upstream
- * source. This is like a `PrefetchIterator` except that the items are
- * returned in randomized order.  Mixing naturally improves as the buffer
- * size increases.
- */
-export class ShuffleIterator<T> extends PrefetchIterator<T> {
-  private readonly random: seedrandom.prng;
-
-  // Strict Promise execution order:
-  // a next() call may not even begin until the previous one completes.
-  private lastRead: Promise<IteratorResult<T>>;
-
-  // Local state that should not be clobbered by out-of-order execution.
-  private upstreamExhausted = false;
-
-  constructor(
-    protected override upstream: LazyIterator<T>, protected windowSize: number,
-      seed?: string) {
-    super(upstream, windowSize);
-    this.random = seedrandom.alea(seed || tf.util.now().toString());
-    this.lastRead = Promise.resolve({value: null, done: false});
-  }
-
-  override async next(): Promise<IteratorResult<T>> {
-    // This sets this.lastRead to a new Promise right away, as opposed to
-    // saying `await this.lastRead; this.lastRead = this.serialNext();` which
-    // would not work because this.nextRead would be updated only after the
-    // promise resolves.
-    this.lastRead = this.lastRead.then(() => this.serialNext());
-    return this.lastRead;
-  }
-
-  private randomInt(max: number) {
-    return Math.floor(this.random() * max);
-  }
-
-  protected chooseIndex(): number {
-    return this.randomInt(this.buffer.length());
-  }
-
-  async serialNext(): Promise<IteratorResult<T>> {
-    // TODO(soergel): consider performance
-    if (!this.upstreamExhausted) {
-      this.refill();
-    }
-    while (!this.buffer.isEmpty()) {
-      const chosenIndex = this.chooseIndex();
-      const result = await this.buffer.shuffleExcise(chosenIndex);
-      if (result.done) {
-        this.upstreamExhausted = true;
-      } else {
-        this.refill();
-        return result;
-      }
-    }
-    return {value: null, done: true};
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/iterators/lazy_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/lazy_iterator_test.ts
deleted file mode 100644
index ccfa1115d..000000000
--- a/tfjs-master/tfjs-data/src/iterators/lazy_iterator_test.ts
+++ /dev/null
@@ -1,500 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {TensorContainer, TensorContainerArray, TensorContainerObject} from '@tensorflow/tfjs-core';
-
-import {iteratorFromConcatenated, iteratorFromConcatenatedFunction, iteratorFromFunction, iteratorFromIncrementing, iteratorFromItems, iteratorFromZipped, LazyIterator, ZipMismatchMode} from './lazy_iterator';
-
-export class TestIntegerIterator extends LazyIterator<number> {
-  currentIndex = 0;
-  data: number[];
-
-  constructor(protected readonly length = 100) {
-    super();
-    this.data = Array.from({length}, (v, k) => k);
-  }
-
-  summary() {
-    return `TestIntegers`;
-  }
-
-  async next(): Promise<IteratorResult<number>> {
-    if (this.currentIndex >= this.length) {
-      return {value: null, done: true};
-    }
-    const result = this.data[this.currentIndex];
-    this.currentIndex++;
-    // Sleep for a millisecond every so often.
-    // This purposely scrambles the order in which these promises are resolved,
-    // to demonstrate that the various methods still process the stream
-    // in the correct order.
-    if (Math.random() < 0.1) {
-      await new Promise(res => setTimeout(res, 1));
-    }
-    return {value: result, done: false};
-  }
-}
-
-describe('LazyIterator', () => {
-  it('collects all stream elements into an array', async () => {
-    const readIterator = new TestIntegerIterator();
-    const result = await readIterator.toArrayForTest();
-    expect(result.length).toEqual(100);
-  });
-
-  it('reads chunks in order', async () => {
-    const readIterator = new TestIntegerIterator();
-    const result = await readIterator.toArrayForTest();
-    expect(result.length).toEqual(100);
-    for (let i = 0; i < 100; i++) {
-      expect(result[i]).toEqual(i);
-    }
-  });
-
-  it('filters elements', async () => {
-    const readIterator = new TestIntegerIterator().filter(x => x % 2 === 0);
-    const result = await readIterator.toArrayForTest();
-    expect(result.length).toEqual(50);
-    for (let i = 0; i < 50; i++) {
-      expect(result[i]).toEqual(2 * i);
-    }
-  });
-
-  it('maps elements', async () => {
-    const readIterator = new TestIntegerIterator().map(x => `item ${x}`);
-    const result = await readIterator.toArrayForTest();
-    expect(result.length).toEqual(100);
-    for (let i = 0; i < 100; i++) {
-      expect(result[i]).toEqual(`item ${i}`);
-    }
-  });
-
-  it('maps elements through an async function', async () => {
-    const readIterator = new TestIntegerIterator().mapAsync(async x => {
-      // Sleep for a millisecond every so often.
-      // This purposely scrambles the order in which these promises are
-      // resolved, to demonstrate that we still process the
-      // stream in the correct order.
-      if (Math.random() < 0.1) {
-        await new Promise(res => setTimeout(res, 1));
-      }
-      return Promise.resolve(`item ${x}`);
-    });
-    // It's important to prefetch in order to test the promise randomization
-    // above.  Note collect() already prefetches by default, but here we do it
-    // explicitly anyway just to be extra clear.
-    const result = await readIterator.prefetch(200).toArray();
-    expect(result.length).toEqual(100);
-    for (let i = 0; i < 100; i++) {
-      expect(result[i]).toEqual(`item ${i}`);
-    }
-  });
-
-  it('flatmaps simple elements', async () => {
-    const readStream = new TestIntegerIterator().flatmap(
-        x => [`item ${x} A`, `item ${x} B`, `item ${x} C`]);
-    const result = await readStream.toArrayForTest();
-    expect(result.length).toEqual(300);
-    for (let i = 0; i < 100; i++) {
-      expect(result[3 * i + 0]).toEqual(`item ${i} A`);
-      expect(result[3 * i + 1]).toEqual(`item ${i} B`);
-      expect(result[3 * i + 2]).toEqual(`item ${i} C`);
-    }
-  });
-
-  it('flatmap flattens object elements but not their contents', async () => {
-    const readStream = new TestIntegerIterator().flatmap(
-        x =>
-            [{foo: `foo ${x} A`, bar: `bar ${x} A`},
-             {foo: `foo ${x} B`, bar: `bar ${x} B`},
-             {foo: `foo ${x} C`, bar: `bar ${x} C`},
-    ]);
-    const result = await readStream.toArrayForTest();
-    expect(result.length).toEqual(300);
-    for (let i = 0; i < 100; i++) {
-      expect(result[3 * i + 0]).toEqual({foo: `foo ${i} A`, bar: `bar ${i} A`});
-      expect(result[3 * i + 1]).toEqual({foo: `foo ${i} B`, bar: `bar ${i} B`});
-      expect(result[3 * i + 2]).toEqual({foo: `foo ${i} C`, bar: `bar ${i} C`});
-    }
-  });
-
-  it('flatmap flattens array elements but not their contents', async () => {
-    const readStream = new TestIntegerIterator().flatmap(
-        x => [
-            [`foo ${x} A`, `bar ${x} A`],
-            [`foo ${x} B`, `bar ${x} B`],
-            [`foo ${x} C`, `bar ${x} C`],
-    ]);
-    const result = await readStream.toArrayForTest();
-    expect(result.length).toEqual(300);
-    for (let i = 0; i < 100; i++) {
-      expect(result[3 * i + 0]).toEqual([`foo ${i} A`, `bar ${i} A`]);
-      expect(result[3 * i + 1]).toEqual([`foo ${i} B`, `bar ${i} B`]);
-      expect(result[3 * i + 2]).toEqual([`foo ${i} C`, `bar ${i} C`]);
-    }
-  });
-
-  it('batches elements to a row-major representation', async () => {
-    const readIterator = new TestIntegerIterator().rowMajorBatch(8);
-    const result = await readIterator.toArrayForTest();
-    expect(result.length).toEqual(13);
-    for (let i = 0; i < 12; i++) {
-      expect(result[i]).toEqual(Array.from({length: 8}, (v, k) => (i * 8) + k));
-    }
-    expect(result[12]).toEqual([96, 97, 98, 99]);
-  });
-
-  it('batches elements to a column-major representation', async () => {
-    const readIterator = new TestIntegerIterator().columnMajorBatch(8);
-    const result = await readIterator.toArrayForTest();
-    expect(result.length).toEqual(13);
-    for (let i = 0; i < 12; i++) {
-      expect(result[i]).toEqual(Array.from({length: 8}, (v, k) => (i * 8) + k));
-    }
-    expect(result[12]).toEqual([96, 97, 98, 99]);
-  });
-
-  it('can be limited to a certain number of elements', async () => {
-    const readIterator = new TestIntegerIterator().take(8);
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual([0, 1, 2, 3, 4, 5, 6, 7]);
-  });
-
-  it('is unaltered by a negative or undefined take() count.', async () => {
-    const baseIterator = new TestIntegerIterator();
-    const readIterator = baseIterator.take(-1);
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual(baseIterator.data);
-    const baseIterator2 = new TestIntegerIterator();
-    const readIterator2 = baseIterator2.take(undefined);
-    const result2 = await readIterator2.toArrayForTest();
-    expect(result2).toEqual(baseIterator2.data);
-  });
-
-  it('can skip a certain number of elements', async () => {
-    const readIterator = new TestIntegerIterator().skip(88).take(8);
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual([88, 89, 90, 91, 92, 93, 94, 95]);
-  });
-
-  it('is unaltered by a negative or undefined skip() count.', async () => {
-    const baseIterator = new TestIntegerIterator();
-    const readIterator = baseIterator.skip(-1);
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual(baseIterator.data);
-    const baseIterator2 = new TestIntegerIterator();
-    const readIterator2 = baseIterator2.skip(undefined);
-    const result2 = await readIterator2.toArrayForTest();
-    expect(result2).toEqual(baseIterator2.data);
-  });
-
-  it('can selectively ignore upstream errors', async () => {
-    const readIterator = new TestIntegerIterator().take(10).map(x => {
-      if (x % 2 === 0) {
-        throw new Error('Oh no, an even number!');
-      }
-      return x;
-    });
-    // The 'true' response means the iterator should continue.
-    const errorIgnoringIterator = readIterator.handleErrors((e) => true);
-    const result = await errorIgnoringIterator.toArrayForTest();
-    expect(result).toEqual([1, 3, 5, 7, 9]);
-  });
-
-  it('can terminate cleanly based on upstream errors', async () => {
-    const readIterator = new TestIntegerIterator().map(x => {
-      if (x % 2 === 0) {
-        throw new Error(`Oh no, an even number: ${x}`);
-      }
-      return x;
-    });
-    // The 'true' response means the iterator should continue.
-    // But in the case of 10, return false, terminating the stream.
-    const errorHandlingIterator = readIterator.handleErrors(
-        (e) => e.message !== 'Oh no, an even number: 10');
-    const result = await errorHandlingIterator.toArrayForTest();
-    expect(result).toEqual([1, 3, 5, 7, 9]);
-  });
-
-  it('can selectively propagate upstream errors', async () => {
-    const readIterator = new TestIntegerIterator().map(x => {
-      if (x % 2 === 0) {
-        throw new Error(`Oh no, an even number: ${x}`);
-      }
-      return x;
-    });
-    const errorHandlingIterator = readIterator.handleErrors((e) => {
-      if (e.message === 'Oh no, an even number: 2') {
-        throw e;
-      }
-      return true;
-    });
-    try {
-      // Using toArray() rather than toArrayForTest().  The prefetch in
-      // the latter, in combination with expecting an exception, causes
-      // unrelated tests to fail (See
-      // https://github.com/tensorflow/tfjs/issues/1330.
-      await errorHandlingIterator.toArray();
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual('Oh no, an even number: 2');
-    }
-  });
-
-  it('can be forced to execute serially', async () => {
-    // This is like FilterIterator, except that it does not enforce serial
-    // execution.  For testing.
-    class ParallelFilterIterator<T> extends LazyIterator<T> {
-      constructor(
-          protected upstream: LazyIterator<T>,
-          protected predicate: (value: T) => boolean) {
-        super();
-      }
-
-      summary() {
-        return `${this.upstream.summary} -> ParallelFilterIterator`;
-      }
-
-      async next(): Promise<IteratorResult<T>> {
-        while (true) {
-          const item = await this.upstream.next();
-          if (item.done || this.predicate(item.value)) {
-            return item;
-          }
-        }
-      }
-    }
-
-    const newReadIterator = () =>
-        new ParallelFilterIterator((new TestIntegerIterator()).take(10), x => {
-          return x % 2 === 0;
-        });
-
-    // Without enforcing serial execution, order gets scrambled; consequently
-    // the done signal arrives before any of the accepted elements!
-    const badResult = await newReadIterator().toArrayForTest();
-    expect(badResult).toEqual([0]);
-
-    // But with serial execution, everything is fine.
-    const goodResult = await newReadIterator().serial().toArrayForTest();
-    expect(goodResult).toEqual([0, 2, 4, 6, 8]);
-  });
-
-  it('can be created from an array', async () => {
-    const readIterator = iteratorFromItems([1, 2, 3, 4, 5, 6]);
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual([1, 2, 3, 4, 5, 6]);
-  });
-
-  it('can be created from a function', async () => {
-    let i = -1;
-    const func = () =>
-        ++i < 7 ? {value: i, done: false} : {value: null, done: true};
-
-    const readIterator = iteratorFromFunction(func);
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual([0, 1, 2, 3, 4, 5, 6]);
-  });
-
-  it('can be created with incrementing integers', async () => {
-    const readIterator = iteratorFromIncrementing(0).take(7);
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual([0, 1, 2, 3, 4, 5, 6]);
-  });
-
-  it('can be concatenated', async () => {
-    const a = iteratorFromItems([1, 2, 3]);
-    const b = iteratorFromItems([4, 5, 6]);
-    const readIterator = a.concatenate(b);
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual([1, 2, 3, 4, 5, 6]);
-  });
-
-  it('can be concatenated after skipping', async () => {
-    const a = iteratorFromItems([1, 2, 3]);
-    const b = iteratorFromItems([4, 5, 6]);
-    const readIterator = a.skip(1).concatenate(b);
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual([2, 3, 4, 5, 6]);
-  });
-
-  it('can be created by concatenating streams', async () => {
-    const a = new TestIntegerIterator();
-    const b = new TestIntegerIterator();
-    const readIterator = iteratorFromConcatenated(iteratorFromItems([a, b]));
-    const result = await readIterator.toArrayForTest();
-    expect(result.length).toEqual(200);
-  });
-
-  it('can be created by concatenating streams from a function', async () => {
-    const readIterator = iteratorFromConcatenatedFunction(
-        () => ({value: new TestIntegerIterator(), done: false}), 3);
-    const expectedResult: number[] = [];
-    for (let i = 0; i < 3; i++) {
-      for (let j = 0; j < 100; j++) {
-        expectedResult[i * 100 + j] = j;
-      }
-    }
-
-    const result = await readIterator.toArrayForTest();
-    expect(result).toEqual(expectedResult);
-  });
-
-  it('can be created by zipping an array of streams', async () => {
-    const a = new TestIntegerIterator();
-    const b = new TestIntegerIterator().map(x => x * 10);
-    const c = new TestIntegerIterator().map(x => `string ${x}`);
-    const readStream = iteratorFromZipped([a, b, c]);
-    const result = await readStream.toArrayForTest();
-    expect(result.length).toEqual(100);
-
-    // each result has the form [x, x * 10, 'string ' + x]
-
-    for (const e of result) {
-      const ee = e as TensorContainerArray;
-      expect(ee[1]).toEqual(ee[0] as number * 10);
-      expect(ee[2]).toEqual(`string ${ee[0]}`);
-    }
-  });
-
-  it('can be created by zipping a dict of streams', async () => {
-    const a = new TestIntegerIterator();
-    const b = new TestIntegerIterator().map(x => x * 10);
-    const c = new TestIntegerIterator().map(x => `string ${x}`);
-    const readStream = iteratorFromZipped({a, b, c});
-    const result = await readStream.toArrayForTest();
-    expect(result.length).toEqual(100);
-
-    // each result has the form {a: x, b: x * 10, c: 'string ' + x}
-
-    for (const e of result) {
-      const ee = e as TensorContainerObject;
-      expect(ee['b']).toEqual(ee['a'] as number * 10);
-      expect(ee['c']).toEqual(`string ${ee['a']}`);
-    }
-  });
-
-  it('can be created by zipping a nested structure of streams', async () => {
-    const a = new TestIntegerIterator().map(x => ({'a': x, 'constant': 12}));
-    const b = new TestIntegerIterator().map(
-        x => ({'b': x * 10, 'array': [x * 100, x * 200]}));
-    const c = new TestIntegerIterator().map(x => ({'c': `string ${x}`}));
-    const readStream = iteratorFromZipped([a, b, c]);
-    const result = await readStream.toArrayForTest();
-    expect(result.length).toEqual(100);
-
-    // each result has the form
-    // [
-    //   {a: x, 'constant': 12}
-    //   {b: x * 10, 'array': [x * 100, x * 200]},
-    //   {c: 'string ' + x}
-    // ]
-
-    for (const e of result) {
-      const ee = e as TensorContainerArray;
-      const aa = ee[0] as TensorContainerObject;
-      const bb = ee[1] as TensorContainerObject;
-      const cc = ee[2] as TensorContainerObject;
-      expect(aa['constant']).toEqual(12);
-      expect(bb['b']).toEqual(aa['a'] as number * 10);
-      expect(bb['array']).toEqual([
-        aa['a'] as number * 100, aa['a'] as number * 200
-      ]);
-      expect(cc['c']).toEqual(`string ${aa['a']}`);
-    }
-  });
-
-  it('zip requires streams of the same length by default', async () => {
-    try {
-      const a = new TestIntegerIterator(10);
-      const b = new TestIntegerIterator(3);
-      const c = new TestIntegerIterator(2);
-      const readStream = iteratorFromZipped([a, b, c]);
-      // Using toArray() rather than toArrayForTest().  The prefetch in
-      // the latter, in combination with expecting an exception, causes
-      // unrelated tests to fail (See
-      // https://github.com/tensorflow/tfjs/issues/1330.
-      await readStream.toArray();
-      fail();
-    } catch (error) {
-      expect(error.message)
-          .toBe(
-              'Zipped streams should have the same length. ' +
-              'Mismatched at element 2.');
-    }
-  });
-
-  it('zip can be told to terminate when the shortest stream terminates',
-     async () => {
-       const a = new TestIntegerIterator(10);
-       const b = new TestIntegerIterator(3);
-       const c = new TestIntegerIterator(2);
-       const readStream =
-           iteratorFromZipped([a, b, c], ZipMismatchMode.SHORTEST);
-       const result = await readStream.toArrayForTest();
-       expect(result.length).toEqual(2);
-     });
-
-  it('zip can be told to terminate when the longest stream terminates',
-     async () => {
-       const a = new TestIntegerIterator(10);
-       const b = new TestIntegerIterator(3);
-       const c = new TestIntegerIterator(2);
-       const readStream =
-           iteratorFromZipped([a, b, c], ZipMismatchMode.LONGEST);
-       const result = await readStream.toArrayForTest();
-       expect(result.length).toEqual(10);
-       expect(result[9]).toEqual([9, null, null]);
-     });
-
-  /**
-   * This test demonstrates behavior that is intrinsic to the tf.data zip()
-   * API, but that may not be what users ultimately want when zipping dicts.
-   * This may merit a convenience function (e.g., maybe flatZip()).
-   */
-  it('zipping TensorContainer streams requires manual merge', async () => {
-    function naiveMerge(xs: TensorContainer[]): TensorContainer {
-      const result = {};
-      for (const x of xs) {
-        // For now, we do nothing to detect name collisions here
-        Object.assign(result, x);
-      }
-      return result;
-    }
-
-    const a = new TestIntegerIterator().map(x => ({'a': x}));
-    const b = new TestIntegerIterator().map(x => ({'b': x * 10}));
-    const c = new TestIntegerIterator().map(x => ({'c': `string ${x}`}));
-    const zippedStream = iteratorFromZipped([a, b, c]);
-    // At first, each result has the form
-    // [{a: x}, {b: x * 10}, {c: 'string ' + x}]
-
-    const readStream =
-        zippedStream.map(e => naiveMerge(e as TensorContainerArray));
-    // Now each result has the form {a: x, b: x * 10, c: 'string ' + x}
-
-    const result = await readStream.toArrayForTest();
-    expect(result.length).toEqual(100);
-
-    for (const e of result) {
-      const ee = e as TensorContainerObject;
-      expect(ee['b']).toEqual(ee['a'] as number * 10);
-      expect(ee['c']).toEqual(`string ${ee['a']}`);
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/iterators/microphone_iterator.ts b/tfjs-master/tfjs-data/src/iterators/microphone_iterator.ts
deleted file mode 100644
index 7d49d7f90..000000000
--- a/tfjs-master/tfjs-data/src/iterators/microphone_iterator.ts
+++ /dev/null
@@ -1,234 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {env, Tensor, tensor, Tensor2D, Tensor3D, TensorContainer, util} from '@tensorflow/tfjs-core';
-import {MicrophoneConfig} from '../types';
-import {LazyIterator} from './lazy_iterator';
-
-/**
- * Provide a stream of tensors from microphone audio stream. The tensors are
- * representing audio data as frequency-domain spectrogram generated with
- * browser's native FFT. Tensors representing time-domain waveform is available
- * based on configuration. Only works in browser environment.
- */
-export class MicrophoneIterator extends LazyIterator<TensorContainer> {
-  private isClosed = false;
-  private stream: MediaStream;
-  private readonly fftSize: number;
-  private readonly columnTruncateLength: number;
-  private freqData: Float32Array;
-  private timeData: Float32Array;
-  private readonly numFrames: number;
-  private analyser: AnalyserNode;
-  private audioContext: AudioContext;
-  private sampleRateHz: number;
-  private readonly audioTrackConstraints: MediaTrackConstraints;
-  private readonly smoothingTimeConstant: number;
-  private readonly includeSpectrogram: boolean;
-  private readonly includeWaveform: boolean;
-
-  private constructor(protected readonly microphoneConfig: MicrophoneConfig) {
-    super();
-    this.fftSize = microphoneConfig.fftSize || 1024;
-    const fftSizeLog2 = Math.log2(this.fftSize);
-    if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||
-        !Number.isInteger(fftSizeLog2)) {
-      throw new Error(
-          `Invalid fftSize: it must be a power of 2 between ` +
-          `2 to 4 and 2 to 14, but got ${this.fftSize}`);
-    }
-
-    this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;
-    this.sampleRateHz = microphoneConfig.sampleRateHz;
-    this.columnTruncateLength =
-        microphoneConfig.columnTruncateLength || this.fftSize;
-    this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;
-    this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;
-
-    this.includeSpectrogram =
-        microphoneConfig.includeSpectrogram === false ? false : true;
-    this.includeWaveform =
-        microphoneConfig.includeWaveform === true ? true : false;
-    if (!this.includeSpectrogram && !this.includeWaveform) {
-      throw new Error(
-          'Both includeSpectrogram and includeWaveform are false. ' +
-          'At least one type of data should be returned.');
-    }
-  }
-
-  summary() {
-    return `microphone`;
-  }
-
-  // Construct a MicrophoneIterator and start the audio stream.
-  static async create(microphoneConfig: MicrophoneConfig = {}) {
-    if (!env().get('IS_BROWSER')) {
-      throw new Error(
-          'microphone API is only supported in browser environment.');
-    }
-
-    const microphoneIterator = new MicrophoneIterator(microphoneConfig);
-
-    // Call async function start() to initialize the audio stream.
-    await microphoneIterator.start();
-
-    return microphoneIterator;
-  }
-
-  // Start the audio stream and FFT.
-  async start(): Promise<void> {
-    try {
-      this.stream = await navigator.mediaDevices.getUserMedia({
-        audio: this.audioTrackConstraints == null ? true :
-                                                    this.audioTrackConstraints,
-        video: false
-      });
-    } catch (e) {
-      throw new Error(
-          `Error thrown while initializing video stream: ${e.message}`);
-    }
-
-    if (!this.stream) {
-      throw new Error('Could not obtain audio from microphone.');
-    }
-
-    const ctxConstructor =
-        // tslint:disable-next-line:no-any
-        (window as any).AudioContext || (window as any).webkitAudioContext;
-    this.audioContext = new ctxConstructor();
-
-    if (!this.sampleRateHz) {
-      // If sample rate is not provided, use the available sample rate on
-      // device.
-      this.sampleRateHz = this.audioContext.sampleRate;
-    } else if (this.audioContext.sampleRate !== this.sampleRateHz) {
-      throw new Error(
-          `Mismatch in sampling rate: ` +
-          `Expected: ${this.sampleRateHz}; ` +
-          `Actual: ${this.audioContext.sampleRate}`);
-    }
-
-    const streamSource = this.audioContext.createMediaStreamSource(this.stream);
-    this.analyser = this.audioContext.createAnalyser();
-    this.analyser.fftSize = this.fftSize * 2;
-    this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;
-    streamSource.connect(this.analyser);
-    this.freqData = new Float32Array(this.fftSize);
-    this.timeData = new Float32Array(this.fftSize);
-    return;
-  }
-
-  async next(): Promise<IteratorResult<TensorContainer>> {
-    if (this.isClosed) {
-      return {value: null, done: true};
-    }
-
-    let spectrogramTensor: Tensor;
-    let waveformTensor: Tensor;
-
-    const audioDataQueue = await this.getAudioData();
-    if (this.includeSpectrogram) {
-      const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);
-      spectrogramTensor = this.getTensorFromAudioDataArray(
-          freqData, [this.numFrames, this.columnTruncateLength, 1]);
-    }
-    if (this.includeWaveform) {
-      const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);
-      waveformTensor = this.getTensorFromAudioDataArray(
-          timeData, [this.numFrames * this.fftSize, 1]);
-    }
-
-    return {
-      value: {'spectrogram': spectrogramTensor, 'waveform': waveformTensor},
-      done: false
-    };
-  }
-
-  // Capture one result from the audio stream, and extract the value from
-  // iterator.next() result.
-  async capture(): Promise<{spectrogram: Tensor3D, waveform: Tensor2D}> {
-    return (await this.next()).value as
-        {spectrogram: Tensor3D, waveform: Tensor2D};
-  }
-
-  private async getAudioData():
-      Promise<{freqDataQueue: Float32Array[], timeDataQueue: Float32Array[]}> {
-    const freqDataQueue: Float32Array[] = [];
-    const timeDataQueue: Float32Array[] = [];
-    let currentFrames = 0;
-    return new Promise(resolve => {
-      const intervalID = setInterval(() => {
-        if (this.includeSpectrogram) {
-          this.analyser.getFloatFrequencyData(this.freqData);
-          // If the audio stream is initializing, return empty queue.
-          if (this.freqData[0] === -Infinity) {
-            resolve({freqDataQueue, timeDataQueue});
-          }
-          freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));
-        }
-        if (this.includeWaveform) {
-          this.analyser.getFloatTimeDomainData(this.timeData);
-          timeDataQueue.push(this.timeData.slice());
-        }
-
-        // Clean interval and return when all frames have been collected
-        if (++currentFrames === this.numFrames) {
-          clearInterval(intervalID);
-          resolve({freqDataQueue, timeDataQueue});
-        }
-      }, this.fftSize / this.sampleRateHz * 1e3);
-    });
-  }
-
-  // Stop the audio stream and pause the iterator.
-  stop(): void {
-    if (!this.isClosed) {
-      this.isClosed = true;
-      this.analyser.disconnect();
-      this.audioContext.close();
-      if (this.stream != null && this.stream.getTracks().length > 0) {
-        this.stream.getTracks()[0].stop();
-      }
-    }
-  }
-
-  // Override toArray() function to prevent collecting.
-  override toArray(): Promise<Tensor[]> {
-    throw new Error('Can not convert infinite audio stream to array.');
-  }
-
-  // Return audio sampling rate in Hz
-  getSampleRate(): number {
-    return this.sampleRateHz;
-  }
-
-  private flattenQueue(queue: Float32Array[]): Float32Array {
-    const frameSize = queue[0].length;
-    const freqData = new Float32Array(queue.length * frameSize);
-    queue.forEach((data, i) => freqData.set(data, i * frameSize));
-    return freqData;
-  }
-
-  private getTensorFromAudioDataArray(freqData: Float32Array, shape: number[]):
-      Tensor {
-    const vals = new Float32Array(util.sizeFromShape(shape));
-    // If the data is less than the output shape, the rest is padded with zeros.
-    vals.set(freqData, vals.length - freqData.length);
-    return tensor(vals, shape);
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/iterators/microphone_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/microphone_iterator_test.ts
deleted file mode 100644
index 82eab1e96..000000000
--- a/tfjs-master/tfjs-data/src/iterators/microphone_iterator_test.ts
+++ /dev/null
@@ -1,253 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {tensor2d, tensor3d, test_util} from '@tensorflow/tfjs-core';
-// TODO(kangyizhang): import from index once microphone is exported.
-import * as tfd from '../readers';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {MEDIA_ENVS, setupFakeAudioStream} from '../util/test_utils';
-
-describeWithFlags('MicrophoneIterator', MEDIA_ENVS, () => {
-  beforeEach(() => {
-    setupFakeAudioStream();
-  });
-
-  it('gets tensor with default shape with no config', async () => {
-    const microphoneIterator = await tfd.microphone();
-    const result = await microphoneIterator.next();
-    expect(result.done).toBeFalsy();
-    expect(result.value.spectrogram.shape).toEqual([43, 1024, 1]);
-  });
-
-  it('throws error when sample rate is not available', async () => {
-    try {
-      await tfd.microphone({sampleRateHz: 48000});
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual(
-          'Mismatch in sampling rate: Expected: 48000; Actual: 44100');
-    }
-  });
-
-  it('gets sample rate', async () => {
-    const microphoneIterator = await tfd.microphone({sampleRateHz: 44100});
-    expect(microphoneIterator.getSampleRate()).toEqual(44100);
-  });
-
-  it('uses available sample rate on device when it is not provided',
-     async () => {
-       const microphoneIterator = await tfd.microphone();
-       expect(microphoneIterator.getSampleRate()).toEqual(44100);
-     });
-
-  it('gets tensor in correct shape with fftSize', async () => {
-    const microphoneIterator = await tfd.microphone({fftSize: 16});
-    const result = await microphoneIterator.next();
-    expect(result.done).toBeFalsy();
-    expect(result.value.spectrogram.shape).toEqual([43, 16, 1]);
-  });
-
-  it('throws error with invalid fftSize', async () => {
-    try {
-      await tfd.microphone({fftSize: 1000});
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual(
-          'Invalid fftSize: it must be a power of 2 between 2 to 4 and ' +
-          '2 to 14, but got 1000');
-    }
-  });
-
-  it('gets tensor in correct shape with columnTruncateLength', async () => {
-    const microphoneIterator =
-        await tfd.microphone({columnTruncateLength: 232, fftSize: 128});
-    const result = await microphoneIterator.next();
-    expect(result.done).toBeFalsy();
-    expect(result.value.spectrogram.shape).toEqual([43, 232, 1]);
-  });
-
-  it('gets tensor in correct shape with numFramesPerSpectrogram',
-     async () => {
-       const microphoneIterator =
-           await tfd.microphone({numFramesPerSpectrogram: 3, fftSize: 16});
-       const result = await microphoneIterator.next();
-       expect(result.done).toBeFalsy();
-  
-       expect(result.value.spectrogram.shape).toEqual([3, 16, 1]);
-     });
-
-  it('gets tensor in correct shape with full spectrogram config',
-     async () => {
-       const microphoneIterator = await tfd.microphone({
-         sampleRateHz: 44100,
-         fftSize: 16,
-         numFramesPerSpectrogram: 10,
-         columnTruncateLength: 10
-       });
-       const result = await microphoneIterator.next();
-       expect(result.done).toBeFalsy();
-  
-       expect(result.value.spectrogram.shape).toEqual([10, 10, 1]);
-     });
-
-  it('provides both spectrogram and waveform', async () => {
-    const microphoneIterator = await tfd.microphone(
-        {includeSpectrogram: true, includeWaveform: true, fftSize: 16});
-    const result = await microphoneIterator.next();
-    expect(result.done).toBeFalsy();
-    expect(result.value.spectrogram.shape).toEqual([43, 16, 1]);
-    expect(result.value.waveform.shape).toEqual([688, 1]);
-  });
-
-  it('stops and restarts microphone', async () => {
-    const microphoneIterator = await tfd.microphone({fftSize: 16});
-    const result1 = await microphoneIterator.next();
-    expect(result1.done).toBeFalsy();
-    expect(result1.value.spectrogram.shape).toEqual([43, 16, 1]);
-    microphoneIterator.stop();
-    const result2 = await microphoneIterator.next();
-    expect(result2.done).toBeTruthy();
-    expect(result2.value).toBeNull();
-    microphoneIterator.start();
-    expect(result1.done).toBeFalsy();
-    expect(result1.value.spectrogram.shape).toEqual([43, 16, 1]);
-  });
-
-  it('stops microphone multiple times', async () => {
-    const microphoneIterator =
-        await tfd.microphone({fftSize: 16, numFramesPerSpectrogram: 2});
-    const result1 = await microphoneIterator.next();
-    expect(result1.done).toBeFalsy();
-    expect(result1.value.spectrogram.shape).toEqual([2, 16, 1]);
-    microphoneIterator.stop();
-    const result2 = await microphoneIterator.next();
-    expect(result2.done).toBeTruthy();
-    expect(result2.value).toBeNull();
-    microphoneIterator.stop();
-    const result3 = await microphoneIterator.next();
-    expect(result3.done).toBeTruthy();
-    expect(result3.value).toBeNull();
-  });
-
-  it('gets spectrogram and waveform tensor with correct value', async () => {
-    const microphoneIterator = await tfd.microphone({
-      numFramesPerSpectrogram: 1,
-      fftSize: 16,
-      includeSpectrogram: true,
-      includeWaveform: true
-    });
-    const result = await microphoneIterator.next();
-    expect(result.done).toBeFalsy();
-    const value = result.value;
-    expect(value.spectrogram.shape).toEqual([1, 16, 1]);
-    test_util.expectArraysClose(
-        await value.spectrogram.array(),
-        await tensor3d([[
-          [0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12],
-          [13], [14], [15]
-        ]]).array());
-    expect(value.waveform.shape).toEqual([16, 1]);
-    test_util.expectArraysClose(
-        await value.waveform.array(),
-        await tensor2d([
-          [-16], [-17], [-18], [-19], [-20], [-21], [-22], [-23], [-24],
-          [-25], [-26], [-27], [-28], [-29], [-30], [-31]
-        ]).array());
-  });
-
-  it('calls iterator.next() concurrently', async () => {
-    let timesRun = 0;
-    let tensorsReturned = 0;
-    const microphoneIterator = await tfd.microphone(
-        {numFramesPerSpectrogram: 10, columnTruncateLength: 10, fftSize: 32});
-
-    // This function will be called 3 times. Between each call there is a
-    // 200ms interval. The spectrogram tensor will be returned after 464ms.
-    /**
-     * The events happen in sequence are:
-     * call 1st at 0ms,    timesRun:1, tensorsReturned:0;
-     * call 2nd at 1ms,  timesRun:2, tensorsReturned:0;
-     * call 3rd at 2ms,  timesRun:3, tensorsReturned:0;
-     * tensor returned from 1st call at ~7ms, timesRun:3, tensorsReturned:1;
-     * tensor returned from 2nd call,  timesRun:3, tensorsReturned:2;
-     * tensor returned from 3rd call,  timesRun:3, tensorsReturned:3.
-     */
-    const getTensor = async () => {
-      // Clear the interval after it ran 3 times.
-      if (timesRun === 3) {
-        clearInterval(interval);
-      } else {
-        timesRun++;
-        expect(tensorsReturned).toBe(0);
-        const result = await microphoneIterator.next();
-        tensorsReturned++;
-        // When the first tensor got returned (~464ms), getTensor() function
-        // should have been called 3 times (at 400ms).
-        expect(timesRun).toBe(3);
-        expect(result.done).toBeFalsy();
-   
-        const value = result.value;
-        expect(value.spectrogram.shape).toEqual([10, 10, 1]);
-      }
-    };
-
-    // Call iterator.next() every 200 milliseconds, stop after 3 times.
-    const interval = setInterval(getTensor, 1);
-
-    // Wait 3 seconds for the intervals to run.
-    await new Promise<void>(resolve => {
-      setTimeout(() => {
-        resolve();
-      }, 100);
-    });
-    // Assert the intervals run 3 times.
-    expect(timesRun).toBe(3);
-    expect(tensorsReturned).toBe(3);
-  });
-
-  it('gets spectrogram from iterator.capture', async () => {
-    const microphoneIterator =
-        await tfd.microphone({fftSize: 16, numFramesPerSpectrogram: 1});
-    const result = await microphoneIterator.capture();
-    expect(result.spectrogram.shape).toEqual([1, 16, 1]);
-  });
-
-  it('gets waveform from iterator.capture', async () => {
-    const microphoneIterator = await tfd.microphone({
-      includeSpectrogram: false,
-      includeWaveform: true,
-      fftSize: 16,
-      numFramesPerSpectrogram: 1
-    });
-    const result = await microphoneIterator.capture();
-    expect(result.waveform.shape).toEqual([16, 1]);
-  });
-
-  it('gets spectrogram and waveform from iterator.capture', async () => {
-    const microphoneIterator = await tfd.microphone({
-      includeSpectrogram: true,
-      includeWaveform: true,
-      fftSize: 16,
-      numFramesPerSpectrogram: 1
-    });
-    const result = await microphoneIterator.capture();
-    expect(result.spectrogram.shape).toEqual([1, 16, 1]);
-    expect(result.waveform.shape).toEqual([16, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/iterators/prefetch_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/prefetch_iterator_test.ts
deleted file mode 100644
index 471085339..000000000
--- a/tfjs-master/tfjs-data/src/iterators/prefetch_iterator_test.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {iteratorFromConcatenatedFunction} from './lazy_iterator';
-import {PrefetchIterator} from './lazy_iterator';
-import {TestIntegerIterator} from './lazy_iterator_test';
-
-describe('PrefetchIterator', () => {
-  it('fetches a stream completely (stream size < buffer size)', async () => {
-    const prefetchIterator =
-        new PrefetchIterator(new TestIntegerIterator(), 500);
-    const expectedResult: number[] = [];
-    for (let j = 0; j < 100; j++) {
-      expectedResult[j] = j;
-    }
-
-    const result = await prefetchIterator.toArray();
-    expect(result).toEqual(expectedResult);
-  });
-
-  it('fetches a chained stream completely (stream size < buffer size)',
-     async () => {
-       const baseIterator = iteratorFromConcatenatedFunction(
-           () => ({value: new TestIntegerIterator(), done: false}), 3);
-
-       const prefetchIterator = new PrefetchIterator(baseIterator, 500);
-
-       const expectedResult: number[] = [];
-       for (let i = 0; i < 3; i++) {
-         for (let j = 0; j < 100; j++) {
-           expectedResult[i * 100 + j] = j;
-         }
-       }
-
-       const result = await prefetchIterator.toArray();
-       expect(result).toEqual(expectedResult);
-     });
-
-  it('fetches a chained stream completely (stream size > buffer size)',
-     async () => {
-       const baseIterator = iteratorFromConcatenatedFunction(
-           () => ({value: new TestIntegerIterator(), done: false}), 3);
-
-       const prefetchIterator = new PrefetchIterator(baseIterator, 122);
-       const expectedResult: number[] = [];
-       for (let i = 0; i < 3; i++) {
-         for (let j = 0; j < 100; j++) {
-           expectedResult[i * 100 + j] = j;
-         }
-       }
-
-       const result = await prefetchIterator.toArray();
-       expect(result).toEqual(expectedResult);
-     });
-});
diff --git a/tfjs-master/tfjs-data/src/iterators/shuffle_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/shuffle_iterator_test.ts
deleted file mode 100644
index 7e42a4611..000000000
--- a/tfjs-master/tfjs-data/src/iterators/shuffle_iterator_test.ts
+++ /dev/null
@@ -1,94 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {ChainedIterator, iteratorFromItems} from './lazy_iterator';
-import {iteratorFromConcatenatedFunction} from './lazy_iterator';
-import {ShuffleIterator} from './lazy_iterator';
-import {TestIntegerIterator} from './lazy_iterator_test';
-
-const LONG_STREAM_LENGTH = 100;
-const SHORT_STREAM_LENGTH = 15;
-
-describe('ShuffleIterator', () => {
-  it('shuffles a stream without replacement', async () => {
-    const baseIterator = new TestIntegerIterator(LONG_STREAM_LENGTH);
-    const shuffleIterator = new ShuffleIterator(baseIterator, 1000);
-    const notExpectedResult: number[] = [];
-    for (let i = 0; i < 1; i++) {
-      for (let j = 0; j < LONG_STREAM_LENGTH; j++) {
-        notExpectedResult[i * LONG_STREAM_LENGTH + j] = j;
-      }
-    }
-    const result = await shuffleIterator.toArrayForTest();
-    expect(result).not.toEqual(notExpectedResult);
-    expect(result.length).toEqual(LONG_STREAM_LENGTH);
-    const counts = new Array<number>(LONG_STREAM_LENGTH);
-    result.forEach((x) => {
-      counts[x] = (counts[x] || 0) + 1;
-    });
-    for (let i = 0; i < LONG_STREAM_LENGTH; i++) {
-      expect(counts[i]).toEqual(1);
-    }
-  });
-
-  it('shuffles a single chained stream without replacement', async () => {
-    const baseIterator = new ChainedIterator(
-        iteratorFromItems([new TestIntegerIterator(SHORT_STREAM_LENGTH)]));
-    const shuffleIterator = new ShuffleIterator(baseIterator, 1000);
-    const notExpectedResult: number[] = [];
-    for (let i = 0; i < 1; i++) {
-      for (let j = 0; j < SHORT_STREAM_LENGTH; j++) {
-        notExpectedResult[i * SHORT_STREAM_LENGTH + j] = j;
-      }
-    }
-    const result = await shuffleIterator.toArrayForTest();
-    expect(result).not.toEqual(notExpectedResult);
-    expect(result.length).toEqual(SHORT_STREAM_LENGTH);
-    const counts = new Array<number>(SHORT_STREAM_LENGTH);
-    result.forEach((x) => {
-      counts[x] = (counts[x] || 0) + 1;
-    });
-    for (let i = 0; i < SHORT_STREAM_LENGTH; i++) {
-      expect(counts[i]).toEqual(1);
-    }
-  });
-
-  it('shuffles multiple chained streams without replacement', async () => {
-    const baseIterator = iteratorFromConcatenatedFunction(
-        () => (
-            {value: new TestIntegerIterator(SHORT_STREAM_LENGTH), done: false}),
-        3);
-    const shuffleIterator = new ShuffleIterator(baseIterator, 1000);
-    const notExpectedResult: number[] = [];
-    for (let i = 0; i < 3; i++) {
-      for (let j = 0; j < SHORT_STREAM_LENGTH; j++) {
-        notExpectedResult[i * SHORT_STREAM_LENGTH + j] = j;
-      }
-    }
-    const result = await shuffleIterator.toArrayForTest();
-    expect(result).not.toEqual(notExpectedResult);
-    expect(result.length).toEqual(3 * SHORT_STREAM_LENGTH);
-    const counts = new Array<number>(SHORT_STREAM_LENGTH);
-    result.forEach((x) => {
-      counts[x] = (counts[x] || 0) + 1;
-    });
-    for (let i = 0; i < SHORT_STREAM_LENGTH; i++) {
-      expect(counts[i]).toEqual(3);
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/iterators/string_iterator.ts b/tfjs-master/tfjs-data/src/iterators/string_iterator.ts
deleted file mode 100644
index 611947ca8..000000000
--- a/tfjs-master/tfjs-data/src/iterators/string_iterator.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {LazyIterator, OneToManyIterator} from './lazy_iterator';
-
-export abstract class StringIterator extends LazyIterator<string> {
-  /**
-   * Splits a string stream on a given separator.
-   *
-   * It is assumed that the incoming chunk boundaries have no semantic meaning,
-   * so conceptually the incoming stream is treated simply as the concatenation
-   * of its elements.
-   *
-   * The outgoing stream provides chunks corresponding to the results of the
-   * standard string split() operation (even if such a chunk spanned incoming
-   * chunks).  The separators are not included.
-   *
-   * A typical usage is to split a text file (represented as a stream with
-   * arbitrary chunk boundaries) into lines.
-   *
-   * @param upstream A readable stream of strings that can be treated as
-   *   concatenated.
-   * @param separator A character to split on.
-   */
-  split(separator: string): StringIterator {
-    return new SplitIterator(this, separator);
-  }
-}
-
-// ============================================================================
-// The following private classes serve to implement the chainable methods
-// on StringIterator.  Unfortunately they can't be placed in separate files, due
-// to resulting trouble with circular imports.
-// ============================================================================
-
-// We wanted multiple inheritance, e.g.
-//   class SplitIterator extends QueueIterator<string>, StringIterator
-// but the TypeScript mixin approach is a bit hacky, so we take this adapter
-// approach instead.
-
-class SplitIterator extends StringIterator {
-  private impl: SplitIteratorImpl;
-
-  constructor(protected upstream: LazyIterator<string>, separator: string) {
-    super();
-    this.impl = new SplitIteratorImpl(upstream, separator);
-  }
-
-  summary() {
-    return this.impl.summary();
-  }
-
-  async next() {
-    return this.impl.next();
-  }
-}
-
-class SplitIteratorImpl extends OneToManyIterator<string> {
-  // A partial string at the end of an upstream chunk
-  carryover = '';
-
-  constructor(
-      protected upstream: LazyIterator<string>, protected separator: string) {
-    super();
-  }
-
-  summary() {
-    return `${this.upstream.summary()} -> Split('${this.separator}')`;
-  }
-
-  async pump(): Promise<boolean> {
-    const chunkResult = await this.upstream.next();
-    if (chunkResult.done) {
-      if (this.carryover === '') {
-        return false;
-      }
-
-      // Pretend that the pump succeeded in order to emit the small last batch.
-      // The next pump() call will actually fail.
-      this.outputQueue.push(this.carryover);
-      this.carryover = '';
-      return true;
-    }
-    const lines = chunkResult.value.split(this.separator) as string[];
-    // Note the behavior: " ab ".split(' ') === ['', 'ab', '']
-    // Thus the carryover may be '' if the separator falls on a chunk
-    // boundary; this produces the correct result.
-
-    lines[0] = this.carryover + lines[0];
-    for (const line of lines.slice(0, -1)) {
-      this.outputQueue.push(line);
-    }
-    this.carryover = lines[lines.length - 1];
-
-    return true;
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/iterators/string_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/string_iterator_test.ts
deleted file mode 100644
index 5f853ad3f..000000000
--- a/tfjs-master/tfjs-data/src/iterators/string_iterator_test.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-import {FileChunkIterator} from './file_chunk_iterator';
-
-const lorem = `Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do
-eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim
-veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo
-consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum
-dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident,
-sunt in culpa qui officia deserunt mollit anim id est laborum.`;
-
-const testData =
-    env().get('IS_BROWSER') ? new Blob([lorem]) : Buffer.from(lorem);
-
-describe('StringIterator.split()', () => {
-  it('Correctly splits lines', async () => {
-    const byteIterator = new FileChunkIterator(testData, {chunkSize: 50});
-    const utf8Iterator = byteIterator.decodeUTF8();
-    const lineIterator = utf8Iterator.split('\n');
-    const expected = lorem.split('\n');
-
-    const result = await lineIterator.toArrayForTest();
-    expect(result.length).toEqual(6);
-    const totalCharacters = result.map(x => x.length).reduce((a, b) => a + b);
-    expect(totalCharacters).toEqual(440);
-    expect(result).toEqual(expected);
-    expect(result.join('\n')).toEqual(lorem);
-  });
-
-  it('Correctly splits strings even when separators fall on chunk boundaries',
-     async () => {
-       const byteIterator = new FileChunkIterator(
-           env().get('IS_BROWSER') ? new Blob(['ab def hi      pq']) :
-                                     Buffer.from('ab def hi      pq'),
-           {chunkSize: 3});
-       // Note the initial chunking will be
-       //   ['ab ', 'def', ' hi', '   ', '   ', 'pq],
-       // so here we are testing for correct behavior when
-       //   * a separator is the last character in a chunk (the first chunk),
-       //   * it is the first character (the third chunk), and
-       //   * when the entire chunk consists of separators (fourth and fifth).
-       const utf8Iterator = byteIterator.decodeUTF8();
-       const lineIterator = utf8Iterator.split(' ');
-       const expected = ['ab', 'def', 'hi', '', '', '', '', '', 'pq'];
-
-       const result = await lineIterator.toArrayForTest();
-       expect(result.length).toEqual(9);
-       const totalCharacters =
-           result.map(x => x.length).reduce((a, b) => a + b);
-       expect(totalCharacters).toEqual(9);
-       expect(result).toEqual(expected);
-       expect(result.join(' ')).toEqual('ab def hi      pq');
-     });
-});
diff --git a/tfjs-master/tfjs-data/src/iterators/url_chunk_iterator.ts b/tfjs-master/tfjs-data/src/iterators/url_chunk_iterator.ts
deleted file mode 100644
index cb138cbdc..000000000
--- a/tfjs-master/tfjs-data/src/iterators/url_chunk_iterator.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {util} from '@tensorflow/tfjs-core';
-import {FileChunkIterator, FileChunkIteratorOptions} from './file_chunk_iterator';
-
-/**
- * Provide a stream of chunks from a URL.
- *
- * Note this class first downloads the entire file into memory before providing
- * the first element from the stream.  This is because the Fetch API does not
- * yet reliably provide a reader stream for the response body.
- */
-export async function urlChunkIterator(
-    url: RequestInfo, options: FileChunkIteratorOptions = {},
-    fetchFunc?: Function) {
-  let urlString;
-  let requestInit;
-  if ((typeof url) === 'string') {
-    urlString = url as string;
-  } else {
-    urlString = (url as Request).url;
-    requestInit = getRequestInitFromRequest(url as Request);
-  }
-  const response = await (fetchFunc || util.fetch)(urlString, requestInit);
-  if (response.ok) {
-    const uint8Array = new Uint8Array(await response.arrayBuffer());
-    return new FileChunkIterator(uint8Array, options);
-  } else {
-    throw new Error(response.statusText);
-  }
-}
-
-// Generate RequestInit from Request to match tf.util.fetch signature.
-const getRequestInitFromRequest = (request: Request) => {
-  const init = {
-    method: request.method,
-    headers: request.headers,
-    body: request.body,
-    mode: request.mode,
-    credentials: request.credentials,
-    cache: request.cache,
-    redirect: request.redirect,
-    referrer: request.referrer,
-    integrity: request.integrity,
-  };
-  return init;
-};
diff --git a/tfjs-master/tfjs-data/src/iterators/url_chunk_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/url_chunk_iterator_test.ts
deleted file mode 100644
index dc5d4791b..000000000
--- a/tfjs-master/tfjs-data/src/iterators/url_chunk_iterator_test.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {urlChunkIterator} from './url_chunk_iterator';
-
-const TEST_STRING = 'abcdefghijklmnopqrstuvwxyz';
-
-describe('URLChunkIterator', () => {
-  const fetchFunc = (path: string) => {
-    const buf = new ArrayBuffer(TEST_STRING.length);
-    const bufView = new Uint8Array(buf);
-    for (let i = 0, strLen = TEST_STRING.length; i < strLen; i++) {
-      bufView[i] = TEST_STRING.charCodeAt(i);
-    }
-
-    return {
-      ok: true,
-      arrayBuffer: async () => {
-        return buf;
-      }
-    };
-  };
-
-  it('Reads the entire file and then closes the stream', async () => {
-    const readIterator = await urlChunkIterator('', {chunkSize: 10}, fetchFunc);
-    const result = await readIterator.toArrayForTest();
-    expect(result.length).toEqual(3);
-    const totalBytes = result.map(x => x.length).reduce((a, b) => a + b);
-    expect(totalBytes).toEqual(26);
-  });
-
-  it('Reads chunks in order', async () => {
-    const readIterator = await urlChunkIterator('', {chunkSize: 10}, fetchFunc);
-
-    const result = await readIterator.toArrayForTest();
-    expect(result[0][0]).toEqual('a'.charCodeAt(0));
-    expect(result[1][0]).toEqual('k'.charCodeAt(0));
-    expect(result[2][0]).toEqual('u'.charCodeAt(0));
-  });
-
-  it('Reads chunks of expected sizes', async () => {
-    const readIterator = await urlChunkIterator('', {chunkSize: 10}, fetchFunc);
-
-    const result = await readIterator.toArrayForTest();
-    expect(result[0].length).toEqual(10);
-    expect(result[1].length).toEqual(10);
-    expect(result[2].length).toEqual(6);
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/iterators/webcam_iterator.ts b/tfjs-master/tfjs-data/src/iterators/webcam_iterator.ts
deleted file mode 100644
index bd445dafb..000000000
--- a/tfjs-master/tfjs-data/src/iterators/webcam_iterator.ts
+++ /dev/null
@@ -1,224 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {browser, cast, env, expandDims, image, reshape, tensor1d, Tensor1D, tensor2d, Tensor2D, Tensor3D, Tensor4D, tidy, util} from '@tensorflow/tfjs-core';
-import {WebcamConfig} from '../types';
-import {LazyIterator} from './lazy_iterator';
-
-/**
- * Provide a stream of image tensors from webcam video stream. Only works in
- * browser environment.
- */
-export class WebcamIterator extends LazyIterator<Tensor3D> {
-  private isClosed = true;
-  private stream: MediaStream;
-  private resize = false;
-  private cropSize: [number, number];
-  private cropBox: Tensor2D;
-  private cropBoxInd: Tensor1D;
-
-  private constructor(
-      protected readonly webcamVideoElement: HTMLVideoElement,
-      protected readonly webcamConfig: WebcamConfig) {
-    super();
-    if (this.needToResize()) {
-      this.resize = true;
-      this.cropSize =
-          [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];
-      this.cropBoxInd = tensor1d([0], 'int32');
-      if (this.webcamConfig.centerCrop) {
-        // Calculate the box based on resizing shape.
-        const widthCroppingRatio =
-            this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;
-        const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /
-            this.webcamVideoElement.height;
-        const widthCropStart = (1 - widthCroppingRatio) / 2;
-        const heightCropStart = (1 - heightCroppingRatio) / 2;
-        const widthCropEnd = widthCropStart + widthCroppingRatio;
-        const heightCropEnd = heightCroppingRatio + heightCropStart;
-        this.cropBox = tensor2d(
-            [heightCropStart, widthCropStart, heightCropEnd, widthCropEnd],
-            [1, 4]);
-      } else {
-        this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);
-      }
-    }
-  }
-
-  summary() {
-    return `webcam`;
-  }
-
-  // Construct a WebcamIterator and start it's video stream.
-  static async create(
-      webcamVideoElement?: HTMLVideoElement, webcamConfig: WebcamConfig = {}) {
-    if (!env().get('IS_BROWSER')) {
-      throw new Error(
-          'tf.data.webcam is only supported in browser environment.');
-    }
-
-    if (!webcamVideoElement) {
-      // If webcam video element is not provided, create a hidden video element
-      // with provided width and height.
-      webcamVideoElement = document.createElement('video');
-      if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {
-        throw new Error(
-            'Please provide webcam video element, or resizeWidth and ' +
-            'resizeHeight to create a hidden video element.');
-      }
-      webcamVideoElement.width = webcamConfig.resizeWidth;
-      webcamVideoElement.height = webcamConfig.resizeHeight;
-    }
-    const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);
-
-    // Call async function to initialize the video stream.
-    await webcamIterator.start();
-
-    return webcamIterator;
-  }
-
-  // Async function to start video stream.
-  async start(): Promise<void> {
-    if (this.webcamConfig.facingMode) {
-      util.assert(
-          (this.webcamConfig.facingMode === 'user') ||
-              (this.webcamConfig.facingMode === 'environment'),
-          () =>
-              `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +
-              `Please provide 'user' or 'environment'`);
-    }
-
-    try {
-      this.stream = await navigator.mediaDevices.getUserMedia({
-        video: {
-          deviceId: this.webcamConfig.deviceId,
-          facingMode: this.webcamConfig.facingMode ?
-              this.webcamConfig.facingMode :
-              'user',
-          width: this.webcamVideoElement.width,
-          height: this.webcamVideoElement.height
-        }
-      });
-    } catch (e) {
-      // Modify the error message but leave the stack trace intact
-      e.message = `Error thrown while initializing video stream: ${e.message}`;
-      throw e;
-    }
-
-    if (!this.stream) {
-      throw new Error('Could not obtain video from webcam.');
-    }
-
-    // Older browsers may not have srcObject
-    try {
-      this.webcamVideoElement.srcObject = this.stream;
-    } catch (error) {
-      console.log(error);
-      this.webcamVideoElement.src = window.URL.createObjectURL(
-        this.stream as unknown as MediaSource);
-    }
-    // Start the webcam video stream
-    this.webcamVideoElement.play();
-
-    this.isClosed = false;
-
-    return new Promise<void>(resolve => {
-      // Add event listener to make sure the webcam has been fully initialized.
-      this.webcamVideoElement.onloadedmetadata = () => {
-        resolve();
-      };
-    });
-  }
-
-  async next(): Promise<IteratorResult<Tensor3D>> {
-    if (this.isClosed) {
-      return {value: null, done: true};
-    }
-
-    let img;
-    try {
-      img = browser.fromPixels(this.webcamVideoElement);
-    } catch (e) {
-      throw new Error(
-          `Error thrown converting video to pixels: ${JSON.stringify(e)}`);
-    }
-    if (this.resize) {
-      try {
-        return {value: this.cropAndResizeFrame(img), done: false};
-      } catch (e) {
-        throw new Error(`Error thrown cropping the video: ${e.message}`);
-      } finally {
-        img.dispose();
-      }
-    } else {
-      return {value: img, done: false};
-    }
-  }
-
-  private needToResize() {
-    // If resizeWidth and resizeHeight are provided, and different from the
-    // width and height of original HTMLVideoElement, then resizing and cropping
-    // is required.
-    if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&
-        (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||
-         this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {
-      return true;
-    }
-    return false;
-  }
-
-  // Cropping and resizing each frame based on config
-  cropAndResizeFrame(img: Tensor3D): Tensor3D {
-    return tidy(() => {
-      const expandedImage: Tensor4D = expandDims(cast(img, 'float32'), (0));
-      let resizedImage;
-      resizedImage = image.cropAndResize(
-          expandedImage, this.cropBox, this.cropBoxInd, this.cropSize,
-          'bilinear');
-      // Extract image from batch cropping.
-      const shape = resizedImage.shape;
-      return reshape(resizedImage, shape.slice(1) as [number, number, number]);
-    });
-  }
-
-  // Capture one frame from the video stream, and extract the value from
-  // iterator.next() result.
-  async capture(): Promise<Tensor3D> {
-    return (await this.next()).value;
-  }
-
-  // Stop the video stream and pause webcam iterator.
-  stop(): void {
-    const tracks = this.stream.getTracks();
-
-    tracks.forEach(track => track.stop());
-
-    try {
-      this.webcamVideoElement.srcObject = null;
-    } catch (error) {
-      console.log(error);
-      this.webcamVideoElement.src = null;
-    }
-    this.isClosed = true;
-  }
-
-  // Override toArray() function to prevent collecting.
-  override toArray(): Promise<Tensor3D[]> {
-    throw new Error('Can not convert infinite video stream to array.');
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/iterators/webcam_iterator_test.ts b/tfjs-master/tfjs-data/src/iterators/webcam_iterator_test.ts
deleted file mode 100644
index 5be30deb0..000000000
--- a/tfjs-master/tfjs-data/src/iterators/webcam_iterator_test.ts
+++ /dev/null
@@ -1,237 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {memory, tensor3d, test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {MEDIA_ENVS, replaceHTMLVideoElementSource, setupFakeVideoStream} from '../util/test_utils';
-import {WebcamIterator} from './webcam_iterator';
-
-describeWithFlags('WebcamIterator', MEDIA_ENVS, () => {
-  beforeEach(() => {
-    setupFakeVideoStream();
-  });
-
-  it('create webcamIterator with html element', async () => {
-    const videoElement = document.createElement('video');
-    videoElement.width = 160;
-    videoElement.height = 90;
-
-    const webcamIterator = await WebcamIterator.create(videoElement);
-
-    await replaceHTMLVideoElementSource(videoElement);
-
-    const result = await webcamIterator.next();
-    expect(result.done).toBeFalsy();
-    expect(result.value.shape).toEqual([90, 160, 3]);
-  });
-
-  it('create webcamIterator with html element and capture', async () => {
-    const videoElement = document.createElement('video');
-    videoElement.width = 160;
-    videoElement.height = 90;
-
-    const webcamIterator = await WebcamIterator.create(videoElement);
-
-    await replaceHTMLVideoElementSource(videoElement);
-
-    const result = await webcamIterator.capture();
-    expect(result.shape).toEqual([90, 160, 3]);
-  });
-
-  it('create webcamIterator with no html element', async () => {
-    const webcamIterator = await WebcamIterator.create(
-        null, {resizeWidth: 300, resizeHeight: 150});
-    const result = await webcamIterator.next();
-    expect(result.done).toBeFalsy();
-    expect(result.value.shape).toEqual([150, 300, 3]);
-  });
-
-  it('create webcamIterator with no html element and capture', async () => {
-    const webcamIterator = await WebcamIterator.create(
-        null, {resizeWidth: 300, resizeHeight: 150});
-    const result = await webcamIterator.capture();
-    expect(result.shape).toEqual([150, 300, 3]);
-  });
-
-  it('create webcamIterator with no html element and no size', async () => {
-    try {
-      await WebcamIterator.create();
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual(
-          'Please provide webcam video element, or resizeWidth and ' +
-          'resizeHeight to create a hidden video element.');
-    }
-  });
-
-  it('resize and center crop has correct shape with html element',
-     async () => {
-       const videoElement = document.createElement('video');
-       videoElement.width = 100;
-       videoElement.height = 200;
-       const webcamIterator = await WebcamIterator.create(
-           videoElement,
-           {resizeWidth: 30, resizeHeight: 40, centerCrop: true});
-       const result = await webcamIterator.next();
-       expect(result.done).toBeFalsy();
-       expect(result.value.shape).toEqual([40, 30, 3]);
-     });
-
-  it('resize and center crop has correct pixel with html element',
-     async () => {
-       const videoElement = document.createElement('video');
-       videoElement.width = 4;
-       videoElement.height = 4;
-       const webcamIterator = await WebcamIterator.create(
-           videoElement, {resizeWidth: 2, resizeHeight: 2, centerCrop: true});
-       const originalImg = tensor3d([
-         [[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]],
-         [
-           [1, 1, 1],
-           [11, 12, 13],
-           [14, 15, 16],
-           [1, 1, 1],
-         ],
-         [
-           [1, 1, 1],
-           [1, 2, 3],
-           [4, 5, 6],
-           [1, 1, 1],
-         ],
-         [[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]]
-       ]);
-       const croppedImg = webcamIterator.cropAndResizeFrame(originalImg);
-       test_util.expectArraysClose(
-           await croppedImg.array(),
-           await tensor3d([
-             [[6.625, 7.1875, 7.75], [8.3125, 8.875, 9.4375]],
-
-             [[1, 1.5625, 2.125], [2.6875, 3.25, 3.8125]]
-           ]).array());
-     });
-
-  it('resize in bilinear method has correct shape with html element',
-     async () => {
-       const videoElement = document.createElement('video');
-       videoElement.width = 100;
-       videoElement.height = 200;
-
-       const webcamIterator = await WebcamIterator.create(
-           videoElement,
-           {resizeWidth: 30, resizeHeight: 40, centerCrop: false});
-       const result = await webcamIterator.next();
-       expect(result.done).toBeFalsy();
-       expect(result.value.shape).toEqual([40, 30, 3]);
-     });
-
-  it('resize in bilinear method has correct pixel with html element',
-     async () => {
-       const videoElement = document.createElement('video');
-       videoElement.width = 4;
-       videoElement.height = 4;
-       const webcamIterator = await WebcamIterator.create(
-           videoElement,
-           {resizeWidth: 2, resizeHeight: 2, centerCrop: false});
-       const originalImg = tensor3d([
-         [[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]],
-         [
-           [1, 1, 1],
-           [11, 12, 13],
-           [14, 15, 16],
-           [1, 1, 1],
-         ],
-         [
-           [1, 1, 1],
-           [1, 2, 3],
-           [4, 5, 6],
-           [1, 1, 1],
-         ],
-         [[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]]
-       ]);
-       const croppedImg = webcamIterator.cropAndResizeFrame(originalImg);
-       test_util.expectArraysClose(
-           await croppedImg.array(), await tensor3d([
-                                       [[1, 1, 1], [1, 1, 1]],
-
-                                       [[1, 1, 1], [1, 1, 1]]
-                                     ]).array());
-     });
-
-  it('webcamIterator could stop', async () => {
-    const videoElement = document.createElement('video');
-    videoElement.width = 160;
-    videoElement.height = 90;
-
-    const webcamIterator = await WebcamIterator.create(videoElement);
-    await replaceHTMLVideoElementSource(videoElement);
-    const result1 = await webcamIterator.next();
-    expect(result1.done).toBeFalsy();
-    expect(result1.value.shape).toEqual([90, 160, 3]);
-
-    await webcamIterator.stop();
-    const result2 = await webcamIterator.next();
-    expect(result2.done).toBeTruthy();
-    expect(result2.value).toBeNull();
-  });
-
-  it('webcamIterator could restart', async () => {
-    const videoElement = document.createElement('video');
-    videoElement.width = 160;
-    videoElement.height = 90;
-
-    const webcamIterator = await WebcamIterator.create(videoElement);
-    await replaceHTMLVideoElementSource(videoElement);
-    const result1 = await webcamIterator.next();
-    expect(result1.done).toBeFalsy();
-    expect(result1.value.shape).toEqual([90, 160, 3]);
-
-    webcamIterator.stop();
-    const result2 = await webcamIterator.next();
-    expect(result2.done).toBeTruthy();
-    expect(result2.value).toBeNull();
-
-    // Skip validation when it's in Firefox and Mac OS, because BrowserStack
-    // for Firefox does not trigger the readyState event when restarting.
-    if (navigator.userAgent.search('Firefox') < 0 &&
-        navigator.userAgent.search('OS X') < 0) {
-      await webcamIterator.start();
-      await replaceHTMLVideoElementSource(videoElement);
-      const result3 = await webcamIterator.next();
-      expect(result3.done).toBeFalsy();
-      expect(result3.value.shape).toEqual([90, 160, 3]);
-    }
-  });
-
-  it('capture with cropAndResize has no memory leaks', async () => {
-    const videoElement = document.createElement('video');
-    videoElement.width = 100;
-    videoElement.height = 200;
-    const webcamIterator = await WebcamIterator.create(
-        videoElement, {resizeWidth: 30, resizeHeight: 40, centerCrop: true});
-
-    const before = memory();
-
-    const result = await webcamIterator.capture();
-    result.dispose();
-
-    const after = memory();
-
-    expect(after.numTensors).toEqual(before.numTensors);
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/readers.ts b/tfjs-master/tfjs-data/src/readers.ts
deleted file mode 100644
index ed93a5557..000000000
--- a/tfjs-master/tfjs-data/src/readers.ts
+++ /dev/null
@@ -1,282 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {TensorContainer} from '@tensorflow/tfjs-core';
-import {Dataset, datasetFromIteratorFn} from './dataset';
-import {CSVDataset} from './datasets/csv_dataset';
-import {iteratorFromFunction} from './iterators/lazy_iterator';
-import {MicrophoneIterator} from './iterators/microphone_iterator';
-import {WebcamIterator} from './iterators/webcam_iterator';
-import {URLDataSource} from './sources/url_data_source';
-import {CSVConfig, MicrophoneConfig, WebcamConfig} from './types';
-
-/**
- * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL
- * or local path if it's in Node environment.
- *
- * Note: If isLabel in columnConfigs is `true` for at least one column, the
- * element in returned `CSVDataset` will be an object of
- * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys
- * is a dict of labels key/value pairs. If no column is marked as label,
- * returns a dict of features only.
- *
- * ```js
- * const csvUrl =
- * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';
- *
- * async function run() {
- *   // We want to predict the column "medv", which represents a median value of
- *   // a home (in $1000s), so we mark it as a label.
- *   const csvDataset = tf.data.csv(
- *     csvUrl, {
- *       columnConfigs: {
- *         medv: {
- *           isLabel: true
- *         }
- *       }
- *     });
- *
- *   // Number of features is the number of column names minus one for the label
- *   // column.
- *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;
- *
- *   // Prepare the Dataset for training.
- *   const flattenedDataset =
- *     csvDataset
- *     .map(({xs, ys}) =>
- *       {
- *         // Convert xs(features) and ys(labels) from object form (keyed by
- *         // column name) to array form.
- *         return {xs:Object.values(xs), ys:Object.values(ys)};
- *       })
- *     .batch(10);
- *
- *   // Define the model.
- *   const model = tf.sequential();
- *   model.add(tf.layers.dense({
- *     inputShape: [numOfFeatures],
- *     units: 1
- *   }));
- *   model.compile({
- *     optimizer: tf.train.sgd(0.000001),
- *     loss: 'meanSquaredError'
- *   });
- *
- *   // Fit the model using the prepared Dataset
- *   return model.fitDataset(flattenedDataset, {
- *     epochs: 10,
- *     callbacks: {
- *       onEpochEnd: async (epoch, logs) => {
- *         console.log(epoch + ':' + logs.loss);
- *       }
- *     }
- *   });
- * }
- *
- * await run();
- * ```
- *
- * @param source URL or local path to get CSV file. If it's a local path, it
- * must have prefix `file://` and it only works in node environment.
- * @param csvConfig (Optional) A CSVConfig object that contains configurations
- *     of reading and decoding from CSV file(s).
- *
- * @doc {
- *   heading: 'Data',
- *   subheading: 'Creation',
- *   namespace: 'data',
- *   configParamIndices: [1]
- *  }
- */
-export function csv(
-    source: RequestInfo, csvConfig: CSVConfig = {}): CSVDataset {
-  return new CSVDataset(new URLDataSource(source), csvConfig);
-}
-
-/**
- * Create a `Dataset` that produces each element by calling a provided function.
- *
- * Note that repeated iterations over this `Dataset` may produce different
- * results, because the function will be called anew for each element of each
- * iteration.
- *
- * Also, beware that the sequence of calls to this function may be out of order
- * in time with respect to the logical order of the Dataset. This is due to the
- * asynchronous lazy nature of stream processing, and depends on downstream
- * transformations (e.g. .shuffle()). If the provided function is pure, this is
- * no problem, but if it is a closure over a mutable state (e.g., a traversal
- * pointer), then the order of the produced elements may be scrambled.
- *
- * ```js
- * let i = -1;
- * const func = () =>
- *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};
- * const ds = tf.data.func(func);
- * await ds.forEachAsync(e => console.log(e));
- * ```
- *
- * @param f A function that produces one data element on each call.
- */
-export function func<T extends TensorContainer>(
-    f: () => IteratorResult<T>| Promise<IteratorResult<T>>): Dataset<T> {
-  const iter = iteratorFromFunction(f);
-  return datasetFromIteratorFn(async () => iter);
-}
-
-/**
- * Create a `Dataset` that produces each element from provided JavaScript
- * generator, which is a function*
- * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),
- * or a function that returns an
- * iterator
- * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).
- *
- * The returned iterator should have `.next()` function that returns element in
- * format of `{value: TensorContainer, done:boolean}`.
- *
- * Example of creating a dataset from an iterator factory:
- * ```js
- * function makeIterator() {
- *   const numElements = 10;
- *   let index = 0;
- *
- *   const iterator = {
- *     next: () => {
- *       let result;
- *       if (index < numElements) {
- *         result = {value: index, done: false};
- *         index++;
- *         return result;
- *       }
- *       return {value: index, done: true};
- *     }
- *   };
- *   return iterator;
- * }
- * const ds = tf.data.generator(makeIterator);
- * await ds.forEachAsync(e => console.log(e));
- * ```
- *
- * Example of creating a dataset from a generator:
- * ```js
- * function* dataGenerator() {
- *   const numElements = 10;
- *   let index = 0;
- *   while (index < numElements) {
- *     const x = index;
- *     index++;
- *     yield x;
- *   }
- * }
- *
- * const ds = tf.data.generator(dataGenerator);
- * await ds.forEachAsync(e => console.log(e));
- * ```
- *
- * @param generator A JavaScript generator function that returns a JavaScript
- *     iterator.
- *
- * @doc {
- *   heading: 'Data',
- *   subheading: 'Creation',
- *   namespace: 'data',
- *   configParamIndices: [1]
- *  }
- */
-export function generator<T extends TensorContainer>(
-    generator: () => Iterator<T>| Promise<Iterator<T>>): Dataset<T> {
-  return datasetFromIteratorFn(async () => {
-    const gen = await generator();
-    return iteratorFromFunction(() => gen.next());
-  });
-}
-
-/**
- * Create an iterator that generates `Tensor`s from webcam video stream. This
- * API only works in Browser environment when the device has webcam.
- *
- * Note: this code snippet only works when the device has a webcam. It will
- * request permission to open the webcam when running.
- * ```js
- * const videoElement = document.createElement('video');
- * videoElement.width = 100;
- * videoElement.height = 100;
- * const cam = await tf.data.webcam(videoElement);
- * const img = await cam.capture();
- * img.print();
- * cam.stop();
- * ```
- *
- * @param webcamVideoElement A `HTMLVideoElement` used to play video from
- *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will
- *     be created. In that case, `resizeWidth` and `resizeHeight` must be
- *     provided to set the generated tensor shape.
- * @param webcamConfig A `WebcamConfig` object that contains configurations of
- *     reading and manipulating data from webcam video stream.
- *
- * @doc {
- *   heading: 'Data',
- *   subheading: 'Creation',
- *   namespace: 'data',
- *   ignoreCI: true
- *  }
- */
-export async function webcam(
-    webcamVideoElement?: HTMLVideoElement,
-    webcamConfig?: WebcamConfig): Promise<WebcamIterator> {
-  return WebcamIterator.create(webcamVideoElement, webcamConfig);
-}
-
-/**
- * Create an iterator that generates frequency-domain spectrogram `Tensor`s from
- * microphone audio stream with browser's native FFT. This API only works in
- * browser environment when the device has microphone.
- *
- * Note: this code snippet only works when the device has a microphone. It will
- * request permission to open the microphone when running.
- * ```js
- * const mic = await tf.data.microphone({
- *   fftSize: 1024,
- *   columnTruncateLength: 232,
- *   numFramesPerSpectrogram: 43,
- *   sampleRateHz:44100,
- *   includeSpectrogram: true,
- *   includeWaveform: true
- * });
- * const audioData = await mic.capture();
- * const spectrogramTensor = audioData.spectrogram;
- * spectrogramTensor.print();
- * const waveformTensor = audioData.waveform;
- * waveformTensor.print();
- * mic.stop();
- * ```
- *
- * @param microphoneConfig A `MicrophoneConfig` object that contains
- *     configurations of reading audio data from microphone.
- *
- * @doc {
- *   heading: 'Data',
- *   subheading: 'Creation',
- *   namespace: 'data',
- *   ignoreCI: true
- *  }
- */
-export async function microphone(microphoneConfig?: MicrophoneConfig):
-    Promise<MicrophoneIterator> {
-  return MicrophoneIterator.create(microphoneConfig);
-}
diff --git a/tfjs-master/tfjs-data/src/readers_test.ts b/tfjs-master/tfjs-data/src/readers_test.ts
deleted file mode 100644
index 740ef2d6c..000000000
--- a/tfjs-master/tfjs-data/src/readers_test.ts
+++ /dev/null
@@ -1,197 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfd from './readers';
-import {describeAllEnvs, describeNodeEnvs, replaceHTMLVideoElementSource, setupFakeVideoStream, MEDIA_ENVS} from './util/test_utils';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-describeAllEnvs('readers', () => {
-  it('generate dataset from function', async () => {
-    let i = -1;
-    const f = () =>
-        ++i < 5 ? {value: i, done: false} : {value: null, done: true};
-    const ds = tfd.func(f);
-    const result = await ds.toArrayForTest();
-    expect(result).toEqual([0, 1, 2, 3, 4]);
-  });
-
-  it('generate dataset from JavaScript generator', async () => {
-    function* dataGenerator() {
-      const numElements = 5;
-      let index = 0;
-      while (index < numElements) {
-        const x = index;
-        index++;
-        yield x;
-      }
-    }
-    const ds = tfd.generator(dataGenerator);
-    const result = await ds.toArrayForTest();
-    expect(result).toEqual([0, 1, 2, 3, 4]);
-  });
-
-  it('generate multiple datasets from JavaScript generator', async () => {
-    function* dataGenerator() {
-      const numElements = 5;
-      let index = 0;
-      while (index < numElements) {
-        const x = index;
-        index++;
-        yield x;
-      }
-    }
-    const ds = tfd.generator(dataGenerator);
-    const result1 = await ds.toArrayForTest();
-    expect(result1).toEqual([0, 1, 2, 3, 4]);
-    const result2 = await ds.toArrayForTest();
-    expect(result2).toEqual([0, 1, 2, 3, 4]);
-  });
-
-  it('generate dataset from JavaScript iterator factory', async () => {
-    function makeIterator() {
-      let iterationCount = 0;
-      const iterator = {
-        next: () => {
-          let result;
-          if (iterationCount < 5) {
-            result = {value: iterationCount, done: false};
-            iterationCount++;
-            return result;
-          }
-          return {value: iterationCount, done: true};
-        }
-      };
-      return iterator;
-    }
-    const ds = tfd.generator(makeIterator);
-    const result = await ds.toArrayForTest();
-    expect(result).toEqual([0, 1, 2, 3, 4]);
-  });
-
-  it('generate multiple datasets from JavaScript iterator factory',
-     async () => {
-       function makeIterator() {
-         let iterationCount = 0;
-         const iterator = {
-           next: () => {
-             let result;
-             if (iterationCount < 5) {
-               result = {value: iterationCount, done: false};
-               iterationCount++;
-               return result;
-             }
-             return {value: iterationCount, done: true};
-           }
-         };
-         return iterator;
-       }
-       const ds = tfd.generator(makeIterator);
-       const result1 = await ds.toArrayForTest();
-       expect(result1).toEqual([0, 1, 2, 3, 4]);
-       const result2 = await ds.toArrayForTest();
-       expect(result2).toEqual([0, 1, 2, 3, 4]);
-     });
-
-  it('generate dataset from async iterator factory', async () => {
-    async function waitAndCreateCount() {
-      return new Promise(resolve => {
-        setTimeout(() => {
-          resolve(3);
-        }, 1000);
-      });
-    }
-    async function makeIterator() {
-      let iterationCount = (await waitAndCreateCount()) as number;
-      const iterator = {
-        next: () => {
-          let result;
-          if (iterationCount < 6) {
-            result = {value: iterationCount, done: false};
-            iterationCount++;
-            return result;
-          }
-          return {value: iterationCount, done: true};
-        }
-      };
-      return iterator;
-    }
-    const ds = tfd.generator(makeIterator);
-    const result = await ds.toArrayForTest();
-    expect(result).toEqual([3, 4, 5]);
-  });
-});
-
-describeWithFlags('readers in browser', MEDIA_ENVS, () => {
-  it('generate data from webcam with HTML element', async () => {
-    setupFakeVideoStream();
-    const videoElement = document.createElement('video');
-    videoElement.width = 160;
-    videoElement.height = 90;
-
-    const webcamIterator = await tfd.webcam(videoElement);
-    await replaceHTMLVideoElementSource(videoElement);
-    const result = await webcamIterator.next();
-    expect(result.done).toBeFalsy();
-    expect(result.value.shape).toEqual([90, 160, 3]);
-  });
-
-  it('generate data from webcam with no HTML element', async () => {
-    setupFakeVideoStream();
-    const webcamIterator =
-        await tfd.webcam(null, {resizeWidth: 300, resizeHeight: 150});
-    const result = await webcamIterator.next();
-    expect(result.done).toBeFalsy();
-    expect(result.value.shape).toEqual([150, 300, 3]);
-  });
-
-  it('generate data from webcam with HTML element and resize', async () => {
-    setupFakeVideoStream();
-    const videoElement = document.createElement('video');
-    videoElement.width = 300;
-    videoElement.height = 500;
-
-    const webcamIterator = await tfd.webcam(
-        videoElement,
-        {resizeWidth: 100, resizeHeight: 200, centerCrop: true});
-    const result = await webcamIterator.next();
-    expect(result.done).toBeFalsy();
-    expect(result.value.shape).toEqual([200, 100, 3]);
-  });
-});
-
-describeNodeEnvs('readers in node', () => {
-  it('webcam only available in browser env', async () => {
-    try {
-      await tfd.webcam();
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual(
-          'tf.data.webcam is only supported in browser environment.');
-    }
-  });
-
-  it('microphone only available in browser env', async () => {
-    try {
-      await tfd.microphone();
-      fail();
-    } catch (e) {
-      expect(e.message).toEqual(
-          'microphone API is only supported in browser environment.');
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/setup_tests.ts b/tfjs-master/tfjs-data/src/setup_tests.ts
deleted file mode 100644
index 4f0f51bfe..000000000
--- a/tfjs-master/tfjs-data/src/setup_tests.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-core';
-// tslint:disable-next-line:no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-
-// Import and run tests from data.
-// tslint:disable-next-line:no-require-imports
-require('./tests');
diff --git a/tfjs-master/tfjs-data/src/sources/file_data_source.ts b/tfjs-master/tfjs-data/src/sources/file_data_source.ts
deleted file mode 100644
index a773b8482..000000000
--- a/tfjs-master/tfjs-data/src/sources/file_data_source.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-import {DataSource} from '../datasource';
-import {ByteChunkIterator} from '../iterators/byte_chunk_iterator';
-import {FileChunkIterator, FileChunkIteratorOptions} from '../iterators/file_chunk_iterator';
-import {FileElement} from '../types';
-import {isLocalPath} from '../util/source_util';
-
-/**
- * Represents a file, blob, or Uint8Array readable as a stream of binary data
- * chunks.
- */
-export class FileDataSource extends DataSource {
-  /**
-   * Create a `FileDataSource`.
-   *
-   * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to
-   *     read. Local file only works in node environment.
-   * @param options Options passed to the underlying `FileChunkIterator`s,
-   *   such as {chunksize: 1024}.
-   */
-  constructor(
-      protected input: FileElement|string,
-      protected readonly options: FileChunkIteratorOptions = {}) {
-    super();
-  }
-
-  async iterator(): Promise<ByteChunkIterator> {
-    if (isLocalPath(this.input) && env().get('IS_NODE')) {
-      // tslint:disable-next-line:no-require-imports
-      const fs = require('fs');
-      this.input = fs.readFileSync((this.input as string).slice(7));
-    }
-    // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming
-    // with file in browser.
-    return new FileChunkIterator(this.input as FileElement, this.options);
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/sources/url_data_source.ts b/tfjs-master/tfjs-data/src/sources/url_data_source.ts
deleted file mode 100644
index 9f8777d60..000000000
--- a/tfjs-master/tfjs-data/src/sources/url_data_source.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {DataSource} from '../datasource';
-import {ByteChunkIterator} from '../iterators/byte_chunk_iterator';
-import {FileChunkIteratorOptions} from '../iterators/file_chunk_iterator';
-import {urlChunkIterator} from '../iterators/url_chunk_iterator';
-import {isLocalPath} from '../util/source_util';
-import {FileDataSource} from './file_data_source';
-
-/*
- * Represents a URL readable as a stream of binary data chunks.
- */
-export class URLDataSource extends DataSource {
-  /**
-   * Create a `URLDataSource`.
-   *
-   * @param url A source URL string, or a `Request` object.
-   * @param options Options passed to the underlying `FileChunkIterator`s,
-   *   such as {chunksize: 1024}.
-   */
-  constructor(
-      protected readonly url: RequestInfo,
-      protected readonly fileOptions: FileChunkIteratorOptions = {}) {
-    super();
-  }
-
-  // TODO(soergel): provide appropriate caching options.  Currently this
-  // will download the URL anew for each call to iterator().  Since we have
-  // to treat the downloaded file as a blob/buffer anyway, we may as well retain
-  // it-- but that raises GC issues.  Also we may want a persistent disk cache.
-  async iterator(): Promise<ByteChunkIterator> {
-    if (isLocalPath(this.url)) {
-      return (new FileDataSource(this.url as string, this.fileOptions))
-          .iterator();
-    } else {
-      return urlChunkIterator(this.url, this.fileOptions);
-    }
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/statistics.ts b/tfjs-master/tfjs-data/src/statistics.ts
deleted file mode 100644
index be08c97cc..000000000
--- a/tfjs-master/tfjs-data/src/statistics.ts
+++ /dev/null
@@ -1,212 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {div, max, min, sub} from '@tensorflow/tfjs-core';
-
-import {Dataset} from './dataset';
-
-// TODO(kangyizhang): eliminate the need for ElementArray and TabularRecord, by
-// computing stats on nested structures via deepMap/deepZip.
-
-/**
- * The value associated with a given key for a single element.
- *
- * Such a value may not have a batch dimension.  A value may be a scalar or an
- * n-dimensional array.
- */
-export type ElementArray = number|number[]|tf.Tensor|string;
-
-/**
- * A map from string keys (aka column names) to values for a single element.
- */
-export type TabularRecord = {
-  [key: string]: ElementArray
-};
-
-// TODO(kangyizhang): Flesh out collected statistics.
-// For numeric columns we should provide mean, stddev, histogram, etc.
-// For string columns we should provide a vocabulary (at least, top-k), maybe a
-// length histogram, etc.
-// Collecting only numeric min and max is just the bare minimum for now.
-
-/** An interface representing numeric statistics of a column. */
-export interface NumericColumnStatistics {
-  min: number;
-  max: number;
-  mean: number;
-  variance: number;
-  stddev: number;
-  length: number;
-}
-
-/**
- * An interface representing column level NumericColumnStatistics for a
- * Dataset.
- */
-export interface DatasetStatistics {
-  [key: string]: NumericColumnStatistics;
-}
-
-/**
- * Provides a function that scales numeric values into the [0, 1] interval.
- *
- * @param min the lower bound of the inputs, which should be mapped to 0.
- * @param max the upper bound of the inputs, which should be mapped to 1,
- * @return A function that maps an input ElementArray to a scaled ElementArray.
- */
-export function scaleTo01(min: number, max: number): (value: ElementArray) =>
-    ElementArray {
-  const range = max - min;
-  const minTensor: tf.Tensor = tf.scalar(min);
-  const rangeTensor: tf.Tensor = tf.scalar(range);
-  return (value: ElementArray): ElementArray => {
-    if (typeof (value) === 'string') {
-      throw new Error('Can\'t scale a string.');
-    } else {
-      if (value instanceof tf.Tensor) {
-        const result = div(sub(value, minTensor), rangeTensor);
-        return result;
-      } else if (value instanceof Array) {
-        return value.map(v => (v - min) / range);
-      } else {
-        return (value - min) / range;
-      }
-    }
-  };
-}
-
-/**
- * Provides a function that calculates column level statistics, i.e. min, max,
- * variance, stddev.
- *
- * @param dataset The Dataset object whose statistics will be calculated.
- * @param sampleSize (Optional) If set, statistics will only be calculated
- *     against a subset of the whole data.
- * @param shuffleWindowSize (Optional) If set, shuffle provided dataset before
- *     calculating statistics.
- * @return A DatasetStatistics object that contains NumericColumnStatistics of
- *     each column.
- */
-export async function computeDatasetStatistics(
-    dataset: Dataset<TabularRecord>, sampleSize?: number,
-    shuffleWindowSize?: number): Promise<DatasetStatistics> {
-  let sampleDataset = dataset;
-  // TODO(soergel): allow for deep shuffle where possible.
-  if (shuffleWindowSize != null) {
-    sampleDataset = sampleDataset.shuffle(shuffleWindowSize);
-  }
-  if (sampleSize != null) {
-    sampleDataset = sampleDataset.take(sampleSize);
-  }
-
-  // TODO(soergel): prepare the column objects based on a schema.
-  const result: DatasetStatistics = {};
-
-  await sampleDataset.forEachAsync(e => {
-    for (const key of Object.keys(e)) {
-      const value = e[key];
-      if (typeof (value) === 'string') {
-        // No statistics for string element.
-      } else {
-        let previousMean = 0;
-        let previousLength = 0;
-        let previousVariance = 0;
-        let columnStats: NumericColumnStatistics = result[key];
-        if (columnStats == null) {
-          columnStats = {
-            min: Number.POSITIVE_INFINITY,
-            max: Number.NEGATIVE_INFINITY,
-            mean: 0,
-            variance: 0,
-            stddev: 0,
-            length: 0
-          };
-          result[key] = columnStats;
-        } else {
-          previousMean = columnStats.mean;
-          previousLength = columnStats.length;
-          previousVariance = columnStats.variance;
-        }
-        let recordMin: number;
-        let recordMax: number;
-
-        // Calculate accumulated mean and variance following tf.Transform
-        // implementation
-        let valueLength = 0;
-        let valueMean = 0;
-        let valueVariance = 0;
-        let combinedLength = 0;
-        let combinedMean = 0;
-        let combinedVariance = 0;
-
-        if (value instanceof tf.Tensor) {
-          recordMin = min(value).dataSync()[0];
-          recordMax = max(value).dataSync()[0];
-          const valueMoment = tf.moments(value);
-          valueMean = valueMoment.mean.dataSync()[0];
-          valueVariance = valueMoment.variance.dataSync()[0];
-          valueLength = value.size;
-
-        } else if (value instanceof Array) {
-          recordMin = value.reduce((a, b) => Math.min(a, b));
-          recordMax = value.reduce((a, b) => Math.max(a, b));
-          const valueMoment = tf.moments(value);
-          valueMean = valueMoment.mean.dataSync()[0];
-          valueVariance = valueMoment.variance.dataSync()[0];
-          valueLength = value.length;
-
-        } else if (!isNaN(value) && isFinite(value)) {
-          recordMin = value;
-          recordMax = value;
-          valueMean = value;
-          valueVariance = 0;
-          valueLength = 1;
-
-        } else {
-          columnStats = null;
-          continue;
-        }
-        combinedLength = previousLength + valueLength;
-        combinedMean = previousMean +
-            (valueLength / combinedLength) * (valueMean - previousMean);
-        combinedVariance = previousVariance +
-            (valueLength / combinedLength) *
-                (valueVariance +
-                 ((valueMean - combinedMean) * (valueMean - previousMean)) -
-                 previousVariance);
-
-        columnStats.min = Math.min(columnStats.min, recordMin);
-        columnStats.max = Math.max(columnStats.max, recordMax);
-        columnStats.length = combinedLength;
-        columnStats.mean = combinedMean;
-        columnStats.variance = combinedVariance;
-        columnStats.stddev = Math.sqrt(combinedVariance);
-      }
-    }
-  });
-  // Variance and stddev should be NaN for the case of a single element.
-  for (const key in result) {
-    const stat: NumericColumnStatistics = result[key];
-    if (stat.length === 1) {
-      stat.variance = NaN;
-      stat.stddev = NaN;
-    }
-  }
-  return result;
-}
diff --git a/tfjs-master/tfjs-data/src/statistics_test.ts b/tfjs-master/tfjs-data/src/statistics_test.ts
deleted file mode 100644
index ef312116e..000000000
--- a/tfjs-master/tfjs-data/src/statistics_test.ts
+++ /dev/null
@@ -1,90 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {Dataset} from './dataset';
-import {TestDataset} from './dataset_test';
-import {computeDatasetStatistics, scaleTo01, TabularRecord} from './statistics';
-import {describeAllEnvs} from './util/test_utils';
-
-describeAllEnvs('makeDatasetStatistics', () => {
-  it('computes numeric min and max over numbers, arrays, and Tensors',
-     async () => {
-       const ds = new TestDataset().skip(55) as Dataset<TabularRecord>;
-       const stats = await computeDatasetStatistics(ds);
-       expect(stats['number'].min).toEqual(55);
-       expect(stats['number'].max).toEqual(99);
-       // The TestDataset includes cubes of the indices
-       expect(stats['numberArray'].min).toEqual(55);
-       expect(stats['numberArray'].max).toEqual(99 * 99 * 99);
-       expect(stats['Tensor'].min).toEqual(55);
-       expect(stats['Tensor'].max).toEqual(99 * 99 * 99);
-     });
-
-  it('computes numeric mean and variance over numbers, arrays, and Tensors',
-     async () => {
-       // According to https://github.com/tensorflow/tfjs/issues/658, unit tests
-       // for mean and variance will use small numbers.
-       const ds = new TestDataset().take(10) as Dataset<TabularRecord>;
-       const stats = await computeDatasetStatistics(ds);
-       expect(stats['number'].min).toEqual(0);
-       expect(stats['number'].max).toEqual(9);
-       expect(stats['number'].mean).toEqual(4.5);
-       expect(stats['number'].variance).toEqual(8.25);
-       expect(stats['number'].stddev).toEqual(2.8722813232690143);
-
-       // The TestDataset includes cubes of the indices
-       expect(stats['numberArray'].min).toEqual(0);
-       expect(stats['numberArray'].max).toEqual(729);
-       tf.test_util.expectNumbersClose(
-           stats['numberArray'].mean, 78.50000066757202);
-       tf.test_util.expectNumbersClose(
-           stats['numberArray'].variance, 26971.849851671857, 0.1);
-       tf.test_util.expectNumbersClose(
-           stats['numberArray'].stddev, 164.2310867396056);
-
-       expect(stats['Tensor'].min).toEqual(0);
-       expect(stats['Tensor'].max).toEqual(729);
-       tf.test_util.expectNumbersClose(stats['Tensor'].mean, 78.50000066757202);
-       tf.test_util.expectNumbersClose(
-           stats['Tensor'].variance, 26971.849851671857, 0.1);
-       tf.test_util.expectNumbersClose(
-           stats['Tensor'].stddev, 164.2310867396056);
-     });
-
-  it('variance should be NaN if there is only one element', async () => {
-    const ds = new TestDataset().take(1) as Dataset<TabularRecord>;
-    const stats = await computeDatasetStatistics(ds);
-    expect(stats['number'].min).toEqual(0);
-    expect(stats['number'].max).toEqual(0);
-    expect(stats['number'].mean).toEqual(0);
-    expect(stats['number'].variance).toEqual(NaN);
-    expect(stats['number'].stddev).toEqual(NaN);
-  });
-});
-
-describeAllEnvs('scaleTo01', () => {
-  it('scales numeric data to the [0, 1] interval', async () => {
-    const ds = new TestDataset().skip(55) as Dataset<TabularRecord>;
-    const scaleFn = scaleTo01(55, 99 * 99 * 99);
-    const scaledDataset = ds.map(x => ({'Tensor': scaleFn(x['Tensor'])}));
-
-    const stats = await computeDatasetStatistics(scaledDataset);
-    expect(stats['Tensor'].min).toBeCloseTo(0);
-    expect(stats['Tensor'].max).toBeCloseTo(1);
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/test_node.ts b/tfjs-master/tfjs-data/src/test_node.ts
deleted file mode 100644
index 7b134fd8c..000000000
--- a/tfjs-master/tfjs-data/src/test_node.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-// tslint:disable-next-line: no-imports-from-dist
-import {setTestEnvs} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-// tslint:disable-next-line:no-require-imports
-const jasmine = require('jasmine');
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-setTestEnvs([{name: 'node', backendName: 'cpu'}]);
-const unitTests = 'tfjs-data/src/**/*_test.js';
-const runner = new jasmine();
-runner.loadConfig({spec_files: [unitTests], random: false});
-runner.execute();
diff --git a/tfjs-master/tfjs-data/src/types.ts b/tfjs-master/tfjs-data/src/types.ts
deleted file mode 100644
index 77b1b4114..000000000
--- a/tfjs-master/tfjs-data/src/types.ts
+++ /dev/null
@@ -1,219 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {DataType, TensorContainer} from '@tensorflow/tfjs-core';
-
-// Maybe this should be called 'NestedContainer'-- that's just a bit unwieldy.
-export type Container<T> = ContainerObject<T>|ContainerArray<T>;
-
-export type ContainerOrT<T> = Container<T>|T;
-
-export interface ContainerObject<T> {
-  [x: string]: ContainerOrT<T>;
-}
-export interface ContainerArray<T> extends Array<ContainerOrT<T>> {}
-
-/**
- * Types supported by FileChunkIterator in both Browser and Node Environment.
- */
-export type FileElement = File|Blob|Uint8Array;
-
-/**
- * A dictionary containing column level configurations when reading and decoding
- * CSV file(s) from csv source.
- * Has the following fields:
- * - `required` If value in this column is required. If set to `true`, throw an
- * error when it finds an empty value.
- *
- * - `dtype` Data type of this column. Could be int32, float32, bool, or string.
- *
- * - `default` Default value of this column.
- *
- * - `isLabel` Whether this column is label instead of features. If isLabel is
- * `true` for at least one column, the .csv() API will return an array of two
- * items: the first item is a dict of features key/value pairs, the second item
- * is a dict of labels key/value pairs. If no column is marked as label returns
- * a dict of features only.
- */
-export interface ColumnConfig {
-  required?: boolean;
-  dtype?: DataType;
-  default?: TensorContainer;
-  isLabel?: boolean;
-}
-
-/**
- * Interface for configuring dataset when reading and decoding from CSV file(s).
- */
-export interface CSVConfig {
-  /**
-   * A boolean value that indicates whether the first row of provided CSV file
-   * is a header line with column names, and should not be included in the data.
-   */
-  hasHeader?: boolean;
-
-  /**
-   * A list of strings that corresponds to the CSV column names, in order. If
-   * provided, it ignores the column names inferred from the header row. If not
-   * provided, infers the column names from the first row of the records. If
-   * `hasHeader` is false and `columnNames` is not provided, this method will
-   * throw an error.
-   */
-  columnNames?: string[];
-
-  /**
-   * A dictionary whose key is column names, value is an object stating if this
-   * column is required, column's data type, default value, and if this column
-   * is label. If provided, keys must correspond to names provided in
-   * `columnNames` or inferred from the file header lines. If any column is
-   * marked as label, the .csv() API will return an array of two items: the
-   * first item is a dict of features key/value pairs, the second item is a dict
-   * of labels key/value pairs. If no column is marked as label returns a dict
-   * of features only.
-   *
-   * Has the following fields:
-   * - `required` If value in this column is required. If set to `true`, throw
-   * an error when it finds an empty value.
-   *
-   * - `dtype` Data type of this column. Could be int32, float32, bool, or
-   * string.
-   *
-   * - `default` Default value of this column.
-   *
-   * - `isLabel` Whether this column is label instead of features. If isLabel is
-   * `true` for at least one column, the element in returned `CSVDataset` will
-   * be an object of {xs: features, ys: labels}: xs is a dict of features
-   * key/value pairs, ys is a dict of labels key/value pairs. If no column is
-   * marked as label, returns a dict of features only.
-   */
-  columnConfigs?: {[key: string]: ColumnConfig};
-
-  /**
-   * If true, only columns provided in `columnConfigs` will be parsed and
-   * provided during iteration.
-   */
-  configuredColumnsOnly?: boolean;
-
-  /**
-   * The string used to parse each line of the input file.
-   */
-  delimiter?: string;
-
-  /**
-   * If true, delimiter field should be null. Parsing delimiter is whitespace
-   * and treat continuous multiple whitespace as one delimiter.
-   */
-  delimWhitespace?: boolean;
-}
-
-/**
- * Interface configuring data from webcam video stream.
- */
-export interface WebcamConfig {
-  /**
-   * A string specifying which camera to use on device. If the value is
-   * 'user', it will use front camera. If the value is 'environment', it will
-   * use rear camera.
-   */
-  facingMode?: 'user'|'environment';
-
-  /**
-   * A string used to request a specific camera. The deviceId can be obtained by
-   * calling `mediaDevices.enumerateDevices()`.
-   */
-  deviceId?: string;
-
-  /**
-   * Specifies the width of the output tensor. The actual width of the
-   * HTMLVideoElement (if provided) can be different and the final image will be
-   * resized to match resizeWidth.
-   */
-  resizeWidth?: number;
-
-  /**
-   * Specifies the height of the output tensor. The actual height of the
-   * HTMLVideoElement (if provided) can be different and the final image will be
-   * resized to match resizeHeight.
-   */
-  resizeHeight?: number;
-
-  /**
-   * A boolean value that indicates whether to crop the video frame from center.
-   * If true, `resizeWidth` and `resizeHeight` must be specified; then an image
-   * of size `[resizeWidth, resizeHeight]` is taken from the center of the frame
-   * without scaling. If false, the entire image is returned (perhaps scaled to
-   * fit in `[resizeWidth, resizeHeight]`, if those are provided).
-   */
-  centerCrop?: boolean;
-}
-
-/**
- * Interface configuring data from microphone audio stream.
- */
-export interface MicrophoneConfig {
-  // A number representing Audio sampling rate in Hz. either 44,100 or 48,000.
-  // If provided sample rate is not available on the device, it will throw an
-  // error. Optional, defaults to the sample rate available on device.
-  sampleRateHz?: 44100|48000;
-
-  // The FFT length of each spectrogram column. A higher value will result in
-  // more details in the frequency domain but fewer details in the time domain.
-  // Must be a power of 2 between 2 to 4 and 2 to 14, so one of: 16, 32, 64,
-  // 128, 256, 512, 1024, 2048, 4096, 8192, and 16384. It will throw an error if
-  // it is an invalid number.Defaults to 1024.
-  fftSize?: number;
-
-  // Truncate each spectrogram column at how many frequency points. Each audio
-  // frame contains fftSize, for example, 1024 samples which covers voice
-  // frequency from 0 to 22,500 Hz. However, the frequency content relevant to
-  // human speech is generally in the frequency range from 0 to 5000 Hz. So each
-  // audio frame only need 232 columns to cover the frequency range of human
-  // voice. This will be part of the output spectrogram tensor shape. Optional,
-  // defaults to null which means no truncation.
-  columnTruncateLength?: number;
-
-  // Number of audio frames per spectrogram. The time duration of one
-  // spectrogram equals to numFramesPerSpectrogram*fftSize/sampleRateHz second.
-  // For example: the device sampling rate is 44,100 Hz, and fftSize is 1024,
-  // then each frame duration between two sampling is 0.023 second. If the
-  // purpose is for an audio model to recognize speech command that last 1
-  // second, each spectrogram should contain 1/0.023, which is 43 frames. This
-  // will be part of the output spectrogram tensor shape. Optional, defaults to
-  // 43 so that each audio data last 1 second.
-  numFramesPerSpectrogram?: number;
-
-  // A dictionary specifying the requirements of audio to request, such as
-  // deviceID, echoCancellation, etc. Optional.
-  audioTrackConstraints?: MediaTrackConstraints;
-
-  // The averaging constant with the last analysis frame -- basically, it makes
-  // the transition between values over time smoother. It is used by
-  // AnalyserNode interface during FFT. Optional, has to be between 0 and 1,
-  // defaults to 0.
-  smoothingTimeConstant?: number;
-
-  // Whether to collect the frequency domain audio spectrogram in
-  // MicrophoneIterator result. If both includeSpectrogram and includeWaveform
-  // are false, it will throw an error. Defaults to true.
-  includeSpectrogram?: boolean;
-
-  // Whether to collect the time domain audio waveform in MicrophoneIterator
-  // result. If both includeSpectrogram and includeWaveform are false, it will
-  // throw an error. Defaults to false.
-  includeWaveform?: boolean;
-}
diff --git a/tfjs-master/tfjs-data/src/util/deep_clone.ts b/tfjs-master/tfjs-data/src/util/deep_clone.ts
deleted file mode 100644
index 178df1306..000000000
--- a/tfjs-master/tfjs-data/src/util/deep_clone.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {deepMap, DeepMapResult, isIterable} from './deep_map';
-
-export function deepClone<T>(container: T): T {
-  return deepMap(container, cloneIfTensor);
-}
-
-// tslint:disable-next-line: no-any
-function cloneIfTensor(item: any): DeepMapResult {
-  if (item instanceof tf.Tensor) {
-    return ({value: item.clone(), recurse: false});
-  } else if (isIterable(item)) {
-    return {value: null, recurse: true};
-  } else {
-    return {value: item, recurse: false};
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/util/deep_map.ts b/tfjs-master/tfjs-data/src/util/deep_map.ts
deleted file mode 100644
index 86385d0f0..000000000
--- a/tfjs-master/tfjs-data/src/util/deep_map.ts
+++ /dev/null
@@ -1,293 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-
-// tslint:disable:no-any
-
-/**
- * A return value for a mapping function that can be applied via deepMap.
- *
- * If recurse is true, the value should be empty, and iteration will continue
- * into the object or array.
- */
-export type DeepMapResult = {
-  value: any,
-  recurse: boolean
-};
-
-/**
- * Apply a mapping function to a nested structure in a recursive manner.
- *
- * The result of the mapping is an object with the same nested structure (i.e.,
- * of arrays and dicts) as the input, except that some subtrees are replaced,
- * according to the results of the mapping function.
- *
- * Mappings are memoized.  Thus, if the nested structure contains the same
- * object in multiple positions, the output will contain the same mapped object
- * in those positions.  Cycles are not supported, however.
- *
- * @param input: The object to which to apply the mapping function.
- * @param mapFn: A function that expects a single node of the object tree, and
- *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a
- *   replacement value for that node (i.e., replacing the subtree), or indicates
- *   that the node should be processed recursively.
- */
-export function deepMap(input: any, mapFn: (x: any) => DeepMapResult): any|
-    any[] {
-  return deepMapInternal(input, mapFn);
-}
-
-/**
- * @param seen: A Map of known object mappings (i.e., memoized results of
- *   `mapFn()`)
- * @param containedIn: An set containing objects on the reference path currently
- *   being processed (used to detect cycles).
- */
-function deepMapInternal(
-    input: any, mapFn: (x: any) => DeepMapResult,
-    seen: Map<any, any> = new Map(), containedIn: Set<{}> = new Set()): any|
-    any[] {
-  if (input == null) {
-    return null;
-  }
-  if (typeof Blob === 'function' && input instanceof Blob) {
-    return input.slice();
-  }
-
-  if (containedIn.has(input)) {
-    throw new Error('Circular references are not supported.');
-  }
-  if (seen.has(input)) {
-    return seen.get(input);
-  }
-  const result = mapFn(input);
-
-  if (result.recurse && result.value !== null) {
-    throw new Error(
-        'A deep map function may not return both a value and recurse=true.');
-  }
-
-  if (!result.recurse) {
-    seen.set(input, result.value);
-    return result.value;
-  } else if (isIterable(input)) {
-    // tslint:disable-next-line:no-any
-    const mappedIterable: any|any[] = Array.isArray(input) ? [] : {};
-    containedIn.add(input);
-    for (const k in input) {
-      const child = input[k];
-      const childResult = deepMapInternal(child, mapFn, seen, containedIn);
-      mappedIterable[k] = childResult;
-    }
-    containedIn.delete(input);
-    if (input.__proto__) {
-      mappedIterable.__proto__ = input.__proto__;
-    }
-    return mappedIterable;
-  } else {
-    throw new Error(`Can't recurse into non-iterable type: ${input}`);
-  }
-}
-
-// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion
-// with zip()
-
-/**
- * Zip nested structures together in a recursive manner.
- *
- * This has the effect of transposing or pivoting data, e.g. converting it from
- * a row-major representation to a column-major representation.
- *
- * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns
- * `{a: [1, 3], b: [2, 4]}`.
- *
- * The inputs should all have the same nested structure (i.e., of arrays and
- * dicts).  The result is a single object with the same nested structure, where
- * the leaves are arrays collecting the values of the inputs at that location
- * (or, optionally, the result of a custom function applied to those arrays).
- *
- * @param inputs: An array of the objects to zip together.
- * @param zipFn: (optional) A function that expects an array of elements at a
- *   single node of the object tree, and returns a `DeepMapResult`.  The
- *   `DeepMapResult` either provides a result value for that node (i.e.,
- *   representing the subtree), or indicates that the node should be processed
- *   recursively.  The default zipFn recurses as far as possible and places
- *   arrays at the leaves.
- */
-export function deepZip(
-    inputs: any[], zipFn: (xs: any[]) => DeepMapResult = zipToList): any|any[] {
-  return deepZipInternal(inputs, zipFn);
-}
-
-/**
- * @param containedIn: An set containing objects on the reference path currently
- *   being processed (used to detect cycles).
- */
-function deepZipInternal(
-    inputs: any[], zipFn: (xs: any[]) => DeepMapResult,
-    containedIn: Set<{}> = new Set()): any|any[] {
-  // The recursion follows the structure of input 0; it's assumed that all the
-  // other inputs have the same structure.
-  const input = inputs[0];
-  if (containedIn.has(input)) {
-    throw new Error('Circular references are not supported.');
-  }
-  const result = zipFn(inputs);
-
-  if (result.recurse && result.value !== null) {
-    throw new Error(
-        'A deep zip function may not return both a value and recurse=true.');
-  }
-
-  if (!result.recurse) {
-    return result.value;
-  } else if (isIterable(input)) {
-    // tslint:disable-next-line:no-any
-    const mappedIterable: any|any[] = Array.isArray(input) ? [] : {};
-    containedIn.add(input);
-    for (const k in input) {
-      const children = inputs.map(x => x[k]);
-      const childResult = deepZipInternal(children, zipFn, containedIn);
-      mappedIterable[k] = childResult;
-    }
-    containedIn.delete(input);
-    return mappedIterable;
-  } else {
-    throw new Error(`Can't recurse into non-iterable type: ${input}`);
-  }
-}
-
-// tslint:disable-next-line:no-any
-export function zipToList(x: any[]): DeepMapResult {
-  if (x === null) {
-    return null;
-  }
-  // TODO(soergel): validate array type?
-
-  if (isIterable(x[0])) {
-    return {value: null, recurse: true};
-  } else {
-    return {value: x, recurse: false};
-  }
-}
-
-/**
- * A return value for an async map function for use with deepMapAndAwaitAll.
- *
- * If recurse is true, the value should be empty, and iteration will continue
- * into the object or array.
- */
-export type DeepMapAsyncResult = {
-  value: Promise<any>,
-  recurse: boolean
-};
-
-/**
- * Apply an async mapping function to a nested structure in a recursive manner.
- *
- * This first creates a nested structure of Promises, and then awaits all of
- * those, resulting in a single Promise for a resolved nested structure.
- *
- * The result of the mapping is an object with the same nested structure (i.e.,
- * of arrays and dicts) as the input, except that some subtrees are replaced,
- * according to the results of the mapping function.
- *
- * Mappings are memoized.  Thus, if the nested structure contains the same
- * object in multiple positions, the output will contain the same mapped object
- * in those positions.  Cycles are not supported, however.
- *
- * @param input: The object to which to apply the mapping function.
- * @param mapFn: A function that expects a single node of the object tree, and
- *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides
- *   a `Promise` for a replacement value for that node (i.e., replacing the
- *   subtree), or indicates that the node should be processed recursively.  Note
- *   that the decision whether or not to recurse must be made immediately; only
- *   the mapped value may be promised.
- */
-export async function deepMapAndAwaitAll(
-    input: any, mapFn: (x: any) => DeepMapAsyncResult): Promise<any|any[]> {
-  const seen: Map<any, any> = new Map();
-
-  // First do a normal deepMap, collecting Promises in 'seen' as a side effect.
-  deepMapInternal(input, mapFn, seen);
-
-  // Replace the Promises in 'seen' in place.
-  // Note TypeScript provides no async map iteration, and regular map iteration
-  // is broken too, so sadly we have to do Array.from() to make it work.
-  // (There's no advantage to Promise.all(), and that would be tricky anyway.)
-  for (const key of Array.from(seen.keys())) {
-    const value = seen.get(key);
-    if (tf.util.isPromise(value)) {
-      const mappedValue = await value;
-      seen.set(key, mappedValue);
-    }
-  }
-
-  // Normal deepMap again, this time filling in the resolved values.
-  // It's unfortunate that we have to do two passes.
-  // TODO(soergel): test performance and think harder about a fast solution.
-  const result = deepMapInternal(input, mapFn, seen);
-  return result;
-}
-
-/**
- * Determine whether the argument is iterable.
- *
- * @returns true if the argument is an array or any non-Tensor object.
- */
-// tslint:disable-next-line:no-any
-export function isIterable(obj: any): boolean {
-  let isTextDecoder = false;
-  if (tf.env().get('IS_BROWSER')) {
-    isTextDecoder = obj instanceof TextDecoder;
-  } else {
-    // tslint:disable-next-line:no-require-imports
-    const {StringDecoder} = require('string_decoder');
-    isTextDecoder = obj instanceof StringDecoder;
-  }
-  return obj != null && (!ArrayBuffer.isView(obj)) &&
-      (Array.isArray(obj) ||
-       (typeof obj === 'object' && !(obj instanceof tf.Tensor) &&
-        !(obj instanceof Promise) && !isTextDecoder));
-}
-
-/**
- * Determine whether the argument can be converted to Tensor.
- *
- * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does
- * not.
- *
- * @returns true if the argument can be converted to Tensor.
- */
-// tslint:disable-next-line:no-any
-export function canTensorify(obj: any): boolean {
-  return obj == null || isPrimitive(obj) || Array.isArray(obj) ||
-      (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||
-      tf.util.isTypedArray(obj);
-}
-
-/**
- * Returns true if the given `value` is a primitive type. Otherwise returns
- * false. This is equivalant to node util.isPrimitive
- */
-function isPrimitive(value: any): boolean {
-  return (
-      value === null ||
-      (typeof value !== 'object' && typeof value !== 'function'));
-}
diff --git a/tfjs-master/tfjs-data/src/util/deep_map_test.ts b/tfjs-master/tfjs-data/src/util/deep_map_test.ts
deleted file mode 100644
index dd4cfc92b..000000000
--- a/tfjs-master/tfjs-data/src/util/deep_map_test.ts
+++ /dev/null
@@ -1,276 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-
-// tslint:disable-next-line:max-line-length
-import {canTensorify, deepMap, deepMapAndAwaitAll, DeepMapAsyncResult, DeepMapResult, deepZip, isIterable} from './deep_map';
-
-const integerNames = [
-  'zero', 'one', 'two', 'three', ['an array representing', 'four'],
-  {five: 'I am a dict'}
-];
-
-// tslint:disable-next-line:no-any
-function transform(x: any): DeepMapResult {
-  if (x === null) {
-    return null;
-  }
-  if (typeof (x) === 'number') {
-    return {value: integerNames[x], recurse: false};
-  }
-
-  if (isIterable(x)) {
-    return {value: null, recurse: true};
-  } else {
-    return {value: x, recurse: false};
-  }
-}
-
-// tslint:disable-next-line:no-any
-function asyncTransform(x: any): DeepMapAsyncResult {
-  const result = transform(x);
-  return {
-    value: result.value === null ? null : Promise.resolve(result.value),
-    recurse: result.recurse
-  };
-}
-
-describe('deepMap', () => {
-  it('maps single mappable objects', () => {
-    expect(deepMap(null, transform)).toEqual(null);
-    expect(deepMap(1, transform)).toEqual('one');
-    expect(deepMap(3, transform)).toEqual('three');
-    expect(deepMap('hello', transform)).toEqual('hello');
-  });
-  it('maps arrays of mappable objects', () => {
-    expect(deepMap([null, 1], transform)).toEqual([null, 'one']);
-    expect(deepMap([1, 2, 3], transform)).toEqual(['one', 'two', 'three']);
-    expect(deepMap([1, 'hello', 3, null], transform)).toEqual([
-      'one', 'hello', 'three', null
-    ]);
-  });
-  it('maps objects containing mappable fields', () => {
-    expect(deepMap({a: null, b: 1}, transform)).toEqual({a: null, b: 'one'});
-    expect(deepMap({a: 1, b: 2, c: 3}, transform))
-        .toEqual({a: 'one', b: 'two', c: 'three'});
-    expect(deepMap({a: 1, b: 'hello', c: 3, d: null}, transform))
-        .toEqual({a: 'one', b: 'hello', c: 'three', d: null});
-  });
-  it('maps nested structures containing mappable fields', () => {
-    const input = {a: 'hello', b: [2, 3, null, {ba: 0, bb: 'world'}]};
-    const expected = {
-      a: 'hello',
-      b: ['two', 'three', null, {ba: 'zero', bb: 'world'}]
-    };
-    expect(deepMap(input, transform)).toEqual(expected);
-  });
-  it('maps elements that produce arrays or objects', () => {
-    const input = {a: 'hello', b: [2, 4, null, {ba: 5, bb: 'world'}]};
-    const expected = {
-      a: 'hello',
-      b: [
-        'two', ['an array representing', 'four'], null,
-        {ba: {five: 'I am a dict'}, bb: 'world'}
-      ]
-    };
-    expect(deepMap(input, transform)).toEqual(expected);
-  });
-  it('handles repetitions', () => {
-    const input = {a1: 4, a2: 4, b1: 5, b2: 5};
-    const result = deepMap(input, transform);
-    expect(result).toEqual({
-      a1: ['an array representing', 'four'],
-      a2: ['an array representing', 'four'],
-      b1: {five: 'I am a dict'},
-      b2: {five: 'I am a dict'}
-    });
-    const b1Mapped = result.b1;
-    const b2Mapped = result.b2;
-    expect(b2Mapped).toBe(b1Mapped);
-  });
-  it('detects and rejects cycles', () => {
-    // tslint:disable-next-line:no-any
-    const b: any[] = [2, 3, null, {ba: 0, bb: 'world'}];
-    const c = {a: 'hello', b};
-    b[4] = c;
-    const input = [b, c];
-    expect(() => deepMap(input, transform)).toThrowError();
-  });
-  it('handle prototype', () => {
-    class Test {
-      first() {
-        return true;
-      }
-    }
-    class ChildTest extends Test {
-      second() {
-        return false;
-      }
-    }
-    // tslint:disable-next-line:no-any
-    const b: ChildTest = new ChildTest();
-    const transformed = deepMap(b, transform);
-    expect(transformed.first).toBeDefined();
-    expect(transformed.second).toBeDefined();
-  });
-});
-
-describe('asyncDeepMap', () => {
-  it('Maps single mappable objects', async () => {
-    expect(await deepMapAndAwaitAll(null, asyncTransform)).toEqual(null);
-    expect(await deepMapAndAwaitAll(1, asyncTransform)).toEqual('one');
-    expect(await deepMapAndAwaitAll(3, asyncTransform)).toEqual('three');
-    expect(await deepMapAndAwaitAll('hello', asyncTransform)).toEqual('hello');
-  });
-  it('Maps arrays of mappable objects', async () => {
-    expect(await deepMapAndAwaitAll([null, 1], asyncTransform)).toEqual([
-      null, 'one'
-    ]);
-    expect(await deepMapAndAwaitAll([1, 2, 3], asyncTransform)).toEqual([
-      'one', 'two', 'three'
-    ]);
-    expect(await deepMapAndAwaitAll([1, 'hello', 3, null], asyncTransform))
-        .toEqual(['one', 'hello', 'three', null]);
-  });
-  it('Maps objects containing mappable fields', async () => {
-    expect(await deepMapAndAwaitAll({a: null, b: 1}, asyncTransform))
-        .toEqual({a: null, b: 'one'});
-    expect(await deepMapAndAwaitAll({a: 1, b: 2, c: 3}, asyncTransform))
-        .toEqual({a: 'one', b: 'two', c: 'three'});
-    expect(await deepMapAndAwaitAll(
-               {a: 1, b: 'hello', c: 3, d: null}, asyncTransform))
-        .toEqual({a: 'one', b: 'hello', c: 'three', d: null});
-  });
-  it('Maps nested structures containing mappable fields', async () => {
-    const input = {a: 'hello', b: [2, 3, null, {ba: 0, bb: 'world'}]};
-    const expected = {
-      a: 'hello',
-      b: ['two', 'three', null, {ba: 'zero', bb: 'world'}]
-    };
-    expect(await deepMapAndAwaitAll(input, asyncTransform)).toEqual(expected);
-  });
-  it('maps elements that produce arrays or objects', async () => {
-    const input = {a: 'hello', b: [2, 4, null, {ba: 5, bb: 'world'}]};
-    const expected = {
-      a: 'hello',
-      b: [
-        'two', ['an array representing', 'four'], null,
-        {ba: {five: 'I am a dict'}, bb: 'world'}
-      ]
-    };
-    expect(await deepMapAndAwaitAll(input, asyncTransform)).toEqual(expected);
-  });
-  it('handles repetitions', async () => {
-    const input = {a1: 4, a2: 4, b1: 5, b2: 5};
-    const result = await deepMapAndAwaitAll(input, asyncTransform);
-    expect(result).toEqual({
-      a1: ['an array representing', 'four'],
-      a2: ['an array representing', 'four'],
-      b1: {five: 'I am a dict'},
-      b2: {five: 'I am a dict'}
-    });
-    const b1Mapped = result.b1;
-    const b2Mapped = result.b2;
-    expect(b2Mapped).toBe(b1Mapped);
-  });
-  it('detects and rejects cycles', async () => {
-    try {
-      // tslint:disable-next-line:no-any
-      const b: any[] = [2, 3, null, {ba: 0, bb: 'world'}];
-      const c = {a: 'hello', b};
-      b[4] = c;
-      const input = [b, c];
-      await deepMapAndAwaitAll(input, asyncTransform);
-      fail();
-    } catch (e) {
-      expect(e.message).toBe('Circular references are not supported.');
-    }
-  });
-});
-
-describe('deepZip', () => {
-  it('zips arrays of primitives', () => {
-    expect(deepZip([1, 2, 3])).toEqual([1, 2, 3]);
-    expect(deepZip([null, 1])).toEqual([null, 1]);
-    expect(deepZip([1, 'hello', 3, null])).toEqual([1, 'hello', 3, null]);
-  });
-  it('zips objects containing simple fields', () => {
-    expect(deepZip([
-      {a: 1, b: 2}, {a: 3, b: 4}
-    ])).toEqual({a: [1, 3], b: [2, 4]});
-    expect(deepZip([
-      {a: 1, b: 'hello', c: 4, d: null}, {a: 2, b: 'world', c: 5, d: 7},
-      {a: 3, b: '!', c: 6, d: null}
-    ])).toEqual({
-      a: [1, 2, 3],
-      b: ['hello', 'world', '!'],
-      c: [4, 5, 6],
-      d: [null, 7, null]
-    });
-  });
-  it('zips arrays containing simple fields', () => {
-    expect(deepZip([[1, 2], [3, 4]])).toEqual([[1, 3], [2, 4]]);
-  });
-  it('zips nested structures', () => {
-    const input = [
-      {a: 'plums', b: [1, 2, null, {ba: 3, bb: 'sweet'}]},
-      {a: 'icebox', b: [3, 4, 5, {ba: 6, bb: 'cold'}]}
-    ];
-    const expected = {
-      a: ['plums', 'icebox'],
-      b: [[1, 3], [2, 4], [null, 5], {ba: [3, 6], bb: ['sweet', 'cold']}]
-    };
-    expect(deepZip(input)).toEqual(expected);
-  });
-  it('detects and rejects cycles', () => {
-    // tslint:disable-next-line:no-any
-    const b: any[] = [2, 3, null, {ba: 0, bb: 'world'}];
-    const c = {a: 'hello', b};
-    b[4] = c;
-    const input = [b, c];
-    expect(() => deepZip(input)).toThrowError();
-  });
-
-  describe('canTensorify', () => {
-    it('returns true for primitives', () => {
-      expect(canTensorify('a')).toBeTruthy();
-      expect(canTensorify(1)).toBeTruthy();
-      expect(canTensorify(true)).toBeTruthy();
-    });
-
-    it('returns true for arrays', () => {
-      expect(canTensorify(['a', 'b', 'c'])).toBeTruthy();
-      expect(canTensorify([1, 2, 3])).toBeTruthy();
-      expect(canTensorify([true, false, true])).toBeTruthy();
-    });
-
-    it('returns true for TypedArrays', () => {
-      expect(canTensorify(new Float32Array([1, 2, 3]))).toBeTruthy();
-      expect(canTensorify(new Int32Array([1, 2, 3]))).toBeTruthy();
-    });
-
-    it('returns true for Tensors', () => {
-      expect(canTensorify(tf.tensor([1, 2, 3]))).toBeTruthy();
-    });
-
-    it('returns false for non-Tensor objects', () => {
-      expect(canTensorify({a: 1, b: 2, c: 3})).toBeFalsy();
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/util/growing_ring_buffer.ts b/tfjs-master/tfjs-data/src/util/growing_ring_buffer.ts
deleted file mode 100644
index e976bf5c0..000000000
--- a/tfjs-master/tfjs-data/src/util/growing_ring_buffer.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {RingBuffer} from './ring_buffer';
-
-export class GrowingRingBuffer<T> extends RingBuffer<T> {
-  private static INITIAL_CAPACITY = 32;
-
-  /**
-   * Constructs a `GrowingRingBuffer`.
-   */
-  constructor() {
-    super(GrowingRingBuffer.INITIAL_CAPACITY);
-  }
-
-  override isFull() {
-    return false;
-  }
-
-  override push(value: T) {
-    if (super.isFull()) {
-      this.expand();
-    }
-    super.push(value);
-  }
-
-  override unshift(value: T) {
-    if (super.isFull()) {
-      this.expand();
-    }
-    super.unshift(value);
-  }
-
-  /**
-   * Doubles the capacity of the buffer.
-   */
-  private expand() {
-    const newCapacity = this.capacity * 2;
-    const newData = new Array<T>(newCapacity);
-    const len = this.length();
-
-    // Rotate the buffer to start at index 0 again, since we can't just
-    // allocate more space at the end.
-    for (let i = 0; i < len; i++) {
-      newData[i] = this.get(this.wrap(this.begin + i));
-    }
-
-    this.data = newData;
-    this.capacity = newCapacity;
-    this.doubledCapacity = 2 * this.capacity;
-    this.begin = 0;
-    this.end = len;
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/util/ring_buffer.ts b/tfjs-master/tfjs-data/src/util/ring_buffer.ts
deleted file mode 100644
index e76fc1c67..000000000
--- a/tfjs-master/tfjs-data/src/util/ring_buffer.ts
+++ /dev/null
@@ -1,176 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-/**
- * A ring buffer, providing O(1) FIFO, LIFO, and related operations.
- */
-export class RingBuffer<T> {
-  // Note we store the indices in the range 0 <= index < 2*capacity.
-  // This allows us to distinguish the full from the empty case.
-  // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/
-  protected begin = 0;  // inclusive
-  protected end = 0;    // exclusive
-  protected doubledCapacity: number;
-
-  protected data: T[];
-
-  /**
-   * Constructs a `RingBuffer`.
-   * @param capacity The number of items that the buffer can accomodate.
-   */
-  constructor(public capacity: number) {
-    if (capacity == null) {
-      throw new RangeError('Can\'t create a ring buffer of unknown capacity.');
-    }
-    if (capacity < 1) {
-      throw new RangeError('Can\'t create ring buffer of capacity < 1.');
-    }
-    this.data = new Array<T>(capacity);
-    this.doubledCapacity = 2 * capacity;
-  }
-
-  /**
-   * Map any index into the range 0 <= index < 2*capacity.
-   */
-  protected wrap(index: number) {
-    // don't trust % on negative numbers
-    while (index < 0) {
-      index += this.doubledCapacity;
-    }
-    return index % this.doubledCapacity;
-  }
-
-  protected get(index: number) {
-    if (index < 0) {
-      throw new RangeError('Can\'t get item at a negative index.');
-    }
-    return this.data[index % this.capacity];
-  }
-
-  protected set(index: number, value: T) {
-    if (index < 0) {
-      throw new RangeError('Can\'t set item at a negative index.');
-    }
-    this.data[index % this.capacity] = value;
-  }
-
-  /**
-   * Returns the current number of items in the buffer.
-   */
-  length(): number {
-    let length = this.end - this.begin;
-    if (length < 0) {
-      length = this.doubledCapacity + length;
-    }
-    return length;
-  }
-
-  /**
-   * Reports whether the buffer is full.
-   * @returns true if the number of items in the buffer equals its capacity, and
-   *   false otherwise.
-   */
-  isFull() {
-    return this.length() === this.capacity;
-  }
-
-  /**
-   * Reports whether the buffer is empty.
-   * @returns true if the number of items in the buffer equals zero, and
-   *   false otherwise.
-   */
-  isEmpty() {
-    return this.length() === 0;
-  }
-
-  /**
-   * Adds an item to the end of the buffer.
-   */
-  push(value: T) {
-    if (this.isFull()) {
-      throw new RangeError('Ring buffer is full.');
-    }
-    this.set(this.end, value);
-    this.end = this.wrap(this.end + 1);
-  }
-
-  /**
-   * Adds many items to the end of the buffer, in order.
-   */
-  pushAll(values: T[]) {
-    for (const value of values) {
-      this.push(value);
-    }
-  }
-
-  /**
-   * Removes and returns the last item in the buffer.
-   */
-  pop(): T {
-    if (this.isEmpty()) {
-      throw new RangeError('Ring buffer is empty.');
-    }
-    this.end = this.wrap(this.end - 1);
-    const result = this.get(this.end);
-    this.set(this.end, undefined);
-    return result;
-  }
-
-  /**
-   * Adds an item to the beginning of the buffer.
-   */
-  unshift(value: T) {
-    if (this.isFull()) {
-      throw new RangeError('Ring buffer is full.');
-    }
-    this.begin = this.wrap(this.begin - 1);
-    this.set(this.begin, value);
-  }
-
-  /**
-   * Removes and returns the first item in the buffer.
-   */
-  shift(): T {
-    if (this.isEmpty()) {
-      throw new RangeError('Ring buffer is empty.');
-    }
-    const result = this.get(this.begin);
-    this.set(this.begin, undefined);
-    this.begin = this.wrap(this.begin + 1);
-    return result;
-  }
-
-  /**
-   * Removes and returns a specific item in the buffer, and moves the last item
-   * to the vacated slot.  This is useful for implementing a shuffling stream.
-   * Note that this operation necessarily scrambles the original order.
-   *
-   * @param relativeIndex: the index of the item to remove, relative to the
-   *   first item in the buffer (e.g., hiding the ring nature of the underlying
-   *   storage).
-   */
-  shuffleExcise(relativeIndex: number): T {
-    if (this.isEmpty()) {
-      throw new RangeError('Ring buffer is empty.');
-    }
-    const index = this.wrap(this.begin + relativeIndex);
-    const result = this.get(index);
-    this.set(index, this.pop());
-    return result;
-  }
-}
diff --git a/tfjs-master/tfjs-data/src/util/ring_buffer_test.ts b/tfjs-master/tfjs-data/src/util/ring_buffer_test.ts
deleted file mode 100644
index a8f2ce556..000000000
--- a/tfjs-master/tfjs-data/src/util/ring_buffer_test.ts
+++ /dev/null
@@ -1,158 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {RingBuffer} from './ring_buffer';
-
-describe('RingBuffer', () => {
-  it('Works as a stack (LIFO)', () => {
-    const ring = new RingBuffer<number>(10);
-    expect(ring.length()).toEqual(0);
-
-    for (let i = 0; i < 10; i++) {
-      ring.push(i);
-    }
-    expect(ring.length()).toEqual(10);
-
-    const result: number[] = [];
-    for (let i = 0; i < 10; i++) {
-      result[i] = ring.pop();
-    }
-    expect(result).toEqual([9, 8, 7, 6, 5, 4, 3, 2, 1, 0]);
-    expect(ring.length()).toEqual(0);
-  });
-
-  it('Works as a queue (FIFO)', () => {
-    const ring = new RingBuffer<number>(10);
-    expect(ring.length()).toEqual(0);
-
-    for (let i = 0; i < 10; i++) {
-      ring.push(i);
-    }
-    expect(ring.length()).toEqual(10);
-
-    const result: number[] = [];
-    for (let i = 0; i < 7; i++) {
-      result[i] = ring.shift();
-    }
-    expect(result).toEqual([0, 1, 2, 3, 4, 5, 6]);
-    expect(ring.length()).toEqual(3);
-
-    // test wrapping
-    for (let i = 10; i < 15; i++) {
-      ring.push(i);
-    }
-    expect(ring.length()).toEqual(8);
-
-    const result2: number[] = [];
-    for (let i = 0; i < 8; i++) {
-      result2[i] = ring.shift();
-    }
-    expect(result2).toEqual([7, 8, 9, 10, 11, 12, 13, 14]);
-    expect(ring.length()).toEqual(0);
-  });
-
-  it('Works as a reverse stack (LIFO)', () => {
-    const ring = new RingBuffer<number>(10);
-    expect(ring.length()).toEqual(0);
-
-    for (let i = 0; i < 10; i++) {
-      ring.unshift(i);
-    }
-    expect(ring.length()).toEqual(10);
-
-    const result: number[] = [];
-    for (let i = 0; i < 10; i++) {
-      result[i] = ring.shift();
-    }
-    expect(result).toEqual([9, 8, 7, 6, 5, 4, 3, 2, 1, 0]);
-    expect(ring.length()).toEqual(0);
-  });
-
-  it('Works as a reverse queue (FIFO)', () => {
-    const ring = new RingBuffer<number>(10);
-    expect(ring.length()).toEqual(0);
-
-    for (let i = 0; i < 10; i++) {
-      ring.unshift(i);
-    }
-    expect(ring.length()).toEqual(10);
-
-    const result: number[] = [];
-    for (let i = 0; i < 10; i++) {
-      result[i] = ring.pop();
-    }
-    expect(result).toEqual([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]);
-    expect(ring.length()).toEqual(0);
-  });
-
-  it('Works as a shuffling queue', () => {
-    const ring = new RingBuffer<number>(10);
-    expect(ring.length()).toEqual(0);
-
-    for (let i = 0; i < 10; i++) {
-      ring.push(i);
-    }
-    expect(ring.length()).toEqual(10);
-
-    expect(ring.shuffleExcise(3)).toEqual(3);
-    expect(ring.shuffleExcise(6)).toEqual(6);
-
-    const result: number[] = [];
-    for (let i = 0; i < 8; i++) {
-      result[i] = ring.shift();
-    }
-    // note how position 3 got the last element (9) and position 6 got the
-    // next-to-last element(8).
-    expect(result).toEqual([0, 1, 2, 9, 4, 5, 8, 7]);
-    expect(ring.length()).toEqual(0);
-  });
-
-  it('Throws error on push over capacity', () => {
-    const ring = new RingBuffer<number>(10);
-    expect(ring.length()).toEqual(0);
-
-    for (let i = 0; i < 10; i++) {
-      ring.push(i);
-    }
-    expect(() => ring.push(10)).toThrowError(/full/);
-  });
-
-  it('Throws error on pop when empty', () => {
-    const ring = new RingBuffer<number>(10);
-    expect(ring.length()).toEqual(0);
-
-    expect(() => ring.pop()).toThrowError(/empty/);
-  });
-
-  it('Throws error on unshift over capacity', () => {
-    const ring = new RingBuffer<number>(10);
-    expect(ring.length()).toEqual(0);
-
-    for (let i = 0; i < 10; i++) {
-      ring.unshift(i);
-    }
-    expect(() => ring.unshift(10)).toThrowError(/full/);
-  });
-
-  it('Throws error on shift when empty', () => {
-    const ring = new RingBuffer<number>(10);
-    expect(ring.length()).toEqual(0);
-
-    expect(() => ring.shift()).toThrowError(/empty/);
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/util/source_util.ts b/tfjs-master/tfjs-data/src/util/source_util.ts
deleted file mode 100644
index a189db430..000000000
--- a/tfjs-master/tfjs-data/src/util/source_util.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-// Skip tslint any type check cause this method is aiming to check type of
-// input.
-// tslint:disable-next-line:no-any
-export function isLocalPath(source: any): boolean {
-  return (typeof source === 'string') && source.slice(0, 7) === 'file://';
-}
diff --git a/tfjs-master/tfjs-data/src/util/source_util_test.ts b/tfjs-master/tfjs-data/src/util/source_util_test.ts
deleted file mode 100644
index 9994e4783..000000000
--- a/tfjs-master/tfjs-data/src/util/source_util_test.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-import {isLocalPath} from './source_util';
-
-const nonPathString = 'iamnotlocalpath';
-const testData = env().get('IS_BROWSER') ? new Blob([nonPathString]) :
-                                           Buffer.from(nonPathString);
-
-describe('source_util', () => {
-  it('returns true if it is local path', () => {
-    const result = isLocalPath('file://testfile');
-    expect(result).toBeTruthy();
-  });
-
-  it('returns false if it is not local path', () => {
-    const result = isLocalPath(nonPathString);
-    expect(result).toBeFalsy();
-  });
-
-  it('returns false if it is not string type', () => {
-    const result = isLocalPath(testData);
-    expect(result).toBeFalsy();
-  });
-});
diff --git a/tfjs-master/tfjs-data/src/util/test_utils.ts b/tfjs-master/tfjs-data/src/util/test_utils.ts
deleted file mode 100644
index 65ce7e305..000000000
--- a/tfjs-master/tfjs-data/src/util/test_utils.ts
+++ /dev/null
@@ -1,157 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- *
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, BROWSER_ENVS, Constraints, describeWithFlags, NODE_ENVS, registerTestEnv} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-// Provide fake video stream
-export function setupFakeVideoStream() {
-  const width = 100;
-  const height = 200;
-  const canvasElement = document.createElement('canvas');
-  const ctx = canvasElement.getContext('2d');
-  ctx.fillStyle = 'rgb(1,2,3)';
-  ctx.fillRect(0, 0, width, height);
-  // tslint:disable-next-line:no-any
-  const stream = (canvasElement as any).captureStream(60);
-  navigator.mediaDevices.getUserMedia = async () => {
-    return stream;
-  };
-}
-
-export async function replaceHTMLVideoElementSource(
-    videoElement: HTMLVideoElement) {
-  const source = document.createElement('source');
-  // tslint:disable:max-line-length
-  source.src =
-      'data:video/mp4;base64,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';
-  source.type = 'video/mp4';
-  videoElement.srcObject = null;
-  videoElement.appendChild(source);
-  videoElement.play();
-
-  if (videoElement.readyState < 2) {
-    await new Promise<void>(resolve => {
-      videoElement.addEventListener('loadeddata', () => resolve());
-    });
-  }
-}
-
-// Register backends.
-registerTestEnv({name: 'cpu', backendName: 'cpu'});
-registerTestEnv({
-  name: 'webgl2',
-  backendName: 'webgl',
-  flags: {
-    'WEBGL_VERSION': 2,
-    'WEBGL_CPU_FORWARD': false,
-    'WEBGL_SIZE_UPLOAD_UNIFORM': 0
-  }
-});
-
-export const MEDIA_ENVS: Constraints = {
-  predicate: (env) => BROWSER_ENVS.predicate(env)
-    && navigator.mediaDevices != null
-};
-
-export function describeAllEnvs(testName: string, tests: () => void) {
-  describeWithFlags(testName, ALL_ENVS, () => {
-    tests();
-  });
-}
-
-export function describeBrowserEnvs(testName: string, tests: () => void) {
-  describeWithFlags(testName, BROWSER_ENVS, () => {
-    tests();
-  });
-}
-
-export function describeNodeEnvs(testName: string, tests: () => void) {
-  describeWithFlags(testName, NODE_ENVS, () => {
-    tests();
-  });
-}
-
-/**
- * Testing Utilities for browser audio stream.
- */
-export function setupFakeAudioStream() {
-  navigator.mediaDevices.getUserMedia = async () => {
-    const stream = new MediaStream();
-    return stream;
-  };
-  // tslint:disable-next-line:no-any
-  (window as any).AudioContext = FakeAudioContext;
-}
-
-export class FakeAudioContext {
-  readonly sampleRate = 44100;
-
-  static createInstance() {
-    return new FakeAudioContext();
-  }
-
-  createMediaStreamSource() {
-    return new FakeMediaStreamAudioSourceNode();
-  }
-
-  createAnalyser() {
-    return new FakeAnalyser();
-  }
-
-  close(): void {}
-}
-
-export class FakeAudioMediaStream {
-  constructor() {}
-  getTracks(): Array<{}> {
-    return [];
-  }
-}
-
-class FakeMediaStreamAudioSourceNode {
-  constructor() {}
-  connect(node: {}): void {}
-}
-
-class FakeAnalyser {
-  fftSize: number;
-  smoothingTimeConstant: number;
-  private x: number;
-  constructor() {
-    this.x = 0;
-  }
-
-  getFloatFrequencyData(data: Float32Array): void {
-    const xs: number[] = [];
-    for (let i = 0; i < this.fftSize / 2; ++i) {
-      xs.push(this.x++);
-    }
-    data.set(new Float32Array(xs));
-  }
-
-  getFloatTimeDomainData(data: Float32Array): void {
-    const xs: number[] = [];
-    for (let i = 0; i < this.fftSize / 2; ++i) {
-      xs.push(-(this.x++));
-    }
-    data.set(new Float32Array(xs));
-  }
-
-  disconnect(): void {}
-}
diff --git a/tfjs-master/tfjs-data/src/version.ts b/tfjs-master/tfjs-data/src/version.ts
deleted file mode 100644
index 5fa574e7d..000000000
--- a/tfjs-master/tfjs-data/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.0';
-export {version};
diff --git a/tfjs-master/tfjs-data/tsconfig.json b/tfjs-master/tfjs-data/tsconfig.json
deleted file mode 100644
index ecb732e21..000000000
--- a/tfjs-master/tfjs-data/tsconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-data/yarn.lock b/tfjs-master/tfjs-data/yarn.lock
deleted file mode 100644
index 172588007..000000000
--- a/tfjs-master/tfjs-data/yarn.lock
+++ /dev/null
@@ -1,101 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@bazel/bazelisk@^1.12.0":
-  version "1.12.0"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.12.0.tgz#f08aebbf4afcb12684422450b0845dd6ef5cfe50"
-  integrity sha512-7oQusq1e4AIyFgotxVV7Pc40Et0QyvoVjujL+7/qV5Vrbfh0Nj3CfqSgl63weEyI4r0+K6RlGVsjfRuBi05p5w==
-
-"@bazel/ibazel@^0.16.2":
-  version "0.16.2"
-  resolved "https://registry.yarnpkg.com/@bazel/ibazel/-/ibazel-0.16.2.tgz#05dd7f06659759fda30f87b15534f1e42f1201bb"
-  integrity sha512-KgqAWMH0emL6f3xH6nqyTryoBMqlJ627LBIe9PT1PRRQPz2FtHib3FIHJPukp1slzF3hJYZvdiVwgPnHbaSOOA==
-
-"@types/node-fetch@^2.1.2":
-  version "2.5.8"
-  resolved "https://registry.yarnpkg.com/@types/node-fetch/-/node-fetch-2.5.8.tgz#e199c835d234c7eb0846f6618012e558544ee2fb"
-  integrity sha512-fbjI6ja0N5ZA8TV53RUqzsKNkl9fv8Oj3T7zxW7FGv1GSH7gwJaNF8dzCjrqKaxKeUpTz4yT1DaJFq/omNpGfw==
-  dependencies:
-    "@types/node" "*"
-    form-data "^3.0.0"
-
-"@types/node@*":
-  version "14.14.36"
-  resolved "https://registry.yarnpkg.com/@types/node/-/node-14.14.36.tgz#5637905dbb15c30a33a3c65b9ef7c20e3c85ebad"
-  integrity sha512-kjivUwDJfIjngzbhooRnOLhGYz6oRFi+L+EpMjxroDYXwDw9lHrJJ43E+dJ6KAd3V3WxWAJ/qZE9XKYHhjPOFQ==
-
-asynckit@^0.4.0:
-  version "0.4.0"
-  resolved "https://registry.yarnpkg.com/asynckit/-/asynckit-0.4.0.tgz#c79ed97f7f34cb8f2ba1bc9790bcc366474b4b79"
-  integrity sha1-x57Zf380y48robyXkLzDZkdLS3k=
-
-combined-stream@^1.0.8:
-  version "1.0.8"
-  resolved "https://registry.yarnpkg.com/combined-stream/-/combined-stream-1.0.8.tgz#c3d45a8b34fd730631a110a8a2520682b31d5a7f"
-  integrity sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==
-  dependencies:
-    delayed-stream "~1.0.0"
-
-delayed-stream@~1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
-  integrity sha1-3zrhmayt+31ECqrgsp4icrJOxhk=
-
-form-data@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/form-data/-/form-data-3.0.1.tgz#ebd53791b78356a99af9a300d4282c4d5eb9755f"
-  integrity sha512-RHkBKtLWUVwd7SqRIvCZMEvAMoGUp0XU+seQiZejj0COz3RI3hWP4sCv3gZWWLjJTd7rGwcsF5eKZGii0r/hbg==
-  dependencies:
-    asynckit "^0.4.0"
-    combined-stream "^1.0.8"
-    mime-types "^2.1.12"
-
-mime-db@1.46.0:
-  version "1.46.0"
-  resolved "https://registry.yarnpkg.com/mime-db/-/mime-db-1.46.0.tgz#6267748a7f799594de3cbc8cde91def349661cee"
-  integrity sha512-svXaP8UQRZ5K7or+ZmfNhg2xX3yKDMUzqadsSqi4NCH/KomcH75MAMYAGVlvXn4+b/xOPhS3I2uHKRUzvjY7BQ==
-
-mime-types@^2.1.12:
-  version "2.1.29"
-  resolved "https://registry.yarnpkg.com/mime-types/-/mime-types-2.1.29.tgz#1d4ab77da64b91f5f72489df29236563754bb1b2"
-  integrity sha512-Y/jMt/S5sR9OaqteJtslsFZKWOIIqMACsJSiHghlCAyhf7jfVYjKBmLiX8OgpWeW+fjJ2b+Az69aPFPkUOY6xQ==
-  dependencies:
-    mime-db "1.46.0"
-
-node-fetch@~2.6.1:
-  version "2.6.7"
-  resolved "https://registry.yarnpkg.com/node-fetch/-/node-fetch-2.6.7.tgz#24de9fba827e3b4ae44dc8b20256a379160052ad"
-  integrity sha512-ZjMPFEfVx5j+y2yF35Kzx5sF7kDzxuDj6ziH4FFbOp87zKDZNx8yExJIb05OGF4Nlt9IHFIMBkRl41VdvcNdbQ==
-  dependencies:
-    whatwg-url "^5.0.0"
-
-safe-buffer@~5.2.0:
-  version "5.2.1"
-  resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.2.1.tgz#1eaf9fa9bdb1fdd4ec75f58f9cdb4e6b7827eec6"
-  integrity sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==
-
-string_decoder@^1.3.0:
-  version "1.3.0"
-  resolved "https://registry.yarnpkg.com/string_decoder/-/string_decoder-1.3.0.tgz#42f114594a46cf1a8e30b0a84f56c78c3edac21e"
-  integrity sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA==
-  dependencies:
-    safe-buffer "~5.2.0"
-
-tr46@~0.0.3:
-  version "0.0.3"
-  resolved "https://registry.yarnpkg.com/tr46/-/tr46-0.0.3.tgz#8184fd347dac9cdc185992f3a6622e14b9d9ab6a"
-  integrity sha1-gYT9NH2snNwYWZLzpmIuFLnZq2o=
-
-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
-
-whatwg-url@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
-  integrity sha1-lmRU6HZUYuN2RNNib2dCzotwll0=
-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
diff --git a/tfjs-master/tfjs-inference/README.md b/tfjs-master/tfjs-inference/README.md
deleted file mode 100644
index 898425332..000000000
--- a/tfjs-master/tfjs-inference/README.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# TensorFlow.js Inference API
-
-This package provides a cli tool for model inferencing in a Node env.
-Additionally, the package is compiled to a binary with the same functionality,
-which allows to use tfjs for ML tasks in any envs and platforms.
-
-The tool can be used to validate TFJS model execution against python results.
-
-**Note**: This package is under development.
-
-## How to use
-
-### Run the cli tool in Node env.
-Checkout the code: `git clone https://github.com/tensorflow/tfjs.git`
-
-Go to tfjs-inference directory: `cd tfjs-inference`
-
-Install dependencies: `yarn`
-
-Run the inference:
-```
-ts-node src/index.ts --model_path=MODEL_PATH --inputs_dir=INPUTS_DIR
- *   --outputs_dir=OUTPUTS_DIR
-```
-The script expects three required arguments: `model_path`, `inputs_dir` and
-`outputs_dir`. There are also optional arguments, see the options below.
-
-**Options**
-
-**model_path**: Directory to a tfjs model json file.
-
-**inputs_dir**: Directory to read the input tensor info and output info files.
-
-**outputs_dir**: Directory to write the output files. Output files include:
-                 data.json, shape.json and dtype.json. Additionally, name.json
-                 is written if the model returns a map. The order of the output
-                 tensors follow the same order as the tf_output_name_file.
-
-**inputs_data_file**: (Optional) Filename of the input data file.
-                      Default to data.json.
-
-**inputs_shape_file**: (Optional) Filename of the input shape file.
-                       Default to shape.json
-
-**inputs_dtype_file**: (Optional) Filename of the input dtype file.
-                       Default to dtype.json
-
-**tf_input_name_file**: (Optional) Filename of the input name of the tf model.
-                        The input names should match the names defined in the
-                        signatureDef of the model.
-                        Default to tf_input_name.json
-
-**tf_output_name_file**: (Optional) Filename of the output name of the tf model.
-                         The output names should match the names defined in
-                         the signatureDef of the model.
-                         Default to tf_output_name.json
-
-**backend**: Choose which tfjs backend to use. Supported backends: cpu|wasm.
-             Default to cpu.
-
-**Notes**:
-* The program requires these files to exist in the inputs_dir:
-  `inputs_data_file`, `inputs_shape_file`, `inputs_dtype_file`, and
-  `tf_input_name_file`.
-* For `model_path`, absolute path is preferred. It also supports relative path,
-  which should be relative to tfjs-inference directory.
-* About the input and output formats. They are represented as array of tensors.
-  The data.json, shape.json, dtype.json and tf_input_name.json files together
-  represent the array of tensors. [Example](https://github.com/tensorflow/tfjs/tree/master/tfjs-inference/test_data)
diff --git a/tfjs-master/tfjs-inference/cloudbuild.yml b/tfjs-master/tfjs-inference/cloudbuild.yml
deleted file mode 100644
index 9020ac4df..000000000
--- a/tfjs-master/tfjs-inference/cloudbuild.yml
+++ /dev/null
@@ -1,40 +0,0 @@
-steps:
-
-# Install packages.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-inference'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-
-# Build binaries.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-inference'
-  entrypoint: 'yarn'
-  id: 'build-binary'
-  args: ['build-binary']
-  waitFor: ['yarn']
-
-# JS Test.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-inference'
-  entrypoint: 'yarn'
-  id: 'test'
-  args: ['test-ci']
-  waitFor: ['yarn']
-
-# Python Test.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-inference'
-  entrypoint: 'yarn'
-  id: 'test-python'
-  args: ['test-python-ci']
-  waitFor: ['build-binary']
-
-timeout: 1800s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/tfjs-inference/package.json b/tfjs-master/tfjs-inference/package.json
deleted file mode 100644
index 0c7d29732..000000000
--- a/tfjs-master/tfjs-inference/package.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-inference",
-  "version": "0.0.0",
-  "description": "Inference API for TensorFlow.js",
-  "private": false,
-  "main": "dist/index.js",
-  "types": "dist/index.d.ts",
-  "bin": "dist/index.js",
-  "engines": {
-    "yarn": ">= 1.3.2"
-  },
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-inference"
-  },
-  "license": "Apache-2.0",
-  "devDependencies": {
-    "@tensorflow/tfjs-backend-cpu": "4.9.0",
-    "@tensorflow/tfjs-backend-wasm": "4.9.0",
-    "@tensorflow/tfjs-converter": "4.9.0",
-    "@tensorflow/tfjs-core": "4.9.0",
-    "@types/jasmine": "~4.3.1",
-    "@types/rimraf": "~4.0.5",
-    "clang-format": "~1.8.0",
-    "jasmine": "~4.6.0",
-    "jasmine-core": "~4.6.0",
-    "rimraf": "~4.1.2",
-    "ts-node": "~10.9.1",
-    "tslint": "~6.1.3",
-    "typescript": "5.0.4"
-  },
-  "scripts": {
-    "build": "tsc",
-    "test": "yarn build && ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/test_node.ts",
-    "test-dev": "tsc && ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/test_node.ts",
-    "test-ci": "ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/test_node.ts",
-    "build-binary": "yarn build && pkg . --targets=node12-macos-x64,node12-linux-x64,node12-win-x64 --out-path=binaries",
-    "test-python": "yarn build-binary && ./scripts/run_python.sh",
-    "test-python-ci": "./scripts/run_python.sh"
-  },
-  "dependencies": {
-    "@types/seedrandom": "^3.0.5",
-    "@types/yargs": "~17.0.24",
-    "pkg": "~5.8.1",
-    "seedrandom": "^3.0.5",
-    "yargs": "~17.7.2"
-  },
-  "pkg": {
-    "assets": "node_modules/@tensorflow/tfjs-backend-wasm/dist/tfjs-backend-wasm.wasm"
-  }
-}
diff --git a/tfjs-master/tfjs-inference/python/__init__.py b/tfjs-master/tfjs-inference/python/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/tfjs-master/tfjs-inference/python/inference.py b/tfjs-master/tfjs-inference/python/inference.py
deleted file mode 100644
index 9e7590160..000000000
--- a/tfjs-master/tfjs-inference/python/inference.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Example showing how to use tfjs binary in python code."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import subprocess
-
-
-def predict(binary_path,
-            model_path,
-            inputs_dir,
-            outputs_dir,
-            backend=None,
-            tf_output_name_file=None):
-  """Use tfjs binary to make inference and store output in file.
-
-  Args:
-    binary_path: Path to the nodejs binary. The path can be an absolute path
-      (preferred) or a relative path from this python script's current
-      directory.
-    model_path: Directory to TensorFlow.js model's json file.
-    inputs_dir: Directory to the inputs files, including data, shape and dtype
-      files.
-    outputs_dir: Directory to write the outputs files, including data, shape
-      and dtype files.
-    backend: Optional. Choose which TensorFlow.js backend to use. Supported
-      backends include cpu and wasm. Default: cpu
-    tf_output_name_file: Optional. File name of the tf_output_name, if file does
-      not exist, will use the default outputs of the model.
-  """
-  model_path_option = '--model_path=' + model_path
-  inputs_dir_option = '--inputs_dir=' + inputs_dir
-  outputs_dir_option = '--outputs_dir=' + outputs_dir
-
-  tfjs_inference_command = [
-      binary_path, model_path_option, inputs_dir_option,
-      outputs_dir_option
-  ]
-
-  if tf_output_name_file:
-    tf_output_name_file_option = '--tf_output_name_file=' + tf_output_name_file
-    tfjs_inference_command.append(tf_output_name_file_option)
-
-  if backend:
-    backend_option = '--backend=' + backend
-    tfjs_inference_command.append(backend_option)
-
-  popen = subprocess.Popen(
-      tfjs_inference_command,
-      stdin=subprocess.PIPE,
-      stdout=subprocess.PIPE,
-      stderr=subprocess.PIPE)
-  stdout, stderr = popen.communicate()
-
-  if popen.returncode != 0:
-    raise ValueError('Inference failed with status %d\nstderr:\n%s' %
-                     (popen.returncode, stderr.decode()))
diff --git a/tfjs-master/tfjs-inference/python/inference_test.py b/tfjs-master/tfjs-inference/python/inference_test.py
deleted file mode 100644
index efa0af31e..000000000
--- a/tfjs-master/tfjs-inference/python/inference_test.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Run `yarn test-python` in the package root directory.
-# This test only supports running in Linux.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import os.path
-import tempfile
-import shutil
-
-import tensorflow as tf
-
-import inference
-
-class InferenceTest(tf.test.TestCase):
-
-  def testInference(self):
-    backends = ['cpu', 'wasm']
-    for backend in backends:
-      binary_path = os.path.join('../binaries', 'tfjs-inference-linux')
-      model_path = os.path.join('../test_data', 'model.json')
-      test_data_dir = os.path.join('../test_data')
-      tmp_dir = tempfile.mkdtemp()
-
-      inference.predict(binary_path, model_path, test_data_dir, tmp_dir, backend=backend)
-
-      with open(os.path.join(tmp_dir, 'data.json'), 'rt') as f:
-        ys_values = json.load(f)
-
-        # The output is a list of tensor data in the form of dict.
-        # Example output:
-        # [{"0":0.7567615509033203,"1":-0.18349379301071167,"2":0.7567615509033203,"3":-0.18349379301071167}]
-        ys_values = [list(y.values()) for y in ys_values]
-
-      with open(os.path.join(tmp_dir, 'shape.json'), 'rt') as f:
-        ys_shapes = json.load(f)
-
-      with open(os.path.join(tmp_dir, 'dtype.json'), 'rt') as f:
-        ys_dtypes = json.load(f)
-
-      self.assertAllClose(ys_values[0], [
-          0.7567615509033203, -0.18349379301071167, 0.7567615509033203,
-          -0.18349379301071167
-      ])
-      self.assertAllEqual(ys_shapes[0], [2, 2])
-      self.assertEqual(ys_dtypes[0], 'float32')
-      self.assertFalse(os.path.exists(os.path.join(tmp_dir, 'name.json')))
-      # Cleanup tmp dir.
-      shutil.rmtree(tmp_dir)
-
-  # Todo(linazhao): Add a test model that outputs multiple tensors.
-  def testInferenceWithOutputNameFile(self):
-    binary_path = os.path.join('../binaries', 'tfjs-inference-linux')
-    model_path = os.path.join('../test_data', 'model.json')
-    test_data_dir = os.path.join('../test_data')
-    tmp_dir = tempfile.mkdtemp()
-
-    inference.predict(binary_path, model_path, test_data_dir, tmp_dir, tf_output_name_file='tf_output_name.json')
-
-    with open(os.path.join(tmp_dir, 'data.json'), 'rt') as f:
-      ys_values = json.load(f)
-
-      # The output is a list of tensor data in the form of dict.
-      # Example output:
-      # [{"0":0.7567615509033203,"1":-0.18349379301071167,"2":0.7567615509033203,"3":-0.18349379301071167}]
-      ys_values = [list(y.values()) for y in ys_values]
-
-    with open(os.path.join(tmp_dir, 'shape.json'), 'rt') as f:
-      ys_shapes = json.load(f)
-
-    with open(os.path.join(tmp_dir, 'dtype.json'), 'rt') as f:
-      ys_dtypes = json.load(f)
-
-    self.assertAllClose(ys_values[0], [
-        0.7567615509033203, -0.18349379301071167, 0.7567615509033203,
-        -0.18349379301071167
-    ])
-    self.assertAllEqual(ys_shapes[0], [2, 2])
-    self.assertEqual(ys_dtypes[0], 'float32')
-    self.assertFalse(os.path.exists(os.path.join(tmp_dir, 'name.json')))
-    # Cleanup tmp dir.
-    shutil.rmtree(tmp_dir)
-
-  def testInferenceWithNonExistingOutputNameFile(self):
-    binary_path = os.path.join('../binaries', 'tfjs-inference-linux')
-    model_path = os.path.join('../test_data', 'model.json')
-    test_data_dir = os.path.join('../test_data')
-    tmp_dir = tempfile.mkdtemp()
-
-    # Throws an error
-    with self.assertRaises(ValueError):
-      inference.predict(binary_path, model_path, test_data_dir, tmp_dir, tf_output_name_file='non_exist.json')
-
-    # ...and does not create an output file.
-    with self.assertRaises(FileNotFoundError):
-      with open(os.path.join(tmp_dir, 'data.json'), 'rt') as f:
-        json.load(f)
-
-    # Cleanup tmp dir.
-    shutil.rmtree(tmp_dir)
-
-  def testInferenceWithStructuredOutputKeys(self):
-    backends = ['cpu', 'wasm']
-    for backend in backends:
-      binary_path = os.path.join('../binaries', 'tfjs-inference-linux')
-      model_path = os.path.join('../test_data', 'model_structured_outputs.json')
-      test_data_dir = os.path.join('../test_data')
-      tmp_dir = tempfile.mkdtemp()
-
-      inference.predict(binary_path, model_path, test_data_dir, tmp_dir, backend=backend)
-
-      with open(os.path.join(tmp_dir, 'data.json'), 'rt') as f:
-        ys_values = json.load(f)
-
-        # The output is a list of tensor data in the form of dict.
-        # Example output:
-        # [{"0":0.7567615509033203,"1":-0.18349379301071167,"2":0.7567615509033203,"3":-0.18349379301071167}]
-        ys_values = [list(y.values()) for y in ys_values]
-
-      with open(os.path.join(tmp_dir, 'shape.json'), 'rt') as f:
-        ys_shapes = json.load(f)
-
-      with open(os.path.join(tmp_dir, 'dtype.json'), 'rt') as f:
-        ys_dtypes = json.load(f)
-
-      with open(os.path.join(tmp_dir, 'name.json'), 'rt') as f:
-        ys_names = json.load(f)
-
-      self.assertAllClose(ys_values[0], [
-          0.7567615509033203, -0.18349379301071167, 0.7567615509033203,
-          -0.18349379301071167
-      ])
-      self.assertAllEqual(ys_shapes[0], [2, 2])
-      self.assertEqual(ys_dtypes[0], 'float32')
-      self.assertEqual(ys_names[0], 'testName')
-      # Cleanup tmp dir.
-      shutil.rmtree(tmp_dir)
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/tfjs-master/tfjs-inference/python/requirements.txt b/tfjs-master/tfjs-inference/python/requirements.txt
deleted file mode 100644
index 7d94982a4..000000000
--- a/tfjs-master/tfjs-inference/python/requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-tensorflow>=2.1.0, <3
diff --git a/tfjs-master/tfjs-inference/scripts/run_python.sh b/tfjs-master/tfjs-inference/scripts/run_python.sh
deleted file mode 100644
index 7455a2838..000000000
--- a/tfjs-master/tfjs-inference/scripts/run_python.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2020 Google LLC
-#
-# Use of this source code is governed by an MIT-style
-# license that can be found in the LICENSE file or at
-# https://opensource.org/licenses/MIT.
-# =============================================================================
-
-cd python/
-
-# Install python env.
-if [[ -z "$(which pip3)" ]]; then
-  echo "pip3 is not on path. Attempting to install it..."
-  apt-get update
-  apt-get install -y python3-pip python3-dev
-fi
-
-echo "Installing virtualenv..."
-pip3 install virtualenv
-
-VENV_DIR="$(mktemp -d)_venv"
-echo "Creating virtualenv at ${VENV_DIR} ..."
-virtualenv -p python3 "${VENV_DIR}"
-source "${VENV_DIR}/bin/activate"
-
-# Install python packages.
-pip3 install -r requirements.txt
-
-# Run python tests.
-python inference_test.py
-
-# Clean up virtualenv directory.
-rm -rf "${VENV_DIR}"
-
-cd ..
diff --git a/tfjs-master/tfjs-inference/src/file_handler.ts b/tfjs-master/tfjs-inference/src/file_handler.ts
deleted file mode 100644
index 384b3f139..000000000
--- a/tfjs-master/tfjs-inference/src/file_handler.ts
+++ /dev/null
@@ -1,142 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import * as fs from 'fs';
-import {dirname, join, resolve} from 'path';
-
-/**
- * Handler for loading model files from local file system.
- */
-export class FileHandler implements tf.io.IOHandler {
-  protected readonly path: string;
-
-  /**
-   * Constructor of the FileHandler.
-   * @param path A single path pointing to the JSON file (usually named
-   *     `model.json`) is expected. The JSON file is expected to contain
-   *     `modelTopology` and/or `weightsManifest`. If `weightsManifest` exists,
-   *     the values of the weights will be loaded from relative paths (relative
-   *     to the directory of `model.json`) as contained in `weightManifest`.
-   */
-  constructor(path: string) {
-    this.path = resolve(path);
-  }
-
-  /**
-   * Load a tfjs model.
-   *
-   *  @return A promise of tfjs model.
-   */
-  async load(): Promise<tf.io.ModelArtifacts> {
-    const path = this.path;
-
-    const fsInfo = fs.statSync(path);
-
-    if (!fsInfo.isFile()) {
-      throw new Error(
-          'The path to load from must be a file. Loading from a directory ' +
-          'is not supported.');
-    } else {
-      const file = fs.readFileSync(path, 'utf8');
-      const modelJSON = JSON.parse(file);
-
-      // Mapping modelJSON to modelArtifacts.
-      const modelArtifacts: tf.io.ModelArtifacts = {
-        modelTopology: modelJSON.modelTopology,
-        format: modelJSON.format,
-        generatedBy: modelJSON.generatedBy,
-        convertedBy: modelJSON.convertedBy
-      };
-
-      if (modelJSON.signature != null) {
-        modelArtifacts.signature = modelJSON.signature;
-      }
-
-      if (modelJSON.userDefinedMetadata != null) {
-        modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;
-      }
-
-      if (modelJSON.modelInitializer != null) {
-        modelArtifacts.modelInitializer = modelJSON.modelInitializer;
-      }
-
-      if (modelJSON.initializerSignature != null) {
-        modelArtifacts.initializerSignature = modelJSON.initializerSignature;
-      }
-
-      if (modelJSON.weightsManifest != null) {
-        const [weightSpecs, weightData] =
-            this.loadWeights(modelJSON.weightsManifest, path);
-        modelArtifacts.weightSpecs = weightSpecs;
-        modelArtifacts.weightData = weightData;
-      }
-      if (modelJSON.trainingConfig != null) {
-        modelArtifacts.trainingConfig = modelJSON.trainingConfig;
-      }
-
-      return modelArtifacts;
-    }
-  }
-
-  /**
-   * Load weights binary files from a local path.
-   *
-   * @param weightsManifest Config file that has
-   *     information of locations of the weight binary files.
-   * @param path Path of the `model.json` file, the weights binary
-   *     files are expected to be in the same directory.
-   * @return [weightSpecs, weightData].
-   */
-  private loadWeights(
-      weightsManifest: tf.io.WeightsManifestConfig,
-      path: string): [tf.io.WeightsManifestEntry[], ArrayBuffer] {
-    const dirName = dirname(path);
-    const buffers: Buffer[] = [];
-    const weightSpecs: tf.io.WeightsManifestEntry[] = [];
-    for (const group of weightsManifest) {
-      for (const path of group.paths) {
-        const weightFilePath = join(dirName, path);
-        const buffer = fs.readFileSync(weightFilePath);
-        buffers.push(buffer);
-      }
-      weightSpecs.push(...group.weights);
-    }
-    return [weightSpecs, toArrayBuffer(buffers)];
-  }
-}
-
-/**
- * Convert a Buffer or an Array of Buffers to an ArrayBuffer.
- *
- * If the input is an Array of Buffers, they will be concatenated in the
- * specified order to form the output ArrayBuffer.
- *
- * @param buf A Buffer or an Array of Buffers.
- * @return An ArrayBuffer.
- */
-function toArrayBuffer(buf: Buffer|Buffer[]): ArrayBuffer {
-  if (Array.isArray(buf)) {
-    const newBuf = Buffer.concat(buf);
-
-    return newBuf.buffer.slice(
-        newBuf.byteOffset, newBuf.byteOffset + newBuf.byteLength);
-  } else {
-    // A single Buffer. Return a copy of the underlying ArrayBuffer slice.
-    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
-  }
-}
diff --git a/tfjs-master/tfjs-inference/src/file_handler_test.ts b/tfjs-master/tfjs-inference/src/file_handler_test.ts
deleted file mode 100644
index 6cdc7b1c4..000000000
--- a/tfjs-master/tfjs-inference/src/file_handler_test.ts
+++ /dev/null
@@ -1,208 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {CompositeArrayBuffer} from '@tensorflow/tfjs-core/dist/io/io';
-import * as fs from 'fs';
-import * as path from 'path';
-import * as rimraf from 'rimraf';
-
-import {FileHandler} from './file_handler';
-
-/**
- * Create a temporary directory for writing files to.
- * @return {string} The absolute path of the tmp dir.
- */
-function makeTmpDir() {
-  return fs.mkdtempSync('tfjs_node_fs_test');
-}
-
-describe('File Handler', () => {
-  const modelTopology1: {} = {
-    'class_name': 'Sequential',
-    'keras_version': '2.1.6',
-    'config': [{
-      'class_name': 'Dense',
-      'config': {
-        'kernel_initializer': {
-          'class_name': 'VarianceScaling',
-          'config': {
-            'distribution': 'uniform',
-            'scale': 1.0,
-            'seed': null,
-            'mode': 'fan_avg'
-          }
-        },
-        'name': 'dense',
-        'kernel_constraint': null,
-        'bias_regularizer': null,
-        'bias_constraint': null,
-        'dtype': 'float32',
-        'activation': 'linear',
-        'trainable': true,
-        'kernel_regularizer': null,
-        'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-        'units': 1,
-        'batch_input_shape': [null, 3],
-        'use_bias': true,
-        'activity_regularizer': null
-      }
-    }],
-    'backend': 'tensorflow'
-  };
-
-  let testDir: string;
-
-  beforeEach(() => {
-    testDir = makeTmpDir();
-  });
-
-  afterEach(() => {
-    rimraf.sync(testDir);
-  });
-
-  describe('load json model', () => {
-    it('load: two weight files', async () => {
-      const weightsManifest: tf.io.WeightsManifestConfig = [
-        {
-          paths: ['weights.1.bin'],
-          weights: [{
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          }],
-        },
-
-        {
-          paths: ['weights.2.bin'],
-          weights: [{
-            name: 'dense/bias',
-            shape: [1],
-            dtype: 'float32',
-          }]
-        }
-      ];
-      const modelJSON: tf.io.ModelJSON = {
-        modelTopology: modelTopology1,
-        weightsManifest,
-        signature: {},
-        userDefinedMetadata: {},
-        modelInitializer: {},
-        initializerSignature: {}
-      };
-
-      // Write model.json file.
-      const modelJSONPath = path.join(testDir, 'model.json');
-      fs.writeFileSync(modelJSONPath, JSON.stringify(modelJSON), 'utf8');
-
-      // Write the two binary weights files.
-      const weightsData1 =
-          Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
-      fs.writeFileSync(
-          path.join(testDir, 'weights.1.bin'), weightsData1, 'binary');
-      const weightsData2 = Buffer.from(new Float32Array([-7.7]).buffer);
-      fs.writeFileSync(
-          path.join(testDir, 'weights.2.bin'), weightsData2, 'binary');
-
-      // Load the artifacts consisting of a model.json and two binary weight
-      // files.
-      const handler = new FileHandler(modelJSONPath);
-      const modelArtifacts = await handler.load();
-
-      expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-      expect(modelArtifacts.signature).toEqual({});
-      expect(modelArtifacts.userDefinedMetadata).toEqual({});
-      expect(modelArtifacts.modelInitializer).toEqual({});
-      expect(modelArtifacts.initializerSignature).toEqual({});
-      expect(modelArtifacts.weightSpecs).toEqual([
-        {
-          name: 'dense/kernel',
-          shape: [3, 1],
-          dtype: 'float32',
-        },
-        {
-          name: 'dense/bias',
-          shape: [1],
-          dtype: 'float32',
-        }
-      ]);
-      tf.test_util.expectArraysClose(
-          new Float32Array(CompositeArrayBuffer.join(modelArtifacts.weightData)),
-          new Float32Array([-1.1, -3.3, -3.3, -7.7]));
-    });
-    it('loading from nonexistent model.json path fails', async () => {
-      const modelJSONPath = path.join(testDir, 'foo', 'model.json');
-      const handler = new FileHandler(modelJSONPath);
-
-      try {
-        await handler.load();
-        fail('Loading from nonexistent file succeeded unexpectedly.');
-      } catch (err) {
-        expect(err.message).toMatch(/.*no such file or directory.*/);
-      }
-    });
-
-    it('loading from missing weights path fails', async () => {
-      const weightsManifest = [
-        {
-          paths: ['weights.1.bin'],
-          weights: [{
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          }],
-        },
-
-        {
-          paths: ['weights.2.bin'],
-          weights: [{
-            name: 'dense/bias',
-            shape: [1],
-            dtype: 'float32',
-          }]
-        }
-      ];
-      const modelJSON = {
-        modelTopology: modelTopology1,
-        weightsManifest,
-      };
-
-      // Write model.json file.
-      const modelJSONPath = path.join(testDir, 'model.json');
-      fs.writeFileSync(modelJSONPath, JSON.stringify(modelJSON), 'utf8');
-
-      // Write only first of the two binary weights files.
-      const weightsData1 =
-          Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
-      fs.writeFileSync(
-          path.join(testDir, 'weights.1.bin'), weightsData1, 'binary');
-
-      // Load the artifacts consisting of a model.json and two binary weight
-      // files.
-      try {
-        const handler = new FileHandler(modelJSONPath);
-        await handler.load();
-
-        fail(
-            'Loading with missing weights file succeeded ' +
-            'unexpectedly.');
-      } catch (err) {
-        expect(err.message).toMatch(/.*no such file or directory.*/);
-      }
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-inference/src/index.ts b/tfjs-master/tfjs-inference/src/index.ts
deleted file mode 100644
index d13b2bf74..000000000
--- a/tfjs-master/tfjs-inference/src/index.ts
+++ /dev/null
@@ -1,239 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  This file is used to load a saved model and perform inference.
- *  Run this script in console:
- *   ts-node index.ts --model_path=MODEL_PATH --inputs_dir=INPUTS_DIR
- *   --outputs_dir=OUTPUTS_DIR
- *
- *  For help, run:
- *   ts-node inference.ts -h
- */
-
-import '@tensorflow/tfjs-backend-wasm';
-import '@tensorflow/tfjs-backend-cpu';
-
-import * as tfconv from '@tensorflow/tfjs-converter';
-import * as tfc from '@tensorflow/tfjs-core';
-import * as fs from 'fs';
-import * as path from 'path';
-import * as yargs from 'yargs';
-
-import {FileHandler} from './file_handler';
-
-
-// Placeholder for g3 import.
-
-// Following cmd options casing tradition.
-// tslint:disable-next-line:enforce-name-casing
-interface Options {
-  model_path: string;
-  inputs_dir: string;
-  outputs_dir: string;
-  inputs_data_file: string;
-  inputs_shape_file: string;
-  inputs_dtype_file: string;
-  tf_input_name_file: string;
-  tf_output_name_file: string;
-  backend: string;
-}
-// tslint:enable:enforce-name-casing
-
-/**
- *   The entry point of tfjs inference api.
- */
-async function main() {
-  const argParser = yargs.options({
-    model_path: {
-      description: 'Path to the tfjs model json file.',
-      type: 'string',
-      demandOption: true
-    },
-    inputs_dir: {
-      description: 'Directory to read the input tensor info and output ' +
-          'info files.',
-      type: 'string',
-      demandOption: true
-    },
-    outputs_dir: {
-      description:
-          'Directory to write the output files. Output files include: ' +
-          'data.json, shape.json and dtype.json. Additionally, name.json is ' +
-          'written if the model returns a map. The order of the output ' +
-          'tensors follow the same order as the tf_output_name_file. If the ' +
-          'file is not provided, the default outputs of the model would be ' +
-          'used.',
-      type: 'string',
-      demandOption: true
-    },
-    inputs_data_file: {
-      description: 'Filename of the input data file. Must provide the file.',
-      type: 'string',
-      default: 'data.json'
-    },
-    inputs_shape_file: {
-      description: 'Filename of the input shape file. Must provide the file.',
-      type: 'string',
-      default: 'shape.json'
-    },
-    inputs_dtype_file: {
-      description: 'Filename of the input dtype file. Must provide the file.',
-      type: 'string',
-      default: 'dtype.json'
-    },
-    tf_input_name_file: {
-      description: 'Filename of the input name of the tf model. The input ' +
-          'names should match the names defined in the signatureDef of the ' +
-          'model. Must provide the file.',
-      type: 'string',
-      default: 'tf_input_name.json'
-    },
-    tf_output_name_file: {
-      description: 'Filename of the output name of the tf model. The output ' +
-          'names should match the names defined in the signatureDef of the ' +
-          'model. If the file is not provided, the default outputs of the ' +
-          'model would be used.',
-      type: 'string'
-    },
-    backend: {
-      description: 'Choose which tfjs backend to use. Supported backends: ' +
-          'cpu|wasm',
-      type: 'string',
-      default: 'cpu'
-    }
-  });
-
-  const options = argParser.argv as unknown as Options;
-
-  if (options.backend === 'wasm') {
-    // Placeholder for g3 specific code.
-    await tfc.setBackend('wasm');
-  } else if (options.backend === 'cpu') {
-    await tfc.setBackend('cpu');
-  } else {
-    throw new Error(
-        'Only cpu and wasm backend is supported, but got ' + options.backend);
-  }
-
-  const model =
-      await tfconv.loadGraphModel(new FileHandler(options.model_path));
-
-  // Read in input tensor info and output info, then convert to json.
-  const inputsDataString = fs.readFileSync(
-      path.join(options.inputs_dir, options.inputs_data_file), 'utf8');
-  const inputsData = JSON.parse(inputsDataString);
-
-  const inputsShapeString = fs.readFileSync(
-      path.join(options.inputs_dir, options.inputs_shape_file), 'utf8');
-  const inputsShape = JSON.parse(inputsShapeString);
-
-  const inputsDtypeString = fs.readFileSync(
-      path.join(options.inputs_dir, options.inputs_dtype_file), 'utf8');
-  const inputsDtype = JSON.parse(inputsDtypeString);
-
-  const tfInputNameString = fs.readFileSync(
-      path.join(options.inputs_dir, options.tf_input_name_file), 'utf8');
-  const inputName = JSON.parse(tfInputNameString);
-
-  let outputName = null;
-  if (options.tf_output_name_file) {
-    const tfOutputNameString = fs.readFileSync(
-        path.join(options.inputs_dir, options.tf_output_name_file), 'utf8');
-    outputName = JSON.parse(tfOutputNameString);
-  }
-
-  const namedInputs =
-      createInputTensors(inputsData, inputsShape, inputsDtype, inputName);
-
-  const result = outputName == null ? await model.predictAsync(namedInputs) :
-                                      await model.executeAsync(namedInputs, outputName);
-
-  // execute can return a single tensor or an
-  // array of tensors. predict can return a map as well.
-  // We wrap the single tensor in an array, and flatten the map, so that
-  // later operation can always assume to operate on an iterable result.
-  let ys: tfc.Tensor[];
-  if (Array.isArray(result)) {
-    ys = result;
-  } else if (result instanceof tfc.Tensor) {
-    ys = [result];
-  } else {
-    const outputNames = model.modelStructuredOutputKeys as string[];
-    fs.writeFileSync(
-        path.join(options.outputs_dir, 'name.json'),
-        JSON.stringify(outputNames));
-
-    ys = outputNames.map(outputName => result[outputName]);
-  }
-
-  // Write out results to file.
-  const ysData = [];
-  const ysShape = [];
-  const ysDtype = [];
-  for (let i = 0; i < ys.length; i++) {
-    const y = ys[i];
-    ysData.push(await y.data());
-    ysShape.push(y.shape);
-    ysDtype.push(y.dtype);
-  }
-
-  fs.writeFileSync(
-      path.join(options.outputs_dir, 'data.json'), JSON.stringify(ysData));
-  fs.writeFileSync(
-      path.join(options.outputs_dir, 'shape.json'), JSON.stringify(ysShape));
-  fs.writeFileSync(
-      path.join(options.outputs_dir, 'dtype.json'), JSON.stringify(ysDtype));
-
-  // Dispose all tensors.
-  Object.keys(namedInputs).forEach(key => namedInputs[key].dispose());
-  ys.forEach(tensor => tensor.dispose());
-}
-
-/**
- * Create a list of input tensors.
- *
- * @private
- * @param inputsData An array with each element being the value to
- *    create a tensor.
- * @param inputsShape An array with each element being
- *    the shape to create a tensor.
- * @param inputsDtype An array with each element being the
- *    dtype to create a tensor.
- * @param inputName An array of input names, identifies the
- *    input tensor in the same order as the other input arrays.
- * @return An array of tensors.
- */
-function createInputTensors(
-    inputsData: tfc.TypedArray[], inputsShape: number[][],
-    inputsDtype: tfc.DataType[], inputName: string[]): tfc.NamedTensorMap {
-  const xs: tfc.Tensor[] = [];
-  for (let i = 0; i < inputsData.length; i++) {
-    const input = tfc.tensor(inputsData[i], inputsShape[i], inputsDtype[i]);
-    xs.push(input);
-  }
-
-  return inputName.reduce((map: tfc.NamedTensorMap, name, index) => {
-    map[name] = xs[index];
-    return map;
-  }, {});
-}
-
-main().catch((err) => {
-  console.error(err);
-  process.exit(1);
-});
diff --git a/tfjs-master/tfjs-inference/src/test_node.ts b/tfjs-master/tfjs-inference/src/test_node.ts
deleted file mode 100644
index fc6788c39..000000000
--- a/tfjs-master/tfjs-inference/src/test_node.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Use the CPU backend for running tests.
-import '@tensorflow/tfjs-backend-cpu';
-// tslint:disable-next-line:no-imports-from-dist
-import * as jasmine_util from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-// tslint:disable-next-line:no-require-imports
-const jasmineCtor = require('jasmine');
-// tslint:disable-next-line:no-require-imports
-
-Error.stackTraceLimit = Infinity;
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-jasmine_util.setTestEnvs(
-    [{name: 'test-inference-api', backendName: 'cpu', flags: {}}]);
-
-const unitTests = 'src/**/*_test.ts';
-
-const runner = new jasmineCtor();
-runner.loadConfig({spec_files: [unitTests], random: false,
-                   jsLoader: 'require'});
-runner.execute();
diff --git a/tfjs-master/tfjs-inference/test_data/data.json b/tfjs-master/tfjs-inference/test_data/data.json
deleted file mode 100644
index 4ce07fe76..000000000
--- a/tfjs-master/tfjs-inference/test_data/data.json
+++ /dev/null
@@ -1 +0,0 @@
-[[1,1,1,1]]
diff --git a/tfjs-master/tfjs-inference/test_data/dtype.json b/tfjs-master/tfjs-inference/test_data/dtype.json
deleted file mode 100644
index e9b8f233f..000000000
--- a/tfjs-master/tfjs-inference/test_data/dtype.json
+++ /dev/null
@@ -1 +0,0 @@
-["float32"]
diff --git a/tfjs-master/tfjs-inference/test_data/model.json b/tfjs-master/tfjs-inference/test_data/model.json
deleted file mode 100644
index 5ad2cdf88..000000000
--- a/tfjs-master/tfjs-inference/test_data/model.json
+++ /dev/null
@@ -1 +0,0 @@
-{"format": "graph-model", "generatedBy": "2.1.0", "convertedBy": "TensorFlow.js Converter v1.7.4r1", "userDefinedMetadata": {"signature": {"inputs": {"Placeholder:0": {"name": "Placeholder:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}, "outputs": {"MatMul:0": {"name": "MatMul:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}}}, "modelTopology": {"node": [{"name": "w", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "Placeholder", "op": "Placeholder", "attr": {"dtype": {"type": "DT_FLOAT"}, "shape": {"shape": {"dim": [{"size": "2"}, {"size": "2"}]}}}}, {"name": "MatMul", "op": "MatMul", "input": ["Placeholder", "w"], "device": "/device:CPU:0", "attr": {"transpose_a": {"b": false}, "transpose_b": {"b": false}, "T": {"type": "DT_FLOAT"}}}], "versions": {"producer": 175}}, "weightsManifest": [{"paths": ["weights.bin"], "weights": [{"name": "w", "shape": [2, 2], "dtype": "float32"}]}]}
diff --git a/tfjs-master/tfjs-inference/test_data/model_structured_outputs.json b/tfjs-master/tfjs-inference/test_data/model_structured_outputs.json
deleted file mode 100644
index 3b269e503..000000000
--- a/tfjs-master/tfjs-inference/test_data/model_structured_outputs.json
+++ /dev/null
@@ -1 +0,0 @@
-{"format": "graph-model", "generatedBy": "2.1.0", "convertedBy": "TensorFlow.js Converter v1.7.4r1", "userDefinedMetadata": {"signature": {"inputs": {"Placeholder:0": {"name": "Placeholder:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}, "outputs": {"MatMul:0": {"name": "MatMul:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}}, "structuredOutputKeys": ["testName"]}, "modelTopology": {"node": [{"name": "w", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "2"}, {"size": "2"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "Placeholder", "op": "Placeholder", "attr": {"dtype": {"type": "DT_FLOAT"}, "shape": {"shape": {"dim": [{"size": "2"}, {"size": "2"}]}}}}, {"name": "MatMul", "op": "MatMul", "input": ["Placeholder", "w"], "device": "/device:CPU:0", "attr": {"transpose_a": {"b": false}, "transpose_b": {"b": false}, "T": {"type": "DT_FLOAT"}}}], "versions": {"producer": 175}}, "weightsManifest": [{"paths": ["weights.bin"], "weights": [{"name": "w", "shape": [2, 2], "dtype": "float32"}]}]}
diff --git a/tfjs-master/tfjs-inference/test_data/shape.json b/tfjs-master/tfjs-inference/test_data/shape.json
deleted file mode 100644
index b2f93cc39..000000000
--- a/tfjs-master/tfjs-inference/test_data/shape.json
+++ /dev/null
@@ -1 +0,0 @@
-[[2,2]]
diff --git a/tfjs-master/tfjs-inference/test_data/tf_input_name.json b/tfjs-master/tfjs-inference/test_data/tf_input_name.json
deleted file mode 100644
index a61c40425..000000000
--- a/tfjs-master/tfjs-inference/test_data/tf_input_name.json
+++ /dev/null
@@ -1 +0,0 @@
-["Placeholder:0"]
diff --git a/tfjs-master/tfjs-inference/test_data/tf_output_name.json b/tfjs-master/tfjs-inference/test_data/tf_output_name.json
deleted file mode 100644
index 35178de4b..000000000
--- a/tfjs-master/tfjs-inference/test_data/tf_output_name.json
+++ /dev/null
@@ -1 +0,0 @@
-["MatMul:0"]
diff --git a/tfjs-master/tfjs-inference/test_data/weights.bin b/tfjs-master/tfjs-inference/test_data/weights.bin
deleted file mode 100644
index 527027940..000000000
Binary files a/tfjs-master/tfjs-inference/test_data/weights.bin and /dev/null differ
diff --git a/tfjs-master/tfjs-inference/tsconfig.json b/tfjs-master/tfjs-inference/tsconfig.json
deleted file mode 100644
index 991473c17..000000000
--- a/tfjs-master/tfjs-inference/tsconfig.json
+++ /dev/null
@@ -1,26 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "commonjs",
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "removeComments": false,
-    "preserveConstEnums": true,
-    "declaration": true,
-    "target": "es5",
-    "lib": ["es2015", "dom"],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false,
-    "experimentalDecorators": true,
-    "skipLibCheck": true
-  },
-  "include": [
-    "src/"
-  ]
-}
diff --git a/tfjs-master/tfjs-inference/tsconfig.test.json b/tfjs-master/tfjs-inference/tsconfig.test.json
deleted file mode 100644
index 76ea3c6cf..000000000
--- a/tfjs-master/tfjs-inference/tsconfig.test.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "extends": "./tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "allowJs": true,
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-inference/tslint.json b/tfjs-master/tfjs-inference/tslint.json
deleted file mode 100644
index ec365f164..000000000
--- a/tfjs-master/tfjs-inference/tslint.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "../tslint.json"
-}
diff --git a/tfjs-master/tfjs-inference/yarn.lock b/tfjs-master/tfjs-inference/yarn.lock
deleted file mode 100644
index 24a2bbfe7..000000000
--- a/tfjs-master/tfjs-inference/yarn.lock
+++ /dev/null
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-  version "0.6.0"
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-
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-  version "2.0.0"
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-    "@tsconfig/node10" "^1.0.7"
-    "@tsconfig/node12" "^1.0.7"
-    "@tsconfig/node14" "^1.0.0"
-    "@tsconfig/node16" "^1.0.2"
-    acorn "^8.4.1"
-    acorn-walk "^8.1.1"
-    arg "^4.1.0"
-    create-require "^1.1.0"
-    diff "^4.0.1"
-    make-error "^1.1.1"
-    v8-compile-cache-lib "^3.0.1"
-    yn "3.1.1"
-
-tslib@^1.13.0, tslib@^1.8.1:
-  version "1.14.1"
-  resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.14.1.tgz#cf2d38bdc34a134bcaf1091c41f6619e2f672d00"
-  integrity sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==
-
-tslint@~6.1.3:
-  version "6.1.3"
-  resolved "https://registry.yarnpkg.com/tslint/-/tslint-6.1.3.tgz#5c23b2eccc32487d5523bd3a470e9aa31789d904"
-  integrity sha512-IbR4nkT96EQOvKE2PW/djGz8iGNeJ4rF2mBfiYaR/nvUWYKJhLwimoJKgjIFEIDibBtOevj7BqCRL4oHeWWUCg==
-  dependencies:
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-    builtin-modules "^1.1.1"
-    chalk "^2.3.0"
-    commander "^2.12.1"
-    diff "^4.0.1"
-    glob "^7.1.1"
-    js-yaml "^3.13.1"
-    minimatch "^3.0.4"
-    mkdirp "^0.5.3"
-    resolve "^1.3.2"
-    semver "^5.3.0"
-    tslib "^1.13.0"
-    tsutils "^2.29.0"
-
-tsutils@^2.29.0:
-  version "2.29.0"
-  resolved "https://registry.yarnpkg.com/tsutils/-/tsutils-2.29.0.tgz#32b488501467acbedd4b85498673a0812aca0b99"
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-  dependencies:
-    tslib "^1.8.1"
-
-tunnel-agent@^0.6.0:
-  version "0.6.0"
-  resolved "https://registry.yarnpkg.com/tunnel-agent/-/tunnel-agent-0.6.0.tgz#27a5dea06b36b04a0a9966774b290868f0fc40fd"
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-typescript@5.0.4:
-  version "5.0.4"
-  resolved "https://registry.yarnpkg.com/typescript/-/typescript-5.0.4.tgz#b217fd20119bd61a94d4011274e0ab369058da3b"
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-universalify@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/universalify/-/universalify-2.0.0.tgz#75a4984efedc4b08975c5aeb73f530d02df25717"
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-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
-  integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=
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-v8-compile-cache-lib@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/v8-compile-cache-lib/-/v8-compile-cache-lib-3.0.1.tgz#6336e8d71965cb3d35a1bbb7868445a7c05264bf"
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-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
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-  version "5.0.0"
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-
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
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-
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-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
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-  version "5.0.8"
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-  version "4.0.0"
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-yargs-parser@^20.2.2:
-  version "20.2.9"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
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-yargs-parser@^21.1.1:
-  version "21.1.1"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-21.1.1.tgz#9096bceebf990d21bb31fa9516e0ede294a77d35"
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-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
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-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yargs@~17.7.2:
-  version "17.7.2"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-17.7.2.tgz#991df39aca675a192b816e1e0363f9d75d2aa269"
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-  dependencies:
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-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.3"
-    y18n "^5.0.5"
-    yargs-parser "^21.1.1"
-
-yn@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
-  integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==
diff --git a/tfjs-master/tfjs-layers/.npmignore b/tfjs-master/tfjs-layers/.npmignore
deleted file mode 100644
index b637aa6a7..000000000
--- a/tfjs-master/tfjs-layers/.npmignore
+++ /dev/null
@@ -1,32 +0,0 @@
-.rpt2_cache/
-.vscode/
-src/**/*_test.ts
-demos/
-python/
-scripts/
-tools/
-coverage/
-package/
-**/node_modules/
-karma.conf.js
-dist/demos/
-dist/**/*_fakes.js
-dist/**/*_test.js
-dist/**/*_test.d.ts
-integration_tests/
-*.tgz
-.travis.yml
-cloudbuild.yml
-.npmignore
-.pylintrc
-CONTRIBUTING.md
-DEVELOPMENT.md
-ISSUE_TEMPLATE.md
-pull_request_template.md
-tslint.json
-tsconfig.json
-rollup.config.js
-WORKSPACE
-yarn.lock
-yarn-error.log
-.yalc/
diff --git a/tfjs-master/tfjs-layers/.vscode/settings.json b/tfjs-master/tfjs-layers/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-layers/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-layers/.vscode/tasks.json b/tfjs-master/tfjs-layers/.vscode/tasks.json
deleted file mode 100644
index 594b0eea8..000000000
--- a/tfjs-master/tfjs-layers/.vscode/tasks.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
-    "version": "2.0.0",
-    "tasks": [
-      {
-        "command": "yarn",
-        "label": "lint",
-        "type": "shell",
-        "args": [
-          "lint"
-        ],
-        "problemMatcher": {
-          "base": "$tslint5",
-          "owner": "tslint-type-checked",
-          "fileLocation": "absolute"
-        }
-      },
-      {
-        "command": "yarn",
-        "label": "build",
-        "type": "shell",
-        "args": ["build", "--pretty", "false", "--noEmit"],
-        "problemMatcher": [
-          "$tsc"
-        ]
-      }
-    ]
-  }
diff --git a/tfjs-master/tfjs-layers/BUILD.bazel b/tfjs-master/tfjs-layers/BUILD.bazel
deleted file mode 100644
index cb1a4e602..000000000
--- a/tfjs-master/tfjs-layers/BUILD.bazel
+++ /dev/null
@@ -1,168 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//rules:copy_file.bzl", "copy_file")
-load("@build_bazel_rules_nodejs//:index.bzl", "js_library", "nodejs_test", "pkg_npm")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-# Allow typescript rules in any package to reference this file
-exports_files([
-    "package.json",
-    "README.md",
-])
-
-# Defined here because chdir must be a subdirectory of the directory the rule is
-# defined in.
-nodejs_test(
-    name = "test_snippets_test",
-    chdir = "tfjs-layers",
-    data = [
-        ":tsconfig.json",
-        "//:tsconfig.json",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-layers/scripts:test_snippets_lib",
-        "//tfjs-layers/src:all_srcs",
-    ],
-    entry_point = "//tfjs-layers/scripts:test_snippets.ts",
-    link_workspace_root = True,
-    tags = ["ci"],
-)
-
-tfjs_web_test(
-    name = "tfjs-layers_webgl2_test",
-    srcs = [
-        "//tfjs-layers/src:tfjs-layers_test_bundle",
-    ],
-    browsers = [
-        "bs_chrome_mac",
-        "bs_firefox_mac",
-        # disabled android test due to training flakiness
-        # "bs_android_10",
-        "win_10_chrome",
-        "bs_ios_15",
-    ],
-    headless = False,
-    seed = "12345",
-    static_files = [
-        # Listed here so sourcemaps are served
-        "//tfjs-layers/src:tfjs-layers_test_bundle",
-    ],
-)
-
-tfjs_web_test(
-    name = "tfjs-layers_webgl1_test",
-    srcs = [
-        "//tfjs-layers/src:tfjs-layers_test_bundle",
-    ],
-    args = [
-        "--testEnv",
-        "webgl1",
-    ],
-    browsers = [
-        "bs_safari_mac",
-    ],
-    headless = False,
-    seed = "12345",
-    static_files = [
-        # Listed here so sourcemaps are served
-        "//tfjs-layers/src:tfjs-layers_test_bundle",
-    ],
-)
-
-tfjs_bundle(
-    name = "tf-layers",
-    entry_point = "//tfjs-layers/src:index.ts",
-    external = [
-        "@tensorflow/tfjs-core",
-    ],
-    globals = {
-        "@tensorflow/tfjs-core": "tf",
-    },
-    umd_name = "tf",
-    deps = [
-        "//tfjs-layers/src:tfjs-layers_lib",
-        "//tfjs-layers/src:tfjs-layers_src_lib",
-    ],
-)
-
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-layers/src:tfjs-layers_lib",
-        "//tfjs-layers/src:tfjs-layers_src_lib",
-    ],
-    root = "src",
-)
-
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-layers",
-        ":tf-layers.es2017",
-        ":tf-layers.es2017.min",
-        ":tf-layers.fesm",
-        ":tf-layers.fesm.min",
-        ":tf-layers.min",
-        ":tf-layers.node",
-    ],
-)
-
-copy_file(
-    name = "copy_miniprogram",
-    src = ":tf-layers.min.js",
-    out = "dist/miniprogram/index.js",
-)
-
-copy_file(
-    name = "copy_miniprogram_map",
-    src = ":tf-layers.min.js.map",
-    out = "dist/miniprogram/index.js.map",
-)
-
-pkg_npm(
-    name = "tfjs-layers_pkg",
-    package_name = "@tensorflow/tfjs-layers",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_miniprogram",
-        ":copy_miniprogram_map",
-        ":copy_src_to_dist",
-    ],
-)
-
-js_library(
-    name = "package_json",
-    package_name = "tfjs-layers",
-    srcs = [
-        ":package.json",
-    ],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        ":tfjs-layers_webgl1_test",
-        ":tfjs-layers_webgl2_test",
-    ],
-)
diff --git a/tfjs-master/tfjs-layers/DEVELOPMENT.md b/tfjs-master/tfjs-layers/DEVELOPMENT.md
deleted file mode 100644
index bce352120..000000000
--- a/tfjs-master/tfjs-layers/DEVELOPMENT.md
+++ /dev/null
@@ -1,44 +0,0 @@
-## Development process
-
-As a preparatory step, run `yarn` which installs all dev dependencies.
-
-Before submitting a PR with a change, make sure the following
-commands succeed:
-* `yarn build` which compiles the project to ES5 Javascript.
-* `yarn format` to format your code.
-* `yarn lint` to check for linter errors.
-* `yarn test` to run unit tests in Chrome and Firefox. Make sure all unit tests pass.
-
-When you send a PR, the above commands will also run on [Cloud Build](https://pantheon.corp.google.com/cloud-build/builds?organizationId=433637338589&project=learnjs-174218)
-and show up as Github checks. If you see Cloud Build failing, click on the `Details`
-link next to the check to open the log.
-
-## Changing @tensorflow/tfjs-layers and testing @tensorflow/tfjs
-
-Often we want to make a change in `tfjs-layers/core` and create a new
-`tfjs` package that reflects that change. There is a 3-step initial process to
-set this up. The instructions below are for `tfjs-layers`, but they should work
-for developing `tfjs-core` if you replace `tfjs-layers` with `tfjs-core`.
-
-1. In the `tfjs-layers` repo, run `yarn publish-local`. This builds the
-project and publishes a new package in a local registry.
-
-2. In the `tfjs` repo, run `yarn link-local @tensorflow/tfjs-layers`. This makes
-`tfjs` depend on the locally published `tfjs-layers` package.
-
-3. In the `tfjs` repo, run `yarn build-npm` to build a new npm package.
-
-Every time you make a change in `tfjs-layers`, re-run:
-- `yarn publish-local` in the `tfjs-layers` repo
-- `yarn build-npm` in the `tfjs` repo to make a new package.
-
-## Running integration tests
-
-### tfjs2keras
-
-This is an integration test that checks the models exported by tfjs-layers
-can be loaded correctly by Keras in Python. To run this test, do:
-
-```sh
-yarn tfjs2keras
-```
diff --git a/tfjs-master/tfjs-layers/LICENSE b/tfjs-master/tfjs-layers/LICENSE
deleted file mode 100644
index 60c5c069d..000000000
--- a/tfjs-master/tfjs-layers/LICENSE
+++ /dev/null
@@ -1,249 +0,0 @@
-TensorFlow.js Layers is licensed under both the MIT and the Apache 2.0 licenses.
-
-================================================================================
-COPYRIGHT
-
-All contributions by François Chollet:
-Copyright (c) 2015 - 2018, François Chollet.
-All rights reserved.
-
-All contributions by Google:
-Copyright (c) 2015 - 2018, Google LLC.
-All rights reserved.
-
-All contributions by Microsoft:
-Copyright (c) 2017 - 2018, Microsoft, LLC
-All rights reserved.
-
-All other contributions:
-Copyright (c) 2015 - 2018, the respective contributors.
-All rights reserved.
-
-Each contributor holds copyright over their respective contributions.
-The project versioning (Git) records all such contribution source information.
-
-LICENSE
-
-The MIT License (MIT)
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-
-================================================================================
-
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-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
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diff --git a/tfjs-master/tfjs-layers/README.md b/tfjs-master/tfjs-layers/README.md
deleted file mode 100644
index cd462791e..000000000
--- a/tfjs-master/tfjs-layers/README.md
+++ /dev/null
@@ -1,94 +0,0 @@
-# TensorFlow.js Layers: High-Level Machine Learning Model API
-
-A part of the TensorFlow.js ecosystem, TensorFlow.js Layers is a high-level
-API built on [TensorFlow.js Core](/tfjs-core),
-enabling users to build, train and execute deep learning models in the browser.
-TensorFlow.js Layers is modeled after
-[Keras](https://keras.io/) and
-[tf.keras](https://www.tensorflow.org/api_docs/python/tf/keras) and can
-load models saved from those libraries.
-
-## Importing
-
-There are three ways to import TensorFlow.js Layers
-
-1. You can access TensorFlow.js Layers through the union package
-   between the TensorFlow.js Core and Layers:
-   [@tensorflow/tfjs](https://www.npmjs.com/package/@tensorflow/tfjs)
-2. You can get [TensorFlow.js](https://github.com/tensorflow/tfjs) Layers as a module:
-   [@tensorflow/tfjs-layers](https://www.npmjs.com/package/@tensorflow/tfjs-layers).
-   Note that `tfjs-layers` has peer dependency on tfjs-core, so if you import
-   `@tensorflow/tfjs-layers`, you also need to import
-   `@tensorflow/tfjs-core`.
-3. As a standalone through [unpkg](https://unpkg.com/).
-
-Option 1 is the most convenient, but leads to a larger bundle size (we will be
-adding more packages to it in the future). Use option 2 if you care about bundle
-size.
-
-## Getting started
-
-### Building, training and executing a model
-
-The following example shows how to build a toy model with only one `dense` layer
-to perform linear regression.
-
-```js
-import * as tf from '@tensorflow/tfjs';
-
-// A sequential model is a container which you can add layers to.
-const model = tf.sequential();
-
-// Add a dense layer with 1 output unit.
-model.add(tf.layers.dense({units: 1, inputShape: [1]}));
-
-// Specify the loss type and optimizer for training.
-model.compile({loss: 'meanSquaredError', optimizer: 'SGD'});
-
-// Generate some synthetic data for training.
-const xs = tf.tensor2d([[1], [2], [3], [4]], [4, 1]);
-const ys = tf.tensor2d([[1], [3], [5], [7]], [4, 1]);
-
-// Train the model.
-await model.fit(xs, ys, {epochs: 500});
-
-// Ater the training, perform inference.
-const output = model.predict(tf.tensor2d([[5]], [1, 1]));
-output.print();
-```
-
-### Loading a pretrained Keras model
-
-You can also load a model previously trained and saved from elsewhere (e.g.,
-from Python Keras) and use it for inference or transfer learning in the browser.
-
-For example, in Python, save your Keras model using
-[tensorflowjs](https://pypi.org/project/tensorflowjs/),
-which can be installed using `pip install tensorflowjs`.
-
-
-```python
-import tensorflowjs as tfjs
-
-# ... Create and train your Keras model.
-
-# Save your Keras model in TensorFlow.js format.
-tfjs.converters.save_keras_model(model, '/path/to/tfjs_artifacts/')
-
-# Then use your favorite web server to serve the directory at a URL, say
-#   http://foo.bar/tfjs_artifacts/model.json
-```
-
-To load the model with TensorFlow.js Layers:
-
-```js
-import * as tf from '@tensorflow/tfjs';
-
-const model = await tf.loadLayersModel('http://foo.bar/tfjs_artifacts/model.json');
-// Now the model is ready for inference, evaluation or re-training.
-```
-
-## For more information
-
-- [TensorFlow.js API documentation](https://js.tensorflow.org/api/latest/)
-- [TensorFlow.js Tutorials](https://js.tensorflow.org/tutorials/)
diff --git a/tfjs-master/tfjs-layers/demos/README.md b/tfjs-master/tfjs-layers/demos/README.md
deleted file mode 100644
index 3930909f7..000000000
--- a/tfjs-master/tfjs-layers/demos/README.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# tfjs-layers benchmarks
-
-To run the benchmark script, first set up your environment.
-
-(You may wish to set up Python the requirements in a virtual environment using
-[pipenv](https://github.com/pypa/pipenv) or [virtualenv](https://virtualenv.pypa.io))
-
-```
-pip install tensorflowjs
-```
-
-Once the development environment is prepared, execute the build script from the root of tfjs-layers.
-
-```
-./scripts/build-benchmarks-demo.sh
-```
-
-The script will construct a number of Keras models in Python and benchmark their training using the TensorFlow backend.  When it is complete, it will bring up a
-local HTTP server.  Navigate to the local URL spcecified in stdout to bring up
-the benchmarks page UI.  There will be a button to begin the JS side of the
-benchmarks.  Clicking the button will run through and time the same models, now
-running in the browser.
-
-Once complete, the models' `fit()` and `predict()` costs are listed in a table.
-
-Prese Ctl-C to end the http-server process.
diff --git a/tfjs-master/tfjs-layers/karma.conf.js b/tfjs-master/tfjs-layers/karma.conf.js
deleted file mode 100644
index 92e2d76e4..000000000
--- a/tfjs-master/tfjs-layers/karma.conf.js
+++ /dev/null
@@ -1,95 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const karmaTypescriptConfig = {
-  tsconfig: 'tsconfig.test.json',
-  // Disable coverage reports and instrumentation by default for tests
-  coverageOptions: {instrumentation: false},
-  reports: {},
-  bundlerOptions: {
-    acornOptions: {ecmaVersion: 8},
-    transforms: [
-      require('karma-typescript-es6-transform')({
-        presets: [
-          // ensure we get es5 by adding IE 11 as a target
-          ['@babel/env', {targets: {browsers: ['defaults', 'IE 11']}}]
-        ]
-      }),
-    ]
-  }
-};
-
-// Enable coverage reports and instrumentation under KARMA_COVERAGE=1 env
-const coverageEnabled = !!process.env.KARMA_COVERAGE;
-if (coverageEnabled) {
-  karmaTypescriptConfig.coverageOptions.instrumentation = true;
-  karmaTypescriptConfig.coverageOptions.exclude = /_test\.ts$/;
-  karmaTypescriptConfig.reports = {html: 'coverage', 'text-summary': ''};
-}
-
-module.exports = function(config) {
-  config.set({
-    frameworks: ['jasmine', 'karma-typescript'],
-    files: [
-      {pattern: './node_modules/@babel/polyfill/dist/polyfill.js'},
-      // Setup the environment for the tests.
-      'src/setup_tests.ts',
-      {pattern: 'src/**/*.ts'},
-    ],
-    preprocessors: {
-      '**/*.ts': ['karma-typescript'],
-    },
-    karmaTypescriptConfig,
-    reporters: ['progress', 'karma-typescript'],
-    browsers: ['Chrome'],
-    port: 9200,
-    browserStack: {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_KEY,
-      timeout: 1800,
-      tunnelIdentifier:
-          `tfjs_layers_${Date.now()}_${Math.floor(Math.random() * 1000)}`
-    },
-    captureTimeout: 3e5,
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 3e5,
-    browserDisconnectTimeout: 3e5,
-    browserDisconnectTolerance: 3,
-    browserSocketTimeout: 1.2e5,
-    customLaunchers: {
-      bs_chrome_mac: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_firefox_mac: {
-        base: 'BrowserStack',
-        browser: 'firefox',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      chrome_with_swift_shader: {
-        base: 'Chrome',
-        flags: ['--blacklist-accelerated-compositing', '--blacklist-webgl']
-      }
-    },
-    client: {jasmine: {random: false}, args: ['--grep', config.grep || '']}
-  });
-};
diff --git a/tfjs-master/tfjs-layers/package.json b/tfjs-master/tfjs-layers/package.json
deleted file mode 100644
index 224b4c409..000000000
--- a/tfjs-master/tfjs-layers/package.json
+++ /dev/null
@@ -1,43 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-layers",
-  "version": "0.0.0",
-  "description": "TensorFlow layers API in JavaScript",
-  "license": "Apache-2.0 AND MIT",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-layers"
-  },
-  "private": false,
-  "main": "dist/tf-layers.node.js",
-  "types": "dist/index.d.ts",
-  "jsnext:main": "dist/index.js",
-  "module": "dist/index.js",
-  "jsdelivr": "dist/tf-layers.min.js",
-  "unpkg": "dist/tf-layers.min.js",
-  "miniprogram": "dist/miniprogram",
-  "devDependencies": {
-    "@bazel/bazelisk": "^1.12.0",
-    "clang-format": "~1.8.0"
-  },
-  "scripts": {
-    "build": "bazel build :tfjs-layers_pkg",
-    "format": "./tools/clang_format_ts.sh",
-    "publish-npm": "bazel run :tfjs-layers_pkg.publish",
-    "test": "bazel test :tests --test_output=all",
-    "test-dev": "ibazel test :tests --test_output=streamed",
-    "test-debug": "yarn test-webgl2-debug",
-    "test-browser": "yarn test-webgl2",
-    "test-browser-debug": "yarn test-webgl2-debug",
-    "test-webgl2": "bazel test :tfjs-layers_webgl2_test --test_output=streamed",
-    "test-webgl2-debug": "bazel run :tfjs-layers_webgl2_test --test_output=streamed",
-    "test-webgl2-dev": "ibazel test :tfjs-layers_webgl2_test --test_output=streamed",
-    "test-webgl1": "bazel test :tfjs-layers_webgl1_test --test_output=streamed",
-    "test-webgl1-debug": "bazel run :tfjs-layers_webgl1_test --test_output=streamed",
-    "test-webgl1-dev": "ibazel test :tfjs-layers_webgl1_test --test_output=streamed",
-    "run-browserstack": "bazel test :browserstack_bs_chrome_mac_tfjs-layers_webgl2_test"
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core"
-  }
-}
diff --git a/tfjs-master/tfjs-layers/scripts/BUILD.bazel b/tfjs-master/tfjs-layers/scripts/BUILD.bazel
deleted file mode 100644
index 3d2a504df..000000000
--- a/tfjs-master/tfjs-layers/scripts/BUILD.bazel
+++ /dev/null
@@ -1,18 +0,0 @@
-load("//tools:defaults.bzl", "ts_library")
-
-package(default_visibility = ["//visibility:public"])
-
-ts_library(
-    name = "test_snippets_lib",
-    testonly = True,
-    srcs = [
-        "test_snippets.ts",
-    ],
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/scripts/test_snippets:test_snippets_util_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-layers/src:tfjs-layers_lib",
-    ],
-)
diff --git a/tfjs-master/tfjs-layers/scripts/test_snippets.ts b/tfjs-master/tfjs-layers/scripts/test_snippets.ts
deleted file mode 100644
index 4ebc5f0ef..000000000
--- a/tfjs-master/tfjs-layers/scripts/test_snippets.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import '@tensorflow/tfjs-backend-cpu';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-
-import * as tfc from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {parseAndEvaluateSnippets} from '@tensorflow/tfjs-core/dist/scripts/test_snippets/util';
-
-import * as tfl from '../src/index';
-
-const tf = {
-  ...tfl,
-  ...tfc
-};
-parseAndEvaluateSnippets(tf);
diff --git a/tfjs-master/tfjs-layers/src/BUILD.bazel b/tfjs-master/tfjs-layers/src/BUILD.bazel
deleted file mode 100644
index 7909b9af0..000000000
--- a/tfjs-master/tfjs-layers/src/BUILD.bazel
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("//tools:defaults.bzl", "esbuild", "ts_library")
-load("//tools:enumerate_tests.bzl", "enumerate_tests")
-
-package(default_visibility = ["//visibility:public"])
-
-# Used for test-snippets
-filegroup(
-    name = "all_srcs",
-    srcs = glob(["**/*.ts"]),
-)
-
-TEST_SRCS = [
-    "**/*_test.ts",
-    "setup_tests.ts",
-]
-
-filegroup(
-    name = "all_test_entrypoints",
-    srcs = glob(
-        ["**/*_test.ts"],
-    ),
-)
-
-# Generates the 'tests.ts' file that imports all test entrypoints.
-enumerate_tests(
-    name = "tests",
-    srcs = [":all_test_entrypoints"],
-    root_path = "tfjs-layers/src",
-)
-
-# Compiles the majority of tfjs-layers using the `@tensorflow/tfjs-layers/dist`
-# module name.
-ts_library(
-    name = "tfjs-layers_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-layers/dist",
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "@npm//@types/seedrandom",
-        "@npm//seedrandom",
-    ],
-)
-
-# Compiles the `index.ts` entrypoint of tfjs-layers separately from the rest of
-# the sources in order to use the `@tensorflow/tfjs-layers` module name instead
-# of `@tensorflow/tfjs-layers/dist`,
-ts_library(
-    name = "tfjs-layers_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-layers",
-    deps = [
-        ":tfjs-layers_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-    ],
-)
-
-ts_library(
-    name = "tfjs-layers_test_lib",
-    testonly = True,
-    srcs = glob(TEST_SRCS) + [":tests"],
-    module_name = "@tensorflow/tfjs-layers/dist",
-    deps = [
-        ":tfjs-layers_lib",
-        ":tfjs-layers_src_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-backend-webgl/src:tfjs-backend-webgl_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-        "//tfjs-core/src:tfjs-core_test_lib",
-        "@npm//@types/jasmine",
-        "@npm//jasmine",
-    ],
-)
-
-esbuild(
-    name = "tfjs-layers_test_bundle",
-    testonly = True,
-    args = {"keepNames": True},
-    entry_point = "setup_tests.ts",
-    external = [
-        # webworker tests call 'require('@tensorflow/tfjs')', which
-        # is external to the test bundle.
-        # Note: This is not a bazel target. It's just a string.
-        "@tensorflow/tfjs",
-        "worker_threads",
-        "util",
-    ],
-    sources_content = True,
-    deps = [
-        ":tfjs-layers_lib",
-        ":tfjs-layers_test_lib",
-        "//tfjs-layers:package_json",
-    ],
-)
diff --git a/tfjs-master/tfjs-layers/src/activations.ts b/tfjs-master/tfjs-layers/src/activations.ts
deleted file mode 100644
index b5758ae27..000000000
--- a/tfjs-master/tfjs-layers/src/activations.ts
+++ /dev/null
@@ -1,278 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-// Layer activation functions
-import * as tfc from '@tensorflow/tfjs-core';
-import {serialization, Tensor, tidy} from '@tensorflow/tfjs-core';
-import * as K from './backend/tfjs_backend';
-import {ActivationIdentifier} from './keras_format/activation_config';
-import {deserializeKerasObject} from './utils/generic_utils';
-
-/**
- * Base class for Activations.
- *
- * Special note: due to cross-language compatibility reasons, the
- * static readonly className field in this family of classes must be set to
- * the initialLowerCamelCase name of the activation.
- */
-export abstract class Activation extends serialization.Serializable {
-  abstract apply(tensor: Tensor, axis?: number): Tensor;
-  getConfig(): serialization.ConfigDict {
-    return {};
-  }
-}
-
-/**
- * Exponential linear unit (ELU).
- * Reference: https://arxiv.org/abs/1511.07289
- */
-export class Elu extends Activation {
-  /** @nocollapse */
-  static readonly className = 'elu';
-  /**
-   * Calculate the activation function.
-   *
-   * @param x: Input.
-   * @param alpha: Scaling factor the negative section.
-   * @return Output of the ELU activation.
-   */
-  apply(x: Tensor, alpha = 1): Tensor {
-    return K.elu(x, alpha);
-  }
-}
-serialization.registerClass(Elu);
-
-/**
- * Scaled Exponential Linear Unit. (Klambauer et al., 2017).
- * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515
- * Notes:
- *   - To be used together with the initialization "lecunNormal".
- *   - To be used together with the dropout variant "AlphaDropout".
- */
-export class Selu extends Activation {
-  /** @nocollapse */
-  static readonly className = 'selu';
-  apply(x: Tensor): Tensor {
-    return tfc.selu(x);
-  }
-}
-serialization.registerClass(Selu);
-
-/**
- *  Rectified linear unit
- */
-export class Relu extends Activation {
-  /** @nocollapse */
-  static readonly className = 'relu';
-  apply(x: Tensor): Tensor {
-    return tfc.relu(x);
-  }
-}
-serialization.registerClass(Relu);
-
-/**
- * Rectified linear unit activation maxing out at 6.0.
- */
-export class Relu6 extends Activation {
-  /** @nocollapse */
-  static readonly className = 'relu6';
-  apply(x: Tensor): Tensor {
-    return tidy(() => tfc.minimum(6.0, tfc.relu(x)));
-  }
-}
-serialization.registerClass(Relu6);
-
-//* Linear activation (no-op) */
-export class Linear extends Activation {
-  /** @nocollapse */
-  static readonly className = 'linear';
-  apply(x: Tensor): Tensor {
-    return x;
-  }
-}
-serialization.registerClass(Linear);
-
-/**
- * Sigmoid activation function.
- */
-export class Sigmoid extends Activation {
-  /** @nocollapse */
-  static readonly className = 'sigmoid';
-  apply(x: Tensor): Tensor {
-    return tfc.sigmoid(x);
-  }
-}
-serialization.registerClass(Sigmoid);
-
-/**
- * Segment-wise linear approximation of sigmoid.
- */
-export class HardSigmoid extends Activation {
-  /** @nocollapse */
-  static readonly className = 'hardSigmoid';
-  apply(x: Tensor): Tensor {
-    return K.hardSigmoid(x);
-  }
-}
-serialization.registerClass(HardSigmoid);
-
-/**
- * Softplus activation function.
- */
-export class Softplus extends Activation {
-  /** @nocollapse */
-  static readonly className = 'softplus';
-  apply(x: Tensor): Tensor {
-    return tfc.softplus(x);
-  }
-}
-serialization.registerClass(Softplus);
-
-/**
- * Softsign activation function.
- */
-export class Softsign extends Activation {
-  /** @nocollapse */
-  static readonly className = 'softsign';
-  apply(x: Tensor): Tensor {
-    return K.softsign(x);
-  }
-}
-serialization.registerClass(Softsign);
-
-/**
- * Hyperbolic tangent function.
- */
-export class Tanh extends Activation {
-  /** @nocollapse */
-  static readonly className = 'tanh';
-  apply(x: Tensor): Tensor {
-    return tfc.tanh(x);
-  }
-}
-serialization.registerClass(Tanh);
-
-/**
- * Softmax activation function
- */
-export class Softmax extends Activation {
-  /** @nocollapse */
-  static readonly className = 'softmax';
-  /**
-   * Calculate the activation function.
-   *
-   * @param x Tensor.
-   * @param axis Integer, axis along which the softmax normalization is applied.
-   * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be
-   * an error.
-   *
-   * @returns a Tensor of the same shape as x
-   *
-   * @throws ValueError: In case `dim(x) < 2`.
-   */
-  apply(x: Tensor, axis: number = (-1)): Tensor {
-    return tfc.softmax(x, axis);
-  }
-}
-serialization.registerClass(Softmax);
-
-/**
- * Log softmax activation function
- */
-export class LogSoftmax extends Activation {
-  /** @nocollapse */
-  static readonly className = 'logSoftmax';
-  /**
-   * Calculate the activation function of log softmax:
-   * log( exp(x_i) / sum(exp(x)) )
-   *
-   * @param x Tensor.
-   * @param axis Integer, axis along which the softmax normalization is applied.
-   * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be
-   * an error.
-   *
-   * @returns a Tensor of the same shape as x
-   *
-   * @throws ValueError: In case `dim(x) < 2`.
-   */
-  apply(x: Tensor, axis: number = (-1)): Tensor {
-    return tfc.logSoftmax(x, axis);
-  }
-}
-serialization.registerClass(LogSoftmax);
-
-/**
- * Swish activation function
- */
-export class Swish extends Activation {
-  /** @nocollapse */
-  static readonly className = 'swish';
-  /**
-   * Calculate the activation function.
-   *
-   * @param x Tensor.
-   * @param alpha Scaling factor for the sigmoid function.
-   * @returns a Tensor of the same shape as x
-   */
-  apply(x: Tensor, alpha = 1): Tensor {
-    return tidy(() => tfc.mul(tfc.sigmoid(tfc.mul(x, alpha)), x));
-  }
-}
-serialization.registerClass(Swish);
-
-/**
- * Mish activation function
- */
-export class Mish extends Activation {
-  /** @nocollapse */
-  static readonly className = 'mish';
-  /**
-   * Calculate the activation function.
-   *
-   * @param x Tensor.
-   * @returns a Tensor of the same shape as x
-   */
-  apply(x: Tensor): Tensor {
-    return tidy(() => tfc.mul(x, tfc.tanh(tfc.softplus(x))));
-  }
-}
-serialization.registerClass(Mish);
-
-export function serializeActivation(activation: Activation): string {
-  return activation.getClassName();
-}
-
-export function deserializeActivation(
-    config: serialization.ConfigDict,
-    customObjects: serialization.ConfigDict = {}): Activation {
-  return deserializeKerasObject(
-      config, serialization.SerializationMap.getMap().classNameMap,
-      customObjects, 'activation');
-}
-
-export function getActivation(identifier: ActivationIdentifier|
-                              serialization.ConfigDict|Activation): Activation {
-  if (identifier == null) {
-    const config: serialization.ConfigDict = {};
-    config['className'] = 'linear';
-    config['config'] = {};
-    return deserializeActivation(config);
-  }
-  if (typeof identifier === 'string') {
-    const config: serialization.ConfigDict = {};
-    config['className'] = identifier;
-    config['config'] = {};
-    return deserializeActivation(config);
-  } else if (identifier instanceof Activation) {
-    return identifier;
-  } else {
-    return deserializeActivation(identifier);
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/activations_test.ts b/tfjs-master/tfjs-layers/src/activations_test.ts
deleted file mode 100644
index bc4d62898..000000000
--- a/tfjs-master/tfjs-layers/src/activations_test.ts
+++ /dev/null
@@ -1,368 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for activations.ts.
- */
-import {scalar, tensor1d, tensor2d, tensor3d} from '@tensorflow/tfjs-core';
-
-import {Elu, HardSigmoid, Linear, LogSoftmax, Relu, Relu6, Selu, Sigmoid, Softmax, Softplus, Softsign, Tanh, Swish, Mish} from './activations';
-import {describeMathCPUAndGPU, expectNoLeakedTensors, expectTensorsClose} from './utils/test_utils';
-
-describeMathCPUAndGPU('linear activation', () => {
-  const initVals = new Float32Array([-1, 2, 0, 4, -5, 6]);
-  const expectedVals = new Float32Array([-1, 2, 0, 4, -5, 6]);
-  const linear = new Linear().apply;
-  it('1D', () => {
-    const initX = tensor1d(initVals);
-    expectTensorsClose(linear(initX), tensor1d(expectedVals));
-  });
-  it('2D', () => {
-    const initX = tensor2d(initVals, [2, 3]);
-    expectTensorsClose(linear(initX), tensor2d(expectedVals, [2, 3]));
-  });
-  it('3D', () => {
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(linear(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => linear(initX), 0);
-  });
-});
-
-describeMathCPUAndGPU('elu activation', () => {
-  const initVals = [-1, 2, 0, 4, -5, 6];
-  const expectedVals = initVals.map(x => x < 0 ? Math.exp(x) - 1 : x);
-  const elu = new Elu().apply;
-  it('1D', () => {
-    const initX = tensor1d(initVals);
-    expectTensorsClose(elu(initX), tensor1d(expectedVals));
-  });
-  it('2D', () => {
-    const initX = tensor2d(initVals, [2, 3]);
-    expectTensorsClose(elu(initX), tensor2d(expectedVals, [2, 3]));
-  });
-  it('3D', () => {
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(elu(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => elu(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('selu activation', () => {
-  const initVals = [-1, 2, 0, 4, -5, 6];
-  const alpha = 1.6732632423543772848170429916717;
-  const scale = 1.0507009873554804934193349852946;
-
-  const expectedVals =
-      initVals.map(x => scale * (x < 0 ? (alpha * (Math.exp(x) - 1)) : x));
-  const selu = new Selu().apply;
-
-  it('1D', () => {
-    const initX = tensor1d(initVals);
-    expectTensorsClose(selu(initX), tensor1d(expectedVals));
-  });
-  it('2D', () => {
-    const initX = tensor2d(initVals, [2, 3]);
-    expectTensorsClose(selu(initX), tensor2d(expectedVals, [2, 3]));
-  });
-  it('3D', () => {
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(selu(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => selu(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('relu activation', () => {
-  const initVals = new Float32Array([-1, 2, 0, 4, -5, 6]);
-  const expectedVals = new Float32Array([0, 2, 0, 4, 0, 6]);
-  const relu = new Relu().apply;
-  it('1D', () => {
-    const initX = tensor1d(initVals);
-    expectTensorsClose(relu(initX), tensor1d(expectedVals));
-  });
-  it('2D', () => {
-    const initX = tensor2d(initVals, [2, 3]);
-    expectTensorsClose(relu(initX), tensor2d(expectedVals, [2, 3]));
-  });
-  it('3D', () => {
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(relu(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => relu(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('relu6 activation', () => {
-  const initVals = new Float32Array([-10, -5, 0, 1, 5, 15]);
-  const expectedVals = new Float32Array([0, 0, 0, 1, 5, 6]);
-  const relu6 = new Relu6().apply;
-  it('1D', () => {
-    const initX = tensor1d(initVals);
-    expectTensorsClose(relu6(initX), tensor1d(expectedVals));
-  });
-  it('2D', () => {
-    const initX = tensor2d(initVals, [2, 3]);
-    expectTensorsClose(relu6(initX), tensor2d(expectedVals, [2, 3]));
-  });
-  it('3D', () => {
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(relu6(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => relu6(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('sigmoid activation', () => {
-  const sigmoid = new Sigmoid().apply;
-  const initVals = [-1, 2, 0, 4, -5, 6];
-  it('Scalar', () => {
-    expectTensorsClose(sigmoid(scalar(0)), scalar(0.5));
-  });
-  it('3D', () => {
-    const expectedVals = initVals.map(v => 1 / (1 + Math.exp(-v)));
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(sigmoid(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => sigmoid(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('hardSigmoid activation', () => {
-  const hardSigmoid = new HardSigmoid().apply;
-  const initVals = [-1, 2, 0, 4, -5, 6];
-  it('Scalar', () => {
-    expectTensorsClose(hardSigmoid(scalar(0)), scalar(0.5));
-  });
-  it('3D', () => {
-    const expectedVals = initVals.map(v => {
-      const y = 0.2 * v + 0.5;
-      if (y > 1) {
-        return 1;
-      } else if (y < 0) {
-        return 0;
-      } else {
-        return y;
-      }
-    });
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(hardSigmoid(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => hardSigmoid(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('softplus activation', () => {
-  const softplus = new Softplus().apply;
-  const initVals = [-1, 2, 0, 4, -5, 6];
-  it('Scalar', () => {
-    expectTensorsClose(softplus(scalar(0)), scalar(Math.log(2)));
-  });
-  it('3D', () => {
-    const expectedVals = initVals.map(v => Math.log(Math.exp(v) + 1));
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(softplus(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => softplus(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('softsign activation', () => {
-  const softsign = new Softsign().apply;
-  const initVals = [-1, 2, 0, 4, -5, 6];
-  it('Scalar', () => {
-    expectTensorsClose(softsign(scalar(0)), scalar(0));
-  });
-  it('3D', () => {
-    const expectedVals = initVals.map(v => v / (Math.abs(v) + 1));
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(softsign(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => softsign(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('tanh activation', () => {
-  const tanh = new Tanh().apply;
-  const initVals = [-1, 2, 0, 4, -5, 6];
-  const expectedVals = initVals.map(x => Math.tanh(x));
-  it('1D', () => {
-    const initX = tensor1d(initVals);
-    expectTensorsClose(tanh(initX), tensor1d(expectedVals));
-  });
-  it('2D', () => {
-    const initX = tensor2d(initVals, [2, 3]);
-    expectTensorsClose(tanh(initX), tensor2d(expectedVals, [2, 3]));
-  });
-  it('3D', () => {
-    const initX = tensor3d(initVals, [1, 2, 3]);
-    expectTensorsClose(tanh(initX), tensor3d(expectedVals, [1, 2, 3]));
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => tanh(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('softmax activation', () => {
-  const softmax = new Softmax().apply;
-  // Setup: Array with initial values.
-  // Execute: Softmax on the last dimension.
-  // Expect: Output array matches size and approximate expected values.
-  it('1D', () => {
-    const initVals = new Float32Array([0, 1, 3, 9]);
-    const expectedVals = new Float32Array([0.000, 0.000, 0.002, 0.997]);
-    const initX = tensor1d(initVals);
-    expectTensorsClose(softmax(initX), tensor1d(expectedVals));
-  });
-  it('1D all equal', () => {
-    const initVals = new Float32Array([-1, -1, -1, -1]);
-    const expectedVals = new Float32Array([0.25, 0.25, 0.25, 0.25]);
-    const initX = tensor1d(initVals);
-    expectTensorsClose(softmax(initX), tensor1d(expectedVals));
-  });
-  it('2D', () => {
-    const initVals = new Float32Array([0, 1, 3, 9, 0, 1, 3, 9]);
-    const expectedVals = new Float32Array(
-        [0.000, 0.000, 0.002, 0.997, 0.000, 0.000, 0.002, 0.997]);
-    const initX = tensor2d(initVals, [2, 4]);
-    expectTensorsClose(softmax(initX), tensor2d(expectedVals, [2, 4]));
-  });
-  it('3D', () => {
-    const initVals = new Float32Array([0, 1, 3, 9, 0, 1, 3, 9]);
-    const expectedVals = new Float32Array(
-        [0.000, 0.000, 0.002, 0.997, 0.000, 0.000, 0.002, 0.997]);
-    const initX = tensor3d(initVals, [1, 2, 4]);
-    expectTensorsClose(softmax(initX), tensor3d(expectedVals, [1, 2, 4]));
-  });
-  it('Does not leak', () => {
-    const initVals = new Float32Array([0, 1, 3, 9]);
-    const initX = tensor1d(initVals);
-    expectNoLeakedTensors(() => softmax(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('logsoftmax activation', () => {
-  const logsoftmax = new LogSoftmax().apply;
-  // Setup: Array with initial values.
-  // Execute: logSoftmax on the last dimension.
-  // Expect: Output array matches size and approximate expected values.
-  it('1D', () => {
-    const initX = tensor1d([0, 1, 3, 9]);
-    const expectedVals = tensor1d([-9.003, -8.003, -6.003, -0.003]);
-    expectTensorsClose(logsoftmax(initX), expectedVals);
-  });
-  it('1D all equal', () => {
-    const initX = tensor1d([-1, -1, -1, -1]);
-    const expectedVals = tensor1d([-1.386, -1.386, -1.386, -1.386]);
-    expectTensorsClose(logsoftmax(initX), expectedVals);
-  });
-  it('2D', () => {
-    const initX = tensor2d([[0, 1, 3, 9], [0, 1, 3, 9]]);
-    const expectedVals = tensor2d(
-        [[-9.003, -8.003, -6.003, -0.003], [-9.003, -8.003, -6.003, -0.003]]);
-    expectTensorsClose(logsoftmax(initX), expectedVals);
-  });
-  it('3D', () => {
-    const initX = tensor3d([[[0, 1, 3, 9], [0, 1, 3, 9]]]);
-    const expectedVals = tensor3d(
-        [[[-9.003, -8.003, -6.003, -0.003], [-9.003, -8.003, -6.003, -0.003]]]);
-    expectTensorsClose(logsoftmax(initX), expectedVals);
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d([0, 1, 3, 9]);
-    expectNoLeakedTensors(() => logsoftmax(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('swish activation', () => {
-  const swish = new Swish().apply;
-  // Setup: Array with initial values.
-  // Execute: Swish on the last dimension.
-  // Expect: Output array matches size and approximate expected values.
-  it('1D', () => {
-    const initX = tensor1d([0, 1, 3, 9]);
-    const expectedVals = tensor1d([0, .731, 2.857, 8.998]);
-    expectTensorsClose(swish(initX), expectedVals);
-  });
-  it('1D all equal', () => {
-    const initX = tensor1d([-1, -1, -1, -1]);
-    const expectedVals = tensor1d([-.268, -.268, -.268, -.268]);
-    expectTensorsClose(swish(initX), expectedVals);
-  });
-  it('2D', () => {
-    const initX = tensor2d([[0, 1, 3, 9], [0, 1, 3, 9]]);
-    const expectedVals = tensor2d(
-        [[0, .731, 2.857, 8.998], [0, .731, 2.857, 8.998]]);
-    expectTensorsClose(swish(initX), expectedVals);
-  });
-  it('3D', () => {
-    const initX = tensor3d([[[0, 1, 3, 9], [0, 1, 3, 9]]]);
-    const expectedVals = tensor3d(
-        [[[0, .731, 2.857, 8.998], [0, .731, 2.857, 8.998]]]);
-    expectTensorsClose(swish(initX), expectedVals);
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d([0, 1, 3, 9]);
-    expectNoLeakedTensors(() => swish(initX), 1);
-  });
-});
-
-describeMathCPUAndGPU('mish activation', () => {
-  const mish = new Mish().apply;
-  // Setup: Array with initial values.
-  // Execute: Mish on the last dimension.
-  // Expect: Output array matches size and approximate expected values.
-  it('1D', () => {
-    const initX = tensor1d([0, 1, 3, 9]);
-    const expectedVals = tensor1d([0., .865, 2.987, 9.]);
-    expectTensorsClose(mish(initX), expectedVals);
-  });
-  it('1D all equal', () => {
-    const initX = tensor1d([-1, -1, -1, -1]);
-    const expectedVals = tensor1d([-0.303, -0.303, -0.303, -0.303]);
-    expectTensorsClose(mish(initX), expectedVals);
-  });
-  it('2D', () => {
-    const initX = tensor2d([[0, 1, 3, 9], [0, 1, 3, 9]]);
-    const expectedVals = tensor2d(
-        [[0., .865, 2.987, 9.], [0., .865, 2.987, 9.]]);
-    expectTensorsClose(mish(initX), expectedVals);
-  });
-  it('3D', () => {
-    const initX = tensor3d([[[0, 1, 3, 9], [0, 1, 3, 9]]]);
-    const expectedVals = tensor3d(
-        [[[0., .865, 2.987, 9.], [0., .865, 2.987, 9.]]]);
-    expectTensorsClose(mish(initX), expectedVals);
-  });
-  it('Does not leak', () => {
-    const initX = tensor1d([0, 1, 3, 9]);
-    expectNoLeakedTensors(() => mish(initX), 1);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/backend/common.ts b/tfjs-master/tfjs-layers/src/backend/common.ts
deleted file mode 100644
index 649f9352f..000000000
--- a/tfjs-master/tfjs-layers/src/backend/common.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {backend} from '@tensorflow/tfjs-core';
-import {DataFormat} from '../keras_format/common';
-
-let _epsilon: number;
-
-/**
- * Returns the value of the fuzz factor used in numeric expressions.
- */
-export function epsilon() {
-  if (_epsilon == null) {
-    _epsilon = backend().epsilon();
-  }
-  return _epsilon;
-}
-
-/**
- * Sets the value of the fuzz factor used in numeric expressions.
- * @param e New value of epsilon.
- */
-export function setEpsilon(e: number) {
-  _epsilon = e;
-}
-
-/**
- * Returns the default image data format convention.
- */
-export function imageDataFormat(): DataFormat {
-  return 'channelsLast';
-}
diff --git a/tfjs-master/tfjs-layers/src/backend/random_seed.ts b/tfjs-master/tfjs-layers/src/backend/random_seed.ts
deleted file mode 100644
index b5dd34c1e..000000000
--- a/tfjs-master/tfjs-layers/src/backend/random_seed.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2023 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Keeps track of seed and handles pseudorandomness
- * Instance created in BaseRandomLayer class
- * Utilized for random preprocessing layers
- */
-
-export class RandomSeed {
-  static className = 'RandomSeed';
-  seed: number | undefined;
-  constructor(seed: number | undefined) { 
-    this.seed = seed; 
-  }
-  next(): number | undefined { 
-    if (this.seed === undefined) {
-      return undefined;
-    }
-    return this.seed++; 
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/backend/random_seed_test.ts b/tfjs-master/tfjs-layers/src/backend/random_seed_test.ts
deleted file mode 100644
index c4f940146..000000000
--- a/tfjs-master/tfjs-layers/src/backend/random_seed_test.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @license
- * Copyright 2023 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {RandomSeed} from './random_seed';
-import {describeMathCPUAndGPU} from '../utils/test_utils';
-
-describeMathCPUAndGPU('RandomSeed', () => {
-  it('Checking if RandomSeed class handles pseudo randomness.', () => {
-    const randomSeed = new RandomSeed(42);
-    const firstSeed = randomSeed.seed;
-    randomSeed.next();
-    const secondSeed = randomSeed.seed;
-    expect(firstSeed).not.toEqual(secondSeed);
-  });
-  it('Checking if RandomSeed class handles undefined seed.', () => {
-    const randomSeed = new RandomSeed(undefined);
-    const firstSeed = randomSeed.seed;
-    const secondSeed = randomSeed.next();
-    expect(firstSeed).toEqual(undefined);
-    expect(secondSeed).toEqual(undefined);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/backend/state.ts b/tfjs-master/tfjs-layers/src/backend/state.ts
deleted file mode 100644
index 41ea8871e..000000000
--- a/tfjs-master/tfjs-layers/src/backend/state.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Utilities related to persistent state in the backend.
- */
-
-/**
- * An ID to track `tf.SymbolicTensor`s and derived classes.
- * Required in different places in engine/topology.ts to identify unique
- * tensors.
- */
-let _nextUniqueTensorId = 0;
-
-export function getNextUniqueTensorId(): number {
-  return _nextUniqueTensorId++;
-}
-
-const _uidPrefixes: {[prefix: string]: number} = {};
-
-/**
- * Provides a unique UID given a string prefix.
- *
- * @param prefix
- */
-export function getUid(prefix = ''): string {
-  if (!(prefix in _uidPrefixes)) {
-    _uidPrefixes[prefix] = 0;
-  }
-  _uidPrefixes[prefix] += 1;
-  return prefix + _uidPrefixes[prefix].toString();
-}
diff --git a/tfjs-master/tfjs-layers/src/backend/state_test.ts b/tfjs-master/tfjs-layers/src/backend/state_test.ts
deleted file mode 100644
index e44f28024..000000000
--- a/tfjs-master/tfjs-layers/src/backend/state_test.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {getUid} from '../backend/state';
-
-describe('getUID ', () => {
-  it('second UID is different.', () => {
-    const name = 'def';
-    const firstUID = getUid(name);
-    const secondUID = getUid(name);
-    expect(secondUID).not.toEqual(firstUID);
-  });
-
-  it('with no prefix works and returns different UIDs.', () => {
-    const firstUID = getUid();
-    const secondUID = getUid();
-    expect(firstUID).not.toEqual(secondUID);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/backend/tfjs_backend.ts b/tfjs-master/tfjs-layers/src/backend/tfjs_backend.ts
deleted file mode 100644
index 127373aab..000000000
--- a/tfjs-master/tfjs-layers/src/backend/tfjs_backend.ts
+++ /dev/null
@@ -1,719 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * deeplearn.js backend.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {onesLike as coreOnesLike, scalar, Tensor, Tensor1D, tensor1d, Tensor2D, Tensor3D, Tensor4D, Tensor5D, tidy, where, zerosLike as coreZerosLike} from '@tensorflow/tfjs-core';
-import {checkDataFormat} from '../common';
-import {NotImplementedError, ValueError} from '../errors';
-import {DataFormat, Shape} from '../keras_format/common';
-import {HasShape} from '../types';
-import * as math_utils from '../utils/math_utils';
-
-import {imageDataFormat} from './common';
-
-// tslint:enable
-
-/* Setting and getting backend from deeplearn.js. */
-
-// Default deeplearn.js backend is WebGL (GPU).
-let backend: 'cpu'|'webgl' = 'webgl';
-
-export function setBackend(requestedBackend: 'cpu'|'webgl') {
-  tfc.setBackend(requestedBackend);
-  backend = requestedBackend;
-}
-
-export function getBackend(): 'cpu'|'webgl' {
-  return backend;
-}
-
-/**
- * Indicates whether the backend is operating symbolically.
- *
- * This function will be used to determine how to interpret user code. If
- * it returns true, calls to the backend construct a symbolic graph; if
- * it returns false, calls to the backend execute immediately.
- */
-export function isBackendSymbolic(): boolean {
-  return false;
-}
-
-/**
- * Get the number of elements in a Tensor.
- * @param x The Tensor.
- * @return Number of elements in `x`.
- */
-export function countParams(x: HasShape): number {
-  const shape = x.shape;
-  if (shape.length > 0) {
-    return shape.reduce((a: number, b: number) => a * b);
-  } else {
-    // Scalar.
-    return 1;
-  }
-}
-
-/**
- * Casts a tensor to a different dtype and returns it.
- * @param x Input tensor.
- * @param dtype String: 'float32'|'int32'|'bool'.
- * @returns Tensor of the specified `dtype`.
- */
-export function cast(x: Tensor, dtype: tfc.DataType): Tensor {
-  return tfc.cast(x, dtype);
-}
-
-/**
- * Adds a 1-sized dimension at index "axis".
- * @param x Input tensor.
- * @param axis Position where to add the new axis.
- * @returns Result of the dimension expansion.
- */
-export function expandDims(x: Tensor, axis = -1): Tensor {
-  const outShape = x.shape.slice();
-  if (axis < 0) {
-    axis = outShape.length + axis + 1;
-  }
-  outShape.splice(axis, 0, 1);
-  return tfc.reshape(x, outShape);
-}
-
-/**
- * Repeats a 2D tensor.
- *
- * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output
- * will have shape `[samples, 2, dim]`.
- *
- * @param x Input tensor.
- * @param n Integer, number of times to repeat.
- * @returns The result of the repeat operation.
- * @throws ValueError: If input tensor is not 2D.
- */
-export function repeat(x: Tensor, n: number): Tensor {
-  return tidy(() => {
-    if (x.shape.length !== 2) {
-      throw new ValueError(
-          `repeat() expects a rank-2 tensor, but received a ` +
-          `rank-${x.shape.length} tensor.`);
-    }
-    const y = expandDims(x, 1);
-    return tile(y, [1, n, 1]);
-  });
-}
-
-/**
- * Flatten a Tensor into 1D.
- * @param x Input tensor.
- * @return The result of the flattening `x`.
- */
-export function flatten(x: Tensor): Tensor {
-  const newShape = [math_utils.arrayProd(x.shape)];
-  return tfc.reshape(x, newShape);
-}
-
-/**
- * Turn a nD tensor into a 2D tensor with same 0th dimension.
- * In other words, it flattens each data samples of a batch.
- *
- * @param x The tensor to flatten. The rank of this tensor is required to be 2
- *   or higher.
- * @return The result of the flattening.
- */
-export function batchFlatten(x: Tensor): Tensor {
-  if (x.rank <= 1) {
-    throw new ValueError(
-        `batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);
-  }
-  const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];
-  return tfc.reshape(x, newShape);
-}
-
-/**
- * Do slicing along the first axis.
- * @param array input `tf.Tensor`.
- * @param start starting index, inclusive.
- * @param size size of the slice along the first axis.
- * @returns result of the slicing.
- * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.
- */
-export function sliceAlongFirstAxis(
-    array: Tensor, start: number, size: number): Tensor {
-  return tidy(() => {
-    switch (array.rank) {
-      case 1:
-        return tfc.slice1d(array as Tensor1D, start, size);
-      case 2:
-        return tfc.slice2d(
-            array as Tensor2D, [start, 0], [size, array.shape[1]]);
-      case 3:
-        return tfc.slice3d(
-            array as Tensor3D, [start, 0, 0],
-            [size, array.shape[1], array.shape[2]]);
-      case 4:
-        return tfc.slice4d(
-            array as Tensor4D, [start, 0, 0, 0],
-            [size, array.shape[1], array.shape[2], array.shape[3]]);
-      case 5:
-        return tfc.slice(array as Tensor5D, [start, 0, 0, 0, 0], [
-          size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]
-        ]);
-      case 6:
-        return tfc.slice(array, [start, 0, 0, 0, 0, 0], [
-          size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],
-          array.shape[5]
-        ]);
-      default:
-        throw new ValueError(
-            `sliceAlongFirstAxis() received an unsupported tensor rank: ` +
-            `${array.rank}`);
-    }
-  });
-}
-
-/**
- * Do slicing along the last axis.
- * @param array input `tf.Tensor`.
- * @param start starting index, inclusive.
- * @param size size of the slice along the last axis.
- * @returns result of the slicing.
- * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.
- */
-export function sliceAlongLastAxis(
-    array: Tensor, start: number, size: number): Tensor {
-  return tidy(() => {
-    switch (array.rank) {
-      case 1:
-        return tfc.slice1d(array as Tensor1D, start, size);
-      case 2:
-        return tfc.slice2d(
-            array as Tensor2D, [0, start], [array.shape[0], size]);
-      case 3:
-        return tfc.slice3d(
-            array as Tensor3D, [0, 0, start],
-            [array.shape[0], array.shape[1], size]);
-      case 4:
-        return tfc.slice4d(
-            array as Tensor4D, [0, 0, 0, start],
-            [array.shape[0], array.shape[1], array.shape[2], size]);
-      default:
-        throw new ValueError(
-            `sliceAlongLastAxis() received an unsupported tensor rank: ` +
-            `${array.rank}`);
-    }
-  });
-}
-
-/**
- * Do slicing along the sepcified axis.
- * @param array input `tf.Tensor`.
- * @param start starting index, inclusive.
- * @param size of the slice along the chosen axis.
- * @param choose an axis.
- * @returns result of the slicing.
- * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.
- */
-export function sliceAlongAxis(
-    array: Tensor, start: number, size: number, axis: number): Tensor {
-  return tidy(() => {
-    switch (array.rank) {
-      case 1:
-        return tfc.slice1d(array as Tensor1D, start, size);
-      case 2:
-        switch (axis) {
-          case 1:
-            return sliceAlongFirstAxis(array, start, size);
-          case 2:
-            return sliceAlongLastAxis(array, start, size);
-          default:
-            throw new ValueError(
-                `The axis is not within the rank of the tensor ` +
-                `${axis}`);
-        }
-      case 3:
-        switch (axis) {
-          case 1:
-            return sliceAlongFirstAxis(array, start, size);
-          case 2:
-            return tfc.slice3d(
-                array as Tensor3D, [0, start, 0],
-                [array.shape[0], size, array.shape[2]]);
-          case 3:
-            return sliceAlongLastAxis(array, start, size);
-          default:
-            throw new ValueError(
-                `The axis is not within the rank of the tensor ` +
-                `${axis}`);
-        }
-      case 4:
-        switch (axis) {
-          case 1:
-            return sliceAlongFirstAxis(array, start, size);
-          case 2:
-            return tfc.slice4d(
-                array as Tensor4D, [0, start, 0, 0],
-                [array.shape[0], size, array.shape[2], array.shape[3]]);
-          case 3:
-            return tfc.slice4d(
-                array as Tensor4D, [0, 0, start, 0],
-                [array.shape[0], array.shape[1], size, array.shape[3]]);
-          case 4:
-            return sliceAlongLastAxis(array, start, size);
-          default:
-            throw new ValueError(
-                `The axis is not within the rank of the tensor ` +
-                `${axis}`);
-        }
-      default:
-        throw new ValueError(
-            `sliceAlongLastAxis() received an unsupported tensor rank: ` +
-            `${array.rank}`);
-    }
-  });
-}
-
-/**
- * Concatenates a list of tensors alongside the specified axis.
- * @param tensors `Array` of tensors to concatenate.
- * @param axis Concatenation axis.
- * @returns The result of the concatenation.
- */
-export function concatenate(tensors: Tensor[], axis = -1): Tensor {
-  let rank: number;
-  if (axis < 0) {
-    rank = tensors[0].rank;
-    if (rank !== 0) {
-      axis = rank;
-    } else {
-      axis = 0;
-    }
-  }
-  if (axis === tensors[0].rank) {
-    // Porting Note: This is necessary because tfc.concat() requires axis to be
-    //   in the interval [-rank, rank).
-    axis = -1;
-  }
-  // Porting Note: Sparse concat is not supported yet.
-  return tfc.concat(tensors, axis);
-}
-
-/**
- * Concatenate two arrays along the first dimension.
- * @param a The 1st `tf.Tensor` to concatenate.
- * @param b The 2nd `tf.Tensor` to concatenate.
- * @returns Result of the concatenation.
- * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.
- */
-export function concatAlongFirstAxis(a: Tensor, b: Tensor): Tensor {
-  switch (a.rank) {
-    case 1:
-      return tfc.concat1d([a as Tensor1D, b as Tensor1D]);
-    case 2:
-      return tfc.concat2d([a as Tensor2D, b as Tensor2D], 0);
-    case 3:
-      return tfc.concat3d([a as Tensor3D, b as Tensor3D], 0);
-    case 4:
-      return tfc.concat4d([a as Tensor4D, b as Tensor4D], 0);
-    default:
-      throw new ValueError(
-          `concatAlongFirstAxis() received an unsupported ` +
-          `tensor rank: ${a.rank}`);
-  }
-}
-
-/**
- * Creates a tensor by tiling `x` by `n`.
- * @param x A tensor.
- * @param n An Array of integers or a single integer. If an Array, the length
- *   must be the same as the number of dimensions in `x`. If a single integer,
- *   it will be treated as an Array of length 1.
- */
-export function tile(x: Tensor, n: number|number[]): Tensor {
-  if (!Array.isArray(n)) {
-    n = [n];
-  }
-  if (x.rank !== n.length) {
-    throw new ValueError(
-        `The length of input n (${n.length}) does not match ` +
-        `the number of dimensions in input x (${x.rank})`);
-  }
-  return tfc.tile(x, n);
-}
-
-/* Creation of random tensors. */
-
-/**
- * Get a tensor with normal distribution of values.
- *
- * @param shape Shape of the tensor.
- * @param mean mean value of the normal distribution.
- * @param stddev standard deviation of the normal distribution.
- * @param dtype
- * @param seed
- * @return The normal tensor.
- */
-export function randomNormal(
-    shape: Shape, mean = 0.0, stddev = 1.0, dtype?: 'float32'|'int32',
-    seed?: number): Tensor {
-  return tfc.randomNormal(shape, mean, stddev, dtype, seed);
-}
-
-/* Linear Algebra */
-
-/**
- * Multiply two tensors and returns the result as a tensor.
- *
- * For 2D tensors, this is equivalent to matrix multiplication (matMul).
- * For tensors of higher ranks, it follows the Theano behavior,
- * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:
- *
- * For N dimensions it is a sum product over the last axis of x and the
- * second-to-last of y:
- *
- * @param a A tensor of at least rank 2.
- * @param b A tensor of at least rank 2.
- * @param activation (optional) A string identifying the activation
- *   function.
- * @return Result of the dot operation.
- */
-export function dot(
-    a: Tensor, b: Tensor, activation?: tfc.fused.Activation,
-    bias?: Tensor): Tensor {
-  if ((a.rank < 2) || (b.rank < 2)) {
-    throw new NotImplementedError(
-        `dot requires both inputs to be rank >= 2` +
-        ` but got x shape = ${a.shape} and y shape = ${b.shape}`);
-  }
-  if (b.rank >= 3) {
-    const xLastDim = a.shape.slice(-1)[0];
-    const ySecondLastDim = b.shape.slice(-2)[0];
-    if (xLastDim !== ySecondLastDim) {
-      throw new NotImplementedError(
-          `If rank y >= 3, then the second last dim` +
-          ` of y must equal the last dim of x but got x shape = ${
-              a.shape} and ` +
-          ` y shape = ${b.shape}`);
-    }
-  }
-  // Handle basic 2D x 2D case.
-  if ((a.rank === 2) && (b.rank === 2)) {
-    const transposeA = false;
-    const transposeB = false;
-    // tfc.fused.matMul only fuses certain activation functions. Unsupported
-    // activation functions are treated as 'linear' activations, which is
-    // equivalent to a no-op.
-    return tfc.fused.matMul({
-      a,
-      b: b as Tensor2D,
-      transposeA,
-      transposeB,
-      bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,
-      activation
-    });
-  } else {
-    // Reshape x into the analogous 2D Tensor.
-    const aFirstDims = a.shape.slice();  // Holds all but the last dim of x.
-    const aLastDim = aFirstDims.pop();
-    a = tfc.reshape(a, [-1, aLastDim]);
-
-    // Reshape y into the analogous 2D Tensor, and keep track of the
-    // required dimensions to reproduce the output shape.
-    const bShape = b.shape.slice();
-    const bLastDim = bShape.pop();
-    const ySecondLastDim = bShape.pop();
-    const yOtherDims = [...bShape, bLastDim];
-    // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]
-    // where r is the rank of y.
-    const perm = Array.from({length: b.rank}, (_, i) => {
-      if (i === 0) {
-        return b.rank - 2;
-      } else if (i <= b.rank - 2) {
-        return i - 1;
-      }
-      return i;
-    });
-    b = tfc.reshape(tfc.transpose(b, perm), [ySecondLastDim, -1]);
-
-    // Multiply x and y as 2D Tensors, and then reshape back to original.
-    const outputShape = [...aFirstDims, ...yOtherDims];
-    const transposeA = false;
-    const transposeB = false;
-    return tfc.reshape(
-        tfc.fused.matMul({
-          a,
-          b,
-          transposeA,
-          transposeB,
-          bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,
-          activation
-        }),
-        outputShape);
-  }
-}
-
-/**
- * Compute the sign Tensor of an input Tensor.
- *
- * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.
- * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.
- * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.
- *
- * @param x Input `tf.Tensor`.
- * @return The sign `tf.Tensor`.
- */
-export function sign(x: Tensor): Tensor {
-  // TODO(cais): Move to the core.
-  return tidy(() => {
-    const zerosLikeX = coreZerosLike(x);
-    const onesLikeX = coreOnesLike(x);
-    return where(
-        tfc.equal(x, zerosLikeX), zerosLikeX,
-        where(
-            tfc.greater(x, coreZerosLike(x)), onesLikeX,
-            tfc.mul(-1, onesLikeX)));
-  });
-}
-
-/**
- * Computes the one-hot representation of an integer tensor.
- * @param indices nD integer tensor of shape
- *   `(batch_size, dim1, dim2, ... dim(n-1))`
- * @param numClasses Integer, number of classes to consider.
- * @returns (n + 1)D one hot representation of the input
- *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`
- */
-export function oneHot(indices: Tensor, numClasses: number): Tensor {
-  return tidy(() => {
-    if (indices.rank !== 1) {
-      throw new Error(
-          'Only 1D one-hot tensors are supported in the ' +
-          'deeplearn backend, at present.');
-    }
-    indices = tfc.cast(indices, 'int32');
-    return tfc.cast(tfc.oneHot(indices as Tensor1D, numClasses), 'float32');
-  });
-}
-
-/* Elementary math functions. */
-
-/**
- * Retrieves the elements of indices `indices` in the tensor `reference`.
- * @param reference A tensor.
- * @param indices An integer tensor of indices or an `Array` of integers.
- * @param axis Axis along which to perform the gather operation.
- * @returns The result of the gathering as a tensor.
- */
-export function gather(
-    reference: Tensor, indices: number[]|Tensor1D, axis?: number): Tensor {
-  return tidy(() => {
-    if (Array.isArray(indices)) {
-      indices = tensor1d(indices, 'int32');
-    } else {
-      indices = tfc.cast(indices, 'int32');
-    }
-    return tfc.gather(reference, indices, axis);
-  });
-}
-
-/**
- * Element-wise square.
- * @param x Input tensor.
- * @return element-wise x^2
- */
-export function square(x: Tensor): Tensor {
-  return tfc.mul(x, x);
-}
-
-/**
- * Element-wise exponentiation.
- *
- * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which
- *   takes advatnage of the backend's (e.g., TensorFlow's) automatic
- * conversion to tensor. Here we allow `a` to be either a number or a tensor.
- *
- * @param x The base tensor.
- * @param a The exponent, tensor or number. If a number, it is rounded to the
- *   nearest integer and converted to a tensor.
- * @returns A tensor of the same shape as `x`.
- */
-export function pow(x: Tensor, a: Tensor|number): Tensor {
-  return tidy(() => {
-    if (typeof (a) === 'number') {
-      a = scalar(Math.round(a), 'int32');
-    }
-    if (a.dtype !== 'int32') {
-      throw new NotImplementedError(
-          `Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);
-    }
-    return tfc.pow(x, a);
-  });
-}
-
-/**
- * Reshapes bias tensor according to rank of x.
- */
-function reshapeBias(xRank: number, bias: Tensor, dataFormat: string) {
-  const biasShape = bias.shape;
-
-  if (bias.rank !== 1 && bias.rank !== xRank) {
-    throw new ValueError(
-        `Unexpected bias dimensions: ${bias.rank}` +
-        `; expected it to be 1 or ${xRank}`);
-  }
-
-  if (xRank === 5) {
-    if (dataFormat === 'channelsFirst') {
-      if (biasShape.length === 1) {
-        return tfc.reshape(bias, [1, biasShape[0], 1, 1, 1]);
-      } else {
-        return tfc.reshape(
-            bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);
-      }
-    } else if (dataFormat === 'channelsLast') {
-      if (biasShape.length === 1) {
-        return tfc.reshape(bias, [1, 1, 1, 1, biasShape[0]]);
-      } else {
-        return tfc.reshape(bias, [1].concat(biasShape));
-      }
-    }
-  } else if (xRank === 4) {
-    if (dataFormat === 'channelsFirst') {
-      if (biasShape.length === 1) {
-        return tfc.reshape(bias, [1, biasShape[0], 1, 1]);
-      } else {
-        return tfc.reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);
-      }
-    } else if (dataFormat === 'channelsLast') {
-      if (biasShape.length === 1) {
-        return tfc.reshape(bias, [1, 1, 1, biasShape[0]]);
-      } else {
-        return tfc.reshape(bias, [1].concat(biasShape));
-      }
-    }
-  } else if (xRank === 3) {
-    if (dataFormat === 'channelsFirst') {
-      if (biasShape.length === 1) {
-        return tfc.reshape(bias, [1, biasShape[0], 1]);
-      } else {
-        return tfc.reshape(bias, [1, biasShape[1], biasShape[0]]);
-      }
-    } else if (dataFormat === 'channelsLast') {
-      if (biasShape.length === 1) {
-        return tfc.reshape(bias, [1, 1, biasShape[0]]);
-      } else {
-        return tfc.reshape(bias, [1].concat(biasShape));
-      }
-    }
-  } else if (xRank < 3) {
-    return bias;
-  }
-  throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);
-}
-
-/* Neural-network operations. */
-
-/**
- * Add a bias to a tensor.
- *
- * @param x The tensor to add the bias to.
- * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.
- * @return Result of the bias adding.
- * @throws ValueError: If the rank of `bias` is incorrect.
- */
-export function biasAdd(
-    x: Tensor, bias: Tensor, dataFormat?: DataFormat): Tensor {
-  return tidy(() => {
-    if (dataFormat == null) {
-      dataFormat = imageDataFormat();
-    }
-    checkDataFormat(dataFormat);
-
-    return tfc.add(x, reshapeBias(x.rank, bias, dataFormat));
-  });
-}
-
-/**
- * Exponential linear unit (ELU).
- * @param x A tensor or variable to compute the activation function for.
- * @param alpha: A scalar, a scaling factor for the negative section.
- * @return Output of the ELU operation.
- */
-export function elu(x: Tensor, alpha = 1): Tensor {
-  // TODO(cais): Add support for alpha values other than 1.
-  if (alpha !== 1) {
-    throw new NotImplementedError(
-        `Support for alpha values other than 1 (${alpha}) is not implemented ` +
-        `yet.`);
-  }
-  return tfc.elu(x);
-}
-
-/**
- * Softsign of a tensor.
- *
- * Defined as x / (abs(x) + 1), element-wise.
- *
- * @param x: Input.
- * @returns Output.
- */
-export function softsign(x: Tensor): Tensor {
-  return tidy(() => tfc.div(x, tfc.add(tfc.abs(x), 1)));
-}
-
-/**
- * Sets entries in `x` to zero at random, while scaling the entire tensor.
- *
- * @param x input tensor.
- * @param level fraction of the entries in the tensor that will be set to 0.
- * @param noiseShape shape of randomly generated keep/drop flags, must be
- *   broadcastable to the shape of `x`. Optional.
- * @param seed random seed to ensure determinism. Optional.
- * @returns Result of the dropout operation.
- */
-export function dropout(
-    x: Tensor, level: number, noiseShape?: number[], seed?: number): Tensor {
-  return tidy(() => tfc.dropout(x, level, noiseShape, seed));
-}
-
-/**
- * Element-wise, segment-wise linear approximation of sigmoid.
- *
- * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.
- * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.
- *
- * @param x Input tensor.
- * @returns Output tensor.
- */
-export function hardSigmoid(x: Tensor): Tensor {
-  return tidy(() => {
-    const y = tfc.add(.5, tfc.mul(.2, x));
-    return tfc.clipByValue(y, 0, 1);
-  });
-}
-
-/**
- * Invoke `x` in the training phase, and `alt` otherwise.
- *
- * Porting Note: We do not create placeholder tensors for the `training`
- * boolean flag here, because there is no such thing in the TF.js imperative
- * backend.
- *
- * @param x The function to invoke iff `training` is `true`.
- * @param alt The function to invoke iff `training` is `false`.
- * @param training Boolean flag for whether training phase is active.
- * @returns The return value of `x()` if `training` is `true`, or the return
- *   value of `alt()` if `training` is `false`.
- */
-export function inTrainPhase<T>(x: () => T, alt: () => T, training = false): T {
-  return training ? x() : alt();
-}
diff --git a/tfjs-master/tfjs-layers/src/backend/tfjs_backend_test.ts b/tfjs-master/tfjs-layers/src/backend/tfjs_backend_test.ts
deleted file mode 100644
index b0850e69f..000000000
--- a/tfjs-master/tfjs-layers/src/backend/tfjs_backend_test.ts
+++ /dev/null
@@ -1,895 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for the TF.js core backend of TF.js Layers.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {DataType, scalar, tensor1d, tensor2d, tensor3d, tensor4d, tensor5d, tensor6d, zeros} from '@tensorflow/tfjs-core';
-
-import {SymbolicTensor} from '../engine/topology';
-import {range} from '../utils/math_utils';
-import {describeMathCPU, describeMathCPUAndGPU, expectNoLeakedTensors, expectTensorsClose} from '../utils/test_utils';
-import {LayerVariable} from '../variables';
-
-import * as K from './tfjs_backend';
-
-describe('TensorMath', () => {
-  it('Setting and getting backend', () => {
-    // Default deeplearn.js backend is WebGL (GPU).
-    const originalBackend = K.getBackend();
-    expect(originalBackend).toEqual('webgl');
-
-    K.setBackend('cpu');
-    expect(K.getBackend()).toEqual('cpu');
-  });
-});
-
-describe('dtype', () => {
-  it('returns float32 for a Tensor', () => {
-    const x = zeros([1]);
-    expect(x.dtype).toEqual('float32');
-  });
-
-  it('returns float32 for a SymbolicTensor', () => {
-    const x = new SymbolicTensor('float32', [1], null, [], {});
-    expect(x.dtype).toEqual('float32');
-  });
-});
-
-describeMathCPU('countParams', () => {
-  it('Scalar', () => {
-    const x = zeros([]);
-    expect(K.countParams(x)).toEqual(1);
-    expect(K.countParams(new LayerVariable(x).read())).toEqual(1);
-  });
-
-  it('Tensor1D', () => {
-    const x = zeros([3]);
-    expect(K.countParams(x)).toEqual(3);
-    expect(K.countParams(new LayerVariable(x).read())).toEqual(3);
-  });
-
-  it('Tensor2D', () => {
-    const x = zeros([3, 2]);
-    expect(K.countParams(x)).toEqual(6);
-    expect(K.countParams(new LayerVariable(x).read())).toEqual(6);
-  });
-
-  it('Tensor3D', () => {
-    const x = zeros([4, 3, 2]);
-    expect(K.countParams(x)).toEqual(24);
-    expect(K.countParams(new LayerVariable(x).read())).toEqual(24);
-  });
-
-  it('Tensor4D', () => {
-    const x = zeros([4, 3, 2, 1]);
-    expect(K.countParams(x)).toEqual(24);
-    expect(K.countParams(new LayerVariable(x).read())).toEqual(24);
-  });
-});
-
-describeMathCPUAndGPU('cast', () => {
-  it('float32 to int32', () => {
-    const x =
-        tensor2d([[-1.1, -1.6], [1.1, 2.2], [3.6, 4.7]], [3, 2], 'float32');
-    const y = K.cast(x, 'int32');
-    expect(y.dtype).toEqual('int32');
-    expect(y.shape).toEqual([3, 2]);
-    expect(Array.from(y.dataSync())).toEqual([-1, -1, 1, 2, 3, 4]);
-  });
-  it('int32 to float32', () => {
-    const x = tensor2d([[-1, -1], [1, 2], [3, 4]], [3, 2], 'int32');
-    const y = K.cast(x, 'float32');
-    expect(y.dtype).toEqual('float32');
-    expect(y.shape).toEqual([3, 2]);
-    expect(Array.from(y.dataSync())).toEqual([-1, -1, 1, 2, 3, 4]);
-  });
-  it('float32 to bool', () => {
-    const x =
-        tensor2d([[-1.1, -1.6], [0.0, 2.2], [3.6, 4.7]], [3, 2], 'float32');
-    const y = K.cast(x, 'bool');
-    expect(y.dtype).toEqual('bool');
-    expect(y.shape).toEqual([3, 2]);
-    expect(Array.from(y.dataSync())).toEqual([1, 1, 0, 1, 1, 1]);
-  });
-  it('bool to float32', () => {
-    const x = tensor2d([[0, 1], [0, 1], [1, 0]], [3, 2], 'bool');
-    const y = K.cast(x, 'float32');
-    expect(y.dtype).toEqual('float32');
-    expect(y.shape).toEqual([3, 2]);
-    expect(Array.from(y.dataSync())).toEqual([0, 1, 0, 1, 1, 0]);
-  });
-  it('int32 to bool', () => {
-    const x = tensor2d([[-1, -2], [0, 2], [3, 4]], [3, 2], 'int32');
-    const y = K.cast(x, 'bool');
-    expect(y.dtype).toEqual('bool');
-    expect(y.shape).toEqual([3, 2]);
-    expect(Array.from(y.dataSync())).toEqual([1, 1, 0, 1, 1, 1]);
-  });
-  it('bool to int32', () => {
-    const x = tensor2d([[0, 1], [0, 1], [1, 0]], [3, 2], 'bool');
-    const y = K.cast(x, 'int32');
-    expect(y.dtype).toEqual('int32');
-    expect(y.shape).toEqual([3, 2]);
-    expect(Array.from(y.dataSync())).toEqual([0, 1, 0, 1, 1, 0]);
-  });
-});
-
-describeMathCPUAndGPU('expandDims', () => {
-  it('Scalar to 1D', () => {
-    const x = scalar(10);
-    expectTensorsClose(K.expandDims(x), tensor1d([10]));
-  });
-  it('1D to 2D: Last dimension', () => {
-    const x = tensor1d([10, 20, 30]);
-    expectTensorsClose(K.expandDims(x), tensor2d([[10], [20], [30]], [3, 1]));
-  });
-  it('1D to 2D: First dimension', () => {
-    const x = tensor1d([10, 20, 30]);
-    expectTensorsClose(K.expandDims(x, 0), tensor2d([[10, 20, 30]], [1, 3]));
-  });
-  it('2D to 3D: Last dimension', () => {
-    const x = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    expectTensorsClose(
-        K.expandDims(x), tensor3d([[[10], [20]], [[30], [40]]], [2, 2, 1]));
-  });
-  it('2D to 3D: Second dimension', () => {
-    const x = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    expectTensorsClose(
-        K.expandDims(x, 1), tensor3d([[[10, 20]], [[30, 40]]], [2, 1, 2]));
-  });
-  it('2D to 3D: First dimension', () => {
-    const x = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    expectTensorsClose(
-        K.expandDims(x, 0), tensor3d([[[10, 20], [30, 40]]], [1, 2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('Repeat', () => {
-  it('2D array', () => {
-    const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const y = K.repeat(x, 3);
-    expectTensorsClose(
-        y,
-        tensor3d(
-            [[[1, 2], [1, 2], [1, 2]], [[3, 4], [3, 4], [3, 4]]], [2, 3, 2]));
-  });
-  it('Non-2D array leads to AssertionError', () => {
-    const x = tensor1d([1, 2, 3]);
-    expect(() => K.repeat(x, 2))
-        .toThrowError(
-            /repeat\(\) expects a rank-2 tensor, but received a rank-1 tensor/);
-  });
-});
-
-describeMathCPUAndGPU('Flatten', () => {
-  it('1D Tensor', () => {
-    const x = tensor1d([1, 3, 3, 7]);
-    const flattend = K.flatten(x);
-    expect(flattend.shape).toEqual([4]);
-    expect(flattend.dataSync()).toEqual(new Float32Array([1, 3, 3, 7]));
-  });
-
-  it('2D Tensor', () => {
-    const x = tensor2d([1, 3, 3, 7], [2, 2]);
-    const flattend = K.flatten(x);
-    expect(flattend.shape).toEqual([4]);
-    expect(flattend.dataSync()).toEqual(new Float32Array([1, 3, 3, 7]));
-  });
-
-  it('3D Tensor', () => {
-    const x = tensor3d(
-        [[[10, 20, 30], [40, 50, 60]], [[-10, -20, -30], [-40, -50, -60]]],
-        [2, 2, 3]);
-    const flattend = K.flatten(x);
-    expect(flattend.shape).toEqual([12]);
-    expect(flattend.dataSync()).toEqual(new Float32Array([
-      10, 20, 30, 40, 50, 60, -10, -20, -30, -40, -50, -60
-    ]));
-  });
-
-  it('4D Tensor', () => {
-    const x = tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -8, -7, -6, -5, -4, -3, -2, -1], [2, 2, 2, 2]);
-    const flattend = K.flatten(x);
-    expect(flattend.shape).toEqual([16]);
-    expect(flattend.dataSync()).toEqual(new Float32Array([
-      1, 2, 3, 4, 5, 6, 7, 8, -8, -7, -6, -5, -4, -3, -2, -1
-    ]));
-  });
-});
-
-describeMathCPUAndGPU('batchFlatten', () => {
-  it('Scalar Tensor leads to error', () => {
-    const x = scalar(1337);
-    expect(() => K.batchFlatten(x))
-        .toThrowError(
-            /batchFlatten requires a minimum rank of 2\. Got rank: 0/);
-  });
-
-  it('1D Tensor leads to error', () => {
-    const x = tensor1d([1, 3, 3, 7]);
-    expect(() => K.batchFlatten(x))
-        .toThrowError(
-            /batchFlatten requires a minimum rank of 2\. Got rank: 1/);
-  });
-
-  it('2D Tensor', () => {
-    const x = tensor2d([1, 3, 3, 7], [2, 2]);
-    const batchFlattened = K.batchFlatten(x);
-    expect(batchFlattened.shape).toEqual([2, 2]);
-    expect(batchFlattened.dataSync()).toEqual(new Float32Array([1, 3, 3, 7]));
-  });
-
-  it('3D Tensor', () => {
-    const x = tensor3d(
-        [[[10, 20, 30], [40, 50, 60]], [[-10, -20, -30], [-40, -50, -60]]],
-        [2, 2, 3]);
-    const batchFlattened = K.batchFlatten(x);
-    expect(batchFlattened.shape).toEqual([2, 6]);
-    expect(batchFlattened.dataSync()).toEqual(new Float32Array([
-      10, 20, 30, 40, 50, 60, -10, -20, -30, -40, -50, -60
-    ]));
-  });
-
-  it('4D Tensor', () => {
-    const x = tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -8, -7, -6, -5, -4, -3, -2, -1], [2, 2, 2, 2]);
-    const batchFlattened = K.batchFlatten(x);
-    expect(batchFlattened.shape).toEqual([2, 8]);
-    expect(batchFlattened.dataSync()).toEqual(new Float32Array([
-      1, 2, 3, 4, 5, 6, 7, 8, -8, -7, -6, -5, -4, -3, -2, -1
-    ]));
-  });
-});
-
-describeMathCPUAndGPU('sliceAlongFirstAxis', () => {
-  const array1DData = [10, 20, 30, 40];
-  it('1D', () => {
-    const x = tensor1d(array1DData);
-    expectTensorsClose(K.sliceAlongFirstAxis(x, 1, 2), tensor1d([20, 30]));
-  });
-
-  const array2DData = [[10, 11], [20, 21], [30, 31], [40, 41]];
-  it('2D', () => {
-    const x = tensor2d(array2DData, [4, 2]);
-    expectTensorsClose(
-        K.sliceAlongFirstAxis(x, 1, 2), tensor2d([[20, 21], [30, 31]], [2, 2]));
-  });
-
-  const array3DData = [[[10]], [[20]], [[30]], [[40]]];
-  it('3D', () => {
-    const x = tensor3d(array3DData, [4, 1, 1]);
-    expectTensorsClose(
-        K.sliceAlongFirstAxis(x, 1, 2), tensor3d([[[20]], [[30]]], [2, 1, 1]));
-  });
-
-  const array4DData = [[[[10]]], [[[20]]], [[[30]]], [[[40]]]];
-  it('4D', () => {
-    const x = tensor4d(array4DData, [4, 1, 1, 1]);
-    expectTensorsClose(
-        K.sliceAlongFirstAxis(x, 1, 2),
-        tensor4d([[[[20]]], [[[30]]]], [2, 1, 1, 1]));
-  });
-
-  const array5DData = [[[[[10]]]], [[[[20]]]], [[[[30]]]], [[[[40]]]]];
-  it('5D', () => {
-    const x = tensor5d(array5DData, [4, 1, 1, 1, 1]);
-    expectTensorsClose(
-        K.sliceAlongFirstAxis(x, 1, 2),
-        tensor5d([[[[[20]]]], [[[[30]]]]], [2, 1, 1, 1, 1]));
-  });
-
-  const array6DData = [[[[[[10]]]]], [[[[[20]]]]], [[[[[30]]]]], [[[[[40]]]]]];
-  it('6D', () => {
-    const x = tensor6d(array6DData, [4, 1, 1, 1, 1, 1]);
-    expectTensorsClose(
-        K.sliceAlongFirstAxis(x, 1, 2),
-        tensor6d([[[[[[20]]]]], [[[[[30]]]]]], [2, 1, 1, 1, 1, 1]));
-  });
-
-  it('Scalar leads to error', () => {
-    expect(() => {
-      K.sliceAlongFirstAxis(scalar(24), 0, 1);
-    }).toThrow();
-  });
-});
-
-describeMathCPUAndGPU('sliceAlongLastAxis', () => {
-  const array1DData = [10, 20, 30, 40];
-  it('1D', () => {
-    const x = tensor1d(array1DData);
-    expectTensorsClose(K.sliceAlongLastAxis(x, 1, 2), tensor1d([20, 30]));
-  });
-
-  const array2DData = [[10, 11, 12, 13], [20, 21, 22, 23]];
-  it('2D', () => {
-    const x = tensor2d(array2DData, [2, 4]);
-    expectTensorsClose(
-        K.sliceAlongLastAxis(x, 1, 2), tensor2d([[11, 12], [21, 22]], [2, 2]));
-  });
-
-  const array3DData = [[[10, 20, 30, 40]]];
-  it('3D', () => {
-    const x = tensor3d(array3DData, [1, 1, 4]);
-    expectTensorsClose(
-        K.sliceAlongLastAxis(x, 1, 2), tensor3d([[[20, 30]]], [1, 1, 2]));
-  });
-
-  const array4DData = [[[[10, 20, 30, 40]]]];
-  it('3D', () => {
-    const x = tensor4d(array4DData, [1, 1, 1, 4]);
-    expectTensorsClose(
-        K.sliceAlongLastAxis(x, 1, 2), tensor4d([[[[20, 30]]]], [1, 1, 1, 2]));
-  });
-});
-
-describeMathCPUAndGPU('sliceAlongAxis', () => {
-  it('1D', () => {
-    const array1DData = [10, 20, 30, 40];
-    const x = tensor1d(array1DData);
-    expectTensorsClose(K.sliceAlongAxis(x, 1, 2, 1), tensor1d([20, 30]));
-  });
-
-  const array2DData = [[10, 11], [20, 21], [30, 31], [40, 41]];
-  it('2D-1', () => {
-    const x = tensor2d(array2DData, [4, 2]);
-    expectTensorsClose(
-        K.sliceAlongAxis(x, 1, 2, 1), tensor2d([[20, 21], [30, 31]], [2, 2]));
-  });
-
-  it('2D-2', () => {
-    const x = tensor2d(array2DData, [4, 2]);
-    expectTensorsClose(
-        K.sliceAlongAxis(x, 0, 1, 2),
-        tensor2d([[10], [20], [30], [40]], [4, 1]));
-  });
-
-  const array3DData = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]];
-  it('3D-1', () => {
-    const x = tensor3d(array3DData, [2, 2, 2]);
-    expectTensorsClose(
-        K.sliceAlongAxis(x, 0, 1, 1), tensor3d([[[1, 2], [3, 4]]], [1, 2, 2]));
-  });
-  it('3D-2', () => {
-    const x = tensor3d(array3DData, [2, 2, 2]);
-    expectTensorsClose(
-        K.sliceAlongAxis(x, 0, 1, 2),
-        tensor3d([[[1, 2]], [[5, 6]]], [2, 1, 2]));
-  });
-  it('3D-3', () => {
-    const x = tensor3d(array3DData, [2, 2, 2]);
-    expectTensorsClose(
-        K.sliceAlongAxis(x, 0, 1, 3),
-        tensor3d([[[1], [3]], [[5], [7]]], [2, 2, 1]));
-  });
-
-  it('4D', () => {
-    const array4DData = [[[[10, 1]]], [[[20, 2]]], [[[30, 3]]], [[[40, 4]]]];
-    const x = tensor4d(array4DData, [4, 1, 1, 2]);
-    expectTensorsClose(
-        K.sliceAlongAxis(x, 0, 1, 4),
-        tensor4d([[[[10]]], [[[20]]], [[[30]]], [[[40]]]], [4, 1, 1, 1]));
-  });
-});
-
-describeMathCPUAndGPU('concatenate', () => {
-  it('1D', () => {
-    const x = tensor1d([1, 2, 3, 4]);
-    const y = tensor1d([-1, -2, -3, -4]);
-    const expected = tensor1d([1, 2, 3, 4, -1, -2, -3, -4]);
-    expectTensorsClose(K.concatenate([x, y]), expected);
-    expectTensorsClose(K.concatenate([x, y], -1), expected);
-    expectTensorsClose(K.concatenate([x, y], 0), expected);
-  });
-  it('2D', () => {
-    const x = tensor2d([1, 2, 3, 4], [2, 2]);
-    const y = tensor2d([-1, -2, -3, -4], [2, 2]);
-    let expected = tensor2d([1, 2, -1, -2, 3, 4, -3, -4], [2, 4]);
-    expectTensorsClose(K.concatenate([x, y]), expected);
-    expectTensorsClose(K.concatenate([x, y], -1), expected);
-    expectTensorsClose(K.concatenate([x, y], 1), expected);
-    expected = tensor2d([1, 2, 3, 4, -1, -2, -3, -4], [4, 2]);
-    expectTensorsClose(K.concatenate([x, y], 0), expected);
-  });
-  it('3D', () => {
-    const x = tensor3d([1, 2, 3, 4], [2, 2, 1]);
-    const y = tensor3d([-1, -2, -3, -4], [2, 2, 1]);
-    let expected = tensor3d([1, -1, 2, -2, 3, -3, 4, -4], [2, 2, 2]);
-    expectTensorsClose(K.concatenate([x, y]), expected);
-    expectTensorsClose(K.concatenate([x, y], -1), expected);
-    expectTensorsClose(K.concatenate([x, y], 2), expected);
-    expected = tensor3d([1, 2, -1, -2, 3, 4, -3, -4], [2, 4, 1]);
-    expectTensorsClose(K.concatenate([x, y], 1), expected);
-    expected = tensor3d([1, 2, 3, 4, -1, -2, -3, -4], [4, 2, 1]);
-    expectTensorsClose(K.concatenate([x, y], 0), expected);
-  });
-  it('3D', () => {
-    const x = tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
-    const y = tensor4d([-1, -2, -3, -4, -5, -6, -7, -8], [2, 2, 2, 1]);
-    let expected = tensor4d(
-        [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8], [2, 2, 2, 2]);
-    expectTensorsClose(K.concatenate([x, y]), expected);
-    expectTensorsClose(K.concatenate([x, y], -1), expected);
-    expectTensorsClose(K.concatenate([x, y], 3), expected);
-    expected = tensor4d(
-        [1, 2, -1, -2, 3, 4, -3, -4, 5, 6, -5, -6, 7, 8, -7, -8], [2, 2, 4, 1]);
-    expectTensorsClose(K.concatenate([x, y], 2), expected);
-    expected = tensor4d(
-        [1, 2, 3, 4, -1, -2, -3, -4, 5, 6, 7, 8, -5, -6, -7, -8], [2, 4, 2, 1]);
-    expectTensorsClose(K.concatenate([x, y], 1), expected);
-    expected = tensor4d(
-        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8], [4, 2, 2, 1]);
-    expectTensorsClose(K.concatenate([x, y], 0), expected);
-  });
-});
-
-describeMathCPUAndGPU('concatAlongFirstAxis', () => {
-  const array1DData1 = [10, 20, 30, 40];
-  const array1DData2 = [-10, -20, -30, -40];
-  it('1D', () => {
-    const a = tensor1d(array1DData1);
-    const b = tensor1d(array1DData2);
-    expectTensorsClose(
-        K.concatAlongFirstAxis(a, b),
-        tensor1d([10, 20, 30, 40, -10, -20, -30, -40]));
-  });
-
-  const array2DData1 = [[10, 11], [20, 21]];
-  const array2DData2 = [[30, 31], [40, 41]];
-  it('2D', () => {
-    const a = tensor2d(array2DData1, [2, 2]);
-    const b = tensor2d(array2DData2, [2, 2]);
-    expectTensorsClose(
-        K.concatAlongFirstAxis(a, b),
-        tensor2d([[10, 11], [20, 21], [30, 31], [40, 41]], [4, 2]));
-  });
-
-  const array3DData1 = [[[10]], [[20]]];
-  const array3DData2 = [[[30]], [[40]]];
-  it('3D', () => {
-    const a = tensor3d(array3DData1, [2, 1, 1]);
-    const b = tensor3d(array3DData2, [2, 1, 1]);
-    expectTensorsClose(
-        K.concatAlongFirstAxis(a, b),
-        tensor3d([[[10]], [[20]], [[30]], [[40]]], [4, 1, 1]));
-  });
-
-  const array4DData1 = [[[[10]]], [[[20]]]];
-  const array4DData2 = [[[[30]]], [[[40]]]];
-  it('4D', () => {
-    const a = tensor4d(array4DData1, [2, 1, 1, 1]);
-    const b = tensor4d(array4DData2, [2, 1, 1, 1]);
-    expectTensorsClose(
-        K.concatAlongFirstAxis(a, b),
-        tensor4d([[[[10]]], [[[20]]], [[[30]]], [[[40]]]], [4, 1, 1, 1]));
-  });
-
-  it('Scalar leads to error', () => {
-    expect(() => {
-      K.concatAlongFirstAxis(scalar(24), scalar(-24));
-    }).toThrow();
-  });
-});
-
-describeMathCPUAndGPU('tile', () => {
-  it('1D, n is number', () => {
-    const x = tensor1d([1, 3, 3, 7]);
-    const n = 3;
-    const y = K.tile(x, n);
-    expectTensorsClose(y, tensor1d([1, 3, 3, 7, 1, 3, 3, 7, 1, 3, 3, 7]));
-  });
-  it('1D, n is number Array', () => {
-    const x = tensor1d([1, 3, 3, 7]);
-    const n = [3];
-    const y = K.tile(x, n);
-    expectTensorsClose(y, tensor1d([1, 3, 3, 7, 1, 3, 3, 7, 1, 3, 3, 7]));
-  });
-  it('2D', () => {
-    const x = tensor2d([[1, 3], [3, 7]], [2, 2]);
-    const n = [2, 3];
-    const y = K.tile(x, n);
-    expectTensorsClose(
-        y,
-        tensor2d(
-            [
-              [1, 3, 1, 3, 1, 3], [3, 7, 3, 7, 3, 7], [1, 3, 1, 3, 1, 3],
-              [3, 7, 3, 7, 3, 7]
-            ],
-            [4, 6]));
-  });
-  it('3D', () => {
-    const x = tensor3d([[[1]]], [1, 1, 1]);
-    const n = [2, 3, 4];
-    const y = K.tile(x, n);
-    expectTensorsClose(y, tfc.ones([2, 3, 4]));
-  });
-  it('Mismatch in x dimensions and n length leads to exception', () => {
-    expect(() => K.tile(tfc.zeros([2, 2]), 1))
-        .toThrowError(/The length of input n \(1\) does not match .*2/);
-  });
-});
-
-describeMathCPUAndGPU('randomNormal', () => {
-  const dtypes: DataType[] = ['float32', 'int32'];
-  for (const dtype of dtypes) {
-    // TODO(bileschi): Add probabilistic assertions on values here.
-    it(`Scalar ${dtype}`, () => {
-      const s = K.randomNormal([], 0, 10, dtype as 'float32' | 'int32');
-      expect(s.shape).toEqual([]);
-    });
-
-    it(`1D ${dtype}`, () => {
-      const v = K.randomNormal([20], 0, 2, dtype as 'float32' | 'int32');
-      expect(v.shape).toEqual([20]);
-    });
-
-    it(`2D ${dtype}`, () => {
-      const x = K.randomNormal([3, 20], -10, 20, dtype as 'float32' | 'int32');
-      expect(x.shape).toEqual([3, 20]);
-    });
-
-    it(`3D ${dtype}`, () => {
-      const y =
-          K.randomNormal([2, 3, 4], 100, 10, dtype as 'float32' | 'int32');
-      expect(y.shape).toEqual([2, 3, 4]);
-    });
-  }
-});
-
-describeMathCPUAndGPU('dot', () => {
-  it('2D x 2D', () => {
-    const x = tensor2d([[1, 0], [0, -1]], [2, 2]);
-    const y = tensor2d([[3], [4]], [2, 1]);
-    const output = K.dot(x, y);
-    expectTensorsClose(output, tensor2d([[3], [-4]], [2, 1]));
-  });
-  it('2D x 2D: Incompatible dimensions', () => {
-    const x = tensor2d([[1, 0], [0, -1]], [2, 2]);
-    const y = tensor2d([[3], [4], [5]], [3, 1]);
-    expect(() => K.dot(x, y)).toThrowError(/inner shapes.*must match/);
-  });
-  it('3D x 2D', () => {
-    const x = tensor3d([[[1, 0], [0, -1]], [[-2, 0], [0, -2]]], [2, 2, 2]);
-    const y = tensor2d([[-1], [1]], [2, 1]);
-    expectTensorsClose(
-        K.dot(x, y), tensor3d([[[-1], [-1]], [[2], [-2]]], [2, 2, 1]));
-  });
-  it('4D x 2D', () => {
-    const x = tensor4d([1, 2, 3, 6], [1, 1, 2, 2]);
-    const y = tensor2d([-1, 1], [2, 1]);
-    expectTensorsClose(K.dot(x, y), tensor4d([1, 3], [1, 1, 2, 1]));
-  });
-  it('5D x 2D', () => {
-    const x = tensor5d([1, 2, 3, 6], [1, 1, 1, 2, 2]);
-    const y = tensor2d([-1, 1], [2, 1]);
-    expectTensorsClose(K.dot(x, y), tensor5d([1, 3], [1, 1, 1, 2, 1]));
-  });
-  it('5D x 3D', () => {
-    const x = tensor5d([1, 2, 3, 6], [1, 1, 1, 2, 2]);
-    const y = tensor3d([-1, 1], [1, 2, 1]);
-    expectTensorsClose(K.dot(x, y), tensor6d([1, 3], [1, 1, 1, 2, 1, 1]));
-  });
-  it('4D x 4D', () => {
-    const x = tensor4d([1, 2, 3, 4], [1, 2, 1, 2]);
-    const y = tensor4d([1, 3], [1, 1, 2, 1]);
-    expectTensorsClose(K.dot(x, y), tensor6d([7, 15], [1, 2, 1, 1, 1, 1]));
-  });
-  it('5D x 3D incompatible shapes yields error', () => {
-    const x = tensor5d([1, 2, 3, 4], [1, 1, 1, 2, 2]);
-    const y = tensor3d([-1, 1, 3], [1, 3, 1]);
-    expect(() => K.dot(x, y))
-        .toThrowError(/the second last dim of y must equal the last dim of x/);
-  });
-  it('2D x 1D leads to error', () => {
-    const x = tensor2d([[1, 0], [0, -1]], [2, 2]);
-    const y = tensor1d([3, 4]);
-    expect(() => K.dot(x, y))
-        .toThrowError(/dot requires both inputs to be rank >= 2/);
-  });
-  it(`1D x 2D leads to error`, () => {
-    const x = tensor1d([3, 4]);
-    const y = tensor2d([[1, 0], [0, -1]], [2, 2]);
-    expect(() => K.dot(x, y))
-        .toThrowError(/dot requires both inputs to be rank >= 2/);
-  });
-  it('2D x Scalar leads to error', () => {
-    const x = tensor2d([[1]], [1, 1]);
-    const y = scalar(10);
-    expect(() => K.dot(x, y))
-        .toThrowError(/dot requires both inputs to be rank >= 2/);
-  });
-  it('1D x 1D leads to error', () => {
-    const x = tensor1d([1, 2]);
-    const y = tensor1d([3, 4]);
-    expect(() => K.dot(x, y))
-        .toThrowError(/dot requires both inputs to be rank >= 2/);
-  });
-});
-
-describeMathCPUAndGPU('sign', () => {
-  it('Scalar', () => {
-    expectTensorsClose(K.sign(scalar(0)), scalar(0));
-    expectTensorsClose(K.sign(scalar(0.5)), scalar(1));
-    expectTensorsClose(K.sign(scalar(-0.5)), scalar(-1));
-  });
-  it('1D', () => {
-    const x = tensor1d([1, 2, -1, 0, 3, -4]);
-    expectTensorsClose(K.sign(x), tensor1d([1, 1, -1, 0, 1, -1]));
-  });
-  it('2D', () => {
-    const x = tensor2d([[1, 2, -1], [0, 3, -4]], [2, 3]);
-    expectTensorsClose(K.sign(x), tensor2d([[1, 1, -1], [0, 1, -1]], [2, 3]));
-  });
-});
-
-describeMathCPUAndGPU('OneHot', () => {
-  it('Unsupported indices', () => {
-    const numClasses = 2;
-    const indices = tensor2d([[-12, 12], [-10, 10]], [2, 2]);
-    expect(() => {
-      K.oneHot(indices, numClasses);
-    }).toThrowError();
-  });
-  it('Unsupported numClasses', () => {
-    const numClasses = 1;
-    const indices = tensor1d([2, 2]);
-    expect(() => {
-      K.oneHot(indices, numClasses);
-    }).toThrowError();
-  });
-  it('Supported use case', () => {
-    const numClasses = 5;
-    const indices = tensor1d([1, 3]);
-    expectTensorsClose(
-        K.oneHot(indices, numClasses).toInt(),
-        tensor2d([[0, 1, 0, 0, 0], [0, 0, 0, 1, 0]], [2, 5], 'int32'));
-  });
-});
-
-describeMathCPUAndGPU('Gather', () => {
-  it('1D, Array of numbers with repeats', () => {
-    expectTensorsClose(
-        K.gather(tensor1d([0, 10, 20, 30]), [2, 2, 3, 1]),
-        tensor1d([20, 20, 30, 10]));
-  });
-  it('2D, Array of numbers', () => {
-    expectTensorsClose(
-        K.gather(tensor2d([[10, 20], [30, 40], [50, 60]], [3, 2]), [2, 0]),
-        tensor2d([[50, 60], [10, 20]], [2, 2]));
-  });
-  it('2D, Tensor1D', () => {
-    expectTensorsClose(
-        K.gather(
-            tensor2d([[10, 20], [30, 40], [50, 60]], [3, 2]), tensor1d([2, 1])),
-        tensor2d([[50, 60], [30, 40]], [2, 2]));
-  });
-  it('3D, Tensor1D', () => {
-    expectTensorsClose(
-        K.gather(
-            tensor3d([[[10, 20], [30, 40]], [[50, 60], [70, 80]]], [2, 2, 2]),
-            tensor1d([1, 0])),
-        tensor3d([[[50, 60], [70, 80]], [[10, 20], [30, 40]]], [2, 2, 2]));
-  });
-  it('2D, Non-default axis', () => {
-    expectTensorsClose(
-        K.gather(tensor2d([[10, 20], [30, 40], [50, 60]], [3, 2]), [1], 1),
-        tensor2d([[20], [40], [60]], [3, 1]));
-  });
-});
-
-describeMathCPUAndGPU('Square', () => {
-  it('Element-wise square', () => {
-    expectTensorsClose(
-        K.square(tensor2d([[1, -2], [-3, 4]], [2, 2])),
-        tensor2d([1, 4, 9, 16], [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('Pow', () => {
-  it('Element-wise Pow: Positive Scalar', () => {
-    expectTensorsClose(
-        K.pow(tensor2d([[1, 1.5], [2, 2.5]], [2, 2]), scalar(2, 'int32')),
-        tensor2d([[1, 2.25], [4, 6.25]], [2, 2]));
-  });
-  it('Element-wise Pow: Negative Scalar', () => {
-    expectTensorsClose(
-        K.pow(tensor2d([[1, 1.5], [2, 2.5]], [2, 2]), scalar(-2, 'int32')),
-        tensor2d(
-            [[1, 1 / (1.5 * 1.5)], [1 / (2 * 2), 1 / (2.5 * 2.5)]], [2, 2]));
-  });
-  it('Element-wise Pow: Zero Scalar', () => {
-    expectTensorsClose(
-        K.pow(tensor2d([[1, 1.5], [2, 2.5]], [2, 2]), scalar(0, 'int32')),
-        tensor2d([[1, 1], [1, 1]], [2, 2]));
-  });
-  it('Element-wise Pow: number', () => {
-    expectTensorsClose(
-        K.pow(tensor2d([[1, 1.5], [2, 2.5]], [2, 2]), 2),
-        tensor2d([[1, 2.25], [4, 6.25]], [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('softsign', () => {
-  it('Element-wise softsign', () => {
-    expectTensorsClose(
-        tfc.tanh(tensor2d([[-2, -1], [1, 2]], [2, 2])),
-        tensor2d(
-            [Math.tanh(-2), Math.tanh(-1), Math.tanh(1), Math.tanh(2)],
-            [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('dropout', () => {
-  const dropoutLevels = [0, 0.75];
-  for (const dropoutLevel of dropoutLevels) {
-    it(`Level = ${dropoutLevel}`, () => {
-      const x = tensor2d(range(1, 21), [10, 2]);
-      const y = K.dropout(x, dropoutLevel);
-      expect(y.dtype).toEqual(x.dtype);
-      expect(y.shape).toEqual(x.shape);
-      const xValue = x.dataSync();
-      const yValue = y.dataSync();
-      let nKept = 0;
-      for (let i = 0; i < xValue.length; ++i) {
-        if (yValue[i] !== 0) {
-          nKept++;
-          expect(yValue[i]).toBeCloseTo(1 / (1 - dropoutLevel) * xValue[i]);
-        }
-      }
-      const numel = K.countParams(x);
-      if (dropoutLevel === 0) {
-        expect(nKept).toEqual(numel);
-      } else {
-        expect(nKept).toBeLessThan(numel);
-      }
-    });
-  }
-
-  it('Level=0.75, with noiseShape', () => {
-    const x = tensor2d(range(1, 21), [10, 2]);
-    const level = 0.75;
-    const noiseShape = [10, 1];
-    const y = K.dropout(x, level, noiseShape);
-    expect(y.dtype).toEqual(x.dtype);
-    expect(y.shape).toEqual(x.shape);
-    const xValue = x.dataSync();
-    const yValue = y.dataSync();
-    let nKept = 0;
-    for (let i = 0; i < x.shape[0]; i++) {
-      const maskedValue = yValue[i * x.shape[1]];
-      for (let j = 0; j < x.shape[1]; j++) {
-        const indice = i * x.shape[1] + j;
-        if (maskedValue !== 0) {
-          nKept++;
-          expect(yValue[indice]).toBeCloseTo(1 / (1 - level) * xValue[indice]);
-        } else {
-          expect(yValue[indice]).toEqual(0);
-        }
-      }
-    }
-    const numel = K.countParams(x);
-    expect(nKept).toBeLessThan(numel);
-  });
-
-  it('Level=0.75, with seed', () => {
-    const x = tensor2d(range(1, 21), [10, 2]);
-    const level = 0.75;
-    const seed = 23;
-    const y = K.dropout(x, level, null, seed);
-    expect(y.dtype).toEqual(x.dtype);
-    expect(y.shape).toEqual(x.shape);
-    const yValuesExpected =
-        [0, 0, 12, 16, 0, 0, 0, 0, 0, 0, 0, 48, 52, 0, 0, 0, 68, 0, 76, 0];
-    expectTensorsClose(y, tensor2d(yValuesExpected, [10, 2]));
-  });
-});
-
-describeMathCPUAndGPU('biasAdd', () => {
-  it('1D + 1D', () => {
-    const x = tfc.ones([2]);
-    const y = tensor1d([-1, 1]);
-    expectTensorsClose(K.biasAdd(x, y), tensor1d([0, 2]));
-  });
-  it('2D + 1D', () => {
-    const x = tfc.ones([2, 2]);
-    const y = tensor1d([-1, 1]);
-    expectTensorsClose(K.biasAdd(x, y), tensor2d([[0, 2], [0, 2]], [2, 2]));
-  });
-  it('3D + 1D', () => {
-    const x = tfc.ones([2, 2, 2]);
-    const y = tensor1d([-1, 1]);
-    expectTensorsClose(
-        K.biasAdd(x, y),
-        tensor3d([[[0, 2], [0, 2]], [[0, 2], [0, 2]]], [2, 2, 2]));
-  });
-  it('4D + 1D', () => {
-    const x = tfc.ones([1, 2, 2, 2]);
-    const y = tensor1d([-1, 1]);
-    expectTensorsClose(
-        K.biasAdd(x, y),
-        tensor4d([[[[0, 2], [0, 2]], [[0, 2], [0, 2]]]], [1, 2, 2, 2]));
-  });
-  it('2D + 1D: Incompatible size', () => {
-    const x = tfc.ones([2, 2]);
-    const y = tensor1d([-1, 0, 1]);
-    expect(() => K.biasAdd(x, y)).toThrowError();
-  });
-  it('3D + 2D leads to error', () => {
-    const x = tfc.ones([2, 2, 2]);
-    const y = tfc.ones([2, 2]);
-    expect(() => K.biasAdd(x, y)).toThrowError();
-  });
-});
-
-describeMathCPUAndGPU('elu', () => {
-  it('elu', () => {
-    const xData = [-1, 0, 1, -1];
-    expectTensorsClose(
-        K.elu(tensor2d(xData, [2, 2])),
-        tensor2d(xData.map(x => x < 0 ? Math.exp(x) - 1 : x), [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('softsign', () => {
-  it('softsign', () => {
-    const xData = [-1, 0, 1, -1];
-    expectTensorsClose(
-        K.softsign(tensor2d(xData, [2, 2])),
-        tensor2d(xData.map(x => x / (Math.abs(x) + 1)), [2, 2]));
-  });
-  it('Does not leak', () => {
-    const input = tensor2d([-1, 0, 1, -1], [2, 2]);
-    expectNoLeakedTensors(() => K.softsign(input), 1);
-  });
-});
-
-describeMathCPUAndGPU('Sigmoid', () => {
-  it('2D', () => {
-    const xValues = [-5, -2, 0, 1, 2, 5];
-    const x = tensor2d(xValues, [2, 3]);
-    const y = tfc.sigmoid(x);
-    const yValuesExpected = xValues.map(v => 1 / (1 + Math.exp(-v)));
-    expectTensorsClose(y, tensor2d(yValuesExpected, [2, 3]));
-  });
-});
-
-describeMathCPUAndGPU('hardSigmoid', () => {
-  it('2D', () => {
-    const xValues = [-5, -2, 0, 1, 2, 5];
-    const x = tensor2d(xValues, [2, 3]);
-    const y = K.hardSigmoid(x);
-    const yValuesExpected = xValues.map(x => {
-      const y = 0.2 * x + 0.5;
-      if (y > 1) {
-        return 1;
-      } else if (y < 0) {
-        return 0;
-      } else {
-        return y;
-      }
-    });
-    expectTensorsClose(y, tensor2d(yValuesExpected, [2, 3]));
-  });
-});
-
-describe('inTrainPhase', () => {
-  it('training = true', () => {
-    expect(K.inTrainPhase(() => -5, () => 5, true)).toEqual(-5);
-  });
-  it('training = false', () => {
-    expect(K.inTrainPhase(() => -5, () => 5, false)).toEqual(5);
-  });
-  it('training = default false', () => {
-    expect(K.inTrainPhase(() => -5, () => 5)).toEqual(5);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/base_callbacks.ts b/tfjs-master/tfjs-layers/src/base_callbacks.ts
deleted file mode 100644
index e79cbf11e..000000000
--- a/tfjs-master/tfjs-layers/src/base_callbacks.ts
+++ /dev/null
@@ -1,609 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original source: keras/callbacks.py */
-
-import {add, div, keep, mul, nextFrame, Scalar, Tensor, tidy, util} from '@tensorflow/tfjs-core';
-
-import {Container} from './engine/container';
-import {ValueError} from './errors';
-import {Logs, resolveScalarsInLogs, UnresolvedLogs} from './logs';
-import * as generic_utils from './utils/generic_utils';
-
-/** Verbosity logging level when fitting a model. */
-export enum ModelLoggingVerbosity {
-  SILENT = 0,
-  VERBOSE = 1
-}
-
-/** How often to yield to the main thread when training (in ms). */
-export const DEFAULT_YIELD_EVERY_MS = 125;
-
-export type Params = {
-  [key: string]: number|string|boolean|number[]|string[]|boolean[];
-};
-
-export type YieldEveryOptions = 'auto'|'batch'|'epoch'|'never'|number;
-
-/**
- * Abstract base class used to build new callbacks.
- *
- * The `logs` dictionary that callback methods take as argument will contain
- * keys for quantities relevant to the current batch or epoch.
- *
- * Currently, the `.fit()` method of the `Sequential` model class
- * will include the following quantities in the `logs` that
- * it passes to its callbacks:
- *
- * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`
- *   (if validation is enabled in `fit`), and `valAcc` (if validation and
- *   accuracy monitoring are enabled).
- * onBatchBegin: Logs include `size`, the number of samples in the current
- *   batch.
- * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring
- *   is enabled).
- */
-export abstract class BaseCallback {
-  // TODO(michaelterry): This type is a best guess.
-  validationData: Tensor|Tensor[] = null;
-  /**
-   * Training parameters (eg. verbosity, batch size, number of epochs...).
-   */
-  params: Params;
-
-  setParams(params: Params): void {
-    this.params = params;
-  }
-
-  async onEpochBegin(epoch: number, logs?: UnresolvedLogs) {}
-
-  async onEpochEnd(epoch: number, logs?: UnresolvedLogs) {}
-
-  async onBatchBegin(batch: number, logs?: UnresolvedLogs) {}
-
-  async onBatchEnd(batch: number, logs?: UnresolvedLogs) {}
-
-  async onTrainBegin(logs?: UnresolvedLogs) {}
-
-  async onTrainEnd(logs?: UnresolvedLogs) {}
-
-  // LayersModel needs to call Callback.setModel(), but cannot actually depend
-  // on Callback because that creates a cyclic dependency.  Providing this no-op
-  // method on BaseCallback breaks the cycle: this way LayersModel can depend on
-  // BaseCallback but not on Callback.  The argument is typed as `Container`
-  // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback
-  // overrides this method and enforces that the argument is really a
-  // LayersModel.
-  setModel(model: Container): void {
-    // Do nothing. Use Callback instead of BaseCallback to track the model.
-  }
-}
-
-/**
- * Container abstracting a list of callbacks.
- */
-export class CallbackList {
-  callbacks: BaseCallback[];
-  queueLength: number;
-
-  // TODO(cais): When the need arises, uncomment the following lines and
-  // implement the queue for time values.
-  // private deltaTBatch: number;
-  // private deltaTsBatchBegin: Array<number>;
-  // private deltaTsBatchEnd: Array<number>;
-
-  /**
-   * Constructor of CallbackList.
-   * @param callbacks Array of `Callback` instances.
-   * @param queueLength Queue length for keeping running statistics over
-   *   callback execution time.
-   */
-  constructor(callbacks?: BaseCallback[], queueLength = 10) {
-    // TODO(cais): Make use of queueLength when implementing the queue for time
-    // values.
-    if (callbacks == null) {
-      callbacks = [];
-    }
-    this.callbacks = callbacks;
-    this.queueLength = queueLength;
-  }
-
-  append(callback: BaseCallback): void {
-    this.callbacks.push(callback);
-  }
-
-  setParams(params: Params): void {
-    for (const callback of this.callbacks) {
-      callback.setParams(params);
-    }
-  }
-
-  setModel(model: Container): void {
-    for (const callback of this.callbacks) {
-      callback.setModel(model);
-    }
-  }
-
-  /**
-   * Called at the start of an epoch.
-   * @param epoch Index of epoch.
-   * @param logs Dictionary of logs.
-   */
-  async onEpochBegin(epoch: number, logs?: UnresolvedLogs) {
-    if (logs == null) {
-      logs = {};
-    }
-    for (const callback of this.callbacks) {
-      await callback.onEpochBegin(epoch, logs);
-    }
-  }
-
-  /**
-   * Called at the end of an epoch.
-   * @param epoch Index of epoch.
-   * @param logs Dictionary of logs.
-   */
-  async onEpochEnd(epoch: number, logs?: UnresolvedLogs) {
-    if (logs == null) {
-      logs = {};
-    }
-    for (const callback of this.callbacks) {
-      await callback.onEpochEnd(epoch, logs);
-    }
-  }
-
-  /**
-   * Called  right before processing a batch.
-   * @param batch Index of batch within the current epoch.
-   * @param logs Dictionary of logs.
-   */
-  async onBatchBegin(batch: number, logs?: UnresolvedLogs) {
-    if (logs == null) {
-      logs = {};
-    }
-    for (const callback of this.callbacks) {
-      await callback.onBatchBegin(batch, logs);
-    }
-  }
-
-  /**
-   * Called at the end of a batch.
-   * @param batch Index of batch within the current epoch.
-   * @param logs Dictionary of logs.
-   */
-  async onBatchEnd(batch: number, logs?: UnresolvedLogs) {
-    if (logs == null) {
-      logs = {};
-    }
-    for (const callback of this.callbacks) {
-      await callback.onBatchEnd(batch, logs);
-    }
-  }
-
-  /**
-   * Called at the beginning of training.
-   * @param logs Dictionary of logs.
-   */
-  async onTrainBegin(logs?: UnresolvedLogs) {
-    if (logs == null) {
-      logs = {};
-    }
-    for (const callback of this.callbacks) {
-      await callback.onTrainBegin(logs);
-    }
-  }
-
-  /**
-   * Called at the end of training.
-   * @param logs Dictionary of logs.
-   */
-  async onTrainEnd(logs?: UnresolvedLogs) {
-    if (logs == null) {
-      logs = {};
-    }
-    for (const callback of this.callbacks) {
-      await callback.onTrainEnd(logs);
-    }
-  }
-}
-
-/**
- * Callback that accumulates epoch averages of metrics.
- *
- * This callback is automatically applied to every LayersModel.
- */
-export class BaseLogger extends BaseCallback {
-  private seen: number;
-  private totals: UnresolvedLogs;
-
-  constructor() {
-    super();
-  }
-
-  override async onEpochBegin(epoch: number) {
-    this.seen = 0;
-    this.totals = {};
-  }
-
-  override async onBatchEnd(batch: number, logs?: UnresolvedLogs) {
-    if (logs == null) {
-      logs = {};
-    }
-    const batchSize = logs['size'] == null ? 0 : logs['size'] as number;
-    this.seen += batchSize;
-    for (const key in logs) {
-      const value = logs[key];
-      if (typeof value === 'number') {
-        if (!this.totals.hasOwnProperty(key)) {
-          this.totals[key] = 0;
-        }
-        this.totals[key] = this.totals[key] as number + value * batchSize;
-      } else {
-        let oldTotalsToDispose: Scalar;
-        if (key in this.totals) {
-          oldTotalsToDispose = this.totals[key] as Scalar;
-        } else {
-          this.totals[key] = 0;
-        }
-        const total: Scalar =
-            tidy(() => add((this.totals[key]), mul(value, batchSize)));
-        this.totals[key] = total;
-        if (oldTotalsToDispose != null) {
-          oldTotalsToDispose.dispose();
-        }
-      }
-    }
-  }
-
-  override async onEpochEnd(epoch: number, logs?: UnresolvedLogs) {
-    if (logs != null) {
-      for (const key of this.params['metrics'] as string[]) {
-        if (this.totals[key] == null) {
-          continue;
-        }
-        if (typeof this.totals[key] === 'number') {
-          logs[key] = this.totals[key] as number / this.seen;
-        } else {
-          tidy(() => {
-            const log: Scalar = mul(div(1, this.seen), this.totals[key]);
-            logs[key] = log;
-            (this.totals[key] as Tensor).dispose();
-            keep(logs[key] as Scalar);
-          });
-        }
-      }
-    }
-  }
-}
-
-/**
- * Callback that records events into a `History` object. This callback is
- * automatically applied to every TF.js Layers model. The `History` object
- * gets returned by the `fit` method of models.
- */
-export class History extends BaseCallback {
-  epoch: number[];
-  history: {[key: string]: Array<number|Tensor>};
-
-  override async onTrainBegin(logs?: UnresolvedLogs) {
-    this.epoch = [];
-    this.history = {};
-  }
-
-  override async onEpochEnd(epoch: number, logs?: UnresolvedLogs) {
-    if (logs == null) {
-      logs = {};
-    }
-    this.epoch.push(epoch);
-    for (const key in logs) {
-      if (this.history[key] == null) {
-        this.history[key] = [];
-      }
-      this.history[key].push(logs[key]);
-    }
-  }
-
-  /**
-   * Await the values of all losses and metrics.
-   */
-  async syncData() {
-    const promises: Array<Promise<Float32Array|Int32Array|Uint8Array>> = [];
-    const keys: string[] = [];
-    const indices: number[] = [];
-    for (const key in this.history) {
-      const valueArray = this.history[key];
-      for (let i = 0; i < valueArray.length; ++i) {
-        if (typeof valueArray[i] !== 'number') {
-          const valueScalar = valueArray[i] as Tensor;
-          promises.push(valueScalar.data());
-          keys.push(key);
-          indices.push(i);
-        }
-      }
-    }
-    const values = await Promise.all(promises);
-    for (let n = 0; n < values.length; ++n) {
-      const tensorToDispose = this.history[keys[n]][indices[n]] as Tensor;
-      tensorToDispose.dispose();
-      this.history[keys[n]][indices[n]] = values[n][0];
-    }
-  }
-}
-
-export interface CustomCallbackArgs {
-  onTrainBegin?: (logs?: Logs) => void | Promise<void>;
-  onTrainEnd?: (logs?: Logs) => void | Promise<void>;
-  onEpochBegin?: (epoch: number, logs?: Logs) => void | Promise<void>;
-  onEpochEnd?: (epoch: number, logs?: Logs) => void | Promise<void>;
-  onBatchBegin?: (batch: number, logs?: Logs) => void | Promise<void>;
-  onBatchEnd?: (batch: number, logs?: Logs) => void | Promise<void>;
-  onYield?: (epoch: number, batch: number, logs: Logs) => void | Promise<void>;
-  // Used for test DI mocking.
-  nowFunc?: Function;
-  nextFrameFunc?: Function;
-}
-
-/**
- * Custom callback for training.
- */
-export class CustomCallback extends BaseCallback {
-  protected readonly trainBegin: (logs?: Logs) => void | Promise<void>;
-  protected readonly trainEnd: (logs?: Logs) => void | Promise<void>;
-  protected readonly epochBegin:
-      (epoch: number, logs?: Logs) => void | Promise<void>;
-  protected readonly epochEnd:
-      (epoch: number, logs?: Logs) => void | Promise<void>;
-  protected readonly batchBegin:
-      (batch: number, logs?: Logs) => void | Promise<void>;
-  protected readonly batchEnd:
-      (batch: number, logs?: Logs) => void | Promise<void>;
-  protected readonly yield:
-      (epoch: number, batch: number, logs: Logs) => void | Promise<void>;
-
-  private yieldEvery: YieldEveryOptions;
-  private currentEpoch = 0;
-  public nowFunc: Function;
-  public nextFrameFunc: Function;
-
-  constructor(args: CustomCallbackArgs, yieldEvery?: YieldEveryOptions) {
-    super();
-    this.nowFunc = args.nowFunc;
-    this.nextFrameFunc = args.nextFrameFunc || nextFrame;
-    this.yieldEvery = yieldEvery || 'auto';
-    if (this.yieldEvery === 'auto') {
-      this.yieldEvery = DEFAULT_YIELD_EVERY_MS;
-    }
-    if (this.yieldEvery === 'never' && args.onYield != null) {
-      throw new Error(
-          'yieldEvery is `never` but you provided an `onYield` callback. ' +
-          'Either change `yieldEvery` or remove the callback');
-    }
-    if (util.isNumber(this.yieldEvery)) {
-      // Decorate `maybeWait` so it will be called at most once every
-      // `yieldEvery` ms.
-      this.maybeWait = generic_utils.debounce(
-          this.maybeWait.bind(this), this.yieldEvery as number, this.nowFunc);
-    }
-    this.trainBegin = args.onTrainBegin;
-    this.trainEnd = args.onTrainEnd;
-    this.epochBegin = args.onEpochBegin;
-    this.epochEnd = args.onEpochEnd;
-    this.batchBegin = args.onBatchBegin;
-    this.batchEnd = args.onBatchEnd;
-    this.yield = args.onYield;
-  }
-
-  async maybeWait(epoch: number, batch: number, logs: UnresolvedLogs) {
-    const ps: Array<void|Promise<void>> = [];
-    if (this.yield != null) {
-      await resolveScalarsInLogs(logs);
-      ps.push(this.yield(epoch, batch, logs as Logs));
-    }
-    ps.push(this.nextFrameFunc());
-    await Promise.all(ps);
-  }
-
-  override async onEpochBegin(epoch: number, logs?: UnresolvedLogs):
-      Promise<void> {
-    this.currentEpoch = epoch;
-    if (this.epochBegin != null) {
-      await resolveScalarsInLogs(logs);
-      await this.epochBegin(epoch, logs as Logs);
-    }
-  }
-
-  override async onEpochEnd(epoch: number, logs?: UnresolvedLogs):
-      Promise<void> {
-    const ps: Array<void|Promise<void>> = [];
-    if (this.epochEnd != null) {
-      await resolveScalarsInLogs(logs);
-      ps.push(this.epochEnd(epoch, logs as Logs));
-    }
-    if (this.yieldEvery === 'epoch') {
-      ps.push(this.nextFrameFunc());
-    }
-    await Promise.all(ps);
-  }
-
-  override async onBatchBegin(batch: number, logs?: UnresolvedLogs):
-      Promise<void> {
-    if (this.batchBegin != null) {
-      await resolveScalarsInLogs(logs);
-      await this.batchBegin(batch, logs as Logs);
-    }
-  }
-
-  override async onBatchEnd(batch: number, logs?: UnresolvedLogs):
-      Promise<void> {
-    const ps: Array<void|Promise<void>> = [];
-    if (this.batchEnd != null) {
-      await resolveScalarsInLogs(logs);
-      ps.push(this.batchEnd(batch, logs as Logs));
-    }
-    if (this.yieldEvery === 'batch') {
-      ps.push(this.nextFrameFunc());
-    } else if (util.isNumber(this.yieldEvery)) {
-      ps.push(this.maybeWait(this.currentEpoch, batch, logs));
-    }
-    await Promise.all(ps);
-  }
-
-  override async onTrainBegin(logs?: UnresolvedLogs): Promise<void> {
-    if (this.trainBegin != null) {
-      await resolveScalarsInLogs(logs);
-      await this.trainBegin(logs as Logs);
-    }
-  }
-
-  override async onTrainEnd(logs?: UnresolvedLogs): Promise<void> {
-    if (this.trainEnd != null) {
-      await resolveScalarsInLogs(logs);
-      await this.trainEnd(logs as Logs);
-    }
-  }
-}
-
-/**
- * Standardize callbacks or configurations of them to an Array of callbacks.
- */
-export function standardizeCallbacks(
-    callbacks: BaseCallback|BaseCallback[]|CustomCallbackArgs|
-    CustomCallbackArgs[],
-    yieldEvery: YieldEveryOptions): BaseCallback[] {
-  if (callbacks == null) {
-    callbacks = {} as BaseCallback;
-  }
-  if (callbacks instanceof BaseCallback) {
-    return [callbacks];
-  }
-  if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {
-    return callbacks as BaseCallback[];
-  }
-  // Convert custom callback configs to custom callback objects.
-  const callbackConfigs =
-      generic_utils.toList<BaseCallback | CustomCallbackArgs>(
-        callbacks) as CustomCallbackArgs[];
-  return callbackConfigs.map(
-      callbackConfig => new CustomCallback(callbackConfig, yieldEvery));
-}
-
-export declare type BaseCallbackConstructor = {
-  new (): BaseCallback
-};
-
-/**
- * A global registry for callback constructors to be used during
- * LayersModel.fit().
- */
-export class CallbackConstructorRegistry {
-  private static constructors:
-      {[verbosityLevel: number]: BaseCallbackConstructor[]} = {};
-
-  /**
-   * Blocks public access to constructor.
-   */
-  private constructor() {}
-
-  /**
-   * Register a tf.LayersModel.fit() callback constructor.
-   *
-   * The registered callback constructor will be used to instantiate
-   * callbacks for every tf.LayersModel.fit() call afterwards.
-   *
-   * @param verbosityLevel Level of verbosity at which the `callbackConstructor`
-   *   is to be reigstered.
-   * @param callbackConstructor A no-arg constructor for `tf.Callback`.
-   * @throws Error, if the same callbackConstructor has been registered before,
-   *   either at the same or a different `verbosityLevel`.
-   */
-  static registerCallbackConstructor(
-      verbosityLevel: number, callbackConstructor: BaseCallbackConstructor) {
-    util.assert(
-        verbosityLevel >= 0 && Number.isInteger(verbosityLevel),
-        () => `Verbosity level is expected to be an integer >= 0, ` +
-            `but got ${verbosityLevel}`);
-    CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);
-    if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {
-      CallbackConstructorRegistry.constructors[verbosityLevel] = [];
-    }
-    CallbackConstructorRegistry.constructors[verbosityLevel].push(
-        callbackConstructor);
-  }
-
-  private static checkForDuplicate(callbackConstructor:
-                                       BaseCallbackConstructor) {
-    for (const levelName in CallbackConstructorRegistry.constructors) {
-      const constructors = CallbackConstructorRegistry.constructors[+levelName];
-      constructors.forEach(ctor => {
-        if (ctor === callbackConstructor) {
-          throw new ValueError('Duplicate callback constructor.');
-        }
-      });
-    }
-  }
-
-  /**
-   * Clear all registered callback constructors.
-   */
-  protected static clear() {
-    CallbackConstructorRegistry.constructors = {};
-  }
-
-  /**
-   * Create callbacks using the registered callback constructors.
-   *
-   * Given `verbosityLevel`, all constructors registered at that level or above
-   * will be called and the instantiated callbacks will be used.
-   *
-   * @param verbosityLevel: Level of verbosity.
-   */
-  static createCallbacks(verbosityLevel: number): BaseCallback[] {
-    const constructors: BaseCallbackConstructor[] = [];
-    for (const levelName in CallbackConstructorRegistry.constructors) {
-      const level = +levelName;
-      if (verbosityLevel >= level) {
-        constructors.push(...CallbackConstructorRegistry.constructors[level]);
-      }
-    }
-    return constructors.map(ctor => new ctor());
-  }
-}
-
-export function configureCallbacks(
-    callbacks: BaseCallback[], verbose: ModelLoggingVerbosity, epochs: number,
-    initialEpoch: number, numTrainSamples: number, stepsPerEpoch: number,
-    batchSize: number, doValidation: boolean,
-    callbackMetrics: string[]): {callbackList: CallbackList, history: History} {
-  const history = new History();
-  const actualCallbacks: BaseCallback[] = [
-    new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)
-  ];
-  if (callbacks != null) {
-    actualCallbacks.push(...callbacks);
-  }
-  actualCallbacks.push(history);
-  const callbackList = new CallbackList(actualCallbacks);
-
-  // TODO(cais): Figure out when this LayersModel instance can have a
-  // dynamically
-  //   set property called 'callback_model' as in PyKeras.
-
-  callbackList.setParams({
-    epochs,
-    initialEpoch,
-    samples: numTrainSamples,
-    steps: stepsPerEpoch,
-    batchSize,
-    verbose,
-    doValidation,
-    metrics: callbackMetrics,
-  });
-  return {callbackList, history};
-}
diff --git a/tfjs-master/tfjs-layers/src/base_callbacks_test.ts b/tfjs-master/tfjs-layers/src/base_callbacks_test.ts
deleted file mode 100644
index f7ed7c4c1..000000000
--- a/tfjs-master/tfjs-layers/src/base_callbacks_test.ts
+++ /dev/null
@@ -1,379 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for callbacks.
- */
-
-import {scalar, zeros} from '@tensorflow/tfjs-core';
-
-import {BaseCallback, BaseLogger, CallbackConstructorRegistry, CallbackList, History} from './base_callbacks';
-import {Callback} from './callbacks';
-import {LayersModel} from './engine/training';
-import * as tfl from './index';
-import {disposeTensorsInLogs, Logs, resolveScalarsInLogs, UnresolvedLogs} from './logs';
-import {describeMathCPUAndGPU} from './utils/test_utils';
-
-describe('BaseLogger Callback', () => {
-  it('Records and averages losses in an epoch', async () => {
-    const baseLogger = new BaseLogger();
-    baseLogger.setParams({metrics: ['loss', 'val_loss']});
-    await baseLogger.onTrainBegin();
-    await baseLogger.onEpochBegin(0);
-    await baseLogger.onBatchBegin(0);
-    await baseLogger.onBatchEnd(0, {batch: 0, size: 10, loss: 5});
-    await baseLogger.onBatchBegin(1);
-    await baseLogger.onBatchEnd(1, {batch: 1, size: 10, loss: 6});
-    await baseLogger.onBatchBegin(2);
-    await baseLogger.onBatchEnd(2, {batch: 2, size: 5, loss: 7});
-    const epochLog: UnresolvedLogs = {val_loss: 3};
-    await baseLogger.onEpochEnd(0, epochLog);
-    expect(epochLog['val_loss'] as number).toEqual(3);
-    expect(epochLog['loss'] as number)
-        .toBeCloseTo((10 * 5 + 10 * 6 + 5 * 7) / (10 + 10 + 5));
-  });
-  it('Forgets old epochs', async () => {
-    const baseLogger = new BaseLogger();
-    baseLogger.setParams({metrics: ['loss', 'val_loss']});
-    const numOldEpochs = 2;
-    await baseLogger.onTrainBegin();
-    for (let i = 0; i < numOldEpochs; ++i) {
-      await baseLogger.onEpochBegin(i);
-      await baseLogger.onBatchBegin(0);
-      await baseLogger.onBatchEnd(0, {batch: 0, size: 10, loss: -5});
-      const epochLog: UnresolvedLogs = {val_loss: 3};
-      await baseLogger.onEpochEnd(i, epochLog);
-    }
-    await baseLogger.onEpochBegin(numOldEpochs);
-    await baseLogger.onBatchBegin(0);
-    await baseLogger.onBatchEnd(0, {batch: 0, size: 10, loss: 5});
-    await baseLogger.onBatchBegin(1);
-    await baseLogger.onBatchEnd(1, {batch: 1, size: 10, loss: 6});
-    await baseLogger.onBatchBegin(2);
-    await baseLogger.onBatchEnd(2, {batch: 2, size: 5, loss: 7});
-    const epochLog: UnresolvedLogs = {val_loss: 3};
-    await baseLogger.onEpochEnd(numOldEpochs, epochLog);
-    expect(epochLog['val_loss'] as number).toEqual(3);
-    expect(epochLog['loss'] as number)
-        .toBeCloseTo((10 * 5 + 10 * 6 + 5 * 7) / (10 + 10 + 5));
-  });
-});
-
-describe('CallbackList', () => {
-  it('Constructor with empty arg', async () => {
-    const callbackList = new CallbackList();
-    await callbackList.onTrainBegin();
-    await callbackList.onTrainEnd();
-  });
-  it('Constructor and setParams with array of callbacks', () => {
-    const history1 = new History();
-    const history2 = new History();
-    const callbackList = new CallbackList([history1, history2]);
-    const params = {'verbose': 3};
-    callbackList.setParams(params);
-    expect(history1.params).toEqual(params);
-    expect(history2.params).toEqual(params);
-  });
-  it('onTrainBegin', async () => {
-    const history1 = new History();
-    const history2 = new History();
-    const callbackList = new CallbackList([history1, history2]);
-    await callbackList.onTrainBegin();
-    expect(history1.epoch).toEqual([]);
-    expect(history1.history).toEqual({});
-    expect(history2.epoch).toEqual([]);
-    expect(history2.history).toEqual({});
-  });
-  it('onEpochEnd', async () => {
-    const history1 = new History();
-    const history2 = new History();
-    const callbackList = new CallbackList([history1, history2]);
-    await callbackList.onTrainBegin();
-    await callbackList.onEpochEnd(100, {'val_loss': 10, 'val_accuracy': 0.1});
-    expect(history1.epoch).toEqual([100]);
-    expect(history1.history).toEqual({'val_loss': [10], 'val_accuracy': [0.1]});
-    expect(history2.epoch).toEqual([100]);
-    expect(history2.history).toEqual({'val_loss': [10], 'val_accuracy': [0.1]});
-    await callbackList.onEpochEnd(101, {'val_loss': 9.5, 'val_accuracy': 0.2});
-    expect(history1.epoch).toEqual([100, 101]);
-    expect(history1.history)
-        .toEqual({'val_loss': [10, 9.5], 'val_accuracy': [0.1, 0.2]});
-    expect(history2.epoch).toEqual([100, 101]);
-    expect(history2.history)
-        .toEqual({'val_loss': [10, 9.5], 'val_accuracy': [0.1, 0.2]});
-  });
-  it('append', async () => {
-    const history1 = new History();
-    const history2 = new History();
-    const callbackList = new CallbackList([history1]);
-    await callbackList.onTrainBegin();
-    expect(history1.epoch).toEqual([]);
-    expect(history1.history).toEqual({});
-    await callbackList.append(history2);
-    await callbackList.onTrainBegin();
-    expect(history2.epoch).toEqual([]);
-    expect(history2.history).toEqual({});
-  });
-});
-
-describeMathCPUAndGPU('resolveScalarsInLogs', () => {
-  it('Resolve mixed numbers and scalars', async () => {
-    const logs: UnresolvedLogs = {
-      'a': 1,
-      'b': scalar(2),
-      'c': -3,
-      'd': scalar(-4),
-    };
-    await resolveScalarsInLogs(logs);
-    expect(logs['a']).toEqual(1);
-    expect(logs['b']).toEqual(2);
-    expect(logs['c']).toEqual(-3);
-    expect(logs['d']).toEqual(-4);
-  });
-
-  it('Resolve null works fine', async () => {
-    const logs: UnresolvedLogs = null;
-    await resolveScalarsInLogs(logs);
-    expect(logs).toEqual(null);
-  });
-
-  it('Resolve empty works fine', async () => {
-    const logs: UnresolvedLogs = {};
-    await resolveScalarsInLogs(logs);
-    expect(logs).toEqual({});
-  });
-});
-
-describeMathCPUAndGPU('disposeTensorsInLogs', () => {
-  it('Resolve mixed numbers and scalars', () => {
-    const logs: UnresolvedLogs = {
-      'a': 1,
-      'b': scalar(2),
-      'c': -3,
-      'd': scalar(-4),
-    };
-    disposeTensorsInLogs(logs);
-    expect(logs['a']).toEqual(1);
-    // tslint:disable-next-line:no-any
-    expect((logs['b'] as any).isDisposed).toEqual(true);
-    expect(logs['c']).toEqual(-3);
-    // tslint:disable-next-line:no-any
-    expect((logs['d'] as any).isDisposed).toEqual(true);
-  });
-});
-
-describe('History Callback', () => {
-  it('onTrainBegin', async () => {
-    const history = new History();
-    await history.onTrainBegin();
-    expect(history.epoch).toEqual([]);
-    expect(history.history).toEqual({});
-  });
-  it('onEpochEnd', async () => {
-    const history = new History();
-    await history.onTrainBegin();
-    await history.onEpochEnd(0, {'val_loss': 10, 'val_accuracy': 0.1});
-    expect(history.epoch).toEqual([0]);
-    expect(history.history).toEqual({'val_loss': [10], 'val_accuracy': [0.1]});
-    await history.onEpochEnd(1, {'val_loss': 9.5, 'val_accuracy': 0.2});
-    expect(history.epoch).toEqual([0, 1]);
-    expect(history.history)
-        .toEqual({'val_loss': [10, 9.5], 'val_accuracy': [0.1, 0.2]});
-  });
-});
-
-class MockLayersModel extends LayersModel {
-  constructor(name: string) {
-    super({inputs: [], outputs: [], name});
-  }
-}
-
-class MockCallback extends Callback {}
-
-describe('CallbackList', () => {
-  it('Constructor and setModel with array of callbacks', () => {
-    const mockCallback1 = new MockCallback();
-    const mockCallback2 = new MockCallback();
-    const callbackList = new CallbackList([mockCallback1, mockCallback2]);
-    const model = new MockLayersModel('MockModelA');
-    callbackList.setModel(model);
-    expect(mockCallback1.model).toEqual(model);
-    expect(mockCallback2.model).toEqual(model);
-  });
-});
-
-let fake1Epochs: number[];
-class FakeCallback1 extends BaseCallback {
-  constructor() {
-    super();
-    fake1Epochs = [];
-  }
-  override async onEpochEnd(epoch: number, logs: Logs) {
-    fake1Epochs.push(epoch);
-  }
-}
-
-let fake2Epochs: number[];
-class FakeCallback2 extends BaseCallback {
-  constructor() {
-    super();
-    fake2Epochs = [];
-  }
-  override async onEpochEnd(epoch: number, logs: Logs) {
-    fake2Epochs.push(epoch);
-  }
-}
-
-describe('CallbackConstructorRegistry', () => {
-  beforeEach(() => {
-    // tslint:disable-next-line:no-any
-    (CallbackConstructorRegistry as any).clear();
-  });
-
-  it('Empty registry creates empty list of callbacks', () => {
-    expect(CallbackConstructorRegistry.createCallbacks(0)).toEqual([]);
-    expect(CallbackConstructorRegistry.createCallbacks(1)).toEqual([]);
-    expect(CallbackConstructorRegistry.createCallbacks(2)).toEqual([]);
-  });
-
-  it('Registry with one element', () => {
-    tfl.registerCallbackConstructor(1, FakeCallback1);
-
-    let callbacks = CallbackConstructorRegistry.createCallbacks(0);
-    expect(callbacks.length).toEqual(0);
-
-    callbacks = CallbackConstructorRegistry.createCallbacks(1);
-    expect(callbacks.length).toEqual(1);
-    expect(callbacks[0] instanceof FakeCallback1).toEqual(true);
-
-    callbacks = CallbackConstructorRegistry.createCallbacks(2);
-    expect(callbacks.length).toEqual(1);
-    expect(callbacks[0] instanceof FakeCallback1).toEqual(true);
-  });
-
-  it('Registry with two elements on two levels', () => {
-    tfl.registerCallbackConstructor(1, FakeCallback1);
-    tfl.registerCallbackConstructor(2, FakeCallback2);
-
-    let callbacks = CallbackConstructorRegistry.createCallbacks(0);
-    expect(callbacks.length).toEqual(0);
-
-    callbacks = CallbackConstructorRegistry.createCallbacks(1);
-    expect(callbacks.length).toEqual(1);
-    expect(callbacks[0] instanceof FakeCallback1).toEqual(true);
-
-    callbacks = CallbackConstructorRegistry.createCallbacks(2);
-    expect(callbacks.length).toEqual(2);
-    expect(callbacks[0] instanceof FakeCallback1).toEqual(true);
-    expect(callbacks[1] instanceof FakeCallback2).toEqual(true);
-  });
-
-  it('Registry with two elements on the same level', () => {
-    tfl.registerCallbackConstructor(2, FakeCallback1);
-    tfl.registerCallbackConstructor(2, FakeCallback2);
-
-    let callbacks = CallbackConstructorRegistry.createCallbacks(0);
-    expect(callbacks.length).toEqual(0);
-
-    callbacks = CallbackConstructorRegistry.createCallbacks(1);
-    expect(callbacks.length).toEqual(0);
-
-    callbacks = CallbackConstructorRegistry.createCallbacks(2);
-    expect(callbacks.length).toEqual(2);
-    expect(callbacks[0] instanceof FakeCallback1).toEqual(true);
-    expect(callbacks[1] instanceof FakeCallback2).toEqual(true);
-  });
-
-  it('Duplicate registration on same level leads to Error', () => {
-    tfl.registerCallbackConstructor(1, FakeCallback1);
-    expect(() => tfl.registerCallbackConstructor(1, FakeCallback1))
-        .toThrowError(/Duplicate callback constructor/);
-  });
-
-  it('Duplicate registration on different level leads to Error', () => {
-    tfl.registerCallbackConstructor(1, FakeCallback1);
-    expect(() => tfl.registerCallbackConstructor(2, FakeCallback1))
-        .toThrowError(/Duplicate callback constructor/);
-  });
-
-  it('Invalid verbosityLevel leads to Error', () => {
-    expect(() => tfl.registerCallbackConstructor(-1, FakeCallback1))
-        .toThrowError(/is expected to be an integer >= 0/);
-    expect(() => tfl.registerCallbackConstructor(0.5, FakeCallback1))
-        .toThrowError(/is expected to be an integer >= 0/);
-    expect(() => tfl.registerCallbackConstructor(NaN, FakeCallback1))
-        .toThrowError(/is expected to be an integer >= 0/);
-  });
-});
-
-describeMathCPUAndGPU('CallbackConstructorRegistry initialization', () => {
-  beforeEach(() => {
-    // tslint:disable-next-line:no-any
-    (CallbackConstructorRegistry as any).clear();
-  });
-  it('CallbackConstructorRegistry is initialized properly', () => {
-    expect(CallbackConstructorRegistry.createCallbacks(1)).toEqual([]);
-  });
-});
-
-describeMathCPUAndGPU('LayersModel.fit and CallbackConstructorRegistry', () => {
-  beforeEach(() => {
-    // tslint:disable-next-line:no-any
-    (CallbackConstructorRegistry as any).clear();
-    fake1Epochs = [];
-    fake2Epochs = [];
-  });
-
-  it('LayersModel.fit call with no callback ctor registered', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [1]}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const xs = zeros([4, 1]);
-    const ys = zeros([4, 1]);
-    await model.fit(xs, ys, {epochs: 3});
-
-    expect(fake1Epochs).toEqual([]);
-  });
-
-  it('LayersModel.fit call with one callback ctor registered', async () => {
-    tfl.registerCallbackConstructor(1, FakeCallback1);
-
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [1]}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const xs = zeros([4, 1]);
-    const ys = zeros([4, 1]);
-    await model.fit(xs, ys, {epochs: 3});
-
-    expect(fake1Epochs).toEqual([0, 1, 2]);
-  });
-
-  it('LayersModel.fit call with two callback ctor registered', async () => {
-    tfl.registerCallbackConstructor(1, FakeCallback1);
-    tfl.registerCallbackConstructor(2, FakeCallback2);
-
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [1]}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const xs = zeros([4, 1]);
-    const ys = zeros([4, 1]);
-
-    await model.fit(xs, ys, {epochs: 3, verbose: 2});
-    expect(fake1Epochs).toEqual([0, 1, 2]);
-    expect(fake2Epochs).toEqual([0, 1, 2]);
-
-    fake1Epochs = [];
-    fake2Epochs = [];
-
-    await model.fit(xs, ys, {epochs: 3, verbose: 1});
-    expect(fake1Epochs).toEqual([0, 1, 2]);
-    expect(fake2Epochs).toEqual([]);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/callbacks.ts b/tfjs-master/tfjs-layers/src/callbacks.ts
deleted file mode 100644
index 102b2f743..000000000
--- a/tfjs-master/tfjs-layers/src/callbacks.ts
+++ /dev/null
@@ -1,255 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original source: keras/callbacks.py */
-
-import {BaseCallback} from './base_callbacks';
-import {Container} from './engine/container';
-import {LayersModel} from './engine/training';
-import {NotImplementedError} from './errors';
-import {Logs, resolveScalarsInLogs} from './logs';
-
-export abstract class Callback extends BaseCallback {
-  /** Instance of `keras.models.Model`. Reference of the model being trained. */
-  model: LayersModel = null;
-
-  override setModel(model: Container): void {
-    if (!(model instanceof LayersModel)) {
-      throw new Error('model must be a LayersModel, not some other Container');
-    }
-    this.model = model;
-  }
-}
-
-export interface EarlyStoppingCallbackArgs {
-  /**
-   * Quantity to be monitored.
-   *
-   * Defaults to 'val_loss'.
-   */
-  monitor?: string;
-
-  /**
-   * Minimum change in the monitored quantity to qualify as improvement,
-   * i.e., an absolute change of less than `minDelta` will count as no
-   * improvement.
-   *
-   * Defaults to 0.
-   */
-  minDelta?: number;
-
-  /**
-   * Number of epochs with no improvement after which training will be stopped.
-   *
-   * Defaults to 0.
-   */
-  patience?: number;
-
-  /** Verbosity mode. */
-  verbose?: number;
-
-  /**
-   * Mode: one of 'min', 'max', and 'auto'.
-   * - In 'min' mode, training will be stopped when the quantity monitored has
-   *   stopped decreasing.
-   * - In 'max' mode, training will be stopped when the quantity monitored has
-   *   stopped increasing.
-   * - In 'auto' mode, the direction is inferred automatically from the name of
-   *   the monitored quantity.
-   *
-   * Defaults to 'auto'.
-   */
-  mode?: 'auto'|'min'|'max';
-
-  /**
-   * Baseline value of the monitored quantity.
-   *
-   * If specified, training will be stopped if the model doesn't show
-   * improvement over the baseline.
-   */
-  baseline?: number;
-
-  /**
-   * Whether to restore model weights from the epoch with the best value
-   * of the monitored quantity. If `False`, the model weights obtained at the
-   * last step of training are used.
-   *
-   * **`True` is not supported yet.**
-   */
-  restoreBestWeights?: boolean;
-}
-
-function less(currVal: number, prevVal: number) {
-  return currVal < prevVal;
-}
-
-function greater(currVal: number, prevVal: number) {
-  return currVal > prevVal;
-}
-
-/**
- * A Callback that stops training when a monitored quantity has stopped
- * improving.
- */
-export class EarlyStopping extends Callback {
-  protected readonly monitor: string;
-  protected readonly minDelta: number;
-  protected readonly patience: number;
-  protected readonly baseline: number;
-  protected readonly verbose: number;
-  protected readonly mode: 'auto'|'min'|'max';
-
-  protected monitorFunc: (currVal: number, prevVal: number) => boolean;
-
-  private wait: number;
-  private stoppedEpoch: number;
-  private best: number;
-
-  constructor(args?: EarlyStoppingCallbackArgs) {
-    super();
-    if (args == null) {
-      args = {};
-    }
-    if (args.restoreBestWeights) {
-      throw new NotImplementedError(
-          'restoreBestWeights = True is not implemented in EarlyStopping yet.');
-    }
-
-    this.monitor = args.monitor || 'val_loss';
-    this.minDelta = Math.abs(args.minDelta || 0);
-    this.patience = args.patience || 0;
-    this.verbose = args.verbose || 0;
-    this.mode = args.mode || 'auto';
-    this.baseline = args.baseline;
-
-    if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {
-      console.warn(
-          `EarlyStopping mode '${this.mode}' is invalid. ` +
-          `Falling back to mode 'auto'.`);
-      this.mode = 'auto';
-    }
-
-    if (this.mode === 'min') {
-      this.monitorFunc = less;
-    } else if (this.mode === 'max') {
-      this.monitorFunc = greater;
-    } else {
-      // For mode === 'auto'.
-      if (this.monitor.indexOf('acc') !== -1) {
-        this.monitorFunc = greater;
-      } else {
-        this.monitorFunc = less;
-      }
-    }
-
-    if (this.monitorFunc === less) {
-      this.minDelta *= -1;
-    }
-  }
-
-  override async onTrainBegin(logs?: Logs) {
-    this.wait = 0;
-    this.stoppedEpoch = 0;
-    if (this.baseline != null) {
-      this.best = this.baseline;
-    } else {
-      this.best = this.monitorFunc === less ? Infinity : -Infinity;
-    }
-  }
-
-  override async onEpochEnd(epoch: number, logs?: Logs) {
-    await resolveScalarsInLogs(logs);
-    const current = this.getMonitorValue(logs);
-    if (current == null) {
-      return;
-    }
-
-    if (this.monitorFunc(current - this.minDelta, this.best)) {
-      this.best = current;
-      this.wait = 0;
-      // TODO(cais): Logic for restoreBestWeights.
-    } else {
-      this.wait++;
-      if (this.wait >= this.patience) {
-        this.stoppedEpoch = epoch;
-        this.model.stopTraining = true;
-      }
-      // TODO(cais): Logic for restoreBestWeights.
-    }
-  }
-
-  override async onTrainEnd(logs?: Logs) {
-    if (this.stoppedEpoch > 0 && this.verbose) {
-      console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);
-    }
-  }
-
-  private getMonitorValue(logs: Logs) {
-    if (logs == null) {
-      logs = {};
-    }
-    const monitorValue = logs[this.monitor];
-    if (monitorValue == null) {
-      console.warn(
-          `Metric for EarlyStopping ${this.monitor} is not available. ` +
-          `Available metrics are: ${Object.keys(logs)}`);
-    }
-    return monitorValue;
-  }
-}
-
-/**
- * Factory function for a Callback that stops training when a monitored
- * quantity has stopped improving.
- *
- * Early stopping is a type of regularization, and protects model against
- * overfitting.
- *
- * The following example based on fake data illustrates how this callback
- * can be used during `tf.LayersModel.fit()`:
- *
- * ```js
- * const model = tf.sequential();
- * model.add(tf.layers.dense({
- *   units: 3,
- *   activation: 'softmax',
- *   kernelInitializer: 'ones',
- *   inputShape: [2]
- * }));
- * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);
- * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);
- * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);
- * model.compile(
- *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});
- *
- * // Without the EarlyStopping callback, the val_acc value would be:
- * //   0.5, 0.5, 0.5, 0.5, ...
- * // With val_acc being monitored, training should stop after the 2nd epoch.
- * const history = await model.fit(xs, ys, {
- *   epochs: 10,
- *   validationData: [xsVal, ysVal],
- *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})
- * });
- *
- * // Expect to see a length-2 array.
- * console.log(history.history.val_acc);
- * ```
- *
- * @doc {
- *   heading: 'Callbacks',
- *   namespace: 'callbacks'
- * }
- */
-export function earlyStopping(args?: EarlyStoppingCallbackArgs) {
-  return new EarlyStopping(args);
-}
-
-export const callbacks = {earlyStopping};
diff --git a/tfjs-master/tfjs-layers/src/callbacks_test.ts b/tfjs-master/tfjs-layers/src/callbacks_test.ts
deleted file mode 100644
index 176f50692..000000000
--- a/tfjs-master/tfjs-layers/src/callbacks_test.ts
+++ /dev/null
@@ -1,294 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {tensor2d} from '@tensorflow/tfjs-core';
-
-import * as tfl from './index';
-import {describeMathCPUAndGPU} from './utils/test_utils';
-
-describe('EarlyStopping', () => {
-  function createDummyModel(): tfl.LayersModel {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [1]}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    return model;
-  }
-
-  it('Default monitor, default mode, increasing val_loss', async () => {
-    const model = createDummyModel();
-    const callback = tfl.callbacks.earlyStopping();
-    callback.setModel(model);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(0);
-    await callback.onEpochEnd(0, {val_loss: 10});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(1);
-    await callback.onEpochEnd(1, {val_loss: 9});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(2);
-    await callback.onEpochEnd(2, {val_loss: 9.5});
-    expect(model.stopTraining).toEqual(true);
-  });
-
-  it('Default monitor, default mode, holding val_loss', async () => {
-    const model = createDummyModel();
-    const callback = tfl.callbacks.earlyStopping();
-    callback.setModel(model);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(0);
-    await callback.onEpochEnd(0, {val_loss: 10});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(1);
-    await callback.onEpochEnd(1, {val_loss: 9});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(2);
-    await callback.onEpochEnd(2, {val_loss: 9});
-    expect(model.stopTraining).toEqual(true);
-  });
-
-  it('Default monitor, default mode, custom minDelta', async () => {
-    const model = createDummyModel();
-    const callback = tfl.callbacks.earlyStopping({minDelta: 1});
-    callback.setModel(model);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(0);
-    await callback.onEpochEnd(0, {val_loss: 10});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(1);
-    await callback.onEpochEnd(1, {val_loss: 8});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(2);
-    // An decrease of 0.5 is < minDelta (1) and should trigger stop.
-    await callback.onEpochEnd(2, {val_loss: 7.5});
-    expect(model.stopTraining).toEqual(true);
-  });
-
-  it('Default monitor, default mode, custom baseline, stopping', async () => {
-    const model = createDummyModel();
-    const callback = tfl.callbacks.earlyStopping({baseline: 5});
-    callback.setModel(model);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(1);
-    // Failure to go below the baseline will stop the training immediately.
-    await callback.onEpochEnd(1, {val_loss: 6});
-    expect(model.stopTraining).toEqual(true);
-  });
-
-  it('Default monitor, default mode, custom baseline, not stopping',
-     async () => {
-       const model = createDummyModel();
-       const callback = tfl.callbacks.earlyStopping({baseline: 5});
-       callback.setModel(model);
-
-       await callback.onTrainBegin();
-       await callback.onEpochBegin(1);
-       // If the loss value goes below the baseline, training should continue.
-       await callback.onEpochEnd(1, {val_loss: 4});
-       expect(model.stopTraining).toBeUndefined();
-       // If the loss value increases, training should stop;
-       await callback.onEpochEnd(1, {val_loss: 4.5});
-       expect(model.stopTraining).toEqual(true);
-     });
-
-  it('Custom monitor, default model, increasing', async () => {
-    const model = createDummyModel();
-    const callback = tfl.callbacks.earlyStopping({monitor: 'aux_loss'});
-    callback.setModel(model);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(0);
-    await callback.onEpochEnd(0, {val_loss: 10, aux_loss: 100});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(1);
-    await callback.onEpochEnd(1, {val_loss: 9, aux_loss: 120});
-    expect(model.stopTraining).toEqual(true);
-  });
-
-  it('Custom monitor, max, increasing', async () => {
-    const model = createDummyModel();
-    const callback =
-        tfl.callbacks.earlyStopping({monitor: 'aux_metric', mode: 'max'});
-    callback.setModel(model);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(0);
-    await callback.onEpochEnd(0, {val_loss: 10, aux_metric: 100});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(1);
-    await callback.onEpochEnd(1, {val_loss: 9, aux_metric: 120});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(2);
-    await callback.onEpochEnd(2, {val_loss: 9, aux_metric: 110});
-    expect(model.stopTraining).toEqual(true);
-  });
-
-  it('Custom monitor, max, custom minDelta', async () => {
-    const model = createDummyModel();
-    const callback = tfl.callbacks.earlyStopping(
-        {monitor: 'aux_metric', mode: 'max', minDelta: 5});
-    callback.setModel(model);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(0);
-    await callback.onEpochEnd(0, {val_loss: 10, aux_metric: 100});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(1);
-    await callback.onEpochEnd(1, {val_loss: 9, aux_metric: 120});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(2);
-    // An increase of 2 is < minDelta (5) and should cause stopping.
-    await callback.onEpochEnd(2, {val_loss: 9, aux_metric: 122});
-    expect(model.stopTraining).toEqual(true);
-  });
-
-  it('Custom monitor, max, custom negative minDelta', async () => {
-    const model = createDummyModel();
-    const callback = tfl.callbacks.earlyStopping(
-        {monitor: 'aux_metric', mode: 'max', minDelta: -5});
-    callback.setModel(model);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(0);
-    await callback.onEpochEnd(0, {val_loss: 10, aux_metric: 100});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(1);
-    await callback.onEpochEnd(1, {val_loss: 9, aux_metric: 120});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(2);
-    // An increase of 2 is < minDelta (5) and should cause stopping.
-    await callback.onEpochEnd(2, {val_loss: 9, aux_metric: 122});
-    expect(model.stopTraining).toEqual(true);
-  });
-
-  it('Patience = 2', async () => {
-    const model = createDummyModel();
-    const callback = tfl.callbacks.earlyStopping({patience: 2});
-    callback.setModel(model);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(0);
-    await callback.onEpochEnd(0, {val_loss: 10});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(1);
-    await callback.onEpochEnd(1, {val_loss: 9});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(2);
-    await callback.onEpochEnd(2, {val_loss: 9.5});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(3);
-    await callback.onEpochEnd(3, {val_loss: 9.6});
-    expect(model.stopTraining).toEqual(true);
-  });
-
-  it('Missing monitor leads to warning', async () => {
-    const model = createDummyModel();
-    const callback = tfl.callbacks.earlyStopping();
-    callback.setModel(model);
-
-    const warnMessages: string[] = [];
-    function fakeWarn(message: string) {
-      warnMessages.push(message);
-    }
-    spyOn(console, 'warn').and.callFake(fakeWarn);
-
-    await callback.onTrainBegin();
-    await callback.onEpochBegin(0);
-    // Note that the default monitor (val_loss) is missing.
-    await callback.onEpochEnd(0, {loss: 100});
-    expect(model.stopTraining).toBeUndefined();
-    await callback.onEpochBegin(1);
-    await callback.onEpochEnd(1, {loss: 100});
-    expect(model.stopTraining).toBeUndefined();
-
-    expect(warnMessages.length).toEqual(2);
-    expect(warnMessages[0]).toMatch(/val_loss is not available/);
-    expect(warnMessages[1]).toMatch(/val_loss is not available/);
-  });
-});
-
-describeMathCPUAndGPU('EarlyStopping LayersModel.fit() integration', () => {
-  it('Functional model, monitor loss, With minDelta', async () => {
-    const input = tfl.input({shape: [1]});
-    const output =
-        tfl.layers.dense({units: 1, kernelInitializer: 'ones'}).apply(input) as
-        tfl.SymbolicTensor;
-    const model = tfl.model({inputs: input, outputs: output});
-    const xs = tensor2d([1, 2, 3, 4], [4, 1]);
-    const ys = tensor2d([2, 3, 4, 5], [4, 1]);
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    // Without the EarlyStopping callback, the loss value would be:
-    //   1, 0.734, 0.549, 0.421, 0.332, ...
-    // With loss being monitored and minDelta set to 0.25, the training should
-    // stop after the 3rd epoch.
-    const history = await model.fit(xs, ys, {
-      epochs: 10,
-      callbacks: tfl.callbacks.earlyStopping({monitor: 'loss', minDelta: 0.25})
-    });
-    expect(history.history.loss.length).toEqual(3);
-  });
-
-  it('Sequential model, monitor val_acc', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      activation: 'softmax',
-      kernelInitializer: 'ones',
-      inputShape: [2]
-    }));
-    const xs = tensor2d([1, 2, 3, 4], [2, 2]);
-    const ys = tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);
-    const xsVal = tensor2d([4, 3, 2, 1], [2, 2]);
-    const ysVal = tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);
-    model.compile(
-        {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});
-
-    // Without the EarlyStopping callback, the val_acc value would be:
-    //   0.5, 0.5, 0.5, 0.5, ...
-    // With val_acc being monitored, training should stop after the 2nd epoch.
-    const history = await model.fit(xs, ys, {
-      epochs: 10,
-      validationData: [xsVal, ysVal],
-      callbacks: tfl.callbacks.earlyStopping({monitor: 'val_acc'})
-    });
-    expect(history.history.loss.length).toEqual(2);
-  });
-
-  it('Sequential model, monitor val_acc, custom patience', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      activation: 'softmax',
-      kernelInitializer: 'ones',
-      inputShape: [2]
-    }));
-    const xs = tensor2d([1, 2, 3, 4], [2, 2]);
-    const ys = tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);
-    const xsVal = tensor2d([4, 3, 2, 1], [2, 2]);
-    const ysVal = tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);
-    model.compile(
-        {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});
-
-    // Without the EarlyStopping callback, the val_acc value would be:
-    //   0.5, 0.5, 0.5, 0.5, ...
-    // With val_acc being monitored and patience set to 4, training should stop
-    // after the 5th epoch.
-    const history = await model.fit(xs, ys, {
-      epochs: 10,
-      validationData: [xsVal, ysVal],
-      callbacks: tfl.callbacks.earlyStopping({monitor: 'val_acc', patience: 4})
-    });
-    expect(history.history.loss.length).toEqual(5);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/common.ts b/tfjs-master/tfjs-layers/src/common.ts
deleted file mode 100644
index bf305e206..000000000
--- a/tfjs-master/tfjs-layers/src/common.ts
+++ /dev/null
@@ -1,120 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Common functions for TensorFlow.js Layers.
- */
-import {VALID_DATA_FORMAT_VALUES, VALID_INTERPOLATION_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES} from './keras_format/common';
-import {checkStringTypeUnionValue} from './utils/generic_utils';
-
-// A map from the requested scoped name of a Tensor to the number of Tensors
-// wanting that name so far.  This allows enforcing name uniqueness by appending
-// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.
-const nameMap: Map<string, number> = new Map<string, number>();
-
-export function checkDataFormat(value?: string): void {
-  checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);
-}
-
-export function checkInterpolationFormat(value?: string): void {
-  checkStringTypeUnionValue(
-      VALID_INTERPOLATION_FORMAT_VALUES, 'InterpolationFormat', value);
-}
-
-export function checkPaddingMode(value?: string): void {
-  checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);
-}
-
-export function checkPoolMode(value?: string): void {
-  checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);
-}
-
-const _nameScopeStack: string[] = [];
-const _nameScopeDivider = '/';
-
-/**
- * Enter namescope, which can be nested.
- */
-export function nameScope<T>(name: string, fn: () => T): T {
-  _nameScopeStack.push(name);
-  try {
-    const val: T = fn();
-    _nameScopeStack.pop();
-    return val;
-  } catch (e) {
-    _nameScopeStack.pop();
-    throw e;
-  }
-}
-
-/**
- * Get the current namescope as a flat, concatenated string.
- */
-function currentNameScopePrefix(): string {
-  if (_nameScopeStack.length === 0) {
-    return '';
-  } else {
-    return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;
-  }
-}
-
-/**
- * Get the name a Tensor (or Variable) would have if not uniqueified.
- * @param tensorName
- * @return Scoped name string.
- */
-export function getScopedTensorName(tensorName: string): string {
-  if (!isValidTensorName(tensorName)) {
-    throw new Error('Not a valid tensor name: \'' + tensorName + '\'');
-  }
-  return currentNameScopePrefix() + tensorName;
-}
-
-/**
- * Get unique names for Tensors and Variables.
- * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by
- *  `getScopedTensorName()`.
- * @return A unique version of the given fully scoped name.
- *   If this is the first time that the scoped name is seen in this session,
- *   then the given `scopedName` is returned unaltered.  If the same name is
- *   seen again (producing a collision), an incrementing suffix is added to the
- *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.
- */
-export function getUniqueTensorName(scopedName: string): string {
-  if (!isValidTensorName(scopedName)) {
-    throw new Error('Not a valid tensor name: \'' + scopedName + '\'');
-  }
-  if (!nameMap.has(scopedName)) {
-    nameMap.set(scopedName, 0);
-  }
-  const index = nameMap.get(scopedName);
-  nameMap.set(scopedName, nameMap.get(scopedName) + 1);
-
-  if (index > 0) {
-    const result = `${scopedName}_${index}`;
-    // Mark the composed name as used in case someone wants
-    // to call getUniqueTensorName("name_1").
-    nameMap.set(result, 1);
-    return result;
-  } else {
-    return scopedName;
-  }
-}
-
-const tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
-
-/**
- * Determine whether a string is a valid tensor name.
- * @param name
- * @returns A Boolean indicating whether `name` is a valid tensor name.
- */
-export function isValidTensorName(name: string): boolean {
-  return !!name.match(tensorNameRegex);
-}
diff --git a/tfjs-master/tfjs-layers/src/common_test.ts b/tfjs-master/tfjs-layers/src/common_test.ts
deleted file mode 100644
index e07f64306..000000000
--- a/tfjs-master/tfjs-layers/src/common_test.ts
+++ /dev/null
@@ -1,172 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for common.ts.
- */
-
-import {checkDataFormat, checkInterpolationFormat, checkPaddingMode, checkPoolMode, getUniqueTensorName, isValidTensorName} from './common';
-import {VALID_DATA_FORMAT_VALUES, VALID_INTERPOLATION_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES} from './keras_format/common';
-
-describe('checkDataFormat', () => {
-  it('Valid values', () => {
-    const extendedValues = VALID_DATA_FORMAT_VALUES.concat([undefined, null]);
-    for (const validValue of extendedValues) {
-      // Using implicit "expect().toNotThrow()" for valid values
-      checkDataFormat(validValue);
-    }
-  });
-  it('Invalid values', () => {
-    // Test invalid values are rejected, and reported in the error.
-    expect(() => checkDataFormat('foo')).toThrowError(/foo/);
-    try {
-      checkDataFormat('bad');
-    } catch (e) {
-      expect(e).toMatch('DataFormat');
-      // Test that the error message contains the list of valid values.
-      for (const validValue of VALID_DATA_FORMAT_VALUES) {
-        expect(e).toMatch(validValue);
-      }
-    }
-  });
-});
-
-describe('checkIntorpolationFormat', () => {
-  it('Valid values', () => {
-    const extendedValues =
-        VALID_INTERPOLATION_FORMAT_VALUES.concat([undefined, null]);
-    for (const validValue of extendedValues) {
-      // Using implicit "expect().toNotThrow()" for valid values
-      checkInterpolationFormat(validValue);
-    }
-  });
-  it('Invalid values', () => {
-    // Test invalid values are rejected, and reported in the error.
-    expect(() => checkInterpolationFormat('foo')).toThrowError(/foo/);
-    try {
-      checkInterpolationFormat('bad');
-    } catch (e) {
-      expect(e).toMatch('InterpolationFormat');
-      // Test that the error message contains the list of valid values.
-      for (const validValue of VALID_INTERPOLATION_FORMAT_VALUES) {
-        expect(e).toMatch(validValue);
-      }
-    }
-  });
-});
-describe('checkPaddingMode', () => {
-  it('Valid values', () => {
-    const extendedValues = VALID_PADDING_MODE_VALUES.concat([undefined, null]);
-    for (const validValue of extendedValues) {
-      // Using implicit "expect().toNotThrow()" for valid values
-      checkPaddingMode(validValue);
-    }
-  });
-  it('Invalid values', () => {
-    // Test invalid values are rejected, and reported in the error.
-    expect(() => checkPaddingMode('foo')).toThrowError(/foo/);
-    try {
-      checkPaddingMode('bad');
-    } catch (e) {
-      expect(e).toMatch('PaddingMode');
-      // Test that the error message contains the list of valid values.
-      for (const validValue of VALID_PADDING_MODE_VALUES) {
-        expect(e).toMatch(validValue);
-      }
-    }
-  });
-});
-
-describe('checkPoolMode', () => {
-  it('Valid values', () => {
-    const extendedValues = VALID_POOL_MODE_VALUES.concat([undefined, null]);
-    for (const validValue of extendedValues) {
-      // Using implicit "expect().toNotThrow()" for valid values
-      checkPoolMode(validValue);
-    }
-  });
-  it('Invalid values', () => {
-    // Test invalid values are rejected, and reported in the error.
-    expect(() => checkPoolMode('foo')).toThrowError(/foo/);
-    try {
-      checkPoolMode('bad');
-    } catch (e) {
-      expect(e).toMatch('PoolMode');
-      // Test that the error message contains the list of valid values.
-      for (const validValue of VALID_POOL_MODE_VALUES) {
-        expect(e).toMatch(validValue);
-      }
-    }
-  });
-});
-
-describe('isValidTensorName', () => {
-  it('Valid tensor names', () => {
-    expect(isValidTensorName('a')).toEqual(true);
-    expect(isValidTensorName('A')).toEqual(true);
-    expect(isValidTensorName('foo1')).toEqual(true);
-    expect(isValidTensorName('Foo2')).toEqual(true);
-    expect(isValidTensorName('n_1')).toEqual(true);
-    expect(isValidTensorName('n.1')).toEqual(true);
-    expect(isValidTensorName('n_1_2')).toEqual(true);
-    expect(isValidTensorName('n.1.2')).toEqual(true);
-    expect(isValidTensorName('a/B/c')).toEqual(true);
-    expect(isValidTensorName('z_1/z_2/z.3')).toEqual(true);
-    expect(isValidTensorName('z-1/z-2/z.3')).toEqual(true);
-    expect(isValidTensorName('1Qux')).toEqual(true);
-    expect(isValidTensorName('5-conv/kernel')).toEqual(true);
-  });
-
-  it('Invalid tensor names: empty', () => {
-    expect(isValidTensorName('')).toEqual(false);
-  });
-
-  it('Invalid tensor names: whitespaces', () => {
-    expect(isValidTensorName('a b')).toEqual(false);
-    expect(isValidTensorName('ab ')).toEqual(false);
-  });
-
-  it('Invalid tensor names: forbidden characters', () => {
-    expect(isValidTensorName('-foo1')).toEqual(false);
-    expect(isValidTensorName('-foo2-')).toEqual(false);
-    expect(isValidTensorName('bar3!4')).toEqual(false);
-  });
-
-  it('Invalid tensor names: invalid first characters', () => {
-    expect(isValidTensorName('/foo/bar')).toEqual(false);
-    expect(isValidTensorName('.baz')).toEqual(false);
-    expect(isValidTensorName('_baz')).toEqual(false);
-  });
-
-  it('Invalid tensor names: non-ASCII', () => {
-    expect(isValidTensorName('フ')).toEqual(false);
-    expect(isValidTensorName('ξ')).toEqual(false);
-  });
-});
-
-describe('getUniqueTensorName', () => {
-  it('Adds unique suffixes to tensor names', () => {
-    expect(getUniqueTensorName('xx')).toEqual('xx');
-    expect(getUniqueTensorName('xx')).toEqual('xx_1');
-    expect(getUniqueTensorName('xx')).toEqual('xx_2');
-    expect(getUniqueTensorName('xx')).toEqual('xx_3');
-  });
-
-  it('Correctly handles preexisting unique suffixes on tensor names', () => {
-    expect(getUniqueTensorName('yy')).toEqual('yy');
-    expect(getUniqueTensorName('yy')).toEqual('yy_1');
-    expect(getUniqueTensorName('yy_1')).toEqual('yy_1_1');
-    expect(getUniqueTensorName('yy')).toEqual('yy_2');
-    expect(getUniqueTensorName('yy_1')).toEqual('yy_1_2');
-    expect(getUniqueTensorName('yy_2')).toEqual('yy_2_1');
-    expect(getUniqueTensorName('yy')).toEqual('yy_3');
-    expect(getUniqueTensorName('yy_1_1')).toEqual('yy_1_1_1');
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/constraints.ts b/tfjs-master/tfjs-layers/src/constraints.ts
deleted file mode 100644
index 412af2c7d..000000000
--- a/tfjs-master/tfjs-layers/src/constraints.ts
+++ /dev/null
@@ -1,263 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original source: keras/contraints.py */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {serialization, Tensor, tidy} from '@tensorflow/tfjs-core';
-import {epsilon} from './backend/common';
-import {deserializeKerasObject, serializeKerasObject} from './utils/generic_utils';
-
-/**
- * Helper function used by many of the Constraints to find the L2Norms.
- */
-function calcL2Norms(w: Tensor, axis: number): Tensor {
-  return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));
-}
-
-/**
- * Base class for functions that impose constraints on weight values
- *
- * @doc {
- *   heading: 'Constraints',
- *   subheading: 'Classes',
- *   namespace: 'constraints'
- * }
- */
-export abstract class Constraint extends serialization.Serializable {
-  /* Porting note: was __call__, apply chosen to match other similar choices */
-  abstract apply(w: Tensor): Tensor;
-  getConfig(): serialization.ConfigDict {
-    return {};
-  }
-}
-
-export interface MaxNormArgs {
-  /**
-   * Maximum norm for incoming weights
-   */
-  maxValue?: number;
-  /**
-   * Axis along which to calculate norms.
-   *
-   *  For instance, in a `Dense` layer the weight matrix
-   *  has shape `[inputDim, outputDim]`,
-   *  set `axis` to `0` to constrain each weight vector
-   *  of length `[inputDim,]`.
-   *  In a `Conv2D` layer with `dataFormat="channels_last"`,
-   *  the weight tensor has shape
-   *  `[rows, cols, inputDepth, outputDepth]`,
-   *  set `axis` to `[0, 1, 2]`
-   *  to constrain the weights of each filter tensor of size
-   *  `[rows, cols, inputDepth]`.
-   */
-  axis?: number;
-}
-
-export class MaxNorm extends Constraint {
-  /** @nocollapse */
-  static readonly className = 'MaxNorm';
-  private maxValue: number;
-  private axis: number;
-  private readonly defaultMaxValue = 2;
-  private readonly defaultAxis = 0;
-
-  constructor(args: MaxNormArgs) {
-    super();
-    this.maxValue =
-        args.maxValue != null ? args.maxValue : this.defaultMaxValue;
-    this.axis = args.axis != null ? args.axis : this.defaultAxis;
-  }
-
-  apply(w: Tensor): Tensor {
-    return tidy(() => {
-      const norms = calcL2Norms(w, this.axis);
-      const desired = tfc.clipByValue(norms, 0, this.maxValue);
-      return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {maxValue: this.maxValue, axis: this.axis};
-  }
-}
-serialization.registerClass(MaxNorm);
-
-export interface UnitNormArgs {
-  /**
-   * Axis along which to calculate norms.
-   *
-   * For instance, in a `Dense` layer the weight matrix
-   * has shape `[inputDim, outputDim]`,
-   * set `axis` to `0` to constrain each weight vector
-   * of length `[inputDim,]`.
-   * In a `Conv2D` layer with `dataFormat="channels_last"`,
-   * the weight tensor has shape
-   * `[rows, cols, inputDepth, outputDepth]`,
-   * set `axis` to `[0, 1, 2]`
-   * to constrain the weights of each filter tensor of size
-   * `[rows, cols, inputDepth]`.
-   */
-  axis?: number;
-}
-
-export class UnitNorm extends Constraint {
-  /** @nocollapse */
-  static readonly className = 'UnitNorm';
-  private axis: number;
-  private readonly defaultAxis = 0;
-  constructor(args: UnitNormArgs) {
-    super();
-    this.axis = args.axis != null ? args.axis : this.defaultAxis;
-  }
-
-  apply(w: Tensor): Tensor {
-    return tidy(
-        () => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {axis: this.axis};
-  }
-}
-serialization.registerClass(UnitNorm);
-
-export class NonNeg extends Constraint {
-  /** @nocollapse */
-  static readonly className = 'NonNeg';
-
-  apply(w: Tensor): Tensor {
-    return tfc.relu(w);
-  }
-}
-serialization.registerClass(NonNeg);
-
-export interface MinMaxNormArgs {
-  /**
-   * Minimum norm for incoming weights
-   */
-  minValue?: number;
-  /**
-   * Maximum norm for incoming weights
-   */
-  maxValue?: number;
-  /**
-   * Axis along which to calculate norms.
-   * For instance, in a `Dense` layer the weight matrix
-   * has shape `[inputDim, outputDim]`,
-   * set `axis` to `0` to constrain each weight vector
-   * of length `[inputDim,]`.
-   * In a `Conv2D` layer with `dataFormat="channels_last"`,
-   * the weight tensor has shape
-   * `[rows, cols, inputDepth, outputDepth]`,
-   * set `axis` to `[0, 1, 2]`
-   * to constrain the weights of each filter tensor of size
-   * `[rows, cols, inputDepth]`.
-   */
-  axis?: number;
-  /**
-   * Rate for enforcing the constraint: weights will be rescaled to yield:
-   * `(1 - rate) * norm + rate * norm.clip(minValue, maxValue)`.
-   * Effectively, this means that rate=1.0 stands for strict
-   * enforcement of the constraint, while rate<1.0 means that
-   * weights will be rescaled at each step to slowly move
-   * towards a value inside the desired interval.
-   */
-  rate?: number;
-}
-
-export class MinMaxNorm extends Constraint {
-  /** @nocollapse */
-  static readonly className = 'MinMaxNorm';
-  private minValue: number;
-  private maxValue: number;
-  private rate: number;
-  private axis: number;
-  private readonly defaultMinValue = 0.0;
-  private readonly defaultMaxValue = 1.0;
-  private readonly defaultRate = 1.0;
-  private readonly defaultAxis = 0;
-
-  constructor(args: MinMaxNormArgs) {
-    super();
-    this.minValue =
-        args.minValue != null ? args.minValue : this.defaultMinValue;
-    this.maxValue =
-        args.maxValue != null ? args.maxValue : this.defaultMaxValue;
-    this.rate = args.rate != null ? args.rate : this.defaultRate;
-    this.axis = args.axis != null ? args.axis : this.defaultAxis;
-  }
-
-  apply(w: Tensor): Tensor {
-    return tidy(() => {
-      const norms = calcL2Norms(w, this.axis);
-      const desired = tfc.add(
-          tfc.mul(
-              this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)),
-          tfc.mul(1.0 - this.rate, norms));
-      return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {
-      minValue: this.minValue,
-      maxValue: this.maxValue,
-      rate: this.rate,
-      axis: this.axis
-    };
-  }
-}
-serialization.registerClass(MinMaxNorm);
-
-/** @docinline */
-export type ConstraintIdentifier =
-    'maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string;
-
-// Maps the JavaScript-like identifier keys to the corresponding registry
-// symbols.
-export const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP:
-    {[identifier in ConstraintIdentifier]: string} = {
-      'maxNorm': 'MaxNorm',
-      'minMaxNorm': 'MinMaxNorm',
-      'nonNeg': 'NonNeg',
-      'unitNorm': 'UnitNorm'
-    };
-
-export function serializeConstraint(constraint: Constraint):
-    serialization.ConfigDictValue {
-  return serializeKerasObject(constraint);
-}
-
-export function deserializeConstraint(
-    config: serialization.ConfigDict,
-    customObjects: serialization.ConfigDict = {}): Constraint {
-  return deserializeKerasObject(
-      config, serialization.SerializationMap.getMap().classNameMap,
-      customObjects, 'constraint');
-}
-
-export function getConstraint(identifier: ConstraintIdentifier|
-                              serialization.ConfigDict|Constraint): Constraint {
-  if (identifier == null) {
-    return null;
-  }
-  if (typeof identifier === 'string') {
-    const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?
-        CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :
-        identifier;
-    const config = {className, config: {}};
-    return deserializeConstraint(config);
-  } else if (identifier instanceof Constraint) {
-    return identifier;
-  } else {
-    return deserializeConstraint(identifier);
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/constraints_test.ts b/tfjs-master/tfjs-layers/src/constraints_test.ts
deleted file mode 100644
index d57d07541..000000000
--- a/tfjs-master/tfjs-layers/src/constraints_test.ts
+++ /dev/null
@@ -1,141 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Unit tests for constraints */
-
-import {serialization, Tensor1D, tensor1d} from '@tensorflow/tfjs-core';
-
-import {ConstraintIdentifier, deserializeConstraint, getConstraint, serializeConstraint} from './constraints';
-import * as tfl from './index';
-import {describeMathCPU, expectNoLeakedTensors, expectTensorsClose} from './utils/test_utils';
-
-describeMathCPU('Built-in Constraints', () => {
-  let initVals: Tensor1D;
-  beforeEach(() => {
-    initVals = tensor1d(new Float32Array([-1, 2, 0, 4, -5, 6]));
-  });
-
-  it('NonNeg', () => {
-    const constraint = getConstraint('NonNeg');
-    const postConstraint = constraint.apply(initVals);
-    expectTensorsClose(
-        postConstraint, tensor1d(new Float32Array([0, 2, 0, 4, 0, 6])));
-    expectNoLeakedTensors(() => constraint.apply(initVals), 1);
-  });
-
-  it('MaxNorm', () => {
-    const constraint = getConstraint('MaxNorm');
-    const postConstraint = constraint.apply(initVals);
-    expectTensorsClose(postConstraint, tensor1d(new Float32Array([
-                         -0.2208630521, 0.4417261043, 0, 0.8834522086,
-                         -1.104315261, 1.325178313
-                       ])));
-    expectNoLeakedTensors(() => constraint.apply(initVals), 1);
-  });
-  it('UnitNorm', () => {
-    const constraint = getConstraint('UnitNorm');
-    const postConstraint = constraint.apply(initVals);
-    expectTensorsClose(postConstraint, tensor1d(new Float32Array([
-                         -0.2208630521 / 2, 0.4417261043 / 2, 0,
-                         0.8834522086 / 2, -1.104315261 / 2, 1.325178313 / 2
-                       ])));
-    expectNoLeakedTensors(() => constraint.apply(initVals), 1);
-  });
-  it('MinMaxNorm', () => {
-    const constraint = getConstraint('MinMaxNorm');
-    const postConstraint = constraint.apply(initVals);
-    expectTensorsClose(postConstraint, tensor1d(new Float32Array([
-                         -0.2208630521 / 2, 0.4417261043 / 2, 0,
-                         0.8834522086 / 2, -1.104315261 / 2, 1.325178313 / 2
-                       ])));
-    expectNoLeakedTensors(() => constraint.apply(initVals), 1);
-  });
-
-  // Lower camel case.
-  it('nonNeg', () => {
-    const constraint = getConstraint('nonNeg');
-    const postConstraint = constraint.apply(initVals);
-    expectTensorsClose(
-        postConstraint, tensor1d(new Float32Array([0, 2, 0, 4, 0, 6])));
-  });
-
-  it('maxNorm', () => {
-    const constraint = getConstraint('maxNorm');
-    const postConstraint = constraint.apply(initVals);
-    expectTensorsClose(postConstraint, tensor1d(new Float32Array([
-                         -0.2208630521, 0.4417261043, 0, 0.8834522086,
-                         -1.104315261, 1.325178313
-                       ])));
-  });
-  it('unitNorm', () => {
-    const constraint = getConstraint('unitNorm');
-    const postConstraint = constraint.apply(initVals);
-    expectTensorsClose(postConstraint, tensor1d(new Float32Array([
-                         -0.2208630521 / 2, 0.4417261043 / 2, 0,
-                         0.8834522086 / 2, -1.104315261 / 2, 1.325178313 / 2
-                       ])));
-  });
-  it('minMaxNorm', () => {
-    const constraint = getConstraint('minMaxNorm');
-    const postConstraint = constraint.apply(initVals);
-    expectTensorsClose(postConstraint, tensor1d(new Float32Array([
-                         -0.2208630521 / 2, 0.4417261043 / 2, 0,
-                         0.8834522086 / 2, -1.104315261 / 2, 1.325178313 / 2
-                       ])));
-  });
-});
-
-describeMathCPU('constraints.get', () => {
-  it('by string', () => {
-    const constraint = getConstraint('maxNorm');
-    const config = serializeConstraint(constraint) as serialization.ConfigDict;
-    const nestedConfig = config.config as serialization.ConfigDict;
-    expect(nestedConfig.maxValue).toEqual(2);
-    expect(nestedConfig.axis).toEqual(0);
-  });
-
-  it('by string, upper case', () => {
-    const constraint = getConstraint('maxNorm');
-    const config = serializeConstraint(constraint) as serialization.ConfigDict;
-    const nestedConfig = config.config as serialization.ConfigDict;
-    expect(nestedConfig.maxValue).toEqual(2);
-    expect(nestedConfig.axis).toEqual(0);
-  });
-
-  it('by existing object', () => {
-    const origConstraint = tfl.constraints.nonNeg();
-    expect(getConstraint(origConstraint)).toEqual(origConstraint);
-  });
-  it('by config dict', () => {
-    const origConstraint = tfl.constraints.minMaxNorm(
-        {minValue: 0, maxValue: 2, rate: 3, axis: 4});
-    const constraint = getConstraint(
-        serializeConstraint(origConstraint) as serialization.ConfigDict);
-    expect(serializeConstraint(constraint))
-        .toEqual(serializeConstraint(origConstraint));
-  });
-});
-
-describe('Constraints Serialization', () => {
-  it('Built-ins', () => {
-    // Test both types of captialization.
-    const constraints: ConstraintIdentifier[] = [
-      'maxNorm', 'nonNeg', 'unitNorm', 'minMaxNorm', 'MaxNorm', 'NonNeg',
-      'UnitNorm', 'MinMaxNorm'
-    ];
-    for (const name of constraints) {
-      const constraint = getConstraint(name);
-      const config =
-          serializeConstraint(constraint) as serialization.ConfigDict;
-      const reconstituted = deserializeConstraint(config);
-      expect(reconstituted).toEqual(constraint);
-    }
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/engine/base_random_layer.ts b/tfjs-master/tfjs-layers/src/engine/base_random_layer.ts
deleted file mode 100644
index baa84fe56..000000000
--- a/tfjs-master/tfjs-layers/src/engine/base_random_layer.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2023 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import { LayerArgs, Layer } from './topology';
-import { RandomSeed } from '../backend/random_seed';
-import { serialization } from '@tensorflow/tfjs-core';
-
-export declare interface BaseRandomLayerArgs extends LayerArgs {
-  seed?: number;
-}
-
-export abstract class BaseRandomLayer extends Layer {
-  // A layer handle the random number creation and savemodel behavior.
-  /** @nocollapse */
-  static className = 'BaseRandomLayer';
-  protected randomGenerator: RandomSeed;
-
-  constructor(args: BaseRandomLayerArgs) {
-    super(args);
-    this.randomGenerator = new RandomSeed(args.seed);
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'seed': this.randomGenerator.seed
-    };
-
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/container.ts b/tfjs-master/tfjs-layers/src/engine/container.ts
deleted file mode 100644
index a91025834..000000000
--- a/tfjs-master/tfjs-layers/src/engine/container.ts
+++ /dev/null
@@ -1,1379 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original source: keras/engine/topology.py */
-
-import {NamedTensorMap, Scalar, serialization, Tensor, tidy} from '@tensorflow/tfjs-core';
-
-import {getUid} from '../backend/state';
-import {NotImplementedError, RuntimeError, ValueError} from '../errors';
-import {Shape} from '../keras_format/common';
-import {TensorKeyWithArgsArray} from '../keras_format/node_config';
-import {PyJsonDict} from '../keras_format/types';
-import {deserialize as deserializeLayer} from '../layers/serialization';
-import {Kwargs} from '../types';
-import * as generic_utils from '../utils/generic_utils';
-import {convertTsToPythonic} from '../utils/serialization_utils';
-import * as types_utils from '../utils/types_utils';
-import {batchSetValue, LayerVariable} from '../variables';
-import {version as layersVersion} from '../version';
-
-import {execute, FeedDict} from './executor';
-import {InputLayer} from './input_layer';
-import {DisposeResult, Layer, Node, SymbolicTensor} from './topology';
-
-/** Constructor config for Container. */
-export interface ContainerArgs {
-  inputs: SymbolicTensor|SymbolicTensor[];
-  outputs: SymbolicTensor|SymbolicTensor[];
-  name?: string;
-}
-
-// get weights key from tensor map in order to check if it is from keras v3.
-// e.g. dense/0
-const isKerasSavedModelFormat = (weights: NamedTensorMap): boolean => {
-  const keys = Object.keys(weights);
-  if (keys.length === 0) {
-    return false;
-  }
-  const key = keys[0].split('/');
-  return !isNaN(parseInt(key[key.length - 1], 10));
-};
-
-/**
- * A Container is a directed acyclic graph of layers.
- *
- * It is the topological form of a "model". A LayersModel
- * is simply a Container with added training routines.
- *
- */
-export abstract class Container extends Layer {
-  inputs: SymbolicTensor[];
-  outputs: SymbolicTensor[];
-
-  inputLayers: Layer[];
-  inputLayersNodeIndices: number[];
-  inputLayersTensorIndices: number[];
-
-  outputLayers: Layer[];
-  outputLayersNodeIndices: number[];
-  outputLayersTensorIndices: number[];
-
-  layers: Layer[];
-  layersByDepth: {[depth: string]: Layer[]};
-  nodesByDepth: {[depth: string]: Node[]};
-
-  internalContainerRefs: Container[];
-
-  containerNodes = new Set<string>();
-
-  // TODO(michaelterry): Add cache support
-  // private outputMaskCache: any;
-  // private outputTensorCache: any;
-  // private outputShapeCache: any;
-
-  inputNames: string[];
-  outputNames: string[];
-  feedInputShapes: Shape[];
-
-  protected internalInputShapes: Shape[];
-  protected internalOutputShapes: Shape[];
-  // TODO(cais): Maybe 'feed' should not in the names of these variables,
-  //   due to the fact that our backend is not symbolic.
-  protected feedInputNames: string[];
-  protected feedOutputNames: string[];
-
-  constructor(args: ContainerArgs) {
-    // No args passed to super's constructor.
-    super({});
-    this.name = args.name;
-    if (this.name == null) {
-      const prefix = this.getClassName().toLowerCase();
-      this.name = getUid(prefix);
-    }
-
-    this.supportsMasking = false;
-    this.trainable_ = true;
-
-    // TODO(michaelterry): Initialize perInputLosses/Updates here.
-
-    // Container-specific properties.
-    if (Array.isArray(args.inputs)) {
-      this.inputs = args.inputs.slice();
-    } else {
-      this.inputs = [args.inputs];
-    }
-    if (Array.isArray(args.outputs)) {
-      this.outputs = args.outputs.slice();
-    } else {
-      this.outputs = [args.outputs];
-    }
-
-    // Check for redundancy in inputs.
-    if (generic_utils.unique(this.inputs).length !== this.inputs.length) {
-      throw new ValueError(
-          'The list of inputs passed to the model is ' +
-          'redundant. All inputs should only appear once. Found: ' +
-          `${this.inputs.map(x => x.name)}`);
-    }
-
-    // Check for redundancy in outputs.
-    if (generic_utils.unique(this.outputs).length !== this.outputs.length) {
-      console.warn(
-          'The list of outputs passed to the model is redundant. ' +
-          'All outputs should only appear once. Found: ' +
-          `${this.outputs.map(x => x.name)}`);
-    }
-
-    /*
-      List of initial layers (1 to 1 mapping with this.inputs, hence the same
-      layer might appear twice)
-    */
-    this.inputLayers = [];
-    this.inputLayersNodeIndices = [];
-    this.inputLayersTensorIndices = [];
-    /*
-      List of layers (1 to 1 mapping with this.outputs, hence the same layer
-      might appear twice)
-    */
-    this.outputLayers = [];
-    this.outputLayersNodeIndices = [];
-    this.outputLayersTensorIndices = [];
-    /*
-      All layers in order of horizontal graph traversal. Entries are unique.
-      Includes input and output layers.
-    */
-    this.layers = [];
-
-    /*
-      References to container layers that were constructed internally. We need
-      these to properly dispose of tensors from nested containers.
-    */
-    this.internalContainerRefs = [];
-
-    // TODO(michaelterry): Determine if caching still needed with eager
-    // backend.
-    /*
-      This is for performance optimization when calling the Container on new
-      inputs. Every time the Container is called on a set on input tensors,
-      we compute the output tensors, output masks and output shapes in one pass,
-      then cache them here. When one of these outputs is queried later,
-      we retrieve it from there instead of recomputing it.
-    */
-    // this.outputTensorCache = {};
-    // this.outputShapeCache = {};
-
-    // Build this.outputLayers:
-    for (const x of this.outputs) {
-      const layer = x.sourceLayer;
-      const nodeIndex = x.nodeIndex;
-      const tensorIndex = x.tensorIndex;
-      this.outputLayers.push(layer);
-      this.outputLayersNodeIndices.push(nodeIndex);
-      this.outputLayersTensorIndices.push(tensorIndex);
-    }
-
-    // TODO(michaelterry): Add output mask cache code.
-
-    // Build this.inputLayers:
-    for (const x of this.inputs) {
-      const layer = x.sourceLayer;
-      const nodeIndex = x.nodeIndex;
-      const tensorIndex = x.tensorIndex;
-      /*
-        It's supposed to be an input layer, so only one node
-        and one tensor output.
-      */
-      generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');
-      generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');
-      this.inputLayers.push(layer);
-      this.inputLayersNodeIndices.push(nodeIndex);
-      this.inputLayersTensorIndices.push(tensorIndex);
-    }
-
-    // Build this.inputNames and this.outputNames.
-    this.inputNames = [];
-    this.outputNames = [];
-    this.feedInputShapes = [];
-    this.feedInputNames = [];
-    this.feedOutputNames = [];
-    for (let i = 0; i < this.inputLayers.length; i++) {
-      const layer = this.inputLayers[i];
-      // Check that layer is an InputLayer.
-      if (!(layer instanceof InputLayer)) {
-        throw new TypeError(
-            'Input layers to a LayersModel must be InputLayer objects. ' +
-            `Received inputs: ${args.inputs}. ` +
-            `Input ${i} (0-based) originates ` +
-            `from layer type ${layer.getClassName()}.`);
-      }
-      this.inputNames.push(layer.name);
-      this.feedInputShapes.push(layer.batchInputShape);
-
-      this.feedInputNames.push(layer.name);
-    }
-    for (const layer of this.outputLayers) {
-      this.outputNames.push(layer.name);
-    }
-
-    this.internalInputShapes = this.inputs.map(x => x.shape);
-    this.internalOutputShapes = this.outputs.map(x => x.shape);
-
-    /*
-      Container_nodes: set of nodes included in the graph (not all nodes
-      included in the layers are relevant to the current graph).
-    */
-    // ids of all nodes relevant to the Container:
-    const nodesDepths: {[nodeID: string]: number} = {};
-    // To recover nodes from their ID.
-    const nodeIDToNode: {[nodeID: string]: Node} = {};
-    const layersDepths: {[layerID: string]: number} = {};
-    // To layers from their ID.
-    const layerIDToLayer: {[layerID: string]: Layer} = {};
-    const layerIndices: {[layerID: string]: number} = {};
-    const nodesInDecreasingDepth: Node[] = [];
-
-    /**
-     * Builds a map of the graph of layers.
-     *
-     * This recursively updates the map `layerIndices`,
-     * the list `nodesInDecreasingDepth` and the set `containerNodes`.
-     *
-     * @param tensor Some tensor in a graph.
-     * @param finishedNodes Set of nodes whose subgraphs have been traversed
-     *         completely. Useful to prevent duplicated work.
-     * @param nodesInProgress Set of nodes that are currently active on the
-     *         recursion stack. Useful to detect cycles.
-     * @param layer Layer from which `tensor` comes from. If not provided,
-     *   will be obtained from tensor.sourceLayer.
-     * @param nodeIndex Node index from which `tensor` comes from.
-     * @param tensorIndex TensorIndex from which `tensor` comes from.
-     *
-     * @exception RuntimeError if a cycle is detected.
-     */
-    const buildMapOfGraph =
-        (tensor: SymbolicTensor, finishedNodes: Node[], nodesInProgress: Node[],
-         layer?: Layer, nodeIndex?: number, tensorIndex?: number) => {
-          if (layer == null || nodeIndex == null || tensorIndex == null) {
-            layer = tensor.sourceLayer;
-            nodeIndex = tensor.nodeIndex;
-            tensorIndex = tensor.tensorIndex;
-          }
-          const node = layer.inboundNodes[nodeIndex];
-
-          // Prevent cycles.
-          if (nodesInProgress.indexOf(node) !== -1) {
-            throw new RuntimeError(
-                `The tensor ${tensor.name} at layer "${layer.name}" ` +
-                'is part of a cycle.');
-          }
-
-          // Don't repeat work for shared subgraphs
-          if (finishedNodes.indexOf(node) !== -1) {
-            return;
-          }
-
-          // Update containerNodes.
-          this.containerNodes.add(Container.nodeKey(layer, nodeIndex));
-
-          // Store the traversal order for layer sorting.
-          if (!(layer.id in layerIndices)) {
-            layerIndices[layer.id] = Object.keys(layerIndices).length;
-          }
-
-          if (nodesInProgress.indexOf(node) === -1) {
-            nodesInProgress.push(node);
-          }
-
-          // Propagate to all previous tensors connected to this node.
-          const numInboundLayers = node.inboundLayers.length;
-          for (let i = 0; i < numInboundLayers; i++) {
-            const x = node.inputTensors[i];
-            const layer = node.inboundLayers[i];
-            const nodeIndex = node.nodeIndices[i];
-            const tensorIndex = node.tensorIndices[i];
-            buildMapOfGraph(
-                x, finishedNodes, nodesInProgress, layer, nodeIndex,
-                tensorIndex);
-          }
-          finishedNodes.push(node);
-          while (nodesInProgress.indexOf(node) >= 0) {
-            nodesInProgress.splice(nodesInProgress.indexOf(node), 1);
-          }
-          nodesInDecreasingDepth.push(node);
-        };
-
-    const finishedNodes: Node[] = [];
-    const nodesInProgress: Node[] = [];
-    for (const x of this.outputs) {
-      buildMapOfGraph(x, finishedNodes, nodesInProgress);
-    }
-
-    const reversedNodesInDecreasingDepth =
-        nodesInDecreasingDepth.slice().reverse();
-    for (const node of reversedNodesInDecreasingDepth) {
-      nodeIDToNode[node.id] = node;
-      // If the depth is not set, the node has no outbound nodes (depth 0).
-      if (!(node.id in nodesDepths)) {
-        nodesDepths[node.id] = 0;
-      }
-      let depth = nodesDepths[node.id];
-
-      // Update the depth of the corresponding layer
-      const previousDepth =
-          (layersDepths[node.outboundLayer.id] == null ?
-               0 :
-               layersDepths[node.outboundLayer.id]);
-
-      /*
-        If we've seen this layer before at a higher depth, we should use that
-        depth instead of the node depth.  This is necessary for shared layers
-        that have inputs at different depth levels in the graph.
-      */
-      depth = Math.max(depth, previousDepth);
-      layersDepths[node.outboundLayer.id] = depth;
-      layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;
-      nodesDepths[node.id] = depth;
-
-      // Update the depth of inbound nodes.
-      for (let i = 0; i < node.inboundLayers.length; i++) {
-        const inboundLayer = node.inboundLayers[i];
-        const nodeIndex = node.nodeIndices[i];
-        const inboundNode = inboundLayer.inboundNodes[nodeIndex];
-        const previousDepth =
-            (nodesDepths[inboundNode.id] == null ? 0 :
-                                                   nodesDepths[inboundNode.id]);
-        nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);
-        nodeIDToNode[inboundNode.id] = inboundNode;
-      }
-    }
-
-    // Build a dict {depth: list of nodes with this depth}
-    const nodesByDepth: {[depth: string]: Node[]} = {};
-    for (const nodeID in nodesDepths) {
-      const depth = nodesDepths[nodeID];
-      if (!(depth in nodesByDepth)) {
-        nodesByDepth[depth] = [];
-      }
-      nodesByDepth[depth].push(nodeIDToNode[nodeID]);
-    }
-
-    // Build a dict {depth: list of layers with this depth}
-    const layersByDepth: {[depth: string]: Layer[]} = {};
-    for (const layerID in layersDepths) {
-      const depth = layersDepths[layerID];
-      if (!(depth in layersByDepth)) {
-        layersByDepth[depth] = [];
-      }
-      layersByDepth[depth].push(layerIDToLayer[layerID]);
-    }
-
-    // Get sorted list of layer depths.
-    let depthKeys = Object.keys(layersByDepth)
-                        .map(x => parseInt(x, 10))
-                        .sort(generic_utils.reverseNumberCompare);
-
-    // Set this.layers and this.layersByDepth.
-    this.layers = [];
-    for (const depth of depthKeys) {
-      const layersForDepth = layersByDepth[depth];
-      // Container.layers needs to have a deterministic order:
-      // here we order them by traversal order.
-      layersForDepth.sort((a, b) => {
-        const aIndex = layerIndices[a.id];
-        const bIndex = layerIndices[b.id];
-        if (aIndex < bIndex) {
-          return -1;
-        }
-        if (aIndex > bIndex) {
-          return 1;
-        }
-        return 0;
-      });
-      for (const layer of layersForDepth) {
-        if (layer instanceof Container) {
-          this.internalContainerRefs.push(layer);
-        }
-        this.layers.push(layer);
-      }
-    }
-    this.layersByDepth = layersByDepth;
-
-    // Get sorted list of node depths;
-    depthKeys = Object.keys(nodesByDepth)
-                    .map(x => parseInt(x, 10))
-                    .sort(generic_utils.reverseNumberCompare);
-
-    // Check that all tensors required are computable.
-    // computable_tensors: all tensors in the graph
-    // that can be computed from the inputs provided.
-    const computableTensors = this.inputs.slice();
-
-    // To provide a better error msg.
-    const layersWithCompleteInput: string[] = [];
-    for (const depth of depthKeys) {
-      for (const node of nodesByDepth[depth]) {
-        const layer = node.outboundLayer;
-        if (layer != null) {
-          for (const x of node.inputTensors) {
-            if (computableTensors.indexOf(x) === -1) {
-              throw new RuntimeError(
-                  `Graph disconnected: cannot obtain value for tensor ${x}` +
-                  ` at layer "${layer.name}". ` +
-                  'The following previous layers were accessed without ' +
-                  `issue: ${layersWithCompleteInput}`);
-            }
-          }
-          for (const x of node.outputTensors) {
-            computableTensors.push(x);
-          }
-          layersWithCompleteInput.push(layer.name);
-        }
-      }
-    }
-
-    // Set this.containerNodes and this.nodesByDepth.
-    this.nodesByDepth = nodesByDepth;
-
-    // Ensure name unicity, which will be crucial for serialization
-    // (since serialized nodes refer to layers by their name).
-    const allNames = this.layers.map(x => x.name);
-    for (const name of allNames) {
-      const numOccurrences = allNames.filter(x => x === name).length;
-      if (numOccurrences !== 1) {
-        throw new RuntimeError(
-            `The name "${name}" is used ${numOccurrences} times ` +
-            'in the model. All layer names should be unique. Layer names: ' +
-            JSON.stringify(allNames));
-      }
-    }
-
-    // Layer parameters.
-    // The new container starts with a single inbound node
-    // for its inputs, and no outbound nodes.
-    // Will be appended to by future calls to apply().
-    this.outboundNodes = [];
-    // Will be appended to below, and by future calls to apply().
-    this.inboundNodes = [];
-
-    // Create the node linking internal inputs to internal outputs.
-    // (This call has side effects.)
-    // tslint:disable-next-line:no-unused-expression
-    new Node({
-      outboundLayer: this,
-      inboundLayers: [],
-      nodeIndices: [],
-      tensorIndices: [],
-      inputTensors: this.inputs,
-      outputTensors: this.outputs,
-      inputMasks: this.inputs.map(x => null),
-      outputMasks: this.outputs.map(x => null),
-      inputShapes: this.inputs.map(x => x.shape),
-      outputShapes: this.outputs.map(x => x.shape)
-    });
-    this.built = true;
-    this._refCount = 1;  // The ref count of a container always start at 1.
-  }
-
-  protected override assertNotDisposed() {
-    if (this._refCount === 0) {
-      throw new Error(`Container '${this.name}' is already disposed.`);
-    }
-  }
-
-  /**
-   * Attempt to dispose a LayersModel's weights.
-   *
-   * This method decrease the reference count of the LayersModel object by 1.
-   *
-   * A LayersModel is reference-counted. Its reference count is incremented by 1
-   * when it is first constructed and when it is used as a Layer of another
-   * LayersModel.
-   *
-   * If the reference count of a LayersModel becomes 0, the `dispose` method of
-   * all its constituent `Layer`s will be called.
-   *
-   * Note: If the reference count is greater than 0 after the decrement, the
-   * `dispose` method of its constituent `Layer`s will *not* be called.
-   *
-   * After a LayersModel is disposed, it cannot be used in calls such as
-   * 'predict`, `evaluate` or `fit` anymore.
-   *
-   * @returns A DisposeResult Object with the following fields:
-   *   - refCountAfterDispose: The reference count of the LayersModel after this
-   *     `dispose()` call.
-   *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed
-   *     during this `dispose()` call.
-   * @throws {Error} If the layer is not built yet, or if the LayersModel has
-   *   already been disposed.
-   */
-  override dispose(): DisposeResult {
-    this.assertNotDisposed();
-    const result:
-        DisposeResult = {refCountAfterDispose: null, numDisposedVariables: 0};
-    if (--this._refCount === 0) {
-      for (const layer of this.layers) {
-        result.numDisposedVariables += layer.dispose().numDisposedVariables;
-      }
-
-      // Call dispose on each internally created container layer again to ensure
-      // their refCounts hit zero and their tensors are subsequently deleted.
-      for (const container of this.internalContainerRefs) {
-        result.numDisposedVariables += container.dispose().numDisposedVariables;
-      }
-    }
-    result.refCountAfterDispose = this._refCount;
-    return result;
-  }
-
-  override get trainable() {
-    return this.trainable_;
-  }
-
-  override set trainable(trainable: boolean) {
-    this.layers.forEach(layer => {
-      // tslint:disable-next-line:no-any
-      ((layer as any)._trainableWeights as LayerVariable[])
-          .forEach(w => w.trainable = trainable);
-    });
-    this.trainable_ = trainable;
-  }
-
-  override get trainableWeights(): LayerVariable[] {
-    // Porting Note: This check below is to prevent errors where the
-    //   _trainableWeights inherited from the parent class (Layer) gets
-    //   inadvertently used.
-    if (this._trainableWeights.length > 0) {
-      throw new ValueError(
-          'Container instance unexpectedly contains _trainableWeights.' +
-          'The trainable weights of a Container are a union of the ' +
-          'trainable weights of its consituent Layers. Its own ' +
-          '_trainableWeights must remain an empty Array.');
-    }
-
-    if (!this.trainable) {
-      return [];
-    }
-    let weights: LayerVariable[] = [];
-    for (const layer of this.layers) {
-      weights = weights.concat(layer.trainableWeights);
-    }
-    return weights;
-  }
-
-  override get nonTrainableWeights(): LayerVariable[] {
-    const weights: LayerVariable[] = [];
-    for (const layer of this.layers) {
-      weights.push(...layer.nonTrainableWeights);
-    }
-    if (!this.trainable) {
-      const trainableWeights: LayerVariable[] = [];
-      for (const layer of this.layers) {
-        trainableWeights.push(...layer.trainableWeights);
-      }
-      return trainableWeights.concat(weights);
-    }
-    return weights;
-  }
-
-  override get weights(): LayerVariable[] {
-    return this.trainableWeights.concat(this.nonTrainableWeights);
-  }
-
-  /**
-   * Loads all layer weights from a JSON object.
-   *
-   * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /
-   *   TypeScript. The utility script at `scripts/pykeras.py` offers means
-   *   to convert them into JSON strings compatible with this method.
-   * Porting Note: TensorFlow.js Layers supports only loading by name currently.
-   *
-   * @param weights A JSON mapping weight names to weight values as nested
-   *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight
-   *   names to `tf.Tensor` objects.
-   * @param strict Require that the provided weights exactly match those
-   *   required by the container.  Default: `true`.  Passing `false` means that
-   *   extra weights and missing weights will be silently ignored.
-   */
-  loadWeights(weights: NamedTensorMap, strict = true) {
-    const nameToWeight: {[name: string]: LayerVariable} = {};
-    let totalWeightsCount = 0;
-    const modelIsKerasSavedModelFormat = isKerasSavedModelFormat(weights);
-    if (modelIsKerasSavedModelFormat) {
-      this.parseWeights(weights);
-    }
-    // Check if weights from keras v3.
-    for (const layer of this.layers) {
-      for (const [index, weight] of layer.weights.entries()) {
-        // Parse the name to layerName/index.
-        // e.g. dense/0, dense/1, dense_1/0, dense_1/1
-        const parsedName = modelIsKerasSavedModelFormat ?
-            `${weight.name.split('/').slice(0, -1).join('/') + '/'}${index}` :
-            weight.originalName;
-        if (nameToWeight[parsedName] != null) {
-          throw new ValueError(`Duplicate weight name: ${parsedName}`);
-        }
-        nameToWeight[parsedName] = weight;
-        totalWeightsCount++;
-      }
-    }
-
-    const weightValueTuples: Array<[LayerVariable, Tensor]> = [];
-    for (const name in weights) {
-      // TF 2.2.0 added cell name to the weight name in the format of
-      // layer_name/cell_name/weight_name, we need to remove
-      // the inner cell name.
-      let validatedName = name;
-      if (nameToWeight[name] == null) {
-        const tokens = name.split('/');
-        const shortenNameArray =
-            tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);
-        validatedName = shortenNameArray.join('/');
-      }
-      if (nameToWeight[validatedName] != null) {
-        weightValueTuples.push([nameToWeight[validatedName], weights[name]]);
-      } else if (strict) {
-        throw new ValueError(
-            `Provided weight data has no target variable: ${name}`);
-      }
-      delete nameToWeight[validatedName];
-    }
-
-    if (strict) {
-      // Check that all weights are set.
-      const unsetNames: string[] = [];
-      for (const name in nameToWeight) {
-        unsetNames.push(name);
-      }
-      if (unsetNames.length > 0) {
-        throw new ValueError(
-            `${unsetNames.length} of ${
-                totalWeightsCount} weights are not set: ` +
-            `${unsetNames}`);
-      }
-    }
-
-    batchSetValue(weightValueTuples);
-  }
-
-  protected parseWeights(weights: NamedTensorMap) {
-    for (const key in Object.keys(weights)) {
-      const listParts = key.split('/');
-      const list = ['vars', 'layer_checkpoint_dependencies'];
-      // For keras v3, the weights name are saved based on the folder structure.
-      // e.g. _backbone/_layer_checkpoint_dependencies/transformer/_self../
-      // _output_dense/vars/0
-      // Therefore we discard the `vars` and `layer_checkpoint_depencies` within
-      // the saved name and only keeps the layer name and weights.
-      // This can help to mapping the actual name of the layers and load each
-      // weight accordingly.
-      const newKey = listParts
-                         .map(str => {
-                           if (str.startsWith('_')) {
-                             return str.slice(1);
-                           }
-                           return str;
-                         })
-                         .filter(str => !list.includes(str))
-                         .join('/');
-      if (newKey !== key) {
-        weights[newKey] = weights[key];
-        delete weights[key];
-      }
-    }
-  }
-
-  /**
-   * Util shared between different serialization methods.
-   * @returns LayersModel config with Keras version information added.
-   */
-  protected updatedConfig(): serialization.ConfigDict {
-    const theConfig = this.getConfig();
-    const modelConfig: serialization.ConfigDict = {};
-    modelConfig['className'] = this.getClassName();
-    modelConfig['config'] = theConfig;
-    modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;
-    // TODO(nielsene): Replace something like K.backend() once
-    // possible.
-    modelConfig['backend'] = 'TensorFlow.js';
-    return modelConfig;
-  }
-
-  /**
-   * Returns a JSON string containing the network configuration.
-   *
-   * To load a network from a JSON save file, use
-   * models.modelFromJSON(jsonString);
-   * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras
-   * @param returnString Whether the return value should be stringified
-   *    (default: `true`).
-   * @returns a JSON string if `returnString` (default), or a JSON object if
-   *   `!returnString`.
-   */
-  // tslint:disable-next-line:no-any
-  toJSON(unused?: any, returnString = true): string|PyJsonDict {
-    const modelConfig = convertTsToPythonic(this.updatedConfig()) as PyJsonDict;
-    return returnString ? JSON.stringify(modelConfig) : modelConfig;
-  }
-
-  /**
-   * Call the model on new inputs.
-   *
-   * In this case `call` just reapplies all ops in the graph to the new inputs
-   * (e.g. build a new computational graph from the provided inputs).
-   *
-   * @param inputs A tensor or list of tensors.
-   * @param mask A mask or list of masks. A mask can be either a tensor or null
-   *   (no mask).
-   *
-   * @return A tensor if there is a single output, or a list of tensors if there
-   *   are more than one outputs.
-   */
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = generic_utils.toList(inputs);
-      const feedDict = new FeedDict();
-      for (let i = 0; i < this.inputs.length; ++i) {
-        feedDict.add(this.inputs[i], inputs[i]);
-      }
-      return execute(this.outputs, feedDict, kwargs) as Tensor | Tensor[];
-    });
-  }
-
-  /**
-   * Computes an output mask tensor.
-   *
-   * @param inputs Tensor or list of tensors.
-   * @param mask Tensor or list of tensors.
-   *
-   * @return null or a tensor (or list of tensors, one per output tensor of the
-   * layer).
-   */
-  override computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]): Tensor
-      |Tensor[] {
-    return tidy(() => {
-      inputs = generic_utils.toList(inputs);
-      let masks: Tensor[];
-      if (mask == null) {
-        masks = generic_utils.pyListRepeat(null, inputs.length);
-      } else {
-        masks = generic_utils.toList(mask);
-      }
-      // TODO(michaelterry): Add support for mask caching.
-      return this.runInternalGraph(inputs, masks)[1];
-    });
-  }
-
-  /**
-   * Computes the output shape of the layer.
-   *
-   * Assumes that the layer will be built to match that input shape provided.
-   *
-   * @param inputShape A shape (tuple of integers) or a list of shape tuples
-   *   (one per output tensor of the layer). Shape tuples can include null for
-   *   free dimensions, instead of an integer.
-   */
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    const inputShapes = types_utils.normalizeShapeList(inputShape);
-    if (inputShapes.length !== this.inputLayers.length) {
-      throw new ValueError(
-          `Invalid inputShape argument ${inputShape}: ` +
-          `model has ${this.inputLayers.length} tensor inputs.`);
-    }
-
-    // TODO(michaelterry): Add caching
-    const layersToOutputShapes: {[shapeKey: string]: Shape} = {};
-    for (let i = 0; i < inputShapes.length; i++) {
-      const layer = this.inputLayers[i];
-      const inputShape = inputShapes[i];
-      // It's an input layer: computeOutputShape is identity,
-      // and there is only one node and one tensor output.
-      const shapeKey = layer.name + '_0_0';
-      layersToOutputShapes[shapeKey] = inputShape;
-    }
-
-    const depthKeys = Object.keys(this.nodesByDepth)
-                          .map(x => parseInt(x, 10))
-                          .sort(generic_utils.reverseNumberCompare);
-    // Iterate over nodes, by depth level.
-    if (depthKeys.length > 1) {
-      for (const depth of depthKeys) {
-        const nodes = this.nodesByDepth[depth];
-        for (const node of nodes) {
-          // This is always a single layer, never a list.
-          const layer = node.outboundLayer;
-          if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {
-            // We've already covered the input layers a few lines above.
-            continue;
-          }
-          // Potentially redundant list, same size of node.inputTensors.
-          const inputShapes: Shape[] = [];
-          for (let j = 0; j < node.inboundLayers.length; j++) {
-            const inboundLayer = node.inboundLayers[j];
-            const nodeIndex = node.nodeIndices[j];
-            const tensorIndex = node.tensorIndices[j];
-            const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;
-            const inputShape = layersToOutputShapes[shapeKey];
-            inputShapes.push(inputShape);
-          }
-
-          const outputShape = layer.computeOutputShape(
-              generic_utils.singletonOrArray(inputShapes));
-
-          const outputShapes = types_utils.normalizeShapeList(outputShape);
-          const nodeIndex = layer.inboundNodes.indexOf(node);
-          for (let j = 0; j < outputShapes.length; j++) {
-            const shapeKey = `${layer.name}_${nodeIndex}_${j}`;
-            layersToOutputShapes[shapeKey] = outputShapes[j];
-          }
-        }
-      }
-    }
-
-    // Read final output shapes from layersToOutputShapes.
-    const outputShapes: Shape[] = [];
-    const outputShapeKeys: string[] = [];
-    for (let i = 0; i < this.outputLayers.length; i++) {
-      const layer = this.outputLayers[i];
-      const nodeIndex = this.outputLayersNodeIndices[i];
-      const tensorIndex = this.outputLayersTensorIndices[i];
-      const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;
-      outputShapeKeys.push(shapeKey);
-    }
-
-    for (let i = 0; i < outputShapeKeys.length; i++) {
-      const key = outputShapeKeys[i];
-      generic_utils.assert(key in layersToOutputShapes);
-      outputShapes.push(layersToOutputShapes[key]);
-    }
-
-    // TODO(michaelterry): Update cache
-    return generic_utils.singletonOrArray(outputShapes);
-  }
-
-  /**
-   * Computes output tensors for new inputs.
-   *
-   * Note:
-   *   - Expects `inputs` to be a list (potentially with 1 element).
-   *
-   * @param inputs List of tensors
-   * @param masks List of masks (tensors or null).
-   * @return Three lists: outputTensors, outputMasks, outputShapes
-   */
-  protected runInternalGraph(inputs: Tensor[], masks?: Tensor[]):
-      [Tensor[], Tensor[], Shape[]] {
-    if (masks == null) {
-      masks = generic_utils.pyListRepeat(null, inputs.length);
-    }
-
-    // Dictionary mapping reference tensors to tuples
-    // (computed tensor, compute mask)
-    // we assume a 1:1 mapping from tensor to mask
-    // TODO: raise exception when a `.computeMask()` call
-    // does not return a list the same size as `call`
-    const tensorMap: {[tensorID: string]: [Tensor, Tensor]} = {};
-    for (let i = 0; i < this.inputs.length; ++i) {
-      const x = this.inputs[i];
-      const y = inputs[i];
-      const mask = masks[i];
-      tensorMap[x.id] = [y, mask];
-    }
-
-    const depthKeys = Object.keys(this.nodesByDepth)
-                          .map(x => parseInt(x, 10))
-                          .sort(generic_utils.reverseNumberCompare);
-    for (const depth of depthKeys) {
-      const nodes = this.nodesByDepth[depth];
-      for (const node of nodes) {
-        // This is always a single layer, never a list.
-        const layer = node.outboundLayer;
-        const referenceInputTensors = node.inputTensors;
-        const referenceOutputTensors = node.outputTensors;
-
-        // If all previous input tensors are available in tensorMap,
-        // then call node.inboundLayer on them.
-        // List of tuples [input, mask]:
-        const computedData = new Array<[Tensor, Tensor]>();
-        for (const x of referenceInputTensors) {
-          if (x.id in tensorMap) {
-            computedData.push(tensorMap[x.id]);
-          }
-        }
-        if (computedData.length === referenceInputTensors.length) {
-          // TODO(michaelterry): Add K.name_scope here, if we need it.
-          let kwargs: Kwargs = {};
-          let computedTensors: Tensor[];
-          let computedMasks: Tensor[];
-          let outputTensors: Tensor[];
-          let outputMasks: Tensor[];
-          // call layer
-          if (node.callArgs != null) {
-            kwargs = node.callArgs;
-          }
-          if (computedData.length === 1) {
-            const [computedTensor, computedMask] = computedData[0];
-            if (kwargs['mask'] == null) {
-              kwargs['mask'] = computedMask;
-            }
-            outputTensors =
-                generic_utils.toList(layer.call(computedTensor, kwargs));
-            outputMasks = generic_utils.toList(
-                layer.computeMask(computedTensor, computedMask));
-            computedTensors = [computedTensor];
-            computedMasks = [computedMask];
-          } else {
-            computedTensors = computedData.map(x => x[0]);
-            computedMasks = computedData.map(x => x[1]);
-            if (kwargs['mask'] == null) {
-              kwargs['mask'] = computedMasks;
-            }
-            outputTensors =
-                generic_utils.toList(layer.call(computedTensors, kwargs));
-            outputMasks = generic_utils.toList(
-                layer.computeMask(computedTensors, computedMasks));
-          }
-
-          if (layer.activityRegularizer) {
-            throw new NotImplementedError(
-                'LayersModel invocation with concrete Tensor value(s) in the ' +
-                'presence of activity regularizer(s) is not supported yet.');
-          }
-          // TODO(michaelterry): Add model updates and losses
-
-          // Update tensor map.
-          for (let i = 0; i < referenceOutputTensors.length; ++i) {
-            const x = referenceOutputTensors[i];
-            const y = outputTensors[i];
-            const mask = outputMasks[i];
-            tensorMap[x.id] = [y, mask];
-          }
-        }
-      }
-    }
-
-    const outputTensors: Tensor[] = [];
-    const outputMasks: Tensor[] = [];
-    const outputShapes: Shape[] = [];
-    for (const x of this.outputs) {
-      generic_utils.assert(
-          x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);
-      const [tensor, mask] = tensorMap[x.id];
-      outputShapes.push(tensor.shape);
-      outputTensors.push(tensor);
-      outputMasks.push(mask);
-    }
-
-    // TODO(michaelterry): Add support for caches.
-    return [outputTensors, outputMasks, outputShapes];
-  }
-
-  /**
-   * Builds a map of internal node keys to node ordering.
-   * Used in serializaion a node orderings may change as unused nodes are
-   * dropped. Porting Note:  This helper method was pulled out of getConfig to
-   * improve readability.
-   * @param layers An array of Layers in the model.
-   * @returns Map of Node Keys to index order within the layer.
-   */
-  private buildNodeConversionMap(layers: Layer[]): {[nodeKey: string]: number} {
-    const nodeConversionMap: {[nodeKey: string]: number} = {};
-    let keptNodes: number;
-    for (const layer of this.layers) {
-      keptNodes = layer instanceof Container ? 1 : 0;
-      for (let originalNodeIndex = 0;
-           originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
-        const nodeKey = Container.nodeKey(layer, originalNodeIndex);
-        if (this.containerNodes.has(nodeKey)) {
-          // i.e. we mark it to be saved
-          nodeConversionMap[nodeKey] = keptNodes;
-          keptNodes += 1;
-        }
-      }
-    }
-    return nodeConversionMap;
-  }
-
-  /**
-   * Retrieves a layer based on either its name (unique) or index.
-   *
-   * Indices are based on order of horizontal graph traversal (bottom-up).
-   *
-   * If both `name` and `index` are specified, `index` takes precedence.
-   *
-   * @param name Name of layer.
-   * @param index Index of layer.
-   * @returns A Layer instance.
-   * @throws ValueError: In case of invalid layer name or index.
-   *
-   * @doc {
-   *    heading: 'Layers',
-   *    subheading: 'Classes',
-   *    namespace: 'layers',
-   *    subclasses: ['LayersModel']
-   * }
-   */
-  getLayer(name: string): Layer;
-  getLayer(index: number): Layer;
-  getLayer(name: string, index: number): Layer;
-  getLayer(nameOrIndex?: string|number, index?: number): Layer {
-    if (index != null) {
-      return this.findLayer(index);
-    } else {
-      if (nameOrIndex == null) {
-        throw new ValueError('Provide either a layer name or layer index');
-      }
-      if (typeof nameOrIndex === 'number') {
-        return this.findLayer(nameOrIndex);
-      }
-    }
-
-    for (const layer of this.layers) {
-      if (layer.name === nameOrIndex) {
-        return layer;
-      }
-    }
-    throw new ValueError(`No such layer: ${nameOrIndex}`);
-  }
-
-  findLayer(index: number): Layer {
-    if (this.layers.length <= index) {
-      throw new ValueError(
-          `Was asked to retrieve layer at index ${index}, but model only ` +
-          `has ${this.layers.length} layer(s).`);
-    } else {
-      return this.layers[index];
-    }
-  }
-
-  /**
-   * Retrieves the Container's current loss values.
-   *
-   * Used for regularizers during training.
-   */
-  override calculateLosses(): Scalar[] {
-    // Porting Node: This is an augmentation to Container.loss in PyKeras.
-    //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete
-    //   Tensor (specifically Scalar) values are returned. This is due to the
-    //   imperative backend.
-    return tidy(() => {
-      const losses: Scalar[] = [];
-      for (const layer of this.layers) {
-        for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length;
-             ++nodeIndex) {
-          const nodeKey = Container.nodeKey(layer, nodeIndex);
-          if (this.containerNodes.has(nodeKey)) {
-            losses.push(...layer.calculateLosses());
-          }
-        }
-      }
-      // TODO(cais): Add any unconditional model-level losses?
-      return losses;
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {name: this.name};
-
-    // Build a map from layer unique name (self._node_key)
-    // to the index of the nodes that are saved in the config.
-    // Only nodes in container_nodes are saved.
-    const nodeConversionMap: {[nodeKey: string]: number} =
-        this.buildNodeConversionMap(this.layers);
-
-    // Serialize and save the layers in layerConfigs
-    const layerConfigs = [];
-    for (const layer of this.layers) {
-      const layerClassName = layer.getClassName();
-      const layerConfig = layer.getConfig();
-      const filteredInboundNodes = [];
-      for (let originalNodeIndex = 0;
-           originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
-        const node = layer.inboundNodes[originalNodeIndex];
-        const nodeKey = Container.nodeKey(layer, originalNodeIndex);
-        let kwargs = {};
-        if (this.containerNodes.has(nodeKey)) {
-          // The node is relevant to the model:
-          // add to filteredInboundNodes.
-          if (node.callArgs) {
-            try {
-              JSON.stringify(node.callArgs);
-              kwargs = node.callArgs;
-            } catch (err) {
-              console.warn(
-                  `Layer ${layer.name} was passed ` +
-                  `non-serializable keyword arguments: ` +
-                  `${node.callArgs}. They will not be included ` +
-                  `in the serialized model (and thus will be ` +
-                  `missing at deserialization time).`);
-              kwargs = {};
-            }
-          }
-          if (node.inboundLayers.length > 0) {
-            const nodeData = [];
-            for (let i = 0; i < node.inboundLayers.length; i++) {
-              const inboundLayer = node.inboundLayers[i];
-              const nodeIndex = node.nodeIndices[i];
-              const tensorIndex = node.tensorIndices[i];
-              const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);
-              let newNodeIndex = nodeConversionMap[nodeKey];
-              if (newNodeIndex == null) {
-                newNodeIndex = 0;
-              }
-              nodeData.push(
-                  [inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);
-            }
-            filteredInboundNodes.push(nodeData);
-          }
-        }
-      }
-      const dict: serialization.ConfigDict = {};
-      dict['name'] = layer.name;
-      dict['className'] = layerClassName;
-      dict['config'] = layerConfig;
-      dict['inboundNodes'] = filteredInboundNodes;
-      layerConfigs.push(dict);
-    }
-    config['layers'] = layerConfigs;
-    // Gather info about inputs and outputs
-    const modelInputs = [];
-    for (let i = 0; i < this.inputLayers.length; i++) {
-      const layer = this.inputLayers[i];
-      const nodeIndex = this.inputLayersNodeIndices[i];
-
-      const nodeKey = Container.nodeKey(layer, nodeIndex);
-      if (!this.containerNodes.has(nodeKey)) {
-        continue;
-      }
-      let newNodeIndex = nodeConversionMap[nodeKey];
-      if (newNodeIndex === null || newNodeIndex === undefined) {
-        newNodeIndex = 0;
-      }
-      const tensorIndex = this.inputLayersTensorIndices[i];
-      modelInputs.push([layer.name, newNodeIndex, tensorIndex]);
-    }
-    config['inputLayers'] = modelInputs;
-
-    const modelOutputs = [];
-    for (let i = 0; i < this.outputLayers.length; i++) {
-      const layer = this.outputLayers[i];
-      const nodeIndex = this.outputLayersNodeIndices[i];
-
-      const nodeKey = Container.nodeKey(layer, nodeIndex);
-      if (!this.containerNodes.has(nodeKey)) {
-        continue;
-      }
-      let newNodeIndex = nodeConversionMap[nodeKey];
-      if (newNodeIndex === null || newNodeIndex === undefined) {
-        newNodeIndex = 0;
-      }
-      const tensorIndex = this.outputLayersTensorIndices[i];
-      modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);
-    }
-    config['outputLayers'] = modelOutputs;
-    return config;
-  }
-
-  /**
-   * Instantiates a LayersModel from its config (output of `get_config()`).
-   * @param cls the class to create
-   * @param config LayersModel config dictionary.
-   * @param customObjects An optional dictionary of custom objects.
-   * @param fastWeightInit Optional flag to use fast weight initialization
-   *   during deserialization. This is applicable to cases in which
-   *   the initialization will be immediately overwritten by loaded weight
-   *   values. Default: `false`.
-   * @returns A LayersModel instance.
-   * @throws ValueError: In case of improperly formatted config dict.
-   */
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict,
-      customObjects = {} as serialization.ConfigDict,
-      fastWeightInit = false): T {
-    // Layer instances created during
-    // the graph reconstruction process
-    const createdLayers: {[layerName: string]: Layer} = {};
-
-    // Dictionary mapping layer instances to
-    // node data that specifies a layer call.
-    // It acts as a queue that maintains any unprocessed
-    // layer call until it becomes possible to process it
-    // (i.e. until the input tensors to the call all exist).
-    const unprocessedNodes: {[layer: string]: TensorKeyWithArgsArray[][]} = {};
-    function addUnprocessedNode(
-        layer: Layer, nodeData: TensorKeyWithArgsArray[]) {
-      if (!(layer.name in unprocessedNodes)) {
-        unprocessedNodes[layer.name] = [nodeData];
-      } else {
-        unprocessedNodes[layer.name].push(nodeData);
-      }
-    }
-
-    function processNode(layer: Layer, nodeData: TensorKeyWithArgsArray[]) {
-      const inputTensors: SymbolicTensor[] = [];
-      let kwargs;
-      for (const inputData of nodeData) {
-        const inboundLayerName = inputData[0];
-        const inboundNodeIndex = inputData[1];
-        const inboundTensorIndex = inputData[2];
-
-        kwargs = inputData[3] == null ?
-            {} :
-            inputData[3] as serialization.ConfigDict;
-        if (!(inboundLayerName in createdLayers)) {
-          addUnprocessedNode(layer, nodeData);
-          return;
-        }
-        const inboundLayer = createdLayers[inboundLayerName];
-        if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {
-          addUnprocessedNode(layer, nodeData);
-          return;
-        }
-        const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];
-        inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);
-      }
-      // Call layer on its inputs, thus creating the node
-      // and building the layer if needed.
-      // Note: This has Eager vs Graph Implications.
-      if (inputTensors.length > 0) {
-        layer.apply(
-            generic_utils.singletonOrArray(inputTensors),
-            kwargs);  // was ** kwargs
-      }
-    }
-
-    /**
-     * Deserialize a layer, then call it on appropriate inputs.
-     * @param layerData: layer config dict.
-     * @throws ValueError: In case of improperly formatted `layer_data`
-     * dict.
-     */
-    function processLayer(layerData: serialization.ConfigDict|null) {
-      const layerName = layerData['name'] as string;
-      // Instantiate layer.
-      const layer =
-          deserializeLayer(
-              layerData,
-              config['customObjects'] != null ?
-                  config['customObjects'] as serialization.ConfigDict :
-                  {}) as Layer;
-      layer.setFastWeightInitDuringBuild(fastWeightInit);
-      createdLayers[layerName] = layer;
-      // Gather layer inputs.
-      const inboundNodesData =
-          layerData['inboundNodes'] as TensorKeyWithArgsArray[][];
-      inboundNodesData.forEach(nodeData => {
-        if (!(nodeData instanceof Array)) {
-          throw new ValueError(
-              `Corrupted configuration, expected array for nodeData: ${
-                  nodeData}`);
-        }
-        // We don't process nodes (i.e. make layer calls)
-        // on the fly because the inbound node may not yet exist,
-        // in case of layer shared at different topological depths
-        // (e.g.a model such as A(B(A(B(x)))))
-        addUnprocessedNode(layer, nodeData);
-      });
-    }
-
-    // First, we create all layers and enqueue nodes to be processed.
-    const name = config['name'];
-    const layersFromConfig = config['layers'] as serialization.ConfigDict[];
-    for (const layerData of layersFromConfig) {
-      processLayer(layerData);
-    }
-
-    // Then we process nodes in order of layer depth.
-    // Nodes that cannot yet be processed(if the inbound node
-    // does not yet exist) are re - enqueued, and the process
-    // is repeated until all nodes are processed.
-    while (!generic_utils.isObjectEmpty(unprocessedNodes)) {
-      for (const layerData of layersFromConfig) {
-        const layer = createdLayers[layerData['name'] as string];
-        if (layer.name in unprocessedNodes) {
-          const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];
-          delete unprocessedNodes[layer.name];
-          for (const nodeData of currentUnprocessedNodesForLayer) {
-            processNode(layer, nodeData);
-          }
-        }
-      }
-    }
-
-    const inputTensors: SymbolicTensor[] = [];
-    const outputTensors: SymbolicTensor[] = [];
-    const inputLayersFromConfig =
-        config['inputLayers'] as serialization.ConfigDict[];
-    for (const layerData of inputLayersFromConfig) {
-      const layerName = layerData[0] as string;
-      const nodeIndex = layerData[1] as number;
-      const tensorIndex = layerData[2] as number;
-      generic_utils.assert(layerName in createdLayers);
-      const layer = createdLayers[layerName];
-      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
-      inputTensors.push(layerOutputTensors[tensorIndex]);
-    }
-    const outputLayersFromConfig =
-        config['outputLayers'] as serialization.ConfigDict[];
-    for (const layerData of outputLayersFromConfig) {
-      const layerName = layerData[0] as string;
-      const nodeIndex = layerData[1] as number;
-      const tensorIndex = layerData[2] as number;
-      generic_utils.assert(layerName in createdLayers);
-      const layer = createdLayers[layerName];
-      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
-      outputTensors.push(layerOutputTensors[tensorIndex]);
-    }
-    return new cls({inputs: inputTensors, outputs: outputTensors, name});
-  }
-
-  /**
-   * Determine whether the container is stateful.
-   *
-   * Porting Note: this is the equivalent of the stateful @property of
-   *   the Container class in PyKeras.
-   */
-  override get stateful(): boolean {
-    // Porting Note: This check is to prevent inadvertent setting of the
-    //   _stateful property of the Container instance.
-    if (this._stateful) {
-      throw new ValueError(
-          'Container instance unexpectedly has _stateful = true. The ' +
-          'statefulness of a Container is determined by the Layers it ' +
-          'contains. Its _stateful property must remain the default false.');
-    }
-    for (const layer of this.layers) {
-      if (layer.stateful) {
-        return true;
-      }
-    }
-    return false;
-  }
-
-  /**
-   * Reset the state of all stateful constituent layers (if any).
-   *
-   * Examples of stateful layers include RNN layers whose `stateful` property
-   * is set as `true`.
-   */
-  override resetStates() {
-    tidy(() => {
-      this.layers.forEach(layer => {
-        // tslint:disable:no-any
-        if (layer.stateful) {
-          layer.resetStates();
-        }
-        // tslint:enable:no-any
-      });
-    });
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/container_test.ts b/tfjs-master/tfjs-layers/src/engine/container_test.ts
deleted file mode 100644
index 4a1013a89..000000000
--- a/tfjs-master/tfjs-layers/src/engine/container_test.ts
+++ /dev/null
@@ -1,695 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {memory, ones, scalar, Tensor, zeros} from '@tensorflow/tfjs-core';
-
-import * as tfl from '../index';
-import {Kwargs} from '../types';
-import {describeMathCPUAndGPU, expectTensorsClose} from '../utils/test_utils';
-
-import {Container, ContainerArgs} from './container';
-import {execute, FeedDict} from './executor';
-import {CallHook, getSourceInputs, Layer, LayerArgs, SymbolicTensor} from './topology';
-
-class LayerForTest extends tfl.layers.Layer {
-  static className = 'LayerForTest';
-  constructor(args: LayerArgs) {
-    super(args);
-  }
-}
-
-class ContainerForTest extends Container {
-  static className = 'ContainerForTest';
-  constructor(args: ContainerArgs) {
-    super(args);
-  }
-}
-
-describeMathCPUAndGPU('Container.fromConfig', () => {
-  it('creates a minimal Container from simplest config', () => {
-    // tslint:disable:no-any
-    const config = {
-      name: 'test',
-      layers: [] as any[],
-      inputLayers: [] as any[],
-      outputLayers: [] as any[]
-    };
-    // tslint:enable
-    const container =
-        Container.fromConfig(ContainerForTest, config) as Container;
-    expect(container.name).toEqual('test');
-  });
-
-  it('creates a simple network', () => {
-    /* python generating code
-    a=Input(shape=(32,))
-    b=Dense(32)(a)
-    model = Container(inputs=a, outputs=b, name="test")
-    pprint.pprint(model.get_config())
-    */
-    const config = {
-      inputLayers: [['input_2', 0, 0]],
-      layers: [
-        {
-          className: 'InputLayer',
-          config: {
-            batchInputShape: [null, 32],
-            dtype: 'float32',
-            name: 'input_2',
-            sparse: false
-          },
-          inboundNodes: [] as string[][],
-          name: 'input_2'
-        },
-        {
-          className: 'Dense',
-          config: {
-            activation: 'linear',
-            activityRegularizer: null as string,
-            biasConstraint: null as string,
-            biasInitializer: {className: 'Zeros', config: {}},
-            biasRegularizer: null as string,
-            kernelConstraint: null as string,
-            kernelInitializer: {
-              className: 'VarianceScaling',
-              config: {
-                distribution: 'uniform',
-                mode: 'fanAvg',
-                scale: 1.0,
-                seed: null as number
-              }
-            },
-            kernelRegularizer: null as string,
-            name: 'dense_2',
-            trainable: null as boolean,
-            units: 32,
-            use_bias: true
-          },
-          inboundNodes: [[['input_2', 0, 0, {}]]],
-          name: 'dense_2'
-        }
-      ],
-      name: 'test',
-      outputLayers: [['dense_2', 0, 0]]
-    };
-    const container =
-        Container.fromConfig(ContainerForTest, config) as Container;
-    expect(container.name).toEqual('test');
-    const allZeros = zeros([1, 32]);
-    expectTensorsClose(container.apply(allZeros) as Tensor, allZeros);
-  });
-});
-
-describeMathCPUAndGPU('Container', () => {
-  const inputLayerName = 'inputLayerName';
-  const layerName = 'layerName';
-  const containerName = 'simpleContainer';
-  let inputTensor: tfl.SymbolicTensor;
-  let layer: Layer;
-  let output: tfl.SymbolicTensor;
-  let simpleContainer: Container;
-
-  beforeEach(() => {
-    inputTensor =
-        tfl.input({shape: [1], name: inputLayerName, dtype: 'float32'});
-    layer = new LayerForTest({name: layerName});
-    output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-    simpleContainer = new ContainerForTest(
-        {inputs: [inputTensor], outputs: [output], name: containerName});
-  });
-
-  it('initializes with no inputs or outputs and a default name', () => {
-    const container = new ContainerForTest({inputs: [], outputs: []});
-    expect(container.name).toMatch(/^container.+$/);
-  });
-
-  it('initializes with no inputs or outputs and a given name', () => {
-    const name = 'xyz';
-    const container = new ContainerForTest({inputs: [], outputs: [], name});
-    expect(container.name).toMatch(name);
-  });
-
-  it('throws an exception if same input provided twice', () => {
-    const makeContainer = () => {
-      // tslint:disable-next-line:no-unused-expression
-      new ContainerForTest({inputs: [inputTensor, inputTensor], outputs: []});
-    };
-    expect(makeContainer).toThrowError(/inputs.*redundant/);
-  });
-
-  it('throws an exception if graph is disconnected', () => {
-    const makeContainer = () => {
-      // tslint:disable-next-line:no-unused-expression
-      new ContainerForTest({inputs: [], outputs: [output]});
-    };
-    expect(makeContainer).toThrowError(/disconnected/);
-  });
-
-  it('creates inputLayers', () => {
-    expect(simpleContainer.inputLayers).toEqual([inputTensor.sourceLayer]);
-  });
-
-  it('creates outputLayers', () => {
-    expect(simpleContainer.outputLayers).toEqual([layer]);
-  });
-
-  it('creates inputNames', () => {
-    expect(simpleContainer.inputNames).toEqual([inputLayerName]);
-  });
-
-  it('creates outputNames', () => {
-    expect(simpleContainer.outputNames).toEqual([layerName]);
-  });
-
-  it('throws exception if given a non-input layer as input', () => {
-    const makeContainer = () => {
-      // tslint:disable-next-line:no-unused-expression
-      new ContainerForTest({inputs: [output], outputs: []});
-    };
-    expect(makeContainer).toThrowError(/must be InputLayer objects/);
-  });
-
-  it('creates layers for simplest case', () => {
-    expect(simpleContainer.layers).toEqual([inputTensor.sourceLayer, layer]);
-  });
-
-  it('creates layers when multiple layers specified', () => {
-    const layer1 = new LayerForTest({name: 'layer1'});
-    const layer2 = new LayerForTest({name: 'layer2'});
-    const output =
-        layer2.apply(layer1.apply(inputTensor)) as tfl.SymbolicTensor;
-    const container =
-        new ContainerForTest({inputs: [inputTensor], outputs: [output]});
-    expect(container.layers).toEqual([inputTensor.sourceLayer, layer1, layer2]);
-  });
-
-  it('correctly creates model with shared subgraphs.', () => {
-    /*
-      The graph:
-
-        A
-      /  \
-      B  X
-      |  |
-      C  B
-         |
-         C
-    */
-    const layerA = new LayerForTest({name: 'A'});
-    const layerB = new LayerForTest({name: 'B'});
-    const layerC = new LayerForTest({name: 'C'});
-    const layerX = new LayerForTest({name: 'X'});
-    const aOutput = layerA.apply(inputTensor);
-    const output1 = layerC.apply(layerB.apply(aOutput)) as tfl.SymbolicTensor;
-    const output2 =
-        layerC.apply(layerB.apply(layerX.apply(aOutput))) as tfl.SymbolicTensor;
-
-    const container = new ContainerForTest(
-        {inputs: [inputTensor], outputs: [output1, output2]});
-
-    const compareFunction = (a: Layer, b: Layer) => {
-      if (a.name < b.name) {
-        return -1;
-      } else if (a.name > b.name) {
-        return 1;
-      } else {
-        return 0;
-      }
-    };
-    const sortedLayers = container.layers.slice().sort(compareFunction);
-    const expectedSortedLayers = [
-      inputTensor.sourceLayer, layerA, layerB, layerC, layerX
-    ].sort(compareFunction);
-
-    expect(sortedLayers).toEqual(expectedSortedLayers);
-  });
-
-  it('throws exception if multiple layers have the same name', () => {
-    const name = 'abc';
-    const layer1 = new LayerForTest({name});
-    const layer2 = new LayerForTest({name});
-    const output =
-        layer2.apply(layer1.apply(inputTensor)) as tfl.SymbolicTensor;
-    const makeContainer = () => {
-      // tslint:disable-next-line:no-unused-expression
-      new ContainerForTest({inputs: [inputTensor], outputs: [output]});
-    };
-    expect(makeContainer).toThrowError(/layer names should be unique/);
-  });
-
-  it('weights gets all weights.', () => {
-    const inputShape = [1, 6];
-    const inputLayer = tfl.layers.input({shape: inputShape});
-    const layer1 = tfl.layers.dense({units: 2, useBias: false});
-    const layer2 = tfl.layers.dense({units: 1, useBias: true});
-    const output = layer2.apply(layer1.apply(inputLayer)) as tfl.SymbolicTensor;
-
-    const container =
-        new ContainerForTest({inputs: [inputLayer], outputs: [output]});
-    expect(container.weights.length).toEqual(3);
-    expect(container.weights[0].name).toEqual(layer1.weights[0].name);
-    expect(container.weights[1].name).toEqual(layer2.weights[0].name);
-    expect(container.weights[2].name).toEqual(layer2.weights[1].name);
-  });
-
-  it('trainableWeights and nonTrainableWeights.', () => {
-    const inputShape = [1, 6];
-    const inputLayer = tfl.layers.input({shape: inputShape});
-    const layer1 = tfl.layers.dense({units: 2, useBias: false});
-    const layer2 = tfl.layers.dense({units: 1, useBias: true});
-    const output = layer2.apply(layer1.apply(inputLayer)) as tfl.SymbolicTensor;
-
-    const container =
-        new ContainerForTest({inputs: [inputLayer], outputs: [output]});
-    expect(container.trainableWeights.length).toEqual(3);
-    expect(container.trainableWeights[0].name).toEqual(layer1.weights[0].name);
-    expect(container.trainableWeights[1].name).toEqual(layer2.weights[0].name);
-    expect(container.trainableWeights[2].name).toEqual(layer2.weights[1].name);
-    expect(container.nonTrainableWeights.length).toEqual(0);
-  });
-
-  it('call() executes all layers.', () => {
-    const inputShape = [1, 6];
-    const finalShape = [3, 2];
-    const inputLayer = tfl.layers.input({shape: inputShape});
-    const layer1 = tfl.layers.reshape({name: 'layer1', targetShape: [2, 3]});
-    const layer2 =
-        tfl.layers.reshape({name: 'layer2', targetShape: finalShape});
-    const output = layer2.apply(layer1.apply(inputLayer)) as tfl.SymbolicTensor;
-
-    const container =
-        new ContainerForTest({inputs: [inputLayer], outputs: [output]});
-    const result = container.call(ones([1, 1, 6]), {}) as Tensor[];
-    const resultShape = [1].concat(finalShape);
-    expectTensorsClose(result[0], ones(resultShape));
-  });
-
-  it('apply() executes all layers with concrete tensors.', () => {
-    const inputShape = [1, 6];
-    const finalShape = [3, 2];
-    const inputLayer = tfl.layers.input({shape: inputShape});
-    const layer1 = tfl.layers.reshape({name: 'layer1', targetShape: [2, 3]});
-    const layer2 =
-        tfl.layers.reshape({name: 'layer2', targetShape: finalShape});
-    const output = layer2.apply(layer1.apply(inputLayer)) as tfl.SymbolicTensor;
-
-    const container =
-        new ContainerForTest({inputs: [inputLayer], outputs: [output]});
-    const result = container.apply(ones([1, 1, 6])) as Tensor;
-    const resultShape = [1].concat(finalShape);
-    expectTensorsClose(result, ones(resultShape));
-  });
-
-  it('apply() executes all layers with symbolic tensors.', () => {
-    const inputShape = [1, 6];
-    const finalShape = [3, 2];
-    const inputLayer = tfl.layers.input({shape: inputShape});
-    const layer1 = tfl.layers.reshape({name: 'layer1', targetShape: [2, 3]});
-    const layer2 =
-        tfl.layers.reshape({name: 'layer2', targetShape: finalShape});
-    const output = layer2.apply(layer1.apply(inputLayer)) as tfl.SymbolicTensor;
-
-    const container =
-        new ContainerForTest({inputs: [inputLayer], outputs: [output]});
-
-    const newInput = tfl.layers.input({shape: [1, 6]});
-    const symbolicResult = container.apply(newInput);
-    expect(symbolicResult instanceof tfl.SymbolicTensor).toEqual(true);
-    const concreteResult = execute(
-        symbolicResult as tfl.SymbolicTensor,
-        new FeedDict([{key: newInput, value: ones([1, 1, 6])}]));
-    const resultShape = [1].concat(finalShape);
-    expectTensorsClose(concreteResult as Tensor, ones(resultShape));
-  });
-
-  it('computeOutputShape() computes the correct outputShape', () => {
-    const inputShape = [2, 3];
-    const finalShape = [3, 2];
-    const inputLayer = tfl.layers.input({shape: inputShape});
-    const layer = tfl.layers.reshape({targetShape: finalShape});
-    const output = layer.apply(inputLayer) as tfl.SymbolicTensor;
-    const container =
-        new ContainerForTest({inputs: [inputLayer], outputs: [output]});
-    expect(container.computeOutputShape([1].concat(inputShape))).toEqual([
-      1
-    ].concat(finalShape));
-  });
-
-  it('trainableWeights is initially an empty Array', () => {
-    expect(simpleContainer.trainableWeights).toEqual([]);
-  });
-
-  it('trainableWeights tracks only trainable weights', () => {
-    const inputShape = [2, 2];
-    const inputLayer = tfl.layers.input({shape: inputShape});
-    const layer1 = tfl.layers.reshape({targetShape: [4], name: 'reshapeLayer'});
-    const layer1Output = layer1.apply(inputLayer) as tfl.SymbolicTensor;
-    const layer2 =
-        tfl.layers.dense({units: 2, useBias: false, name: 'denseLayer'});
-    const layer2Output = layer2.apply(layer1Output) as tfl.SymbolicTensor;
-    const container =
-        new ContainerForTest({inputs: [inputLayer], outputs: [layer2Output]});
-    expect(container.trainableWeights.length).toEqual(1);
-  });
-
-  it('stateful is initially false', () => {
-    expect(simpleContainer.stateful).toEqual(false);
-  });
-
-  function createSimpleTwoLayerContainer(): [Container, Layer[]] {
-    const inputShape = [2, 2];
-    const inputLayer = tfl.layers.input({shape: inputShape});
-    const layer1 = tfl.layers.reshape({targetShape: [4], name: 'reshapeLayer'});
-    const layer1Output = layer1.apply(inputLayer) as tfl.SymbolicTensor;
-    const layer2 =
-        tfl.layers.dense({units: 2, useBias: false, name: 'denseLayer'});
-    const layer2Output = layer2.apply(layer1Output) as tfl.SymbolicTensor;
-    const container =
-        new ContainerForTest({inputs: [inputLayer], outputs: [layer2Output]});
-    return [container, [container.inputLayers[0], layer1, layer2]];
-  }
-
-  it('getLayer works by name', () => {
-    const [container, layers] = createSimpleTwoLayerContainer();
-    expect(container.getLayer(layers[0].name)).toEqual(layers[0]);
-    expect(container.getLayer(layers[1].name)).toEqual(layers[1]);
-    expect(container.getLayer(layers[2].name)).toEqual(layers[2]);
-  });
-
-  it('getLayer works by index', () => {
-    const [container, layers] = createSimpleTwoLayerContainer();
-    expect(container.getLayer(null, 0)).toEqual(layers[0]);
-    expect(container.getLayer(null, 1)).toEqual(layers[1]);
-    expect(container.getLayer(null, 2)).toEqual(layers[2]);
-  });
-
-  it('getLayer works by index when index is the first argument', () => {
-    const [container, layers] = createSimpleTwoLayerContainer();
-    expect(container.getLayer(0)).toEqual(layers[0]);
-    expect(container.getLayer(1)).toEqual(layers[1]);
-    expect(container.getLayer(2)).toEqual(layers[2]);
-  });
-
-  it('getLayer throws error for nonexistent layer name', () => {
-    const [container, layers] = createSimpleTwoLayerContainer();
-    expect(
-        () => container.getLayer(
-            layers[0].name + '_suffixToMakeLayerNameNonexistent'))
-        .toThrowError(/No such layer/);
-  });
-
-  it('getLayer throws error for index out of bound', () => {
-    const container = createSimpleTwoLayerContainer()[0];
-    expect(() => container.getLayer(3)).toThrowError(/only has 3 layer/);
-  });
-
-  it('getLayer throws error when neither name or index is specified', () => {
-    const container = createSimpleTwoLayerContainer()[0];
-    expect(() => container.getLayer(null))
-        .toThrowError(/Provide either a layer name or layer index/);
-  });
-});
-
-describeMathCPUAndGPU('Container.calculateLosses', () => {
-  function createSimpleOneLayerContainer(useRegularizers: boolean):
-      [Container, Layer[]] {
-    const inputShape = [2];
-    const inputLayer = tfl.layers.input({shape: inputShape});
-    const kernelRegularizer =
-        useRegularizers ? tfl.regularizers.l1({l1: 2}) : null;
-    const biasRegularizer =
-        useRegularizers ? tfl.regularizers.l2({l2: 3}) : null;
-    const denseLayer = tfl.layers.dense({
-      units: 2,
-      kernelInitializer: 'ones',
-      biasInitializer: 'ones',
-      kernelRegularizer,
-      biasRegularizer,
-      name: 'denseLayer'
-    });
-    const layer2Output = denseLayer.apply(inputLayer) as tfl.SymbolicTensor;
-    const container =
-        new ContainerForTest({inputs: [inputLayer], outputs: [layer2Output]});
-    return [container, [denseLayer]];
-  }
-
-  it('L1 and L2', () => {
-    const container = createSimpleOneLayerContainer(true)[0];
-    const losses = container.calculateLosses();
-    expect(losses.length).toEqual(2);
-    expectTensorsClose(losses[0], scalar(2 * (1 + 1 + 1 + 1)));
-    expectTensorsClose(losses[1], scalar(3 * (1 + 1)));
-  });
-
-  it('No regularizers', () => {
-    const container = createSimpleOneLayerContainer(false)[0];
-    const losses = container.calculateLosses();
-    expect(losses.length).toEqual(0);
-  });
-});
-
-describe('getSourceInputs()', () => {
-  it('returns the single source input', () => {
-    const inputTensor = tfl.layers.input({shape: [1]});
-    const layer1 = new LayerForTest({name: 'layer1'});
-    const layer2 = new LayerForTest({name: 'layer2'});
-    const output =
-        layer2.apply(layer1.apply(inputTensor)) as tfl.SymbolicTensor;
-    expect(getSourceInputs(output)).toEqual([inputTensor]);
-  });
-
-  it('returns all inputs', () => {
-    const input1 = tfl.layers.input({shape: [1], name: 'input1'});
-    const input2 = tfl.layers.input({shape: [1], name: 'input2'});
-    const layer = new LayerForTest({});
-    const output1 = layer.apply(input1) as tfl.SymbolicTensor;
-    const output2 = layer.apply(input2) as tfl.SymbolicTensor;
-    expect(getSourceInputs(output1)).toEqual([input1]);
-    expect(getSourceInputs(output2)).toEqual([input2]);
-  });
-});
-
-describeMathCPUAndGPU('LayersModel-dispose', () => {
-  it('Dispose Sequential model frees memory', () => {
-    const numTensors0 = memory().numTensors;
-    const model = tfl.sequential();
-    model.add(
-        tfl.layers.dense({units: 2, inputShape: [3], activation: 'relu'}));
-    model.add(tfl.layers.dense({units: 1}));
-    model.build([3, 3]);
-
-    const result = model.dispose();
-    expect(result.refCountAfterDispose).toEqual(0);
-    expect(result.numDisposedVariables).toEqual(4);
-    // The four weight variables of the two layers should have been disposed.
-    expect(memory().numTensors).toEqual(numTensors0);
-  });
-
-  it('Dispose Sequential model twice leads to Error', () => {
-    const model = tfl.sequential();
-    model.add(
-        tfl.layers.dense({units: 2, inputShape: [3], activation: 'relu'}));
-    model.add(tfl.layers.dense({units: 1}));
-    model.build([3, 3]);
-
-    model.dispose();
-    expect(() => model.dispose()).toThrowError(/Container .* already disposed/);
-  });
-
-  it('Using disposed Sequential model leads to Error', async () => {
-    const model = tfl.sequential();
-    model.add(
-        tfl.layers.dense({units: 2, inputShape: [3], activation: 'relu'}));
-    model.add(tfl.layers.dense({units: 1, activation: 'sigmoid'}));
-    model.build([3, 3]);
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    model.dispose();
-
-    const xs = zeros([3, 3]);
-    const ys = zeros([3, 1]);
-    expect(() => model.predict(xs)).toThrowError(/already disposed/);
-    expect(() => model.evaluate(xs, ys)).toThrowError(/already disposed/);
-    let errorCaughtDuringFit = false;
-    try {
-      await model.fit(xs, ys);
-    } catch (err) {
-      errorCaughtDuringFit = true;
-    }
-    expect(errorCaughtDuringFit).toEqual(true);
-  });
-
-  it('Dispose functional model frees memory', () => {
-    const input = tfl.input({shape: [4]});
-    const dense1 =
-        tfl.layers.dense({units: 3}).apply(input) as tfl.SymbolicTensor;
-    const dense2 = tfl.layers.dense({units: 2, useBias: false}).apply(input) as
-        tfl.SymbolicTensor;
-    const model = tfl.model({inputs: [input], outputs: [dense1, dense2]});
-    // Call predict once to make sure that the model's weights are initialized.
-    model.predict(zeros([2, 4]));
-
-    const numTensors0 = memory().numTensors;
-    const result = model.dispose();
-
-    expect(result.refCountAfterDispose).toEqual(0);
-    expect(result.numDisposedVariables).toEqual(3);
-    // The 2 + 1 = 3 weight variables of the two layers should have been
-    // disposed.
-    expect(memory().numTensors).toEqual(numTensors0 - 3);
-  });
-
-  it('Dispose functional model twice leads to Error', () => {
-    const input = tfl.input({shape: [4]});
-    const dense1 =
-        tfl.layers.dense({units: 3}).apply(input) as tfl.SymbolicTensor;
-    const dense2 = tfl.layers.dense({units: 2, useBias: false}).apply(input) as
-        tfl.SymbolicTensor;
-    const model = tfl.model({inputs: [input], outputs: [dense1, dense2]});
-    // Call predict once to make sure that the model's weights are  initialized.
-    model.predict(zeros([2, 4]));
-
-    model.dispose();
-    expect(() => model.dispose()).toThrowError(/Container .* already disposed/);
-  });
-
-  it('Layer shared between two functional models is not disposed', () => {
-    const input1 = tfl.input({shape: [4]});
-    const input2 = tfl.input({shape: [4]});
-    const sharedDenseLayer = tfl.layers.dense({units: 3, activation: 'relu'});
-    const nonSharedDenseLayer1 = tfl.layers.dense({units: 1, useBias: false});
-    const nonSharedDenseLayer2 = tfl.layers.dense({units: 1, useBias: false});
-    const output1 = nonSharedDenseLayer1.apply(
-                        sharedDenseLayer.apply(input1)) as tfl.SymbolicTensor;
-    const output2 = nonSharedDenseLayer2.apply(
-                        sharedDenseLayer.apply(input2)) as tfl.SymbolicTensor;
-
-    const model1 = tfl.model({inputs: [input1], outputs: [output1]});
-    const model2 = tfl.model({inputs: [input2], outputs: [output2]});
-
-    // Call predict once to make sure that both models' weights are initialized.
-    model1.predict(zeros([2, 4]));
-    model2.predict(zeros([2, 4]));
-    const xs = zeros([2, 4]);
-
-    const numTensors0 = memory().numTensors;
-    const result1 = model1.dispose();
-
-    expect(result1.refCountAfterDispose).toEqual(0);
-    expect(result1.numDisposedVariables).toEqual(1);
-    // After model1 is disposed, only the single weight of
-    // `nonSharedDenseLayer1` should have been freed.
-    expect(memory().numTensors).toEqual(numTensors0 - 1);
-
-    // At this point, calling predict() on model1 should fail, but doing the
-    // same on model2 should still work.
-    expect(() => model1.predict(xs)).toThrowError(/already disposed/);
-    const ys = model2.predict(xs) as Tensor;
-    expect(ys.shape).toEqual([2, 1]);
-    ys.dispose();
-
-    const result2 = model2.dispose();
-
-    expect(result2.refCountAfterDispose).toEqual(0);
-    expect(result2.numDisposedVariables).toEqual(3);
-    // After model2 is disposed, the single weight of `nonSharedDenseLayer2`
-    // and the two weights o `sharedDenseLayer` should be freed.
-    expect(memory().numTensors).toEqual(numTensors0 - 4);
-
-    // At this point, calling predict() on both model1 and model2 should fail.
-    expect(() => model1.predict(xs)).toThrowError(/already disposed/);
-    expect(() => model2.predict(xs)).toThrowError(/already disposed/);
-  });
-
-  it('Disposing nested sequential model preserves the inner model', () => {
-    const innerModel = tfl.sequential();
-    innerModel.add(tfl.layers.reshape({targetShape: [10], inputShape: [2, 5]}));
-    innerModel.add(tfl.layers.dense({units: 6, activation: 'relu'}));
-    innerModel.add(tfl.layers.dense({units: 4, activation: 'relu'}));
-
-    const outerModel = tfl.sequential();
-    outerModel.add(
-        tfl.layers.reshape({targetShape: [2, 5], inputShape: [5, 2]}));
-    outerModel.add(innerModel);
-    outerModel.add(tfl.layers.dense({units: 3, activation: 'softmax'}));
-
-    const xsOuter = zeros([1, 5, 2]);
-    const xsInner = zeros([1, 2, 5]);
-    outerModel.predict(xsOuter);  // Call predict() to initialize the weights.
-    const numTensors0 = memory().numTensors;
-
-    const result1 = outerModel.dispose();
-
-    expect(result1.refCountAfterDispose).toEqual(0);
-    expect(result1.numDisposedVariables).toEqual(2);
-    // Calling dispose on the outer model should have freed the two weights that
-    // belong to only the outer model and not to the inner model.
-    expect(memory().numTensors).toEqual(numTensors0 - 2);
-
-    // Calling dispose on the outer model again should lead to Error.
-    expect(() => outerModel.dispose())
-        .toThrowError(/Container .* already disposed/);
-    // Calling predict on the outer model should fail.
-    expect(() => outerModel.predict(xsOuter)).toThrowError(/already disposed/);
-
-    // At this point, the inner model is still usable.
-    const ysInner = innerModel.predict(xsInner) as Tensor;
-    expect(ysInner.shape).toEqual([1, 4]);
-    ysInner.dispose();
-
-    // Calling dispose on innerModel should finally freed all the weights.
-    const result2 = innerModel.dispose();
-
-    expect(result2.refCountAfterDispose).toEqual(0);
-    expect(result2.numDisposedVariables).toEqual(4);
-    expect(memory().numTensors).toEqual(numTensors0 - 6);
-
-    // At this point, the inner model should have become unusable.
-    expect(() => innerModel.predict(xsInner)).toThrowError(/already disposed/);
-  });
-
-  it('Nested model gets the correct kwargs', async () => {
-    const innerModel = tfl.sequential();
-    const layer =
-        tfl.layers.dense({units: 1, inputShape: [5], activation: 'sigmoid'});
-    innerModel.add(layer);
-
-    const input = tfl.input({shape: [5]});
-    const output = innerModel.apply(input) as SymbolicTensor;
-    const model = tfl.model({inputs: input, outputs: output});
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const kwargsArray: Kwargs[] = [];
-    const recordKwargsHook: CallHook = (inputs: Tensor|Tensor[], kwargs: {}) =>
-        kwargsArray.push(kwargs);
-    layer.setCallHook(recordKwargsHook);
-
-    const xs = ones([3, 5]);
-    const ys = ones([3, 1]);
-    await model.trainOnBatch(xs, ys);
-    expect(kwargsArray.length).toEqual(1);
-    expect(kwargsArray[0]['training']).toEqual(true);
-  });
-  it('Dispose Sequential model with a Dropout', () => {
-    const numTensors0 = memory().numTensors;
-    const model = tfl.sequential();
-    model.add(
-        tfl.layers.dense({units: 2, inputShape: [3], activation: 'relu'}));
-    model.add(tfl.layers.dense({units: 1}));
-    model.add(tfl.layers.dropout({rate: 0.8}));
-    model.build([3, 3]);
-
-    const result = model.dispose();
-    expect(result.refCountAfterDispose).toEqual(0);
-    expect(result.numDisposedVariables).toEqual(4);
-    // The four weight variables of the two layers should have been disposed.
-    // + the rate from the dropout tensor
-    expect(memory().numTensors).toEqual(numTensors0);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/engine/dataset_fakes.ts b/tfjs-master/tfjs-layers/src/engine/dataset_fakes.ts
deleted file mode 100644
index 222a89353..000000000
--- a/tfjs-master/tfjs-layers/src/engine/dataset_fakes.ts
+++ /dev/null
@@ -1,245 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-
-import {Shape} from '../keras_format/common';
-import {TensorOrArrayOrMap} from '../types';
-
-import {Dataset, LazyIterator} from './dataset_stub';
-import {FitDatasetElement} from './training_dataset';
-
-export interface FakeDatasetArgs {
-  /**
-   * The shape(s) of the features of a single example.
-   *
-   * Use an object mapping name to shape, if more than one feature tensors
-   * are required.
-   */
-  xShape: Shape|{[name: string]: Shape};
-
-  /**
-   * The shape of the target(s) of a single exapmle.
-   */
-  yShape: Shape|{[name: string]: Shape};
-
-  /**
-   * A function that generates preset sequence of X tensors.
-   *
-   * This function is invoked each time a new iterator is created.
-   */
-  xTensorsFunc?: () => tfc.Tensor[] | {[name: string]: tfc.Tensor[]};
-
-  /**
-   * A function that generates preset sequence of Y tensors.
-   *
-   * This function is invoked each time a new iterator is created.
-   */
-  yTensorsFunc?: () => tfc.Tensor[] | {[name: string]: tfc.Tensor[]};
-
-  /**
-   * The size of each batch generated by the iterator.
-   */
-  batchSize: number;
-
-  /**
-   * The number of batches an iterator generates before declaring done to be
-   * true.
-   */
-  numBatches: number;
-}
-
-function mergeBatchSizeAndShape(
-    batchSize: number, shape: Shape|{[name: string]: Shape}): Shape|
-    {[name: string]: Shape} {
-  if (Array.isArray(shape)) {
-    return [batchSize].concat(shape);
-  } else {
-    const output: {[name: string]: Shape} = {};
-    for (const name in shape) {
-      output[name] = [batchSize].concat(shape[name]);
-    }
-    return output;
-  }
-}
-
-function generateRandomTensorContainer(shape: Shape|{[name: string]: Shape}):
-    tfc.Tensor|{[name: string]: tfc.Tensor} {
-  let output: tfc.Tensor|{[name: string]: tfc.Tensor};
-  if (Array.isArray(shape)) {
-    output = tfc.randomNormal(shape);
-  } else {
-    output = {};
-    for (const name in shape) {
-      output[name] = tfc.randomNormal(shape[name]);
-    }
-  }
-  return output;
-}
-
-class FakeNumericIterator extends LazyIterator<FitDatasetElement> {
-  private xBatchShape: Shape|{[name: string]: Shape};
-  private yBatchShape: Shape|{[name: string]: Shape};
-  private numBatches: number;
-  private batchCount: number;
-  private xTensorsFunc: () => tfc.Tensor[] | {[name: string]: tfc.Tensor[]};
-  private yTensorsFunc: () => tfc.Tensor[] | {[name: string]: tfc.Tensor[]};
-  private xTensorValues: tfc.Tensor[]|{[name: string]: tfc.Tensor[]};
-  private yTensorValues: tfc.Tensor[]|{[name: string]: tfc.Tensor[]};
-  private tensorIndex = 0;
-
-  constructor(args: FakeDatasetArgs) {
-    super();
-    this.xBatchShape = mergeBatchSizeAndShape(args.batchSize, args.xShape);
-    this.yBatchShape = mergeBatchSizeAndShape(args.batchSize, args.yShape);
-    this.numBatches = args.numBatches;
-    this.batchCount = 0;
-    this.xTensorsFunc = args.xTensorsFunc;
-    this.yTensorsFunc = args.yTensorsFunc;
-
-    // Sanity check on the preset tensors.
-    tfc.util.assert(
-        this.xTensorsFunc == null && this.yTensorsFunc == null ||
-            this.xTensorsFunc != null && this.yTensorsFunc != null,
-        () => 'presetXTensors and presetYTensors must be both null/undefined ' +
-            'or both set.');
-  }
-
-  async next(): Promise<IteratorResult<FitDatasetElement>> {
-    const done = ++this.batchCount > this.numBatches;
-    if (done) {
-      return {done, value: null};
-    }
-    if (this.xTensorsFunc == null) {
-      // Generate data randomly.
-      return {
-        done,
-        value: done ? null : {
-          xs: generateRandomTensorContainer(this.xBatchShape),
-          ys: generateRandomTensorContainer(this.yBatchShape)
-        }
-      };
-    } else {
-      // Use preset tensors.
-      if ((this.batchCount - 1) % this.numBatches === 0) {
-        this.xTensorValues = this.xTensorsFunc();
-        this.yTensorValues = this.yTensorsFunc();
-        this.tensorIndex = 0;
-      }
-      const index = this.tensorIndex++;
-
-      let xs: tfc.Tensor|{[name: string]: tfc.Tensor};
-      if (Array.isArray(this.xTensorValues)) {
-        xs = this.xTensorValues[index];
-        tfc.util.assert(
-            tfc.util.arraysEqual(xs.shape, this.xBatchShape as Shape),
-            () => `Shape mismatch: expected: ${
-                      JSON.stringify(this.xBatchShape)}; ` +
-                `actual: ${JSON.stringify((xs as tfc.Tensor).shape)}`);
-      } else {
-        xs = {};
-        for (const key in this.xTensorValues) {
-          xs[key] = this.xTensorValues[key][index];
-          tfc.util.assert(
-              tfc.util.arraysEqual(xs[key].shape, this.xBatchShape as Shape),
-              () => `Shape mismatch: expected: ${
-                        JSON.stringify(this.xBatchShape)}; ` +
-                  `actual: ${JSON.stringify((xs as tfc.Tensor).shape)}`);
-        }
-      }
-
-      let ys: tfc.Tensor|{[name: string]: tfc.Tensor};
-      if (Array.isArray(this.yTensorValues)) {
-        // Get preset ys tensors for single-output models.
-        ys = this.yTensorValues[index];
-        tfc.util.assert(
-            tfc.util.arraysEqual(ys.shape, this.yBatchShape as Shape),
-            () => `Shape mismatch: expected: ${
-                      JSON.stringify(this.yBatchShape)}; ` +
-                `actual: ${JSON.stringify((ys as tfc.Tensor).shape)}`);
-      } else {
-        // Get preset ys tensors for multi-output models.
-        ys = {};
-        this.yBatchShape = this.yBatchShape as {[name: string]: Shape};
-        for (const key in this.yTensorValues) {
-          ys[key] = this.yTensorValues[key][index];
-          tfc.util.assert(
-              tfc.util.arraysEqual(ys[key].shape, this.yBatchShape[key]),
-              () => `Shape mismatch: expected: ${
-                        JSON.stringify(this.yBatchShape)}; ` +
-                  `actual: ${
-                        JSON.stringify(
-                            (ys as {[name: string]: tfc.Tensor})[key].shape)}`);
-        }
-      }
-
-      return {done, value: {xs, ys}};
-    }
-  }
-}
-
-/**
- * A fake dataset with configurable feature and target shapes.
- *
- * The batch size and # of batches are also configurable.
- *
- * The iterator from the dataset always generate random-normal float32 values.
- */
-export class FakeNumericDataset extends Dataset<FitDatasetElement> {
-  constructor(readonly args: FakeDatasetArgs) {
-    super();
-    tfc.util.assert(
-        args.batchSize > 0 && Number.isInteger(args.batchSize),
-        () =>
-            `batchSize must be a positive integer, but got ${args.batchSize}`);
-    tfc.util.assert(
-        args.numBatches > 0 && Number.isInteger(args.numBatches),
-        () =>
-            `numBatches must be positive integer, but got ${args.numBatches}`);
-    this.size = args.numBatches;
-  }
-
-  async iterator(): Promise<LazyIterator<FitDatasetElement>> {
-    return new FakeNumericIterator(this.args);
-  }
-}
-
-// We can't use Dataset.map(...) because we don't depend on tfjs-data here,
-// so we manually transform the above {xs, ys} dataset to the [xs, ys] form.
-export class FakeNumericDatasetLegacyArrayForm extends
-    Dataset<[TensorOrArrayOrMap, TensorOrArrayOrMap]> {
-  ds: FakeNumericDataset;
-  constructor(readonly args: FakeDatasetArgs) {
-    super();
-    this.ds = new FakeNumericDataset(args);
-  }
-
-  async iterator():
-      Promise<LazyIterator<[TensorOrArrayOrMap, TensorOrArrayOrMap]>> {
-    const it = await this.ds.iterator();
-    return new FakeNumericIteratorLegacyArrayForm(it);
-  }
-}
-
-class FakeNumericIteratorLegacyArrayForm extends
-    LazyIterator<[TensorOrArrayOrMap, TensorOrArrayOrMap]> {
-  constructor(private readonly it: LazyIterator<FitDatasetElement>) {
-    super();
-  }
-
-  async next():
-      Promise<IteratorResult<[TensorOrArrayOrMap, TensorOrArrayOrMap]>> {
-    const result = await this.it.next();
-    return {
-      done: result.done,
-      value: result.value == null ? null : [result.value.xs, result.value.ys]
-    };
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/dataset_fakes_test.ts b/tfjs-master/tfjs-layers/src/engine/dataset_fakes_test.ts
deleted file mode 100644
index 9333aa67c..000000000
--- a/tfjs-master/tfjs-layers/src/engine/dataset_fakes_test.ts
+++ /dev/null
@@ -1,123 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {NamedTensorMap, Tensor} from '@tensorflow/tfjs-core';
-
-import {describeMathCPUAndGPU} from '../utils/test_utils';
-
-import {FakeNumericDataset} from './dataset_fakes';
-
-describeMathCPUAndGPU('FakeNumericDataset', () => {
-  it('1D features, 1D targets', async () => {
-    const dataset = new FakeNumericDataset(
-        {xShape: [3], yShape: [1], batchSize: 8, numBatches: 5});
-    for (let k = 0; k < 2; ++k) {
-      // Run twice to make sure that calling iteartor() multiple times works.
-      const iterator = await dataset.iterator();
-      for (let i = 0; i < 5; ++i) {
-        const result = await iterator.next();
-        expect((result.value.xs as Tensor).shape).toEqual([8, 3]);
-        expect((result.value.ys as Tensor).shape).toEqual([8, 1]);
-        expect(result.done).toEqual(false);
-      }
-      for (let i = 0; i < 3; ++i) {
-        const result = await iterator.next();
-        expect(result.value).toBeNull();
-        expect(result.done).toEqual(true);
-      }
-    }
-  });
-
-  it('2D features, 1D targets', async () => {
-    const dataset = new FakeNumericDataset(
-        {xShape: [3, 4], yShape: [2], batchSize: 8, numBatches: 5});
-    for (let k = 0; k < 2; ++k) {
-      // Run twice to make sure that calling iteartor() multiple times works.
-      const iterator = await dataset.iterator();
-      for (let i = 0; i < 5; ++i) {
-        const result = await iterator.next();
-        expect((result.value.xs as Tensor).shape).toEqual([8, 3, 4]);
-        expect((result.value.ys as Tensor).shape).toEqual([8, 2]);
-        expect(result.done).toEqual(false);
-      }
-      for (let i = 0; i < 3; ++i) {
-        const result = await iterator.next();
-        expect(result.value).toBeNull();
-        expect(result.done).toEqual(true);
-      }
-    }
-  });
-
-  it('Multiple 2D features, 1D targets', async () => {
-    const dataset = new FakeNumericDataset({
-      xShape: {'input1': [3, 4], 'input2': [2, 3]},
-      yShape: [2],
-      batchSize: 8,
-      numBatches: 5
-    });
-    for (let k = 0; k < 2; ++k) {
-      // Run twice to make sure that calling iteartor() multiple times works.
-      const iterator = await dataset.iterator();
-      for (let i = 0; i < 5; ++i) {
-        const result = await iterator.next();
-        const xs = result.value.xs as NamedTensorMap;
-        expect(xs['input1'].shape).toEqual([8, 3, 4]);
-        expect(xs['input2'].shape).toEqual([8, 2, 3]);
-        expect((result.value.ys as Tensor).shape).toEqual([8, 2]);
-        expect(result.done).toEqual(false);
-      }
-      for (let i = 0; i < 3; ++i) {
-        const result = await iterator.next();
-        expect(result.value).toBeNull();
-        expect(result.done).toEqual(true);
-      }
-    }
-  });
-
-  it('Invalid batchSize leads to Error', () => {
-    expect(
-        () => new FakeNumericDataset(
-            {xShape: [3], yShape: [1], batchSize: -8, numBatches: 5}))
-        .toThrow();
-    expect(
-        () => new FakeNumericDataset(
-            {xShape: [3], yShape: [1], batchSize: 8.5, numBatches: 5}))
-        .toThrow();
-    expect(
-        () => new FakeNumericDataset(
-            {xShape: [3], yShape: [1], batchSize: 0, numBatches: 5}))
-        .toThrow();
-    expect(
-        () => new FakeNumericDataset(
-            // tslint:disable-next-line:no-any
-            {xShape: [3], yShape: [1], batchSize: 'foo' as any, numBatches: 5}))
-        .toThrow();
-  });
-
-  it('Invalid numBatches leads to Error', () => {
-    expect(
-        () => new FakeNumericDataset(
-            {xShape: [3], yShape: [1], batchSize: 8, numBatches: -5}))
-        .toThrow();
-    expect(
-        () => new FakeNumericDataset(
-            {xShape: [3], yShape: [1], batchSize: 8, numBatches: 5.5}))
-        .toThrow();
-    expect(
-        () => new FakeNumericDataset(
-            {xShape: [3], yShape: [1], batchSize: 8, numBatches: 0}))
-        .toThrow();
-    expect(
-        () => new FakeNumericDataset(
-            // tslint:disable-next-line:no-any
-            {xShape: [3], yShape: [1], batchSize: 8, numBatches: 'foo' as any}))
-        .toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/engine/dataset_stub.ts b/tfjs-master/tfjs-layers/src/engine/dataset_stub.ts
deleted file mode 100644
index 5546c7af0..000000000
--- a/tfjs-master/tfjs-layers/src/engine/dataset_stub.ts
+++ /dev/null
@@ -1,25 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Stub interfaces and classes for testing tf.LayersModel.fitDataset().
- *
- * TODO(cais, soergel): Remove this in favor of actual interfaces and classes
- *   when ready.
- */
-
-export abstract class LazyIterator<T> {
-  abstract next(): Promise<IteratorResult<T>>;
-}
-
-export abstract class Dataset<T> {
-  abstract iterator(): Promise<LazyIterator<T>>;
-  size: number;
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/executor.ts b/tfjs-master/tfjs-layers/src/engine/executor.ts
deleted file mode 100644
index 608537f73..000000000
--- a/tfjs-master/tfjs-layers/src/engine/executor.ts
+++ /dev/null
@@ -1,541 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Executor: Evaluates SymbolicTensor based on feeds.
- */
-
-import {cast, dispose, memory, Tensor, util} from '@tensorflow/tfjs-core';
-
-import {ValueError} from '../errors';
-import {Kwargs} from '../types';
-import {LruCache} from '../utils/executor_utils';
-import {toList} from '../utils/generic_utils';
-
-import {InputLayer} from './input_layer';
-import {SymbolicTensor} from './topology';
-
-/**
- * Helper function to check the dtype and shape compatibility of a feed value.
- */
-function assertFeedCompatibility(key: SymbolicTensor, val: Tensor): Tensor {
-  // Check dtype compatibility.
-  if (key.dtype == null || key.dtype === val.dtype) {
-    //  a.  If types match, return val tensor as is.
-    return val;
-  }
-  try {
-    //  b. Attempt to convert to expected type.
-    return cast(val, key.dtype);
-  } catch (err) {
-    //  c. If conversion fails, return helpful error.
-    throw new ValueError(
-        `The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +
-        `of the key '${key.name}' (${key.dtype}).`);
-  }
-}
-
-/**
- * A concrete Tensor value for a symbolic tensor as the key.
- */
-export interface Feed {
-  key: SymbolicTensor;
-  value: Tensor;
-}
-
-/**
- * FeedDict: A mapping from unique SymbolicTensors to feed values for them.
- * A feed value is a concrete value represented as an `Tensor`.
- */
-export class FeedDict {
-  private id2Value: {[id: number]: Tensor} = {};
-  private id2Mask: {[id: number]: Tensor} = {};
-  private name2Id: {[name: string]: number} = {};
-
-  /**
-   * Constructor, optionally does copy-construction.
-   * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case
-   *   copy-construction will be performed.
-   */
-  constructor(feeds?: Feed[]|FeedDict) {
-    if (feeds instanceof FeedDict) {
-      for (const id in feeds.id2Value) {
-        this.id2Value[id] = feeds.id2Value[id];
-        if (id in feeds.id2Mask) {
-          this.id2Mask[id] = feeds.id2Mask[id];
-        }
-      }
-    } else {
-      if (feeds == null) {
-        return;
-      }
-      for (const feed of feeds) {
-        this.add(feed.key, feed.value);
-      }
-    }
-  }
-
-  /**
-   * Add a key-value pair to the FeedDict.
-   *
-   * @param key The key of the feed.
-   * @param value The value of the tensor feed.
-   * @param mask The value of the mask feed (optional).
-   * @returns This `FeedDict`.
-   * @throws ValueError: If the key `SymbolicTensor` already exists in the
-   *   `FeedDict`.
-   */
-  add(key: SymbolicTensor, value: Tensor, mask?: Tensor): FeedDict {
-    if (this.id2Value[key.id] == null) {
-      this.id2Value[key.id] = assertFeedCompatibility(key, value);
-      this.name2Id[key.name] = key.id;
-      if (mask != null) {
-        this.id2Mask[key.id] = mask;
-      }
-    } else {
-      throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);
-    }
-    return this;
-  }
-
-  /**
-   * Add a Feed to the FeedDict.
-   * @param feed The new `Feed` to add.
-   * @returns This `FeedDict`.
-   */
-  addFeed(feed: Feed) {
-    this.add(feed.key, feed.value);
-  }
-
-  /**
-   * Probe whether a key already exists in the FeedDict.
-   * @param key
-   */
-  hasKey(key: SymbolicTensor): boolean {
-    return this.id2Value[key.id] != null;
-  }
-
-  /**
-   * Get all the SymbolicTensor available in this FeedDict.
-   */
-  names(): string[] {
-    return Object.keys(this.name2Id);
-  }
-
-  /**
-   * Get the feed value for given key.
-   * @param key The SymbolicTensor, or its name (as a string), of which the
-   *     value is sought.
-   * @returns If `key` exists, the corresponding feed value.
-   * @throws ValueError: If `key` does not exist in this `FeedDict`.
-   */
-  getValue(key: SymbolicTensor|string): Tensor {
-    if (key instanceof SymbolicTensor) {
-      if (this.id2Value[key.id] == null) {
-        throw new ValueError(`Nonexistent key: ${key.name}`);
-      } else {
-        return this.id2Value[key.id];
-      }
-    } else {
-      const id = this.name2Id[key];
-      if (id == null) {
-        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
-      }
-      return this.id2Value[id];
-    }
-  }
-
-  /**
-   * Get the feed mask for given key.
-   * @param key The SymbolicTensor, or its name (as a string), of which the
-   *     value is sought.
-   * @returns If `key` exists, the corresponding feed mask.
-   * @throws ValueError: If `key` does not exist in this `FeedDict`.
-   */
-  getMask(key: SymbolicTensor|string): Tensor {
-    if (key instanceof SymbolicTensor) {
-      if (this.id2Value[key.id] == null) {
-        throw new ValueError(`Nonexistent key: ${key.name}`);
-      } else {
-        return this.id2Mask[key.id];
-      }
-    } else {
-      const id = this.name2Id[key];
-      if (id == null) {
-        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
-      }
-      return this.id2Mask[id];
-    }
-  }
-
-  /** Dispose all mask Tensors held by this object. */
-  disposeMasks() {
-    if (this.id2Mask != null) {
-      dispose(this.id2Mask);
-    }
-  }
-}
-
-// Cache for topologically sorted SymbolicTensors for given execution
-// targets (i.e., fetches).
-export const cachedSorted: LruCache<SymbolicTensor[]> =
-    new LruCache<SymbolicTensor[]>();
-
-// Cache for recipient count maps for given execution targets (i.e., fetches).
-export const cachedRecipientCounts: LruCache<RecipientCounts> =
-    new LruCache<RecipientCounts>();
-
-export function updateCacheMaxEntries(maxEntries: number) {
-  if (cachedSorted != null) {
-    cachedSorted.setMaxEntries(maxEntries);
-  }
-  if (cachedRecipientCounts != null) {
-    cachedRecipientCounts.setMaxEntries(maxEntries);
-  }
-}
-
-/**
- * Interface for the optional object used for probing the memory
- * usage and other statistics during execution.
- */
-export interface ExecutionProbe {
-  /**
-   * Maximum number of tensors that exist during all steps of the
-   * execution. Tensor counts are measured at the beginning of every
-   * step.
-   */
-  maxNumTensors?: number;
-
-  /**
-   * Minimum number of tensors that exist during all steps of the
-   * execution. Tensor counts are measured at the beginning of every
-   * step.
-   */
-  minNumTensors?: number;
-}
-
-/**
- * Execute a SymbolicTensor by using concrete feed values.
- *
- * A `SymbolicTensor` object is a node in a computation graph of TF.js
- * Layers. The object is backed by a source layer and input
- * `SymbolicTensor`s to the source layer. This method evaluates
- * the `call()` method of the source layer, using concrete values of the
- * inputs obtained from either
- * * `feedDict`, if the input key exists in `feedDict`, or else,
- * * a recursive call to `execute()` itself.
- *
- * @param x: The `SymbolicTensor` to execute.
- * @param feedDict: The feed values, as base condition of the recursion.
- *   execution.
- * @param kwargs: Optional keyword arguments.
- * @param probe: A probe object (of interface `ExecutionProbe`) used for
- *   testing memory footprint of `execute` calls.
- * @returns Result of the execution.
- * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s
- *   encountered during the execution lacks a feed value in `feedDict`.
- */
-export function execute(
-    fetches: SymbolicTensor|SymbolicTensor[], feedDict: FeedDict,
-    kwargs?: Kwargs, probe?: ExecutionProbe): Tensor|
-    Tensor[]|[Tensor | Tensor[]] {
-  const training: boolean = kwargs == null ? false : kwargs['training'];
-
-  const arrayFetches = Array.isArray(fetches);
-  const fetchArray: SymbolicTensor[] =
-      arrayFetches ? fetches : [fetches];
-
-  const outputNames = fetchArray.map(t => t.name);
-  const finalOutputs: Tensor[] = [];
-  const feedNames = feedDict.names();
-  for (const outputName of outputNames) {
-    if (feedNames.indexOf(outputName) !== -1) {
-      finalOutputs.push(feedDict.getValue(outputName));
-    } else {
-      finalOutputs.push(null);
-    }
-  }
-
-  if (probe != null) {
-    // For optional probing of memory footprint during execution.
-    probe.maxNumTensors = -Infinity;
-    probe.minNumTensors = Infinity;
-  }
-
-  // Check cache.
-  const fetchAndFeedKey =
-      outputNames.join(',') + '|' + feedDict.names().sort().join(',');
-  let sorted: SymbolicTensor[] = cachedSorted.get(fetchAndFeedKey);
-  let recipientCounts: {[fetchName: string]: number};
-  if (sorted == null) {
-    // Cache doesn't contain the desired combination of fetches. Compute
-    // topological sort for the combination for the first time.
-    const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);
-    sorted = out.sorted;
-    recipientCounts = out.recipientCounts;
-
-    // Store results in cache for future use.
-    cachedSorted.put(fetchAndFeedKey, sorted);
-    cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);
-  }
-  recipientCounts = {};
-  if (!training) {
-    Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));
-  }
-
-  const internalFeedDict = new FeedDict(feedDict);
-
-  // Start iterative execution on the topologically-sorted SymbolicTensors.
-  for (let i = 0; i < sorted.length; ++i) {
-    if (probe != null) {
-      // For optional probing of memory usage during execution.
-      const numTensors = memory().numTensors;
-      if (numTensors > probe.maxNumTensors) {
-        probe.maxNumTensors = numTensors;
-      }
-      if (numTensors < probe.minNumTensors) {
-        probe.minNumTensors = numTensors;
-      }
-    }
-
-    const symbolic = sorted[i];
-    const srcLayer = symbolic.sourceLayer;
-    if (srcLayer instanceof InputLayer) {
-      continue;
-    }
-    const inputValues: Tensor[] = [];
-    const inputMasks: Tensor[] = [];
-    const tensorsToDispose: Tensor[] = [];
-
-    let maskExists = false;
-    for (const input of symbolic.inputs) {
-      const value = internalFeedDict.getValue(input);
-      const mask = internalFeedDict.getMask(input);
-      inputValues.push(value);
-      inputMasks.push(mask);
-      if (mask != null) {
-        maskExists = true;
-      }
-      if (!training) {
-        recipientCounts[input.name]--;
-        if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&
-            outputNames.indexOf(input.name) === -1 && !value.isDisposed &&
-            input.sourceLayer.stateful !== true) {
-          tensorsToDispose.push(value);
-        }
-      }
-    }
-
-    if (maskExists) {
-      kwargs = kwargs || {};
-      kwargs['mask'] = inputMasks[0];
-    }
-    const outputTensors =
-        toList(srcLayer.apply(inputValues, kwargs)) as Tensor[];
-    let outputMask: Tensor|Tensor[] = null;
-    if (srcLayer.supportsMasking) {
-      outputMask = srcLayer.computeMask(inputValues, inputMasks);
-    }
-    const layerOutputs = getNodeOutputs(symbolic);
-    const outputSymbolicTensors =
-        Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];
-    for (let i = 0; i < outputSymbolicTensors.length; ++i) {
-      if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {
-        internalFeedDict.add(
-            outputSymbolicTensors[i], outputTensors[i],
-            Array.isArray(outputMask) ? outputMask[0] : outputMask);
-      }
-      const index = outputNames.indexOf(outputSymbolicTensors[i].name);
-      if (index !== -1) {
-        finalOutputs[index] = outputTensors[i];
-      }
-    }
-
-    if (!training) {
-      // Clean up Tensors that are no longer needed.
-      dispose(tensorsToDispose);
-    }
-  }
-  // NOTE(cais): Unlike intermediate tensors, we don't discard mask
-  // tensors as we go, because these tensors are sometimes passed over a
-  // series of mutliple layers, i.e., not obeying the immediate input
-  // relations in the graph. If this becomes a memory-usage concern,
-  // we can improve this in the future.
-  internalFeedDict.disposeMasks();
-
-  return arrayFetches ? finalOutputs : finalOutputs[0];
-}
-
-type RecipientCounts = {
-  [fetchName: string]: number
-};
-
-export type RecipientMap = {
-  [fetchName: string]: Set<string>;
-};
-
-/**
- * Sort the `SymbolicTensor`s topologically, for an array of fetches.
- *
- * This function calls getTopologicalSortAndRecipientCountsForOneFetch and
- * merges their results.
- *
- * @param fetch The array of fetches requested. Must be a non-empty array.
- * @param feedDict The dictionary of fed values.
- * @returns sorted: Topologically-sorted array of SymbolicTensors.
- *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.
- */
-function getTopologicalSortAndRecipientCounts(
-    fetches: SymbolicTensor[], feedDict: FeedDict):
-    {sorted: SymbolicTensor[], recipientCounts: RecipientCounts} {
-  util.assert(
-      fetches != null && fetches.length > 0,
-      () => `Expected at least one fetch, got none`);
-
-  let finalSorted: SymbolicTensor[] = [];
-  let finalRecipientMap: RecipientMap = {};
-  if (fetches.length === 1) {
-    // Special-casing 1 fetch for efficiency.
-    const out =
-        getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);
-    finalSorted = out.sorted;
-    finalRecipientMap = out.recipientMap;
-  } else {
-    const visited = new Set<string>();
-    for (const fetch of fetches) {
-      const {sorted, recipientMap} =
-          getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);
-
-      // Merge sorted SymbolicTensor Arrays.
-      for (const symbolicTensor of sorted) {
-        if (!visited.has(symbolicTensor.name)) {
-          finalSorted.push(symbolicTensor);
-          visited.add(symbolicTensor.name);
-        }
-      }
-
-      // Merge recipient maps.
-      for (const name in recipientMap) {
-        if (finalRecipientMap[name] == null) {
-          finalRecipientMap[name] = new Set<string>();
-        }
-        recipientMap[name].forEach(
-            recipient => finalRecipientMap[name].add(recipient));
-      }
-    }
-  }
-  return {
-    sorted: finalSorted,
-    recipientCounts: recipientMap2Counts(finalRecipientMap)
-  };
-}
-
-function recipientMap2Counts(recipientMap: RecipientMap): RecipientCounts {
-  const recipientCounts: RecipientCounts = {};
-  for (const name in recipientMap) {
-    recipientCounts[name] = recipientMap[name].size;
-  }
-  return recipientCounts;
-}
-
-/**
- * Sort the `SymbolicTensor`s topologically, for a single fetch.
- *
- * This helper function processes the upstream SymbolicTensors of a single
- * fetch.
- *
- * @param fetch The single fetch requested.
- * @param feedDict The dictionary of fed values.
- * @returns sorted: Topologically-sorted array of SymbolicTensors.
- *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.
- */
-export function getTopologicalSortAndRecipientCountsForOneFetch(
-    fetch: SymbolicTensor, feedDict: FeedDict):
-    {sorted: SymbolicTensor[], recipientMap: RecipientMap} {
-  const visited = new Set<string>();
-  const sorted: SymbolicTensor[] = [];
-  const recipientMap: RecipientMap = {};
-
-  // Put keys of the feedDict into visited first, so they don't have to be
-  // walked. This is needed in case where there are feeds for intermediate
-  // SymbolicTensors of the graph.
-  for (const key of feedDict.names()) {
-    visited.add(key);
-  }
-
-  const stack: SymbolicTensor[] = [];
-  const marks: number[] = [];
-
-  // Initial population of stack and marks.
-  stack.push(fetch);
-
-  while (stack.length > 0) {
-    const top = stack[stack.length - 1];
-    if (visited.has(top.name)) {
-      stack.pop();
-      continue;
-    }
-    const topIsMarked = marks[marks.length - 1] === stack.length - 1;
-    if (top.inputs.length === 0 || topIsMarked) {
-      // Input SymbolicTensor or all children have been visited.
-      stack.pop();
-      sorted.push(top);
-      visited.add(top.name);
-      if (topIsMarked) {
-        marks.pop();
-      }
-    } else {
-      // A non-input SymbolicTensor whose upstream SymbolicTensors haven't
-      // been visited yet. Push them onto the stack.
-      marks.push(stack.length - 1);
-      for (const input of top.inputs) {
-        // Increment the recipient count. Note that this needs to happen
-        // regardless of whether the SymbolicTensor has been visited before.
-        if (recipientMap[input.name] == null) {
-          recipientMap[input.name] = new Set<string>();
-        }
-        recipientMap[input.name].add(top.name);
-
-        if (visited.has(input.name)) {
-          continue;  // Avoid repeated visits to the same SymbolicTensor.
-        }
-        stack.push(input);
-      }
-    }
-  }
-  return {sorted, recipientMap};
-}
-
-/**
- * Get the symbolic output tensors of the node to which a given fetch belongs.
- * @param fetch The fetched symbolic tensor.
- * @returns The Array of symbolic tensors output by the node to which `fetch`
- *   belongs.
- */
-function getNodeOutputs(fetch: SymbolicTensor): SymbolicTensor|
-    SymbolicTensor[] {
-  let layerOutputs: SymbolicTensor|SymbolicTensor[];
-  if (fetch.sourceLayer.inboundNodes.length === 1) {
-    layerOutputs = fetch.sourceLayer.output;
-  } else {
-    let nodeIndex: number = null;
-    for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {
-      for (const outputTensor of fetch.sourceLayer.inboundNodes[i]
-               .outputTensors) {
-        if (outputTensor.id === fetch.id) {
-          nodeIndex = i;
-          break;
-        }
-      }
-    }
-    layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);
-  }
-  return layerOutputs;
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/executor_test.ts b/tfjs-master/tfjs-layers/src/engine/executor_test.ts
deleted file mode 100644
index 4134e0ef6..000000000
--- a/tfjs-master/tfjs-layers/src/engine/executor_test.ts
+++ /dev/null
@@ -1,255 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for executor_test.ts.
- */
-
-import {dispose, env, memory, ones, Tensor, tensor1d, tensor2d, tensor3d, zeros} from '@tensorflow/tfjs-core';
-
-import * as tfl from '../index';
-import {describeMathCPU, describeMathCPUAndGPU, expectTensorsClose} from '../utils/test_utils';
-
-import {cachedRecipientCounts, execute, ExecutionProbe, FeedDict, getTopologicalSortAndRecipientCountsForOneFetch} from './executor';
-
-// tslint:enable
-
-describeMathCPU('FeedDict', () => {
-  const x = tfl.input({shape: [], name: 'x', dtype: 'float32'});
-  const y = tfl.input({shape: [], name: 'y', dtype: 'float32'});
-  const xValue = tensor1d([42]);
-  const yValue = tensor1d([21]);
-
-  it('FeedDict from a single Feed', () => {
-    const feedDict = new FeedDict([{key: x, value: xValue}]);
-
-    expect(feedDict.hasKey(x)).toBe(true);
-    expect(feedDict.hasKey(y)).toBe(false);
-    expect(feedDict.getValue(x)).toEqual(xValue);
-    expect(() => feedDict.getValue(y)).toThrowError();
-  });
-  it('FeedDict from duplicate Feeds throws error', () => {
-    const feed = {key: x, value: xValue};
-    expect(() => new FeedDict([feed, feed])).toThrowError(/Duplicate key/);
-  });
-  it('Add key and value', () => {
-    const feedDict = new FeedDict();
-    expect(feedDict.hasKey(x)).toBe(false);
-    expect(feedDict.hasKey(y)).toBe(false);
-
-    expect(feedDict.add(x, xValue)).toEqual(feedDict);
-    expect(feedDict.hasKey(x)).toBe(true);
-    expect(feedDict.hasKey(y)).toBe(false);
-
-    expect(feedDict.add(y, yValue)).toEqual(feedDict);
-    expect(feedDict.hasKey(x)).toBe(true);
-    expect(feedDict.hasKey(y)).toBe(true);
-    expect(feedDict.getValue(x)).toEqual(xValue);
-    expect(feedDict.getValue(y)).toEqual(yValue);
-  });
-  it('getValue by tensor name', () => {
-    const feedDict = new FeedDict();
-    expect(feedDict.add(x, xValue)).toEqual(feedDict);
-    expect(feedDict.add(y, yValue)).toEqual(feedDict);
-
-    expect(feedDict.getValue(x.name)).toEqual(xValue);
-    expect(feedDict.getValue(y.name)).toEqual(yValue);
-  });
-  it('Copy constructor', () => {
-    const feedDict1 = new FeedDict().add(x, xValue);
-    const feedDict2 = new FeedDict(feedDict1);
-    expect(feedDict2.hasKey(x)).toBe(true);
-    expect(feedDict2.getValue(x)).toEqual(xValue);
-    expect(feedDict2.hasKey(y)).toBe(false);
-
-    feedDict2.add(y, yValue);
-    expect(feedDict2.hasKey(y)).toBe(true);
-    expect(feedDict2.getValue(y)).toEqual(yValue);
-    expect(feedDict1.hasKey(y)).toBe(false);
-  });
-  it('Add duplicate key and value leads to error', () => {
-    const feedDict = new FeedDict();
-
-    expect(feedDict.add(x, xValue)).toEqual(feedDict);
-    expect(() => feedDict.add(x, xValue)).toThrowError(/Duplicate key/);
-  });
-  it('Feeding compatible value with undetermined dimension works', () => {
-    const s = tfl.input({shape: [null, 4], name: 's', dtype: 'float32'});
-    const sValue = tensor3d([1, 3, 3, 7], [1, 1, 4]);
-    const feedDict = new FeedDict([{key: s, value: sValue}]);
-    expect(feedDict.getValue(s)).toEqual(sValue);
-  });
-});
-
-describeMathCPU('getTopologicalSortAndRecipientCountsForOneFetch', () => {
-  it('Triangular topology', () => {
-    const input = tfl.input({shape: [2, 6]});
-    const f1 = tfl.layers.flatten().apply(input) as tfl.SymbolicTensor;
-    const r1 = tfl.layers.reLU().apply(f1) as tfl.SymbolicTensor;
-    const c1 = tfl.layers.concatenate().apply([f1, r1]) as tfl.SymbolicTensor;
-    const relu2 = tfl.layers.reLU().apply(c1) as tfl.SymbolicTensor;
-
-    const {sorted, recipientMap} =
-        getTopologicalSortAndRecipientCountsForOneFetch(relu2, new FeedDict());
-    expect(sorted).toEqual([input, f1, r1, c1, relu2]);
-    expect(recipientMap[input.name].size).toEqual(1);
-    expect(recipientMap[f1.name].size).toEqual(2);
-    expect(recipientMap[r1.name].size).toEqual(1);
-    expect(recipientMap[c1.name].size).toEqual(1);
-  });
-
-  it('Double triangular topology', () => {
-    const input = tfl.input({shape: [2, 6]});
-    const f1 = tfl.layers.flatten().apply(input) as tfl.SymbolicTensor;
-    const r1 = tfl.layers.reLU().apply(f1) as tfl.SymbolicTensor;
-    const c1 = tfl.layers.concatenate().apply([f1, r1]) as tfl.SymbolicTensor;
-    const r2 = tfl.layers.reLU().apply(c1) as tfl.SymbolicTensor;
-    const c2 = tfl.layers.concatenate().apply([f1, r2]) as tfl.SymbolicTensor;
-    const r3 = tfl.layers.reLU().apply(c2) as tfl.SymbolicTensor;
-
-    const {sorted, recipientMap} =
-        getTopologicalSortAndRecipientCountsForOneFetch(r3, new FeedDict());
-    expect(sorted).toEqual([input, f1, r1, c1, r2, c2, r3]);
-    expect(recipientMap[input.name].size).toEqual(1);
-    expect(recipientMap[f1.name].size).toEqual(3);
-    expect(recipientMap[r1.name].size).toEqual(1);
-    expect(recipientMap[c1.name].size).toEqual(1);
-    expect(recipientMap[r2.name].size).toEqual(1);
-    expect(recipientMap[c2.name].size).toEqual(1);
-  });
-});
-
-describeMathCPUAndGPU('Executor', () => {
-  describe('Linear Graph Topology', () => {
-    let x: tfl.SymbolicTensor;
-    let y: {};
-    let u: tfl.SymbolicTensor;
-    let v: {};
-    let w: {};
-
-    beforeEach(() => {
-      x = tfl.input({shape: [2], name: 'fooInput', dtype: 'float32'});
-      const denseLayer1 = tfl.layers.dense(
-          {units: 5, activation: 'linear', kernelInitializer: 'ones'});
-      y = denseLayer1.apply(x);
-      u = tfl.input({shape: [2], name: 'footInput', dtype: 'float32'});
-      const denseLayer2 = tfl.layers.dense(
-          {units: 5, activation: 'linear', kernelInitializer: 'ones'});
-      const denseLayer3 = tfl.layers.dense(
-          {units: 3, activation: 'linear', kernelInitializer: 'ones'});
-      v = denseLayer2.apply(u);
-      w = denseLayer3.apply(v as tfl.SymbolicTensor);
-    });
-
-    it('Execute Input directly', () => {
-      const xValue = ones([2, 2]);
-      const feedDict = new FeedDict().add(x, xValue);
-      expectTensorsClose(
-          execute(x, feedDict) as Tensor, tensor2d([1, 1, 1, 1], [2, 2]));
-    });
-    it('Input to Dense', () => {
-      const xValue = ones([2, 2]);
-      const feedDict = new FeedDict([{key: x, value: xValue}]);
-      expectTensorsClose(
-          execute(y as tfl.SymbolicTensor, feedDict) as Tensor,
-          tensor2d([2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 5]));
-    });
-    it('Input to Dense1 to Dense2', () => {
-      const uValue = ones([2, 2]);
-      const feedDict = new FeedDict([{key: u, value: uValue}]);
-      expectTensorsClose(
-          execute(w as tfl.SymbolicTensor, feedDict) as Tensor,
-          tensor2d([10, 10, 10, 10, 10, 10], [2, 3]));
-    });
-    it('Feed value to intermediate layers is supported', () => {
-      const vValue = ones([3, 5]);
-      const feedDict =
-          new FeedDict([{key: v as tfl.SymbolicTensor, value: vValue}]);
-      expectTensorsClose(
-          execute(w as tfl.SymbolicTensor, feedDict) as Tensor,
-          tensor2d([5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 3]));
-    });
-    it('Calling execute without all Input feeds available leads to error',
-       () => {
-         const feedDict = new FeedDict();
-         expect(() => execute(y as tfl.SymbolicTensor, feedDict)).toThrow();
-       });
-
-    it('Maximum memory use under linear graph topology', () => {
-      const input = tfl.input({shape: [2, 3]});
-      let y: tfl.SymbolicTensor = input;
-      for (let i = 0; i < 10; ++i) {
-        y = tfl.layers.reshape({targetShape:　i % 2 === 0 ? [6] : [3, 2]})
-                .apply(y) as tfl.SymbolicTensor;
-      }
-      const feedDict = new FeedDict([{key: input, value: zeros([4, 2, 3])}]);
-      const numTensors0 = memory().numTensors;
-      const probe: ExecutionProbe = {};
-      dispose(execute(y, feedDict, null, probe));
-      // Assert no memory leak.
-      expect(memory().numTensors).toEqual(numTensors0);
-      // Assert that intermediate tensors are cleaned up properly during
-      // execution.
-      expect(probe.maxNumTensors).toBeLessThanOrEqual(numTensors0 + 1);
-    });
-  });
-
-  describe('Diamond Graph Topology', () => {
-    it('Calling execute with two fetches and diamond graph works', () => {
-      const x = tfl.input({shape: [2], name: 'fooInput', dtype: 'float32'});
-      const denseLayer1 = tfl.layers.dense({
-        units: 5,
-        activation: 'linear',
-        kernelInitializer: 'ones',
-        name: 'denseLayer1'
-      });
-      const y = denseLayer1.apply(x);
-      const denseLayer2 = tfl.layers.dense({
-        units: 4,
-        activation: 'linear',
-        kernelInitializer: 'ones',
-        name: 'denseLayer2'
-      });
-      const denseLayer3 = tfl.layers.dense({
-        units: 3,
-        activation: 'linear',
-        kernelInitializer: 'ones',
-        name: 'denseLayer3'
-      });
-      const z1 = denseLayer2.apply(y) as tfl.SymbolicTensor;
-      const z2 = denseLayer3.apply(y) as tfl.SymbolicTensor;
-
-      const xValue = ones([2, 2]);
-      const feedDict = new FeedDict([{key: x, value: xValue}]);
-      let callCounter = 0;
-      denseLayer1.setCallHook(() => {
-        callCounter++;
-      });
-
-      const outputs = execute([z1, z2], feedDict) as Tensor[];
-      expectTensorsClose(
-          outputs[0], tensor2d([10, 10, 10, 10, 10, 10, 10, 10], [2, 4]));
-      expectTensorsClose(
-          outputs[1], tensor2d([10, 10, 10, 10, 10, 10], [2, 3]));
-      expect(callCounter).toEqual(1);
-    });
-  });
-
-  describe('Caches', () => {
-    it('changes the maxEntries of caches while updating the TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES flag',
-       () => {
-         const ENV = env();
-         expect(cachedRecipientCounts.getMaxEntries()).toBe(100);
-         ENV.set('TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES', 50);
-         expect(cachedRecipientCounts.getMaxEntries()).toBe(50);
-         ENV.set('TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES', 100);
-       });
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/engine/input_layer.ts b/tfjs-master/tfjs-layers/src/engine/input_layer.ts
deleted file mode 100644
index d93739622..000000000
--- a/tfjs-master/tfjs-layers/src/engine/input_layer.ts
+++ /dev/null
@@ -1,208 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {DataType, serialization, Tensor} from '@tensorflow/tfjs-core';
-
-import {getUid} from '../backend/state';
-import {ValueError} from '../errors';
-import {Shape} from '../keras_format/common';
-import {Kwargs} from '../types';
-
-import {DisposeResult, Layer, Node, SymbolicTensor} from './topology';
-
-/**
- * Constructor arguments for InputLayer.
- *
- * Note: You should provide only inputShape or batchInputShape (not both).
- * If only inputShape is provided, then the batchInputShape is determined by
- * the batchSize argument and the inputShape: [batchSize].concat(inputShape).
- */
-export declare interface InputLayerArgs {
-  /** Input shape, not including the batch axis. */
-  inputShape?: Shape;
-  /** Optional input batch size (integer or null). */
-  batchSize?: number;
-  /** Batch input shape, including the batch axis. */
-  batchInputShape?: Shape;
-  /** Datatype of the input.  */
-  dtype?: DataType;
-  /**
-   * Whether the placeholder created is meant to be sparse.
-   */
-  sparse?: boolean;  // TODO(michaelterry): Not clear whether we'll need this.
-
-  /** Name of the layer. */
-  name?: string;
-}
-
-export class InputLayer extends Layer {
-  /** @nocollapse */
-  static readonly className = 'InputLayer';
-  sparse: boolean;
-  constructor(args: InputLayerArgs) {
-    super({
-      dtype: args.dtype,
-      name: args.name != null ? args.name : getUid('input').toString()
-    });
-    // Normalize config.batchSize and config.sparse
-    if (args.batchSize == null) {
-      args.batchSize = null;
-    }
-    if (args.sparse == null) {
-      args.sparse = false;
-    }
-
-    this.trainable = false;
-    this.built = true;
-    this.sparse = args.sparse;
-
-    if (args.inputShape != null && args.batchInputShape != null) {
-      throw new ValueError(
-          'Only provide the inputShape OR ' +
-          'batchInputShape argument to inputLayer, not both at the same time.');
-    }
-    let batchInputShape = args.batchInputShape;
-    if (batchInputShape == null) {
-      if (args.inputShape == null) {
-        throw new ValueError(
-            'An InputLayer should be passed either a ' +
-            '`batchInputShape` or an `inputShape`.');
-      } else {
-        batchInputShape = [args.batchSize].concat(args.inputShape);
-      }
-    } else {
-      // TODO(michaelterry): Backport to PyKeras
-      if (args.batchSize != null) {
-        throw new ValueError(
-            'Cannot specify batchSize if batchInputShape is ' +
-            'specified when creating an InputLayer.');
-      }
-    }
-
-    const dtype = args.dtype || 'float32';
-
-    this.batchInputShape = batchInputShape;
-    this.dtype = dtype;
-    // TODO(michaelterry): Backport this to PyKeras?
-    this.inputSpec = [{shape: batchInputShape}];
-
-    const inputTensor = new SymbolicTensor(
-        this.dtype, this.batchInputShape, this, [], {}, this.name);
-    inputTensor.nodeIndex = 0;
-    inputTensor.tensorIndex = 0;
-
-    // Create an input node to add to this.outboundNode.
-    // (This call has side effects.)
-    // tslint:disable-next-line:no-unused-expression
-    new Node({
-      outboundLayer: this,
-      inboundLayers: [],
-      nodeIndices: [],
-      tensorIndices: [],
-      inputTensors: [inputTensor],
-      outputTensors: [inputTensor],
-      inputMasks: [null],
-      outputMasks: [null],
-      inputShapes: [batchInputShape],
-      outputShapes: [batchInputShape]
-    });
-  }
-
-  override apply(
-      inputs: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],
-      kwargs?: Kwargs): Tensor|Tensor[]|SymbolicTensor {
-    throw new ValueError(
-        'Cannot pass any input to an ' +
-        `InputLayer's apply() method. InputLayer name: ${this.name}`);
-  }
-
-  override dispose(): DisposeResult {
-    // dispose() for InputLayer is overridden as no-op.
-    return {refCountAfterDispose: this._refCount, numDisposedVariables: 0};
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {
-      batchInputShape: this.batchInputShape,
-      dtype: this.dtype,
-      sparse: this.sparse,
-      name: this.name
-    };
-  }
-}
-serialization.registerClass(InputLayer);
-
-/**
- * Config for the Input function.
- *
- * Note: You should provide only shape or batchShape (not both).
- * If only shape is provided, then the batchShape becomes
- * [null].concat(inputShape).
- */
-export interface InputConfig {
-  /**
-   * A shape, not including the batch size. For instance, `shape=[32]`
-   * indicates that the expected input will be batches of 32-dimensional
-   * vectors.
-   */
-  shape?: Shape;
-  /**
-   * A shape tuple (integer), including the batch size. For instance,
-   * `batchShape=[10, 32]` indicates that the expected input will be batches of
-   * 10 32-dimensional vectors. `batchShape=[null, 32]` indicates batches of an
-   * arbitrary number of 32-dimensional vectors.
-   */
-  batchShape?: Shape;
-  /**
-   * An optional name string for the layer. Should be unique in a model (do not
-   * reuse the same name twice). It will be autogenerated if it isn't provided.
-   */
-  name?: string;
-  dtype?: DataType;
-  /**
-   * A boolean specifying whether the placeholder to be created is sparse.
-   */
-  sparse?: boolean;
-}
-
-export function Input(config: InputConfig): SymbolicTensor {
-  if (config.batchShape == null && config.shape == null) {
-    throw new Error(
-        'Please provide to Input either a `shape`' +
-        ' or a `batchShape` argument. Note that ' +
-        '`shape` does not include the batch ' +
-        'dimension.');
-  }
-  if (config.batchShape != null && config.shape != null) {
-    // TODO(michaelterry): Backport to PyKeras.
-    throw new ValueError(
-        'Please provide either a `shape` or `batchShape` ' +
-        'argument to Input, but not both.');
-  }
-  let batchShape = config.batchShape;
-  if (config.shape != null && batchShape == null) {
-    batchShape = [null].concat(config.shape);
-  }
-
-  let dtype = config.dtype;
-  if (dtype == null) {
-    dtype = 'float32';
-  }
-
-  const inputLayer = new InputLayer({
-    batchInputShape: batchShape,
-    name: config.name,
-    dtype,
-    sparse: config.sparse
-  });
-
-  const outputs = inputLayer.inboundNodes[0].outputTensors;
-  return outputs[0];
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/input_layer_test.ts b/tfjs-master/tfjs-layers/src/engine/input_layer_test.ts
deleted file mode 100644
index 206677850..000000000
--- a/tfjs-master/tfjs-layers/src/engine/input_layer_test.ts
+++ /dev/null
@@ -1,245 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {ones} from '@tensorflow/tfjs-core';
-
-import * as tfl from '../index';
-import {describeMathCPU} from '../utils/test_utils';
-
-import {Input} from './input_layer';
-import {LayerArgs} from './topology';
-
-describeMathCPU('InputLayer', () => {
-  it('when initialized to its defaults throws an exception', () => {
-    expect(() => tfl.layers.inputLayer({}))
-        .toThrowError(/InputLayer should be passed either/);
-  });
-  describe('initialized with only an inputShape', () => {
-    const inputShape = [1];
-    const inputLayer = tfl.layers.inputLayer({inputShape});
-
-    it('is not trainable.', () => {
-      expect(inputLayer.trainable).toBe(false);
-    });
-
-    it('is built.', () => {
-      expect(inputLayer.built).toBe(true);
-    });
-
-    it('is not sparse.', () => {
-      expect(inputLayer.sparse).toBe(false);
-    });
-
-    it('automatically assigns a name.', () => {
-      expect(inputLayer.name).toMatch(/^input.*$/);
-    });
-
-    it('creates a batchInputShape of [null].concat(inputShape).', () => {
-      expect(inputLayer.batchInputShape).toEqual([null].concat(inputShape));
-    });
-
-    it('has no outboundNodes', () => {
-      expect(inputLayer.outboundNodes.length).toEqual(0);
-    });
-
-    it('has one inboundNode', () => {
-      expect(inputLayer.inboundNodes.length).toEqual(1);
-    });
-
-    describe('creates an inbound Node', () => {
-      const inboundNode = inputLayer.inboundNodes[0];
-      it('with no inboundLayers, nodeIndices, or tensorIndices', () => {
-        expect(inboundNode.inboundLayers.length).toEqual(0);
-        expect(inboundNode.nodeIndices.length).toEqual(0);
-        expect(inboundNode.tensorIndices.length).toEqual(0);
-      });
-
-      it('with [null] inputMasks and outputMasks', () => {
-        expect(inboundNode.inputMasks).toEqual([null]);
-        expect(inboundNode.outputMasks).toEqual([null]);
-      });
-
-      it('with equal inputShapes and outputShapes', () => {
-        expect(inboundNode.inputShapes).toEqual(inboundNode.outputShapes);
-        expect(inboundNode.inputShapes).toEqual([[null].concat(inputShape)]);
-      });
-
-      describe('with a SymbolicTensor', () => {
-        const symbolicTensor = inboundNode.inputTensors[0];
-
-        it('that is defined.', () => {
-          expect(symbolicTensor instanceof tfl.SymbolicTensor).toBe(true);
-        });
-
-        it('assigned to both the input and outputTensors.', () => {
-          expect(inboundNode.inputTensors.length).toEqual(1);
-          expect(inboundNode.outputTensors.length).toEqual(1);
-          expect(inboundNode.inputTensors).toEqual(inboundNode.outputTensors);
-        });
-
-        it('with a node and tensorIndex of 0.', () => {
-          expect(symbolicTensor.nodeIndex).toEqual(0);
-          expect(symbolicTensor.tensorIndex).toEqual(0);
-        });
-
-        it('with a sourceLayer of the inputLayer.', () => {
-          expect(symbolicTensor.sourceLayer).toEqual(inputLayer);
-        });
-
-        it('with a name matching the inputLayer name.', () => {
-          expect(symbolicTensor.name).toEqual(inputLayer.name);
-        });
-
-        it('with a dtype equal to the inputLayer.', () => {
-          expect(symbolicTensor.dtype).toEqual(inputLayer.dtype);
-        });
-
-        it('with a shape matching the inputLayer.batchInputShape', () => {
-          expect(symbolicTensor.shape).toEqual(inputLayer.batchInputShape);
-        });
-      });
-    });
-  });
-
-  // See https://github.com/tensorflow/tfjs/issues/1341
-  it('allow `null` in shape', () => {
-    const inputShape = [null, 2];
-    const inputs = tfl.layers.inputLayer({inputShape});
-    expect(inputs.inputSpec[0].shape).toEqual([null].concat(inputShape));
-  });
-
-  it('throws an exception if both inputShape and batchInputShape ' +
-         'are specified during initialization.',
-     () => {
-       expect(
-           () => tfl.layers.inputLayer({inputShape: [1], batchInputShape: [1]}))
-           .toThrowError(/Only provide the inputShape OR batchInputShape/);
-     });
-
-  for (const batchSize of [null, 5]) {
-    it('initializes with batchSize when inputShape specified', () => {
-      const inputShape = [1];
-      const inputLayer = tfl.layers.inputLayer({inputShape, batchSize});
-      expect(inputLayer.batchInputShape).toEqual([
-        batchSize
-      ].concat(inputShape));
-    });
-  }
-
-  it('initializes with batchInputShape if specified.', () => {
-    const batchInputShape = [1, 2];
-    const inputLayer = tfl.layers.inputLayer({batchInputShape});
-    expect(inputLayer.batchInputShape).toEqual(batchInputShape);
-  });
-
-  it('initializes with batchInputShape if null specified for the batch size.',
-     () => {
-       const batchInputShape = [1, 2];
-       const inputLayer = tfl.layers.inputLayer({batchInputShape});
-       expect(inputLayer.batchInputShape).toEqual(batchInputShape);
-     });
-
-  it('throws exception if batchSize and batchInputShape are specified.', () => {
-    expect(() => tfl.layers.inputLayer({batchInputShape: [1], batchSize: 5}))
-        .toThrowError(/Cannot specify batchSize if batchInputShape/);
-  });
-
-  for (const sparse of [true, false]) {
-    it('uses config.sparse during initialization.', () => {
-      const inputLayer =
-          tfl.layers.inputLayer({inputShape: [1], sparse});
-      expect(inputLayer.sparse).toEqual(sparse);
-    });
-  }
-
-  it('use config.dtype during initialization.', () => {
-    const dtype = 'float32';
-    const inputLayer = tfl.layers.inputLayer({inputShape: [1], dtype});
-    expect(inputLayer.dtype).toEqual(dtype);
-  });
-
-  it('use config.name during initialization.', () => {
-    const name = 'abc';
-    const inputLayer = tfl.layers.inputLayer({inputShape: [1], name});
-    expect(inputLayer.name).toEqual(name);
-  });
-
-  it('throws an exception if apply() is called with any input.', () => {
-    const inputLayer = tfl.layers.inputLayer({inputShape: [1]});
-    const symbolicTensor = new tfl.SymbolicTensor('float32', [2], null, [], {});
-    expect(() => inputLayer.apply(symbolicTensor))
-        .toThrowError(/Cannot pass any input to an InputLayer's apply/);
-  });
-
-  it('throws an exception if its inputs differ in shape to what it ' +
-         'was initialized to.',
-     () => {
-       const inputLayer = tfl.layers.inputLayer({inputShape: [1]});
-       const inputs = ones([2, 2]);
-       expect(() => inputLayer.apply(inputs)).toThrowError();
-     });
-
-  it('returns a serializable config.', () => {
-    const batchInputShape = [1];
-    const dtype = 'float32';
-    const sparse = true;
-    const name = 'my_name';
-    const inputLayer =
-        tfl.layers.inputLayer({batchInputShape, dtype, sparse, name});
-    expect(inputLayer.getConfig())
-        .toEqual({batchInputShape, dtype, sparse, name});
-  });
-});
-
-class LayerForTest extends tfl.layers.Layer {
-  static className = 'LayerForTest';
-  constructor(args: LayerArgs) {
-    super(args);
-  }
-}
-
-describe('Input()', () => {
-  it('throws an exception if neither shape nor batchShape are specified',
-     () => {
-       expect(() => tfl.layers.input({}))
-           .toThrowError(/Please provide to Input either/);
-     });
-
-  const shape = [1];
-  const batchShape = [2, 2];
-  const name = 'abc';
-  const dtype = 'float32';
-
-  it('returns an initialized symbolicTensor given a shape.', () => {
-    const symbolicTensor = tfl.layers.input({shape, name, dtype});
-    expect(symbolicTensor instanceof tfl.SymbolicTensor).toBe(true);
-    expect(symbolicTensor.shape).toEqual([null].concat(shape));
-    expect(symbolicTensor.name).toMatch(/abc/);
-    expect(symbolicTensor.dtype).toEqual(dtype);
-  });
-
-  it('returns a SymbolicTensor given a batchShape', () => {
-    const symbolicTensor = tfl.layers.input({batchShape});
-    expect(symbolicTensor.shape).toEqual(batchShape);
-  });
-
-  it('throws exception if both shape and batchShape are specified.', () => {
-    expect(() => tfl.layers.input({shape, batchShape}))
-        .toThrowError(/Please provide either a `shape`/);
-  });
-
-  it('produces output that can feed into a Layer.', () => {
-    const inputTensor = Input({shape, name});
-    const otherLayer = new LayerForTest({name: 'firstLayer'});
-    const output = otherLayer.apply(inputTensor) as tfl.SymbolicTensor;
-    expect(output instanceof tfl.SymbolicTensor).toBe(true);
-    expect(output.name).toEqual('firstLayer/firstLayer');
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/engine/topology.ts b/tfjs-master/tfjs-layers/src/engine/topology.ts
deleted file mode 100644
index 0bc4fb843..000000000
--- a/tfjs-master/tfjs-layers/src/engine/topology.ts
+++ /dev/null
@@ -1,1683 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original source: keras/engine/topology.py */
-
-import {DataType, Scalar, serialization, Tensor, tidy, util} from '@tensorflow/tfjs-core';
-
-import {getNextUniqueTensorId, getUid} from '../backend/state';
-import {getScopedTensorName, getUniqueTensorName, nameScope} from '../common';
-import {Constraint} from '../constraints';
-import {AttributeError, NotImplementedError, RuntimeError, ValueError} from '../errors';
-import {getInitializer, Initializer} from '../initializers';
-import {Shape} from '../keras_format/common';
-import {Regularizer} from '../regularizers';
-import {Kwargs, RegularizerFn} from '../types';
-import * as generic_utils from '../utils/generic_utils';
-import * as types_utils from '../utils/types_utils';
-import * as variable_utils from '../utils/variable_utils';
-import {batchGetValue, batchSetValue, LayerVariable} from '../variables';
-
-// TODO(michaelterry): This is a stub until it's defined.
-export type Op = (x: LayerVariable) => LayerVariable;
-
-/**
- * Constructor arguments for InputSpec.
- */
-export interface InputSpecArgs {
-  /** Expected datatype of the input. */
-  dtype?: DataType;
-  /** Expected shape of the input (may include null for unchecked axes). */
-  shape?: Shape;
-  /** Expected rank of the input. */
-  ndim?: number;
-  /** Maximum rank of the input. */
-  maxNDim?: number;
-  /** Minimum rank of the input. */
-  minNDim?: number;
-  /** Dictionary mapping integer axes to a specific dimension value. */
-  axes?: {[axis: number]: number};
-}
-
-/**
- * Specifies the ndim, dtype and shape of every input to a layer.
- *
- * Every layer should expose (if appropriate) an `inputSpec` attribute:
- * a list of instances of InputSpec (one per input tensor).
- *
- * A null entry in a shape is compatible with any dimension,
- * a null shape is compatible with any shape.
- */
-export class InputSpec {
-  /** Expected datatype of the input. */
-  dtype?: DataType;
-  /** Expected shape of the input (may include null for unchecked axes). */
-  shape?: Shape;
-  /** Expected rank of the input. */
-  ndim?: number;
-  /** Maximum rank of the input. */
-  maxNDim?: number;
-  /** Minimum rank of the input. */
-  minNDim?: number;
-  /** Dictionary mapping integer axes to a specific dimension value. */
-  axes?: {[axis: number]: number};
-
-  constructor(args: InputSpecArgs) {
-    this.dtype = args.dtype;
-    this.shape = args.shape;
-    /*
-      TODO(michaelterry): Could throw error if ndim and shape are both defined
-        (then backport).
-    */
-    if (args.shape != null) {
-      this.ndim = args.shape.length;
-    } else {
-      this.ndim = args.ndim;
-    }
-    this.maxNDim = args.maxNDim;
-    this.minNDim = args.minNDim;
-    this.axes = args.axes || {};
-  }
-}
-
-/**
- * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.
- *
- * They are most often encountered when building a graph of `Layer`s for a
- * `tf.LayersModel` and the input data's shape, but not values are known.
- *
- * @doc {heading: 'Models', 'subheading': 'Classes'}
- */
-export class SymbolicTensor {
-  /* A unique ID for the tensor to be able to differentiate tensors. */
-  readonly id: number;
-  // The fully scoped name of this Variable, including a unique suffix if needed
-  readonly name: string;
-  // The originally requested fully scoped name of this Variable, not including
-  // any unique suffix.  This may be needed when restoring weights because this
-  // original name is used as a key.
-  readonly originalName?: string;
-  /**
-   * Rank/dimensionality of the tensor.
-   */
-  readonly rank: number;
-  /**
-   * Replacement for _keras_history.
-   */
-  nodeIndex: number;
-  /**
-   * Replacement for _keras_history.
-   */
-  tensorIndex: number;
-
-  /**
-   *
-   * @param dtype
-   * @param shape
-   * @param sourceLayer The Layer that produced this symbolic tensor.
-   * @param inputs The inputs passed to sourceLayer's __call__() method.
-   * @param nodeIndex
-   * @param tensorIndex
-   * @param callArgs The keyword arguments passed to the __call__() method.
-   * @param name
-   * @param outputTensorIndex The index of this tensor in the list of outputs
-   *   returned by apply().
-   */
-  constructor(
-      readonly dtype: DataType, readonly shape: Shape,
-      public sourceLayer: Layer, readonly inputs: SymbolicTensor[],
-      readonly callArgs: Kwargs, name?: string,
-      readonly outputTensorIndex?: number) {
-    this.id = getNextUniqueTensorId();
-    if (name != null) {
-      this.originalName = getScopedTensorName(name);
-      this.name = getUniqueTensorName(this.originalName);
-    }
-    this.rank = shape.length;
-  }
-}
-
-/**
- * Constructor arguments for Node.
- */
-export interface NodeArgs {
-  /**
-   * The layer that takes `inputTensors` and turns them into `outputTensors`.
-   * (the node gets created when the `call` method of the layer is called).
-   */
-  outboundLayer: Layer;
-  /**
-   * A list of layers, the same length as `inputTensors`, the layers from where
-   * `inputTensors` originate.
-   */
-  inboundLayers: Layer[];
-  /**
-   * A list of integers, the same length as `inboundLayers`. `nodeIndices[i]` is
-   * the origin node of `inputTensors[i]` (necessary since each inbound layer
-   * might have several nodes, e.g. if the layer is being shared with a
-   * different data stream).
-   */
-  nodeIndices: number[];
-  /**
-   * A list of integers, the same length as `inboundLayers`. `tensorIndices[i]`
-   * is the index of `inputTensors[i]` within the output of the inbound layer
-   * (necessary since each inbound layer might have multiple tensor outputs,
-   * with each one being independently manipulable).
-   */
-  tensorIndices: number[];
-  /** List of input tensors. */
-  inputTensors: SymbolicTensor[];
-  /** List of output tensors. */
-  outputTensors: SymbolicTensor[];
-  /** List of input masks (a mask can be a tensor, or null). */
-  inputMasks: Tensor[];
-  /** List of output masks (a mask can be a tensor, or null). */
-  outputMasks: Tensor[];
-  /** List of input shape tuples. */
-  inputShapes: Shape|Shape[];
-  /** List of output shape tuples. */
-  outputShapes: Shape|Shape[];
-}
-
-/**
- * The type of the return value of Layer.dispose() and Container.dispose().
- */
-export interface DisposeResult {
-  /**
-   * Reference count after the dispose call.
-   */
-  refCountAfterDispose: number;
-
-  /**
-   * Number of variables dispose in this dispose call.
-   */
-  numDisposedVariables: number;
-}
-
-let _nextNodeID = 0;
-
-/**
- * A `Node` describes the connectivity between two layers.
- *
- * Each time a layer is connected to some new input,
- * a node is added to `layer.inboundNodes`.
- *
- * Each time the output of a layer is used by another layer,
- * a node is added to `layer.outboundNodes`.
- *
- * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates
- * describing the origin of the `inputTensors`, verifying the following:
- *
- * `inputTensors[i] ==
- * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[
- *   tensorIndices[i]]`
- *
- * A node from layer A to layer B is added to:
- *     A.outboundNodes
- *     B.inboundNodes
- */
-export class Node {
-  /**
-   * The layer that takes `inputTensors` and turns them into `outputTensors`
-   * (the node gets created when the `call` method of the layer is called).
-   */
-  outboundLayer: Layer;
-  /**
-   * A list of layers, the same length as `inputTensors`, the layers from where
-   * `inputTensors` originate.
-   */
-  inboundLayers: Layer[];
-  /**
-   * A list of integers, the same length as `inboundLayers`. `nodeIndices[i]` is
-   * the origin node of `inputTensors[i]` (necessary since each inbound layer
-   * might have several nodes, e.g. if the layer is being shared with a
-   * different data stream).
-   */
-  nodeIndices: number[];
-  /**
-   * A list of integers, the same length as `inboundLayers`. `tensorIndices[i]`
-   * is the index of `inputTensors[i]` within the output of the inbound layer
-   * (necessary since each inbound layer might have multiple tensor outputs,
-   * with each one being independently manipulable).
-   */
-  tensorIndices: number[];
-  /** List of input tensors. */
-  inputTensors: SymbolicTensor[];
-  /** List of output tensors. */
-  outputTensors: SymbolicTensor[];
-  /** List of input masks (a mask can be a tensor, or null). */
-  inputMasks: Tensor[];
-  /** List of output masks (a mask can be a tensor, or null). */
-  outputMasks: Tensor[];
-  /** List of input shape tuples. */
-  inputShapes: Shape|Shape[];
-  /** List of output shape tuples. */
-  outputShapes: Shape|Shape[];
-
-  readonly id: number;
-
-  constructor(
-      args: NodeArgs,
-      // TODO(michaelterry): Define actual type for this.
-      public callArgs?: Kwargs) {
-    this.id = _nextNodeID++;
-    /*
-      Layer instance (NOT a list).
-      this is the layer that takes a list of input tensors
-      and turns them into a list of output tensors.
-      the current node will be added to
-      the inboundNodes of outboundLayer.
-    */
-    this.outboundLayer = args.outboundLayer;
-
-    /*
-        The following 3 properties describe where
-        the input tensors come from: which layers,
-        and for each layer, which node and which
-        tensor output of each node.
-    */
-
-    // List of layer instances.
-    this.inboundLayers = args.inboundLayers;
-    // List of integers, 1:1 mapping with inboundLayers.
-    this.nodeIndices = args.nodeIndices;
-    // List of integers, 1:1 mapping with inboundLayers.
-    this.tensorIndices = args.tensorIndices;
-
-    /*
-        Following 2 properties:
-        tensor inputs and outputs of outboundLayer.
-    */
-
-    // List of tensors. 1:1 mapping with inboundLayers.
-    this.inputTensors = args.inputTensors;
-    // List of tensors, created by outboundLayer.call().
-    this.outputTensors = args.outputTensors;
-
-    /*
-        Following 2 properties: input and output masks.
-        List of tensors, 1:1 mapping with inputTensor.
-    */
-    this.inputMasks = args.inputMasks;
-    // List of tensors, created by outboundLayer.computeMask().
-    this.outputMasks = args.outputMasks;
-
-    // Following 2 properties: input and output shapes.
-
-    // List of shape tuples, shapes of inputTensors.
-    this.inputShapes = args.inputShapes;
-    // List of shape tuples, shapes of outputTensors.
-    this.outputShapes = args.outputShapes;
-
-    // Add nodes to all layers involved.
-    for (const layer of args.inboundLayers) {
-      if (layer != null) {
-        layer.outboundNodes.push(this);
-      }
-    }
-    args.outboundLayer.inboundNodes.push(this);
-  }
-
-  getConfig(): serialization.ConfigDict {
-    const inboundNames: string[] = [];
-    for (const layer of this.inboundLayers) {
-      if (layer != null) {
-        inboundNames.push(layer.name);
-      } else {
-        inboundNames.push(null);
-      }
-    }
-    return {
-      outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,
-      inboundLayers: inboundNames,
-      nodeIndices: this.nodeIndices,
-      tensorIndices: this.tensorIndices
-    };
-  }
-}
-
-/** Constructor arguments for Layer. */
-export declare interface LayerArgs {
-  /**
-   * If defined, will be used to create an input layer to insert before this
-   * layer. If both `inputShape` and `batchInputShape` are defined,
-   * `batchInputShape` will be used. This argument is only applicable to input
-   * layers (the first layer of a model).
-   */
-  inputShape?: Shape;
-  /**
-   * If defined, will be used to create an input layer to insert before this
-   * layer. If both `inputShape` and `batchInputShape` are defined,
-   * `batchInputShape` will be used. This argument is only applicable to input
-   * layers (the first layer of a model).
-   */
-  batchInputShape?: Shape;
-  /**
-   * If `inputShape` is specified and `batchInputShape` is *not* specified,
-   * `batchSize` is used to construct the `batchInputShape`: `[batchSize,
-   * ...inputShape]`
-   */
-  batchSize?: number;
-  /**
-   * The data-type for this layer. Defaults to 'float32'.
-   * This argument is only applicable to input layers (the first layer of a
-   * model).
-   */
-  dtype?: DataType;
-  /** Name for this layer. */
-  name?: string;
-  /**
-   * Whether the weights of this layer are updatable by `fit`.
-   * Defaults to true.
-   */
-  trainable?: boolean;
-  /**
-   * Initial weight values of the layer.
-   */
-  weights?: Tensor[];
-  /** Legacy support. Do not use for new code. */
-  inputDType?: DataType;
-}
-
-// If necessary, add `output` arguments to the CallHook function.
-// This is currently used for testing only, but may be used for debugger-related
-// purposes in the future.
-export type CallHook = (inputs: Tensor|Tensor[], kwargs: Kwargs) => void;
-
-let _nextLayerID = 0;
-
-/**
- * A layer is a grouping of operations and weights that can be composed to
- * create a `tf.LayersModel`.
- *
- * Layers are constructed by using the functions under the
- * [tf.layers](#Layers-Basic) namespace.
- *
- * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}
- */
-export abstract class Layer extends serialization.Serializable {
-  /** Name for this layer. Must be unique within a model. */
-  name: string;
-  /**
-   * List of InputSpec class instances.
-   *
-   * Each entry describes one required input:
-   * - ndim
-   * - dtype
-   * A layer with `n` input tensors must have an `inputSpec` of length `n`.
-   */
-  inputSpec: InputSpec[];
-  supportsMasking: boolean;
-  /** Whether the layer weights will be updated during training. */
-  protected trainable_: boolean;
-  batchInputShape: Shape;
-  dtype: DataType;
-  initialWeights: Tensor[];
-
-  inboundNodes: Node[];
-  outboundNodes: Node[];
-
-  activityRegularizer: Regularizer;
-
-  protected _trainableWeights: LayerVariable[];
-  private _nonTrainableWeights: LayerVariable[];
-  private _losses: RegularizerFn[];
-  // TODO(cais): _updates is currently unused.
-  private _updates: Tensor[];
-  private _built: boolean;
-  private _callHook: CallHook = null;
-
-  private _addedWeightNames: string[] = [];
-
-  readonly id: number;
-
-  // Porting Notes: PyKeras does not have this property in this base Layer
-  //   class. Instead lets Layer subclass set it dynamically and checks the
-  //   value with `hasattr`. In tfjs-layers, we let this be a member of this
-  //   base class.
-  protected _stateful = false;
-
-  protected _refCount: number|null;
-
-  // A flag for whether fast (i.e., all-zero) weight initialization is to
-  // be used during `build()` call. This speeds up weight initialization
-  // by saving unnecessary calls to expensive initializers in cases where
-  // the initialized values will be overwritten by loaded weight values
-  // during model loading.
-  private fastWeightInitDuringBuild: boolean;
-
-  constructor(args: LayerArgs = {}) {
-    super();
-    this.id = _nextLayerID++;
-
-    this.activityRegularizer = null;
-
-    this.inputSpec = null;
-    this.supportsMasking = false;
-
-    // These properties will be set upon call of this.build()
-    this._trainableWeights = [];
-    this._nonTrainableWeights = [];
-    this._losses = [];
-    this._updates = [];
-    this._built = false;
-
-    /*
-      These lists will be filled via successive calls
-      to this.addInboundNode().
-     */
-    this.inboundNodes = [];
-    this.outboundNodes = [];
-
-    let name = args.name;
-    if (!name) {
-      const prefix = this.getClassName();
-      name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);
-    }
-    this.name = name;
-
-    this.trainable_ = args.trainable == null ? true : args.trainable;
-
-    if (args.inputShape != null || args.batchInputShape != null) {
-      /*
-        In this case we will later create an input layer
-        to insert before the current layer
-       */
-      let batchInputShape: Shape;
-      if (args.batchInputShape != null) {
-        batchInputShape = args.batchInputShape;
-      } else if (args.inputShape != null) {
-        let batchSize: number = null;
-        if (args.batchSize != null) {
-          batchSize = args.batchSize;
-        }
-        batchInputShape = [batchSize].concat(args.inputShape);
-      }
-      this.batchInputShape = batchInputShape;
-
-      // Set dtype.
-      let dtype = args.dtype;
-      if (dtype == null) {
-        dtype = args.inputDType;
-      }
-      if (dtype == null) {
-        dtype = 'float32';
-      }
-      this.dtype = dtype;
-    }
-
-    if (args.weights != null) {
-      this.initialWeights = args.weights;
-    } else {
-      this.initialWeights = null;
-    }
-
-    // The value of `_refCount` is initialized to null. When the layer is used
-    // in a symbolic way for the first time, it will be set to 1.
-    this._refCount = null;
-
-    this.fastWeightInitDuringBuild = false;
-  }
-
-  /**
-   * Converts a layer and its index to a unique (immutable type) name.
-   * This function is used internally with `this.containerNodes`.
-   * @param layer The layer.
-   * @param nodeIndex The layer's position (e.g. via enumerate) in a list of
-   *   nodes.
-   *
-   * @returns The unique name.
-   */
-  protected static nodeKey(layer: Layer, nodeIndex: number) {
-    return layer.name + '_ib-' + nodeIndex.toString();
-  }
-
-  /**
-   * Returns this.inboundNode at index nodeIndex.
-   *
-   * Porting note: This is a replacement for _get_node_attribute_at_index()
-   * @param nodeIndex
-   * @param attrName The name of the attribute related to request for this node.
-   */
-  private getNodeAtIndex(nodeIndex: number, attrName: string): Node {
-    if (this.inboundNodes.length === 0) {
-      throw new RuntimeError(
-          'The layer has never been called ' +
-          `and thus has no defined ${attrName}.`);
-    }
-    if (this.inboundNodes.length <= nodeIndex) {
-      throw new ValueError(
-          `Asked to get ${attrName} at node ${nodeIndex}, ` +
-          `but the layer has only ${this.inboundNodes.length} inbound nodes.`);
-    }
-    return this.inboundNodes[nodeIndex];
-  }
-
-  /**
-   * Retrieves the input tensor(s) of a layer at a given node.
-   *
-   * @param nodeIndex Integer, index of the node from which to retrieve the
-   *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer
-   *   was called.
-   *
-   * @return A tensor (or list of tensors if the layer has multiple inputs).
-   */
-  getInputAt(nodeIndex: number): SymbolicTensor|SymbolicTensor[] {
-    return generic_utils.singletonOrArray(
-        this.getNodeAtIndex(nodeIndex, 'input').inputTensors);
-  }
-
-  /**
-   * Retrieves the output tensor(s) of a layer at a given node.
-   *
-   * @param nodeIndex Integer, index of the node from which to retrieve the
-   *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer
-   *   was called.
-   *
-   * @return A tensor (or list of tensors if the layer has multiple outputs).
-   */
-  getOutputAt(nodeIndex: number): SymbolicTensor|SymbolicTensor[] {
-    return generic_utils.singletonOrArray(
-        this.getNodeAtIndex(nodeIndex, 'output').outputTensors);
-  }
-
-  // Properties
-
-  /**
-   * Retrieves the input tensor(s) of a layer.
-   *
-   * Only applicable if the layer has exactly one inbound node,
-   * i.e. if it is connected to one incoming layer.
-   *
-   * @return Input tensor or list of input tensors.
-   *
-   * @exception AttributeError if the layer is connected to more than one
-   *   incoming layers.
-   */
-  get input(): SymbolicTensor|SymbolicTensor[] {
-    if (this.inboundNodes.length > 1) {
-      throw new AttributeError(
-          `Layer ${this.name}` +
-          ' has multiple inbound nodes, ' +
-          'hence the notion of "layer input" ' +
-          'is ill-defined. ' +
-          'Use `getInputAt(nodeIndex)` instead.');
-    } else if (this.inboundNodes.length === 0) {
-      throw new AttributeError(
-          `Layer ${this.name}` +
-          ' is not connected, no input to return.');
-    }
-    return generic_utils.singletonOrArray(
-        this.getNodeAtIndex(0, 'input').inputTensors);
-  }
-
-  /**
-   * Retrieves the output tensor(s) of a layer.
-   *
-   * Only applicable if the layer has exactly one inbound node,
-   * i.e. if it is connected to one incoming layer.
-   *
-   * @return Output tensor or list of output tensors.
-   *
-   * @exception AttributeError if the layer is connected to more than one
-   *   incoming layers.
-   */
-  get output(): SymbolicTensor|SymbolicTensor[] {
-    if (this.inboundNodes.length === 0) {
-      throw new AttributeError(
-          `Layer ${this.name}` +
-          ' has no inbound nodes.');
-    }
-    if (this.inboundNodes.length > 1) {
-      throw new AttributeError(
-          `Layer ${this.name}` +
-          ' has multiple inbound nodes, ' +
-          'hence the notion of "layer output" ' +
-          'is ill-defined. ' +
-          'Use `getOutputAt(nodeIndex)` instead.');
-    }
-    return generic_utils.singletonOrArray(
-        this.getNodeAtIndex(0, 'output').outputTensors);
-  }
-
-  get losses(): RegularizerFn[] {
-    return this._losses;
-  }
-
-  /**
-   * Retrieves the Layer's current loss values.
-   *
-   * Used for regularizers during training.
-   */
-  calculateLosses(): Scalar[] {
-    // Porting Node: This is an augmentation to Layer.loss in PyKeras.
-    //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete
-    //   Tensor (specifically Scalar) values are returned. This is due to the
-    //   imperative backend.
-    return this.losses.map(lossFn => lossFn());
-  }
-
-  get updates(): Tensor[] {
-    return this._updates;
-  }
-
-  get built(): boolean {
-    return this._built;
-  }
-
-  set built(built: boolean) {
-    this._built = built;
-  }
-
-  get trainable(): boolean {
-    return this.trainable_;
-  }
-
-  set trainable(trainable: boolean) {
-    this._trainableWeights.forEach(w => w.trainable = trainable);
-    this.trainable_ = trainable;
-  }
-
-  get trainableWeights(): LayerVariable[] {
-    if (this.trainable_) {
-      return this._trainableWeights.filter(w => w.trainable);
-    } else {
-      return [];
-    }
-  }
-
-  set trainableWeights(weights: LayerVariable[]) {
-    this._trainableWeights = weights;
-  }
-
-  get nonTrainableWeights(): LayerVariable[] {
-    if (this.trainable) {
-      return this._trainableWeights.filter(w => !w.trainable)
-          .concat(this._nonTrainableWeights);
-    } else {
-      return this._trainableWeights.concat(this._nonTrainableWeights);
-    }
-  }
-
-  set nonTrainableWeights(weights: LayerVariable[]) {
-    this._nonTrainableWeights = weights;
-  }
-
-  /**
-   * The concatenation of the lists trainableWeights and nonTrainableWeights
-   * (in this order).
-   */
-  get weights(): LayerVariable[] {
-    return this.trainableWeights.concat(this.nonTrainableWeights);
-  }
-
-  get stateful(): boolean {
-    return this._stateful;
-  }
-
-  /**
-   * Reset the states of the layer.
-   *
-   * This method of the base Layer class is essentially a no-op.
-   * Subclasses that are stateful (e.g., stateful RNNs) should override this
-   * method.
-   */
-  resetStates(): void {
-    if (!this.stateful) {
-      throw new Error(
-          'Cannot call the resetStates() method of a non-stateful Layer ' +
-          'object.');
-    }
-  }
-
-  /**
-   * Checks compatibility between the layer and provided inputs.
-   *
-   * This checks that the tensor(s) `input`
-   * verify the input assumptions of the layer
-   * (if any). If not, exceptions are raised.
-   *
-   * @param inputs Input tensor or list of input tensors.
-   *
-   * @exception ValueError in case of mismatch between
-   *   the provided inputs and the expectations of the layer.
-   */
-  protected assertInputCompatibility(inputs: Tensor|Tensor[]|SymbolicTensor|
-                                     SymbolicTensor[]): void {
-    const inputsList = generic_utils.toList(inputs);
-    if (this.inputSpec == null || this.inputSpec.length === 0) {
-      return;
-    }
-    const inputSpec = generic_utils.toList(this.inputSpec);
-    if (inputsList.length !== inputSpec.length) {
-      throw new ValueError(
-          `Layer ${this.name} expects ${inputSpec.length} inputs, ` +
-          `but it received ${inputsList.length} input tensors. ` +
-          `Input received: ${inputs}`);
-    }
-    for (let inputIndex = 0; inputIndex < inputsList.length; inputIndex++) {
-      const x = inputsList[inputIndex];
-      const spec: InputSpec = inputSpec[inputIndex];
-      if (spec == null) {
-        continue;
-      }
-
-      // Check ndim.
-      const ndim = x.rank;
-      if (spec.ndim != null) {
-        if (ndim !== spec.ndim) {
-          throw new ValueError(
-              `Input ${inputIndex} is incompatible with layer ${this.name}: ` +
-              `expected ndim=${spec.ndim}, found ndim=${ndim}`);
-        }
-      }
-      if (spec.maxNDim != null) {
-        if (ndim > spec.maxNDim) {
-          throw new ValueError(
-              `Input ${inputIndex} is incompatible with layer ${this.name}` +
-              `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);
-        }
-      }
-      if (spec.minNDim != null) {
-        if (ndim < spec.minNDim) {
-          throw new ValueError(
-              `Input ${inputIndex} is incompatible with layer ${this.name}` +
-              `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);
-        }
-      }
-
-      // Check dtype.
-      if (spec.dtype != null) {
-        if (x.dtype !== spec.dtype) {
-          throw new ValueError(
-              `Input ${inputIndex} is incompatible with layer ${this.name} ` +
-              `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);
-        }
-      }
-
-      // Check specific shape axes.
-      if (spec.axes) {
-        const xShape = x.shape;
-        for (const key in spec.axes) {
-          const axis = Number(key);
-          const value = spec.axes[key];
-          // Perform Python-style slicing in case axis < 0;
-          // TODO(cais): Use https://github.com/alvivi/typescript-underscore to
-          // ensure type safety through Underscore calls.
-          const xShapeAtAxis =
-              axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];
-          if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {
-            throw new ValueError(
-                `Input ${inputIndex} is incompatible with layer ` +
-                `${this.name}: expected axis ${axis} of input shape to ` +
-                `have value ${value} but got shape ${xShape}.`);
-          }
-        }
-      }
-
-      // Check shape.
-      if (spec.shape != null) {
-        for (let i = 0; i < spec.shape.length; ++i) {
-          const specDim = spec.shape[i];
-          const dim = x.shape[i];
-          if (specDim != null && dim != null) {
-            if (specDim !== dim) {
-              throw new ValueError(
-                  `Input ${inputIndex} is incompatible with layer ` +
-                  `${this.name}: expected shape=${spec.shape}, ` +
-                  `found shape=${x.shape}.`);
-            }
-          }
-        }
-      }
-    }
-  }
-
-  /**
-   * This is where the layer's logic lives.
-   *
-   * @param inputs Input tensor, or list/tuple of input tensors.
-   * @param kwargs Additional keyword arguments.
-   *
-   * @return A tensor or list/tuple of tensors.
-   */
-  call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return inputs;
-  }
-
-  protected invokeCallHook(inputs: Tensor|Tensor[], kwargs: Kwargs) {
-    if (this._callHook != null) {
-      this._callHook(inputs, kwargs);
-    }
-  }
-
-  /**
-   * Set call hook.
-   * This is currently used for testing only.
-   * @param callHook
-   */
-  setCallHook(callHook: CallHook) {
-    this._callHook = callHook;
-  }
-
-  /**
-   * Clear call hook.
-   * This is currently used for testing only.
-   */
-  clearCallHook() {
-    this._callHook = null;
-  }
-
-  /**
-   * Builds or executes a `Layer`'s logic.
-   *
-   * When called with `tf.Tensor`(s), execute the `Layer`'s computation and
-   * return Tensor(s). For example:
-   *
-   * ```js
-   * const denseLayer = tf.layers.dense({
-   *   units: 1,
-   *   kernelInitializer: 'zeros',
-   *   useBias: false
-   * });
-   *
-   * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete
-   * // numeric values).
-   * const input = tf.ones([2, 2]);
-   * const output = denseLayer.apply(input);
-   *
-   * // The output's value is expected to be [[0], [0]], due to the fact that
-   * // the dense layer has a kernel initialized to all-zeros and does not have
-   * // a bias.
-   * output.print();
-   * ```
-   *
-   * When called with `tf.SymbolicTensor`(s), this will prepare the layer for
-   * future execution.  This entails internal book-keeping on shapes of
-   * expected Tensors, wiring layers together, and initializing weights.
-   *
-   * Calling `apply` with `tf.SymbolicTensor`s are typically used during the
-   * building of non-`tf.Sequential` models. For example:
-   *
-   * ```js
-   * const flattenLayer = tf.layers.flatten();
-   * const denseLayer = tf.layers.dense({units: 1});
-   *
-   * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().
-   * const input = tf.input({shape: [2, 2]});
-   * const output1 = flattenLayer.apply(input);
-   *
-   * // output1.shape is [null, 4]. The first dimension is the undetermined
-   * // batch size. The second dimension comes from flattening the [2, 2]
-   * // shape.
-   * console.log(JSON.stringify(output1.shape));
-   *
-   * // The output SymbolicTensor of the flatten layer can be used to call
-   * // the apply() of the dense layer:
-   * const output2 = denseLayer.apply(output1);
-   *
-   * // output2.shape is [null, 1]. The first dimension is the undetermined
-   * // batch size. The second dimension matches the number of units of the
-   * // dense layer.
-   * console.log(JSON.stringify(output2.shape));
-   *
-   * // The input and output can be used to construct a model that consists
-   * // of the flatten and dense layers.
-   * const model = tf.model({inputs: input, outputs: output2});
-   * ```
-   *
-   * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.
-   * @param kwargs Additional keyword arguments to be passed to `call()`.
-   *
-   * @return Output of the layer's `call` method.
-   *
-   * @exception ValueError error in case the layer is missing shape information
-   *   for its `build` call.
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  // Porting Note: This is a replacement for __call__() in Python.
-  apply(
-      inputs: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],
-      kwargs?: Kwargs): Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[] {
-    kwargs = kwargs || {};
-
-    this.assertNotDisposed();
-
-    // Ensure inputs are all the same type.
-    const inputsList = generic_utils.toList(inputs);
-
-    const allAreSymbolic = checkAllSymbolic(inputs);
-    const noneAreSymbolic = checkNoneSymbolic(inputs);
-
-    if (allAreSymbolic === noneAreSymbolic) {
-      throw new ValueError(
-          'Arguments to apply() must be all ' +
-          'SymbolicTensors or all Tensors');
-    }
-
-    // TODO(michaelterry): nameScope() may not be necessary.
-    return nameScope(this.name, () => {
-      // Handle laying building (weight creating, input spec locking).
-      if (!this.built) {
-        /*
-          Throw exceptions in case the input is not compatible
-          with the inputSpec specified in the layer constructor.
-         */
-        this.assertInputCompatibility(inputs);
-
-        // Collect input shapes to build layer.
-        const inputShapes: Shape[] = [];
-        for (const xElem of generic_utils.toList(inputs)) {
-          inputShapes.push(xElem.shape);
-        }
-        this.build(generic_utils.singletonOrArray(inputShapes));
-        this.built = true;
-
-        // Load weights that were specified at layer instantiation.
-        if (this.initialWeights) {
-          this.setWeights(this.initialWeights);
-        }
-
-        if (this._refCount === null && noneAreSymbolic) {
-          // The first use of this layer is a non-symbolic call, set ref count
-          // to 1 so the Layer can be properly disposed if its dispose() method
-          // is called.
-          this._refCount = 1;
-        }
-      }
-
-      /*
-        Throw exceptions in case the input is not compatible
-        with the inputSpec set at build time.
-      */
-      this.assertInputCompatibility(inputs);
-
-      // Handle mask propagation.
-      // TODO(michaelterry): Mask propagation not currently implemented.
-
-      // Actually call the layer, collecting output(s), mask(s), and shape(s).
-      if (noneAreSymbolic) {
-        let output = this.call(inputs, kwargs);
-
-        // Apply masks to the output tensors if the layer supports it.
-        if (this.supportsMasking) {
-          // TODO(mattsoulanille): pass the input tensors' masks to computeMask
-          this.setMaskMetadata(inputs, output);
-        }
-
-        // If the layer returns tensors from its inputs, unmodified,
-        // we copy them to avoid loss of tensor metadata.
-        const outputList: Tensor[] = generic_utils.toList(output);
-        const outputListCopy: Tensor[] = [];
-        // TODO(michaelterry): This copying may not be necessary given our eager
-        // backend.
-        for (let x of outputList) {
-          if (inputsList.indexOf(x) !== -1) {
-            x = x.clone();
-          }
-          outputListCopy.push(x);
-        }
-        output = generic_utils.singletonOrArray(outputListCopy);
-
-        if (this.activityRegularizer != null) {
-          throw new NotImplementedError(
-              'Layer invocation in the presence of activity ' +
-              'regularizer(s) is not supported yet.');
-        }
-
-        // TODO(michaelterry): Call addInboundNode()?
-        return output;
-      } else {
-        const inputShape = collectInputShape(inputs);
-        const outputShape = this.computeOutputShape(inputShape);
-        let output: SymbolicTensor|SymbolicTensor[];
-        const outputDType = guessOutputDType(inputs);
-        this.warnOnIncompatibleInputShape(
-            Array.isArray(inputs) ? inputShape[0] as Shape :
-                                    inputShape as Shape);
-
-        if (outputShape != null && outputShape.length > 0 &&
-            Array.isArray(outputShape[0])) {
-          // We have multiple output shapes. Create multiple output tensors.
-          output = (outputShape as Shape[])
-                       .map(
-                           (shape, index) => new SymbolicTensor(
-                               outputDType, shape, this,
-                               generic_utils.toList(inputs), kwargs, this.name,
-                               index));
-        } else {
-          output = new SymbolicTensor(
-              outputDType, outputShape as Shape, this,
-              generic_utils.toList(inputs), kwargs, this.name);
-        }
-
-        /*
-          Add an inbound node to the layer, so that it keeps track
-          of the call and of all new variables created during the call.
-          This also updates the layer history of the output tensor(s).
-          If the input tensor(s) had no previous history,
-          this does nothing.
-        */
-        this.addInboundNode(
-            inputs, output, null, null, inputShape, outputShape, kwargs);
-        this._refCount++;
-
-        if (this.activityRegularizer != null) {
-          throw new NotImplementedError(
-              'Layer invocation in the presence of activity ' +
-              'regularizer(s) is not supported yet.');
-        }
-
-        return output;
-      }
-    });
-  }
-
-  /**
-   * Check compatibility between input shape and this layer's batchInputShape.
-   *
-   * Print warning if any incompatibility is found.
-   *
-   * @param inputShape Input shape to be checked.
-   */
-  protected warnOnIncompatibleInputShape(inputShape: Shape) {
-    if (this.batchInputShape == null) {
-      return;
-    } else if (inputShape.length !== this.batchInputShape.length) {
-      console.warn(
-          `The rank of the input tensor provided (shape: ` +
-          `${JSON.stringify(inputShape)}) does not match that of the ` +
-          `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +
-          `of the layer ${this.name}`);
-    } else {
-      let dimMismatch = false;
-      this.batchInputShape.forEach((dimension, i) => {
-        if (dimension != null && inputShape[i] != null &&
-            inputShape[i] !== dimension) {
-          dimMismatch = true;
-        }
-      });
-      if (dimMismatch) {
-        console.warn(
-            `The shape of the input tensor ` +
-            `(${JSON.stringify(inputShape)}) does not ` +
-            `match the expectation of layer ${this.name}: ` +
-            `${JSON.stringify(this.batchInputShape)}`);
-      }
-    }
-  }
-
-  /**
-   * Retrieves the output shape(s) of a layer.
-   *
-   * Only applicable if the layer has only one inbound node, or if all inbound
-   * nodes have the same output shape.
-   *
-   * @returns Output shape or shapes.
-   * @throws AttributeError: if the layer is connected to more than one incoming
-   *   nodes.
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  get outputShape(): Shape|Shape[] {
-    if (this.inboundNodes == null || this.inboundNodes.length === 0) {
-      throw new AttributeError(
-          `The layer ${this.name} has never been called and thus has no ` +
-          `defined output shape.`);
-    }
-    const allOutputShapes: string[] = [];
-    for (const node of this.inboundNodes) {
-      const shapeString = JSON.stringify(node.outputShapes);
-      if (allOutputShapes.indexOf(shapeString) === -1) {
-        allOutputShapes.push(shapeString);
-      }
-    }
-    if (allOutputShapes.length === 1) {
-      const outputShapes = this.inboundNodes[0].outputShapes;
-      if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&
-          outputShapes.length === 1) {
-        return (outputShapes as Shape[])[0];
-      } else {
-        return outputShapes;
-      }
-
-    } else {
-      throw new AttributeError(
-          `The layer ${this.name} has multiple inbound nodes with different ` +
-          `output shapes. Hence the notion of "output shape" is ill-defined ` +
-          `for the layer.`);
-      // TODO(cais): Implement getOutputShapeAt().
-    }
-  }
-
-  /**
-   * Counts the total number of numbers (e.g., float32, int32) in the
-   * weights.
-   *
-   * @returns An integer count.
-   * @throws RuntimeError: If the layer is not built yet (in which case its
-   *   weights are not defined yet.)
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  countParams(): number {
-    if (!this.built) {
-      throw new RuntimeError(
-          `You tried to call countParams() on ${this.name}, ` +
-          `but the layer is not built yet. Build it first by calling ` +
-          `build(batchInputShape).`);
-    }
-    return variable_utils.countParamsInWeights(this.weights);
-  }
-
-  /**
-   * Creates the layer weights.
-   *
-   * Must be implemented on all layers that have weights.
-   *
-   * Called when apply() is called to construct the weights.
-   *
-   * @param inputShape A `Shape` or array of `Shape` (unused).
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  build(inputShape: Shape|Shape[]) {
-    this.built = true;
-  }
-
-  /**
-   * Returns the current values of the weights of the layer.
-   *
-   * @param trainableOnly Whether to get the values of only trainable weights.
-   * @returns Weight values as an `Array` of `tf.Tensor`s.
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  getWeights(trainableOnly = false): Tensor[] {
-    return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);
-  }
-
-  /**
-   * Sets the weights of the layer, from Tensors.
-   *
-   * @param weights a list of Tensors. The number of arrays and their shape
-   *   must match number of the dimensions of the weights of the layer (i.e.
-   *   it should match the output of `getWeights`).
-   *
-   * @exception ValueError If the provided weights list does not match the
-   *   layer's specifications.
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  setWeights(weights: Tensor[]): void {
-    tidy(() => {
-      const params = this.weights;
-      if (params.length !== weights.length) {
-        // TODO(cais): Restore the following and use `providedWeights`, instead
-        // of `weights` in the error message, once the deeplearn.js bug is
-        // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const
-        // providedWeights = JSON.stringify(weights).slice(0, 50);
-        throw new ValueError(
-            `You called setWeights(weights) on layer "${this.name}" ` +
-            `with a weight list of length ${weights.length}, ` +
-            `but the layer was expecting ${params.length} weights. ` +
-            `Provided weights: ${weights}...`);
-      }
-      if (params.length === 0) {
-        return;
-      }
-      const weightValueTuples: Array<[LayerVariable, Tensor]> = [];
-      const paramValues = batchGetValue(params);
-      for (let i = 0; i < paramValues.length; ++i) {
-        const pv = paramValues[i];
-        const p = params[i];
-        const w = weights[i];
-        if (!util.arraysEqual(pv.shape, w.shape)) {
-          throw new ValueError(
-              `Layer weight shape ${pv.shape} ` +
-              `not compatible with provided weight shape ${w.shape}`);
-        }
-        weightValueTuples.push([p, w]);
-      }
-      batchSetValue(weightValueTuples);
-    });
-  }
-
-  /**
-   * Adds a weight variable to the layer.
-   *
-   * @param name Name of the new weight variable.
-   * @param shape The shape of the weight.
-   * @param dtype The dtype of the weight.
-   * @param initializer An initializer instance.
-   * @param regularizer A regularizer instance.
-   * @param trainable Whether the weight should be trained via backprop or not
-   *   (assuming that the layer itself is also trainable).
-   * @param constraint An optional trainable.
-   * @return The created weight variable.
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  protected addWeight(
-      name: string, shape: Shape, dtype?: DataType, initializer?: Initializer,
-      regularizer?: Regularizer, trainable?: boolean, constraint?: Constraint,
-      getInitializerFunc?: Function): LayerVariable {
-    // Reject duplicate weight names.
-    if (this._addedWeightNames.indexOf(name) !== -1) {
-      throw new ValueError(
-          `Duplicate weight name ${name} for layer ${this.name}`);
-    }
-    this._addedWeightNames.push(name);
-
-    if (dtype == null) {
-      dtype = 'float32';
-    }
-
-    if (this.fastWeightInitDuringBuild) {
-      initializer = getInitializerFunc != null ? getInitializerFunc() :
-                                                 getInitializer('zeros');
-    }
-    const initValue = initializer.apply(shape, dtype);
-    const weight =
-        new LayerVariable(initValue, dtype, name, trainable, constraint);
-    initValue.dispose();
-    // Request backend not to dispose the weights of the model on scope() exit.
-    if (regularizer != null) {
-      this.addLoss(() => regularizer.apply(weight.read()));
-    }
-    if (trainable == null) {
-      trainable = true;
-    }
-    if (trainable) {
-      this._trainableWeights.push(weight);
-    } else {
-      this._nonTrainableWeights.push(weight);
-    }
-    return weight;
-  }
-
-  /**
-   * Set the fast-weight-initialization flag.
-   *
-   * In cases where the initialized weight values will be immediately
-   * overwritten by loaded weight values during model loading, setting
-   * the flag to `true` saves unnecessary calls to potentially expensive
-   * initializers and speeds up the loading process.
-   *
-   * @param value Target value of the flag.
-   */
-  setFastWeightInitDuringBuild(value: boolean) {
-    this.fastWeightInitDuringBuild = value;
-  }
-
-  /**
-   * Add losses to the layer.
-   *
-   * The loss may potentially be conditional on some inputs tensors,
-   * for instance activity losses are conditional on the layer's inputs.
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  addLoss(losses: RegularizerFn|RegularizerFn[]): void {
-    if (losses == null || Array.isArray(losses) && losses.length === 0) {
-      return;
-    }
-    // Update this.losses
-    losses = generic_utils.toList(losses);
-    if (this._losses !== undefined && this._losses !== null) {
-      this.losses.push(...losses);
-    }
-  }
-
-  /**
-   * Computes the output shape of the layer.
-   *
-   * Assumes that the layer will be built to match that input shape provided.
-   *
-   * @param inputShape A shape (tuple of integers) or a list of shape tuples
-   *   (one per output tensor of the layer). Shape tuples can include null for
-   *   free dimensions, instead of an integer.
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  /**
-   * Computes an output mask tensor.
-   *
-   * @param inputs Tensor or list of tensors.
-   * @param mask Tensor or list of tensors.
-   *
-   * @return null or a tensor (or list of tensors, one per output tensor of the
-   * layer).
-   */
-  computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]): Tensor
-      |Tensor[] {
-    if (!this.supportsMasking) {
-      if (mask != null) {
-        if (Array.isArray(mask)) {
-          mask.forEach(maskElement => {
-            if (maskElement != null) {
-              throw new TypeError(
-                  `Layer ${this.name} does not support masking, ` +
-                  'but was passed an inputMask.');
-            }
-          });
-        } else {
-          throw new TypeError(
-              `Layer ${this.name} does not support masking, ` +
-              'but was passed an inputMask.');
-        }
-      }
-      // masking not explicitly supported: return null as mask
-      return null;
-    }
-    // if masking is explictly supported, by default
-    // carry over the input mask
-    return mask;
-  }
-
-  private setMaskMetadata(
-      inputs: Tensor|Tensor[], outputs: Tensor|Tensor[],
-      previousMask?: Tensor|Tensor[]): void {
-    if (!this.supportsMasking) {
-      return;
-    }
-
-    const outputMasks = this.computeMask(inputs, previousMask);
-    const outputsList = generic_utils.toList(outputs);
-    const outputMasksList = generic_utils.toList(outputMasks);
-
-    if (outputsList.length !== outputMasksList.length) {
-      throw new Error(
-          `${this.name} outputs ${outputsList.length} tensors ` +
-          `but ${outputsList.length} masks for those tensors`);
-    }
-    for (let i = 0; i < outputsList.length; i++) {
-      outputsList[i].kerasMask = outputMasksList[i];
-    }
-  }
-
-  /**
-   * Internal method to create an inbound node for the layer.
-   *
-   * @param inputTensors List of input tensors.
-   * @param outputTensors List of output tensors.
-   * @param inputMasks List of input masks (a mask can be a tensor, or null).
-   * @param outputMasks List of output masks (a mask can be a tensor, or null).
-   * @param inputShapes List of input shape tuples.
-   * @param outputShapes List of output shape tuples.
-   * @param kwargs Dictionary of keyword arguments that were passed to the
-   *   `call` method of the layer at the call that created the node.
-   */
-  private addInboundNode(
-      inputTensors: SymbolicTensor|SymbolicTensor[],
-      outputTensors: SymbolicTensor|SymbolicTensor[],
-      inputMasks: Tensor|Tensor[], outputMasks: Tensor|Tensor[],
-      inputShapes: Shape|Shape[], outputShapes: Shape|Shape[],
-      kwargs: {} = null): void {
-    const inputTensorList: SymbolicTensor[] =
-        generic_utils.toList(inputTensors);
-    outputTensors = generic_utils.toList(outputTensors);
-    inputMasks = generic_utils.toList(inputMasks);
-    outputMasks = generic_utils.toList(outputMasks);
-    inputShapes = types_utils.normalizeShapeList(inputShapes);
-    outputShapes = types_utils.normalizeShapeList(outputShapes);
-
-    // Collect input tensor(s) coordinates.
-    const inboundLayers: Layer[] = [];
-    const nodeIndices: number[] = [];
-    const tensorIndices: number[] = [];
-    for (const x of inputTensorList) {
-      /*
-       * TODO(michaelterry): Keras adds this value to tensors; it's not
-       * clear whether we'll use this or not.
-       */
-      inboundLayers.push(x.sourceLayer);
-      nodeIndices.push(x.nodeIndex);
-      tensorIndices.push(x.tensorIndex);
-    }
-
-    // Create node, add it to inbound nodes.
-    // (This call has side effects.)
-    // tslint:disable-next-line:no-unused-expression
-    new Node(
-        {
-          outboundLayer: this,
-          inboundLayers,
-          nodeIndices,
-          tensorIndices,
-          inputTensors: inputTensorList,
-          outputTensors,
-          inputMasks,
-          outputMasks,
-          inputShapes,
-          outputShapes
-        },
-        kwargs);
-
-    // Update tensor history
-    for (let i = 0; i < outputTensors.length; i++) {
-      // TODO(michaelterry: _uses_learning_phase not tracked.
-      outputTensors[i].sourceLayer = this;
-      outputTensors[i].nodeIndex = this.inboundNodes.length - 1;
-      outputTensors[i].tensorIndex = i;
-    }
-  }
-
-  /**
-   * Returns the config of the layer.
-   *
-   * A layer config is a TS dictionary (serializable)
-   * containing the configuration of a layer.
-   * The same layer can be reinstantiated later
-   * (without its trained weights) from this configuration.
-   *
-   * The config of a layer does not include connectivity
-   * information, nor the layer class name.  These are handled
-   * by 'Container' (one layer of abstraction above).
-   *
-   * Porting Note: The TS dictionary follows TS naming standards for
-   * keys, and uses tfjs-layers type-safe Enums.  Serialization methods
-   * should use a helper function to convert to the pythonic storage
-   * standard. (see serialization_utils.convertTsToPythonic)
-   *
-   * @returns TS dictionary of configuration.
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  getConfig(): serialization.ConfigDict {
-    const config:
-        serialization.ConfigDict = {name: this.name, trainable: this.trainable};
-    if (this.batchInputShape != null) {
-      config['batchInputShape'] = this.batchInputShape;
-    }
-    if (this.dtype != null) {
-      config['dtype'] = this.dtype;
-    }
-    return config;
-  }
-
-  /**
-   * Dispose the weight variables that this Layer instance holds.
-   *
-   * @returns {number} Number of disposed variables.
-   */
-  protected disposeWeights(): number {
-    this.weights.forEach(weight => weight.dispose());
-    return this.weights.length;
-  }
-
-  protected assertNotDisposed() {
-    if (this._refCount === 0) {
-      throw new Error(`Layer '${this.name}' is already disposed.`);
-    }
-  }
-
-  /**
-   * Attempt to dispose layer's weights.
-   *
-   * This method decreases the reference count of the Layer object by 1.
-   *
-   * A Layer is reference-counted. Its reference count is incremented by 1
-   * the first item its `apply()` method is called and when it becomes a part
-   * of a new `Node` (through calling the `apply()` method on a
-   * `tf.SymbolicTensor`).
-   *
-   * If the reference count of a Layer becomes 0, all the weights will be
-   * disposed and the underlying memory (e.g., the textures allocated in WebGL)
-   * will be freed.
-   *
-   * Note: If the reference count is greater than 0 after the decrement, the
-   * weights of the Layer will *not* be disposed.
-   *
-   * After a Layer is disposed, it cannot be used in calls such as `apply()`,
-   * `getWeights()` or `setWeights()` anymore.
-   *
-   * @returns A DisposeResult Object with the following fields:
-   *   - refCountAfterDispose: The reference count of the Container after this
-   *     `dispose()` call.
-   *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed
-   *     during this `dispose()` call.
-   * @throws {Error} If the layer is not built yet, or if the layer has already
-   *   been disposed.
-   *
-   * @doc {heading: 'Models', 'subheading': 'Classes'}
-   */
-  dispose(): DisposeResult {
-    if (!this.built) {
-      throw new Error(
-          `Cannot dispose Layer ${this.name} because it has not been ` +
-          `built yet.`);
-    }
-
-    if (this._refCount === null) {
-      throw new Error(
-          `Cannot dispose Layer ${this.name} because it has not been used ` +
-          `yet.`);
-    }
-
-    this.assertNotDisposed();
-
-    let numDisposedVariables = 0;
-    if (--this._refCount === 0) {
-      numDisposedVariables = this.disposeWeights();
-    }
-
-    return {refCountAfterDispose: this._refCount, numDisposedVariables};
-  }
-}
-
-/**
- * Collects the input shape(s) of a list of `tf.Tensor`s or
- * `tf.SymbolicTensor`s.
- *
- * TODO(michaelterry): Update PyKeras docs (backport).
- *
- * @param inputTensors List of input tensors (or single input tensor).
- *
- * @return List of shape tuples (or single tuple), one tuple per input.
- */
-function collectInputShape(inputTensors: SymbolicTensor|SymbolicTensor[]|Tensor|
-                           Tensor[]): Shape|Shape[] {
-  inputTensors =
-      generic_utils.toList(inputTensors) as SymbolicTensor[] | Tensor[];
-  const shapes: Shape[] = [];
-  for (const x of inputTensors) {
-    shapes.push(x.shape);
-  }
-  return generic_utils.singletonOrArray(shapes);
-}
-
-/**
- * Guesses output dtype based on inputs.
- *
- * At present, just returns 'float32' for any input.
- *
- * @param inputTensors List of input tensors (or single input tensor).
- *
- * @return The guessed DType. At present, always returns 'float32'.
- */
-function guessOutputDType(inputTensors: SymbolicTensor|SymbolicTensor[]|Tensor|
-                          Tensor[]): DataType {
-  return 'float32';
-}
-
-/**
- * Returns the list of input tensors necessary to compute `tensor`.
- *
- * Output will always be a list of tensors (potentially with 1 element).
- *
- * @param tensor The tensor to start from.
- * @param layer Origin layer of the tensor.
- * @param nodeIndex Origin node index of the tensor.
- *
- * @return Array of input tensors.
- */
-export function getSourceInputs(
-    tensor: SymbolicTensor, layer?: Layer,
-    nodeIndex?: number): SymbolicTensor[] {
-  if (layer == null || (nodeIndex != null && nodeIndex > 0)) {
-    layer = tensor.sourceLayer;
-    nodeIndex = tensor.nodeIndex;
-  }
-  if (layer.inboundNodes.length === 0) {
-    return [tensor];
-  } else {
-    const node = layer.inboundNodes[nodeIndex];
-    if (node.inboundLayers.length === 0) {
-      return node.inputTensors;
-    } else {
-      const sourceTensors: SymbolicTensor[] = [];
-      for (let i = 0; i < node.inboundLayers.length; i++) {
-        const x = node.inputTensors[i];
-        const layer = node.inboundLayers[i];
-        const nodeIndex = node.nodeIndices[i];
-        const previousSources = getSourceInputs(x, layer, nodeIndex);
-        // Avoid input redundancy.
-        for (const x of previousSources) {
-          if (sourceTensors.indexOf(x) === -1) {
-            sourceTensors.push(x);
-          }
-        }
-      }
-      return sourceTensors;
-    }
-  }
-}
-
-type MaybeSymbolic = SymbolicTensor|Tensor;
-
-function checkAllSymbolic(tensors: MaybeSymbolic|MaybeSymbolic[]):
-    tensors is SymbolicTensor|SymbolicTensor[] {
-  let allAreSymbolic = true;
-  for (const tensor of generic_utils.toList(tensors)) {
-    if (!(tensor instanceof SymbolicTensor)) {
-      allAreSymbolic = false;
-      break;
-    }
-  }
-  return allAreSymbolic;
-}
-
-function checkNoneSymbolic(tensors: MaybeSymbolic|
-                           MaybeSymbolic[]): tensors is Tensor|Tensor[] {
-  let noneAreSymbolic = true;
-  for (const tensor of generic_utils.toList(tensors)) {
-    if (tensor instanceof SymbolicTensor) {
-      noneAreSymbolic = false;
-      break;
-    }
-  }
-  return noneAreSymbolic;
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/topology_test.ts b/tfjs-master/tfjs-layers/src/engine/topology_test.ts
deleted file mode 100644
index eaa93449e..000000000
--- a/tfjs-master/tfjs-layers/src/engine/topology_test.ts
+++ /dev/null
@@ -1,1181 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {eye, memory, NamedTensorMap, ones, Tensor, tensor1d, tensor2d, zeros} from '@tensorflow/tfjs-core';
-
-import * as tfl from '../index';
-import * as initializers from '../initializers';
-import {Shape} from '../keras_format/common';
-import {describeMathCPU, describeMathCPUAndGPU, expectTensorsClose} from '../utils/test_utils';
-import {LayerVariable, onesVariable, zerosVariable} from '../variables';
-
-import {InputSpec, Layer, LayerArgs, Node, SymbolicTensor} from './topology';
-
-class LayerForTest extends tfl.layers.Layer {
-  static className = 'LayerForTest';
-  constructor(args: LayerArgs = {}) {
-    super(args);
-  }
-}
-
-describe('InputSpec', () => {
-  it('initializes with expected default values.', () => {
-    const inputSpec = new InputSpec({});
-    expect(inputSpec.dtype).toBeUndefined();
-    expect(inputSpec.shape).toBeUndefined();
-    expect(inputSpec.ndim).toBeUndefined();
-    expect(inputSpec.maxNDim).toBeUndefined();
-    expect(inputSpec.minNDim).toBeUndefined();
-    expect(inputSpec.axes).toEqual({});
-  });
-
-  it('initializes with inputSpec.ndim = shape.length when shape is specified.',
-     () => {
-       const shape = [1, 2, 3];
-       const expectedValue = shape.length;
-       const inputSpec = new InputSpec({shape: [1, 2, 3], ndim: -1});
-       expect(inputSpec.ndim).toEqual(expectedValue);
-     });
-
-  it('initializes inputSpec.axes when axes specified.', () => {
-    const expectedValue = {1: 2};
-    const inputSpec = new InputSpec({axes: expectedValue});
-    expect(inputSpec.axes).toEqual(expectedValue);
-  });
-});
-
-describe('Node', () => {
-  it('initializes object as expected.', () => {
-    const outboundLayerName = 'outboundLayer';
-    const inboundLayerName = 'inboundLayer';
-    const outboundLayer = new LayerForTest({name: outboundLayerName});
-    const inboundLayers = [new LayerForTest({name: inboundLayerName})];
-    const nodeIndices = [0];
-    const tensorIndices = [0];
-    const inputTensors = [new tfl.SymbolicTensor('float32', [1], null, [], {})];
-    const outputTensors =
-        [new tfl.SymbolicTensor('float32', [2, 2], null, [], {})];
-    const inputMasks = [zeros([1])];
-    const outputMasks = [zeros([1])];
-    const inputShapes = [[1]];
-    const outputShapes = [[1], [1]];
-    const callArgs = {mask: zeros([1])};
-    const node = new Node(
-        {
-          outboundLayer,
-          inboundLayers,
-          nodeIndices,
-          tensorIndices,
-          inputTensors,
-          outputTensors,
-          inputMasks,
-          outputMasks,
-          inputShapes,
-          outputShapes
-        },
-        callArgs);
-    expect(node.outboundLayer).toEqual(outboundLayer);
-    expect(node.inboundLayers).toEqual(inboundLayers);
-    expect(node.nodeIndices).toEqual(nodeIndices);
-    expect(node.tensorIndices).toEqual(tensorIndices);
-    expect(node.inputTensors).toEqual(inputTensors);
-    expect(node.outputTensors).toEqual(outputTensors);
-    expect(node.inputMasks).toEqual(inputMasks);
-    expect(node.outputMasks).toEqual(outputMasks);
-    expect(node.inputShapes).toEqual(inputShapes);
-    expect(node.outputShapes).toEqual(outputShapes);
-    expect(node.callArgs).toEqual(callArgs);
-    expect(inboundLayers[0].outboundNodes).toEqual([node]);
-    expect(node.outboundLayer.inboundNodes).toEqual([node]);
-  });
-
-  it('generates expected SerializableNodeConfig.', () => {
-    const outboundLayerName = 'outboundLayer';
-    const inboundLayerName = 'inboundLayer';
-    const outboundLayer = new LayerForTest({name: outboundLayerName});
-    const inboundLayers = [new LayerForTest({name: inboundLayerName})];
-    const nodeIndices = [0];
-    const tensorIndices = [0];
-    const inputTensors = [new tfl.SymbolicTensor('float32', [1], null, [], {})];
-    const outputTensors =
-        [new tfl.SymbolicTensor('float32', [2, 2], null, [], {})];
-    const inputMasks = [zeros([1])];
-    const outputMasks = [zeros([1])];
-    const inputShapes = [[1]];
-    const outputShapes = [[1], [1]];
-    const callArgs = {mask: zeros([1])};
-    const node = new Node(
-        {
-          outboundLayer,
-          inboundLayers,
-          nodeIndices,
-          tensorIndices,
-          inputTensors,
-          outputTensors,
-          inputMasks,
-          outputMasks,
-          inputShapes,
-          outputShapes
-        },
-        callArgs);
-    const nodeConfig = node.getConfig();
-    expect(nodeConfig.outboundLayer).toEqual(outboundLayerName);
-    expect(nodeConfig.inboundLayers).toEqual([inboundLayerName]);
-    expect(nodeConfig.nodeIndices).toEqual(nodeIndices);
-    expect(nodeConfig.tensorIndices).toEqual(tensorIndices);
-  });
-
-  it('generates unique IDs', () => {
-    const outboundLayerName = 'outboundLayer';
-    const inboundLayerName = 'inboundLayer';
-    const outboundLayer = new LayerForTest({name: outboundLayerName});
-    const inboundLayers = [new LayerForTest({name: inboundLayerName})];
-    const nodeIndices = [0];
-    const tensorIndices = [0];
-    const inputTensors = [new tfl.SymbolicTensor('float32', [1], null, [], {})];
-    const outputTensors =
-        [new tfl.SymbolicTensor('float32', [2, 2], null, [], {})];
-    const inputMasks = [zeros([1])];
-    const outputMasks = [zeros([1])];
-    const inputShapes = [[1]];
-    const outputShapes = [[1], [1]];
-    const callArgs = {mask: zeros([1])};
-    const node = new Node(
-        {
-          outboundLayer,
-          inboundLayers,
-          nodeIndices,
-          tensorIndices,
-          inputTensors,
-          outputTensors,
-          inputMasks,
-          outputMasks,
-          inputShapes,
-          outputShapes
-        },
-        callArgs);
-    const secondNode = new Node(
-        {
-          outboundLayer,
-          inboundLayers,
-          nodeIndices,
-          tensorIndices,
-          inputTensors,
-          outputTensors,
-          inputMasks,
-          outputMasks,
-          inputShapes,
-          outputShapes
-        },
-        callArgs);
-    expect(secondNode.id).not.toEqual(node.id);
-  });
-});
-
-describeMathCPU('Layer', () => {
-  describe('initialized to its defaults', () => {
-    // TODO(bileschi): This should be tfl.layers.Layer for some future version
-    // of TS that doesn't fail to compile.
-    let defaultLayer: Layer;
-
-    beforeEach(() => {
-      defaultLayer = new LayerForTest();
-    });
-
-    it('has a default layer name of layer_....', () => {
-      expect(defaultLayer.name).toMatch(/^layer_.+$/);
-    });
-
-    it('has null inputSpecs.', () => {
-      expect(defaultLayer.inputSpec).toBeNull();
-    });
-
-    it('does not support masking (supportsMasking == false).', () => {
-      expect(defaultLayer.supportsMasking).toEqual(false);
-    });
-
-    it('is trainable.', () => {
-      expect(defaultLayer.trainable).toEqual(true);
-    });
-
-    it('has an undefined batchInputShape.', () => {
-      expect(defaultLayer.batchInputShape).toBeUndefined();
-    });
-
-    it('has an undefined dtype.', () => {
-      expect(defaultLayer.dtype).toBeUndefined();
-    });
-
-    it('has null initialWeights.', () => {
-      expect(defaultLayer.initialWeights).toBeNull();
-    });
-
-    it('has an empty inboundNodes list.', () => {
-      expect(defaultLayer.inboundNodes).toEqual([]);
-    });
-
-    it('has an empty outboundNodes list.', () => {
-      expect(defaultLayer.outboundNodes).toEqual([]);
-    });
-
-    it('has an empty losses list.', () => {
-      expect(defaultLayer.losses).toEqual([]);
-    });
-
-    it('has an empty updates list.', () => {
-      expect(defaultLayer.updates).toEqual([]);
-    });
-
-    it('is not built (built == false).', () => {
-      expect(defaultLayer.built).toEqual(false);
-    });
-
-    it('has an empty trainableWeights list.', () => {
-      expect(defaultLayer.trainableWeights).toEqual([]);
-    });
-
-    it('has an empty nonTrainableWeights list.', () => {
-      expect(defaultLayer.nonTrainableWeights).toEqual([]);
-    });
-
-    it('has an empty weights list.', () => {
-      expect(defaultLayer.weights).toEqual([]);
-    });
-
-    it('produces a unique ID', () => {
-      const secondLayer = new LayerForTest();
-      expect(defaultLayer.id).not.toEqual(secondLayer.id);
-    });
-
-    it('stateful is false by default', () => {
-      const layer = new LayerForTest();
-      expect(layer.stateful).toBe(false);
-    });
-
-    it('returns null if it doesn`t support masking and no mask is passed in.',
-       () => {
-         expect(defaultLayer.computeMask([], null)).toBeNull();
-       });
-
-    it('throws exception if it doesn`t support masking and a ' +
-           'mask is passed in.',
-       () => {
-         const mask = ones([1]);
-         expect(() => defaultLayer.computeMask([], mask))
-             .toThrowError(/does not support masking/);
-       });
-
-    it('returns the same mask passed in if it supports masking', () => {
-      const mask = ones([1]);
-      defaultLayer.supportsMasking = true;
-      expect(defaultLayer.computeMask([], mask)).toEqual(mask);
-    });
-
-    it('correctly generates a config for serialization', () => {
-      const config = defaultLayer.getConfig();
-      expect(config.name).toEqual(defaultLayer.name);
-      expect(config.trainable).toEqual(defaultLayer.trainable);
-      expect(config.batchInputShape).toBeUndefined();
-      expect(config.dtype).toBeUndefined();
-    });
-  });
-
-  describe('A layer with non-default arguments', () => {
-    it('initializes layer with given name.', () => {
-      const name = 'layer name';
-      const layer = new LayerForTest({name});
-      expect(layer.name).toMatch(name);
-      const config = layer.getConfig();
-      expect(config.name).toEqual(layer.name);
-    });
-
-    for (const trainable of [true, false]) {
-      it('initializes layer as trainable, if specified.', () => {
-        const layer = new LayerForTest({trainable});
-        expect(layer.trainable).toEqual(trainable);
-        const config = layer.getConfig();
-        expect(config.trainable).toEqual(layer.trainable);
-      });
-    }
-
-    for (const batchInputShape of [[], [1]]) {
-      it('initializes batchInputShape to layerConfig.batchInputShape.', () => {
-        const layer = new LayerForTest({batchInputShape});
-        expect(layer.batchInputShape).toEqual(batchInputShape);
-        const config = layer.getConfig();
-        expect(config.batchInputShape).toEqual(layer.batchInputShape);
-      });
-    }
-
-    it('initializes batchInputShape to layerConfig.batchInputShape even if ' +
-           'layerConfig.inputShape is defined.',
-       () => {
-         const batchInputShape = [1];
-         const inputShape = [2, 3];
-         const layer = new LayerForTest({batchInputShape, inputShape});
-         expect(layer.batchInputShape).toEqual(batchInputShape);
-       });
-
-    for (const [batchSize, inputShape, expectedBatchInputShape] of [
-             [null, [], [null]], [null, [1], [null, 1]], [3, [], [3]],
-             [3, [1], [3, 1]]]) {
-      it('initializes batchInputShape to layerConfig.inputShape.', () => {
-        const layer = new LayerForTest(
-            {batchSize: batchSize as number, inputShape: inputShape as Shape});
-        expect(layer.batchInputShape).toEqual(expectedBatchInputShape as Shape);
-      });
-    }
-
-    it('initializes dtype to float32 if layerConfig.inputShape is set.', () => {
-      const layer = new LayerForTest({inputShape: []});
-      expect(layer.dtype).toEqual('float32');
-      const config = layer.getConfig();
-      expect(config.dtype).toEqual(layer.dtype);
-    });
-
-    it('initializes dtype to float32 if layerConfig.batchInputShape is set.',
-       () => {
-         const layer = new LayerForTest({batchInputShape: []});
-         expect(layer.dtype).toEqual('float32');
-       });
-
-    it('initializes initialWeights if present.', () => {
-      const weights = [zeros([1])];
-      const layer = new LayerForTest({weights});
-      expect(layer.initialWeights).toEqual(weights);
-    });
-
-    it('Layer with duplicate weight names throws error', () => {
-      class LayerForTest extends tfl.layers.Layer {
-        static className = 'LayerForTest';
-        constructor(args: LayerArgs = {}) {
-          super(args);
-          this.addWeight(
-              'foo', [1, 2], 'float32', initializers.getInitializer('zeros'));
-          this.addWeight(
-              'foo', [2, 3], 'float32', initializers.getInitializer('zeros'));
-        }
-      }
-      expect(() => new LayerForTest()).toThrowError(/[Dd]uplicate weight name/);
-    });
-  });
-
-  it('can be set to built.', () => {
-    const layer = new LayerForTest();
-    layer.built = true;
-    expect(layer.built).toEqual(true);
-  });
-
-  // Weights used for subsequent tests
-  const trainableWeights = [zerosVariable([1])];
-  const nonTrainableWeights = [onesVariable([1])];
-  it('can set trainableWeights.', () => {
-    const layer = new LayerForTest();
-    layer.trainableWeights = trainableWeights;
-    expect(layer.trainableWeights).toEqual(trainableWeights);
-  });
-
-  it('doesn\'t return trainableWeights if layer is not trainable, even ' +
-         'if they exist',
-     () => {
-       const layer = new LayerForTest({trainable: false});
-       layer.trainableWeights = trainableWeights;
-       expect(layer.trainableWeights).toEqual([]);
-     });
-
-  it('can set nonTrainableWeights.', () => {
-    const layer = new LayerForTest();
-    layer.nonTrainableWeights = nonTrainableWeights;
-    expect(layer.nonTrainableWeights).toEqual(nonTrainableWeights);
-  });
-
-  it('only returns nonTrainableWeights for nonTrainableWeights if the layer ' +
-         'is trainable.',
-     () => {
-       const layer = new LayerForTest({trainable: true});
-       layer.trainableWeights = trainableWeights;
-       layer.nonTrainableWeights = nonTrainableWeights;
-       expect(layer.nonTrainableWeights).toEqual(nonTrainableWeights);
-     });
-
-  it('concats trainable and nonTrainableWeights for nonTrainableWeights if ' +
-         'not trainable.',
-     () => {
-       const layer = new LayerForTest({trainable: false});
-       const expectedWeights = trainableWeights.concat(nonTrainableWeights);
-       layer.trainableWeights = trainableWeights;
-       layer.nonTrainableWeights = nonTrainableWeights;
-       expect(layer.nonTrainableWeights).toEqual(expectedWeights);
-     });
-
-  it('Setting trainable of layer sets Variable.trainable', () => {
-    const layer1 = tfl.layers.dense({units: 3});
-    layer1.build([4]);
-    // The weights should start from a trainable = true state.
-    expect(layer1.weights[0].trainable).toEqual(true);
-    expect(layer1.weights[1].trainable).toEqual(true);
-    layer1.trainable = false;
-    expect(layer1.weights[0].trainable).toEqual(false);
-    expect(layer1.weights[1].trainable).toEqual(false);
-    layer1.trainable = true;
-    expect(layer1.weights[0].trainable).toEqual(true);
-    expect(layer1.weights[1].trainable).toEqual(true);
-  });
-
-  for (const trainable of [true, false]) {
-    it('concats trainable and nonTrainableWeights for weights regardless of ' +
-           'whether the layer is trainable trainable.',
-       () => {
-         const layer = new LayerForTest({trainable});
-         const expectedWeights = trainableWeights.concat(nonTrainableWeights);
-         layer.trainableWeights = trainableWeights;
-         layer.nonTrainableWeights = nonTrainableWeights;
-         expect(layer.weights).toEqual(expectedWeights);
-       });
-  }
-
-  describe('assertInputCompatibility()', () => {
-    function runAssert(
-        layer: Layer,
-        inputs: Tensor|Tensor[]|tfl.SymbolicTensor|tfl.SymbolicTensor[]) {
-      // tslint:disable-next-line:no-any
-      (layer as any).assertInputCompatibility(inputs);
-    }
-    const testInputs = [
-      () => ones([1]), () => [ones([1])],
-      () => new tfl.SymbolicTensor('float32', [1], null, [], {}),
-      () => [new tfl.SymbolicTensor('float32', [1], null, [], {})]
-    ];
-
-    for (const inputs of testInputs) {
-      it('doesn\'t raise an exception if no inputSpec is provided.', () => {
-        const layer = new LayerForTest();
-        runAssert(layer, inputs());
-      });
-
-      it('doesn\'t raise exception if number of inputs == number of ' +
-             'inputSpecs.',
-         () => {
-           const inputSpecs = [new InputSpec({})];
-           const layer = new LayerForTest();
-           layer.inputSpec = inputSpecs;
-           expect(() => runAssert(layer, inputs())).not.toThrowError();
-         });
-
-      it('throws exception if number of inputs != number of inputSpecs.',
-         () => {
-           const inputSpecs = [new InputSpec({}), new InputSpec({})];
-           const layer = new LayerForTest();
-           layer.inputSpec = inputSpecs;
-           expect(() => runAssert(layer, inputs()))
-               .toThrowError(/expects [0-9]+ inputs/);
-         });
-
-      it('doesn\'t raise exception if inputs\' ndim == inputSpecs.ndim.',
-         () => {
-           const inputSpecs = [new InputSpec({ndim: 1})];
-           const layer = new LayerForTest();
-           layer.inputSpec = inputSpecs;
-           expect(() => runAssert(layer, inputs())).not.toThrowError();
-         });
-
-      it('throws exception if inputs\' ndim != inputSpecs.ndim.', () => {
-        const inputSpecs = [new InputSpec({ndim: 2})];
-        const layer = new LayerForTest();
-        layer.inputSpec = inputSpecs;
-        expect(() => runAssert(layer, inputs())).toThrowError(/expected ndim=/);
-      });
-
-      it('doesn\'t raise exception if inputs\' ndim <= inputSpecs.maxNdim.',
-         () => {
-           const inputSpecs = [new InputSpec({maxNDim: 1})];
-           const layer = new LayerForTest();
-           layer.inputSpec = inputSpecs;
-           expect(() => runAssert(layer, inputs())).not.toThrowError();
-         });
-
-      it('throws exception if inputs\' ndim > inputSpecs.maxNdim.', () => {
-        const inputSpecs = [new InputSpec({maxNDim: 0})];
-        const layer = new LayerForTest();
-        layer.inputSpec = inputSpecs;
-        expect(() => runAssert(layer, inputs()))
-            .toThrowError(/expected max_ndim=/);
-      });
-
-      it('doesn\'t raise exception if inputs\' ndim >= inputSpecs.minNdim.',
-         () => {
-           const inputSpecs = [new InputSpec({minNDim: 1})];
-           const layer = new LayerForTest();
-           layer.inputSpec = inputSpecs;
-           expect(() => runAssert(layer, inputs())).not.toThrowError();
-         });
-
-      it('throws exception if inputs\' ndim < inputSpecs.minNdim.', () => {
-        const inputSpecs = [new InputSpec({minNDim: 2})];
-        const layer = new LayerForTest();
-        layer.inputSpec = inputSpecs;
-        expect(() => runAssert(layer, inputs()))
-            .toThrowError(/expected min_ndim=/);
-      });
-
-      it('doesn\'t raise exception if inputs\' dtype == inputSpecs.dtype.',
-         () => {
-           const inputSpecs = [new InputSpec({dtype: 'float32'})];
-           const layer = new LayerForTest();
-           layer.inputSpec = inputSpecs;
-           expect(() => runAssert(layer, inputs())).not.toThrowError();
-         });
-
-      // TODO(michaelterry): Add dtype test once more dtypes supported.
-
-      it('doesn\'t raise exception if inputs\' dimensions == inputSpecs.axes.',
-         () => {
-           const inputSpecs = [new InputSpec({axes: {0: 1}})];
-           const layer = new LayerForTest();
-           layer.inputSpec = inputSpecs;
-           expect(() => runAssert(layer, inputs())).not.toThrowError();
-         });
-
-      it('throws exception if inputs\' dimensions != inputSpecs.axes.', () => {
-        const inputSpecs = [new InputSpec({axes: {0: 2}})];
-        const layer = new LayerForTest();
-        layer.inputSpec = inputSpecs;
-        expect(() => runAssert(layer, inputs())).toThrowError(/expected axis/);
-      });
-
-      it('throws exception if inputs\' dimensions don\'t have the same ' +
-             'number of inputSpecs.axes.',
-         () => {
-           const inputSpecs = [new InputSpec({axes: {0: 1, 2: 1}})];
-           const layer = new LayerForTest();
-           layer.inputSpec = inputSpecs;
-           expect(() => runAssert(layer, inputs()))
-               .toThrowError(/expected axis/);
-         });
-
-      it('doesn\'t raise exception if inputs\' shape == inputSpecs.shape.',
-         () => {
-           const inputSpecs = [new InputSpec({shape: [1]})];
-           const layer = new LayerForTest();
-           layer.inputSpec = inputSpecs;
-           expect(() => runAssert(layer, inputs())).not.toThrowError();
-         });
-
-      it('throws exception if inputs\' shape != inputSpecs.shape.', () => {
-        const inputSpecs = [new InputSpec({shape: [2]})];
-        const layer = new LayerForTest();
-        layer.inputSpec = inputSpecs;
-        expect(() => runAssert(layer, inputs())).toThrowError(/expected shape/);
-      });
-    }
-  });
-
-  describe('apply() passed 1 SymbolicTensor', () => {
-    const firstLayer = new LayerForTest({name: 'firstLayer'});
-    const secondLayer = new LayerForTest({name: 'secondLayer'});
-    const callArgs = {a: 1};
-    const singleSymbolicTensor =
-        new tfl.SymbolicTensor('float32', [1], firstLayer, [], {});
-    const returnedTensor =
-        secondLayer.apply(singleSymbolicTensor, callArgs) as tfl.SymbolicTensor;
-
-    it('returns a SymbolicTensor.', () => {
-      expect(returnedTensor instanceof tfl.SymbolicTensor).toBe(true);
-    });
-
-    it('returns a SymbolicTensor with a reference to the source layer.', () => {
-      expect(returnedTensor.sourceLayer).toEqual(secondLayer);
-    });
-
-    it('returns a SymbolicTensor with a reference to the inputs passed ' +
-           'to apply().',
-       () => {
-         expect(returnedTensor.inputs).toEqual([singleSymbolicTensor]);
-         expect(returnedTensor.callArgs).toEqual(callArgs);
-       });
-
-    it('returns a SymbolicTensor with nodeIndex and tensorIndex set.', () => {
-      expect(returnedTensor.nodeIndex).toBeDefined();
-      expect(returnedTensor.tensorIndex).toBeDefined();
-    });
-
-    it('returns a SymbolicTensor with the name set.', () => {
-      expect(returnedTensor.name).toMatch(/secondLayer/);
-    });
-
-    it('is built.', () => {
-      expect(secondLayer.built).toBe(true);
-    });
-
-    it('Incompatible inputShape leads to warning', () => {
-      let recordedWarnMessage: string;
-      spyOn(console, 'warn')
-          .and.callFake((message: string) => recordedWarnMessage = message);
-      const layer1 = tfl.layers.dense({units: 2, inputShape: [5]});
-      layer1.apply(tfl.input({shape: [4]}));
-      expect(recordedWarnMessage)
-          .toMatch(/shape of the input tensor .*null,4.* not match .*null,5.*/);
-    });
-
-    it('Incompatible inputShape leads to warning: batchInputShape', () => {
-      let recordedWarnMessage: string;
-      spyOn(console, 'warn')
-          .and.callFake((message: string) => recordedWarnMessage = message);
-      const layer1 = tfl.layers.dense({units: 2, batchInputShape: [2, 3, 5]});
-      layer1.apply(tfl.input({shape: [4, 5]}));
-      expect(recordedWarnMessage)
-          .toMatch(
-              /shape of the input tensor .*null,4,5.* not match .*2,3,5.*/);
-    });
-
-    it('Incompatible inputShape rank leads to warning', () => {
-      let recordedWarnMessage: string;
-      spyOn(console, 'warn')
-          .and.callFake((message: string) => recordedWarnMessage = message);
-      const layer1 = tfl.layers.dense({units: 2, inputShape: [5]});
-      layer1.apply(tfl.input({shape: [4, 3]}));
-      expect(recordedWarnMessage)
-          .toMatch(/rank .*null,4,3.* does not match .*null,5.*/);
-    });
-
-    it('Incompatible inputShape rank leads to warning: batchInputShape', () => {
-      let recordedWarnMessage: string;
-      spyOn(console, 'warn')
-          .and.callFake((message: string) => recordedWarnMessage = message);
-      const layer1 = tfl.layers.dense({units: 2, batchInputShape: [3, 5]});
-      layer1.apply(tfl.input({shape: [4, 3]}));
-      expect(recordedWarnMessage)
-          .toMatch(/rank .*null,4,3.* does not match .*3,5.*/);
-    });
-
-    it('Compatible inputShape leads to NO warning', () => {
-      let recordedWarnMessage: string;
-      spyOn(console, 'warn')
-          .and.callFake((message: string) => recordedWarnMessage = message);
-      const layer1 = tfl.layers.dense({units: 2, inputShape: [5]});
-      layer1.apply(tfl.input({shape: [5]}));
-      expect(recordedWarnMessage).toEqual(undefined);
-    });
-  });
-
-  describe('apply() passed >1 SymbolicTensor', () => {
-    it('throws an exception for multiple symbolic inputs.', () => {
-      const firstLayer = new LayerForTest({name: 'first layer'});
-      const secondLayer = new LayerForTest({name: 'second layer'});
-      const symbolicTensorList = [
-        new tfl.SymbolicTensor(
-            'float32', [1], firstLayer, [], {}, 'first_symbolic_tensor'),
-        new tfl.SymbolicTensor(
-            'float32', [1], firstLayer, [], {}, 'second_symbolic_tensor')
-      ];
-      // TODO(michaelterry): Update this once multiple symbolic tensors are
-      // allowed.
-      expect(() => secondLayer.apply(symbolicTensorList)).toThrowError();
-    });
-  });
-
-  describe('apply() passed SymbolicTensor and Tensor', () => {
-    it('throws an exception.', () => {
-      const layer = new LayerForTest();
-      const inputs = [
-        new tfl.SymbolicTensor(
-            'float32', [1], null, [], {}, 'first_symbolic_tensor'),
-        ones([1])
-      ];
-      expect(() => layer.apply(inputs as Tensor[]))
-          .toThrowError(/must be all SymbolicTensors or all Tensors/);
-    });
-  });
-
-  it('apply() returns multiple symbolic tensors for multiple ' +
-         'output shapes',
-     () => {
-       const layer = new LayerForTest();
-       const outputShapes = [[1], [2, 3]];
-       const input = new tfl.SymbolicTensor('float32', [1], null, [], {});
-       // tslint:disable-next-line:no-any
-       spyOn((layer as any), 'computeOutputShape').and.callFake(() => {
-         return outputShapes;
-       });
-       const results = layer.apply(input) as tfl.SymbolicTensor[];
-       expect(results.length).toEqual(2);
-       expect(results.map(x => x.shape)).toEqual(outputShapes);
-       expect(results.map(x => x.outputTensorIndex)).toEqual([0, 1]);
-     });
-
-  describe('apply() passed 1+ Tensors', () => {
-    it('returns new values for output if the same as the input.', () => {
-      const anArray = ones([1]);
-      // Test with both a Tensor and an array of Tensors.
-      for (const inputs of [anArray, [anArray, anArray]]) {
-        const layer = new LayerForTest();
-        const result = layer.apply(inputs) as Tensor | Tensor[];
-
-        expect(result instanceof Tensor || (result[0] instanceof Tensor))
-            .toBe(true);
-
-        expect(layer.built).toBe(true);
-
-        if (result instanceof Array) {
-          const inputArray = inputs as Tensor[];
-          for (let i = 0; i < result.length; i++) {
-            expectTensorsClose(result[i], inputArray[i]);
-          }
-        } else {
-          expectTensorsClose(result, inputs as Tensor);
-        }
-        expect(result === inputs).toBe(false);
-      }
-    });
-  });
-
-  describe('initialized with weights at construction time', () => {
-    it('sets those weights after calling apply().', () => {
-      const initialWeights = eye(2);
-      const arrayInput = zeros([1]);
-      const symbolicInput =
-          new tfl.SymbolicTensor('float32', [1], null, [], {});
-      // Test with symbolic and concrete input.
-      for (const inputs of [arrayInput, symbolicInput]) {
-        const layer = new LayerForTest({weights: [initialWeights]});
-        // Fake the build() method to test assignment to initialWeights.
-        // tslint:disable-next-line:no-any
-        spyOn((layer as any), 'build').and.callFake(() => {
-          layer.built = true;
-          layer.trainableWeights = [new LayerVariable(zeros([2, 2]))];
-        });
-        expect(layer.weights.length).toEqual(0);
-        layer.apply(inputs);
-        expect(layer.weights.length).toEqual(1);
-        expectTensorsClose(layer.weights[0].read(), initialWeights);
-      }
-    });
-  });
-
-  describe('apply() (nodes)', () => {
-    it('doesn\'t change inboundNodes or outboundNodes when called with ' +
-           'concrete input',
-       () => {
-         const layer = new LayerForTest();
-         expect(layer.inboundNodes.length).toEqual(0);
-         expect(layer.outboundNodes.length).toEqual(0);
-         layer.apply(eye(1));
-         expect(layer.inboundNodes.length).toEqual(0);
-         expect(layer.outboundNodes.length).toEqual(0);
-       });
-
-    it('changes inboundNodes and outboundNodes when called with ' +
-           'symbolic input',
-       () => {
-         const layer = new LayerForTest();
-         const input = new tfl.SymbolicTensor('float32', [1], null, [], {});
-         expect(layer.inboundNodes.length).toEqual(0);
-         expect(layer.outboundNodes.length).toEqual(0);
-         layer.apply(input);
-         expect(layer.inboundNodes.length).toEqual(1);
-         expect(layer.outboundNodes.length).toEqual(0);
-         expect(layer.inboundNodes[0].outboundLayer).toEqual(layer);
-       });
-
-    it('updates inbound and outboundNodes when there are multiple layers',
-       () => {
-         const firstLayer = new LayerForTest({name: 'first_layer'});
-         const secondLayer = new LayerForTest({name: 'second_layer'});
-         const initialInput =
-             new tfl.SymbolicTensor('float32', [1], null, [], {});
-         const firstOutput = firstLayer.apply(initialInput);
-         secondLayer.apply(firstOutput);
-
-         expect(firstLayer.inboundNodes.length).toEqual(1);
-         expect(firstLayer.outboundNodes.length).toEqual(1);
-         expect(secondLayer.inboundNodes.length).toEqual(1);
-         expect(secondLayer.outboundNodes.length).toEqual(0);
-         expect(firstLayer.outboundNodes[0].outboundLayer).toEqual(secondLayer);
-       });
-  });
-
-  describe('Layer.outputShape', () => {
-    it('Layers with one output', () => {
-      const layer = tfl.layers.dense({units: 3});
-      layer.apply(new tfl.SymbolicTensor('float32', [null, 4], null, [], {}));
-      expect(layer.outputShape).toEqual([null, 3]);
-    });
-
-    it('Layers with two outputs', () => {
-      const layer = tfl.layers.simpleRNN({units: 3, returnState: true});
-      layer.apply(
-          new tfl.SymbolicTensor('float32', [null, 4, 5], null, [], {}));
-      expect(layer.outputShape).toEqual([[null, 3], [null, 3]]);
-    });
-
-    it('Layers with two inboundNodes of the same outputShape', () => {
-      const layer = tfl.layers.dense({units: 3});
-      layer.apply(new tfl.SymbolicTensor('float32', [null, 4], null, [], {}));
-      layer.apply(new tfl.SymbolicTensor('float32', [null, 4], null, [], {}));
-      expect(layer.inboundNodes.length).toEqual(2);
-      expect(layer.outputShape).toEqual([null, 3]);
-    });
-
-    it('Layers with two inboundNodes of different outputShapes', () => {
-      const layer = tfl.layers.dense({units: 3});
-      layer.apply(
-          new tfl.SymbolicTensor('float32', [null, 5, 4], null, [], {}));
-      layer.apply(
-          new tfl.SymbolicTensor('float32', [null, 6, 4], null, [], {}));
-      expect(layer.inboundNodes.length).toEqual(2);
-      expect(() => layer.outputShape)
-          .toThrowError(/has multiple inbound nodes/);
-    });
-
-    it('Unbuilt layer throws Error', () => {
-      const layer = tfl.layers.dense({units: 3});
-      expect(() => layer.outputShape).toThrowError(/has never been called/);
-    });
-  });
-
-  describe('Layer.countParams', () => {
-    it('Layers with weights', () => {
-      const units = 3;
-      const inputSize = 4;
-      const layer = tfl.layers.dense({units});
-      layer.apply(zeros([1, inputSize]));
-      const numParams = layer.countParams();
-      expect(numParams).toEqual(units * inputSize + units);
-    });
-
-    it('Layer without weights', () => {
-      const layer = tfl.layers.flatten();
-      layer.apply(zeros([2, 2, 2]));
-      const numParams = layer.countParams();
-      expect(numParams).toEqual(0);
-    });
-  });
-
-  describe('setWeights', () => {
-    it('throws exception if weights are not the same length ' +
-           'as existing weights',
-       () => {
-         const layer = new LayerForTest();
-         layer.trainableWeights = [new LayerVariable(zeros([2, 2]))];
-         const onesTensor = ones([1]);
-         expect(() => layer.setWeights([
-           onesTensor, onesTensor
-         ])).toThrowError(/with a weight list of length/);
-       });
-
-    it('throws exception if weights are not the same shape ' +
-           'as existing weights',
-       () => {
-         const layer = new LayerForTest();
-         const onesTensor = ones([1]);
-         layer.trainableWeights = [new LayerVariable(zeros([2, 2]))];
-         expect(() => layer.setWeights([onesTensor]))
-             .toThrowError(/not compatible with provided weight shape/);
-       });
-
-    it('updates weights.', () => {
-      const layer = new LayerForTest();
-      const onesTensor = ones([1]);
-      layer.trainableWeights = [new LayerVariable(zeros([1]))];
-      layer.setWeights([onesTensor]);
-      expectTensorsClose(layer.trainableWeights[0].read(), onesTensor);
-    });
-  });
-
-  describe('computeOutputShape()', () => {
-    it('returns the inputShape in the base class', () => {
-      const layer = new LayerForTest();
-      const shape = [1];
-      expect(layer.computeOutputShape(shape)).toEqual(shape);
-    });
-  });
-
-  describe('input and output properties: ', () => {
-    let input: tfl.SymbolicTensor;
-    let layer: Layer;
-    let output: tfl.SymbolicTensor;
-
-    beforeEach(() => {
-      input =
-          new tfl.SymbolicTensor('float32', [1], null, [], {}, 'firstInput');
-      layer = new LayerForTest();
-      output = layer.apply(input) as tfl.SymbolicTensor;
-    });
-
-    it('input retrieves layer\'s inputs.', () => {
-      expect(layer.input).toEqual(input);
-    });
-
-    it('input retrieves layer\'s outputs.', () => {
-      expect(layer.output).toEqual(output);
-    });
-
-    it('input throws exception if there is more than one input', () => {
-      const secondInput =
-          new tfl.SymbolicTensor('float32', [1], null, [], {}, 'secondInput');
-      layer.apply(secondInput);
-      expect(() => layer.input).toThrowError(/"layer input" is ill-defined/);
-    });
-
-    it('output throws exception if there is more than one output', () => {
-      const secondInput =
-          new tfl.SymbolicTensor('float32', [1], null, [], {}, 'secondInput');
-      layer.apply(secondInput);
-      expect(() => layer.output).toThrowError(/"layer output" is ill-defined/);
-    });
-  });
-
-  describe('getInputAt and getOutputAt: ', () => {
-    let input: tfl.SymbolicTensor;
-    let layer: Layer;
-    let output: tfl.SymbolicTensor;
-
-    beforeEach(() => {
-      input =
-          new tfl.SymbolicTensor('float32', [1], null, [], {}, 'firstInput');
-      layer = new LayerForTest();
-      output = layer.apply(input) as tfl.SymbolicTensor;
-    });
-
-    it('getInputAt() retrieves layer\'s inputs.', () => {
-      expect(layer.getInputAt(0)).toEqual(input);
-    });
-
-    it('getOutputAt() retrieves layer\'s outputs.', () => {
-      expect(layer.getOutputAt(0)).toEqual(output);
-    });
-
-    it('getInputAt() throws exception ask for incorrect index.', () => {
-      expect(() => layer.getInputAt(1))
-          .toThrowError(/Asked to get input at node 1, but/);
-    });
-
-    it('getOutputAt() throws exception ask for incorrect index.', () => {
-      expect(() => layer.getOutputAt(1))
-          .toThrowError(/Asked to get output at node 1, but/);
-    });
-  });
-});
-
-describeMathCPUAndGPU('Layer-dispose', () => {
-  it('Dispose Dense Layer before build leads to Error', () => {
-    const dense = tfl.layers.dense({units: 1, inputShape: [4]});
-    expect(() => dense.dispose()).toThrowError(/has not been built/);
-  });
-
-  it('Dispose Dense Layer after one tensor call frees memory', () => {
-    const dense = tfl.layers.dense({units: 1, inputShape: [4]});
-    dense.apply(zeros([2, 4]));
-    const numTensors0 = memory().numTensors;
-    const result = dense.dispose();
-
-    expect(result.refCountAfterDispose).toEqual(0);
-    expect(result.numDisposedVariables).toEqual(2);
-    // Two variables should have been freed: the kernel and the bias.
-    expect(memory().numTensors).toEqual(numTensors0 - 2);
-  });
-
-  it('Symbolic apply() call after Dense disposal leads to Error', () => {
-    const dense = tfl.layers.dense({units: 1, inputShape: [4]});
-    dense.apply(zeros([2, 4]));
-    const result = dense.dispose();
-    // This dispose() call should dispose the layer.
-
-    expect(result.refCountAfterDispose).toEqual(0);
-    expect(result.numDisposedVariables).toEqual(2);
-    expect(
-        () => dense.apply(
-            new tfl.SymbolicTensor('float32', [2, 4], null, [], {})))
-        .toThrowError(/Layer .* is already disposed/);
-  });
-
-  it('Non-symbolic apply() call after Dense disposal leads to Error', () => {
-    const dense = tfl.layers.dense({units: 1, inputShape: [4]});
-    dense.apply(zeros([2, 4]));
-    dense.dispose();  // This dispose() call should dispose the layer.
-
-    expect(() => dense.apply(ones([2, 4])))
-        .toThrowError(/Layer .* is already disposed/);
-  });
-
-  it('Calling defRec() repeatedly for two-Node Layer frees memory', () => {
-    const dense = tfl.layers.dense({units: 1, inputShape: [4]});
-    dense.apply(new tfl.SymbolicTensor('float32', [2, 4], null, [], {}));
-    dense.apply(new tfl.SymbolicTensor('float32', [2, 4], null, [], {}));
-    const numTensors0 = memory().numTensors;
-
-    const result1 = dense.dispose();
-    // After the first dispose call, no memory should have been freed.
-    expect(memory().numTensors).toEqual(numTensors0);
-    expect(result1.refCountAfterDispose).toEqual(1);
-    expect(result1.numDisposedVariables).toEqual(0);
-
-    const result2 = dense.dispose();
-    // After the second dispose call, memory for the kernel and the bias should
-    // have been freed.
-    expect(memory().numTensors).toEqual(numTensors0 - 2);
-    expect(result2.refCountAfterDispose).toEqual(0);
-    expect(result2.numDisposedVariables).toEqual(2);
-  });
-
-  it('Calling dispose on already-disposed Layer leads to Error', () => {
-    const dense = tfl.layers.dense({units: 1, inputShape: [4]});
-    dense.apply(zeros([2, 4]));
-    dense.dispose();
-    expect(() => dense.dispose()).toThrowError(/Layer .* is already disposed/);
-  });
-
-  it('Symbolic apply() call after Flatten disposal leads to Error', () => {
-    const dense = tfl.layers.flatten();
-    dense.apply(zeros([2, 3, 4]));
-    dense.dispose();  // This dispose() call should dispose the layer.
-
-    expect(
-        () => dense.apply(
-            new tfl.SymbolicTensor('float32', [2, 4], null, [], {})))
-        .toThrowError(/Layer .* is already disposed/);
-  });
-
-  it('Non-symbolic apply() call after Flatten disposal leads to Error', () => {
-    const dense = tfl.layers.flatten();
-    dense.apply(zeros([2, 3, 4]));
-    dense.dispose();  // This dispose() call should dispose the layer.
-
-    expect(() => dense.apply(zeros([2, 3, 4])))
-        .toThrowError(/Layer .* is already disposed/);
-  });
-
-  it('dispose() call works on Input Layer', () => {
-    const input = tfl.layers.input({shape: [2, 3]});
-    const output = tfl.layers.reshape({targetShape: [3, 2]}).apply(input) as
-        tfl.SymbolicTensor;
-    const model = tfl.model({inputs: [input], outputs: [output]});
-
-    const result = model.dispose();
-    // This model, consiting of only an input layer and a reshape layer, does
-    // not have any weights to dispose.
-    expect(result.numDisposedVariables).toEqual(0);
-    expect(() => model.predict(zeros([1, 2, 3])))
-        .toThrowError(/already disposed/);
-  });
-});
-
-describeMathCPUAndGPU('loadWeightsFromNamedTensorMap', () => {
-  const inputTensor =
-      tfl.layers.input({shape: [3], name: 'inputLayer', dtype: 'float32'});
-
-  it('One layer', () => {
-    const denseLayer =
-        tfl.layers.dense({units: 2, useBias: true, name: 'dense_layer'});
-    const output = denseLayer.apply(inputTensor) as SymbolicTensor;
-    const model = tfl.model({inputs: inputTensor, outputs: output});
-
-    const namedWeightsMap: NamedTensorMap = {};
-    namedWeightsMap[denseLayer.weights[0].originalName] =
-        tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    namedWeightsMap[denseLayer.weights[1].originalName] = tensor1d([10, 20]);
-    model.loadWeights(namedWeightsMap);
-    expectTensorsClose(
-        denseLayer.weights[0].read(), tensor2d([1, 2, 3, 4, 5, 6], [3, 2]));
-    expectTensorsClose(denseLayer.weights[1].read(), tensor1d([10, 20]));
-  });
-
-  it('load keras weights', () => {
-    const denseLayer =
-        tfl.layers.dense({units: 2, useBias: true, name: 'dense_layer'});
-    const output = denseLayer.apply(inputTensor) as SymbolicTensor;
-    const model = tfl.model({inputs: inputTensor, outputs: output});
-
-    const namedWeightsMap: NamedTensorMap = {};
-    namedWeightsMap[denseLayer.weights[0].originalName.split('/')[0] + '/0'] =
-        tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    namedWeightsMap[denseLayer.weights[1].originalName.split('/')[0] + '/1'] =
-        tensor1d([10, 20]);
-    model.loadWeights(namedWeightsMap);
-    expectTensorsClose(
-        denseLayer.weights[0].read(), tensor2d([1, 2, 3, 4, 5, 6], [3, 2]));
-    expectTensorsClose(denseLayer.weights[1].read(), tensor1d([10, 20]));
-  });
-
-  it('Mismatching shape throws an error even in non-strict mode', () => {
-    const denseLayer =
-        tfl.layers.dense({units: 2, useBias: true, name: 'dense_layer'});
-    const output = denseLayer.apply(inputTensor) as SymbolicTensor;
-    const model = tfl.model({inputs: inputTensor, outputs: output});
-
-    const namedWeightsMap: NamedTensorMap = {};
-    namedWeightsMap[denseLayer.weights[0].originalName] =
-        tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2]);
-    namedWeightsMap[denseLayer.weights[1].originalName] = tensor1d([10, 20]);
-    const strict = false;
-    expect(() => model.loadWeights(namedWeightsMap, strict))
-        .toThrowError('Shape mismatch: [3,2] vs. [4,2]');
-  });
-
-  it('Extra weights leads to error', () => {
-    const denseLayer =
-        tfl.layers.dense({units: 2, useBias: true, name: 'dense_layer'});
-    const output = denseLayer.apply(inputTensor) as SymbolicTensor;
-    const model = tfl.model({inputs: inputTensor, outputs: output});
-
-    const namedWeightsMap: NamedTensorMap = {};
-    namedWeightsMap[denseLayer.weights[0].originalName] =
-        tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    namedWeightsMap[denseLayer.weights[1].originalName] = tensor1d([10, 20]);
-    namedWeightsMap['extra'] = tensor1d([10, 20]);
-    expect(() => model.loadWeights(namedWeightsMap))
-        .toThrowError(/Provided weight data has no target variable: extra/);
-  });
-
-  it('Extra weights are allowed in non-strict mode', () => {
-    const denseLayer =
-        tfl.layers.dense({units: 2, useBias: true, name: 'dense_layer'});
-    const output = denseLayer.apply(inputTensor) as SymbolicTensor;
-    const model = tfl.model({inputs: inputTensor, outputs: output});
-
-    const namedWeightsMap: NamedTensorMap = {};
-    namedWeightsMap[denseLayer.weights[0].originalName] =
-        tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    namedWeightsMap[denseLayer.weights[1].originalName] = tensor1d([10, 20]);
-    namedWeightsMap['extra'] = tensor1d([10, 20]);
-    const strict = false;
-    model.loadWeights(namedWeightsMap, strict);
-    expectTensorsClose(
-        denseLayer.weights[0].read(), tensor2d([1, 2, 3, 4, 5, 6], [3, 2]));
-    expectTensorsClose(denseLayer.weights[1].read(), tensor1d([10, 20]));
-  });
-
-  it('Unset weights leads to error', () => {
-    const denseLayer =
-        tfl.layers.dense({units: 2, useBias: true, name: 'dense_layer'});
-    const output = denseLayer.apply(inputTensor) as SymbolicTensor;
-    const model = tfl.model({inputs: inputTensor, outputs: output});
-
-    const namedWeightsMap: NamedTensorMap = {};
-    namedWeightsMap[denseLayer.weights[0].originalName] =
-        tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    expect(() => model.loadWeights(namedWeightsMap))
-        .toThrowError(/1 of 2 weights are not set: .*bias.*/);
-  });
-
-  it('Unset weights are allowed in non-strict mode', () => {
-    const denseLayer =
-        tfl.layers.dense({units: 2, useBias: true, name: 'dense_layer'});
-    const output = denseLayer.apply(inputTensor) as SymbolicTensor;
-    const model = tfl.model({inputs: inputTensor, outputs: output});
-
-    const namedWeightsMap: NamedTensorMap = {};
-    namedWeightsMap[denseLayer.weights[0].originalName] =
-        tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
-    const strict = false;
-    model.loadWeights(namedWeightsMap, strict);
-    // No exception thrown.
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/engine/training.ts b/tfjs-master/tfjs-layers/src/engine/training.ts
deleted file mode 100644
index f15bc4ef1..000000000
--- a/tfjs-master/tfjs-layers/src/engine/training.ts
+++ /dev/null
@@ -1,2208 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original Source: engine/training.py */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {io, ModelPredictConfig as ModelPredictArgs, NamedTensorMap, Optimizer, Scalar, scalar, serialization, Tensor, Tensor1D, tensor1d, util} from '@tensorflow/tfjs-core';
-
-import * as K from '../backend/tfjs_backend';
-import {BaseCallback, configureCallbacks, History, ModelLoggingVerbosity, standardizeCallbacks} from '../base_callbacks';
-import {nameScope} from '../common';
-import {NotImplementedError, RuntimeError, ValueError} from '../errors';
-import {Shape} from '../keras_format/common';
-import {LossIdentifier} from '../keras_format/loss_config';
-import {OptimizerSerialization} from '../keras_format/optimizer_config';
-import {MetricsIdentifier, TrainingConfig} from '../keras_format/training_config';
-import {deserialize} from '../layers/serialization';
-import { disposeTensorsInLogs, UnresolvedLogs } from '../logs';
-import * as losses from '../losses';
-import * as Metrics from '../metrics';
-import * as optimizers from '../optimizers';
-import {LossOrMetricFn, NamedTensor} from '../types';
-import {checkUserDefinedMetadata} from '../user_defined_metadata';
-import {count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique} from '../utils/generic_utils';
-import {printSummary} from '../utils/layer_utils';
-import {range} from '../utils/math_utils';
-import {convertPythonicToTs} from '../utils/serialization_utils';
-import {LayerVariable} from '../variables';
-import {version} from '../version';
-
-import {Container, ContainerArgs} from './container';
-import {Dataset} from './dataset_stub';
-import {execute, FeedDict} from './executor';
-import {DisposeResult, SymbolicTensor} from './topology';
-import {evaluateDataset, fitDataset, ModelEvaluateDatasetArgs, ModelFitDatasetArgs} from './training_dataset';
-import {checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, makeBatches, ModelFitArgs, sliceArrays, sliceArraysByIndices} from './training_tensors';
-import {ClassWeight, ClassWeightMap, computeWeightedLoss, standardizeClassWeights, standardizeWeights} from './training_utils';
-
-/**
- * Helper function for polymorphic input data: 1. singleton Tensor.
- */
-export function isDataTensor(x: Tensor|Tensor[]|{[inputName: string]: Tensor}|
-                             {[inputName: string]: Tensor[]}): boolean {
-  return x instanceof Tensor;
-}
-
-/**
- * Helper function for polymorphic input data: 2. Array of Tensor.
- */
-export function isDataArray(x: Tensor|Tensor[]|
-                            {[inputName: string]: Tensor}): boolean {
-  return Array.isArray(x);
-}
-
-/**
- * Helper function for polymorphic input data: 3. "dict" of Tensor.
- */
-export function isDataDict(x: Tensor|Tensor[]|
-                           {[inputName: string]: Tensor}): boolean {
-  return !isDataTensor(x) && !isDataArray(x);
-}
-
-/**
- * Normalizes inputs and targets provided by users.
- * @param data User-provided input data (polymorphic).
- * @param names An Array of expected Tensor names.
- * @param shapes Optional Array of expected Tensor shapes.
- * @param checkBatchAxis Whether to check that the batch axis of the arrays
- *   match  the expected value found in `shapes`.
- * @param exceptionPrefix String prefix used for exception formatting.
- * @returns List of standardized input Tensors (one Tensor per model input).
- * @throws ValueError: in case of improperly formatted user data.
- */
-export function standardizeInputData(
-    data: Tensor|Tensor[]|{[inputName: string]: Tensor}, names: string[],
-    shapes?: Shape[], checkBatchAxis = true, exceptionPrefix = ''): Tensor[] {
-  if (names == null || names.length === 0) {
-    // Check for the case where the model expected no data, but some data got
-    // sent.
-    if (data != null) {
-      let gotUnexpectedData = false;
-      if (isDataArray(data) && (data as Tensor[]).length > 0) {
-        gotUnexpectedData = true;
-      } else if (isDataDict(data)) {
-        for (const key in data) {
-          if (data.hasOwnProperty(key)) {
-            gotUnexpectedData = true;
-            break;
-          }
-        }
-      } else {
-        // `data` is a singleton Tensor in this case.
-        gotUnexpectedData = true;
-      }
-      if (gotUnexpectedData) {
-        throw new ValueError(
-            `Error when checking model ${exceptionPrefix} expected no data, ` +
-            `but got ${data}`);
-      }
-    }
-    return [];
-  }
-  if (data == null) {
-    return names.map(name => null);
-  }
-
-  let arrays: Tensor[];
-  if (isDataDict(data)) {
-    data = data as {[inputName: string]: Tensor};
-    arrays = [];
-    for (const name of names) {
-      if (data[name] == null) {
-        throw new ValueError(
-            `No data provided for "${name}". Need data for each key in: ` +
-            `${names}`);
-      }
-      arrays.push(data[name]);
-    }
-  } else if (isDataArray(data)) {
-    data = data as Tensor[];
-    if (data.length !== names.length) {
-      throw new ValueError(
-          `Error when checking model ${exceptionPrefix}: the Array of ` +
-          `Tensors that you are passing to your model is not the size the ` +
-          `model expected. Expected to see ${names.length} Tensor(s), but ` +
-          `instead got the following list of Tensor(s): ${data}`);
-    }
-    arrays = data;
-  } else {
-    data = data as Tensor;
-    if (names.length > 1) {
-      throw new ValueError(
-          `The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +
-          `but only received one Tensor. Found: Tensor with shape ${
-              data.shape}`);
-    }
-    arrays = [data];
-  }
-
-  arrays = ensureTensorsRank2OrHigher(arrays);
-
-  // Check shape compatibility.
-  if (shapes != null) {
-    for (let i = 0; i < names.length; ++i) {
-      if (shapes[i] == null) {
-        continue;
-      }
-      const array = arrays[i];
-      if (array.shape.length !== shapes[i].length) {
-        throw new ValueError(
-            `Error when checking ${exceptionPrefix}: expected ${names[i]} ` +
-            `to have ${shapes[i].length} dimension(s). but got array with ` +
-            `shape ${array.shape}`);
-      }
-      for (let j = 0; j < shapes[i].length; ++j) {
-        if (j === 0 && !checkBatchAxis) {
-          // Skip the first (batch) axis.
-          continue;
-        }
-        const dim = array.shape[j];
-        const refDim = shapes[i][j];
-        if (refDim != null && refDim >= 0 && dim !== refDim) {
-          throw new ValueError(
-              `${exceptionPrefix} expected a batch of elements where each ` +
-              `example has shape [${shapes[i].slice(1, shapes[i].length)}] ` +
-              `(i.e.,tensor shape [*,${
-                  shapes[i].slice(1, shapes[i].length)}])` +
-              ` but the ${exceptionPrefix} received an input with ${
-                  array.shape[0]}` +
-              ` examples, each with shape [${
-                  array.shape.slice(1, array.shape.length)}]` +
-              ` (tensor shape [${array.shape}])`);
-        }
-      }
-    }
-  }
-  return arrays;
-}
-
-/**
- * User input validation for Tensors.
- * @param inputs `Array` of `tf.Tensor`s for inputs.
- * @param targets `Array` of `tf.Tensor`s for targets.
- * @param weights Optional `Array` of `tf.Tensor`s for sample weights.
- * @throws ValueError: in case of incorrectly formatted data.
- */
-export function checkArrayLengths(
-    inputs: Tensor[], targets: Tensor[], weights?: Tensor[]) {
-  const setX = unique(inputs.map(input => input.shape[0]));
-  setX.sort();
-  const setY = unique(targets.map(target => target.shape[0]));
-  setY.sort();
-  // TODO(cais): Check `weights` as well.
-  if (setX.length > 1) {
-    throw new ValueError(
-        `All input Tensors (x) should have the same number of samples. ` +
-        `Got array shapes: ` +
-        `${JSON.stringify(inputs.map(input => input.shape))}`);
-  }
-  if (setY.length > 1) {
-    throw new ValueError(
-        `All target Tensors (y) should have the same number of samples. ` +
-        `Got array shapes: ` +
-        `${JSON.stringify(targets.map(target => target.shape))}`);
-  }
-  if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {
-    throw new ValueError(
-        `Input Tensors should have the same number of samples as target ` +
-        `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +
-        `sample(s).`);
-  }
-}
-
-/**
- * Validation on the compatibility of targes and loss functions.
- *
- * This helps prevent users from using loss functions incorrectly.
- *
- * @param targets `Array` of `tf.Tensor`s of targets.
- * @param lossFns `Array` of loss functions.
- * @param outputShapes `Array` of shapes of model outputs.
- */
-function checkLossAndTargetCompatibility(
-    targets: Tensor[], lossFns: LossOrMetricFn[], outputShapes: Shape[]) {
-  // TODO(cais): Dedicated test coverage?
-  const keyLosses = [
-    losses.meanSquaredError, losses.binaryCrossentropy,
-    losses.categoricalCrossentropy
-  ];
-  for (let i = 0; i < targets.length; ++i) {
-    const y = targets[i];
-    const loss = lossFns[i];
-    const shape = outputShapes[i];
-    if (loss == null) {
-      continue;
-    }
-    if (loss === losses.categoricalCrossentropy) {
-      if (y.shape[y.shape.length - 1] === 1) {
-        throw new ValueError(
-            `You are passing a target array of shape ${y.shape} while using ` +
-            `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +
-            `expects targets to be binary matrices (1s and 0s) of shape ` +
-            `[samples, classes].`);
-        // TODO(cais): Example code in error message.
-      }
-    }
-    if (keyLosses.indexOf(loss) !== -1) {
-      const slicedYShape = y.shape.slice(1);
-      const slicedShape = shape.slice(1);
-      for (let j = 0; j < slicedYShape.length; ++j) {
-        const targetDim = slicedYShape[j];
-        const outDim = slicedShape[j];
-        if (outDim != null && targetDim !== outDim) {
-          throw new ValueError(
-              `A target Tensor with shape ${y.shape} was passed for an ` +
-              `output of shape ${shape}, while using a loss function that ` +
-              `expects targets to have the same shape as the output.`);
-        }
-      }
-    }
-  }
-}
-
-/**
- * Check inputs provided by the user.
- *
- * Porting Note: This corresponds to _standardize_input_data() in Python
- *   Keras. Because of the strong typing in TF.js, we do not need to convert
- *   the data. Specifically:
- *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for
- *      example. We don't need to worry about that here because there is no
- *      widely popular javascript/typesdcript equivalent of pandas (so far).
- *      If one becomes available in the future, we can add support.
- *   2) in PyKeras, inputs can be Python dict. But here we are stipulating
- * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We
- * may add support for `Object` data inputs in the future when the need
- * arises.
- *
- * Instead, we perform basic checks for number of parameters and shapes.
- *
- * @param data: The input data.
- * @param names: Name for the inputs, from the model.
- * @param shapes: Expected shapes for the input data, from the model.
- * @param checkBatchAxis: Whether the size along the batch axis (i.e., the
- *   first dimension) will be checked for matching.
- * @param exceptionPrefix: Execption prefix message, used in generating error
- *   messages.
- * @throws ValueError: on incorrect number of inputs or mismatches in shapes.
- */
-function checkInputData(
-    data: Tensor|Tensor[], names: string[], shapes?: Shape[],
-    checkBatchAxis = true, exceptionPrefix = '') {
-  let arrays: Tensor[];
-  if (Array.isArray(data)) {
-    if (data.length !== names.length) {
-      throw new ValueError(
-          `Error when checking model ${exceptionPrefix}: the Array of ` +
-          `Tensors that you are passing to your model is not the size the ` +
-          `the model expected. Expected to see ${names.length} Tensor(s),` +
-          ` but instead got ${data.length} Tensors(s).`);
-    }
-    arrays = data;
-  } else {
-    if (names.length > 1) {
-      throw new ValueError(
-          `The model expects ${names.length} ${exceptionPrefix} Tensors, ` +
-          `but only received one Tensor. Found: array with shape ` +
-          `${JSON.stringify(data.shape)}.`);
-    }
-    arrays = [data];
-  }
-
-  if (shapes != null) {
-    for (let i = 0; i < names.length; ++i) {
-      if (shapes[i] == null) {
-        continue;
-      }
-      const array = arrays[i];
-      if (array.shape.length !== shapes[i].length) {
-        throw new ValueError(
-            `Error when checking ${exceptionPrefix}: expected ${names[i]} ` +
-            `to have ${shapes[i].length} dimension(s), but got array with ` +
-            `shape ${JSON.stringify(array.shape)}`);
-      }
-      for (let j = 0; j < shapes[i].length; ++j) {
-        if (j === 0 && !checkBatchAxis) {
-          continue;
-        }
-        const dim = array.shape[j];
-        const refDim = shapes[i][j];
-        if (refDim != null) {
-          if (refDim !== dim) {
-            throw new ValueError(
-                `Error when checking ${exceptionPrefix}: expected ` +
-                `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +
-                `got array with shape ${JSON.stringify(array.shape)}.`);
-          }
-        }
-      }
-    }
-  }
-}
-
-/**
- * Maps metric functions to model outputs.
- * @param metrics An shortcut strings name, metric function, `Array` or dict
- *   (`Object`) of metric functions.
- * @param outputNames An `Array` of the names of model outputs.
- * @returns An `Array` (one entry per model output) of `Array` of metric
- *   functions. For instance, if the model has 2 outputs, and for the first
- *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,
- *   and just `binaryAccuracy` for the second output, the `Array` would look
- *   like:
- *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`
- * @throws TypeError: incompatible metrics format.
- */
-export function collectMetrics(
-    metrics: string|LossOrMetricFn|Array<string|LossOrMetricFn>|
-    {[outputName: string]: string | LossOrMetricFn},
-    outputNames: string[]): Array<Array<string|LossOrMetricFn>> {
-  if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {
-    return outputNames.map(name => []);
-  }
-
-  let wrappedMetrics: Array<string|LossOrMetricFn>|
-      {[outputName: string]: string | LossOrMetricFn};
-  if (typeof metrics === 'string' || typeof metrics === 'function') {
-    wrappedMetrics = [metrics];
-  } else if (Array.isArray(metrics) || typeof metrics === 'object') {
-    wrappedMetrics = metrics as Array<string|LossOrMetricFn>|
-        {[outputName: string]: string} | {[outputName: string]: LossOrMetricFn};
-  } else {
-    throw new TypeError(
-        'Type of metrics argument not understood. Expected an string,' +
-        `function, Array, or Object, found: ${metrics}`);
-  }
-
-  if (Array.isArray(wrappedMetrics)) {
-    // We then apply all metrics to all outputs.
-    return outputNames.map(
-        name => wrappedMetrics as Array<string|LossOrMetricFn>);
-  } else {
-    // In this case, metrics is a dict.
-    const nestedMetrics: Array<Array<string|LossOrMetricFn>> = [];
-    for (const name of outputNames) {
-      let outputMetrics: string|LossOrMetricFn|Array<string|LossOrMetricFn> =
-          wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];
-      if (!Array.isArray(outputMetrics)) {
-        outputMetrics = [outputMetrics];
-      }
-      nestedMetrics.push(outputMetrics);
-    }
-    return nestedMetrics;
-  }
-}
-
-export interface ModelEvaluateArgs {
-  /**
-   * Batch size (Integer). If unspecified, it will default to 32.
-   */
-  batchSize?: number;
-
-  /**
-   * Verbosity mode.
-   */
-  verbose?: ModelLoggingVerbosity;
-
-  /**
-   * Tensor of weights to weight the contribution of different samples to the
-   * loss and metrics.
-   */
-  sampleWeight?: Tensor;
-
-  /**
-   * integer: total number of steps (batches of samples)
-   * before declaring the evaluation round finished. Ignored with the default
-   * value of `undefined`.
-   */
-  steps?: number;
-}
-
-/**
- * Configuration for calls to `LayersModel.compile()`.
- */
-export interface ModelCompileArgs {
-  /**
-   * An instance of `tf.train.Optimizer` or a string name for an Optimizer.
-   */
-  optimizer: string|Optimizer;
-
-  /**
-   * Object function(s) or name(s) of object function(s).
-   * If the model has multiple outputs, you can use a different loss
-   * on each output by passing a dictionary or an Array of losses.
-   * The loss value that will be minimized by the model will then be the sum
-   * of all individual losses.
-   */
-  loss: string|string[]|{[outputName: string]: string}|LossOrMetricFn|
-      LossOrMetricFn[]|{[outputName: string]: LossOrMetricFn};
-
-  /**
-   * List of metrics to be evaluated by the model during training and testing.
-   * Typically you will use `metrics=['accuracy']`.
-   * To specify different metrics for different outputs of a multi-output
-   * model, you could also pass a dictionary.
-   */
-  metrics?: string|LossOrMetricFn|Array<string|LossOrMetricFn>|
-      {[outputName: string]: string | LossOrMetricFn};
-
-  // TODO(cais): Add lossWeights, sampleWeightMode, weightedMetrics, and
-  //   targetTensors.
-}
-
-const LAYERS_MODEL_FORMAT_NAME = 'layers-model';
-
-/**
- * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods
- * for training, evaluation, prediction and saving.
- *
- * `tf.LayersModel` is the basic unit of training, inference and evaluation in
- * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.
- *
- * See also:
- *   `tf.Sequential`, `tf.loadLayersModel`.
- *
- * @doc {heading: 'Models', subheading: 'Classes'}
- */
-export class LayersModel extends Container implements tfc.InferenceModel {
-  // The class name is 'Model' rather than 'LayersModel' for backwards
-  // compatibility since this class name shows up in the serialization format.
-  /** @nocollapse */
-  static className = 'Model';
-  protected optimizer_: Optimizer;
-  // Whether the model instance owns the optimizer: `true` if and only if
-  // `optimizer` is created from a string parameter during `compile()` call.
-  protected isOptimizerOwned: boolean;
-
-  loss: string|string[]|{[outputName: string]: string}|LossOrMetricFn|
-      LossOrMetricFn[]|{[outputName: string]: LossOrMetricFn};
-  lossFunctions: LossOrMetricFn[];
-
-  // TODO(cais): These private variables should probably not have the string
-  //   'feed' in their names, because we are not dealing with a symbolic
-  //   backend.
-  private feedOutputShapes: Shape[];
-  private feedLossFns: LossOrMetricFn[];
-  private collectedTrainableWeights: LayerVariable[];
-  private testFunction: (data: Tensor[]) => Scalar[];
-  history: History;
-
-  // A public property that can be set by Callbacks to order early stopping
-  // during `fit()` calls.
-  protected stopTraining_: boolean;
-  protected isTraining: boolean;
-
-  metrics: string|LossOrMetricFn|Array<string|LossOrMetricFn>|
-      {[outputName: string]: string | LossOrMetricFn};
-  metricsNames: string[];
-  // Porting Note: `metrics_tensors` in PyKeras is a symbolic tensor. But given
-  //   the imperative nature of tfjs-core, `metricsTensors` is a
-  //   TypeScript function here.
-  //   Also note that due to the imperative nature of tfjs-core, `metricsTensor`
-  //   here needs an output index to keep track of which output of the
-  //   LayersModel a metric belongs to. This is unlike `metrics_tensors` in
-  //   PyKeras, which is a `list` of symbolic tensors, each of which has
-  //   implicit "knowledge" of the outputs it depends on.
-  metricsTensors: Array<[LossOrMetricFn, number]>;
-
-  // User defind metadata (if any).
-  private userDefinedMetadata: {};
-
-  constructor(args: ContainerArgs) {
-    super(args);
-    this.isTraining = false;
-  }
-
-  /**
-   * Print a text summary of the model's layers.
-   *
-   * The summary includes
-   * - Name and type of all layers that comprise the model.
-   * - Output shape(s) of the layers
-   * - Number of weight parameters of each layer
-   * - If the model has non-sequential-like topology, the inputs each layer
-   *   receives
-   * - The total number of trainable and non-trainable parameters of the model.
-   *
-   * ```js
-   * const input1 = tf.input({shape: [10]});
-   * const input2 = tf.input({shape: [20]});
-   * const dense1 = tf.layers.dense({units: 4}).apply(input1);
-   * const dense2 = tf.layers.dense({units: 8}).apply(input2);
-   * const concat = tf.layers.concatenate().apply([dense1, dense2]);
-   * const output =
-   *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);
-   *
-   * const model = tf.model({inputs: [input1, input2], outputs: output});
-   * model.summary();
-   * ```
-   *
-   * @param lineLength Custom line length, in number of characters.
-   * @param positions Custom widths of each of the columns, as either
-   *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number
-   *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to
-   *   right-most (i.e., ending) position of a column.
-   * @param printFn Custom print function. Can be used to replace the default
-   *   `console.log`. For example, you can use `x => {}` to mute the printed
-   *   messages in the console.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  summary(
-      lineLength?: number, positions?: number[],
-      printFn:
-          // tslint:disable-next-line:no-any
-      (message?: any, ...optionalParams: any[]) => void = console.log) {
-    if (!this.built) {
-      throw new ValueError(
-          `This model has never been called, thus its weights have not been ` +
-          `created yet. So no summary can be displayed. Build the model ` +
-          `first (e.g., by calling it on some test data).`);
-    }
-    printSummary(this, lineLength, positions, printFn);
-  }
-
-  /**
-   * Configures and prepares the model for training and evaluation.  Compiling
-   * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`
-   * or `evaluate` on an un-compiled model will throw an error.
-   *
-   * @param args a `ModelCompileArgs` specifying the loss, optimizer, and
-   * metrics to be used for fitting and evaluating this model.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  compile(args: ModelCompileArgs): void {
-    if (args.loss == null) {
-      args.loss = [];
-    }
-    this.loss = args.loss;
-
-    if (typeof args.optimizer === 'string') {
-      this.optimizer_ = optimizers.getOptimizer(args.optimizer);
-      this.isOptimizerOwned = true;
-    } else {
-      if (!(args.optimizer instanceof Optimizer)) {
-        throw new ValueError(
-            `User-defined optimizer must be an instance of tf.Optimizer.`);
-      }
-      this.optimizer_ = args.optimizer;
-      this.isOptimizerOwned = false;
-    }
-
-    // TODO(cais): Add lossWeights.
-    // TODO(cais): Add sampleWeightMode.
-
-    // Prepare loss functions.
-    let lossFunctions: LossOrMetricFn[] = [];
-    if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&
-        typeof args.loss !== 'function') {
-      args.loss = args.loss as {[outputName: string]: string};
-      for (const name in args.loss) {
-        if (this.outputNames.indexOf(name) === -1) {
-          throw new ValueError(
-              `Unknown entry in loss dictionary: "${name}". ` +
-              `Only expected the following keys: ${this.outputNames}`);
-        }
-      }
-      for (const name of this.outputNames) {
-        if (args.loss[name] == null) {
-          console.warn(
-              `Output "${name}" is missing from loss dictionary. We assume ` +
-              `this was done on purpose, and we will not be expecting data ` +
-              `to be passed to ${name} during training`);
-        }
-        lossFunctions.push(losses.get(args.loss[name]));
-      }
-    } else if (Array.isArray(args.loss)) {
-      if (args.loss.length !== this.outputs.length) {
-        throw new ValueError(
-            `When passing an Array as loss, it should have one entry per ` +
-            `model output. The model has ${this.outputs.length} output(s), ` +
-            `but you passed loss=${args.loss}.`);
-      }
-      const theLosses = args.loss as Array<string|LossOrMetricFn>;
-      lossFunctions = theLosses.map(l => losses.get(l));
-    } else {
-      const lossFunction = losses.get(args.loss);
-      this.outputs.forEach(_ => {
-        lossFunctions.push(lossFunction);
-      });
-    }
-
-    this.lossFunctions = lossFunctions;
-
-    this.feedOutputNames = [];
-    this.feedOutputShapes = [];
-    this.feedLossFns = [];
-    for (let i = 0; i < this.outputs.length; ++i) {
-      // TODO(cais): Logic for skipping target(s).
-      const shape = this.internalOutputShapes[i];
-      const name = this.outputNames[i];
-      this.feedOutputNames.push(name);
-      this.feedOutputShapes.push(shape);
-      this.feedLossFns.push(this.lossFunctions[i]);
-    }
-
-    // TODO(cais): Add logic for output masks.
-    // TODO(cais): Add logic for sample weights.
-    const skipTargetIndices: number[] = [];
-
-    // Prepare metrics.
-    this.metrics = args.metrics;
-    // TODO(cais): Add weightedMetrics.
-    this.metricsNames = ['loss'];
-    this.metricsTensors = [];
-
-    // Compute total loss.
-    // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.
-    //   Here, metricsTensors are TypeScript functions. This difference is due
-    //   to the difference in symbolic/imperative property of the backends.
-    nameScope('loss', () => {
-      for (let i = 0; i < this.outputs.length; ++i) {
-        if (skipTargetIndices.indexOf(i) !== -1) {
-          continue;
-        }
-        // TODO(cais): Add weightedLoss, sampleWeight and mask.
-        //   The following line should be weightedLoss
-        const weightedLoss = this.lossFunctions[i];
-        if (this.outputs.length > 1) {
-          this.metricsTensors.push([weightedLoss, i]);
-          this.metricsNames.push(this.outputNames[i] + '_loss');
-        }
-      }
-
-      // Porting Note: Due to the imperative nature of the backend, we calculate
-      //   the regularizer penalties in the totalLossFunction, instead of here.
-    });
-
-    const nestedMetrics = collectMetrics(args.metrics, this.outputNames);
-    // TODO(cais): Add nestedWeightedMetrics.
-
-    /**
-     * Helper function used in loop below.
-     */
-    const appendMetric =
-        (outputIndex: number, metricName: string,
-         metricTensor: LossOrMetricFn) => {
-          if (this.outputNames.length > 1) {
-            metricName = this.outputNames[outputIndex] + '_' + metricName;
-          }
-          this.metricsNames.push(metricName);
-          this.metricsTensors.push([metricTensor, outputIndex]);
-        };
-
-    nameScope('metric', () => {
-      for (let i = 0; i < this.outputs.length; ++i) {
-        if (skipTargetIndices.indexOf(i) !== -1) {
-          continue;
-        }
-        const outputMetrics = nestedMetrics[i];
-        // TODO(cais): Add weights and outputWeightedMetrics.
-
-        // TODO(cais): Add optional arg `weights` to the following function.
-        const handleMetrics = (metrics: Array<string|LossOrMetricFn>) => {
-          const metricNamePrefix = '';
-          let metricName: string;
-          let accFn: LossOrMetricFn;
-          let weightedMetricFn: LossOrMetricFn;
-          //  TODO(cais): Use 'weights_' for weighted metrics.
-
-          for (const metric of metrics) {
-            if (typeof metric === 'string' &&
-                ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==
-                    -1) {
-              const outputShape = this.internalOutputShapes[i];
-
-              if (outputShape[outputShape.length - 1] === 1 ||
-                  this.lossFunctions[i] === losses.binaryCrossentropy) {
-                // case: binary accuracy/crossentropy.
-                if (['accuracy', 'acc'].indexOf(metric) !== -1) {
-                  accFn = Metrics.binaryAccuracy;
-                } else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {
-                  accFn = Metrics.binaryCrossentropy;
-                }
-              } else if (
-                  this.lossFunctions[i] ===
-                  losses.sparseCategoricalCrossentropy) {
-                // case: categorical accuracy / crossentropy with sparse
-                // targets.
-                if (['accuracy', 'acc'].indexOf(metric) !== -1) {
-                  accFn = Metrics.sparseCategoricalAccuracy;
-                } else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {
-                  accFn = Metrics.sparseCategoricalCrossentropy;
-                }
-              } else {
-                // case: categorical accuracy / crossentropy.
-                if (['accuracy', 'acc'].indexOf(metric) !== -1) {
-                  accFn = Metrics.categoricalAccuracy;
-                } else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {
-                  accFn = Metrics.categoricalCrossentropy;
-                }
-              }
-              let suffix: string;
-              if (['accuracy', 'acc'].indexOf(metric) !== -1) {
-                suffix = 'acc';
-              } else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {
-                suffix = 'ce';
-              }
-              // TODO(cais): Add weighting actually.
-              weightedMetricFn = accFn;
-              metricName = metricNamePrefix + suffix;
-            } else {
-              const metricFn = Metrics.get(metric);
-              // TODO(cais): Add weighting actually.
-              weightedMetricFn = metricFn;
-              metricName =
-                  metricNamePrefix + Metrics.getLossOrMetricName(metric);
-            }
-
-            // TODO(cais): Add weighting and masking to metricResult.
-            let metricResult: LossOrMetricFn;
-            nameScope(metricName, () => {
-              metricResult = weightedMetricFn;
-            });
-            appendMetric(i, metricName, metricResult);
-          }
-        };
-
-        handleMetrics(outputMetrics);
-        // TODO(cais): Call handleMetrics with weights.
-      }
-    });
-
-    // Porting Notes: Given the imperative backend of tfjs-core,
-    //   there is no need for constructing the symbolic graph and placeholders.
-    this.collectedTrainableWeights = this.trainableWeights;
-  }
-
-  /**
-   * Check trainable weights count consistency.
-   *
-   * This will raise a warning if `this.trainableWeights` and
-   * `this.collectedTrainableWeights` are inconsistent (i.e., have different
-   * numbers of parameters).
-   * Inconsistency will typically arise when one modifies `model.trainable`
-   * without calling `model.compile()` again.
-   */
-  protected checkTrainableWeightsConsistency(): void {
-    if (this.collectedTrainableWeights == null) {
-      return;
-    }
-    if (this.trainableWeights.length !==
-        this.collectedTrainableWeights.length) {
-      console.warn(
-          'Discrepancy between trainableweights and collected trainable ' +
-          'weights. Did you set `model.trainable` without calling ' +
-          '`model.compile()` afterwards?');
-    }
-  }
-
-  /**
-   * Returns the loss value & metrics values for the model in test mode.
-   *
-   * Loss and metrics are specified during `compile()`, which needs to happen
-   * before calls to `evaluate()`.
-   *
-   * Computation is done in batches.
-   *
-   * ```js
-   * const model = tf.sequential({
-   *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]
-   * });
-   * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-   * const result = model.evaluate(
-   *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});
-   * result.print();
-   * ```
-   *
-   * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the
-   * model has multiple inputs.
-   * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the
-   * model has multiple outputs.
-   * @param args A `ModelEvaluateArgs`, containing optional fields.
-   *
-   * @return `Scalar` test loss (if the model has a single output and no
-   *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs
-   *   and/or metrics). The attribute `model.metricsNames`
-   *   will give you the display labels for the scalar outputs.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  evaluate(
-      x: Tensor|Tensor[], y: Tensor|Tensor[],
-      args: ModelEvaluateArgs = {}): Scalar|Scalar[] {
-    const batchSize = args.batchSize == null ? 32 : args.batchSize;
-    checkBatchSize(batchSize);
-
-    // TODO(cais): Standardize `config.sampleWeights` as well.
-    // Validate user data.
-    const checkBatchAxis = true;
-    const standardizedOuts =
-        this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
-    try {
-      // TODO(cais): If uses `useLearningPhase`, set the corresponding element
-      // of the input to 0.
-      const ins = standardizedOuts[0].concat(standardizedOuts[1]);
-      this.makeTestFunction();
-      const f = this.testFunction;
-      const testOuts =
-          this.testLoop(f, ins, batchSize, args.verbose, args.steps);
-      return singletonOrArray(testOuts);
-    } finally {
-      disposeNewTensors(standardizedOuts[0], x);
-      disposeNewTensors(standardizedOuts[1], y);
-    }
-  }
-
-  // TODO(cais): Add code snippet below once real dataset objects are
-  //   available.
-  /**
-   * Evaluate model using a dataset object.
-   *
-   * Note: Unlike `evaluate()`, this method is asynchronous (`async`).
-   *
-   * @param dataset A dataset object. Its `iterator()` method is expected
-   *   to generate a dataset iterator object, the `next()` method of which
-   *   is expected to produce data batches for evaluation. The return value
-   *   of the `next()` call ought to contain a boolean `done` field and a
-   *   `value` field. The `value` field is expected to be an array of two
-   *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former
-   *   case is for models with exactly one input and one output (e.g.
-   *   a sequential model). The latter case is for models with multiple
-   *   inputs and/or multiple outputs. Of the two items in the array, the
-   *   first is the input feature(s) and the second is the output target(s).
-   * @param args A configuration object for the dataset-based evaluation.
-   * @returns Loss and metric values as an Array of `Scalar` objects.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  async evaluateDataset(dataset: Dataset<{}>, args?: ModelEvaluateDatasetArgs):
-      Promise<Scalar|Scalar[]> {
-    this.makeTestFunction();
-    return evaluateDataset(this, dataset, args);
-  }
-
-  /**
-   * Get number of samples provided for training, evaluation or prediction.
-   *
-   * @param ins Input `tf.Tensor`.
-   * @param batchSize Integer batch size, optional.
-   * @param steps Total number of steps (batches of samples) before
-   * declaring loop finished. Optional.
-   * @param stepsName The public API's parameter name for `steps`.
-   * @returns Number of samples provided.
-   */
-  private checkNumSamples(
-      ins: Tensor|Tensor[], batchSize?: number, steps?: number,
-      stepsName = 'steps'): number {
-    let numSamples: number;
-    if (steps != null) {
-      numSamples = null;
-      if (batchSize != null) {
-        throw new ValueError(
-            `If ${stepsName} is set, batchSize must be null or undefined.` +
-            `Got batchSize = ${batchSize}`);
-      }
-    } else if (ins != null) {
-      if (Array.isArray(ins)) {
-        numSamples = ins[0].shape[0];
-      } else {
-        numSamples = ins.shape[0];
-      }
-    } else {
-      throw new ValueError(
-          `Either the input data should have a defined shape, or ` +
-          `${stepsName} shoud be specified.`);
-    }
-    return numSamples;
-  }
-
-  /**
-   * Execute internal tensors of the model with input data feed.
-   * @param inputs Input data feed. Must match the inputs of the model.
-   * @param outputs Names of the output tensors to be fetched. Must match
-   *   names of the SymbolicTensors that belong to the graph.
-   * @returns Fetched values for `outputs`.
-   */
-  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs: string|string[]):
-      Tensor|Tensor[] {
-    if (Array.isArray(outputs) && outputs.length === 0) {
-      throw new ValueError(
-          '`outputs` is an empty Array, which is not allowed.');
-    }
-
-    const outputsIsArray = Array.isArray(outputs);
-    const outputNames =
-        (outputsIsArray ? outputs : [outputs]);
-    const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);
-
-    // Format the input into a FeedDict.
-    const feedDict = new FeedDict();
-    if (inputs instanceof Tensor) {
-      inputs = [inputs];
-    }
-    if (Array.isArray(inputs)) {
-      if (inputs.length !== this.inputs.length) {
-        throw new ValueError(
-            `The number of inputs provided (${inputs.length}) ` +
-            `does not match the number of inputs of this model ` +
-            `(${this.inputs.length}).`);
-      }
-      for (let i = 0; i < this.inputs.length; ++i) {
-        feedDict.add(this.inputs[i], inputs[i]);
-      }
-    } else {
-      for (const input of this.inputs) {
-        const tensorValue = inputs[input.name];
-        if (tensorValue == null) {
-          throw new ValueError(
-              `No value is provided for the model's input ${input.name}`);
-        }
-        feedDict.add(input, tensorValue);
-      }
-    }
-
-    // Run execution.
-    const executeOutputs = execute(outputSymbolicTensors, feedDict) as Tensor[];
-    return outputsIsArray ? executeOutputs : executeOutputs[0];
-  }
-
-  /**
-   * Retrieve the model's internal symbolic tensors from symbolic-tensor names.
-   */
-  private retrieveSymbolicTensors(symbolicTensorNames: string[]):
-      SymbolicTensor[] {
-    const outputSymbolicTensors: SymbolicTensor[] =
-        pyListRepeat(null, symbolicTensorNames.length);
-    let outputsRemaining = symbolicTensorNames.length;
-    for (const layer of this.layers) {
-      const layerOutputs: SymbolicTensor[] =
-          Array.isArray(layer.output) ? layer.output : [layer.output];
-      const layerOutputNames = layerOutputs.map(output => output.name);
-      for (let i = 0; i < symbolicTensorNames.length; ++i) {
-        const index = layerOutputNames.indexOf(symbolicTensorNames[i]);
-        if (index !== -1) {
-          outputSymbolicTensors[i] = layerOutputs[index];
-          outputsRemaining--;
-        }
-        if (outputsRemaining === 0) {
-          break;
-        }
-      }
-      if (outputsRemaining === 0) {
-        break;
-      }
-    }
-
-    if (outputsRemaining > 0) {
-      const remainingNames: string[] = [];
-      outputSymbolicTensors.forEach((tensor, i) => {
-        if (tensor == null) {
-          remainingNames.push(symbolicTensorNames[i]);
-        }
-      });
-      throw new ValueError(
-          `Cannot find SymbolicTensors for output name(s): ` +
-          `${JSON.stringify(remainingNames)}`);
-    }
-    return outputSymbolicTensors;
-  }
-
-  /**
-   * Helper method to loop over some data in batches.
-   *
-   * Porting Note: Not using the functional approach in the Python equivalent
-   *   due to the imperative backend.
-   * Porting Note: Does not support step mode currently.
-   *
-   * @param ins: input data
-   * @param batchSize: integer batch size.
-   * @param verbose: verbosity model
-   * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of
-   *   `tf.Tensor` (if multipe outputs).
-   */
-  private predictLoop(ins: Tensor|Tensor[], batchSize = 32, verbose = false):
-      Tensor|Tensor[] {
-    return tfc.tidy(() => {
-      const numSamples = this.checkNumSamples(ins);
-      if (verbose) {
-        throw new NotImplementedError(
-            'Verbose predictLoop() is not implemented yet.');
-      }
-
-      // Sample-based predictions.
-      // Porting Note: Tensor currently does not support sliced assignments as
-      //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while
-      //   iterating over the batches.
-
-      const batches = makeBatches(numSamples, batchSize);
-      const outsBatches: Tensor[][] = this.outputs.map(output => []);
-
-      // TODO(cais): Can the scope() be pushed down inside the for loop?
-      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
-        const batchOuts = tfc.tidy(() => {
-          const batchStart = batches[batchIndex][0];
-          const batchEnd = batches[batchIndex][1];
-          // TODO(cais): Take care of the case of the last element is a flag for
-          //   training/test.
-          const insBatch = sliceArrays(ins, batchStart, batchEnd);
-
-          // Construct the feeds for execute();
-          const feeds = [];
-          if (Array.isArray(insBatch)) {
-            for (let i = 0; i < insBatch.length; ++i) {
-              feeds.push({key: this.inputs[i], value: insBatch[i]});
-            }
-          } else {
-            feeds.push({key: this.inputs[0], value: insBatch});
-          }
-          const feedDict = new FeedDict(feeds);
-          return execute(this.outputs, feedDict) as Tensor[];
-        });
-        batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));
-      }
-      return singletonOrArray(
-          outsBatches.map(batches => tfc.concat(batches, 0)));
-    });
-  }
-
-  /**
-   * Generates output predictions for the input samples.
-   *
-   * Computation is done in batches.
-   *
-   * Note: the "step" mode of predict() is currently not supported.
-   *   This is because the TensorFlow.js core backend is imperative only.
-   *
-   * ```js
-   * const model = tf.sequential({
-   *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]
-   * });
-   * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();
-   * ```
-   *
-   * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if
-   *   the model has multiple inputs.
-   * @param args A `ModelPredictArgs` object containing optional fields.
-   *
-   * @return Prediction results as a `tf.Tensor`(s).
-   *
-   * @exception ValueError In case of mismatch between the provided input data
-   *   and the model's expectations, or in case a stateful model receives a
-   *   number of samples that is not a multiple of the batch size.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  predict(x: Tensor|Tensor[], args: ModelPredictArgs = {}): Tensor|Tensor[] {
-    const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);
-    checkInputData(
-        xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);
-    try {
-      // TODO(cais): Take care of stateful models.
-      //   if (this.stateful) ...
-      // TODO(cais): Take care of the learning_phase boolean flag.
-      //   if (this.useLearningPhase) ...
-      const batchSize = args.batchSize == null ? 32 : args.batchSize;
-      checkBatchSize(batchSize);
-      return this.predictLoop(xsRank2OrHigher, batchSize);
-    } finally {
-      disposeNewTensors(xsRank2OrHigher, x);
-    }
-  }
-
-  /**
-   * Returns predictions for a single batch of samples.
-   *
-   * ```js
-   * const model = tf.sequential({
-   *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]
-   * });
-   * model.predictOnBatch(tf.ones([8, 10])).print();
-   * ```
-   * @param x: Input samples, as a Tensor (for models with exactly one
-   *   input) or an array of Tensors (for models with more than one input).
-   * @return Tensor(s) of predictions
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  predictOnBatch(x: Tensor|Tensor[]): Tensor|Tensor[] {
-    checkInputData(x, this.inputNames, this.feedInputShapes, true);
-    // TODO(cais): Take care of the learning_phase boolean flag.
-    //   if (this.useLearningPhase) ...
-    const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];
-    return this.predictLoop(x, batchSize);
-  }
-
-  protected standardizeUserDataXY(
-      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
-      y: Tensor|Tensor[]|{[inputName: string]: Tensor}, checkBatchAxis = true,
-      batchSize?: number): [Tensor[], Tensor[]] {
-    // TODO(cais): Add sampleWeight, classWeight
-    if (this.optimizer_ == null) {
-      throw new RuntimeError(
-          'You must compile a model before training/testing. Use ' +
-          'LayersModel.compile(modelCompileArgs).');
-    }
-    const outputShapes: Shape[] = [];
-    for (let i = 0; i < this.feedOutputShapes.length; ++i) {
-      const outputShape = this.feedOutputShapes[i];
-      const lossFn = this.feedLossFns[i];
-      if (lossFn === losses.sparseCategoricalCrossentropy) {
-        outputShapes.push(
-            outputShape.slice(0, outputShape.length - 1).concat([1]));
-      } else {
-        // Porting Note: Because of strong typing `lossFn` must be a function.
-        outputShapes.push(outputShape);
-      }
-    }
-    x = standardizeInputData(
-        x, this.feedInputNames, this.feedInputShapes, false, 'input');
-    y = standardizeInputData(
-        y, this.feedOutputNames, outputShapes, false, 'target');
-    // TODO(cais): Standardize sampleWeights & classWeights.
-    checkArrayLengths(x, y, null);
-    // TODO(cais): Check sampleWeights as well.
-    checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);
-    if (this.stateful && batchSize != null && batchSize > 0) {
-      if (x[0].shape[0] % batchSize !== 0) {
-        throw new ValueError(
-            `In a stateful network, you should only pass inputs with a ` +
-            `number of samples that is divisible by the batch size ` +
-            `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);
-      }
-    }
-    return [x, y];
-  }
-
-  protected async standardizeUserData(
-      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
-      y: Tensor|Tensor[]|{[inputName: string]: Tensor},
-      sampleWeight?: Tensor|Tensor[]|{[outputName: string]: Tensor},
-      classWeight?: ClassWeight|ClassWeight[]|ClassWeightMap,
-      checkBatchAxis = true,
-      batchSize?: number): Promise<[Tensor[], Tensor[], Tensor[]]> {
-    const [standardXs, standardYs] =
-        this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
-    // TODO(cais): Handle sampleWeights.
-    if (sampleWeight != null) {
-      throw new Error('sample weight is not supported yet.');
-    }
-
-    let standardSampleWeights: Tensor[] = null;
-    if (classWeight != null) {
-      const classWeights =
-          standardizeClassWeights(classWeight, this.outputNames);
-      standardSampleWeights = [];
-      for (let i = 0; i < classWeights.length; ++i) {
-        standardSampleWeights.push(
-            await standardizeWeights(standardYs[i], null, classWeights[i]));
-      }
-    }
-
-    // TODO(cais): Deal with the case of model.stateful == true.
-    return [standardXs, standardYs, standardSampleWeights];
-  }
-
-  /**
-   * Loop over some test data in batches.
-   * @param f A Function returning a list of tensors.
-   * @param ins Array of tensors to be fed to `f`.
-   * @param batchSize Integer batch size or `null` / `undefined`.
-   * @param verbose verbosity mode.
-   * @param steps Total number of steps (batches of samples) before
-   * declaring test finished. Ignored with the default value of `null` /
-   * `undefined`.
-   * @returns Array of Scalars.
-   */
-  private testLoop(
-      f: (data: Tensor[]) => Scalar[], ins: Tensor[], batchSize?: number,
-      verbose = 0, steps?: number): Scalar[] {
-    return tfc.tidy(() => {
-      const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');
-      const outs: Scalar[] = [];
-      if (verbose > 0) {
-        throw new NotImplementedError('Verbose mode is not implemented yet.');
-      }
-      // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.
-      if (steps != null) {
-        throw new NotImplementedError(
-            'steps mode in testLoop() is not implemented yet');
-      } else {
-        const batches = makeBatches(numSamples, batchSize);
-        const indexArray = tensor1d(range(0, numSamples));
-        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
-          const batchStart = batches[batchIndex][0];
-          const batchEnd = batches[batchIndex][1];
-          const batchIds =
-              K.sliceAlongFirstAxis(
-                  indexArray, batchStart, batchEnd - batchStart) as Tensor1D;
-          // TODO(cais): In ins, train flag can be a number, instead of an
-          //   Tensor? Do we need to handle this in tfjs-layers?
-          const insBatch = sliceArraysByIndices(ins, batchIds) as Scalar[];
-          const batchOuts = f(insBatch);
-          if (batchIndex === 0) {
-            for (let i = 0; i < batchOuts.length; ++i) {
-              outs.push(scalar(0));
-            }
-          }
-          for (let i = 0; i < batchOuts.length; ++i) {
-            const batchOut = batchOuts[i];
-            outs[i] =
-                tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));
-          }
-        }
-        for (let i = 0; i < outs.length; ++i) {
-          outs[i] = tfc.div(outs[i], numSamples);
-        }
-      }
-      return outs;
-    });
-  }
-
-  protected getDedupedMetricsNames(): string[] {
-    const outLabels = this.metricsNames;
-    // Rename duplicated metrics names (can happen with an output layer
-    // shared among multiple dataflows).
-    const dedupedOutLabels = [];
-    for (let i = 0; i < outLabels.length; ++i) {
-      const label = outLabels[i];
-      let newLabel = label;
-      if (count(outLabels, label) > 1) {
-        const dupIndex = count(outLabels.slice(0, i), label);
-        newLabel += `_${dupIndex}`;
-      }
-      dedupedOutLabels.push(newLabel);
-    }
-    return dedupedOutLabels;
-  }
-
-  /**
-   * Creates a function that performs the following actions:
-   *
-   * 1. computes the losses
-   * 2. sums them to get the total loss
-   * 3. call the optimizer computes the gradients of the LayersModel's
-   *    trainable weights w.r.t. the total loss and update the variables
-   * 4. calculates the metrics
-   * 5. returns the values of the losses and metrics.
-   */
-  protected makeTrainFunction(): (data: Tensor[]) => Scalar[] {
-    return (data: Tensor[]) => {
-      const lossValues: Scalar[] = [];
-
-      const inputs = data.slice(0, this.inputs.length);
-      const targets = data.slice(
-          this.inputs.length, this.inputs.length + this.outputs.length);
-      const sampleWeights = data.slice(
-          this.inputs.length + this.outputs.length,
-          this.inputs.length + this.outputs.length * 2);
-
-      const metricsValues: Scalar[] = [];
-
-      // Create a function that computes the total loss based on the
-      // inputs. This function is used for obtaining gradients through
-      // backprop.
-      const totalLossFunction = () => {
-        const feeds = [];
-        for (let i = 0; i < this.inputs.length; ++i) {
-          feeds.push({key: this.inputs[i], value: inputs[i]});
-        }
-        const feedDict = new FeedDict(feeds);
-        const outputs =
-            execute(this.outputs, feedDict, {'training': true}) as Tensor[];
-        // TODO(cais): Take care of the case of multiple outputs from a
-        //   single layer?
-
-        let totalLoss: Tensor;
-        for (let i = 0; i < this.lossFunctions.length; ++i) {
-          const lossFunction = this.lossFunctions[i];
-          let loss = lossFunction(targets[i], outputs[i]);
-          if (sampleWeights[i] != null) {
-            loss = computeWeightedLoss(loss, sampleWeights[i]);
-          }
-
-          // TODO(cais): push Scalar instead.
-          const meanLoss: Scalar = tfc.mean(loss);
-          // TODO(cais): Use a scope() instead, to avoid ownership.
-          lossValues.push(meanLoss);
-          if (i === 0) {
-            totalLoss = loss;
-          } else {
-            totalLoss = tfc.add(totalLoss, loss);
-          }
-        }
-
-        // Compute the metrics.
-        // TODO(cais): These should probably be calculated outside
-        //   totalLossFunction to benefit speed?
-        for (let i = 0; i < this.metricsTensors.length; ++i) {
-          let weightedMetric: Scalar;
-
-          if (this.outputs.length > 1 && i < this.outputs.length) {
-            weightedMetric = lossValues[i];
-          } else {
-            const metric = this.metricsTensors[i][0];
-            const outputIndex = this.metricsTensors[i][1];
-            weightedMetric =
-                tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));
-          }
-
-          tfc.keep(weightedMetric);
-          // TODO(cais): Use a scope() instead, to avoid ownership.
-          metricsValues.push(weightedMetric);
-        }
-
-        totalLoss = tfc.mean(totalLoss);
-
-        // Add regularizer penalties.
-        this.calculateLosses().forEach(regularizerLoss => {
-          totalLoss = tfc.add(totalLoss, regularizerLoss);
-        });
-
-        return totalLoss as Scalar;
-      };
-
-      const variables = this.collectedTrainableWeights.map(
-          param => param.read() as tfc.Variable);
-      const returnCost = true;
-      const totalLossValue =
-          this.optimizer_.minimize(totalLossFunction, returnCost, variables);
-
-      return [totalLossValue].concat(metricsValues);
-    };
-  }
-
-  /**
-   * Create a function which, when invoked with an array of `tf.Tensor`s as a
-   * batch of inputs, returns the prespecified loss and metrics of the model
-   * under the batch of input data.
-   */
-  private makeTestFunction() {
-    this.testFunction = (data: Tensor[]) => {
-      return tfc.tidy(() => {
-        const valOutputs: Scalar[] = [];
-        let totalLoss: Scalar;
-        const inputs = data.slice(0, this.inputs.length);
-        const targets = data.slice(
-            this.inputs.length, this.inputs.length + this.outputs.length);
-        const feeds = [];
-        for (let i = 0; i < this.inputs.length; ++i) {
-          feeds.push({key: this.inputs[i], value: inputs[i]});
-        }
-        const feedDict = new FeedDict(feeds);
-        const outputs = execute(this.outputs, feedDict) as Tensor[];
-        // Compute total loss.
-        for (let i = 0; i < this.lossFunctions.length; ++i) {
-          const lossFunction = this.lossFunctions[i];
-          // TODO(cais): Add sample weighting and replace the simple
-          // averaging.
-          const loss: Scalar = tfc.mean(lossFunction(targets[i], outputs[i]));
-          if (i === 0) {
-            totalLoss = loss;
-          } else {
-            totalLoss = tfc.add(totalLoss, loss);
-          }
-          valOutputs.push(totalLoss);
-        }
-        // Compute the metrics.
-        for (let i = 0; i < this.metricsTensors.length; ++i) {
-          const metric = this.metricsTensors[i][0];
-          const outputIndex = this.metricsTensors[i][1];
-          // TODO(cais): Replace K.mean() with a proper weighting function.
-          const meanMetric =
-              tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));
-          valOutputs.push(meanMetric as Scalar);
-        }
-        return valOutputs;
-      });
-    };
-  }
-
-  /**
-   * Trains the model for a fixed number of epochs (iterations on a
-   * dataset).
-   *
-   * ```js
-   * const model = tf.sequential({
-   *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]
-   * });
-   * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-   * for (let i = 1; i < 5 ; ++i) {
-   *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {
-   *       batchSize: 4,
-   *       epochs: 3
-   *   });
-   *   console.log("Loss after Epoch " + i + " : " + h.history.loss[0]);
-   * }
-   * ```
-   *
-   * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the
-   * model has multiple inputs. If all inputs in the model are named, you
-   * can also pass a dictionary mapping input names to `tf.Tensor`s.
-   * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if
-   * the model has multiple outputs. If all outputs in the model are named,
-   * you can also pass a dictionary mapping output names to `tf.Tensor`s.
-   * @param args A `ModelFitArgs`, containing optional fields.
-   *
-   * @return A `History` instance. Its `history` attribute contains all
-   *   information collected during training.
-   *
-   * @exception ValueError In case of mismatch between the provided input
-   * data and what the model expects.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  async fit(
-      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
-      y: Tensor|Tensor[]|{[inputName: string]: Tensor},
-      args: ModelFitArgs = {}): Promise<History> {
-    if (this.isTraining) {
-      throw new Error(
-          'Cannot start training because another fit() call is ongoing.');
-    }
-    this.isTraining = true;
-    let inputs: Tensor[];
-    let targets: Tensor[];
-    let originalInputs: Tensor[];
-    let originalTargets: Tensor[];
-    let inputValX: Tensor|Tensor[];
-    let inputValY: Tensor|Tensor[];
-    let valX: Tensor|Tensor[];
-    let valY: Tensor|Tensor[];
-    let sampleWeights: Tensor[];
-    try {
-      const batchSize = args.batchSize == null ? 32 : args.batchSize;
-      checkBatchSize(batchSize);
-
-      // Validate user data.
-      // TODO(cais): Support sampleWeight.
-      const checkBatchAxis = false;
-      const standardizedOuts =
-          await this.standardizeUserData(
-              x, y, args.sampleWeight, args.classWeight, checkBatchAxis,
-              batchSize) as [Tensor[], Tensor[], Tensor[]];
-      inputs = standardizedOuts[0];
-      targets = standardizedOuts[1];
-      sampleWeights = standardizedOuts[2];
-
-      // Prepare validation data.
-      let doValidation = false;
-      let valIns: Tensor[];
-      if (args.validationData != null && args.validationData.length > 0) {
-        doValidation = true;
-        if (args.validationData.length === 2) {
-          // config.validationData consists of valX and valY.
-          inputValX = args.validationData[0];
-          inputValY = args.validationData[1];
-        } else if (args.validationData.length === 3) {
-          throw new NotImplementedError(
-              'validationData including sample weights is not supported yet.');
-        } else {
-          throw new ValueError(
-              `When passing validation data, it must contain 2 (valX, valY) ` +
-              `or 3 (valX, valY, valSampleWeight) items; ` +
-              `${args.validationData} is invalid.`);
-        }
-
-        const checkBatchAxis = true;
-        const valStandardized =
-            await this.standardizeUserData(
-                inputValX, inputValY, null, /** Unused sample weights. */
-                null,                       /** Unused class weights. */
-                checkBatchAxis, batchSize) as [Tensor[], Tensor[], Tensor[]];
-        valX = valStandardized[0];
-        valY = valStandardized[1];
-        valIns = valX.concat(valY);
-        // TODO(cais): Add useLearningPhase data properly.
-      } else if (
-          args.validationSplit != null && args.validationSplit > 0 &&
-          args.validationSplit < 1) {
-        doValidation = true;
-        // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.
-        const splitAt =
-            Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));
-        const originalBatchSize = inputs[0].shape[0];
-        valX = sliceArrays(inputs, splitAt, originalBatchSize) as Tensor[];
-        originalInputs = inputs;
-        inputs = sliceArrays(inputs, 0, splitAt) as Tensor[];
-        valY = sliceArrays(targets, splitAt, originalBatchSize) as Tensor[];
-        originalTargets = targets;
-        targets = sliceArrays(targets, 0, splitAt) as Tensor[];
-        // TODO(cais): Once sampleWeights becomes available, slice it to get
-        //   valSampleWeights.
-        valIns = valX.concat(valY);
-
-        // TODO(cais): Add useLearningPhase data properly.
-      } else if (args.validationSteps != null) {
-        doValidation = true;
-        // TODO(cais): Add useLearningPhase.
-      }
-
-      const ins = inputs.concat(targets).concat(sampleWeights);
-
-      this.checkTrainableWeightsConsistency();
-
-      // TODO(cais): Handle use_learning_phase and learning_phase?
-
-      // Porting Note: Here we see a key deviation of tfjs-layers from
-      // Keras.
-      //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),
-      //  we do not construct symbolic computation graphs to embody the
-      //  training process. Instead, we define a function that performs the
-      //  training action. In PyKeras, the data (inputs and targets) are fed
-      //  through graph placeholders. In tfjs-layers, the data are fed as
-      //  function arguments. Since the function are defined below in the
-      //  scope, we don't have equivalents of PyKeras's
-      //  `_make_train_funciton`.
-      const trainFunction = this.makeTrainFunction();
-      const outLabels = this.getDedupedMetricsNames();
-
-      let valFunction: (data: Tensor[]) => Scalar[];
-      let callbackMetrics: string[];
-      if (doValidation) {
-        this.makeTestFunction();
-        valFunction = this.testFunction;
-        callbackMetrics =
-            outLabels.slice().concat(outLabels.map(n => 'val_' + n));
-      } else {
-        valFunction = null;
-        valIns = [];
-        callbackMetrics = outLabels.slice();
-      }
-
-      const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);
-      const out = await this.fitLoop(
-          trainFunction, ins, outLabels, batchSize, args.epochs,
-          args.verbose, callbacks, valFunction, valIns, args.shuffle,
-          callbackMetrics, args.initialEpoch, null, null);
-      return out;
-    } finally {
-      this.isTraining = false;
-      // Memory clean up.
-      disposeNewTensors(inputs, x);
-      disposeNewTensors(targets, y);
-      disposeNewTensors(originalInputs, x);
-      disposeNewTensors(originalTargets, y);
-      disposeNewTensors(valX as Tensor[], inputValX);
-      disposeNewTensors(valY as Tensor[], inputValY);
-      if (sampleWeights != null) {
-        tfc.dispose(sampleWeights);
-      }
-    }
-    // TODO(cais): Add value to outLabels.
-  }
-
-  /**
-   * Abstract fit function for `f(ins)`.
-   * @param f A Function returning a list of tensors. For training, this
-   *   function is expected to perform the updates to the variables.
-   * @param ins List of tensors to be fed to `f`.
-   * @param outLabels List of strings, display names of the outputs of `f`.
-   * @param batchSize Integer batch size or `== null` if unknown. Default : 32.
-   * @param epochs Number of times to iterate over the data. Default : 1.
-   * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.
-   * @param callbacks List of callbacks to be called during training.
-   * @param valF Function to call for validation.
-   * @param valIns List of tensors to be fed to `valF`.
-   * @param shuffle Whether to shuffle the data at the beginning of every
-   * epoch. Default : true.
-   * @param callbackMetrics List of strings, the display names of the metrics
-   *   passed to the callbacks. They should be the concatenation of the
-   *   display names of the outputs of `f` and the list of display names
-   *   of the outputs of `valF`.
-   * @param initialEpoch Epoch at which to start training (useful for
-   *   resuming a previous training run). Default : 0.
-   * @param stepsPerEpoch Total number of steps (batches on samples) before
-   *   declaring one epoch finished and starting the next epoch. Ignored with
-   *   the default value of `undefined` or `null`.
-   * @param validationSteps Number of steps to run validation for (only if
-   *   doing validation from data tensors). Not applicable for tfjs-layers.
-   * @returns A `History` object.
-   */
-  async fitLoop(
-      f: (data: Tensor[]) => Scalar[], ins: Tensor[], outLabels?:
-      string[], batchSize?: number, epochs?: number, verbose?: number,
-      callbacks?: BaseCallback[], valF?: (data: Tensor[]) => Scalar[], valIns?:
-      Tensor[], shuffle?: boolean|string, callbackMetrics?: string[],
-      initialEpoch?: number, stepsPerEpoch?: number, validationSteps?: number):
-      Promise<History> {
-    if (batchSize == null) {
-      batchSize = 32;
-    }
-    if (epochs == null) {
-      epochs = 1;
-    }
-    if (shuffle == null) {
-      shuffle = true;
-    }
-    if (initialEpoch == null) {
-      initialEpoch = 0;
-    }
-
-    // TODO(cais): Change const to let below when implementing validation.
-    let doValidation = false;
-    if (valF != null && valIns != null) {
-      doValidation = true;
-      // TODO(cais): verbose message.
-    }
-    if (validationSteps != null) {
-      doValidation = true;
-      if (stepsPerEpoch == null) {
-        throw new ValueError(
-            'Can only use `validationSteps` when doing step-wise training, ' +
-            'i.e., `stepsPerEpoch` must be set.');
-      }
-    }
-
-    const numTrainSamples =
-        this.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');
-    let indexArray: number[];
-    if (numTrainSamples != null) {
-      indexArray = range(0, numTrainSamples);
-    }
-
-    if (verbose == null) {
-      verbose = 1;
-    }
-
-    const {callbackList, history} = configureCallbacks(
-        callbacks, verbose, epochs, initialEpoch, numTrainSamples,
-        stepsPerEpoch, batchSize, doValidation, callbackMetrics);
-    callbackList.setModel(this);
-    this.history = history;
-    await callbackList.onTrainBegin();
-    this.stopTraining_ = false;
-    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.
-    // TODO(cais): Pre-convert feeds for performance as in PyKeras.
-
-    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {
-      await callbackList.onEpochBegin(epoch);
-      const epochLogs: UnresolvedLogs = {};
-      if (stepsPerEpoch != null) {
-        throw new NotImplementedError(
-            'stepsPerEpoch mode is not implemented yet.');
-      } else {
-        if (shuffle === 'batch') {
-          throw new NotImplementedError('batch shuffling is not implemneted'
-                                        + ' yet');
-        } else if (shuffle) {
-          util.shuffle(indexArray);
-        }
-        // Convert the potentially shuffled indices to Tensor1D, to avoid the
-        // cost of repeated creation of Array1Ds later on.
-        const epochIndexArray1D = tensor1d(indexArray);
-
-        const batches = makeBatches(numTrainSamples, batchSize);
-        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
-          const batchLogs: UnresolvedLogs = {};
-          await callbackList.onBatchBegin(batchIndex, batchLogs);
-
-          tfc.tidy(() => {
-            const batchStart = batches[batchIndex][0];
-            const batchEnd = batches[batchIndex][1];
-            const batchIds = K.sliceAlongFirstAxis(
-                                 epochIndexArray1D, batchStart,
-                                 batchEnd - batchStart) as Tensor1D;
-            batchLogs['batch'] = batchIndex;
-            batchLogs['size'] = batchEnd - batchStart;
-
-            // TODO(cais): In ins, train flag can be a number, instead of an
-            //   Tensor? Do we need to handle this in tfjs-layers?
-            const insBatch = sliceArraysByIndices(ins, batchIds) as Tensor[];
-            const outs = f(insBatch);
-            for (let i = 0; i < outLabels.length; ++i) {
-              const label = outLabels[i];
-              const out = outs[i];
-              batchLogs[label] = out;
-              tfc.keep(out);
-              // TODO(cais): Use scope() to avoid ownership.
-            }
-
-            if (batchIndex === batches.length - 1) {  // Last batch.
-              if (doValidation) {
-                const valOuts = this.testLoop(valF, valIns, batchSize);
-                // Porting Notes: In tfjs-layers, valOuts is always an Array.
-                for (let i = 0; i < outLabels.length; ++i) {
-                  const label = outLabels[i];
-                  const out = valOuts[i];
-                  tfc.keep(out);
-                  // TODO(cais): Use scope() to avoid ownership.
-                  epochLogs['val_' + label] = out;
-                }
-              }
-            }
-          });
-
-          await callbackList.onBatchEnd(batchIndex, batchLogs);
-          disposeTensorsInLogs(batchLogs);
-
-          if (this.stopTraining_) {
-            break;
-          }
-          // TODO(cais): return outs as list of Tensor.
-        }
-
-        epochIndexArray1D.dispose();
-      }
-      // TODO(cais): Run validation at the end of the epoch.
-      await callbackList.onEpochEnd(epoch, epochLogs);
-      if (this.stopTraining_) {
-        break;
-      }
-    }
-    await callbackList.onTrainEnd();
-
-    await this.history.syncData();
-    return this.history;
-  }
-
-  // TODO(cais): Add code snippet below when it's possible to instantiate
-  //   actual dataset objects.
-  /**
-   * Trains the model using a dataset object.
-   *
-   * @param dataset A dataset object. Its `iterator()` method is expected
-   *   to generate a dataset iterator object, the `next()` method of which
-   *   is expected to produce data batches for training. The return value
-   *   of the `next()` call ought to contain a boolean `done` field and a
-   *   `value` field. The `value` field is expected to be an array of two
-   *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former
-   *   case is for models with exactly one input and one output (e.g.
-   *   a sequential model). The latter case is for models with multiple
-   *   inputs and/or multiple outputs.
-   *   Of the two items in the array, the first is the input feature(s) and
-   *   the second is the output target(s).
-   * @param args A `ModelFitDatasetArgs`, containing optional fields.
-   *
-   * @return A `History` instance. Its `history` attribute contains all
-   *   information collected during training.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  async fitDataset<T>(dataset: Dataset<T>, args: ModelFitDatasetArgs<T>):
-      Promise<History> {
-    return fitDataset(this, dataset, args);
-  }
-
-  /**
-   * Runs a single gradient update on a single batch of data.
-   *
-   * This method differs from `fit()` and `fitDataset()` in the following
-   * regards:
-   *   - It operates on exactly one batch of data.
-   *   - It returns only the loss and metric values, instead of
-   *     returning the batch-by-batch loss and metric values.
-   *   - It doesn't support fine-grained options such as verbosity and
-   *     callbacks.
-   *
-   * @param x Input data. It could be one of the following:
-   *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has
-   *     multiple inputs).
-   *   - An Object mapping input names to corresponding `tf.Tensor` (if the
-   *     model has named inputs).
-   * @param y Target data. It could be either a `tf.Tensor` or multiple
-   *   `tf.Tensor`s. It should be consistent with `x`.
-   * @returns Training loss or losses (in case the model has
-   *   multiple outputs), along with metrics (if any), as numbers.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  async trainOnBatch(
-      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
-      y: Tensor|Tensor[]|
-      {[inputName: string]: Tensor}): Promise<number|number[]> {
-    // TODO(cais): Support sampleWeight and classWeight.
-    // TODO(cais): Support Dataset objects.
-    const standardizeOut = await this.standardizeUserData(x, y);
-    const inputs = standardizeOut[0];
-    const targets = standardizeOut[1];
-    const trainFunction = this.makeTrainFunction();
-    const losses = trainFunction(inputs.concat(targets));
-    const lossValues: number[] = [];
-    for (const loss of losses) {
-      const v = await loss.data();
-      lossValues.push(v[0]);
-    }
-    tfc.dispose(losses);
-    disposeNewTensors(standardizeOut[0], x);
-    disposeNewTensors(standardizeOut[1], y);
-    return singletonOrArray(lossValues);
-  }
-
-  /**
-   * Extract weight values of the model.
-   *
-   * @param config: An instance of `io.SaveConfig`, which specifies
-   * model-saving options such as whether only trainable weights are to be
-   * saved.
-   * @returns A `NamedTensorMap` mapping original weight names (i.e.,
-   *   non-uniqueified weight names) to their values.
-   */
-  protected getNamedWeights(config?: io.SaveConfig): NamedTensor[] {
-    const namedWeights: NamedTensor[] = [];
-
-    const trainableOnly = config != null && config.trainableOnly;
-    const weights = trainableOnly ? this.trainableWeights : this.weights;
-    const weightValues = this.getWeights(trainableOnly);
-    for (let i = 0; i < weights.length; ++i) {
-      if (trainableOnly && !weights[i].trainable) {
-        // Optionally skip non-trainable weights.
-        continue;
-      }
-      namedWeights.push(
-          {name: weights[i].originalName, tensor: weightValues[i]});
-    }
-    return namedWeights;
-  }
-
-  /**
-   * Setter used for force stopping of LayersModel.fit() (i.e., training).
-   *
-   * Example:
-   *
-   * ```js
-   * const input = tf.input({shape: [10]});
-   * const output = tf.layers.dense({units: 1}).apply(input);
-   * const model = tf.model({inputs: [input], outputs: [output]});
-   * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-   * const xs = tf.ones([8, 10]);
-   * const ys = tf.zeros([8, 1]);
-   *
-   * const history = await model.fit(xs, ys, {
-   *   epochs: 10,
-   *   callbacks: {
-   *     onEpochEnd: async (epoch, logs) => {
-   *       if (epoch === 2) {
-   *         model.stopTraining = true;
-   *       }
-   *     }
-   *   }
-   * });
-   *
-   * // There should be only 3 values in the loss array, instead of 10
-   * values,
-   * // due to the stopping after 3 epochs.
-   * console.log(history.history.loss);
-   * ```
-   */
-  set stopTraining(stop: boolean) {
-    this.stopTraining_ = stop;
-  }
-
-  get stopTraining(): boolean {
-    return this.stopTraining_;
-  }
-
-  get optimizer(): Optimizer {
-    return this.optimizer_;
-  }
-
-  set optimizer(optimizer: Optimizer) {
-    if (this.optimizer_ !== optimizer) {
-      this.optimizer_ = optimizer;
-      this.isOptimizerOwned = false;
-    }
-  }
-
-  override dispose(): DisposeResult {
-    const result = super.dispose();
-    if (result.refCountAfterDispose === 0 && this.optimizer != null &&
-        this.isOptimizerOwned) {
-      const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;
-      this.optimizer_.dispose();
-      result.numDisposedVariables +=
-          numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;
-    }
-    return result;
-  }
-
-  private getLossIdentifiers(): LossIdentifier|LossIdentifier[]|
-      {[outputName: string]: LossIdentifier} {
-    let lossNames: LossIdentifier|LossIdentifier[]|
-        {[outputName: string]: LossIdentifier};
-    if (typeof this.loss === 'string') {
-      lossNames = toSnakeCase(this.loss) as LossIdentifier;
-    } else if (Array.isArray(this.loss)) {
-      for (const loss of this.loss) {
-        if (typeof loss !== 'string') {
-          throw new Error('Serialization of non-string loss is not supported.');
-        }
-      }
-      lossNames = (this.loss as string[]).map(name => toSnakeCase(name)) as
-          LossIdentifier[];
-    } else {
-      const outputNames = Object.keys(this.loss);
-      lossNames = {} as {[outputName: string]: LossIdentifier};
-      const losses =
-          this.loss as {[outputName: string]: LossOrMetricFn | string};
-      for (const outputName of outputNames) {
-        if (typeof losses[outputName] === 'string') {
-          lossNames[outputName] =
-              toSnakeCase(losses[outputName] as string) as LossIdentifier;
-        } else {
-          throw new Error('Serialization of non-string loss is not supported.');
-        }
-      }
-    }
-    return lossNames;
-  }
-
-  private getMetricIdentifiers(): MetricsIdentifier[]|
-      {[key: string]: MetricsIdentifier} {
-    if (typeof this.metrics === 'string' ||
-        typeof this.metrics === 'function') {
-      return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];
-    } else if (Array.isArray(this.metrics)) {
-      return this.metrics.map(
-          metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));
-    } else {
-      const metricsIdentifiers: {[key: string]: MetricsIdentifier} = {};
-      for (const key in this.metrics) {
-        metricsIdentifiers[key] =
-            toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));
-      }
-      return metricsIdentifiers;
-    }
-  }
-
-  protected getTrainingConfig(): TrainingConfig {
-    return {
-      loss: this.getLossIdentifiers(),
-      metrics: this.getMetricIdentifiers(),
-      optimizer_config: {
-        class_name: this.optimizer.getClassName(),
-        config: this.optimizer.getConfig()
-      } as OptimizerSerialization
-    };
-    // TODO(cais): Add weight_metrics when they are supported.
-    // TODO(cais): Add sample_weight_mode when it's supported.
-    // TODO(cais): Add loss_weights when it's supported.
-  }
-
-  loadTrainingConfig(trainingConfig: TrainingConfig) {
-    if (trainingConfig.weighted_metrics != null) {
-      throw new Error('Loading weight_metrics is not supported yet.');
-    }
-    if (trainingConfig.loss_weights != null) {
-      throw new Error('Loading loss_weights is not supported yet.');
-    }
-    if (trainingConfig.sample_weight_mode != null) {
-      throw new Error('Loading sample_weight_mode is not supported yet.');
-    }
-
-    const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config) as
-        serialization.ConfigDict;
-    const optimizer = deserialize(tsConfig) as Optimizer;
-
-    let loss;
-    if (typeof trainingConfig.loss === 'string') {
-      loss = toCamelCase(trainingConfig.loss);
-    } else if (Array.isArray(trainingConfig.loss)) {
-      loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));
-    } else if (trainingConfig.loss != null) {
-      loss = {} as {[outputName: string]: LossIdentifier};
-      for (const key in trainingConfig.loss) {
-        loss[key] = toCamelCase(trainingConfig.loss[key]) as LossIdentifier;
-      }
-    }
-
-    let metrics;
-    if (Array.isArray(trainingConfig.metrics)) {
-      metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));
-    } else if (trainingConfig.metrics != null) {
-      metrics = {} as {[outputName: string]: MetricsIdentifier};
-      for (const key in trainingConfig.metrics) {
-        metrics[key] = toCamelCase(trainingConfig.metrics[key]);
-      }
-    }
-
-    this.compile({loss, metrics, optimizer});
-  }
-
-  /**
-   * Save the configuration and/or weights of the LayersModel.
-   *
-   * An `IOHandler` is an object that has a `save` method of the proper
-   * signature defined. The `save` method manages the storing or
-   * transmission of serialized data ("artifacts") that represent the
-   * model's topology and weights onto or via a specific medium, such as
-   * file downloads, local storage, IndexedDB in the web browser and HTTP
-   * requests to a server. TensorFlow.js provides `IOHandler`
-   * implementations for a number of frequently used saving mediums, such as
-   * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
-   * for more details.
-   *
-   * This method also allows you to refer to certain types of `IOHandler`s
-   * as URL-like string shortcuts, such as 'localstorage://' and
-   * 'indexeddb://'.
-   *
-   * Example 1: Save `model`'s topology and weights to browser [local
-   * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
-   * then load it back.
-   *
-   * ```js
-   * const model = tf.sequential(
-   *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
-   * console.log('Prediction from original model:');
-   * model.predict(tf.ones([1, 3])).print();
-   *
-   * const saveResults = await model.save('localstorage://my-model-1');
-   *
-   * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');
-   * console.log('Prediction from loaded model:');
-   * loadedModel.predict(tf.ones([1, 3])).print();
-   * ```
-   *
-   * Example 2. Saving `model`'s topology and weights to browser
-   * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);
-   * then load it back.
-   *
-   * ```js
-   * const model = tf.sequential(
-   *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
-   * console.log('Prediction from original model:');
-   * model.predict(tf.ones([1, 3])).print();
-   *
-   * const saveResults = await model.save('indexeddb://my-model-1');
-   *
-   * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');
-   * console.log('Prediction from loaded model:');
-   * loadedModel.predict(tf.ones([1, 3])).print();
-   * ```
-   *
-   * Example 3. Saving `model`'s topology and weights as two files
-   * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from
-   * browser.
-   *
-   * ```js
-   * const model = tf.sequential(
-   *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
-   * const saveResults = await model.save('downloads://my-model-1');
-   * ```
-   *
-   * Example 4. Send  `model`'s topology and weights to an HTTP server.
-   * See the documentation of `tf.io.http` for more details
-   * including specifying request parameters and implementation of the
-   * server.
-   *
-   * ```js
-   * const model = tf.sequential(
-   *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
-   * const saveResults = await model.save('http://my-server/model/upload');
-   * ```
-   *
-   * @param handlerOrURL An instance of `IOHandler` or a URL-like,
-   * scheme-based string shortcut for `IOHandler`.
-   * @param config Options for saving the model.
-   * @returns A `Promise` of `SaveResult`, which summarizes the result of
-   * the saving, such as byte sizes of the saved artifacts for the model's
-   *   topology and weight values.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
-   */
-  async save(handlerOrURL: io.IOHandler|string, config?: io.SaveConfig):
-      Promise<io.SaveResult> {
-    if (typeof handlerOrURL === 'string') {
-      const handlers = io.getSaveHandlers(handlerOrURL);
-      if (handlers.length === 0) {
-        throw new ValueError(
-            `Cannot find any save handlers for URL '${handlerOrURL}'`);
-      } else if (handlers.length > 1) {
-        throw new ValueError(
-            `Found more than one (${handlers.length}) save handlers for ` +
-            `URL '${handlerOrURL}'`);
-      }
-      handlerOrURL = handlers[0];
-    }
-    if (handlerOrURL.save == null) {
-      throw new ValueError(
-          'LayersModel.save() cannot proceed because the IOHandler ' +
-          'provided does not have the `save` attribute defined.');
-    }
-
-    const weightDataAndSpecs =
-        await io.encodeWeights(this.getNamedWeights(config));
-
-    const returnString = false;
-    const unusedArg: {} = null;
-    const modelConfig = this.toJSON(unusedArg, returnString);
-    const modelArtifacts: io.ModelArtifacts = {
-      modelTopology: modelConfig,
-      format: LAYERS_MODEL_FORMAT_NAME,
-      generatedBy: `TensorFlow.js tfjs-layers v${version}`,
-      convertedBy: null,
-    };
-
-    const includeOptimizer = config == null ? false : config.includeOptimizer;
-    if (includeOptimizer && this.optimizer != null) {
-      modelArtifacts.trainingConfig = this.getTrainingConfig();
-      const weightType = 'optimizer';
-      const {data: optimizerWeightData, specs: optimizerWeightSpecs} =
-          await io.encodeWeights(await this.optimizer.getWeights(), weightType);
-      weightDataAndSpecs.specs.push(...optimizerWeightSpecs);
-      weightDataAndSpecs.data = io.concatenateArrayBuffers(
-          [weightDataAndSpecs.data, optimizerWeightData]);
-    }
-
-    if (this.userDefinedMetadata != null) {
-      // Check serialized size of user-defined metadata.
-      const checkSize = true;
-      checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);
-      modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;
-    }
-
-    modelArtifacts.weightData = weightDataAndSpecs.data;
-    modelArtifacts.weightSpecs = weightDataAndSpecs.specs;
-    return handlerOrURL.save(modelArtifacts);
-  }
-
-  /**
-   * Set user-defined metadata.
-   *
-   * The set metadata will be serialized together with the topology
-   * and weights of the model during `save()` calls.
-   *
-   * @param setUserDefinedMetadata
-   */
-  setUserDefinedMetadata(userDefinedMetadata: {}): void {
-    checkUserDefinedMetadata(userDefinedMetadata, this.name);
-    this.userDefinedMetadata = userDefinedMetadata;
-  }
-
-  /**
-   * Get user-defined metadata.
-   *
-   * The metadata is supplied via one of the two routes:
-   *   1. By calling `setUserDefinedMetadata()`.
-   *   2. Loaded during model loading (if the model is constructed
-   *      via `tf.loadLayersModel()`.)
-   *
-   * If no user-defined metadata is available from either of the
-   * two routes, this function will return `undefined`.
-   */
-  getUserDefinedMetadata(): {} {
-    return this.userDefinedMetadata;
-  }
-}
-serialization.registerClass(LayersModel);
-
-/**
- * A `tf.Functional` is an alias to `tf.LayersModel`.
- *
- * See also:
- *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.
- */
-/** @doc {heading: 'Models', subheading: 'Classes'} */
-export class Functional extends LayersModel {
-  static override className = 'Functional';
-}
-serialization.registerClass(Functional);
diff --git a/tfjs-master/tfjs-layers/src/engine/training_dataset.ts b/tfjs-master/tfjs-layers/src/engine/training_dataset.ts
deleted file mode 100644
index 45ce98a5d..000000000
--- a/tfjs-master/tfjs-layers/src/engine/training_dataset.ts
+++ /dev/null
@@ -1,615 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Interfaces and methods for training models using TensorFlow.js datasets.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {scalar} from '@tensorflow/tfjs-core';
-import {BaseCallback, configureCallbacks, CustomCallbackArgs, History, ModelLoggingVerbosity, standardizeCallbacks, YieldEveryOptions} from '../base_callbacks';
-import {NotImplementedError, ValueError} from '../errors';
-import {disposeTensorsInLogs, UnresolvedLogs} from '../logs';
-import {TensorOrArrayOrMap} from '../types';
-import {singletonOrArray, toList} from '../utils/generic_utils';
-
-import {Dataset, LazyIterator} from './dataset_stub';
-import {ClassWeight, ClassWeightMap, standardizeClassWeights, standardizeWeights} from './training_utils';
-
-/**
- * Interface for configuring model training based on a dataset object.
- */
-export interface ModelFitDatasetArgs<T> {
-  /**
-   * (Optional) Total number of steps (batches of samples) before
-   * declaring one epoch finished and starting the next epoch. It should
-   * typically be equal to the number of samples of your dataset divided by
-   * the batch size, so that `fitDataset`() call can utilize the entire dataset.
-   * If it is not provided, use `done` return value in `iterator.next()` as
-   * signal to finish an epoch.
-   */
-  batchesPerEpoch?: number;
-
-  /**
-   * Integer number of times to iterate over the training dataset.
-   */
-  epochs: number;
-
-  /**
-   * Verbosity level.
-   *
-   * Expected to be 0, 1, or 2. Default: 1.
-   *
-   * 0 - No printed message during fit() call.
-   * 1 - In Node.js (tfjs-node), prints the progress bar, together with
-   *     real-time updates of loss and metric values and training speed.
-   *     In the browser: no action. This is the default.
-   * 2 - Not implemented yet.
-   */
-  verbose?: ModelLoggingVerbosity;
-
-  /**
-   * List of callbacks to be called during training.
-   * Can have one or more of the following callbacks:
-   *   - `onTrainBegin(logs)`: called when training starts.
-   *   - `onTrainEnd(logs)`: called when training ends.
-   *   - `onEpochBegin(epoch, logs)`: called at the start of every epoch.
-   *   - `onEpochEnd(epoch, logs)`: called at the end of every epoch.
-   *   - `onBatchBegin(batch, logs)`: called at the start of every batch.
-   *   - `onBatchEnd(batch, logs)`: called at the end of every batch.
-   *   - `onYield(epoch, batch, logs)`: called every `yieldEvery` milliseconds
-   *      with the current epoch, batch and logs. The logs are the same
-   *      as in `onBatchEnd()`. Note that `onYield` can skip batches or
-   *      epochs. See also docs for `yieldEvery` below.
-   */
-  callbacks?: BaseCallback[]|CustomCallbackArgs|CustomCallbackArgs[];
-
-  /**
-   * Data on which to evaluate the loss and any model
-   * metrics at the end of each epoch. The model will not be trained on this
-   * data. This could be any of the following:
-   *
-   *   - An array `[xVal, yVal]`, where the two values may be `tf.Tensor`,
-   *     an array of Tensors, or a map of string to Tensor.
-   *   - Similarly, an array ` [xVal, yVal, valSampleWeights]`
-   *     (not implemented yet).
-   *   - a `Dataset` object with elements of the form `{xs: xVal, ys: yVal}`,
-   *     where `xs` and `ys` are the feature and label tensors, respectively.
-   *
-   * If `validationData` is an Array of Tensor objects, each `tf.Tensor` will be
-   * sliced into batches during validation, using the parameter
-   * `validationBatchSize` (which defaults to 32). The entirety of the
-   * `tf.Tensor` objects will be used in the validation.
-   *
-   * If `validationData` is a dataset object, and the `validationBatches`
-   * parameter is specified, the validation will use `validationBatches` batches
-   * drawn from the dataset object. If `validationBatches` parameter is not
-   * specified, the validation will stop when the dataset is exhausted.
-   *
-   * The model will not be trained on this data.
-   */
-  validationData?: [
-    TensorOrArrayOrMap, TensorOrArrayOrMap
-  ]|[TensorOrArrayOrMap, TensorOrArrayOrMap, TensorOrArrayOrMap]|Dataset<T>;
-
-  /**
-   * Optional batch size for validation.
-   *
-   * Used only if `validationData` is an array of `tf.Tensor` objects, i.e., not
-   * a dataset object.
-   *
-   * If not specified, its value defaults to 32.
-   */
-  validationBatchSize?: number;
-
-  /**
-   * (Optional) Only relevant if `validationData` is specified and is a dataset
-   * object.
-   *
-   * Total number of batches of samples to draw from `validationData` for
-   * validation purpose before stopping at the end of every epoch. If not
-   * specified, `evaluateDataset` will use `iterator.next().done` as signal to
-   * stop validation.
-   */
-  validationBatches?: number;
-
-  /**
-   * Configures the frequency of yielding the main thread to other tasks.
-   *
-   * In the browser environment, yielding the main thread can improve the
-   * responsiveness of the page during training. In the Node.js environment,
-   * it can ensure tasks queued in the event loop can be handled in a timely
-   * manner.
-   *
-   * The value can be one of the following:
-   *   - `'auto'`: The yielding happens at a certain frame rate (currently set
-   *               at 125ms). This is the default.
-   *   - `'batch'`: yield every batch.
-   *   - `'epoch'`: yield every epoch.
-   *   - a `number`: Will yield every `number` milliseconds.
-   *   - `'never'`: never yield. (But yielding can still happen through `await
-   *      nextFrame()` calls in custom callbacks.)
-   */
-  yieldEvery?: YieldEveryOptions;
-
-  /**
-   * Epoch at which to start training (useful for resuming a previous training
-   * run). When this is used, `epochs` is the index of the "final epoch".
-   * The model is not trained for a number of iterations given by `epochs`,
-   * but merely until the epoch of index `epochs` is reached.
-   */
-  initialEpoch?: number;
-
-  /**
-   * Optional object mapping class indices (integers) to
-   * a weight (float) to apply to the model's loss for the samples from this
-   * class during training. This can be useful to tell the model to "pay more
-   * attention" to samples from an under-represented class.
-   *
-   * If the model has multiple outputs, a class weight can be specified for
-   * each of the outputs by setting this field an array of weight object
-   * or an object that maps model output names (e.g., `model.outputNames[0]`)
-   * to weight objects.
-   */
-  classWeight?: ClassWeight|ClassWeight[]|ClassWeightMap;
-}
-
-export interface FitDatasetElement {
-  xs: TensorOrArrayOrMap;
-  ys: TensorOrArrayOrMap;
-}
-
-/**
- * Interface for configuring model evaluation based on a dataset object.
- */
-export interface ModelEvaluateDatasetArgs {
-  /**
-   * Number of batches to draw from the dataset object before ending the
-   * evaluation.
-   */
-  batches?: number;
-
-  /**
-   * Verbosity mode.
-   */
-  verbose?: ModelLoggingVerbosity;
-}
-
-// Default batch size used during tensor-based validation.
-const DEFAULT_VALIDATION_BATCH_SIZE = 32;
-
-/**
- * Standardize the output of a dataset iterator for use by
- * LayersModel.fitDataset().
- *
- * @param model: A `tf.LayersModel` object.
- * @param iteratorOut The output of a dataset iterator. It is required to be
- *   an object of the form `{xs: TensorOrArrayOrMap, ys:
- * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,
- * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.
- * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s
- *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided
- *   as a map, the order in the resulting array is taken from the `inputNames`
- *   and `outputNames` of the model.
- */
-function standardizeDataIteratorOutput(
-    // Type `model` as `any` here to avoid circular dependency w/
-    // training.ts.
-    // tslint:disable-next-line:no-any
-    model: any, iteratorOut: {}): {xs: tfc.Tensor[], ys: tfc.Tensor[]} {
-  let xs: TensorOrArrayOrMap;
-  let ys: TensorOrArrayOrMap;
-
-  const iteratorOutObj = iteratorOut as FitDatasetElement;
-  xs = iteratorOutObj['xs'];
-  ys = iteratorOutObj['ys'];
-  tfc.util.assert(
-      xs != null && ys != null,
-      () => 'A Dataset iterator for fitDataset() is expected to generate ' +
-          'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +
-          'values may be `tf.Tensor`, an array of Tensors, or a map of ' +
-          'string to Tensor.  The provided Dataset instead generates ' +
-          `${iteratorOut}`);
-
-  const flattenedXs: tfc.Tensor[] =
-      flattenTensorOrArrayOrMap('input', model.inputNames, xs);
-  const flattenedYs: tfc.Tensor[] =
-      flattenTensorOrArrayOrMap('output', model.outputNames, ys);
-
-  const batchSize: number = flattenedXs[0].shape[0];
-
-  tfc.util.assert(
-      flattenedXs.length === model.inputs.length,
-      () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +
-          `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +
-          `${JSON.stringify(model.inputNames)})`);
-
-  tfc.util.assert(
-      flattenedYs.length === model.outputs.length,
-      () =>
-          `LayersModel has ${model.outputs.length} outputs, but the dataset ` +
-          `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +
-          `${JSON.stringify(model.outputNames)})`);
-
-  for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {
-    tfc.util.assert(
-        flattenedXs[xIndex].shape[0] === batchSize,
-        () => `Batch size mismatch: input ` +
-            `${model.inputNames[xIndex]} has ${
-                  flattenedXs[xIndex].shape[0]}; ` +
-            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);
-  }
-
-  for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {
-    tfc.util.assert(
-        flattenedYs[yIndex].shape[0] === batchSize,
-        () => `Batch size mismatch: output ` +
-            `${model.outputNames[yIndex]} has ${
-                  flattenedYs[yIndex].shape[0]}; ` +
-            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);
-  }
-
-  return {xs: flattenedXs, ys: flattenedYs};
-}
-
-function flattenTensorOrArrayOrMap(
-    inputOrOutput: string, names: string[], values: TensorOrArrayOrMap) {
-  if (values instanceof tfc.Tensor) {
-    return [values];
-  } else if (Array.isArray(values)) {
-    tfc.util.assert(
-        values.length === names.length,
-        () => `Received an array of ${values.length} Tensors, but expected ${
-            names.length} to match the ${inputOrOutput} keys ${names}.`);
-    return values;
-  } else {
-    const result: tfc.Tensor[] = [];
-    // Check that all the required keys are available.
-    for (const name of names) {
-      if (values[name] == null) {
-        throw new ValueError(
-            `The feature data generated by the dataset lacks the required ` +
-            `${inputOrOutput} key '${name}'.`);
-      }
-      result.push(values[name]);
-    }
-    return result;
-  }
-}
-
-function standardizeTensorValidationData<T>(
-    data:
-        [
-          tfc.Tensor|tfc.Tensor[], tfc.Tensor|tfc.Tensor[]
-        ]|[tfc.Tensor | tfc.Tensor[], tfc.Tensor | tfc.Tensor[],
-           tfc.Tensor | tfc.Tensor[]]):
-    {xs: tfc.Tensor|tfc.Tensor[], ys: tfc.Tensor|tfc.Tensor[]} {
-  if (data.length === 3) {
-    throw new NotImplementedError(
-        'Validation with sample weights is not implemented yet.');
-  }
-  return {xs: data[0], ys: data[1]};
-}
-
-export async function fitDataset<T>(
-    // Type `model` as `any` here to avoid circular dependency w/
-    // training.ts.
-    // tslint:disable-next-line:no-any
-    model: any, dataset: Dataset<T>,
-    args: ModelFitDatasetArgs<T>): Promise<History> {
-  const hasBatchesPerEpoch = args.batchesPerEpoch != null;
-  tfc.util.assert(
-      model.optimizer != null,
-      () => 'You must compile a model before training/testing. Use ' +
-          'LayersModel.compile(modelCompileConfig).');
-
-  tfc.util.assert(
-      args != null,
-      () => `For fitDataset(), the 2nd argument (config) is required, ` +
-          `but it is not provided in this call.`);
-  tfc.util.assert(
-      args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs),
-      () => `For fitDataset(), config.epochs is expected to be a positive ` +
-          `integer, but got ${args.epochs}`);
-  tfc.util.assert(
-      !hasBatchesPerEpoch ||
-          (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)),
-      () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +
-          `positive integer if specified, but got ${args.batchesPerEpoch}`);
-  tfc.util.assert(
-      // tslint:disable-next-line:no-any
-      (args as any)['validationSplit'] == null,
-      () => '`validationSplit` is not supported by `fitDataset()`. ' +
-          'Use validationData instead.');
-
-  if (model.isTraining) {
-    throw new Error(
-        'Cannot start training because another fit() call is ongoing.');
-  }
-  model.isTraining = true;
-
-  try {
-    const doValidation = args.validationData != null;
-    let valXs: tfc.Tensor|tfc.Tensor[];
-    let valYs: tfc.Tensor|tfc.Tensor[];
-    if (doValidation) {
-      if (isDatasetObject(args.validationData)) {
-        tfc.util.assert(
-            args.validationBatches == null ||
-                (args.validationBatches > 0 &&
-                 Number.isInteger(args.validationBatches)),
-            () => `For fitDataset() with dataset-based validation, ` +
-                `config.validationBatches is expected not to be provided, ` +
-                `or to be a positive integer, ` +
-                `but got ${args.validationBatches}`);
-      } else {
-        const validationData = standardizeTensorValidationData(
-            args.validationData as
-                    [tfc.Tensor | tfc.Tensor[], tfc.Tensor | tfc.Tensor[]] |
-            [
-              tfc.Tensor | tfc.Tensor[], tfc.Tensor | tfc.Tensor[],
-              tfc.Tensor | tfc.Tensor[]
-            ]);
-        valXs = validationData.xs;
-        valYs = validationData.ys;
-      }
-    }
-
-    const trainFunction = model.makeTrainFunction();
-    const outLabels = model.getDedupedMetricsNames() as string[];
-
-    let callbackMetrics: string[];
-    if (doValidation) {
-      callbackMetrics =
-          outLabels.slice().concat(outLabels.map(n => 'val_' + n));
-    } else {
-      callbackMetrics = outLabels.slice();
-    }
-
-    const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);
-    const verbose = args.verbose == null ? 1 : args.verbose;
-    const {callbackList, history} = configureCallbacks(
-        callbacks, verbose, args.epochs, null, null,
-        getStepsPerEpoch(dataset, args),
-        null,  // Batch size determined by the dataset itself.
-        doValidation, callbackMetrics);
-    callbackList.setModel(model);
-    model.history = history;
-
-    await callbackList.onTrainBegin();
-    model.stopTraining_ = false;
-    let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;
-
-    let dataIterator = await dataset.iterator();
-    while (epoch < args.epochs) {
-      const epochLogs: UnresolvedLogs = {};
-      await callbackList.onEpochBegin(epoch);
-      let stepsDone = 0;
-      let batchIndex = 0;
-      if (!hasBatchesPerEpoch) {
-        dataIterator = await dataset.iterator();
-      }
-      while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {
-        const iteratorOut = await dataIterator.next();
-
-        // If `batchesPerEpoch` is specified, the dataset should not be
-        // exhausted until all epoches are done.
-        if (hasBatchesPerEpoch && iteratorOut.done) {
-          console.warn(
-              'You provided `batchesPerEpoch` as ' +
-              `${args.batchesPerEpoch}, ` +
-              'but your dataset iterator ran out of data after ' +
-              `${stepsDone} batches; ` +
-              'interrupting training. Make sure that your ' +
-              'dataset can generate at least `batchesPerEpoch * epochs` ' +
-              'batches (in this case, ' +
-              `${args.batchesPerEpoch * args.epochs} batches). ` +
-              'You may need to use the repeat() function when building ' +
-              'your dataset.');
-          break;
-        }
-
-        if (iteratorOut.value != null) {
-          const {xs, ys} =
-              standardizeDataIteratorOutput(model, iteratorOut.value);
-          const batchLogs: UnresolvedLogs = {};
-          batchLogs['batch'] = batchIndex;
-          batchLogs['size'] = xs[0].shape[0];
-
-          await callbackList.onBatchBegin(batchIndex, batchLogs);
-
-          const sampleWeights: tfc.Tensor[] = [];
-          if (args.classWeight != null) {
-            const standardClassWeights =
-                standardizeClassWeights(args.classWeight, model.outputNames);
-            for (let i = 0; i < standardClassWeights.length; ++i) {
-              sampleWeights.push(await standardizeWeights(
-                  ys[i], null, standardClassWeights[i]));
-            }
-          }
-
-          // Train on batch.
-          const ins = xs.concat(ys).concat(sampleWeights);
-          const outs = trainFunction(ins);
-          tfc.dispose(ins);
-          for (let i = 0; i < outLabels.length; ++i) {
-            const label = outLabels[i];
-            const out = outs[i];
-            batchLogs[label] = out;
-            tfc.keep(out);
-          }
-
-          await callbackList.onBatchEnd(batchIndex, batchLogs);
-          disposeTensorsInLogs(batchLogs);
-
-          batchIndex++;
-          stepsDone++;
-        }
-
-        if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :
-                                 iteratorOut.done) {
-          // Epoch finished. Perform validation.
-          if (doValidation) {
-            let valOuts: tfc.Scalar[];
-            if (isDatasetObject(args.validationData)) {
-              valOuts = toList(await model.evaluateDataset(
-                  args.validationData, {batches: args.validationBatches}));
-            } else {
-              valOuts = toList(model.evaluate(valXs, valYs, {
-                batchSize: args.validationBatchSize == null ?
-                    DEFAULT_VALIDATION_BATCH_SIZE :
-                    args.validationBatchSize,
-                verbose: 0
-              }));
-            }
-            for (let i = 0; i < model.metricsNames.length; ++i) {
-              epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];
-            }
-          }
-          // Call `break` to exit one epoch lopp after validation is done. If
-          // config.batchesPerEpoch is specified, an epoch while loop will
-          // stop when `stepsDone >= config.batchesPerEpoch`. When
-          // config.batchesPerEpoch is not provided, the following `break` is
-          // required to exit the while lopp after dataset is exhausted.
-          break;
-        }
-
-        if (model.stopTraining_) {
-          break;
-        }
-      }
-      await callbackList.onEpochEnd(epoch, epochLogs);
-      epoch++;
-      if (model.stopTraining_) {
-        break;
-      }
-    }
-    await callbackList.onTrainEnd();
-    await model.history.syncData();
-    return model.history;
-  } finally {
-    model.isTraining = false;
-  }
-}
-
-/** Helper function that determines number of steps (batches) per epoch. */
-function getStepsPerEpoch<T>(
-    dataset: Dataset<T>, args: ModelFitDatasetArgs<T>): number {
-  // Attempt to determine # of batches in an epoch.
-  let stepsPerEpoch: number = null;
-  if (args.batchesPerEpoch != null) {
-    stepsPerEpoch = args.batchesPerEpoch;
-  } else if (Number.isFinite(dataset.size)) {
-    stepsPerEpoch = dataset.size;
-  }
-  return stepsPerEpoch;
-}
-
-// Check if provided object is a Dataset object by checking its .iterator
-// element.
-function isDatasetObject<T>(
-    dataset:
-        [
-          TensorOrArrayOrMap, TensorOrArrayOrMap
-        ]|[TensorOrArrayOrMap, TensorOrArrayOrMap, TensorOrArrayOrMap]|
-    Dataset<T>): boolean {
-  return (typeof (dataset as Dataset<T>).iterator === 'function');
-}
-
-// Check if provided object is a LazyIterator object by checking it's .next
-// element.
-function isLazyIteratorObject<T>(iterator: Dataset<T>|
-                                 LazyIterator<T>): boolean {
-  return (typeof (iterator as LazyIterator<T>).next === 'function');
-}
-
-export async function evaluateDataset<T>(
-    // Type `model` as `any` here to avoid circular dependency w/
-    // training.ts.
-    // tslint:disable-next-line:no-any
-    model: any, dataset: Dataset<T>|LazyIterator<T>,
-    args: ModelEvaluateDatasetArgs): Promise<tfc.Scalar|tfc.Scalar[]> {
-  args = args || {};
-  const hasBatches = args.batches != null;
-  const f = model.testFunction;
-  let outs: tfc.Scalar[] = [];
-  if (args.verbose > 0) {
-    throw new NotImplementedError('Verbose mode is not implemented yet.');
-  }
-
-  tfc.util.assert(
-      !hasBatches || (args.batches > 0 && Number.isInteger(args.batches)),
-      () => 'Test loop expects `batches` to be a positive integer, but ' +
-          `received ${JSON.stringify(args.batches)}`);
-  const dataIterator = isLazyIteratorObject(dataset) ?
-      dataset as LazyIterator<T>:
-      await (dataset as Dataset<T>).iterator();
-  // Keeps track of number of examples used in this evaluation.
-  let numExamples = 0;
-  let batch = 0;
-
-  while (hasBatches ? batch < args.batches : true) {
-    const iteratorOut = await dataIterator.next();
-    outs = tfc.tidy(() => {
-      if (iteratorOut.value) {
-        // TODO(cais): Once real dataset is available, use
-        //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.
-        const {xs, ys} =
-            standardizeDataIteratorOutput(model, iteratorOut.value);
-        const xsAndYs = xs.concat(ys);
-        const batchOuts = tfc.tidy(() => f(xsAndYs));
-        tfc.dispose(xsAndYs);
-
-        if (batch === 0) {
-          for (let i = 0; i < batchOuts.length; ++i) {
-            outs.push(scalar(0));
-          }
-        }
-
-        const batchSize = xsAndYs[0].shape[0];
-        for (let i = 0; i < batchOuts.length; ++i) {
-          const batchOut = batchOuts[i];
-          const oldScalar = outs[i];
-          outs[i] =
-              tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));
-          if (batch > 0) {
-            tfc.dispose(oldScalar);
-          }
-        }
-        tfc.dispose(batchOuts);
-        numExamples += batchSize;
-
-        ++batch;
-      }
-      return outs;
-    });
-
-    if (iteratorOut.done) {
-      if (hasBatches) {
-        console.warn(
-            'Your dataset iterator ran out of data during evaluateDataset(). ' +
-            'Interrupting evalution. Make sure that your ' +
-            'dataset can generate at least `batches` ' +
-            `batches (in this case, ${args.batches} batches). ` +
-            'You may need to use the repeat() function when building ' +
-            'your dataset.');
-      }
-      break;
-    }
-  }
-
-  for (let i = 0; i < outs.length; ++i) {
-    const oldScalar = outs[i];
-    outs[i] = tfc.div(outs[i], numExamples);
-    tfc.dispose(oldScalar);
-  }
-
-  return singletonOrArray(outs);
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/training_dataset_test.ts b/tfjs-master/tfjs-layers/src/engine/training_dataset_test.ts
deleted file mode 100644
index 4df34f991..000000000
--- a/tfjs-master/tfjs-layers/src/engine/training_dataset_test.ts
+++ /dev/null
@@ -1,3414 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for training.ts, focusing on the tf.LayersModel.fitDataset() and
- * tf.LayersModel.evaluateDataset() methods.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-
-import {CustomCallback, DEFAULT_YIELD_EVERY_MS} from '../base_callbacks';
-import * as tfl from '../index';
-import {Logs} from '../logs';
-import {describeMathCPUAndGPU, expectTensorsClose} from '../utils/test_utils';
-
-import {FakeNumericDataset} from './dataset_fakes';
-
-function createDenseModel(): tfl.LayersModel {
-  const model = tfl.sequential();
-  model.add(tfl.layers.dense(
-      {units: 1, inputShape: [1], kernelInitializer: 'zeros'}));
-  return model;
-}
-
-describeMathCPUAndGPU('LayersModel.fitDataset', () => {
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices((xs, ys)).batch(batch_size)
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     kernel_initializer='zeros'))
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // history = model.fit(dataset, steps_per_epoch=num_batches, epochs=epochs)
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // ```
-  it('1 input, 1 output, no metric, no validation, with batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const yTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {batchesPerEpoch, epochs: 1});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history =
-           await model.fitDataset(dataset, {batchesPerEpoch, epochs});
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history)).toEqual(['loss']);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-     });
-
-  it('1 input, 1 output, no metric, no validation, no batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {epochs: 1});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history = await model.fitDataset(dataset, {epochs});
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history)).toEqual(['loss']);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-     });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution():
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices((xs, ys)).batch(batch_size)
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     kernel_initializer='zeros'))
-  // model.compile(loss='mean_squared_error',
-  //               optimizer='sgd',
-  //               metrics=['acc'])
-  //
-  // history = model.fit(dataset, steps_per_epoch=num_batches, epochs=epochs)
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // ```
-  it('1 input, 1 output, 1 metric, no validation, with batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const yTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {batchesPerEpoch, epochs: 1});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history =
-           await model.fitDataset(dataset, {batchesPerEpoch, epochs});
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history)).toEqual(['loss', 'acc']);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-     });
-
-  it('1 input, 1 output, 1 metric, no validation, no batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {epochs: 1});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history = await model.fitDataset(dataset, {epochs});
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history)).toEqual(['loss', 'acc']);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-     });
-
-  // Reference Python tf.keras code.
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution():
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices((xs, ys)).batch(batch_size)
-  // val_xs = np.zeros([batch_size * 2, 1])
-  // val_ys = np.zeros([batch_size * 2, 1])
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     kernel_initializer='zeros'))
-  // model.compile(loss='mean_squared_error', optimizer='sgd',
-  // metrics=['accuracy'])
-  //
-  // class CustomCallback(tf.keras.callbacks.Callback):
-  //   def on_epoch_end(self, epoch, logs):
-  //     print('epoch = %d; logs = %s' % (epoch, logs))
-  //
-  // history = model.fit(dataset,
-  //                     steps_per_epoch=num_batches,
-  //                     epochs=epochs,
-  //                     validation_steps=2,
-  //                     validation_data=(val_xs, val_ys),
-  //                     callbacks=[CustomCallback()])
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // ```
-  it('1 input, 1 output, 1 metric, tensor validation, callback, ' +
-         'with batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const yTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-       const valXs = tfc.zeros([batchSize * 2, 1]);
-       const valYs = tfc.zeros([batchSize * 2, 1]);
-
-       // Do a burn-in call to account for initialization of cached
-       // tensors (for the memory-leak check below).
-       await model.fitDataset(
-           dataset,
-           {batchesPerEpoch, epochs: 1, validationData: [valXs, valYs]});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const epochEndValLosses: number[] = [];
-       const epochEndValAccs: number[] = [];
-       const history = await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs,
-         validationData: [valXs, valYs],
-         callbacks: {
-           onEpochEnd: async (epoch, logs) => {
-             epochEndValLosses.push(logs.val_loss);
-             epochEndValAccs.push(logs.val_acc);
-           }
-         }
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history).sort()).toEqual([
-         'loss', 'acc', 'val_loss', 'val_acc'
-       ].sort());
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003321);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.011799);
-       expect(history.history.val_acc.length).toEqual(2);
-       expect(history.history.val_acc[0]).toBeCloseTo(1);
-       expect(history.history.val_acc[1]).toBeCloseTo(1);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-
-       expect(epochEndValLosses.length).toEqual(2);
-       expect(epochEndValLosses[0]).toBeCloseTo(0.003321);
-       expect(epochEndValLosses[1]).toBeCloseTo(0.011799);
-       expect(epochEndValAccs.length).toEqual(2);
-       expect(epochEndValAccs[0]).toBeCloseTo(1);
-       expect(epochEndValAccs[1]).toBeCloseTo(1);
-     });
-
-  it('1 input, 1 output, 1 metric, tensor validation, callback, ' +
-         'no batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-       const valXs = tfc.zeros([batchSize * 2, 1]);
-       const valYs = tfc.zeros([batchSize * 2, 1]);
-
-       // Do a burn-in call to account for initialization of cached
-       // tensors (for the memory-leak check below).
-       await model.fitDataset(
-           dataset, {epochs: 1, validationData: [valXs, valYs]});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const epochEndValLosses: number[] = [];
-       const epochEndValAccs: number[] = [];
-       const history = await model.fitDataset(dataset, {
-         epochs,
-         validationData: [valXs, valYs],
-         callbacks: {
-           onEpochEnd: async (epoch, logs) => {
-             epochEndValLosses.push(logs.val_loss);
-             epochEndValAccs.push(logs.val_acc);
-           }
-         }
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history).sort()).toEqual([
-         'loss', 'acc', 'val_loss', 'val_acc'
-       ].sort());
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003321);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.011799);
-       expect(history.history.val_acc.length).toEqual(2);
-       expect(history.history.val_acc[0]).toBeCloseTo(1);
-       expect(history.history.val_acc[1]).toBeCloseTo(1);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-
-       expect(epochEndValLosses.length).toEqual(2);
-       expect(epochEndValLosses[0]).toBeCloseTo(0.003321);
-       expect(epochEndValLosses[1]).toBeCloseTo(0.011799);
-       expect(epochEndValAccs.length).toEqual(2);
-       expect(epochEndValAccs[0]).toBeCloseTo(1);
-       expect(epochEndValAccs[1]).toBeCloseTo(1);
-     });
-
-  it('Earlier logs are not overwritten', async () => {
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-    const batchSize = 8;
-    const epochs = 2;
-    const batchesPerEpoch = 3;
-    const xTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    const trainLogs: Logs[] = [];
-    await model.fitDataset(dataset, {
-      epochs,
-      callbacks: {
-        onEpochEnd: async (epoch, logs) => {
-          trainLogs.push(logs);
-        }
-      }
-    });
-    expect(trainLogs.length).toEqual(2);
-    // Assert that the the first log and the second logs do not overwrite each
-    // other.
-    expect(trainLogs[0].loss).not.toEqual(trainLogs[1].loss);
-  });
-
-  it('dataset.size != null feeds stepPerEpoch to callbacks', async () => {
-    const batchSize = 8;
-    const batchesPerEpoch = 3;
-    const xTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    let recordedSteps: number;
-    class TestCallback extends CustomCallback {
-      constructor() {
-        super({
-          onTrainBegin: async (logs?: Logs) => {
-            recordedSteps = this.params.steps as number;
-          }
-        });
-      }
-    }
-
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-    const epochs = 1;
-    await model.fitDataset(dataset, {epochs, callbacks: new TestCallback()});
-
-    expect(dataset.size).toEqual(batchesPerEpoch);
-    expect(recordedSteps).toEqual(batchesPerEpoch);
-  });
-
-  it('explicit stepPerEpoch overrides dataset.size for callbacks', async () => {
-    const batchSize = 8;
-    const numBatches = 3;
-    const xTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    let recordedSteps: number;
-    class TestCallback extends CustomCallback {
-      constructor() {
-        super({
-          onTrainBegin: async (logs?: Logs) => {
-            recordedSteps = this.params.steps as number;
-          }
-        });
-      }
-    }
-
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-    const epochs = 1;
-    await model.fitDataset(dataset, {
-      epochs,
-      batchesPerEpoch: numBatches - 1,
-      callbacks: new TestCallback()
-    });
-
-    expect(dataset.size).toEqual(numBatches);
-    expect(recordedSteps).toEqual(numBatches - 1);
-  });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices((xs, ys)).batch(batch_size)
-  // val_xs = np.zeros([batch_size * 2, 1])
-  // val_ys = np.zeros([batch_size * 2, 1])
-  // val_dataset = tf.data.Dataset.from_tensor_slices(
-  //     (val_xs, val_ys)).batch(batch_size)
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     kernel_initializer='zeros'))
-  // model.compile(loss='mean_squared_error',
-  //               optimizer=tf.train.GradientDescentOptimizer(0.01),
-  //               metrics=['accuracy'])
-  //
-  // class CustomCallback(tf.keras.callbacks.Callback):
-  //   def on_batch_end(self, batch, logs):
-  //     print('batch = %d; logs = %s' % (batch, logs))
-  //
-  //   def on_epoch_end(self, epoch, logs):
-  //     print('epoch = %d; logs = %s' % (epoch, logs))
-  //
-  // history = model.fit(dataset,
-  //                     steps_per_epoch=num_batches,
-  //                     epochs=epochs,
-  //                     batch_size=4,
-  //                     validation_steps=2,
-  //                     validation_data=val_dataset,
-  //                     callbacks=[CustomCallback()])
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // ```
-  it('1 input, 1 output, 1 metric, dataset validation, callback, ' +
-         'with batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       // Training dataset.
-       const xTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const yTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Validation dataset.
-       const valXTensorsFunc = () =>
-           [tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1]),
-            tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1]),
-            tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1])];
-       const valYTensorsFunc = () =>
-           [tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1]),
-            tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1]),
-            tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1])];
-       const valDataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc: valXTensorsFunc,
-         yTensorsFunc: valYTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached
-       // tensors (for the memory-leak check below).
-       await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs,
-         validationData: valDataset,
-         validationBatches: batchesPerEpoch * epochs
-       });
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const epochEndValLosses: number[] = [];
-       const epochEndValAccs: number[] = [];
-       const history = await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs,
-         validationData: valDataset,
-         validationBatches: batchesPerEpoch * epochs,
-         callbacks: {
-           onEpochEnd: async (epoch, logs) => {
-             epochEndValLosses.push(logs.val_loss);
-             epochEndValAccs.push(logs.val_acc);
-           }
-         }
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history).sort()).toEqual([
-         'loss', 'acc', 'val_loss', 'val_acc'
-       ].sort());
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003321);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.011799);
-       expect(history.history.val_acc.length).toEqual(2);
-       expect(history.history.val_acc[0]).toBeCloseTo(1);
-       expect(history.history.val_acc[1]).toBeCloseTo(1);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-
-       expect(epochEndValLosses.length).toEqual(2);
-       expect(epochEndValLosses[0]).toBeCloseTo(0.003321);
-       expect(epochEndValLosses[1]).toBeCloseTo(0.011799);
-       expect(epochEndValAccs.length).toEqual(2);
-       expect(epochEndValAccs[0]).toBeCloseTo(1);
-       expect(epochEndValAccs[1]).toBeCloseTo(1);
-     });
-
-  it('1 input, 1 output, 1 metric, dataset validation, callback, ' +
-         'no batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       // Training dataset.
-       const xTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Validation dataset.
-       const valXTensorsFunc = () =>
-           [tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1]),
-            tfc.zeros([batchSize, 1])];
-       const valYTensorsFunc = () =>
-           [tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1]),
-            tfc.zeros([batchSize, 1])];
-       const valDataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc: valXTensorsFunc,
-         yTensorsFunc: valYTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached
-       // tensors (for the memory-leak check below).
-       await model.fitDataset(dataset, {epochs, validationData: valDataset});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const epochEndValLosses: number[] = [];
-       const epochEndValAccs: number[] = [];
-       const history = await model.fitDataset(dataset, {
-         epochs,
-         validationData: valDataset,
-         callbacks: {
-           onEpochEnd: async (epoch, logs) => {
-             epochEndValLosses.push(logs.val_loss);
-             epochEndValAccs.push(logs.val_acc);
-           }
-         }
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history).sort()).toEqual([
-         'loss', 'acc', 'val_loss', 'val_acc'
-       ].sort());
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003321);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.011799);
-       expect(history.history.val_acc.length).toEqual(2);
-       expect(history.history.val_acc[0]).toBeCloseTo(1);
-       expect(history.history.val_acc[1]).toBeCloseTo(1);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-
-       expect(epochEndValLosses.length).toEqual(2);
-       expect(epochEndValLosses[0]).toBeCloseTo(0.003321);
-       expect(epochEndValLosses[1]).toBeCloseTo(0.011799);
-       expect(epochEndValAccs.length).toEqual(2);
-       expect(epochEndValAccs[0]).toBeCloseTo(1);
-       expect(epochEndValAccs[1]).toBeCloseTo(1);
-     });
-
-  it('Memory leak check with metric and validation, with batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 3;
-       const batchesPerEpoch = 3;
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs
-       });
-       const valXs = tfc.zeros([batchSize * 2, 1]);
-       const valYs = tfc.zeros([batchSize * 2, 1]);
-
-       // Do a burn-in call to account for initialization of cached
-       // tensors (for the memory-leak check below).
-       await model.fitDataset(
-           dataset,
-           {batchesPerEpoch, epochs: 1, validationData: [valXs, valYs]});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs,
-         validationData: [valXs, valYs],
-         callbacks: {
-           onEpochEnd: async (epoch, logs) => {
-             expect(tfc.memory().numTensors).toEqual(numTensors0);
-           }
-         }
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-     });
-
-  it('Memory leak check with metric and validation, no batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 3;
-       const batchesPerEpoch = 3;
-       const dataset = new FakeNumericDataset(
-           {xShape: [1], yShape: [1], batchSize, numBatches: batchesPerEpoch});
-       const valXs = tfc.zeros([batchSize * 2, 1]);
-       const valYs = tfc.zeros([batchSize * 2, 1]);
-
-       // Do a burn-in call to account for initialization of cached
-       // tensors (for the memory-leak check below).
-       await model.fitDataset(
-           dataset, {epochs: 1, validationData: [valXs, valYs]});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       await model.fitDataset(dataset, {
-         epochs,
-         validationData: [valXs, valYs],
-         callbacks: {
-           onEpochEnd: async (epoch, logs) => {
-             expect(tfc.memory().numTensors).toEqual(numTensors0);
-           }
-         }
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-     });
-
-  // Refence Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution():
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices((xs, ys)).batch(batch_size)
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     kernel_initializer='zeros'))
-  // model.compile(loss='mean_squared_error', optimizer='sgd',
-  // metrics=['accuracy'])
-  //
-  // class CustomCallback(tf.keras.callbacks.Callback):
-  //   def on_batch_end(self, batch, logs):
-  //     print('batch = %d; logs = %s' % (batch, logs))
-  //
-  // history = model.fit(dataset,
-  //                     steps_per_epoch=num_batches,
-  //                     epochs=epochs,
-  //                     callbacks=[CustomCallback()])
-  // print(history.history)
-  // ```
-  it('1 input, 1 output, 1 metric, no validation, callback, ' +
-         'with batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const yTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {batchesPerEpoch, epochs: 1});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       let onTrainBeginCalls = 0;
-       let onTrainEndCalls = 0;
-       const epochBeginEpochs: number[] = [];
-       const epochEndEpochs: number[] = [];
-       const batchBeginBatches: number[] = [];
-       const batchEndBatches: number[] = [];
-       const epochEndLosses: number[] = [];
-       const epochEndAccs: number[] = [];
-       const batchEndLosses: number[] = [];
-       const batchEndAccs: number[] = [];
-       const history = await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs,
-         callbacks: {
-           onTrainBegin: async () => {
-             onTrainBeginCalls++;
-           },
-           onTrainEnd: async () => {
-             onTrainEndCalls++;
-           },
-           onEpochBegin: async (epoch) => {
-             epochBeginEpochs.push(epoch);
-           },
-           onEpochEnd: async (epoch, logs) => {
-             epochEndEpochs.push(epoch);
-             epochEndLosses.push(logs.loss);
-             epochEndAccs.push(logs.acc);
-           },
-           onBatchBegin: async (batch, logs) => {
-             batchBeginBatches.push(batch);
-           },
-           onBatchEnd: async (batch, logs) => {
-             batchEndBatches.push(batch);
-             batchEndLosses.push(logs.loss);
-             batchEndAccs.push(logs.acc);
-           },
-         }
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history)).toEqual(['loss', 'acc']);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-
-       expect(onTrainBeginCalls).toEqual(1);
-       expect(onTrainEndCalls).toEqual(1);
-       expect(epochBeginEpochs).toEqual([0, 1]);
-       expect(epochEndEpochs).toEqual([0, 1]);
-       expect(batchBeginBatches).toEqual([0, 1, 2, 0, 1, 2]);
-       expect(batchEndBatches).toEqual([0, 1, 2, 0, 1, 2]);
-       expect(epochEndLosses.length).toEqual(2);
-       expect(epochEndLosses[0]).toBeCloseTo(0.923649);
-       expect(epochEndLosses[1]).toBeCloseTo(0.722993);
-       expect(epochEndAccs.length).toEqual(2);
-       expect(epochEndAccs[0]).toBeCloseTo(0);
-       expect(epochEndAccs[1]).toBeCloseTo(0);
-       expectTensorsClose(
-           batchEndLosses, [1, 0.9216, 0.849347, 0.782758, 0.721390, 0.664832]);
-     });
-
-  it('1 input, 1 output, 1 metric, no validation, callback, no batchesPerEpoch',
-     async () => {
-       const model = createDenseModel();
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {epochs: 1});
-       model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       let onTrainBeginCalls = 0;
-       let onTrainEndCalls = 0;
-       const epochBeginEpochs: number[] = [];
-       const epochEndEpochs: number[] = [];
-       const batchBeginBatches: number[] = [];
-       const batchEndBatches: number[] = [];
-       const epochEndLosses: number[] = [];
-       const epochEndAccs: number[] = [];
-       const batchEndLosses: number[] = [];
-       const batchEndAccs: number[] = [];
-       const history = await model.fitDataset(dataset, {
-         epochs,
-         callbacks: {
-           onTrainBegin: async () => {
-             onTrainBeginCalls++;
-           },
-           onTrainEnd: async () => {
-             onTrainEndCalls++;
-           },
-           onEpochBegin: async (epoch) => {
-             epochBeginEpochs.push(epoch);
-           },
-           onEpochEnd: async (epoch, logs) => {
-             epochEndEpochs.push(epoch);
-             epochEndLosses.push(logs.loss);
-             epochEndAccs.push(logs.acc);
-           },
-           onBatchBegin: async (batch, logs) => {
-             batchBeginBatches.push(batch);
-           },
-           onBatchEnd: async (batch, logs) => {
-             batchEndBatches.push(batch);
-             batchEndLosses.push(logs.loss);
-             batchEndAccs.push(logs.acc);
-           },
-         }
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history)).toEqual(['loss', 'acc']);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.923649);
-       expect(history.history.loss[1]).toBeCloseTo(0.722993);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-
-       expect(onTrainBeginCalls).toEqual(1);
-       expect(onTrainEndCalls).toEqual(1);
-       expect(epochBeginEpochs).toEqual([0, 1]);
-       expect(epochEndEpochs).toEqual([0, 1]);
-       expect(batchBeginBatches).toEqual([0, 1, 2, 0, 1, 2]);
-       expect(batchEndBatches).toEqual([0, 1, 2, 0, 1, 2]);
-       expect(epochEndLosses.length).toEqual(2);
-       expect(epochEndLosses[0]).toBeCloseTo(0.923649);
-       expect(epochEndLosses[1]).toBeCloseTo(0.722993);
-       expect(epochEndAccs.length).toEqual(2);
-       expect(epochEndAccs[0]).toBeCloseTo(0);
-       expect(epochEndAccs[1]).toBeCloseTo(0);
-       expectTensorsClose(
-           batchEndLosses, [1, 0.9216, 0.849347, 0.782758, 0.721390, 0.664832]);
-     });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // input1 = tf.keras.Input(shape = [1], name = 'x1')
-  // input2 = tf.keras.Input(shape = [1], name = 'x2')
-  // concat = tf.keras.layers.concatenate([input1, input2])
-  // y = tf.keras.layers.Dense(
-  //     1, kernel_initializer = 'zeros')(concat)
-  // model = tf.keras.Model(inputs = [input1, input2], outputs = y)
-  // model.compile(
-  //     loss = 'mean_squared_error', optimizer = 'sgd', metrics =
-  //     ['accuracy'])
-  // model.summary()
-  // print(input1.name)
-  // print(input2.name)
-  //
-  // xs1 = np.ones([batch_size * num_batches * epochs, 1])
-  // xs2 = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     ({'x1': xs1, 'x2': xs2}, ys)).batch(batch_size)
-  //
-  // history = model.fit(dataset,
-  //                     steps_per_epoch=num_batches,
-  //                     epochs=epochs)
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // ```
-  it('2 inputs, 1 output, 1 metric, no validation, with batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 inputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                     .apply(concat) as tfl.SymbolicTensor;
-       const model = tfl.model({inputs: [input1, input2], outputs: y});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {batchesPerEpoch, epochs: 1});
-       model.setWeights([tfc.zeros([2, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history =
-           await model.fitDataset(dataset, {batchesPerEpoch, epochs});
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history)).toEqual(['loss', 'acc']);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.888116);
-       expect(history.history.loss[1]).toBeCloseTo(0.612685);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.103377, 0.103377]);
-       expectTensorsClose(model.getWeights()[1], [0.103377]);
-     });
-
-  it('2 inputs, 1 output, 1 metric, no validation, no batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 inputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                     .apply(concat) as tfl.SymbolicTensor;
-       const model = tfl.model({inputs: [input1, input2], outputs: y});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {epochs: 1});
-       model.setWeights([tfc.zeros([2, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history = await model.fitDataset(dataset, {epochs});
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history)).toEqual(['loss', 'acc']);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.888116);
-       expect(history.history.loss[1]).toBeCloseTo(0.612685);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.103377, 0.103377]);
-       expectTensorsClose(model.getWeights()[1], [0.103377]);
-     });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // input1 = tf.keras.Input(shape = [1], name = 'x1')
-  // input2 = tf.keras.Input(shape = [1], name = 'x2')
-  // concat = tf.keras.layers.concatenate([input1, input2])
-  // y = tf.keras.layers.Dense(
-  //     1, kernel_initializer = 'zeros')(concat)
-  // model = tf.keras.Model(inputs = [input1, input2], outputs = y)
-  // model.compile(
-  //     loss='mean_squared_error',
-  //     optimizer=tf.train.GradientDescentOptimizer(0.01),
-  //     metrics=['accuracy'])
-  // model.summary()
-  // print(input1.name)
-  // print(input2.name)
-  //
-  // xs1 = np.ones([batch_size * num_batches * epochs, 1])
-  // xs2 = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     ({'x1': xs1, 'x2': xs2}, ys)).batch(batch_size)
-  //
-  // val_xs = [np.zeros([batch_size, 1]),
-  //           np.zeros([batch_size, 1])]
-  // val_ys = np.zeros([batch_size, 1])
-  //
-  // history = model.fit(dataset,
-  //                     steps_per_epoch=num_batches,
-  //                     epochs=epochs,
-  //                     batch_size=batch_size,
-  //                     validation_data=[val_xs, val_ys])
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // ```
-  it('2 inputs, 1 output, 1 metric, tensor array validation, ' +
-         'with batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 inputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                     .apply(concat) as tfl.SymbolicTensor;
-       const model = tfl.model({inputs: [input1, input2], outputs: y});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       // Training data.
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Validation data.
-       const valXs: tfc.Tensor[] =
-           [tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1])];
-       const valYs = tfc.zeros([batchSize, 1]);
-
-       // Do a burn-in call to account for initialization of cached tensors
-       // (for the memory-leak check below).
-       await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs: 1,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       model.setWeights([tfc.zeros([2, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history = await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history).sort()).toEqual([
-         'acc', 'loss', 'val_acc', 'val_loss'
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.888116);
-       expect(history.history.loss[1]).toBeCloseTo(0.612685);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003189);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.010687);
-       expect(history.history.val_acc.length).toEqual(2);
-       expect(history.history.val_acc[0]).toBeCloseTo(1.0);
-       expect(history.history.val_acc[1]).toBeCloseTo(1.0);
-       expectTensorsClose(model.getWeights()[0], [0.103377, 0.103377]);
-       expectTensorsClose(model.getWeights()[1], [0.103377]);
-     });
-
-  it('2 inputs, 1 output, 1 metric, tensor array validation, ' +
-         'no batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 inputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                     .apply(concat) as tfl.SymbolicTensor;
-       const model = tfl.model({inputs: [input1, input2], outputs: y});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       // Training data.
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Validation data.
-       const valXs: tfc.Tensor[] =
-           [tfc.zeros([batchSize, 1]), tfc.zeros([batchSize, 1])];
-       const valYs = tfc.zeros([batchSize, 1]);
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {
-         epochs: 1,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       model.setWeights([tfc.zeros([2, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history = await model.fitDataset(dataset, {
-         epochs,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history).sort()).toEqual([
-         'acc', 'loss', 'val_acc', 'val_loss'
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.888116);
-       expect(history.history.loss[1]).toBeCloseTo(0.612685);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003189);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.010687);
-       expect(history.history.val_acc.length).toEqual(2);
-       expect(history.history.val_acc[0]).toBeCloseTo(1.0);
-       expect(history.history.val_acc[1]).toBeCloseTo(1.0);
-       expectTensorsClose(model.getWeights()[0], [0.103377, 0.103377]);
-       expectTensorsClose(model.getWeights()[1], [0.103377]);
-     });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // input1 = tf.keras.Input(shape = [1], name = 'x1')
-  // input2 = tf.keras.Input(shape = [1], name = 'x2')
-  // concat = tf.keras.layers.concatenate([input1, input2])
-  // y = tf.keras.layers.Dense(
-  //     1, kernel_initializer = 'zeros')(concat)
-  // model = tf.keras.Model(inputs = [input1, input2], outputs = y)
-  // model.compile(
-  //     loss='mean_squared_error',
-  //     optimizer=tf.train.GradientDescentOptimizer(0.01),
-  //     metrics=['accuracy'])
-  // model.summary()
-  // print(input1.name)
-  // print(input2.name)
-  //
-  // xs1 = np.ones([batch_size * num_batches * epochs, 1])
-  // xs2 = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     ({'x1': xs1, 'x2': xs2}, ys)).batch(batch_size)
-  //
-  // val_xs = {
-  //     'x1': np.zeros([batch_size, 1]),
-  //     'x2': np.zeros([batch_size, 1])
-  // }
-  // val_ys = np.zeros([batch_size, 1])
-  //
-  // history = model.fit(dataset,
-  //                     steps_per_epoch=num_batches,
-  //                     epochs=epochs,
-  //                     batch_size=batch_size,
-  //                     validation_data=[val_xs, val_ys])
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // ```
-  it('2 input, 1 output, 1 metric, tensor array validation, ' +
-         'with batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 inputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                     .apply(concat) as tfl.SymbolicTensor;
-       const model = tfl.model({inputs: [input1, input2], outputs: y});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       // Training data.
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Validation data.
-       const valXs: tfc.NamedTensorMap = {};
-       valXs[input1.name] = tfc.zeros([batchSize, 1]);
-       valXs[input2.name] = tfc.zeros([batchSize, 1]);
-       const valYs = tfc.zeros([batchSize, 1]);
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs: 1,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       model.setWeights([tfc.zeros([2, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history = await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history).sort()).toEqual([
-         'acc', 'loss', 'val_acc', 'val_loss'
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.888116);
-       expect(history.history.loss[1]).toBeCloseTo(0.612685);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003189);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.010687);
-       expect(history.history.val_acc.length).toEqual(2);
-       expect(history.history.val_acc[0]).toBeCloseTo(1.0);
-       expect(history.history.val_acc[1]).toBeCloseTo(1.0);
-       expectTensorsClose(model.getWeights()[0], [0.103377, 0.103377]);
-       expectTensorsClose(model.getWeights()[1], [0.103377]);
-     });
-
-  it('2 input, 1 output, 1 metric, tensor array validation, ' +
-         'no batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 inputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                     .apply(concat) as tfl.SymbolicTensor;
-       const model = tfl.model({inputs: [input1, input2], outputs: y});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       // Training data.
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Validation data.
-       const valXs: tfc.NamedTensorMap = {};
-       valXs[input1.name] = tfc.zeros([batchSize, 1]);
-       valXs[input2.name] = tfc.zeros([batchSize, 1]);
-       const valYs = tfc.zeros([batchSize, 1]);
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {
-         epochs: 1,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       model.setWeights([tfc.zeros([2, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history = await model.fitDataset(dataset, {
-         epochs,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-       expect(Object.keys(history.history).sort()).toEqual([
-         'acc', 'loss', 'val_acc', 'val_loss'
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(0.888116);
-       expect(history.history.loss[1]).toBeCloseTo(0.612685);
-       expect(history.history.acc.length).toEqual(2);
-       expect(history.history.acc[0]).toBeCloseTo(0);
-       expect(history.history.acc[1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003189);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.010687);
-       expect(history.history.val_acc.length).toEqual(2);
-       expect(history.history.val_acc[0]).toBeCloseTo(1.0);
-       expect(history.history.val_acc[1]).toBeCloseTo(1.0);
-       expectTensorsClose(model.getWeights()[0], [0.103377, 0.103377]);
-       expectTensorsClose(model.getWeights()[1], [0.103377]);
-     });
-
-  it('2 input, 1 missing input in dataset, with batchesPerEpoch', async () => {
-    // Create a functional model with 2 inputs.
-    const input1 = tfl.layers.input({shape: [1]});
-    const input2 = tfl.layers.input({shape: [1]});
-    const concat = tfl.layers.concatenate().apply([input1, input2]);
-    const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                  .apply(concat) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: [input1, input2], outputs: y});
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const batchSize = 8;
-    const epochs = 2;
-    const batchesPerEpoch = 3;
-    const xTensorsFunc = () => {
-      const output: {[name: string]: tfc.Tensor[]} = {};
-      output[input1.name] = [
-        tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-        tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-        tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-      ];
-      // Note: input2 is missing from the data, by intention.
-      return output;
-    };
-    const yTensorsFunc = () =>
-        [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch * epochs,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    let errorCaught: Error;
-    try {
-      await model.fitDataset(dataset, {batchesPerEpoch, epochs});
-    } catch (err) {
-      errorCaught = err;
-    }
-    expect(errorCaught.message)
-        .toEqual(
-            'The feature data generated by the dataset lacks the required ' +
-            `input key '${input2.name}'.`);
-  });
-
-  it('2 input, 1 missing input in dataset, no batchesPerEpoch', async () => {
-    // Create a functional model with 2 inputs.
-    const input1 = tfl.layers.input({shape: [1]});
-    const input2 = tfl.layers.input({shape: [1]});
-    const concat = tfl.layers.concatenate().apply([input1, input2]);
-    const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                  .apply(concat) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: [input1, input2], outputs: y});
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const batchSize = 8;
-    const epochs = 2;
-    const batchesPerEpoch = 3;
-    const xTensorsFunc = () => {
-      const output: {[name: string]: tfc.Tensor[]} = {};
-      output[input1.name] = [
-        tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-        tfc.ones([batchSize, 1])
-      ];
-      // Note: input2 is missing from the data, by intention.
-      return output;
-    };
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    let errorCaught: Error;
-    try {
-      await model.fitDataset(dataset, {epochs});
-    } catch (err) {
-      errorCaught = err;
-    }
-    expect(errorCaught.message)
-        .toEqual(
-            'The feature data generated by the dataset lacks the required ' +
-            `input key '${input2.name}'.`);
-  });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // x = tf.keras.Input(shape=[1], name='x')
-  //
-  // output1 = tf.keras.layers.Dense(
-  //     1, kernel_initializer='zeros')(x)
-  // output2 = tf.keras.layers.Dense(
-  //     1, kernel_initializer='zeros')(x)
-  //
-  // model = tf.keras.Model(inputs=x, outputs=[output1, output2])
-  // model.compile(
-  //     loss = 'mean_squared_error', optimizer = 'sgd', metrics =
-  //     ['accuracy'])
-  // model.summary()
-  // output1_name = model.output_names[0]
-  // output2_name = model.output_names[1]
-  // print(output1_name)
-  // print(output2_name)
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys1 = np.ones([batch_size * num_batches * epochs, 1])
-  // ys2 = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     (xs, {output1_name: ys1, output2_name: ys2})).batch(batch_size)
-  //
-  // history = model.fit(dataset,
-  //                     steps_per_epoch=num_batches,
-  //                     epochs=epochs)
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // print(model.get_weights()[2])
-  // print(model.get_weights()[3])
-  // ```
-  it('1 input, 2 outputs, 1 metric, no validation, with batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 outputs.
-       const x = tfl.layers.input({shape: [1]});
-       const output1 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const output2 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const model = tfl.model({inputs: x, outputs: [output1, output2]});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       const xTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const yTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[model.outputNames[0]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         output[model.outputNames[1]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-
-       const yShape: {[name: string]: number[]} = {};
-       yShape[model.outputNames[0]] = [1];
-       yShape[model.outputNames[1]] = [1];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape,
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {batchesPerEpoch, epochs: 1});
-       model.setWeights([
-         tfc.zeros([1, 1]), tfc.zeros([1]), tfc.zeros([1, 1]), tfc.zeros([1])
-       ]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history =
-           await model.fitDataset(dataset, {batchesPerEpoch, epochs});
-
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-
-       const output1AccName = model.outputNames[0] + '_acc';
-       const output1LossName = model.outputNames[0] + '_loss';
-       const output2AccName = model.outputNames[1] + '_acc';
-       const output2LossName = model.outputNames[1] + '_loss';
-
-       expect(Object.keys(history.history)).toEqual([
-         'loss', output1LossName, output2LossName, output1AccName,
-         output2AccName
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(1.847297);
-       expect(history.history.loss[1]).toBeCloseTo(1.445986);
-       expect(history.history[output1LossName].length).toEqual(2);
-       expect(history.history[output1LossName][0]).toBeCloseTo(0.923648);
-       expect(history.history[output1LossName][1]).toBeCloseTo(0.722993);
-       expect(history.history[output2LossName].length).toEqual(2);
-       expect(history.history[output2LossName][0]).toBeCloseTo(0.923648);
-       expect(history.history[output2LossName][1]).toBeCloseTo(0.722993);
-       expect(history.history[output1AccName].length).toEqual(2);
-       expect(history.history[output1AccName][0]).toBeCloseTo(0);
-       expect(history.history[output1AccName][1]).toBeCloseTo(0);
-       expect(history.history[output2AccName].length).toEqual(2);
-       expect(history.history[output2AccName][0]).toBeCloseTo(0);
-       expect(history.history[output2AccName][1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-       expectTensorsClose(model.getWeights()[2], [0.108621]);
-       expectTensorsClose(model.getWeights()[3], [0.108621]);
-     });
-
-  it('1 input, 2 outputs, 1 metric, no validation, no batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 outputs.
-       const x = tfl.layers.input({shape: [1]});
-       const output1 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const output2 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const model = tfl.model({inputs: x, outputs: [output1, output2]});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       const xTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const yTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[model.outputNames[0]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         output[model.outputNames[1]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-
-       const yShape: {[name: string]: number[]} = {};
-       yShape[model.outputNames[0]] = [1];
-       yShape[model.outputNames[1]] = [1];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape,
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {epochs: 1});
-       model.setWeights([
-         tfc.zeros([1, 1]), tfc.zeros([1]), tfc.zeros([1, 1]), tfc.zeros([1])
-       ]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history = await model.fitDataset(dataset, {epochs});
-
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-
-       const output1AccName = model.outputNames[0] + '_acc';
-       const output1LossName = model.outputNames[0] + '_loss';
-       const output2AccName = model.outputNames[1] + '_acc';
-       const output2LossName = model.outputNames[1] + '_loss';
-
-       expect(Object.keys(history.history)).toEqual([
-         'loss', output1LossName, output2LossName, output1AccName,
-         output2AccName
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(1.847297);
-       expect(history.history.loss[1]).toBeCloseTo(1.445986);
-       expect(history.history[output1LossName].length).toEqual(2);
-       expect(history.history[output1LossName][0]).toBeCloseTo(0.923648);
-       expect(history.history[output1LossName][1]).toBeCloseTo(0.722993);
-       expect(history.history[output2LossName].length).toEqual(2);
-       expect(history.history[output2LossName][0]).toBeCloseTo(0.923648);
-       expect(history.history[output2LossName][1]).toBeCloseTo(0.722993);
-       expect(history.history[output1AccName].length).toEqual(2);
-       expect(history.history[output1AccName][0]).toBeCloseTo(0);
-       expect(history.history[output1AccName][1]).toBeCloseTo(0);
-       expect(history.history[output2AccName].length).toEqual(2);
-       expect(history.history[output2AccName][0]).toBeCloseTo(0);
-       expect(history.history[output2AccName][1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-       expectTensorsClose(model.getWeights()[2], [0.108621]);
-       expectTensorsClose(model.getWeights()[3], [0.108621]);
-     });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // x = tf.keras.Input(shape=[1], name='x')
-  //
-  // output1 = tf.keras.layers.Dense(
-  //     1, kernel_initializer='zeros')(x)
-  // output2 = tf.keras.layers.Dense(
-  //     1, kernel_initializer='zeros')(x)
-  //
-  // model = tf.keras.Model(inputs=x, outputs=[output1, output2])
-  // model.compile(
-  //     loss = 'mean_squared_error', optimizer = 'sgd', metrics =
-  //     ['accuracy'])
-  // model.summary()
-  // output1_name = model.output_names[0]
-  // output2_name = model.output_names[1]
-  // print(output1_name)
-  // print(output2_name)
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys1 = np.ones([batch_size * num_batches * epochs, 1])
-  // ys2 = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     (xs, {output1_name: ys1, output2_name: ys2})).batch(batch_size)
-  //
-  // val_xs = np.zeros([batch_size, 1])
-  // val_ys = {output1_name: np.zeros([batch_size, 1]),
-  //           output1_name: np.zeros([batch_size, 1])}
-  //
-  // history = model.fit(dataset,
-  //                     steps_per_epoch=num_batches,
-  //                     epochs=epochs,
-  //                     validation_data=[val_xs, val_ys])
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // print(model.get_weights()[2])
-  // print(model.get_weights()[3])
-  // ```
-  it('1 input, 2 outputs, 1 metric, tensor array validation, ' +
-         'with batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 outputs.
-       const x = tfl.layers.input({shape: [1]});
-       const output1 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const output2 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const model = tfl.model({inputs: x, outputs: [output1, output2]});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       const xTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const yTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[model.outputNames[0]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         output[model.outputNames[1]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-
-       const yShape: {[name: string]: number[]} = {};
-       yShape[model.outputNames[0]] = [1];
-       yShape[model.outputNames[1]] = [1];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape,
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Validation data.
-       const valXs = tfc.zeros([batchSize, 1]);
-       const valYs: tfc.NamedTensorMap = {};
-       valYs[model.outputNames[0]] = tfc.zeros([batchSize, 1]);
-       valYs[model.outputNames[1]] = tfc.zeros([batchSize, 1]);
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs: 1,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       model.setWeights([
-         tfc.zeros([1, 1]), tfc.zeros([1]), tfc.zeros([1, 1]), tfc.zeros([1])
-       ]);
-
-       const history = await model.fitDataset(dataset, {
-         batchesPerEpoch,
-         epochs,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-
-       const output1AccName = model.outputNames[0] + '_acc';
-       const output1LossName = model.outputNames[0] + '_loss';
-       const output2AccName = model.outputNames[1] + '_acc';
-       const output2LossName = model.outputNames[1] + '_loss';
-       const valOutput1AccName = 'val_' + output1AccName;
-       const valOutput1LossName = 'val_' + output1LossName;
-       const valOutput2AccName = 'val_' + output2AccName;
-       const valOutput2LossName = 'val_' + output2LossName;
-
-       expect(Object.keys(history.history)).toEqual([
-         'val_loss', valOutput1LossName, valOutput2LossName, valOutput1AccName,
-         valOutput2AccName, 'loss', output1LossName, output2LossName,
-         output1AccName, output2AccName
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(1.847297);
-       expect(history.history.loss[1]).toBeCloseTo(1.445986);
-       expect(history.history[output1LossName].length).toEqual(2);
-       expect(history.history[output1LossName][0]).toBeCloseTo(0.923648);
-       expect(history.history[output1LossName][1]).toBeCloseTo(0.722993);
-       expect(history.history[output2LossName].length).toEqual(2);
-       expect(history.history[output2LossName][0]).toBeCloseTo(0.923648);
-       expect(history.history[output2LossName][1]).toBeCloseTo(0.722993);
-       expect(history.history[output1AccName].length).toEqual(2);
-       expect(history.history[output1AccName][0]).toBeCloseTo(0);
-       expect(history.history[output1AccName][1]).toBeCloseTo(0);
-       expect(history.history[output2AccName].length).toEqual(2);
-       expect(history.history[output2AccName][0]).toBeCloseTo(0);
-       expect(history.history[output2AccName][1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003321);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.011798);
-       expect(history.history[valOutput1LossName].length).toEqual(2);
-       expect(history.history[valOutput1LossName][0]).toBeCloseTo(0.006642);
-       expect(history.history[valOutput1LossName][1]).toBeCloseTo(0.023597);
-       expect(history.history[valOutput2LossName].length).toEqual(2);
-       expect(history.history[valOutput2LossName][0]).toBeCloseTo(0.003321);
-       expect(history.history[valOutput2LossName][1]).toBeCloseTo(0.011798);
-       expect(history.history[valOutput1AccName].length).toEqual(2);
-       expect(history.history[valOutput1AccName][0]).toBeCloseTo(0.003321);
-       expect(history.history[valOutput1AccName][1]).toBeCloseTo(0.011798);
-       expect(history.history[valOutput2AccName].length).toEqual(2);
-       expect(history.history[valOutput2AccName][0]).toBeCloseTo(1);
-       expect(history.history[valOutput2AccName][1]).toBeCloseTo(1);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-       expectTensorsClose(model.getWeights()[2], [0.108621]);
-       expectTensorsClose(model.getWeights()[3], [0.108621]);
-     });
-
-  it('1 input, 2 outputs, 1 metric, tensor array validation, ' +
-         'no batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 outputs.
-       const x = tfl.layers.input({shape: [1]});
-       const output1 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const output2 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const model = tfl.model({inputs: x, outputs: [output1, output2]});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       const xTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const yTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[model.outputNames[0]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         output[model.outputNames[1]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-
-       const yShape: {[name: string]: number[]} = {};
-       yShape[model.outputNames[0]] = [1];
-       yShape[model.outputNames[1]] = [1];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape,
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Validation data.
-       const valXs = tfc.zeros([batchSize, 1]);
-       const valYs: tfc.NamedTensorMap = {};
-       valYs[model.outputNames[0]] = tfc.zeros([batchSize, 1]);
-       valYs[model.outputNames[1]] = tfc.zeros([batchSize, 1]);
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {
-         epochs: 1,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-       model.setWeights([
-         tfc.zeros([1, 1]), tfc.zeros([1]), tfc.zeros([1, 1]), tfc.zeros([1])
-       ]);
-
-       const history = await model.fitDataset(dataset, {
-         epochs,
-         validationData: [valXs, valYs],
-         validationBatchSize: batchSize
-       });
-
-       const output1AccName = model.outputNames[0] + '_acc';
-       const output1LossName = model.outputNames[0] + '_loss';
-       const output2AccName = model.outputNames[1] + '_acc';
-       const output2LossName = model.outputNames[1] + '_loss';
-       const valOutput1AccName = 'val_' + output1AccName;
-       const valOutput1LossName = 'val_' + output1LossName;
-       const valOutput2AccName = 'val_' + output2AccName;
-       const valOutput2LossName = 'val_' + output2LossName;
-
-       expect(Object.keys(history.history)).toEqual([
-         'val_loss', valOutput1LossName, valOutput2LossName, valOutput1AccName,
-         valOutput2AccName, 'loss', output1LossName, output2LossName,
-         output1AccName, output2AccName
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(1.847297);
-       expect(history.history.loss[1]).toBeCloseTo(1.445986);
-       expect(history.history[output1LossName].length).toEqual(2);
-       expect(history.history[output1LossName][0]).toBeCloseTo(0.923648);
-       expect(history.history[output1LossName][1]).toBeCloseTo(0.722993);
-       expect(history.history[output2LossName].length).toEqual(2);
-       expect(history.history[output2LossName][0]).toBeCloseTo(0.923648);
-       expect(history.history[output2LossName][1]).toBeCloseTo(0.722993);
-       expect(history.history[output1AccName].length).toEqual(2);
-       expect(history.history[output1AccName][0]).toBeCloseTo(0);
-       expect(history.history[output1AccName][1]).toBeCloseTo(0);
-       expect(history.history[output2AccName].length).toEqual(2);
-       expect(history.history[output2AccName][0]).toBeCloseTo(0);
-       expect(history.history[output2AccName][1]).toBeCloseTo(0);
-       expect(history.history.val_loss.length).toEqual(2);
-       expect(history.history.val_loss[0]).toBeCloseTo(0.003321);
-       expect(history.history.val_loss[1]).toBeCloseTo(0.011798);
-       expect(history.history[valOutput1LossName].length).toEqual(2);
-       expect(history.history[valOutput1LossName][0]).toBeCloseTo(0.006642);
-       expect(history.history[valOutput1LossName][1]).toBeCloseTo(0.023597);
-       expect(history.history[valOutput2LossName].length).toEqual(2);
-       expect(history.history[valOutput2LossName][0]).toBeCloseTo(0.003321);
-       expect(history.history[valOutput2LossName][1]).toBeCloseTo(0.011798);
-       expect(history.history[valOutput1AccName].length).toEqual(2);
-       expect(history.history[valOutput1AccName][0]).toBeCloseTo(0.003321);
-       expect(history.history[valOutput1AccName][1]).toBeCloseTo(0.011798);
-       expect(history.history[valOutput2AccName].length).toEqual(2);
-       expect(history.history[valOutput2AccName][0]).toBeCloseTo(1);
-       expect(history.history[valOutput2AccName][1]).toBeCloseTo(1);
-       expectTensorsClose(model.getWeights()[0], [0.108621]);
-       expectTensorsClose(model.getWeights()[1], [0.108621]);
-       expectTensorsClose(model.getWeights()[2], [0.108621]);
-       expectTensorsClose(model.getWeights()[3], [0.108621]);
-     });
-
-  it('2 outputs, 1 missing output in dataset, with batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 outputs.
-       const x = tfl.layers.input({shape: [1]});
-       const output1 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const output2 =
-           tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-           tfl.SymbolicTensor;
-       const model = tfl.model({inputs: x, outputs: [output1, output2]});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       const xTensorsFunc = () =>
-           [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-            tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-       const yTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[model.outputNames[0]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-
-       const yShape: {[name: string]: number[]} = {};
-       yShape[model.outputNames[0]] = [1];
-       yShape[model.outputNames[1]] = [1];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape,
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       let errorCaught: Error;
-       try {
-         await model.fitDataset(dataset, {batchesPerEpoch, epochs});
-       } catch (err) {
-         errorCaught = err;
-       }
-       expect(errorCaught.message)
-           .toEqual(
-               'The feature data generated by the dataset lacks the required ' +
-               `output key '${model.outputNames[1]}'.`);
-     });
-
-  it('2 outputs, 1 missing output in dataset, no batchesPerEpoch', async () => {
-    // Create a functional model with 2 outputs.
-    const x = tfl.layers.input({shape: [1]});
-    const output1 =
-        tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-        tfl.SymbolicTensor;
-    const output2 =
-        tfl.layers.dense({units: 1, kernelInitializer: 'zeros'}).apply(x) as
-        tfl.SymbolicTensor;
-    const model = tfl.model({inputs: x, outputs: [output1, output2]});
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-    const batchSize = 8;
-    const epochs = 2;
-    const batchesPerEpoch = 3;
-
-    const xTensorsFunc = () =>
-        [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-    const yTensorsFunc = () => {
-      const output: {[name: string]: tfc.Tensor[]} = {};
-      output[model.outputNames[0]] = [
-        tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-        tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-        tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-      ];
-      return output;
-    };
-
-    const yShape: {[name: string]: number[]} = {};
-    yShape[model.outputNames[0]] = [1];
-    yShape[model.outputNames[1]] = [1];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape,
-      batchSize,
-      numBatches: batchesPerEpoch,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    let errorCaught: Error;
-    try {
-      await model.fitDataset(dataset, {epochs});
-    } catch (err) {
-      errorCaught = err;
-    }
-    expect(errorCaught.message)
-        .toEqual(
-            'The feature data generated by the dataset lacks the required ' +
-            `output key '${model.outputNames[1]}'.`);
-  });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // input1 = tf.keras.Input(shape=[1], name='x1')
-  // input2 = tf.keras.Input(shape=[1], name='x2')
-  // concat = tf.keras.layers.concatenate([input1, input2])
-  //
-  // output1 = tf.keras.layers.Dense(
-  //     1, kernel_initializer='zeros')(concat)
-  // output2 = tf.keras.layers.Dense(
-  //     1, kernel_initializer='zeros')(concat)
-  //
-  // model = tf.keras.Model(
-  //      inputs=[input1, input2],
-  //      outputs=[output1, output2])
-  // model.compile(
-  //     loss = 'mean_squared_error', optimizer = 'sgd', metrics =
-  //     ['accuracy'])
-  // model.summary()
-  // output1_name = model.output_names[0]
-  // output2_name = model.output_names[1]
-  // print(x1)
-  // print(x2)
-  // print(output1_name)
-  // print(output2_name)
-  //
-  // xs1 = np.ones([batch_size * num_batches * epochs, 1])
-  // xs2 = np.ones([batch_size * num_batches * epochs, 1])
-  // ys1 = np.ones([batch_size * num_batches * epochs, 1])
-  // ys2 = np.ones([batch_size * num_batches * epochs, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     ({
-  //        x1: xs1,
-  //        x2: xs2
-  //      },{
-  //        output1_name: ys1,
-  //        output2_name: ys2
-  //      })).batch(batch_size)
-  //
-  // history = model.fit(dataset,
-  //                     steps_per_epoch=num_batches,
-  //                     epochs=epochs)
-  // print(history.history)
-  // print(model.get_weights()[0])
-  // print(model.get_weights()[1])
-  // print(model.get_weights()[2])
-  // print(model.get_weights()[3])
-  // ```
-  it('2 inputs, 2 outputs, 1 metric, no validation, with batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 inputs and 2 outputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const output1 = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                           .apply(concat) as tfl.SymbolicTensor;
-       const output2 = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                           .apply(concat) as tfl.SymbolicTensor;
-       const model =
-           tfl.model({inputs: [input1, input2], outputs: [output1, output2]});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       // Training data.
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[model.outputNames[0]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         output[model.outputNames[1]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-
-       const yShape: {[name: string]: number[]} = {};
-       yShape[model.outputNames[0]] = [1];
-       yShape[model.outputNames[1]] = [1];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape,
-         batchSize,
-         numBatches: batchesPerEpoch * epochs,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {batchesPerEpoch, epochs: 1});
-       model.setWeights([
-         tfc.zeros([2, 1]), tfc.zeros([1]), tfc.zeros([2, 1]), tfc.zeros([1])
-       ]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history =
-           await model.fitDataset(dataset, {batchesPerEpoch, epochs});
-
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-
-       const output1AccName = model.outputNames[0] + '_acc';
-       const output1LossName = model.outputNames[0] + '_loss';
-       const output2AccName = model.outputNames[1] + '_acc';
-       const output2LossName = model.outputNames[1] + '_loss';
-
-       expect(Object.keys(history.history)).toEqual([
-         'loss', output1LossName, output2LossName, output1AccName,
-         output2AccName
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(1.776232);
-       expect(history.history.loss[1]).toBeCloseTo(1.225369);
-       expect(history.history[output1LossName].length).toEqual(2);
-       expect(history.history[output1LossName][0]).toBeCloseTo(0.888116);
-       expect(history.history[output1LossName][1]).toBeCloseTo(0.612684);
-       expect(history.history[output2LossName].length).toEqual(2);
-       expect(history.history[output2LossName][0]).toBeCloseTo(0.888116);
-       expect(history.history[output2LossName][1]).toBeCloseTo(0.612684);
-       expect(history.history[output1AccName].length).toEqual(2);
-       expect(history.history[output1AccName][0]).toBeCloseTo(0);
-       expect(history.history[output1AccName][1]).toBeCloseTo(0);
-       expect(history.history[output2AccName].length).toEqual(2);
-       expect(history.history[output2AccName][0]).toBeCloseTo(0);
-       expect(history.history[output2AccName][1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.103376, 0.103376]);
-       expectTensorsClose(model.getWeights()[1], [0.103376]);
-       expectTensorsClose(model.getWeights()[2], [0.103376, 0.103376]);
-       expectTensorsClose(model.getWeights()[3], [0.103376]);
-     });
-
-  it('2 inputs, 2 outputs, 1 metric, no validation, no batchesPerEpoch',
-     async () => {
-       // Create a functional model with 2 inputs and 2 outputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const output1 = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                           .apply(concat) as tfl.SymbolicTensor;
-       const output2 = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                           .apply(concat) as tfl.SymbolicTensor;
-       const model =
-           tfl.model({inputs: [input1, input2], outputs: [output1, output2]});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const epochs = 2;
-       const batchesPerEpoch = 3;
-
-       // Training data.
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[model.outputNames[0]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         output[model.outputNames[1]] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-
-       const yShape: {[name: string]: number[]} = {};
-       yShape[model.outputNames[0]] = [1];
-       yShape[model.outputNames[1]] = [1];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape,
-         batchSize,
-         numBatches: batchesPerEpoch,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.fitDataset(dataset, {epochs: 1});
-       model.setWeights([
-         tfc.zeros([2, 1]), tfc.zeros([1]), tfc.zeros([2, 1]), tfc.zeros([1])
-       ]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const history = await model.fitDataset(dataset, {epochs});
-
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-
-       const output1AccName = model.outputNames[0] + '_acc';
-       const output1LossName = model.outputNames[0] + '_loss';
-       const output2AccName = model.outputNames[1] + '_acc';
-       const output2LossName = model.outputNames[1] + '_loss';
-
-       expect(Object.keys(history.history)).toEqual([
-         'loss', output1LossName, output2LossName, output1AccName,
-         output2AccName
-       ]);
-       expect(history.history.loss.length).toEqual(2);
-       expect(history.history.loss[0]).toBeCloseTo(1.776232);
-       expect(history.history.loss[1]).toBeCloseTo(1.225369);
-       expect(history.history[output1LossName].length).toEqual(2);
-       expect(history.history[output1LossName][0]).toBeCloseTo(0.888116);
-       expect(history.history[output1LossName][1]).toBeCloseTo(0.612684);
-       expect(history.history[output2LossName].length).toEqual(2);
-       expect(history.history[output2LossName][0]).toBeCloseTo(0.888116);
-       expect(history.history[output2LossName][1]).toBeCloseTo(0.612684);
-       expect(history.history[output1AccName].length).toEqual(2);
-       expect(history.history[output1AccName][0]).toBeCloseTo(0);
-       expect(history.history[output1AccName][1]).toBeCloseTo(0);
-       expect(history.history[output2AccName].length).toEqual(2);
-       expect(history.history[output2AccName][0]).toBeCloseTo(0);
-       expect(history.history[output2AccName][1]).toBeCloseTo(0);
-       expectTensorsClose(model.getWeights()[0], [0.103376, 0.103376]);
-       expectTensorsClose(model.getWeights()[1], [0.103376]);
-       expectTensorsClose(model.getWeights()[2], [0.103376, 0.103376]);
-       expectTensorsClose(model.getWeights()[3], [0.103376]);
-     });
-
-  it('Exhausting iterator with batchesPerEpoch throws warning', async () => {
-    const model = createDenseModel();
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const batchSize = 8;
-    const batchesPerEpoch = 3;
-    const dataset = new FakeNumericDataset(
-        {xShape: [1], yShape: [1], batchSize, numBatches: batchesPerEpoch});
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below).
-    await model.fitDataset(dataset, {batchesPerEpoch, epochs: 1});
-    model.setWeights([tfc.zeros([1, 1]), tfc.zeros([1])]);
-    const warningMessages: string[] = [];
-    spyOn(console, 'warn')
-        .and.callFake((msg: string) => warningMessages.push(msg));
-    const numTensors0 = tfc.memory().numTensors;
-    const epochs = 3;
-    const history = await model.fitDataset(dataset, {batchesPerEpoch, epochs});
-    const numTensors1 = tfc.memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-    expect(Object.keys(history.history)).toEqual(['loss']);
-    // Only the loss value from the first epoch should be logged.
-    // The 2nd and 3rd epochs are cut short because of dataset iterator
-    // exhaustion.
-    expect(history.history.loss.length).toEqual(1);
-    expect(warningMessages.length).toEqual(2);
-    expect(warningMessages[0])
-        .toMatch(/You provided `batchesPerEpoch` as .* 9 batches/);
-    expect(warningMessages[1])
-        .toMatch(/You provided `batchesPerEpoch` as .* 9 batches/);
-  });
-
-  it('Calling fitDataset() without calling compile() errors', async () => {
-    const model = createDenseModel();
-
-    const batchSize = 8;
-    const numBatches = 3;
-    const epochs = 2;
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches,
-    });
-
-    let errorCaught: Error;
-    try {
-      await model.fitDataset(dataset, {epochs});
-    } catch (err) {
-      errorCaught = err;
-    }
-    expect(errorCaught.message)
-        .toEqual('The model needs to be compiled before being used.');
-  });
-
-  it('Wrong validationBatches leads to Error', async () => {
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-    const batchSize = 8;
-    const epochs = 2;
-    const batchesPerEpoch = 3;
-    // Training dataset.
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch * epochs
-    });
-    // Validation dataset.
-    const valDataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch * epochs
-    });
-    // Do a burn-in call to account for initialization of cached
-    // tensors (for the memory-leak check below).
-    let errorCaught: Error;
-    try {
-      await model.fitDataset(dataset, {
-        batchesPerEpoch,
-        epochs,
-        validationData: valDataset,
-        validationBatches: 0
-      });
-    } catch (err) {
-      errorCaught = err;
-    }
-    expect(errorCaught.message)
-        .toMatch(/fitDataset.*dataset-based validation.*not to be provided.*0/);
-  });
-
-  it('Calling fitDataset with validationSplit leads to Error', async () => {
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-    const batchSize = 8;
-    const epochs = 2;
-    const batchesPerEpoch = 3;
-
-    // Training dataset.
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch * epochs
-    });
-
-    let errorCaught: Error;
-    try {
-      await model.fitDataset(
-          dataset,
-          // tslint:disable-next-line:no-any
-          {epochs: 1, batchesPerEpoch: 2, validationSplit: 0.25} as any);
-    } catch (err) {
-      errorCaught = err;
-    }
-    expect(errorCaught.message)
-        .toMatch(/.*validationSplit.*not supported.*validationData/);
-  });
-
-  class StopAfterNBatches extends tfl.Callback {
-    private readonly batchesToTrain: number;
-    constructor(epochsToTrain: number) {
-      super();
-      this.batchesToTrain = epochsToTrain;
-    }
-
-    override async onBatchEnd(batch: number, logs?: Logs) {
-      if (batch === this.batchesToTrain - 1) {
-        this.model.stopTraining = true;
-      }
-    }
-  }
-
-  it('Stop training resets at start of LayersModel.fitDataset()', async () => {
-    const model = createDenseModel();
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const batchSize = 8;
-    const epochs = 2;
-    const batchesPerEpoch = 1;
-    const xTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch * epochs,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-    // Order 2 epochs of training, but the training should stop after only one
-    // epochs due to the callback that orders the training to stop after one
-    // batches.
-    let history = await model.fitDataset(
-        dataset,
-        {batchesPerEpoch, epochs, callbacks: [new StopAfterNBatches(1)]});
-    expect(history.history.loss.length).toEqual(1);
-
-    // Running fitDataset again should now run to completion
-    history = await model.fitDataset(dataset, {batchesPerEpoch, epochs});
-    expect(history.history.loss.length).toEqual(2);
-  });
-
-  it('onYield with yieldEvery: auto', async () => {
-    const wait = DEFAULT_YIELD_EVERY_MS;
-    const timeBetweenCalls = [
-      0,
-      1,
-      1,
-      wait + 1,  // Should call.
-      wait + 1,  // Should call.
-      1,
-      1,
-    ];
-    let counter = 0;
-    let prevTime = 0;
-    const nowFunc = jasmine.createSpy('now').and.callFake(() => {
-      prevTime += timeBetweenCalls[counter++];
-      return prevTime;
-    });
-    let nextFrameCallCount = 0;
-    const nextFrameFunc =
-        jasmine.createSpy('nextFrame').and.callFake(async () => {
-          nextFrameCallCount++;
-        });
-
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-    const epochs = 2;
-    const batchSize = 8;
-    const xTensorsFunc = () =>
-        [tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]),
-    ];
-    const yTensorsFunc = () =>
-        [tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]),
-    ];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: 3,
-      xTensorsFunc,
-      yTensorsFunc,
-    });
-    const onYieldEpochIds: number[] = [];
-    const onYieldBatchesIds: number[] = [];
-
-    const history = await model.fitDataset(dataset, {
-      epochs,
-      callbacks: {
-        nowFunc,
-        nextFrameFunc,
-        onYield: async (epoch, batch, _logs) => {
-          onYieldBatchesIds.push(batch);
-          onYieldEpochIds.push(epoch);
-        }
-      }
-    });
-    expect(history.history.loss.length).toEqual(epochs);
-    // There are 5 batches in total (1 batch per epoch). We expect next frame
-    // to be called twice, after epoch 1 and after epoch 2.
-    expect(nextFrameCallCount).toBe(2);
-    expect(onYieldEpochIds).toEqual([0, 1]);
-    expect(onYieldBatchesIds).toEqual([2, 0]);
-  });
-
-  it('fails when onYield is provided, but yieldEvery is never', async () => {
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-    const epochs = 2;
-    const batchSize = 8;
-    const xTensorsFunc = () =>
-        [tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]),
-    ];
-    const yTensorsFunc = () =>
-        [tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]),
-         tfc.ones([batchSize, 1]),
-    ];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: 3,
-      xTensorsFunc,
-      yTensorsFunc,
-    });
-    try {
-      await model.fitDataset(dataset, {
-        epochs,
-        yieldEvery: 'never',
-        callbacks: {onYield: async (_epoch, _batch, _logs) => {}},
-      });
-      fail('Model.fit should fail');
-    } catch {}
-  });
-});
-
-// TODO(cais): The corresponding test for predict() and evaluate().
-
-describeMathCPUAndGPU('LayersModel.evaluateDataset', () => {
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // batch_size = 8
-  // num_batches = 3
-  //
-  // xs = np.ones([batch_size * num_batches, 1])
-  // ys = np.ones([batch_size * num_batches, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices((xs, ys)).batch(batch_size)
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     kernel_initializer='zeros'))
-  // model.compile(loss='mean_squared_error',
-  //               optimizer=tf.train.GradientDescentOptimizer(0.01))
-  //
-  // out = model.evaluate(dataset, steps=3, verbose=0)
-  // print(out)
-  // ```
-  it('1 input, 1 output, no metric, with batches specified', async () => {
-    const model = createDenseModel();
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const batchSize = 8;
-    const batches = 3;
-    const xTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batches,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below).
-    tfc.dispose(
-        await model.evaluateDataset(dataset, {batches}) as tfc.Scalar[]);
-
-    const numTensors0 = tfc.memory().numTensors;
-    const evalOut =
-        await model.evaluateDataset(dataset, {batches}) as tfc.Scalar;
-    const expectedLoss = tfc.scalar(1.0);
-    expectTensorsClose(evalOut, expectedLoss);
-    tfc.dispose(evalOut);
-    tfc.dispose(expectedLoss);
-    const numTensors1 = tfc.memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-  });
-
-  it('1 input, 1 output, no metric, no batches specified', async () => {
-    const model = createDenseModel();
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const batchSize = 8;
-    const batches = 3;
-    const xTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batches,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below).
-    tfc.dispose(await model.evaluateDataset(dataset, {}) as tfc.Scalar[]);
-
-    const numTensors0 = tfc.memory().numTensors;
-    const evalOut = await model.evaluateDataset(dataset, {}) as tfc.Scalar;
-    const expectedLoss = tfc.scalar(1.0);
-    expectTensorsClose(evalOut, expectedLoss);
-    tfc.dispose(evalOut);
-    tfc.dispose(expectedLoss);
-    const numTensors1 = tfc.memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-  });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // batch_size = 8
-  // num_batches = 3
-  //
-  // xs = np.ones([batch_size * num_batches, 1])
-  // ys = np.ones([batch_size * num_batches, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices((xs, ys)).batch(batch_size)
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     kernel_initializer='zeros'))
-  // model.compile(loss='mean_squared_error',
-  //               optimizer=tf.train.GradientDescentOptimizer(0.01),
-  //               metrics=['accuracy'])
-  //
-  // out = model.evaluate(dataset, steps=3, verbose=0)
-  // print(out)
-  // ```
-  it('1 input, 1 output, 1 metric, with batches specified', async () => {
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['acc']});
-
-    const batchSize = 8;
-    const batches = 3;
-    const xTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batches,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below).
-    tfc.dispose(
-        await model.evaluateDataset(dataset, {batches}) as tfc.Scalar[]);
-
-    const numTensors0 = tfc.memory().numTensors;
-    const evalOut =
-        await model.evaluateDataset(dataset, {batches}) as tfc.Scalar[];
-    expect(evalOut.length).toEqual(2);
-    const expectedLoss = tfc.scalar(1.0);
-    const expectedAcc = tfc.scalar(0.0);
-    expectTensorsClose(evalOut[0], expectedLoss);
-    expectTensorsClose(evalOut[1], expectedAcc);
-    tfc.dispose(evalOut);
-    tfc.dispose([expectedLoss, expectedAcc]);
-    const numTensors1 = tfc.memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-  });
-
-  it('1 input, 1 output, 1 metric, no batches specified', async () => {
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['acc']});
-
-    const batchSize = 8;
-    const batches = 3;
-    const xTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batches,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below).
-    tfc.dispose(await model.evaluateDataset(dataset, {}) as tfc.Scalar[]);
-
-    const numTensors0 = tfc.memory().numTensors;
-    const evalOut = await model.evaluateDataset(dataset, {}) as tfc.Scalar[];
-    expect(evalOut.length).toEqual(2);
-    const expectedLoss = tfc.scalar(1.0);
-    const expectedAcc = tfc.scalar(0.0);
-    expectTensorsClose(evalOut[0], expectedLoss);
-    expectTensorsClose(evalOut[1], expectedAcc);
-    tfc.dispose(evalOut);
-    tfc.dispose([expectedLoss, expectedAcc]);
-    const numTensors1 = tfc.memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-  });
-
-  it('1 input, 1 output, 1 metric, no batches, only 1 arg', async () => {
-    const model = createDenseModel();
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['acc']});
-
-    const batchSize = 8;
-    const batches = 3;
-    const xTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const yTensorsFunc =
-        () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-          batchSize, 1
-        ])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batches,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below). Use 1-arg call.
-    tfc.dispose(await model.evaluateDataset(dataset) as tfc.Scalar[]);
-
-    const numTensors0 = tfc.memory().numTensors;
-    // Use 1-arg call, omitting the config object.
-    const evalOut = await model.evaluateDataset(dataset) as tfc.Scalar[];
-    expect(evalOut.length).toEqual(2);
-    const expectedLoss = tfc.scalar(1.0);
-    const expectedAcc = tfc.scalar(0.0);
-    expectTensorsClose(evalOut[0], expectedLoss);
-    expectTensorsClose(evalOut[1], expectedAcc);
-    tfc.dispose(evalOut);
-    tfc.dispose([expectedLoss, expectedAcc]);
-    const numTensors1 = tfc.memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-  });
-
-  it('1 input, 1 output, iterator exhaustion with batches', async () => {
-    const model = createDenseModel();
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const batchSize = 8;
-    const batches = 3;
-    const dataset = new FakeNumericDataset(
-        {xShape: [1], yShape: [1], batchSize, numBatches: batches});
-
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below).
-    tfc.dispose(
-        await model.evaluateDataset(dataset, {batches}) as tfc.Scalar[]);
-
-    const warningMessages: string[] = [];
-    spyOn(console, 'warn')
-        .and.callFake((msg: string) => warningMessages.push(msg));
-
-    const numTensors0 = tfc.memory().numTensors;
-    tfc.dispose(await model.evaluateDataset(dataset, {batches: batches + 2}));
-    const numTensors1 = tfc.memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-
-    expect(warningMessages.length).toEqual(1);
-    expect(warningMessages[0])
-        .toMatch(
-            /dataset iterator ran out of data during evaluate.* 5 batches/);
-  });
-
-  // Reference Python tf.keras code:
-  //
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // batch_size = 8
-  // num_batches = 3
-  //
-  // xs1 = np.ones([batch_size * num_batches, 1])
-  // xs2 = np.ones([batch_size * num_batches, 1])
-  // ys = np.ones([batch_size * num_batches, 1])
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     ({'input1': xs1, 'input2': xs2}, ys)).batch(batch_size)
-  //
-  // input1 = tf.keras.Input(shape=[1], name='input1')
-  // input2 = tf.keras.Input(shape=[1], name='input2')
-  // concat = tf.keras.layers.concatenate([input1, input2])
-  // output = tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     kernel_initializer='zeros').apply(concat)
-  // model = tf.keras.Model(inputs=[input1, input2], outputs=output)
-  // model.compile(loss='mean_squared_error',
-  //               optimizer=tf.train.GradientDescentOptimizer(0.01),
-  //               metrics=['accuracy'])
-  //
-  // out = model.evaluate(dataset, steps=3, verbose=0)
-  // print(out)
-  // ```
-  it('2 inputs, 1 output, 1 metric, no validation, with batches specified',
-     async () => {
-       // Create a functional model with 2 inputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                     .apply(concat) as tfl.SymbolicTensor;
-       const model = tfl.model({inputs: [input1, input2], outputs: y});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const batches = 3;
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batches,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.evaluateDataset(dataset, {batches});
-       model.setWeights([tfc.zeros([2, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const evalOut =
-           await model.evaluateDataset(dataset, {batches}) as tfc.Scalar[];
-       const expectedLoss = tfc.scalar(1.0);
-       const expectedAcc = tfc.scalar(0.0);
-       expectTensorsClose(evalOut[0], expectedLoss);
-       expectTensorsClose(evalOut[1], expectedAcc);
-       tfc.dispose(evalOut);
-       tfc.dispose([expectedLoss, expectedAcc]);
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-     });
-
-  it('2 inputs, 1 output, 1 metric, no validation, no batches specified',
-     async () => {
-       // Create a functional model with 2 inputs.
-       const input1 = tfl.layers.input({shape: [1]});
-       const input2 = tfl.layers.input({shape: [1]});
-       const concat = tfl.layers.concatenate().apply([input1, input2]);
-       const y = tfl.layers.dense({units: 1, kernelInitializer: 'zeros'})
-                     .apply(concat) as tfl.SymbolicTensor;
-       const model = tfl.model({inputs: [input1, input2], outputs: y});
-       model.compile(
-           {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
-
-       const batchSize = 8;
-       const batches = 3;
-       const xTensorsFunc = () => {
-         const output: {[name: string]: tfc.Tensor[]} = {};
-         output[input1.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         output[input2.name] = [
-           tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]),
-           tfc.ones([batchSize, 1])
-         ];
-         return output;
-       };
-       const yTensorsFunc =
-           () => [tfc.ones([batchSize, 1]), tfc.ones([batchSize, 1]), tfc.ones([
-             batchSize, 1
-           ])];
-       const dataset = new FakeNumericDataset({
-         xShape: [1],
-         yShape: [1],
-         batchSize,
-         numBatches: batches,
-         xTensorsFunc,
-         yTensorsFunc
-       });
-
-       // Do a burn-in call to account for initialization of cached tensors (for
-       // the memory-leak check below).
-       await model.evaluateDataset(dataset, {});
-       model.setWeights([tfc.zeros([2, 1]), tfc.zeros([1])]);
-
-       const numTensors0 = tfc.memory().numTensors;
-       const evalOut = await model.evaluateDataset(dataset, {}) as tfc.Scalar[];
-       const expectedLoss = tfc.scalar(1.0);
-       const expectedAcc = tfc.scalar(0.0);
-       expectTensorsClose(evalOut[0], expectedLoss);
-       expectTensorsClose(evalOut[1], expectedAcc);
-       tfc.dispose(evalOut);
-       tfc.dispose([expectedLoss, expectedAcc]);
-       const numTensors1 = tfc.memory().numTensors;
-       expect(numTensors1).toEqual(numTensors0);
-     });
-});
diff --git a/tfjs-master/tfjs-layers/src/engine/training_tensors.ts b/tfjs-master/tfjs-layers/src/engine/training_tensors.ts
deleted file mode 100644
index 1a3e68610..000000000
--- a/tfjs-master/tfjs-layers/src/engine/training_tensors.ts
+++ /dev/null
@@ -1,329 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Interfaces and methods for training models using tf.Tensor objects.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {Tensor, Tensor1D} from '@tensorflow/tfjs-core';
-import {expandDims, gather, sliceAlongFirstAxis} from '../backend/tfjs_backend';
-import {BaseCallback, CustomCallbackArgs, ModelLoggingVerbosity, YieldEveryOptions} from '../base_callbacks';
-import {ClassWeight, ClassWeightMap} from './training_utils';
-
-/**
- * Interface configuration model training based on data as `tf.Tensor`s.
- */
-export interface ModelFitArgs {
-  /**
-   * Number of samples per gradient update. If unspecified, it
-   * will default to 32.
-   */
-  batchSize?: number;
-
-  /**
-   * Integer number of times to iterate over the training data arrays.
-   */
-  epochs?: number;
-
-  /**
-   * Verbosity level.
-   *
-   * Expected to be 0, 1, or 2. Default: 1.
-   *
-   * 0 - No printed message during fit() call.
-   * 1 - In Node.js (tfjs-node), prints the progress bar, together with
-   *     real-time updates of loss and metric values and training speed.
-   *     In the browser: no action. This is the default.
-   * 2 - Not implemented yet.
-   */
-  verbose?: ModelLoggingVerbosity | 2;
-
-  /**
-   * List of callbacks to be called during training.
-   * Can have one or more of the following callbacks:
-   *   - `onTrainBegin(logs)`: called when training starts.
-   *   - `onTrainEnd(logs)`: called when training ends.
-   *   - `onEpochBegin(epoch, logs)`: called at the start of every epoch.
-   *   - `onEpochEnd(epoch, logs)`: called at the end of every epoch.
-   *   - `onBatchBegin(batch, logs)`: called at the start of every batch.
-   *   - `onBatchEnd(batch, logs)`: called at the end of every batch.
-   *   - `onYield(epoch, batch, logs)`: called every `yieldEvery` milliseconds
-   *      with the current epoch, batch and logs. The logs are the same
-   *      as in `onBatchEnd()`. Note that `onYield` can skip batches or
-   *      epochs. See also docs for `yieldEvery` below.
-   */
-  callbacks?: BaseCallback[]|CustomCallbackArgs|CustomCallbackArgs[];
-
-  /**
-   * Float between 0 and 1: fraction of the training data
-   * to be used as validation data. The model will set apart this fraction of
-   * the training data, will not train on it, and will evaluate the loss and
-   * any model metrics on this data at the end of each epoch.
-   * The validation data is selected from the last samples in the `x` and `y`
-   * data provided, before shuffling.
-   */
-  validationSplit?: number;
-
-  /**
-   * Data on which to evaluate the loss and any model
-   * metrics at the end of each epoch. The model will not be trained on this
-   * data. This could be a tuple [xVal, yVal] or a tuple [xVal, yVal,
-   * valSampleWeights]. The model will not be trained on this data.
-   * `validationData` will override `validationSplit`.
-   */
-  validationData?: [
-    Tensor|Tensor[], Tensor|Tensor[]
-  ]|[Tensor | Tensor[], Tensor|Tensor[], Tensor|Tensor[]];
-
-  /**
-   * Whether to shuffle the training data before each epoch. Has
-   * no effect when `stepsPerEpoch` is not `null`.
-   */
-  shuffle?: boolean;
-
-  /**
-   * Optional object mapping class indices (integers) to
-   * a weight (float) to apply to the model's loss for the samples from this
-   * class during training. This can be useful to tell the model to "pay more
-   * attention" to samples from an under-represented class.
-   *
-   * If the model has multiple outputs, a class weight can be specified for
-   * each of the outputs by setting this field an array of weight object
-   * or an object that maps model output names (e.g., `model.outputNames[0]`)
-   * to weight objects.
-   */
-  classWeight?: ClassWeight|ClassWeight[]|ClassWeightMap;
-
-  /**
-   * Optional array of the same length as x, containing
-   * weights to apply to the model's loss for each sample. In the case of
-   * temporal data, you can pass a 2D array with shape (samples,
-   * sequenceLength), to apply a different weight to every timestep of every
-   * sample. In this case you should make sure to specify
-   * sampleWeightMode="temporal" in compile().
-   */
-  sampleWeight?: Tensor;
-
-  /**
-   * Epoch at which to start training (useful for resuming a previous training
-   * run). When this is used, `epochs` is the index of the "final epoch".
-   * The model is not trained for a number of iterations given by `epochs`,
-   * but merely until the epoch of index `epochs` is reached.
-   */
-  initialEpoch?: number;
-
-  /**
-   * Total number of steps (batches of samples) before
-   * declaring one epoch finished and starting the next epoch. When training
-   * with Input Tensors such as TensorFlow data tensors, the default `null` is
-   * equal to the number of unique samples in your dataset divided by the
-   * batch size, or 1 if that cannot be determined.
-   */
-  stepsPerEpoch?: number;
-
-  /**
-   * Only relevant if `stepsPerEpoch` is specified. Total number of steps
-   * (batches of samples) to validate before stopping.
-   */
-  validationSteps?: number;
-
-  /**
-   * Configures the frequency of yielding the main thread to other tasks.
-   *
-   * In the browser environment, yielding the main thread can improve the
-   * responsiveness of the page during training. In the Node.js environment,
-   * it can ensure tasks queued in the event loop can be handled in a timely
-   * manner.
-   *
-   * The value can be one of the following:
-   *   - `'auto'`: The yielding happens at a certain frame rate (currently set
-   *               at 125ms). This is the default.
-   *   - `'batch'`: yield every batch.
-   *   - `'epoch'`: yield every epoch.
-   *   - any `number`: yield every `number` milliseconds.
-   *   - `'never'`: never yield. (yielding can still happen through `await
-   *      nextFrame()` calls in custom callbacks.)
-   */
-  yieldEvery?: YieldEveryOptions;
-}
-
-export function checkBatchSize(batchSize: number) {
-  tfc.util.assert(
-      batchSize > 0 && Number.isInteger(batchSize),
-      () => `batchSize is required to be a positive integer, but got ${
-          batchSize}`);
-}
-
-/**
- * Slice a Tensor or an Array of Tensors, by start and stop indices.
- *
- * Porting Note: The `_slice_arrays` function in PyKeras is covered by this
- *   function and `sliceArraysByIndices()` together.
- *
- * @param arrays: the input.
- * @param start: the starting index (inclusive).
- * @param stop: the stopping index (exclusive).
- * @returns The result of the slicing. If `arrays` is an `Array` of
- *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`
- *   in the same way.
- */
-export function sliceArrays(
-    arrays: Tensor|Tensor[], start: number, stop: number): Tensor|Tensor[] {
-  if (arrays == null) {
-    return [null];
-  } else if (Array.isArray(arrays)) {
-    return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));
-  } else {  // Tensor.
-    return sliceAlongFirstAxis(arrays, start, stop - start);
-  }
-}
-
-/**
- * Slice a Tensor or an Array of Tensors, by random-order indices.
- *
- * Porting Note: The `_slice_arrays` function in PyKeras is covered by this
- *   function and `sliceArrays()` together.
- *
- * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.
- *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the
- *   same fashion.
- * @param indices The indices to use for slicing along the first (batch)
- *   dimension.
- * @returns Result(s) of the slicing.
- */
-export function sliceArraysByIndices(
-    arrays: Tensor|Tensor[], indices: Tensor1D): Tensor|Tensor[] {
-  return tfc.tidy(() => {
-    if (arrays == null) {
-      return null;
-    } else if (Array.isArray(arrays)) {
-      return arrays.map(
-          array => (sliceArraysByIndices(array, indices) as Tensor));
-    } else {
-      // TODO(cais): indices should be a pre-constructed Tensor1D to avoid
-      //   tensor1d() calls.
-      return gather(
-          arrays,
-          indices.dtype === 'int32' ? indices : tfc.cast(indices, 'int32'));
-    }
-  });
-}
-
-/**
- * Returns a list of batch indices (tuples of indices).
- * @param size: Integer, total size of the data to slice into batches.
- * @param batchSize: Integer, batch size.
- * @returns An Array of [batchStart, batchEnd] tuples. batchStart is
- *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x
- *   that satisfy batchStart <= x < batchEnd.
- */
-export function makeBatches(
-    size: number, batchSize: number): Array<[number, number]> {
-  const output: Array<[number, number]> = [];
-  let batchStart = 0;
-  let batchEnd: number = null;
-  while (batchStart < size) {
-    batchEnd = batchStart + batchSize;
-    if (batchEnd >= size) {
-      batchEnd = size;
-    }
-    output.push([batchStart, batchEnd]);
-    batchStart = batchEnd;
-  }
-  return output;
-}
-
-/**
- * Ensure tensors all have a rank of at least 2.
- *
- * If a tensor has a rank of 1, it is dimension-expanded to rank 2.
- * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.
- */
-export function ensureTensorsRank2OrHigher(tensors: Tensor|Tensor[]): Tensor[] {
-  const outs: Tensor[] = [];
-  if (tensors instanceof Tensor) {
-    tensors = [tensors];
-  }
-
-  // Make Tensors at least 2D.
-  for (let i = 0; i < tensors.length; ++i) {
-    const tensor = tensors[i];
-    if (tensor.rank === 1) {
-      outs.push(expandDims(tensor, 1));
-    } else if (tensor.rank === 0) {
-      throw new Error(
-          'Expected tensor to be at least 1D, but received a 0D tensor ' +
-          '(scalar).');
-    } else {
-      outs.push(tensor);
-    }
-  }
-  return outs;
-}
-
-/**
- * Compare a set of tensors with a reference (old) set, discard the ones
- * in the new set that are not present in the reference set.
- *
- * This method is used for memory clenaup during calls such as
- * LayersModel.fit().
- *
- * @param tensors New set which may contain Tensors not present in
- *   `refTensors`.
- * @param refTensors Reference Tensor set.
- */
-// TODO(cais, kangyizhang): Deduplicate with tfjs-data.
-export function disposeNewTensors(
-    tensors: Tensor|Tensor[]|{[inputName: string]: Tensor},
-    refTensors: Tensor|Tensor[]|{[inputName: string]: Tensor}): void {
-  if (tensors == null) {
-    return;
-  }
-  const oldTensorIds: number[] = [];
-  if (refTensors instanceof Tensor) {
-    oldTensorIds.push(refTensors.id);
-  } else if (Array.isArray(refTensors)) {
-    refTensors.forEach(t => oldTensorIds.push(t.id));
-  } else if (refTensors != null) {
-    // `oldTensors` is a map from string name to Tensor.
-    for (const name in refTensors) {
-      const oldTensor = refTensors[name];
-      oldTensorIds.push(oldTensor.id);
-    }
-  }
-
-  const tensorsToDispose: Tensor[] = [];
-  if (tensors instanceof Tensor) {
-    if (oldTensorIds.indexOf(tensors.id) === -1) {
-      tensorsToDispose.push(tensors);
-    }
-  } else if (Array.isArray(tensors)) {
-    tensors.forEach(t => {
-      if (oldTensorIds.indexOf(t.id) === -1) {
-        tensorsToDispose.push(t);
-      }
-    });
-  } else if (tensors != null) {
-    // `oldTensors` is a map from string name to Tensor.
-    for (const name in tensors) {
-      const tensor = tensors[name];
-      if (oldTensorIds.indexOf(tensor.id) === -1) {
-        tensorsToDispose.push(tensor);
-      }
-    }
-  }
-
-  tensorsToDispose.forEach(t => {
-    if (!t.isDisposed) {
-      t.dispose();
-    }
-  });
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/training_test.ts b/tfjs-master/tfjs-layers/src/engine/training_test.ts
deleted file mode 100644
index ce3a8f452..000000000
--- a/tfjs-master/tfjs-layers/src/engine/training_test.ts
+++ /dev/null
@@ -1,2991 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for training.ts
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {abs, mean, memory, mul, NamedTensorMap, ones, Scalar, scalar, SGDOptimizer, Tensor, tensor1d, tensor2d, tensor3d, test_util, zeros} from '@tensorflow/tfjs-core';
-
-import * as K from '../backend/tfjs_backend';
-import {CustomCallback, CustomCallbackArgs, DEFAULT_YIELD_EVERY_MS, Params} from '../base_callbacks';
-import * as tfl from '../index';
-import * as logs from '../logs';
-import {Logs, UnresolvedLogs} from '../logs';
-import {Regularizer} from '../regularizers';
-import {Kwargs} from '../types';
-import {pyListRepeat, stringsEqual, unique} from '../utils/generic_utils';
-import {describeMathCPU, describeMathCPUAndGPU, describeMathCPUAndWebGL2, describeMathGPU, expectTensorsClose} from '../utils/test_utils';
-
-// TODO(bileschi): Use external version of Layer.
-import {Layer, SymbolicTensor} from './topology';
-import {checkArrayLengths, collectMetrics, isDataArray, isDataDict, isDataTensor, standardizeInputData} from './training';
-import {makeBatches, sliceArraysByIndices} from './training_tensors';
-
-describeMathCPU('isDataTensor', () => {
-  const x = tfc.tensor2d([[3.14]]);
-
-  it('Positive case', () => {
-    expect(isDataTensor(x)).toEqual(true);
-  });
-  it('Negative cases', () => {
-    expect(isDataTensor([x, x])).toEqual(false);
-    expect(isDataTensor({'Pie': x})).toEqual(false);
-    expect(isDataTensor({})).toEqual(false);
-  });
-});
-
-describeMathCPU('isDataArray', () => {
-  const x = tfc.tensor2d([[3.14]]);
-
-  it('Positive case', () => {
-    expect(isDataArray([x, x])).toEqual(true);
-    expect(isDataArray([])).toEqual(true);
-  });
-  it('Negative cases', () => {
-    expect(isDataArray(x)).toEqual(false);
-    expect(isDataArray({'Pie': x})).toEqual(false);
-    expect(isDataArray({})).toEqual(false);
-  });
-});
-
-describeMathCPU('isDataDict', () => {
-  const x = tfc.tensor2d([[3.14]]);
-  it('Positive case', () => {
-    expect(isDataDict({'Pie': x})).toEqual(true);
-    expect(isDataDict({})).toEqual(true);
-  });
-  it('Negative cases', () => {
-    expect(isDataDict(x)).toEqual(false);
-    expect(isDataDict([x, x])).toEqual(false);
-    expect(isDataDict([])).toEqual(false);
-  });
-});
-
-describeMathCPU('standardizeInputData', () => {
-  const getX = () => tfc.tensor2d([[42]]);
-  const getY = () => tfc.tensor2d([[21]]);
-
-  it('Singleton Tensor, Array of one name', () => {
-    const outputs = standardizeInputData(getX(), ['Foo']);
-    expect(outputs.length).toEqual(1);
-    expectTensorsClose(outputs[0], getX());
-  });
-  it('Array of one Tensor, Array of one name', () => {
-    const outputs = standardizeInputData([getX()], ['Foo']);
-    expect(outputs.length).toEqual(1);
-    expectTensorsClose(outputs[0], getX());
-  });
-  it('Array of two Tensors, Array of two names', () => {
-    const outputs = standardizeInputData([getX(), getY()], ['Foo', 'Bar']);
-    expect(outputs.length).toEqual(2);
-    expectTensorsClose(outputs[0], getX());
-    expectTensorsClose(outputs[1], getY());
-  });
-  it('Dict of two Tensors, Array of two names', () => {
-    const outputs =
-        standardizeInputData({'Foo': getX(), 'Bar': getY()}, ['Foo', 'Bar']);
-    expect(outputs.length).toEqual(2);
-    expectTensorsClose(outputs[0], getX());
-    expectTensorsClose(outputs[1], getY());
-  });
-  it('Unexpected data leads to exception: singleton Tensor', () => {
-    expect(() => standardizeInputData(getX(), []))
-        .toThrowError(/expected no data/);
-  });
-  it('Unexpected data leads to exception: Array of two Tensors', () => {
-    expect(() => standardizeInputData([getX(), getY()], []))
-        .toThrowError(/expected no data/);
-  });
-  it('Unexpected data leads to exception: Dict', () => {
-    expect(() => standardizeInputData({'Pie': getX()}, []))
-        .toThrowError(/expected no data/);
-  });
-  it('Length mismatch: 1 singleton Scalar vs two names', () => {
-    expect(() => standardizeInputData(getX(), ['Foo', 'Bar']))
-        .toThrowError(/expects 2 Tensor.* but only received one/);
-  });
-  it('Length mismatch: Array of 2 Scalars vs one name', () => {
-    expect(() => standardizeInputData([getX(), scalar(-42)], ['Foo']))
-        .toThrowError(/Expected to see 1 Tensor/);
-  });
-  it('Length mismatch: Dict of 1 Scalar vs 2 names', () => {
-    expect(() => standardizeInputData({'Foo': getX()}, ['Foo', 'Bar']))
-        .toThrowError(/No data provided for \"Bar\"/);
-  });
-  it('Shape mismatch: input shape [1,1] vs expected shape [*,2]', () => {
-    expect(() => standardizeInputData(getX(), ['Foo'], [[null, 2]]))
-        .toThrowError(
-            / expected a batch of elements where each example has shape \[2\]/);
-  });
-});
-
-describeMathCPU('checkArrayLengths', () => {
-  it('Batch mismatch in inputs', () => {
-    const inputs = [zeros([2, 1]), zeros([3, 1])];
-    const targets = [zeros([2, 1]), zeros([2, 1])];
-    expect(() => checkArrayLengths(inputs, targets))
-        .toThrowError(/All input .* should have the same number of samples/);
-  });
-  it('Batch mismatch in targets', () => {
-    const inputs = [zeros([2, 1]), zeros([2, 1])];
-    const targets = [zeros([2, 1]), zeros([3, 1])];
-    expect(() => checkArrayLengths(inputs, targets))
-        .toThrowError(/All target .* should have the same number of samples/);
-  });
-  it('Batch mismatch between inputs and targets', () => {
-    const inputs = [zeros([2, 1]), zeros([2, 1])];
-    const targets = [zeros([3, 1]), zeros([3, 1])];
-    expect(() => checkArrayLengths(inputs, targets))
-        .toThrowError(
-            /Input Tensors should have the same number of samples as target/);
-  });
-});
-
-describeMathCPUAndWebGL2('collectMetrics', () => {
-  it('shortcut strings name', () => {
-    const metrics = 'mse';
-    const outputNames = ['output'];
-    const collectedMetrics = collectMetrics(metrics, outputNames);
-    expect(collectedMetrics.length).toEqual(1);
-    expect(collectedMetrics[0].length).toEqual(1);
-    expect(collectedMetrics[0][0]).toEqual('mse');
-  });
-  it('metric function', () => {
-    const metrics = tfl.metrics.meanSquaredError;
-    const outputNames = ['output'];
-    const collectedMetrics = collectMetrics(metrics, outputNames);
-    expect(collectedMetrics.length).toEqual(1);
-    expect(collectedMetrics[0].length).toEqual(1);
-    expect(collectedMetrics[0][0]).toEqual(metrics);
-  });
-  it('Array of shortcut string names', () => {
-    const metrics = ['mse', 'crossentropy'];
-    const outputNames = ['output'];
-    const collectedMetrics = collectMetrics(metrics, outputNames);
-    expect(collectedMetrics.length).toEqual(1);
-    expect(collectedMetrics[0].length).toEqual(2);
-    expect(collectedMetrics[0][0]).toEqual('mse');
-    expect(collectedMetrics[0][1]).toEqual('crossentropy');
-  });
-  it('Array of metric functions', () => {
-    const metrics = [tfl.metrics.meanSquaredError, tfl.metrics.precision];
-    const outputNames = ['output'];
-    const collectedMetrics = collectMetrics(metrics, outputNames);
-    expect(collectedMetrics.length).toEqual(1);
-    expect(collectedMetrics[0].length).toEqual(2);
-    expect(collectedMetrics[0][0]).toEqual(metrics[0]);
-    expect(collectedMetrics[0][1]).toEqual(metrics[1]);
-  });
-  it('Array of mixing shortcut string names and metric functions', () => {
-    const metrics = ['mse', tfl.metrics.precision];
-    const outputNames = ['output'];
-    const collectedMetrics = collectMetrics(metrics, outputNames);
-    expect(collectedMetrics.length).toEqual(1);
-    expect(collectedMetrics[0].length).toEqual(2);
-    expect(collectedMetrics[0][0]).toEqual('mse');
-    expect(collectedMetrics[0][1]).toEqual(metrics[1]);
-  });
-  it('Dict of shortcut string names', () => {
-    const metrics = {'output': 'mse'};
-    const outputNames = ['output'];
-    const collectedMetrics = collectMetrics(metrics, outputNames);
-    expect(collectedMetrics.length).toEqual(1);
-    expect(collectedMetrics[0].length).toEqual(1);
-    expect(collectedMetrics[0][0]).toEqual('mse');
-  });
-  it('Dict of metric functions', () => {
-    const metrics = {'output': tfl.metrics.meanSquaredError};
-    const outputNames = ['output'];
-    const collectedMetrics = collectMetrics(metrics, outputNames);
-    expect(collectedMetrics.length).toEqual(1);
-    expect(collectedMetrics[0].length).toEqual(1);
-    expect(collectedMetrics[0][0]).toEqual(metrics['output']);
-  });
-  it('metrics null', () => {
-    const outputNames = ['output'];
-    const collectedMetrics = collectMetrics(null, outputNames);
-    expect(collectedMetrics.length).toEqual(1);
-    expect(collectedMetrics[0].length).toEqual(0);
-  });
-  it('metrics is array, length = 0', () => {
-    const outputNames = ['output'];
-    const collectedMetrics = collectMetrics([], outputNames);
-    expect(collectedMetrics.length).toEqual(1);
-    expect(collectedMetrics[0].length).toEqual(0);
-  });
-  it('multiple output names', () => {
-    const metrics = ['mse'];
-    const outputNames = ['output1', 'output2'];
-    const collectedMetrics = collectMetrics(metrics, outputNames);
-    expect(collectedMetrics.length).toEqual(2);
-    expect(collectedMetrics[0].length).toEqual(1);
-    expect(collectedMetrics[0][0]).toEqual('mse');
-    expect(collectedMetrics[1].length).toEqual(1);
-    expect(collectedMetrics[1][0]).toEqual('mse');
-  });
-});
-
-describeMathCPUAndGPU('sliceArraysByIndices', () => {
-  it('Single 2D', () => {
-    const x = tensor2d([[1, 2], [3, 4], [5, 6]], [3, 2]);
-    const y = sliceArraysByIndices(x, tensor1d([0, 2])) as Tensor;
-    expectTensorsClose(y, tensor2d([[1, 2], [5, 6]], [2, 2]));
-  });
-  it('Array of two 2Ds', () => {
-    const xs = [
-      tensor2d([[1, 2], [3, 4], [5, 6]], [3, 2]),
-      tensor2d([[10, 20], [30, 40], [50, 60]], [3, 2])
-    ];
-    const ys = sliceArraysByIndices(xs, tensor1d([0, 2])) as Tensor[];
-    expect(ys.length).toEqual(2);
-    expectTensorsClose(ys[0], tensor2d([[1, 2], [5, 6]], [2, 2]));
-    expectTensorsClose(ys[1], tensor2d([[10, 20], [50, 60]], [2, 2]));
-  });
-  it('Array of two 3Ds', () => {
-    const xs = [
-      tensor3d([[[1]], [[2]], [[3]]], [3, 1, 1]),
-      tensor3d([[[10]], [[20]], [[30]]], [3, 1, 1]),
-    ];
-    const ys = sliceArraysByIndices(xs, tensor1d([0, 2])) as Tensor[];
-    expect(ys.length).toEqual(2);
-    expectTensorsClose(ys[0], tensor3d([[[1]], [[3]]], [2, 1, 1]));
-    expectTensorsClose(ys[1], tensor3d([[[10]], [[30]]], [2, 1, 1]));
-  });
-  it('null array input', () => {
-    expect(sliceArraysByIndices(null, tensor1d([0, 2]))).toBeNull();
-  });
-  it('casts indices automatically', () => {
-    const x = tensor2d([[1, 2], [3, 4], [5, 6]], [3, 2]);
-    const y =
-        sliceArraysByIndices(x, tensor1d([0.1, 2.0], 'float32')) as Tensor;
-    expectTensorsClose(y, tensor2d([[1, 2], [5, 6]], [2, 2]));
-  });
-});
-
-describe('makeBatches', () => {
-  it('divisible', () => {
-    expect(makeBatches(6, 3)).toEqual([[0, 3], [3, 6]]);
-  });
-
-  it('indivisible', () => {
-    expect(makeBatches(7, 3)).toEqual([[0, 3], [3, 6], [6, 7]]);
-    expect(makeBatches(2, 4)).toEqual([[0, 2]]);
-  });
-
-  it('empty size', () => {
-    expect(makeBatches(0, 4)).toEqual([]);
-  });
-});
-
-describeMathCPUAndGPU('LayersModel.predict', () => {
-  it('1 input, 1 output', () => {
-    const inputTensor = tfl.layers.input(
-        {shape: [3, 4], name: 'inputLayer1', dtype: 'float32'});
-    const layer = tfl.layers.reshape({targetShape: [2, 6]});
-    const output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel(
-        {inputs: [inputTensor], outputs: [output], name: 'model1x1'});
-    const xs = ones([10, 3, 4]);
-    const ys = model.predict(xs, {batchSize: 4}) as Tensor;
-    expectTensorsClose(ys, ones([10, 2, 6]));
-  });
-
-  it('1D tensors as inputs', () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [1]}));
-
-    const xs = ones([10]);  // A batch of 10.
-    // Do a burn-in call first.
-    tfc.dispose(model.predict(xs, {batchSize: 4}));
-    const numTensors0 = memory().numTensors;
-    const ys = model.predict(xs, {batchSize: 4}) as Tensor;
-    expect(ys.shape).toEqual([10, 1]);
-    ys.dispose();
-    // Assert no memory leak.
-    expect(memory().numTensors).toEqual(numTensors0);
-  });
-
-  it('1 input, 1 output, tensor as input argument', () => {
-    const inputTensor = tfl.layers.input(
-        {shape: [3, 4], name: 'inputLayer1', dtype: 'float32'});
-    const layer = tfl.layers.reshape({targetShape: [2, 6]});
-    const output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel(
-        {inputs: [inputTensor], outputs: [output], name: 'model1x1'});
-    const xs = ones([10, 3, 4]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(ys, ones([10, 2, 6]));
-  });
-
-  it('1 input as Array, 1 output', () => {
-    const inputTensor = tfl.layers.input(
-        {shape: [3, 4], name: 'inputLayer1', dtype: 'float32'});
-    const layer = tfl.layers.reshape({targetShape: [2, 6]});
-    const output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel(
-        {inputs: [inputTensor], outputs: [output], name: 'model1x1'});
-    const xs = ones([10, 3, 4]);
-    const ys = model.predict([xs], {batchSize: 4}) as Tensor;
-    expectTensorsClose(ys, ones([10, 2, 6]));
-  });
-
-  it('1 input, 2 outputs', () => {
-    const inputTensor = tfl.layers.input(
-        {shape: [3, 4], name: 'inputLayer2', dtype: 'float32'});
-    const layer1 = tfl.layers.reshape({targetShape: [2, 6]});
-    const layer2 = tfl.layers.flatten();
-    const output1 = layer1.apply(inputTensor) as tfl.SymbolicTensor;
-    const output2 = layer2.apply(output1) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel(
-        {inputs: [inputTensor], outputs: [output1, output2], name: 'model1x2'});
-    const xs = ones([10, 3, 4]);
-    const ys = model.predict(xs, {batchSize: 4}) as Tensor[];
-    expect(ys.length).toEqual(2);
-    expectTensorsClose(ys[0], ones([10, 2, 6]));
-    expectTensorsClose(ys[1], ones([10, 12]));
-  });
-
-  it('2 inputs, 2 outputs', () => {
-    const inputTensor1 = tfl.layers.input(
-        {shape: [3, 4], name: 'inputLayer3', dtype: 'float32'});
-    const inputTensor2 = tfl.layers.input(
-        {shape: [3, 3], name: 'inputLayer4', dtype: 'float32'});
-    const layer1 = tfl.layers.reshape({targetShape: [2, 6]});
-    const layer2 = tfl.layers.flatten();
-    const output1 = layer1.apply(inputTensor1) as tfl.SymbolicTensor;
-    const output2 = layer2.apply(inputTensor2) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel({
-      inputs: [inputTensor1, inputTensor2],
-      outputs: [output1, output2],
-      name: 'model2x2'
-    });
-    const xs1 = ones([10, 3, 4]);
-    const xs2 = ones([10, 3, 3]);
-    const ys = model.predict([xs1, xs2], {batchSize: 4}) as Tensor[];
-    expect(ys.length).toEqual(2);
-    expectTensorsClose(ys[0], ones([10, 2, 6]));
-    expectTensorsClose(ys[1], ones([10, 9]));
-  });
-
-  it('Incorrect number of inputs leads to exception: 1 vs 2', () => {
-    const inputTensor = tfl.layers.input(
-        {shape: [3, 4], name: 'inputLayer_inc_1', dtype: 'float32'});
-    const layer = tfl.layers.reshape({targetShape: [2, 6]});
-    const output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel(
-        {inputs: [inputTensor], outputs: [output], name: 'model_inc_1x1'});
-    const xs1 = ones([10, 3, 4]);
-
-    expect(() => model.predict([
-      xs1, xs1
-    ])).toThrowError(/.*Expected.*1 Tensor.*got 2 Tensor.*/);
-  });
-
-  it('Incorrect number of inputs leads to exception: 2 vs 3', () => {
-    const inputTensor1 = tfl.layers.input(
-        {shape: [3, 4], name: 'inputLayer_inc_3', dtype: 'float32'});
-    const inputTensor2 = tfl.layers.input(
-        {shape: [3, 3], name: 'inputLayer_inc_4', dtype: 'float32'});
-    const layer1 = tfl.layers.reshape({targetShape: [2, 6]});
-    const layer2 = tfl.layers.flatten();
-    const output1 = layer1.apply(inputTensor1) as tfl.SymbolicTensor;
-    const output2 = layer2.apply(inputTensor2) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel({
-      inputs: [inputTensor1, inputTensor2],
-      outputs: [output1, output2],
-      name: 'model_inc_2x2'
-    });
-    const xs1 = ones([10, 3, 4]);
-
-    expect(() => model.predict([
-      xs1, xs1, xs1
-    ])).toThrowError(/.*Expected.*2 Tensor.*got 3 Tensor.*/);
-  });
-
-  it('Incorrect input shape leads to exception', () => {
-    const inputTensor = tfl.layers.input(
-        {shape: [3, 4], name: 'inputLayer_inc_1', dtype: 'float32'});
-    const layer = tfl.layers.reshape({targetShape: [2, 6]});
-    const output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel(
-        {inputs: [inputTensor], outputs: [output], name: 'model_inc_1x1'});
-    const xs1 = ones([2, 4, 3]);
-
-    expect(() => model.predict(xs1))
-        .toThrowError(/.*expected.* shape \[null,3,4\].*but got.*\[2,4,3\]/);
-  });
-
-  it('Invalid batchSize value leads to Error', () => {
-    const model = tfl.sequential(
-        {layers: [tfl.layers.dense({units: 1, inputShape: [2]})]});
-    const xs = tfc.zeros([5, 2]);
-    expect(() => model.predict(xs, {batchSize: 0}))
-        .toThrowError(
-            /batchSize is required to be a positive integer, but got 0/);
-    expect(() => model.predict(xs, {batchSize: -2}))
-        .toThrowError(
-            /batchSize is required to be a positive integer, but got -2/);
-    expect(() => model.predict(xs, {batchSize: 3.14}))
-        .toThrowError(
-            /batchSize is required to be a positive integer, but got 3\.14/);
-    // tslint:disable-next-line:no-any
-    expect(() => model.predict(xs, {batchSize: 'a' as any}))
-        .toThrowError(
-            /batchSize is required to be a positive integer, but got a/);
-  });
-});
-describeMathCPUAndWebGL2('LayersModel.fit long', () => {
-  let originalTimeout: number;
-
-  beforeEach(() => {
-    originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-  });
-
-  afterEach(() => {
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout;
-  });
-  it('Return sequences; Fit with metric', async () => {
-    // The golden values for history used in the assertion below can be obtained
-    // with the following Python Keras code.
-    // Ran with Python Keras verion 2.1.2 and TensorFlow (CPU) version
-    // 1.7.0-dev20180226.
-    // ```python
-    // import keras
-    // import numpy as np
-    //
-    // sequenceLength = 3
-    // inputSize = 4
-    // dataSize = 16
-    // validationSplit = 0.5
-    // batchSize = 3
-    // outputSize = 2
-    //
-    // model = keras.Sequential()
-    //
-    // model.add(keras.layers.SimpleRNN(
-    //     outputSize,
-    //     kernel_initializer='ones',
-    //     recurrent_initializer='ones',
-    //     use_bias=False,
-    //     return_sequences=True,
-    //     input_shape=[sequenceLength, inputSize]))
-    // model.add(keras.layers.TimeDistributed(
-    //     keras.layers.Dense(
-    //         outputSize,
-    //         kernel_initializer='ones',
-    //         use_bias=False)))
-    //
-    // model.compile(optimizer='sgd',
-    //               loss='categorical_crossentropy',
-    //               metrics=['accuracy'])
-    // history = model.fit(np.ones([dataSize, sequenceLength, inputSize]),
-    //                     np.ones([dataSize, sequenceLength, outputSize]),
-    //                     batch_size=batchSize,
-    //                     epochs=2,
-    //                     validation_split=validationSplit)
-    // print(history.history)
-    // ```
-    const sequenceLength = 3;
-    const inputSize = 4;
-    const dataSize = 16;
-    const validationSplit = 0.5;
-    const batchSize = 3;
-    // So there are 8 examples for train and validation, respectivly. The actual
-    // batches during training and validation will be 3, 3 and 2. This tests the
-    // correct averaging of the loss values happens without broadcasting.
-    const outputSize = 2;
-    const simpleRNN = tfl.layers.simpleRNN({
-      units: outputSize,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      useBias: false,
-      returnSequences: true,
-    });
-    const timeDistributed = tfl.layers.timeDistributed({
-      layer: tfl.layers.dense(
-          {units: outputSize, kernelInitializer: 'ones', useBias: false})
-    });
-    const input = tfl.layers.input({shape: [sequenceLength, inputSize]});
-    const output =
-        timeDistributed.apply(simpleRNN.apply(input)) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel({inputs: input, outputs: output});
-    model.compile({
-      optimizer: 'sgd',
-      loss: 'categoricalCrossentropy',
-      metrics: ['accuracy'],
-    });
-    const history = await model.fit(
-        ones([dataSize, sequenceLength, inputSize]),
-        ones([dataSize, sequenceLength, outputSize]), {
-          batchSize,
-          epochs: 1,
-          validationSplit,
-        });
-    expectTensorsClose(
-        history.history['loss'] as number[], [1.3862943649291992]);
-    expectTensorsClose(
-        history.history['val_loss'] as number[], [1.3862943649291992]);
-    expectTensorsClose(history.history['acc'] as number[], [1.0]);
-    expectTensorsClose(history.history['val_acc'] as number[], [1.0]);
-  });
-});
-describeMathCPUAndWebGL2('LayersModel.fit', () => {
-  const inputSize = 4;   // Input vector size for model with one input.
-  const inputSize1 = 3;  // 1st input vector size for model with two inputs.
-  const inputSize2 = 4;  // 2nd input vector size for model with two inputs.
-  const numSamples = 5;  // Number of samples in a batch.
-
-  const inputTensor = tfl.layers.input(
-      {shape: [inputSize], name: 'inputLayer1', dtype: 'float32'});
-  const inputTensor1 = tfl.layers.input(
-      {shape: [inputSize1], name: 'inputLayer1of2', dtype: 'float32'});
-  const inputTensor2 = tfl.layers.input(
-      {shape: [inputSize2], name: 'inputLayer2of2', dtype: 'float32'});
-
-  // For model with one input.
-  let model: tfl.LayersModel;
-  let inputs: Tensor;
-  let targets: Tensor;
-
-  // For model with two inputs (and two outputs).
-  let twoOutputModel: tfl.LayersModel;
-  let inputs1: Tensor;
-  let inputs2: Tensor;
-  let targets1: Tensor;
-  let targets2: Tensor;
-
-  function createDenseModelAndData(
-      useBias = false,
-      kernelRegularizer?: string|Regularizer,
-      biasRegularizer?: string|Regularizer,
-      ): void {
-    const layer = tfl.layers.dense(
-        {units: 1, useBias, kernelInitializer: 'ones', kernelRegularizer});
-    const output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-    model = new tfl.LayersModel({inputs: [inputTensor], outputs: [output]});
-    inputs = ones([numSamples, inputSize]);
-    targets = ones([numSamples, 1]);
-  }
-
-  function createDenseCategoricalModelAndData(useBias = false): void {
-    const layer =
-        tfl.layers.dense({units: 2, useBias, kernelInitializer: 'ones'});
-    const output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-    model = new tfl.LayersModel({inputs: [inputTensor], outputs: [output]});
-    inputs = ones([numSamples, inputSize]);
-    targets = K.oneHot(ones([numSamples]), 2);
-  }
-
-  function createTwoLayerDenseModelAndData(useBias = false): [Layer, Layer] {
-    const layer1 =
-        tfl.layers.dense({units: 10, useBias, kernelInitializer: 'ones'});
-    const layer2 =
-        tfl.layers.dense({units: 1, useBias, kernelInitializer: 'ones'});
-    const output =
-        layer2.apply(layer1.apply(inputTensor)) as tfl.SymbolicTensor;
-    model = new tfl.LayersModel({inputs: [inputTensor], outputs: [output]});
-    inputs = ones([numSamples, inputSize]);
-    targets = ones([numSamples, 1]);
-    return [layer1, layer2];
-  }
-
-  function createDenseModelWithTwoOutputsAndData(): void {
-    const layer1 =
-        tfl.layers.dense({units: 1, useBias: false, kernelInitializer: 'ones'});
-    const layer2 =
-        tfl.layers.dense({units: 1, useBias: false, kernelInitializer: 'ones'});
-    const output1 = layer1.apply(inputTensor1) as tfl.SymbolicTensor;
-    const output2 = layer2.apply(inputTensor2) as tfl.SymbolicTensor;
-    twoOutputModel = new tfl.LayersModel(
-        {inputs: [inputTensor1, inputTensor2], outputs: [output1, output2]});
-    inputs1 = ones([numSamples, inputSize1]);
-    inputs2 = ones([numSamples, inputSize2]);
-    targets1 = ones([numSamples, 1]);
-    targets2 = ones([numSamples, 1]);
-  }
-
-  it('1 input, 1 output, dense, 1 weight, string optimizer, 1 batch',
-     async () => {
-       createDenseModelAndData();
-
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       // Use batchSize === numSamples to get exactly one batch.
-       const history =
-           await model.fit(inputs, targets, {batchSize: numSamples, epochs: 1});
-
-       expect(history.epoch).toEqual([0]);
-       const newWeightsValue = model.trainableWeights[0].read();
-
-       const lr = 0.01;  // This is the default learning rate of SGD.
-       const expectedValueArray =
-           pyListRepeat([1.0 - (inputSize - 1) * 2 * lr], inputSize);
-       expectTensorsClose(
-           newWeightsValue, tensor2d(expectedValueArray, [inputSize, 1]));
-     });
-
-  it('1D tensor as inputs, targets and validationData', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [1]}));
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-
-    // Use 1D tensor shapes.
-    inputs = ones([numSamples]);
-    targets = ones([numSamples]);
-    const valInputs = ones([numSamples]);
-    const valTargets = ones([numSamples]);
-
-    // Do a burn-in run before checking memory to give any cached
-    // tensors a chance to be created first.
-    await model.fit(inputs, targets, {
-      batchSize: numSamples,
-      epochs: 2,
-      validationData: [valInputs, valTargets]
-    });
-
-    for (let i = 0; i < 2; ++i) {
-      const numTensors0 = memory().numTensors;
-      const history = await model.fit(inputs, targets, {
-        batchSize: numSamples,
-        epochs: 2,
-        validationData: [valInputs, valTargets]
-      });
-      expect(memory().numTensors).toEqual(numTensors0);
-      // Assert no memory leak.
-      expect(history.epoch).toEqual([0, 1]);
-      expect(history.history.loss.length).toEqual(2);
-      expect(history.history.val_loss.length).toEqual(2);
-    }
-  });
-
-  it('training with cosineProximity loss', async () => {
-    createDenseCategoricalModelAndData();
-    model.compile({optimizer: 'SGD', loss: 'cosineProximity'});
-    // Use batchSize === numSamples to get exactly one batch.
-    const history = await model.fit(
-        inputs, targets,
-        {batchSize: numSamples, epochs: 2, validationSplit: 0.2});
-    expect(history.epoch).toEqual([0, 1]);
-    expect(history.history.loss.length).toEqual(2);
-    expect(history.history.val_loss.length).toEqual(2);
-    test_util.expectArraysClose(
-        history.history['loss'] as number[],
-        [-0.70710688829422, -0.7077317237854004]);
-    test_util.expectArraysClose(
-        history.history['val_loss'] as number[],
-        [-0.70710688829422, -0.7077317237854004]);
-  });
-
-  it('training with custom loss', async () => {
-    // Use the following Python code snippet to get reference values
-    // for assertion:
-    //
-    // ```python
-    // import keras
-    // import keras.backend as K;
-    // import numpy as np
-    //
-    // def abs_diff_loss(x, y):
-    //   return K.mean(K.abs(x - y))
-    //
-    // input1 = keras.Input(shape=[4])
-    // layer = keras.layers.Dense(
-    //     units=1, use_bias=False, kernel_initializer='ones')
-    // output = layer(input1)
-    // model = keras.Model(input1, output)
-    // model.compile(optimizer='SGD', loss=abs_diff_loss)
-    // inputs = np.ones([5, 4])
-    // targets = np.ones([5])
-    // history = model.fit(
-    //     inputs, targets, batch_size=5, epochs=2,
-    //     validation_split=0.2)
-    // print(history.history)
-    // ```
-
-    createDenseModelAndData();
-
-    const absDiffLoss = (x: Tensor, y: Tensor) => mean(abs(x.sub(y)));
-
-    model.compile({optimizer: 'SGD', loss: absDiffLoss});
-    // Use batchSize === numSamples to get exactly one batch.
-    const history = await model.fit(
-        inputs, targets,
-        {batchSize: numSamples, epochs: 2, validationSplit: 0.2});
-    test_util.expectArraysClose(history.history['loss'] as number[], [3, 2.96]);
-    test_util.expectArraysClose(
-        history.history['val_loss'] as number[], [2.96, 2.92]);
-  });
-
-  it('Using only x and y input arguments', async () => {
-    createDenseModelAndData();
-
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    const history = await model.fit(inputs, targets, {epochs: 10});
-    // 100 is the default number of epochs.
-    expect(history.epoch.length).toEqual(10);
-    for (let i = 0; i < 10; ++i) {
-      expect(history.epoch[i]).toEqual(i);
-    }
-  });
-
-  it('Default LayersModel.fit epochs is 1', async () => {
-    createDenseModelAndData();
-
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    const history = await model.fit(inputs, targets);
-    expect(history.epoch.length).toEqual(1);
-    expect(history.epoch[0]).toEqual(0);
-  });
-
-  it('1 input, 1 output, dense, 1 weight, string optimizer, 2 epochs',
-     async () => {
-       createDenseModelAndData();
-
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       const history =
-           await model.fit(inputs, targets, {batchSize: numSamples, epochs: 2});
-       expect(history.epoch).toEqual([0, 1]);
-     });
-
-  it('1 input, 1 output, dense, 1 weight, string optimizer, 2 epochs, ' +
-         '1 initialEpoch',
-     async () => {
-       createDenseModelAndData();
-
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       const history = await model.fit(
-           inputs, targets,
-           {batchSize: numSamples, epochs: 2, initialEpoch: 1});
-       expect(history.epoch).toEqual([1]);
-       expect(history.history.loss.length).toEqual(1);
-     });
-
-  it('Training with Dropout layer', async () => {
-    const inputSize = 2;
-    const batchSize = 4;
-    const input = tfl.layers.input({shape: [inputSize]});
-    const dense1 =
-        tfl.layers.dense({units: 2, kernelInitializer: 'ones', useBias: false});
-    const dropout = tfl.layers.dropout({rate: 0.5});
-    const dense2 =
-        tfl.layers.dense({units: 1, kernelInitializer: 'ones', useBias: false});
-    const output =
-        dense2.apply(dropout.apply(dense1.apply(input))) as tfl.SymbolicTensor;
-    const model = new tfl.LayersModel({inputs: input, outputs: output});
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const x = ones([batchSize, inputSize]);
-    const y = ones([batchSize, 1]);
-    await model.fit(x, y, {batchSize, epochs: 1});
-  });
-
-  it('Calling fit twice in a row leads to Error', async () => {
-    createDenseModelAndData();
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    // Do not use `await` in the following `model.fit` call, so that
-    // the two model.fit() calls may interleave.
-    const firstFit =
-        model.fit(inputs, targets, {batchSize: numSamples, epochs: 8});
-    let errorCaught: Error;
-    try {
-      await model.fit(inputs, targets);
-    } catch (err) {
-      errorCaught = err;
-    }
-    expect(errorCaught.message)
-        .toEqual(
-            'Cannot start training because another fit() call is ongoing.');
-    await firstFit;
-  });
-
-  const validationSplits = [0.2, 0.01];
-  for (const validationSplit of validationSplits) {
-    const testTitle =
-        '1 input, 1 output, dense, 1 weight, string optimizer, 2 epochs, ' +
-        `validationSplit=${validationSplit}`;
-    it(testTitle, async () => {
-      createDenseModelAndData();
-      model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-      const history = await model.fit(
-          inputs, targets, {batchSize: numSamples, epochs: 2, validationSplit});
-      expect(history.epoch).toEqual([0, 1]);
-      const losses = history.history['loss'];
-      expect(losses.length).toEqual(2);
-      const valLosses = history.history['val_loss'];
-      expect(valLosses.length).toEqual(2);
-      // Reference values of the losses can be obtained from PyKeras:
-      // ```python
-      // import keras
-      // import numpy as np
-      // input1 = keras.Input(shape=[4])
-      // layer = keras.layers.Dense(
-      //     units=1, use_bias=False, kernel_initializer='ones')
-      // output = layer(input1)
-      // model = keras.Model(input1, output)
-      // model.compile(optimizer='SGD', loss='mean_squared_error')
-      // inputs = np.ones([5, 4])
-      // targets = np.ones([5])
-      // history = model.fit(
-      //     inputs, targets, batch_size=5, epochs=2,
-      //     validation_split=0.2)
-      // print(history.history)
-      // ```
-      expectTensorsClose(losses as number[], [9, 7.617599964141846]);
-      expectTensorsClose(
-          valLosses as number[], [7.617599964141846, 6.447536945343018]);
-    });
-  }
-
-  it('1 input, 1 output, dense, 1 weight, string optimizer, 2 epochs, ' +
-         'use validationData',
-     async () => {
-       createDenseModelAndData();
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       const history = await model.fit(inputs, targets, {
-         batchSize: numSamples,
-         epochs: 2,
-         validationData: [zeros(inputs.shape as [number, number]), targets]
-       });
-       expect(history.epoch).toEqual([0, 1]);
-       const losses = history.history['loss'];
-       expect(losses.length).toEqual(2);
-       const valLosses = history.history['val_loss'];
-       expect(valLosses.length).toEqual(2);
-       expectTensorsClose(losses as number[], [9, 7.617599964141846]);
-     });
-
-  it('1 input, 1 output, dense, 1 weight, string optimizer, 2 epochs, ' +
-         'validationSplit = 0.2, with additional metric',
-     async () => {
-       createDenseModelAndData();
-       model.compile(
-           {optimizer: 'SGD', loss: 'meanSquaredError', metrics: ['accuracy']});
-       expect(model.metricsNames).toEqual(['loss', 'acc']);
-       const history = await model.fit(inputs, targets, {
-         batchSize: numSamples,
-         epochs: 2,
-         validationSplit: 0.2,
-       });
-
-       expect(history.epoch).toEqual([0, 1]);
-       const losses = history.history['loss'];
-       expect(losses.length).toEqual(2);
-       const valLosses = history.history['val_loss'];
-       expect(valLosses.length).toEqual(2);
-       expectTensorsClose(losses as number[], [9, 7.617599964141846]);
-       expectTensorsClose(
-           valLosses as number[], [7.617599964141846, 6.447536945343018]);
-     });
-
-  // TODO(cais): Test metric as a "dict", for models with >1 outputs.
-
-  const metricsToTest: string[][] = [['acc'], ['accuracy']];
-  // TODO(cais): Add 'acc', 'accuracy' and assertion acc_1, acc_2.
-  for (const metrics of metricsToTest) {
-    const testTitle = `categoricalCrossentropy model, validationSplit = 0.2, ` +
-        `${JSON.stringify(metrics)}`;
-    it(testTitle, async () => {
-      createDenseCategoricalModelAndData();
-      model.compile(
-          {optimizer: 'SGD', loss: 'categoricalCrossentropy', metrics});
-      if (stringsEqual(metrics, ['acc']) ||
-          stringsEqual(metrics, ['accuracy'])) {
-        expect(model.metricsNames).toEqual(['loss', 'acc']);
-      } else if (stringsEqual(metrics, ['acc', 'accuracy'])) {
-        expect(model.metricsNames).toEqual(['loss', 'acc', 'acc']);
-      }
-      const history = await model.fit(
-          inputs, targets,
-          {batchSize: numSamples, epochs: 2, validationSplit: 0.2});
-      const losses = history.history['loss'];
-      expectTensorsClose(
-          losses as number[], [0.6931471824645996, 0.6918979287147522]);
-      const valLosses = history.history['val_loss'];
-      expectTensorsClose(
-          valLosses as number[], [0.6918979287147522, 0.6906517744064331]);
-      const acc = history.history['acc'];
-      expectTensorsClose(acc as number[], [0, 1]);
-      const valAcc = history.history['val_acc'];
-      expectTensorsClose(valAcc as number[], [1, 1]);
-    });
-  }
-
-  it('categoricalCrossentropy model, validationSplit = 0.2, ' +
-         'tf.metrics.meanSquareError metric function',
-     async () => {
-       createDenseCategoricalModelAndData();
-       model.compile({
-         optimizer: 'SGD',
-         loss: 'categoricalCrossentropy',
-         metrics: tfl.metrics.meanSquaredError
-       });
-       expect(model.metricsNames).toEqual(['loss', 'meanSquaredError']);
-       const history = await model.fit(
-           inputs, targets,
-           {batchSize: numSamples, epochs: 2, validationSplit: 0.2});
-       const losses = history.history['loss'];
-       expectTensorsClose(
-           losses as number[], [0.6931471824645996, 0.6918979287147522]);
-       const meanSquareError = history.history['meanSquaredError'];
-       expectTensorsClose(
-           meanSquareError as number[], [12.5, 12.495024681091309]);
-     });
-
-  it('categoricalCrossentropy model, validationSplit = 0.2, ' +
-         'tf.metrics.meanSquareError metric function && acc',
-     async () => {
-       createDenseCategoricalModelAndData();
-       model.compile({
-         optimizer: 'SGD',
-         loss: 'categoricalCrossentropy',
-         metrics: [tfl.metrics.meanSquaredError, 'acc']
-       });
-       expect(model.metricsNames).toEqual(['loss', 'meanSquaredError', 'acc']);
-       const history = await model.fit(
-           inputs, targets,
-           {batchSize: numSamples, epochs: 2, validationSplit: 0.2});
-       const losses = history.history['loss'];
-       expectTensorsClose(
-           losses as number[], [0.6931471824645996, 0.6918979287147522]);
-       const meanSquareError = history.history['meanSquaredError'];
-       expectTensorsClose(
-           meanSquareError as number[], [12.5, 12.495024681091309]);
-       const acc = history.history['acc'];
-       expectTensorsClose(acc as number[], [0, 1]);
-     });
-
-  it('Two layers, freeze one layer', async () => {
-    // The golden values used below can be obtained with the following PyKeras
-    // code.
-    // ```python
-    // import keras
-    // import numpy as np
-    //
-    // input_size = 4
-    // num_samples = 5
-    //
-    // input_tensor = keras.Input([input_size], name='inputLayer1')
-    // layer1 = keras.layers.Dense(
-    //     units=10, use_bias=False, kernel_initializer='ones')
-    // layer2 = keras.layers.Dense(
-    //     units=1, use_bias=False, kernel_initializer='ones')
-    // output = layer2(layer1(input_tensor))
-    // model = keras.Model(input_tensor, output)
-    //
-    // inputs = np.ones([num_samples, input_size])
-    // targets = np.ones([num_samples, 1])
-    //
-    // optimizer = keras.optimizers.SGD(lr=1e-2)
-    // model.compile(optimizer=optimizer, loss='mean_squared_error')
-    // history = model.fit(inputs,
-    //                     targets,
-    //                     batch_size=num_samples,
-    //                     epochs=2,
-    //                     validation_split=0.2)
-    //
-    // print(history.history)
-    // print(layer1.get_weights())
-    // print(layer2.get_weights())
-    //
-    // # Freeze layer 1.
-    // layer1.trainable = False
-    // model.compile(optimizer=optimizer, loss='mean_squared_error')
-    // history = model.fit(inputs,
-    //                     targets,
-    //                     batch_size=num_samples,
-    //                     epochs=2,
-    //                     validation_split=0.2)
-    //
-    // print(history.history)
-    // print(layer1.get_weights())
-    // print(layer2.get_weights())
-    // ```
-    const layers = createTwoLayerDenseModelAndData();
-    const layer1 = layers[0];
-    const layer2 = layers[1];
-    const optimizer = new SGDOptimizer(1e-2);
-    model.compile({optimizer, loss: 'meanSquaredError'});
-    let history = await model.fit(
-        inputs, targets,
-        {batchSize: numSamples, epochs: 2, validationSplit: 0.2});
-    let losses = history.history['loss'];
-    expectTensorsClose(losses as number[], [1521.0, 386.35842895507812]);
-    let valLosses = history.history['val_loss'];
-    expectTensorsClose(
-        valLosses as number[], [386.35848999023438, 1808.7342529296875]);
-    expectTensorsClose(
-        layer1.getWeights()[0], mul(scalar(-0.61341441), ones([4, 10])));
-    expectTensorsClose(
-        layer2.getWeights()[0], mul(scalar(-1.77405429), ones([10, 1])));
-
-    // Freeze the 1st layer and compile the model again.
-    layer1.trainable = false;
-    model.compile({optimizer, loss: 'meanSquaredError'});
-
-    history = await model.fit(
-        inputs, targets,
-        {batchSize: numSamples, epochs: 2, validationSplit: 0.2});
-    losses = history.history['loss'];
-    expectTensorsClose(
-        losses as number[], [1808.7342529296875, 75.336509704589844]);
-    valLosses = history.history['val_loss'];
-    expectTensorsClose(
-        valLosses as number[], [75.336524963378906, 3.1378798484802246]);
-    // Expect no change in the value of layer1's kernel, due to the freezing.
-    expectTensorsClose(
-        layer1.getWeights()[0], mul(scalar(-0.61341441), ones([4, 10])));
-    // Expect change in the value of layer2's kernel.
-    expectTensorsClose(
-        layer2.getWeights()[0], mul(scalar(-0.11295), ones([10, 1])));
-  });
-
-  it('Setting trainable of layer from fit callback', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      activation: 'relu',
-      inputShape: [4],
-      kernelInitializer: 'ones'
-    }));
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    expect(model.trainableWeights.length).toEqual(4);
-    const xs = tfc.ones([5, 4]);
-    const ys = tfc.ones([5, 1]);
-    const layer1KernelValues: Float32Array[] = [];
-    const layer2KernelValues: Float32Array[] = [];
-    await model.fit(xs, ys, {
-      epochs: 3,
-      callbacks: {
-        onEpochEnd: async (epoch, logs) => {
-          layer1KernelValues.push(
-              model.layers[0].getWeights()[0].dataSync() as Float32Array);
-          layer2KernelValues.push(
-              model.layers[1].getWeights()[0].dataSync() as Float32Array);
-          // Freeze the first dense layer after the 2nd epoch and unfreeze it
-          // after the 3rd epoch.
-          if (epoch === 1) {
-            model.layers[0].trainable = false;
-          } else if (epoch === 2) {
-            model.layers[0].trainable = true;
-          }
-          if (epoch > 0) {
-            // The 2nd dense layer is never frozen. So its kernel should
-            // be updated in every training epoch.
-            // TODO(cais): Use `expectArraysNotClose()` when available.
-            expect(tensor1d(layer2KernelValues[epoch])
-                       .sub(tensor1d(layer2KernelValues[epoch - 1]))
-                       .abs()
-                       .max()
-                       .dataSync()[0])
-                .toBeGreaterThan(0);
-          }
-          // The 1st dense layer is frozen after the 2nd epoch (epoch === 1),
-          // and is then unfrozen after the 3rd (epoch === 2).
-          // So its kernel value should not change between epoch === 1 and epoch
-          // === 2.
-          if (epoch === 2) {
-            expect(tensor1d(layer1KernelValues[epoch])
-                       .sub(tensor1d(layer1KernelValues[epoch - 1]))
-                       .abs()
-                       .max()
-                       .dataSync()[0])
-                .toEqual(0);
-          } else if (epoch > 0) {
-            expect(tensor1d(layer1KernelValues[epoch])
-                       .sub(tensor1d(layer1KernelValues[epoch - 1]))
-                       .abs()
-                       .max()
-                       .dataSync()[0])
-                .toBeGreaterThan(0);
-          }
-        }
-      }
-    });
-  });
-
-  it('Unknown metric', () => {
-    createDenseCategoricalModelAndData();
-    expect(() => model.compile({
-      optimizer: 'SGD',
-      loss: 'categoricalCrossentropy',
-      metrics: ['foo']
-    })).toThrowError(/Unknown metric foo/);
-  });
-
-  it('1 input, 1 output, dense, 2 weights, string optimizer, 1 batch',
-     async () => {
-       createDenseModelAndData(true);
-
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       const history =
-           await model.fit(inputs, targets, {batchSize: numSamples, epochs: 1});
-       expect(history.epoch).toEqual([0]);
-
-       expect(model.trainableWeights.length).toEqual(2);
-       const lr = 0.01;  // This is the default learning rate of SGD.
-       const newKernelValue = model.trainableWeights[0].read();
-       const expectedKernelArray =
-           pyListRepeat([1.0 - (inputSize - 1) * 2 * lr], inputSize);
-       expectTensorsClose(
-           newKernelValue, tensor2d(expectedKernelArray, [inputSize, 1]));
-       const newBiasValue = model.trainableWeights[1].read();
-       const expectedBiasArray = [0.0 - (inputSize - 1) * 2 * lr];
-       expectTensorsClose(newBiasValue, tensor1d(expectedBiasArray));
-     });
-
-  it('1 input, 1 output, dense, 1 weight, optimizer object, 1 batch',
-     async () => {
-       createDenseModelAndData();
-
-       // Use a custom learning rate for SGD.
-       const lr = 0.025;
-       model.compile(
-           {optimizer: new SGDOptimizer(lr), loss: 'meanSquaredError'});
-       const history =
-           await model.fit(inputs, targets, {batchSize: numSamples, epochs: 1});
-       expect(history.epoch).toEqual([0]);
-       const newWeightsValue = model.trainableWeights[0].read();
-
-       const expectedValueArray =
-           pyListRepeat([1.0 - (inputSize - 1) * 2 * lr], inputSize);
-       expectTensorsClose(
-           newWeightsValue, tensor2d(expectedValueArray, [inputSize, 1]));
-     });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // input1 = keras.Input(shape=[2])
-  // output1 = keras.layers.Dense(1,
-  //                             kernel_initializer='ones',
-  //                             use_bias=False)(input1)
-  // output2 = keras.layers.Dense(1,
-  //                             kernel_initializer='ones',
-  //                             use_bias=False)(input1)
-  // model = keras.Model(input1, [output1, output2])
-  //
-  // model.compile(loss={model.output_names[0]: 'mean_squared_error',
-  //                     model.output_names[1]: 'mean_absolute_error'},
-  //               optimizer='sgd')
-  //
-  // xs = np.ones([2, 2])
-  // ys1 = np.zeros([2, 1])
-  // ys2 = np.zeros([2, 1])
-  // history = model.fit(xs, [ys1, ys2], epochs=1)
-  // print(history.history)
-  // ```
-  it('2 outputs, losses by output name', async () => {
-    const input1 = tfl.input({shape: [2]});
-    const output1 =
-        tfl.layers.dense({units: 1, kernelInitializer: 'ones', useBias: false})
-            .apply(input1) as SymbolicTensor;
-    const output2 =
-        tfl.layers.dense({units: 1, kernelInitializer: 'ones', useBias: false})
-            .apply(input1) as SymbolicTensor;
-    const model = tfl.model({inputs: input1, outputs: [output1, output2]});
-    const loss: {[outputName: string]: string} = {};
-    loss[model.outputNames[0]] = 'meanSquaredError';
-    loss[model.outputNames[1]] = 'meanAbsoluteError';
-    model.compile({loss, optimizer: 'sgd'});
-
-    const xs = ones([2, 2]);
-    const ys1 = zeros([2, 1]);
-    const ys2 = zeros([2, 1]);
-    const history = await model.fit(xs, [ys1, ys2], {epochs: 1});
-    expect(history.history.loss[0]).toBeCloseTo(6);
-    expect(history.history[`${model.outputNames[0]}_loss`][0]).toBeCloseTo(4);
-    expect(history.history[`${model.outputNames[1]}_loss`][0]).toBeCloseTo(2);
-  });
-
-  it('2 inputs, 2 outputs, dense, optimizer object, 1 batch', async () => {
-    createDenseModelWithTwoOutputsAndData();
-
-    const lr = 0.01;
-    twoOutputModel.compile({
-      optimizer: new SGDOptimizer(lr),
-      loss: ['meanSquaredError', 'meanSquaredError']
-    });
-    const trainableWeights = twoOutputModel.trainableWeights;
-    let newWeightsValue1 = trainableWeights[0].read();
-    let newWeightsValue2 = trainableWeights[1].read();
-    await twoOutputModel.fit(
-        [inputs1, inputs2], [targets1, targets2],
-        {batchSize: numSamples, epochs: 1});
-
-    expect(twoOutputModel.trainableWeights.length).toEqual(2);
-    newWeightsValue1 = twoOutputModel.trainableWeights[0].read();
-    newWeightsValue2 = twoOutputModel.trainableWeights[1].read();
-
-    // Check the weight updates to layer1.
-    const expectedValueArray1 =
-        pyListRepeat([1.0 - (inputSize1 - 1) * 2 * lr], inputSize1);
-    expectTensorsClose(
-        newWeightsValue1, tensor2d(expectedValueArray1, [inputSize1, 1]));
-    // Check the weight updates to layer2 (different from those to
-    // layer1).
-    const expectedValueArray2 =
-        pyListRepeat([1.0 - (inputSize2 - 1) * 2 * lr], inputSize2);
-    expectTensorsClose(
-        newWeightsValue2, tensor2d(expectedValueArray2, [inputSize2, 1]));
-  });
-
-  const isCustomCallbackArgs = [false, true];
-  const isCustomCallbackArray = [false, true];
-  for (const isArgs of isCustomCallbackArgs) {
-    for (const isArray of isCustomCallbackArray) {
-      const testTitle = `Fit with custom callback object: isConfig=${
-          isArgs}, isArray=${isArray}`;
-      it(testTitle, async () => {
-        createDenseModelAndData();
-        const trainBeginLogs: Logs[] = [];
-        const trainEndLogs: Logs[] = [];
-        const epochBeginEpochs: number[] = [];
-        const epochEndEpochs: number[] = [];
-        const batchBeginBatches: number[] = [];
-        const batchEndBatches: number[] = [];
-        const batchEndLosses: number[] = [];
-        const epochEndLosses: number[] = [];
-        const customCallbackArgs: CustomCallbackArgs = {
-          onTrainBegin: async (logs?: Logs) => {
-            trainBeginLogs.push(logs);
-          },
-          onTrainEnd: async (logs?: Logs) => {
-            trainEndLogs.push(logs);
-          },
-          onEpochBegin: async (epoch: number, logs?: Logs) => {
-            epochBeginEpochs.push(epoch);
-          },
-          onEpochEnd: async (epoch: number, logs?: Logs) => {
-            epochEndEpochs.push(epoch);
-            epochEndLosses.push(logs['loss']);
-          },
-          onBatchBegin: async (batch: number, logs?: Logs) => {
-            batchBeginBatches.push(batch);
-          },
-          onBatchEnd: async (batch: number, logs?: Logs) => {
-            batchEndBatches.push(batch);
-            batchEndLosses.push(logs['loss']);
-          }
-        };
-        const customCallback = isArgs ? customCallbackArgs :
-                                        new CustomCallback(customCallbackArgs);
-        model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-        await model.fit(inputs, targets, {
-          batchSize: 2,
-          epochs: 2,
-          callbacks: isArray ? [customCallback] : customCallback,
-        });
-        expect(trainBeginLogs.length).toEqual(1);
-        expect(trainEndLogs.length).toEqual(1);
-        expect(epochBeginEpochs).toEqual([0, 1]);
-        expect(epochEndEpochs).toEqual([0, 1]);
-        expect(batchBeginBatches).toEqual([0, 1, 2, 0, 1, 2]);
-        expect(batchEndBatches).toEqual([0, 1, 2, 0, 1, 2]);
-
-        // The optimization problem is a convex one (a single Dense layer),
-        // the learning rate low (default 0.01 for SGD). So it should be fine to
-        // assert monotonic assert monotonic decrease in loss value.
-        expect(batchEndLosses.length).toEqual(6);
-        for (let i = 1; i < batchEndLosses.length; ++i) {
-          expect(batchEndLosses[i]).toBeLessThan(batchEndLosses[i - 1]);
-        }
-        expect(epochEndLosses.length).toEqual(2);
-        expect(epochEndLosses[1]).toBeLessThan(epochEndLosses[0]);
-      });
-    }
-  }
-
-  it('Using custom regularizer', async () => {
-    // The golden values used for assertion can be obtained with PyKeras code:
-    //
-    // ```python
-    // import keras
-    // import numpy as np
-    //
-    // model = keras.Sequential([
-    //     keras.layers.Dense(
-    //         1, kernel_initializer='ones', use_bias=False, input_shape=[4],
-    //         kernel_regularizer=keras.regularizers.l1_l2(1, 1))
-    // ]);
-    //
-    // xs = np.ones([5, 4])
-    // ys = np.ones([5, 1])
-    //
-    // model.compile(optimizer='sgd', loss='mean_squared_error')
-    //
-    // history = model.fit(xs, ys, epochs=2)
-    // print(model.get_weights()[0])
-    // print(history.history)
-    //
-    // ```
-    createDenseModelAndData(false, tfl.regularizers.l1l2({l1: 1, l2: 1}));
-
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    // Use batchSize === numSamples to get exactly one batch.
-    const history =
-        await model.fit(inputs, targets, {batchSize: numSamples, epochs: 2});
-    expectTensorsClose(
-        model.layers[1].getWeights()[0],
-        tensor2d([0.829, 0.829, 0.829, 0.829], [4, 1]));
-    expect(history.history.loss.length).toEqual(2);
-    expect(history.history.loss[0]).toBeCloseTo(17);
-    expect(history.history.loss[1]).toBeCloseTo(13.92);
-  });
-
-  it('Using string regularizer', async () => {
-    // The golden values used for assertion can be obtained with PyKeras code:
-    //
-    // ```python
-    // import keras
-    // import numpy as np
-    //
-    // model = keras.Sequential([
-    //     keras.layers.Dense(
-    //         1, kernel_initializer='ones', use_bias=False, input_shape=[4],
-    //         kernel_regularizer='l1l2')
-    // ]);
-    //
-    // xs = np.ones([5, 4])
-    // ys = np.ones([5, 1])
-    //
-    // model.compile(optimizer='sgd', loss='mean_squared_error')
-    //
-    // history = model.fit(xs, ys, epochs=2)
-    // print(model.get_weights()[0])
-    // print(history.history)
-    //
-    // ```
-    createDenseModelAndData(false, 'l1l2');
-
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    // Use batchSize === numSamples to get exactly one batch.
-    const history =
-        await model.fit(inputs, targets, {batchSize: numSamples, epochs: 2});
-    expectTensorsClose(
-        model.layers[1].getWeights()[0],
-        tensor2d([0.884, 0.884, 0.884, 0.884], [4, 1]));
-    expect(history.history.loss.length).toEqual(2);
-    expect(history.history.loss[0]).toBeCloseTo(9.08);
-    expect(history.history.loss[1]).toBeCloseTo(7.68);
-  });
-
-  it('and then set weights to new weights', async () => {
-    createDenseModelAndData(false, 'l1l2');
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    await model.fit(inputs, targets, {batchSize: numSamples, epochs: 2});
-    const w = zeros([4, 1]);
-    model.layers[1].setWeights([w]);
-    expectTensorsClose(model.layers[1].getWeights()[0], w);
-  });
-
-  it('and then set weights to own weights', async () => {
-    createDenseModelAndData(false, 'l1l2');
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    await model.fit(inputs, targets, {batchSize: numSamples, epochs: 2});
-    const w = model.layers[1].getWeights()[0];
-    model.layers[1].setWeights([w]);
-    expectTensorsClose(model.layers[1].getWeights()[0], w);
-  });
-
-  class CustomCallbackForTest extends tfl.CustomCallback {
-    constructor(readonly recordedParams: Params[]) {
-      super({
-        onTrainBegin: async () => {
-          recordedParams.push(this.params);
-        }
-      });
-    }
-  }
-
-  it('Custom callback params: no validation', async () => {
-    createDenseModelAndData();
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    const recordedParams: Params[] = [];
-    const epochs = 3;
-    const batchSize = 2;
-    await model.fit(inputs, targets, {
-      epochs,
-      batchSize,
-      callbacks: new CustomCallbackForTest(recordedParams)
-    });
-    expect(recordedParams[0].epochs).toEqual(epochs);
-    expect(recordedParams[0].initialEpoch).toEqual(0);
-    expect(recordedParams[0].samples).toEqual(inputs.shape[0]);
-    expect(recordedParams[0].steps).toEqual(null);
-    expect(recordedParams[0].batchSize).toEqual(batchSize);
-    expect(recordedParams[0].doValidation).toEqual(false);
-    expect(recordedParams[0].metrics).toEqual(['loss']);
-  });
-
-  it('Custom callback params: has validation', async () => {
-    createDenseModelAndData();
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    const recordedParams: Params[] = [];
-    const epochs = 3;
-    const batchSize = 2;
-    const validationSplit = 0.2;
-    await model.fit(inputs, targets, {
-      epochs,
-      batchSize,
-      validationSplit,
-      callbacks: new CustomCallbackForTest(recordedParams)
-    });
-    expect(recordedParams[0].epochs).toEqual(epochs);
-    expect(recordedParams[0].initialEpoch).toEqual(0);
-    expect(recordedParams[0].samples)
-        .toEqual(Math.round(inputs.shape[0] * (1 - validationSplit)));
-    expect(recordedParams[0].steps).toEqual(null);
-    expect(recordedParams[0].batchSize).toEqual(batchSize);
-    expect(recordedParams[0].doValidation).toEqual(true);
-    expect(recordedParams[0].metrics).toEqual(['loss', 'val_loss']);
-  });
-
-  class StopAfterNEpochs extends tfl.Callback {
-    private readonly epochsToTrain: number;
-    constructor(epochsToTrain: number) {
-      super();
-      this.epochsToTrain = epochsToTrain;
-    }
-
-    override async onEpochEnd(epoch: number, logs?: UnresolvedLogs) {
-      if (epoch === this.epochsToTrain - 1) {
-        this.model.stopTraining = true;
-      }
-    }
-  }
-
-  it('Stop training at the end of an epoch: Functional model', async () => {
-    createDenseModelAndData(true);
-    model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    // Order 10 epochs of training, but the training should stop after two
-    // epochs due to the callback.
-    const history = await model.fit(inputs, targets, {
-      batchSize: numSamples,
-      epochs: 10,
-      callbacks: [new StopAfterNEpochs(2)]
-    });
-    expect(history.history.loss.length).toEqual(2);
-  });
-
-  class StopAfterNBatches extends tfl.Callback {
-    private readonly batchesToTrain: number;
-    constructor(batchesToTrain: number) {
-      super();
-      this.batchesToTrain = batchesToTrain;
-    }
-
-    override async onBatchEnd(batch: number, logs?: Logs) {
-      if (batch === this.batchesToTrain - 1) {
-        this.model.stopTraining = true;
-      }
-    }
-  }
-
-  it('Stop training at the end of a batch: Sequential model', async () => {
-    const sequentialModel = tfl.sequential();
-    sequentialModel.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', inputShape: [inputSize]}));
-    // numSamples is 5.
-    inputs = ones([numSamples, inputSize]);
-    targets = ones([numSamples, 1]);
-    sequentialModel.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    // Order 10 epochs of training, but the training should stop after only one
-    // epochs due to the callback that orders the training to stop after two
-    // batches. The first epoch should have five batches  due to a batchSize
-    // of 1.
-    const history = await sequentialModel.fit(
-        inputs, targets,
-        {batchSize: 1, epochs: 10, callbacks: [new StopAfterNBatches(2)]});
-    expect(history.history.loss.length).toEqual(1);
-  });
-
-  it('Stop LayersModel.fit() using non-class object callback function',
-     async () => {
-       createDenseModelAndData();
-
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-
-       let numEpochsDone = 0;
-       const epochs = 8;
-       const stopAfterEpoch = 3;
-       let history = await model.fit(inputs, targets, {
-         epochs,
-         callbacks: {
-           onEpochEnd: async (epoch: number, logs?: UnresolvedLogs) => {
-             numEpochsDone++;
-             if (epoch === stopAfterEpoch) {
-               model.stopTraining = true;
-             }
-           }
-         }
-       });
-       expect(numEpochsDone).toEqual(stopAfterEpoch + 1);
-       expect(history.history.loss.length).toEqual(stopAfterEpoch + 1);
-
-       // Check that model.fit can still be called after force stopping.
-       history = await model.fit(inputs, targets, {epochs: 2});
-       expect(history.history.loss.length).toEqual(2);
-     });
-
-  it('Stop training resets at start of LayersModel.fit()', async () => {
-    const sequentialModel = tfl.sequential();
-    sequentialModel.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', inputShape: [inputSize]}));
-    // numSamples is 5.
-    inputs = ones([numSamples, inputSize]);
-    targets = ones([numSamples, 1]);
-    sequentialModel.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-    // Order 10 epochs of training, but the training should stop after only one
-    // epochs due to the callback that orders the training to stop after two
-    // batches. The first epoch should have five batches due to a batchSize
-    // of 1.
-    let history = await sequentialModel.fit(
-        inputs, targets,
-        {batchSize: 1, epochs: 10, callbacks: [new StopAfterNBatches(2)]});
-    expect(history.history.loss.length).toEqual(1);
-
-    // Running fit again should now run to completion
-    history =
-        await sequentialModel.fit(inputs, targets, {batchSize: 1, epochs: 10});
-    expect(history.history.loss.length).toEqual(10);
-  });
-
-  it('Model.dispose() cleans up owned optimizer: Functional', async () => {
-    const input1 = tfl.input({shape: [2]});
-    const input2 = tfl.input({shape: [2]});
-    const y1 = tfl.layers.add().apply([input1, input2]);
-    const y2 = tfl.layers.concatenate().apply([input1, input2]);
-    const output1 =
-        tfl.layers
-            .dense({units: 1, activation: 'linear', kernelInitializer: 'zeros'})
-            .apply(y1) as tfl.SymbolicTensor;
-    const output2 =
-        tfl.layers
-            .dense(
-                {units: 1, activation: 'sigmoid', kernelInitializer: 'zeros'})
-            .apply(y2) as tfl.SymbolicTensor;
-    const model =
-        tfl.model({inputs: [input1, input2], outputs: [output1, output2]});
-    model.compile(
-        {loss: ['meanSquaredError', 'binaryCrossentropy'], optimizer: 'adam'});
-
-    const xs: Tensor[] = [zeros([4, 2]), zeros([4, 2])];
-    const ys: Tensor[] = [zeros([4, 1]), zeros([4, 1])];
-
-    await model.fit(xs, ys, {epochs: 1});
-    const numTensors1 = memory().numTensors;
-
-    const disposalResult = model.dispose();
-    const numTensors2 = memory().numTensors;
-    // The optimizerNumGlobalVariables comes from the intrinsic weights of the
-    // ADAM optimizer, e.g., accBeta1, accBeta2.
-    const optimizerNumGlobalVariables = 2;
-    // The optimizerNumVariablesPerWeight comes from accumulatedFirstMoment and
-    // accumulatedSecondMoment, which are computed per model weight.
-    const optimizerNumVariablesPerWeight = 2;
-    const numModelWeights = 4;
-    expect(disposalResult.numDisposedVariables)
-        .toEqual(
-            numModelWeights + optimizerNumGlobalVariables +
-            numModelWeights * optimizerNumVariablesPerWeight);
-    expect(disposalResult.refCountAfterDispose).toEqual(0);
-    expect(numTensors1 - numTensors2)
-        .toEqual(
-            numModelWeights + optimizerNumGlobalVariables +
-            numModelWeights * optimizerNumVariablesPerWeight);
-  });
-
-  it('Model.dispose() cleans up owned optimizer: Sequential', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [2]}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'adam'});
-
-    const xs = zeros([4, 2]);
-    const ys = zeros([4, 1]);
-
-    await model.fit(xs, ys, {epochs: 1});
-    const numTensors1 = memory().numTensors;
-
-    const disposalResult = model.dispose();
-    const numTensors2 = memory().numTensors;
-
-    // The optimizerNumGlobalVariables comes from the intrinsic weights of the
-    // ADAM optimizer, e.g., accBeta1, accBeta2.
-    const optimizerNumGlobalVariables = 2;
-    // The optimizerNumVariablesPerWeight comes from accumulatedFirstMoment and
-    // accumulatedSecondMoment, which are computed per model weight.
-    const optimizerNumVariablesPerWeight = 2;
-    const numModelWeights = 2;
-    expect(disposalResult.numDisposedVariables)
-        .toEqual(
-            numModelWeights + optimizerNumGlobalVariables +
-            numModelWeights * optimizerNumVariablesPerWeight);
-    expect(disposalResult.refCountAfterDispose).toEqual(0);
-    expect(numTensors1 - numTensors2)
-        .toEqual(
-            numModelWeights + optimizerNumGlobalVariables +
-            numModelWeights * optimizerNumVariablesPerWeight);
-  });
-
-  it('Model.dispose() skips non-owned optimizer: Functional', async () => {
-    const input1 = tfl.input({shape: [2]});
-    const input2 = tfl.input({shape: [2]});
-    const y1 = tfl.layers.add().apply([input1, input2]);
-    const y2 = tfl.layers.concatenate().apply([input1, input2]);
-    const output1 =
-        tfl.layers
-            .dense({units: 1, activation: 'linear', kernelInitializer: 'zeros'})
-            .apply(y1) as tfl.SymbolicTensor;
-    const output2 =
-        tfl.layers
-            .dense(
-                {units: 1, activation: 'sigmoid', kernelInitializer: 'zeros'})
-            .apply(y2) as tfl.SymbolicTensor;
-    const model =
-        tfl.model({inputs: [input1, input2], outputs: [output1, output2]});
-    const optimizer = new tfc.AdamOptimizer(1e-3, 0.9, 0.999, 1e-6);
-    model.compile(
-        {loss: ['meanSquaredError', 'binaryCrossentropy'], optimizer});
-
-    const xs: Tensor[] = [zeros([4, 2]), zeros([4, 2])];
-    const ys: Tensor[] = [zeros([4, 1]), zeros([4, 1])];
-
-    await model.fit(xs, ys, {epochs: 1});
-    const numTensors1 = memory().numTensors;
-
-    const disposalResult = model.dispose();
-    const numTensors2 = memory().numTensors;
-    // Only the weights of the model (not including the optimizer) should have
-    // been disposed.
-    expect(disposalResult.numDisposedVariables).toEqual(4);
-    expect(disposalResult.refCountAfterDispose).toEqual(0);
-    expect(numTensors1 - numTensors2).toEqual(4);
-  });
-
-  it('Model.dispose() skips non-owned optimizer: Sequential', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [2]}));
-    const optimizer = new tfc.AdamOptimizer(1e-3, 0.9, 0.999, 1e-6);
-    model.compile({loss: 'meanSquaredError', optimizer});
-
-    const xs = zeros([4, 2]);
-    const ys = zeros([4, 1]);
-
-    await model.fit(xs, ys, {epochs: 1});
-    const numTensors1 = memory().numTensors;
-
-    const disposalResult = model.dispose();
-    const numTensors2 = memory().numTensors;
-
-    // Only the Model's own weights should have been disposed.
-    expect(disposalResult.numDisposedVariables).toEqual(2);
-    expect(disposalResult.refCountAfterDispose).toEqual(0);
-    expect(numTensors1 - numTensors2).toEqual(2);
-  });
-
-  it('Invalid dict loss: nonexistent output name', () => {
-    createDenseModelAndData();
-    expect(() => model.compile({
-      optimizer: 'SGD',
-      loss: {'Foo': 'meanSquaredError'}
-    })).toThrowError(/Unknown entry in loss dictionary:.*Foo.*/);
-  });
-
-  it('Invalid Array loss: missing loss for an output', () => {
-    createDenseModelWithTwoOutputsAndData();
-    expect(() => twoOutputModel.compile({
-      optimizer: 'SGD',
-      loss: ['meanSquaredError']
-    })).toThrowError(/should have one entry per model output.*has 2 output/);
-  });
-
-  it('Calling fit without compile leads to error', () => {
-    createDenseModelAndData(true);
-    const fitPromise =
-        model.fit(inputs, targets, {batchSize: numSamples, epochs: 1});
-    fitPromise.catch(error => {
-      expect(error.message).toContain('You must compile a model before');
-    });
-  });
-
-  it('Invalid batchSize leads to Error', async () => {
-    createDenseModelAndData();
-    const badBatchSizeValues: Array<number|string> = [0, -1, 3.14, 'a'];
-    for (const batchSize of badBatchSizeValues) {
-      let errorCaught: Error;
-      try {
-        // tslint:disable-next-line:no-any
-        await model.fit(inputs, targets, {batchSize: batchSize as any});
-      } catch (err) {
-        errorCaught = err;
-      }
-      expect(errorCaught.message)
-          .toEqual(`batchSize is required to be a positive integer, but got ${
-              batchSize}`);
-    }
-  });
-});
-
-describeMathCPUAndWebGL2(
-    'LayersModel.fit with training-sensitive layers', () => {
-      it('Correct training arg during fit/evaluate/predict', async () => {
-        const inputTensor = tfl.layers.input(
-            {shape: [1], name: 'inputLayer1', dtype: 'float32'});
-        const layer1 = tfl.layers.dense({units: 1});
-        const layer2 = tfl.layers.dropout({rate: 0.5});
-
-        // Hook the dropout layer to observe the training arg values during the
-        // fit(), evaluate() and predict() calls.
-        const dropoutLayerTrainingFlags: boolean[] = [];
-        const recordDropoutTrainingArgHook =
-            (inputs: Tensor|Tensor[], kwargs: Kwargs) => {
-              dropoutLayerTrainingFlags.push(kwargs.training as boolean);
-            };
-        layer2.setCallHook(recordDropoutTrainingArgHook);
-
-        const output =
-            layer2.apply(layer1.apply(inputTensor)) as tfl.SymbolicTensor;
-        const model =
-            new tfl.LayersModel({inputs: [inputTensor], outputs: [output]});
-        model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-        const xs = ones([4, 1]);
-        const ys = ones([4, 1]);
-
-        // 1. Call fit: Dropout layer should be called twice, with training as
-        // true.
-        await model.fit(xs, ys, {epochs: 2, batchSize: 4});
-        expect(dropoutLayerTrainingFlags).toEqual([true, true]);
-
-        // 2. Call evaluate, Dropout layer should be called once, without
-        // training defined.
-        model.evaluate(xs, ys, {batchSize: 4});
-        expect(dropoutLayerTrainingFlags).toEqual([true, true, undefined]);
-
-        // 3. Call predict, Dropout layer should be called once, without
-        // training
-        //   defined.
-        model.predict(xs, {batchSize: 4});
-        expect(dropoutLayerTrainingFlags).toEqual([
-          true, true, undefined, undefined
-        ]);
-      });
-    });
-
-describeMathCPUAndGPU(
-    'LayersModel.predict and LayersModel.evaluate: No memory leak', () => {
-      const inputSize = 4;  // Input vector size for model with one input.
-
-      const inputTensor = tfl.layers.input(
-          {shape: [inputSize], name: 'inputLayer1', dtype: 'float32'});
-      let model: tfl.LayersModel;
-      let inputs: Tensor;
-      let targets: Tensor;
-
-      function createDenseModelAndData(
-          numSamples: number,
-          kernelRegularizer?: string|Regularizer,
-          biasRegularizer?: string|Regularizer,
-          ): void {
-        const layer = tfl.layers.dense(
-            {units: 1, kernelInitializer: 'ones', kernelRegularizer});
-        const output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-        model = new tfl.LayersModel({inputs: [inputTensor], outputs: [output]});
-        inputs = ones([numSamples, inputSize]);
-        targets = ones([numSamples, 1]);
-      }
-
-      it('predict: Single batch', () => {
-        const numExamples = 5;
-        const batchSize = 32;  // batchSize >= numExamples ==> a single batch.
-        createDenseModelAndData(numExamples);
-        // Burn-in call.
-        let out = model.predict(inputs, {batchSize}) as Tensor;
-        out.dispose();
-        const numTensors0 = memory().numTensors;
-
-        // Actual call.
-        out = model.predict(inputs, {batchSize}) as Tensor;
-        out.dispose();
-        const numTensors1 = memory().numTensors;
-        expect(numTensors1).toEqual(numTensors0);
-      });
-
-      it('predict: Two batches', () => {
-        const numExamples = 5;
-        const batchSize = 3;  // batchSize < numExamples ==> multiple batches.
-        createDenseModelAndData(numExamples);
-        // Burn-in call.
-        let out = model.predict(inputs, {batchSize}) as Tensor;
-        out.dispose();
-        const numTensors0 = memory().numTensors;
-
-        // Actual call.
-        out = model.predict(inputs, {batchSize}) as Tensor;
-        out.dispose();
-        const numTensors1 = memory().numTensors;
-        expect(numTensors1).toEqual(numTensors0);
-      });
-
-      it('evaluate: Single batch, no metric', () => {
-        const numExamples = 5;
-        createDenseModelAndData(numExamples);
-        model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-        const batchSize = 32;  // batchSize >= numExamples ==> a single batch.
-        // Burn-in call.
-        let out = model.evaluate(inputs, targets, {batchSize}) as Tensor;
-        out.dispose();
-        const numTensors0 = memory().numTensors;
-
-        // Actual call.
-        out = model.evaluate(inputs, targets, {batchSize}) as Tensor;
-        out.dispose();
-        const numTensors1 = memory().numTensors;
-        expect(numTensors1).toEqual(numTensors0);
-      });
-
-      it('evaluate: Two batches, no metric', () => {
-        const numExamples = 5;
-        createDenseModelAndData(numExamples);
-        model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-        const batchSize = 3;  // batchSize < numExamples ==> multiple batches.
-        // Burn-in call.
-        let out = model.evaluate(inputs, targets, {batchSize}) as Tensor;
-        out.dispose();
-        const numTensors0 = memory().numTensors;
-
-        // Actual call.
-        out = model.evaluate(inputs, targets, {batchSize}) as Tensor;
-        out.dispose();
-        const numTensors1 = memory().numTensors;
-        expect(numTensors1).toEqual(numTensors0);
-      });
-
-      it('evaluate: Two batches, with metric', () => {
-        const numExamples = 5;
-        createDenseModelAndData(numExamples);
-        model.compile(
-            {optimizer: 'SGD', loss: 'meanSquaredError', metrics: ['mae']});
-        const batchSize = 3;  // batchSize < numExamples ==> multiple batches.
-        // Burn-in call.
-        let out = model.evaluate(inputs, targets, {batchSize}) as Tensor[];
-        out.forEach(tensor => tensor.dispose());
-        const numTensors0 = memory().numTensors;
-
-        // Actual call.
-        out = model.evaluate(inputs, targets, {batchSize}) as Tensor[];
-        out.forEach(tensor => tensor.dispose());
-        const numTensors1 = memory().numTensors;
-        expect(numTensors1).toEqual(numTensors0);
-      });
-    });
-
-describeMathCPUAndWebGL2('LayersModel.fit: No memory leak', () => {
-  const inputSize = 4;   // Input vector size for model with one input.
-  const numSamples = 5;  // Number of samples in a batch.
-
-  const inputTensor = tfl.layers.input(
-      {shape: [inputSize], name: 'inputLayer1', dtype: 'float32'});
-  let model: tfl.LayersModel;
-  let inputs: Tensor;
-  let targets: Tensor;
-  let valInputs: Tensor;
-  let valTargets: Tensor;
-
-  function createDenseModelAndData(
-      useBias = false,
-      kernelRegularizer?: string|Regularizer,
-      biasRegularizer?: string|Regularizer,
-      ): void {
-    const layer = tfl.layers.dense(
-        {units: 1, useBias, kernelInitializer: 'ones', kernelRegularizer});
-    const output = layer.apply(inputTensor) as tfl.SymbolicTensor;
-    model = new tfl.LayersModel({inputs: [inputTensor], outputs: [output]});
-    inputs = ones([numSamples, inputSize]);
-    targets = ones([numSamples, 1]);
-    valInputs = zeros([numSamples, inputSize]);
-    valTargets = zeros([numSamples, 1]);
-  }
-
-  it('Repeated fit calls leads to no memory leak: no validation or metrics',
-     async () => {
-       createDenseModelAndData();
-
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       // Use batchSize === numSamples to get exactly one batch.
-       await model.fit(inputs, targets, {batchSize: numSamples, epochs: 1});
-       const numTensors0 = memory().numTensors;
-       for (let i = 0; i < 2; ++i) {
-         await model.fit(inputs, targets, {batchSize: numSamples, epochs: 1});
-         const numTensorsNow = memory().numTensors;
-         if (numTensorsNow > numTensors0) {
-           fail(
-               `Memory leak detected during fit(): Leaked ` +
-               `${numTensorsNow - numTensors0} tensor(s) after the ` +
-               `${i + 1}-th fit() call.`);
-         }
-       }
-     });
-
-  it('Repeated fit calls leads to no memory leak: batchSize=1, ' +
-         'no validation or metrics',
-     async () => {
-       createDenseModelAndData();
-
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       const batchSize = 1;  // Use batchSize = 1.
-       await model.fit(inputs, targets, {batchSize, epochs: 1});
-       const numTensors0 = memory().numTensors;
-       for (let i = 0; i < 2; ++i) {
-         await model.fit(inputs, targets, {batchSize, epochs: 1});
-         const numTensorsNow = memory().numTensors;
-         if (numTensorsNow > numTensors0) {
-           fail(
-               `Memory leak detected during fit(): Leaked ` +
-               `${numTensorsNow - numTensors0} tensor(s) after the ` +
-               `${i + 1}-th fit() call.`);
-         }
-       }
-     });
-
-  it('Repeated fit calls leads to no memory leak: with metrics', async () => {
-    createDenseModelAndData();
-
-    model.compile(
-        {optimizer: 'SGD', loss: 'meanSquaredError', metrics: ['mse']});
-    // Use batchSize === numSamples to get exactly one batch.
-    await model.fit(inputs, targets, {batchSize: numSamples, epochs: 1});
-    const numTensors0 = memory().numTensors;
-    for (let i = 0; i < 2; ++i) {
-      await model.fit(inputs, targets, {batchSize: numSamples, epochs: 1});
-      const numTensorsNow = memory().numTensors;
-      if (numTensorsNow > numTensors0) {
-        fail(
-            `Memory leak detected during fit(): Leaked ` +
-            `${numTensorsNow - numTensors0} tensor(s) after the ` +
-            `${i + 1}-th fit() call.`);
-      }
-    }
-  });
-
-  it('Repeated fit calls leads to no memory leak: validationSplit',
-     async () => {
-       createDenseModelAndData();
-
-       const validationSplit = 0.4;
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       // Use batchSize === numSamples to get exactly one batch.
-       await model.fit(
-           inputs, targets,
-           {batchSize: numSamples, epochs: 1, validationSplit});
-       const numTensors0 = memory().numTensors;
-       for (let i = 0; i < 2; ++i) {
-         await model.fit(
-             inputs, targets,
-             {batchSize: numSamples, epochs: 1, validationSplit});
-         const numTensorsNow = memory().numTensors;
-         if (numTensorsNow > numTensors0) {
-           fail(
-               `Memory leak detected during fit(): Leaked ` +
-               `${numTensorsNow - numTensors0} tensor(s) after the ` +
-               `${i + 1}-th fit() call.`);
-         }
-       }
-     });
-
-  it('Repeated fit calls of 1d target leads to no memory leak: validationSplit',
-     async () => {
-       createDenseModelAndData();
-
-       const validationSplit = 0.4;
-       targets = ones([numSamples]);
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       const numTensors0 = memory().numTensors;
-       // Use batchSize === numSamples to get exactly one batch.
-       await model.fit(
-           inputs, targets,
-           {batchSize: 2, epochs: 10, validationSplit, shuffle: true});
-       for (let i = 0; i < 2; ++i) {
-         await model.fit(
-             inputs, targets,
-             {batchSize: 2, epochs: 10, validationSplit, shuffle: true});
-         const numTensorsNow = memory().numTensors;
-         if (numTensorsNow > numTensors0) {
-           fail(
-               `Memory leak detected during fit(): Leaked ` +
-               `${numTensorsNow - numTensors0} tensor(s) after the ` +
-               `${i + 1}-th fit() call.`);
-         }
-       }
-     });
-
-  it('Repeated fit calls leads to no memory leak: validationData',
-     async () => {
-       createDenseModelAndData();
-
-       const validationData: [Tensor, Tensor] = [valInputs, valTargets];
-       model.compile({optimizer: 'SGD', loss: 'meanSquaredError'});
-       // Use batchSize === numSamples to get exactly one batch.
-       await model.fit(
-           inputs, targets, {batchSize: numSamples, epochs: 1, validationData});
-       const numTensors0 = memory().numTensors;
-       for (let i = 0; i < 2; ++i) {
-         await model.fit(
-             inputs, targets,
-             {batchSize: numSamples, epochs: 1, validationData});
-         const numTensorsNow = memory().numTensors;
-         if (numTensorsNow > numTensors0) {
-           fail(
-               `Memory leak detected during fit(): Leaked ` +
-               `${numTensorsNow - numTensors0} tensor(s) after the ` +
-               `${i + 1}-th fit() call.`);
-         }
-       }
-     });
-
-  it('Repeated fit calls leads to no memory leak: metrics & validationSplit',
-     async () => {
-       createDenseModelAndData();
-
-       const validationSplit = 0.4;
-       model.compile(
-           {optimizer: 'SGD', loss: 'meanSquaredError', metrics: ['mse']});
-       // Use batchSize === numSamples to get exactly one batch.
-       await model.fit(
-           inputs, targets,
-           {batchSize: numSamples, epochs: 1, validationSplit});
-       const numTensors0 = memory().numTensors;
-       for (let i = 0; i < 2; ++i) {
-         await model.fit(
-             inputs, targets,
-             {batchSize: numSamples, epochs: 1, validationSplit});
-         const numTensorsNow = memory().numTensors;
-         if (numTensorsNow > numTensors0) {
-           fail(
-               `Memory leak detected during fit(): Leaked ` +
-               `${numTensorsNow - numTensors0} tensor(s) after the ` +
-               `${i + 1}-th fit() call.`);
-         }
-       }
-     });
-
-  it('Repeated fit calls leads to no memory leak: batchSize=2, ' +
-         'metrics & validationSplit',
-     async () => {
-       createDenseModelAndData();
-
-       const validationSplit = 0.4;
-       model.compile(
-           {optimizer: 'SGD', loss: 'meanSquaredError', metrics: ['mse']});
-       const batchSize = 2;  // Use batchSize < numSamples.
-       const epochsPerIter = 2;
-       await model.fit(
-           inputs, targets, {batchSize, epochs: 1, validationSplit});
-       const numTensors0 = memory().numTensors;
-       for (let i = 0; i < 2; ++i) {
-         const history = await model.fit(
-             inputs, targets,
-             {batchSize, epochs: epochsPerIter, validationSplit});
-         expect(history.history['loss'].length).toEqual(epochsPerIter);
-         expect(history.history['val_loss'].length).toEqual(epochsPerIter);
-         expect(history.history['mse'].length).toEqual(epochsPerIter);
-         expect(history.history['val_mse'].length).toEqual(epochsPerIter);
-         const numTensorsNow = memory().numTensors;
-         if (numTensorsNow > numTensors0) {
-           fail(
-               `Memory leak detected during fit(): Leaked ` +
-               `${numTensorsNow - numTensors0} tensor(s) after the ` +
-               `${i + 1}-th fit() call.`);
-         }
-       }
-     });
-
-  it('Fit with onEpochEnd callback: no memory leak: validation & metrics',
-     async () => {
-       createDenseModelAndData();
-
-       model.compile(
-           {optimizer: 'SGD', loss: 'meanSquaredError', metrics: ['mse']});
-       const validationSplit = 0.4;
-
-       // First, perform a burn-in call.
-       await model.fit(
-           inputs, targets,
-           {batchSize: numSamples, epochs: 1, validationSplit});
-       const numTensors0 = memory().numTensors;
-
-       // Perform actual testing calls.
-       const numFitCalls = 2;
-       for (let n = 0; n < numFitCalls; ++n) {
-         const tensorCounts: number[] = [];
-         await model.fit(inputs, targets, {
-           batchSize: numSamples,
-           epochs: 4,
-           validationSplit,
-           callbacks: {
-             onEpochEnd: async () => {
-               tensorCounts.push(memory().numTensors);
-             }
-           }
-         });
-         expect(tensorCounts.length).toEqual(4);
-         if (unique(tensorCounts).length !== 1) {
-           fail(
-               `Detected WebGL memory leak during fit() call with ` +
-               `onEpochEnd callback: tensor counts: ${tensorCounts}.`);
-         }
-         const numTensors1 = memory().numTensors;
-         if (numTensors1 > numTensors0) {
-           fail(
-               `Detected memory leak of ${numTensors1 - numTensors0} ` +
-               `tensor(s) after fit() call ${n + 1} of ${numFitCalls} ` +
-               `with onEpochEnd callback.`);
-         }
-       }
-     });
-
-  it('Fit with onBatchEnd callback: no memory leak: validation & metrics',
-     async () => {
-       createDenseModelAndData();
-
-       model.compile(
-           {optimizer: 'SGD', loss: 'meanSquaredError', metrics: ['mse']});
-       const validationSplit = 0.4;
-       const epochs = 3;
-       const batchesPerEpoch = numSamples * (1 - validationSplit);
-
-       // First, perform a burn-in call.
-       await model.fit(inputs, targets, {
-         batchSize: 1,
-         epochs,
-         validationSplit,
-       });
-       const numTensors0 = memory().numTensors;
-
-       // Perform actual testing calls.
-       for (let n = 0; n < 2; ++n) {
-         const tensorCounts: number[] = [];
-         await model.fit(inputs, targets, {
-           batchSize: 1,
-           epochs,
-           validationSplit,
-           callbacks: {
-             onBatchEnd: async (batch: number, logs: Logs) => {
-               tensorCounts.push(memory().numTensors);
-             }
-           }
-         });
-         for (let epochIndex = 0; epochIndex < epochs; ++epochIndex) {
-           // Get the tensor counts within an epoch (i.e., from the first
-           // batch till the penultimate one.) Assert that the counts are
-           // constant, i.e., no increase in the tensor count within the
-           // epoch. N.B.: Even though the tensor count is expected to be
-           // constant across batches, across epochs, the count will increase,
-           // due to the per-epoch loss and metric values stored for the
-           // returned history object, which are currently downloaded via
-           // data() calls only at the end of the fit() call.
-           const beginBatch = batchesPerEpoch * epochIndex;
-           const endBatch = batchesPerEpoch * (epochIndex + 1);
-           const inEpochTensorCounts =
-               tensorCounts.slice(beginBatch, endBatch - 1);
-           if (unique(inEpochTensorCounts).length !== 1) {
-             fail(
-                 `Detected WebGL memory leak within epoch ${epochIndex + 1} ` +
-                 `of ${epochs} of the fit() call with ` +
-                 `onBatchEnd callback: tensor counts: ${inEpochTensorCounts}.`);
-           }
-         }
-         expect(tensorCounts.length).toEqual(batchesPerEpoch * epochs);
-         const numTensors1 = memory().numTensors;
-         if (numTensors1 > numTensors0) {
-           fail(
-               `Detected memory leak of ${numTensors1 - numTensors0} ` +
-               `tensor(s) after fit() call with callback.`);
-         }
-       }
-     });
-});
-
-describeMathGPU('LayersModel.fit: yieldEvery', () => {
-  function createDummyModel(inputSize: number): tfl.LayersModel {
-    const model = tfl.sequential();
-    const layerSize = 10;
-    model.add(tfl.layers.dense(
-        {units: layerSize, inputShape: [inputSize], activation: 'relu'}));
-    model.add(tfl.layers.dense({units: 1}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    return model;
-  }
-
-  it('auto: 1 batch per epoch; 5 epochs', async () => {
-    const wait = DEFAULT_YIELD_EVERY_MS;
-    const timeBetweenCalls = [
-      0,
-      1,
-      wait + 1,  // Should call.
-      wait + 1,  // Should call.
-      1,
-      1,
-    ];
-    let counter = 0;
-    let prevTime = 0;
-    const nowFunc = jasmine.createSpy('now').and.callFake(() => {
-      prevTime += timeBetweenCalls[counter++];
-      return prevTime;
-    });
-    let nextFrameCallCount = 0;
-    const nextFrameFunc =
-        jasmine.createSpy('nextFrame').and.callFake(async () => {
-          nextFrameCallCount++;
-        });
-
-    const inputSize = 2;
-    const numExamples = 10;
-    const epochs = 5;
-    const model = createDummyModel(inputSize);
-    const xs = ones([numExamples, inputSize]);
-    const ys = ones([numExamples, 1]);
-    const onYieldEpochIds: number[] = [];
-    const onYieldBatchesIds: number[] = [];
-    const history = await model.fit(xs, ys, {
-      epochs,
-      batchSize: numExamples,
-      callbacks: {
-        nowFunc,
-        nextFrameFunc,
-        onYield: async (epoch, batch, _logs) => {
-          onYieldBatchesIds.push(batch);
-          onYieldEpochIds.push(epoch);
-        }
-      }
-    });
-    expect(history.history.loss.length).toEqual(epochs);
-    // There are 5 batches in total (1 batch per epoch). We expect next frame
-    // to be called twice, after epoch 1 and after epoch 2.
-    expect(nextFrameCallCount).toBe(2);
-    expect(onYieldEpochIds).toEqual([1, 2]);
-    expect(onYieldBatchesIds).toEqual([0, 0]);
-  });
-
-  it('auto: 2 batches per epoch; 4 epochs', async () => {
-    const yieldEvery = DEFAULT_YIELD_EVERY_MS;
-    const timeBetweenCalls = [
-      0,
-      1,
-      yieldEvery + 1,  // Should call.
-      yieldEvery + 1,  // Should call.
-      1,
-      yieldEvery + 1,  // SHould call.
-      yieldEvery + 1,  // Should call.
-      1,
-      1,
-    ];
-    let counter = 0;
-    let prevTime = 0;
-    const nowFunc = jasmine.createSpy('now').and.callFake(() => {
-      prevTime += timeBetweenCalls[counter++];
-      return prevTime;
-    });
-    let nextFrameCallCount = 0;
-    const nextFrameFunc =
-        jasmine.createSpy('nextFrame').and.callFake(async () => {
-          nextFrameCallCount++;
-        });
-
-    const inputSize = 2;
-    const numExamples = 10;
-    const epochs = 4;
-    const model = createDummyModel(inputSize);
-    const xs = ones([numExamples, inputSize]);
-    const ys = ones([numExamples, 1]);
-    const onYieldEpochIds: number[] = [];
-    const onYieldBatchesIds: number[] = [];
-    const history = await model.fit(xs, ys, {
-      epochs,
-      batchSize: numExamples / 2,
-      callbacks: {
-        nowFunc,
-        nextFrameFunc,
-        onYield: async (epoch, batch, _logs) => {
-          onYieldBatchesIds.push(batch);
-          onYieldEpochIds.push(epoch);
-        }
-      }
-    });
-    expect(history.history.loss.length).toEqual(epochs);
-    // There are 8 batches in total (2 batches per epoch). We expect next
-    // frame to be called 3 times, after (epoch 0, batch 1), (epoch 1, batch 0)
-    // (epoch 2, batch 0) and (epoch 2, batch 1).
-    expect(nextFrameCallCount).toBe(4);
-    expect(onYieldEpochIds).toEqual([0, 1, 2, 2]);
-    expect(onYieldBatchesIds).toEqual([1, 0, 0, 1]);
-  });
-
-  it('yieldEvery: 5, 1 batch per epoch; 5 epochs', async () => {
-    const yieldEvery = 5;
-    const timeBetweenCalls = [
-      0, 1,
-      yieldEvery + 1,     // Should call.
-      1, yieldEvery + 1,  // Should call.
-      yieldEvery + 1,     // Should call.
-    ];
-    let counter = 0;
-    let prevTime = 0;
-    const nowFunc = jasmine.createSpy('now').and.callFake(() => {
-      prevTime += timeBetweenCalls[counter++];
-      return prevTime;
-    });
-    let nextFrameCallCount = 0;
-    const nextFrameFunc =
-        jasmine.createSpy('nextFrame').and.callFake(async () => {
-          nextFrameCallCount++;
-        });
-    const inputSize = 2;
-    const numExamples = 10;
-    const epochs = 5;
-    const model = createDummyModel(inputSize);
-    const xs = ones([numExamples, inputSize]);
-    const ys = ones([numExamples, 1]);
-    const onYieldEpochIds: number[] = [];
-    const onYieldBatchesIds: number[] = [];
-    const history = await model.fit(xs, ys, {
-      epochs,
-      batchSize: numExamples,
-      yieldEvery,
-      callbacks: {
-        nowFunc,
-        nextFrameFunc,
-        onYield: async (epoch, batch, _logs) => {
-          onYieldBatchesIds.push(batch);
-          onYieldEpochIds.push(epoch);
-        }
-      }
-    });
-    expect(history.history.loss.length).toEqual(epochs);
-    // There are 5 batches in total (1 batch per epoch). We expect next frame
-    // to be called 3 times, after epoch 1, epoch 3 and epoch 4.
-    expect(nextFrameCallCount).toBe(3);
-    expect(onYieldEpochIds).toEqual([1, 3, 4]);
-    expect(onYieldBatchesIds).toEqual([0, 0, 0]);
-  });
-
-  it('fails when onYield is provided, but yieldEvery is never', async () => {
-    const inputSize = 2;
-    const numExamples = 10;
-    const epochs = 5;
-    const model = createDummyModel(inputSize);
-    const xs = ones([numExamples, inputSize]);
-    const ys = ones([numExamples, 1]);
-    try {
-      await model.fit(xs, ys, {
-        epochs,
-        batchSize: numExamples,
-        yieldEvery: 'never',
-        callbacks: {onYield: async (_epoch, _batch, _logs) => {}}
-      });
-      fail('Model.fit should fail');
-    } catch {
-
-    }
-  });
-
-  it('batch: uneven 9 batches per epoch; 2 epochs', async () => {
-    const presetBatchTimestamps = [0, 2, 4, 6, 8, 10];
-    let counter = 0;
-    const nowFunc = jasmine.createSpy('now').and.callFake(
-        () => presetBatchTimestamps[counter++]);
-    let nextFrameCallCount = 0;
-    const nextFrameFunc =
-        jasmine.createSpy('nextFrame').and.callFake(async () => {
-          nextFrameCallCount++;
-        });
-
-    const inputSize = 1;
-    const numExamples = 10;
-    const epochs = 2;
-    const model = createDummyModel(inputSize);
-    const xs = ones([numExamples, inputSize]);
-    const ys = ones([numExamples, 1]);
-    const history = await model.fit(xs, ys, {
-      epochs,
-      batchSize: 4,
-      yieldEvery: 'batch',
-      callbacks: {nowFunc, nextFrameFunc}
-    });
-    expect(history.history.loss.length).toEqual(epochs);
-    // There are `ceil(10 / 4)` batches per epoch.
-    expect(nextFrameCallCount).toEqual(Math.ceil(10 / 4) * epochs);
-  });
-
-  it('epoch: 10 batches per epoch; 2 epochs', async () => {
-    let nextFrameCallCount = 0;
-    const nextFrameFunc =
-        jasmine.createSpy('nextFrame').and.callFake(async () => {
-          nextFrameCallCount++;
-        });
-
-    const inputSize = 5;
-    const numExamples = 10;
-    const epochs = 2;
-    const model = createDummyModel(inputSize);
-    const xs = ones([numExamples, inputSize]);
-    const ys = ones([numExamples, 1]);
-    const history = await model.fit(xs, ys, {
-      callbacks: {nextFrameFunc},
-      epochs,
-      batchSize: numExamples / 10,
-      yieldEvery: 'epoch'
-    });
-    expect(history.history.loss.length).toEqual(epochs);
-    expect(nextFrameCallCount).toEqual(epochs);
-  });
-
-  it('never: 2 batches per epoch; 20 epochs', async () => {
-    let nextFrameCallCount = 0;
-    const nextFrameFunc =
-        jasmine.createSpy('nextFrame').and.callFake(async () => {
-          nextFrameCallCount++;
-        });
-
-    const inputSize = 5;
-    const numExamples = 10;
-    const epochs = 4;
-    const model = createDummyModel(inputSize);
-    const xs = ones([numExamples, inputSize]);
-    const ys = ones([numExamples, 1]);
-    const history = await model.fit(xs, ys, {
-      callbacks: {nextFrameFunc},
-      epochs,
-      batchSize: numExamples / 2,
-      yieldEvery: 'never'
-    });
-    expect(history.history.loss.length).toEqual(epochs);
-    // Due to yieldEvery = 'never', no `await nextFrame()` call should have
-    // happened.
-    expect(nextFrameCallCount).toEqual(0);
-  });
-
-  // TODO: disabled the test, since it will be very hard to inject dependency of
-  // resolveScalarsInLogs, it will be better to test result instead of internal
-  // code structure.
-  // tslint:disable-next-line: ban
-  xit('resolveScalarInLogs is not called if no custom callbacks', async () => {
-    const inputSize = 1;
-    const numExamples = 10;
-    const batchSize = 2;
-    const epochs = 2;
-    const model = createDummyModel(inputSize);
-    const xs = ones([numExamples, inputSize]);
-    const ys = ones([numExamples, 1]);
-
-    const spy = spyOn(logs, 'resolveScalarsInLogs').and.callThrough();
-    await model.fit(xs, ys, {epochs, batchSize, yieldEvery: 'never'});
-    expect(spy).not.toHaveBeenCalled();
-  });
-});
-
-describeMathCPUAndWebGL2('LayersModel.trainOnBatch', () => {
-  // Reference Python Keras code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Dense(
-  //     1, input_shape=[3], kernel_initializer='zeros'))
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // batch_size = 4
-  // xs = np.ones([batch_size, 3])
-  // ys = np.ones([batch_size, 1])
-  //
-  // for _ in range(3):
-  //   loss = model.train_on_batch(xs, ys)
-  //   print(loss)
-  // ```
-  it('Sequential MLP: correctness', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense(
-        {units: 1, inputShape: [3], kernelInitializer: 'zeros'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const batchSize = 4;
-    const xs = tfc.ones([batchSize, 3]);
-    const ys = tfc.ones([batchSize, 1]);
-    let loss = await model.trainOnBatch(xs, ys) as number;
-    expect(loss).toBeCloseTo(1.0);
-    loss = await model.trainOnBatch(xs, ys) as number;
-    expect(loss).toBeCloseTo(0.8464);
-    loss = await model.trainOnBatch(xs, ys) as number;
-    expect(loss).toBeCloseTo(0.716393);
-  });
-
-  // Reference Python Keras code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // input1 = keras.Input(shape=[2])
-  // input2 = keras.Input(shape=[2])
-  // y1 = keras.layers.Add()([input1, input2])
-  // y2 = keras.layers.Concatenate()([input1, input2])
-  // output1 = keras.layers.Dense(
-  //     units=1,
-  //     activation='linear',
-  //     kernel_initializer='zeros')(y1)
-  // output2 = keras.layers.Dense(
-  //     units=1,
-  //     activation='sigmoid',
-  //     kernel_initializer='zeros')(y2)
-  // model = keras.Model(inputs=[input1, input2], outputs=[output1, output2])
-  // model.compile(loss=['mean_squared_error', 'binary_crossentropy'],
-  //               optimizer='sgd')
-  //
-  // batch_size = 4
-  // xs1 = np.ones([batch_size, 2])
-  // xs2 = np.ones([batch_size, 2])
-  // ys1 = np.ones([batch_size, 1])
-  // ys2 = np.ones([batch_size, 1])
-  //
-  // for _ in range(3):
-  //   losses = model.train_on_batch([xs1, xs2], [ys1, ys2])
-  //   print(losses)
-  // ```
-  it('Functional two inputs and two outputs: correctness', async () => {
-    const input1 = tfl.input({shape: [2]});
-    const input2 = tfl.input({shape: [2]});
-    const y1 = tfl.layers.add().apply([input1, input2]);
-    const y2 = tfl.layers.concatenate().apply([input1, input2]);
-    const output1 =
-        tfl.layers
-            .dense({units: 1, activation: 'linear', kernelInitializer: 'zeros'})
-            .apply(y1) as tfl.SymbolicTensor;
-    const output2 =
-        tfl.layers
-            .dense(
-                {units: 1, activation: 'sigmoid', kernelInitializer: 'zeros'})
-            .apply(y2) as tfl.SymbolicTensor;
-    const model =
-        tfl.model({inputs: [input1, input2], outputs: [output1, output2]});
-    model.compile(
-        {loss: ['meanSquaredError', 'binaryCrossentropy'], optimizer: 'sgd'});
-
-    const batchSize = 4;
-    const xs1 = tfc.ones([batchSize, 2]);
-    const xs2 = tfc.ones([batchSize, 2]);
-    const ys1 = tfc.ones([batchSize, 1]);
-    const ys2 = tfc.ones([batchSize, 1]);
-    let losses = await model.trainOnBatch([xs1, xs2], [ys1, ys2]) as number[];
-    expect(losses.length).toEqual(3);
-    expect(losses[0]).toBeCloseTo(1.6931472);
-    expect(losses[1]).toBeCloseTo(1.0);
-    expect(losses[2]).toBeCloseTo(0.6931472);
-    losses = await model.trainOnBatch([xs1, xs2], [ys1, ys2]) as number[];
-    expect(losses.length).toEqual(3);
-    expect(losses[0]).toBeCloseTo(1.3531253);
-    expect(losses[1]).toBeCloseTo(0.6724);
-    expect(losses[2]).toBeCloseTo(0.68072534);
-    losses = await model.trainOnBatch([xs1, xs2], [ys1, ys2]) as number[];
-    expect(losses.length).toEqual(3);
-    expect(losses[0]).toBeCloseTo(1.1207337);
-    expect(losses[1]).toBeCloseTo(0.45212176);
-    expect(losses[2]).toBeCloseTo(0.66861194);
-  });
-
-  // Reference Python Keras code.
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Dense(
-  //     3, input_shape=[2], activation='softmax', kernel_initializer='ones'))
-  // model.compile(loss='categorical_crossentropy', optimizer='sgd')
-  //
-  // xs = np.array([[0.5, 0.5], [1, 1], [0, 1]], dtype=np.float32)
-  // ys = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.float32)
-  //
-  // for _ in range(3):
-  //   loss = model.train_on_batch(xs, ys)
-  //   print(loss)
-  // ```
-  it('Categorical: Correctness and no memory leak', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [2],
-      activation: 'softmax',
-      kernelInitializer: 'ones'
-    }));
-    model.compile({loss: 'categoricalCrossentropy', optimizer: 'sgd'});
-
-    const xs = tfc.tensor2d([[0.5, 0.5], [1, 1], [0, 1]]);
-    const ys = tfc.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]]);
-    // Perform burn-in.
-    model.trainOnBatch(xs, ys);
-    const numTensors0 = memory().numTensors;
-    for (let i = 0; i < 3; ++i) {
-      const loss = await model.trainOnBatch(xs, ys);
-      tfc.tidy(() => {
-        if (i === 0) {
-          expect(loss).toBeCloseTo(1.0986123);
-        } else if (i === 1) {
-          expect(loss).toBeCloseTo(1.0978721);
-        } else {
-          expect(loss).toBeCloseTo(1.0971345);
-        }
-      });
-      // Assert no tensor memory leak.
-      expect(memory().numTensors).toBeLessThanOrEqual(numTensors0);
-    }
-  });
-
-  // Reference Python Keras code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Dense(
-  //     units=3,
-  //     input_shape=[2],
-  //     activation='softmax',
-  //     kernel_initializer='ones'
-  // ))
-  // model.compile(
-  //     loss='sparse_categorical_crossentropy',
-  //     optimizer='sgd',
-  //     metrics=['acc'])
-  // model.summary()
-  //
-  // xs = np.array([[0, 0.5], [0.5, 1], [0, 1]], dtype=np.float32)
-  // ys = np.array([[2], [1], [0]], dtype=np.float32)
-  //
-  // for _ in range(3):
-  //   print(model.train_on_batch(xs, ys))
-  // ```
-  it('Sparse categorical: w/ metrics: correctness and no leak', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [2],
-      activation: 'softmax',
-      kernelInitializer: 'ones'
-    }));
-    model.compile({
-      loss: 'sparseCategoricalCrossentropy',
-      optimizer: 'sgd',
-      metrics: ['acc']
-    });
-
-    const xs = tfc.tensor2d([[0, 0.5], [0.5, 1], [0, 1]]);
-    const ys = tfc.tensor2d([[2], [1], [0]]);
-
-    for (let i = 0; i < 3; ++i) {
-      const [loss, acc] = await model.trainOnBatch(xs, ys) as number[];
-      if (i === 0) {
-        expect(loss).toBeCloseTo(1.0986123);
-        expect(acc).toBeCloseTo(0.3333333);
-      } else if (i === 1) {
-        expect(loss).toBeCloseTo(1.0982422);
-        expect(acc).toBeCloseTo(0.6666667);
-      } else if (i === 2) {
-        expect(loss).toBeCloseTo(1.0978734);
-        expect(acc).toBeCloseTo(0.6666667);
-      }
-    }
-  });
-});
-
-describeMathCPUAndGPU('LayersModel.evaluate', () => {
-  const numExamples = 8;
-  const inputSize = 2;
-  const outputSize = 1;
-  let model: tfl.LayersModel;
-  let x: Tensor;
-  let y: Tensor;
-  function prepModel() {
-    const input = tfl.layers.input({shape: [inputSize]});
-    const dense = tfl.layers.dense(
-        {units: outputSize, kernelInitializer: 'ones', useBias: false});
-    const output = dense.apply(input) as tfl.SymbolicTensor;
-    model = new tfl.LayersModel({inputs: input, outputs: output});
-  }
-  function prepData() {
-    x = ones([numExamples, inputSize]);
-    y = ones([numExamples, outputSize]);
-  }
-
-  it('Calling evaluate before compile leads to error', () => {
-    prepModel();
-    prepData();
-
-    expect(() => model.evaluate(x, y))
-        .toThrowError(/must compile a model before/);
-  });
-
-  const metricsValues: string[][] = [null, ['mse']];
-  const batchSizes = [null, 4, 16];
-  for (const metrics of metricsValues) {
-    for (const batchSize of batchSizes) {
-      const testTitle =
-          `metrics=${JSON.stringify(metrics)}, batchSize=${batchSize}`;
-      it(testTitle, () => {
-        prepModel();
-        prepData();
-        model.compile({optimizer: 'sgd', loss: 'meanSquaredError', metrics});
-        const losses = model.evaluate(x, y, {batchSize});
-        if (metrics == null) {
-          expectTensorsClose(losses as Scalar, scalar(1));
-        } else {
-          const lossesArray = losses as Scalar[];
-          expect(lossesArray.length).toEqual(2);
-          expectTensorsClose(lossesArray[0], scalar(1));
-          expectTensorsClose(lossesArray[1], scalar(1));
-        }
-      });
-    }
-  }
-
-  it('1D tensors as inputs and targets', () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [1]}));
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['acc']});
-
-    const xs = ones([10]);   // A batch of 10, as a 1D tensor.
-    const ys = zeros([10]);  // A batch of 10, as a 1D tensor.
-    // Do a burn-in call first.
-    tfc.dispose(model.evaluate(xs, ys, {batchSize: 4}));
-    const numTensors0 = memory().numTensors;
-    const evalOuts = model.evaluate(xs, ys, {batchSize: 4}) as Tensor[];
-    expect(evalOuts.length).toEqual(2);     // Loss and acc.
-    expect(evalOuts[0].shape).toEqual([]);  // Loss as a scalar.
-    expect(evalOuts[1].shape).toEqual([]);  // Acc as a scalar.
-    tfc.dispose(evalOuts);
-    // Assert no memory leak.
-    expect(memory().numTensors).toEqual(numTensors0);
-  });
-
-  it('Invalid batchSize value leads to Error', () => {
-    prepModel();
-    prepData();
-    expect(() => model.evaluate(x, y, {batchSize: 0}))
-        .toThrowError(
-            /batchSize is required to be a positive integer, but got 0/);
-    expect(() => model.evaluate(x, y, {batchSize: -2}))
-        .toThrowError(
-            /batchSize is required to be a positive integer, but got -2/);
-    expect(() => model.evaluate(x, y, {batchSize: 3.14}))
-        .toThrowError(
-            /batchSize is required to be a positive integer, but got 3\.14/);
-    // tslint:disable-next-line:no-any
-    expect(() => model.evaluate(x, y, {batchSize: 'a' as any}))
-        .toThrowError(
-            /batchSize is required to be a positive integer, but got a/);
-  });
-});
-
-describe('LayersModel trainable setter and getter', () => {
-  it('Setting trainable does not affect Layers', () => {
-    const model = tfl.sequential({
-      layers: [
-        tfl.layers.flatten({inputShape: [2, 5]}),
-        // Initially non-trainable.
-        tfl.layers.dense({units: 3, activation: 'relu', trainable: false}),
-        tfl.layers.dense({units: 1}),
-      ]
-    });
-
-    model.trainable = false;
-    expect(model.trainable).toEqual(false);
-    // The trainable property of the layers should be unaffected.
-    expect(model.layers[0].trainable).toEqual(true);
-    expect(model.layers[1].trainable).toEqual(false);
-    expect(model.layers[2].trainable).toEqual(true);
-
-    model.trainable = true;
-    expect(model.trainable).toEqual(true);
-    expect(model.layers[0].trainable).toEqual(true);
-    expect(model.layers[1].trainable).toEqual(false);
-    expect(model.layers[2].trainable).toEqual(true);
-  });
-
-  it('Setting trainable of model sets trainable bit of Variable', async () => {
-    const model = tfl.sequential();
-    model.add(
-        tfl.layers.dense({units: 3, activation: 'relu', inputShape: [4]}));
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.trainable = false;
-    expect(model.layers[0].weights[0].trainable).toEqual(false);
-    expect(model.layers[0].weights[1].trainable).toEqual(false);
-    expect(model.layers[1].weights[0].trainable).toEqual(false);
-    expect(model.layers[1].weights[1].trainable).toEqual(false);
-    model.trainable = true;
-    expect(model.layers[0].weights[0].trainable).toEqual(true);
-    expect(model.layers[0].weights[1].trainable).toEqual(true);
-    expect(model.layers[1].weights[0].trainable).toEqual(true);
-    expect(model.layers[1].weights[1].trainable).toEqual(true);
-  });
-
-  it('model.trainable respects layer.trainable', async () => {
-    const model = tfl.sequential();
-    model.add(
-        tfl.layers.dense({units: 3, activation: 'relu', inputShape: [4]}));
-    model.add(tfl.layers.dense({units: 1}));
-    expect(model.trainableWeights.length).toEqual(4);
-    model.layers[0].trainable = false;
-    expect(model.trainableWeights.length).toEqual(2);
-    model.trainable = false;
-    expect(model.trainableWeights.length).toEqual(0);
-    model.trainable = true;
-    expect(model.trainableWeights.length).toEqual(2);
-    model.layers[0].trainable = true;
-    expect(model.trainableWeights.length).toEqual(4);
-    model.trainable = false;
-    expect(model.trainableWeights.length).toEqual(0);
-  });
-});
-
-describeMathCPUAndGPU('LayersModel.execute', () => {
-  function createFunctionalModel():
-      [tfl.LayersModel, {[name: string]: tfl.SymbolicTensor}] {
-    const input1 = tfl.input({shape: [2, 3]});
-    const reshape1 = tfl.layers.reshape({targetShape: [3, 2]}).apply(input1) as
-        tfl.SymbolicTensor;
-    const input2 = tfl.input({shape: [3, 4]});
-    const concat =
-        tfl.layers.concatenate({axis: -1}).apply([reshape1, input2]) as
-        tfl.SymbolicTensor;
-    const model = tfl.model({inputs: [input1, input2], outputs: concat});
-
-    return [model, {input1, reshape1, input2, concat}];
-  }
-
-  function createSequentialModel(): tfl.Sequential {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 6,
-      inputShape: [4],
-      kernelInitializer: 'zeros',
-      useBias: false
-    }));
-    model.add(tfl.layers.dense(
-        {units: 3, kernelInitializer: 'zeros', useBias: false}));
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'zeros', useBias: false}));
-    return model;
-  }
-
-  it('Functional model: single output', () => {
-    const [model, layers] = createFunctionalModel();
-    const inputs = [zeros([1, 2, 3]), zeros([1, 3, 4])];
-    const outputs = model.execute(inputs, layers['reshape1'].name) as Tensor;
-    expectTensorsClose(outputs, zeros([1, 3, 2]));
-  });
-
-  it('Functional model: multiple outputs', () => {
-    const [model, layers] = createFunctionalModel();
-    const inputs = [zeros([1, 2, 3]), zeros([1, 3, 4])];
-    const outputs = model.execute(inputs, [
-      layers['reshape1'].name, layers['concat'].name, layers['input2'].name
-    ]) as Tensor[];
-    expectTensorsClose(outputs[0], zeros([1, 3, 2]));
-    expectTensorsClose(outputs[1], zeros([1, 3, 6]));
-    expectTensorsClose(outputs[2], zeros([1, 3, 4]));
-  });
-
-  it('Functional model: Dictionary of inputs', () => {
-    const [model, layers] = createFunctionalModel();
-    const inputName1 = model.inputs[0].name;
-    const inputName2 = model.inputs[1].name;
-    const inputs: NamedTensorMap = {};
-    inputs[inputName1] = zeros([1, 2, 3]);
-    inputs[inputName2] = zeros([1, 3, 4]);
-    const outputs = model.execute(inputs, [
-      layers['reshape1'].name, layers['concat'].name, layers['input2'].name
-    ]) as Tensor[];
-    expectTensorsClose(outputs[0], zeros([1, 3, 2]));
-    expectTensorsClose(outputs[1], zeros([1, 3, 6]));
-    expectTensorsClose(outputs[2], zeros([1, 3, 4]));
-  });
-
-  it('Functional model: missing input in dictionary throws Error', () => {
-    const [model, layers] = createFunctionalModel();
-    const inputName2 = model.inputs[1].name;
-    const inputs: NamedTensorMap = {};
-    inputs[inputName2] = zeros([1, 3, 4]);
-    expect(() => model.execute(inputs, layers['reshape1'].name))
-        .toThrowError(/No value is provided for .* input/);
-  });
-
-  it('Functional model: Incorrect number of inputs throws Error', () => {
-    const [model, layers] = createFunctionalModel();
-    const inputs = [zeros([1, 2, 3])];
-    expect(() => model.execute(inputs, layers['reshape1'].name))
-        .toThrowError(/The number of inputs provided \(1\) does not match .*2/);
-  });
-
-  it('Functional model: nonexistent tensor name throws Error', () => {
-    const [model, layers] = createFunctionalModel();
-    const inputs = [zeros([1, 2, 3]), zeros([1, 3, 4])];
-    const nonexistentTensorName =
-        layers['reshape1'].name + Math.random().toFixed(4);
-    expect(() => model.execute(inputs, nonexistentTensorName))
-        .toThrowError(/Cannot find SymbolicTensors for output name/);
-    expect(() => model.execute(inputs, [
-      layers['reshape1'].name, nonexistentTensorName
-    ])).toThrowError(/Cannot find SymbolicTensors for output name/);
-  });
-
-  it('Functional model: empty outputs string throws Error', () => {
-    const model = createFunctionalModel()[0];
-    const inputs = [zeros([1, 2, 3]), zeros([1, 3, 4])];
-    expect(() => model.execute(inputs, [])).toThrowError(/empty Array/);
-  });
-
-  it('Sequential model: singleton input', () => {
-    const model = createSequentialModel();
-    const input = zeros([2, 4]);
-    const outputs = model.execute(input, [
-      (model.layers[2].output as tfl.SymbolicTensor).name,
-      (model.layers[1].output as tfl.SymbolicTensor).name,
-      (model.layers[0].output as tfl.SymbolicTensor).name,
-    ]) as Tensor[];
-    expectTensorsClose(outputs[0], zeros([2, 1]));
-    expectTensorsClose(outputs[1], zeros([2, 3]));
-    expectTensorsClose(outputs[2], zeros([2, 6]));
-  });
-
-  it('Sequential model: length-1 Array input', () => {
-    const model = createSequentialModel();
-    const input = [zeros([2, 4])];
-    const output = model.execute(
-                       input,
-                       (model.layers[1].output as tfl.SymbolicTensor).name,
-                       ) as Tensor;
-    expectTensorsClose(output, zeros([2, 3]));
-  });
-
-  it('Sequential model: length-1 dictionary input', () => {
-    const model = createSequentialModel();
-    const inputs: NamedTensorMap = {};
-    inputs[(model.input as SymbolicTensor).name] = zeros([2, 4]);
-    const output = model.execute(
-                       inputs,
-                       (model.layers[1].output as tfl.SymbolicTensor).name,
-                       ) as Tensor;
-    expectTensorsClose(output, zeros([2, 3]));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/engine/training_utils.ts b/tfjs-master/tfjs-layers/src/engine/training_utils.ts
deleted file mode 100644
index 09836ba18..000000000
--- a/tfjs-master/tfjs-layers/src/engine/training_utils.ts
+++ /dev/null
@@ -1,190 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {argMax, clone, dispose, mul, reshape, Tensor, Tensor1D, tensor1d, tidy} from '@tensorflow/tfjs-core';
-
-/**
- * For multi-class classification problems, this object is designed to store a
- * mapping from class index to the "weight" of the class, where higher weighted
- * classes have larger impact on loss, accuracy, and other metrics.
- *
- * This is useful for cases in which you want the model to "pay more attention"
- * to examples from an under-represented class, e.g., in unbalanced datasets.
- */
-export type ClassWeight = {
-  [classIndex: number]: number
-};
-
-/**
- * Class weighting for a model with multiple outputs.
- *
- * This object maps each output name to a class-weighting object.
- */
-export type ClassWeightMap = {
-  [outputName: string]: ClassWeight
-};
-
-function standardizeSampleOrClassWeights(
-    xWeight: ClassWeight|ClassWeight[]|ClassWeightMap, outputNames: string[],
-    weightType: 'sampleWeight'|'classWeight'): ClassWeight[] {
-  const numOutputs = outputNames.length;
-  if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {
-    return outputNames.map(name => null);
-  }
-  if (numOutputs === 1) {
-    if (Array.isArray(xWeight) && xWeight.length === 1) {
-      return xWeight;
-    } else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {
-      return [(xWeight as ClassWeightMap)[outputNames[0]]];
-    } else {
-      return [xWeight as ClassWeight];
-    }
-  }
-  if (Array.isArray(xWeight)) {
-    if (xWeight.length !== numOutputs) {
-      throw new Error(
-          `Provided ${weightType} is an array of ${xWeight.length} ` +
-          `element(s), but the model has ${numOutputs} outputs. ` +
-          `Make sure a set of weights is provided for each model output.`);
-    }
-    return xWeight;
-  } else if (
-      typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&
-      typeof (xWeight as ClassWeightMap)[Object.keys(xWeight)[0]] ===
-          'object') {
-    const output: ClassWeight[] = [];
-    outputNames.forEach(outputName => {
-      if (outputName in xWeight) {
-        output.push((xWeight as ClassWeightMap)[outputName]);
-      } else {
-        output.push(null);
-      }
-    });
-    return output;
-  } else {
-    throw new Error(
-        `The model has multiple (${numOutputs}) outputs, ` +
-        `so ${weightType} must be either an array with ` +
-        `${numOutputs} elements or an object with ${outputNames} keys. ` +
-        `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);
-  }
-}
-
-/**
- * Standardize class weighting objects.
- *
- * This function takes a single class-weighting object, an array of them,
- * or a map from output name to class-weighting object. It compares it to the
- * output name(s) of the model, base on which it outputs an array of
- * class-weighting objects of which the length matches the number of outputs.
- *
- * @param classWeight Input class-weighting object(s).
- * @param outputNames All output name(s) of the model.
- * @return An array of class-weighting objects. The length of the array matches
- *   the model's number of outputs.
- */
-export function standardizeClassWeights(
-    classWeight: ClassWeight|ClassWeight[]|ClassWeightMap,
-    outputNames: string[]): ClassWeight[] {
-  return standardizeSampleOrClassWeights(
-      classWeight, outputNames, 'classWeight');
-}
-
-export function standardizeSampleWeights(
-    classWeight: ClassWeight|ClassWeight[]|ClassWeightMap,
-    outputNames: string[]): ClassWeight[] {
-  return standardizeSampleOrClassWeights(
-      classWeight, outputNames, 'sampleWeight');
-}
-
-/**
- * Standardize by-sample and/or by-class weights for training.
- *
- * Note that this function operates on one model output at a time. For a model
- * with multiple outputs, you must call this function multiple times.
- *
- * @param y The target tensor that the by-sample and/or by-class weight is for.
- *     The values of y are assumed to encode the classes, either directly
- *     as an integer index, or as one-hot encoding.
- * @param sampleWeight By-sample weights.
- * @param classWeight By-class weights: an object mapping class indices
- *     (integers) to a weight (float) to apply to the model's loss for the
- *     samples from this class during training. This can be useful to tell the
- *     model to "pay more attention" to samples from an under-represented class.
- * @param sampleWeightMode The mode for the sample weights.
- * @return A Promise of weight tensor, of which the size of the first dimension
- *     matches that of `y`.
- */
-export async function standardizeWeights(
-    y: Tensor, sampleWeight?: Tensor, classWeight?: ClassWeight,
-    sampleWeightMode?: 'temporal'): Promise<Tensor> {
-  if (sampleWeight != null || sampleWeightMode != null) {
-    // TODO(cais): Once 'temporal' mode is implemented, document it in the doc
-    // string.
-    throw new Error('Support sampleWeight is not implemented yet');
-  }
-
-  if (classWeight != null) {
-    // Apply class weights per sample.
-    const yClasses: Tensor1D = tidy(() => {
-      if (y.shape.length === 1) {
-        // Assume class indices.
-        return clone(y) as Tensor1D;
-      } else if (y.shape.length === 2) {
-        if (y.shape[1] > 1) {
-          // Assume one-hot encoding of classes.
-          const axis = 1;
-          return argMax(y, axis);
-        } else if (y.shape[1] === 1) {
-          // Class index.
-          return reshape(y, [y.shape[0]]);
-        } else {
-          throw new Error(
-              `Encountered unexpected last-dimension size (${y.shape[1]}) ` +
-              `during handling of class weights. The size is expected to be ` +
-              `>= 1.`);
-        }
-      } else {
-        throw new Error(
-            `Unexpected rank of target (y) tensor (${y.rank}) during ` +
-            `handling of class weights. The rank is expected to be 1 or 2.`);
-      }
-    });
-
-    const yClassIndices = Array.from(await yClasses.data());
-    dispose(yClasses);
-    const classSampleWeight: number[] = [];
-    yClassIndices.forEach(classIndex => {
-      if (classWeight[classIndex] == null) {
-        throw new Error(
-            `classWeight must contain all classes in the training data. ` +
-            `The class ${classIndex} exists in the data but not in ` +
-            `classWeight`);
-      } else {
-        classSampleWeight.push(classWeight[classIndex]);
-      }
-    });
-
-    return tensor1d(classSampleWeight, 'float32');
-  } else {
-    return null;
-  }
-}
-
-/**
- * Apply per-sample weights on the loss values from a number of samples.
- *
- * @param losses Loss tensor of shape `[batchSize]`.
- * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.
- * @returns Tensor of the same shape as`losses`.
- */
-export function computeWeightedLoss(losses: Tensor, sampleWeights: Tensor) {
-  return mul(losses, sampleWeights);
-}
diff --git a/tfjs-master/tfjs-layers/src/engine/training_utils_test.ts b/tfjs-master/tfjs-layers/src/engine/training_utils_test.ts
deleted file mode 100644
index 33dcebf81..000000000
--- a/tfjs-master/tfjs-layers/src/engine/training_utils_test.ts
+++ /dev/null
@@ -1,153 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {memory, tensor1d, tensor2d} from '@tensorflow/tfjs-core';
-
-import {describeMathCPU, expectTensorsClose} from '../utils/test_utils';
-
-import {ClassWeight, ClassWeightMap, standardizeClassWeights, standardizeWeights} from './training_utils';
-
-describeMathCPU('standardizeWeights', () => {
-  it('classWeights with 1D class-index target', async () => {
-    const y = tensor1d([0, 1, 2, 1, 0]);
-    const classWeight: ClassWeight = {0: 10, 1: 1, 2: 0.1};
-    const numTensors0 = memory().numTensors;
-    const classSampleWeight = await standardizeWeights(y, null, classWeight);
-    // Assert no memory leak. The extra tensor is `classSampleWeight` itself.
-    expect(memory().numTensors).toEqual(numTensors0 + 1);
-    expectTensorsClose(classSampleWeight, tensor1d([10, 1, 0.1, 1, 10]));
-    expect(y.isDisposed).toEqual(false);
-  });
-
-  it('classWeights with 2D class-index target', async () => {
-    const y = tensor2d([[3], [2], [0]]);
-    const classWeight: ClassWeight = {0: 10, 1: 1, 2: 0.1, 3: 0.01};
-    const numTensors0 = memory().numTensors;
-    const classSampleWeight = await standardizeWeights(y, null, classWeight);
-    // Assert no memory leak. The extra tensor is `classSampleWeight` itself.
-    expect(memory().numTensors).toEqual(numTensors0 + 1);
-    expectTensorsClose(classSampleWeight, tensor1d([0.01, 0.1, 10]));
-    expect(y.isDisposed).toEqual(false);
-  });
-
-  it('classWeights with 2D one-hot target', async () => {
-    const y = tensor2d([[0, 0, 0, 1], [0, 0, 1, 0], [1, 0, 0, 0]]);
-    const classWeight: ClassWeight = {0: 10, 1: 1, 2: 0.1, 3: 0.01};
-    const numTensors0 = memory().numTensors;
-    const classSampleWeight = await standardizeWeights(y, null, classWeight);
-    // Assert no memory leak. The extra tensor is `classSampleWeight` itself.
-    expect(memory().numTensors).toEqual(numTensors0 + 1);
-    expectTensorsClose(classSampleWeight, tensor1d([0.01, 0.1, 10]));
-    expect(y.isDisposed).toEqual(false);
-  });
-
-  it('classWeights with 1D class-index target: Missing class', async () => {
-    const y = tensor1d([0, 1, 2, 3, 2, 1, 0]);
-    const classWeight: ClassWeight = {0: 10, 1: 1, 2: 0.1};
-
-    let caughtError: Error;
-    try {
-      await standardizeWeights(y, null, classWeight);
-    } catch (error) {
-      caughtError = error;
-    }
-    expect(caughtError.message)
-        .toMatch(/classWeight must contain all classes.* class 3 .*/);
-  });
-
-  it('classWeights with 2D class-index target: Missing class', async () => {
-    const y = tensor2d([[3], [2], [0], [4]]);
-    const classWeight: ClassWeight = {0: 10, 1: 1, 2: 0.1, 3: 0.01};
-
-    let caughtError: Error;
-    try {
-      await standardizeWeights(y, null, classWeight);
-    } catch (error) {
-      caughtError = error;
-    }
-    expect(caughtError.message)
-        .toMatch(/classWeight must contain all classes.* class 4 .*/);
-  });
-
-  it('classWeights with 2D one-hot target: missing weight', async () => {
-    const y = tensor2d([[0, 0, 0, 1], [0, 0, 1, 0], [1, 0, 0, 0]]);
-    const classWeight: ClassWeight = {0: 10, 1: 1, 3: 0.01};
-
-    let caughtError: Error;
-    try {
-      await standardizeWeights(y, null, classWeight);
-    } catch (error) {
-      caughtError = error;
-    }
-    expect(caughtError.message)
-        .toMatch(/classWeight must contain all classes.* class 2 .*/);
-  });
-});
-
-describe('standardizeClassWeights', () => {
-  it('One output, ClassWeight singleton', () => {
-    const outputNames = ['output1'];
-    const classWeight: ClassWeight = {0: 1, 1: 2};
-    const output = standardizeClassWeights(classWeight, outputNames);
-    expect(output).toEqual([{0: 1, 1: 2}]);
-  });
-
-  it('One output, ClassWeight array', () => {
-    const outputNames = ['output1'];
-    const classWeight: ClassWeight[] = [{0: 1, 1: 2}];
-    const output = standardizeClassWeights(classWeight, outputNames);
-    expect(output).toEqual([{0: 1, 1: 2}]);
-  });
-
-  it('One output, ClassWeight dict', () => {
-    const outputNames = ['output1'];
-    const classWeight: ClassWeightMap = {'output1': {0: 1, 1: 2}};
-    const output = standardizeClassWeights(classWeight, outputNames);
-    expect(output).toEqual([{0: 1, 1: 2}]);
-  });
-
-  it('Two outputs, ClassWeight array', () => {
-    const outputNames = ['output1', 'output2'];
-    const classWeight: ClassWeight[] = [{0: 1, 1: 2}, {0: 10, 1: 20}];
-    const output = standardizeClassWeights(classWeight, outputNames);
-    expect(output).toEqual([{0: 1, 1: 2}, {0: 10, 1: 20}]);
-  });
-
-  it('Two outputs, ClassWeight dict', () => {
-    const outputNames = ['output1', 'output2'];
-    const classWeight:
-        ClassWeightMap = {'output2': {0: 10, 1: 20}, 'output1': {0: 1, 1: 2}};
-    const output = standardizeClassWeights(classWeight, outputNames);
-    expect(output).toEqual([{0: 1, 1: 2}, {0: 10, 1: 20}]);
-  });
-
-  it('Two outputs, ClassWeight singleton leads to Error', () => {
-    const outputNames = ['output1', 'output2'];
-    const classWeight: ClassWeight = {0: 10, 1: 20};
-    expect(() => standardizeClassWeights(classWeight, outputNames))
-        .toThrowError(/.*has multiple \(2\) outputs.*/);
-  });
-
-  it('Three outputs, ClassWeight array missing element', () => {
-    const outputNames = ['output1', 'output2', 'output3'];
-    const classWeight: ClassWeight[] = [{0: 1, 1: 2}, {0: 10, 1: 20}];
-    expect(() => standardizeClassWeights(classWeight, outputNames))
-        .toThrowError(
-            /.*classWeight is an array of 2 element.* model has 3 outputs/);
-  });
-
-  it('Three outputs, ClassWeight dict missing element is okay', () => {
-    const outputNames = ['output1', 'output2', 'output3'];
-    const classWeight:
-        ClassWeightMap = {'output1': {0: 1, 1: 2}, 'output3': {0: 10, 1: 20}};
-    const output = standardizeClassWeights(classWeight, outputNames);
-    expect(output).toEqual([{0: 1, 1: 2}, null, {0: 10, 1: 20}]);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/engine/training_weighted_test.ts b/tfjs-master/tfjs-layers/src/engine/training_weighted_test.ts
deleted file mode 100644
index f4abc3c0d..000000000
--- a/tfjs-master/tfjs-layers/src/engine/training_weighted_test.ts
+++ /dev/null
@@ -1,856 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/** Unit tests for training with {sample, class} weights. */
-
-import {memory, ones, Tensor, tensor2d, train, zeros} from '@tensorflow/tfjs-core';
-
-import * as tfl from '../index';
-
-import {describeMathCPUAndWebGL2, expectTensorsClose} from '../utils/test_utils';
-import {FakeNumericDataset} from './dataset_fakes';
-import {ClassWeight, ClassWeightMap} from './training_utils';
-
-describeMathCPUAndWebGL2('LayersModel.fit() with classWeight', () => {
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     units=3,
-  //     input_shape=[2],
-  //     kernel_initializer='zeros',
-  //     activation='softmax'))
-  // model.compile(loss='categorical_crossentropy',
-  //               metrics=['acc'],
-  //               optimizer=tf.keras.optimizers.SGD(1.0))
-  // model.summary()
-  //
-  // xs = np.array([[0, 1], [0, 2], [1, 10], [1, 20], [2, -10], [2, -20]],
-  //               dtype=np.float32)
-  // ys = np.array([[1, 0, 0],
-  //                [1, 0, 0],
-  //                [0, 1, 0],
-  //                [0, 1, 0],
-  //                [0, 0, 1],
-  //                [0, 0, 1]], dtype=np.float32)
-  //
-  // model.fit(xs,
-  //           ys,
-  //           epochs=2,
-  //           class_weight=[{
-  //             0: 1,
-  //             1: 10,
-  //             2: 1
-  //           }])
-  // print(model.get_weights()[0])
-  // ```
-  it('One output, multi-class, one-hot encoding', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [2],
-      kernelInitializer: 'zeros',
-      activation: 'softmax'
-    }));
-    model.compile({
-      loss: 'categoricalCrossentropy',
-      metrics: ['acc'],
-      optimizer: train.sgd(1)
-    });
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20], [2, -10], [2, -20]]);
-    const ys = tensor2d(
-        [[1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]]);
-    const numTensors0 = memory().numTensors;
-    const history = await model.fit(
-        xs, ys, {epochs: 2, classWeight: [{0: 1, 1: 10, 2: 1}]});
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    expect(history.history.loss.length).toEqual(2);
-    // These loss values are different than what the values would be
-    // if there is no class weighting.
-    expect(history.history.loss[0]).toBeCloseTo(4.3944);
-    expect(history.history.loss[1]).toBeCloseTo(5.3727);
-    expect(history.history.acc.length).toEqual(2);
-    expect(history.history.acc[0]).toBeCloseTo(0.3333);
-    expect(history.history.acc[1]).toBeCloseTo(0.6667);
-  });
-
-  it('One output, multi-class, one-hot encoding, batchSize = 2', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [2],
-      kernelInitializer: 'zeros',
-      activation: 'softmax'
-    }));
-    model.compile({
-      loss: 'categoricalCrossentropy',
-      metrics: ['acc'],
-      optimizer: train.sgd(1)
-    });
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20], [2, -10], [2, -20]]);
-    const ys = tensor2d(
-        [[1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]]);
-    const numTensors0 = memory().numTensors;
-    const history = await model.fit(xs, ys, {
-      epochs: 2,
-      batchSize: 2,
-      shuffle: false,
-      classWeight: [{0: 1, 1: 10, 2: 1}]
-    });
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    expect(history.history.loss.length).toEqual(2);
-    // These loss values are different than what the values would be
-    // if there is no class weighting.
-    expect(history.history.loss[0]).toBeCloseTo(59.2691);
-    expect(history.history.loss[1]).toBeCloseTo(10.7454);
-    expect(history.history.acc.length).toEqual(2);
-    expect(history.history.acc[0]).toBeCloseTo(0.3333);
-    expect(history.history.acc[1]).toBeCloseTo(0.3333);
-  });
-
-  it('One output, multi-class, one-hot encoding, validationData', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [2],
-      kernelInitializer: 'zeros',
-      activation: 'softmax'
-    }));
-    model.compile({
-      loss: 'categoricalCrossentropy',
-      metrics: ['acc'],
-      optimizer: train.sgd(1)
-    });
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20], [2, -10], [2, -20]]);
-    const ys = tensor2d(
-        [[1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]]);
-    const numTensors0 = memory().numTensors;
-    const history = await model.fit(xs, ys, {
-      epochs: 2,
-      classWeight: [{0: 1, 1: 10, 2: 1}],
-      validationData: [xs, ys]
-    });
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    expect(history.history.loss.length).toEqual(2);
-    // These loss values are different than what the values would be
-    // if there is no class weighting.
-    expect(history.history.loss[0]).toBeCloseTo(4.3944);
-    expect(history.history.loss[1]).toBeCloseTo(5.3727);
-    expect(history.history.acc.length).toEqual(2);
-    expect(history.history.acc[0]).toBeCloseTo(0.3333);
-    expect(history.history.acc[1]).toBeCloseTo(0.6667);
-
-    expect(history.history.val_loss.length).toEqual(2);
-    expect(history.history.val_loss[0]).toBeCloseTo(5.3727);
-    expect(history.history.val_loss[1]).toBeCloseTo(5.3727);
-    expect(history.history.val_acc.length).toEqual(2);
-    expect(history.history.val_acc[0]).toBeCloseTo(0.6667);
-    expect(history.history.val_acc[1]).toBeCloseTo(0.6667);
-  });
-
-  it('One output, multi-class, one-hot encoding, validationSplit', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [2],
-      kernelInitializer: 'zeros',
-      activation: 'softmax'
-    }));
-    model.compile({
-      loss: 'categoricalCrossentropy',
-      metrics: ['acc'],
-      optimizer: train.sgd(1)
-    });
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20], [2, -10], [2, -20]]);
-    const ys = tensor2d(
-        [[1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]]);
-    const numTensors0 = memory().numTensors;
-    const history = await model.fit(
-        xs, ys,
-        {epochs: 2, classWeight: [{0: 1, 1: 10, 2: 1}], validationSplit: 0.5});
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    expect(history.history.loss.length).toEqual(2);
-    // These loss values are different than what the values would be
-    // if there is no class weighting.
-    expect(history.history.loss[0]).toBeCloseTo(4.3944);
-    expect(history.history.loss[1]).toBeCloseTo(10.7454);
-    expect(history.history.acc.length).toEqual(2);
-    expect(history.history.acc[0]).toBeCloseTo(0.6667);
-    expect(history.history.acc[1]).toBeCloseTo(0.3333);
-
-    expect(history.history.val_loss.length).toEqual(2);
-    expect(history.history.val_loss[0]).toBeCloseTo(2.9903e-05);
-    expect(history.history.val_loss[1]).toBeCloseTo(2.9903e-05);
-    expect(history.history.val_acc.length).toEqual(2);
-    expect(history.history.val_acc[0]).toBeCloseTo(1);
-    expect(history.history.val_acc[1]).toBeCloseTo(1);
-  });
-
-  it('One output, multi-class, sparse encoding', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [2],
-      kernelInitializer: 'zeros',
-      activation: 'softmax'
-    }));
-    model.compile(
-        {loss: 'sparseCategoricalCrossentropy', optimizer: train.sgd(1)});
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20], [2, -10], [2, -20]]);
-    const ys = tensor2d([[0], [0], [1], [1], [2], [2]]);
-    const numTensors0 = memory().numTensors;
-    const history =
-        await model.fit(xs, ys, {epochs: 2, classWeight: {0: 1, 1: 10, 2: 1}});
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    expect(history.history.loss.length).toEqual(2);
-    // These loss values are different than what the values would be
-    // if there is no class weighting.
-    expect(history.history.loss[0]).toBeCloseTo(4.3944);
-    expect(history.history.loss[1]).toBeCloseTo(5.3727);
-  });
-
-  // Reference Python code.
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     units=1,
-  //     input_shape=[2],
-  //     kernel_initializer='zeros',
-  //     activation='sigmoid'))
-  // model.compile(loss='binary_crossentropy',
-  //               optimizer=tf.keras.optimizers.SGD(1.0))
-  // model.summary()
-  //
-  // xs = np.array([[0, 1], [0, 2], [1, 10], [1, 20]],
-  //               dtype=np.float32)
-  // ys = np.array([[0], [0], [1], [1]], dtype=np.float32)
-  //
-  // # model.fit(xs, ys, epochs=1)
-  // model.fit(xs,
-  //           ys,
-  //           epochs=3,
-  //           class_weight=[{
-  //               0: 0.1,
-  //               1: 0.9
-  //           }])
-  // print(model.get_weights()[0])
-  // ```
-  it('One output, binary classes, sparse encoding', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 1,
-      inputShape: [2],
-      kernelInitializer: 'zeros',
-      activation: 'sigmoid'
-    }));
-    model.compile({loss: 'binaryCrossentropy', optimizer: train.sgd(1)});
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20]]);
-    const ys = tensor2d([[0], [0], [1], [1]]);
-    const numTensors0 = memory().numTensors;
-    const history =
-        await model.fit(xs, ys, {epochs: 2, classWeight: [{0: 0.1, 1: 0.9}]});
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    expect(history.history.loss.length).toEqual(2);
-    // These loss values are different than what the values would be
-    // if there is no class weighting.
-    expect(history.history.loss[0]).toBeCloseTo(0.3466);
-    expect(history.history.loss[1]).toBeCloseTo(0.2611);
-  });
-
-  // Python Reference Code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // inp = tf.keras.Input(shape=[2])
-  // y1 = tf.keras.layers.Dense(
-  //     units=3,
-  //     input_shape=[2],
-  //     kernel_initializer='zeros',
-  //     activation='softmax')(inp)
-  // y2 = tf.keras.layers.Dense(
-  //     units=1,
-  //     input_shape=[2],
-  //     kernel_initializer='zeros',
-  //     activation='sigmoid')(inp)
-  // model = tf.keras.Model(inp, [y1, y2])
-  // model.compile(
-  //     loss=['sparse_categorical_crossentropy', 'binary_crossentropy'],
-  //     optimizer=tf.keras.optimizers.SGD(1.0))
-  // model.summary()
-  //
-  // xs = np.array(
-  //     [[0, 1], [0, 2], [1, 10], [1, 20], [2, 10], [2, 20]],
-  //     dtype=np.float32)
-  // y1s = np.array(
-  //     [[0], [0], [1], [1], [2], [2]], dtype=np.float32)
-  // y2s = np.array(
-  //     [[0], [0], [1], [1], [1], [1]], dtype=np.float32)
-  //
-  // # model.fit(xs, ys, epochs=1)
-  // model.fit(xs,
-  //           [y1s, y2s],
-  //           epochs=3,
-  //           class_weight=[{
-  //               0: 0.1,
-  //               1: 0.2,
-  //               2: 0.7
-  //           }, {
-  //               0: 0.1,
-  //               1: 0.9
-  //           }])
-  // ```
-  it('Two outputs, classWeight as array', async () => {
-    const inp = tfl.input({shape: [2]});
-    const y1 = tfl.layers
-                   .dense({
-                     units: 3,
-                     inputShape: [2],
-                     kernelInitializer: 'zeros',
-                     activation: 'softmax'
-                   })
-                   .apply(inp) as tfl.SymbolicTensor;
-    const y2 = tfl.layers
-                   .dense({
-                     units: 1,
-                     inputShape: [2],
-                     kernelInitializer: 'zeros',
-                     activation: 'sigmoid'
-                   })
-                   .apply(inp) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: inp, outputs: [y1, y2]});
-    model.compile({
-      loss: ['sparseCategoricalCrossentropy', 'binaryCrossentropy'],
-      optimizer: train.sgd(1)
-    });
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20], [2, 10], [2, 20]]);
-    const y1s = tensor2d([[0], [0], [1], [1], [2], [2]]);
-    const y2s = tensor2d([[0], [0], [1], [1], [1], [1]]);
-
-    const numTensors0 = memory().numTensors;
-    const history = await model.fit(
-        xs, [y1s, y2s],
-        {epochs: 3, classWeight: [{0: 0.1, 1: 0.2, 2: 0.7}, {0: 0.1, 1: 0.9}]});
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    expect(history.history.loss.length).toEqual(3);
-    expect(history.history.loss[0]).toBeCloseTo(0.8052);
-    expect(history.history.loss[1]).toBeCloseTo(1.4887);
-    expect(history.history.loss[2]).toBeCloseTo(1.4782);
-    const lossKey0 = `${model.outputNames[0]}_loss`;
-    expect(history.history[lossKey0].length).toEqual(3);
-    expect(history.history[lossKey0][0]).toBeCloseTo(0.3662);
-    expect(history.history[lossKey0][1]).toBeCloseTo(1.2553);
-    expect(history.history[lossKey0][2]).toBeCloseTo(1.2485);
-    const lossKey1 = `${model.outputNames[1]}_loss`;
-    expect(history.history[lossKey1].length).toEqual(3);
-    expect(history.history[lossKey1][0]).toBeCloseTo(0.4390);
-    expect(history.history[lossKey1][1]).toBeCloseTo(0.2333);
-    expect(history.history[lossKey1][2]).toBeCloseTo(0.2297);
-  });
-
-  it('Two outputs, classWeight as array, one being null', async () => {
-    const inp = tfl.input({shape: [2]});
-    const y1 = tfl.layers
-                   .dense({
-                     units: 3,
-                     inputShape: [2],
-                     kernelInitializer: 'zeros',
-                     activation: 'softmax'
-                   })
-                   .apply(inp) as tfl.SymbolicTensor;
-    const y2 = tfl.layers
-                   .dense({
-                     units: 1,
-                     inputShape: [2],
-                     kernelInitializer: 'zeros',
-                     activation: 'sigmoid'
-                   })
-                   .apply(inp) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: inp, outputs: [y1, y2]});
-    model.compile({
-      loss: ['sparseCategoricalCrossentropy', 'binaryCrossentropy'],
-      optimizer: train.sgd(1)
-    });
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20], [2, 10], [2, 20]]);
-    const y1s = tensor2d([[0], [0], [1], [1], [2], [2]]);
-    const y2s = tensor2d([[0], [0], [1], [1], [1], [1]]);
-
-    const numTensors0 = memory().numTensors;
-    const history = await model.fit(
-        xs, [y1s, y2s],
-        {epochs: 3, classWeight: [null, {0: 0.1, 1: 0.9}], shuffle: false});
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    // Note that the following values don't match results from Python,
-    // which is a bug in Python TensorFlow / tf.keras. But the final
-    // kernel value does match Python results. (See b/136123157).
-    expect(history.history.loss.length).toEqual(3);
-    expect(history.history.loss[0]).toBeCloseTo(1.5376);
-    expect(history.history.loss[1]).toBeCloseTo(3.1466);
-    expect(history.history.loss[2]).toBeCloseTo(7.9620);
-    const lossKey0 = `${model.outputNames[0]}_loss`;
-    expect(history.history[lossKey0].length).toEqual(3);
-    expect(history.history[lossKey0][0]).toBeCloseTo(1.0986);
-    expect(history.history[lossKey0][1]).toBeCloseTo(2.9113);
-    expect(history.history[lossKey0][2]).toBeCloseTo(7.7323);
-    const lossKey1 = `${model.outputNames[1]}_loss`;
-    expect(history.history[lossKey1].length).toEqual(3);
-    expect(history.history[lossKey1][0]).toBeCloseTo(0.4390);
-    expect(history.history[lossKey1][1]).toBeCloseTo(0.2333);
-    expect(history.history[lossKey1][2]).toBeCloseTo(0.2298);
-    expectTensorsClose(model.getWeights()[0], tensor2d([
-                         [-0.3333333, -0.03197281, 0.3653062],
-                         [-2.0025878, 1.9823718, 0.02021614]
-                       ]));
-  });
-
-  it('Two outputs, classWeight as map, one output no weighting', async () => {
-    const inp = tfl.input({shape: [2]});
-    const y1 = tfl.layers
-                   .dense({
-                     units: 3,
-                     inputShape: [2],
-                     kernelInitializer: 'zeros',
-                     activation: 'softmax'
-                   })
-                   .apply(inp) as tfl.SymbolicTensor;
-    const y2 = tfl.layers
-                   .dense({
-                     units: 1,
-                     inputShape: [2],
-                     kernelInitializer: 'zeros',
-                     activation: 'sigmoid'
-                   })
-                   .apply(inp) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: inp, outputs: [y1, y2]});
-    model.compile({
-      loss: ['sparseCategoricalCrossentropy', 'binaryCrossentropy'],
-      optimizer: train.sgd(1)
-    });
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20], [2, 10], [2, 20]]);
-    const y1s = tensor2d([[0], [0], [1], [1], [2], [2]]);
-    const y2s = tensor2d([[0], [0], [1], [1], [1], [1]]);
-
-    const numTensors0 = memory().numTensors;
-    const history = await model.fit(xs, [y1s, y2s], {
-      epochs: 3,
-      classWeight: {
-        [model.outputNames[1]]: {0: 0.1, 1: 0.9},
-      },
-      shuffle: false
-    });
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    // Note that the following values don't match results from Python,
-    // which is a bug in Python TensorFlow / tf.keras. But the final
-    // kernel value does match Python results.
-    expect(history.history.loss.length).toEqual(3);
-    expect(history.history.loss[0]).toBeCloseTo(1.5376);
-    expect(history.history.loss[1]).toBeCloseTo(3.1466);
-    expect(history.history.loss[2]).toBeCloseTo(7.9620);
-    const lossKey0 = `${model.outputNames[0]}_loss`;
-    expect(history.history[lossKey0].length).toEqual(3);
-    expect(history.history[lossKey0][0]).toBeCloseTo(1.0986);
-    expect(history.history[lossKey0][1]).toBeCloseTo(2.9113);
-    expect(history.history[lossKey0][2]).toBeCloseTo(7.7323);
-    const lossKey1 = `${model.outputNames[1]}_loss`;
-    expect(history.history[lossKey1].length).toEqual(3);
-    expect(history.history[lossKey1][0]).toBeCloseTo(0.4390);
-    expect(history.history[lossKey1][1]).toBeCloseTo(0.2333);
-    expect(history.history[lossKey1][2]).toBeCloseTo(0.2298);
-    expectTensorsClose(model.getWeights()[0], tensor2d([
-                         [-0.3333333, -0.03197281, 0.3653062],
-                         [-2.0025878, 1.9823718, 0.02021614]
-                       ]));
-  });
-
-  it('Two outputs, classWeight as map', async () => {
-    const inp = tfl.input({shape: [2]});
-    const y1 = tfl.layers
-                   .dense({
-                     units: 3,
-                     inputShape: [2],
-                     kernelInitializer: 'zeros',
-                     activation: 'softmax'
-                   })
-                   .apply(inp) as tfl.SymbolicTensor;
-    const y2 = tfl.layers
-                   .dense({
-                     units: 1,
-                     inputShape: [2],
-                     kernelInitializer: 'zeros',
-                     activation: 'sigmoid'
-                   })
-                   .apply(inp) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: inp, outputs: [y1, y2]});
-    model.compile({
-      loss: ['sparseCategoricalCrossentropy', 'binaryCrossentropy'],
-      optimizer: train.sgd(1)
-    });
-
-    const xs = tensor2d([[0, 1], [0, 2], [1, 10], [1, 20], [2, 10], [2, 20]]);
-    const y1s = tensor2d([[0], [0], [1], [1], [2], [2]]);
-    const y2s = tensor2d([[0], [0], [1], [1], [1], [1]]);
-
-    const numTensors0 = memory().numTensors;
-    const history = await model.fit(xs, [y1s, y2s], {
-      epochs: 3,
-      classWeight: {
-        [model.outputNames[0]]: {0: 0.1, 1: 0.2, 2: 0.7},
-        [model.outputNames[1]]: {0: 0.1, 1: 0.9}
-      }
-    });
-    expect(memory().numTensors).toEqual(numTensors0);  // Assert no memory leak.
-    expect(history.history.loss.length).toEqual(3);
-    expect(history.history.loss[0]).toBeCloseTo(0.8052);
-    expect(history.history.loss[1]).toBeCloseTo(1.4887);
-    expect(history.history.loss[2]).toBeCloseTo(1.4782);
-    const lossKey0 = `${model.outputNames[0]}_loss`;
-    expect(history.history[lossKey0].length).toEqual(3);
-    expect(history.history[lossKey0][0]).toBeCloseTo(0.3662);
-    expect(history.history[lossKey0][1]).toBeCloseTo(1.2553);
-    expect(history.history[lossKey0][2]).toBeCloseTo(1.2485);
-    const lossKey1 = `${model.outputNames[1]}_loss`;
-    expect(history.history[lossKey1].length).toEqual(3);
-    expect(history.history[lossKey1][0]).toBeCloseTo(0.4390);
-    expect(history.history[lossKey1][1]).toBeCloseTo(0.2333);
-    expect(history.history[lossKey1][2]).toBeCloseTo(0.2297);
-  });
-});
-
-describeMathCPUAndWebGL2('LayersModel.fitDataset() with classWeight', () => {
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.concatenate([
-  //     np.zeros([int(batch_size * num_batches * epochs / 2), 1]),
-  //     np.ones([int(batch_size * num_batches * epochs / 2), 1])],
-  //     axis=0)
-  //
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     (xs, ys)).batch(batch_size)
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     activation='sigmoid',
-  //     kernel_initializer='zeros'))
-  // model.compile(loss='binary_crossentropy',
-  //               metrics=['acc'],
-  //               optimizer='sgd')
-  //
-  // history = model.fit(
-  //     dataset,
-  //     steps_per_epoch=num_batches,
-  //     epochs=epochs,
-  //     class_weight={0: 0.1, 1: 2})
-  // print(history.history)
-  // ```
-  it('One output, binary crossentropy, acc metric', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 1,
-      inputShape: [1],
-      kernelInitializer: 'zeros',
-      activation: 'sigmoid'
-    }));
-    model.compile(
-        {loss: 'binaryCrossentropy', metrics: ['acc'], optimizer: 'sgd'});
-
-    const batchSize = 8;
-    const batchesPerEpoch = 3;
-    const epochs = 2;
-    const xTensorsFunc = () =>
-        [ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1]),
-         ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1])];
-    const yTensorsFunc = () =>
-        [zeros([batchSize, 1]), zeros([batchSize, 1]), zeros([batchSize, 1]),
-         ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch * epochs,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    const classWeight: ClassWeight = {0: 0.1, 1: 2};
-
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below).
-    await model.fitDataset(dataset, {batchesPerEpoch, epochs: 1, classWeight});
-    model.setWeights([zeros([1, 1]), zeros([1])]);
-
-    const numTensors0 = memory().numTensors;
-    const history =
-        await model.fitDataset(dataset, {batchesPerEpoch, epochs, classWeight});
-    const numTensors1 = memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-
-    // The loss and acc values are different if no classWeight is used.
-    expect(history.history.loss.length).toEqual(2);
-    expect(history.history.loss[0]).toBeCloseTo(0.0693);
-    expect(history.history.loss[1]).toBeCloseTo(1.3695);
-
-    expect(history.history.acc.length).toEqual(2);
-    expect(history.history.acc[0]).toBeCloseTo(1);
-    expect(history.history.acc[1]).toBeCloseTo(0.6667);
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys = np.concatenate([
-  //     np.zeros([int(batch_size * num_batches * epochs / 2), 1]),
-  //     np.ones([int(batch_size * num_batches * epochs / 2), 1])],
-  //     axis=0)
-  //
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     (xs, ys)).batch(batch_size)
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     activation='sigmoid',
-  //     kernel_initializer='zeros'))
-  // model.compile(loss='binary_crossentropy',
-  //               metrics=['acc'],
-  //               optimizer='sgd')
-  //
-  // history = model.fit(
-  //     dataset,
-  //     steps_per_epoch=num_batches,
-  //     epochs=epochs,
-  //     class_weight={0: 0.1, 1: 2})
-  // print(history.history)
-  // ```
-  it('One output, binary crossentropy，validation', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 1,
-      inputShape: [1],
-      kernelInitializer: 'zeros',
-      activation: 'sigmoid'
-    }));
-    model.compile(
-        {loss: 'binaryCrossentropy', metrics: ['acc'], optimizer: 'sgd'});
-
-    const batchSize = 8;
-    const batchesPerEpoch = 3;
-    const epochs = 2;
-    const xTensorsFunc = () =>
-        [ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1]),
-         ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1])];
-    const yTensorsFunc = () =>
-        [zeros([batchSize, 1]), zeros([batchSize, 1]), zeros([batchSize, 1]),
-         ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1])];
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: [1],
-      batchSize,
-      numBatches: batchesPerEpoch * epochs,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    const classWeight: ClassWeight = {0: 0.1, 1: 2};
-
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below).
-    await model.fitDataset(
-        dataset,
-        {batchesPerEpoch, epochs: 1, classWeight, validationData: dataset});
-    model.setWeights([zeros([1, 1]), zeros([1])]);
-
-    const numTensors0 = memory().numTensors;
-    const history = await model.fitDataset(
-        dataset,
-        {batchesPerEpoch, epochs, classWeight, validationData: dataset});
-    const numTensors1 = memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-
-    // The loss and acc values are different if no classWeight is used.
-    expect(history.history.loss.length).toEqual(2);
-    expect(history.history.loss[0]).toBeCloseTo(0.0693);
-    expect(history.history.loss[1]).toBeCloseTo(1.3695);
-
-    expect(history.history.acc.length).toEqual(2);
-    expect(history.history.acc[0]).toBeCloseTo(1);
-    expect(history.history.acc[1]).toBeCloseTo(0.6667);
-
-    expect(history.history.val_loss.length).toEqual(2);
-    expect(history.history.val_loss[0]).toBeCloseTo(0.693148);
-    expect(history.history.val_loss[1]).toBeCloseTo(0.693546);
-
-    expect(history.history.val_acc.length).toEqual(2);
-    expect(history.history.val_acc[0]).toBeCloseTo(0.5);
-    expect(history.history.val_acc[1]).toBeCloseTo(0.5);
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // batch_size = 8
-  // num_batches = 3
-  // epochs = 2
-  //
-  // xs = np.ones([batch_size * num_batches * epochs, 1])
-  // ys1 = np.concatenate([
-  //     np.zeros([int(batch_size * num_batches * epochs / 2), 1]),
-  //     np.ones([int(batch_size * num_batches * epochs / 2), 1])],
-  //     axis=0)
-  // ys2 = np.concatenate([
-  //     np.ones([int(batch_size * num_batches * epochs / 2), 1]),
-  //     np.zeros([int(batch_size * num_batches * epochs / 2), 1])],
-  //     axis=0)
-  //
-  // dataset = tf.data.Dataset.from_tensor_slices(
-  //     (xs, {'out1': ys1, 'out2': ys2})).batch(batch_size)
-  //
-  // inp = tf.keras.Input(shape=[1])
-  // out1 = tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     activation='sigmoid',
-  //     kernel_initializer='zeros',
-  //     name='out1')(inp)
-  // out2 = tf.keras.layers.Dense(
-  //     1,
-  //     input_shape=[1],
-  //     activation='sigmoid',
-  //     kernel_initializer='zeros',
-  //     name='out2')(inp)
-  //
-  // model = tf.keras.Model(inp, [out1, out2])
-  // model.compile(
-  //     loss=['binary_crossentropy', 'binary_crossentropy'],
-  //     optimizer='sgd')
-  //
-  // history = model.fit(
-  //     dataset,
-  //     steps_per_epoch=num_batches,
-  //     epochs=epochs,
-  //     class_weight=[{0: 0.1, 1: 2}, {0: 5, 1: 0.5}])
-  // print(history.history)
-  // ```
-  it('Two outputs, binary crossentropy, acc metric', async () => {
-    const inp = tfl.input({shape: [1]});
-    const out1 = tfl.layers
-                     .dense({
-                       units: 1,
-                       inputShape: [1],
-                       kernelInitializer: 'zeros',
-                       activation: 'sigmoid'
-                     })
-                     .apply(inp) as tfl.SymbolicTensor;
-    const out2 = tfl.layers
-                     .dense({
-                       units: 1,
-                       inputShape: [1],
-                       kernelInitializer: 'zeros',
-                       activation: 'sigmoid'
-                     })
-                     .apply(inp) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: inp, outputs: [out1, out2]});
-    model.compile(
-        {loss: ['binaryCrossentropy', 'binaryCrossentropy'], optimizer: 'sgd'});
-
-    const batchSize = 8;
-    const batchesPerEpoch = 3;
-    const epochs = 2;
-    const xTensorsFunc = () =>
-        [ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1]),
-         ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1])];
-    const yTensorsFunc = () => {
-      const output: {[name: string]: Tensor[]} = {};
-      output[model.outputNames[0]] = [
-        zeros([batchSize, 1]), zeros([batchSize, 1]), zeros([batchSize, 1]),
-        ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1])
-      ];
-      output[model.outputNames[1]] = [
-        ones([batchSize, 1]), ones([batchSize, 1]), ones([batchSize, 1]),
-        zeros([batchSize, 1]), zeros([batchSize, 1]), zeros([batchSize, 1])
-      ];
-      return output;
-    };
-    const dataset = new FakeNumericDataset({
-      xShape: [1],
-      yShape: {[model.outputNames[0]]: [1], [model.outputNames[1]]: [1]},
-      batchSize,
-      numBatches: batchesPerEpoch * epochs,
-      xTensorsFunc,
-      yTensorsFunc
-    });
-
-    const classWeight: ClassWeightMap = {
-      [model.outputNames[0]]: {0: 0.1, 1: 2},
-      [model.outputNames[1]]: {0: 5, 1: 0.5}
-    };
-
-    // Do a burn-in call to account for initialization of cached tensors (for
-    // the memory-leak check below).
-    await model.fitDataset(dataset, {batchesPerEpoch, epochs: 1, classWeight});
-    // model.setWeights([zeros([1, 1]), zeros([1])]);
-
-    const numTensors0 = memory().numTensors;
-    const history =
-        await model.fitDataset(dataset, {batchesPerEpoch, epochs, classWeight});
-    const numTensors1 = memory().numTensors;
-    expect(numTensors1).toEqual(numTensors0);
-
-    // The loss and acc values are different if no classWeight is used.
-    const totalLoss = history.history.loss;
-    expect(totalLoss.length).toEqual(2);
-    expect(totalLoss[0]).toBeCloseTo(0.4146);
-    expect(totalLoss[1]).toBeCloseTo(4.7882);
-
-    const loss0 = history.history[`${model.outputNames[0]}_loss`];
-    expect(loss0.length).toEqual(2);
-    expect(loss0[0]).toBeCloseTo(0.0693);
-    expect(loss0[1]).toBeCloseTo(1.3695);
-
-    const loss1 = history.history[`${model.outputNames[1]}_loss`];
-    expect(loss1.length).toEqual(2);
-    expect(loss1[0]).toBeCloseTo(0.3453);
-    expect(loss1[1]).toBeCloseTo(3.4158);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/errors.ts b/tfjs-master/tfjs-layers/src/errors.ts
deleted file mode 100644
index f0065a740..000000000
--- a/tfjs-master/tfjs-layers/src/errors.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Explicit error types.
- *
- * See the following link for more information about why the code includes
- * calls to setPrototypeOf:
- *
- * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work
- */
-// tslint:enable
-
-/**
- * Equivalent of Python's AttributeError.
- */
-export class AttributeError extends Error {
-  constructor(message?: string) {
-    super(message);
-    // Set the prototype explicitly.
-    Object.setPrototypeOf(this, AttributeError.prototype);
-  }
-}
-
-/**
- * Equivalent of Python's RuntimeError.
- */
-export class RuntimeError extends Error {
-  constructor(message?: string) {
-    super(message);
-    // Set the prototype explicitly.
-    Object.setPrototypeOf(this, RuntimeError.prototype);
-  }
-}
-
-/**
- * Equivalent of Python's ValueError.
- */
-export class ValueError extends Error {
-  constructor(message?: string) {
-    super(message);
-    // Set the prototype explicitly.
-    Object.setPrototypeOf(this, ValueError.prototype);
-  }
-}
-
-/**
- * Equivalent of Python's NotImplementedError.
- */
-export class NotImplementedError extends Error {
-  constructor(message?: string) {
-    super(message);
-    // Set the prototype explicitly.
-    Object.setPrototypeOf(this, NotImplementedError.prototype);
-  }
-}
-
-/**
- * Equivalent of Python's AssertionError.
- */
-export class AssertionError extends Error {
-  constructor(message?: string) {
-    super(message);
-    // Set the prototype explicitly.
-    Object.setPrototypeOf(this, AssertionError.prototype);
-  }
-}
-
-/**
- * Equivalent of Python's IndexError.
- */
-export class IndexError extends Error {
-  constructor(message?: string) {
-    super(message);
-    // Set the prototype explicitly.
-    Object.setPrototypeOf(this, IndexError.prototype);
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/errors_test.ts b/tfjs-master/tfjs-layers/src/errors_test.ts
deleted file mode 100644
index 4366d41f2..000000000
--- a/tfjs-master/tfjs-layers/src/errors_test.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {AttributeError, RuntimeError, ValueError} from './errors';
-
-describe('Error classes', () => {
-  // tslint:disable-next-line:variable-name
-  for (const SomeClass of [AttributeError, RuntimeError, ValueError]) {
-    it('pass instanceof tests.', () => {
-      const msg = 'Some message';
-      const e = new SomeClass(msg);
-      expect(e.message).toEqual(msg);
-      expect(e instanceof SomeClass).toBe(true);
-    });
-  }
-});
diff --git a/tfjs-master/tfjs-layers/src/exports.ts b/tfjs-master/tfjs-layers/src/exports.ts
deleted file mode 100644
index 81a39137c..000000000
--- a/tfjs-master/tfjs-layers/src/exports.ts
+++ /dev/null
@@ -1,170 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Exported functions.
- */
-
-import {BaseCallbackConstructor, CallbackConstructorRegistry} from './base_callbacks';
-import {ContainerArgs} from './engine/container';
-import {Input, InputConfig,} from './engine/input_layer';
-import {SymbolicTensor} from './engine/topology';
-import {LayersModel} from './engine/training';
-import {Sequential, SequentialArgs} from './models';
-
-export {loadLayersModel} from './models';
-
-// TODO(cais): Add doc string to all the public static functions in this
-//   class; include exectuable JavaScript code snippets where applicable
-//   (b/74074458).
-
-// LayersModel and related factory methods.
-
-/**
- * A model is a data structure that consists of `Layers` and defines inputs
- * and outputs.
- *
- * The key difference between `tf.model` and `tf.sequential` is that
- * `tf.model` is more generic, supporting an arbitrary graph (without
- * cycles) of layers. `tf.sequential` is less generic and supports only a linear
- * stack of layers.
- *
- * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers
- * are used to wire input(s) to output(s).
- *
- * For example, the following code snippet defines a model consisting of
- * two `dense` layers, with 10 and 4 units, respectively.
- *
- * ```js
- * // Define input, which has a size of 5 (not including batch dimension).
- * const input = tf.input({shape: [5]});
- *
- * // First dense layer uses relu activation.
- * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});
- * // Second dense layer uses softmax activation.
- * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});
- *
- * // Obtain the output symbolic tensor by applying the layers on the input.
- * const output = denseLayer2.apply(denseLayer1.apply(input));
- *
- * // Create the model based on the inputs.
- * const model = tf.model({inputs: input, outputs: output});
- *
- * // The model can be used for training, evaluation and prediction.
- * // For example, the following line runs prediction with the model on
- * // some fake data.
- * model.predict(tf.ones([2, 5])).print();
- * ```
- * See also:
- *   `tf.sequential`, `tf.loadLayersModel`.
- *
- * @doc {heading: 'Models', subheading: 'Creation'}
- */
-export function model(args: ContainerArgs): LayersModel {
-  return new LayersModel(args);
-}
-
-/**
- * Creates a `tf.Sequential` model.  A sequential model is any model where the
- * outputs of one layer are the inputs to the next layer, i.e. the model
- * topology is a simple 'stack' of layers, with no branching or skipping.
- *
- * This means that the first layer passed to a `tf.Sequential` model should have
- * a defined input shape. What that means is that it should have received an
- * `inputShape` or `batchInputShape` argument, or for some type of layers
- * (recurrent, Dense...) an `inputDim` argument.
- *
- * The key difference between `tf.model` and `tf.sequential` is that
- * `tf.sequential` is less generic, supporting only a linear stack of layers.
- * `tf.model` is more generic and supports an arbitrary graph (without
- * cycles) of layers.
- *
- * Examples:
- *
- * ```js
- * const model = tf.sequential();
- *
- * // First layer must have an input shape defined.
- * model.add(tf.layers.dense({units: 32, inputShape: [50]}));
- * // Afterwards, TF.js does automatic shape inference.
- * model.add(tf.layers.dense({units: 4}));
- *
- * // Inspect the inferred shape of the model's output, which equals
- * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the
- * // 2nd is the output size of the model's last layer.
- * console.log(JSON.stringify(model.outputs[0].shape));
- * ```
- *
- * It is also possible to specify a batch size (with potentially undetermined
- * batch dimension, denoted by "null") for the first layer using the
- * `batchInputShape` key. The following example is equivalent to the above:
- *
- * ```js
- * const model = tf.sequential();
- *
- * // First layer must have a defined input shape
- * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));
- * // Afterwards, TF.js does automatic shape inference.
- * model.add(tf.layers.dense({units: 4}));
- *
- * // Inspect the inferred shape of the model's output.
- * console.log(JSON.stringify(model.outputs[0].shape));
- * ```
- *
- * You can also use an `Array` of already-constructed `Layer`s to create
- * a `tf.Sequential` model:
- *
- * ```js
- * const model = tf.sequential({
- *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),
- *            tf.layers.dense({units: 4})]
- * });
- * console.log(JSON.stringify(model.outputs[0].shape));
- * ```
- *
- * @doc {heading: 'Models', subheading: 'Creation'}
- */
-export function sequential(config?: SequentialArgs): Sequential {
-  return new Sequential(config);
-}
-
-/**
- * Used to instantiate an input to a model as a `tf.SymbolicTensor`.
- *
- * Users should call the `input` factory function for
- * consistency with other generator functions.
- *
- * Example:
- *
- * ```js
- * // Defines a simple logistic regression model with 32 dimensional input
- * // and 3 dimensional output.
- * const x = tf.input({shape: [32]});
- * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);
- * const model = tf.model({inputs: x, outputs: y});
- * model.predict(tf.ones([2, 32])).print();
- * ```
- *
- * Note: `input` is only necessary when using `model`. When using
- * `sequential`, specify `inputShape` for the first layer or use `inputLayer`
- * as the first layer.
- *
- * @doc {heading: 'Models', subheading: 'Inputs'}
- */
-export function input(config: InputConfig): SymbolicTensor {
-  return Input(config);
-}
-
-export function registerCallbackConstructor(
-    verbosityLevel: number,
-    callbackConstructor: BaseCallbackConstructor): void {
-  CallbackConstructorRegistry.registerCallbackConstructor(
-      verbosityLevel, callbackConstructor);
-}
diff --git a/tfjs-master/tfjs-layers/src/exports_constraints.ts b/tfjs-master/tfjs-layers/src/exports_constraints.ts
deleted file mode 100644
index e42a36439..000000000
--- a/tfjs-master/tfjs-layers/src/exports_constraints.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-// tslint:disable-next-line:max-line-length
-import {Constraint, MaxNorm, MaxNormArgs, MinMaxNorm, MinMaxNormArgs, NonNeg, UnitNorm, UnitNormArgs} from './constraints';
-
-/**
- * MaxNorm weight constraint.
- *
- * Constrains the weights incident to each hidden unit
- * to have a norm less than or equal to a desired value.
- *
- * References
- *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- * Srivastava, Hinton, et al.
- * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
- *
- * @doc {heading: 'Constraints',namespace: 'constraints'}
- */
-export function maxNorm(args: MaxNormArgs): Constraint {
-  return new MaxNorm(args);
-}
-
-/**
- * Constrains the weights incident to each hidden unit to have unit norm.
- *
- * @doc {heading: 'Constraints', namespace: 'constraints'}
- */
-export function unitNorm(args: UnitNormArgs): Constraint {
-  return new UnitNorm(args);
-}
-
-/**
- * Constrains the weight to be non-negative.
- *
- * @doc {heading: 'Constraints', namespace: 'constraints'}
- */
-export function nonNeg(): Constraint {
-  return new NonNeg();
-}
-
-/** @doc {heading: 'Constraints', namespace: 'constraints'} */
-export function minMaxNorm(config: MinMaxNormArgs): Constraint {
-  return new MinMaxNorm(config);
-}
diff --git a/tfjs-master/tfjs-layers/src/exports_initializers.ts b/tfjs-master/tfjs-layers/src/exports_initializers.ts
deleted file mode 100644
index 583e3ccf2..000000000
--- a/tfjs-master/tfjs-layers/src/exports_initializers.ts
+++ /dev/null
@@ -1,210 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-// tslint:disable-next-line:max-line-length
-import {Constant, ConstantArgs, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, IdentityArgs, Initializer, LeCunNormal, LeCunUniform, Ones, Orthogonal, OrthogonalArgs, RandomNormal, RandomNormalArgs, RandomUniform, RandomUniformArgs, SeedOnlyInitializerArgs, TruncatedNormal, TruncatedNormalArgs, VarianceScaling, VarianceScalingArgs, Zeros} from './initializers';
-
-/**
- * Initializer that generates tensors initialized to 0.
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function zeros(): Zeros {
-  return new Zeros();
-}
-
-/**
- * Initializer that generates tensors initialized to 1.
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function ones(): Initializer {
-  return new Ones();
-}
-
-/**
- * Initializer that generates values initialized to some constant.
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function constant(args: ConstantArgs): Initializer {
-  return new Constant(args);
-}
-
-/**
- * Initializer that generates random values initialized to a uniform
- * distribution.
- *
- * Values will be distributed uniformly between the configured minval and
- * maxval.
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function randomUniform(args: RandomUniformArgs): Initializer {
-  return new RandomUniform(args);
-}
-
-/**
- * Initializer that generates random values initialized to a normal
- * distribution.
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function randomNormal(args: RandomNormalArgs): Initializer {
-  return new RandomNormal(args);
-}
-
-/**
- * Initializer that generates random values initialized to a truncated normal
- * distribution.
- *
- * These values are similar to values from a `RandomNormal` except that values
- * more than two standard deviations from the mean are discarded and re-drawn.
- * This is the recommended initializer for neural network weights and filters.
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function truncatedNormal(args: TruncatedNormalArgs): Initializer {
-  return new TruncatedNormal(args);
-}
-
-/**
- * Initializer that generates the identity matrix.
- * Only use for square 2D matrices.
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function identity(args: IdentityArgs): Initializer {
-  return new Identity(args);
-}
-
-/**
- * Initializer capable of adapting its scale to the shape of weights.
- * With distribution=NORMAL, samples are drawn from a truncated normal
- * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:
- *   - number of input units in the weight tensor, if mode = FAN_IN.
- *   - number of output units, if mode = FAN_OUT.
- *   - average of the numbers of input and output units, if mode = FAN_AVG.
- * With distribution=UNIFORM,
- * samples are drawn from a uniform distribution
- * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.
- *
- * @doc {heading: 'Initializers',namespace: 'initializers'}
- */
-export function varianceScaling(config: VarianceScalingArgs): Initializer {
-  return new VarianceScaling(config);
-}
-
-/**
- * Glorot uniform initializer, also called Xavier uniform initializer.
- * It draws samples from a uniform distribution within [-limit, limit]
- * where `limit` is `sqrt(6 / (fan_in + fan_out))`
- * where `fan_in` is the number of input units in the weight tensor
- * and `fan_out` is the number of output units in the weight tensor
- *
- * Reference:
- *   Glorot & Bengio, AISTATS 2010
- *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function glorotUniform(args: SeedOnlyInitializerArgs): Initializer {
-  return new GlorotUniform(args);
-}
-
-/**
- * Glorot normal initializer, also called Xavier normal initializer.
- * It draws samples from a truncated normal distribution centered on 0
- * with `stddev = sqrt(2 / (fan_in + fan_out))`
- * where `fan_in` is the number of input units in the weight tensor
- * and `fan_out` is the number of output units in the weight tensor.
- *
- * Reference:
- *   Glorot & Bengio, AISTATS 2010
- *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function glorotNormal(args: SeedOnlyInitializerArgs): Initializer {
-  return new GlorotNormal(args);
-}
-
-/**
- * He normal initializer.
- *
- * It draws samples from a truncated normal distribution centered on 0
- * with `stddev = sqrt(2 / fanIn)`
- * where `fanIn` is the number of input units in the weight tensor.
- *
- * Reference:
- *     He et al., http://arxiv.org/abs/1502.01852
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function heNormal(args: SeedOnlyInitializerArgs): Initializer {
-  return new HeNormal(args);
-}
-
-/**
- * He uniform initializer.
- *
- * It draws samples from a uniform distribution within [-limit, limit]
- * where `limit` is `sqrt(6 / fan_in)`
- * where `fanIn` is the number of input units in the weight tensor.
- *
- * Reference:
- *     He et al., http://arxiv.org/abs/1502.01852
- *
- * @doc {heading: 'Initializers',namespace: 'initializers'}
- */
-export function heUniform(args: SeedOnlyInitializerArgs): Initializer {
-  return new HeUniform(args);
-}
-
-/**
- * LeCun normal initializer.
- *
- * It draws samples from a truncated normal distribution centered on 0
- * with `stddev = sqrt(1 / fanIn)`
- * where `fanIn` is the number of input units in the weight tensor.
- *
- * References:
- *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
- *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function leCunNormal(args: SeedOnlyInitializerArgs): Initializer {
-  return new LeCunNormal(args);
-}
-
-/**
- * LeCun uniform initializer.
- *
- * It draws samples from a uniform distribution in the interval
- * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,
- * where `fanIn` is the number of input units in the weight tensor.
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function leCunUniform(args: SeedOnlyInitializerArgs): Initializer {
-  return new LeCunUniform(args);
-}
-
-/**
- * Initializer that generates a random orthogonal matrix.
- *
- * Reference:
- * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)
- *
- * @doc {heading: 'Initializers', namespace: 'initializers'}
- */
-export function orthogonal(args: OrthogonalArgs): Initializer {
-  return new Orthogonal(args);
-}
diff --git a/tfjs-master/tfjs-layers/src/exports_layers.ts b/tfjs-master/tfjs-layers/src/exports_layers.ts
deleted file mode 100644
index 4af5634bf..000000000
--- a/tfjs-master/tfjs-layers/src/exports_layers.ts
+++ /dev/null
@@ -1,1889 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {InputLayer, InputLayerArgs} from './engine/input_layer';
-import {Layer, LayerArgs} from './engine/topology';
-import {input} from './exports';
-import {ELU, ELULayerArgs, LeakyReLU, LeakyReLULayerArgs, PReLU, PReLULayerArgs, ReLU, ReLULayerArgs, Softmax, SoftmaxLayerArgs, ThresholdedReLU, ThresholdedReLULayerArgs} from './layers/advanced_activations';
-import {Conv1D, Conv2D, Conv2DTranspose, Conv3D, ConvLayerArgs, Cropping2D, Cropping2DLayerArgs, SeparableConv2D, SeparableConvLayerArgs, UpSampling2D, UpSampling2DLayerArgs, Conv3DTranspose} from './layers/convolutional';
-import {DepthwiseConv2D, DepthwiseConv2DLayerArgs} from './layers/convolutional_depthwise';
-import {ConvLSTM2D, ConvLSTM2DArgs, ConvLSTM2DCell, ConvLSTM2DCellArgs} from './layers/convolutional_recurrent';
-import {Activation, ActivationLayerArgs, Dense, DenseLayerArgs, Dropout, DropoutLayerArgs, Flatten, FlattenLayerArgs, Masking, MaskingArgs, Permute, PermuteLayerArgs, RepeatVector, RepeatVectorLayerArgs, Reshape, ReshapeLayerArgs, SpatialDropout1D, SpatialDropout1DLayerConfig} from './layers/core';
-import {Embedding, EmbeddingLayerArgs} from './layers/embeddings';
-import {Add, Average, Concatenate, ConcatenateLayerArgs, Dot, DotLayerArgs, Maximum, Minimum, Multiply} from './layers/merge';
-import {AlphaDropout, AlphaDropoutArgs, GaussianDropout, GaussianDropoutArgs, GaussianNoise, GaussianNoiseArgs} from './layers/noise';
-import {BatchNormalization, BatchNormalizationLayerArgs, LayerNormalization, LayerNormalizationLayerArgs} from './layers/normalization';
-import {ZeroPadding2D, ZeroPadding2DLayerArgs} from './layers/padding';
-import {AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, GlobalPooling2DLayerArgs, MaxPooling1D, MaxPooling2D, MaxPooling3D, Pooling1DLayerArgs, Pooling2DLayerArgs, Pooling3DLayerArgs} from './layers/pooling';
-import {GRU, GRUCell, GRUCellLayerArgs, GRULayerArgs, LSTM, LSTMCell, LSTMCellLayerArgs, LSTMLayerArgs, RNN, RNNCell, RNNLayerArgs, SimpleRNN, SimpleRNNCell, SimpleRNNCellLayerArgs, SimpleRNNLayerArgs, StackedRNNCells, StackedRNNCellsArgs} from './layers/recurrent';
-import {Bidirectional, BidirectionalLayerArgs, TimeDistributed, WrapperLayerArgs} from './layers/wrappers';
-import {Rescaling, RescalingArgs} from './layers/preprocessing/image_preprocessing';
-import {CenterCrop, CenterCropArgs} from './layers/preprocessing/center_crop';
-import {CategoryEncoding, CategoryEncodingArgs} from './layers/preprocessing/category_encoding';
-import {Resizing, ResizingArgs} from './layers/preprocessing/image_resizing';
-import {RandomWidth, RandomWidthArgs} from './layers/preprocessing/random_width';
-
-// TODO(cais): Add doc string to all the public static functions in this
-//   class; include exectuable JavaScript code snippets where applicable
-//   (b/74074458).
-
-// Input Layer.
-/**
- * An input layer is an entry point into a `tf.LayersModel`.
- *
- * `InputLayer` is generated automatically for `tf.Sequential` models by
- * specifying the `inputshape` or `batchInputShape` for the first layer.  It
- * should not be specified explicitly. However, it can be useful sometimes,
- * e.g., when constructing a sequential model from a subset of another
- * sequential model's layers. Like the code snippet below shows.
- *
- * ```js
- * // Define a model which simply adds two inputs.
- * const model1 = tf.sequential();
- * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));
- * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));
- * model1.summary();
- * model1.predict(tf.zeros([1, 4])).print();
- *
- * // Construct another model, reusing the second layer of `model1` while
- * // not using the first layer of `model1`. Note that you cannot add the second
- * // layer of `model` directly as the first layer of the new sequential model,
- * // because doing so will lead to an error related to the fact that the layer
- * // is not an input layer. Instead, you need to create an `inputLayer` and add
- * // it to the new sequential model before adding the reused layer.
- * const model2 = tf.sequential();
- * // Use an inputShape that matches the input shape of `model1`'s second
- * // layer.
- * model2.add(tf.layers.inputLayer({inputShape: [3]}));
- * model2.add(model1.layers[1]);
- * model2.summary();
- * model2.predict(tf.zeros([1, 3])).print();
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}
- */
-export function inputLayer(args: InputLayerArgs) {
-  return new InputLayer(args);
-}
-
-// Advanced Activation Layers.
-
-/**
- * Exponential Linear Unit (ELU).
- *
- * It follows:
- * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,
- * `f(x) = x for x >= 0`.
- *
- * Input shape:
- *   Arbitrary. Use the configuration `inputShape` when using this layer as the
- *   first layer in a model.
- *
- * Output shape:
- *   Same shape as the input.
- *
- * References:
- *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units
- * (ELUs)](https://arxiv.org/abs/1511.07289v1)
- *
- * @doc {
- *   heading: 'Layers',
- *   subheading: 'Advanced Activation',
- *   namespace: 'layers'
- * }
- */
-export function elu(args?: ELULayerArgs) {
-  return new ELU(args);
-}
-
-/**
- * Rectified Linear Unit activation function.
- *
- * Input shape:
- *   Arbitrary. Use the config field `inputShape` (Array of integers, does
- *   not include the sample axis) when using this layer as the first layer
- *   in a model.
- *
- * Output shape:
- *   Same shape as the input.
- *
- * @doc {
- *   heading: 'Layers',
- *   subheading: 'Advanced Activation',
- *   namespace: 'layers'
- * }
- */
-export function reLU(args?: ReLULayerArgs) {
-  return new ReLU(args);
-}
-
-/**
- * Leaky version of a rectified linear unit.
- *
- * It allows a small gradient when the unit is not active:
- * `f(x) = alpha * x for x < 0.`
- * `f(x) = x for x >= 0.`
- *
- * Input shape:
- *   Arbitrary. Use the configuration `inputShape` when using this layer as the
- *   first layer in a model.
- *
- * Output shape:
- *   Same shape as the input.
- *
- * @doc {
- *   heading: 'Layers',
- *   subheading: 'Advanced Activation',
- *   namespace: 'layers'
- * }
- */
-export function leakyReLU(args?: LeakyReLULayerArgs) {
-  return new LeakyReLU(args);
-}
-
-/**
- * Parameterized version of a leaky rectified linear unit.
- *
- * It follows
- * `f(x) = alpha * x for x < 0.`
- * `f(x) = x for x >= 0.`
- * wherein `alpha` is a trainable weight.
- *
- * Input shape:
- *   Arbitrary. Use the configuration `inputShape` when using this layer as the
- *   first layer in a model.
- *
- * Output shape:
- *   Same shape as the input.
- *
- * @doc {
- *   heading: 'Layers',
- *   subheading: 'Advanced Activation',
- *   namespace: 'layers'
- * }
- */
-export function prelu(args?: PReLULayerArgs) {
-  return new PReLU(args);
-}
-
-/**
- * Softmax activation layer.
- *
- * Input shape:
- *   Arbitrary. Use the configuration `inputShape` when using this layer as the
- *   first layer in a model.
- *
- * Output shape:
- *   Same shape as the input.
- *
- * @doc {
- *   heading: 'Layers',
- *   subheading: 'Advanced Activation',
- *   namespace: 'layers'
- * }
- */
-export function softmax(args?: SoftmaxLayerArgs) {
-  return new Softmax(args);
-}
-
-/**
- * Thresholded Rectified Linear Unit.
- *
- * It follows:
- * `f(x) = x for x > theta`,
- * `f(x) = 0 otherwise`.
- *
- * Input shape:
- *   Arbitrary. Use the configuration `inputShape` when using this layer as the
- *   first layer in a model.
- *
- * Output shape:
- *   Same shape as the input.
- *
- * References:
- *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting
- * Features](http://arxiv.org/abs/1402.3337)
- *
- * @doc {
- *   heading: 'Layers',
- *   subheading: 'Advanced Activation',
- *   namespace: 'layers'
- * }
- */
-export function thresholdedReLU(args?: ThresholdedReLULayerArgs) {
-  return new ThresholdedReLU(args);
-}
-
-// Convolutional Layers.
-
-/**
- * 1D convolution layer (e.g., temporal convolution).
- *
- * This layer creates a convolution kernel that is convolved
- * with the layer input over a single spatial (or temporal) dimension
- * to produce a tensor of outputs.
- *
- * If `use_bias` is True, a bias vector is created and added to the outputs.
- *
- * If `activation` is not `null`, it is applied to the outputs as well.
- *
- * When using this layer as the first layer in a model, provide an
- * `inputShape` argument `Array` or `null`.
- *
- * For example, `inputShape` would be:
- * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors
- * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.
- *
- * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}
- */
-export function conv1d(args: ConvLayerArgs) {
-  return new Conv1D(args);
-}
-
-/**
- * 2D convolution layer (e.g. spatial convolution over images).
- *
- * This layer creates a convolution kernel that is convolved
- * with the layer input to produce a tensor of outputs.
- *
- * If `useBias` is True, a bias vector is created and added to the outputs.
- *
- * If `activation` is not `null`, it is applied to the outputs as well.
- *
- * When using this layer as the first layer in a model,
- * provide the keyword argument `inputShape`
- * (Array of integers, does not include the sample axis),
- * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures
- * in `dataFormat='channelsLast'`.
- *
- * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}
- */
-export function conv2d(args: ConvLayerArgs) {
-  return new Conv2D(args);
-}
-
-/**
- * Transposed convolutional layer (sometimes called Deconvolution).
- *
- * The need for transposed convolutions generally arises
- * from the desire to use a transformation going in the opposite direction of
- * a normal convolution, i.e., from something that has the shape of the output
- * of some convolution to something that has the shape of its input while
- * maintaining a connectivity pattern that is compatible with said
- * convolution.
- *
- * When using this layer as the first layer in a model, provide the
- * configuration `inputShape` (`Array` of integers, does not include the
- * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in
- * `dataFormat: 'channelsLast'`.
- *
- * Input shape:
- *   4D tensor with shape:
- *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.
- *   or 4D tensor with shape
- *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast'`.
- *
- * Output shape:
- *   4D tensor with shape:
- *   `[batch, filters, newRows, newCols]` if `dataFormat` is
- * `'channelsFirst'`. or 4D tensor with shape:
- *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.
- *
- * References:
- *   - [A guide to convolution arithmetic for deep
- * learning](https://arxiv.org/abs/1603.07285v1)
- *   - [Deconvolutional
- * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
- *
- * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}
- */
-export function conv2dTranspose(args: ConvLayerArgs) {
-  return new Conv2DTranspose(args);
-}
-
-/**
- * 3D convolution layer (e.g. spatial convolution over volumes).
- *
- * This layer creates a convolution kernel that is convolved
- * with the layer input to produce a tensor of outputs.
- *
- * If `useBias` is True, a bias vector is created and added to the outputs.
- *
- * If `activation` is not `null`, it is applied to the outputs as well.
- *
- * When using this layer as the first layer in a model,
- * provide the keyword argument `inputShape`
- * (Array of integers, does not include the sample axis),
- * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes
- * in `dataFormat='channelsLast'`.
- *
- * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}
- */
-export function conv3d(args: ConvLayerArgs) {
-  return new Conv3D(args);
-}
-
-export function conv3dTranspose(args: ConvLayerArgs): Layer {
-  return new Conv3DTranspose(args);
-}
-
-/**
- * Depthwise separable 2D convolution.
- *
- * Separable convolution consists of first performing
- * a depthwise spatial convolution
- * (which acts on each input channel separately)
- * followed by a pointwise convolution which mixes together the resulting
- * output channels. The `depthMultiplier` argument controls how many
- * output channels are generated per input channel in the depthwise step.
- *
- * Intuitively, separable convolutions can be understood as
- * a way to factorize a convolution kernel into two smaller kernels,
- * or as an extreme version of an Inception block.
- *
- * Input shape:
- *   4D tensor with shape:
- *     `[batch, channels, rows, cols]` if data_format='channelsFirst'
- *   or 4D tensor with shape:
- *     `[batch, rows, cols, channels]` if data_format='channelsLast'.
- *
- * Output shape:
- *   4D tensor with shape:
- *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'
- *   or 4D tensor with shape:
- *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.
- *     `rows` and `cols` values might have changed due to padding.
- *
- * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}
- */
-export function separableConv2d(args: SeparableConvLayerArgs) {
-  return new SeparableConv2D(args);
-}
-
-/**
- * Cropping layer for 2D input (e.g., image).
- *
- * This layer can crop an input
- * at the top, bottom, left and right side of an image tensor.
- *
- * Input shape:
- *   4D tensor with shape:
- *   - If `dataFormat` is `"channelsLast"`:
- *     `[batch, rows, cols, channels]`
- *   - If `data_format` is `"channels_first"`:
- *     `[batch, channels, rows, cols]`.
- *
- * Output shape:
- *   4D with shape:
- *   - If `dataFormat` is `"channelsLast"`:
- *     `[batch, croppedRows, croppedCols, channels]`
- *    - If `dataFormat` is `"channelsFirst"`:
- *     `[batch, channels, croppedRows, croppedCols]`.
- *
- * Examples
- * ```js
- *
- * const model = tf.sequential();
- * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],
- *                                inputShape: [128, 128, 3]}));
- * //now output shape is [batch, 124, 124, 3]
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}
- */
-export function cropping2D(args: Cropping2DLayerArgs) {
-  return new Cropping2D(args);
-}
-
-/**
- * Upsampling layer for 2D inputs.
- *
- * Repeats the rows and columns of the data
- * by size[0] and size[1] respectively.
- *
- *
- * Input shape:
- *    4D tensor with shape:
- *     - If `dataFormat` is `"channelsLast"`:
- *         `[batch, rows, cols, channels]`
- *     - If `dataFormat` is `"channelsFirst"`:
- *        `[batch, channels, rows, cols]`
- *
- * Output shape:
- *     4D tensor with shape:
- *     - If `dataFormat` is `"channelsLast"`:
- *        `[batch, upsampledRows, upsampledCols, channels]`
- *     - If `dataFormat` is `"channelsFirst"`:
- *         `[batch, channels, upsampledRows, upsampledCols]`
- *
- *
- * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}
- */
-export function upSampling2d(args: UpSampling2DLayerArgs) {
-  return new UpSampling2D(args);
-}
-
-// Convolutional(depthwise) Layers.
-
-/**
- * Depthwise separable 2D convolution.
- *
- * Depthwise Separable convolutions consists in performing just the first step
- * in a depthwise spatial convolution (which acts on each input channel
- * separately). The `depthMultiplier` argument controls how many output channels
- * are generated per input channel in the depthwise step.
- *
- * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}
- */
-export function depthwiseConv2d(args: DepthwiseConv2DLayerArgs) {
-  return new DepthwiseConv2D(args);
-}
-
-// Basic Layers.
-
-/**
- * Applies an activation function to an output.
- *
- * This layer applies element-wise activation function.  Other layers, notably
- * `dense` can also apply activation functions.  Use this isolated activation
- * function to extract the values before and after the
- * activation. For instance:
- *
- * ```js
- * const input = tf.input({shape: [5]});
- * const denseLayer = tf.layers.dense({units: 1});
- * const activationLayer = tf.layers.activation({activation: 'relu6'});
- *
- * // Obtain the output symbolic tensors by applying the layers in order.
- * const denseOutput = denseLayer.apply(input);
- * const activationOutput = activationLayer.apply(denseOutput);
- *
- * // Create the model based on the inputs.
- * const model = tf.model({
- *     inputs: input,
- *     outputs: [denseOutput, activationOutput]
- * });
- *
- * // Collect both outputs and print separately.
- * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));
- * denseOut.print();
- * activationOut.print();
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}
- */
-export function activation(args: ActivationLayerArgs) {
-  return new Activation(args);
-}
-
-/**
- * Creates a dense (fully connected) layer.
- *
- * This layer implements the operation:
- *   `output = activation(dot(input, kernel) + bias)`
- *
- * `activation` is the element-wise activation function
- *   passed as the `activation` argument.
- *
- * `kernel` is a weights matrix created by the layer.
- *
- * `bias` is a bias vector created by the layer (only applicable if `useBias`
- * is `true`).
- *
- * **Input shape:**
- *
- *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.
- *
- *   The most common situation would be
- *   a 2D input with shape `(batchSize, inputDim)`.
- *
- * **Output shape:**
- *
- *   nD tensor with shape: `(batchSize, ..., units)`.
- *
- *   For instance, for a 2D input with shape `(batchSize, inputDim)`,
- *   the output would have shape `(batchSize, units)`.
- *
- * Note: if the input to the layer has a rank greater than 2, then it is
- * flattened prior to the initial dot product with the kernel.
- *
- * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}
- */
-export function dense(args: DenseLayerArgs) {
-  return new Dense(args);
-}
-
-/**
- * Applies
- * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to
- * the input.
- *
- * Dropout consists in randomly setting a fraction `rate` of input units to 0 at
- * each update during training time, which helps prevent overfitting.
- *
- * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}
- */
-export function dropout(args: DropoutLayerArgs) {
-  return new Dropout(args);
-}
-
-/**
- * Spatial 1D version of Dropout.
- *
- * This Layer type performs the same function as the Dropout layer, but it drops
- * entire 1D feature maps instead of individual elements. For example, if an
- * input example consists of 3 timesteps and the feature map for each timestep
- * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps
- * of the 1st timesteps and 2nd timesteps completely while sparing all feature
- * elements of the 3rd timestep.
- *
- * If adjacent frames (timesteps) are strongly correlated (as is normally the
- * case in early convolution layers), regular dropout will not regularize the
- * activation and will otherwise just result in merely an effective learning
- * rate decrease. In this case, `spatialDropout1d` will help promote
- * independence among feature maps and should be used instead.
- *
- * **Arguments:**
- *   rate: A floating-point number >=0 and <=1. Fraction of the input elements
- *     to drop.
- *
- * **Input shape:**
- *   3D tensor with shape `(samples, timesteps, channels)`.
- *
- * **Output shape:**
- *   Same as the input shape.
- *
- * References:
- *   - [Efficient Object Localization Using Convolutional
- *      Networks](https://arxiv.org/abs/1411.4280)
- *
- * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}
- */
-export function spatialDropout1d(args: SpatialDropout1DLayerConfig) {
-  return new SpatialDropout1D(args);
-}
-
-/**
- * Flattens the input. Does not affect the batch size.
- *
- * A `Flatten` layer flattens each batch in its inputs to 1D (making the output
- * 2D).
- *
- * For example:
- *
- * ```js
- * const input = tf.input({shape: [4, 3]});
- * const flattenLayer = tf.layers.flatten();
- * // Inspect the inferred output shape of the flatten layer, which
- * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the
- * // flattening. (The 1st dimension is the undermined batch size.)
- * console.log(JSON.stringify(flattenLayer.apply(input).shape));
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}
- */
-export function flatten(args?: FlattenLayerArgs) {
-  return new Flatten(args);
-}
-
-/**
- * Repeats the input n times in a new dimension.
- *
- * ```js
- *  const model = tf.sequential();
- *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));
- *  const x = tf.tensor2d([[10, 20]]);
- *  // Use the model to do inference on a data point the model hasn't seen
- *  model.predict(x).print();
- *  // output shape is now [batch, 2, 4]
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}
- */
-export function repeatVector(args: RepeatVectorLayerArgs) {
-  return new RepeatVector(args);
-}
-
-/**
- * Reshapes an input to a certain shape.
- *
- * ```js
- * const input = tf.input({shape: [4, 3]});
- * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});
- * // Inspect the inferred output shape of the Reshape layer, which
- * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)
- * console.log(JSON.stringify(reshapeLayer.apply(input).shape));
- * ```
- *
- * Input shape:
- *   Arbitrary, although all dimensions in the input shape must be fixed.
- *   Use the configuration `inputShape` when using this layer as the
- *   first layer in a model.
- *
- *
- * Output shape:
- *   [batchSize, targetShape[0], targetShape[1], ...,
- *    targetShape[targetShape.length - 1]].
- *
- * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}
- */
-export function reshape(args: ReshapeLayerArgs) {
-  return new Reshape(args);
-}
-
-/**
- * Permutes the dimensions of the input according to a given pattern.
- *
- * Useful for, e.g., connecting RNNs and convnets together.
- *
- * Example:
- *
- * ```js
- * const model = tf.sequential();
- * model.add(tf.layers.permute({
- *   dims: [2, 1],
- *   inputShape: [10, 64]
- * }));
- * console.log(model.outputShape);
- * // Now model's output shape is [null, 64, 10], where null is the
- * // unpermuted sample (batch) dimension.
- * ```
- *
- * Input shape:
- *   Arbitrary. Use the configuration field `inputShape` when using this
- *   layer as the first layer in a model.
- *
- * Output shape:
- *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,
- *   permuted) according to the `dims` configuration of this layer.
- *
- * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}
- */
-export function permute(args: PermuteLayerArgs) {
-  return new Permute(args);
-}
-
-/**
- * Maps positive integers (indices) into dense vectors of fixed size.
- * E.g. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]
- *
- * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.
- *
- * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,
- * outputDim]`.
- *
- * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}
- */
-export function embedding(args: EmbeddingLayerArgs) {
-  return new Embedding(args);
-}
-
-// Merge Layers.
-
-/**
- * Layer that performs element-wise addition on an `Array` of inputs.
- *
- * It takes as input a list of tensors, all of the same shape, and returns a
- * single tensor (also of the same shape). The inputs are specified as an
- * `Array` when the `apply` method of the `Add` layer instance is called. For
- * example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const addLayer = tf.layers.add();
- * const sum = addLayer.apply([input1, input2]);
- * console.log(JSON.stringify(sum.shape));
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}
- */
-export function add(args?: LayerArgs) {
-  return new Add(args);
-}
-
-/**
- * Layer that performs element-wise averaging on an `Array` of inputs.
- *
- * It takes as input a list of tensors, all of the same shape, and returns a
- * single tensor (also of the same shape). For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const averageLayer = tf.layers.average();
- * const average = averageLayer.apply([input1, input2]);
- * console.log(JSON.stringify(average.shape));
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}
- */
-export function average(args?: LayerArgs) {
-  return new Average(args);
-}
-
-/**
- * Layer that concatenates an `Array` of inputs.
- *
- * It takes a list of tensors, all of the same shape except for the
- * concatenation axis, and returns a single tensor, the concatenation
- * of all inputs. For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 3]});
- * const concatLayer = tf.layers.concatenate();
- * const output = concatLayer.apply([input1, input2]);
- * console.log(JSON.stringify(output.shape));
- * // You get [null, 2, 5], with the first dimension as the undetermined batch
- * // dimension. The last dimension (5) is the result of concatenating the
- * // last dimensions of the inputs (2 and 3).
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}
- */
-export function concatenate(args?: ConcatenateLayerArgs) {
-  return new Concatenate(args);
-}
-
-/**
- * Layer that computes the element-wise maximum of an `Array` of inputs.
- *
- * It takes as input a list of tensors, all of the same shape, and returns a
- * single tensor (also of the same shape). For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const maxLayer = tf.layers.maximum();
- * const max = maxLayer.apply([input1, input2]);
- * console.log(JSON.stringify(max.shape));
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}
- */
-export function maximum(args?: LayerArgs) {
-  return new Maximum(args);
-}
-
-/**
- * Layer that computes the element-wise minimum of an `Array` of inputs.
- *
- * It takes as input a list of tensors, all of the same shape, and returns a
- * single tensor (also of the same shape). For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const minLayer = tf.layers.minimum();
- * const min = minLayer.apply([input1, input2]);
- * console.log(JSON.stringify(min.shape));
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}
- */
-export function minimum(args?: LayerArgs) {
-  return new Minimum(args);
-}
-
-/**
- * Layer that multiplies (element-wise) an `Array` of inputs.
- *
- * It takes as input an Array of tensors, all of the same
- * shape, and returns a single tensor (also of the same shape).
- * For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const input3 = tf.input({shape: [2, 2]});
- * const multiplyLayer = tf.layers.multiply();
- * const product = multiplyLayer.apply([input1, input2, input3]);
- * console.log(product.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- *
- * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}
- */
-export function multiply(args?: LayerArgs) {
-  return new Multiply(args);
-}
-
-/**
- * Layer that computes a dot product between samples in two tensors.
- *
- * E.g., if applied to a list of two tensors `a` and `b` both of shape
- * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,
- * where each entry at index `[i, 0]` will be the dot product between
- * `a[i, :]` and `b[i, :]`.
- *
- * Example:
- *
- * ```js
- * const dotLayer = tf.layers.dot({axes: -1});
- * const x1 = tf.tensor2d([[10, 20], [30, 40]]);
- * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);
- *
- * // Invoke the layer's apply() method in eager (imperative) mode.
- * const y = dotLayer.apply([x1, x2]);
- * y.print();
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}
- */
-export function dot(args: DotLayerArgs) {
-  return new Dot(args);
-}
-
-// Normalization Layers.
-
-/**
- * Batch normalization layer (Ioffe and Szegedy, 2014).
- *
- * Normalize the activations of the previous layer at each batch,
- * i.e. applies a transformation that maintains the mean activation
- * close to 0 and the activation standard deviation close to 1.
- *
- * Input shape:
- *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does
- *   not include the sample axis) when calling the constructor of this class,
- *   if this layer is used as a first layer in a model.
- *
- * Output shape:
- *   Same shape as input.
- *
- * References:
- *   - [Batch Normalization: Accelerating Deep Network Training by Reducing
- * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
- *
- * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}
- */
-export function batchNormalization(args?: BatchNormalizationLayerArgs) {
-  return new BatchNormalization(args);
-}
-
-/**
- * Layer-normalization layer (Ba et al., 2016).
- *
- * Normalizes the activations of the previous layer for each given example in a
- * batch independently, instead of across a batch like in `batchNormalization`.
- * In other words, this layer applies a transformation that maintains the mean
- * activation within each example close to 0 and activation variance close to 1.
- *
- * Input shape:
- *   Arbitrary. Use the argument `inputShape` when using this layer as the first
- *   layer in a model.
- *
- * Output shape:
- *   Same as input.
- *
- * References:
- *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)
- *
- * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}
- */
-export function layerNormalization(args?: LayerNormalizationLayerArgs) {
-  return new LayerNormalization(args);
-}
-
-// Padding Layers.
-
-/**
- * Zero-padding layer for 2D input (e.g., image).
- *
- * This layer can add rows and columns of zeros
- * at the top, bottom, left and right side of an image tensor.
- *
- * Input shape:
- *   4D tensor with shape:
- *   - If `dataFormat` is `"channelsLast"`:
- *     `[batch, rows, cols, channels]`
- *   - If `data_format` is `"channels_first"`:
- *     `[batch, channels, rows, cols]`.
- *
- * Output shape:
- *   4D with shape:
- *   - If `dataFormat` is `"channelsLast"`:
- *     `[batch, paddedRows, paddedCols, channels]`
- *    - If `dataFormat` is `"channelsFirst"`:
- *     `[batch, channels, paddedRows, paddedCols]`.
- *
- * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}
- */
-export function zeroPadding2d(args?: ZeroPadding2DLayerArgs) {
-  return new ZeroPadding2D(args);
-}
-
-// Pooling Layers.
-
-/**
- * Average pooling operation for spatial data.
- *
- * Input shape: `[batchSize, inLength, channels]`
- *
- * Output shape: `[batchSize, pooledLength, channels]`
- *
- * `tf.avgPool1d` is an alias.
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function averagePooling1d(args: Pooling1DLayerArgs) {
-  return new AveragePooling1D(args);
-}
-export function avgPool1d(args: Pooling1DLayerArgs) {
-  return averagePooling1d(args);
-}
-// For backwards compatibility.
-// See https://github.com/tensorflow/tfjs/issues/152
-export function avgPooling1d(args: Pooling1DLayerArgs) {
-  return averagePooling1d(args);
-}
-
-/**
- * Average pooling operation for spatial data.
- *
- * Input shape:
- *  - If `dataFormat === CHANNEL_LAST`:
- *      4D tensor with shape:
- *      `[batchSize, rows, cols, channels]`
- *  - If `dataFormat === CHANNEL_FIRST`:
- *      4D tensor with shape:
- *      `[batchSize, channels, rows, cols]`
- *
- * Output shape
- *  - If `dataFormat === CHANNEL_LAST`:
- *      4D tensor with shape:
- *      `[batchSize, pooledRows, pooledCols, channels]`
- *  - If `dataFormat === CHANNEL_FIRST`:
- *      4D tensor with shape:
- *      `[batchSize, channels, pooledRows, pooledCols]`
- *
- * `tf.avgPool2d` is an alias.
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function averagePooling2d(args: Pooling2DLayerArgs) {
-  return new AveragePooling2D(args);
-}
-export function avgPool2d(args: Pooling2DLayerArgs) {
-  return averagePooling2d(args);
-}
-// For backwards compatibility.
-// See https://github.com/tensorflow/tfjs/issues/152
-export function avgPooling2d(args: Pooling2DLayerArgs) {
-  return averagePooling2d(args);
-}
-
-/**
- * Average pooling operation for 3D data.
- *
- * Input shape
- *   - If `dataFormat === channelsLast`:
- *       5D tensor with shape:
- *       `[batchSize, depths, rows, cols, channels]`
- *   - If `dataFormat === channelsFirst`:
- *      4D tensor with shape:
- *       `[batchSize, channels, depths, rows, cols]`
- *
- * Output shape
- *   - If `dataFormat=channelsLast`:
- *       5D tensor with shape:
- *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`
- *   - If `dataFormat=channelsFirst`:
- *       5D tensor with shape:
- *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function averagePooling3d(args: Pooling3DLayerArgs) {
-  return new AveragePooling3D(args);
-}
-export function avgPool3d(args: Pooling3DLayerArgs) {
-  return averagePooling3d(args);
-}
-// For backwards compatibility.
-// See https://github.com/tensorflow/tfjs/issues/152
-export function avgPooling3d(args: Pooling3DLayerArgs) {
-  return averagePooling3d(args);
-}
-
-/**
- * Global average pooling operation for temporal data.
- *
- * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.
- *
- * Output Shape: 2D tensor with shape: `[batchSize, features]`.
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function globalAveragePooling1d(args?: LayerArgs) {
-  return new GlobalAveragePooling1D(args);
-}
-
-/**
- * Global average pooling operation for spatial data.
- *
- * Input shape:
- *   - If `dataFormat` is `CHANNEL_LAST`:
- *       4D tensor with shape: `[batchSize, rows, cols, channels]`.
- *   - If `dataFormat` is `CHANNEL_FIRST`:
- *       4D tensor with shape: `[batchSize, channels, rows, cols]`.
- *
- * Output shape:
- *   2D tensor with shape: `[batchSize, channels]`.
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function globalAveragePooling2d(args: GlobalPooling2DLayerArgs) {
-  return new GlobalAveragePooling2D(args);
-}
-
-/**
- * Global max pooling operation for temporal data.
- *
- * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.
- *
- * Output Shape: 2D tensor with shape: `[batchSize, features]`.
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function globalMaxPooling1d(args?: LayerArgs) {
-  return new GlobalMaxPooling1D(args);
-}
-
-/**
- * Global max pooling operation for spatial data.
- *
- * Input shape:
- *   - If `dataFormat` is `CHANNEL_LAST`:
- *       4D tensor with shape: `[batchSize, rows, cols, channels]`.
- *   - If `dataFormat` is `CHANNEL_FIRST`:
- *       4D tensor with shape: `[batchSize, channels, rows, cols]`.
- *
- * Output shape:
- *   2D tensor with shape: `[batchSize, channels]`.
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function globalMaxPooling2d(args: GlobalPooling2DLayerArgs) {
-  return new GlobalMaxPooling2D(args);
-}
-
-/**
- * Max pooling operation for temporal data.
- *
- * Input shape:  `[batchSize, inLength, channels]`
- *
- * Output shape: `[batchSize, pooledLength, channels]`
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function maxPooling1d(args: Pooling1DLayerArgs) {
-  return new MaxPooling1D(args);
-}
-
-/**
- * Max pooling operation for spatial data.
- *
- * Input shape
- *   - If `dataFormat === CHANNEL_LAST`:
- *       4D tensor with shape:
- *       `[batchSize, rows, cols, channels]`
- *   - If `dataFormat === CHANNEL_FIRST`:
- *      4D tensor with shape:
- *       `[batchSize, channels, rows, cols]`
- *
- * Output shape
- *   - If `dataFormat=CHANNEL_LAST`:
- *       4D tensor with shape:
- *       `[batchSize, pooledRows, pooledCols, channels]`
- *   - If `dataFormat=CHANNEL_FIRST`:
- *       4D tensor with shape:
- *       `[batchSize, channels, pooledRows, pooledCols]`
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function maxPooling2d(args: Pooling2DLayerArgs) {
-  return new MaxPooling2D(args);
-}
-
-/**
- * Max pooling operation for 3D data.
- *
- * Input shape
- *   - If `dataFormat === channelsLast`:
- *       5D tensor with shape:
- *       `[batchSize, depths, rows, cols, channels]`
- *   - If `dataFormat === channelsFirst`:
- *      5D tensor with shape:
- *       `[batchSize, channels, depths, rows, cols]`
- *
- * Output shape
- *   - If `dataFormat=channelsLast`:
- *       5D tensor with shape:
- *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`
- *   - If `dataFormat=channelsFirst`:
- *       5D tensor with shape:
- *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`
- *
- * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}
- */
-export function maxPooling3d(args: Pooling3DLayerArgs) {
-  return new MaxPooling3D(args);
-}
-
-// Recurrent Layers.
-
-/**
- * Gated Recurrent Unit - Cho et al. 2014.
- *
- * This is an `RNN` layer consisting of one `GRUCell`. However, unlike
- * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates
- * on a sequence of inputs. The shape of the input (not including the first,
- * batch dimension) needs to be at least 2-D, with the first dimension being
- * time steps. For example:
- *
- * ```js
- * const rnn = tf.layers.gru({units: 8, returnSequences: true});
- *
- * // Create an input with 10 time steps.
- * const input = tf.input({shape: [10, 20]});
- * const output = rnn.apply(input);
- *
- * console.log(JSON.stringify(output.shape));
- * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
- * // same as the sequence length of `input`, due to `returnSequences`: `true`;
- * // 3rd dimension is the `GRUCell`'s number of units.
- *
- * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}
- */
-export function gru(args: GRULayerArgs) {
-  return new GRU(args);
-}
-
-/**
- * Cell class for `GRU`.
- *
- * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its
- * `apply` method takes the input data of only a single time step and returns
- * the cell's output at the time step, while `GRU` takes the input data
- * over a number of time steps. For example:
- *
- * ```js
- * const cell = tf.layers.gruCell({units: 2});
- * const input = tf.input({shape: [10]});
- * const output = cell.apply(input);
- *
- * console.log(JSON.stringify(output.shape));
- * // [null, 10]: This is the cell's output at a single time step. The 1st
- * // dimension is the unknown batch size.
- * ```
- *
- * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The
- * most typical use of this workflow is to combine a number of cells into a
- * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an
- * RNN. For example:
- *
- * ```js
- * const cells = [
- *   tf.layers.gruCell({units: 4}),
- *   tf.layers.gruCell({units: 8}),
- * ];
- * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});
- *
- * // Create an input with 10 time steps and a length-20 vector at each step.
- * const input = tf.input({shape: [10, 20]});
- * const output = rnn.apply(input);
- *
- * console.log(JSON.stringify(output.shape));
- * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
- * // same as the sequence length of `input`, due to `returnSequences`: `true`;
- * // 3rd dimension is the last `gruCell`'s number of units.
- * ```
- *
- * To create an `RNN` consisting of only *one* `GRUCell`, use the
- * `tf.layers.gru`.
- *
- * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}
- */
-export function gruCell(args: GRUCellLayerArgs) {
-  return new GRUCell(args);
-}
-
-/**
- * Long-Short Term Memory layer - Hochreiter 1997.
- *
- * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike
- * the underlying `LSTMCell`, the `apply` method of `LSTM` operates
- * on a sequence of inputs. The shape of the input (not including the first,
- * batch dimension) needs to be at least 2-D, with the first dimension being
- * time steps. For example:
- *
- * ```js
- * const lstm = tf.layers.lstm({units: 8, returnSequences: true});
- *
- * // Create an input with 10 time steps.
- * const input = tf.input({shape: [10, 20]});
- * const output = lstm.apply(input);
- *
- * console.log(JSON.stringify(output.shape));
- * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
- * // same as the sequence length of `input`, due to `returnSequences`: `true`;
- * // 3rd dimension is the `LSTMCell`'s number of units.
- *
- * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}
- */
-export function lstm(args: LSTMLayerArgs) {
-  return new LSTM(args);
-}
-
-/**
- * Cell class for `LSTM`.
- *
- * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its
- * `apply` method takes the input data of only a single time step and returns
- * the cell's output at the time step, while `LSTM` takes the input data
- * over a number of time steps. For example:
- *
- * ```js
- * const cell = tf.layers.lstmCell({units: 2});
- * const input = tf.input({shape: [10]});
- * const output = cell.apply(input);
- *
- * console.log(JSON.stringify(output.shape));
- * // [null, 10]: This is the cell's output at a single time step. The 1st
- * // dimension is the unknown batch size.
- * ```
- *
- * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The
- * most typical use of this workflow is to combine a number of cells into a
- * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an
- * RNN. For example:
- *
- * ```js
- * const cells = [
- *   tf.layers.lstmCell({units: 4}),
- *   tf.layers.lstmCell({units: 8}),
- * ];
- * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});
- *
- * // Create an input with 10 time steps and a length-20 vector at each step.
- * const input = tf.input({shape: [10, 20]});
- * const output = rnn.apply(input);
- *
- * console.log(JSON.stringify(output.shape));
- * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
- * // same as the sequence length of `input`, due to `returnSequences`: `true`;
- * // 3rd dimension is the last `lstmCell`'s number of units.
- * ```
- *
- * To create an `RNN` consisting of only *one* `LSTMCell`, use the
- * `tf.layers.lstm`.
- *
- * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}
- */
-export function lstmCell(args: LSTMCellLayerArgs) {
-  return new LSTMCell(args);
-}
-
-/**
- * Fully-connected RNN where the output is to be fed back to input.
- *
- * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike
- * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates
- * on a sequence of inputs. The shape of the input (not including the first,
- * batch dimension) needs to be at least 2-D, with the first dimension being
- * time steps. For example:
- *
- * ```js
- * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});
- *
- * // Create an input with 10 time steps.
- * const input = tf.input({shape: [10, 20]});
- * const output = rnn.apply(input);
- *
- * console.log(JSON.stringify(output.shape));
- * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
- * // same as the sequence length of `input`, due to `returnSequences`: `true`;
- * // 3rd dimension is the `SimpleRNNCell`'s number of units.
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}
- */
-export function simpleRNN(args: SimpleRNNLayerArgs) {
-  return new SimpleRNN(args);
-}
-
-/**
- * Cell class for `SimpleRNN`.
- *
- * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its
- * `apply` method takes the input data of only a single time step and returns
- * the cell's output at the time step, while `SimpleRNN` takes the input data
- * over a number of time steps. For example:
- *
- * ```js
- * const cell = tf.layers.simpleRNNCell({units: 2});
- * const input = tf.input({shape: [10]});
- * const output = cell.apply(input);
- *
- * console.log(JSON.stringify(output.shape));
- * // [null, 10]: This is the cell's output at a single time step. The 1st
- * // dimension is the unknown batch size.
- * ```
- *
- * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The
- * most typical use of this workflow is to combine a number of cells into a
- * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an
- * RNN. For example:
- *
- * ```js
- * const cells = [
- *   tf.layers.simpleRNNCell({units: 4}),
- *   tf.layers.simpleRNNCell({units: 8}),
- * ];
- * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});
- *
- * // Create an input with 10 time steps and a length-20 vector at each step.
- * const input = tf.input({shape: [10, 20]});
- * const output = rnn.apply(input);
- *
- * console.log(JSON.stringify(output.shape));
- * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
- * // same as the sequence length of `input`, due to `returnSequences`: `true`;
- * // 3rd dimension is the last `SimpleRNNCell`'s number of units.
- * ```
- *
- * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the
- * `tf.layers.simpleRNN`.
- *
- * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}
- */
-export function simpleRNNCell(args: SimpleRNNCellLayerArgs) {
-  return new SimpleRNNCell(args);
-}
-
-/**
- * Convolutional LSTM layer - Xingjian Shi 2015.
- *
- * This is a `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,
- * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`
- * operates on a sequence of inputs. The shape of the input (not including the
- * first, batch dimension) needs to be 4-D, with the first dimension being time
- * steps. For example:
- *
- * ```js
- * const filters = 3;
- * const kernelSize = 3;
- *
- * const batchSize = 4;
- * const sequenceLength = 2;
- * const size = 5;
- * const channels = 3;
- *
- * const inputShape = [batchSize, sequenceLength, size, size, channels];
- * const input = tf.ones(inputShape);
- *
- * const layer = tf.layers.convLstm2d({filters, kernelSize});
- *
- * const output = layer.apply(input);
- * ```
- */
-/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */
-export function convLstm2d(args: ConvLSTM2DArgs) {
-  return new ConvLSTM2D(args);
-}
-
-/**
- * Cell class for `ConvLSTM2D`.
- *
- * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in
- * that its `call` method takes the input data of only a single time step and
- * returns the cell's output at the time step, while `ConvLSTM2D` takes the
- * input data over a number of time steps. For example:
- *
- * ```js
- * const filters = 3;
- * const kernelSize = 3;
- *
- * const sequenceLength = 1;
- * const size = 5;
- * const channels = 3;
- *
- * const inputShape = [sequenceLength, size, size, channels];
- * const input = tf.ones(inputShape);
- *
- * const cell = tf.layers.convLstm2dCell({filters, kernelSize});
- *
- * cell.build(input.shape);
- *
- * const outputSize = size - kernelSize + 1;
- * const outShape = [sequenceLength, outputSize, outputSize, filters];
- *
- * const initialH = tf.zeros(outShape);
- * const initialC = tf.zeros(outShape);
- *
- * const [o, h, c] = cell.call([input, initialH, initialC], {});
- * ```
- */
-/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */
-export function convLstm2dCell(args: ConvLSTM2DCellArgs) {
-  return new ConvLSTM2DCell(args);
-}
-
-/**
- * Base class for recurrent layers.
- *
- * Input shape:
- *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.
- *
- * Output shape:
- *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first
- *     tensor is the output. The remaining tensors are the states at the
- *     last time step, each with shape `[batchSize, units]`.
- *   - if `returnSequences`, the output will have shape
- *     `[batchSize, timeSteps, units]`.
- *   - else, the output will have shape `[batchSize, units]`.
- *
- * Masking:
- *   This layer supports masking for input data with a variable number
- *   of timesteps. To introduce masks to your data,
- *   use an embedding layer with the `mask_zero` parameter
- *   set to `True`.
- *
- * Notes on using statefulness in RNNs:
- *   You can set RNN layers to be 'stateful', which means that the states
- *   computed for the samples in one batch will be reused as initial states
- *   for the samples in the next batch. This assumes a one-to-one mapping
- *   between samples in different successive batches.
- *
- *   To enable statefulness:
- *     - specify `stateful: true` in the layer constructor.
- *     - specify a fixed batch size for your model, by passing
- *       if sequential model:
- *         `batchInputShape=[...]` to the first layer in your model.
- *       else for functional model with 1 or more Input layers:
- *         `batchShape=[...]` to all the first layers in your model.
- *       This is the expected shape of your inputs *including the batch size*.
- *       It should be a tuple of integers, e.g. `(32, 10, 100)`.
- *     - specify `shuffle=False` when calling fit().
- *
- *   To reset the states of your model, call `.resetStates()` on either
- *   a specific layer, or on your entire model.
- *
- * Note on specifying the initial state of RNNs
- *   You can specify the initial state of RNN layers symbolically by
- *   calling them with the option `initialState`. The value of
- *   `initialState` should be a tensor or list of tensors representing
- *   the initial state of the RNN layer.
- *
- *   You can specify the initial state of RNN layers numerically by
- *   calling `resetStates` with the keyword argument `states`. The value of
- *   `states` should be a numpy array or list of numpy arrays representing
- *   the initial state of the RNN layer.
- *
- * Note on passing external constants to RNNs
- *   You can pass "external" constants to the cell using the `constants`
- *   keyword argument of `RNN.call` method. This requires that the `cell.call`
- *   method accepts the same keyword argument `constants`. Such constants
- *   can be used to condition the cell transformation on additional static
- *   inputs (not changing over time), a.k.a. an attention mechanism.
- *
- * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}
- */
-export function rnn(args: RNNLayerArgs) {
-  return new RNN(args);
-}
-
-/**
- * Wrapper allowing a stack of RNN cells to behave as a single cell.
- *
- * Used to implement efficient stacked RNNs.
- *
- * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}
- */
-export function stackedRNNCells(args: StackedRNNCellsArgs){
-  return new StackedRNNCells(args);
-}
-
-// Wrapper Layers.
-
-/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */
-export function bidirectional(args: BidirectionalLayerArgs) {
-  return new Bidirectional(args);
-}
-
-/**
- * This wrapper applies a layer to every temporal slice of an input.
- *
- * The input should be at least 3D,  and the dimension of the index `1` will be
- * considered to be the temporal dimension.
- *
- * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors
- * of 16 dimensions. The batch input shape of the layer is then `[32,  10,
- * 16]`, and the `inputShape`, not including the sample dimension, is
- * `[10, 16]`.
- *
- * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10
- * timesteps, independently:
- *
- * ```js
- * const model = tf.sequential();
- * model.add(tf.layers.timeDistributed({
- *   layer: tf.layers.dense({units: 8}),
- *   inputShape: [10, 16],
- * }));
- *
- * // Now model.outputShape = [null, 10, 8].
- * // The output will then have shape `[32, 10, 8]`.
- *
- * // In subsequent layers, there is no need for `inputShape`:
- * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));
- * console.log(JSON.stringify(model.outputs[0].shape));
- * // Now model.outputShape = [null, 10, 32].
- * ```
- *
- * The output will then have shape `[32, 10, 32]`.
- *
- * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for
- * instance a `Conv2D` layer.
- *
- * ```js
- * const model = tf.sequential();
- * model.add(tf.layers.timeDistributed({
- *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),
- *   inputShape: [10, 299, 299, 3],
- * }));
- * console.log(JSON.stringify(model.outputs[0].shape));
- * ```
- *
- * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}
- */
-export function timeDistributed(args: WrapperLayerArgs) {
-  return new TimeDistributed(args);
-}
-
-// Aliases for pooling.
-export const globalMaxPool1d = globalMaxPooling1d;
-export const globalMaxPool2d = globalMaxPooling2d;
-export const maxPool1d = maxPooling1d;
-export const maxPool2d = maxPooling2d;
-
-export {Layer, RNN, RNNCell, input /* alias for tf.input */};
-
-/**
- * Apply additive zero-centered Gaussian noise.
- *
- * As it is a regularization layer, it is only active at training time.
- *
- * This is useful to mitigate overfitting
- * (you could see it as a form of random data augmentation).
- * Gaussian Noise (GS) is a natural choice as corruption process
- * for real valued inputs.
- *
- * # Arguments
- * stddev: float, standard deviation of the noise distribution.
- *
- * # Input shape
- * Arbitrary. Use the keyword argument `input_shape`
- * (tuple of integers, does not include the samples axis)
- * when using this layer as the first layer in a model.
- *
- * # Output shape
- * Same shape as input.
- *
- * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}
- */
-export function gaussianNoise(args: GaussianNoiseArgs) {
-  return new GaussianNoise(args);
-}
-
-/**
- * Apply multiplicative 1-centered Gaussian noise.
- *
- * As it is a regularization layer, it is only active at training time.
- *
- * Arguments:
- *   - `rate`: float, drop probability (as with `Dropout`).
- *     The multiplicative noise will have
- *     standard deviation `sqrt(rate / (1 - rate))`.
- *
- * Input shape:
- *   Arbitrary. Use the keyword argument `inputShape`
- *   (tuple of integers, does not include the samples axis)
- *   when using this layer as the first layer in a model.
- *
- * Output shape:
- *   Same shape as input.
- *
- * References:
- *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](
- *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
- *
- * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}
- */
-export function gaussianDropout(args: GaussianDropoutArgs) {
-  return new GaussianDropout(args);
-}
-
-/**
- * Applies Alpha Dropout to the input.
- *
- * As it is a regularization layer, it is only active at training time.
- *
- * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs
- * to their original values, in order to ensure the self-normalizing property
- * even after this dropout.
- * Alpha Dropout fits well to Scaled Exponential Linear Units
- * by randomly setting activations to the negative saturation value.
- *
- * Arguments:
- *   - `rate`: float, drop probability (as with `Dropout`).
- *     The multiplicative noise will have
- *     standard deviation `sqrt(rate / (1 - rate))`.
- *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the
- *     shape for randomly generated keep/drop flags.
- *
- * Input shape:
- *   Arbitrary. Use the keyword argument `inputShape`
- *   (tuple of integers, does not include the samples axis)
- *   when using this layer as the first layer in a model.
- *
- * Output shape:
- *   Same shape as input.
- *
- * References:
- *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
- *
- * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}
- */
-export function alphaDropout(args: AlphaDropoutArgs) {
-  return new AlphaDropout(args);
-}
-
-/**
- * Masks a sequence by using a mask value to skip timesteps.
- *
- * If all features for a given sample timestep are equal to `mask_value`,
- * then the sample timestep will be masked (skipped) in all downstream layers
- * (as long as they support masking).
- *
- * If any downstream layer does not support masking yet receives such
- * an input mask, an exception will be raised.
- *
- * Arguments:
- *   - `maskValue`: Either None or mask value to skip.
- *
- * Input shape:
- *   Arbitrary. Use the keyword argument `inputShape`
- *   (tuple of integers, does not include the samples axis)
- *   when using this layer as the first layer in a model.
- *
- * Output shape:
- *   Same shape as input.
- *
- * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}
- */
-export function masking(args?: MaskingArgs) {
-  return new Masking(args);
-}
-
-/**
- * A preprocessing layer which rescales input values to a new range.
- *
- * This layer rescales every value of an input (often an image) by multiplying
- * by `scale` and adding `offset`.
- *
- * For instance:
- * 1. To rescale an input in the ``[0, 255]`` range
- * to be in the `[0, 1]` range, you would pass `scale=1/255`.
- * 2. To rescale an input in the ``[0, 255]`` range to be in the `[-1, 1]`
- * range, you would pass `scale=1./127.5, offset=-1`.
- * The rescaling is applied both during training and inference. Inputs can be
- * of integer or floating point dtype, and by default the layer will output
- * floats.
- *
- * Arguments:
- *   - `scale`: Float, the scale to apply to the inputs.
- *   - `offset`: Float, the offset to apply to the inputs.
- *
- * Input shape:
- *   Arbitrary.
- *
- * Output shape:
- *   Same as input.
- *
- * @doc {heading: 'Layers', subheading: 'Rescaling', namespace: 'layers'}
- */
-export function rescaling(args?: RescalingArgs) {
-  return new Rescaling(args);
-}
-
-/**
- *  A preprocessing layer which center crops images.
- *
- *   This layers crops the central portion of the images to a target size. If an
- *   image is smaller than the target size, it will be resized and cropped so as
- *   to return the largest possible window in the image that matches the target
- *   aspect ratio.
- *
- *   Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
- *   of integer or floating point dtype.
- *
- *   If the input height/width is even and the target height/width is odd (or
- *   inversely), the input image is left-padded by 1 pixel.
- *
- *   Arguments:
- *     `height`: Integer, the height of the output shape.
- *     `width`: Integer, the width of the output shape.
- *
- *   Input shape:
- *     3D (unbatched) or 4D (batched) tensor with shape:
- *     `(..., height, width, channels)`, in `channelsLast` format.
- *
- *   Output shape:
- *     3D (unbatched) or 4D (batched) tensor with shape:
- *     `(..., targetHeight, targetWidth, channels)`.
- *
- *
- *  @doc {heading: 'Layers', subheading: 'CenterCrop', namespace: 'layers'}
- */
-export function centerCrop(args?: CenterCropArgs) {
-   return new CenterCrop(args);
-  }
-
-/**
- * A preprocessing layer which resizes images.
- * This layer resizes an image input to a target height and width. The input
- * should be a 4D (batched) or 3D (unbatched) tensor in `"channels_last"`
- * format.  Input pixel values can be of any range (e.g. `[0., 1.)` or `[0,
- * 255]`) and of interger or floating point dtype. By default, the layer will
- * output floats.
- *
- * Arguments:
- *   - `height`: number, the height for the output tensor.
- *   - `width`: number, the width for the output tensor.
- *   - `interpolation`: string, the method for image resizing interpolation.
- *   - `cropToAspectRatio`: boolean, whether to keep image aspect ratio.
- *
- * Input shape:
- *   Arbitrary.
- *
- * Output shape:
- *   height, width, num channels.
- *
- * @doc {heading: 'Layers', subheading: 'Resizing', namespace: 'layers'}
- */
-export function resizing(args?: ResizingArgs) {
-  return new Resizing(args);
-}
-
-/**
- * A preprocessing layer which encodes integer features.
- *
- * This layer provides options for condensing data into a categorical encoding
- * when the total number of tokens are known in advance. It accepts integer
- * values as inputs, and it outputs a dense representation of those
- * inputs.
- *
- * Arguments:
- *
- * numTokens: The total number of tokens the layer should support. All
- *  inputs to the layer must integers in the range `0 <= value <
- *  numTokens`, or an error will be thrown.
- *
- * outputMode: Specification for the output of the layer.
- *  Defaults to `multiHot`. Values can be `oneHot`, `multiHot` or
- *  `count`, configuring the layer as follows:
- *
- *    oneHot: Encodes each individual element in the input into an
- *      array of `numTokens` size, containing a 1 at the element index. If
- *      the last dimension is size 1, will encode on that dimension. If the
- *      last dimension is not size 1, will append a new dimension for the
- *      encoded output.
- *
- *    multiHot: Encodes each sample in the input into a single array
- *     of `numTokens` size, containing a 1 for each vocabulary term
- *     present in the sample. Treats the last dimension as the sample
- *     dimension, if input shape is `(..., sampleLength)`, output shape
- *     will be `(..., numTokens)`.
- *
- *    count: Like `multiHot`, but the int array contains a count of
- *     the number of times the token at that index appeared in the sample.
- *
- *  For all output modes, currently only output up to rank 2 is supported.
- *   Call arguments:
- *    inputs: A 1D or 2D tensor of integer inputs.
- *    countWeights: A tensor in the same shape as `inputs` indicating the
- *    weight for each sample value when summing up in `count` mode. Not used
- *    in `multiHot` or `oneHot` modes.
- *
- *
- * @doc {heading: 'Layers', subheading: 'CategoryEncoding', namespace: 'layers'}
- */
-export function categoryEncoding(args: CategoryEncodingArgs) {
-  return new CategoryEncoding(args);
-}
-
- /**
-  * A preprocessing layer which randomly varies image width during training.
-  *
-  * This layer will randomly adjusts the width of a batch of images of a batch
-  * of images by a random factor.
-  *
-  * The input should be a 3D (unbatched) or 4D (batched) tensor in
-  * the `"channels_last"` image data format. Input pixel values can be of any
-  * range (e.g. `[0., 1.)` or `[0, 255]`) and of integer or floating point
-  * dtype. By default, the layer will output floats. By default, this layer is
-  * inactive during inference. For an overview and full list of preprocessing
-  * layers, see the preprocessing [guide]
-  * (https://www.tensorflow.org/guide/keras/preprocessing_layers).
-  *
-  * Arguments:
-  *
-  * factor:
-  *   A positive float (fraction of original width), or a tuple of size 2
-  *   representing lower and upper bound for resizing vertically.
-  *   When represented as a single float, this value is used for both the upper
-  *   and lower bound. For instance, `factor=(0.2, 0.3)` results in an output
-  *   with width changed by a random amount in the range `[20%, 30%]`.
-  *   `factor=(-0.2, 0.3)` results in an output with width changed by a random
-  *   amount in the range `[-20%, +30%]`. `factor=0.2` results in an output
-  *   with width changed by a random amount in the range `[-20%, +20%]`.
-  * interpolation:
-  *   String, the interpolation method.
-  *   Defaults to `bilinear`.
-  *   Supports `"bilinear"`, `"nearest"`.
-  *   The tf methods `"bicubic"`, `"area"`, `"lanczos3"`, `"lanczos5"`,
-  *   `"gaussian"`, `"mitchellcubic"` are unimplemented in tfjs.
-  * seed:
-  *   Integer. Used to create a random seed.
-  *
-  * Input shape:
-  *     3D (unbatched) or 4D (batched) tensor with shape:
-  *     `(..., height, width, channels)`, in `"channels_last"` format.
-  * Output shape:
-  *     3D (unbatched) or 4D (batched) tensor with shape:
-  *     `(..., height, random_width, channels)`.
-  *
-  *
-  * @doc {heading: 'Layers', subheading: 'RandomWidth', namespace: 'layers'}
-  */
-  export function randomWidth(args: RandomWidthArgs) {
-    return new RandomWidth(args);
-  }
diff --git a/tfjs-master/tfjs-layers/src/exports_metrics.ts b/tfjs-master/tfjs-layers/src/exports_metrics.ts
deleted file mode 100644
index dd6472c34..000000000
--- a/tfjs-master/tfjs-layers/src/exports_metrics.ts
+++ /dev/null
@@ -1,316 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-import {Tensor} from '@tensorflow/tfjs-core';
-
-import * as losses from './losses';
-import * as metrics from './metrics';
-
-/**
- * Binary accuracy metric function.
- *
- * `yTrue` and `yPred` can have 0-1 values. Example:
- * ```js
- * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);
- * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);
- * const accuracy = tf.metrics.binaryAccuracy(x, y);
- * accuracy.print();
- * ```
- *
- * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in
- * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,
- * a value >= 0.5 and <= 1.0 is interpreted as 1).
- *
- * Example:
- * ```js
- * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);
- * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);
- * const accuracy = tf.metrics.binaryAccuracy(x, y);
- * accuracy.print();
- * ```
- *
- * @param yTrue Binary Tensor of truth.
- * @param yPred Binary Tensor of prediction.
- * @return Accuracy Tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function binaryAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {
-  return metrics.binaryAccuracy(yTrue, yPred);
-}
-
-/**
- * Binary crossentropy metric function.
- *
- * Example:
- * ```js
- * const x = tf.tensor2d([[0], [1], [1], [1]]);
- * const y = tf.tensor2d([[0], [0], [0.5], [1]]);
- * const crossentropy = tf.metrics.binaryCrossentropy(x, y);
- * crossentropy.print();
- * ```
- *
- * @param yTrue Binary Tensor of truth.
- * @param yPred Binary Tensor of prediction, probabilities for the `1` case.
- * @return Accuracy Tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function binaryCrossentropy(yTrue: Tensor, yPred: Tensor): Tensor {
-  return metrics.binaryCrossentropy(yTrue, yPred);
-}
-
-/**
- * Sparse categorical accuracy metric function.
- *
- * Example:
- * ```js
- *
- * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);
- * const yPred = tf.tensor2d(
- *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);
- * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);
- * crossentropy.print();
- * ```
- *
- * @param yTrue True labels: indices.
- * @param yPred Predicted probabilities or logits.
- * @returns Accuracy tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function sparseCategoricalAccuracy(
-    yTrue: Tensor, yPred: Tensor): Tensor {
-  return metrics.sparseCategoricalAccuracy(yTrue, yPred);
-}
-
-/**
- * Categorical accuracy metric function.
- *
- * Example:
- * ```js
- * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);
- * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);
- * const accuracy = tf.metrics.categoricalAccuracy(x, y);
- * accuracy.print();
- * ```
- *
- * @param yTrue Binary Tensor of truth: one-hot encoding of categories.
- * @param yPred Binary Tensor of prediction: probabilities or logits for the
- *   same categories as in `yTrue`.
- * @return Accuracy Tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function categoricalAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {
-  return metrics.categoricalAccuracy(yTrue, yPred);
-}
-
-/**
- * Categorical crossentropy between an output tensor and a target tensor.
- *
- * @param target A tensor of the same shape as `output`.
- * @param output A tensor resulting from a softmax (unless `fromLogits` is
- *  `true`, in which case `output` is expected to be the logits).
- * @param fromLogits Boolean, whether `output` is the result of a softmax, or is
- *   a tensor of logits.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function categoricalCrossentropy(yTrue: Tensor, yPred: Tensor): Tensor {
-  return metrics.categoricalCrossentropy(yTrue, yPred);
-}
-
-/**
- * Computes the precision of the predictions with respect to the labels.
- *
- * Example:
- * ```js
- * const x = tf.tensor2d(
- *    [
- *      [0, 0, 0, 1],
- *      [0, 1, 0, 0],
- *      [0, 0, 0, 1],
- *      [1, 0, 0, 0],
- *      [0, 0, 1, 0]
- *    ]
- * );
- *
- * const y = tf.tensor2d(
- *    [
- *      [0, 0, 1, 0],
- *      [0, 1, 0, 0],
- *      [0, 0, 0, 1],
- *      [0, 1, 0, 0],
- *      [0, 1, 0, 0]
- *    ]
- * );
- *
- * const precision = tf.metrics.precision(x, y);
- * precision.print();
- * ```
- *
- * @param yTrue The ground truth values. Expected to contain only 0-1 values.
- * @param yPred The predicted values. Expected to contain only 0-1 values.
- * @return Precision Tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function precision(yTrue: Tensor, yPred: Tensor): Tensor {
-  return metrics.precision(yTrue, yPred);
-}
-
-/**
- * Computes the recall of the predictions with respect to the labels.
- *
- * Example:
- * ```js
- * const x = tf.tensor2d(
- *    [
- *      [0, 0, 0, 1],
- *      [0, 1, 0, 0],
- *      [0, 0, 0, 1],
- *      [1, 0, 0, 0],
- *      [0, 0, 1, 0]
- *    ]
- * );
- *
- * const y = tf.tensor2d(
- *    [
- *      [0, 0, 1, 0],
- *      [0, 1, 0, 0],
- *      [0, 0, 0, 1],
- *      [0, 1, 0, 0],
- *      [0, 1, 0, 0]
- *    ]
- * );
- *
- * const recall = tf.metrics.recall(x, y);
- * recall.print();
- * ```
- *
- * @param yTrue The ground truth values. Expected to contain only 0-1 values.
- * @param yPred The predicted values. Expected to contain only 0-1 values.
- * @return Recall Tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function recall(yTrue: Tensor, yPred: Tensor): Tensor {
-  return metrics.recall(yTrue, yPred);
-}
-
-/**
- * Loss or metric function: Cosine proximity.
- *
- * Mathematically, cosine proximity is defined as:
- *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,
- * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`
- * represents element-wise multiplication.
- *
- * ```js
- * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);
- * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);
- * const proximity = tf.metrics.cosineProximity(yTrue, yPred);
- * proximity.print();
- * ```
- *
- * @param yTrue Truth Tensor.
- * @param yPred Prediction Tensor.
- * @return Cosine proximity Tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function cosineProximity(yTrue: Tensor, yPred: Tensor): Tensor {
-  return losses.cosineProximity(yTrue, yPred);
-}
-
-/**
- * Loss or metric function: Mean absolute error.
- *
- * Mathematically, mean absolute error is defined as:
- *   `mean(abs(yPred - yTrue))`,
- * wherein the `mean` is applied over feature dimensions.
- *
- * ```js
- * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);
- * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);
- * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);
- * mse.print();
- * ```
- *
- * @param yTrue Truth Tensor.
- * @param yPred Prediction Tensor.
- * @return Mean absolute error Tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function meanAbsoluteError(yTrue: Tensor, yPred: Tensor): Tensor {
-  return losses.meanAbsoluteError(yTrue, yPred);
-}
-
-/**
- * Loss or metric function: Mean absolute percentage error.
- *
- * ```js
- * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);
- * const yPred = tf.tensor2d([[0, 1], [11, 24]]);
- * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);
- * mse.print();
- * ```
- *
- * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.
- *
- * @param yTrue Truth Tensor.
- * @param yPred Prediction Tensor.
- * @return Mean absolute percentage error Tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function meanAbsolutePercentageError(
-    yTrue: Tensor, yPred: Tensor): Tensor {
-  return losses.meanAbsolutePercentageError(yTrue, yPred);
-}
-
-export function MAPE(yTrue: Tensor, yPred: Tensor): Tensor {
-  return losses.meanAbsolutePercentageError(yTrue, yPred);
-}
-
-export function mape(yTrue: Tensor, yPred: Tensor): Tensor {
-  return losses.meanAbsolutePercentageError(yTrue, yPred);
-}
-
-/**
- * Loss or metric function: Mean squared error.
- *
- * ```js
- * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);
- * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);
- * const mse = tf.metrics.meanSquaredError(yTrue, yPred);
- * mse.print();
- * ```
- *
- * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.
- *
- * @param yTrue Truth Tensor.
- * @param yPred Prediction Tensor.
- * @return Mean squared error Tensor.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export function meanSquaredError(yTrue: Tensor, yPred: Tensor): Tensor {
-  return losses.meanSquaredError(yTrue, yPred);
-}
-
-export function MSE(yTrue: Tensor, yPred: Tensor): Tensor {
-  return losses.meanSquaredError(yTrue, yPred);
-}
-
-export function mse(yTrue: Tensor, yPred: Tensor): Tensor {
-  return losses.meanSquaredError(yTrue, yPred);
-}
diff --git a/tfjs-master/tfjs-layers/src/exports_models.ts b/tfjs-master/tfjs-layers/src/exports_models.ts
deleted file mode 100644
index 68377038f..000000000
--- a/tfjs-master/tfjs-layers/src/exports_models.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-export {modelFromJSON} from './models';
diff --git a/tfjs-master/tfjs-layers/src/exports_regularizers.ts b/tfjs-master/tfjs-layers/src/exports_regularizers.ts
deleted file mode 100644
index eecd0c206..000000000
--- a/tfjs-master/tfjs-layers/src/exports_regularizers.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-import * as regularizers from './regularizers';
-// tslint:disable-next-line:max-line-length
-import {L1Args, L1L2, L1L2Args, L2Args, Regularizer} from './regularizers';
-
-/**
- * Regularizer for L1 and L2 regularization.
- *
- * Adds a term to the loss to penalize large weights:
- * loss += sum(l1 * abs(x)) + sum(l2 * x^2)
- *
- * @doc {heading: 'Regularizers', namespace: 'regularizers'}
- */
-export function l1l2(config?: L1L2Args): Regularizer {
-  return new L1L2(config);
-}
-
-/**
- * Regularizer for L1 regularization.
- *
- * Adds a term to the loss to penalize large weights:
- * loss += sum(l1 * abs(x))
- * @param args l1 config.
- *
- * @doc {heading: 'Regularizers', namespace: 'regularizers'}
- */
-export function l1(config?: L1Args): Regularizer {
-  return regularizers.l1(config);
-}
-
-/**
- * Regularizer for L2 regularization.
- *
- * Adds a term to the loss to penalize large weights:
- * loss += sum(l2 * x^2)
- * @param args l2 config.
- *
- * @doc {heading: 'Regularizers', namespace: 'regularizers'}
- */
-export function l2(config?: L2Args): Regularizer {
-  return regularizers.l2(config);
-}
diff --git a/tfjs-master/tfjs-layers/src/flags_layers.ts b/tfjs-master/tfjs-layers/src/flags_layers.ts
deleted file mode 100644
index 4026af6d9..000000000
--- a/tfjs-master/tfjs-layers/src/flags_layers.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {env} from '@tensorflow/tfjs-core';
-
-import {updateCacheMaxEntries} from './engine/executor';
-
-export const ENV = env();
-
-/** The max number of entries for the caches of layers' topological sort. */
-ENV.registerFlag(
-    'TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES', () => 100, updateCacheMaxEntries);
diff --git a/tfjs-master/tfjs-layers/src/index.ts b/tfjs-master/tfjs-layers/src/index.ts
deleted file mode 100644
index fd2e01631..000000000
--- a/tfjs-master/tfjs-layers/src/index.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import './flags_layers';
-import '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-
-// This file lists all exports of TensorFlow.js Layers
-
-import * as constraints from './exports_constraints';
-import * as initializers from './exports_initializers';
-import * as layers from './exports_layers';
-import * as metrics from './exports_metrics';
-import * as models from './exports_models';
-import * as regularizers from './exports_regularizers';
-
-export {CallbackList, CustomCallback, CustomCallbackArgs, History} from './base_callbacks';
-export {Callback, callbacks, EarlyStopping, EarlyStoppingCallbackArgs} from './callbacks';
-export {InputSpec, SymbolicTensor} from './engine/topology';
-export {LayersModel, ModelCompileArgs, ModelEvaluateArgs} from './engine/training';
-export {ModelFitDatasetArgs} from './engine/training_dataset';
-export {ModelFitArgs} from './engine/training_tensors';
-export {ClassWeight, ClassWeightMap} from './engine/training_utils';
-export {input, loadLayersModel, model, registerCallbackConstructor, sequential} from './exports';
-export {Shape} from './keras_format/common';
-export {GRUCellLayerArgs, GRULayerArgs, LSTMCellLayerArgs, LSTMLayerArgs, RNN, RNNLayerArgs, SimpleRNNCellLayerArgs, SimpleRNNLayerArgs} from './layers/recurrent';
-export {Logs} from './logs';
-export {ModelAndWeightsConfig, Sequential, SequentialArgs} from './models';
-export {LayerVariable} from './variables';
-export {version as version_layers} from './version';
-export {constraints, initializers, layers, metrics, models, regularizers};
diff --git a/tfjs-master/tfjs-layers/src/initializers.ts b/tfjs-master/tfjs-layers/src/initializers.ts
deleted file mode 100644
index 369be829c..000000000
--- a/tfjs-master/tfjs-layers/src/initializers.ts
+++ /dev/null
@@ -1,668 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {DataType, eye, linalg, mul, ones, randomUniform, scalar, serialization, Tensor, tidy, truncatedNormal, util, zeros} from '@tensorflow/tfjs-core';
-
-import * as K from './backend/tfjs_backend';
-import {checkDataFormat} from './common';
-import {NotImplementedError, ValueError} from './errors';
-import {DataFormat, Shape} from './keras_format/common';
-import {Distribution, FanMode, VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES} from './keras_format/initializer_config';
-import {checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject} from './utils/generic_utils';
-import {arrayProd} from './utils/math_utils';
-
-export function checkFanMode(value?: string): void {
-  checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);
-}
-
-export function checkDistribution(value?: string): void {
-  checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);
-}
-
-/**
- * Initializer base class.
- *
- * @doc {
- *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}
- */
-export abstract class Initializer extends serialization.Serializable {
-  public fromConfigUsesCustomObjects(): boolean {
-    return false;
-  }
-  /**
-   * Generate an initial value.
-   * @param shape
-   * @param dtype
-   * @return The init value.
-   */
-  abstract apply(shape: Shape, dtype?: DataType): Tensor;
-
-  getConfig(): serialization.ConfigDict {
-    return {};
-  }
-}
-
-export class Zeros extends Initializer {
-  /** @nocollapse */
-  static className = 'Zeros';
-
-  apply(shape: Shape, dtype?: DataType): Tensor {
-    return zeros(shape, dtype);
-  }
-}
-serialization.registerClass(Zeros);
-
-export class Ones extends Initializer {
-  /** @nocollapse */
-  static className = 'Ones';
-
-  apply(shape: Shape, dtype?: DataType): Tensor {
-    return ones(shape, dtype);
-  }
-}
-serialization.registerClass(Ones);
-
-export interface ConstantArgs {
-  /** The value for each element in the variable. */
-  value: number;
-}
-
-export class Constant extends Initializer {
-  /** @nocollapse */
-  static className = 'Constant';
-  private value: number;
-  constructor(args: ConstantArgs) {
-    super();
-    if (typeof args !== 'object') {
-      throw new ValueError(
-          `Expected argument of type ConstantConfig but got ${args}`);
-    }
-    if (args.value === undefined) {
-      throw new ValueError(`config must have value set but got ${args}`);
-    }
-    this.value = args.value;
-  }
-
-  apply(shape: Shape, dtype?: DataType): Tensor {
-    return tidy(() => mul(scalar(this.value), ones(shape, dtype)));
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {
-      value: this.value,
-    };
-  }
-}
-serialization.registerClass(Constant);
-
-export interface RandomUniformArgs {
-  /** Lower bound of the range of random values to generate. */
-  minval?: number;
-  /** Upper bound of the range of random values to generate. */
-  maxval?: number;
-  /** Used to seed the random generator. */
-  seed?: number;
-}
-
-export class RandomUniform extends Initializer {
-  /** @nocollapse */
-  static className = 'RandomUniform';
-  readonly DEFAULT_MINVAL = -0.05;
-  readonly DEFAULT_MAXVAL = 0.05;
-  private minval: number;
-  private maxval: number;
-  private seed: number;
-
-  constructor(args: RandomUniformArgs) {
-    super();
-    this.minval = args.minval || this.DEFAULT_MINVAL;
-    this.maxval = args.maxval || this.DEFAULT_MAXVAL;
-    this.seed = args.seed;
-  }
-
-  apply(shape: Shape, dtype?: DataType): Tensor {
-    return randomUniform(shape, this.minval, this.maxval, dtype, this.seed);
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {minval: this.minval, maxval: this.maxval, seed: this.seed};
-  }
-}
-serialization.registerClass(RandomUniform);
-
-export interface RandomNormalArgs {
-  /** Mean of the random values to generate. */
-  mean?: number;
-  /** Standard deviation of the random values to generate. */
-  stddev?: number;
-  /** Used to seed the random generator. */
-  seed?: number;
-}
-
-export class RandomNormal extends Initializer {
-  /** @nocollapse */
-  static className = 'RandomNormal';
-  readonly DEFAULT_MEAN = 0.;
-  readonly DEFAULT_STDDEV = 0.05;
-  private mean: number;
-  private stddev: number;
-  private seed: number;
-
-  constructor(args: RandomNormalArgs) {
-    super();
-    this.mean = args.mean || this.DEFAULT_MEAN;
-    this.stddev = args.stddev || this.DEFAULT_STDDEV;
-    this.seed = args.seed;
-  }
-
-  apply(shape: Shape, dtype?: DataType): Tensor {
-    dtype = dtype || 'float32';
-    if (dtype !== 'float32' && dtype !== 'int32') {
-      throw new NotImplementedError(
-          `randomNormal does not support dType ${dtype}.`);
-    }
-
-    return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {mean: this.mean, stddev: this.stddev, seed: this.seed};
-  }
-}
-serialization.registerClass(RandomNormal);
-
-export interface TruncatedNormalArgs {
-  /** Mean of the random values to generate. */
-  mean?: number;
-  /** Standard deviation of the random values to generate. */
-  stddev?: number;
-  /** Used to seed the random generator. */
-  seed?: number;
-}
-
-export class TruncatedNormal extends Initializer {
-  /** @nocollapse */
-  static className = 'TruncatedNormal';
-
-  readonly DEFAULT_MEAN = 0.;
-  readonly DEFAULT_STDDEV = 0.05;
-  private mean: number;
-  private stddev: number;
-  private seed: number;
-
-  constructor(args: TruncatedNormalArgs) {
-    super();
-    this.mean = args.mean || this.DEFAULT_MEAN;
-    this.stddev = args.stddev || this.DEFAULT_STDDEV;
-    this.seed = args.seed;
-  }
-
-  apply(shape: Shape, dtype?: DataType): Tensor {
-    dtype = dtype || 'float32';
-    if (dtype !== 'float32' && dtype !== 'int32') {
-      throw new NotImplementedError(
-          `truncatedNormal does not support dType ${dtype}.`);
-    }
-    return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {mean: this.mean, stddev: this.stddev, seed: this.seed};
-  }
-}
-serialization.registerClass(TruncatedNormal);
-
-export interface IdentityArgs {
-  /**
-   * Multiplicative factor to apply to the identity matrix.
-   */
-  gain?: number;
-}
-
-export class Identity extends Initializer {
-  /** @nocollapse */
-  static className = 'Identity';
-  private gain: number;
-  constructor(args: IdentityArgs) {
-    super();
-    this.gain = args.gain != null ? args.gain : 1.0;
-  }
-
-  apply(shape: Shape, dtype?: DataType): Tensor {
-    return tidy(() => {
-      if (shape.length !== 2 || shape[0] !== shape[1]) {
-        throw new ValueError(
-            'Identity matrix initializer can only be used for' +
-            ' 2D square matrices.');
-      } else {
-        return mul(this.gain, eye(shape[0]));
-      }
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {gain: this.gain};
-  }
-}
-serialization.registerClass(Identity);
-
-/**
- * Computes the number of input and output units for a weight shape.
- * @param shape Shape of weight.
- * @param dataFormat data format to use for convolution kernels.
- *   Note that all kernels in Keras are standardized on the
- *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).
- * @return An length-2 array: fanIn, fanOut.
- */
-function computeFans(
-    shape: Shape, dataFormat: DataFormat = 'channelsLast'): number[] {
-  let fanIn: number;
-  let fanOut: number;
-  checkDataFormat(dataFormat);
-  if (shape.length === 2) {
-    fanIn = shape[0];
-    fanOut = shape[1];
-  } else if ([3, 4, 5].indexOf(shape.length) !== -1) {
-    if (dataFormat === 'channelsFirst') {
-      const receptiveFieldSize = arrayProd(shape, 2);
-      fanIn = shape[1] * receptiveFieldSize;
-      fanOut = shape[0] * receptiveFieldSize;
-    } else if (dataFormat === 'channelsLast') {
-      const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);
-      fanIn = shape[shape.length - 2] * receptiveFieldSize;
-      fanOut = shape[shape.length - 1] * receptiveFieldSize;
-    }
-  } else {
-    const shapeProd = arrayProd(shape);
-    fanIn = Math.sqrt(shapeProd);
-    fanOut = Math.sqrt(shapeProd);
-  }
-
-  return [fanIn, fanOut];
-}
-
-export interface VarianceScalingArgs {
-  /** Scaling factor (positive float). */
-  scale?: number;
-
-  /** Fanning mode for inputs and outputs. */
-  mode?: FanMode;
-
-  /** Probabilistic distribution of the values. */
-  distribution?: Distribution;
-
-  /** Random number generator seed. */
-  seed?: number;
-}
-
-export class VarianceScaling extends Initializer {
-  /** @nocollapse */
-  static className = 'VarianceScaling';
-  private scale: number;
-  private mode: FanMode;
-  private distribution: Distribution;
-  private seed: number;
-
-  /**
-   * Constructor of VarianceScaling.
-   * @throws ValueError for invalid value in scale.
-   */
-  constructor(args: VarianceScalingArgs) {
-    super();
-    if (args.scale < 0.0) {
-      throw new ValueError(
-          `scale must be a positive float. Got: ${args.scale}`);
-    }
-    this.scale = args.scale == null ? 1.0 : args.scale;
-    this.mode = args.mode == null ? 'fanIn' : args.mode;
-    checkFanMode(this.mode);
-    this.distribution =
-        args.distribution == null ? 'normal' : args.distribution;
-    checkDistribution(this.distribution);
-    this.seed = args.seed;
-  }
-
-  apply(shape: Shape, dtype?: DataType): Tensor {
-    const fans = computeFans(shape);
-    const fanIn = fans[0];
-    const fanOut = fans[1];
-    let scale = this.scale;
-    if (this.mode === 'fanIn') {
-      scale /= Math.max(1, fanIn);
-    } else if (this.mode === 'fanOut') {
-      scale /= Math.max(1, fanOut);
-    } else {
-      scale /= Math.max(1, (fanIn + fanOut) / 2);
-    }
-
-    if (this.distribution === 'normal') {
-      const stddev = Math.sqrt(scale);
-      dtype = dtype || 'float32';
-      if (dtype !== 'float32' && dtype !== 'int32') {
-        throw new NotImplementedError(
-            `${this.getClassName()} does not support dType ${dtype}.`);
-      }
-      return truncatedNormal(shape, 0, stddev, dtype, this.seed);
-    } else {
-      const limit = Math.sqrt(3 * scale);
-      return randomUniform(shape, -limit, limit, dtype, this.seed);
-    }
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {
-      scale: this.scale,
-      mode: this.mode,
-      distribution: this.distribution,
-      seed: this.seed
-    };
-  }
-}
-serialization.registerClass(VarianceScaling);
-
-export interface SeedOnlyInitializerArgs {
-  /** Random number generator seed. */
-  seed?: number;
-}
-
-export class GlorotUniform extends VarianceScaling {
-  /** @nocollapse */
-  static override className = 'GlorotUniform';
-
-  /**
-   * Constructor of GlorotUniform
-   * @param scale
-   * @param mode
-   * @param distribution
-   * @param seed
-   */
-  constructor(args?: SeedOnlyInitializerArgs) {
-    super({
-      scale: 1.0,
-      mode: 'fanAvg',
-      distribution: 'uniform',
-      seed: args == null ? null : args.seed
-    });
-  }
-
-  override getClassName(): string {
-    // In Python Keras, GlorotUniform is not a class, but a helper method
-    // that creates a VarianceScaling object. Use 'VarianceScaling' as
-    // class name to be compatible with that.
-    return VarianceScaling.className;
-  }
-}
-serialization.registerClass(GlorotUniform);
-
-export class GlorotNormal extends VarianceScaling {
-  /** @nocollapse */
-  static override className = 'GlorotNormal';
-
-  /**
-   * Constructor of GlorotNormal.
-   * @param scale
-   * @param mode
-   * @param distribution
-   * @param seed
-   */
-  constructor(args?: SeedOnlyInitializerArgs) {
-    super({
-      scale: 1.0,
-      mode: 'fanAvg',
-      distribution: 'normal',
-      seed: args == null ? null : args.seed
-    });
-  }
-
-  override getClassName(): string {
-    // In Python Keras, GlorotNormal is not a class, but a helper method
-    // that creates a VarianceScaling object. Use 'VarianceScaling' as
-    // class name to be compatible with that.
-    return VarianceScaling.className;
-  }
-}
-serialization.registerClass(GlorotNormal);
-
-export class HeNormal extends VarianceScaling {
-  /** @nocollapse */
-  static override className = 'HeNormal';
-
-  constructor(args?: SeedOnlyInitializerArgs) {
-    super({
-      scale: 2.0,
-      mode: 'fanIn',
-      distribution: 'normal',
-      seed: args == null ? null : args.seed
-    });
-  }
-
-  override getClassName(): string {
-    // In Python Keras, HeNormal is not a class, but a helper method
-    // that creates a VarianceScaling object. Use 'VarianceScaling' as
-    // class name to be compatible with that.
-    return VarianceScaling.className;
-  }
-}
-serialization.registerClass(HeNormal);
-
-export class HeUniform extends VarianceScaling {
-  /** @nocollapse */
-  static override className = 'HeUniform';
-
-  constructor(args?: SeedOnlyInitializerArgs) {
-    super({
-      scale: 2.0,
-      mode: 'fanIn',
-      distribution: 'uniform',
-      seed: args == null ? null : args.seed
-    });
-  }
-
-  override getClassName(): string {
-    // In Python Keras, HeUniform is not a class, but a helper method
-    // that creates a VarianceScaling object. Use 'VarianceScaling' as
-    // class name to be compatible with that.
-    return VarianceScaling.className;
-  }
-}
-serialization.registerClass(HeUniform);
-
-export class LeCunNormal extends VarianceScaling {
-  /** @nocollapse */
-  static override className = 'LeCunNormal';
-
-  constructor(args?: SeedOnlyInitializerArgs) {
-    super({
-      scale: 1.0,
-      mode: 'fanIn',
-      distribution: 'normal',
-      seed: args == null ? null : args.seed
-    });
-  }
-
-  override getClassName(): string {
-    // In Python Keras, LeCunNormal is not a class, but a helper method
-    // that creates a VarianceScaling object. Use 'VarianceScaling' as
-    // class name to be compatible with that.
-    return VarianceScaling.className;
-  }
-}
-serialization.registerClass(LeCunNormal);
-
-export class LeCunUniform extends VarianceScaling {
-  /** @nocollapse */
-  static override className = 'LeCunUniform';
-
-  constructor(args?: SeedOnlyInitializerArgs) {
-    super({
-      scale: 1.0,
-      mode: 'fanIn',
-      distribution: 'uniform',
-      seed: args == null ? null : args.seed
-    });
-  }
-
-  override getClassName(): string {
-    // In Python Keras, LeCunUniform is not a class, but a helper method
-    // that creates a VarianceScaling object. Use 'VarianceScaling' as
-    // class name to be compatible with that.
-    return VarianceScaling.className;
-  }
-}
-serialization.registerClass(LeCunUniform);
-
-export interface OrthogonalArgs extends SeedOnlyInitializerArgs {
-  /**
-   * Multiplicative factor to apply to the orthogonal matrix. Defaults to 1.
-   */
-  gain?: number;
-}
-
-export class Orthogonal extends Initializer {
-  /** @nocollapse */
-  static className = 'Orthogonal';
-  readonly DEFAULT_GAIN = 1;
-  readonly ELEMENTS_WARN_SLOW = 2000;
-  protected readonly gain: number;
-  protected readonly seed: number;
-
-  constructor(args?: OrthogonalArgs) {
-    super();
-    this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;
-    this.seed = args.seed;
-  }
-
-  apply(shape: Shape, dtype?: DataType): Tensor {
-    return tidy(() => {
-      if (shape.length < 2) {
-        throw new NotImplementedError('Shape must be at least 2D.');
-      }
-      if (dtype !== 'int32' && dtype !== 'float32' && dtype !== undefined) {
-        throw new TypeError(`Unsupported data type ${dtype}.`);
-      }
-      dtype = dtype as 'int32' | 'float32' | undefined;
-
-      // flatten the input shape with the last dimension remaining its
-      // original shape so it works for conv2d
-      const numRows = util.sizeFromShape(shape.slice(0, -1));
-      const numCols = shape[shape.length - 1];
-      const numElements = numRows * numCols;
-      if (numElements > this.ELEMENTS_WARN_SLOW) {
-        console.warn(
-            `Orthogonal initializer is being called on a matrix with more ` +
-            `than ${this.ELEMENTS_WARN_SLOW} (${numElements}) elements: ` +
-            `Slowness may result.`);
-      }
-      const flatShape =
-          [Math.max(numCols, numRows), Math.min(numCols, numRows)];
-
-      // Generate a random matrix
-      const randNormalMat = K.randomNormal(flatShape, 0, 1, dtype, this.seed);
-
-      // Compute QR factorization
-      const qr = linalg.qr(randNormalMat, false);
-      let qMat = qr[0];
-      const rMat = qr[1];
-
-      // Make Q uniform
-      const diag = rMat.flatten().stridedSlice(
-          [0], [Math.min(numCols, numRows) * Math.min(numCols, numRows)],
-          [Math.min(numCols, numRows) + 1]);
-      qMat = mul(qMat, diag.sign());
-      if (numRows < numCols) {
-        qMat = qMat.transpose();
-      }
-
-      return mul(scalar(this.gain), qMat.reshape(shape));
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {
-      gain: this.gain,
-      seed: this.seed,
-    };
-  }
-}
-serialization.registerClass(Orthogonal);
-
-/** @docinline */
-export type InitializerIdentifier =
-    'constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'|
-    'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'|
-    'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string;
-
-// Maps the JavaScript-like identifier keys to the corresponding registry
-// symbols.
-export const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP:
-    {[identifier in InitializerIdentifier]: string} = {
-      'constant': 'Constant',
-      'glorotNormal': 'GlorotNormal',
-      'glorotUniform': 'GlorotUniform',
-      'heNormal': 'HeNormal',
-      'heUniform': 'HeUniform',
-      'identity': 'Identity',
-      'leCunNormal': 'LeCunNormal',
-      'leCunUniform': 'LeCunUniform',
-      'ones': 'Ones',
-      'orthogonal': 'Orthogonal',
-      'randomNormal': 'RandomNormal',
-      'randomUniform': 'RandomUniform',
-      'truncatedNormal': 'TruncatedNormal',
-      'varianceScaling': 'VarianceScaling',
-      'zeros': 'Zeros'
-    };
-
-function deserializeInitializer(
-    config: serialization.ConfigDict,
-    customObjects: serialization.ConfigDict = {}): Initializer {
-  return deserializeKerasObject(
-      config, serialization.SerializationMap.getMap().classNameMap,
-      customObjects, 'initializer');
-}
-
-export function serializeInitializer(initializer: Initializer):
-    serialization.ConfigDictValue {
-  return serializeKerasObject(initializer);
-}
-
-export function getInitializer(identifier: InitializerIdentifier|Initializer|
-                               serialization.ConfigDict): Initializer {
-  if (typeof identifier === 'string') {
-    const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?
-        INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :
-        identifier;
-    /* We have four 'helper' classes for common initializers that
-    all get serialized as 'VarianceScaling' and shouldn't go through
-    the deserializeInitializer pathway. */
-    if (className === 'GlorotNormal') {
-      return new GlorotNormal();
-    } else if (className === 'GlorotUniform') {
-      return new GlorotUniform();
-    } else if (className === 'HeNormal') {
-      return new HeNormal();
-    } else if (className === 'HeUniform') {
-      return new HeUniform();
-    } else if (className === 'LeCunNormal') {
-      return new LeCunNormal();
-    } else if (className === 'LeCunUniform') {
-      return new LeCunUniform();
-    } else {
-      const config: serialization.ConfigDict = {};
-      config['className'] = className;
-      config['config'] = {};
-      return deserializeInitializer(config);
-    }
-  } else if (identifier instanceof Initializer) {
-    return identifier;
-  } else {
-    return deserializeInitializer(identifier);
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/initializers_test.ts b/tfjs-master/tfjs-layers/src/initializers_test.ts
deleted file mode 100644
index 04e57a78d..000000000
--- a/tfjs-master/tfjs-layers/src/initializers_test.ts
+++ /dev/null
@@ -1,851 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for initializers.
- */
-
-import {eye, randomNormal, serialization, Tensor, Tensor2D, tensor2d} from '@tensorflow/tfjs-core';
-
-import * as tfl from './index';
-import {checkDistribution, checkFanMode, getInitializer, serializeInitializer, VarianceScaling} from './initializers';
-import {VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES} from './keras_format/initializer_config';
-import {PyJsonDict} from './keras_format/types';
-import {deserialize} from './layers/serialization';
-import * as math_utils from './utils/math_utils';
-import {convertPythonicToTs} from './utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndWebGL2, expectNoLeakedTensors, expectTensorsClose, expectTensorsValuesInRange} from './utils/test_utils';
-
-describeMathCPU('Zeros initializer', () => {
-  it('1D', () => {
-    const init = getInitializer('zeros');
-    const weights = init.apply([3], 'float32');
-    expect(weights.shape).toEqual([3]);
-    expect(weights.dtype).toEqual('float32');
-    expect(weights.dataSync()).toEqual(new Float32Array([0, 0, 0]));
-  });
-
-  it('1D, upper case', () => {
-    const init = getInitializer('Zeros');
-    const weights = init.apply([3], 'float32');
-    expect(weights.shape).toEqual([3]);
-    expect(weights.dtype).toEqual('float32');
-    expect(weights.dataSync()).toEqual(new Float32Array([0, 0, 0]));
-  });
-
-  it('2D', () => {
-    const init = getInitializer('zeros');
-    const weights = init.apply([2, 2], 'float32');
-    expect(weights.shape).toEqual([2, 2]);
-    expect(weights.dtype).toEqual('float32');
-    expect(weights.dataSync()).toEqual(new Float32Array([0, 0, 0, 0]));
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('zeros').apply([3]), 1);
-  });
-});
-
-describeMathCPU('Ones initializer', () => {
-  it('1D', () => {
-    const init = getInitializer('ones');
-    const weights = init.apply([3], 'float32');
-    expect(weights.shape).toEqual([3]);
-    expect(weights.dtype).toEqual('float32');
-    expect(weights.dataSync()).toEqual(new Float32Array([1, 1, 1]));
-  });
-
-  it('1D, upper case', () => {
-    const init = getInitializer('Ones');
-    const weights = init.apply([3], 'float32');
-    expect(weights.shape).toEqual([3]);
-    expect(weights.dtype).toEqual('float32');
-    expect(weights.dataSync()).toEqual(new Float32Array([1, 1, 1]));
-  });
-
-  it('2D', () => {
-    const init = getInitializer('ones');
-    const weights = init.apply([2, 2], 'float32');
-    expect(weights.shape).toEqual([2, 2]);
-    expect(weights.dtype).toEqual('float32');
-    expect(weights.dataSync()).toEqual(new Float32Array([1, 1, 1, 1]));
-  });
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('ones').apply([3]), 1);
-  });
-});
-
-describeMathCPU('Constant initializer', () => {
-  it('1D, from config dict', () => {
-    const initializerConfig:
-        serialization.ConfigDict = {className: 'Constant', config: {value: 5}};
-    const init = getInitializer(initializerConfig);
-    const weights = init.apply([3], 'float32');
-    expect(weights.shape).toEqual([3]);
-    expect(weights.dtype).toEqual('float32');
-    expect(weights.dataSync()).toEqual(new Float32Array([5, 5, 5]));
-  });
-
-  it('1D, from builder function', () => {
-    const init = tfl.initializers.constant({value: 5});
-    const weights = init.apply([3], 'float32');
-    expect(weights.shape).toEqual([3]);
-    expect(weights.dtype).toEqual('float32');
-    expect(weights.dataSync()).toEqual(new Float32Array([5, 5, 5]));
-  });
-
-  it('1D, from builder function: passing a direct value throws error', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.initializers.constant(5 as any))
-        .toThrowError(/Expected.*ConstantConfig/);
-  });
-
-  it('2D, from config dict', () => {
-    const initializerConfig:
-        serialization.ConfigDict = {className: 'Constant', config: {value: 5}};
-    const init = getInitializer(initializerConfig);
-    const weights = init.apply([2, 2], 'float32');
-    expect(weights.shape).toEqual([2, 2]);
-    expect(weights.dtype).toEqual('float32');
-    expect(weights.dataSync()).toEqual(new Float32Array([5, 5, 5, 5]));
-  });
-
-  it('Does not leak', () => {
-    const initializerConfig:
-        serialization.ConfigDict = {className: 'Constant', config: {value: 5}};
-    expectNoLeakedTensors(
-        () => getInitializer(initializerConfig).apply([3]), 1);
-  });
-});
-
-describeMathCPU('Identity initializer', () => {
-  it('1D', () => {
-    const initializerConfig:
-        serialization.ConfigDict = {className: 'Identity', config: {gain: 5}};
-    const init = getInitializer(initializerConfig);
-    expect(() => init.apply([4])).toThrowError(/2D square/);
-  });
-
-  it('1D, from config', () => {
-    const initializerConfig:
-        serialization.ConfigDict = {className: 'Identity', config: {gain: 5}};
-    const init = getInitializer(initializerConfig);
-    expect(() => init.apply([4])).toThrowError(/2D square/);
-  });
-
-  it('2D', () => {
-    const initializerConfig:
-        serialization.ConfigDict = {className: 'Identity', config: {gain: 5}};
-    const init = getInitializer(initializerConfig);
-    const weights = init.apply([2, 2], 'float32');
-    expect(weights.shape).toEqual([2, 2]);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsClose(weights, tensor2d([5, 0, 0, 5], [2, 2]));
-  });
-});
-
-describeMathCPU('RandomUniform initializer', () => {
-  const shape = [7, 2];
-  it('default', () => {
-    const init = getInitializer('randomUniform');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -0.05, 0.05);
-  });
-
-  it('default, upper case', () => {
-    const init = getInitializer('RandomUniform');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -0.05, 0.05);
-  });
-
-  it('with configured min max val', () => {
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'RandomUniform',
-      config: {minval: 17, maxval: 47}
-    };
-    const init = getInitializer(initializerConfig);
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, 17, 47);
-  });
-
-  it('with configured seed', () => {
-
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'RandomUniform',
-      config: { seed: 42 }
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply(shape, 'float32');
-    const actual = actualInitializer.apply(shape, 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('RandomUniform').apply([3]), 1);
-  });
-});
-
-describeMathCPU('RandomNormal initializer', () => {
-  const shape = [7, 2];
-  it('default', () => {
-    const init = getInitializer('randomNormal');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    // TODO(bileschi): Add test to assert the values match expectations.
-  });
-
-  it('default, upper case', () => {
-    const init = getInitializer('RandomNormal');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    // TODO(bileschi): Add test to assert the values match expectations.
-  });
-
-  it('with configured min max val', () => {
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'RandomNormal',
-      config: {mean: 1.0, stddev: 0.001}
-    };
-    const init = getInitializer(initializerConfig);
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    // TODO(bileschi): Add test to assert the values match expectations.
-  });
-
-  it('with configured seed', () => {
-
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'RandomNormal',
-      config: { seed: 42 }
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply(shape, 'float32');
-    const actual = actualInitializer.apply(shape, 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('RandomNormal').apply([3]), 1);
-  });
-});
-
-describeMathCPU('HeNormal initializer', () => {
-  const shape = [7, 2];
-  const stddev = Math.sqrt(2 / shape[0]);
-  it('default', () => {
-    const init = getInitializer('heNormal');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -2 * stddev, 2 * stddev);
-    expect(init.getClassName()).toEqual(VarianceScaling.className);
-  });
-
-  it('default, upper case', () => {
-    const init = getInitializer('HeNormal');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -2 * stddev, 2 * stddev);
-  });
-
-  it('with configured seed', () => {
-
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'HeNormal',
-      config: { seed: 42 }
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply(shape, 'float32');
-    const actual = actualInitializer.apply(shape, 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('HeNormal').apply([3]), 1);
-  });
-});
-
-describeMathCPU('HeUniform initializer', () => {
-  const shape = [7, 2];
-  const bound = Math.sqrt(6 / shape[0]);
-  it('default', () => {
-    const init = getInitializer('heUniform');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -bound, bound);
-    expect(init.getClassName()).toEqual(VarianceScaling.className);
-  });
-
-  it('default, upper case', () => {
-    const init = getInitializer('HeUniform');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -bound, bound);
-  });
-
-  it('with configured seed', () => {
-
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'HeUniform',
-      config: { seed: 42 }
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply(shape, 'float32');
-    const actual = actualInitializer.apply(shape, 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('heUniform').apply([3]), 1);
-  });
-});
-
-describeMathCPU('LecunNormal initializer', () => {
-  const shape = [7, 2];
-  const stddev = Math.sqrt(1 / shape[0]);
-  it('default', () => {
-    const init = getInitializer('leCunNormal');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -2 * stddev, 2 * stddev);
-    expect(init.getClassName()).toEqual(VarianceScaling.className);
-  });
-
-  it('default, upper case', () => {
-    const init = getInitializer('LeCunNormal');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -2 * stddev, 2 * stddev);
-  });
-
-  it('with configured seed', () => {
-
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'LeCunNormal',
-      config: { seed: 42 }
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply(shape, 'float32');
-    const actual = actualInitializer.apply(shape, 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('LeCunNormal').apply([3]), 1);
-  });
-});
-
-describeMathCPU('LeCunUniform initializer', () => {
-  const shape = [7, 2];
-  const bound = Math.sqrt(3 / shape[0]);
-  it('default', () => {
-    const init = getInitializer('leCunUniform');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -bound, bound);
-    expect(init.getClassName()).toEqual(VarianceScaling.className);
-  });
-
-  it('default, upper case', () => {
-    const init = getInitializer('LeCunUniform');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -bound, bound);
-  });
-
-  it('with configured seed', () => {
-
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'LeCunUniform',
-      config: { seed: 42 }
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply(shape, 'float32');
-    const actual = actualInitializer.apply(shape, 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('LeCunUniform').apply([3]), 1);
-  });
-});
-
-describeMathCPU('TruncatedNormal initializer', () => {
-  const shape = [7, 2];
-  it('default', () => {
-    const init = getInitializer('truncatedNormal');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -0.1, 0.1);
-  });
-
-  it('default, upper case', () => {
-    const init = getInitializer('TruncatedNormal');
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, -0.1, 0.1);
-  });
-
-  it('with configured min max val', () => {
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'TruncatedNormal',
-      config: {mean: 1.0, stddev: 0.5}
-    };
-    const init = getInitializer(initializerConfig);
-    const weights = init.apply(shape, 'float32');
-    expect(weights.shape).toEqual(shape);
-    expect(weights.dtype).toEqual('float32');
-    expectTensorsValuesInRange(weights, 0.0, 2.0);
-  });
-
-  it('with configured seed', () => {
-
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'TruncatedNormal',
-      config: { seed: 42 }
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply(shape, 'float32');
-    const actual = actualInitializer.apply(shape, 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(
-        () => getInitializer('TruncatedNormal').apply([3]), 1);
-  });
-});
-
-describeMathCPU('Glorot uniform initializer', () => {
-  ['glorotUniform', 'GlorotUniform'].forEach(initializer => {
-    it('1D ' + initializer, () => {
-      const init = getInitializer(initializer);
-      let weights = init.apply([3], 'float32');
-      expect(weights.shape).toEqual([3]);
-      expect(weights.dtype).toEqual('float32');
-      let scale = 1 / ((Math.sqrt(3) + Math.sqrt(3)) / 2);
-      let limit = Math.sqrt(3 * scale);
-      expect(math_utils.max(weights.dataSync() as Float32Array))
-          .toBeLessThan(limit);
-      expect(math_utils.min(weights.dataSync() as Float32Array))
-          .toBeGreaterThan(-limit);
-
-      weights = init.apply([30], 'float32');
-      expect(weights.shape).toEqual([30]);
-      expect(weights.dtype).toEqual('float32');
-      scale = 1 / ((Math.sqrt(30) + Math.sqrt(30)) / 2);
-      limit = Math.sqrt(3 * scale);
-      expect(math_utils.max(weights.dataSync() as Float32Array))
-          .toBeLessThan(limit);
-      expect(math_utils.min(weights.dataSync() as Float32Array))
-          .toBeGreaterThan(-limit);
-      expect(init.getClassName()).toEqual(VarianceScaling.className);
-    });
-
-    it('2D ' + initializer, () => {
-      const init = getInitializer(initializer);
-      let weights = init.apply([2, 2], 'float32');
-      expect(weights.shape).toEqual([2, 2]);
-      expect(weights.dtype).toEqual('float32');
-      let scale = 1 / ((Math.sqrt(2) + Math.sqrt(2)) / 2);
-      let limit = Math.sqrt(3 * scale);
-      expect(math_utils.max(weights.dataSync() as Float32Array))
-          .toBeLessThan(limit);
-      expect(math_utils.min(weights.dataSync() as Float32Array))
-          .toBeGreaterThan(-limit);
-
-      weights = init.apply([20, 20], 'float32');
-      expect(weights.shape).toEqual([20, 20]);
-      expect(weights.dtype).toEqual('float32');
-      scale = 1 / ((Math.sqrt(20) + Math.sqrt(20)) / 2);
-      limit = Math.sqrt(3 * scale);
-      expect(math_utils.max(weights.dataSync() as Float32Array))
-          .toBeLessThan(limit);
-      expect(math_utils.min(weights.dataSync() as Float32Array))
-          .toBeGreaterThan(-limit);
-    });
-  });
-
-  it('with configured seed', () => {
-
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'GlorotUniform',
-      config: { seed: 42 }
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply([7, 2], 'float32');
-    const actual = actualInitializer.apply([7, 2], 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('GlorotUniform').apply([3]), 1);
-  });
-});
-
-describeMathCPU('VarianceScaling initializer', () => {
-  ['varianceScaling', 'VarianceScaling'].forEach(initializer => {
-    it('Default argument values', () => {
-      const init = getInitializer(initializer);
-      const config = init.getConfig();
-      expect(config['scale']).toEqual(1);
-      expect(config['mode']).toEqual('fanIn');
-      expect(config['distribution']).toEqual('normal');
-      expect(config['seed']).toEqual(undefined);
-    });
-  });
-
-  ['uniform', 'normal', 'truncatedNormal'].forEach(distribution => {
-    it('Can be constructed from config dict', () => {
-      const baseInit = getInitializer('varianceScaling');
-      const baseConfig = baseInit.getConfig();
-      baseConfig.distribution = distribution;
-      const newInit = new VarianceScaling(baseConfig);
-      const newConfig = newInit.getConfig();
-      expect(newConfig['distribution']).toEqual(baseConfig['distribution']);
-    });
-
-    it(`${distribution} with configured seed`, () => {
-
-      const initializerConfig: serialization.ConfigDict = {
-        className: 'VarianceScaling',
-        config: {
-          distribution,
-          seed: 42
-        }
-      };
-
-      const expectedInitializer = getInitializer(initializerConfig);
-      const actualInitializer = getInitializer(initializerConfig);
-
-      const expected = expectedInitializer.apply([7, 2], 'float32');
-      const actual = actualInitializer.apply([7, 2], 'float32');
-
-      expect(actual.shape).toEqual(expected.shape);
-      expect(actual.dtype).toEqual(expected.dtype);
-      expectTensorsClose(actual, expected);
-    });
-  });
-});
-
-describeMathCPU('Glorot normal initializer', () => {
-  ['glorotNormal', 'GlorotNormal'].forEach(initializer => {
-    it('1D ' + initializer, () => {
-      const init = getInitializer(initializer);
-      const NUM_TRIALS = 4;
-      const varianceArr1: number[] = [];
-      const varianceArr2: number[] = [];
-
-      for (let i = 0; i < NUM_TRIALS; ++i) {
-        let weights = init.apply([30], 'float32');
-        expect(weights.shape).toEqual([30]);
-        expect(weights.dtype).toEqual('float32');
-        varianceArr1.push(
-            math_utils.variance(weights.dataSync() as Float32Array));
-
-        weights = init.apply([1200], 'float32');
-        expect(weights.shape).toEqual([1200]);
-        expect(weights.dtype).toEqual('float32');
-        varianceArr2.push(
-            math_utils.variance(weights.dataSync() as Float32Array));
-        expect(init.getClassName()).toEqual(VarianceScaling.className);
-      }
-
-      const variance1 = math_utils.median(varianceArr1);
-      const variance2 = math_utils.median(varianceArr2);
-      expect(variance2).toBeLessThan(variance1);
-    });
-
-    it('2D ' + initializer, () => {
-      const init = getInitializer(initializer);
-      const NUM_TRIALS = 4;
-      const varianceArr1: number[] = [];
-      const varianceArr2: number[] = [];
-
-      for (let i = 0; i < NUM_TRIALS; ++i) {
-        let weights = init.apply([5, 6], 'float32');
-        expect(weights.shape).toEqual([5, 6]);
-        expect(weights.dtype).toEqual('float32');
-        varianceArr1.push(
-            math_utils.variance(weights.dataSync() as Float32Array));
-
-        weights = init.apply([30, 50], 'float32');
-        expect(weights.shape).toEqual([30, 50]);
-        expect(weights.dtype).toEqual('float32');
-        varianceArr2.push(
-            math_utils.variance(weights.dataSync() as Float32Array));
-      }
-
-      const variance1 = math_utils.median(varianceArr1);
-      const variance2 = math_utils.median(varianceArr2);
-      expect(variance2).toBeLessThan(variance1);
-    });
-  });
-
-  it('with configured seed', () => {
-
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'GlorotNormal',
-      config: { seed: 42 }
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply([7, 2], 'float32');
-    const actual = actualInitializer.apply([7, 2], 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-
-  it('Does not leak', () => {
-    expectNoLeakedTensors(() => getInitializer('GlorotNormal').apply([3]), 1);
-  });
-});
-
-describeMathCPU('initializers.get', () => {
-  it('by string', () => {
-    const initializer = getInitializer('glorotNormal');
-    const config =
-        serializeInitializer(initializer) as serialization.ConfigDict;
-    const nestedConfig = config.config as serialization.ConfigDict;
-    expect(nestedConfig.scale).toEqual(1.0);
-    expect(nestedConfig.mode).toEqual('fanAvg');
-    expect(nestedConfig.distribution).toEqual('normal');
-  });
-  it('by existing object', () => {
-    const origInit = tfl.initializers.zeros();
-    const initializer = getInitializer(origInit);
-    expect(initializer).toEqual(origInit);
-  });
-  it('by config dict', () => {
-    const origInit = tfl.initializers.glorotUniform({seed: 10});
-    const initializer = getInitializer(
-        serializeInitializer(origInit) as serialization.ConfigDict);
-    expect(serializeInitializer(initializer))
-        .toEqual(serializeInitializer(origInit));
-  });
-});
-
-describe('Invalid intializer identifier', () => {
-  it('Throws exception', () => {
-    expect(() => {
-      getInitializer('invalid_initializer_id');
-    }).toThrowError();
-  });
-});
-
-describe('checkFanMode', () => {
-  it('Valid values', () => {
-    const extendedValues = VALID_FAN_MODE_VALUES.concat([undefined, null]);
-    for (const validValue of extendedValues) {
-      // Using implicit "expect().toNotThrow()" for valid values
-      checkFanMode(validValue);
-    }
-  });
-  it('Invalid values', () => {
-    // Test invalid values are rejected, and reported in the error.
-    expect(() => checkFanMode('foo')).toThrowError(/foo/);
-    try {
-      checkFanMode('bad');
-    } catch (e) {
-      expect(e).toMatch('FanMode');
-      // Test that the error message contains the list of valid values.
-      for (const validValue of VALID_FAN_MODE_VALUES) {
-        expect(e).toMatch(validValue);
-      }
-    }
-  });
-});
-
-describe('checkDistribution', () => {
-  it('Valid values', () => {
-    const extendedValues = VALID_DISTRIBUTION_VALUES.concat([undefined, null]);
-    for (const validValue of extendedValues) {
-      // Using implicit "expect().toNotThrow()" for valid values
-      checkDistribution(validValue);
-    }
-  });
-  it('Invalid values', () => {
-    // Test invalid values are rejected, and reported in the error.
-    expect(() => checkDistribution('foo')).toThrowError(/foo/);
-    try {
-      checkDistribution('bad');
-    } catch (e) {
-      expect(e).toMatch('Distribution');
-      // Test that the error message contains the list of valid values.
-      for (const validValue of VALID_DISTRIBUTION_VALUES) {
-        expect(e).toMatch(validValue);
-      }
-    }
-  });
-});
-
-describeMathCPUAndWebGL2('Orthogonal Initializer', () => {
-  it('2x2', () => {
-    const init = getInitializer('Orthogonal');
-    const w = init.apply([2, 2], 'float32') as Tensor2D;
-    expect(w.shape).toEqual([2, 2]);
-    expect(w.dtype).toEqual('float32');
-    // Assert that columns of w are orthogonal (w is a unitary matrix).
-    expectTensorsClose(w.transpose().matMul(w), eye(2));
-  });
-
-  it('1x1 with gain', () => {
-    const init = tfl.initializers.orthogonal({gain: 3});
-    const w = init.apply([1, 1], 'float32') as Tensor2D;
-    expect(w.shape).toEqual([1, 1]);
-    expect(w.dtype).toEqual('float32');
-    // Assert that columns of w are orthogonal (w is a unitary matrix) and the
-    // gain has been reflected.
-    expectTensorsClose(w.transpose().matMul(w), tensor2d([[9]], [1, 1]));
-  });
-
-  it('4x2', () => {
-    const init = getInitializer('Orthogonal');
-    const w = init.apply([4, 2], 'float32') as Tensor2D;
-    expect(w.shape).toEqual([4, 2]);
-    expect(w.dtype).toEqual('float32');
-    // Assert that columns of w are orthogonal.
-    expectTensorsClose(w.transpose().matMul(w), eye(2));
-  });
-
-  it('2x4', () => {
-    const init = getInitializer('Orthogonal');
-    const w = init.apply([2, 4], 'float32') as Tensor2D;
-    expect(w.shape).toEqual([2, 4]);
-    expect(w.dtype).toEqual('float32');
-    const wT: Tensor2D = w.transpose();
-    // Assert that columns of w are orthogonal.
-    expectTensorsClose(w.matMul(wT), eye(2));
-  });
-
-  it('64x64', () => {
-    // Disable console warning during this test.
-    // Silence the large-size orthogonal matrix warnings.
-    spyOn(console, 'warn').and.callFake((message: string) => {});
-    const n = 64;
-    const init = getInitializer('Orthogonal');
-    const w = init.apply([n, n], 'float32') as Tensor2D;
-    expect(w.shape).toEqual([n, n]);
-    expect(w.dtype).toEqual('float32');
-    // Assert that columns of w are orthogonal.
-    const wT: Tensor2D = w.transpose();
-    expectTensorsClose(w.matMul(wT), eye(n));
-  });
-  it('Does not leak', () => {
-    const init = getInitializer('Orthogonal');
-    expectNoLeakedTensors(() => init.apply([3, 3]), 1);
-  });
-
-  it('Deserialize model containing GlorotUniform initializer', () => {
-    // From https://github.com/tensorflow/tfjs/issues/798
-    const testModelJSON =
-        // tslint:disable-next-line:max-line-length
-        `{"modelTopology": {"keras_version": "2.1.6-tf", "backend": "tensorflow", "model_config": {"class_name": "Model", "config": {"name": "emoji_autoencoder", "layers": [{"name": "input_1", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 128, 128, 1], "dtype": "float32", "sparse": false, "name": "input_1"}, "inbound_nodes": []}, {"name": "Encoder", "class_name": "Model", "config": {"name": "Encoder", "layers": [{"name": "input_128x128", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 128, 128, 1], "dtype": "float32", "sparse": false, "name": "input_128x128"}, "inbound_nodes": []}, {"name": "Convolution1", "class_name": "Conv2D", "config": {"name": "Convolution1", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": [5, 5], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["input_128x128", 0, 0, {}]]]}, {"name": "shrink_64x64", "class_name": "MaxPooling2D", "config": {"name": "shrink_64x64", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "same", "strides": [2, 2], "data_format": "channels_last"}, "inbound_nodes": [[["Convolution1", 0, 0, {}]]]}, {"name": "Convolution2", "class_name": "Conv2D", "config": {"name": "Convolution2", "trainable": true, "dtype": "float32", "filters": 8, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["shrink_64x64", 0, 0, {}]]]}, {"name": "shrink_32x32", "class_name": "MaxPooling2D", "config": {"name": "shrink_32x32", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "same", "strides": [2, 2], "data_format": "channels_last"}, "inbound_nodes": [[["Convolution2", 0, 0, {}]]]}, {"name": "Convolution3", "class_name": "Conv2D", "config": {"name": "Convolution3", "trainable": true, "dtype": "float32", "filters": 8, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["shrink_32x32", 0, 0, {}]]]}, {"name": "shrink_8x8", "class_name": "MaxPooling2D", "config": {"name": "shrink_8x8", "trainable": true, "dtype": "float32", "pool_size": [4, 4], "padding": "same", "strides": [4, 4], "data_format": "channels_last"}, "inbound_nodes": [[["Convolution3", 0, 0, {}]]]}, {"name": "conv2d", "class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "filters": 4, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["shrink_8x8", 0, 0, {}]]]}, {"name": "shrink_4x4", "class_name": "MaxPooling2D", "config": {"name": "shrink_4x4", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "same", "strides": [2, 2], "data_format": "channels_last"}, "inbound_nodes": [[["conv2d", 0, 0, {}]]]}, {"name": "matrix-to-vector", "class_name": "Flatten", "config": {"name": "matrix-to-vector", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "inbound_nodes": [[["shrink_4x4", 0, 0, {}]]]}, {"name": "link_flat_to_64x1", "class_name": "Dense", "config": {"name": "link_flat_to_64x1", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["matrix-to-vector", 0, 0, {}]]]}, {"name": "output_8x1", "class_name": "Dense", "config": {"name": "output_8x1", "trainable": true, "dtype": "float32", "units": 8, "activation": "tanh", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["link_flat_to_64x1", 0, 0, {}]]]}], "input_layers": [["input_128x128", 0, 0]], "output_layers": [["output_8x1", 0, 0]]}, "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"name": "Decoder", "class_name": "Model", "config": {"name": "Decoder", "layers": [{"name": "input_8x1", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 8], "dtype": "float32", "sparse": false, "name": "input_8x1"}, "inbound_nodes": []}, {"name": "activate_input", "class_name": "Dense", "config": {"name": "activate_input", "trainable": true, "dtype": "float32", "units": 64, "activation": "tanh", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["input_8x1", 0, 0, {}]]]}, {"name": "link_reshape_64x1", "class_name": "Dense", "config": {"name": "link_reshape_64x1", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activate_input", 0, 0, {}]]]}, {"name": "reshape_8x8", "class_name": "Reshape", "config": {"name": "reshape_8x8", "trainable": true, "dtype": "float32", "target_shape": [8, 8, 1]}, "inbound_nodes": [[["link_reshape_64x1", 0, 0, {}]]]}, {"name": "conv2d_1", "class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 8, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["reshape_8x8", 0, 0, {}]]]}, {"name": "grow_16x16", "class_name": "UpSampling2D", "config": {"name": "grow_16x16", "trainable": true, "dtype": "float32", "size": [2, 2], "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_1", 0, 0, {}]]]}, {"name": "conv2d_2", "class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 8, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["grow_16x16", 0, 0, {}]]]}, {"name": "grow_32x32", "class_name": "UpSampling2D", "config": {"name": "grow_32x32", "trainable": true, "dtype": "float32", "size": [2, 2], "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_2", 0, 0, {}]]]}, {"name": "conv2d_3", "class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 8, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["grow_32x32", 0, 0, {}]]]}, {"name": "grow_64x64", "class_name": "UpSampling2D", "config": {"name": "grow_64x64", "trainable": true, "dtype": "float32", "size": [2, 2], "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_3", 0, 0, {}]]]}, {"name": "conv2d_4", "class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["grow_64x64", 0, 0, {}]]]}, {"name": "grow_128x128", "class_name": "UpSampling2D", "config": {"name": "grow_128x128", "trainable": true, "dtype": "float32", "size": [2, 2], "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_4", 0, 0, {}]]]}, {"name": "output_128x128", "class_name": "Conv2D", "config": {"name": "output_128x128", "trainable": true, "dtype": "float32", "filters": 1, "kernel_size": [5, 5], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "tanh", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["grow_128x128", 0, 0, {}]]]}], "input_layers": [["input_8x1", 0, 0]], "output_layers": [["output_128x128", 0, 0]]}, "inbound_nodes": [[["Encoder", 1, 0, {}]]]}], "input_layers": [["input_1", 0, 0]], "output_layers": [["Decoder", 1, 0]]}}, "training_config": {"optimizer_config": {"class_name": "Adadelta", "config": {"lr": 1.0, "rho": 0.95, "decay": 0.0, "epsilon": 1e-07}}, "loss": "mean_squared_error", "metrics": [], "weighted_metrics": null, "sample_weight_mode": null, "loss_weights": null}}, "weightsManifest": [{"paths": ["group1-shard1of1"], "weights": [{"name": "activate_input/kernel", "shape": [8, 64], "dtype": "float32"}, {"name": "activate_input/bias", "shape": [64], "dtype": "float32"}, {"name": "link_reshape_64x1/kernel", "shape": [64, 64], "dtype": "float32"}, {"name": "link_reshape_64x1/bias", "shape": [64], "dtype": "float32"}, {"name": "conv2d_1/kernel", "shape": [3, 3, 1, 8], "dtype": "float32"}, {"name": "conv2d_1/bias", "shape": [8], "dtype": "float32"}, {"name": "conv2d_2/kernel", "shape": [3, 3, 8, 8], "dtype": "float32"}, {"name": "conv2d_2/bias", "shape": [8], "dtype": "float32"}, {"name": "conv2d_3/kernel", "shape": [3, 3, 8, 8], "dtype": "float32"}, {"name": "conv2d_3/bias", "shape": [8], "dtype": "float32"}, {"name": "conv2d_4/kernel", "shape": [3, 3, 8, 16], "dtype": "float32"}, {"name": "conv2d_4/bias", "shape": [16], "dtype": "float32"}, {"name": "output_128x128/kernel", "shape": [5, 5, 16, 1], "dtype": "float32"}, {"name": "output_128x128/bias", "shape": [1], "dtype": "float32"}, {"name": "Convolution1/kernel", "shape": [5, 5, 1, 16], "dtype": "float32"}, {"name": "Convolution1/bias", "shape": [16], "dtype": "float32"}, {"name": "Convolution2/kernel", "shape": [3, 3, 16, 8], "dtype": "float32"}, {"name": "Convolution2/bias", "shape": [8], "dtype": "float32"}, {"name": "Convolution3/kernel", "shape": [3, 3, 8, 8], "dtype": "float32"}, {"name": "Convolution3/bias", "shape": [8], "dtype": "float32"}, {"name": "conv2d/kernel", "shape": [3, 3, 8, 4], "dtype": "float32"}, {"name": "conv2d/bias", "shape": [4], "dtype": "float32"}, {"name": "link_flat_to_64x1/kernel", "shape": [64, 64], "dtype": "float32"}, {"name": "link_flat_to_64x1/bias", "shape": [64], "dtype": "float32"}, {"name": "output_8x1/kernel", "shape": [64, 8], "dtype": "float32"}, {"name": "output_8x1/bias", "shape": [8], "dtype": "float32"}]}]}`;
-    const modelConfig = convertPythonicToTs(
-        JSON.parse(testModelJSON).modelTopology.model_config);
-
-    const model = deserialize(modelConfig as PyJsonDict) as tfl.LayersModel;
-    expect(model.layers.length).toEqual(3);
-    expect(model.layers[0] instanceof tfl.LayersModel).toEqual(false);
-    expect(model.layers[1] instanceof tfl.LayersModel).toEqual(true);
-    expect(model.layers[2] instanceof tfl.LayersModel).toEqual(true);
-    expect(model.inputs[0].shape).toEqual([null, 128, 128, 1]);
-    expect(model.outputs[0].shape).toEqual([null, 128, 128, 1]);
-    expect((model.predict(randomNormal([1, 128, 128, 1])) as Tensor).shape)
-        .toEqual([1, 128, 128, 1]);
-  });
-
-  it('with configured seed', () => {
-    const initializerConfig: serialization.ConfigDict = {
-      className: 'Orthogonal',
-      config: {seed: 666013}
-    };
-
-    const expectedInitializer = getInitializer(initializerConfig);
-    const actualInitializer = getInitializer(initializerConfig);
-
-    const expected = expectedInitializer.apply([7, 2], 'float32');
-    const actual = actualInitializer.apply([7, 2], 'float32');
-
-    expect(actual.shape).toEqual(expected.shape);
-    expect(actual.dtype).toEqual(expected.dtype);
-    expectTensorsClose(actual, expected);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/keras_format/README.md b/tfjs-master/tfjs-layers/src/keras_format/README.md
deleted file mode 100644
index cc2117514..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/README.md
+++ /dev/null
@@ -1,53 +0,0 @@
-TypeScript Interfaces describing the Keras JSON format
-------------------------------------------------------
-
-This directory contains a description of the current Keras JSON serialization
-format, in the form of TypeScript interfaces.  The intent is that any valid
-Keras JSON file can be parsed in a type-safe manner using these types.
-
-The Keras JSON format originated in the Python Keras implementation.  The basic
-design is that the format mirrors the Python API.  Each class instance in a
-Python model is serialized as a JSON object containing the class name and its
-serialized constructor arguments.
-
-Here, we provide a type called `*Serialization` to describe the on-disk JSON
-representation for each class.  It always provides a `class_name` and a `config`
-representing the constructor arguments required to reconstruct a given instance.
-
-The constructor arguments may be primitives, arrays of primitives, or plain
-key-value dictionaries, in which case the JSON serialization is straightforward.
-
-If a constructor argument is another object, then it is represented by a nested
-`*Serialization`.  This structure is illustrated below:
-
-    FooSerialization {
-      class_name: 'Foo';
-      config: {
-        bar: string;
-        baz: number[];
-        qux: QuxSerialization;
-      }
-    }
-
-Deserializing such a nested object configuration requires recursively
-deserializing any object arguments, and finally calling the top-level
-constructor using the reconstructed object arguments.
-
-In general this means that deserialization is purely tree-like, so instances
-cannot be reused.  (The deserialization code for Models is an exception to this
-principle, because it allows Layers to refer to each other in order to describe
-a DAG).
-
-As a consequence of this design, our deserialization code requires an `*Args`
-type mirroring each of the `*Serialization` types here.  `*Args` types represent
-the actual arguments passed to a constructor, after any nested objects have been
-deserialized.  For instance, the above `FooSerialization` will be resolved into
- `FooArgs` like this:
-
-     FooArgs {
-       bar: string;
-       baz: number[];
-       qux: Qux;
-     }
-
-which can then be passed to the `Foo` constructor.
diff --git a/tfjs-master/tfjs-layers/src/keras_format/activation_config.ts b/tfjs-master/tfjs-layers/src/keras_format/activation_config.ts
deleted file mode 100644
index 791d622f1..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/activation_config.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {stringLiteralArray} from './utils';
-
-/**
- * List of all known activation names.
- */
-export const activationOptions = stringLiteralArray([
-  'elu', 'hard_sigmoid', 'linear', 'relu', 'relu6', 'selu', 'sigmoid',
-  'softmax', 'softplus', 'softsign', 'tanh', 'swish', 'mish'
-]);
-
-/**
- * A type representing the strings that are valid loss names.
- */
-export type ActivationSerialization = typeof activationOptions[number];
-
-// Sad that we have to do all this just for hard_sigmoid vs. hardSigmoid.
-// TODO(soergel): Move the CamelCase versions back out of keras_format
-// e.g. to src/common.ts.  Maybe even duplicate *all* of these to be pedantic?
-/** @docinline */
-export type ActivationIdentifier = 'elu'|'hardSigmoid'|'linear'|'relu'|'relu6'|
-    'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh'|'swish'|'mish';
diff --git a/tfjs-master/tfjs-layers/src/keras_format/common.ts b/tfjs-master/tfjs-layers/src/keras_format/common.ts
deleted file mode 100644
index 5c279a160..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/common.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-// TODO(huan): add layer-specific input shape types (see: https://github.com/tensorflow/tfjs-layers/pull/492)
-/** @docalias (null | number)[] */
-export type Shape = Array<null | number>;
-
-// The tfjs-core version of DataType must stay synced with this.
-export type DataType = 'float32'|'int32'|'bool'|'complex64'|'string';
-
-// TODO(soergel): Move the CamelCase versions back out of keras_format
-// e.g. to src/common.ts.  Maybe even duplicate *all* of these to be pedantic?
-/** @docinline */
-export type DataFormat = 'channelsFirst'|'channelsLast';
-export const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];
-
-export type InterpolationFormat = 'nearest'|'bilinear';
-export const VALID_INTERPOLATION_FORMAT_VALUES = ['nearest', 'bilinear'];
-// These constants have a snake vs. camel distinction.
-export type DataFormatSerialization = 'channels_first'|'channels_last';
-
-/** @docinline */
-export type PaddingMode = 'valid'|'same'|'causal';
-export const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];
-
-/** @docinline */
-export type PoolMode = 'max'|'avg';
-export const VALID_POOL_MODE_VALUES = ['max', 'avg'];
-
-/** @docinline */
-export type BidirectionalMergeMode = 'sum'|'mul'|'concat'|'ave';
-export const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];
-
-/** @docinline */
-export type SampleWeightMode = 'temporal';
-export const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/constraint_config.ts b/tfjs-master/tfjs-layers/src/keras_format/constraint_config.ts
deleted file mode 100644
index 0ab1018c3..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/constraint_config.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {BaseSerialization} from './types';
-
-export type MaxNormConfig = {
-  max_value?: number;
-  axis?: number;
-};
-
-export type MaxNormSerialization = BaseSerialization<'MaxNorm', MaxNormConfig>;
-
-export type UnitNormConfig = {
-  axis?: number;
-};
-
-export type UnitNormSerialization =
-    BaseSerialization<'UnitNorm', UnitNormConfig>;
-
-export type NonNegSerialization = BaseSerialization<'NonNeg', null>;
-
-export type MinMaxNormConfig = {
-  min_value?: number;
-  max_value?: number;
-  axis?: number;
-  rate?: number;
-};
-
-export type MinMaxNormSerialization =
-    BaseSerialization<'MinMaxNorm', MinMaxNormConfig>;
-
-// Update constraintClassNames below in concert with this.
-export type ConstraintSerialization = MaxNormSerialization|NonNegSerialization|
-    UnitNormSerialization|MinMaxNormSerialization;
-
-export type ConstraintClassName = ConstraintSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid
-// and that we have the right number of them.
-
-/**
- * A string array of valid Constraint class names.
- *
- * This is guaranteed to match the `ConstraintClassName` union type.
- */
-export const constraintClassNames: ConstraintClassName[] =
-    ['MaxNorm', 'UnitNorm', 'NonNeg', 'MinMaxNorm'];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/initializer_config.ts b/tfjs-master/tfjs-layers/src/keras_format/initializer_config.ts
deleted file mode 100644
index e471a4dc0..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/initializer_config.ts
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {BaseSerialization} from './types';
-
-// TODO(soergel): Move the CamelCase versions back out of keras_format
-// e.g. to src/common.ts.  Maybe even duplicate *all* of these to be pedantic?
-/** @docinline */
-export type FanMode = 'fanIn'|'fanOut'|'fanAvg';
-export const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];
-
-// These constants have a snake vs. camel distinction.
-export type FanModeSerialization = 'fan_in'|'fan_out'|'fan_avg';
-
-/** @docinline */
-export type Distribution = 'normal'|'uniform'|'truncatedNormal';
-export const VALID_DISTRIBUTION_VALUES =
-    ['normal', 'uniform', 'truncatedNormal'];
-// These constants have a snake vs. camel distinction.
-export type DistributionSerialization = 'normal'|'uniform'|'truncated_normal';
-
-export type ZerosSerialization = BaseSerialization<'Zeros', {}>;
-
-export type OnesSerialization = BaseSerialization<'Ones', {}>;
-
-export type ConstantConfig = {
-  value: number;
-};
-
-export type ConstantSerialization =
-    BaseSerialization<'Constant', ConstantConfig>;
-
-export type RandomNormalConfig = {
-  mean?: number;
-  stddev?: number;
-  seed?: number;
-};
-
-export type RandomNormalSerialization =
-    BaseSerialization<'RandomNormal', RandomNormalConfig>;
-
-export type RandomUniformConfig = {
-  minval?: number;
-  maxval?: number;
-  seed?: number;
-};
-
-export type RandomUniformSerialization =
-    BaseSerialization<'RandomUniform', RandomUniformConfig>;
-
-export type TruncatedNormalConfig = {
-  mean?: number;
-  stddev?: number;
-  seed?: number;
-};
-
-export type TruncatedNormalSerialization =
-    BaseSerialization<'TruncatedNormal', TruncatedNormalConfig>;
-
-export type VarianceScalingConfig = {
-  scale?: number;
-
-  mode?: FanModeSerialization;
-  distribution?: DistributionSerialization;
-  seed?: number;
-};
-
-export type VarianceScalingSerialization =
-    BaseSerialization<'VarianceScaling', VarianceScalingConfig>;
-
-export type OrthogonalConfig = {
-  seed?: number;
-  gain?: number;
-};
-
-export type OrthogonalSerialization =
-    BaseSerialization<'Orthogonal', OrthogonalConfig>;
-
-export type IdentityConfig = {
-  gain?: number;
-};
-
-export type IdentitySerialization =
-    BaseSerialization<'Identity', IdentityConfig>;
-
-// Update initializerClassNames below in concert with this.
-export type InitializerSerialization = ZerosSerialization|OnesSerialization|
-    ConstantSerialization|RandomUniformSerialization|RandomNormalSerialization|
-    TruncatedNormalSerialization|IdentitySerialization|
-    VarianceScalingSerialization|OrthogonalSerialization;
-
-export type InitializerClassName = InitializerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid
-// and that we have the right number of them.
-
-/**
- * A string array of valid Initializer class names.
- *
- * This is guaranteed to match the `InitializerClassName` union type.
- */
-export const initializerClassNames: InitializerClassName[] = [
-  'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',
-  'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/input_config.ts b/tfjs-master/tfjs-layers/src/keras_format/input_config.ts
deleted file mode 100644
index 1c76830c9..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/input_config.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {DataType} from '@tensorflow/tfjs-core';
-import {Shape} from './common';
-import {BaseLayerSerialization} from './topology_config';
-
-export type InputLayerConfig = {
-  name?: string;
-  input_shape?: Shape;
-  batch_size?: number;
-  batch_input_shape?: Shape;
-  dtype?: DataType;
-  sparse?: boolean;
-};
-
-// This really should be BaseSerialization because an input layer has no
-// inbound_nodes. But, that makes type safety more difficult.
-
-// Update inputLayerClassNames below in concert with this.
-export type InputLayerSerialization =
-    BaseLayerSerialization<'InputLayer', InputLayerConfig>;
-
-export type InputLayerClassName = InputLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid InputLayer class names.
- *
- * This is guaranteed to match the `InputLayerClassName` union type.
- */
-export const inputLayerClassNames: InputLayerClassName[] = [
-  'InputLayer',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/keras_class_names.ts b/tfjs-master/tfjs-layers/src/keras_format/keras_class_names.ts
deleted file mode 100644
index ef0deefa8..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/keras_class_names.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {constraintClassNames, ConstraintSerialization} from './constraint_config';
-import {initializerClassNames, InitializerSerialization} from './initializer_config';
-import {layerClassNames, LayerSerialization} from './layers/layer_serialization';
-import {optimizerClassNames, OptimizerSerialization} from './optimizer_config';
-import {regularizerClassNames, RegularizerSerialization} from './regularizer_config';
-
-/**
- * A type representing all possible Serializations of Keras objects, including
- * Layers, Constraints, Optimizers, etc.
- */
-export type KerasSerialization = LayerSerialization|ConstraintSerialization|
-    InitializerSerialization|RegularizerSerialization|OptimizerSerialization;
-
-/**
- * A type representing all valid values of `class_name` in a Keras JSON file
- * (regardless of context, which will naturally further restrict the valid
- * values).
- */
-export type KerasClassName = KerasSerialization['class_name'];
-
-export const kerasClassNames: KerasClassName[] = [
-  ...layerClassNames, ...constraintClassNames, ...initializerClassNames,
-  ...regularizerClassNames, ...optimizerClassNames
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/advanced_activation_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/advanced_activation_serialization.ts
deleted file mode 100644
index 4540f8fd7..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/advanced_activation_serialization.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {ConstraintSerialization} from '../constraint_config';
-import {InitializerSerialization} from '../initializer_config';
-import {RegularizerSerialization} from '../regularizer_config';
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-
-export interface ReLULayerConfig extends LayerConfig {
-  max_value?: number;
-}
-
-export type ReLULayerSerialization =
-    BaseLayerSerialization<'ReLU', ReLULayerConfig>;
-
-export interface LeakyReLULayerConfig extends LayerConfig {
-  alpha?: number;
-}
-
-export type LeakyReLULayerSerialization =
-    BaseLayerSerialization<'LeakyReLU', LeakyReLULayerConfig>;
-
-export interface PReLULayerConfig extends LayerConfig {
-  alpha_initializer?: InitializerSerialization;
-  alpha_regularizer?: RegularizerSerialization;
-  alpha_constraint?: ConstraintSerialization;
-  shared_axes?: number|number[];
-}
-
-export type PReLULayerSerialization =
-    BaseLayerSerialization<'PReLU', PReLULayerConfig>;
-
-export interface ELULayerConfig extends LayerConfig {
-  alpha?: number;
-}
-
-export type ELULayerSerialization =
-    BaseLayerSerialization<'ELU', ELULayerConfig>;
-
-export interface ThresholdedReLULayerConfig extends LayerConfig {
-  theta?: number;
-}
-
-export type ThresholdedReLULayerSerialization =
-    BaseLayerSerialization<'ThresholdedReLU', ThresholdedReLULayerConfig>;
-
-export interface SoftmaxLayerConfig extends LayerConfig {
-  axis?: number;
-}
-
-export type SoftmaxLayerSerialization =
-    BaseLayerSerialization<'Softmax', SoftmaxLayerConfig>;
-
-// Update advancedActivationLayerClassNames below in concert with this.
-export type AdvancedActivationLayerSerialization = ReLULayerSerialization|
-    LeakyReLULayerSerialization|PReLULayerSerialization|ELULayerSerialization|
-    ThresholdedReLULayerSerialization|SoftmaxLayerSerialization;
-
-export type AdvancedActivationLayerClassName =
-    AdvancedActivationLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid AdvancedActivationLayer class names.
- *
- * This is guaranteed to match the `AdvancedActivationLayerClassName` union
- * type.
- */
-export const advancedActivationLayerClassNames:
-    AdvancedActivationLayerClassName[] = [
-      'ReLU',
-      'LeakyReLU',
-      'PReLU',
-      'ELU',
-      'ThresholdedReLU',
-      'Softmax',
-    ];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/convolutional_depthwise_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/convolutional_depthwise_serialization.ts
deleted file mode 100644
index c8176a454..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/convolutional_depthwise_serialization.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {ConstraintSerialization} from '../constraint_config';
-import {InitializerSerialization} from '../initializer_config';
-import {RegularizerSerialization} from '../regularizer_config';
-import {BaseLayerSerialization} from '../topology_config';
-import {BaseConvLayerConfig} from './convolutional_serialization';
-
-export interface DepthwiseConv2DLayerConfig extends BaseConvLayerConfig {
-  kernel_size: number|[number, number];
-  depth_multiplier?: number;
-  depthwise_initializer?: InitializerSerialization;
-  depthwise_constraint?: ConstraintSerialization;
-  depthwise_regularizer?: RegularizerSerialization;
-}
-
-// Update depthwiseConv2DLayerClassNames below in concert with this.
-export type DepthwiseConv2DLayerSerialization =
-    BaseLayerSerialization<'DepthwiseConv2D', DepthwiseConv2DLayerConfig>;
-
-export type ConvolutionalDepthwiseLayerSerialization =
-    DepthwiseConv2DLayerSerialization;
-
-export type ConvolutionalDepthwiseLayerClassName =
-    ConvolutionalDepthwiseLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid ConvolutionalDepthwiseLayer class names.
- *
- * This is guaranteed to match the `ConvolutionalDepthwiseLayerClassName` union
- * type.
- */
-export const convolutionalDepthwiseLayerClassNames:
-    ConvolutionalDepthwiseLayerClassName[] = [
-      'DepthwiseConv2D',
-    ];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/convolutional_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/convolutional_serialization.ts
deleted file mode 100644
index 77c251ee9..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/convolutional_serialization.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {DataFormatSerialization, PaddingMode} from '../common';
-import {ConstraintSerialization} from '../constraint_config';
-import {InitializerSerialization} from '../initializer_config';
-import {RegularizerSerialization} from '../regularizer_config';
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-
-export interface BaseConvLayerConfig extends LayerConfig {
-  kernel_size: number|number[];
-  strides?: number|number[];
-  padding?: PaddingMode;
-  data_format?: DataFormatSerialization;
-  dilation_rate?: number|[number]|[number, number];
-  activation?: string;
-  use_bias?: boolean;
-  kernel_initializer?: InitializerSerialization;
-  bias_initializer?: InitializerSerialization;
-  kernel_constraint?: ConstraintSerialization;
-  bias_constraint?: ConstraintSerialization;
-  kernel_regularizer?: RegularizerSerialization;
-  bias_regularizer?: RegularizerSerialization;
-  activity_regularizer?: RegularizerSerialization;
-}
-
-export interface ConvLayerConfig extends BaseConvLayerConfig {
-  filters: number;
-}
-
-export type Conv1DLayerSerialization =
-    BaseLayerSerialization<'Conv1D', ConvLayerConfig>;
-
-export type Conv2DLayerSerialization =
-    BaseLayerSerialization<'Conv2D', ConvLayerConfig>;
-
-export type Conv2DTransposeLayerSerialization =
-    BaseLayerSerialization<'Conv2DTranspose', ConvLayerConfig>;
-
-export interface SeparableConvLayerConfig extends ConvLayerConfig {
-  depth_multiplier?: number;
-  depthwise_initializer?: InitializerSerialization;
-  pointwise_initializer?: InitializerSerialization;
-  depthwise_regularizer?: RegularizerSerialization;
-  pointwise_regularizer?: RegularizerSerialization;
-  depthwise_constraint?: ConstraintSerialization;
-  pointwise_constraint?: ConstraintSerialization;
-}
-
-export type SeparableConv2DLayerSerialization =
-    BaseLayerSerialization<'SeparableConv2D', ConvLayerConfig>;
-
-export interface Cropping2DLayerConfig extends LayerConfig {
-  cropping: number|[number, number]|[[number, number], [number, number]];
-  data_format?: DataFormatSerialization;
-}
-
-export type Cropping2DLayerSerialization =
-    BaseLayerSerialization<'Cropping2D', Cropping2DLayerConfig>;
-
-export interface UpSampling2DLayerConfig extends LayerConfig {
-  size?: number[];
-  data_format?: DataFormatSerialization;
-}
-
-export type UpSampling2DLayerSerialization =
-    BaseLayerSerialization<'UpSampling2D', UpSampling2DLayerConfig>;
-
-// Update convolutionalLayerClassNames below in concert with this.
-export type ConvolutionalLayerSerialization =
-    Conv1DLayerSerialization|Conv2DLayerSerialization|
-    Conv2DTransposeLayerSerialization|SeparableConv2DLayerSerialization|
-    Cropping2DLayerSerialization|UpSampling2DLayerSerialization;
-
-export type ConvolutionalLayerClassName =
-    ConvolutionalLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid ConvolutionalLayer class names.
- *
- * This is guaranteed to match the `ConvolutionalLayerClassName` union type.
- */
-export const convolutionalLayerClassNames: ConvolutionalLayerClassName[] = [
-  'Conv1D',
-  'Conv2D',
-  'Conv2DTranspose',
-  'Cropping2D',
-  'SeparableConv2D',
-  'UpSampling2D',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/core_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/core_serialization.ts
deleted file mode 100644
index 596193ae3..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/core_serialization.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {ActivationSerialization} from '../activation_config';
-import {Shape} from '../common';
-import {ConstraintSerialization} from '../constraint_config';
-import {InitializerSerialization} from '../initializer_config';
-import {RegularizerSerialization} from '../regularizer_config';
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-
-export interface DropoutLayerConfig extends LayerConfig {
-  rate: number;
-  noise_shape?: number[];
-  seed?: number;
-}
-
-export type DropoutLayerSerialization =
-    BaseLayerSerialization<'Dropout', DropoutLayerConfig>;
-
-export interface DenseLayerConfig extends LayerConfig {
-  units: number;
-  activation?: ActivationSerialization;
-  use_bias?: boolean;
-  input_dim?: number;
-  kernel_initializer?: InitializerSerialization;
-  bias_initializer?: InitializerSerialization;
-  kernel_constraint?: ConstraintSerialization;
-  bias_constraint?: ConstraintSerialization;
-  kernel_regularizer?: RegularizerSerialization;
-  bias_regularizer?: RegularizerSerialization;
-  activity_regularizer?: RegularizerSerialization;
-}
-
-export type DenseLayerSerialization =
-    BaseLayerSerialization<'Dense', DenseLayerConfig>;
-
-export type FlattenLayerSerialization =
-    BaseLayerSerialization<'Flatten', LayerConfig>;
-
-export interface ActivationLayerConfig extends LayerConfig {
-  activation: ActivationSerialization;
-}
-
-export type ActivationLayerSerialization =
-    BaseLayerSerialization<'Activation', ActivationLayerConfig>;
-
-export interface RepeatVectorLayerConfig extends LayerConfig {
-  n: number;
-}
-
-export type RepeatVectorLayerSerialization =
-    BaseLayerSerialization<'RepeatVector', RepeatVectorLayerConfig>;
-
-export interface ReshapeLayerConfig extends LayerConfig {
-  target_shape: Shape;
-}
-
-export type ReshapeLayerSerialization =
-    BaseLayerSerialization<'Reshape', ReshapeLayerConfig>;
-
-export interface PermuteLayerConfig extends LayerConfig {
-  dims: number[];
-}
-
-export type PermuteLayerSerialization =
-    BaseLayerSerialization<'Permute', PermuteLayerConfig>;
-
-export interface MaskingLayerConfig extends LayerConfig {
-  maskValue: number;
-}
-
-export type MaskingLayerSerialization =
-    BaseLayerSerialization<'Masking', MaskingLayerConfig>;
-
-// Update coreLayerClassNames below in concert with this.
-export type CoreLayerSerialization =
-    DropoutLayerSerialization|DenseLayerSerialization|FlattenLayerSerialization|
-    ActivationLayerSerialization|RepeatVectorLayerSerialization|
-    ReshapeLayerSerialization|PermuteLayerSerialization|
-    MaskingLayerSerialization;
-
-export type CoreLayerClassName = CoreLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid CoreLayer class names.
- *
- * This is guaranteed to match the `CoreLayerClassName` union type.
- */
-export const coreLayerClassNames: CoreLayerClassName[] = [
-  'Activation',
-  'Dense',
-  'Dropout',
-  'Flatten',
-  'Permute',
-  'RepeatVector',
-  'Reshape',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/embeddings_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/embeddings_serialization.ts
deleted file mode 100644
index 238fca8e4..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/embeddings_serialization.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {ConstraintSerialization} from '../constraint_config';
-import {InitializerSerialization} from '../initializer_config';
-import {RegularizerSerialization} from '../regularizer_config';
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-
-export interface EmbeddingLayerConfig extends LayerConfig {
-  input_dim: number;
-  output_dim: number;
-  embeddings_initializer?: InitializerSerialization;
-  embeddings_regularizer?: RegularizerSerialization;
-  activity_regularizer?: RegularizerSerialization;
-  embeddings_constraint?: ConstraintSerialization;
-  mask_zero?: boolean;
-  input_length?: number|number[];
-}
-
-// Update embeddingLayerClassNames below in concert with this.
-export type EmbeddingLayerSerialization =
-    BaseLayerSerialization<'Embedding', EmbeddingLayerConfig>;
-
-export type EmbeddingLayerClassName = EmbeddingLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid EmbeddingLayer class names.
- *
- * This is guaranteed to match the `EmbeddingLayerClassName` union type.
- */
-export const embeddingLayerClassNames: EmbeddingLayerClassName[] = [
-  'Embedding',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/layer_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/layer_serialization.ts
deleted file mode 100644
index e8bd41aa7..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/layer_serialization.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {inputLayerClassNames, InputLayerSerialization} from '../input_config';
-import {advancedActivationLayerClassNames, AdvancedActivationLayerSerialization} from './advanced_activation_serialization';
-import {convolutionalDepthwiseLayerClassNames, ConvolutionalDepthwiseLayerSerialization} from './convolutional_depthwise_serialization';
-import {convolutionalLayerClassNames, ConvolutionalLayerSerialization} from './convolutional_serialization';
-import {coreLayerClassNames, CoreLayerSerialization} from './core_serialization';
-import {embeddingLayerClassNames, EmbeddingLayerSerialization} from './embeddings_serialization';
-import {mergeLayerClassNames, MergeLayerSerialization} from './merge_serialization';
-import {normalizationLayerClassNames, NormalizationLayerSerialization} from './normalization_serialization';
-import {paddingLayerClassNames, PaddingLayerSerialization} from './padding_serialization';
-import {poolingLayerClassNames, PoolingLayerSerialization} from './pooling_serialization';
-import {recurrentLayerClassNames, RecurrentLayerSerialization} from './recurrent_serialization';
-
-export type LayerSerialization = AdvancedActivationLayerSerialization|
-    ConvolutionalDepthwiseLayerSerialization|ConvolutionalLayerSerialization|
-    CoreLayerSerialization|EmbeddingLayerSerialization|MergeLayerSerialization|
-    NormalizationLayerSerialization|PaddingLayerSerialization|
-    PoolingLayerSerialization|RecurrentLayerSerialization|
-    InputLayerSerialization;
-
-export type LayerClassName = LayerSerialization['class_name'];
-
-/**
- * A string array of valid Layer class names.
- *
- * This is guaranteed to match the `LayerClassName` union type.
- */
-export const layerClassNames: LayerClassName[] = [
-  ...advancedActivationLayerClassNames,
-  ...convolutionalDepthwiseLayerClassNames, ...convolutionalLayerClassNames,
-  ...coreLayerClassNames, ...embeddingLayerClassNames, ...mergeLayerClassNames,
-  ...normalizationLayerClassNames, ...paddingLayerClassNames,
-  ...poolingLayerClassNames, ...recurrentLayerClassNames,
-  ...inputLayerClassNames
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/merge_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/merge_serialization.ts
deleted file mode 100644
index 4e6f142d9..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/merge_serialization.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-
-export type AddLayerSerialization = BaseLayerSerialization<'Add', LayerConfig>;
-
-export type MultiplyLayerSerialization =
-    BaseLayerSerialization<'Multiply', LayerConfig>;
-
-export type AverageLayerSerialization =
-    BaseLayerSerialization<'Average', LayerConfig>;
-
-export type MaximumLayerSerialization =
-    BaseLayerSerialization<'Maximum', LayerConfig>;
-
-export type MinimumLayerSerialization =
-    BaseLayerSerialization<'Minimum', LayerConfig>;
-
-export interface ConcatenateLayerConfig extends LayerConfig {
-  axis?: number;
-}
-
-export type ConcatenateLayerSerialization =
-    BaseLayerSerialization<'Concatenate', ConcatenateLayerConfig>;
-
-export interface DotLayerConfig extends LayerConfig {
-  axes: number|[number, number];
-  normalize?: boolean;
-}
-
-export type DotLayerSerialization =
-    BaseLayerSerialization<'Dot', DotLayerConfig>;
-
-// Update mergeLayerClassNames below in concert with this.
-export type MergeLayerSerialization =
-    AddLayerSerialization|MultiplyLayerSerialization|AverageLayerSerialization|
-    MaximumLayerSerialization|MinimumLayerSerialization|
-    ConcatenateLayerSerialization|DotLayerSerialization;
-
-export type MergeLayerClassName = MergeLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid MergeLayer class names.
- *
- * This is guaranteed to match the `MergeLayerClassName` union type.
- */
-export const mergeLayerClassNames: MergeLayerClassName[] = [
-  'Add',
-  'Average',
-  'Concatenate',
-  'Dot',
-  'Maximum',
-  'Minimum',
-  'Multiply',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/normalization_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/normalization_serialization.ts
deleted file mode 100644
index 1725d291c..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/normalization_serialization.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {ConstraintSerialization} from '../constraint_config';
-import {InitializerSerialization} from '../initializer_config';
-import {RegularizerSerialization} from '../regularizer_config';
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-
-export interface BatchNormalizationLayerConfig extends LayerConfig {
-  axis?: number;
-  momentum?: number;
-  epsilon?: number;
-  center?: boolean;
-  scale?: boolean;
-  beta_initializer?: InitializerSerialization;
-  gamma_initializer?: InitializerSerialization;
-  moving_mean_initializer?: InitializerSerialization;
-  moving_variance_initializer?: InitializerSerialization;
-  beta_constraint?: ConstraintSerialization;
-  gamma_constraint?: ConstraintSerialization;
-  beta_regularizer?: RegularizerSerialization;
-  gamma_regularizer?: RegularizerSerialization;
-}
-
-// Update batchNormalizationLayerClassNames below in concert with this.
-export type BatchNormalizationLayerSerialization =
-    BaseLayerSerialization<'BatchNormalization', BatchNormalizationLayerConfig>;
-
-export type NormalizationLayerSerialization =
-    BatchNormalizationLayerSerialization;
-
-export type NormalizationLayerClassName =
-    NormalizationLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid NormalizationLayer class names.
- *
- * This is guaranteed to match the `NormalizationLayerClassName` union
- * type.
- */
-export const normalizationLayerClassNames: NormalizationLayerClassName[] = [
-  'BatchNormalization',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/padding_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/padding_serialization.ts
deleted file mode 100644
index 200897058..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/padding_serialization.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {DataFormatSerialization} from '../common';
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-
-export interface ZeroPadding2DLayerConfig extends LayerConfig {
-  padding?: number|[number, number]|[[number, number], [number, number]];
-  data_format?: DataFormatSerialization;
-}
-
-// Update paddingLayerClassNames below in concert with this.
-export type ZeroPadding2DLayerSerialization =
-    BaseLayerSerialization<'ZeroPadding2D', ZeroPadding2DLayerConfig>;
-
-export type PaddingLayerSerialization = ZeroPadding2DLayerSerialization;
-
-export type PaddingLayerClassName = PaddingLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid PaddingLayer class names.
- *
- * This is guaranteed to match the `PaddingLayerClassName` union type.
- */
-export const paddingLayerClassNames: PaddingLayerClassName[] = [
-  'ZeroPadding2D',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/pooling_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/pooling_serialization.ts
deleted file mode 100644
index 7a31f4dc1..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/pooling_serialization.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {DataFormatSerialization, PaddingMode} from '../common';
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-
-export interface Pooling1DLayerConfig extends LayerConfig {
-  pool_size?: [number];
-  strides?: [number];
-  padding?: PaddingMode;
-}
-
-export type MaxPooling1DLayerSerialization =
-    BaseLayerSerialization<'MaxPooling1D', Pooling1DLayerConfig>;
-
-export type AveragePooling1DLayerSerialization =
-    BaseLayerSerialization<'AveragePooling1D', Pooling1DLayerConfig>;
-
-export interface Pooling2DLayerConfig extends LayerConfig {
-  pool_size?: number|[number, number];
-  strides?: number|[number, number];
-  padding?: PaddingMode;
-  data_format?: DataFormatSerialization;
-}
-
-export type MaxPooling2DLayerSerialization =
-    BaseLayerSerialization<'MaxPooling2D', Pooling2DLayerConfig>;
-
-export type AveragePooling2DLayerSerialization =
-    BaseLayerSerialization<'AveragePooling2D', Pooling2DLayerConfig>;
-
-export type GlobalAveragePooling1DLayerSerialization =
-    BaseLayerSerialization<'GlobalAveragePooling1D', LayerConfig>;
-
-export type GlobalMaxPooling1DLayerSerialization =
-    BaseLayerSerialization<'GlobalMaxPooling1D', LayerConfig>;
-
-export interface GlobalPooling2DLayerConfig extends LayerConfig {
-  data_format?: DataFormatSerialization;
-}
-
-export type GlobalAveragePooling2DLayerSerialization = BaseLayerSerialization<
-    'GlobalAveragePooling2D', GlobalPooling2DLayerConfig>;
-
-export type GlobalMaxPooling2DLayerSerialization =
-    BaseLayerSerialization<'GlobalMaxPooling2D', GlobalPooling2DLayerConfig>;
-
-// Update poolingLayerClassNames below in concert with this.
-export type PoolingLayerSerialization = MaxPooling1DLayerSerialization|
-    AveragePooling1DLayerSerialization|MaxPooling2DLayerSerialization|
-    AveragePooling2DLayerSerialization|GlobalAveragePooling1DLayerSerialization|
-    GlobalMaxPooling1DLayerSerialization|
-    GlobalAveragePooling2DLayerSerialization|
-    GlobalMaxPooling2DLayerSerialization;
-
-export type PoolingLayerClassName = PoolingLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid PoolingLayer class names.
- *
- * This is guaranteed to match the `PoolingLayerClassName` union type.
- */
-export const poolingLayerClassNames: PoolingLayerClassName[] = [
-  'AveragePooling1D',
-  'AveragePooling2D',
-  'GlobalAveragePooling1D',
-  'GlobalAveragePooling2D',
-  'GlobalMaxPooling1D',
-  'GlobalMaxPooling2D',
-  'MaxPooling1D',
-  'MaxPooling2D',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/recurrent_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/recurrent_serialization.ts
deleted file mode 100644
index 7fd735344..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/recurrent_serialization.ts
+++ /dev/null
@@ -1,131 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {ActivationSerialization} from '../activation_config';
-import {ConstraintSerialization} from '../constraint_config';
-import {InitializerSerialization} from '../initializer_config';
-import {RegularizerSerialization} from '../regularizer_config';
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-import {BaseSerialization} from '../types';
-
-export interface BaseRNNLayerConfig extends LayerConfig {
-  cell?: RNNCellSerialization|RNNCellSerialization[];
-  return_sequences?: boolean;
-  return_state?: boolean;
-  go_backwards?: boolean;
-  stateful?: boolean;
-  unroll?: boolean;
-  input_dim?: number;
-  input_length?: number;
-}
-
-export interface SimpleRNNCellConfig extends LayerConfig {
-  units: number;
-  activation?: ActivationSerialization;
-  use_bias?: boolean;
-  kernel_initializer?: InitializerSerialization;
-  recurrent_initializer?: InitializerSerialization;
-  bias_initializer?: InitializerSerialization;
-  kernel_regularizer?: RegularizerSerialization;
-  recurrent_regularizer?: RegularizerSerialization;
-  bias_regularizer?: RegularizerSerialization;
-  kernel_constraint?: ConstraintSerialization;
-  recurrent_constraint?: ConstraintSerialization;
-  bias_constraint?: ConstraintSerialization;
-  dropout?: number;
-  recurrent_dropout?: number;
-}
-
-export type SimpleRNNCellSerialization =
-    BaseSerialization<'SimpleRNNCell', SimpleRNNCellConfig>;
-
-export interface SimpleRNNLayerConfig extends BaseRNNLayerConfig {
-  units: number;
-  activation?: ActivationSerialization;
-  use_bias?: boolean;
-  kernel_initializer?: InitializerSerialization;
-  recurrent_initializer?: InitializerSerialization;
-  bias_initializer?: InitializerSerialization;
-  kernel_regularizer?: RegularizerSerialization;
-  recurrent_regularizer?: RegularizerSerialization;
-  bias_regularizer?: RegularizerSerialization;
-  kernel_constraint?: ConstraintSerialization;
-  recurrent_constraint?: ConstraintSerialization;
-  bias_constraint?: ConstraintSerialization;
-  dropout?: number;
-  recurrent_dropout?: number;
-}
-
-export type SimpleRNNLayerSerialization =
-    BaseLayerSerialization<'SimpleRNN', SimpleRNNLayerConfig>;
-
-export interface GRUCellConfig extends SimpleRNNCellConfig {
-  recurrent_activation?: string;
-  implementation?: number;
-}
-
-export type GRUCellSerialization = BaseSerialization<'GRUCell', GRUCellConfig>;
-
-export interface GRULayerConfig extends SimpleRNNLayerConfig {
-  recurrent_activation?: ActivationSerialization;
-  implementation?: number;
-}
-
-export type GRULayerSerialization =
-    BaseLayerSerialization<'GRU', GRULayerConfig>;
-
-export interface LSTMCellConfig extends SimpleRNNCellConfig {
-  recurrent_activation?: ActivationSerialization;
-  unit_forget_bias?: boolean;
-  implementation?: number;
-}
-
-export type LSTMCellSerialization =
-    BaseSerialization<'LSTMCell', LSTMCellConfig>;
-
-export interface LSTMLayerConfig extends SimpleRNNLayerConfig {
-  recurrent_activation?: ActivationSerialization;
-  unit_forget_bias?: boolean;
-  implementation?: number;
-}
-export type LSTMLayerSerialization =
-    BaseLayerSerialization<'LSTM', LSTMLayerConfig>;
-
-export interface StackedRNNCellsConfig extends LayerConfig {
-  // TODO(soergel): consider whether we can avoid improperly mixing
-  // Simple / LSTM / GRU cells here and in the above Layer serializations.
-  cells: RNNCellSerialization[];
-}
-
-export type StackedRNNCellsSerialization =
-    BaseSerialization<'StackedRNNCells', StackedRNNCellsConfig>;
-
-export type RNNCellSerialization = SimpleRNNCellSerialization|
-    GRUCellSerialization|LSTMCellSerialization|StackedRNNCellsSerialization;
-
-// Update recurrentLayerClassNames below in concert with this.
-export type RecurrentLayerSerialization =
-    SimpleRNNLayerSerialization|LSTMLayerSerialization|GRULayerSerialization;
-
-export type RecurrentLayerClassName = RecurrentLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid RecurrentLayer class names.
- *
- * This is guaranteed to match the `RecurrentLayerClassName` union type.
- */
-export const recurrentLayerClassNames: RecurrentLayerClassName[] = [
-  'GRU',
-  'LSTM',
-  'SimpleRNN',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/layers/wrappers_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/layers/wrappers_serialization.ts
deleted file mode 100644
index be2d29ae6..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/layers/wrappers_serialization.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {BidirectionalMergeMode} from '../common';
-import {BaseLayerSerialization, LayerConfig} from '../topology_config';
-import {LayerSerialization} from './layer_serialization';
-import {RecurrentLayerSerialization} from './recurrent_serialization';
-
-export type TimeDistributedLayerSerialization =
-    BaseLayerSerialization<'TimeDistributed', TimeDistributedLayerConfig>;
-
-export interface TimeDistributedLayerConfig extends LayerConfig {
-  layer: LayerSerialization;
-}
-
-export type BidirectionalLayerSerialization =
-    BaseLayerSerialization<'Bidirectional', BidirectionalLayerConfig>;
-
-export interface BidirectionalLayerConfig extends LayerConfig {
-  layer: RecurrentLayerSerialization;
-  merge_mode?: BidirectionalMergeMode;
-}
-
-// Update wrapperLayerClassNames below in concert with this.
-export type WrapperLayerSerialization =
-    TimeDistributedLayerSerialization|BidirectionalLayerSerialization;
-
-export type WrapperLayerClassName = WrapperLayerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid WrapperLayer class names.
- *
- * This is guaranteed to match the `WrapperLayerClassName` union type.
- */
-export const wrapperLayerClassNames: WrapperLayerClassName[] = [
-  'Bidirectional',
-  'TimeDistributed',
-];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/loss_config.ts b/tfjs-master/tfjs-layers/src/keras_format/loss_config.ts
deleted file mode 100644
index 8fca8e8d2..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/loss_config.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {stringLiteralArray} from './utils';
-
-/**
- * List of all known loss names.
- */
-export const lossOptions = stringLiteralArray([
-  'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error',
-  'mean_squared_logarithmic_error', 'squared_hinge', 'hinge',
-  'categorical_hinge', 'logcosh', 'categorical_crossentropy',
-  'sparse_categorical_crossentropy', 'kullback_leibler_divergence', 'poisson',
-  'cosine_proximity'
-]);
-
-/**
- * A type representing the strings that are valid loss names.
- */
-export type LossIdentifier = typeof lossOptions[number];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/model_serialization.ts b/tfjs-master/tfjs-layers/src/keras_format/model_serialization.ts
deleted file mode 100644
index 9b9b006d2..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/model_serialization.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {LayerSerialization} from './layers/layer_serialization';
-import {TensorKeyArray} from './node_config';
-import {TrainingConfig} from './training_config';
-import {BaseSerialization} from './types';
-
-export type ModelConfig = {
-  name: string,
-  layers: LayerSerialization[],
-  input_layers: TensorKeyArray[],
-  output_layers: TensorKeyArray[],
-};
-
-/**
- * A standard Keras JSON 'Model' configuration.
- */
-export interface ModelSerialization extends
-    BaseSerialization<'Model', ModelConfig> {
-  backend?: string;
-  keras_version?: string;
-}
-
-export type SequentialConfig = {
-  layers: LayerSerialization[]
-};
-
-/**
- * A standard Keras JSON 'Sequential' configuration.
- */
-export interface SequentialSerialization extends
-    BaseSerialization<'Sequential', SequentialConfig> {
-  backend?: string;
-  keras_version?: string;
-}
-
-/**
- * A legacy Keras JSON 'Sequential' configuration.
- *
- * It was a bug that Keras Sequential models were recorded with
- * model_config.config as an array of layers, instead of a dict containing a
- * 'layers' entry.  While the bug has been fixed, we still need to be able to
- * read this legacy format.
- */
-export type LegacySequentialSerialization = {
-  // Note this cannot extend `BaseSerialization` because of the bug.
-  class_name: 'Sequential';
-
-  config: LayerSerialization[];
-  backend?: string;
-  keras_version?: string;
-};
-
-/**
- * Contains the description of a KerasModel, as well as the configuration
- * necessary to train that model.
- */
-export type KerasFileSerialization = {
-  // aka ModelTopology?
-  model_config: ModelSerialization|SequentialSerialization|
-  LegacySequentialSerialization;
-  training_config: TrainingConfig;
-};
diff --git a/tfjs-master/tfjs-layers/src/keras_format/node_config.ts b/tfjs-master/tfjs-layers/src/keras_format/node_config.ts
deleted file mode 100644
index a0581fcb2..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/node_config.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {PyJsonDict} from './types';
-
-/**
- * The unique string name of a Layer.
- */
-export type LayerName = string;
-
-/**
- * The index of a Node, identifying a specific invocation of a given Layer.
- */
-export type NodeIndex = number;
-
-/**
- * The index of a Tensor output by a given Node of a given Layer.
- */
-export type TensorIndex = number;
-
-/**
- * Arguments to the apply(...) method that produced a specific Node.
- */
-// tslint:disable-next-line:no-empty-interface
-export interface NodeArgs extends PyJsonDict {}
-
-/**
- * A reference to a specific Tensor, given by its Layer name, Node index, and
- * output index, including the apply() arguments associated with the Node.
- *
- * This is used in `NodeConfig` to specify the inputs to each Node.
- */
-export type TensorKeyWithArgsArray =
-    [LayerName, NodeIndex, TensorIndex, NodeArgs];
-
-// TODO(soergel): verify behavior of Python Keras; maybe PR to standardize it.
-/**
- * A reference to a specific Tensor, given by its Layer name, Node index, and
- * output index.
- *
- * This does not include the apply() arguments associated with the Node.  It is
- * used in the LayersModel config to specify the inputLayers and outputLayers.
- * It seems to be an idiosyncrasy of Python Keras that the node arguments are
- * not included here.
- */
-export type TensorKeyArray = [LayerName, NodeIndex, TensorIndex];
-
-/**
- * A Keras JSON entry representing a Node, i.e. a specific instance of a Layer.
- *
- * By Keras JSON convention, a Node is specified as an array of Tensor keys
- * (i.e., references to Tensors output by other Layers) providing the inputs to
- * this Layer in order.
- */
-export type NodeConfig = TensorKeyWithArgsArray[];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/optimizer_config.ts b/tfjs-master/tfjs-layers/src/keras_format/optimizer_config.ts
deleted file mode 100644
index c1e6e2096..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/optimizer_config.ts
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {BaseSerialization} from './types';
-
-// Because of the limitations in the current Keras spec, there is no clear
-// definition of what may or may not be the configuration of an optimizer.
-//
-// For now we'll represent the ones available in TF.js--but it will take more
-// thought to get this right in a cross-platform way.
-//
-// See internal issue: b/121033602
-
-// TODO(soergel): This is a stopgap that needs further thought.
-// Does it belong here?
-// Does it belong in tfjs-core?
-// See also the dormant https://github.com/tensorflow/tfjs-core/pull/1404
-
-export type AdadeltaOptimizerConfig = {
-  learning_rate: number; rho: number; epsilon: number;
-};
-
-export type AdadeltaSerialization =
-    BaseSerialization<'Adadelta', AdadeltaOptimizerConfig>;
-
-export type AdagradOptimizerConfig = {
-  learning_rate: number;
-  initial_accumulator_value?: number;
-};
-
-export type AdagradSerialization =
-    BaseSerialization<'Adagrad', AdagradOptimizerConfig>;
-
-export type AdamOptimizerConfig = {
-  learning_rate: number; beta1: number; beta2: number;
-  epsilon?: number;
-};
-
-export type AdamSerialization = BaseSerialization<'Adam', AdamOptimizerConfig>;
-
-export type AdamaxOptimizerConfig = {
-  learning_rate: number; beta1: number; beta2: number;
-  epsilon?: number;
-  decay?: number;
-};
-
-export type AdamaxSerialization =
-    BaseSerialization<'Adamax', AdamaxOptimizerConfig>;
-
-export type MomentumOptimizerConfig = {
-  // extends SGDOptimizerConfig {
-  learning_rate: number; momentum: number;
-  use_nesterov?: boolean;
-};
-
-export type MomentumSerialization =
-    BaseSerialization<'Momentum', MomentumOptimizerConfig>;
-
-export type RMSPropOptimizerConfig = {
-  learning_rate: number;
-  decay?: number;
-  momentum?: number;
-  epsilon?: number;
-  centered?: boolean;
-};
-
-export type RMSPropSerialization =
-    BaseSerialization<'RMSProp', RMSPropOptimizerConfig>;
-
-export type SGDOptimizerConfig = {
-  learning_rate: number;
-};
-
-export type SGDSerialization = BaseSerialization<'SGD', SGDOptimizerConfig>;
-
-// Update optimizerClassNames below in concert with this.
-export type OptimizerSerialization = AdadeltaSerialization|AdagradSerialization|
-    AdamSerialization|AdamaxSerialization|MomentumSerialization|
-    RMSPropSerialization|SGDSerialization;
-
-export type OptimizerClassName = OptimizerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid.
-
-/**
- * A string array of valid Optimizer class names.
- *
- * This is guaranteed to match the `OptimizerClassName` union type.
- */
-export const optimizerClassNames: OptimizerClassName[] =
-    ['Adadelta', 'Adagrad', 'Adam', 'Adamax', 'Momentum', 'RMSProp', 'SGD'];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/regularizer_config.ts b/tfjs-master/tfjs-layers/src/keras_format/regularizer_config.ts
deleted file mode 100644
index aca7a703b..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/regularizer_config.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {BaseSerialization} from './types';
-
-export type L1L2Config = {
-  l1?: number;
-  l2?: number;
-};
-
-export type L1L2Serialization = BaseSerialization<'L1L2', L1L2Config>;
-
-// Update regularizerClassNames below in concert with this.
-export type RegularizerSerialization = L1L2Serialization;
-
-export type RegularizerClassName = RegularizerSerialization['class_name'];
-
-// We can't easily extract a string[] from the string union type, but we can
-// recapitulate the list, enforcing at compile time that the values are valid
-// and that we have the right number of them.
-
-/**
- * A string array of valid Regularizer class names.
- *
- * This is guaranteed to match the `RegularizerClassName` union type.
- */
-export const regularizerClassNames: RegularizerClassName[] = ['L1L2'];
diff --git a/tfjs-master/tfjs-layers/src/keras_format/topology_config.ts b/tfjs-master/tfjs-layers/src/keras_format/topology_config.ts
deleted file mode 100644
index c8e208995..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/topology_config.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {DataType} from '@tensorflow/tfjs-core';
-
-import {Shape} from './common';
-import {NodeConfig} from './node_config';
-import {BaseSerialization, PyJson, PyJsonDict} from './types';
-
-/** Constructor arguments for Layer. */
-export interface LayerConfig extends PyJsonDict {
-  input_shape?: Shape;
-  batch_input_shape?: Shape;
-  batch_size?: number;
-  dtype?: DataType;
-  name?: string;
-  trainable?: boolean;
-  input_dtype?: DataType;
-}
-
-/**
- * Converts a subtype of `LayerConfig` to a variant with restricted keys.
- *
- * This is a bit tricky because `keyof` obtains only local fields, not inherited
- * fields.  Thus, this type combines the keys from the `LayerConfig` supertype
- * with those of the specific subtype.
- *
- * See ./types.ts for an explanation of the PyJson type.
- */
-export type JsonLayer<C extends LayerConfig> = C&LayerConfig&
-    PyJson<Extract<keyof C, string>|Extract<keyof LayerConfig, string>>;
-
-/**
- * A Keras JSON entry representing a layer.
- *
- * The Keras JSON convention is to provide the `class_name` (i.e., the layer
- * type) at the top level, and then to place the layer-specific configuration in
- * a `config` subtree.  These layer-specific configurations are provided by
- * subtypes of `LayerConfig`.  Thus, this `*Serialization` has a type parameter
- * giving the specific type of the wrapped `LayerConfig`.
- */
-export interface BaseLayerSerialization<N extends string, C extends LayerConfig>
-    extends BaseSerialization<N, JsonLayer<C>> {
-  name: string;
-  inbound_nodes?: NodeConfig[];
-}
diff --git a/tfjs-master/tfjs-layers/src/keras_format/training_config.ts b/tfjs-master/tfjs-layers/src/keras_format/training_config.ts
deleted file mode 100644
index bf7b220b2..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/training_config.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-import {SampleWeightMode} from './common';
-import {LossIdentifier} from './loss_config';
-import {OptimizerSerialization} from './optimizer_config';
-import {PyJsonDict} from './types';
-
-// TODO(soergel): flesh out known metrics options
-export type MetricsIdentifier = string;
-
-/**
- * a type for valid values of the `loss_weights` field.
- */
-export type LossWeights = number[]|{[key: string]: number};
-
-/**
- * Configuration of the Keras trainer. This includes the configuration to the
- * optimizer, the loss, any metrics to be calculated, etc.
- */
-export interface TrainingConfig extends PyJsonDict {
-  // tslint:disable-next-line:no-any
-  optimizer_config: OptimizerSerialization;
-  loss: LossIdentifier|LossIdentifier[]|{[key: string]: LossIdentifier};
-  metrics?: MetricsIdentifier[]|{[key: string]: MetricsIdentifier};
-  weighted_metrics?: MetricsIdentifier[];
-  sample_weight_mode?: SampleWeightMode;
-  loss_weights?: LossWeights;
-}
diff --git a/tfjs-master/tfjs-layers/src/keras_format/types.ts b/tfjs-master/tfjs-layers/src/keras_format/types.ts
deleted file mode 100644
index caa01670f..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/types.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * A value within the JSON-serialized form of a serializable object.
- *
- * The keys of any nested dicts should be in snake_case (i.e., using Python
- * naming conventions) for compatibility with Python Keras.
- *
- * @see PyJsonDict
- */
-export type PyJsonValue = boolean|number|string|null|PyJsonArray|PyJsonDict;
-
-/**
- * A key-value dict within the JSON-serialized form of a serializable object.
- *
- * Serialization/deserialization uses stringified-JSON as the storage
- * representation. Typically this should be used for materialized JSON
- * stored on disk or sent/received over the wire.
- *
- * The keys of this dict and of any nested dicts should be in snake_case (i.e.,
- * using Python naming conventions) for compatibility with Python Keras.
- *
- * Internally this is normally converted to a ConfigDict that has CamelCase keys
- * (using TypeScript naming conventions) and support for Enums.
- */
-export interface PyJsonDict {
-  [key: string]: PyJsonValue;
-}
-
-/**
- * A key-value dict like @see PyJsonDict, but with restricted keys.
- *
- * This makes it possible to create subtypes that have only the specified
- * fields, while requiring that the values are JSON-compatible.
- *
- * That is in contrast to extending `PyJsonDict`, or using an intersection type
- * `Foo & PyJsonDict`.  In both of those cases, the fields of Foo are actually
- * allowed to be of types that are incompatible with `PyJsonValue`.  Worse, the
- * index signature of `PyJsonValue` means that *any* key is accepted: eg.
- * `const foo: Foo = ...; foo.bogus = 12; const x = foo.bogus` works for both
- * reading and assignment, even if `bogus` is not a field of the type `Foo`,
- * because the index signature inherited from `PyJsonDict` accepts all strings.
- *
- * Here, we *both* restrict the keys to known values, *and* guarantee that the
- * values associated with those keys are compatible with `PyJsonValue`.
- *
- * This guarantee is easiest to apply via an additional incantation:
- *
- * ```
- * export interface Foo extends PyJson<keyof Foo> {
- *   a: SomeType;
- *   b: SomeOtherType;
- * }
- * ```
- *
- * Now instances of `Foo` have *only* the fields `a` and `b`, and furthermore,
- * if either the type `SomeType` or `SomeOtherType` is incompatible with
- * `PyJsonValue`, the compiler produces a typing error.
- */
-export type PyJson<Keys extends string> = {
-  [x in Keys]?: PyJsonValue;
-};
-
-/**
- * An array of values within the JSON-serialized form of a serializable object.
- *
- * The keys of any nested dicts should be in snake_case (i.e., using Python
- * naming conventions) for compatibility with Python Keras.
- *
- * @see PyJsonDict
- */
-export interface PyJsonArray extends Array<PyJsonValue> {}
-
-/**
- * A Keras JSON entry representing a Keras object such as a Layer.
- *
- * The Keras JSON convention is to provide the `class_name` (e.g., the layer
- * type) at the top level, and then to place the class-specific configuration in
- * a `config` subtree.  These class-specific configurations are provided by
- * subtypes of `PyJsonDict`.  Thus, this `*Serialization` has a type parameter
- * giving the specific type of the wrapped `PyJsonDict`.
- */
-export interface BaseSerialization<
-    N extends string, T extends PyJson<Extract<keyof T, string>>> extends
-    PyJsonDict {
-  // The above type voodoo does this:
-  // * `keyof T` obtains the known keys of the specific config type.
-  //   `keyof` returns `string | number | symbol`; see
-  //   (https://www.typescriptlang.org/docs/handbook/release-notes/typescript-2-9.html)
-  // * `Extract<keyof T, string>` selects the string values.  This amounts to
-  //    assuming that we are dealing with a type with string keys, as opposed to
-  //    an array.  In our usage, this assumption always holds.
-  // * `PyJson<Extract<keyof T, string>>` is a type whose keys are constrained
-  //    to the provided ones, and whose values must be JSON-compatible.
-  // * `T extends PyJson<Extract<keyof T, string>> means that we can provide any
-  //    config type with known keys, provided that the associated values are
-  //    JSON-compatible.
-  //
-  // The upshot is that we can extend `BaseSerialization` with whatever config
-  // type `T` that we like-- remaining confident that the result can be
-  // trivially rendered as JSON, because the compiler will produce a typing
-  // error if that guarantee does not hold.
-  //
-  // To test this, try adding a field with a non-JSON-like value (e.g., Tensor)
-  // to any subclass of `BaseSerialization`.  A compilation error will result.
-  class_name: N;
-  config: T;
-}
diff --git a/tfjs-master/tfjs-layers/src/keras_format/utils.ts b/tfjs-master/tfjs-layers/src/keras_format/utils.ts
deleted file mode 100644
index ac26ee0a1..000000000
--- a/tfjs-master/tfjs-layers/src/keras_format/utils.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Infers a string union type from an array of string literals, and returns
- * the array as an array of that type.
- *
- * For instance:
- *
- * ```
- * const fruits = stringLiteralArray(['apple', 'banana', 'orange']);
- * type Fruit = typeof activationOptions[number];
- * ```
- *
- * now `Fruit` is the union type `'apple'|'banana'|'orange'`.
- *
- * https://stackoverflow.com/questions/52085454/typescript-define-a-union-type-from-an-array-of-strings/52085658
- */
-export function stringLiteralArray<T extends string>(a: T[]) {
-  return a;
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/advanced_activation_test.ts b/tfjs-master/tfjs-layers/src/layers/advanced_activation_test.ts
deleted file mode 100644
index d297f07e1..000000000
--- a/tfjs-master/tfjs-layers/src/layers/advanced_activation_test.ts
+++ /dev/null
@@ -1,248 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit Tests for Advanced Activation Layers.
- */
-
-import {ones, Tensor, tensor1d, tensor2d, zeros} from '@tensorflow/tfjs-core';
-
-import * as tfl from '../index';
-import {convertPythonicToTs, convertTsToPythonic} from '../utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndGPU, describeMathCPUAndWebGL2, expectTensorsClose} from '../utils/test_utils';
-
-describeMathCPU('ReLU: Symbolic', () => {
-  it('Correct output shape', () => {
-    const layer = tfl.layers.reLU({});
-    const x = new tfl.SymbolicTensor('float32', [2, 3, 4], null, null, null);
-    const y = layer.apply(x) as tfl.SymbolicTensor;
-    expect(y.shape).toEqual(x.shape);
-  });
-
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.reLU({maxValue: 28});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.reLU(tsConfig);
-    expect(layerPrime.getConfig().maxValue).toEqual(28);
-  });
-});
-
-describeMathCPUAndGPU('ReLU: Tensor', () => {
-  it('No maxValue', () => {
-    const layer = tfl.layers.reLU();
-    const x = tensor2d([[-100, -200], [0, 300], [200, 200]]);
-    const y = layer.apply(x) as Tensor;
-    expectTensorsClose(y, tensor2d([[0, 0], [0, 300], [200, 200]]));
-  });
-
-  it('Finite maxValue', () => {
-    const layer = tfl.layers.reLU({maxValue: 250});
-    const x = tensor1d([-100, -200, 0, 300, 200, 200]);
-    const y = layer.apply(x) as Tensor;
-    expectTensorsClose(y, tensor1d([0, 0, 0, 250, 200, 200]));
-  });
-});
-
-describeMathCPU('PReLU: Symbolic', () => {
-  it('Correct output shape: no-arg constructor', () => {
-    const layer = tfl.layers.prelu();
-    const x = new tfl.SymbolicTensor('float32', [2, 3, 4], null, null, null);
-    const y = layer.apply(x) as tfl.SymbolicTensor;
-    expect(y.shape).toEqual(x.shape);
-  });
-
-  it('Correct output shape: constructor with arg', () => {
-    const layer = tfl.layers.prelu({});
-    const x = new tfl.SymbolicTensor('float32', [2, 3, 4], null, null, null);
-    const y = layer.apply(x) as tfl.SymbolicTensor;
-    expect(y.shape).toEqual(x.shape);
-  });
-
-  it('Serialization round trip', () => {
-    const layer =
-        tfl.layers.prelu({alphaInitializer: 'ones', sharedAxes: [1, 2]});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.prelu(tsConfig);
-    expect(layerPrime.getConfig().sharedAxes).toEqual([1, 2]);
-  });
-});
-
-describeMathCPUAndWebGL2('PReLU: Tensor', () => {
-  it('Forward pass', () => {
-    const layer = tfl.layers.prelu({alphaInitializer: 'ones'});
-    const x = tensor2d([[-100, -200], [0, 300], [200, 200]]);
-    const y = layer.apply(x) as Tensor;
-    expectTensorsClose(y, tensor2d([[-100, -200], [0, 300], [200, 200]]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Dense(
-  //     3, kernel_initializer='ones', input_shape=[4]))
-  // model.add(keras.layers.PReLU(alpha_initializer='ones'))
-  // model.add(keras.layers.Dense(1, kernel_initializer='ones'))
-  // model.compile(optimizer='sgd', loss='mean_squared_error')
-  //
-  // xs = -np.ones([2, 4])
-  // ys = np.zeros([2, 1])
-  // history = model.fit(xs, ys, batch_size=2, epochs=3)
-  // print(history.history)
-  // print(model.get_weights()[2])
-  // ```
-  it('Training: no sharedAxes', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense(
-        {units: 3, kernelInitializer: 'ones', inputShape: [4]}));
-    model.add(tfl.layers.prelu({alphaInitializer: 'ones'}));
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-
-    const xs = ones([2, 4]).neg();
-    const ys = zeros([2, 1]);
-    const history = await model.fit(xs, ys, {batchSize: 2, epochs: 3});
-    expect(history.history.loss.length).toEqual(3);
-    expect(history.history.loss[0]).toBeCloseTo(144);
-    expect(history.history.loss[1]).toBeCloseTo(0.051329);
-    expect(history.history.loss[2]).toBeCloseTo(0.049144);
-    expectTensorsClose(
-        model.getWeights()[2], tensor1d([0.0410104, 0.0410104, 0.0410104]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Dense(
-  //     3, kernel_initializer='ones', input_shape=[2, 2]))
-  // model.add(keras.layers.PReLU(alpha_initializer='ones', shared_axes=[1]))
-  // model.add(keras.layers.Dense(1, kernel_initializer='ones'))
-  // model.compile(optimizer='sgd', loss='mean_squared_error')
-  //
-  // xs = -np.ones([2, 2, 2])
-  // ys = np.zeros([2, 2, 1])
-  // history = model.fit(xs, ys, batch_size=2, epochs=3)
-  // print(history.history)
-  // print(model.get_weights()[2])
-  // ```
-  it('Training, with sharedAxes', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense(
-        {units: 3, kernelInitializer: 'ones', inputShape: [2, 2]}));
-    model.add(tfl.layers.prelu({alphaInitializer: 'ones', sharedAxes: [1]}));
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-
-    const xs = ones([2, 2, 2]).neg();
-    const ys = zeros([2, 2, 1]);
-    const history = await model.fit(xs, ys, {batchSize: 2, epochs: 3});
-    expect(history.history.loss.length).toEqual(3);
-    expect(history.history.loss[0]).toBeCloseTo(36);
-    expect(history.history.loss[1]).toBeCloseTo(7.408153);
-    expect(history.history.loss[2]).toBeCloseTo(4.190359);
-    expectTensorsClose(
-        model.getWeights()[2], tensor2d([[0.648351, 0.648351, 0.648351]]));
-  });
-});
-
-describeMathCPU('leakyReLU: Symbolic', () => {
-  it('Correct output shape', () => {
-    const layer = tfl.layers.leakyReLU({alpha: 0.1});
-    const x = new tfl.SymbolicTensor('float32', [2, 3, 4], null, null, null);
-    const y = layer.apply(x) as tfl.SymbolicTensor;
-    expect(y.shape).toEqual(x.shape);
-  });
-});
-
-describeMathCPUAndGPU('leakyReLU: Tensor', () => {
-  it('alpha = default 0.3', () => {
-    const layer = tfl.layers.leakyReLU();
-    const x = tensor2d([[-1, -2], [0, 3]], [2, 2]);
-    const y = layer.apply(x) as Tensor;
-    expectTensorsClose(y, tensor2d([[-0.3, -0.6], [0, 3]], [2, 2]));
-  });
-
-  it('alpha = 0.1', () => {
-    const layer = tfl.layers.leakyReLU({alpha: 0.1});
-    const x = tensor2d([[-1, -2], [0, 3]], [2, 2]);
-    const y = layer.apply(x) as Tensor;
-    expectTensorsClose(y, tensor2d([[-0.1, -0.2], [0, 3]], [2, 2]));
-  });
-});
-
-describeMathCPU('elu: Symbolic', () => {
-  it('Correct output shape', () => {
-    const layer = tfl.layers.elu();
-    const x = new tfl.SymbolicTensor('float32', [2, 3, 4], null, null, null);
-    const y = layer.apply(x) as tfl.SymbolicTensor;
-    expect(y.shape).toEqual(x.shape);
-  });
-});
-
-describeMathCPUAndGPU('elu: Tensor', () => {
-  it('alpha = default 1.0', () => {
-    const layer = tfl.layers.elu({});
-    const x = tensor2d([[-1, -2], [0, 3]], [2, 2]);
-    const y = layer.apply(x) as Tensor;
-    expectTensorsClose(
-        y, tensor2d([[Math.exp(-1) - 1, Math.exp(-2) - 1], [0, 3]], [2, 2]));
-  });
-});
-
-describeMathCPU('thresholdedReLU: Symbolic', () => {
-  it('Correct output shape', () => {
-    const layer = tfl.layers.thresholdedReLU();
-    const x = new tfl.SymbolicTensor('float32', [2, 3, 4], null, null, null);
-    const y = layer.apply(x) as tfl.SymbolicTensor;
-    expect(y.shape).toEqual(x.shape);
-  });
-});
-
-describeMathCPUAndGPU('thresholdedReLU: Tensor', () => {
-  it('theta = default 1.0', () => {
-    const layer = tfl.layers.thresholdedReLU({});
-    const x = tensor2d([[-1, 0], [1, 3]], [2, 2]);
-    const y = layer.apply(x) as Tensor;
-    expectTensorsClose(y, tensor2d([[0, 0], [0, 3]], [2, 2]));
-  });
-});
-
-describeMathCPU('softmax: Symbolic', () => {
-  const axisValues = [0, 1, 2, -1, null];
-  for (const axis of axisValues) {
-    it(`Correct output shape, axis=${axis}`, () => {
-      const layer = tfl.layers.softmax({axis});
-      const x = new tfl.SymbolicTensor('float32', [2, 3, 4], null, null, null);
-      const y = layer.apply(x) as tfl.SymbolicTensor;
-      expect(y.shape).toEqual(x.shape);
-    });
-  }
-});
-
-describeMathCPUAndGPU('softmax: Tensor', () => {
-  it('theta = default 1.0', () => {
-    const layer = tfl.layers.softmax({});
-    const x = tensor2d([[0, 1], [5, 5]], [2, 2]);
-    const y = layer.apply(x) as Tensor;
-    expectTensorsClose(
-        y,
-        tensor2d(
-            [[1 / (1 + Math.E), Math.E / (1 + Math.E)], [0.5, 0.5]], [2, 2]));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/advanced_activations.ts b/tfjs-master/tfjs-layers/src/layers/advanced_activations.ts
deleted file mode 100644
index de9180c77..000000000
--- a/tfjs-master/tfjs-layers/src/layers/advanced_activations.ts
+++ /dev/null
@@ -1,366 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- *  Advanced activation layers.
- */
-
-import {add, cast, clipByValue, elu, exp, greater, leakyRelu, logSumExp, mul, ones, prelu, relu, scalar, serialization, sub, Tensor, tidy} from '@tensorflow/tfjs-core';
-
-import {Softmax as softmaxActivation} from '../activations';
-import {Constraint, getConstraint, serializeConstraint} from '../constraints';
-import {InputSpec, Layer, LayerArgs} from '../engine/topology';
-import {NotImplementedError, ValueError} from '../errors';
-import {getInitializer, Initializer, InitializerIdentifier, serializeInitializer} from '../initializers';
-import {Shape} from '../keras_format/common';
-import {getRegularizer, Regularizer, serializeRegularizer} from '../regularizers';
-import {Kwargs} from '../types';
-import {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';
-import {LayerVariable} from '../variables';
-
-export declare interface ReLULayerArgs extends LayerArgs {
-  /**
-   * Float, the maximum output value.
-   */
-  maxValue?: number;
-}
-
-export class ReLU extends Layer {
-  /** @nocollapse */
-  static className = 'ReLU';
-  maxValue: number;
-
-  constructor(args?: ReLULayerArgs) {
-    super(args == null ? {} : args);
-    this.supportsMasking = true;
-    if (args != null) {
-      this.maxValue = args.maxValue;
-    }
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    inputs = getExactlyOneTensor(inputs);
-    let output = relu(inputs);
-    if (this.maxValue != null) {
-      output = clipByValue(output, 0, this.maxValue);
-    }
-    return output;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {maxValue: this.maxValue};
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(ReLU);
-
-export declare interface LeakyReLULayerArgs extends LayerArgs {
-  /**
-   * Float `>= 0`. Negative slope coefficient. Defaults to `0.3`.
-   */
-  alpha?: number;
-}
-
-export class LeakyReLU extends Layer {
-  /** @nocollapse */
-  static className = 'LeakyReLU';
-  readonly alpha: number;
-
-  readonly DEFAULT_ALPHA = 0.3;
-
-  constructor(args?: LeakyReLULayerArgs) {
-    super(args == null ? {} : args);
-    if (args == null) {
-      args = {};
-    }
-    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    const x = getExactlyOneTensor(inputs);
-    return leakyRelu(x, this.alpha);
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {alpha: this.alpha};
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(LeakyReLU);
-
-export declare interface PReLULayerArgs extends LayerArgs {
-  /**
-   * Initializer for the learnable alpha.
-   */
-  alphaInitializer?: Initializer|InitializerIdentifier;
-
-  /**
-   * Regularizer for the learnable alpha.
-   */
-  alphaRegularizer?: Regularizer;
-
-  /**
-   * Constraint for the learnable alpha.
-   */
-  alphaConstraint?: Constraint;
-
-  /**
-   * The axes along which to share learnable parameters for the activation
-   * function. For example, if the incoming feature maps are from a 2D
-   * convolution with output shape `[numExamples, height, width, channels]`,
-   * and you wish to share parameters across space (height and width) so that
-   * each filter channels has only one set of parameters, set
-   * `shared_axes: [1, 2]`.
-   */
-  sharedAxes?: number|number[];
-}
-
-export class PReLU extends Layer {
-  /** @nocollapse */
-  static className = 'PReLU';
-  private readonly alphaInitializer: Initializer;
-  private readonly alphaRegularizer: Regularizer;
-  private readonly alphaConstraint: Constraint;
-  private readonly sharedAxes: number[];
-  private alpha: LayerVariable;
-
-  readonly DEFAULT_ALPHA_INITIALIZER: InitializerIdentifier = 'zeros';
-
-  constructor(args?: PReLULayerArgs) {
-    super(args == null ? {} : args);
-    if (args == null) {
-      args = {};
-    }
-
-    this.supportsMasking = true;
-    this.alphaInitializer =
-        getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);
-    this.alphaRegularizer = getRegularizer(args.alphaRegularizer);
-    this.alphaConstraint = getConstraint(args.alphaConstraint);
-    if (args.sharedAxes == null) {
-      this.sharedAxes = null;
-    } else if (Array.isArray(args.sharedAxes)) {
-      this.sharedAxes = args.sharedAxes;
-    } else if (typeof args.sharedAxes === 'number') {
-      this.sharedAxes = [args.sharedAxes];
-    } else {
-      throw new ValueError(
-          `Expected sharedAxes to be a number or an array of numbers, ` +
-          `but got ${args.sharedAxes}`);
-    }
-  }
-
-  override build(inputShape: Shape|Shape[]) {
-    inputShape = getExactlyOneShape(inputShape);
-    const paramShape: Shape = inputShape.slice(1);
-    if (this.sharedAxes != null) {
-      for (const i of this.sharedAxes) {
-        paramShape[i - 1] = 1;
-      }
-    }
-    this.alpha = this.addWeight(
-        'alpha', paramShape, 'float32', this.alphaInitializer,
-        this.alphaRegularizer, true, this.alphaConstraint);
-    // Set input spec.
-    const axes: {[axis: number]: number} = {};
-    if (this.sharedAxes != null) {
-      for (let i = 1; i < inputShape.length; ++i) {
-        axes[i] = inputShape[i];
-      }
-    }
-    this.inputSpec = [new InputSpec({
-      ndim: inputShape.length,
-      axes,
-    })];
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    inputs = getExactlyOneTensor(inputs);
-    return prelu(inputs, this.alpha.read());
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      alphaInitializer: serializeInitializer(this.alphaInitializer),
-      alphaRegularizer: serializeRegularizer(this.alphaRegularizer),
-      alphaConstraint: serializeConstraint(this.alphaConstraint),
-      sharedAxes: this.sharedAxes
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(PReLU);
-
-export declare interface ELULayerArgs extends LayerArgs {
-  /**
-   * Float `>= 0`. Negative slope coefficient. Defaults to `1.0`.
-   */
-  alpha?: number;
-}
-
-export class ELU extends Layer {
-  /** @nocollapse */
-  static className = 'ELU';
-  readonly alpha: number;
-
-  readonly DEFAULT_ALPHA = 1.0;
-
-  constructor(args?: ELULayerArgs) {
-    super(args == null ? {} : args);
-    if (args == null) {
-      args = {};
-    }
-
-    if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {
-      throw new NotImplementedError(
-          `Non-default alpha value (${args.alpha}) is not supported by the ` +
-          `ELU layer yet.`);
-    }
-
-    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    const x = getExactlyOneTensor(inputs);
-    return elu(x);
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {alpha: this.alpha};
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(ELU);
-
-export declare interface ThresholdedReLULayerArgs extends LayerArgs {
-  /**
-   * Float >= 0. Threshold location of activation.
-   */
-  theta?: number;
-}
-
-export class ThresholdedReLU extends Layer {
-  /** @nocollapse */
-  static className = 'ThresholdedReLU';
-  readonly theta: number;
-
-  readonly DEFAULT_THETA = 1.0;
-
-  constructor(args?: ThresholdedReLULayerArgs) {
-    super(args == null ? {} : args);
-    if (args == null) {
-      args = {};
-    }
-
-    this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    const x = getExactlyOneTensor(inputs);
-    return mul(x, cast(greater(x, this.theta), 'float32'));
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {theta: this.theta};
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(ThresholdedReLU);
-
-export declare interface SoftmaxLayerArgs extends LayerArgs {
-  /**
-   * Integer, axis along which the softmax normalization is applied.
-   * Defaults to `-1` (i.e., the last axis).
-   */
-  axis?: number|number[];
-}
-
-export class Softmax extends Layer {
-  /** @nocollapse */
-  static className = 'Softmax';
-  readonly axis: number|number[];
-  readonly softmax: (t: Tensor, a?: number) => Tensor;
-  readonly DEFAULT_AXIS = 1.0;
-
-  constructor(args?: SoftmaxLayerArgs) {
-    super(args == null ? {} : args);
-    if (args == null) {
-      args = {};
-    }
-    this.softmax = new softmaxActivation().apply;
-    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    // TODO(pforderique): Add tests for when `this.axis` is a number[].
-    return tidy(() => {
-      let x = getExactlyOneTensor(inputs);
-      const mask = kwargs['mask'] as Tensor;
-      if (mask != null) {
-        // Since mask is 1.0 for positions we want to keep and 0.0 for masked
-        // positions, this operation will create a tensor which is 0.0 for
-        // positions we want to attend and -1e.9 for masked positions.
-        const adder =
-          mul(sub(ones(x.shape), cast(mask, x.dtype)), scalar(-1e9));
-
-        // Since we are adding it to the raw scores before the softmax, this
-        // is effectively the same as removing these entirely.
-        x = add(x, adder);
-      }
-      if (this.axis instanceof Array) {
-        if (this.axis.length > 1) {
-          return exp(sub(x, logSumExp(x, this.axis, true)));
-        } else {
-          return this.softmax(x, this.axis[0]);
-        }
-      }
-      return this.softmax(x, this.axis);
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {axis: this.axis};
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Softmax);
diff --git a/tfjs-master/tfjs-layers/src/layers/convolutional.ts b/tfjs-master/tfjs-layers/src/layers/convolutional.ts
deleted file mode 100644
index c75240e1f..000000000
--- a/tfjs-master/tfjs-layers/src/layers/convolutional.ts
+++ /dev/null
@@ -1,1448 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * TensorFlow.js Layers: Convolutional Layers
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {fused, serialization, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, tidy} from '@tensorflow/tfjs-core';
-
-import {Activation, getActivation, serializeActivation} from '../activations';
-import {imageDataFormat} from '../backend/common';
-import * as K from '../backend/tfjs_backend';
-import {checkDataFormat, checkInterpolationFormat, checkPaddingMode} from '../common';
-import {Constraint, ConstraintIdentifier, getConstraint, serializeConstraint} from '../constraints';
-import {InputSpec, Layer, LayerArgs} from '../engine/topology';
-import {NotImplementedError, ValueError} from '../errors';
-import {getInitializer, Initializer, InitializerIdentifier, serializeInitializer} from '../initializers';
-import {ActivationIdentifier} from '../keras_format/activation_config';
-import {DataFormat, InterpolationFormat, PaddingMode, Shape} from '../keras_format/common';
-import {getRegularizer, Regularizer, RegularizerIdentifier, serializeRegularizer} from '../regularizers';
-import {Kwargs} from '../types';
-import {convOutputLength, deconvLength, normalizeArray} from '../utils/conv_utils';
-import * as generic_utils from '../utils/generic_utils';
-import {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';
-import {LayerVariable} from '../variables';
-
-/**
- * Transpose and cast the input before the conv2d.
- * @param x Input image tensor.
- * @param dataFormat
- */
-export function preprocessConv2DInput(
-    x: Tensor, dataFormat: DataFormat): Tensor {
-  // TODO(cais): Cast type to float32 if not.
-  return tidy(() => {
-    checkDataFormat(dataFormat);
-    if (dataFormat === 'channelsFirst') {
-      return tfc.transpose(x, [0, 2, 3, 1]);  // NCHW -> NHWC.
-    } else {
-      return x;
-    }
-  });
-}
-
-/**
- * Transpose and cast the input before the conv3d.
- * @param x Input image tensor.
- * @param dataFormat
- */
-export function preprocessConv3DInput(
-    x: Tensor, dataFormat: DataFormat): Tensor {
-  return tidy(() => {
-    checkDataFormat(dataFormat);
-    if (dataFormat === 'channelsFirst') {
-      return tfc.transpose(x, [0, 2, 3, 4, 1]);  // NCDHW -> NDHWC.
-    } else {
-      return x;
-    }
-  });
-}
-
-/**
- * 1D-convolution with bias added.
- *
- * Porting Note: This function does not exist in the Python Keras backend.
- *   It is exactly the same as `conv2d`, except the added `bias`.
- *
- * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.
- * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.
- * @param bias Bias, rank-3, of shape `[outDepth]`.
- * @param strides
- * @param padding Padding mode.
- * @param dataFormat Data format.
- * @param dilationRate
- * @returns The result of the 1D convolution.
- * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.
- */
-export function conv1dWithBias(
-    x: Tensor, kernel: Tensor, bias: Tensor, strides = 1, padding = 'valid',
-    dataFormat?: DataFormat, dilationRate = 1): Tensor {
-  return tidy(() => {
-    if (dataFormat == null) {
-      dataFormat = imageDataFormat();
-    }
-    checkDataFormat(dataFormat);
-    // Check the ranks of x, kernel and bias.
-    if (x.shape.length !== 3) {
-      throw new ValueError(
-          `The input of a conv1dWithBias operation should be 3, but is ` +
-          `${x.shape.length} instead.`);
-    }
-    if (kernel.shape.length !== 3) {
-      throw new ValueError(
-          `The kernel for a conv1dWithBias operation should be 3, but is ` +
-          `${kernel.shape.length} instead`);
-    }
-    if (bias != null && bias.shape.length !== 1) {
-      throw new ValueError(
-          `The bias for a conv1dWithBias operation should be 1, but is ` +
-          `${kernel.shape.length} instead`);
-    }
-    // TODO(cais): Support CAUSAL padding mode.
-    if (dataFormat === 'channelsFirst') {
-      x = tfc.transpose(x, [0, 2, 1]);  // NCW -> NWC.
-    }
-    if (padding === 'causal') {
-      throw new NotImplementedError(
-          'The support for CAUSAL padding mode in conv1dWithBias is not ' +
-          'implemented yet.');
-    }
-    let y: Tensor = tfc.conv1d(
-        x as Tensor2D | Tensor3D, kernel as Tensor3D, strides,
-        padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);
-    if (bias != null) {
-      y = K.biasAdd(y, bias);
-    }
-    return y;
-  });
-}
-
-/**
- * 1D-convolution.
- *
- * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.
- * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s
- * @param strides
- * @param padding Padding mode.
- * @param dataFormat Data format.
- * @param dilationRate
- * @returns The result of the 1D convolution.
- * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.
- */
-export function conv1d(
-    x: Tensor, kernel: Tensor, strides = 1, padding = 'valid',
-    dataFormat?: DataFormat, dilationRate = 1): Tensor {
-  return tidy(() => {
-    checkDataFormat(dataFormat);
-    return conv1dWithBias(
-        x, kernel, null, strides, padding, dataFormat, dilationRate);
-  });
-}
-
-/**
- * 2D Convolution
- * @param x
- * @param kernel kernel of the convolution.
- * @param strides strides array.
- * @param padding padding mode. Default to 'valid'.
- * @param dataFormat data format. Defaults to 'channelsLast'.
- * @param dilationRate dilation rate array.
- * @returns Result of the 2D pooling.
- */
-export function conv2d(
-    x: Tensor, kernel: Tensor, strides = [1, 1], padding = 'valid',
-    dataFormat?: DataFormat, dilationRate?: [number, number]): Tensor {
-  return tidy(() => {
-    checkDataFormat(dataFormat);
-    return conv2dWithBiasActivation(
-        x, kernel, null, strides, padding, dataFormat, dilationRate);
-  });
-}
-
-/**
- * 2D Convolution with an added bias and optional activation.
- * Note: This function does not exist in the Python Keras Backend. This function
- * is exactly the same as `conv2d`, except the added `bias`.
- */
-export function conv2dWithBiasActivation(
-    x: Tensor, kernel: Tensor, bias: Tensor, strides = [1, 1],
-    padding = 'valid', dataFormat?: DataFormat, dilationRate?: [number, number],
-    activation: fused.Activation = null): Tensor {
-  return tidy(() => {
-    if (dataFormat == null) {
-      dataFormat = imageDataFormat();
-    }
-    checkDataFormat(dataFormat);
-    if (x.rank !== 3 && x.rank !== 4) {
-      throw new ValueError(
-          `conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +
-          `but received ${x.rank}.`);
-    }
-    if (kernel.rank !== 3 && kernel.rank !== 4) {
-      throw new ValueError(
-          `conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +
-          `but received ${x.rank}.`);
-    }
-    let y = preprocessConv2DInput(x, dataFormat);
-    if (padding === 'causal') {
-      throw new NotImplementedError(
-          'The support for CAUSAL padding mode in conv1dWithBias is not ' +
-          'implemented yet.');
-    }
-    y = tfc.fused.conv2d({
-      x: y as Tensor3D | Tensor4D,
-      filter: kernel as Tensor4D,
-      strides: strides as [number, number],
-      pad: padding === 'same' ? 'same' : 'valid',
-      dilations: dilationRate,
-      dataFormat: 'NHWC',
-      bias,
-      activation
-    });
-    if (dataFormat === 'channelsFirst') {
-      y = tfc.transpose(y, [0, 3, 1, 2]);
-    }
-    return y;
-  });
-}
-
-/**
- * 3D Convolution.
- * @param x
- * @param kernel kernel of the convolution.
- * @param strides strides array.
- * @param padding padding mode. Default to 'valid'.
- * @param dataFormat data format. Defaults to 'channelsLast'.
- * @param dilationRate dilation rate array.
- * @returns Result of the 3D convolution.
- */
-export function conv3d(
-    x: Tensor, kernel: Tensor, strides = [1, 1, 1], padding = 'valid',
-    dataFormat?: DataFormat, dilationRate?: [number, number, number]): Tensor {
-  return tidy(() => {
-    checkDataFormat(dataFormat);
-    return conv3dWithBias(
-        x, kernel, null, strides, padding, dataFormat, dilationRate);
-  });
-}
-
-/**
- * 3D Convolution with an added bias.
- * Note: This function does not exist in the Python Keras Backend. This function
- * is exactly the same as `conv3d`, except the added `bias`.
- */
-export function conv3dWithBias(
-    x: Tensor, kernel: Tensor, bias: Tensor, strides = [1, 1, 1],
-    padding = 'valid', dataFormat?: DataFormat,
-    dilationRate?: [number, number, number]): Tensor {
-  return tidy(() => {
-    if (dataFormat == null) {
-      dataFormat = imageDataFormat();
-    }
-    checkDataFormat(dataFormat);
-    if (x.rank !== 4 && x.rank !== 5) {
-      throw new ValueError(
-          `conv3dWithBias expects input to be of rank 4 or 5, but received ` +
-          `${x.rank}.`);
-    }
-    if (kernel.rank !== 4 && kernel.rank !== 5) {
-      throw new ValueError(
-          `conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +
-          `${x.rank}.`);
-    }
-    let y = preprocessConv3DInput(x, dataFormat);
-    if (padding === 'causal') {
-      throw new NotImplementedError(
-          'The support for CAUSAL padding mode in conv3dWithBias is not ' +
-          'implemented yet.');
-    }
-    y = tfc.conv3d(
-        y as Tensor4D | tfc.Tensor<tfc.Rank.R5>,
-        kernel as tfc.Tensor<tfc.Rank.R5>, strides as [number, number, number],
-        padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);
-    if (bias != null) {
-      y = K.biasAdd(y, bias as Tensor1D);
-    }
-    if (dataFormat === 'channelsFirst') {
-      y = tfc.transpose(y, [0, 4, 1, 2, 3]);
-    }
-    return y;
-  });
-}
-
-/**
- * Base LayerConfig for depthwise and non-depthwise convolutional layers.
- */
-export declare interface BaseConvLayerArgs extends LayerArgs {
-  /**
-   * The dimensions of the convolution window. If kernelSize is a number, the
-   * convolutional window will be square.
-   */
-  kernelSize: number|number[];
-
-  /**
-   * The strides of the convolution in each dimension. If strides is a number,
-   * strides in both dimensions are equal.
-   *
-   * Specifying any stride value != 1 is incompatible with specifying any
-   * `dilationRate` value != 1.
-   */
-  strides?: number|number[];
-
-  /**
-   * Padding mode.
-   */
-  padding?: PaddingMode;
-
-  /**
-   * Format of the data, which determines the ordering of the dimensions in
-   * the inputs.
-   *
-   * `channels_last` corresponds to inputs with shape
-   *   `(batch, ..., channels)`
-   *
-   *  `channels_first` corresponds to inputs with shape `(batch, channels,
-   * ...)`.
-   *
-   * Defaults to `channels_last`.
-   */
-  dataFormat?: DataFormat;
-
-  /**
-   * The dilation rate to use for the dilated convolution in each dimension.
-   * Should be an integer or array of two or three integers.
-   *
-   * Currently, specifying any `dilationRate` value != 1 is incompatible with
-   * specifying any `strides` value != 1.
-   */
-  dilationRate?: number|[number]|[number, number]|[number, number, number];
-
-  /**
-   * Activation function of the layer.
-   *
-   * If you don't specify the activation, none is applied.
-   */
-  activation?: ActivationIdentifier;
-
-  /**
-   * Whether the layer uses a bias vector. Defaults to `true`.
-   */
-  useBias?: boolean;
-
-  /**
-   * Initializer for the convolutional kernel weights matrix.
-   */
-  kernelInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the bias vector.
-   */
-  biasInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Constraint for the convolutional kernel weights.
-   */
-  kernelConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Constraint for the bias vector.
-   */
-  biasConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Regularizer function applied to the kernel weights matrix.
-   */
-  kernelRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer function applied to the bias vector.
-   */
-  biasRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer function applied to the activation.
-   */
-  activityRegularizer?: RegularizerIdentifier|Regularizer;
-}
-
-/**
- * LayerConfig for non-depthwise convolutional layers.
- * Applies to non-depthwise convolution of all ranks (e.g, Conv1D, Conv2D,
- * Conv3D).
- */
-export declare interface ConvLayerArgs extends BaseConvLayerArgs {
-  /**
-   * The dimensionality of the output space (i.e. the number of filters in the
-   * convolution).
-   */
-  filters: number;
-}
-
-/**
- * Abstract convolution layer.
- */
-export abstract class BaseConv extends Layer {
-  protected readonly rank: number;
-  protected readonly kernelSize: number[];
-  protected readonly strides: number[];
-  protected readonly padding: PaddingMode;
-  protected readonly dataFormat: DataFormat;
-  protected readonly activation: Activation;
-  protected readonly useBias: boolean;
-  protected readonly dilationRate: number[];
-
-  // Bias-related members are here because all convolution subclasses use the
-  // same configuration parmeters to control bias.  Kernel-related members
-  // are in subclass `Conv` because some subclasses use different parameters to
-  // control kernel properties, for instance, `DepthwiseConv2D` uses
-  // `depthwiseInitializer` instead of `kernelInitializer`.
-  protected readonly biasInitializer?: Initializer;
-  protected readonly biasConstraint?: Constraint;
-  protected readonly biasRegularizer?: Regularizer;
-
-  protected bias: LayerVariable = null;
-
-  readonly DEFAULT_KERNEL_INITIALIZER: InitializerIdentifier = 'glorotNormal';
-  readonly DEFAULT_BIAS_INITIALIZER: InitializerIdentifier = 'zeros';
-
-  constructor(rank: number, args: BaseConvLayerArgs) {
-    super(args as LayerArgs);
-    BaseConv.verifyArgs(args);
-    this.rank = rank;
-    generic_utils.assertPositiveInteger(this.rank, 'rank');
-    if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {
-      throw new NotImplementedError(
-          `Convolution layer for rank other than 1, 2, or 3 (${
-              this.rank}) is ` +
-          `not implemented yet.`);
-    }
-    this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');
-    this.strides = normalizeArray(
-        args.strides == null ? 1 : args.strides, rank, 'strides');
-    this.padding = args.padding == null ? 'valid' : args.padding;
-    checkPaddingMode(this.padding);
-    this.dataFormat =
-        args.dataFormat == null ? 'channelsLast' : args.dataFormat;
-    checkDataFormat(this.dataFormat);
-    this.activation = getActivation(args.activation);
-    this.useBias = args.useBias == null ? true : args.useBias;
-    this.biasInitializer =
-        getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
-    this.biasConstraint = getConstraint(args.biasConstraint);
-    this.biasRegularizer = getRegularizer(args.biasRegularizer);
-    this.activityRegularizer = getRegularizer(args.activityRegularizer);
-    this.dilationRate = normalizeArray(
-        args.dilationRate == null ? 1 : args.dilationRate, rank,
-        'dilationRate');
-    if (this.rank === 1 &&
-        (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {
-      throw new ValueError(
-          `dilationRate must be a number or an array of a single number ` +
-          `for 1D convolution, but received ` +
-          `${JSON.stringify(this.dilationRate)}`);
-    } else if (this.rank === 2) {
-      if (typeof this.dilationRate === 'number') {
-        this.dilationRate = [this.dilationRate, this.dilationRate];
-      } else if (this.dilationRate.length !== 2) {
-        throw new ValueError(
-            `dilationRate must be a number or array of two numbers for 2D ` +
-            `convolution, but received ${JSON.stringify(this.dilationRate)}`);
-      }
-    } else if (this.rank === 3) {
-      if (typeof this.dilationRate === 'number') {
-        this.dilationRate =
-            [this.dilationRate, this.dilationRate, this.dilationRate];
-      } else if (this.dilationRate.length !== 3) {
-        throw new ValueError(
-            `dilationRate must be a number or array of three numbers for 3D ` +
-            `convolution, but received ${JSON.stringify(this.dilationRate)}`);
-      }
-    }
-  }
-
-  protected static verifyArgs(args: BaseConvLayerArgs) {
-    // Check config.kernelSize type and shape.
-    generic_utils.assert(
-        'kernelSize' in args, `required key 'kernelSize' not in config`);
-    if (typeof args.kernelSize !== 'number' &&
-        !generic_utils.checkArrayTypeAndLength(
-            args.kernelSize, 'number', 1, 3)) {
-      throw new ValueError(
-          `BaseConv expects config.kernelSize to be number or number[] with ` +
-          `length 1, 2, or 3, but received ${
-              JSON.stringify(args.kernelSize)}.`);
-    }
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      kernelSize: this.kernelSize,
-      strides: this.strides,
-      padding: this.padding,
-      dataFormat: this.dataFormat,
-      dilationRate: this.dilationRate,
-      activation: serializeActivation(this.activation),
-      useBias: this.useBias,
-      biasInitializer: serializeInitializer(this.biasInitializer),
-      biasRegularizer: serializeRegularizer(this.biasRegularizer),
-      activityRegularizer: serializeRegularizer(this.activityRegularizer),
-      biasConstraint: serializeConstraint(this.biasConstraint)
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-
-/**
- * Abstract nD convolution layer.  Ancestor of convolution layers which reduce
- * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.
- */
-export abstract class Conv extends BaseConv {
-  protected readonly filters: number;
-
-  protected kernel: LayerVariable = null;
-
-  // Bias-related properties are stored in the superclass `BaseConv` because all
-  // convolution subclasses use the same configuration parameters to control
-  // bias. Kernel-related properties are defined here rather than in the
-  // superclass because some convolution subclasses use different names and
-  // configuration parameters for their internal kernel state.
-  protected readonly kernelInitializer?: Initializer;
-  protected readonly kernelConstraint?: Constraint;
-  protected readonly kernelRegularizer?: Regularizer;
-
-  constructor(rank: number, args: ConvLayerArgs) {
-    super(rank, args as BaseConvLayerArgs);
-    Conv.verifyArgs(args);
-    this.filters = args.filters;
-    generic_utils.assertPositiveInteger(this.filters, 'filters');
-    this.kernelInitializer = getInitializer(
-        args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
-    this.kernelConstraint = getConstraint(args.kernelConstraint);
-    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    const channelAxis =
-        this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;
-    if (inputShape[channelAxis] == null) {
-      throw new ValueError(
-          `The channel dimension of the input should be defined. ` +
-          `Found ${inputShape[channelAxis]}`);
-    }
-    const inputDim = inputShape[channelAxis];
-
-    const kernelShape = this.kernelSize.concat([inputDim, this.filters]);
-
-    this.kernel = this.addWeight(
-        'kernel', kernelShape, null, this.kernelInitializer,
-        this.kernelRegularizer, true, this.kernelConstraint);
-    if (this.useBias) {
-      this.bias = this.addWeight(
-          'bias', [this.filters], null, this.biasInitializer,
-          this.biasRegularizer, true, this.biasConstraint);
-    }
-
-    this.inputSpec = [{ndim: this.rank + 2, axes: {[channelAxis]: inputDim}}];
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = getExactlyOneTensor(inputs);
-      let outputs: Tensor;
-      const biasValue = this.bias == null ? null : this.bias.read();
-      const fusedActivationName = generic_utils.mapActivationToFusedKernel(
-          this.activation.getClassName());
-
-      if (fusedActivationName != null && this.rank === 2) {
-        outputs = conv2dWithBiasActivation(
-            inputs, this.kernel.read(), biasValue, this.strides, this.padding,
-            this.dataFormat, this.dilationRate as [number, number],
-            fusedActivationName);
-      } else {
-        if (this.rank === 1) {
-          outputs = conv1dWithBias(
-              inputs, this.kernel.read(), biasValue, this.strides[0],
-              this.padding, this.dataFormat, this.dilationRate[0]);
-        } else if (this.rank === 2) {
-          // TODO(cais): Move up to constructor.
-          outputs = conv2dWithBiasActivation(
-              inputs, this.kernel.read(), biasValue, this.strides, this.padding,
-              this.dataFormat, this.dilationRate as [number, number]);
-        } else if (this.rank === 3) {
-          outputs = conv3dWithBias(
-              inputs, this.kernel.read(), biasValue, this.strides, this.padding,
-              this.dataFormat, this.dilationRate as [number, number, number]);
-        } else {
-          throw new NotImplementedError(
-              'convolutions greater than 3D are not implemented yet.');
-        }
-
-        if (this.activation != null) {
-          outputs = this.activation.apply(outputs);
-        }
-      }
-
-      return outputs;
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const newSpace: number[] = [];
-    const space = (this.dataFormat === 'channelsLast') ?
-        inputShape.slice(1, inputShape.length - 1) :
-        inputShape.slice(2);
-    for (let i = 0; i < space.length; ++i) {
-      const newDim = convOutputLength(
-          space[i], this.kernelSize[i], this.padding, this.strides[i],
-          typeof this.dilationRate === 'number' ? this.dilationRate :
-                                                  this.dilationRate[i]);
-      newSpace.push(newDim);
-    }
-
-    let outputShape = [inputShape[0]];
-    if (this.dataFormat === 'channelsLast') {
-      outputShape = outputShape.concat(newSpace);
-      outputShape.push(this.filters);
-    } else {
-      outputShape.push(this.filters);
-      outputShape = outputShape.concat(newSpace);
-    }
-    return outputShape;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      filters: this.filters,
-      kernelInitializer: serializeInitializer(this.kernelInitializer),
-      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
-      kernelConstraint: serializeConstraint(this.kernelConstraint)
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  protected static override verifyArgs(args: ConvLayerArgs) {
-    // Check config.filters type, shape, and value.
-    if (!('filters' in args) || typeof args.filters !== 'number' ||
-        args.filters < 1) {
-      throw new ValueError(
-          `Convolution layer expected config.filters to be a 'number' > 0 ` +
-          `but got ${JSON.stringify(args.filters)}`);
-    }
-  }
-}
-
-export class Conv2D extends Conv {
-  /** @nocollapse */
-  static className = 'Conv2D';
-  constructor(args: ConvLayerArgs) {
-    super(2, args);
-    Conv2D.verifyArgs(args);
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = super.getConfig();
-    delete config['rank'];
-    return config;
-  }
-
-  protected static override verifyArgs(args: ConvLayerArgs) {
-    // config.kernelSize must be a number or array of numbers.
-    if ((typeof args.kernelSize !== 'number') &&
-        !generic_utils.checkArrayTypeAndLength(
-            args.kernelSize, 'number', 1, 2)) {
-      throw new ValueError(
-          `Conv2D expects config.kernelSize to be number or number[] with ` +
-          `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);
-    }
-  }
-}
-serialization.registerClass(Conv2D);
-
-export class Conv3D extends Conv {
-  /** @nocollapse */
-  static className = 'Conv3D';
-  constructor(args: ConvLayerArgs) {
-    super(3, args);
-    Conv3D.verifyArgs(args);
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = super.getConfig();
-    delete config['rank'];
-    return config;
-  }
-
-  protected static override verifyArgs(args: ConvLayerArgs) {
-    // config.kernelSize must be a number or array of numbers.
-    if (typeof args.kernelSize !== 'number') {
-      if (!(Array.isArray(args.kernelSize) &&
-            (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {
-        throw new ValueError(
-            `Conv3D expects config.kernelSize to be number or` +
-            ` [number, number, number], but received ${
-                JSON.stringify(args.kernelSize)}.`);
-      }
-    }
-  }
-}
-serialization.registerClass(Conv3D);
-
-export class Conv2DTranspose extends Conv2D {
-  /** @nocollapse */
-  static override className = 'Conv2DTranspose';
-
-  constructor(args: ConvLayerArgs) {
-    super(args);
-    this.inputSpec = [new InputSpec({ndim: 4})];
-
-    if (this.padding !== 'same' && this.padding !== 'valid') {
-      throw new ValueError(
-          `Conv2DTranspose currently supports only padding modes 'same' ` +
-          `and 'valid', but received padding mode ${this.padding}`);
-    }
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-
-    if (inputShape.length !== 4) {
-      throw new ValueError(
-          'Input should have rank 4; Received input shape: ' +
-          JSON.stringify(inputShape));
-    }
-
-    const channelAxis =
-        this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;
-    if (inputShape[channelAxis] == null) {
-      throw new ValueError(
-          'The channel dimension of the inputs should be defined. ' +
-          'Found `None`.');
-    }
-    const inputDim = inputShape[channelAxis];
-    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);
-
-    this.kernel = this.addWeight(
-        'kernel', kernelShape, 'float32', this.kernelInitializer,
-        this.kernelRegularizer, true, this.kernelConstraint);
-    if (this.useBias) {
-      this.bias = this.addWeight(
-          'bias', [this.filters], 'float32', this.biasInitializer,
-          this.biasRegularizer, true, this.biasConstraint);
-    }
-
-    // Set input spec.
-    this.inputSpec =
-        [new InputSpec({ndim: 4, axes: {[channelAxis]: inputDim}})];
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tfc.tidy(() => {
-      let input = getExactlyOneTensor(inputs);
-      if (input.shape.length !== 4) {
-        throw new ValueError(
-            `Conv2DTranspose.call() expects input tensor to be rank-4, but ` +
-            `received a tensor of rank-${input.shape.length}`);
-      }
-
-      const inputShape = input.shape;
-      const batchSize = inputShape[0];
-
-      let hAxis: number;
-      let wAxis: number;
-      if (this.dataFormat === 'channelsFirst') {
-        hAxis = 2;
-        wAxis = 3;
-      } else {
-        hAxis = 1;
-        wAxis = 2;
-      }
-
-      const height = inputShape[hAxis];
-      const width = inputShape[wAxis];
-      const kernelH = this.kernelSize[0];
-      const kernelW = this.kernelSize[1];
-      const strideH = this.strides[0];
-      const strideW = this.strides[1];
-
-      // Infer the dynamic output shape.
-      const outHeight = deconvLength(height, strideH, kernelH, this.padding);
-      const outWidth = deconvLength(width, strideW, kernelW, this.padding);
-
-      // Porting Note: We don't branch based on `this.dataFormat` here,
-      // because
-      //   the tjfs-core function `conv2dTranspose` called below always
-      //   assumes channelsLast.
-      const outputShape: [number, number, number, number] =
-          [batchSize, outHeight, outWidth, this.filters];
-
-      if (this.dataFormat !== 'channelsLast') {
-        input = tfc.transpose(input, [0, 2, 3, 1]);
-      }
-      let outputs = tfc.conv2dTranspose(
-          input as Tensor4D, this.kernel.read() as Tensor4D, outputShape,
-          this.strides as [number, number], this.padding as 'same' | 'valid');
-      if (this.dataFormat !== 'channelsLast') {
-        outputs = tfc.transpose(outputs, [0, 3, 1, 2]);
-      }
-
-      if (this.bias != null) {
-        outputs =
-            K.biasAdd(outputs, this.bias.read(), this.dataFormat) as Tensor4D;
-      }
-      if (this.activation != null) {
-        outputs = this.activation.apply(outputs) as Tensor4D;
-      }
-      return outputs;
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const outputShape = inputShape.slice();
-
-    let channelAxis: number;
-    let heightAxis: number;
-    let widthAxis: number;
-    if (this.dataFormat === 'channelsFirst') {
-      channelAxis = 1;
-      heightAxis = 2;
-      widthAxis = 3;
-    } else {
-      channelAxis = 3;
-      heightAxis = 1;
-      widthAxis = 2;
-    }
-
-    const kernelH = this.kernelSize[0];
-    const kernelW = this.kernelSize[1];
-    const strideH = this.strides[0];
-    const strideW = this.strides[1];
-
-    outputShape[channelAxis] = this.filters;
-    outputShape[heightAxis] =
-        deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);
-    outputShape[widthAxis] =
-        deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);
-    return outputShape;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = super.getConfig();
-    delete config['dilationRate'];
-    return config;
-  }
-}
-serialization.registerClass(Conv2DTranspose);
-
-export class Conv3DTranspose extends Conv3D {
-  /** @nocollapse */
-  static override className = 'Conv3DTranspose';
-
-  constructor(args: ConvLayerArgs) {
-    super(args);
-    this.inputSpec = [new InputSpec({ndim: 5})];
-
-    if (this.padding !== 'same' && this.padding !== 'valid') {
-      throw new ValueError(
-          `Conv3DTranspose currently supports only padding modes 'same' ` +
-          `and 'valid', but received padding mode ${this.padding}`);
-    }
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-
-    if (inputShape.length !== 5) {
-      throw new ValueError(
-          'Input should have rank 5; Received input shape: ' +
-          JSON.stringify(inputShape));
-    }
-
-    const channelAxis =
-        this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;
-    if (inputShape[channelAxis] == null) {
-      throw new ValueError(
-          'The channel dimension of the inputs should be defined. ' +
-          'Found `None`.');
-    }
-    const inputDim = inputShape[channelAxis];
-    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);
-
-    this.kernel = this.addWeight(
-        'kernel', kernelShape, 'float32', this.kernelInitializer,
-        this.kernelRegularizer, true, this.kernelConstraint);
-    if (this.useBias) {
-      this.bias = this.addWeight(
-          'bias', [this.filters], 'float32', this.biasInitializer,
-          this.biasRegularizer, true, this.biasConstraint);
-    }
-
-    // Set input spec.
-    this.inputSpec =
-        [new InputSpec({ndim: 5, axes: {[channelAxis]: inputDim}})];
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tfc.tidy<tfc.Tensor5D>(() => {
-      let input = getExactlyOneTensor(inputs);
-      if (input.shape.length !== 5) {
-        throw new ValueError(
-            `Conv3DTranspose.call() expects input tensor to be rank-4, but ` +
-            `received a tensor of rank-${input.shape.length}`);
-      }
-
-      const inputShape = input.shape;
-      const batchSize = inputShape[0];
-
-      let hAxis: number;
-      let wAxis: number;
-      let dAxis: number;
-
-      if (this.dataFormat === 'channelsFirst') {
-        dAxis = 2;
-        hAxis = 3;
-        wAxis = 4;
-      } else {
-        dAxis = 1;
-        hAxis = 2;
-        wAxis = 3;
-      }
-
-      const depth = inputShape[dAxis];
-      const height = inputShape[hAxis];
-      const width = inputShape[wAxis];
-      const kernelD = this.kernelSize[0];
-      const kernelH = this.kernelSize[1];
-      const kernelW = this.kernelSize[2];
-      const strideD = this.strides[0];
-      const strideH = this.strides[1];
-      const strideW = this.strides[2];
-
-      // Infer the dynamic output shape.
-      const outDepth = deconvLength(depth, strideD, kernelD, this.padding);
-      const outHeight = deconvLength(height, strideH, kernelH, this.padding);
-      const outWidth = deconvLength(width, strideW, kernelW, this.padding);
-
-      // Same as `conv2dTranspose`. We always assumes channelsLast.
-      const outputShape: [number, number, number, number, number] =
-          [batchSize, outDepth, outHeight, outWidth, this.filters];
-      if (this.dataFormat !== 'channelsLast') {
-        input = tfc.transpose(input, [0, 2, 3, 4, 1]);
-      }
-      let outputs = tfc.conv3dTranspose(
-          input as Tensor5D, this.kernel.read() as Tensor5D, outputShape,
-          this.strides as [number, number, number],
-          this.padding as 'same' | 'valid');
-      if (this.dataFormat !== 'channelsLast') {
-        outputs = tfc.transpose(outputs, [0, 4, 1, 2, 3]);
-      }
-
-      if (this.bias !== null) {
-        outputs =
-            K.biasAdd(outputs, this.bias.read(), this.dataFormat) as Tensor5D;
-      }
-      if (this.activation !== null) {
-        outputs = this.activation.apply(outputs) as Tensor5D;
-      }
-      return outputs;
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const outputShape = inputShape.slice();
-
-    let channelAxis: number;
-    let depthAxis: number;
-    let heightAxis: number;
-    let widthAxis: number;
-    if (this.dataFormat === 'channelsFirst') {
-      channelAxis = 1;
-      depthAxis = 2;
-      heightAxis = 3;
-      widthAxis = 4;
-    } else {
-      channelAxis = 4;
-      depthAxis = 1;
-      heightAxis = 2;
-      widthAxis = 3;
-    }
-
-    const kernelD = this.kernelSize[0];
-    const kernelH = this.kernelSize[1];
-    const kernelW = this.kernelSize[2];
-    const strideD = this.strides[0];
-    const strideH = this.strides[1];
-    const strideW = this.strides[2];
-
-    outputShape[channelAxis] = this.filters;
-    outputShape[depthAxis] =
-        deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding);
-    outputShape[heightAxis] =
-        deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);
-    outputShape[widthAxis] =
-        deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);
-    return outputShape;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = super.getConfig();
-    delete config['dilationRate'];
-    return config;
-  }
-}
-serialization.registerClass(Conv3DTranspose);
-
-export declare interface SeparableConvLayerArgs extends ConvLayerArgs {
-  /**
-   * The number of depthwise convolution output channels for each input
-   * channel.
-   * The total number of depthwise convolution output channels will be equal
-   * to `filtersIn * depthMultiplier`. Default: 1.
-   */
-  depthMultiplier?: number;
-
-  /**
-   * Initializer for the depthwise kernel matrix.
-   */
-  depthwiseInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the pointwise kernel matrix.
-   */
-  pointwiseInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Regularizer function applied to the depthwise kernel matrix.
-   */
-  depthwiseRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer function applied to the pointwise kernel matrix.
-   */
-  pointwiseRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Constraint function applied to the depthwise kernel matrix.
-   */
-  depthwiseConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Constraint function applied to the pointwise kernel matrix.
-   */
-  pointwiseConstraint?: ConstraintIdentifier|Constraint;
-}
-
-export class SeparableConv extends Conv {
-  /** @nocollapse */
-  static className = 'SeparableConv';
-
-  readonly depthMultiplier: number;
-
-  protected readonly depthwiseInitializer?: Initializer;
-  protected readonly depthwiseRegularizer?: Regularizer;
-  protected readonly depthwiseConstraint?: Constraint;
-  protected readonly pointwiseInitializer?: Initializer;
-  protected readonly pointwiseRegularizer?: Regularizer;
-  protected readonly pointwiseConstraint?: Constraint;
-
-  readonly DEFAULT_DEPTHWISE_INITIALIZER: InitializerIdentifier =
-      'glorotUniform';
-  readonly DEFAULT_POINTWISE_INITIALIZER: InitializerIdentifier =
-      'glorotUniform';
-
-  protected depthwiseKernel: LayerVariable = null;
-  protected pointwiseKernel: LayerVariable = null;
-
-  constructor(rank: number, config?: SeparableConvLayerArgs) {
-    super(rank, config);
-
-    if (config.filters == null) {
-      throw new ValueError(
-          'The `filters` configuration field is required by SeparableConv, ' +
-          'but is unspecified.');
-    }
-    if (config.kernelInitializer != null || config.kernelRegularizer != null ||
-        config.kernelConstraint != null) {
-      throw new ValueError(
-          'Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +
-          'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +
-          'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +
-          'pointwiseRegularizer and pointwiseConstraint instead.');
-    }
-    if (config.padding != null && config.padding !== 'same' &&
-        config.padding !== 'valid') {
-      throw new ValueError(
-          `SeparableConv${this.rank}D supports only padding modes: ` +
-          `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);
-    }
-
-    this.depthMultiplier =
-        config.depthMultiplier == null ? 1 : config.depthMultiplier;
-    this.depthwiseInitializer = getInitializer(
-        config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);
-    this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);
-    this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);
-    this.pointwiseInitializer = getInitializer(
-        config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);
-    this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);
-    this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    if (inputShape.length < this.rank + 2) {
-      throw new ValueError(
-          `Inputs to SeparableConv${this.rank}D should have rank ` +
-          `${this.rank + 2}, but received input shape: ` +
-          `${JSON.stringify(inputShape)}`);
-    }
-    const channelAxis =
-        this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;
-    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
-      throw new ValueError(
-          `The channel dimension of the inputs should be defined, ` +
-          `but found ${JSON.stringify(inputShape[channelAxis])}`);
-    }
-
-    const inputDim = inputShape[channelAxis];
-    const depthwiseKernelShape =
-        this.kernelSize.concat([inputDim, this.depthMultiplier]);
-    const pointwiseKernelShape = [];
-    for (let i = 0; i < this.rank; ++i) {
-      pointwiseKernelShape.push(1);
-    }
-    pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);
-
-    const trainable = true;
-    this.depthwiseKernel = this.addWeight(
-        'depthwise_kernel', depthwiseKernelShape, 'float32',
-        this.depthwiseInitializer, this.depthwiseRegularizer, trainable,
-        this.depthwiseConstraint);
-    this.pointwiseKernel = this.addWeight(
-        'pointwise_kernel', pointwiseKernelShape, 'float32',
-        this.pointwiseInitializer, this.pointwiseRegularizer, trainable,
-        this.pointwiseConstraint);
-    if (this.useBias) {
-      this.bias = this.addWeight(
-          'bias', [this.filters], 'float32', this.biasInitializer,
-          this.biasRegularizer, trainable, this.biasConstraint);
-    } else {
-      this.bias = null;
-    }
-
-    this.inputSpec =
-        [new InputSpec({ndim: this.rank + 2, axes: {[channelAxis]: inputDim}})];
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = getExactlyOneTensor(inputs);
-
-      let output: Tensor;
-      if (this.rank === 1) {
-        throw new NotImplementedError(
-            '1D separable convolution is not implemented yet.');
-      } else if (this.rank === 2) {
-        if (this.dataFormat === 'channelsFirst') {
-          inputs = tfc.transpose(inputs, [0, 2, 3, 1]);  // NCHW -> NHWC.
-        }
-
-        output = tfc.separableConv2d(
-            inputs as Tensor4D, this.depthwiseKernel.read() as Tensor4D,
-            this.pointwiseKernel.read() as Tensor4D,
-            this.strides as [number, number], this.padding as 'same' | 'valid',
-            this.dilationRate as [number, number], 'NHWC');
-      }
-
-      if (this.useBias) {
-        output = K.biasAdd(output, this.bias.read(), this.dataFormat);
-      }
-      if (this.activation != null) {
-        output = this.activation.apply(output);
-      }
-
-      if (this.dataFormat === 'channelsFirst') {
-        output = tfc.transpose(output, [0, 3, 1, 2]);  // NHWC -> NCHW.
-      }
-      return output;
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = super.getConfig();
-    delete config['rank'];
-    delete config['kernelInitializer'];
-    delete config['kernelRegularizer'];
-    delete config['kernelConstraint'];
-    config['depthwiseInitializer'] =
-        serializeInitializer(this.depthwiseInitializer);
-    config['pointwiseInitializer'] =
-        serializeInitializer(this.pointwiseInitializer);
-    config['depthwiseRegularizer'] =
-        serializeRegularizer(this.depthwiseRegularizer);
-    config['pointwiseRegularizer'] =
-        serializeRegularizer(this.pointwiseRegularizer);
-    config['depthwiseConstraint'] =
-        serializeConstraint(this.depthwiseConstraint);
-    config['pointwiseConstraint'] =
-        serializeConstraint(this.pointwiseConstraint);
-    return config;
-  }
-}
-
-export class SeparableConv2D extends SeparableConv {
-  /** @nocollapse */
-  static override className = 'SeparableConv2D';
-  constructor(args?: SeparableConvLayerArgs) {
-    super(2, args);
-  }
-}
-serialization.registerClass(SeparableConv2D);
-
-export class Conv1D extends Conv {
-  /** @nocollapse */
-  static className = 'Conv1D';
-  constructor(args: ConvLayerArgs) {
-    super(1, args);
-    Conv1D.verifyArgs(args);
-    this.inputSpec = [{ndim: 3}];
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = super.getConfig();
-    delete config['rank'];
-    delete config['dataFormat'];
-    return config;
-  }
-
-  protected static override verifyArgs(args: ConvLayerArgs) {
-    // config.kernelSize must be a number or array of numbers.
-    if (typeof args.kernelSize !== 'number' &&
-        !generic_utils.checkArrayTypeAndLength(
-            args.kernelSize, 'number', 1, 1)) {
-      throw new ValueError(
-          `Conv1D expects config.kernelSize to be number or number[] with ` +
-          `length 1, but received ${JSON.stringify(args.kernelSize)}.`);
-    }
-  }
-}
-serialization.registerClass(Conv1D);
-
-export declare interface Cropping2DLayerArgs extends LayerArgs {
-  /**
-   * Dimension of the cropping along the width and the height.
-   * - If integer: the same symmetric cropping
-   *  is applied to width and height.
-   * - If list of 2 integers:
-   *   interpreted as two different
-   *   symmetric cropping values for height and width:
-   *   `[symmetric_height_crop, symmetric_width_crop]`.
-   * - If a list of 2 lists of 2 integers:
-   *   interpreted as
-   *   `[[top_crop, bottom_crop], [left_crop, right_crop]]`
-   */
-  cropping: number|[number, number]|[[number, number], [number, number]];
-
-  /**
-   * Format of the data, which determines the ordering of the dimensions in
-   * the inputs.
-   *
-   * `channels_last` corresponds to inputs with shape
-   *   `(batch, ..., channels)`
-   *
-   * `channels_first` corresponds to inputs with shape
-   *   `(batch, channels, ...)`
-   *
-   * Defaults to `channels_last`.
-   */
-  dataFormat?: DataFormat;
-}
-
-export class Cropping2D extends Layer {
-  /** @nocollapse */
-  static className = 'Cropping2D';
-  protected readonly cropping: [[number, number], [number, number]];
-  protected readonly dataFormat: DataFormat;
-
-  constructor(args: Cropping2DLayerArgs) {
-    super(args);
-    if (typeof args.cropping === 'number') {
-      this.cropping =
-          [[args.cropping, args.cropping], [args.cropping, args.cropping]];
-    } else if (typeof args.cropping[0] === 'number') {
-      this.cropping = [
-        [args.cropping[0], args.cropping[0]],
-        [args.cropping[1] as number, args.cropping[1] as number]
-      ];
-    } else {
-      this.cropping = args.cropping as [[number, number], [number, number]];
-    }
-    this.dataFormat =
-        args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;
-    this.inputSpec = [{ndim: 4}];
-  }
-
-  override computeOutputShape(inputShape: Shape): Shape {
-    if (this.dataFormat === 'channelsFirst') {
-      return [
-        inputShape[0], inputShape[1],
-        inputShape[2] - this.cropping[0][0] - this.cropping[0][1],
-        inputShape[3] - this.cropping[1][0] - this.cropping[1][1]
-      ];
-    } else {
-      return [
-        inputShape[0],
-        inputShape[1] - this.cropping[0][0] - this.cropping[0][1],
-        inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]
-      ];
-    }
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = getExactlyOneTensor(inputs);
-
-      if (this.dataFormat === 'channelsLast') {
-        const hSliced = K.sliceAlongAxis(
-            inputs, this.cropping[0][0],
-            inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
-        return K.sliceAlongAxis(
-            hSliced, this.cropping[1][0],
-            inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
-      } else {
-        const hSliced = K.sliceAlongAxis(
-            inputs, this.cropping[0][0],
-            inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
-        return K.sliceAlongAxis(
-            hSliced, this.cropping[1][0],
-            inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
-      }
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {cropping: this.cropping, dataFormat: this.dataFormat};
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Cropping2D);
-
-export declare interface UpSampling2DLayerArgs extends LayerArgs {
-  /**
-   * The upsampling factors for rows and columns.
-   *
-   * Defaults to `[2, 2]`.
-   */
-  size?: number[];
-  /**
-   * Format of the data, which determines the ordering of the dimensions in
-   * the inputs.
-   *
-   * `"channelsLast"` corresponds to inputs with shape
-   *   `[batch, ..., channels]`
-   *
-   *  `"channelsFirst"` corresponds to inputs with shape `[batch, channels,
-   * ...]`.
-   *
-   * Defaults to `"channelsLast"`.
-   */
-  dataFormat?: DataFormat;
-  /**
-   * The interpolation mechanism, one of `"nearest"` or `"bilinear"`, default
-   * to `"nearest"`.
-   */
-  interpolation?: InterpolationFormat;
-}
-
-export class UpSampling2D extends Layer {
-  /** @nocollapse */
-  static className = 'UpSampling2D';
-  protected readonly DEFAULT_SIZE = [2, 2];
-  protected readonly size: number[];
-  protected readonly dataFormat: DataFormat;
-  protected readonly interpolation: InterpolationFormat;
-
-  constructor(args: UpSampling2DLayerArgs) {
-    super(args);
-    this.inputSpec = [{ndim: 4}];
-    this.size = args.size == null ? this.DEFAULT_SIZE : args.size;
-    this.dataFormat =
-        args.dataFormat == null ? 'channelsLast' : args.dataFormat;
-    checkDataFormat(this.dataFormat);
-    this.interpolation =
-        args.interpolation == null ? 'nearest' : args.interpolation;
-    checkInterpolationFormat(this.interpolation);
-  }
-
-  override computeOutputShape(inputShape: Shape): Shape {
-    if (this.dataFormat === 'channelsFirst') {
-      const height =
-          inputShape[2] == null ? null : this.size[0] * inputShape[2];
-      const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];
-      return [inputShape[0], inputShape[1], height, width];
-    } else {
-      const height =
-          inputShape[1] == null ? null : this.size[0] * inputShape[1];
-      const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];
-      return [inputShape[0], height, width, inputShape[3]];
-    }
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tfc.tidy(() => {
-      let input = getExactlyOneTensor(inputs) as Tensor4D;
-      const inputShape = input.shape;
-
-      if (this.dataFormat === 'channelsFirst') {
-        input = tfc.transpose(input, [0, 2, 3, 1]);
-        const height = this.size[0] * inputShape[2];
-        const width = this.size[1] * inputShape[3];
-
-        const resized = this.interpolation === 'nearest' ?
-            tfc.image.resizeNearestNeighbor(input, [height, width]) :
-            tfc.image.resizeBilinear(input, [height, width]);
-        return tfc.transpose(resized, [0, 3, 1, 2]);
-      } else {
-        const height = this.size[0] * inputShape[1];
-        const width = this.size[1] * inputShape[2];
-        return this.interpolation === 'nearest' ?
-            tfc.image.resizeNearestNeighbor(input, [height, width]) :
-            tfc.image.resizeBilinear(input, [height, width]);
-      }
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-        size: this.size,
-        dataFormat: this.dataFormat,
-        interpolation: this.interpolation
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(UpSampling2D);
diff --git a/tfjs-master/tfjs-layers/src/layers/convolutional_depthwise.ts b/tfjs-master/tfjs-layers/src/layers/convolutional_depthwise.ts
deleted file mode 100644
index ea27eebda..000000000
--- a/tfjs-master/tfjs-layers/src/layers/convolutional_depthwise.ts
+++ /dev/null
@@ -1,211 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * TensorFlow.js Layers: Depthwise Convolutional Layers
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {serialization, Tensor, Tensor4D, tidy} from '@tensorflow/tfjs-core';
-
-import {imageDataFormat} from '../backend/common';
-import * as K from '../backend/tfjs_backend';
-import {checkDataFormat} from '../common';
-import {Constraint, ConstraintIdentifier, getConstraint, serializeConstraint} from '../constraints';
-import {ValueError} from '../errors';
-import {getInitializer, Initializer, InitializerIdentifier, serializeInitializer} from '../initializers';
-import {DataFormat, Shape} from '../keras_format/common';
-import {getRegularizer, Regularizer, RegularizerIdentifier, serializeRegularizer} from '../regularizers';
-import {Kwargs} from '../types';
-import {convOutputLength} from '../utils/conv_utils';
-import {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';
-import {LayerVariable} from '../variables';
-
-import {BaseConv, BaseConvLayerArgs, ConvLayerArgs, preprocessConv2DInput} from './convolutional';
-
-/**
- * 2D convolution with separable filters.
- * @param x Input tensor.
- * @param depthwiseKernel Convolution kernel for depthwise convolution.
- * @param strides Strides (Array of two integers).
- * @param padding Padding model.
- * @param dataFormat Data format.
- * @param dilationRate Array of two integers, dilation rates for the separable
- *   convolution.
- * @returns Output tensor.
- * @throws ValueError If depthwiseKernel is not a 4D array.
- */
-export function depthwiseConv2d(
-    x: Tensor, depthwiseKernel: Tensor, strides: [number, number] = [1, 1],
-    padding = 'valid', dataFormat?: DataFormat,
-    dilationRate?: [number, number]): Tensor {
-  return tidy(() => {
-    if (dataFormat == null) {
-      dataFormat = imageDataFormat();
-    }
-    checkDataFormat(dataFormat);
-    let y = preprocessConv2DInput(x, dataFormat);
-    if (x.rank !== 4) {
-      throw new ValueError(
-          `Input for depthwiseConv2d is required to be 4-D, but is instead ` +
-          `${x.rank}-D`);
-    }
-    if (depthwiseKernel.rank !== 4) {
-      throw new ValueError(
-          `depthwiseKernel is required to be 4-D, but is instead ` +
-          `${depthwiseKernel.rank}-D`);
-    }
-    y = tfc.depthwiseConv2d(
-        y as Tensor4D, depthwiseKernel as Tensor4D, strides,
-        padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);
-    if (dataFormat === 'channelsFirst') {
-      y = tfc.transpose(y, [0, 3, 1, 2]);
-    }
-    return y;
-  });
-}
-
-export declare interface DepthwiseConv2DLayerArgs extends BaseConvLayerArgs {
-  /**
-   * An integer or Array of 2 integers, specifying the width and height of the
-   * 2D convolution window. Can be a single integer to specify the same value
-   * for all spatial dimensions.
-   */
-  kernelSize: number|[number, number];
-
-  /**
-   * The number of depthwise convolution output channels for each input
-   * channel.
-   * The total number of depthwise convolution output channels will be equal to
-   * `filtersIn * depthMultiplier`.
-   * Default: 1.
-   */
-  depthMultiplier?: number;
-
-  /**
-   * Initializer for the depthwise kernel matrix.
-   * Default: GlorotNormal.
-   */
-  depthwiseInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Constraint for the depthwise kernel matrix.
-   */
-  depthwiseConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Regularizer function for the depthwise kernel matrix.
-   */
-  depthwiseRegularizer?: RegularizerIdentifier|Regularizer;
-}
-
-export class DepthwiseConv2D extends BaseConv {
-  /** @nocollapse */
-  static className = 'DepthwiseConv2D';
-  private readonly depthMultiplier: number;
-  private readonly depthwiseInitializer: Initializer;
-  private readonly depthwiseConstraint: Constraint;
-  private readonly depthwiseRegularizer: Regularizer;
-
-  private depthwiseKernel: LayerVariable = null;
-
-  constructor(args: DepthwiseConv2DLayerArgs) {
-    super(2, args as ConvLayerArgs);
-    this.depthMultiplier =
-        args.depthMultiplier == null ? 1 : args.depthMultiplier;
-    this.depthwiseInitializer = getInitializer(
-        args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);
-    this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);
-    this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    if (inputShape.length < 4) {
-      throw new ValueError(
-          `Inputs to DepthwiseConv2D should have rank 4. ` +
-          `Received input shape: ${JSON.stringify(inputShape)}.`);
-    }
-    const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;
-    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
-      throw new ValueError(
-          'The channel dimension of the inputs to DepthwiseConv2D should ' +
-          `be defined, but is not (${inputShape[channelAxis]}).`);
-    }
-    const inputDim = inputShape[channelAxis];
-    const depthwiseKernelShape: Shape = [
-      this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier
-    ];
-
-    this.depthwiseKernel = this.addWeight(
-        'depthwise_kernel', depthwiseKernelShape, null,
-        this.depthwiseInitializer, this.depthwiseRegularizer, true,
-        this.depthwiseConstraint);
-    if (this.useBias) {
-      this.bias = this.addWeight(
-          'bias', [inputDim * this.depthMultiplier], null, this.biasInitializer,
-          this.biasRegularizer, true, this.biasConstraint);
-    } else {
-      this.bias = null;
-    }
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = getExactlyOneTensor(inputs);
-      let outputs = depthwiseConv2d(
-          inputs, this.depthwiseKernel.read(), this.strides as [number, number],
-          this.padding, this.dataFormat, null);
-      // TODO(cais): Add support for dilation.
-      if (this.useBias) {
-        outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);
-      }
-      if (this.activation != null) {
-        outputs = this.activation.apply(outputs);
-      }
-      return outputs;
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const rows =
-        this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];
-    const cols =
-        this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];
-    const outFilters = this.dataFormat === 'channelsFirst' ?
-        inputShape[1] * this.depthMultiplier :
-        inputShape[3] * this.depthMultiplier;
-    const outRows = convOutputLength(
-        rows, this.kernelSize[0], this.padding, this.strides[0]);
-    const outCols = convOutputLength(
-        cols, this.kernelSize[1], this.padding, this.strides[1]);
-    if (this.dataFormat === 'channelsFirst') {
-      return [inputShape[0], outFilters, outRows, outCols];
-    } else {
-      // In this case, assume 'channelsLast'.
-      return [inputShape[0], outRows, outCols, outFilters];
-    }
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = super.getConfig();
-    config['depthMultiplier'] = this.depthMultiplier;
-    config['depthwiseInitializer'] =
-        serializeInitializer(this.depthwiseInitializer);
-    config['depthwiseRegularizer'] =
-        serializeRegularizer(this.depthwiseRegularizer);
-    config['depthwiseConstraint'] =
-        serializeConstraint(this.depthwiseRegularizer);
-    return config;
-  }
-}
-serialization.registerClass(DepthwiseConv2D);
diff --git a/tfjs-master/tfjs-layers/src/layers/convolutional_depthwise_test.ts b/tfjs-master/tfjs-layers/src/layers/convolutional_depthwise_test.ts
deleted file mode 100644
index 5573bcb22..000000000
--- a/tfjs-master/tfjs-layers/src/layers/convolutional_depthwise_test.ts
+++ /dev/null
@@ -1,246 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for convolutional_depthwise.ts.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {Tensor, tensor4d, Tensor4D} from '@tensorflow/tfjs-core';
-
-import * as tfl from '../index';
-import {InitializerIdentifier} from '../initializers';
-import {DataFormat, PaddingMode} from '../keras_format/common';
-import {convertPythonicToTs, convertTsToPythonic} from '../utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndGPU, expectTensorsClose} from '../utils/test_utils';
-
-import {depthwiseConv2d} from './convolutional_depthwise';
-
-describeMathCPUAndGPU('depthwiseConv2d', () => {
-  const x4by4Data = [[[
-    [10, 30, 50, 70], [20, 40, 60, 80], [-10, -30, -50, -70],
-    [-20, -40, -60, -80]
-  ]]];
-
-  const dataFormats: DataFormat[] =
-      [undefined, 'channelsFirst', 'channelsLast'];
-  const paddingModes: PaddingMode[] = [undefined, 'same', 'valid'];
-  const stridesArray = [1, 2];
-  const depthMultipliers = [1, 2];
-
-  for (const dataFormat of dataFormats) {
-    for (const paddingMode of paddingModes) {
-      for (const stride of stridesArray) {
-        for (const depthMultiplier of depthMultipliers) {
-          const testTitle = `stride=${stride}, ${paddingMode}, ` +
-              `${dataFormat}, depthMultiplier=${depthMultiplier}`;
-          it(testTitle, () => {
-            let x: Tensor = tensor4d(x4by4Data, [1, 1, 4, 4]);
-            if (dataFormat !== 'channelsFirst') {
-              x = tfc.transpose(x, [0, 2, 3, 1]);  // NCHW -> NHWC.
-            }
-
-            let kernel: Tensor4D;
-            if (depthMultiplier === 1) {
-              kernel = tensor4d([1, 0, 0, -1], [2, 2, 1, 1]);
-            } else if (depthMultiplier === 2) {
-              // Two kernels of the same absolute values but opposite signs:
-              //   [[1, 0], [0, -1]] and [[-1, 0], [0, 1]].
-              kernel = tensor4d([1, -1, 0, 0, 0, 0, -1, 1], [2, 2, 1, 2]);
-            }
-            const y = depthwiseConv2d(
-                x, kernel, [stride, stride], 'valid', dataFormat);
-
-            let yExpected: Tensor;
-            if (stride === 1) {
-              if (depthMultiplier === 1) {
-                yExpected = tensor4d(
-                    [[[[-30, -30, -30], [50, 90, 130], [30, 30, 30]]]],
-                    [1, 1, 3, 3]);
-              } else if (depthMultiplier === 2) {
-                yExpected = tensor4d(
-                    [[
-                      [[-30, -30, -30], [50, 90, 130], [30, 30, 30]],
-                      [[30, 30, 30], [-50, -90, -130], [-30, -30, -30]]
-                    ]],
-                    [1, 2, 3, 3]);
-              }
-            } else if (stride === 2) {
-              if (depthMultiplier === 1) {
-                yExpected = tensor4d([[[[-30, -30], [30, 30]]]], [1, 1, 2, 2]);
-              } else if (depthMultiplier === 2) {
-                yExpected = tensor4d(
-                    [[[[-30, -30], [30, 30]], [[30, 30], [-30, -30]]]],
-                    [1, 2, 2, 2]);
-              }
-            }
-            if (dataFormat !== 'channelsFirst') {
-              yExpected = tfc.transpose(yExpected, [0, 2, 3, 1]);
-            }
-            expectTensorsClose(y, yExpected);
-          });
-        }
-      }
-    }
-  }
-
-  it('Non-4D kernel leads to exception', () => {
-    const x = tfc.zeros([1, 1, 4, 4]);
-    expect(() => depthwiseConv2d(x, tfc.zeros([1, 2, 2]), [1, 1]))
-        .toThrowError(/.* is required to be 4-D, but is instead 3-D/);
-  });
-});
-
-describeMathCPU('DepthwiseConv2D-Symbolic', () => {
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const kernelSizes: [number, [number, number]] = [2, [2, 2]];
-  const depthMultipliers = [1, 3];
-  const paddingModes: PaddingMode[] = ['valid', 'same'];
-
-  for (const dataFormat of dataFormats) {
-    for (const kernelSize of kernelSizes) {
-      for (const depthMultiplier of depthMultipliers) {
-        for (const padding of paddingModes) {
-          const testTitle = `dataFormat=${dataFormat}, ` +
-              `kernelSize=${JSON.stringify(kernelSize)}, ` +
-              `depthMultiplier=${depthMultiplier}, ` +
-              `paddingMode=${padding}`;
-          it(testTitle, () => {
-            const depthwiseConvLayer = tfl.layers.depthwiseConv2d(
-                {dataFormat, kernelSize, depthMultiplier, padding});
-            const inputShape = dataFormat === 'channelsFirst' ? [1, 8, 10, 10] :
-                                                                [1, 10, 10, 8];
-            const symbolicInput =
-                new tfl.SymbolicTensor('float32', inputShape, null, [], null);
-            const symbolicOutput =
-                depthwiseConvLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-
-            const outputImageSize = padding === 'valid' ? 9 : 10;
-            let expectedShape: [number, number, number, number];
-            if (dataFormat === 'channelsFirst') {
-              expectedShape =
-                  [1, 8 * depthMultiplier, outputImageSize, outputImageSize];
-            } else {
-              expectedShape =
-                  [1, outputImageSize, outputImageSize, 8 * depthMultiplier];
-            }
-            expect(symbolicOutput.shape).toEqual(expectedShape);
-          });
-        }
-      }
-    }
-  }
-
-  it('Non-4D Array Input leads to exception', () => {
-    const depthwiseConvLayer = tfl.layers.depthwiseConv2d({kernelSize: 2});
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [1, 10, 10], null, [], null);
-    expect(() => depthwiseConvLayer.apply(symbolicInput))
-        .toThrowError(
-            /Inputs to DepthwiseConv2D should have rank 4\. Received .*/);
-  });
-
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.depthwiseConv2d(
-        {kernelSize: 3, depthMultiplier: 4, activation: 'relu'});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.depthwiseConv2d(tsConfig);
-    const configPrime = layerPrime.getConfig();
-    expect(configPrime.kernelSize).toEqual([3, 3]);
-    expect(configPrime.depthMultiplier).toEqual(4);
-    expect(configPrime.activation).toEqual('relu');
-  });
-});
-
-describeMathCPUAndGPU('DepthwiseConv2D-Tensor:', () => {
-  const x4by4Data = [[[
-    [10, 30, 50, 70], [20, 40, 60, 80], [-10, -30, -50, -70],
-    [-20, -40, -60, -80]
-  ]]];
-
-  const depthMultipliers = [1, 2];
-  const useBiases = [false];
-  const biasInitializers: InitializerIdentifier[] = ['zeros', 'ones'];
-
-  for (const depthMultiplier of depthMultipliers) {
-    for (const useBias of useBiases) {
-      for (const biasInitializer of biasInitializers) {
-        const testTitle =
-            `channelsFirst, depthMultiplier=${depthMultiplier}, ` +
-            `useBias=${useBias}, biasInitializer=${biasInitializer}, ` +
-            `activation=relu`;
-        it(testTitle, () => {
-          const x = tensor4d(x4by4Data, [1, 1, 4, 4]);
-          const conv2dLayer = tfl.layers.depthwiseConv2d({
-            kernelSize: [2, 2],
-            depthMultiplier,
-            strides: [2, 2],
-            dataFormat: 'channelsFirst',
-            useBias,
-            depthwiseInitializer: 'ones',
-            biasInitializer,
-            activation: 'relu'
-          });
-          const y = conv2dLayer.apply(x) as Tensor;
-
-          let yExpectedShape: [number, number, number, number];
-          let yExpectedData: number[];
-          if (depthMultiplier === 1) {
-            yExpectedShape = [1, 1, 2, 2];
-            yExpectedData = [100, 260, -100, -260];
-          } else if (depthMultiplier === 2) {
-            yExpectedShape = [1, 2, 2, 2];
-            yExpectedData = [100, 260, -100, -260, 100, 260, -100, -260];
-          }
-          if (useBias && biasInitializer === 'ones') {
-            yExpectedData = yExpectedData.map(element => element + 1);
-          }
-          // relu.
-          yExpectedData =
-              yExpectedData.map(element => element >= 0 ? element : 0);
-          const yExpected = tensor4d(yExpectedData, yExpectedShape);
-          expectTensorsClose(y, yExpected);
-        });
-      }
-    }
-  }
-
-  it('channelsLast', () => {
-    // Convert input to channelsLast.
-    const x = tfc.transpose(tensor4d(x4by4Data, [1, 1, 4, 4]), [0, 2, 3, 1]);
-    const conv2dLayer = tfl.layers.depthwiseConv2d({
-      depthMultiplier: 2,
-      kernelSize: [2, 2],
-      strides: [2, 2],
-      dataFormat: 'channelsLast',
-      useBias: false,
-      depthwiseInitializer: 'ones',
-      activation: 'linear'
-    });
-    const y = conv2dLayer.apply(x) as Tensor;
-    const yExpected =
-        tensor4d([100, 100, 260, 260, -100, -100, -260, -260], [1, 2, 2, 2]);
-    expectTensorsClose(y, yExpected);
-  });
-
-  it('missing config.kernelSize throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect((filters: 1) => tfl.layers.depthwiseConv2d({} as any))
-        .toThrowError(/kernelSize/);
-  });
-  it('bad config.kernelSize throws exception', () => {
-    expect(
-        // tslint:disable-next-line:no-any
-        () => tfl.layers.depthwiseConv2d({kernelSize: [1]} as any))
-        .toThrowError(/kernelSize/);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/convolutional_recurrent.ts b/tfjs-master/tfjs-layers/src/layers/convolutional_recurrent.ts
deleted file mode 100644
index fb3406acd..000000000
--- a/tfjs-master/tfjs-layers/src/layers/convolutional_recurrent.ts
+++ /dev/null
@@ -1,580 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {Tensor, util} from '@tensorflow/tfjs-core';
-
-import {Activation} from '../activations';
-import * as K from '../backend/tfjs_backend';
-import {checkDataFormat, checkPaddingMode} from '../common';
-import {Constraint} from '../constraints';
-import {InputSpec} from '../engine/topology';
-import {AttributeError, NotImplementedError, ValueError} from '../errors';
-import {Initializer} from '../initializers';
-import {DataFormat, DataType, PaddingMode, Shape} from '../keras_format/common';
-import {Regularizer} from '../regularizers';
-import {Kwargs} from '../types';
-import {convOutputLength, normalizeArray} from '../utils/conv_utils';
-import {assertPositiveInteger} from '../utils/generic_utils';
-import {getExactlyOneShape} from '../utils/types_utils';
-
-import {BaseRNNLayerArgs, generateDropoutMask, LSTMCell, LSTMCellLayerArgs, LSTMLayerArgs, RNN, RNNCell, RNNLayerArgs, SimpleRNNCellLayerArgs} from './recurrent';
-
-declare interface ConvRNN2DCellArgs extends
-    Omit<SimpleRNNCellLayerArgs, 'units'> {
-  /**
-   * The dimensionality of the output space (i.e. the number of filters in the
-   * convolution).
-   */
-  filters: number;
-
-  /**
-   * The dimensions of the convolution window. If kernelSize is a number, the
-   * convolutional window will be square.
-   */
-  kernelSize: number|number[];
-
-  /**
-   * The strides of the convolution in each dimension. If strides is a number,
-   * strides in both dimensions are equal.
-   *
-   * Specifying any stride value != 1 is incompatible with specifying any
-   * `dilationRate` value != 1.
-   */
-  strides?: number|number[];
-
-  /**
-   * Padding mode.
-   */
-  padding?: PaddingMode;
-
-  /**
-   * Format of the data, which determines the ordering of the dimensions in
-   * the inputs.
-   *
-   * `channels_last` corresponds to inputs with shape
-   *   `(batch, ..., channels)`
-   *
-   *  `channels_first` corresponds to inputs with shape `(batch, channels,
-   * ...)`.
-   *
-   * Defaults to `channels_last`.
-   */
-  dataFormat?: DataFormat;
-
-  /**
-   * The dilation rate to use for the dilated convolution in each dimension.
-   * Should be an integer or array of two or three integers.
-   *
-   * Currently, specifying any `dilationRate` value != 1 is incompatible with
-   * specifying any `strides` value != 1.
-   */
-  dilationRate?: number|[number]|[number, number];
-}
-
-abstract class ConvRNN2DCell extends RNNCell {
-  readonly filters: number;
-  readonly kernelSize: number[];
-  readonly strides: number[];
-  readonly padding: PaddingMode;
-  readonly dataFormat: DataFormat;
-  readonly dilationRate: number[];
-
-  readonly activation: Activation;
-  readonly useBias: boolean;
-
-  readonly kernelInitializer: Initializer;
-  readonly recurrentInitializer: Initializer;
-  readonly biasInitializer: Initializer;
-
-  readonly kernelConstraint: Constraint;
-  readonly recurrentConstraint: Constraint;
-  readonly biasConstraint: Constraint;
-
-  readonly kernelRegularizer: Regularizer;
-  readonly recurrentRegularizer: Regularizer;
-  readonly biasRegularizer: Regularizer;
-
-  readonly dropout: number;
-  readonly recurrentDropout: number;
-}
-
-declare interface ConvRNN2DLayerArgs extends BaseRNNLayerArgs,
-                                             ConvRNN2DCellArgs {}
-
-/**
- * Base class for convolutional-recurrent layers.
- */
-class ConvRNN2D extends RNN {
-  /** @nocollapse */
-  static override className = 'ConvRNN2D';
-
-  declare readonly cell: ConvRNN2DCell;
-
-  constructor(args: ConvRNN2DLayerArgs) {
-    if (args.unroll) {
-      throw new NotImplementedError(
-          'Unrolling is not possible with convolutional RNNs.');
-    }
-
-    if (Array.isArray(args.cell)) {
-      throw new NotImplementedError(
-          'It is not possible at the moment to stack convolutional cells.');
-    }
-
-    super(args as RNNLayerArgs);
-
-    this.inputSpec = [new InputSpec({ndim: 5})];
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tfc.tidy(() => {
-      if (this.cell.dropoutMask != null) {
-        tfc.dispose(this.cell.dropoutMask);
-
-        this.cell.dropoutMask = null;
-      }
-
-      if (this.cell.recurrentDropoutMask != null) {
-        tfc.dispose(this.cell.recurrentDropoutMask);
-
-        this.cell.recurrentDropoutMask = null;
-      }
-
-      if (kwargs && kwargs['constants']) {
-        throw new ValueError('ConvRNN2D cell does not support constants');
-      }
-
-      const mask = kwargs == null ? null : kwargs['mask'];
-
-      const training = kwargs == null ? null : kwargs['training'];
-
-      const initialState: Tensor[] =
-          kwargs == null ? null : kwargs['initialState'];
-
-      return super.call(inputs, {mask, training, initialState});
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape): Shape|Shape[] {
-    let outShape: Shape = this.computeSingleOutputShape(inputShape);
-
-    if (!this.returnSequences) {
-      outShape = [outShape[0], ...outShape.slice(2)];
-    }
-
-    if (this.returnState) {
-      outShape =
-          [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];
-    }
-
-    return outShape;
-  }
-
-  override getInitialState(inputs: tfc.Tensor): tfc.Tensor[] {
-    return tfc.tidy(() => {
-      const {stateSize} = this.cell;
-
-      const inputShape = inputs.shape;
-
-      const outputShape = this.computeSingleOutputShape(inputShape);
-
-      const stateShape = [outputShape[0], ...outputShape.slice(2)];
-
-      const initialState = tfc.zeros(stateShape);
-
-      if (Array.isArray(stateSize)) {
-        return Array(stateSize.length).fill(initialState);
-      }
-
-      return [initialState];
-    });
-  }
-
-  override resetStates(states?: Tensor|Tensor[], training = false): void {
-    tfc.tidy(() => {
-      if (!this.stateful) {
-        throw new AttributeError(
-            'Cannot call resetStates() on an RNN Layer that is not stateful.');
-      }
-
-      const inputShape = this.inputSpec[0].shape;
-
-      const outputShape = this.computeSingleOutputShape(inputShape);
-
-      const stateShape = [outputShape[0], ...outputShape.slice(2)];
-
-      const batchSize = inputShape[0];
-
-      if (batchSize == null) {
-        throw new ValueError(
-            'If an RNN is stateful, it needs to know its batch size. Specify ' +
-            'the batch size of your input tensors: \n' +
-            '- If using a Sequential model, specify the batch size by ' +
-            'passing a `batchInputShape` option to your first layer.\n' +
-            '- If using the functional API, specify the batch size by ' +
-            'passing a `batchShape` option to your Input layer.');
-      }
-
-      // Initialize state if null.
-      if (this.getStates() == null) {
-        if (Array.isArray(this.cell.stateSize)) {
-          this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));
-        } else {
-          this.states_ = [tfc.zeros(stateShape)];
-        }
-      } else if (states == null) {
-        // Dispose old state tensors.
-        tfc.dispose(this.states_);
-
-        // For stateful RNNs, fully dispose kept old states.
-        if (this.keptStates != null) {
-          tfc.dispose(this.keptStates);
-          this.keptStates = [];
-        }
-
-        if (Array.isArray(this.cell.stateSize)) {
-          this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));
-        } else {
-          this.states_[0] = tfc.zeros(stateShape);
-        }
-      } else {
-        if (!Array.isArray(states)) {
-          states = [states];
-        }
-
-        if (states.length !== this.states_.length) {
-          throw new ValueError(
-              `Layer ${this.name} expects ${this.states_.length} state(s), ` +
-              `but it received ${states.length} state value(s). Input ` +
-              `received: ${states}`);
-        }
-
-        if (training) {
-          // Store old state tensors for complete disposal later, i.e., during
-          // the next no-arg call to this method. We do not dispose the old
-          // states immediately because that BPTT (among other things) require
-          // them.
-          this.keptStates.push(this.states_.slice());
-        } else {
-          tfc.dispose(this.states_);
-        }
-
-        for (let index = 0; index < this.states_.length; ++index) {
-          const value = states[index];
-
-          const expectedShape = stateShape;
-
-          if (!util.arraysEqual(value.shape, expectedShape)) {
-            throw new ValueError(
-                `State ${index} is incompatible with layer ${this.name}: ` +
-                `expected shape=${expectedShape}, received shape=${
-                    value.shape}`);
-          }
-
-          this.states_[index] = value;
-        }
-      }
-
-      this.states_ = this.states_.map(state => tfc.keep(state.clone()));
-    });
-  }
-
-  protected computeSingleOutputShape(inputShape: Shape): Shape {
-    const {dataFormat, filters, kernelSize, padding, strides, dilationRate} =
-        this.cell;
-
-    const isChannelsFirst = dataFormat === 'channelsFirst';
-
-    const h = inputShape[isChannelsFirst ? 3 : 2];
-    const w = inputShape[isChannelsFirst ? 4 : 3];
-
-    const hOut = convOutputLength(
-        h, kernelSize[0], padding, strides[0], dilationRate[0]);
-    const wOut = convOutputLength(
-        w, kernelSize[1], padding, strides[1], dilationRate[1]);
-
-    const outShape: Shape = [
-      ...inputShape.slice(0, 2),
-      ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])
-    ];
-
-    return outShape;
-  }
-}
-
-export declare interface ConvLSTM2DCellArgs extends
-    Omit<LSTMCellLayerArgs, 'units'>, ConvRNN2DCellArgs {}
-
-export class ConvLSTM2DCell extends LSTMCell implements ConvRNN2DCell {
-  /** @nocollapse */
-  static override className = 'ConvLSTM2DCell';
-
-  readonly filters: number;
-  readonly kernelSize: number[];
-  readonly strides: number[];
-  readonly padding: PaddingMode;
-  readonly dataFormat: DataFormat;
-  readonly dilationRate: number[];
-
-  constructor(args: ConvLSTM2DCellArgs) {
-    const {
-      filters,
-      kernelSize,
-      strides,
-      padding,
-      dataFormat,
-      dilationRate,
-    } = args;
-
-    super({...args, units: filters});
-
-    this.filters = filters;
-    assertPositiveInteger(this.filters, 'filters');
-
-    this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');
-    this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));
-
-    this.strides = normalizeArray(strides || 1, 2, 'strides');
-    this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));
-
-    this.padding = padding || 'valid';
-    checkPaddingMode(this.padding);
-
-    this.dataFormat = dataFormat || 'channelsLast';
-    checkDataFormat(this.dataFormat);
-
-    this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');
-    this.dilationRate.forEach(
-        rate => assertPositiveInteger(rate, 'dilationRate'));
-  }
-
-  public override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-
-    const channelAxis =
-        this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;
-
-    if (inputShape[channelAxis] == null) {
-      throw new ValueError(
-          `The channel dimension of the input should be defined. ` +
-          `Found ${inputShape[channelAxis]}`);
-    }
-
-    const inputDim = inputShape[channelAxis];
-
-    const numOfKernels = 4;
-
-    const kernelShape =
-        this.kernelSize.concat([inputDim, this.filters * numOfKernels]);
-
-    this.kernel = this.addWeight(
-        'kernel', kernelShape, null, this.kernelInitializer,
-        this.kernelRegularizer, true, this.kernelConstraint);
-
-    const recurrentKernelShape =
-        this.kernelSize.concat([this.filters, this.filters * numOfKernels]);
-
-    this.recurrentKernel = this.addWeight(
-        'recurrent_kernel', recurrentKernelShape, null,
-        this.recurrentInitializer, this.recurrentRegularizer, true,
-        this.recurrentConstraint);
-
-    if (this.useBias) {
-      let biasInitializer: Initializer;
-
-      if (this.unitForgetBias) {
-        const init = this.biasInitializer;
-
-        const filters = this.filters;
-
-        biasInitializer = new (class CustomInit extends Initializer {
-          /** @nocollapse */
-          static className = 'CustomInit';
-
-          apply(shape: Shape, dtype?: DataType): tfc.Tensor {
-            const biasI = init.apply([filters]);
-            const biasF = tfc.ones([filters]);
-            const biasCAndO = init.apply([filters * 2]);
-            return K.concatenate([biasI, biasF, biasCAndO]);
-          }
-        })();
-      } else {
-        biasInitializer = this.biasInitializer;
-      }
-
-      this.bias = this.addWeight(
-          'bias', [this.filters * numOfKernels], null, biasInitializer,
-          this.biasRegularizer, true, this.biasConstraint);
-    }
-
-    this.built = true;
-  }
-
-  override call(inputs: tfc.Tensor[], kwargs: Kwargs): tfc.Tensor[] {
-    return tfc.tidy(() => {
-      if (inputs.length !== 3) {
-        throw new ValueError(
-            `ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +
-            `${inputs.length}.`);
-      }
-
-      const training = kwargs['training'] || false;
-
-      const x = inputs[0];         // Current input
-      const hTMinus1 = inputs[1];  // Previous memory state.
-      const cTMinus1 = inputs[2];  // Previous carry state.
-
-      const numOfKernels = 4;
-
-      type DropoutMasks = [tfc.Tensor, tfc.Tensor, tfc.Tensor, tfc.Tensor];
-
-      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
-        this.dropoutMask = generateDropoutMask({
-                             ones: () => tfc.onesLike(x),
-                             rate: this.dropout,
-                             training,
-                             count: numOfKernels,
-                             dropoutFunc: this.dropoutFunc
-                           }) as tfc.Tensor[];
-      }
-
-      const dropoutMask = this.dropoutMask as DropoutMasks;
-
-      const applyDropout =
-          (x: tfc.Tensor, mask: tfc.Tensor[], index: number) => {
-            if (!mask || !mask[index]) {
-              return x;
-            }
-
-            return tfc.mul(mask[index], x);
-          };
-
-      let xI = applyDropout(x, dropoutMask, 0);
-      let xF = applyDropout(x, dropoutMask, 1);
-      let xC = applyDropout(x, dropoutMask, 2);
-      let xO = applyDropout(x, dropoutMask, 3);
-
-      if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&
-          this.recurrentDropoutMask == null) {
-        this.recurrentDropoutMask = generateDropoutMask({
-                                      ones: () => tfc.onesLike(hTMinus1),
-                                      rate: this.recurrentDropout,
-                                      training,
-                                      count: numOfKernels,
-                                      dropoutFunc: this.dropoutFunc
-                                    }) as tfc.Tensor[];
-      }
-
-      const recDropoutMask = this.recurrentDropoutMask as DropoutMasks;
-
-      let hI = applyDropout(hTMinus1, recDropoutMask, 0);
-      let hF = applyDropout(hTMinus1, recDropoutMask, 1);
-      let hC = applyDropout(hTMinus1, recDropoutMask, 2);
-      let hO = applyDropout(hTMinus1, recDropoutMask, 3);
-
-      const kernelChannelAxis = 3;
-
-      const [kernelI, kernelF, kernelC, kernelO]: tfc.Tensor[] =
-          tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);
-
-      const [biasI, biasF, biasC, biasO]: tfc.Tensor[] = this.useBias ?
-          tfc.split(this.bias.read(), numOfKernels) :
-          [null, null, null, null];
-
-      xI = this.inputConv(xI, kernelI, biasI, this.padding);
-      xF = this.inputConv(xF, kernelF, biasF, this.padding);
-      xC = this.inputConv(xC, kernelC, biasC, this.padding);
-      xO = this.inputConv(xO, kernelO, biasO, this.padding);
-
-      const [recKernelI, recKernelF, recKernelC, recKernelO]: tfc.Tensor[] =
-          tfc.split(
-              this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);
-
-      hI = this.recurrentConv(hI, recKernelI);
-      hF = this.recurrentConv(hF, recKernelF);
-      hC = this.recurrentConv(hC, recKernelC);
-      hO = this.recurrentConv(hO, recKernelO);
-
-      const i = this.recurrentActivation.apply(tfc.add(xI, hI));
-      const f = this.recurrentActivation.apply(tfc.add(xF, hF));
-      const c = tfc.add(
-          tfc.mul(f, cTMinus1),
-          tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));
-      const h = tfc.mul(
-          this.recurrentActivation.apply(tfc.add(xO, hO)),
-          this.activation.apply(c));
-
-      return [h, h, c];
-    });
-  }
-
-  override getConfig(): tfc.serialization.ConfigDict {
-    const {'units': _, ...baseConfig} = super.getConfig();
-
-    const config: tfc.serialization.ConfigDict = {
-      filters: this.filters,
-      kernelSize: this.kernelSize,
-      padding: this.padding,
-      dataFormat: this.dataFormat,
-      dilationRate: this.dilationRate,
-      strides: this.strides,
-    };
-
-    return {...baseConfig, ...config};
-  }
-
-  inputConv(x: Tensor, w: Tensor, b?: Tensor, padding?: PaddingMode) {
-    const out = tfc.conv2d(
-        x as tfc.Tensor3D, w as tfc.Tensor4D, this.strides as [number, number],
-        (padding || 'valid') as 'same' | 'valid',
-        this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC',
-        this.dilationRate as [number, number]);
-
-    if (b) {
-      return K.biasAdd(out, b, this.dataFormat) as tfc.Tensor3D;
-    }
-
-    return out;
-  }
-
-  recurrentConv(x: Tensor, w: Tensor) {
-    const strides = 1;
-
-    return tfc.conv2d(
-        x as tfc.Tensor3D, w as tfc.Tensor4D, strides, 'same',
-        this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');
-  }
-}
-
-tfc.serialization.registerClass(ConvLSTM2DCell);
-
-export declare interface ConvLSTM2DArgs extends
-    Omit<LSTMLayerArgs, 'units'|'cell'>, ConvRNN2DLayerArgs {}
-
-export class ConvLSTM2D extends ConvRNN2D {
-  /** @nocollapse */
-  static override className = 'ConvLSTM2D';
-
-  constructor(args: ConvLSTM2DArgs) {
-    const cell = new ConvLSTM2DCell(args);
-
-    super({...args, cell} as ConvRNN2DLayerArgs);
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends tfc.serialization.Serializable>(
-      cls: tfc.serialization.SerializableConstructor<T>,
-      config: tfc.serialization.ConfigDict): T {
-    return new cls(config);
-  }
-}
-
-tfc.serialization.registerClass(ConvLSTM2D);
diff --git a/tfjs-master/tfjs-layers/src/layers/convolutional_recurrent_test.ts b/tfjs-master/tfjs-layers/src/layers/convolutional_recurrent_test.ts
deleted file mode 100644
index f80739e5c..000000000
--- a/tfjs-master/tfjs-layers/src/layers/convolutional_recurrent_test.ts
+++ /dev/null
@@ -1,891 +0,0 @@
-
-/**
- * @license
- * Copyright 2020 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {Tensor} from '@tensorflow/tfjs-core';
-
-import {serializeActivation, Tanh} from '../activations';
-import {NonNeg, serializeConstraint, UnitNorm} from '../constraints';
-import {sequential} from '../exports';
-import * as tfl from '../index';
-import {GlorotUniform, HeUniform, Ones, serializeInitializer} from '../initializers';
-import {DataFormat, PaddingMode} from '../keras_format/common';
-import {modelFromJSON} from '../models';
-import {L1L2, serializeRegularizer} from '../regularizers';
-import {getCartesianProductOfValues} from '../utils/generic_utils';
-import {convertPythonicToTs, convertTsToPythonic} from '../utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndGPU, describeMathCPUAndWebGL2, expectTensorsClose} from '../utils/test_utils';
-
-import {ConvLSTM2DArgs, ConvLSTM2DCellArgs} from './convolutional_recurrent';
-
-describeMathCPUAndGPU('ConvLSTM2DCell', () => {
-  /**
-   * The tensor values (output, h, c) can be obtained by the following Python
-   * code
-   *
-   * sequence_len = 1
-   * data_size = 8
-   * data_channel = 3
-   *
-   * data_format = "channels_first"
-   * filters = 9
-   * kernel_size = 5
-   * padding = "same"
-   *
-   * inputs = np.ones([1, sequence_len, data_channel, data_size, data_size])
-   *
-   * x = keras.Input(batch_shape=inputs.shape)
-   *
-   * kwargs = {'data_format': data_format,
-   *           'return_state': True,
-   *           'filters': filters,
-   *           'kernel_size': kernel_size,
-   *           'padding': padding}
-   *
-   * layer = keras.layers.ConvLSTM2D(kernel_initializer='ones',
-   * bias_initializer="ones", recurrent_initializer='ones', **kwargs)
-   * layer.build(inputs.shape)
-   *
-   * outputs = layer(x)
-   *
-   * model = keras.models.Model(x, outputs)
-   *
-   * y = model.predict(inputs)
-   *
-   * y[0].mean(), y[1].mean(), y[2].mean()
-   */
-  describe('should return the correct outputs', () => {
-    const sequenceLength = 1;
-    const inputSize = 8;
-    const channels = 3;
-
-    const dataFormatValues: DataFormat[] = ['channelsFirst', 'channelsLast'];
-    const filterValues = [3, 5, 9];
-    const kernelSizeValues = [3, 5];
-    const paddingValues: PaddingMode[] = ['valid', 'same'];
-
-    const testArgs = getCartesianProductOfValues(
-                         dataFormatValues,
-                         filterValues,
-                         kernelSizeValues,
-                         paddingValues,
-                         ) as Array<[DataFormat, number, number, PaddingMode]>;
-
-    for (const [dataFormat, filters, kernelSize, padding] of testArgs) {
-      const testTitle = `for dataFormat=${dataFormat}, filters=${
-          filters}, kernelSize=${kernelSize}, padding=${padding}`;
-
-      it(testTitle, () => {
-        const inputShape = dataFormat === 'channelsFirst' ?
-            [sequenceLength, channels, inputSize, inputSize] :
-            [sequenceLength, inputSize, inputSize, channels];
-
-        const x = tfc.ones(inputShape);
-
-        const cell = tfl.layers.convLstm2dCell({
-          dataFormat,
-          filters,
-          kernelSize,
-          padding,
-          kernelInitializer: 'ones',
-          recurrentInitializer: 'ones',
-          biasInitializer: 'ones',
-        });
-
-        cell.build(x.shape);
-
-        const outputSize =
-            padding === 'same' ? inputSize : (inputSize - kernelSize + 1);
-
-        const outShape = dataFormat === 'channelsFirst' ?
-            [sequenceLength, filters, outputSize, outputSize] :
-            [sequenceLength, outputSize, outputSize, filters];
-
-        const initialH = tfc.zeros(outShape);
-
-        const initialC = tfc.zeros(outShape);
-
-        const [o, h, c] = cell.call([x, initialH, initialC], {});
-
-        expect(o.shape).toEqual(outShape);
-        expect(h.shape).toEqual(outShape);
-        expect(c.shape).toEqual(outShape);
-
-        expectTensorsClose(o.mean().flatten(), tfc.tensor1d([0.7615942]));
-        expectTensorsClose(h.mean().flatten(), tfc.tensor1d([0.7615942]));
-        expectTensorsClose(c.mean().flatten(), tfc.tensor1d([1]));
-      });
-    }
-  });
-});
-
-describeMathCPU('ConvLSTM2D Symbolic', () => {
-  const dtype = 'float32';
-
-  const batchSize = 8;
-  const sequenceLength = 10;
-  const inputSize = 8;
-  const channels = 3;
-
-  const inputShape = [
-    batchSize,
-    sequenceLength,
-    inputSize,
-    inputSize,
-    channels,
-  ];
-
-  const filters = 5;
-  const kernelSize = [3, 3];
-
-  const outputSize = inputSize - kernelSize[0] + 1;
-
-  describe('should return the correct output shape', () => {
-    it('for returnSequences=false, returnState=false', () => {
-      const input =
-          new tfl.SymbolicTensor('float32', inputShape, null, [], null);
-      const convLstm = tfl.layers.convLstm2d({filters, kernelSize});
-      const output = convLstm.apply(input) as tfl.SymbolicTensor;
-      expect(output.shape).toEqual([8, 6, 6, 5]);
-    });
-
-    it('for returnSequences=false, returnState=true', () => {
-      const returnState = true;
-
-      const input = new tfl.SymbolicTensor(dtype, inputShape, null, [], null);
-
-      const convLstm =
-          tfl.layers.convLstm2d({filters, kernelSize, returnState});
-
-      const output = convLstm.apply(input) as tfl.SymbolicTensor[];
-
-      const outputShape = [batchSize, outputSize, outputSize, filters];
-
-      expect(output.length).toEqual(3);
-      expect(output[0].shape).toEqual(outputShape);
-      expect(output[1].shape).toEqual(outputShape);
-      expect(output[2].shape).toEqual(outputShape);
-    });
-
-    it('for returnSequences=true, returnState=false', () => {
-      const returnSequences = true;
-
-      const input = new tfl.SymbolicTensor(dtype, inputShape, null, [], null);
-
-      const convLstm =
-          tfl.layers.convLstm2d({filters, kernelSize, returnSequences});
-
-      const output = convLstm.apply(input) as tfl.SymbolicTensor;
-
-      const outputShape =
-          [batchSize, sequenceLength, outputSize, outputSize, filters];
-
-      expect(output.shape).toEqual(outputShape);
-    });
-
-    it('for returnSequences=true, returnState=true', () => {
-      const returnSequences = true;
-      const returnState = true;
-
-      const input = new tfl.SymbolicTensor(dtype, inputShape, null, [], null);
-
-      const convLstm = tfl.layers.convLstm2d(
-          {filters, kernelSize, returnSequences, returnState});
-
-      const output = convLstm.apply(input) as tfl.SymbolicTensor[];
-
-      const outputShape =
-          [batchSize, sequenceLength, outputSize, outputSize, filters];
-
-      const stateShape = [batchSize, outputSize, outputSize, filters];
-
-      expect(output.length).toEqual(3);
-      expect(output[0].shape).toEqual(outputShape);
-      expect(output[1].shape).toEqual(stateShape);
-      expect(output[2].shape).toEqual(stateShape);
-    });
-  });
-
-  it('should contain the correct number of weights', () => {
-    const input = new tfl.SymbolicTensor(dtype, inputShape, null, [], null);
-
-    const convLstm = tfl.layers.convLstm2d(
-        {filters, kernelSize, returnSequences: true, returnState: true});
-
-    convLstm.apply(input);
-
-    expect(convLstm.trainable).toEqual(true);
-    expect(convLstm.trainableWeights.length).toEqual(3);
-    expect(convLstm.nonTrainableWeights.length).toEqual(0);
-    expect(convLstm.weights.length).toEqual(3);
-  });
-
-  describe('should build the correct layer from exported config', () => {
-    for (const implementation of [1, 2]) {
-      it(`for implementation=${implementation}`, () => {
-        const layer = tfl.layers.convLstm2d({
-          filters,
-          kernelSize,
-          inputShape: inputShape.slice(1),
-          implementation,
-          padding: 'same',
-          returnSequences: true,
-        });
-
-        const pythonicConfig = convertTsToPythonic(layer.getConfig());
-
-        const tsConfig = convertPythonicToTs(pythonicConfig);
-
-        const layerPrime =
-            tfl.layers.convLstm2d(tsConfig as unknown as ConvLSTM2DArgs);
-
-        expect(layerPrime.getConfig().filters).toEqual(filters);
-        expect(layerPrime.getConfig().kernelSize).toEqual(kernelSize);
-        expect(layerPrime.getConfig().implementation).toEqual(implementation);
-      });
-    }
-  });
-
-  describe('should return equal outputs with loaded model', () => {
-    it('for simple model', async () => {
-      const model = tfl.sequential();
-
-      const layer = tfl.layers.convLstm2d({
-        filters,
-        kernelSize,
-        padding: 'same',
-        inputShape: inputShape.slice(1),
-        returnSequences: true,
-      });
-
-      model.add(layer);
-
-      const x = tfc.randomNormal(inputShape);
-
-      const y = model.predict(x) as tfc.Tensor;
-
-      let savedArtifacts: tfc.io.ModelArtifacts;
-
-      await model.save(tfc.io.withSaveHandler(async (artifacts) => {
-        savedArtifacts = artifacts;
-        return null;
-      }));
-
-      const loadedModel =
-          await tfl.loadLayersModel(tfc.io.fromMemory(savedArtifacts));
-
-      const yPrime = loadedModel.predict(x) as tfc.Tensor;
-
-      expect(model.inputs[0].shape).toEqual(loadedModel.inputs[0].shape);
-      expect(model.outputs[0].shape).toEqual(loadedModel.outputs[0].shape);
-      expectTensorsClose(yPrime, y);
-    });
-
-    it('for more complex model', async () => {
-      const model = tfl.sequential();
-      model.add(tfl.layers.convLstm2d({
-        filters,
-        kernelSize,
-        inputShape: inputShape.slice(1),
-      }));
-      model.add(tfl.layers.dropout({rate: 0.2}));
-      model.add(tfl.layers.flatten());
-      model.add(tfl.layers.dense({units: 256, activation: 'relu'}));
-      model.add(tfl.layers.dropout({rate: 0.3}));
-      model.add(tfl.layers.dense({units: 6, activation: 'softmax'}));
-
-      const x = tfc.randomNormal(inputShape);
-
-      const y = model.predict(x) as tfc.Tensor;
-
-      let savedArtifacts: tfc.io.ModelArtifacts;
-
-      await model.save(tfc.io.withSaveHandler(async (artifacts) => {
-        savedArtifacts = artifacts;
-        return null;
-      }));
-
-      const loadedModel =
-          await tfl.loadLayersModel(tfc.io.fromMemory(savedArtifacts));
-
-      const yPrime = loadedModel.predict(x) as tfc.Tensor;
-
-      expect(model.inputs[0].shape).toEqual(loadedModel.inputs[0].shape);
-      expect(model.outputs[0].shape).toEqual(loadedModel.outputs[0].shape);
-      expectTensorsClose(yPrime, y);
-    });
-  });
-});
-
-describeMathCPUAndWebGL2('ConvLSTM2D Tensor', () => {
-  const filters = 5;
-  const kernelSize = 3;
-
-  const batchSize = 4;
-  const sequenceLength = 2;
-  const inputSize = 5;
-  const channels = 3;
-
-  const inputShape =
-      [batchSize, sequenceLength, inputSize, inputSize, channels];
-
-  const outputSize = inputSize - kernelSize + 1;
-
-  describe('should run as expected', () => {
-    const dropoutValues = [0.0, 0.1];
-    const recurrentDropoutValues = [0.0, 0.1];
-    const trainingValues = [true, false];
-    const implementationValues = [1, 2];
-
-    const args = getCartesianProductOfValues(
-                     dropoutValues,
-                     recurrentDropoutValues,
-                     trainingValues,
-                     implementationValues,
-                     ) as Array<[number, number, boolean, number]>;
-
-    for (const [dropout, recurrentDropout, training, implementation] of args) {
-      const testTitle = `for dropout=${dropout}, recurrentDropout=${
-          recurrentDropout},implementation=${implementation}, training=${
-          training}, implementation=${implementation}`;
-
-      it(testTitle, () => {
-        const dropoutFunc = jasmine.createSpy('dropout').and.callFake(
-            (x: Tensor, level: number, noiseShape?: number[], seed?: number) =>
-                tfc.tidy(() => tfc.dropout(x, level, noiseShape, seed)));
-
-        const convLstm = tfl.layers.convLstm2d({
-          filters,
-          kernelSize,
-          kernelInitializer: 'ones',
-          recurrentInitializer: 'ones',
-          biasInitializer: 'ones',
-          dropout,
-          recurrentDropout,
-          implementation,
-          dropoutFunc,
-        });
-
-        const input = tfc.ones(inputShape);
-
-        let dropoutCall = 0;
-        if (dropout !== 0.0 && training) {
-          dropoutCall += 4;
-        }
-        if (recurrentDropout !== 0.0 && training) {
-          dropoutCall += 4;
-        }
-
-        let numTensors = 0;
-
-        for (let i = 0; i < 2; i++) {
-          tfc.dispose(convLstm.apply(input, {training}) as tfc.Tensor);
-
-          expect(dropoutFunc).toHaveBeenCalledTimes((i + 1) * dropoutCall);
-
-          if (i === 0) {
-            numTensors = tfc.memory().numTensors;
-          } else {
-            expect(tfc.memory().numTensors).toEqual(numTensors);
-          }
-        }
-      });
-    }
-
-    it('for stateful forward');
-  });
-
-  describe('should return the correct outputs', () => {
-    const returnStateValues = [true, false];
-    const returnSequencesValues = [true, false];
-    const implementationValues = [1, 2];
-
-    const args = getCartesianProductOfValues(
-                     returnStateValues,
-                     returnSequencesValues,
-                     implementationValues,
-                     ) as Array<[boolean, boolean, number]>;
-
-    for (const [returnState, returnSequences, implementation] of args) {
-      const testTitle = `for returnState=${returnState}, returnSequences=${
-          returnSequences}, implementation=${implementation}`;
-
-      it(testTitle, () => {
-        const convLstm = tfl.layers.convLstm2d({
-          filters,
-          kernelSize,
-          returnState,
-          returnSequences,
-          implementation,
-          kernelInitializer: 'ones',
-          recurrentInitializer: 'ones',
-          biasInitializer: 'ones',
-        });
-
-        const input = tfc.ones(inputShape);
-
-        let output = convLstm.apply(input);
-
-        const expectedOutputValueAtT0 = 0.76159424;
-        const expectedOutputValueAtT1 = 0.96402746;
-        const expectedH = expectedOutputValueAtT1;
-        const expectedC = 2.0;
-
-        let expectedOutput: tfc.Tensor;
-        if (returnSequences) {
-          const outputAtT0 = tfc.mul(
-              tfc.scalar(expectedOutputValueAtT0),
-              tfc.ones([batchSize, 1, outputSize, outputSize, filters]));
-
-          const outputAtT1 = tfc.mul(
-              tfc.scalar(expectedOutputValueAtT1),
-              tfc.ones([batchSize, 1, outputSize, outputSize, filters]));
-
-          expectedOutput = tfc.concat([outputAtT0, outputAtT1], 1);
-        } else {
-          expectedOutput = tfc.mul(
-              tfc.scalar(expectedOutputValueAtT1),
-              tfc.ones([batchSize, outputSize, outputSize, filters]));
-        }
-
-        if (returnState) {
-          output = output as tfc.Tensor[];
-
-          expect(output.length).toEqual(3);
-
-          expectTensorsClose(output[0], expectedOutput);
-
-          expectTensorsClose(
-              output[1],
-              tfc.mul(
-                  tfc.scalar(expectedH),
-                  tfc.ones([batchSize, outputSize, outputSize, filters])));
-          expectTensorsClose(
-              output[2],
-              tfc.mul(
-                  tfc.scalar(expectedC),
-                  tfc.ones([batchSize, outputSize, outputSize, filters])));
-        } else {
-          output = output as tfc.Tensor;
-
-          expectTensorsClose(output, expectedOutput);
-        }
-      });
-    }
-
-    it('for nested model', () => {
-      const model = tfl.sequential();
-
-      const nestedModel = tfl.sequential();
-
-      nestedModel.add(tfl.layers.convLstm2d({
-        filters,
-        kernelSize,
-        kernelInitializer: 'ones',
-        recurrentInitializer: 'ones',
-        biasInitializer: 'ones',
-        batchInputShape: inputShape
-      }));
-
-      model.add(nestedModel);
-
-      const input = tfc.ones(inputShape);
-
-      const output = model.apply(input) as tfc.Tensor;
-
-      const expectedOutput = tfc.mul(
-          tfc.scalar(0.96402746),
-          tfc.ones([batchSize, outputSize, outputSize, filters]));
-
-      expectTensorsClose(output, expectedOutput);
-    });
-  });
-});
-
-// TODO: Add GPU test once Gather supports 5 rank tensor.
-describeMathCPU('should run BPTT correctly', () => {
-  const filters = 3;
-  const kernelSize = 3;
-  const padding = 'same';
-
-  const batchSize = 4;
-  const sequenceLength = 2;
-  const inputSize = 5;
-  const channels = 1;
-
-  const inputShape =
-      [batchSize, sequenceLength, inputSize, inputSize, channels];
-
-  /**
-   * batch_size = 4
-   * sequence_len = 2
-   * data_size = 5
-   * data_channel = 1
-   *
-   * filters = 3
-   * kernel_size = 3
-   * padding = "same"
-   *
-   * kwargs = {'filters': filters, 'kernel_size': kernel_size, 'padding':
-   * padding, 'batch_input_shape': [batch_size, sequence_len, data_size,
-   * data_size, data_channel], 'stateful': True}
-   *
-   * model = keras.Sequential()
-   *
-   * model.add(keras.layers.ConvLSTM2D(kernel_initializer='ones',
-   * bias_initializer="ones", recurrent_initializer='ones', **kwargs))
-   *
-   * model.add(keras.layers.Flatten())
-   *
-   * model.add(keras.layers.Dense(units=1, kernel_initializer='zeros',
-   * use_bias=False))
-   *
-   * model.compile(loss='mean_squared_error', optimizer='sgd')
-   *
-   * xs_1 = np.ones([batch_size, sequence_len, data_size, data_size,
-   * data_channel])
-   * xs_2 = np.zeros([batch_size, sequence_len, data_size, data_size,
-   * data_channel])
-   * xs = np.concatenate([xs_1, xs_2], 0)
-   *
-   * ys = np.array([[1], [1], [1], [1], [0], [0], [0], [0]])
-   *
-   * history = model.fit(xs, ys, batch_size=batch_size, shuffle=False, epochs=3)
-   * print(history.history)
-   */
-  it('for stateful BPTT', async () => {
-    const model = tfl.sequential();
-
-    model.add(tfl.layers.convLstm2d({
-      filters,
-      kernelSize,
-      padding,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      biasInitializer: 'ones',
-      stateful: true,
-      batchInputShape: inputShape
-    }));
-
-    model.add(tfl.layers.flatten());
-
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'zeros', useBias: false}));
-
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const input = tfc.concat([tfc.ones(inputShape), tfc.zeros(inputShape)], 0);
-
-    const output = tfc.tensor([1, 1, 1, 1, 0, 0, 0, 0]);
-
-    const history =
-        await model.fit(input, output, {batchSize, shuffle: false, epochs: 3});
-
-    expect(history.history.loss.length).toBe(3);
-    expect(history.history.loss[0]).toBeCloseTo(1.5441135168075562);
-    expect(history.history.loss[1]).toBeCloseTo(3.209195613861084);
-    expect(history.history.loss[2]).toBeCloseTo(3.7930736541748047);
-  });
-
-  /**
-   * batch_size = 4
-   * sequence_len = 2
-   * data_size = 5
-   * data_channel = 1
-   *
-   * filters = 3
-   * kernel_size = 3
-   * padding = "same"
-   *
-   * kwargs = {'filters': filters, 'kernel_size': kernel_size, 'padding':
-   * padding, 'batch_input_shape': [batch_size, sequence_len, data_size,
-   * data_size, data_channel]}
-   *
-   * model = keras.Sequential()
-   *
-   * model.add(keras.layers.ConvLSTM2D(kernel_initializer='ones',
-   * bias_initializer="ones", recurrent_initializer='ones', **kwargs))
-   *
-   * model.add(keras.layers.Flatten())
-   *
-   * model.add(keras.layers.Dense(units=1, kernel_initializer='zeros',
-   * use_bias=False))
-   *
-   * model.compile(loss='mean_squared_error', optimizer='sgd')
-   *
-   * xs_1 = np.ones([batch_size, sequence_len, data_size, data_size,
-   * data_channel])
-   * xs_2 = np.zeros([batch_size, sequence_len, data_size,
-   * data_size, data_channel])
-   * xs = np.concatenate([xs_1, xs_2], 0)
-   *
-   * ys = np.array([[1], [1], [1], [1], [0], [0], [0], [0]])
-   *
-   * history = model.fit(xs, ys, batch_size=batch_size, shuffle=False, epochs=3)
-   * print(history.history)
-   */
-  it('for normal BPTT', async () => {
-    const model = tfl.sequential();
-
-    model.add(tfl.layers.convLstm2d({
-      filters,
-      kernelSize,
-      padding,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      biasInitializer: 'ones',
-      batchInputShape: inputShape
-    }));
-
-    model.add(tfl.layers.flatten());
-
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'zeros', useBias: false}));
-
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const input = tfc.concat([tfc.ones(inputShape), tfc.zeros(inputShape)], 0);
-
-    const output = tfc.tensor([1, 1, 1, 1, 0, 0, 0, 0]);
-
-    const history =
-        await model.fit(input, output, {batchSize, shuffle: false, epochs: 3});
-
-    expect(history.history.loss.length).toBe(3);
-    expect(history.history.loss[0]).toBeCloseTo(1.367607831954956);
-    expect(history.history.loss[1]).toBeCloseTo(1.9423290491104126);
-    expect(history.history.loss[2]).toBeCloseTo(2.00433349609375);
-  });
-
-  /**
-   * batch_size = 4
-   * sequence_len = 2
-   * data_size = 5
-   * data_channel = 1
-   *
-   * filters = 3
-   * kernel_size = 3
-   * padding = "same"
-   *
-   * kwargs = {'filters': filters, 'kernel_size': kernel_size, 'padding':
-   * padding, 'batch_input_shape': [batch_size, sequence_len, data_size,
-   * data_size, data_channel]}
-   *
-   * model = keras.Sequential()
-   *
-   * model.add(keras.layers.ConvLSTM2D(kernel_initializer='ones',
-   * bias_initializer="ones", recurrent_initializer='ones', **kwargs))
-   *
-   * model.add(keras.layers.Flatten())
-   *
-   * model.add(keras.layers.Dense(units=1, kernel_initializer='zeros',
-   * use_bias=False))
-   *
-   * model.compile(loss='mean_squared_error', optimizer='sgd')
-   *
-   * xs_1 = np.ones([batch_size, sequence_len, data_size, data_size,
-   * data_channel])
-   * xs_2 = np.zeros([batch_size, sequence_len, data_size,
-   * data_size, data_channel])
-   * xs = np.concatenate([xs_1, xs_2], 0)
-   *
-   * ys = np.array([[1], [1], [1], [1], [0], [0], [0], [0]])
-   *
-   * model.fit(xs, ys, batch_size=batch_size, shuffle=False, epochs=2)
-   *
-   * history = model.fit(xs, ys, batch_size=batch_size, shuffle=False, epochs=3)
-   * print(history.history)
-   */
-  it('with no leak', async () => {
-    const model = tfl.sequential();
-
-    model.add(tfl.layers.convLstm2d({
-      filters,
-      kernelSize,
-      padding,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      biasInitializer: 'ones',
-      batchInputShape: inputShape
-    }));
-
-    model.add(tfl.layers.flatten());
-
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'zeros', useBias: false}));
-
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const input = tfc.concat([tfc.ones(inputShape), tfc.zeros(inputShape)], 0);
-
-    const output = tfc.tensor([1, 1, 1, 1, 0, 0, 0, 0]);
-
-    // Serves as burn-in call for subsequent tracking of memory leak.
-    await model.fit(input, output, {epochs: 2, batchSize, shuffle: false});
-
-    const numTensors0 = tfc.memory().numTensors;
-    const history =
-        await model.fit(input, output, {epochs: 3, batchSize, shuffle: false});
-    const numTensors1 = tfc.memory().numTensors;
-
-    // Assert no memory leak.
-    expect(numTensors1).toEqual(numTensors0);
-    expect(history.history.loss.length).toEqual(3);
-    expect(history.history.loss[0]).toBeCloseTo(2.00433349609375);
-    expect(history.history.loss[1]).toBeCloseTo(2.0098776817321777);
-    expect(history.history.loss[2]).toBeCloseTo(2.0099055767059326);
-  });
-});
-
-describeMathCPU('ConvLSTM2D Serialization and Deserialization', () => {
-  const cellConfig: ConvLSTM2DCellArgs = {
-    filters: 32,
-    kernelSize: 3,
-    dataFormat: 'channelsLast',
-    dilationRate: 2,
-    padding: 'same',
-    strides: 2,
-    activation: 'tanh',
-    recurrentActivation: 'tanh',
-    useBias: true,
-    kernelInitializer: 'glorotUniform',
-    recurrentInitializer: 'heUniform',
-    biasInitializer: 'ones',
-    kernelRegularizer: 'l1l2',
-    recurrentRegularizer: 'l1l2',
-    biasRegularizer: 'l1l2',
-    kernelConstraint: 'unitNorm',
-    recurrentConstraint: 'unitNorm',
-    biasConstraint: 'nonNeg',
-    dropout: 0.1,
-    recurrentDropout: 0.2,
-    name: 'cell_1',
-    batchSize: 12,
-    batchInputShape: [12, 8, 8],
-    inputShape: [8, 8],
-    dtype: 'int32',
-    inputDType: 'int32',
-    trainable: true,
-    implementation: 1,
-    unitForgetBias: true,
-  };
-
-  const expectedCellConfigPrime = {
-    name: 'cell_1',
-    trainable: true,
-    batchInputShape: [12, 8, 8],
-    dtype: 'int32',
-    filters: 32,
-    kernelSize: [3, 3],
-    dataFormat: 'channelsLast',
-    dilationRate: [2, 2],
-    padding: 'same',
-    strides: [2, 2],
-    activation: serializeActivation(new Tanh()),
-    recurrentActivation: serializeActivation(new Tanh()),
-    useBias: true,
-    kernelInitializer: serializeInitializer(new GlorotUniform()),
-    recurrentInitializer: serializeInitializer(new HeUniform()),
-    biasInitializer: serializeInitializer(new Ones()),
-    kernelRegularizer: serializeRegularizer(new L1L2()),
-    recurrentRegularizer: serializeRegularizer(new L1L2()),
-    biasRegularizer: serializeRegularizer(new L1L2()),
-    activityRegularizer: serializeRegularizer(null),
-    kernelConstraint: serializeConstraint(new UnitNorm({})),
-    recurrentConstraint: serializeConstraint(new UnitNorm({})),
-    biasConstraint: serializeConstraint(new NonNeg()),
-    implementation: 1,
-    unitForgetBias: true,
-  };
-
-  describe('ConvLSTM2DCell.getConfig', () => {
-    it('should return the expected values', () => {
-      const cell = tfl.layers.convLstm2dCell(cellConfig);
-
-      const {dropout, recurrentDropout, ...configPrime} = cell.getConfig();
-
-      expect(configPrime).toEqual(expectedCellConfigPrime);
-      expect(dropout).toBeCloseTo(0.1);
-      expect(recurrentDropout).toBeCloseTo(0.2);
-    });
-  });
-
-  describe('ConvLSTM2D.getConfig', () => {
-    it('should return the expected values', () => {
-      const config: ConvLSTM2DArgs = {
-        ...cellConfig,
-        name: 'layer_1',
-        ...{
-          returnSequences: true,
-          returnState: true,
-          stateful: true,
-          unroll: false,
-          goBackwards: true,
-          inputDim: 8,
-          inputLength: 8,
-        } as Omit<ConvLSTM2DArgs, keyof ConvLSTM2DCellArgs>
-      };
-
-      const cell = tfl.layers.convLstm2d(config);
-
-      const {dropout, recurrentDropout, ...configPrime} = cell.getConfig();
-
-      expect(configPrime).toEqual({
-        ...expectedCellConfigPrime,
-        name: 'layer_1',
-        returnSequences: true,
-        returnState: true,
-        stateful: true,
-        unroll: false,
-        goBackwards: true,
-      });
-      expect(dropout).toBeCloseTo(0.1);
-      expect(recurrentDropout).toBeCloseTo(0.2);
-    });
-  });
-
-  it('should return equal outputs before and after', async () => {
-    const model = sequential();
-
-    const batchSize = 8;
-    const sequenceLength = 1;
-    const inputSize = 8;
-    const channels = 3;
-
-    const filters = 5;
-    const kernelSize = 3;
-
-    const layer = tfl.layers.convLstm2d({
-      filters,
-      kernelSize,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      returnSequences: true,
-      dataFormat: 'channelsFirst',
-      inputShape: [sequenceLength, channels, inputSize, inputSize]
-    });
-
-    model.add(layer);
-
-    const x =
-        tfc.ones([batchSize, sequenceLength, channels, inputSize, inputSize]);
-
-    const y = model.predict(x) as tfc.Tensor;
-
-    const json = model.toJSON(null, false);
-
-    const modelPrime = await modelFromJSON({modelTopology: json});
-
-    const yPrime = modelPrime.predict(x) as tfc.Tensor;
-
-    expectTensorsClose(yPrime, y);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/convolutional_test.ts b/tfjs-master/tfjs-layers/src/layers/convolutional_test.ts
deleted file mode 100644
index 8d59c1781..000000000
--- a/tfjs-master/tfjs-layers/src/layers/convolutional_test.ts
+++ /dev/null
@@ -1,1758 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for convolutional.ts.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {scalar, Tensor, tensor1d, tensor3d, tensor4d, tensor5d, util} from '@tensorflow/tfjs-core';
-
-import * as tfl from '../index';
-import {InitializerIdentifier} from '../initializers';
-import {ActivationIdentifier} from '../keras_format/activation_config';
-import {DataFormat, InterpolationFormat, PaddingMode, Shape} from '../keras_format/common';
-import {describeMathCPU, describeMathCPUAndGPU, describeMathGPU, expectTensorsClose} from '../utils/test_utils';
-
-import {conv1d, conv1dWithBias, conv2d, conv2dWithBiasActivation, conv3d, conv3dWithBias} from './convolutional';
-
-describeMathCPUAndGPU('conv1dWithBias', () => {
-  const xLength4Data = [10, 20, 40, 80];
-  const kernelLength2Data = [1, -1];
-  const biasScalarData = 2.2;
-  // In the basic case, this convolves [10, 20, 40, 80] with the kernel [1, -1],
-  // producing [-10, -20, -40], and adds the bias 2.2, producing
-  // [-7.8, -17.8, -37.7].  The test is reproduced for either 1 or 2 output
-  // channels, and several reasonable data formats.
-
-  const outChannelsArray = [1, 2];
-  const dataFormats: DataFormat[] =
-      [undefined, 'channelsFirst', 'channelsLast'];
-  const paddingModes: PaddingMode[] = [undefined, 'same', 'valid'];
-  const stride = 1;
-
-  for (const outChannels of outChannelsArray) {
-    for (const dataFormat of dataFormats) {
-      for (const paddingMode of paddingModes) {
-        const testTitle = `outChannels=${outChannels}, stride=${stride}, ` +
-            `${paddingMode}, ${dataFormat}`;
-        it(testTitle, () => {
-          let x: Tensor = tensor3d(xLength4Data, [1, 4, 1]);
-          if (dataFormat === 'channelsFirst') {
-            x = tfc.transpose(x, [0, 2, 1]);  // NWC -> NCW.
-          }
-
-          let kernelData: number[] = [];
-          let biasData: number[] = [];
-          for (let i = 0; i < outChannels; ++i) {
-            kernelData = kernelData.concat(kernelLength2Data);
-            biasData = biasData.concat([biasScalarData + i]);
-          }
-          const kernel = tfc.transpose(
-              tensor3d(kernelData, [1, outChannels, 2]), [2, 0, 1]);
-          const bias = tensor1d(biasData);
-
-          const y =
-              conv1dWithBias(x, kernel, bias, stride, paddingMode, dataFormat);
-
-          let yExpectedShape: [number, number, number];
-          let yExpectedData: number[];
-          if (paddingMode === 'valid' || paddingMode === undefined) {
-            if (outChannels === 1) {
-              yExpectedShape = [1, 3, 1];
-              yExpectedData = [-7.8, -17.8, -37.8];
-            } else if (outChannels === 2) {
-              yExpectedShape = [1, 3, 2];
-              yExpectedData = [-7.8, -6.8, -17.8, -16.8, -37.8, -36.8];
-            }
-          } else if (paddingMode === 'same') {
-            if (outChannels === 1) {
-              yExpectedShape = [1, 4, 1];
-              yExpectedData = [-7.8, -17.8, -37.8, 82.2];
-            } else if (outChannels === 2) {
-              yExpectedShape = [1, 4, 2];
-              yExpectedData =
-                  [-7.8, -6.8, -17.8, -16.8, -37.8, -36.8, 82.2, 83.2];
-            }
-          }
-          expectTensorsClose(y, tensor3d(yExpectedData, yExpectedShape));
-        });
-      }
-    }
-  }
-});
-
-describeMathCPUAndGPU('conv1d', () => {
-  const xLength4Data = [10, 20, 40, 80];
-  const kernelLength2Data = [1, -1];
-
-  const outChannels = 2;
-  const dataFormat = 'channelsLast';
-  const paddingMode = 'valid';
-  const strides = [2, 1];
-  const dilations = [1, 2];
-  const expectations = [[-10, -10, -40, -40], [-30, -30, -60, -60]];
-
-  for (let i = 0; i < strides.length; ++i) {
-    const stride = strides[i];
-    const dilationRate = dilations[i];
-    const expectation = expectations[i];
-    const testTitle = `outChannels=${outChannels}, stride=${stride}, ` +
-        `${paddingMode}, dilationRate=${dilationRate}, ${dataFormat}`;
-    it(testTitle, () => {
-      const x = tensor3d(xLength4Data, [1, 4, 1]);
-      let kernelData: number[] = [];
-      for (let i = 0; i < outChannels; ++i) {
-        kernelData = kernelData.concat(kernelLength2Data);
-      }
-      const kernel =
-          tfc.transpose(tensor3d(kernelData, [1, outChannels, 2]), [2, 0, 1]);
-      const y =
-          conv1d(x, kernel, stride, paddingMode, dataFormat, dilationRate);
-      expectTensorsClose(y, tensor3d(expectation, [1, 2, 2]));
-    });
-  }
-});
-
-describeMathCPUAndGPU('conv2d', () => {
-  const x4by4Data = [[[
-    [10, 30, 50, 70], [20, 40, 60, 80], [-10, -30, -50, -70],
-    [-20, -40, -60, -80]
-  ]]];
-  const kernel2by2Data = [1, 0, 0, -1];
-
-  const dataFormats: DataFormat[] =
-      [undefined, 'channelsFirst', 'channelsLast'];
-  const paddingModes: PaddingMode[] = [undefined, 'same', 'valid'];
-  const stridesArray = [1, 2];
-
-  for (const dataFormat of dataFormats) {
-    for (const paddingMode of paddingModes) {
-      for (const stride of stridesArray) {
-        const testTitle = `stride=${stride}, ${paddingMode}, ` +
-            `${dataFormat}`;
-        it(testTitle, () => {
-          let x: Tensor = tensor4d(x4by4Data, [1, 1, 4, 4]);
-          if (dataFormat !== 'channelsFirst') {
-            x = tfc.transpose(x, [0, 2, 3, 1]);  // NCHW -> NHWC.
-          }
-          const kernel = tensor4d(kernel2by2Data, [2, 2, 1, 1]);
-          const y = conv2d(x, kernel, [stride, stride], 'valid', dataFormat);
-
-          let yExpected: Tensor;
-          if (stride === 1) {
-            yExpected = tensor4d(
-                [[[[-30, -30, -30], [50, 90, 130], [30, 30, 30]]]],
-                [1, 1, 3, 3]);
-          } else if (stride === 2) {
-            yExpected = tensor4d([[[[-30, -30], [30, 30]]]], [1, 1, 2, 2]);
-          }
-          if (dataFormat !== 'channelsFirst') {
-            yExpected = tfc.transpose(yExpected, [0, 2, 3, 1]);
-          }
-          expectTensorsClose(y, yExpected);
-        });
-      }
-    }
-  }
-
-  it('Invalid filters leads to Error', () => {
-    expect(() => tfl.layers.conv2d({filters: 2.5, kernelSize: 3}))
-        .toThrowError(/filters.*positive integer.*2\.5\.$/);
-  });
-});
-
-describeMathCPUAndGPU('conv2dWithBiasActivation', () => {
-  const x4by4Data = [[[
-    [10, 30, 50, 70], [20, 40, 60, 80], [-10, -30, -50, -70],
-    [-20, -40, -60, -80]
-  ]]];
-  const kernel2by2Data = [1, 0, 0, -1];
-  const biasScalarData = [2.2];
-
-  const outChannelsArray = [2, 3];
-  const dataFormats: DataFormat[] =
-      [undefined, 'channelsFirst', 'channelsLast'];
-  const paddingModes: PaddingMode[] = [undefined, 'same', 'valid'];
-  const stridesArray = [1, 2];
-
-  for (const outChannels of outChannelsArray) {
-    for (const dataFormat of dataFormats) {
-      for (const paddingMode of paddingModes) {
-        for (const stride of stridesArray) {
-          const testTitle = `outChannels=${outChannels}, stride=${stride}, ` +
-              `${paddingMode}, ${dataFormat}`;
-          it(testTitle, () => {
-            let x: Tensor = tensor4d(x4by4Data, [1, 1, 4, 4]);
-            if (dataFormat !== 'channelsFirst') {
-              x = tfc.transpose(x, [0, 2, 3, 1]);  // NCHW -> NHWC.
-            }
-
-            let kernelData: number[] = [];
-            let biasData: number[] = [];
-            for (let i = 0; i < outChannels; ++i) {
-              kernelData = kernelData.concat(kernel2by2Data);
-              biasData = biasData.concat(biasScalarData);
-            }
-            const kernel = tfc.transpose(
-                tensor4d(kernelData, [outChannels, 2, 2, 1]), [1, 2, 3, 0]);
-            const bias = tensor1d(biasData);
-
-            const y = conv2dWithBiasActivation(
-                x, kernel, bias, [stride, stride], 'valid', dataFormat);
-
-            let yExpectedShape: [number, number, number, number];
-            let yExpectedDataPerChannel: number[];
-            if (stride === 1) {
-              yExpectedShape = [1, outChannels, 3, 3];
-              yExpectedDataPerChannel =
-                  [-30, -30, -30, 50, 90, 130, 30, 30, 30];
-            } else if (stride === 2) {
-              yExpectedShape = [1, outChannels, 2, 2];
-              yExpectedDataPerChannel = [-30, -30, 30, 30];
-            }
-            for (let i = 0; i < yExpectedDataPerChannel.length; ++i) {
-              yExpectedDataPerChannel[i] += biasScalarData[0];
-            }
-            let yExpectedData: number[] = [];
-            for (let i = 0; i < outChannels; ++i) {
-              yExpectedData = yExpectedData.concat(yExpectedDataPerChannel);
-            }
-            let yExpected: Tensor = tensor4d(yExpectedData, yExpectedShape);
-            if (dataFormat !== 'channelsFirst') {
-              yExpected = tfc.transpose(yExpected, [0, 2, 3, 1]);
-            }
-            expectTensorsClose(y, yExpected);
-          });
-        }
-      }
-    }
-  }
-});
-
-describeMathCPU('Conv2D Layers: Symbolic', () => {
-  const filtersArray = [1, 64];
-  const paddingModes: PaddingMode[] = [undefined, 'valid', 'same'];
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const kernelSizes = [[2, 2], [3, 4]];
-  // In this test suite, `undefined` means strides is the same as kernelSize.
-  const stridesArray = [undefined, 1];
-
-  for (const filters of filtersArray) {
-    for (const padding of paddingModes) {
-      for (const dataFormat of dataFormats) {
-        for (const kernelSize of kernelSizes) {
-          for (const stride of stridesArray) {
-            const strides = stride || kernelSize;
-            const testTitle = `filters=${filters}, kernelSize=${
-                                  JSON.stringify(kernelSize)}, ` +
-                `strides=${JSON.stringify(strides)}, ` +
-                `${dataFormat}, ${padding}`;
-            it(testTitle, () => {
-              const inputShape = dataFormat === 'channelsFirst' ?
-                  [2, 16, 11, 9] :
-                  [2, 11, 9, 16];
-              const symbolicInput =
-                  new tfl.SymbolicTensor('float32', inputShape, null, [], null);
-
-              const conv2dLayer = tfl.layers.conv2d({
-                filters,
-                kernelSize,
-                strides,
-                padding,
-                dataFormat,
-              });
-
-              const output =
-                  conv2dLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-
-              let outputRows: number;
-              let outputCols: number;
-              if (stride === undefined) {  // Same strides as kernelSize.
-                outputRows = kernelSize[0] === 2 ? 5 : 3;
-                if (padding === 'same') {
-                  outputRows++;
-                }
-                outputCols = kernelSize[1] === 2 ? 4 : 2;
-                if (padding === 'same') {
-                  outputCols++;
-                }
-              } else {  // strides: 1.
-                outputRows = kernelSize[0] === 2 ? 10 : 9;
-                if (padding === 'same') {
-                  outputRows += kernelSize[0] - 1;
-                }
-                outputCols = kernelSize[1] === 2 ? 8 : 6;
-                if (padding === 'same') {
-                  outputCols += kernelSize[1] - 1;
-                }
-              }
-              let expectedShape: [number, number, number, number];
-              if (dataFormat === 'channelsFirst') {
-                expectedShape = [2, filters, outputRows, outputCols];
-              } else {
-                expectedShape = [2, outputRows, outputCols, filters];
-              }
-
-              expect(output.shape).toEqual(expectedShape);
-              expect(output.dtype).toEqual(symbolicInput.dtype);
-            });
-          }
-        }
-      }
-    }
-  }
-
-  it('missing config.kernelSize throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect((filters: 1) => tfl.layers.conv2d({filters: 1} as any))
-        .toThrowError(/kernelSize/);
-  });
-  it('bad config.kernelSize shape throws exception', () => {
-    expect(() => tfl.layers.conv2d({filters: 1, kernelSize: [1, 1, 1]}))
-        .toThrowError(
-            /kernelSize argument must be an integer or tuple of 2 integers/);
-  });
-  it('bad config.kernelSize shape throws exception', () => {
-    expect(() => tfl.layers.conv2d({filters: 1, kernelSize: [1, 1, 1, 1]}))
-        .toThrowError(
-            /kernelSize to be number or number\[\] with length 1, 2, or 3/);
-  });
-  it('missing config.filters throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.conv2d({kernelSize: 1} as any))
-        .toThrowError(/filters to be a 'number' > 0/);
-  });
-  it('bad config.filters value throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.conv2d({kernelSize: 1, filters: 0} as any))
-        .toThrowError(/filters to be a 'number' > 0/);
-  });
-});
-
-describeMathCPUAndGPU('Conv2D Layer: Tensor', () => {
-  const x4by4Data = [[[
-    [10, 30, 50, 70], [20, 40, 60, 80], [-10, -30, -50, -70],
-    [-20, -40, -60, -80]
-  ]]];
-
-  const useBiases = [false, true];
-  const biasInitializers: InitializerIdentifier[] = ['zeros', 'ones'];
-  const activations: ActivationIdentifier[] = [null, 'linear', 'relu'];
-
-  for (const useBias of useBiases) {
-    for (const biasInitializer of biasInitializers) {
-      for (const activation of activations) {
-        const testTitle =
-            `useBias=${useBias}, biasInitializer=${biasInitializer}, ` +
-            `activation=${activation}`;
-        it(testTitle, () => {
-          const x = tensor4d(x4by4Data, [1, 1, 4, 4]);
-          const conv2dLayer = tfl.layers.conv2d({
-            filters: 1,
-            kernelSize: [2, 2],
-            strides: [2, 2],
-            dataFormat: 'channelsFirst',
-            useBias,
-            kernelInitializer: 'ones',
-            biasInitializer,
-            activation
-          });
-          const y = conv2dLayer.apply(x) as Tensor;
-
-          let yExpectedData = [100, 260, -100, -260];
-          if (useBias && biasInitializer === 'ones') {
-            yExpectedData = yExpectedData.map(element => element + 1);
-          }
-          if (activation === 'relu') {
-            yExpectedData =
-                yExpectedData.map(element => element >= 0 ? element : 0);
-          }
-          const yExpected = tensor4d(yExpectedData, [1, 1, 2, 2]);
-          expectTensorsClose(y, yExpected);
-        });
-      }
-    }
-  }
-
-  it('CHANNEL_LAST', () => {
-    // Convert input to CHANNEL_LAST.
-    const x = tfc.transpose(tensor4d(x4by4Data, [1, 1, 4, 4]), [0, 2, 3, 1]);
-    const conv2dLayer = tfl.layers.conv2d({
-      filters: 1,
-      kernelSize: [2, 2],
-      strides: [2, 2],
-      dataFormat: 'channelsLast',
-      useBias: false,
-      kernelInitializer: 'ones',
-      activation: 'linear'
-    });
-    const y = conv2dLayer.apply(x) as Tensor;
-    const yExpected = tensor4d([100, 260, -100, -260], [1, 2, 2, 1]);
-    expectTensorsClose(y, yExpected);
-  });
-
-  const dilationRateValues: Array<number|[number, number]> = [2, [2, 2]];
-  for (const dilationRate of dilationRateValues) {
-    it(`CHANNEL_LAST, dilationRate=${dilationRate}`, () => {
-      const x = tensor4d(
-          [[
-            [
-              [0.89240986], [0.54892443], [0.24670805], [0.03983783],
-              [0.56602233]
-            ],
-
-            [
-              [0.21421895], [0.58529864], [0.60060781], [0.66895784],
-              [0.08855761]
-            ],
-
-            [
-              [0.56657235], [0.25803428], [0.17971111], [0.65166403],
-              [0.70492866]
-            ],
-
-            [
-              [0.46641512], [0.05765411], [0.52517211], [0.62557303],
-              [0.30612501]
-            ],
-
-            [
-              [0.8406994], [0.56932724], [0.96028134], [0.34666753],
-              [0.04458038]
-            ]
-          ]],
-          [1, 5, 5, 1]);
-      const conv2dLayer = tfl.layers.conv2d({
-        filters: 1,
-        kernelSize: [2, 2],
-        strides: 1,
-        dataFormat: 'channelsLast',
-        useBias: false,
-        kernelInitializer: 'ones',
-        activation: 'linear',
-        dilationRate
-      });
-      const y = conv2dLayer.apply(x) as Tensor;
-      const yExpected = tensor4d(
-          [[
-            [[1.8854014], [1.4984605], [1.6973702]],
-
-            [[1.8064139], [1.9374835], [1.5204625]],
-
-            [[2.547264], [1.8256931], [1.8895016]]
-          ]],
-          [1, 3, 3, 1]);
-      expectTensorsClose(y, yExpected);
-    });
-  }
-
-  const explicitDefaultDilations: Array<number|[number, number]> = [1, [1, 1]];
-  for (const explicitDefaultDilation of explicitDefaultDilations) {
-    const testTitle = 'Explicit default dilation rate: ' +
-        JSON.stringify(explicitDefaultDilation);
-    it(testTitle, () => {
-      const conv2dLayer = tfl.layers.conv2d({
-        filters: 1,
-        kernelSize: [2, 2],
-        strides: [2, 2],
-        dataFormat: 'channelsFirst',
-        useBias: false,
-        kernelInitializer: 'ones',
-        dilationRate: explicitDefaultDilation
-      });
-      const x = tensor4d(x4by4Data, [1, 1, 4, 4]);
-      const y = conv2dLayer.apply(x) as Tensor;
-      const yExpected = tensor4d([100, 260, -100, -260], [1, 1, 2, 2]);
-      expectTensorsClose(y, yExpected);
-    });
-  }
-});
-
-describeMathCPUAndGPU('conv3d', () => {
-  // # The following TensorFlow Python code is used to verify the results of
-  // # the 3D convolutions.
-  //
-  // ```python
-  // import numpy as np
-  // import tensorflow as tf
-  // tf.enable_eager_execution()
-  //
-  // x = np.arange(64, dtype=np.float32).reshape(1, 4, 4, 4, 1)
-  // # Shape of kernel is (height, width, depth, input_ch, output_ch)
-  // kernel = np.array([
-  //     1, 1, 1, -1, -1, 1, 1, 1
-  //   ], dtype=np.float32).reshape(2, 2, 2, 1, 1)
-  //
-  // # Strides (1, 1, 1)
-  // outputs = tf.nn.conv3d(
-  //     input=x, filter=kernel, strides=[1, 1, 1, 1, 1], padding='VALID')
-  // outputs.numpy().flatten()
-  // # Output:
-  // #   array([ 42.,  46.,  50.,  58.,  62.,  66.,  74.,  78.,  82., 106.,
-  // #           110., 114., 122., 126., 130., 138., 142., 146., 170., 174.,
-  // #           178., 186., 190., 194., 202., 206., 210.], dtype=float32)
-  // outputs.numpy().shape  # (1, 3, 3, 3, 1)
-  //
-  // # Strides (2, 2, 2)
-  // outputs = tf.nn.conv3d(
-  //     input=x, filter=kernel, strides=[1, 2, 2, 2, 1], padding='VALID')
-  // outputs.numpy().flatten()
-  // # Output:
-  // #   array([ 42.,  50.,  74.,  82., 170., 178., 202., 210.], dtype=float32)
-  // outputs.numpy().shape  # (1, 2, 2, 2, 1)
-  // ```
-
-  const x4by4by4Data = [
-    0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
-    16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
-    32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
-    48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63
-  ];
-  const kernel2by2by2Data = [1, 1, 1, -1, -1, 1, 1, 1];
-
-  const dataFormats: DataFormat[] =
-      [undefined, 'channelsFirst', 'channelsLast'];
-  const paddingModes: PaddingMode[] = [undefined, 'same', 'valid'];
-  const stridesArray = [1, 2];
-
-  for (const dataFormat of dataFormats) {
-    for (const paddingMode of paddingModes) {
-      for (const stride of stridesArray) {
-        const testTitle = `stride=${stride}, ${paddingMode}, ` +
-            `${dataFormat}`;
-        it(testTitle, () => {
-          let x = tensor5d(x4by4by4Data, [1, 1, 4, 4, 4]);
-          if (dataFormat !== 'channelsFirst') {
-            x = tfc.transpose(x, [0, 2, 3, 4, 1]);  // NCDHW -> NDHWC.
-          }
-          const kernel = tensor5d(kernel2by2by2Data, [2, 2, 2, 1, 1]);
-          const y =
-              conv3d(x, kernel, [stride, stride, stride], 'valid', dataFormat);
-
-          let yExpected: Tensor;
-          if (stride === 1) {
-            // See validation Python code in comment above.
-            yExpected = tensor5d(
-                [
-                  42.,  46.,  50.,  58.,  62.,  66.,  74.,  78.,  82.,
-                  106., 110., 114., 122., 126., 130., 138., 142., 146.,
-                  170., 174., 178., 186., 190., 194., 202., 206., 210.
-                ],
-                [1, 1, 3, 3, 3]);
-          } else if (stride === 2) {
-            // See validation Python code in comment above.
-            yExpected = tensor5d(
-                [42., 50., 74., 82., 170., 178., 202., 210.], [1, 1, 2, 2, 2]);
-          }
-          if (dataFormat !== 'channelsFirst') {
-            yExpected = tfc.transpose(yExpected, [0, 2, 3, 4, 1]);
-          }
-          expectTensorsClose(y, yExpected);
-        });
-      }
-    }
-  }
-
-  const biasScalarData = [2.2];
-  const outChannelsArray = [2, 3];
-
-  for (const outChannels of outChannelsArray) {
-    for (const dataFormat of dataFormats) {
-      for (const paddingMode of paddingModes) {
-        for (const stride of stridesArray) {
-          const testTitle = `outChannels=${outChannels}, stride=${stride}, ` +
-              `${paddingMode}, ${dataFormat}`;
-          it(testTitle, () => {
-            let x: Tensor = tensor5d(x4by4by4Data, [1, 1, 4, 4, 4]);
-            if (dataFormat !== 'channelsFirst') {
-              x = tfc.transpose(x, [0, 2, 3, 4, 1]);  // NCDHW -> NDHWC.
-            }
-
-            let kernelData: number[] = [];
-            let biasData: number[] = [];
-            for (let i = 0; i < outChannels; ++i) {
-              kernelData = kernelData.concat(kernel2by2by2Data);
-              biasData = biasData.concat(biasScalarData);
-            }
-            const kernel = tfc.transpose(
-                tensor5d(kernelData, [outChannels, 2, 2, 2, 1]),
-                [1, 2, 3, 4, 0]);
-            const bias = tensor1d(biasData);
-
-            const y = conv3dWithBias(
-                x, kernel, bias, [stride, stride, stride], 'valid', dataFormat);
-
-            let yExpectedShape: [number, number, number, number, number];
-            let yExpectedDataPerChannel: number[];
-            if (stride === 1) {
-              yExpectedShape = [1, outChannels, 3, 3, 3];
-              // See validation Python code above.
-              yExpectedDataPerChannel = [
-                42.,  46.,  50.,  58.,  62.,  66.,  74.,  78.,  82.,
-                106., 110., 114., 122., 126., 130., 138., 142., 146.,
-                170., 174., 178., 186., 190., 194., 202., 206., 210.
-              ];
-            } else if (stride === 2) {
-              yExpectedShape = [1, outChannels, 2, 2, 2];
-              // See validation Python code above.
-              yExpectedDataPerChannel =
-                  [42., 50., 74., 82., 170., 178., 202., 210.];
-            }
-            for (let i = 0; i < yExpectedDataPerChannel.length; ++i) {
-              yExpectedDataPerChannel[i] += biasScalarData[0];
-            }
-            let yExpectedData: number[] = [];
-            for (let i = 0; i < outChannels; ++i) {
-              yExpectedData = yExpectedData.concat(yExpectedDataPerChannel);
-            }
-            let yExpected: Tensor = tensor5d(yExpectedData, yExpectedShape);
-            if (dataFormat !== 'channelsFirst') {
-              yExpected = tfc.transpose(yExpected, [0, 2, 3, 4, 1]);
-            }
-            expectTensorsClose(y, yExpected);
-          });
-        }
-      }
-    }
-  }
-});
-
-describeMathCPU('Conv3D Layers: Symbolic', () => {
-  const filtersArray = [1, 64];
-  const paddingModes: PaddingMode[] = [undefined, 'valid', 'same'];
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const kernelSizes = [[2, 2, 2], [3, 4, 4]];
-  // In this test suite, `undefined` means strides is the same as kernelSize.
-  const stridesArray = [undefined, 1];
-
-  for (const filters of filtersArray) {
-    for (const padding of paddingModes) {
-      for (const dataFormat of dataFormats) {
-        for (const kernelSize of kernelSizes) {
-          for (const stride of stridesArray) {
-            const strides = stride || kernelSize;
-            const testTitle = `filters=${filters}, kernelSize=${
-                                  JSON.stringify(kernelSize)}, ` +
-                `strides=${JSON.stringify(strides)}, ` +
-                `${dataFormat}, ${padding}`;
-            it(testTitle, () => {
-              const inputShape = dataFormat === 'channelsFirst' ?
-                  [2, 16, 11, 9, 9] :
-                  [2, 11, 9, 9, 16];
-              const symbolicInput =
-                  new tfl.SymbolicTensor('float32', inputShape, null, [], null);
-
-              const conv3dLayer = tfl.layers.conv3d({
-                filters,
-                kernelSize,
-                strides,
-                padding,
-                dataFormat,
-              });
-
-              const output =
-                  conv3dLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-
-              let outputRows: number;
-              let outputCols: number;
-              let outputDepth: number;
-              if (stride === undefined) {  // Same strides as kernelSize.
-                outputRows = kernelSize[0] === 2 ? 5 : 3;
-                if (padding === 'same') {
-                  outputRows++;
-                }
-                outputCols = kernelSize[1] === 2 ? 4 : 2;
-                if (padding === 'same') {
-                  outputCols++;
-                }
-                outputDepth = kernelSize[2] === 2 ? 4 : 2;
-                if (padding === 'same') {
-                  outputDepth++;
-                }
-              } else {  // strides: 1.
-                outputRows = kernelSize[0] === 2 ? 10 : 9;
-                if (padding === 'same') {
-                  outputRows += kernelSize[0] - 1;
-                }
-                outputCols = kernelSize[1] === 2 ? 8 : 6;
-                if (padding === 'same') {
-                  outputCols += kernelSize[1] - 1;
-                }
-                outputDepth = kernelSize[2] === 2 ? 8 : 6;
-                if (padding === 'same') {
-                  outputDepth += kernelSize[1] - 1;
-                }
-              }
-              let expectedShape: [number, number, number, number, number];
-              if (dataFormat === 'channelsFirst') {
-                expectedShape =
-                    [2, filters, outputRows, outputCols, outputDepth];
-              } else {
-                expectedShape =
-                    [2, outputRows, outputCols, outputDepth, filters];
-              }
-
-              expect(output.shape).toEqual(expectedShape);
-              expect(output.dtype).toEqual(symbolicInput.dtype);
-            });
-          }
-        }
-      }
-    }
-  }
-
-  it('Invalid filters leads to Error', () => {
-    expect(() => tfl.layers.conv3d({filters: 2.5, kernelSize: 3}))
-        .toThrowError(/filters.*positive integer.*2\.5\.$/);
-  });
-  it('missing config.kernelSize throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect((filters: 1) => tfl.layers.conv3d({filters: 1} as any))
-        .toThrowError(/kernelSize/);
-  });
-  it('scalar config.kernelSize shape does not throw exception', () => {
-    expect(() => tfl.layers.conv3d({filters: 1, kernelSize: 1}))
-        .not.toThrowError();
-  });
-  it('bad config.kernelSize shape throws exception', () => {
-    expect(() => tfl.layers.conv3d({filters: 1, kernelSize: [1, 1]}))
-        .toThrowError(
-            /kernelSize argument must be an integer or tuple of 3 integers/);
-  });
-  it('bad config.kernelSize shape throws exception', () => {
-    expect(() => tfl.layers.conv2d({filters: 1, kernelSize: [1, 1, 1, 1]}))
-        .toThrowError(
-            /kernelSize to be number or number\[\] with length 1, 2, or 3/);
-  });
-  it('missing config.filters throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.conv3d({kernelSize: 1} as any))
-        .toThrowError(/filters to be a 'number' > 0/);
-  });
-  it('bad config.filters value throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.conv3d({kernelSize: 1, filters: 0} as any))
-        .toThrowError(/filters to be a 'number' > 0/);
-  });
-});
-
-describeMathCPUAndGPU('Conv3D Layer: Tensor', () => {
-  // # The following TensorFlow Python code is used to verify the results of
-  // # the 3D convolution layer.
-  //
-  // ```python
-  // import numpy as np
-  // import tensorflow as tf
-  // tf.enable_eager_execution()
-  //
-  // x = np.arange(64, dtype=np.float32).reshape(1, 4, 4, 4, 1)
-  // layer = tf.keras.layers.Conv3D(
-  //     filters=1, kernel_size=(2, 2, 2), strides=(2, 2, 2),
-  //     use_bias=True, kernel_initializer='ones', bias_initializer='zeros')
-  // outputs = layer(x)
-  // outputs.numpy().flatten()
-  // # Output:
-  // #  array([ 84., 100., 148., 164., 340., 356., 404., 420.], dtype=float32)
-  // outputs.numpy().shape
-  // # Output:
-  // #  (1, 2, 2, 2, 1)
-  // # TensorFlow Keras (Python) does not have a CPU implementation for channels
-  // # first yet, so the validation code uses channels last. Axes 1 and 4 would
-  // # be swapped to get channels first.
-  // ```
-
-  const x4by4by4Data = [
-    0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
-    16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
-    32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
-    48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63
-  ];
-
-  const useBiases = [false, true];
-  const biasInitializers: InitializerIdentifier[] = ['zeros', 'ones'];
-  const activations: ActivationIdentifier[] = [null, 'linear', 'relu'];
-
-  for (const useBias of useBiases) {
-    for (const biasInitializer of biasInitializers) {
-      for (const activation of activations) {
-        const testTitle =
-            `useBias=${useBias}, biasInitializer=${biasInitializer}, ` +
-            `activation=${activation}`;
-        it(testTitle, () => {
-          const x = tensor5d(x4by4by4Data, [1, 1, 4, 4, 4]);
-          const conv3dLayer = tfl.layers.conv3d({
-            filters: 1,
-            kernelSize: [2, 2, 2],
-            strides: [2, 2, 2],
-            dataFormat: 'channelsFirst',
-            useBias,
-            kernelInitializer: 'ones',
-            biasInitializer,
-            activation
-          });
-          const y = conv3dLayer.apply(x) as Tensor;
-          // See validation Python code above.
-          let yExpectedData = [84., 100., 148., 164., 340., 356., 404., 420.];
-          if (useBias && biasInitializer === 'ones') {
-            yExpectedData = yExpectedData.map(element => element + 1);
-          }
-          if (activation === 'relu') {
-            yExpectedData =
-                yExpectedData.map(element => element >= 0 ? element : 0);
-          }
-          const yExpected = tensor5d(yExpectedData, [1, 1, 2, 2, 2]);
-          expectTensorsClose(y, yExpected);
-        });
-      }
-    }
-  }
-
-  it('CHANNEL_LAST', () => {
-    // Convert input to CHANNEL_LAST.
-    const x =
-        tfc.transpose(tensor5d(x4by4by4Data, [1, 1, 4, 4, 4]), [0, 2, 3, 4, 1]);
-    const conv3dLayer = tfl.layers.conv3d({
-      filters: 1,
-      kernelSize: [2, 2, 2],
-      strides: [2, 2, 2],
-      dataFormat: 'channelsLast',
-      useBias: false,
-      kernelInitializer: 'ones',
-      activation: 'linear'
-    });
-    const y = conv3dLayer.apply(x) as Tensor;
-    // See validation Python code above.
-    const yExpected = tensor5d(
-        [84., 100., 148., 164., 340., 356., 404., 420.], [1, 2, 2, 2, 1]);
-    expectTensorsClose(y, yExpected);
-  });
-
-  // # The following TensorFlow Python code is used to verify the results of
-  // # the 3D convolution layer with dilation.
-  //
-  // ```python
-  // import numpy as np
-  // import tensorflow as tf
-  // tf.enable_eager_execution()
-  //
-  // np.random.seed(42)
-  // x = np.random.rand(1, 4, 4, 4, 1)
-  // layer = tf.keras.layers.Conv3D(
-  //     filters=1, kernel_size=(2, 2, 2), strides=1, dilation_rate=2,
-  //     use_bias=False, kernel_initializer='ones', activation='linear')
-  // outputs = layer(x)
-  // outputs.numpy().flatten()
-  // # Output:
-  // #   array([2.91534395, 6.38913542, 2.78770771, 3.1383292 , 3.04194573,
-  // #          3.59669644, 4.85877068, 3.16763418])
-  // outputs.numpy().shape
-  // # Output:
-  // #   (1, 2, 2, 2, 1)
-  // ```
-
-  const dilationRateValues: Array<number|[number, number, number]> =
-      [2, [2, 2, 2]];
-  for (const dilationRate of dilationRateValues) {
-    it(`CHANNEL_LAST, dilationRate=${dilationRate}`, () => {
-      const x = tensor5d(
-          [
-            0.37454012, 0.95071431, 0.73199394, 0.59865848, 0.15601864,
-            0.15599452, 0.05808361, 0.86617615, 0.60111501, 0.70807258,
-            0.02058449, 0.96990985, 0.83244264, 0.21233911, 0.18182497,
-            0.18340451, 0.30424224, 0.52475643, 0.43194502, 0.29122914,
-            0.61185289, 0.13949386, 0.29214465, 0.36636184, 0.45606998,
-            0.78517596, 0.19967378, 0.51423444, 0.59241457, 0.04645041,
-            0.60754485, 0.17052412, 0.06505159, 0.94888554, 0.96563203,
-            0.80839735, 0.30461377, 0.09767211, 0.68423303, 0.44015249,
-            0.12203823, 0.49517691, 0.03438852, 0.9093204,  0.25877998,
-            0.66252228, 0.31171108, 0.52006802, 0.54671028, 0.18485446,
-            0.96958463, 0.77513282, 0.93949894, 0.89482735, 0.59789998,
-            0.92187424, 0.0884925,  0.19598286, 0.04522729, 0.32533033,
-            0.38867729, 0.27134903, 0.82873751, 0.35675333
-          ],
-          [1, 4, 4, 4, 1]);
-      const conv3dLayer = tfl.layers.conv3d({
-        filters: 1,
-        kernelSize: [2, 2, 2],
-        strides: 1,
-        dataFormat: 'channelsLast',
-        useBias: false,
-        kernelInitializer: 'ones',
-        activation: 'linear',
-        dilationRate
-      });
-      const y = conv3dLayer.apply(x) as Tensor;
-      // See validation Python code above.
-      const yExpected = tensor5d(
-          [
-            2.91534395, 6.38913542, 2.78770771, 3.1383292, 3.04194573,
-            3.59669644, 4.85877068, 3.16763418
-          ],
-          [1, 2, 2, 2, 1]);
-      expectTensorsClose(y, yExpected);
-    });
-  }
-
-  const explicitDefaultDilations: Array<number|[number, number, number]> =
-      [1, [1, 1, 1]];
-  for (const explicitDefaultDilation of explicitDefaultDilations) {
-    const testTitle = 'Explicit default dilation rate: ' +
-        JSON.stringify(explicitDefaultDilation);
-    it(testTitle, () => {
-      const conv3dLayer = tfl.layers.conv3d({
-        filters: 1,
-        kernelSize: [2, 2, 2],
-        strides: [2, 2, 2],
-        dataFormat: 'channelsFirst',
-        useBias: false,
-        kernelInitializer: 'ones',
-        dilationRate: explicitDefaultDilation
-      });
-      const x = tensor5d(x4by4by4Data, [1, 1, 4, 4, 4]);
-      const y = conv3dLayer.apply(x) as Tensor;
-      const yExpected = tensor5d(
-          [84., 100., 148., 164., 340., 356., 404., 420.], [1, 1, 2, 2, 2]);
-      expectTensorsClose(y, yExpected);
-    });
-  }
-});
-
-describeMathCPU('Conv2DTranspose: Symbolic', () => {
-  const filtersArray = [1, 64];
-  const paddingModes: PaddingMode[] = [undefined, 'valid', 'same'];
-  const kernelSizes = [2, [2, 2], [3, 4]];
-  const stridesArray = [undefined, 2];
-
-  for (const filters of filtersArray) {
-    for (const padding of paddingModes) {
-      for (const kernelSize of kernelSizes) {
-        for (const strides of stridesArray) {
-          const testTitle = `filters=${filters}, paddingMode=${padding},` +
-              `kernelSize=${JSON.stringify(kernelSize)}, strides=${strides}`;
-          it(testTitle, () => {
-            const inputShape = [2, 11, 9, 16];
-            const x =
-                new tfl.SymbolicTensor('float32', inputShape, null, [], null);
-
-            const layer = tfl.layers.conv2dTranspose(
-                {filters, kernelSize, padding, strides});
-            const y = layer.apply(x) as tfl.SymbolicTensor;
-
-            let expectedShape: [number, number, number, number];
-            if (strides === undefined) {
-              if (padding === 'valid' || padding === undefined) {
-                if (kernelSize as number === 2 ||
-                    util.arraysEqual(kernelSize as number[], [2, 2])) {
-                  expectedShape = [2, 12, 10, filters];
-                } else if (util.arraysEqual(kernelSize as number[], [3, 4])) {
-                  expectedShape = [2, 13, 12, filters];
-                }
-              } else if (padding === 'same') {
-                expectedShape = [2, 11, 9, filters];
-              }
-            } else {
-              if (padding === 'valid' || padding === undefined) {
-                if (kernelSize as number === 2 ||
-                    util.arraysEqual(kernelSize as number[], [2, 2])) {
-                  expectedShape = [2, 22, 18, filters];
-                } else if (util.arraysEqual(kernelSize as number[], [3, 4])) {
-                  expectedShape = [2, 23, 20, filters];
-                }
-              } else if (padding === 'same') {
-                expectedShape = [2, 22, 18, filters];
-              }
-            }
-            expect(y.shape).toEqual(expectedShape);
-          });
-        }
-      }
-    }
-  }
-
-  it('Correct weight names', () => {
-    const x = new tfl.SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-    const layer = tfl.layers.conv2dTranspose({filters: 2, kernelSize: [3, 3]});
-    layer.apply(x);  // Let the layer build first.
-
-    expect(layer.weights.length).toEqual(2);
-    expect(layer.weights[0].name.indexOf('/kernel')).toBeGreaterThan(0);
-    expect(layer.weights[1].name.indexOf('/bias')).toBeGreaterThan(0);
-  });
-});
-
-describeMathCPUAndGPU('Conv2DTranspose: Tensor', () => {
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const stridesArray = [2, [2, 2]];
-  for (const dataFormat of dataFormats) {
-    for (const strides of stridesArray) {
-      const testTitle =
-          `filters=8, kernelSize=[2,2], padding=valid, strides=${strides}` +
-          `dataFormat=${dataFormat}`;
-      it(testTitle, () => {
-        const filters = 8;
-        const kernelSize = [2, 2];
-        const padding = 'valid';
-        const strides = 2;
-        const layer = tfl.layers.conv2dTranspose({
-          filters,
-          kernelSize,
-          padding,
-          strides,
-          dataFormat,
-          kernelInitializer: 'ones',
-          biasInitializer: 'ones'
-        });
-
-        const x = tfc.ones([2, 3, 4, 2]);
-        const y = layer.apply(x) as Tensor;
-        if (dataFormat === 'channelsLast') {
-          expectTensorsClose(y, tfc.ones([2, 6, 8, 8]).mul(scalar(3)));
-        } else {
-          expectTensorsClose(y, tfc.ones([2, 8, 8, 4]).mul(scalar(4)));
-        }
-      });
-    }
-  }
-});
-
-describeMathCPU('Conv1D Layers: Symbolic', () => {
-  const filtersArray = [1, 4];
-  const paddingModes: PaddingMode[] = [undefined, 'valid', 'same'];
-  const stridesArray = [undefined, 1];
-
-  for (const filters of filtersArray) {
-    for (const padding of paddingModes) {
-      for (const strides of stridesArray) {
-        const testTitle = `filters=${filters}, padding=${padding}, ` +
-            `strides=${strides}`;
-        it(testTitle, () => {
-          const inputShape = [2, 8, 3];
-          const symbolicInput =
-              new tfl.SymbolicTensor('float32', inputShape, null, [], null);
-
-          const conv1dLayer = tfl.layers.conv1d({
-            filters,
-            kernelSize: 2,
-            strides,
-            padding,
-            dataFormat: 'channelsLast',
-          });
-
-          const output = conv1dLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-
-          const expectedShape = [2, 7, filters];
-          if (padding === 'same') {
-            expectedShape[1] = 8;
-          }
-          expect(output.shape).toEqual(expectedShape);
-          expect(output.dtype).toEqual(symbolicInput.dtype);
-        });
-      }
-    }
-  }
-
-  it('bad config.kernelSize shape throws exception', () => {
-    expect(() => tfl.layers.conv1d({filters: 1, kernelSize: [1, 1]}))
-        .toThrowError(/kernelSize.*1/);
-  });
-});
-
-describeMathCPUAndGPU('Conv1D Layer: Tensor', () => {
-  const xLength4Data = [10, -30, -50, 70];
-  // In the most basic case, applying an all-ones convolutional kernel to
-  // the 1D input above gives [-20, -80, 20]. Then adding all-ones bias to
-  // it gives [-19, -79, 21].
-
-  const stridesValues = [1, 2];
-  const activations: ActivationIdentifier[] = ['linear', 'relu'];
-  for (const strides of stridesValues) {
-    for (const activation of activations) {
-      const testTitle = `useBias=true, biasInitializer=ones, ` +
-          `activation=${activation}; strides=${strides}`;
-      it(testTitle, () => {
-        const x = tensor3d(xLength4Data, [1, 4, 1]);
-        const conv1dLayer = tfl.layers.conv1d({
-          filters: 1,
-          kernelSize: 2,
-          strides,
-          dataFormat: 'channelsLast',
-          useBias: true,
-          kernelInitializer: 'ones',
-          biasInitializer: 'ones',
-          activation
-        });
-        const y = conv1dLayer.apply(x) as Tensor;
-
-        let yExpectedShape: [number, number, number];
-        let yExpectedData: number[];
-        if (strides === 1) {
-          yExpectedShape = [1, 3, 1];
-          yExpectedData = [-19, -79, 21];
-        } else {
-          yExpectedShape = [1, 2, 1];
-          yExpectedData = [-19, 21];
-        }
-        if (activation === 'relu') {
-          yExpectedData = yExpectedData.map(x => x > 0 ? x : 0);
-        }
-        const yExpected = tensor3d(yExpectedData, yExpectedShape);
-        expectTensorsClose(y, yExpected);
-      });
-    }
-  }
-
-  const dilationRates: Array<number|[number]> = [2, [2]];
-  for (const dilationRate of dilationRates) {
-    it(`dilationRate = ${dilationRate}`, () => {
-      const x = tensor3d(
-          [
-            0.0024236, 0.54829558, 0.47628448, 0.2971449, 0.7984293, 0.71802861,
-            0.53109141, 0.85882819
-          ],
-          [1, 8, 1]);
-      const conv1dLayer = tfl.layers.conv1d({
-        filters: 1,
-        kernelSize: 2,
-        strides: 1,
-        useBias: true,
-        kernelInitializer: 'ones',
-        biasInitializer: 'ones',
-        dilationRate,
-      });
-      const y = conv1dLayer.apply(x) as Tensor;
-      const yExpected = tensor3d(
-          [1.478708, 1.8454404, 2.2747138, 2.0151734, 2.3295207, 2.5768569],
-          [1, 6, 1]);
-      expectTensorsClose(y, yExpected);
-    });
-  }
-
-  it('missing config.kernelSize throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect((filters: 1) => tfl.layers.conv1d({filters: 1} as any))
-        .toThrowError(/required key 'kernelSize' not in config/);
-  });
-  it('bad config.kernelSize throws exception', () => {
-    expect(() => tfl.layers.conv1d({filters: 1, kernelSize: [1, 1, 1]}))
-        .toThrowError(
-            /kernelSize argument must be an integer or tuple of 1 integers/);
-  });
-  it('missing config.filters throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.conv1d({kernelSize: 1} as any))
-        .toThrowError(/filters to be a 'number' > 0/);
-  });
-  it('bad config.filters throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.conv1d({kernelSize: 1, filters: 0} as any))
-        .toThrowError(/filters to be a 'number' > 0/);
-  });
-});
-
-describeMathCPU('SeparableConv2D Layers: Symbolic', () => {
-  const filtersArray = [1, 8];
-  const paddingModes: PaddingMode[] = [undefined, 'valid', 'same'];
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const kernelSizes = [[2, 2], [3, 4]];
-  // In this test suite, `undefined` means strides is the same as kernelSize.
-  const stridesArray = [undefined, 1];
-  const dilationRates = [undefined, 2];
-
-  for (const filters of filtersArray) {
-    for (const padding of paddingModes) {
-      for (const dataFormat of dataFormats) {
-        for (const kernelSize of kernelSizes) {
-          for (const stride of stridesArray) {
-            for (const dilationRate of dilationRates) {
-              const strides = stride || kernelSize;
-              const testTitle = `filters=${filters}, kernelSize=${
-                                    JSON.stringify(kernelSize)}, ` +
-                  `strides=${JSON.stringify(strides)}, ` +
-                  `dataFormat=${dataFormat}, padding=${padding}, ` +
-                  `dilationRate=${dilationRate}`;
-              it(testTitle, () => {
-                const inputShape = dataFormat === 'channelsFirst' ?
-                    [2, 16, 11, 9] :
-                    [2, 11, 9, 16];
-                const symbolicInput = new tfl.SymbolicTensor(
-                    'float32', inputShape, null, [], null);
-
-                const layer = tfl.layers.separableConv2d({
-                  filters,
-                  kernelSize,
-                  strides,
-                  padding,
-                  dataFormat,
-                  dilationRate,
-                });
-
-                const output = layer.apply(symbolicInput) as tfl.SymbolicTensor;
-
-                let outputRows: number;
-                let outputCols: number;
-                if (dilationRate == null) {
-                  if (stride === undefined) {  // Same strides as kernelSize.
-                    outputRows = kernelSize[0] === 2 ? 5 : 3;
-                    if (padding === 'same') {
-                      outputRows++;
-                    }
-                    outputCols = kernelSize[1] === 2 ? 4 : 2;
-                    if (padding === 'same') {
-                      outputCols++;
-                    }
-                  } else {  // strides: 1.
-                    outputRows = kernelSize[0] === 2 ? 10 : 9;
-                    if (padding === 'same') {
-                      outputRows += kernelSize[0] - 1;
-                    }
-                    outputCols = kernelSize[1] === 2 ? 8 : 6;
-                    if (padding === 'same') {
-                      outputCols += kernelSize[1] - 1;
-                    }
-                  }
-                } else {
-                  if (padding === 'same') {
-                    if (stride === undefined) {  // Same strides as kernelSize.
-                      outputRows = kernelSize[0] === 2 ? 6 : 4;
-                      outputCols = kernelSize[1] === 2 ? 5 : 3;
-                    } else {  // strides: 1.
-                      outputRows = 11;
-                      outputCols = 9;
-                    }
-                  } else {
-                    if (stride === undefined) {  // Same strides as kernelSize.
-                      outputRows = kernelSize[0] === 2 ? 5 : 3;
-                      outputCols = kernelSize[1] === 2 ? 4 : 1;
-                    } else {  // strides: 1.
-                      outputRows = kernelSize[0] === 2 ? 9 : 7;
-                      outputCols = kernelSize[1] === 2 ? 7 : 3;
-                    }
-                  }
-                }
-                let expectedShape: [number, number, number, number];
-                if (dataFormat === 'channelsFirst') {
-                  expectedShape = [2, filters, outputRows, outputCols];
-                } else {
-                  expectedShape = [2, outputRows, outputCols, filters];
-                }
-
-                expect(output.shape).toEqual(expectedShape);
-                expect(output.dtype).toEqual(symbolicInput.dtype);
-              });
-            }
-          }
-        }
-      }
-    }
-  }
-
-  it('Incorrect input rank throws error', () => {
-    const layer = tfl.layers.separableConv2d({
-      filters: 1,
-      kernelSize: [2, 2],
-      strides: 1,
-    });
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [2, 3, 4], null, [], null);
-    expect(() => layer.apply(symbolicInput)).toThrowError(/rank 4/);
-  });
-
-  it('Undefined channel axis throws error', () => {
-    const layer = tfl.layers.separableConv2d({
-      filters: 1,
-      kernelSize: [2, 2],
-      strides: 1,
-    });
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [1, , 2, 3, null], null, [], null);
-    expect(() => layer.apply(symbolicInput))
-        .toThrowError(/channel dimension .* should be defined/);
-  });
-});
-
-describeMathGPU('SeparableConv2D Layer: Tensor', () => {
-  const x5by5Data = [
-    1,  3,  5,  7,  9,  2,  4,   6,  8, 10, -1, -3, -5,
-    -7, -9, -2, -4, -6, -8, -10, -1, 1, -1, 1,  -1
-  ];
-
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const dilationRates: number[] = [undefined, 2];
-  const useBiases = [false, true];
-  const biasInitializers: InitializerIdentifier[] = ['zeros', 'ones'];
-  const activations: ActivationIdentifier[] = [null, 'linear', 'relu'];
-
-  for (const dataFormat of dataFormats) {
-    for (const dilationRate of dilationRates) {
-      for (const useBias of useBiases) {
-        for (const biasInitializer of biasInitializers) {
-          for (const activation of activations) {
-            const testTitle = `dataFormat=${dataFormat}, ` +
-                `dilationRate=${dilationRate}, ` +
-                `useBias=${useBias}, biasInitializer=${biasInitializer}, ` +
-                `activation=${activation}`;
-            it(testTitle, () => {
-              let x = tensor4d(x5by5Data, [1, 5, 5, 1]);
-              if (dataFormat === 'channelsFirst') {
-                x = tfc.transpose(x, [0, 3, 1, 2]);  // NHWC -> NCHW.
-              }
-
-              const conv2dLayer = tfl.layers.separableConv2d({
-                depthMultiplier: 1,
-                filters: 1,
-                kernelSize: [2, 2],
-                strides: 1,
-                dilationRate,
-                dataFormat,
-                useBias,
-                depthwiseInitializer: 'ones',
-                pointwiseInitializer: 'ones',
-                biasInitializer,
-                activation
-              });
-              const y = conv2dLayer.apply(x) as Tensor;
-
-              let yExpectedData: number[];
-              if (dilationRate === 2) {
-                yExpectedData = [0, 0, 0, 0, 0, 0, -8, -8, -16];
-              } else {
-                yExpectedData = [
-                  10, 18, 26, 34, 2, 2, 2, 2, -10, -18, -26, -34, -6, -10, -14,
-                  -18
-                ];
-              }
-              if (useBias && biasInitializer === 'ones') {
-                yExpectedData = yExpectedData.map(element => element + 1);
-              }
-              if (activation === 'relu') {
-                yExpectedData =
-                    yExpectedData.map(element => element >= 0 ? element : 0);
-              }
-
-              let yExpected = dilationRate === 2 ?
-                  tensor4d(yExpectedData, [1, 3, 3, 1]) :
-                  tensor4d(yExpectedData, [1, 4, 4, 1]);
-              if (dataFormat === 'channelsFirst') {
-                // NHWC -> NCHW.
-                yExpected = tfc.transpose(yExpected, [0, 3, 1, 2]);
-              }
-              expectTensorsClose(y, yExpected);
-            });
-          }
-        }
-      }
-    }
-  }
-  it('missing config.kernelSize throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect((filters: 1) => tfl.layers.separableConv2d({filters: 1} as any))
-        .toThrowError(/kernelSize/);
-  });
-  it('bad config.kernelSize throws exception', () => {
-    expect(
-        () => tfl.layers.separableConv2d({filters: 1, kernelSize: [1, 1, 1]}))
-        .toThrowError(/kernelSize/);
-  });
-  it('missing config.filters throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.separableConv2d({kernelSize: 1} as any))
-        .toThrowError(/filters/);
-  });
-  it('bad config.filters throws exception', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.separableConv2d({kernelSize: 1, filters: 0} as any))
-        .toThrowError(/filters/);
-  });
-});
-
-describeMathCPUAndGPU('Cropping2D Layer', () => {
-  it('check with undefined channels type', () => {
-    const layer = tfl.layers.cropping2D({cropping: [[1, 0], [1, 0]]});
-    const x = tensor4d(
-        [
-          [[[1], [2], [3]], [[4], [5], [6]], [[7], [8], [9]]],
-        ],
-        [1, 3, 3, 1]);
-
-    const y = tensor4d(
-        [
-          [[[5], [6]], [[8], [9]]],
-        ],
-        [1, 2, 2, 1]);
-
-    expectTensorsClose(layer.apply(x, null) as Tensor, y);
-  });
-
-  it('check with channels last', () => {
-    const layer = tfl.layers.cropping2D(
-        {cropping: [[1, 1], [1, 1]], dataFormat: 'channelsLast'});
-    const x = tensor4d(
-        [
-          [[[1], [2], [3]], [[4], [5], [6]], [[7], [8], [9]]],
-        ],
-        [1, 3, 3, 1]);
-    const y = tensor4d(
-        [
-          [[[5]]],
-        ],
-        [1, 1, 1, 1]);
-
-    expectTensorsClose(layer.apply(x, null) as Tensor, y);
-  });
-
-  it('check with channels first', () => {
-    const layer = tfl.layers.cropping2D(
-        {cropping: [[1, 1], [1, 1]], dataFormat: 'channelsFirst'});
-    const x = tensor4d(
-        [
-          [[[1, 2, 3], [3, 4, 5], [6, 7, 8]]],
-        ],
-        [1, 1, 3, 3]);
-    const y = tensor4d(
-        [
-          [[[4]]],
-        ],
-        [1, 1, 1, 1]);
-
-    expectTensorsClose(layer.apply(x, null) as Tensor, y);
-  });
-
-  it('check with non-square tensor', () => {
-    const layer = tfl.layers.cropping2D(
-        {cropping: [[1, 1], [1, 1]], dataFormat: 'channelsFirst'});
-    const x = tensor4d(
-        [
-          [[[1, 2, 3, 4], [3, 4, 5, 6], [6, 7, 8, 9]]],
-        ],
-        [1, 1, 3, 4]);
-    const y = tensor4d(
-        [
-          [[[4, 5]]],
-        ],
-        [1, 1, 1, 2]);
-
-    expect(layer.computeOutputShape(x.shape)).toEqual(y.shape);
-    expectTensorsClose(layer.apply(x, null) as Tensor, y);
-  });
-});
-
-describeMathCPU('UpSampling2D Layer: Symbolic', () => {
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const interpolatinFormats: InterpolationFormat[] = ['nearest', 'bilinear'];
-  const sizes = [undefined, [2, 2]];
-  const undeterminedDimensionArray: string[] = [null, 'height', 'both'];
-
-  for (const dataFormat of dataFormats) {
-    for (const interpolation of interpolatinFormats) {
-      for (const size of sizes) {
-        for (const undeterminedDimension of undeterminedDimensionArray) {
-          const testTitle = `size = ${size}, ${dataFormat}, ${interpolation}` +
-              `undetermined dimension = ${
-                                JSON.stringify(undeterminedDimension)}`;
-          it(testTitle, () => {
-            let inputShape: Shape;
-            if (undeterminedDimension == null) {
-              inputShape = dataFormat === 'channelsFirst' ? [2, 16, 11, 9] :
-                                                            [2, 11, 9, 16];
-            } else if (undeterminedDimension === 'height') {
-              inputShape = dataFormat === 'channelsFirst' ? [2, 16, null, 9] :
-                                                            [2, null, 9, 16];
-            } else if (undeterminedDimension === 'both') {
-              inputShape = dataFormat === 'channelsFirst' ?
-                  [2, 16, null, null] :
-                  [2, null, null, 16];
-            }
-            const symbolicInput =
-                new tfl.SymbolicTensor('float32', inputShape, null, [], null);
-
-            const upSampling2dLayer = tfl.layers.upSampling2d({
-              size,
-              dataFormat,
-              interpolation,
-            });
-
-            const output =
-                upSampling2dLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-
-            let outputRows: number;
-            let outputCols: number;
-            if (size === undefined) {
-              outputRows = 2;
-              outputCols = 2;
-            } else {
-              outputRows = size[0];
-              outputCols = size[1];
-            }
-            let expectedShape: [number, number, number, number];
-            if (undeterminedDimension == null) {
-              if (dataFormat === 'channelsFirst') {
-                outputRows *= inputShape[2];
-                outputCols *= inputShape[3];
-                expectedShape = [2, 16, outputRows, outputCols];
-              } else {
-                outputRows *= inputShape[1];
-                outputCols *= inputShape[2];
-                expectedShape = [2, outputRows, outputCols, 16];
-              }
-            } else if (undeterminedDimension === 'height') {
-              if (dataFormat === 'channelsFirst') {
-                outputCols *= inputShape[3];
-                expectedShape = [2, 16, null, outputCols];
-              } else {
-                outputCols *= inputShape[2];
-                expectedShape = [2, null, outputCols, 16];
-              }
-            } else if (undeterminedDimension === 'both') {
-              if (dataFormat === 'channelsFirst') {
-                expectedShape = [2, 16, null, null];
-              } else {
-                outputCols *= inputShape[2];
-                expectedShape = [2, null, null, 16];
-              }
-            }
-
-            expect(output.shape).toEqual(expectedShape);
-          });
-        }
-      }
-    }
-  }
-});
-
-describeMathCPUAndGPU('UpSampling2D Layer', () => {
-  it('check with default values', () => {
-    const layer = tfl.layers.upSampling2d({});
-    const x = tensor4d(
-        [
-          [[[1], [2]], [[3], [4]]],
-        ],
-        [1, 2, 2, 1]);
-
-    const y = tensor4d(
-        [
-          [
-            [[1], [1], [2], [2]], [[1], [1], [2], [2]], [[3], [3], [4], [4]],
-            [[3], [3], [4], [4]]
-          ],
-        ],
-        [1, 4, 4, 1]);
-
-    expectTensorsClose(layer.apply(x, null) as Tensor, y);
-  });
-
-  it('channels last', () => {
-    const layer =
-        tfl.layers.upSampling2d({size: [2, 2], dataFormat: 'channelsLast'});
-    const x = tensor4d(
-        [
-          [[[1], [2]], [[3], [4]]],
-        ],
-        [1, 2, 2, 1]);
-
-    const y = tensor4d(
-        [
-          [
-            [[1], [1], [2], [2]], [[1], [1], [2], [2]], [[3], [3], [4], [4]],
-            [[3], [3], [4], [4]]
-          ],
-        ],
-        [1, 4, 4, 1]);
-
-    expectTensorsClose(layer.apply(x, null) as Tensor, y);
-  });
-
-  it('channels first', () => {
-    const layer =
-        tfl.layers.upSampling2d({size: [2, 2], dataFormat: 'channelsFirst'});
-    const x = tensor4d(
-        [
-          [[[1, 2], [3, 4]]],
-        ],
-        [1, 1, 2, 2]);
-
-    const y = tensor4d(
-        [
-          [[[1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4]]],
-        ],
-        [1, 1, 4, 4]);
-
-    expectTensorsClose(layer.apply(x, null) as Tensor, y);
-  });
-
-  it('varying input image sizes', () => {
-    const layer =
-        tfl.layers.upSampling2d({size: [2, 2], dataFormat: 'channelsLast'});
-    const x1 = tensor4d(
-        [
-          [[[1], [2]], [[3], [4]]],
-        ],
-        [1, 2, 2, 1]);
-    const y1 = tensor4d(
-        [
-          [
-            [[1], [1], [2], [2]], [[1], [1], [2], [2]], [[3], [3], [4], [4]],
-            [[3], [3], [4], [4]]
-          ],
-        ],
-        [1, 4, 4, 1]);
-    expectTensorsClose(layer.apply(x1, null) as Tensor, y1);
-
-    const x2 = tensor4d(
-        [
-          [[[1], [2]]],
-        ],
-        [1, 1, 2, 1]);
-
-    const y2 = tensor4d(
-        [
-          [
-            [[1], [1], [2], [2]],
-            [[1], [1], [2], [2]],
-          ],
-        ],
-        [1, 2, 4, 1]);
-    expectTensorsClose(layer.apply(x2, null) as Tensor, y2);
-  });
-  it('interpolation bilinear', () => {
-    const layer =
-        tfl.layers.upSampling2d({size: [2, 2], interpolation: 'bilinear'});
-    const x = tensor4d(
-        [
-          [[[1], [2]], [[3], [4]]],
-        ],
-        [1, 2, 2, 1]);
-    const y = tensor4d(
-        [
-          [
-            [[1], [1.5], [2], [2]], [[2], [2.5], [3], [3]],
-            [[3], [3.5], [4], [4]], [[3], [3.5], [4], [4]]
-          ],
-        ],
-        [1, 4, 4, 1]);
-    expectTensorsClose(layer.apply(x, null) as Tensor, y);
-  });
-
-  it('interpolation nearest', () => {
-    const layer = tfl.layers.upSampling2d(
-        {size: [2, 2], dataFormat: 'channelsFirst', interpolation: 'nearest'});
-    const x = tensor4d(
-        [
-          [[[1, 2], [3, 4]]],
-        ],
-        [1, 1, 2, 2]);
-
-    const y = tensor4d(
-        [
-          [[[1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4]]],
-        ],
-        [1, 1, 4, 4]);
-
-    expectTensorsClose(layer.apply(x, null) as Tensor, y);
-  });
-});
-
-describeMathCPU('Conv3DTranspose: Symbolic', () => {
-  const filtersArray = [1, 64];
-  const paddingModes: PaddingMode[] = ['valid', 'same'];
-  const kernelSizes = [[2, 2, 2], [3, 4, 4]];
-  const stridesArray = [1, 3];
-
-  for (const filters of filtersArray) {
-    for (const padding of paddingModes) {
-      for (const kernelSize of kernelSizes) {
-        for (const strides of stridesArray) {
-          const testTitle = `filters=${filters}, paddingMode=${padding},` +
-              `kernelSize=${JSON.stringify(kernelSize)}, strides=${strides}`;
-          it(testTitle, () => {
-            const inputShape = [1, 2, 11, 9, 16];
-            const x =
-                new tfl.SymbolicTensor('float32', inputShape, null, [], null);
-
-            const layer = tfl.layers.conv3dTranspose(
-                {filters, kernelSize, padding, strides});
-            const y = layer.apply(x) as tfl.SymbolicTensor;
-
-            let expectedShape: [number, number, number, number, number];
-            if (strides === 1) {
-              if (padding === 'same') {
-                expectedShape = [1, 2, 11, 9, filters];
-              } else {
-                if (kernelSize[0] === 2) {
-                  expectedShape = [1, 3, 12, 10, filters];
-                } else {
-                  expectedShape = [1, 4, 14, 12, filters];
-                }
-              }
-            } else if (strides === 3) {
-              if (padding === 'same') {
-                expectedShape = [1, 6, 33, 27, filters];
-              } else {
-                if (kernelSize[0] === 2) {
-                  expectedShape = [1, 6, 33, 27, filters];
-                } else {
-                  expectedShape = [1, 6, 34, 28, filters];
-                }
-              }
-            }
-            expect(y.shape).toEqual(expectedShape);
-          });
-        }
-      }
-    }
-  }
-
-  it('Correct weight names', () => {
-    const x =
-        new tfl.SymbolicTensor('float32', [1, 2, 3, 4, 5], null, [], null);
-    const layer =
-        tfl.layers.conv3dTranspose({filters: 2, kernelSize: [3, 3, 3]});
-    layer.apply(x);  // Let the layer build first.
-
-    expect(layer.weights.length).toEqual(2);
-    expect(layer.weights[0].name.indexOf('/kernel')).toBeGreaterThan(0);
-    expect(layer.weights[1].name.indexOf('/bias')).toBeGreaterThan(0);
-  });
-});
-
-describeMathCPUAndGPU('Conv3DTranspose: Tensor', () => {
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const stridesArray = [1, 2];
-  for (const dataFormat of dataFormats) {
-    for (const strides of stridesArray) {
-      const testTitle =
-          `filters=8, kernelSize=[2,2], padding=valid, strides=${strides}` +
-          `dataFormat=${dataFormat}`;
-      it(testTitle, () => {
-        const filters = 8;
-        const kernelSize = [2, 2, 2];
-        const padding = 'valid';
-        const strides = 2;
-        const layer = tfl.layers.conv3dTranspose({
-          filters,
-          kernelSize,
-          padding,
-          strides,
-          dataFormat,
-          kernelInitializer: 'ones',
-          biasInitializer: 'ones'
-        });
-
-        const x = tfc.ones([2, 3, 4, 5, 2]);
-        const y = layer.apply(x) as Tensor;
-        if (dataFormat === 'channelsLast') {
-          expectTensorsClose(y, tfc.ones([2, 6, 8, 10, 8]).mul(scalar(3)));
-        } else {
-          expectTensorsClose(y, tfc.ones([2, 8, 8, 10, 4]).mul(scalar(4)));
-        }
-      });
-    }
-  }
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/core.ts b/tfjs-master/tfjs-layers/src/layers/core.ts
deleted file mode 100644
index 11fe3a28c..000000000
--- a/tfjs-master/tfjs-layers/src/layers/core.ts
+++ /dev/null
@@ -1,676 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * TensorFlow.js Layers: Basic Layers.
- */
-
-import {any, cast, mul, notEqual, reshape, serialization, Tensor, tidy, transpose, util} from '@tensorflow/tfjs-core';
-
-import {Activation as ActivationFn, getActivation, serializeActivation} from '../activations';
-import * as K from '../backend/tfjs_backend';
-import {Constraint, ConstraintIdentifier, getConstraint, serializeConstraint} from '../constraints';
-import {DisposeResult, InputSpec, Layer, LayerArgs} from '../engine/topology';
-import {ValueError} from '../errors';
-import {getInitializer, Initializer, InitializerIdentifier, serializeInitializer} from '../initializers';
-import {ActivationIdentifier} from '../keras_format/activation_config';
-import {DataFormat, Shape} from '../keras_format/common';
-import {LayerConfig} from '../keras_format/topology_config';
-import {getRegularizer, Regularizer, RegularizerIdentifier, serializeRegularizer} from '../regularizers';
-import {Kwargs} from '../types';
-import {assertPositiveInteger, mapActivationToFusedKernel} from '../utils/generic_utils';
-import {arrayProd, range} from '../utils/math_utils';
-import {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';
-import {LayerVariable} from '../variables';
-
-export declare interface DropoutLayerArgs extends LayerArgs {
-  /** Float between 0 and 1. Fraction of the input units to drop. */
-  rate: number;
-
-  /**
-   * Integer array representing the shape of the binary dropout mask that will
-   * be multiplied with the input.
-   *
-   * For instance, if your inputs have shape `(batchSize, timesteps, features)`
-   * and you want the dropout mask to be the same for all timesteps, you can use
-   * `noise_shape=(batch_size, 1, features)`.
-   */
-  noiseShape?: number[];
-
-  /** An integer to use as random seed. */
-  seed?: number;
-}
-
-export class Dropout extends Layer {
-  /** @nocollapse */
-  static className = 'Dropout';
-  private readonly rate: number;
-  private readonly noiseShape: number[];
-  private readonly seed: number;
-
-  constructor(args: DropoutLayerArgs) {
-    super(args);
-    this.rate = Math.max(Math.min(args.rate, 1), 0);
-    // So that the scalar doesn't get tidied up between executions.
-    this.noiseShape = args.noiseShape;
-    this.seed = args.seed;
-    this.supportsMasking = true;
-  }
-
-  protected getNoiseShape(input: Tensor): Shape {
-    if (this.noiseShape == null) {
-      return this.noiseShape;
-    }
-    const inputShape = input.shape;
-    const noiseShape: Shape = [];
-    for (let i = 0; i < this.noiseShape.length; ++i) {
-      noiseShape.push(
-          this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);
-    }
-    return noiseShape;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      const input = getExactlyOneTensor(inputs);
-      if (0 < this.rate && this.rate < 1) {
-        const training =
-            kwargs['training'] == null ? false : kwargs['training'];
-        const noiseShape = this.getNoiseShape(input);
-        const output = K.inTrainPhase(
-            () => K.dropout(input, this.rate, noiseShape, this.seed),
-            () => input, training);
-        return output;
-      }
-      return inputs;
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      rate: this.rate,
-      noiseShape: this.noiseShape,
-      seed: this.seed,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override dispose(): DisposeResult {
-    return super.dispose();
-  }
-}
-serialization.registerClass(Dropout);
-
-export declare interface DenseLayerArgs extends LayerArgs {
-  /** Positive integer, dimensionality of the output space. */
-  units: number;
-  /**
-   * Activation function to use.
-   *
-   * If unspecified, no activation is applied.
-   */
-  activation?: ActivationIdentifier;
-  /** Whether to apply a bias. */
-  useBias?: boolean;
-  /**
-   * Initializer for the dense kernel weights matrix.
-   */
-  kernelInitializer?: InitializerIdentifier|Initializer;
-  /**
-   * Initializer for the bias vector.
-   */
-  biasInitializer?: InitializerIdentifier|Initializer;
-  /**
-   * If specified, defines inputShape as `[inputDim]`.
-   */
-  inputDim?: number;
-
-  /**
-   * Constraint for the kernel weights.
-   */
-  kernelConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Constraint for the bias vector.
-   */
-  biasConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Regularizer function applied to the dense kernel weights matrix.
-   */
-  kernelRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer function applied to the bias vector.
-   */
-  biasRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer function applied to the activation.
-   */
-  activityRegularizer?: RegularizerIdentifier|Regularizer;
-}
-
-export interface SpatialDropout1DLayerConfig extends LayerConfig {
-  /** Float between 0 and 1. Fraction of the input units to drop. */
-  rate: number;
-
-  /** An integer to use as random seed. */
-  seed?: number;
-}
-
-export class SpatialDropout1D extends Dropout {
-  /** @nocollapse */
-  static override className = 'SpatialDropout1D';
-
-  constructor(args: SpatialDropout1DLayerConfig) {
-    super(args);
-    this.inputSpec = [{ndim: 3}];
-  }
-
-  protected override getNoiseShape(input: Tensor): Shape {
-    const inputShape = input.shape;
-    return [inputShape[0], 1, inputShape[2]];
-  }
-}
-serialization.registerClass(SpatialDropout1D);
-
-export class Dense extends Layer {
-  /** @nocollapse */
-  static className = 'Dense';
-  private units: number;
-  // Default activation: Linear (none).
-  private activation: ActivationFn = null;
-  private useBias = true;
-  private kernelInitializer: Initializer;
-  private biasInitializer: Initializer;
-  private kernel: LayerVariable = null;
-  private bias: LayerVariable = null;
-
-  readonly DEFAULT_KERNEL_INITIALIZER: InitializerIdentifier = 'glorotNormal';
-  readonly DEFAULT_BIAS_INITIALIZER: InitializerIdentifier = 'zeros';
-  private readonly kernelConstraint?: Constraint;
-  private readonly biasConstraint?: Constraint;
-  private readonly kernelRegularizer?: Regularizer;
-  private readonly biasRegularizer?: Regularizer;
-
-  constructor(args: DenseLayerArgs) {
-    super(args);
-    if (args.batchInputShape == null && args.inputShape == null &&
-        args.inputDim != null) {
-      // This logic is copied from Layer's constructor, since we can't
-      // do exactly what the Python constructor does for Dense().
-      let batchSize: number = null;
-      if (args.batchSize != null) {
-        batchSize = args.batchSize;
-      }
-      this.batchInputShape = [batchSize, args.inputDim];
-    }
-
-    this.units = args.units;
-    assertPositiveInteger(this.units, 'units');
-    this.activation = getActivation(args.activation);
-    if (args.useBias != null) {
-      this.useBias = args.useBias;
-    }
-    this.kernelInitializer = getInitializer(
-        args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
-    this.biasInitializer =
-        getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
-    this.kernelConstraint = getConstraint(args.kernelConstraint);
-    this.biasConstraint = getConstraint(args.biasConstraint);
-    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
-    this.biasRegularizer = getRegularizer(args.biasRegularizer);
-    this.activityRegularizer = getRegularizer(args.activityRegularizer);
-    this.supportsMasking = true;
-
-    this.inputSpec = [{minNDim: 2}];
-  }
-
-  public override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    const inputLastDim = inputShape[inputShape.length - 1];
-    if (this.kernel == null) {
-      this.kernel = this.addWeight(
-          'kernel', [inputLastDim, this.units], null, this.kernelInitializer,
-          this.kernelRegularizer, true, this.kernelConstraint);
-      if (this.useBias) {
-        this.bias = this.addWeight(
-            'bias', [this.units], null, this.biasInitializer,
-            this.biasRegularizer, true, this.biasConstraint);
-      }
-    }
-
-    this.inputSpec = [{minNDim: 2, axes: {[-1]: inputLastDim}}];
-    this.built = true;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const outputShape = inputShape.slice();
-    outputShape[outputShape.length - 1] = this.units;
-    return outputShape;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      // Dense layer accepts only a single input.
-      const input = getExactlyOneTensor(inputs);
-      const fusedActivationName =
-          mapActivationToFusedKernel(this.activation.getClassName());
-      let output: Tensor;
-
-      if (fusedActivationName != null) {
-        output = K.dot(
-            input, this.kernel.read(), fusedActivationName,
-            this.bias ? this.bias.read() : null);
-      } else {
-        output = K.dot(input, this.kernel.read());
-        if (this.bias != null) {
-          output = K.biasAdd(output, this.bias.read());
-        }
-        if (this.activation != null) {
-          output = this.activation.apply(output);
-        }
-      }
-
-      return output;
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      units: this.units,
-      activation: serializeActivation(this.activation),
-      useBias: this.useBias,
-      kernelInitializer: serializeInitializer(this.kernelInitializer),
-      biasInitializer: serializeInitializer(this.biasInitializer),
-      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
-      biasRegularizer: serializeRegularizer(this.biasRegularizer),
-      activityRegularizer: serializeRegularizer(this.activityRegularizer),
-      kernelConstraint: serializeConstraint(this.kernelConstraint),
-      biasConstraint: serializeConstraint(this.biasConstraint)
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Dense);
-
-export declare interface FlattenLayerArgs extends LayerArgs {
-  /** Image data format: channelsLast (default) or channelsFirst. */
-  dataFormat?: DataFormat;
-}
-
-export class Flatten extends Layer {
-  private dataFormat: DataFormat;
-
-  /** @nocollapse */
-  static className = 'Flatten';
-  constructor(args?: FlattenLayerArgs) {
-    args = args || {};
-    super(args);
-    this.inputSpec = [{minNDim: 3}];
-    this.dataFormat = args.dataFormat;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    for (const dim of inputShape.slice(1)) {
-      if (dim == null) {
-        throw new ValueError(
-            `The shape of the input to "Flatten" is not fully defined ` +
-            `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +
-            `"input_shape" or "batch_input_shape" argument to the first ` +
-            `layer in your model.`);
-      }
-    }
-    return [inputShape[0], arrayProd(inputShape, 1)];
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-
-      let input = getExactlyOneTensor(inputs);
-      if (this.dataFormat === 'channelsFirst' && input.rank > 1) {
-        const permutation: number[] = [0];
-        for (let i = 2; i < input.rank; ++i) {
-          permutation.push(i);
-        }
-        permutation.push(1);
-        input = transpose(input, permutation);
-      }
-
-      return K.batchFlatten(input);
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {};
-    if (this.dataFormat != null) {
-      config['dataFormat'] = this.dataFormat;
-    }
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Flatten);
-
-export declare interface ActivationLayerArgs extends LayerArgs {
-  /**
-   * Name of the activation function to use.
-   */
-  activation: ActivationIdentifier;
-}
-
-export class Activation extends Layer {
-  /** @nocollapse */
-  static className = 'Activation';
-  activation: ActivationFn;
-
-  constructor(args: ActivationLayerArgs) {
-    super(args);
-    this.supportsMasking = true;
-    this.activation = getActivation(args.activation);
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      const input = getExactlyOneTensor(inputs);
-      return this.activation.apply(input);
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {activation: serializeActivation(this.activation)};
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Activation);
-
-export declare interface ReshapeLayerArgs extends LayerArgs {
-  /** The target shape. Does not include the batch axis. */
-  targetShape: Shape;
-}
-
-export declare interface RepeatVectorLayerArgs extends LayerArgs {
-  /**
-   * The integer number of times to repeat the input.
-   */
-  n: number;
-}
-
-export class RepeatVector extends Layer {
-  /** @nocollapse */
-  static className = 'RepeatVector';
-  readonly n: number;
-
-  constructor(args: RepeatVectorLayerArgs) {
-    super(args);
-    this.n = args.n;
-    this.inputSpec = [{ndim: 2}];
-  }
-
-  override computeOutputShape(inputShape: Shape): Shape {
-    return [inputShape[0], this.n, inputShape[1]];
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = getExactlyOneTensor(inputs);
-      return K.repeat(inputs, this.n);
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      n: this.n,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(RepeatVector);
-
-export class Reshape extends Layer {
-  /** @nocollapse */
-  static className = 'Reshape';
-  private targetShape: Shape;
-
-  constructor(args: ReshapeLayerArgs) {
-    super(args);
-    this.targetShape = args.targetShape;
-
-    // Make sure that all unknown dimensions are represented as `null`.
-    for (let i = 0; i < this.targetShape.length; ++i) {
-      if (this.isUnknown(this.targetShape[i])) {
-        this.targetShape[i] = null;
-      }
-    }
-  }
-
-  private isUnknown(dim: number): boolean {
-    return dim < 0 || dim == null;
-  }
-
-  /**
-   * Finds and replaces a missing dimension in output shape.
-   *
-   * This is a near direct port of the internal Numpy function
-   * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.
-   *
-   * @param inputShape: Original shape of array begin reshape.
-   * @param outputShape: Target shape of the array, with at most a single
-   * `null` or negative number, which indicates an underdetermined dimension
-   * that should be derived from `inputShape` and the known dimensions of
-   *   `outputShape`.
-   * @returns: The output shape with `null` replaced with its computed value.
-   * @throws: ValueError: If `inputShape` and `outputShape` do not match.
-   */
-  private fixUnknownDimension(inputShape: Shape, outputShape: Shape): Shape {
-    const errorMsg = 'Total size of new array must be unchanged.';
-    const finalShape = outputShape.slice();
-    let known = 1;
-    let unknown = null;
-    for (let i = 0; i < finalShape.length; ++i) {
-      const dim = finalShape[i];
-      if (this.isUnknown(dim)) {
-        if (unknown === null) {
-          unknown = i;
-        } else {
-          throw new ValueError('Can only specifiy one unknown dimension.');
-        }
-      } else {
-        known *= dim;
-      }
-    }
-
-    const originalSize = arrayProd(inputShape);
-    if (unknown !== null) {
-      if (known === 0 || originalSize % known !== 0) {
-        throw new ValueError(errorMsg);
-      }
-      finalShape[unknown] = originalSize / known;
-    } else if (originalSize !== known) {
-      throw new ValueError(errorMsg);
-    }
-
-    return finalShape;
-  }
-
-  override computeOutputShape(inputShape: Shape): Shape {
-    let anyUnknownDims = false;
-    for (let i = 0; i < inputShape.length; ++i) {
-      if (this.isUnknown(inputShape[i])) {
-        anyUnknownDims = true;
-        break;
-      }
-    }
-
-    if (anyUnknownDims) {
-      return inputShape.slice(0, 1).concat(this.targetShape);
-    } else {
-      return inputShape.slice(0, 1).concat(
-          this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
-    }
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      const input = getExactlyOneTensor(inputs);
-      const inputShape = input.shape;
-      const outputShape = inputShape.slice(0, 1).concat(
-          this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
-      return reshape(input, outputShape);
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      targetShape: this.targetShape,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Reshape);
-
-export declare interface PermuteLayerArgs extends LayerArgs {
-  /**
-   * Array of integers. Permutation pattern. Does not include the
-   * sample (batch) dimension. Index starts at 1.
-   * For instance, `[2, 1]` permutes the first and second dimensions
-   * of the input.
-   */
-  dims: number[];
-}
-
-export class Permute extends Layer {
-  /** @nocollapse */
-  static className = 'Permute';
-  readonly dims: number[];
-  private readonly dimsIncludingBatch: number[];
-
-  constructor(args: PermuteLayerArgs) {
-    super(args);
-    if (args.dims == null) {
-      throw new Error(
-          'Required configuration field `dims` is missing during Permute ' +
-          'constructor call.');
-    }
-    if (!Array.isArray(args.dims)) {
-      throw new Error(
-          'Permute constructor requires `dims` to be an Array, but received ' +
-          `${args.dims} instead.`);
-    }
-
-    // Check the validity of the permutation indices.
-    const expectedSortedIndices = range(1, args.dims.length + 1);
-    if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {
-      throw new Error(
-          'Invalid permutation `dims`: ' + JSON.stringify(args.dims) +
-          ' `dims` must contain consecutive integers starting from 1.');
-    }
-
-    this.dims = args.dims;
-    this.dimsIncludingBatch = [0].concat(this.dims);
-    this.inputSpec = [new InputSpec({ndim: this.dims.length + 1})];
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const outputShape = inputShape.slice();
-    this.dims.forEach((dim: number, i: number) => {
-      outputShape[i + 1] = (inputShape as Shape)[dim];
-    });
-    return outputShape;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      dims: this.dims,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Permute);
-
-export declare interface MaskingArgs extends LayerArgs {
-  /**
-   * Masking Value. Defaults to `0.0`.
-   */
-  maskValue?: number;
-}
-
-export class Masking extends Layer {
-  /** @nocollapse */
-  static className = 'Masking';
-  maskValue: number;
-
-  constructor(args?: MaskingArgs) {
-    super(args == null ? {} : args);
-    this.supportsMasking = true;
-    if (args != null) {
-      this.maskValue = args.maskValue == null ? 0 : args.maskValue;
-    } else {
-      this.maskValue = 0;
-    }
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  override getConfig() {
-    const baseConfig = super.getConfig();
-    const config = {maskValue: this.maskValue};
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]):
-      Tensor {
-    const input = getExactlyOneTensor(inputs);
-    const axis = -1;
-    return any(notEqual(input, this.maskValue), axis);
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      const input = getExactlyOneTensor(inputs);
-      const axis = -1;
-      const keepDims = true;
-      const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);
-      const output = mul(input, cast(booleanMask, input.dtype));
-      return output;
-    });
-  }
-}
-serialization.registerClass(Masking);
diff --git a/tfjs-master/tfjs-layers/src/layers/core_test.ts b/tfjs-master/tfjs-layers/src/layers/core_test.ts
deleted file mode 100644
index 96379b400..000000000
--- a/tfjs-master/tfjs-layers/src/layers/core_test.ts
+++ /dev/null
@@ -1,871 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for core.ts.
- */
-import {mul, ones, scalar, Tensor, tensor2d, tensor3d, tensor4d, zeros} from '@tensorflow/tfjs-core';
-
-import * as K from '../backend/tfjs_backend';
-import {SymbolicTensor} from '../engine/topology';
-import * as tfl from '../index';
-import {InitializerIdentifier} from '../initializers';
-import {ActivationIdentifier} from '../keras_format/activation_config';
-import {pyListRepeat} from '../utils/generic_utils';
-import {arrayProd} from '../utils/math_utils';
-import {convertPythonicToTs, convertTsToPythonic} from '../utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndGPU, describeMathCPUAndWebGL2, expectTensorsClose} from '../utils/test_utils';
-
-describe('Dropout Layer: Symbolic', () => {
-  const dropoutRates = [0, 0.5];
-  const symbolicInputs = [
-    new tfl.SymbolicTensor('float32', [10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [null, 4], null, [], null),
-  ];
-
-  for (const rate of dropoutRates) {
-    for (const symbolicInput of symbolicInputs) {
-      const testTitle = `dropoutRate=${rate}; ` +
-          `input shape=${JSON.stringify(symbolicInput.shape)}`;
-      it(testTitle, () => {
-        const dropoutLayer = tfl.layers.dropout({rate});
-        const output = dropoutLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-        expect(output.dtype).toEqual(symbolicInput.dtype);
-        expect(output.shape).toEqual(symbolicInput.shape);
-        expect(output.sourceLayer).toEqual(dropoutLayer);
-        expect(output.inputs).toEqual([symbolicInput]);
-      });
-    }
-  }
-});
-
-describeMathCPUAndGPU('Dropout Layer', () => {
-  describe('tensor', () => {
-    const inputShape = [2, 3, 4];
-    const trainingValues = [false, true];
-    const dropoutRates = [0, 0.5];
-    const noiseShapes = [null, inputShape, [2, 3, 1]];
-    const seed = 0;
-
-    for (const training of trainingValues) {
-      for (const rate of dropoutRates) {
-        for (const noiseShape of noiseShapes) {
-          const testTitle = `training=${training}, dropoutRate=${rate}, ` +
-              `noiseShape=${JSON.stringify(noiseShape)}`;
-          it(testTitle, () => {
-            const x = ones(inputShape);
-            const dropoutLayer = tfl.layers.dropout({rate, noiseShape, seed});
-            const y = dropoutLayer.apply(x, {training}) as Tensor;
-            expect(x.dtype).toEqual(y.dtype);
-            expect(x.shape).toEqual(y.shape);
-            const xValue = x.dataSync();
-            const yValue = y.dataSync();
-            let nKept = 0;
-            if (noiseShape === noiseShapes[2]) {  // customized noiseShape
-              for (let i = 0; i < x.shape[0]; ++i) {
-                for (let j = 0; j < x.shape[1]; ++j) {
-                  const maskedValue =
-                      yValue[i * x.shape[1] * x.shape[2] + j * x.shape[2]];
-                  for (let k = 0; k < x.shape[2]; ++k) {
-                    const indice =
-                        i * x.shape[1] * x.shape[2] + j * x.shape[2] + k;
-                    if (training) {
-                      if (maskedValue === 0) {
-                        expect(yValue[indice]).toEqual(0);
-                      } else {
-                        nKept++;
-                        expect(yValue[indice]).toBeCloseTo(1 / (1 - rate));
-                      }
-                    } else {
-                      nKept++;
-                      expect(yValue[indice]).toEqual(1);
-                    }
-                  }
-                }
-              }
-            } else {  // default noiseShape
-              for (let i = 0; i < xValue.length; ++i) {
-                if (yValue[i] !== 0) {
-                  nKept++;
-                  if (training) {
-                    expect(yValue[i]).toBeCloseTo(1 / (1 - rate));
-                  } else {
-                    expect(yValue[i]).toBeCloseTo(1);
-                  }
-                }
-              }
-            }
-            const numel = K.countParams(x);
-            if (rate === 0 || !training) {
-              expect(nKept).toEqual(numel);
-            } else {
-              expect(nKept).toBeLessThan(numel);
-            }
-          });
-        }
-      }
-    }
-  });
-
-  describe('tensor with seed', () => {
-    it('get specific value.', () => {
-      const training = true;
-      const rate = 0.5;
-      const noiseShape = [2, 3, 4];
-      const x = ones([2, 3, 4]);
-      const seed = 23;
-      const dropoutLayer = tfl.layers.dropout({rate, noiseShape, seed});
-      const y = dropoutLayer.apply(x, {training}) as Tensor;
-      const yValuesExpected = [
-        0, 2, 2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 2, 0, 2, 0, 2, 2, 2, 0
-      ];
-      expectTensorsClose(y, tensor3d(yValuesExpected, [2, 3, 4]));
-    });
-  });
-});
-
-describeMathCPUAndGPU('SpatialDropout1D Layer', () => {
-  for (const training of [false, true]) {
-    for (const rate of [0.5, 0]) {
-      it(`Forward: rate=${rate}; training=${training}`, () => {
-        const layer = tfl.layers.spatialDropout1d({rate, seed: 1337});
-        const xs = ones([2, 3, 4]);
-        const ys = layer.apply(xs, {training}) as Tensor;
-        if (!training || rate === 0) {
-          expectTensorsClose(ys, xs);
-        } else {
-          expectTensorsClose(ys, tensor3d([
-                               [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
-                               [[0, 2, 0, 0], [0, 2, 0, 0], [0, 2, 0, 0]]
-                             ]));
-        }
-      });
-    }
-  }
-
-  it('Incorrect input shape: Symbolic', () => {
-    const layer = tfl.layers.spatialDropout1d({rate: 0.5});
-    const x = new SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-    expect(() => layer.apply(x))
-        .toThrowError(/.*expected ndim=3.*found ndim=4.*/);
-  });
-
-  it('Incorrect input shape: Concrete Tensor', () => {
-    const layer = tfl.layers.spatialDropout1d({rate: 0.5});
-    const x = ones([1, 2, 3, 4]);
-    expect(() => layer.apply(x))
-        .toThrowError(/.*expected ndim=3.*found ndim=4.*/);
-  });
-
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.spatialDropout1d({rate: 0.3, seed: 1337});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.spatialDropout1d(tsConfig);
-    expect(layerPrime.getConfig().rate).toEqual(0.3);
-    expect(layerPrime.getConfig().seed).toEqual(1337);
-  });
-});
-
-describeMathCPU('Dense Layer: Symbolic', () => {
-  const units = 3;
-  const activations: ActivationIdentifier[] =
-      [null, 'linear', 'relu', 'softmax'];
-  const symbolicInputs = [
-    new tfl.SymbolicTensor('float32', [10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [14, 12, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [null, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [null, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [null, 12, 10, 4], null, [], null),
-  ];
-
-  for (const activation of activations) {
-    for (const symbolicInput of symbolicInputs) {
-      it(`Generates correct symbolic output: ` +
-             `activation=${activation}, ` +
-             `input shape=${JSON.stringify(symbolicInput.shape)}`,
-         () => {
-           const denseLayer = tfl.layers.dense({units, activation});
-           const output = denseLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-
-           const expectedShape = symbolicInput.shape;
-           expectedShape[expectedShape.length - 1] = units;
-           expect(output.shape).toEqual(expectedShape);
-           expect(output.sourceLayer).toEqual(denseLayer);
-           expect(output.inputs).toEqual([symbolicInput]);
-         });
-    }
-  }
-
-  it('2D cascade: With undetermined dimension', () => {
-    const input1 = new tfl.SymbolicTensor('float32', [null, 4], null, [], null);
-    const denseLayer1 = tfl.layers.dense({units: 3});
-    const output1 = denseLayer1.apply(input1) as tfl.SymbolicTensor;
-
-    const denseLayer2 = tfl.layers.dense({units: 6});
-    const output2 = denseLayer2.apply(output1) as tfl.SymbolicTensor;
-
-    expect(output1.shape).toEqual([null, 3]);
-    expect(output1.sourceLayer).toEqual(denseLayer1);
-    expect(output1.inputs).toEqual([input1]);
-    expect(output2.shape).toEqual([null, 6]);
-    expect(output2.sourceLayer).toEqual(denseLayer2);
-    expect(output2.inputs).toEqual([output1]);
-  });
-
-  it('Using 1D input leads to error', () => {
-    const input = new tfl.SymbolicTensor('float32', [4], null, [], null);
-    const denseLayer = tfl.layers.dense({units: 3});
-    expect(() => denseLayer.apply(input)).toThrowError();
-  });
-
-  it('Different rank but compatible shape works', () => {
-    const denseLayer = tfl.layers.dense({units: 3});
-    const input1 = new tfl.SymbolicTensor('float32', [null, 4], null, [], null);
-    const input2 =
-        new tfl.SymbolicTensor('float32', [null, 6, 4], null, [], null);
-    const output1 = denseLayer.apply(input1) as tfl.SymbolicTensor;
-    expect(output1.shape).toEqual([null, 3]);
-    expect(output1.sourceLayer).toEqual(denseLayer);
-    expect(output1.inputs).toEqual([input1]);
-
-    const output2 = denseLayer.apply(input2) as tfl.SymbolicTensor;
-    expect(output2.shape).toEqual([null, 6, 3]);
-    expect(output2.sourceLayer).toEqual(denseLayer);
-    expect(output2.inputs).toEqual([input2]);
-  });
-
-  it('2D incompatible shape leads to error', () => {
-    const denseLayer = tfl.layers.dense({units: 3});
-    const input1 = new tfl.SymbolicTensor('float32', [null, 4], null, [], null);
-    const input2 = new tfl.SymbolicTensor('float32', [null, 5], null, [], null);
-    const output1 = denseLayer.apply(input1) as tfl.SymbolicTensor;
-    expect(output1.shape).toEqual([null, 3]);
-    expect(output1.sourceLayer).toEqual(denseLayer);
-    expect(output1.inputs).toEqual([input1]);
-
-    expect(() => {
-      // tslint:disable-next-line:no-unused-expression
-      denseLayer.apply(input2);
-    }).toThrowError(/incompatible with layer .* axis -1/);
-  });
-
-  it('Invalid kernelInitializer', () => {
-    expect(() => {
-      // tslint:disable-next-line:no-unused-expression
-      tfl.layers.dense({units: 4, kernelInitializer: 'invalid_initializer!'});
-    }).toThrowError(/Unknown initializer/);
-  });
-
-  it('Invalid activation', () => {
-    expect(() => {
-      // tslint:disable-next-line:no-unused-expression no-any
-      tfl.layers.dense({units: 4, activation: 'invalid_activation!' as any});
-    }).toThrowError(/Unknown activation/);
-  });
-
-  it('Invalid units leads to Error', () => {
-    expect(() => tfl.layers.dense({
-      units: 10.9
-    })).toThrowError(/units.*positive integer.*10\.9\.$/);
-    expect(() => tfl.layers.dense({
-      units: 0.5
-    })).toThrowError(/units.*positive integer.*0\.5\.$/);
-    expect(() => tfl.layers.dense({
-      units: 0
-    })).toThrowError(/units.*positive integer.*0\.$/);
-    expect(() => tfl.layers.dense({
-      units: -2
-    })).toThrowError(/units.*positive integer.*-2\.$/);
-    expect(() => tfl.layers.dense({
-      // tslint:disable-next-line:no-any
-      units: '2' as any
-    })).toThrowError(/units.*positive integer.*\"2\"\.$/);
-    expect(() => tfl.layers.dense({
-      // tslint:disable-next-line:no-any
-      units: [] as any
-    })).toThrowError('units is unexpectedly an empty array.');
-  });
-});
-
-describeMathCPUAndGPU('Dense Layer: Tensor', () => {
-  const units = 6;
-  const useBiases = [null, false, true];
-  const biasInitializers: InitializerIdentifier[] = ['zeros', 'ones'];
-  const activations: ActivationIdentifier[] =
-      [null, 'linear', 'relu', 'softmax'];
-  const inputLastDims = [5, 8];
-  // TODO(cais): Test Tensor1D, Tensor3D, Tensor4D once those are supported by
-  // the backend.
-
-  for (const useBias of useBiases) {
-    for (const biasInitializer of biasInitializers) {
-      for (const activation of activations) {
-        for (const inputLastDim of inputLastDims) {
-          it(`Call once: useBias=${useBias}, ` +
-                 `biasInitializer=${biasInitializer}, ` +
-                 `activation=${activation}, ` +
-                 `inputLastDim=${JSON.stringify(inputLastDim)}`,
-             () => {
-               const input = ones([2, inputLastDim]);
-               const denseLayer = tfl.layers.dense({
-                 units,
-                 useBias,
-                 biasInitializer,
-                 activation,
-                 kernelInitializer: 'ones'
-               });
-               let expectedElementValue: number;
-               if (activation === 'softmax') {
-                 expectedElementValue = 1 / units;
-               } else {
-                 expectedElementValue = input.shape[input.shape.length - 1];
-                 if (useBias !== false && biasInitializer === 'ones') {
-                   expectedElementValue += 1;
-                 }
-               }
-               const expectedShape = input.shape.slice();
-               expectedShape[expectedShape.length - 1] = units;
-               let expectedOutput;
-               if (input.rank === 2) {
-                 expectedOutput = tensor2d(
-                     pyListRepeat(
-                         expectedElementValue, arrayProd(expectedShape)),
-                     [expectedShape[0], expectedShape[1]]);
-               }
-               expectTensorsClose(
-                   denseLayer.apply(input, null) as Tensor, expectedOutput);
-             });
-        }
-      }
-    }
-  }
-
-  it('Calling apply again with incompatible shape leads to error', () => {
-    const input1 = ones([2, 2]);  // First call.
-    const input2 = ones([3, 2]);  // Okay.
-    const input3 = ones([3, 3]);  // Leads to error.
-
-    const denseLayer = tfl.layers.dense({units: 4, kernelInitializer: 'ones'});
-    expectTensorsClose(
-        denseLayer.apply(input1) as Tensor,
-        tensor2d([2, 2, 2, 2, 2, 2, 2, 2], [2, 4]));
-    expectTensorsClose(
-        denseLayer.apply(input2) as Tensor,
-        tensor2d([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [3, 4]));
-    expect(() => denseLayer.apply(input3)).toThrowError();
-  });
-  it('Calling apply with compatible symbolic input after Tensor input works',
-     () => {
-       const concreteInput = ones([2, 2]);
-       const symbolicInput =
-           new tfl.SymbolicTensor('float32', [2, 2], null, [], null);
-       const denseLayer =
-           tfl.layers.dense({units: 4, kernelInitializer: 'ones'});
-
-       expectTensorsClose(
-           denseLayer.apply(concreteInput) as Tensor,
-           tensor2d([2, 2, 2, 2, 2, 2, 2, 2], [2, 4]));
-
-       const symbolicOuptut =
-           denseLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-       expect(symbolicOuptut.shape).toEqual([2, 4]);
-       expect(symbolicOuptut.sourceLayer).toEqual(denseLayer);
-       expect(symbolicOuptut.inputs).toEqual([symbolicInput]);
-     });
-  it('Calling apply with incompatible symbolic input after Tensor', () => {
-    const concreteInput = ones([2, 2]);
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [2, 3], null, [], null);
-    const denseLayer = tfl.layers.dense({units: 4, kernelInitializer: 'ones'});
-
-    expectTensorsClose(
-        denseLayer.apply(concreteInput) as Tensor,
-        tensor2d([2, 2, 2, 2, 2, 2, 2, 2], [2, 4]));
-
-    expect(() => {
-      // tslint:disable-next-line:no-unused-expression
-      denseLayer.apply(symbolicInput);
-    }).toThrowError(/incompatible with layer .* axis -1/);
-  });
-});
-
-describe('Flatten Layer: Symbolic', () => {
-  const symbolicInputs = [
-    new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [14, 12, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [null, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [null, 12, 10, 4], null, [], null),
-  ];
-
-  for (const symbolicInput of symbolicInputs) {
-    it(`Generates correct symbolic output: no-arg constructor: ` +
-           `input shape=${JSON.stringify(symbolicInput.shape)}`,
-       () => {
-         const flattenLayer = tfl.layers.flatten();
-         const output = flattenLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-         const expectedShape =
-             [symbolicInput.shape[0], arrayProd(symbolicInput.shape, 1)];
-         expect(output.shape).toEqual(expectedShape);
-         expect(output.sourceLayer).toEqual(flattenLayer);
-         expect(output.inputs).toEqual([symbolicInput]);
-       });
-
-    it(`Generates correct symbolic output: empty one-arg constructor: ` +
-           `input shape=${JSON.stringify(symbolicInput.shape)}`,
-       () => {
-         const flattenLayer = tfl.layers.flatten({});
-         const output = flattenLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-         const expectedShape =
-             [symbolicInput.shape[0], arrayProd(symbolicInput.shape, 1)];
-         expect(output.shape).toEqual(expectedShape);
-         expect(output.sourceLayer).toEqual(flattenLayer);
-         expect(output.inputs).toEqual([symbolicInput]);
-       });
-  }
-
-  it('2D tfl.SymbolicTensor leads to error', () => {
-    const flattenLayer = tfl.layers.flatten();
-    const x = new tfl.SymbolicTensor('float32', [null, 4], null, [], null);
-    expect(() => flattenLayer.apply(x)).toThrowError();
-  });
-
-  it('3D with undetermined input size leads to error', () => {
-    const flattenLayer = tfl.layers.flatten({});
-    const x = new tfl.SymbolicTensor('float32', [8, 4, null], null, [], null);
-    expect(() => flattenLayer.apply(x)).toThrowError(/not fully defined/);
-  });
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.flatten({dataFormat: 'channelsFirst'});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.flatten(tsConfig);
-    expect(layerPrime.getConfig().dataFormat).toEqual('channelsFirst');
-  });
-});
-
-describeMathCPUAndGPU('Flatten Layer: Tensor', () => {
-  it('Attempt to apply on Tensor2D leads to error', () => {
-    const flattenLayer = tfl.layers.flatten();
-    const x = tensor2d([[1, 3], [3, 3]], [2, 2]);
-    expect(() => flattenLayer.apply(x)).toThrowError();
-  });
-  it('Flattens Tensor3D', () => {
-    const flattenLayer = tfl.layers.flatten();
-    const x =
-        tensor3d([[[10, 20], [30, 40]], [[-10, -20], [-30, -40]]], [2, 2, 2]);
-    const expectedOutput =
-        tensor2d([[10, 20, 30, 40], [-10, -20, -30, -40]], [2, 4]);
-    expectTensorsClose(flattenLayer.apply(x, null) as Tensor, expectedOutput);
-  });
-  it('Flattens Tensor4D', () => {
-    const flattenLayer = tfl.layers.flatten();
-    const x = tensor4d(
-        [
-          [[[10, 20], [30, 40]], [[-10, -20], [-30, -40]]],
-          [[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]
-        ],
-        [2, 2, 2, 2]);
-    const expectedOutput = tensor2d(
-        [10, 20, 30, 40, -10, -20, -30, -40, 1, 2, 3, 4, -1, -2, -3, -4],
-        [2, 8]);
-    expectTensorsClose(flattenLayer.apply(x, null) as Tensor, expectedOutput);
-  });
-  it('Flattens Tensor4D, channelFirst', () => {
-    const flattenLayer = tfl.layers.flatten({dataFormat: 'channelsFirst'});
-    const x = tensor4d(
-        [
-          [[[10, 20], [30, 40]], [[-10, -20], [-30, -40]]],
-          [[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]
-        ],
-        [2, 2, 2, 2]);
-    const expectedOutput = tensor2d(
-        [10, -10, 20, -20, 30, -30, 40, -40, 1, -1, 2, -2, 3, -3, 4, -4],
-        [2, 8]);
-    expectTensorsClose(flattenLayer.apply(x, null) as Tensor, expectedOutput);
-  });
-});
-
-describeMathCPUAndGPU('Activation Layer: Tensor', () => {
-  const inputShape = [1];
-
-  it('linear', () => {
-    const x = mul(scalar(10), ones(inputShape));
-    const layer = tfl.layers.activation({activation: 'linear'});
-    const output = layer.apply(x) as Tensor;
-    expectTensorsClose(output, x);
-  });
-
-  it('relu', () => {
-    const x = mul(scalar(-5), ones(inputShape));
-    const expectedValue = zeros(inputShape);
-    const layer = tfl.layers.activation({activation: 'relu'});
-    const output = layer.apply(x) as Tensor;
-    expectTensorsClose(output, expectedValue);
-  });
-
-  it('sigmoid', () => {
-    const val = 10;
-    const x = mul(scalar(val), ones(inputShape));
-    const expectedValue =
-        mul(scalar(1 / (1 + Math.exp(-1 * val))), ones(inputShape));
-    const layer = tfl.layers.activation({activation: 'sigmoid'});
-    const output = layer.apply(x) as Tensor;
-    expectTensorsClose(output, expectedValue);
-  });
-
-  it('softmax', () => {
-    const x = mul(scalar(10), ones(inputShape));
-    const expectedValue = ones(inputShape);
-    const layer = tfl.layers.activation({activation: 'softmax'});
-    const output = layer.apply(x) as Tensor;
-    expectTensorsClose(output, expectedValue);
-  });
-
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.activation({activation: 'relu'});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.activation(tsConfig);
-    expect(layerPrime.getConfig().activation).toEqual('relu');
-  });
-});
-
-describe('RepeatVector Layer: Symbolic', () => {
-  it('All dimensions known.', () => {
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [3, 4], null, [], null);
-    const repeatVectorLayer = tfl.layers.repeatVector({n: 2});
-    const output = repeatVectorLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([3, 2, 4]);
-    expect(output.sourceLayer).toEqual(repeatVectorLayer);
-    expect(output.inputs).toEqual([symbolicInput]);
-  });
-});
-
-describeMathCPUAndGPU('RepeatVector Layer: Tensor', () => {
-  it('With 2D tensor', () => {
-    const repeatVectorLayer = tfl.layers.repeatVector({n: 3});
-    const x = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const expectedOutput = tensor3d(
-        [[[10, 20], [10, 20], [10, 20]], [[30, 40], [30, 40], [30, 40]]],
-        [2, 3, 2]);
-    expectTensorsClose(
-        repeatVectorLayer.apply(x, null) as Tensor, expectedOutput);
-  });
-});
-
-describe('Reshape Layer: Symbolic', () => {
-  it('All dimensions known.', () => {
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null);
-    const targetShape = [5, 8];
-    const flattenLayer = tfl.layers.reshape({targetShape});
-    const output = flattenLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([12, 5, 8]);
-    expect(output.sourceLayer).toEqual(flattenLayer);
-    expect(output.inputs).toEqual([symbolicInput]);
-  });
-
-  it('One unknown dimension.', () => {
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null);
-    const targetShape = [5, null];
-    const flattenLayer = tfl.layers.reshape({targetShape});
-    const output = flattenLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([12, 5, 8]);
-    expect(output.sourceLayer).toEqual(flattenLayer);
-    expect(output.inputs).toEqual([symbolicInput]);
-  });
-
-  it('Incompatible size.', () => {
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null);
-    const targetShape = [8, 8];
-    const flattenLayer = tfl.layers.reshape({targetShape});
-    expect(() => flattenLayer.apply(symbolicInput))
-        .toThrowError(/Total size of new array must be unchanged/);
-  });
-
-  it('Two unknown dimensions.', () => {
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null);
-    const targetShape: number[] = [null, null];
-    const flattenLayer = tfl.layers.reshape({targetShape});
-    expect(() => flattenLayer.apply(symbolicInput))
-        .toThrowError(/Can only specifiy one unknown dimension/);
-  });
-
-  it('One unknown with indivisible size.', () => {
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null);
-    const targetShape = [7, null];
-    const flattenLayer = tfl.layers.reshape({targetShape});
-    expect(() => flattenLayer.apply(symbolicInput))
-        .toThrowError(/Total size of new array must be unchanged/);
-  });
-
-  it('Serialization round-trip', () => {
-    const layer = tfl.layers.reshape({targetShape: [2, 3]});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.reshape(tsConfig);
-    expect(layerPrime.getConfig().targetShape).toEqual([2, 3]);
-  });
-});
-
-describeMathCPUAndGPU('Reshape Layer: Tensor', () => {
-  it('Reshape Tensor3D to Tensor3D: All dimensions known', () => {
-    const reshapeLayer = tfl.layers.reshape({targetShape: [4, 1]});
-    const x =
-        tensor3d([[[10, 20], [30, 40]], [[-10, -20], [-30, -40]]], [2, 2, 2]);
-    const expectedOutput =
-        tensor3d([10, 20, 30, 40, -10, -20, -30, -40], [2, 4, 1]);
-    expectTensorsClose(reshapeLayer.apply(x, null) as Tensor, expectedOutput);
-  });
-
-  it('Reshape Tensor3D to Tensor2D: All dimensions known', () => {
-    const reshapeLayer = tfl.layers.reshape({targetShape: [4]});
-    const x =
-        tensor3d([[[10, 20], [30, 40]], [[-10, -20], [-30, -40]]], [2, 2, 2]);
-    const expectedOutput =
-        tensor2d([10, 20, 30, 40, -10, -20, -30, -40], [2, 4]);
-    expectTensorsClose(reshapeLayer.apply(x, null) as Tensor, expectedOutput);
-  });
-
-  it('Reshape Tensor2D to Tensor3D: All dimensions known', () => {
-    const reshapeLayer = tfl.layers.reshape({targetShape: [3, 2]});
-    const x = tensor2d(
-        [[10, 20, 30, 40, 50, 60], [-10, -20, -30, -40, -50, -60]], [2, 6]);
-    const expectedOutput = tensor3d(
-        [10, 20, 30, 40, 50, 60, -10, -20, -30, -40, -50, -60], [2, 3, 2]);
-    expectTensorsClose(reshapeLayer.apply(x, null) as Tensor, expectedOutput);
-  });
-
-  for (const unknownDim of [-1, null]) {
-    it(`Reshape Tensor2D to Tensor3D: Last dimension unknown as ${unknownDim}`,
-       () => {
-         const reshapeLayer =
-             tfl.layers.reshape({targetShape: [3, unknownDim]});
-         const x = tensor2d(
-             [[10, 20, 30, 40, 50, 60], [-10, -20, -30, -40, -50, -60]],
-             [2, 6]);
-         const expectedOutput = tensor3d(
-             [10, 20, 30, 40, 50, 60, -10, -20, -30, -40, -50, -60], [2, 3, 2]);
-         expectTensorsClose(
-             reshapeLayer.apply(x, null) as Tensor, expectedOutput);
-       });
-
-    it(`Reshape Tensor2D to Tensor3D: First dimension unknown as ${unknownDim}`,
-       () => {
-         const reshapeLayer =
-             tfl.layers.reshape({targetShape: [unknownDim, 3]});
-         const x = tensor2d(
-             [[10, 20, 30, 40, 50, 60], [-10, -20, -30, -40, -50, -60]],
-             [2, 6]);
-         const expectedOutput = tensor3d(
-             [10, 20, 30, 40, 50, 60, -10, -20, -30, -40, -50, -60], [2, 2, 3]);
-         expectTensorsClose(
-             reshapeLayer.apply(x, null) as Tensor, expectedOutput);
-       });
-  }
-
-  it('Known but incompatible dimensions', () => {
-    const reshapeLayer = tfl.layers.reshape({targetShape: [3, 3]});
-    const x =
-        tensor3d([[[10, 20], [30, 40]], [[-10, -20], [-30, -40]]], [2, 2, 2]);
-    expect(() => reshapeLayer.apply(x, null))
-        .toThrowError(/Total size of new array must be unchanged/);
-  });
-
-  it('Unknown and incompatible dimensions', () => {
-    const reshapeLayer = tfl.layers.reshape({targetShape: [3, null]});
-    const x =
-        tensor3d([[[10, 20], [30, 40]], [[-10, -20], [-30, -40]]], [2, 2, 2]);
-    expect(() => reshapeLayer.apply(x, null))
-        .toThrowError(/Total size of new array must be unchanged/);
-  });
-
-  it('More than one unknown dimension.', () => {
-    const reshapeLayer = tfl.layers.reshape({targetShape: [null, null]});
-    const x =
-        tensor3d([[[10, 20], [30, 40]], [[-10, -20], [-30, -40]]], [2, 2, 2]);
-    expect(() => reshapeLayer.apply(x, null))
-        .toThrowError(/Can only specifiy one unknown dimension/);
-  });
-});
-
-describe('Permute Layer: Symbolic', () => {
-  it('1D Trivial', () => {
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [null, 4], null, [], null);
-    const dims = [1];
-    const permuteLayer = tfl.layers.permute({dims});
-    const output = permuteLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([null, 4]);
-    expect(output.sourceLayer).toEqual(permuteLayer);
-    expect(output.inputs).toEqual([symbolicInput]);
-  });
-
-  it('2D', () => {
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [null, 4, 6], null, [], null);
-    const dims = [2, 1];
-    const permuteLayer = tfl.layers.permute({dims});
-    const output = permuteLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([null, 6, 4]);
-    expect(output.sourceLayer).toEqual(permuteLayer);
-    expect(output.inputs).toEqual([symbolicInput]);
-  });
-
-  it('3D', () => {
-    const symbolicInput =
-        new tfl.SymbolicTensor('float32', [null, 4, 6, 8], null, [], null);
-    const dims = [3, 1, 2];
-    const permuteLayer = tfl.layers.permute({dims});
-    const output = permuteLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([null, 8, 4, 6]);
-    expect(output.sourceLayer).toEqual(permuteLayer);
-    expect(output.inputs).toEqual([symbolicInput]);
-  });
-
-  it('Missing dims config leads to Error', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.permute({} as any)).toThrowError(/dims.* missing/);
-  });
-
-  it('Non-Array dims config leads to Error', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.permute({dims: 1} as any))
-        .toThrowError(/requires.*dims.* to be an Array/);
-  });
-
-  it('Non-consecutive dims values leads to Error', () => {
-    expect(() => tfl.layers.permute({
-      dims: [1, 3]
-    })).toThrowError(/Invalid permutation .*dims/);
-  });
-
-  it('Repeating dims values leads to Error', () => {
-    expect(() => tfl.layers.permute({
-      dims: [1, 1, 3]
-    })).toThrowError(/Invalid permutation .*dims/);
-  });
-
-  it('Dims values containing 0 leads to Error', () => {
-    expect(() => tfl.layers.permute({
-      dims: [0, 1, 2]
-    })).toThrowError(/Invalid permutation .*dims/);
-  });
-
-  it('Serialization round-trip', () => {
-    const layer = tfl.layers.permute({dims: [1, 3, 2]});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.permute(tsConfig);
-    expect(layerPrime.getConfig().dims).toEqual([1, 3, 2]);
-  });
-});
-
-describe('Masking Layer: Symbolic', () => {
-  it('computeOutputShape', () => {
-    const layer = tfl.layers.masking();
-    const inputShape = [null, 4, 6];
-    const outputShape = layer.computeOutputShape(inputShape);
-    expect(outputShape).toEqual([null, 4, 6]);
-  });
-
-  it('Serialization round-trip', () => {
-    const layer = tfl.layers.masking({maskValue: -3});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.masking(tsConfig);
-    expect(layerPrime.getConfig().maskValue).toEqual(-3);
-  });
-});
-
-describeMathCPUAndGPU('Permute Layer: Tensor', () => {
-  it('2D', () => {
-    const permuteLayer = tfl.layers.permute({dims: [2, 1]});
-    const x =
-        tensor3d([[[10, 20], [30, 40]], [[-10, -20], [-30, -40]]], [2, 2, 2]);
-    const expectedOutput =
-        tensor3d([[[10, 30], [20, 40]], [[-10, -30], [-20, -40]]], [2, 2, 2]);
-    expectTensorsClose(permuteLayer.apply(x) as Tensor, expectedOutput);
-  });
-});
-
-describeMathCPUAndWebGL2('Masking Layer: Tensor', () => {
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Masking(input_shape=[3, 2]))
-  // model.add(tf.keras.layers.SimpleRNN(
-  //   units=1,
-  //   recurrent_initializer='ones',
-  //   kernel_initializer='ones'))
-  //
-  // xs = np.array([[[1, 1], [1, 0], [0, 0]]], dtype=np.float32)
-  // print(xs.shape)
-  // ys = model.predict(xs)
-  //
-  // print(ys)
-  // ```
-  it('3D, default maskValue', () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.masking({inputShape: [3, 2]}));
-    model.add(tfl.layers.simpleRNN(
-        {units: 1, recurrentInitializer: 'ones', kernelInitializer: 'ones'}));
-
-    const xs = tensor3d([[[1, 1], [1, 0], [0, 0]]]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(ys, tensor2d([[0.961396]]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Masking(mask_value=-1, input_shape=[3, 2]))
-  // model.add(tf.keras.layers.SimpleRNN(
-  //   units=1,
-  //   recurrent_initializer='ones',
-  //   kernel_initializer='ones'))
-  //
-  // xs = np.array([[[1, 1], [1, -1], [-1, -1]]], dtype=np.float32)
-  // print(xs.shape)
-  // ys = model.predict(xs)
-  //
-  // print(ys)
-  // ```
-  it('3D, custom maskValue', () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.masking({maskValue: -1, inputShape: [3, 2]}));
-    model.add(tfl.layers.simpleRNN(
-        {units: 1, recurrentInitializer: 'ones', kernelInitializer: 'ones'}));
-
-    const xs = tensor3d([[[1, 1], [1, -1], [-1, -1]]]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(ys, tensor2d([[0.746068]]));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/embeddings.ts b/tfjs-master/tfjs-layers/src/layers/embeddings.ts
deleted file mode 100644
index 61c428311..000000000
--- a/tfjs-master/tfjs-layers/src/layers/embeddings.ts
+++ /dev/null
@@ -1,212 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * TensorFlow.js Layers: Embedding Layer.
- *
- * Original source: keras/constraints.py
- */
-import {notEqual, reshape, serialization, Tensor, tidy, zerosLike} from '@tensorflow/tfjs-core';
-
-import * as K from '../backend/tfjs_backend';
-import {Constraint, ConstraintIdentifier, getConstraint, serializeConstraint} from '../constraints';
-import {Layer, LayerArgs} from '../engine/topology';
-import {ValueError} from '../errors';
-import {getInitializer, Initializer, InitializerIdentifier, serializeInitializer} from '../initializers';
-import {Shape} from '../keras_format/common';
-import {getRegularizer, Regularizer, RegularizerIdentifier, serializeRegularizer} from '../regularizers';
-import {Kwargs} from '../types';
-import * as generic_utils from '../utils/generic_utils';
-import {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';
-import {LayerVariable} from '../variables';
-
-export declare interface EmbeddingLayerArgs extends LayerArgs {
-  /**
-   * Integer > 0. Size of the vocabulary, i.e. maximum integer index + 1.
-   */
-  inputDim: number;
-  /**
-   * Integer >= 0. Dimension of the dense embedding.
-   */
-  outputDim: number;
-  /**
-   * Initializer for the `embeddings` matrix.
-   */
-  embeddingsInitializer?: InitializerIdentifier|Initializer;
-  /**
-   * Regularizer function applied to the `embeddings` matrix.
-   */
-  embeddingsRegularizer?: RegularizerIdentifier|Regularizer;
-  /**
-   * Regularizer function applied to the activation.
-   */
-  activityRegularizer?: RegularizerIdentifier|Regularizer;
-  /**
-   * Constraint function applied to the `embeddings` matrix.
-   */
-  embeddingsConstraint?: ConstraintIdentifier|Constraint;
-  /**
-   * Whether the input value 0 is a special "padding" value that should be
-   * masked out. This is useful when using recurrent layers which may take
-   * variable length input.
-   *
-   * If this is `True` then all subsequent layers in the model need to support
-   * masking or an exception will be raised. If maskZero is set to `True`, as a
-   * consequence, index 0 cannot be used in the vocabulary (inputDim should
-   * equal size of vocabulary + 1).
-   */
-  maskZero?: boolean;
-  /**
-   * Length of input sequences, when it is constant.
-   *
-   * This argument is required if you are going to connect `flatten` then
-   * `dense` layers upstream (without it, the shape of the dense outputs cannot
-   * be computed).
-   */
-  inputLength?: number|number[];
-}
-
-export class Embedding extends Layer {
-  /** @nocollapse */
-  static className = 'Embedding';
-  private inputDim: number;
-  private outputDim: number;
-  private embeddingsInitializer: Initializer;
-  private maskZero: boolean;
-  private inputLength: number|number[];
-
-  private embeddings: LayerVariable = null;
-
-  readonly DEFAULT_EMBEDDINGS_INITIALIZER: InitializerIdentifier =
-      'randomUniform';
-  private readonly embeddingsRegularizer?: Regularizer;
-  private readonly embeddingsConstraint?: Constraint;
-
-  constructor(args: EmbeddingLayerArgs) {
-    super(args);
-    if (args.batchInputShape == null && args.inputShape == null) {
-      // Porting Note: This logic is copied from Layer's constructor, since we
-      // can't do exactly what the Python constructor does for Embedding().
-      // Specifically, the super constructor can not be called after the
-      // mutation of the `config` argument.
-      let batchSize: number = null;
-      if (args.batchSize != null) {
-        batchSize = args.batchSize;
-      }
-      if (args.inputLength == null) {
-        // Fix super-constructor to what it would have done if
-        // 'config.inputShape' were (None, )
-        this.batchInputShape = [batchSize, null];
-      } else {
-        // Fix super-constructor to what it would have done if
-        // 'config.inputShape' were (config.inputLength, )
-        this.batchInputShape =
-            [batchSize].concat(generic_utils.toList(args.inputLength));
-      }
-    }
-    this.inputDim = args.inputDim;
-    generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');
-    this.outputDim = args.outputDim;
-    generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');
-    this.embeddingsInitializer = getInitializer(
-        args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);
-    this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);
-    this.activityRegularizer = getRegularizer(args.activityRegularizer);
-    this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);
-    this.maskZero = args.maskZero;
-    this.supportsMasking = args.maskZero;
-    this.inputLength = args.inputLength;
-  }
-
-  public override build(inputShape: Shape|Shape[]): void {
-    this.embeddings = this.addWeight(
-        'embeddings', [this.inputDim, this.outputDim], this.dtype,
-        this.embeddingsInitializer, this.embeddingsRegularizer, true,
-        this.embeddingsConstraint);
-    this.built = true;
-  }
-
-  // Override warnOnIncompatibleInputShape because an embedding layer allows
-  // the input to have varying ranks.
-  protected override warnOnIncompatibleInputShape(inputShape: Shape) {}
-
-  override computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]):
-      Tensor {
-    return tidy(() => {
-      if (!this.maskZero) {
-        return null;
-      } else {
-        inputs = getExactlyOneTensor(inputs);
-        return notEqual(inputs, zerosLike(inputs));
-      }
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    if (this.inputLength == null) {
-      return [...inputShape, this.outputDim];
-    }
-    // inputLength can be an array if input is 3D or higher.
-    const inLens: number[] = generic_utils.toList(this.inputLength);
-    if (inLens.length !== inputShape.length - 1) {
-      throw new ValueError(
-          `"inputLength" is ${this.inputLength}, but received ` +
-          `input shape has shape ${inputShape}`);
-    } else {
-      let i = 0;
-      for (let k = 0; k < inLens.length; ++k) {
-        const s1 = inLens[k];
-        const s2 = inputShape[k + 1];
-        if ((s1 != null) && (s2 != null) && (s1 !== s2)) {
-          throw new ValueError(
-              `"inputLength" is ${this.inputLength}, but received ` +
-              `input shape has shape ${inputShape}`);
-        } else if (s1 == null) {
-          inLens[i] = s2;
-        }
-        i++;
-      }
-    }
-    return [inputShape[0], ...inLens, this.outputDim];
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      // Embedding layer accepts only a single input.
-      let input = getExactlyOneTensor(inputs);
-      if (input.dtype !== 'int32') {
-        input = K.cast(input, 'int32');
-      }
-      const output =
-          K.gather(this.embeddings.read(), reshape(input, [input.size]));
-      return reshape(
-          output, getExactlyOneShape(this.computeOutputShape(input.shape)));
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      inputDim: this.inputDim,
-      outputDim: this.outputDim,
-      embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),
-      embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),
-      activityRegularizer: serializeRegularizer(this.activityRegularizer),
-      embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),
-      maskZero: this.maskZero,
-      inputLength: this.inputLength
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Embedding);
diff --git a/tfjs-master/tfjs-layers/src/layers/embeddings_test.ts b/tfjs-master/tfjs-layers/src/layers/embeddings_test.ts
deleted file mode 100644
index 44fa81350..000000000
--- a/tfjs-master/tfjs-layers/src/layers/embeddings_test.ts
+++ /dev/null
@@ -1,163 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for embedding.ts.
- */
-
-import {DataType, Tensor, tensor2d, tensor3d, test_util} from '@tensorflow/tfjs-core';
-
-import * as tfl from '../index';
-import {describeMathCPU, expectTensorsClose} from '../utils/test_utils';
-
-const expectArraysClose = test_util.expectArraysClose;
-
-describeMathCPU('Embedding Layers: Symbolic 1D, 2D & 3D', () => {
-  const inputShapes = [[1], [5], [1, 20], [2, 3, 4]];
-  const outputDims = [1, 7, 47];
-  const batchDim = 17;
-  const inputDim = 100;
-  for (const inputShape of inputShapes) {
-    for (const outputDim of outputDims) {
-      const testTitle = `inputShape=${inputShape}, outputDim=${outputDim}`;
-      it(testTitle, () => {
-        const embeddingLayer = tfl.layers.embedding({inputDim, outputDim});
-        const fullInputShape = [batchDim].concat(inputShape);
-        const symbolicInput =
-            new tfl.SymbolicTensor('float32', fullInputShape, null, [], null);
-        const output =
-            embeddingLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-        const expectedShape = [batchDim].concat(inputShape).concat([outputDim]);
-        expect(output.shape).toEqual(expectedShape);
-      });
-    }
-  }
-
-  it('Invalid inputDim leads to Error', () => {
-    expect(() => tfl.layers.embedding({inputDim: 12.5, outputDim: 4}))
-        .toThrowError(/inputDim.*positive integer.*12\.5\.$/);
-    expect(() => tfl.layers.embedding({inputDim: 0, outputDim: 4}))
-        .toThrowError(/inputDim.*positive integer.*0\.$/);
-    expect(() => tfl.layers.embedding({inputDim: -123, outputDim: 4}))
-        .toThrowError(/inputDim.*positive integer.*-123\.$/);
-  });
-
-  it('Invalid outputDim leads to Error', () => {
-    expect(() => tfl.layers.embedding({inputDim: 8, outputDim: 16.5}))
-        .toThrowError(/outputDim.*positive integer.*16\.5\.$/);
-    expect(() => tfl.layers.embedding({inputDim: 8, outputDim: 0}))
-        .toThrowError(/outputDim.*positive integer.*0\.$/);
-    expect(() => tfl.layers.embedding({inputDim: 8, outputDim: -32}))
-        .toThrowError(/outputDim.*positive integer.*-32\.$/);
-  });
-});
-
-describeMathCPU('Embedding Layers: With explicit inputLength', () => {
-  const inputShape = [null, 4, 5];
-  const outputDim = 7;
-  const inputLengths = [[null, 4, 5], [null, null, 5], [null, null, null]];
-  const batchDim = 17;
-  for (const inputLength of inputLengths) {
-    const testTitle = `inputLength=${inputLength}`;
-    it(testTitle, () => {
-      const inputDim = 100;
-      const embeddingLayer =
-          tfl.layers.embedding({inputDim, outputDim, inputLength});
-      const fullInputShape = [batchDim].concat(inputShape);
-      const symbolicInput =
-          new tfl.SymbolicTensor('float32', fullInputShape, null, [], null);
-      const output = embeddingLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-      const expectedShape = [batchDim].concat(inputShape).concat([outputDim]);
-      expect(output.shape).toEqual(expectedShape);
-      expect(output.dtype).toEqual(symbolicInput.dtype);
-    });
-  }
-});
-
-describeMathCPU('Embedding Layers: Tensor', () => {
-  it('check value equality', () => {
-    const x = tensor3d([0, 5, 1, 1, 1, 1, 1, 1], [1, 2, 4]);
-    const embeddingLayer = tfl.layers.embedding(
-        {inputDim: 6, outputDim: 3, embeddingsInitializer: 'randomUniform'});
-    const y = embeddingLayer.apply(x) as Tensor;
-    const yExpectedShape = [1, 2, 4, 3];
-    expect(y.shape).toEqual(yExpectedShape);
-    const weights = embeddingLayer.getWeights()[0];
-    expect(embeddingLayer.computeOutputShape(x.shape)).toEqual(yExpectedShape);
-    // Collect embedded output elements.
-    const yData0 = y.slice([0, 0, 0, 0], [1, 1, 1, 3]).dataSync();
-    const yData1 = y.slice([0, 0, 1, 0], [1, 1, 1, 3]).dataSync();
-    const yData2 = y.slice([0, 0, 2, 0], [1, 1, 1, 3]).dataSync();
-    const yData3 = y.slice([0, 0, 3, 0], [1, 1, 1, 3]).dataSync();
-    // Collect sample embedding rows.
-    const wData0 = weights.slice([0, 0], [1, 3]).dataSync();
-    const wData1 = weights.slice([1, 0], [1, 3]).dataSync();
-    const wData5 = weights.slice([5, 0], [1, 3]).dataSync();
-    // First output should match first embedding row.
-    expectArraysClose(yData0, wData0);
-    // First output should *not* match second embedding row
-    expect(() => {
-      expectArraysClose(yData0, wData1);
-    }).toThrow();
-    // Second output should match 6th embedding row.
-    expectArraysClose(yData1, wData5);
-    // Second output should *not* match first embedding row.
-    expect(() => {
-      expectArraysClose(yData1, wData0);
-    }).toThrow();
-    // Third output should match fourth output (same embedding index);
-    expectArraysClose(yData2, yData3);
-  });
-
-  const dtypes: DataType[] = ['int32', 'float32'];
-  for (const dtype of dtypes) {
-    it(`Works with '${dtype}' dtype input`, () => {
-      // Input values are [0, 0, 1] with type 'float32'
-      const x = tensor2d([[0], [0], [1]], [3, 1], dtype);
-      const embeddingLayer = tfl.layers.embedding(
-          {inputDim: 6, outputDim: 4, embeddingsInitializer: 'randomUniform'});
-      const y = embeddingLayer.apply(x) as Tensor;
-      // Collect embedded output elements.
-      const yData0 = y.slice([0, 0, 0], [1, 1, 3]).dataSync();
-      const yData1 = y.slice([1, 0, 0], [1, 1, 3]).dataSync();
-      const yData2 = y.slice([2, 0, 0], [1, 1, 3]).dataSync();
-      // First output should match second output.
-      expectArraysClose(yData0, yData1);
-      // First output should not match third output.
-      expect(() => {
-        expectArraysClose(yData0, yData2);
-      }).toThrow();
-    });
-
-    it('computeMask: maskZero = false', () => {
-      const embeddingLayer = tfl.layers.embedding({
-        inputDim: 6,
-        outputDim: 4,
-        embeddingsInitializer: 'randomUniform',
-        maskZero: false
-      });
-      const x = tensor2d([[0], [0], [2]], [3, 1], dtype);
-      const y = embeddingLayer.computeMask(x);
-      expect(y).toBeNull();
-    });
-
-    it('computeMask: maskZero = true', () => {
-      const embeddingLayer = tfl.layers.embedding({
-        inputDim: 6,
-        outputDim: 4,
-        embeddingsInitializer: 'randomUniform',
-        maskZero: true
-      });
-      const x = tensor2d([[0], [0], [2]], [3, 1], dtype);
-      const y = embeddingLayer.computeMask(x);
-      expectTensorsClose(y, tensor2d([[0], [0], [1]], [3, 1], 'bool'));
-    });
-  }
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/merge.ts b/tfjs-master/tfjs-layers/src/layers/merge.ts
deleted file mode 100644
index e01cd8aa1..000000000
--- a/tfjs-master/tfjs-layers/src/layers/merge.ts
+++ /dev/null
@@ -1,1079 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * TensorFlow.js Layers: Merge Layers.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {serialization, Tensor, tidy, util} from '@tensorflow/tfjs-core';
-import * as K from '../backend/tfjs_backend';
-import {Layer, LayerArgs, SymbolicTensor} from '../engine/topology';
-import {NotImplementedError, ValueError} from '../errors';
-import {Shape} from '../keras_format/common';
-import {l2Normalize} from '../losses';
-import {Kwargs} from '../types';
-import * as generic_utils from '../utils/generic_utils';
-import * as mathUtils from '../utils/math_utils';
-import {getExactlyOneShape} from '../utils/types_utils';
-
-/**
- * Generic Merge layer for element-wise merge functions.
- *
- * Used to implement `Sum`, `Average`, `Concatenate`, etc.
- */
-export abstract class Merge extends Layer {
-  protected reshapeRequired: boolean;
-
-  constructor(args?: LayerArgs) {
-    super(args || {});
-    this.supportsMasking = true;
-  }
-
-  /**
-   * Logic for merging multiple tensors, to be overridden by subclasses.
-   * @param inputs
-   */
-  protected mergeFunction(inputs: Tensor[]): Tensor {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * Computes the shape of the result of an elementwise operation.
-   *
-   * @param shape1: Shape of the first tensor.
-   * @param shape2: Shape of the second tensor.
-   * @returns Expected output shape when an elementwise operation is carried
-   *   out on 2 tensors with shapes `shape1` and `shape2`.
-   * @throws ValueError: If `shape1` and `shape2` are not compatible for
-   *   element-wise operations.
-   */
-  private computeElementwiseOpOutputShape(shape1: Shape, shape2: Shape): Shape {
-    if (shape1 == null || shape2 == null) {
-      return null;
-    } else if (shape1.length < shape2.length) {
-      return this.computeElementwiseOpOutputShape(shape2, shape1);
-    } else if (shape2.length === 0) {
-      return shape1;
-    }
-    const outputShape: Shape = shape1.slice(0, shape1.length - shape2.length);
-    for (let k = 0; k < shape2.length; ++k) {
-      const i = shape1[shape1.length - shape2.length + k];
-      const j = shape2[k];
-      if (i == null || j == null || i < 0 || j < 0) {
-        outputShape.push(null);
-      } else if (i === 1) {
-        outputShape.push(j);
-      } else if (j === 1) {
-        outputShape.push(i);
-      } else {
-        if (i !== j) {
-          throw new ValueError(
-              'Operands could not be broadcast together with shapes ' +
-              JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));
-        }
-        outputShape.push(i);
-      }
-    }
-    return outputShape;
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    // Used purely for shape validation.
-    if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {
-      // Make sure that inputShape is an Array of shape.
-      inputShape = [getExactlyOneShape(inputShape)];
-    }
-    inputShape = inputShape as Shape[];
-    if (inputShape.length < 2) {
-      throw new ValueError(
-          'A merge layer should be called on an Array of at least 2 inputs.' +
-          ` Got ${inputShape.length} input(s).`);
-    }
-
-    // Make sure that there is at most one unique batch size among the input
-    // shapes.
-    let batchSizes: number[] = [];
-    for (const shape of inputShape) {
-      if (shape != null && shape[0] !== null) {
-        batchSizes.push(shape[0]);
-      }
-    }
-    batchSizes = generic_utils.unique(batchSizes);
-    if (batchSizes.length > 1) {
-      throw new ValueError(
-          `Can not merge tensors with different batch sizes. ` +
-          `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);
-    }
-
-    let outputShape: Shape =
-        inputShape[0] == null ? null : inputShape[0].slice(1);
-    for (let i = 1; i < inputShape.length; ++i) {
-      const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
-      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
-    }
-    // If the inputs have different ranks, we have to reshape them to make them
-    // broadcastable.
-    const allRanks = inputShape.map(shape => shape.length);
-    if (inputShape.indexOf(null) === -1 &&
-        generic_utils.unique(allRanks).length === 1) {
-      this.reshapeRequired = false;
-    } else {
-      this.reshapeRequired = true;
-    }
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = inputs as Tensor[];
-      if (this.reshapeRequired) {
-        const reshapedInputs: Tensor[] = [];
-        const inputDims = inputs.map(input => input.rank);
-        if (inputDims.indexOf(null) === -1) {
-          // If ranks of all inputs are available, we simply expand each of them
-          // at axis=1 until all of them have the same rank.
-          const maxNDim = mathUtils.max(inputDims);
-          for (let x of inputs) {
-            const xNDim = x.rank;
-            for (let k = 0; k < maxNDim - xNDim; ++k) {
-              x = K.expandDims(x, 1);
-            }
-            reshapedInputs.push(x);
-          }
-          return this.mergeFunction(reshapedInputs);
-        } else {
-          // Transpose all inputs so that batch size is the last dimension.
-          // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]
-          let transposed = false;
-          for (const x of inputs) {
-            const xNDim = x.rank;
-            if (xNDim == null) {
-              const xShape = x.shape;
-              const batchSize = xShape[0];
-              const newShape = xShape.slice(1).concat([batchSize]);
-              let xTransposed = tfc.reshape(
-                  x, [batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));
-              xTransposed = tfc.transpose(xTransposed, [1, 0]);
-              xTransposed = tfc.reshape(xTransposed, newShape);
-              reshapedInputs.push(xTransposed);
-              transposed = true;
-            } else if (xNDim > 1) {
-              const dims = mathUtils.range(1, xNDim).concat([0]);
-              reshapedInputs.push(tfc.transpose(x, dims));
-              transposed = true;
-            } else {
-              // We don't transpose inputs if they are 1D vectors or scalars.
-              reshapedInputs.push(x);
-            }
-          }
-          let y = this.mergeFunction(reshapedInputs);
-          const yNDim = y.rank;
-          if (transposed) {
-            // If inputs have been transposed, we have to transpose the output
-            // too.
-            if (yNDim == null) {
-              const yShape = y.shape;
-              const yNDim = yShape.length;
-              const batchSize = yShape[yNDim - 1];
-              const newShape =
-                  [batchSize].concat(yShape.slice(0, yShape.length - 1));
-              y = tfc.reshape(
-                  tfc.transpose(tfc.reshape(y, [-1, batchSize]), [1, 0]),
-                  newShape);
-            } else if (yNDim > 1) {
-              const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));
-              y = tfc.transpose(y, dims);
-            }
-          }
-          return y;
-        }
-      } else {
-        return this.mergeFunction(inputs);
-      }
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = inputShape as Shape[];
-    let outputShape: Shape;
-    if (inputShape[0] == null) {
-      outputShape = null;
-    } else {
-      outputShape = inputShape[0].slice(1);
-    }
-    for (let i = 1; i < inputShape.length; ++i) {
-      const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
-      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
-    }
-
-    let batchSizes: number[] = [];
-    for (const shape of inputShape) {
-      if (shape != null && shape[0] !== null) {
-        batchSizes.push(shape[0]);
-      }
-    }
-    batchSizes = generic_utils.unique(batchSizes);
-    if (batchSizes.length === 1) {
-      outputShape = batchSizes.concat(outputShape);
-    } else {
-      outputShape = [null].concat(outputShape);
-    }
-    return outputShape;
-  }
-
-  override computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]):
-      Tensor {
-    return tfc.tidy(() => {
-      if (mask == null) {
-        return null;
-      }
-      if (!Array.isArray(mask)) {
-        throw new ValueError('`mask` should be an Array');
-      }
-      if (!Array.isArray(inputs)) {
-        throw new ValueError('`inputs` should be an Array');
-      }
-      if (mask.length !== inputs.length) {
-        throw new ValueError(
-            `The Array 'inputs' and 'mask' are expected to have the same ` +
-            `length, but have different lengths ` +
-            `(${inputs.length} vs ${mask.length})`);
-      }
-      if (mask.every(m => m == null)) {
-        return null;
-      }
-      mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));
-      let output = mask[0];
-      for (let i = 1; i < mask.length - 1; ++i) {
-        output = tfc.logicalAnd(output, mask[i]);
-      }
-      return output;
-    });
-  }
-}
-
-export class Add extends Merge {
-  /** @nocollapse */
-  static className = 'Add';
-  constructor(args?: LayerArgs) {
-    super(args);
-  }
-
-  protected override mergeFunction(inputs: Tensor[]): Tensor {
-    return tidy(() => {
-      let output = inputs[0].clone();
-      for (let i = 1; i < inputs.length; ++i) {
-        output = tfc.add(output, inputs[i]);
-      }
-      return output;
-    });
-  }
-}
-serialization.registerClass(Add);
-
-/**
- * Calculate the element-wise sum of inputs, which all have the same shape.
- *
- * This function can be invoked in three ways.
- *
- * 1. Construct an instance of `Add` layer, by using no input argument
- *    or a single configuration argument. The resultant `Add` layer can then
- *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
- *
- * ```js
- * const addLayer = tf.layers.add();
- *
- * // The layer can be applied to inputs.
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = addLayer.apply([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.SymbolicTensor`. For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = tf.layers.add([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.Tensor` as the result of the computation. For
- * example:
- *
- * ```js
- * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);
- * tf.layers.add([input1, input2]).print();
- * // Gives [[11, 22], [33, 44]].
- *
- */
-export function add(config?: SymbolicTensor[]|Tensor[]|LayerArgs): Layer|
-    SymbolicTensor|Tensor {
-  if (Array.isArray(config)) {
-    const layer = new Add({});
-    return layer.apply(config) as SymbolicTensor | Tensor;
-  } else {
-    return new Add(config);
-  }
-}
-
-export class Multiply extends Merge {
-  /** @nocollapse */
-  static className = 'Multiply';
-  constructor(args?: LayerArgs) {
-    super(args);
-  }
-
-  protected override mergeFunction(inputs: Tensor[]): Tensor {
-    return tidy(() => {
-      let output = inputs[0].clone();
-      for (let i = 1; i < inputs.length; ++i) {
-        output = tfc.mul(output, inputs[i]);
-      }
-      return output;
-    });
-  }
-}
-serialization.registerClass(Multiply);
-
-/**
- * Calculate the element-wise product of inputs, which all have the same shape.
- *
- * This function can be invoked in three ways.
- *
- * 1. Construct an instance of `Multiply` layer, by using no input argument
- *    or a single configuration argument. The resultant `Multiply` layer can
- *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
- *
- * ```js
- * const multiplyLayer = tf.layers.multiply();
- *
- * // The layer can be applied to inputs.
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = multiplyLayer.apply([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.SymbolicTensor`. For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = tf.layers.multiply([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.Tensor` as the result of the computation. For
- * example:
- *
- * ```js
- * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);
- * tf.layers.multiply([input1, input2]).print();
- * // Gives [[10, 40], [90, 160]].
- *
- */
-export function multiply(config?: SymbolicTensor[]|Tensor[]|LayerArgs): Layer|
-    SymbolicTensor|Tensor {
-  if (Array.isArray(config)) {
-    const layer = new Multiply({});
-    return layer.apply(config) as SymbolicTensor | Tensor;
-  } else {
-    return new Multiply(config);
-  }
-}
-
-export class Average extends Merge {
-  /** @nocollapse */
-  static className = 'Average';
-  constructor(args?: LayerArgs) {
-    super(args);
-  }
-
-  protected override mergeFunction(inputs: Tensor[]): Tensor {
-    return tidy(() => {
-      let output = inputs[0].clone();
-      for (let i = 1; i < inputs.length; ++i) {
-        output = tfc.add(output, inputs[i]);
-      }
-      return tfc.mul(1 / inputs.length, output);
-    });
-  }
-}
-serialization.registerClass(Average);
-
-/**
- * Calculate the element-wise arithmetic mean of inputs, which all have the same
- * shape.
- *
- * This function can be invoked in three ways.
- *
- * 1. Construct an instance of `Average` layer, by using no input argument
- *    or a single configuration argument. The resultant `Average` layer can then
- *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
- *
- * ```js
- * const averageLayer = tf.layers.average();
- *
- * // The layer can be applied to inputs.
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = averageLayer.apply([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.SymbolicTensor`. For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = tf.layers.average([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.Tensor` as the result of the computation. For
- * example:
- *
- * ```js
- * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);
- * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);
- * tf.layers.average([input1, input2]).print();
- * // Gives [[5.5, 11], [16.5, 22]].
- *
- */
-export function average(config?: SymbolicTensor[]|Tensor[]|LayerArgs): Layer|
-    SymbolicTensor|Tensor {
-  if (Array.isArray(config)) {
-    const layer = new Average({});
-    return layer.apply(config) as SymbolicTensor | Tensor;
-  } else {
-    return new Average(config);
-  }
-}
-
-export class Maximum extends Merge {
-  /** @nocollapse */
-  static className = 'Maximum';
-  constructor(args?: LayerArgs) {
-    super(args);
-  }
-
-  protected override mergeFunction(inputs: Tensor[]): Tensor {
-    return tidy(() => {
-      let output = inputs[0];
-      for (let i = 1; i < inputs.length; ++i) {
-        output = tfc.maximum(output, inputs[i]);
-      }
-      return output;
-    });
-  }
-}
-serialization.registerClass(Maximum);
-
-/**
- * Calculate the element-wise maximum of inputs, which all have the same shape.
- *
- * This function can be invoked in three ways.
- *
- * 1. Construct an instance of `Maximum` layer, by using no input argument
- *    or a single configuration argument. The resultant `Maximum` layer can then
- *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
- *
- * ```js
- * const maximumLayer = tf.layers.maximum();
- *
- * // The layer can be applied to inputs.
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = maximumLayer.apply([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.SymbolicTensor`. For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = tf.layers.maximum([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.Tensor` as the result of the computation. For
- * example:
- *
- * ```js
- * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);
- * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);
- * tf.layers.maximum([input1, input2]).print();
- * // Gives [[10, 20], [30, 40]].
- *
- */
-export function maximum(config?: SymbolicTensor[]|Tensor[]|LayerArgs): Layer|
-    SymbolicTensor|Tensor {
-  if (Array.isArray(config)) {
-    const layer = new Maximum({});
-    return layer.apply(config) as SymbolicTensor | Tensor;
-  } else {
-    return new Maximum(config);
-  }
-}
-
-export class Minimum extends Merge {
-  /** @nocollapse */
-  static className = 'Minimum';
-  constructor(args?: LayerArgs) {
-    super(args);
-  }
-
-  protected override mergeFunction(inputs: Tensor[]): Tensor {
-    return tidy(() => {
-      let output = inputs[0];
-      for (let i = 1; i < inputs.length; ++i) {
-        output = tfc.minimum(output, inputs[i]);
-      }
-      return output;
-    });
-  }
-}
-serialization.registerClass(Minimum);
-
-/**
- * Calculate the element-wise minimum of inputs, which all have the same shape.
- *
- * This function can be invoked in three ways.
- *
- * 1. Construct an instance of `Minimum` layer, by using no input argument
- *    or a single configuration argument. The resultant `Minimum` layer can then
- *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
- *
- * ```js
- * const minimumLayer = tf.layers.minimum();
- *
- * // The layer can be applied to inputs.
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = minimumLayer.apply([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.SymbolicTensor`. For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 2]});
- * const input2 = tf.input({shape: [2, 2]});
- * const output = tf.layers.minimum([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension.
- * ```
- *
- * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.Tensor` as the result of the computation. For
- * example:
- *
- * ```js
- * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);
- * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);
- * tf.layers.minimum([input1, input2]).print();
- * // Gives [[1, 2], [3, 4]].
- *
- */
-export function minimum(config?: SymbolicTensor[]|Tensor[]|LayerArgs): Layer|
-    SymbolicTensor|Tensor {
-  if (Array.isArray(config)) {
-    const layer = new Minimum({});
-    return layer.apply(config) as SymbolicTensor | Tensor;
-  } else {
-    return new Minimum(config);
-  }
-}
-
-export declare interface ConcatenateLayerArgs extends LayerArgs {
-  /**
-   * Axis along which to concatenate.
-   */
-  axis?: number;
-}
-
-export class Concatenate extends Merge {
-  /** @nocollapse */
-  static className = 'Concatenate';
-  readonly DEFAULT_AXIS = -1;
-  private readonly axis: number;
-
-  constructor(args?: ConcatenateLayerArgs) {
-    super(args);
-    if (args == null) {
-      args = {};
-    }
-    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
-    this.supportsMasking = true;
-    this.reshapeRequired = false;
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    // Used purely for shape validation.]
-    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||
-        inputShape.length === 1) {
-      throw new ValueError(
-          'A `Concatenate` layer should be called on a list of at least 2 ' +
-          'inputs');
-    }
-    inputShape = inputShape as Shape[];
-
-    let allNoneShape = true;
-    for (const shape of inputShape) {
-      if (shape != null) {
-        allNoneShape = false;
-        break;
-      }
-    }
-    if (allNoneShape) {
-      return;
-    }
-
-    const shapeSet: Shape[] = [];
-    for (let i = 0; i < inputShape.length; ++i) {
-      const shapeWithoutConcatAxis = inputShape[i].slice();
-      shapeWithoutConcatAxis.splice(this.axis, 1);
-      let exists = false;
-      for (const shape of shapeSet) {
-        if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {
-          exists = true;
-          break;
-        }
-      }
-      if (!exists) {
-        shapeSet.push(shapeWithoutConcatAxis);
-      }
-    }
-    if (shapeSet.length > 1) {
-      throw new ValueError(
-          'A `Concatenate` layer requires inputs with matching shapes ' +
-          'except for the concat axis. Got input shapes: ' +
-          JSON.stringify(inputShape));
-    }
-  }
-
-  protected override mergeFunction(inputs: Tensor[]): Tensor {
-    return tidy(() => {
-      return K.concatenate(inputs, this.axis);
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {
-      throw new ValueError(
-          'A `Concatenate` layer should be called on a list of inputs.');
-    }
-    const inputShapes = inputShape as Shape[];
-    const outputShape = inputShapes[0].slice();
-    const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;
-    // Porting Note: the line above is because TypeScript doesn't support
-    //   negative indices.
-    for (const shape of inputShapes.slice(1)) {
-      if (outputShape[axis] == null || shape[axis] == null) {
-        outputShape[axis] = null;
-        break;
-      }
-      outputShape[axis] += shape[axis];
-    }
-    return outputShape;
-  }
-
-  override computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]):
-      Tensor {
-    if (mask == null) {
-      return null;
-    }
-    if (!Array.isArray(mask)) {
-      throw new ValueError('`mask` should be an array for Concatenate');
-    }
-    if (!Array.isArray(inputs)) {
-      throw new ValueError('`inputs` should be an array for Concatenate');
-    }
-    if (mask.length !== inputs.length) {
-      throw new ValueError(
-          `Mismatch in the length of mask (${mask.length}) ` +
-          `and the legnth of inputs (${inputs.length})`);
-    }
-    return tfc.tidy(() => {
-      let allNullMasks = true;
-      mask.forEach(m => {
-        if (m != null) {
-          allNullMasks = false;
-          return;
-        }
-      });
-      if (allNullMasks) {
-        return null;
-      }
-      const outputMasks: Tensor[] = [];
-      for (let i = 0; i < inputs.length; ++i) {
-        if (mask[i] == null) {
-          // Input is unmasked. Append all 1's to masks.
-          outputMasks.push(tfc.cast(tfc.onesLike(inputs[i]), 'bool'));
-        } else if (mask[i].rank < inputs[i].rank) {
-          // Mask is smaller than the input, expand it.
-          outputMasks.push(tfc.expandDims(mask[i], -1));
-        } else {
-          outputMasks.push(mask[i]);
-        }
-      }
-      const concatenatedMasks = tfc.concat(outputMasks, this.axis);
-      return tfc.all(concatenatedMasks, -1, false);
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'axis': this.axis,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Concatenate);
-
-/**
- * Concatenate an `Array` of inputs.
- *
- * This function can be invoked in three ways.
- *
- * 1. Construct an instance of `Concatenate` layer, by using no input argument
- *    or a single configuration argument. The resultant `Concatenate` layer can
- *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
- *
- * ```js
- * const concatLayer = tf.layers.concatenate();
- *
- * // The layer can be applied to inputs.
- * const input1 = tf.input({shape: [2, 3]});
- * const input2 = tf.input({shape: [2, 4]});
- * const output = concatLayer.apply([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 7], with the first dimension as the undetermined batch
- * // dimension and the last dimension as the result of concatenating the
- * // last dimensions of the two inputs.
- * ```
- *
- * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.SymbolicTensor`. For example:
- *
- * ```js
- * const input1 = tf.input({shape: [2, 3]});
- * const input2 = tf.input({shape: [2, 4]});
- * const output = tf.layers.concatenate([input1, input2]);
- * console.log(output.shape);
- * // You get [null, 2, 2], with the first dimension as the undetermined batch
- * // dimension and the last dimension as the result of concatenating the
- * // last dimensions of the two inputs.
- * ```
- *
- * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
- *    an `Layer` object internally and calls its `apply` method on the inputs,
- *    generating a new `tf.Tensor` as the result of the computation. For
- * example:
- *
- * ```js
- * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
- * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);
- * tf.layers.concatenate([input1, input2]).print();
- * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].
- *
- */
-export function concatenate(config?: SymbolicTensor[]|Tensor[]|
-                            ConcatenateLayerArgs): Layer|SymbolicTensor|Tensor {
-  if (Array.isArray(config)) {
-    const layer = new Concatenate({});
-    return layer.apply(config) as SymbolicTensor | Tensor;
-  } else {
-    return new Concatenate(config);
-  }
-}
-
-export declare interface DotLayerArgs extends LayerArgs {
-  /**
-   * Axis or axes along which the dot product will be taken.
-   *
-   * Integer or an Array of integers.
-   */
-  axes: number|[number, number];
-
-  /**
-   * Whether to L2-normalize samples along the dot product axis
-   * before taking the dot product.
-   *
-   * If set to `true`, the output of the dot product is the cosine
-   * proximity between the two samples.
-   */
-  normalize?: boolean;
-}
-
-/**
- * Interpretable potentially negative axis index.
- *
- * For example, given axis = -1, and dim = 3, this function will return 2.
- *
- * @param axis The axis index, may be a positive, zero or negative integer.
- * @param dim Total number of dimensions, a positive integer.
- * @returns A non-negative axis index equivalent to the input `axis`.
- */
-function interpretAxis(axis: number, dim: number): number {
-  while (axis < 0) {
-    axis += dim;
-  }
-  return axis;
-}
-
-function batchDot(x: Tensor, y: Tensor, axes: number|[number, number]): Tensor {
-  if (x.shape.length > 3 || y.shape.length > 3) {
-    throw new NotImplementedError(
-        'batchDot is not implemented for tensors of 4D or higher rank yet');
-  }
-  tfc.util.assert(
-      x.shape.length >= 2,
-      () => `batchDot requires the rank of x to be >= 2, ` +
-          `but got ${x.shape.length}`);
-  tfc.util.assert(
-      x.shape.length >= 2,
-      () => `batchDot requires the rank of y to be >= 2, ` +
-          `but got ${y.shape.length}`);
-
-  if (typeof axes === 'number') {
-    axes = [axes, axes];
-  }
-
-  if (x.dtype === 'complex64' || y.dtype === 'complex64') {
-    throw new NotImplementedError(
-        'batchDot is not implemented for complex64-type Tensors yet.');
-  }
-
-  const xNDim = x.shape.length;
-  const yNDim = y.shape.length;
-  if (axes == null) {
-    // Behave like batchMatmul by default.
-    axes = [xNDim - 1, yNDim - 2];
-  }
-  const axesArray = axes as [number, number];
-
-  return tfc.tidy(() => {
-    let diff: number;
-    if (xNDim > yNDim) {
-      diff = xNDim - yNDim;
-      const diffShape: Shape = [];
-      for (let i = 0; i < diff; ++i) {
-        diffShape.push(1);
-      }
-      y = tfc.reshape(y, y.shape.concat(diffShape));
-    } else if (yNDim > xNDim) {
-      diff = yNDim - xNDim;
-      const diffShape: Shape = [];
-      for (let i = 0; i < diff; ++i) {
-        diffShape.push(1);
-      }
-      x = tfc.reshape(x, x.shape.concat(diffShape));
-    } else {
-      diff = 0;
-    }
-
-    let out: Tensor;
-    if (x.shape.length === 2 && y.shape.length === 2) {
-      if (axesArray[0] === axesArray[1]) {
-        out = tfc.sum(tfc.mul(x, y), axesArray[0]);
-      } else {
-        out = tfc.sum(tfc.mul(tfc.transpose(x, [1, 0]), y), axesArray[1]);
-      }
-    } else {
-      const adjX = axesArray[0] !== x.shape.length - 1;
-      const adjY = axesArray[1] === y.shape.length - 1;
-      out = tfc.matMul(x, y, adjX, adjY);
-    }
-
-    if (diff > 0) {
-      let idx: number;
-      if (xNDim > yNDim) {
-        idx = xNDim + yNDim - 3;
-      } else {
-        idx = xNDim - 1;
-      }
-      const squeezeAxes: number[] = [];
-      for (let i = idx; i < idx + diff; ++i) {
-        squeezeAxes.push(i);
-      }
-      out = tfc.squeeze(out, squeezeAxes);
-    }
-    if (out.shape.length === 1) {
-      out = tfc.expandDims(out, 1);
-    }
-    return out;
-  });
-}
-
-export class Dot extends Merge {
-  /** @nocollapse */
-  static className = 'Dot';
-
-  private axes: number|[number, number];
-  private normalize: boolean;
-
-  constructor(args: DotLayerArgs) {
-    super(args);
-    this.axes = args.axes;
-    this.normalize = args.normalize == null ? false : args.normalize;
-    this.supportsMasking = true;
-    this.reshapeRequired = false;
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    tfc.util.assert(
-        Array.isArray(inputShape) && inputShape.length === 2 &&
-            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]),
-        () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');
-    const shape1 = inputShape[0] as Shape;
-    const shape2 = inputShape[1] as Shape;
-    if (shape1.length > 3 || shape2.length > 3) {
-      throw new NotImplementedError(
-          'Dot layer does not support tensors of 4D or higher rank yet.');
-    }
-
-    const axes = this.interpretAxes(shape1, shape2);
-    if (shape1[axes[0]] !== shape2[axes[1]]) {
-      throw new ValueError(
-          `Dimension incompatibility: ` +
-          `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);
-    }
-  }
-
-  protected override mergeFunction(inputs: Tensor[]): Tensor {
-    if (inputs.length !== 2) {
-      throw new ValueError(
-          'A `Dot` layer must be called on exactly 2 inputs, ' +
-          `but received ${inputs.length} input(s).`);
-    }
-
-    let x1 = inputs[0];
-    let x2 = inputs[1];
-    let axes: [number, number];
-    if (!Array.isArray(this.axes)) {
-      axes = [
-        interpretAxis(this.axes, x1.shape.length),
-        interpretAxis(this.axes, x2.shape.length)
-      ];
-    } else {
-      axes = this.axes.map(
-                 (axis, i) => interpretAxis(
-                     axis, inputs[i].shape.length)) as [number, number];
-    }
-    if (this.normalize) {
-      x1 = l2Normalize(x1, axes[0]);
-      x2 = l2Normalize(x2, axes[1]);
-    }
-    return batchDot(x1, x2, axes);
-  }
-
-  private interpretAxes(shape1: Shape, shape2: Shape): number[] {
-    let axes: number[];
-    if (!Array.isArray(this.axes)) {
-      // `this.axes` is a single integer.
-      axes = [
-        interpretAxis(this.axes, shape1.length),
-        interpretAxis(this.axes, shape2.length)
-      ];
-    } else {
-      // `this.axes` is an Array of integers.
-      axes = this.axes;
-    }
-    return axes;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    tfc.util.assert(
-        Array.isArray(inputShape) && inputShape.length === 2 &&
-            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]),
-        () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');
-    const shape1 = (inputShape[0] as Shape).slice();
-    const shape2 = (inputShape[1] as Shape).slice();
-    if (shape1.length > 3 || shape2.length > 3) {
-      throw new NotImplementedError(
-          'Dot layer does not support tensors of 4D or higher rank yet.');
-    }
-
-    const axes = this.interpretAxes(shape1, shape2);
-    shape1.splice(axes[0], 1);
-    shape2.splice(axes[1], 1);
-    shape2.splice(0, 1);
-    const outputShape = shape1.concat(shape2);
-    if (outputShape.length === 1) {
-      outputShape.push(1);
-    }
-    return outputShape;
-  }
-
-  override computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]):
-      Tensor {
-    return null;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'axes': this.axes,
-      'normalize': this.normalize
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(Dot);
-
-// TODO(cais): Add functional interfaces for the merge layers.
diff --git a/tfjs-master/tfjs-layers/src/layers/merge_test.ts b/tfjs-master/tfjs-layers/src/layers/merge_test.ts
deleted file mode 100644
index 7432ee885..000000000
--- a/tfjs-master/tfjs-layers/src/layers/merge_test.ts
+++ /dev/null
@@ -1,1011 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for core.ts.
- */
-
-import {cast, ones, serialization, Tensor, tensor1d, Tensor2D, tensor2d, tensor3d} from '@tensorflow/tfjs-core';
-
-import {SymbolicTensor} from '../engine/topology';
-import * as tfl from '../index';
-import {Shape} from '../keras_format/common';
-import {deserialize} from '../layers/serialization';
-import {convertPythonicToTs, convertTsToPythonic} from '../utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndGPU, describeMathCPUAndWebGL2, expectTensorsClose} from '../utils/test_utils';
-
-import {Add, Average, Concatenate, Maximum, Minimum, Multiply} from './merge';
-
-describeMathCPU('Merge Layers Except Concatenate: Symbolic', () => {
-  const layers = [Add, Average, Multiply, Maximum, Minimum];
-  const symbolicInputShapes: Shape[] = [
-    [10, 3],
-    [10, 2, 2],
-  ];
-  const numInputsArray: number[] = [2, 4];
-
-  for (const layer of layers) {
-    for (const inputShape of symbolicInputShapes) {
-      for (const numInputs of numInputsArray) {
-        const testTitle =
-            `layer=${layer.name}; inputShape=${JSON.stringify(inputShape)}; ` +
-            `numInputs=${numInputs}`;
-        it(testTitle, () => {
-          const addLayer = new layer({name: layer.name});
-          const symbolicInputs: tfl.SymbolicTensor[] = [];
-          for (let i = 0; i < numInputs; ++i) {
-            symbolicInputs.push(
-                new tfl.SymbolicTensor('float32', inputShape, null, [], null));
-          }
-          const output = addLayer.apply(symbolicInputs) as tfl.SymbolicTensor;
-          expect(output.dtype).toEqual(symbolicInputs[0].dtype);
-          expect(output.shape).toEqual(inputShape);
-        });
-      }
-    }
-  }
-
-  it('Single input leads to exception', () => {
-    const x = new tfl.SymbolicTensor('float32', [2, 2], null, [], null);
-    const addLayer = tfl.layers.add({name: 'Add'});
-    expect(() => {
-      addLayer.apply([x]);
-    }).toThrowError(/.*at least 2 inputs\. Got 1 input.*/);
-  });
-
-  it('Non-unique batch sizes to exception', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [1, 2], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [2, 2], null, [], null);
-    const addLayer = tfl.layers.add({name: 'Add'});
-    expect(() => {
-      addLayer.apply([x1, x2]);
-    }).toThrowError(/Can not merge tensors with different batch sizes/);
-  });
-});
-
-describeMathCPUAndGPU('Add-Functional', () => {
-  it('Calling without arg returns Layer', () => {
-    expect((tfl.layers.add()).getClassName()).toEqual('Add');
-  });
-
-  it('Calling with config arg returns Layer', () => {
-    expect((tfl.layers.add({name: 'addLayer'})).name.indexOf('addLayer'))
-        .toEqual(0);
-  });
-
-  it('Calling with symbolic tensors returns symbolic tensor', () => {
-    const input1 = tfl.layers.input({shape: [2, 2]});
-    const input2 = tfl.layers.input({shape: [2, 2]});
-    const output =
-        tfl.layers.add().apply([input1, input2]) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([null, 2, 2]);
-  });
-
-  it('Calling with tensors returns tensor', () => {
-    const input1 = tensor2d([1, 2, 3, 4], [2, 2]);
-    const input2 = tensor2d([10, 20, 30, 40], [2, 2]);
-    const output = tfl.layers.add().apply([input1, input2]) as Tensor;
-    expectTensorsClose(output, tensor2d([11, 22, 33, 44], [2, 2]));
-  });
-
-  it('predict() with functional model with Add layer works', () => {
-    const input = tfl.layers.input({shape: [24, 24, 3]});
-    const conv1 =
-        tfl.layers.conv2d({filters: 4, kernelSize: [3, 3]}).apply(input) as
-        tfl.SymbolicTensor;
-    const conv2 =
-        tfl.layers.conv2d({filters: 4, kernelSize: [3, 3]}).apply(input) as
-        tfl.SymbolicTensor;
-    const sum = tfl.layers.add().apply([conv1, conv2]) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: [input], outputs: sum});
-    const x = ones([1, 24, 24, 3]);
-    const y = model.predict(x) as Tensor;
-    expect(y.shape).toEqual([1, 22, 22, 4]);
-  });
-});
-
-describeMathCPUAndGPU('Multiply-Functional', () => {
-  it('Calling without arg returns Layer', () => {
-    expect(tfl.layers.multiply().getClassName()).toEqual('Multiply');
-  });
-
-  it('Calling with config arg returns Layer', () => {
-    expect(tfl.layers.multiply({name: 'multiplyLayer'})
-               .name.indexOf('multiplyLayer'))
-        .toEqual(0);
-  });
-
-  it('Calling with symbolic tensors returns symbolic tensor', () => {
-    const input1 = tfl.layers.input({shape: [2, 2]});
-    const input2 = tfl.layers.input({shape: [2, 2]});
-    const output =
-        tfl.layers.multiply().apply([input1, input2]) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([null, 2, 2]);
-  });
-
-  it('Calling with tensors returns tensor', () => {
-    const input1 = tensor2d([1, 2, 3, 4], [2, 2]);
-    const input2 = tensor2d([10, 20, 30, 40], [2, 2]);
-    const output = tfl.layers.multiply().apply([input1, input2]) as Tensor;
-    expectTensorsClose(output, tensor2d([10, 40, 90, 160], [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('Average-Functional', () => {
-  it('Calling without arg returns Layer', () => {
-    expect(tfl.layers.average().getClassName()).toEqual('Average');
-  });
-
-  it('Calling with config arg returns Layer', () => {
-    expect(
-        tfl.layers.average({name: 'averageLayer'}).name.indexOf('averageLayer'))
-        .toEqual(0);
-  });
-
-  it('Calling with symbolic tensors returns symbolic tensor', () => {
-    const input1 = tfl.layers.input({shape: [2, 2]});
-    const input2 = tfl.layers.input({shape: [2, 2]});
-    const output =
-        tfl.layers.average().apply([input1, input2]) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([null, 2, 2]);
-  });
-
-  it('Calling with tensors returns tensor', () => {
-    const input1 = tensor2d([1, 2, 3, 4], [2, 2]);
-    const input2 = tensor2d([10, 20, 30, 40], [2, 2]);
-    const output = tfl.layers.average().apply([input1, input2]) as Tensor;
-    expectTensorsClose(output, tensor2d([5.5, 11, 16.5, 22], [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('Maximum-Functional', () => {
-  it('Calling without arg returns Layer', () => {
-    expect(tfl.layers.maximum().getClassName()).toEqual('Maximum');
-  });
-
-  it('Calling with config arg returns Layer', () => {
-    expect(
-        tfl.layers.maximum({name: 'maximumLayer'}).name.indexOf('maximumLayer'))
-        .toEqual(0);
-  });
-
-  it('Calling with symbolic tensors returns symbolic tensor', () => {
-    const input1 = tfl.layers.input({shape: [2, 2]});
-    const input2 = tfl.layers.input({shape: [2, 2]});
-    const output =
-        tfl.layers.maximum().apply([input1, input2]) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([null, 2, 2]);
-  });
-
-  it('Calling with tensors returns tensor', () => {
-    const input1 = tensor2d([1, 20, 3, 40], [2, 2]);
-    const input2 = tensor2d([10, 2, 30, 4], [2, 2]);
-    const output = tfl.layers.maximum().apply([input1, input2]) as Tensor;
-    expectTensorsClose(output, tensor2d([10, 20, 30, 40], [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('Minimum-Functional', () => {
-  it('Calling without arg returns Layer', () => {
-    expect(tfl.layers.minimum().getClassName()).toEqual('Minimum');
-  });
-
-  it('Calling with config arg returns Layer', () => {
-    expect(
-        tfl.layers.minimum({name: 'minimumLayer'}).name.indexOf('minimumLayer'))
-        .toEqual(0);
-  });
-
-  it('Calling with symbolic tensors returns symbolic tensor', () => {
-    const input1 = tfl.layers.input({shape: [2, 2]});
-    const input2 = tfl.layers.input({shape: [2, 2]});
-    const output =
-        tfl.layers.minimum().apply([input1, input2]) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([null, 2, 2]);
-  });
-
-  it('Calling with tensors returns tensor', () => {
-    const input1 = tensor2d([1, 20, 3, 40], [2, 2]);
-    const input2 = tensor2d([10, 2, 30, 4], [2, 2]);
-    const output = tfl.layers.minimum().apply([input1, input2]) as Tensor;
-    expectTensorsClose(output, tensor2d([1, 2, 3, 4], [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('Concatenate-Functional', () => {
-  it('Calling without arg returns Layer', () => {
-    expect(tfl.layers.concatenate().getClassName()).toEqual('Concatenate');
-  });
-
-  it('Calling with config arg returns Layer', () => {
-    expect(tfl.layers.concatenate({name: 'concatenateLayer'})
-               .name.indexOf('concatenateLayer'))
-        .toEqual(0);
-  });
-
-  it('Calling with symbolic tensors returns symbolic tensor', () => {
-    const input1 = tfl.layers.input({shape: [2, 3]});
-    const input2 = tfl.layers.input({shape: [2, 4]});
-    const output =
-        tfl.layers.concatenate().apply([input1, input2]) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([null, 2, 7]);
-  });
-
-  it('Calling with tensors returns tensor', () => {
-    const input1 = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const input2 = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const output = tfl.layers.concatenate().apply([input1, input2]) as Tensor;
-    expectTensorsClose(
-        output, tensor2d([[1, 2, 10, 20], [3, 4, 30, 40]], [2, 4]));
-  });
-});
-
-describeMathCPU('Concatenate Layer: Symbolic', () => {
-  it('All known shapes', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [2, 3, 4], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [2, 3, 4], null, [], null);
-    const layer0 = tfl.layers.concatenate({});
-    expect((layer0.apply([x1, x2]) as tfl.SymbolicTensor).shape).toEqual([
-      2, 3, 8
-    ]);
-    const layer1 = tfl.layers.concatenate({axis: -1});
-    expect((layer1.apply([x1, x2]) as tfl.SymbolicTensor).shape).toEqual([
-      2, 3, 8
-    ]);
-    const layer2 = tfl.layers.concatenate({axis: 0});
-    expect((layer2.apply([x1, x2]) as tfl.SymbolicTensor).shape).toEqual([
-      4, 3, 4
-    ]);
-    const layer3 = tfl.layers.concatenate({axis: 1});
-    expect((layer3.apply([x1, x2]) as tfl.SymbolicTensor).shape).toEqual([
-      2, 6, 4
-    ]);
-  });
-  it('Concat axis has unknown shape', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [2, null, 4], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [2, null, 4], null, [], null);
-    const layer = tfl.layers.concatenate({axis: 1});
-    expect((layer.apply([x1, x2]) as tfl.SymbolicTensor).shape).toEqual([
-      2, null, 4
-    ]);
-  });
-  it('Non-concat axis has unknown shape', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 3, 4], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 5, 4], null, [], null);
-    const layer = tfl.layers.concatenate({axis: 1});
-    expect((layer.apply([x1, x2]) as tfl.SymbolicTensor).shape).toEqual([
-      null, 8, 4
-    ]);
-  });
-  it('Incompatible shape leads to error', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [2, 3, 5], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [2, 4, 5], null, [], null);
-    const layer = tfl.layers.concatenate({});
-    expect(() => layer.apply([
-      x1, x2
-    ])).toThrowError(/requires inputs with matching shapes except/);
-  });
-  it('Single shape leads to error', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [2, 3, 5], null, [], null);
-    const layer = tfl.layers.concatenate({});
-    expect(() => layer.apply([x1]))
-        .toThrowError(/should be called on a list of at least 2 inputs/);
-  });
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.concatenate({axis: 2});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.concatenate(tsConfig);
-    expect(layerPrime.getConfig().axis).toEqual(2);
-  });
-});
-
-describeMathCPUAndGPU('Add Layer: Tensor', () => {
-  it('2D plus 2D', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const x2 = tensor2d([[-1, -2], [-3, -4]], [2, 2]);
-    const addLayer = tfl.layers.add({});
-    const y = addLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[9, 18], [27, 36]], [2, 2]));
-  });
-  it('2D plus 2D, with broadcast', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const x2 = tensor2d([[-2], [-4]], [2, 1]);
-    const addLayer = tfl.layers.add({});
-    const y = addLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[8, 18], [26, 36]], [2, 2]));
-  });
-  it('2D plus 2D, with dimension expansion', () => {
-    const x1 =
-        tensor3d([[[10, 20], [30, 40]], [[50, 60], [70, 80]]], [2, 2, 2]);
-    const x2 = tensor2d([[-2], [-4]], [2, 1]);
-    const addLayer = tfl.layers.add({});
-    const y = addLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(
-        y, tensor3d([[[8, 18], [28, 38]], [[46, 56], [66, 76]]], [2, 2, 2]));
-  });
-  it('computeMask', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]]);
-    const x2 = tensor2d([[-2, -1], [-4, -3]]);
-    const addLayer = tfl.layers.add({});
-    const m1 = tensor1d([true, false], 'bool');
-    const m2 = tensor1d([true, true], 'bool');
-    const mask = addLayer.computeMask([x1, x2], [m1, m2]);
-    expectTensorsClose(mask, tensor2d([[true, false]], [1, 2], 'bool'));
-  });
-  it('computeMask error condition: non-array input', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]]);
-    const x2 = tensor2d([[-2, -1], [-4, -3]]);
-    const addLayer = tfl.layers.add({});
-    const m1 = tensor1d([true, false], 'bool');
-    const m2 = tensor1d([true, true], 'bool');
-    expect(() => addLayer.computeMask(x1, [
-      m1, m2
-    ])).toThrowError(/inputs.*should be an Array/);
-    expect(() => addLayer.computeMask([x1, x2], m1))
-        .toThrowError(/mask.*should be an Array/);
-  });
-  it('computeMask error condition: incorrect number of masks', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]]);
-    const x2 = tensor2d([[-2, -1], [-4, -3]]);
-    const addLayer = tfl.layers.add({});
-    const m1 = tensor1d([true, false], 'bool');
-    expect(() => addLayer.computeMask([x1, x2], [m1]))
-        .toThrowError(/ are expected to have the same/);
-  });
-});
-
-describeMathCPUAndGPU('Multiply Layer: Tensor', () => {
-  it('2D times 2D', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const x2 = tensor2d([[-1, -2], [-3, -4]], [2, 2]);
-    const multipyLayer = tfl.layers.multiply({});
-    const y = multipyLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[-10, -40], [-90, -160]], [2, 2]));
-  });
-  // TODO(cais): Reinstate when this issue is fixed:
-  //   https://github.com/PAIR-code/deeplearnjs/issues/457
-  // it('2D times 2D, with broadcast', () => {
-  //   const x1 = tensor2d([[10, 20], [30, 40]], [2, 2]);
-  //   const x2 = tensor2d([[-2], [-4]], [2, 1]);
-  //   const multiplyLayer = new Multiply({});
-  //   const y = multiplyLayer.apply([x1, x2]) as Tensor;
-  //   expectTensorsClose(y, tensor2d([[-20, -40], [-120, -160]], [2, 2]));
-  // });
-});
-
-describeMathCPUAndGPU('Average Layer: Tensor', () => {
-  it('2D and 2D', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const x2 = tensor2d([[-2, -4], [-6, -8]], [2, 2]);
-    const averageLayer = tfl.layers.average({});
-    const y = averageLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[4, 8], [12, 16]], [2, 2]));
-  });
-  it('2D and 2D, with broadcast', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const x2 = tensor2d([[-2], [-4]], [2, 1]);
-    const averageLayer = tfl.layers.average({});
-    const y = averageLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[4, 9], [13, 18]], [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('Maximum Layer: Tensor', () => {
-  it('2D and 2D', () => {
-    const x1 = tensor2d([[10, 20], [-6, -8]], [2, 2]);
-    const x2 = tensor2d([[-2, -4], [30, 40]], [2, 2]);
-    const averageLayer = tfl.layers.maximum({});
-    const y = averageLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[10, 20], [30, 40]], [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('Minimum Layer: Tensor', () => {
-  it('2D and 2D', () => {
-    const x1 = tensor2d([[10, 20], [-6, -8]], [2, 2]);
-    const x2 = tensor2d([[-2, -4], [30, 40]], [2, 2]);
-    const averageLayer = tfl.layers.minimum({});
-    const y = averageLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[-2, -4], [-6, -8]], [2, 2]));
-  });
-});
-
-describeMathCPUAndWebGL2('Concatenate Layer: Tensor', () => {
-  let x1: Tensor2D;
-  let x2: Tensor2D;
-
-  function createData() {
-    x1 = tensor2d([1, 2, 3, 4], [2, 2]);
-    x2 = tensor2d([-1, -2, -3, -4], [2, 2]);
-  }
-
-  const axisValues: number[] = [null, undefined, 0, 1, -1];
-  for (const axis of axisValues) {
-    it(`axis=${axis}`, () => {
-      createData();
-      const layer = tfl.layers.concatenate({axis});
-      const expected = axis === 0 ?
-          tensor2d([1, 2, 3, 4, -1, -2, -3, -4], [4, 2]) :
-          tensor2d([1, 2, -1, -2, 3, 4, -3, -4], [2, 4]);
-      expectTensorsClose(layer.apply([x1, x2]) as Tensor, expected);
-    });
-  }
-
-  it('computeMask', () => {
-    const layer = tfl.layers.concatenate();
-    const x1 = tensor2d([[1], [0], [1]]);
-    const x2 = tensor2d([[1], [0], [0]]);
-    const mask =
-        layer.computeMask([x1, x2], [cast(x1, 'bool'), cast(x2, 'bool')]);
-    expectTensorsClose(mask, tensor1d([true, false, false], 'bool'));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // input1 = keras.Input(shape=[4])
-  // input2 = keras.Input(shape=[4])
-  // y1 = keras.layers.Embedding(10,
-  //                             3,
-  //                             input_length=4,
-  //                             mask_zero=True,
-  //                             embeddings_initializer='ones')(input1)
-  // y1 = keras.layers.LSTM(3,
-  //                       recurrent_initializer='ones',
-  //                       kernel_initializer='ones',
-  //                       bias_initializer='zeros')(y1)
-  // y2 = keras.layers.Embedding(10,
-  //                             3,
-  //                             input_length=4,
-  //                             mask_zero=True,
-  //                             embeddings_initializer='ones')(input2)
-  // y2 = keras.layers.LSTM(3,
-  //                       recurrent_initializer='ones',
-  //                       kernel_initializer='ones',
-  //                       bias_initializer='zeros')(y2)
-  //
-  // y = keras.layers.Concatenate()([y1, y2])
-  // y = keras.layers.Dense(1,
-  //                       kernel_initializer='ones',
-  //                       bias_initializer='zeros')(y)
-  //
-  // model = keras.Model(inputs=[input1, input2], outputs=y)
-  // model.summary()
-  //
-  // xs1 = np.array([[0, 0, 0, 0],
-  //                 [1, 0, 0, 0],
-  //                 [1, 2, 0, 0],
-  //                 [1, 2, 3, 0]])
-  // xs2 = np.array([[0, 0, 0, 0],
-  //                 [0, 0, 0, 0],
-  //                 [1, 0, 0, 0],
-  //                 [1, 2, 0, 0]])
-  //
-  // ys = model.predict([xs1, xs2])
-  // print(ys)
-  // ```
-  it('With masking', () => {
-    const input1 = tfl.input({shape: [4]});
-    const input2 = tfl.input({shape: [4]});
-    let y1 = tfl.layers
-                 .embedding({
-                   inputDim: 10,
-                   outputDim: 3,
-                   inputLength: 4,
-                   maskZero: true,
-                   embeddingsInitializer: 'ones'
-                 })
-                 .apply(input1) as SymbolicTensor;
-    y1 = tfl.layers
-             .lstm({
-               units: 3,
-               recurrentInitializer: 'ones',
-               kernelInitializer: 'ones',
-               biasInitializer: 'zeros'
-             })
-             .apply(y1) as SymbolicTensor;
-    let y2 = tfl.layers
-                 .embedding({
-                   inputDim: 10,
-                   outputDim: 3,
-                   inputLength: 4,
-                   maskZero: true,
-                   embeddingsInitializer: 'ones'
-                 })
-                 .apply(input2) as SymbolicTensor;
-    y2 = tfl.layers
-             .lstm({
-               units: 3,
-               recurrentInitializer: 'ones',
-               kernelInitializer: 'ones',
-               biasInitializer: 'zeros'
-             })
-             .apply(y2) as SymbolicTensor;
-    let y = tfl.layers.concatenate().apply([y1, y2]) as SymbolicTensor;
-    y = tfl.layers
-            .dense(
-                {units: 1, kernelInitializer: 'ones', biasInitializer: 'zeros'})
-            .apply(y) as SymbolicTensor;
-    const model = tfl.model({inputs: [input1, input2], outputs: y});
-
-    const xs1 =
-        tensor2d([[0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0], [1, 2, 3, 0]]);
-    // Notice the mask of xs2 is different from that of xs1.
-    const xs2 =
-        tensor2d([[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0]]);
-    const ys = model.predict([xs1, xs2]) as Tensor;
-    expectTensorsClose(
-        ys, tensor2d([[0], [2.2785282], [5.169547], [5.8760333]]));
-  });
-});
-
-describeMathCPU('Deserialize Merge Layers', () => {
-  it('LayersModel with Add Layer', () => {
-    // The following model config JSON can be obtained with Python code:
-    // ```python
-    // import keras
-    //
-    // input1 = keras.Input(shape=[4])
-    // input2 = keras.Input(shape=[4])
-    //
-    // output = keras.layers.add([input1, input2])
-    // model = keras.Model([input1, input2], output)
-    //
-    // model_json = model.to_json()
-    // print(model_json)
-
-    const modelWithMergeJSON: {} = {
-      'class_name': 'Model',
-      'keras_version': '2.1.5',
-      'config': {
-        'layers': [
-          {
-            'class_name': 'InputLayer',
-            'config': {
-              'dtype': 'float32',
-              'batch_input_shape': [null, 4],
-              'name': 'input_1',
-              'sparse': false
-            },
-            'inbound_nodes': [],
-            'name': 'input_1'
-          },
-          {
-            'class_name': 'InputLayer',
-            'config': {
-              'dtype': 'float32',
-              'batch_input_shape': [null, 4],
-              'name': 'input_2',
-              'sparse': false
-            },
-            'inbound_nodes': [],
-            'name': 'input_2'
-          },
-          {
-            'class_name': 'Add',
-            'config': {'trainable': true, 'name': 'add_1'},
-            'inbound_nodes': [[['input_1', 0, 0, {}], ['input_2', 0, 0, {}]]],
-            'name': 'add_1'
-          }
-        ],
-        'input_layers': [['input_1', 0, 0], ['input_2', 0, 0]],
-        'output_layers': [['add_1', 0, 0]],
-        'name': 'model_1'
-      },
-      'backend': 'tensorflow'
-    };
-
-    const tsConfig =
-        convertPythonicToTs(modelWithMergeJSON) as serialization.ConfigDict;
-    const model = deserialize(tsConfig) as tfl.LayersModel;
-    expect(model.inputs.length).toEqual(2);
-    expect(model.inputs[0].shape).toEqual([null, 4]);
-    expect(model.inputs[1].shape).toEqual([null, 4]);
-    expect(model.layers.length).toEqual(3);
-    expect(model.layers[2] instanceof Add);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 4]);
-  });
-
-  it('LayersModel with Concatenate Layer', () => {
-    // The following model config JSON can be obtained with Python code:
-    // ```python
-    // import keras
-    //
-    // input1 = keras.Input(shape=[4])
-    // input2 = keras.Input(shape=[4])
-    //
-    // output = keras.layers.concatenate([input1, input2])
-    // model = keras.Model([input1, input2], output)
-    //
-    // model_json = model.to_json()
-    // print(model_json)
-
-    const modelWithMergeJSON: {} = {
-      'class_name': 'Model',
-      'keras_version': '2.1.5',
-      'config': {
-        'layers': [
-          {
-            'class_name': 'InputLayer',
-            'config': {
-              'dtype': 'float32',
-              'batch_input_shape': [null, 4],
-              'name': 'input_1',
-              'sparse': false
-            },
-            'inbound_nodes': [],
-            'name': 'input_1'
-          },
-          {
-            'class_name': 'InputLayer',
-            'config': {
-              'dtype': 'float32',
-              'batch_input_shape': [null, 4],
-              'name': 'input_2',
-              'sparse': false
-            },
-            'inbound_nodes': [],
-            'name': 'input_2'
-          },
-          {
-            'class_name': 'Concatenate',
-            'config': {'trainable': true, 'name': 'concatenate_1', 'axis': -1},
-            'inbound_nodes': [[['input_1', 0, 0, {}], ['input_2', 0, 0, {}]]],
-            'name': 'concatenate_1'
-          }
-        ],
-        'input_layers': [['input_1', 0, 0], ['input_2', 0, 0]],
-        'output_layers': [['concatenate_1', 0, 0]],
-        'name': 'model_1'
-      },
-      'backend': 'tensorflow'
-    };
-
-    const tsConfig =
-        convertPythonicToTs(modelWithMergeJSON) as serialization.ConfigDict;
-    const model = deserialize(tsConfig) as tfl.LayersModel;
-    expect(model.inputs.length).toEqual(2);
-    expect(model.inputs[0].shape).toEqual([null, 4]);
-    expect(model.inputs[1].shape).toEqual([null, 4]);
-    expect(model.layers.length).toEqual(3);
-    expect(model.layers[2] instanceof Concatenate);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 8]);
-  });
-});
-
-describeMathCPU('Dot-Layer: Symbolic', () => {
-  // Example refernce Python Keras code:
-  //
-  // ```py
-  // import keras
-  //
-  // x1 = keras.Input(shape=[3, 4])
-  // x2 = keras.Input(shape=[3])
-  // dot_layer = keras.layers.Dot(1)
-  // y = dot_layer([x1, x2])
-  //
-  // print(x1.shape)
-  // print(x2.shape)
-  // print(y.shape)
-  // ```
-
-  it('2D x 2D', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 8], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 8], null, [], null);
-    const y1 = tfl.layers.dot({axes: -1}).apply([x1, x2]) as tfl.SymbolicTensor;
-    expect(y1.shape).toEqual([null, 1]);
-    const y2 = tfl.layers.dot({axes: 1}).apply([x1, x2]) as tfl.SymbolicTensor;
-    expect(y2.shape).toEqual([null, 1]);
-  });
-
-  it('3D x 3D, axes = -1', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const y = tfl.layers.dot({axes: -1}).apply([x1, x2]) as tfl.SymbolicTensor;
-    expect(y.shape).toEqual([null, 2, 2]);
-  });
-
-  it('3D x 3D, axes = 1', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const y2 = tfl.layers.dot({axes: 1}).apply([x1, x2]) as tfl.SymbolicTensor;
-    expect(y2.shape).toEqual([null, 3, 3]);
-  });
-
-  it('3D x 3D, axes = 2', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const y2 = tfl.layers.dot({axes: 2}).apply([x1, x2]) as tfl.SymbolicTensor;
-    expect(y2.shape).toEqual([null, 2, 2]);
-  });
-
-  it('2D x 3D, axes = -1', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 3], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const y2 = tfl.layers.dot({axes: -1}).apply([x1, x2]) as tfl.SymbolicTensor;
-    expect(y2.shape).toEqual([null, 2]);
-  });
-
-  it('2D x 3D, axes = 1', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 3], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 3, 4], null, [], null);
-    const y2 = tfl.layers.dot({axes: 1}).apply([x1, x2]) as tfl.SymbolicTensor;
-    expect(y2.shape).toEqual([null, 4]);
-  });
-
-  it('3D x 2D, axes = -1', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 3], null, [], null);
-    const y2 = tfl.layers.dot({axes: -1}).apply([x1, x2]) as tfl.SymbolicTensor;
-    expect(y2.shape).toEqual([null, 2]);
-  });
-
-  it('3D x 2D, axes = -1', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 3, 4], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 3], null, [], null);
-    const y2 = tfl.layers.dot({axes: 1}).apply([x1, x2]) as tfl.SymbolicTensor;
-    expect(y2.shape).toEqual([null, 4]);
-  });
-
-  it('computeOutputShape() does not alter input shape', () => {
-    const dotLayer = tfl.layers.dot({axes: 1});
-    const inputShape1: Shape = [null, 3, 4];
-    const inputShape2: Shape = [null, 3];
-    const outputShape = dotLayer.computeOutputShape([inputShape1, inputShape2]);
-    expect(outputShape).toEqual([null, 4]);
-    expect(inputShape1).toEqual([null, 3, 4]);
-    expect(inputShape2).toEqual([null, 3]);
-  });
-
-  // TODO(cais): Uncomment the follow test case when 4D and higher is supported
-  //   by the Dot layer.
-  // it('4D x 4D, axes = -1', () => {
-  //   const x1 = new tfl.SymbolicTensor(
-  //       'float32', [null, 2, 3, 4], null, [], null);
-  //   const x2 = new tfl.SymbolicTensor(
-  //       'float32', [null, 2, 3, 4], null, [], null);
-  //   const y = tfl.layers.dot({axes: -1}).apply([x1, x2]) as
-  //       tfl.SymbolicTensor;
-  //   expect(y.shape).toEqual([null, 2, 3, 2, 3]);
-  // });
-
-  it('Dimension mismatch leads to error', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 4], null, [], null);
-    expect(() => tfl.layers.dot({axes: -1}).apply([
-      x1, x2
-    ])).toThrowError('Dimension incompatibility: 3 !== 4');
-  });
-
-  it('Incorrect number of inputs leads to error', () => {
-    const x1 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const x2 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const x3 = new tfl.SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    expect(() => tfl.layers.dot({axes: -1}).apply([x1]))
-        .toThrowError(/should be called on a list of exactly 2 inputs/);
-    expect(() => tfl.layers.dot({axes: -1}).apply(x1))
-        .toThrowError(/should be called on a list of exactly 2 inputs/);
-    expect(() => tfl.layers.dot({axes: -1}).apply([
-      x1, x2, x3
-    ])).toThrowError(/should be called on a list of exactly 2 inputs/);
-  });
-
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.dot({axes: -1, normalize: true});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.dot(tsConfig);
-    expect(layerPrime.getConfig().axes).toEqual(-1);
-    expect(layerPrime.getConfig().normalize).toEqual(true);
-  });
-});
-
-describeMathCPUAndWebGL2('Dot-Layer: Tensor', () => {
-  // Example reference Python Keras code:
-  //
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // x1 = keras.Input(shape=[2])
-  // x2 = keras.Input(shape=[2])
-  // dot_layer = keras.layers.Dot(-11)
-  // y = dot_layer([x1, x2])
-  //
-  // model = keras.Model([x1, x2], y)
-  // model.summary()
-  //
-  // xs1 = np.array([[10, 20], [30, 40]], dtype=np.float32)
-  // xs2 = np.array([[-1, -2], [-3, -4]], dtype=np.float32)
-  // print(model.predict([xs1, xs2]))
-  // ```
-
-  it('2D x 2D, axis = -1', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]]);
-    const x2 = tensor2d([[-1, -2], [-3, -4]]);
-    const dotLayer = tfl.layers.dot({axes: -1});
-    const y = dotLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[-50], [-250]]));
-  });
-
-  it('2D x 2D, axis = -1, normalize = true', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]]);
-    const x2 = tensor2d([[-1, -2], [-4, -3]]);
-    const dotLayer = tfl.layers.dot({axes: -1, normalize: true});
-    const y = dotLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[-1], [-0.96]]));
-  });
-
-  it('2D x 2D, axis = 1', () => {
-    const x1 = tensor2d([[10, 20], [30, 40]]);
-    const x2 = tensor2d([[-1, -2], [-3, -4]]);
-    const dotLayer = tfl.layers.dot({axes: 1});
-    const y = dotLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[-50], [-250]]));
-  });
-
-  it('3D x 2D, axis = -1', () => {
-    const x1 = tensor3d([[[10, 20], [30, 40]], [[4, 3], [2, 1]]]);
-    const x2 = tensor2d([[-1, -2], [-3, -4]]);
-    const dotLayer = tfl.layers.dot({axes: -1});
-    const y1 = dotLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y1, tensor2d([[-50, -110], [-24, -10]]));
-    const x3 = tensor2d([[1, 2], [3, 4]]);
-    const y2 = dotLayer.apply([x1, x3]) as Tensor;
-    expectTensorsClose(y2, tensor2d([[50, 110], [24, 10]]));
-  });
-
-  it('2D x 3D, axis = -1', () => {
-    const x1 = tensor2d([[-1, -2], [-3, -4]]);
-    const x2 = tensor3d([[[10, 20], [30, 40]], [[4, 3], [2, 1]]]);
-    const dotLayer = tfl.layers.dot({axes: -1});
-    const y = dotLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[-50, -110], [-24, -10]]));
-  });
-
-  it('2D x 3D, axis = 1', () => {
-    const x1 = tensor2d([[-1, -2], [-3, -4]]);
-    const x2 = tensor3d([[[10, 20], [30, 40]], [[4, 3], [2, 1]]]);
-    const dotLayer = tfl.layers.dot({axes: 1});
-    const y = dotLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(y, tensor2d([[-70, -100], [-20, -13]]));
-  });
-
-  it('3D x 3D, axis = -1', () => {
-    const x1 = tensor3d([[[-1, -2], [-3, -4]], [[5, 6], [7, 8]]]);
-    const x2 = tensor3d([[[10, 20], [30, 40]], [[4, 3], [2, 1]]]);
-    const dotLayer = tfl.layers.dot({axes: -1});
-    const y = dotLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(
-        y, tensor3d([[[-50, -110], [-110, -250]], [[38, 16], [52, 22]]]));
-  });
-
-  it('3D x 3D, axis = 1', () => {
-    const x1 = tensor3d([[[-1, -2], [-3, -4]], [[5, 6], [7, 8]]]);
-    const x2 = tensor3d([[[10, 20], [30, 40]], [[4, 3], [2, 1]]]);
-    const dotLayer = tfl.layers.dot({axes: 1});
-    const y = dotLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(
-        y, tensor3d([[[-100, -140], [-140, -200]], [[34, 22], [40, 26]]]));
-  });
-
-  it('3D x 3D, axis = [1, 2]', () => {
-    const x1 = tensor3d([[[-1, -2], [-3, -4]], [[5, 6], [7, 8]]]);
-    const x2 = tensor3d([[[10, 20], [30, 40]], [[4, 3], [2, 1]]]);
-    const dotLayer = tfl.layers.dot({axes: [1, 2]});
-    const y = dotLayer.apply([x1, x2]) as Tensor;
-    expectTensorsClose(
-        y, tensor3d([[[-70, -150], [-100, -220]], [[41, 17], [48, 20]]]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // input1 = keras.Input(shape=[4])
-  // input2 = keras.Input(shape=[4])
-  // y1 = keras.layers.Embedding(10,
-  //                             3,
-  //                             input_length=4,
-  //                             mask_zero=True,
-  //                             embeddings_initializer='ones')(input1)
-  // y1 = keras.layers.LSTM(3,
-  //                       recurrent_initializer='ones',
-  //                       kernel_initializer='ones',
-  //                       bias_initializer='zeros')(y1)
-  // y2 = keras.layers.Embedding(10,
-  //                             3,
-  //                             input_length=4,
-  //                             mask_zero=True,
-  //                             embeddings_initializer='ones')(input2)
-  // y2 = keras.layers.LSTM(3,
-  //                       recurrent_initializer='ones',
-  //                       kernel_initializer='ones',
-  //                       bias_initializer='zeros')(y2)
-  //
-  // y = keras.layers.Dot(axes=[-1, -1])([y1, y2])
-  // y = keras.layers.Dense(1,
-  //                       kernel_initializer='ones',
-  //                       bias_initializer='zeros')(y)
-  //
-  // model = keras.Model(inputs=[input1, input2], outputs=y)
-  //
-  // xs1 = np.array([[0, 0, 0, 0],
-  //                 [1, 0, 0, 0],
-  //                 [1, 2, 0, 0],
-  //                 [1, 2, 3, 0]])
-  // xs2 = np.array([[0, 0, 0, 0],
-  //                 [0, 0, 0, 0],
-  //                 [1, 0, 0, 0],
-  //                 [1, 2, 0, 0]])
-  // ys = model.predict([xs1, xs2])
-  // print(ys)
-  // ```
-  it('With masking', () => {
-    const input1 = tfl.input({shape: [4]});
-    const input2 = tfl.input({shape: [4]});
-    let y1 = tfl.layers
-                 .embedding({
-                   inputDim: 10,
-                   outputDim: 3,
-                   inputLength: 4,
-                   maskZero: true,
-                   embeddingsInitializer: 'ones'
-                 })
-                 .apply(input1) as SymbolicTensor;
-    y1 = tfl.layers
-             .lstm({
-               units: 3,
-               recurrentInitializer: 'ones',
-               kernelInitializer: 'ones',
-               biasInitializer: 'zeros'
-             })
-             .apply(y1) as SymbolicTensor;
-    let y2 = tfl.layers
-                 .embedding({
-                   inputDim: 10,
-                   outputDim: 3,
-                   inputLength: 4,
-                   maskZero: true,
-                   embeddingsInitializer: 'ones'
-                 })
-                 .apply(input2) as SymbolicTensor;
-    y2 = tfl.layers
-             .lstm({
-               units: 3,
-               recurrentInitializer: 'ones',
-               kernelInitializer: 'ones',
-               biasInitializer: 'zeros'
-             })
-             .apply(y2) as SymbolicTensor;
-    let y = tfl.layers.dot({axes: [-1, -1]}).apply([y1, y2]) as SymbolicTensor;
-    y = tfl.layers
-            .dense(
-                {units: 1, kernelInitializer: 'ones', biasInitializer: 'zeros'})
-            .apply(y) as SymbolicTensor;
-    const model = tfl.model({inputs: [input1, input2], outputs: y});
-
-    const xs1 =
-        tensor2d([[0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0], [1, 2, 3, 0]]);
-    // Notice the mask of xs2 is different from that of xs1.
-    const xs2 =
-        tensor2d([[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0]]);
-    const ys = model.predict([xs1, xs2]) as Tensor;
-    expectTensorsClose(ys, tensor2d([[0], [0], [2.195756], [2.8765779]]));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/einsum_dense.ts b/tfjs-master/tfjs-layers/src/layers/nlp/einsum_dense.ts
deleted file mode 100644
index 6fd744ef0..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/einsum_dense.ts
+++ /dev/null
@@ -1,447 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  TFJS-based einsum dense layer.
- */
-
-/* Original source: keras/layers/core/einsum_dense.py */
-import { Tensor, Tensor2D, einsum, serialization, tidy } from '@tensorflow/tfjs-core';
-
-import { Activation, getActivation, serializeActivation } from '../../activations';
-import { Constraint, ConstraintIdentifier, getConstraint, serializeConstraint } from '../../constraints';
-import { Layer, LayerArgs } from '../../engine/topology';
-import { ValueError } from '../../errors';
-import { Initializer, InitializerIdentifier, getInitializer, serializeInitializer } from '../../initializers';
-import { ActivationIdentifier } from '../../keras_format/activation_config';
-import { Shape } from '../../keras_format/common';
-import { Regularizer, RegularizerIdentifier, getRegularizer, serializeRegularizer } from '../../regularizers';
-import { Kwargs } from '../../types';
-import { LayerVariable } from '../../variables';
-
-/**
- * Analyzes an einsum string to determine the required weight shape.
- */
-export function analyzeEinsumString(
-  equation: string,
-  biasAxes: string,
-  inputShape: Shape,
-  outputShape: Shape
-): [Shape, Shape, Shape] {
-  const dotReplacedString = equation.replace(/\.\.\./g, '0');
-
-  // This is the case where no ellipses are present in the string.
-  let splitString =
-    dotReplacedString.match(/([a-zA-Z]+),([a-zA-Z]+)->([a-zA-Z]+)/);
-  if (splitString) {
-    return analyzeSplitString(
-      splitString, biasAxes, inputShape, outputShape);
-  }
-
-  // This is the case where ellipses are present on the left.
-  splitString =
-    dotReplacedString.match(/0([a-zA-Z]+),([a-zA-Z]+)->0([a-zA-Z]+)/);
-  if (splitString) {
-    return analyzeSplitString(
-      splitString, biasAxes, inputShape, outputShape, true);
-  }
-
-  // This is the case where ellipses are present on the right.
-  splitString =
-    dotReplacedString.match(/([a-zA-Z]{2,})0,([a-zA-Z]+)->([a-zA-Z]+)0/);
-  if (splitString) {
-    return analyzeSplitString(
-      splitString, biasAxes, inputShape, outputShape);
-  }
-
-  throw new ValueError(
-    `Invalid einsum equation '${equation}'. Equations must be in the form ` +
-    '[X],[Y]->[Z], ...[X],[Y]->...[Z], or [X]...,[Y]->[Z]....'
-  );
-}
-
-/**
- * Analyze an pre-split einsum string to find the weight shape.
- */
-export function analyzeSplitString(
-  splitString: RegExpMatchArray,
-  biasAxes: string,
-  inputShape: Shape,
-  outputShape: Shape|number,
-  leftElided = false
-): [Shape, Shape, Shape] {
-  const inputSpec = splitString[1];
-  const weightSpec = splitString[2];
-  const outputSpec = splitString[3];
-  const elided = inputShape.length - inputSpec.length;
-
-  const newOutputShape: Shape = Array.isArray(outputShape) ?
-    outputShape.slice() : [outputShape];
-  newOutputShape.unshift(inputShape[0]);
-
-  if (elided > 0 && leftElided) {
-    for(let i = 1; i < elided; i++) {
-      // We already inserted the 0th input dimension at dim 0, so we need
-      // to start at location 1 here.
-      newOutputShape.splice(1, 0, inputShape[i]);
-    }
-  } else if (elided > 0 && !leftElided) {
-    for(let i = inputShape.length - elided; i < inputShape.length; i++) {
-      newOutputShape.push(inputShape[i]);
-    }
-  }
-
-  const inputSpecArr = Array.from(inputSpec);
-  const outputSpecArr = Array.from(outputSpec);
-  let inputDimMap, outputDimMap;
-
-  if (leftElided) {
-    // If we have beginning dimensions elided, we need to use negative
-    // indexing to determine where in the input dimension our values are.
-    inputDimMap = new Map<string, number>(
-      inputSpecArr.map((dim, i) => {
-        // This converts any negative indices to positive ones.
-        const idx = i + elided - inputShape.length;
-        const positiveIdx =
-          ((idx % inputShape.length) + inputShape.length) % inputShape.length;
-        return [dim, positiveIdx];
-      })
-    );
-
-    // Because we've constructed the full output shape already, we don't need
-    // to do negative indexing.
-    outputDimMap = new Map<string, number>(
-      outputSpecArr.map((dim, i) => [dim, i + elided])
-    );
-  } else {
-    inputDimMap = new Map<string, number>(
-      inputSpecArr.map((dim, i) => [dim, i])
-    );
-    outputDimMap = new Map<string, number>(
-      outputSpecArr.map((dim, i) => [dim, i])
-    );
-  }
-
-  for (const dim of inputSpec) {
-    const inputShapeAtDim = inputShape[inputDimMap.get(dim)];
-    if (outputDimMap.has(dim)) {
-      const outputShapeAtDim = newOutputShape[outputDimMap.get(dim)];
-      if (outputShapeAtDim !== null && outputShapeAtDim !== inputShapeAtDim) {
-        throw new ValueError(
-          `Input shape and output shape do not match at shared dimension `+
-          `'${dim}'. Input shape is ${inputShapeAtDim}, and output shape ` +
-          `is ${outputShapeAtDim}.`
-        );
-      }
-    }
-  }
-
-  for (const dim of outputSpec) {
-    if (!inputSpec.includes(dim) && !weightSpec.includes(dim)) {
-      throw new ValueError(
-        `Dimension '${dim}' was specified in the output '${outputSpec}' ` +
-        `but has no corresponding dimension in the input spec ` +
-        `'${inputSpec}' or weight spec '${weightSpec}'`
-      );
-    }
-  }
-
-  const weightShape: Shape = [];
-  for (const dim of weightSpec) {
-    if (inputDimMap.has(dim)) {
-      weightShape.push(inputShape[inputDimMap.get(dim)]);
-    } else if (outputDimMap.has(dim)) {
-      weightShape.push(newOutputShape[outputDimMap.get(dim)]);
-    } else {
-      throw new ValueError(
-        `Weight dimension '${dim}' did not have a match in either the ` +
-        `input spec '${inputSpec}' or the output spec '${outputSpec}'. For ` +
-        `this layer, the weight must be fully specified.`
-      );
-    }
-  }
-
-  let biasShape: Shape;
-  if (biasAxes != null) {
-    const numLeftElided = leftElided ? elided : 0;
-    const idxMap: { [char: string]: number } = {};
-    for (let i = 0; i < outputSpec.length; i++) {
-      idxMap[outputSpec[i]] = newOutputShape[i + numLeftElided];
-    }
-
-    for (const char of biasAxes) {
-      if (!outputSpec.includes(char)) {
-        throw new ValueError(
-          `Bias dimension '${char}' was requested, but is not part of the ` +
-          `output spec '${outputSpec}'`
-        );
-      }
-    }
-
-    const firstBiasLocation = Math.min(
-      ...biasAxes.split('').map(char => outputSpec.indexOf(char))
-    );
-    const biasOutputSpec = outputSpec.slice(firstBiasLocation);
-
-    biasShape = biasOutputSpec.split('').map(char =>
-      biasAxes.includes(char) ? idxMap[char] : 1
-    );
-
-    if (!leftElided) {
-      for (let i = 0; i < elided; i++) {
-        biasShape.push(1);
-      }
-    }
-  } else {
-    biasShape = null;
-  }
-  return [weightShape, biasShape, newOutputShape];
-}
-
-export declare interface EinsumDenseArgs extends LayerArgs {
-  /**
-   * An equation describing the einsum to perform. This equation must be a
-   * valid einsum string of the form `ab,bc->ac`, `...ab,bc->...ac`, or
-   * `ab...,bc->ac...` where 'ab', 'bc', and 'ac' can be any valid einsum
-   * axis expression sequence.
-   */
-  equation: string;
-
-  /**
-   * The expected shape of the output tensor (excluding the batch dimension and
-   * any dimensions represented by ellipses). You can specify None for any
-   * dimension that is unknown or can be inferred from the input shape.
-   */
-  outputShape: Shape|number;
-
-  /**
-   * Activation function to use. If you don't specify anything, no activation
-   * is applied (that is, a "linear" activation: `a(x) = x`).
-   */
-  activation?: ActivationIdentifier;
-
-  /**
-   * A string containing the output dimension(s) to apply a bias to. Each
-   * character in the `biasAxes` string should correspond to a character
-   * in the output portion of the `equation` string.
-   */
-  biasAxes?: string;
-
-  /**
-   * Initializer for the `kernel` weights matrix.
-   * Defaults to `"glorotUniform"`.
-   */
-  kernelInitializer?: InitializerIdentifier;
-
-  /**
-   * Initializer for the bias vector.
-   * Defaults to `"zeros"`.
-   */
-  biasInitializer?: InitializerIdentifier;
-
-  /**
-   * Regularizer function applied to the `kernel` weights matrix.
-   */
-  kernelRegularizer?: RegularizerIdentifier;
-
-  /**
-   * Regularizer function applied to the bias vector.
-   */
-  biasRegularizer?: RegularizerIdentifier;
-
-  /**
-   * Regularizer function applied to the output of the layer (its "activation").
-   */
-  activityRegularizer?: RegularizerIdentifier;
-
-  /**
-   * Constraint function applied to the `kernel` weights matrix.
-   */
-  kernelConstraint?: ConstraintIdentifier;
-
-  /**
-   * Constraint function applied to the bias vector.
-   */
-  biasConstraint?: ConstraintIdentifier;
-}
-
-/**
- * A layer that uses `tf.einsum` as the backing computation.
- *
- * This layer can perform einsum calculations of arbitrary dimensionality.
- *
- * Examples:
- *
- * **Biased dense layer with einsums**
- *
- * This example shows how to instantiate a standard Keras dense layer using
- * einsum operations. This example is equivalent to
- * tf.layers.Dense({units: 64, useBias: true})`.
- *
- * const layer = new EinsumDense({
- *    equation: "ab,bc->ac", outputShape: 4, biasAxes: "c"});
- * const inputTensor = tf.input({shape: [32]});
- * const outputTensor = layer.call(inputTensor);
- * console.log(outputTensor);  // [null, 64]
- *
- * **Applying a dense layer to a sequence**
- *
- * This example shows how to instantiate a layer that applies the same dense
- * operation to every element in a sequence. Here, the `outputShape` has two
- * values (since there are two non-batch dimensions in the output); the first
- * dimension in the `outputShape` is `null`, because the sequence dimension
- * `b` has an unknown shape.
- *
- * ```js
- * const layer = new EinsumDense({
- *    equation: "abc,cd->abd", outputShape: [null, 64], biasAxes: "d"});
- * const inputTensor = tf.input({shape: [32, 128]});
- * const outputTensor = layer.call(inputTensor);
- * console.log(outputTensor);  // [null, 32, 64]
- * ```
- *
- * **Applying a dense layer to a sequence using ellipses**
- *
- * This example shows how to instantiate a layer that applies the same dense
- * operation to every element in a sequence, but uses the ellipsis notation
- * instead of specifying the batch and sequence dimensions.
- *
- * Because we are using ellipsis notation and have specified only one axis, the
- * `outputShape` arg is a single value. When instantiated in this way, the
- * layer can handle any number of sequence dimensions - including the case
- * where no sequence dimension exists.
- *
- * ```js
- * const layer = new EinsumDense({
- *    equation: "...x,xy->...y", outputShape: 64, biasAxes: "y"});
- * const inputTensor = tf.input({shape: [32, 128]});
- * const outputTensor = layer.call(inputTensor);
- * console.log(outputTensor);  // [null, 32, 64]
- * ``
- */
-export class EinsumDense extends Layer {
-  /** @nocollapse */
-  static readonly className = 'EinsumDense';
-  private readonly equation: string;
-  private readonly biasAxes: string;
-  private readonly partialOutputShape: Shape;
-  private readonly activation: Activation;
-  private readonly kernelInitializer: Initializer;
-  private readonly biasInitializer: Initializer;
-  private readonly kernelRegularizer: Regularizer;
-  private readonly biasRegularizer: Regularizer;
-  private readonly kernelConstraint: Constraint;
-  private readonly biasConstraint: Constraint;
-  private fullOutputShape: Shape;
-  private _kernel: LayerVariable;
-  private _bias: LayerVariable;
-
-  constructor(args: EinsumDenseArgs) {
-    super(args);
-    this.equation = args.equation;
-    this.biasAxes = args.biasAxes;
-    this.partialOutputShape =
-      Array.isArray(args.outputShape) ? args.outputShape : [args.outputShape];
-    this.activation = getActivation(args.activation);
-    this.kernelInitializer = getInitializer(
-      args.kernelInitializer ?? 'glorotUniform');
-    this.biasInitializer = getInitializer(args.biasInitializer ?? 'zeros');
-    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
-    this.biasRegularizer = getRegularizer(args.biasRegularizer);
-    this.kernelConstraint = getConstraint(args.kernelConstraint);
-    this.biasConstraint = getConstraint(args.biasConstraint);
-  }
-
-  get kernel(): LayerVariable {
-    return this._kernel;
-  }
-
-  get bias(): LayerVariable {
-    return this._bias;
-  }
-
-  override build(inputShape: Shape): void {
-    const [kernelShape, biasShape, fullOutputShape] = analyzeEinsumString(
-      this.equation,
-      this.biasAxes,
-      inputShape,
-      this.partialOutputShape
-    );
-    this.fullOutputShape = fullOutputShape;
-    this._kernel = this.addWeight(
-      'kernel',
-      kernelShape,
-      this.dtype,
-      this.kernelInitializer,
-      this.kernelRegularizer,
-      true,
-      this.kernelConstraint,
-    );
-
-    if (biasShape != null) {
-      this._bias = this.addWeight(
-        'bias',
-        biasShape,
-        this.dtype,
-        this.biasInitializer,
-        this.biasRegularizer,
-        true,
-        this.biasConstraint,
-      );
-    } else {
-      this._bias = null;
-    }
-    super.build(inputShape);
-  }
-
-  override computeOutputShape(_: Shape): Shape {
-    return this.fullOutputShape;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      outputShape: this.partialOutputShape,
-      equation: this.equation,
-      activation: serializeActivation(this.activation),
-      biasAxes: this.biasAxes,
-      kernelInitializer: serializeInitializer(this.kernelInitializer),
-      biasInitializer: serializeInitializer(this.biasInitializer),
-      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
-      biasRegularizer: serializeRegularizer(this.biasRegularizer),
-      kernelConstraint: serializeConstraint(this.kernelConstraint),
-      biasConstraint: serializeConstraint(this.biasConstraint),
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor2D {
-    return tidy(() => {
-      inputs = Array.isArray(inputs) ? inputs : [inputs];
-      let ret = einsum(this.equation, ...inputs, this.kernel.read());
-      if (this.bias != null) {
-        ret = ret.add(this.bias.read());
-      }
-      if (this.activation != null) {
-        ret = this.activation.apply(ret);
-      }
-      return ret;
-    });
-  }
-}
-serialization.registerClass(EinsumDense);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/einsum_dense_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/einsum_dense_test.ts
deleted file mode 100644
index f209c0c7c..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/einsum_dense_test.ts
+++ /dev/null
@@ -1,317 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for TFJS-based EinsumDense Layer.
- */
-
-import { memory, Tensor } from '@tensorflow/tfjs-core';
-
-import { analyzeEinsumString, EinsumDense } from './einsum_dense';
-import { Shape } from '../../keras_format/common';
-import { input } from '../../exports';
-
-declare interface EinsumDenseTestCaseArgs {
-  testcaseName: string;
-  equation: string;
-  biasAxes: string;
-  inputShape: Shape;
-  outputShape: Shape;
-  expectedWeightShape: Shape;
-  expectedBiasShape: Shape;
-  expectedOutputShape: Shape;
-}
-
-describe('EinsumDense', () => {
-  const combinations: EinsumDenseTestCaseArgs[] = [
-    {
-      testcaseName: '_1d_end_weight',
-      equation: 'ab,b->a',
-      biasAxes: null,
-      inputShape: [null, 32],
-      outputShape: [],
-      expectedWeightShape: [32],
-      expectedBiasShape: null,
-      expectedOutputShape: [null],
-    },
-    {
-      testcaseName: '_2d_middle_weight',
-      equation: 'ab,bc->ac',
-      biasAxes: null,
-      inputShape: [null, 32],
-      outputShape: [64],
-      expectedWeightShape: [32, 64],
-      expectedBiasShape: null,
-      expectedOutputShape: [null, 64],
-    },
-    {
-        testcaseName: '_3d_bert',
-        equation: 'abc,cde->abde',
-        biasAxes: null,
-        inputShape: [null, 1, 2],
-        outputShape: [1, 3, 4],
-        expectedWeightShape: [2, 3, 4],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, 1, 3, 4],
-    },
-    {
-        testcaseName: '_3d_3_bias',
-        equation: 'abc,cde->abde',
-        biasAxes: 'e',
-        inputShape: [null, 1, 2],
-        outputShape: [1, 3, 4],
-        expectedWeightShape: [2, 3, 4],
-        expectedBiasShape: [4],
-        expectedOutputShape: [null, 1, 3, 4],
-    },
-    {
-        testcaseName: '_3d_2_bias',
-        equation: 'abc,cde->abde',
-        biasAxes: 'd',
-        inputShape: [null, 1, 2],
-        outputShape: [1, 3, 4],
-        expectedWeightShape: [2, 3, 4],
-        expectedBiasShape: [3, 1],
-        expectedOutputShape: [null, 1, 3, 4],
-    },
-    {
-        testcaseName: '_3d_1_3_bias',
-        equation: 'abc,cde->abde',
-        biasAxes: 'be',
-        inputShape: [null, 7, 2],
-        outputShape: [7, 3, 4],
-        expectedWeightShape: [2, 3, 4],
-        expectedBiasShape: [7, 1, 4],
-        expectedOutputShape: [null, 7, 3, 4],
-    },
-    {
-        testcaseName: '_3d_bert_projection',
-        equation: 'BFNH,NHD->BFD',
-        biasAxes: null,
-        inputShape: [null, 1, 2, 3],
-        outputShape: [1, 4],
-        expectedWeightShape: [2, 3, 4],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, 1, 4],
-    },
-    {
-        testcaseName: '_2d_bert',
-        equation: 'abc,cd->abd',
-        biasAxes: null,
-        inputShape: [null, 1, 2],
-        outputShape: [1, 4],
-        expectedWeightShape: [2, 4],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, 1, 4],
-    },
-    {
-        testcaseName: '_embedding_1d',
-        equation: 'i,d->id',
-        biasAxes: null,
-        inputShape: [null],
-        outputShape: [2],
-        expectedWeightShape: [2],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, 2],
-    },
-    {
-        testcaseName: '_xlnet_lm',
-        equation: 'ibd,nd->ibn',
-        biasAxes: null,
-        inputShape: [null, null, 1],
-        outputShape: [null, 2],
-        expectedWeightShape: [2, 1],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, null, 2],
-    },
-    {
-        testcaseName: '_2d_precast',
-        equation: '...b,bc->...c',
-        biasAxes: null,
-        inputShape: [null, 32],
-        outputShape: [64],
-        expectedWeightShape: [32, 64],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, 64],
-    },
-    {
-        testcaseName: '_2d_precast_elided_input_used_in_output',
-        equation: '...bc,bc->...b',
-        biasAxes: null,
-        inputShape: [null, 32, 64],
-        outputShape: [32],
-        expectedWeightShape: [32, 64],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, 32],
-    },
-    {
-        testcaseName: '_2d_precast_multiple_elided_dims',
-        equation: '...b,bc->...c',
-        biasAxes: null,
-        inputShape: [null, null, 32],
-        outputShape: [64],
-        expectedWeightShape: [32, 64],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, null, 64],
-    },
-    {
-        testcaseName: '_3d_precast',
-        equation: '...c,cde->...de',
-        biasAxes: null,
-        inputShape: [null, 1, 2],
-        outputShape: [3, 4],
-        expectedWeightShape: [2, 3, 4],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, 1, 3, 4],
-    },
-    {
-        testcaseName: '_3d_precast_3_bias',
-        equation: '...c,cde->...de',
-        biasAxes: 'e',
-        inputShape: [null, 1, 2],
-        outputShape: [3, 4],
-        expectedWeightShape: [2, 3, 4],
-        expectedBiasShape: [4],
-        expectedOutputShape: [null, 1, 3, 4],
-    },
-    {
-        testcaseName: '_3d_precast_2_bias',
-        equation: '...c,cde->...de',
-        biasAxes: 'd',
-        inputShape: [null, 1, 2],
-        outputShape: [3, 4],
-        expectedWeightShape: [2, 3, 4],
-        expectedBiasShape: [3, 1],
-        expectedOutputShape: [null, 1, 3, 4],
-    },
-    {
-        testcaseName: '_3d_precast_2_3_bias',
-        equation: '...c,cde->...de',
-        biasAxes: 'de',
-        inputShape: [null, 1, 2],
-        outputShape: [3, 4],
-        expectedWeightShape: [2, 3, 4],
-        expectedBiasShape: [3, 4],
-        expectedOutputShape: [null, 1, 3, 4],
-    },
-    {
-        testcaseName: '_2d_postcast',
-        equation: 'bc...,cd->bd...',
-        biasAxes: null,
-        inputShape: [null, 1, 2, 3],
-        outputShape: [4],
-        expectedWeightShape: [1, 4],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, 4, 2, 3],
-    },
-    {
-        testcaseName: '_3d_postcast',
-        equation: 'bc...,cde->bde...',
-        biasAxes: null,
-        inputShape: [null, 1, 2],
-        outputShape: [3, 4],
-        expectedWeightShape: [1, 3, 4],
-        expectedBiasShape: null,
-        expectedOutputShape: [null, 3, 4, 2],
-    },
-    {
-        testcaseName: '_3d_postcast_1_bias',
-        equation: 'bc...,cde->bde...',
-        biasAxes: 'd',
-        inputShape: [null, 1, 2],
-        outputShape: [3, 4],
-        expectedWeightShape: [1, 3, 4],
-        expectedBiasShape: [3, 1, 1],
-        expectedOutputShape: [null, 3, 4, 2],
-    },
-    {
-        testcaseName: '_3d_postcast_2_bias',
-        equation: 'bc...,cde->bde...',
-        biasAxes: 'e',
-        inputShape: [null, 1, 2],
-        outputShape: [3, 4],
-        expectedWeightShape: [1, 3, 4],
-        expectedBiasShape: [4, 1],
-        expectedOutputShape: [null, 3, 4, 2],
-    },
-    {
-        testcaseName: '_3d_postcast_1_2_bias',
-        equation: 'bc...,cde->bde...',
-        biasAxes: 'de',
-        inputShape: [null, 1, 2],
-        outputShape: [3, 4],
-        expectedWeightShape: [1, 3, 4],
-        expectedBiasShape: [3, 4, 1],
-        expectedOutputShape: [null, 3, 4, 2],
-    },
-  ];
-
-  function testWeightShape(combo: EinsumDenseTestCaseArgs) {
-    it(`${combo.testcaseName} weight shape`, () => {
-      const [weightShape, biasShape, _] = analyzeEinsumString(
-        combo.equation, combo.biasAxes, combo.inputShape, combo.outputShape
-      );
-      expect(weightShape).toEqual(combo.expectedWeightShape);
-      expect(biasShape).toEqual(combo.expectedBiasShape);
-    });
-  }
-
-  function testLayerCreation(combo: EinsumDenseTestCaseArgs) {
-    it(`${combo.testcaseName} layer creation`, () => {
-      const nonBatchInputShape = combo.inputShape.slice(1);
-      const inputTensor = input({shape: nonBatchInputShape});
-
-      const layer = new EinsumDense({
-        equation: combo.equation,
-        biasAxes: combo.biasAxes,
-        outputShape: combo.outputShape,
-      });
-      const outputTensor = layer.apply(inputTensor) as Tensor;
-
-      expect(layer.kernel.shape).toEqual(combo.expectedWeightShape);
-      if (combo.expectedBiasShape === null) {
-        expect(layer.bias).toBeNull();
-      } else {
-        expect(layer.bias.shape).toEqual(combo.expectedBiasShape);
-      }
-      expect(outputTensor.shape).toEqual(combo.expectedOutputShape);
-    });
-  }
-
-  for (const combo of combinations) {
-    testWeightShape(combo);
-    testLayerCreation(combo);
-  }
-
-  it('Does not leak memory', () => {
-    const combo = combinations[0];
-    const layer = new EinsumDense({
-      equation: combo.equation,
-      biasAxes: combo.biasAxes,
-      outputShape: combo.outputShape,
-    });
-    const nonBatchInputShape = combo.inputShape.slice(1);
-    const inputTensor = input({shape: nonBatchInputShape});
-
-    const numTensors = memory().numTensors;
-    layer.apply(inputTensor);
-
-    expect(memory().numTensors).toEqual(numTensors + 1);
-  });
-
-  // TODO(pforderique): Test serialization.
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/match_all_polyfill.ts b/tfjs-master/tfjs-layers/src/layers/nlp/match_all_polyfill.ts
deleted file mode 100644
index 81109f718..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/match_all_polyfill.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// TODO(mattSoulanille): Replace this with automatic polyfilling using core-js.
-export function *matchAll(str: string, regexp: RegExp): IterableIterator<RegExpMatchArray> {
-  // Remove the global flag since str.match does not work with it.
-  const flags = regexp.flags.replace(/g/g, '');
-  regexp = new RegExp(regexp, flags);
-
-  let match = str.match(regexp);
-  let offset = 0;
-  let restOfStr = str;
-  while (match != null) {
-    if (match.index == null) {
-      console.error(match);
-      throw new Error(`Matched string '${match[0]}' has no index`);
-    }
-
-    // Remove up to and including the first match from the input string
-    // so the next loop can find the next match.
-    const matchEnd = match.index + match[0].length;
-    restOfStr = restOfStr.slice(matchEnd);
-
-    // Adjust the match to look like a result from matchAll.
-    match.index += offset;
-    match.input = str;
-
-    offset += matchEnd;
-    yield match;
-    match = restOfStr.match(regexp);
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/match_all_polyfill_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/match_all_polyfill_test.ts
deleted file mode 100644
index 5bccbbab3..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/match_all_polyfill_test.ts
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {matchAll} from './match_all_polyfill';
-
-function format({match, index, input}: {match: string, index: number,
-                                        input: string}) {
-  const res: RegExpMatchArray = [match];
-  res.index = index;
-  res.input = input;
-  return jasmine.objectContaining(res);
-}
-
-describe('matchAll', () => {
-  it('finds all matches of a regexp on a string', () => {
-    const input = 'asdfasdfasdfasdf';
-    expect([...matchAll(input , /asd/g)]).toEqual([
-      {match: 'asd', index: 0, input},
-      {match: 'asd', index: 4, input},
-      {match: 'asd', index: 8, input},
-      {match: 'asd', index: 12, input},
-    ].map(format));
-  });
-
-  it('supports regexp flags', () => {
-    const input = 'asdfASDFasdfASDF';
-    // Case sensitive
-    expect([...matchAll(input, /asd/g)]).toEqual([
-      {match: 'asd', index: 0, input},
-      {match: 'asd', index: 8, input},
-    ].map(format));
-
-    // case insensitive
-    expect([...matchAll('asdfASDFasdfASDF', /asd/gi)]).toEqual([
-      {match: 'asd', index: 0, input},
-      {match: 'ASD', index: 4, input},
-      {match: 'asd', index: 8, input},
-      {match: 'ASD', index: 12, input},
-    ].map(format));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/cached_multihead_attention.ts b/tfjs-master/tfjs-layers/src/layers/nlp/modeling/cached_multihead_attention.ts
deleted file mode 100644
index 590f3a736..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/cached_multihead_attention.ts
+++ /dev/null
@@ -1,181 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Cached MHA layer based on `MultiHeadAttention`.
- */
-
-/* Original source: keras_nlp/layers/modeling/cached_multi_head_attention.py */
-import { Tensor, cast, einsum, mul, reciprocal, serialization, sqrt, stack, tidy } from '@tensorflow/tfjs-core';
-
-import { ValueError } from '../../../errors';
-import { MultiHeadAttention } from '../multihead_attention';
-import { sliceUpdate } from '../utils';
-
-export declare interface CachedMultiHeadAttentionOptions {
-  /**
-   * Query `Tensor` of shape `(B, T, dim)`.
-   */
-
-  /**
-   * Value `Tensor` of shape `(B, S*, dim)`. If `cache` is `null`, `S*`
-   * must equal `S` and match the shape of `attentionMask`. If `cache` is
-   * not `null`, `S*` can be any length less than `S`, and the computed
-   * value will be spliced into `cache` at `cacheUpdateIndex`.
-   */
-  value: Tensor;
-
-  /**
-   * Key `Tensor` of shape `(B, S*, dim)`.  If `cache` is `null`, `S*` must
-   * equal `S` and match the shape of `attentionMask`. If `cache` is not `null`,
-   * `S*` can be any length less than `S`, and the computed value will be
-   * spliced into `cache` at `cacheUpdateIndex`.
-   */
-  key?: Tensor;
-
-  /**
-   * A boolean mask of shape `(B, T, S)`. `attentionMask` prevents
-   * attention to certain positions. The boolean mask specifies which
-   * query elements can attend to which key elements, 1 indicates
-   * attention and 0 indicates no attention. Broadcasting can happen for
-   * the missing batch dimensions and the head dimension.
-   */
-  attentionMask?: Tensor;
-
-  /**
-   * A dense float Tensor. The key/value cache, of shape
-   * `[B, 2, S, numHeads, keyDims]`, where `S` must agree with the
-   * `attentionMask` shape. This argument is intended for use during
-   * generation to avoid recomputing intermediate state.
-   */
-  cache?: Tensor;
-
-  /**
-   * Integer or Integer `Tensor`. The index at which to update `cache`
-   * (usually the index of the current token being processed when running
-   * generation). If `cacheUpdateIndex=null` while `cache` is set, the cache
-   * will not be updated.
-   */
-  cacheUpdateIndex?: number;
-}
-
-/**
- * MultiHeadAttention layer with cache support.
- *
- * This layer is suitable for use in autoregressive decoding. It can be use
- * to cache decoder self-attention and cross-attention. The forward pass
- * can happen in one of three modes:
- * - No cache, same as regular multi-head attention.
- * - Static cache (`cacheUpdateIndex` is None). In this case, the
- *     cached key/value projections will be used and the input values will
- *     be ignored.
- * - Updated cache (`cacheUpdateIndex` is not None). In this case, new
- *     key/value projections are computed using the input, and spliced into
- *     the cache at the specified index.
- *
- * Note that caching is useful only during inference and should not be used
- * during training.
- *
- * We use the notation `B`, `T`, `S` below, where `B` is the batch dimension,
- * `T` is the target sequence length, and `S` in the source sequence length.
- * Note that during generative decoding, `T` is usually 1 (you are
- * generating a target sequence of length one to predict the next token).
- *
- * Returns:
- *     An `(attentionOutput, cache)` tuple. `attentionOutput` is the result
- *     of the computation, of shape `(B, T, dim)`, where `T` is for target
- *     sequence shapes and `dim` is the query input last dimension if
- *     `outputShape` is `null`. Otherwise, the multi-head outputs are
- *     projected to the shape specified by `outputShape`. `cache` is the
- *     updated cache.
- */
-export class CachedMultiHeadAttention extends MultiHeadAttention {
-
-  override call(
-    query: Tensor, kwargs: CachedMultiHeadAttentionOptions
-  ): Tensor {
-    return this.callAndReturnCache(query, kwargs)[0];
-  }
-
-  /**
-   * Exactly like `call` except also returns the updated cache.
-   */
-  callAndReturnCache(
-    query: Tensor,
-    {
-      value,
-      key,
-      attentionMask,
-      cache,
-      cacheUpdateIndex
-    } : CachedMultiHeadAttentionOptions
-  ): [Tensor, Tensor] {
-    return tidy(() => {
-      if (!this.builtFromSignature) {
-        this.buildFromSignature(
-          query.shape, value.shape, key ? key.shape : null);
-      }
-      if (key == null) {
-        key = value;
-      }
-
-      query = this.queryDense.apply(query) as Tensor;
-      // If cache is not `null`, we will use the cache to compute the final key
-      // and value tensors. If `cacheUpdateIndex` is not `null`, we will first
-      // update the cache before use. To do this, we first call the
-      // `keyDense` and `valueDense` layers, and copy the outputs into the
-      // cache at the specified index. `cache = null` handles the training
-      // case, where we don't use the cache at all.
-      if (cache != null) {
-        const keyCache = cache.gather([0], 1).squeeze();
-        const valueCache = cache.gather([1], 1).squeeze();
-        if (cacheUpdateIndex == null) {
-          key = keyCache;
-          value = valueCache;
-        } else {
-          const keyUpdate = this.keyDense.apply(key) as Tensor;
-          const valueUpdate = this.valueDense.apply(value) as Tensor;
-          const start = [0, cacheUpdateIndex, 0, 0];
-          key = sliceUpdate(keyCache, start, keyUpdate);
-          value = sliceUpdate(valueCache, start, valueUpdate);
-          cache = stack([key, value], 1);
-        }
-      } else {
-        if (cacheUpdateIndex != null) {
-          throw new ValueError(
-            '`cacheUpdateIndex` should not be set if `cache` is `null`. ' +
-            `Received: cache=${cache}, cacheUpdateIndex=${cacheUpdateIndex}`
-          );
-        }
-        key = this.keyDense.apply(key) as Tensor;
-        value = this.valueDense.apply(value) as Tensor;
-      }
-
-      query = mul(query, reciprocal(sqrt(cast(this.keyDim, query.dtype))));
-      let attentionScores = einsum(this.dotProductEquation, key, query);
-      attentionScores = this.maskedSoftmax(attentionScores, attentionMask);
-      attentionScores = this.dropoutLayer.apply(attentionScores) as Tensor;
-
-      let attentionOutput =
-        einsum(this.combineEquation, attentionScores, value);
-      attentionOutput = this.outputDense.apply(attentionOutput) as Tensor;
-
-      return [attentionOutput, cache];
-    });
-  }
-}
-serialization.registerClass(CachedMultiHeadAttention);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/cached_multihead_attention_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/modeling/cached_multihead_attention_test.ts
deleted file mode 100644
index dc41c10df..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/cached_multihead_attention_test.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for CachedMultiHeadAttention layer.
- */
-import { Tensor, linalg, memory, ones, randomUniform, zeros, zerosLike } from '@tensorflow/tfjs-core';
-
-import { expectTensorsClose } from '../../../utils/test_utils';
-import { sliceUpdate } from '../utils';
-
-import { CachedMultiHeadAttention } from './cached_multihead_attention';
-
-describe('CachedMultiHeadAttention', () => {
-  it('valid call', () => {
-    const layer = new CachedMultiHeadAttention({numHeads: 2, keyDim: 4});
-    const query = randomUniform([2, 2, 8]);
-
-    expect(() => layer.call(query, {value: query})).not.toThrow();
-  });
-
-  it('cache call is correct', () => {
-    const batchSize = 2;
-    const seqLen = 5;
-    const numHeads = 2;
-    const keyDim = 4;
-    const hiddenDim = numHeads * keyDim;
-    const inputShape = [batchSize, seqLen, hiddenDim];
-
-    const x = randomUniform(inputShape);
-    const inputCache = zeros([batchSize, 2, seqLen, numHeads, keyDim]);
-    // Use a causal mask.
-    const mask = linalg.bandPart(ones([seqLen, seqLen]), -1, 0);
-    const outputs = zerosLike(x);
-
-    const layer = new CachedMultiHeadAttention({numHeads, keyDim});
-    const [noLoopOutputs, noLoopCache] = layer.callAndReturnCache(
-      x,
-      {
-        value: x,
-        cache: inputCache,
-        cacheUpdateIndex: 0,
-        attentionMask: mask
-      }
-    );
-
-    function call(outputs: Tensor, cache: Tensor) {
-      for (let i = 0; i < seqLen; i++) {
-        // Compute the rest tokens.
-        const nextInput = x.slice([0, i, 0], [batchSize, 1, hiddenDim]);
-        const nextMask = mask.slice([i, 0], [1, seqLen]);
-        const [nextOutput, nextCache] = layer.callAndReturnCache(
-          nextInput,
-          {
-            value: nextInput,
-            cache,
-            cacheUpdateIndex: i,
-            attentionMask: nextMask,
-          }
-        );
-        outputs = sliceUpdate(outputs, [0, i, 0], nextOutput);
-        cache = nextCache;
-      }
-      return [outputs, cache];
-    }
-    const [output, outputCache] = call(outputs, inputCache);
-
-    expectTensorsClose(output, noLoopOutputs);
-    expectTensorsClose(outputCache, noLoopCache);
-  });
-
-  it('does not leak memory', () => {
-    const layer = new CachedMultiHeadAttention({numHeads: 2, keyDim: 2});
-    const query = ones([1, 4, 8]);
-    // Initial call that builds sublayers and necessary tensors.
-    layer.call(query, {value: query});
-
-    const numTensors = memory().numTensors;
-    layer.call(query, {value: query});
-
-    expect(memory().numTensors).toEqual(numTensors + 1);
-  });
-  // TODO(pforderique): Test serialization.
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/position_embedding.ts b/tfjs-master/tfjs-layers/src/layers/nlp/modeling/position_embedding.ts
deleted file mode 100644
index 35a873651..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/position_embedding.ts
+++ /dev/null
@@ -1,147 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Position embedding implementation based on `tf.layers.Layer`.
- */
-
-/* Original source: keras_nlp/layers/modeling/position_embedding.py */
-import { Tensor, serialization, tidy } from '@tensorflow/tfjs-core';
-
-import { Shape } from '../../../keras_format/common';
-import { Layer, LayerArgs } from '../../../engine/topology';
-import { ValueError } from '../../../errors';
-import { Initializer, InitializerIdentifier, getInitializer, serializeInitializer } from '../../../initializers';
-import { getExactlyOneTensor } from '../../../utils/types_utils';
-import { LayerVariable } from '../../../variables';
-
-export declare interface PositionEmbeddingArgs extends LayerArgs {
-  /**
-   * Integer. The maximum length of the dynamic sequence.
-   */
-  sequenceLength: number;
-
-  /**
-   * The initializer to use for the embedding weights.
-   * Defaults to `"glorotUniform"`.
-   */
-  initializer?: Initializer|InitializerIdentifier;
-}
-
-export declare interface PositionEmbeddingOptions {
-  /**
-   * Integer. Index to start the position embeddings at.
-   * Defaults to 0.
-   */
-  startIndex?: number;
-}
-
-/**
- * A layer which learns a position embedding for input sequences.
- *
- * This class assumes that in the input tensor, the last dimension corresponds
- * to the features, and the dimension before the last corresponds to the
- * sequence.
- *
- * Examples:
- *
- * Called directly on input.
- * ```js
- * const layer = new PositionEmbedding({sequenceLength=10});
- * layer.call(tf.zeros([8, 10, 16]));
- * ```
- *
- * Combine with a token embedding.
- * ```js
- * const seqLength = 50;
- * const vocabSize = 5000;
- * const embedDim = 128;
- * const inputs = tf.input({shape: [seqLength]});
- * const tokenEmbeddings = tf.layers.embedding({
- *     inputDim=vocabSize, outputDim=embedDim
- * }).apply(inputs);
- * const positionEmbeddings = new PositionEmbedding({
- *     sequenceLength: seqLength
- * }).apply(tokenEmbeddings);
- * const outputs = tf.add(tokenEmbeddings, positionEmbeddings);
- * ```
- *
- * Reference:
- *  - [Devlin et al., 2019](https://arxiv.org/abs/1810.04805)
- */
-export class PositionEmbedding extends Layer {
-  /** @nocollapse */
-  static readonly className = 'PositionEmbedding';
-  private sequenceLength: number;
-  private initializer: Initializer;
-  protected positionEmbeddings: LayerVariable;
-
-  constructor(args: PositionEmbeddingArgs) {
-    super(args);
-    if (args.sequenceLength == null) {
-      throw new ValueError(
-        '`sequenceLength` must be an Integer, received `null`.');
-    }
-    this.sequenceLength = args.sequenceLength;
-    this.initializer = getInitializer(args.initializer || 'glorotUniform');
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      'sequenceLength': this.sequenceLength,
-      'initializer': serializeInitializer(this.initializer),
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override build(inputShape: Shape): void {
-    const featureSize = inputShape[inputShape.length - 1];
-    this.positionEmbeddings = this.addWeight(
-      'embeddings',
-      [this.sequenceLength, featureSize],
-      null,
-      this.initializer,
-      null,
-      true
-    );
-    super.build(inputShape);
-  }
-
-  override call(
-    inputs: Tensor|Tensor[],
-    kwargs?: PositionEmbeddingOptions
-  ): Tensor {
-    return tidy(() => {
-      kwargs.startIndex = kwargs.startIndex ?? 0;
-      const shape = getExactlyOneTensor(inputs).shape;
-      const featureLength = shape[shape.length - 1];
-      const sequenceLength = shape[shape.length - 2];
-      // trim to match the length of the input sequence, which might be less
-      // than the sequence_length of the layer.
-      const positionEmbeddings = this.positionEmbeddings.read().slice(
-        [kwargs.startIndex, 0], [sequenceLength, featureLength]);
-      return positionEmbeddings.broadcastTo(shape);
-    });
-  }
-
-  override computeOutputShape(inputShape: Shape): Shape {
-    return inputShape;
-  }
-}
-serialization.registerClass(PositionEmbedding);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/position_embedding_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/modeling/position_embedding_test.ts
deleted file mode 100644
index cde6f6a3e..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/position_embedding_test.ts
+++ /dev/null
@@ -1,177 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Tests for position embedding layer..
- */
-
-import { Tensor, memory, ones, randomUniform } from '@tensorflow/tfjs-core';
-
-import { SymbolicTensor } from '../../../engine/topology';
-import { input, model } from '../../../exports';
-import { PositionEmbedding } from './position_embedding';
-
-describe('PositionEmbedding', () => {
-  it('static layer output shape', () => {
-    // Create a 3-dimensional input (the first dimension is implicit).
-    const sequenceLength = 21;
-    const featureSize = 30;
-    const testLayer = new PositionEmbedding({sequenceLength});
-    const inputTensor = input({shape: [sequenceLength, featureSize]});
-    const outputTensor = testLayer.apply(inputTensor) as SymbolicTensor;
-
-    // When using static position embedding shapes, the output is expected to
-    // be the same shape as the input shape in all dimensions save batch.
-    const expectedOutputShape = [null, sequenceLength, featureSize];
-
-    expect(outputTensor.shape).toEqual(expectedOutputShape);
-  });
-
-  it('more than 3 dimensions static', () => {
-    // Create a 4-dimensional input (the first dimension is implicit).
-    const sequenceLength = 21;
-    const featureSize = 30;
-    const testLayer = new PositionEmbedding({sequenceLength});
-    const inputTensor =
-      input({shape: [featureSize, sequenceLength, featureSize]});
-    const outputTensor = testLayer.apply(inputTensor) as SymbolicTensor;
-
-    // When using static position embedding shapes, the output is expected
-    // to be the same as the input shape in all dimensions save batch.
-    const expectedOutputShape =
-      [null, featureSize, sequenceLength, featureSize];
-
-    expect(outputTensor.shape).toEqual(expectedOutputShape);
-  });
-
-  it('float32 dtype', () => {
-    // Create a 3-dimensional input (the first dimension is implicit).
-    const sequenceLength = 21;
-    const featureSize = 30;
-    const testLayer = new PositionEmbedding({sequenceLength, dtype: 'float32'});
-    const inputTensor = input({shape: [sequenceLength, featureSize]});
-    const outputTensor = testLayer.apply(inputTensor) as SymbolicTensor;
-
-    // When using static position embedding shapes, the output is expected
-    // to be the same as the input shape in all dimensions save batch.
-    const expectedOutputShape =
-      [null, sequenceLength, featureSize];
-
-    expect(outputTensor.shape).toEqual(expectedOutputShape);
-    // The output dtype for this layer should match the compute dtype.
-    expect(outputTensor.dtype).toEqual('float32');
-  });
-
-  it('dynamic layer output shape', () => {
-    const maxSequenceLength = 21;
-    const featureSize = 30;
-    const testLayer =
-      new PositionEmbedding({sequenceLength: maxSequenceLength});
-    // Create a 3-dimensional input (the first dimension is implicit).
-    const inputTensor = input({shape: [null, featureSize]});
-    const outputTensor = testLayer.apply(inputTensor) as SymbolicTensor;
-
-    // When using dynamic position embedding shapes, the output is expected to
-    // be the same shape as the input shape in all dimensions - but may be
-    // null if the input shape is null there.
-    const expectedOutputShape = [null, null, featureSize];
-
-    expect(outputTensor.shape).toEqual(expectedOutputShape);
-  });
-
-  it('more than 3 dimensions dynamic', () => {
-    const maxSequenceLength = 60;
-    const featureSize = 30;
-    const testLayer =
-      new PositionEmbedding({sequenceLength: maxSequenceLength});
-    // Create a 4-dimensional input (the first dimension is implicit).
-    const inputTensor = input({shape: [null, null, featureSize]});
-    const outputTensor = testLayer.apply(inputTensor) as SymbolicTensor;
-
-    // When using dynamic position embedding shapes, the output is expected
-    // to be the same as the input shape in all dimensions save batch.
-    const expectedOutputShape = [null, null, null, featureSize];
-
-    expect(outputTensor.shape).toEqual(expectedOutputShape);
-  });
-
-  it('dynamic layer slicing', () => {
-    const maxSequenceLength = 40;
-    const featureSize = 30;
-    const testLayer =
-      new PositionEmbedding({sequenceLength: maxSequenceLength});
-    // Create a 3-dimensional input (the first dimension is implicit).
-    const inputTensor = input({shape: [null, featureSize]});
-    const outputTensor = testLayer.apply(inputTensor) as SymbolicTensor;
-
-    const pmodel = model({inputs: inputTensor, outputs: outputTensor});
-
-    // Create input data that is shorter than maxSequenceLength, which
-    // should trigger a down-slice.
-    const inputLength = 17;
-    // Note: In practice, this layer should be used inside a model, where it can
-    // be projected when added to another tensor.
-    const inputData = ones([1, inputLength, featureSize]);
-    const outputData = pmodel.predict(inputData) as Tensor;
-
-    expect(outputData.shape).toEqual([1, inputLength, featureSize]);
-  });
-
-  it('one training step', async () => {
-    const maxSequenceLength = 4;
-    const featureSize = 3;
-    const inputs = input({shape: [maxSequenceLength, featureSize]});
-    const testLayer =
-      new PositionEmbedding({sequenceLength: maxSequenceLength});
-    const outputs = testLayer.apply(inputs) as SymbolicTensor;
-    const pmodel = model({inputs, outputs});
-
-    const batchSize = 2;
-    const data = randomUniform([batchSize, maxSequenceLength, featureSize]);
-    const label = randomUniform([batchSize, maxSequenceLength, featureSize]);
-
-    pmodel.compile({optimizer: 'adam', loss: 'meanSquaredError'});
-    const loss = pmodel.trainOnBatch(data, label);
-
-    expect(await loss).toBeGreaterThan(0);
-  });
-
-  it('serialization round trip', () => {
-    const maxSequenceLength = 40;
-    const testLayer = new PositionEmbedding({
-      sequenceLength: maxSequenceLength,
-    });
-    const original = testLayer.getConfig();
-    const restored =
-      PositionEmbedding.fromConfig(PositionEmbedding, original).getConfig();
-
-    expect(original['sequenceLength']).toEqual(restored['sequenceLength']);
-  });
-
-  it('does not leak memory', () => {
-    const sequenceLength = 4;
-    const batchSize = 2;
-    const testLayer = new PositionEmbedding({sequenceLength});
-    const data = randomUniform([batchSize, sequenceLength, 3]);
-    testLayer.build(data.shape);
-
-    const numTensors = memory().numTensors;
-    testLayer.apply(data);
-
-    expect(memory().numTensors).toEqual(numTensors + 1);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_decoder.ts b/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_decoder.ts
deleted file mode 100644
index f7d8b0ecf..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_decoder.ts
+++ /dev/null
@@ -1,494 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Transformer decoder block implementation based on TFJS `Layer`.
- */
-
-/* Original source: keras_nlp/layers/modeling/transformer_decoder.py */
-import { Tensor, add, serialization, tidy } from '@tensorflow/tfjs-core';
-
-import { Activation, getActivation, serializeActivation } from '../../../activations';
-import { Layer, LayerArgs, SymbolicTensor, } from '../../../engine/topology';
-import { ValueError } from '../../../errors';
-import { Initializer, InitializerIdentifier, getInitializer, serializeInitializer } from '../../../initializers';
-import { ActivationIdentifier } from '../../../keras_format/activation_config';
-import { Shape } from '../../../keras_format/common';
-import { Dense, Dropout } from '../../core';
-import { LayerNormalization } from '../../normalization';
-
-import { CachedMultiHeadAttention } from './cached_multihead_attention';
-import { computeCausalMask, mergePaddingAndAttentionMask } from './transformer_layer_utils';
-
-export declare interface TransformerDecoderArgs extends LayerArgs {
-  /**
-   * Integer. The hidden size of feedforward network.
-   */
-  intermediateDim: number;
-
-  /**
-   * Integer. The number of heads in MultiHeadAttention.
-   */
-  numHeads: number;
-
-  /**
-   * The dropout value, shared by MultiHeadAttention and feedforward network.
-   * Defaults to `0.`.
-   */
-  dropout?: number;
-
-  /**
-   * The activation function of feedforward network.
-   * Defaults to `"relu"`.
-   */
-  activation?: Activation|ActivationIdentifier;
-
-  /**
-   * The eps value in layer normalization components.
-   * Defaults to `1e-5`.
-   */
-  layerNormEpsilon?: number;
-
-  /**
-   * The kernel initializer for the dense and multiheaded attention layers.
-   * Defaults to `"glorotUniform"`.
-   */
-  kernelInitializer?: Initializer|InitializerIdentifier;
-
-  /**
-   * The bias initializer for the dense and multiheaded attention layers.
-   * Defaults to `"zeros"`.
-   */
-  biasInitializer?: Initializer|InitializerIdentifier;
-
-  /**
-   * If true, the inputs to the attention layer(s) and the intermediate dense
-   * layer are normalized (similar to GPT-2). If set to false, outputs of
-   * attention layer and intermediate dense layer are normalized
-   * (similar to BERT).
-   * Defaults to `false`.
-   */
-  normalizeFirst?: boolean;
-}
-
-export declare interface TransformerDecoderOptions {
-  /**
-   * decoderSequence: The decode input sequence.
-   */
-
-  /**
-   * The encoder input sequence. For decoder only models (like GPT2), this
-   * should be left `null`. Once the model is called without an encoderSequence,
-   * you cannot call it again with encoderSequence.
-   */
-  encoderSequence?: Tensor|SymbolicTensor;
-
-  /**
-   * A boolean Tensor, the padding mask of decoder sequence, must be of shape
-   * `[batchSize, decoderSequenceLength]`.
-   */
-  decoderPaddingMask?: Tensor|SymbolicTensor;
-
-  /**
-   * A boolean Tensor. Customized decoder sequence mask, must be of shape
-   * `[batchSize, decoderSequenceLength, decoderSequenceLength]`.
-   */
-  decoderAttentionMask?: Tensor;
-
-  /**
-   * A boolean Tensor, the padding mask of encoder sequence, must be of shape
-   * `[batchSize, encoderSequenceLength]`.
-   */
-  encoderPaddingMask?: Tensor;
-
-  /**
-   * A boolean Tensor. Customized encoder sequence mask, must be of shape
-   * `[batchSize, encoderSequenceLength, encoderSequenceLength]`.
-   */
-  encoderAttentionMask?: Tensor;
-
-  /**
-   * A dense float Tensor. The cache of key/values pairs in the self-attention
-   * layer. Has shape `[batchSize, 2, maxSeqLen, numHeads, keyDims]`.
-   */
-  selfAttentionCache?: Tensor;
-
-  /**
-   * Integer or Integer Tensor. The index at which to update the
-   * `selfAttentionCache`. Usually, this is the index of the current token
-   * being processed during decoding.
-   */
-  selfAttentionCacheUpdateIndex?: number;
-
-  /**
-   * A dense float Tensor. The cache of key/value pairs in the cross-attention
-   * layer. Has shape `[batchSize, 2, S, numHeads, keyDims]`.
-   */
-  crossAttentionCache?: Tensor;
-
-  /**
-   * Integer or Integer Tensor. The index at which to update the
-   * `crossAttentionCache`. Usually, this is either `0` (compute the entire
-   * `crossAttentionCache`), or `null` (reuse a previously computed
-   * `crossAttentionCache`).
-   */
-  crossAttentionCacheUpdateIndex?: number;
-
-  /**
-   * If true, a causal mask (masking out future input) is applied on the decoder
-   * sequence.
-   * Defaults to `true`.
-   */
-  useCausalMask?: boolean;
-}
-
-/**
- * Transformer decoder.
- *
- * This class follows the architecture of the transformer decoder layer in the
- * paper [Attention is All You Need](https://arxiv.org/abs/1706.03762). Users
- * can instantiate multiple instances of this class to stack up a decoder.
- *
- * By default, this layer will apply a causal mask to the decoder attention
- * layer. This layer will correctly compute an attention mask from an implicit
- * padding mask (for example, by passing `maskZero=true` to a
- * `tf.layers.embedding` layer). See the Masking and Padding
- * [guide](https://keras.io/guides/understanding_masking_and_padding/)
- * for more details.
- *
- * This layer can be called with either one or two inputs. The number of inputs
- * must be consistent across all calls. The options are as follows:
- *    `layer.call(decoderSequence)`: no cross-attention will be built into the
- *         decoder block. This is useful when building a "decoder-only"
- *         transformer such as GPT-2.
- *    `layer.call(decoderSequence, {encoderSequence})`: cross-attention will be
- *         built into the decoder block. This is useful when building an
- *         "encoder-decoder" transformer, such as the original transformer
- *         model described in Attention is All You Need.
- *
- * Examples:
- * ```js
- * // Create a single transformer decoder layer.
- * const decoder = new TransformerDecoder({intermediateDim: 64, numHeads: 8});
- *
- * // Create a simple model containing the decoder.
- * const decoderInput = tf.input({shape: [10, 64]});
- * const encoderInput = tf.input({shape: {[10, 64]});
- * const output = decoder.call(decoderInput, {encoderInput});
- * const model = tf.model({
- *     inputs: [decoderInput, encoderInput],
- *     outputs: output,
- * );
- *
- * // Call decoder on the inputs.
- * const decoderInputData = tf.randomUniform([2, 10, 64]);
- * const encoderInputData = tf.randomUniform([2, 10, 64]);
- * const decoderOutput = model.predict([decoderInputData, encoderInputData]);
- * ```
- *
- * References:
- *  - [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)
- */
-export class TransformerDecoder extends Layer {
-  /** @nocollapse */
-  static readonly className = 'TransformerDecoder';
-
-  protected intermediateDim: number;
-  protected numHeads: number;
-  protected dropout: number;
-  protected activation: Activation;
-  protected layerNormEpsilon: number;
-  protected kernelInitializer: Initializer;
-  protected biasInitializer: Initializer;
-  protected normalizeFirst: boolean;
-  protected decoderSequenceShape: Shape;
-  protected encoderSequenceShape: Shape;
-
-  protected selfAttentionLayer: CachedMultiHeadAttention;
-  protected selfAttentionLayernorm: LayerNormalization;
-  protected selfAttentionDropout: Dropout;
-
-  protected selfCrossAttentionLayer: CachedMultiHeadAttention;
-  protected selfCrossAttentionLayernorm: LayerNormalization;
-  protected selfCrossAttentionDropout: Dropout;
-
-  protected feedforwardIntermediateDense: Dense;
-  protected feedforwardOutputDense: Dense;
-  protected feedforwardLayernorm: LayerNormalization;
-  protected feedforwardDropout: Dropout;
-
-  constructor(args: TransformerDecoderArgs) {
-    super(args);
-    this.intermediateDim = args.intermediateDim;
-    this.numHeads = args.numHeads;
-    this.dropout = args.dropout ?? 0;
-    this.activation = getActivation(args.activation ?? 'relu');
-    this.layerNormEpsilon = args.layerNormEpsilon ?? 1e-05;
-    this.kernelInitializer =
-      getInitializer(args.kernelInitializer ?? 'glorotUniform');
-    this.biasInitializer = getInitializer(args.biasInitializer ?? 'zeros');
-    this.normalizeFirst = args.normalizeFirst ?? false;
-  }
-
-  /**
-   *
-   * @param inputShape decoderSequenceShape or
-   *  [decoderSequenceShape, encoderSequenceShape]
-   */
-  override build(inputShape: Shape|[Shape, Shape]): void {
-    if (Array.isArray(inputShape[0])) {
-      // `inputShape` is of type [Shape, Shape].
-      [this.decoderSequenceShape, this.encoderSequenceShape] =
-        inputShape as [Shape, Shape];
-    } else {
-      this.decoderSequenceShape = inputShape as Shape;
-    }
-    // Infer the dimension of our hidden feature size from the build shape.
-    const hiddenDim =
-      this.decoderSequenceShape[this.decoderSequenceShape.length - 1];
-    // Attention head size is `hiddenDim` over the number of heads.
-    const headDim = Math.floor(hiddenDim / this.numHeads);
-
-    // Self attention layers.
-    this.selfAttentionLayer = new CachedMultiHeadAttention({
-      numHeads: this.numHeads,
-      keyDim: headDim,
-      dropout: this.dropout,
-      kernelInitializer: getInitializer(this.kernelInitializer.getClassName()),
-      biasInitializer: getInitializer(this.biasInitializer.getClassName()),
-    });
-
-    this.selfAttentionLayer.buildFromSignature(
-      this.decoderSequenceShape, this.decoderSequenceShape);
-
-    this.selfAttentionLayernorm =
-      new LayerNormalization({epsilon: this.layerNormEpsilon});
-
-    this.selfAttentionLayernorm.build(this.decoderSequenceShape);
-    this.selfAttentionDropout = new Dropout({rate: this.dropout});
-
-    // Cross attention layers are optional.
-    // TODO(pforderique): Add cross attention layers.
-
-    // Feedforward layers.
-    this.feedforwardIntermediateDense = new Dense({
-      units: this.intermediateDim,
-      activation: this.activation.getClassName() as ActivationIdentifier,
-      kernelInitializer: getInitializer(this.kernelInitializer.getClassName()),
-      biasInitializer: getInitializer(this.biasInitializer.getClassName()),
-    });
-    this.feedforwardIntermediateDense.build(this.decoderSequenceShape);
-    this.feedforwardOutputDense = new Dense({
-      units: hiddenDim,
-      kernelInitializer: getInitializer(this.kernelInitializer.getClassName()),
-      biasInitializer: getInitializer(this.biasInitializer.getClassName()),
-    });
-    const intermediateShape = this.decoderSequenceShape.slice();
-    intermediateShape[intermediateShape.length - 1] = this.intermediateDim;
-    this.feedforwardOutputDense.build(intermediateShape);
-    this.feedforwardLayernorm =
-      new LayerNormalization({epsilon: this.layerNormEpsilon});
-    this.feedforwardLayernorm.build(this.decoderSequenceShape);
-    this.feedforwardDropout = new Dropout({rate: this.dropout});
-    // Create layers based on input shape.
-    this.built = true;
-  }
-
-  override apply(
-      decoderSequence: Tensor|SymbolicTensor,
-      kwargs?: TransformerDecoderOptions): Tensor|SymbolicTensor {
-    if (!this.built) {
-      const decoderSequenceShape = decoderSequence.shape;
-      const encoderSequenceShape =
-        kwargs && kwargs.encoderSequence ? kwargs.encoderSequence.shape : null;
-      this.build([decoderSequenceShape, encoderSequenceShape]);
-    }
-    return super.apply(decoderSequence, kwargs) as Tensor|SymbolicTensor;
-  }
-
-  override call(
-      decoderSequence: Tensor, kwargs: TransformerDecoderOptions): Tensor {
-    return this.callAndReturnCaches(decoderSequence, kwargs)[0];
-  }
-
-  /**
-   * Forward pass of the TransformerDecoder.
-   *
-   * @returns One of three things, depending on call arguments:
-   *   - `[outputs, null, null]`, if `selfAttentionCache` is `null`.
-   *   - `[outputs, selfAttentionCache, null]`, if `selfAttentionCache` is
-   *     set and the layer has no cross-attention.
-   *   - `[outputs, selfAttentionCache, crossAttentionCache]`, if
-   *     `selfAttentionCache` and `crossAttentionCache` are set and
-   *     the layer has cross-attention.
-   */
-  callAndReturnCaches(
-    decoderSequence: Tensor, kwargs: TransformerDecoderOptions
-  ): [Tensor, Tensor, Tensor] {
-    return tidy(() => {
-      const hasEncoderSequence = kwargs.encoderSequence != null;
-      const hasCrossAttention = this.selfCrossAttentionLayer != null;
-
-      if (!hasCrossAttention && hasEncoderSequence) {
-        throw new ValueError(
-          'The number of call arguments to `TransformerDecoder` should ' +
-          'not change. Use `layer.apply(decoderSequence, {encoderSequence})` ' +
-          'to build a layer with cross attention, or ' +
-          '`layer.apply (decoderSequence)` to build a layer without. ' +
-          'This layer has been built without cross attention, but ' +
-          'you are trying to call it with encoderSequence.'
-        );
-      } else if (hasCrossAttention && !hasEncoderSequence) {
-        throw new ValueError(
-          'The number of call arguments to `TransformerDecoder` should not ' +
-          'change. Use `layer.apply(decoderSequence, {encoderSequence})` ' +
-          'to build a layer with cross attention, or ' +
-          '`layer.apply(decoderSequence)` to build a layer without. ' +
-          'This layer has been built with cross attention, but ' +
-          'you did not provide encoderSequence.'
-        );
-      }
-
-      const hasSelfAttentionCache = kwargs.selfAttentionCache != null;
-      const hasCrossAttentionCache = kwargs.crossAttentionCache != null;
-      if (hasCrossAttention && (
-        hasSelfAttentionCache !== hasCrossAttentionCache
-      )) {
-        throw new ValueError(
-          'When calling `TransformerDecoder` with cross-attention (with both ' +
-          '`encoderSequence` and `decoderSequence`), `selfAttentionCache` ' +
-          'and `crossAttentionCache` should both be set or both be `null`.  ' +
-          'One cannot be `null` while the other is not. Received: ' +
-          `selfAttentionCache=${kwargs.selfAttentionCache}, ` +
-          `crossAttentionCache=${kwargs.crossAttentionCache}.`
-        );
-      }
-
-      const selfAttentionMask = this.computeSelfAttentionMask(
-        decoderSequence,
-        kwargs.decoderPaddingMask as Tensor,
-        kwargs.decoderAttentionMask,
-        kwargs.useCausalMask,
-        kwargs.selfAttentionCache,
-        kwargs.selfAttentionCacheUpdateIndex,
-      );
-
-      let x = decoderSequence; // Intermediate result.
-      let selfAttentionCache = kwargs.selfAttentionCache;
-
-      // Self attention block.
-      let residual = x;
-      if (this.normalizeFirst) {
-        x = this.selfAttentionLayernorm.apply(x) as Tensor;
-      }
-      [x, selfAttentionCache] = this.selfAttentionLayer.callAndReturnCache(
-        x,
-        {
-          value: x,
-          attentionMask: selfAttentionMask,
-          cache: selfAttentionCache,
-          cacheUpdateIndex: kwargs.selfAttentionCacheUpdateIndex,
-        }
-      );
-      x = this.selfAttentionDropout.apply(x) as Tensor;
-      x = add(x, residual);
-      if (!this.normalizeFirst) {
-        x = this.selfAttentionLayernorm.apply(x) as Tensor;
-      }
-
-      // Cross attention is optional.
-      // TODO(pforderique): Add cross attention logic for encoder-decoder arch.
-
-      // Feedforward block.
-      residual = x;
-      if (this.normalizeFirst) {
-        x = this.selfAttentionLayernorm.apply(x) as Tensor;
-      }
-      x = this.feedforwardIntermediateDense.apply(x) as Tensor;
-      x = this.feedforwardOutputDense.apply(x) as Tensor;
-      x = this.feedforwardDropout.apply(x) as Tensor;
-      x = add(x, residual);
-      if (!this.normalizeFirst) {
-        x = this.selfAttentionLayernorm.apply(x) as Tensor;
-      }
-
-      if (selfAttentionCache != null) {
-        if (hasCrossAttention) {
-          return [x, selfAttentionCache, kwargs.crossAttentionCache];
-        } else {
-          return [x, selfAttentionCache, null];
-        }
-      }
-      return [x, null, null];
-    });
-  }
-
-  private computeSelfAttentionMask(
-    decoderSequence: Tensor,
-    decoderPaddingMask: Tensor,
-    decoderAttentionMask: Tensor,
-    useCasualMask: boolean,
-    selfAttentionCache: Tensor,
-    selfAttentionCacheUpdateIndex: number
-  ): Tensor {
-    const decoderMask = mergePaddingAndAttentionMask(
-      decoderSequence, decoderPaddingMask, decoderAttentionMask);
-    if(useCasualMask) {
-      const batchSize = decoderSequence.shape[0];
-      let inputLength = decoderSequence.shape[1];
-      const outputLength = decoderSequence.shape[1];
-      // We need to handle a rectangular causal mask when doing cached
-      // decoding. For generative inference, `decoderSequence` will
-      // generally be length 1, and `cache` will be the full generation length.
-      if(selfAttentionCache != null) {
-        inputLength = selfAttentionCache.shape[2];
-      }
-
-      const causalMask = computeCausalMask(
-        batchSize,
-        inputLength,
-        outputLength,
-        selfAttentionCacheUpdateIndex ?? 0
-      );
-      return decoderMask != null ? decoderMask.minimum(causalMask) : causalMask;
-    }
-    return decoderMask;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      'intermediateDim': this.intermediateDim,
-      'numHeads': this.numHeads,
-      'dropout': this.dropout,
-      'activation': serializeActivation(this.activation),
-      'layerNormEpsilon': this.layerNormEpsilon,
-      'kernelInitializer': serializeInitializer(this.kernelInitializer),
-      'biasInitializer': serializeInitializer(this.biasInitializer),
-      'normalizeFirst': this.normalizeFirst,
-      'decoderSequenceShape': this.decoderSequenceShape,
-      'encoderSequenceShape': this.encoderSequenceShape,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override computeOutputShape(decoderSequenceShape: Shape): Shape {
-    return decoderSequenceShape;
-  }
-}
-serialization.registerClass(TransformerDecoder);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_decoder_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_decoder_test.ts
deleted file mode 100644
index 191392363..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_decoder_test.ts
+++ /dev/null
@@ -1,171 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for Transformer Decoder.
- */
-import { Tensor, memory, randomUniform, randomUniformInt, serialization, zeros, zerosLike } from '@tensorflow/tfjs-core';
-
-import { SymbolicTensor } from '../../../engine/topology';
-import { input, model } from '../../../exports';
-import { expectTensorsClose } from '../../../utils/test_utils';
-import { Dense } from '../../core';
-import { sliceUpdate } from '../utils';
-
-import { TransformerDecoder } from './transformer_decoder';
-
-describe('TransformerDecoder', () => {
-  let originalTimeout: number;
-
-  beforeAll(() => {
-    // This test needs more time to finish the async fetch, adjusting
-    // jasmine timeout for this test to avoid flakiness. See jasmine
-    // documentation for detail:
-    // https://jasmine.github.io/2.0/introduction.html#section-42
-    originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-  });
-
-  afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-  describe('valid call', () => {
-    function testValidCallWithoutCrossAttention(
-        testcaseName: string, normalizeFirst: boolean) {
-      it(`${testcaseName} without cross attention`, () => {
-        const decoderInput = randomUniform([4, 6]);
-        const decoder = new TransformerDecoder({
-          intermediateDim: 4,
-          numHeads: 2,
-          normalizeFirst,
-        });
-        expect(() => decoder.apply(decoderInput)).not.toThrow();
-      });
-    }
-
-    testValidCallWithoutCrossAttention('without_norm_first', false);
-    testValidCallWithoutCrossAttention('with_norm_first', true);
-  });
-
-  it('invalid call', () => {
-    const encoderInput = zeros([4, 6]);
-    const decoderInput = zeros([4, 6]);
-
-    // Without cross-attention.
-    const decoder = new TransformerDecoder({intermediateDim: 4, numHeads: 2});
-    decoder.apply(decoderInput);
-
-    // Should raise ValueError if encoderInput is provided.
-    expect(
-      () => decoder.apply(decoderInput, {encoderSequence: encoderInput})
-    ).toThrow();
-  });
-
-  it('error when invalid kernel initializer', () => {
-    expect(() => new TransformerDecoder({
-      intermediateDim: 4,
-      numHeads: 2,
-      dropout: 0.5,
-      kernelInitializer: 'Invalid',
-    })).toThrow();
-  });
-
-  it('one training step of transformer without cross attention', async () => {
-    const decoderInput = input({shape: [4, 6]});
-    const decoder = new TransformerDecoder({intermediateDim: 4, numHeads: 2});
-    let outputs = decoder.apply(decoderInput);
-    outputs = new Dense({
-      units: 10, activation: 'softmax'}).apply(outputs) as SymbolicTensor;
-    const tModel = model({inputs: decoderInput, outputs});
-
-    const decoderSequence = randomUniform([2, 4, 6]);
-    const label = randomUniformInt([2, 4, 1], 0, 10).asType('float32');
-
-    tModel.compile({loss: 'sparseCategoricalCrossentropy', optimizer: 'adam'});
-    const loss = tModel.trainOnBatch(decoderSequence, label);
-
-    expect(await loss).toBeGreaterThan(0);
-  });
-
-  it('serialization round trip', () => {
-    const testLayer = new TransformerDecoder({intermediateDim: 4, numHeads: 2});
-
-    const config = testLayer.getConfig();
-    const restored = TransformerDecoder.fromConfig(TransformerDecoder, config);
-
-    // Initializers don't get serailized with customObjects.
-    delete ((config['kernelInitializer'] as serialization.ConfigDict
-      )['config'] as serialization.ConfigDict)['customObjects'];
-    delete ((config['biasInitializer'] as serialization.ConfigDict
-      )['config'] as serialization.ConfigDict)['customObjects'];
-
-    expect(restored.getConfig()).toEqual(config);
-  });
-
-  it('does not leak memory', () => {
-    const decoderInput = randomUniform([4, 6]);
-    const decoder = new TransformerDecoder({intermediateDim: 4, numHeads: 2});
-    // Initial apply to make sure layer is built.
-    decoder.apply(decoderInput);
-
-    const numTensors = memory().numTensors;
-    decoder.apply(decoderInput);
-
-    expect(memory().numTensors).toEqual(numTensors + 1);
-  });
-
-  it('cache call is correct', () => {
-    const batchSize = 2;
-    const seqLen = 5;
-    const numHeads = 2;
-    const headDim = 4;
-    const hiddenDim = numHeads * headDim;
-
-    const layer = new TransformerDecoder({intermediateDim: 4, numHeads});
-    const dtype = 'float32';
-
-    const x = randomUniform([batchSize, seqLen, hiddenDim], null, null, dtype);
-    const cache = zeros([batchSize, 2, seqLen, numHeads, headDim], dtype);
-    const outputs = zerosLike(x);
-
-    layer.build(x.shape);
-    const [noLoopOutputs, noLoopCache] = layer.callAndReturnCaches(
-      x, {selfAttentionCache: cache, selfAttentionCacheUpdateIndex: 0});
-
-    function call(outputs: Tensor, cache: Tensor) {
-      for (let i = 0; i < seqLen; i++) {
-        // Compute the rest tokens.
-        const nextInput = x.slice([0, i, 0], [batchSize, 1, hiddenDim]);
-        const [nextOutput, nextCache] = layer.callAndReturnCaches(
-          nextInput,
-          {
-            selfAttentionCache: cache,
-            selfAttentionCacheUpdateIndex: i
-          }
-        );
-        outputs = sliceUpdate(outputs, [0, i, 0], nextOutput);
-        cache = nextCache;
-      }
-      return [outputs, cache];
-    }
-    const [output, outputCache] = call(outputs, cache);
-
-    expectTensorsClose(output, noLoopOutputs);
-    expectTensorsClose(outputCache, noLoopCache);
-  });
-
-  // TODO(pforderique): Test mask propogation once supported.
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_layer_utils.ts b/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_layer_utils.ts
deleted file mode 100644
index c06d7ad52..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_layer_utils.ts
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Utility functions for `TransformerDecoder`.
- */
-
-/* Original source: keras_nlp/layers/modeling/transformer_layer_utils.py */
-import { Tensor, add, expandDims, tensor, tidy } from '@tensorflow/tfjs-core';
-
-import { ValueError } from '../../../errors';
-
-function checkMasksShapes(
-    inputs: Tensor, paddingMask: Tensor, attentionMask: Tensor): void {
-  if (paddingMask != null) {
-    if (paddingMask.shape.length !==2) {
-      throw new ValueError(
-        '`paddingMask` should have shape ' +
-        `[batchSize, targetLength]. Received shape ${paddingMask.shape}.`
-      );
-    }
-  }
-  if (attentionMask != null) {
-    if (attentionMask.shape.length !== 3) {
-      throw new ValueError(
-        '`attentionMask` should have shape ' +
-        `[batchSize, targetLength, sourceLength]. ` +
-        `Received shape ${attentionMask.shape}.`
-      );
-    }
-  }
-}
-
-/**
- * Compute a causal attention mask for a transformer decoder.
- *
- * @param batchSize batch size for the mask.
- * @param inputLength the length of key/value tensors in the attention layer.
- * @param outputLength the length of query tensor in the attention layer.
- * @param cacheIndex the current index for cached generation. If passed, the
- *  query sequence will be considered to start at `cacheIndex` rather than zero.
- *  For example, a casual mask with `outputLength=1` and `cacheIndex=5` would
- *  allow the query tensor to attend to the first five positions of the
- *  key/value tensors.
- *
- * @returns a causal attention mask with shape
- *  `[batchSize, outputLength, inputLength]` that can be passed to a attention
- *  layer.
- */
-export function computeCausalMask(
-    batchSize: number,
-    inputLength: number,
-    outputLength: number,
-    cacheIndex = 0
-  ): Tensor {
-  return tidy(() => {
-    const i = add(
-      expandDims(Array.from({length: outputLength}, (_, i) => i), 1),
-      cacheIndex,
-    );
-    const j = tensor(Array.from({length: inputLength}, (_, i) => i));
-    const mask = i.greaterEqual(j).cast('int32').expandDims(0);
-    return mask.broadcastTo([batchSize, outputLength, inputLength]);
-  });
-}
-
-/**
- * Merge the padding mask with a customized attention mask.
- *
- * @param inputs the input sequence.
- * @param paddingMask the 1D padding mask, of shape
- *          [batchSize, sequenceLength].
- * @param attentionMask the 2D customized mask, of shape
- *          [batchSize, sequenceLength, sequence2_length].
- * @returns
- *  A merged 2D mask or null. If only `paddingMask` is provided, the
- *  returned mask is paddingMask with one additional axis.
- */
-export function mergePaddingAndAttentionMask(
-    inputs: Tensor, paddingMask: Tensor, attentionMask: Tensor): Tensor {
-  return tidy(() => {
-    checkMasksShapes(inputs, paddingMask, attentionMask);
-    let mask: Tensor;
-    if (paddingMask != null) {
-      // Add an axis for broadcasting, the attention mask should be 2D
-      // (not including the batch axis).
-      mask = paddingMask.expandDims(1).cast('int32');
-    }
-    if (attentionMask != null) {
-      attentionMask = attentionMask.cast('int32');
-      if (mask == null) {
-        return attentionMask;
-      } else {
-        return mask.minimum(attentionMask);
-      }
-    }
-    return mask;
-  });
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_layer_utils_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_layer_utils_test.ts
deleted file mode 100644
index 37348047d..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/modeling/transformer_layer_utils_test.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for Transformer Decoder.
- */
-import { memory, randomUniform, tensor } from '@tensorflow/tfjs-core';
-
-import { expectTensorsClose } from '../../../utils/test_utils';
-
-import { computeCausalMask, mergePaddingAndAttentionMask } from './transformer_layer_utils';
-
-describe('TransformerLayerUtils', () => {
-  it('compute causal mask', () => {
-    const mask = computeCausalMask(1, 2, 2);
-    expect(mask.arraySync()).toEqual([[[1, 0], [1, 1]]]);
-  });
-
-  it('merge padding and attention mask', () => {
-    const paddingMask = tensor([[1, 1, 0]]);
-    const attentionMask = tensor([[[0, 0, 1], [0, 1, 0], [1, 0, 0]]]);
-    const inputs = randomUniform([1, 3, 2]);
-
-    const mergedMask =
-      mergePaddingAndAttentionMask(inputs, paddingMask, attentionMask);
-
-    expectTensorsClose(
-      mergedMask,
-      tensor([[[0, 0, 0], [0, 1, 0], [1, 0, 0]]], null, 'int32')
-    );
-  });
-
-  it('bad mask shapes', () => {
-    let paddingMask = tensor([[[1, 1, 0], [1, 0, 0]]]);
-    let attentionMask = tensor([[[0, 0, 1], [0, 1, 0], [1, 0, 0]]]);
-    let inputs = randomUniform([1, 3, 2]);
-
-    expect(
-      () => mergePaddingAndAttentionMask(inputs, paddingMask, attentionMask)
-    ).toThrow();
-
-    paddingMask = tensor([[1, 1, 0]]);
-    attentionMask = tensor([[0, 0, 1], [1, 0, 0]]);
-    inputs = randomUniform([1, 3, 2]);
-
-    expect(
-      () => mergePaddingAndAttentionMask(inputs, paddingMask, attentionMask)
-    ).toThrow();
-  });
-
-  it('does not leak memory', () => {
-    // computeCausalMask
-    let numTensorsBefore = memory().numTensors;
-    computeCausalMask(1, 2, 2);
-    expect(memory().numTensors).toEqual(numTensorsBefore + 1);
-
-    // mergePaddingAndAttentionMask
-    const paddingMask = tensor([[1, 1, 0]]);
-    const attentionMask = tensor([[[0, 0, 1], [0, 1, 0], [1, 0, 0]]]);
-    const inputs = randomUniform([1, 3, 2]);
-    numTensorsBefore = memory().numTensors;
-    mergePaddingAndAttentionMask(inputs, paddingMask, attentionMask);
-    expect(memory().numTensors).toEqual(numTensorsBefore + 1);
-  });
-  // TODO(pforderique): Test serialization.
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/backbone.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/backbone.ts
deleted file mode 100644
index 72ae1c546..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/backbone.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Base class for Backbone models.
- */
-
-/* Original source: keras_nlp/models/backbone.py */
-import { serialization } from '@tensorflow/tfjs-core';
-
-import { ContainerArgs } from '../../../engine/container';
-import { LayersModel } from '../../../engine/training';
-import { NotImplementedError } from '../../../errors';
-import { Embedding } from '../../embeddings';
-
-export class Backbone extends LayersModel {
-  /** @nocollapse */
-  static override className = 'Backbone';
-
-  constructor(args: ContainerArgs) {
-    super(args);
-  }
-
-  /**
-   * A `tf.layers.embedding` instance for embedding token ids.
-   */
-  get tokenEmbedding(): Embedding {
-    throw new NotImplementedError();
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    return {
-      name: this.name,
-      trainable: this.trainable,
-    };
-  }
-
-  static override fromConfig<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>,
-    config: serialization.ConfigDict): T {
-
-    return new cls(config);
-  }
-}
-serialization.registerClass(Backbone);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/generative_task.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/generative_task.ts
deleted file mode 100644
index f83116bc3..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/generative_task.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Base class for Generative Task models.
- */
-
-/* Original source: keras_nlp/models/generative_task.py */
-import { NamedTensorMap, Tensor } from '@tensorflow/tfjs-core';
-
-import { NotImplementedError } from '../../../errors';
-import { ModelCompileArgs } from '../../../engine/training';
-
-import { Task } from './task';
-
-export type GenerateFn =
-  (inputs: NamedTensorMap, endTokenId?: number) => NamedTensorMap;
-
-/**
- *  Base class for Generative Task models.
- */
-export class GenerativeTask extends Task {
-  /** @nocollapse */
-  static override className = 'GenerativeTask';
-
-  protected generateFunction: GenerateFn;
-
-  override compile(args: ModelCompileArgs): void {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * Run the generation on a single batch of input.
-   */
-  generateStep(
-    inputs: NamedTensorMap,
-    endTokenId: number
-  ): NamedTensorMap {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * Create or return the compiled generation function.
-   */
-  makeGenerateFunction(): GenerateFn {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * Normalize user input to the generate function.
-   *
-   * This function converts all inputs to tensors, adds a batch dimension if
-   * necessary, and returns a iterable "dataset like" object.
-   */
-  protected normalizeGenerateInputs(inputs: Tensor): [Tensor, boolean] {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * Normalize user output from the generate function.
-   *
-   * This function converts all output to numpy (for integer output), or
-   * python strings (for string output). If a batch dimension was added to
-   * the input, it is removed from the output (so generate can be string in,
-   * string out).
-   */
-  protected normalizeGenerateOutputs(
-    outputs: Tensor,
-    inputIsScalar: boolean
-  ): Tensor {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * Generate text given prompt `inputs`.
-   *
-   * This method generates text based on given `inputs`. The sampling method
-   * used for generation can be set via the `compile()` method.
-   *
-   * `inputs` will be handled as a single batch.
-   *
-   * If a `preprocessor` is attached to the model, `inputs` will be
-   * preprocessed inside the `generate()` function and should match the
-   * structure expected by the `preprocessor` layer (usually raw strings).
-   * If a `preprocessor` is not attached, inputs should match the structure
-   * expected by the `backbone`. See the example usage above for a
-   * demonstration of each.
-   *
-   * @param inputs tensor data. If a `preprocessor` is attached to the model,
-   *  `inputs` should match the structure expected by the `preprocessor` layer.
-   *  If a `preprocessor` is not attached, `inputs` should match the structure
-   *  expected the the `backbone` model.
-   * @param maxLength Integer. The max length of the generated sequence.
-   *  Will default to the max configured `sequenceLength` of the
-   *  `preprocessor`. If `preprocessor` is `null`, `inputs` should be
-   *  should be padded to the desired maximum length and this argument
-   *  will be ignored.
-   */
-  generate(inputs: Tensor, maxLength?: number) {
-    throw new NotImplementedError();
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_backbone.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_backbone.ts
deleted file mode 100644
index 971f8868c..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_backbone.ts
+++ /dev/null
@@ -1,222 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Base class for Backbone models.
- */
-
-/* Original source: keras_nlp/models/gpt2/gpt2_backbone.py */
-import { serialization } from '@tensorflow/tfjs-core';
-
-import { RandomNormal } from '../../../../initializers';
-import { input } from '../../../../exports';
-import { Embedding } from '../../../embeddings';
-import { SymbolicTensor } from '../../../../engine/topology';
-import { PositionEmbedding } from '../../modeling/position_embedding';
-import { add } from '../../../../exports_layers';
-import { Dropout } from '../../../core';
-import { TransformerDecoder } from '../../modeling/transformer_decoder';
-import { getActivation } from '../../../../activations';
-import { LayerNormalization } from '../../../normalization';
-import { Backbone } from '../backbone';
-
-function gpt2KernelInitializer(stddev = 0.02) {
-  return new RandomNormal({stddev});
-}
-
-export interface GPT2BackboneArgs  {
-  /**
-   * Integer. The size of the token vocabulary.
-   */
-  vocabularySize: number;
-
-  /**
-   * Integer. The number of transformer layers.
-   */
-  numLayers: number;
-
-  /**
-   * Integer. The number of attention heads for each transformer.
-   * The hidden size must be divisible by the number of attention heads.
-   */
-  numHeads: number;
-
-  /**
-   * Integer. The size of the transformer encoding and pooler layers.
-   */
-  hiddenDim: number;
-
-  /**
-   * Integer. The output dimension of the first Dense layer in a two-layer
-   * feedforward network for each transformer.
-   */
-  intermediateDim: number;
-
-  /**
-   * Float. Dropout probability for the Transformer encoder.
-   * Defaults to 0.2.
-   */
-  dropout?: number;
-
-  /**
-   * Integer. The maximum sequence length that this encoder can consume.
-   * If `null`, `maxSequenceLength` uses the value from sequence length.
-   * This determines the variable shape for positional embeddings.
-   * Defaults to 1024.
-   */
-  maxSequenceLength?: number;
-}
-
-/**
- * GPT-2 core network with hyperparameters.
- *
- * This network implements a Transformer-based decoder network,
- * Generative Pretrained Transformer-2 (GPT-2), as described in
- * ["Language Models are Unsupervised Multitask Learners"](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf).
- * It includes the embedding lookups and transformer layers.
- *
- * The default constructor gives a fully customizable, randomly initialized
- * GPT-2 model with any number of layers, heads, and embedding
- * dimensions. To load preset architectures and weights, use the `fromPreset`
- * constructor.
- *
- * Disclaimer: Pre-trained models are provided on an "as is" basis, without
- * warranties or conditions of any kind. The underlying model is provided by a
- * third party and subject to a separate license, available
- * [here](https://github.com/openai/gpt-2).
- *
- *
- * Example usage:
- * ```js
- * const tokenIds = tf.ones([1, 12]), dtype="int32");
- * const paddingMask = tf.tensor(
- *  [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]], 'int32');
- *
- * # Pretrained GPT-2 decoder.
- * model = GPT2Backbone.fromPreset("gpt2_base_en");
- * model.apply(inputData, {paddingMask});
- *
- * # Randomly initialized GPT-2 decoder with custom config.
- * model = kerasNlp.models.GPT2Backbone({
- *     vocabularySize: 50257,
- *     numLayers: 12,
- *     numHeads: 12,
- *     hiddenDim: 768,
- *     intermediateDim: 3072,
- *     maxSequenceLength: 1024,
- * });
- * model.apply(inputData, {paddingMask});
- * ```
- */
-export class GPT2Backbone extends Backbone {
-  /** @nocollapse */
-  static override className = 'GPT2Backbone';
-
-  private vocabularySize: number;
-  private numLayers: number;
-  private numHeads: number;
-  private hiddenDim: number;
-  private intermediateDim: number;
-  private dropout: number;
-  private maxSequenceLength: number;
-
-  constructor(args: GPT2BackboneArgs) {
-    args.dropout = args.dropout ?? 0.1;
-    args.maxSequenceLength = args.maxSequenceLength ?? 1024;
-
-    // Inputs
-    const tokenIds = input({shape: [null], dtype: 'int32', name: 'token_ids'});
-    const paddingMask =
-      input({shape: [null], dtype: 'int32', name: 'padding_mask'});
-
-    // Embed tokens, positions.
-    const tokenEmbedding = new Embedding({
-      inputDim: args.vocabularySize,
-      outputDim: args.hiddenDim,
-      embeddingsInitializer: gpt2KernelInitializer(0.01),
-      name: 'token_embedding',
-    }).apply(tokenIds) as SymbolicTensor;
-
-    const positionEmbedding = new PositionEmbedding({
-      initializer: gpt2KernelInitializer(0.02),
-      sequenceLength: args.maxSequenceLength,
-      name: 'position_embedding',
-    }).apply(tokenEmbedding) as SymbolicTensor;
-
-    // Sum and apply dropout to embeddings.
-    let x = add({name: 'embeddings_add'})
-      .apply([tokenEmbedding, positionEmbedding]) as SymbolicTensor;
-    x = new Dropout({rate: args.dropout, name: 'embeddings_dropout'})
-      .apply(x) as SymbolicTensor;
-
-    // Apply successive transformer decoder blocks.
-    for(let i = 0; i < args.numLayers; i++) {
-      x = new TransformerDecoder({
-        intermediateDim: args.intermediateDim,
-        numHeads: args.numHeads,
-        dropout: args.dropout,
-        layerNormEpsilon: 1e-05,
-        // TODO(pforderique): Implement gelu.
-        activation: getActivation('relu'),
-        kernelInitializer: gpt2KernelInitializer(0.02),
-        normalizeFirst: true,
-        name: `transformer_layer_${i}`,
-      }).apply(x, {decoderPaddingMask: paddingMask}) as SymbolicTensor;
-    }
-
-    const sequenceOutput = new LayerNormalization({
-      name: 'layer_norm',
-      axis: -1,
-      epsilon: 1e-05,
-      dtype: 'float32',
-    }).apply(x) as SymbolicTensor;
-
-    // Instantiate using Functional API Model constructor.
-    super({
-      inputs: [tokenIds, paddingMask],
-      outputs: sequenceOutput,
-      name: 'gpt2_backbone'
-    });
-    this.vocabularySize = args.vocabularySize;
-    this.numLayers = args.numLayers;
-    this.numHeads = args.numHeads;
-    this.hiddenDim = args.hiddenDim;
-    this.intermediateDim = args.intermediateDim;
-    this.dropout = args.dropout ?? 0.1;
-    this.maxSequenceLength = args.maxSequenceLength ?? 1024;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      vocabularySize: this.vocabularySize,
-      numLayers: this.numLayers,
-      numHeads: this.numHeads,
-      hiddenDim: this.hiddenDim,
-      intermediateDim: this.intermediateDim,
-      dropout: this.dropout,
-      maxSequenceLength: this.maxSequenceLength,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override get tokenEmbedding(): Embedding {
-    return this.getLayer('token_embedding') as Embedding;
-  }
-}
-serialization.registerClass(GPT2Backbone);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_backbone_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_backbone_test.ts
deleted file mode 100644
index 020b9f3c2..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_backbone_test.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { Tensor, memory, ones } from '@tensorflow/tfjs-core';
-import { GPT2Backbone } from './gpt2_backbone';
-
-/**
- *  Tests for GPT-2 backbone models.
- */
-
-describe('GPT2Backbone', () => {
-  let backbone: GPT2Backbone;
-  // let inputBatch: {[name: string]: Tensor};
-  let inputBatch: Tensor[];
-
-  beforeAll(() => {
-    backbone = new GPT2Backbone({
-      vocabularySize: 10,
-      numLayers: 2,
-      numHeads: 2,
-      hiddenDim: 2,
-      intermediateDim: 4,
-      maxSequenceLength: 5,
-    });
-    inputBatch = [
-      ones([2, 5], 'int32'),  // tokenIds
-      ones([2, 5], 'int32'),  // paddingMask
-    ];
-  });
-
-  it('call', () => {
-    expect(() => backbone.apply(inputBatch)).not.toThrow();
-  });
-
-  it('token embedding', () => {
-    const output = backbone.tokenEmbedding.apply(inputBatch[0]) as Tensor;
-    expect(output.shape).toEqual([2, 5, 2]);
-  });
-
-  it('name', () => {
-    // Check default name passed through.
-    expect(backbone.name).toMatch('gpt2_backbone');
-  });
-
-  it('variable sequence length', () => {
-    let inputData: Tensor[];
-    for (const seqLength of [2, 3, 4]) {
-      inputData = [
-        ones([2, seqLength], 'int32'),  // tokenIds
-        ones([2, seqLength], 'int32'),  // paddingMask
-      ];
-      expect(() => backbone.apply(inputData)).not.toThrow();
-    }
-  });
-
-  it('predict', () => {
-    expect(() => backbone.predict(inputBatch)).not.toThrow();
-  });
-
-  it('does not leak memory', () => {
-    const numTensors = memory().numTensors;
-    backbone.apply(inputBatch);
-
-    expect(memory().numTensors).toEqual(numTensors + 1);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_causal_lm.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_causal_lm.ts
deleted file mode 100644
index b4a096e31..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_causal_lm.ts
+++ /dev/null
@@ -1,224 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * GPT2 Causal LM (Language Model).
- */
-
-/* Original source: keras-nlp/models/gpt2/gpt2_causal_lm.py */
-import { NamedTensorMap, Tensor, serialization } from '@tensorflow/tfjs-core';
-
-import { GPT2Preprocessor } from './gpt2_preprocessor';
-import { NotImplementedError } from '../../../../errors';
-import { Layer } from '../../../../exports_layers';
-import { LayerArgs } from '../../../../engine/topology';
-import { Embedding } from '../../../../layers/embeddings';
-import { Shape } from '../../../../keras_format/common';
-import { GenerativeTask } from '../generative_task';
-import { GPT2Backbone } from './gpt2_backbone';
-import { PipelineModelArgs } from '../../utils';
-import { Kwargs } from '../../../../types';
-
-declare interface ReverseEmbeddingArgs extends LayerArgs {
-  embedding: Embedding;
-}
-
-class ReverseEmbedding extends Layer {
-  protected embedding: Embedding;
-
-  constructor(args: ReverseEmbeddingArgs) {
-    super(args);
-    this.embedding = args.embedding;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    throw new NotImplementedError();
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    throw new NotImplementedError();
-  }
-
-}
-
-export declare interface GPT2CausalLMArgs extends PipelineModelArgs {
-  /**
-   * A `GPT2Backbone` instance.
-   */
-  backbone: GPT2Backbone;
-
-  /**
-   * Optional `GPT2CausalLMPreprocessor`.
-   * If `null`, this model will not apply preprocessing, and inputs should be
-   * preprocessed before calling the model.
-   */
-  preprocessor?: GPT2Preprocessor;
-}
-
-/**
- * An end-to-end GPT2 model for causal langauge modeling.
- *
- * A causal language model (LM) predicts the next token based on previous
- * tokens. This task setup can be used to train the model unsupervised on
- * plain text input, or to autoregressively generate plain text similar to
- * the data used for training. This task can be used for pre-training or
- * fine-tuning a GPT-2 model, simply by calling `fit()`.
- *
- * This model has a `generate()` method, which generates text based on a
- * prompt. The generation strategy used is controlled by an additional
- * sampler` argument on `compile()`.
- * By default, the top k results will be returned.
- *
- * This model can optionally be configured with a `preprocessor` layer, in
- * which case it will automatically apply preprocessing to string inputs during
- * fit()`, `predict()`, `evaluate()` and `generate()`. This is done by default
- * when creating the model with `fromPreset()`.
- *
- * Disclaimer: Pre-trained models are provided on an "as is" basis, without
- * warranties or conditions of any kind. The underlying model is provided by a
- * third party and subject to a separate license, available
- * here](https://github.com/openai/gpt-2).
- *
- * Use `generate()` to do text generation.
- * ```js
- * const gpt2LM = GPT2CausalLM.fromPreset('gpt2_base_en');
- * gpt2LM.generate("I want to say", max_length=30);
- * // Generate with batched prompts.
- * gpt2LM.generate(["This is a", "Where are you"], max_length=30);
- * ```
- *
- * Use `generate()` without preprocessing.
- * ```js
- * // Prompt the model with `5338, 318` (the token ids for `"Who is"`).
- * // Use `"paddingMask"` to indicate values that should not be overridden.
- * const prompt = {
- *  tokenIds: tf.tensor([[5338, 318, 0, 0, 0], [5338, 318, 0, 0, 0]]),
- *  paddingMask: tf.tensor([[1, 1, 0, 0, 0], [1, 1, 0, 0, 0]]]),
- * };
- * const gpt2LM = GPT2CausalLM.from_preset('gpt2_base_en', null);
- * gpt2LM.generate(prompt);
- * ```
- *
- * Call `fit()` on a single batch.
- * ```js
- * const features = ['The quick brown fox jumped.', 'I forgot my homework.'];
- * const gpt2LM = GPT2CausalLM.from_preset('gpt2_base_en');
- * gpt2LM.fit(features, {batchSize: 2});
- * ```
- *
- * Call `fit()` without preprocessing.
- * ```js
- * const x = {
- *  tokenIds: tf.tensor([[50256, 1, 2, 3, 4], [50256, 1, 2, 3, 4]]),
- *  paddingMask: tf.tensor([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]),
- * };
- * const y = tf.tensor([[1, 2, 3, 4, 50256], [1, 2, 3, 4, 50256]]);
- * const sw = tf.tensor([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]);
- * const gpt2LM = GPT2CausalLM.from_preset('gpt2_base_en', null);
- * gpt2LM.fit(x, y, {sampleWeight: sw, batchSize: 2});
- * ```
- *
- * Custom backbone and vocabulary.
- * ```js
- * const features = ["a quick fox.", "a fox quick."];
- * const vocab = {"<|endoftext|>": 0, "a": 4, "Ġquick": 5, "Ġfox": 6};
- * const merges = [
- *  "Ġ q", "u i", "c k", "ui ck", "Ġq uick", "Ġ f", "o x", "Ġf ox"
- * ];
- * const tokenizer = new GPT2Tokenizer({vocabulary: vocab, merges});
- * const preprocessor =  new GPT2CausalLMPreprocessor({
- *  tokenizer,
- *  sequence_length: 128,
- * });
- * const backbone = new GPT2Backbone({
- *  vocabularysize: 30552,
- *  numlayers: 4,
- *  numheads: 4,
- *  hiddendim: 256,
- *  intermediatedim: 512,
- *  maxSequenceLength: 128,
- * });
- * const gpt2LM = new GPT2CausalLM({backbone, preprocessor});
- * gpt2LM.fit(features, {batch_size: 2});
- * ```
- */
-export class GPT2CausalLM extends GenerativeTask {
-  /** @nocollapse */
-  static override className = 'GPT2CausalLM';
-
-  constructor(args: GPT2CausalLMArgs) {
-    super(args);
-    throw new NotImplementedError(`Uses ${ReverseEmbedding}.`);
-  }
-
-  static override presets<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>
-  ): {} {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * Forward pass of `GPT2CausalLM` with cache.
-   *
-   * `callWithCache` adds an additional forward pass for the model for
-   * autoregressive inference. Unlike calling the model directly, this method
-   * allows caching previous key/value Tensors in multi-head attention layer,
-   * and avoids recomputing the outputs of seen tokens.
-   *
-   * @param tokenIds a dense int Tensor with shape `[batchSize, maxLength]`.
-   * @param cache a dense float Tensor, the cache of key and value.
-   * @param cacheUpdateIndex Integer. The index of current inputs in the whole
-   *  sequence.
-   * @returns [logits, hiddenStates, cache], where `logits` is the
-   *  language model logits for the input tokenIds, `hiddenStates` is
-   *  the final hidden representation of the input tokens, and `cache` is
-   *  the decoding cache.
-   */
-  callWithCache(
-    tokenIds: Tensor,
-    cache: Tensor,
-    cacheUpdateIndex: number
-  ): [Tensor, Tensor, Tensor] {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * Build an empty cache for use with `callWithCache()`.
-   */
-  private buildCache(tokenIds: Tensor): [Tensor, Tensor] {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * A compilable generation function for a single batch of inputs.
-   *
-   * This function represents the inner generation function for a single batch
-   *  of inputs.
-   *
-   * @param inputs An object with two keys `tokenIds` and `paddingMask` and
-   *  batched tensor values.
-   * @param endTokenId The id of the end token to stop on. If all
-   *  sequences have produced a new `endTokenId`, generation will stop.
-   */
-  override generateStep(
-    inputs: NamedTensorMap,
-    endTokenId: number
-  ): NamedTensorMap {
-    throw new NotImplementedError(`Uses ${this.buildCache}`);
-  }
-}
-serialization.registerClass(GPT2CausalLM);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_causal_lm_preprocessor.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_causal_lm_preprocessor.ts
deleted file mode 100644
index cba521314..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_causal_lm_preprocessor.ts
+++ /dev/null
@@ -1,119 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * GPT2 Causal LM preprocessor layer.
- */
-
-/* Original source: keras-nlp/models/gpt2/gpt2_causal_lm_preprocessor.py */
-import { NamedTensorMap, Tensor, serialization } from '@tensorflow/tfjs-core';
-
-import { GPT2Preprocessor, GPT2PreprocessorOptions, packXYSampleWeight } from './gpt2_preprocessor';
-import { NotImplementedError } from '../../../../errors';
-
-/**
- * GPT2 Causal LM preprocessor.
- *
- * This preprocessing layer is meant for use with
- * `GPT2CausalLM`. By default, it will take in batches of
- * strings, and return outputs in a `[x, y, sampleWeight]` format, where the
- * `y` label is the next token id in the `x` sequence.
- *
- * For use with generation, the layer also exposes two methods
- * generatePreprocess()` and `generatePostprocess()`. When this preprocessor
- * is attached to a `GPT2CausalLM` instance, these methods
- * will be called implicitly in `generate()`. They can also be called
- * standalone (e.g. to precompute preprocessing inputs for generation in a
- * separate process).
- *
- * Examples:
- * ```js
- * // Load the preprocessor from a preset.
- * const preprocessor = GPT2CausalLMPreprocessor.from_preset('gpt2_base_en');
- *
- * // Tokenize and pack a single sentence.
- * const sentence = tf.scalar('League of legends');
- * preprocessor.apply(sentence);
- * // Same output.
- * preprocessor('League of legends');
- *
- * // Tokenize a batch of sentences.
- * const sentences = tf.constant(['Taco tuesday', 'Fish taco please!']);
- * preprocessor.apply(sentences);
- * // Same output.
- * preprocessor.apply(['Taco tuesday', 'Fish taco please!']);
- * ```
- */
-export class GPT2CausalLMPreprocessor extends GPT2Preprocessor {
-  /** @nocollapse */
-  static override className = 'GPT2CausalLMPreprocessor';
-
-  override call(
-    inputs: Tensor|Tensor[],
-    kwargs: GPT2PreprocessorOptions
-  ): Tensor|Tensor[] {
-    const output = this.callAndPackArgs(inputs, kwargs);
-    if (kwargs.y) {
-      return (output as [NamedTensorMap, Tensor])[0]['tokenIds'];
-    }
-    return (output as NamedTensorMap)['tokenIds'];
-  }
-
-  /**
-   * Calls the layer and returns extra information like the paddingMask used to
-   * pack the sequence, the label data, and the sample weights used.
-   */
-  override callAndPackArgs(
-    inputs: Tensor|Tensor[],
-    kwargs: GPT2PreprocessorOptions
-  ):
-    NamedTensorMap
-    | [NamedTensorMap, Tensor]
-    | [NamedTensorMap, Tensor, Tensor] {
-
-    throw new NotImplementedError(`Uses ${packXYSampleWeight}`);
-  }
-
-  /**
-   * Covert strings to integer token input for generation.
-   *
-   * Similar to calling the layer for training, this method takes in strings
-   * or tensor strings, tokenizes and packs the input, and computes a padding
-   * mask masking all inputs not filled in with a padded value.
-   *
-   * Unlike calling the the layer for training, this method does not compute
-   * labels and will never append a `tokenizer.endTokenId` to the end of
-   * the sequence (as generation is expected to continue at the end of the
-   * inputted prompt).
-   */
-  generatePreprocess(x: Tensor, sequenceLength?: number): NamedTensorMap {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * Covert integer token output to strings for generation.
-   *
-   * This method reverses `generatePreprocess()`, by first removing all
-   * padding and start/end tokens, and then converting the integer sequence
-   * back to a string.
-   */
-  generatePostprocess(x: NamedTensorMap): Tensor {
-    throw new NotImplementedError();
-  }
-
-}
-serialization.registerClass(GPT2CausalLMPreprocessor);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_preprocessor.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_preprocessor.ts
deleted file mode 100644
index 87e1a9262..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_preprocessor.ts
+++ /dev/null
@@ -1,218 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * GPT-2 preprocessor layer.
- */
-
-/* Original source: keras-nlp/models/gpt2/gpt2_preprocessor.py */
-import { NamedTensorMap, Tensor, Tensor2D, serialization, tidy } from '@tensorflow/tfjs-core';
-
-import { LayerArgs } from '../../../../engine/topology';
-import { Preprocessor } from '../preprocessor';
-import { GPT2Tokenizer } from './gpt2_tokenizer';
-import { StartEndPacker } from '../../preprocessing/start_end_packer';
-import { ValueError } from '../../../../errors';
-
-export declare interface GPT2PreprocessorArgs extends LayerArgs {
-  /**
-   * A GPT2Tokenizer instance.
-   */
-  tokenizer: GPT2Tokenizer;
-
-  /**
-   * The length of the packed inputs.
-   * Defaults to 1024.
-   */
-  sequenceLength?: number;
-
-  /**
-   * If `true`, the preprocessor will prepend the tokenizer start token to each
-   * input sequence.
-   * Defaults to `true`.
-   */
-  addStartToken?: boolean;
-
-  /**
-   * If `true`, the preprocessor will prepend the tokenizer end token to each
-   * input sequence.
-   * Defaults to `true`.
-   */
-  addEndToken?: boolean;
-}
-
-export declare interface GPT2PreprocessorOptions {
-  /**
-   * Any label data. Will be passed through unaltered.
-   */
-  y?: Tensor;
-
-  /**
-   * Any label weight data. Will be passed through unaltered.
-   */
-  sampleWeight?: Tensor;
-
-  /**
-   * Pass to override the configured `sequenceLength` of the layer.
-   */
-  sequenceLength?: number;
-}
-
-export function packXYSampleWeight(
-  x: NamedTensorMap, y?: Tensor, sampleWeight?: Tensor):
-  NamedTensorMap
-  | [NamedTensorMap, Tensor]
-  | [NamedTensorMap, Tensor, Tensor] {
-
-  if (y === undefined) {
-    return x;
-  } else if (sampleWeight === undefined) {
-    return [x, y];
-  } else {
-    return [x, y, sampleWeight];
-  }
-}
-
-/**
- * GPT2 preprocessing layer which tokenizes and packs inputs.
- *
- * This preprocessing layer will do 2 things:
- *
- * - Tokenize the inputs using the `tokenizer`.
- * - Construct a dictionary with keys `"tokenIds"`, `"paddingMask"`, that can
- *     be passed directly to a `GPT2Backbone`.
- *
- * The call method of this layer accepts three arguments, `x`, `y`, and
- * `sampleWeight`. `x` can be a string or tensor representing a single
- * segment, a list of strings representing a batch of single segments,
- * or a list of tensors representing multiple segments to be packed together.
- * `y` and `sampleWeight` are both optional, can have any format, and will be
- * passed through unaltered.
- *
- * `GPT2Preprocessor` forces the input to have only one segment, as GPT2 is
- * mainly used for generation tasks. For tasks having multi-segment inputs
- * like "glue/mnli", please use a model designed for classification purposes
- * such as BERT or RoBERTa.
- *
- * Examples:
- *
- * Directly calling the layer on data.
- * ```js
- * const features =  ['a quick fox.', 'a fox quick.'];
- * const vocabulary =
- *    new Map([['<|endoftext|>', 0], ['a', 4], ['Ġquick', 5], ['Ġfox', 6]]);
- * const merges =
- *    ['Ġ q', 'u i', 'c k', 'ui ck', 'Ġq uick', 'Ġ f', 'o x', 'Ġf ox'];
- * const tokenizer = GPT2Tokenizer({vocabulary, merges});
- *
- * const preprocessor = GPT2Preprocessor({tokenizer});
- * preprocessor.call(tensor(['the quick brown fox jumped.']))[0].print();
- * ```
- */
-export class GPT2Preprocessor extends Preprocessor {
-  /** @nocollapse */
-  static override className = 'GPT2Preprocessor';
-
-  protected readonly sequenceLength: number;
-  protected readonly addStartToken: boolean;
-  protected readonly addEndToken: boolean;
-  protected readonly packer: StartEndPacker;
-
-  constructor(args: GPT2PreprocessorArgs) {
-    super(args);
-    this.tokenizer = args.tokenizer;
-    this.sequenceLength = args.sequenceLength ?? 1024;
-    this.addStartToken = args.addStartToken ?? true;
-    this.addEndToken = args.addEndToken ?? true;
-
-    const gpt2Tokenizer = this.tokenizer as GPT2Tokenizer;
-    this.packer = new StartEndPacker({
-      startValue: gpt2Tokenizer.startTokenId,
-      endValue: gpt2Tokenizer.endTokenId,
-      padValue: gpt2Tokenizer.padTokenId,
-      sequenceLength: this.sequenceLength,
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      sequenceLength: this.sequenceLength,
-      addStartToken: this.addStartToken,
-      addEndToken: this.addEndToken,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override call(
-    inputs: Tensor|Tensor[], kwargs: GPT2PreprocessorOptions): Tensor|Tensor[] {
-    return this.callAndReturnPaddingMask(inputs, kwargs).tokenIds;
-  }
-
-  private callAndReturnPaddingMask(
-    inputs: Tensor|Tensor[],
-    kwargs: GPT2PreprocessorOptions
-  ): NamedTensorMap {
-    return tidy(() => {
-      if (inputs instanceof Array) {
-        if (inputs.length !== 1) {
-          throw new ValueError(
-            'GPT2 requires each input feature to contain only ' +
-            `one segment, but received ${inputs.length}. If you are using ` +
-            'GPT2 for a multi-segment classification task, please refer to ' +
-            'classification models like BERT or RoBERTa.'
-          );
-        }
-        inputs = inputs[0];
-      }
-
-      const sequenceLength = kwargs.sequenceLength ?? this.sequenceLength;
-      const [tokenIds, paddingMask] = this.packer.callAndReturnPaddingMask(
-        this.tokenizer.call(inputs),
-        {
-          sequenceLength,
-          addStartValue: this.addStartToken,
-          addEndValue: this.addEndToken
-        }
-      );
-
-      return {
-        tokenIds: tokenIds as Tensor2D,
-        paddingMask: paddingMask as Tensor2D
-      };
-    });
-  }
-
-  /**
-   * Calls the layer and returns extra information like the paddingMask used to
-   * pack the sequence, the label data, and the sample weights used.
-   */
-  callAndPackArgs(inputs: Tensor|Tensor[], kwargs: GPT2PreprocessorOptions):
-    NamedTensorMap
-    | [NamedTensorMap, Tensor]
-    | [NamedTensorMap, Tensor, Tensor] {
-    const x = this.callAndReturnPaddingMask(inputs, kwargs);
-    return packXYSampleWeight(x, kwargs.y, kwargs.sampleWeight);
-  }
-
-  static override tokenizerCls<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>) {
-    return GPT2Tokenizer;
-  }
-}
-serialization.registerClass(GPT2Preprocessor);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_preprocessor_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_preprocessor_test.ts
deleted file mode 100644
index b464c386c..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_preprocessor_test.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for GPT2Preprocessor.
- */
-
-import { NamedTensorMap, Tensor, memory, serialization, tensor, tensor2d } from '@tensorflow/tfjs-core';
-
-import { GPT2Preprocessor } from './gpt2_preprocessor';
-import { GPT2Tokenizer } from './gpt2_tokenizer';
-import { expectTensorsClose } from '../../../../utils/test_utils';
-
-describe('GPT2Preprocessor', () => {
-  let vocabulary: Map<string, number>;
-  let merges: string[];
-  let preprocessor: GPT2Preprocessor;
-
-  beforeEach(() => {
-    vocabulary = new Map([
-      ['!', 0],
-      ['air', 1],
-      ['Ġair', 2],
-      ['plane', 3],
-      ['Ġat', 4],
-      ['port', 5],
-      ['<|endoftext|>', 6],
-    ]);
-
-    merges = ['Ġ a', 'Ġ t', 'Ġ i', 'Ġ b', 'a i', 'p l', 'n e'].concat(
-      ['Ġa t', 'p o', 'r t', 'Ġt h', 'ai r', 'pl a', 'po rt'],
-      ['Ġai r', 'Ġa i', 'pla ne']
-    );
-    preprocessor = new GPT2Preprocessor({
-      tokenizer: new GPT2Tokenizer({vocabulary, merges}),
-      sequenceLength: 8
-    });
-  });
-
-  it('tokenize', () => {
-    const inputData = tensor(['airplane at airport']);
-
-    const output =
-      preprocessor.callAndPackArgs(inputData, {}) as NamedTensorMap;
-
-    expectTensorsClose(output.tokenIds, tensor2d([[6, 1, 3, 4, 2, 5, 6, 0]]));
-    expectTensorsClose(
-      output.paddingMask, tensor2d([[1, 1, 1, 1, 1, 1, 1, 0]], [1, 8], 'bool'));
-  });
-
-  it('no start end token', () => {
-    const inputData = tensor(Array<string>(4).fill('airplane at airport'));
-    preprocessor = new GPT2Preprocessor({
-      tokenizer: new GPT2Tokenizer({vocabulary, merges}),
-      sequenceLength: 8,
-      addStartToken: false,
-      addEndToken: false,
-    });
-    const expectedOutput = {
-      tokenIds: tensor2d(Array<number[]>(4).fill([1, 3, 4, 2, 5, 0, 0, 0])),
-      paddingMask: tensor2d(
-        Array<number[]>(4).fill([1, 1, 1, 1, 1, 0, 0, 0]), [4, 8], 'bool'),
-    };
-
-    const output =
-      preprocessor.callAndPackArgs(inputData, {}) as NamedTensorMap;
-
-    expectTensorsClose(output.tokenIds, expectedOutput.tokenIds);
-    expectTensorsClose(output.paddingMask, expectedOutput.paddingMask);
-  });
-
-  it('tokenize labeled batch', () => {
-    const inputData = tensor(Array<string>(4).fill('airplane at airport'));
-    const yIn = tensor([1, 1, 1, 1]);
-    const swIn = tensor([1., 1., 1., 1.]);
-    const expectedX = {
-      tokenIds: tensor2d(Array<number[]>(4).fill([6, 1, 3, 4, 2, 5, 6, 0])),
-      paddingMask: tensor2d(
-        Array<number[]>(4).fill([1, 1, 1, 1, 1, 1, 1, 0]), [4, 8], 'bool'),
-    };
-
-    const output = preprocessor.callAndPackArgs(
-      inputData, {y: yIn, sampleWeight: swIn}
-    ) as [NamedTensorMap, Tensor, Tensor];
-
-    expectTensorsClose(output[0].tokenIds, expectedX.tokenIds);
-    expectTensorsClose(output[0].paddingMask, expectedX.paddingMask);
-    expectTensorsClose(output[1], yIn);
-    expectTensorsClose(output[2], swIn);
-  });
-
-  it('sequence length override', () => {
-    const inputData = tensor(['airplane at airport']);
-
-    const output = preprocessor.callAndPackArgs(
-      inputData, {sequenceLength: 4}
-    ) as NamedTensorMap;
-
-    expectTensorsClose(output.tokenIds, tensor2d([[6, 1, 3, 6]]));
-  });
-
-  it('does not leak memory', () => {
-    const inputData = tensor(['airplane at airport']);
-
-    const numTensorsBefore = memory().numTensors;
-    preprocessor.callAndPackArgs(inputData, {sequenceLength: 4});
-    const numTensorsAfter = memory().numTensors;
-    expect(numTensorsAfter).toEqual(numTensorsBefore + 2);
-  });
-
-  it('serialization round-trip', () => {
-    const reserialized = GPT2Preprocessor.fromConfig(
-      GPT2Preprocessor, preprocessor.getConfig());
-
-    const originalConfig = preprocessor.getConfig();
-    const reserializedConfig = reserialized.getConfig();
-
-    // TODO(pforderique): Verify any tokenizer name consistency issues.
-    delete ((originalConfig['tokenizer'] as serialization.ConfigDict
-      )['config'] as serialization.ConfigDict) ['name'];
-    delete ((reserializedConfig['tokenizer'] as serialization.ConfigDict
-      )['config'] as serialization.ConfigDict) ['name'];
-
-    expect(reserializedConfig).toEqual(originalConfig);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_tokenizer.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_tokenizer.ts
deleted file mode 100644
index 6d1dcff23..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_tokenizer.ts
+++ /dev/null
@@ -1,117 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * GPT-2 tokenizer layer.
- */
-
-/* Original source: keras-nlp/models/gpt2/gpt2_tokenizer.py */
-import { serialization } from '@tensorflow/tfjs-core';
-
-import { LayerArgs } from '../../../../engine/topology';
-import { BytePairTokenizer } from '../../tokenizers';
-import { ValueError } from '../../../../errors';
-
-export declare interface GPT2TokenizerArgs extends LayerArgs {
-  /**
-   * Maps token to integer ids
-   */
-  vocabulary: Map<string, number>;
-
-  /**
-   * Array. Contains the merge rule.
-   */
-  merges: string[];
-}
-
-/**
- * A GPT-2 tokenizer using Byte-Pair Encoding subword segmentation.
- *
- * This tokenizer class will tokenize raw strings into integer sequences and
- * is based on `BytePairTokenizer`. Unlike the underlying tokenizer, it will
- * check for all special tokens needed by GPT-2 models.
- *
- * This tokenizer does not provide truncation or padding of inputs.
- *
- * When given an input of a batch of strings (`tf.Tensor`), the layer will
- * output a `tf.Tensor[]`.
- *
- * Examples:
- *
- * ```js
- * const vocabulary = new Map([
- *    ['<|endoftext|>', 0], ['butter', 1], ['fly', 2]]);
- * const merges = ['b u', 't t', 'e r', 'bu tt', 'butt er', 'f l', 'fl y'];
- * const tokenizer = new BytePairTokenizer({vocabulary, merges});
- *
- * tokenizer.tokenize(tensor(['butterfly']))[0].print();
- * tokenizer.tokenize(tensor(['butterfly, butter<|endoftext|>']))[1].print();
- *
- * tokenizer.detokenize([tensor([1, 2, 0])]).print();
- */
-export class GPT2Tokenizer extends BytePairTokenizer {
-  private readonly _endTokenId: number;
-  private readonly _startTokenId: number;
-  private readonly _padTokenId: number;
-
-  constructor(args: GPT2TokenizerArgs) {
-
-    // Special tokens.
-    const endToken = '<|endoftext|>';
-
-    super({
-      vocabulary: args.vocabulary,
-      merges: args.merges,
-      unsplittableTokens: [endToken]
-    });
-
-    // Check whether special tokens are present in the vocabulary.
-    if (!this.vocabulary.includes(endToken)) {
-      throw new ValueError(
-        `Cannot find token '${endToken}' in the provided 'vocabulary'. Please` +
-        ` provide '${endToken}' in your 'vocabulary' or use a pretrained` +
-        ` 'vocabulary' name.`
-      );
-    }
-
-    this._endTokenId = this.tokenToId(endToken);
-    this._startTokenId = this._endTokenId;
-    this._padTokenId = 0;
-  }
-
-  get endTokenId() {
-    return this._endTokenId;
-  }
-
-  get startTokenId() {
-    return this._startTokenId;
-  }
-
-  get padTokenId() {
-    return this._padTokenId;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = super.getConfig();
-    // In the constructor, we pass the list of special tokens to the
-    // `unsplittableTokens` arg of the superclass' constructor. Hence, we
-    // delete it from the config here.
-    delete config.unsplittableTokens;
-    return config;
-  }
-}
-serialization.registerClass(GPT2Tokenizer);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_tokenizer_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_tokenizer_test.ts
deleted file mode 100644
index 3aa607ced..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/gpt2/gpt2_tokenizer_test.ts
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for GPT2Tokenizer.
- */
-
-import { tensor } from '@tensorflow/tfjs-core';
-
-import { expectTensorsClose } from '../../../../utils/test_utils';
-import { GPT2Tokenizer } from './gpt2_tokenizer';
-
-describe('GPT2Tokenizer', () => {
-  let vocabulary: Map<string, number>;
-  let merges: string[];
-  let tokenizer: GPT2Tokenizer;
-
-  beforeEach(() => {
-    vocabulary = new Map([
-      ['<|endoftext|>', 0],
-      ['Ġair', 1],
-      ['plane', 2],
-      ['Ġat', 3],
-      ['port', 4],
-      ['Ġkoh', 5],
-      ['li', 6],
-      ['Ġis', 7],
-      ['Ġthe', 8],
-      ['Ġbest', 9],
-    ]);
-
-    merges = ['Ġ a', 'Ġ t', 'Ġ k', 'Ġ i', 'Ġ b', 'Ġa i', 'p l', 'n e'].concat(
-      ['Ġa t', 'p o', 'r t', 'o h', 'l i', 'Ġi s', 'Ġb e', 's t'],
-      ['Ġt h', 'Ġai r', 'pl a', 'Ġk oh', 'Ġth e', 'Ġbe st', 'po rt'],
-      ['pla ne']);
-
-    tokenizer = new GPT2Tokenizer({vocabulary, merges});
-  });
-
-  it('tokenize', () => {
-    const inputData = tensor([' airplane at airport']);
-    const expectedOutput = [tensor([1, 2, 3, 1, 4])];
-
-    const output = tokenizer.tokenize(inputData);
-
-    expect(output.length).toBe(1);
-    expectTensorsClose(output[0], expectedOutput[0]);
-  });
-
-  it('tokenize end token', () => {
-    const inputData = tensor([' airplane at airport<|endoftext|>']);
-    const expectedOutput = [tensor([1, 2, 3, 1, 4, 0])];
-
-    const output = tokenizer.tokenize(inputData);
-
-    expect(output.length).toBe(1);
-    expectTensorsClose(output[0], expectedOutput[0]);
-  });
-
-  it('tokenize batch', () => {
-    const inputData = tensor([' airplane at airport', ' kohli is the best']);
-    const expectedOutput = [tensor([1, 2, 3, 1, 4]), tensor([5, 6, 7, 8, 9])];
-
-    const output = tokenizer.tokenize(inputData);
-
-    expect(output.length).toBe(2);
-    expectTensorsClose(output[0], expectedOutput[0]);
-    expectTensorsClose(output[1], expectedOutput[1]);
-  });
-
-  it('detokenize', () => {
-    const inputData = [tensor([1, 2, 3, 1, 4])];
-    const expectedOutput = tensor([' airplane at airport']);
-
-    const output = tokenizer.detokenize(inputData);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('vocabulary size', () => {
-    expect(tokenizer.vocabularySize).toBe(10);
-  });
-
-  it('errors with missing special tokens', () => {
-    vocabulary = new Map([['a', 1], ['b', 2], ['c', 3]]);
-    merges = [];
-    expect(() => new GPT2Tokenizer({vocabulary, merges})).toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/preprocessor.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/preprocessor.ts
deleted file mode 100644
index 7b8e0937e..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/preprocessor.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/* Original source: keras-nlp/models/preprocessor.py */
-import { serialization } from '@tensorflow/tfjs-core';
-
-import { Layer, LayerArgs } from '../../../engine/topology';
-import { Tokenizer } from '../tokenizers';
-import { Kwargs } from '../../../types';
-import { deserializeKerasObject, serializeKerasObject } from '../../../utils/generic_utils';
-
-/**
- * Base class for model Preprocessors.
- */
-export class Preprocessor extends Layer {
-  /** @nocollapse */
-  static className = 'Preprocessor';
-
-  private _tokenizer: Tokenizer;
-
-  constructor(args: LayerArgs) {
-    super(args);
-  }
-
-  /**
-   * The tokenizer used to tokenize strings.
-   */
-  get tokenizer() {
-    return this._tokenizer;
-  }
-
-  set tokenizer(value: Tokenizer) {
-    this._tokenizer = value;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = super.getConfig();
-    config.tokenizer = serializeKerasObject(this.tokenizer);
-    return config;
-  }
-
-  static override fromConfig<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>,
-    config: serialization.ConfigDict
-  ): T {
-    const kwargs: Kwargs = config;
-
-    if (config.tokenizer != null && !(config.tokenizer instanceof Tokenizer)) {
-      const tokenizerConfigDict = config.tokenizer as serialization.ConfigDict;
-
-      kwargs.tokenizer = deserializeKerasObject(
-        tokenizerConfigDict,
-        serialization.SerializationMap.getMap().classNameMap,
-        {}, 'preprocessor');
-    }
-    return new cls(kwargs);
-  }
-
-  static tokenizerCls<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>) {}
-}
-serialization.registerClass(Preprocessor);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/preprocessor_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/preprocessor_test.ts
deleted file mode 100644
index 1676c2a98..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/preprocessor_test.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for Preprocessor Layers.
- */
-import { Preprocessor } from './preprocessor';
-
-describe('Preprocessor', () => {
-  let preprocessor: Preprocessor;
-
-  beforeEach(() => {
-    preprocessor = new Preprocessor({});
-  });
-
-  it('serialization round-trip with no set tokenizer', () => {
-    const reserialized = Preprocessor.fromConfig(
-      Preprocessor, preprocessor.getConfig());
-    expect(reserialized.getConfig()).toEqual(preprocessor.getConfig());
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/models/task.ts b/tfjs-master/tfjs-layers/src/layers/nlp/models/task.ts
deleted file mode 100644
index d601c9329..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/models/task.ts
+++ /dev/null
@@ -1,126 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Base class for Task models.
- */
-
-/* Original source: keras_nlp/models/task.py */
-import { Tensor, serialization } from '@tensorflow/tfjs-core';
-
-import { NotImplementedError } from '../../../errors';
-
-import { PipelineModel, PipelineModelArgs } from '../utils';
-import { Backbone } from './backbone';
-import { Preprocessor } from './preprocessor';
-import { ModelCompileArgs } from '../../../engine/training';
-import { LossOrMetricFn } from '../../../types';
-
-export class Task extends PipelineModel {
-  /** @nocollapse */
-  static override className = 'Task';
-
-  protected _backbone: Backbone;
-  protected _preprocessor: Preprocessor;
-
-  constructor(args: PipelineModelArgs) {
-    super(args);
-  }
-
-  private checkForLossMismatch(
-    loss: string|string[]|{[outputName: string]: string}|LossOrMetricFn|
-          LossOrMetricFn[]|{[outputName: string]: LossOrMetricFn}
-  ) {
-    throw new NotImplementedError();
-  }
-
-  override compile(args: ModelCompileArgs): void {
-    this.checkForLossMismatch(args.loss);
-    super.compile(args);
-  }
-
-  override preprocessSamples(x: Tensor, y?: Tensor, sampleWeight?: Tensor):
-      Tensor | [Tensor, Tensor] | [Tensor, Tensor, Tensor] {
-    throw new NotImplementedError();
-  }
-
-  /**
-   * A `LayersModel` instance providing the backbone submodel.
-   */
-  get backbone(): Backbone {
-    return this._backbone;
-  }
-
-  set backbone(value: Backbone) {
-    this._backbone = value;
-  }
-
-  /**
-   * A `LayersModel` instance used to preprocess inputs.
-   */
-  get preprocessor(): Preprocessor {
-    return this._preprocessor;
-  }
-
-  set preprocessor(value: Preprocessor) {
-    this.includePreprocessing = value != null;
-    this._preprocessor = value;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    // Don't chain to super here. The default `getConfig()` for functional
-    // models is nested and cannot be passed to our Task constructors.
-    throw new NotImplementedError();
-  }
-
-  static override fromConfig<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>,
-    config: serialization.ConfigDict
-  ): T {
-    throw new NotImplementedError();
-  }
-
-  static backboneCls<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>
-  ): serialization.SerializableConstructor<T> {
-    return null;
-  }
-
-  static preprocessorCls<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>
-  ): serialization.SerializableConstructor<T> {
-    return null;
-  }
-
-  static presets<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>
-  ) {
-    return {};
-  }
-
-  getLayers() {
-    throw new NotImplementedError();
-  }
-
-  override summary(
-    lineLength?: number, positions?: number[],
-    printFn:
-        // tslint:disable-next-line:no-any
-    (message?: any, ...optionalParams: any[]) => void = console.log) {
-    throw new NotImplementedError();
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/multihead_attention.ts b/tfjs-master/tfjs-layers/src/layers/nlp/multihead_attention.ts
deleted file mode 100644
index 46253c117..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/multihead_attention.ts
+++ /dev/null
@@ -1,897 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  TFJS-based multi-head attention layer.
- */
-
-/* Original source: keras/layers/attention/multi_head_attention.py */
-import { Tensor, einsum, linalg, logicalAnd, mul, ones, serialization, tidy, util } from '@tensorflow/tfjs-core';
-
-import { cast, expandDims } from '../../backend/tfjs_backend';
-import { Constraint, ConstraintIdentifier, getConstraint, serializeConstraint } from '../../constraints';
-import { Layer, LayerArgs, SymbolicTensor } from '../../engine/topology';
-import { ValueError } from '../../errors';
-import { Initializer, InitializerIdentifier, getInitializer, serializeInitializer } from '../../initializers';
-import { Shape } from '../../keras_format/common';
-import { Regularizer, RegularizerIdentifier, getRegularizer, serializeRegularizer } from '../../regularizers';
-import { Kwargs } from '../../types';
-import { Softmax } from '../advanced_activations';
-import { Dropout } from '../core';
-import { EinsumDense } from './einsum_dense';
-
-const _CHR_IDX = 'abcdefghijklmnopqrstuvwxyz'.split('');
-/**
- * Builds einsum equations for the attention computation.
- *
- * Query, key, value inputs after projection are expected to have the shape as:
- * `(bs, <non-attention dims>, <attention dims>, numHeads, channels)`.
- * `bs` and `<non-attention dims>` are treated as `<batch dims>`.
- *
- * The attention operations can be generalized:
- * (1) Query-key dot product:
- * `(<batch dims>, <query attention dims>, numHeads, channels), (<batch dims>,
- * <key attention dims>, numHeads, channels) -> (<batch dims>,
- * numHeads, <query attention dims>, <key attention dims>)`
- * (2) Combination:
- * `(<batch dims>, numHeads, <query attention dims>, <key attention dims>),
- * (<batch dims>, <value attention dims>, numHeads, channels) -> (<batch
- * dims>, <query attention dims>, numHeads, channels)`
- *
- * @param rank Rank of query, key, value tensors.
- * @param attnAxes Array of axes, `[-1, rank)`,
- *    that attention will be applied to.
- * @returns Einsum equations.
- */
-function buildAttentionEquation(
-  rank: number, attnAxes: number[]
-): [string, string, number] {
-  const targetNotationArr = _CHR_IDX.slice(0, rank);
-  // `batchDims` includes the head dim.
-  const excludeIndices = [...attnAxes, rank - 1];
-  const batchDims = [];
-  for (const e of Array(rank).keys()) {
-    if (!excludeIndices.includes(e)) {
-      batchDims.push(e);
-    }
-  }
-  let letterOffset = rank;
-  let sourceNotation = '';
-  for (let i = 0; i < rank; i++) {
-    if (batchDims.includes(i) || i === rank - 1) {
-      sourceNotation += targetNotationArr[i];
-    } else {
-      sourceNotation += _CHR_IDX[letterOffset];
-      letterOffset++;
-    }
-  }
-
-  const productNotation =
-    batchDims.map(i => targetNotationArr[i]).concat(
-    attnAxes.map(i => targetNotationArr[i]),
-    attnAxes.map(i => sourceNotation[i]),
-  ).join('');
-  const targetNotation = targetNotationArr.join('');
-
-  const dotProductEquation =
-    `${sourceNotation},${targetNotation}->${productNotation}`;
-  const attnScoresRank = productNotation.length;
-  const combineEquation =
-    `${productNotation},${sourceNotation}->${targetNotation}`;
-
-  return [dotProductEquation, combineEquation, attnScoresRank];
-}
-
-/**
- * Builds an einsum equation for projections inside multi-head attention.
- */
-function buildProjectionEquation(
-  freeDims: number, boundDims: number, outputDims: number
-): [string, string, number] {
-  let inputStr = '';
-  let kernelStr = '';
-  let outputStr = '';
-  let biasAxes = '';
-  let letterOffset = 0;
-
-  for (let i = 0; i < freeDims; i++) {
-    const char = _CHR_IDX[i + letterOffset];
-    inputStr += char;
-    outputStr += char;
-  }
-
-  letterOffset += freeDims;
-  for (let i = 0; i < boundDims; i++) {
-    const char = _CHR_IDX[i + letterOffset];
-    inputStr += char;
-    kernelStr += char;
-  }
-
-  letterOffset += boundDims;
-  for (let i = 0; i < outputDims; i++) {
-    const char = _CHR_IDX[i + letterOffset];
-    kernelStr += char;
-    outputStr += char;
-    biasAxes += char;
-  }
-
-  const equation = `${inputStr},${kernelStr}->${outputStr}`;
-  return [equation, biasAxes, outputStr.length];
-}
-
-function getOutputShape(
-  outputRank: number, knownLastDims: number[]
-): Shape {
-  const outputShape =
-    Array(outputRank - knownLastDims.length).fill(null).concat(knownLastDims);
-  return outputShape;
-}
-
-export declare interface MultiHeadAttentionArgs extends LayerArgs {
-  /**
-   * Integer. Number of attention heads.
-   */
-  numHeads: number;
-
-  /**
-   * Integer. Size of each attention head for query and key.
-   */
-  keyDim: number;
-
-  /**
-   * Integer. Size of each attention head for value.
-   * Defaults to `keyDim`.
-   */
-  valueDim?: number;
-
-  /**
-   * Dropout probability.
-   * Defaults to 0.0.
-   */
-  dropout?: number;
-
-  /**
-   * Whether the dense layers use bias vectors/matrices.
-   * Defaults to true.
-   */
-  useBias?: boolean;
-
-  /**
-   * The expected shape of an output tensor, besides the batch
-   * and sequence dims. If not specified, projects back to the query
-   * feature dim (the query input's last dimension).
-   */
-  outputShape?: Shape;
-
-  /**
-   * Axes over which the attention is applied. `null` means attention over
-   * all axes, but batch, heads, and features.
-   */
-  attentionAxes?: number[]|number;
-
-  /**
-   * Initializer for dense layer kernels.
-   * Defaults to `"glorotUniform"`.
-   */
-  kernelInitializer?: Initializer|InitializerIdentifier;
-
-  /**
-   * Initializer for dense layer biases.
-   * Defaults to `"zeros"`.
-   */
-  biasInitializer?: Initializer|InitializerIdentifier;
-
-  /**
-   * Regularizer for dense layer kernels.
-   */
-  kernelRegularizer?: Regularizer|RegularizerIdentifier;
-
-  /**
-   * Regularizer for dense layer biases.
-   */
-  biasRegularizer?: Regularizer|RegularizerIdentifier;
-
-  /**
-   * Regularizer for dense layer activity.
-   */
-  activityRegularizer?: Regularizer|RegularizerIdentifier;
-
-  /**
-   * Constraint for dense layer kernels.
-   */
-  kernelConstraint?: Constraint|ConstraintIdentifier;
-
-  /**
-   * Constraint for dense layer kernels.
-   */
-  biasConstraint?: Constraint|ConstraintIdentifier;
-}
-
-export declare interface MultiHeadAttentionOptions {
-  /**
-   * Query `Tensor` of shape `(B, T, dim)`.
-   */
-
-  /**
-   * Value `Tensor` of shape `(B, S, dim)`.
-   */
-  value: Tensor;
-
-  /**
-   * Key `Tensor` of shape `(B, S, dim)`. If not given, will use `value` for
-   * both `key` and `value`, which is the most common case.
-   */
-  key?: Tensor;
-
-  /**
-   * A boolean mask of shape `(B, T, S)`, that prevents
-   * attention to certain positions. The boolean mask specifies which
-   * query elements can attend to which key elements, 1 indicates
-   * attention and 0 indicates no attention. Broadcasting can happen for
-   * the missing batch dimensions and the head dimension.
-   */
-  attentionMask?: Tensor;
-
-  /**
-   * Indicates whether the layer should behave in training mode
-   * (adding dropout) or in inference mode (no dropout).
-   * Will go with either using the training mode of the parent
-   * layer/model, or false (inference) if there is no parent layer.
-   */
-  training?: boolean;
-
-  /**
-   * Indicates whether to apply a causal mask to prevent tokens from attending
-   * to future tokens (e.g., used in a decoder Transformer).
-   * Defaults to false.
-   */
-  useCausalMask?: boolean;
-}
-
-/**
- * MultiHeadAttention layer.
- *
- * This is an implementation of multi-headed attention as described in the
- * paper "Attention is all you Need" (Vaswani et al., 2017).
- * If `query`, `key,` `value` are the same, then
- * this is self-attention. Each timestep in `query` attends to the
- * corresponding sequence in `key`, and returns a fixed-width vector.
- *
- * This layer first projects `query`, `key` and `value`. These are
- * (effectively) a list of tensors of length `numAttentionHeads`, where the
- * corresponding shapes are `(batchSize, <query dimensions>, keyDim)`,
- * `(batchSize, <key/value dimensions>, keyDim)`,
- * `(batchSize, <key/value dimensions>, valueDim)`.
- *
- * Then, the query and key tensors are dot-producted and scaled. These are
- * softmaxed to obtain attention probabilities. The value tensors are then
- * interpolated by these probabilities, then concatenated back to a single
- * tensor.
- *
- * Finally, the result tensor with the last dimension as valueDim can take an
- * linear projection and return.
- *
- * When using `MultiHeadAttention` inside a custom layer, the custom layer must
- * implement its own `build()` method and call `MultiHeadAttention`'s
- * `buildFromSignature()` there.
- * This enables weights to be restored correctly when the model is loaded.
- *
- * Examples:
- *
- * Performs 1D cross-attention over two sequence inputs with an attention mask.
- * Returns the additional attention weights over heads.
- *
- * ```js
- * const layer = new MultiHeadAttention({numHeads: 2, keyDim: 2});
- * const target = tf.input({shape: [8, 16]});
- * const source = tf.input({shape: [4, 16]});
- * const outputTensor, weights = layer.callAndReturnAttentionScores(
- *     target, {value: source});
- * console.log(outputTensor.shape);  // [null, 8, 16]
- * console.log(weights.shape);  // [null, 2, 8, 4]
- * ```
- *
- * Performs 2D self-attention over a 5D input tensor on axes 2 and 3.
- *
- * ```js
- * const layer = new MultiHeadAttention({
- *    numHeads: 2, keyDim: 2, attentionAxes: [2, 3]});
- * const inputTensor = tf.input({shape: [5, 3, 4, 16]});
- * const outputTensor = layer.call(inputTensor, {value: inputTensor});
- * console.log(outputTensor.shape);  // [null, 5, 3, 4, 16]
- * ```
- *
- * Returns:
- *    attentionOutput: The result of the computation, of shape `(B, T, E)`,
- *        where `T` is for target sequence shapes and `E` is the query input
- *        last dimension if `outputShape` is `None`. Otherwise, the
- *        multi-head outputs are projected to the shape specified by
- *        `outputShape`.
- *    attentionScores: multi-head attention coefficients over attention axes.
- */
-export class MultiHeadAttention extends Layer {
-  /** @nocollapse */
-  static readonly className = 'MultiHeadAttention';
-
-  protected readonly numHeads: number;
-  protected readonly keyDim: number;
-  protected readonly valueDim: number;
-  protected readonly dropout: number;
-  protected readonly useBias: boolean;
-  protected readonly _outputShape: Shape;
-  protected readonly kernelInitializer: Initializer;
-  protected readonly biasInitializer: Initializer;
-  protected readonly kernelRegularizer: Regularizer;
-  protected readonly biasRegularizer: Regularizer;
-  protected readonly kernelConstraint: Constraint;
-  protected readonly biasConstraint: Constraint;
-  protected dotProductEquation: string;
-  protected combineEquation: string;
-  protected attentionAxes: number[];
-  protected builtFromSignature: boolean;
-  protected softmax: Softmax;
-  protected dropoutLayer: Dropout;
-  protected queryShape: Shape;
-  protected keyShape: Shape;
-  protected valueShape: Shape;
-  protected queryDense: EinsumDense;
-  protected keyDense: EinsumDense;
-  protected valueDense: EinsumDense;
-  protected outputDense: EinsumDense;
-
-  constructor(args: MultiHeadAttentionArgs) {
-    super(args);
-    this.supportsMasking = true;
-    this.numHeads = args.numHeads;
-    this.keyDim = args.keyDim;
-    this.valueDim = args.valueDim ?? args.keyDim;
-    this.dropout = args.dropout ?? 0;
-    this.useBias = args.useBias ?? true;
-    this._outputShape = args.outputShape;
-    this.kernelInitializer = getInitializer(
-      args.kernelInitializer ?? 'glorotUniform');
-    this.biasInitializer = getInitializer(args.biasInitializer ?? 'zeros');
-    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
-    this.biasRegularizer = getRegularizer(args.biasRegularizer);
-    this.activityRegularizer = getRegularizer(args.activityRegularizer);
-    this.kernelConstraint = getConstraint(args.kernelConstraint);
-    this.biasConstraint = getConstraint(args.biasConstraint);
-    if (args.attentionAxes != null && !Array.isArray(args.attentionAxes)) {
-      this.attentionAxes = [args.attentionAxes];
-    } else {
-      this.attentionAxes = args.attentionAxes as number[];
-    }
-    this.builtFromSignature = false;
-    this.queryShape = null;
-    this.keyShape = null;
-    this.valueShape = null;
-  }
-
-  /**
-   * Should be used for testing purposes only.
-   */
-  get _queryDense() {
-    return this.queryDense;
-  }
-
-  /**
-   * Should be used for testing purposes only.
-   */
-  get _keyDense() {
-    return this.keyDense;
-  }
-
-  /**
-   * Should be used for testing purposes only.
-   */
-  get _valueDense() {
-    return this.valueDense;
-  }
-
-  /**
-   * Should be used for testing purposes only.
-   */
-  get _outputDense() {
-    return this.outputDense;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      numHeads: this.numHeads,
-      keyDim: this.keyDim,
-      valueDim: this.valueDim,
-      dropout: this.dropout,
-      useBias: this.useBias,
-      outputShape: this._outputShape,
-      attentionAxes: this.attentionAxes,
-      kernelInitializer: serializeInitializer(this.kernelInitializer),
-      biasInitializer: serializeInitializer(this.biasInitializer),
-      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
-      biasRegularizer: serializeRegularizer(this.biasRegularizer),
-      activityRegularizer: serializeRegularizer(this.activityRegularizer),
-      kernelConstraint: serializeConstraint(this.kernelConstraint),
-      biasConstraint: serializeConstraint(this.biasConstraint),
-      queryShape: this.queryShape,
-      keyShape: this.keyShape,
-      valueShape: this.valueShape,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  static override fromConfig<T extends serialization.Serializable>(
-    cls: serialization.SerializableConstructor<T>,
-    config: serialization.ConfigDict
-  ): T {
-    // If the layer has a different build() function from the default,
-    // we need to trigger the customized build to create weights.
-    const queryShape = config['queryShape'] as Shape;
-    const keyShape = config['keyShape'] as Shape;
-    const valueShape = config['valueShape'] as Shape;
-    delete config['queryShape'];
-    delete config['keyShape'];
-    delete config['valueShape'];
-
-    const layer = new cls(config);
-    if ([queryShape, keyShape, valueShape].includes(null)) {
-        console.warn(
-            'One of dimensions of the input shape is missing. It ' +
-            'should have been memorized when the layer was serialized. ' +
-            `${cls.toString()} is created without weights.`
-        );
-    } else {
-      (layer as unknown as MultiHeadAttention).buildFromSignature(
-        queryShape, valueShape, keyShape);
-    }
-    return layer;
-  }
-
-  /**
-   * Builds layers and variables.
-   *
-   * Once the method is called, this.builtFromSignature will be set to true.
-   */
-  buildFromSignature(
-    queryShape: Shape,
-    valueShape: Shape,
-    keyShape?: Shape
-  ) {
-    this.builtFromSignature = true;
-
-    if (keyShape == null) {
-      keyShape = valueShape;
-    }
-
-    this.queryShape = queryShape;
-    this.valueShape = valueShape;
-    this.keyShape = keyShape;
-
-    // Not using SymbolicTensors since tf.input() adds a batch dimension to the
-    // given shape, therefore giving the tensor the wrong rank.
-    const queryRank = queryShape.length;
-    const valueRank = valueShape.length;
-    const keyRank = keyShape.length;
-
-    const freeDims = queryRank - 1;
-    let [einsumEquation, biasAxes, outputRank] =
-      buildProjectionEquation(freeDims, 1, 2);
-    this.queryDense = new EinsumDense({
-      equation: einsumEquation,
-      outputShape: getOutputShape(outputRank - 1, [this.numHeads, this.keyDim]),
-      biasAxes: this.useBias ? biasAxes : null,
-      name: 'query',
-      ...this.getCommonKwargsForSublayer(),
-    });
-
-    [einsumEquation, biasAxes, outputRank] =
-      buildProjectionEquation(keyRank - 1, 1, 2);
-    this.keyDense = new EinsumDense({
-      equation: einsumEquation,
-      outputShape: getOutputShape(outputRank - 1, [this.numHeads, this.keyDim]),
-      biasAxes: this.useBias ? biasAxes : null,
-      name: 'key',
-      ...this.getCommonKwargsForSublayer(),
-    });
-
-    [einsumEquation, biasAxes, outputRank] =
-      buildProjectionEquation(valueRank - 1, 1, 2);
-    this.valueDense = new EinsumDense({
-      equation: einsumEquation,
-      outputShape: getOutputShape(
-        outputRank - 1, [this.numHeads, this.valueDim]),
-      biasAxes: this.useBias ? biasAxes : null,
-      name: 'value',
-      ...this.getCommonKwargsForSublayer(),
-    });
-
-    // Builds the attention computations for multi-head dot product attention.
-    this.buildAttention(outputRank);
-    this.outputDense = this.makeOutputDense(
-      freeDims,
-      this.getCommonKwargsForSublayer(),
-      'attentionOutput'
-    );
-  }
-
-  private getCommonKwargsForSublayer(): Kwargs {
-    // Create new clone of kernel/bias initializer, so that we don't reuse
-    // the initializer instance, which could lead to same init value since
-    // initializer is stateless.
-    const kernelInitializer = getInitializer({
-      className: this.kernelInitializer.getClassName(),
-      config: this.kernelInitializer.getConfig(),
-    });
-    const biasInitializer = getInitializer({
-      className: this.biasInitializer.getClassName(),
-      config: this.biasInitializer.getConfig(),
-    });
-
-    const commonKwargs = {
-      kernelInitializer,
-      biasInitializer,
-      kernelRegularizer: this.kernelRegularizer,
-      biasRegularizer: this.biasRegularizer,
-      activityRegularizer: this.activityRegularizer,
-      kernelConstraint: this.kernelConstraint,
-      biasConstraint: this.biasConstraint,
-    };
-    return commonKwargs;
-  }
-
-  /**
-   * Builds the output projection matrix.
-   *
-   * @param freeDims Number of free dimensions for einsum equation building.
-   * @param commonKwargs Common keyword arguments for einsum layer.
-   * @param name Name for the projection layer.
-   * @returns Projection layer.
-   */
-  private makeOutputDense(
-    freeDims: number, commonKwargs: Kwargs, name?: string
-  ): EinsumDense {
-    let outputShape: Shape;
-    if (this._outputShape) {
-      if (!Array.isArray(this._outputShape)) {
-        outputShape = [this._outputShape];
-      } else {
-        outputShape = this._outputShape;
-      }
-    } else {
-      outputShape = [this.queryShape[this.queryShape.length - 1]];
-    }
-
-    const [einsumEquation, biasAxes, outputRank] =
-      buildProjectionEquation(freeDims, 2, outputShape.length);
-
-    return new EinsumDense({
-      equation: einsumEquation,
-      outputShape: getOutputShape(outputRank - 1, outputShape),
-      biasAxes: this.useBias ? biasAxes : null,
-      name,
-      ...commonKwargs,
-    });
-  }
-
-  /**
-   * Builds multi-head dot-product attention computations.
-   *
-   * This function builds attributes necessary for `computeAttention` to
-   * customize attention computation to replace the default dot-product
-   * attention.
-   *
-   * @param rank The rank of query, key, value tensors.
-   */
-  protected buildAttention(rank: number) {
-    if (this.attentionAxes == null) {
-      this.attentionAxes = [];
-      for (let i = 1; i < rank - 2; i++) {
-        this.attentionAxes.push(i);
-      }
-    } else {
-      this.attentionAxes = [...this.attentionAxes];
-    }
-
-    const [dotProductEquation, combineEquation, attnScoresRank] =
-      buildAttentionEquation(rank, this.attentionAxes);
-    this.dotProductEquation = dotProductEquation;
-    this.combineEquation = combineEquation;
-
-    const normAxes: number[] = [];
-    const startIdx = attnScoresRank - this.attentionAxes.length;
-    for (let i = startIdx; i < attnScoresRank; i++) {
-      normAxes.push(i);
-    }
-    this.softmax = new Softmax({axis: normAxes});
-    this.dropoutLayer = new Dropout({rate: this.dropout});
-  }
-
-  protected maskedSoftmax(
-    attentionScores: Tensor, attentionMask?: Tensor
-  ): Tensor {
-    return tidy(() => {
-      // Normalize the attention scores to probabilities.
-      // `attentionScores` = [B, N, T, S]
-      if (attentionMask != null) {
-        // The expand dim happens starting from the `numHeads` dimension,
-        // (<batchDims>, numHeads, <queryAttentionDims, keyAttentionDims>)
-        const maskExpansionAxis = -this.attentionAxes.length * 2 - 1;
-        const endIdx =
-          attentionScores.shape.length - attentionMask.shape.length;
-        for (let _ = 0; _ < endIdx; _++) {
-          attentionMask = expandDims(attentionMask, maskExpansionAxis);
-        }
-      }
-      return this.softmax.apply(
-        attentionScores, {mask: attentionMask}) as Tensor;
-    });
-  }
-
-  /**
-   * Applies Dot-product attention with query, key, value tensors.
-   *
-   * This function defines the computation inside `call` with projected
-   * multi-head Q, K, V inputs. Users can override this function for
-   * customized attention implementation.
-   *
-   * @param query Projected query `Tensor` of shape `(B, T, N, keyDim)`.
-   * @param key  Projected key `Tensor` of shape `(B, S, N, keyDim)`.
-   * @param value Projected value `Tensor` of shape `(B, S, N, valueDim)`.
-   * @param attentionMask A boolean mask of shape `(B, T, S)`, that prevents
-   *    attention to certain positions. It is generally not needed if
-   *    the `query` and `value` (and/or `key`) are masked.
-   * @param training Boolean indicating whether the layer should behave
-   *    in training mode (adding dropout) or in inference mode (doing
-   *    nothing).
-   * @returns attentionOutput: Multi-headed outputs of attention computation.
-   * @returns attentionScores: Multi-headed attention weights.
-   */
-  protected computeAttention(
-    query: Tensor,
-    key: Tensor,
-    value: Tensor,
-    attentionMask?: Tensor,
-    training?: boolean
-  ): [Tensor, Tensor] {
-    return tidy(() => {
-      // Note: Applying scalar multiply at the smaller end of einsum improves
-      // XLA performance, but may introduce slight numeric differences in
-      // the Transformer attention head.
-      query = mul(query, 1.0 / Math.sqrt(this.keyDim));
-
-      // Take the dot product between "query" and "key" to get the raw
-      // attention scores.
-      let attentionScores = einsum(this.dotProductEquation, key, query);
-
-      attentionScores = this.maskedSoftmax(attentionScores, attentionMask);
-
-      // This is actually dropping out entire tokens to attend to, which might
-      // seem a bit unusual, but is taken from the original Transformer paper.
-      const attentionScoresDropout =
-        this.dropoutLayer.apply(attentionScores, {training}) as Tensor;
-
-      // `contextLayer` = [B, T, N, H]
-      const attentionOutput =
-        einsum(this.combineEquation, attentionScoresDropout, value);
-
-      return [attentionOutput, attentionScores];
-    });
-  }
-
-  override apply(
-    inputs: Tensor | SymbolicTensor,
-    kwargs?: Kwargs
-  ): Tensor | Tensor[] | SymbolicTensor | SymbolicTensor[] {
-    if (!kwargs || !kwargs['value']) {
-      throw new ValueError('Must pass in `value` argument in `kwargs.`');
-    }
-    let newInputs: Tensor[]|SymbolicTensor[];
-
-    newInputs = [inputs, kwargs['value']].concat(kwargs['key'] ?? []);
-
-    // TODO(pforderique): Support mask propogation.
-    return super.apply(newInputs, kwargs);
-  }
-
-  override call(
-    query: Tensor, kwargs: MultiHeadAttentionOptions
-  ): Tensor {
-    return tidy(() => {
-      return this.callAndReturnAttentionScores(query, kwargs)[0];
-    });
-  }
-
-  /**
-   * Exactly like `call` except also returns the attention scores.
-   */
-  callAndReturnAttentionScores(
-    query: Tensor,
-    {
-      value,
-      key,
-      useCausalMask,
-      attentionMask,
-      training
-    }: MultiHeadAttentionOptions
-  ): [Tensor, Tensor] {
-    return tidy(() => {
-      if (!this.builtFromSignature) {
-        this.buildFromSignature(
-          query.shape,
-          value.shape,
-          key ? key.shape : null
-        );
-      }
-      if (key == null) {
-        key = value;
-      }
-
-      // TODO(pforderique): Support RaggedTensor inputs.
-
-      attentionMask = this.computeAttentionMask(
-        query,
-        value,
-        attentionMask,
-        useCausalMask,
-      );
-
-      //   N = `numAttentionHeads`
-      //   H = `sizePerHead`
-      // `query` = [B, T, N ,H]
-      query = this.queryDense.apply(query) as Tensor;
-
-      // `key` = [B, S, N, H]
-      key = this.keyDense.apply(key) as Tensor;
-
-      // `value` = [B, S, N, H]
-      value = this.valueDense.apply(value) as Tensor;
-
-      const [attentionOutputPreDense, attentionScores] = this.computeAttention(
-        query,
-        key,
-        value,
-        attentionMask,
-        training
-      );
-      const attentionOutput =
-        this.outputDense.apply(attentionOutputPreDense) as Tensor;
-
-      return [attentionOutput, attentionScores];
-    });
-  }
-
-  /**
-   * Computes the attention mask.
-   *
-   * * The `query`'s mask is reshaped from [B, T] to [B, T, 1].
-   * * The `value`'s mask is reshaped from [B, S] to [B, 1, S].
-   * * The `key`'s mask is reshaped from [B, S] to [B, 1, S]. The `key`'s
-   *   mask is ignored if `key` is `None` or if `key is value`.
-   * * If `useCausalMask=true`, then the causal mask is computed. Its shape
-   *   is [1, T, S].
-   *
-   * All defined masks are merged using a logical AND operation (`&`).
-   *
-   * In general, if the `query` and `value` are masked, then there is no need
-   * to define the `attentionMask`.
-   *
-   * @param query Projected query `Tensor` of shape `(B, T, N, keyDim)`.
-   * @param key  Projected key `Tensor` of shape `(B, S, N, keyDim)`.
-   * @param value Projected value `Tensor` of shape `(B, S, N, valueDim)`.
-   * @param attentionMask A boolean mask of shape `(B, T, S)`, that prevents
-   *    attention to certain positions.
-   * @param useCausalMask  A boolean to indicate whether to apply a causal
-   *    mask to prevent tokens from attending to future tokens (e.g.,
-   *    used in a decoder Transformer).
-   * @returns attentionMask: A boolean mask of shape `(B, T, S)`, that prevents
-   *    attention to certain positions, based on the Keras masks of the
-   *    `query`, `key`, `value`, and `attentionMask` tensors, and the
-   *    causal mask if `useCausalMask=true`.
-   */
-  private computeAttentionMask(
-    query: Tensor,
-    value: Tensor,
-    attentionMask?: Tensor,
-    useCausalMask = false
-  ): Tensor {
-    return tidy(() => {
-      let autoMask: Tensor;
-
-      const queryMask = query.kerasMask;
-      const valueMask = value.kerasMask;
-      if (queryMask != null) {
-        autoMask = queryMask.expandDims(2); // Shape is [B, T, 1]
-      }
-      if (valueMask != null) {
-        const mask = valueMask.expandDims(1); // Shape is [B, 1, S]
-        autoMask = autoMask ? logicalAnd(autoMask, mask) : mask;
-      }
-      if (useCausalMask) {
-        // the shape of the causal mask is [1, T, S]
-        const mask = this.computeCausalMask(query, value);
-        autoMask = autoMask ? logicalAnd(autoMask, mask) : mask;
-      }
-      if (autoMask != null) {
-        // Merge attentionMask & automatic mask, to shape [B, T, S]
-        attentionMask = attentionMask ?
-          cast(attentionMask, 'bool').logicalAnd(autoMask) : autoMask;
-      }
-
-      return attentionMask;
-    });
-  }
-
-  /**
-   * Computes a causal mask (e.g., for masked self-attention layers).
-   *
-   * For example, if query and value both contain sequences of length 4,
-   * this function returns a boolean `Tensor` equal to:
-   *
-   * ```
-   * [[[true,  false, false, false],
-   *   [true,  true,  false, false],
-   *   [true,  true,  true,  false],
-   *   [true,  true,  true,  true]]]
-   * ```
-   *
-   * @param query query `Tensor` of shape `(B, T, ...)`.
-   * @param value value `Tensor` of shape `(B, S, ...)` (defaults to query).
-   * @returns mask: A boolean `Tensor` of shape [1, T, S] containing a lower
-   *    triangular matrix of shape [T, S].
-   */
-  private computeCausalMask(query: Tensor, value?: Tensor): Tensor {
-    return tidy(() => {
-      const qSeqLength = query.shape[1];
-      const vSeqLength = value ? value.shape[1] : qSeqLength;
-      // Create a lower triangular matrix.
-      return linalg.bandPart(ones([1, qSeqLength, vSeqLength], 'bool'), -1, 0);
-    });
-  }
-
-  /**
-   *
-   * @param inputShapes A list of [queryShape, valueShape] or
-   *    [queryShape, valueShape, keyShape]. If no keyShape provided, valueShape
-   *    is assumed as the keyShape.
-   */
-  override computeOutputShape(inputShapes: [Shape, Shape, Shape|null]): Shape {
-    const [queryShape, valueShape, maybeKeyShape] = inputShapes;
-    const keyShape = maybeKeyShape ?? valueShape;
-
-    if (queryShape.slice(-1)[0] !== valueShape.slice(-1)[0]) {
-      throw new ValueError(
-        `The last dimension of 'queryShape' and 'valueShape' must be equal, ` +
-        `but are ${queryShape.slice(-1)[0]}, ${valueShape.slice(-1)[0]}. ` +
-        `Received: queryShape=${queryShape}, valueShape=${valueShape}`
-      );
-    }
-
-    if (!util.arraysEqual(valueShape.slice(1, -1), keyShape.slice(1, -1))) {
-      throw new Error(
-        `All dimensions of 'value' and 'key', except the last one, must be ` +
-        `equal. Received ${valueShape} and ${keyShape}`
-      );
-    }
-
-    if (this._outputShape) {
-      return queryShape.slice(0, -1).concat(this._outputShape);
-    }
-
-    return queryShape;
-  }
-}
-serialization.registerClass(MultiHeadAttention);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/multihead_attention_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/multihead_attention_test.ts
deleted file mode 100644
index 1cc5d77f0..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/multihead_attention_test.ts
+++ /dev/null
@@ -1,517 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for MultiHeadAttention layer.
- */
-
-import { Tensor, memory, ones, randomUniform, randomUniformInt, tensor, tensor2d } from '@tensorflow/tfjs-core';
-
-import { TruncatedNormal } from '../../initializers';
-import { input } from '../../exports';
-import { Shape } from '../../keras_format/common';
-import { MultiHeadAttention } from './multihead_attention';
-import { describeMathCPU, describeMathCPUAndGPU, expectTensorsClose, expectTensorsNotClose } from '../../utils/test_utils';
-import { Embedding } from '../embeddings';
-
-describeMathCPUAndGPU('MultiHeadAttention', () => {
-
-  describe('Non Masked Attention', () => {
-    interface NonMaskedAttentionArgs {
-      testcaseName: string;
-      valueDim: number;
-      outputShape: Shape;
-      outputDims: Shape;
-    }
-    /**
-     * Test that the attention layer can be created without a mask tensor.
-     */
-    function testNonMaskedAttention(
-      {testcaseName, valueDim, outputShape, outputDims}: NonMaskedAttentionArgs
-    ) {
-      it(`${testcaseName} non masked attention`, () => {
-        const testLayer = new MultiHeadAttention({
-          numHeads: 12,
-          keyDim: 64,
-          valueDim,
-          outputShape,
-        });
-        // Create a 3-dimensional input (the first dimension is implicit).
-        const query = input({shape: [40, 80]});
-        const value = input({shape: [20, 80]});
-        const output = testLayer.apply(query, {value}) as Tensor;
-        expect(output.shape).toEqual([null].concat(outputDims));
-      });
-    }
-
-    const params: NonMaskedAttentionArgs[] = [
-      {
-        testcaseName: 'key value same proj',
-        valueDim: null,
-        outputShape: null,
-        outputDims: [40, 80],
-      },
-      {
-        testcaseName: 'key value different proj',
-        valueDim: 32,
-        outputShape: [60],
-        outputDims: [40, 60],
-      }
-    ];
-    for (const param of params) {
-      testNonMaskedAttention(param);
-    }
-  });
-
-  // Test with one input (self-attention) and no mask tensor.
-  it('non masked self attention', () => {
-    const testLayer = new MultiHeadAttention({numHeads: 12, keyDim: 64});
-    // Create a 3-dimensional input (the first dimension is implicit).
-    const query = input({shape: [40, 80]});
-    const output = testLayer.apply(query, {value: query}) as Tensor;
-    expect(output.shape).toEqual([null, 40, 80]);
-  });
-
-  // Test attention outputs with coefficients.
-  it('attention scores', () => {
-    const testLayer = new MultiHeadAttention({numHeads: 12, keyDim: 64});
-    const query = ones([1, 40, 80]);
-    const [output, coef] =
-      testLayer.callAndReturnAttentionScores(query, {value: query});
-    expect(output.shape).toEqual([1, 40, 80]);
-    expect(coef.shape).toEqual([1, 12, 40, 40]);
-  });
-
-  // Test attention outputs with coefficients.
-  it('attention scores with values', () => {
-    const testLayer = new MultiHeadAttention({numHeads: 12, keyDim: 64});
-    const query = ones([1, 40, 80]);
-    const value = ones([1, 60, 80]);
-    const [output, coef] =
-      testLayer.callAndReturnAttentionScores(query, {value});
-    expect(output.shape).toEqual([1, 40, 80]);
-    expect(coef.shape).toEqual([1, 12, 40, 60]);
-  });
-
-  describe('Masked Attention', () => {
-    interface MaskedAttentionArgs {
-      testcaseName: string;
-      useBias: boolean;
-    }
-    /**
-     * Test with a mask tensor.
-     */
-    function testMaskedAttention({testcaseName, useBias}: MaskedAttentionArgs) {
-      it(`${testcaseName}`, () => {
-        const testLayer = new MultiHeadAttention({
-          numHeads: 2,
-          keyDim: 2,
-          useBias,
-        });
-        // Create a 3-dimensional input (the first dimension is implicit).
-        const batchSize = 4;
-        const query = randomUniform([batchSize, 4, 8]);
-        const value = randomUniform([batchSize, 2, 8]);
-
-        // Invoke the data with a random set of mask data. This should mask at
-        // least one element.
-        const maskData = randomUniformInt([batchSize, 4, 2], 0, 2);
-        const maskedOutputData = testLayer.call(
-          query, {value, attentionMask: maskData});
-
-        // Invoke the same data, but with a null mask (where no elements are
-        // masked).
-        const nullMaskData = ones([batchSize, 4, 2]);
-        const unmaskedOutputData = testLayer.call(
-          query, {value, attentionMask: nullMaskData});
-
-        expectTensorsNotClose(maskedOutputData, unmaskedOutputData);
-
-        if (useBias) {
-          expect(testLayer._queryDense.trainableWeights.length).toEqual(2);
-          expect(testLayer._outputDense.trainableWeights.length).toEqual(2);
-        } else {
-          expect(testLayer._queryDense.trainableWeights.length).toEqual(1);
-          expect(testLayer._outputDense.trainableWeights.length).toEqual(1);
-        }
-      });
-    }
-    const params: MaskedAttentionArgs[] = [
-      {
-        testcaseName: 'with bias',
-        useBias: true,
-      },
-      {
-        testcaseName: 'no bias',
-        useBias: false,
-      }
-    ];
-    for (const param of params) {
-      testMaskedAttention(param);
-    }
-  });
-
-  // Test with a specified initializer.
-  it('initializers', () => {
-    const testLayer = new MultiHeadAttention({
-      numHeads: 12,
-      keyDim: 64,
-      kernelInitializer: new TruncatedNormal({stddev: 0.02}),
-    });
-    const query = ones([1, 40, 80]);
-    const output = testLayer.call(query, {value: query});
-    expect(output.shape).toEqual([1, 40, 80]);
-
-    // Make sure the sub layers have different kernel init value, and not
-    // reusing the initializers.
-    const queryKernel = testLayer._queryDense.kernel.read();
-    const keyKernel = testLayer._keyDense.kernel.read();
-    const valueKernel = testLayer._valueDense.kernel.read();
-    const outputKernel = testLayer._outputDense.kernel.read();
-
-    expectTensorsNotClose(queryKernel, keyKernel, 1e-6);
-    expectTensorsNotClose(queryKernel, valueKernel, 1e-6);
-    expectTensorsNotClose(queryKernel, outputKernel, 1e-6);
-  });
-
-  it('dropout', () => {
-    const testLayer = new MultiHeadAttention({
-      numHeads: 2,
-      keyDim: 2,
-      dropout: 0.5,
-    });
-    const fromData = ones([32, 4, 8]);
-    const toData = ones([32, 2, 8]);
-
-    const trainOut = testLayer.call(fromData, {value: toData, training: true});
-    const testOut = testLayer.call(fromData, {value: toData, training: false});
-
-    expectTensorsNotClose(trainOut, testOut);
-  });
-
-  describe('Causal Mask Value', () => {
-    let testLayer: MultiHeadAttention;
-    let maskedQuery: Tensor;
-    let maskedValue: Tensor;
-    let mask: Tensor;
-
-    beforeEach(() => {
-      testLayer = new MultiHeadAttention({numHeads: 2, keyDim: 2});
-      const query = tensor2d([
-        [1, 2, 3, 0, 0], [3, 3, 1, 1, 2], [1, 0, 0, 0, 0]
-      ]);
-      const maskedQueryLayer = new Embedding(
-        {inputDim: 4, outputDim: 8, maskZero: true});
-      maskedQuery = maskedQueryLayer.apply(query) as Tensor;
-      const value = tensor2d([[5, 4, 0], [3, 0, 0], [2, 1, 1]]);
-      maskedValue = new Embedding(
-        {inputDim: 6, outputDim: 8, maskZero: true}).apply(value) as Tensor;
-
-      mask = tensor([
-        Array<boolean[]>(3).fill([true, true, false]).concat(
-          Array<boolean[]>(2).fill([false, false, false])),
-        Array<boolean[]>(5).fill([true, false, false]),
-        [[true, true, true]].concat(
-          Array<boolean[]>(4).fill([false, false, false]))
-      ]);
-    });
-
-    /**
-     * Test that the value and causal masks are taken into account.
-     */
-    it('causal', () => {
-      const output = testLayer.call(
-        maskedQuery, {value: maskedValue, useCausalMask: true});
-
-      mask = mask.logicalAnd(tensor([
-        [[true, false, false], [true, true, false]].concat(
-          [[true, true, true], [true, true, true], [true, true, true]])
-      ]));
-
-      const outputWithManualMask = testLayer.call(
-        maskedQuery, {value: maskedValue, attentionMask: mask});
-
-      expectTensorsClose(output, outputWithManualMask);
-    });
-
-    it('not_causal', () => {
-      const output = testLayer.call(
-        maskedQuery, {value: maskedValue, useCausalMask: false});
-
-      const outputWithManualMask = testLayer.call(
-        maskedQuery, {value: maskedValue, attentionMask: mask});
-
-      expectTensorsClose(output, outputWithManualMask);
-    });
-  });
-
-  describe('Compute Output Shape', () => {
-    interface ComputeOutputShapeArgs {
-      testcaseName: string;
-      queryDims: Shape;
-      valueDims: Shape;
-      keyDims?: Shape;
-      outputShape: Shape;
-    }
-    /**
-     * Test computed shape is equal to the layer output's shape.
-     */
-    function testComputeOutputShape({
-      testcaseName, queryDims, valueDims, keyDims, outputShape,
-    }: ComputeOutputShapeArgs) {
-      it(testcaseName, () => {
-        const testLayer = new MultiHeadAttention({
-          numHeads: 2,
-          keyDim: 2,
-          valueDim: 2,
-          outputShape
-        });
-        const batchSize = 1;
-
-        const queryShape = [batchSize].concat(queryDims);
-        const valueShape = [batchSize].concat(valueDims);
-        const keyShape = keyDims ? [batchSize].concat(keyDims) : null;
-
-        const query = randomUniform(queryShape);
-        const value = randomUniform(valueShape);
-        const key = keyShape ? randomUniform(keyShape) : null;
-
-        const output = testLayer.call(query, {value, key});
-        const computedOutputShape = testLayer.computeOutputShape(
-          [queryShape, valueShape, keyShape]);
-
-        expect(output.shape).toEqual(computedOutputShape);
-      });
-    }
-    const params: ComputeOutputShapeArgs[] = [
-      {
-        testcaseName: 'without_key_same_proj',
-        queryDims: [40, 80],
-        valueDims: [20, 80],
-        keyDims: null,
-        outputShape: null
-      },
-      {
-        testcaseName: 'with_key_same_proj',
-        queryDims: [40, 80],
-        valueDims: [20, 80],
-        keyDims: [20, 30],
-        outputShape: null
-      },
-      {
-        testcaseName: 'wihtout_key_different_proj',
-        queryDims: [40, 80],
-        valueDims: [20, 80],
-        keyDims: null,
-        outputShape: [30, 40]
-      },
-      {
-        testcaseName: 'with_key_different_proj',
-        queryDims: [40, 80],
-        valueDims: [20, 80],
-        keyDims: [20, 30],
-        outputShape: [15, 50]
-      },
-    ];
-    for (const param of params) {
-      testComputeOutputShape(param);
-    }
-  });
-
-  describe('Compute Output Shape Raises Error', () => {
-    interface ComputeOutputShapeErrorArgs {
-      testcaseName: string;
-      queryShape: Shape;
-      valueShape: Shape;
-      keyShape?: Shape;
-    }
-    /**
-     * Test dimension mismatches.
-     */
-    function testComputeOutputShapeError({
-      testcaseName, queryShape, valueShape, keyShape,
-    }: ComputeOutputShapeErrorArgs) {
-      it(testcaseName, () => {
-        const testLayer = new MultiHeadAttention({
-          numHeads: 4,
-          keyDim: 2,
-          valueDim: 2,
-        });
-
-        expect(() => testLayer.computeOutputShape(
-          [queryShape, valueShape, keyShape])).toThrow();
-      });
-    }
-    const params: ComputeOutputShapeErrorArgs[] = [
-      {
-        testcaseName: 'query_value_dim_mismatch',
-        queryShape: [null, 40, 80],
-        valueShape: [null, 20, 70],
-        keyShape: null
-      },
-      {
-        testcaseName: 'key_value_dim_mismatch',
-        queryShape: [null, 40, 80],
-        valueShape: [null, 20, 80],
-        keyShape: [null, 10, 70],
-      },
-      {
-        testcaseName:'key_value_dim_mismatch_high_dim',
-        queryShape: [null, 40, 20, 30, 80],
-        valueShape: [null, 10, 10, 50, 80],
-        keyShape: [null, 10, 15, 50, 20],
-      },
-    ];
-    for (const param of params) {
-      testComputeOutputShapeError(param);
-    }
-  });
-
-  it('does not leak memory', () => {
-    const layer = new MultiHeadAttention({numHeads: 2, keyDim: 2});
-    const query = ones([1, 4, 8]);
-    // Initial call that builds sublayers and necessary tensors.
-    layer.call(query, {value: query});
-
-    const numTensors = memory().numTensors;
-    layer.call(query, {value: query});
-
-    expect(memory().numTensors).toEqual(numTensors + 1);
-  });
-  // TODO(pforderique): Test serialization.
-});
-
-describeMathCPU('High Dimensional Attention', () => {
-  interface HighDimAttentionArgs {
-    testcaseName: string;
-    qDims: Shape;
-    vDims: Shape;
-    maskDims: Shape;
-    attentionAxes: number[];
-  }
-  /**
-   * Test with high dimensional inputs.
-   */
-  function testHighDimAttention({
-    testcaseName, qDims, vDims, maskDims, attentionAxes,
-  }: HighDimAttentionArgs) {
-    it(testcaseName, () => {
-      const testLayer = new MultiHeadAttention({
-        numHeads: 2, keyDim: 2, attentionAxes,
-      });
-      const batchSize = 3;
-      const hiddenSize = 8;
-      // Generate data for the input (non-mask) tensors.
-      const queryShape = [batchSize].concat(qDims).concat(hiddenSize);
-      const valueShape = [batchSize].concat(vDims).concat(hiddenSize);
-      const maskShape = [batchSize].concat(maskDims);
-      const query = randomUniform(queryShape, 0, 10);
-      const value = randomUniform(valueShape, 0, 10);
-
-      // Invoke the data with a random set of mask data. This should mask at
-      // least one element.
-      const maskData = randomUniformInt(maskShape, 0, 2).asType('bool');
-
-      // Invoke the same data, but with a null mask (where no elements are
-      // masked).
-      const nullMaskData = ones(maskShape);
-
-      // Because one data is masked and one is not, the outputs should not be
-      // the same.
-
-      const outputWithMask = testLayer.call(
-        query, {value, attentionMask: maskData});
-      const outputWithNullMask = testLayer.call(
-        query, {value, attentionMask: nullMaskData});
-
-      expectTensorsNotClose(outputWithMask, outputWithNullMask);
-    });
-  }
-  const params: HighDimAttentionArgs[] = [
-    {
-      testcaseName: '4d_inputs_1freebatch_mask2',
-      qDims: [3, 4],
-      vDims: [3, 2],
-      maskDims: [4, 2],
-      attentionAxes: [2],
-    },
-    {
-      testcaseName: '4d_inputs_1freebatch_mask3',
-      qDims: [3, 4],
-      vDims: [3, 2],
-      maskDims: [3, 4, 2],
-      attentionAxes: [2],
-    },
-    {
-      testcaseName: '4d_inputs_1freebatch_mask4',
-      qDims: [3, 4],
-      vDims: [3, 2],
-      maskDims: [3, 2, 4, 2],
-      attentionAxes: [2],
-    },
-    {
-      testcaseName: '4D_inputs_2D_attention',
-      qDims: [3, 4],
-      vDims: [3, 2],
-      maskDims: [3, 4, 3, 2],
-      attentionAxes: [1, 2],
-    },
-    {
-      testcaseName: '5D_inputs_2D_attention',
-      qDims: [5, 3, 4],
-      vDims: [5, 3, 2],
-      maskDims: [3, 4, 3, 2],
-      attentionAxes: [2, 3],
-    },
-    {
-      testcaseName: '5D_inputs_2D_attention_fullmask',
-      qDims: [5, 3, 4],
-      vDims: [5, 3, 2],
-      maskDims: [5, 3, 4, 3, 2],
-      attentionAxes: [2, 3],
-    },
-  ];
-  for (const param of params) {
-    testHighDimAttention(param);
-  }
-});
-
-class SubclassAttention extends MultiHeadAttention {
-  protected override buildAttention(qkvRank: number) {}
-
-  protected override computeAttention(
-    query: Tensor,
-    key: Tensor,
-    value: Tensor,
-    attentionMask?: Tensor,
-    training?: boolean
-  ): [Tensor, Tensor] {
-    return [value, null];
-  }
-}
-
-describe('AttentionSubclass', () => {
-  // Test with a specified initializer.
-  it('initializer', () => {
-    const testLayer = new SubclassAttention({numHeads: 12, keyDim: 64});
-    // Create a 3-dimensional input.
-    const query = ones([1, 40, 80]);
-    const output = testLayer.call(query, {value: query});
-
-    expect(output.shape).toEqual([1, 40, 80]);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/preprocessing/start_end_packer.ts b/tfjs-master/tfjs-layers/src/layers/nlp/preprocessing/start_end_packer.ts
deleted file mode 100644
index 3146d6815..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/preprocessing/start_end_packer.ts
+++ /dev/null
@@ -1,198 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Start End Packer implementation based on `tf.layers.Layer`.
- */
-
-/* Original source: keras-nlp/start_end_packer.py */
-import { Tensor, Tensor1D, Tensor2D, concat, serialization, stack, tensor, tidy } from '@tensorflow/tfjs-core';
-
-import { Layer, LayerArgs } from '../../../engine/topology';
-import { ValueError } from '../../../errors';
-
-export declare interface StartEndPackerArgs extends LayerArgs {
-  /**
-   * Integer. The desired output length.
-   */
-  sequenceLength: number;
-
-  /**
-   * Integer or string. The ID or token that is to be placed at the start of
-   * each sequence. The dtype must match the dtype of the input tensors to the
-   * layer. If undefined, no start value will be added.
-   */
-  startValue?: number|string;
-
-  /**
-   * Integer or string. The ID or token that is to be placed at the end of each
-   * input segment. The dtype must match the dtype of the input tensors to the
-   * layer. If undefined, no end value will be added.
-   */
-  endValue?: number|string;
-
-  /**
-   * Integer or string. The ID or token that is to be placed into the unused
-   * positions after the last segment in the sequence. If undefined, 0 or ''
-   * will be added depending on the dtype of the input tensor.
-   */
-  padValue?: number|string;
-}
-
-export declare interface StartEndPackerOptions {
-  /**
-   * Pass to override the configured `sequenceLength` of the layer.
-   */
-  sequenceLength?: number;
-
-  /**
-   * Pass `false` to not append a start value for this input.
-   * Defaults to true.
-   */
-  addStartValue?: boolean;
-
-  /**
-   * Pass `false` to not append an end value for this input.
-   * Defaults to true.
-   */
-  addEndValue?: boolean;
-}
-
-/**
- * Adds start and end tokens to a sequence and pads to a fixed length.
- *
- *  This layer is useful when tokenizing inputs for tasks like translation,
- *  where each sequence should include a start and end marker. It should
- *  be called after tokenization. The layer will first trim inputs to fit, then
- *  add start/end tokens, and finally pad, if necessary, to `sequence_length`.
- *
- *  Input should be either a `tf.Tensor[]` or a dense `tf.Tensor`, and
- *  either rank-1 or rank-2.
- */
-export class StartEndPacker extends Layer {
-  /** @nocollapse */
-  static readonly className = 'StartEndPacker';
-
-  private sequenceLength: number;
-  private startValue?: number|string;
-  private endValue?: number|string;
-  private padValue?: number|string;
-
-  constructor(args: StartEndPackerArgs) {
-    super(args);
-
-    this.sequenceLength = args.sequenceLength;
-    this.startValue = args.startValue;
-    this.endValue = args.endValue;
-    this.padValue = args.padValue;
-  }
-
-  override call(
-    inputs: Tensor|Tensor[],
-    kwargs: StartEndPackerOptions={addStartValue: true, addEndValue: true}
-  ): Tensor|Tensor2D {
-    return this.callAndReturnPaddingMask(inputs, kwargs)[0];
-  }
-
-  /**
-   * Exactly like `call` except also returns a boolean padding mask of all
-   * locations that are filled in with the `padValue`.
-   */
-  callAndReturnPaddingMask(
-    inputs: Tensor|Tensor[],
-    kwargs: StartEndPackerOptions={addStartValue: true, addEndValue: true}
-  ): [Tensor1D|Tensor2D, Tensor1D|Tensor2D] {
-    return tidy(() => {
-      // Add a new axis at the beginning if needed.
-      let x = inputs instanceof Tensor ? [inputs] : inputs;
-
-      const inputIs1d = inputs instanceof Tensor && inputs.rank === 1;
-
-      if (x.some(t => t.rank !== 1)) {
-        throw new ValueError(
-          'Input must either be a rank 1 Tensor or an array of rank 1 Tensors.'
-        );
-      }
-      const sequenceLength = kwargs.sequenceLength ?? this.sequenceLength;
-
-      // Concatenate start and end tokens.
-      if (kwargs.addStartValue && this.startValue != null) {
-        const startTokenIdTensor = tensor([this.startValue]);
-        x = x.map(t => concat([startTokenIdTensor, t]));
-      }
-      if (kwargs.addEndValue && this.endValue != null) {
-        const endTokenIdTensor = tensor([this.endValue]);
-        // Trim to leave room for end token.
-        x = x.map(t => {
-          const sliced = t.slice(0, Math.min(t.shape[0], sequenceLength - 1));
-          const padded = concat([sliced, endTokenIdTensor]);
-          return padded;
-        });
-      }
-
-      // tf.pad does not allow padding on Tensors with dtype='string'
-      function ensureLength(
-        input: Tensor, length: number, padValue?: string|number) {
-        if (padValue === undefined) {
-          padValue = input.dtype === 'string' ? '' : 0;
-        }
-        if (typeof padValue === 'number') {
-          return input.pad([[0, length - input.size]], padValue);
-        }
-
-        const strInput = input.arraySync() as unknown as string[];
-
-        if (strInput.length <= length) {
-          const pads = Array(length - strInput.length).fill(padValue);
-          return tensor(strInput.concat(pads));
-        }
-
-        return tensor(strInput.slice(0, strInput.length - length));
-      }
-
-      const paddedMask: Tensor[] = x.map(t => {
-        // `onesLike` not used since it does not support string tensors.
-        const ones = tensor(Array(t.shape[0]).fill(1));
-        return ensureLength(ones, sequenceLength, 0).cast('bool');
-      });
-      const mask = inputIs1d ?
-        paddedMask[0] as Tensor1D
-        : stack(paddedMask) as Tensor2D;
-
-      const paddedTensors: Tensor[] =
-        x.map(t => ensureLength(t, sequenceLength, this.padValue));
-      const outputs = inputIs1d ?
-        paddedTensors[0] as Tensor1D
-        : stack(paddedTensors) as Tensor2D;
-
-      return [outputs, mask];
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      sequenceLength: this.sequenceLength,
-      startValue: this.startValue,
-      endValue: this.endValue,
-      padValue: this.padValue,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(StartEndPacker);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/preprocessing/start_end_packer_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/preprocessing/start_end_packer_test.ts
deleted file mode 100644
index 5a6668428..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/preprocessing/start_end_packer_test.ts
+++ /dev/null
@@ -1,191 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for StartEndPacker Layer.
- */
-
-import { Tensor1D, Tensor2D, memory, tensor, tensor2d } from '@tensorflow/tfjs-core';
-
-import { StartEndPacker } from './start_end_packer';
-import { expectTensorsClose } from '../../../utils/test_utils';
-import { ValueError } from '../../../errors';
-
-describe('StartEndPacker', () => {
-  it('tensor input', () => {
-    const inputData = tensor([5, 6, 7]);
-    const startEndPacker = new StartEndPacker({sequenceLength: 5});
-
-    const output = startEndPacker.call(inputData) as Tensor1D;
-    const expectedOutput = tensor([5, 6, 7, 0, 0]);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('tensor array input', () => {
-    const inputData = [tensor([5, 6, 7])];
-    const startEndPacker = new StartEndPacker({sequenceLength: 5});
-
-    const output = startEndPacker.call(inputData) as Tensor2D;
-    const expectedOutput = tensor2d([[5, 6, 7, 0, 0]]);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('uneven tensor array input', () => {
-    const inputData = [tensor([5, 6, 7]), tensor([8, 9, 10, 11])];
-    const startEndPacker = new StartEndPacker({sequenceLength: 5});
-
-    const output = startEndPacker.call(inputData) as Tensor2D;
-    const expectedOutput = tensor2d([[5, 6, 7, 0, 0], [8, 9, 10, 11, 0]]);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('tensor array truncation', () => {
-    const inputData = [tensor([5, 6, 7]), tensor([8, 9, 10, 11])];
-    const startEndPacker = new StartEndPacker({sequenceLength: 3});
-
-    const output = startEndPacker.call(inputData) as Tensor2D;
-    const expectedOutput = tensor2d([[5, 6, 7], [8, 9, 10]]);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('tensor array input error', () => {
-    const inputData = [tensor([[5, 6, 7], [8, 9, 10, 11]])];
-    const startEndPacker = new StartEndPacker({sequenceLength: 5});
-
-    expect(() => startEndPacker.call(inputData))
-      .toThrow(new ValueError(
-        'Input must either be a rank 1 Tensor or an array of rank 1 Tensors.'));
-  });
-
-  it('start only', () => {
-    const inputData = [tensor([5, 6, 7]), tensor([8, 9, 10, 11])];
-    const startEndPacker = new StartEndPacker({
-      sequenceLength: 3, startValue: -1
-    });
-
-    const output = startEndPacker.call(inputData) as Tensor2D;
-    const expectedOutput = tensor2d([[-1, 5, 6], [-1, 8, 9]]);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('start end token', () => {
-    const inputData = [tensor([5, 6, 7]), tensor([8, 9, 10, 11])];
-    const startEndPacker = new StartEndPacker({
-      sequenceLength: 6, startValue: 1, endValue: 2,
-    });
-
-    const output = startEndPacker.call(inputData) as Tensor2D;
-    const expectedOutput = tensor2d([[1, 5, 6, 7, 2, 0], [1, 8, 9, 10, 11, 2]]);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('start end and padding', () => {
-    const inputData = [tensor([5, 6, 7]), tensor([8, 9, 10, 11])];
-    const startEndPacker = new StartEndPacker({
-      sequenceLength: 7, startValue: 1, endValue: 2, padValue: 3,
-    });
-
-    const output = startEndPacker.call(inputData) as Tensor2D;
-    const expectedOutput = tensor2d([
-      [1, 5, 6, 7, 2, 3, 3],
-      [1, 8, 9, 10, 11, 2, 3],
-    ]);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('end token value during truncation', () => {
-    const inputData = [tensor([5, 6]), tensor([8, 9, 10, 11, 12, 13])];
-    const startEndPacker = new StartEndPacker({
-      sequenceLength: 5, startValue: 1, endValue: 2, padValue: 0,
-    });
-
-    const output = startEndPacker.call(inputData) as Tensor2D;
-    const expectedOutput = tensor2d([[1, 5, 6, 2, 0], [1, 8, 9, 10, 2]]);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('string input', () => {
-    const inputData = [
-      tensor(['TensorflowJS', 'is', 'awesome']),
-      tensor(['amazing'])
-    ];
-    const startEndPacker = new StartEndPacker({
-      sequenceLength: 5,
-      startValue: '[START]',
-      endValue: '[END]',
-      padValue: '[PAD]',
-    });
-
-    const output = startEndPacker.call(inputData) as Tensor2D;
-    const expectedOutput = tensor2d([
-      ['[START]', 'TensorflowJS', 'is', 'awesome', '[END]'],
-      ['[START]', 'amazing', '[END]', '[PAD]', '[PAD]'],
-    ]);
-
-    expectTensorsClose(output, expectedOutput);
-  });
-
-  it('correct mask', () => {
-    const inputData = [tensor([5, 6]), tensor([8, 9, 10, 11])];
-    const startEndPacker = new StartEndPacker({
-      sequenceLength: 6, startValue: 1, endValue: 2
-    });
-
-    const outputMask =
-      startEndPacker.callAndReturnPaddingMask(inputData)[1] as Tensor2D;
-    const expectedMask = tensor2d([
-      [true, true, true, true, false, false],
-      [true, true, true, true, true, true],
-    ]);
-
-    expectTensorsClose(outputMask, expectedMask);
-  });
-
-  it('does not leak memory', () => {
-    const inputData = [tensor([5, 6]), tensor([8, 9, 10, 11])];
-    const startEndPacker = new StartEndPacker({
-      sequenceLength: 6, startValue: 1, endValue: 2
-    });
-
-    const numTensorsBefore = memory().numTensors;
-    startEndPacker.callAndReturnPaddingMask(inputData);
-    const numTensorsAfter = memory().numTensors;
-
-    expect(numTensorsAfter).toEqual(numTensorsBefore + 2);
-  });
-
-  it('correct getConfig', () => {
-    const config = {
-      sequenceLength: 512,
-      startValue: 10,
-      endValue: 20,
-      padValue: 100,
-    };
-    const startEndPacker = new StartEndPacker(config);
-    const outputConfig = startEndPacker.getConfig();
-
-    expect(outputConfig).toEqual(jasmine.objectContaining(config));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers.ts b/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers.ts
deleted file mode 100644
index b4f395cff..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers.ts
+++ /dev/null
@@ -1,572 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- *  Tokenizer layers.
- */
-
-/* Original source: keras-nlp/tokenizer.py */
-import { Tensor, serialization, tensor, tidy} from '@tensorflow/tfjs-core';
-
-import { Layer, LayerArgs } from '../../engine/topology';
-import { NotImplementedError, ValueError } from '../../errors';
-import { BytePairTokenizerCache, StaticHashTable, bytesToUnicode, createStaticHashtable, removeStringsFromInputs, splitStringsForBpe } from './tokenizers_utils';
-import { tensorToArr, tensorArrTo2DArr } from './utils';
-
-export declare interface TokenizerOptions {
-  mode?: 'tokenize' | 'detokenize';
-}
-
-/**
- * Base class for Tokenizers.
- *
- *  Tokenizers in the tfjs library should all subclass this layer.
- *  The class provides two core methods `tokenize()` and `detokenize()` for
- *  going from plain text to sequences and back. A tokenizer is a subclass of
- *  `Layer` and can be combined with other layers in a `tf.sequential` model.
- *
- *  Subclassers should always implement the `tokenize()` method, which will also
- *  be the default when calling the layer directly on inputs.
- *
- *  Subclassers can optionally implement the `detokenize()` method if the
- *  tokenization is reversible. Otherwise, this can be skipped.
- *
- *  Subclassers should implement `get_vocabulary()`, `vocabulary_size()`,
- *  `token_to_id()` and `id_to_token()` if applicable. For some simple
- *  "vocab free" tokenizers, such as a whitespace splitter shown below, these
- *  methods do not apply and can be skipped.
- *
- *  Example:
- *
- *  ```js
- *  class WhitespaceSplitterTokenizer extends Tokenizer {
- *    tokenize(inputs: Tensor): Tensor[] {
- *      const stringInputs = inputs.dataSync() as unknown as string[];
- *      return stringInputs.map(input => Tensor(input.split(' ')));
- *    }
- *
- *    override detokenize(inputs: Tensor[]): Tensor {
- *      const stringInputs = inputs.map(
- *        input => input.dataSync() as unknown as string[]);
- *      return Tensor(stringInputs.map(str => str.join(' ')));
- *    }
- *  }
- *
- * const tokenizer = new WhitespaceSplitterTokenizer();
- *
- * tokenizer.tokenize(tensor(['this is a test']))[0].print();
- *
- * tokenizer.detokenize([tensor(['this', 'is', 'a', 'test'])]).print();
- * ```
- */
-export abstract class Tokenizer extends Layer {
-  /**
-   * Transform input tensors of strings into output tokens.
-   *
-   * @param inputs Input tensor.
-   * @param kwargs Additional keyword arguments.
-   */
-  abstract tokenize(inputs: Tensor): Tensor[];
-
-  /**
-   * Transform tokens back into strings.
-   *
-   * @param inputs Input tensor.
-   * @param kwargs Additional keyword arguments.
-   */
-  detokenize(inputs: Tensor[]): Tensor {
-    throw new NotImplementedError(
-      `No implementation of 'detokenize()' was found for
-      ${this.constructor.name}.`
-    );
-  }
-
-  /**
-   * Get the tokenizer vocabulary as a list of strings terms.
-   */
-  get vocabulary(): string[] {
-    throw new NotImplementedError(
-      `No implementation of 'vocabulary()' was found for
-      ${this.constructor.name}.`
-    );
-  }
-
-  /**
-   * Returns the total size of the token id space.
-   */
-  get vocabularySize(): number {
-    throw new NotImplementedError(
-      `No implementation of 'vocabularySize()' was found for
-      ${this.constructor.name}.`
-    );
-  }
-
-  /**
-   * Convert an integer id to a string token.
-   */
-  idToToken(id: number): string {
-    throw new NotImplementedError(
-      `No implementation of 'idToToken()' was found for
-      ${this.constructor.name}.`
-    );
-  }
-
-  /**
-   * Convert an integer id to a string token.
-   */
-  tokenToId(token: string): number {
-    throw new NotImplementedError(
-      `No implementation of 'tokenToId()' was found for
-      ${this.constructor.name}.`
-    );
-  }
-
-  override call(
-    inputs: Tensor|Tensor[],
-    {mode = 'tokenize'}: TokenizerOptions={}
-  ): Tensor|Tensor[] {
-
-    if (mode === 'tokenize') {
-      if (inputs instanceof Array) {
-        throw new ValueError(`tokenize expects Tensor, not Tensor[].`);
-      }
-      return this.tokenize(inputs);
-    }
-
-    if (mode === 'detokenize') {
-      if (!(inputs instanceof Array)) {
-        throw new ValueError(`detokenize expects Tensor[], not Tensor.`);
-      }
-      return this.detokenize(inputs);
-    }
-
-    throw new ValueError(`Input mode=${mode} is not supported.`);
-  }
-}
-
-/* Original source: keras-nlp/byte_pair_tokenizer.py */
-// TODO(pforderique): Support filename string inputs for vocabulary and merges.
-export declare interface BytePairTokenizerArgs extends LayerArgs {
-  /**
-   * Maps token to integer ids
-   */
-  vocabulary: Map<string, number>;
-
-  /**
-   * Array. Contains the merge rule.
-   */
-  merges: string[];
-
-  /**
-   * Integer. If set, the output will be padded or truncated to the
-   * `sequenceLength`. Defaults to `null`.
-   */
-  sequenceLength?: number;
-
-  /**
-   * Boolean. Whether to add an initial space to the input. This tokenizer is
-   * whitespace aware, and will tokenize a word with a leading space
-   * differently. Adding a prefix space to the first word will cause it to be
-   * tokenized equivalently to all subsequent words in the sequence.
-   * Defaults to `false`.
-   */
-  addPrefixSpace?: boolean;
-
-  /**
-   * Array. A list of strings that will never be split during the word-level
-   * splitting applied before the byte-pair encoding. This can be used to ensure
-   * special tokens map to unique indices in the vocabulary, even if these
-   * special tokens contain splittable characters such as punctuation. Special
-   * tokens must still be included in `vocabulary`. Defaults to `None`.
-   */
-  unsplittableTokens?: string[];
-}
-
-/**
- * Byte-pair encoding tokenizer layer.
- *
- * This BPE tokenizer provides the same functionality as the official GPT-2
- * tokenizer. Given the same `vocabulary` which maps tokens to ids, and `merges`
- * which describes BPE merge rules, it should provide the same output as OpenAI
- * implementation (https://github.com/openai/gpt-2/blob/master/src/encoder.py).
- *
- * If input is a batch of strings (rank > 0):
- * By default, the layer will output a `Tensor[]`.
- * If `sequenceLength` is set, the layer will output a `Tensor[]` where all
- * inputs have been padded or truncated to `sequenceLength`.
- *
- * Examples:
- *
- * Tokenize
- * ```js
- * const vocabulary = new Map([['butter', 1], ['fly', 2]]);
- * const merges = ['b u', 't t', 'e r', 'bu tt', 'butt er', 'f l', 'fl y'];
- * const tokenizer = new BytePairTokenizer({vocabulary, merges});
- *
- * tokenizer.tokenize(tensor(['butterfly']))[0].print();
- * tokenizer.tokenize(tensor(['butterfly, butter']))[1].print();
- * ```
- *
- * Detokenize
- * ```js
- * const vocabulary = new Map([['butter', 1], ['fly', 2]]);
- * const merges = ['b u', 't t', 'e r', 'bu tt', 'butt er', 'f l', 'fl y'];
- * const tokenizer = new BytePairTokenizer({vocabulary, merges});
- *
- * tokenizer.detokenize([[1, 2]]).print();
- * ```
- */
-export class BytePairTokenizer extends Tokenizer {
-  /** @nocollapse */
-  static readonly className = 'BytePairTokenizer';
-
-  private _vocabulary: Map<string, number>;
-  private merges: string[];
-
-  private readonly sequenceLength: number;
-  private readonly addPrefixSpace: boolean;
-  private readonly unsplittableTokens: string[];
-
-  private readonly byte2Unicode: StaticHashTable<number, string>;
-  private readonly cache = new BytePairTokenizerCache();
-
-  private readonly tokenToIdMap: StaticHashTable<string, number>;
-  private readonly idToTokenMap: StaticHashTable<number, string>;
-
-  private readonly mergeRanksLookupDefault: number;
-  private readonly mergeRanks: StaticHashTable<string, number>;
-
-  constructor(args: BytePairTokenizerArgs) {
-    super(args);
-
-    this._vocabulary = new Map(args.vocabulary);
-    this.merges = [...args.merges];
-
-    this.sequenceLength = args.sequenceLength || null;
-    this.addPrefixSpace = args.addPrefixSpace || false;
-    this.unsplittableTokens = args.unsplittableTokens || null;
-
-    // Create byte <=> unicode mapping. This is useful for handling
-    // whitespace tokens.
-    const [byteList, unicodeList] = bytesToUnicode();
-    this.byte2Unicode = createStaticHashtable(
-      Array.from(byteList), unicodeList, '');
-
-    if (this.unsplittableTokens) {
-      // Put unsplittable tokens into cache, so it won't be further split and
-      // merged.
-      this.cache.insert(this.unsplittableTokens, this.unsplittableTokens);
-    }
-
-    // Create mapping between string tokens to int ids, and vice versa.
-    const bytePairs = [...this._vocabulary.keys()];
-    const bytePairEncodingIndicies = [...this._vocabulary.values()];
-
-    this.tokenToIdMap = createStaticHashtable(
-      bytePairs, bytePairEncodingIndicies, -1);
-
-    this.idToTokenMap = createStaticHashtable(
-      bytePairEncodingIndicies, bytePairs, '');
-
-    // Create ranking of merge rules, this is the same as order of merge pairs
-    // in `this.merges`.
-    this.mergeRanksLookupDefault = this.merges.length + 1;
-    this.mergeRanks = createStaticHashtable(
-      this.merges,
-      [...Array(this.merges.length).keys()],
-      this.mergeRanksLookupDefault
-    );
-  }
-
-  /**
-   * Get the tokenizer vocabulary as a list of string tokens.
-   */
-  override get vocabulary(): string[] {
-    return [...this._vocabulary.keys()];
-  }
-
-  /**
-   * Get the size of the tokenizer vocabulary.
-   */
-  override get vocabularySize(): number {
-    return this._vocabulary.size;
-  }
-
-  /**
-   * Convert an integer id to a string token.
-   */
-  override idToToken(id: number): string | undefined {
-    // This will be slow, but keep memory usage down compared to building a
-    // dict. Assuming the main use case is looking up a few special tokens
-    // early in the vocab, this should be fine.
-    const keys = this.vocabulary;
-    for (const token of keys) {
-      if (this._vocabulary.get(token) === id) {
-        return token;
-      }
-    }
-    return undefined;
-  }
-
-  /**
-   * Convert a string token to an integer id.
-   */
-  override tokenToId(token: string): number | undefined {
-    return this._vocabulary.get(token);
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      vocabulary: Array.from(this._vocabulary.entries()),
-      merges: this.merges,
-      sequenceLength: this.sequenceLength,
-      addPrefixSpace: this.addPrefixSpace,
-      unsplittableTokens: this.unsplittableTokens,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  /**
-   * Perform one step of byte-pair merge.
-   */
-  private bpeMergeOneStep(
-    words: Tensor[], mask: boolean[]): [Tensor[], boolean[]] {
-
-    const wordsStr = tensorArrTo2DArr(words) as string[][];
-
-    // Get all word pairs.
-    const first = wordsStr.map(arr => arr.slice(0, -1));
-    const second = wordsStr.map(arr => arr.slice(1, arr.length));
-
-    // Mask empty.
-    const nonEmptyMask = second.map(arr => arr.length > 0);
-    mask = mask.map((a, idx) => a && nonEmptyMask[idx]);
-    if (!mask.some(e => e)) {
-      return [words, mask];
-    }
-    const nonEmptyIndices = mask
-      .map((bool, idx) => bool ? idx : -1)
-      .filter(e => e !== -1);
-
-    const filteredFirst = nonEmptyIndices.map(idx => first[idx]);
-    const filteredSecond = nonEmptyIndices.map(idx => second[idx]);
-
-    // Get byte pair ranking in merge rules.
-    const pairs: string[][] = filteredFirst.map((firstSubArr, idx) => {
-      const secondSubArr = filteredSecond[idx];
-
-      return firstSubArr.map((char, idx) => `${char} ${secondSubArr[idx]}`);
-    });
-    const pairRanksTensor = this.mergeRanks.lookup(
-      pairs.map(arr => tensor(arr)));
-    const pairRanks = tensorArrTo2DArr(pairRanksTensor) as number[][];
-
-    // Get BPE pair ranks.
-    const minPairRank = pairRanks.map(
-      arr => arr.reduce((a, b) => Math.min(a, b), Infinity));
-    const pairFoundMask = minPairRank.map(
-      rank => rank !== this.mergeRanksLookupDefault);
-
-    // Tokens that cannot be further merged are marked as finished.
-    for (const [idx, index] of nonEmptyIndices.entries()) {
-      const update = pairFoundMask[idx];
-      mask[index] = update;
-    }
-    if (!mask.some(e => e)) {
-      return [words, mask];
-    }
-
-    function argMin(arr: number[]): number {
-      return arr.indexOf(arr.reduce((a, b) => Math.min(a, b), Infinity));
-    }
-
-    const maskedPairRanks = pairRanks.filter((_, idx) => pairFoundMask[idx]);
-    const minPairRankIndices = maskedPairRanks.map(arr => argMin(arr));
-
-    // Get words and pairs to process.
-    const unfinishedWords = wordsStr.filter((_, idx) => mask[idx]);
-
-    const pairLeft = unfinishedWords.map(
-      (word, idx) => word[minPairRankIndices[idx]]);
-
-    const pairRight = unfinishedWords.map(
-      (word, idx) => word[minPairRankIndices[idx] + 1]);
-
-    const mergedPairs = pairLeft.map((left, idx) => {
-      const right = pairRight[idx];
-      return `${left}${right}`;
-    });
-    const unfinishedWordsIndices = mask
-      .map((_, idx) => idx)
-      .filter((_, idx) => mask[idx]);
-
-    const mergedPairIndices = unfinishedWordsIndices.map(
-      (index, idx) => [index, minPairRankIndices[idx]]);
-    const emptyStringIndices = unfinishedWordsIndices.map(
-      (index, idx) => [index, minPairRankIndices[idx] + 1]);
-
-    for (const [idx, indices] of mergedPairIndices.entries()) {
-      const [wordIdx, charIdx] = indices;
-      const mergedPair = mergedPairs[idx];
-      wordsStr[wordIdx][charIdx] = mergedPair;
-    }
-
-    for (const indices of emptyStringIndices) {
-      const [wordIdx, charIdx] = indices;
-      wordsStr[wordIdx][charIdx] = '';
-    }
-
-    words = wordsStr.map(word => tensor(word));
-    words = removeStringsFromInputs(words, '');
-
-    return [words, mask];
-  }
-
-  /**
-   * Perform byte-pair merge for each word in the inputs.
-   */
-  private bpeMerge(words: Tensor[]): Tensor[] {
-    const numWords = words.length;
-
-    // Merge bytes.
-    function loopCondition(mask: boolean[]): boolean {
-      return mask.some(e => e);
-    }
-
-    const initialMask: boolean[] = Array(numWords).fill(true);
-
-    let mergedWords = words;
-    let mask = initialMask;
-    while (loopCondition(mask)) {
-      [mergedWords, mask] = this.bpeMergeOneStep(mergedWords, mask);
-    }
-
-    return mergedWords;
-  }
-
-  /**
-   * Map token bytes to unicode using `byte2unicode`.
-   */
-  private transformBytes(tokens: Tensor): Tensor[] {
-    const tokensStr = tensorToArr(tokens) as string[];
-
-    const splitBytes = tokensStr.map(
-      token => tensor(token.split('').map(c => c.charCodeAt(0))));
-    const splitUnicode = this.byte2Unicode.lookup(splitBytes);
-
-    return splitUnicode;
-  }
-
-  /**
-   * Process unseen tokens and add to cache.
-   */
-  private bpeMergeAndUpdateCache(tokens: Tensor) {
-    const words = this.transformBytes(tokens);
-    const tokenizedWordsTensor = this.bpeMerge(words);
-    const tokenizedWords = tensorArrTo2DArr(tokenizedWordsTensor) as string[][];
-
-    // For each word, join all its token by a whitespace,
-    // e.g., ["dragon", "fly"] => "dragon fly" for hash purpose.
-    const joinedTokens = tokenizedWords.map(word => word.join(' '));
-
-    this.cache.insert(tokens, joinedTokens);
-  }
-
-  tokenize(inputs: Tensor): Tensor[] {
-    return tidy(() => {
-      if (this.addPrefixSpace) {
-        const strInputs = tensorToArr(inputs) as string[];
-        inputs = tensor(strInputs.map(word => ' ' + word));
-      }
-
-      const rawTokensTensor =
-        splitStringsForBpe(inputs, this.unsplittableTokens);
-      const rawTokens = tensorArrTo2DArr(rawTokensTensor) as string[][];
-
-      const tokenRowSplits = [0];
-      for (const [idx, token] of rawTokens.entries()) {
-        tokenRowSplits.push(tokenRowSplits[idx] + token.length);
-      }
-
-      const flatTokens = rawTokens.reduce((acc, e) => acc.concat(e), []);
-
-      // Check cache.
-      const cacheLookup = this.cache.lookup(flatTokens);
-      const cacheMask = cacheLookup.map(e => e === '');
-
-      const hasUnseenWords = cacheMask.some(
-        (bool, idx) => bool && flatTokens[idx] !== '');
-
-      const processUnseenTokens = (): string[]  => {
-        const unseenTokens = flatTokens.filter((_, idx) => cacheMask[idx]);
-        this.bpeMergeAndUpdateCache(tensor(unseenTokens));
-        return this.cache.lookup(flatTokens);
-      };
-
-      // If `has_unseen_words == True`, it means not all tokens are in cache,
-      // we will process the unseen tokens. Otherwise return the cache lookup.
-      const tokenizedWords =
-        hasUnseenWords ? processUnseenTokens() : cacheLookup;
-
-      const tokensTensor = this.tokenToIdMap.lookup(
-        tokenizedWords.map(word => tensor(word.split(' '))));
-      const tokens = tokensTensor.map(t => Array.from(t.dataSync()));
-
-      // Unflatten to match input.
-      const newTokenRowSplits = [0];
-      for (const [idx, token] of tokens.entries()) {
-        newTokenRowSplits.push(newTokenRowSplits[idx] + token.length);
-      }
-      const newFlatTokens = tokens.reduce((acc, e) => acc.concat(e), []);
-      const gatheredIndices =
-        tokenRowSplits.map(index => newTokenRowSplits[index]);
-
-      let tokens2D: Tensor[] = [];
-      for (let i = 0; i < gatheredIndices.length - 1; i++) {
-        const [start, end] = [gatheredIndices[i], gatheredIndices[i+1]];
-        const row = newFlatTokens.slice(start, end);
-        tokens2D.push(tensor(row));
-      }
-
-      // Convert to a dense output if `sequenceLength` is set.
-      if (this.sequenceLength) {
-        // pad or truncate
-        tokens2D = tokens2D.map(t => {
-          if (t.size === this.sequenceLength) {
-            return t;
-          } else if (t.size > this.sequenceLength) {
-            return t.slice(0, this.sequenceLength);
-          } else {
-            return t.pad([[0, this.sequenceLength - t.size]]);
-          }
-        });
-      }
-
-      return tokens2D;
-    });
-  }
-
-  override detokenize(inputs: Tensor[]): Tensor {
-    const unicodeText = this.idToTokenMap.lookup(inputs)
-      .map(t => (tensorToArr(t) as string[]).join(''));
-
-    return tensor(unicodeText);
-  }
-}
-serialization.registerClass(BytePairTokenizer);
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers_test.ts
deleted file mode 100644
index 2608c8209..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers_test.ts
+++ /dev/null
@@ -1,241 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Unit Tests for Tokenizer Layers.
- */
-
-import { Tensor, memory, tensor, test_util } from '@tensorflow/tfjs-core';
-
-import { BytePairTokenizer, Tokenizer } from './tokenizers';
-import { expectTensorsClose } from '../../utils/test_utils';
-
-class SimpleTokenizer extends Tokenizer {
-  /** @nocollapse */
-  static className = 'SimpleTokenizer';
-
-  tokenize(inputs: Tensor): Tensor[] {
-    const stringInputs = inputs.dataSync() as unknown as string[];
-    return stringInputs.map(input => tensor(input.split(' ')));
-  }
-
-  override detokenize(inputs: Tensor[]): Tensor {
-    const stringInputs = inputs.map(
-      input => input.dataSync() as unknown as string[]);
-    return tensor(stringInputs.map(str => str.join(' ')));
-  }
-}
-
-describe('Tokenizer', () => {
-  let tokenizer: SimpleTokenizer;
-
-  beforeEach(() => {
-    tokenizer = new SimpleTokenizer();
-  });
-
-  it('tokenize', () => {
-    const inputData = tensor(['the quick brown fox']);
-    const expectedOutput = [tensor(['the', 'quick', 'brown', 'fox'])];
-
-    const tokenizeOutput = tokenizer.tokenize(inputData);
-    const callOutput = tokenizer.call(inputData) as Tensor[];
-
-    expect(tokenizeOutput.length).toBe(1);
-    expectTensorsClose(tokenizeOutput[0], expectedOutput[0]);
-
-    expect(callOutput.length).toBe(1);
-    expectTensorsClose(callOutput[0], expectedOutput[0]);
-  });
-
-  it('detokenize', () => {
-    const inputData = [tensor(['the', 'quick', 'brown', 'fox'])];
-    const expectedOutput = tensor(['the quick brown fox']);
-
-    const detokenizeOutput = tokenizer.detokenize(inputData);
-    const callOutput = tokenizer.call(
-      inputData, {mode: 'detokenize'}) as Tensor;
-
-    expectTensorsClose(detokenizeOutput, expectedOutput);
-    expectTensorsClose(callOutput, expectedOutput);
-  });
-
-  it('detokenize(tokenize) composition', () => {
-    const inputData = tensor(['the quick brown fox']);
-
-    expectTensorsClose(
-      tokenizer.detokenize(tokenizer.tokenize(inputData)), inputData);
-  });
-});
-
-describe('BytePairTokenizer', () => {
-  it('gets correct set up', () => {
-    const vocabulary = new Map([['butter', 1], ['fly', 2]]);
-    const merges = ['b u', 't t', 'e r', 'bu tt', 'butt er', 'f l', 'fl y'];
-    const tokenizer = new BytePairTokenizer({vocabulary, merges});
-    const config = tokenizer.getConfig();
-
-    expect(tokenizer.vocabulary).toEqual(['butter', 'fly']);
-    expect(tokenizer.vocabularySize).toEqual(2);
-    expect(tokenizer.idToToken(1)).toEqual('butter');
-    expect(tokenizer.idToToken(3)).toEqual(undefined);
-    expect(tokenizer.tokenToId('butter')).toEqual(1);
-    test_util.expectArraysEqual(config.merges as string[], merges);
-  });
-
-  it('tokenize with special tokens', () => {
-    const vocabulary = new Map([['sp', 0], ['s', 1], ['p', 2]]);
-    const merges = ['s p'];
-    let tokenizer = new BytePairTokenizer({
-      vocabulary,
-      merges,
-      unsplittableTokens: ['s', 'p'],
-    });
-    const inputData = tensor(['sp']);
-    const expectedOutput = [tensor([1, 2])];
-
-    const tokenizeOutput = tokenizer.tokenize(inputData);
-    const callOutput = tokenizer.call(inputData) as Tensor[];
-
-    expect(tokenizeOutput.length).toBe(1);
-    expectTensorsClose(tokenizeOutput[0], expectedOutput[0]);
-
-    expect(callOutput.length).toBe(1);
-    expectTensorsClose(callOutput[0], expectedOutput[0]);
-
-    // If not setting special tokens, "sp" is one token.
-    tokenizer = new BytePairTokenizer({
-      vocabulary,
-      merges,
-    });
-    const output = tokenizer.tokenize(inputData);
-    expect(output.length).toBe(1);
-    expectTensorsClose(output[0], tensor([0]));
-  });
-
-  it('tokenize with merges', () => {
-    const vocabulary = new Map([
-      ['br', 0], ['wn', 1], ['ck', 2], ['b', 3], ['r', 4], ['o', 5], ['w', 6],
-      ['n', 7], ['.', 8], ['l', 9], ['a', 10], ['c', 11], ['d', 12]
-    ]);
-    const merges = ['b r', 'w n', 'c k'];
-    const tokenizer = new BytePairTokenizer({vocabulary, merges});
-    const inputData = tensor(['brown.', 'black.']);
-    const expectedOutput = [tensor([0, 5, 1, 8]), tensor([3, 9, 10, 2, 8])];
-
-    const tokenizeOutput = tokenizer.tokenize(inputData);
-    const callOutput = tokenizer.call(inputData) as Tensor[];
-
-    expect(tokenizeOutput.length).toBe(2);
-    expectTensorsClose(tokenizeOutput[0], expectedOutput[0]);
-    expectTensorsClose(tokenizeOutput[1], expectedOutput[1]);
-
-    expect(callOutput.length).toBe(2);
-    expectTensorsClose(callOutput[0], expectedOutput[0]);
-    expectTensorsClose(callOutput[1], expectedOutput[1]);
-  });
-
-  it('tokenize with prefix space', () => {
-    const vocabulary = new Map([
-      ['br', 0], ['wn', 1], ['ck', 2], ['b', 3], ['r', 4], ['o', 5], ['w', 6],
-      ['n', 7], ['.', 8], ['l', 9], ['a', 10], ['c', 11], ['d', 12]
-    ]);
-    const merges = ['b r', 'w n', 'c k'];
-    const tokenizer = new BytePairTokenizer({
-      vocabulary,
-      merges,
-      addPrefixSpace: true,
-    });
-    const inputData = tensor(['brown.', 'black.']);
-    const expectedOutput = [
-      tensor([-1, 0, 5, 1, 8]),
-      tensor([-1, 3, 9, 10, 2, 8])
-    ];
-
-    const tokenizeOutput = tokenizer.tokenize(inputData);
-
-    expect(tokenizeOutput.length).toBe(2);
-    expectTensorsClose(tokenizeOutput[0], expectedOutput[0]);
-    expectTensorsClose(tokenizeOutput[1], expectedOutput[1]);
-  });
-
-  it('tokenize with whitespace split', () => {
-    const vocabulary = new Map([['\n\n', 0], ['\n', 1], [' ', 2], ['s', 3]]);
-    const merges = ['\n \n'];
-    const tokenizer = new BytePairTokenizer({vocabulary, merges});
-    const inputData = tensor(['\n\n\n  s']);
-    const expectedOutput = [tensor([-1, -1, -1, -1, -1, 3])];
-
-    const tokenizeOutput = tokenizer.tokenize(inputData);
-
-    expect(tokenizeOutput.length).toBe(1);
-    expectTensorsClose(tokenizeOutput[0], expectedOutput[0]);
-  });
-
-  it('tokenize with unsplittable tokens', () => {
-    const vocabulary = new Map([['<|end|>', 0], ['butter', 1], ['fly', 2]]);
-    const merges = ['b u', 't t', 'e r', 'bu tt', 'butt er', 'f l', 'fl y'];
-    const tokenizer = new BytePairTokenizer({
-      vocabulary, merges, unsplittableTokens: ['<|end|>']});
-    const inputData = tensor(['butterfly<|end|>']);
-    const expectedOutput = [tensor([1, 2, 0])];
-
-    const tokenizeOutput = tokenizer.tokenize(inputData);
-
-    expect(tokenizeOutput.length).toBe(1);
-    expectTensorsClose(tokenizeOutput[0], expectedOutput[0]);
-  });
-
-  it('tokenize with sequence length', () => {
-    const vocabulary = new Map([['butter', 1], ['fly', 2]]);
-    const merges = ['b u', 't t', 'e r', 'bu tt', 'butt er', 'f l', 'fl y'];
-    const tokenizer = new BytePairTokenizer({
-      vocabulary, merges, sequenceLength: 2});
-    const inputData = tensor(['butterfly', 'butter', 'butterflybutter']);
-    const expectedOutput = [tensor([1, 2]), tensor([1, 0]), tensor([1, 2])];
-
-    const tokenizeOutput = tokenizer.tokenize(inputData);
-
-    expect(tokenizeOutput.length).toBe(3);
-    expectTensorsClose(tokenizeOutput[0], expectedOutput[0]);
-    expectTensorsClose(tokenizeOutput[1], expectedOutput[1]);
-    expectTensorsClose(tokenizeOutput[2], expectedOutput[2]);
-  });
-
-  it('tokenize does not leak memory', () => {
-    const vocabulary = new Map([['butter', 1], ['fly', 2]]);
-    const merges = ['b u', 't t', 'e r', 'bu tt', 'butt er', 'f l', 'fl y'];
-    const tokenizer = new BytePairTokenizer({vocabulary, merges});
-    const inputData = tensor(['butterfly']);
-
-    const numTensorsBefore = memory().numTensors;
-    tokenizer.tokenize(inputData);
-    const numTensorsAfter = memory().numTensors;
-
-    expect(numTensorsAfter).toEqual(numTensorsBefore + 1);
-  });
-
-  it('detokenize with multiple inputs', () => {
-    const vocabulary = new Map([['butter', 1], ['fly', 2]]);
-    const merges = ['b u', 't t', 'e r', 'bu tt', 'butt er', 'f l', 'fl y'];
-    const tokenizer = new BytePairTokenizer({vocabulary, merges});
-    const inputData = tensor(['butterfly', 'butter']);
-
-    const output = tokenizer.detokenize(tokenizer.tokenize(inputData));
-
-    expectTensorsClose(output, inputData);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers_utils.ts b/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers_utils.ts
deleted file mode 100644
index 685f412a5..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers_utils.ts
+++ /dev/null
@@ -1,303 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/* Original source: keras-nlp/byte_pair_tokenizer.py */
-
-import { Tensor, tensor } from '@tensorflow/tfjs-core';
-import { ValueError } from '../../errors';
-import { matchAll } from './match_all_polyfill';
-import { tensorArrTo2DArr, tensorToArr } from './utils';
-
-export function bytesToUnicode(): [Uint8Array, string[]] {
-  const inclusiveRange = (start: number, end: number) =>
-    Array.from({ length: (end - start + 1) }, (v, k) => k + start);
-
-  const bs = [
-    ...inclusiveRange('!'.charCodeAt(0), '~'.charCodeAt(0)),
-    ...inclusiveRange('¡'.charCodeAt(0), '¬'.charCodeAt(0)),
-    ...inclusiveRange('®'.charCodeAt(0), 'ÿ'.charCodeAt(0))
-  ];
-
-  const cs = [...bs];
-  let n = 0;
-
-  // Removes mapping an int to a whitespace character
-  for (let b = 0; b < 2 ** 8; b++) {
-    if (!bs.includes(b)) {
-      bs.push(b);
-      cs.push(2 ** 8 + n);
-      n++;
-    }
-  }
-
-  const chars = cs.map(n => String.fromCharCode(n));
-
-  // TODO(orderique): Verify same functionality.
-  const bytes = Uint8Array.from(bs);
-
-  return [bytes, chars];
-}
-
-/**
- * StaticHashTable includes a `lookup` function for multiple keys at once.
- */
-export class StaticHashTable<K, V extends number|string> {
-  private _map: Map<K, V>;
-
-  constructor(keys: K[], values: V[], private readonly defaultValue: V) {
-    if (keys.length !== values.length) {
-      throw new ValueError(`keys and values arrays must be same length.
-        Instead got lengths ${keys.length} and ${values.length}.`
-      );
-    }
-    const keyValPairs: Array<[K, V]> = [];
-    for (let idx = 0; idx < keys.length; idx++) {
-      const key = keys[idx];
-      const val = values[idx];
-      keyValPairs.push([key, val]);
-    }
-
-    this._map = new Map(keyValPairs);
-  }
-
-  get(key: K): V {
-    if (this._map.has(key)) {
-      return this._map.get(key);
-    }
-    return this.defaultValue;
-  }
-
-  lookup(keys: Tensor[]): Tensor[] {
-    const values = keys.map(t => {
-      const innerValues: V[] = [];
-      for (const key of t.dataSync() as unknown as K[]) {
-        innerValues.push(this.get(key));
-      }
-
-      return tensor(innerValues, t.shape);
-    });
-
-    return values;
-  }
-}
-
-export function createStaticHashtable<K, V extends number|string>(
-  keys: K[], values: V[], defaultVal: V): StaticHashTable<K, V> {
-
-  return new StaticHashTable(keys, values, defaultVal);
-}
-
-/**
- * Cache that stores the encoded result of seen tokens.
- *
- * The cache key is string tensor or python strings, and the value is split
- * tokens joined by whitespace. For example, "dragonfly" => "dragon fly"
- *
- * Examples:
- *
- * ```js
- * const cache = new BytePairTokenizerCache();
- * cache.insert(["butterfly", "dragonfly"], ["but ter fly", "dragon fly"]);
- * cache.lookup(["butterfly"]);
- * ```
- */
-export class BytePairTokenizerCache {
-  // TODO(orderique): modify to use id2value map. Debug for correct behavior.
-  private _cache: Map<string, string>;
-
-  constructor() {
-    this._cache = new Map();
-  }
-
-  get(key: string): string {
-    if (this._cache.has(key)) {
-      return this._cache.get(key);
-    }
-    return '';
-  }
-
-  /**
-   * Insert token <=> encoded outputs pairs.
-   */
-  insert(
-    keys: Tensor|string[], values: string[]): BytePairTokenizerCache {
-    const arrKeys = keys instanceof Tensor ?
-      keys.dataSync() as unknown as string[] : keys;
-
-    for (const [idx, key] of arrKeys.entries()) {
-      this._cache.set(key, values[idx]);
-    }
-    return this;
-  }
-
-  /**
-   * Look up the encoded outputs of given tokens.
-   */
-  lookup(keys: Tensor|string[]): string[] {
-    const arrKeys = keys instanceof Tensor ?
-      keys.dataSync() as unknown as string[] : keys;
-    return arrKeys.map(key => this.get(key));
-  }
-}
-
-/**
- * Remove certain strings from input tensor.
- */
-export function removeStringsFromInputs(
-  inputs: Tensor[], stringToRemove: string): Tensor[] {
-
-  const stringArrInputs = tensorArrTo2DArr(inputs) as string[][];
-  const filteredStrArrays = stringArrInputs
-    .map(arr => arr.filter(s => s !== stringToRemove));
-
-  const filteredTensors = filteredStrArrays.map(arr => tensor(arr));
-
-  return filteredTensors;
-}
-
-/**
- * Create alternates for all special tokens that will be not split during
- * tokenization.
- */
-export function createAltsForUnsplittableTokens(
-  unsplittableTokens: string[]): string[] {
-
-  const prefix = 'ĵ';
-
-  // Trim out splitters.
-  const replacePattern: RegExp = /'|\s+|[^\p{L}\p{N}]+/gu;
-  return unsplittableTokens.map(
-    token => prefix + token.replace(replacePattern, ''));
-}
-
-// Typescript and TF handles special spaces differently, we need to
-// manually handle special spaces during string split.
-const SPECIAL_WHITESPACES = /\u00A0\u2009\u202f\u3000/;
-
-// String splitting regex pattern.
-const pL = 'a-zA-ZáàâäãåçéèêëíìîïñóòôöõúùûüýÿæœÁÀÂÄÃÅÇÉÈÊËÍÌÎÏÑÓÒÔÖÕÚÙÛÜÝŸÆŒĵ';
-const pN = '0-9';
-export const SPLIT_PATTERN_1 = new RegExp(
-  `'s|'t|'re|'ve|'m|'ll|'d` +
-  `|[\\s${SPECIAL_WHITESPACES.source}]+` +
-  `[\\n\\r\\t\\f६${SPECIAL_WHITESPACES.source}]| ?${pL}+|`+
-  ` ?${pN}+| ?[^\\s${pL}${pN}${SPECIAL_WHITESPACES.source}]+`,
-  'gu'
-);
-
-const SPLIT_PATTERN_2 = new RegExp(`[\\s६${SPECIAL_WHITESPACES.source}]\$`);
-
-function flatten<T>(inputs: T[][]): T[] {
-  return inputs.reduce(
-    (accumulator, value) => accumulator.concat(value), []);
-}
-
-export function regexSplit(
-  strs: string[]|string[][],
-  delimRegexPattern: RegExp | string,
-  keepDelimRegexPattern = false): string[][] {
-
-  if (strs[0] instanceof Array) {
-    const mapped = strs.map(arr => regexSplit(
-      arr as string[], delimRegexPattern, keepDelimRegexPattern));
-    return mapped.map(flatten);
-  }
-
-  strs = strs as string[];
-
-  if (!(delimRegexPattern instanceof RegExp)) {
-    if (keepDelimRegexPattern) {
-      delimRegexPattern = new RegExp(`(${delimRegexPattern})`);
-    }
-    return strs.map(str => str.split(delimRegexPattern).filter(s => s));
-  }
-
-  const regexPattern = delimRegexPattern.flags.includes('g') ?
-    delimRegexPattern
-    : new RegExp(delimRegexPattern.source, delimRegexPattern.flags + 'g');
-
-  return strs.map(str => {
-    const matches = matchAll(str, regexPattern);
-
-    const splitString = [];
-    let currIdx = 0;
-    for (const match of matches) {
-      splitString.push(str.slice(currIdx, match.index));
-      if (keepDelimRegexPattern) {
-        splitString.push(
-          str.slice(match.index, match.index! + match[0].length));
-      }
-      currIdx = match.index! + match[0].length;
-    }
-    if (currIdx !== str.length) {
-      splitString.push(str.slice(currIdx, str.length));
-    }
-    return splitString.filter(s => s);
-  });
-}
-
-export function splitStringsForBpe(
-  inputs: Tensor, unsplittableTokens?: string[]): Tensor[] {
-
-  // We need to recreate the exact behavior of token presplitting in the
-  // original gpt2 implementation which uses a lookahead. We are using an
-  // alternative by inserting a special token "६" before leading space of
-  // non-space characters and after the trailing space, e.g.,
-  // " tf" will be "६ tf".
-  const pattern1 = new RegExp(`( )([^\s${SPECIAL_WHITESPACES}])`);
-  const pattern2 = new RegExp(`(\s${SPECIAL_WHITESPACES})\$`);
-
-  const inputsStr = (tensorToArr(inputs) as string[]).map(str =>
-    str.replace(pattern1, `६$1$2`).replace(pattern2, `$1६`)
-  );
-
-  let alts: string[];
-  let rawTokens: string[][];
-
-  function escape(input: string): string {
-    return input.replace(/[-\/\\^$*+?.()|[\]{}]/g, '\\$&');
-  }
-
-  if (unsplittableTokens && unsplittableTokens.length > 0) {
-    alts = createAltsForUnsplittableTokens(unsplittableTokens);
-    for (const [idx, token] of unsplittableTokens.entries()) {
-      const alt = alts[idx];
-      const escapedToken = escape(token);
-
-      rawTokens = regexSplit(rawTokens !== undefined ?
-        rawTokens : inputsStr, escapedToken, true);
-      rawTokens = rawTokens.map(
-        arr => arr.map(t => t.replace(new RegExp(escapedToken), alt)));
-    }
-  }
-  rawTokens = regexSplit(rawTokens !== undefined ?
-    rawTokens : inputsStr, SPLIT_PATTERN_1, true);
-  // Second pass splits out the last whilespace char or "६".
-  rawTokens  = regexSplit(rawTokens, SPLIT_PATTERN_2, true);
-
-  if (unsplittableTokens && unsplittableTokens.length > 0) {
-    // Replace special tokens alternate with originals.
-    for (const [idx, token] of unsplittableTokens.entries()) {
-      const alt = alts[idx];
-      const escapedAlt = escape(alt);
-      rawTokens = rawTokens.map(
-        arr => arr.map(t => t.replace(new RegExp(escapedAlt), token)));
-    }
-  }
-
-  return removeStringsFromInputs(rawTokens.map(tokens => tensor(tokens)), '६');
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers_utils_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers_utils_test.ts
deleted file mode 100644
index 1f87ef1cc..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/tokenizers_utils_test.ts
+++ /dev/null
@@ -1,232 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { tensor, test_util } from '@tensorflow/tfjs-core';
-
-import { BytePairTokenizerCache, SPLIT_PATTERN_1, bytesToUnicode,
-  createAltsForUnsplittableTokens, createStaticHashtable, regexSplit,
-  removeStringsFromInputs, splitStringsForBpe } from './tokenizers_utils';
-import { expectTensorsClose } from '../../utils/test_utils';
-
-describe('bytesToUnicode', () => {
-  it('returns correct output', () => {
-    const [bytesList, charsList] = bytesToUnicode();
-    const expectedChars = ['!', '"', '#', '$', '%', '&', '\'', '(', ')', '*',
-      '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
-      ':', ';', '<', '=', '>', '?', '@', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',
-      'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W',
-      'X','Y', 'Z', '[', '\\', ']', '^', '_', '`', 'a', 'b', 'c', 'd', 'e', 'f',
-      'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u',
-      'v', 'w', 'x', 'y', 'z', '{', '|', '}', '~', '¡', '¢', '£', '¤', '¥', '¦',
-      '§', '¨', '©', 'ª', '«', '¬', '®', '¯', '°', '±', '²', '³', '´', 'µ', '¶',
-      '·', '¸', '¹', 'º', '»', '¼', '½', '¾', '¿', 'À', 'Á', 'Â', 'Ã', 'Ä', 'Å',
-      'Æ', 'Ç', 'È', 'É', 'Ê', 'Ë', 'Ì', 'Í', 'Î', 'Ï', 'Ð', 'Ñ', 'Ò', 'Ó', 'Ô',
-      'Õ', 'Ö', '×', 'Ø', 'Ù', 'Ú', 'Û', 'Ü', 'Ý', 'Þ', 'ß', 'à', 'á', 'â', 'ã',
-      'ä', 'å', 'æ', 'ç', 'è', 'é', 'ê', 'ë', 'ì', 'í', 'î', 'ï', 'ð', 'ñ', 'ò',
-      'ó', 'ô', 'õ', 'ö', '÷', 'ø', 'ù', 'ú', 'û', 'ü', 'ý', 'þ', 'ÿ', 'Ā', 'ā',
-      'Ă', 'ă', 'Ą', 'ą', 'Ć', 'ć', 'Ĉ', 'ĉ', 'Ċ', 'ċ', 'Č', 'č', 'Ď', 'ď', 'Đ',
-      'đ', 'Ē', 'ē', 'Ĕ', 'ĕ', 'Ė', 'ė', 'Ę', 'ę', 'Ě', 'ě', 'Ĝ', 'ĝ', 'Ğ', 'ğ',
-      'Ġ', 'ġ', 'Ģ', 'ģ', 'Ĥ', 'ĥ', 'Ħ', 'ħ', 'Ĩ', 'ĩ', 'Ī', 'ī', 'Ĭ', 'ĭ', 'Į',
-      'į', 'İ', 'ı', 'IJ', 'ij', 'Ĵ', 'ĵ', 'Ķ', 'ķ', 'ĸ', 'Ĺ', 'ĺ', 'Ļ', 'ļ', 'Ľ',
-      'ľ', 'Ŀ', 'ŀ', 'Ł', 'ł', 'Ń'];
-    const expectedBytes = [33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
-      46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-      64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
-      82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-      100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114,
-      115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 161, 162, 163,
-      164, 165, 166, 167, 168, 169, 170, 171, 172, 174, 175, 176, 177, 178, 179,
-      180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
-      195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,
-      210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,
-      225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239,
-      240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254,
-      255, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-      20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 127, 128, 129, 130,
-      131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,
-      146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160,
-      173];
-
-    test_util.expectArraysClose(bytesList, expectedBytes);
-    test_util.expectArraysClose(charsList, expectedChars);
-  });
-});
-
-describe('createStaticHashtable', () => {
-  it('creates StaticHashTable<number, string> correctly', () => {
-    const [bytesList, charsList] = bytesToUnicode();
-    const byte2Unicode = createStaticHashtable(
-      Array.from(bytesList), charsList, '');
-
-    expect(byte2Unicode.get(33)).toBe('!');
-    expect(byte2Unicode.get(-1)).toBe('');
-
-    expectTensorsClose(
-      (byte2Unicode.lookup([tensor([33, 133])]))[0],
-      tensor(['!', '\x85']));
-  });
-
-  it('creates StaticHashTable<string, number> correctly', () => {
-    const [bytesList, charsList] = bytesToUnicode();
-    const unicode2Byte = createStaticHashtable(
-      charsList, Array.from(bytesList), -1);
-
-    expect(unicode2Byte.get('Û')).toBe(219);
-    expect(unicode2Byte.get('😁')).toBe(-1);
-
-    expectTensorsClose(
-      (unicode2Byte.lookup([tensor(['!', '{'])]))[0],
-      tensor([33, 123]));
-  });
-});
-
-describe('BytePairTokenizerCache', () => {
-  let cache: BytePairTokenizerCache;
-
-  beforeEach(() => {
-    cache = new BytePairTokenizerCache();
-  });
-
-  it('inserts strings and retrieves correctly', () => {
-    cache.insert(['butterfly', 'dragonfly'], ['but ter fly', 'dragon fly']);
-
-    test_util.expectArraysEqual(cache.lookup(['butterfly']), ['but ter fly']);
-  });
-
-  it('inserts tensors and retrieves correctly', () => {
-    cache.insert(
-      tensor(['butterfly', 'dragonfly']), ['but ter fly', 'dragon fly']);
-
-    test_util.expectArraysEqual(
-      cache.lookup(tensor(['dragonfly'])), ['dragon fly']);
-  });
-});
-
-describe('removeStringsFromInputs', () => {
-  it ('removes nothing successfully', () => {
-    const inputs = [tensor(['butterfly']), tensor(['butter'])];
-    const stringToRemove = '६';
-
-    const result = removeStringsFromInputs(inputs, stringToRemove);
-
-    expect(result.length).toBe(2);
-    expectTensorsClose(result[0], tensor(['butterfly']));
-    expectTensorsClose(result[1], tensor(['butter']));
-  });
-
-  it ('removes strings successfully', () => {
-    const inputs = [tensor(['butterfly']), tensor(['butter'])];
-    const stringToRemove = 'butter';
-
-    const result = removeStringsFromInputs(inputs, stringToRemove);
-
-    expect(result.length).toBe(2);
-    expectTensorsClose(result[0], tensor(['butterfly']));
-    expectTensorsClose(result[1], tensor([]));
-  });
-});
-
-describe('createAltsForUnsplittableTokens', () => {
-  it ('creates alts with no matching regex pattern', () => {
-    const unsplittableTokens = ['s', 'p'];
-
-    const result = createAltsForUnsplittableTokens(unsplittableTokens);
-
-    expect(result.length).toBe(2);
-    test_util.expectArraysEqual(result, ['ĵs', 'ĵp']);
-  });
-
-  it ('creates alts with matching regex pattern', () => {
-    const unsplittableTokens = [' s', 'p'];
-
-    const result = createAltsForUnsplittableTokens(unsplittableTokens);
-
-    expect(result.length).toBe(2);
-    test_util.expectArraysEqual(result, ['ĵs', 'ĵp']);
-  });
-
-  it ('regex works correctly', () => {
-    const unsplittableTokens = ['😊,_五$ñü]aA5{\'\n~`'];
-
-    const result = createAltsForUnsplittableTokens(unsplittableTokens);
-
-    expect(result.length).toBe(1);
-    test_util.expectArraysEqual(result, ['ĵ五ñüaA5']);
-  });
-});
-
-describe('regexSplit', () => {
-  it ('splits with regex and string', () => {
-    const strResult = regexSplit(['hello there'], /\s/g);
-    const regexResult = regexSplit(['hello there'], ' ');
-    const expected = [['hello', 'there']];
-
-    test_util.expectArraysEqual(strResult, expected);
-    test_util.expectArraysEqual(regexResult, expected);
-  });
-
-  it ('keeps string delimiter', () => {
-    test_util.expectArraysEqual(regexSplit(['sp'], 's', true), [['s', 'p']]);
-    test_util.expectArraysEqual(
-      regexSplit(['\xc4\xb4s', 'p'], 'p', true), [['Ä´s'], ['p']] );
-  });
-
-  it('splits regex delimiter', () => {
-    const result = regexSplit(['ĵs', 'ĵp'], SPLIT_PATTERN_1, true);
-
-    test_util.expectArraysEqual(result, [['ĵs'], ['ĵp']]);
-  });
-
-  it('works with periods', () => {
-    const result = regexSplit(
-      ['brown.', 'black.'], SPLIT_PATTERN_1, true);
-
-    test_util.expectArraysEqual(result, [['brown', '.'], ['black', '.']]);
-  });
-});
-
-describe('splitStringsForBpe', () => {
-  it ('splits with unsplittable tokens', () => {
-    const inputs = tensor(['sp']);
-    const unsplittableTokens = ['s', 'p'];
-
-    const result = splitStringsForBpe(inputs, unsplittableTokens);
-
-    expect(result.length).toBe(1);
-    expectTensorsClose(result[0], tensor(['s', 'p']));
-  });
-
-  it ('splits with multicharacter unsplittable tokens', () => {
-    const inputs = tensor(['butterfly<|endOfText|>']);
-    const unsplittableTokens = ['<|endOfText|>'];
-
-    const result = splitStringsForBpe(inputs, unsplittableTokens);
-
-    expect(result.length).toBe(1);
-    expectTensorsClose(result[0], tensor(['butterfly', '<|endOfText|>']));
-  });
-
-  it ('splits with no unsplittable tokens', () => {
-    const inputs = tensor(['brown.', 'black.']);
-
-    const result = splitStringsForBpe(inputs);
-
-    expect(result.length).toBe(2);
-    expectTensorsClose(result[0], tensor(['brown', '.']));
-    expectTensorsClose(result[1], tensor(['black', '.']));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/utils.ts b/tfjs-master/tfjs-layers/src/layers/nlp/utils.ts
deleted file mode 100644
index c68075675..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/utils.ts
+++ /dev/null
@@ -1,180 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { ModelPredictConfig, Scalar, Tensor, tensorScatterUpdate, tidy } from '@tensorflow/tfjs-core';
-
-import { History } from '../../base_callbacks';
-import { ContainerArgs } from '../../engine/container';
-import { LayersModel, ModelEvaluateArgs } from '../../engine/training';
-import { ModelFitArgs } from '../../engine/training_tensors';
-import { NotImplementedError } from '../../errors';
-
-export function tensorToArr(input: Tensor): unknown[] {
-  return Array.from(input.dataSync()) as unknown as unknown[];
-}
-
-export function tensorArrTo2DArr(inputs: Tensor[]): unknown[][] {
-  return inputs.map(input => tensorToArr(input));
-}
-
-/**
- * Returns a new Tensor with `updates` inserted into `inputs` starting at the
- * index `startIndices`.
- *
- * @param inputs Tensor to "modify"
- * @param startIndices the starting index to insert the slice.
- *  Length must be equal to `inputs.rank`;
- * @param updates the update tensor. Shape must fit within `inputs` shape.
- * @returns a new tensor with the modification.
- */
-export function sliceUpdate(
-    inputs: Tensor, startIndices: number[], updates: Tensor): Tensor {
-  return tidy(() => {
-    const indices: number[][] = [];
-    /**
-     * Computes the update indices by iterating through all indices from
-     * `startIndices` to `startIndices + updates.shape`.
-     */
-    function createIndices(idx: number, curr: number[]): void {
-      if (curr.length === startIndices.length) {
-        indices.push(curr.slice());
-        return;
-      }
-      const start = startIndices[idx];
-      const end = start + updates.shape[idx];
-      for (let i = start; i < end; i++) {
-        curr.push(i);
-        createIndices(idx + 1, curr);
-        curr.pop();
-      }
-    }
-    createIndices(0, []);
-    // Flatten the updates to match length of its update indices.
-    updates = updates.reshape([updates.size]);
-    return tensorScatterUpdate(inputs, indices, updates);
-  });
-}
-
-function packXYSampleWeight(x: Tensor, y?: Tensor, sampleWeight?: Tensor):
-  Tensor
-  | [Tensor, Tensor]
-  | [Tensor, Tensor, Tensor] {
-  throw new NotImplementedError();
-}
-
-function unPackXYSampleWeight(
-  data: [Tensor]|[Tensor, Tensor]|[Tensor, Tensor, Tensor]
-) {
-  throw new NotImplementedError();
-}
-
-// TODO(pforderique): Figure out a workaround for `tf.data.Dataset`.
-function convertInputsToDataset(
-  x?: Tensor, y?: Tensor, sampleWeight?: Tensor, batchSize?: number
-) {
-  throw new NotImplementedError();
-}
-
-function trainValidationSplit(arrays: Tensor[], validationSplit: number) {
-  throw new NotImplementedError();
-}
-
-/**
- * A model which allows automatically applying preprocessing.
- */
-export interface PipelineModelArgs extends ContainerArgs {
-  /**
-   * Defaults to true.
-   */
-  includePreprocessing?: boolean;
-}
-
-export class PipelineModel extends LayersModel {
-  /** @nocollapse */
-  static override className = 'PipelineModel';
-
-  protected includePreprocessing: boolean;
-
-  constructor(args: PipelineModelArgs) {
-    super(args);
-    this.includePreprocessing = args.includePreprocessing ?? true;
-  }
-
-  /**
-   * An overridable function which preprocesses features.
-   */
-  preprocessFeatures(x: Tensor) {
-    return x;
-  }
-
-  /**
-   * An overridable function which preprocesses labels.
-   */
-  preprocessLabels(y: Tensor) {
-    return y;
-  }
-
-  /**
-   * An overridable function which preprocesses entire samples.
-   */
-  preprocessSamples(x: Tensor, y?: Tensor, sampleWeight?: Tensor):
-    Tensor
-    | [Tensor, Tensor]
-    | [Tensor, Tensor, Tensor] {
-    throw new NotImplementedError();
-  }
-
-  // ---------------------------------------------------------------------------
-  // Below are overrides to LayersModel methods to apply the functions above.
-  // ---------------------------------------------------------------------------
-  override fit(
-    x: Tensor|Tensor[]|{[inputName: string]: Tensor},
-    y: Tensor|Tensor[]|{[inputName: string]: Tensor},
-    args: ModelFitArgs = {}
-  ): Promise<History> {
-    throw new NotImplementedError(
-      `Uses ${convertInputsToDataset}, ${trainValidationSplit} ` +
-      `${packXYSampleWeight}, and ${unPackXYSampleWeight}`);
-  }
-
-  override evaluate(
-    x: Tensor|Tensor[],
-    y: Tensor|Tensor[],
-    args?: ModelEvaluateArgs
-  ): Scalar | Scalar[] {
-    throw new NotImplementedError();
-  }
-
-  override predict(
-    x: Tensor | Tensor[],
-    args?: ModelPredictConfig
-  ): Tensor | Tensor[] {
-    throw new NotImplementedError();
-  }
-
-  override trainOnBatch(
-    x: Tensor|Tensor[]|{[inputName: string]: Tensor},
-    y: Tensor|Tensor[]|{[inputName: string]: Tensor},
-    sampleWeight?: Tensor
-  ): Promise<number|number[]> {
-    throw new NotImplementedError();
-  }
-
-  override predictOnBatch(x: Tensor|Tensor[]): Tensor|Tensor[] {
-    throw new NotImplementedError();
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/nlp/utils_test.ts b/tfjs-master/tfjs-layers/src/layers/nlp/utils_test.ts
deleted file mode 100644
index 4cf619736..000000000
--- a/tfjs-master/tfjs-layers/src/layers/nlp/utils_test.ts
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { ones, tensor, test_util, zeros } from '@tensorflow/tfjs-core';
-import { sliceUpdate, tensorArrTo2DArr, tensorToArr } from './utils';
-import { expectTensorsClose } from '../../utils/test_utils';
-
-describe('tensor to array functions', () => {
-  it('tensorToArr', () => {
-    const inputStr = tensor(['these', 'are', 'strings', '.']);
-    const inputNum = tensor([2, 11, 15]);
-
-    test_util.expectArraysEqual(
-      tensorToArr(inputStr) as string[], ['these', 'are', 'strings', '.']);
-    test_util.expectArraysEqual(tensorToArr(inputNum) as number[], [2, 11, 15]);
-  });
-
-  it('tensorArrTo2DArr', () => {
-    const inputStr = [tensor(['these', 'are']), tensor(['strings', '.'])];
-    const inputNum = [tensor([2, 11]), tensor([15])];
-
-    test_util.expectArraysEqual(
-      tensorArrTo2DArr(inputStr) as string[][],
-      [['these', 'are'], ['strings', '.']]
-    );
-    test_util.expectArraysEqual(
-      tensorArrTo2DArr(inputNum) as number[][], [[2, 11], [15]]);
-  });
-});
-
-describe('sliceUpdate', () => {
-  it('1D', () => {
-    const inputs = tensor([1, 2, 3, 4, 5]);
-    const startIndices = [2];
-    const updates = tensor([-1, -2]);
-    const expected = tensor([1, 2, -1, -2, 5]);
-
-    const result = sliceUpdate(inputs, startIndices, updates);
-
-    expectTensorsClose(result, expected);
-  });
-
-  it('2D', () => {
-    const inputs = zeros([2, 5]);
-    const startIndices = [0, 1];
-    const updates = tensor([
-      [-1, -2],
-      [-4, -3],
-    ]);
-    const expected = tensor([
-      [0, -1, -2, 0, 0],
-      [0, -4, -3, 0, 0]
-    ]);
-    const result = sliceUpdate(inputs, startIndices, updates);
-
-    expectTensorsClose(result, expected);
-  });
-
-  it('3D', () => {
-    const inputs = zeros([2, 3, 4]);
-    const startIndices = [0, 0, 0];
-    const updates = ones([2, 1, 4]);
-    const expected = tensor([
-      [[1, 1, 1, 1],
-       [0, 0, 0, 0],
-       [0, 0, 0, 0]],
-      [[1, 1, 1, 1],
-       [0, 0, 0, 0],
-       [0, 0, 0, 0]],
-    ]);
-    const result = sliceUpdate(inputs, startIndices, updates);
-
-    expectTensorsClose(result, expected);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/noise.ts b/tfjs-master/tfjs-layers/src/layers/noise.ts
deleted file mode 100644
index c488ca052..000000000
--- a/tfjs-master/tfjs-layers/src/layers/noise.ts
+++ /dev/null
@@ -1,209 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * TensorFlow.js Layers: Noise Layers.
- */
-
-import {add, greaterEqual, mul, randomUniform, serialization, Tensor, tidy} from '@tensorflow/tfjs-core';
-
-import * as K from '../backend/tfjs_backend';
-import {Layer, LayerArgs} from '../engine/topology';
-import {Shape} from '../keras_format/common';
-import {Kwargs} from '../types';
-import {getExactlyOneTensor} from '../utils/types_utils';
-
-export declare interface GaussianNoiseArgs extends LayerArgs {
-  /** Standard Deviation.  */
-  stddev: number;
-}
-
-export class GaussianNoise extends Layer {
-  /** @nocollapse */
-  static className = 'GaussianNoise';
-  readonly stddev: number;
-
-  constructor(args: GaussianNoiseArgs) {
-    super(args);
-    this.supportsMasking = true;
-    this.stddev = args.stddev;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  override getConfig() {
-    const baseConfig = super.getConfig();
-    const config = {stddev: this.stddev};
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      const input = getExactlyOneTensor(inputs);
-      const noised = () =>
-          add(K.randomNormal(input.shape, 0, this.stddev), input);
-      const output =
-          K.inTrainPhase(noised, () => input, kwargs['training'] || false);
-      return output;
-    });
-  }
-}
-serialization.registerClass(GaussianNoise);
-
-export declare interface GaussianDropoutArgs extends LayerArgs {
-  /** drop probability.  */
-  rate: number;
-}
-
-export class GaussianDropout extends Layer {
-  /** @nocollapse */
-  static className = 'GaussianDropout';
-  readonly rate: number;
-
-  constructor(args: GaussianDropoutArgs) {
-    super(args);
-    this.supportsMasking = true;
-    this.rate = args.rate;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  override getConfig() {
-    const baseConfig = super.getConfig();
-    const config = {rate: this.rate};
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      const input = getExactlyOneTensor(inputs);
-      if (this.rate > 0 && this.rate < 1) {
-        const noised = () => {
-          const stddev = Math.sqrt(this.rate / (1 - this.rate));
-          return mul(input, K.randomNormal(input.shape, 1, stddev));
-        };
-        return K.inTrainPhase(noised, () => input, kwargs['training'] || false);
-      }
-      return input;
-    });
-  }
-}
-serialization.registerClass(GaussianDropout);
-
-export declare interface AlphaDropoutArgs extends LayerArgs {
-  /** drop probability.  */
-  rate: number;
-  /**
-   * A 1-D `Tensor` of type `int32`, representing the
-   * shape for randomly generated keep/drop flags.
-   */
-  noiseShape?: Shape;
-}
-
-/**
- * Applies Alpha Dropout to the input.
- *
- * As it is a regularization layer, it is only active at training time.
- *
- * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs
- * to their original values, in order to ensure the self-normalizing property
- * even after this dropout.
- * Alpha Dropout fits well to Scaled Exponential Linear Units
- * by randomly setting activations to the negative saturation value.
- *
- * Arguments:
- *   - `rate`: float, drop probability (as with `Dropout`).
- *     The multiplicative noise will have
- *     standard deviation `sqrt(rate / (1 - rate))`.
- *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the
- *     shape for randomly generated keep/drop flags.
- *
- * Input shape:
- *   Arbitrary. Use the keyword argument `inputShape`
- *   (tuple of integers, does not include the samples axis)
- *   when using this layer as the first layer in a model.
- *
- * Output shape:
- *   Same shape as input.
- *
- * References:
- *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
- */
-export class AlphaDropout extends Layer {
-  /** @nocollapse */
-  static className = 'AlphaDropout';
-  readonly rate: number;
-  readonly noiseShape: Shape;
-
-  constructor(args: AlphaDropoutArgs) {
-    super(args);
-    this.supportsMasking = true;
-    this.rate = args.rate;
-    this.noiseShape = args.noiseShape;
-  }
-
-  _getNoiseShape(inputs: Tensor|Tensor[]) {
-    return this.noiseShape || getExactlyOneTensor(inputs).shape;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    return inputShape;
-  }
-
-  override getConfig() {
-    const baseConfig = super.getConfig();
-    const config = {rate: this.rate};
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      if (this.rate < 1 && this.rate > 0) {
-        const noiseShape = this._getNoiseShape(inputs);
-
-        const droppedInputs = () => {
-          const input = getExactlyOneTensor(inputs);
-
-          const alpha = 1.6732632423543772848170429916717;
-          const scale = 1.0507009873554804934193349852946;
-
-          const alphaP = -alpha * scale;
-
-          let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);
-
-          keptIdx = K.cast(keptIdx, 'float32');  // get default dtype.
-
-          // Get affine transformation params.
-          const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;
-          const b = -a * alphaP * this.rate;
-
-          // Apply mask.
-          const x = add(mul(input, keptIdx), mul(add(keptIdx, -1), alphaP));
-
-          return add(mul(x, a), b);
-        };
-        return K.inTrainPhase(
-            droppedInputs, () => getExactlyOneTensor(inputs),
-            kwargs['training'] || false);
-      }
-      return inputs;
-    });
-  }
-}
-serialization.registerClass(AlphaDropout);
diff --git a/tfjs-master/tfjs-layers/src/layers/noise_test.ts b/tfjs-master/tfjs-layers/src/layers/noise_test.ts
deleted file mode 100644
index cf9189a75..000000000
--- a/tfjs-master/tfjs-layers/src/layers/noise_test.ts
+++ /dev/null
@@ -1,191 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit Tests for Noise Layers.
- */
-
-import {describeMathCPU, describeMathCPUAndGPU, expectTensorsClose} from '../utils/test_utils';
-import * as tfl from '../index';
-import {ones, Tensor} from '@tensorflow/tfjs-core';
-import {getExactlyOneTensor} from '../utils/types_utils';
-
-describeMathCPU('GaussianNoise: Symbolic', () => {
-  const stddevs = [0, 1, 0.5];
-  const symbolicInputs = [
-    new tfl.SymbolicTensor('float32', [10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [null, 4], null, [], null),
-  ];
-
-  for (const stddev of stddevs) {
-    for (const symbolicInput of symbolicInputs) {
-      const testTitle = `std=${stddev}; ` +
-        `input shape=${JSON.stringify(symbolicInput.shape)}`;
-      it(testTitle, () => {
-        const gaussianNoiseLayer = tfl.layers.gaussianNoise({stddev});
-        const output =
-            gaussianNoiseLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-        expect(output.dtype).toEqual(symbolicInput.dtype);
-        expect(output.shape).toEqual(symbolicInput.shape);
-        expect(output.sourceLayer).toEqual(gaussianNoiseLayer);
-        expect(output.inputs).toEqual([symbolicInput]);
-      });
-    }
-  }
-});
-
-describeMathCPUAndGPU('GaussianNoise: Tensor', () => {
-  it('GaussianNoise: Predict', () => {
-    const input = ones([2, 2]);
-    const gaussianNoiseLayer = tfl.layers.gaussianNoise({stddev: 1});
-    let output = gaussianNoiseLayer.apply(input, {training: false}) as Tensor;
-    output = getExactlyOneTensor(output);
-    expectTensorsClose(input, output);
-  });
-
-  it('GaussianNoise: Train', () => {
-    const input = ones([2, 2]);
-    const gaussianNoiseLayer = tfl.layers.gaussianNoise({stddev: 1});
-    let output = gaussianNoiseLayer.apply(input, {training: true}) as Tensor;
-    output = getExactlyOneTensor(output);
-    const diff = output.sub(input).abs().max();
-    expect(diff.dataSync()).toBeGreaterThan(0);
-    expect(output.shape).toEqual(input.shape);
-    expect(output.dtype).toEqual(input.dtype);
-  });
-
-  it('GaussianNoise: Successive Call', () => {
-    const training = true;
-    const inputA = ones([2, 2]);
-    const gaussianNoiseLayer = tfl.layers.gaussianNoise({stddev: 1});
-    let outputA = gaussianNoiseLayer.apply(inputA, {training}) as Tensor;
-    outputA = getExactlyOneTensor(outputA);
-    const inputB = ones([2, 2]);
-    let outputB = gaussianNoiseLayer.apply(inputB, {training}) as Tensor;
-    outputB = getExactlyOneTensor(outputA);
-    expectTensorsClose(outputA, outputB);
-  });
-});
-
-describeMathCPU('GaussianDropout: Symbolic', () => {
-  const rates = [0, 1, 0.5];
-  const symbolicInputs = [
-    new tfl.SymbolicTensor('float32', [10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [null, 4], null, [], null),
-  ];
-
-  for (const rate of rates) {
-    for (const symbolicInput of symbolicInputs) {
-      const testTitle = `dropoutRate=${rate}; ` +
-        `input shape=${JSON.stringify(symbolicInput.shape)}`;
-      it(testTitle, () => {
-        const gaussianDropout = tfl.layers.gaussianDropout({rate});
-        const output =
-          gaussianDropout.apply(symbolicInput) as tfl.SymbolicTensor;
-        expect(output.dtype).toEqual(symbolicInput.dtype);
-        expect(output.shape).toEqual(symbolicInput.shape);
-        expect(output.sourceLayer).toEqual(gaussianDropout);
-        expect(output.inputs).toEqual([symbolicInput]);
-      });
-    }
-  }
-});
-
-describeMathCPUAndGPU('GaussianDropout: Tensor', () => {
-  it('GaussianDropout: Predict', () => {
-    const input = ones([1, 2]);
-    const gaussianDropoutLayer = tfl.layers.gaussianDropout({rate: 0.5});
-    let output = gaussianDropoutLayer.apply(input, {training: false}) as Tensor;
-    output = getExactlyOneTensor(output);
-    expectTensorsClose(input, output);
-  });
-
-  it('GaussianDropout: Train', () => {
-    const input = ones([1, 2]);
-    const gaussianDropoutLayer = tfl.layers.gaussianDropout({rate: 0.5});
-    let output = gaussianDropoutLayer.apply(input, {training: true}) as Tensor;
-    output = getExactlyOneTensor(output);
-    const diff = output.sub(input).abs().max();
-    expect(diff.dataSync()).toBeGreaterThan(0);
-    expect(output.shape).toEqual(input.shape);
-    expect(output.dtype).toEqual(input.dtype);
-  });
-
-  it('GaussianDropout: Successive Call', () => {
-    const training = true;
-    const inputA = ones([1, 2]);
-    const gaussianDropoutLayer = tfl.layers.gaussianDropout({rate: 0.5});
-    let outputA = gaussianDropoutLayer.apply(inputA, {training}) as Tensor;
-    outputA = getExactlyOneTensor(outputA);
-    const inputB = ones([1, 2]);
-    let outputB = gaussianDropoutLayer.apply(inputB, {training}) as Tensor;
-    outputB = getExactlyOneTensor(outputA);
-    expectTensorsClose(outputA, outputB);
-  });
-});
-
-describeMathCPU('AlphaDropout: Symbolic', () => {
-  const rates = [0, 1, 0.5];
-  const symbolicInputs = [
-    new tfl.SymbolicTensor('float32', [10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [12, 10, 4], null, [], null),
-    new tfl.SymbolicTensor('float32', [null, 4], null, [], null),
-  ];
-
-  for (const rate of rates) {
-    for (const symbolicInput of symbolicInputs) {
-      const testTitle = `dropoutRate=${rate}; ` +
-        `input shape=${JSON.stringify(symbolicInput.shape)}`;
-      it(testTitle, () => {
-        const alphaDropout = tfl.layers.alphaDropout({rate});
-        const output = alphaDropout.apply(symbolicInput) as tfl.SymbolicTensor;
-        expect(output.dtype).toEqual(symbolicInput.dtype);
-        expect(output.shape).toEqual(symbolicInput.shape);
-        expect(output.sourceLayer).toEqual(alphaDropout);
-        expect(output.inputs).toEqual([symbolicInput]);
-      });
-    }
-  }
-});
-
-describeMathCPUAndGPU('AlphaDropout: Tensor', () => {
-  it('AlphaDropout: Predict', () => {
-    const input = ones([1, 2]);
-    const alphaDropoutLayer = tfl.layers.alphaDropout({rate: 0.5});
-    let output = alphaDropoutLayer.apply(input, {training: false}) as Tensor;
-    output = getExactlyOneTensor(output);
-    expectTensorsClose(input, output);
-  });
-
-  it('AlphaDropout: Train', () => {
-    const input = ones([1, 2]);
-    const alphaDropoutLayer = tfl.layers.alphaDropout({rate: 0.5});
-    let output = alphaDropoutLayer.apply(input, {training: true}) as Tensor;
-    output = getExactlyOneTensor(output);
-    const diff = output.sub(input).abs().max();
-    expect(diff.dataSync()).toBeGreaterThan(0);
-    expect(output.shape).toEqual(input.shape);
-    expect(output.dtype).toEqual(input.dtype);
-  });
-
-  it('AlphaDropout: Successive Call', () => {
-    const training = true;
-    const inputA = ones([1, 2]);
-    const alphaDropoutLayer = tfl.layers.alphaDropout({rate: 0.5});
-    let outputA = alphaDropoutLayer.apply(inputA, {training}) as Tensor;
-    outputA = getExactlyOneTensor(outputA);
-    const inputB = ones([1, 2]);
-    let outputB = alphaDropoutLayer.apply(inputB, {training}) as Tensor;
-    outputB = getExactlyOneTensor(outputA);
-    expectTensorsClose(outputA, outputB);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/normalization.ts b/tfjs-master/tfjs-layers/src/layers/normalization.ts
deleted file mode 100644
index 03193c83a..000000000
--- a/tfjs-master/tfjs-layers/src/layers/normalization.ts
+++ /dev/null
@@ -1,642 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Normalization layers.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {moments, reshape, serialization, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, tidy, util} from '@tensorflow/tfjs-core';
-
-import {Constraint, ConstraintIdentifier, getConstraint, serializeConstraint} from '../constraints';
-import {InputSpec, Layer, LayerArgs} from '../engine/topology';
-import {NotImplementedError, ValueError} from '../errors';
-import {getInitializer, Initializer, InitializerIdentifier, serializeInitializer} from '../initializers';
-import {Shape} from '../keras_format/common';
-import {getRegularizer, Regularizer, RegularizerIdentifier, serializeRegularizer} from '../regularizers';
-import {Kwargs} from '../types';
-import * as generic_utils from '../utils/generic_utils';
-import * as math_utils from '../utils/math_utils';
-import {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';
-import {LayerVariable} from '../variables';
-
-/**
- * Applies batch normalization on x given mean, var, beta and gamma.
- *
- * I.e. returns:
- *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`
- *
- * @param x Input tensor.
- * @param mean Mean of batch.
- * @param variance Variance of batch.
- * @param beta Tensor with which to center the input.
- * @param gamma Tensor by which to scale the input.
- * @param epsilon Fuzz factor.
- * @returns The result of the batch normalization.
- */
-export function batchNormalization(
-    x: Tensor, mean: Tensor, variance: Tensor, beta?: Tensor, gamma?: Tensor,
-    epsilon = 1e-3): Tensor {
-  let out: Tensor;
-  if (x.rank === 2) {
-    out = tfc.batchNorm2d(
-        x as Tensor2D, mean as Tensor2D | Tensor1D,
-        variance as Tensor2D | Tensor1D, beta as Tensor2D | Tensor1D,
-        gamma as Tensor2D | Tensor1D, epsilon);
-  } else if (x.rank === 3) {
-    // TODO(cais): Check rank; give proper error message.
-    out = tfc.batchNorm3d(
-        x as Tensor3D, mean as Tensor3D | Tensor1D,
-        variance as Tensor3D | Tensor1D, beta as Tensor3D | Tensor1D,
-        gamma as Tensor3D | Tensor1D, epsilon);
-  } else if (x.rank === 4) {
-    out = tfc.batchNorm4d(
-        x as Tensor4D, mean as Tensor4D | Tensor1D,
-        variance as Tensor4D | Tensor1D, beta as Tensor4D | Tensor1D,
-        gamma as Tensor4D | Tensor1D, epsilon);
-  } else {
-    throw new NotImplementedError(
-        `batchNormalization is not implemented for array of rank ${x.rank} ` +
-        `yet`);
-  }
-  return out;
-}
-
-/**
- * Non-broadcasting batch normalization for use in training (not inference).
- *
- * The input is normalized to zero mean and unit variance along the
- * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.
- * The result of that is returned as the first element
- * of the returned `Array`. The other two elements are the mean and variance,
- * respectively.
- *
- * @param x Input tensor to be normalized.
- * @param gamma Tensor by which to scale the input.
- * @param beta Tensor by which to center the input.
- * @param reductionAxes Axes over which to normalize.
- * @param epsilon Fuzz factor.
- * @returns An `Array` of three `Tensors`:
- *   [normalized tensor, mean of input, variance of input].
- */
-function regularNormalizeBatchInTraining(
-    x: Tensor, gamma: Tensor, beta: Tensor, reductionAxes: number[],
-    epsilon = 1e-3): [Tensor, Tensor, Tensor] {
-  return tidy(() => {
-           const meanAndVariance = tfc.moments(x, reductionAxes);
-           const mean = meanAndVariance.mean;
-           const variance = meanAndVariance.variance;
-           const normed =
-               batchNormalization(x, mean, variance, beta, gamma, epsilon);
-           return [normed, mean, variance];
-         }) as [Tensor, Tensor, Tensor];
-}
-
-/**
- * Broadcasting batch normalization for use in training (not inference).
- *
- * The input is normalized to zero mean and unit variance along the
- * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.
- * The result of that is returned as the first element
- * of the returned `Array`. The other two elements are the mean and variance,
- * respectively.
- *
- * @param x Input tensor to be normalized.
- * @param gamma Tensor by which to scale the input.
- * @param beta Tensor by which to center the input.
- * @param reductionAxes Axes over which to normalize.
- * @param epsilon Fuzz factor.
- * @returns An `Array` of three `Tensors`:
- *   [normalized tensor, mean of input, variance of input].
- */
-function broadcastNormalizeBatchInTraining(
-    x: Tensor, gamma: Tensor, beta: Tensor, reductionAxes: number[],
-    epsilon = 1e-3): [Tensor, Tensor, Tensor] {
-  return tidy(() => {
-           const meanAndVariance = tfc.moments(x, reductionAxes);
-           const mean = meanAndVariance.mean;
-           const variance = meanAndVariance.variance;
-           const targetShape: number[] = [];
-           for (const axis of math_utils.range(0, x.rank)) {
-             if (reductionAxes.indexOf(axis) !== -1) {
-               targetShape.push(1);
-             } else {
-               targetShape.push(x.shape[axis]);
-             }
-           }
-           const broadcastMean = reshape(mean, targetShape);
-           const broadcastVariance = reshape(variance, targetShape);
-           const broadcastGamma =
-               gamma == null ? null : reshape(gamma, targetShape);
-           const broadcastBeta =
-               beta == null ? null : reshape(beta, targetShape);
-           const normed = batchNormalization(
-               x, broadcastMean, broadcastVariance, broadcastBeta,
-               broadcastGamma, epsilon);
-           return [normed, mean, variance];
-         }) as [Tensor, Tensor, Tensor];
-}
-
-/**
- * Batch normalization for use in training (not inference).
- *
- * @param x Input tensor to be normalized.
- * @param gamma Tensor by which to scale the input.
- * @param beta Tensor by which to center the input.
- * @param reductionAxes Axes over which to normalize.
- * @param epsilon Fuzz factor.
- * @returns An `Array` of three `Tensors`:
- *   [normalized tensor, mean of input, variance of input].
- */
-export function normalizeBatchInTraining(
-    x: Tensor, gamma: Tensor, beta: Tensor, reductionAxes: number[],
-    epsilon = 1e-3): [Tensor, Tensor, Tensor] {
-  if (util.arraysEqual(
-          reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {
-    return regularNormalizeBatchInTraining(
-        x, gamma, beta, reductionAxes, epsilon);
-  } else {
-    return broadcastNormalizeBatchInTraining(
-        x, gamma, beta, reductionAxes, epsilon);
-  }
-}
-
-export declare interface BatchNormalizationLayerArgs extends LayerArgs {
-  /**
-   * The integer axis that should be normalized (typically the features axis).
-   * Defaults to -1.
-   *
-   * For instance, after a `Conv2D` layer with `data_format="channels_first"`,
-   * set `axis=1` in `batchNormalization`.
-   */
-  axis?: number;
-
-  /**
-   * Momentum of the moving average. Defaults to 0.99.
-   */
-  momentum?: number;
-
-  /**
-   * Small float added to the variance to avoid dividing by zero. Defaults to
-   * 1e-3.
-   */
-  epsilon?: number;
-
-  /**
-   * If `true`, add offset of `beta` to normalized tensor.
-   * If `false`, `beta` is ignored.
-   * Defaults to `true`.
-   */
-  center?: boolean;
-
-  /**
-   * If `true`, multiply by `gamma`.
-   * If `false`, `gamma` is not used.
-   * When the next layer is linear (also e.g. `nn.relu`),
-   * this can be disabled since the scaling will be done by the next layer.
-   * Defaults to `true`.
-   */
-  scale?: boolean;
-
-  /**
-   * Initializer for the beta weight.
-   *  Defaults to 'zeros'.
-   */
-  betaInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the gamma weight.
-   *  Defaults to `ones`.
-   */
-  gammaInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the moving mean.
-   * Defaults to `zeros`
-   */
-  movingMeanInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the moving variance.
-   *  Defaults to 'Ones'.
-   */
-  movingVarianceInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Constraint for the beta weight.
-   */
-  betaConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Constraint for gamma weight.
-   */
-  gammaConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Regularizer for the beta weight.
-   */
-  betaRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer for the gamma weight.
-   */
-  gammaRegularizer?: RegularizerIdentifier|Regularizer;
-}
-
-export class BatchNormalization extends Layer {
-  /** @nocollapse */
-  static className = 'BatchNormalization';
-  private readonly axis: number;
-  private readonly momentum: number;
-  private readonly epsilon: number;
-  private readonly center: boolean;
-  private readonly scale: boolean;
-  private readonly betaInitializer: Initializer;
-  private readonly gammaInitializer: Initializer;
-  private readonly movingMeanInitializer: Initializer;
-  private readonly movingVarianceInitializer: Initializer;
-  private readonly betaConstraint: Constraint;
-  private readonly gammaConstraint: Constraint;
-  private readonly betaRegularizer: Regularizer;
-  private readonly gammaRegularizer: Regularizer;
-  private gamma: LayerVariable;
-  private beta: LayerVariable;
-  private movingMean: LayerVariable;
-  private movingVariance: LayerVariable;
-
-  constructor(args?: BatchNormalizationLayerArgs) {
-    if (args == null) {
-      args = {};
-    }
-    super(args);
-
-    this.supportsMasking = true;
-    this.axis = args.axis == null ? -1 : args.axis;
-    this.momentum = args.momentum == null ? 0.99 : args.momentum;
-    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
-    this.center = args.center == null ? true : args.center;
-    this.scale = args.scale == null ? true : args.scale;
-    this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');
-    this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');
-    this.movingMeanInitializer =
-        getInitializer(args.movingMeanInitializer || 'zeros');
-    this.movingVarianceInitializer =
-        getInitializer(args.movingVarianceInitializer || 'ones');
-    this.betaConstraint = getConstraint(args.betaConstraint);
-    this.gammaConstraint = getConstraint(args.gammaConstraint);
-    this.betaRegularizer = getRegularizer(args.betaRegularizer);
-    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
-  }
-
-  public override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);
-    const dim = inputShape[axis];
-    if (dim == null) {
-      throw new ValueError(
-          `Axis ${axis} of input tensor should have a defined dimension but ` +
-          `the layer received an input with shape ` +
-          `${JSON.stringify(inputShape)}.`);
-    }
-    this.inputSpec =
-        [new InputSpec({ndim: inputShape.length, axes: {[axis]: dim}})];
-    const shape = [dim];
-    if (this.scale) {
-      this.gamma = this.addWeight(
-          'gamma', shape, null, this.gammaInitializer, this.gammaRegularizer,
-          true, this.gammaConstraint);
-    }
-    if (this.center) {
-      this.beta = this.addWeight(
-          'beta', shape, null, this.betaInitializer, this.betaRegularizer, true,
-          this.betaConstraint);
-    }
-    this.movingMean = this.addWeight(
-        'moving_mean', shape, null, this.movingMeanInitializer, null, false);
-    this.movingVariance = this.addWeight(
-        'moving_variance', shape, null, this.movingVarianceInitializer, null,
-        false);
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      const training = kwargs['training'] == null ? false : kwargs['training'];
-      const input = getExactlyOneTensor(inputs);
-      const inputShape = input.shape;
-      const ndim = inputShape.length;
-      const reductionAxes = math_utils.range(0, ndim);
-      const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);
-      reductionAxes.splice(axis, 1);
-      const broadcastShape = generic_utils.pyListRepeat(1, ndim);
-      broadcastShape[axis] = inputShape[axis];
-
-      const sortedReductionAxes = reductionAxes.slice();
-      sortedReductionAxes.sort();
-      const needsBroadcasting = !util.arraysEqual(
-          sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));
-
-      const normalizeInference: () => Tensor = () => {
-        if (needsBroadcasting) {
-          const broadcastMovingMean =
-              reshape(this.movingMean.read(), broadcastShape);
-          const broadcastMovingVariance =
-              reshape(this.movingVariance.read(), broadcastShape);
-          const broadcastBeta =
-              this.center ? reshape(this.beta.read(), broadcastShape) : null;
-          const broadcastGamma =
-              this.scale ? reshape(this.gamma.read(), broadcastShape) : null;
-          return batchNormalization(
-              input, broadcastMovingMean, broadcastMovingVariance,
-              broadcastBeta, broadcastGamma, this.epsilon);
-        } else {
-          return batchNormalization(
-              input, this.movingMean.read(), this.movingVariance.read(),
-              this.beta == null ? null : this.beta.read(),
-              this.gamma == null ? null : this.gamma.read(), this.epsilon);
-        }
-      };
-
-      if (!training) {
-        return normalizeInference();
-      }
-
-      const [normedTraining, mean, variance] = normalizeBatchInTraining(
-          input, this.gamma.read(), this.beta.read(), reductionAxes,
-          this.epsilon);
-
-      const doMovingAverage =
-          (variable: LayerVariable, value: Tensor, momentum: number): void => {
-            tfc.tidy(() => {
-              const decay = 1 - momentum;
-              const origValue = variable.read();
-              const updateDelta = tfc.mul(tfc.sub(origValue, value), decay);
-              variable.write(tfc.sub(origValue, updateDelta));
-            });
-          };
-
-      // Perform updates to moving mean and moving variance for training.
-      // Porting Note: In PyKeras, these updates to `movingMean` and
-      //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s
-      //   `update`s using the `add_update()` method. Here we do it imperatively
-      //   and encapsulate the updates in a function that is invoked
-      //   immediately.
-      const updateMovingMeanAndVariance = () => {
-        doMovingAverage(this.movingMean, mean, this.momentum);
-        doMovingAverage(this.movingVariance, variance, this.momentum);
-      };
-      updateMovingMeanAndVariance();
-
-      return normedTraining;
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      axis: this.axis,
-      momentum: this.momentum,
-      epsilon: this.epsilon,
-      center: this.center,
-      scale: this.scale,
-      betaInitializer: serializeInitializer(this.betaInitializer),
-      gammaInitializer: serializeInitializer(this.gammaInitializer),
-      movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),
-      movingVarianceInitializer:
-          serializeInitializer(this.movingVarianceInitializer),
-      betaRegularizer: serializeRegularizer(this.betaRegularizer),
-      gammaRegularizer: serializeRegularizer(this.gammaRegularizer),
-      betaConstraint: serializeConstraint(this.betaConstraint),
-      gammaConstraint: serializeConstraint(this.gammaConstraint)
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(BatchNormalization);
-
-export interface LayerNormalizationLayerArgs extends LayerArgs {
-  /**
-   * The axis or axes that should be normalized (typically, the feature axis).
-   * Defaults to -1 (the last axis).
-   */
-  axis?: number|number[];
-
-  /**
-   * A small positive float added to variance to avoid divison by zero.
-   * Defaults to 1e-3.
-   */
-  epsilon?: number;
-
-  /**
-   * If `true`, add offset of `beta` to normalized tensor.
-   * If `false`, `beta` is ignored.
-   * Default: `true`.
-   */
-  center?: boolean;
-
-  /**
-   * If `true`, multiply output by `gamma`.
-   * If `false`, `gamma` is not used.
-   * When the next layer is linear, this can be disabled since scaling will
-   * be done by the next layer.
-   * Default: `true`.
-   */
-  scale?: boolean;
-
-  /**
-   * Initializer for the beta weight.
-   * Default: `'zeros'`.
-   */
-  betaInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the gamma weight.
-   * Default: `'ones'`.
-   */
-  gammaInitializer?: InitializerIdentifier|Initializer;
-
-  /** Regularizer for the beta weight. */
-  betaRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /** Regularizer for the gamma weight. */
-  gammaRegularizer?: RegularizerIdentifier|Regularizer;
-}
-
-export class LayerNormalization extends Layer {
-  /** @nocollapse */
-  static className = 'LayerNormalization';
-
-  private axis: number|number[];
-  readonly epsilon: number;
-  readonly center: boolean;
-  readonly scale: boolean;
-  readonly betaInitializer: Initializer;
-  readonly gammaInitializer: Initializer;
-  readonly betaRegularizer: Regularizer;
-  readonly gammaRegularizer: Regularizer;
-
-  private gamma: LayerVariable;
-  private beta: LayerVariable;
-
-  constructor(args?: LayerNormalizationLayerArgs) {
-    if (args == null) {
-      args = {};
-    }
-    super(args);
-
-    this.axis = args.axis == null ? -1 : args.axis;
-    if (typeof this.axis === 'number') {
-      if (!Number.isInteger(this.axis)) {
-        throw new Error(
-            `Expected axis to be an integer, but received ${this.axis}`);
-      }
-    } else if (Array.isArray(this.axis)) {
-      for (const axis of this.axis) {
-        if (!Number.isInteger(axis)) {
-          throw new Error(
-              `Expected axis to be an array of integers, ` +
-              `but received ${JSON.stringify(this.axis)}`);
-        }
-      }
-    } else {
-      throw new Error(
-          `Expected axis to be an integer or an array of integers, ` +
-          `but received ${JSON.stringify(this.axis)}`);
-    }
-
-    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
-    this.center = args.center == null ? true : args.center;
-    this.scale = args.scale == null ? true : args.scale;
-    this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');
-    this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');
-    this.betaRegularizer = getRegularizer(args.betaRegularizer);
-    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
-
-    this.supportsMasking = true;
-  }
-
-  public override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    const nDims = inputShape.length;
-
-    // Convert axis to array and resolve negatives.
-    if (typeof this.axis === 'number') {
-      this.axis = [this.axis];
-    }
-    for (let i = 0; i < this.axis.length; ++i) {
-      if (this.axis[i] < 0) {
-        this.axis[i] += nDims;
-      }
-    }
-
-    // Further validate axes.
-    for (const axis of this.axis) {
-      if (axis < 0 || axis >= nDims) {
-        throw new Error(`Invalid axis: ${axis}`);
-      }
-    }
-    if (this.axis.length !== generic_utils.unique(this.axis).length) {
-      throw new Error(`Found duplicate axes in: ${this.axis}`);
-    }
-
-    const paramShape = this.axis.map(axis => inputShape[axis]) as number[];
-
-    const trainable = true;
-    if (this.scale) {
-      this.gamma = this.addWeight(
-          'gamma', paramShape, 'float32', this.gammaInitializer,
-          this.gammaRegularizer, trainable);
-    } else {
-      this.gamma = null;
-    }
-    if (this.center) {
-      this.beta = this.addWeight(
-          'beta', paramShape, 'float32', this.betaInitializer,
-          this.betaRegularizer, trainable);
-    } else {
-      this.beta = null;
-    }
-
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    const input = getExactlyOneTensor(inputs);
-    const inputShape = input.shape;
-    const nDims = inputShape.length;
-
-    return tidy(() => {
-      const keepDims = true;
-      let {mean, variance} = moments(input, this.axis, keepDims);
-      const broadcastShape = generic_utils.pyListRepeat(1, nDims);
-      for (const dim of this.axis as number[]) {
-        broadcastShape[dim] = inputShape[dim];
-      }
-
-      const broadcast = (v: Tensor) => {
-        if (v != null && v.shape.length !== nDims) {
-          return tfc.reshape(v, broadcastShape);
-        } else {
-          return v;
-        }
-      };
-
-      let scale = this.scale ? broadcast(this.gamma.read()) : null;
-      let offset = this.center ? broadcast(this.beta.read()) : null;
-
-      // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below
-      // is a workaround for the limitation of core's batchNormalization?d don't
-      // support broadcasting in their gradients. In addition, the tiling is
-      // necessary to ensure correctness on the browser CPU backend regardless
-      // of forward or backward computation. Remove this workaround once the
-      // limitation is addressed. See .
-      const momentsTiling: number[] = [];
-      const scaleOffsetTiling: number[] = [];
-      for (let i = 0; i < nDims; ++i) {
-        if ((this.axis as number[]).indexOf(i) !== -1) {
-          momentsTiling.push(inputShape[i]);
-          scaleOffsetTiling.push(1);
-        } else {
-          momentsTiling.push(1);
-          scaleOffsetTiling.push(inputShape[i]);
-        }
-      }
-      mean = tfc.tile(mean, momentsTiling);
-      variance = tfc.tile(variance, momentsTiling);
-      if (scale != null) {
-        scale = tfc.tile(scale, scaleOffsetTiling);
-      }
-      if (offset != null) {
-        offset = tfc.tile(offset, scaleOffsetTiling);
-      }
-
-      return batchNormalization(
-          input, mean, variance, offset, scale, this.epsilon);
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      axis: this.axis,
-      epsilon: this.epsilon,
-      center: this.center,
-      scale: this.scale,
-      betaInitializer: serializeInitializer(this.betaInitializer),
-      gammaInitializer: serializeInitializer(this.gammaInitializer),
-      betaRegularizer: serializeRegularizer(this.betaRegularizer),
-      gammaRegularizer: serializeRegularizer(this.gammaRegularizer)
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(LayerNormalization);
diff --git a/tfjs-master/tfjs-layers/src/layers/normalization_test.ts b/tfjs-master/tfjs-layers/src/layers/normalization_test.ts
deleted file mode 100644
index b7e3db11c..000000000
--- a/tfjs-master/tfjs-layers/src/layers/normalization_test.ts
+++ /dev/null
@@ -1,1012 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for normalization layers.
- */
-
-import {dispose, div, memory, onesLike, scalar, sub, Tensor, tensor1d, tensor2d, tensor3d, tensor4d, test_util, train, zeros, zerosLike} from '@tensorflow/tfjs-core';
-
-import {SymbolicTensor} from '../engine/topology';
-import * as tfl from '../index';
-import {convertPythonicToTs, convertTsToPythonic} from '../utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndGPU, describeMathCPUAndWebGL2, expectTensorsClose} from '../utils/test_utils';
-
-import {batchNormalization, normalizeBatchInTraining} from './normalization';
-
-describeMathCPUAndGPU('normalizeBatchInTraining', () => {
-  // The reference values for assertion below can be obtained with Python code
-  // as the following:
-  // ```python
-  // import keras
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // with tf.Session() as sess:
-  //   x = tf.Variable(np.array(
-  //       [[1, 2, 3, 4], [2, 4, 6, 8], [12, 11, 10, 9]], dtype=np.float32))
-  //   gamma = tf.Variable(np.array([1, 1, 1, 1], dtype=np.float32))
-  //   beta = tf.Variable(np.array([0, 0, 0, 0], dtype=np.float32))
-  //   reduction_axes = [0]
-  //   normed, mean, variance = keras.backend.normalize_batch_in_training(
-  //       x, gamma, beta, reduction_axes)
-  //   print(normed)
-  //   print(mean)
-  //   print(variance)
-  // ```
-
-  it('2D, no broadcasting', () => {
-    const x = tensor2d([[1, 2, 3, 4], [2, 4, 6, 8], [12, 11, 10, 9]], [3, 4]);
-    const gamma = tensor1d([1, 1, 1, 1]);
-    const beta = tensor1d([0, 0, 0, 0]);
-    const reductionAxes = [0];
-    const [normed, mean, variance] =
-        normalizeBatchInTraining(x, gamma, beta, reductionAxes);
-    expectTensorsClose(
-        normed,
-        tensor2d(
-            [
-              [-0.805371, -0.9502233, -1.1624058, -1.3885813],
-              [-0.6040282, -0.4319197, -0.11624074, 0.46286058],
-              [1.4093992, 1.3821429, 1.2786462, 0.92572117]
-            ],
-            [3, 4]));
-    expectTensorsClose(mean, tensor1d([5.0, 5.6666665, 6.3333335, 7.0]));
-    expectTensorsClose(
-        variance, tensor1d([24.666666, 14.888889, 8.222222, 4.6666665]));
-  });
-
-  it('3D, no broadcasting', () => {
-    const x = tensor3d(
-        [[[1, 2], [3, 4]], [[2, 4], [6, 8]], [[12, 11], [10, 9]]], [3, 2, 2]);
-    const gamma = tensor1d([1, 1]);
-    const beta = tensor1d([0, 0]);
-    const reductionAxes = [0, 1];
-    const [normed, mean, variance] =
-        normalizeBatchInTraining(x, gamma, beta, reductionAxes);
-    expectTensorsClose(
-        normed,
-        tensor3d(
-            [
-              [[-1.1355163, -1.3552775], [-0.6488664, -0.7297648]],
-              [[-0.8921913, -0.7297648], [0.08110833, 0.5212605]],
-              [[1.5410578, 1.4595294], [1.0544081, 0.8340168]]
-            ],
-            [3, 2, 2]));
-    expectTensorsClose(mean, tensor1d([5.6666665, 6.3333335]));
-    expectTensorsClose(variance, tensor1d([16.88889, 10.222222]));
-  });
-
-  it('3D, broadcasting', () => {
-    const x = tensor3d(
-        [[[1, 2], [3, 4]], [[2, 4], [6, 8]], [[12, 11], [10, 9]]], [3, 2, 2]);
-    const gamma = tensor2d([[1, 1], [1, 1]], [2, 2]);
-    const beta = tensor2d([[0, 0], [0, 0]], [2, 2]);
-    const reductionAxes = [0];
-    const [normed, mean, variance] =
-        normalizeBatchInTraining(x, gamma, beta, reductionAxes);
-    expectTensorsClose(
-        normed,
-        tensor3d(
-            [
-              [[-0.805371, -0.9502233], [-1.1624058, -1.3885813]],
-              [[-0.6040282, -0.4319197], [-0.11624074, 0.46286058]],
-              [[1.4093992, 1.3821429], [1.2786462, 0.92572117]]
-            ],
-            [3, 2, 2]));
-    expectTensorsClose(
-        mean, tensor2d([[5, 5.6666665], [6.3333335, 7]], [2, 2]));
-    expectTensorsClose(
-        variance,
-        tensor2d([[24.666666, 14.888889], [8.222222, 4.6666665]], [2, 2]));
-  });
-
-  it('4D, broadcasting', () => {
-    const x = tensor4d(
-        [[[[1, 2], [3, 4]], [[2, 4], [6, 8]], [[12, 11], [10, 9]]]],
-        [1, 3, 2, 2]);
-    const gamma = tensor2d([[1, 1], [1, 1]], [2, 2]);
-    const beta = tensor2d([[0, 0], [0, 0]], [2, 2]);
-    const reductionAxes = [0, 1];
-    const [normed, mean, variance] =
-        normalizeBatchInTraining(x, gamma, beta, reductionAxes);
-    expectTensorsClose(
-        normed,
-        tensor4d(
-            [[
-              [[-0.805371, -0.9502233], [-1.1624058, -1.3885813]],
-              [[-0.6040282, -0.4319197], [-0.11624074, 0.46286058]],
-              [[1.4093992, 1.3821429], [1.2786462, 0.92572117]]
-            ]],
-            [1, 3, 2, 2]));
-    expectTensorsClose(
-        mean, tensor2d([[5, 5.6666665], [6.3333335, 7]], [2, 2]));
-    expectTensorsClose(
-        variance,
-        tensor2d([[24.666666, 14.888889], [8.222222, 4.6666665]], [2, 2]));
-  });
-});
-
-describeMathCPUAndGPU('batchNormalization', () => {
-  it('2D, no broadcast, no gamma, no beta', () => {
-    const x = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const mean = tensor2d([[5, 5], [5, 5]], [2, 2]);
-    const variance = tensor2d([[4, 16], [4, 16]], [2, 2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, null, null, 0),
-        tensor2d([[2.5, 3.75], [12.5, 8.75]], [2, 2]));
-  });
-  it('2D, no broadcast, no gamma, no beta, custom epsilon', () => {
-    const x = tensor2d([[30, 30], [60, 60]], [2, 2]);
-    const mean = tensor2d([[0, 0], [0, 0]], [2, 2]);
-    const variance = tensor2d([[7, 7], [7, 7]], [2, 2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, null, null, 2),
-        tensor2d([[10, 10], [20, 20]], [2, 2]));
-  });
-  it('2D, no broadcast, gamma, no beta', () => {
-    const x = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const mean = tensor2d([[5, 5], [5, 5]], [2, 2]);
-    const variance = tensor2d([[4, 16], [4, 16]], [2, 2]);
-    const gamma = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, null, gamma, 0),
-        tensor2d([[2.5, 7.5], [37.5, 35]], [2, 2]));
-  });
-  it('2D, no broadcast, gamma, beta', () => {
-    const x = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const mean = tensor2d([[5, 5], [5, 5]], [2, 2]);
-    const variance = tensor2d([[4, 16], [4, 16]], [2, 2]);
-    const gamma = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const beta = tensor2d([[-1, -1], [-2, -2]], [2, 2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, beta, gamma, 0),
-        tensor2d([[1.5, 6.5], [35.5, 33]], [2, 2]));
-  });
-  it('2D, broadcast, gamma, beta', () => {
-    const x = tensor2d([[10, 20], [30, 40]], [2, 2]);
-    const mean = tensor1d([2, 5]);
-    const variance = tensor1d([1, 4]);
-    const gamma = tensor1d([3, 4]);
-    const beta = tensor1d([-1, -2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, beta, gamma, 0),
-        tensor2d([[23, 28], [83, 68]], [2, 2]));
-  });
-  it('3D, no broadcast, no gamma, no beta', () => {
-    const x = tensor3d([[[10, 20], [30, 40]], [[10, 20], [30, 40]]], [2, 2, 2]);
-    const mean = tensor3d([[[5, 5], [5, 5]], [[5, 5], [5, 5]]], [2, 2, 2]);
-    const variance =
-        tensor3d([[[4, 16], [4, 16]], [[16, 25], [16, 25]]], [2, 2, 2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, null, null, 0),
-        tensor3d(
-            [[[2.5, 3.75], [12.5, 8.75]], [[1.25, 3], [6.25, 7]]], [2, 2, 2]));
-  });
-  it('3D, no broadcast, gamma, beta', () => {
-    const x = tensor3d([[[10, 20], [30, 40]], [[10, 20], [30, 40]]], [2, 2, 2]);
-    const mean = tensor3d([[[5, 5], [5, 5]], [[5, 5], [5, 5]]], [2, 2, 2]);
-    const variance =
-        tensor3d([[[4, 16], [4, 16]], [[16, 25], [16, 25]]], [2, 2, 2]);
-    const gamma = tensor3d([[[2, 2], [2, 2]], [[4, 4], [4, 4]]], [2, 2, 2]);
-    const beta =
-        tensor3d([[[-1, -1], [-2, -2]], [[-1, -1], [-2, -2]]], [2, 2, 2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, beta, gamma, 0),
-        tensor3d([[[4, 6.5], [23, 15.5]], [[4, 11], [23, 26]]], [2, 2, 2]));
-  });
-  it('3D, broadcast, gamma, beta', () => {
-    const x = tensor3d([[[10, 20], [30, 40]], [[10, 20], [30, 40]]], [2, 2, 2]);
-    const mean = tensor1d([5, 5]);
-    const variance = tensor1d([4, 16]);
-    const gamma = tensor1d([2, 4]);
-    const beta = tensor1d([-1, -2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, beta, gamma, 0),
-        tensor3d([[[4, 13], [24, 33]], [[4, 13], [24, 33]]], [2, 2, 2]));
-  });
-  it('4D, no broadcast, no gamma, no beta', () => {
-    const x = tensor4d(
-        [
-          [[[10, 20], [30, 40]], [[10, 20], [30, 40]]],
-          [[[-10, -20], [-30, -40]], [[-10, -20], [-30, -40]]]
-        ],
-        [2, 2, 2, 2]);
-    const mean = tensor4d(
-        [
-          [[[5, 5], [5, 5]], [[5, 5], [5, 5]]],
-          [[[-5, -5], [-5, -5]], [[-5, -5], [-5, -5]]]
-        ],
-        [2, 2, 2, 2]);
-    const variance = tensor4d(
-        [
-          [[[4, 16], [4, 16]], [[16, 25], [16, 25]]],
-          [[[4, 16], [4, 16]], [[16, 25], [16, 25]]]
-        ],
-        [2, 2, 2, 2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, null, null, 0),
-        tensor4d(
-            [
-              [[[2.5, 3.75], [12.5, 8.75]], [[1.25, 3], [6.25, 7]]],
-              [[[-2.5, -3.75], [-12.5, -8.75]], [[-1.25, -3], [-6.25, -7]]]
-            ],
-            [2, 2, 2, 2]));
-  });
-  it('4D, no broadcast, gamma, beta', () => {
-    const x = tensor4d(
-        [
-          [[[10, 20], [30, 40]], [[10, 20], [30, 40]]],
-          [[[-10, -20], [-30, -40]], [[-10, -20], [-30, -40]]]
-        ],
-        [2, 2, 2, 2]);
-    const mean = tensor4d(
-        [
-          [[[5, 5], [5, 5]], [[5, 5], [5, 5]]],
-          [[[-5, -5], [-5, -5]], [[-5, -5], [-5, -5]]]
-        ],
-        [2, 2, 2, 2]);
-    const variance = tensor4d(
-        [
-          [[[4, 16], [4, 16]], [[16, 25], [16, 25]]],
-          [[[4, 16], [4, 16]], [[16, 25], [16, 25]]]
-        ],
-        [2, 2, 2, 2]);
-    const gamma = tensor4d(
-        [
-          [[[2, 2], [2, 2]], [[4, 4], [4, 4]]],
-          [[[2, 2], [2, 2]], [[4, 4], [4, 4]]]
-        ],
-        [2, 2, 2, 2]);
-    const beta = tensor4d(
-        [
-          [[[-1, -1], [-2, -2]], [[-1, -1], [-2, -2]]],
-          [[[1, 1], [2, 2]], [[1, 1], [2, 2]]]
-        ],
-        [2, 2, 2, 2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, beta, gamma, 0),
-        tensor4d(
-            [
-              [[[4, 6.5], [23, 15.5]], [[4, 11], [23, 26]]],
-              [[[-4, -6.5], [-23, -15.5]], [[-4, -11], [-23, -26]]]
-            ],
-            [2, 2, 2, 2]));
-  });
-  it('4D, broadcast, gamma, beta', () => {
-    const x = tensor4d(
-        [[[[10, 20], [30, 40]]], [[[10, 20], [30, 40]]]], [2, 1, 2, 2]);
-    const mean = tensor1d([5, 5]);
-    const variance = tensor1d([4, 16]);
-    const gamma = tensor1d([2, 4]);
-    const beta = tensor1d([-1, -2]);
-    expectTensorsClose(
-        batchNormalization(x, mean, variance, beta, gamma, 0),
-        tensor4d([[[[4, 13], [24, 33]]], [[[4, 13], [24, 33]]]], [2, 1, 2, 2]));
-  });
-});
-
-describeMathCPU('BatchNormalization Layers: Symbolic', () => {
-  const validInputShapes = [[4, 6], [2, 3, 4], [2, 3, 4, 5]];
-  for (const inputShape of validInputShapes) {
-    const testTitle = `shape=${JSON.stringify(inputShape)}`;
-    it(testTitle, () => {
-      const x = new SymbolicTensor('float32', inputShape, null, [], null);
-      const layer = tfl.layers.batchNormalization({});
-      const y = layer.apply(x) as SymbolicTensor;
-      expect(y.dtype).toEqual(x.dtype);
-      expect(y.shape).toEqual(x.shape);
-    });
-  }
-
-  it('Undetermined dim axis leads to ValueError', () => {
-    const x = new SymbolicTensor('float32', [null, 2, 3], null, [], null);
-    const layer = tfl.layers.batchNormalization({axis: 0});
-    expect(() => layer.apply(x))
-        .toThrowError(
-            /Axis 0 of input tensor should have a defined dimension.*/);
-  });
-
-  it('batchNormalization constructor works without arg', () => {
-    const layer = tfl.layers.batchNormalization();
-    expect(layer.getConfig().axis).toEqual(-1);
-  });
-});
-
-describeMathCPUAndWebGL2('BatchNormalization Layers: Tensor', () => {
-  const dimensions = [2, 3, 4];
-  const axisValues = [0, -1];
-
-  for (const dim of dimensions) {
-    for (const axis of axisValues) {
-      const testTitle = `Inference, ${dim}D, axis=${axis}`;
-      it(testTitle, () => {
-        const layer = tfl.layers.batchNormalization({axis});
-        let x: Tensor;
-        if (dim === 2) {
-          x = tensor2d([[1, 2], [3, 4]], [2, 2]);
-        } else if (dim === 3) {
-          x = tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]], [2, 2, 2]);
-        } else if (dim === 4) {
-          x = tensor4d(
-              [
-                [[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]],
-                [[[-1, -2], [-3, -4]], [[1, 2], [3, 4]]]
-              ],
-              [2, 2, 2, 2]);
-        }
-        const y = layer.apply(x, {training: false}) as Tensor;
-        expectTensorsClose(y, x, 0.01);
-      });
-    }
-  }
-
-  it('no center', () => {
-    const layer = tfl.layers.batchNormalization({center: false, axis: 0});
-    const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    expectTensorsClose(layer.apply(x) as Tensor, x, 0.01);
-    expect(layer.getWeights().length).toEqual(3);
-    // Firt weight is gamma.
-    expectTensorsClose(layer.getWeights()[0], onesLike(layer.getWeights()[0]));
-    // Second weight is moving mean.
-    expectTensorsClose(layer.getWeights()[1], zerosLike(layer.getWeights()[1]));
-    // Third weight is moving variance.
-    expectTensorsClose(layer.getWeights()[2], onesLike(layer.getWeights()[2]));
-  });
-
-  it('no scale', () => {
-    const layer = tfl.layers.batchNormalization({scale: false, axis: 0});
-    const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    expectTensorsClose(layer.apply(x) as Tensor, x, 0.01);
-    expect(layer.getWeights().length).toEqual(3);
-    // Firt weight is beta.
-    expectTensorsClose(layer.getWeights()[0], zerosLike(layer.getWeights()[0]));
-    // Second weight is moving mean.
-    expectTensorsClose(layer.getWeights()[1], zerosLike(layer.getWeights()[1]));
-    // Third weight is moving variance.
-    expectTensorsClose(layer.getWeights()[2], onesLike(layer.getWeights()[2]));
-  });
-
-  it('no center, no scale', () => {
-    const layer = tfl.layers.batchNormalization({scale: false, center: false});
-    const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    expectTensorsClose(layer.apply(x) as Tensor, x, 0.01);
-    expect(layer.getWeights().length).toEqual(2);
-    // First weight is moving mean.
-    expectTensorsClose(layer.getWeights()[0], zerosLike(layer.getWeights()[0]));
-    // Second weight is moving variance.
-    expectTensorsClose(layer.getWeights()[1], onesLike(layer.getWeights()[1]));
-  });
-
-  // Use the following Python code to get the reference values for assertion:
-  // ```python
-  // from tensorflow import keras
-  // import numpy as np
-  //
-  // layer1 = keras.layers.BatchNormalization(input_shape=(4,))
-  // model = keras.Sequential([layer1])
-  //
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // xs = np.array(
-  //     [[1, 2, 3, 4], [2, 4, 6, 8], [12, 11, 10, 9]], dtype=np.float32)
-  // ys = np.zeros([3, 4])
-  // print(layer1.get_weights())
-  // history = model.fit(xs, ys, epochs=2, batch_size=3)
-  // print(history.history)
-  // print(layer1.get_weights())
-  // ```
-  it('Fit: 2D, BatchNorm Layer Only', async () => {
-    const layer1 = tfl.layers.batchNormalization({inputShape: [4]});
-    const model = tfl.sequential({layers: [layer1]});
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const xs1 = tensor2d([[1, 2, 3, 4], [2, 4, 6, 8], [12, 11, 10, 9]], [3, 4]);
-    const ys = zeros([3, 4]);
-    const history = await model.fit(xs1, ys, {epochs: 2, batchSize: 3});
-    expect(history.history['loss'][0]).toBeCloseTo(0.9998891353607178);
-    expect(history.history['loss'][1]).toBeCloseTo(0.9899163246154785);
-    const gammaValue = layer1.getWeights()[0];
-    expectTensorsClose(
-        gammaValue, [0.9900254, 0.9900257, 0.9900262, 0.9900271]);
-    const betaValue = layer1.getWeights()[1];
-    expectTensorsClose(
-        betaValue,
-        [2.9802322e-10, 1.4901161e-10, 9.1269614e-10, -7.4505802e-10]);
-    const movingMeanValue = layer1.getWeights()[2];
-    expectTensorsClose(
-        movingMeanValue, [0.09949999, 0.11276666, 0.12603334, 0.13929999]);
-    const movingVarianceValue = layer1.getWeights()[3];
-    expectTensorsClose(
-        movingVarianceValue, [1.4709667, 1.2763889, 1.1437222, 1.0729666]);
-  });
-
-  // Use the following Python code to get the reference values for
-  // assertion:
-  // ```python
-  // from tensorflow import keras
-  // import numpy as np
-  //
-  // layer1 = keras.layers.Dense(
-  //     4, kernel_initializer='ones', use_bias=False, input_shape=(4,))
-  // layer2 = keras.layers.BatchNormalization()
-  // layer3 = keras.layers.Dense(1, kernel_initializer='ones',
-  //                             use_bias=False)
-  // model = keras.Sequential([layer1, layer2, layer3])
-  //
-  // optimizer = keras.optimizers.sgd(lr=0.1)
-  // model.compile(loss='mean_squared_error', optimizer=optimizer)
-  //
-  // xs = np.array([[1, 2, 3, 4], [2, 4, 6, 8], [12, 11, 10, 9]],
-  //                dtype=np.float32)
-  // ys = np.zeros([3, 1])
-  // history = model.fit(xs, ys, epochs=3, batch_size=3)
-  //
-  // print(history.history)
-  // print(layer1.get_weights())
-  // print(layer2.get_weights())
-  // print(layer3.get_weights())
-  // ```
-  it('Fit: 2D, BatchNorm Layer between two Dense Layers', async () => {
-    const layer1 = tfl.layers.dense(
-        {units: 4, kernelInitializer: 'ones', useBias: false, inputShape: [4]});
-    const layer2 = tfl.layers.batchNormalization({inputShape: [4]});
-    const layer3 =
-        tfl.layers.dense({units: 1, kernelInitializer: 'ones', useBias: false});
-    const model = tfl.sequential({layers: [layer1, layer2, layer3]});
-
-    const optimizer = train.sgd(0.1);
-    model.compile({loss: 'meanSquaredError', optimizer});
-
-    const xs1 = tensor2d([[1, 2, 3, 4], [2, 4, 6, 8], [12, 11, 10, 9]], [3, 4]);
-    const ys = zeros([3, 1]);
-    const history = await model.fit(xs1, ys, {epochs: 3, batchSize: 3});
-    expect(history.history['loss'][0]).toBeCloseTo(15.999907493591309);
-    expect(history.history['loss'][1]).toBeCloseTo(0.025602197274565697);
-    expect(history.history['loss'][2]).toBeCloseTo(0.022478966042399406);
-    const dense1KernelValue = layer1.getWeights()[0];
-    expectTensorsClose(
-        dense1KernelValue,
-        tensor2d(
-            [
-              [0.99999833, 0.99999833, 0.99999833, 0.99999833],
-              [0.9999987, 0.9999987, 0.9999987, 0.9999987],
-              [0.999999, 0.999999, 0.999999, 0.999999],
-              [0.99999934, 0.99999934, 0.99999934, 0.99999934]
-            ],
-            [4, 4]));
-    const gammaValue = layer2.getWeights()[0];
-    expectTensorsClose(
-        gammaValue, [0.18779878, 0.18779878, 0.18779878, 0.18779878]);
-    const betaValue = layer2.getWeights()[1];
-    expectTensorsClose(
-        betaValue,
-        [5.5367128e-08, 5.5367128e-08, 5.5367128e-08, 5.5367128e-08]);
-    const movingMeanValue = layer2.getWeights()[2];
-    // TODO(cais): Update this to tf.keras.
-    expectTensorsClose(
-        movingMeanValue, [0.7128234, 0.7128234, 0.7128234, 0.7128234]);
-    const movingVarianceValue = layer2.getWeights()[3];
-    expectTensorsClose(
-        movingVarianceValue, [6.276868, 6.276868, 6.276868, 6.276868]);
-    const dense2KernelValue = layer3.getWeights()[0];
-    expectTensorsClose(
-        dense2KernelValue,
-        tensor2d(
-            [[0.18779878], [0.18779878], [0.18779878], [0.18779878]], [4, 1]));
-  });
-
-  // Python reference code:
-  // ```python
-  // import numpy as np
-  // from tensorflow import keras
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Conv2D(
-  //     4,
-  //     2,
-  //     kernel_initializer='ones',
-  //     bias_initializer='zeros',
-  //     input_shape=[5, 5, 1]))
-  // model.add(keras.layers.BatchNormalization())
-  // model.add(keras.layers.Flatten())
-  // model.add(keras.layers.Dense(
-  //     1,
-  //     kernel_initializer='ones',
-  //     bias_initializer='zeros'))
-  //
-  // model.compile(loss='mse', optimizer='sgd')
-  //
-  // xs = np.arange(2 * 5 * 5 * 1).reshape([2, 5, 5, 1])
-  // ys = np.array([[0], [1]])
-  // h = model.fit(xs, ys, epochs=3)
-  // print(h.history)
-  // ```
-  it('Fit: Wtih conv2d layer', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.conv2d({
-      filters: 4,
-      kernelSize: 2,
-      kernelInitializer: 'ones',
-      biasInitializer: 'zeros',
-      inputShape: [5, 5, 1]
-    }));
-    model.add(tfl.layers.batchNormalization());
-    model.add(tfl.layers.flatten());
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', biasInitializer: 'zeros'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const xsData = [];
-    for (let i = 0; i < 2 * 5 * 5 * 1; ++i) {
-      xsData.push(i);
-    }
-    const xs = tensor4d(xsData, [2, 5, 5, 1]);
-    const ys = tensor2d([0, 1], [2, 1]);
-
-    const h = await model.fit(xs, ys, {epochs: 2});
-    expectTensorsClose(
-        h.history.loss as number[], [3332.9971, 2122.5361], 0.01);
-  });
-
-  // Reference Python code:
-  // ```python
-  // import numpy as np
-  // import tensorflow as tf
-  // from tensorflow import keras
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Conv2DTranspose(
-  //     4,
-  //     2,
-  //     kernel_initializer='ones',
-  //     bias_initializer='zeros',
-  //     input_shape=[5, 5, 1]))
-  // model.add(keras.layers.BatchNormalization())
-  // model.add(keras.layers.Flatten())
-  // model.add(keras.layers.Dense(
-  //     1,
-  //     kernel_initializer='ones',
-  //     bias_initializer='zeros'))
-  //
-  // model.compile(loss='mse', optimizer='sgd')
-  //
-  // xs = np.arange(2 * 5 * 5 * 1).reshape([2, 5, 5, 1]).astype(np.float32)
-  // xs = (xs - 25.0) / 100.0
-  // ys = np.array([[0], [1]])
-  //
-  // print(model.layers[1].get_weights())
-  // h = model.fit(xs, ys, epochs=2)
-  // print(h.history)
-  // print(model.layers[1].get_weights())
-  // ```
-  it('Fit: Wtih conv2dTranspose layer', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.conv2dTranspose({
-      filters: 4,
-      kernelSize: 2,
-      kernelInitializer: 'ones',
-      biasInitializer: 'zeros',
-      inputShape: [5, 5, 1]
-    }));
-    model.add(tfl.layers.batchNormalization());
-    model.add(tfl.layers.flatten());
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', biasInitializer: 'zeros'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const xsData = [];
-    for (let i = 0; i < 2 * 5 * 5 * 1; ++i) {
-      xsData.push(i);
-    }
-    const xs =
-        div(sub(tensor4d(xsData, [2, 5, 5, 1]), scalar(25)), scalar(100));
-    const ys = tensor2d([0, 1], [2, 1]);
-
-    const h = await model.fit(xs, ys, {epochs: 2});
-    expect(h.history.loss[0]).toBeCloseTo(13922.4492);
-    expect(h.history.loss[1]).toBeCloseTo(106532048, -3);
-    const weights = model.layers[1].getWeights();
-    expect(weights.length).toEqual(4);
-    expectTensorsClose(
-        weights[0], tensor1d([7661.0874, 7661.0874, 7661.0874, 7661.0874]),
-        1e-2);
-    expectTensorsClose(
-        weights[1], tensor1d([-118.35103, -118.35103, -118.35103, -118.35103]),
-        1e-2);
-    expectTensorsClose(
-        weights[2],
-        tensor1d([-0.00026271, -0.00026271, -0.00026271, -0.00026271]));
-    expectTensorsClose(
-        weights[3], tensor1d([0.98333836, 0.98333836, 0.98333836, 0.98333836]));
-  });
-
-  // Use the following Python code to get the reference values for assertion:
-  // ```python
-  // from tensorflow import keras
-  // import numpy as np
-  //
-  // layer1 = keras.layers.BatchNormalization(input_shape=[2, 2])
-  // model = keras.Sequential([layer1])
-  //
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // xs = np.array(
-  //     [[[1, 2], [3, 4]], [[2, 4], [6, 8]], [[12, 11], [10, 9]]],
-  //     dtype=np.float32)
-  // ys = np.zeros([3, 2, 2], dtype=np.float32)
-  // print(layer1.get_weights())
-  // history = model.fit(xs, ys, epochs=2, batch_size=3)
-  // print(history.history)
-  // print(layer1.get_weights())
-  // ```
-  it('Fit: 3D, BatchNorm Layer Only', async () => {
-    const layer1 = tfl.layers.batchNormalization({inputShape: [2, 2]});
-    const model = tfl.sequential({layers: [layer1]});
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const xs1 = tensor3d(
-        [[[1, 2], [3, 4]], [[2, 4], [6, 8]], [[12, 11], [10, 9]]], [3, 2, 2]);
-    const ys = zeros([3, 2, 2]);
-    const history = await model.fit(xs1, ys, {epochs: 2, batchSize: 3});
-    expect(history.history['loss'][0]).toBeCloseTo(0.9999215006828308);
-    expect(history.history['loss'][1]).toBeCloseTo(0.980024516582489);
-    const gammaValue = layer1.getWeights()[0];
-    expectTensorsClose(gammaValue, [0.98010117, 0.98010194]);
-    const betaValue = layer1.getWeights()[1];
-    expectTensorsClose(betaValue, [-1.1175870e-09, 8.1956386e-10]);
-    const movingMeanValue = layer1.getWeights()[2];
-    expectTensorsClose(movingMeanValue, [0.11276666, 0.12603334]);
-    const movingVarianceValue = layer1.getWeights()[3];
-    expectTensorsClose(movingVarianceValue, [1.3161889, 1.1835222], 1e-5);
-  });
-});
-
-describeMathCPUAndGPU('LayerNormalization Layer: Symbolic', () => {
-  it('Invalid axis value leads to constructor error', () => {
-    expect(() => tfl.layers.layerNormalization({
-      // tslint:disable-next-line:no-any
-      axis: 'foo' as any
-    })).toThrowError(/Expected axis to be an integer/);
-    expect(() => tfl.layers.layerNormalization({
-      axis: 1.2
-    })).toThrowError(/Expected axis to be an integer/);
-    expect(() => tfl.layers.layerNormalization({
-      axis: [1, 1.5]
-    })).toThrowError(/Expected axis to be an array of integers/);
-  });
-
-  it('Serialization round trip', async () => {
-    const layer = tfl.layers.layerNormalization(
-        {axis: [-2, -1], center: true, scale: false});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.layerNormalization(tsConfig);
-    expect(layerPrime.getConfig()).toEqual(layer.getConfig());
-  });
-
-  it('Deserialize model with BatchNorm Layer', async () => {
-    // tslint:disable:max-line-length
-    const modelJSONString =
-        `{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "Dense", "config": {"name": "dense", "trainable": true, "batch_input_shape": [null, 5], "dtype": "float32", "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.2.4-tf", "backend": "tensorflow"}`;
-    // tslint:enable:max-line-length
-    const model = await tfl.models.modelFromJSON(JSON.parse(modelJSONString));
-    const ys = model.predict(zeros([3, 5])) as Tensor;
-    expect(ys.shape).toEqual([3, 1]);
-    expect(model.layers[1].getWeights().length).toEqual(4);
-  });
-});
-
-describeMathCPUAndGPU('LayerNormalization Layer: Tensor', () => {
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // layer = tf.keras.layers.LayerNormalization()
-  // xs = np.array([[1, 2, 3], [3, 6, 24]], dtype=np.float32)
-  // ys = layer(xs)
-  // print(ys)
-  // ```
-  it('Forward, 2D input, default axis', () => {
-    const layer = tfl.layers.layerNormalization();
-    const xs = tensor2d([[1, 2, 3], [3, 6, 24]]);
-    const ys = layer.apply(xs) as Tensor;
-    expectTensorsClose(
-        ys,
-        tensor2d(
-            [[-1.2238274, 0, 1.2238274], [-0.8626572, -0.5391607, 1.401818]]));
-  });
-
-  it('Forward: no memory leak', () => {
-    const layer = tfl.layers.layerNormalization();
-    const xs = tensor2d([[1, 2, 3], [3, 6, 24]]);
-    dispose(layer.apply(xs) as Tensor);  // Warm up.
-    const numTensors0 = memory().numTensors;
-    dispose(layer.apply(xs) as Tensor);
-    expect(memory().numTensors).toEqual(numTensors0);
-  });
-
-  it('Forward: configuration change', () => {
-    const layer = tfl.layers.layerNormalization({scale: false, center: false});
-    const xs = tensor2d([[1, 2, 3], [3, 6, 24]]);
-    dispose(layer.apply(xs) as Tensor);  // Warm up.
-    const numTensors0 = memory().numTensors;
-    dispose(layer.apply(xs, {scale: true, center: true}) as Tensor);
-    expect(memory().numTensors).toEqual(numTensors0);
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // layer = tf.keras.layers.LayerNormalization()
-  // xs = np.array([1, 2, 3, 6, 5, 4, 3, 6, 24, -10, 0, 5],
-  //               dtype=np.float32).reshape((2, 2, 3))
-  // ys = layer(xs)
-  // print(ys)
-  // ```
-  it('Forward, 3D input, default axis', () => {
-    const layer = tfl.layers.layerNormalization();
-    const xs = tensor3d([1, 2, 3, 6, 5, 4, 3, 6, 24, -10, 0, 5], [2, 2, 3]);
-    const ys = layer.apply(xs) as Tensor;
-    expectTensorsClose(
-        ys, tensor3d([
-          [[-1.2238274, 0, 1.2238274], [1.2238274, 0, -1.2238274]],
-          [
-            [-0.8626572, -0.5391607, 1.401818],
-            [-1.3362889, 0.26725778, 1.0690311]
-          ]
-        ]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // layer = tf.keras.layers.LayerNormalization(axis=[1, 2])
-  // xs = np.array([1, 2, 3, 6, 5, 4, 3, 6, 24, -10, 0, 5],
-  //               dtype=np.float32).reshape((2, 2, 3))
-  // ys = layer(xs)
-  // print(ys)
-  // ```
-  const nonDefaultAxisValues: number[][] = [[1, 2], [-2, -1]];
-  for (const nonDefaultAxis of nonDefaultAxisValues) {
-    it(`Forward, 3D input, non-default axis: ${nonDefaultAxis}`, () => {
-      const layer = tfl.layers.layerNormalization({axis: nonDefaultAxis});
-      const xs = tensor3d([1, 2, 3, 6, 5, 4, 3, 6, 24, -10, 0, 5], [2, 2, 3]);
-      const ys = layer.apply(xs) as Tensor;
-      expectTensorsClose(ys, tensor3d([
-                           [
-                             [-1.4635992, -0.8781595, -0.29271984],
-                             [1.4635992, 0.8781595, 0.29271984]
-                           ],
-                           [
-                             [-0.1645762, 0.13166097, 1.909084],
-                             [-1.4482707, -0.46081337, 0.03291526]
-                           ]
-                         ]));
-    });
-  }
-
-  it('Duplicate items in axis leads to constructor error', () => {
-    const layers = tfl.layers.layerNormalization({axis: [-2, -1, -1]});
-    const xs = tensor3d([1, 2, 3, 6, 5, 4, 3, 6, 24, -10, 0, 5], [2, 2, 3]);
-    expect(() => layers.apply(xs)).toThrowError(/duplicate axes/);
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.LayerNormalization(input_shape=(3,)))
-  // model.add(tf.keras.layers.Dense(1, kernel_initializer='ones'))
-  // model.compile(loss='mse', optimizer='sgd')
-  //
-  // xs = np.array([[1, 2, 3], [3, 6, 24], [10, 5, 0]], dtype=np.float32)
-  // ys = np.array([[0], [-1], [2]], dtype=np.float32)
-  // history = model.fit(xs, ys, epochs=5, verbose=0)
-  // print(history.history)
-  // ```
-  it('Training: 2D: default axis', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.layerNormalization({inputShape: [3]}));
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const xs = tensor2d([[1, 2, 3], [3, 6, 24], [10, 5, 0]]);
-    const ys = tensor2d([[0], [-1], [2]]);
-    const history = await model.fit(xs, ys, {epochs: 5});
-    test_util.expectArraysClose(history.history.loss as number[], [
-      1.6666666269302368, 1.4296358823776245, 1.2372404336929321,
-      1.0793765783309937, 0.9486551880836487
-    ]);
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.Dense(10, kernel_initializer='ones',
-  // input_shape=(3,))) model.add(tf.keras.layers.LayerNormalization())
-  // model.add(tf.keras.layers.Dense(1, kernel_initializer='ones'))
-  // model.compile(loss="mse", optimizer="sgd")
-  //
-  // xs = np.array([[1, 2, 3], [3, 6, 24], [10, 5, 0], [2, 7, 8]],
-  // dtype=np.float32) ys = np.array([[0], [-1], [2], [3]], dtype=np.float32)
-  // history = model.fit(xs, ys, epochs=5, verbose=0)
-  // print(history.history)
-  // ```
-  it('Training: 2D: as intermediate layer: default axis', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense(
-        {units: 10, kernelInitializer: 'ones', inputShape: [3]}));
-    model.add(tfl.layers.layerNormalization());
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const xs = tensor2d([[1, 2, 3], [3, 6, 24], [10, 5, 0], [2, 7, 8]]);
-    const ys = tensor2d([[0], [-1], [2], [3]]);
-    const history = await model.fit(xs, ys, {epochs: 5});
-    test_util.expectArraysClose(history.history.loss as number[], [
-      3.5, 3.1083502769470215, 2.8706729412078857, 2.7243311405181885,
-      2.6366190910339355
-    ]);
-  });
-
-  it('Training: no memory leak', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.layerNormalization({inputShape: [3]}));
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const xs = tensor2d([[1, 2, 3], [3, 6, 24], [10, 5, 0], [2, 7, 8]]);
-    const ys = tensor2d([[0], [-1], [2], [3]]);
-    await model.fit(xs, ys, {epochs: 1});  // Warm up.
-
-    const numTensors0 = memory().numTensors;
-    await model.fit(xs, ys, {epochs: 1});
-    expect(memory().numTensors).toEqual(numTensors0);
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.LayerNormalization(input_shape=(2, 3)))
-  // model.add(tf.keras.layers.Flatten())
-  // model.add(tf.keras.layers.Dense(1, kernel_initializer='ones'))
-  // model.compile(loss='mse', optimizer='sgd')
-  //
-  // xs = np.array([[[1, 2, 3], [3, 6, 24]], [[10, 5, 0], [2, 7, 8]]],
-  // dtype=np.float32) ys = np.array([[0], [-1]], dtype=np.float32) history =
-  // model.fit(xs, ys, epochs=5, verbose=0) print(history.history)
-  // ```
-  it('Training: 3D: default axis', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.layerNormalization({inputShape: [2, 3]}));
-    model.add(tfl.layers.flatten());
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const xs = tensor3d([[[1, 2, 3], [3, 6, 24]], [[10, 5, 0], [2, 7, 8]]]);
-    const ys = tensor2d([[0], [-1]]);
-    const history = await model.fit(xs, ys, {epochs: 5});
-    test_util.expectArraysClose(history.history.loss as number[], [
-      0.5, 0.33119967579841614, 0.23371894657611847, 0.171361044049263,
-      0.12831644713878632
-    ]);
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // model = tf.keras.Sequential()
-  // model.add(tf.keras.layers.LayerNormalization(input_shape=(2, 3), axis=[-2,
-  // -1])) model.add(tf.keras.layers.Flatten())
-  // model.add(tf.keras.layers.Dense(1, kernel_initializer='ones'))
-  // model.compile(loss='mse', optimizer='sgd')
-  //
-  // xs = np.array([[[1, 2, 3], [3, 6, 24]], [[10, 5, 0], [2, 7, 8]]],
-  // dtype=np.float32) ys = np.array([[0], [-1]], dtype=np.float32) history =
-  // model.fit(xs, ys, epochs=5, verbose=0) print(history.history)
-  // ```
-  it('Training: 3D: non-default axis', async () => {
-    const model = tfl.sequential();
-    model.add(
-        tfl.layers.layerNormalization({inputShape: [2, 3], axis: [-2, -1]}));
-    model.add(tfl.layers.flatten());
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const xs = tensor3d([[[1, 2, 3], [3, 6, 24]], [[10, 5, 0], [2, 7, 8]]]);
-    const ys = tensor2d([[0], [-1]]);
-    const history = await model.fit(xs, ys, {epochs: 5});
-    test_util.expectArraysClose(history.history.loss as number[], [
-      0.5, 0.3337608873844147, 0.23789873719215393, 0.17923809587955475,
-      0.1408553570508957
-    ]);
-  });
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // model = tf.keras.Sequential()
-  // embedding_layer = tf.keras.layers.Embedding(
-  //     input_dim=4, output_dim=3, input_length=4, mask_zero=True,
-  //     embeddings_initializer='ones')
-  // model.add(embedding_layer)
-  // model.add(tf.keras.layers.LayerNormalization())
-  //
-  // xs = np.array([[0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0], [1, 2, 3, 0]],
-  //               dtype=np.float32)
-  // model.predict(xs)
-  //
-  // embedding_layer.set_weights([
-  //     np.array([[1, 2, 3], [3, 2, 1], [2, 3, 1], [3, 1, 2]],
-  //     dtype=np.float32)])
-  // ys = model.predict(xs)
-  // print(ys)
-  // ```
-  it('Forward, with masking', () => {
-    const model = tfl.sequential();
-    const embeddingLayer = tfl.layers.embedding({
-      inputDim: 4,
-      outputDim: 3,
-      inputLength: 4,
-      maskZero: true,
-      embeddingsInitializer: 'ones'
-    });
-    model.add(embeddingLayer);
-    // model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.add(tfl.layers.layerNormalization());
-
-    const xs =
-        tensor2d([[0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0], [1, 2, 3, 0]]);
-    model.predict(xs);  // Make sure the embedding layer is built first.
-
-    embeddingLayer.setWeights(
-        [tensor2d([[1, 2, 3], [3, 2, 1], [2, 3, 1], [3, 1, 2]])]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(
-        ys, tensor3d([
-          [
-            [-1.2238274, 0, 1.2238274], [-1.2238274, 0, 1.2238274],
-            [-1.2238274, 0, 1.2238274], [-1.2238274, 0, 1.2238274]
-          ],
-          [
-            [1.2238274, 0, -1.2238274], [-1.2238274, 0, 1.2238274],
-            [-1.2238274, 0, 1.2238274], [-1.2238274, 0, 1.2238274]
-          ],
-          [
-            [1.2238274, 0, -1.2238274], [0, 1.2238274, -1.2238274],
-            [-1.2238274, 0, 1.2238274], [-1.2238274, 0, 1.2238274]
-          ],
-          [
-            [1.2238274, 0, -1.2238274], [0, 1.2238274, -1.2238274],
-            [1.2238274, -1.2238274, 0], [-1.2238274, 0, 1.2238274]
-          ]
-        ]));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/padding.ts b/tfjs-master/tfjs-layers/src/layers/padding.ts
deleted file mode 100644
index cdb3584f7..000000000
--- a/tfjs-master/tfjs-layers/src/layers/padding.ts
+++ /dev/null
@@ -1,237 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Padding Layers.
- */
-
-// Porting Note: In Python Keras, the padding layers are in convolutional.py,
-//   but we decided to put them in a separate file (padding.ts) for clarity.
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {serialization, Tensor, tidy} from '@tensorflow/tfjs-core';
-
-import {imageDataFormat} from '../backend/common';
-import {InputSpec, Layer, LayerArgs} from '../engine/topology';
-import {ValueError} from '../errors';
-import {DataFormat, Shape} from '../keras_format/common';
-import {Kwargs} from '../types';
-import {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';
-
-/**
- * Pads the middle dimension of a 3D tensor.
- *
- * @param x Input `tf.Tensor` to be padded.
- * @param padding `Array` of 2 integers, how many zeros to add at the start and
- *   end of the middle dimension (i.e., dimension 1).
- * @return A padded 3D `tf.Tensor`.
- */
-export function temporalPadding(x: Tensor, padding?: [number, number]): Tensor {
-  return tidy(() => {
-    if (x.rank !== 3) {
-      throw new ValueError(
-          `temporalPadding expects input tensor to be 3-D, but received a ` +
-          `${x.rank}-D tensor.`);
-    }
-
-    if (padding == null) {
-      padding = [1, 1];
-    }
-    if (padding.length !== 2) {
-      throw new ValueError(
-          `temporalPadding expects input padding pattern to be a length-2 ` +
-          `array, but received a length-${padding.length} array.`);
-    }
-
-    const pattern: Array<[number, number]> = [[0, 0], padding, [0, 0]];
-    return tfc.pad(x, pattern);
-  });
-}
-
-/**
- * Pads the 2nd and 3rd dimensions of a 4D tensor.
- *
- * @param x Input `tf.Tensor` to be padded.
- * @param padding `Array` of two `Array`s, each of which is an `Array` of two
- *   integers. The amount of padding at the beginning and end of the 2nd and 3rd
- *   dimensions, respectively.
- * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.
- * @return Padded 4D `tf.Tensor`.
- */
-export function spatial2dPadding(
-    x: Tensor, padding?: [[number, number], [number, number]],
-    dataFormat?: DataFormat): Tensor {
-  return tidy(() => {
-    if (x.rank !== 4) {
-      throw new ValueError(
-          `temporalPadding expects input tensor to be 4-D, but received a ` +
-          `${x.rank}-D tensor.`);
-    }
-
-    if (padding == null) {
-      padding = [[1, 1], [1, 1]];
-    }
-    if (padding.length !== 2 || padding[0].length !== 2 ||
-        padding[1].length !== 2) {
-      throw new ValueError(
-          'spatial2dPadding expects `padding` to be an Array of two Arrays, ' +
-          'each of which is an Array of two integers.');
-    }
-
-    if (dataFormat == null) {
-      dataFormat = imageDataFormat();
-    }
-    if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {
-      throw new ValueError(
-          `Unknown data format: ${dataFormat}. ` +
-          `Supported data formats are 'channelsLast' and 'channelsFirst.`);
-    }
-
-    let pattern: Array<[number, number]>;
-    if (dataFormat === 'channelsFirst') {
-      pattern = [[0, 0], [0, 0], padding[0], padding[1]];
-    } else {
-      pattern = [[0, 0], padding[0], padding[1], [0, 0]];
-    }
-
-    return tfc.pad(x, pattern);
-  });
-}
-
-export declare interface ZeroPadding2DLayerArgs extends LayerArgs {
-  /**
-   * Integer, or `Array` of 2 integers, or `Array` of 2 `Array`s, each of
-   * which is an `Array` of 2 integers.
-   * - If integer, the same symmetric padding is applied to width and height.
-   * - If `Array` of 2 integers, interpreted as two different symmetric values
-   *   for height and width:
-   *   `[symmetricHeightPad, symmetricWidthPad]`.
-   * - If `Array` of 2 `Array`s, interpreted as:
-   *   `[[topPad, bottomPad], [leftPad, rightPad]]`.
-   */
-  padding?: number|[number, number]|[[number, number], [number, number]];
-
-  /**
-   * One of `'channelsLast'` (default) and `'channelsFirst'`.
-   *
-   * The ordering of the dimensions in the inputs.
-   * `channelsLast` corresponds to inputs with shape
-   * `[batch, height, width, channels]` while `channelsFirst`
-   * corresponds to inputs with shape
-   * `[batch, channels, height, width]`.
-   */
-  dataFormat?: DataFormat;
-}
-
-export class ZeroPadding2D extends Layer {
-  /** @nocollapse */
-  static className = 'ZeroPadding2D';
-  readonly dataFormat: DataFormat;
-  readonly padding: [[number, number], [number, number]];
-
-  constructor(args?: ZeroPadding2DLayerArgs) {
-    if (args == null) {
-      args = {};
-    }
-    super(args);
-
-    this.dataFormat =
-        args.dataFormat == null ? imageDataFormat() : args.dataFormat;
-    // TODO(cais): Maybe refactor the following logic surrounding `padding`
-    //   into a helper method.
-    if (args.padding == null) {
-      this.padding = [[1, 1], [1, 1]];
-    } else if (typeof args.padding === 'number') {
-      this.padding =
-          [[args.padding, args.padding], [args.padding, args.padding]];
-    } else {
-      args.padding = args.padding;
-      if (args.padding.length !== 2) {
-        throw new ValueError(
-            `ZeroPadding2D expects padding to be a length-2 array, but ` +
-            `received a length-${args.padding.length} array.`);
-      }
-
-      let heightPadding: [number, number];
-      let widthPadding: [number, number];
-      if (typeof args.padding[0] === 'number') {
-        heightPadding = [args.padding[0], args.padding[0]];
-        widthPadding = [args.padding[1] as number, args.padding[1] as number];
-      } else {
-        args.padding = args.padding as [[number, number], [number, number]];
-
-        if (args.padding[0].length !== 2) {
-          throw new ValueError(
-              `ZeroPadding2D expects height padding to be a length-2 array, ` +
-              `but received a length-${args.padding[0].length} array.`);
-        }
-        heightPadding = args.padding[0] as [number, number];
-
-        if (args.padding[1].length !== 2) {
-          throw new ValueError(
-              `ZeroPadding2D expects width padding to be a length-2 array, ` +
-              `but received a length-${args.padding[1].length} array.`);
-        }
-        widthPadding = args.padding[1] as [number, number];
-      }
-      this.padding = [heightPadding, widthPadding];
-    }
-    this.inputSpec = [new InputSpec({ndim: 4})];
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-
-    let rows: number;
-    let cols: number;
-    if (this.dataFormat === 'channelsFirst') {
-      if (inputShape[2] != null && inputShape[2] >= 0) {
-        rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];
-      } else {
-        rows = null;
-      }
-      if (inputShape[3] != null && inputShape[3] >= 0) {
-        cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];
-      } else {
-        cols = null;
-      }
-      return [inputShape[0], inputShape[1], rows, cols];
-    } else {
-      if (inputShape[1] != null && inputShape[1] >= 0) {
-        rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];
-      } else {
-        rows = null;
-      }
-      if (inputShape[2] != null && inputShape[2] >= 0) {
-        cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];
-      } else {
-        cols = null;
-      }
-      return [inputShape[0], rows, cols, inputShape[3]];
-    }
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(
-        () => spatial2dPadding(
-            getExactlyOneTensor(inputs), this.padding, this.dataFormat));
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      padding: this.padding,
-      dataFormat: this.dataFormat,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-serialization.registerClass(ZeroPadding2D);
diff --git a/tfjs-master/tfjs-layers/src/layers/padding_test.ts b/tfjs-master/tfjs-layers/src/layers/padding_test.ts
deleted file mode 100644
index bb256a35f..000000000
--- a/tfjs-master/tfjs-layers/src/layers/padding_test.ts
+++ /dev/null
@@ -1,233 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit Tests for Padding Layers.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {Tensor, tensor3d} from '@tensorflow/tfjs-core';
-
-import {SymbolicTensor} from '../engine/topology';
-import * as tfl from '../index';
-import {DataFormat} from '../keras_format/common';
-import {convertPythonicToTs, convertTsToPythonic} from '../utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndGPU, expectTensorsClose} from '../utils/test_utils';
-
-import {spatial2dPadding, temporalPadding, ZeroPadding2DLayerArgs} from './padding';
-
-describeMathCPUAndGPU('temporalPadding', () => {
-  it('default padding 1-1', () => {
-    const x = tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]);
-    const y = temporalPadding(x);
-    expectTensorsClose(
-        y, tensor3d([
-          [[0, 0], [1, 2], [3, 4], [0, 0]], [[0, 0], [-1, -2], [-3, -4], [0, 0]]
-        ]));
-  });
-  it('custom padding 2-2', () => {
-    const x = tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]);
-    const y = temporalPadding(x, [2, 2]);
-    expectTensorsClose(y, tensor3d([
-                         [[0, 0], [0, 0], [1, 2], [3, 4], [0, 0], [0, 0]],
-                         [[0, 0], [0, 0], [-1, -2], [-3, -4], [0, 0], [0, 0]]
-                       ]));
-  });
-});
-
-describeMathCPUAndGPU('spatial2dPadding', () => {
-  it('default padding 1-1-1-1', () => {
-    const x = tfc.ones([2, 3, 4, 3]);
-    const y = spatial2dPadding(x);
-
-    expect(y.shape).toEqual([2, 5, 6, 3]);
-    expectTensorsClose(tfc.slice(y, [0, 1, 1, 0], [2, 3, 4, 3]), x);
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 1, 6, 3]), tfc.zeros([2, 1, 6, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 4, 0, 0], [2, 1, 6, 3]), tfc.zeros([2, 1, 6, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 5, 1, 3]), tfc.zeros([2, 5, 1, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 5, 0], [2, 5, 1, 3]), tfc.zeros([2, 5, 1, 3]));
-  });
-
-  it('custom padding 2-2-3-0', () => {
-    const x = tfc.ones([2, 3, 4, 3]);
-    const y = spatial2dPadding(x, [[2, 2], [3, 0]]);
-    expect(y.shape).toEqual([2, 7, 7, 3]);
-
-    expectTensorsClose(tfc.slice(y, [0, 2, 3, 0], [2, 3, 4, 3]), x);
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 2, 7, 3]), tfc.zeros([2, 2, 7, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 5, 0, 0], [2, 2, 7, 3]), tfc.zeros([2, 2, 7, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 7, 3, 3]), tfc.zeros([2, 7, 3, 3]));
-  });
-});
-
-describeMathCPU('ZeroPadding2D: Symbolic', () => {
-  const dataFormats: DataFormat[] =
-      [undefined, 'channelsFirst', 'channelsLast'];
-
-  for (const dataFormat of dataFormats) {
-    it('Default padding 1-1-1-1, dataFormat=' + dataFormat, () => {
-      const x = new SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-      const layer = tfl.layers.zeroPadding2d({dataFormat});
-      const y = layer.apply(x) as SymbolicTensor;
-      expect(y.dtype).toEqual('float32');
-      if (dataFormat === 'channelsFirst') {
-        expect(y.shape).toEqual([1, 2, 5, 6]);
-      } else {
-        expect(y.shape).toEqual([1, 4, 5, 4]);
-      }
-    });
-
-    it('All symmetric padding 2, dataFormat=' + dataFormat, () => {
-      const x = new SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-      const layer = tfl.layers.zeroPadding2d({dataFormat, padding: 2});
-      const y = layer.apply(x) as SymbolicTensor;
-      expect(y.dtype).toEqual('float32');
-      if (dataFormat === 'channelsFirst') {
-        expect(y.shape).toEqual([1, 2, 7, 8]);
-      } else {
-        expect(y.shape).toEqual([1, 6, 7, 4]);
-      }
-    });
-
-    it('Symmetric padding 2-3, dataFormat=' + dataFormat, () => {
-      const x = new SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-      const layer = tfl.layers.zeroPadding2d({dataFormat, padding: [2, 3]});
-      const y = layer.apply(x) as SymbolicTensor;
-      expect(y.dtype).toEqual('float32');
-      if (dataFormat === 'channelsFirst') {
-        expect(y.shape).toEqual([1, 2, 7, 10]);
-      } else {
-        expect(y.shape).toEqual([1, 6, 9, 4]);
-      }
-    });
-
-    it('Asymmetric padding 2-3-4-5, dataFormat=' + dataFormat, () => {
-      const x = new SymbolicTensor('float32', [1, 2, 3, 4], null, [], null);
-      const layer =
-          tfl.layers.zeroPadding2d({dataFormat, padding: [[2, 3], [4, 5]]});
-      const y = layer.apply(x) as SymbolicTensor;
-      expect(y.dtype).toEqual('float32');
-      if (dataFormat === 'channelsFirst') {
-        expect(y.shape).toEqual([1, 2, 8, 13]);
-      } else {
-        expect(y.shape).toEqual([1, 7, 12, 4]);
-      }
-    });
-  }
-
-  it('Incorrect array length leads to error', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.zeroPadding2d({padding: [2, 3, 4]} as any))
-        .toThrowError(/length-2 array/);
-  });
-
-  it('Incorrect height array length leads to error', () => {
-    // tslint:disable:no-any
-    expect(
-        () => tfl.layers.zeroPadding2d({padding: [[2, 3, 4], [5, 6]]} as any))
-        .toThrowError(/height.*length-2 array/);
-    // tslint:enable:no-any
-  });
-
-  it('Incorrect height array length leads to error', () => {
-    // tslint:disable:no-any
-    expect(
-        () => tfl.layers.zeroPadding2d({padding: [[1, 1], [2, 3, 4]]} as any))
-        .toThrowError(/width.*length-2 array/);
-    // tslint:enable:no-any
-  });
-
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.zeroPadding2d({padding: [2, 4]});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    const tsConfig = convertPythonicToTs(pythonicConfig);
-    const layerPrime = tfl.layers.zeroPadding2d(
-                           tsConfig as ZeroPadding2DLayerArgs);
-    expect(layerPrime.padding).toEqual(layer.padding);
-    expect(layerPrime.dataFormat).toEqual(layer.dataFormat);
-  });
-});
-
-describeMathCPUAndGPU('ZeroPadding2D: Tensor', () => {
-  it('Default padding 1-1-1-1, channelsLast', () => {
-    const x = tfc.ones([2, 2, 2, 3]);
-    const layer = tfl.layers.zeroPadding2d();
-    const y = layer.apply(x) as Tensor;
-    expect(y.shape).toEqual([2, 4, 4, 3]);
-
-    expectTensorsClose(tfc.slice(y, [0, 1, 1, 0], [2, 2, 2, 3]), x);
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 1, 4, 3]), tfc.zeros([2, 1, 4, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 3, 0, 0], [2, 1, 4, 3]), tfc.zeros([2, 1, 4, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 4, 1, 3]), tfc.zeros([2, 4, 1, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 3, 0], [2, 4, 1, 3]), tfc.zeros([2, 4, 1, 3]));
-  });
-
-  it('Default padding 1-1-1-1, channelFirst', () => {
-    const x = tfc.ones([2, 3, 2, 2]);
-    const layer = tfl.layers.zeroPadding2d({dataFormat: 'channelsFirst'});
-    const y = layer.apply(x) as Tensor;
-    expect(y.shape).toEqual([2, 3, 4, 4]);
-
-    expectTensorsClose(tfc.slice(y, [0, 0, 1, 1], [2, 3, 2, 2]), x);
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 3, 1, 4]), tfc.zeros([2, 3, 1, 4]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 3, 0], [2, 3, 1, 4]), tfc.zeros([2, 3, 1, 4]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 3, 4, 1]), tfc.zeros([2, 3, 4, 1]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 3], [2, 3, 4, 1]), tfc.zeros([2, 3, 4, 1]));
-  });
-
-  it('Symmetric padding 2-2, channelsLast', () => {
-    const x = tfc.ones([2, 2, 2, 3]);
-    const layer = tfl.layers.zeroPadding2d({padding: [2, 2]});
-    const y = layer.apply(x) as Tensor;
-    expect(y.shape).toEqual([2, 6, 6, 3]);
-
-    expectTensorsClose(tfc.slice(y, [0, 2, 2, 0], [2, 2, 2, 3]), x);
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 2, 6, 3]), tfc.zeros([2, 2, 6, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 4, 0, 0], [2, 2, 6, 3]), tfc.zeros([2, 2, 6, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 6, 2, 3]), tfc.zeros([2, 6, 2, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 4, 0], [2, 6, 2, 3]), tfc.zeros([2, 6, 2, 3]));
-  });
-
-  it('Asymmetric padding 2-1-2-1, channelsLast', () => {
-    const x = tfc.ones([2, 2, 2, 3]);
-    const layer = tfl.layers.zeroPadding2d({padding: [[2, 1], [2, 1]]});
-    const y = layer.apply(x) as Tensor;
-    expect(y.shape).toEqual([2, 5, 5, 3]);
-
-    expectTensorsClose(tfc.slice(y, [0, 2, 2, 0], [2, 2, 2, 3]), x);
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 2, 5, 3]), tfc.zeros([2, 2, 5, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 4, 0, 0], [2, 1, 5, 3]), tfc.zeros([2, 1, 5, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 0, 0], [2, 5, 2, 3]), tfc.zeros([2, 5, 2, 3]));
-    expectTensorsClose(
-        tfc.slice(y, [0, 0, 4, 0], [2, 5, 1, 3]), tfc.zeros([2, 5, 1, 3]));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/pooling.ts b/tfjs-master/tfjs-layers/src/layers/pooling.ts
deleted file mode 100644
index e6072f2d2..000000000
--- a/tfjs-master/tfjs-layers/src/layers/pooling.ts
+++ /dev/null
@@ -1,701 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * TensorFlow.js Layers: Pooling Layers.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {serialization, Tensor, Tensor3D, Tensor4D, Tensor5D, tidy} from '@tensorflow/tfjs-core';
-
-import {imageDataFormat} from '../backend/common';
-import * as K from '../backend/tfjs_backend';
-import {checkDataFormat, checkPaddingMode, checkPoolMode} from '../common';
-import {InputSpec} from '../engine/topology';
-import {Layer, LayerArgs} from '../engine/topology';
-import {NotImplementedError, ValueError} from '../errors';
-import {DataFormat, PaddingMode, PoolMode, Shape} from '../keras_format/common';
-import {Kwargs} from '../types';
-import {convOutputLength} from '../utils/conv_utils';
-import {assertPositiveInteger} from '../utils/generic_utils';
-import {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';
-
-import {preprocessConv2DInput, preprocessConv3DInput} from './convolutional';
-
-/**
- * 2D pooling.
- * @param x
- * @param poolSize
- * @param strides strides. Defaults to [1, 1].
- * @param padding padding. Defaults to 'valid'.
- * @param dataFormat data format. Defaults to 'channelsLast'.
- * @param poolMode Mode of pooling. Defaults to 'max'.
- * @returns Result of the 2D pooling.
- */
-export function pool2d(
-    x: Tensor, poolSize: [number, number], strides?: [number, number],
-    padding?: PaddingMode, dataFormat?: DataFormat,
-    poolMode?: PoolMode): Tensor {
-  return tidy(() => {
-    checkDataFormat(dataFormat);
-    checkPoolMode(poolMode);
-    checkPaddingMode(padding);
-    if (strides == null) {
-      strides = [1, 1];
-    }
-    if (padding == null) {
-      padding = 'valid';
-    }
-    if (dataFormat == null) {
-      dataFormat = imageDataFormat();
-    }
-    if (poolMode == null) {
-      poolMode = 'max';
-    }
-
-    // TODO(cais): Remove the preprocessing step once deeplearn.js supports
-    // dataFormat as an input argument.
-    x = preprocessConv2DInput(x, dataFormat);  // x is NHWC after preprocessing.
-    let y: Tensor;
-    const paddingString = (padding === 'same') ? 'same' : 'valid';
-    if (poolMode === 'max') {
-      // TODO(cais): Rank check?
-      y = tfc.maxPool(x as Tensor4D, poolSize, strides, paddingString);
-    } else {  // 'avg'
-      // TODO(cais): Check the dtype and rank of x and give clear error message
-      //   if those are incorrect.
-      y = tfc.avgPool(
-          // TODO(cais): Rank check?
-          x as Tensor3D | Tensor4D, poolSize, strides, paddingString);
-    }
-    if (dataFormat === 'channelsFirst') {
-      y = tfc.transpose(y, [0, 3, 1, 2]);  // NHWC -> NCHW.
-    }
-    return y;
-  });
-}
-
-/**
- * 3D pooling.
- * @param x
- * @param poolSize. Default to [1, 1, 1].
- * @param strides strides. Defaults to [1, 1, 1].
- * @param padding padding. Defaults to 'valid'.
- * @param dataFormat data format. Defaults to 'channelsLast'.
- * @param poolMode Mode of pooling. Defaults to 'max'.
- * @returns Result of the 3D pooling.
- */
-export function pool3d(
-    x: Tensor5D, poolSize: [number, number, number],
-    strides?: [number, number, number], padding?: PaddingMode,
-    dataFormat?: DataFormat, poolMode?: PoolMode): Tensor {
-  return tidy(() => {
-    checkDataFormat(dataFormat);
-    checkPoolMode(poolMode);
-    checkPaddingMode(padding);
-    if (strides == null) {
-      strides = [1, 1, 1];
-    }
-    if (padding == null) {
-      padding = 'valid';
-    }
-    if (dataFormat == null) {
-      dataFormat = imageDataFormat();
-    }
-    if (poolMode == null) {
-      poolMode = 'max';
-    }
-
-    // x is NDHWC after preprocessing.
-    x = preprocessConv3DInput(x as Tensor, dataFormat) as Tensor5D;
-    let y: Tensor;
-    const paddingString = (padding === 'same') ? 'same' : 'valid';
-    if (poolMode === 'max') {
-      y = tfc.maxPool3d(x, poolSize, strides, paddingString);
-    } else {  // 'avg'
-      y = tfc.avgPool3d(x, poolSize, strides, paddingString);
-    }
-    if (dataFormat === 'channelsFirst') {
-      y = tfc.transpose(y, [0, 4, 1, 2, 3]);  // NDHWC -> NCDHW.
-    }
-    return y;
-  });
-}
-
-export declare interface Pooling1DLayerArgs extends LayerArgs {
-  /**
-   * Size of the window to pool over, should be an integer.
-   */
-  poolSize?: number|[number];
-  /**
-   * Period at which to sample the pooled values.
-   *
-   * If `null`, defaults to `poolSize`.
-   */
-  strides?: number|[number];
-  /** How to fill in data that's not an integer multiple of poolSize. */
-  padding?: PaddingMode;
-}
-
-/**
- * Abstract class for different pooling 1D layers.
- */
-export abstract class Pooling1D extends Layer {
-  protected readonly poolSize: [number];
-  protected readonly strides: [number];
-  protected readonly padding: PaddingMode;
-
-  /**
-   *
-   * @param args Parameters for the Pooling layer.
-   *
-   * config.poolSize defaults to 2.
-   */
-  constructor(args: Pooling1DLayerArgs) {
-    if (args.poolSize == null) {
-      args.poolSize = 2;
-    }
-    super(args);
-    if (typeof args.poolSize === 'number') {
-      this.poolSize = [args.poolSize];
-    } else if (
-        Array.isArray(args.poolSize) &&
-        (args.poolSize as number[]).length === 1 &&
-        typeof (args.poolSize as number[])[0] === 'number') {
-      this.poolSize = args.poolSize;
-    } else {
-      throw new ValueError(
-          `poolSize for 1D convolutional layer must be a number or an ` +
-          `Array of a single number, but received ` +
-          `${JSON.stringify(args.poolSize)}`);
-    }
-    assertPositiveInteger(this.poolSize, 'poolSize');
-    if (args.strides == null) {
-      this.strides = this.poolSize;
-    } else {
-      if (typeof args.strides === 'number') {
-        this.strides = [args.strides];
-      } else if (
-          Array.isArray(args.strides) &&
-          (args.strides as number[]).length === 1 &&
-          typeof (args.strides as number[])[0] === 'number') {
-        this.strides = args.strides;
-      } else {
-        throw new ValueError(
-            `strides for 1D convolutional layer must be a number or an ` +
-            `Array of a single number, but received ` +
-            `${JSON.stringify(args.strides)}`);
-      }
-    }
-    assertPositiveInteger(this.strides, 'strides');
-
-    this.padding = args.padding == null ? 'valid' : args.padding;
-    checkPaddingMode(this.padding);
-    this.inputSpec = [new InputSpec({ndim: 3})];
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const length = convOutputLength(
-        inputShape[1], this.poolSize[0], this.padding, this.strides[0]);
-    return [inputShape[0], length, inputShape[2]];
-  }
-
-  protected abstract poolingFunction(
-      inputs: Tensor, poolSize: [number, number], strides: [number, number],
-      padding: PaddingMode, dataFormat: DataFormat): Tensor;
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      // Add dummy last dimension.
-      inputs = K.expandDims(getExactlyOneTensor(inputs), 2);
-      const output = this.poolingFunction(
-          getExactlyOneTensor(inputs), [this.poolSize[0], 1],
-          [this.strides[0], 1], this.padding, 'channelsLast');
-      // Remove dummy last dimension.
-      return tfc.squeeze(output, [2]);
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      poolSize: this.poolSize,
-      padding: this.padding,
-      strides: this.strides,
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-
-export class MaxPooling1D extends Pooling1D {
-  /** @nocollapse */
-  static className = 'MaxPooling1D';
-  constructor(args: Pooling1DLayerArgs) {
-    super(args);
-  }
-
-  protected poolingFunction(
-      inputs: Tensor, poolSize: [number, number], strides: [number, number],
-      padding: PaddingMode, dataFormat: DataFormat): Tensor {
-    checkDataFormat(dataFormat);
-    checkPaddingMode(padding);
-    return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');
-  }
-}
-serialization.registerClass(MaxPooling1D);
-
-export class AveragePooling1D extends Pooling1D {
-  /** @nocollapse */
-  static className = 'AveragePooling1D';
-  constructor(args: Pooling1DLayerArgs) {
-    super(args);
-  }
-
-  protected poolingFunction(
-      inputs: Tensor, poolSize: [number, number], strides: [number, number],
-      padding: PaddingMode, dataFormat: DataFormat): Tensor {
-    checkDataFormat(dataFormat);
-    checkPaddingMode(padding);
-    return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');
-  }
-}
-serialization.registerClass(AveragePooling1D);
-
-export declare interface Pooling2DLayerArgs extends LayerArgs {
-  /**
-   * Factors by which to downscale in each dimension [vertical, horizontal].
-   * Expects an integer or an array of 2 integers.
-   *
-   * For example, `[2, 2]` will halve the input in both spatial dimensions.
-   * If only one integer is specified, the same window length
-   * will be used for both dimensions.
-   */
-  poolSize?: number|[number, number];
-
-  /**
-   * The size of the stride in each dimension of the pooling window. Expects
-   * an integer or an array of 2 integers. Integer, tuple of 2 integers, or
-   * None.
-   *
-   * If `null`, defaults to `poolSize`.
-   */
-  strides?: number|[number, number];
-
-  /** The padding type to use for the pooling layer. */
-  padding?: PaddingMode;
-  /** The data format to use for the pooling layer. */
-  dataFormat?: DataFormat;
-}
-
-/**
- * Abstract class for different pooling 2D layers.
- */
-export abstract class Pooling2D extends Layer {
-  protected readonly poolSize: [number, number];
-  protected readonly strides: [number, number];
-  protected readonly padding: PaddingMode;
-  protected readonly dataFormat: DataFormat;
-
-  constructor(args: Pooling2DLayerArgs) {
-    if (args.poolSize == null) {
-      args.poolSize = [2, 2];
-    }
-    super(args);
-    this.poolSize = Array.isArray(args.poolSize) ?
-        args.poolSize :
-        [args.poolSize, args.poolSize];
-    if (args.strides == null) {
-      this.strides = this.poolSize;
-    } else if (Array.isArray(args.strides)) {
-      if (args.strides.length !== 2) {
-        throw new ValueError(
-            `If the strides property of a 2D pooling layer is an Array, ` +
-            `it is expected to have a length of 2, but received length ` +
-            `${args.strides.length}.`);
-      }
-      this.strides = args.strides;
-    } else {
-      // `config.strides` is a number.
-      this.strides = [args.strides, args.strides];
-    }
-    assertPositiveInteger(this.poolSize, 'poolSize');
-    assertPositiveInteger(this.strides, 'strides');
-    this.padding = args.padding == null ? 'valid' : args.padding;
-    this.dataFormat =
-        args.dataFormat == null ? 'channelsLast' : args.dataFormat;
-    checkDataFormat(this.dataFormat);
-    checkPaddingMode(this.padding);
-
-    this.inputSpec = [new InputSpec({ndim: 4})];
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    let rows =
-        this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];
-    let cols =
-        this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];
-    rows =
-        convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);
-    cols =
-        convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);
-    if (this.dataFormat === 'channelsFirst') {
-      return [inputShape[0], inputShape[1], rows, cols];
-    } else {
-      return [inputShape[0], rows, cols, inputShape[3]];
-    }
-  }
-
-  protected abstract poolingFunction(
-      inputs: Tensor, poolSize: [number, number], strides: [number, number],
-      padding: PaddingMode, dataFormat: DataFormat): Tensor;
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      return this.poolingFunction(
-          getExactlyOneTensor(inputs), this.poolSize, this.strides,
-          this.padding, this.dataFormat);
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      poolSize: this.poolSize,
-      padding: this.padding,
-      strides: this.strides,
-      dataFormat: this.dataFormat
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-
-export class MaxPooling2D extends Pooling2D {
-  /** @nocollapse */
-  static className = 'MaxPooling2D';
-  constructor(args: Pooling2DLayerArgs) {
-    super(args);
-  }
-
-  protected poolingFunction(
-      inputs: Tensor, poolSize: [number, number], strides: [number, number],
-      padding: PaddingMode, dataFormat: DataFormat): Tensor {
-    checkDataFormat(dataFormat);
-    checkPaddingMode(padding);
-    return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');
-  }
-}
-serialization.registerClass(MaxPooling2D);
-
-export class AveragePooling2D extends Pooling2D {
-  /** @nocollapse */
-  static className = 'AveragePooling2D';
-  constructor(args: Pooling2DLayerArgs) {
-    super(args);
-  }
-
-  protected poolingFunction(
-      inputs: Tensor, poolSize: [number, number], strides: [number, number],
-      padding: PaddingMode, dataFormat: DataFormat): Tensor {
-    checkDataFormat(dataFormat);
-    checkPaddingMode(padding);
-    return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');
-  }
-}
-serialization.registerClass(AveragePooling2D);
-
-export declare interface Pooling3DLayerArgs extends LayerArgs {
-  /**
-   * Factors by which to downscale in each dimension [depth, height, width].
-   * Expects an integer or an array of 3 integers.
-   *
-   * For example, `[2, 2, 2]` will halve the input in three dimensions.
-   * If only one integer is specified, the same window length
-   * will be used for all dimensions.
-   */
-  poolSize?: number|[number, number, number];
-
-  /**
-   * The size of the stride in each dimension of the pooling window. Expects
-   * an integer or an array of 3 integers. Integer, tuple of 3 integers, or
-   * None.
-   *
-   * If `null`, defaults to `poolSize`.
-   */
-  strides?: number|[number, number, number];
-
-  /** The padding type to use for the pooling layer. */
-  padding?: PaddingMode;
-  /** The data format to use for the pooling layer. */
-  dataFormat?: DataFormat;
-}
-
-/**
- * Abstract class for different pooling 3D layers.
- */
-export abstract class Pooling3D extends Layer {
-  protected readonly poolSize: [number, number, number];
-  protected readonly strides: [number, number, number];
-  protected readonly padding: PaddingMode;
-  protected readonly dataFormat: DataFormat;
-
-  constructor(args: Pooling3DLayerArgs) {
-    if (args.poolSize == null) {
-      args.poolSize = [2, 2, 2];
-    }
-    super(args);
-    this.poolSize = Array.isArray(args.poolSize) ?
-        args.poolSize :
-        [args.poolSize, args.poolSize, args.poolSize];
-    if (args.strides == null) {
-      this.strides = this.poolSize;
-    } else if (Array.isArray(args.strides)) {
-      if (args.strides.length !== 3) {
-        throw new ValueError(
-            `If the strides property of a 3D pooling layer is an Array, ` +
-            `it is expected to have a length of 3, but received length ` +
-            `${args.strides.length}.`);
-      }
-      this.strides = args.strides;
-    } else {
-      // `config.strides` is a number.
-      this.strides = [args.strides, args.strides, args.strides];
-    }
-    assertPositiveInteger(this.poolSize, 'poolSize');
-    assertPositiveInteger(this.strides, 'strides');
-    this.padding = args.padding == null ? 'valid' : args.padding;
-    this.dataFormat =
-        args.dataFormat == null ? 'channelsLast' : args.dataFormat;
-    checkDataFormat(this.dataFormat);
-    checkPaddingMode(this.padding);
-
-    this.inputSpec = [new InputSpec({ndim: 5})];
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    let depths =
-        this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];
-    let rows =
-        this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];
-    let cols =
-        this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];
-    depths = convOutputLength(
-        depths, this.poolSize[0], this.padding, this.strides[0]);
-    rows =
-        convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);
-    cols =
-        convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);
-    if (this.dataFormat === 'channelsFirst') {
-      return [inputShape[0], inputShape[1], depths, rows, cols];
-    } else {
-      return [inputShape[0], depths, rows, cols, inputShape[4]];
-    }
-  }
-
-  protected abstract poolingFunction(
-      inputs: Tensor, poolSize: [number, number, number],
-      strides: [number, number, number], padding: PaddingMode,
-      dataFormat: DataFormat): Tensor;
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      this.invokeCallHook(inputs, kwargs);
-      return this.poolingFunction(
-          getExactlyOneTensor(inputs), this.poolSize, this.strides,
-          this.padding, this.dataFormat);
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {
-      poolSize: this.poolSize,
-      padding: this.padding,
-      strides: this.strides,
-      dataFormat: this.dataFormat
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-
-export class MaxPooling3D extends Pooling3D {
-  /** @nocollapse */
-  static className = 'MaxPooling3D';
-  constructor(args: Pooling3DLayerArgs) {
-    super(args);
-  }
-
-  protected poolingFunction(
-      inputs: Tensor, poolSize: [number, number, number],
-      strides: [number, number, number], padding: PaddingMode,
-      dataFormat: DataFormat): Tensor {
-    checkDataFormat(dataFormat);
-    checkPaddingMode(padding);
-    return pool3d(
-        inputs as Tensor5D, poolSize, strides, padding, dataFormat, 'max');
-  }
-}
-serialization.registerClass(MaxPooling3D);
-
-export class AveragePooling3D extends Pooling3D {
-  /** @nocollapse */
-  static className = 'AveragePooling3D';
-  constructor(args: Pooling3DLayerArgs) {
-    super(args);
-  }
-
-  protected poolingFunction(
-      inputs: Tensor, poolSize: [number, number, number],
-      strides: [number, number, number], padding: PaddingMode,
-      dataFormat: DataFormat): Tensor {
-    checkDataFormat(dataFormat);
-    checkPaddingMode(padding);
-    return pool3d(
-        inputs as Tensor5D, poolSize, strides, padding, dataFormat, 'avg');
-  }
-}
-serialization.registerClass(AveragePooling3D);
-
-/**
- * Abstract class for different global pooling 1D layers.
- */
-export abstract class GlobalPooling1D extends Layer {
-  constructor(args: LayerArgs) {
-    super(args);
-    this.inputSpec = [new InputSpec({ndim: 3})];
-  }
-
-  override computeOutputShape(inputShape: Shape): Shape {
-    return [inputShape[0], inputShape[2]];
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    throw new NotImplementedError();
-  }
-}
-
-export class GlobalAveragePooling1D extends GlobalPooling1D {
-  /** @nocollapse */
-  static className = 'GlobalAveragePooling1D';
-  constructor(args?: LayerArgs) {
-    super(args || {});
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      const input = getExactlyOneTensor(inputs);
-      return tfc.mean(input, 1);
-    });
-  }
-}
-serialization.registerClass(GlobalAveragePooling1D);
-
-export class GlobalMaxPooling1D extends GlobalPooling1D {
-  /** @nocollapse */
-  static className = 'GlobalMaxPooling1D';
-  constructor(args: LayerArgs) {
-    super(args || {});
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      const input = getExactlyOneTensor(inputs);
-      return tfc.max(input, 1);
-    });
-  }
-}
-serialization.registerClass(GlobalMaxPooling1D);
-
-export declare interface GlobalPooling2DLayerArgs extends LayerArgs {
-  /**
-   * One of `CHANNEL_LAST` (default) or `CHANNEL_FIRST`.
-   *
-   * The ordering of the dimensions in the inputs. `CHANNEL_LAST` corresponds
-   * to inputs with shape `[batch, height, width, channels]` while
-   * `CHANNEL_FIRST` corresponds to inputs with shape
-   * `[batch, channels, height, width]`.
-   */
-  dataFormat?: DataFormat;
-}
-
-/**
- * Abstract class for different global pooling 2D layers.
- */
-export abstract class GlobalPooling2D extends Layer {
-  protected dataFormat: DataFormat;
-  constructor(args: GlobalPooling2DLayerArgs) {
-    super(args);
-    this.dataFormat =
-        args.dataFormat == null ? 'channelsLast' : args.dataFormat;
-    checkDataFormat(this.dataFormat);
-    this.inputSpec = [new InputSpec({ndim: 4})];
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = inputShape as Shape;
-    if (this.dataFormat === 'channelsLast') {
-      return [inputShape[0], inputShape[3]];
-    } else {
-      return [inputShape[0], inputShape[1]];
-    }
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    throw new NotImplementedError();
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config = {dataFormat: this.dataFormat};
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-}
-
-export class GlobalAveragePooling2D extends GlobalPooling2D {
-  /** @nocollapse */
-  static className = 'GlobalAveragePooling2D';
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      const input = getExactlyOneTensor(inputs);
-      if (this.dataFormat === 'channelsLast') {
-        return tfc.mean(input, [1, 2]);
-      } else {
-        return tfc.mean(input, [2, 3]);
-      }
-    });
-  }
-}
-serialization.registerClass(GlobalAveragePooling2D);
-
-export class GlobalMaxPooling2D extends GlobalPooling2D {
-  /** @nocollapse */
-  static className = 'GlobalMaxPooling2D';
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      const input = getExactlyOneTensor(inputs);
-      if (this.dataFormat === 'channelsLast') {
-        return tfc.max(input, [1, 2]);
-      } else {
-        return tfc.max(input, [2, 3]);
-      }
-    });
-  }
-}
-serialization.registerClass(GlobalMaxPooling2D);
diff --git a/tfjs-master/tfjs-layers/src/layers/pooling_test.ts b/tfjs-master/tfjs-layers/src/layers/pooling_test.ts
deleted file mode 100644
index 4217c9614..000000000
--- a/tfjs-master/tfjs-layers/src/layers/pooling_test.ts
+++ /dev/null
@@ -1,927 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for pooling.ts.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {Tensor, tensor2d, Tensor2D, tensor3d, tensor4d, tensor5d, Tensor4D, Tensor5D, util} from '@tensorflow/tfjs-core';
-
-import {SymbolicTensor} from '../engine/topology';
-import * as tfl from '../index';
-import {DataFormat, PaddingMode, PoolMode} from '../keras_format/common';
-import {convOutputLength} from '../utils/conv_utils';
-import {describeMathCPUAndGPU, expectTensorsClose} from '../utils/test_utils';
-
-import {pool2d, pool3d} from './pooling';
-
-describeMathCPUAndGPU('pool2d', () => {
-  const x4by4Data = [[[
-    [10, 30, 50, 70], [20, 40, 60, 80], [-10, -30, -50, -70],
-    [-20, -40, -60, -80]
-  ]]];
-  const x5by5Data = [[[
-    [0, 1, 3, 5, 7], [0, 2, 4, 6, 8], [0, 0, 0, 0, 0], [0, -1, -3, -5, -7],
-    [0, -2, -4, -6, -8]
-  ]]];
-
-  const poolModes: PoolMode[] = [undefined, 'max', 'avg'];
-  const dataFormats: DataFormat[] =
-      [undefined, 'channelsFirst', 'channelsLast'];
-  const stridesArray = [1, 2];
-  for (const poolMode of poolModes) {
-    for (const dataFormat of dataFormats) {
-      for (const stride of stridesArray) {
-        const testTitle = `4x4, ${stride}, same, ${dataFormat}, ` +
-            `${poolMode}`;
-        it(testTitle, () => {
-          let x: Tensor = tensor4d(x4by4Data, [1, 1, 4, 4]);
-          if (dataFormat !== 'channelsFirst') {
-            x = tfc.transpose(x, [0, 2, 3, 1]);  // NCHW -> NHWC.
-          }
-          let yExpected: Tensor;
-          if (poolMode === 'avg') {
-            if (stride === 1) {
-              yExpected = tensor4d(
-                  [[[
-                    [25, 45, 65, 75], [5, 5, 5, 5], [-25, -45, -65, -75],
-                    [-30, -50, -70, -80]
-                  ]]],
-                  [1, 1, 4, 4]);
-            } else {
-              yExpected = tensor4d([[[[25, 65], [-25, -65]]]], [1, 1, 2, 2]);
-            }
-          } else {
-            if (stride === 1) {
-              yExpected = tensor4d(
-                  [[[
-                    [40, 60, 80, 80], [40, 60, 80, 80], [-10, -30, -50, -70],
-                    [-20, -40, -60, -80]
-                  ]]],
-                  [1, 1, 4, 4]);
-            } else if (stride === 2) {
-              yExpected = tensor4d([[[[40, 80], [-10, -50]]]], [1, 1, 2, 2]);
-            }
-          }
-          if (dataFormat !== 'channelsFirst') {
-            yExpected = tfc.transpose(yExpected, [0, 2, 3, 1]);
-          }
-          const y =
-              pool2d(x, [2, 2], [stride, stride], 'same', dataFormat, poolMode);
-          expectTensorsClose(y, yExpected);
-        });
-      }
-    }
-  }
-
-  for (const poolMode of poolModes) {
-    it(`5x5, 2, same, CHANNEL_FIRST, ${poolMode}`, () => {
-      const x5by5 = tensor4d(x5by5Data, [1, 1, 5, 5]);
-      let yExpected = tensor4d(x4by4Data, [1, 1, 4, 4]);
-      if (poolMode === 'avg') {
-        yExpected = tensor4d(
-            [[[[0.75, 4.5, 7.5], [-0.25, -2, -3.5], [-1, -5, -8]]]],
-            [1, 1, 3, 3]);
-      } else {
-        yExpected =
-            tensor4d([[[[2, 6, 8], [0, 0, 0], [0, -4, -8]]]], [1, 1, 3, 3]);
-      }
-      const y =
-          pool2d(x5by5, [2, 2], [2, 2], 'same', 'channelsFirst', poolMode);
-      expectTensorsClose(y, yExpected);
-    });
-  }
-
-  for (const poolMode of poolModes) {
-    it(`5x5, 2, valid, CHANNEL_LAST, ${poolMode}`, () => {
-      const x5by5 =
-          tfc.transpose(tensor4d(x5by5Data, [1, 1, 5, 5]), [0, 2, 3, 1]);
-      let yExpected: Tensor4D;
-      if (poolMode === 'avg') {
-        yExpected = tensor4d([[[[0.75, 4.5], [-0.25, -2]]]], [1, 1, 2, 2]);
-      } else {
-        yExpected = tensor4d([[[[2, 6], [0, 0]]]], [1, 1, 2, 2]);
-      }
-      const y =
-          pool2d(x5by5, [2, 2], [2, 2], 'valid', 'channelsLast', poolMode);
-      expectTensorsClose(y, tfc.transpose(yExpected, [0, 2, 3, 1]));
-    });
-  }
-});
-
-describeMathCPUAndGPU('pool3d', () => {
-  const x4by4by4Data = [[[
-    [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]],
-    [[17, 18, 19, 20], [21, 22, 23, 24], [25, 26, 27, 28], [19, 30, 31, 32]],
-    [[33 ,34, 35 ,36], [37, 38, 39, 40], [41, 42, 43, 44], [45, 46, 47, 48]],
-    [[49, 50, 51, 52], [53, 54, 55, 56], [57, 58, 59, 60], [61, 62, 63 ,64]]
-  ]]];
-  const x5by5by5Data = [[[
-    [
-      [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15],
-      [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]
-    ], [
-      [26, 27, 28, 29, 30], [31, 32, 33, 34, 35], [36, 37, 38, 39, 40],
-      [41, 42, 43, 44, 45], [46, 47, 48, 49, 50]
-    ], [
-      [51, 52, 53, 54, 55], [56, 57, 58, 59, 60], [61, 62, 63, 64, 65],
-      [66, 67, 68, 69, 70], [71, 72, 73, 74, 75]
-    ], [
-      [76, 77, 78, 79, 80], [81, 82, 83, 84, 85], [86, 87, 88, 89, 90],
-      [91, 92, 93, 94, 95], [96, 97, 98, 99, 100]
-    ], [
-      [101, 102, 103, 104, 105], [106, 107, 108, 109, 110],
-      [111, 112, 113, 114, 115], [116, 117, 118, 119, 120],
-      [121, 122, 123, 124, 125]
-    ]
-  ]]];
-
-  const poolModes: PoolMode[] = [undefined, 'max', 'avg'];
-  const dataFormats: DataFormat[] =
-      [undefined, 'channelsFirst', 'channelsLast'];
-  const stridesArray = [1, 2];
-  for (const poolMode of poolModes) {
-    for (const dataFormat of dataFormats) {
-      for (const stride of stridesArray) {
-        const testTitle = `4x4x4, ${stride}, same, ${dataFormat}, ` +
-            `${poolMode}`;
-        it(testTitle, () => {
-          let x: Tensor5D = tensor5d(x4by4by4Data, [1, 1, 4, 4, 4]);
-          if (dataFormat !== 'channelsFirst') {
-            x = tfc.transpose(x, [0, 2, 3, 4, 1]);  // NDHWC -> NCDHW.
-          }
-          let yExpected: Tensor5D;
-          if (poolMode === 'avg') {
-            if (stride === 1) {
-              yExpected = tensor5d(
-                  [[[
-                    [
-                      [11.5, 12.5, 13.5, 14], [15.5, 16.5, 17.5, 18],
-                      [18.25, 20.5, 21.5, 22], [19, 22.5, 23.5, 24]
-                    ], [
-                      [27.5, 28.5, 29.5, 30], [31.5, 32.5, 33.5, 34],
-                      [34.25, 36.5, 37.5, 38], [35, 38.5, 39.5, 40]
-                    ], [
-                      [43.5, 44.5, 45.5, 46], [47.5, 48.5, 49.5, 50],
-                      [51.5, 52.5, 53.5, 54], [53.5, 54.5, 55.5, 56]
-                    ], [
-                      [51.5, 52.5, 53.5, 54], [55.5, 56.5, 57.5, 58],
-                      [59.5, 60.5, 61.5, 62], [61.5, 62.5, 63.5, 64]]
-                  ]]],
-                  [1, 1, 4, 4, 4]);
-            } else {
-              yExpected = tensor5d([[[[[11.5, 13.5], [18.25, 21.5]],
-                    [[43.5, 45.5], [51.5, 53.5]]]]], [1, 1, 2, 2, 2]);
-            }
-          } else {
-            if (stride === 1) {
-              yExpected = tensor5d(
-                  [[[
-                    [
-                      [22, 23, 24, 24], [26, 27, 28, 28],
-                      [30, 31, 32, 32], [30, 31, 32, 32]
-                    ], [
-                      [38, 39, 40, 40], [42, 43, 44, 44],
-                      [46, 47, 48, 48], [46, 47, 48, 48]
-                    ], [
-                      [54, 55, 56, 56], [58, 59, 60, 60],
-                      [62, 63, 64, 64], [62, 63, 64, 64]
-                    ], [
-                      [54, 55, 56, 56], [58, 59, 60, 60],
-                      [62, 63, 64, 64], [62, 63, 64, 64]]
-                  ]]], [1, 1, 4, 4, 4]);
-            } else if (stride === 2) {
-              yExpected =
-                  tensor5d([[[[[22, 24], [30, 32]], [[54, 56], [62, 64]]]]],
-                      [1, 1, 2, 2, 2]);
-            }
-          }
-          if (dataFormat !== 'channelsFirst') {
-            yExpected = tfc.transpose(yExpected, [0, 2, 3, 4, 1]);
-          }
-          const y = pool3d(x, [2, 2, 2], [stride, stride, stride], 'same',
-              dataFormat, poolMode);
-          expectTensorsClose(y, yExpected as Tensor);
-        });
-      }
-    }
-  }
-
-  for (const poolMode of poolModes) {
-    it(`5x5x5, 2, same, CHANNEL_FIRST, ${poolMode}`, () => {
-      const x5by5by5 = tensor5d(x5by5by5Data, [1, 1, 5, 5, 5]);
-      let yExpected: Tensor5D;
-      if (poolMode === 'avg') {
-        yExpected = tensor5d([[[
-          [[16.5, 18.5, 20], [26.5, 28.5, 30], [34, 36, 37.5]],
-          [[66.5, 68.5, 70], [76.5, 78.5, 80], [84, 86, 87.5]],
-          [[104, 106, 107.5], [114, 116, 117.5], [121.5, 123.5, 125]]
-        ]]], [1, 1, 3, 3, 3]);
-      } else {
-        yExpected = tensor5d([[[
-          [[32, 34, 35], [42, 44, 45], [47, 49, 50]],
-          [[82, 84, 85], [92, 94, 95], [97, 99, 100]],
-          [[107, 109, 110], [117, 119, 120], [122, 124, 125]]
-        ]]], [1, 1, 3, 3, 3]);
-      }
-      const y = pool3d(x5by5by5, [2, 2, 2], [2, 2, 2], 'same', 'channelsFirst',
-          poolMode);
-      expectTensorsClose(y, yExpected as Tensor);
-    });
-  }
-
-  for (const poolMode of poolModes) {
-    it(`5x5x5, 2, valid, CHANNEL_LAST, ${poolMode}`, () => {
-      const x5by5by5 = tfc.transpose(
-          tensor5d(x5by5by5Data, [1, 1, 5, 5, 5]), [0, 2, 3, 4, 1]);
-      let yExpected: Tensor5D;
-      if (poolMode === 'avg') {
-        yExpected = tensor5d(
-            [[[[[16.5, 18.5], [26.5, 28.5]], [[66.5, 68.5], [76.5, 78.5]]]]],
-            [1, 1, 2, 2, 2]);
-      } else {
-        yExpected = tensor5d(
-            [[[[[32, 34], [42, 44]], [[82, 84], [92, 94]]]]],
-            [1, 1, 2, 2, 2]);
-      }
-      const y = pool3d(x5by5by5, [2, 2, 2], [2, 2, 2], 'valid', 'channelsLast',
-          poolMode);
-      expectTensorsClose(y, tfc.transpose(yExpected, [0, 2, 3, 4, 1]));
-    });
-  }
-});
-
-describe('Pooling Layers 1D: Symbolic', () => {
-  const poolSizes = [2, 3];
-  const stridesList = [null, 1, 2];
-  const poolModes: PoolMode[] = ['avg', 'max'];
-  const paddingModes: PaddingMode[] = [undefined, 'valid', 'same'];
-
-  for (const poolMode of poolModes) {
-    for (const paddingMode of paddingModes) {
-      for (const poolSize of poolSizes) {
-        for (const strides of stridesList) {
-          const testTitle = `poolSize=${poolSize}, ` +
-              `${paddingMode}, ${poolMode}`;
-          it(testTitle, () => {
-            const inputLength = 16;
-            const inputNumChannels = 11;
-            const inputBatchSize = 2;
-            const inputShape = [inputBatchSize, inputLength, inputNumChannels];
-            const symbolicInput =
-                new SymbolicTensor('float32', inputShape, null, [], null);
-            const poolConstructor = poolMode === 'avg' ?
-                tfl.layers.averagePooling1d :
-                tfl.layers.maxPooling1d;
-            const poolingLayer = poolConstructor({
-              poolSize,
-              strides,
-              padding: paddingMode,
-            });
-            const output = poolingLayer.apply(symbolicInput) as SymbolicTensor;
-            const expectedOutputLength = convOutputLength(
-                inputLength, poolSize, paddingMode,
-                strides ? strides : poolSize);
-            const expectedShape =
-                [inputBatchSize, expectedOutputLength, inputNumChannels];
-
-            expect(output.shape).toEqual(expectedShape);
-            expect(output.dtype).toEqual(symbolicInput.dtype);
-          });
-        }
-      }
-    }
-  }
-});
-
-describeMathCPUAndGPU('Pooling Layers 1D: Tensor', () => {
-  const poolModes = ['avg', 'max'];
-  const strides = [2, 4];
-  const poolSizes = [2, 4];
-  const batchSize = 2;
-  for (const poolMode of poolModes) {
-    for (const stride of strides) {
-      for (const poolSize of poolSizes) {
-        const testTitle = `stride=${stride}, ${poolMode}, ` +
-            `poolSize=${poolSize}`;
-        it(testTitle, () => {
-          const x2by8 = tensor2d([
-            [10, 30, 50, 70, 20, 40, 60, 80],
-            [-10, -30, -50, -70, -20, -40, -60, -80]
-          ]);
-          const x2by8by1 = tfc.expandDims(x2by8, 2);
-          const poolConstructor = poolMode === 'avg' ?
-              tfl.layers.averagePooling1d :
-              tfl.layers.maxPooling1d;
-          const poolingLayer = poolConstructor({
-            poolSize,
-            strides: stride,
-            padding: 'valid',
-          });
-          const output = poolingLayer.apply(x2by8by1) as Tensor;
-          let outputLength: number;
-          let expectedOutputVals: number[][][];
-          if (poolSize === 2) {
-            if (stride === 2) {
-              outputLength = 4;
-              if (poolMode === 'avg') {
-                expectedOutputVals =
-                    [[[20], [60], [30], [70]], [[-20], [-60], [-30], [-70]]];
-              } else {
-                expectedOutputVals =
-                    [[[30], [70], [40], [80]], [[-10], [-50], [-20], [-60]]];
-              }
-            } else if (stride === 4) {
-              outputLength = 2;
-              if (poolMode === 'avg') {
-                expectedOutputVals = [[[20], [30]], [[-20], [-30]]];
-              } else {
-                expectedOutputVals = [[[30], [40]], [[-10], [-20]]];
-              }
-            }
-          } else if (poolSize === 4) {
-            if (stride === 2) {
-              outputLength = 3;
-              if (poolMode === 'avg') {
-                expectedOutputVals =
-                    [[[40], [45], [50]], [[-40], [-45], [-50]]];
-              } else {
-                expectedOutputVals =
-                    [[[70], [70], [80]], [[-10], [-20], [-20]]];
-              }
-            } else if (stride === 4) {
-              outputLength = 2;
-              if (poolMode === 'avg') {
-                expectedOutputVals = [[[40], [50]], [[-40], [-50]]];
-              } else {
-                expectedOutputVals = [[[70], [80]], [[-10], [-20]]];
-              }
-            }
-          }
-          const expectedShape: [number, number, number] =
-              [batchSize, outputLength, 1];
-          expectTensorsClose(
-              output, tensor3d(expectedOutputVals, expectedShape));
-        });
-      }
-    }
-  }
-
-  it('Handles poolSize and strides passed as number arrays', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.maxPool1d(
-        {poolSize: [2], strides: [2], inputShape: [4, 3]}));
-    const xs = tfc.ones([1, 4, 3]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(ys, [1, 1, 1, 1, 1, 1]);
-    expect(ys.shape).toEqual([1, 2, 3]);
-
-    const config = model.layers[0].getConfig();
-    expect(config['poolSize']).toEqual([2]);
-    expect(config['strides']).toEqual([2]);
-  });
-});
-
-describe('Pooling Layers 2D: Symbolic', () => {
-  const poolSizes = [2, 3];
-  const poolModes = ['avg', 'max'];
-  const paddingModes: PaddingMode[] = [undefined, 'valid', 'same'];
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const poolSizeIsNumberValues = [false, true];
-
-  for (const poolMode of poolModes) {
-    for (const paddingMode of paddingModes) {
-      for (const dataFormat of dataFormats) {
-        for (const poolSize of poolSizes) {
-          for (const poolSizeIsNumber of poolSizeIsNumberValues) {
-            const testTitle = `poolSize=${poolSize}, ` +
-                `${dataFormat}, ${paddingMode}, ` +
-                `${poolMode}, ` +
-                `poollSizeIsNumber=${poolSizeIsNumber}`;
-            it(testTitle, () => {
-              const inputShape = dataFormat === 'channelsFirst' ?
-                  [2, 16, 11, 9] :
-                  [2, 11, 9, 16];
-              const symbolicInput =
-                  new SymbolicTensor('float32', inputShape, null, [], null);
-
-              const poolConstructor = poolMode === 'avg' ?
-                  tfl.layers.averagePooling2d :
-                  tfl.layers.maxPooling2d;
-              const poolingLayer = poolConstructor({
-                poolSize: poolSizeIsNumber ? poolSize : [poolSize, poolSize],
-                padding: paddingMode,
-                dataFormat,
-              });
-
-              const output =
-                  poolingLayer.apply(symbolicInput) as SymbolicTensor;
-
-              let outputRows = poolSize === 2 ? 5 : 3;
-              if (paddingMode === 'same') {
-                outputRows++;
-              }
-              let outputCols = poolSize === 2 ? 4 : 3;
-              if (paddingMode === 'same' && poolSize === 2) {
-                outputCols++;
-              }
-
-              let expectedShape: [number, number, number, number];
-              if (dataFormat === 'channelsFirst') {
-                expectedShape = [2, 16, outputRows, outputCols];
-              } else {
-                expectedShape = [2, outputRows, outputCols, 16];
-              }
-
-              expect(output.shape).toEqual(expectedShape);
-              expect(output.dtype).toEqual(symbolicInput.dtype);
-            });
-          }
-        }
-      }
-    }
-  }
-
-  const stridesValues: Array<number|[number, number]> = [1, [1, 1], [2, 1]];
-  for (const strides of stridesValues) {
-    it(`custom strides: ${strides}`, () => {
-      const inputShape = [2, 16, 11, 3];
-      const symbolicInput =
-          new SymbolicTensor('float32', inputShape, null, [], null);
-      const poolingLayer = tfl.layers.maxPooling2d({poolSize: [2, 2], strides});
-      const output = poolingLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-      if (Array.isArray(strides) && util.arraysEqual(strides, [1, 1])) {
-        expect(output.shape).toEqual([2, 15, 10, 3]);
-      } else if (Array.isArray(strides) && util.arraysEqual(strides, [2, 1])) {
-        expect(output.shape).toEqual([2, 8, 10, 3]);
-      } else {
-        // strides = 1
-        expect(output.shape).toEqual([2, 15, 10, 3]);
-      }
-    });
-  }
-
-  it('Incorrect strides array length leads to error', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.maxPooling2d({poolSize: 2, strides: [2]} as any))
-        .toThrowError(/expected to have a length of 2/);
-    expect(
-        // tslint:disable-next-line:no-any
-        () => tfl.layers.maxPooling2d({poolSize: 2, strides: [2, 3, 3]} as any))
-        .toThrowError(/expected to have a length of 2/);
-  });
-
-  it('Invalid poolSize', () => {
-    expect(() => tfl.layers.maxPooling2d({poolSize: 2.5, strides: 2}))
-        .toThrowError(/poolSize.*positive integer.*2\.5\.$/);
-    expect(() => tfl.layers.maxPooling2d({poolSize: 0, strides: 2}))
-        .toThrowError(/poolSize.*positive integer.*0\.$/);
-    expect(() => tfl.layers.maxPooling2d({poolSize: -2, strides: 2}))
-        .toThrowError(/poolSize.*positive integer.*-2\.$/);
-  });
-
-  it('Invalid strides leads to Error', () => {
-    expect(() => tfl.layers.maxPooling2d({poolSize: 3, strides: 2.5}))
-        .toThrowError(/strides.*positive integer.*2\.5\.$/);
-    expect(() => tfl.layers.maxPooling2d({poolSize: 3, strides: 0}))
-        .toThrowError(/strides.*positive integer.*0\.$/);
-    expect(() => tfl.layers.maxPooling2d({poolSize: 3, strides: -2}))
-        .toThrowError(/strides.*positive integer.*-2\.$/);
-  });
-});
-
-describeMathCPUAndGPU('Pooling Layers 2D: Tensor', () => {
-  const x4by4Data =
-      [10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80];
-
-  const poolModes: PoolMode[] = ['avg', 'max'];
-  const strides = [1, 2];
-  const batchSizes = [2, 4];
-  const channelsArray = [1, 3];
-  for (const poolMode of poolModes) {
-    for (const stride of strides) {
-      for (const batchSize of batchSizes) {
-        for (const channels of channelsArray) {
-          const testTitle = `stride=${stride}, ${poolMode}, ` +
-              `batchSize=${batchSize}, channels=${channels}`;
-          it(testTitle, () => {
-            let xArrayData: number[] = [];
-            for (let b = 0; b < batchSize; ++b) {
-              for (let c = 0; c < channels; ++c) {
-                xArrayData = xArrayData.concat(x4by4Data);
-              }
-            }
-            const x4by4 = tensor4d(xArrayData, [batchSize, channels, 4, 4]);
-
-            const poolConstructor = poolMode === 'avg' ?
-                tfl.layers.averagePooling2d :
-                tfl.layers.maxPooling2d;
-            const poolingLayer = poolConstructor({
-              poolSize: [2, 2],
-              strides: [stride, stride],
-              padding: 'valid',
-              dataFormat: 'channelsFirst',
-            });
-
-            const output = poolingLayer.apply(x4by4) as Tensor;
-
-            let expectedShape: [number, number, number, number];
-            let expectedOutputSlice: number[];
-            if (poolMode === 'avg') {
-              if (stride === 1) {
-                expectedShape = [batchSize, channels, 3, 3];
-                expectedOutputSlice = [25, 45, 65, 5, 5, 5, -25, -45, -65];
-              } else if (stride === 2) {
-                expectedShape = [batchSize, channels, 2, 2];
-                expectedOutputSlice = [25, 65, -25, -65];
-              }
-            } else {
-              if (stride === 1) {
-                expectedShape = [batchSize, channels, 3, 3];
-                expectedOutputSlice = [40, 60, 80, 40, 60, 80, -10, -30, -50];
-              } else if (stride === 2) {
-                expectedShape = [batchSize, channels, 2, 2];
-                expectedOutputSlice = [40, 80, -10, -50];
-              }
-            }
-            let expectedOutputArray: number[] = [];
-            for (let b = 0; b < batchSize; ++b) {
-              for (let c = 0; c < channels; ++c) {
-                expectedOutputArray =
-                    expectedOutputArray.concat(expectedOutputSlice);
-              }
-            }
-            expectTensorsClose(
-                output, tensor4d(expectedOutputArray, expectedShape));
-          });
-        }
-      }
-    }
-  }
-
-  it('Handles strides passed as number arrays', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.maxPooling2d(
-        {poolSize: 2, strides: [2, 2], inputShape: [4, 4, 3]}));
-    const xs = tfc.ones([1, 4, 4, 3]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(ys, tfc.ones([1, 2, 2, 3]));
-    expect(ys.shape).toEqual([1, 2, 2, 3]);
-    const config = model.layers[0].getConfig();
-    expect(config['poolSize']).toEqual([2, 2]);
-    expect(config['strides']).toEqual([2, 2]);
-  });
-});
-
-describe('Pooling Layers 3D: Symbolic', () => {
-  const poolSizes = [2, 3];
-  const poolModes = ['avg', 'max'];
-  const paddingModes: PaddingMode[] = [undefined, 'valid', 'same'];
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-  const poolSizeIsNumberValues = [false, true];
-
-  for (const poolMode of poolModes) {
-    for (const paddingMode of paddingModes) {
-      for (const dataFormat of dataFormats) {
-        for (const poolSize of poolSizes) {
-          for (const poolSizeIsNumber of poolSizeIsNumberValues) {
-            const testTitle = `poolSize=${poolSize}, ` +
-                `${dataFormat}, ${paddingMode}, ` +
-                `${poolMode}, ` +
-                `poollSizeIsNumber=${poolSizeIsNumber}`;
-            it(testTitle, () => {
-              const inputShape = dataFormat === 'channelsFirst' ?
-                  [2, 16, 11, 9, 7] :
-                  [2, 11, 9, 7, 16];
-              const symbolicInput =
-                  new SymbolicTensor('float32', inputShape, null, [], null);
-
-              const poolConstructor = poolMode === 'avg' ?
-                  tfl.layers.averagePooling3d :
-                  tfl.layers.maxPooling3d;
-              const poolingLayer = poolConstructor({
-                poolSize: poolSizeIsNumber ? poolSize :
-                    [poolSize, poolSize, poolSize],
-                padding: paddingMode,
-                dataFormat,
-              });
-
-              const output =
-                  poolingLayer.apply(symbolicInput) as SymbolicTensor;
-
-              let outputDepths = poolSize === 2 ? 5: 3;
-              if (paddingMode === 'same') {
-                outputDepths++;
-              }
-
-              let outputRows = poolSize === 2 ? 4 : 3;
-              if (paddingMode === 'same' && poolSize === 2) {
-                outputRows++;
-              }
-              let outputCols = poolSize === 2 ? 3 : 2;
-              if (paddingMode === 'same') {
-                outputCols++;
-              }
-
-              let expectedShape: [number, number, number, number, number];
-              if (dataFormat === 'channelsFirst') {
-                expectedShape = [2, 16, outputDepths, outputRows, outputCols];
-              } else {
-                expectedShape = [2, outputDepths, outputRows, outputCols, 16];
-              }
-
-              expect(output.shape).toEqual(expectedShape);
-              expect(output.dtype).toEqual(symbolicInput.dtype);
-            });
-          }
-        }
-      }
-    }
-  }
-
-  const stridesValues: Array<number|[number, number, number]> =
-      [1, [1, 1, 1], [2, 1, 2]];
-  for (const strides of stridesValues) {
-    it(`custom strides: ${strides}`, () => {
-      const inputShape = [2, 16, 11, 15, 3];
-      const symbolicInput =
-          new SymbolicTensor('float32', inputShape, null, [], null);
-      const poolingLayer =
-          tfl.layers.maxPooling3d({poolSize: [2, 2, 2], strides});
-      const output = poolingLayer.apply(symbolicInput) as tfl.SymbolicTensor;
-      if (Array.isArray(strides) && util.arraysEqual(strides, [1, 1, 1])) {
-        expect(output.shape).toEqual([2, 15, 10, 14, 3]);
-      } else if (Array.isArray(strides) &&
-          util.arraysEqual(strides, [2, 1, 2])) {
-        expect(output.shape).toEqual([2, 8, 10, 7, 3]);
-      } else {
-        // strides = 1
-        expect(output.shape).toEqual([2, 15, 10, 14, 3]);
-      }
-    });
-  }
-
-  it('Incorrect strides array length leads to error', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.layers.maxPooling3d({poolSize: 2, strides: [2]} as any))
-        .toThrowError(/expected to have a length of 3/);
-    expect(
-        // tslint:disable-next-line:no-any
-        () => tfl.layers.maxPooling3d({poolSize: 2, strides: [2, 3]} as any))
-        .toThrowError(/expected to have a length of 3/);
-    expect(() => tfl.layers.averagePooling3d(
-        // tslint:disable-next-line:no-any
-        {poolSize: 2, strides: [2]} as any))
-        .toThrowError(/expected to have a length of 3/);
-    expect(() => tfl.layers.averagePooling3d(
-            // tslint:disable-next-line:no-any
-            {poolSize: 2, strides: [2, 3]} as any))
-        .toThrowError(/expected to have a length of 3/);
-  });
-
-  it('Invalid poolSize', () => {
-    expect(() => tfl.layers.maxPooling3d({poolSize: 2.5, strides: 2}))
-        .toThrowError(/poolSize.*positive integer.*2\.5\.$/);
-    expect(() => tfl.layers.maxPooling3d({poolSize: 0, strides: 2}))
-        .toThrowError(/poolSize.*positive integer.*0\.$/);
-    expect(() => tfl.layers.maxPooling3d({poolSize: -2, strides: 2}))
-        .toThrowError(/poolSize.*positive integer.*-2\.$/);
-    expect(() => tfl.layers.averagePooling3d({poolSize: 2.5, strides: 2}))
-        .toThrowError(/poolSize.*positive integer.*2\.5\.$/);
-    expect(() => tfl.layers.averagePooling3d({poolSize: 0, strides: 2}))
-        .toThrowError(/poolSize.*positive integer.*0\.$/);
-    expect(() => tfl.layers.averagePooling3d({poolSize: -2, strides: 2}))
-        .toThrowError(/poolSize.*positive integer.*-2\.$/);
-  });
-
-  it('Invalid strides leads to Error', () => {
-    expect(() => tfl.layers.maxPooling3d({poolSize: 3, strides: 2.5}))
-        .toThrowError(/strides.*positive integer.*2\.5\.$/);
-    expect(() => tfl.layers.maxPooling3d({poolSize: 3, strides: 0}))
-        .toThrowError(/strides.*positive integer.*0\.$/);
-    expect(() => tfl.layers.maxPooling3d({poolSize: 3, strides: -2}))
-        .toThrowError(/strides.*positive integer.*-2\.$/);
-    expect(() => tfl.layers.averagePooling3d({poolSize: 3, strides: 2.5}))
-        .toThrowError(/strides.*positive integer.*2\.5\.$/);
-    expect(() => tfl.layers.averagePooling3d({poolSize: 3, strides: 0}))
-        .toThrowError(/strides.*positive integer.*0\.$/);
-    expect(() => tfl.layers.averagePooling3d({poolSize: 3, strides: -2}))
-        .toThrowError(/strides.*positive integer.*-2\.$/);
-  });
-});
-
-describeMathCPUAndGPU('Pooling Layers 3D: Tensor', () => {
-  const x4by4by4Data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
-        16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
-        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
-        52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64];
-
-  const poolModes: PoolMode[] = ['avg', 'max'];
-  const strides = [1, 2];
-  const batchSizes = [2, 4];
-  const channelsArray = [1, 3];
-  for (const poolMode of poolModes) {
-    for (const stride of strides) {
-      for (const batchSize of batchSizes) {
-        for (const channels of channelsArray) {
-          const testTitle = `stride=${stride}, ${poolMode}, ` +
-              `batchSize=${batchSize}, channels=${channels}`;
-          it(testTitle, () => {
-            let xArrayData: number[] = [];
-            for (let b = 0; b < batchSize; ++b) {
-              for (let c = 0; c < channels; ++c) {
-                xArrayData = xArrayData.concat(x4by4by4Data);
-              }
-            }
-            const x4by4by4 =
-                tensor5d(xArrayData, [batchSize, channels, 4, 4, 4]);
-
-            const poolConstructor = poolMode === 'avg' ?
-                tfl.layers.averagePooling3d :
-                tfl.layers.maxPooling3d;
-            const poolingLayer = poolConstructor({
-              poolSize: [2, 2, 2],
-              strides: [stride, stride, stride],
-              padding: 'valid',
-              dataFormat: 'channelsFirst',
-            });
-
-            const output = poolingLayer.apply(x4by4by4 as Tensor) as Tensor;
-
-            let expectedShape: [number, number, number, number, number];
-            let expectedOutputSlice: number[];
-            if (poolMode === 'avg') {
-              if (stride === 1) {
-                expectedShape = [batchSize, channels, 3, 3, 3];
-                expectedOutputSlice = [11.5, 12.5, 13.5, 15.5, 16.5, 17.5,
-                  19.5, 20.5, 21.5, 27.5, 28.5, 29.5, 31.5, 32.5, 33.5, 35.5,
-                  36.5, 37.5, 43.5, 44.5, 45.5, 47.5, 48.5, 49.5, 51.5, 52.5,
-                  53.5];
-              } else if (stride === 2) {
-                expectedShape = [batchSize, channels, 2, 2, 2];
-                expectedOutputSlice =
-                    [11.5, 13.5, 19.5, 21.5, 43.5, 45.5, 51.5, 53.5];
-              }
-            } else {
-              if (stride === 1) {
-                expectedShape = [batchSize, channels, 3, 3, 3];
-                expectedOutputSlice = [22, 23, 24, 26, 27, 28, 30, 31, 32, 38,
-                  39, 40, 42, 43, 44, 46, 47, 48, 54, 55, 56, 58, 59, 60, 62,
-                  63, 64];
-              } else if (stride === 2) {
-                expectedShape = [batchSize, channels, 2, 2, 2];
-                expectedOutputSlice = [22, 24, 30, 32, 54, 56, 62, 64];
-              }
-            }
-            let expectedOutputArray: number[] = [];
-            for (let b = 0; b < batchSize; ++b) {
-              for (let c = 0; c < channels; ++c) {
-                expectedOutputArray =
-                    expectedOutputArray.concat(expectedOutputSlice);
-              }
-            }
-            expectTensorsClose(output,
-                tensor5d(expectedOutputArray, expectedShape) as Tensor);
-          });
-        }
-      }
-    }
-  }
-});
-
-describe('1D Global pooling Layers: Symbolic', () => {
-  const globalPoolingLayers =
-      [tfl.layers.globalAveragePooling1d, tfl.layers.globalMaxPooling1d];
-
-  for (const globalPoolingLayer of globalPoolingLayers) {
-    for (const hasArgs of [true, false]) {
-      const testTitle = `layer=${globalPoolingLayer.name}; hasArgs=${hasArgs}`;
-      it(testTitle, () => {
-        const inputShape = [2, 11, 9];
-        const symbolicInput =
-            new SymbolicTensor('float32', inputShape, null, [], null);
-
-        const layer = globalPoolingLayer(hasArgs ? {} : undefined);
-        const output = layer.apply(symbolicInput) as SymbolicTensor;
-
-        const expectedShape = [2, 9];
-        expect(output.shape).toEqual(expectedShape);
-        expect(output.dtype).toEqual(symbolicInput.dtype);
-      });
-    }
-  }
-
-  it('Invalid poolSize', () => {
-    expect(() => tfl.layers.avgPooling1d({
-      poolSize: 2.5
-    })).toThrowError(/poolSize.*positive integer.*2\.5/);
-    expect(() => tfl.layers.avgPooling1d({
-      poolSize: 0
-    })).toThrowError(/poolSize.*positive integer.*0\.$/);
-    expect(() => tfl.layers.avgPooling1d({
-      poolSize: -3
-    })).toThrowError(/poolSize.*positive integer.*-3\.$/);
-  });
-
-  it('Invalid strides leads to Error', () => {
-    expect(() => tfl.layers.avgPooling1d({poolSize: 3, strides: 4.5}))
-        .toThrowError(/strides.*positive integer.*4\.5\.$/);
-  });
-});
-
-describeMathCPUAndGPU('1D Global Pooling Layers: Tensor', () => {
-  const x3DimData = [
-    [[4, -1], [0, -2], [40, -10], [0, -20]],
-    [[-4, 1], [0, 2], [-40, 10], [0, 20]]
-  ];
-  const globalPoolingLayers =
-      [tfl.layers.globalAveragePooling1d, tfl.layers.globalMaxPooling1d];
-  for (const globalPoolingLayer of globalPoolingLayers) {
-    const testTitle = `globalPoolingLayer=${globalPoolingLayer.name}`;
-    it(testTitle, () => {
-      const x = tensor3d(x3DimData, [2, 4, 2]);
-      const layer = globalPoolingLayer({});
-      const output = layer.apply(x) as Tensor;
-
-      let expectedOutput: Tensor2D;
-      if (globalPoolingLayer === tfl.layers.globalAveragePooling1d) {
-        expectedOutput = tensor2d([[11, -8.25], [-11, 8.25]], [2, 2]);
-      } else {
-        expectedOutput = tensor2d([[40, -1], [0, 20]], [2, 2]);
-      }
-      expectTensorsClose(output, expectedOutput);
-    });
-  }
-});
-
-describe('2D Global pooling Layers: Symbolic', () => {
-  const globalPoolingLayers =
-      [tfl.layers.globalAveragePooling2d, tfl.layers.globalMaxPooling2d];
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-
-  for (const globalPoolingLayer of globalPoolingLayers) {
-    for (const dataFormat of dataFormats) {
-      const testTitle = `layer=${globalPoolingLayer.name}, ${dataFormat}`;
-      it(testTitle, () => {
-        const inputShape = [2, 16, 11, 9];
-        const symbolicInput =
-            new SymbolicTensor('float32', inputShape, null, [], null);
-
-        const layer = globalPoolingLayer({dataFormat});
-        const output = layer.apply(symbolicInput) as SymbolicTensor;
-
-        const expectedShape = dataFormat === 'channelsLast' ? [2, 9] : [2, 16];
-        expect(output.shape).toEqual(expectedShape);
-        expect(output.dtype).toEqual(symbolicInput.dtype);
-      });
-    }
-  }
-});
-
-describeMathCPUAndGPU('2D Global Pooling Layers: Tensor', () => {
-  const x4DimData = [
-    [[[4, -1], [0, -2]], [[40, -10], [0, -20]]],
-    [[[4, -1], [0, -2]], [[40, -10], [0, -20]]]
-  ];
-  const dataFormats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-
-  const globalPoolingLayers =
-      [tfl.layers.globalAveragePooling2d, tfl.layers.globalMaxPooling2d];
-  for (const globalPoolingLayer of globalPoolingLayers) {
-    for (const dataFormat of dataFormats) {
-      const testTitle =
-          `globalPoolingLayer=${globalPoolingLayer.name}, ${dataFormat}`;
-      it(testTitle, () => {
-        const x = tensor4d(x4DimData, [2, 2, 2, 2]);
-        const layer = globalPoolingLayer({dataFormat});
-        const output = layer.apply(x) as Tensor;
-
-        let expectedOutput: Tensor2D;
-        if (globalPoolingLayer === tfl.layers.globalAveragePooling2d) {
-          if (dataFormat === 'channelsFirst') {
-            expectedOutput = tensor2d([[0.25, 2.5], [0.25, 2.5]], [2, 2]);
-          } else {
-            expectedOutput = tensor2d([[11, -8.25], [11, -8.25]], [2, 2]);
-          }
-        } else {
-          if (dataFormat === 'channelsFirst') {
-            expectedOutput = tensor2d([[4, 40], [4, 40]], [2, 2]);
-          } else {
-            expectedOutput = tensor2d([[40, -1], [40, -1]], [2, 2]);
-          }
-        }
-        expectTensorsClose(output, expectedOutput);
-        const config = layer.getConfig();
-        expect(config.dataFormat).toEqual(dataFormat);
-      });
-    }
-  }
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/category_encoding.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/category_encoding.ts
deleted file mode 100644
index d40f8ab2f..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/category_encoding.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import { LayerArgs, Layer } from '../../engine/topology';
-import { serialization, Tensor, tidy, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';
-import { greater, greaterEqual, max, min} from '@tensorflow/tfjs-core';
-import { Shape } from '../../keras_format/common';
-import { getExactlyOneShape, getExactlyOneTensor } from '../../utils/types_utils';
-import { Kwargs } from '../../types';
-import { ValueError } from '../../errors';
-import * as K from '../../backend/tfjs_backend';
-import * as utils from './preprocessing_utils';
-import { OutputMode } from './preprocessing_utils';
-
-export declare interface CategoryEncodingArgs extends LayerArgs {
-  numTokens: number;
-  outputMode?: OutputMode;
- }
-
-export class CategoryEncoding extends Layer {
-  /** @nocollapse */
-  static className = 'CategoryEncoding';
-  private readonly numTokens: number;
-  private readonly outputMode: OutputMode;
-
-  constructor(args: CategoryEncodingArgs) {
-    super(args);
-    this.numTokens = args.numTokens;
-
-    if(args.outputMode) {
-    this.outputMode = args.outputMode;
-    } else {
-      this.outputMode = 'multiHot';
-    }
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'numTokens': this.numTokens,
-      'outputMode': this.outputMode,
-    };
-
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-
-    if(inputShape == null) {
-      return [this.numTokens];
-    }
-
-    if(this.outputMode === 'oneHot' && inputShape[inputShape.length - 1] !== 1){
-      inputShape.push(this.numTokens);
-      return inputShape;
-    }
-
-    inputShape[inputShape.length - 1] = this.numTokens;
-    return inputShape;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor[]|Tensor {
-    return tidy(() => {
-
-        inputs = getExactlyOneTensor(inputs);
-        if(inputs.dtype !== 'int32') {
-          inputs = K.cast(inputs, 'int32');
-      }
-
-        let countWeights: Tensor1D | Tensor2D;
-
-        if((typeof kwargs['countWeights']) !== 'undefined') {
-
-          if(this.outputMode !== 'count') {
-            throw new ValueError(
-              `countWeights is not used when outputMode !== count.
-              Received countWeights=${kwargs['countWeights']}`);
-          }
-
-          countWeights
-            =  getExactlyOneTensor(kwargs['countWeights']) as Tensor1D|Tensor2D;
-        }
-
-        const maxValue = max(inputs);
-        const minValue = min(inputs);
-        const greaterEqualMax = greater(this.numTokens, maxValue)
-                                                    .bufferSync().get(0);
-
-        const greaterMin = greaterEqual(minValue, 0).bufferSync().get(0);
-
-        if(!(greaterEqualMax && greaterMin)) {
-
-          throw new ValueError('Input values must be between 0 < values <='
-            + ` numTokens with numTokens=${this.numTokens}`);
-        }
-
-        return utils.encodeCategoricalInputs(inputs,
-          this.outputMode, this.numTokens, countWeights);
-    });
-  }
-}
-
-serialization.registerClass(CategoryEncoding);
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/category_encoding_test.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/category_encoding_test.ts
deleted file mode 100644
index f2564d1e6..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/category_encoding_test.ts
+++ /dev/null
@@ -1,152 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import { describeMathCPUAndGPU, expectTensorsClose} from '../../utils/test_utils';
-import { Tensor, tensor} from '@tensorflow/tfjs-core';
-import { CategoryEncoding } from './category_encoding';
-
-describeMathCPUAndGPU('Category Encoding', () => {
-
-   it('Calculates correct output for Count outputMode rank 0', () => {
-    const categoryData = tensor(0);
-    const expectedOutput = tensor([1,0,0,0]);
-    const numTokens = 4;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                outputMode: 'count'});
-    const computedOutput = encodingLayer.
-                          apply(categoryData) as Tensor;
-
-    expectTensorsClose(computedOutput, expectedOutput);
-  });
-
-  it('Calculates correct output for Count outputMode rank 1 (weights)', () => {
-    const categoryData = tensor([1, 2, 3, 3, 0]);
-    const weightData = tensor([1, 2, 3, 1, 7]);
-    const numTokens = 6;
-    const expectedOutput = tensor([7, 1, 2, 4, 0, 0]);
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                            outputMode: 'count'});
-
-    const computedOutput = encodingLayer.apply(categoryData,
-                      {countWeights: weightData}) as Tensor;
-
-    expectTensorsClose(computedOutput, expectedOutput);
-  });
-
-  it('Calculates correct output for Count outputMode rank 2', () => {
-    const categoryData   = tensor([[1, 2, 3, 1], [0, 3, 1, 0]]);
-    const expectedOutput = tensor([[0, 2, 1, 1, 0, 0], [2, 1, 0, 1, 0, 0]]);
-    const numTokens = 6;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                            outputMode: 'count'});
-    const computedOutput = encodingLayer.apply(categoryData) as Tensor;
-    expectTensorsClose(computedOutput, expectedOutput);
-  });
-
-  it('Calculates correct output for oneHot outputMode rank 0', () => {
-    const categoryData = tensor(3);
-    const expectedOutput = tensor([0, 0, 0, 1]);
-    const numTokens = 4;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                          outputMode: 'oneHot'});
-    const computedOutput = encodingLayer.apply(categoryData) as Tensor;
-    expectTensorsClose(computedOutput, expectedOutput);
-  });
-
-  it('Calculates correct output and shape for oneHot outputMode rank 1', () => {
-    const categoryData   = tensor([3, 2, 0, 1]);
-    const expectedOutput = tensor([[0, 0, 0, 1],
-                                   [0, 0, 1, 0],
-                                   [1, 0, 0, 0],
-                                   [0, 1, 0, 0]]);
-    const numTokens = 4;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                          outputMode: 'oneHot'});
-    const computedOutput = encodingLayer.apply(categoryData) as Tensor;
-    expectTensorsClose(computedOutput, expectedOutput);
-  });
-
-  it('Calculates correct output and shape for oneHot outputMode rank 2', () => {
-    const categoryData   = tensor([[3], [2], [0], [1]]);
-    const expectedOutput = tensor([[0, 0, 0, 1],
-                                   [0, 0, 1, 0],
-                                   [1, 0, 0, 0],
-                                   [0, 1, 0, 0]]);
-    const numTokens = 4;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                          outputMode: 'oneHot'});
-    const computedOutput = encodingLayer.apply(categoryData) as Tensor;
-    expectTensorsClose(computedOutput, expectedOutput);
-  });
-
-  it('Calculates correct output for multiHot outputMode rank 0', () => {
-    const categoryData = tensor(3);
-    const expectedOutput = tensor([0, 0, 0, 1, 0, 0]);
-    const numTokens = 6;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                outputMode: 'multiHot'});
-    const computedOutput = encodingLayer.apply(categoryData) as Tensor;
-    expectTensorsClose(computedOutput, expectedOutput);
-  });
-
-  it('Calculates correct output for multiHot outputMode rank 1', () => {
-    const categoryData   = tensor([3, 2, 0, 1]);
-    const expectedOutput = tensor([1, 1, 1, 1, 0, 0]);
-    const numTokens = 6;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                        outputMode: 'multiHot'});
-    const computedOutput = encodingLayer.apply(categoryData) as Tensor;
-    expectTensorsClose(computedOutput, expectedOutput);
-  });
-
-  it('Calculates correct output for multiHot outputMode rank 2', () => {
-    const categoryData   = tensor([[0, 1], [0, 0], [1, 2], [3, 1]]);
-    const expectedOutput = tensor([[1, 1, 0, 0],
-                                   [1, 0, 0, 0],
-                                   [0, 1, 1, 0],
-                                   [0, 1, 0, 1]]);
-    const numTokens = 4;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                        outputMode: 'multiHot'});
-    const computedOutput = encodingLayer.apply(categoryData) as Tensor;
-    expectTensorsClose(computedOutput, expectedOutput);
-  });
-
-  it('Raises Value Error if input Tensor has Rank > 2', () =>{
-    const categoryData = tensor([[[1], [2]], [[3], [4]]]);
-    const numTokens = 6;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                        outputMode: 'multiHot'});
-    expect(() => encodingLayer.apply(categoryData))
-    .toThrowError(`When outputMode is not int, maximum output rank is 2`
-    + ` Received outputMode ${'multiHot'} and input shape ${categoryData.shape}`
-    + ` which would result in output rank ${categoryData.rank}.`);
-  });
-
-  it('Raises Value Error if max input value !<= numTokens', () => {
-    const categoryData   = tensor([7, 2, 0, 1]);
-    const numTokens = 3;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                        outputMode: 'multiHot'});
-    expect(() => encodingLayer.apply(categoryData))
-    .toThrowError('Input values must be between 0 < values <= numTokens'
-      + ` with numTokens=${numTokens}`);
-  });
-
-  it('Raises Value Error if min input value < 0', () => {
-    const categoryData   = tensor([7, 2, -1, 1]);
-    const numTokens = 3;
-    const encodingLayer = new CategoryEncoding({numTokens,
-                                        outputMode: 'multiHot'});
-    expect(() => encodingLayer.apply(categoryData))
-    .toThrowError(`Input values must be between 0 < values <= numTokens`
-      + ` with numTokens=${numTokens}`);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/center_crop.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/center_crop.ts
deleted file mode 100644
index 3a5700d48..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/center_crop.ts
+++ /dev/null
@@ -1,141 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {serialization,DataType,unstack,stack,tensor,Tensor,Tensor1D,Tensor2D, Tensor3D, Tensor4D, tidy, range, image} from '@tensorflow/tfjs-core';
-import {getExactlyOneShape, getExactlyOneTensor} from '../../utils/types_utils';
-import {LayerArgs, Layer} from '../../engine/topology';
-import {Kwargs} from '../../types';
-import {Shape} from '../../keras_format/common';
-import * as K from '../../backend/tfjs_backend';
-
-const {resizeBilinear, cropAndResize} = image;
-
-export declare interface CenterCropArgs extends LayerArgs{
-  height: number;
-  width: number;
-}
-
-export class CenterCrop extends Layer {
-  /** @nocollapse */
-  static className = 'CenterCrop';
-  private readonly height: number;
-  private readonly width: number;
-  constructor(args: CenterCropArgs) {
-    super(args);
-    this.height = args.height;
-    this.width = args.width;
-  }
-
-  centerCrop(inputs: Tensor3D | Tensor4D, hBuffer: number, wBuffer: number,
-            height: number, width: number, inputHeight: number,
-            inputWidth: number, dtype: DataType): Tensor | Tensor[] {
-
-    return tidy(() => {
-      let input: Tensor4D;
-      let isRank3      = false;
-      const top      = hBuffer / inputHeight;
-      const left     = wBuffer / inputWidth;
-      const bottom   = ((height) + hBuffer) / inputHeight;
-      const right    = ((width) + wBuffer) / inputWidth;
-      const bound    = [top, left, bottom, right];
-      const boxesArr = [];
-
-      if(inputs.rank === 3) {
-        isRank3  = true;
-        input  = stack([inputs]) as Tensor4D;
-      } else {
-        input = inputs as Tensor4D;
-      }
-
-      for (let i = 0; i < input.shape[0]; i++) {
-        boxesArr.push(bound);
-      }
-
-      const boxes: Tensor2D  = tensor(boxesArr, [boxesArr.length, 4]);
-      const boxInd: Tensor1D = range(0, boxesArr.length, 1, 'int32');
-
-      const cropSize: [number, number] = [height, width];
-      const cropped = cropAndResize(input, boxes, boxInd, cropSize, 'nearest');
-
-      if(isRank3) {
-        return K.cast(getExactlyOneTensor(unstack(cropped)), dtype);
-      }
-      return K.cast(cropped, dtype);
-   });
-
-  }
-
-  upsize(inputs : Tensor3D | Tensor4D, height: number,
-         width: number, dtype: DataType): Tensor | Tensor[] {
-
-    return tidy(() => {
-      const outputs = resizeBilinear(inputs, [height, width]);
-      return K.cast(outputs, dtype);
-  });
-
-}
-
-  override call(inputs: Tensor3D | Tensor4D , kwargs: Kwargs):
-      Tensor[] | Tensor {
-    return tidy(() => {
-      const rankedInputs = getExactlyOneTensor(inputs) as Tensor3D | Tensor4D;
-      const dtype       = rankedInputs.dtype;
-      const inputShape  = rankedInputs.shape;
-      const inputHeight = inputShape[inputShape.length - 3];
-      const inputWidth  =  inputShape[inputShape.length - 2];
-
-      let hBuffer = 0;
-      if (inputHeight !== this.height) {
-        hBuffer =  Math.floor((inputHeight - this.height) / 2);
-      }
-
-      let wBuffer = 0;
-      if (inputWidth !== this.width) {
-        wBuffer = Math.floor((inputWidth - this.width) / 2);
-
-        if (wBuffer === 0) {
-          wBuffer = 1;
-        }
-      }
-
-      if(hBuffer >= 0 && wBuffer >= 0) {
-        return this.centerCrop(rankedInputs, hBuffer, wBuffer,
-                              this.height, this.width, inputHeight,
-                              inputWidth, dtype);
-      } else {
-        return this.upsize(inputs, this.height, this.width, dtype);
-      }
-   });
-
-  }
-
-  override getConfig(): serialization.ConfigDict{
-
-    const config: serialization.ConfigDict = {
-      'height' : this.height,
-      'width' : this.width
-    };
-
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override computeOutputShape(inputShape: Shape | Shape[]): Shape | Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const hAxis = inputShape.length - 3;
-    const wAxis = inputShape.length - 2;
-    inputShape[hAxis] = this.height;
-    inputShape[wAxis] = this.width;
-    return inputShape;
-  }
-}
-
-serialization.registerClass(CenterCrop);
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/center_crop_test.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/center_crop_test.ts
deleted file mode 100644
index f836e55b4..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/center_crop_test.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import { tensor, Tensor3D, Tensor4D, range} from '@tensorflow/tfjs-core';
-import { CenterCrop } from './center_crop';
-import { describeMathCPUAndGPU, expectTensorsClose } from '../../utils/test_utils';
-
-describeMathCPUAndGPU('CenterCrop Layer', () => {
-
-  it('Crops batched image with even crop lengths as expected', () => {
-    const inputTensor = range(0, 16).reshape([1, 4, 4, 1]);
-    const height = 2;
-    const width = 2;
-    const expectedOutput = tensor([ [[5,6],
-                                     [9,10]] ], [1,2,2,1]);
-    const centerCrop = new CenterCrop({height, width});
-    const actualOutput = centerCrop.apply(inputTensor) as Tensor4D;
-    expectTensorsClose(actualOutput, expectedOutput);
-  });
-
-  it('Crops batched image with odd crop lengths as expected', () => {
-    const inputTensor = range(0, 16).reshape([1, 4, 4, 1]);
-    const height = 3;
-    const width = 3;
-    const expectedOutput = tensor( [[[1,2,3],
-                                     [5,6,7],
-                                     [9,10,11]]], [1,3,3,1]);
-
-    const centerCrop = new CenterCrop({height, width});
-    const actualOutput = centerCrop.apply(inputTensor) as Tensor4D;
-    expectTensorsClose(actualOutput, expectedOutput);
-  });
-
-  it('Upsizes image when crop boundaries are larger than image dims', () => {
-    const inputArr =  [ [[4,  7],
-                         [21, 9]],
-
-                        [[8,  9],
-                         [1, 33]] ];
-    const inputTensor = tensor([inputArr], [1,2,2,2]);
-    const height = 3;
-    const width = 4;
-    const centerCrop = new CenterCrop({height, width});
-    const layerOutputTensor = centerCrop.apply(inputTensor) as Tensor4D;
-
-    const expectedArr = [[[4,7], [12.5, 8], [21, 9],[21, 9]],
-                        [[6.666667 , 8.333333 ], [7.1666665, 16.666666],
-                        [7.666666 , 25], [7.666666 , 25]],
-                        [[8, 9],[4.5, 21],[1, 33],[1, 33]]];
-
-    const expectedOutput = tensor([expectedArr], [1,3,4,2]);
-    expectTensorsClose(layerOutputTensor, expectedOutput);
-  });
-
-  it('Crops unbatched image with even crop lengths as expected', () => {
-    const inputTensor = range(0, 16).reshape([4, 4, 1]);
-    const height = 2;
-    const width = 2;
-    const expectedOutput = tensor([[5,6],
-                                   [9,10]], [2,2,1]);
-
-    const centerCrop = new CenterCrop({height, width});
-    const actualOutput = centerCrop.apply(inputTensor) as Tensor3D;
-    expectTensorsClose(actualOutput, expectedOutput);
-  });
-
-  it('Crops unbatched image with odd crop lengths as expected', () => {
-    const inputTensor = range(0, 16).reshape([ 4, 4, 1]);
-    const height = 3;
-    const width = 3;
-    const expectedOutput = tensor([[1,2,3],
-                                   [5,6,7],
-                                   [9,10,11]], [3,3,1]);
-
-    const centerCrop = new CenterCrop({height, width});
-    const actualOutput = centerCrop.apply(inputTensor) as Tensor3D;
-    expectTensorsClose(actualOutput, expectedOutput);
-  });
-
-  it('Crops batched image with non-square crop as expected', () => {
-    const inputTensor = range(0, 16).reshape([1, 4, 4, 1]);
-    const height = 2;
-    const width = 3;
-    const expectedOutput = tensor([ [[5,6,7],
-                                     [9,10,11]] ], [1,2,3,1]);
-    const centerCrop = new CenterCrop({height, width});
-    const actualOutput = centerCrop.apply(inputTensor) as Tensor4D;
-    expectTensorsClose(actualOutput, expectedOutput);
-  });
-
-  it('Config holds correct name', () => {
-    const height = 1;
-    const width = 1;
-    const resizingLayer = new CenterCrop({height, width, name:'CenterCrop'});
-    const config = resizingLayer.getConfig();
-    expect(config.name).toEqual('CenterCrop');
-  });
-
-  it('Returns Tensor with correct dtype', () => {
-    const inputTensorInt = range(0,16,1,'int32').reshape([1, 4, 4, 1]);
-    const inputTensorFloat = range(0,16,1,'float32').reshape([1, 4, 4, 1]);
-    const height = 2;
-    const width = 2;
-    const centerCrop = new CenterCrop({height, width});
-    const actualOutputInt = centerCrop.apply(inputTensorInt) as Tensor4D;
-    const actualOutputFloat = centerCrop.apply(inputTensorFloat) as Tensor4D;
-    expect(inputTensorInt.dtype).toBe(actualOutputInt.dtype);
-    expect(inputTensorFloat.dtype).toBe(actualOutputFloat.dtype);
-  });
-
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/image_preprocessing.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/image_preprocessing.ts
deleted file mode 100644
index a8308be15..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/image_preprocessing.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {LayerArgs, Layer} from '../../engine/topology';
-import { serialization, Tensor, mul, add, tidy } from '@tensorflow/tfjs-core';
-import { getExactlyOneTensor } from '../../utils/types_utils';
-import * as K from '../../backend/tfjs_backend';
-import { Kwargs } from '../../types';
-
-export declare interface RescalingArgs extends LayerArgs {
-  scale: number;
-  offset?: number;
-}
-
-/**
- * Preprocessing Rescaling Layer
- *
- * This rescales images by a scaling and offset factor
- */
-export class Rescaling extends Layer {
-  /** @nocollapse */
-  static className = 'Rescaling';
-  private readonly scale: number;
-  private readonly offset: number;
-  constructor(args: RescalingArgs) {
-    super(args);
-
-    this.scale = args.scale;
-
-    if(args.offset) {
-    this.offset = args.offset;
-    } else {
-      this.offset = 0;
-    }
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'scale': this.scale,
-      'offset': this.offset
-    };
-
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor[]|Tensor {
-    return tidy(() => {
-      inputs = getExactlyOneTensor(inputs);
-      if(inputs.dtype !== 'float32') {
-          inputs = K.cast(inputs, 'float32');
-      }
-      return add(mul(inputs, this.scale), this.offset);
-    });
-  }
-}
-
-serialization.registerClass(Rescaling);
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/image_preprocessing_test.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/image_preprocessing_test.ts
deleted file mode 100644
index 24628dbdb..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/image_preprocessing_test.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import { Tensor, randomNormal, mul, add} from '@tensorflow/tfjs-core';
-import { Rescaling } from './image_preprocessing';
-import { describeMathCPUAndGPU, expectTensorsClose } from '../../utils/test_utils';
-
-describeMathCPUAndGPU('Rescaling Layer', () => {
-
-  it('Check if input shape matches output shape', () => {
-    const scale = 1.0 / 127.5;
-    const offset = 0;
-    const input = randomNormal([2, 4, 5, 3]);
-    const expectedOutputTensor = add(mul(input, scale), offset);
-    const scalingLayer = new Rescaling({scale, offset});
-    const layerOutputTensor = scalingLayer.apply(input) as Tensor;
-    expect(expectedOutputTensor.shape).toEqual(layerOutputTensor.shape);
-  });
-
-  it('Rescales input layer based on scaling factor and offset', () => {
-    const scale = 1.0 / 127.5;
-    const offset = -1.0;
-    const input = randomNormal([2, 4, 5, 3]);
-    const expectedOutputTensor = add(mul(input, scale), offset);
-    const scalingLayer = new Rescaling({scale, offset});
-    const layerOutputTensor = scalingLayer.apply(input) as Tensor;
-    expectTensorsClose(layerOutputTensor, expectedOutputTensor);
-  });
-
-  it('Recasts dtype to float32', () => {
-    const scale = 1.0 / 127.5;
-    const offset = -1.0;
-    const intTensor = randomNormal([2, 4, 5, 3], 7, 2, 'int32');
-    const expectedOutputTensor = add(mul(intTensor, scale), offset);
-    const scalingLayer = new Rescaling({scale, offset});
-    const outputTensor = scalingLayer.apply(intTensor) as Tensor;
-    expect(outputTensor.dtype).toBe('float32'); 
-    expectTensorsClose(outputTensor, expectedOutputTensor); 
-  });
-
-  it('Config holds correct name', () => {
-    const scale = 1.0 / 127.5;
-    const offset = -1.0;
-    const scalingLayer = new Rescaling({scale, offset, name: 'Rescaling'});
-    const config = scalingLayer.getConfig();
-    expect(config.name).toEqual('Rescaling'); 
-  });
-
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/image_resizing.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/image_resizing.ts
deleted file mode 100644
index bd5b7f192..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/image_resizing.ts
+++ /dev/null
@@ -1,102 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {image, Rank, serialization, Tensor, tidy} from '@tensorflow/tfjs-core';  // mul, add
-
-import {Layer, LayerArgs} from '../../engine/topology';
-import {ValueError} from '../../errors';
-import {Shape} from '../../keras_format/common';
-import {Kwargs} from '../../types';
-import {getExactlyOneShape} from '../../utils/types_utils';  //, getExactlyOneTensor
-
-// tf methods unimplemented in tfjs: 'bicubic', 'area', 'lanczos3', 'lanczos5',
-//                                   'gaussian', 'mitchellcubic'
-const INTERPOLATION_KEYS = ['bilinear', 'nearest'] as const;
-const INTERPOLATION_METHODS = new Set(INTERPOLATION_KEYS);
-type InterpolationType = typeof INTERPOLATION_KEYS[number];
-
-export declare interface ResizingArgs extends LayerArgs {
-  height: number;
-  width: number;
-  interpolation?: InterpolationType; // default = 'bilinear';
-  cropToAspectRatio?: boolean;       // default = false;
-}
-
-/**
- * Preprocessing Resizing Layer
- *
- * This resizes images by a scaling and offset factor
- */
-
-export class Resizing extends Layer {
-  /** @nocollapse */
-  static className = 'Resizing';
-  private readonly height: number;
-  private readonly width: number;
-  // method of interpolation to be used; default = "bilinear";
-  private readonly interpolation: InterpolationType;
-  // toggle whether the aspect ratio should be preserved; default = false;
-  private readonly cropToAspectRatio: boolean;
-
-  constructor(args: ResizingArgs) {
-    super(args);
-
-    this.height = args.height;
-    this.width = args.width;
-
-    if (args.interpolation) {
-      if (INTERPOLATION_METHODS.has(args.interpolation)) {
-        this.interpolation = args.interpolation;
-      } else {
-        throw new ValueError(`Invalid interpolation parameter: ${
-            args.interpolation} is not implemented`);
-      }
-    } else {
-      this.interpolation = 'bilinear';
-    }
-    this.cropToAspectRatio = Boolean(args.cropToAspectRatio);
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const numChannels = inputShape[2];
-    return [this.height, this.width, numChannels];
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'height': this.height,
-      'width': this.width,
-      'interpolation': this.interpolation,
-      'cropToAspectRatio': this.cropToAspectRatio
-    };
-
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override call(inputs: Tensor<Rank.R3>|Tensor<Rank.R4>, kwargs: Kwargs):
-      Tensor[]|Tensor {
-    return tidy(() => {
-      const size: [number, number] = [this.height, this.width];
-      if (this.interpolation === 'bilinear') {
-        return image.resizeBilinear(inputs, size, !this.cropToAspectRatio);
-      } else if (this.interpolation === 'nearest') {
-        return image.resizeNearestNeighbor(
-            inputs, size, !this.cropToAspectRatio);
-      } else {
-        throw new Error(`Interpolation is ${this.interpolation} but only ${[...INTERPOLATION_METHODS]} are supported`);
-      }
-    });
-  }
-}
-
-serialization.registerClass(Resizing);
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/image_resizing_test.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/image_resizing_test.ts
deleted file mode 100644
index 9afc0b7f8..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/image_resizing_test.ts
+++ /dev/null
@@ -1,125 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit Tests for image resizing layer.
- */
-
-import {image, Rank, Tensor, tensor, zeros, range, reshape} from '@tensorflow/tfjs-core';
-
-// import {Shape} from '../../keras_format/common';
-import {describeMathCPUAndGPU, expectTensorsClose} from '../../utils/test_utils';
-
-import {Resizing, ResizingArgs} from './image_resizing';
-
-describeMathCPUAndGPU('Resizing Layer', () => {
-  it('Check if output shape matches specifications', () => {
-    // resize and check output shape
-    const maxHeight = 40;
-    const height = Math.floor(Math.random() * maxHeight);
-    const maxWidth = 60;
-    const width = Math.floor(Math.random() * maxWidth);
-    const numChannels = 3;
-    const inputTensor = zeros([height * 2, width * 2, numChannels]);
-    const expectedOutputShape = [height, width, numChannels];
-    const resizingLayer = new Resizing({height, width});
-    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
-    expect(layerOutputTensor.shape).toEqual(expectedOutputShape);
-  });
-
-  it('Returns correctly downscaled tensor', () => {
-    // resize and check output content (not batched)
-    const rangeTensor = range(0, 16);
-    const inputTensor = reshape(rangeTensor, [4,4,1]);
-    const height = 2;
-    const width = 2;
-    const interpolation = 'nearest';
-    const resizingLayer = new Resizing({height, width, interpolation});
-    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
-    const expectedArr = [[0, 3], [12, 15]];
-    const expectedOutput = tensor(expectedArr, [2,2,1]);
-    expectTensorsClose(layerOutputTensor, expectedOutput);
-  });
-
-  it('Returns correctly downscaled tensor', () => {
-    // resize and check output content (batched)
-    const rangeTensor = range(0, 36);
-    const inputTensor = reshape(rangeTensor, [1,6,6,1]);
-    const height = 3;
-    const width = 3;
-    const interpolation = 'nearest';
-    const resizingLayer = new Resizing({height, width, interpolation});
-    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
-    const expectedArr = [[0,3,5], [18,21,23], [30,33,35]];
-    const expectedOutput = tensor([expectedArr], [1,3,3,1]);
-    expectTensorsClose(layerOutputTensor, expectedOutput);
-  });
-
-  it('Returns correctly upscaled tensor', () => {
-    const rangeTensor = range(0, 4);
-    const inputTensor = reshape(rangeTensor, [1, 2, 2, 1]);
-    const height = 4;
-    const width = 4;
-    const interpolation = 'nearest';
-    const resizingLayer = new Resizing({height, width, interpolation});
-    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
-    const expectedArr = [[0,0,1,1], [0,0,1,1], [2,2,3,3], [2,2,3,3]];
-    const expectedOutput = tensor([expectedArr], [1,4,4,1]);
-    expectTensorsClose(layerOutputTensor, expectedOutput);
-  });
-
-  it('Returns the same tensor when given same shape as input', () => {
-    // create a resizing layer with same shape as input
-    const height = 64;
-    const width = 32;
-    const numChannels = 1;
-    const rangeTensor = range(0, height * width);
-    const inputTensor = reshape(rangeTensor, [height, width, numChannels]);
-    const resizingLayer = new Resizing({height, width});
-    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
-    expectTensorsClose(layerOutputTensor, inputTensor);
-  });
-
-  it('Returns a tensor of the correct dtype', () => {
-    // do a same resizing operation, cheeck tensors dtypes and content
-    const height = 40;
-    const width = 60;
-    const numChannels = 3;
-    const inputTensor: Tensor<Rank.R3> =
-        zeros([height, width, numChannels]);
-    const size: [number, number] = [height, width];
-    const expectedOutputTensor = image.resizeBilinear(inputTensor, size);
-    const resizingLayer = new Resizing({height, width});
-    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
-    expect(layerOutputTensor.dtype).toBe(inputTensor.dtype);
-    expectTensorsClose(layerOutputTensor, expectedOutputTensor);
-  });
-
-  it('Throws an error given incorrect parameters', () => {
-    // pass incorrect interpolation method string to layer init
-    const height = 16;
-    const width = 16;
-    const interpolation = 'unimplemented';
-    const incorrectArgs = {height, width, interpolation};
-    const expectedError =
-        `Invalid interpolation parameter: ${interpolation} is not implemented`;
-    expect(() => new Resizing(incorrectArgs as ResizingArgs))
-        .toThrowError(expectedError);
-  });
-
-  it('Config holds correct name', () => {
-    // layer name property set properly
-    const height = 40;
-    const width = 60;
-    const resizingLayer = new Resizing({height, width, name:'Resizing'});
-    const config = resizingLayer.getConfig();
-    expect(config.name).toEqual('Resizing');
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/preprocessing_utils.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/preprocessing_utils.ts
deleted file mode 100644
index 84ef0374d..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/preprocessing_utils.ts
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import { Tensor, denseBincount, Tensor1D, Tensor2D, TensorLike, mul} from '@tensorflow/tfjs-core';
-import { getExactlyOneTensor } from '../../utils/types_utils';
-import { expandDims} from '@tensorflow/tfjs-core';
-import { ValueError } from '../../errors';
-import * as K from '../../backend/tfjs_backend';
-
-export type OutputMode = 'int' | 'oneHot' | 'multiHot' | 'count' | 'tfIdf';
-
-export function encodeCategoricalInputs(inputs: Tensor|Tensor[],
-                                        outputMode: OutputMode,
-                                        depth: number,
-                                        weights?: Tensor1D|Tensor2D|TensorLike):
-                                        Tensor|Tensor[] {
-
-  let input = getExactlyOneTensor(inputs);
-
-  if(input.dtype !== 'int32') {
-    input = K.cast(input, 'int32');
-    }
-
-  if(outputMode === 'int') {
-    return input;
-  }
-
-  const originalShape = input.shape;
-
-  if(input.rank === 0) {
-    input = expandDims(input, -1);
-  }
-
-  if(outputMode === 'oneHot') {
-    if(input.shape[input.shape.length - 1] !== 1) {
-      input = expandDims(input, -1);
-    }
-  }
-
-  if(input.rank > 2) {
-    throw new ValueError(`When outputMode is not int, maximum output rank is 2`
-    + ` Received outputMode ${outputMode} and input shape ${originalShape}`
-    + ` which would result in output rank ${input.rank}.`);
-  }
-
-  const binaryOutput = ['multiHot', 'oneHot'].includes(outputMode);
-
-  const denseBincountInput = input as Tensor1D | Tensor2D;
-
-  let binCounts: Tensor1D | Tensor2D;
-
-  if ((typeof weights) !== 'undefined' && outputMode === 'count') {
-    binCounts = denseBincount(denseBincountInput, weights, depth, binaryOutput);
-   } else {
-    binCounts = denseBincount(denseBincountInput, [], depth, binaryOutput);
-   }
-
-  if(outputMode !== 'tfIdf') {
-    return binCounts;
-  }
-
-  if (weights) {
-    return mul(binCounts, weights);
-  } else {
-      throw new ValueError(
-        `When outputMode is 'tfIdf', weights must be provided.`
-      );
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/preprocessing_utils_test.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/preprocessing_utils_test.ts
deleted file mode 100644
index 7bb58c52a..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/preprocessing_utils_test.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2022 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import { describeMathCPUAndGPU, expectTensorsClose} from '../../utils/test_utils';
-import { Tensor, tensor, tensor1d} from '@tensorflow/tfjs-core';
-import * as utils from './preprocessing_utils';
-
-describeMathCPUAndGPU('Preprocessing Utils', () => {
-
-  it('Peforms int encoding correctly', () => {
-    const inputs = tensor([0, 1, 2], [3], 'int32');
-    const outputs = utils.
-    encodeCategoricalInputs(inputs, 'int', 4) as Tensor;
-    expectTensorsClose(outputs, inputs);
-  });
-
-  it('Peforms oneHot encoding correctly', () => {
-    const inputs = tensor([0, 1, 2],  [3], 'int32');
-    const outputs = utils.
-    encodeCategoricalInputs(inputs, 'oneHot', 4) as Tensor;
-    const expectedOutput = tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]);
-    expectTensorsClose(outputs, expectedOutput);
-  });
-
-  it('Peforms multiHot encoding correctly', () => {
-    const inputs = tensor([0, 1, 2], [3], 'int32');
-    const outputs = utils.
-    encodeCategoricalInputs(inputs, 'multiHot', 4) as Tensor;
-    const expectedOutput = tensor([1, 1, 1, 0]);
-    expectTensorsClose(outputs, expectedOutput);
-  });
-
-  it('Peforms count encoding correctly', () => {
-    const inputs = tensor([0, 1, 1, 2, 2, 2], [6], 'int32');
-    const outputs = utils.
-    encodeCategoricalInputs(inputs, 'count', 4) as Tensor;
-    const expectedOutput = tensor([1, 2, 3, 0]);
-    expectTensorsClose(outputs, expectedOutput);
-  });
-
-  it('Peforms tfIdf encoding correctly', () => {
-    const inputs = tensor([0, 1, 1, 2, 2, 2]);
-    const idfWeights = tensor1d([0.1, 1.0, 10.0, 0]);
-    const outputs = utils.encodeCategoricalInputs(inputs, 'tfIdf',
-                                                  4, idfWeights) as Tensor;
-    const expectedOutput = tensor([0.1, 2, 30, 0]);
-    expectTensorsClose(outputs, expectedOutput);
-  });
-
-  it('Thows an error if input rank > 2', () => {
-    const inputs = tensor([[[1], [2]], [[3], [1]]]);
-    expect(() =>utils.encodeCategoricalInputs(inputs, 'multiHot', 4))
-    .toThrowError(`When outputMode is not int, maximum output rank is 2`
-    + ` Received outputMode ${'multiHot'} and input shape ${inputs.shape}`
-    + ` which would result in output rank ${inputs.rank}.`);
-  });
-
-  it('Thows an error if weights are not supplied for tfIdf', () => {
-    const inputs = tensor([0, 1, 1, 2, 2, 2]);
-    expect(() =>utils.encodeCategoricalInputs(inputs, 'tfIdf', 4)).
-    toThrowError(`When outputMode is 'tfIdf', weights must be provided.`);
-  });
-
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/random_height.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/random_height.ts
deleted file mode 100644
index fa21f371e..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/random_height.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-/**
- * @license
- * Copyright 2023 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import { image, Rank, serialization, Tensor, tidy } from '@tensorflow/tfjs-core';
-import { getExactlyOneTensor, getExactlyOneShape } from '../../utils/types_utils';
-import { Shape } from '../../keras_format/common';
-import { Kwargs } from '../../types';
-import { ValueError } from '../../errors';
-import { BaseRandomLayerArgs, BaseRandomLayer } from '../../engine/base_random_layer';
-import { randomUniform } from '@tensorflow/tfjs-core';
-
-export declare interface RandomHeightArgs extends BaseRandomLayerArgs {
-   factor: number | [number, number];
-   interpolation?: InterpolationType; // default = 'bilinear';
-   seed?: number; // default = null;
-   autoVectorize?: boolean;
-}
-
-const INTERPOLATION_KEYS = ['bilinear', 'nearest'] as const;
-export const INTERPOLATION_METHODS = new Set(INTERPOLATION_KEYS);
-type InterpolationType = typeof INTERPOLATION_KEYS[number];
-
-/**
- * Preprocessing Layer with randomly varies image during training
- *
- * This layer randomly adjusts the height of a
- * batch of images by a random factor.
- *
- * The input should be a 3D (unbatched) or
- * 4D (batched) tensor in the `"channels_last"` image data format. Input pixel
- * values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger
- * or floating point dtype. By default, the layer will output floats.
- *
- * tf methods implemented in tfjs: 'bilinear', 'nearest',
- * tf methods unimplemented in tfjs: 'bicubic', 'area', 'lanczos3', 'lanczos5',
- *                                   'gaussian', 'mitchellcubic'
- *
- */
-
-export class RandomHeight extends BaseRandomLayer {
-  /** @nocollapse */
-  static override className = 'RandomHeight';
-  private readonly factor: number | [number, number];
-  private readonly interpolation?: InterpolationType;  // defualt = 'bilinear
-  private heightLower: number;
-  private heightUpper: number;
-  private imgWidth: number;
-  private heightFactor: Tensor<Rank.R1>;
-
-  constructor(args: RandomHeightArgs) {
-    super(args);
-    const {factor, interpolation = 'bilinear'} = args;
-
-    this.factor = factor;
-
-    if (Array.isArray(this.factor) && this.factor.length === 2) {
-      this.heightLower = this.factor[0];
-      this.heightUpper = this.factor[1];
-    } else if (!Array.isArray(this.factor) && this.factor > 0){
-      this.heightLower = -this.factor;
-      this.heightUpper = this.factor;
-    } else {
-      throw new ValueError(
-        `Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`
-      );
-    }
-    if (this.heightLower < -1.0 || this.heightUpper < -1.0) {
-      throw new ValueError(
-        `factor must have values larger than -1. Got: ${this.factor}`
-      );
-    }
-
-    if (this.heightUpper < this.heightLower) {
-      throw new ValueError(
-        `factor cannot have upper bound less than lower bound.
-        Got upper bound: ${this.heightUpper}.
-        Got lower bound: ${this.heightLower}
-      `);
-    }
-
-    if (interpolation) {
-      if (INTERPOLATION_METHODS.has(interpolation)) {
-        this.interpolation = interpolation;
-      } else {
-        throw new ValueError(`Invalid interpolation parameter: ${
-            interpolation} is not implemented`);
-      }
-    } 
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'factor': this.factor,
-      'interpolation': this.interpolation,
-    };
-
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const numChannels = inputShape[2];
-    return [-1, this.imgWidth, numChannels];
-  }
-
-  override call(inputs: Tensor<Rank.R3>|Tensor<Rank.R4>,
-    kwargs: Kwargs): Tensor[]|Tensor {
-
-    return tidy(() => {
-      const input = getExactlyOneTensor(inputs);
-      this.imgWidth = input.shape[input.shape.length - 2];
-      const imgHeight = input.shape[input.shape.length - 3];
-
-      this.heightFactor = randomUniform([1],
-        (1.0 + this.heightLower), (1.0 + this.heightUpper),
-        'float32', this.randomGenerator.next()
-      );
-
-      let adjustedHeight = this.heightFactor.dataSync()[0] * imgHeight;
-      adjustedHeight = Math.round(adjustedHeight);
-
-      const size:[number, number] = [adjustedHeight, this.imgWidth];
-
-      switch (this.interpolation) {
-        case 'bilinear':
-          return image.resizeBilinear(inputs, size);
-        case 'nearest':
-          return image.resizeNearestNeighbor(inputs, size);
-        default:
-          throw new Error(`Interpolation is ${this.interpolation}
-          but only ${[...INTERPOLATION_METHODS]} are supported`);
-      }
-    });
-  }
-}
-
-serialization.registerClass(RandomHeight);
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/random_height_test.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/random_height_test.ts
deleted file mode 100644
index edbfa418b..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/random_height_test.ts
+++ /dev/null
@@ -1,159 +0,0 @@
-/**
- * @license
- * Copyright 2023 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit Tests for random height layer.
- */
-
-import { Tensor, range, image, Rank, zeros, randomUniform, tensor } from '@tensorflow/tfjs-core';
-import { describeMathCPUAndGPU, expectTensorsClose } from '../../utils/test_utils';
-
-import { RandomHeight, RandomHeightArgs } from './random_height';
-
-describeMathCPUAndGPU('RandomHeight Layer', () => {
-  it('Returns correct, randomly scaled height of Rank 3 Tensor', () => {
-    const inputTensor = range(0, 16).reshape([4, 4, 1]);
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomHeightLayer = new RandomHeight({factor, interpolation, seed});
-    const layerOutputTensor = randomHeightLayer.apply(inputTensor) as Tensor;
-
-    expect(layerOutputTensor.shape).toEqual([5,4,1]);
-  });
-
-  it('Returns correct, randomly scaled height of Rank 4 Tensor', () => {
-    const inputTensor = range(0, 16).reshape([1,4,4,1]);
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomHeightLayer = new RandomHeight({factor, interpolation, seed});
-    const layerOutputTensor = randomHeightLayer.apply(inputTensor) as Tensor;
-
-    expect(layerOutputTensor.shape).toEqual([1,5,4,1]);
-  });
-
-  it('Returns a tensor of the correct dtype and compares class to standard ops',
-    () => {
-    // perform random height resizing ops, check tensors dtypes and content
-    const inputTensor: Tensor<Rank.R3> =
-    zeros([4, 4, 1]);
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomHeightLayer = new RandomHeight({factor, interpolation, seed});
-    const layerOutputTensor = randomHeightLayer.apply(inputTensor) as Tensor;
-    const lower = 1 + -factor;
-    const upper = 1 + factor;
-    const heightFactor = randomUniform([1], lower, upper, 'float32', seed);
-    const adjustedHeight = Math.round(heightFactor.dataSync()[0] * 4);
-    const size: [number, number] = [adjustedHeight, 4];
-    const expectedOutputTensor = image.resizeNearestNeighbor(inputTensor, size);
-    expect(layerOutputTensor.dtype).toBe(inputTensor.dtype);
-    expectTensorsClose(layerOutputTensor, expectedOutputTensor);
-  });
-
-  it('Returns correctly scaled tensor; not batched', () => {
-    // randomly resize height and check output content
-    const inputTensor = range(0, 16).reshape([4, 4, 1]);
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomHeightLayer = new RandomHeight({factor, interpolation, seed});
-    const layerOutputTensor = randomHeightLayer.apply(inputTensor) as Tensor;
-    const expectedArr = [
-      [0, 1, 2, 3],
-      [0, 1, 2, 3],
-      [4, 5, 6, 7],
-      [8, 9, 10, 11],
-      [12, 13, 14, 15]
-    ];
-    const expectedOutput = tensor(expectedArr, [5, 4, 1]);
-    expectTensorsClose(layerOutputTensor, expectedOutput);
-  });
-
-  it('Check unimplemented interpolation method', () => {
-    const factor = 0.5;
-    const interpolation = 'unimplemented';
-    const seed = 42;
-    const incorrectArgs = {factor, interpolation, seed };
-    const expectedError = `Invalid interpolation parameter: ${
-    interpolation} is not implemented`;
-    expect(() => new RandomHeight(incorrectArgs as RandomHeightArgs))
-      .toThrowError(expectedError);
-  });
-
-  it('Validate thrown error when factor is less than -1', () => {
-    // test for factor array, first item in array must be >= -1
-    const factor = [-5, 0.3];
-    const interpolation = 'nearest';
-    const seed = 42;
-    const incorrectArgs = { factor, interpolation, seed };
-    const expectedError =  `factor must have values larger than -1. Got: ${factor}`;
-    expect(
-      () => new RandomHeight(incorrectArgs as RandomHeightArgs)
-    ).toThrowError(expectedError);
-  });
-
-  it('Validate thrown error when factor contains more than 2 elements', () => {
-    // test for factor array, first item in array must be >= -1
-    const factor = [-5, 0.3, 7];
-    const interpolation = 'nearest';
-    const seed = 42;
-    const incorrectArgs = { factor, interpolation, seed };
-    const expectedError =
-    `Invalid factor: ${factor}. Must be positive number or tuple of 2 numbers`;
-
-    expect(
-      () => new RandomHeight(incorrectArgs as RandomHeightArgs)
-    ).toThrowError(expectedError);
-  });
-
-  it('Throws error if factor is an array, upper height < lower height', () => {
-    // test for upper height < lower height
-    const factor = [0.5, 0.3];
-    const interpolation = 'nearest';
-    const seed = 42;
-    const incorrectArgs = { factor, interpolation, seed };
-    const expectedError = `factor cannot have upper bound less than lower bound.
-        Got upper bound: ${factor[1]}.
-        Got lower bound: ${factor[0]}
-      `;
-    expect(
-      () => new RandomHeight(incorrectArgs as RandomHeightArgs)
-    ).toThrowError(expectedError);
-  });
-
-  it('Throws an error if factor is a negative number', () => {
-    // test factor, if factor is a number, it must be positive
-    const factor = -0.5;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const incorrectArgs = { factor, interpolation, seed };
-    const expectedError =
-    `Invalid factor: ${factor}. Must be positive number or tuple of 2 numbers`;
-
-    expect(
-      () => new RandomHeight(incorrectArgs as RandomHeightArgs)
-    ).toThrowError(expectedError);
-  });
-
-  it('Config holds correct name', () => {
-    // layer name property set properly
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomHeightLayer = new RandomHeight({
-      factor, interpolation, seed, name: 'RandomHeight'
-    });
-    const config = randomHeightLayer.getConfig();
-    expect(config.name).toEqual('RandomHeight');
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/random_width.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/random_width.ts
deleted file mode 100644
index 60b71490e..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/random_width.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-/**
- * @license
- * Copyright 2023 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import { image, Rank, serialization, Tensor, tidy } from '@tensorflow/tfjs-core';
-import { getExactlyOneTensor, getExactlyOneShape } from '../../utils/types_utils';
-import { Shape } from '../../keras_format/common';
-import { Kwargs } from '../../types';
-import { ValueError } from '../../errors';
-import { BaseRandomLayerArgs, BaseRandomLayer } from '../../engine/base_random_layer';
-import { randomUniform } from '@tensorflow/tfjs-core';
-
-export declare interface RandomWidthArgs extends BaseRandomLayerArgs {
-   factor: number | [number, number];
-   interpolation?: InterpolationType; // default = 'bilinear';
-   seed?: number; // default = null;
-   autoVectorize?: boolean;
-}
-
-const INTERPOLATION_KEYS = ['bilinear', 'nearest'] as const;
-export const INTERPOLATION_METHODS = new Set(INTERPOLATION_KEYS);
-type InterpolationType = typeof INTERPOLATION_KEYS[number];
-
-/**
- * Preprocessing Layer with randomly varies image during training
- *
- * This layer randomly adjusts the width of a batch of images of a
- * batch of images by a random factor.
- *
- * The input should be a 3D (unbatched) or
- * 4D (batched) tensor in the `"channels_last"` image data format. Input pixel
- * values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger
- * or floating point dtype. By default, the layer will output floats.
- *
- * tf methods implemented in tfjs: 'bilinear', 'nearest',
- * tf methods unimplemented in tfjs: 'bicubic', 'area', 'lanczos3', 'lanczos5',
- *                                   'gaussian', 'mitchellcubic'
- *
- */
-
-export class RandomWidth extends BaseRandomLayer {
-  /** @nocollapse */
-  static override className = 'RandomWidth';
-  private readonly factor: number | [number, number];
-  private readonly interpolation?: InterpolationType;  // defualt = 'bilinear
-  private widthLower: number;
-  private widthUpper: number;
-  private imgHeight: number;
-  private widthFactor: Tensor<Rank.R1>;
-
-  constructor(args: RandomWidthArgs) {
-    super(args);
-    const {factor, interpolation = 'bilinear'} = args;
-
-    this.factor = factor;
-
-    if (Array.isArray(this.factor) && this.factor.length === 2) {
-      this.widthLower = this.factor[0];
-      this.widthUpper = this.factor[1];
-    } else if (!Array.isArray(this.factor) && this.factor > 0){
-      this.widthLower = -this.factor;
-      this.widthUpper = this.factor;
-    } else {
-      throw new ValueError(
-        `Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`
-      );
-    }
-    if (this.widthLower < -1.0 || this.widthUpper < -1.0) {
-      throw new ValueError(
-        `factor must have values larger than -1. Got: ${this.factor}`
-      );
-    }
-
-    if (this.widthUpper < this.widthLower) {
-      throw new ValueError(
-        `factor cannot have upper bound less than lower bound.
-        Got upper bound: ${this.widthUpper}.
-        Got lower bound: ${this.widthLower}
-      `);
-    }
-
-    if (interpolation) {
-      if (INTERPOLATION_METHODS.has(interpolation)) {
-        this.interpolation = interpolation;
-      } else {
-        throw new ValueError(`Invalid interpolation parameter: ${
-            interpolation} is not implemented`);
-      }
-    } 
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'factor': this.factor,
-      'interpolation': this.interpolation,
-    };
-
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const numChannels = inputShape[2];
-    return [this.imgHeight, -1, numChannels];
-  }
-
-  override call(inputs: Tensor<Rank.R3>|Tensor<Rank.R4>,
-    kwargs: Kwargs): Tensor[]|Tensor {
-
-    return tidy(() => {
-      const input = getExactlyOneTensor(inputs);
-      this.imgHeight = input.shape[input.shape.length - 3];
-      const imgWidth = input.shape[input.shape.length - 2];
-
-      this.widthFactor = randomUniform([1],
-        (1.0 + this.widthLower), (1.0 + this.widthUpper),
-        'float32', this.randomGenerator.next()
-      );
-
-      let adjustedWidth = this.widthFactor.dataSync()[0] * imgWidth;
-      adjustedWidth = Math.round(adjustedWidth);
-
-      const size:[number, number] = [this.imgHeight, adjustedWidth];
-
-      switch (this.interpolation) {
-        case 'bilinear':
-          return image.resizeBilinear(inputs, size);
-        case 'nearest':
-          return image.resizeNearestNeighbor(inputs, size);
-        default:
-          throw new Error(`Interpolation is ${this.interpolation}
-          but only ${[...INTERPOLATION_METHODS]} are supported`);
-      }
-    });
-  }
-}
-
-serialization.registerClass(RandomWidth);
diff --git a/tfjs-master/tfjs-layers/src/layers/preprocessing/random_width_test.ts b/tfjs-master/tfjs-layers/src/layers/preprocessing/random_width_test.ts
deleted file mode 100644
index 2729a67f5..000000000
--- a/tfjs-master/tfjs-layers/src/layers/preprocessing/random_width_test.ts
+++ /dev/null
@@ -1,158 +0,0 @@
-/**
- * @license
- * Copyright 2023 CodeSmith LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit Tests for random width layer.
- */
-
-import { Tensor, range, image, Rank, zeros, randomUniform, tensor } from '@tensorflow/tfjs-core';
-import { describeMathCPUAndGPU, expectTensorsClose } from '../../utils/test_utils';
-
-import { RandomWidth, RandomWidthArgs } from './random_width';
-
-describeMathCPUAndGPU('RandomWidth Layer', () => {
-  it('Returns correct, randomly scaled width of Rank 3 Tensor', () => {
-    const inputTensor = range(0, 16).reshape([4, 4, 1]);
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomWidthLayer = new RandomWidth({factor, interpolation, seed});
-    const layerOutputTensor = randomWidthLayer.apply(inputTensor) as Tensor;
-
-    expect(layerOutputTensor.shape).toEqual([4,5,1]);
-  });
-
-  it('Returns correct, randomly scaled width of Rank 4 Tensor', () => {
-    const inputTensor = range(0, 16).reshape([1,4,4,1]);
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomWidthLayer = new RandomWidth({factor, interpolation, seed});
-    const layerOutputTensor = randomWidthLayer.apply(inputTensor) as Tensor;
-
-    expect(layerOutputTensor.shape).toEqual([1,4,5,1]);
-  });
-
-  it('Returns a tensor of the correct dtype and compares class to standard ops',
-    () => {
-    // perform random width resizing ops, check tensors dtypes and content
-    const inputTensor: Tensor<Rank.R3> =
-    zeros([4, 4, 1]);
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomWidthLayer = new RandomWidth({factor, interpolation, seed});
-    const layerOutputTensor = randomWidthLayer.apply(inputTensor) as Tensor;
-    const lower = 1 + -factor;
-    const upper = 1 + factor;
-    const widthFactor = randomUniform([1], lower, upper, 'float32', seed);
-    const adjustedWidth = Math.round(widthFactor.dataSync()[0] * 4);
-    const size: [number, number] = [4, adjustedWidth];
-    const expectedOutputTensor = image.resizeNearestNeighbor(inputTensor, size);
-    expect(layerOutputTensor.dtype).toBe(inputTensor.dtype);
-    expectTensorsClose(layerOutputTensor, expectedOutputTensor);
-  });
-
-  it('Returns correctly scaled tensor; not batched', () => {
-    // randomly resize width and check output content
-    const inputTensor = range(0, 16).reshape([4, 4, 1]);
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomWidthLayer = new RandomWidth({factor, interpolation, seed});
-    const layerOutputTensor = randomWidthLayer.apply(inputTensor) as Tensor;
-    const expectedArr = [
-      [0, 0, 1, 2, 3],
-      [4, 4, 5, 6, 7],
-      [8, 8, 9, 10, 11],
-      [12, 12, 13, 14, 15]
-    ];
-    const expectedOutput = tensor(expectedArr, [4, 5, 1]);
-    expectTensorsClose(layerOutputTensor, expectedOutput);
-  });
-
-  it('Check unimplemented interpolation method', () => {
-    const factor = 0.5;
-    const interpolation = 'unimplemented';
-    const seed = 42;
-    const incorrectArgs = {factor, interpolation, seed };
-    const expectedError = `Invalid interpolation parameter: ${
-    interpolation} is not implemented`;
-    expect(() => new RandomWidth(incorrectArgs as RandomWidthArgs))
-      .toThrowError(expectedError);
-  });
-
-  it('Validate thrown error when factor is less than -1', () => {
-    // test for factor array, first item in array must be >= -1
-    const factor = [-5, 0.3];
-    const interpolation = 'nearest';
-    const seed = 42;
-    const incorrectArgs = { factor, interpolation, seed };
-    const expectedError =  `factor must have values larger than -1. Got: ${factor}`;
-    expect(
-      () => new RandomWidth(incorrectArgs as RandomWidthArgs)
-    ).toThrowError(expectedError);
-  });
-
-  it('Validate thrown error when factor contains more than 2 elements', () => {
-    // test for factor array, first item in array must be >= -1
-    const factor = [-5, 0.3, 7];
-    const interpolation = 'nearest';
-    const seed = 42;
-    const incorrectArgs = { factor, interpolation, seed };
-    const expectedError =
-    `Invalid factor: ${factor}. Must be positive number or tuple of 2 numbers`;
-
-    expect(
-      () => new RandomWidth(incorrectArgs as RandomWidthArgs)
-    ).toThrowError(expectedError);
-  });
-
-  it('Throws an error if factor is an array, upper width < lower width', () => {
-    // test for upper width < lower width
-    const factor = [0.5, 0.3];
-    const interpolation = 'nearest';
-    const seed = 42;
-    const incorrectArgs = { factor, interpolation, seed };
-    const expectedError = `factor cannot have upper bound less than lower bound.
-        Got upper bound: ${factor[1]}.
-        Got lower bound: ${factor[0]}
-      `;
-    expect(
-      () => new RandomWidth(incorrectArgs as RandomWidthArgs)
-    ).toThrowError(expectedError);
-  });
-
-  it('Throws an error if factor is a negative number', () => {
-    // test factor, if factor is a number, it must be positive
-    const factor = -0.5;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const incorrectArgs = { factor, interpolation, seed };
-    const expectedError =
-    `Invalid factor: ${factor}. Must be positive number or tuple of 2 numbers`;
-
-    expect(
-      () => new RandomWidth(incorrectArgs as RandomWidthArgs)
-    ).toThrowError(expectedError);
-  });
-
-  it('Config holds correct name', () => {
-    // layer name property set properly
-    const factor = 0.4;
-    const interpolation = 'nearest';
-    const seed = 42;
-    const randomWidthLayer = new RandomWidth({
-      factor, interpolation, seed, name: 'RandomWidth'
-    });
-    const config = randomWidthLayer.getConfig();
-    expect(config.name).toEqual('RandomWidth');
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/recurrent.ts b/tfjs-master/tfjs-layers/src/layers/recurrent.ts
deleted file mode 100644
index b2af94772..000000000
--- a/tfjs-master/tfjs-layers/src/layers/recurrent.ts
+++ /dev/null
@@ -1,2127 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * TensorFlow.js Layers: Recurrent Neural Network Layers.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {DataType, serialization, Tensor, tidy, util} from '@tensorflow/tfjs-core';
-
-import {Activation, getActivation, serializeActivation} from '../activations';
-import * as K from '../backend/tfjs_backend';
-import {nameScope} from '../common';
-import {Constraint, ConstraintIdentifier, getConstraint, serializeConstraint} from '../constraints';
-import {InputSpec, SymbolicTensor} from '../engine/topology';
-import {Layer, LayerArgs} from '../engine/topology';
-import {AttributeError, NotImplementedError, ValueError} from '../errors';
-import {getInitializer, Initializer, InitializerIdentifier, Ones, serializeInitializer} from '../initializers';
-import {ActivationIdentifier} from '../keras_format/activation_config';
-import {Shape} from '../keras_format/common';
-import {getRegularizer, Regularizer, RegularizerIdentifier, serializeRegularizer} from '../regularizers';
-import {Kwargs, RnnStepFunction} from '../types';
-import {assertPositiveInteger} from '../utils/generic_utils';
-import * as math_utils from '../utils/math_utils';
-import {getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes} from '../utils/types_utils';
-import {batchGetValue, batchSetValue, LayerVariable} from '../variables';
-
-import {deserialize} from './serialization';
-
-/**
- * Standardize `apply()` args to a single list of tensor inputs.
- *
- * When running a model loaded from file, the input tensors `initialState` and
- * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the
- * dedicated kwargs fields. `inputs` consists of
- * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this
- * case.
- * This method makes sure that arguments are
- * separated and that `initialState` and `constants` are `Array`s of tensors
- * (or None).
- *
- * @param inputs Tensor or `Array` of  tensors.
- * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.
- * @param constants Tensor or `Array` of tensors or `null`/`undefined`.
- * @returns An object consisting of
- *   inputs: A tensor.
- *   initialState: `Array` of tensors or `null`.
- *   constants: `Array` of tensors or `null`.
- * @throws ValueError, if `inputs` is an `Array` but either `initialState` or
- *   `constants` is provided.
- */
-export function standardizeArgs(
-    inputs: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],
-    initialState: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],
-    constants: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],
-    numConstants?: number): {
-  inputs: Tensor|SymbolicTensor,
-  initialState: Tensor[]|SymbolicTensor[],
-  constants: Tensor[]|SymbolicTensor[]
-} {
-  if (Array.isArray(inputs)) {
-    if (initialState != null || constants != null) {
-      throw new ValueError(
-          'When inputs is an array, neither initialState or constants ' +
-          'should be provided');
-    }
-    if (numConstants != null) {
-      constants = inputs.slice(inputs.length - numConstants, inputs.length);
-      inputs = inputs.slice(0, inputs.length - numConstants);
-    }
-    if (inputs.length > 1) {
-      initialState = inputs.slice(1, inputs.length);
-    }
-    inputs = inputs[0];
-  }
-
-  function toListOrNull(x: Tensor|Tensor[]|SymbolicTensor|
-                        SymbolicTensor[]): Tensor[]|SymbolicTensor[] {
-    if (x == null || Array.isArray(x)) {
-      return x as Tensor[] | SymbolicTensor[];
-    } else {
-      return [x] as Tensor[] | SymbolicTensor[];
-    }
-  }
-
-  initialState = toListOrNull(initialState);
-  constants = toListOrNull(constants);
-
-  return {inputs, initialState, constants};
-}
-
-/**
- * Iterates over the time dimension of a tensor.
- *
- * @param stepFunction RNN step function.
- *   Parameters:
- *     inputs: tensor with shape `[samples, ...]` (no time dimension),
- *       representing input for the batch of samples at a certain time step.
- *     states: an Array of tensors.
- *   Returns:
- *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).
- *     newStates: list of tensors, same length and shapes as `states`. The first
- *       state in the list must be the output tensor at the previous timestep.
- * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at
- *   least 3D).
- * @param initialStates Tensor with shape `[samples, outputDim]` (no time
- *   dimension), containing the initial values of the states used in the step
- *   function.
- * @param goBackwards If `true`, do the iteration over the time dimension in
- *   reverse order and return the reversed sequence.
- * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for
- *   every element that is masked.
- * @param constants An Array of constant values passed at each step.
- * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*
- *   applicable to this imperative deeplearn.js backend. Its value is ignored.
- * @param needPerStepOutputs Whether the per-step outputs are to be
- *   concatenated into a single tensor and returned (as the second return
- *   value). Default: `false`. This arg is included so that the relatively
- *   expensive concatenation of the stepwise outputs can be omitted unless
- *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which
- *   `returnSequence` is `true`.)
- * @returns An Array: `[lastOutput, outputs, newStates]`.
- *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.
- *   outputs: tensor with shape `[samples, time, ...]` where each entry
- *     `output[s, t]` is the output of the step function at time `t` for sample
- *     `s`. This return value is provided if and only if the
- *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this
- *     return value will be `undefined`.
- *   newStates: Array of tensors, latest states returned by the step function,
- *      of shape `(samples, ...)`.
- * @throws ValueError If input dimension is less than 3.
- *
- * TODO(nielsene): This needs to be tidy-ed.
- */
-export function rnn(
-    stepFunction: RnnStepFunction, inputs: Tensor, initialStates: Tensor[],
-    goBackwards = false, mask?: Tensor, constants?: Tensor[], unroll = false,
-    needPerStepOutputs = false): [Tensor, Tensor, Tensor[]] {
-  return tfc.tidy(() => {
-    const ndim = inputs.shape.length;
-    if (ndim < 3) {
-      throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);
-    }
-
-    // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,
-    // ...].
-    const axes = [1, 0].concat(math_utils.range(2, ndim));
-    inputs = tfc.transpose(inputs, axes);
-
-    if (constants != null) {
-      throw new NotImplementedError(
-          'The rnn() functoin of the deeplearn.js backend does not support ' +
-          'constants yet.');
-    }
-
-    // Porting Note: the unroll option is ignored by the imperative backend.
-    if (unroll) {
-      console.warn(
-          'Backend rnn(): the unroll = true option is not applicable to the ' +
-          'imperative deeplearn.js backend.');
-    }
-
-    if (mask != null) {
-      mask = tfc.cast(tfc.cast(mask, 'bool'), 'float32');
-      if (mask.rank === ndim - 1) {
-        mask = tfc.expandDims(mask, -1);
-      }
-      mask = tfc.transpose(mask, axes);
-    }
-
-    if (goBackwards) {
-      inputs = tfc.reverse(inputs, 0);
-      if (mask != null) {
-        mask = tfc.reverse(mask, 0);
-      }
-    }
-
-    // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop
-    //   (tf.while_loop). But for the imperative deeplearn.js backend, we just
-    //   use the usual TypeScript control flow to iterate over the time steps in
-    //   the inputs.
-    // Porting Note: PyKeras patches a "_use_learning_phase" attribute to
-    // outputs.
-    //   This is not idiomatic in TypeScript. The info regarding whether we are
-    //   in a learning (i.e., training) phase for RNN is passed in a different
-    //   way.
-
-    const perStepOutputs: Tensor[] = [];
-    let lastOutput: Tensor;
-    let states = initialStates;
-    const timeSteps = inputs.shape[0];
-    const perStepInputs = tfc.unstack(inputs);
-    let perStepMasks: Tensor[];
-    if (mask != null) {
-      perStepMasks = tfc.unstack(mask);
-    }
-
-    for (let t = 0; t < timeSteps; ++t) {
-      const currentInput = perStepInputs[t];
-      const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));
-
-      if (mask == null) {
-        lastOutput = stepOutputs[0];
-        states = stepOutputs[1];
-      } else {
-        const maskedOutputs = tfc.tidy(() => {
-          const stepMask = perStepMasks[t];
-          const negStepMask = tfc.sub(tfc.onesLike(stepMask), stepMask);
-          // TODO(cais): Would tfc.where() be better for performance?
-          const output = tfc.add(
-              tfc.mul(stepOutputs[0], stepMask),
-              tfc.mul(states[0], negStepMask));
-          const newStates = states.map((state, i) => {
-            return tfc.add(
-                tfc.mul(stepOutputs[1][i], stepMask),
-                tfc.mul(state, negStepMask));
-          });
-          return {output, newStates};
-        });
-        lastOutput = maskedOutputs.output;
-        states = maskedOutputs.newStates;
-      }
-
-      if (needPerStepOutputs) {
-        perStepOutputs.push(lastOutput);
-      }
-    }
-    let outputs: Tensor;
-    if (needPerStepOutputs) {
-      const axis = 1;
-      outputs = tfc.stack(perStepOutputs, axis);
-    }
-    return [lastOutput, outputs, states] as [Tensor, Tensor, Tensor[]];
-  });
-}
-
-export declare interface BaseRNNLayerArgs extends LayerArgs {
-  /**
-   * A RNN cell instance. A RNN cell is a class that has:
-   *   - a `call()` method, which takes `[Tensor, Tensor]` as the
-   *     first input argument. The first item is the input at time t, and
-   *     second item is the cell state at time t.
-   *     The `call()` method returns `[outputAtT, statesAtTPlus1]`.
-   *     The `call()` method of the cell can also take the argument `constants`,
-   *     see section "Note on passing external constants" below.
-   *     Porting Node: PyKeras overrides the `call()` signature of RNN cells,
-   *       which are Layer subtypes, to accept two arguments. tfjs-layers does
-   *       not do such overriding. Instead we preseve the `call()` signature,
-   *       which due to its `Tensor|Tensor[]` argument and return value is
-   *       flexible enough to handle the inputs and states.
-   *   - a `stateSize` attribute. This can be a single integer (single state)
-   *     in which case it is the size of the recurrent state (which should be
-   *     the same as the size of the cell output). This can also be an Array of
-   *     integers (one size per state). In this case, the first entry
-   *     (`stateSize[0]`) should be the same as the size of the cell output.
-   * It is also possible for `cell` to be a list of RNN cell instances, in which
-   * case the cells get stacked on after the other in the RNN, implementing an
-   * efficient stacked RNN.
-   */
-  cell?: RNNCell|RNNCell[];
-
-  /**
-   * Whether to return the last output in the output sequence, or the full
-   * sequence.
-   */
-  returnSequences?: boolean;
-
-  /**
-   * Whether to return the last state in addition to the output.
-   */
-  returnState?: boolean;
-
-  /**
-   * If `true`, process the input sequence backwards and return the reversed
-   * sequence (default: `false`).
-   */
-  goBackwards?: boolean;
-
-  /**
-   * If `true`, the last state for each sample at index i in a batch will be
-   * used as initial state of the sample of index i in the following batch
-   * (default: `false`).
-   *
-   * You can set RNN layers to be "stateful", which means that the states
-   * computed for the samples in one batch will be reused as initial states
-   * for the samples in the next batch. This assumes a one-to-one mapping
-   * between samples in different successive batches.
-   *
-   * To enable "statefulness":
-   *   - specify `stateful: true` in the layer constructor.
-   *   - specify a fixed batch size for your model, by passing
-   *     - if sequential model:
-   *       `batchInputShape: [...]` to the first layer in your model.
-   *     - else for functional model with 1 or more Input layers:
-   *       `batchShape: [...]` to all the first layers in your model.
-   *     This is the expected shape of your inputs
-   *     *including the batch size*.
-   *     It should be a tuple of integers, e.g., `[32, 10, 100]`.
-   *   - specify `shuffle: false` when calling `LayersModel.fit()`.
-   *
-   * To reset the state of your model, call `resetStates()` on either the
-   * specific layer or on the entire model.
-   */
-  stateful?: boolean;
-  // TODO(cais): Explore whether we can warn users when they fail to set
-  //   `shuffle: false` when training a model consisting of stateful RNNs
-  //   and any stateful Layers in general.
-
-  /**
-   * If `true`, the network will be unrolled, else a symbolic loop will be
-   * used. Unrolling can speed up a RNN, although it tends to be more
-   * memory-intensive. Unrolling is only suitable for short sequences (default:
-   * `false`).
-   * Porting Note: tfjs-layers has an imperative backend. RNNs are executed with
-   *   normal TypeScript control flow. Hence this property is inapplicable and
-   *   ignored in tfjs-layers.
-   */
-  unroll?: boolean;
-
-  /**
-   * Dimensionality of the input (integer).
-   *   This option (or alternatively, the option `inputShape`) is required when
-   *   this layer is used as the first layer in a model.
-   */
-  inputDim?: number;
-
-  /**
-   * Length of the input sequences, to be specified when it is constant.
-   * This argument is required if you are going to connect `Flatten` then
-   * `Dense` layers upstream (without it, the shape of the dense outputs cannot
-   * be computed). Note that if the recurrent layer is not the first layer in
-   * your model, you would need to specify the input length at the level of the
-   * first layer (e.g., via the `inputShape` option).
-   */
-  inputLength?: number;
-}
-
-export class RNN extends Layer {
-  /** @nocollapse */
-  static className = 'RNN';
-  public readonly cell: RNNCell;
-  public readonly returnSequences: boolean;
-  public readonly returnState: boolean;
-  public readonly goBackwards: boolean;
-  public readonly unroll: boolean;
-
-  public stateSpec: InputSpec[];
-  protected states_: Tensor[];
-
-  // NOTE(cais): For stateful RNNs, the old states cannot be disposed right
-  // away when new states are set, because the old states may need to be used
-  // later for backpropagation through time (BPTT) and other purposes. So we
-  // keep them here for final disposal when the state is reset completely
-  // (i.e., through no-arg call to `resetStates()`).
-  protected keptStates: Tensor[][];
-
-  private numConstants: number;
-
-  constructor(args: RNNLayerArgs) {
-    super(args);
-    let cell: RNNCell;
-    if (args.cell == null) {
-      throw new ValueError(
-          'cell property is missing for the constructor of RNN.');
-    } else if (Array.isArray(args.cell)) {
-      cell = new StackedRNNCells({cells: args.cell});
-    } else {
-      cell = args.cell;
-    }
-    if (cell.stateSize == null) {
-      throw new ValueError(
-          'The RNN cell should have an attribute `stateSize` (tuple of ' +
-          'integers, one integer per RNN state).');
-    }
-    this.cell = cell;
-    this.returnSequences =
-        args.returnSequences == null ? false : args.returnSequences;
-    this.returnState = args.returnState == null ? false : args.returnState;
-    this.goBackwards = args.goBackwards == null ? false : args.goBackwards;
-    this._stateful = args.stateful == null ? false : args.stateful;
-    this.unroll = args.unroll == null ? false : args.unroll;
-
-    this.supportsMasking = true;
-    this.inputSpec = [new InputSpec({ndim: 3})];
-    this.stateSpec = null;
-    this.states_ = null;
-    // TODO(cais): Add constantsSpec and numConstants.
-    this.numConstants = null;
-    // TODO(cais): Look into the use of initial_state in the kwargs of the
-    //   constructor.
-
-    this.keptStates = [];
-  }
-
-  // Porting Note: This is the equivalent of `RNN.states` property getter in
-  //   PyKeras.
-  getStates(): Tensor[] {
-    if (this.states_ == null) {
-      const numStates =
-          Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
-      return math_utils.range(0, numStates).map(x => null);
-    } else {
-      return this.states_;
-    }
-  }
-
-  // Porting Note: This is the equivalent of the `RNN.states` property setter in
-  //   PyKeras.
-  setStates(states: Tensor[]): void {
-    this.states_ = states;
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    if (isArrayOfShapes(inputShape)) {
-      inputShape = (inputShape as Shape[])[0];
-    }
-    inputShape = inputShape as Shape;
-
-    // TODO(cais): Remove the casting once stacked RNN cells become supported.
-    let stateSize = this.cell.stateSize;
-    if (!Array.isArray(stateSize)) {
-      stateSize = [stateSize];
-    }
-    const outputDim = stateSize[0];
-    let outputShape: Shape|Shape[];
-    if (this.returnSequences) {
-      outputShape = [inputShape[0], inputShape[1], outputDim];
-    } else {
-      outputShape = [inputShape[0], outputDim];
-    }
-
-    if (this.returnState) {
-      const stateShape: Shape[] = [];
-      for (const dim of stateSize) {
-        stateShape.push([inputShape[0], dim]);
-      }
-      return [outputShape].concat(stateShape);
-    } else {
-      return outputShape;
-    }
-  }
-
-  override computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]): Tensor
-      |Tensor[] {
-    return tfc.tidy(() => {
-      if (Array.isArray(mask)) {
-        mask = mask[0];
-      }
-      const outputMask = this.returnSequences ? mask : null;
-
-      if (this.returnState) {
-        const stateMask = this.states.map(s => null);
-        return [outputMask].concat(stateMask);
-      } else {
-        return outputMask;
-      }
-    });
-  }
-
-  /**
-   * Get the current state tensors of the RNN.
-   *
-   * If the state hasn't been set, return an array of `null`s of the correct
-   * length.
-   */
-  get states(): Tensor[] {
-    if (this.states_ == null) {
-      const numStates =
-          Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
-      const output: Tensor[] = [];
-      for (let i = 0; i < numStates; ++i) {
-        output.push(null);
-      }
-      return output;
-    } else {
-      return this.states_;
-    }
-  }
-
-  set states(s: Tensor[]) {
-    this.states_ = s;
-  }
-
-  public override build(inputShape: Shape|Shape[]): void {
-    // Note inputShape will be an Array of Shapes of initial states and
-    // constants if these are passed in apply().
-    const constantShape: Shape[] = null;
-    if (this.numConstants != null) {
-      throw new NotImplementedError(
-          'Constants support is not implemented in RNN yet.');
-    }
-
-    if (isArrayOfShapes(inputShape)) {
-      inputShape = (inputShape as Shape[])[0];
-    }
-    inputShape = inputShape as Shape;
-
-    const batchSize: number = this.stateful ? inputShape[0] : null;
-    const inputDim = inputShape.slice(2);
-    this.inputSpec[0] = new InputSpec({shape: [batchSize, null, ...inputDim]});
-
-    // Allow cell (if RNNCell Layer) to build before we set or validate
-    // stateSpec.
-    const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));
-    if (constantShape != null) {
-      throw new NotImplementedError(
-          'Constants support is not implemented in RNN yet.');
-    } else {
-      this.cell.build(stepInputShape);
-    }
-
-    // Set or validate stateSpec.
-    let stateSize: number[];
-    if (Array.isArray(this.cell.stateSize)) {
-      stateSize = this.cell.stateSize;
-    } else {
-      stateSize = [this.cell.stateSize];
-    }
-
-    if (this.stateSpec != null) {
-      if (!util.arraysEqual(
-              this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]),
-              stateSize)) {
-        throw new ValueError(
-            `An initialState was passed that is not compatible with ` +
-            `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +
-            `However cell.stateSize is ${this.cell.stateSize}`);
-      }
-    } else {
-      this.stateSpec =
-          stateSize.map(dim => new InputSpec({shape: [null, dim]}));
-    }
-    if (this.stateful) {
-      this.resetStates();
-    }
-  }
-
-  /**
-   * Reset the state tensors of the RNN.
-   *
-   * If the `states` argument is `undefined` or `null`, will set the
-   * state tensor(s) of the RNN to all-zero tensors of the appropriate
-   * shape(s).
-   *
-   * If `states` is provided, will set the state tensors of the RNN to its
-   * value.
-   *
-   * @param states Optional externally-provided initial states.
-   * @param training Whether this call is done during training. For stateful
-   *   RNNs, this affects whether the old states are kept or discarded. In
-   *   particular, if `training` is `true`, the old states will be kept so
-   *   that subsequent backpropgataion through time (BPTT) may work properly.
-   *   Else, the old states will be discarded.
-   */
-  override resetStates(states?: Tensor|Tensor[], training = false): void {
-    tidy(() => {
-      if (!this.stateful) {
-        throw new AttributeError(
-            'Cannot call resetStates() on an RNN Layer that is not stateful.');
-      }
-      const batchSize = this.inputSpec[0].shape[0];
-      if (batchSize == null) {
-        throw new ValueError(
-            'If an RNN is stateful, it needs to know its batch size. Specify ' +
-            'the batch size of your input tensors: \n' +
-            '- If using a Sequential model, specify the batch size by ' +
-            'passing a `batchInputShape` option to your first layer.\n' +
-            '- If using the functional API, specify the batch size by ' +
-            'passing a `batchShape` option to your Input layer.');
-      }
-      // Initialize state if null.
-      if (this.states_ == null) {
-        if (Array.isArray(this.cell.stateSize)) {
-          this.states_ =
-              this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));
-        } else {
-          this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];
-        }
-      } else if (states == null) {
-        // Dispose old state tensors.
-        tfc.dispose(this.states_);
-        // For stateful RNNs, fully dispose kept old states.
-        if (this.keptStates != null) {
-          tfc.dispose(this.keptStates);
-          this.keptStates = [];
-        }
-
-        if (Array.isArray(this.cell.stateSize)) {
-          this.states_ =
-              this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));
-        } else {
-          this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);
-        }
-      } else {
-        if (!Array.isArray(states)) {
-          states = [states];
-        }
-        if (states.length !== this.states_.length) {
-          throw new ValueError(
-              `Layer ${this.name} expects ${this.states_.length} state(s), ` +
-              `but it received ${states.length} state value(s). Input ` +
-              `received: ${states}`);
-        }
-
-        if (training === true) {
-          // Store old state tensors for complete disposal later, i.e., during
-          // the next no-arg call to this method. We do not dispose the old
-          // states immediately because that BPTT (among other things) require
-          // them.
-          this.keptStates.push(this.states_.slice());
-        } else {
-          tfc.dispose(this.states_);
-        }
-
-        for (let index = 0; index < this.states_.length; ++index) {
-          const value = states[index];
-          const dim = Array.isArray(this.cell.stateSize) ?
-              this.cell.stateSize[index] :
-              this.cell.stateSize;
-          const expectedShape = [batchSize, dim];
-          if (!util.arraysEqual(value.shape, expectedShape)) {
-            throw new ValueError(
-                `State ${index} is incompatible with layer ${this.name}: ` +
-                `expected shape=${expectedShape}, received shape=${
-                    value.shape}`);
-          }
-          this.states_[index] = value;
-        }
-      }
-      this.states_ = this.states_.map(state => tfc.keep(state.clone()));
-    });
-  }
-
-  override apply(
-      inputs: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],
-      kwargs?: Kwargs): Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[] {
-    // TODO(cais): Figure out whether initialState is in kwargs or inputs.
-    let initialState: Tensor[]|SymbolicTensor[] =
-        kwargs == null ? null : kwargs['initialState'];
-    let constants: Tensor[]|SymbolicTensor[] =
-        kwargs == null ? null : kwargs['constants'];
-    if (kwargs == null) {
-      kwargs = {};
-    }
-
-    const standardized =
-        standardizeArgs(inputs, initialState, constants, this.numConstants);
-    inputs = standardized.inputs;
-    initialState = standardized.initialState;
-    constants = standardized.constants;
-
-    // If any of `initial_state` or `constants` are specified and are
-    // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify
-    // the input_spec to include them.
-
-    let additionalInputs: Array<Tensor|SymbolicTensor> = [];
-    let additionalSpecs: InputSpec[] = [];
-    if (initialState != null) {
-      kwargs['initialState'] = initialState;
-      additionalInputs = additionalInputs.concat(initialState);
-      this.stateSpec = [];
-      for (const state of initialState) {
-        this.stateSpec.push(new InputSpec({shape: state.shape}));
-      }
-      // TODO(cais): Use the following instead.
-      // this.stateSpec = initialState.map(state => new InputSpec({shape:
-      // state.shape}));
-      additionalSpecs = additionalSpecs.concat(this.stateSpec);
-    }
-    if (constants != null) {
-      kwargs['constants'] = constants;
-      additionalInputs = additionalInputs.concat(constants);
-      // TODO(cais): Add this.constantsSpec.
-      this.numConstants = constants.length;
-    }
-
-    const isTensor = additionalInputs[0] instanceof SymbolicTensor;
-    if (isTensor) {
-      // Compute full input spec, including state and constants.
-      const fullInput =
-          [inputs].concat(additionalInputs) as Tensor[] | SymbolicTensor[];
-      const fullInputSpec = this.inputSpec.concat(additionalSpecs);
-      // Perform the call with temporarily replaced inputSpec.
-      const originalInputSpec = this.inputSpec;
-      this.inputSpec = fullInputSpec;
-      const output = super.apply(fullInput, kwargs);
-      this.inputSpec = originalInputSpec;
-      return output;
-    } else {
-      return super.apply(inputs, kwargs);
-    }
-  }
-
-  // tslint:disable-next-line:no-any
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    // Input shape: `[samples, time (padded with zeros), input_dim]`.
-    // Note that the .build() method of subclasses **must** define
-    // this.inputSpec and this.stateSpec owith complete input shapes.
-    return tidy(() => {
-      const mask = kwargs == null ? null : kwargs['mask'] as Tensor;
-      const training = kwargs == null ? null : kwargs['training'];
-      let initialState: Tensor[] =
-          kwargs == null ? null : kwargs['initialState'];
-
-      inputs = getExactlyOneTensor(inputs);
-      if (initialState == null) {
-        if (this.stateful) {
-          initialState = this.states_;
-        } else {
-          initialState = this.getInitialState(inputs);
-        }
-      }
-
-      const numStates =
-          Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
-      if (initialState.length !== numStates) {
-        throw new ValueError(
-            `RNN Layer has ${numStates} state(s) but was passed ` +
-            `${initialState.length} initial state(s).`);
-      }
-      if (this.unroll) {
-        console.warn(
-            'Ignoring unroll = true for RNN layer, due to imperative backend.');
-      }
-
-      const cellCallKwargs: Kwargs = {training};
-
-      // TODO(cais): Add support for constants.
-      const step = (inputs: Tensor, states: Tensor[]) => {
-        // `inputs` and `states` are concatenated to form a single `Array` of
-        // `tf.Tensor`s as the input to `cell.call()`.
-        const outputs =
-            this.cell.call([inputs].concat(states), cellCallKwargs) as Tensor[];
-        // Marshall the return value into output and new states.
-        return [outputs[0], outputs.slice(1)] as [Tensor, Tensor[]];
-      };
-
-      // TODO(cais): Add support for constants.
-
-      const rnnOutputs =
-          rnn(step, inputs, initialState, this.goBackwards, mask, null,
-              this.unroll, this.returnSequences);
-      const lastOutput = rnnOutputs[0];
-      const outputs = rnnOutputs[1];
-      const states = rnnOutputs[2];
-
-      if (this.stateful) {
-        this.resetStates(states, training);
-      }
-
-      const output = this.returnSequences ? outputs : lastOutput;
-
-      // TODO(cais): Porperty set learning phase flag.
-
-      if (this.returnState) {
-        return [output].concat(states);
-      } else {
-        return output;
-      }
-    });
-  }
-
-  getInitialState(inputs: Tensor): Tensor[] {
-    return tidy(() => {
-      // Build an all-zero tensor of shape [samples, outputDim].
-      // [Samples, timeSteps, inputDim].
-      let initialState = tfc.zeros(inputs.shape);
-      // [Samples].
-      initialState = tfc.sum(initialState, [1, 2]);
-      initialState = K.expandDims(initialState);  // [Samples, 1].
-
-      if (Array.isArray(this.cell.stateSize)) {
-        return this.cell.stateSize.map(
-            dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);
-      } else {
-        return this.cell.stateSize > 1 ?
-            [K.tile(initialState, [1, this.cell.stateSize])] :
-            [initialState];
-      }
-    });
-  }
-
-  override get trainableWeights(): LayerVariable[] {
-    if (!this.trainable) {
-      return [];
-    }
-    // Porting Note: In TypeScript, `this` is always an instance of `Layer`.
-    return this.cell.trainableWeights;
-  }
-
-  override get nonTrainableWeights(): LayerVariable[] {
-    // Porting Note: In TypeScript, `this` is always an instance of `Layer`.
-    if (!this.trainable) {
-      return this.cell.weights;
-    }
-    return this.cell.nonTrainableWeights;
-  }
-
-  override setFastWeightInitDuringBuild(value: boolean) {
-    super.setFastWeightInitDuringBuild(value);
-    if (this.cell != null) {
-      this.cell.setFastWeightInitDuringBuild(value);
-    }
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const baseConfig = super.getConfig();
-
-    const config: serialization.ConfigDict = {
-      returnSequences: this.returnSequences,
-      returnState: this.returnState,
-      goBackwards: this.goBackwards,
-      stateful: this.stateful,
-      unroll: this.unroll,
-    };
-
-    if (this.numConstants != null) {
-      config['numConstants'] = this.numConstants;
-    }
-
-    const cellConfig = this.cell.getConfig();
-
-    if (this.getClassName() === RNN.className) {
-      config['cell'] = {
-        'className': this.cell.getClassName(),
-        'config': cellConfig,
-      } as serialization.ConfigDictValue;
-    }
-
-    // this order is necessary, to prevent cell name from replacing layer name
-    return {...cellConfig, ...baseConfig, ...config};
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict,
-      customObjects = {} as serialization.ConfigDict): T {
-    const cellConfig = config['cell'] as serialization.ConfigDict;
-    const cell = deserialize(cellConfig, customObjects) as RNNCell;
-    return new cls(Object.assign(config, {cell}));
-  }
-}
-serialization.registerClass(RNN);
-
-// Porting Note: This is a common parent class for RNN cells. There is no
-// equivalent of this in PyKeras. Having a common parent class forgoes the
-//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.
-/**
- * An RNNCell layer.
- *
- * @doc {heading: 'Layers', subheading: 'Classes'}
- */
-export abstract class RNNCell extends Layer {
-  /**
-   * Size(s) of the states.
-   * For RNN cells with only a single state, this is a single integer.
-   */
-  // See
-  // https://www.typescriptlang.org/docs/handbook/release-notes/typescript-4-0.html#properties-overriding-accessors-and-vice-versa-is-an-error
-  public abstract stateSize: number|number[];
-  public dropoutMask: Tensor|Tensor[];
-  public recurrentDropoutMask: Tensor|Tensor[];
-}
-
-export declare interface SimpleRNNCellLayerArgs extends LayerArgs {
-  /**
-   * units: Positive integer, dimensionality of the output space.
-   */
-  units: number;
-
-  /**
-   * Activation function to use.
-   * Default: hyperbolic tangent ('tanh').
-   * If you pass `null`,  'linear' activation will be applied.
-   */
-  activation?: ActivationIdentifier;
-
-  /**
-   * Whether the layer uses a bias vector.
-   */
-  useBias?: boolean;
-
-  /**
-   * Initializer for the `kernel` weights matrix, used for the linear
-   * transformation of the inputs.
-   */
-  kernelInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the `recurrentKernel` weights matrix, used for
-   * linear transformation of the recurrent state.
-   */
-  recurrentInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the bias vector.
-   */
-  biasInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Regularizer function applied to the `kernel` weights matrix.
-   */
-  kernelRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer function applied to the `recurrent_kernel` weights matrix.
-   */
-  recurrentRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer function applied to the bias vector.
-   */
-  biasRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Constraint function applied to the `kernel` weights matrix.
-   */
-  kernelConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Constraint function applied to the `recurrentKernel` weights matrix.
-   */
-  recurrentConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Constraint function applied to the bias vector.
-   */
-  biasConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Float number between 0 and 1. Fraction of the units to drop for the linear
-   * transformation of the inputs.
-   */
-  dropout?: number;
-
-  /**
-   * Float number between 0 and 1. Fraction of the units to drop for the linear
-   * transformation of the recurrent state.
-   */
-  recurrentDropout?: number;
-
-  /**
-   * This is added for test DI purpose.
-   */
-  dropoutFunc?: Function;
-}
-
-export class SimpleRNNCell extends RNNCell {
-  /** @nocollapse */
-  static className = 'SimpleRNNCell';
-  readonly units: number;
-  readonly activation: Activation;
-  readonly useBias: boolean;
-
-  readonly kernelInitializer: Initializer;
-  readonly recurrentInitializer: Initializer;
-  readonly biasInitializer: Initializer;
-
-  readonly kernelConstraint: Constraint;
-  readonly recurrentConstraint: Constraint;
-  readonly biasConstraint: Constraint;
-
-  readonly kernelRegularizer: Regularizer;
-  readonly recurrentRegularizer: Regularizer;
-  readonly biasRegularizer: Regularizer;
-
-  readonly dropout: number;
-  readonly recurrentDropout: number;
-  readonly dropoutFunc: Function;
-
-  readonly stateSize: number;
-
-  kernel: LayerVariable;
-  recurrentKernel: LayerVariable;
-  bias: LayerVariable;
-
-  readonly DEFAULT_ACTIVATION = 'tanh';
-  readonly DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';
-  readonly DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';
-  readonly DEFAULT_BIAS_INITIALIZER: InitializerIdentifier = 'zeros';
-
-  constructor(args: SimpleRNNCellLayerArgs) {
-    super(args);
-    this.units = args.units;
-    assertPositiveInteger(this.units, `units`);
-    this.activation = getActivation(
-        args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);
-    this.useBias = args.useBias == null ? true : args.useBias;
-
-    this.kernelInitializer = getInitializer(
-        args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
-    this.recurrentInitializer = getInitializer(
-        args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
-
-    this.biasInitializer =
-        getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
-
-    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
-    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
-    this.biasRegularizer = getRegularizer(args.biasRegularizer);
-
-    this.kernelConstraint = getConstraint(args.kernelConstraint);
-    this.recurrentConstraint = getConstraint(args.recurrentConstraint);
-    this.biasConstraint = getConstraint(args.biasConstraint);
-
-    this.dropout = math_utils.min(
-        [1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);
-    this.recurrentDropout = math_utils.min([
-      1,
-      math_utils.max(
-          [0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
-    ]);
-    this.dropoutFunc = args.dropoutFunc;
-    this.stateSize = this.units;
-    this.dropoutMask = null;
-    this.recurrentDropoutMask = null;
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    // TODO(cais): Use regularizer.
-    this.kernel = this.addWeight(
-        'kernel', [inputShape[inputShape.length - 1], this.units], null,
-        this.kernelInitializer, this.kernelRegularizer, true,
-        this.kernelConstraint);
-    this.recurrentKernel = this.addWeight(
-        'recurrent_kernel', [this.units, this.units], null,
-        this.recurrentInitializer, this.recurrentRegularizer, true,
-        this.recurrentConstraint);
-    if (this.useBias) {
-      this.bias = this.addWeight(
-          'bias', [this.units], null, this.biasInitializer,
-          this.biasRegularizer, true, this.biasConstraint);
-    } else {
-      this.bias = null;
-    }
-    this.built = true;
-  }
-
-  // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:
-  //   `inputs` and `states`. Here, the two tensors are combined into an
-  //   `Tensor[]` Array as the first input argument.
-  //   Similarly, PyKeras' equivalent of this method returns two values:
-  //    `output` and `[output]`. Here the two are combined into one length-2
-  //    `Tensor[]`, consisting of `output` repeated.
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = inputs as Tensor[];
-      if (inputs.length !== 2) {
-        throw new ValueError(
-            `SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);
-      }
-      let prevOutput = inputs[1];
-      inputs = inputs[0];
-      const training = kwargs['training'] == null ? false : kwargs['training'];
-
-      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
-        this.dropoutMask = generateDropoutMask({
-                             ones: () => tfc.onesLike(inputs as Tensor),
-                             rate: this.dropout,
-                             training,
-                             dropoutFunc: this.dropoutFunc,
-                           }) as Tensor;
-      }
-      if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&
-          this.recurrentDropoutMask == null) {
-        this.recurrentDropoutMask = generateDropoutMask({
-                                      ones: () => tfc.onesLike(prevOutput),
-                                      rate: this.recurrentDropout,
-                                      training,
-                                      dropoutFunc: this.dropoutFunc,
-                                    }) as Tensor;
-      }
-      let h: Tensor;
-      const dpMask: Tensor = this.dropoutMask as Tensor;
-      const recDpMask: Tensor = this.recurrentDropoutMask as Tensor;
-      if (dpMask != null) {
-        h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());
-      } else {
-        h = K.dot(inputs, this.kernel.read());
-      }
-      if (this.bias != null) {
-        h = K.biasAdd(h, this.bias.read());
-      }
-      if (recDpMask != null) {
-        prevOutput = tfc.mul(prevOutput, recDpMask);
-      }
-      let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));
-      if (this.activation != null) {
-        output = this.activation.apply(output);
-      }
-
-      // TODO(cais): Properly set learning phase on output tensor?
-      return [output, output];
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const baseConfig = super.getConfig();
-
-    const config: serialization.ConfigDict = {
-      units: this.units,
-      activation: serializeActivation(this.activation),
-      useBias: this.useBias,
-      kernelInitializer: serializeInitializer(this.kernelInitializer),
-      recurrentInitializer: serializeInitializer(this.recurrentInitializer),
-      biasInitializer: serializeInitializer(this.biasInitializer),
-      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
-      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
-      biasRegularizer: serializeRegularizer(this.biasRegularizer),
-      activityRegularizer: serializeRegularizer(this.activityRegularizer),
-      kernelConstraint: serializeConstraint(this.kernelConstraint),
-      recurrentConstraint: serializeConstraint(this.recurrentConstraint),
-      biasConstraint: serializeConstraint(this.biasConstraint),
-      dropout: this.dropout,
-      recurrentDropout: this.recurrentDropout,
-    };
-
-    return {...baseConfig, ...config};
-  }
-}
-serialization.registerClass(SimpleRNNCell);
-
-export declare interface SimpleRNNLayerArgs extends BaseRNNLayerArgs {
-  /**
-   * Positive integer, dimensionality of the output space.
-   */
-  units: number;
-
-  /**
-   * Activation function to use.
-   *
-   * Defaults to  hyperbolic tangent (`tanh`)
-   *
-   * If you pass `null`, no activation will be applied.
-   */
-  activation?: ActivationIdentifier;
-
-  /**
-   * Whether the layer uses a bias vector.
-   */
-  useBias?: boolean;
-
-  /**
-   * Initializer for the `kernel` weights matrix, used for the linear
-   * transformation of the inputs.
-   */
-  kernelInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the `recurrentKernel` weights matrix, used for
-   * linear transformation of the recurrent state.
-   */
-  recurrentInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Initializer for the bias vector.
-   */
-  biasInitializer?: InitializerIdentifier|Initializer;
-
-  /**
-   * Regularizer function applied to the kernel weights matrix.
-   */
-  kernelRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer function applied to the recurrentKernel weights matrix.
-   */
-  recurrentRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Regularizer function applied to the bias vector.
-   */
-  biasRegularizer?: RegularizerIdentifier|Regularizer;
-
-  /**
-   * Constraint function applied to the kernel weights matrix.
-   */
-  kernelConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Constraint function applied to the recurrentKernel weights matrix.
-   */
-  recurrentConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Constraint function applied to the bias vector.
-   */
-  biasConstraint?: ConstraintIdentifier|Constraint;
-
-  /**
-   * Number between 0 and 1. Fraction of the units to drop for the linear
-   * transformation of the inputs.
-   */
-  dropout?: number;
-
-  /**
-   * Number between 0 and 1. Fraction of the units to drop for the linear
-   * transformation of the recurrent state.
-   */
-  recurrentDropout?: number;
-
-  /**
-   * This is added for test DI purpose.
-   */
-  dropoutFunc?: Function;
-}
-
-/**
- * RNNLayerConfig is identical to BaseRNNLayerConfig, except it makes the
- * `cell` property required. This interface is to be used with constructors
- * of concrete RNN layer subtypes.
- */
-export declare interface RNNLayerArgs extends BaseRNNLayerArgs {
-  cell: RNNCell|RNNCell[];
-}
-
-export class SimpleRNN extends RNN {
-  /** @nocollapse */
-  static override className = 'SimpleRNN';
-  constructor(args: SimpleRNNLayerArgs) {
-    args.cell = new SimpleRNNCell(args);
-    super(args as RNNLayerArgs);
-    // TODO(cais): Add activityRegularizer.
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      if (this.cell.dropoutMask != null) {
-        tfc.dispose(this.cell.dropoutMask);
-        this.cell.dropoutMask = null;
-      }
-      if (this.cell.recurrentDropoutMask != null) {
-        tfc.dispose(this.cell.recurrentDropoutMask);
-        this.cell.recurrentDropoutMask = null;
-      }
-      const mask = kwargs == null ? null : kwargs['mask'];
-      const training = kwargs == null ? null : kwargs['training'];
-      const initialState: Tensor[] =
-          kwargs == null ? null : kwargs['initialState'];
-      return super.call(inputs, {mask, training, initialState});
-    });
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict): T {
-    return new cls(config);
-  }
-}
-serialization.registerClass(SimpleRNN);
-
-// Porting Note: Since this is a superset of SimpleRNNLayerConfig, we extend
-//   that interface instead of repeating the fields.
-export declare interface GRUCellLayerArgs extends SimpleRNNCellLayerArgs {
-  /**
-   * Activation function to use for the recurrent step.
-   *
-   * Defaults to hard sigmoid (`hardSigmoid`).
-   *
-   * If `null`, no activation is applied.
-   */
-  recurrentActivation?: ActivationIdentifier;
-
-  /**
-   * Implementation mode, either 1 or 2.
-   *
-   * Mode 1 will structure its operations as a larger number of
-   *   smaller dot products and additions.
-   *
-   * Mode 2 will batch them into fewer, larger operations. These modes will
-   * have different performance profiles on different hardware and
-   * for different applications.
-   *
-   * Note: For superior performance, TensorFlow.js always uses implementation
-   * 2, regardless of the actual value of this configuration field.
-   */
-  implementation?: number;
-
-  /**
-   * GRU convention (whether to apply reset gate after or before matrix
-   * multiplication). false = "before", true = "after" (only false is
-   * supported).
-   */
-  resetAfter?: boolean;
-}
-
-export class GRUCell extends RNNCell {
-  /** @nocollapse */
-  static className = 'GRUCell';
-  readonly units: number;
-  readonly activation: Activation;
-  readonly recurrentActivation: Activation;
-  readonly useBias: boolean;
-
-  readonly kernelInitializer: Initializer;
-  readonly recurrentInitializer: Initializer;
-  readonly biasInitializer: Initializer;
-
-  readonly kernelRegularizer: Regularizer;
-  readonly recurrentRegularizer: Regularizer;
-  readonly biasRegularizer: Regularizer;
-
-  readonly kernelConstraint: Constraint;
-  readonly recurrentConstraint: Constraint;
-  readonly biasConstraint: Constraint;
-
-  readonly dropout: number;
-  readonly recurrentDropout: number;
-  readonly dropoutFunc: Function;
-
-  readonly stateSize: number;
-  readonly implementation: number;
-
-  readonly DEFAULT_ACTIVATION = 'tanh';
-  readonly DEFAULT_RECURRENT_ACTIVATION: ActivationIdentifier = 'hardSigmoid';
-
-  readonly DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';
-  readonly DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';
-  readonly DEFAULT_BIAS_INITIALIZER: InitializerIdentifier = 'zeros';
-
-  kernel: LayerVariable;
-  recurrentKernel: LayerVariable;
-  bias: LayerVariable;
-
-  constructor(args: GRUCellLayerArgs) {
-    super(args);
-    if (args.resetAfter) {
-      throw new ValueError(
-          `GRUCell does not support reset_after parameter set to true.`);
-    }
-    this.units = args.units;
-    assertPositiveInteger(this.units, 'units');
-    this.activation = getActivation(
-        args.activation === undefined ? this.DEFAULT_ACTIVATION :
-                                        args.activation);
-    this.recurrentActivation = getActivation(
-        args.recurrentActivation === undefined ?
-            this.DEFAULT_RECURRENT_ACTIVATION :
-            args.recurrentActivation);
-    this.useBias = args.useBias == null ? true : args.useBias;
-
-    this.kernelInitializer = getInitializer(
-        args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
-    this.recurrentInitializer = getInitializer(
-        args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
-
-    this.biasInitializer =
-        getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
-
-    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
-    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
-    this.biasRegularizer = getRegularizer(args.biasRegularizer);
-
-    this.kernelConstraint = getConstraint(args.kernelConstraint);
-    this.recurrentConstraint = getConstraint(args.recurrentConstraint);
-    this.biasConstraint = getConstraint(args.biasConstraint);
-
-    this.dropout = math_utils.min(
-        [1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);
-    this.recurrentDropout = math_utils.min([
-      1,
-      math_utils.max(
-          [0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
-    ]);
-    this.dropoutFunc = args.dropoutFunc;
-    this.implementation = args.implementation;
-    this.stateSize = this.units;
-    this.dropoutMask = null;
-    this.recurrentDropoutMask = null;
-  }
-
-  public override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    const inputDim = inputShape[inputShape.length - 1];
-    this.kernel = this.addWeight(
-        'kernel', [inputDim, this.units * 3], null, this.kernelInitializer,
-        this.kernelRegularizer, true, this.kernelConstraint);
-    this.recurrentKernel = this.addWeight(
-        'recurrent_kernel', [this.units, this.units * 3], null,
-        this.recurrentInitializer, this.recurrentRegularizer, true,
-        this.recurrentConstraint);
-    if (this.useBias) {
-      this.bias = this.addWeight(
-          'bias', [this.units * 3], null, this.biasInitializer,
-          this.biasRegularizer, true, this.biasConstraint);
-    } else {
-      this.bias = null;
-    }
-    // Porting Notes: Unlike the PyKeras implementation, we perform slicing
-    //   of the weights and bias in the call() method, at execution time.
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = inputs as Tensor[];
-      if (inputs.length !== 2) {
-        throw new ValueError(
-            `GRUCell expects 2 input Tensors (inputs, h, c), got ` +
-            `${inputs.length}.`);
-      }
-
-      const training = kwargs['training'] == null ? false : kwargs['training'];
-      let hTMinus1 = inputs[1];  // Previous memory state.
-      inputs = inputs[0];
-
-      // Note: For superior performance, TensorFlow.js always uses
-      // implementation 2, regardless of the actual value of
-      // config.implementation.
-      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
-        this.dropoutMask = generateDropoutMask({
-                             ones: () => tfc.onesLike(inputs as Tensor),
-                             rate: this.dropout,
-                             training,
-                             count: 3,
-                             dropoutFunc: this.dropoutFunc,
-                           }) as Tensor[];
-      }
-      if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&
-          this.recurrentDropoutMask == null) {
-        this.recurrentDropoutMask = generateDropoutMask({
-                                      ones: () => tfc.onesLike(hTMinus1),
-                                      rate: this.recurrentDropout,
-                                      training,
-                                      count: 3,
-                                      dropoutFunc: this.dropoutFunc,
-                                    }) as Tensor[];
-      }
-      const dpMask = this.dropoutMask as [Tensor, Tensor, Tensor];
-      const recDpMask = this.recurrentDropoutMask as [Tensor, Tensor, Tensor];
-      let z: Tensor;
-      let r: Tensor;
-      let hh: Tensor;
-
-      if (0 < this.dropout && this.dropout < 1) {
-        inputs = tfc.mul(inputs, dpMask[0]);
-      }
-      let matrixX = K.dot(inputs, this.kernel.read());
-      if (this.useBias) {
-        matrixX = K.biasAdd(matrixX, this.bias.read());
-      }
-      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
-        hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);
-      }
-
-      const recurrentKernelValue = this.recurrentKernel.read();
-      const [rk1, rk2] = tfc.split(
-          recurrentKernelValue, [2 * this.units, this.units],
-          recurrentKernelValue.rank - 1);
-      const matrixInner = K.dot(hTMinus1, rk1);
-
-      const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);
-      const [recurrentZ, recurrentR] =
-          tfc.split(matrixInner, 2, matrixInner.rank - 1);
-      z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));
-      r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));
-
-      const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);
-      hh = this.activation.apply(tfc.add(xH, recurrentH));
-
-      const h =
-          tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));
-      // TODO(cais): Add use_learning_phase flag properly.
-      return [h, h];
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const baseConfig = super.getConfig();
-
-    const config: serialization.ConfigDict = {
-      units: this.units,
-      activation: serializeActivation(this.activation),
-      recurrentActivation: serializeActivation(this.recurrentActivation),
-      useBias: this.useBias,
-      kernelInitializer: serializeInitializer(this.kernelInitializer),
-      recurrentInitializer: serializeInitializer(this.recurrentInitializer),
-      biasInitializer: serializeInitializer(this.biasInitializer),
-      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
-      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
-      biasRegularizer: serializeRegularizer(this.biasRegularizer),
-      activityRegularizer: serializeRegularizer(this.activityRegularizer),
-      kernelConstraint: serializeConstraint(this.kernelConstraint),
-      recurrentConstraint: serializeConstraint(this.recurrentConstraint),
-      biasConstraint: serializeConstraint(this.biasConstraint),
-      dropout: this.dropout,
-      recurrentDropout: this.recurrentDropout,
-      implementation: this.implementation,
-      resetAfter: false
-    };
-
-    return {...baseConfig, ...config};
-  }
-}
-serialization.registerClass(GRUCell);
-
-// Porting Note: Since this is a superset of SimpleRNNLayerConfig, we inherit
-//   from that interface instead of repeating the fields here.
-export declare interface GRULayerArgs extends SimpleRNNLayerArgs {
-  /**
-   * Activation function to use for the recurrent step.
-   *
-   * Defaults to hard sigmoid (`hardSigmoid`).
-   *
-   * If `null`, no activation is applied.
-   */
-  recurrentActivation?: ActivationIdentifier;
-
-  /**
-   * Implementation mode, either 1 or 2.
-   *
-   * Mode 1 will structure its operations as a larger number of
-   * smaller dot products and additions.
-   *
-   * Mode 2 will batch them into fewer, larger operations. These modes will
-   * have different performance profiles on different hardware and
-   * for different applications.
-   *
-   * Note: For superior performance, TensorFlow.js always uses implementation
-   * 2, regardless of the actual value of this configuration field.
-   */
-  implementation?: number;
-}
-
-export class GRU extends RNN {
-  /** @nocollapse */
-  static override className = 'GRU';
-  constructor(args: GRULayerArgs) {
-    if (args.implementation === 0) {
-      console.warn(
-          '`implementation=0` has been deprecated, and now defaults to ' +
-          '`implementation=1`. Please update your layer call.');
-    }
-    args.cell = new GRUCell(args);
-    super(args as RNNLayerArgs);
-    // TODO(cais): Add activityRegularizer.
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      if (this.cell.dropoutMask != null) {
-        tfc.dispose(this.cell.dropoutMask);
-        this.cell.dropoutMask = null;
-      }
-      if (this.cell.recurrentDropoutMask != null) {
-        tfc.dispose(this.cell.recurrentDropoutMask);
-        this.cell.recurrentDropoutMask = null;
-      }
-      const mask = kwargs == null ? null : kwargs['mask'];
-      const training = kwargs == null ? null : kwargs['training'];
-      const initialState: Tensor[] =
-          kwargs == null ? null : kwargs['initialState'];
-      return super.call(inputs, {mask, training, initialState});
-    });
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict): T {
-    if (config['implmentation'] === 0) {
-      config['implementation'] = 1;
-    }
-    return new cls(config);
-  }
-}
-serialization.registerClass(GRU);
-
-// Porting Note: Since this is a superset of SimpleRNNLayerConfig, we extend
-//   that interface instead of repeating the fields.
-export declare interface LSTMCellLayerArgs extends SimpleRNNCellLayerArgs {
-  /**
-   * Activation function to use for the recurrent step.
-   *
-   * Defaults to hard sigmoid (`hardSigmoid`).
-   *
-   * If `null`, no activation is applied.
-   */
-  recurrentActivation?: ActivationIdentifier;
-
-  /**
-   * If `true`, add 1 to the bias of the forget gate at initialization.
-   * Setting it to `true` will also force `biasInitializer = 'zeros'`.
-   * This is recommended in
-   * [Jozefowicz et
-   * al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
-   */
-  unitForgetBias?: boolean;
-
-  /**
-   * Implementation mode, either 1 or 2.
-   *
-   * Mode 1 will structure its operations as a larger number of
-   *   smaller dot products and additions.
-   *
-   * Mode 2 will batch them into fewer, larger operations. These modes will
-   * have different performance profiles on different hardware and
-   * for different applications.
-   *
-   * Note: For superior performance, TensorFlow.js always uses implementation
-   * 2, regardless of the actual value of this configuration field.
-   */
-  implementation?: number;
-}
-
-export class LSTMCell extends RNNCell {
-  /** @nocollapse */
-  static className = 'LSTMCell';
-  readonly units: number;
-  readonly activation: Activation;
-  readonly recurrentActivation: Activation;
-  readonly useBias: boolean;
-
-  readonly kernelInitializer: Initializer;
-  readonly recurrentInitializer: Initializer;
-  readonly biasInitializer: Initializer;
-  readonly unitForgetBias: boolean;
-
-  readonly kernelConstraint: Constraint;
-  readonly recurrentConstraint: Constraint;
-  readonly biasConstraint: Constraint;
-
-  readonly kernelRegularizer: Regularizer;
-  readonly recurrentRegularizer: Regularizer;
-  readonly biasRegularizer: Regularizer;
-
-  readonly dropout: number;
-  readonly recurrentDropout: number;
-  readonly dropoutFunc: Function;
-
-  readonly stateSize: number[];
-  readonly implementation: number;
-
-  readonly DEFAULT_ACTIVATION = 'tanh';
-  readonly DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';
-  readonly DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';
-  readonly DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';
-
-  readonly DEFAULT_BIAS_INITIALIZER = 'zeros';
-
-  kernel: LayerVariable;
-  recurrentKernel: LayerVariable;
-  bias: LayerVariable;
-
-  constructor(args: LSTMCellLayerArgs) {
-    super(args);
-
-    this.units = args.units;
-    assertPositiveInteger(this.units, 'units');
-    this.activation = getActivation(
-        args.activation === undefined ? this.DEFAULT_ACTIVATION :
-                                        args.activation);
-    this.recurrentActivation = getActivation(
-        args.recurrentActivation === undefined ?
-            this.DEFAULT_RECURRENT_ACTIVATION :
-            args.recurrentActivation);
-    this.useBias = args.useBias == null ? true : args.useBias;
-
-    this.kernelInitializer = getInitializer(
-        args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
-    this.recurrentInitializer = getInitializer(
-        args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
-
-    this.biasInitializer =
-        getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
-    this.unitForgetBias = args.unitForgetBias;
-
-    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
-    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
-    this.biasRegularizer = getRegularizer(args.biasRegularizer);
-
-    this.kernelConstraint = getConstraint(args.kernelConstraint);
-    this.recurrentConstraint = getConstraint(args.recurrentConstraint);
-    this.biasConstraint = getConstraint(args.biasConstraint);
-
-    this.dropout = math_utils.min(
-        [1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);
-    this.recurrentDropout = math_utils.min([
-      1,
-      math_utils.max(
-          [0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
-    ]);
-    this.dropoutFunc = args.dropoutFunc;
-    this.implementation = args.implementation;
-    this.stateSize = [this.units, this.units];
-    this.dropoutMask = null;
-    this.recurrentDropoutMask = null;
-  }
-
-  public override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    const inputDim = inputShape[inputShape.length - 1];
-    this.kernel = this.addWeight(
-        'kernel', [inputDim, this.units * 4], null, this.kernelInitializer,
-        this.kernelRegularizer, true, this.kernelConstraint);
-    this.recurrentKernel = this.addWeight(
-        'recurrent_kernel', [this.units, this.units * 4], null,
-        this.recurrentInitializer, this.recurrentRegularizer, true,
-        this.recurrentConstraint);
-    let biasInitializer: Initializer;
-    if (this.useBias) {
-      if (this.unitForgetBias) {
-        const capturedBiasInit = this.biasInitializer;
-        const capturedUnits = this.units;
-        biasInitializer = new (class CustomInit extends Initializer {
-          /** @nocollapse */
-          static className = 'CustomInit';
-
-          apply(shape: Shape, dtype?: DataType): Tensor {
-            // TODO(cais): More informative variable names?
-            const bI = capturedBiasInit.apply([capturedUnits]);
-            const bF = (new Ones()).apply([capturedUnits]);
-            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);
-            return K.concatAlongFirstAxis(
-                K.concatAlongFirstAxis(bI, bF), bCAndH);
-          }
-        })();
-      } else {
-        biasInitializer = this.biasInitializer;
-      }
-      this.bias = this.addWeight(
-          'bias', [this.units * 4], null, biasInitializer, this.biasRegularizer,
-          true, this.biasConstraint);
-    } else {
-      this.bias = null;
-    }
-    // Porting Notes: Unlike the PyKeras implementation, we perform slicing
-    //   of the weights and bias in the call() method, at execution time.
-    this.built = true;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      const training = kwargs['training'] == null ? false : kwargs['training'];
-      inputs = inputs as Tensor[];
-      if (inputs.length !== 3) {
-        throw new ValueError(
-            `LSTMCell expects 3 input Tensors (inputs, h, c), got ` +
-            `${inputs.length}.`);
-      }
-      let hTMinus1 = inputs[1];    // Previous memory state.
-      const cTMinus1 = inputs[2];  // Previous carry state.
-      inputs = inputs[0];
-      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
-        this.dropoutMask = generateDropoutMask({
-                             ones: () => tfc.onesLike(inputs as Tensor),
-                             rate: this.dropout,
-                             training,
-                             count: 4,
-                             dropoutFunc: this.dropoutFunc
-                           }) as Tensor[];
-      }
-      if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&
-          this.recurrentDropoutMask == null) {
-        this.recurrentDropoutMask = generateDropoutMask({
-                                      ones: () => tfc.onesLike(hTMinus1),
-                                      rate: this.recurrentDropout,
-                                      training,
-                                      count: 4,
-                                      dropoutFunc: this.dropoutFunc
-                                    }) as Tensor[];
-      }
-      const dpMask = this.dropoutMask as [Tensor, Tensor, Tensor, Tensor];
-      const recDpMask =
-          this.recurrentDropoutMask as [Tensor, Tensor, Tensor, Tensor];
-
-      // Note: For superior performance, TensorFlow.js always uses
-      // implementation 2 regardless of the actual value of
-      // config.implementation.
-      let i: Tensor;
-      let f: Tensor;
-      let c: Tensor;
-      let o: Tensor;
-      if (0 < this.dropout && this.dropout < 1) {
-        inputs = tfc.mul(inputs, dpMask[0]);
-      }
-      let z = K.dot(inputs, this.kernel.read());
-      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
-        hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);
-      }
-      z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));
-      if (this.useBias) {
-        z = K.biasAdd(z, this.bias.read());
-      }
-
-      const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);
-
-      i = this.recurrentActivation.apply(z0);
-      f = this.recurrentActivation.apply(z1);
-      c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));
-      o = this.recurrentActivation.apply(z3);
-
-      const h = tfc.mul(o, this.activation.apply(c));
-      // TODO(cais): Add use_learning_phase flag properly.
-      return [h, h, c];
-    });
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const baseConfig = super.getConfig();
-
-    const config: serialization.ConfigDict = {
-      units: this.units,
-      activation: serializeActivation(this.activation),
-      recurrentActivation: serializeActivation(this.recurrentActivation),
-      useBias: this.useBias,
-      kernelInitializer: serializeInitializer(this.kernelInitializer),
-      recurrentInitializer: serializeInitializer(this.recurrentInitializer),
-      biasInitializer: serializeInitializer(this.biasInitializer),
-      unitForgetBias: this.unitForgetBias,
-      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
-      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
-      biasRegularizer: serializeRegularizer(this.biasRegularizer),
-      activityRegularizer: serializeRegularizer(this.activityRegularizer),
-      kernelConstraint: serializeConstraint(this.kernelConstraint),
-      recurrentConstraint: serializeConstraint(this.recurrentConstraint),
-      biasConstraint: serializeConstraint(this.biasConstraint),
-      dropout: this.dropout,
-      recurrentDropout: this.recurrentDropout,
-      implementation: this.implementation,
-    };
-
-    return {...baseConfig, ...config};
-  }
-}
-serialization.registerClass(LSTMCell);
-
-// Porting Note: Since this is a superset of SimpleRNNLayerConfig, we inherit
-//   from that interface instead of repeating the fields here.
-export declare interface LSTMLayerArgs extends SimpleRNNLayerArgs {
-  /**
-   * Activation function to use for the recurrent step.
-   *
-   * Defaults to hard sigmoid (`hardSigmoid`).
-   *
-   * If `null`, no activation is applied.
-   */
-  recurrentActivation?: ActivationIdentifier;
-
-  /**
-   * If `true`, add 1 to the bias of the forget gate at initialization.
-   * Setting it to `true` will also force `biasInitializer = 'zeros'`.
-   * This is recommended in
-   * [Jozefowicz et
-   * al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
-   */
-  unitForgetBias?: boolean;
-
-  /**
-   * Implementation mode, either 1 or 2.
-   *   Mode 1 will structure its operations as a larger number of
-   *   smaller dot products and additions, whereas mode 2 will
-   *   batch them into fewer, larger operations. These modes will
-   *   have different performance profiles on different hardware and
-   *   for different applications.
-   *
-   * Note: For superior performance, TensorFlow.js always uses implementation
-   * 2, regardless of the actual value of this config field.
-   */
-  implementation?: number;
-}
-
-export class LSTM extends RNN {
-  /** @nocollapse */
-  static override className = 'LSTM';
-  constructor(args: LSTMLayerArgs) {
-    if (args.implementation === 0) {
-      console.warn(
-          '`implementation=0` has been deprecated, and now defaults to ' +
-          '`implementation=1`. Please update your layer call.');
-    }
-    args.cell = new LSTMCell(args);
-    super(args as RNNLayerArgs);
-    // TODO(cais): Add activityRegularizer.
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      if (this.cell.dropoutMask != null) {
-        tfc.dispose(this.cell.dropoutMask);
-        this.cell.dropoutMask = null;
-      }
-      if (this.cell.recurrentDropoutMask != null) {
-        tfc.dispose(this.cell.recurrentDropoutMask);
-        this.cell.recurrentDropoutMask = null;
-      }
-      const mask = kwargs == null ? null : kwargs['mask'];
-      const training = kwargs == null ? null : kwargs['training'];
-      const initialState: Tensor[] =
-          kwargs == null ? null : kwargs['initialState'];
-      return super.call(inputs, {mask, training, initialState});
-    });
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict): T {
-    if (config['implmentation'] === 0) {
-      config['implementation'] = 1;
-    }
-    return new cls(config);
-  }
-}
-serialization.registerClass(LSTM);
-
-export declare interface StackedRNNCellsArgs extends LayerArgs {
-  /**
-   * An `Array` of `RNNCell` instances.
-   */
-  cells: RNNCell[];
-}
-
-export class StackedRNNCells extends RNNCell {
-  /** @nocollapse */
-  static className = 'StackedRNNCells';
-  protected cells: RNNCell[];
-
-  constructor(args: StackedRNNCellsArgs) {
-    super(args);
-    this.cells = args.cells;
-  }
-
-  get stateSize(): number[] {
-    // States are a flat list in reverse order of the cell stack.
-    // This allows perserving the requirement `stack.statesize[0] ===
-    // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,
-    // assuming one LSTM has states `[h, c]`.
-    const stateSize: number[] = [];
-    for (const cell of this.cells.slice().reverse()) {
-      if (Array.isArray(cell.stateSize)) {
-        stateSize.push(...cell.stateSize);
-      } else {
-        stateSize.push(cell.stateSize);
-      }
-    }
-    return stateSize;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      inputs = inputs as Tensor[];
-      let states = inputs.slice(1);
-
-      // Recover per-cell states.
-      const nestedStates: Tensor[][] = [];
-      for (const cell of this.cells.slice().reverse()) {
-        if (Array.isArray(cell.stateSize)) {
-          nestedStates.push(states.splice(0, cell.stateSize.length));
-        } else {
-          nestedStates.push(states.splice(0, 1));
-        }
-      }
-      nestedStates.reverse();
-
-      // Call the cells in order and store the returned states.
-      const newNestedStates: Tensor[][] = [];
-      let callInputs: Tensor[];
-      for (let i = 0; i < this.cells.length; ++i) {
-        const cell = this.cells[i];
-        states = nestedStates[i];
-        // TODO(cais): Take care of constants.
-        if (i === 0) {
-          callInputs = [inputs[0]].concat(states);
-        } else {
-          callInputs = [callInputs[0]].concat(states);
-        }
-        callInputs = cell.call(callInputs, kwargs) as Tensor[];
-        newNestedStates.push(callInputs.slice(1));
-      }
-
-      // Format the new states as a flat list in reverse cell order.
-      states = [];
-      for (const cellStates of newNestedStates.slice().reverse()) {
-        states.push(...cellStates);
-      }
-      return [callInputs[0]].concat(states);
-    });
-  }
-
-  public override build(inputShape: Shape|Shape[]): void {
-    if (isArrayOfShapes(inputShape)) {
-      // TODO(cais): Take care of input constants.
-      // const constantShape = inputShape.slice(1);
-      inputShape = (inputShape as Shape[])[0];
-    }
-    inputShape = inputShape as Shape;
-    let outputDim: number;
-    this.cells.forEach((cell, i) => {
-      nameScope(`RNNCell_${i}`, () => {
-        // TODO(cais): Take care of input constants.
-
-        cell.build(inputShape);
-        if (Array.isArray(cell.stateSize)) {
-          outputDim = cell.stateSize[0];
-        } else {
-          outputDim = cell.stateSize;
-        }
-        inputShape = [inputShape[0], outputDim] as Shape;
-      });
-    });
-    this.built = true;
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const baseConfig = super.getConfig();
-
-    const getCellConfig = (cell: RNNCell) => {
-      return {
-        'className': cell.getClassName(),
-        'config': cell.getConfig(),
-      };
-    };
-
-    const cellConfigs = this.cells.map(getCellConfig);
-
-    const config = {'cells': cellConfigs};
-
-    return {...baseConfig, ...config};
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict,
-      customObjects = {} as serialization.ConfigDict): T {
-    const cells: RNNCell[] = [];
-    for (const cellConfig of (config['cells'] as serialization.ConfigDict[])) {
-      cells.push(deserialize(cellConfig, customObjects) as RNNCell);
-    }
-    return new cls({cells});
-  }
-
-  override get trainableWeights(): LayerVariable[] {
-    if (!this.trainable) {
-      return [];
-    }
-    const weights: LayerVariable[] = [];
-    for (const cell of this.cells) {
-      weights.push(...cell.trainableWeights);
-    }
-    return weights;
-  }
-
-  override get nonTrainableWeights(): LayerVariable[] {
-    const weights: LayerVariable[] = [];
-    for (const cell of this.cells) {
-      weights.push(...cell.nonTrainableWeights);
-    }
-    if (!this.trainable) {
-      const trainableWeights: LayerVariable[] = [];
-      for (const cell of this.cells) {
-        trainableWeights.push(...cell.trainableWeights);
-      }
-      return trainableWeights.concat(weights);
-    }
-    return weights;
-  }
-
-  /**
-   * Retrieve the weights of a the model.
-   *
-   * @returns A flat `Array` of `tf.Tensor`s.
-   */
-  override getWeights(): Tensor[] {
-    const weights: LayerVariable[] = [];
-    for (const cell of this.cells) {
-      weights.push(...cell.weights);
-    }
-    return batchGetValue(weights);
-  }
-
-  /**
-   * Set the weights of the model.
-   *
-   * @param weights An `Array` of `tf.Tensor`s with shapes and types matching
-   *     the output of `getWeights()`.
-   */
-  override setWeights(weights: Tensor[]): void {
-    const tuples: Array<[LayerVariable, Tensor]> = [];
-    for (const cell of this.cells) {
-      const numParams = cell.weights.length;
-      const inputWeights = weights.splice(numParams);
-      for (let i = 0; i < cell.weights.length; ++i) {
-        tuples.push([cell.weights[i], inputWeights[i]]);
-      }
-    }
-    batchSetValue(tuples);
-  }
-
-  // TODO(cais): Maybe implemnt `losses` and `getLossesFor`.
-}
-serialization.registerClass(StackedRNNCells);
-
-export function generateDropoutMask(args: {
-  ones: () => tfc.Tensor,
-  rate: number,
-  training?: boolean,
-  count?: number,
-  dropoutFunc?: Function,
-}): tfc.Tensor|tfc.Tensor[] {
-  const {ones, rate, training = false, count = 1, dropoutFunc} = args;
-
-  const droppedInputs = () =>
-      dropoutFunc != null ? dropoutFunc(ones(), rate) : K.dropout(ones(), rate);
-
-  const createMask = () => K.inTrainPhase(droppedInputs, ones, training);
-
-  // just in case count is provided with null or undefined
-  if (!count || count <= 1) {
-    return tfc.keep(createMask().clone());
-  }
-
-  const masks = Array(count).fill(undefined).map(createMask);
-
-  return masks.map(m => tfc.keep(m.clone()));
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/recurrent_test.ts b/tfjs-master/tfjs-layers/src/layers/recurrent_test.ts
deleted file mode 100644
index 0a6e9024e..000000000
--- a/tfjs-master/tfjs-layers/src/layers/recurrent_test.ts
+++ /dev/null
@@ -1,3198 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for recurrent.ts.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {io, randomNormal, scalar, Tensor, tensor1d, tensor2d, tensor3d, tensor4d} from '@tensorflow/tfjs-core';
-
-import {serializeActivation, Tanh} from '../activations';
-import * as K from '../backend/tfjs_backend';
-import {NonNeg, serializeConstraint, UnitNorm} from '../constraints';
-import * as tfl from '../index';
-import {GlorotUniform, HeUniform, Ones, serializeInitializer} from '../initializers';
-import {ActivationIdentifier} from '../keras_format/activation_config';
-import {ModelAndWeightsConfig, modelFromJSON} from '../models';
-import {L1L2, serializeRegularizer} from '../regularizers';
-import {Kwargs} from '../types';
-import {convertPythonicToTs, convertTsToPythonic} from '../utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndGPU, describeMathCPUAndWebGL2, describeMathWebGL2, expectTensorsClose} from '../utils/test_utils';
-
-import {GRUCellLayerArgs, GRULayerArgs, LSTMCellLayerArgs, LSTMLayerArgs, rnn, RNN, RNNCell, SimpleRNNCellLayerArgs, SimpleRNNLayerArgs} from './recurrent';
-
-/**
- * A simplistic RNN step function for testing.
- * This step function simply
- * - calculates a reduced mean over all input elements, for each sample.
- * - adds that mean to the state tensor(s),
- * - take the negative of the 1st current state tensor and use it as the
- *   output.
- * @param inputs
- * @param states
- */
-function rnnStepForTest(inputs: Tensor, states: Tensor[]): [Tensor, Tensor[]] {
-  const mean = tfc.mean(inputs);
-  const newStates = states.map(state => tfc.add(mean, state));
-  const output = tfc.neg(newStates[0]);
-  return [output, newStates];
-}
-
-describeMathCPUAndGPU('rnn', () => {
-  it('Simple step function: 3D inputs, 1 state', () => {
-    const inputs = tensor3d(
-        [[[1, 2], [3, 4], [5, 6]], [[10, 20], [30, 40], [50, 60]]], [2, 3, 2]);
-    const initialStates = [tfc.zeros([2, 4])];
-    const rnnOutputs =
-        rnn(rnnStepForTest, inputs, initialStates, false /* goBackwards */,
-            null /* mask */, null /* constants */, false /* unroll */,
-            true /* needPerStepOutputs */);
-    const lastOutput = rnnOutputs[0];
-    const outputs = rnnOutputs[1];
-    const newStates = rnnOutputs[2];
-    expectTensorsClose(
-        lastOutput,
-        tensor2d(
-            [
-              [-57.75, -57.75, -57.75, -57.75], [-57.75, -57.75, -57.75, -57.75]
-            ],
-            [2, 4]));
-    expectTensorsClose(
-        outputs,
-        tensor3d(
-            [
-              [
-                [-8.25, -8.25, -8.25, -8.25], [-27.5, -27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75, -57.75]
-              ],
-              [
-                [-8.25, -8.25, -8.25, -8.25], [-27.5, -27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75, -57.75]
-              ]
-            ],
-            [2, 3, 4]));
-    expect(newStates.length).toEqual(1);
-    expectTensorsClose(
-        newStates[0],
-        tensor2d(
-            [[57.75, 57.75, 57.75, 57.75], [57.75, 57.75, 57.75, 57.75]],
-            [2, 4]));
-  });
-
-  it('Simple step function: 3D inputs, 2 states', () => {
-    const inputs = tensor3d(
-        [[[1, 2], [3, 4], [5, 6]], [[10, 20], [30, 40], [50, 60]]], [2, 3, 2]);
-    // The two state tensors have different shapes.
-    const initialStates = [tfc.zeros([2, 4]), tfc.ones([2, 3])];
-    const rnnOutputs =
-        rnn(rnnStepForTest, inputs, initialStates, false /* goBackwards */,
-            null /* mask */, null /* constants */, false /* unroll */,
-            true /* needPerStepOutputs */);
-    const lastOutput = rnnOutputs[0];
-    const outputs = rnnOutputs[1];
-    const newStates = rnnOutputs[2];
-    expectTensorsClose(
-        lastOutput,
-        tensor2d(
-            [
-              [-57.75, -57.75, -57.75, -57.75], [-57.75, -57.75, -57.75, -57.75]
-            ],
-            [2, 4]));
-    expectTensorsClose(
-        outputs,
-        tensor3d(
-            [
-              [
-                [-8.25, -8.25, -8.25, -8.25], [-27.5, -27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75, -57.75]
-              ],
-              [
-                [-8.25, -8.25, -8.25, -8.25], [-27.5, -27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75, -57.75]
-              ]
-            ],
-            [2, 3, 4]));
-    expect(newStates.length).toEqual(2);
-    expectTensorsClose(
-        newStates[0],
-        tensor2d(
-            [[57.75, 57.75, 57.75, 57.75], [57.75, 57.75, 57.75, 57.75]],
-            [2, 4]));
-    expectTensorsClose(
-        newStates[1],
-        tensor2d([[58.75, 58.75, 58.75], [58.75, 58.75, 58.75]], [2, 3]));
-  });
-
-  it('Simple step function: 4D inputs, 2 states', () => {
-    const inputs = tensor4d(
-        [
-          [[[1], [2]], [[3], [4]], [[5], [6]]],
-          [[[10], [20]], [[30], [40]], [[50], [60]]]
-        ],
-        [2, 3, 2, 1]);
-    // The two state tensors have different shapes.
-    const initialStates = [tfc.zeros([2, 4]), tfc.ones([2, 3])];
-    const rnnOutputs =
-        rnn(rnnStepForTest, inputs, initialStates, false /* goBackwards */,
-            null /* mask */, null /* constants */, false /* unroll */,
-            true /* needPerStepOutputs */);
-    const lastOutput = rnnOutputs[0];
-    const outputs = rnnOutputs[1];
-    const newStates = rnnOutputs[2];
-    expectTensorsClose(
-        lastOutput,
-        tensor2d(
-            [
-              [-57.75, -57.75, -57.75, -57.75], [-57.75, -57.75, -57.75, -57.75]
-            ],
-            [2, 4]));
-    expectTensorsClose(
-        outputs,
-        tensor3d(
-            [
-              [
-                [-8.25, -8.25, -8.25, -8.25], [-27.5, -27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75, -57.75]
-              ],
-              [
-                [-8.25, -8.25, -8.25, -8.25], [-27.5, -27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75, -57.75]
-              ]
-            ],
-            [2, 3, 4]));
-    expect(newStates.length).toEqual(2);
-    expectTensorsClose(
-        newStates[0],
-        tensor2d(
-            [[57.75, 57.75, 57.75, 57.75], [57.75, 57.75, 57.75, 57.75]],
-            [2, 4]));
-    expectTensorsClose(
-        newStates[1],
-        tensor2d([[58.75, 58.75, 58.75], [58.75, 58.75, 58.75]], [2, 3]));
-  });
-
-  it('Using inputs <3D leads to ValueError', () => {
-    const inputs = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const initialStates = [tfc.zeros([4]), tfc.ones([3])];
-    expect(() => rnn(rnnStepForTest, inputs, initialStates)).toThrowError();
-  });
-});
-
-/**
- * A simplistic RNNCell for testing.
- *
- * This RNNCell performs the following with the inputs and states.
- * - calculates a reduced mean over all input elements,
- * - adds that mean to the state tensor(s),
- * - take the negative of the 1st current state tensor and use it as the
- *   output.
- */
-class RNNCellForTest extends RNNCell {
-  /** @nocollapse */
-  static className = 'RNNCellForTest';
-  stateSize: number|number[];
-  constructor(stateSizes: number|number[]) {
-    super({});
-    this.stateSize = stateSizes;
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    inputs = inputs as Tensor[];
-    const dataInputs = inputs[0];
-    const states = inputs.slice(1);
-    const mean = tfc.mean(dataInputs);
-    const newStates = states.map(state => tfc.add(mean, state));
-    const output = tfc.neg(newStates[0]);
-    return [output].concat(newStates);
-  }
-}
-
-describeMathCPU('RNN-Layer', () => {
-  // TODO(cais): Add tests for stacked RNN cell (i.e., multiple cells) once it
-  //   implemented.
-  // TODO(cais): Add tests for masks once implemented.
-  // TODO(cais): Add tests for constants once implemented.
-
-  it('constructor: only cell', () => {
-    const cell = new RNNCellForTest(5);
-    const rnn = tfl.layers.rnn({cell});
-    expect(rnn.returnSequences).toEqual(false);
-    expect(rnn.returnState).toEqual(false);
-    expect(rnn.goBackwards).toEqual(false);
-  });
-
-  it('constructor: cell and custom options', () => {
-    const cell = new RNNCellForTest(5);
-    const rnn = tfl.layers.rnn(
-        {cell, returnSequences: true, returnState: true, goBackwards: true});
-    expect(rnn.returnSequences).toEqual(true);
-    expect(rnn.returnState).toEqual(true);
-    expect(rnn.goBackwards).toEqual(true);
-  });
-
-  it('computeOutputShape: 1 state, returnSequences=false, returnState=false',
-     () => {
-       const cell = new RNNCellForTest(5);
-       const rnn = tfl.layers.rnn({cell});
-       const inputShape = [4, 3, 2];
-       expect(rnn.computeOutputShape(inputShape)).toEqual([4, 5]);
-     });
-
-  it('computeOutputShape: 1 state, returnSequences=true, returnState=false',
-     () => {
-       const cell = new RNNCellForTest([5, 6]);
-       const rnn = tfl.layers.rnn({cell, returnSequences: true});
-       const inputShape = [4, 3, 2];
-       expect(rnn.computeOutputShape(inputShape)).toEqual([4, 3, 5]);
-     });
-
-  it('computeOutputShape: 1 state, returnSequences=true, returnState=true',
-     () => {
-       const cell = new RNNCellForTest(6);
-       const rnn =
-           tfl.layers.rnn({cell, returnSequences: true, returnState: true});
-       const inputShape = [4, 3, 2];
-       expect(rnn.computeOutputShape(inputShape)).toEqual([[4, 3, 6], [4, 6]]);
-     });
-
-  it('computeOutputShape: 2 states, returnSequences=true, returnState=true',
-     () => {
-       const cell = new RNNCellForTest([5, 6]);
-       const rnn =
-           tfl.layers.rnn({cell, returnSequences: true, returnState: true});
-       const inputShape = [4, 3, 2];
-       expect(rnn.computeOutputShape(inputShape)).toEqual([
-         [4, 3, 5], [4, 5], [4, 6]
-       ]);
-     });
-
-  it('apply: Symbolic: 1 state, returnSequences=false, returnState=false',
-     () => {
-       const cell = new RNNCellForTest(6);
-       const rnn = tfl.layers.rnn({cell});
-       const input =
-           new tfl.SymbolicTensor('float32', [16, 10, 8], null, [], null);
-       const output = rnn.apply(input) as tfl.SymbolicTensor;
-       expect(output.shape).toEqual([16, 6]);
-     });
-
-  it('apply: Symbolic: 1 state, returnSequences=true, returnState=false',
-     () => {
-       const cell = new RNNCellForTest(6);
-       const rnn = tfl.layers.rnn({cell, returnSequences: true});
-       const input =
-           new tfl.SymbolicTensor('float32', [16, 10, 8], null, [], null);
-       const output = rnn.apply(input) as tfl.SymbolicTensor;
-       expect(output.shape).toEqual([16, 10, 6]);
-     });
-
-  it('apply: Symbolic: 1 state, returnSequences=true, returnState=true', () => {
-    const cell = new RNNCellForTest(6);
-    const rnn =
-        tfl.layers.rnn({cell, returnSequences: true, returnState: true});
-    const input =
-        new tfl.SymbolicTensor('float32', [16, 10, 8], null, [], null);
-    const output = rnn.apply(input) as tfl.SymbolicTensor[];
-    expect(output.length).toEqual(2);
-    expect(output[0].shape).toEqual([16, 10, 6]);
-    expect(output[1].shape).toEqual([16, 6]);
-  });
-
-  it('apply: Symbolic: 1 state, returnSequences=false, returnState=true',
-     () => {
-       const cell = new RNNCellForTest(6);
-       const rnn =
-           tfl.layers.rnn({cell, returnSequences: false, returnState: true});
-       const input =
-           new tfl.SymbolicTensor('float32', [16, 10, 8], null, [], null);
-       const output = rnn.apply(input) as tfl.SymbolicTensor[];
-       expect(output.length).toEqual(2);
-       expect(output[0].shape).toEqual([16, 6]);
-       expect(output[1].shape).toEqual([16, 6]);
-     });
-
-  it('apply: Symbolic: 2 states, returnSequences=true, returnState=true',
-     () => {
-       const cell = new RNNCellForTest([5, 6]);
-       const rnn =
-           tfl.layers.rnn({cell, returnSequences: true, returnState: true});
-       const input =
-           new tfl.SymbolicTensor('float32', [16, 10, 8], null, [], null);
-       const output = rnn.apply(input) as tfl.SymbolicTensor[];
-       expect(output.length).toEqual(3);
-       expect(output[0].shape).toEqual([16, 10, 5]);
-       expect(output[1].shape).toEqual([16, 5]);
-       expect(output[2].shape).toEqual([16, 6]);
-     });
-});
-
-describeMathCPUAndWebGL2('RNN-Layer-Math', () => {
-  it('getInitialState: 1 state', () => {
-    const cell = new RNNCellForTest(5);
-    const inputs = tfc.zeros([4, 3, 2]);
-    const rnn = tfl.layers.rnn({cell});
-    const initialStates = rnn.getInitialState(inputs);
-    expect(initialStates.length).toEqual(1);
-    expectTensorsClose(initialStates[0], tfc.zeros([4, 5]));
-  });
-
-  it('getInitialState: 2 states', () => {
-    const cell = new RNNCellForTest([5, 6]);
-    const inputs = tfc.zeros([4, 3, 2]);
-    const rnn = tfl.layers.rnn({cell});
-    const initialStates = rnn.getInitialState(inputs);
-    expect(initialStates.length).toEqual(2);
-    expectTensorsClose(initialStates[0], tfc.zeros([4, 5]));
-    expectTensorsClose(initialStates[1], tfc.zeros([4, 6]));
-  });
-
-  it('call: 1 state: returnSequences=false, returnState=false', () => {
-    const cell = new RNNCellForTest(4);
-    const rnn = tfl.layers.rnn({cell});
-    const inputs = tensor3d(
-        [[[1, 2], [3, 4], [5, 6]], [[10, 20], [30, 40], [50, 60]]], [2, 3, 2]);
-    const outputs = rnn.apply(inputs) as Tensor;
-    expectTensorsClose(outputs, tfc.mul(scalar(-57.75), tfc.ones([2, 4])));
-  });
-
-  it('apply: 1 state: returnSequences=true, returnState=false', () => {
-    const cell = new RNNCellForTest(3);
-    const rnn = tfl.layers.rnn({cell, returnSequences: true});
-    const inputs = tensor3d(
-        [[[1, 2], [3, 4], [5, 6]], [[10, 20], [30, 40], [50, 60]]], [2, 3, 2]);
-    const outputs = rnn.apply(inputs) as Tensor;
-    expectTensorsClose(
-        outputs,
-        tensor3d(
-            [
-              [
-                [-8.25, -8.25, -8.25], [-27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75]
-              ],
-              [
-                [-8.25, -8.25, -8.25], [-27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75]
-              ],
-            ],
-            [2, 3, 3]));
-  });
-
-  it('apply: 1 state: returnSequences=true, returnState=true', () => {
-    const cell = new RNNCellForTest(3);
-    const rnn =
-        tfl.layers.rnn({cell, returnSequences: true, returnState: true});
-    const inputs = tensor3d(
-        [[[1, 2], [3, 4], [5, 6]], [[10, 20], [30, 40], [50, 60]]], [2, 3, 2]);
-    const outputs = rnn.apply(inputs) as Tensor[];
-    expect(outputs.length).toEqual(2);
-    expectTensorsClose(
-        outputs[0],
-        tensor3d(
-            [
-              [
-                [-8.25, -8.25, -8.25], [-27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75]
-              ],
-              [
-                [-8.25, -8.25, -8.25], [-27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75]
-              ],
-            ],
-            [2, 3, 3]));
-    expectTensorsClose(
-        outputs[1],
-        tensor2d([[57.75, 57.75, 57.75], [57.75, 57.75, 57.75]], [2, 3]));
-  });
-
-  it('apply: 2 states: returnSequences=true, returnState=true', () => {
-    const cell = new RNNCellForTest([3, 4]);
-    const rnn =
-        tfl.layers.rnn({cell, returnSequences: true, returnState: true});
-    const inputs = tensor3d(
-        [[[1, 2], [3, 4], [5, 6]], [[10, 20], [30, 40], [50, 60]]], [2, 3, 2]);
-    const outputs = rnn.apply(inputs) as Tensor[];
-    expect(outputs.length).toEqual(3);
-    expectTensorsClose(
-        outputs[0],
-        tensor3d(
-            [
-              [
-                [-8.25, -8.25, -8.25], [-27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75]
-              ],
-              [
-                [-8.25, -8.25, -8.25], [-27.5, -27.5, -27.5],
-                [-57.75, -57.75, -57.75]
-              ],
-            ],
-            [2, 3, 3]));
-    expectTensorsClose(
-        outputs[1],
-        tensor2d([[57.75, 57.75, 57.75], [57.75, 57.75, 57.75]], [2, 3]));
-    expectTensorsClose(
-        outputs[2],
-        tensor2d(
-            [[57.75, 57.75, 57.75, 57.75], [57.75, 57.75, 57.75, 57.75]],
-            [2, 4]));
-  });
-
-  it('call: with 1 initialState', () => {
-    const cell = new RNNCellForTest(4);
-    const rnn = tfl.layers.rnn({cell});
-    const inputs = tensor3d(
-        [[[1, 2], [3, 4], [5, 6]], [[10, 20], [30, 40], [50, 60]]], [2, 3, 2]);
-    const outputs =
-        rnn.apply(inputs, {'initialState': [tfc.ones([2, 4])]}) as Tensor;
-    expectTensorsClose(outputs, tfc.mul(scalar(-58.75), tfc.ones([2, 4])));
-  });
-
-  it('call: with 2 initialStates', () => {
-    const cell = new RNNCellForTest([4, 5]);
-    const rnn = tfl.layers.rnn({cell, returnState: true});
-    const inputs = tensor3d(
-        [[[1, 2], [3, 4], [5, 6]], [[10, 20], [30, 40], [50, 60]]], [2, 3, 2]);
-    const outputs = rnn.apply(inputs, {
-      'initialState': [tfc.ones([2, 4]), tfc.mul(scalar(2), tfc.ones([2, 5]))]
-    }) as Tensor[];
-    expect(outputs.length).toEqual(3);
-    expectTensorsClose(outputs[0], tfc.mul(scalar(-58.75), tfc.ones([2, 4])));
-    expectTensorsClose(outputs[1], tfc.mul(scalar(58.75), tfc.ones([2, 4])));
-    expectTensorsClose(outputs[2], tfc.mul(scalar(59.75), tfc.ones([2, 5])));
-  });
-
-  it('call with incorrect number of initialStates leads to ValueError', () => {
-    const cell = new RNNCellForTest([4, 5]);
-    const rnn = tfl.layers.rnn({cell, returnState: true});
-    const inputs = tensor3d(
-        [[[1, 2], [3, 4], [5, 6]], [[10, 20], [30, 40], [50, 60]]], [2, 3, 2]);
-    expect(() => rnn.apply(inputs, {
-      'initialState': [tfc.ones([2, 4])]
-    })).toThrowError(/An initialState was passed that is not compatible with/);
-  });
-});
-
-describeMathCPU('SimpleRNN Symbolic', () => {
-  it('returnSequences=false, returnState=false', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const simpleRNN = tfl.layers.simpleRNN({units: 5});
-    const output = simpleRNN.apply(input) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([9, 5]);
-  });
-
-  it('returnSequences=false, returnState=true', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const simpleRNN = tfl.layers.simpleRNN({units: 5, returnState: true});
-    const output = simpleRNN.apply(input) as tfl.SymbolicTensor[];
-    expect(output.length).toEqual(2);
-    expect(output[0].shape).toEqual([9, 5]);
-    expect(output[1].shape).toEqual([9, 5]);
-  });
-
-  it('returnSequences=true, returnState=false', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const simpleRNN = tfl.layers.simpleRNN({units: 5, returnSequences: true});
-    const output = simpleRNN.apply(input) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([9, 10, 5]);
-  });
-
-  it('returnSequences=true, returnState=true', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const simpleRNN = tfl.layers.simpleRNN({
-      units: 5,
-      returnSequences: true,
-      returnState: true,
-    });
-    const output = simpleRNN.apply(input) as tfl.SymbolicTensor[];
-    expect(output.length).toEqual(2);
-    expect(output[0].shape).toEqual([9, 10, 5]);
-    expect(output[1].shape).toEqual([9, 5]);
-  });
-
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.simpleRNN({units: 4});
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.simpleRNN(tsConfig);
-    expect(layerPrime.getConfig().units).toEqual(4);
-  });
-
-  it('Invalid units leads to Error', () => {
-    expect(() => tfl.layers.simpleRNN({
-      units: 12.5
-    })).toThrowError(/units.*positive integer.*12\.5\.$/);
-    expect(() => tfl.layers.simpleRNN({
-      units: 0
-    })).toThrowError(/units.*positive integer.*0\.$/);
-    expect(() => tfl.layers.simpleRNN({
-      units: -25
-    })).toThrowError(/units.*positive integer.*-25\.$/);
-  });
-});
-
-describeMathCPUAndWebGL2('SimpleRNN Tensor', () => {
-  const units = 5;
-  const batchSize = 4;
-  const inputSize = 2;
-
-  // TODO(cais): Add test for the default recurrent initializer ('Orthogonal')
-  //   when it becomes available.
-
-  const dropouts = [0.0, 0.1];
-  const trainings = [true, false];
-  for (const training of trainings) {
-    for (const dropout of dropouts) {
-      const testTitle =
-          `returnSequences=false, returnState=false, useBias=true,` +
-          ` ${training}, dropout=${dropout}`;
-      it(testTitle, () => {
-        const timeSteps = 3;
-        const dropoutFunc = jasmine.createSpy('dropout').and.callFake(
-            (x: Tensor, level: number, noiseShape?: number[], seed?: number) =>
-                tfc.tidy(() => tfc.dropout(x, level, noiseShape, seed)));
-        const simpleRNN = tfl.layers.simpleRNN({
-          units,
-          kernelInitializer: 'ones',
-          recurrentInitializer: 'ones',
-          biasInitializer: 'ones',
-          dropout,
-          dropoutFunc
-        });
-        const kwargs: Kwargs = {};
-        if (training) {
-          kwargs['training'] = true;
-        }
-        const input = tfc.ones([batchSize, timeSteps, inputSize]);
-        let numTensors = 0;
-        for (let i = 0; i < 2; i++) {
-          tfc.dispose(simpleRNN.apply(input, kwargs) as Tensor);
-          if (dropout !== 0.0 && training) {
-            expect(dropoutFunc).toHaveBeenCalledTimes(1 * (i + 1));
-          } else {
-            expect(dropoutFunc).toHaveBeenCalledTimes(0);
-          }
-          if (i === 0) {
-            numTensors = tfc.memory().numTensors;
-          } else {
-            expect(tfc.memory().numTensors).toEqual(numTensors);
-          }
-        }
-      });
-    }
-  }
-
-  const recurrentDropouts = [0.0, 0.1];
-  for (const training of trainings) {
-    for (const recurrentDropout of recurrentDropouts) {
-      const testTitle =
-          `returnSequences=false, returnState=false, useBias=true,` +
-          ` ${training}, recurrentDropout=${recurrentDropout}`;
-      it(testTitle, () => {
-        const timeSteps = 3;
-        const dropoutFunc = jasmine.createSpy('dropout').and.callFake(
-            (x: Tensor, level: number, noiseShape?: number[], seed?: number) =>
-                tfc.tidy(() => tfc.dropout(x, level, noiseShape, seed)));
-        const simpleRNN = tfl.layers.simpleRNN({
-          units,
-          kernelInitializer: 'ones',
-          recurrentInitializer: 'ones',
-          biasInitializer: 'ones',
-          recurrentDropout,
-          dropoutFunc
-        });
-        const kwargs: Kwargs = {};
-        if (training) {
-          kwargs['training'] = true;
-        }
-        const input = tfc.ones([batchSize, timeSteps, inputSize]);
-        let numTensors = 0;
-        for (let i = 0; i < 2; i++) {
-          tfc.dispose(simpleRNN.apply(input, kwargs) as Tensor);
-          if (recurrentDropout !== 0.0 && training) {
-            expect(dropoutFunc).toHaveBeenCalledTimes(1 * (i + 1));
-          } else {
-            expect(dropoutFunc).toHaveBeenCalledTimes(0);
-          }
-          if (i === 0) {
-            numTensors = tfc.memory().numTensors;
-          } else {
-            expect(tfc.memory().numTensors).toEqual(numTensors);
-          }
-        }
-      });
-    }
-  }
-
-  const activations: ActivationIdentifier[] = ['linear', 'tanh'];
-  for (const activation of activations) {
-    const testTitle =
-        `returnSequences=false, returnState=false, useBias=true, ${activation}`;
-    it(testTitle, () => {
-      const timeSteps = 1;
-      const simpleRNN = tfl.layers.simpleRNN({
-        units,
-        kernelInitializer: 'ones',
-        recurrentInitializer: 'ones',
-        biasInitializer: 'ones',
-        activation
-      });
-      const input = tfc.ones([batchSize, timeSteps, inputSize]);
-      const output = simpleRNN.apply(input) as Tensor;
-      let expectedElementValue = inputSize + 1;
-      if (activation === 'tanh') {
-        expectedElementValue = Math.tanh(expectedElementValue);
-      }
-      expectTensorsClose(
-          output,
-          tfc.mul(scalar(expectedElementValue), tfc.ones([batchSize, units])));
-    });
-  }
-
-  const returnStateValues = [false, true];
-  for (const returnState of returnStateValues) {
-    const testTitle = `returnSequences=true, ` +
-        `returnState=${returnState}, useBias=true, linear`;
-    it(testTitle, () => {
-      const timeSteps = 2;
-      const simpleRNN = tfl.layers.simpleRNN({
-        units,
-        returnSequences: true,
-        returnState,
-        kernelInitializer: 'ones',
-        recurrentInitializer: 'ones',
-        biasInitializer: 'ones',
-        activation: 'linear'
-      });
-      const input = tfc.ones([batchSize, timeSteps, inputSize]);
-      let output = simpleRNN.apply(input);
-      let finalState: Tensor;
-      if (returnState) {
-        output = output as Tensor[];
-        expect(output.length).toEqual(2);
-        finalState = output[1];
-        output = output[0];
-      } else {
-        output = output as Tensor;
-      }
-
-      expect(output.shape).toEqual([batchSize, timeSteps, units]);
-      const timeMajorOutput = tfc.transpose(output, [1, 0, 2]);
-      const outputT0 = K.sliceAlongFirstAxis(timeMajorOutput, 0, 1);
-      const outputT1 = K.sliceAlongFirstAxis(timeMajorOutput, 1, 1);
-      expectTensorsClose(
-          outputT0,
-          tfc.mul(scalar(inputSize + 1), tfc.ones([1, batchSize, units])));
-      expectTensorsClose(
-          outputT1,
-          tfc.mul(
-              scalar((inputSize + 1) * (units + 1)),
-              tfc.ones([1, batchSize, units])));
-      if (returnState) {
-        expectTensorsClose(finalState, outputT1.reshape([batchSize, units]));
-      }
-    });
-
-    it('stateful missing batchInputShape leads to error', () => {
-      const sequenceLength = 3;
-      const simpleRNN = tfl.layers.simpleRNN({
-        units,
-        kernelInitializer: 'ones',
-        recurrentInitializer: 'ones',
-        biasInitializer: 'zeros',
-        activation: 'linear',
-        stateful: true,
-        inputShape: [sequenceLength, inputSize]
-      }) as RNN;
-      const model = tfl.sequential();
-      expect(() => model.add(simpleRNN))
-          .toThrowError(/needs to know its batch size/);
-    });
-
-    // The reference values below can be obtained with PyKeras code:
-    // ```python
-    // import keras
-    // import numpy as np
-    //
-    // units = 5
-    // batch_size = 4
-    // input_size = 2
-    // sequence_length = 3
-    //
-    // rnn = keras.layers.SimpleRNN(units,
-    //                             kernel_initializer='ones',
-    //                             recurrent_initializer='ones',
-    //                             bias_initializer='zeros',
-    //                             activation='linear',
-    //                             stateful=True,
-    //                             batch_input_shape=[batch_size,
-    //                                                 sequence_length,
-    //                                                 input_size])
-    //
-    // x = np.ones([batch_size, sequence_length, input_size])
-    //
-    // model = keras.Sequential()
-    // model.add(rnn)
-    // print(model.predict(x))
-    // print(model.predict(x))
-    // model.reset_states()
-    // print(model.predict(x))
-    // print(model.predict(x))
-    // ```
-    it('stateful forward: only RNN layer', async () => {
-      const sequenceLength = 3;
-      const rnn = tfl.layers.simpleRNN({
-        units,
-        kernelInitializer: 'ones',
-        recurrentInitializer: 'ones',
-        biasInitializer: 'zeros',
-        activation: 'linear',
-        stateful: true,
-        batchInputShape: [batchSize, sequenceLength, inputSize]
-      });
-      const model = tfl.sequential();
-      model.add(rnn);
-      const x = tfc.ones([batchSize, sequenceLength, inputSize]);
-      const scalar1 = tfc.scalar(62);
-      const scalar2 = tfc.scalar(7812);
-
-      let y1 = model.predict(x) as Tensor;
-      expect(y1.kept).toBe(false);
-      let y1Expected =
-          tfc.tidy(() => tfc.ones([batchSize, units]).mul(scalar1));
-      expectTensorsClose(y1, y1Expected);
-
-      let y2 = model.predict(x) as Tensor;
-      expect(y2.kept).toBe(false);
-      // Future predicts should not dispose previous outputs.
-      expect(y1.isDisposed).toBe(false);
-      let y2Expected =
-          tfc.tidy(() => tfc.ones([batchSize, units]).mul(scalar2));
-      expectTensorsClose(y2, y2Expected);
-      tfc.dispose([y1, y2, y1Expected, y2Expected]);
-
-      model.resetStates();
-      const numTensors0 = tfc.memory().numTensors;
-
-      y1 = model.predict(x) as Tensor;
-      expect(y1.kept).toBe(false);
-      y1Expected = tfc.tidy(() => tfc.ones([batchSize, units]).mul(scalar1));
-      expectTensorsClose(y1, y1Expected);
-
-      y2 = model.predict(x) as Tensor;
-      expect(y2.kept).toBe(false);
-      // Future predicts should not dispose previous outputs.
-      expect(y1.isDisposed).toBe(false);
-      y2Expected = tfc.tidy(() => tfc.ones([batchSize, units]).mul(scalar2));
-      expectTensorsClose(y2, y2Expected);
-      tfc.dispose([y1, y2, y1Expected, y2Expected]);
-
-      // Assert no memory leak, even without resetStates() being called.
-      expect(tfc.memory().numTensors).toEqual(numTensors0);
-    });
-  }
-
-  // Reference Python code:
-  // ```py
-  // import numpy as np
-  // from tensorflow import keras
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.SimpleRNN(
-  //     units=5,
-  //     kernel_initializer='ones',
-  //     recurrent_initializer='ones',
-  //     bias_initializer='zeros',
-  //     activation='linear',
-  //     stateful=True,
-  //     batch_input_shape=[4, 3, 2]))
-  // model.add(keras.layers.Dense(units=1, kernel_initializer='ones'))
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  // model.summary()
-  //
-  // xs = np.ones([4, 3, 2])
-  // y1 = model.predict(xs)
-  // print(y1)
-  // y2 = model.predict(xs)
-  // print(y2)
-  //
-  // history = model.fit(xs, ys, batch_size=4, epochs=3)
-  // print(history.history)
-  // ```
-  it('stateful forward: RNN and dense layers', async () => {
-    const sequenceLength = 3;
-    const model = tfl.sequential();
-    model.add(tfl.layers.simpleRNN({
-      units,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      biasInitializer: 'zeros',
-      activation: 'linear',
-      stateful: true,
-      batchInputShape: [batchSize, sequenceLength, inputSize]
-    }));
-    model.add(tfl.layers.dense({units: 1, kernelInitializer: 'ones'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const xs = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const ys = tfc.ones([batchSize, 1]);
-    let y1: Tensor;
-    let y2: Tensor;
-    tfc.tidy(() => {
-      y1 = model.predict(xs) as Tensor;
-      expectTensorsClose(y1, tfc.tensor2d([310, 310, 310, 310], [4, 1]));
-      y2 = model.predict(xs) as Tensor;
-      expectTensorsClose(
-          y2, tfc.tensor2d([39060, 39060, 39060, 39060], [4, 1]));
-    });
-    model.resetStates();
-    const numTensors0 = tfc.memory().numTensors;
-
-    tfc.tidy(() => {
-      y1 = model.predict(xs) as Tensor;
-      expect(y1.shape).toEqual([batchSize, 1]);
-      expectTensorsClose(y1, tfc.tensor2d([310, 310, 310, 310], [4, 1]));
-      y2 = model.predict(xs) as Tensor;
-      expectTensorsClose(
-          y2, tfc.tensor2d([39060, 39060, 39060, 39060], [4, 1]));
-    });
-    // Assert no memory leak, even without resetStates() being called.
-    expect(tfc.memory().numTensors).toEqual(numTensors0);
-
-    const history = await model.fit(xs, ys, {epochs: 1, batchSize: 4});
-    expect(history.history.loss[0]).toBeGreaterThanOrEqual(23841822736384);
-  });
-
-  it('computeMask: returnSequence = false, returnState = false', () => {
-    const sequenceLength = 3;
-    const rnn = tfl.layers.simpleRNN({
-      units,
-      returnSequences: false,
-      returnState: false,
-      batchInputShape: [batchSize, sequenceLength, inputSize]
-    });
-    const x = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const m = tfc.ones([sequenceLength, 1]);
-    const mask = rnn.computeMask(x, m);
-    expect(mask).toBeNull();
-  });
-
-  it('computeMask: returnSequence = true, returnState = false', () => {
-    const sequenceLength = 3;
-    const rnn = tfl.layers.simpleRNN({
-      units,
-      returnSequences: true,
-      returnState: false,
-      batchInputShape: [batchSize, sequenceLength, inputSize]
-    });
-    const x = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const m = tfc.ones([sequenceLength, 1]);
-    const mask = rnn.computeMask(x, m) as Tensor;
-    expectTensorsClose(mask, m);
-  });
-
-  it('computeMask: returnSequence = true, returnState = true', () => {
-    const sequenceLength = 3;
-    const rnn = tfl.layers.simpleRNN({
-      units,
-      returnSequences: true,
-      returnState: true,
-      stateful: true,
-      batchInputShape: [batchSize, sequenceLength, inputSize]
-    });
-    const x = tfc.ones([batchSize, sequenceLength, inputSize]);
-    rnn.apply(x);
-    rnn.resetStates();  // Let the RNN layer object construct its state first.
-    const m = tfc.ones([sequenceLength, 1]);
-    const masks = rnn.computeMask(x, m) as Tensor[];
-    expect(masks.length).toEqual(2);
-    expectTensorsClose(masks[0], m);
-    expect(masks[1]).toBeNull();
-  });
-
-  // The reference values can be obtained with the following PyKeras code:
-  // ```python
-  // import keras
-  // import numpy as np
-  //
-  // batch_size = 4
-  // sequence_length = 3
-  // input_size = 2
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.SimpleRNN(
-  //     units=5,
-  //     batch_input_shape=[batch_size, sequence_length, input_size],
-  //     kernel_initializer='ones',
-  //     recurrent_initializer='ones',
-  //     bias_initializer='zeros',
-  //     stateful=True))
-  // model.add(keras.layers.Dense(1,
-  //                              kernel_initializer='zeros',
-  //                              use_bias=False))
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // xs_1 = np.ones([batch_size, sequence_length, input_size])
-  // xs_2 = np.zeros([batch_size, sequence_length, input_size])
-  // xs = np.concatenate([xs_1, xs_2], 0)
-  // ys = np.array([[-1], [-2], [0], [1], [1], [2], [0], [-1]])
-  //
-  // history = model.fit(xs, ys, batch_size=batch_size, shuffle=False)
-  // print(history.history)
-  // ```
-  it('stateful BPTT', async () => {
-    const sequenceLength = 3;
-    const model = tfl.sequential();
-    model.add(tfl.layers.simpleRNN({
-      units,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      biasInitializer: 'zeros',
-      stateful: true,
-      batchInputShape: [batchSize, sequenceLength, inputSize]
-    }));
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'zeros', useBias: false}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const xs1 = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const xs2 = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const xs = tfc.concat([xs1, xs2], 0);
-    const ys = tfc.tensor2d([[-1], [-2], [0], [1], [1], [2], [0], [-1]]);
-
-    const history = await model.fit(xs, ys, {batchSize, shuffle: false});
-    // See code snippet above for the code used to obtain this loss value.
-    expect(history.history.loss[0]).toBeCloseTo(1.5262475);
-  });
-
-  it('BPTT', async () => {
-    // The following golden values for assertion can be obtained with the
-    // following Python Keras code.
-    // ```python
-    // import keras
-    // import numpy as np
-    //
-    // sequence_length = 3
-    // input_size = 4
-    // batch_size = 5
-    //
-    // t_input = keras.Input([sequence_length, input_size])
-    // simple_rnn = keras.layers.SimpleRNN(1,
-    //                                     kernel_initializer='ones',
-    //                                     recurrent_initializer='ones',
-    //                                     use_bias=False)
-    // dense = keras.layers.Dense(1,
-    //                            kernel_initializer='ones',
-    //                            use_bias=False)
-    // output = dense(simple_rnn(t_input))
-    // model = keras.Model(t_input, output)
-    // optimizer = keras.optimizers.SGD(5)
-    // model.compile(optimizer=optimizer, loss='mean_squared_error')
-    //
-    // x = np.ones([batch_size, sequence_length, input_size])
-    // y = np.zeros([batch_size, 1])
-    // model.fit(x, y, batch_size=batch_size, epochs=2)
-    // print(simple_rnn.get_weights()[0])
-    // print(simple_rnn.get_weights()[1])
-    // print(dense.get_weights()[0])
-    // ```
-    const sequenceLength = 3;
-    const inputSize = 4;
-    const batchSize = 5;
-    const model = tfl.sequential();
-    model.add(tfl.layers.simpleRNN({
-      units: 1,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      useBias: false,
-      inputShape: [sequenceLength, inputSize],
-    }));
-    model.add(tfl.layers.dense({
-      units: 1,
-      kernelInitializer: 'ones',
-      useBias: false,
-    }));
-
-    const x = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const y = tfc.zeros([batchSize, 1]);
-    const sgd = tfc.train.sgd(5);
-    model.compile({loss: 'meanSquaredError', optimizer: sgd});
-    await model.fit(x, y, {epochs: 2});
-
-    expectTensorsClose(
-        model.layers[0].getWeights()[0],
-        tfc.mul(scalar(0.8484658), tfc.ones([4, 1])));
-    expectTensorsClose(
-        model.layers[0].getWeights()[1],
-        tfc.mul(scalar(0.8484799), tfc.ones([1, 1])));
-    expectTensorsClose(
-        model.layers[1].getWeights()[0],
-        tfc.mul(scalar(80.967026), tfc.ones([1, 1])));
-  });
-});
-
-describeMathCPU('SimpleRNN Serialization', () => {
-  const cellConfig: SimpleRNNCellLayerArgs = {
-    units: 8,
-    activation: 'tanh',
-    useBias: true,
-    kernelInitializer: 'glorotUniform',
-    recurrentInitializer: 'heUniform',
-    biasInitializer: 'ones',
-    kernelRegularizer: 'l1l2',
-    recurrentRegularizer: 'l1l2',
-    biasRegularizer: 'l1l2',
-    kernelConstraint: 'unitNorm',
-    recurrentConstraint: 'unitNorm',
-    biasConstraint: 'nonNeg',
-    dropout: 0.1,
-    recurrentDropout: 0.2,
-    name: 'cell_1',
-    batchSize: 12,
-    batchInputShape: [12, 8, 8],
-    inputShape: [8, 8],
-    dtype: 'int32',
-    inputDType: 'int32',
-    trainable: true,
-  };
-
-  const expectedCellConfigPrime = {
-    name: 'cell_1',
-    trainable: true,
-    batchInputShape: [12, 8, 8],
-    dtype: 'int32',
-    units: 8,
-    activation: serializeActivation(new Tanh()),
-    useBias: true,
-    kernelInitializer: serializeInitializer(new GlorotUniform()),
-    recurrentInitializer: serializeInitializer(new HeUniform()),
-    biasInitializer: serializeInitializer(new Ones()),
-    kernelRegularizer: serializeRegularizer(new L1L2()),
-    recurrentRegularizer: serializeRegularizer(new L1L2()),
-    biasRegularizer: serializeRegularizer(new L1L2()),
-    activityRegularizer: serializeRegularizer(null),
-    kernelConstraint: serializeConstraint(new UnitNorm({})),
-    recurrentConstraint: serializeConstraint(new UnitNorm({})),
-    biasConstraint: serializeConstraint(new NonNeg()),
-  };
-
-  describe('SimpleRNNCell.getConfig', () => {
-    it('should return the expected values', () => {
-      const cell = tfl.layers.simpleRNNCell(cellConfig);
-
-      const {dropout, recurrentDropout, ...configPrime} = cell.getConfig();
-
-      expect(configPrime).toEqual(expectedCellConfigPrime);
-      expect(dropout).toBeCloseTo(0.1);
-      expect(recurrentDropout).toBeCloseTo(0.2);
-    });
-  });
-
-  describe('SimpleRNN.getConfig', () => {
-    it('should return the expected values', () => {
-      const config: SimpleRNNLayerArgs = {
-        ...cellConfig,
-        name: 'layer_1',
-        ...{
-          returnSequences: true,
-          returnState: true,
-          stateful: true,
-          unroll: true,
-          goBackwards: true,
-          inputDim: 8,
-          inputLength: 8,
-        } as Omit<SimpleRNNLayerArgs, keyof SimpleRNNCellLayerArgs>
-      };
-
-      const cell = tfl.layers.simpleRNN(config);
-
-      const {dropout, recurrentDropout, ...configPrime} = cell.getConfig();
-
-      expect(configPrime).toEqual({
-        ...expectedCellConfigPrime,
-        name: 'layer_1',
-        returnSequences: true,
-        returnState: true,
-        stateful: true,
-        unroll: true,
-        goBackwards: true,
-      });
-      expect(dropout).toBeCloseTo(0.1);
-      expect(recurrentDropout).toBeCloseTo(0.2);
-    });
-  });
-});
-
-describeMathCPU('GRU Symbolic', () => {
-  it('returnSequences=false, returnState=false', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const gru = tfl.layers.gru({units: 5});
-    const output = gru.apply(input) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([9, 5]);
-  });
-
-  it('returnSequences=false, returnState=true', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const gru = tfl.layers.gru({units: 5, returnState: true});
-    const output = gru.apply(input) as tfl.SymbolicTensor[];
-    expect(output.length).toEqual(2);
-    expect(output[0].shape).toEqual([9, 5]);
-    expect(output[1].shape).toEqual([9, 5]);
-  });
-
-  it('returnSequences=true, returnState=false', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const gru = tfl.layers.gru({units: 5, returnSequences: true});
-    const output = gru.apply(input) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([9, 10, 5]);
-  });
-
-  it('returnSequences=true, returnState=true', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const gru = tfl.layers.gru({
-      units: 5,
-      returnSequences: true,
-      returnState: true,
-    });
-    const output = gru.apply(input) as tfl.SymbolicTensor[];
-    expect(output.length).toEqual(2);
-    expect(output[0].shape).toEqual([9, 10, 5]);
-    expect(output[1].shape).toEqual([9, 5]);
-  });
-
-  it('trainableWeights, nonTrainableWeights and weights give correct outputs',
-     () => {
-       const input =
-           new tfl.SymbolicTensor('float32', [2, 3, 4], null, [], null);
-       const gru = tfl.layers.gru({units: 5, returnState: true});
-       gru.apply(input);
-       expect(gru.trainable).toEqual(true);
-       // Trainable weights: kernel, recurrent kernel and bias.
-       expect(gru.trainableWeights.length).toEqual(3);
-       expect(gru.nonTrainableWeights.length).toEqual(0);
-       expect(gru.weights.length).toEqual(3);
-     });
-
-  for (const implementation of [1, 2]) {
-    it('Serialization round trip', () => {
-      const layer = tfl.layers.gru({units: 4, implementation});
-      const pythonicConfig = convertTsToPythonic(layer.getConfig());
-      // tslint:disable-next-line:no-any
-      const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-      const layerPrime = tfl.layers.gru(tsConfig);
-      expect(layerPrime.getConfig().units).toEqual(4);
-      expect(layerPrime.getConfig().implementation).toEqual(implementation);
-    });
-  }
-
-  it('Invalid units leads to Error', () => {
-    expect(() => tfl.layers.gru({
-      units: 12.5
-    })).toThrowError(/units.*positive integer.*12\.5\.$/);
-    expect(() => tfl.layers.gru({
-      units: 0
-    })).toThrowError(/units.*positive integer.*0\.$/);
-    expect(() => tfl.layers.gru({
-      units: -25
-    })).toThrowError(/units.*positive integer.*-25\.$/);
-  });
-});
-
-describeMathCPUAndWebGL2('GRU Tensor', () => {
-  // Note:
-  // The golden expected values used for assertions in these unit tests can be
-  // obtained through running the Python code similar to the following example.
-  // TensorFlow 1.5 was used to obtain the values.
-  //
-  // ```python
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // units = 5
-  // batch_size = 4
-  // input_size = 2
-  // time_steps = 3
-  //
-  // with tf.Session() as sess:
-  //   lstm = tf.keras.layers.GRU(units,
-  //                              kernel_initializer="ones",
-  //                              recurrent_initializer="ones",
-  //                              bias_initializer="ones",
-  //                              return_sequences=True,
-  //                              return_state=True)
-  //   inputs = tf.placeholder(tf.float32, shape=[None, None, input_size])
-  //   outputs = lstm(inputs)
-  //
-  //   sess.run(tf.global_variables_initializer())
-  //   feed_inputs = np.ones([batch_size, time_steps, input_size])
-  //   print(sess.run(outputs, feed_dict={inputs: feed_inputs}))
-  // ```
-
-  const units = 5;
-  const batchSize = 4;
-  const inputSize = 2;
-  const timeSteps = 3;
-  const goldenOutputElementValues = [0.22847827, 0.2813754, 0.29444352];
-
-  // TODO(cais): Add test for the default recurrent initializer ('Orthogonal')
-  //   when it becomes available.
-
-  const dropouts = [0.0, 0.1];
-  const trainings = [true, false];
-  for (const training of trainings) {
-    for (const dropout of dropouts) {
-      const testTitle =
-          `returnSequences=false, returnState=false, useBias=true,` +
-          ` ${training}, dropout=${dropout}`;
-      it(testTitle, () => {
-        const dropoutFunc = jasmine.createSpy('dropout').and.callFake(
-            (x: Tensor, level: number, noiseShape?: number[], seed?: number) =>
-                tfc.tidy(() => tfc.dropout(x, level, noiseShape, seed)));
-        const gru = tfl.layers.gru({
-          units,
-          kernelInitializer: 'ones',
-          recurrentInitializer: 'ones',
-          biasInitializer: 'ones',
-          dropout,
-          implementation: 1,
-          dropoutFunc
-        });
-        const kwargs: Kwargs = {};
-        if (training) {
-          kwargs['training'] = true;
-        }
-        const input = tfc.ones([batchSize, timeSteps, inputSize]);
-        let numTensors = 0;
-        for (let i = 0; i < 2; i++) {
-          tfc.dispose(gru.apply(input, kwargs) as Tensor);
-          if (dropout !== 0.0 && training) {
-            expect(dropoutFunc).toHaveBeenCalledTimes(3 * (i + 1));
-          } else {
-            expect(dropoutFunc).toHaveBeenCalledTimes(0);
-          }
-          if (i === 0) {
-            numTensors = tfc.memory().numTensors;
-          } else {
-            expect(tfc.memory().numTensors).toEqual(numTensors);
-          }
-        }
-      });
-    }
-  }
-
-  const recurrentDropouts = [0.0, 0.1];
-  for (const training of trainings) {
-    for (const recurrentDropout of recurrentDropouts) {
-      const testTitle =
-          `returnSequences=false, returnState=false, useBias=true,` +
-          ` ${training}, recurrentDropout=${recurrentDropout}`;
-      it(testTitle, () => {
-        const dropoutFunc = jasmine.createSpy('dropout').and.callFake(
-            (x: Tensor, level: number, noiseShape?: number[], seed?: number) =>
-                tfc.tidy(() => tfc.dropout(x, level, noiseShape, seed)));
-        const gru = tfl.layers.gru({
-          units,
-          kernelInitializer: 'ones',
-          recurrentInitializer: 'ones',
-          biasInitializer: 'ones',
-          recurrentDropout,
-          implementation: 1,
-          dropoutFunc
-        });
-        const kwargs: Kwargs = {};
-        if (training) {
-          kwargs['training'] = true;
-        }
-        const input = tfc.ones([batchSize, timeSteps, inputSize]);
-        let numTensors = 0;
-        for (let i = 0; i < 2; i++) {
-          tfc.dispose(gru.apply(input, kwargs) as Tensor);
-          if (recurrentDropout !== 0.0 && training) {
-            expect(dropoutFunc).toHaveBeenCalledTimes(3 * (i + 1));
-          } else {
-            expect(dropoutFunc).toHaveBeenCalledTimes(0);
-          }
-          if (i === 0) {
-            numTensors = tfc.memory().numTensors;
-          } else {
-            expect(tfc.memory().numTensors).toEqual(numTensors);
-          }
-        }
-      });
-    }
-  }
-
-  const implementations = [1, 2];
-  const returnStateValues = [false, true];
-  const returnSequencesValues = [false, true];
-  for (const implementation of implementations) {
-    for (const returnState of returnStateValues) {
-      for (const returnSequences of returnSequencesValues) {
-        const testTitle = `implementation=${implementation}, ` +
-            `returnSequences=${returnSequences}, ` +
-            `returnState=${returnState}`;
-        it(testTitle, () => {
-          const gru = tfl.layers.gru({
-            units,
-            kernelInitializer: 'ones',
-            recurrentInitializer: 'ones',
-            biasInitializer: 'ones',
-            returnState,
-            returnSequences,
-            implementation
-          });
-          const input = tfc.zeros([batchSize, timeSteps, inputSize]);
-          let output = gru.apply(input);
-
-          const goldenOutputElementValueFinal =
-              goldenOutputElementValues[goldenOutputElementValues.length - 1];
-
-          let expectedOutput: Tensor;
-          if (returnSequences) {
-            const outputs = goldenOutputElementValues.map(
-                value =>
-                    tfc.mul(scalar(value), tfc.ones([1, batchSize, units])));
-            expectedOutput = tfc.transpose(
-                K.concatAlongFirstAxis(
-                    K.concatAlongFirstAxis(outputs[0], outputs[1]), outputs[2]),
-                [1, 0, 2]);
-          } else {
-            expectedOutput = tfc.mul(
-                scalar(goldenOutputElementValueFinal),
-                tfc.ones([batchSize, units]));
-          }
-          if (returnState) {
-            output = output as Tensor[];
-            expect(output.length).toEqual(2);
-            expectTensorsClose(output[0], expectedOutput);
-            expectTensorsClose(
-                output[1],
-                tfc.mul(
-                    scalar(goldenOutputElementValueFinal),
-                    tfc.ones([batchSize, units])));
-          } else {
-            output = output as Tensor;
-            expectTensorsClose(output, expectedOutput);
-          }
-        });
-      }
-    }
-  }
-
-  // The reference values below can be obtained with PyKeras code:
-  // ```python
-  // import keras
-  // import numpy as np
-  //
-  // units = 5
-  // batch_size = 4
-  // input_size = 2
-  // sequence_length = 3
-  //
-  // rnn = keras.layers.GRU(units,
-  //                        kernel_initializer='ones',
-  //                        recurrent_initializer='zeros',
-  //                        bias_initializer='zeros',
-  //                        activation='linear',
-  //                        stateful=True,
-  //                        batch_input_shape=[batch_size,
-  //                                           sequence_length,
-  //                                           input_size])
-  //
-  // x = np.ones([batch_size, sequence_length, input_size])
-  //
-  // model = keras.Sequential()
-  // model.add(rnn)
-  // print(model.predict(x))
-  // print(model.predict(x))
-  // model.reset_states()
-  // print(model.predict(x))
-  // print(model.predict(x))
-  // ```
-  it('stateful forward', async () => {
-    const sequenceLength = 3;
-    const rnn = tfl.layers.gru({
-      units,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'zeros',
-      biasInitializer: 'zeros',
-      activation: 'linear',
-      stateful: true,
-      batchInputShape: [batchSize, sequenceLength, inputSize]
-    });
-    const model = tfl.sequential();
-    model.add(rnn);
-    const x = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const y1 = model.predict(x) as Tensor;
-    expect(y1.kept).toBe(false);
-    const y1Expected =
-        tfc.tidy(() => tfc.ones([batchSize, units]).mul(tfc.scalar(0.542)));
-    expectTensorsClose(y1, y1Expected);
-    const y2 = model.predict(x) as Tensor;
-    expect(y2.kept).toBe(false);
-    // Future predicts should not dispose previous outputs.
-    expect(y1.isDisposed).toBe(false);
-    const y2Expected =
-        tfc.tidy(() => tfc.ones([batchSize, units]).mul(tfc.scalar(0.9371182)));
-    expectTensorsClose(y2, y2Expected);
-
-    tfc.dispose([y1, y2, y1Expected, y2Expected]);
-    model.resetStates();
-    const numTensors0 = tfc.memory().numTensors;
-
-    const y3 = model.predict(x) as Tensor;
-    expect(y3.kept).toBe(false);
-    const y3Expected =
-        tfc.tidy(() => tfc.ones([batchSize, units]).mul(tfc.scalar(0.542)));
-    expectTensorsClose(y3, y3Expected);
-    const y4 = model.predict(x) as Tensor;
-    expect(y4.kept).toBe(false);
-    // Future predicts should not dispose previous outputs.
-    expect(y3.isDisposed).toBe(false);
-    const y4Expected =
-        tfc.tidy(() => tfc.ones([batchSize, units]).mul(tfc.scalar(0.9371182)));
-    expectTensorsClose(y4, y4Expected);
-    tfc.dispose([y3, y3Expected, y4, y4Expected]);
-    // Assert no memory leak, even without resetStates() being called.
-    expect(tfc.memory().numTensors).toEqual(numTensors0);
-  });
-
-  // The reference values can be obtained with the following PyKeras code:
-  // ```python
-  // import keras
-  // import numpy as np
-  //
-  // batch_size = 4
-  // sequence_length = 3
-  // input_size = 2
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.GRU(
-  //     units=5,
-  //     batch_input_shape=[batch_size, sequence_length, input_size],
-  //     kernel_initializer='ones',
-  //     recurrent_initializer='ones',
-  //     bias_initializer='zeros',
-  //     stateful=True))
-  // model.add(keras.layers.Dense(1,
-  //                              kernel_initializer='zeros',
-  //                              use_bias=False))
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // xs_1 = np.ones([batch_size, sequence_length, input_size])
-  // xs_2 = np.zeros([batch_size, sequence_length, input_size])
-  // xs = np.concatenate([xs_1, xs_2], 0)
-  // ys = np.array([[-1], [-2], [0], [1], [1], [2], [0], [-1]])
-  //
-  // history = model.fit(xs, ys, batch_size=batch_size, shuffle=False)
-  // print(history.history)
-  // ```
-  it('stateful BPTT', async () => {
-    const sequenceLength = 3;
-    const model = tfl.sequential();
-    model.add(tfl.layers.gru({
-      units,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      biasInitializer: 'zeros',
-      stateful: true,
-      batchInputShape: [batchSize, sequenceLength, inputSize]
-    }));
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'zeros', useBias: false}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const xs1 = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const xs2 = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const xs = tfc.concat([xs1, xs2], 0);
-    const ys = tfc.tensor2d([[-1], [-2], [0], [1], [1], [2], [0], [-1]]);
-
-    const history = await model.fit(xs, ys, {batchSize, shuffle: false});
-    // See code snippet above for the code used to obtain this loss value.
-    expect(history.history.loss[0]).toBeCloseTo(1.501);
-  });
-
-  it('BPTT', async () => {
-    // The following golden values for assertion can be obtained with the
-    // following Python Keras code.
-    // ```python
-    // import keras
-    // import numpy as np
-
-    // sequence_length = 3
-    // input_size = 4
-    // batch_size = 5
-
-    // t_input = keras.Input([sequence_length, input_size])
-    // gru = keras.layers.GRU(1,
-    //                        kernel_initializer='zeros',
-    //                        recurrent_initializer='zeros',
-    //                        use_bias=False)
-    // dense = keras.layers.Dense(1,
-    //                            kernel_initializer='ones',
-    //                            use_bias=False)
-    // output = dense(gru(t_input))
-    // model = keras.Model(t_input, output)
-    // optimizer = keras.optimizers.SGD(1)
-    // model.compile(optimizer=optimizer, loss='mean_squared_error')
-
-    // x = np.ones([batch_size, sequence_length, input_size])
-    // y = np.ones([batch_size, 1])
-    // model.fit(x, y, batch_size=batch_size, epochs=2)
-    // print(gru.get_weights()[0])
-    // print(gru.get_weights()[1])
-    // print(dense.get_weights()[0])
-    // ```
-    const sequenceLength = 3;
-    const inputSize = 4;
-    const batchSize = 5;
-    const model = tfl.sequential();
-    model.add(tfl.layers.gru({
-      units: 1,
-      kernelInitializer: 'zeros',
-      recurrentInitializer: 'zeros',
-      useBias: false,
-      inputShape: [sequenceLength, inputSize]
-    }));
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', useBias: false}));
-
-    const sgd = tfc.train.sgd(1);
-    model.compile({loss: 'meanSquaredError', optimizer: sgd});
-    const x = tfc.ones([batchSize, sequenceLength, inputSize]);
-    const y = tfc.ones([batchSize, 1]);
-    await model.fit(x, y, {epochs: 2});
-    expectTensorsClose(
-        model.layers[0].getWeights()[0],
-        K.tile(tensor2d([[-0.03750037, 0, 1.7500007]], [1, 3]), [4, 1]));
-    expectTensorsClose(
-        model.layers[0].getWeights()[1],
-        tensor2d([[-1.562513e-02, 0, 2.086183e-07]], [1, 3]));
-    expectTensorsClose(
-        model.layers[1].getWeights()[0], tensor2d([[1.2187521]], [1, 1]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // in1 = keras.Input(shape=[5])
-  // out1 = keras.layers.Dense(2,
-  //                           kernel_initializer='ones',
-  //                           bias_initializer='zeros')(in1)
-  // in2 = keras.Input(shape=[3, 4])
-  // out2 = keras.layers.GRU(2,
-  //                         recurrent_initializer='ones',
-  //                         kernel_initializer='ones',
-  //                         bias_initializer='zeros')(in2, initial_state=out1)
-  // model = keras.Model(inputs=[in1, in2], outputs=[out1, out2])
-  //
-  // xs1 = np.array([[0.1, 0.2, 0.3, 0.4, 0.5]])
-  // xs2 = np.array([[[0.1, 0.2, 0.3, 0.4], [-0.1, -0.2, -0.3, -0.4],
-  //                 [0.3, 0.4, 0.5, 0.6]]])
-  // print(model.predict([xs1, xs2]))
-  // ```
-  it('SymbolicTensor as initialState thru kwargs; Save & Load', async () => {
-    const in1 = tfl.input({shape: [5]});
-    const out1 =
-        tfl.layers
-            .dense(
-                {units: 2, kernelInitializer: 'ones', biasInitializer: 'zeros'})
-            .apply(in1) as tfl.SymbolicTensor;
-    const in2 = tfl.input({shape: [3, 4]});
-    const out2 = tfl.layers
-                     .gru({
-                       units: 2,
-                       recurrentInitializer: 'ones',
-                       kernelInitializer: 'ones',
-                       biasInitializer: 'zeros'
-                     })
-                     .apply(in2, {initialState: out1}) as tfl.SymbolicTensor;
-
-    const model = tfl.model({inputs: [in1, in2], outputs: [out1, out2]});
-
-    const xs1 = tensor2d([[0.1, 0.2, 0.3, 0.4, 0.5]]);
-    const xs2 = tensor3d([
-      [[0.1, 0.2, 0.3, 0.4], [-0.1, -0.2, -0.3, -0.4], [0.3, 0.4, 0.5, 0.6]]
-    ]);
-    const ys = model.predict([xs1, xs2]) as Tensor[];
-    expect(ys.length).toEqual(2);
-    expectTensorsClose(ys[0], tensor2d([[1.5, 1.5]]));
-    expectTensorsClose(ys[1], tensor2d([[1.4435408, 1.4435408]]));
-
-    // NOTE: Here on down, i.e., the part that tests serialization and
-    // deserialization of the model, has no counterpart in the Python
-    // code snippet above.
-    const modelJSON = model.toJSON(null, false);
-    const modelPrime =
-        await tfl.models.modelFromJSON({modelTopology: modelJSON});
-    const ysPrime = modelPrime.predict([xs1, xs2]) as Tensor[];
-    expect(ysPrime.length).toEqual(2);
-    expectTensorsClose(ysPrime[0], ys[0]);
-    expectTensorsClose(ysPrime[1], ys[1]);
-  });
-});
-
-describeMathCPU('GRU-deserialization', () => {
-  it('Default recurrentActivation round trip', () => {
-    const x = randomNormal([1, 2, 3]);
-    const layer = tfl.layers.gru({units: 4});
-    const y = layer.apply(x) as Tensor;
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.gru(tsConfig);
-    const yPrime = layer.apply(x) as Tensor;
-    expectTensorsClose(yPrime, y);
-    expect(layerPrime.getConfig()['recurrentActivation'])
-        .toEqual(layer.getConfig()['recurrentActivation']);
-  });
-
-  it('Non-default recurrentActivation round trip', () => {
-    const x = randomNormal([1, 2, 3]);
-    const layer = tfl.layers.gru({units: 4, recurrentActivation: 'tanh'});
-    const y = layer.apply(x) as Tensor;
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.gru(tsConfig);
-    const yPrime = layer.apply(x) as Tensor;
-    expectTensorsClose(yPrime, y);
-    expect(layerPrime.getConfig()['recurrentActivation'])
-        .toEqual(layer.getConfig()['recurrentActivation']);
-  });
-});
-
-describeMathCPU('GRU Serialization', () => {
-  const cellConfig: GRUCellLayerArgs = {
-    units: 8,
-    activation: 'tanh',
-    recurrentActivation: 'tanh',
-    useBias: true,
-    kernelInitializer: 'glorotUniform',
-    recurrentInitializer: 'heUniform',
-    biasInitializer: 'ones',
-    kernelRegularizer: 'l1l2',
-    recurrentRegularizer: 'l1l2',
-    biasRegularizer: 'l1l2',
-    kernelConstraint: 'unitNorm',
-    recurrentConstraint: 'unitNorm',
-    biasConstraint: 'nonNeg',
-    dropout: 0.1,
-    recurrentDropout: 0.2,
-    name: 'cell_1',
-    batchSize: 12,
-    batchInputShape: [12, 8, 8],
-    inputShape: [8, 8],
-    dtype: 'int32',
-    inputDType: 'int32',
-    trainable: true,
-    implementation: 1,
-  };
-
-  const expectedCellConfigPrime = {
-    name: 'cell_1',
-    trainable: true,
-    batchInputShape: [12, 8, 8],
-    dtype: 'int32',
-    units: 8,
-    activation: serializeActivation(new Tanh()),
-    recurrentActivation: serializeActivation(new Tanh()),
-    useBias: true,
-    kernelInitializer: serializeInitializer(new GlorotUniform()),
-    recurrentInitializer: serializeInitializer(new HeUniform()),
-    biasInitializer: serializeInitializer(new Ones()),
-    kernelRegularizer: serializeRegularizer(new L1L2()),
-    recurrentRegularizer: serializeRegularizer(new L1L2()),
-    biasRegularizer: serializeRegularizer(new L1L2()),
-    activityRegularizer: serializeRegularizer(null),
-    kernelConstraint: serializeConstraint(new UnitNorm({})),
-    recurrentConstraint: serializeConstraint(new UnitNorm({})),
-    biasConstraint: serializeConstraint(new NonNeg()),
-    implementation: 1,
-    resetAfter: false,
-  };
-
-  describe('GRUCell.getConfig', () => {
-    it('should return the expected values', () => {
-      const cell = tfl.layers.gruCell(cellConfig);
-
-      const {dropout, recurrentDropout, ...configPrime} = cell.getConfig();
-
-      expect(configPrime).toEqual(expectedCellConfigPrime);
-      expect(dropout).toBeCloseTo(0.1);
-      expect(recurrentDropout).toBeCloseTo(0.2);
-    });
-  });
-
-  describe('GRU.getConfig', () => {
-    it('should return the expected values', () => {
-      const config: GRULayerArgs = {
-        ...cellConfig,
-        name: 'layer_1',
-        ...{
-          returnSequences: true,
-          returnState: true,
-          stateful: true,
-          unroll: true,
-          goBackwards: true,
-          inputDim: 8,
-          inputLength: 8,
-        } as Omit<GRULayerArgs, keyof GRUCellLayerArgs>
-      };
-
-      const cell = tfl.layers.gru(config);
-
-      const {dropout, recurrentDropout, ...configPrime} = cell.getConfig();
-
-      expect(configPrime).toEqual({
-        ...expectedCellConfigPrime,
-        name: 'layer_1',
-        returnSequences: true,
-        returnState: true,
-        stateful: true,
-        unroll: true,
-        goBackwards: true,
-      });
-      expect(dropout).toBeCloseTo(0.1);
-      expect(recurrentDropout).toBeCloseTo(0.2);
-    });
-  });
-});
-
-describeMathCPU('LSTM Symbolic', () => {
-  it('returnSequences=false, returnState=false', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const lstm = tfl.layers.lstm({units: 5});
-    const output = lstm.apply(input) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([9, 5]);
-  });
-
-  it('returnSequences=false, returnState=true', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const lstm = tfl.layers.lstm({units: 5, returnState: true});
-    const output = lstm.apply(input) as tfl.SymbolicTensor[];
-    expect(output.length).toEqual(3);
-    expect(output[0].shape).toEqual([9, 5]);
-    expect(output[1].shape).toEqual([9, 5]);
-    expect(output[2].shape).toEqual([9, 5]);
-  });
-
-  it('returnSequences=true, returnState=false', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const lstm = tfl.layers.lstm({units: 5, returnSequences: true});
-    const output = lstm.apply(input) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([9, 10, 5]);
-  });
-
-  it('returnSequences=true, returnState=true', () => {
-    const input = new tfl.SymbolicTensor('float32', [9, 10, 8], null, [], null);
-    const lstm = tfl.layers.lstm({
-      units: 5,
-      returnSequences: true,
-      returnState: true,
-    });
-    const output = lstm.apply(input) as tfl.SymbolicTensor[];
-    expect(output.length).toEqual(3);
-    expect(output[0].shape).toEqual([9, 10, 5]);
-    expect(output[1].shape).toEqual([9, 5]);
-    expect(output[2].shape).toEqual([9, 5]);
-  });
-
-  it('trainableWeights, nonTrainableWeights and weights give correct outputs',
-     () => {
-       const input =
-           new tfl.SymbolicTensor('float32', [2, 3, 4], null, [], null);
-       const lstm = tfl.layers.lstm({units: 5, returnState: true});
-       lstm.apply(input);
-       expect(lstm.trainable).toEqual(true);
-       // Trainable weights: kernel, recurrent kernel and bias.
-       expect(lstm.trainableWeights.length).toEqual(3);
-       expect(lstm.nonTrainableWeights.length).toEqual(0);
-       expect(lstm.weights.length).toEqual(3);
-     });
-
-  for (const implementation of [1, 2]) {
-    it('Serialization round trip', () => {
-      const layer = tfl.layers.lstm({units: 4, implementation});
-      const pythonicConfig = convertTsToPythonic(layer.getConfig());
-      // tslint:disable-next-line:no-any
-      const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-      const layerPrime = tfl.layers.lstm(tsConfig);
-      expect(layerPrime.getConfig().units).toEqual(4);
-      expect(layerPrime.getConfig().implementation).toEqual(implementation);
-    });
-  }
-
-  it('LSTM Cells save and load', async () => {
-    const inputShape = [2, 3];
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape}));
-    const cells = [
-      tfl.layers.lstmCell({units: 3}),
-      tfl.layers.lstmCell({units: 4}),
-    ];
-    const rnn = tfl.layers.rnn({cell: cells, returnSequences: true});
-    model.add(rnn);
-
-    const numExamples = 5;
-    const xs = randomNormal([numExamples].concat(inputShape));
-    const ys = model.predict(xs) as Tensor;
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model.save(tfc.io.withSaveHandler(async (artifacts) => {
-      savedArtifacts = artifacts;
-      return null;
-    }));
-
-    const loadedModel =
-        await tfl.loadLayersModel(tfc.io.fromMemory(savedArtifacts));
-
-    expect(model.inputs[0].shape).toEqual(loadedModel.inputs[0].shape);
-    expect(model.outputs[0].shape).toEqual(loadedModel.outputs[0].shape);
-    expectTensorsClose(loadedModel.predict(xs) as Tensor, ys);
-  });
-
-  it('Invalid units leads to Error', () => {
-    expect(() => tfl.layers.lstm({
-      units: 12.5
-    })).toThrowError(/units.*positive integer.*12\.5\.$/);
-    expect(() => tfl.layers.lstm({
-      units: 0
-    })).toThrowError(/units.*positive integer.*0\.$/);
-    expect(() => tfl.layers.lstm({
-      units: -25
-    })).toThrowError(/units.*positive integer.*-25\.$/);
-  });
-});
-
-describeMathCPUAndWebGL2('LSTM Tensor', () => {
-  // Note:
-  // The golden expected values used for assertions in these unit tests can be
-  // obtained through running the Python code similar to the following
-  // example. TensorFlow 1.5 was used to obtain the values.
-  //
-  // ```python
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // units = 5
-  // batch_size = 4
-  // input_size = 2
-  // time_steps = 2
-  //
-  // with tf.Session() as sess:
-  //   lstm = tf.keras.layers.LSTM(units,
-  //                               kernel_initializer="ones",
-  //                               recurrent_initializer="ones",
-  //                               bias_initializer="ones",
-  //                               return_sequences=True,
-  //                               return_state=True,
-  //                               unit_forget_bias=True)
-  //   inputs = tf.placeholder(tf.float32, shape=[None, None, input_size])
-  //   outputs = lstm(inputs)
-  //
-  //   sess.run(tf.global_variables_initializer())
-  //   feed_inputs = np.ones([batch_size, time_steps, input_size])
-  //   print(sess.run(outputs, feed_dict={inputs: feed_inputs}))
-  // ```
-
-  const units = 5;
-  const batchSize = 4;
-  const inputSize = 2;
-  const timeSteps = 2;
-
-  // TODO(cais): Add test for the default recurrent initializer ('Orthogonal')
-  //   when it becomes available.
-
-  const dropouts = [0.0, 0.1];
-  const trainings = [true, false];
-  for (const training of trainings) {
-    for (const dropout of dropouts) {
-      const testTitle =
-          `returnSequences=false, returnState=false, useBias=true,` +
-          ` ${training}, dropout=${dropout}`;
-      it(testTitle, () => {
-        const dropoutFunc = jasmine.createSpy('dropout').and.callFake(
-            (x: Tensor, level: number, noiseShape?: number[], seed?: number) =>
-                tfc.tidy(() => tfc.dropout(x, level, noiseShape, seed)));
-        const lstm = tfl.layers.lstm({
-          units,
-          kernelInitializer: 'ones',
-          recurrentInitializer: 'ones',
-          biasInitializer: 'ones',
-          dropout,
-          implementation: 1,
-          dropoutFunc
-        });
-        const kwargs: Kwargs = {};
-        if (training) {
-          kwargs['training'] = true;
-        }
-        const input = tfc.ones([batchSize, timeSteps, inputSize]);
-        let numTensors = 0;
-        for (let i = 0; i < 2; i++) {
-          tfc.dispose(lstm.apply(input, kwargs) as Tensor);
-          if (dropout !== 0.0 && training) {
-            expect(dropoutFunc).toHaveBeenCalledTimes(4 * (i + 1));
-          } else {
-            expect(dropoutFunc).toHaveBeenCalledTimes(0);
-          }
-          if (i === 0) {
-            numTensors = tfc.memory().numTensors;
-          } else {
-            expect(tfc.memory().numTensors).toEqual(numTensors);
-          }
-        }
-      });
-    }
-  }
-
-  const recurrentDropouts = [0.0, 0.1];
-  for (const training of trainings) {
-    for (const recurrentDropout of recurrentDropouts) {
-      const testTitle =
-          `returnSequences=false, returnState=false, useBias=true,` +
-          ` ${training}, recurrentDropout=${recurrentDropout}`;
-      it(testTitle, () => {
-        const dropoutFunc = jasmine.createSpy('dropout').and.callFake(
-            (x: Tensor, level: number, noiseShape?: number[], seed?: number) =>
-                tfc.tidy(() => tfc.dropout(x, level, noiseShape, seed)));
-        const lstm = tfl.layers.lstm({
-          units,
-          kernelInitializer: 'ones',
-          recurrentInitializer: 'ones',
-          biasInitializer: 'ones',
-          recurrentDropout,
-          implementation: 1,
-          dropoutFunc
-        });
-        const kwargs: Kwargs = {};
-        if (training) {
-          kwargs['training'] = true;
-        }
-        const input = tfc.ones([batchSize, timeSteps, inputSize]);
-        let numTensors = 0;
-        for (let i = 0; i < 2; i++) {
-          tfc.dispose(lstm.apply(input, kwargs) as Tensor);
-          if (recurrentDropout !== 0.0 && training) {
-            expect(dropoutFunc).toHaveBeenCalledTimes(4 * (i + 1));
-          } else {
-            expect(dropoutFunc).toHaveBeenCalledTimes(0);
-          }
-          if (i === 0) {
-            numTensors = tfc.memory().numTensors;
-          } else {
-            expect(tfc.memory().numTensors).toEqual(numTensors);
-          }
-        }
-      });
-    }
-  }
-
-  const implementations: Array<(1 | 2)> = [1, 2];
-  const returnStateValues = [false, true];
-  const returnSequencesValues = [false, true];
-  for (const implementation of implementations) {
-    for (const returnState of returnStateValues) {
-      for (const returnSequences of returnSequencesValues) {
-        const testTitle = `implementation=${implementation}, ` +
-            `returnSequences=${returnSequences}, ` +
-            `returnState=${returnState}`;
-        it(testTitle, () => {
-          const lstm = tfl.layers.lstm({
-            units,
-            kernelInitializer: 'ones',
-            recurrentInitializer: 'ones',
-            biasInitializer: 'ones',
-            returnState,
-            returnSequences,
-            implementation
-          });
-          const input = tfc.ones([batchSize, timeSteps, inputSize]);
-          let output = lstm.apply(input);
-
-          // See comments at the beginning of this describe() block on how
-          // these golden expected values can be obtained.
-          const goldenOutputElementValueAtT0 = 0.7595095;
-          const goldenOutputElementValueAtT1 = 0.96367633;
-          const goldenHStateElementValue = goldenOutputElementValueAtT1;
-          const goldenCStateElementValue = 1.99505234;
-
-          let expectedOutput: Tensor;
-          if (returnSequences) {
-            const outputAtT0 = tfc.mul(
-                scalar(goldenOutputElementValueAtT0),
-                tfc.ones([1, batchSize, units]));
-            const outputAtT1 = tfc.mul(
-                scalar(goldenOutputElementValueAtT1),
-                tfc.ones([1, batchSize, units]));
-            expectedOutput = tfc.transpose(
-                K.concatAlongFirstAxis(outputAtT0, outputAtT1), [1, 0, 2]);
-          } else {
-            expectedOutput = tfc.mul(
-                scalar(goldenOutputElementValueAtT1),
-                tfc.ones([batchSize, units]));
-          }
-          if (returnState) {
-            output = output as Tensor[];
-            expect(output.length).toEqual(3);
-            expectTensorsClose(output[0], expectedOutput);
-            expectTensorsClose(
-                output[1],
-                tfc.mul(
-                    scalar(goldenHStateElementValue),
-                    tfc.ones([batchSize, units])));
-            expectTensorsClose(
-                output[2],
-                tfc.mul(
-                    scalar(goldenCStateElementValue),
-                    tfc.ones([batchSize, units])));
-          } else {
-            output = output as Tensor;
-            expectTensorsClose(output, expectedOutput);
-          }
-        });
-      }
-    }
-
-    // The reference values below can be obtained with PyKeras code:
-    // ```python
-    // import keras
-    // import numpy as np
-    //
-    // units = 5
-    // batch_size = 4
-    // input_size = 2
-    // sequence_length = 3
-    //
-    // rnn = keras.layers.LSTM(units,
-    //                         kernel_initializer='ones',
-    //                         recurrent_initializer='ones',
-    //                         bias_initializer='ones',
-    //                         stateful=True,
-    //                         batch_input_shape=[batch_size,
-    //                                            sequence_length,
-    //                                            input_size])
-    //
-    // x = np.ones([batch_size, sequence_length, input_size])
-    //
-    // model = keras.Sequential()
-    // model.add(rnn)
-    // print(model.predict(x))
-    // print(model.predict(x))
-    // model.reset_states()
-    // print(model.predict(x))
-    // print(model.predict(x))
-    // ```
-    it('stateful forward', async () => {
-      const sequenceLength = 3;
-      const rnn = tfl.layers.lstm({
-        units,
-        kernelInitializer: 'ones',
-        recurrentInitializer: 'ones',
-        biasInitializer: 'ones',
-        stateful: true,
-        batchInputShape: [batchSize, sequenceLength, inputSize]
-      });
-      const model = tfl.sequential();
-      model.add(rnn);
-      const x = tfc.ones([batchSize, sequenceLength, inputSize]);
-
-      let y1 = model.predict(x) as Tensor;
-      expect(y1.kept).toBe(false);
-      let y1Expected =
-          tfc.tidy(() => tfc.ones([batchSize, units]).mul(tfc.scalar(0.995)));
-      expectTensorsClose(y1, y1Expected);
-      let y2 = model.predict(x) as Tensor;
-      expect(y2.kept).toBe(false);
-      // Future predicts should not dispose previous outputs.
-      expect(y1.isDisposed).toBe(false);
-      let y2Expected = tfc.tidy(
-          () => tfc.ones([batchSize, units]).mul(tfc.scalar(0.99998766)));
-      expectTensorsClose(y2, y2Expected);
-
-      tfc.dispose([y1, y2, y1Expected, y2Expected]);
-      model.resetStates();
-      const numTensors0 = tfc.memory().numTensors;
-
-      y1 = model.predict(x) as Tensor;
-      expect(y1.kept).toBe(false);
-      y1Expected =
-          tfc.tidy(() => tfc.ones([batchSize, units]).mul(tfc.scalar(0.995)));
-      expectTensorsClose(y1, y1Expected);
-      y2 = model.predict(x) as Tensor;
-      expect(y2.kept).toBe(false);
-      // Future predicts should not dispose previous outputs.
-      expect(y1.isDisposed).toBe(false);
-      y2Expected = tfc.tidy(
-          () => tfc.ones([batchSize, units]).mul(tfc.scalar(0.99998766)));
-      expectTensorsClose(y2, y2Expected);
-      tfc.dispose([y1, y2, y1Expected, y2Expected]);
-      // Assert no memory leak, even without resetStates() being called.
-      expect(tfc.memory().numTensors).toEqual(numTensors0);
-    });
-
-    // The reference values can be obtained with the following PyKeras code:
-    // ```python
-    // import keras
-    // import numpy as np
-    //
-    // batch_size = 4
-    // sequence_length = 3
-    // input_size = 2
-    //
-    // model = keras.Sequential()
-    // model.add(keras.layers.LSTM(
-    //     5,
-    //     batch_input_shape=[batch_size, sequence_length, input_size],
-    //     kernel_initializer='ones',
-    //     recurrent_initializer='ones',
-    //     bias_initializer='ones',
-    //     stateful=True))
-    // model.add(keras.layers.Dense(1,
-    //                              kernel_initializer='ones',
-    //                              use_bias=False))
-    // model.compile(loss='mean_squared_error', optimizer='sgd')
-    //
-    // xs_1 = np.ones([batch_size, sequence_length, input_size])
-    // xs_2 = np.ones([batch_size, sequence_length, input_size])
-    // xs = np.concatenate([xs_1, xs_2], 0)
-    // ys = np.array([[1], [2], [3], [4], [5], [6], [7], [8]])
-    //
-    // history = model.fit(xs, ys, batch_size=batch_size, shuffle=False)
-    // print(history.history)
-    // ```
-    it('stateful BPTT', async () => {
-      const sequenceLength = 3;
-      const model = tfl.sequential();
-      model.add(tfl.layers.lstm({
-        units,
-        kernelInitializer: 'ones',
-        recurrentInitializer: 'ones',
-        biasInitializer: 'ones',
-        stateful: true,
-        batchInputShape: [batchSize, sequenceLength, inputSize]
-      }));
-      model.add(tfl.layers.dense(
-          {units: 1, kernelInitializer: 'ones', useBias: false}));
-      model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-      const xs1 = tfc.ones([batchSize, sequenceLength, inputSize]);
-      const xs2 = tfc.ones([batchSize, sequenceLength, inputSize]);
-      const xs = tfc.concat([xs1, xs2], 0);
-      const ys = tfc.tensor2d([[1], [2], [3], [4], [5], [6], [7], [8]]);
-
-      const history = await model.fit(xs, ys, {batchSize, shuffle: false});
-      // See code snippet above for the code used to obtain this loss value.
-      expect(history.history.loss[0]).toBeCloseTo(5.8377);
-    });
-
-    it('BPTT', async () => {
-      // The following golden values for assertion can be obtained with the
-      // following Python Keras code.
-      // ```python
-      // import keras
-      // import numpy as np
-      //
-      // sequence_length = 3
-      // input_size = 4
-      // batch_size = 5
-      //
-      // t_input = keras.Input([sequence_length, input_size])
-      // lstm = keras.layers.LSTM(1,
-      //                          kernel_initializer='zeros',
-      //                          recurrent_initializer='zeros',
-      //                          use_bias=False)
-      // dense = keras.layers.Dense(1,
-      //                            kernel_initializer='ones',
-      //                            use_bias=False)
-      // output = dense(lstm(t_input))
-      // model = keras.Model(t_input, output)
-      // optimizer = keras.optimizers.SGD(1)
-      // model.compile(optimizer=optimizer, loss='mean_squared_error')
-      //
-      // x = np.ones([batch_size, sequence_length, input_size])
-      // y = np.ones([batch_size, 1])
-      // model.fit(x, y, batch_size=batch_size, epochs=2)
-      // print(lstm.get_weights()[0])
-      // print(lstm.get_weights()[1])
-      // print(dense.get_weights()[0])
-      // ```
-      const sequenceLength = 3;
-      const inputSize = 4;
-      const batchSize = 5;
-      const model = tfl.sequential();
-      model.add(tfl.layers.lstm({
-        units: 1,
-        kernelInitializer: 'zeros',
-        recurrentInitializer: 'zeros',
-        useBias: false,
-        inputShape: [sequenceLength, inputSize]
-      }));
-      model.add(tfl.layers.dense({
-        units: 1,
-        kernelInitializer: 'ones',
-        useBias: false,
-      }));
-
-      const sgd = tfc.train.sgd(1);
-      model.compile({loss: 'meanSquaredError', optimizer: sgd});
-
-      const x = tfc.ones([batchSize, sequenceLength, inputSize]);
-      const y = tfc.ones([batchSize, 1]);
-      await model.fit(x, y, {epochs: 2});
-
-      expectTensorsClose(
-          model.layers[0].getWeights()[0],
-          K.tile(
-              tensor2d(
-                  [[0.11455188, 0.06545822, 0.8760446, 0.18237013]], [1, 4]),
-              [4, 1]));
-      expectTensorsClose(
-          model.layers[0].getWeights()[1],
-          tensor2d([[0.02831176, 0.01934617, 0.00025817, 0.05784169]], [1, 4]));
-      expectTensorsClose(
-          model.layers[1].getWeights()[0], tensor2d([[1.4559253]], [1, 1]));
-    });
-  }
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Embedding(10,
-  //                                  4,
-  //                                  input_length=6,
-  //                                  mask_zero=True,
-  //                                  embeddings_initializer='ones'))
-  // model.add(keras.layers.LSTM(3,
-  //                             recurrent_initializer='ones',
-  //                             kernel_initializer='ones',
-  //                             bias_initializer='zeros'))
-  // model.add(keras.layers.Dense(1,
-  //                              kernel_initializer='ones',
-  //                              bias_initializer='zero'))
-  //
-  // xs = np.array([[0, 0, 0, 0, 0, 0],
-  //                [1, 0, 0, 0, 0, 0],
-  //                [1, 2, 0, 0, 0, 0],
-  //                [1, 2, 3, 0, 0, 0]])
-  // ys = model.predict(xs)
-  // print(ys)
-  // ```
-  it('With mask', () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.embedding({
-      inputDim: 10,
-      outputDim: 4,
-      inputLength: 6,
-      maskZero: true,
-      embeddingsInitializer: 'ones'
-    }));
-    model.add(tfl.layers.lstm({
-      units: 3,
-      recurrentInitializer: 'ones',
-      kernelInitializer: 'ones',
-      biasInitializer: 'zeros'
-    }));
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', biasInitializer: 'zeros'}));
-
-    const xs = tensor2d([
-      [0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0],
-      [1, 2, 3, 0, 0, 0]
-    ]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(
-        ys, tensor2d([[0], [2.283937], [2.891939], [2.9851441]]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  //
-  // model = keras.Sequential()
-  // embedding_layer = keras.layers.Embedding(4,
-  //                                          2,
-  //                                          input_length=3,
-  //                                          mask_zero=True)
-  // model.add(embedding_layer)
-  // lstm_layer = keras.layers.LSTM(2, go_backwards=True)
-  // model.add(lstm_layer)
-  // model.add(keras.layers.Dense(1,
-  //                              kernel_initializer='ones',
-  //                              bias_initializer='zero'))
-  //
-  // embedding_layer.set_weights([
-  //     np.array([[0.1, 0.2], [0.3, 0.4], [-0.1, -0.2], [-0.3, -0.4]])])
-  // print(lstm_layer.get_weights())
-  // lstm_layer.set_weights([
-  //     np.array([[1, 2, 3, 4, 5, 6, 7, 8],
-  //               [-1, -2, -3, -4, -5, -6, -7, -8]]),
-  //     np.array([[1, 2, 3, 4, 5, 6, 7, 8],
-  //               [-1, -2, -3, -4, -5, -6, -7, -8]]),
-  //     np.array([1, 2, 3, 4, 5, 6, 7, 8])])
-  //
-  // xs = np.array([[0, 0, 0],
-  //                [1, 0, 0],
-  //                [1, 2, 0],
-  //                [1, 2, 3]])
-  // ys = model.predict(xs)
-  // print(ys)
-  // ```
-  it('With mask, goBackwards = true', () => {
-    const model = tfl.sequential();
-    const embeddingLayer = tfl.layers.embedding(
-        {inputDim: 4, outputDim: 2, inputLength: 3, maskZero: true});
-    model.add(embeddingLayer);
-    const lstmLayer = tfl.layers.lstm({units: 2, goBackwards: true});
-    model.add(lstmLayer);
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', biasInitializer: 'zeros'}));
-
-    // Setting weights to asymmetric, so that the effect of goBackwards=true
-    // can show.
-    embeddingLayer.setWeights(
-        [tensor2d([[0.1, 0.2], [0.3, 0.4], [-0.1, -0.2], [-0.3, -0.4]])]);
-    lstmLayer.setWeights([
-      tensor2d([[1, 2, 3, 4, 5, 6, 7, 8], [-1, -2, -3, -4, -5, -6, -7, -8]]),
-      tensor2d([[1, 2, 3, 4, 5, 6, 7, 8], [-1, -2, -3, -4, -5, -6, -7, -8]]),
-      tensor1d([1, 2, 3, 4, 5, 6, 7, 8])
-    ]);
-
-    const xs = tensor2d([[0, 0, 0], [1, 0, 0], [1, 2, 0], [1, 2, 3]]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(
-        ys, tensor2d([[0], [1.2876499], [1.8165315], [1.9599037]]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Reshape(target_shape=[6], input_shape=[6]))
-  // nested_model = keras.Sequential()
-  // nested_model.add(keras.layers.Embedding(10,
-  //                                         4,
-  //                                         input_length=6,
-  //                                         mask_zero=True,
-  //                                         embeddings_initializer='ones'))
-  // nested_model.add(keras.layers.LSTM(3,
-  //                                    recurrent_initializer='ones',
-  //                                    kernel_initializer='ones',
-  //                                    bias_initializer='zeros'))
-  // model.add(nested_model)
-  // model.add(keras.layers.Dense(1,
-  //                              kernel_initializer='ones',
-  //                              bias_initializer='zero'))
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // xs = np.array([[0, 0, 0, 0, 0, 0],
-  //               [1, 0, 0, 0, 0, 0],
-  //               [1, 2, 0, 0, 0, 0],
-  //               [1, 2, 3, 0, 0, 0]])
-  // ys = model.predict(xs)
-  // print(ys)
-  // ```
-  it('With mask and a nested model', () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.reshape(
-        {targetShape: [6], inputShape: [6]}));  // A dummy input layer.
-    const nestedModel = tfl.sequential();
-    nestedModel.add(tfl.layers.embedding({
-      inputDim: 10,
-      outputDim: 4,
-      inputLength: 6,
-      maskZero: true,
-      embeddingsInitializer: 'ones'
-    }));
-    nestedModel.add(tfl.layers.lstm({
-      units: 3,
-      recurrentInitializer: 'ones',
-      kernelInitializer: 'ones',
-      biasInitializer: 'zeros'
-    }));
-    model.add(nestedModel);
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', biasInitializer: 'zeros'}));
-
-    const xs = tensor2d([
-      [0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0],
-      [1, 2, 3, 0, 0, 0]
-    ]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(
-        ys, tensor2d([[0], [2.283937], [2.891939], [2.9851441]]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Embedding(10,
-  //                                  4,
-  //                                  input_length=6,
-  //                                  mask_zero=True,
-  //                                 embeddings_initializer='ones'))
-  // model.add(keras.layers.LSTM(3,
-  //                             recurrent_initializer='ones',
-  //                             kernel_initializer='ones',
-  //                             bias_initializer='zeros'))
-  // model.add(keras.layers.Dense(1,
-  //                             kernel_initializer='ones',
-  //                             bias_initializer='zero'))
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // xs = np.array([[0, 0, 0, 0, 0, 0],
-  //               [1, 0, 0, 0, 0, 0],
-  //               [1, 2, 0, 0, 0, 0],
-  //               [1, 2, 3, 0, 0, 0]])
-  // ys = np.array([[1], [2], [3], [4]])
-  //
-  // model.fit(xs, ys, epochs=2, batch_size=4)
-  // history = model.fit(xs, ys, epochs=2, batch_size=4)
-  // print(history.history)
-  // ```
-  it('BPTT with mask: correctness and no leak', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.embedding({
-      inputDim: 10,
-      outputDim: 4,
-      inputLength: 6,
-      maskZero: true,
-      embeddingsInitializer: 'ones'
-    }));
-    model.add(tfl.layers.lstm({
-      units: 3,
-      recurrentInitializer: 'ones',
-      kernelInitializer: 'ones',
-      biasInitializer: 'zeros'
-    }));
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', biasInitializer: 'zeros'}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const xs = tensor2d([
-      [0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0],
-      [1, 2, 3, 0, 0, 0]
-    ]);
-    const ys = tensor2d([[1], [2], [3], [4]]);
-
-    // Serves as burn-in call for subsequent tracking of memory leak.
-    await model.fit(xs, ys, {epochs: 2, batchSize: 4});
-
-    const numTensors0 = tfc.memory().numTensors;
-    const history = await model.fit(xs, ys, {epochs: 2, batchSize: 4});
-    const numTensors1 = tfc.memory().numTensors;
-    // Assert no memory leak.
-    expect(numTensors1).toEqual(numTensors0);
-    expect(history.history.loss.length).toEqual(2);
-    expect(history.history.loss[0]).toBeCloseTo(0.503677);
-    expect(history.history.loss[1]).toBeCloseTo(0.492173);
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // inp = keras.Input(shape=[6])
-  // y = keras.layers.Embedding(10,
-  //                            4,
-  //                            input_length=6,
-  //                            mask_zero=True,
-  //                            embeddings_initializer='ones')(inp)
-  // y = keras.layers.LSTM(3,
-  //                       return_state=True,
-  //                       recurrent_initializer='ones',
-  //                       kernel_initializer='ones',
-  //                       bias_initializer='zeros')(y)
-  //
-  // model = keras.Model(inputs=inp, outputs=y)
-  //
-  // xs = np.array([[0, 0, 0, 0, 0, 0],
-  //                [1, 0, 0, 0, 0, 0],
-  //                [1, 2, 0, 0, 0, 0],
-  //                [1, 2, 3, 0, 0, 0]])
-  // ys = model.predict(xs)
-  // print(ys)
-  // ```
-  it('With mask, returnStates = true', () => {
-    const inp = tfl.input({shape: [6]});
-    let y: tfl.SymbolicTensor|tfl.SymbolicTensor[] =
-        tfl.layers
-            .embedding({
-              inputDim: 10,
-              outputDim: 4,
-              inputLength: 6,
-              maskZero: true,
-              embeddingsInitializer: 'ones'
-            })
-            .apply(inp) as tfl.SymbolicTensor;
-    y = tfl.layers
-            .lstm({
-              units: 3,
-              returnState: true,
-              recurrentInitializer: 'ones',
-              kernelInitializer: 'ones',
-              biasInitializer: 'zeros'
-            })
-            .apply(y) as tfl.SymbolicTensor[];
-    const model = tfl.model({inputs: inp, outputs: y});
-    const xs = tensor2d([
-      [0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0],
-      [1, 2, 3, 0, 0, 0]
-    ]);
-    const ys = model.predict(xs) as Tensor[];
-    expect(ys.length).toEqual(3);
-    expectTensorsClose(ys[0], tensor2d([
-                         [0, 0, 0], [0.76131237, 0.76131237, 0.76131237],
-                         [0.9639796, 0.9639796, 0.9639796],
-                         [0.99504817, 0.99504817, 0.99504817]
-                       ]));
-    expectTensorsClose(ys[1], tensor2d([
-                         [0, 0, 0], [0.76131237, 0.76131237, 0.76131237],
-                         [0.9639796, 0.9639796, 0.9639796],
-                         [0.99504817, 0.99504817, 0.99504817]
-                       ]));
-    expectTensorsClose(
-        ys[2], tensor2d([
-          [0, 0, 0], [0.9993292, 0.9993292, 0.9993292],
-          [1.9993222, 1.9993222, 1.9993222], [2.9993203, 2.9993203,　2.9993203]
-        ]));
-  });
-
-  // Referernce Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Embedding(10,
-  //                                 4,
-  //                                 input_length=3,
-  //                                 mask_zero=True,
-  //                                 embeddings_initializer='ones'))
-  // model.add(keras.layers.LSTM(3,
-  //                             return_sequences=True,
-  //                             recurrent_initializer='ones',
-  //                             kernel_initializer='ones',
-  //                             bias_initializer='zeros'))
-  // model.add(keras.layers.Dense(1,
-  //                             kernel_initializer='ones',
-  //                             bias_initializer='zeros'))
-  //
-  // xs = np.array([[0, 0, 0],
-  //               [1, 0, 0],
-  //               [1, 2, 0],
-  //               [1, 2, 3]])
-  // ys = model.predict(xs)
-  // print(ys)
-  // ```
-  it('With mask, returnSequences = true', () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.embedding({
-      inputDim: 10,
-      outputDim: 4,
-      inputLength: 3,
-      maskZero: true,
-      embeddingsInitializer: 'ones'
-    }));
-    model.add(tfl.layers.lstm({
-      units: 3,
-      returnSequences: true,
-      recurrentInitializer: 'ones',
-      kernelInitializer: 'ones',
-      biasInitializer: 'zeros'
-    }));
-    model.add(tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', biasInitializer: 'zeros'}));
-
-    const xs = tensor2d([[0, 0, 0], [1, 0, 0], [1, 2, 0], [1, 2, 3]]);
-    const ys = model.predict(xs) as Tensor;
-    expectTensorsClose(ys, tensor3d([
-                         [[0], [0], [0]], [[2.283937], [2.283937], [2.283937]],
-                         [[2.283937], [2.8919387], [2.8919387]],
-                         [[2.283937], [2.8919387], [2.9851446]]
-                       ]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model = keras.Sequential()
-  // model.add(keras.layers.Embedding(10,
-  //                                  4,
-  //                                  input_length=3,
-  //                                  mask_zero=True,
-  //                                  embeddings_initializer='ones'))
-  // model.add(keras.layers.SimpleRNN(3,
-  //                                  return_sequences=True,
-  //                                  recurrent_initializer='ones',
-  //                                  kernel_initializer='ones',
-  //                                  bias_initializer='zeros'))
-  // model.add(keras.layers.LSTM(3,
-  //                             recurrent_initializer='ones',
-  //                             kernel_initializer='ones',
-  //                             bias_initializer='zeros'))
-  //
-  // xs = np.array([[0, 0, 0],
-  //                [1, 0, 0],
-  //                [1, 2, 0],
-  //                [1, 2, 3]])
-  // ys = model.predict(xs)
-  // print(ys)
-  // ```
-  it('Stacked RNNs with masking: correctness and no leak', () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.embedding({
-      inputDim: 10,
-      outputDim: 4,
-      inputLength: 3,
-      maskZero: true,
-      embeddingsInitializer: 'ones'
-    }));
-    model.add(tfl.layers.simpleRNN({
-      units: 3,
-      returnSequences: true,
-      recurrentInitializer: 'ones',
-      kernelInitializer: 'ones',
-      biasInitializer: 'zeros'
-    }));
-    model.add(tfl.layers.lstm({
-      units: 3,
-      recurrentInitializer: 'ones',
-      kernelInitializer: 'ones',
-      biasInitializer: 'zeros'
-    }));
-
-    const xs = tensor2d([[0, 0, 0], [1, 0, 0], [1, 2, 0], [1, 2, 3]]);
-
-    // Burn-in call for subsequent memory leak check.
-    model.predict(xs);
-
-    const numTensors0 = tfc.memory().numTensors;
-    const ys = model.predict(xs) as Tensor;
-    const numTensors1 = tfc.memory().numTensors;
-    expectTensorsClose(ys, tensor2d([
-                         [0, 0, 0], [0.75950104, 0.75950104, 0.75950104],
-                         [0.96367145, 0.96367145, 0.96367145],
-                         [0.9950049, 0.9950049, 0.9950049]
-                       ]));
-    ys.dispose();
-    // Assert no memory leak.
-    expect(numTensors1).toEqual(numTensors0 + 1);
-  });
-});
-
-describeMathCPU('LSTM-deserialization', () => {
-  it('modelFromConfig', async () => {
-    const model = await modelFromJSON(fakeLSTMModel);
-    const encoderInputs = tfc.zeros([1, 3, 71], 'float32');
-    const decoderInputs = tfc.zeros([1, 3, 94], 'float32');
-    const outputs = model.predict([encoderInputs, decoderInputs]) as Tensor;
-    expect(outputs.shape).toEqual([1, 3, 94]);
-  });
-
-  it('Default recurrentActivation round trip', () => {
-    const x = randomNormal([1, 2, 3]);
-    const layer = tfl.layers.lstm({units: 4});
-    const y = layer.apply(x) as Tensor;
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.lstm(tsConfig);
-    const yPrime = layer.apply(x) as Tensor;
-    expectTensorsClose(yPrime, y);
-    expect(layerPrime.getConfig()['recurrentActivation'])
-        .toEqual(layer.getConfig()['recurrentActivation']);
-  });
-
-  it('Non-default recurrentActivation round trip', () => {
-    const x = randomNormal([1, 2, 3]);
-    const layer = tfl.layers.lstm({units: 4, recurrentActivation: 'tanh'});
-    const y = layer.apply(x) as Tensor;
-    const pythonicConfig = convertTsToPythonic(layer.getConfig());
-    // tslint:disable-next-line:no-any
-    const tsConfig = convertPythonicToTs(pythonicConfig) as any;
-    const layerPrime = tfl.layers.lstm(tsConfig);
-    const yPrime = layer.apply(x) as Tensor;
-    expectTensorsClose(yPrime, y);
-    expect(layerPrime.getConfig()['recurrentActivation'])
-        .toEqual(layer.getConfig()['recurrentActivation']);
-  });
-});
-
-const fakeLSTMModel: ModelAndWeightsConfig = {
-  modelTopology: {
-    'class_name': 'Model',
-    'keras_version': '2.1.2',
-    'config': {
-      'layers': [
-        {
-          'class_name': 'InputLayer',
-          'config': {
-            'dtype': 'float32',
-            'batch_input_shape': [null, null, 71],
-            'name': 'input_1',
-            'sparse': false
-          },
-          'inbound_nodes': [],
-          'name': 'input_1'
-        },
-        {
-          'class_name': 'InputLayer',
-          'config': {
-            'dtype': 'float32',
-            'batch_input_shape': [null, null, 94],
-            'name': 'input_2',
-            'sparse': false
-          },
-          'inbound_nodes': [],
-          'name': 'input_2'
-        },
-        {
-          'class_name': 'LSTM',
-          'config': {
-            'recurrent_activation': 'hard_sigmoid',
-            'trainable': true,
-            'recurrent_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'seed': null,
-                'mode': 'fan_avg'
-              }
-            },
-            'use_bias': true,
-            'bias_regularizer': null,
-            'return_state': true,
-            'unroll': false,
-            'activation': 'tanh',
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'units': 256,
-            'unit_forget_bias': true,
-            'activity_regularizer': null,
-            'recurrent_dropout': 0.0,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'seed': null,
-                'mode': 'fan_avg'
-              }
-            },
-            'kernel_constraint': null,
-            'dropout': 0.0,
-            'stateful': false,
-            'recurrent_regularizer': null,
-            'name': 'lstm_1',
-            'bias_constraint': null,
-            'go_backwards': false,
-            'implementation': 1,
-            'kernel_regularizer': null,
-            'return_sequences': false,
-            'recurrent_constraint': null
-          },
-          'inbound_nodes': [[['input_1', 0, 0, {}]]],
-          'name': 'lstm_1'
-        },
-        {
-          'class_name': 'LSTM',
-          'config': {
-            'recurrent_activation': 'hard_sigmoid',
-            'trainable': true,
-            'recurrent_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'seed': null,
-                'mode': 'fan_avg'
-              }
-            },
-            'use_bias': true,
-            'bias_regularizer': null,
-            'return_state': true,
-            'unroll': false,
-            'activation': 'tanh',
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'units': 256,
-            'unit_forget_bias': true,
-            'activity_regularizer': null,
-            'recurrent_dropout': 0.0,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'seed': null,
-                'mode': 'fan_avg'
-              }
-            },
-            'kernel_constraint': null,
-            'dropout': 0.0,
-            'stateful': false,
-            'recurrent_regularizer': null,
-            'name': 'lstm_2',
-            'bias_constraint': null,
-            'go_backwards': false,
-            'implementation': 1,
-            'kernel_regularizer': null,
-            'return_sequences': true,
-            'recurrent_constraint': null
-          },
-          'inbound_nodes': [
-            [['input_2', 0, 0, {}], ['lstm_1', 0, 1, {}], ['lstm_1', 0, 2, {}]]
-          ],
-          'name': 'lstm_2'
-        },
-        {
-          'class_name': 'Dense',
-          'config': {
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'seed': null,
-                'mode': 'fan_avg'
-              }
-            },
-            'name': 'dense_1',
-            'kernel_constraint': null,
-            'bias_regularizer': null,
-            'bias_constraint': null,
-            'activation': 'softmax',
-            'trainable': true,
-            'kernel_regularizer': null,
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'units': 94,
-            'use_bias': true,
-            'activity_regularizer': null
-          },
-          'inbound_nodes': [[['lstm_2', 0, 0, {}]]],
-          'name': 'dense_1'
-        }
-      ],
-      'input_layers': [['input_1', 0, 0], ['input_2', 0, 0]],
-      'output_layers': [['dense_1', 0, 0]],
-      'name': 'model_1'
-    },
-    'backend': 'tensorflow'
-  }
-};
-
-describeMathCPU('LSTM Serialization', () => {
-  const cellConfig: LSTMCellLayerArgs = {
-    units: 8,
-    activation: 'tanh',
-    recurrentActivation: 'tanh',
-    useBias: true,
-    kernelInitializer: 'glorotUniform',
-    recurrentInitializer: 'heUniform',
-    biasInitializer: 'ones',
-    kernelRegularizer: 'l1l2',
-    recurrentRegularizer: 'l1l2',
-    biasRegularizer: 'l1l2',
-    kernelConstraint: 'unitNorm',
-    recurrentConstraint: 'unitNorm',
-    biasConstraint: 'nonNeg',
-    dropout: 0.1,
-    recurrentDropout: 0.2,
-    name: 'cell_1',
-    batchSize: 12,
-    batchInputShape: [12, 8, 8],
-    inputShape: [8, 8],
-    dtype: 'int32',
-    inputDType: 'int32',
-    trainable: true,
-    implementation: 1,
-    unitForgetBias: true,
-  };
-
-  const expectedCellConfigPrime = {
-    name: 'cell_1',
-    trainable: true,
-    batchInputShape: [12, 8, 8],
-    dtype: 'int32',
-    units: 8,
-    activation: serializeActivation(new Tanh()),
-    recurrentActivation: serializeActivation(new Tanh()),
-    useBias: true,
-    kernelInitializer: serializeInitializer(new GlorotUniform()),
-    recurrentInitializer: serializeInitializer(new HeUniform()),
-    biasInitializer: serializeInitializer(new Ones()),
-    kernelRegularizer: serializeRegularizer(new L1L2()),
-    recurrentRegularizer: serializeRegularizer(new L1L2()),
-    biasRegularizer: serializeRegularizer(new L1L2()),
-    activityRegularizer: serializeRegularizer(null),
-    kernelConstraint: serializeConstraint(new UnitNorm({})),
-    recurrentConstraint: serializeConstraint(new UnitNorm({})),
-    biasConstraint: serializeConstraint(new NonNeg()),
-    implementation: 1,
-    unitForgetBias: true,
-  };
-
-  describe('LSTMCell.getConfig', () => {
-    it('should return the expected values', () => {
-      const cell = tfl.layers.lstmCell(cellConfig);
-
-      const {dropout, recurrentDropout, ...configPrime} = cell.getConfig();
-
-      expect(configPrime).toEqual(expectedCellConfigPrime);
-      expect(dropout).toBeCloseTo(0.1);
-      expect(recurrentDropout).toBeCloseTo(0.2);
-    });
-  });
-
-  describe('LSTM.getConfig', () => {
-    it('should return the expected values', () => {
-      const config: LSTMLayerArgs = {
-        ...cellConfig,
-        name: 'layer_1',
-        ...{
-          returnSequences: true,
-          returnState: true,
-          stateful: true,
-          unroll: true,
-          goBackwards: true,
-          inputDim: 8,
-          inputLength: 8,
-        } as Omit<LSTMLayerArgs, keyof LSTMCellLayerArgs>
-      };
-
-      const cell = tfl.layers.lstm(config);
-
-      const {dropout, recurrentDropout, ...configPrime} = cell.getConfig();
-
-      expect(configPrime).toEqual({
-        ...expectedCellConfigPrime,
-        name: 'layer_1',
-        returnSequences: true,
-        returnState: true,
-        stateful: true,
-        unroll: true,
-        goBackwards: true,
-      });
-      expect(dropout).toBeCloseTo(0.1);
-      expect(recurrentDropout).toBeCloseTo(0.2);
-    });
-  });
-});
-
-describeMathCPU('StackedRNNCells Symbolic', () => {
-  it('With SimpleRNNCell', () => {
-    const stackedRNN = tfl.layers.rnn({
-      cell: tfl.layers.stackedRNNCells({
-        cells: [
-          tfl.layers.simpleRNNCell(
-              {units: 3, recurrentInitializer: 'glorotNormal'}),
-          tfl.layers.simpleRNNCell(
-              {units: 2, recurrentInitializer: 'glorotNormal'})
-        ],
-      })
-    });
-    const input =
-        new tfl.SymbolicTensor('float32', [16, 10, 7], null, [], null);
-    const output = stackedRNN.apply(input) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([16, 2]);
-
-    // 3 trainable weights from each cell.
-    expect(stackedRNN.trainableWeights.length).toEqual(6);
-    expect(stackedRNN.nonTrainableWeights.length).toEqual(0);
-    // Kernel, recurrent kernel and bias of 1st cell.
-    expect(stackedRNN.getWeights()[0].shape).toEqual([7, 3]);
-    expect(stackedRNN.getWeights()[1].shape).toEqual([3, 3]);
-    expect(stackedRNN.getWeights()[2].shape).toEqual([3]);
-    // Kernel, recurrent kernel and bias of 2nd cell.
-    expect(stackedRNN.getWeights()[3].shape).toEqual([3, 2]);
-    expect(stackedRNN.getWeights()[4].shape).toEqual([2, 2]);
-    expect(stackedRNN.getWeights()[5].shape).toEqual([2]);
-  });
-
-  it('With LSTMCell', () => {
-    const stackedRNN = tfl.layers.rnn({
-      cell: tfl.layers.stackedRNNCells({
-        cells: [
-          tfl.layers.lstmCell({units: 3, recurrentInitializer: 'glorotNormal'}),
-          tfl.layers.lstmCell({units: 2, recurrentInitializer: 'glorotNormal'})
-        ],
-      })
-    });
-    const input =
-        new tfl.SymbolicTensor('float32', [16, 10, 7], null, [], null);
-    const output = stackedRNN.apply(input) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([16, 2]);
-
-    // 3 trainable weights from each cell.
-    expect(stackedRNN.trainableWeights.length).toEqual(6);
-    expect(stackedRNN.nonTrainableWeights.length).toEqual(0);
-    // Kernel, recurrent kernel and bias of 1st cell.
-    expect(stackedRNN.getWeights()[0].shape).toEqual([7, 12]);
-    expect(stackedRNN.getWeights()[1].shape).toEqual([3, 12]);
-    expect(stackedRNN.getWeights()[2].shape).toEqual([12]);
-    // expect(stackedRNN.getWeights()[2].shape).toEqual([3]);
-    // Kernel, recurrent kernel and bias of 2nd cell.
-    expect(stackedRNN.getWeights()[3].shape).toEqual([3, 8]);
-    expect(stackedRNN.getWeights()[4].shape).toEqual([2, 8]);
-    expect(stackedRNN.getWeights()[5].shape).toEqual([8]);
-  });
-
-  it('RNN with cell array creates StackedRNNCell', () => {
-    const stackedRNN = tfl.layers.rnn({
-      cell: [
-        tfl.layers.gruCell({units: 3, recurrentInitializer: 'glorotNormal'}),
-        tfl.layers.gruCell({units: 2, recurrentInitializer: 'glorotNormal'}),
-      ],
-    });
-    const input =
-        new tfl.SymbolicTensor('float32', [16, 10, 7], null, [], null);
-    const output = stackedRNN.apply(input) as tfl.SymbolicTensor;
-    expect(output.shape).toEqual([16, 2]);
-
-    // 3 trainable weights from each cell.
-    expect(stackedRNN.trainableWeights.length).toEqual(6);
-    expect(stackedRNN.nonTrainableWeights.length).toEqual(0);
-    // Kernel, recurrent kernel and bias of 1st cell.
-    expect(stackedRNN.getWeights()[0].shape).toEqual([7, 9]);
-    expect(stackedRNN.getWeights()[1].shape).toEqual([3, 9]);
-    expect(stackedRNN.getWeights()[2].shape).toEqual([9]);
-    // expect(stackedRNN.getWeights()[2].shape).toEqual([3]);
-    // Kernel, recurrent kernel and bias of 2nd cell.
-    expect(stackedRNN.getWeights()[3].shape).toEqual([3, 6]);
-    expect(stackedRNN.getWeights()[4].shape).toEqual([2, 6]);
-    expect(stackedRNN.getWeights()[5].shape).toEqual([6]);
-  });
-});
-
-describeMathCPU('Stacked RNN serialization', () => {
-  it('StackedRNNCells', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense(
-        {units: 1, inputShape: [3, 4], kernelInitializer: 'ones'}));
-    const cells = [
-      tfl.layers.lstmCell(
-          {units: 5, kernelInitializer: 'ones', recurrentInitializer: 'ones'}),
-      tfl.layers.lstmCell(
-          {units: 6, kernelInitializer: 'ones', recurrentInitializer: 'ones'})
-    ];
-    const rnn = tfl.layers.rnn({cell: cells, returnSequences: true});
-    model.add(rnn);
-    const xs = tfc.ones([1, 3, 4]).mul(0.1);
-    const ys = model.predict(xs) as Tensor;
-
-    const modelJSON = model.toJSON(null, false);
-    const modelPrime =
-        await tfl.models.modelFromJSON({modelTopology: modelJSON});
-    const ysPrime = modelPrime.predict(xs) as Tensor;
-    expectTensorsClose(ysPrime, ys);
-  });
-});
-
-describeMathWebGL2('StackedRNNCells Tensor', () => {
-  // The golden values for assertion below can be obtained with the following
-  // Python Keras code:
-  //
-  // ```python
-  // import keras
-  // import numpy as np
-  //
-  // stacked_rnn = keras.layers.RNN(
-  //     [
-  //       keras.layers.SimpleRNNCell(
-  //           3,
-  //           kernel_initializer='ones',
-  //           recurrent_initializer='ones',
-  //           use_bias=False),
-  //       keras.layers.GRUCell(
-  //           2,
-  //           kernel_initializer='ones',
-  //           recurrent_initializer='ones',
-  //           use_bias=False),
-  //       keras.layers.LSTMCell(
-  //           1,
-  //           kernel_initializer='ones',
-  //           recurrent_initializer='ones',
-  //           use_bias=False),
-  //     ])
-  //
-  // t_input = keras.layers.Input(batch_shape=(2, 3, 4))
-  // t_output = stacked_rnn(t_input)
-  // print(t_input.shape)
-  // print(t_output.shape)
-  //
-  // model = keras.Model(t_input, t_output)
-  //
-  // input_val = np.array([
-  //     [
-  //         [0.1, -0.1, 0.2, -0.2], [-0.1, 0.1, -0.2, 0.2],
-  //         [0.1, 0.1, -0.2, -0.2]
-  //     ],
-  //     [
-  //         [0.05, -0.05, 0.1, -0.1], [-0.05, 0.05, -0.1, 0.1],
-  //         [0.05, 0.05, -0.1, -0.1]
-  //     ]
-  // ])
-  // print(model.predict(input_val))
-  // ```
-  it('Forward pass', () => {
-    const stackedRNN = tfl.layers.rnn({
-      cell: tfl.layers.stackedRNNCells({
-        cells: [
-          tfl.layers.simpleRNNCell({
-            units: 3,
-            recurrentInitializer: 'ones',
-            kernelInitializer: 'ones',
-            useBias: false
-          }),
-          tfl.layers.gruCell({
-            units: 2,
-            recurrentInitializer: 'ones',
-            kernelInitializer: 'ones',
-            useBias: false
-          }),
-          tfl.layers.lstmCell({
-            units: 1,
-            recurrentInitializer: 'ones',
-            kernelInitializer: 'ones',
-            useBias: false
-          }),
-        ],
-      })
-    });
-    const input = tensor3d(
-        [
-          [
-            [0.1, -0.1, 0.2, -0.2], [-0.1, 0.1, -0.2, 0.2],
-            [0.1, 0.1, -0.2, -0.2]
-          ],
-          [
-            [0.05, -0.05, 0.1, -0.1], [-0.05, 0.05, -0.1, 0.1],
-            [0.05, 0.05, -0.1, -0.1]
-          ]
-        ],
-        [2, 3, 4]);
-    const output = stackedRNN.apply(input) as Tensor;
-    expectTensorsClose(
-        output, tensor2d([[-0.07715216], [-0.05906887]], [2, 1]));
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/serialization.ts b/tfjs-master/tfjs-layers/src/layers/serialization.ts
deleted file mode 100644
index 2f71caca9..000000000
--- a/tfjs-master/tfjs-layers/src/layers/serialization.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original Source layers/__init__.py */
-import {serialization} from '@tensorflow/tfjs-core';
-
-import {deserializeKerasObject} from '../utils/generic_utils';
-
-/**
- * Instantiate a layer from a config dictionary.
- * @param config dict of the form {class_name: str, config: dict}
- * @param customObjects dict mapping class names (or function names)
- *   of custom (non-Keras) objects to class/functions
- * @param fastWeightInit Optional flag to use fast weight initialization
- *   during deserialization. This is applicable to cases in which
- *   the initialization will be immediately overwritten by loaded weight
- *   values. Default: `false`.
- * @returns Layer instance (may be LayersModel, Sequential, Layer...)
- */
-export function deserialize(
-    config: serialization.ConfigDict,
-    customObjects = {} as serialization.ConfigDict,
-    fastWeightInit = false): serialization.Serializable {
-  return deserializeKerasObject(
-      config, serialization.SerializationMap.getMap().classNameMap,
-      customObjects, 'layer', fastWeightInit);
-}
diff --git a/tfjs-master/tfjs-layers/src/layers/serialization_test.ts b/tfjs-master/tfjs-layers/src/layers/serialization_test.ts
deleted file mode 100644
index 995c2df40..000000000
--- a/tfjs-master/tfjs-layers/src/layers/serialization_test.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {serialization} from '@tensorflow/tfjs-core';
-
-import {Initializer, Ones, Zeros} from '../initializers';
-import {deserialize} from './serialization';
-
-describe('Deserialization', () => {
-  it('Zeros Initialzer', () => {
-    const config: serialization.ConfigDict = {};
-    config['className'] = 'Zeros';
-    config.config = {};
-    const initializer: Zeros = deserialize(config) as Initializer;
-    expect(initializer instanceof (Zeros)).toEqual(true);
-  });
-  it('Ones Initialzer', () => {
-    const config: serialization.ConfigDict = {};
-    config['className'] = 'Ones';
-    config.config = {};
-    const initializer: Ones = deserialize(config) as Initializer;
-    expect(initializer instanceof (Ones)).toEqual(true);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/layers/wrappers.ts b/tfjs-master/tfjs-layers/src/layers/wrappers.ts
deleted file mode 100644
index 1daccd5d3..000000000
--- a/tfjs-master/tfjs-layers/src/layers/wrappers.ts
+++ /dev/null
@@ -1,590 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Layers that augment the functionality of a base layer.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {serialization, Tensor, tidy} from '@tensorflow/tfjs-core';
-import * as K from '../backend/tfjs_backend';
-import {nameScope} from '../common';
-import {InputSpec, Layer, LayerArgs, SymbolicTensor} from '../engine/topology';
-import {NotImplementedError, ValueError} from '../errors';
-import {BidirectionalMergeMode, Shape, VALID_BIDIRECTIONAL_MERGE_MODES} from '../keras_format/common';
-import {Kwargs} from '../types';
-import {RegularizerFn, RnnStepFunction} from '../types';
-import * as generic_utils from '../utils/generic_utils';
-import {getExactlyOneShape, getExactlyOneTensor} from '../utils/types_utils';
-import {LayerVariable} from '../variables';
-
-import {rnn, RNN, standardizeArgs} from './recurrent';
-import {deserialize} from './serialization';
-
-export declare interface WrapperLayerArgs extends LayerArgs {
-  /**
-   * The layer to be wrapped.
-   */
-  layer: Layer;
-}
-
-/**
- * Abstract wrapper base class.
- *
- * Wrappers take another layer and augment it in various ways.
- * Do not use this class as a layer, it is only an abstract base class.
- * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.
- */
-export abstract class Wrapper extends Layer {
-  readonly layer: Layer;
-
-  constructor(args: WrapperLayerArgs) {
-    // Porting Note: In PyKeras, `self.layer` is set prior to the calling
-    //   `super()`. But we can't do that here due to TypeScript's restriction.
-    //   See: https://github.com/Microsoft/TypeScript/issues/8277
-    //   As a result, we have to add checks in `get trainable()` and
-    //   `set trainable()` below in order to prevent using `this.layer` when
-    //   its value is `undefined`. The super constructor does use the getter
-    //   and the setter of `this.layer`.
-    super(args);
-    this.layer = args.layer;
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    this.built = true;
-  }
-
-  // TODO(cais): Implement activityRegularizer getter.
-
-  override get trainable(): boolean {
-    // Porting Note: the check of `this.layer` here is necessary due to the
-    //   way the `constructor` of this class is written (see Porting Note
-    //   above).
-    if (this.layer != null) {
-      return this.layer.trainable;
-    } else {
-      return false;
-    }
-  }
-
-  override set trainable(value: boolean) {
-    // Porting Note: the check of `this.layer` here is necessary due to the
-    //   way the `constructor` of this class is written (see Porting Note
-    //   above).
-    if (this.layer != null) {
-      this.layer.trainable = value;
-    }
-  }
-
-  override get trainableWeights(): LayerVariable[] {
-    return this.layer.trainableWeights;
-  }
-  // TODO(cais): Implement setter for trainableWeights.
-
-  override get nonTrainableWeights(): LayerVariable[] {
-    return this.layer.nonTrainableWeights;
-  }
-  // TODO(cais): Implement setter for nonTrainableWeights.
-
-  override get updates(): Tensor[] {
-    // tslint:disable-next-line:no-any
-    return (this.layer as any)._updates;
-  }
-
-  // TODO(cais): Implement getUpdatesFor().
-
-  override get losses(): RegularizerFn[] {
-    return this.layer.losses;
-  }
-
-  // TODO(cais): Implement getLossesFor().
-
-  override getWeights(): Tensor[] {
-    return this.layer.getWeights();
-  }
-
-  override setWeights(weights: Tensor[]): void {
-    this.layer.setWeights(weights);
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'layer': {
-        'className': this.layer.getClassName(),
-        'config': this.layer.getConfig(),
-      }
-    };
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  override setFastWeightInitDuringBuild(value: boolean) {
-    super.setFastWeightInitDuringBuild(value);
-    if (this.layer != null) {
-      this.layer.setFastWeightInitDuringBuild(value);
-    }
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict,
-      customObjects = {} as serialization.ConfigDict): T {
-    const layerConfig = config['layer'] as serialization.ConfigDict;
-    const layer = deserialize(layerConfig, customObjects) as Layer;
-    delete config['layer'];
-    const newConfig = {layer};
-    Object.assign(newConfig, config);
-    return new cls(newConfig);
-  }
-}
-
-export class TimeDistributed extends Wrapper {
-  /** @nocollapse */
-  static className = 'TimeDistributed';
-  constructor(args: WrapperLayerArgs) {
-    super(args);
-    this.supportsMasking = true;
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    inputShape = getExactlyOneShape(inputShape);
-    if (inputShape.length < 3) {
-      throw new ValueError(
-          `TimeDistributed layer expects an input shape >= 3D, but received ` +
-          `input shape ${JSON.stringify(inputShape)}`);
-    }
-    this.inputSpec = [{shape: inputShape}];
-    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
-    if (!this.layer.built) {
-      this.layer.build(childInputShape);
-      this.layer.built = true;
-    }
-    super.build(inputShape);
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    inputShape = getExactlyOneShape(inputShape);
-    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
-    const childOutputShape =
-        this.layer.computeOutputShape(childInputShape) as Shape;
-    const timesteps = inputShape[1];
-    return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.
-      inputs = getExactlyOneTensor(inputs);
-      // Porting Note: In tfjs-layers, `inputs` are always concrete tensor
-      // values. Hence the inputs can't have an undetermined first (batch)
-      // dimension, which is why we always use the K.rnn approach here.
-      const step: RnnStepFunction = (inputs: Tensor, states: Tensor[]) => {
-        // TODO(cais): Add useLearningPhase.
-        // NOTE(cais): `layer.call` may return a length-1 array of Tensor in
-        //   some cases (e.g., `layer` is a `Sequential` instance), which is
-        //   why `getExactlyOneTensor` is used below.
-        const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));
-        return [output, []];
-      };
-      const rnnOutputs =
-          rnn(step, inputs, [], false /* goBackwards */, null /* mask */,
-              null /* constants */, false /* unroll */,
-              true /* needPerStepOutputs */);
-      const y = rnnOutputs[1];
-      // TODO(cais): Add activity regularization.
-      // TODO(cais): Add useLearningPhase.
-      return y;
-    });
-  }
-
-  // TODO(cais): Implement detailed computeMask() logic.
-}
-serialization.registerClass(TimeDistributed);
-
-export function checkBidirectionalMergeMode(value?: string): void {
-  generic_utils.checkStringTypeUnionValue(
-      VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);
-}
-
-export declare interface BidirectionalLayerArgs extends WrapperLayerArgs {
-  /**
-   * The instance of an `RNN` layer to be wrapped.
-   */
-  layer: RNN;
-
-  /**
-   * Mode by which outputs of the forward and backward RNNs are
-   * combined. If `null` or `undefined`, the output will not be
-   * combined, they will be returned as an `Array`.
-   *
-   * If `undefined` (i.e., not provided), defaults to `'concat'`.
-   */
-  mergeMode?: BidirectionalMergeMode;
-}
-
-const DEFAULT_BIDIRECTIONAL_MERGE_MODE: BidirectionalMergeMode = 'concat';
-
-export class Bidirectional extends Wrapper {
-  /** @nocollapse */
-  static className = 'Bidirectional';
-  mergeMode: BidirectionalMergeMode;
-  private forwardLayer: RNN;
-  private backwardLayer: RNN;
-  private returnSequences: boolean;
-  private returnState: boolean;
-  private numConstants?: number;
-  private _trainable: boolean;
-
-  constructor(args: BidirectionalLayerArgs) {
-    super(args);
-
-    // Note: When creating `this.forwardLayer`, the original Layer object
-    //   (`config.layer`) ought to be cloned. This is why we call
-    //   `getConfig()` followed by `deserialize()`. Without this cloning,
-    //   the layer names saved during serialization will incorrectly contain
-    //   the 'forward_' prefix. In Python Keras, this is done using
-    //   `copy.copy` (shallow copy), which does not have a simple equivalent
-    //   in JavaScript. JavaScript's `Object.assign()` does not copy
-    //   methods.
-    const layerConfig = args.layer.getConfig();
-    const forwDict: serialization.ConfigDict = {};
-    forwDict['className'] = args.layer.getClassName();
-    forwDict['config'] = layerConfig;
-    this.forwardLayer = deserialize(forwDict) as RNN;
-    layerConfig['goBackwards'] =
-        layerConfig['goBackwards'] === true ? false : true;
-    const backDict: serialization.ConfigDict = {};
-    backDict['className'] = args.layer.getClassName();
-    backDict['config'] = layerConfig;
-    this.backwardLayer = deserialize(backDict) as RNN;
-    this.forwardLayer.name = 'forward_' + this.forwardLayer.name;
-    this.backwardLayer.name = 'backward_' + this.backwardLayer.name;
-
-    this.mergeMode = args.mergeMode === undefined ?
-        DEFAULT_BIDIRECTIONAL_MERGE_MODE :
-        args.mergeMode;
-    checkBidirectionalMergeMode(this.mergeMode);
-    if (args.weights) {
-      throw new NotImplementedError(
-          'weights support is not implemented for Bidirectional layer yet.');
-    }
-    this._stateful = args.layer.stateful;
-    this.returnSequences = args.layer.returnSequences;
-    this.returnState = args.layer.returnState;
-    this.supportsMasking = true;
-    this._trainable = true;
-    this.inputSpec = args.layer.inputSpec;
-    this.numConstants = null;
-  }
-
-  override get trainable(): boolean {
-    return this._trainable;
-  }
-
-  override set trainable(value: boolean) {
-    // Porting Note: the check of `this.layer` here is necessary due to the
-    //   way the `constructor` of this class is written (see Porting Note
-    //   above).
-    this._trainable = value;
-    if (this.forwardLayer != null) {
-      this.forwardLayer.trainable = value;
-    }
-    if (this.backwardLayer != null) {
-      this.backwardLayer.trainable = value;
-    }
-  }
-
-  override getWeights(): Tensor[] {
-    return this.forwardLayer.getWeights().concat(
-        this.backwardLayer.getWeights());
-  }
-
-  override setWeights(weights: Tensor[]): void {
-    const numWeights = weights.length;
-    const numeightsOver2 = Math.floor(numWeights / 2);
-    this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));
-    this.backwardLayer.setWeights(weights.slice(numeightsOver2));
-  }
-
-  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
-    let layerShapes: Shape|Shape[] =
-        this.forwardLayer.computeOutputShape(inputShape);
-    if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {
-      layerShapes = [layerShapes as Shape];
-    }
-    layerShapes = layerShapes as Shape[];
-
-    let outputShape: Shape;
-    let outputShapes: Shape[];
-    let stateShape: Shape[];
-    if (this.returnState) {
-      stateShape = layerShapes.slice(1);
-      outputShape = layerShapes[0];
-    } else {
-      outputShape = layerShapes[0];
-    }
-    outputShape = outputShape;
-    if (this.mergeMode === 'concat') {
-      outputShape[outputShape.length - 1] *= 2;
-      outputShapes = [outputShape];
-    } else if (this.mergeMode == null) {
-      outputShapes = [outputShape, outputShape.slice()];
-    } else {
-      outputShapes = [outputShape];
-    }
-
-    if (this.returnState) {
-      if (this.mergeMode == null) {
-        return outputShapes.concat(stateShape).concat(stateShape.slice());
-      }
-      return [outputShape].concat(stateShape).concat(stateShape.slice());
-    }
-    return generic_utils.singletonOrArray(outputShapes);
-  }
-
-  override apply(
-      inputs: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],
-      kwargs?: Kwargs): Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[] {
-    let initialState: Tensor[]|SymbolicTensor[] =
-        kwargs == null ? null : kwargs['initialState'];
-    let constants: Tensor[]|SymbolicTensor[] =
-        kwargs == null ? null : kwargs['constants'];
-    if (kwargs == null) {
-      kwargs = {};
-    }
-    const standardized =
-        standardizeArgs(inputs, initialState, constants, this.numConstants);
-    inputs = standardized.inputs;
-    initialState = standardized.initialState;
-    constants = standardized.constants;
-
-    if (Array.isArray(inputs)) {
-      initialState = (inputs as Tensor[] | SymbolicTensor[]).slice(1);
-      inputs = (inputs as Tensor[] | SymbolicTensor[])[0];
-    }
-
-    if ((initialState == null || initialState.length === 0) &&
-        constants == null) {
-      return super.apply(inputs, kwargs);
-    }
-    const additionalInputs: Array<Tensor|SymbolicTensor> = [];
-    const additionalSpecs: InputSpec[] = [];
-    if (initialState != null) {
-      const numStates = initialState.length;
-      if (numStates % 2 > 0) {
-        throw new ValueError(
-            'When passing `initialState` to a Bidrectional RNN, ' +
-            'the state should be an Array containing the states of ' +
-            'the underlying RNNs.');
-      }
-      kwargs['initialState'] = initialState;
-      additionalInputs.push(...initialState);
-      const stateSpecs = (initialState as Array<Tensor|SymbolicTensor>)
-                             .map(state => new InputSpec({shape: state.shape}));
-      this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);
-      this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);
-      additionalSpecs.push(...stateSpecs);
-    }
-    if (constants != null) {
-      throw new NotImplementedError(
-          'Support for constants in Bidirectional layers is not ' +
-          'implemented yet.');
-    }
-
-    const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;
-    for (const tensor of additionalInputs) {
-      if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {
-        throw new ValueError(
-            'The initial state of a Bidirectional layer cannot be ' +
-            'specified as a mix of symbolic and non-symbolic tensors');
-      }
-    }
-
-    if (isSymbolicTensor) {
-      // Compute the full input and specs, including the states.
-      const fullInput = [inputs].concat(additionalInputs);
-      const fullInputSpec = this.inputSpec.concat(additionalSpecs);
-      // Perform the call temporarily and replace inputSpec.
-      // Note: with initial states symbolic calls and non-symbolic calls to
-      // this method differ in how the initial states are passed. For
-      // symbolic calls, the initial states are passed in the first arg, as
-      // an Array of SymbolicTensors; for non-symbolic calls, they are
-      // passed in the second arg as a part of the kwargs. Hence the need to
-      // temporarily modify inputSpec here.
-      // TODO(cais): Make refactoring so that this hacky code below is no
-      // longer needed.
-      const originalInputSpec = this.inputSpec;
-      this.inputSpec = fullInputSpec;
-      const output =
-          super.apply(fullInput as Tensor[] | SymbolicTensor[], kwargs);
-      this.inputSpec = originalInputSpec;
-      return output;
-    } else {
-      return super.apply(inputs, kwargs);
-    }
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    return tidy(() => {
-      const initialState = kwargs['initialState'];
-
-      let y: Tensor|Tensor[];
-      let yRev: Tensor|Tensor[];
-      if (initialState == null) {
-        y = this.forwardLayer.call(inputs, kwargs);
-        yRev = this.backwardLayer.call(inputs, kwargs);
-      } else {
-        const forwardState = initialState.slice(0, initialState.length / 2);
-        const backwardState = initialState.slice(initialState.length / 2);
-        y = this.forwardLayer.call(
-            inputs, Object.assign(kwargs, {initialState: forwardState}));
-        yRev = this.backwardLayer.call(
-            inputs, Object.assign(kwargs, {initialState: backwardState}));
-      }
-
-      let states: Tensor[];
-      if (this.returnState) {
-        if (Array.isArray(y)) {
-          states = y.slice(1).concat((yRev as Tensor[]).slice(1));
-        } else {
-        }
-        y = (y as Tensor[])[0];
-        yRev = (yRev as Tensor[])[0];
-      }
-
-      if (this.returnSequences) {
-        yRev = tfc.reverse(yRev as Tensor, 1);
-      }
-
-      let output: Tensor|Tensor[];
-      if (this.mergeMode === 'concat') {
-        output = K.concatenate([y as Tensor, yRev as Tensor]);
-      } else if (this.mergeMode === 'sum') {
-        output = tfc.add(y as Tensor, yRev as Tensor);
-      } else if (this.mergeMode === 'ave') {
-        output = tfc.mul(.5, tfc.add(y as Tensor, yRev as Tensor));
-      } else if (this.mergeMode === 'mul') {
-        output = tfc.mul(y as Tensor, yRev as Tensor);
-      } else if (this.mergeMode == null) {
-        output = [y as Tensor, yRev as Tensor];
-      }
-
-      // TODO(cais): Properly set learning phase.
-      if (this.returnState) {
-        if (this.mergeMode == null) {
-          return (output as Tensor[]).concat(states);
-        }
-        return [output as Tensor].concat(states);
-      }
-      return output;
-    });
-  }
-
-  override resetStates(states?: Tensor|Tensor[]): void {
-    this.forwardLayer.resetStates();
-    this.backwardLayer.resetStates();
-  }
-
-  override build(inputShape: Shape|Shape[]): void {
-    nameScope(this.forwardLayer.name, () => {
-      this.forwardLayer.build(inputShape);
-    });
-    nameScope(this.backwardLayer.name, () => {
-      this.backwardLayer.build(inputShape);
-    });
-    this.built = true;
-  }
-
-  override computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]): Tensor
-      |Tensor[] {
-    if (Array.isArray(mask)) {
-      mask = mask[0];
-    }
-    let outputMask: Tensor|Tensor[];
-    if (this.returnSequences) {
-      if (this.mergeMode == null) {
-        outputMask = [mask, mask];
-      } else {
-        outputMask = mask;
-      }
-    } else {
-      if (this.mergeMode == null) {
-        outputMask = [null, null];
-      } else {
-        outputMask = null;
-      }
-    }
-    if (this.returnState) {
-      const states = this.forwardLayer.states;
-      const stateMask: Tensor[] = states.map(state => null);
-      if (Array.isArray(outputMask)) {
-        return outputMask.concat(stateMask).concat(stateMask);
-      } else {
-        return [outputMask].concat(stateMask).concat(stateMask);
-      }
-    } else {
-      return outputMask;
-    }
-  }
-
-  override get trainableWeights(): LayerVariable[] {
-    return this.forwardLayer.trainableWeights.concat(
-        this.backwardLayer.trainableWeights);
-  }
-
-  override get nonTrainableWeights(): LayerVariable[] {
-    return this.forwardLayer.nonTrainableWeights.concat(
-        this.backwardLayer.nonTrainableWeights);
-  }
-
-  // TODO(cais): Implement constraints().
-
-  override setFastWeightInitDuringBuild(value: boolean) {
-    super.setFastWeightInitDuringBuild(value);
-    if (this.forwardLayer != null) {
-      this.forwardLayer.setFastWeightInitDuringBuild(value);
-    }
-    if (this.backwardLayer != null) {
-      this.backwardLayer.setFastWeightInitDuringBuild(value);
-    }
-  }
-
-  override getConfig(): serialization.ConfigDict {
-    const config: serialization.ConfigDict = {
-      'mergeMode': this.mergeMode,
-    };
-    // TODO(cais): Add logic for `numConstants` once the property is added.
-    const baseConfig = super.getConfig();
-    Object.assign(config, baseConfig);
-    return config;
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict): T {
-    const rnnLayer =
-        deserialize(config['layer'] as serialization.ConfigDict) as RNN;
-    delete config['layer'];
-    // TODO(cais): Add logic for `numConstants` once the property is added.
-    if (config['numConstants'] != null) {
-      throw new NotImplementedError(
-          `Deserialization of a Bidirectional layer with numConstants ` +
-          `present is not supported yet.`);
-    }
-    // tslint:disable-next-line:no-any
-    const newConfig: {[key: string]: any} = config;
-    newConfig['layer'] = rnnLayer;
-    return new cls(newConfig);
-  }
-}
-serialization.registerClass(Bidirectional);
diff --git a/tfjs-master/tfjs-layers/src/layers/wrappers_test.ts b/tfjs-master/tfjs-layers/src/layers/wrappers_test.ts
deleted file mode 100644
index fec2bfee7..000000000
--- a/tfjs-master/tfjs-layers/src/layers/wrappers_test.ts
+++ /dev/null
@@ -1,785 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for wrapper layers.
- */
-
-import {ones, scalar, serialization, Tensor, tensor2d, Tensor3D, tensor3d} from '@tensorflow/tfjs-core';
-
-import {Layer, SymbolicTensor} from '../engine/topology';
-import {LayersModel} from '../engine/training';
-import * as tfl from '../index';
-import {BidirectionalMergeMode, VALID_BIDIRECTIONAL_MERGE_MODES} from '../keras_format/common';
-import {convertPythonicToTs} from '../utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndWebGL2, expectTensorsClose} from '../utils/test_utils';
-
-import {Dense, Reshape} from './core';
-import {RNN, SimpleRNN} from './recurrent';
-import {deserialize} from './serialization';
-import {Bidirectional, checkBidirectionalMergeMode, TimeDistributed} from './wrappers';
-
-describeMathCPU('TimeDistributed Layer: Symbolic', () => {
-  it('3D input: Dense', () => {
-    const input = new tfl.SymbolicTensor('float32', [10, 8, 2], null, [], null);
-    const wrapper = tfl.layers.timeDistributed({layer: new Dense({units: 3})});
-    const output = wrapper.apply(input) as tfl.SymbolicTensor;
-    expect(wrapper.trainable).toEqual(true);
-    expect(wrapper.getWeights().length).toEqual(2);  // kernel and bias.
-    expect(output.dtype).toEqual(input.dtype);
-    expect(output.shape).toEqual([10, 8, 3]);
-  });
-  it('4D input: Reshape', () => {
-    const input =
-        new tfl.SymbolicTensor('float32', [10, 8, 2, 3], null, [], null);
-    const wrapper =
-        tfl.layers.timeDistributed({layer: new Reshape({targetShape: [6]})});
-    const output = wrapper.apply(input) as tfl.SymbolicTensor;
-    expect(output.dtype).toEqual(input.dtype);
-    expect(output.shape).toEqual([10, 8, 6]);
-  });
-  it('2D input leads to exception', () => {
-    const input = new tfl.SymbolicTensor('float32', [10, 2], null, [], null);
-    const wrapper = tfl.layers.timeDistributed({layer: new Dense({units: 3})});
-    expect(() => wrapper.apply(input))
-        .toThrowError(
-            /TimeDistributed .*expects an input shape >= 3D, .* \[10,.*2\]/);
-  });
-  it('getConfig and fromConfig: round trip', () => {
-    const wrapper = tfl.layers.timeDistributed({layer: new Dense({units: 3})});
-    const config = wrapper.getConfig();
-    const wrapperPrime = TimeDistributed.fromConfig(TimeDistributed, config);
-    expect(wrapperPrime.getConfig()).toEqual(wrapper.getConfig());
-  });
-});
-
-describeMathCPUAndWebGL2('TimeDistributed Layer: Tensor', () => {
-  it('3D input: Dense', () => {
-    const input = tensor3d(
-        [
-          [[1, 2], [3, 4], [5, 6], [7, 8]],
-          [[-1, -2], [-3, -4], [-5, -6], [-7, -8]]
-        ],
-        [2, 4, 2]);
-    // Given an all-ones Dense kernel and no bias, the output at each timestep
-    // is expected to be [3, 7, 11, 15], give or take a minus sign.
-    const wrapper = tfl.layers.timeDistributed({
-      layer: new Dense({units: 1, kernelInitializer: 'ones', useBias: false})
-    });
-    const output = wrapper.apply(input) as Tensor;
-    expectTensorsClose(
-        output,
-        tensor3d(
-            [[[3], [7], [11], [15]], [[-3], [-7], [-11], [-15]]], [2, 4, 1]));
-  });
-
-  // Reference Python Keras code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // model_as_layer = keras.Sequential(
-  //     layers=[keras.layers.Dense(
-  //         units=3,
-  //         input_shape=[2, 5],
-  //         kernel_initializer='ones',
-  //         activation='softmax')])
-  //
-  // td = keras.layers.TimeDistributed(
-  //     layer=model_as_layer, input_shape=[2, 5])
-  // model = keras.Sequential(layers=[td])
-  // model.summary()
-  //
-  // xs = np.ones([1, 2, 5])
-  // ys = model.predict(xs)
-  // print(ys)
-  // ```
-  it('LayersModel as constituent layer', () => {
-    const modelAsLayer = tfl.sequential({
-      layers: [tfl.layers.dense({
-        activation: 'softmax',
-        units: 3,
-        inputShape: [2, 5],
-        kernelInitializer: 'ones'
-      })],
-    });
-    const td =
-        tfl.layers.timeDistributed({layer: modelAsLayer, inputShape: [2, 5]});
-    const model = tfl.sequential({layers: [td]});
-    const ys = model.predict(ones([1, 2, 5])) as Tensor;
-    expect(ys.shape).toEqual([1, 2, 3]);
-    expectTensorsClose(ys, tensor3d([[
-                         [0.33333334, 0.33333334, 0.33333334],
-                         [0.33333334, 0.33333334, 0.33333334]
-                       ]]));
-  });
-
-  // Reference Python code:
-  // ```py
-  // import keras
-  // import numpy as np
-  //
-  // input1 = keras.Input(shape=[4])
-  // input2 = keras.Input(shape=[4])
-  // y1 = keras.layers.Embedding(10,
-  //                             3,
-  //                             input_length=4,
-  //                             mask_zero=True,
-  //                             embeddings_initializer='ones')(input1)
-  // y1 = keras.layers.LSTM(3,
-  //                        return_sequences=True,
-  //                        recurrent_initializer='ones',
-  //                        kernel_initializer='ones',
-  //                        bias_initializer='zeros')(y1)
-  // z1 = keras.layers.Embedding(10,
-  //                             3,
-  //                             input_length=4,
-  //                             mask_zero=True,
-  //                             embeddings_initializer='ones')(input2)
-  // z1 = keras.layers.LSTM(3,
-  //                        return_sequences=True,
-  //                        recurrent_initializer='ones',
-  //                        kernel_initializer='ones',
-  //                        bias_initializer='zeros')(z1)
-  // y2 = keras.layers.Dot(axes=[2, 2])([y1, z1])
-  // y3 = keras.layers.Concatenate()([y1, y2])
-  // y3 = keras.layers.TimeDistributed(keras.layers.Dense(
-  //     1,
-  //     kernel_initializer='ones',
-  //     bias_initializer='zeros'))(y3)
-  // model = keras.Model(inputs=[input1, input2], outputs=y3)
-  // model.summary()
-  //
-  // xs1 = np.array([[0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0], [1, 2, 3, 0]])
-  // xs2 = np.ones([4, 4])
-  // ys = model.predict([xs1, xs2])
-  // print(ys)
-  // ```
-  it('With masking', () => {
-    const input1 = tfl.input({shape: [4]});
-    const input2 = tfl.input({shape: [4]});
-    let y1 = tfl.layers
-                 .embedding({
-                   inputDim: 10,
-                   outputDim: 3,
-                   inputLength: 4,
-                   maskZero: true,
-                   embeddingsInitializer: 'ones'
-                 })
-                 .apply(input1) as SymbolicTensor;
-    y1 = tfl.layers
-             .lstm({
-               units: 3,
-               returnSequences: true,
-               recurrentInitializer: 'ones',
-               kernelInitializer: 'ones',
-               biasInitializer: 'zeros'
-             })
-             .apply(y1) as SymbolicTensor;
-    let z1 = tfl.layers
-                 .embedding({
-                   inputDim: 10,
-                   outputDim: 3,
-                   inputLength: 4,
-                   maskZero: true,
-                   embeddingsInitializer: 'ones'
-                 })
-                 .apply(input2) as SymbolicTensor;
-    z1 = tfl.layers
-             .lstm({
-               units: 3,
-               returnSequences: true,
-               recurrentInitializer: 'ones',
-               kernelInitializer: 'ones',
-               biasInitializer: 'zeros'
-             })
-             .apply(z1) as SymbolicTensor;
-    const y2 = tfl.layers.dot({axes: [2, 2]}).apply([y1, z1]) as SymbolicTensor;
-    let y3 = tfl.layers.concatenate().apply([y1, y2]) as SymbolicTensor;
-    y3 = tfl.layers
-             .timeDistributed({
-               layer: tfl.layers.dense({
-                 units: 1,
-                 kernelInitializer: 'ones',
-                 biasInitializer: 'zeros'
-               })
-             })
-             .apply(y3) as SymbolicTensor;
-    const model = tfl.model({inputs: [input1, input2], outputs: y3});
-
-    const xs1 =
-        tensor2d([[0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0], [1, 2, 3, 0]]);
-    const xs2 = ones([4, 4]);
-    const ys = model.predict([xs1, xs2]) as Tensor;
-    expectTensorsClose(
-        ys, tensor3d([
-          [[0], [0], [0], [0]],
-          [[10.7489796], [10.7489796], [10.7489796], [10.7489796]],
-          [[10.7489796], [13.6384077], [13.6384077], [13.6384077]],
-          [[10.7489796], [13.6384077], [14.0818386], [14.0818386]]
-        ]));
-  });
-});
-
-describeMathCPU('Bidirectional Layer: Symbolic', () => {
-  const mergeModes: BidirectionalMergeMode[] = [
-    null,
-    'concat',
-    'ave',
-    'mul',
-    'sum',
-  ];
-  const returnStateValues: boolean[] = [false, true];
-
-  for (const mergeMode of mergeModes) {
-    for (const returnState of returnStateValues) {
-      const testTitle = `3D input: returnSequence=false, ` +
-          `mergeMode=${mergeMode}; returnState=${returnState}`;
-      it(testTitle, () => {
-        const input =
-            new tfl.SymbolicTensor('float32', [10, 8, 2], null, [], null);
-        const bidi = tfl.layers.bidirectional({
-          layer: new SimpleRNN(
-              {units: 3, recurrentInitializer: 'glorotNormal', returnState}),
-          mergeMode,
-        });
-        // TODO(cais): Remove recurrentInitializer once Orthogonal initializer
-        // is available.
-        let outputs = bidi.apply(input);
-        expect(bidi.trainable).toEqual(true);
-        // {kernel, recurrentKernel, bias} * {forward, backward}.
-        expect(bidi.getWeights().length).toEqual(6);
-        if (!returnState) {
-          if (mergeMode === null) {
-            outputs = outputs as tfl.SymbolicTensor[];
-            expect(outputs.length).toEqual(2);
-            expect(outputs[0].shape).toEqual([10, 3]);
-            expect(outputs[1].shape).toEqual([10, 3]);
-          } else if (mergeMode === 'concat') {
-            outputs = outputs as tfl.SymbolicTensor;
-            expect(outputs.shape).toEqual([10, 6]);
-          } else {
-            outputs = outputs as tfl.SymbolicTensor;
-            expect(outputs.shape).toEqual([10, 3]);
-          }
-        } else {
-          if (mergeMode === null) {
-            outputs = outputs as tfl.SymbolicTensor[];
-            expect(outputs.length).toEqual(4);
-            expect(outputs[0].shape).toEqual([10, 3]);
-            expect(outputs[1].shape).toEqual([10, 3]);
-            expect(outputs[2].shape).toEqual([10, 3]);
-            expect(outputs[3].shape).toEqual([10, 3]);
-          } else if (mergeMode === 'concat') {
-            outputs = outputs as tfl.SymbolicTensor[];
-            expect(outputs.length).toEqual(3);
-            expect(outputs[0].shape).toEqual([10, 6]);
-            expect(outputs[1].shape).toEqual([10, 3]);
-            expect(outputs[2].shape).toEqual([10, 3]);
-          } else {
-            outputs = outputs as tfl.SymbolicTensor[];
-            expect(outputs.length).toEqual(3);
-            expect(outputs[0].shape).toEqual([10, 3]);
-            expect(outputs[1].shape).toEqual([10, 3]);
-            expect(outputs[2].shape).toEqual([10, 3]);
-          }
-        }
-      });
-    }
-  }
-  it('returnSequence=true', () => {
-    const input = new tfl.SymbolicTensor('float32', [10, 8, 2], null, [], null);
-    const bidi = tfl.layers.bidirectional({
-      layer: new SimpleRNN({
-        units: 3,
-        recurrentInitializer: 'glorotNormal',
-        returnSequences: true,
-        returnState: true
-      }),
-      mergeMode: 'ave'
-    });
-    const outputs = bidi.apply(input) as tfl.SymbolicTensor[];
-    expect(outputs.length).toEqual(3);
-    expect(outputs[0].shape).toEqual([10, 8, 3]);
-    expect(outputs[1].shape).toEqual([10, 3]);
-    expect(outputs[2].shape).toEqual([10, 3]);
-  });
-  it('Serialization round trip', () => {
-    const layer = tfl.layers.bidirectional({
-      layer: new SimpleRNN({units: 3}),
-      mergeMode: 'concat',
-      inputShape: [4, 4],
-    });
-    const model = tfl.sequential({layers: [layer]});
-    const unused: {} = null;
-    const modelJSON = model.toJSON(unused, false);
-    const modelPrime = deserialize(
-                           convertPythonicToTs(modelJSON) as
-                           serialization.ConfigDict) as tfl.Sequential;
-    expect((modelPrime.layers[0] as Bidirectional).getConfig().mergeMode)
-        .toEqual('concat');
-  });
-  it('mergeMode defaults to concat', () => {
-    const bidi = tfl.layers.bidirectional({layer: new SimpleRNN({units: 3})});
-    expect(bidi.mergeMode).toEqual('concat');
-    expect(bidi.getConfig().mergeMode).toEqual('concat');
-  });
-});
-
-describe('checkBidirectionalMergeMode', () => {
-  it('Valid values', () => {
-    const extendedValues =
-        VALID_BIDIRECTIONAL_MERGE_MODES.concat([undefined, null]);
-    for (const validValue of extendedValues) {
-      // Using implicit "expect().toNotThrow()" for valid values
-      checkBidirectionalMergeMode(validValue);
-    }
-  });
-  it('Invalid values', () => {
-    // Test invalid values are rejected, and reported in the error.
-    expect(() => checkBidirectionalMergeMode('foo')).toThrowError(/foo/);
-    try {
-      checkBidirectionalMergeMode('bad');
-    } catch (e) {
-      expect(e).toMatch('BidirectionalMergeMode');
-      // Test that the error message contains the list of valid values.
-      for (const validValue of VALID_BIDIRECTIONAL_MERGE_MODES) {
-        expect(e).toMatch(validValue);
-      }
-    }
-  });
-});
-
-describeMathCPUAndWebGL2('Bidirectional Layer: Tensor', () => {
-  // The golden tensor values used in the tests below can be obtained with
-  // PyKeras code such as the following:
-  // ```python
-  // import keras
-  // import numpy as np
-  //
-  // bidi = keras.layers.Bidirectional(
-  //     keras.layers.SimpleRNN(
-  //         3,
-  //         kernel_initializer='ones',
-  //         recurrent_initializer='ones',
-  //         return_state=True),
-  //     merge_mode='ave')
-  //
-  // time_steps = 4
-  // input_size = 2
-  // inputs = keras.Input([time_steps, input_size])
-  // outputs = bidi(inputs)
-  // model = keras.Model(inputs, outputs)
-  //
-  // x = np.array(
-  //     [[[0.05, 0.05],
-  //       [-0.05, -0.05],
-  //       [0.1, 0.1],
-  //       [-0.1, -0.1]]])
-  // print(model.predict(x))
-  // ```
-
-  // TODO(bileschi): This should be tfl.layers.Layer.
-  let bidi: Layer;
-  let x: Tensor3D;
-  function createLayerAndData(
-      mergeMode: BidirectionalMergeMode, returnState: boolean) {
-    const units = 3;
-    bidi = tfl.layers.bidirectional({
-      layer: new SimpleRNN({
-        units,
-        kernelInitializer: 'ones',
-        recurrentInitializer: 'ones',
-        useBias: false,
-        returnState
-      }),
-      mergeMode,
-    });
-    const timeSteps = 4;
-    const inputSize = 2;
-    x = tensor3d(
-        [[[0.05, 0.05], [-0.05, -0.05], [0.1, 0.1], [-0.1, -0.1]]],
-        [1, timeSteps, inputSize]);
-  }
-
-  const mergeModes: BidirectionalMergeMode[] =
-      [null, undefined, 'concat', 'mul'];
-  for (const mergeMode of mergeModes) {
-    it(`No returnState, mergeMode=${mergeMode}`, () => {
-      createLayerAndData(mergeMode, false);
-      let y = bidi.apply(x);
-      if (mergeMode === null) {
-        y = y as Tensor[];
-        expect(y.length).toEqual(2);
-        expectTensorsClose(
-            y[0], tensor2d([[0.9440416, 0.9440416, 0.9440416]], [1, 3]));
-        expectTensorsClose(
-            y[1], tensor2d([[-0.9842659, -0.9842659, -0.9842659]], [1, 3]));
-      } else if (mergeMode === undefined || mergeMode === 'concat') {
-        y = y as Tensor;
-        expectTensorsClose(
-            y,
-            tensor2d(
-                [[
-                  0.9440416, 0.9440416, 0.9440416, -0.9842659, -0.9842659,
-                  -0.9842659
-                ]],
-                [1, 6]));
-      } else if (mergeMode === 'mul') {
-        y = y as Tensor;
-        expectTensorsClose(
-            y, tensor2d([[-0.929188, -0.929188, -0.929188]], [1, 3]));
-      }
-    });
-  }
-  it('returnState', () => {
-    createLayerAndData('ave', true);
-    const y = bidi.apply(x) as Tensor[];
-    expect(y.length).toEqual(3);
-    expectTensorsClose(
-        y[0], tensor2d([[-0.02011216, -0.02011216, -0.02011216]], [1, 3]));
-    expectTensorsClose(
-        y[1], tensor2d([[0.9440416, 0.9440416, 0.9440416]], [1, 3]));
-    expectTensorsClose(
-        y[2], tensor2d([[-0.9842659, -0.9842659, -0.9842659]], [1, 3]));
-  });
-
-  // The golden values in the test below can be obtained with the following
-  // Python Keras code.
-  //
-  // ```python
-  // import keras
-  // import numpy
-  //
-  // rnn = keras.layers.LSTM(
-  //     1,
-  //     kernel_initializer='ones',
-  //     recurrent_initializer='ones',
-  //     bias_initializer='ones',
-  //     go_backwards=True)
-  // bidi = keras.layers.Bidirectional(
-  //     rnn, merge_mode='concat', input_shape=[2, 2])
-  // model = keras.Sequential([bidi])
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // x = np.array([[[0.1, 0.2],
-  //               [-0.1, 0.1]]])
-  // y = np.array([[0.3, 0.5]])
-  // print(model.predict(x))
-  //
-  // history = model.fit(x, y)
-  // print(history.history)
-  // ```
-  it('Backwards LSTM: predict and fit: concat', async () => {
-    const lstm = tfl.layers.lstm({
-      units: 1,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      biasInitializer: 'ones',
-      goBackwards: true
-    }) as RNN;
-    const bidi = tfl.layers.bidirectional(
-        {layer: lstm, inputShape: [2, 2], mergeMode: 'concat'});
-    const model = tfl.sequential({layers: [bidi]});
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const x = tensor3d([[[0.1, 0.2], [-0.1, 0.1]]]);
-    const y = tensor2d([[0.3, 0.5]]);
-    expectTensorsClose(
-        model.predict(x) as Tensor, tensor2d([[0.69299805, 0.66088]]));
-    const history = await model.fit(x, y);
-    expect(history.history.loss[0]).toBeCloseTo(0.0901649);
-    expectTensorsClose(
-        model.predict(x) as Tensor, tensor2d([[0.6927189, 0.66077083]]));
-  });
-
-  // The golden values in the test below can be obtained with the following
-  // Python Keras code.
-  //
-  // ```python
-  // import keras
-  // import numpy
-  //
-  // rnn = keras.layers.LSTM(
-  //     1,
-  //     kernel_initializer='ones',
-  //     recurrent_initializer='ones',
-  //     bias_initializer='ones',
-  //     go_backwards=True)
-  // bidi = keras.layers.Bidirectional(
-  //     rnn, merge_mode='ave', input_shape=[2, 2])
-  // model = keras.Sequential([bidi])
-  // model.compile(loss='mean_squared_error', optimizer='sgd')
-  //
-  // x = np.array([[[0.1, 0.2],
-  //               [-0.1, 0.1]]])
-  // y = np.array([[0.4]])
-  // print(model.predict(x))
-  //
-  // history = model.fit(x, y)
-  // print(history.history)
-  // ```
-  it('Backwards LSTM: predict and fit: ave', done => {
-    const lstm = tfl.layers.lstm({
-      units: 1,
-      kernelInitializer: 'ones',
-      recurrentInitializer: 'ones',
-      biasInitializer: 'ones',
-      goBackwards: true
-    }) as RNN;
-    const bidi = tfl.layers.bidirectional(
-        {layer: lstm, inputShape: [2, 2], mergeMode: 'ave'});
-    const model = tfl.sequential({layers: [bidi]});
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const x = tensor3d([[[0.1, 0.2], [-0.1, 0.1]]]);
-    const y = tensor2d([[0.4]]);
-    expectTensorsClose(model.predict(x) as Tensor, tensor2d([[0.676939]]));
-    model.fit(x, y)
-        .then(history => {
-          expect(history.history.loss[0]).toBeCloseTo(0.0766952);
-          expectTensorsClose(
-              model.predict(x) as Tensor, tensor2d([[0.6767467]]));
-          done();
-        })
-        .catch(err => {
-          done.fail(err.stack);
-        });
-  });
-});
-
-describeMathCPUAndWebGL2('Bidirectional with initial state', () => {
-  const sequenceLength = 4;
-  const recurrentUnits = 3;
-  const inputDim = 2;
-
-  function createLayerAndTensors(): {
-    bidiLayer: Bidirectional,
-    initState1: SymbolicTensor,
-    initState2: SymbolicTensor,
-    inputTensor: SymbolicTensor,
-    outputTensors: SymbolicTensor[],
-  } {
-    const initState1 = tfl.input({shape: [recurrentUnits]});
-    const initState2 = tfl.input({shape: [recurrentUnits]});
-    const inputTensor = tfl.input({shape: [sequenceLength, inputDim]});
-    const bidiLayer = tfl.layers.bidirectional({
-      layer: tfl.layers.gru({
-        units: recurrentUnits,
-        kernelInitializer: 'zeros',
-        recurrentInitializer: 'zeros',
-        biasInitializer: 'ones'
-      }) as RNN,
-      mergeMode: null,
-    });
-    const outputTensors =
-        bidiLayer.apply(
-            inputTensor, {initialState: [initState1, initState2]}) as
-        SymbolicTensor[];
-    return {bidiLayer, initState1, initState2, inputTensor, outputTensors};
-  }
-
-  it('Correct shapes', () => {
-    const layerAndTensors = createLayerAndTensors();
-    expect(layerAndTensors.outputTensors.length).toEqual(2);
-    expect(layerAndTensors.outputTensors[0].shape).toEqual([
-      null, recurrentUnits
-    ]);
-    expect(layerAndTensors.outputTensors[1].shape).toEqual([
-      null, recurrentUnits
-    ]);
-  });
-
-  it('apply() with concrete tensors', () => {
-    const layerAndTensors = createLayerAndTensors();
-    const xVal = ones([1, sequenceLength, inputDim]);
-    const initState1Val = ones([1, recurrentUnits]).mul(scalar(-1));
-    const initState2Val = ones([1, recurrentUnits]);
-    const yVals =
-        layerAndTensors.bidiLayer.apply(
-            xVal, {initialState: [initState1Val, initState2Val]}) as Tensor[];
-    expect(yVals.length).toEqual(2);
-    expectTensorsClose(
-        yVals[0], tensor2d([[0.33863544, 0.33863544, 0.33863544]]));
-    expectTensorsClose(yVals[1], tensor2d([[0.8188354, 0.8188354, 0.8188354]]));
-  });
-
-  it('LayersModel predict', () => {
-    const layerAndTensors = createLayerAndTensors();
-    const model = tfl.model({
-      inputs: [
-        layerAndTensors.inputTensor, layerAndTensors.initState1,
-        layerAndTensors.initState2
-      ],
-      outputs: layerAndTensors.outputTensors
-    });
-    const xVal = ones([1, sequenceLength, inputDim]);
-    const initState1Val = ones([1, recurrentUnits]).mul(scalar(-1));
-    const initState2Val = ones([1, recurrentUnits]);
-    const yVals =
-        model.predict([xVal, initState1Val, initState2Val]) as Tensor[];
-    expect(yVals.length).toEqual(2);
-    expectTensorsClose(
-        yVals[0], tensor2d([[0.33863544, 0.33863544, 0.33863544]]));
-    expectTensorsClose(yVals[1], tensor2d([[0.8188354, 0.8188354, 0.8188354]]));
-  });
-
-  it('LayersModel serialization round trip', () => {
-    // Disable the console warning about the unserialization SymbolicTensor in
-    // the CallArgs.
-    spyOn(console, 'warn');
-    const layerAndTensors = createLayerAndTensors();
-    const model = tfl.model({
-      inputs: [
-        layerAndTensors.inputTensor, layerAndTensors.initState1,
-        layerAndTensors.initState2
-      ],
-      outputs: layerAndTensors.outputTensors
-    });
-    const json1 = model.toJSON(null, false);
-    const model2 =
-        deserialize(convertPythonicToTs(json1) as serialization.ConfigDict) as
-        LayersModel;
-    const json2 = model2.toJSON(null, false);
-    expect(json2).toEqual(json1);
-  });
-
-  it('Incorrect number of initial-state tensors leads to error', () => {
-    const initState1 = tfl.input({shape: [recurrentUnits]});
-    const x = tfl.input({shape: [sequenceLength, inputDim]});
-    const bidi = tfl.layers.bidirectional({
-      layer: tfl.layers.gru({
-        units: recurrentUnits,
-      }) as RNN,
-      mergeMode: null,
-    });
-    expect(() => bidi.apply(x, {
-      initialState: [initState1]
-    })).toThrowError(/the state should be .*RNNs/);
-  });
-});
-
-describeMathCPUAndWebGL2('Bidirectional with masking', () => {
-  for (const rnnLayerType of [tfl.layers.simpleRNN, tfl.layers.lstm]) {
-    // Reference Python TensorFlow code (v1.15.0):
-    // ```py
-    // import numpy as np
-    // import tensorflow as tf
-    //
-    // tf.enable_v2_behavior()
-    // tf.enable_eager_execution()
-    //
-    // model = tf.keras.Sequential()
-    // model.add(tf.keras.layers.Embedding(
-    //     3, 3, input_length=2, embeddings_initializer="ones", mask_zero=True))
-    // model.add(tf.keras.layers.Bidirectional(tf.keras.layers.SimpleRNN(
-    //     4,
-    //     kernel_initializer="ones",
-    //     recurrent_initializer="ones")))
-    // model.add(tf.keras.layers.Dense(1, kernel_initializer="ones"))
-    //
-    // xs = np.array([[0, 1], [0, 2], [0, 0], [1, 2]], dtype=np.int32)
-    // print(model.predict(xs))
-    // ```
-    it(`Inference ${rnnLayerType.name}`, () => {
-      const model = tfl.sequential();
-      model.add(tfl.layers.embedding({
-        inputLength: 2,
-        inputDim: 3,
-        outputDim: 3,
-        embeddingsInitializer: 'ones',
-        maskZero: true
-      }));
-      model.add(tfl.layers.bidirectional({
-        layer: rnnLayerType({
-                 units: 4,
-                 kernelInitializer: 'ones',
-                 recurrentInitializer: 'ones'
-               }) as RNN
-      }));
-      model.add(tfl.layers.dense({
-        units: 1,
-        kernelInitializer: 'ones',
-      }));
-      const xs = tensor2d([[0, 1], [0, 2], [0, 0], [1, 2]]);
-      const ys = model.predict(xs) as Tensor;
-      if (rnnLayerType.name === 'lstm') {
-        expectTensorsClose(
-            ys, tensor2d([[6.076076], [6.076076], [0], [7.709405]]));
-      } else if (rnnLayerType.name === 'simpleRNN') {
-        expectTensorsClose(
-            ys, tensor2d([[7.960438], [7.960438], [0], [7.9999857]]));
-      } else {
-        throw new Error(`Unexpected rnnLayerType name: ${rnnLayerType.name}`);
-      }
-    });
-
-    // Reference Python TensorFlow code (v1.15.0):
-    // ```py
-    // import numpy as np
-    // import tensorflow as tf
-    //
-    // tf.enable_v2_behavior()
-    // tf.enable_eager_execution()
-    //
-    // model = tf.keras.Sequential()
-    // model.add(tf.keras.layers.Embedding(
-    //     3, 3, input_length=2, embeddings_initializer="ones", mask_zero=True))
-    // model.add(tf.keras.layers.Bidirectional(tf.keras.layers.SimpleRNN(
-    //     4,
-    //     kernel_initializer="ones",
-    //     recurrent_initializer="ones")))
-    // model.add(tf.keras.layers.Dense(1, kernel_initializer="ones"))
-    // model.compile(optimizer="sgd", loss="mse")
-    //
-    // xs = np.array([[0, 1], [0, 2], [0, 0], [1, 2]], dtype=np.int32)
-    // ys = np.array([[0], [0], [0], [0]], dtype=np.float32)
-    // history = model.fit(xs, ys, epochs=5)
-    // print(history.history)
-    // ```
-    it(`Training ${rnnLayerType.name}`, async () => {
-      const model = tfl.sequential();
-      model.add(tfl.layers.embedding({
-        inputLength: 2,
-        inputDim: 3,
-        outputDim: 3,
-        embeddingsInitializer: 'ones',
-        maskZero: true
-      }));
-      model.add(tfl.layers.bidirectional({
-        layer: rnnLayerType({
-                 units: 4,
-                 kernelInitializer: 'ones',
-                 recurrentInitializer: 'ones'
-               }) as RNN
-      }));
-      model.add(tfl.layers.dense({
-        units: 1,
-        kernelInitializer: 'ones',
-      }));
-      model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-      const xs = tensor2d([[0, 1], [0, 2], [0, 0], [1, 2]]);
-      const ys = tensor2d([[0], [0], [0], [0]]);
-      const history = await model.fit(xs, ys, {epochs: 5});
-      if (rnnLayerType.name === 'lstm') {
-        expectTensorsClose(
-            history.history['loss'] as number[],
-            [33.318, 27.104, 22.058, 17.961, 14.634]);
-      } else if (rnnLayerType.name === 'simpleRNN') {
-        expectTensorsClose(
-            history.history['loss'] as number[],
-            [47.684, 35.741, 26.804, 20.115, 15.108]);
-      } else {
-        throw new Error(`Unexpected rnnLayerType name:
-          ${rnnLayerType.name}`);
-      }
-    });
-  }
-});
diff --git a/tfjs-master/tfjs-layers/src/logs.ts b/tfjs-master/tfjs-layers/src/logs.ts
deleted file mode 100644
index 2e011bb98..000000000
--- a/tfjs-master/tfjs-layers/src/logs.ts
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {dispose, Scalar} from '@tensorflow/tfjs-core';
-
-/**
- * Logs in which values can be either numbers or Tensors (Scalars).
- *
- * Used internally.
- */
-export type UnresolvedLogs = {
-  [key: string]: number|Scalar;
-};
-
-/**
- * Turn any Scalar values in a Logs object into actual number values.
- *
- * @param logs The `Logs` object to be resolved in place.
- */
-export async function resolveScalarsInLogs(logs: UnresolvedLogs) {
-  if (logs == null) {
-    return;
-  }
-  const promises: Array<Promise<Float32Array|Int32Array|Uint8Array>> = [];
-  const keys: string[] = [];
-  const scalarsToDispose: Scalar[] = [];
-  for (const key in logs) {
-    const value = logs[key];
-    if (typeof value !== 'number') {
-      const valueScalar = value;
-      promises.push(valueScalar.data());
-      keys.push(key);
-      scalarsToDispose.push(valueScalar);
-    }
-  }
-  if (promises.length > 0) {
-    const values = await Promise.all(promises);
-    for (let i = 0; i < values.length; ++i) {
-      logs[keys[i]] = values[i][0];
-    }
-    // Dispose the original scalar tensors.
-    dispose(scalarsToDispose);
-  }
-}
-
-/**
- * Dispose all Tensors in an UnresolvedLogs object.
- *
- * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in
- *   places where the values can be `tf.Tensor` or `number`.
- */
-export function disposeTensorsInLogs(logs: UnresolvedLogs) {
-  if (logs == null) {
-    return;
-  }
-  for (const key in logs) {
-    const value = logs[key];
-    if (typeof value !== 'number') {
-      value.dispose();
-    }
-  }
-}
-
-/**
- * Logs in which values can only be numbers.
- *
- * Used when calling client-provided custom callbacks.
- */
-export type Logs = {
-  [key: string]: number;
-};
diff --git a/tfjs-master/tfjs-layers/src/losses.ts b/tfjs-master/tfjs-layers/src/losses.ts
deleted file mode 100644
index 80e990b26..000000000
--- a/tfjs-master/tfjs-layers/src/losses.ts
+++ /dev/null
@@ -1,276 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original Source: losses.py */
-import * as tfc from '@tensorflow/tfjs-core';
-import {Tensor, Tensor1D, tidy, util} from '@tensorflow/tfjs-core';
-
-import {epsilon} from './backend/common';
-import * as K from './backend/tfjs_backend';
-import {ValueError} from './errors';
-import {LossOrMetricFn} from './types';
-
-/**
- * Normalizes a tensor wrt the L2 norm alongside the specified axis.
- * @param x
- * @param axis Axis along which to perform normalization.
- */
-export function l2Normalize(x: Tensor, axis?: number): Tensor {
-  return tidy(() => {
-    if (x.dtype !== 'float32') {
-      x = tfc.cast(x, 'float32');
-    }
-    const squareSum = tfc.sum(K.square(x), axis, true);
-    const epsilonTensor = tfc.fill(squareSum.shape, epsilon());
-    const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));
-    return tfc.div(x, norm);
-  });
-}
-
-export function meanSquaredError(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));
-}
-
-export function meanAbsoluteError(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));
-}
-
-export function meanAbsolutePercentageError(
-    yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const diff = tfc.sub(yTrue, yPred);
-    const clippedTrue =
-        tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);
-    const absResult = tfc.abs(tfc.div(diff, clippedTrue));
-    return tfc.mul(100, tfc.mean(absResult, -1));
-  });
-}
-
-export function meanSquaredLogarithmicError(
-    yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);
-    const firstLog = tfc.log(tfc.add(1, clippedPred));
-
-    const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);
-    const secondLog = tfc.log(tfc.add(1, clippedTrue));
-
-    return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);
-  });
-}
-
-export function squaredHinge(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));
-    return tfc.mean(K.square(maxResult), -1);
-  });
-}
-
-export function hinge(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));
-    return tfc.mean(maxResult, -1);
-  });
-}
-
-export function categoricalHinge(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);
-    const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);
-    return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));
-  });
-}
-
-/**
- * Logarithm of the hyperbolic cosine of the prediction error.
- *
- * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and
- * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly
- * like the mean squared error, but will not be so strongly affected by the
- * occasional wildly incorrect prediction.
- */
-export function logcosh(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const log2 = Math.log(2);
-    const predictionDiff = tfc.sub(yPred, yTrue);
-    const logcoshResult = tfc.sub(
-        tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))),
-        log2);
-    return tfc.mean(logcoshResult, -1);
-  });
-}
-
-export function categoricalCrossentropy(
-    target: Tensor, output: Tensor, fromLogits = false): Tensor {
-  return tidy(() => {
-    if (fromLogits) {
-      output = tfc.softmax(output);
-    } else {
-      // scale preds so that the class probabilities of each sample sum to 1.
-      const outputSum = tfc.sum(output, output.shape.length - 1, true);
-      output = tfc.div(output, outputSum);
-    }
-    output = tfc.clipByValue(output, epsilon(), 1 - epsilon());
-    return tfc.neg(tfc.sum(
-        tfc.mul(tfc.cast(target, 'float32'), tfc.log(output)),
-        output.shape.length - 1));
-  });
-}
-
-/**
- * Categorical crossentropy with integer targets.
- *
- * @param target An integer tensor.
- * @param output A tensor resulting from a softmax (unless `fromLogits` is
- *  `true`, in which case `output` is expected to be the logits).
- * @param fromLogits Boolean, whether `output` is the result of a softmax, or is
- *   a tensor of logits.
- */
-export function sparseCategoricalCrossentropy(
-    target: Tensor, output: Tensor, fromLogits = false): Tensor {
-  return tidy(() => {
-    const flatTarget =
-        tfc.cast(tfc.floor(K.flatten(target)), 'int32') as Tensor1D;
-    output = tfc.clipByValue(output, epsilon(), 1 - epsilon());
-    const outputShape = output.shape;
-    const oneHotTarget = tfc.reshape(
-        tfc.oneHot(flatTarget, outputShape[outputShape.length - 1]),
-        outputShape);
-    return categoricalCrossentropy(oneHotTarget, output, fromLogits);
-  });
-}
-
-/**
- * From TensorFlow's implementation in nn_impl.py:
- *
- * For brevity, let `x = logits`, `z = labels`.  The logistic loss is
- *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
- *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
- *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
- *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
- *    = (1 - z) * x + log(1 + exp(-x))
- *    = x - x * z + log(1 + exp(-x))
- * For x < 0, to avoid overflow in exp(-x), we reformulate the above
- *      x - x * z + log(1 + exp(-x))
- *    = log(exp(x)) - x * z + log(1 + exp(-x))
- *    = - x * z + log(1 + exp(x))
- * Hence, to ensure stability and avoid overflow, the implementation uses this
- * equivalent formulation
- *    max(x, 0) - x * z + log(1 + exp(-abs(x)))
- *
- * @param labels The labels.
- * @param logits The logits.
- */
-export function sigmoidCrossEntropyWithLogits(
-    labels: Tensor, logits: Tensor): Tensor {
-  if (!util.arraysEqual(labels.shape, logits.shape)) {
-    throw new ValueError(
-        `logits and labels must have the same shape, but got shapes ` +
-        `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);
-  }
-  return tidy(() => {
-    // The logistic loss formula from above is
-    //   x - x * z + log(1 + exp(-x))
-    // For x < 0, a more numerically stable formula is
-    //   -x * z + log(1 + exp(x))
-    // Note that these two expressions can be combined into the following:
-    //   max(x, 0) - x * z + log(1 + exp(-abs(x)))
-    const reluLogits = tfc.relu(logits);
-    const negAbsLogits = tfc.neg(tfc.abs(logits));
-    return tfc.add(
-        tfc.sub(reluLogits, tfc.mul(logits, labels)),
-        tfc.log1p(tfc.exp(negAbsLogits)));
-  });
-}
-
-export function binaryCrossentropy(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    let y: Tensor;
-    y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());
-    y = tfc.log(tfc.div(y, tfc.sub(1, y)));
-    return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);
-  });
-}
-
-export function kullbackLeiblerDivergence(
-    yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);
-    const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);
-    return tfc.sum(
-        tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);
-  });
-}
-
-export function poisson(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const logPred = tfc.log(tfc.add(epsilon(), yPred));
-    return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);
-  });
-}
-
-export function cosineProximity(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const trueNormalized = l2Normalize(yTrue, -1);
-    const predNormalized = l2Normalize(yPred, -1);
-    const trueXPred = tfc.mul(trueNormalized, predNormalized);
-    return tfc.neg(tfc.sum(trueXPred, -1));
-  });
-}
-
-export const mse = meanSquaredError;
-export const MSE = meanSquaredError;
-export const mae = meanAbsoluteError;
-export const MAE = meanAbsoluteError;
-export const mape = meanAbsolutePercentageError;
-export const MAPE = meanAbsolutePercentageError;
-export const msle = meanSquaredLogarithmicError;
-export const MSLE = meanSquaredLogarithmicError;
-export const kld = kullbackLeiblerDivergence;
-export const KLD = kullbackLeiblerDivergence;
-export const cosine = cosineProximity;
-
-// TODO(michaelterry): Add deserialize() function.
-
-export const lossesMap: {[functionName: string]: LossOrMetricFn} = {
-  meanSquaredError,
-  meanAbsoluteError,
-  meanAbsolutePercentageError,
-  meanSquaredLogarithmicError,
-  squaredHinge,
-  hinge,
-  categoricalHinge,
-  logcosh,
-  categoricalCrossentropy,
-  sparseCategoricalCrossentropy,
-  binaryCrossentropy,
-  kullbackLeiblerDivergence,
-  poisson,
-  cosineProximity
-};
-
-// Porting note: This diverges from the PyKeras implementation and may need to
-// change based on (de)serialization requirements.
-export function get(identifierOrFn: string|LossOrMetricFn): LossOrMetricFn {
-  if (typeof identifierOrFn === 'string') {
-    if (identifierOrFn in lossesMap) {
-      return lossesMap[identifierOrFn];
-    }
-    let errMsg = `Unknown loss ${identifierOrFn}`;
-    if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {
-      errMsg = `Unknown loss ${identifierOrFn}. ` +
-          'Use "categoricalCrossentropy" as the string name for ' +
-          'tf.losses.softmaxCrossEntropy';
-    }
-    throw new ValueError(errMsg);
-  } else {
-    return identifierOrFn;
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/losses_test.ts b/tfjs-master/tfjs-layers/src/losses_test.ts
deleted file mode 100644
index 559693a77..000000000
--- a/tfjs-master/tfjs-layers/src/losses_test.ts
+++ /dev/null
@@ -1,443 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for activations.ts.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {scalar, Tensor, tensor1d, tensor2d, tensor3d} from '@tensorflow/tfjs-core';
-
-import {epsilon} from './backend/common';
-import * as losses from './losses';
-import {describeMathCPUAndGPU, describeMathCPUAndWebGL2, expectTensorsClose} from './utils/test_utils';
-
-describeMathCPUAndGPU('meanSquaredError', () => {
-  it('1D', () => {
-    const yTrue = tfc.zeros([3]);
-    const yPred = tensor1d([1, 2, 3]);
-    const expectedVal = scalar((1 * 1 + 2 * 2 + 3 * 3) / 3);
-    const result = losses.meanSquaredError(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-
-  it('2D', () => {
-    const yTrue = tfc.zeros([2, 2]);
-    const yPred = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const expectedVal = tensor1d([(1 * 1 + 2 * 2) / 2, (3 * 3 + 4 * 4) / 2]);
-    const result = losses.meanSquaredError(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('meanAbsoluteError', () => {
-  it('1D', () => {
-    const yTrue = tfc.zeros([3]);
-    const yPred = tensor1d([-1, -2, -3]);
-    const expectedVal = scalar((1 + 2 + 3) / 3);
-    const result = losses.meanAbsoluteError(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-
-  it('2D', () => {
-    const yTrue = tfc.zeros([2, 2]);
-    const yPred = tensor2d([[-1, -2], [-3, -4]], [2, 2]);
-    const expectedVal = tensor1d([(1 + 2) / 2, (3 + 4) / 2]);
-    const result = losses.meanAbsoluteError(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('meanAbsolutePercentageError', () => {
-  it('1D', () => {
-    const yTrue = tensor1d([-1, -2, -3]);
-    const yPred = tfc.zeros([3]);
-    const expectedVal = scalar((1 + 2 + 3) / (1 + 2 + 3) * 100);
-    const result = losses.meanAbsolutePercentageError(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-
-  it('2D', () => {
-    const yTrue = tensor2d([[-1, -2], [-3, -4]], [2, 2]);
-    const yPred = tfc.zeros([2, 2]);
-    const expectedVal =
-        tensor1d([(1 + 2) / (1 + 2) * 100, (3 + 4) / (3 + 4) * 100]);
-    const result = losses.meanAbsolutePercentageError(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('meanSquaredLogarithmicError', () => {
-  function meanSquaredLogErrorFor1DArray(x: number[], y: number[]): number {
-    const calcLog = (val: number) => Math.log(Math.max(val, epsilon()) + 1);
-    const logX = x.map(calcLog);
-    const logY = y.map(calcLog);
-    let acc = 0.0;
-    for (let i = 0; i < x.length; i++) {
-      const diff = logX[i] - logY[i];
-      acc += diff * diff;
-    }
-    return acc / x.length;
-  }
-
-  it('2D', () => {
-    const yTrue = tfc.zeros([2, 2]);
-    const yPred = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const expectedVal = tensor1d([
-      meanSquaredLogErrorFor1DArray([1, 2], [0, 0]),
-      meanSquaredLogErrorFor1DArray([3, 4], [0, 0])
-    ]);
-    const result = losses.meanSquaredLogarithmicError(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('squaredHinge', () => {
-  it('2D', () => {
-    const yTrue = tensor2d([[-1, 2], [-3, 2]], [2, 2]);
-    const yPred = tensor2d([[-3, 5], [3, -2]], [2, 2]);
-    // First row has correct predictions, so loss is 0.
-    const secondRow = [1 - (-3 * 3), 1 - (2 * -2)].map(x => x * x);
-    const expectedVal = tensor1d([0, (secondRow[0] + secondRow[1]) / 2]);
-    const result = losses.squaredHinge(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('hinge', () => {
-  it('2D', () => {
-    const yTrue = tensor2d([[-1, 2], [-3, 2]], [2, 2]);
-    const yPred = tensor2d([[-3, 5], [3, -2]], [2, 2]);
-    // First row has correct predictions, so loss is 0.
-    const secondRow = [1 - (-3 * 3), 1 - (2 * -2)];
-    const expectedVal = tensor1d([0, (secondRow[0] + secondRow[1]) / 2]);
-    const result = losses.hinge(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('categoricalHinge', () => {
-  it('2D', () => {
-    const yTrue = tensor2d([[0, 1, 0], [1, 0, 0]], [2, 3]);
-    const yPred = tensor2d([[0, 2, 0], [1, 3, 2]], [2, 3]);
-    // First row has correct predictions, so loss is 0.
-    const secondRowPos = 1 * 1;
-    const secondRowNeg = Math.max(1 * 3, 1 * 2);
-    const secondRowVal = secondRowNeg - secondRowPos + 1;
-    const expectedVal = tensor1d([0, secondRowVal]);
-    const result = losses.categoricalHinge(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('logcosh', () => {
-  function _logcosh(x: number): number {
-    return x + Math.log(Math.exp(-2 * x) + 1) - Math.log(2);
-  }
-  it('2D', () => {
-    const yTrue = tfc.zeros([2, 2]);
-    const yPred = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const firstRow = [1, 2].map(_logcosh);
-    const secondRow = [3, 4].map(_logcosh);
-    const firstVal = (firstRow[0] + firstRow[1]) / 2;
-    const secondVal = (secondRow[0] + secondRow[1]) / 2;
-    const expectedVal = tensor1d([firstVal, secondVal]);
-    const result = losses.logcosh(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndWebGL2('categoricalCrossentropy ', () => {
-  it('from logits', () => {
-    const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const target = tensor2d([[0.25, 0.75], [0.1, 0.9]], [2, 2]);
-    const expected = tensor1d([
-      -1 *
-          (Math.log(Math.exp(1) / (Math.exp(1) + Math.exp(2))) * 0.25 +
-           Math.log(Math.exp(2) / (Math.exp(1) + Math.exp(2))) * 0.75),
-      -1 *
-          (Math.log(Math.exp(3) / (Math.exp(3) + Math.exp(4))) * 0.1 +
-           Math.log(Math.exp(4) / (Math.exp(3) + Math.exp(4))) * 0.9)
-    ]);
-    const result = losses.categoricalCrossentropy(target, x, true);
-    expectTensorsClose(result, expected);
-  });
-
-  it('from softmax', () => {
-    const x = tensor2d([[0.3, 0.7], [0.4, 0.6]], [2, 2]);
-    const target = tensor2d([[0.25, 0.75], [0.1, 0.9]], [2, 2]);
-    const expected = tensor1d([
-      -1 * (Math.log(0.3) * 0.25 + Math.log(0.7) * 0.75),
-      -1 * (Math.log(0.4) * 0.1 + Math.log(0.6) * 0.9)
-    ]);
-    const result = losses.categoricalCrossentropy(target, x, false);
-    expectTensorsClose(result, expected);
-  });
-
-  // Reference Python code
-  // ```python
-  // import numpy as np
-  // import tensorflow as tf
-  // from tensorflow import keras
-  //
-  // x_value = np.array([[[0.3, 0.7], [0.4, 0.6]], [[0.2, 0.8], [0.55, 0.45]]])
-  // target_value = np.array([[[0, 1], [1, 0]], [[0, 1], [1, 0]]])
-  //
-  // with tf.Session() as sess:
-  //   x = tf.placeholder(tf.float32, [2, 2, 2])
-  //   target = tf.placeholder(tf.float32, [2, 2, 2])
-  //   y = keras.backend.categorical_crossentropy(target, x)
-  //   print(sess.run(y, feed_dict={
-  //       x: x_value,
-  //       target: target_value
-  //   }))
-  // ```
-  it('from softmax, sequence output', () => {
-    const x = tensor3d([[[0.3, 0.7], [0.4, 0.6]], [[0.2, 0.8], [0.55, 0.45]]], [
-      2,
-      2,
-      2,
-    ]);
-    const target = tensor3d([[[0, 1], [1, 0]], [[0, 1], [1, 0]]], [2, 2, 2]);
-    const result = losses.categoricalCrossentropy(target, x, false);
-    expectTensorsClose(
-        result, tensor2d([[0.35667497, 0.9162907], [0.22314353, 0.597837]]));
-  });
-});
-
-describeMathCPUAndWebGL2('sparseCategoricalCrossentropy ', () => {
-  // Reference Python TensorFlow code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // with tf.Session() as sess:
-  //   x = tf.placeholder(tf.float32, [None, 3])
-  //   target = tf.placeholder(tf.float32, [None])
-  //   crossentropy = tf.keras.backend.sparse_categorical_crossentropy(
-  //       target, x)
-  //   out = sess.run(
-  //       crossentropy,
-  //       feed_dict={
-  //           x: np.array([[0.1, 0.2, 0.7], [0.2, 0.3, 0.5]],
-  //                       dtype=np.float32),
-  //           target: np.array([0, 2], dtype=np.float32)
-  //           })
-  //   print(out)
-  // ```
-  it('sparseCategoricalCrossentropy', () => {
-    const x = tensor2d([[0.1, 0.2, 0.7], [0.2, 0.3, 0.5]], [2, 3]);
-    const target = tensor1d([0, 2]);
-    const result = losses.sparseCategoricalCrossentropy(target, x);
-    expectTensorsClose(result, tensor1d([2.3025851, 0.6931472]));
-  });
-});
-
-describeMathCPUAndGPU('sigmoidCrossEntropyWithLogits', () => {
-  it('outputs sigmoid cross-entropy', () => {
-    const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const target = tensor2d([[0.25, 0.75], [0.1, 0.9]], [2, 2]);
-    const targetComplement = tfc.add(scalar(1), tfc.neg(target));
-    const sigmoidX = tfc.sigmoid(x);
-    const sigmoidXComplement = tfc.add(scalar(1), tfc.neg(sigmoidX));
-    const expected = tfc.add(
-        tfc.mul(target, tfc.neg(tfc.log(sigmoidX))),
-        tfc.mul(targetComplement, tfc.neg(tfc.log(sigmoidXComplement))));
-    const result = losses.sigmoidCrossEntropyWithLogits(target, x);
-    expectTensorsClose(result, expected);
-  });
-
-  // Python TensorFlow reference code:
-  // ```py
-  // import numpy as np
-  // import tensorflow as tf
-  //
-  // tf.enable_eager_execution()
-  //
-  // logits = np.array([[-10, -10, -10],
-  //                    [-5, -5, -5],
-  //                    [0, 0, 0],
-  //                    [0.5, 0.5, 0.5],
-  //                    [2, 2, 2]], dtype=np.float32)
-  // labels = np.array([[0, 0.5, 1],
-  //                    [0, 0.5, 1],
-  //                    [0, 0.5, 1],
-  //                    [0, 0.5, 1],
-  //                    [0, 0.5, 1]], dtype=np.float32)
-  //
-  // print(tf.nn.sigmoid_cross_entropy_with_logits(
-  //     logits=logits, labels=labels))
-  // ```
-  it('Comparison with TensorFlow references values', () => {
-    const logits = tensor2d(
-        [[-10, -10, -10], [-5, -5, -5], [0, 0, 0], [0.5, 0.5, 0.5], [2, 2, 2]]);
-    const labels = tensor2d(
-        [[0, 0.5, 1], [0, 0.5, 1], [0, 0.5, 1], [0, 0.5, 1], [0, 0.5, 1]]);
-    const outputs = losses.sigmoidCrossEntropyWithLogits(labels, logits);
-    expectTensorsClose(outputs, tensor2d([
-                         [4.5398901e-05, 5.0000453e+00, 1.0000046e+01],
-                         [6.7153485e-03, 2.5067153e+00, 5.0067153e+00],
-                         [6.9314718e-01, 6.9314718e-01, 6.9314718e-01],
-                         [9.7407699e-01, 7.2407699e-01, 4.7407699e-01],
-                         [2.1269281e+00, 1.1269280e+00, 1.2692800e-01]
-                       ]));
-  });
-});
-
-describeMathCPUAndWebGL2('categoricalCrossentropy', () => {
-  it('2D', () => {
-    const yTrue = tensor2d([[1, 0], [0, 1]], [2, 2]);
-    const yPred = yTrue;
-    const expectedVal = tfc.zeros([2]);
-    const result = losses.categoricalCrossentropy(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndWebGL2('sparseCategoricalCrossentropy', () => {
-  it('2D', () => {
-    const yTrue = tensor1d([0, 1]);
-    const yPred = tensor2d([[1, 0], [0, 1]], [2, 2]);
-    const expectedVal = tfc.zeros([2]);
-    const result = losses.sparseCategoricalCrossentropy(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('binaryCrossentropy', () => {
-  function _binaryCrossentropy(target: Tensor, output: Tensor): Tensor {
-    const targetComplement = tfc.add(scalar(1), tfc.neg(target));
-    const outputComplement = tfc.add(scalar(1), tfc.neg(output));
-    return tfc.mean(
-        tfc.neg(tfc.add(
-            tfc.mul(target, tfc.log(output)),
-            tfc.mul(targetComplement, tfc.log(outputComplement)))),
-        -1);
-  }
-
-  it('from sigmoid', () => {
-    const x = tensor2d([[0.3, 0.7], [0.4, 0.6]], [2, 2]);
-    const target = tensor2d([[0.25, 0.75], [0.1, 0.9]], [2, 2]);
-    const expected = _binaryCrossentropy(target, x);
-    const result = losses.binaryCrossentropy(target, x);
-    expectTensorsClose(result, expected);
-  });
-});
-
-describeMathCPUAndWebGL2('kullbackLeiblerDivergence', () => {
-  function klElement(actual: number, predicted: number): number {
-    actual = Math.max(actual, epsilon());
-    predicted = Math.max(predicted, epsilon());
-    return actual * Math.log(actual / predicted);
-  }
-
-  it('2D', () => {
-    const yTrue = tensor2d([[1, 0], [1, 0]], [2, 2]);
-    const yPred = tensor2d([[0.25, 0.75], [0.9, 0.1]], [2, 2]);
-    const expectedVal = tensor1d([
-      klElement(1, 0.25) + klElement(0, 0.75),
-      klElement(1, 0.9) + klElement(0, 0.1),
-    ]);
-    const result = losses.kullbackLeiblerDivergence(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('poisson', () => {
-  function poissonElement(actual: number, predicted: number): number {
-    return predicted - actual * Math.log(predicted + epsilon());
-  }
-
-  it('2D', () => {
-    const yTrue = tensor2d([[1, 0], [1, 0]], [2, 2]);
-    const yPred = tensor2d([[0.25, 0.75], [0.9, 0.1]], [2, 2]);
-    const expectedVal = tensor1d([
-      (poissonElement(1, 0.25) + poissonElement(0, 0.75)) / 2,
-      (poissonElement(1, 0.9) + poissonElement(0, 0.1)) / 2,
-    ]);
-    const result = losses.poisson(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describeMathCPUAndGPU('cosineProximity', () => {
-  it('2D', () => {
-    const z = Math.sqrt(2) / 2;
-    const yTrue = tensor2d([[1, 0], [1, 0]], [2, 2]);
-    const yPred = tensor2d([[z, z], [0, 1]], [2, 2]);
-    const expectedVal = tensor1d([-1 * z, 0]);
-    const result = losses.cosineProximity(yTrue, yPred);
-    expectTensorsClose(result, expectedVal);
-  });
-});
-
-describe('losses get', () => {
-  for (const lossName
-           of ['meanSquaredError', 'meanAbsoluteError',
-               'meanAbsolutePercentageError', 'meanSquaredLogarithmicError',
-               'squaredHinge', 'hinge', 'categoricalHinge', 'logcosh',
-               'categoricalCrossentropy', 'sparseCategoricalCrossentropy',
-               'binaryCrossentropy', 'kullbackLeiblerDivergence', 'poisson',
-               'cosineProximity']) {
-    it(`can get ${lossName}`, () => {
-      losses.get(lossName);
-    });
-  }
-
-  it(`get custom loss works`, () => {
-    const customLoss = (x: Tensor, y: Tensor) => scalar(42.0);
-    expect(losses.get(customLoss)).toEqual(customLoss);
-  });
-});
-
-describeMathCPUAndWebGL2('l2Normalize', () => {
-  it('normalizes with no axis defined.', () => {
-    const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const norm = Math.sqrt(1 * 1 + 2 * 2 + 3 * 3 + 4 * 4);
-    const expected =
-        tensor2d([[1 / norm, 2 / norm], [3 / norm, 4 / norm]], [2, 2]);
-    const result = losses.l2Normalize(x);
-    expectTensorsClose(result, expected);
-  });
-
-  it('normalizes along axis = -1.', () => {
-    const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
-    const firstNorm = Math.sqrt(1 * 1 + 2 * 2);
-    const secondNorm = Math.sqrt(3 * 3 + 4 * 4);
-    const expected = tensor2d(
-        [[1 / firstNorm, 2 / firstNorm], [3 / secondNorm, 4 / secondNorm]],
-        [2, 2]);
-    const result = losses.l2Normalize(x, -1);
-    expectTensorsClose(result, expected);
-  });
-
-  it('normalizes with zeros.', () => {
-    const x = tfc.zeros([2, 2]);
-    const result = losses.l2Normalize(x);
-    expectTensorsClose(result, x);
-  });
-
-  it('normalizes casts int32 as float32.', () => {
-    const x = tensor2d([[1, 2], [3, 4]], [2, 2], 'int32');
-    const norm = Math.sqrt(1 * 1 + 2 * 2 + 3 * 3 + 4 * 4);
-    const expected =
-        tensor2d([[1 / norm, 2 / norm], [3 / norm, 4 / norm]], [2, 2]);
-    const result = losses.l2Normalize(x);
-    expectTensorsClose(result, expected);
-  });
-
-  it('normalizes casts bool as float32.', () => {
-    const x = tensor2d([[1, 1]], [1, 2], 'bool');
-    const norm = Math.sqrt(1 * 1 + 1 * 1);
-    const expected = tensor2d([[1 / norm, 1 / norm]], [1, 2]);
-    const result = losses.l2Normalize(x);
-    expectTensorsClose(result, expected);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/metrics.ts b/tfjs-master/tfjs-layers/src/metrics.ts
deleted file mode 100644
index a8080d8bf..000000000
--- a/tfjs-master/tfjs-layers/src/metrics.ts
+++ /dev/null
@@ -1,196 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Built-in metrics.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {Tensor, tidy} from '@tensorflow/tfjs-core';
-
-import * as K from './backend/tfjs_backend';
-import {NotImplementedError, ValueError} from './errors';
-import {categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss} from './losses';
-import {binaryCrossentropy as lossBinaryCrossentropy} from './losses';
-import {lossesMap} from './losses';
-import {LossOrMetricFn} from './types';
-import * as util from './utils/generic_utils';
-
-export function binaryAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const threshold = tfc.mul(.5, tfc.onesLike(yPred));
-    const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);
-    return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);
-  });
-}
-
-export function categoricalAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(
-      () => K.cast(
-          tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));
-}
-
-function truePositives(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    return tfc.cast(
-        tfc.sum(tfc.logicalAnd(tfc.equal(yTrue, 1), tfc.equal(yPred, 1))),
-        'float32');
-  });
-}
-
-function falseNegatives(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    return tfc.cast(
-        tfc.sum(tfc.logicalAnd(tfc.equal(yTrue, 1), tfc.equal(yPred, 0))),
-        'float32');
-  });
-}
-
-function falsePositives(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    return tfc.cast(
-        tfc.sum(tfc.logicalAnd(tfc.equal(yTrue, 0), tfc.equal(yPred, 1))),
-        'float32');
-  });
-}
-
-export function precision(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const tp = truePositives(yTrue, yPred);
-    const fp = falsePositives(yTrue, yPred);
-
-    const denominator = tfc.add(tp, fp);
-
-    return tfc.cast(
-        tfc.where(tfc.greater(denominator, 0), tfc.div(tp, denominator), 0),
-        'float32');
-  });
-}
-
-export function recall(yTrue: Tensor, yPred: Tensor): Tensor {
-  return tidy(() => {
-    const tp = truePositives(yTrue, yPred);
-    const fn = falseNegatives(yTrue, yPred);
-
-    const denominator = tfc.add(tp, fn);
-
-    return tfc.cast(
-        tfc.where(tfc.greater(denominator, 0), tfc.div(tp, denominator), 0),
-        'float32');
-  });
-}
-
-export function binaryCrossentropy(yTrue: Tensor, yPred: Tensor): Tensor {
-  return lossBinaryCrossentropy(yTrue, yPred);
-}
-
-export function sparseCategoricalAccuracy(
-    yTrue: Tensor, yPred: Tensor): Tensor {
-  if (yTrue.rank === yPred.rank) {
-    yTrue = tfc.squeeze(yTrue, [yTrue.rank - 1]);
-  }
-  yPred = tfc.argMax(yPred, -1);
-  if (yPred.dtype !== yTrue.dtype) {
-    yPred = tfc.cast(yPred, yTrue.dtype);
-  }
-  return tfc.cast(tfc.equal(yTrue, yPred), 'float32');
-}
-
-export function topKCategoricalAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {
-  throw new NotImplementedError();
-}
-
-export function sparseTopKCategoricalAccuracy(
-    yTrue: Tensor, yPred: Tensor): Tensor {
-  throw new NotImplementedError();
-}
-
-// Aliases.
-export const mse = meanSquaredError;
-export const MSE = meanSquaredError;
-export const mae = meanAbsoluteError;
-export const MAE = meanAbsoluteError;
-export const mape = meanAbsolutePercentageError;
-export const MAPE = meanAbsolutePercentageError;
-export const categoricalCrossentropy = categoricalCrossentropyLoss;
-export const cosine = cosineProximity;
-export const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;
-
-// TODO(cais, nielsene): Add serialize().
-
-export const metricsMap: {[functionName: string]: LossOrMetricFn} = {
-  binaryAccuracy,
-  categoricalAccuracy,
-  precision,
-  categoricalCrossentropy,
-  sparseCategoricalCrossentropy,
-  mse,
-  MSE,
-  mae,
-  MAE,
-  mape,
-  MAPE,
-  cosine
-};
-
-export function get(identifier: string|LossOrMetricFn): LossOrMetricFn {
-  if (typeof identifier === 'string' && identifier in metricsMap) {
-    return metricsMap[identifier];
-  } else if (typeof identifier !== 'string' && identifier != null) {
-    return identifier;
-  } else {
-    throw new ValueError(`Unknown metric ${identifier}`);
-  }
-}
-
-/**
- * Get the shortcut function name.
- *
- * If the fn name is a string,
- *   directly return the string name.
- * If the function is included in metricsMap or lossesMap,
- *   return key of the map.
- *   - If the function relative to multiple keys,
- *     return the first found key as the function name.
- *   - If the function exists in both lossesMap and metricsMap,
- *     search lossesMap first.
- * If the function is not included in metricsMap or lossesMap,
- *   return the function name.
- *
- * @param fn loss function, metric function, or short cut name.
- * @returns Loss or Metric name in string.
- */
-export function getLossOrMetricName(fn: string|LossOrMetricFn): string {
-  util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);
-  if (typeof fn === 'string') {
-    return fn;
-  } else {
-    let fnName;
-    for (const key of Object.keys(lossesMap)) {
-      if (lossesMap[key] === fn) {
-        fnName = key;
-        break;
-      }
-    }
-    if (fnName !== undefined) {
-      return fnName;
-    }
-    for (const key of Object.keys(metricsMap)) {
-      if (metricsMap[key] === fn) {
-        fnName = key;
-        break;
-      }
-    }
-    if (fnName !== undefined) {
-      return fnName;
-    }
-    return (fn as Function).name;
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/metrics_test.ts b/tfjs-master/tfjs-layers/src/metrics_test.ts
deleted file mode 100644
index 3bcb3308b..000000000
--- a/tfjs-master/tfjs-layers/src/metrics_test.ts
+++ /dev/null
@@ -1,328 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for metrics.ts.
- */
-
-import {scalar, Tensor, tensor, tensor1d, tensor2d} from '@tensorflow/tfjs-core';
-
-import {setEpsilon} from './backend/common';
-import * as tfl from './index';
-import {binaryAccuracy, categoricalAccuracy, get, getLossOrMetricName} from './metrics';
-import {LossOrMetricFn} from './types';
-import {describeMathCPUAndGPU, describeMathCPUAndWebGL2, expectTensorsClose} from './utils/test_utils';
-
-describeMathCPUAndGPU('binaryAccuracy', () => {
-  it('1D exact', () => {
-    const x = tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);
-    const y = tensor1d([1, 0, 1, 0, 0, 1, 0, 1]);
-    const accuracy = tfl.metrics.binaryAccuracy(x, y);
-    expectTensorsClose(accuracy, scalar(0.5));
-  });
-  it('2D thresholded', () => {
-    const x = tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);
-    const y = tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);
-    const accuracy = tfl.metrics.binaryAccuracy(x, y);
-    expectTensorsClose(accuracy, scalar(5 / 8));
-  });
-  it('2D exact', () => {
-    const x = tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);
-    const y = tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);
-    const accuracy = tfl.metrics.binaryAccuracy(x, y);
-    expectTensorsClose(accuracy, tensor1d([0.5, 0.75]));
-  });
-  it('2D thresholded', () => {
-    const x = tensor2d([[1, 1], [1, 1], [0, 0], [0, 0]], [4, 2]);
-    const y =
-        tensor2d([[0.2, 0.4], [0.6, 0.8], [0.2, 0.3], [0.4, 0.7]], [4, 2]);
-    const accuracy = tfl.metrics.binaryAccuracy(x, y);
-    expectTensorsClose(accuracy, tensor1d([0, 1, 1, 0.5]));
-  });
-});
-
-describeMathCPUAndGPU('sparseCategoricalAccuracy', () => {
-  it('1D float32 yTrue, 2D yPred', () => {
-    const yTrue = tensor1d([1, 1, 2, 2, 0]);
-    const yPred = tensor2d(
-        [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);
-    const accuracy = tfl.metrics.sparseCategoricalAccuracy(yTrue, yPred);
-    expectTensorsClose(accuracy.toBool(), tensor1d([1, 0, 1, 0, 1], 'bool'));
-  });
-  it('1D int32 yTrue, 2D yPred', () => {
-    const yTrue = tensor1d([1, 1, 2, 2, 0], 'int32');
-    const yPred = tensor2d(
-        [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);
-    const accuracy = tfl.metrics.sparseCategoricalAccuracy(yTrue, yPred);
-    expectTensorsClose(accuracy.toBool(), tensor1d([1, 0, 1, 0, 1], 'bool'));
-  });
-  it('2D int32 yTrue, 2D yPred', () => {
-    const yTrue = tensor2d([1, 1, 2, 2, 0], [5, 1], 'int32');
-    const yPred = tensor2d(
-        [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);
-    const accuracy = tfl.metrics.sparseCategoricalAccuracy(yTrue, yPred);
-    expectTensorsClose(accuracy.toBool(), tensor1d([1, 0, 1, 0, 1], 'bool'));
-  });
-});
-
-describeMathCPUAndWebGL2('binaryCrossentropy', () => {
-  beforeEach(() => {
-    setEpsilon(1e-7);
-  });
-
-  it('2D single-value yTrue', () => {
-    // Use the following Python code to generate the reference values:
-    // ```python
-    // import keras
-    // import numpy as np
-    // import tensorflow as tf
-    //
-    // with tf.Session() as sess:
-    //   x = tf.Variable(np.array(
-    //       [[0], [0], [0], [1], [1], [1]],
-    //       dtype=np.float32))
-    //   y = tf.Variable(np.array(
-    //       [[0], [0.5], [1], [0], [0.5], [1]],
-    //       dtype=np.float32))
-    //   z = keras.metrics.binary_crossentropy(x, y)
-    //
-    //   sess.run(tf.global_variables_initializer())
-    //   print(sess.run(z))
-    // ```
-    const x = tensor2d([[0], [0], [0], [1], [1], [1]]);
-    const y = tensor2d([[0], [0.5], [1], [0], [0.5], [1]]);
-    const accuracy = tfl.metrics.binaryCrossentropy(x, y);
-    expectTensorsClose(accuracy, tensor1d([
-                         1.00000015e-07, 6.93147182e-01, 1.59423847e+01,
-                         1.61180954e+01, 6.93147182e-01, 1.19209332e-07
-                       ]));
-  });
-  it('2D one-hot binary yTrue', () => {
-    // Use the following Python code to generate the reference values:
-    // ```python
-    // import keras
-    // import numpy as np
-    // import tensorflow as tf
-    //
-    // with tf.Session() as sess:
-    //   x = tf.Variable(np.array(
-    //       [[1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1]],
-    //       dtype=np.float32))
-    //   y = tf.Variable(np.array(
-    //       [[1, 0], [0.5, 0.5], [0, 1], [1, 0], [0.5, 0.5], [0, 1]],
-    //       dtype=np.float32))
-    //   z = keras.metrics.binary_crossentropy(x, y)
-    //
-    //   sess.run(tf.global_variables_initializer())
-    //   print(sess.run(z))
-    // ```
-    const x = tensor2d([[1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1]]);
-    const y =
-        tensor2d([[1, 0], [0.5, 0.5], [0, 1], [1, 0], [0.5, 0.5], [0, 1]]);
-    const accuracy = tfl.metrics.binaryCrossentropy(x, y);
-    expectTensorsClose(accuracy, tensor1d([
-                         1.0960467e-07, 6.9314718e-01, 1.6030239e+01,
-                         1.6030239e+01, 6.9314718e-01, 1.0960467e-07
-                       ]));
-  });
-});
-
-describeMathCPUAndGPU('categoricalAccuracy', () => {
-  it('1D', () => {
-    const x = tensor1d([0, 0, 0, 1]);
-    const y = tensor1d([0.1, 0.8, 0.05, 0.05]);
-    const accuracy = tfl.metrics.categoricalAccuracy(x, y);
-    expect(accuracy.dtype).toEqual('float32');
-    expect(accuracy.shape).toEqual([]);
-    expect(Array.from(accuracy.dataSync())).toEqual([0]);
-  });
-  it('2D', () => {
-    const x = tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]], [2, 4]);
-    const y =
-        tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]], [2, 4]);
-    const accuracy = tfl.metrics.categoricalAccuracy(x, y);
-    expect(accuracy.dtype).toEqual('float32');
-    expect(accuracy.shape).toEqual([2]);
-    expect(Array.from(accuracy.dataSync())).toEqual([0, 1]);
-  });
-});
-
-describeMathCPUAndWebGL2('categoricalCrossentropy metric', () => {
-  it('1D', () => {
-    const x = tensor1d([0, 0, 0, 1]);
-    const y = tensor1d([0.1, 0.8, 0.05, 0.05]);
-    const accuracy = tfl.metrics.categoricalCrossentropy(x, y);
-    expect(accuracy.dtype).toEqual('float32');
-    expectTensorsClose(accuracy, scalar(2.995732));
-  });
-  it('2D', () => {
-    const x = tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);
-    const y = tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);
-    const accuracy = tfl.metrics.categoricalCrossentropy(x, y);
-    expect(accuracy.dtype).toEqual('float32');
-    expectTensorsClose(accuracy, tensor1d([2.995732, 0.22314353]));
-  });
-});
-
-describeMathCPUAndGPU('precision metric', () => {
-  it('1D', () => {
-    const x = tensor1d([0, 0, 0, 1]);
-    const y = tensor1d([0, 0, 0, 1]);
-    const precision = tfl.metrics.precision(x, y);
-    expect(precision.dtype).toEqual('float32');
-    expectTensorsClose(precision, scalar(1));
-  });
-  it('2D', () => {
-    // Use the following Python code to generate the reference values:
-    // ```python
-    //
-    // import tensorflow as tf
-    // import numpy as np
-    //
-    // with tf.Session() as session:
-    //     labels = tf.constant([
-    //       [0, 0, 1],
-    //       [0, 0, 1],
-    //       [1, 0, 0],
-    //       [0, 1, 0]
-    //     ])
-    //     prediction = tf.constant([
-    //       [0, 0, 1],
-    //       [1, 0, 0],
-    //       [1, 0, 0],
-    //       [0, 1, 0]
-    //     ])
-    //
-    //     output = tf.metrics.precision(
-    //       labels,
-    //       prediction
-    //     )
-    //
-    //     init = tf.local_variables_initializer()
-    //     session.run(init)
-    //
-    //     result = session.run(output)
-    //     print(result[0])
-    // ```
-    const x = tensor2d([[0, 0, 1], [0, 0, 1], [1, 0, 0], [0, 1, 0]]);
-    const y = tensor2d([[0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 1, 0]]);
-    const precision = tfl.metrics.precision(x, y);
-    expect(precision.dtype).toEqual('float32');
-    expectTensorsClose(precision, scalar(0.75));
-  });
-
-  it('2D edge case', () => {
-    const x = tensor2d([[0, 0, 1], [0, 0, 1], [1, 0, 0], [0, 1, 0]]);
-    const y = tensor2d([[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]);
-    const precision = tfl.metrics.precision(x, y);
-    expect(precision.dtype).toEqual('float32');
-    expectTensorsClose(precision, scalar(0));
-  });
-});
-
-describeMathCPUAndGPU('recall metric', () => {
-  it('1D', () => {
-    const x = tensor1d([1, 0, 1, 0, 0, 0, 1]);
-    const y = tensor1d([0, 0, 1, 1, 1, 1, 0]);
-    const recall = tfl.metrics.recall(x, y);
-    expect(recall.dtype).toEqual('float32');
-    expectTensorsClose(recall, scalar(0.33333334));
-  });
-  it('2D', () => {
-    // Use the following Python code to generate the reference values:
-    // ```python
-    //
-    // import tensorflow as tf
-    // import numpy as np
-    //
-    // with tf.Session() as session:
-    //     labels = tf.constant([
-    //       [0, 0, 1],
-    //       [0, 0, 1],
-    //       [1, 0, 0],
-    //       [0, 1, 0]
-    //     ])
-    //     prediction = tf.constant([
-    //       [0, 0, 1],
-    //       [1, 0, 0],
-    //       [1, 0, 0],
-    //       [0, 1, 0]
-    //     ])
-    //
-    //     output = tf.metrics.recall(
-    //       labels,
-    //       prediction
-    //     )
-    //
-    //     init = tf.local_variables_initializer()
-    //     session.run(init)
-    //
-    //     result = session.run(output)
-    //     print(result[0])
-    // ```
-    const x = tensor2d([[0, 0, 1], [0, 0, 1], [1, 0, 0], [0, 1, 0]]);
-    const y = tensor2d([[0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 1, 0]]);
-    const recall = tfl.metrics.recall(x, y);
-    expect(recall.dtype).toEqual('float32');
-    expectTensorsClose(recall, scalar(0.75));
-  });
-
-  it('2D edge case', () => {
-    const x = tensor2d([[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]);
-    const y = tensor2d([[0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 1, 0]]);
-    const recall = tfl.metrics.recall(x, y);
-    expect(recall.dtype).toEqual('float32');
-    expectTensorsClose(recall, scalar(0));
-  });
-});
-
-describe('metrics.get', () => {
-  it('valid name, not alias', () => {
-    expect(get('binaryAccuracy') === get('categoricalAccuracy')).toEqual(false);
-  });
-  it('valid name, alias', () => {
-    expect(get('mse') === get('MSE')).toEqual(true);
-  });
-  it('invalid name', () => {
-    expect(() => get('InvalidMetricName')).toThrowError(/Unknown metric/);
-  });
-  it('LossOrMetricFn input', () => {
-    expect(get(binaryAccuracy)).toEqual(binaryAccuracy);
-    expect(get(categoricalAccuracy)).toEqual(categoricalAccuracy);
-  });
-});
-
-describe('getLossOrMetricName', () => {
-  it('string short cut name', async () => {
-    const fnName = getLossOrMetricName('meanSquaredError');
-    expect(fnName).toMatch(/meanSquaredError/);
-  });
-
-  it('function included in losses map', async () => {
-    const fnName = getLossOrMetricName(tfl.metrics.meanSquaredError);
-    expect(fnName).toMatch(/meanSquaredError/);
-  });
-
-  it('function included in metrics map', async () => {
-    const fnName = getLossOrMetricName(tfl.metrics.categoricalAccuracy);
-    expect(fnName).toMatch(/categoricalAccuracy/);
-  });
-
-  it('function not included in losses map or metrics map', async () => {
-    const fakeMetric: LossOrMetricFn = (yTrue: Tensor, yPred: Tensor) =>
-        tensor([1]);
-    const fnName = getLossOrMetricName(fakeMetric);
-    expect(fnName).toEqual('fakeMetric');
-  });
-
-  it('throws null', async () => {
-    expect(() => getLossOrMetricName(null)).toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/model_save_test.ts b/tfjs-master/tfjs-layers/src/model_save_test.ts
deleted file mode 100644
index 026c0e88c..000000000
--- a/tfjs-master/tfjs-layers/src/model_save_test.ts
+++ /dev/null
@@ -1,579 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {io, linalg, randomNormal, Tensor, zeros} from '@tensorflow/tfjs-core';
-
-import * as tfl from './index';
-// tslint:disable-next-line:max-line-length
-import {describeMathCPUAndGPU, describeMathCPUAndWebGL2, expectTensorsClose} from './utils/test_utils';
-import {version} from './version';
-
-describeMathCPUAndGPU('LayersModel.save', () => {
-  class IOHandlerForTest implements io.IOHandler {
-    savedArtifacts: io.ModelArtifacts;
-
-    async save(modelArtifacts: io.ModelArtifacts): Promise<io.SaveResult> {
-      this.savedArtifacts = modelArtifacts;
-      return {modelArtifactsInfo: null};
-    }
-  }
-
-  class EmptyIOHandler implements io.IOHandler {}
-
-  it('Model artifacts contains meta-information: Sequential', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 3, inputShape: [5]}));
-    const handler = new IOHandlerForTest();
-
-    await model.save(handler);
-    expect(handler.savedArtifacts.format).toEqual('layers-model');
-    expect(handler.savedArtifacts.generatedBy)
-        .toEqual(`TensorFlow.js tfjs-layers v${version}`);
-    expect(handler.savedArtifacts.convertedBy).toEqual(null);
-  });
-
-  it('Model artifacts contains meta-information: Functional', async () => {
-    const input = tfl.input({shape: [5]});
-    const output =
-        tfl.layers.dense({units: 3}).apply(input) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: input, outputs: output});
-    const handler = new IOHandlerForTest();
-
-    await model.save(handler);
-    expect(handler.savedArtifacts.format).toEqual('layers-model');
-    expect(handler.savedArtifacts.generatedBy)
-        .toEqual(`TensorFlow.js tfjs-layers v${version}`);
-    expect(handler.savedArtifacts.convertedBy).toEqual(null);
-  });
-
-  it('Saving all weights succeeds', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 3, inputShape: [5]}));
-    const handler = new IOHandlerForTest();
-
-    await model.save(handler);
-    expect(handler.savedArtifacts.modelTopology)
-        .toEqual(model.toJSON(null, false));
-    expect(handler.savedArtifacts.weightSpecs.length).toEqual(2);
-    expect(handler.savedArtifacts.weightSpecs[0].name.indexOf('/kernel'))
-        .toBeGreaterThan(0);
-    expect(handler.savedArtifacts.weightSpecs[0].shape).toEqual([5, 3]);
-    expect(handler.savedArtifacts.weightSpecs[0].dtype).toEqual('float32');
-    expect(handler.savedArtifacts.weightSpecs[1].name.indexOf('/bias'))
-        .toBeGreaterThan(0);
-    expect(handler.savedArtifacts.weightSpecs[1].shape).toEqual([3]);
-    expect(handler.savedArtifacts.weightSpecs[1].dtype).toEqual('float32');
-  });
-
-  it('Saving only trainable weights succeeds', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 3, inputShape: [5], trainable: false}));
-    model.add(tfl.layers.dense({units: 2}));
-    const handler = new IOHandlerForTest();
-
-    await model.save(handler, {trainableOnly: true});
-    expect(handler.savedArtifacts.modelTopology)
-        .toEqual(model.toJSON(null, false));
-    // Verify that only the trainable weights (i.e., weights from the
-    // 2nd, trainable Dense layer) are saved.
-    expect(handler.savedArtifacts.weightSpecs.length).toEqual(2);
-    expect(handler.savedArtifacts.weightSpecs[0].name.indexOf('/kernel'))
-        .toBeGreaterThan(0);
-    expect(handler.savedArtifacts.weightSpecs[0].shape).toEqual([3, 2]);
-    expect(handler.savedArtifacts.weightSpecs[0].dtype).toEqual('float32');
-    expect(handler.savedArtifacts.weightSpecs[1].name.indexOf('/bias'))
-        .toBeGreaterThan(0);
-    expect(handler.savedArtifacts.weightSpecs[1].shape).toEqual([2]);
-    expect(handler.savedArtifacts.weightSpecs[1].dtype).toEqual('float32');
-  });
-
-  it('Saving to a handler without save method fails', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 3, inputShape: [5]}));
-    const handler = new EmptyIOHandler();
-    model.save(handler)
-        .then(saveResult => {
-          fail(
-              'Saving with an IOHandler without `save` succeeded ' +
-              'unexpectedly.');
-        })
-        .catch(err => {
-          expect(err.message)
-              .toEqual(
-                  'LayersModel.save() cannot proceed because the IOHandler ' +
-                  'provided does not have the `save` attribute defined.');
-        });
-  });
-});
-
-describeMathCPUAndWebGL2('Save-load round trips', () => {
-  it('Sequential model, Local storage', async () => {
-    const model1 = tfl.sequential();
-    model1.add(
-        tfl.layers.dense({units: 2, inputShape: [2], activation: 'relu'}));
-    model1.add(tfl.layers.dense({units: 1, useBias: false}));
-
-    // Use a randomly generated model path to prevent collision.
-    const path = `testModel${new Date().getTime()}_${Math.random()}`;
-
-    // First save the model to local storage.
-    const modelURL = `localstorage://${path}`;
-    await model1.save(modelURL);
-    // Once the saving succeeds, load the model back.
-    const model2 = await tfl.loadLayersModel(modelURL);
-    // Verify that the topology of the model is correct.
-    expect(model2.toJSON(null, false)).toEqual(model1.toJSON(null, false));
-
-    // Check the equality of the two models' weights.
-    const weights1 = model1.getWeights();
-    const weights2 = model2.getWeights();
-    expect(weights2.length).toEqual(weights1.length);
-    for (let i = 0; i < weights1.length; ++i) {
-      expectTensorsClose(weights1[i], weights2[i]);
-    }
-  });
-
-  it('loadLayersModel: save and load a model with empty weights', async () => {
-    // https://github.com/tensorflow/tfjs/issues/7865
-    // Models without weights should still be valid models
-    const model1 = tfl.sequential();
-    model1.add(
-      tfl.layers.upSampling2d({
-        size: [2, 2],
-        dataFormat: 'channelsLast',
-        inputShape: [null, null, 3],
-      })
-    );
-
-    // Use a randomly generated model path to prevent collision.
-    const path = `testModel${new Date().getTime()}_${Math.random()}`;
-
-    // First save the model to local storage.
-    const modelURL = `localstorage://${path}`;
-    await model1.save(modelURL);
-    // Once the saving succeeds, load the model back.
-    const model2 = await tfl.loadLayersModel(modelURL);
-    // Verify that the topology of the model is correct.
-    expect(model2.toJSON(null, false)).toEqual(model1.toJSON(null, false));
-
-    // Check the equality of the two models' weights.
-    const weights1 = model1.getWeights();
-    const weights2 = model2.getWeights();
-    expect(weights2.length).toEqual(weights1.length);
-    for (let i = 0; i < weights1.length; ++i) {
-      expectTensorsClose(weights1[i], weights2[i]);
-    }
-  });
-
-  it('Functional model, IndexedDB', async () => {
-    const input = tfl.input({shape: [2, 2]});
-    const layer1 = tfl.layers.flatten().apply(input);
-    const layer2 =
-        tfl.layers.dense({units: 2}).apply(layer1) as tfl.SymbolicTensor;
-    const model1 = tfl.model({inputs: input, outputs: layer2});
-    // Use a randomly generated model path to prevent collision.
-    const path = `testModel${new Date().getTime()}_${Math.random()}`;
-
-    // First save the model to local storage.
-    const modelURL = `indexeddb://${path}`;
-    await model1.save(modelURL);
-    // Once the saving succeeds, load the model back.
-    const model2 = await tfl.loadLayersModel(modelURL);
-    // Verify that the topology of the model is correct.
-    expect(model2.toJSON(null, false)).toEqual(model1.toJSON(null, false));
-
-    // Check the equality of the two models' weights.
-    const weights1 = model1.getWeights();
-    const weights2 = model2.getWeights();
-    expect(weights2.length).toEqual(weights1.length);
-    for (let i = 0; i < weights1.length; ++i) {
-      expectTensorsClose(weights1[i], weights2[i]);
-    }
-  });
-
-  it('Call predict() and fit() after load: conv2d model', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.conv2d({
-      filters: 8,
-      kernelSize: 4,
-      inputShape: [28, 28, 1],
-      padding: 'same',
-      activation: 'relu'
-    }));
-    model.add(tfl.layers.maxPooling2d({
-      poolSize: 2,
-      padding: 'same',
-    }));
-    model.add(tfl.layers.flatten());
-    model.add(tfl.layers.dense({units: 1}));
-
-    const x = randomNormal([1, 28, 28, 1]);
-    const y = model.predict(x) as Tensor;
-
-    const path = `testModel${new Date().getTime()}_${Math.random()}`;
-    const url = `indexeddb://${path}`;
-    await model.save(url);
-    // Load the model back.
-    const modelPrime = await tfl.loadLayersModel(url);
-    // Call predict() on the loaded model and assert the result
-    // equals the original predict() result.
-    const yPrime = modelPrime.predict(x) as Tensor;
-    expectTensorsClose(y, yPrime);
-
-    // Call compile and fit() on the loaded model.
-    modelPrime.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-    const trainExamples = 10;
-    await modelPrime.fit(
-        randomNormal([trainExamples, 28, 28, 1]),
-        randomNormal([trainExamples, 1]), {epochs: 4});
-  });
-
-  it('Call predict() and fit() after load: conv1d model', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.conv1d({
-      filters: 8,
-      kernelSize: 4,
-      inputShape: [100, 1],
-      padding: 'same',
-      activation: 'relu'
-    }));
-    model.add(tfl.layers.maxPooling1d({
-      poolSize: 2,
-      padding: 'same',
-    }));
-    model.add(tfl.layers.flatten());
-    model.add(tfl.layers.dense({units: 1}));
-
-    const x = randomNormal([1, 100, 1]);
-    const y = model.predict(x) as Tensor;
-
-    const path = `testModel${new Date().getTime()}_${Math.random()}`;
-    const url = `indexeddb://${path}`;
-    await model.save(url);
-    // Load the model back.
-    const modelPrime = await tfl.loadLayersModel(url);
-    // Call predict() on the loaded model and assert the
-    // result equals the original predict() result.
-    const yPrime = modelPrime.predict(x) as Tensor;
-    expectTensorsClose(y, yPrime);
-
-    // Call compile and fit() on the loaded model.
-    modelPrime.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-    const trainExamples = 10;
-    await modelPrime.fit(
-        randomNormal([trainExamples, 100, 1]), randomNormal([trainExamples, 1]),
-        {epochs: 4});
-  });
-
-  it('Call predict() and fit() after load: Bidirectional LSTM', async () => {
-    const model = tfl.sequential();
-    const lstmUnits = 3;
-    const sequenceLength = 4;
-    const inputDims = 5;
-    model.add(tfl.layers.bidirectional({
-      layer: tfl.layers.lstm({units: lstmUnits}) as tfl.RNN,
-      mergeMode: 'concat',
-      inputShape: [sequenceLength, inputDims]
-    }));
-
-    const x = randomNormal([2, 4, 5]);
-    const y = model.predict(x) as Tensor;
-
-    const path = `testModel${new Date().getTime()}_${Math.random()}`;
-    const url = `indexeddb://${path}`;
-    await model.save(url);
-    const modelPrime = await tfl.loadLayersModel(url);
-    const yPrime = modelPrime.predict(x) as Tensor;
-    expectTensorsClose(y, yPrime);
-
-    // Call compile and fit() on the loaded model.
-    modelPrime.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-    const trainExamples = 2;
-    await modelPrime.fit(
-        randomNormal([trainExamples, sequenceLength, inputDims]),
-        randomNormal([trainExamples, lstmUnits * 2]), {epochs: 2});
-  });
-
-  it('Load model: Fast init w/ weights: Sequential & LSTM', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.lstm({
-      units: 2,
-      inputShape: [3, 4],
-      recurrentInitializer: 'orthogonal',
-      kernelInitializer: 'orthogonal',
-      biasInitializer: 'randomNormal',
-    }));
-    let savedArtifacts: io.ModelArtifacts;
-    await model.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return {modelArtifactsInfo: null};
-        }));
-    const weights = model.getWeights();
-
-    const modelPrime = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    const weightsPrime = modelPrime.getWeights();
-    expect(weightsPrime.length).toEqual(weights.length);
-    for (let i = 0; i < weights.length; ++i) {
-      expectTensorsClose(weightsPrime[i], weights[i]);
-    }
-  });
-
-  it('Loading model: Fast init w/ weights: timeDistributed', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.timeDistributed({
-      inputShape: [3, 4],
-      layer: tfl.layers.dense(
-          {units: 4, kernelInitializer: 'orthogonal', useBias: false})
-    }));
-    let savedArtifacts: io.ModelArtifacts;
-    await model.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return {modelArtifactsInfo: null};
-        }));
-    const weights = model.getWeights();
-
-    const modelPrime = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    const weightsPrime = modelPrime.getWeights();
-    expect(weightsPrime.length).toEqual(weights.length);
-    for (let i = 0; i < weights.length; ++i) {
-      expectTensorsClose(weightsPrime[i], weights[i]);
-    }
-  });
-
-  it('Loading model: Fast init w/ weights: bidirectional', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.bidirectional({
-      inputShape: [3, 4],
-      mergeMode: 'concat',
-      layer: tfl.layers.lstm({
-        units: 4,
-        kernelInitializer: 'orthogonal',
-        recurrentInitializer: 'orthogonal',
-        biasInitializer: 'glorotNormal'
-      }) as tfl.RNN
-    }));
-    let savedArtifacts: io.ModelArtifacts;
-    await model.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return {modelArtifactsInfo: null};
-        }));
-    const weights = model.getWeights();
-
-    const modelPrime = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    const weightsPrime = modelPrime.getWeights();
-    expect(weightsPrime.length).toEqual(weights.length);
-    for (let i = 0; i < weights.length; ++i) {
-      expectTensorsClose(weightsPrime[i], weights[i]);
-    }
-  });
-
-  it('Loading model: Fast init w/ weights: functional model', async () => {
-    const input1 = tfl.input({shape: [3, 2]});
-    const input2 = tfl.input({shape: [3, 2]});
-    let y =
-        tfl.layers.concatenate().apply([input1, input2]) as tfl.SymbolicTensor;
-    y = tfl.layers
-            .lstm({
-              units: 4,
-              kernelInitializer: 'orthogonal',
-              recurrentInitializer: 'orthogonal',
-              biasInitializer: 'glorotNormal'
-            })
-            .apply(y) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: [input1, input2], outputs: y});
-    let savedArtifacts: io.ModelArtifacts;
-    await model.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return {modelArtifactsInfo: null};
-        }));
-    const weights = model.getWeights();
-
-    const modelPrime = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    const weightsPrime = modelPrime.getWeights();
-    expect(weightsPrime.length).toEqual(weights.length);
-    for (let i = 0; i < weights.length; ++i) {
-      expectTensorsClose(weightsPrime[i], weights[i]);
-    }
-  });
-
-  it('modelFromJSON calls correct weight initializers', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.lstm({
-      units: 2,
-      inputShape: [3, 4],
-      recurrentInitializer: 'orthogonal',
-      kernelInitializer: 'orthogonal',
-      biasInitializer: 'randomNormal',
-    }));
-    const modelJSON = model.toJSON(null, false);
-
-    const qrSpy = spyOn(linalg, 'qr').and.callThrough();
-    const modelPrime =
-        await tfl.models.modelFromJSON({modelTopology: modelJSON});
-    // Make sure modelPrime builds.
-    modelPrime.predict(zeros([2, 3, 4]));
-    // Assert the orthogonal initializer has been called.
-    expect(qrSpy).toHaveBeenCalled();
-  });
-
-  it('Partial non-strict load calls weight initializers', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.lstm({
-      units: 2,
-      inputShape: [3, 4],
-      recurrentInitializer: 'orthogonal',
-      kernelInitializer: 'orthogonal',
-      biasInitializer: 'randomNormal',
-    }));
-    let savedArtifacts: io.ModelArtifacts;
-    await model.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return {modelArtifactsInfo: null};
-        }));
-    const weights = model.getWeights();
-
-    expect(savedArtifacts.weightSpecs.length).toEqual(3);
-    savedArtifacts.weightSpecs = savedArtifacts.weightSpecs.slice(0, 1);
-
-    const qrSpy = spyOn(linalg, 'qr').and.callThrough();
-    const strict = false;
-    const modelPrime =
-        await tfl.loadLayersModel(io.fromMemory(savedArtifacts), {strict});
-    const weightsPrime = modelPrime.getWeights();
-    expect(weightsPrime.length).toEqual(weights.length);
-    expectTensorsClose(weightsPrime[0], weights[0]);
-    // Assert the orthogonal initializer has been called.
-    expect(qrSpy).toHaveBeenCalled();
-  });
-
-  it('loadLayersModel: non-strict load calls weight initializers', async () => {
-    const model = tfl.sequential();
-    model.add(tfl.layers.lstm({
-      units: 2,
-      inputShape: [3, 4],
-      recurrentInitializer: 'orthogonal',
-      kernelInitializer: 'orthogonal',
-      biasInitializer: 'randomNormal',
-    }));
-    let savedArtifacts: io.ModelArtifacts;
-    await model.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return {modelArtifactsInfo: null};
-        }));
-    const weights = model.getWeights();
-
-    expect(savedArtifacts.weightSpecs.length).toEqual(3);
-    savedArtifacts.weightSpecs = savedArtifacts.weightSpecs.slice(0, 1);
-
-    const qrSpy = spyOn(linalg, 'qr').and.callThrough();
-    const strict = false;
-    const modelPrime =
-        await tfl.loadLayersModel(io.fromMemory(savedArtifacts), {strict});
-    const weightsPrime = modelPrime.getWeights();
-    expect(weightsPrime.length).toEqual(weights.length);
-    expectTensorsClose(weightsPrime[0], weights[0]);
-    // Assert the orthogonal initializer has been called.
-    expect(qrSpy).toHaveBeenCalled();
-  });
-
-  it('Load model artifact with ndarray-format scalar objects', async () => {
-    // The following model config contains a scalar parameter serialized in the
-    // ndarray-style format: `{"type": "ndarray", "value": 6}`.
-    const modelJSON = JSON.stringify({
-      'class_name': 'Sequential',
-      'keras_version': '2.2.4',
-      'config': {
-        'layers': [
-          {
-            'class_name': 'Dense',
-            'config': {
-              'kernel_initializer': {
-                'class_name': 'VarianceScaling',
-                'config': {
-                  'distribution': 'uniform',
-                  'scale': 1.0,
-                  'seed': null,
-                  'mode': 'fan_avg'
-                }
-              },
-              'name': 'dense_1',
-              'kernel_constraint': null,
-              'bias_regularizer': null,
-              'bias_constraint': null,
-              'dtype': 'float32',
-              'activation': 'linear',
-              'trainable': true,
-              'kernel_regularizer': null,
-              'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-              'units': 2,
-              'batch_input_shape': [null, 3],
-              'use_bias': true,
-              'activity_regularizer': null
-            }
-          },
-          {
-            'class_name': 'ReLU',
-            'config': {
-              'threshold': 0.0,
-              'max_value': {'type': 'ndarray', 'value': 6},
-              'trainable': true,
-              'name': 're_lu_1',
-              'negative_slope': 0.0
-            }
-          },
-          {
-            'class_name': 'Dense',
-            'config': {
-              'kernel_initializer': {
-                'class_name': 'VarianceScaling',
-                'config': {
-                  'distribution': 'uniform',
-                  'scale': 1.0,
-                  'seed': null,
-                  'mode': 'fan_avg'
-                }
-              },
-              'name': 'dense_2',
-              'kernel_constraint': null,
-              'bias_regularizer': null,
-              'bias_constraint': null,
-              'activation': 'linear',
-              'trainable': true,
-              'kernel_regularizer': null,
-              'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-              'units': 1,
-              'use_bias': true,
-              'activity_regularizer': null
-            }
-          }
-        ],
-        'name': 'sequential_1'
-      },
-      'backend': 'tensorflow'
-    });
-    const model =
-        await tfl.models.modelFromJSON({modelTopology: JSON.parse(modelJSON)});
-    expect(model.layers.length).toEqual(3);
-    expect(model.layers[1].getConfig().maxValue).toEqual(6);
-
-    const xs = randomNormal([5].concat(model.inputs[0].shape.slice(1)));
-    const ys = model.predict(xs) as Tensor;
-    expect(ys.shape).toEqual([5, 1]);
-  });
-
-  // TODO(cais): Test fast initialization of models consisting of
-  //   StackedRNN layers.
-});
diff --git a/tfjs-master/tfjs-layers/src/model_summary_test.ts b/tfjs-master/tfjs-layers/src/model_summary_test.ts
deleted file mode 100644
index 3b8b52dc8..000000000
--- a/tfjs-master/tfjs-layers/src/model_summary_test.ts
+++ /dev/null
@@ -1,337 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-// Unit tests for tf.LayersModel.summary() and tf.Sequential.summary().
-
-import * as tfl from './index';
-import {describeMathCPU} from './utils/test_utils';
-
-function getRandomLayerOrModelName(length = 12) {
-  return 'L' + Math.random().toFixed(length - 1).slice(2);
-}
-
-describeMathCPU('LayersModel.summary', () => {
-  let consoleLogHistory: string[];
-
-  beforeEach(() => {
-    consoleLogHistory = [];
-    spyOn(console, 'log').and.callFake((message?: string) => {
-      consoleLogHistory.push(message);
-    });
-  });
-
-  afterEach(() => {
-    consoleLogHistory = [];
-  });
-
-  it('Sequential model: one layer', () => {
-    const layerName = getRandomLayerOrModelName();
-    const model = tfl.sequential({
-      layers: [tfl.layers.dense({units: 3, inputShape: [10], name: layerName})]
-    });
-    model.summary();
-    expect(consoleLogHistory).toEqual([
-      '__________________________________________________________________________________________',
-      'Layer (type)                Input Shape               Output shape              Param #   ',
-      '==========================================================================================',
-      `${layerName} (Dense)        [[null,10]]               [null,3]                  33        `,
-      '==========================================================================================',
-      'Total params: 33', 'Trainable params: 33', 'Non-trainable params: 0',
-      '__________________________________________________________________________________________'
-    ]);
-  });
-
-  it('Sequential model: one layer: custom lineLength', () => {
-    const layerName = getRandomLayerOrModelName();
-    const model = tfl.sequential({
-      layers: [tfl.layers.dense({units: 3, inputShape: [10], name: layerName})]
-    });
-    const lineLength = 120;
-    model.summary(lineLength);
-    expect(consoleLogHistory).toEqual([
-      '________________________________________________________________________________________________________________________',
-      'Layer (type)                          Input Shape                        Output shape                     Param #       ',
-      '========================================================================================================================',
-      `${layerName} (Dense)                  [[null,10]]                        [null,3]                         33            `,
-      '========================================================================================================================',
-      'Total params: 33', 'Trainable params: 33', 'Non-trainable params: 0',
-      '________________________________________________________________________________________________________________________'
-    ]);
-  });
-
-  it('Sequential model: one layer: custom positions', () => {
-    const layerName = getRandomLayerOrModelName();
-    const model = tfl.sequential({
-      layers: [tfl.layers.dense({units: 3, inputShape: [10], name: layerName})]
-    });
-    const lineLength = 120;
-    const positions: number[] = [0.3, 0.5, 0.8, 1.0];
-    model.summary(lineLength, positions);
-    expect(consoleLogHistory).toEqual([
-      '________________________________________________________________________________________________________________________',
-      'Layer (type)                        Input Shape             Output shape                        Param #                 ',
-      '========================================================================================================================',
-      `${layerName} (Dense)                [[null,10]]             [null,3]                            33                      `,
-      '========================================================================================================================',
-      'Total params: 33', 'Trainable params: 33', 'Non-trainable params: 0',
-      '________________________________________________________________________________________________________________________'
-    ]);
-  });
-
-  it('Sequential model: one layer: custom printFn', () => {
-    const layerName = getRandomLayerOrModelName();
-    const model = tfl.sequential({
-      layers: [tfl.layers.dense({units: 3, inputShape: [10], name: layerName})]
-    });
-
-    const messages: string[] = [];
-    // tslint:disable-next-line:no-any
-    function rerouteLog(message?: any, ...optionalParams: any[]) {
-      messages.push(message);
-    }
-
-    model.summary(null, null, rerouteLog);
-    expect(messages).toEqual([
-      '__________________________________________________________________________________________',
-      'Layer (type)                Input Shape               Output shape              Param #   ',
-      '==========================================================================================',
-      `${layerName} (Dense)        [[null,10]]               [null,3]                  33        `,
-      '==========================================================================================',
-      'Total params: 33', 'Trainable params: 33', 'Non-trainable params: 0',
-      '__________________________________________________________________________________________'
-    ]);
-
-    // console.log should have received no calls.
-    expect(consoleLogHistory).toEqual([]);
-  });
-
-  it('Sequential model: three layers', () => {
-    const lyrName01 = getRandomLayerOrModelName();
-    const lyrName02 = getRandomLayerOrModelName();
-    const lyrName03 = getRandomLayerOrModelName();
-    const model = tfl.sequential({
-      layers: [
-        tfl.layers.flatten({inputShape: [2, 5], name: lyrName01}),
-        tfl.layers.dense({units: 3, name: lyrName02}),
-        tfl.layers.dense({units: 1, name: lyrName03}),
-      ]
-    });
-    model.summary();
-    expect(consoleLogHistory).toEqual([
-      '__________________________________________________________________________________________',
-      'Layer (type)                Input Shape               Output shape              Param #   ',
-      '==========================================================================================',
-      `${lyrName01} (Flatten)      [[null,2,5]]              [null,10]                 0         `,
-      '__________________________________________________________________________________________',
-      `${lyrName02} (Dense)        [[null,10]]               [null,3]                  33        `,
-      '__________________________________________________________________________________________',
-      `${lyrName03} (Dense)        [[null,3]]                [null,1]                  4         `,
-      '==========================================================================================',
-      'Total params: 37',
-      'Trainable params: 37',
-      'Non-trainable params: 0',
-      '__________________________________________________________________________________________',
-    ]);
-  });
-
-  it('Sequential model: with non-trainable layers', () => {
-    const lyrName01 = getRandomLayerOrModelName();
-    const lyrName02 = getRandomLayerOrModelName();
-    const lyrName03 = getRandomLayerOrModelName();
-    const model = tfl.sequential({
-      layers: [
-        tfl.layers.flatten({inputShape: [2, 5], name: lyrName01}),
-        tfl.layers.dense({units: 3, name: lyrName02, trainable: false}),
-        tfl.layers.dense({units: 1, name: lyrName03}),
-      ]
-    });
-    model.summary();
-    expect(consoleLogHistory).toEqual([
-      '__________________________________________________________________________________________',
-      'Layer (type)                Input Shape               Output shape              Param #   ',
-      '==========================================================================================',
-      `${lyrName01} (Flatten)      [[null,2,5]]              [null,10]                 0         `,
-      '__________________________________________________________________________________________',
-      `${lyrName02} (Dense)        [[null,10]]               [null,3]                  33        `,
-      '__________________________________________________________________________________________',
-      `${lyrName03} (Dense)        [[null,3]]                [null,1]                  4         `,
-      '==========================================================================================',
-      'Total params: 37',
-      'Trainable params: 4',
-      'Non-trainable params: 33',
-      '__________________________________________________________________________________________',
-    ]);
-    consoleLogHistory = [];
-
-    // Setting the entire model to non-trainable should be reflected
-    // in the summary.
-    model.trainable = false;
-    model.summary();
-    expect(consoleLogHistory).toEqual([
-      '__________________________________________________________________________________________',
-      'Layer (type)                Input Shape               Output shape              Param #   ',
-      '==========================================================================================',
-      `${lyrName01} (Flatten)      [[null,2,5]]              [null,10]                 0         `,
-      '__________________________________________________________________________________________',
-      `${lyrName02} (Dense)        [[null,10]]               [null,3]                  33        `,
-      '__________________________________________________________________________________________',
-      `${lyrName03} (Dense)        [[null,3]]                [null,1]                  4         `,
-      '==========================================================================================',
-      'Total params: 37',
-      'Trainable params: 0',
-      'Non-trainable params: 37',
-      '__________________________________________________________________________________________',
-    ]);
-    consoleLogHistory = [];
-
-    // Setting the model's trainable property should be reflected in the
-    // new summary. But the initially untrainable layer should still stay
-    // untrainable.
-    model.trainable = true;
-    model.summary();
-    expect(consoleLogHistory).toEqual([
-      '__________________________________________________________________________________________',
-      'Layer (type)                Input Shape               Output shape              Param #   ',
-      '==========================================================================================',
-      `${lyrName01} (Flatten)      [[null,2,5]]              [null,10]                 0         `,
-      '__________________________________________________________________________________________',
-      `${lyrName02} (Dense)        [[null,10]]               [null,3]                  33        `,
-      '__________________________________________________________________________________________',
-      `${lyrName03} (Dense)        [[null,3]]                [null,1]                  4         `,
-      '==========================================================================================',
-      'Total params: 37',
-      'Trainable params: 4',
-      'Non-trainable params: 33',
-      '__________________________________________________________________________________________',
-    ]);
-    consoleLogHistory = [];
-  });
-
-  it('Sequential model with Embedding layer', () => {
-    const lyrName01 = getRandomLayerOrModelName();
-    const lyrName02 = getRandomLayerOrModelName();
-    const model = tfl.sequential({
-      layers: [
-        tfl.layers.embedding({
-          inputDim: 10,
-          outputDim: 8,
-          inputShape: [null, 5],
-          name: lyrName01
-        }),
-        tfl.layers.dense({units: 3, name: lyrName02}),
-      ]
-    });
-    model.summary();
-    expect(consoleLogHistory).toEqual([
-      '__________________________________________________________________________________________',
-      'Layer (type)                Input Shape               Output shape              Param #   ',
-      '==========================================================================================',
-      `${lyrName01} (Embedding)    [[null,null,5]]           [null,null,5,8]           80        `,
-      '__________________________________________________________________________________________',
-      `${lyrName02} (Dense)        [[null,null,5,8]]         [null,null,5,3]           27        `,
-      '==========================================================================================',
-      'Total params: 107', 'Trainable params: 107', 'Non-trainable params: 0',
-      '__________________________________________________________________________________________'
-    ]);
-  });
-
-  it('Sequential model: nested', () => {
-    const mdlName01 = getRandomLayerOrModelName();
-    const innerModel = tfl.sequential({
-      layers: [tfl.layers.dense({units: 3, inputShape: [10]})],
-      name: mdlName01
-    });
-    const outerModel = tfl.sequential();
-    outerModel.add(innerModel);
-
-    const lyrName02 = getRandomLayerOrModelName();
-    outerModel.add(tfl.layers.dense({units: 1, name: lyrName02}));
-
-    outerModel.summary();
-    expect(consoleLogHistory).toEqual([
-      '__________________________________________________________________________________________',
-      'Layer (type)                Input Shape               Output shape              Param #   ',
-      '==========================================================================================',
-      `${mdlName01} (Sequential)   [[null,10]]               [null,3]                  33        `,
-      '__________________________________________________________________________________________',
-      `${lyrName02} (Dense)        [[null,3]]                [null,1]                  4         `,
-      '==========================================================================================',
-      'Total params: 37',
-      'Trainable params: 37',
-      'Non-trainable params: 0',
-      '__________________________________________________________________________________________',
-    ]);
-  });
-
-  it('Functional model', () => {
-    const lyrName01 = getRandomLayerOrModelName();
-    const input1 = tfl.input({shape: [3], name: lyrName01});
-    const lyrName02 = getRandomLayerOrModelName();
-    const input2 = tfl.input({shape: [4], name: lyrName02});
-    const lyrName03 = getRandomLayerOrModelName();
-    const input3 = tfl.input({shape: [5], name: lyrName03});
-    const lyrName04 = getRandomLayerOrModelName();
-    const concat1 =
-        tfl.layers.concatenate({name: lyrName04}).apply([input1, input2]) as
-        tfl.SymbolicTensor;
-    const lyrName05 = getRandomLayerOrModelName();
-    const output =
-        tfl.layers.concatenate({name: lyrName05}).apply([concat1, input3]) as
-        tfl.SymbolicTensor;
-    const model =
-        tfl.model({inputs: [input1, input2, input3], outputs: output});
-
-    const lineLength = 120;
-    const positions: number[] = [0.24, 0.48, 0.70, 0.80, 1];
-    model.summary(lineLength, positions);
-    expect(consoleLogHistory).toEqual([
-      '________________________________________________________________________________________________________________________',
-      'Layer (type)                Input Shape                  Output shape               Param #     Receives inputs         ',
-      '========================================================================================================================',
-      `${lyrName01} (InputLayer)   [[null,3]]                   [null,3]                   0                                   `,
-      '________________________________________________________________________________________________________________________',
-      `${lyrName02} (InputLayer)   [[null,4]]                   [null,4]                   0                                   `,
-      '________________________________________________________________________________________________________________________',
-      `${lyrName04} (Concatenate)  [[null,3],[null,4]]          [null,7]                   0           ${lyrName01}[0][0]      `,
-      `                                                                                                ${lyrName02}[0][0]      `,
-      '________________________________________________________________________________________________________________________',
-      `${lyrName03} (InputLayer)   [[null,5]]                   [null,5]                   0                                   `,
-      '________________________________________________________________________________________________________________________',
-      `${lyrName05} (Concatenate)  [[null,7],[null,5]]          [null,12]                  0           ${lyrName04}[0][0]      `,
-      `                                                                                                ${lyrName03}[0][0]      `,
-      '========================================================================================================================',
-      'Total params: 0', 'Trainable params: 0', 'Non-trainable params: 0',
-      '________________________________________________________________________________________________________________________'
-    ]);
-  });
-
-  it('LayersModel with multiple outputs', () => {
-    const lyrName01 = getRandomLayerOrModelName();
-    const input1 = tfl.input({shape: [3, 4], name: lyrName01});
-    const lyrName02 = getRandomLayerOrModelName();
-    const outputs =
-        tfl.layers.simpleRNN({units: 2, returnState: true, name: lyrName02})
-            .apply(input1) as tfl.SymbolicTensor[];
-    const model = tfl.model({inputs: input1, outputs});
-    const lineLength = 100;
-    model.summary(lineLength);
-    expect(consoleLogHistory).toEqual([
-      '____________________________________________________________________________________________________',
-      'Layer (type)                    Input Shape                  Output shape                Param #    ',
-      '====================================================================================================',
-      `${lyrName01} (InputLayer)       [[null,3,4]]                 [null,3,4]                  0          `,
-      '____________________________________________________________________________________________________',
-      `${lyrName02} (SimpleRNN)        [[null,3,4]]                 [[null,2],[null,2]]         14         `,
-      '====================================================================================================',
-      'Total params: 14', 'Trainable params: 14', 'Non-trainable params: 0',
-      '____________________________________________________________________________________________________'
-    ]);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/models.ts b/tfjs-master/tfjs-layers/src/models.ts
deleted file mode 100644
index 40d6e79a3..000000000
--- a/tfjs-master/tfjs-layers/src/models.ts
+++ /dev/null
@@ -1,1114 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original source keras/models.py */
-
-import {dispose, io, NamedTensorMap, Optimizer, Scalar, serialization, Tensor, util} from '@tensorflow/tfjs-core';
-
-import {getUid} from './backend/state';
-import {History} from './base_callbacks';
-import {Dataset} from './engine/dataset_stub';
-import {Input} from './engine/input_layer';
-import {getSourceInputs, Layer, Node, SymbolicTensor} from './engine/topology';
-import {LayersModel, ModelCompileArgs, ModelEvaluateArgs} from './engine/training';
-import {ModelEvaluateDatasetArgs, ModelFitDatasetArgs} from './engine/training_dataset';
-import {ModelFitArgs} from './engine/training_tensors';
-import {NotImplementedError, RuntimeError, ValueError} from './errors';
-import {Shape} from './keras_format/common';
-import {TrainingConfig} from './keras_format/training_config';
-import {PyJsonDict} from './keras_format/types';
-import {deserialize} from './layers/serialization';
-import {Kwargs, NamedTensor} from './types';
-import * as generic_utils from './utils/generic_utils';
-import {convertPythonicToTs} from './utils/serialization_utils';
-import {getExactlyOneShape} from './utils/types_utils';
-
-/**
- * Parses a JSON model configuration file and returns a model instance.
- *
- * ```js
- * // This example shows how to serialize a model using `toJSON()` and
- * // deserialize it as another model using `tf.models.modelFromJSON()`.
- * // Note: this example serializes and deserializes only the topology
- * // of the model; the weights of the loaded model will be different
- * // from those of the the original model, due to random weight
- * // initialization.
- * // To load the topology and weights of a model, use `tf.loadLayersModel()`.
- * const model1 = tf.sequential();
- * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));
- * // Serialize `model1` as a JSON object.
- * const model1JSON = model1.toJSON(null, false);
- * model1.summary();
- *
- * const model2 = await tf.models.modelFromJSON(model1JSON);
- * model2.summary();
- * ```
- *
- *  @param modelAndWeightsConfig JSON object or string encoding a model and
- *       weights configuration. It can also be only the topology JSON of the
- *       model, in which case the weights will not be loaded.
- *  @param custom_objects Optional dictionary mapping names
- *       (strings) to custom classes or functions to be
- *       considered during deserialization.
- * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).
- */
-export async function modelFromJSON(
-    modelAndWeightsConfig: ModelAndWeightsConfig|PyJsonDict,
-    customObjects?: serialization.ConfigDict): Promise<LayersModel> {
-  if (!('modelTopology' in modelAndWeightsConfig)) {
-    modelAndWeightsConfig = {modelTopology: modelAndWeightsConfig};
-  }
-  modelAndWeightsConfig = modelAndWeightsConfig as ModelAndWeightsConfig;
-
-  let modelTopology = modelAndWeightsConfig.modelTopology;
-  if (modelTopology['model_config'] != null) {
-    // If the model-topology JSON contains a 'model_config' field, then it is
-    // a full model JSON (e.g., from `keras.Model.save()`), which contains
-    // not only the model's architecture in its 'model_config' field, but
-    // additional information such as the model's optimizer. We use only the
-    // 'model_config' field currently.
-    modelTopology = modelTopology['model_config'] as PyJsonDict;
-  }
-  const tsConfig =
-      convertPythonicToTs(modelTopology) as serialization.ConfigDict;
-  const model = deserialize(tsConfig, customObjects) as LayersModel;
-
-  if (modelAndWeightsConfig.weightsManifest != null) {
-    // Load the weight values keyed by the original tensor names in the model
-    // file that was loaded.  These should match the keys of the weight
-    // manifest.
-    const weightValues = await io.loadWeights(
-        modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix,
-        model.weights.map(weight => weight.originalName));
-
-    // Map the weights to the unique tensor names generated during model loading
-    const uniqueWeightValues: NamedTensorMap = {};
-    for (const weight of model.weights) {
-      uniqueWeightValues[weight.originalName] =
-          weightValues[weight.originalName];
-    }
-
-    model.loadWeights(uniqueWeightValues);
-    // Dispose temporary weight values.
-    dispose(weightValues);
-  }
-  return model;
-}
-
-/**
- * Options for loading a saved mode in TensorFlow.js format.
- */
-export interface ModelAndWeightsConfig {
-  /**
-   * A JSON object or JSON string containing the model config.
-   *
-   * This can be either of the following two formats:
-   *   - A model archiecture-only config,  i.e., a format consistent with the
-   *     return value of`keras.Model.to_json()`.
-   *   - A full model config, containing not only model architecture, but also
-   *     training options and state, i.e., a format consistent with the return
-   *     value of `keras.models.save_model()`.
-   */
-  modelTopology: PyJsonDict;
-
-  /**
-   * A weights manifest in TensorFlow.js format.
-   */
-  weightsManifest?: io.WeightsManifestConfig;
-
-  /**
-   * Path to prepend to the paths in `weightManifest` before fetching.
-   *
-   * The path may optionally end in a slash ('/').
-   */
-  pathPrefix?: string;
-}
-
-// TODO(nielsene): Remove after: https://github.com/tensorflow/tfjs/issues/400
-export interface ModelPredictArgs {
-  /**
-   * Optional. Batch size (Integer). If unspecified, it will default to 32.
-   */
-  batchSize?: number;
-
-  /**
-   * Optional. Verbosity mode. Defaults to false.
-   */
-  verbose?: boolean;
-}
-
-/**
- * Load a model composed of Layer objects, including its topology and optionally
- * weights. See the Tutorial named "How to import a Keras Model" for usage
- * examples.
- *
- * This method is applicable to:
- *
- * 1. Models created with the `tf.layers.*`, `tf.sequential`, and
- * `tf.model` APIs of TensorFlow.js and later saved with the
- * `tf.LayersModel.save` method.
- * 2. Models converted from Keras or TensorFlow tf.keras using the
- * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).
- *
- * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted
- * forms. For those models, use `tf.loadGraphModel`.
- *
- * Example 1. Load a model from an HTTP server.
- *
- * ```js
- * const model = await tf.loadLayersModel(
- *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');
- * model.summary();
- * ```
- *
- * Example 2: Save `model`'s topology and weights to browser [local
- * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
- * then load it back.
- *
- * ```js
- * const model = tf.sequential(
- *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
- * console.log('Prediction from original model:');
- * model.predict(tf.ones([1, 3])).print();
- *
- * const saveResults = await model.save('localstorage://my-model-1');
- *
- * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');
- * console.log('Prediction from loaded model:');
- * loadedModel.predict(tf.ones([1, 3])).print();
- * ```
- *
- * Example 3. Saving `model`'s topology and weights to browser
- * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);
- * then load it back.
- *
- * ```js
- * const model = tf.sequential(
- *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
- * console.log('Prediction from original model:');
- * model.predict(tf.ones([1, 3])).print();
- *
- * const saveResults = await model.save('indexeddb://my-model-1');
- *
- * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');
- * console.log('Prediction from loaded model:');
- * loadedModel.predict(tf.ones([1, 3])).print();
- * ```
- *
- * Example 4. Load a model from user-selected files from HTML
- * [file input
- * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).
- *
- * ```js
- * // Note: this code snippet will not work without the HTML elements in the
- * //   page
- * const jsonUpload = document.getElementById('json-upload');
- * const weightsUpload = document.getElementById('weights-upload');
- *
- * const model = await tf.loadLayersModel(
- *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));
- * ```
- *
- * @param pathOrIOHandler Can be either of the two formats
- *   1. A string path to the `ModelAndWeightsConfig` JSON describing
- *      the model in the canonical TensorFlow.js format. For file://
- *      (tfjs-node-only), http:// and https:// schemas, the path can be
- *      either absolute or relative. The content of the JSON file is assumed to
- *      be a JSON object with the following fields and values:
- *      - 'modelTopology': A JSON object that can be either of:
- *        1. a model architecture JSON consistent with the format of the return
- *            value of `keras.Model.to_json()`
- *        2. a full model JSON in the format of `keras.models.save_model()`.
- *      - 'weightsManifest': A TensorFlow.js weights manifest.
- *      See the Python converter function `save_model()` for more details.
- *      It is also assumed that model weights can be accessed from relative
- *      paths described by the `paths` fields in weights manifest.
- *   2. A `tf.io.IOHandler` object that loads model artifacts with its `load`
- *      method.
- * @param options Optional configuration arguments for the model loading,
- *   including:
- *   - `strict`: Require that the provided weights exactly match those required
- *     by the layers.  Default true.  Passing false means that both extra
- *     weights and missing weights will be silently ignored.
- *   - `onProgress`: A progress callback of the form:
- *     `(fraction: number) => void`. This callback can be used to monitor the
- *     model-loading process.
- * @returns A `Promise` of `tf.LayersModel`, with the topology and weights
- *     loaded.
- *
- * @doc {heading: 'Models', subheading: 'Loading'}
- */
-export async function loadLayersModel(
-    pathOrIOHandler: string|io.IOHandler,
-    options?: io.LoadOptions): Promise<LayersModel> {
-  if (options == null) {
-    options = {};
-  }
-  if (typeof pathOrIOHandler === 'string') {
-    const handlers = io.getLoadHandlers(pathOrIOHandler, options);
-    if (handlers.length === 0) {
-      // For backward compatibility: if no load handler can be found,
-      // assume it is a relative http path.
-      // TODO(cais): Reformat the args into a single `LoadOptions` once the core
-      // is refactored.
-      handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));
-    } else if (handlers.length > 1) {
-      throw new ValueError(
-          `Found more than one (${handlers.length}) load handlers for ` +
-          `URL '${pathOrIOHandler}'`);
-    }
-    pathOrIOHandler = handlers[0];
-  }
-  return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);
-}
-
-/**
- * Load a model and optionally its weights, using an IOHandler object.
- *
- * @param handler The instance of `IOHandler` to be used during the model
- *   loading.
- * @param customObjects Any optional custom objects to be used during model
- *   loading.
- * @param strict Whether the weight loading will be done in strict mode.
- *   Default: `true`.
- */
-export async function loadLayersModelFromIOHandler(
-    handler: io.IOHandler, customObjects?: serialization.ConfigDict,
-    options?: io.LoadOptions): Promise<LayersModel> {
-  if (options == null) {
-    options = {};
-  }
-  if (handler.load == null) {
-    throw new ValueError(
-        'Cannot proceed with model loading because the IOHandler provided ' +
-        'does not have the `load` method implemented.');
-  }
-  const artifacts = await handler.load();
-  let modelTopology = artifacts.modelTopology as PyJsonDict;
-  if (modelTopology['model_config'] != null) {
-    modelTopology = modelTopology['model_config'] as PyJsonDict;
-  }
-
-  const strict = options.strict == null ? true : options.strict;
-  // If weights are provided and the weight-loading mode is strict, use
-  // fast weight initialization. This skips costly initializers such as
-  // 'orthogonal' and saves unnecessary computation in cases where
-  // the initialized weight values will immediately be overwritten by
-  // loaded weight values.
-  const fastWeightInit =
-      artifacts.weightData != null && artifacts.weightSpecs != null && strict;
-  const model =
-      deserialize(
-          convertPythonicToTs(modelTopology) as serialization.ConfigDict,
-          customObjects, fastWeightInit) as LayersModel;
-
-  const trainingConfig = artifacts.trainingConfig as TrainingConfig;
-  if (trainingConfig != null) {
-    model.loadTrainingConfig(trainingConfig);
-  }
-  if (artifacts.userDefinedMetadata != null) {
-    model.setUserDefinedMetadata(artifacts.userDefinedMetadata);
-  }
-
-  // If weightData is present, load the weights into the model.
-  if (artifacts.weightData != null) {
-    // Loading weights requires weightSpecs.
-    if (artifacts.weightSpecs == null) {
-      throw new ValueError(
-          'LayersModel artifacts contains weight data, but not weight specs. ' +
-          'Therefore loading of weights cannot proceed.');
-    }
-
-    const {modelWeights, optimizerWeights} = decodeModelAndOptimizerWeights(
-        artifacts.weightData, artifacts.weightSpecs);
-    model.loadWeights(modelWeights, strict);
-
-    if (model.optimizer != null && optimizerWeights.length > 0) {
-      await model.optimizer.setWeights(optimizerWeights);
-    }
-
-    // Dispose temporary weight values.
-    dispose(modelWeights);
-    dispose(optimizerWeights.map(w => w.tensor));
-  }
-  return model;
-}
-
-function decodeModelAndOptimizerWeights(
-    weightData: io.WeightData, specs: io.WeightsManifestEntry[]):
-    {modelWeights: NamedTensorMap, optimizerWeights: NamedTensor[]} {
-  const name2Tensor = io.decodeWeights(weightData, specs);
-  const modelWeights: NamedTensorMap = {};
-  const optimizerWeights: NamedTensor[] = [];
-  specs.forEach(spec => {
-    if (spec.group === 'optimizer') {
-      optimizerWeights.push({name: spec.name, tensor: name2Tensor[spec.name]});
-    } else {
-      modelWeights[spec.name] = name2Tensor[spec.name];
-    }
-  });
-  return {modelWeights, optimizerWeights};
-}
-
-/**
- * Configuration for a Sequential model.
- */
-export interface SequentialArgs {
-  /** Stack of layers for the model. */
-  layers?: Layer[];
-
-  /** The name of this model. */
-  name?: string;
-}
-
-/**
- * A model with a stack of layers, feeding linearly from one to the next.
- *
- * `tf.sequential` is a factory function that creates an instance of
- * `tf.Sequential`.
- *
- * ```js
- *  // Define a model for linear regression.
- *  const model = tf.sequential();
- *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));
- *
- *  // Prepare the model for training: Specify the loss and the optimizer.
- *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
- *
- *  // Generate some synthetic data for training.
- *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
- *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
- *
- *  // Train the model using the data then do inference on a data point the
- *  // model hasn't seen:
- *  await model.fit(xs, ys);
- *  model.predict(tf.tensor2d([5], [1, 1])).print();
- * ```
- *
- * @doc {heading: 'Models', subheading: 'Classes'}
- */
-export class Sequential extends LayersModel {
-  /** @nocollapse */
-  static override className = 'Sequential';
-  private model: LayersModel;
-  constructor(args?: SequentialArgs) {
-    super({inputs: [], outputs: []});
-    args = args || {};
-
-    this.trainable = true;
-    this.built = false;
-
-    // Set model name.
-    this.name = (args.name != null) ? args.name : getUid('sequential_');
-
-    // Add to the model any layers passed to the constructor.
-    if (args.layers != null) {
-      for (const layer of args.layers) {
-        this.add(layer);
-      }
-    }
-  }
-
-  // Helper function to Sequential.add  Throws if the new output shape will be
-  // invalid.
-  private checkShape(layer: Layer) {
-    const shape = layer.inboundNodes[0].outputTensors[0].shape;
-    if (shape.some(x => x < 0)) {
-      throw new ValueError(
-          'Negative dimension size caused by adding layer ' +
-          `${layer.name} with input shape [` +
-          `${layer.inboundNodes[0].inputTensors[0].shape}]`);
-    }
-  }
-
-  /**
-   * Adds a layer instance on top of the layer stack.
-   *
-   * ```js
-   *  const model = tf.sequential();
-   *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));
-   *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));
-   *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));
-   *  // Note that the untrained model is random at this point.
-   *  model.predict(tf.randomNormal([10, 1])).print();
-   * ```
-   * @param layer Layer instance.
-   *
-   * @exception ValueError In case the `layer` argument does not know its
-   * input shape.
-   * @exception ValueError In case the `layer` argument has multiple output
-   *   tensors, or is already connected somewhere else (forbidden in
-   *   `Sequential` models).
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  add(layer: Layer): void {
-    const isLayerModelInstance =
-        layer instanceof Sequential || layer instanceof LayersModel;
-    let modelLayer: LayersModel;
-    if (isLayerModelInstance) {
-      modelLayer = layer as LayersModel;
-      if (modelLayer.outputs.length !== 1) {
-        throw new ValueError(
-            'All layers in a Sequential model ' +
-            'should have a single output tensor. ' +
-            'For multi-output layers, ' +
-            'use the functional API.');
-      }
-      if (modelLayer.inputs.length !== 1) {
-        throw new ValueError(
-            'All layers in a Sequential model ' +
-            'should have a single input tensor. ' +
-            'For multi-input layers, ' +
-            'use the functional API.');
-      }
-    }
-
-    if (this.outputs.length === 0) {
-      // first layer in model: check that it is an input layer
-      if (layer.inboundNodes.length === 0) {
-        // create an input layer
-        if (layer.batchInputShape == null) {
-          throw new ValueError(
-              'The first layer in a Sequential model must ' +
-              'get an `inputShape` or `batchInputShape` argument.');
-        }
-        // Instantiate the input layer.
-        const x = Input({
-          batchShape: layer.batchInputShape,
-          dtype: layer.dtype,
-          name: layer.name + '_input'
-        });
-        // This will build the current layer and create the node connecting
-        // the current layer to the input layer we just created.
-        layer.apply(x);
-      }
-
-      if (isLayerModelInstance) {
-        this.outputs = modelLayer.outputs;
-        this.inputs = modelLayer.inputs;
-      } else {
-        if (layer.inboundNodes.length !== 1) {
-          throw new ValueError(
-              'A layer added to a Sequential model must not already be ' +
-              `connected somewhere else. LayersModel received layer ${
-                  layer.name} ` +
-              `which has ${layer.inboundNodes.length} pre-existing inbound ` +
-              'connections.');
-        }
-
-        if (layer.inboundNodes[0].outputTensors.length !== 1) {
-          throw new ValueError(
-              'All layers in a Sequential model ' +
-              'should have a single output tensor. ' +
-              'For multi-output layers, ' +
-              'use the functional API.');
-        }
-        this.checkShape(layer);
-        this.outputs = [layer.inboundNodes[0].outputTensors[0]];
-        this.inputs = getSourceInputs(this.outputs[0]);
-      }
-
-      this.inboundNodes = [];
-      // We create an input node, which we will keep updated
-      // as we add more layers.
-      // (This call has side effects.)
-      // tslint:disable-next-line:no-unused-expression
-      new Node({
-        outboundLayer: this,
-        inboundLayers: [],
-        nodeIndices: [],
-        tensorIndices: [],
-        inputTensors: this.inputs,
-        outputTensors: this.outputs,
-        // no model-level masking for now
-        inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),
-        outputMasks: [null],
-        inputShapes: this.inputs.map(x => x.shape),
-        outputShapes: this.outputs[0].shape
-      });
-    } else {
-      const outputTensor = layer.apply(this.outputs[0]);
-      if (Array.isArray(outputTensor)) {
-        throw new TypeError(
-            'All layers in a Sequential model ' +
-            'should have a single output tensor. ' +
-            'For multi-output layers, ' +
-            'use the functional API.');
-      }
-      this.checkShape(layer);
-      this.outputs = [outputTensor as SymbolicTensor];
-      // update self.inbound_nodes
-      this.inboundNodes[0].outputTensors = this.outputs;
-      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
-    }
-
-    this.layers.push(layer);
-    this.built = false;
-  }
-
-  /**
-   * Removes the last layer in the model.
-   *
-   * @exception TypeError if there are no layers in the model.
-   */
-  pop(): void {
-    if (this.layers.length === 0) {
-      throw new TypeError('There are no layers in the model.');
-    }
-
-    this.layers.pop();
-    if (this.layers.length === 0) {
-      this.outputs = [];
-      this.inboundNodes = [];
-      this.outboundNodes = [];
-    } else {
-      const lastLayerIndex = this.layers.length - 1;
-      this.layers[lastLayerIndex].outboundNodes = [];
-      this.outputs = [this.layers[lastLayerIndex].output as SymbolicTensor];
-      // update self.inbound_nodes
-      this.inboundNodes[0].outputTensors = this.outputs;
-      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
-    }
-  }
-
-  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
-    if (this.model == null) {
-      this.build();
-    }
-    return this.model.call(inputs, kwargs);
-  }
-
-  override build(inputShape?: Shape|Shape[]) {
-    // Call `getExactlyOneShape` without using its return value,
-    // to verify that exactly one input shape is provided.
-    getExactlyOneShape(inputShape);
-
-    if (this.inputs.length === 0 || this.outputs.length === 0) {
-      throw new TypeError(
-          'Sequential model cannot be built: model is empty.' +
-          ' Add some layers first.');
-    }
-    // actually create the model
-    this.model = new LayersModel({
-      inputs: this.inputs,
-      outputs: this.outputs[0],
-      name: this.name + '_model'
-    });
-    this.model.trainable = this.trainable;
-
-    // mirror model attributes
-    this.supportsMasking = this.model.supportsMasking;
-    // TODO(michaelterry): Add caches
-    this.inputLayers = this.model.inputLayers;
-    this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;
-    this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;
-    this.outputLayers = this.model.outputLayers;
-    this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;
-    this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;
-    this.nodesByDepth = this.model.nodesByDepth;
-    this.containerNodes = this.model.containerNodes;
-    this.outputNames = this.model.outputNames;
-    this.inputNames = this.model.inputNames;
-    // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.
-    // TODO(michaelterry): Add callbackModel if needed.
-    this.built = true;
-  }
-
-  override countParams(): number {
-    if (!this.built) {
-      this.build();
-    }
-    return super.countParams();
-  }
-
-  /**
-   * Print a text summary of the Sequential model's layers.
-   *
-   * The summary includes
-   * - Name and type of all layers that comprise the model.
-   * - Output shape(s) of the layers
-   * - Number of weight parameters of each layer
-   * - The total number of trainable and non-trainable parameters of the
-   * model.
-   *
-   * ```js
-   * const model = tf.sequential();
-   * model.add(
-   *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));
-   * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));
-   *
-   * model.summary();
-   * ```
-   *
-   * @param lineLength Custom line length, in number of characters.
-   * @param positions Custom widths of each of the columns, as either
-   *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number
-   *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to
-   *   right-most (i.e., ending) position of a column.
-   * @param printFn Custom print function. Can be used to replace the default
-   *   `console.log`. For example, you can use `x => {}` to mute the printed
-   *   messages in the console.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  override summary(
-      lineLength?: number, positions?: number[],
-      printFn:
-          // tslint:disable-next-line:no-any
-      (message?: any, ...optionalParams: any[]) => void = console.log) {
-    if (!this.built) {
-      this.build();
-    }
-    super.summary(lineLength, positions, printFn);
-  }
-
-  /**
-   * Sets the weights of the model.
-   *
-   * @param weights Should be a list of Tensors with shapes and types matching
-   *   the output of `model.getWeights()`.
-   */
-  override setWeights(weights: Tensor[]): void {
-    if (this.model == null) {
-      this.build();
-    }
-    this.model.setWeights(weights);
-  }
-
-  /**
-   * Returns the loss value & metrics values for the model in test mode.
-   *
-   * Loss and metrics are specified during `compile()`, which needs to happen
-   * before calls to `evaluate()`.
-   *
-   * Computation is done in batches.
-   *
-   * ```js
-   * const model = tf.sequential({
-   *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]
-   * });
-   * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-   * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {
-   *   batchSize: 4,
-   * });
-   * result.print();
-   * ```
-   *
-   * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the
-   * model has multiple inputs.
-   * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the
-   * model has multiple outputs.
-   * @param args A `ModelEvaluateConfig`, containing optional fields.
-   *
-   * @return `Scalar` test loss (if the model has a single output and no
-   *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs
-   *   and/or metrics). The attribute `model.metricsNames`
-   *   will give you the display labels for the scalar outputs.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  override evaluate(
-      x: Tensor|Tensor[], y: Tensor|Tensor[],
-      args: ModelEvaluateArgs = {}): Scalar|Scalar[] {
-    if (!this.built) {
-      throw new RuntimeError(
-          'The model needs to be compiled before being used.');
-    }
-    return this.model.evaluate(x, y, args);
-  }
-
-  // TODO(cais): Add code snippet below once real dataset objects are
-  //   available.
-  /**
-   * Evaluate model using a dataset object.
-   *
-   * Note: Unlike `evaluate()`, this method is asynchronous (`async`).
-   *
-   * @param dataset A dataset object. Its `iterator()` method is expected
-   *   to generate a dataset iterator object, the `next()` method of which
-   *   is expected to produce data batches for evaluation. The return value
-   *   of the `next()` call ought to contain a boolean `done` field and a
-   *   `value` field. The `value` field is expected to be an array of two
-   *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former
-   *   case is for models with exactly one input and one output (e.g.
-   *   a sequential model). The latter case is for models with multiple
-   *   inputs and/or multiple outputs. Of the two items in the array, the
-   *   first is the input feature(s) and the second is the output target(s).
-   * @param args A configuration object for the dataset-based evaluation.
-   * @returns Loss and metric values as an Array of `Scalar` objects.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  override async evaluateDataset(dataset: Dataset<{}>,
-      args: ModelEvaluateDatasetArgs): Promise<Scalar|Scalar[]> {
-    if (!this.built) {
-      throw new RuntimeError(
-          'The model needs to be compiled before being used.');
-    }
-    return this.model.evaluateDataset(dataset, args);
-  }
-
-  /**
-   * Generates output predictions for the input samples.
-   *
-   * Computation is done in batches.
-   *
-   * Note: the "step" mode of predict() is currently not supported.
-   *   This is because the TensorFlow.js core backend is imperative only.
-   *
-   * ```js
-   * const model = tf.sequential({
-   *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]
-   * });
-   * model.predict(tf.ones([2, 10])).print();
-   * ```
-   *
-   * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if
-   *   the model has multiple inputs.
-   * @param conifg A `ModelPredictConfig` object containing optional fields.
-   *
-   * @return `tf.Tensor`(s) of predictions.
-   *
-   * @exception ValueError In case of mismatch between the provided input data
-   *   and the model's expectations, or in case a stateful model receives a
-   *   number of samples that is not a multiple of the batch size.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  override predict(x: Tensor|Tensor[], args: ModelPredictArgs = {}):
-      Tensor|Tensor[] {
-    if (this.model == null) {
-      this.build();
-    }
-    return this.model.predict(x, args);
-  }
-
-  /**
-   * Returns predictions for a single batch of samples.
-   *
-   * @param x: Input samples, as a Tensor, or list of Tensors (if the model
-   *   has multiple inputs).
-   * @return Tensor(s) of predictions
-   */
-  override predictOnBatch(x: Tensor): Tensor|Tensor[] {
-    if (this.model == null) {
-      this.build();
-    }
-    return this.model.predictOnBatch(x);
-  }
-
-  /**
-   * See `LayersModel.compile`.
-   *
-   * @param args
-   */
-  override compile(args: ModelCompileArgs): void {
-    this.build();
-    this.model.compile(args);
-    this.optimizer_ = this.model.optimizer;
-    // tslint:disable-next-line:no-any
-    this.isOptimizerOwned = (this.model as any).isOptimizerOwned;
-    this.loss = this.model.loss;
-    this.metrics = this.model.metrics;
-    // TODO(cais): Add this.lossWeights, this.sampleWeightMode,
-    //   this.weightedMetrics, this.targets.
-    this.metricsTensors = this.model.metricsTensors;
-    this.metricsNames = this.model.metricsNames;
-    // TODO(cais): Add sampleWeights.
-  }
-
-  override get optimizer(): Optimizer {
-    return this.model == null ? undefined : this.model.optimizer;
-  }
-
-  override set optimizer(optimizer: Optimizer) {
-    this.model.optimizer = optimizer;
-  }
-
-  /**
-   * Trains the model for a fixed number of epochs (iterations on a dataset).
-   *
-   * ```js
-   * const model = tf.sequential({
-   *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]
-   * });
-   * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-   * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {
-   *   batchSize: 4,
-   *   epochs: 3
-   * });
-   * console.log(history.history.loss[0]);
-   * ```
-   *
-   * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the
-   * model has multiple inputs. If all inputs in the model are named, you can
-   * also pass a dictionary mapping input names to `tf.Tensor`s.
-   * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if
-   * the model has multiple outputs. If all outputs in the model are named, you
-   *  can also pass a dictionary mapping output names to `tf.Tensor`s.
-   * @param args  A `ModelFitConfig`, containing optional fields.
-   *
-   * @return A `History` instance. Its `history` attribute contains all
-   *   information collected during training.
-   *
-   * @exception ValueError In case of mismatch between the provided input data
-   *   and what the model expects.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  override async fit(
-      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
-      y: Tensor|Tensor[]|{[inputName: string]: Tensor},
-      args: ModelFitArgs = {}): Promise<History> {
-    if (!this.built) {
-      throw new RuntimeError(
-          'The model needs to be compiled before ' +
-          'being used.');
-    }
-    return this.model.fit(x, y, args);
-  }
-
-  /**
-   * Trains the model using a dataset object.
-   *
-   * ```js
-   * const xArray = [
-   *   [1, 1, 1, 1, 1, 1, 1, 1, 1],
-   *   [1, 1, 1, 1, 1, 1, 1, 1, 1],
-   *   [1, 1, 1, 1, 1, 1, 1, 1, 1],
-   *   [1, 1, 1, 1, 1, 1, 1, 1, 1],
-   * ];
-   * const yArray = [1, 1, 1, 1];
-   * // Create a dataset from the JavaScript array.
-   * const xDataset = tf.data.array(xArray);
-   * const yDataset = tf.data.array(yArray);
-   * // Zip combines the `x` and `y` Datasets into a single Dataset, the
-   * // iterator of which will return an object containing of two tensors,
-   * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle
-   * // four such samples into a single object, with the same keys now pointing
-   * // to tensors that hold 4 examples, organized along the batch dimension.
-   * // The call to `shuffle(4)` causes each iteration through the dataset to
-   * // happen in a different order.  The size of the shuffle window is 4.
-   * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})
-   *     .batch(4)
-   *     .shuffle(4);
-   * const model = tf.sequential({
-   *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]
-   * });
-   * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-   * const history = await model.fitDataset(xyDataset, {
-   *   epochs: 4,
-   *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}
-   * });
-   * ```
-   *
-   * @param dataset A dataset object. Its `iterator()` method is expected to
-   *   generate a dataset iterator object, the `next()` method of which is
-   *   expected to produce data batches for evaluation. The return value of the
-   *   `next()` call ought to contain a boolean `done` field and a `value`
-   *   field.
-   *
-   *   The `value` field is expected to be an object of with fields
-   *   `xs` and `ys`, which point to the feature tensor and the target tensor,
-   *   respectively. This case is for models with exactly one input and one
-   *   output (e.g. a sequential model). For example:
-   *   ```js
-   *   {value: {xs: xsTensor, ys: ysTensor}, done: false}
-   *   ```
-   *
-   *   If the model has multiple inputs, the `xs` field of `value` should
-   *   be an object mapping input names to their respective feature tensors.
-   *   For example:
-   *   ```js
-   *   {
-   *     value: {
-   *       xs: {
-   *         input_1: xsTensor1,
-   *         input_2: xsTensor2
-   *       },
-   *       ys: ysTensor
-   *     },
-   *     done: false
-   *   }
-   *   ```
-   *   If the model has multiple outputs, the `ys` field of `value` should
-   *   be an object mapping output names to their respective target tensors.
-   *   For example:
-   *   ```js
-   *   {
-   *     value: {
-   *       xs: xsTensor,
-   *       ys: {
-   *         output_1: ysTensor1,
-   *         output_2: ysTensor2
-   *       },
-   *     },
-   *     done: false
-   *   }
-   *   ```
-   * @param args A `ModelFitDatasetArgs`, containing optional fields.
-   *
-   * @return A `History` instance. Its `history` attribute contains all
-   *   information collected during training.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
-   */
-  override async fitDataset<T>(dataset: Dataset<T>,
-      args: ModelFitDatasetArgs<T>): Promise<History> {
-    if (!this.built) {
-      throw new RuntimeError(
-          'The model needs to be compiled before ' +
-          'being used.');
-    }
-    return this.model.fitDataset(dataset, args);
-  }
-
-  /**
-   * Runs a single gradient update on a single batch of data.
-   *
-   * This method differs from `fit()` and `fitDataset()` in the following
-   * regards:
-   *   - It operates on exactly one batch of data.
-   *   - It returns only the loss and metric values, instead of
-   *     returning the batch-by-batch loss and metric values.
-   *   - It doesn't support fine-grained options such as verbosity and
-   *     callbacks.
-   *
-   * @param x Input data. It could be one of the following:
-   *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has
-   *     multiple inputs).
-   *   - An Object mapping input names to corresponding `tf.Tensor` (if the
-   *     model has named inputs).
-   * @param y Target data. It could be either a `tf.Tensor` or multiple
-   *   `tf.Tensor`s. It should be consistent with `x`.
-   * @returns Training loss or losses (in case the model has
-   *   multiple outputs), along with metrics (if any), as numbers.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  override async trainOnBatch(
-      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
-      y: Tensor|Tensor[]|
-      {[inputName: string]: Tensor}): Promise<number|number[]> {
-    return this.model.trainOnBatch(x, y);
-  }
-
-  /* See parent class for JsDoc */
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict,
-      customObjects = {} as serialization.ConfigDict,
-      fastWeightInit = false): T {
-    let configArray: serialization.ConfigDictArray;
-    let extraModelConfig: serialization.ConfigDict = {};
-    if (config instanceof Array) {
-      if (!(config[0].className != null) ||
-          config[0]['className'] === 'Merge') {
-        throw new ValueError('Legacy serialization format not supported yet.');
-      }
-      configArray = config;
-    } else {
-      util.assert(
-          config['layers'] != null,
-          () =>
-              `When the config data for a Sequential model is not an Array, ` +
-              `it must be an Object that contains the 'layers' field.`);
-      configArray = config['layers'] as serialization.ConfigDictArray;
-      delete config['layers'];
-      extraModelConfig = config;
-    }
-
-    const model = new cls(extraModelConfig);
-    if (!(model instanceof Sequential)) {
-      throw new NotImplementedError(
-          `Sequential.fromConfig called on non-Sequential input: ${model}`);
-    }
-    for (const conf of configArray) {
-      const customObjects: serialization.ConfigDict = undefined;
-      const layer = deserialize(
-                        conf as serialization.ConfigDict, customObjects,
-                        fastWeightInit) as Layer;
-      if (fastWeightInit) {
-        layer.setFastWeightInitDuringBuild(true);
-      }
-      model.add(layer);
-    }
-    return model;
-  }
-
-  /**
-   * Setter used for force stopping of LayersModel.fit() (i.e., training).
-   *
-   * Example:
-   *
-   * ```js
-   * const model = tf.sequential();
-   * model.add(tf.layers.dense({units: 1, inputShape: [10]}));
-   * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-   * const xs = tf.ones([8, 10]);
-   * const ys = tf.zeros([8, 1]);
-   *
-   * const history = await model.fit(xs, ys, {
-   *   epochs: 10,
-   *   callbacks: {
-   *     onEpochEnd: async (epoch, logs) => {
-   *       if (epoch === 2) {
-   *         model.stopTraining = true;
-   *       }
-   *     }
-   *   }
-   * });
-   *
-   * // There should be only 3 values in the loss array, instead of 10 values,
-   * // due to the stopping after 3 epochs.
-   * console.log(history.history.loss);
-   * ```
-   */
-  override set stopTraining(stop: boolean) {
-    // TODO(cais): When refactoring to remove the composition pattern happens,
-    // remove this method overriding.
-    if (this.model == null) {
-      throw new ValueError(
-          'Cannot set the stopTraining property of a sequential model before ' +
-          'it is compiled.');
-    }
-    this.model.stopTraining = stop;
-  }
-
-  override get stopTraining(): boolean {
-    if (this.model == null) {
-      throw new ValueError(
-          'Cannot get the stopTraining property of a sequential model before ' +
-          'it is compiled.');
-    }
-    return this.model.stopTraining;
-  }
-
-  // TODO(cais): Override get trainableWeights() here
-
-  // tslint:disable-next-line:no-any
-  override getConfig(): any {
-    // NOTE(cais): We override the return type of getConfig() to `any` here,
-    //   because the `Sequential` class is a special case among `Container`
-    //   subtypes in that its getConfig() method returns an Array (not a
-    //   dict).
-    const layers: serialization.ConfigDict[] = [];
-    for (const layer of this.layers) {
-      const dict: serialization.ConfigDict = {};
-      dict['className'] = layer.getClassName();
-      dict['config'] = layer.getConfig();
-      layers.push(dict);
-    }
-    return {name: this.name, layers};
-  }
-}
-serialization.registerClass(Sequential);
diff --git a/tfjs-master/tfjs-layers/src/models_test.ts b/tfjs-master/tfjs-layers/src/models_test.ts
deleted file mode 100644
index 41edc6582..000000000
--- a/tfjs-master/tfjs-layers/src/models_test.ts
+++ /dev/null
@@ -1,3236 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {DataType, env, io, memory, ones, randomNormal, Scalar, scalar, serialization, sum, Tensor, tensor1d, tensor2d, tensor3d, train, zeros} from '@tensorflow/tfjs-core';
-
-import {LayersModel} from './engine/training';
-import * as tfl from './index';
-import {PyJsonDict} from './keras_format/types';
-import {Reshape} from './layers/core';
-import {deserialize} from './layers/serialization';
-import {loadLayersModel, ModelAndWeightsConfig, modelFromJSON} from './models';
-import {convertPythonicToTs, convertTsToPythonic} from './utils/serialization_utils';
-import {describeMathCPU, describeMathCPUAndGPU, describeMathCPUAndWebGL2, expectTensorsClose} from './utils/test_utils';
-import {version as layersVersion} from './version';
-
-const OCTET_STREAM_TYPE = 'application/octet-stream';
-const JSON_TYPE = 'application/json';
-
-describeMathCPU('Nested model topology', () => {
-  it('Nested Sequential model: Sequential as first layer', done => {
-    const modelLevel1 = tfl.sequential(
-        {layers: [tfl.layers.dense({units: 2, inputShape: [3]})]});
-    const x = ones([1, 3]);
-    const y = modelLevel1.predict(x) as Tensor;
-
-    const modelLevel2 = tfl.sequential();
-    modelLevel2.add(modelLevel1);
-    expectTensorsClose(modelLevel2.predict(x) as Tensor, y);
-
-    const modelLevel3 = tfl.sequential();
-    modelLevel3.add(modelLevel2);
-    expectTensorsClose(modelLevel3.predict(x) as Tensor, y);
-
-    const xs = ones([8, 3]);
-    const ys = zeros([8, 2]);
-    modelLevel3.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    modelLevel3.fit(xs, ys)
-        .then(history => {
-          const newY = modelLevel1.predict(x) as Tensor;
-          expectTensorsClose(modelLevel2.predict(x) as Tensor, newY);
-          expectTensorsClose(modelLevel3.predict(x) as Tensor, newY);
-          done();
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  it('Nested Sequential model: Functional model as first layer', done => {
-    const input = tfl.input({shape: [3]});
-    const output =
-        tfl.layers.dense({units: 2}).apply(input) as tfl.SymbolicTensor;
-    const modelLevel1 = tfl.model({inputs: input, outputs: output});
-    const x = ones([1, 3]);
-    const y = modelLevel1.predict(x) as Tensor;
-
-    const modelLevel2 = tfl.sequential();
-    modelLevel2.add(modelLevel1);
-    expectTensorsClose(modelLevel2.predict(x) as Tensor, y);
-
-    const modelLevel3 = tfl.sequential();
-    modelLevel3.add(modelLevel2);
-    expectTensorsClose(modelLevel3.predict(x) as Tensor, y);
-
-    const xs = ones([8, 3]);
-    const ys = zeros([8, 2]);
-    modelLevel3.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    modelLevel3.fit(xs, ys)
-        .then(history => {
-          const newY = modelLevel1.predict(x) as Tensor;
-          expectTensorsClose(modelLevel2.predict(x) as Tensor, newY);
-          expectTensorsClose(modelLevel3.predict(x) as Tensor, newY);
-          done();
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  it('Nested Sequential model: Sequential as second layer', done => {
-    const innerModel = tfl.sequential(
-        {layers: [tfl.layers.dense({units: 2, inputShape: [4]})]});
-    const x = ones([1, 4]);
-    const y = innerModel.predict(x) as Tensor;
-
-    const x2By2 = ones([1, 2, 2]);
-
-    const outerModel = tfl.sequential(
-        {layers: [tfl.layers.reshape({targetShape: [4], inputShape: [2, 2]})]});
-    outerModel.add(innerModel);
-    expectTensorsClose(outerModel.predict(x2By2) as Tensor, y);
-
-    const xs = ones([8, 2, 2]);
-    const ys = zeros([8, 2]);
-    outerModel.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    outerModel.fit(xs, ys)
-        .then(history => {
-          const newY = innerModel.predict(x) as Tensor;
-          expectTensorsClose(outerModel.predict(x2By2) as Tensor, newY);
-          done();
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  it('Nested Sequential model: Sequential as middle layer', done => {
-    const innerModel = tfl.sequential(
-        {layers: [tfl.layers.dense({units: 4, inputShape: [4]})]});
-    const x = ones([1, 4]);
-    const y = innerModel.predict(x) as Tensor;
-
-    const x2By2 = ones([1, 2, 2]);
-
-    const outerModel = tfl.sequential({
-      layers: [
-        tfl.layers.reshape({targetShape: [4], inputShape: [2, 2]}), innerModel,
-        tfl.layers.reshape({targetShape: [2, 2]})
-      ]
-    });
-    expectTensorsClose(
-        outerModel.predict(x2By2) as Tensor, y.reshape([1, 2, 2]));
-
-    const xs = ones([8, 2, 2]);
-    const ys = zeros([8, 2, 2]);
-    outerModel.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    outerModel.fit(xs, ys)
-        .then(history => {
-          const newY = innerModel.predict(x) as Tensor;
-          expectTensorsClose(
-              outerModel.predict(x2By2) as Tensor, newY.reshape([1, 2, 2]));
-          done();
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  it('getLayer() works for nested sequential model', () => {
-    const innerModel = tfl.sequential({
-      layers: [
-        tfl.layers.dense({
-          units: 4,
-          inputShape: [4],
-          activation: 'relu',
-          kernelInitializer: 'ones',
-          biasInitializer: 'ones'
-        }),
-        tfl.layers.dense({
-          units: 2,
-          activation: 'tanh',
-          kernelInitializer: 'ones',
-          biasInitializer: 'ones'
-        })
-      ]
-    });
-    const outerModel = tfl.sequential({
-      layers: [
-        innerModel,
-        tfl.layers.dense(
-            {units: 1, kernelInitializer: 'ones', biasInitializer: 'ones'})
-      ]
-    });
-    expect(outerModel.getLayer(0) instanceof tfl.Sequential).toEqual(true);
-    // Expect all-one values based on the kernel and bias initializers specified
-    // above.
-    expectTensorsClose(outerModel.getLayer(1).getWeights()[0], ones([2, 1]));
-    expectTensorsClose(outerModel.getLayer(1).getWeights()[1], ones([1]));
-    // Expect there to be only two layers.
-    expect(() => outerModel.getLayer(2)).toThrow();
-  });
-
-  it('getWeights() works for nested sequential model', () => {
-    const innerModel = tfl.sequential({
-      layers: [
-        tfl.layers.dense({
-          units: 4,
-          inputShape: [4],
-          activation: 'relu',
-          kernelInitializer: 'ones',
-          biasInitializer: 'ones'
-        }),
-        tfl.layers.dense({
-          units: 2,
-          activation: 'tanh',
-          kernelInitializer: 'ones',
-          biasInitializer: 'ones'
-        })
-      ]
-    });
-    const outerModel =
-        tfl.sequential({layers: [innerModel, tfl.layers.dense({units: 1})]});
-    const weights = outerModel.getWeights();
-    expect(weights.length).toEqual(6);
-    expect(weights[0].shape).toEqual([4, 4]);
-    expect(weights[1].shape).toEqual([4]);
-    expect(weights[2].shape).toEqual([4, 2]);
-    expect(weights[3].shape).toEqual([2]);
-    expect(weights[4].shape).toEqual([2, 1]);
-    expect(weights[5].shape).toEqual([1]);
-  });
-
-  it('setWeights() works for nested sequential model', () => {
-    const innerModel = tfl.sequential({
-      layers: [
-        tfl.layers.dense({
-          units: 2,
-          inputShape: [3],
-          activation: 'relu',
-          kernelInitializer: 'zeros',
-          useBias: false
-        }),
-      ]
-    });
-    const outerModel = tfl.sequential({
-      layers: [
-        innerModel,
-        tfl.layers.dense({units: 1, kernelInitializer: 'zeros', useBias: false})
-      ]
-    });
-
-    // The kernel start off as zeros. Set them all to ones.
-    outerModel.setWeights([ones([3, 2]), ones([2, 1])]);
-    expectTensorsClose(outerModel.getWeights()[0], ones([3, 2]));
-    expectTensorsClose(outerModel.getWeights()[1], ones([2, 1]));
-    expectTensorsClose(
-        outerModel.predict(ones([1, 3])) as Tensor, tensor2d([[6]]));
-  });
-
-  it('Sequential as layer: save-load round trip', () => {
-    const innerModel = tfl.sequential({
-      layers: [
-        tfl.layers.dense({
-          units: 4,
-          inputShape: [4],
-          activation: 'relu',
-          kernelInitializer: 'ones',
-          biasInitializer: 'ones'
-        }),
-        tfl.layers.dense({
-          units: 4,
-          activation: 'tanh',
-          kernelInitializer: 'ones',
-          biasInitializer: 'ones'
-        })
-      ]
-    });
-    const outerModel = tfl.sequential({
-      layers: [
-        tfl.layers.reshape({targetShape: [4], inputShape: [2, 2]}), innerModel,
-        tfl.layers.dense(
-            {units: 1, kernelInitializer: 'ones', biasInitializer: 'ones'})
-      ]
-    });
-    const x = randomNormal([1, 2, 2]);
-    const y = outerModel.predict(x) as Tensor;
-
-    const unusedArg: {} = null;
-    const returnString = false;
-    const outerModelJSON = outerModel.toJSON(unusedArg, returnString);
-    const reconstructedModel =
-        deserialize(convertPythonicToTs(outerModelJSON) as PyJsonDict) as
-        tfl.Sequential;
-    expect(reconstructedModel.toJSON(unusedArg, returnString))
-        .toEqual(outerModelJSON);
-    expectTensorsClose(reconstructedModel.predict(x) as Tensor, y);
-  });
-
-  it('Functional model as layer: save-load round trip', () => {
-    const input = tfl.input({shape: [4]});
-    const layer1 = tfl.layers.dense({
-      units: 4,
-      activation: 'relu',
-      kernelInitializer: 'ones',
-      biasInitializer: 'ones'
-    });
-    const layer2 = tfl.layers.dense({
-      units: 4,
-      activation: 'tanh',
-      kernelInitializer: 'ones',
-      biasInitializer: 'ones'
-    });
-    const output = layer2.apply(layer1.apply(input)) as tfl.SymbolicTensor;
-    const innerModel = tfl.model({inputs: input, outputs: output});
-    const outerModel = tfl.sequential({
-      layers: [
-        tfl.layers.reshape({targetShape: [4], inputShape: [2, 2]}), innerModel,
-        tfl.layers.reshape({targetShape: [2, 2]})
-      ]
-    });
-    const x = randomNormal([1, 2, 2]);
-    const y = outerModel.predict(x) as Tensor;
-
-    const unusedArg: {} = null;
-    const returnString = false;
-    const outerModelJSON = outerModel.toJSON(unusedArg, returnString);
-    const reconstructedModel =
-        deserialize(convertPythonicToTs(outerModelJSON) as PyJsonDict) as
-        tfl.Sequential;
-    expect(reconstructedModel.toJSON(unusedArg, returnString))
-        .toEqual(outerModelJSON);
-    expectTensorsClose(reconstructedModel.predict(x) as Tensor, y);
-  });
-
-  it('Attempt to nest two-input functional model fails', () => {
-    const input1 = tfl.input({shape: [4]});
-    const input2 = tfl.input({shape: [5]});
-    const output =
-        tfl.layers.concatenate().apply([input1, input2]) as tfl.SymbolicTensor;
-    const innerModel = tfl.model({inputs: [input1, input2], outputs: output});
-
-    const outerModel = tfl.sequential();
-
-    // Adding the two-input model as the first layer of the new model should
-    // fail.
-    expect(() => outerModel.add(innerModel))
-        .toThrowError(/should have a single input tensor/);
-
-    // Adding the two-input model as the second layer of the new model should
-    // fail.
-    const outerModel2 = tfl.sequential(
-        {layers: [tfl.layers.dense({units: 4, inputShape: [8]})]});
-    expect(() => outerModel2.add(innerModel))
-        .toThrowError(/should have a single input tensor/);
-
-    // Creating a sequential model consisting of only the two-input model as
-    // a layer should fail.
-    expect(() => tfl.sequential({
-      layers: [innerModel]
-    })).toThrowError(/should have a single input tensor/);
-  });
-
-  it('Attempt to nest two-output functional model fails', () => {
-    const input = tfl.input({shape: [12]});
-    const output1 = tfl.layers.reshape({targetShape: [2, 6]}).apply(input) as
-        tfl.SymbolicTensor;
-    const output2 = tfl.layers.reshape({targetShape: [3, 4]}).apply(input) as
-        tfl.SymbolicTensor;
-    const innerModel = tfl.model({inputs: input, outputs: [output1, output2]});
-
-    // Adding the two-output model as the first layer of the new model should
-    // fail.
-    const outerModel = tfl.sequential();
-    expect(() => outerModel.add(innerModel))
-        .toThrowError(/should have a single output tensor/);
-
-    // Adding the two-output model as the second layer of the new model should
-    // fail.
-    const outerModel2 = tfl.sequential(
-        {layers: [tfl.layers.dense({units: 4, inputShape: [8]})]});
-    expect(() => outerModel2.add(innerModel))
-        .toThrowError(/should have a single output tensor/);
-
-    // Creating a sequential model consisting of only the two-output model as
-    // a layer should fail.
-    expect(() => tfl.sequential({
-      layers: [innerModel]
-    })).toThrowError(/should have a single output tensor/);
-  });
-});
-
-describeMathCPU('modelFromJSON', () => {
-  it('reconstitutes pythonic json string', done => {
-    /* python generating code
-        a=Input(shape=(32,))
-        b=Dense(32)(a)
-        model = Model(inputs=a, outputs=b, name="test")
-        model.to_json())
-    */
-    modelFromJSON(fakeSequentialModel)
-        .then(model => {
-          expect(model.name).toEqual('test');
-          const allZeros = zeros([1, 32]);
-          expectTensorsClose(model.apply(allZeros) as Tensor, allZeros);
-          done();
-        })
-        .catch(done.fail);
-  });
-
-  it('modelFromJSON with only model topology (no weights)', async () => {
-    const model = await modelFromJSON(fakeSequentialModel.modelTopology);
-    expect(model.name).toEqual('test');
-    expect(model.layers.length).toEqual(2);
-    expect(model.inputs.length).toEqual(1);
-    expect(model.inputs[0].shape).toEqual([null, 32]);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 32]);
-  });
-
-  it('toJSON and modelFromJSON', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.repeatVector({inputShape: [2], n: 4}));
-    const model1JSON = model1.toJSON(null, false) as PyJsonDict;
-    const model2 = await tfl.models.modelFromJSON(model1JSON);
-    expect(model2.toJSON(null, false)).toEqual(model1JSON);
-  });
-
-  // TODO(bileschi): Uncomment once we are able to load the full RNN model.
-  /*
-  it('reconstitutes rnn model', done => {
-    modelFromJSON(fakeRNNModel)
-        .then(model => {
-          console.log(model);
-          done();
-        })
-        .catch(done.fail);
-  });*/
-
-  it('reconstitutes mnist non-sequential mode.', async () => {
-    /*
-    input_shape = (28,28,1)
-    num_classes=10
-
-    input_layer= Input(shape=input_shape)
-    layer1 = Conv2D(32, kernel_size=(3, 3),
-                     activation='relu',
-                     input_shape=input_shape)
-    layer1_result = layer1(input_layer)
-    layer2= Conv2D(64, (3,3), activation='relu')
-    layer2_result = layer2(layer1_result)
-    layer3 = MaxPooling2D(pool_size=(2, 2))
-    layer3_result = layer3(layer2_result)
-    layer4 = Dropout(0.25)
-    layer4_result = layer4(layer3_result)
-    layer5 = Flatten()
-    layer5_result = layer5(layer4_result)
-    layer6 = Dense(128, activation='relu')
-    layer6_result = layer6(layer5_result)
-    layer7=Dropout(0.5)
-    layer7_result = layer7(layer6_result)
-    layer8=Dense(num_classes, activation='softmax')
-    layer8_result = layer8(layer7_result)
-    model = Model(inputs=input_layer, outputs=layer8_result, name='mnist')
-    model.to_json()
-      */
-    const model = await modelFromJSON(fakeNonSequentialModel);
-    expect(model.name).toEqual('mnist');
-    expect(model.layers.length).toEqual(9);
-    const prediction = model.predict(zeros([1, 28, 28, 1])) as Tensor;
-    expect(prediction.shape).toEqual([1, 10]);
-    expect(sum(prediction).dataSync()).toBeCloseTo(1);
-  });
-  it('reconstitutes mnist sequential mode.', async () => {
-    /*
-    input_shape = (28,28,1)
-    num_classes = 10
-
-    model = Sequential()
-    model.add(Conv2D(32, kernel_size=(3, 3),
-                     activation='relu',
-                     input_shape=input_shape))
-    model.add(Conv2D(64, (3,3), activation='relu'))
-    model.add(MaxPooling2D(pool_size=(2, 2)))
-    model.add(Dropout(0.25))
-    model.add(Flatten())
-    model.add(Dense(128, activation='relu'))
-    model.add(Dropout(0.5))
-    model.add(Dense(num_classes, activation='softmax'))
-
-    model.to_json())
-    */
-    const model = await modelFromJSON(fakeMnistModel);
-    expect(model.layers.length).toEqual(8);
-    const prediction = model.predict(zeros([1, 28, 28, 1])) as Tensor;
-    expect(prediction.shape).toEqual([1, 10]);
-    expect(sum(prediction).dataSync()).toBeCloseTo(1);
-  });
-
-  it('Serialization round-tripping', async () => {
-    const model = await modelFromJSON(fakeRoundtripModel);
-    const serializedModel = model.toJSON() as string;
-    // toJSON() returns a string by default.
-    expect(typeof serializedModel).toEqual('string');
-    const reparsedJson = JSON.parse(serializedModel);
-    expect(reparsedJson['class_name'])
-        .toEqual(fakeRoundtripModel.modelTopology['class_name']);
-    // Intentionally skipping backend and keras_version fields.
-    expect(reparsedJson['config'])
-        .toEqual(fakeRoundtripModel.modelTopology['config']);
-  });
-
-  it('toJSON with returnString = false', async () => {
-    const model = await modelFromJSON(fakeRoundtripModel);
-    const serializedModel = model.toJSON(null, false) as PyJsonDict;
-    expect(serializedModel['class_name'])
-        .toEqual(fakeRoundtripModel.modelTopology['class_name']);
-    expect(serializedModel['config'])
-        .toEqual(fakeRoundtripModel.modelTopology['config']);
-  });
-
-  it('toJSON return value includes correct versions', async () => {
-    const model = await modelFromJSON(fakeRoundtripModel);
-    const serializedModel = model.toJSON(null, false) as PyJsonDict;
-    expect(serializedModel['keras_version'])
-        .toEqual(`tfjs-layers ${layersVersion}`);
-  });
-
-  it('Load sequential model with non-array config', async () => {
-    const modelTopology: {} = {
-      'class_name': 'Sequential',
-      'keras_version': '2.1.6-tf',
-      'config': {
-        'name': 'BarSequential123',
-        'layers': [{
-          'class_name': 'Dense',
-          'config': {
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'seed': null,
-                'mode': 'fan_avg'
-              }
-            },
-            'name': 'dense_1',
-            'kernel_constraint': null,
-            'bias_regularizer': null,
-            'bias_constraint': null,
-            'dtype': 'float32',
-            'activation': 'sigmoid',
-            'trainable': true,
-            'kernel_regularizer': null,
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'units': 1,
-            'batch_input_shape': [null, 4],
-            'use_bias': true,
-            'activity_regularizer': null
-          }
-        }]
-      },
-      'backend': 'tensorflow'
-    };
-    const model = deserialize(
-                      convertPythonicToTs(modelTopology) as
-                      serialization.ConfigDict) as LayersModel;
-
-    expect(model.name.indexOf('BarSequential123')).toEqual(0);
-    expect(model.inputs.length).toEqual(1);
-    expect(model.inputs[0].shape).toEqual([null, 4]);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 1]);
-  });
-});
-
-describeMathCPU('loadLayersModel from URL', () => {
-  const setupFakeWeightFiles =
-      (fileBufferMap:
-           {[filename: string]: Float32Array|Int32Array|ArrayBuffer}) => {
-        spyOn(env().platform, 'fetch')
-            .and.callFake((path: string) => new Promise(resolve => {
-                            resolve(new Response(fileBufferMap[path], {
-                              'headers': {'Content-Type': OCTET_STREAM_TYPE}
-                            }));
-                          }));
-      };
-
-  const isModelConfigNestedValues = [false, true];
-  const pathPrefixes = ['.', './', './model-home', './model-home/'];
-  for (const isModelConfigNested of isModelConfigNestedValues) {
-    for (const pathPrefix of pathPrefixes) {
-      it(`pathPrefix=${pathPrefix}`, done => {
-        const path0 = pathPrefix.endsWith('/') ? `${pathPrefix}weight_0` :
-                                                 `${pathPrefix}/weight_0`;
-        const path1 = pathPrefix.endsWith('/') ? `${pathPrefix}weight_1` :
-                                                 `${pathPrefix}/weight_1`;
-        const fileBufferMap:
-            {[filename: string]: Float32Array|Int32Array|ArrayBuffer} = {};
-        fileBufferMap[path0] =
-            ones([32, 32], 'float32').dataSync() as Float32Array;
-        fileBufferMap[path1] = ones([32], 'float32').dataSync() as Float32Array;
-        setupFakeWeightFiles(fileBufferMap);
-        // Use a randomly generated layer name to prevent interaction with
-        // other unit tests that load the same sample JSON.
-        const denseLayerName = `dense_${Math.floor(Math.random() * 1e9)}`;
-        const weightsManifest: io.WeightsManifestConfig = [
-          {
-            'paths': ['weight_0'],
-            'weights': [{
-              'name': `${denseLayerName}/kernel`,
-              'dtype': 'float32',
-              'shape': [32, 32]
-            }],
-          },
-          {
-            'paths': ['weight_1'],
-            'weights': [{
-              'name': `${denseLayerName}/bias`,
-              'dtype': 'float32',
-              'shape': [32]
-            }],
-          }
-        ];
-        // JSON.parse and stringify to deep copy fakeSequentialModel.
-        let modelTopology =
-            JSON.parse(JSON.stringify(fakeSequentialModel)).modelTopology;
-        modelTopology['config']['layers'][1]['config']['name'] = denseLayerName;
-        if (isModelConfigNested) {
-          // Simulate case in which `configJson` contains not only
-          // `model_config`, but also other data, such as training.
-          modelTopology = {'model_config': modelTopology};
-        }
-        modelFromJSON({modelTopology, weightsManifest, pathPrefix})
-            .then(model => {
-              expectTensorsClose(
-                  model.weights[0].read(), ones([32, 32], 'float32'));
-              expectTensorsClose(
-                  model.weights[1].read(), ones([32], 'float32'));
-            })
-            .then(done)
-            .catch(done.fail);
-      });
-    }
-  }
-
-  it('load topology and weights from implicit relative http path', async () => {
-    const modelTopology =
-        JSON.parse(JSON.stringify(fakeSequentialModel)).modelTopology;
-    const weightsManifest: io.WeightsManifestConfig = [
-      {
-        'paths': ['weight_0'],
-        'weights':
-            [{'name': `dense_6/kernel`, 'dtype': 'float32', 'shape': [32, 32]}],
-      },
-      {
-        'paths': ['weight_1'],
-        'weights':
-            [{'name': `dense_6/bias`, 'dtype': 'float32', 'shape': [32]}],
-      }
-    ];
-
-    spyOn(env().platform, 'fetch').and.callFake((path: string) => {
-      return new Promise((resolve, reject) => {
-        if (path === 'model/model.json') {
-          resolve(new Response(
-              JSON.stringify({
-                modelTopology,
-                weightsManifest,
-              }),
-              {'headers': {'Content-Type': JSON_TYPE}}));
-        } else if (path === 'model/weight_0') {
-          resolve(new Response(
-              ones([32, 32], 'float32').dataSync() as Float32Array,
-              {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else if (path === 'model/weight_1') {
-          resolve(new Response(
-              zeros([32], 'float32').dataSync() as Float32Array,
-              {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else {
-          reject(new Error(`Invalid path: ${path}`));
-        }
-      });
-    });
-
-    const model = await loadLayersModel('model/model.json');
-    expect(model.layers.length).toEqual(2);
-    expect(model.inputs.length).toEqual(1);
-    expect(model.inputs[0].shape).toEqual([null, 32]);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 32]);
-    const weightValues = model.getWeights();
-    expect(weightValues.length).toEqual(2);
-    expectTensorsClose(weightValues[0], ones([32, 32]));
-    expectTensorsClose(weightValues[1], zeros([32]));
-  });
-
-  it('loadLayersModel: with onProgress callback from relative path',
-     async () => {
-       const modelTopology =
-           JSON.parse(JSON.stringify(fakeSequentialModel)).modelTopology;
-       const weightsManifest: io.WeightsManifestConfig = [
-         {
-           'paths': ['weight_0'],
-           'weights': [
-             {'name': `dense_6/kernel`, 'dtype': 'float32', 'shape': [32, 32]}
-           ],
-         },
-         {
-           'paths': ['weight_1'],
-           'weights':
-               [{'name': `dense_6/bias`, 'dtype': 'float32', 'shape': [32]}],
-         }
-       ];
-
-       spyOn(env().platform, 'fetch').and.callFake((path: string) => {
-         return new Promise((resolve, reject) => {
-           if (path === 'model/model.json') {
-             resolve(new Response(
-                 JSON.stringify({
-                   modelTopology,
-                   weightsManifest,
-                 }),
-                 {'headers': {'Content-Type': JSON_TYPE}}));
-           } else if (path === 'model/weight_0') {
-             resolve(new Response(
-                 ones([32, 32], 'float32').dataSync() as Float32Array,
-                 {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-           } else if (path === 'model/weight_1') {
-             resolve(new Response(
-                 zeros([32], 'float32').dataSync() as Float32Array,
-                 {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-           } else {
-             reject(new Error(`Invalid path: ${path}`));
-           }
-         });
-       });
-
-       const progressFractions: number[] = [];
-       const model = await tfl.loadLayersModel('model/model.json', {
-         onProgress: (fraction: number) => {
-           progressFractions.push(fraction);
-         }
-       });
-       expect(model.layers.length).toEqual(2);
-       expect(model.inputs.length).toEqual(1);
-       expect(model.inputs[0].shape).toEqual([null, 32]);
-       expect(model.outputs.length).toEqual(1);
-       expect(model.outputs[0].shape).toEqual([null, 32]);
-       const weightValues = model.getWeights();
-       expect(weightValues.length).toEqual(2);
-       expectTensorsClose(weightValues[0], ones([32, 32]));
-       expectTensorsClose(weightValues[1], zeros([32]));
-       // There are three files: a JSON file and two weight files. So the
-       // progress callback should have been called four times (twice the weight
-       // files' number).
-       expect(progressFractions).toEqual([0.25, 0.5, 0.75, 1]);
-     });
-
-  it('loadLayersModel: with onProgress callback from URL', async () => {
-    const modelTopology =
-        JSON.parse(JSON.stringify(fakeSequentialModel)).modelTopology;
-    const weightsManifest: io.WeightsManifestConfig = [
-      {
-        'paths': ['weight_0'],
-        'weights':
-            [{'name': `dense_6/kernel`, 'dtype': 'float32', 'shape': [32, 32]}],
-      },
-      {
-        'paths': ['weight_1'],
-        'weights':
-            [{'name': `dense_6/bias`, 'dtype': 'float32', 'shape': [32]}],
-      }
-    ];
-
-    spyOn(env().platform, 'fetch').and.callFake((path: string) => {
-      return new Promise((resolve, reject) => {
-        if (path === 'https://url/to/model/model.json') {
-          resolve(new Response(
-              JSON.stringify({
-                modelTopology,
-                weightsManifest,
-              }),
-              {'headers': {'Content-Type': JSON_TYPE}}));
-        } else if (path === 'https://url/to/model/weight_0') {
-          resolve(new Response(
-              ones([32, 32], 'float32').dataSync() as Float32Array,
-              {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else if (path === 'https://url/to/model/weight_1') {
-          resolve(new Response(
-              zeros([32], 'float32').dataSync() as Float32Array,
-              {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else {
-          reject(new Error(`Invalid path: ${path}`));
-        }
-      });
-    });
-
-    const progressFractions: number[] = [];
-    const model = await tfl.loadLayersModel('https://url/to/model/model.json', {
-      onProgress: (fraction: number) => {
-        progressFractions.push(fraction);
-      }
-    });
-    expect(model.layers.length).toEqual(2);
-    expect(model.inputs.length).toEqual(1);
-    expect(model.inputs[0].shape).toEqual([null, 32]);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 32]);
-    const weightValues = model.getWeights();
-    expect(weightValues.length).toEqual(2);
-    expectTensorsClose(weightValues[0], ones([32, 32]));
-    expectTensorsClose(weightValues[1], zeros([32]));
-    // There are three files: a JSON file and two weight files. So the progress
-    // callback should have been called four times (twice the weight files'
-    // number).
-    expect(progressFractions).toEqual([0.25, 0.5, 0.75, 1]);
-  });
-
-  it('loadLayersModel: no memory leak during model loading', async () => {
-    const modelTopology =
-        JSON.parse(JSON.stringify(fakeSequentialModel)).modelTopology;
-    const weightsManifest: io.WeightsManifestConfig = [
-      {
-        'paths': ['weight_0'],
-        'weights':
-            [{'name': `dense_6/kernel`, 'dtype': 'float32', 'shape': [32, 32]}],
-      },
-      {
-        'paths': ['weight_1'],
-        'weights':
-            [{'name': `dense_6/bias`, 'dtype': 'float32', 'shape': [32]}],
-      }
-    ];
-
-    const kernelData = ones([32, 32], 'float32').dataSync() as Float32Array;
-    const biasData = zeros([32], 'float32').dataSync() as Float32Array;
-    spyOn(env().platform, 'fetch').and.callFake((path: string) => {
-      return new Promise((resolve, reject) => {
-        if (path === 'model/model.json') {
-          resolve(new Response(
-              JSON.stringify({
-                modelTopology,
-                weightsManifest,
-              }),
-              {'headers': {'Content-Type': JSON_TYPE}}));
-        } else if (path === 'model/weight_0') {
-          resolve(new Response(
-              kernelData, {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else if (path === 'model/weight_1') {
-          resolve(new Response(
-              biasData, {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else {
-          reject(new Error(`Invalid path: ${path}`));
-        }
-      });
-    });
-
-    const numTensors0 = memory().numTensors;
-    const model1 = await tfl.loadLayersModel('model/model.json');
-    const numTensors1 = memory().numTensors;
-    // The increase in the number of tensors should be equal to the
-    // number of weights possessed by the model. No extra tensors
-    // should have been created.
-    expect(numTensors1).toEqual(numTensors0 + 2);
-
-    model1.dispose();
-    const numTensors2 = memory().numTensors;
-    // The dispose() call should have brought us to the initial number
-    // of tensors (i.e., before the model was created).
-    expect(numTensors2).toEqual(numTensors0);
-  });
-
-  it('loadLayersModel: no memory leak with nested LayersModel layer',
-     async () => {
-       const modelTopology =
-           JSON.parse(JSON.stringify(fakeSequentialModelWithNestedContainer))
-               .modelTopology;
-
-       const weightsManifest: io.WeightsManifestConfig = [{
-         'paths': ['weight_0'],
-         'weights': [
-           {'name': `dense_1/kernel`, 'dtype': 'float32', 'shape': [4, 2]},
-           {'name': `dense_1/bias`, 'dtype': 'float32', 'shape': [2]},
-           {'name': `dense_2/kernel`, 'dtype': 'float32', 'shape': [2, 1]},
-           {'name': `dense_2/bias`, 'dtype': 'float32', 'shape': [1]}
-         ],
-       }];
-
-       spyOn(env().platform, 'fetch').and.callFake((path: string) => {
-         return new Promise((resolve, reject) => {
-           if (path === 'model/model.json') {
-             resolve(new Response(
-                 JSON.stringify({
-                   modelTopology,
-                   weightsManifest,
-                 }),
-                 {'headers': {'Content-Type': JSON_TYPE}}));
-           } else if (path === 'model/weight_0') {
-             resolve(new Response(
-                 new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),
-                 {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-           } else {
-             reject(new Error(`Invalid path: ${path}`));
-           }
-         });
-       });
-
-       const numTensors0 = memory().numTensors;
-       const model = await tfl.loadLayersModel('model/model.json');
-       const mem = await memory();
-       const numTensors1 = mem.numTensors;
-       expect(numTensors1).toEqual(numTensors0 + 4);
-
-       const disposeResult = model.dispose();
-       expect(disposeResult.numDisposedVariables).toEqual(4);
-       const numTensors2 = memory().numTensors;
-       expect(numTensors2).toEqual(numTensors0);
-     });
-
-  it('load topology and weights from implicit relative http path: HDF5 format',
-     async () => {
-       const modelTopology =
-           JSON.parse(JSON.stringify(fakeSequentialModelFromHDF5))
-               .modelTopology;
-       const weightsManifest: io.WeightsManifestConfig = [
-         {
-           'paths': ['weight_0'],
-           'weights': [
-             {'name': `dense_1/kernel`, 'dtype': 'float32', 'shape': [10, 2]}
-           ],
-         },
-         {
-           'paths': ['weight_1'],
-           'weights':
-               [{'name': `dense_1/bias`, 'dtype': 'float32', 'shape': [2]}],
-         },
-         {
-           'paths': ['weight_2'],
-           'weights': [
-             {'name': `dense_2/kernel`, 'dtype': 'float32', 'shape': [2, 1]}
-           ],
-         },
-         {
-           'paths': ['weight_3'],
-           'weights':
-               [{'name': `dense_2/bias`, 'dtype': 'float32', 'shape': [1]}],
-         }
-       ];
-       spyOn(env().platform, 'fetch').and.callFake((path: string) => {
-         return new Promise((resolve, reject) => {
-           if (path === 'model/model.json') {
-             resolve(new Response(
-                 JSON.stringify({
-                   modelTopology,
-                   weightsManifest,
-                 }),
-                 {'headers': {'Content-Type': JSON_TYPE}}));
-           } else if (path === 'model/weight_0') {
-             resolve(new Response(
-                 ones([10, 2], 'float32').dataSync() as Float32Array,
-                 {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-           } else if (path === 'model/weight_1') {
-             resolve(new Response(
-                 zeros([2], 'float32').dataSync() as Float32Array,
-                 {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-           } else if (path === 'model/weight_2') {
-             resolve(new Response(
-                 zeros([2, 1], 'float32').dataSync() as Float32Array,
-                 {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-           } else if (path === 'model/weight_3') {
-             resolve(new Response(
-                 ones([1], 'float32').dataSync() as Float32Array,
-                 {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-           } else {
-             reject(new Error(`Invalid path: ${path}`));
-           }
-         });
-       });
-
-       const model = await loadLayersModel('model/model.json');
-       expect(model.layers.length).toEqual(2);
-       expect(model.inputs.length).toEqual(1);
-       expect(model.inputs[0].shape).toEqual([null, 10]);
-       expect(model.outputs.length).toEqual(1);
-       expect(model.outputs[0].shape).toEqual([null, 1]);
-       const weightValues = model.getWeights();
-       expect(weightValues.length).toEqual(4);
-       expectTensorsClose(weightValues[0], ones([10, 2]));
-       expectTensorsClose(weightValues[1], zeros([2]));
-       expectTensorsClose(weightValues[2], zeros([2, 1]));
-       expectTensorsClose(weightValues[3], ones([1]));
-     });
-
-  it('load topology and weights: non-array Sequential config', async () => {
-    const modelTopology =
-        JSON.parse(JSON.stringify(fakeNonArrayConfigSequentialModelFromHDF5))
-            .modelTopology;
-    const weightsManifest: io.WeightsManifestConfig = [
-      {
-        'paths': ['weight_0'],
-        'weights':
-            [{'name': `dense_1/kernel`, 'dtype': 'float32', 'shape': [10, 2]}],
-      },
-      {
-        'paths': ['weight_1'],
-        'weights': [{'name': `dense_1/bias`, 'dtype': 'float32', 'shape': [2]}],
-      },
-      {
-        'paths': ['weight_2'],
-        'weights':
-            [{'name': `dense_2/kernel`, 'dtype': 'float32', 'shape': [2, 1]}],
-      },
-      {
-        'paths': ['weight_3'],
-        'weights': [{'name': `dense_2/bias`, 'dtype': 'float32', 'shape': [1]}],
-      }
-    ];
-    spyOn(env().platform, 'fetch').and.callFake((path: string) => {
-      return new Promise((resolve, reject) => {
-        if (path === 'model/model.json') {
-          resolve(new Response(
-              JSON.stringify({
-                modelTopology,
-                weightsManifest,
-              }),
-              {'headers': {'Content-Type': JSON_TYPE}}));
-        } else if (path === 'model/weight_0') {
-          resolve(new Response(
-              ones([10, 2], 'float32').dataSync() as Float32Array,
-              {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else if (path === 'model/weight_1') {
-          resolve(new Response(
-              zeros([2], 'float32').dataSync() as Float32Array,
-              {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else if (path === 'model/weight_2') {
-          resolve(new Response(
-              zeros([2, 1], 'float32').dataSync() as Float32Array,
-              {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else if (path === 'model/weight_3') {
-          resolve(new Response(
-              ones([1], 'float32').dataSync() as Float32Array,
-              {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-        } else {
-          reject(new Error(`Invalid path: ${path}`));
-        }
-      });
-    });
-
-    const model = await loadLayersModel('model/model.json');
-    expect(model.name.indexOf('Foo123Sequential')).toEqual(0);
-    expect(model.layers.length).toEqual(2);
-    expect(model.inputs.length).toEqual(1);
-    expect(model.inputs[0].shape).toEqual([null, 10]);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 1]);
-    const weightValues = model.getWeights();
-    expect(weightValues.length).toEqual(4);
-    expectTensorsClose(weightValues[0], ones([10, 2]));
-    expectTensorsClose(weightValues[1], zeros([2]));
-    expectTensorsClose(weightValues[2], zeros([2, 1]));
-    expectTensorsClose(weightValues[3], ones([1]));
-  });
-
-  it('load topology and weights with browserHTTPRequest with requestInit',
-     async () => {
-       const modelTopology =
-           JSON.parse(JSON.stringify(fakeSequentialModel)).modelTopology;
-       const weightsManifest: io.WeightsManifestConfig = [
-         {
-           'paths': ['weight_0'],
-           'weights': [
-             {'name': `dense_6/kernel`, 'dtype': 'float32', 'shape': [32, 32]}
-           ],
-         },
-         {
-           'paths': ['weight_1'],
-           'weights':
-               [{'name': `dense_6/bias`, 'dtype': 'float32', 'shape': [32]}],
-         }
-       ];
-
-       const requestHeaders: Array<{[key: string]: string | {}}> = [];
-       const requestCredentials: string[] = [];
-       spyOn(env().platform, 'fetch')
-           .and.callFake((path: string, requestInit?: RequestInit) => {
-             if (requestInit != null) {
-               requestHeaders.push(requestInit.headers as {});
-               requestCredentials.push(requestInit.credentials);
-             }
-             return new Promise((resolve, reject) => {
-               if (path === 'model/model.json') {
-                 resolve(new Response(
-                     JSON.stringify({
-                       modelTopology,
-                       weightsManifest,
-                     }),
-                     {'headers': {'Content-Type': JSON_TYPE}}));
-               } else if (path === 'model/weight_0') {
-                 resolve(new Response(
-                     ones([32, 32], 'float32').dataSync() as Float32Array,
-                     {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-               } else if (path === 'model/weight_1') {
-                 resolve(new Response(
-                     zeros([32], 'float32').dataSync() as Float32Array,
-                     {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-               } else {
-                 reject(new Error(`Invalid path: ${path}`));
-               }
-             });
-           });
-
-       const model =
-           await loadLayersModel(io.browserHTTPRequest('model/model.json', {
-             requestInit: {
-               headers: {'header_key_1': 'header_value_1'},
-               credentials: 'include',
-             }
-           }));
-       expect(model.layers.length).toEqual(2);
-       expect(model.inputs.length).toEqual(1);
-       expect(model.inputs[0].shape).toEqual([null, 32]);
-       expect(model.outputs.length).toEqual(1);
-       expect(model.outputs[0].shape).toEqual([null, 32]);
-       const weightValues = model.getWeights();
-       expect(weightValues.length).toEqual(2);
-       expectTensorsClose(weightValues[0], ones([32, 32]));
-       expectTensorsClose(weightValues[1], zeros([32]));
-
-       // Verify that the headers and credentials are sent via
-       // `fetch` properly.
-       expect(requestHeaders[0]).toEqual(jasmine.objectContaining({
-         'header_key_1': 'header_value_1'
-       }));
-       expect(requestHeaders[1]).toEqual(jasmine.objectContaining({
-         'header_key_1': 'header_value_1'
-       }));
-       expect(requestHeaders[2]).toEqual(jasmine.objectContaining({
-         'header_key_1': 'header_value_1'
-       }));
-       expect(requestCredentials).toEqual(['include', 'include', 'include']);
-     });
-
-  const httpProtocols = ['http://', 'https://'];
-  for (const protocol of httpProtocols) {
-    it(`load topology and weights: explicit relative ${protocol} path`,
-       async () => {
-         const modelTopology =
-             JSON.parse(JSON.stringify(fakeSequentialModel)).modelTopology;
-         const weightsManifest: io.WeightsManifestConfig = [
-           {
-             'paths': ['weight_0'],
-             'weights': [
-               {'name': `dense_6/kernel`, 'dtype': 'float32', 'shape': [32, 32]}
-             ],
-           },
-           {
-             'paths': ['weight_1'],
-             'weights':
-                 [{'name': `dense_6/bias`, 'dtype': 'float32', 'shape': [32]}],
-           }
-         ];
-         spyOn(env().platform, 'fetch').and.callFake((path: string) => {
-           return new Promise((resolve, reject) => {
-             if (path === `${protocol}localhost:8888/models/model.json`) {
-               resolve(new Response(
-                   JSON.stringify({
-                     modelTopology,
-                     weightsManifest,
-                   }),
-                   {'headers': {'Content-Type': JSON_TYPE}}));
-             } else if (path === `${protocol}localhost:8888/models/weight_0`) {
-               resolve(new Response(
-                   ones([32, 32], 'float32').dataSync() as Float32Array,
-                   {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-             } else if (path === `${protocol}localhost:8888/models/weight_1`) {
-               resolve(new Response(
-                   zeros([32], 'float32').dataSync() as Float32Array,
-                   {'headers': {'Content-Type': OCTET_STREAM_TYPE}}));
-             } else {
-               reject(new Error(`Invalid path: ${path}`));
-             }
-           });
-         });
-
-         const model = await loadLayersModel(
-             `${protocol}localhost:8888/models/model.json`);
-         expect(model.layers.length).toEqual(2);
-         expect(model.inputs.length).toEqual(1);
-         expect(model.inputs[0].shape).toEqual([null, 32]);
-         expect(model.outputs.length).toEqual(1);
-         expect(model.outputs[0].shape).toEqual([null, 32]);
-         const weightValues = model.getWeights();
-         expect(weightValues.length).toEqual(2);
-         expectTensorsClose(weightValues[0], ones([32, 32]));
-         expectTensorsClose(weightValues[1], zeros([32]));
-       });
-  }
-
-  it('Missing weight in manifest leads to error', done => {
-    setupFakeWeightFiles({
-      './weight_0': ones([32, 32], 'float32').dataSync() as Float32Array,
-      './weight_1': ones([32], 'float32').dataSync() as Float32Array,
-    });
-    // Use a randomly generated layer name to prevent interaction with other
-    // unit tests that load the same sample JSON.
-    const denseLayerName = `dense_${Math.floor(Math.random() * 1e9)}`;
-    const weightsManifest: io.WeightsManifestConfig = [
-      {
-        'paths': ['weight_0'],
-        'weights': [{
-          'name': `${denseLayerName}/kernel`,
-          'dtype': 'float32',
-          'shape': [32, 32]
-        }],
-      },
-    ];  // Missing bias.
-    // JSON.parse and stringify to deep copy fakeSequentialModel.
-    const configJson =
-        JSON.parse(JSON.stringify(fakeSequentialModel)).modelTopology;
-    configJson['config']['layers'][1]['config']['name'] = denseLayerName;
-    modelFromJSON({modelTopology: configJson, weightsManifest, pathPrefix: '.'})
-        .then(() => done.fail())
-        .catch(() => done());
-  });
-
-  it('Loads weights despite uniqueified tensor names', async () => {
-    setupFakeWeightFiles({
-      './weight_0': ones([32, 32], 'float32').dataSync() as Float32Array,
-      './weight_1': ones([32], 'float32').dataSync() as Float32Array,
-    });
-    const denseLayerName = 'dense_uniqueify';
-    const weightsManifest: io.WeightsManifestConfig = [
-      {
-        'paths': ['weight_0'],
-        'weights': [{
-          'name': `${denseLayerName}/kernel`,
-          'dtype': 'float32',
-          'shape': [32, 32]
-        }],
-      },
-      {
-        'paths': ['weight_1'],
-        'weights': [
-          {'name': `${denseLayerName}/bias`, 'dtype': 'float32', 'shape': [32]}
-        ],
-      }
-    ];
-    // JSON.parse and stringify to deep copy fakeSequentialModel.
-    const configJson =
-        JSON.parse(JSON.stringify(fakeSequentialModel)).modelTopology;
-    configJson['config']['layers'][1]['config']['name'] = denseLayerName;
-    const model1 = await modelFromJSON(
-        {modelTopology: configJson, weightsManifest, pathPrefix: '.'});
-    expect(model1.weights[0].name).toEqual('dense_uniqueify/kernel');
-    expect(model1.weights[0].originalName).toEqual('dense_uniqueify/kernel');
-    expect(model1.weights[1].name).toEqual('dense_uniqueify/bias');
-    expect(model1.weights[1].originalName).toEqual('dense_uniqueify/bias');
-    expectTensorsClose(model1.weights[0].read(), ones([32, 32], 'float32'));
-    expectTensorsClose(model1.weights[1].read(), ones([32], 'float32'));
-
-    // On the second load, the variable names will be uniqueified. This
-    // test succeeds only because we maintain the name mapping, so we
-    // can load weights--keyed by non-unique names in the weight
-    // manifest--into variables with newly uniqueified names.
-    const model2 = await modelFromJSON(
-        {modelTopology: configJson, weightsManifest, pathPrefix: '.'});
-    // note unique suffix
-    expect(model2.weights[0].name).toEqual('dense_uniqueify/kernel_1');
-    expect(model2.weights[0].originalName).toEqual('dense_uniqueify/kernel');
-    // note unique suffix
-    expect(model2.weights[1].name).toEqual('dense_uniqueify/bias_1');
-    expect(model2.weights[1].originalName).toEqual('dense_uniqueify/bias');
-    expectTensorsClose(model2.weights[0].read(), ones([32, 32], 'float32'));
-    expectTensorsClose(model2.weights[1].read(), ones([32], 'float32'));
-  });
-
-  it('Repeated saving and loading of LayersModel works', () => {
-    const model1 = tfl.sequential();
-    model1.add(
-        tfl.layers.dense({units: 3, inputShape: [4], activation: 'relu'}));
-    model1.add(tfl.layers.dense({units: 1, activation: 'sigmoid'}));
-    const json1 = model1.toJSON(null, false);
-    const model2 =
-        deserialize(convertPythonicToTs(json1) as serialization.ConfigDict) as
-        LayersModel;
-    const json2 = model2.toJSON(null, false);
-    expect(json2).toEqual(json1);
-  });
-});
-
-describeMathCPUAndWebGL2('Saving+loading model with optimizer', () => {
-  it('SGD', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.dense(
-        {units: 1, inputShape: [8], kernelInitializer: 'ones'}));
-    const learningRate = 0.02;
-    const optimizer = train.sgd(learningRate);
-    model1.compile({loss: 'meanSquaredError', optimizer});
-
-    const xs = ones([4, 8]);
-    const ys = zeros([4, 1]);
-    await model1.fit(xs, ys, {epochs: 1});
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual('mean_squared_error');
-
-    const weightSpecs = savedArtifacts.weightSpecs;
-    // The first two weights belong to the model proper.
-    // The next weight is the iterations counter.
-    expect(weightSpecs.length).toEqual(2 + 1);
-    expect(weightSpecs[2].name).toEqual('iter');
-    // The first part comes from the kernel of the dense layer, which has a
-    // 8 elements and each is 4 bytes.
-    // The second part comes from the bias of the dense layer, which has 1
-    // element and is also 4 bytes.
-    const weightData = savedArtifacts.weightData;
-    expect(new io.CompositeArrayBuffer(weightData).byteLength)
-        .toEqual(4 * 8 + 4 * 1 + 4);
-
-    // Load the model back, with the optimizer.
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    expect(model2.optimizer.getConfig()['learningRate']).toEqual(learningRate);
-
-    const optimizer1Weights = await model1.optimizer.getWeights();
-    const optimizer2Weights = await model2.optimizer.getWeights();
-    expect(optimizer2Weights.length).toEqual(optimizer1Weights.length);
-    for (let i = 0; i < optimizer1Weights.length; ++i) {
-      expectTensorsClose(
-          optimizer2Weights[i].tensor, optimizer1Weights[i].tensor);
-    }
-
-    // Call fit() on the loaded model and assert that the effect is the
-    // same as calling fit() again on the original model.
-    const history = await model2.fit(xs, ys, {epochs: 1});
-    expect(history.history.loss[0]).toBeCloseTo(26.2144);
-  });
-
-  it('RMSProp', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.dense(
-        {units: 1, inputShape: [8], kernelInitializer: 'ones'}));
-    const learningRate = 0.02;
-    const decay = 0.95;
-    const optimizer = train.rmsprop(learningRate, decay);
-    model1.compile({loss: 'meanSquaredError', optimizer});
-
-    const xs = ones([4, 8]);
-    const ys = zeros([4, 1]);
-    await model1.fit(xs, ys, {epochs: 1});
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual('mean_squared_error');
-
-    const weightSpecs = savedArtifacts.weightSpecs;
-    // The first two weights belong to the model proper.
-    // The next weight is the iterations counter.
-    // The last four weights belong to the rmsprop optimizer.
-    expect(weightSpecs.length).toEqual(2 + 1 + 2 * 2);
-    expect(weightSpecs[2].name).toEqual('iter');
-    expect(weightSpecs[3].name).toEqual(`${weightSpecs[0].name}/rms`);
-    expect(weightSpecs[4].name).toEqual(`${weightSpecs[1].name}/rms`);
-    expect(weightSpecs[5].name).toEqual(`${weightSpecs[0].name}/momentum`);
-    expect(weightSpecs[6].name).toEqual(`${weightSpecs[1].name}/momentum`);
-    // The first part comes from the kernel of the dense layer, which has a
-    // 8 elements and each is 4 bytes.
-    // The second part comes from the bias of the dense layer, which has 1
-    // element and is also 4 bytes.
-    const weightData = savedArtifacts.weightData;
-    expect(new io.CompositeArrayBuffer(weightData).byteLength)
-        .toEqual(4 + 4 * 8 * 3 + 4 * 1 * 3);
-
-    // Load the model back, with the optimizer.
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    expect(model2.optimizer.getConfig()['learningRate']).toEqual(learningRate);
-    expect(model2.optimizer.getConfig()['decay']).toEqual(decay);
-
-    const optimizer1Weights = await model1.optimizer.getWeights();
-    const optimizer2Weights = await model2.optimizer.getWeights();
-    expect(optimizer2Weights.length).toEqual(optimizer1Weights.length);
-    for (let i = 0; i < optimizer1Weights.length; ++i) {
-      expectTensorsClose(
-          optimizer2Weights[i].tensor, optimizer1Weights[i].tensor);
-    }
-
-    // Call fit() on the loaded model and assert that the effect is the
-    // same as calling fit() again on the original model.
-    const history = await model2.fit(xs, ys, {epochs: 1});
-    expect(history.history.loss[0]).toBeCloseTo(51.768246);
-  });
-
-  it('Adadelta', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.dense(
-        {units: 1, inputShape: [8], kernelInitializer: 'ones'}));
-    const learningRate = 0.02;
-    const optimizer = train.adadelta(learningRate);
-    model1.compile({loss: 'meanSquaredError', optimizer});
-
-    const xs = ones([4, 8]);
-    const ys = zeros([4, 1]);
-    await model1.fit(xs, ys, {epochs: 1});
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual('mean_squared_error');
-
-    const weightSpecs = savedArtifacts.weightSpecs;
-    // The first two weights belong to the model proper.
-    // The next weight is the iterations counter.
-    // The last four weights belong to the rmsprop optimizer.
-    expect(weightSpecs.length).toEqual(2 + 1 + 2 * 2);
-    expect(weightSpecs[2].name).toEqual('iter');
-    expect(weightSpecs[3].name).toEqual(`${weightSpecs[0].name}/accum_grad`);
-    expect(weightSpecs[4].name).toEqual(`${weightSpecs[1].name}/accum_grad`);
-    expect(weightSpecs[5].name).toEqual(`${weightSpecs[0].name}/accum_var`);
-    expect(weightSpecs[6].name).toEqual(`${weightSpecs[1].name}/accum_var`);
-    // The first part comes from the kernel of the dense layer, which has a
-    // 8 elements and each is 4 bytes.
-    // The second part comes from the bias of the dense layer, which has 1
-    // element and is also 4 bytes.
-    const weightData = savedArtifacts.weightData;
-    expect(new io.CompositeArrayBuffer(weightData).byteLength)
-        .toEqual(4 + 4 * 8 * 3 + 4 * 1 * 3);
-
-    // Load the model back, with the optimizer.
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    expect(model2.optimizer.getConfig()['learningRate']).toEqual(learningRate);
-
-    const optimizer1Weights = await model1.optimizer.getWeights();
-    const optimizer2Weights = await model2.optimizer.getWeights();
-    expect(optimizer2Weights.length).toEqual(optimizer1Weights.length);
-    for (let i = 0; i < optimizer1Weights.length; ++i) {
-      expectTensorsClose(
-          optimizer2Weights[i].tensor, optimizer1Weights[i].tensor);
-    }
-
-    // Call fit() on the loaded model and assert that the effect is the
-    // same as calling fit() again on the original model.
-    const history = await model2.fit(xs, ys, {epochs: 1});
-    expect(history.history.loss[0]).toBeCloseTo(26.2144);
-  });
-
-  it('Adagrad', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.dense(
-        {units: 1, inputShape: [8], kernelInitializer: 'ones'}));
-    const learningRate = 0.02;
-    const initialAccumulatorValue = 0.15;
-    const optimizer = train.adagrad(learningRate, initialAccumulatorValue);
-    model1.compile({loss: 'meanSquaredError', optimizer});
-
-    const xs = ones([4, 8]);
-    const ys = zeros([4, 1]);
-    await model1.fit(xs, ys, {epochs: 1});
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual('mean_squared_error');
-
-    const weightSpecs = savedArtifacts.weightSpecs;
-    // The first two weights belong to the model proper.
-    // The next weight is the iterations counter.
-    // The last two weights belong to the rmsprop optimizer.
-    expect(weightSpecs.length).toEqual(2 + 1 + 2);
-    expect(weightSpecs[2].name).toEqual('iter');
-    expect(weightSpecs[3].name).toEqual(`${weightSpecs[0].name}/accumulator`);
-    expect(weightSpecs[4].name).toEqual(`${weightSpecs[1].name}/accumulator`);
-    // The first part comes from the kernel of the dense layer, which has a
-    // 8 elements and each is 4 bytes.
-    // The second part comes from the bias of the dense layer, which has 1
-    // element and is also 4 bytes.
-    const weightData = savedArtifacts.weightData;
-    expect(new io.CompositeArrayBuffer(weightData).byteLength)
-        .toEqual(4 + 4 * 8 * 2 + 4 * 1 * 2);
-
-    // Load the model back, with the optimizer.
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    expect(model2.optimizer.getConfig()['learningRate']).toEqual(learningRate);
-    expect(model2.optimizer.getConfig()['initialAccumulatorValue'])
-        .toEqual(initialAccumulatorValue);
-
-    const optimizer1Weights = await model1.optimizer.getWeights();
-    const optimizer2Weights = await model2.optimizer.getWeights();
-    expect(optimizer2Weights.length).toEqual(optimizer1Weights.length);
-    for (let i = 0; i < optimizer1Weights.length; ++i) {
-      expectTensorsClose(
-          optimizer2Weights[i].tensor, optimizer1Weights[i].tensor);
-    }
-
-    // Call fit() on the loaded model and assert that the effect is the
-    // same as calling fit() again on the original model.
-    const history = await model2.fit(xs, ys, {epochs: 1});
-    expect(history.history.loss[0]).toBeCloseTo(61.153225);
-  });
-
-  it('Adam as explicit object', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.dense(
-        {units: 1, inputShape: [8], kernelInitializer: 'ones'}));
-    const learningRate = 0.02;
-    const beta1 = 0.95;
-    const beta2 = 0.98;
-    const optimizer = train.adam(learningRate, beta1, beta2);
-    model1.compile({loss: 'meanSquaredError', optimizer});
-
-    const xs = ones([4, 8]);
-    const ys = zeros([4, 1]);
-    await model1.fit(xs, ys, {epochs: 1});
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual('mean_squared_error');
-
-    const weightSpecs = savedArtifacts.weightSpecs;
-    // The first two weights belong to the model proper.
-    // The next weight is the iterations counter.
-    // The last four weights belong to the rmsprop optimizer.
-    expect(weightSpecs.length).toEqual(2 + 1 + 2 * 2);
-    expect(weightSpecs[2].name).toEqual('iter');
-    expect(weightSpecs[3].name).toEqual(`${weightSpecs[0].name}/m`);
-    expect(weightSpecs[4].name).toEqual(`${weightSpecs[1].name}/m`);
-    expect(weightSpecs[5].name).toEqual(`${weightSpecs[0].name}/v`);
-    expect(weightSpecs[6].name).toEqual(`${weightSpecs[1].name}/v`);
-    // The first part comes from the kernel of the dense layer, which has a
-    // 8 elements and each is 4 bytes.
-    // The second part comes from the bias of the dense layer, which has 1
-    // element and is also 4 bytes.
-    const weightData = savedArtifacts.weightData;
-    expect(new io.CompositeArrayBuffer(weightData).byteLength)
-        .toEqual(4 + 4 * 8 * 3 + 4 * 1 * 3);
-
-    // Load the model back, with the optimizer.
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    expect(model2.optimizer.getConfig()['learningRate']).toEqual(learningRate);
-    expect(model2.optimizer.getConfig()['beta1']).toEqual(beta1);
-    expect(model2.optimizer.getConfig()['beta2']).toEqual(beta2);
-
-    const optimizer1Weights = await model1.optimizer.getWeights();
-    const optimizer2Weights = await model2.optimizer.getWeights();
-    expect(optimizer2Weights.length).toEqual(optimizer1Weights.length);
-    for (let i = 0; i < optimizer1Weights.length; ++i) {
-      expectTensorsClose(
-          optimizer2Weights[i].tensor, optimizer1Weights[i].tensor);
-    }
-
-    // Call fit() on the loaded model and assert that the effect is the
-    // same as calling fit() again on the original model.
-    const history = await model2.fit(xs, ys, {epochs: 1});
-    expect(history.history.loss[0]).toBeCloseTo(61.1524);
-  });
-
-  it('Adam as string name', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.dense(
-        {units: 1, inputShape: [8], kernelInitializer: 'ones'}));
-    model1.compile({loss: 'meanSquaredError', optimizer: 'adam'});
-
-    const xs = ones([4, 8]);
-    const ys = zeros([4, 1]);
-    await model1.fit(xs, ys, {epochs: 1});
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual('mean_squared_error');
-
-    const weightSpecs = savedArtifacts.weightSpecs;
-    // The first two weights belong to the model proper.
-    // The next weight is the iterations counter.
-    // The last four weights belong to the rmsprop optimizer.
-    expect(weightSpecs.length).toEqual(2 + 1 + 2 * 2);
-    expect(weightSpecs[2].name).toEqual('iter');
-    expect(weightSpecs[3].name).toEqual(`${weightSpecs[0].name}/m`);
-    expect(weightSpecs[4].name).toEqual(`${weightSpecs[1].name}/m`);
-    expect(weightSpecs[5].name).toEqual(`${weightSpecs[0].name}/v`);
-    expect(weightSpecs[6].name).toEqual(`${weightSpecs[1].name}/v`);
-    // The first part comes from the kernel of the dense layer, which has a
-    // 8 elements and each is 4 bytes.
-    // The second part comes from the bias of the dense layer, which has 1
-    // element and is also 4 bytes.
-    const weightData = savedArtifacts.weightData;
-    expect(new io.CompositeArrayBuffer(weightData).byteLength)
-        .toEqual(4 + 4 * 8 * 3 + 4 * 1 * 3);
-
-    // Load the model back, with the optimizer.
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    expect(model2.optimizer.getConfig()['learningRate']).toEqual(1e-3);
-
-    const optimizer1Weights = await model1.optimizer.getWeights();
-    const optimizer2Weights = await model2.optimizer.getWeights();
-    expect(optimizer2Weights.length).toEqual(optimizer1Weights.length);
-    for (let i = 0; i < optimizer1Weights.length; ++i) {
-      expectTensorsClose(
-          optimizer2Weights[i].tensor, optimizer1Weights[i].tensor);
-    }
-
-    // Call fit() on the loaded model and assert that the effect is the
-    // same as calling fit() again on the original model.
-    const history = await model2.fit(xs, ys, {epochs: 1});
-    expect(history.history.loss[0]).toBeCloseTo(63.8561);
-  });
-
-  it('Momentum', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.dense(
-        {units: 1, inputShape: [8], kernelInitializer: 'ones'}));
-    const learningRate = 0.02;
-    const momentum = 0.91;
-    const optimizer = train.momentum(learningRate, momentum);
-    model1.compile({loss: 'meanSquaredError', optimizer});
-
-    const xs = ones([4, 8]);
-    const ys = zeros([4, 1]);
-    await model1.fit(xs, ys, {epochs: 1});
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual('mean_squared_error');
-
-    const weightSpecs = savedArtifacts.weightSpecs;
-    // The first two weights belong to the model proper.
-    // The next weight is the iterations counter.
-    // The last four weights belong to the rmsprop optimizer.
-    expect(weightSpecs.length).toEqual(2 + 1 + 2);
-    expect(weightSpecs[2].name).toEqual('iter');
-    expect(weightSpecs[3].name).toEqual(`${weightSpecs[0].name}/momentum`);
-    expect(weightSpecs[4].name).toEqual(`${weightSpecs[1].name}/momentum`);
-    // The first part comes from the kernel of the dense layer, which has a
-    // 8 elements and each is 4 bytes.
-    // The second part comes from the bias of the dense layer, which has 1
-    // element and is also 4 bytes.
-    const weightData = savedArtifacts.weightData;
-    expect(new io.CompositeArrayBuffer(weightData).byteLength)
-        .toEqual(4 + 4 * 8 * 2 + 4 * 1 * 2);
-
-    // Load the model back, with the optimizer.
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    expect(model2.optimizer.getConfig()['learningRate']).toEqual(learningRate);
-
-    const optimizer1Weights = await model1.optimizer.getWeights();
-    const optimizer2Weights = await model2.optimizer.getWeights();
-    expect(optimizer2Weights.length).toEqual(optimizer1Weights.length);
-    for (let i = 0; i < optimizer1Weights.length; ++i) {
-      expectTensorsClose(
-          optimizer2Weights[i].tensor, optimizer1Weights[i].tensor);
-    }
-
-    // Call fit() on the loaded model and assert that the effect is the
-    // same as calling fit() again on the original model.
-    const history = await model2.fit(xs, ys, {epochs: 1});
-    expect(history.history.loss[0]).toBeCloseTo(26.2144);
-  });
-
-  it('Saving model with metrics', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [8],
-      kernelInitializer: 'ones',
-      activation: 'softmax'
-    }));
-    model1.compile(
-        {loss: 'categoricalCrossentropy', optimizer: 'adam', metrics: ['acc']});
-
-    const xs = ones([4, 8]);
-    const ys = tensor2d([[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]]);
-    let h = await model1.fit(xs, ys, {epochs: 1});
-    expect(h.history.loss[0]).toBeCloseTo(1.0986123);
-    expect(h.history.acc).toEqual([0]);
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual('categorical_crossentropy');
-    expect(trainingConfig['metrics']).toEqual(['acc']);
-
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-    h = await model2.fit(xs, ys, {epochs: 1});
-    expect(h.history.loss.length).toEqual(1);
-    expect(h.history.loss[0]).toBeCloseTo(1.086648);
-    expect(h.history.acc).toEqual([1]);
-  });
-
-  it('Saving model without calling fit first', async () => {
-    const model1 = tfl.sequential();
-    model1.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [8],
-      kernelInitializer: 'ones',
-      activation: 'softmax'
-    }));
-    model1.compile(
-        {loss: 'categoricalCrossentropy', optimizer: 'adam', metrics: ['acc']});
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual('categorical_crossentropy');
-    expect(trainingConfig['metrics']).toEqual(['acc']);
-
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-
-    const xs = ones([4, 8]);
-    const ys = tensor2d([[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]]);
-    const h = await model2.fit(xs, ys, {epochs: 1});
-    expect(h.history.loss.length).toEqual(1);
-    expect(h.history.loss[0]).toBeCloseTo(1.0986123);
-    expect(h.history.acc).toEqual([0]);
-    expect(model2.optimizer.iterations).toEqual(1);
-  });
-
-  it('Saving functional model with more than one output', async () => {
-    const input = tfl.input({shape: [8]});
-    const x =
-        tfl.layers
-            .dense({units: 5, kernelInitializer: 'ones', activation: 'relu'})
-            .apply(input) as tfl.SymbolicTensor;
-    const outLayer1 = tfl.layers.dense(
-        {units: 3, kernelInitializer: 'ones', activation: 'softmax'});
-    const y1 = outLayer1.apply(x) as tfl.SymbolicTensor;
-    const outLayer2 = tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', activation: 'sigmoid'});
-    const y2 = outLayer2.apply(x) as tfl.SymbolicTensor;
-    const model1 = tfl.model({inputs: input, outputs: [y1, y2]});
-    model1.compile({
-      loss: ['categoricalCrossentropy', 'binaryCrossentropy'],
-      optimizer: 'adam',
-      metrics: ['acc']
-    });
-
-    const xs = ones([4, 8]);
-    const ys1 = tensor2d([[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]]);
-    const ys2 = tensor2d([[1], [1], [1], [1]]);
-
-    let h = await model1.fit(xs, [ys1, ys2], {epochs: 1});
-
-    let savedArtifacts: io.ModelArtifacts;
-    await model1.save(
-        io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-          savedArtifacts = artifacts;
-          return null;
-        }),
-        {includeOptimizer: true});
-
-    const trainingConfig = savedArtifacts.trainingConfig;
-    expect(trainingConfig['loss']).toEqual([
-      'categorical_crossentropy', 'binary_crossentropy'
-    ]);
-    expect(trainingConfig['metrics']).toEqual(['acc']);
-
-    const model2 = await tfl.loadLayersModel(io.fromMemory(savedArtifacts));
-
-    h = await model2.fit(xs, [ys1, ys2], {epochs: 1});
-    expect(h.history.loss.length).toEqual(1);
-    expect(h.history.loss[0]).toBeCloseTo(1.044699);
-
-    const lossKey1 = `${outLayer1.name}_loss`;
-    const outputLoss1 = h.history[lossKey1] as number[];
-    expect(outputLoss1.length).toEqual(1);
-    expect(outputLoss1[0]).toBeCloseTo(1.0446988);
-    const lossKey2 = `${outLayer2.name}_loss`;
-    const outputLoss2 = h.history[lossKey2] as number[];
-    expect(outputLoss2.length).toEqual(1);
-    expect(outputLoss2[0]).toBeCloseTo(1.936124e-7);
-
-    const metricKey1 = `${outLayer1.name}_acc`;
-    const metricValues1 = h.history[metricKey1] as number[];
-    expect(metricValues1.length).toEqual(1);
-    expect(metricValues1[0]).toEqual(1);
-    const metricKey2 = `${outLayer2.name}_acc`;
-    const metricValues2 = h.history[metricKey2] as number[];
-    expect(metricValues2.length).toEqual(1);
-    expect(metricValues2[0]).toBeCloseTo(1);
-  });
-});
-
-describeMathCPU('loadLayersModel from IOHandler', () => {
-  // The model topology JSON can be obtained with the following Python Keras
-  // code:
-  //
-  // ```python
-  // import keras
-  // model = keras.Sequential([
-  //     keras.layers.Dense(1, input_shape=[4], activation='sigmoid')
-  // ])
-  // print(model.to_json())
-  // ```
-  const modelTopology: {} = {
-    'class_name': 'Sequential',
-    'keras_version': '2.1.4',
-    'config': [{
-      'class_name': 'Dense',
-      'config': {
-        'kernel_initializer': {
-          'class_name': 'VarianceScaling',
-          'config': {
-            'distribution': 'uniform',
-            'scale': 1.0,
-            'seed': null,
-            'mode': 'fan_avg'
-          }
-        },
-        'name': 'dense_1',
-        'kernel_constraint': null,
-        'bias_regularizer': null,
-        'bias_constraint': null,
-        'dtype': 'float32',
-        'activation': 'sigmoid',
-        'trainable': true,
-        'kernel_regularizer': null,
-        'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-        'units': 1,
-        'batch_input_shape': [null, 4],
-        'use_bias': true,
-        'activity_regularizer': null
-      }
-    }],
-    'backend': 'tensorflow'
-  };
-  const weightSpecs: io.WeightsManifestEntry[] = [
-    {
-      name: 'dense_1/kernel',
-      shape: [4, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense_1/bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-  const weightData = new Float32Array([1.1, 2.2, 3.3, 4.4, 5.5]).buffer;
-
-  // A dummy IOHandler that returns hard-coded model artifacts when its
-  // `load` method is called.
-  class IOHandlerForTest implements io.IOHandler {
-    private readonly includeWeights: boolean;
-
-    constructor(includeWeights = true) {
-      this.includeWeights = includeWeights;
-    }
-
-    async load(): Promise<io.ModelArtifacts> {
-      return this.includeWeights ? {modelTopology, weightSpecs, weightData} :
-                                   {modelTopology};
-    }
-  }
-
-  // A dummy IOHandler that doesn't have the `load` method implemented and
-  // is expected to cause `loadLayersModel` or `loadLayersModelInternal` to
-  // fail.
-  class IOHandlerWithoutLoad implements io.IOHandler {
-    constructor() {}
-  }
-
-  it('load topology and weights', async () => {
-    const model = await loadLayersModel(new IOHandlerForTest(true));
-    expect(model.layers.length).toEqual(1);
-    expect(model.inputs.length).toEqual(1);
-    expect(model.inputs[0].shape).toEqual([null, 4]);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 1]);
-    const weightValues = model.getWeights();
-    expect(weightValues.length).toEqual(2);
-    expectTensorsClose(weightValues[0], tensor2d([1.1, 2.2, 3.3, 4.4], [4, 1]));
-    expectTensorsClose(weightValues[1], tensor1d([5.5]));
-  });
-
-  it('load topology only', async () => {
-    const model = await loadLayersModel(new IOHandlerForTest(false));
-    expect(model.layers.length).toEqual(1);
-    expect(model.inputs.length).toEqual(1);
-    expect(model.inputs[0].shape).toEqual([null, 4]);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 1]);
-  });
-
-  it('IOHandler without load method causes error', async () => {
-    loadLayersModel(new IOHandlerWithoutLoad())
-        .then(model => {
-          fail(
-              'Loading with an IOHandler without load method succeeded ' +
-              'unexpectedly.');
-        })
-        .catch(err => {
-          expect(err.message).toMatch(/does not have .*load.* method/);
-        });
-  });
-});
-
-describeMathCPUAndGPU('Sequential', () => {
-  const inputShape = [1, 6];
-  const batchInputShape = [1].concat(inputShape);
-  const firstReshape = [2, 3];
-  const secondReshape = [3, 2];
-  const layers = [
-    new Reshape({targetShape: firstReshape, batchInputShape, name: 'layer1'}),
-    new Reshape({targetShape: secondReshape, name: 'layer2'})
-  ];
-
-  function getInputs(): Tensor {
-    return ones(batchInputShape);
-  }
-
-  function getExpectedOutputs(): Tensor {
-    return ones([1].concat(secondReshape));
-  }
-
-  it('throws an exception if the first layer is not an input layer', () => {
-    const layer = new Reshape({targetShape: firstReshape});
-    expect(() => tfl.sequential({layers: [layer]}))
-        .toThrowError(
-            /The first layer in a Sequential model must get an `inputShape`/);
-  });
-
-  it('can accept a list of layers in constructor', () => {
-    const model = tfl.sequential({layers});
-    expect(model.layers).toEqual(layers);
-  });
-
-  it('can add layers', () => {
-    const model = tfl.sequential();
-    for (const layer of layers) {
-      model.add(layer);
-    }
-    expect(model.layers).toEqual(layers);
-  });
-
-  it('can pop layers', () => {
-    const model = tfl.sequential({layers});
-    model.pop();
-    expect(model.layers).toEqual(layers.slice(0, 1));
-  });
-
-  it('Incompatible inputShape leads to warning', () => {
-    let recordedWarnMessage: string;
-    spyOn(console, 'warn')
-        .and.callFake((message: string) => recordedWarnMessage = message);
-    tfl.sequential({
-      layers: [
-        tfl.layers.reshape({targetShape: [8], inputShape: [2, 4]}),
-        tfl.layers.dense({units: 1, inputShape: [7]})
-      ]
-    });
-    expect(recordedWarnMessage)
-        .toMatch(/shape of the input tensor .*null,8.* not match .*null,7.*/);
-  });
-
-  it('Incompatible inputShape rank leads to warning', () => {
-    let recordedWarnMessage: string;
-    spyOn(console, 'warn')
-        .and.callFake((message: string) => recordedWarnMessage = message);
-    tfl.sequential({
-      layers: [
-        tfl.layers.reshape({targetShape: [8], inputShape: [2, 4]}),
-        tfl.layers.dense({units: 1, inputShape: [3, 7]})
-      ]
-    });
-    expect(recordedWarnMessage)
-        .toMatch(/rank .*null,8.* does not match .*null,3,7.* /);
-  });
-
-  it('Compatible inputShape leads to NO warning', () => {
-    let recordedWarnMessage: string;
-    spyOn(console, 'warn')
-        .and.callFake((message: string) => recordedWarnMessage = message);
-    tfl.sequential({
-      layers: [
-        tfl.layers.reshape({targetShape: [8], inputShape: [2, 4]}),
-        tfl.layers.dense({units: 1, inputShape: [8]})
-      ]
-    });
-    expect(recordedWarnMessage).toEqual(undefined);
-  });
-
-  it('throws error if try to pop too many layers', () => {
-    const model = tfl.sequential();
-    expect(() => model.pop()).toThrowError(/There are no layers in the model/);
-  });
-
-  it('throws error if first layer would result in negative shape', () => {
-    const model = tfl.sequential();
-    const layer = tfl.layers.conv2d({
-      filters: 1,
-      kernelSize: [10, 10],
-      strides: 1,
-      padding: 'valid',
-      dataFormat: 'channelsLast',
-      inputShape: [4, 4, 1]
-    });
-    // Adding the layer would result in a shpae of [null, -5, -5, 1]
-    expect(() => model.add(layer)).toThrowError(/Negative dimension size/);
-  });
-
-  it('throws error if adding layer would result in negative shape', () => {
-    const model = tfl.sequential();
-    model.add(
-        tfl.layers.activation({inputShape: [4, 4, 1], activation: 'relu'}));
-    const layer = tfl.layers.conv2d({
-      filters: 1,
-      kernelSize: [10, 10],
-      strides: 1,
-      padding: 'valid',
-      dataFormat: 'channelsLast',
-    });
-    // Adding the layer would result in a shpae of [null, -5, -5, 1]
-    expect(() => model.add(layer)).toThrowError(/Negative dimension size/);
-  });
-
-  it('apply() threads data through the model.', () => {
-    const model = tfl.sequential({layers});
-    expectTensorsClose(
-        model.apply(getInputs()) as Tensor, getExpectedOutputs());
-  });
-
-  it('predict() threads data through the model.', () => {
-    const model = tfl.sequential({layers});
-    expectTensorsClose(
-        model.predict(getInputs()) as Tensor, getExpectedOutputs());
-  });
-
-  const dtypes: DataType[] = ['int32', 'float32'];
-  // TODO(bileschi): Add test for casting incompatible types, once they exist.
-  for (const dtype of dtypes) {
-    it(`predict() works with input dtype ${dtype}.`, () => {
-      const embModel = tfl.sequential();
-      embModel.add(
-          tfl.layers.embedding({inputShape: [1], inputDim: 10, outputDim: 2}));
-      const x = tensor2d([[0], [0], [1]], [3, 1], dtype);
-      const y = embModel.predict(x) as Tensor;
-      expect(y.dtype).toBe('float32');
-    });
-
-    it(`fit() works with input dtype ${dtype}.`, async () => {
-      const embModel = tfl.sequential();
-      embModel.add(
-          tfl.layers.embedding({inputShape: [1], inputDim: 10, outputDim: 2}));
-      embModel.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-      const x = tensor2d([[0]], [1, 1], dtype);
-      const y = tensor3d([[[0.5, 0.5]]], [1, 1, 2], 'float32');
-      await embModel.fit(x, y);
-    });
-  }
-
-  it('predictOnBatch() threads data through the model.', () => {
-    const batchSize = 10;
-    const inputShape = [1, 6];
-    const batchInputShape = [batchSize].concat(inputShape);
-    const firstReshape = [2, 3];
-    const secondReshape = [3, 2];
-    const layers = [
-      new Reshape({targetShape: firstReshape, batchInputShape, name: 'layer1'}),
-      new Reshape({targetShape: secondReshape, name: 'layer2'})
-    ];
-    const inputBatch = ones([batchSize].concat(inputShape));
-    const expectedOutput = ones([batchSize].concat(secondReshape));
-    const model = tfl.sequential({layers});
-    expectTensorsClose(
-        model.predictOnBatch(inputBatch) as Tensor, expectedOutput);
-  });
-
-  it('predictOnBatch() works with multi-input model.', () => {
-    const input1 = tfl.input({shape: [3]});
-    const input2 = tfl.input({shape: [4]});
-    const dense1 = tfl.layers.dense({units: 1, activation: 'sigmoid'});
-    const y1 = dense1.apply(input1) as tfl.SymbolicTensor;
-    const dense2 = tfl.layers.dense({units: 1, activation: 'sigmoid'});
-    const y2 = dense2.apply(input2) as tfl.SymbolicTensor;
-    const y = tfl.layers.concatenate().apply([y1, y2]) as tfl.SymbolicTensor;
-    const model = tfl.model({inputs: [input1, input2], outputs: y});
-
-    const batchSize = 5;
-    const x1 = zeros([batchSize, 3]);
-    const x2 = ones([batchSize, 4]);
-    const out = model.predictOnBatch([x1, x2]) as Tensor;
-    expect(out.shape).toEqual([5, 2]);
-  });
-
-  it('compile() and fit()', async () => {
-    const batchSize = 5;
-    const inputSize = 4;
-    const xs = ones([batchSize, inputSize]);
-    const ys = ones([batchSize, 1]);
-    const denseLayer1 = tfl.layers.dense({
-      units: 3,
-      useBias: false,
-      kernelInitializer: 'ones',
-      inputShape: [inputSize]
-    });
-    const denseLayer2 =
-        tfl.layers.dense({units: 1, useBias: false, kernelInitializer: 'ones'});
-    const model = tfl.sequential({layers: [denseLayer1, denseLayer2]});
-    model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-    const history = await model.fit(xs, ys, {batchSize, epochs: 2});
-    expectTensorsClose(
-        history.history['loss'] as number[], [121, 0.015178224071860313]);
-  });
-
-  it('calling fit twice in a row leads to error', async () => {
-    const batchSize = 5;
-    const inputSize = 4;
-    const xs = ones([batchSize, inputSize]);
-    const ys = ones([batchSize, 1]);
-    const denseLayer1 = tfl.layers.dense({units: 3, inputShape: [inputSize]});
-    const denseLayer2 = tfl.layers.dense({units: 1});
-    const model = tfl.sequential({layers: [denseLayer1, denseLayer2]});
-    model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-    // Do not call `await` below, so the two fit() calls may interleave.
-    const firstFit = model.fit(xs, ys, {batchSize, epochs: 8});
-
-    let errorCaught: Error;
-    try {
-      await model.fit(xs, ys);
-    } catch (err) {
-      errorCaught = err;
-    }
-    expect(errorCaught.message)
-        .toEqual(
-            'Cannot start training because another fit() call is ongoing.');
-    await firstFit;
-  });
-
-  it('Stop Sequential.fit() using non-class callback function', async () => {
-    const batchSize = 5;
-    const inputSize = 4;
-    const xs = ones([batchSize, inputSize]);
-    const ys = ones([batchSize, 1]);
-    const denseLayer1 = tfl.layers.dense({units: 3, inputShape: [inputSize]});
-    const denseLayer2 = tfl.layers.dense({units: 1});
-    const model = tfl.sequential({layers: [denseLayer1, denseLayer2]});
-    model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-
-    let numEpochsDone = 0;
-    const epochs = 8;
-    const stopAfterEpoch = 3;
-    let history = await model.fit(xs, ys, {
-      epochs,
-      callbacks: {
-        onEpochEnd: async (epoch, logs) => {
-          numEpochsDone++;
-          if (epoch === stopAfterEpoch) {
-            model.stopTraining = true;
-          }
-        }
-      }
-    });
-    expect(numEpochsDone).toEqual(stopAfterEpoch + 1);
-    expect(history.history.loss.length).toEqual(stopAfterEpoch + 1);
-
-    // Check that model.fit can still be called after force stopping.
-    history = await model.fit(xs, ys, {epochs: 2});
-    expect(history.history.loss.length).toEqual(2);
-  });
-
-  it('Calling evaluate before compile leads to error', () => {
-    const batchSize = 5;
-    const inputSize = 4;
-    const denseLayer1 = tfl.layers.dense({units: 3, inputShape: [inputSize]});
-    const model = tfl.sequential({layers: [denseLayer1]});
-    const xs = ones([batchSize, inputSize]);
-    const ys = ones([batchSize, 1]);
-    expect(() => model.evaluate(xs, ys))
-        .toThrowError(/needs to be compiled before/);
-  });
-
-  it('compile() and evaluate()', () => {
-    const batchSize = 5;
-    const inputSize = 4;
-    const xs = ones([batchSize, inputSize]);
-    const ys = ones([batchSize, 1]);
-    const denseLayer1 = tfl.layers.dense({
-      units: 3,
-      useBias: false,
-      kernelInitializer: 'ones',
-      inputShape: [inputSize]
-    });
-    const denseLayer2 =
-        tfl.layers.dense({units: 1, useBias: false, kernelInitializer: 'ones'});
-    const model = tfl.sequential({layers: [denseLayer1, denseLayer2]});
-    model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-    const losses = model.evaluate(xs, ys, {batchSize}) as Scalar;
-    expectTensorsClose(losses, scalar(121));
-  });
-
-  it('getConfig returns an Array', () => {
-    const model = tfl.sequential({layers});
-    const config = model.getConfig();
-    expect(Array.isArray(config.layers)).toEqual(true);
-    expect(config.layers.length).toEqual(layers.length);
-  });
-});
-
-// Fake models.
-
-const fakeSequentialModel: ModelAndWeightsConfig = {
-  modelTopology: {
-    'class_name': 'Model',
-    'keras_version': '2.0.7',
-    'config': {
-      'layers': [
-        {
-          'class_name': 'InputLayer',
-          'config': {
-            'dtype': 'float32',
-            'batch_input_shape': [null, 32],
-            'name': 'input_6',
-            'sparse': false
-          },
-          'inbound_nodes': [],
-          'name': 'input_6'
-        },
-        {
-          'class_name': 'Dense',
-          'config': {
-            'units': 32,
-            'bias_constraint': null,
-            'use_bias': true,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1,
-                'seed': null,
-                'mode': 'fan_avg'
-              }
-            },
-            'activation': 'linear',
-            'bias_regularizer': null,
-            'activity_regularizer': null,
-            'trainable': true,
-            'kernel_constraint': null,
-            'kernel_regularizer': null,
-            'name': 'dense_6',
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}}
-          },
-          'inbound_nodes': [[['input_6', 0, 0, {}]]],
-          'name': 'dense_6'
-        }
-      ],
-      'input_layers': [['input_6', 0, 0]],
-      'output_layers': [['dense_6', 0, 0]],
-      'name': 'test'
-    },
-    'backend': 'tensorflow'
-  }
-};
-
-const fakeSequentialModelWithNestedContainer: ModelAndWeightsConfig = {
-  modelTopology: {
-    'keras_version': '2.2.4',
-    'backend': 'tensorflow',
-    'model_config': {
-      'class_name': 'Model',
-      'config': {
-        'name': 'model_1',
-        'layers': [
-          {
-            'name': 'dense_1_input',
-            'class_name': 'InputLayer',
-            'config': {
-              'batch_input_shape': [null, 4],
-              'dtype': 'float32',
-              'sparse': false,
-              'name': 'dense_1_input'
-            },
-            'inbound_nodes': []
-          },
-          {
-            'name': 'dense_1',
-            'class_name': 'Dense',
-            'config': {
-              'name': 'dense_1',
-              'trainable': true,
-              'batch_input_shape': [null, 4],
-              'dtype': 'float32',
-              'units': 2,
-              'activation': 'relu',
-              'use_bias': true,
-              'kernel_initializer': {
-                'class_name': 'VarianceScaling',
-                'config': {
-                  'scale': 1.0,
-                  'mode': 'fan_avg',
-                  'distribution': 'uniform',
-                  'seed': null
-                }
-              },
-              'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-              'kernel_regularizer': null,
-              'bias_regularizer': null,
-              'activity_regularizer': null,
-              'kernel_constraint': null,
-              'bias_constraint': null
-            },
-            'inbound_nodes': [[['dense_1_input', 0, 0, {}]]]
-          },
-          {
-            'name': 'sequential_2',
-            'class_name': 'Sequential',
-            'config': {
-              'name': 'sequential_2',
-              'layers': [{
-                'class_name': 'Dense',
-                'config': {
-                  'name': 'dense_2',
-                  'trainable': true,
-                  'batch_input_shape': [null, 2],
-                  'dtype': 'float32',
-                  'units': 1,
-                  'activation': 'softmax',
-                  'use_bias': true,
-                  'kernel_initializer': {
-                    'class_name': 'VarianceScaling',
-                    'config': {
-                      'scale': 1.0,
-                      'mode': 'fan_avg',
-                      'distribution': 'uniform',
-                      'seed': null
-                    }
-                  },
-                  'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-                  'kernel_regularizer': null,
-                  'bias_regularizer': null,
-                  'activity_regularizer': null,
-                  'kernel_constraint': null,
-                  'bias_constraint': null
-                }
-              }]
-            },
-            'inbound_nodes': [[['dense_1', 0, 0, {}]]]
-          }
-        ],
-        'input_layers': [['dense_1_input', 0, 0]],
-        'output_layers': [['sequential_2', 1, 0]]
-      }
-    }
-  }
-};
-
-const fakeSequentialModelFromHDF5: ModelAndWeightsConfig = {
-  modelTopology: {
-    'backend': 'tensorflow',
-    'keras_version': '2.1.4',
-    'model_config': {
-      'class_name': 'Sequential',
-      'config': [
-        {
-          'class_name': 'Dense',
-          'config': {
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'seed': null,
-                'mode': 'fan_avg'
-              }
-            },
-            'name': 'dense_1',
-            'kernel_constraint': null,
-            'bias_regularizer': null,
-            'bias_constraint': null,
-            'dtype': 'float32',
-            'activation': 'relu',
-            'trainable': true,
-            'kernel_regularizer': null,
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'units': 2,
-            'batch_input_shape': [null, 10],
-            'use_bias': true,
-            'activity_regularizer': null
-          }
-        },
-        {
-          'class_name': 'Dense',
-          'config': {
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'seed': null,
-                'mode': 'fan_avg'
-              }
-            },
-            'name': 'dense_2',
-            'kernel_constraint': null,
-            'bias_regularizer': null,
-            'bias_constraint': null,
-            'activation': 'sigmoid',
-            'trainable': true,
-            'kernel_regularizer': null,
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'units': 1,
-            'use_bias': true,
-            'activity_regularizer': null
-          }
-        }
-      ]
-    },
-  }
-};
-
-const fakeNonArrayConfigSequentialModelFromHDF5: ModelAndWeightsConfig = {
-  modelTopology: {
-    'backend': 'tensorflow',
-    'keras_version': '2.1.6-tf',
-    'model_config': {
-      'class_name': 'Sequential',
-      'config': {
-        'name': 'Foo123Sequential',
-        'layers': [
-          {
-            'class_name': 'Dense',
-            'config': {
-              'kernel_initializer': {
-                'class_name': 'VarianceScaling',
-                'config': {
-                  'distribution': 'uniform',
-                  'scale': 1.0,
-                  'seed': null,
-                  'mode': 'fan_avg'
-                }
-              },
-              'name': 'dense_1',
-              'kernel_constraint': null,
-              'bias_regularizer': null,
-              'bias_constraint': null,
-              'dtype': 'float32',
-              'activation': 'relu',
-              'trainable': true,
-              'kernel_regularizer': null,
-              'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-              'units': 2,
-              'batch_input_shape': [null, 10],
-              'use_bias': true,
-              'activity_regularizer': null
-            }
-          },
-          {
-            'class_name': 'Dense',
-            'config': {
-              'kernel_initializer': {
-                'class_name': 'VarianceScaling',
-                'config': {
-                  'distribution': 'uniform',
-                  'scale': 1.0,
-                  'seed': null,
-                  'mode': 'fan_avg'
-                }
-              },
-              'name': 'dense_2',
-              'kernel_constraint': null,
-              'bias_regularizer': null,
-              'bias_constraint': null,
-              'activation': 'sigmoid',
-              'trainable': true,
-              'kernel_regularizer': null,
-              'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-              'units': 1,
-              'use_bias': true,
-              'activity_regularizer': null
-            }
-          }
-        ]
-      },
-    }
-  }
-};
-
-// TODO(bileschi): Uncomment once we are able to load a model of this size.
-/*
-const fakeRNNModel: ModelAndWeightsConfig = {
-  modelTopology: {
-    'keras_version': '2.1.5',
-    'training_config': {
-      'optimizer_config': {
-        'config': {
-          'rho': 0.8999999761581421,
-          'epsilon': 1e-07,
-          'lr': 0.009999999776482582,
-          'decay': 0.0
-        },
-        'class_name': 'RMSprop'
-      },
-      'loss': 'categorical_crossentropy',
-      'metrics': ['acc'],
-      'sample_weight_mode': null,
-      'loss_weights': null
-    },
-    'backend': 'tensorflow',
-    'model_config': {
-      'config': [
-        {
-          'config': {
-            'unroll': false,
-            'name': 'lstm_9',
-            'trainable': true,
-            'implementation': 1,
-            'recurrent_constraint': null,
-            'stateful': false,
-            'return_sequences': false,
-            'recurrent_initializer': {
-              'config': {'gain': 1.0, 'seed': null},
-              'class_name': 'Orthogonal'
-            },
-            'bias_regularizer': null,
-            'kernel_constraint': null,
-            'dtype': 'float32',
-            'bias_initializer': {'config': {}, 'class_name': 'Zeros'},
-            'kernel_regularizer': null,
-            'go_backwards': false,
-            'units': 256,
-            'recurrent_regularizer': null,
-            'kernel_initializer': {
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              },
-              'class_name': 'VarianceScaling'
-            },
-            'use_bias': true,
-            'bias_constraint': null,
-            'activation': 'tanh',
-            'recurrent_activation': 'hard_sigmoid',
-            'recurrent_dropout': 0.0,
-            'return_state': false,
-            'dropout': 0.0,
-            'batch_input_shape': [null, 10, 393],
-            'activity_regularizer': null,
-            'unit_forget_bias': true
-          },
-          'class_name': 'LSTM'
-        },
-        {
-          'config': {
-            'rate': 0.2,
-            'name': 'dropout_4',
-            'noise_shape': null,
-            'seed': null,
-            'trainable': true
-          },
-          'class_name': 'Dropout'
-        },
-        {
-          'config': {
-            'name': 'dense_4',
-            'trainable': true,
-            'use_bias': true,
-            'bias_constraint': null,
-            'units': 393,
-            'kernel_initializer': {
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              },
-              'class_name': 'VarianceScaling'
-            },
-            'activation': 'linear',
-            'bias_regularizer': null,
-            'kernel_constraint': null,
-            'bias_initializer': {'config': {}, 'class_name': 'Zeros'},
-            'activity_regularizer': null,
-            'kernel_regularizer': null
-          },
-          'class_name': 'Dense'
-        },
-        {
-          'config': {
-            'name': 'activation_4',
-            'trainable': true,
-            'activation': 'softmax'
-          },
-          'class_name': 'Activation'
-        }
-      ],
-      'class_name': 'Sequential'
-    }
-  }
-} */
-
-const fakeNonSequentialModel: ModelAndWeightsConfig = {
-  modelTopology: {
-    'backend': 'tensorflow',
-    'class_name': 'Model',
-    'keras_version': '2.1.1',
-    'config': {
-      'name': 'mnist',
-      'output_layers': [['dense_16', 0, 0]],
-      'layers': [
-        {
-          'class_name': 'InputLayer',
-          'name': 'input_6',
-          'inbound_nodes': [],
-          'config': {
-            'batch_input_shape': [null, 28, 28, 1],
-            'sparse': false,
-            'name': 'input_6',
-            'dtype': 'float32'
-          }
-        },
-        {
-          'class_name': 'Conv2D',
-          'name': 'conv2d_15',
-          'inbound_nodes': [[['input_6', 0, 0, {}]]],
-          'config': {
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'padding': 'valid',
-            'use_bias': true,
-            'strides': [1, 1],
-            'bias_regularizer': null,
-            'activity_regularizer': null,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              }
-            },
-            'data_format': 'channels_last',
-            'dilation_rate': [1, 1],
-            'kernel_constraint': null,
-            'kernel_regularizer': null,
-            'kernel_size': [3, 3],
-            'activation': 'relu',
-            'name': 'conv2d_15',
-            'filters': 32,
-            'trainable': true,
-            'bias_constraint': null
-          }
-        },
-        {
-          'class_name': 'Conv2D',
-          'name': 'conv2d_16',
-          'inbound_nodes': [[['conv2d_15', 0, 0, {}]]],
-          'config': {
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'padding': 'valid',
-            'use_bias': true,
-            'strides': [1, 1],
-            'bias_regularizer': null,
-            'activity_regularizer': null,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              }
-            },
-            'data_format': 'channels_last',
-            'dilation_rate': [1, 1],
-            'kernel_constraint': null,
-            'kernel_regularizer': null,
-            'kernel_size': [3, 3],
-            'activation': 'relu',
-            'name': 'conv2d_16',
-            'filters': 64,
-            'trainable': true,
-            'bias_constraint': null
-          }
-        },
-        {
-          'class_name': 'MaxPooling2D',
-          'name': 'max_pooling2d_8',
-          'inbound_nodes': [[['conv2d_16', 0, 0, {}]]],
-          'config': {
-            'padding': 'valid',
-            'strides': [2, 2],
-            'pool_size': [2, 2],
-            'data_format': 'channels_last',
-            'name': 'max_pooling2d_8',
-            'trainable': true
-          }
-        },
-        {
-          'class_name': 'Dropout',
-          'name': 'dropout_15',
-          'inbound_nodes': [[['max_pooling2d_8', 0, 0, {}]]],
-          'config': {
-            'rate': 0.25,
-            'noise_shape': null,
-            'name': 'dropout_15',
-            'trainable': true,
-            'seed': null
-          }
-        },
-        {
-          'class_name': 'Flatten',
-          'name': 'flatten_8',
-          'inbound_nodes': [[['dropout_15', 0, 0, {}]]],
-          'config': {'name': 'flatten_8', 'trainable': true}
-        },
-        {
-          'class_name': 'Dense',
-          'name': 'dense_15',
-          'inbound_nodes': [[['flatten_8', 0, 0, {}]]],
-          'config': {
-            'use_bias': true,
-            'bias_regularizer': null,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              }
-            },
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'kernel_constraint': null,
-            'bias_constraint': null,
-            'kernel_regularizer': null,
-            'activation': 'relu',
-            'name': 'dense_15',
-            'activity_regularizer': null,
-            'trainable': true,
-            'units': 128
-          }
-        },
-        {
-          'class_name': 'Dropout',
-          'name': 'dropout_16',
-          'inbound_nodes': [[['dense_15', 0, 0, {}]]],
-          'config': {
-            'rate': 0.5,
-            'noise_shape': null,
-            'name': 'dropout_16',
-            'trainable': true,
-            'seed': null
-          }
-        },
-        {
-          'class_name': 'Dense',
-          'name': 'dense_16',
-          'inbound_nodes': [[['dropout_16', 0, 0, {}]]],
-          'config': {
-            'use_bias': true,
-            'bias_regularizer': null,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              }
-            },
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'kernel_constraint': null,
-            'bias_constraint': null,
-            'kernel_regularizer': null,
-            'activation': 'softmax',
-            'name': 'dense_16',
-            'activity_regularizer': null,
-            'trainable': true,
-            'units': 10
-          }
-        }
-      ],
-      'input_layers': [['input_6', 0, 0]]
-    }
-  }
-};
-
-const fakeMnistModel: ModelAndWeightsConfig = {
-  modelTopology: {
-    'backend': 'tensorflow',
-    'config': [
-      {
-        'config': {
-          'kernel_size': [3, 3],
-          'use_bias': true,
-          'batch_input_shape': [null, 28, 28, 1],
-          'filters': 32,
-          'kernel_regularizer': null,
-          'dilation_rate': [1, 1],
-          'strides': [1, 1],
-          'padding': 'valid',
-          'bias_constraint': null,
-          'kernel_constraint': null,
-          'data_format': 'channels_last',
-          'trainable': true,
-          'activation': 'relu',
-          'dtype': 'float32',
-          'bias_initializer': {'config': {}, 'class_name': 'Zeros'},
-          'bias_regularizer': null,
-          'name': 'conv2d_1',
-          'kernel_initializer': {
-            'config': {
-              'scale': 1.0,
-              'mode': 'fan_avg',
-              'seed': null,
-              'distribution': 'uniform'
-            },
-            'class_name': 'VarianceScaling'
-          },
-          'activity_regularizer': null
-        },
-        'class_name': 'Conv2D'
-      },
-      {
-        'config': {
-          'kernel_size': [3, 3],
-          'use_bias': true,
-          'filters': 64,
-          'kernel_regularizer': null,
-          'dilation_rate': [1, 1],
-          'strides': [1, 1],
-          'padding': 'valid',
-          'bias_constraint': null,
-          'data_format': 'channels_last',
-          'trainable': true,
-          'activation': 'relu',
-          'kernel_constraint': null,
-          'bias_initializer': {'config': {}, 'class_name': 'Zeros'},
-          'bias_regularizer': null,
-          'name': 'conv2d_2',
-          'kernel_initializer': {
-            'config': {
-              'scale': 1.0,
-              'mode': 'fan_avg',
-              'seed': null,
-              'distribution': 'uniform'
-            },
-            'class_name': 'VarianceScaling'
-          },
-          'activity_regularizer': null
-        },
-        'class_name': 'Conv2D'
-      },
-      {
-        'config': {
-          'strides': [2, 2],
-          'padding': 'valid',
-          'pool_size': [2, 2],
-          'data_format': 'channels_last',
-          'trainable': true,
-          'name': 'max_pooling2d_1'
-        },
-        'class_name': 'MaxPooling2D'
-      },
-      {
-        'config': {
-          'seed': null,
-          'name': 'dropout_1',
-          'trainable': true,
-          'noise_shape': null,
-          'rate': 0.25
-        },
-        'class_name': 'Dropout'
-      },
-      {
-        'config': {'name': 'flatten_1', 'trainable': true},
-        'class_name': 'Flatten'
-      },
-      {
-        'config': {
-          'use_bias': true,
-          'units': 128,
-          'bias_initializer': {'config': {}, 'class_name': 'Zeros'},
-          'kernel_regularizer': null,
-          'bias_regularizer': null,
-          'trainable': true,
-          'activation': 'relu',
-          'bias_constraint': null,
-          'kernel_constraint': null,
-          'name': 'dense_1',
-          'kernel_initializer': {
-            'config': {
-              'scale': 1.0,
-              'mode': 'fan_avg',
-              'seed': null,
-              'distribution': 'uniform'
-            },
-            'class_name': 'VarianceScaling'
-          },
-          'activity_regularizer': null
-        },
-        'class_name': 'Dense'
-      },
-      {
-        'config': {
-          'seed': null,
-          'name': 'dropout_2',
-          'trainable': true,
-          'noise_shape': null,
-          'rate': 0.5
-        },
-        'class_name': 'Dropout'
-      },
-      {
-        'config': {
-          'use_bias': true,
-          'units': 10,
-          'bias_initializer': {'config': {}, 'class_name': 'Zeros'},
-          'kernel_regularizer': null,
-          'bias_regularizer': null,
-          'trainable': true,
-          'activation': 'softmax',
-          'bias_constraint': null,
-          'kernel_constraint': null,
-          'name': 'dense_2',
-          'kernel_initializer': {
-            'config': {
-              'scale': 1.0,
-              'mode': 'fan_avg',
-              'seed': null,
-              'distribution': 'uniform'
-            },
-            'class_name': 'VarianceScaling'
-          },
-          'activity_regularizer': null
-        },
-        'class_name': 'Dense'
-      }
-    ],
-    'keras_version': '2.1.1',
-    'class_name': 'Sequential'
-  }
-};
-
-const fakeRoundtripModel: ModelAndWeightsConfig = {
-  modelTopology: {
-    'backend': 'tensorflow',
-    'class_name': 'Model',
-    'keras_version': '2.1.1',
-    'config': {
-      'name': 'mnist',
-      'output_layers': [['dense_16', 0, 0]],
-      'layers': [
-        {
-          'class_name': 'InputLayer',
-          'name': 'input_6',
-          'inbound_nodes': [],
-          'config': {
-            'batch_input_shape': [null, 28, 28, 1],
-            'sparse': false,
-            'name': 'input_6',
-            'dtype': 'float32'
-          }
-        },
-        {
-          'class_name': 'Conv2D',
-          'name': 'conv2d_15',
-          'inbound_nodes': [[['input_6', 0, 0, {}]]],
-          'config': {
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'padding': 'valid',
-            'use_bias': true,
-            'strides': [1, 1],
-            'bias_regularizer': null,
-            'activity_regularizer': null,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              }
-            },
-            'data_format': 'channels_last',
-            'dilation_rate': [1, 1],
-            'kernel_constraint': null,
-            'kernel_regularizer': null,
-            'kernel_size': [3, 3],
-            'activation': 'relu',
-            'name': 'conv2d_15',
-            'filters': 32,
-            'trainable': true,
-            'bias_constraint': null
-          }
-        },
-        {
-          'class_name': 'Conv2D',
-          'name': 'conv2d_16',
-          'inbound_nodes': [[['conv2d_15', 0, 0, {}]]],
-          'config': {
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'padding': 'valid',
-            'use_bias': true,
-            'strides': [1, 1],
-            'bias_regularizer': null,
-            'activity_regularizer': null,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              }
-            },
-            'data_format': 'channels_last',
-            'dilation_rate': [1, 1],
-            'kernel_constraint': null,
-            'kernel_regularizer': null,
-            'kernel_size': [3, 3],
-            'activation': 'relu',
-            'name': 'conv2d_16',
-            'filters': 64,
-            'trainable': true,
-            'bias_constraint': null
-          }
-        },
-        {
-          'class_name': 'MaxPooling2D',
-          'name': 'max_pooling2d_8',
-          'inbound_nodes': [[['conv2d_16', 0, 0, {}]]],
-          'config': {
-            'padding': 'valid',
-            'strides': [2, 2],
-            'pool_size': [2, 2],
-            'data_format': 'channels_last',
-            'name': 'max_pooling2d_8',
-            'trainable': true
-          }
-        },
-        {
-          'class_name': 'Dropout',
-          'name': 'dropout_15',
-          'inbound_nodes': [[['max_pooling2d_8', 0, 0, {}]]],
-          'config': {
-            'rate': 0.25,
-            'noise_shape': null,
-            'name': 'dropout_15',
-            'trainable': true,
-            'seed': null
-          }
-        },
-        {
-          'class_name': 'Flatten',
-          'name': 'flatten_8',
-          'inbound_nodes': [[['dropout_15', 0, 0, {}]]],
-          'config': {'name': 'flatten_8', 'trainable': true}
-        },
-        {
-          'class_name': 'Dense',
-          'name': 'dense_15',
-          'inbound_nodes': [[['flatten_8', 0, 0, {}]]],
-          'config': {
-            'use_bias': true,
-            'bias_regularizer': null,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              }
-            },
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'kernel_constraint': null,
-            'bias_constraint': null,
-            'kernel_regularizer': null,
-            'activation': 'relu',
-            'name': 'dense_15',
-            'activity_regularizer': null,
-            'trainable': true,
-            'units': 128
-          }
-        },
-        {
-          'class_name': 'Dropout',
-          'name': 'dropout_16',
-          'inbound_nodes': [[['dense_15', 0, 0, {}]]],
-          'config': {
-            'rate': 0.5,
-            'noise_shape': null,
-            'name': 'dropout_16',
-            'trainable': true,
-            'seed': null
-          }
-        },
-        {
-          'class_name': 'Dense',
-          'name': 'dense_16',
-          'inbound_nodes': [[['dropout_16', 0, 0, {}]]],
-          'config': {
-            'use_bias': true,
-            'bias_regularizer': null,
-            'kernel_initializer': {
-              'class_name': 'VarianceScaling',
-              'config': {
-                'distribution': 'uniform',
-                'scale': 1.0,
-                'mode': 'fan_avg',
-                'seed': null
-              }
-            },
-            'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-            'kernel_constraint': null,
-            'bias_constraint': null,
-            'kernel_regularizer': null,
-            'activation': 'softmax',
-            'name': 'dense_16',
-            'activity_regularizer': null,
-            'trainable': true,
-            'units': 10
-          }
-        }
-      ],
-      'input_layers': [['input_6', 0, 0]]
-    }
-  }
-};
-
-describeMathCPU('Functional-model saving and loading', () => {
-  it('Save-load round trip: multi-node layer', async () => {
-    const input1 = tfl.input({shape: [2, 3]});
-    const input2 = tfl.input({shape: [3, 2]});
-
-    const reshape = tfl.layers.reshape({targetShape: [6]});
-    const output1 = reshape.apply(input1) as tfl.SymbolicTensor;
-    const output2 = reshape.apply(input2) as tfl.SymbolicTensor;
-
-    const model1 =
-        tfl.model({inputs: [input1, input2], outputs: [output1, output2]});
-
-    const model1JSON = model1.toJSON(null, false) as PyJsonDict;
-    const model2 = await modelFromJSON({modelTopology: model1JSON});
-
-    expect(model2.inputs.length).toEqual(model1.inputs.length);
-    expect(model2.inputs[0].shape).toEqual(model1.inputs[0].shape);
-    expect(model2.inputs[1].shape).toEqual(model1.inputs[1].shape);
-    expect(model2.outputs.length).toEqual(model1.outputs.length);
-    expect(model2.outputs[0].shape).toEqual(model1.outputs[0].shape);
-    expect(model2.outputs[1].shape).toEqual(model1.outputs[1].shape);
-    expect(model2.toJSON(null, false)).toEqual(model1JSON);
-
-    const x1 = randomNormal([1, 2, 3]);
-    const x2 = randomNormal([1, 3, 2]);
-    const ys1 = model1.apply([x1, x2]) as Tensor[];
-
-    const ys2 = model2.apply([x1, x2]) as Tensor[];
-
-    expectTensorsClose(ys1[0], ys2[0]);
-    expectTensorsClose(ys1[1], ys2[1]);
-  });
-
-  it('Save-load round trip: layer with loopy invocation', async () => {
-    const input = tfl.layers.input({shape: [1]});
-    const dense = tfl.layers.dense(
-        {units: 1, kernelInitializer: 'ones', biasInitializer: 'ones'});
-    let output = dense.apply(input) as tfl.SymbolicTensor;
-    for (let i = 0; i < 3; ++i) {
-      output = dense.apply(output) as tfl.SymbolicTensor;
-    }
-    const model1 = tfl.model({inputs: input, outputs: output});
-
-    const xs = ones([10, 1]);
-    const ys1 = model1.predict(xs) as Tensor;
-
-    const model1JSON = model1.toJSON(null, false) as PyJsonDict;
-    const model2 = await modelFromJSON({modelTopology: model1JSON});
-    expect(model2.toJSON(null, false)).toEqual(model1JSON);
-
-    const ys2 = model2.predict(xs) as Tensor;
-    expectTensorsClose(ys1, ys2);
-  });
-
-  it('Deserialization with truncated_normal', async () => {
-    // From https://github.com/tensorflow/tfjs/issues/146
-    const modelJSON = JSON.parse(
-        // tslint:disable-next-line:max-line-length
-        `{"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":1,"activation":"linear","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"truncated_normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true,"batch_input_shape":[null,1],"dtype":"float32"}}],"keras_version":"tfjs-layers 1.0.2","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["weights.bin"],"weights":[{"name":"dense_Dense1/kernel","shape":[1,1],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[1],"dtype":"float32"}]}]}`);
-    const modelTopology = modelJSON.modelTopology;
-    expect(() => modelFromJSON({modelTopology})).not.toThrow();
-  });
-
-  it('Load attention model', async () => {
-    // From https://github.com/tensorflow/tfjs/issues/794
-    const modelJSON = JSON.parse(
-        // tslint:disable-next-line:max-line-length
-        `{"modelTopology":{"keras_version":"2.1.6","backend":"tensorflow","model_config":{"class_name":"Model","config":{"input_layers":[["input_1",0,0],["s0",0,0],["c0",0,0]],"name":"model_1","layers":[{"class_name":"InputLayer","inbound_nodes":[],"name":"input_1","config":{"dtype":"float32","name":"input_1","sparse":false,"batch_input_shape":[null,30,38]}},{"class_name":"InputLayer","inbound_nodes":[],"name":"s0","config":{"dtype":"float32","name":"s0","sparse":false,"batch_input_shape":[null,64]}},{"class_name":"Bidirectional","inbound_nodes":[[["input_1",0,0,{}]]],"name":"bidirectional_1","config":{"trainable":true,"name":"bidirectional_1","merge_mode":"concat","layer":{"class_name":"LSTM","config":{"stateful":false,"units":32,"activation":"tanh","recurrent_activation":"hard_sigmoid","dropout":0,"recurrent_dropout":0,"use_bias":true,"trainable":true,"recurrent_initializer":{"class_name":"Orthogonal","config":{"seed":null,"gain":1}},"bias_constraint":null,"unroll":false,"kernel_initializer":{"class_name":"VarianceScaling","config":{"seed":null,"distribution":"uniform","mode":"fan_avg","scale":1}},"unit_forget_bias":true,"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_constraint":null,"activity_regularizer":null,"return_sequences":true,"recurrent_constraint":null,"recurrent_regularizer":null,"bias_regularizer":null,"go_backwards":false,"implementation":1,"name":"lstm_2","kernel_regularizer":null,"return_state":false}}}},{"class_name":"RepeatVector","inbound_nodes":[[["s0",0,0,{}]],[["lstm_1",0,0,{}]],[["lstm_1",1,0,{}]],[["lstm_1",2,0,{}]],[["lstm_1",3,0,{}]],[["lstm_1",4,0,{}]],[["lstm_1",5,0,{}]],[["lstm_1",6,0,{}]],[["lstm_1",7,0,{}]],[["lstm_1",8,0,{}]]],"name":"repeat_vector_1","config":{"n":30,"trainable":true,"name":"repeat_vector_1"}},{"class_name":"Concatenate","inbound_nodes":[[["bidirectional_1",0,0,{}],["repeat_vector_1",0,0,{}]],[["bidirectional_1",0,0,{}],["repeat_vector_1",1,0,{}]],[["bidirectional_1",0,0,{}],["repeat_vector_1",2,0,{}]],[["bidirectional_1",0,0,{}],["repeat_vector_1",3,0,{}]],[["bidirectional_1",0,0,{}],["repeat_vector_1",4,0,{}]],[["bidirectional_1",0,0,{}],["repeat_vector_1",5,0,{}]],[["bidirectional_1",0,0,{}],["repeat_vector_1",6,0,{}]],[["bidirectional_1",0,0,{}],["repeat_vector_1",7,0,{}]],[["bidirectional_1",0,0,{}],["repeat_vector_1",8,0,{}]],[["bidirectional_1",0,0,{}],["repeat_vector_1",9,0,{}]]],"name":"concatenate_1","config":{"trainable":true,"name":"concatenate_1","axis":-1}},{"class_name":"Dense","inbound_nodes":[[["concatenate_1",0,0,{}]],[["concatenate_1",1,0,{}]],[["concatenate_1",2,0,{}]],[["concatenate_1",3,0,{}]],[["concatenate_1",4,0,{}]],[["concatenate_1",5,0,{}]],[["concatenate_1",6,0,{}]],[["concatenate_1",7,0,{}]],[["concatenate_1",8,0,{}]],[["concatenate_1",9,0,{}]]],"name":"dense_1","config":{"bias_constraint":null,"kernel_constraint":null,"units":10,"activity_regularizer":null,"use_bias":true,"bias_regularizer":null,"trainable":true,"activation":"tanh","name":"dense_1","kernel_initializer":{"class_name":"VarianceScaling","config":{"seed":null,"distribution":"uniform","mode":"fan_avg","scale":1}},"kernel_regularizer":null,"bias_initializer":{"class_name":"Zeros","config":{}}}},{"class_name":"Dense","inbound_nodes":[[["dense_1",0,0,{}]],[["dense_1",1,0,{}]],[["dense_1",2,0,{}]],[["dense_1",3,0,{}]],[["dense_1",4,0,{}]],[["dense_1",5,0,{}]],[["dense_1",6,0,{}]],[["dense_1",7,0,{}]],[["dense_1",8,0,{}]],[["dense_1",9,0,{}]]],"name":"dense_2","config":{"bias_constraint":null,"kernel_constraint":null,"units":1,"activity_regularizer":null,"use_bias":true,"bias_regularizer":null,"trainable":true,"activation":"relu","name":"dense_2","kernel_initializer":{"class_name":"VarianceScaling","config":{"seed":null,"distribution":"uniform","mode":"fan_avg","scale":1}},"kernel_regularizer":null,"bias_initializer":{"class_name":"Zeros","config":{}}}},{"class_name":"Activation","inbound_nodes":[[["dense_2",0,0,{}]],[["dense_2",1,0,{}]],[["dense_2",2,0,{}]],[["dense_2",3,0,{}]],[["dense_2",4,0,{}]],[["dense_2",5,0,{}]],[["dense_2",6,0,{}]],[["dense_2",7,0,{}]],[["dense_2",8,0,{}]],[["dense_2",9,0,{}]]],"name":"attention_weights","config":{"trainable":true,"activation":"softmax","name":"attention_weights"}},{"class_name":"Dot","inbound_nodes":[[["attention_weights",0,0,{}],["bidirectional_1",0,0,{}]],[["attention_weights",1,0,{}],["bidirectional_1",0,0,{}]],[["attention_weights",2,0,{}],["bidirectional_1",0,0,{}]],[["attention_weights",3,0,{}],["bidirectional_1",0,0,{}]],[["attention_weights",4,0,{}],["bidirectional_1",0,0,{}]],[["attention_weights",5,0,{}],["bidirectional_1",0,0,{}]],[["attention_weights",6,0,{}],["bidirectional_1",0,0,{}]],[["attention_weights",7,0,{}],["bidirectional_1",0,0,{}]],[["attention_weights",8,0,{}],["bidirectional_1",0,0,{}]],[["attention_weights",9,0,{}],["bidirectional_1",0,0,{}]]],"name":"dot_1","config":{"trainable":true,"name":"dot_1","normalize":false,"axes":1}},{"class_name":"InputLayer","inbound_nodes":[],"name":"c0","config":{"dtype":"float32","name":"c0","sparse":false,"batch_input_shape":[null,64]}},{"class_name":"LSTM","inbound_nodes":[[["dot_1",0,0,{}],["s0",0,0,{}],["c0",0,0,{}]],[["dot_1",1,0,{}],["lstm_1",0,0,{}],["lstm_1",0,2,{}]],[["dot_1",2,0,{}],["lstm_1",1,0,{}],["lstm_1",1,2,{}]],[["dot_1",3,0,{}],["lstm_1",2,0,{}],["lstm_1",2,2,{}]],[["dot_1",4,0,{}],["lstm_1",3,0,{}],["lstm_1",3,2,{}]],[["dot_1",5,0,{}],["lstm_1",4,0,{}],["lstm_1",4,2,{}]],[["dot_1",6,0,{}],["lstm_1",5,0,{}],["lstm_1",5,2,{}]],[["dot_1",7,0,{}],["lstm_1",6,0,{}],["lstm_1",6,2,{}]],[["dot_1",8,0,{}],["lstm_1",7,0,{}],["lstm_1",7,2,{}]],[["dot_1",9,0,{}],["lstm_1",8,0,{}],["lstm_1",8,2,{}]]],"name":"lstm_1","config":{"stateful":false,"units":64,"activation":"tanh","recurrent_activation":"hard_sigmoid","dropout":0,"recurrent_dropout":0,"use_bias":true,"trainable":true,"recurrent_initializer":{"class_name":"Orthogonal","config":{"seed":null,"gain":1}},"bias_constraint":null,"unroll":false,"kernel_initializer":{"class_name":"VarianceScaling","config":{"seed":null,"distribution":"uniform","mode":"fan_avg","scale":1}},"unit_forget_bias":true,"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_constraint":null,"activity_regularizer":null,"return_sequences":false,"recurrent_constraint":null,"recurrent_regularizer":null,"bias_regularizer":null,"go_backwards":false,"implementation":1,"name":"lstm_1","kernel_regularizer":null,"return_state":true}},{"class_name":"Dense","inbound_nodes":[[["lstm_1",0,0,{}]],[["lstm_1",1,0,{}]],[["lstm_1",2,0,{}]],[["lstm_1",3,0,{}]],[["lstm_1",4,0,{}]],[["lstm_1",5,0,{}]],[["lstm_1",6,0,{}]],[["lstm_1",7,0,{}]],[["lstm_1",8,0,{}]],[["lstm_1",9,0,{}]]],"name":"dense_3","config":{"bias_constraint":null,"kernel_constraint":null,"units":11,"activity_regularizer":null,"use_bias":true,"bias_regularizer":null,"trainable":true,"activation":"softmax","name":"dense_3","kernel_initializer":{"class_name":"VarianceScaling","config":{"seed":null,"distribution":"uniform","mode":"fan_avg","scale":1}},"kernel_regularizer":null,"bias_initializer":{"class_name":"Zeros","config":{}}}}],"output_layers":[["dense_3",0,0],["dense_3",1,0],["dense_3",2,0],["dense_3",3,0],["dense_3",4,0],["dense_3",5,0],["dense_3",6,0],["dense_3",7,0],["dense_3",8,0],["dense_3",9,0]]}},"training_config":{"loss":"categorical_crossentropy","sample_weight_mode":null,"metrics":["accuracy"],"optimizer_config":{"class_name":"Adam","config":{"beta_1":0.8999999761581421,"decay":0.009999999776482582,"beta_2":0.9990000128746033,"lr":0.004999999888241291,"amsgrad":false,"epsilon":1e-7}},"loss_weights":null}},"weightsManifest":[{"paths":["group1-shard1of1"],"weights":[{"shape":[38,128],"dtype":"float32","name":"bidirectional_1/forward_lstm_2/kernel"},{"shape":[32,128],"dtype":"float32","name":"bidirectional_1/forward_lstm_2/recurrent_kernel"},{"shape":[128],"dtype":"float32","name":"bidirectional_1/forward_lstm_2/bias"},{"shape":[38,128],"dtype":"float32","name":"bidirectional_1/backward_lstm_2/kernel"},{"shape":[32,128],"dtype":"float32","name":"bidirectional_1/backward_lstm_2/recurrent_kernel"},{"shape":[128],"dtype":"float32","name":"bidirectional_1/backward_lstm_2/bias"},{"shape":[128,10],"dtype":"float32","name":"dense_1/kernel"},{"shape":[10],"dtype":"float32","name":"dense_1/bias"},{"shape":[10,1],"dtype":"float32","name":"dense_2/kernel"},{"shape":[1],"dtype":"float32","name":"dense_2/bias"},{"shape":[64,11],"dtype":"float32","name":"dense_3/kernel"},{"shape":[11],"dtype":"float32","name":"dense_3/bias"},{"shape":[64,256],"dtype":"float32","name":"lstm_1/kernel"},{"shape":[64,256],"dtype":"float32","name":"lstm_1/recurrent_kernel"},{"shape":[256],"dtype":"float32","name":"lstm_1/bias"}]}]}`);
-    const modelTopology = modelJSON.modelTopology;
-    const model = await modelFromJSON({modelTopology});
-
-    expect(model.inputs.length).toEqual(3);
-    expect(model.inputs[0].shape).toEqual([null, 30, 38]);
-    expect(model.inputs[1].shape).toEqual([null, 64]);
-    expect(model.inputs[2].shape).toEqual([null, 64]);
-    expect(model.outputs.length).toEqual(10);
-    for (const output of model.outputs) {
-      expect(output.shape).toEqual([null, 11]);
-    }
-    expect(convertTsToPythonic(model.getConfig()))
-        .toEqual(modelTopology['model_config']['config']);
-
-    const x = randomNormal([2, 30, 38]);
-    const s = randomNormal([2, 64]);
-    const c = randomNormal([2, 64]);
-    const output = model.predict([x, s, c]) as Tensor[];
-    expect(output.length).toEqual(10);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/optimizers.ts b/tfjs-master/tfjs-layers/src/optimizers.ts
deleted file mode 100644
index 386edab78..000000000
--- a/tfjs-master/tfjs-layers/src/optimizers.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Optimizers.
- */
-
-import {Optimizer, train} from '@tensorflow/tfjs-core';
-
-import {epsilon} from './backend/common';
-
-import {ValueError} from './errors';
-
-// Add (de)serialize()
-
-// Porting note: This diverges from the PyKeras implementation and may need to
-// change based on (de)serialization requirements.
-export function getOptimizer(identifier: string): Optimizer {
-  const optimizerMap: {[optimizerName: string]: () => Optimizer} = {
-    'Adagrad': () => train.adagrad(0.01),
-    'Adadelta': () => train.adadelta(1, 0.95, epsilon()),
-    'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),
-    'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),
-    'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),
-    'SGD': () => train.sgd(0.01)
-  };
-  optimizerMap['adagrad'] = optimizerMap['Adagrad'];
-  optimizerMap['adadelta'] = optimizerMap['Adadelta'];
-  optimizerMap['adam'] = optimizerMap['Adam'];
-  optimizerMap['adamax'] = optimizerMap['Adamax'];
-  optimizerMap['rmsprop'] = optimizerMap['RMSProp'];
-  optimizerMap['sgd'] = optimizerMap['SGD'];
-
-  if (identifier in optimizerMap) {
-    return optimizerMap[identifier]();
-  }
-  throw new ValueError(`Unknown Optimizer ${identifier}`);
-}
diff --git a/tfjs-master/tfjs-layers/src/optimizers_test.ts b/tfjs-master/tfjs-layers/src/optimizers_test.ts
deleted file mode 100644
index a86a5add4..000000000
--- a/tfjs-master/tfjs-layers/src/optimizers_test.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for optimizers.ts.
- */
-
-import {AdadeltaOptimizer, AdagradOptimizer, AdamaxOptimizer, AdamOptimizer, RMSPropOptimizer, SGDOptimizer} from '@tensorflow/tfjs-core';
-
-import {getOptimizer} from './optimizers';
-import {describeMathCPU} from './utils/test_utils';
-
-describeMathCPU('getOptimizer', () => {
-  // TODO(nsthorat): Assert defaults by getting config from the optimizer.
-
-  it(`can instantiate SGD`, () => {
-    const optimizer = getOptimizer('SGD');
-    expect(optimizer instanceof SGDOptimizer).toBe(true);
-  });
-  it(`can instantiate sgd`, () => {
-    const optimizer = getOptimizer('sgd');
-    expect(optimizer instanceof SGDOptimizer).toBe(true);
-  });
-  it(`can instantiate Adam`, () => {
-    const optimizer = getOptimizer('Adam');
-    expect(optimizer instanceof AdamOptimizer).toBe(true);
-  });
-  it(`can instantiate adam`, () => {
-    const optimizer = getOptimizer('adam');
-    expect(optimizer instanceof AdamOptimizer).toBe(true);
-  });
-  it(`can instantiate RMSProp`, () => {
-    const optimizer = getOptimizer('RMSProp');
-    expect(optimizer instanceof RMSPropOptimizer).toBe(true);
-  });
-  it(`can instantiate rmsprop`, () => {
-    const optimizer = getOptimizer('rmsprop');
-    expect(optimizer instanceof RMSPropOptimizer).toBe(true);
-  });
-  it(`can instantiate Adagrad`, () => {
-    const optimizer = getOptimizer('Adagrad');
-    expect(optimizer instanceof AdagradOptimizer).toBe(true);
-  });
-  it(`can instantiate adagrad`, () => {
-    const optimizer = getOptimizer('adagrad');
-    expect(optimizer instanceof AdagradOptimizer).toBe(true);
-  });
-  it(`can instantiate Adadelta`, () => {
-    const optimizer = getOptimizer('Adadelta');
-    expect(optimizer instanceof AdadeltaOptimizer).toBe(true);
-  });
-  it(`can instantiate adadelta`, () => {
-    const optimizer = getOptimizer('adadelta');
-    expect(optimizer instanceof AdadeltaOptimizer).toBe(true);
-  });
-  it(`can instantiate Adamax`, () => {
-    const optimizer = getOptimizer('Adamax');
-    expect(optimizer instanceof AdamaxOptimizer).toBe(true);
-  });
-  it(`can instantiate adamax`, () => {
-    const optimizer = getOptimizer('adamax');
-    expect(optimizer instanceof AdamaxOptimizer).toBe(true);
-  });
-  it('throws for non-existent optimizer', () => {
-    expect(() => getOptimizer('not an optimizer'))
-        .toThrowError(/Unknown Optimizer/);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/regularizers.ts b/tfjs-master/tfjs-layers/src/regularizers.ts
deleted file mode 100644
index 6b4bd1c74..000000000
--- a/tfjs-master/tfjs-layers/src/regularizers.ts
+++ /dev/null
@@ -1,149 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* original source: keras/regularizers.py */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {abs, add, Scalar, serialization, sum, Tensor, tidy, zeros} from '@tensorflow/tfjs-core';
-import * as K from './backend/tfjs_backend';
-import {deserializeKerasObject, serializeKerasObject} from './utils/generic_utils';
-
-function assertObjectArgs(args: L1Args|L2Args|L1L2Args): void {
-  if (args != null && typeof args !== 'object') {
-    throw new Error(
-        `Argument to L1L2 regularizer's constructor is expected to be an ` +
-        `object, but received: ${args}`);
-  }
-}
-
-/**
- * Regularizer base class.
- */
-export abstract class Regularizer extends serialization.Serializable {
-  abstract apply(x: Tensor): Scalar;
-}
-
-export interface L1L2Args {
-  /** L1 regularization rate. Defaults to 0.01. */
-  l1?: number;
-  /** L2 regularization rate. Defaults to 0.01. */
-  l2?: number;
-}
-
-export interface L1Args {
-  /** L1 regularization rate. Defaults to 0.01. */
-  l1: number;
-}
-
-export interface L2Args {
-  /** L2 regularization rate. Defaults to 0.01. */
-  l2: number;
-}
-
-export class L1L2 extends Regularizer {
-  /** @nocollapse */
-  static className = 'L1L2';
-
-  private readonly l1: number;
-  private readonly l2: number;
-  private readonly hasL1: boolean;
-  private readonly hasL2: boolean;
-  constructor(args?: L1L2Args) {
-    super();
-
-    assertObjectArgs(args);
-
-    this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;
-    this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;
-    this.hasL1 = this.l1 !== 0;
-    this.hasL2 = this.l2 !== 0;
-  }
-
-  /**
-   * Porting note: Renamed from __call__.
-   * @param x Variable of which to calculate the regularization score.
-   */
-  apply(x: Tensor): Scalar {
-    return tidy(() => {
-      let regularization: Tensor = zeros([1]);
-      if (this.hasL1) {
-        regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));
-      }
-      if (this.hasL2) {
-        regularization =
-            add(regularization, sum(tfc.mul(this.l2, K.square(x))));
-      }
-      return tfc.reshape(regularization, []);
-    });
-  }
-
-  getConfig(): serialization.ConfigDict {
-    return {'l1': this.l1, 'l2': this.l2};
-  }
-
-  /** @nocollapse */
-  static override fromConfig<T extends serialization.Serializable>(
-      cls: serialization.SerializableConstructor<T>,
-      config: serialization.ConfigDict): T {
-    return new cls({l1: config['l1'] as number, l2: config['l2'] as number});
-  }
-}
-serialization.registerClass(L1L2);
-
-export function l1(args?: L1Args) {
-  assertObjectArgs(args);
-  return new L1L2({l1: args != null ? args.l1 : null, l2: 0});
-}
-
-export function l2(args: L2Args) {
-  assertObjectArgs(args);
-  return new L1L2({l2: args != null ? args.l2 : null, l1: 0});
-}
-
-/** @docinline */
-export type RegularizerIdentifier = 'l1l2'|string;
-
-// Maps the JavaScript-like identifier keys to the corresponding keras symbols.
-export const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP:
-    {[identifier in RegularizerIdentifier]: string} = {
-      'l1l2': 'L1L2'
-    };
-
-export function serializeRegularizer(constraint: Regularizer):
-    serialization.ConfigDictValue {
-  return serializeKerasObject(constraint);
-}
-
-export function deserializeRegularizer(
-    config: serialization.ConfigDict,
-    customObjects: serialization.ConfigDict = {}): Regularizer {
-  return deserializeKerasObject(
-      config, serialization.SerializationMap.getMap().classNameMap,
-      customObjects, 'regularizer');
-}
-
-export function getRegularizer(identifier: RegularizerIdentifier|
-                               serialization.ConfigDict|
-                               Regularizer): Regularizer {
-  if (identifier == null) {
-    return null;
-  }
-  if (typeof identifier === 'string') {
-    const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?
-        REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :
-        identifier;
-    const config = {className, config: {}};
-    return deserializeRegularizer(config);
-  } else if (identifier instanceof Regularizer) {
-    return identifier;
-  } else {
-    return deserializeRegularizer(identifier);
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/regularizers_test.ts b/tfjs-master/tfjs-layers/src/regularizers_test.ts
deleted file mode 100644
index 4b3395cf9..000000000
--- a/tfjs-master/tfjs-layers/src/regularizers_test.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Unit tests for constraints */
-
-import {scalar, serialization, Tensor, tensor1d} from '@tensorflow/tfjs-core';
-
-import * as tfl from './index';
-import {deserializeRegularizer, getRegularizer, serializeRegularizer} from './regularizers';
-import {describeMathCPU, expectTensorsClose} from './utils/test_utils';
-
-describeMathCPU('Built-in Regularizers', () => {
-  it('l1_l2', () => {
-    const x = tensor1d([1, -2, 3, -4]);
-    const regularizer = tfl.regularizers.l1l2();
-    const score = regularizer.apply(x);
-    expectTensorsClose(
-        score, scalar(0.01 * (1 + 2 + 3 + 4) + 0.01 * (1 + 4 + 9 + 16)));
-  });
-  it('l1', () => {
-    const x = tensor1d([1, -2, 3, -4]);
-    const regularizer = tfl.regularizers.l1();
-    const score = regularizer.apply(x);
-    expectTensorsClose(score, scalar(0.01 * (1 + 2 + 3 + 4)));
-  });
-  it('l2', () => {
-    const x = tensor1d([1, -2, 3, -4]);
-    const regularizer = tfl.regularizers.l2();
-    const score = regularizer.apply(x);
-    expectTensorsClose(score, scalar(0.01 * (1 + 4 + 9 + 16)));
-  });
-  it('l1_l2 non default', () => {
-    const x = tensor1d([1, -2, 3, -4]);
-    const regularizer = tfl.regularizers.l1l2({l1: 1, l2: 2});
-    const score = regularizer.apply(x);
-    expectTensorsClose(
-        score, scalar(1 * (1 + 2 + 3 + 4) + 2 * (1 + 4 + 9 + 16)));
-  });
-  it('Using number arg for constructor leads to error', () => {
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.regularizers.l1(0.001 as any))
-        .toThrowError(/expected.*object.*received.*0\.001/);
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.regularizers.l2(0.001 as any))
-        .toThrowError(/expected.*object.*received.*0\.001/);
-    // tslint:disable-next-line:no-any
-    expect(() => tfl.regularizers.l1l2(0.001 as any))
-        .toThrowError(/expected.*object.*received.*0\.001/);
-  });
-});
-
-describeMathCPU('regularizers.get', () => {
-  let x: Tensor;
-  beforeEach(() => {
-    x = tensor1d([1, -2, 3, -4]);
-  });
-
-  it('by string - lower camel', () => {
-    const regularizer = getRegularizer('l1l2');
-    expectTensorsClose(regularizer.apply(x), tfl.regularizers.l1l2().apply(x));
-  });
-  it('by string - upper camel', () => {
-    const regularizer = getRegularizer('L1L2');
-    expectTensorsClose(regularizer.apply(x), tfl.regularizers.l1l2().apply(x));
-  });
-
-  it('by existing object', () => {
-    const origReg = tfl.regularizers.l1l2({l1: 1, l2: 2});
-    const regularizer = getRegularizer(origReg);
-    expect(regularizer).toEqual(origReg);
-  });
-  it('by config dict', () => {
-    const origReg = tfl.regularizers.l1l2({l1: 1, l2: 2});
-    const regularizer = getRegularizer(
-        serializeRegularizer(origReg) as serialization.ConfigDict);
-    expectTensorsClose(regularizer.apply(x), origReg.apply(x));
-  });
-});
-
-describeMathCPU('Regularizer Serialization', () => {
-  it('Built-ins', () => {
-    const regularizer = tfl.regularizers.l1l2({l1: 1, l2: 2});
-    const config =
-        serializeRegularizer(regularizer) as serialization.ConfigDict;
-    const reconstituted = deserializeRegularizer(config);
-    const roundTripConfig =
-        serializeRegularizer(reconstituted) as serialization.ConfigDict;
-    expect(roundTripConfig.className).toEqual('L1L2');
-    const nestedConfig = roundTripConfig.config as serialization.ConfigDict;
-    expect(nestedConfig.l1).toEqual(1);
-    expect(nestedConfig.l2).toEqual(2);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/setup_tests.ts b/tfjs-master/tfjs-layers/src/setup_tests.ts
deleted file mode 100644
index b9ef137e2..000000000
--- a/tfjs-master/tfjs-layers/src/setup_tests.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Register Layers' flags.
-import './flags_layers';
-import '@tensorflow/tfjs-core';
-// tslint:disable-next-line:no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-import '@tensorflow/tfjs-backend-cpu';
-import '@tensorflow/tfjs-backend-webgl';
-
-// tslint:disable-next-line: no-imports-from-dist
-import {parseTestEnvFromKarmaFlags, registerTestEnv, setTestEnvs, TEST_ENVS} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-// Register test environments.
-const webgl1TestEnv = {
-  name: 'webgl1',
-  backendName: 'webgl',
-  flags: {
-    'WEBGL_VERSION': 1,
-    'WEBGL_CPU_FORWARD': false,
-    'WEBGL_SIZE_UPLOAD_UNIFORM': 0
-  },
-  isDataSync: true
-};
-registerTestEnv(webgl1TestEnv);
-registerTestEnv({name: 'cpu', backendName: 'cpu'});
-registerTestEnv({
-  name: 'webgl2',
-  backendName: 'webgl',
-  flags: {
-    'WEBGL_VERSION': 2,
-    'WEBGL_CPU_FORWARD': false,
-    'WEBGL_SIZE_UPLOAD_UNIFORM': 0
-  }
-});
-
-// Allow flags to override test envs
-// tslint:disable-next-line:no-any
-declare let __karma__: any;
-if (typeof __karma__ !== 'undefined') {
-  const testEnv = parseTestEnvFromKarmaFlags(__karma__.config.args, TEST_ENVS);
-
-  if (testEnv != null) {
-    setTestEnvs([testEnv]);
-  } else {
-    // Exclude webgl1 unless it is specifically requested because it causes
-    // test flakiness when switching between webgl1 and webgl2.
-    setTestEnvs(TEST_ENVS.filter(env => env !== webgl1TestEnv));
-  }
-}
-
-// Import and run tests from layers.
-// tslint:disable-next-line:no-require-imports
-require('./tests');
diff --git a/tfjs-master/tfjs-layers/src/types.ts b/tfjs-master/tfjs-layers/src/types.ts
deleted file mode 100644
index d5f530622..000000000
--- a/tfjs-master/tfjs-layers/src/types.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/** Defines allowable data types for tensors. */
-
-import {NamedTensorMap, Scalar, Tensor} from '@tensorflow/tfjs-core';
-
-import {Shape} from './keras_format/common';
-
-export interface NamedTensor {
-  name: string;
-  tensor: Tensor;
-}
-
-export type HasShape = {
-  shape: Shape;
-};
-
-/**
- * Type for loss a metric function.
- *
- * Takes a true value and a predicted value, and returns a loss or metric value.
- */
-export type LossOrMetricFn = (yTrue: Tensor, yPred: Tensor) => Tensor;
-
-/**
- * Type for a regularizer function.
- */
-export type RegularizerFn = () => Scalar;
-
-/*
- * The type for an RNN step function.
- * The arguments are:
- *   - inputs: Input data, with shape `[sapmles, ...]`.
- * The return values are:
- *   - outputs: tensor with shape `[samples, outputDim]` (no time dimension).
- *   - newStates: Array of tensors. The `Array` has the same length as `states`
- *     in the input arguments. Each `tf.Tensor` has the same shape as the
- *     corresponding element in `states`.
- */
-export type RnnStepFunction =
-    (inputs: Tensor, states: Tensor[]) => [Tensor, Tensor[]];
-
-/**
- * A single Tensor or a non-nested collection of Tensors.
- *
- * An object of this type can always be reduced to `Tensor[]`.  A single
- * 'Tensor' becomes `[Tensor]`.  A `Tensor[]` is unchanged.  A `NamedTensorMap`
- * can be converted with the help of a list of names, providing the order in
- * which the Tensors should appear in the resulting array.
- */
-export type TensorOrArrayOrMap = Tensor|Tensor[]|NamedTensorMap;
-
-/**
- * Type representing a loosely-typed bundle of keyword arguments.
- *
- * This is a looser type than PyJsonDict/serialization.ConfigDict as it
- * can contain arbitrary objects as its values.  It is most appropriate
- * for functions that pass through keyword arguments to other functions
- * without knowledge of the structure.  If the function can place type
- * restrictions on the keyword arguments, it should via the Config
- * interface convention used throughout.
- */
-export type Kwargs = {
-  // tslint:disable-next-line:no-any
-  [key: string]: any
-};
diff --git a/tfjs-master/tfjs-layers/src/types_test.ts b/tfjs-master/tfjs-layers/src/types_test.ts
deleted file mode 100644
index 249ecd85e..000000000
--- a/tfjs-master/tfjs-layers/src/types_test.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for -specific types.
- */
-
-import {SymbolicTensor} from './engine/topology';
-
-/**
- * Unit tests for SymbolicTensor.
- */
-describe('SymbolicTensor Test', () => {
-  it('Correct dtype and shape properties', () => {
-    const st1 = new SymbolicTensor('float32', [4, 6], null, [], {});
-    expect(st1.dtype).toEqual('float32');
-    expect(st1.shape).toEqual([4, 6]);
-    expect(st1.rank).toEqual(2);
-  });
-  it('Correct when operating on scalars', () => {
-    const scalar = new SymbolicTensor('float32', [], null, [], {});
-    expect(scalar.dtype).toEqual('float32');
-    expect(scalar.shape).toEqual([]);
-    expect(scalar.rank).toEqual(0);
-  });
-
-  it('Correct names and ids', () => {
-    const st1 = new SymbolicTensor(
-        'float32', [2, 2], null, [], {}, 'TestSymbolicTensor');
-    const st2 = new SymbolicTensor(
-        'float32', [2, 2], null, [], {}, 'TestSymbolicTensor');
-    expect(st1.name.indexOf('TestSymbolicTensor')).toEqual(0);
-    expect(st2.name.indexOf('TestSymbolicTensor')).toEqual(0);
-    // Explicit names of symbolic tensors should be unique.
-    expect(st1 === st2).toBe(false);
-
-    expect(st1.id).toBeGreaterThanOrEqual(0);
-    expect(st2.id).toBeGreaterThanOrEqual(0);
-    expect(st1.id === st2.id).toBe(false);
-  });
-
-  it('Invalid tensor name leads to error', () => {
-    expect(() => new SymbolicTensor('float32', [2, 2], null, [], {}, '!'))
-        .toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/user_defined_metadata.ts b/tfjs-master/tfjs-layers/src/user_defined_metadata.ts
deleted file mode 100644
index 398ce18c2..000000000
--- a/tfjs-master/tfjs-layers/src/user_defined_metadata.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/** Utility functions related to user-defined metadata. */
-
-// Maximum recommended serialized size for user-defined metadata.
-// Beyond this limit, a warning message will be printed during model loading and
-// saving.
-export const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;
-
-/**
- * Check validity of user-defined metadata.
- *
- * @param userDefinedMetadata
- * @param modelName Name of the model that the user-defined metadata belongs to.
- *   Used during construction of error messages.
- * @param checkSize Whether to check the size of the metadata is under
- *   recommended limit. Default: `false`. If `true`, will try stringify the
- *   JSON object and print a console warning if the serialzied size is above the
- *   limit.
- * @throws Error if `userDefinedMetadata` is not a plain JSON object.
- */
-export function checkUserDefinedMetadata(
-    userDefinedMetadata: {}, modelName: string, checkSize = false): void {
-  if (userDefinedMetadata == null ||
-      typeof userDefinedMetadata !== 'object' ||
-      Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||
-      !plainObjectCheck(userDefinedMetadata)) {
-    throw new Error(
-        'User-defined metadata is expected to be a JSON object, but is not.');
-  }
-
-  if (checkSize) {
-    const out = JSON.stringify(userDefinedMetadata);
-    if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {
-      console.warn(
-          `User-defined metadata of model "${modelName}" is too large in ` +
-          `size (length=${out.length} when serialized). It is not ` +
-          `recommended to store such large objects in user-defined metadata. ` +
-          `Please make sure its serialized length is <= ` +
-          `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);
-    }
-  }
-}
-
-/**
- * Check if an input is plain JSON object or any valid subfield of it.
- *
- * @param x The input to be checked.
- * @param assertObject Whether to assert `x` is a JSON object, i.e., reject
- *   cases of arrays and primitives.
- * @return Returns `true` if and only if `x` is a plain JSON object,
- *   a JSON-valid primitive including string, number, boolean and null,
- *   or an array of the said types.
- */
-// tslint:disable-next-line:no-any
-export function plainObjectCheck(x: any): boolean {
-  if (x === null) {
-    // Note: typeof `null` is 'object', and `null` is valid in JSON.
-    return true;
-  } else if (typeof x === 'object') {
-    if (Object.getPrototypeOf(x) === Object.prototype) {
-      // `x` is a JavaScript object and its prototype is Object.
-      const keys = Object.keys(x);
-      for (const key of keys) {
-        if (typeof key !== 'string') {
-          // JSON keys must be strings.
-          return false;
-        }
-        if (!plainObjectCheck(x[key])) {  // Recursive call.
-          return false;
-        }
-      }
-      return true;
-    } else {
-      // `x` is a JavaScript object but its prototype is not Object.
-      if (Array.isArray(x)) {
-        // `x` is a JavaScript array.
-        for (const item of x) {
-          if (!plainObjectCheck(item)) {  // Recursive call.
-            return false;
-          }
-        }
-        return true;
-      } else {
-        // `x` is a JavaScript object and its prototype is not Object,
-        // and it's not an Array. I.e., it's a complex object such as
-        // `Error` and `Date`.
-        return false;
-      }
-    }
-  } else {
-    // `x` is not a JavaScript object or `null`.
-    const xType = typeof x;
-    return xType === 'string' || xType === 'number' || xType === 'boolean';
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/user_defined_metadata_test.ts b/tfjs-master/tfjs-layers/src/user_defined_metadata_test.ts
deleted file mode 100644
index 5854322e8..000000000
--- a/tfjs-master/tfjs-layers/src/user_defined_metadata_test.ts
+++ /dev/null
@@ -1,184 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {io, zeros} from '@tensorflow/tfjs-core';
-
-import * as tfl from './index';
-import {Sequential} from './models';
-import {plainObjectCheck, MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH} from './user_defined_metadata';
-
-describe('plainObjectCheck', () => {
-  it('Primitives', () => {
-    // `undefined` is not valid JSON.
-    expect(plainObjectCheck(undefined)).toEqual(false);
-    // `null` is valid JSON.
-    expect(plainObjectCheck(null)).toEqual(true);
-    expect(plainObjectCheck(true)).toEqual(true);
-    expect(plainObjectCheck(1337)).toEqual(true);
-    expect(plainObjectCheck('foo')).toEqual(true);
-  });
-  it('Complex objects lead to false', () => {
-    expect(plainObjectCheck(new Date())).toEqual(false);
-    expect(plainObjectCheck(new Float32Array([1, 2])))
-        .toEqual(false);
-    expect(plainObjectCheck(new ArrayBuffer(4))).toEqual(false);
-    expect(plainObjectCheck(new Error())).toEqual(false);
-    expect(plainObjectCheck(zeros([2, 3]))).toEqual(false);
-  });
-  it('POJOs lead to true', () => {
-    expect(plainObjectCheck({})).toEqual(true);
-    expect(plainObjectCheck({
-      'key1': 'foo',
-      'key2': 1337,
-      'key3': false
-    })).toEqual(true);
-    expect(plainObjectCheck({
-      'key1': {
-        'key1_1': [1, 3, 3, 7, [42]],
-        'key1_2': null,
-        'key1_3': ['foo', 'bar', null, {}, {'qux': 233}],
-      },
-      'key2': 1337,
-      'key3': false
-    })).toEqual(true);
-  });
-  it('POJOs with invalid value types lead to false', () => {
-    expect(plainObjectCheck({
-      'key1': new Date(),
-      'key2': 1337
-    })).toEqual(false);
-    expect(plainObjectCheck({
-      'key1': new ArrayBuffer(3),
-      'key2': 1337
-    })).toEqual(false);
-    expect(plainObjectCheck({
-      'key1': 'foo',
-      'key2': undefined
-    })).toEqual(false);
-    expect(plainObjectCheck({
-      'key1': 'foo',
-      'key2': {
-        'tensor': zeros([2, 3])
-      }
-    })).toEqual(false);
-  });
-  it('Arrays of POJO lead to true', () => {
-    expect(plainObjectCheck([])).toEqual(true);
-    expect(plainObjectCheck([{}, {}])).toEqual(true);
-    expect(plainObjectCheck([{
-      'key1': 'foo',
-      'key2': 1337,
-      'key3': false
-    }])).toEqual(true);
-    expect(plainObjectCheck([{
-      'key1': {
-        'key1_1': [1, 3, 3, 7],
-        'key1_2': null,
-        'key1_3': ['foo', 'bar', null, {}, {'qux': 233}],
-      },
-      'key2': 1337,
-      'key3': false
-    }])).toEqual(true);
-  });
-});
-
-describe('Save and load model with metadata', () => {
-
-  function createSequentialModelForTest(): Sequential {
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({
-      units: 3,
-      inputShape: [10],
-      activation: 'softmax'
-    }));
-    return model;
-  }
-
-  function createFunctionalModelForTest(): tfl.LayersModel {
-    const input1 = tfl.input({shape: [3]});
-    const input2 = tfl.input({shape: [4]});
-    const dense1 = tfl.layers.dense({units: 2, inputShape: [3]});
-    const dense2 = tfl.layers.dense({units: 1, inputShape: [4]});
-    const y1 = dense1.apply(input1) as tfl.SymbolicTensor;
-    const y2 = dense2.apply(input2) as tfl.SymbolicTensor;
-    const output = tfl.layers.concatenate().apply([y1, y2]) as
-        tfl.SymbolicTensor;
-    return tfl.model({inputs: [input1, input2], outputs: output});
-  }
-
-  for (const modelType of ['sequential', 'functional']) {
-    it(`Valid user-defined metadata round trip: ${modelType}`, async () => {
-      const model = modelType === 'sequential' ?
-          createSequentialModelForTest() : createFunctionalModelForTest();
-      const userDefinedMetadata = {'outputLabels': ['foo', 'bar', 'baz']};
-      model.setUserDefinedMetadata(userDefinedMetadata);
-      expect(model.getUserDefinedMetadata()).toEqual(userDefinedMetadata);
-      let savedArtifacts: io.ModelArtifacts;
-      await model.save(
-          io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-            savedArtifacts = artifacts;
-            return {modelArtifactsInfo: null};
-          }));
-      const reloadedModel = await tfl.loadLayersModel(
-          io.fromMemory(savedArtifacts));
-      expect(reloadedModel.getUserDefinedMetadata())
-          .toEqual(userDefinedMetadata);
-    });
-  }
-  for (const modelType of ['sequential', 'functional']) {
-    it(`Invalid user metadata leads to error: ${modelType}`, async () => {
-      const model = modelType === 'sequential' ?
-          createSequentialModelForTest() : createFunctionalModelForTest();
-      expect(() => model.setUserDefinedMetadata(
-          JSON.stringify({'outputLabels': ['foo', 'bar', 'baz']})))
-          .toThrowError(/is expected to be a JSON object, but is not/);
-      expect(() => model.setUserDefinedMetadata(
-          ['foo', 'bar', 'baz']))
-          .toThrowError(/is expected to be a JSON object, but is not/);
-      expect(() => model.setUserDefinedMetadata(
-          {'foo': zeros([2, 3]), 'outputLabels': ['foo', 'bar', 'baz']}))
-          .toThrowError(/is expected to be a JSON object, but is not/);
-      expect(() => model.setUserDefinedMetadata(undefined))
-          .toThrowError(/is expected to be a JSON object, but is not/);
-      expect(() => model.setUserDefinedMetadata(null))
-          .toThrowError(/is expected to be a JSON object, but is not/);
-      expect(() => model.setUserDefinedMetadata('foo'))
-          .toThrowError(/is expected to be a JSON object, but is not/);
-      expect(() => model.setUserDefinedMetadata(1337))
-          .toThrowError(/is expected to be a JSON object, but is not/);
-    });
-  }
-  for (const modelType of ['sequential', 'functional']) {
-    it(`Large metadata size leads to warning: ${modelType}`, async () => {
-      const warningMessages: string[] = [];
-      spyOn(console, 'warn').and.callFake((message: string) => {
-        warningMessages.push(message);
-      });
-      const largeMetadata: {} = {
-        'metadata': 'x'.repeat(MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH)
-      };
-      const model = modelType === 'sequential' ?
-          createSequentialModelForTest() : createFunctionalModelForTest();
-      model.setUserDefinedMetadata(largeMetadata);
-      await model.save(
-          io.withSaveHandler(async (artifacts: io.ModelArtifacts) => {
-            return {modelArtifactsInfo: null};
-          }));
-      expect(warningMessages.length).toEqual(1);
-      expect(warningMessages).toMatch(/is too large in size/);
-    });
-  }
-  it('MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH value', () => {
-    expect(MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH).toBeGreaterThan(0);
-    expect(Number.isInteger(MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH))
-        .toEqual(true);
-
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/utils/conv_utils.ts b/tfjs-master/tfjs-layers/src/utils/conv_utils.ts
deleted file mode 100644
index 5a62a2c3d..000000000
--- a/tfjs-master/tfjs-layers/src/utils/conv_utils.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {ValueError} from '../errors';
-import {PaddingMode} from '../keras_format/common';
-
-import {pyListRepeat} from './generic_utils';
-import {isInteger, max} from './math_utils';
-
-/**
- * Transforms a single number of array of numbers into an array of numbers.
- * @param value
- * @param n: The size of the tuple to be returned.
- * @param name: Name of the parameter, used for generating error messages.
- * @returns An array of numbers.
- */
-export function normalizeArray(
-    value: number|number[], n: number, name: string): number[] {
-  if (typeof value === 'number') {
-    return pyListRepeat(value, n);
-  } else {
-    if (value.length !== n) {
-      throw new ValueError(
-          `The ${name} argument must be an integer or tuple of ${n} integers.` +
-          ` Received: ${value.length} elements.`);
-    }
-    for (let i = 0; i < n; ++i) {
-      const singleValue = value[i];
-      if (!isInteger(singleValue)) {
-        throw new ValueError(
-            `The ${name} argument must be an integer or tuple of ${n}` +
-            ` integers. Received: ${JSON.stringify(value)} including a` +
-            ` non-integer number ${singleValue}`);
-      }
-    }
-    return value;
-  }
-}
-
-/**
- * Determines output length of a convolution given input length.
- * @param inputLength
- * @param filterSize
- * @param padding
- * @param stride
- * @param dilation: dilation rate.
- */
-export function convOutputLength(
-    inputLength: number, filterSize: number, padding: PaddingMode,
-    stride: number, dilation = 1): number {
-  if (inputLength == null) {
-    return inputLength;
-  }
-  const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);
-  let outputLength: number;
-  if (padding === 'same') {
-    outputLength = inputLength;
-  } else {  // VALID
-    outputLength = inputLength - dilatedFilterSize + 1;
-  }
-  return Math.floor((outputLength + stride - 1) / stride);
-}
-
-export function deconvLength(
-    dimSize: number, strideSize: number, kernelSize: number,
-    padding: PaddingMode): number {
-  if (dimSize == null) {
-    return null;
-  }
-
-  if (padding === 'valid') {
-    dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);
-  } else if (padding === 'same') {
-    dimSize = dimSize * strideSize;
-  } else {
-    throw new ValueError(`Unsupport padding mode: ${padding}.`);
-  }
-  return dimSize;
-}
diff --git a/tfjs-master/tfjs-layers/src/utils/executor_utils.ts b/tfjs-master/tfjs-layers/src/utils/executor_utils.ts
deleted file mode 100644
index 8a79e8189..000000000
--- a/tfjs-master/tfjs-layers/src/utils/executor_utils.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-/**
- * LruCache: A mapping from the String to T. If the number of the entries is
- * exceeding the `maxEntries`, the LruCache will delete the least recently
- * used entry.
- */
-
-export class LruCache<T> {
-  private cache: Map<string, T>;
-  private maxEntries: number;
-
-  constructor(maxEntries?: number) {
-    this.maxEntries = maxEntries || 100;
-    this.cache = new Map<string, T>();
-  }
-
-  /**
-   * Get the entry for the key and mark it as used recently.
-   */
-  public get(key: string): T {
-    let entry: T;
-    if (this.cache.has(key)) {
-      entry = this.cache.get(key);
-      this.cache.delete(key);
-      this.cache.set(key, entry);
-    }
-    return entry;
-  }
-
-  /**
-   * Put the entry into the cache. If the key already existed, mark the key as
-   * used recently.
-   */
-  public put(key: string, value: T): void {
-    if (this.cache.has(key)) {
-      this.cache.delete(key);
-    } else if (this.cache.size >= this.maxEntries) {
-      const keyToDelete = this.cache.keys().next().value;
-      this.cache.delete(keyToDelete);
-    }
-    this.cache.set(key, value);
-  }
-
-  /**
-   * Get the MaxEntries of the cache.
-   */
-  public getMaxEntries(): number {
-    return this.maxEntries;
-  }
-
-  /**
-   * Set the MaxEntries of the cache. If the maxEntries is decreased, reduce
-   * entries in the cache.
-   */
-  public setMaxEntries(maxEntries: number): void {
-    if (maxEntries < 0) {
-      throw new Error(
-          `The maxEntries of LRU caches must be at least 0, but got ${
-              maxEntries}.`);
-    }
-
-    if (this.maxEntries > maxEntries) {
-      for (let i = 0; i < this.maxEntries - maxEntries; i++) {
-        const keyToDelete = this.cache.keys().next().value;
-        this.cache.delete(keyToDelete);
-      }
-    }
-
-    this.maxEntries = maxEntries;
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/utils/executor_utils_test.ts b/tfjs-master/tfjs-layers/src/utils/executor_utils_test.ts
deleted file mode 100644
index 22191e733..000000000
--- a/tfjs-master/tfjs-layers/src/utils/executor_utils_test.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {LruCache} from '../utils/executor_utils';
-import {describeMathCPU} from '../utils/test_utils';
-
-// tslint:enable
-
-describeMathCPU('LruCache', () => {
-  it('Delete the leaset recent used entry when exceeding the size', () => {
-    const maxEntries = 3;
-    const cache = new LruCache<number>(maxEntries);
-    cache.put('1', 1);  // Caching [1]
-    cache.put('2', 2);  // Caching [1, 2]
-    cache.put('3', 3);  // Caching [1, 2, 3]
-    cache.get('1');     // Caching [2, 3, 1]
-    cache.put('4', 4);  // Caching [3, 1, 4]
-
-    expect(cache.get('2')).toBeUndefined();
-    expect(cache.get('1')).toBe(1);
-    expect(cache.get('3')).toBe(3);
-    expect(cache.get('4')).toBe(4);
-  });
-
-  it('Reduce cache entries while decreasing maxEntries', () => {
-    const cache = new LruCache<number>(100);
-    for (let i = 0; i < 100; i++) {
-      cache.put(i.toString(), i);
-    }
-    cache.setMaxEntries(99);
-
-    // The first entry should be deleted, because the number of entries exceeded
-    // the maxEntries.
-    expect(cache.get('0')).toBeUndefined();
-    expect(cache.get('1')).toBe(1);
-    expect(cache.get('99')).toBe(99);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/utils/generic_utils.ts b/tfjs-master/tfjs-layers/src/utils/generic_utils.ts
deleted file mode 100644
index c128097f5..000000000
--- a/tfjs-master/tfjs-layers/src/utils/generic_utils.ts
+++ /dev/null
@@ -1,563 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original source: utils/generic_utils.py */
-
-import {DataType, fused, serialization, util} from '@tensorflow/tfjs-core';
-
-import {AssertionError, ValueError} from '../errors';
-
-// tslint:enable
-
-/**
- * If `value` is an Array, equivalent to Python's `value * numValues`.
- * If `value` is not an Array, equivalent to Python's `[value] * numValues`
- */
-// tslint:disable-next-line:no-any
-export function pyListRepeat(value: any, numValues: number): any[] {
-  if (Array.isArray(value)) {
-    // tslint:disable-next-line:no-any
-    let newArray: any[] = [];
-    for (let i = 0; i < numValues; i++) {
-      newArray = newArray.concat(value);
-    }
-    return newArray;
-  } else {
-    const newArray = new Array(numValues);
-    newArray.fill(value);
-    return newArray;
-  }
-}
-
-export function assert(val: boolean, message?: string): void {
-  if (!val) {
-    throw new AssertionError(message);
-  }
-}
-
-/**
- * Count the number of elements of the `array` that are equal to `reference`.
- */
-export function count<T>(array: T[], refernce: T) {
-  let counter = 0;
-  for (const item of array) {
-    if (item === refernce) {
-      counter++;
-    }
-  }
-  return counter;
-}
-
-/**
- * If an array is of length 1, just return the first element. Otherwise, return
- * the full array.
- * @param tensors
- */
-export function singletonOrArray<T>(xs: T[]): T|T[] {
-  if (xs.length === 1) {
-    return xs[0];
-  }
-  return xs;
-}
-
-/**
- * Normalizes a list/tensor into a list.
- *
- * If a tensor is passed, we return
- * a list of size 1 containing the tensor.
- *
- * @param x target object to be normalized.
- */
-// tslint:disable-next-line:no-any
-export function toList<T>(x: T|T[]): T[] {
-  if (Array.isArray(x)) {
-    return x;
-  }
-  return [x];
-}
-
-/**
- * Generate a UID for a list
- */
-// tslint:disable-next-line:no-any
-export function objectListUid(objs: any|any[]): string {
-  const objectList = toList(objs);
-  let retVal = '';
-  for (const obj of objectList) {
-    if (obj.id == null) {
-      throw new ValueError(
-          `Object ${obj} passed to objectListUid without an id`);
-    }
-    if (retVal !== '') {
-      retVal = retVal + ', ';
-    }
-    retVal = `${retVal}${Math.abs(obj.id)}`;
-  }
-  return retVal;
-}
-/**
- * Converts string to snake-case.
- * @param name
- */
-export function toSnakeCase(name: string): string {
-  const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');
-  const insecure =
-      intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();
-  /*
-   If the class is private the name starts with "_" which is not secure
-   for creating scopes. We prefix the name with "private" in this case.
-   */
-  if (insecure[0] !== '_') {
-    return insecure;
-  }
-  return 'private' + insecure;
-}
-
-export function toCamelCase(identifier: string): string {
-  // quick return for empty string or single character strings
-  if (identifier.length <= 1) {
-    return identifier;
-  }
-  // Check for the underscore indicating snake_case
-  if (identifier.indexOf('_') === -1) {
-    return identifier;
-  }
-  return identifier.replace(/[_]+(\w|$)/g, (m, p1) => p1.toUpperCase());
-}
-
-// tslint:disable-next-line:no-any
-let _GLOBAL_CUSTOM_OBJECTS = {} as {[objName: string]: any};
-
-export function serializeKerasObject(instance: serialization.Serializable):
-    serialization.ConfigDictValue {
-  if (instance === null || instance === undefined) {
-    return null;
-  }
-  const dict: serialization.ConfigDictValue = {};
-  dict['className'] = instance.getClassName();
-  dict['config'] = instance.getConfig();
-  return dict;
-}
-
-/**
- * Replace ndarray-style scalar objects in serialization objects with numbers.
- *
- * Background: In some versions of tf.keras, certain scalar values in the HDF5
- * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,
- * where in `num` is a plain number. This method converts such serialization
- * to a `number`.
- *
- * @param config The keras-format serialization object to be processed
- *   (in place).
- */
-function convertNDArrayScalarsInConfig(config: serialization.ConfigDictValue):
-    void {
-  if (config == null || typeof config !== 'object') {
-    return;
-  } else if (Array.isArray(config)) {
-    config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));
-  } else {
-    const fields = Object.keys(config);
-    for (const field of fields) {
-      const value = config[field];
-      if (value != null && typeof value === 'object') {
-        if (!Array.isArray(value) && value['type'] === 'ndarray' &&
-            typeof value['value'] === 'number') {
-          config[field] = value['value'];
-        } else {
-          convertNDArrayScalarsInConfig(value as serialization.ConfigDict);
-        }
-      }
-    }
-  }
-}
-
-/**
- * Deserialize a saved Keras Object
- * @param identifier either a string ID or a saved Keras dictionary
- * @param moduleObjects a list of Python class names to object constructors
- * @param customObjects a list of Python class names to object constructors
- * @param printableModuleName debug text for the object being reconstituted
- * @param fastWeightInit Optional flag to use fast weight initialization
- *   during deserialization. This is applicable to cases in which
- *   the initialization will be immediately overwritten by loaded weight
- *   values. Default: `false`.
- * @returns a TensorFlow.js Layers object
- */
-// tslint:disable:no-any
-export function deserializeKerasObject(
-    identifier: string|serialization.ConfigDict,
-    moduleObjects = {} as {[objName: string]: any},
-    customObjects = {} as {[objName: string]: any},
-    printableModuleName = 'object', fastWeightInit = false): any {
-  // tslint:enable
-  if (typeof identifier === 'string') {
-    const functionName = identifier;
-    let fn;
-    if (functionName in customObjects) {
-      fn = customObjects[functionName];
-    } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {
-      fn = _GLOBAL_CUSTOM_OBJECTS[functionName];
-    } else {
-      fn = moduleObjects[functionName];
-      if (fn == null) {
-        throw new ValueError(
-            `Unknown ${printableModuleName}: ${identifier}. ` +
-            `This may be due to one of the following reasons:\n` +
-            `1. The ${printableModuleName} is defined in Python, in which ` +
-            `case it needs to be ported to TensorFlow.js or your JavaScript ` +
-            `code.\n` +
-            `2. The custom ${printableModuleName} is defined in JavaScript, ` +
-            `but is not registered properly with ` +
-            `tf.serialization.registerClass().`);
-        // TODO(cais): Add link to tutorial page on custom layers.
-      }
-    }
-    return fn;
-  } else {
-    // In this case we are dealing with a Keras config dictionary.
-    const config = identifier;
-    if (config['className'] == null || config['config'] == null) {
-      throw new ValueError(
-          `${printableModuleName}: Improper config format: ` +
-          `${JSON.stringify(config)}.\n` +
-          `'className' and 'config' must set.`);
-    }
-    const className = config['className'] as string;
-    let cls, fromConfig;
-    if (className in customObjects) {
-      [cls, fromConfig] = customObjects[className];
-    } else if (className in _GLOBAL_CUSTOM_OBJECTS) {
-      [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];
-    } else if (className in moduleObjects) {
-      [cls, fromConfig] = moduleObjects[className];
-    }
-    if (cls == null) {
-      throw new ValueError(
-          `Unknown ${printableModuleName}: ${className}. ` +
-          `This may be due to one of the following reasons:\n` +
-          `1. The ${printableModuleName} is defined in Python, in which ` +
-          `case it needs to be ported to TensorFlow.js or your JavaScript ` +
-          `code.\n` +
-          `2. The custom ${printableModuleName} is defined in JavaScript, ` +
-          `but is not registered properly with ` +
-          `tf.serialization.registerClass().`);
-      // TODO(cais): Add link to tutorial page on custom layers.
-    }
-    if (fromConfig != null) {
-      // Porting notes: Instead of checking to see whether fromConfig accepts
-      // customObjects, we create a customObjects dictionary and tack it on to
-      // config['config'] as config['config'].customObjects. Objects can use it,
-      // if they want.
-
-      // tslint:disable-next-line:no-any
-      const customObjectsCombined = {} as {[objName: string]: any};
-      for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {
-        customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];
-      }
-      for (const key of Object.keys(customObjects)) {
-        customObjectsCombined[key] = customObjects[key];
-      }
-      // Add the customObjects to config
-      const nestedConfig = config['config'] as serialization.ConfigDict;
-      nestedConfig['customObjects'] = customObjectsCombined;
-
-      const backupCustomObjects = {..._GLOBAL_CUSTOM_OBJECTS};
-      for (const key of Object.keys(customObjects)) {
-        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
-      }
-      convertNDArrayScalarsInConfig(config['config']);
-      const returnObj =
-          fromConfig(cls, config['config'], customObjects, fastWeightInit);
-      _GLOBAL_CUSTOM_OBJECTS = {...backupCustomObjects};
-
-      return returnObj;
-    } else {
-      // Then `cls` may be a function returning a class.
-      // In this case by convention `config` holds
-      // the kwargs of the function.
-      const backupCustomObjects = {..._GLOBAL_CUSTOM_OBJECTS};
-      for (const key of Object.keys(customObjects)) {
-        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
-      }
-      // In python this is **config['config'], for tfjs-layers we require
-      // classes that use this fall-through construction method to take
-      // a config interface that mimics the expansion of named parameters.
-      const returnObj = new cls(config['config']);
-      _GLOBAL_CUSTOM_OBJECTS = {...backupCustomObjects};
-      return returnObj;
-    }
-  }
-}
-
-/**
- * Compares two numbers for sorting.
- * @param a
- * @param b
- */
-export function numberCompare(a: number, b: number) {
-  return (a < b) ? -1 : ((a > b) ? 1 : 0);
-}
-
-/**
- * Comparison of two numbers for reverse sorting.
- * @param a
- * @param b
- */
-export function reverseNumberCompare(a: number, b: number) {
-  return -1 * numberCompare(a, b);
-}
-
-/**
- * Convert a string into the corresponding DType.
- * @param dtype
- * @returns An instance of DType.
- */
-export function stringToDType(dtype: string): DataType {
-  switch (dtype) {
-    case 'float32':
-      return 'float32';
-    default:
-      throw new ValueError(`Invalid dtype: ${dtype}`);
-  }
-}
-
-/**
- * Test the element-by-element equality of two Arrays of strings.
- * @param xs First array of strings.
- * @param ys Second array of strings.
- * @returns Wether the two arrays are all equal, element by element.
- */
-export function stringsEqual(xs: string[], ys: string[]): boolean {
-  if (xs == null || ys == null) {
-    return xs === ys;
-  }
-  if (xs.length !== ys.length) {
-    return false;
-  }
-  for (let i = 0; i < xs.length; ++i) {
-    if (xs[i] !== ys[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-
-/**
- * Get the unique elements of an array.
- * @param xs Array.
- * @returns An Array consisting of the unique elements in `xs`.
- */
-export function unique<T>(xs: T[]): T[] {
-  if (xs == null) {
-    return xs;
-  }
-  const out: T[] = [];
-  // TODO(cais): Maybe improve performance by sorting.
-  for (const x of xs) {
-    if (out.indexOf(x) === -1) {
-      out.push(x);
-    }
-  }
-  return out;
-}
-
-/**
- * Determine if an Object is empty (i.e., does not have own properties).
- * @param obj Object
- * @returns Whether the Object is empty.
- * @throws ValueError: If object is `null` or `undefined`.
- */
-export function isObjectEmpty(obj: {}): boolean {
-  if (obj == null) {
-    throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);
-  }
-  for (const key in obj) {
-    if (obj.hasOwnProperty(key)) {
-      return false;
-    }
-  }
-  return true;
-}
-
-/**
- * Helper function used to build type union/enum run-time checkers.
- * @param values The list of allowed values.
- * @param label A string name for the type
- * @param value The value to test.
- * @throws ValueError: If the value is not in values nor `undefined`/`null`.
- */
-export function checkStringTypeUnionValue(
-    values: string[], label: string, value: string): void {
-  if (value == null) {
-    return;
-  }
-  if (values.indexOf(value) < 0) {
-    throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${
-        values} or null/undefined.`);
-  }
-}
-
-/**
- * Helper function for verifying the types of inputs.
- *
- * Ensures that the elements of `x` are all of type `expectedType`.
- * Also verifies that the length of `x` is within bounds.
- *
- * @param x Object to test.
- * @param expectedType The string expected type of all of the elements in the
- * Array.
- * @param minLength Return false if x.length is less than this.
- * @param maxLength Return false if x.length is greater than this.
- * @returns true if and only if `x` is an `Array<expectedType>` with
- * length >= `minLength` and <= `maxLength`.
- */
-// tslint:disable:no-any
-export function checkArrayTypeAndLength(
-    x: any, expectedType: string, minLength = 0,
-    maxLength = Infinity): boolean {
-  assert(minLength >= 0);
-  assert(maxLength >= minLength);
-  return (
-      Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&
-      x.every(e => typeof e === expectedType));
-}
-// tslint:enable:no-any
-
-/**
- * Assert that a value or an array of value are positive integer.
- *
- * @param value The value being asserted on. May be a single number or an array
- *   of numbers.
- * @param name Name of the value, used to make the error message.
- */
-export function assertPositiveInteger(value: number|number[], name: string) {
-  if (Array.isArray(value)) {
-    util.assert(
-        value.length > 0, () => `${name} is unexpectedly an empty array.`);
-    value.forEach(
-        (v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));
-  } else {
-    util.assert(
-        Number.isInteger(value) && value > 0,
-        () => `Expected ${name} to be a positive integer, but got ` +
-            `${formatAsFriendlyString(value)}.`);
-  }
-}
-
-/**
- * Format a value into a display-friendly, human-readable fashion.
- *
- * - `null` is formatted as `'null'`
- * - Strings are formated with flanking pair of quotes.
- * - Arrays are formatted with flanking pair of square brackets.
- *
- * @param value The value to display.
- * @return Formatted string.
- */
-// tslint:disable-next-line:no-any
-export function formatAsFriendlyString(value: any): string {
-  if (value === null) {
-    return 'null';
-  } else if (Array.isArray(value)) {
-    return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';
-  } else if (typeof value === 'string') {
-    return `"${value}"`;
-  } else {
-    return `${value}`;
-  }
-}
-
-/**
- * Returns a function `f2` (decorator) which wraps the original function
- * `f`. `f2` guarantees that `f` can be called at most once
- * every `waitMs` ms. If `f2` is called more often, it will return
- * the last returned result of `f`.
- *
- * @param f The original function `f` to wrap.
- * @param waitMs The time between two consecutive calls to `f` in ms.
- */
-export function debounce<T>(
-    f: (...args: Array<{}>) => T, waitMs: number,
-    nowFunc?: Function): (...args: Array<{}>) => T {
-  let lastTime = nowFunc != null ? nowFunc() : util.now();
-  let lastResult: T;
-  const f2 = (...args: Array<{}>) => {
-    const now = nowFunc != null ? nowFunc() : util.now();
-    if (now - lastTime < waitMs) {
-      return lastResult;
-    }
-    lastTime = now;
-    lastResult = f(...args);
-    return lastResult;
-  };
-  return f2;
-}
-
-/**
- * Returns the fusable activation given a layers identifier.
- *
- * @param activationName The layers identifier string.
- * @return The name of the fusable activation.
- */
-export function mapActivationToFusedKernel(activationName: string):
-    fused.Activation {
-  if (activationName === 'relu') {
-    return 'relu';
-  }
-  if (activationName === 'linear') {
-    return 'linear';
-  }
-  if (activationName === 'elu') {
-    return 'elu';
-  }
-  return null;
-}
-
-type PossibleValues = Array<Array<boolean|string|number>>;
-
-/**
- * Returns the cartesian product of sets of values.
- * This works the same as itertools.product in Python.
- *
- * Example:
- *
- * filters = [128, 256, 512]
- * paddings = ['same', 'valid']
- *
- * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],
- * [512, 'same'], [512, 'valid']]
- *
- * @param arrayOfValues List/array of values.
- * @return The cartesian product.
- */
-export function getCartesianProductOfValues(...arrayOfValues: PossibleValues):
-    PossibleValues {
-  assert(arrayOfValues.length > 0, 'arrayOfValues is empty');
-
-  for (const values of arrayOfValues) {
-    assert(Array.isArray(values), 'one of the values is not an array');
-    assert(values.length > 0, 'one of the values is empty');
-  }
-
-  return arrayOfValues.reduce((products, values) => {
-    if (products.length === 0) {
-      return values.map(value => [value]);
-    }
-
-    return values
-        .map(value => {
-          return products.map((prevValue) => [...prevValue, value]);
-        })
-        .reduce((flattenedProduct, unflattenedProduct) => {
-          return flattenedProduct.concat(unflattenedProduct);
-        }, []);
-  }, [] as PossibleValues);
-}
diff --git a/tfjs-master/tfjs-layers/src/utils/generic_utils_test.ts b/tfjs-master/tfjs-layers/src/utils/generic_utils_test.ts
deleted file mode 100644
index c9fc29bd0..000000000
--- a/tfjs-master/tfjs-layers/src/utils/generic_utils_test.ts
+++ /dev/null
@@ -1,369 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {AssertionError} from '../errors';
-import {DataFormat, PaddingMode} from '../keras_format/common';
-
-import * as utils from './generic_utils';
-
-describe('pyListRepeat() ', () => {
-  it('creates an empty array for 0 numValues', () => {
-    expect(utils.pyListRepeat(null, 0)).toEqual([]);
-  });
-
-  it('creates an array with 1 value for 1 numValues', () => {
-    const value = 'a';
-    expect(utils.pyListRepeat(value, 1)).toEqual([value]);
-  });
-
-  it('creates an array with 3 values for 3 numValues', () => {
-    const value = 'a';
-    const numValues = 3;
-    const expectedValue = [value, value, value];
-    expect(utils.pyListRepeat(value, numValues)).toEqual(expectedValue);
-  });
-
-  it('throws an exception when numValues <0', () => {
-    const fillFn = () => utils.pyListRepeat(null, -1);
-    expect(fillFn).toThrowError();
-  });
-
-  it('takes an existing array and replicates its contents.', () => {
-    const value = [1, 2];
-    const numValues = 2;
-    const expectedValue = [1, 2, 1, 2];
-    expect(utils.pyListRepeat(value, numValues)).toEqual(expectedValue);
-  });
-});
-
-describe('assert', () => {
-  for (const x of [false, null, undefined]) {
-    it('throws error for false conditions', () => {
-      expect(() => utils.assert(x)).toThrowError();
-    });
-  }
-
-  it('doesn\'t throw error for true conditions', () => {
-    expect(() => utils.assert(true)).not.toThrowError();
-  });
-});
-
-describe('count', () => {
-  it('string array, non-empty', () => {
-    const array: string[] = ['foo', 'bar', 'foo'];
-    expect(utils.count(array, 'foo')).toEqual(2);
-    expect(utils.count(array, 'bar')).toEqual(1);
-    expect(utils.count(array, 'baz')).toEqual(0);
-    expect(utils.count(array, '')).toEqual(0);
-  });
-  it('number array, non-empty', () => {
-    const array: number[] = [-1, 1, 3, 3, 7, -1, 1.337, -1];
-    expect(utils.count(array, 1)).toEqual(1);
-    expect(utils.count(array, 3)).toEqual(2);
-    expect(utils.count(array, 1.337)).toEqual(1);
-    expect(utils.count(array, -1)).toEqual(3);
-    expect(utils.count(array, 0)).toEqual(0);
-  });
-  it('string array, empty', () => {
-    const array: string[] = [];
-    expect(utils.count(array, 'foo')).toEqual(0);
-    expect(utils.count(array, 'bar')).toEqual(0);
-    expect(utils.count(array, 'baz')).toEqual(0);
-    expect(utils.count(array, '')).toEqual(0);
-  });
-});
-
-describe('Compare functions', () => {
-  const inputs =
-      [[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 1, 2], [3, 2, 1]];
-
-  for (const input of inputs) {
-    it('cmp sorts numbers in ascending order', () => {
-      const expected = [1, 2, 3];
-      expect(input.slice().sort(utils.numberCompare)).toEqual(expected);
-    });
-  }
-
-  for (const input of inputs) {
-    it('reverseCmp sorts numbers in ascending order', () => {
-      const expected = [3, 2, 1];
-      expect(input.slice().sort(utils.reverseNumberCompare)).toEqual(expected);
-    });
-  }
-});
-
-describe('toList', () => {
-  it('creates array from non-array.', () => {
-    const value = 1;
-    expect(utils.toList(value)).toEqual([value]);
-  });
-
-  it('returns array if passed an array.', () => {
-    const value = [1];
-    expect(utils.toList(value)).toEqual(value);
-  });
-});
-
-describe('toSnakeCase', () => {
-  for (const [inputString, expectedOutput] of [
-           ['', ''], ['A', 'a'], ['AA', 'aa'], ['AAA', 'aaa'], ['AAa', 'a_aa'],
-           ['AA0', 'a_a0'], ['aB', 'a_b'], ['aBC', 'a_bc'], ['aBc', 'a_bc'],
-           ['_', 'private_'], ['a', 'a'], ['_a', 'private_a']]) {
-    it('creates expected output', () => {
-      expect(utils.toSnakeCase(inputString)).toEqual(expectedOutput);
-    });
-  }
-});
-
-describe('toCamelCase', () => {
-  for (const [inputString, expectedOutput] of [
-           ['', ''], ['A', 'A'], ['aa', 'aa'], ['a_a', 'aA'], ['a_aa', 'aAa'],
-           ['aAa', 'aAa']]) {
-    it('creates expected output', () => {
-      expect(utils.toCamelCase(inputString)).toEqual(expectedOutput);
-    });
-  }
-});
-
-describe('stringsEqual', () => {
-  it('null and undefined', () => {
-    expect(utils.stringsEqual(null, null)).toEqual(true);
-    expect(utils.stringsEqual(undefined, undefined)).toEqual(true);
-    expect(utils.stringsEqual(undefined, null)).toEqual(false);
-    expect(utils.stringsEqual(undefined, [])).toEqual(false);
-    expect(utils.stringsEqual(null, [])).toEqual(false);
-    expect(utils.stringsEqual(null, ['a'])).toEqual(false);
-  });
-  it('Empty arrays', () => {
-    expect(utils.stringsEqual([], [])).toEqual(true);
-    expect(utils.stringsEqual([], ['a'])).toEqual(false);
-  });
-  it('Non-empty arrays', () => {
-    expect(utils.stringsEqual(['a', 'b', 'c', null], [
-      'a', 'b', 'c', null
-    ])).toEqual(true);
-    expect(utils.stringsEqual(['a', 'b', 'c', ''], [
-      'a', 'b', 'c', ''
-    ])).toEqual(true);
-    expect(utils.stringsEqual(['a', 'b', 'c', null], [
-      'a', 'b', 'c', undefined
-    ])).toEqual(false);
-    expect(utils.stringsEqual(['a', 'b', 'c', ''], [
-      'a', 'c', 'b', ''
-    ])).toEqual(false);
-  });
-});
-
-describe('unique', () => {
-  it('null or undefined', () => {
-    expect(utils.unique(null)).toEqual(null);
-    expect(utils.unique(undefined)).toEqual(undefined);
-  });
-  it('empty array', () => {
-    expect(utils.unique([])).toEqual([]);
-  });
-  it('Non-empty array: string', () => {
-    expect(utils.unique(['foo', 'bar', 'foo'])).toEqual(['foo', 'bar']);
-    expect(utils.unique(['foo', 'bar', ''])).toEqual(['foo', 'bar', '']);
-    expect(utils.unique(['foo', 'bar', null, ''])).toEqual([
-      'foo', 'bar', null, ''
-    ]);
-  });
-  it('Non-empty array: number', () => {
-    expect(utils.unique([1, 2, -1, 2])).toEqual([1, 2, -1]);
-    expect(utils.unique([2, 3, 2, null])).toEqual([2, 3, null]);
-  });
-});
-
-describe('isObjectEmpty', () => {
-  it('null or undefined', () => {
-    expect(() => utils.isObjectEmpty(null)).toThrowError();
-    expect(() => utils.isObjectEmpty(undefined)).toThrowError();
-  });
-  it('empty object', () => {
-    expect(utils.isObjectEmpty({})).toEqual(true);
-  });
-  it('Non-empty object', () => {
-    expect(utils.isObjectEmpty({'a': 12})).toEqual(false);
-    expect(utils.isObjectEmpty({'a': 12, 'b': 34})).toEqual(false);
-  });
-});
-
-describe('checkArrayTypeAndLength', () => {
-  it('checks types', () => {
-    // [1,2,3] is made of all 'number's.
-    expect(utils.checkArrayTypeAndLength([1, 2, 3], 'number')).toEqual(true);
-    // ['hello', 'world'] is made of all 'strings's.
-    expect(utils.checkArrayTypeAndLength(['hello', 'world'], 'string'))
-        .toEqual(true);
-    // [1,2,[3]] is not made of all 'number's.
-    expect(utils.checkArrayTypeAndLength([1, 2, [3]], 'number')).toEqual(false);
-  });
-  it('checks lengths', () => {
-    // length of [1,2,3] is >= 1.
-    expect(utils.checkArrayTypeAndLength([1, 2, 3], 'number', 1)).toEqual(true);
-    // length of [1,2,3] is >= 1 and <= 3.
-    expect(utils.checkArrayTypeAndLength([1, 2, 3], 'number', 1, 3))
-        .toEqual(true);
-    // length of [1,2,3,4,5] is not >= 1 and <= 3.
-    expect(utils.checkArrayTypeAndLength([1, 2, 3, 4, 5], 'number', 1, 3))
-        .toEqual(false);
-    // length of [1,2,3,4,5] is not >= 7 and <= 10.
-    expect(utils.checkArrayTypeAndLength([1, 2, 3, 4, 5], 'number', 7, 10))
-        .toEqual(false);
-    // Length of the empty array is >= 0 and <= 0.
-    expect(utils.checkArrayTypeAndLength([], 'does_not_matter', 0, 0))
-        .toEqual(true);
-  });
-  it('rejects negative length limits', () => {
-    expect(() => utils.checkArrayTypeAndLength([1, 2, 3], 'number', -1))
-        .toThrowError();
-  });
-  it('rejects maxLength < minLength', () => {
-    expect(() => utils.checkArrayTypeAndLength([1, 2, 3], 'number', 100, 2))
-        .toThrowError();
-  });
-});
-
-describe('formatValueAsFriendlyString', () => {
-  it('null input', () => {
-    expect(utils.formatAsFriendlyString(null)).toEqual('null');
-  });
-
-  it('string input', () => {
-    expect(utils.formatAsFriendlyString('')).toEqual('\"\"');
-    expect(utils.formatAsFriendlyString('foo')).toEqual('\"foo\"');
-  });
-
-  it('array input', () => {
-    expect(utils.formatAsFriendlyString([])).toEqual('[]');
-    expect(utils.formatAsFriendlyString([null, 3])).toEqual('[null,3]');
-    expect(utils.formatAsFriendlyString([1, 3, 3, 7])).toEqual('[1,3,3,7]');
-    expect(utils.formatAsFriendlyString([1, 3, 3, 'a'])).toEqual('[1,3,3,"a"]');
-    expect(utils.formatAsFriendlyString([
-      [1], 3, [3], 7
-    ])).toEqual('[[1],3,[3],7]');
-  });
-});
-
-describe('debouce', () => {
-  it('first call happens after waitMs', async () => {
-    const timestamps = [0, 1, 11, 12];
-    let counter = 0;
-    const nowFunc =
-        jasmine.createSpy('now').and.callFake(() => timestamps[counter++]);
-    let numCalls = 0;
-    const f = () => numCalls++;
-    const waitMs = 10;
-    const f2 = utils.debounce(f, waitMs, nowFunc);
-    // The first call is ignored since waitMs hasn't passed yet.
-    f2();
-    expect(numCalls).toBe(0);
-    f2();
-    expect(numCalls).toBe(1);
-    // The second call is ignored.
-    f2();
-    expect(numCalls).toBe(1);
-  });
-
-  it('allows at most period/wait calls in a given period', async () => {
-    const timestamps = [0, 2, 4, 6, 8, 10];
-    let counter = 0;
-    const nowFunc =
-        jasmine.createSpy('now').and.callFake(() => timestamps[counter++]);
-
-    let numCalls = 0;
-    const f = () => numCalls++;
-    const waitMs = 3;
-    const f2 = utils.debounce(f, waitMs, nowFunc);
-    // Call f2 5 times.
-    for (let i = 1; i < 5; i++) {
-      f2();
-    }
-    // Expect f to be called 2 times (between timestamps 4 and 2, and 8 and 4).
-    expect(numCalls).toBe(2);
-  });
-});
-
-describe('getCartesianProductOfValues', () => {
-  it('should return correct cartesian product of 2 group of values', () => {
-    const filters = [128, 256, 512];
-    const paddings: PaddingMode[] = ['same', 'valid'];
-
-    const product = utils.getCartesianProductOfValues(filters, paddings);
-
-    expect(Array.isArray(product)).toBe(true);
-    expect(product.length).toBe(6);
-    expect(product.sort()).toEqual([
-      [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],
-      [512, 'same'], [512, 'valid']
-    ].sort());
-  });
-
-  it('should return correct cartesian product of 3 group of values', () => {
-    const filters = [128, 256, 512];
-    const kernels = [3, 5, 7];
-    const paddings: PaddingMode[] = ['same', 'valid'];
-
-    const product =
-        utils.getCartesianProductOfValues(filters, kernels, paddings);
-
-    expect(Array.isArray(product)).toBe(true);
-    expect(product.length).toBe(18);
-    expect(product.sort()).toEqual([
-      [128, 3, 'same'], [128, 3, 'valid'], [128, 5, 'same'], [128, 5, 'valid'],
-      [128, 7, 'same'], [128, 7, 'valid'], [256, 3, 'same'], [256, 3, 'valid'],
-      [256, 5, 'same'], [256, 5, 'valid'], [256, 7, 'same'], [256, 7, 'valid'],
-      [512, 3, 'same'], [512, 3, 'valid'], [512, 5, 'same'], [512, 5, 'valid'],
-      [512, 7, 'same'], [512, 7, 'valid']
-    ].sort());
-  });
-
-  it('should return correct cartesian product of 5 group of values', () => {
-    const filters = [128, 256, 512];
-    const kernels = [3, 5, 7];
-    const paddings: PaddingMode[] = ['same', 'valid'];
-    const formats: DataFormat[] = ['channelsFirst', 'channelsLast'];
-    const dilation = [1, 2];
-
-    const product = utils.getCartesianProductOfValues(
-        filters, kernels, paddings, formats, dilation);
-
-    expect(Array.isArray(product)).toBe(true);
-    expect(product.length).toBe(72);
-    // expect(product[0].length).toBe(5);
-    expect(product.every(p => p.length === 5)).toBe(true);
-    expect(product[0].map(p => typeof p)).toEqual([
-      'number', 'number', 'string', 'string', 'number'
-    ]);
-  });
-
-  it('should raise AssertionError if array is not valid', () => {
-    expect(() => {
-      utils.getCartesianProductOfValues(undefined);
-    }).toThrowError(AssertionError);
-  });
-
-  it('should raise AssertionError if array is empty', () => {
-    expect(() => {
-      utils.getCartesianProductOfValues([]);
-    }).toThrowError(AssertionError);
-  });
-
-  it('should raise AssertionError if 1 group of values is empty', () => {
-    expect(() => {
-      const filters = [128, 256, 512];
-      const kernels: string[] = [];
-      const paddings: PaddingMode[] = ['same', 'valid'];
-
-      utils.getCartesianProductOfValues(filters, kernels, paddings);
-    }).toThrowError(AssertionError);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/utils/layer_utils.ts b/tfjs-master/tfjs-layers/src/utils/layer_utils.ts
deleted file mode 100644
index d58729acf..000000000
--- a/tfjs-master/tfjs-layers/src/utils/layer_utils.ts
+++ /dev/null
@@ -1,240 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {Container} from '../engine/container';
-import {Layer, Node} from '../engine/topology';
-import {countParamsInWeights} from './variable_utils';
-
-/**
- * Print the summary of a LayersModel object.
- *
- * @param model tf.LayersModel instance.
- * @param lineLength Total length of printed lines. Set this to adapt to the
- *   display to different terminal or console sizes.
- * @param positions Relative or absolute positions of log elements in each
- *   line. Each number corresponds to right-most (i.e., ending) position of a
- *   column.
- *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like
- *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.
- * @param printFn Print function to use.
- *   It will be called on each line of the summary. You can provide a custom
- *   function in order to capture the string summary. Defaults to `console.log`.
- */
-export function printSummary(
-    model: Container, lineLength?: number, positions?: number[],
-    // tslint:disable-next-line:no-any
-    printFn: (message?: any, ...optionalParams: any[]) => void =
-        console.log): void {
-  const sequentialLike = isModelSequentialLike(model);
-
-  // Header names for different log elements.
-  const toDisplay: string[] = ['Layer (type)', 'Input Shape', 'Output shape', 'Param #'];
-  if (sequentialLike) {
-    lineLength = lineLength || 90;
-    positions = positions || [0.32, 0.61, 0.89, 1];
-  } else {
-    lineLength = lineLength || 115;
-    positions = positions || [0.24, 0.48, 0.70, 0.80, 1];
-    // Header names for different log elements.
-  }
-
-  if (positions[positions.length - 1] <= 1) {
-    // `positions` is relative. Convert it to absolute positioning.
-    positions = positions.map(p => Math.floor(lineLength * p));
-  }
-
-  let relevantNodes: Node[];
-  if (!sequentialLike) {
-    toDisplay.push('Receives inputs');
-    relevantNodes = [];
-    for (const depth in model.nodesByDepth) {
-      relevantNodes.push(...model.nodesByDepth[depth]);
-    }
-  }
-
-  printFn('_'.repeat(lineLength));
-  printRow(toDisplay, positions, printFn);
-  printFn('='.repeat(lineLength));
-
-  const layers = model.layers;
-  for (let i = 0; i < layers.length; ++i) {
-    if (sequentialLike) {
-      printLayerSummary(layers[i], positions, printFn);
-    } else {
-      printLayerSummaryWithConnections(
-          layers[i], positions, relevantNodes, printFn);
-    }
-    printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));
-  }
-
-  // tslint:disable-next-line:no-any
-  (model as any).checkTrainableWeightsConsistency();
-
-  const trainableCount = countTrainableParams(model);
-  const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);
-
-  printFn(`Total params: ${trainableCount + nonTrainableCount}`);
-  printFn(`Trainable params: ${trainableCount}`);
-  printFn(`Non-trainable params: ${nonTrainableCount}`);
-  printFn('_'.repeat(lineLength));
-}
-
-function countTrainableParams(model: Container): number {
-  let trainableCount: number;
-  // tslint:disable:no-any
-  if ((model as any).collectedTrainableWeights != null) {
-    trainableCount =
-        countParamsInWeights((model as any).collectedTrainableWeights);
-  } else {
-    trainableCount = countParamsInWeights(model.trainableWeights);
-  }
-  // tslint:enable:no-any
-  return trainableCount;
-}
-
-function isModelSequentialLike(model: Container): boolean {
-  let sequentialLike = true;
-  const nodesByDepth: Node[][] = [];
-  const nodes: Node[] = [];
-  for (const depth in model.nodesByDepth) {
-    nodesByDepth.push(model.nodesByDepth[depth]);
-  }
-  for (const depthNodes of nodesByDepth) {
-    if (depthNodes.length > 1 ||
-        depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {
-      sequentialLike = false;
-      break;
-    }
-    nodes.push(...depthNodes);
-  }
-  if (sequentialLike) {
-    // Search for shared layers.
-    for (const layer of model.layers) {
-      let flag = false;
-      for (const node of layer.inboundNodes) {
-        if (nodes.indexOf(node) !== -1) {
-          if (flag) {
-            sequentialLike = false;
-            break;
-          } else {
-            flag = true;
-          }
-        }
-      }
-      if (!sequentialLike) {
-        break;
-      }
-    }
-  }
-  return sequentialLike;
-}
-
-function printRow(
-    fields: string[], positions: number[],
-    // tslint:disable-next-line:no-any
-    printFn: (message?: any, ...optionalParams: any[]) => void = console.log) {
-  let line = '';
-  for (let i = 0; i < fields.length; ++i) {
-    if (i > 0) {
-      line = line.slice(0, line.length - 1) + ' ';
-    }
-    line += fields[i];
-    line = line.slice(0, positions[i]);
-    line += ' '.repeat(positions[i] - line.length);
-  }
-  printFn(line);
-}
-
-/**
- * Prints a summary for a single Layer, without connectivity information.
- *
- * @param layer: Layer instance to print.
- */
-function printLayerSummary(
-    layer: Layer, positions: number[],
-    // tslint:disable-next-line:no-any
-    printFn: (message?: any, ...optionalParams: any[]) => void) {
-  let outputShape: string;
-  let inputShape: string;
-
-  try {
-    inputShape = (layer.inboundNodes.map(
-      x => JSON.stringify(x.inputShapes)
-    )).join(',');
-  } catch (err) {
-    inputShape = 'multiple';
-  }
-
-  try {
-    outputShape = JSON.stringify(layer.outputShape);
-  } catch (err) {
-    outputShape = 'multiple';
-  }
-
-  const name = layer.name;
-  const className = layer.getClassName();
-  const fields: string[] =
-      [`${name} (${className})`, inputShape,
-      outputShape, layer.countParams().toString()];
-  printRow(fields, positions, printFn);
-}
-
-/**
- * Prints a summary for a single Layer, with connectivity information.
- */
-function printLayerSummaryWithConnections(
-    layer: Layer, positions: number[], relevantNodes: Node[],
-    // tslint:disable-next-line:no-any
-    printFn: (message?: any, ...optionalParams: any[]) => void) {
-  let outputShape: string;
-  let inputShape: string;
-
-  try {
-    inputShape = (layer.inboundNodes.map(
-      x => JSON.stringify(x.inputShapes)
-    )).join(',');
-  } catch (err) {
-    inputShape = 'multiple';
-  }
-
-  try {
-    outputShape = JSON.stringify(layer.outputShape);
-  } catch (err) {
-    outputShape = 'multiple';
-  }
-
-  const connections: string[] = [];
-  for (const node of layer.inboundNodes) {
-    if (relevantNodes != null && relevantNodes.length > 0 &&
-        relevantNodes.indexOf(node) === -1) {
-      continue;
-    }
-    for (let i = 0; i < node.inboundLayers.length; ++i) {
-      const inboundLayer = node.inboundLayers[i].name;
-      const inboundLayerIndex = node.nodeIndices[i];
-      const inboundTensorIndex = node.tensorIndices[i];
-      connections.push(
-          `${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);
-    }
-  }
-  const name = layer.name;
-  const className = layer.getClassName();
-  const firstConnection = connections.length === 0 ? '' : connections[0];
-  const fields: string[] = [
-    `${name} (${className})`, inputShape,
-    outputShape, layer.countParams().toString(),
-    firstConnection
-  ];
-
-  printRow(fields, positions, printFn);
-  for (let i = 1; i < connections.length; ++i) {
-    printRow(['', '', '', '', connections[i]], positions, printFn);
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/utils/math_utils.ts b/tfjs-master/tfjs-layers/src/utils/math_utils.ts
deleted file mode 100644
index 629f73f92..000000000
--- a/tfjs-master/tfjs-layers/src/utils/math_utils.ts
+++ /dev/null
@@ -1,166 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Math utility functions.
- *
- * This file contains some frequently used math function that operates on
- * number[] or Float32Array and return a number. Many of these functions are
- * not-so-thick wrappers around TF.js Core functions. But they offer the
- * convenience of
- * 1) not having to convert the inputs into Tensors,
- * 2) not having to convert the returned Tensors to numbers.
- */
-
-import {ValueError} from '../errors';
-
-export type ArrayTypes = Uint8Array|Int32Array|Float32Array;
-
-/**
- * Determine if a number is an integer.
- */
-export function isInteger(x: number): boolean {
-  return x === parseInt(x.toString(), 10);
-}
-
-/**
- * Calculate the product of an array of numbers.
- * @param array The array to calculate the product over.
- * @param begin Beginning index, inclusive.
- * @param end Ending index, exclusive.
- * @return The product.
- */
-export function arrayProd(
-    array: number[]|ArrayTypes, begin?: number, end?: number): number {
-  if (begin == null) {
-    begin = 0;
-  }
-  if (end == null) {
-    end = array.length;
-  }
-
-  let prod = 1;
-  for (let i = begin; i < end; ++i) {
-    prod *= array[i];
-  }
-  return prod;
-}
-
-/**
- * Compute minimum value.
- * @param array
- * @return minimum value.
- */
-export function min(array: number[]|Float32Array): number {
-  // same behavior as tf.min()
-  if (array.length === 0) {
-    return Number.NaN;
-  }
-  let min = Number.POSITIVE_INFINITY;
-  for (let i = 0; i < array.length; i++) {
-    const value = array[i];
-    if (value < min) {
-      min = value;
-    }
-  }
-  return min;
-}
-
-/**
- * Compute maximum value.
- * @param array
- * @return maximum value
- */
-export function max(array: number[]|Float32Array): number {
-  // same behavior as tf.max()
-  if (array.length === 0) {
-    return Number.NaN;
-  }
-  let max = Number.NEGATIVE_INFINITY;
-  for (let i = 0; i < array.length; i++) {
-    const value = array[i];
-    if (value > max) {
-      max = value;
-    }
-  }
-  return max;
-}
-
-/**
- * Compute sum of array.
- * @param array
- * @return The sum.
- */
-export function sum(array: number[]|Float32Array): number {
-  let sum = 0;
-  for (let i = 0; i < array.length; i++) {
-    const value = array[i];
-    sum += value;
-  }
-  return sum;
-}
-
-/**
- * Compute mean of array.
- * @param array
- * @return The mean.
- */
-export function mean(array: number[]|Float32Array): number {
-  return sum(array) / array.length;
-}
-
-/**
- * Compute variance of array.
- * @param array
- * @return The variance.
- */
-export function variance(array: number[]|Float32Array): number {
-  const meanValue = mean(array);
-  const demeaned = array.map((value: number) => value - meanValue);
-  let sumSquare = 0;
-  for (let i = 0; i < demeaned.length; i++) {
-    const value = demeaned[i];
-    sumSquare += value * value;
-  }
-  return sumSquare / array.length;
-}
-
-/**
- * Compute median of array.
- * @param array
- * @return The median value.
- */
-export function median(array: number[]|Float32Array): number {
-  const arraySorted = array.slice().sort((a, b) => a - b);
-  const lowIdx = Math.floor((arraySorted.length - 1) / 2);
-  const highIdx = Math.ceil((arraySorted.length - 1) / 2);
-  if (lowIdx === highIdx) {
-    return arraySorted[lowIdx];
-  }
-  return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;
-}
-
-/**
- * Generate an array of integers in [begin, end).
- * @param begin Beginning integer, inclusive.
- * @param end Ending integer, exclusive.
- * @returns Range array.
- * @throws ValueError, iff `end` < `begin`.
- */
-export function range(begin: number, end: number): number[] {
-  if (end < begin) {
-    throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);
-  }
-  const out: number[] = [];
-  for (let i = begin; i < end; ++i) {
-    out.push(i);
-  }
-  return out;
-}
diff --git a/tfjs-master/tfjs-layers/src/utils/math_utils_test.ts b/tfjs-master/tfjs-layers/src/utils/math_utils_test.ts
deleted file mode 100644
index 6227ecf5e..000000000
--- a/tfjs-master/tfjs-layers/src/utils/math_utils_test.ts
+++ /dev/null
@@ -1,141 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for math_utils.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-
-import * as math_utils from './math_utils';
-import {describeMathCPU} from './test_utils';
-
-describe('isInteger', () => {
-  it('True cases', () => {
-    expect(math_utils.isInteger(-103)).toBe(true);
-    expect(math_utils.isInteger(0)).toBe(true);
-    expect(math_utils.isInteger(1337)).toBe(true);
-  });
-
-  it('False cases', () => {
-    expect(math_utils.isInteger(-1.03)).toBe(false);
-    expect(math_utils.isInteger(0.008)).toBe(false);
-    expect(math_utils.isInteger(133.7)).toBe(false);
-  });
-});
-
-describe('arrayProd', () => {
-  it('Full length', () => {
-    expect(math_utils.arrayProd([2, 3, 4])).toEqual(24);
-    expect(math_utils.arrayProd(new Float32Array([2, 3, 4]))).toEqual(24);
-  });
-
-  it('Partial from beginning', () => {
-    expect(math_utils.arrayProd([2, 3, 4], null, 2)).toEqual(6);
-    expect(math_utils.arrayProd([2, 3, 4], 0, 2)).toEqual(6);
-  });
-
-  it('Partial to end', () => {
-    expect(math_utils.arrayProd([2, 3, 4], 1)).toEqual(12);
-    expect(math_utils.arrayProd([2, 3, 4], 1, 3)).toEqual(12);
-  });
-
-  it('Partial no beginninng no end', () => {
-    expect(math_utils.arrayProd([2, 3, 4, 5], 1, 3)).toEqual(12);
-  });
-
-  it('Empty array', () => {
-    expect(math_utils.arrayProd([])).toEqual(1);
-  });
-});
-
-describeMathCPU('min', () => {
-  it('Number array', () => {
-    expect(math_utils.min([-100, -200, 150])).toEqual(-200);
-  });
-
-  it('Float32Array', () => {
-    expect(math_utils.min(new Float32Array([-100, -200, 150]))).toEqual(-200);
-  });
-});
-
-describeMathCPU('max', () => {
-  it('Number array', () => {
-    expect(math_utils.max([-100, -200, 150])).toEqual(150);
-  });
-
-  it('Float32Array', () => {
-    expect(math_utils.max(new Float32Array([-100, -200, 150]))).toEqual(150);
-  });
-});
-
-describeMathCPU('sum', () => {
-  it('Number array', () => {
-    expect(math_utils.sum([-100, -200, 150])).toEqual(-150);
-  });
-
-  it('Float32Array', () => {
-    expect(math_utils.sum(new Float32Array([-100, -200, 150]))).toEqual(-150);
-  });
-});
-
-describeMathCPU('mean', () => {
-  it('Number array', () => {
-    expect(math_utils.mean([-100, -200, 150])).toEqual(-50);
-  });
-
-  it('Float32Array', () => {
-    expect(math_utils.mean(new Float32Array([-100, -200, 150]))).toEqual(-50);
-  });
-});
-
-describeMathCPU('variance', () => {
-  it('Number array', () => {
-    expect(math_utils.variance([-100, -200, 150, 50])).toEqual(18125);
-  });
-
-  it('Float32Array', () => {
-    expect(math_utils.variance(new Float32Array([-100, -200, 150, 50])))
-        .toEqual(18125);
-  });
-});
-
-describeMathCPU('median', () => {
-  it('Number array', () => {
-    expect(math_utils.median([-100, -200, 150, 50])).toEqual(-25);
-  });
-
-  it('Float32Array', () => {
-    expect(math_utils.median(new Float32Array([-100, -200, 150, 50])))
-        .toEqual(-25);
-  });
-
-  it('does not mutate input array', () => {
-    const numbers = [-100, -200, 150, 50];
-    math_utils.median(numbers);
-    tfc.test_util.expectArraysClose(numbers, [-100, -200, 150, 50]);
-  });
-});
-
-describe('range', () => {
-  it('end > begin', () => {
-    expect(math_utils.range(0, 1)).toEqual([0]);
-    expect(math_utils.range(0, 5)).toEqual([0, 1, 2, 3, 4]);
-    expect(math_utils.range(-10, -5)).toEqual([-10, -9, -8, -7, -6]);
-    expect(math_utils.range(-3, 3)).toEqual([-3, -2, -1, 0, 1, 2]);
-  });
-  it('end === begin', () => {
-    expect(math_utils.range(0, 0)).toEqual([]);
-    expect(math_utils.range(-2, -2)).toEqual([]);
-  });
-  it('end < begin throws error', () => {
-    expect(() => math_utils.range(0, -2)).toThrowError(/.*-2.*0.*forbidden/);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/utils/serialization_utils.ts b/tfjs-master/tfjs-layers/src/utils/serialization_utils.ts
deleted file mode 100644
index 30f80ccfa..000000000
--- a/tfjs-master/tfjs-layers/src/utils/serialization_utils.ts
+++ /dev/null
@@ -1,128 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-// Porting note: This file doesn't exist in PyKeras.
-// Its purpose here is to centralize the boundary layer between
-// tfjs-layers's internal Config TS-Centric format and PyKeras's
-// serialized Python Config format.
-
-import {serialization} from '@tensorflow/tfjs-core';
-
-import {PyJsonValue} from '../keras_format/types';
-import * as generic_utils from '../utils/generic_utils';
-// tslint:enable
-
-/**
- * Test whether a value in an array is the name of a LayersModel or Layer.
- * @param key The key name that the value is found under. Note that the key
- *   may not be at the level immediately above the value, if the value is in a
- *   nested array.
- * @param index Index of the value in the Array that it is found in.
- * @param value The value object.
- * @returns A boolean indicating whether value is a name.
- */
-function isArrayItemInputOrOutputName<T>(
-    key: string, index: number, value: T): boolean {
-  return (key === 'inboundNodes' || key === 'outputLayers' ||
-          key === 'inputLayers') &&
-      index === 0 && typeof value === 'string';
-}
-
-/**
- * Convert a Pythonic config object to TypeScript config object.
- * @param pythonicConfig The config object to convert.
- * @param key Optional key name of the object being converted.
- * @returns Result of the conversion.
- */
-export function convertPythonicToTs(
-    pythonicConfig: PyJsonValue, key?: string): serialization.ConfigDictValue {
-  if (pythonicConfig === null) {
-    return null;
-  } else if (typeof pythonicConfig === 'string') {
-    return generic_utils.toCamelCase(pythonicConfig);
-  } else if (
-      (typeof pythonicConfig === 'number') ||
-      (typeof pythonicConfig === 'boolean')) {
-    return pythonicConfig;
-  } else if (pythonicConfig instanceof Array) {
-    const tsArray = [];
-    const arrayLength = pythonicConfig.length;
-    for (let i = 0; i < arrayLength; ++i) {
-      const item = pythonicConfig[i];
-      if (isArrayItemInputOrOutputName(key, i, item)) {
-        tsArray.push(item);
-      } else {
-        tsArray.push(convertPythonicToTs(item, key));
-      }
-    }
-    return tsArray;
-  } else {
-    const tsDict: serialization.ConfigDict = {};
-    for (const pythonicKey of Object.keys(pythonicConfig)) {
-      const pythonicValue = pythonicConfig[pythonicKey];
-      if (pythonicKey === 'name' && typeof pythonicValue === 'string') {
-        // Special case the 'name' key with a string value. Name values, such as
-        // the names of LayersModel and Layer instances, should not undergo the
-        // camel-case conversion.
-        tsDict[pythonicKey] = pythonicValue;
-      } else {
-        const tsKey = generic_utils.toCamelCase(pythonicKey);
-        tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);
-      }
-    }
-    return tsDict;
-  }
-}
-
-/**
- * Convert a TypeScript config object to Python config object.
- * @param tsConfig The config object to convert.
- * @param key Optional key name of the object being converted.
- * @returns Result of the conversion.
- */
-export function convertTsToPythonic(
-    tsConfig: serialization.ConfigDictValue, key?: string): PyJsonValue {
-  if (tsConfig === null || tsConfig === undefined) {
-    return null;
-  } else if (typeof tsConfig === 'string') {
-    return generic_utils.toSnakeCase(tsConfig);
-  } else if (
-      (typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {
-    return tsConfig;
-  } else if (tsConfig instanceof Array) {
-    const pyArray = [];
-    const arrayLength = tsConfig.length;
-    for (let i = 0; i < arrayLength; ++i) {
-      const item = tsConfig[i];
-      if (isArrayItemInputOrOutputName(key, i, item)) {
-        pyArray.push(item);
-      } else {
-        pyArray.push(convertTsToPythonic(item, key));
-      }
-    }
-    return pyArray;
-  } else {
-    const pyDict: serialization.ConfigDict = {};
-    for (const tsKey of Object.keys(tsConfig)) {
-      const tsValue = tsConfig[tsKey];
-      const pyKey = generic_utils.toSnakeCase(tsKey);
-      if ((tsKey === 'name' || tsKey === 'className') &&
-          typeof tsValue === 'string') {
-        // Special case the 'name' key with a string value. Name values, such as
-        // the names of LayersModel and Layer instances, should not undergo the
-        // snake-case conversion.
-        pyDict[pyKey] = tsValue;
-      } else {
-        pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);
-      }
-    }
-    return pyDict;
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/utils/serialization_utils_test.ts b/tfjs-master/tfjs-layers/src/utils/serialization_utils_test.ts
deleted file mode 100644
index da0680d60..000000000
--- a/tfjs-master/tfjs-layers/src/utils/serialization_utils_test.ts
+++ /dev/null
@@ -1,148 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit Tests for serialization_utils
- * Porting Note: serialization_utils is a tfjs-layers only file, not found
- * int the original PyKeras.
- */
-import {serialization} from '@tensorflow/tfjs-core';
-
-import {PyJsonValue} from '../keras_format/types';
-
-import {convertPythonicToTs, convertTsToPythonic} from './serialization_utils';
-
-describe('convertPythonToTs', () => {
-  it('primitives', () => {
-    expect(convertPythonicToTs(null)).toEqual(null);
-    expect(convertPythonicToTs(true)).toEqual(true);
-    expect(convertPythonicToTs(false)).toEqual(false);
-    expect(convertPythonicToTs(4)).toEqual(4);
-  });
-  it('strings w/o name tags', () => {
-    expect(convertPythonicToTs('abc')).toEqual('abc');
-    expect(convertPythonicToTs('ABC')).toEqual('ABC');
-    expect(convertPythonicToTs('one_two')).toEqual('oneTwo');
-    expect(convertPythonicToTs('OneTwo')).toEqual('OneTwo');
-  });
-  it('simple arrays', () => {
-    expect(convertPythonicToTs([])).toEqual([]);
-    expect(convertPythonicToTs([null])).toEqual([null]);
-    expect(convertPythonicToTs(['one_two'])).toEqual(['oneTwo']);
-    expect(convertPythonicToTs([null, true, false, 4, 'abc'])).toEqual([
-      null, true, false, 4, 'abc'
-    ]);
-    expect(convertPythonicToTs([[[]]])).toEqual([[[]]]);
-  });
-  // We have to special case strings that are layer_names to not be converted
-  it('layer tuple (array) with key', () => {
-    for (const key of ['inboundNodes', 'inputLayers', 'outputLayers']) {
-      expect(convertPythonicToTs(['layer_name', 'meta_data', 0], key)).toEqual([
-        'layer_name', 'metaData', 0
-      ]);
-    }
-  });
-  it('dictionary', () => {
-    expect(convertPythonicToTs({})).toEqual({});
-    expect(convertPythonicToTs({key: null})).toEqual({key: null});
-    expect(convertPythonicToTs({key_one: 4})).toEqual({keyOne: 4});
-    expect(convertPythonicToTs({key_two: 'abc_def'})).toEqual({
-      keyTwo: 'abcDef'
-    });
-    expect(convertPythonicToTs({key_one: true, key_two: false}))
-        .toEqual({keyOne: true, keyTwo: false});
-    // values keyed by 'name' are special and don't get converted.
-    expect(convertPythonicToTs({name: 'layer_name'})).toEqual({
-      name: 'layer_name'
-    });
-  });
-  it('dictionary keys are passed down the stack', () => {
-    const dict: PyJsonValue = {inbound_nodes: ['DoNotChange_Me', 0, null]};
-    expect(convertPythonicToTs(dict)).toEqual({
-      inboundNodes: ['DoNotChange_Me', 0, null]
-    });
-  });
-  // We promote certan fields to enums
-  it('enum promotion', () => {
-    expect(convertPythonicToTs({mode: 'fan_out'})).toEqual({mode: 'fanOut'});
-    expect(convertPythonicToTs({distribution: 'normal'})).toEqual({
-      distribution: 'normal'
-    });
-    expect(convertPythonicToTs({data_format: 'channels_last'})).toEqual({
-      dataFormat: 'channelsLast'
-    });
-    expect(convertPythonicToTs({padding: 'valid'})).toEqual({padding: 'valid'});
-  });
-});
-
-describe('convertTsToPythonic', () => {
-  it('primitives', () => {
-    expect(convertTsToPythonic(null)).toEqual(null);
-    expect(convertTsToPythonic(true)).toEqual(true);
-    expect(convertTsToPythonic(false)).toEqual(false);
-    expect(convertTsToPythonic(4)).toEqual(4);
-  });
-  it('strings w/o name tags', () => {
-    expect(convertTsToPythonic('abc')).toEqual('abc');
-    expect(convertTsToPythonic('ABC')).toEqual('abc');
-    expect(convertTsToPythonic('oneTwo')).toEqual('one_two');
-    expect(convertTsToPythonic('OneTwo')).toEqual('one_two');
-  });
-  it('simple arrays', () => {
-    expect(convertTsToPythonic([])).toEqual([]);
-    expect(convertTsToPythonic([null])).toEqual([null]);
-    expect(convertTsToPythonic(['oneTwo'])).toEqual(['one_two']);
-    expect(convertTsToPythonic([null, true, false, 4, 'abc'])).toEqual([
-      null, true, false, 4, 'abc'
-    ]);
-    expect(convertTsToPythonic([[[]]])).toEqual([[[]]]);
-  });
-  // We have to special case strings that are layer_names to not be converted
-  it('layer tuple (array) with key', () => {
-    for (const key of ['inboundNodes', 'inputLayers', 'outputLayers']) {
-      expect(convertTsToPythonic(['layerName', 'metaData', 0], key)).toEqual([
-        'layerName', 'meta_data', 0
-      ]);
-    }
-  });
-  it('dictionary', () => {
-    expect(convertTsToPythonic({})).toEqual({});
-    expect(convertTsToPythonic({key: null})).toEqual({key: null});
-    expect(convertTsToPythonic({keyOne: 4})).toEqual({key_one: 4});
-    expect(convertTsToPythonic({keyTwo: 'abcDef'})).toEqual({
-      key_two: 'abc_def'
-    });
-    expect(convertTsToPythonic({keyOne: true, keyTwo: false}))
-        .toEqual({key_one: true, key_two: false});
-    // values keyed by 'name' are special and don't get converted.
-    expect(convertTsToPythonic({name: 'layerName'})).toEqual({
-      name: 'layerName'
-    });
-  });
-  it('dictionary keys are passed down the stack', () => {
-    const dict: serialization.ConfigDictValue = {
-      inboundNodes: ['DoNotChange_Me', 0, null]
-    };
-    expect(convertTsToPythonic(dict)).toEqual({
-      inbound_nodes: ['DoNotChange_Me', 0, null]
-    });
-  });
-  // We need to stringify our enums
-  it('enum promotion', () => {
-    expect(convertTsToPythonic({mode: 'fanOut'})).toEqual({mode: 'fan_out'});
-    expect(convertTsToPythonic({distribution: 'normal'})).toEqual({
-      distribution: 'normal'
-    });
-    expect(convertTsToPythonic({dataFormat: 'channelsLast'})).toEqual({
-      data_format: 'channels_last'
-    });
-    expect(convertTsToPythonic({padding: 'valid'})).toEqual({padding: 'valid'});
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/utils/test_utils.ts b/tfjs-master/tfjs-layers/src/utils/test_utils.ts
deleted file mode 100644
index 29b691848..000000000
--- a/tfjs-master/tfjs-layers/src/utils/test_utils.ts
+++ /dev/null
@@ -1,175 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Testing utilities.
- */
-
-import {memory, Tensor, test_util, util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {ALL_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {ValueError} from '../errors';
-
-/**
- * Expect values are close between a Tensor or number array.
- * @param actual
- * @param expected
- */
-export function expectTensorsClose(
-    actual: Tensor|number[], expected: Tensor|number[], epsilon?: number) {
-  if (actual == null) {
-    throw new ValueError(
-        'First argument to expectTensorsClose() is not defined.');
-  }
-  if (expected == null) {
-    throw new ValueError(
-        'Second argument to expectTensorsClose() is not defined.');
-  }
-  if (actual instanceof Tensor && expected instanceof Tensor) {
-    if (actual.dtype !== expected.dtype) {
-      throw new Error(
-          `Data types do not match. Actual: '${actual.dtype}'. ` +
-          `Expected: '${expected.dtype}'`);
-    }
-    if (!util.arraysEqual(actual.shape, expected.shape)) {
-      throw new Error(
-          `Shapes do not match. Actual: [${actual.shape}]. ` +
-          `Expected: [${expected.shape}].`);
-    }
-  }
-  const actualData = actual instanceof Tensor ? actual.dataSync() : actual;
-  const expectedData =
-      expected instanceof Tensor ? expected.dataSync() : expected;
-  test_util.expectArraysClose(actualData, expectedData, epsilon);
-}
-
-/**
- * Expect values are not close between a Tensor or number array.
- * @param t1
- * @param t2
- */
-export function expectTensorsNotClose(
-  t1: Tensor|number[], t2: Tensor|number[], epsilon?: number) {
-try {
-  expectTensorsClose(t1, t2, epsilon);
-} catch (error) {
-  return;
-}
-throw new Error('The two values are close at all elements.');
-}
-
-/**
- * Expect values in array are within a specified range, boundaries inclusive.
- * @param actual
- * @param expected
- */
-export function expectTensorsValuesInRange(
-    actual: Tensor, low: number, high: number) {
-  if (actual == null) {
-    throw new ValueError(
-        'First argument to expectTensorsClose() is not defined.');
-  }
-  test_util.expectValuesInRange(actual.dataSync(), low, high);
-}
-
-/**
- * Describe tests to be run on CPU and GPU.
- * @param testName
- * @param tests
- */
-export function describeMathCPUAndGPU(testName: string, tests: () => void) {
-  describeWithFlags(testName, ALL_ENVS, () => {
-    tests();
-  });
-}
-
-/**
- * Describe tests to be run on CPU and GPU WebGL2.
- * @param testName
- * @param tests
- */
-export function describeMathCPUAndWebGL2(testName: string, tests: () => void) {
-  describeWithFlags(
-      testName, {
-        predicate: testEnv =>
-            (testEnv.flags == null || testEnv.flags['WEBGL_VERSION'] === 2)
-      },
-      () => {
-        tests();
-      });
-}
-
-/**
- * Describe tests to be run on CPU only.
- * @param testName
- * @param tests
- */
-export function describeMathCPU(testName: string, tests: () => void) {
-  describeWithFlags(
-      testName, {predicate: testEnv => testEnv.backendName === 'cpu'}, () => {
-        tests();
-      });
-}
-
-/**
- * Describe tests to be run on GPU only.
- * @param testName
- * @param tests
- */
-export function describeMathGPU(testName: string, tests: () => void) {
-  describeWithFlags(
-      testName, {predicate: testEnv => testEnv.backendName === 'webgl'}, () => {
-        tests();
-      });
-}
-
-/**
- * Describe tests to be run on WebGL2 GPU only.
- * @param testName
- * @param tests
- */
-export function describeMathWebGL2(testName: string, tests: () => void) {
-  describeWithFlags(
-      testName, {
-        predicate: testEnv => testEnv.backendName === 'webgl' &&
-            (testEnv.flags == null || testEnv.flags['WEBGL_VERSION'] === 2)
-
-      },
-      () => {
-        tests();
-      });
-}
-
-/**
- * Check that a function only generates the expected number of new Tensors.
- *
- * The test  function is called twice, once to prime any regular constants and
- * once to ensure that additional copies aren't created/tensors aren't leaked.
- *
- * @param testFunc A fully curried (zero arg) version of the function to test.
- * @param numNewTensors The expected number of new Tensors that should exist.
- */
-export function expectNoLeakedTensors(
-    // tslint:disable-next-line:no-any
-    testFunc: () => any, numNewTensors: number) {
-  testFunc();
-  const numTensorsBefore = memory().numTensors;
-  testFunc();
-  const numTensorsAfter = memory().numTensors;
-  const actualNewTensors = numTensorsAfter - numTensorsBefore;
-  if (actualNewTensors !== numNewTensors) {
-    throw new ValueError(
-        `Created an unexpected number of new ` +
-        `Tensors.  Expected: ${numNewTensors}, created : ${
-            actualNewTensors}. ` +
-        `Please investigate the discrepency and/or use tidy.`);
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/utils/types_utils.ts b/tfjs-master/tfjs-layers/src/utils/types_utils.ts
deleted file mode 100644
index 1d690a7e8..000000000
--- a/tfjs-master/tfjs-layers/src/utils/types_utils.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/* Original source: utils/generic_utils.py */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-import {ValueError} from '../errors';
-import {Shape} from '../keras_format/common';
-// tslint:enable
-
-/**
- * Determine whether the input is an Array of Shapes.
- */
-export function isArrayOfShapes(x: Shape|Shape[]): boolean {
-  return Array.isArray(x) && Array.isArray(x[0]);
-}
-
-/**
- * Special case of normalizing shapes to lists.
- *
- * @param x A shape or list of shapes to normalize into a list of Shapes.
- * @return A list of Shapes.
- */
-export function normalizeShapeList(x: Shape|Shape[]): Shape[] {
-  if (x.length === 0) {
-    return [];
-  }
-  if (!Array.isArray(x[0])) {
-    return [x] as Shape[];
-  }
-  return x as Shape[];
-}
-
-/**
- * Helper function to obtain exactly one Tensor.
- * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.
- * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.
- * @throws ValueError: If `xs` is an `Array` and its length is not 1.
- */
-export function getExactlyOneTensor(xs: Tensor|Tensor[]): Tensor {
-  let x: Tensor;
-  if (Array.isArray(xs)) {
-    if (xs.length !== 1) {
-      throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);
-    }
-    x = xs[0];
-  } else {
-    x = xs;
-  }
-  return x;
-}
-
-/**
- * Helper function to obtain exactly on instance of Shape.
- *
- * @param shapes Input single `Shape` or Array of `Shape`s.
- * @returns If input is a single `Shape`, return it unchanged. If the input is
- *   an `Array` containing exactly one instance of `Shape`, return the instance.
- *   Otherwise, throw a `ValueError`.
- * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not
- *   1.
- */
-export function getExactlyOneShape(shapes: Shape|Shape[]): Shape {
-  if (Array.isArray(shapes) && Array.isArray(shapes[0])) {
-    if (shapes.length === 1) {
-      shapes = shapes as Shape[];
-      return shapes[0];
-    } else {
-      throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);
-    }
-  } else {
-    return shapes as Shape;
-  }
-}
diff --git a/tfjs-master/tfjs-layers/src/utils/types_utils_test.ts b/tfjs-master/tfjs-layers/src/utils/types_utils_test.ts
deleted file mode 100644
index 5fd7c43cc..000000000
--- a/tfjs-master/tfjs-layers/src/utils/types_utils_test.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import * as types_utils from './types_utils';
-
-describe('isArrayOfShapes', () => {
-  it('returns false for a single non-empty shape', () => {
-    expect(types_utils.isArrayOfShapes([1, 2, 3])).toEqual(false);
-  });
-  it('returns false for a single empty shape', () => {
-    expect(types_utils.isArrayOfShapes([])).toEqual(false);
-  });
-  it('returns true for an array of shapes', () => {
-    expect(types_utils.isArrayOfShapes([[1], [2, 3]])).toEqual(true);
-  });
-  it('returns true for an array of shapes that includes empty shapes', () => {
-    expect(types_utils.isArrayOfShapes([[], [2, 3]])).toEqual(true);
-    expect(types_utils.isArrayOfShapes([[]])).toEqual(true);
-    expect(types_utils.isArrayOfShapes([[], []])).toEqual(true);
-  });
-});
-
-describe('normalizeShapeList', () => {
-  it('returns an empty list if an empty list is passed in.', () => {
-    expect(types_utils.normalizeShapeList([])).toEqual([]);
-  });
-
-  it('returns a list of shapes if a single shape is passed in.', () => {
-    expect(types_utils.normalizeShapeList([1])).toEqual([[1]]);
-  });
-
-  it('returns a list of shapes if an empty shape is passed in.', () => {
-    expect(types_utils.normalizeShapeList([[]])).toEqual([[]]);
-  });
-
-  it('returns a list of shapes if a list of shapes is passed in.', () => {
-    expect(types_utils.normalizeShapeList([[1]])).toEqual([[1]]);
-  });
-});
-
-describe('getExactlyOneShape', () => {
-  it('single instance', () => {
-    expect(types_utils.getExactlyOneShape([1, 2, 3])).toEqual([1, 2, 3]);
-    expect(types_utils.getExactlyOneShape([null, 8])).toEqual([null, 8]);
-    expect(types_utils.getExactlyOneShape([])).toEqual([]);
-  });
-  it('Array of length 1', () => {
-    expect(types_utils.getExactlyOneShape([[1, 2]])).toEqual([1, 2]);
-    expect(types_utils.getExactlyOneShape([[]])).toEqual([]);
-  });
-  it('Array of length 2: ValueError', () => {
-    expect(() => types_utils.getExactlyOneShape([
-      [1], [2]
-    ])).toThrowError(/Expected exactly 1 Shape; got 2/);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/utils/variable_utils.ts b/tfjs-master/tfjs-layers/src/utils/variable_utils.ts
deleted file mode 100644
index 2bf690007..000000000
--- a/tfjs-master/tfjs-layers/src/utils/variable_utils.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {LayerVariable} from '../variables';
-
-/**
- * Count the elements in an Array of LayerVariables.
- *
- * @param weights: The LayerVariables of which the constituent numbers are to
- *   be counted.
- * @returns A count of the elements in all the LayerVariables
- */
-export function countParamsInWeights(weights: LayerVariable[]): number {
-  let count = 0;
-  for (const weight of weights) {
-    if (weight.shape.length === 0) {
-      count += 1;
-    } else {
-      count += weight.shape.reduce((a, b) => a * b);
-    }
-  }
-  return count;
-}
diff --git a/tfjs-master/tfjs-layers/src/utils/variable_utils_test.ts b/tfjs-master/tfjs-layers/src/utils/variable_utils_test.ts
deleted file mode 100644
index a76197d2a..000000000
--- a/tfjs-master/tfjs-layers/src/utils/variable_utils_test.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import {scalar, zeros} from '@tensorflow/tfjs-core';
-
-import {LayerVariable} from '../variables';
-
-import * as variable_utils from './variable_utils';
-
-describe('countParamsInWeights', () => {
-  it('Zero weights', () => {
-    expect(variable_utils.countParamsInWeights([])).toEqual(0);
-  });
-
-  it('One float32 weight', () => {
-    const weight1 = new LayerVariable(zeros([2, 3]));
-    expect(variable_utils.countParamsInWeights([weight1])).toEqual(6);
-  });
-
-  it('One float32 scalar weight', () => {
-    const weight1 = new LayerVariable(scalar(42));
-    expect(variable_utils.countParamsInWeights([weight1])).toEqual(1);
-  });
-
-  it('One int32 weight', () => {
-    const weight1 = new LayerVariable(zeros([1, 3, 4], 'int32'), 'int32');
-    expect(variable_utils.countParamsInWeights([weight1])).toEqual(12);
-  });
-
-  it('Two weights, mixed types and shapes', () => {
-    const weight1 = new LayerVariable(scalar(42));
-    const weight2 = new LayerVariable(zeros([2, 3]));
-    const weight3 = new LayerVariable(zeros([1, 3, 4], 'int32'), 'int32');
-    expect(variable_utils.countParamsInWeights([
-      weight1, weight2, weight3
-    ])).toEqual(19);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/variables.ts b/tfjs-master/tfjs-layers/src/variables.ts
deleted file mode 100644
index b1ddc6489..000000000
--- a/tfjs-master/tfjs-layers/src/variables.ts
+++ /dev/null
@@ -1,359 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {DataType, Tensor, variableGrads} from '@tensorflow/tfjs-core';
-
-import {getNextUniqueTensorId} from './backend/state';
-import {getScopedTensorName, getUniqueTensorName} from './common';
-import {Constraint} from './constraints';
-import {NotImplementedError} from './errors';
-import {Shape} from './keras_format/common';
-import {HasShape} from './types';
-
-const DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';
-
-/**
- * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a
- * dtype and shape, but its value is mutable.  The value is itself represented
- * as a`tf.Tensor`, and can be read with the `read()` method and updated with
- * the `write()` method.
- */
-export class LayerVariable {
-  readonly dtype: DataType;
-  readonly shape: Shape;
-
-  readonly id: number;
-  // The fully scoped name of this Variable, including a unique suffix if needed
-  readonly name: string;
-  // The originally requested fully scoped name of this Variable, not including
-  // any unique suffix.  This may be needed when restoring weights because this
-  // original name is used as a key.
-  readonly originalName: string;
-  private trainable_: boolean;
-
-  protected readonly val: tfc.Variable;
-  readonly constraint: Constraint;
-  /**
-   * Construct Variable from a `tf.Tensor`.
-   *
-   * If not explicitly named, the Variable will be given a name with the
-   * prefix 'Variable'. Variable names are unique. In the case of name
-   * collision, suffixies '_<num>' will be added to the name.
-   *
-   * @param val Initial value of the Variable.
-   * @param name Name of the variable. If `null` or `undefined` is provided, it
-   *   will default a name with the prefix 'Variable'.
-   * @param constraint Optional, projection function to be applied to the
-   * variable after optimize updates
-   * @throws ValueError if `name` is `null` or `undefined`.
-   */
-  constructor(
-      val: Tensor, dtype: DataType = 'float32',
-      name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true,
-      constraint: Constraint = null) {
-    this.dtype = dtype == null ? 'float32' : dtype;
-    this.shape = val.shape;
-    this.id = getNextUniqueTensorId();
-
-    name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;
-    this.originalName = getScopedTensorName(name);
-    this.name = getUniqueTensorName(this.originalName);
-
-    this.trainable_ = trainable;
-    this.constraint = constraint;
-
-    this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);
-  }
-
-  /**
-   * Get a snapshot of the Variable's value.
-   *
-   * The returned value is a snapshot of the Variable's value at the time of
-   * the invocation. Future mutations in the value of the tensor will only
-   * be reflected by future calls to this method.
-   */
-  read(): Tensor {
-    this.assertNotDisposed();
-    return this.val;
-  }
-
-  /**
-   * Update the value of the Variable.
-   *
-   * @param newVal: The new value to update to. Must be consistent with the
-   *   dtype and shape of the Variable.
-   * @return This Variable.
-   */
-  write(newVal: Tensor) {
-    // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.
-    this.assertNotDisposed();
-    checkShapesMatch(this.val, newVal);
-    // Skip updating if this is the exact same tensor.
-    if (this.val.id !== newVal.id) {
-      this.val.assign(newVal);
-      if (this.constraint != null) {
-        this.val.assign(this.constraint.apply(this.val));
-      }
-    }
-    return this;
-  }
-
-  /**
-   * Dispose this LayersVariable instance from memory.
-   */
-  dispose(): void {
-    this.assertNotDisposed();
-    this.val.dispose();
-  }
-
-  protected assertNotDisposed(): void {
-    if (this.val.isDisposed) {
-      throw new Error(`LayersVariable ${this.name} is already disposed.`);
-    }
-  }
-
-  get trainable(): boolean {
-    return this.trainable_;
-  }
-
-  set trainable(trainable: boolean) {
-    this.trainable_ = trainable;
-    this.val.trainable = trainable;
-  }
-}
-
-function checkShapesMatch(x: HasShape, y: HasShape): void {
-  if (x.shape.toString() !== y.shape.toString()) {
-    throw new Error(
-        'Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +
-        JSON.stringify(y.shape));
-  }
-}
-
-/**
- * Create a Variable.
- * @param x The initial value of the `Variable`.
- * @param dtype optional, the type of the variable.
- * @param name optional, the name of the variable, default provided by
- * Variable.
- * @param constraint optional, a constraint to be applied after every update.
- * @return The newly instantiated `Variable`.
- */
-export function variable(
-    x: Tensor, dtype?: DataType, name?: string,
-    constraint?: Constraint): LayerVariable {
-  return new LayerVariable(x, dtype, name, true, constraint);
-}
-
-/**
- * Instantiates an all-zeros Variable and returns it.
- *
- * @param shape Shape of the tensor.
- * @param dtype DType of the tensor.
- * @param name Name of the tensor.
- * @return An all-zero Variable.
- */
-export function zerosVariable(
-    shape: Shape, dtype?: DataType, name?: string): LayerVariable {
-  // TODO(cais): Implement logic for dtype.
-  return new LayerVariable(tfc.zeros(shape), dtype, name);
-}
-
-/**
- * Instantiates an all-zeros tensor of the same shape as another tensor.
- *
- * @param x The other tensor.
- * @param dtype DType of the tensor.
- * @param name Name of the tensor.
- * @return A newly instantiated Variable.
- */
-export function zerosLike(
-    x: Tensor, dtype?: DataType, name?: string): LayerVariable {
-  return new LayerVariable(tfc.zerosLike(x), dtype, name);
-}
-
-/**
- * Instantiates an all-ones tensor and returns it.
- *
- * @param shape Shape of the tensor.
- * @param dtype DType of the tensor.
- * @param name Name of the tensor.
- * @return An all-ones Variable.
- */
-export function onesVariable(
-    shape: Shape, dtype?: DataType, name?: string): LayerVariable {
-  // TODO(cais): Implement logic for dtype.
-  const allocated = tfc.ones(shape);
-  return new LayerVariable(allocated, dtype, name);
-}
-
-/**
- * Instantiates an all-ones tensor of the same shape as another tensor.
- *
- * @param x The other tensor.
- * @param dtype DType of the tensor.
- * @param name Name of the tensor.
- * @return A newly instantiated Variable.
- */
-export function onesLike(
-    x: Tensor, dtype?: DataType, name?: string): LayerVariable {
-  const allocated = tfc.onesLike(x);
-  return new LayerVariable(allocated, dtype, name);
-}
-
-/**
- * Instantiate an identity matrix and returns it, as a Variable
- *
- * @param size Number of rows/columns.
- * @param dtype Data type of returned Variable.
- * @param name Name of returned Variable.
- * @return A Variable, an identity matrix.
- */
-export function eyeVariable(
-    size: number, dtype?: DataType, name?: string): LayerVariable {
-  return new LayerVariable(tfc.eye(size), dtype, name);
-}
-
-/**
- * Get a Variable with uniform distribution of values.
- * @param shape Shape of the tensor.
- * @param minval Lower bound of the uniform distribution.
- * @param maxval Upper bound of the uniform distribution.
- * @param dtype
- * @param seed
- * @param name Optional name.
- * @return The uniform-random Variable.
- */
-export function randomUniformVariable(
-    shape: Shape, minval: number, maxval: number, dtype?: DataType,
-    seed?: number, name = 'randomUniform'): LayerVariable {
-  return new LayerVariable(
-      tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);
-}
-
-/**
- * Get a Variable with truncated-normal distribution of values.
- * @param shape Shape of the tensor.
- * @param mean mean value of the normal distribution.
- * @param stddev standard deviation of the normal distribution.
- * @param dtype
- * @param seed
- * @param name Optional name.
- * @return The truncated-normal-random Variable.
- */
-export function truncatedNormalVariable(
-    shape: Shape, mean = 0.0, stddev = 1.0, dtype?: DataType, seed?: number,
-    name = 'truncatedNormal'): LayerVariable {
-  // TODO(cais): Implement logic for dtype and seed once they are supported
-  // by deeplearn.js.
-  dtype = dtype || 'float32';
-  if (dtype !== 'float32' && dtype !== 'int32') {
-    throw new NotImplementedError(
-        `randomNormal does not support dType ${dtype}.`);
-  }
-  return new LayerVariable(
-      tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);
-}
-/**
- * Get a Variable with normal distribution of values.
- * @param shape Shape of the tensor.
- * @param mean mean value of the normal distribution.
- * @param stddev standard deviation of the normal distribution.
- * @param dtype
- * @param seed
- * @param name Optional name.
- * @return The truncated-normal-random Variable.
- */
-export function randomNormalVariable(
-    shape: Shape, mean = 0.0, stddev = 1.0, dtype?: DataType, seed?: number,
-    name = 'randomNormal'): LayerVariable {
-  dtype = dtype || 'float32';
-  if (dtype !== 'float32' && dtype !== 'int32') {
-    throw new NotImplementedError(
-        `randomNormalVariable does not support dType ${dtype}.`);
-  }
-  return new LayerVariable(
-      tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);
-}
-
-/**
- * Update the value of a Variable.
- * @param x The Variable to be updated.
- * @param xNew The new value to update to.
- * @return The Variable updated.
- */
-export function update(x: LayerVariable, xNew: Tensor): LayerVariable {
-  return x.write(xNew);
-}
-
-/**
- * Update the value of a Variable by adding an increment.
- * @param x The Variable to be updated.
- * @param increment The incrment to add to `x`.
- * @return The Variable updated.
- */
-export function updateAdd(x: LayerVariable, increment: Tensor): LayerVariable {
-  return x.write(tfc.add(x.read(), increment));
-}
-
-/**
- * Update the value of a Variable by subtracting a decrement.
- * @param x The Variable to be updated.
- * @param decrement The decrement to subtract from `x`.
- * @return The Variable updated.
- */
-export function updateSub(x: LayerVariable, decrement: Tensor): LayerVariable {
-  return x.write(tfc.sub(x.read(), decrement));
-}
-
-/**
- * Get the values of an array of Variables.
- *
- * @param tensors An `Array` of `Variable`s to get the values of.
- * @return The values of the inputs, as an `Array` of`tf.Tensor`s.
- */
-export function batchGetValue(xs: LayerVariable[]): Tensor[] {
-  return xs.map(x => x.read());
-}
-
-/**
- * Update the value of multiple Variables at once.
- *
- * @param variablesAndValues An `Array`, each element is of type
- *   [Variable, Tensor]. The first item is the
- *   `Variable` of which the value is to be updated. The second item
- *   carries the new value.
- */
-export function batchSetValue(
-    variablesAndValues: Array<[LayerVariable, Tensor]>): void {
-  variablesAndValues.forEach(variableAndValue => {
-    const variable: LayerVariable = variableAndValue[0];
-    variable.write(variableAndValue[1]);
-  });
-}
-
-/**
- * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.
- * @param lossFn A function which returns a Scalar to be used as the function
- *   value (i.e., numerator) for differentiation.
- * @param variables List of variables to be used as the independent variables
- *   (i.e., denominator) for differentiation.
- * @returns An Array of gradients tensors.
- */
-export function gradients(
-    lossFn: () => tfc.Scalar, variables: LayerVariable[]): Tensor[] {
-  // TODO(cais): The return type signature can be simplified if deeplearn makes
-  //   the corresponding type public.
-  const variableList =
-      variables.map(variable => variable.read() as tfc.Variable);
-  const valudAndGrads = variableGrads(lossFn, variableList);
-  return variables.map(variable => valudAndGrads.grads[variable.name]);
-}
diff --git a/tfjs-master/tfjs-layers/src/variables_test.ts b/tfjs-master/tfjs-layers/src/variables_test.ts
deleted file mode 100644
index 8915a690d..000000000
--- a/tfjs-master/tfjs-layers/src/variables_test.ts
+++ /dev/null
@@ -1,457 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-/**
- * Unit tests for LayerVariables.
- */
-
-import * as tfc from '@tensorflow/tfjs-core';
-import {randomUniform, scalar, tensor1d, tensor2d, zeros} from '@tensorflow/tfjs-core';
-
-import * as K from './backend/tfjs_backend';
-import {nameScope} from './common';
-import * as tfl from './index';
-import {describeMathCPU, describeMathCPUAndGPU, expectTensorsClose} from './utils/test_utils';
-import * as V from './variables';
-
-/**
- * Unit tests for Variable.
- */
-describeMathCPU('Variable', () => {
-  it('Variable constructor: no explicit name', () => {
-    const v1 = new V.LayerVariable(zeros([2]));
-    expect(v1.name.indexOf('Variable')).toEqual(0);
-    expect(v1.dtype).toEqual('float32');
-    expect(v1.shape).toEqual([2]);
-    expect(v1.trainable).toEqual(true);
-    expect(v1.read().dataSync()).toEqual(new Float32Array([0, 0]));
-
-    const v2 = new V.LayerVariable(zeros([2, 2]));
-    expect(v2.name.indexOf('Variable')).toEqual(0);
-    expect(v2.dtype).toEqual('float32');
-    expect(v2.shape).toEqual([2, 2]);
-    expect(v2.read().dataSync()).toEqual(new Float32Array([0, 0, 0, 0]));
-
-    expect(v2.name === v1.name).toBe(false);
-  });
-
-  it('Variable constructor: explicit name', () => {
-    const v1 = new V.LayerVariable(zeros([]), undefined, 'foo');
-    expect(v1.name.indexOf('foo')).toEqual(0);
-    expect(v1.dtype).toEqual('float32');
-    expect(v1.shape).toEqual([]);
-    expect(v1.trainable).toEqual(true);
-    expect(v1.read().dataSync()).toEqual(new Float32Array([0]));
-
-    const v2 = new V.LayerVariable(zeros([2, 2, 1]));
-    expect(v1.name.indexOf('foo')).toEqual(0);
-    expect(v2.dtype).toEqual('float32');
-    expect(v2.shape).toEqual([2, 2, 1]);
-    expect(v2.read().dataSync()).toEqual(new Float32Array([0, 0, 0, 0]));
-    expect(v2.name.length).toBeGreaterThan(0);
-
-    expect(v2.name === v1.name).toBe(false);
-  });
-
-  it('Variable constructor: explicit name with name scope', () => {
-    let v1: V.LayerVariable;
-    nameScope('barScope', () => {
-      nameScope('bazScope', () => {
-        v1 = new V.LayerVariable(scalar(0), undefined, 'foo');
-      });
-    });
-    expect(v1.name.indexOf('barScope/bazScope/foo')).toEqual(0);
-    expect(v1.dtype).toEqual('float32');
-    expect(v1.shape).toEqual([]);
-    expect(v1.trainable).toEqual(true);
-    expect(v1.read().dataSync()).toEqual(new Float32Array([0]));
-  });
-
-  it('Variable trainable property', () => {
-    const v1 = new V.LayerVariable(zeros([]), null, 'foo', false);
-    expect(v1.trainable).toEqual(false);
-  });
-
-  it('Variable works if name is null or undefined', () => {
-    expect((new V.LayerVariable(zeros([]), null)).name.indexOf('Variable'))
-        .toEqual(0);
-    expect((new V.LayerVariable(zeros([]), undefined)).name.indexOf('Variable'))
-        .toEqual(0);
-  });
-
-  it('int32 dtype', () => {
-    expect(new V.LayerVariable(zeros([]), 'int32').dtype).toEqual('int32');
-  });
-
-  it('bool dtype', () => {
-    expect(new V.LayerVariable(zeros([]), 'bool').dtype).toEqual('bool');
-  });
-
-  it('Read value', () => {
-    const v1 = new V.LayerVariable(scalar(10), null, 'foo');
-    expect(v1.read().dataSync()).toEqual(new Float32Array([10]));
-  });
-
-  it('Update value: Compatible shape', () => {
-    const v = new V.LayerVariable(tensor1d([10, -10]), null, 'bar');
-    expect(v.name.indexOf('bar')).toEqual(0);
-    expect(v.shape).toEqual([2]);
-    expect(v.read().dataSync()).toEqual(new Float32Array([10, -10]));
-
-    v.write(tensor1d([10, 50]));
-    expect(v.name.indexOf('bar')).toEqual(0);
-    expect(v.shape).toEqual([2]);
-    expect(v.read().dataSync()).toEqual(new Float32Array([10, 50]));
-  });
-
-  it('Update value: w/ constraint', () => {
-    const v = new V.LayerVariable(
-        tensor1d([10, -10]), null, 'bar', true, tfl.constraints.nonNeg());
-
-    v.write(tensor1d([-10, 10]));
-    expect(v.read().dataSync()).toEqual(new Float32Array([0, 10]));
-  });
-
-  it('Update value: Incompatible shape', () => {
-    const v = new V.LayerVariable(zeros([2, 2]), null, 'qux');
-    expect(() => {
-      v.write(zeros([4]));
-    }).toThrowError();
-  });
-
-  it('Generates unique ID', () => {
-    const v1 = new V.LayerVariable(scalar(1), null, 'foo');
-    const v2 = new V.LayerVariable(scalar(1), null, 'foo');
-    expect(v1.id).not.toEqual(v2.id);
-  });
-
-  it('Generates unique IDs for Tensors and Variables', () => {
-    const v1 = scalar(1);
-    const v2 = new V.LayerVariable(scalar(1), null, 'foo');
-    expect(v1.id).not.toEqual(v2.id);
-  });
-
-  it('dispose() frees memory', () => {
-    const v = new V.LayerVariable(tensor1d([10, -10]), null, 'gralk');
-    const numTensors0 = tfc.memory().numTensors;
-    v.dispose();
-    expect(tfc.memory().numTensors).toEqual(numTensors0 - 1);
-  });
-
-  it('Disposing LayersVariable twices leads to Error', () => {
-    const v = new V.LayerVariable(tensor1d([10, -10]), null, 'gralk');
-    v.dispose();
-    expect(() => v.dispose()).toThrowError(/LayersVariable .*gralk.* disposed/);
-  });
-
-  it('read() after dispose() leads to Error', () => {
-    const v = new V.LayerVariable(tensor1d([10, -10]), null, 'gralk');
-    v.dispose();
-    expect(() => v.read()).toThrowError(/LayersVariable .*gralk.* disposed/);
-  });
-
-  it('write() after dispose() leads to Error', () => {
-    const v = new V.LayerVariable(tensor1d([10, -10]), null, 'gralk');
-    v.dispose();
-    expect(() => v.write(tensor1d([20, -20])))
-        .toThrowError(/LayersVariable .*gralk.* disposed/);
-  });
-
-  it('Setting trainable sets the trainable of underlying tfc.Variable', () => {
-    const v1 = new V.LayerVariable(scalar(1), null, 'foo', true);
-    // tslint:disable-next-line:no-any
-    const coreVariable = (v1 as any).val as tfc.Variable;
-    expect(coreVariable.trainable).toEqual(true);
-    v1.trainable = false;
-    expect(coreVariable.trainable).toEqual(false);
-    v1.trainable = true;
-    expect(coreVariable.trainable).toEqual(true);
-  });
-});
-
-describeMathCPUAndGPU('Create Variable', () => {
-  it('From Tensor, no explicit name', () => {
-    const v = V.variable(zeros([2, 2]));
-    expect(v.name.indexOf('Variable')).toEqual(0);
-    expect(v.shape).toEqual([2, 2]);
-    expect(v.read().dataSync()).toEqual(new Float32Array([0, 0, 0, 0]));
-  });
-
-  it('From Tensor, no explicit name', () => {
-    const v = V.variable(zeros([3]));
-    expect(v.name.indexOf('Variable')).toEqual(0);
-    expect(v.shape).toEqual([3]);
-    expect(v.read().dataSync()).toEqual(new Float32Array([0, 0, 0]));
-  });
-
-  it('From Tensor, explicit name', () => {
-    const v = V.variable(zeros([3]), undefined, 'Var1');
-    expect(v.name.indexOf('Var1')).toEqual(0);
-    expect(v.shape).toEqual([3]);
-    expect(v.read().dataSync()).toEqual(new Float32Array([0, 0, 0]));
-  });
-});
-
-describeMathCPUAndGPU('ZerosVariable', () => {
-  it('Scalar', () => {
-    const s = V.zerosVariable([], 'float32', 'Scalar');
-    expect(s.name.indexOf('Scalar')).toEqual(0);
-    expect(s.read().shape).toEqual([]);
-    expect(s.read().dataSync()).toEqual(new Float32Array([0]));
-  });
-
-  it('Vector', () => {
-    const v = V.zerosVariable([3], 'float32', 'Vector');
-    expect(v.name.indexOf('Vector')).toEqual(0);
-    expect(v.read().shape).toEqual([3]);
-    expect(v.read().dataSync()).toEqual(new Float32Array([0, 0, 0]));
-  });
-
-  it('Matrix', () => {
-    const m = V.zerosVariable([2, 2], 'float32', 'Matrix');
-    expect(m.name.indexOf('Matrix')).toEqual(0);
-    expect(m.read().shape).toEqual([2, 2]);
-    expect(m.read().dataSync()).toEqual(new Float32Array([0, 0, 0, 0]));
-  });
-
-  it('3D', () => {
-    const t = V.zerosVariable([2, 2, 2], 'float32', 'Tertiary');
-    expect(t.name.indexOf('Tertiary')).toEqual(0);
-    expect(t.read().shape).toEqual([2, 2, 2]);
-    expect(t.read().dataSync()).toEqual(new Float32Array([
-      0, 0, 0, 0, 0, 0, 0, 0
-    ]));
-  });
-
-  it('4D', () => {
-    const q = V.zerosVariable([1, 2, 1, 3], 'float32', 'Quaternary');
-    expect(q.name.indexOf('Quaternary')).toEqual(0);
-    expect(q.read().shape).toEqual([1, 2, 1, 3]);
-    expect(q.read().dataSync()).toEqual(new Float32Array([0, 0, 0, 0, 0, 0]));
-  });
-});
-
-describeMathCPUAndGPU('OnesVariable', () => {
-  it('Scalar', () => {
-    const s = V.onesVariable([], 'float32', 'Scalar');
-    expect(s.name.indexOf('Scalar')).toEqual(0);
-    expect(s.read().shape).toEqual([]);
-    expect(s.read().dataSync()).toEqual(new Float32Array([1]));
-  });
-  it('Vector', () => {
-    const v = V.onesVariable([3], 'float32', 'Vector');
-    expect(v.name.indexOf('Vector')).toEqual(0);
-    expect(v.read().shape).toEqual([3]);
-    expect(v.read().dataSync()).toEqual(new Float32Array([1, 1, 1]));
-  });
-  it('Matrix', () => {
-    const m = V.onesVariable([2, 2], 'float32', 'Matrix');
-    expect(m.name.indexOf('Matrix')).toEqual(0);
-    expect(m.read().shape).toEqual([2, 2]);
-    expect(m.read().dataSync()).toEqual(new Float32Array([1, 1, 1, 1]));
-  });
-  it('3D', () => {
-    const t = V.onesVariable([2, 2, 2], 'float32', 'Tertiary');
-    expect(t.name.indexOf('Tertiary')).toEqual(0);
-    expect(t.read().shape).toEqual([2, 2, 2]);
-    expect(t.read().dataSync()).toEqual(new Float32Array([
-      1, 1, 1, 1, 1, 1, 1, 1
-    ]));
-  });
-  it('4D', () => {
-    const q = V.onesVariable([1, 2, 1, 3], 'float32', 'Quaternary');
-    expect(q.name.indexOf('Quaternary')).toEqual(0);
-    expect(q.read().shape).toEqual([1, 2, 1, 3]);
-    expect(q.read().dataSync()).toEqual(new Float32Array([1, 1, 1, 1, 1, 1]));
-  });
-});
-
-describeMathCPUAndGPU('ZerosLike', () => {
-  it('Scalar', () => {
-    const s = V.zerosLike(randomUniform([], -10, 10));
-    expect(s.read().shape).toEqual([]);
-    expect(s.read().dataSync()).toEqual(new Float32Array([0]));
-  });
-
-  it('Vector', () => {
-    const v = V.zerosLike(randomUniform([3], -10, 10));
-    expect(v.read().shape).toEqual([3]);
-    expect(v.read().dataSync()).toEqual(new Float32Array([0, 0, 0]));
-  });
-
-  it('Matrix', () => {
-    const m = V.zerosLike(randomUniform([2, 2], -10, 10));
-    expect(m.read().shape).toEqual([2, 2]);
-    expect(m.read().dataSync()).toEqual(new Float32Array([0, 0, 0, 0]));
-  });
-
-  it('3D', () => {
-    const t = V.zerosLike(randomUniform([2, 2, 2], -10, 10));
-    expect(t.read().shape).toEqual([2, 2, 2]);
-    expect(t.read().dataSync()).toEqual(new Float32Array([
-      0, 0, 0, 0, 0, 0, 0, 0
-    ]));
-  });
-
-  it('4D', () => {
-    const q = V.zerosLike(randomUniform([1, 2, 1, 3], -10, 10));
-    expect(q.read().shape).toEqual([1, 2, 1, 3]);
-    expect(q.read().dataSync()).toEqual(new Float32Array([0, 0, 0, 0, 0, 0]));
-  });
-});
-
-describeMathCPUAndGPU('OnesLike', () => {
-  it('Scalar', () => {
-    const s = V.onesLike(randomUniform([], -10, 10));
-    expect(s.read().shape).toEqual([]);
-    expect(s.read().dataSync()).toEqual(new Float32Array([1]));
-  });
-
-  it('Vector', () => {
-    const v = V.onesLike(randomUniform([3], -10, 10));
-    expect(v.read().shape).toEqual([3]);
-    expect(v.read().dataSync()).toEqual(new Float32Array([1, 1, 1]));
-  });
-
-  it('Matrix', () => {
-    const m = V.onesLike(randomUniform([2, 2], -10, 10));
-    expect(m.read().shape).toEqual([2, 2]);
-    expect(m.read().dataSync()).toEqual(new Float32Array([1, 1, 1, 1]));
-  });
-
-  it('3D', () => {
-    const t = V.onesLike(randomUniform([2, 2, 2], -10, 10));
-    expect(t.read().shape).toEqual([2, 2, 2]);
-    expect(t.read().dataSync()).toEqual(new Float32Array([
-      1, 1, 1, 1, 1, 1, 1, 1
-    ]));
-  });
-
-  it('4D', () => {
-    const q = V.onesLike(randomUniform([1, 2, 1, 3], -10, 10));
-    expect(q.read().shape).toEqual([1, 2, 1, 3]);
-    expect(q.read().dataSync()).toEqual(new Float32Array([1, 1, 1, 1, 1, 1]));
-  });
-});
-
-describeMathCPUAndGPU('eye (I-matrix builder)', () => {
-  it('Variable Zero sized 2D matrix works', () => {
-    const v = V.eyeVariable(0);
-    expect(v.shape).toEqual([0, 0]);
-  });
-  it('Variable 1 sized 2D matrix', () => {
-    const I = V.eyeVariable(1);
-    expect(I.shape).toEqual([1, 1]);
-    expect(I.read().dataSync()).toEqual(new Float32Array([1]));
-  });
-  it('Variable 2 sized 2D matrix', () => {
-    const I = V.eyeVariable(2);
-    expect(I.shape).toEqual([2, 2]);
-    expect(I.read().dataSync()).toEqual(new Float32Array([1, 0, 0, 1]));
-  });
-});
-
-describeMathCPUAndGPU('Variable update', () => {
-  it('Update', () => {
-    const v = new V.LayerVariable(scalar(10.0));
-    V.update(v, scalar(20.0));
-    expectTensorsClose(v.read(), scalar(20.0));
-  });
-  it('Update: Incompatible shape', () => {
-    const v = new V.LayerVariable(tensor1d([10.0, 20.0]));
-    const x = tensor1d([10.0, 20.0, 30.0]);
-    expect(() => V.update(v, x)).toThrowError();
-  });
-  it('UpdateAdd', () => {
-    const v = new V.LayerVariable(scalar(10.0));
-    V.updateAdd(v, scalar(20.0));
-    expectTensorsClose(v.read(), scalar(30.0));
-  });
-  it('UpdateAdd: Incompatible shape', () => {
-    const v = new V.LayerVariable(tensor1d([10.0, 20.0]));
-    const x = tensor1d([0.0, 10.0, 20.0]);
-    expect(() => V.updateAdd(v, x)).toThrowError();
-  });
-  it('UpdateSub', () => {
-    const v = new V.LayerVariable(scalar(10.0));
-    V.updateSub(v, scalar(20.0));
-    const vNew = v.read();
-    expectTensorsClose(vNew, scalar(-10.0));
-  });
-  it('UpdateSub: Incompatible shape', () => {
-    const v = new V.LayerVariable(tensor1d([10.0, 20.0]));
-    const x = tensor1d([0.0, 10.0, 20.0]);
-    expect(() => V.updateSub(v, x)).toThrowError();
-  });
-});
-
-describeMathCPUAndGPU('batchGetValue', () => {
-  it('Legnth-3 Array, Mixed Tensor and Variable', () => {
-    const v1 = V.variable(zeros([]));
-    const v2 = V.variable(zeros([2]));
-    const v3 = V.variable(zeros([2, 2]));
-    const values = V.batchGetValue([v1, v2, v3]);
-    expect(values.length).toEqual(3);
-    expect(values[0].shape).toEqual([]);
-    expect(values[0].dataSync()).toEqual(new Float32Array([0]));
-    expect(values[1].shape).toEqual([2]);
-    expect(values[1].dataSync()).toEqual(new Float32Array([0, 0]));
-    expect(values[2].shape).toEqual([2, 2]);
-    expect(values[2].dataSync()).toEqual(new Float32Array([0, 0, 0, 0]));
-  });
-});
-
-describeMathCPUAndGPU('batchSetValue', () => {
-  it('Update using Tensor values', () => {
-    const v1 = V.randomUniformVariable([2], 0, 1);
-    const v2 = V.randomUniformVariable([2, 2], 0, 1);
-    V.batchSetValue([[v1, zeros([2])], [v2, zeros([2, 2])]]);
-    expect(v1.shape).toEqual([2]);
-    expect(v1.read().dataSync()).toEqual(new Float32Array([0, 0]));
-    expect(v2.shape).toEqual([2, 2]);
-    expect(v2.read().dataSync()).toEqual(new Float32Array([0, 0, 0, 0]));
-  });
-
-  it('Update using Tensor values', () => {
-    const v1 = V.randomUniformVariable([], 0, 1);
-    const v2 = V.randomUniformVariable([2, 2, 1], 0, 1);
-    V.batchSetValue([[v1, zeros([])], [v2, zeros([2, 2, 1])]]);
-    expect(v1.shape).toEqual([]);
-    expect(v1.read().dataSync()).toEqual(new Float32Array([0]));
-    expect(v2.shape).toEqual([2, 2, 1]);
-    expect(v2.read().dataSync()).toEqual(new Float32Array([0, 0, 0, 0]));
-  });
-
-  it('Update empty Array', () => {
-    V.batchSetValue([]);
-  });
-});
-
-describeMathCPUAndGPU('gradients', () => {
-  it('Simple mean: 1 variable', () => {
-    const var1 = new V.LayerVariable(tfc.mul(scalar(2.0), tfc.ones([2, 2])));
-    const gradients = V.gradients(() => tfc.mean(var1.read()), [var1]);
-    expect(gradients.length).toEqual(1);
-    expectTensorsClose(
-        tensor2d([[0.25, 0.25], [0.25, 0.25]], [2, 2]), gradients[0]);
-  });
-  it('Simple matmul and mean: 2 variables', () => {
-    const var1 = new V.LayerVariable(tensor2d([[1, 0], [0, 0]], [2, 2]));
-    const var2 = new V.LayerVariable(tensor2d([[1, 0], [0, 1]], [2, 2]));
-    const gradients = V.gradients(
-        () => tfc.mean(K.dot(var1.read(), var2.read())), [var1, var2]);
-    expect(gradients.length).toEqual(2);
-    // d(loss) / d(var1).
-    expectTensorsClose(
-        tensor2d([[0.25, 0.25], [0.25, 0.25]], [2, 2]), gradients[0]);
-    // d(loss) / d(var2).
-    expectTensorsClose(tensor2d([[0.25, 0.25], [0, 0]], [2, 2]), gradients[1]);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/src/version.ts b/tfjs-master/tfjs-layers/src/version.ts
deleted file mode 100644
index 5fa574e7d..000000000
--- a/tfjs-master/tfjs-layers/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.0';
-export {version};
diff --git a/tfjs-master/tfjs-layers/src/version_test.ts b/tfjs-master/tfjs-layers/src/version_test.ts
deleted file mode 100644
index 9ea7f5653..000000000
--- a/tfjs-master/tfjs-layers/src/version_test.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC
- *
- * Use of this source code is governed by an MIT-style
- * license that can be found in the LICENSE file or at
- * https://opensource.org/licenses/MIT.
- * =============================================================================
- */
-
-// tslint:disable-next-line:no-require-imports
-import {version_layers} from './index';
-
-describe('tfjs-core version consistency', () => {
-  it('version.ts matches package version', () => {
-    // tslint:disable-next-line:no-require-imports
-    const expected = require('../package.json').version;
-    expect(version_layers).toBe(expected);
-  });
-});
diff --git a/tfjs-master/tfjs-layers/tools/clang_format_ts.sh b/tfjs-master/tfjs-layers/tools/clang_format_ts.sh
deleted file mode 100644
index 37afb7823..000000000
--- a/tfjs-master/tfjs-layers/tools/clang_format_ts.sh
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/bin/bash
-
-# Copyright 2018 Google LLC
-#
-# Use of this source code is governed by an MIT-style
-# license that can be found in the LICENSE file or at
-# https://opensource.org/licenses/MIT.
-# ==============================================================================
-
-
-# This script applies google-style clang format on all TypeScript (.ts) files
-# within a certain scope.
-#
-# Usage examples:
-# 1. Format all .ts files touched by this change (unstaged or staged in git).
-#   clang_format_ts.sh
-#
-# 2. Format all .ts files under the source tree.
-#   clang_format_ts.sh -a
-#
-# 3. Format specific files.
-#   clang_format_ts.sh src/types.ts
-
-set -e
-
-FILE_SCOPE=""
-
-if [[ "$#" -gt 0 ]]; then
-  while true; do
-    if [[ -z "$1" ]]; then
-      break
-    fi
-    if [[ "$1" == "-a" ]]; then
-      if [[ -z "${FILE_SCOPE}" ]]; then
-        FILE_SCOPE="__all__"
-      else
-        echo "ERROR: -a flag should not be used with file names"
-        exit 1
-      fi
-    else
-      if [[ "${FILE_SCOPE}" != "__all__" ]]; then
-        FILE_SCOPE="${FILE_SCOPE} $1"
-      else
-        echo "ERROR: -a flag should not be used with file names"
-        exit 1
-      fi
-    fi
-    shift
-  done
-else
-  FILE_SCOPE="__touched__"
-fi
-
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-CLANG_FORMAT_PREFIX="${SCRIPT_DIR}/../node_modules/.bin/clang-format -i --verbose --style=google"
-if [[ "${FILE_SCOPE}" == "__touched__" ]]; then
-  TOUCHED_TS_FILES="$(git status --porcelain | grep '.*\.ts$' | sed s/^...//)"
-
-  if [[ -z "${TOUCHED_TS_FILES}" ]]; then
-    exit 0
-  else
-    pushd "${SCRIPT_DIR}/.." > /dev/null
-    for TS_FILE in ${TOUCHED_TS_FILES}; do
-      if [[ -f ${TS_FILE} ]]; then
-        ${CLANG_FORMAT_PREFIX} "${TS_FILE}"
-      fi
-    done
-    popd > /dev/null
-  fi
-elif [[ "${FILE_SCOPE}" == "__all__" ]]; then
-  ALL_TS_FILES="$(find "${SCRIPT_DIR}/../src" "${SCRIPT_DIR}/../demos" -name '*.ts')"
-  for TS_FILE in ${ALL_TS_FILES}; do
-    ${CLANG_FORMAT_PREFIX} "${TS_FILE}"
-  done
-
-else
-  ${CLANG_FORMAT_PREFIX} ${FILE_SCOPE}
-fi
diff --git a/tfjs-master/tfjs-layers/tsconfig.json b/tfjs-master/tfjs-layers/tsconfig.json
deleted file mode 100644
index 71a1c14ec..000000000
--- a/tfjs-master/tfjs-layers/tsconfig.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-layers/yarn.lock b/tfjs-master/tfjs-layers/yarn.lock
deleted file mode 100644
index ace10ffbf..000000000
--- a/tfjs-master/tfjs-layers/yarn.lock
+++ /dev/null
@@ -1,126 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@bazel/bazelisk@^1.12.0":
-  version "1.12.0"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.12.0.tgz#f08aebbf4afcb12684422450b0845dd6ef5cfe50"
-  integrity sha512-7oQusq1e4AIyFgotxVV7Pc40Et0QyvoVjujL+7/qV5Vrbfh0Nj3CfqSgl63weEyI4r0+K6RlGVsjfRuBi05p5w==
-
-async@^3.2.3:
-  version "3.2.3"
-  resolved "https://registry.yarnpkg.com/async/-/async-3.2.3.tgz#ac53dafd3f4720ee9e8a160628f18ea91df196c9"
-  integrity sha512-spZRyzKL5l5BZQrr/6m/SqFdBN0q3OCI0f9rjfBzCMBIP4p75P620rR3gTmaksNOhmzgdxcaxdNfMy6anrbM0g==
-
-balanced-match@^1.0.0:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
-  integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
-
-brace-expansion@^1.1.7:
-  version "1.1.11"
-  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd"
-  integrity sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==
-  dependencies:
-    balanced-match "^1.0.0"
-    concat-map "0.0.1"
-
-clang-format@~1.8.0:
-  version "1.8.0"
-  resolved "https://registry.yarnpkg.com/clang-format/-/clang-format-1.8.0.tgz#7779df1c5ce1bc8aac1b0b02b4e479191ef21d46"
-  integrity sha512-pK8gzfu55/lHzIpQ1givIbWfn3eXnU7SfxqIwVgnn5jEM6j4ZJYjpFqFs4iSBPNedzRMmfjYjuQhu657WAXHXw==
-  dependencies:
-    async "^3.2.3"
-    glob "^7.0.0"
-    resolve "^1.1.6"
-
-concat-map@0.0.1:
-  version "0.0.1"
-  resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
-  integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
-
-fs.realpath@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
-  integrity sha1-FQStJSMVjKpA20onh8sBQRmU6k8=
-
-function-bind@^1.1.1:
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.1.tgz#a56899d3ea3c9bab874bb9773b7c5ede92f4895d"
-  integrity sha512-yIovAzMX49sF8Yl58fSCWJ5svSLuaibPxXQJFLmBObTuCr0Mf1KiPopGM9NiFjiYBCbfaa2Fh6breQ6ANVTI0A==
-
-glob@^7.0.0:
-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/glob/-/glob-7.2.0.tgz#d15535af7732e02e948f4c41628bd910293f6023"
-  integrity sha512-lmLf6gtyrPq8tTjSmrO94wBeQbFR3HbLHbuyD69wuyQkImp2hWqMGB47OX65FBkPffO641IP9jWa1z4ivqG26Q==
-  dependencies:
-    fs.realpath "^1.0.0"
-    inflight "^1.0.4"
-    inherits "2"
-    minimatch "^3.0.4"
-    once "^1.3.0"
-    path-is-absolute "^1.0.0"
-
-has@^1.0.3:
-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/has/-/has-1.0.3.tgz#722d7cbfc1f6aa8241f16dd814e011e1f41e8796"
-  integrity sha512-f2dvO0VU6Oej7RkWJGrehjbzMAjFp5/VKPp5tTpWIV4JHHZK1/BxbFRtf/siA2SWTe09caDmVtYYzWEIbBS4zw==
-  dependencies:
-    function-bind "^1.1.1"
-
-inflight@^1.0.4:
-  version "1.0.6"
-  resolved "https://registry.yarnpkg.com/inflight/-/inflight-1.0.6.tgz#49bd6331d7d02d0c09bc910a1075ba8165b56df9"
-  integrity sha1-Sb1jMdfQLQwJvJEKEHW6gWW1bfk=
-  dependencies:
-    once "^1.3.0"
-    wrappy "1"
-
-inherits@2:
-  version "2.0.4"
-  resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
-  integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
-
-is-core-module@^2.2.0:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/is-core-module/-/is-core-module-2.2.0.tgz#97037ef3d52224d85163f5597b2b63d9afed981a"
-  integrity sha512-XRAfAdyyY5F5cOXn7hYQDqh2Xmii+DEfIcQGxK/uNwMHhIkPWO0g8msXcbzLe+MpGoR951MlqM/2iIlU4vKDdQ==
-  dependencies:
-    has "^1.0.3"
-
-minimatch@^3.0.4:
-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.1.2.tgz#19cd194bfd3e428f049a70817c038d89ab4be35b"
-  integrity sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==
-  dependencies:
-    brace-expansion "^1.1.7"
-
-once@^1.3.0:
-  version "1.4.0"
-  resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
-  integrity sha1-WDsap3WWHUsROsF9nFC6753Xa9E=
-  dependencies:
-    wrappy "1"
-
-path-is-absolute@^1.0.0:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
-  integrity sha1-F0uSaHNVNP+8es5r9TpanhtcX18=
-
-path-parse@^1.0.6:
-  version "1.0.7"
-  resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.7.tgz#fbc114b60ca42b30d9daf5858e4bd68bbedb6735"
-  integrity sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==
-
-resolve@^1.1.6:
-  version "1.20.0"
-  resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.20.0.tgz#629a013fb3f70755d6f0b7935cc1c2c5378b1975"
-  integrity sha512-wENBPt4ySzg4ybFQW2TT1zMQucPK95HSh/nq2CFTZVOGut2+pQvSsgtda4d26YrYcr067wjbmzOG8byDPBX63A==
-  dependencies:
-    is-core-module "^2.2.0"
-    path-parse "^1.0.6"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
diff --git a/tfjs-master/tfjs-node-gpu/.npmignore b/tfjs-master/tfjs-node-gpu/.npmignore
deleted file mode 100644
index d732fadac..000000000
--- a/tfjs-master/tfjs-node-gpu/.npmignore
+++ /dev/null
@@ -1 +0,0 @@
-../tfjs-node/.npmignore
\ No newline at end of file
diff --git a/tfjs-master/tfjs-node-gpu/.nycrc b/tfjs-master/tfjs-node-gpu/.nycrc
deleted file mode 100644
index 743ee11c2..000000000
--- a/tfjs-master/tfjs-node-gpu/.nycrc
+++ /dev/null
@@ -1 +0,0 @@
-../tfjs-node/.nycrc
\ No newline at end of file
diff --git a/tfjs-master/tfjs-node-gpu/README.md b/tfjs-master/tfjs-node-gpu/README.md
deleted file mode 100644
index 408f7109e..000000000
--- a/tfjs-master/tfjs-node-gpu/README.md
+++ /dev/null
@@ -1 +0,0 @@
-For information please refer [here](https://github.com/tensorflow/tfjs/blob/master/tfjs-node/README.md).
diff --git a/tfjs-master/tfjs-node-gpu/cloudbuild.yml b/tfjs-master/tfjs-node-gpu/cloudbuild.yml
deleted file mode 100644
index 3846c3a31..000000000
--- a/tfjs-master/tfjs-node-gpu/cloudbuild.yml
+++ /dev/null
@@ -1,77 +0,0 @@
-steps:
-# Prepare tfjs-node-gpu folder, copy files from tfjs-node.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node-gpu'
-  id: 'prep-gpu'
-  entrypoint: 'yarn'
-  args: ['prep-gpu']
-
-# Install common dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'yarn-common'
-  entrypoint: 'yarn'
-  args: ['install']
-  waitFor: ['prep-gpu']
-
-# Install packages.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node-gpu'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-  waitFor: ['yarn-common', 'prep-gpu']
-
-# Build add-on.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node-gpu'
-  entrypoint: 'yarn'
-  id: 'build-addon'
-  args: ['build-addon-from-source']
-  waitFor: ['yarn']
-
-# Build deps.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node-gpu'
-  entrypoint: 'yarn'
-  id: 'build-deps'
-  args: ['build-deps-ci']
-  waitFor: ['yarn-common', 'prep-gpu']
-
-# Build.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node-gpu'
-  entrypoint: 'yarn'
-  id: 'build'
-  args: ['build-ci']
-  waitFor: ['yarn', 'build-deps']
-
-# Lint.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node-gpu'
-  entrypoint: 'yarn'
-  id: 'lint'
-  args: ['lint']
-  waitFor: ['yarn', 'build-deps']
-
-# Unit tests.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node-gpu'
-  entrypoint: 'yarn'
-  id: 'test'
-  args: ['test-ci']
-  waitFor: ['yarn', 'build-addon', 'build-deps', 'lint']
-
-# CPU / GPU package alignment.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node-gpu'
-  entrypoint: 'yarn'
-  id: 'ensure-cpu-gpu-packages-align'
-  args: ['ensure-cpu-gpu-packages-align']
-  waitFor: ['yarn-common', 'prep-gpu']
-
-timeout: 1800s
-logsBucket: 'gs://tfjs-build-logs'
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'N1_HIGHCPU_8'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/tfjs-node-gpu/package.json b/tfjs-master/tfjs-node-gpu/package.json
deleted file mode 100644
index 111260824..000000000
--- a/tfjs-master/tfjs-node-gpu/package.json
+++ /dev/null
@@ -1,89 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-node-gpu",
-  "version": "0.0.0",
-  "main": "dist/index.js",
-  "types": "dist/index.d.ts",
-  "gypfile": true,
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-node-gpu"
-  },
-  "license": "Apache-2.0",
-  "engines": {
-    "node": ">=8.11.0"
-  },
-  "scripts": {
-    "build": "tsc && npx mkdirp dist/proto && cp src/proto/api_pb.js dist/proto/api_pb.js",
-    "build-ci": "tsc && npx mkdirp dist/proto && cp src/proto/api_pb.js dist/proto/api_pb.js",
-    "build-link-package": "cd ../link-package && yarn build-deps-for tfjs-node",
-    "build-union": "cd ../tfjs && yarn && yarn build",
-    "build-union-ci": "cd ../tfjs && yarn && yarn build-ci",
-    "build-deps": "yarn build-link-package && yarn build-union",
-    "build-deps-ci": "yarn build-link-package && yarn build-union-ci",
-    "build-npm": "yarn prep-gpu && ./scripts/build-npm.sh",
-    "build-and-upload-addon": "./scripts/build-and-upload-addon.sh",
-    "build-addon-from-source": "node-pre-gyp install --build-from-source",
-    "clean-deps": "rm -rf deps && rm -rf lib",
-    "coverage": "nyc yarn ts-node -P tsconfig.test.json src/run_tests.ts",
-    "enable-gpu": "node scripts/install.js gpu download && yarn && yarn build-addon-from-source",
-    "ensure-cpu-gpu-packages-align": "node scripts/ensure-cpu-gpu-packages-align.js",
-    "format": "clang-format -i -style=Google binding/*.cc binding/*.h",
-    "install": "node scripts/install.js gpu download",
-    "install-from-source": "yarn clean-deps && yarn && yarn build-addon-from-source",
-    "link-local": "yalc link",
-    "lint": "tslint -p . -t verbose",
-    "prep": "cd node_modules/@tensorflow/tfjs-core && yarn && yarn build",
-    "prep-gpu": "./prep-gpu.sh",
-    "prep-gpu-windows": "./prep-gpu-windows.bat",
-    "publish-local": "yarn prep && yalc push",
-    "publish-npm": "yarn build-and-upload-addon publish && npm publish",
-    "test": "yarn && yarn build-deps && yarn build && ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts",
-    "test-dev": "tsc && ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts",
-    "test-ci": "ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts",
-    "upload-windows-addon": "prep-gpu-windows.bat && ./scripts/build-and-upload-windows-addon-gpu.bat"
-  },
-  "devDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core",
-    "@types/jasmine": "~4.0.3",
-    "@types/node": "^10.5.1",
-    "@types/progress": "^2.0.1",
-    "@types/rimraf": "~2.0.2",
-    "@types/yargs": "^13.0.3",
-    "clang-format": "~1.8.0",
-    "jasmine": "~4.2.1",
-    "node-fetch": "~2.6.1",
-    "nyc": "^15.1.0",
-    "tmp": "^0.0.33",
-    "ts-node": "~8.8.2",
-    "tslint": "~6.1.3",
-    "tslint-no-circular-imports": "^0.7.0",
-    "typescript": "5.0.4",
-    "yalc": "~1.0.0-pre.50",
-    "yargs": "^16.2.0"
-  },
-  "dependencies": {
-    "@mapbox/node-pre-gyp": "1.0.9",
-    "@tensorflow/tfjs": "link:../tfjs",
-    "adm-zip": "^0.5.2",
-    "google-protobuf": "^3.9.2",
-    "https-proxy-agent": "^2.2.1",
-    "progress": "^2.0.0",
-    "rimraf": "^2.6.2",
-    "tar": "^4.4.6"
-  },
-  "binary": {
-    "module_name": "tfjs_binding",
-    "module_path": "./lib/napi-v{napi_build_version}",
-    "host": "https://storage.googleapis.com/tf-builds/pre-built-binary",
-    "remote_path": "./napi-v{napi_build_version}/{version}/",
-    "napi_versions": [
-      3,
-      4,
-      5,
-      6,
-      7,
-      8
-    ]
-  }
-}
diff --git a/tfjs-master/tfjs-node-gpu/prep-gpu-windows.bat b/tfjs-master/tfjs-node-gpu/prep-gpu-windows.bat
deleted file mode 100644
index 43b1ee52e..000000000
--- a/tfjs-master/tfjs-node-gpu/prep-gpu-windows.bat
+++ /dev/null
@@ -1,25 +0,0 @@
-::!/usr/bin/env bash
-:: Copyright 2019 Google LLC. All Rights Reserved.
-::
-:: Licensed under the Apache License, Version 2.0 (the "License");
-:: you may not use this file except in compliance with the License.
-:: You may obtain a copy of the License at
-::
-::     http://www.apache.org/licenses/LICENSE-2.0
-::
-:: Unless required by applicable law or agreed to in writing, software
-:: distributed under the License is distributed on an "AS IS" BASIS,
-:: WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-:: See the License for the specific language governing permissions and
-:: limitations under the License.
-:: =============================================================================
-
-DEL /Q/S .\src
-DEL /Q/S .\binding
-DEL /Q/S .\scripts
-DEL /Q binding.gyp
-
-Xcopy /E ..\tfjs-node\src .\src
-Xcopy /E ..\tfjs-node\binding .\binding
-Xcopy /E ..\tfjs-node\scripts .\scripts
-Xcopy ..\tfjs-node\binding.gyp .
diff --git a/tfjs-master/tfjs-node-gpu/prep-gpu.sh b/tfjs-master/tfjs-node-gpu/prep-gpu.sh
deleted file mode 100644
index bf76ec6fe..000000000
--- a/tfjs-master/tfjs-node-gpu/prep-gpu.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-rm -rf ./src
-rm -rf ./binding
-rm -rf ./scripts
-rm -f binding.gyp
-
-cp -R ../tfjs-node/src ./src
-cp -R ../tfjs-node/binding ./binding
-cp -R ../tfjs-node/scripts ./scripts
-cp -R ../tfjs-node/binding.gyp .
-
diff --git a/tfjs-master/tfjs-node-gpu/test_objects/images/gif_test.gif b/tfjs-master/tfjs-node-gpu/test_objects/images/gif_test.gif
deleted file mode 100644
index 9b2882e74..000000000
Binary files a/tfjs-master/tfjs-node-gpu/test_objects/images/gif_test.gif and /dev/null differ
diff --git a/tfjs-master/tfjs-node-gpu/test_objects/images/image_bmp_4_channel_test.bmp b/tfjs-master/tfjs-node-gpu/test_objects/images/image_bmp_4_channel_test.bmp
deleted file mode 100644
index 2bce84ac4..000000000
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diff --git a/tfjs-master/tfjs-node-gpu/test_objects/saved_model/uint8_multiply/variables/variables.index b/tfjs-master/tfjs-node-gpu/test_objects/saved_model/uint8_multiply/variables/variables.index
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diff --git a/tfjs-master/tfjs-node-gpu/tsconfig.json b/tfjs-master/tfjs-node-gpu/tsconfig.json
deleted file mode 100644
index c3913754a..000000000
--- a/tfjs-master/tfjs-node-gpu/tsconfig.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "commonjs",
-    "moduleResolution": "node",
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "removeComments": false,
-    "preserveConstEnums": true,
-    "declaration": true,
-    "target": "es5",
-    "lib": [
-      "es2015",
-      "dom"
-    ],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false
-  },
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ]
-}
diff --git a/tfjs-master/tfjs-node-gpu/tsconfig.test.json b/tfjs-master/tfjs-node-gpu/tsconfig.test.json
deleted file mode 100644
index 76ea3c6cf..000000000
--- a/tfjs-master/tfjs-node-gpu/tsconfig.test.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "extends": "./tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "allowJs": true,
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-node-gpu/tslint.json b/tfjs-master/tfjs-node-gpu/tslint.json
deleted file mode 100644
index 7b7590f6f..000000000
--- a/tfjs-master/tfjs-node-gpu/tslint.json
+++ /dev/null
@@ -1 +0,0 @@
-../tfjs-node/tslint.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-node-gpu/yarn.lock b/tfjs-master/tfjs-node-gpu/yarn.lock
deleted file mode 100644
index 6ac00ffb5..000000000
--- a/tfjs-master/tfjs-node-gpu/yarn.lock
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@@ -1,1889 +0,0 @@
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-  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd"
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-    balanced-match "^1.0.0"
-    concat-map "0.0.1"
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-  version "4.20.0"
-  resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.20.0.tgz#35951e3541078c125d36df76056e94738a52ebe9"
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-    electron-to-chromium "^1.4.76"
-    escalade "^3.1.1"
-    node-releases "^2.0.2"
-    picocolors "^1.0.0"
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-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.1.tgz#32713bc028f75c02fdb710d7c7bcec1f2c6070ef"
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-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/builtin-modules/-/builtin-modules-1.1.1.tgz#270f076c5a72c02f5b65a47df94c5fe3a278892f"
-  integrity sha1-Jw8HbFpywC9bZaR9+Uxf46J4iS8=
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-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/caching-transform/-/caching-transform-4.0.0.tgz#00d297a4206d71e2163c39eaffa8157ac0651f0f"
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-    make-dir "^3.0.0"
-    package-hash "^4.0.0"
-    write-file-atomic "^3.0.0"
-
-camelcase@^5.0.0, camelcase@^5.3.1:
-  version "5.3.1"
-  resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-5.3.1.tgz#e3c9b31569e106811df242f715725a1f4c494320"
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-  version "1.0.30001316"
-  resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001316.tgz#b44a1f419f82d2e119aa0bbdab5ec15471796358"
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-
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-  version "2.4.2"
-  resolved "https://registry.yarnpkg.com/chalk/-/chalk-2.4.2.tgz#cd42541677a54333cf541a49108c1432b44c9424"
-  integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==
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-    escape-string-regexp "^1.0.5"
-    supports-color "^5.3.0"
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-chalk@^4.1.0:
-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.0.tgz#4e14870a618d9e2edd97dd8345fd9d9dc315646a"
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-  dependencies:
-    ansi-styles "^4.1.0"
-    supports-color "^7.1.0"
-
-chownr@^1.1.4:
-  version "1.1.4"
-  resolved "https://registry.yarnpkg.com/chownr/-/chownr-1.1.4.tgz#6fc9d7b42d32a583596337666e7d08084da2cc6b"
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-
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/chownr/-/chownr-2.0.0.tgz#15bfbe53d2eab4cf70f18a8cd68ebe5b3cb1dece"
-  integrity sha512-bIomtDF5KGpdogkLd9VspvFzk9KfpyyGlS8YFVZl7TGPBHL5snIOnxeshwVgPteQ9b4Eydl+pVbIyE1DcvCWgQ==
-
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-  version "1.8.0"
-  resolved "https://registry.yarnpkg.com/clang-format/-/clang-format-1.8.0.tgz#7779df1c5ce1bc8aac1b0b02b4e479191ef21d46"
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-    async "^3.2.3"
-    glob "^7.0.0"
-    resolve "^1.1.6"
-
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-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/clean-stack/-/clean-stack-2.2.0.tgz#ee8472dbb129e727b31e8a10a427dee9dfe4008b"
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-  version "6.0.0"
-  resolved "https://registry.yarnpkg.com/cliui/-/cliui-6.0.0.tgz#511d702c0c4e41ca156d7d0e96021f23e13225b1"
-  integrity sha512-t6wbgtoCXvAzst7QgXxJYqPt0usEfbgQdftEPbLL/cvv6HPE5VgvqCuAIDR0NgU52ds6rFwqrgakNLrHEjCbrQ==
-  dependencies:
-    string-width "^4.2.0"
-    strip-ansi "^6.0.0"
-    wrap-ansi "^6.2.0"
-
-cliui@^7.0.2:
-  version "7.0.4"
-  resolved "https://registry.yarnpkg.com/cliui/-/cliui-7.0.4.tgz#a0265ee655476fc807aea9df3df8df7783808b4f"
-  integrity sha512-OcRE68cOsVMXp1Yvonl/fzkQOyjLSu/8bhPDfQt0e0/Eb283TKP20Fs2MqoPsr9SwA595rRCA+QMzYc9nBP+JQ==
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-    string-width "^4.2.0"
-    strip-ansi "^6.0.0"
-    wrap-ansi "^7.0.0"
-
-color-convert@^1.9.0:
-  version "1.9.3"
-  resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-1.9.3.tgz#bb71850690e1f136567de629d2d5471deda4c1e8"
-  integrity sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==
-  dependencies:
-    color-name "1.1.3"
-
-color-convert@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
-  integrity sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==
-  dependencies:
-    color-name "~1.1.4"
-
-color-name@1.1.3:
-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.3.tgz#a7d0558bd89c42f795dd42328f740831ca53bc25"
-  integrity sha1-p9BVi9icQveV3UIyj3QIMcpTvCU=
-
-color-name@~1.1.4:
-  version "1.1.4"
-  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
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-
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-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/color-support/-/color-support-1.1.3.tgz#93834379a1cc9a0c61f82f52f0d04322251bd5a2"
-  integrity sha512-qiBjkpbMLO/HL68y+lh4q0/O1MZFj2RX6X/KmMa3+gJD3z+WwI1ZzDHysvqHGS3mP6mznPckpXmw1nI9cJjyRg==
-
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-  version "1.0.8"
-  resolved "https://registry.yarnpkg.com/combined-stream/-/combined-stream-1.0.8.tgz#c3d45a8b34fd730631a110a8a2520682b31d5a7f"
-  integrity sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==
-  dependencies:
-    delayed-stream "~1.0.0"
-
-commander@^2.12.1:
-  version "2.20.3"
-  resolved "https://registry.yarnpkg.com/commander/-/commander-2.20.3.tgz#fd485e84c03eb4881c20722ba48035e8531aeb33"
-  integrity sha512-GpVkmM8vF2vQUkj2LvZmD35JxeJOLCwJ9cUkugyk2nuhbv3+mJvpLYYt+0+USMxE+oj+ey/lJEnhZw75x/OMcQ==
-
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-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/commondir/-/commondir-1.0.1.tgz#ddd800da0c66127393cca5950ea968a3aaf1253b"
-  integrity sha1-3dgA2gxmEnOTzKWVDqloo6rxJTs=
-
-concat-map@0.0.1:
-  version "0.0.1"
-  resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
-  integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
-
-console-control-strings@^1.0.0, console-control-strings@^1.1.0:
-  version "1.1.0"
-  resolved "https://registry.yarnpkg.com/console-control-strings/-/console-control-strings-1.1.0.tgz#3d7cf4464db6446ea644bf4b39507f9851008e8e"
-  integrity sha1-PXz0Rk22RG6mRL9LOVB/mFEAjo4=
-
-convert-source-map@^1.7.0:
-  version "1.7.0"
-  resolved "https://registry.yarnpkg.com/convert-source-map/-/convert-source-map-1.7.0.tgz#17a2cb882d7f77d3490585e2ce6c524424a3a442"
-  integrity sha512-4FJkXzKXEDB1snCFZlLP4gpC3JILicCpGbzG9f9G7tGqGCzETQ2hWPrcinA9oU4wtf2biUaEH5065UnMeR33oA==
-  dependencies:
-    safe-buffer "~5.1.1"
-
-core-js@3:
-  version "3.9.1"
-  resolved "https://registry.yarnpkg.com/core-js/-/core-js-3.9.1.tgz#cec8de593db8eb2a85ffb0dbdeb312cb6e5460ae"
-  integrity sha512-gSjRvzkxQc1zjM/5paAmL4idJBFzuJoo+jDjF1tStYFMV2ERfD02HhahhCGXUyHxQRG4yFKVSdO6g62eoRMcDg==
-
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-  version "7.0.3"
-  resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.3.tgz#f73a85b9d5d41d045551c177e2882d4ac85728a6"
-  integrity sha512-iRDPJKUPVEND7dHPO8rkbOnPpyDygcDFtWjpeWNCgy8WP2rXcxXL8TskReQl6OrB2G7+UJrags1q15Fudc7G6w==
-  dependencies:
-    path-key "^3.1.0"
-    shebang-command "^2.0.0"
-    which "^2.0.1"
-
-debug@4, debug@^4.1.0, debug@^4.1.1:
-  version "4.3.1"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.1.tgz#f0d229c505e0c6d8c49ac553d1b13dc183f6b2ee"
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-  dependencies:
-    ms "2.1.2"
-
-debug@^3.1.0:
-  version "3.2.7"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-3.2.7.tgz#72580b7e9145fb39b6676f9c5e5fb100b934179a"
-  integrity sha512-CFjzYYAi4ThfiQvizrFQevTTXHtnCqWfe7x1AhgEscTz6ZbLbfoLRLPugTQyBth6f8ZERVUSyWHFD/7Wu4t1XQ==
-  dependencies:
-    ms "^2.1.1"
-
-decamelize@^1.2.0:
-  version "1.2.0"
-  resolved "https://registry.yarnpkg.com/decamelize/-/decamelize-1.2.0.tgz#f6534d15148269b20352e7bee26f501f9a191290"
-  integrity sha1-9lNNFRSCabIDUue+4m9QH5oZEpA=
-
-default-require-extensions@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/default-require-extensions/-/default-require-extensions-3.0.0.tgz#e03f93aac9b2b6443fc52e5e4a37b3ad9ad8df96"
-  integrity sha512-ek6DpXq/SCpvjhpFsLFRVtIxJCRw6fUR42lYMVZuUMK7n8eMz4Uh5clckdBjEpLhn/gEBZo7hDJnJcwdKLKQjg==
-  dependencies:
-    strip-bom "^4.0.0"
-
-delayed-stream@~1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
-  integrity sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ==
-
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/delegates/-/delegates-1.0.0.tgz#84c6e159b81904fdca59a0ef44cd870d31250f9a"
-  integrity sha1-hMbhWbgZBP3KWaDvRM2HDTElD5o=
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-detect-indent@^6.0.0:
-  version "6.0.0"
-  resolved "https://registry.yarnpkg.com/detect-indent/-/detect-indent-6.0.0.tgz#0abd0f549f69fc6659a254fe96786186b6f528fd"
-  integrity sha512-oSyFlqaTHCItVRGK5RmrmjB+CmaMOW7IaNA/kdxqhoa6d17j/5ce9O9eWXmV/KEdRwqpQA+Vqe8a8Bsybu4YnA==
-
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-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/detect-libc/-/detect-libc-2.0.1.tgz#e1897aa88fa6ad197862937fbc0441ef352ee0cd"
-  integrity sha512-463v3ZeIrcWtdgIg6vI6XUncguvr2TnGl4SzDXinkt9mSLpBJKXT3mW6xT3VQdDN11+WVs29pgvivTc4Lp8v+w==
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-diff@^3.1.0:
-  version "3.5.0"
-  resolved "https://registry.yarnpkg.com/diff/-/diff-3.5.0.tgz#800c0dd1e0a8bfbc95835c202ad220fe317e5a12"
-  integrity sha512-A46qtFgd+g7pDZinpnwiRJtxbC1hpgf0uzP3iG89scHk0AUC7A1TGxf5OiiOUv/JMZR8GOt8hL900hV0bOy5xA==
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-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/diff/-/diff-4.0.2.tgz#60f3aecb89d5fae520c11aa19efc2bb982aade7d"
-  integrity sha512-58lmxKSA4BNyLz+HHMUzlOEpg09FV+ev6ZMe3vJihgdxzgcwZ8VoEEPmALCZG9LmqfVoNMMKpttIYTVG6uDY7A==
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-  version "1.4.82"
-  resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.4.82.tgz#51e123ca434b1eba8c434ece2b54f095b304a651"
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-
-emoji-regex@^8.0.0:
-  version "8.0.0"
-  resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-8.0.0.tgz#e818fd69ce5ccfcb404594f842963bf53164cc37"
-  integrity sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==
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-  version "4.1.1"
-  resolved "https://registry.yarnpkg.com/es6-error/-/es6-error-4.1.1.tgz#9e3af407459deed47e9a91f9b885a84eb05c561d"
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-  version "4.2.8"
-  resolved "https://registry.yarnpkg.com/es6-promise/-/es6-promise-4.2.8.tgz#4eb21594c972bc40553d276e510539143db53e0a"
-  integrity sha512-HJDGx5daxeIvxdBxvG2cb9g4tEvwIk3i8+nhX0yGrYmZUzbkdg8QbDevheDB8gd0//uPj4c1EQua8Q+MViT0/w==
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-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/es6-promisify/-/es6-promisify-5.0.0.tgz#5109d62f3e56ea967c4b63505aef08291c8a5203"
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-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.1.1.tgz#d8cfdc7000965c5a0174b4a82eaa5c0552742e40"
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-escape-string-regexp@^1.0.5:
-  version "1.0.5"
-  resolved "https://registry.yarnpkg.com/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz#1b61c0562190a8dff6ae3bb2cf0200ca130b86d4"
-  integrity sha1-G2HAViGQqN/2rjuyzwIAyhMLhtQ=
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-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/esprima/-/esprima-4.0.1.tgz#13b04cdb3e6c5d19df91ab6987a8695619b0aa71"
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-  version "3.3.1"
-  resolved "https://registry.yarnpkg.com/find-cache-dir/-/find-cache-dir-3.3.1.tgz#89b33fad4a4670daa94f855f7fbe31d6d84fe880"
-  integrity sha512-t2GDMt3oGC/v+BMwzmllWDuJF/xcDtE5j/fCGbqDD7OLuJkj0cfh1YSA5VKPvwMeLFLNDBkwOKZ2X85jGLVftQ==
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-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/find-up/-/find-up-4.1.0.tgz#97afe7d6cdc0bc5928584b7c8d7b16e8a9aa5d19"
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-    webidl-conversions "^3.0.0"
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-  version "3.0.3"
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-    typedarray-to-buffer "^3.1.5"
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-  version "5.0.5"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.5.tgz#8769ec08d03b1ea2df2500acef561743bbb9ab18"
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-
-yalc@~1.0.0-pre.50:
-  version "1.0.0-pre.50"
-  resolved "https://registry.yarnpkg.com/yalc/-/yalc-1.0.0-pre.50.tgz#e654e5af5f739cf255eedad1d66ba05a17de78f9"
-  integrity sha512-HGFjFFUhXSpQcxyOwJQl3jhERMn7XBgSCCoJ1k3dDPMbH6n56onv6Cp6cDGMiTho8tLIF4x02/Kb4g6pHavzgA==
-  dependencies:
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-    detect-indent "^6.0.0"
-    fs-extra "^8.0.1"
-    glob "^7.1.4"
-    ignore "^5.0.4"
-    ini "^2.0.0"
-    npm-packlist "^1.4.1"
-    yargs "^16.1.1"
-
-yallist@^3.0.0, yallist@^3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.1.1.tgz#dbb7daf9bfd8bac9ab45ebf602b8cbad0d5d08fd"
-  integrity sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==
-
-yallist@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
-  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
-
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-  version "18.1.3"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-18.1.3.tgz#be68c4975c6b2abf469236b0c870362fab09a7b0"
-  integrity sha512-o50j0JeToy/4K6OZcaQmW6lyXXKhq7csREXcDwk2omFPJEwUNOVtJKvmDr9EI1fAJZUyZcRF7kxGBWmRXudrCQ==
-  dependencies:
-    camelcase "^5.0.0"
-    decamelize "^1.2.0"
-
-yargs-parser@^20.2.2:
-  version "20.2.7"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.7.tgz#61df85c113edfb5a7a4e36eb8aa60ef423cbc90a"
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-
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-  version "15.4.1"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-15.4.1.tgz#0d87a16de01aee9d8bec2bfbf74f67851730f4f8"
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-  dependencies:
-    cliui "^6.0.0"
-    decamelize "^1.2.0"
-    find-up "^4.1.0"
-    get-caller-file "^2.0.1"
-    require-directory "^2.1.1"
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-    which-module "^2.0.0"
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-
-yn@^2.0.0:
-  version "2.0.0"
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diff --git a/tfjs-master/tfjs-node/.bazelversion b/tfjs-master/tfjs-node/.bazelversion
deleted file mode 100644
index 2094a100c..000000000
--- a/tfjs-master/tfjs-node/.bazelversion
+++ /dev/null
@@ -1 +0,0 @@
-0.24.0
diff --git a/tfjs-master/tfjs-node/.npmignore b/tfjs-master/tfjs-node/.npmignore
deleted file mode 100644
index 4073833d2..000000000
--- a/tfjs-master/tfjs-node/.npmignore
+++ /dev/null
@@ -1,28 +0,0 @@
-.vscode/
-src/**/*_test.ts
-demo/
-deps/lib/*
-deps/include/*
-scripts/build-npm.sh
-scripts/make-version
-scripts/tag-version
-scripts/publish-npm.sh
-build/
-integration/
-node_modules/
-test_objects/
-*.tgz
-.travis.yml
-.npmignore
-cloudbuild.yml
-CONTRIBUTING.md
-tslint.json
-yarn.lock
-yalc.lock
-yarn-error.log
-.yalc/
-lib/
-deps/
-build-tmp-napi-v*/
-*.tar.gz
-*.zip
diff --git a/tfjs-master/tfjs-node/.nycrc b/tfjs-master/tfjs-node/.nycrc
deleted file mode 100644
index e99df8422..000000000
--- a/tfjs-master/tfjs-node/.nycrc
+++ /dev/null
@@ -1,20 +0,0 @@
-{
-    "extension": [
-      ".ts"
-    ],
-    "reporter": [
-      "html",
-      "text-summary"
-    ],
-    "include": [
-      "src/**/*"
-    ],
-    "exclude": [
-      "src/run_tests.ts",
-      "src/**/*_test.ts",
-      "src/proto/*"
-    ],
-    "cache": false,
-    "sourceMap": true,
-    "instrument": true
-}
diff --git a/tfjs-master/tfjs-node/.vscode/c_cpp_properties.json b/tfjs-master/tfjs-node/.vscode/c_cpp_properties.json
deleted file mode 100644
index a3816b147..000000000
--- a/tfjs-master/tfjs-node/.vscode/c_cpp_properties.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "configurations": [
-    {
-      "name": "Mac",
-      "includePath": [
-        "${workspaceFolder}",
-        "${workspaceFolder}/deps/include",
-        "${workspaceFolder}/deps/tensorflow/include/tensorflow/c",
-        "${workspaceFolder}/deps/tensorflow/include/tensorflow/c/eager",
-        "${env.HOME}/.node-gyp/10.15.3/include/node"
-      ],
-      "defines": [],
-      "intelliSenseMode": "clang-x64",
-      "browse": {
-        "path": [
-          "${workspaceFolder}",
-          "${workspaceFolder}/deps/include",
-          "${workspaceFolder}/deps/tensorflow/include/tensorflow/c",
-          "${workspaceFolder}/deps/tensorflow/include/tensorflow/c/eager",
-          "${env.HOME}/.node-gyp/10.15.3/include/node"
-        ],
-        "limitSymbolsToIncludedHeaders": true,
-        "databaseFilename": ""
-      },
-      "macFrameworkPath": [
-        "/System/Library/Frameworks",
-        "/Library/Frameworks"
-      ],
-      "compilerPath": "/usr/bin/clang",
-      "cStandard": "c11",
-      "cppStandard": "c++17"
-    },
-    {
-      "name": "Linux",
-      "includePath": [
-        "/usr/include",
-        "/usr/local/include",
-        "${workspaceFolder}",
-        "${env.HOME}/.node-gyp/10.15.0/include/node",
-        "${workspaceFolder}/deps/include"
-      ],
-      "defines": [],
-      "intelliSenseMode": "clang-x64",
-      "browse": {
-        "path": [
-          "/usr/include",
-          "/usr/local/include",
-          "${workspaceFolder}",
-          "${workspaceFolder}/deps/include/tensorflow/c",
-          "${workspaceFolder}/deps/include/tensorflow/c/eager",
-          "${env.HOME}/.node-gyp/10.15.0/include/node"
-        ],
-        "limitSymbolsToIncludedHeaders": true,
-        "databaseFilename": ""
-      },
-      "compilerPath": "/usr/bin/clang++-3.8",
-      "cStandard": "c11",
-      "cppStandard": "c++17"
-    },
-    {
-      "name": "Win32",
-      "includePath": [
-        "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/include",
-        "${workspaceFolder}",
-        "${workspaceFolder}/deps/include",
-        "${env.HOMEPATH}/.node-gyp/10.15.2/include/node"
-      ],
-      "defines": [
-        "_DEBUG",
-        "UNICODE",
-        "_UNICODE"
-      ],
-      "intelliSenseMode": "msvc-x64",
-      "browse": {
-        "path": [
-          "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/include/*",
-          "${workspaceFolder}",
-          "${workspaceFolder}/deps/include/tensorflow/c",
-          "${workspaceFolder}/deps/include/tensorflow/c/eager",
-          "${env.HOMEPATH}/.node-gyp/10.15.2/include/node"
-        ],
-        "limitSymbolsToIncludedHeaders": true,
-        "databaseFilename": ""
-      }
-    }
-  ],
-  "version": 4
-}
diff --git a/tfjs-master/tfjs-node/.vscode/launch.json b/tfjs-master/tfjs-node/.vscode/launch.json
deleted file mode 100644
index 811523fa9..000000000
--- a/tfjs-master/tfjs-node/.vscode/launch.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
-  // Use IntelliSense to learn about possible attributes.
-  // Hover to view descriptions of existing attributes.
-  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-  "version": "0.2.0",
-  "configurations": [
-    {
-      "name": "Run tests",
-      "type": "node",
-      "request": "launch",
-      "args": [
-        "${workspaceFolder}/src/run_tests.ts"
-      ],
-      "runtimeArgs": [
-        "--nolazy",
-        "-r",
-        "ts-node/register"
-      ],
-      "sourceMaps": true,
-      "cwd": "${workspaceRoot}",
-      "protocol": "inspector",
-    }
-  ]
-}
diff --git a/tfjs-master/tfjs-node/.vscode/settings.json b/tfjs-master/tfjs-node/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-node/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-node/DEVELOPMENT.md b/tfjs-master/tfjs-node/DEVELOPMENT.md
deleted file mode 100644
index 5aa56258f..000000000
--- a/tfjs-master/tfjs-node/DEVELOPMENT.md
+++ /dev/null
@@ -1,116 +0,0 @@
-# TensorFlow.js Node.js bindings development.
-
-The @tensorflow/tfjs-node repo supports npm package @tensorflow/tfjs-node and @tensorflow/tfjs-node-gpu on Windows/Mac/Linux. This guide lists commands to use when developing this package.
-
-## Install
-
-#### Dependencies and addon module
-
-```sh
-$ yarn
-```
-
-This command installs all dependencies and devDependencies listed in package.json. It also downloads the TensorFlow C library and native node addon.
-
-#### Compile native addon from source files
-
-```sh
-$ yarn build-addon-from-source
-```
-
-This command will compile a new native node addon from source files.
-
-####
-
-```sh
-$ yarn install-from-source
-```
-
-This command does the following:
-
-1. Clears local binary and addon resources
-2. Downloads the TensorFlow C library
-3. Compiles the native addon from source files (instead of downloading pre-compile addon)
-
-#### Switching local workflow to CUDA/GPU
-
-```sh
-$ yarn enable-gpu
-```
-
-This command is the same as `yarn install-from-source` except it uses the TensorFlow GPU library.
-
-## Build and test
-
-#### Compile javascript files from typescript
-
-```sh
-$ yarn build
-```
-
-#### Publish locally through yalc to test this package in another repo
-
-```sh
-$ yarn publish-local
-```
-
-This command packs the `tfjs-node` package and publishes locally through [yalc](https://github.com/whitecolor/yalc).
-NOTE: Dependent packages must install this locally published package through yalc and compile the node native addon locally. In the dependent package run the following command to link local published `tfjs-node` package:
-
-```sh
-$ yalc link @tensorflow/tfjs-node
-$ cd .yalc/@tensorflow/tfjs-node
-$ yarn && yarn build-addon-from-source
-$ cd ../../..
-```
-
-#### Run tests
-
-```sh
-$ yarn test
-```
-
-## Prepare and publish
-
-#### Prerequisite: install GCP command line tool
-
-Publishing this package requires uploading objects to GCP bucket. Developers need to install GCP command line tool [gsutil](https://cloud.google.com/storage/docs/gsutil_install) before publishing. Please ask TFJS developers for GCP project ID.
-
-#### Build and upload node addon to Google Cloud Platform
-
-```sh
-$ yarn build-and-upload-addon publish
-```
-
-This command will compile, compress, and upload a new node addon to GCP bucket. Please read [build-and-upload-addon.sh](./scripts/build-and-upload-addon.sh) for details.
-
-#### Build NPM package
-
-```sh
-$ yarn build-npm
-```
-
-This command will build a new version of tfjs-node/tfjs-node-gpu NPM tarball. NOTE: this command does not update the pre-compiled node addon to GCP (see `yarn build-and-upload-addon publish`).
-
-#### Publish NPM package
-
-```sh
-$ yarn publish-npm
-```
-
-This command compiles a new node addon, upload it to GCP, then builds and publishes a new npm package. Please read instruction in [publish-npm.sh](./scripts/publish.sh) before publishing.
-
-#### Build and upload node addon on Windows
-
-```sh
-$ yarn upload-windows-addon
-```
-
-Most times the NPM package is published on Linux machine, and only the Linux node addon is compiled and uploaded to GCP bucket. To build and upload the native node addon for Windows, developers should run the above commands on Windows machine. Please read [build-and-upload-windows-addon.bat](./scripts/build-and-upload-windows-addon.bat) for details.
-
-#### Build and upload libtensorflow for custom platforms
-Some platforms need a custom version of libtensorflow built for them because tensorflow does not host binaries for them. Right now, the only automated platform is linux-arm64, which can be built with the following command:
-
-```sh
-gcloud builds submit . --config=scripts/build-libtensorflow-arm64.yml
-```
diff --git a/tfjs-master/tfjs-node/README.md b/tfjs-master/tfjs-node/README.md
deleted file mode 100644
index 71dbe07e1..000000000
--- a/tfjs-master/tfjs-node/README.md
+++ /dev/null
@@ -1,189 +0,0 @@
-# TensorFlow backend for TensorFlow.js via Node.js
-This repository provides native TensorFlow execution in backend JavaScript applications under the Node.js runtime,
-accelerated by the TensorFlow C binary under the hood. It provides the same API as [TensorFlow.js](https://js.tensorflow.org/api/latest/).
-
-This package will work on Linux, Windows, and Mac platforms where TensorFlow is supported.
-
-## Installing
-
-TensorFlow.js for Node currently supports the following platforms:
-- Mac OS X CPU (10.12.6 Siera or higher)
-- Linux CPU (Ubuntu 14.04 or higher)
-- Linux GPU (Ubuntu 14.04 or higher and Cuda 11.2 w/ CUDNN v8) ([see installation instructions](https://www.tensorflow.org/install/gpu#software_requirements))
-- Windows CPU (Win 7 or higher)
-- Windows GPU (Win 7 or higher and Cuda 11.2 w/ CUDNN v8) ([see installation instructions](https://www.tensorflow.org/install/gpu#windows_setup))
-
-For GPU support, tfjs-node-gpu@1.2.4 or later requires the following NVIDIA® software installed on your system:
-
-| Name | Version |
-| ------------- | ------------- |
-| [NVIDIA® GPU drivers](https://www.nvidia.com/Download/index.aspx?lang=en-us) | >450.x  |
-| [CUDA® Toolkit](https://developer.nvidia.com/cuda-toolkit-archive)  | 11.2  |
-| [cuDNN SDK](https://developer.nvidia.com/rdp/cudnn-download)  | 8.1.0  |
-
-*Other Linux variants might also work but this project matches [core TensorFlow installation requirements](https://www.tensorflow.org/install/source).*
-
-#### Installing CPU TensorFlow.js for Node:
-
-```sh
-npm install @tensorflow/tfjs-node
-(or)
-yarn add @tensorflow/tfjs-node
-```
-
-#### Installing Linux/Windows GPU TensorFlow.js for Node:
-
-```sh
-npm install @tensorflow/tfjs-node-gpu
-(or)
-yarn add @tensorflow/tfjs-node-gpu
-```
-
-#### Windows / Mac OS X Requires Python 2.7
-
-Windows & OSX build support for `node-gyp` requires Python 2.7. Be sure to have this version before installing `@tensorflow/tfjs-node` or `@tensorflow/tfjs-node-gpu`. Machines with Python 3.x will not install the bindings properly.
-
-*For more troubleshooting on Windows, check out [WINDOWS_TROUBLESHOOTING.md](./WINDOWS_TROUBLESHOOTING.md).*
-
-#### Mac OS X Requires Xcode
-
-If you do not have Xcode setup on your machine, please run the following commands:
-
-```sh
-$ xcode-select --install
-```
-For Mac OS Catalina please follow [this guide](https://github.com/nodejs/node-gyp/blob/master/macOS_Catalina.md#installing-node-gyp-using-the-xcode-command-line-tools-via-manual-download) to install node-gyp.
-
-After that operation completes, re-run `yarn add` or `npm install` for the `@tensorflow/tfjs-node` package.
-
-You only need to include `@tensorflow/tfjs-node` or `@tensorflow/tfjs-node-gpu` in the package.json file, since those packages ship with `@tensorflow/tfjs` already.
-
-#### Mac OS X with M1 chip
-For Mac with M1 chip, tfjs-node only support arm64 build.
-To install tfjs-node, you need to ensure rosetta has been turned off on your terminal app.
-Start your terminal and verify following command shows `arm64` as response:
-```
-uname -m
-```
-Install your node version with arm64 binary. You can verify that with following command also shows `arm64`:
-```
-node -e 'console.log(os.arch())'
-```
-Now you can install tfjs-node as described before.
-
-#### Rebuild the package on Raspberry Pi
-
-To use this package on Raspberry Pi, you need to rebuild the node native addon with the following command after you installed the package:
-
-```sh
-$ npm rebuild @tensorflow/tfjs-node --build-from-source
-```
-
-#### Custom binaries URI
-
-If you happen to be using a mirror for the libtensorflow binaries (default is [https://storage.googleapis.com/]), you have 3 options (in order of priority):
-
-1. Set the environment variable `TFJS_NODE_CDN_STORAGE`. This has the same behavior as `CDN_STORAGE`, but introduced to prevent collisions with other npm packages that might use `CDN_STORAGE`.
-
-```sh
-TFJS_NODE_CDN_STORAGE="https://yourmirrorofchoice.com/" npm install <package>
-(or)
-TFJS_NODE_CDN_STORAGE="https://yourmirrorofchoice.com/" yarn install <package>
-```
-
-2. Add the variable `TFJS_NODE_CDN_STORAGE` to your `.npmrc` file.
-
-```
-TFJS_NODE_CDN_STORAGE=https://yourmirrorofchoice.com/
-```
-
-3. Set the environment variable `CDN_STORAGE`. This option is deprecated in favor of the `TFJS_NODE_` prefix version above and will be removed in a future release.
-
-```sh
-CDN_STORAGE="https://yourmirrorofchoice.com/" npm install <package>
-(or)
-CDN_STORAGE="https://yourmirrorofchoice.com/" yarn install <package>
-```
-
-If your "mirror" uses a custom URI path that doesn't match the default, you have 2 options (in order of priority):
-
-1. Set the environment variable `TFJS_NODE_BASE_URI`
-
-```sh
-TFJS_NODE_BASE_URI="https://yourhost.com/your/path/libtensorflow-" npm install <package>
-(or)
-TFJS_NODE_BASE_URI="https://yourhost.com/your/path/libtensorflow-" yarn install <package>
-```
-
-2. Add the variable `TFJS_NODE_BASE_URI` to your `.npmrc` file
-
-```
-TFJS_NODE_BASE_URI=https://yourhost.com/your/path/libtensorflow-
-```
-
-## Using the binding
-
-Before executing any TensorFlow.js code, import the node package:
-
-```js
-// Load the binding
-const tf = require('@tensorflow/tfjs-node');
-
-// Or if running with GPU:
-const tf = require('@tensorflow/tfjs-node-gpu');
-```
-
-Note: you do not need to add the `@tensorflow/tfjs` package to your dependencies or import it directly.
-
-## Development
-
-```sh
-# Download and install JS dependencies, including libtensorflow 1.8.
-yarn
-
-# Run TFJS tests against Node.js backend:
-yarn test
-```
-
-```sh
-# Switch to GPU for local development:
-yarn enable-gpu
-```
-
-
-## MNIST demo for Node.js
-
-See the [tfjs-examples repository](https://github.com/tensorflow/tfjs-examples/tree/master/mnist-node) for training the MNIST dataset using the Node.js bindings.
-
-### Optional: Build optimal TensorFlow from source
-
-To get the most optimal TensorFlow build that can take advantage of your specific hardware (AVX512, MKL-DNN), you can build the `libtensorflow` library from source:
-- [Install bazel](https://docs.bazel.build/versions/master/install.html)
-- Checkout the [main tensorflow repo](https://github.com/tensorflow/tensorflow) and follow the instructions in [here](https://www.tensorflow.org/install/source) with **one difference**: instead of building the pip package, build `libtensorflow`:
-
-```sh
-./configure
-bazel build --config=opt --config=monolithic //tensorflow/tools/lib_package:libtensorflow
-```
-
-The build might take a while and will produce a `bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz` file, which should be unpacked and replace the files in `deps` folder of `tfjs-node` repo:
-```sh
-cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz ~/myproject/node_modules/@tensorflow/tfjs-node/deps
-cd path-to-my-project/node_modules/@tensorflow/tfjs-node/deps
-tar -xf libtensorflow.tar.gz
-```
-
-If you want to publish an addon library with your own libtensorflow binary, you can host the custom libtensorflow binary and optional pre-compiled node addon module on the cloud service you choose, and add a `custom-binary.json` file in `scripts` folder with the following information:
-
-```js
-{
-  "tf-lib": "url-to-download-customized-binary",
-  "addon": {
-    "host": "host-of-pre-compiled-addon",
-    "remote_path": "remote-path-of-pre-compiled-addon",
-    "package_name": "file-name-of-pre-compile-addon"
-  }
-}
-```
-
-The installation scripts will automatically catch this file and use the custom libtensorflow binary and addon. If `addon` is not provided, the installation script will compile addon from source.
diff --git a/tfjs-master/tfjs-node/WINDOWS_TROUBLESHOOTING.md b/tfjs-master/tfjs-node/WINDOWS_TROUBLESHOOTING.md
deleted file mode 100644
index 3e7a36aa7..000000000
--- a/tfjs-master/tfjs-node/WINDOWS_TROUBLESHOOTING.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# TensorFlow.js Node.js bindings Windows troubleshooting.
-
-The tfjs-node package uses the [node-gyp](https://github.com/nodejs/node-gyp) package to handle cross-compiling the C++ code required to bind TensorFlow to Node.js. This cross platform solution can be somewhat tricky on Windows platforms. This guide helps reference solutions that have been triaged through Issues on the main [tfjs repo](https://github.com/tensorflow/tfjs).
-
-
-## 'The system cannot find the patch specified' Exceptions
-
-This can happen for a variety of reasons. First, to inspect what is missing either `cd node_modules/@tensorflow/tfjs-node` or clone the [tensorflow/tfjs repo](https://github.com/tensorflow/tfjs).
-
-After `cd`'ing or cloning, run the following command (you might need node-gyp installed globablly `npm install -g node-gyp`):
-
-```sh
-node-gyp configure --verbose
-```
-
-### Missing `python2`
-
-From the verbose output, if you see something like:
-
-```sh
-gyp verb check python checking for Python executable "python2" in the PATH
-gyp verb `which` failed Error: not found: python2
-```
-
-This means that node-gyp expects a 'python2' exe somewhere in `%PATH%`. Try running this command from an Admin (elevated privilaged prompt):
-
-You can try running this from an Adminstrative prompt:
-
-```sh
-$ npm --add-python-to-path='true' --debug install --global windows-build-tools
-```
-
-### Something else?
-
-If another missing component shows up - please file an issue on [tensorflow/tfjs](https://github.com/tensorflow/tfjs/issues/new) with the output from the `node-gyp configure --verbose` command.
-
-## msbuild.exe Exceptions
-
-Check the full stack trace from `npm install` (or `yarn`) command. If you see something like:
-
-```
-gyp ERR! stack Error: C:\Program Files (x86)\MSBuild\14.0\bin\msbuild.exe failed with exit code: 1
-```
-
-You might need to install the system tools manually. This can be done via:
-
-```
-npm install -g --production windows-build-tools
-```
-
-If that still does not work - try re-running the command above in a privileged shell.
diff --git a/tfjs-master/tfjs-node/binding.gyp b/tfjs-master/tfjs-node/binding.gyp
deleted file mode 100644
index 4e0e7df3e..000000000
--- a/tfjs-master/tfjs-node/binding.gyp
+++ /dev/null
@@ -1,108 +0,0 @@
-##
-# @license
-# Copyright 2018 Google LLC. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Node.js TensorFlow Binding config:
-{
-  'variables' : {
-    'tensorflow_include_dir' : '<(module_root_dir)/deps/include',
-    'tensorflow_headers' : [
-      '<@(tensorflow_include_dir)/tensorflow/c/c_api.h',
-      '<@(tensorflow_include_dir)/tensorflow/c/eager/c_api.h',
-    ],
-    'tensorflow-library-action': 'move'
-  },
-  'targets' : [{
-    'target_name' : 'tfjs_binding',
-    'sources' : [
-      'binding/tfjs_backend.cc',
-      'binding/tfjs_binding.cc'
-    ],
-    'include_dirs' : [ '..', '<(tensorflow_include_dir)' ],
-    'conditions' : [
-      [
-        'OS=="linux"', {
-          'libraries' : [
-            '-Wl,-rpath,\$$ORIGIN/../../deps/lib',
-            '-ltensorflow',
-            '-ltensorflow_framework',
-          ],
-          'library_dirs' : ['<(module_root_dir)/deps/lib'],
-        }
-      ],
-      [
-        'OS=="mac"', {
-          'libraries' : [
-            '<(module_root_dir)/deps/lib/libtensorflow.2.dylib',
-            '<(module_root_dir)/deps/lib/libtensorflow_framework.2.dylib',
-          ],
-          'postbuilds': [
-            {
-              'postbuild_name': 'Adjust libtensorflow load path',
-              'action': [
-                'install_name_tool',
-                "-change",
-                "@rpath/libtensorflow.2.dylib",
-                "@loader_path/../../deps/lib/libtensorflow.2.dylib",
-                "<(PRODUCT_DIR)/tfjs_binding.node"
-              ]
-            },
-            {
-              'postbuild_name': 'Adjust libtensorflow_framework load path',
-              'action': [
-                'install_name_tool',
-                "-change",
-                "@rpath/libtensorflow_framework.2.dylib",
-                "@loader_path/../../deps/lib/libtensorflow_framework.2.dylib",
-                "<(PRODUCT_DIR)/tfjs_binding.node"
-              ]
-            }
-          ],
-        }
-      ],
-      [
-        'OS=="win"', {
-          'defines': ['COMPILER_MSVC'],
-          'libraries': ['tensorflow'],
-          'library_dirs' : ['<(module_root_dir)/deps/lib'],
-          'variables': {
-            'tensorflow-library-target': 'windows'
-          },
-          'msvs_disabled_warnings': [
-            # Warning	C4190: 'TF_NewWhile' has C-linkage specified, but returns
-            # UDT 'TF_WhileParams' which is incompatible with C.
-            # (in include/tensorflow/c/c_api.h)
-            4190
-          ]
-        },
-      ]
-    ],
-  }
-  , {
-      "target_name": "action_after_build",
-      "type": "none",
-      "dependencies": [ "<(module_name)" ],
-      "copies": [
-        {
-          "files": [ "<(PRODUCT_DIR)/<(module_name).node" ],
-          "destination": "<(module_path)"
-        }
-      ]
-    }
-    ],
-  "defines": [
-      "NAPI_VERSION=<(napi_build_version)"
-  ]
-}
diff --git a/tfjs-master/tfjs-node/binding/napi_auto_ref.h b/tfjs-master/tfjs-node/binding/napi_auto_ref.h
deleted file mode 100644
index 07f69d230..000000000
--- a/tfjs-master/tfjs-node/binding/napi_auto_ref.h
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#ifndef TF_NODEJS_NAPI_AUTO_REF_H_
-#define TF_NODEJS_NAPI_AUTO_REF_H_
-
-#include <node_api.h>
-#include "utils.h"
-
-namespace tfnodejs {
-
-// Helper class to automatically cleanup napi_ref instances.
-class NapiAutoRef {
- public:
-  NapiAutoRef() : env_(nullptr), ref_(nullptr) {}
-
-  napi_status Init(napi_env env, napi_value value) {
-    env_ = env;
-    return napi_create_reference(env_, value, 1, &ref_);
-  }
-
-  napi_status Cleanup() {
-    if (!env_ || !ref_) {
-#if DEBUG
-      NAPI_THROW_ERROR(env_, "Uninitialized reference attempted to cleanup");
-#endif
-      return napi_invalid_arg;
-    }
-
-    napi_status nstatus = napi_delete_reference(env_, ref_);
-    env_ = nullptr;
-    ref_ = nullptr;
-    return nstatus;
-  }
-
-  virtual ~NapiAutoRef() {
-    if (env_) {
-#if DEBUG
-      NAPI_THROW_ERROR(env_, "Non-cleaned up napi_env instance");
-#endif
-    }
-    if (ref_) {
-#if DEBUG
-      NAPI_THROW_ERROR(env_, "Non-cleaned up napi_ref instance");
-#endif
-    }
-  }
-
- private:
-  napi_env env_;
-  napi_ref ref_;
-};
-
-}  // namespace tfnodejs
-
-#endif  // TF_NODEJS_NAPI_AUTO_REF_H_
diff --git a/tfjs-master/tfjs-node/binding/tf_auto_status.h b/tfjs-master/tfjs-node/binding/tf_auto_status.h
deleted file mode 100644
index 81b512fca..000000000
--- a/tfjs-master/tfjs-node/binding/tf_auto_status.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#ifndef TF_NODEJS_TF_AUTO_STATUS_H_
-#define TF_NODEJS_TF_AUTO_STATUS_H_
-
-#include "tensorflow/c/c_api.h"
-
-namespace tfnodejs {
-
-// Automatically cleans up a TF_Status instance.
-class TF_AutoStatus {
- public:
-  TF_AutoStatus() : status(TF_NewStatus()) {}
-  virtual ~TF_AutoStatus() { TF_DeleteStatus(status); }
-
-  TF_Status* status;
-};
-
-}  // namespace tfnodejs
-
-#endif  // TF_NODEJS_TF_AUTO_STATUS_H_
diff --git a/tfjs-master/tfjs-node/binding/tf_auto_tensor.h b/tfjs-master/tfjs-node/binding/tf_auto_tensor.h
deleted file mode 100644
index ee8229335..000000000
--- a/tfjs-master/tfjs-node/binding/tf_auto_tensor.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#ifndef TF_NODEJS_TF_AUTO_TENSOR_H_
-#define TF_NODEJS_TF_AUTO_TENSOR_H_
-
-#include "tensorflow/c/c_api.h"
-
-namespace tfnodejs {
-
-// Automatically cleans up a TF_Tensor instance.
-class TF_AutoTensor {
- public:
-  TF_AutoTensor(TF_Tensor* tensor) : tensor(tensor) {}
-  virtual ~TF_AutoTensor() { TF_DeleteTensor(tensor); }
-
-  TF_Tensor* tensor;
-};
-
-}  // namespace tfnodejs
-
-#endif  // TF_NODEJS_TF_AUTO_TENSOR_H_
diff --git a/tfjs-master/tfjs-node/binding/tfe_auto_op.h b/tfjs-master/tfjs-node/binding/tfe_auto_op.h
deleted file mode 100644
index 1b43fa490..000000000
--- a/tfjs-master/tfjs-node/binding/tfe_auto_op.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#ifndef TF_NODEJS_TFE_AUTO_OP_H_
-#define TF_NODEJS_TFE_AUTO_OP_H_
-
-#include "tensorflow/c/eager/c_api.h"
-
-namespace tfnodejs {
-
-// Automatically cleans up a TF_Op instance.
-class TFE_AutoOp {
- public:
-  TFE_AutoOp(TFE_Op* op) : op(op) {}
-  virtual ~TFE_AutoOp() { TFE_DeleteOp(op); }
-
-  TFE_Op* op;
-};
-
-}  // namespace tfnodejs
-
-#endif  // TF_NODEJS_TFE_AUTO_OP_H_
diff --git a/tfjs-master/tfjs-node/binding/tfjs_backend.cc b/tfjs-master/tfjs-node/binding/tfjs_backend.cc
deleted file mode 100644
index f17efd2df..000000000
--- a/tfjs-master/tfjs-node/binding/tfjs_backend.cc
+++ /dev/null
@@ -1,1220 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#include "tfjs_backend.h"
-
-#include <algorithm>
-#include <cstring>
-#include <memory>
-#include <set>
-#include <string>
-#include "napi_auto_ref.h"
-#include "tensorflow/c/eager/c_api.h"
-#include "tensorflow/c/tf_datatype.h"
-#include "tensorflow/c/tf_status.h"
-#include "tensorflow/c/tf_tensor.h"
-#include "tensorflow/core/platform/ctstring_internal.h"
-#include "tf_auto_tensor.h"
-#include "tfe_auto_op.h"
-#include "utils.h"
-
-namespace tfnodejs {
-
-// Used to hold strings beyond the lifetime of a JS call.
-static std::set<std::string> ATTR_NAME_SET;
-
-// Callback to cleanup extra reference count for shared V8/TF tensor memory:
-static void DeallocTensor(void *data, size_t len, void *arg) {
-  NapiAutoRef *auto_ref = static_cast<NapiAutoRef *>(arg);
-  if (!auto_ref) {
-#if DEBUG
-    fprintf(stderr, "Invalid NapiAutoRef reference passed to V8 cleanup\n");
-#endif
-    return;
-  }
-  if (auto_ref->Cleanup() != napi_ok) {
-#if DEBUG
-    fprintf(stderr, "Exception cleaning up napi_ref instance\n");
-#endif
-  }
-  delete auto_ref;
-}
-
-// Creates a TFE_TensorHandle from a JS typed array.
-TFE_TensorHandle *CreateTFE_TensorHandleFromTypedArray(napi_env env,
-                                                       int64_t *shape,
-                                                       uint32_t shape_length,
-                                                       TF_DataType dtype,
-                                                       napi_value array_value) {
-  napi_status nstatus;
-  napi_typedarray_type array_type;
-  size_t array_length;
-  void *array_data;
-  nstatus =
-      napi_get_typedarray_info(env, array_value, &array_type, &array_length,
-                               &array_data, nullptr, nullptr);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // Double check the underlying TF_Tensor type matches the supplied
-  // typed-array.
-  size_t width = 0;
-  switch (array_type) {
-    case napi_float32_array:
-      if (dtype != TF_FLOAT) {
-        NAPI_THROW_ERROR(env, "Tensor type does not match Float32Array");
-        return nullptr;
-      }
-      width = sizeof(float);
-      break;
-    case napi_int32_array:
-      if (dtype != TF_INT32 && dtype != TF_INT64) {
-        // Currently, both int32- and int64-type Tensors are represented
-        // as Int32Arrays in JavaScript. See int64_tensors.ts for details
-        // about the latter.
-        NAPI_THROW_ERROR(env, "Tensor type does not match Int32Array");
-        return nullptr;
-      }
-      width = sizeof(int32_t);
-      break;
-    case napi_uint8_array:
-      if (dtype != TF_BOOL && dtype != TF_UINT8) {
-        NAPI_THROW_ERROR(env, "Tensor type does not match Uint8Array");
-        return nullptr;
-      }
-      width = sizeof(uint8_t);
-      break;
-    default:
-      REPORT_UNKNOWN_TYPED_ARRAY_TYPE(env, array_type);
-      return nullptr;
-  }
-
-  // Double check that width matches TF data type size:
-  if (dtype == TF_INT64) {
-    // Currently, int64-type Tensors are represented as Int32Arrays. So the
-    // logic for comparing the byte size of the typed-array representation and
-    // the byte size of the tensor dtype needs to be special-cased for int64.
-    if (width * 2 != TF_DataTypeSize(dtype)) {
-      NAPI_THROW_ERROR(
-          env,
-          "Byte size of elements differs between JavaScript VM "
-          "(%zu * 2 = %zu) and TensorFlow (%zu) for int64-type tensor",
-          width, width * 2, TF_DataTypeSize(dtype));
-      return nullptr;
-    }
-  } else {
-    if (width != TF_DataTypeSize(dtype)) {
-      NAPI_THROW_ERROR(env,
-                       "Byte size of elements differs between JavaScript VM "
-                       "(%zu) and TensorFlow (%zu)",
-                       width, TF_DataTypeSize(dtype));
-      return nullptr;
-    }
-  }
-
-  // Determine the size of the buffer based on the dimensions.
-  size_t num_elements = 1;
-  for (size_t i = 0; i < shape_length; i++) {
-    num_elements *= shape[i];
-  }
-
-  // Ensure the shape matches the length of the passed in typed-array.
-  if (dtype == TF_INT64) {
-    // Currently, int64-type Tensors are represented as Int32Arrays.
-    // To represent a int64-type Tensor of `n` elements, an Int32Array of
-    // length `2 * n` is requried. This is why the length-match checking
-    // logic is special-cased for int64.
-    if (array_length != num_elements * 2) {
-      NAPI_THROW_ERROR(
-          env,
-          "Shape does not match two times typed-array in bindData() "
-          "(num_elements * 2 = %zu, array_length=%zu) for int64 data type",
-          num_elements * 2, array_length);
-      return nullptr;
-    }
-  } else {
-    if (num_elements != array_length) {
-      NAPI_THROW_ERROR(env,
-                       "Shape does not match typed-array in bindData() "
-                       "(num_elements=%zu, array_length=%zu)",
-                       num_elements, array_length);
-      return nullptr;
-    }
-  }
-
-  // Sharing V8 memory with the underlying TensorFlow tensor requires adding an
-  // additional refcount. When the Tensor is deleted, the refcount will be
-  // reduced in the callback helper.
-  NapiAutoRef *auto_ref = new NapiAutoRef();
-  nstatus = auto_ref->Init(env, array_value);
-  if (nstatus != napi_ok) {
-    delete auto_ref;
-  }
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // Currently, int64-type Tensors are represented as Int32Arrays.
-  // So the logic for comparing the byte size of the typed-array representation
-  // and the byte size of the tensor dtype needs to be special-cased for int64.
-  const size_t byte_size =
-      dtype == TF_INT64 ? num_elements * width * 2 : num_elements * width;
-
-  TF_AutoTensor tensor(TF_NewTensor(dtype, shape, shape_length, array_data,
-                                    byte_size, DeallocTensor, auto_ref));
-
-  TF_AutoStatus tf_status;
-  TFE_TensorHandle *tfe_tensor_handle =
-      TFE_NewTensorHandle(tensor.tensor, tf_status.status);
-  if (TF_GetCode(tf_status.status) != TF_OK) {
-    delete auto_ref;
-    TFE_DeleteTensorHandle(tfe_tensor_handle);
-  }
-  ENSURE_TF_OK_RETVAL(env, tf_status, nullptr);
-
-  return tfe_tensor_handle;
-}
-
-// Creates a TFE_TensorHandle from a JS array of Uint8Array values.
-TFE_TensorHandle *CreateTFE_TensorHandleFromStringArray(
-    napi_env env, int64_t *shape, uint32_t shape_length, TF_DataType dtype,
-    napi_value array_value) {
-  napi_status nstatus;
-
-  uint32_t array_length;
-  nstatus = napi_get_array_length(env, array_value, &array_length);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  TF_AutoTensor tensor(TF_AllocateTensor(TF_DataType::TF_STRING, shape,
-                                         shape_length,
-                                         array_length * sizeof(TF_TString)));
-
-  TF_TString *t = reinterpret_cast<TF_TString *>(TF_TensorData(tensor.tensor));
-
-  for (uint32_t i = 0; i < array_length; ++i) {
-    napi_value cur_value;
-    nstatus = napi_get_element(env, array_value, i, &cur_value);
-    ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-    ENSURE_VALUE_IS_TYPED_ARRAY_RETVAL(env, cur_value, nullptr);
-
-    napi_typedarray_type array_type;
-    size_t cur_array_length;
-    void *buffer = nullptr;
-
-    nstatus =
-        napi_get_typedarray_info(env, cur_value, &array_type, &cur_array_length,
-                                 &buffer, nullptr, nullptr);
-    ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-    // Only Uint8 typed arrays are supported.
-    if (array_type != napi_uint8_array) {
-      NAPI_THROW_ERROR(env, "Unsupported array type - expecting Uint8Array");
-      return nullptr;
-    }
-
-    TF_TString_Init(t);
-    TF_TString_Copy(t, reinterpret_cast<char *>(buffer), cur_array_length);
-    t += 1;
-  }
-
-  TF_AutoStatus tf_status;
-
-  TFE_TensorHandle *tfe_tensor_handle =
-      TFE_NewTensorHandle(tensor.tensor, tf_status.status);
-  ENSURE_TF_OK_RETVAL(env, tf_status, nullptr);
-  return tfe_tensor_handle;
-}
-
-TFE_TensorHandle *CreateTFE_TensorHandleFromJSValues(napi_env env,
-                                                     int64_t *shape,
-                                                     uint32_t shape_length,
-                                                     TF_DataType dtype,
-                                                     napi_value array_value) {
-  bool is_typed_array;
-  napi_status nstatus = napi_is_typedarray(env, array_value, &is_typed_array);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  if (is_typed_array) {
-    return CreateTFE_TensorHandleFromTypedArray(env, shape, shape_length, dtype,
-                                                array_value);
-  } else {
-    return CreateTFE_TensorHandleFromStringArray(env, shape, shape_length,
-                                                 dtype, array_value);
-  }
-}
-
-TFE_TensorHandle *CopyTFE_TensorHandleToDevice(napi_env env,
-                                               const char *device_name,
-                                               TFE_TensorHandle *handle,
-                                               TFE_Context *tfe_context) {
-  TF_AutoStatus tf_status;
-
-  TFE_TensorHandle *new_handle = TFE_TensorHandleCopyToDevice(
-      handle, tfe_context, device_name, tf_status.status);
-  ENSURE_TF_OK_RETVAL(env, tf_status, nullptr);
-
-  return new_handle;
-}
-
-void CopyTFE_TensorHandleDataToTypedArray(napi_env env,
-                                          TFE_Context *tfe_context,
-                                          TFE_TensorHandle *tfe_tensor_handle,
-                                          TF_DataType tensor_data_type,
-                                          napi_typedarray_type array_type,
-                                          napi_value *result) {
-  TF_AutoStatus tf_status;
-
-  TF_AutoTensor tensor(
-      TFE_TensorHandleResolve(tfe_tensor_handle, tf_status.status));
-  ENSURE_TF_OK(env, tf_status);
-
-  // Determine the length of the array based on the shape of the tensor.
-  size_t num_elements = GetTensorNumElements(tensor.tensor);
-
-  if (tensor_data_type == TF_COMPLEX64) {
-    // Dimension length will be double for Complex 64.
-    num_elements *= 2;
-  }
-
-  size_t byte_length = TF_TensorByteSize(tensor.tensor);
-
-  napi_value array_buffer_value;
-  void *array_buffer_data;
-  napi_status nstatus;
-  nstatus = napi_create_arraybuffer(env, byte_length, &array_buffer_data,
-                                    &array_buffer_value);
-  ENSURE_NAPI_OK(env, nstatus);
-
-  // TFE_TensorHandleResolve can use a shared data pointer, memcpy() the
-  // current value to the newly allocated NAPI buffer.
-  memcpy(array_buffer_data, TF_TensorData(tensor.tensor), byte_length);
-
-  nstatus = napi_create_typedarray(env, array_type, num_elements,
-                                   array_buffer_value, 0, result);
-  ENSURE_NAPI_OK(env, nstatus);
-}
-
-void CopyTFE_TensorHandleDataToStringArray(napi_env env,
-                                           TFE_Context *tfe_context,
-                                           TFE_TensorHandle *tfe_tensor_handle,
-                                           napi_value *result) {
-  TF_AutoStatus tf_status;
-
-  TF_AutoTensor tensor(
-      TFE_TensorHandleResolve(tfe_tensor_handle, tf_status.status));
-  ENSURE_TF_OK(env, tf_status);
-
-  if (TF_TensorType(tensor.tensor) != TF_STRING) {
-    NAPI_THROW_ERROR(env, "Tensor is not of type TF_STRING");
-    return;
-  }
-
-  size_t num_elements = TF_TensorElementCount(tensor.tensor);
-
-  TF_TString *ts = static_cast<TF_TString *>(TF_TensorData(tensor.tensor));
-  TF_AutoStatus status;
-
-  // Create a JS string to stash strings into
-  napi_status nstatus;
-  nstatus = napi_create_array_with_length(env, num_elements, result);
-
-  for (uint64_t i = 0; i < num_elements; i++) {
-    napi_value array_buffer_value;
-
-    const char *raw_string = TF_TString_GetDataPointer(ts);
-    size_t raw_string_size = TF_TString_GetSize(ts);
-    void *array_buffer_data;
-    nstatus = napi_create_arraybuffer(env, raw_string_size, &array_buffer_data,
-                                      &array_buffer_value);
-    ENSURE_NAPI_OK(env, nstatus);
-
-    memcpy(array_buffer_data, raw_string, raw_string_size);
-
-    napi_value typed_array_value;
-    nstatus = napi_create_typedarray(env, napi_uint8_array, raw_string_size,
-                                     array_buffer_value, 0, &typed_array_value);
-    ENSURE_NAPI_OK(env, nstatus);
-
-    nstatus = napi_set_element(env, *result, i, typed_array_value);
-    ENSURE_NAPI_OK(env, nstatus);
-
-    ts += 1;
-  }
-}
-
-void CopyTFE_TensorHandleDataToResourceArray(
-    napi_env env, TFE_Context *tfe_context, TFE_TensorHandle *tfe_tensor_handle,
-    napi_value *result) {
-  TF_AutoStatus tf_status;
-
-  TF_AutoTensor tensor(
-      TFE_TensorHandleResolve(tfe_tensor_handle, tf_status.status));
-  ENSURE_TF_OK(env, tf_status);
-
-  if (TF_TensorType(tensor.tensor) != TF_RESOURCE) {
-    NAPI_THROW_ERROR(env, "Tensor is not of type TF_RESOURCE");
-    return;
-  }
-
-  void *tensor_data = TF_TensorData(tensor.tensor);
-  ENSURE_VALUE_IS_NOT_NULL(env, tensor_data);
-
-  size_t num_elements = GetTensorNumElements(tensor.tensor);
-  if (num_elements != 1) {
-    NAPI_THROW_ERROR(env,
-                     "For DT_RESOURCE tensors, Node.js binding currently "
-                     "supports only exactly 1 element, but encountered "
-                     "DT_RESOURCE tensor with %zu elements.",
-                     num_elements);
-  }
-
-  TF_AutoStatus status;
-
-  // Create a JS string to stash the resouce handle into.
-  napi_status nstatus;
-  size_t byte_length = TF_TensorByteSize(tensor.tensor);
-  nstatus = napi_create_array_with_length(env, byte_length, result);
-  ENSURE_NAPI_OK(env, nstatus);
-
-  napi_value array_buffer_value;
-  void *array_buffer_data = nullptr;
-  nstatus = napi_create_arraybuffer(env, byte_length, &array_buffer_data,
-                                    &array_buffer_value);
-  ENSURE_NAPI_OK(env, nstatus);
-
-  // TFE_TensorHandleResolve can use a shared data pointer, memcpy() the
-  // current value to the newly allocated NAPI buffer.
-  memcpy(array_buffer_data, tensor_data, byte_length);
-
-  // This method will only return uint8 arrays.
-  nstatus = napi_create_typedarray(env, napi_uint8_array, byte_length,
-                                   array_buffer_value, 0, result);
-  ENSURE_NAPI_OK(env, nstatus);
-}
-
-// Handles converting the stored TF_Tensor data into the correct JS value.
-void CopyTFE_TensorHandleDataToJSData(napi_env env, TFE_Context *tfe_context,
-                                      TFE_TensorHandle *tfe_tensor_handle,
-                                      napi_value *result) {
-  if (tfe_context == nullptr) {
-    NAPI_THROW_ERROR(env, "Invalid TFE_Context");
-    return;
-  }
-  if (tfe_tensor_handle == nullptr) {
-    NAPI_THROW_ERROR(env, "Invalid TFE_TensorHandle");
-    return;
-  }
-
-  // Determine the type of the array
-  napi_typedarray_type typed_array_type;
-  bool is_string = false;
-  bool is_resource = false;
-  TF_DataType tensor_data_type = TFE_TensorHandleDataType(tfe_tensor_handle);
-  switch (tensor_data_type) {
-    case TF_COMPLEX64:
-    case TF_FLOAT:
-      typed_array_type = napi_float32_array;
-      break;
-    case TF_INT32:
-      typed_array_type = napi_int32_array;
-      break;
-    case TF_INT64:
-      typed_array_type = napi_bigint64_array;
-      break;
-    case TF_BOOL:
-      typed_array_type = napi_uint8_array;
-      break;
-    case TF_STRING:
-      is_string = true;
-      break;
-    case TF_RESOURCE:
-      // We currently represent a resource handle as an `Uint8Array`.
-      typed_array_type = napi_uint8_array;
-      is_resource = true;
-      break;
-    default:
-      REPORT_UNKNOWN_TF_DATA_TYPE(env,
-                                  TFE_TensorHandleDataType(tfe_tensor_handle));
-      return;
-  }
-
-  if (is_string) {
-    CopyTFE_TensorHandleDataToStringArray(env, tfe_context, tfe_tensor_handle,
-                                          result);
-  } else if (is_resource) {
-    CopyTFE_TensorHandleDataToResourceArray(env, tfe_context, tfe_tensor_handle,
-                                            result);
-  } else {
-    CopyTFE_TensorHandleDataToTypedArray(env, tfe_context, tfe_tensor_handle,
-                                         tensor_data_type, typed_array_type,
-                                         result);
-  }
-}
-
-void GetTFE_TensorHandleShape(napi_env env, TFE_TensorHandle *handle,
-                              napi_value *result) {
-  napi_status nstatus;
-
-  TF_AutoStatus tf_status;
-  uint32_t num_dims = TFE_TensorHandleNumDims(handle, tf_status.status);
-  ENSURE_TF_OK(env, tf_status);
-
-  if (num_dims == 0) {
-    nstatus = napi_create_array_with_length(env, 0, result);
-    ENSURE_NAPI_OK(env, nstatus);
-  } else {
-    nstatus = napi_create_array_with_length(env, num_dims, result);
-    ENSURE_NAPI_OK(env, nstatus);
-
-    for (uint32_t i = 0; i < num_dims; i++) {
-      napi_value cur_dim;
-      nstatus = napi_create_int64(
-          env, TFE_TensorHandleDim(handle, i, tf_status.status), &cur_dim);
-      ENSURE_TF_OK(env, tf_status);
-      ENSURE_NAPI_OK(env, nstatus);
-
-      nstatus = napi_set_element(env, *result, i, cur_dim);
-      ENSURE_NAPI_OK(env, nstatus);
-    }
-  }
-}
-
-inline bool IsArray(napi_env env, napi_status &nstatus, napi_value *val) {
-  bool is_array;
-  nstatus = napi_is_array(env, *val, &is_array);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, false);
-  return is_array;
-}
-
-void GetTFE_TensorHandleType(napi_env env, TFE_TensorHandle *handle,
-                             napi_value *result) {
-  napi_status nstatus;
-
-  TF_DataType dtype = TFE_TensorHandleDataType(handle);
-  nstatus = napi_create_int32(env, dtype, result);
-  ENSURE_NAPI_OK(env, nstatus);
-}
-
-void AssignOpAttr(napi_env env, TFE_Op *tfe_op, napi_value attr_value) {
-  napi_status nstatus;
-
-  napi_value attr_name_value;
-  nstatus = napi_get_named_property(env, attr_value, "name", &attr_name_value);
-  ENSURE_NAPI_OK(env, nstatus);
-
-  std::string attr_name_string;
-  nstatus = GetStringParam(env, attr_name_value, attr_name_string);
-  ENSURE_NAPI_OK(env, nstatus);
-
-  // OpAttr will be used beyond the scope of this function call. Stash ops in
-  // a set for re-use instead of dynamically reallocating strings for
-  // operations.
-  const char *attr_name =
-      ATTR_NAME_SET.insert(attr_name_string.c_str()).first->c_str();
-
-  napi_value attr_type_value;
-  nstatus = napi_get_named_property(env, attr_value, "type", &attr_type_value);
-  ENSURE_NAPI_OK(env, nstatus);
-
-  TF_AttrType tf_attr_type;
-  nstatus = napi_get_value_int32(env, attr_type_value,
-                                 reinterpret_cast<int32_t *>(&tf_attr_type));
-  ENSURE_NAPI_OK(env, nstatus);
-
-  napi_value js_value;
-  nstatus = napi_get_named_property(env, attr_value, "value", &js_value);
-  ENSURE_NAPI_OK(env, nstatus);
-
-  switch (tf_attr_type) {
-    case TF_ATTR_STRING: {
-      // NOTE: String attribute values do not have to be utf8 encoded strings
-      // (could be arbitrary byte sequences).
-      std::string str_value;
-      nstatus = GetStringParam(env, js_value, str_value);
-      ENSURE_NAPI_OK(env, nstatus);
-
-      TFE_OpSetAttrString(tfe_op, attr_name, str_value.c_str(),
-                          str_value.size());
-      break;
-    }
-
-    case TF_ATTR_INT: {
-      if (IsArray(env, nstatus, &js_value)) {
-        uint32_t length;
-        nstatus = napi_get_array_length(env, js_value, &length);
-        ENSURE_NAPI_OK(env, nstatus);
-        std::unique_ptr<int64_t[]> data(new int64_t[length]);
-        for (uint32_t i = 0; i < length; ++i) {
-          napi_value element;
-          nstatus = napi_get_element(env, js_value, i, &element);
-          ENSURE_NAPI_OK(env, nstatus);
-          int32_t value;
-          nstatus = napi_get_value_int32(env, element, &value);
-          ENSURE_NAPI_OK(env, nstatus);
-          data[i] = value;
-        }
-        TFE_OpSetAttrIntList(tfe_op, attr_name, data.get(),
-                             static_cast<int>(length));
-      } else {
-        int64_t value;
-        nstatus = napi_get_value_int64(env, js_value, &value);
-        ENSURE_NAPI_OK(env, nstatus);
-
-        TFE_OpSetAttrInt(tfe_op, attr_name, value);
-      }
-      break;
-    }
-
-    case TF_ATTR_FLOAT: {
-      if (IsArray(env, nstatus, &js_value)) {
-        uint32_t length;
-        nstatus = napi_get_array_length(env, js_value, &length);
-        ENSURE_NAPI_OK(env, nstatus);
-        std::unique_ptr<float[]> data(new float[length]);
-        for (uint32_t i = 0; i < length; ++i) {
-          napi_value element;
-          nstatus = napi_get_element(env, js_value, i, &element);
-          ENSURE_NAPI_OK(env, nstatus);
-          double value;
-          nstatus = napi_get_value_double(env, element, &value);
-          ENSURE_NAPI_OK(env, nstatus);
-          data[i] = static_cast<float>(value);
-        }
-        TFE_OpSetAttrFloatList(tfe_op, attr_name, data.get(),
-                               static_cast<int>(length));
-      } else {
-        double value;
-        nstatus = napi_get_value_double(env, js_value, &value);
-        ENSURE_NAPI_OK(env, nstatus);
-        TFE_OpSetAttrFloat(tfe_op, attr_name, static_cast<float>(value));
-      }
-      break;
-    }
-
-    case TF_ATTR_BOOL: {
-      if (IsArray(env, nstatus, &js_value)) {
-        uint32_t length;
-        nstatus = napi_get_array_length(env, js_value, &length);
-        ENSURE_NAPI_OK(env, nstatus);
-        std::unique_ptr<unsigned char[]> data(new unsigned char[length]);
-        for (uint32_t i = 0; i < length; ++i) {
-          napi_value element;
-          nstatus = napi_get_element(env, js_value, i, &element);
-          ENSURE_NAPI_OK(env, nstatus);
-          bool value;
-          nstatus = napi_get_value_bool(env, element, &value);
-          ENSURE_NAPI_OK(env, nstatus);
-          data[i] = value ? 1 : 0;
-        }
-        TFE_OpSetAttrBoolList(tfe_op, attr_name, data.get(),
-                              static_cast<int>(length));
-      } else {
-        bool value;
-        nstatus = napi_get_value_bool(env, js_value, &value);
-        ENSURE_NAPI_OK(env, nstatus);
-        TFE_OpSetAttrBool(tfe_op, attr_name, value ? 1 : 0);
-      }
-      break;
-    }
-
-    case TF_ATTR_TYPE: {
-      TF_DataType tf_data_type;
-      nstatus = napi_get_value_int32(
-          env, js_value, reinterpret_cast<int32_t *>(&tf_data_type));
-      ENSURE_NAPI_OK(env, nstatus);
-
-      TFE_OpSetAttrType(tfe_op, attr_name, tf_data_type);
-      break;
-    }
-
-    case TF_ATTR_SHAPE: {
-      std::vector<int64_t> shape_vector;
-      ExtractArrayShape(env, js_value, &shape_vector);
-
-      TF_AutoStatus tf_status;
-      TFE_OpSetAttrShape(tfe_op, attr_name, shape_vector.data(),
-                         shape_vector.size(), tf_status.status);
-      ENSURE_TF_OK(env, tf_status);
-      break;
-    }
-
-    default:
-      REPORT_UNKNOWN_TF_ATTR_TYPE(env, tf_attr_type);
-      break;
-  }
-}
-
-TFJSBackend::TFJSBackend(napi_env env)
-    : next_tensor_id_(0), next_savedmodel_id_(0) {
-  TF_AutoStatus tf_status;
-  TFE_ContextOptions *tfe_options = TFE_NewContextOptions();
-  tfe_context_ = TFE_NewContext(tfe_options, tf_status.status);
-  if (TF_GetCode(tf_status.status) != TF_OK) {
-    NAPI_THROW_ERROR(env, "Exception creating TFE_Context");
-  }
-
-  TFE_DeleteContextOptions(tfe_options);
-
-  TF_DeviceList *device_list =
-      TFE_ContextListDevices(tfe_context_, tf_status.status);
-  if (TF_GetCode(tf_status.status) != TF_OK) {
-    NAPI_THROW_ERROR(env, "Exception creating TFE_Context");
-  }
-
-  // TODO(kreeger): Add better support for this in the future through the JS
-  // API. https://github.com/tensorflow/tfjs/issues/320
-  std::string cpu_device_name;
-  const int num_devices = TF_DeviceListCount(device_list);
-  for (int i = 0; i < num_devices; i++) {
-    const char *device_type =
-        TF_DeviceListType(device_list, i, tf_status.status);
-    ENSURE_TF_OK(env, tf_status);
-
-    // Keep a reference to the host CPU device:
-    if (strcmp(device_type, "CPU") == 0) {
-      cpu_device_name =
-          std::string(TF_DeviceListName(device_list, i, tf_status.status));
-      ENSURE_TF_OK(env, tf_status);
-    } else if (strcmp(device_type, "GPU") == 0) {
-      device_name =
-          std::string(TF_DeviceListName(device_list, i, tf_status.status));
-      ENSURE_TF_OK(env, tf_status);
-    }
-  }
-
-  // If no GPU devices found, fallback to host CPU:
-  if (device_name.empty()) {
-    device_name = cpu_device_name;
-    is_gpu_device = false;
-  } else {
-    is_gpu_device = true;
-  }
-  TF_DeleteDeviceList(device_list);
-}
-
-TFJSBackend::~TFJSBackend() {
-  for (auto &kv : tfe_handle_map_) {
-    TFE_DeleteTensorHandle(kv.second);
-  }
-  for (auto &kv : tf_savedmodel_map_) {
-    TF_AutoStatus tf_status;
-    TF_DeleteSession(kv.second.first, tf_status.status);
-    TF_DeleteGraph(kv.second.second);
-  }
-  if (tfe_context_ != nullptr) {
-    TFE_DeleteContext(tfe_context_);
-  }
-}
-
-TFJSBackend *TFJSBackend::Create(napi_env env) { return new TFJSBackend(env); }
-
-int32_t TFJSBackend::InsertHandle(TFE_TensorHandle *tfe_handle) {
-  return tfe_handle_map_.insert(std::make_pair(next_tensor_id_++, tfe_handle))
-      .first->first;
-}
-
-int32_t TFJSBackend::InsertSavedModel(TF_Session *tf_session,
-                                      TF_Graph *tf_graph) {
-  // Both TF_Session and TF_Graph are required when executing SavedModel.
-  // TF_Graph is used to find input/output operation from string name.
-  return tf_savedmodel_map_
-      .insert(std::make_pair(next_savedmodel_id_++,
-                             std::make_pair(tf_session, tf_graph)))
-      .first->first;
-}
-
-napi_value TFJSBackend::CreateTensor(napi_env env, napi_value shape_value,
-                                     napi_value dtype_value,
-                                     napi_value array_value) {
-  napi_status nstatus;
-
-  std::vector<int64_t> shape_vector;
-  ExtractArrayShape(env, shape_value, &shape_vector);
-  // Check to see if an exception exists, if so return a failure.
-  if (IsExceptionPending(env)) {
-    return nullptr;
-  }
-
-  int32_t dtype_int32;
-  nstatus = napi_get_value_int32(env, dtype_value, &dtype_int32);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  TFE_TensorHandle *tfe_handle = CreateTFE_TensorHandleFromJSValues(
-      env, shape_vector.data(), shape_vector.size(),
-      static_cast<TF_DataType>(dtype_int32), array_value);
-
-  // Check to see if an exception exists, if so return a failure.
-  if (IsExceptionPending(env)) {
-    return nullptr;
-  }
-
-  // Copy non-int32 and non-string tensors to a device. Most GPU kernels expect
-  // to have int32 tensors in host memory.
-  if (dtype_int32 != TF_INT32 && dtype_int32 != TF_STRING) {
-    // Note that this is a shallow copy and will share the underlying buffer
-    // if copying to the same device.
-    TFE_TensorHandle *new_handle = CopyTFE_TensorHandleToDevice(
-        env, device_name.c_str(), tfe_handle, tfe_context_);
-
-    TFE_DeleteTensorHandle(tfe_handle);
-    tfe_handle = new_handle;
-  }
-
-  napi_value output_tensor_id;
-  nstatus = napi_create_int32(env, InsertHandle(tfe_handle), &output_tensor_id);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  return output_tensor_id;
-}
-
-void TFJSBackend::DeleteTensor(napi_env env, napi_value tensor_id_value) {
-  int32_t tensor_id;
-  ENSURE_NAPI_OK(env, napi_get_value_int32(env, tensor_id_value, &tensor_id));
-
-  auto tensor_entry = tfe_handle_map_.find(tensor_id);
-  if (tensor_entry == tfe_handle_map_.end()) {
-    NAPI_THROW_ERROR(env,
-                     "Delete called on a Tensor not referenced (tensor_id: %d)",
-                     tensor_id);
-    return;
-  }
-
-  TFE_TensorHandle *tensor_handle = tensor_entry->second;
-  if (TFE_TensorHandleDataType(tensor_handle) == TF_STRING) {
-    TF_AutoStatus tf_status;
-    TF_AutoTensor tensor(
-        TFE_TensorHandleResolve(tensor_handle, tf_status.status));
-    ENSURE_TF_OK(env, tf_status);
-    size_t num_elements = GetTensorNumElements(tensor.tensor);
-    TF_TString *data = reinterpret_cast<TF_TString *>(TF_TensorData(tensor.tensor));
-
-    // Deallocate each string
-    for (size_t i = 0; i < num_elements; i++) {
-      TF_TString_Dealloc(data + i);
-    }
-  };
-  TFE_DeleteTensorHandle(tensor_entry->second);
-  tfe_handle_map_.erase(tensor_entry);
-}
-
-napi_value TFJSBackend::GetNumOfTensors(napi_env env) {
-  napi_status nstatus;
-  napi_value num_of_tensors;
-  nstatus =
-      napi_create_int32(env, tfe_handle_map_.size(), &num_of_tensors);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  return num_of_tensors;
-}
-
-napi_value TFJSBackend::GetTensorData(napi_env env,
-                                      napi_value tensor_id_value) {
-  int32_t tensor_id;
-  ENSURE_NAPI_OK_RETVAL(
-      env, napi_get_value_int32(env, tensor_id_value, &tensor_id), nullptr);
-
-  auto tensor_entry = tfe_handle_map_.find(tensor_id);
-  if (tensor_entry == tfe_handle_map_.end()) {
-    NAPI_THROW_ERROR(
-        env, "Get data called on a Tensor not referenced (tensor_id: %d)",
-        tensor_id);
-    return nullptr;
-  }
-
-  napi_value js_value;
-  CopyTFE_TensorHandleDataToJSData(env, tfe_context_, tensor_entry->second,
-                                   &js_value);
-  return js_value;
-}
-
-napi_value TFJSBackend::ExecuteOp(napi_env env, napi_value op_name_value,
-                                  napi_value op_attr_inputs,
-                                  napi_value input_tensor_ids,
-                                  napi_value num_output_values) {
-  napi_status nstatus;
-
-  std::string op_name;
-  nstatus = GetStringParam(env, op_name_value, op_name);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  TF_AutoStatus tf_status;
-  TFE_AutoOp tfe_op(TFE_NewOp(tfe_context_, op_name.c_str(), tf_status.status));
-  ENSURE_TF_OK_RETVAL(env, tf_status, nullptr);
-
-  uint32_t num_input_ids;
-  nstatus = napi_get_array_length(env, input_tensor_ids, &num_input_ids);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  for (uint32_t i = 0; i < num_input_ids; i++) {
-    napi_value cur_input_id;
-    nstatus = napi_get_element(env, input_tensor_ids, i, &cur_input_id);
-    ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-    int32_t cur_input_tensor_id;
-    nstatus = napi_get_value_int32(env, cur_input_id, &cur_input_tensor_id);
-    ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-    auto input_tensor_entry = tfe_handle_map_.find(cur_input_tensor_id);
-    if (input_tensor_entry == tfe_handle_map_.end()) {
-      NAPI_THROW_ERROR(env, "Input Tensor ID not referenced (tensor_id: %d)",
-                       cur_input_tensor_id);
-      return nullptr;
-    }
-
-    TFE_OpAddInput(tfe_op.op, input_tensor_entry->second, tf_status.status);
-    ENSURE_TF_OK_RETVAL(env, tf_status, nullptr);
-  }
-
-  uint32_t op_attrs_length;
-  nstatus = napi_get_array_length(env, op_attr_inputs, &op_attrs_length);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  for (uint32_t i = 0; i < op_attrs_length; i++) {
-    napi_value cur_op_attr;
-    nstatus = napi_get_element(env, op_attr_inputs, i, &cur_op_attr);
-    ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-    AssignOpAttr(env, tfe_op.op, cur_op_attr);
-
-    // Check to see if an exception exists, if so return a failure.
-    if (IsExceptionPending(env)) {
-      return nullptr;
-    }
-  }
-
-  int32_t num_outputs;
-  nstatus = napi_get_value_int32(env, num_output_values, &num_outputs);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // Push `nullptr` to get a valid pointer in the call to `TFE_Execute()`
-  // below.
-  std::vector<TFE_TensorHandle *> result_handles(num_outputs, nullptr);
-
-  int size = result_handles.size();
-  TFE_Execute(tfe_op.op, result_handles.data(), &size, tf_status.status);
-  ENSURE_TF_OK_RETVAL(env, tf_status, nullptr);
-
-  napi_value output_tensor_infos;
-  nstatus = napi_create_array_with_length(env, size, &output_tensor_infos);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  for (int32_t i = 0; i < num_outputs; i++) {
-    TFE_TensorHandle *handle = result_handles[i];
-    napi_value tensor_info_value = GenerateOutputTensorInfo(env, handle);
-    // Push into output array
-    nstatus = napi_set_element(env, output_tensor_infos, i, tensor_info_value);
-    ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  }
-
-  return output_tensor_infos;
-}
-
-/* Helper function to generate TensorInfo(used for JavaScript) from
- * TFE_TensorHandle. This helper function is used by ExecuteOp() and
- * RunSavedModel().
- */
-napi_value TFJSBackend::GenerateOutputTensorInfo(napi_env env,
-                                                 TFE_TensorHandle *handle) {
-  napi_status nstatus;
-
-  // Output tensor info object:
-  napi_value tensor_info_value;
-  nstatus = napi_create_object(env, &tensor_info_value);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // Output tensor ID:
-  napi_value output_tensor_id_value;
-  nstatus =
-      napi_create_int32(env, InsertHandle(handle), &output_tensor_id_value);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  nstatus = napi_set_named_property(env, tensor_info_value, "id",
-                                    output_tensor_id_value);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // Output tensor shape:
-  napi_value shape_value;
-  GetTFE_TensorHandleShape(env, handle, &shape_value);
-
-  nstatus =
-      napi_set_named_property(env, tensor_info_value, "shape", shape_value);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // Output tensor dtype:
-  napi_value type_value;
-  GetTFE_TensorHandleType(env, handle, &type_value);
-
-  nstatus =
-      napi_set_named_property(env, tensor_info_value, "dtype", type_value);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  return tensor_info_value;
-}
-
-napi_value TFJSBackend::LoadSavedModel(napi_env env,
-                                       napi_value export_dir_value,
-                                       napi_value tags_value) {
-  TF_SessionOptions *session_options = TF_NewSessionOptions();
-
-  TF_Buffer *run_options = TF_NewBufferFromString("", 0);
-
-  std::string export_dir_string;
-  napi_status nstatus;
-  nstatus = GetStringParam(env, export_dir_value, export_dir_string);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  const char *export_dir = export_dir_string.c_str();
-
-  std::string tags;
-  nstatus = GetStringParam(env, tags_value, tags);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  std::vector<const char *> tags_ptrs = splitStringByComma(tags);
-
-  TF_Graph *graph = TF_NewGraph();
-
-  TF_Buffer *metagraph = TF_NewBuffer();
-
-  TF_AutoStatus tf_status;
-
-  TF_Session *session = TF_LoadSessionFromSavedModel(
-      session_options, run_options, export_dir, tags_ptrs.data(),
-      tags_ptrs.size(), graph, metagraph, tf_status.status);
-  // Delete objects that are necessary when loading the SavedModel but not gonna
-  // be used later.
-  TF_DeleteSessionOptions(session_options);
-  TF_DeleteBuffer(run_options);
-  TF_DeleteBuffer(metagraph);
-
-  if (TF_GetCode(tf_status.status) != TF_OK) {
-    NAPI_THROW_ERROR(env, "Failed to load SavedModel: %s",
-                     TF_Message(tf_status.status));
-    return nullptr;
-  }
-
-  napi_value output_session_id;
-  nstatus = napi_create_int32(env, InsertSavedModel(session, graph),
-                              &output_session_id);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // clean up the tags strings.
-  for (uint32_t i = 0; i < tags_ptrs.size(); i++) {
-    delete tags_ptrs[i];
-  }
-  return output_session_id;
-}
-
-void TFJSBackend::DeleteSavedModel(napi_env env,
-                                   napi_value savedmodel_id_value) {
-  int32_t savedmodel_id;
-  ENSURE_NAPI_OK(
-      env, napi_get_value_int32(env, savedmodel_id_value, &savedmodel_id));
-
-  auto savedmodel_entry = tf_savedmodel_map_.find(savedmodel_id);
-  if (savedmodel_entry == tf_savedmodel_map_.end()) {
-    NAPI_THROW_ERROR(
-        env, "Delete called on a SavedModel not found (savedmodel_id: %d)",
-        savedmodel_id);
-    return;
-  }
-
-  TF_AutoStatus tf_status;
-  TF_DeleteSession(savedmodel_entry->second.first, tf_status.status);
-  if (TF_GetCode(tf_status.status) != TF_OK) {
-    NAPI_THROW_ERROR(env, "Failed to delete SavedModel: %s",
-                     TF_Message(tf_status.status));
-    return;
-  }
-  // TODO(kangyizhang): Add tests to validate TF_Session and TF_Graph are
-  // deleted.
-  TF_DeleteGraph(savedmodel_entry->second.second);
-  tf_savedmodel_map_.erase(savedmodel_entry);
-}
-
-napi_value TFJSBackend::RunSavedModel(napi_env env,
-                                      napi_value savedmodel_id_value,
-                                      napi_value input_tensor_ids,
-                                      napi_value input_op_names_value,
-                                      napi_value output_op_names_value) {
-  napi_status nstatus;
-  TF_AutoStatus tf_status;
-
-  int32_t savedmodel_id;
-  nstatus = napi_get_value_int32(env, savedmodel_id_value, &savedmodel_id);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // Get corresponding SavedModel session and graph.
-  auto savedmodel_entry = tf_savedmodel_map_.find(savedmodel_id);
-  if (savedmodel_entry == tf_savedmodel_map_.end()) {
-    NAPI_THROW_ERROR(env, "SavedModel ID not found (savedmodel_id: %d)",
-                     savedmodel_id);
-    return nullptr;
-  }
-
-  std::string input_op_names;
-  nstatus = GetStringParam(env, input_op_names_value, input_op_names);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  std::string output_op_names;
-  nstatus = GetStringParam(env, output_op_names_value, output_op_names);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // Get input/output op names as vector
-  std::vector<const char *> input_op_name_array =
-      splitStringByComma(input_op_names);
-  std::vector<const char *> output_op_name_array =
-      splitStringByComma(output_op_names);
-
-  std::vector<TF_Output> inputs;
-  std::vector<TF_Output> outputs;
-
-  uint32_t num_input_ids;
-  nstatus = napi_get_array_length(env, input_tensor_ids, &num_input_ids);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  if (input_op_name_array.size() != num_input_ids) {
-    NAPI_THROW_ERROR(env,
-                     "Length of input op names (%d) does not match the length "
-                     "of input tensors (%d).",
-                     input_op_name_array.size(), num_input_ids);
-    return nullptr;
-  }
-
-  std::vector<TF_Tensor *> input_values;
-
-  for (uint32_t i = 0; i < num_input_ids; i++) {
-    napi_value cur_input_id;
-    nstatus = napi_get_element(env, input_tensor_ids, i, &cur_input_id);
-    ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-    int32_t cur_input_tensor_id;
-    nstatus = napi_get_value_int32(env, cur_input_id, &cur_input_tensor_id);
-    ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-    // Find input tensor based on tensor id.
-    auto tensor_entry = tfe_handle_map_.find(cur_input_tensor_id);
-    if (tensor_entry == tfe_handle_map_.end()) {
-      NAPI_THROW_ERROR(env, "Input Tensor ID not found (tensor_id: %d)",
-                       cur_input_tensor_id);
-      return nullptr;
-    }
-    TF_Tensor *inputTensor =
-        TFE_TensorHandleResolve(tensor_entry->second, tf_status.status);
-
-    if (TF_GetCode(tf_status.status) != TF_OK) {
-      NAPI_THROW_ERROR(
-          env, "Failed to get input tensor (tensor_id: %d) for session.",
-          cur_input_tensor_id);
-      return nullptr;
-    }
-
-    // Add input tensor into input values list.
-    input_values.push_back(inputTensor);
-
-    // The item in input_op_name_array is something like "serving_default_x:0".
-    // Parse it into input op name and index for provided tensor.
-    std::string name(input_op_name_array[i]);
-    int index = name.find(":");
-    std::string input_op_name = name.substr(0, index);
-    std::string input_op_index = name.substr(index + 1);
-    int input_tensor_index;
-    if (input_op_index.length() == 0) {
-      input_tensor_index = 0;
-    } else {
-      input_tensor_index = atoi(input_op_index.c_str());
-    }
-
-    // Add input op into input ops list.
-    // TODO(kangyizhang): Store these TF_Operations somewhere so they don't need
-    // to be generated  every time.
-    TF_Operation *input_op = TF_GraphOperationByName(
-        savedmodel_entry->second.second, input_op_name.c_str());
-    if (input_op == nullptr) {
-      NAPI_THROW_ERROR(env, "Input op name can not be found in the graph.");
-      return nullptr;
-    }
-    TF_Output in = {input_op, input_tensor_index};
-    inputs.push_back(in);
-  }
-
-  // Add output op into output ops list.
-  for (uint32_t i = 0; i < output_op_name_array.size(); i++) {
-    // The item in output_op_name_array is something like
-    // "StatefulPartitionedCall:0". Parse it into output op name and index.
-    std::string name(output_op_name_array[i]);
-    int index = name.find(":");
-    std::string output_op_name = name.substr(0, index);
-    std::string output_op_index = name.substr(index + 1);
-    int output_tensor_index;
-    if (output_op_index.length() == 0) {
-      output_tensor_index = 0;
-    } else {
-      output_tensor_index = atoi(output_op_index.c_str());
-    }
-
-    TF_Operation *output_op = TF_GraphOperationByName(
-        savedmodel_entry->second.second, output_op_name.c_str());
-    if (output_op == nullptr) {
-      NAPI_THROW_ERROR(env, "Output op name can not be found in the graph.");
-      return nullptr;
-    }
-    TF_Output out = {output_op, output_tensor_index};
-    outputs.push_back(out);
-  }
-
-  std::vector<TF_Tensor *> output_values(outputs.size(), nullptr);
-
-  TF_SessionRun(savedmodel_entry->second.first, nullptr, inputs.data(),
-                input_values.data(), num_input_ids, outputs.data(),
-                output_values.data(), output_op_name_array.size(), nullptr, 0,
-                nullptr, tf_status.status);
-
-  if (TF_GetCode(tf_status.status) != TF_OK) {
-    NAPI_THROW_ERROR(env, "Session fail to run with error: %s",
-                     TF_Message(tf_status.status));
-    return nullptr;
-  }
-
-  napi_value output_tensor_infos;
-  nstatus = napi_create_array_with_length(env, 1, &output_tensor_infos);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  // Generate output tensors for JS.
-  for (uint32_t i = 0; i < output_op_name_array.size(); i++) {
-    TFE_TensorHandle *tfe_handle =
-        TFE_NewTensorHandle(output_values[i], tf_status.status);
-    // Deallocate output TF_Tensor in C++.
-    TF_DeleteTensor(output_values[i]);
-
-    napi_value tensor_info_value = GenerateOutputTensorInfo(env, tfe_handle);
-    // Push into output array
-    nstatus = napi_set_element(env, output_tensor_infos, i, tensor_info_value);
-    ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-    // delete output op name string
-    delete output_op_name_array[i];
-  }
-
-  for (uint32_t i = 0; i < num_input_ids; i++) {
-    // Deallocate input TF_Tensor in C++.
-    TF_DeleteTensor(input_values[i]);
-    // delete input op name string
-    delete input_op_name_array[i];
-  }
-
-  return output_tensor_infos;
-}
-
-napi_value TFJSBackend::GetNumOfSavedModels(napi_env env) {
-  napi_status nstatus;
-  napi_value num_saved_models;
-  nstatus =
-      napi_create_int32(env, tf_savedmodel_map_.size(), &num_saved_models);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  return num_saved_models;
-}
-
-}  // namespace tfnodejs
diff --git a/tfjs-master/tfjs-node/binding/tfjs_backend.h b/tfjs-master/tfjs-node/binding/tfjs_backend.h
deleted file mode 100644
index ca85bc211..000000000
--- a/tfjs-master/tfjs-node/binding/tfjs_backend.h
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#ifndef TF_NODEJS_TFJS_BACKEND_H_
-#define TF_NODEJS_TFJS_BACKEND_H_
-
-#include <node_api.h>
-#include <memory>
-#include <string>
-#include <unordered_map>
-#include "tensorflow/c/c_api.h"
-#include "tensorflow/c/eager/c_api.h"
-
-namespace tfnodejs {
-
-class TFJSBackend {
- public:
-  ~TFJSBackend();
-
-  // Creates, initializes, and returns a TFJSBackend instance. If initialization
-  // fails, a nullptr is returned.
-  static TFJSBackend *Create(napi_env env);
-
-  // Creates a new Tensor with given shape and data and returns an ID that
-  // refernces the new Tensor.
-  // - shape_value (number[])
-  // - dtype_value (number)
-  // - array_value (TypedArray|Array)
-  napi_value CreateTensor(napi_env env, napi_value shape_value,
-                          napi_value dtype_value, napi_value array_value);
-
-  // Deletes a created Tensor.
-  // - tensor_id_value (number)
-  void DeleteTensor(napi_env env, napi_value tensor_id_value);
-
-  // Returns a typed-array as a `napi_value` with the data associated with the
-  // TF/TFE pointers.
-  // - tensor_id_value (number)
-  napi_value GetTensorData(napi_env env, napi_value tensor_id_value);
-
-  // Executes a TFE Op and returns an array of objects containing tensor
-  // attributes (id, dtype, shape).
-  // - op_name_value (string)
-  // - op_attr_inputs (array of TFE Op attributes)
-  // - input_tensor_ids (array of input tensor IDs)
-  // - num_output_values (number)
-  napi_value ExecuteOp(napi_env env, napi_value op_name_value,
-                       napi_value op_attr_inputs, napi_value input_tensor_ids,
-                       napi_value num_output_values);
-
-  // Load a SavedModel from a path:
-  // - export_dir (string)
-  // - tags_value (string)
-  napi_value LoadSavedModel(napi_env env, napi_value export_dir,
-                            napi_value tags_value);
-
-  // Delete the SavedModel corresponding TF_Session and TF_Graph
-  // - saved_model_id (number)
-  void DeleteSavedModel(napi_env env, napi_value saved_model_id);
-
-  // Execute a session from SavedModel with the provided inputs:
-  // - saved_model_id (number)
-  // - input_tensor_ids (array of input tensor IDs)
-  // - input_op_names (array of input op names)
-  // - output_op_names (array of output op names)
-  napi_value RunSavedModel(napi_env env, napi_value saved_model_id,
-                           napi_value input_tensor_ids,
-                           napi_value input_op_names,
-                           napi_value output_op_names);
-
-  // Get number of loaded SavedModel in the backend:
-  napi_value GetNumOfSavedModels(napi_env env);
-
-  // Get number of tensor handles in the backend:
-  napi_value GetNumOfTensors(napi_env env);
-
- private:
-  TFJSBackend(napi_env env);
-
-  int32_t InsertHandle(TFE_TensorHandle *tfe_handle);
-  int32_t InsertSavedModel(TF_Session *tf_session, TF_Graph *tf_graph);
-  napi_value GenerateOutputTensorInfo(napi_env env, TFE_TensorHandle *handle);
-
-  TFE_Context *tfe_context_;
-  std::unordered_map<int32_t, TFE_TensorHandle *> tfe_handle_map_;
-  std::unordered_map<int32_t, std::pair<TF_Session *, TF_Graph *>>
-      tf_savedmodel_map_;
-  int32_t next_tensor_id_;
-  int32_t next_savedmodel_id_;
-  std::string device_name;
-
- public:
-  bool is_gpu_device;
-};
-
-}  // namespace tfnodejs
-
-#endif  // TF_NODEJS_TFJS_BACKEND_H_
diff --git a/tfjs-master/tfjs-node/binding/tfjs_binding.cc b/tfjs-master/tfjs-node/binding/tfjs_binding.cc
deleted file mode 100644
index ad00b79e7..000000000
--- a/tfjs-master/tfjs-node/binding/tfjs_binding.cc
+++ /dev/null
@@ -1,358 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#include <node_api.h>
-#include "tfjs_backend.h"
-#include "utils.h"
-
-namespace tfnodejs {
-
-/**
- * Get the TFJSBackend associated with this environment instance.
- * While this does throw a JS error if the instance data is not found,
- * the caller should still check if the return value is a `nullptr`.
- */
-static inline TFJSBackend *GetTFJSBackend(napi_env env)
-{
-  TFJSBackend *backend = nullptr;
-  napi_status nstatus = napi_get_instance_data(env, reinterpret_cast<void **>(&backend));
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  return backend;
-}
-
-static void AssignIntProperty(napi_env env, napi_value exports,
-                              const char *name, int32_t value) {
-
-  napi_value js_value;
-  napi_status nstatus = napi_create_int32(env, value, &js_value);
-  ENSURE_NAPI_OK(env, nstatus);
-
-  napi_property_descriptor property = {name,         nullptr, nullptr,
-                                       nullptr,      nullptr, js_value,
-                                       napi_default, nullptr};
-  nstatus = napi_define_properties(env, exports, 1, &property);
-  ENSURE_NAPI_OK(env, nstatus);
-}
-
-static napi_value CreateTensor(napi_env env, napi_callback_info info) {
-  napi_status nstatus;
-
-  // Create tensor takes 3 params: shape, dtype, typed-array/array:
-  size_t argc = 3;
-  napi_value args[3];
-  napi_value js_this;
-  nstatus = napi_get_cb_info(env, info, &argc, args, &js_this, nullptr);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  if (argc < 3) {
-    NAPI_THROW_ERROR(env,
-                     "Invalid number of args passed to createTensor(). "
-                     "Expecting 3 args but got %d.",
-                     argc);
-    return nullptr;
-  }
-
-  ENSURE_VALUE_IS_ARRAY_RETVAL(env, args[0], nullptr);
-  ENSURE_VALUE_IS_NUMBER_RETVAL(env, args[1], nullptr);
-
-  // The third array can either be a typed array or an array:
-  bool is_typed_array;
-  nstatus = napi_is_typedarray(env, args[2], &is_typed_array);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-  if (!is_typed_array) {
-    ENSURE_VALUE_IS_ARRAY_RETVAL(env, args[2], nullptr);
-  }
-
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-
-  return backend->CreateTensor(env, args[0], args[1], args[2]);
-}
-
-static napi_value DeleteTensor(napi_env env, napi_callback_info info) {
-  napi_status nstatus;
-
-  // Delete tensor takes 1 param: tensor ID;
-  size_t argc = 1;
-  napi_value args[1];
-  napi_value js_this;
-  nstatus = napi_get_cb_info(env, info, &argc, args, &js_this, nullptr);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, js_this);
-
-  if (argc < 1) {
-    NAPI_THROW_ERROR(env,
-                     "Invalid number of args passed to deleteTensor(). "
-                     "Expecting 1 arg but got %d.",
-                     argc);
-    return js_this;
-  }
-
-  ENSURE_VALUE_IS_NUMBER_RETVAL(env, args[0], js_this);
-
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-
-  backend->DeleteTensor(env, args[0]);
-  return js_this;
-}
-
-static napi_value TensorDataSync(napi_env env, napi_callback_info info) {
-  napi_status nstatus;
-
-  // Tensor data-sync takes 1 param: tensor ID;
-  size_t argc = 1;
-  napi_value args[1];
-  napi_value js_this;
-  nstatus = napi_get_cb_info(env, info, &argc, args, &js_this, nullptr);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, js_this);
-
-  if (argc < 1) {
-    NAPI_THROW_ERROR(env,
-                     "Invalid number of args passed to tensorDataSync(). "
-                     "Expecting 1 arg but got %d.",
-                     argc);
-    return nullptr;
-  }
-
-  ENSURE_VALUE_IS_NUMBER_RETVAL(env, args[0], js_this);
-
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-
-  return backend->GetTensorData(env, args[0]);
-}
-
-static napi_value ExecuteOp(napi_env env, napi_callback_info info) {
-  napi_status nstatus;
-
-  // Create tensor takes 4 params: op-name, op-attrs, input-tensor-ids,
-  // num-outputs:
-  size_t argc = 4;
-  napi_value args[4];
-  napi_value js_this;
-  nstatus = napi_get_cb_info(env, info, &argc, args, &js_this, nullptr);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  if (argc < 4) {
-    NAPI_THROW_ERROR(env,
-                     "Invalid number of args passed to executeOp(). Expecting "
-                     "4 args but got %d.",
-                     argc);
-    return nullptr;
-  }
-
-  ENSURE_VALUE_IS_STRING_RETVAL(env, args[0], nullptr);
-  ENSURE_VALUE_IS_ARRAY_RETVAL(env, args[1], nullptr);
-  ENSURE_VALUE_IS_ARRAY_RETVAL(env, args[2], nullptr);
-  ENSURE_VALUE_IS_NUMBER_RETVAL(env, args[3], nullptr);
-
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-
-  return backend->ExecuteOp(env, args[0], args[1], args[2], args[3]);
-}
-
-static napi_value IsUsingGPUDevice(napi_env env, napi_callback_info info) {
-  napi_value result;
-
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-
-  napi_status nstatus;
-  nstatus = napi_get_boolean(env, backend->is_gpu_device, &result);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  return result;
-}
-
-static napi_value LoadSavedModel(napi_env env, napi_callback_info info) {
-  napi_status nstatus;
-
-  // Load saved model takes 2 params: export_dir, tags:
-  size_t argc = 2;
-  napi_value args[2];
-  napi_value js_this;
-  nstatus = napi_get_cb_info(env, info, &argc, args, &js_this, nullptr);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  if (argc < 2) {
-    NAPI_THROW_ERROR(env,
-                     "Invalid number of args passed to LoadSavedModel(). "
-                     "Expecting 2 args but got %d.",
-                     argc);
-    return nullptr;
-  }
-
-  ENSURE_VALUE_IS_STRING_RETVAL(env, args[0], nullptr);
-  ENSURE_VALUE_IS_STRING_RETVAL(env, args[1], nullptr);
-
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-
-  return backend->LoadSavedModel(env, args[0], args[1]);
-}
-
-static napi_value DeleteSavedModel(napi_env env, napi_callback_info info) {
-  napi_status nstatus;
-
-  // Delete SavedModel takes 1 param: savedModel ID;
-  size_t argc = 1;
-  napi_value args[1];
-  napi_value js_this;
-  nstatus = napi_get_cb_info(env, info, &argc, args, &js_this, nullptr);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, js_this);
-
-  if (argc < 1) {
-    NAPI_THROW_ERROR(env,
-                     "Invalid number of args passed to deleteSavedModel(). "
-                     "Expecting 1 arg but got %d.",
-                     argc);
-    return js_this;
-  }
-
-  ENSURE_VALUE_IS_NUMBER_RETVAL(env, args[0], js_this);
-
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-
-  backend->DeleteSavedModel(env, args[0]);
-  return js_this;
-}
-
-static napi_value RunSavedModel(napi_env env, napi_callback_info info) {
-  napi_status nstatus;
-
-  // Run SavedModel takes 4 params: session_id, input_tensor_ids,
-  // input_op_names, output_op_names.
-  size_t argc = 4;
-  napi_value args[4];
-  napi_value js_this;
-  nstatus = napi_get_cb_info(env, info, &argc, args, &js_this, nullptr);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nullptr);
-
-  if (argc < 4) {
-    NAPI_THROW_ERROR(env, "Invalid number of args passed to RunSavedModel()");
-    return nullptr;
-  }
-
-  ENSURE_VALUE_IS_NUMBER_RETVAL(env, args[0], nullptr);
-  ENSURE_VALUE_IS_ARRAY_RETVAL(env, args[1], nullptr);
-  ENSURE_VALUE_IS_STRING_RETVAL(env, args[2], nullptr);
-  ENSURE_VALUE_IS_STRING_RETVAL(env, args[3], nullptr);
-
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-
-  return backend->RunSavedModel(env, args[0], args[1], args[2], args[3]);
-}
-
-static napi_value GetNumOfTensors(napi_env env, napi_callback_info info) {
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-  // Delete SavedModel takes 0 param;
-  return backend->GetNumOfTensors(env);
-}
-
-static napi_value GetNumOfSavedModels(napi_env env, napi_callback_info info) {
-  TFJSBackend *const backend = GetTFJSBackend(env);
-  if (!backend) return nullptr;
-  // Delete SavedModel takes 0 param;
-  return backend->GetNumOfSavedModels(env);
-}
-
-/**
- * Called by Node to cleanup our instance data, which is
- * the TFJSBackend allocated in `InitTFNodeJSBinding`.
- */
-static void FinalizeTFNodeJSBinding(napi_env env, void *finalize_data, void *finalize_hint)
-{
-  delete reinterpret_cast<TFJSBackend *>(finalize_data);
-}
-
-static napi_value InitTFNodeJSBinding(napi_env env, napi_value exports) {
-
-  napi_status nstatus;
-
-  TFJSBackend *const backend = TFJSBackend::Create(env);
-  ENSURE_VALUE_IS_NOT_NULL_RETVAL(env, backend, nullptr);
-
-  // Store the backend in node's instance data for this addon
-  nstatus = napi_set_instance_data(env, backend, &FinalizeTFNodeJSBinding, nullptr);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, exports);
-
-  // TF version
-  napi_value tf_version;
-  nstatus = napi_create_string_latin1(env, TF_Version(), -1, &tf_version);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, exports);
-
-  // Set all export values list here.
-  napi_property_descriptor exports_properties[] = {
-      {"createTensor", nullptr, CreateTensor, nullptr, nullptr, nullptr,
-       napi_default, nullptr},
-      {"deleteTensor", nullptr, DeleteTensor, nullptr, nullptr, nullptr,
-       napi_default, nullptr},
-      {"tensorDataSync", nullptr, TensorDataSync, nullptr, nullptr, nullptr,
-       napi_default, nullptr},
-      {"executeOp", nullptr, ExecuteOp, nullptr, nullptr, nullptr, napi_default,
-       nullptr},
-      {"loadSavedModel", nullptr, LoadSavedModel, nullptr, nullptr, nullptr,
-       napi_default, nullptr},
-      {"deleteSavedModel", nullptr, DeleteSavedModel, nullptr, nullptr, nullptr,
-       napi_default, nullptr},
-      {"runSavedModel", nullptr, RunSavedModel, nullptr, nullptr, nullptr,
-       napi_default, nullptr},
-      {"TF_Version", nullptr, nullptr, nullptr, nullptr, tf_version,
-       napi_default, nullptr},
-      {"isUsingGpuDevice", nullptr, IsUsingGPUDevice, nullptr, nullptr, nullptr,
-       napi_default, nullptr},
-      {"getNumOfSavedModels", nullptr, GetNumOfSavedModels, nullptr, nullptr,
-       nullptr, napi_default, nullptr},
-      {"getNumOfTensors", nullptr, GetNumOfTensors, nullptr, nullptr,
-       nullptr, napi_default, nullptr},
-
-  };
-  nstatus = napi_define_properties(env, exports, ARRAY_SIZE(exports_properties),
-                                   exports_properties);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, exports);
-
-  // Export TF property types to JS
-#define EXPORT_INT_PROPERTY(v) AssignIntProperty(env, exports, #v, v)
-  // Types
-  EXPORT_INT_PROPERTY(TF_FLOAT);
-  EXPORT_INT_PROPERTY(TF_INT32);
-  EXPORT_INT_PROPERTY(TF_INT64);
-  EXPORT_INT_PROPERTY(TF_BOOL);
-  EXPORT_INT_PROPERTY(TF_COMPLEX64);
-  EXPORT_INT_PROPERTY(TF_STRING);
-  EXPORT_INT_PROPERTY(TF_RESOURCE);
-  EXPORT_INT_PROPERTY(TF_UINT8);
-
-  // Op AttrType
-  EXPORT_INT_PROPERTY(TF_ATTR_STRING);
-  EXPORT_INT_PROPERTY(TF_ATTR_INT);
-  EXPORT_INT_PROPERTY(TF_ATTR_FLOAT);
-  EXPORT_INT_PROPERTY(TF_ATTR_BOOL);
-  EXPORT_INT_PROPERTY(TF_ATTR_TYPE);
-  EXPORT_INT_PROPERTY(TF_ATTR_SHAPE);
-#undef EXPORT_INT_PROPERTY
-
-  return exports;
-}
-
-NAPI_MODULE(tfe_binding, InitTFNodeJSBinding)
-
-}  // namespace tfnodejs
diff --git a/tfjs-master/tfjs-node/binding/utils.h b/tfjs-master/tfjs-node/binding/utils.h
deleted file mode 100644
index b620845a9..000000000
--- a/tfjs-master/tfjs-node/binding/utils.h
+++ /dev/null
@@ -1,329 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-#ifndef TF_NODEJS_UTILS_H_
-#define TF_NODEJS_UTILS_H_
-
-#include <node_api.h>
-#include <stdarg.h>
-#include <stdio.h>
-#include <cstdlib>
-#include <cstring>
-#include <vector>
-#include "tensorflow/c/c_api.h"
-#include "tf_auto_status.h"
-
-#define MAX_TENSOR_SHAPE 4
-
-#define ARRAY_SIZE(array) (sizeof(array) / sizeof(array[0]))
-
-#ifndef DEBUG
-#define DEBUG 0
-#endif
-
-#define DEBUG_LOG(message, file, line_number)                             \
-  do {                                                                    \
-    if (DEBUG)                                                            \
-      fprintf(stderr, "** -%s:%zu\n-- %s\n", file, line_number, message); \
-  } while (0)
-
-namespace tfnodejs {
-
-#define NAPI_THROW_ERROR(env, message, ...) \
-  NapiThrowError(env, __FILE__, __LINE__, message, ##__VA_ARGS__);
-
-inline void NapiThrowError(napi_env env, const char *file,
-                           const size_t line_number, const char *message, ...) {
-  char buffer[500];
-  va_list args;
-  va_start(args, message);
-  std::vsnprintf(buffer, 500, message, args);
-  va_end(args);
-  DEBUG_LOG(buffer, file, line_number);
-  napi_throw_error(env, nullptr, buffer);
-}
-
-#define ENSURE_NAPI_OK(env, status) \
-  if (!EnsureNapiOK(env, status, __FILE__, __LINE__)) return;
-#define ENSURE_NAPI_OK_RETVAL(env, status, retval) \
-  if (!EnsureNapiOK(env, status, __FILE__, __LINE__)) return retval;
-
-inline bool EnsureNapiOK(napi_env env, napi_status status, const char *file,
-                         const size_t line_number) {
-  if (status != napi_ok) {
-    const napi_extended_error_info *error_info = 0;
-    napi_get_last_error_info(env, &error_info);
-    NapiThrowError(
-        env, file, line_number, "Invalid napi_status: %s\n",
-        error_info->error_message ? error_info->error_message : "unknown");
-  }
-  return status == napi_ok;
-}
-
-#define ENSURE_TF_OK(env, status) \
-  if (!EnsureTFOK(env, status, __FILE__, __LINE__)) return;
-#define ENSURE_TF_OK_RETVAL(env, status, retval) \
-  if (!EnsureTFOK(env, status, __FILE__, __LINE__)) return retval;
-
-inline bool EnsureTFOK(napi_env env, TF_AutoStatus &status, const char *file,
-                       const size_t line_number) {
-  TF_Code tf_code = TF_GetCode(status.status);
-  if (tf_code != TF_OK) {
-    NapiThrowError(env, file, line_number, "Invalid TF_Status: %u\nMessage: %s",
-                   TF_GetCode(status.status), TF_Message(status.status));
-  }
-  return tf_code == TF_OK;
-}
-
-#define ENSURE_CONSTRUCTOR_CALL(env, info) \
-  if (!EnsureConstructorCall(env, info, __FILE__, __LINE__)) return;
-#define ENSURE_CONSTRUCTOR_CALL_RETVAL(env, info, retval) \
-  if (!EnsureConstructorCall(env, info, __FILE__, __LINE__)) return retval;
-
-inline bool EnsureConstructorCall(napi_env env, napi_callback_info info,
-                                  const char *file, const size_t line_number) {
-  napi_value js_target;
-  napi_status nstatus = napi_get_new_target(env, info, &js_target);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, false);
-  bool is_target = js_target != nullptr;
-  if (!is_target) {
-    NapiThrowError(env, file, line_number,
-                   "Function not used as a constructor!");
-  }
-  return is_target;
-}
-
-#define ENSURE_VALUE_IS_OBJECT(env, value) \
-  if (!EnsureValueIsObject(env, value, __FILE__, __LINE__)) return;
-#define ENSURE_VALUE_IS_OBJECT_RETVAL(env, value, retval) \
-  if (!EnsureValueIsObject(env, value, __FILE__, __LINE__)) return retval;
-
-inline bool EnsureValueIsObject(napi_env env, napi_value value,
-                                const char *file, const size_t line_number) {
-  napi_valuetype type;
-  ENSURE_NAPI_OK_RETVAL(env, napi_typeof(env, value, &type), false);
-  bool is_object = type == napi_object;
-  if (!is_object) {
-    NapiThrowError(env, file, line_number, "Argument is not an object!");
-  }
-  return is_object;
-}
-
-#define ENSURE_VALUE_IS_STRING(env, value) \
-  if (!EnsureValueIsString(env, value, __FILE__, __LINE__)) return;
-#define ENSURE_VALUE_IS_STRING_RETVAL(env, value, retval) \
-  if (!EnsureValueIsString(env, value, __FILE__, __LINE__)) return retval;
-
-inline bool EnsureValueIsString(napi_env env, napi_value value,
-                                const char *file, const size_t line_number) {
-  napi_valuetype type;
-  ENSURE_NAPI_OK_RETVAL(env, napi_typeof(env, value, &type), false);
-  bool is_string = type == napi_string;
-  if (!is_string) {
-    NapiThrowError(env, file, line_number, "Argument is not a string!");
-  }
-  return is_string;
-}
-
-#define ENSURE_VALUE_IS_NUMBER(env, value) \
-  if (!EnsureValueIsNumber(env, value, __FILE__, __LINE__)) return;
-#define ENSURE_VALUE_IS_NUMBER_RETVAL(env, value, retval) \
-  if (!EnsureValueIsNumber(env, value, __FILE__, __LINE__)) return retval;
-
-inline bool EnsureValueIsNumber(napi_env env, napi_value value,
-                                const char *file, const size_t line_number) {
-  napi_valuetype type;
-  ENSURE_NAPI_OK_RETVAL(env, napi_typeof(env, value, &type), false);
-  bool is_number = type == napi_number;
-  if (!is_number) {
-    NapiThrowError(env, file, line_number, "Argument is not a number!");
-  }
-  return is_number;
-}
-
-#define ENSURE_VALUE_IS_ARRAY(env, value) \
-  if (!EnsureValueIsArray(env, value, __FILE__, __LINE__)) return;
-#define ENSURE_VALUE_IS_ARRAY_RETVAL(env, value, retval) \
-  if (!EnsureValueIsArray(env, value, __FILE__, __LINE__)) return retval;
-
-inline bool EnsureValueIsArray(napi_env env, napi_value value, const char *file,
-                               const size_t line_number) {
-  bool is_array;
-  ENSURE_NAPI_OK_RETVAL(env, napi_is_array(env, value, &is_array), false);
-  if (!is_array) {
-    NapiThrowError(env, file, line_number, "Argument is not an array!");
-  }
-  return is_array;
-}
-
-#define ENSURE_VALUE_IS_TYPED_ARRAY(env, value) \
-  if (!EnsureValueIsTypedArray(env, value, __FILE__, __LINE__)) return;
-#define ENSURE_VALUE_IS_TYPED_ARRAY_RETVAL(env, value, retval) \
-  if (!EnsureValueIsTypedArray(env, value, __FILE__, __LINE__)) return retval;
-
-inline bool EnsureValueIsTypedArray(napi_env env, napi_value value,
-                                    const char *file,
-                                    const size_t line_number) {
-  bool is_array;
-  ENSURE_NAPI_OK_RETVAL(env, napi_is_typedarray(env, value, &is_array), false);
-  if (!is_array) {
-    NapiThrowError(env, file, line_number, "Argument is not a typed-array!");
-  }
-  return is_array;
-}
-
-#define ENSURE_VALUE_IS_LESS_THAN(env, value, max) \
-  if (!EnsureValueIsLessThan(env, value, max, __FILE__, __LINE__)) return;
-#define ENSURE_VALUE_IS_LESS_THAN_RETVAL(env, value, max, retval)  \
-  if (!EnsureValueIsLessThan(env, value, max, __FILE__, __LINE__)) \
-    return retval;
-
-inline bool EnsureValueIsLessThan(napi_env env, uint32_t value, uint32_t max,
-                                  const char *file, const size_t line_number) {
-  if (value > max) {
-    NapiThrowError(env, file, line_number,
-                   "Argument is greater than max: %u > %u", value, max);
-    return false;
-  } else {
-    return true;
-  }
-}
-
-#define REPORT_UNKNOWN_TF_DATA_TYPE(env, type) \
-  ReportUnknownTFDataType(env, type, __FILE__, __LINE__)
-
-inline void ReportUnknownTFDataType(napi_env env, TF_DataType type,
-                                    const char *file,
-                                    const size_t line_number) {
-  NapiThrowError(env, file, line_number, "Unhandled TF_DataType: %u\n", type);
-}
-
-#define REPORT_UNKNOWN_TF_ATTR_TYPE(env, type) \
-  ReportUnknownTFAttrType(env, type, __FILE__, __LINE__)
-
-inline void ReportUnknownTFAttrType(napi_env env, TF_AttrType type,
-                                    const char *file,
-                                    const size_t line_number) {
-  NapiThrowError(env, file, line_number, "Unhandled TF_AttrType: %u\n", type);
-}
-
-#define REPORT_UNKNOWN_TYPED_ARRAY_TYPE(env, type) \
-  ReportUnknownTypedArrayType(env, type, __FILE__, __LINE__)
-
-inline void ReportUnknownTypedArrayType(napi_env env, napi_typedarray_type type,
-                                        const char *file,
-                                        const size_t line_number) {
-  NapiThrowError(env, file, line_number, "Unhandled napi typed_array_type: %u",
-                 type);
-}
-
-// Returns a vector with the shape values of an array.
-inline void ExtractArrayShape(napi_env env, napi_value array_value,
-                              std::vector<int64_t> *result) {
-  napi_status nstatus;
-
-  uint32_t array_length;
-  nstatus = napi_get_array_length(env, array_value, &array_length);
-  ENSURE_NAPI_OK(env, nstatus);
-
-  for (uint32_t i = 0; i < array_length; i++) {
-    napi_value dimension_value;
-    nstatus = napi_get_element(env, array_value, i, &dimension_value);
-    ENSURE_NAPI_OK(env, nstatus);
-
-    int64_t dimension;
-    nstatus = napi_get_value_int64(env, dimension_value, &dimension);
-    ENSURE_NAPI_OK(env, nstatus);
-
-    result->push_back(dimension);
-  }
-}
-
-inline bool IsExceptionPending(napi_env env) {
-  bool has_exception = false;
-  ENSURE_NAPI_OK_RETVAL(env, napi_is_exception_pending(env, &has_exception),
-                        has_exception);
-  return has_exception;
-}
-
-#define ENSURE_VALUE_IS_NOT_NULL(env, value) \
-  if (!EnsureValueIsNotNull(env, value, __FILE__, __LINE__)) return;
-#define ENSURE_VALUE_IS_NOT_NULL_RETVAL(env, value, retval) \
-  if (!EnsureValueIsNotNull(env, value, __FILE__, __LINE__)) return retval;
-
-inline bool EnsureValueIsNotNull(napi_env env, void *value, const char *file,
-                                 const size_t line_number) {
-  bool is_null = value == nullptr;
-  if (is_null) {
-    NapiThrowError(env, file, line_number, "Argument is null!");
-  }
-  return !is_null;
-}
-
-inline napi_status GetStringParam(napi_env env, napi_value string_value,
-                                  std::string &string) {
-  ENSURE_VALUE_IS_STRING_RETVAL(env, string_value, napi_invalid_arg);
-
-  napi_status nstatus;
-
-  size_t str_length;
-  nstatus =
-      napi_get_value_string_utf8(env, string_value, nullptr, 0, &str_length);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nstatus);
-
-  char *buffer = (char *)(malloc(sizeof(char) * (str_length + 1)));
-  ENSURE_VALUE_IS_NOT_NULL_RETVAL(env, buffer, napi_generic_failure);
-
-  nstatus = napi_get_value_string_utf8(env, string_value, buffer,
-                                       str_length + 1, &str_length);
-  ENSURE_NAPI_OK_RETVAL(env, nstatus, nstatus);
-
-  string.assign(buffer, str_length);
-  free(buffer);
-  return napi_ok;
-}
-
-// Returns the number of elements in a Tensor.
-inline size_t GetTensorNumElements(TF_Tensor *tensor) {
-  size_t ret = 1;
-  for (int i = 0; i < TF_NumDims(tensor); ++i) {
-    ret *= TF_Dim(tensor, i);
-  }
-  return ret;
-}
-
-// Split a string into an array of characters array with `,` as delimiter.
-inline std::vector<const char *> splitStringByComma(const std::string &str) {
-  std::vector<const char *> tokens;
-  size_t prev = 0, pos = 0;
-  do {
-    pos = str.find(',', prev);
-    if (pos == std::string::npos) pos = str.length();
-    std::string token = str.substr(prev, pos - prev);
-    if (!token.empty()) {
-      char *cstr = new char[str.length() + 1];
-      std::strcpy(cstr, token.c_str());
-      tokens.push_back(cstr);
-    }
-    prev = pos + 1;
-  } while (pos < str.length() && prev < str.length());
-  return tokens;
-}
-
-}  // namespace tfnodejs
-
-#endif  // TF_NODEJS_UTILS_H_
diff --git a/tfjs-master/tfjs-node/cloudbuild.yml b/tfjs-master/tfjs-node/cloudbuild.yml
deleted file mode 100644
index c841b812e..000000000
--- a/tfjs-master/tfjs-node/cloudbuild.yml
+++ /dev/null
@@ -1,69 +0,0 @@
-steps:
-# Install common dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'yarn-common'
-  entrypoint: 'yarn'
-  args: ['install']
-
-# Install packages.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-  waitFor: ['yarn-common']
-
-# Build add-on.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node'
-  entrypoint: 'yarn'
-  id: 'build-addon'
-  args: ['build-addon-from-source']
-  waitFor: ['yarn']
-
-# Build deps.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node'
-  entrypoint: 'yarn'
-  id: 'build-deps'
-  args: ['build-deps-ci']
-  waitFor: ['yarn-common']
-
-# Build.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node'
-  entrypoint: 'yarn'
-  id: 'build'
-  args: ['build-ci']
-  waitFor: ['yarn', 'build-deps']
-
-# Lint.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node'
-  entrypoint: 'yarn'
-  id: 'lint'
-  args: ['lint']
-  waitFor: ['yarn', 'build-deps']
-
-# Unit tests.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node'
-  entrypoint: 'yarn'
-  id: 'test'
-  args: ['test-ci']
-  waitFor: ['yarn', 'build-addon', 'build-deps', 'lint']
-
-# CPU / GPU package alignment.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node'
-  entrypoint: 'yarn'
-  id: 'ensure-cpu-gpu-packages-align'
-  args: ['ensure-cpu-gpu-packages-align']
-  waitFor: ['yarn-common']
-
-timeout: 1800s
-logsBucket: 'gs://tfjs-build-logs'
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'N1_HIGHCPU_8'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/tfjs-node/package.json b/tfjs-master/tfjs-node/package.json
deleted file mode 100644
index f04616480..000000000
--- a/tfjs-master/tfjs-node/package.json
+++ /dev/null
@@ -1,87 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-node",
-  "version": "0.0.0",
-  "main": "dist/index.js",
-  "types": "dist/index.d.ts",
-  "gypfile": true,
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-node"
-  },
-  "license": "Apache-2.0",
-  "engines": {
-    "node": ">=8.11.0"
-  },
-  "scripts": {
-    "build": "tsc && npx mkdirp dist/proto && cp src/proto/api_pb.js dist/proto/api_pb.js",
-    "build-ci": "tsc && npx mkdirp dist/proto && cp src/proto/api_pb.js dist/proto/api_pb.js",
-    "build-link-package": "cd ../link-package && yarn build-deps-for tfjs-node",
-    "build-union": "cd ../tfjs && yarn && yarn build",
-    "build-union-ci": "cd ../tfjs && yarn && yarn build-ci",
-    "build-deps": "yarn build-link-package && yarn build-union",
-    "build-deps-ci": "yarn build-link-package && yarn build-union-ci",
-    "build-npm": "./scripts/build-npm.sh",
-    "build-and-upload-addon": "./scripts/build-and-upload-addon.sh",
-    "build-addon-from-source": "node-pre-gyp install --build-from-source",
-    "clean-deps": "rm -rf deps && rm -rf lib",
-    "coverage": "nyc yarn ts-node -P tsconfig.test.json src/run_tests.ts",
-    "enable-gpu": "node scripts/install.js gpu download && yarn && yarn build-addon-from-source",
-    "ensure-cpu-gpu-packages-align": "node scripts/ensure-cpu-gpu-packages-align.js",
-    "format": "clang-format -i -style=Google binding/*.cc binding/*.h",
-    "install": "node scripts/install.js",
-    "install-from-source": "yarn clean-deps && yarn && yarn build-addon-from-source",
-    "link-local": "yalc link",
-    "lint": "tslint -p . -t verbose",
-    "prep": "cd node_modules/@tensorflow/tfjs-core && yarn && yarn build",
-    "publish-local": "yarn prep && yalc push",
-    "publish-npm": "yarn build-and-upload-addon publish && npm publish",
-    "test": "yarn && yarn build-deps && yarn build && ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts",
-    "test-dev": "tsc && ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts",
-    "test-ci": "ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts",
-    "upload-windows-addon": "./scripts/build-and-upload-windows-addon.bat"
-  },
-  "devDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core",
-    "@types/jasmine": "~4.0.3",
-    "@types/node": "^10.5.1",
-    "@types/progress": "^2.0.1",
-    "@types/rimraf": "~2.0.2",
-    "@types/yargs": "^13.0.3",
-    "clang-format": "~1.8.0",
-    "jasmine": "~4.2.1",
-    "node-fetch": "~2.6.1",
-    "nyc": "^15.1.0",
-    "tmp": "^0.0.33",
-    "ts-node": "~8.8.2",
-    "tslint": "~6.1.3",
-    "tslint-no-circular-imports": "^0.7.0",
-    "typescript": "5.0.4",
-    "yalc": "~1.0.0-pre.50",
-    "yargs": "^16.2.0"
-  },
-  "dependencies": {
-    "@mapbox/node-pre-gyp": "1.0.9",
-    "@tensorflow/tfjs": "link:../tfjs",
-    "adm-zip": "^0.5.2",
-    "google-protobuf": "^3.9.2",
-    "https-proxy-agent": "^2.2.1",
-    "progress": "^2.0.0",
-    "rimraf": "^2.6.2",
-    "tar": "^4.4.6"
-  },
-  "binary": {
-    "module_name": "tfjs_binding",
-    "module_path": "./lib/napi-v{napi_build_version}",
-    "host": "https://storage.googleapis.com/tf-builds/pre-built-binary",
-    "remote_path": "./napi-v{napi_build_version}/{version}/",
-    "napi_versions": [
-      3,
-      4,
-      5,
-      6,
-      7,
-      8
-    ]
-  }
-}
diff --git a/tfjs-master/tfjs-node/python/int64_model.py b/tfjs-master/tfjs-node/python/int64_model.py
deleted file mode 100644
index f9a50a02f..000000000
--- a/tfjs-master/tfjs-node/python/int64_model.py
+++ /dev/null
@@ -1,36 +0,0 @@
-    # Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Python script for creating Tensorflow SavedModel with UINT64 input."""
-
-import os
-
-import tensorflow as tf
-from tensorflow.python.eager import def_function
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
-from tensorflow.python.ops import variables
-from tensorflow.python.trackable import autotrackable
-from tensorflow.python.saved_model.save import save
-
-"""Test a basic model with functions to make sure functions are inlined."""
-input_data = constant_op.constant(1, shape=[2], dtype=tf.int64)
-root = autotrackable.AutoTrackable()
-root.v1 = variables.Variable(3, dtype=tf.int64)
-root.v2 = variables.Variable(2, dtype=tf.int64)
-root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
-to_save = root.f.get_concrete_function(input_data)
-
-save_dir = os.path.join('..', 'test_objects', 'saved_model', 'int64_multiply')
-save(root, save_dir, to_save)
diff --git a/tfjs-master/tfjs-node/python/unint8_model.py b/tfjs-master/tfjs-node/python/unint8_model.py
deleted file mode 100644
index 9d7ebd5b9..000000000
--- a/tfjs-master/tfjs-node/python/unint8_model.py
+++ /dev/null
@@ -1,35 +0,0 @@
-    # Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Python script for creating Tensorflow SavedModel with UINT8 input."""
-
-import os
-
-import tensorflow as tf
-from tensorflow.python.eager import def_function
-from tensorflow.python.framework import constant_op
-from tensorflow.python.ops import variables
-from tensorflow.python.trackable import autotrackable
-from tensorflow.python.saved_model.save import save
-
-"""Test a basic model with functions to make sure functions are inlined."""
-input_data = constant_op.constant(1, shape=[1], dtype=tf.uint8)
-root = autotrackable.AutoTrackable()
-root.v1 = variables.Variable(3)
-root.v2 = variables.Variable(2)
-root.f = def_function.function(lambda x: root.v1 * root.v2 * tf.cast(x, tf.int32))
-to_save = root.f.get_concrete_function(input_data)
-
-save_dir = os.path.join('..', 'test_objects', 'saved_model', 'uint8_multiply')
-save(root, save_dir, to_save)
diff --git a/tfjs-master/tfjs-node/scripts/build-and-upload-addon.sh b/tfjs-master/tfjs-node/scripts/build-and-upload-addon.sh
deleted file mode 100644
index f9185f7a1..000000000
--- a/tfjs-master/tfjs-node/scripts/build-and-upload-addon.sh
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# The following commands do these steps:
-# 1) Remove the existing pre-built addon tarball.
-# 2) Run `yarn install-from-source` to build binding from source
-# 3) Compress and upload the pre-built addon tarball.
-# Please read DEVELOPMENT.md before developing and publishing.
-
-set -e
-
-# get package name based on os and processor
-PACKAGE_NAME=$(node scripts/get-addon-name.js)
-# get NAPI version
-NAPI_VERSION=`node -p "process.versions.napi"`
-# remove the pre-built addon tarball if it already exist
-rm -f $PACKAGE_NAME
-# Build the native binding in docker to use a known version of GLIBC
-docker run --rm --mount type=bind,source=$(pwd),target=/tfjs-node/ --user "$(id -u):$(id -g)" gcr.io/learnjs-174218/release  bash -c "cd tfjs-node && yarn build-addon-from-source"
-if [ "$1" = "publish" ]; then
-  # build a new pre-built addon tarball
-  tar -czvf $PACKAGE_NAME -C lib napi-v$NAPI_VERSION/tfjs_binding.node
-  # upload pre-built addon tarball to gcloud
-  PACKAGE_HOST=`node -p "require('./package.json').binary.host.split('.com/')[1] + '/napi-v' + process.versions.napi + '/' + require('./package.json').version + '/'"`
-  gsutil cp $PACKAGE_NAME gs://$PACKAGE_HOST
-fi
diff --git a/tfjs-master/tfjs-node/scripts/build-and-upload-windows-addon-gpu.bat b/tfjs-master/tfjs-node/scripts/build-and-upload-windows-addon-gpu.bat
deleted file mode 100644
index 61cf01d7b..000000000
--- a/tfjs-master/tfjs-node/scripts/build-and-upload-windows-addon-gpu.bat
+++ /dev/null
@@ -1,12 +0,0 @@
-:: Download TensorFlow GPU library and compile native node addon
-node scripts/install.js gpu download
-call yarn
-call yarn build-addon-from-source
-:: Compress and upload the GPU node addon to GCP bucket.
-for /f %%i in ('node scripts/get-addon-name.js') do set PACKAGE_NAME=%%i
-:: Update addon tarball name to GPU
-set PACKAGE_NAME=%PACKAGE_NAME:CPU=GPU%
-for /f %%i in ('node -p "process.versions.napi"') do set NAPI_VERSION=%%i
-tar -czvf %PACKAGE_NAME% -C lib napi-v%NAPI_VERSION%/tfjs_binding.node
-for /f %%i in ('node scripts/print-full-package-host') do set PACKAGE_HOST=%%i
-gsutil cp %PACKAGE_NAME% gs://%PACKAGE_HOST%
diff --git a/tfjs-master/tfjs-node/scripts/build-and-upload-windows-addon.bat b/tfjs-master/tfjs-node/scripts/build-and-upload-windows-addon.bat
deleted file mode 100644
index ded2ea6f3..000000000
--- a/tfjs-master/tfjs-node/scripts/build-and-upload-windows-addon.bat
+++ /dev/null
@@ -1,12 +0,0 @@
-:: Delete deps and lib folder
-if exist deps rmdir /s /q deps
-if exist lib rmdir /s /q lib
-:: Install dependencies, download TensorFlow c library and compile native node addon
-call yarn
-call yarn build-addon-from-source
-:: Compress and upload the node addon to GCP bucket.
-for /f %%i in ('node scripts/get-addon-name.js') do set PACKAGE_NAME=%%i
-for /f %%i in ('node -p "process.versions.napi"') do set NAPI_VERSION=%%i
-tar -czvf %PACKAGE_NAME% -C lib napi-v%NAPI_VERSION%/tfjs_binding.node
-for /f %%i in ('node scripts/print-full-package-host') do set PACKAGE_HOST=%%i
-gsutil cp %PACKAGE_NAME% gs://%PACKAGE_HOST%
diff --git a/tfjs-master/tfjs-node/scripts/build-libtensorflow-arm64.yml b/tfjs-master/tfjs-node/scripts/build-libtensorflow-arm64.yml
deleted file mode 100644
index e403ae793..000000000
--- a/tfjs-master/tfjs-node/scripts/build-libtensorflow-arm64.yml
+++ /dev/null
@@ -1,84 +0,0 @@
-steps:
-# Clone tensorflow
-# TODO(mattsoulanille): Specify branch with variable.
-- name: 'tensorflow/build@sha256:47f8603647612f797f9ad9279c55620dc4ceb9980a5f9fe6ae35882b96ec0ac0'
-  entrypoint: 'git'
-  id: 'clone'
-  args: ['clone', '--branch', 'r2.7', '--single-branch', '--depth', '1', 'https://github.com/tensorflow/tensorflow.git']
-
-# Configure tensorflow
-- name: 'tensorflow/build@sha256:47f8603647612f797f9ad9279c55620dc4ceb9980a5f9fe6ae35882b96ec0ac0'
-  entrypoint: 'bash'
-  id: 'configure'
-  dir: 'tensorflow'
-  args: ['./configure']
-  env:
-    - 'PYTHON_BIN_PATH=/usr/bin/python3'
-    - 'PYTHON_LIB_PATH=/usr/lib/python3/dist-packages'
-    - 'TF_NEED_ROCM=false'
-    - 'TF_NEED_CUDA=false'
-    - 'TF_DOWNLOAD_CLANG=false'
-    - 'CC_OPT_FLAGS=-Wno-sign-compare'
-    - 'TF_SET_ANDROID_WORKSPACE=false'
-
-
-# Build tensorflow
-# First, hide system openssl.
-# https://github.com/tensorflow/tensorflow/issues/48401#issuecomment-818377995
-- name: 'tensorflow/build@sha256:47f8603647612f797f9ad9279c55620dc4ceb9980a5f9fe6ae35882b96ec0ac0'
-  entrypoint: 'bash'
-  id: 'build'
-  dir: 'tensorflow'
-  args:
-    - '-eEuo'
-    - 'pipefail'
-    - '-c'
-    - |-
-      mv /usr/include/openssl /usr/include/openssl.original
-      bazel build --jobs=16 -c opt --config=elinux_aarch64 //tensorflow:libtensorflow.so //tensorflow:libtensorflow_framework.so
-
-# Copy results to a staging directory and tar them
-- name: 'tensorflow/build@sha256:47f8603647612f797f9ad9279c55620dc4ceb9980a5f9fe6ae35882b96ec0ac0'
-  entrypoint: 'bash'
-  id: 'copy-results'
-  args:
-    - '-eEuo'
-    - 'pipefail'
-    - '-c'
-    - |-
-      mkdir tf_build
-      mkdir tf_build/lib
-      cp -P tensorflow/bazel-bin/tensorflow/libtensorflow*.so* tf_build/lib
-      mkdir -p tf_build/include/tensorflow/core/platform
-      mkdir -p tf_build/include/tensorflow/c/eager
-      cp tensorflow/tensorflow/core/platform/ctstring.h tf_build/include/tensorflow/core/platform/
-      cp tensorflow/tensorflow/core/platform/ctstring_internal.h tf_build/include/tensorflow/core/platform/
-      cp tensorflow/tensorflow/c/c_api.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/c_api_experimental.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/c_api_macros.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/tensor_interface.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/tf_attrtype.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/tf_datatype.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/tf_file_statistics.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/tf_status.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/tf_tensor.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/tf_tstring.h tf_build/include/tensorflow/c/
-      cp tensorflow/tensorflow/c/eager/c_api.h tf_build/include/tensorflow/c/eager/
-      cp tensorflow/tensorflow/c/eager/c_api_experimental.h tf_build/include/tensorflow/c/eager/
-      cp tensorflow/tensorflow/c/eager/c_api_unified_experimental.h tf_build/include/tensorflow/c/eager/
-      cp tensorflow/tensorflow/c/eager/dlpack.h tf_build/include/tensorflow/c/eager/
-      cd tf_build
-      tar czf libtensorflow_r2_7_linux_arm64.tar.gz ./lib ./include
-
-# Push results to bucket
-- name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
-  entrypoint: 'gsutil'
-  id: 'push'
-  args: ['cp', 'tf_build/libtensorflow_r2_7_linux_arm64.tar.gz', 'gs://tf-builds']
-
-timeout: 7200s
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'E2_HIGHCPU_32'
-  substitution_option: 'ALLOW_LOOSE'
-
diff --git a/tfjs-master/tfjs-node/scripts/build-npm.sh b/tfjs-master/tfjs-node/scripts/build-npm.sh
deleted file mode 100644
index a322fb744..000000000
--- a/tfjs-master/tfjs-node/scripts/build-npm.sh
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2018 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-# Clean the build.
-yarn rimraf dist/
-yarn rimraf deps/
-yarn rimraf lib/
-
-# Download the tensorflow headers and lib.
-yarn install
-# Build the pre-built addon. Do not publish it yet.
-yarn build-addon-from-source
-
-tsc --sourceMap false
-# Manual copy src/proto/api_pb.js until both allowJs and declaration are
-# supported in tsconfig: https://github.com/microsoft/TypeScript/pull/32372
-mkdir -p dist/proto && cp src/proto/api_pb.js dist/proto/api_pb.js
-
-npm pack
diff --git a/tfjs-master/tfjs-node/scripts/custom-binary.sample.json b/tfjs-master/tfjs-node/scripts/custom-binary.sample.json
deleted file mode 100644
index 1e1eee11d..000000000
--- a/tfjs-master/tfjs-node/scripts/custom-binary.sample.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "tf-lib": "url-to-download-customized-binary",
-  "addon": {
-    "host": "host-of-pre-compiled-addon",
-    "remote_path": "remote-path-of-pre-compiled-addon",
-    "package_name": "file-name-of-pre-compile-addon"
-  }
-}
diff --git a/tfjs-master/tfjs-node/scripts/deps-constants.js b/tfjs-master/tfjs-node/scripts/deps-constants.js
deleted file mode 100644
index 6c5c8ef54..000000000
--- a/tfjs-master/tfjs-node/scripts/deps-constants.js
+++ /dev/null
@@ -1,122 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const os = require('os');
-const fs = require('fs');
-const join = require('path').join;
-const module_path_napi = require('../package.json').binary.module_path;
-const modulePath =
-    module_path_napi.replace('{napi_build_version}', process.versions.napi);
-
-/** Version of the libtensorflow shared library to depend on. */
-const LIBTENSORFLOW_VERSION = '2.9.1';
-
-/** Map the os.arch() to arch string in a file name */
-const ARCH_MAPPING = {
-  'x64': 'x86_64',
-  'arm64': 'arm64'
-};
-/** Map the os.platform() to the platform value in a file name */
-const PLATFORM_MAPPING = {
-  'darwin': 'darwin',
-  'linux': 'linux',
-  'win32': 'windows'
-};
-/** The extension of a compressed file */
-const PLATFORM_EXTENSION = os.platform() === 'win32' ? 'zip' : 'tar.gz';
-/**
- * Current supported type, platform and architecture combinations
- * `tf-lib` represents tensorflow shared libraries and `binding` represents
- * node binding.
- */
-const ALL_SUPPORTED_COMBINATION = [
-  'cpu-darwin-x86_64', 'gpu-linux-x86_64', 'cpu-linux-x86_64',
-  'cpu-windows-x86_64', 'gpu-windows-x86_64'
-];
-
-/** Get the MAJOR.MINOR-only version of libtensorflow. */
-function getLibTensorFlowMajorDotMinorVersion() {
-  const items = LIBTENSORFLOW_VERSION.split('.');
-  if (items.length < 3) {
-    throw new Error(
-        `Invalid version string for libtensorflow: ${LIBTENSORFLOW_VERSION}`);
-  }
-  return `${items[0]}.${items[1]}`;
-}
-
-// Determine constants for deps folder names and destination (build) path names.
-let depsLibTensorFlowName = 'libtensorflow';
-let depsLibTensorFlowFrameworkName = 'libtensorflow_framework';
-
-let destLibTensorFlowName = depsLibTensorFlowName;
-let destLibTensorFlowFrameworkName = depsLibTensorFlowFrameworkName;
-
-if (os.platform() === 'win32') {
-  depsLibTensorFlowName = 'tensorflow.dll';
-  depsLibTensorFlowFrameworkName = '';  // Not supported on Windows
-
-  destLibTensorFlowName = depsLibTensorFlowName;
-  destLibTensorFlowFrameworkName = '';  // Not supported on Windows
-} else if (os.platform() === 'darwin') {
-  depsLibTensorFlowName += '.dylib';
-  depsLibTensorFlowFrameworkName += '.dylib';
-
-  destLibTensorFlowName = depsLibTensorFlowName;
-  destLibTensorFlowFrameworkName = depsLibTensorFlowFrameworkName;
-} else if (os.platform() === 'linux') {
-  // Linux has a hard-coded version number, make the destination name simpler:
-  depsLibTensorFlowName += `.so.${LIBTENSORFLOW_VERSION}`;
-  depsLibTensorFlowFrameworkName += `.so.${LIBTENSORFLOW_VERSION}`;
-  destLibTensorFlowName += '.so';
-  destLibTensorFlowFrameworkName += '.so';
-} else {
-  throw new Error('Unsupported platform: ' + os.platform());
-}
-
-const depsPath = join(__dirname, '..', 'deps');
-const depsLibPath = join(depsPath, 'lib');
-
-const depsLibTensorFlowPath = join(depsLibPath, depsLibTensorFlowName);
-const depsLibTensorFlowFrameworkPath =
-    join(depsLibPath, depsLibTensorFlowFrameworkName);
-
-// Get information for custom binary
-const CUSTOM_BINARY_FILENAME = 'custom-binary.json';
-function loadCustomBinary() {
-  const cfg = join(__dirname, CUSTOM_BINARY_FILENAME);
-  return fs.existsSync(cfg) ? require(cfg) : {};
-}
-const customBinaries = loadCustomBinary();
-
-module.exports = {
-  depsPath,
-  depsLibPath,
-  depsLibTensorFlowFrameworkName,
-  depsLibTensorFlowFrameworkPath,
-  depsLibTensorFlowName,
-  depsLibTensorFlowPath,
-  destLibTensorFlowFrameworkName,
-  destLibTensorFlowName,
-  getLibTensorFlowMajorDotMinorVersion,
-  modulePath,
-  LIBTENSORFLOW_VERSION,
-  ARCH_MAPPING,
-  PLATFORM_MAPPING,
-  PLATFORM_EXTENSION,
-  ALL_SUPPORTED_COMBINATION,
-  customTFLibUri : customBinaries['tf-lib'],
-  customAddon : customBinaries['addon']
-};
diff --git a/tfjs-master/tfjs-node/scripts/deps-stage.js b/tfjs-master/tfjs-node/scripts/deps-stage.js
deleted file mode 100644
index 0ec0d113b..000000000
--- a/tfjs-master/tfjs-node/scripts/deps-stage.js
+++ /dev/null
@@ -1,97 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const fs = require('fs');
-const path = require('path');
-const util = require('util');
-
-const copy = util.promisify(fs.copyFile);
-const os = require('os');
-const rename = util.promisify(fs.rename);
-const symlink = util.promisify(fs.symlink);
-const {
-  depsLibTensorFlowFrameworkPath,
-  depsLibTensorFlowPath,
-  destLibTensorFlowFrameworkName,
-  destLibTensorFlowName
-} = require('./deps-constants.js');
-
-const action = process.argv[2];
-let targetDir = process.argv[3];
-
-// This file is Windows only - the libraries must be placed in the correct
-// directory to work.
-if (os.platform() !== 'win32') {
-  throw new Error('Dep staging is only supported on Windows');
-}
-
-// Some windows machines contain a trailing " char:
-if (targetDir != undefined && targetDir.endsWith('"')) {
-  targetDir = targetDir.slice(0, -1);
-}
-
-// Setup dest binary paths:
-const destLibTensorFlowPath = path.join(targetDir, destLibTensorFlowName);
-const destLibTensorFlowFrameworkPath =
-    path.join(targetDir, destLibTensorFlowFrameworkName);
-
-/**
- * Symlinks the extracted libtensorflow library to the destination path. If the
- * symlink fails, a copy is made.
- */
-async function symlinkDepsLib() {
-  if (destLibTensorFlowPath === undefined) {
-    throw new Error('Destination path not supplied!');
-  }
-  try {
-    await symlink(
-        path.relative(
-            path.dirname(destLibTensorFlowPath), depsLibTensorFlowPath),
-        destLibTensorFlowPath);
-  } catch (e) {
-    console.error(`  * Symlink of ${
-        destLibTensorFlowPath} failed, creating a copy on disk.`);
-    await copy(depsLibTensorFlowPath, destLibTensorFlowPath);
-  }
-}
-
-/**
- * Moves the deps library path to the destination path.
- */
-async function moveDepsLib() {
-  await rename(depsLibTensorFlowPath, destLibTensorFlowPath);
-  if (os.platform() !== 'win32') {
-    await rename(
-        depsLibTensorFlowFrameworkPath, destLibTensorFlowFrameworkPath);
-  }
-}
-
-/**
- * Symlink or move libtensorflow for building the binding.
- */
-async function run(action) {
-  if (action.endsWith('symlink')) {
-    // Symlink will happen during `node-gyp rebuild`
-    await symlinkDepsLib();
-  } else if (action.endsWith('move')) {
-    // Move action is used when installing this module as a package.
-    await moveDepsLib();
-  } else {
-    throw new Error('Invalid action: ' + action);
-  }
-}
-
-run(action);
diff --git a/tfjs-master/tfjs-node/scripts/ensure-cpu-gpu-packages-align.js b/tfjs-master/tfjs-node/scripts/ensure-cpu-gpu-packages-align.js
deleted file mode 100644
index f50fee4b6..000000000
--- a/tfjs-master/tfjs-node/scripts/ensure-cpu-gpu-packages-align.js
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Ensures that the GPU and CPU packages align.
- */
-const fs = require('fs');
-const cpuPackage = require('../../tfjs-node/package.json');
-const gpuPackage = require('../../tfjs-node-gpu/package.json');
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-/**
- * Ensure package.json aligns.
- */
-const FIELDS_TO_IGNORE = [
-  'name', 'scripts/install', 'scripts/test', 'scripts/prepare', 'scripts/prep',
-  'scripts/upload-windows-addon', 'scripts/build-npm',
-  'scripts/prep-gpu-windows', 'binary/package_name', 'repository/directory',
-];
-
-const cpuPackageKeys = Object.keys(cpuPackage);
-const gpuPackageKeys = Object.keys(gpuPackage);
-cpuPackageKeys.forEach((key, i) => {
-  if (gpuPackageKeys[i] != cpuPackageKeys[i]) {
-    throw new Error(
-        `CPU and GPU package have different keys: ` +
-        `${gpuPackageKeys[i]} and ${cpuPackageKeys[i]}.`);
-  }
-});
-if (cpuPackageKeys.length != gpuPackageKeys.length) {
-  throw new Error(`CPU and GPU package.jsons have different top-level fields.`);
-}
-
-// Ensure the cpu and gpu packages have the same keys.
-cpuPackageKeys.forEach(key => {
-  const cpuPackageValue = cpuPackage[key];
-  const gpuPackageValue = gpuPackage[key];
-
-  if (typeof cpuPackageValue !== 'object') {
-    if (cpuPackageValue != gpuPackageValue &&
-        FIELDS_TO_IGNORE.indexOf(key) === -1) {
-      throw new Error(
-          `CPU package key '${key}' with value ` +
-          `${JSON.stringify(cpuPackageValue)} does not match GPU value ` +
-          `${JSON.stringify(gpuPackageValue)}.`);
-    }
-  } else {
-    const cpuFieldKeys = Object.keys(cpuPackageValue);
-    cpuFieldKeys.forEach(fieldKey => {
-      const cpuFieldValue = '' + cpuPackageValue[fieldKey];
-      const gpuFieldValue = '' + gpuPackageValue[fieldKey];
-      const deepKey = `${key}/${fieldKey}`;
-      if (cpuFieldValue !== gpuFieldValue &&
-          FIELDS_TO_IGNORE.indexOf(deepKey) === -1) {
-        throw new Error(
-            `CPU package key '${deepKey}' with value ` +
-            `${JSON.stringify(cpuFieldValue)} does not match GPU value ` +
-            `${JSON.stringify(gpuFieldValue)}.`);
-      }
-    });
-  }
-});
diff --git a/tfjs-master/tfjs-node/scripts/get-addon-name.js b/tfjs-master/tfjs-node/scripts/get-addon-name.js
deleted file mode 100644
index 65066599f..000000000
--- a/tfjs-master/tfjs-node/scripts/get-addon-name.js
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-const os = require('os');
-const name = require('../package.json').name;
-const version = require('../package.json').version;
-
-const platform = os.platform();
-
-const {PLATFORM_MAPPING, PLATFORM_EXTENSION} = require('./deps-constants.js');
-
-const type = name.includes('gpu') ? 'GPU' : 'CPU';
-const addonName = `${type}-${PLATFORM_MAPPING[platform]}-` +
-    `${version}.${PLATFORM_EXTENSION}`;
-
-// Print out the addon tarball name so that it can be used in bash script when
-// uploading the tarball to GCP bucket.
-console.log(addonName);
-
-module.exports = {
-  addonName: addonName
-};
diff --git a/tfjs-master/tfjs-node/scripts/install.js b/tfjs-master/tfjs-node/scripts/install.js
deleted file mode 100644
index 88b55dd14..000000000
--- a/tfjs-master/tfjs-node/scripts/install.js
+++ /dev/null
@@ -1,216 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const fs = require('fs');
-let path = require('path');
-const rimraf = require('rimraf');
-const util = require('util');
-const cp = require('child_process');
-const os = require('os');
-const {
-  depsPath,
-  depsLibPath,
-  depsLibTensorFlowPath,
-  LIBTENSORFLOW_VERSION,
-  PLATFORM_MAPPING,
-  ARCH_MAPPING,
-  PLATFORM_EXTENSION,
-  ALL_SUPPORTED_COMBINATION,
-  modulePath,
-  customTFLibUri,
-  customAddon
-} = require('./deps-constants.js');
-const resources = require('./resources');
-const {addonName} = require('./get-addon-name.js');
-
-const exists = util.promisify(fs.exists);
-const mkdir = util.promisify(fs.mkdir);
-const rename = util.promisify(fs.rename);
-const rimrafPromise = util.promisify(rimraf);
-
-const CDN_STORAGE = process.env.TFJS_NODE_CDN_STORAGE ||
-    process.env.npm_config_TFJS_NODE_CDN_STORAGE || process.env.CDN_STORAGE;
-const BASE_HOST = CDN_STORAGE || 'https://storage.googleapis.com/';
-const BASE_URI = process.env.TFJS_NODE_BASE_URI ||
-    process.env.npm_config_TFJS_NODE_BASE_URI ||
-    `${BASE_HOST}tensorflow/libtensorflow/libtensorflow-`;
-
-const platform = os.platform();
-// Use windows path
-if (platform === 'win32') {
-  path = path.win32;
-}
-let libType = process.argv[2] === undefined ? 'cpu' : process.argv[2];
-let system = `${libType}-${PLATFORM_MAPPING[platform]}-` +
-    `${ARCH_MAPPING[os.arch()]}`;
-let forceDownload = process.argv[3] === undefined ? undefined : process.argv[3];
-
-let packageJsonFile;
-
-function setPackageJsonFile() {
-  packageJsonFile =
-      JSON.parse(fs.readFileSync(`${__dirname}/../package.json`).toString());
-}
-
-function updateAddonName() {
-  if (customAddon !== undefined) {
-    Object.assign(packageJsonFile['binary'], customAddon);
-  } else {
-    packageJsonFile['binary']['package_name'] = addonName;
-  }
-  const stringFile = JSON.stringify(packageJsonFile, null, 2);
-  fs.writeFileSync((`${__dirname}/../package.json`), stringFile);
-}
-
-function revertAddonName(orig) {
-  packageJsonFile['binary'] = orig;
-  const stringFile = JSON.stringify(packageJsonFile, null, 2).concat('\n');
-  fs.writeFileSync((`${__dirname}/../package.json`), stringFile);
-}
-
-/**
- * Returns the libtensorflow hosted path of the current platform.
- */
-function getPlatformLibtensorflowUri() {
-  // Exception for mac+gpu user
-  if (platform === 'darwin') {
-    if (os.arch() === 'arm64') {
-      return `${BASE_HOST}tf-builds/libtensorflow_r2_7_darwin_arm64_cpu.tar.gz`;
-    }
-    system = `cpu-${PLATFORM_MAPPING[platform]}-${ARCH_MAPPING[os.arch()]}`;
-  }
-
-  if (customTFLibUri !== undefined) {
-    return customTFLibUri;
-  }
-
-  if (platform === 'linux') {
-    if (os.arch() === 'arm') {
-      return `${BASE_HOST}tf-builds/libtensorflow_r2_5_linux_arm7l.tar.gz`;
-    } else if (os.arch() === 'arm64') {
-      return `${BASE_HOST}tf-builds/libtensorflow_r2_7_linux_arm64.tar.gz`;
-    }
-  }
-
-  if (ALL_SUPPORTED_COMBINATION.indexOf(system) === -1) {
-    throw new Error(`Unsupported system: ${libType}-${platform}-${os.arch()}`);
-  }
-
-  return `${BASE_URI}${system}-${LIBTENSORFLOW_VERSION}.${PLATFORM_EXTENSION}`;
-}
-
-/**
- * Ensures a directory exists, creates as needed.
- */
-async function ensureDir(dirPath) {
-  if (!await exists(dirPath)) {
-    await mkdir(dirPath);
-  }
-}
-
-/**
- * Deletes the deps directory if it exists, and creates a fresh deps folder.
- */
-async function cleanDeps() {
-  if (await exists(depsPath)) {
-    await rimrafPromise(depsPath);
-  }
-  await mkdir(depsPath);
-}
-
-/**
- * Downloads libtensorflow and notifies via a callback when unpacked.
- */
-async function downloadLibtensorflow(callback) {
-  // Ensure dependencies staged directory is available:
-  await ensureDir(depsPath);
-
-  console.warn('* Downloading libtensorflow');
-  console.log(getPlatformLibtensorflowUri());
-  resources.downloadAndUnpackResource(
-      getPlatformLibtensorflowUri(), depsPath, async () => {
-        if (platform === 'win32') {
-          // Some windows libtensorflow zip files are missing structure and the
-          // eager headers. Check, restructure, and download resources as
-          // needed.
-          if (!await exists(depsLibTensorFlowPath)) {
-            // Verify that tensorflow.dll exists
-            const libtensorflowDll = path.join(depsPath, 'tensorflow.dll');
-            if (!await exists(libtensorflowDll)) {
-              throw new Error('Could not find libtensorflow.dll');
-            }
-
-            await ensureDir(depsLibPath);
-            await rename(libtensorflowDll, depsLibTensorFlowPath);
-          }
-        }
-        // No other work is required on other platforms.
-        if (callback !== undefined) {
-          callback();
-        }
-      });
-}
-
-/**
- * Calls node-gyp for Node.js Tensorflow binding after lib is downloaded.
- */
-async function build() {
-  // Load package.json file
-  setPackageJsonFile();
-  // Update addon name in package.json file
-  const origBinary = JSON.parse(JSON.stringify(packageJsonFile['binary']));
-  updateAddonName();
-  console.error('* Building TensorFlow Node.js bindings');
-  let buildOption = '--fallback-to-build';
-  if (customTFLibUri !== undefined && customAddon === undefined) {
-    // Has custom tensorflow shared libs but no addon. Then build it from source
-    buildOption = '--build-from-source';
-  }
-  cp.exec(`node-pre-gyp install ${buildOption}`, (err) => {
-    if (err) {
-      console.log('node-pre-gyp install failed with error: ' + err);
-      process.exit(1);
-    }
-    if (platform === 'win32') {
-      // Move libtensorflow to module path, where tfjs_binding.node locates.
-      cp.exec('node scripts/deps-stage.js symlink ' + modulePath, (error) => {
-        if (error) {
-          console.error('symlink ' + modulePath + ' failed: ', error);
-          process.exit(1);
-        }
-      });
-    }
-    revertAddonName(origBinary);
-  });
-}
-
-/**
- * Ensures libtensorflow requirements are met for building the binding.
- */
-async function run() {
-  // First check if deps library exists:
-  if (forceDownload !== 'download' && await exists(depsLibTensorFlowPath)) {
-    // Library has already been downloaded, then compile and simlink:
-    await build();
-  } else {
-    // Library has not been downloaded, download, then compile and symlink:
-    await cleanDeps();
-    await downloadLibtensorflow(build);
-  }
-}
-
-run();
diff --git a/tfjs-master/tfjs-node/scripts/make-version b/tfjs-master/tfjs-node/scripts/make-version
deleted file mode 100644
index c3379eb91..000000000
--- a/tfjs-master/tfjs-node/scripts/make-version
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2018 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-
-// Run this script from the base directory (not the script directory):
-// ./scripts/make-version
-
-const fs = require('fs');
-const version = JSON.parse(fs.readFileSync('package.json', 'utf8')).version;
-
-const versionCode =
-`/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '${version}';
-export {version};
-`
-
-fs.writeFile('src/version.ts', versionCode, err => {
-  if (err) {
-    throw new Error(`Could not save version file ${version}: ${err}`);
-  }
-  console.log(`Version file for version ${version} saved sucessfully.`);
-});
diff --git a/tfjs-master/tfjs-node/scripts/print-full-package-host.js b/tfjs-master/tfjs-node/scripts/print-full-package-host.js
deleted file mode 100644
index 0447d63cd..000000000
--- a/tfjs-master/tfjs-node/scripts/print-full-package-host.js
+++ /dev/null
@@ -1,9 +0,0 @@
-// This file generate and print out the full path of the pre-compiled addon in
-// GCP bucket. It is used by bash script when uploading pre-compiled addon to
-// GCP bucket.
-console.log(
-  require('../package.json').binary.host.split('.com/')[1] +
-  '/napi-v' +
-  process.versions.napi +
-  '/' +
-  require('../package.json').version + '/');
diff --git a/tfjs-master/tfjs-node/scripts/publish-npm-gpu.sh b/tfjs-master/tfjs-node/scripts/publish-npm-gpu.sh
deleted file mode 100644
index b621ab347..000000000
--- a/tfjs-master/tfjs-node/scripts/publish-npm-gpu.sh
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2017 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Before you run this script, do this:
-# 1) Update the version in package.json
-# 2) Run ./scripts/make-version from the base dir of the project.
-# 3) Run `yarn` to update `yarn.lock`, in case you updated dependencies
-# 4) Commit to the master branch.
-
-# Then:
-# 5) Checkout the master branch of this repo.
-# 6) Run this script as `./scripts/publish-npm-gpu.sh` from the project base dir.
-
-set -e
-
-BRANCH=`git rev-parse --abbrev-ref HEAD`
-ORIGIN=`git config --get remote.origin.url`
-
-if [ "$BRANCH" != "master" ] && [ "$BRANCH" != "0.3.x" ]; then
-  echo "Error: Switch to the master or a release branch before publishing."
-  exit
-fi
-
-if ! [[ "$ORIGIN" =~ tensorflow/tfjs-node ]]; then
-  echo "Error: Switch to the main repo (tensorflow/tfjs-node) before publishing."
-  exit
-fi
-
-./scripts/make-version # This is for safety in case you forgot to do 2).
-yarn build-npm-gpu upload
-
-GPU_TARBALLS=$(ls tensorflow-tfjs-node-gpu*.tgz)
-GPU_TARBALL_COUNT=$(echo $GPU_TARBALLS | wc -w | xargs)
-if [ "$GPU_TARBALL_COUNT" != "1" ]; then
-  echo "Error: Please make sure there is exactly one GPU tarball, found:"
-  echo $GPU_TARBALLS
-  exit
-fi
-
-# Publish the GPU package
-npm publish $GPU_TARBALLS
-
-# Cleanup
-git checkout .
-
-echo 'Yay! Published the tfjs-node-gpu package to npm.'
diff --git a/tfjs-master/tfjs-node/scripts/publish-npm.sh b/tfjs-master/tfjs-node/scripts/publish-npm.sh
deleted file mode 100644
index ec63ca251..000000000
--- a/tfjs-master/tfjs-node/scripts/publish-npm.sh
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2017 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-################################################################################
-# NOTE: This does *not* publish the GPU package. Please run
-# ./scripts/publish-npm-gpu.sh separately to publish the GPU package.
-################################################################################
-
-# Before you run this script, do this:
-# 1) Update the version in package.json
-# 2) Run ./scripts/make-version from the base dir of the project.
-# 3) Run `yarn` to update `yarn.lock`, in case you updated dependencies
-# 4) Commit to the master branch.
-
-# Then:
-# 5) Checkout the master branch of this repo.
-# 6) Run this script as `./scripts/publish-npm.sh` from the project base dir.
-
-set -e
-
-BRANCH=`git rev-parse --abbrev-ref HEAD`
-ORIGIN=`git config --get remote.origin.url`
-
-if [ "$BRANCH" != "master" ] && [ "$BRANCH" != "0.3.x" ]; then
-  echo "Error: Switch to the master or a release branch before publishing."
-  exit
-fi
-
-if ! [[ "$ORIGIN" =~ tensorflow/tfjs-node ]]; then
-  echo "Error: Switch to the main repo (tensorflow/tfjs-node) before publishing."
-  exit
-fi
-
-# build, compress and upload pre-built addon
-yarn build-npm upload
-./scripts/make-version # This is for safety in case you forgot to do 2).
-
-# Publish the CPU package
-npm publish
-./scripts/tag-version
-echo 'Yay! Published the tfjs-node package to npm.'
-
-echo '#########################################################################'
-echo '# NOTE: This does *not* publish the GPU package. Please run'
-echo '# ./scripts/publish-npm-gpu.sh separately to publish the GPU package.'
-echo '#########################################################################'
diff --git a/tfjs-master/tfjs-node/scripts/resources.js b/tfjs-master/tfjs-node/scripts/resources.js
deleted file mode 100644
index 0611d8f9a..000000000
--- a/tfjs-master/tfjs-node/scripts/resources.js
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const fs = require('fs');
-const http = require('http');
-const https = require('https');
-const HttpsProxyAgent = require('https-proxy-agent');
-const path = require('os').platform() === 'win32' ? require('path') :
-                                                    require('path').win32;
-const ProgressBar = require('progress');
-const tar = require('tar');
-const url = require('url');
-const util = require('util');
-const zip = require('adm-zip');
-
-const unlink = util.promisify(fs.unlink);
-
-/**
- * Downloads and unpacks a given tarball or zip file at a given path.
- * @param {string} uri The path of the compressed file to download and extract.
- * @param {string} destPath The destination path for the compressed content.
- * @param {Function} callback Handler for when downloading and extraction is
- *     complete.
- */
-async function downloadAndUnpackResource(uri, destPath, callback) {
-  const httpClient = uri.startsWith('https') ? https : http;
-
-  // If HTTPS_PROXY, https_proxy, HTTP_PROXY, or http_proxy is set
-  const proxy = process.env['HTTPS_PROXY'] || process.env['https_proxy'] ||
-      process.env['HTTP_PROXY'] || process.env['http_proxy'] || '';
-
-  // Using object destructuring to construct the options object for the
-  // http request.  the '...url.parse(targetUri)' part fills in the host,
-  // path, protocol, etc from the targetUri and then we set the agent to the
-  // default agent which is overridden a few lines down if there is a proxy
-  const options = {...url.parse(uri), agent: httpClient.globalAgent};
-
-  if (proxy !== '') {
-    options.agent = new HttpsProxyAgent(proxy);
-  }
-
-  const request = httpClient.get(options, response => {
-    const bar = new ProgressBar('[:bar] :rate/bps :percent :etas', {
-      complete: '=',
-      incomplete: ' ',
-      width: 30,
-      total: parseInt(response.headers['content-length'], 10)
-    });
-
-    if (uri.endsWith('.zip')) {
-      // Save zip file to disk, extract, and delete the downloaded zip file.
-      const tempFileName = path.join(__dirname, '_tmp.zip');
-      const outputFile = fs.createWriteStream(tempFileName);
-
-      response.on('data', chunk => bar.tick(chunk.length))
-          .pipe(outputFile)
-          .on('close', async () => {
-            const zipFile = new zip(tempFileName);
-            zipFile.extractAllTo(destPath, true /* overwrite */);
-
-            await unlink(tempFileName);
-
-            if (callback !== undefined) {
-              callback();
-            }
-          });
-    } else if (uri.endsWith('.tar.gz')) {
-      response.on('data', chunk => bar.tick(chunk.length))
-          .pipe(tar.x({C: destPath, strict: true}))
-          .on('close', () => {
-            if (callback !== undefined) {
-              callback();
-            }
-          });
-    } else {
-      throw new Error(`Unsupported packed resource: ${uri}`);
-    }
-  });
-  request.end();
-}
-
-module.exports = {downloadAndUnpackResource};
diff --git a/tfjs-master/tfjs-node/scripts/tag-version b/tfjs-master/tfjs-node/scripts/tag-version
deleted file mode 100644
index 19f0194ff..000000000
--- a/tfjs-master/tfjs-node/scripts/tag-version
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2018 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-
-// Run this script from the base directory (not the script directory):
-// ./scripts/tag-version
-
-var fs = require('fs');
-var exec = require('child_process').exec;
-
-var version = JSON.parse(fs.readFileSync('package.json', 'utf8')).version;
-var tag = `v${version}`;
-
-exec(`git tag ${tag}`, err => {
-  if (err) {
-    throw new Error(`Could not git tag with ${tag}: ${err.message}.`);
-  }
-  console.log(`Successfully tagged with ${tag}.`);
-});
-
-exec(`git push --tags`, err => {
-  if (err) {
-    throw new Error(`Could not push git tags: ${err.message}.`);
-  }
-  console.log(`Successfully pushed tags.`);
-});
diff --git a/tfjs-master/tfjs-node/scripts/tsconfig.json b/tfjs-master/tfjs-node/scripts/tsconfig.json
deleted file mode 100644
index c5e8c9340..000000000
--- a/tfjs-master/tfjs-node/scripts/tsconfig.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "extends": "../tsconfig.test",
-  "compilerOptions": {
-    "module": "commonjs"
-  }
-}
diff --git a/tfjs-master/tfjs-node/src/callbacks.ts b/tfjs-master/tfjs-node/src/callbacks.ts
deleted file mode 100644
index eabd3a382..000000000
--- a/tfjs-master/tfjs-node/src/callbacks.ts
+++ /dev/null
@@ -1,366 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {CustomCallback, LayersModel, Logs, nextFrame, util} from '@tensorflow/tfjs';
-import * as path from 'path';
-import * as ProgressBar from 'progress';
-
-import {summaryFileWriter, SummaryFileWriter} from './tensorboard';
-
-type LogFunction = (message: string) => void;
-// A helper class created for testing with the jasmine `spyOn` method, which
-// operates only on member methods of objects.
-// tslint:disable-next-line:no-any
-export const progressBarHelper: {ProgressBar: any, log: LogFunction} = {
-  ProgressBar,
-  log: console.log
-};
-
-/**
- * Terminal-based progress bar callback for tf.Model.fit().
- */
-export class ProgbarLogger extends CustomCallback {
-  private numTrainBatchesPerEpoch: number;
-  private progressBar: ProgressBar;
-  private currentEpochBegin: number;
-  private epochDurationMillis: number;
-  private usPerStep: number;
-  private batchesInLatestEpoch: number;
-
-  private terminalWidth: number;
-
-  private readonly RENDER_THROTTLE_MS = 50;
-
-  /**
-   * Construtor of LoggingCallback.
-   */
-  constructor() {
-    super({
-      onTrainBegin: async (logs?: Logs) => {
-        const samples = this.params.samples as number;
-        const batchSize = this.params.batchSize as number;
-        const steps = this.params.steps as number;
-        if (samples != null || steps != null) {
-          this.numTrainBatchesPerEpoch =
-              samples != null ? Math.ceil(samples / batchSize) : steps;
-        } else {
-          // Undetermined number of batches per epoch, e.g., due to
-          // `fitDataset()` without `batchesPerEpoch`.
-          this.numTrainBatchesPerEpoch = 0;
-        }
-      },
-      onEpochBegin: async (epoch: number, logs?: Logs) => {
-        progressBarHelper.log(`Epoch ${epoch + 1} / ${this.params.epochs}`);
-        this.currentEpochBegin = util.now();
-        this.epochDurationMillis = null;
-        this.usPerStep = null;
-        this.batchesInLatestEpoch = 0;
-        this.terminalWidth = process.stderr.columns;
-      },
-      onBatchEnd: async (batch: number, logs?: Logs) => {
-        this.batchesInLatestEpoch++;
-        if (batch === 0) {
-          this.progressBar = new progressBarHelper.ProgressBar(
-              'eta=:eta :bar :placeholderForLossesAndMetrics', {
-                width: Math.floor(0.5 * this.terminalWidth),
-                total: this.numTrainBatchesPerEpoch + 1,
-                head: `>`,
-                renderThrottle: this.RENDER_THROTTLE_MS
-              });
-        }
-        const maxMetricsStringLength =
-            Math.floor(this.terminalWidth * 0.5 - 12);
-        const tickTokens = {
-          placeholderForLossesAndMetrics:
-              this.formatLogsAsMetricsContent(logs, maxMetricsStringLength)
-        };
-        if (this.numTrainBatchesPerEpoch === 0) {
-          // Undetermined number of batches per epoch.
-          this.progressBar.tick(0, tickTokens);
-        } else {
-          this.progressBar.tick(tickTokens);
-        }
-        await nextFrame();
-        if (batch === this.numTrainBatchesPerEpoch - 1) {
-          this.epochDurationMillis = util.now() - this.currentEpochBegin;
-          this.usPerStep = this.params.samples != null ?
-              this.epochDurationMillis / (this.params.samples as number) * 1e3 :
-              this.epochDurationMillis / this.batchesInLatestEpoch * 1e3;
-        }
-      },
-      onEpochEnd: async (epoch: number, logs?: Logs) => {
-        if (this.epochDurationMillis == null) {
-          // In cases where the number of batches per epoch is not determined,
-          // the calculation of the per-step duration is done at the end of the
-          // epoch. N.B., this includes the time spent on validation.
-          this.epochDurationMillis = util.now() - this.currentEpochBegin;
-          this.usPerStep =
-              this.epochDurationMillis / this.batchesInLatestEpoch * 1e3;
-        }
-        this.progressBar.tick({placeholderForLossesAndMetrics: ''});
-        const lossesAndMetricsString = this.formatLogsAsMetricsContent(logs);
-        progressBarHelper.log(
-            `${this.epochDurationMillis.toFixed(0)}ms ` +
-            `${this.usPerStep.toFixed(0)}us/step - ` +
-            `${lossesAndMetricsString}`);
-        await nextFrame();
-      },
-    });
-  }
-
-  private formatLogsAsMetricsContent(logs: Logs, maxMetricsLength?: number):
-      string {
-    let metricsContent = '';
-    const keys = Object.keys(logs).sort();
-    for (const key of keys) {
-      if (this.isFieldRelevant(key)) {
-        const value = logs[key];
-        metricsContent += `${key}=${getSuccinctNumberDisplay(value)} `;
-      }
-    }
-
-    if (maxMetricsLength != null && metricsContent.length > maxMetricsLength) {
-      // Cut off metrics strings that are too long to avoid new lines being
-      // constantly created.
-      metricsContent = metricsContent.slice(0, maxMetricsLength - 3) + '...';
-    }
-    return metricsContent;
-  }
-
-  private isFieldRelevant(key: string) {
-    return key !== 'batch' && key !== 'size';
-  }
-}
-
-const BASE_NUM_DIGITS = 2;
-const MAX_NUM_DECIMAL_PLACES = 4;
-
-/**
- * Get a succint string representation of a number.
- *
- * Uses decimal notation if the number isn't too small.
- * Otherwise, use engineering notation.
- *
- * @param x Input number.
- * @return Succinct string representing `x`.
- */
-export function getSuccinctNumberDisplay(x: number): string {
-  const decimalPlaces = getDisplayDecimalPlaces(x);
-  return decimalPlaces > MAX_NUM_DECIMAL_PLACES ?
-      x.toExponential(BASE_NUM_DIGITS) :
-      x.toFixed(decimalPlaces);
-}
-
-/**
- * Determine the number of decimal places to display.
- *
- * @param x Number to display.
- * @return Number of decimal places to display for `x`.
- */
-export function getDisplayDecimalPlaces(x: number): number {
-  if (!Number.isFinite(x) || x === 0 || x > 1 || x < -1) {
-    return BASE_NUM_DIGITS;
-  } else {
-    return BASE_NUM_DIGITS - Math.floor(Math.log10(Math.abs(x)));
-  }
-}
-
-export interface TensorBoardCallbackArgs {
-  /**
-   * The frequency at which loss and metric values are written to logs.
-   *
-   * Currently supported options are:
-   *
-   * - 'batch': Write logs at the end of every batch of training, in addition
-   *   to the end of every epoch of training.
-   * - 'epoch': Write logs at the end of every epoch of training.
-   *
-   * Note that writing logs too often slows down the training.
-   *
-   * Default: 'epoch'.
-   */
-  updateFreq?: 'batch'|'epoch';
-
-  /**
-   * The frequency (in epochs) at which to compute activation and weight
-   * histograms for the layers of the model.
-   *
-   * If set to 0, histograms won't be computed.
-   *
-   * Validation data (or split) must be specified for histogram visualizations.
-   *
-   * Default: 0.
-   */
-  histogramFreq?: number;
-}
-
-/**
- * Callback for logging to TensorBoard during training.
- *
- * Users are expected to access this class through the `tensorBoardCallback()`
- * factory method instead.
- */
-export class TensorBoardCallback extends CustomCallback {
-  private model: LayersModel = null;
-  private trainWriter: SummaryFileWriter;
-  private valWriter: SummaryFileWriter;
-  private batchesSeen: number;
-  private readonly args: TensorBoardCallbackArgs;
-
-  constructor(readonly logdir = './logs', args?: TensorBoardCallbackArgs) {
-    super({
-      onBatchEnd: async (batch: number, logs?: Logs) => {
-        this.batchesSeen++;
-        if (this.args.updateFreq !== 'epoch') {
-          this.logMetrics(logs, 'batch_', this.batchesSeen);
-        }
-      },
-      onEpochEnd: async (epoch: number, logs?: Logs) => {
-        this.logMetrics(logs, 'epoch_', epoch + 1);
-        if (this.args.histogramFreq > 0 &&
-            epoch % this.args.histogramFreq === 0) {
-          this.logWeights(epoch);
-        }
-      },
-      onTrainEnd: async (logs?: Logs) => {
-        if (this.trainWriter != null) {
-          this.trainWriter.flush();
-        }
-        if (this.valWriter != null) {
-          this.valWriter.flush();
-        }
-      }
-    });
-
-    this.args = args == null ? {} : args;
-    if (this.args.updateFreq == null) {
-      this.args.updateFreq = 'epoch';
-    }
-    util.assert(
-        ['batch', 'epoch'].indexOf(this.args.updateFreq) !== -1,
-        () => `Expected updateFreq to be 'batch' or 'epoch', but got ` +
-            `${this.args.updateFreq}`);
-    if (this.args.histogramFreq == null) {
-      this.args.histogramFreq = 0;
-    }
-    util.assert(
-        Number.isInteger(this.args.histogramFreq) &&
-            this.args.histogramFreq >= 0,
-        () => `Expected histogramFreq to be a positive integer, but got ` +
-            `${this.args.histogramFreq}`);
-    this.batchesSeen = 0;
-  }
-
-  setModel(model: LayersModel): void {
-    // This method is inherited from BaseCallback. To avoid cyclical imports,
-    // that class uses Container instead of LayersModel, and uses a run-time
-    // check to make sure the model is a LayersModel.
-    // Since this subclass isn't imported by tfjs-layers, we can safely use type
-    // the parameter as a LayersModel.
-    this.model = model;
-  }
-
-  private logMetrics(logs: Logs, prefix: string, step: number) {
-    for (const key in logs) {
-      if (key === 'batch' || key === 'size' || key === 'num_steps') {
-        continue;
-      }
-
-      const VAL_PREFIX = 'val_';
-      if (key.startsWith(VAL_PREFIX)) {
-        this.ensureValWriterCreated();
-        const scalarName = prefix + key.slice(VAL_PREFIX.length);
-        this.valWriter.scalar(scalarName, logs[key], step);
-      } else {
-        this.ensureTrainWriterCreated();
-        this.trainWriter.scalar(`${prefix}${key}`, logs[key], step);
-      }
-    }
-  }
-
-  private logWeights(step: number) {
-    for (const weights of this.model.weights) {
-      this.trainWriter.histogram(weights.name, weights.read(), step);
-    }
-  }
-
-  private ensureTrainWriterCreated() {
-    this.trainWriter = summaryFileWriter(path.join(this.logdir, 'train'));
-  }
-
-  private ensureValWriterCreated() {
-    this.valWriter = summaryFileWriter(path.join(this.logdir, 'val'));
-  }
-}
-
-/**
- * Callback for logging to TensorBoard during training.
- *
- * Writes the loss and metric values (if any) to the specified log directory
- * (`logdir`) which can be ingested and visualized by TensorBoard.
- * This callback is usually passed as a callback to `tf.Model.fit()` or
- * `tf.Model.fitDataset()` calls during model training. The frequency at which
- * the values are logged can be controlled with the `updateFreq` field of the
- * configuration object (2nd argument).
- *
- * Usage example:
- * ```js
- * // Constructor a toy multilayer-perceptron regressor for demo purpose.
- * const model = tf.sequential();
- * model.add(
- *     tf.layers.dense({units: 100, activation: 'relu', inputShape: [200]}));
- * model.add(tf.layers.dense({units: 1}));
- * model.compile({
- *   loss: 'meanSquaredError',
- *   optimizer: 'sgd',
- *   metrics: ['MAE']
- * });
- *
- * // Generate some random fake data for demo purpose.
- * const xs = tf.randomUniform([10000, 200]);
- * const ys = tf.randomUniform([10000, 1]);
- * const valXs = tf.randomUniform([1000, 200]);
- * const valYs = tf.randomUniform([1000, 1]);
- *
- * // Start model training process.
- * await model.fit(xs, ys, {
- *   epochs: 100,
- *   validationData: [valXs, valYs],
- *    // Add the tensorBoard callback here.
- *   callbacks: tf.node.tensorBoard('/tmp/fit_logs_1')
- * });
- * ```
- *
- * Then you can use the following commands to point tensorboard
- * to the logdir:
- *
- * ```sh
- * pip install tensorboard  # Unless you've already installed it.
- * tensorboard --logdir /tmp/fit_logs_1
- * ```
- *
- * @param logdir Directory to which the logs will be written.
- * @param args Optional configuration arguments.
- * @returns An instance of `TensorBoardCallback`, which is a subclass of
- *   `tf.CustomCallback`.
- *
- * @doc {heading: 'TensorBoard', namespace: 'node'}
- */
-export function tensorBoard(
-    logdir = './logs', args?: TensorBoardCallbackArgs): TensorBoardCallback {
-  return new TensorBoardCallback(logdir, args);
-}
diff --git a/tfjs-master/tfjs-node/src/callbacks_test.ts b/tfjs-master/tfjs-node/src/callbacks_test.ts
deleted file mode 100644
index c6d60a03a..000000000
--- a/tfjs-master/tfjs-node/src/callbacks_test.ts
+++ /dev/null
@@ -1,338 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-
-import {getDisplayDecimalPlaces, getSuccinctNumberDisplay, progressBarHelper} from './callbacks';
-
-describe('progbarLogger', () => {
-  // Fake progbar class written for testing.
-  class FakeProgbar {
-    readonly tickConfigs: Array<{placeholderForLossesAndMetrics: string}> = [];
-
-    constructor(readonly specs: string, readonly config?: {}) {}
-
-    tick(tickConfig: {placeholderForLossesAndMetrics: string}) {
-      this.tickConfigs.push(tickConfig);
-    }
-  }
-
-  let originalStderrColumns: number;
-
-  beforeEach(() => {
-    // In some CI environments, process.stderr.columns has a null value.
-    originalStderrColumns = process.stderr.columns;
-    process.stderr.columns = 100;
-  });
-
-  afterEach(() => {
-    process.stderr.columns = originalStderrColumns;
-  });
-
-  it('Model.fit with loss, no metric, no validation, verobse = 1', async () => {
-    const fakeProgbars: FakeProgbar[] = [];
-    spyOn(progressBarHelper, 'ProgressBar')
-        .and.callFake((specs: string, config: {}) => {
-          const fakeProgbar = new FakeProgbar(specs, config);
-          fakeProgbars.push(fakeProgbar);
-          return fakeProgbar;
-        });
-    const consoleMessages: string[] = [];
-    spyOn(progressBarHelper, 'log').and.callFake((message: string) => {
-      consoleMessages.push(message);
-    });
-
-    const model = tf.sequential();
-    model.add(
-        tf.layers.dense({units: 10, inputShape: [8], activation: 'relu'}));
-    model.add(tf.layers.dense({units: 1}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const numSamples = 14;
-    const epochs = 3;
-    const batchSize = 8;
-    const xs = tf.randomNormal([numSamples, 8]);
-    const ys = tf.randomNormal([numSamples, 1]);
-    await model.fit(xs, ys, {epochs, batchSize, verbose: 1});
-
-    // A progbar object is created for each epoch.
-    expect(fakeProgbars.length).toEqual(3);
-    for (const fakeProgbar of fakeProgbars) {
-      const tickConfigs = fakeProgbar.tickConfigs;
-      // There are ceil(14 / 8) = 2 batchs per epoch. There should be 1 tick
-      // for epoch batch, plus a tick at the end of the epoch.
-      expect(tickConfigs.length).toEqual(3);
-      for (let i = 0; i < 2; ++i) {
-        expect(Object.keys(tickConfigs[i])).toEqual([
-          'placeholderForLossesAndMetrics'
-        ]);
-        expect(tickConfigs[i]['placeholderForLossesAndMetrics'])
-            .toMatch(/^loss=.*/);
-      }
-      expect(tickConfigs[2]).toEqual({placeholderForLossesAndMetrics: ''});
-    }
-    expect(consoleMessages.length).toEqual(6);
-    expect(consoleMessages[0]).toEqual('Epoch 1 / 3');
-    expect(consoleMessages[1]).toMatch(/.*ms .*us\/step - loss=.*/);
-    expect(consoleMessages[2]).toEqual('Epoch 2 / 3');
-    expect(consoleMessages[3]).toMatch(/.*ms .*us\/step - loss=.*/);
-    expect(consoleMessages[4]).toEqual('Epoch 3 / 3');
-    expect(consoleMessages[5]).toMatch(/.*ms .*us\/step - loss=.*/);
-  });
-
-  it('Model.fit with loss, metric and validation, verbose = 2', async () => {
-    const fakeProgbars: FakeProgbar[] = [];
-    spyOn(progressBarHelper, 'ProgressBar')
-        .and.callFake((specs: string, config: {}) => {
-          const fakeProgbar = new FakeProgbar(specs, config);
-          fakeProgbars.push(fakeProgbar);
-          return fakeProgbar;
-        });
-    const consoleMessages: string[] = [];
-    spyOn(progressBarHelper, 'log').and.callFake((message: string) => {
-      consoleMessages.push(message);
-    });
-
-    const model = tf.sequential();
-    model.add(
-        tf.layers.dense({units: 10, inputShape: [8], activation: 'relu'}));
-    model.add(tf.layers.dense({units: 1}));
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['acc']});
-
-    const numSamples = 40;
-    const epochs = 2;
-    const batchSize = 8;
-    const validationSplit = 0.15;
-    const xs = tf.randomNormal([numSamples, 8]);
-    const ys = tf.randomNormal([numSamples, 1]);
-    await model.fit(xs, ys, {epochs, batchSize, validationSplit, verbose: 2});
-
-    // A progbar object is created for each epoch.
-    expect(fakeProgbars.length).toEqual(2);
-    for (const fakeProgbar of fakeProgbars) {
-      const tickConfigs = fakeProgbar.tickConfigs;
-      // There are 5 batchs per epoch. There should be 1 tick for epoch batch,
-      // plus a tick at the end of the epoch.
-      expect(tickConfigs.length).toEqual(6);
-      for (let i = 0; i < 5; ++i) {
-        expect(Object.keys(tickConfigs[i])).toEqual([
-          'placeholderForLossesAndMetrics'
-        ]);
-        expect(tickConfigs[i]['placeholderForLossesAndMetrics'])
-            .toMatch(/^acc=.* loss=.*/);
-      }
-      expect(tickConfigs[5]).toEqual({placeholderForLossesAndMetrics: ''});
-    }
-    expect(consoleMessages.length).toEqual(4);
-    expect(consoleMessages[0]).toEqual('Epoch 1 / 2');
-    expect(consoleMessages[1])
-        .toMatch(/.*ms .*us\/step - acc=.* loss=.* val_acc=.* val_loss=.*/);
-    expect(consoleMessages[2]).toEqual('Epoch 2 / 2');
-    expect(consoleMessages[3])
-        .toMatch(/.*ms .*us\/step - acc=.* loss=.* val_acc=.* val_loss=.*/);
-  });
-
-  it('Model.fit does not create ProgbarLogger if verbose is 0', async () => {
-    const fakeProgbars: FakeProgbar[] = [];
-    spyOn(progressBarHelper, 'ProgressBar')
-        .and.callFake((specs: string, config: {}) => {
-          const fakeProgbar = new FakeProgbar(specs, config);
-          fakeProgbars.push(fakeProgbar);
-          return fakeProgbar;
-        });
-    const consoleMessages: string[] = [];
-    spyOn(progressBarHelper, 'log').and.callFake((message: string) => {
-      consoleMessages.push(message);
-    });
-
-    const model = tf.sequential();
-    model.add(
-        tf.layers.dense({units: 10, inputShape: [8], activation: 'relu'}));
-    model.add(tf.layers.dense({units: 1}));
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['acc']});
-
-    const numSamples = 40;
-    const epochs = 2;
-    const batchSize = 8;
-    const validationSplit = 0.15;
-    const xs = tf.randomNormal([numSamples, 8]);
-    const ys = tf.randomNormal([numSamples, 1]);
-    await model.fit(xs, ys, {epochs, batchSize, validationSplit, verbose: 0});
-
-    expect(fakeProgbars.length).toEqual(0);
-  });
-
-  it('Model.fitDataset: batchesPerEpoch specified, verbose = 1', async () => {
-    const fakeProgbars: FakeProgbar[] = [];
-    spyOn(progressBarHelper, 'ProgressBar')
-        .and.callFake((specs: string, config: {}) => {
-          const fakeProgbar = new FakeProgbar(specs, config);
-          fakeProgbars.push(fakeProgbar);
-          return fakeProgbar;
-        });
-    const consoleMessages: string[] = [];
-    spyOn(progressBarHelper, 'log').and.callFake((message: string) => {
-      consoleMessages.push(message);
-    });
-
-    const epochs = 2;
-    const xDataset = tf.data.array([[1, 2], [3, 4], [5, 6], [7, 8]])
-                         .map(x => tf.tensor2d(x, [1, 2]));
-    const yDataset =
-        tf.data.array([[1], [2], [3], [4]]).map(y => tf.tensor2d(y, [1, 1]));
-    const dataset = tf.data.zip({xs: xDataset, ys: yDataset}).repeat(epochs);
-
-    const model = tf.sequential();
-    model.add(tf.layers.dense({units: 1, inputShape: [2]}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    await model.fitDataset(dataset, {batchesPerEpoch: 4, epochs, verbose: 1});
-
-    expect(consoleMessages.length).toEqual(4);
-    expect(consoleMessages[0]).toEqual('Epoch 1 / 2');
-    expect(consoleMessages[1]).toMatch(/.*ms .*us\/step - loss=.*/);
-    expect(consoleMessages[2]).toEqual('Epoch 2 / 2');
-    expect(consoleMessages[3]).toMatch(/.*ms .*us\/step - loss=.*/);
-  });
-
-  it('Model.fitDataset: batchesPerEpoch unavailable, verbose = 1', async () => {
-    const fakeProgbars: FakeProgbar[] = [];
-    spyOn(progressBarHelper, 'ProgressBar')
-        .and.callFake((specs: string, config: {}) => {
-          const fakeProgbar = new FakeProgbar(specs, config);
-          fakeProgbars.push(fakeProgbar);
-          return fakeProgbar;
-        });
-    const consoleMessages: string[] = [];
-    spyOn(progressBarHelper, 'log').and.callFake((message: string) => {
-      consoleMessages.push(message);
-    });
-
-    const epochs = 2;
-    const xDataset = tf.data.array([[1, 2], [3, 4], [5, 6], [7, 8]])
-                         .map(x => tf.tensor2d(x, [1, 2]));
-    const yDataset =
-        tf.data.array([[1], [2], [3], [4]]).map(y => tf.tensor2d(y, [1, 1]));
-    const dataset = tf.data.zip({xs: xDataset, ys: yDataset}).repeat(epochs);
-
-    const model = tf.sequential();
-    model.add(tf.layers.dense({units: 1, inputShape: [2]}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    // `batchesPerEpoch` is not specified. Instead, `fitDataset()` relies on
-    // the `done` field being `true` to terminate the epoch(s).
-    await model.fitDataset(dataset, {epochs, verbose: 1});
-
-    expect(consoleMessages.length).toEqual(4);
-    expect(consoleMessages[0]).toEqual('Epoch 1 / 2');
-    expect(consoleMessages[1]).toMatch(/.*ms .*us\/step - loss=.*/);
-    expect(consoleMessages[2]).toEqual('Epoch 2 / 2');
-    expect(consoleMessages[3]).toMatch(/.*ms .*us\/step - loss=.*/);
-  });
-
-  it('Model.fitDataset: verbose = 0 leads to no logging', async () => {
-    const fakeProgbars: FakeProgbar[] = [];
-    spyOn(progressBarHelper, 'ProgressBar')
-        .and.callFake((specs: string, config: {}) => {
-          const fakeProgbar = new FakeProgbar(specs, config);
-          fakeProgbars.push(fakeProgbar);
-          return fakeProgbar;
-        });
-    const consoleMessages: string[] = [];
-    spyOn(progressBarHelper, 'log').and.callFake((message: string) => {
-      consoleMessages.push(message);
-    });
-
-    const xDataset = tf.data.array([[1, 2], [3, 4], [5, 6], [7, 8]])
-                         .map(x => tf.tensor2d(x, [1, 2]));
-    const yDataset =
-        tf.data.array([[1], [2], [3], [4]]).map(y => tf.tensor2d(y, [1, 1]));
-    const dataset = tf.data.zip({xs: xDataset, ys: yDataset});
-
-    const model = tf.sequential();
-    model.add(tf.layers.dense({units: 1, inputShape: [2]}));
-    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-    const history = await model.fitDataset(dataset, {epochs: 1, verbose: 0});
-    expect(history.history.loss.length).toEqual(1);
-    expect(consoleMessages.length)
-        .toEqual(0);  // No logging should have happened.
-  });
-});
-
-describe('getSuccinctNumberDisplay', () => {
-  it('Not finite', () => {
-    expect(getSuccinctNumberDisplay(Infinity)).toEqual('Infinity');
-    expect(getSuccinctNumberDisplay(-Infinity)).toEqual('-Infinity');
-    expect(getSuccinctNumberDisplay(NaN)).toEqual('NaN');
-  });
-
-  it('zero', () => {
-    expect(getSuccinctNumberDisplay(0)).toEqual('0.00');
-  });
-
-  it('Finite and positive', () => {
-    expect(getSuccinctNumberDisplay(300)).toEqual('300.00');
-    expect(getSuccinctNumberDisplay(30)).toEqual('30.00');
-    expect(getSuccinctNumberDisplay(1)).toEqual('1.00');
-    expect(getSuccinctNumberDisplay(1e-2)).toEqual('0.0100');
-    expect(getSuccinctNumberDisplay(1e-3)).toEqual('1.00e-3');
-    expect(getSuccinctNumberDisplay(4e-3)).toEqual('4.00e-3');
-    expect(getSuccinctNumberDisplay(1e-6)).toEqual('1.00e-6');
-  });
-
-  it('Finite and negative', () => {
-    expect(getSuccinctNumberDisplay(-300)).toEqual('-300.00');
-    expect(getSuccinctNumberDisplay(-30)).toEqual('-30.00');
-    expect(getSuccinctNumberDisplay(-1)).toEqual('-1.00');
-    expect(getSuccinctNumberDisplay(-1e-2)).toEqual('-0.0100');
-    expect(getSuccinctNumberDisplay(-1e-3)).toEqual('-1.00e-3');
-    expect(getSuccinctNumberDisplay(-4e-3)).toEqual('-4.00e-3');
-    expect(getSuccinctNumberDisplay(-1e-6)).toEqual('-1.00e-6');
-  });
-});
-
-describe('getDisplayDecimalPlaces', () => {
-  it('Not finite', () => {
-    expect(getDisplayDecimalPlaces(Infinity)).toEqual(2);
-    expect(getDisplayDecimalPlaces(-Infinity)).toEqual(2);
-    expect(getDisplayDecimalPlaces(NaN)).toEqual(2);
-  });
-
-  it('zero', () => {
-    expect(getDisplayDecimalPlaces(0)).toEqual(2);
-  });
-
-  it('Finite and positive', () => {
-    expect(getDisplayDecimalPlaces(300)).toEqual(2);
-    expect(getDisplayDecimalPlaces(30)).toEqual(2);
-    expect(getDisplayDecimalPlaces(1)).toEqual(2);
-    expect(getDisplayDecimalPlaces(1e-2)).toEqual(4);
-    expect(getDisplayDecimalPlaces(1e-3)).toEqual(5);
-    expect(getDisplayDecimalPlaces(4e-3)).toEqual(5);
-    expect(getDisplayDecimalPlaces(1e-6)).toEqual(8);
-  });
-
-  it('Finite and negative', () => {
-    expect(getDisplayDecimalPlaces(-300)).toEqual(2);
-    expect(getDisplayDecimalPlaces(-30)).toEqual(2);
-    expect(getDisplayDecimalPlaces(-1)).toEqual(2);
-    expect(getDisplayDecimalPlaces(-1e-2)).toEqual(4);
-    expect(getDisplayDecimalPlaces(-1e-3)).toEqual(5);
-    expect(getDisplayDecimalPlaces(-4e-3)).toEqual(5);
-    expect(getDisplayDecimalPlaces(-1e-6)).toEqual(8);
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/canvas_test.ts b/tfjs-master/tfjs-node/src/canvas_test.ts
deleted file mode 100644
index f03bfae13..000000000
--- a/tfjs-master/tfjs-node/src/canvas_test.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-
-class MockContext {
-  getImageData(x: number, y: number, width: number, height: number) {
-    const data = new Uint8ClampedArray(width * height * 4);
-    for (let i = 0; i < data.length; ++i) {
-      data[i] = i + 1;
-    }
-    return {data};
-  }
-}
-
-class MockCanvas {
-  constructor(public width: number, public height: number) {}
-  getContext(type: '2d'): MockContext {
-    return new MockContext();
-  }
-}
-
-describe('tf.browser.fromPixels with polyfills', () => {
-  it('accepts a canvas-like element', async () => {
-    const c = new MockCanvas(2, 2);
-    // tslint:disable-next-line:no-any
-    const t = tf.browser.fromPixels(c as any);
-    expect(t.dtype).toBe('int32');
-    expect(t.shape).toEqual([2, 2, 3]);
-    tf.test_util.expectArraysEqual(
-        await t.data(), [1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15]);
-  });
-
-  it('accepts a canvas-like element, numChannels=4', async () => {
-    const c = new MockCanvas(2, 2);
-    // tslint:disable-next-line:no-any
-    const t = tf.browser.fromPixels(c as any, 4);
-    expect(t.dtype).toBe('int32');
-    expect(t.shape).toEqual([2, 2, 4]);
-    tf.test_util.expectArraysEqual(
-        await t.data(),
-        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);
-  });
-
-  it('errors when passed a non-canvas object', () => {
-    const c = 5;
-    // tslint:disable-next-line:no-any
-    expect(() => tf.browser.fromPixels(c as any))
-        .toThrowError(
-            /pixels passed to tf\.browser\.fromPixels\(\) must be either an/);
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/image.ts b/tfjs-master/tfjs-node/src/image.ts
deleted file mode 100644
index 2d40ece62..000000000
--- a/tfjs-master/tfjs-node/src/image.ts
+++ /dev/null
@@ -1,299 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor, Tensor3D, Tensor4D, tidy, util} from '@tensorflow/tfjs';
-import {ensureTensorflowBackend, nodeBackend} from './nodejs_kernel_backend';
-
-export enum ImageType {
-  JPEG = 'jpeg',
-  PNG = 'png',
-  GIF = 'gif',
-  BMP = 'BMP'
-}
-
-/**
- * Decode a JPEG-encoded image to a 3D Tensor of dtype `int32`.
- *
- * @param contents The JPEG-encoded image in an Uint8Array.
- * @param channels An optional int. Defaults to 0. Accepted values are
- *     0: use the number of channels in the JPEG-encoded image.
- *     1: output a grayscale image.
- *     3: output an RGB image.
- * @param ratio An optional int. Defaults to 1. Downscaling ratio. It is used
- *     when image is type Jpeg.
- * @param fancyUpscaling An optional bool. Defaults to True. If true use a
- *     slower but nicer upscaling of the chroma planes. It is used when image is
- *     type Jpeg.
- * @param tryRecoverTruncated An optional bool. Defaults to False. If true try
- *     to recover an image from truncated input. It is used when image is type
- *     Jpeg.
- * @param acceptableFraction An optional float. Defaults to 1. The minimum
- *     required fraction of lines before a truncated input is accepted. It is
- *     used when image is type Jpeg.
- * @param dctMethod An optional string. Defaults to "". string specifying a hint
- *     about the algorithm used for decompression. Defaults to "" which maps to
- *     a system-specific default. Currently valid values are ["INTEGER_FAST",
- *     "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal jpeg
- *     library changes to a version that does not have that specific option.) It
- *     is used when image is type Jpeg.
- * @returns A 3D Tensor of dtype `int32` with shape [height, width, 1/3].
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
- */
-export function decodeJpeg(
-    contents: Uint8Array, channels = 0, ratio = 1, fancyUpscaling = true,
-    tryRecoverTruncated = false, acceptableFraction = 1,
-    dctMethod = ''): Tensor3D {
-  ensureTensorflowBackend();
-  return tidy(() => {
-    return nodeBackend()
-        .decodeJpeg(
-            contents, channels, ratio, fancyUpscaling, tryRecoverTruncated,
-            acceptableFraction, dctMethod)
-        .toInt();
-  });
-}
-
-/**
- * Decode a PNG-encoded image to a 3D Tensor of dtype `int32`.
- *
- * @param contents The PNG-encoded image in an Uint8Array.
- * @param channels An optional int. Defaults to 0. Accepted values are
- *      0: use the number of channels in the PNG-encoded image.
- *      1: output a grayscale image.
- *      3: output an RGB image.
- *      4: output an RGBA image.
- * @param dtype The data type of the result. Only `int32` is supported at this
- *     time.
- * @returns A 3D Tensor of dtype `int32` with shape [height, width, 1/3/4].
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
- */
-export function decodePng(
-    contents: Uint8Array, channels = 0, dtype = 'int32'): Tensor3D {
-  util.assert(
-      dtype === 'int32',
-      () => 'decodeImage could only return Tensor of type `int32` for now.');
-  ensureTensorflowBackend();
-  return tidy(() => {
-    return nodeBackend().decodePng(contents, channels).toInt();
-  });
-}
-
-/**
- * Decode the first frame of a BMP-encoded image to a 3D Tensor of dtype
- * `int32`.
- *
- * @param contents The BMP-encoded image in an Uint8Array.
- * @param channels An optional int. Defaults to 0. Accepted values are
- *      0: use the number of channels in the BMP-encoded image.
- *      3: output an RGB image.
- *      4: output an RGBA image.
- * @returns A 3D Tensor of dtype `int32` with shape [height, width, 3/4].
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
- */
-export function decodeBmp(contents: Uint8Array, channels = 0): Tensor3D {
-  ensureTensorflowBackend();
-  return tidy(() => {
-    return nodeBackend().decodeBmp(contents, channels).toInt();
-  });
-}
-
-/**
- * Decode the frame(s) of a GIF-encoded image to a 4D Tensor of dtype `int32`.
- *
- * @param contents The GIF-encoded image in an Uint8Array.
- * @returns A 4D Tensor of dtype `int32` with shape [num_frames, height, width,
- *     3]. RGB channel order.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
- */
-export function decodeGif(contents: Uint8Array): Tensor4D {
-  ensureTensorflowBackend();
-  return tidy(() => {
-    return nodeBackend().decodeGif(contents).toInt();
-  });
-}
-
-/**
- * Given the encoded bytes of an image, it returns a 3D or 4D tensor of the
- * decoded image. Supports BMP, GIF, JPEG and PNG formats.
- *
- * @param content The encoded image in an Uint8Array.
- * @param channels An optional int. Defaults to 0, use the number of channels in
- *     the image. Number of color channels for the decoded image. It is used
- *     when image is type Png, Bmp, or Jpeg.
- * @param dtype The data type of the result. Only `int32` is supported at this
- *     time.
- * @param expandAnimations A boolean which controls the shape of the returned
- *     op's output. If True, the returned op will produce a 3-D tensor for PNG,
- *     JPEG, and BMP files; and a 4-D tensor for all GIFs, whether animated or
- *     not. If, False, the returned op will produce a 3-D tensor for all file
- *     types and will truncate animated GIFs to the first frame.
- * @returns A Tensor with dtype `int32` and a 3- or 4-dimensional shape,
- *     depending on the file type. For gif file the returned Tensor shape is
- *     [num_frames, height, width, 3], and for jpeg/png/bmp the returned Tensor
- *     shape is [height, width, channels]
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
- */
-export function decodeImage(
-    content: Uint8Array, channels = 0, dtype = 'int32',
-    expandAnimations = true): Tensor3D|Tensor4D {
-  util.assert(
-      dtype === 'int32',
-      () => 'decodeImage could only return Tensor of type `int32` for now.');
-
-  const imageType = getImageType(content);
-
-  // The return tensor has dtype uint8, which is not supported in
-  // TensorFlow.js, casting it to int32 which is the default dtype for image
-  // tensor. If the image is BMP, JPEG or PNG type, expanding the tensors
-  // shape so it becomes Tensor4D, which is the default tensor shape for image
-  // ([batch,imageHeight,imageWidth, depth]).
-  switch (imageType) {
-    case ImageType.JPEG:
-      return decodeJpeg(content, channels);
-    case ImageType.PNG:
-      return decodePng(content, channels);
-    case ImageType.GIF:
-      // If not to expand animations, take first frame of the gif and return
-      // as a 3D tensor.
-      return tidy(() => {
-        const img = decodeGif(content);
-        return expandAnimations ? img : img.slice(0, 1).squeeze([0]);
-      });
-    case ImageType.BMP:
-      return decodeBmp(content, channels);
-    default:
-      return null;
-  }
-}
-
-/**
- * Encodes an image tensor to JPEG.
- *
- * @param image A 3-D uint8 Tensor of shape [height, width, channels].
- * @param format An optional string from: "", "grayscale", "rgb".
- *     Defaults to "". Per pixel image format.
- *     - '': Use a default format based on the number of channels in the image.
- *     - grayscale: Output a grayscale JPEG image. The channels dimension of
- *       image must be 1.
- *     - rgb: Output an RGB JPEG image. The channels dimension of image must
- *       be 3.
- * @param quality An optional int. Defaults to 95. Quality of the compression
- *     from 0 to 100 (higher is better and slower).
- * @param progressive An optional bool. Defaults to False. If True, create a
- *     JPEG that loads progressively (coarse to fine).
- * @param optimizeSize An optional bool. Defaults to False. If True, spend
- *     CPU/RAM to reduce size with no quality change.
- * @param chromaDownsampling  An optional bool. Defaults to True.
- *     See http://en.wikipedia.org/wiki/Chroma_subsampling.
- * @param densityUnit An optional string from: "in", "cm". Defaults to "in".
- *     Unit used to specify x_density and y_density: pixels per inch ('in') or
- *     centimeter ('cm').
- * @param xDensity An optional int. Defaults to 300. Horizontal pixels per
- *     density unit.
- * @param yDensity An optional int. Defaults to 300. Vertical pixels per
- *     density unit.
- * @param xmpMetadata An optional string. Defaults to "". If not empty, embed
- *     this XMP metadata in the image header.
- * @returns The JPEG encoded data as an Uint8Array.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
- */
-export async function encodeJpeg(
-    image: Tensor3D, format: ''|'grayscale'|'rgb' = '', quality = 95,
-    progressive = false, optimizeSize = false, chromaDownsampling = true,
-    densityUnit: 'in'|'cm' = 'in', xDensity = 300, yDensity = 300,
-    xmpMetadata = ''): Promise<Uint8Array> {
-  ensureTensorflowBackend();
-
-  const backendEncodeImage = (imageData: Uint8Array) =>
-      nodeBackend().encodeJpeg(
-          imageData, image.shape, format, quality, progressive, optimizeSize,
-          chromaDownsampling, densityUnit, xDensity, yDensity, xmpMetadata);
-
-  return encodeImage(image, backendEncodeImage);
-}
-
-/**
- * Encodes an image tensor to PNG.
- *
- * @param image A 3-D uint8 Tensor of shape [height, width, channels].
- * @param compression An optional int. Defaults to 1. Compression level.
- * @returns The PNG encoded data as an Uint8Array.
- *
- * @doc {heading: 'Operations', subheading: 'Images', namespace: 'node'}
- */
-export async function encodePng(
-    image: Tensor3D, compression = 1): Promise<Uint8Array> {
-  ensureTensorflowBackend();
-
-  const backendEncodeImage = (imageData: Uint8Array) =>
-      nodeBackend().encodePng(imageData, image.shape, compression);
-  return encodeImage(image, backendEncodeImage);
-}
-
-async function encodeImage(
-    image: Tensor3D, backendEncodeImage: (imageData: Uint8Array) => Tensor):
-    Promise<Uint8Array> {
-  const encodedDataTensor =
-      backendEncodeImage(new Uint8Array(await image.data()));
-
-  const encodedPngData =
-      (
-          // tslint:disable-next-line:no-any
-          await encodedDataTensor.data())[0] as any as Uint8Array;
-  encodedDataTensor.dispose();
-  return encodedPngData;
-}
-
-/**
- * Helper function to get image type based on starting bytes of the image file.
- */
-export function getImageType(content: Uint8Array): string {
-  // Classify the contents of a file based on starting bytes (aka magic number:
-  // https://en.wikipedia.org/wiki/Magic_number_(programming)#Magic_numbers_in_files)
-  // This aligns with TensorFlow Core code:
-  // https://github.com/tensorflow/tensorflow/blob/4213d5c1bd921f8d5b7b2dc4bbf1eea78d0b5258/tensorflow/core/kernels/decode_image_op.cc#L44
-  if (content.length > 3 && content[0] === 255 && content[1] === 216 &&
-      content[2] === 255) {
-    // JPEG byte chunk starts with `ff d8 ff`
-    return ImageType.JPEG;
-  } else if (
-      content.length > 4 && content[0] === 71 && content[1] === 73 &&
-      content[2] === 70 && content[3] === 56) {
-    // GIF byte chunk starts with `47 49 46 38`
-    return ImageType.GIF;
-  } else if (
-      content.length > 8 && content[0] === 137 && content[1] === 80 &&
-      content[2] === 78 && content[3] === 71 && content[4] === 13 &&
-      content[5] === 10 && content[6] === 26 && content[7] === 10) {
-    // PNG byte chunk starts with `\211 P N G \r \n \032 \n (89 50 4E 47 0D 0A
-    // 1A 0A)`
-    return ImageType.PNG;
-  } else if (content.length > 3 && content[0] === 66 && content[1] === 77) {
-    // BMP byte chunk starts with `42 4d`
-    return ImageType.BMP;
-  } else {
-    throw new Error(
-        'Expected image (BMP, JPEG, PNG, or GIF), but got unsupported ' +
-        'image type');
-  }
-}
diff --git a/tfjs-master/tfjs-node/src/image_test.ts b/tfjs-master/tfjs-node/src/image_test.ts
deleted file mode 100644
index 59889e32b..000000000
--- a/tfjs-master/tfjs-node/src/image_test.ts
+++ /dev/null
@@ -1,414 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {memory, registerBackend, setBackend, test_util} from '@tensorflow/tfjs';
-// tslint:disable-next-line: no-imports-from-dist
-import {TestKernelBackend} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import * as fs from 'fs';
-import {promisify} from 'util';
-
-import {getImageType, ImageType} from './image';
-import * as tf from './index';
-import {NodeJSKernelBackend} from './nodejs_kernel_backend';
-
-const readFile = promisify(fs.readFile);
-
-describe('decode images', () => {
-  it('decode png', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const uint8array =
-        await getUint8ArrayFromImage('test_objects/images/image_png_test.png');
-    const imageTensor = tf.node.decodePng(uint8array);
-
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 3]);
-    test_util.expectArraysEqual(
-        await imageTensor.data(),
-        [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode png 1 channels', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const uint8array =
-        await getUint8ArrayFromImage('test_objects/images/image_png_test.png');
-    const imageTensor = tf.node.decodeImage(uint8array, 1);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 1]);
-    test_util.expectArraysEqual(await imageTensor.data(), [130, 50, 59, 124]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode png 3 channels', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const uint8array =
-        await getUint8ArrayFromImage('test_objects/images/image_png_test.png');
-    const imageTensor = tf.node.decodeImage(uint8array);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 3]);
-    test_util.expectArraysEqual(
-        await imageTensor.data(),
-        [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode png 4 channels', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const uint8array = await getUint8ArrayFromImage(
-        'test_objects/images/image_png_4_channel_test.png');
-    const imageTensor = tf.node.decodeImage(uint8array, 4);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 4]);
-    test_util.expectArraysEqual(await imageTensor.data(), [
-      238, 101, 0, 255, 50, 50, 50, 255, 100, 50, 0, 255, 200, 100, 50, 255
-    ]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode bmp', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const uint8array =
-        await getUint8ArrayFromImage('test_objects/images/image_bmp_test.bmp');
-    const imageTensor = tf.node.decodeBmp(uint8array);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 3]);
-    test_util.expectArraysEqual(
-        await imageTensor.data(),
-        [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode bmp through decodeImage', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const uint8array =
-        await getUint8ArrayFromImage('test_objects/images/image_bmp_test.bmp');
-    const imageTensor = tf.node.decodeImage(uint8array);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 3]);
-    test_util.expectArraysEqual(
-        await imageTensor.data(),
-        [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode jpg', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-
-    const uint8array = await getUint8ArrayFromImage(
-        'test_objects/images/image_jpeg_test.jpeg');
-    const imageTensor = tf.node.decodeJpeg(uint8array);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 3]);
-    test_util.expectArraysEqual(
-        await imageTensor.data(),
-        [239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode jpeg node bindings do not leak', async () => {
-    const uint8array = await getUint8ArrayFromImage(
-        'test_objects/images/image_jpeg_test.jpeg');
-
-    // Warm up the node bindings
-    for (let i = 0; i < 10_000; i++) {
-      const imageTensor = tf.node.decodeJpeg(uint8array);
-      imageTensor.dispose();
-    }
-
-    // Check if decodeJpeg leaks memory by running it many times.
-    const beforeMem = process.memoryUsage().rss;
-    for (let i = 0; i < 100_000; i++) {
-      const imageTensor = tf.node.decodeJpeg(uint8array);
-      imageTensor.dispose();
-    }
-    const afterMem = process.memoryUsage().rss;
-
-    // Due to GC fluctuations, There has to be a large 1Mb margain for this
-    // test, but if decodeJpeg leaks more than 10 bytes per run, it will be
-    // detected.
-    expect(afterMem).toBeLessThan(beforeMem + 1e6 /* 1Mb */);
-  });
-
-  it('decode jpg 1 channel', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-
-    const uint8array = await getUint8ArrayFromImage(
-        'test_objects/images/image_jpeg_test.jpeg');
-    const imageTensor = tf.node.decodeImage(uint8array, 1);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 1]);
-    test_util.expectArraysEqual(await imageTensor.data(), [130, 47, 56, 121]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode jpg 3 channels', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const uint8array = await getUint8ArrayFromImage(
-        'test_objects/images/image_jpeg_test.jpeg');
-    const imageTensor = tf.node.decodeImage(uint8array, 3);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 3]);
-    test_util.expectArraysEqual(
-        await imageTensor.data(),
-        [239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode jpg with 0 channels, use the number of channels in the ' +
-         'JPEG-encoded image',
-     async () => {
-       const beforeNumTensors = memory().numTensors;
-       const beforeNumTFTensors =
-           (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-       const uint8array = await getUint8ArrayFromImage(
-           'test_objects/images/image_jpeg_test.jpeg');
-       const imageTensor = tf.node.decodeImage(uint8array);
-       expect(imageTensor.dtype).toBe('int32');
-       expect(imageTensor.shape).toEqual([2, 2, 3]);
-       test_util.expectArraysEqual(
-           await imageTensor.data(),
-           [239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]);
-       expect(memory().numTensors).toBe(beforeNumTensors + 1);
-       expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-           .toBe(beforeNumTFTensors + 1);
-     });
-
-  it('decode jpg with downscale', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const uint8array = await getUint8ArrayFromImage(
-        'test_objects/images/image_jpeg_test.jpeg');
-    const imageTensor = tf.node.decodeJpeg(uint8array, 0, 2);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([1, 1, 3]);
-    test_util.expectArraysEqual(await imageTensor.data(), [147, 75, 25]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode gif', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const uint8array =
-        await getUint8ArrayFromImage('test_objects/images/gif_test.gif');
-    const imageTensor = tf.node.decodeImage(uint8array);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 2, 3]);
-    test_util.expectArraysEqual(await imageTensor.data(), [
-      238, 101, 0,  50, 50, 50,  100, 50, 0,   200, 100, 50,
-      200, 100, 50, 34, 68, 102, 170, 0,  102, 255, 255, 255
-    ]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('decode gif with no expandAnimation', async () => {
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-
-    const uint8array =
-        await getUint8ArrayFromImage('test_objects/images/gif_test.gif');
-    const imageTensor = tf.node.decodeImage(uint8array, 3, 'int32', false);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 3]);
-    test_util.expectArraysEqual(
-        await imageTensor.data(),
-        [238, 101, 0, 50, 50, 50, 100, 50, 0, 200, 100, 50]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors + 1);
-  });
-
-  it('throw error if request non int32 dtype', async () => {
-    const uint8array = await getUint8ArrayFromImage(
-      'test_objects/images/image_png_test.png');
-    expect(() => tf.node.decodeImage(uint8array, 0, 'uint8')).toThrowError(
-      'decodeImage could only return Tensor of type `int32` for now.');
-  });
-
-  it('throw error if decode invalid image type', async () => {
-    const uint8array = await getUint8ArrayFromImage('package.json');
-    expect(() => tf.node.decodeImage(uint8array)).toThrowError(
-      'Expected image (BMP, JPEG, PNG, or GIF), ' +
-        'but got unsupported image type');
-  });
-
-  it('throw error if backend is not tensorflow', async () => {
-    const testBackend = new TestKernelBackend();
-    registerBackend('fake', () => testBackend);
-    setBackend('fake');
-    try {
-      const uint8array = await getUint8ArrayFromImage(
-        'test_objects/images/image_png_test.png');
-      expect(() => tf.node.decodeImage(uint8array)).toThrowError(
-        'Expect the current backend to be "tensorflow", but got "fake"');
-    } finally {
-      setBackend('tensorflow');
-    }
-  });
-});
-
-describe('encode images', () => {
-  it('encodeJpeg', async () => {
-    const imageTensor = tf.tensor3d(
-        new Uint8Array([239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]),
-        [2, 2, 3]);
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const jpegEncodedData = await tf.node.encodeJpeg(imageTensor);
-    expect(memory().numTensors).toBe(beforeNumTensors);
-    expect(getImageType(jpegEncodedData)).toEqual(ImageType.JPEG);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors);
-    imageTensor.dispose();
-  });
-
-  it('encodeJpeg grayscale', async () => {
-    const imageTensor = tf.tensor3d(new Uint8Array([239, 0, 47, 0]), [2, 2, 1]);
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const jpegEncodedData = await tf.node.encodeJpeg(imageTensor, 'grayscale');
-    expect(memory().numTensors).toBe(beforeNumTensors);
-    expect(getImageType(jpegEncodedData)).toEqual(ImageType.JPEG);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors);
-    imageTensor.dispose();
-  });
-
-  it('encodeJpeg with parameters', async () => {
-    const imageTensor = tf.tensor3d(
-        new Uint8Array([239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]),
-        [2, 2, 3]);
-    const format = 'rgb';
-    const quality = 50;
-    const progressive = true;
-    const optimizeSize = true;
-    const chromaDownsampling = false;
-    const densityUnit = 'cm';
-    const xDensity = 500;
-    const yDensity = 500;
-
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const jpegEncodedData = await tf.node.encodeJpeg(
-        imageTensor, format, quality, progressive, optimizeSize,
-        chromaDownsampling, densityUnit, xDensity, yDensity);
-    expect(memory().numTensors).toBe(beforeNumTensors);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors);
-    expect(getImageType(jpegEncodedData)).toEqual(ImageType.JPEG);
-    imageTensor.dispose();
-  });
-
-  it('encodePng', async () => {
-    const imageTensor = tf.tensor3d(
-        new Uint8Array([239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]),
-        [2, 2, 3]);
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const pngEncodedData = await tf.node.encodePng(imageTensor);
-    const pngDecodedTensor = await tf.node.decodePng(pngEncodedData);
-    const pngDecodedData = await pngDecodedTensor.data();
-    pngDecodedTensor.dispose();
-    expect(memory().numTensors).toBe(beforeNumTensors);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors);
-    expect(getImageType(pngEncodedData)).toEqual(ImageType.PNG);
-    test_util.expectArraysEqual(await imageTensor.data(), pngDecodedData);
-    imageTensor.dispose();
-  });
-
-  it('encodePng grayscale', async () => {
-    const imageTensor = tf.tensor3d(new Uint8Array([239, 0, 47, 0]), [2, 2, 1]);
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const pngEncodedData = await tf.node.encodePng(imageTensor);
-    expect(memory().numTensors).toBe(beforeNumTensors);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors);
-    expect(getImageType(pngEncodedData)).toEqual(ImageType.PNG);
-    imageTensor.dispose();
-  });
-
-  it('encodePng with parameters', async () => {
-    const imageTensor = tf.tensor3d(
-        new Uint8Array([239, 100, 0, 46, 48, 47, 92, 49, 0, 194, 98, 47]),
-        [2, 2, 3]);
-    const compression = 4;
-
-    const beforeNumTensors = memory().numTensors;
-    const beforeNumTFTensors =
-        (tf.backend() as NodeJSKernelBackend).getNumOfTFTensors();
-    const pngEncodedData = await tf.node.encodePng(imageTensor, compression);
-    expect(memory().numTensors).toBe(beforeNumTensors);
-    expect((tf.backend() as NodeJSKernelBackend).getNumOfTFTensors())
-        .toBe(beforeNumTFTensors);
-    expect(getImageType(pngEncodedData)).toEqual(ImageType.PNG);
-    imageTensor.dispose();
-  });
-});
-
-async function getUint8ArrayFromImage(path: string) {
-  const image = await readFile(path);
-  const buf = Buffer.from(image);
-  return new Uint8Array(buf);
-}
diff --git a/tfjs-master/tfjs-node/src/index.ts b/tfjs-master/tfjs-node/src/index.ts
deleted file mode 100644
index c81bddd7b..000000000
--- a/tfjs-master/tfjs-node/src/index.ts
+++ /dev/null
@@ -1,86 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Register all kernels.
-import './register_all_kernels';
-
-import * as tf from '@tensorflow/tfjs';
-import * as path from 'path';
-
-import {ProgbarLogger} from './callbacks';
-import {nodeFileSystemRouter} from './io/file_system';
-import * as nodeIo from './io/index';
-import {NodeJSKernelBackend} from './nodejs_kernel_backend';
-import {TFJSBinding} from './tfjs_binding';
-import * as nodeVersion from './version';
-
-// tslint:disable-next-line:no-require-imports
-const binary = require('@mapbox/node-pre-gyp');
-const bindingPath =
-    binary.find(path.resolve(path.join(__dirname, '/../package.json')));
-
-// Check if the node native addon module exists.
-// tslint:disable-next-line:no-require-imports
-const fs = require('fs');
-if (!fs.existsSync(bindingPath)) {
-  throw new Error(
-      `The Node.js native addon module (tfjs_binding.node) can not ` +
-      `be found at path: ` + String(bindingPath) + `. \nPlease run command ` +
-      `'npm rebuild @tensorflow/tfjs-node` +
-      (String(bindingPath).indexOf('tfjs-node-gpu') > 0 ? `-gpu` : ``) +
-      ` --build-addon-from-source' to ` +
-      `rebuild the native addon module. \nIf you have problem with building ` +
-      `the addon module, please check ` +
-      `https://github.com/tensorflow/tfjs/blob/master/tfjs-node/` +
-      `WINDOWS_TROUBLESHOOTING.md or file an issue.`);
-}
-// tslint:disable-next-line:no-require-imports
-const bindings = require(bindingPath);
-
-// Merge version and io namespaces.
-export const version = {
-  ...tf.version,
-  'tfjs-node': nodeVersion.version
-};
-export const io = {
-  ...tf.io,
-  ...nodeIo
-};
-
-// Export all union package symbols
-export * from '@tensorflow/tfjs';
-export * from './node';
-
-// tslint:disable-next-line:no-require-imports
-const pjson = require('../package.json');
-
-// Side effects for default initialization of Node backend.
-tf.registerBackend('tensorflow', () => {
-  return new NodeJSKernelBackend(bindings as TFJSBinding, pjson.name);
-}, 3 /* priority */);
-
-const success = tf.setBackend('tensorflow');
-if (!success) {
-  throw new Error(`Could not initialize TensorFlow backend.`);
-}
-
-// Register the model saving and loading handlers for the 'file://' URL scheme.
-tf.io.registerLoadRouter(nodeFileSystemRouter);
-tf.io.registerSaveRouter(nodeFileSystemRouter);
-
-// Register the ProgbarLogger for Model.fit() at verbosity level 1.
-tf.registerCallbackConstructor(1, ProgbarLogger);
diff --git a/tfjs-master/tfjs-node/src/index_test.ts b/tfjs-master/tfjs-node/src/index_test.ts
deleted file mode 100644
index b551d0d3d..000000000
--- a/tfjs-master/tfjs-node/src/index_test.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-
-describe('Ops are exported from index and work', () => {
-  it('tf.mul works', async () => {
-    const a = tf.tensor1d([1, 2, 3]);
-    const b = tf.tensor1d([3, 4, 5]);
-    tf.test_util.expectArraysClose(await a.mul(b).data(), [3, 8, 15]);
-  });
-});
-
-describe('packages merge', () => {
-  it('versions', () => {
-    expect(tf.version['tfjs']).toBeDefined();
-    expect(tf.version['tfjs-core']).toBeDefined();
-
-    const expectedNodeVersion =
-        // tslint:disable-next-line:no-require-imports
-        require('../package.json').version;
-    expect(tf.version['tfjs-node']).toBe(expectedNodeVersion);
-  });
-
-  it('symbols merge', () => {
-    expect(tf.mul).toBeDefined();
-    expect(tf.layers.dense).toBeDefined();
-    expect(tf.io.listModels).toBeDefined();
-    expect(tf.io.nodeHTTPRequest).toBeDefined();
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/int64_tensors.ts b/tfjs-master/tfjs-node/src/int64_tensors.ts
deleted file mode 100644
index 406d9ccbd..000000000
--- a/tfjs-master/tfjs-node/src/int64_tensors.ts
+++ /dev/null
@@ -1,102 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Shape, util} from '@tensorflow/tfjs';
-import {endianness} from 'os';
-
-const INT32_MAX = 2147483648;
-
-/**
- * Node.js-specific tensor type: int64-type scalar.
- *
- * This class is created for a specific purpose: to support
- * writing `step`s to TensorBoard via op-kernel bindings.
- * `step` is required to have an int64 dtype, but TensorFlow.js
- * (tfjs-core) doesn't have a built-in int64 dtype. This is
- * related to a lack of `Int64Array` or `Uint64Array` typed
- * array in basic JavaScript.
- *
- * This class is introduced as a workaround.
- */
-export class Int64Scalar {
-  readonly dtype: string = 'int64';
-  readonly rank: number = 1;
-  private valueArray_: Int32Array;
-
-  private static endiannessOkay_: boolean;
-
-  constructor(readonly value: number) {
-    // The reason why we need to check endianness of the machine here is
-    // negative int64 values and the way in which we represent them
-    // using Int32Arrays in JavaScript. We represent each int64 value with
-    // two consecutive elements of an Int32Array. For positive values,
-    // the high part is simply zero; for negative values, the high part
-    // should be -1. The ordering of the low and high parts assumes
-    // little endian (i.e., least significant digits appear first).
-    // This assumption is checked by the lines below.
-    if (Int64Scalar.endiannessOkay_ == null) {
-      if (endianness() !== 'LE') {
-        throw new Error(
-            `Int64Scalar does not support endianness of this machine: ` +
-            `${endianness()}`);
-      }
-      Int64Scalar.endiannessOkay_ = true;
-    }
-
-    util.assert(
-        value > -INT32_MAX && value < INT32_MAX - 1,
-        () =>
-            `Got a value outside of the bound of values supported for int64 ` +
-            `dtype ([-${INT32_MAX}, ${INT32_MAX - 1}]): ${value}`);
-    util.assert(
-        Number.isInteger(value),
-        () => `Expected value to be an integer, but got ${value}`);
-
-    // We use two int32 elements to represent a int64 value. This assumes
-    // little endian, which is checked above.
-    const highPart = value >= 0 ? 0 : -1;
-    const lowPart = value % INT32_MAX;
-    this.valueArray_ = new Int32Array([lowPart, highPart]);
-  }
-
-  get shape(): Shape {
-    return [];
-  }
-
-  /** Get the Int32Array that represents the int64 value. */
-  get valueArray(): Int32Array {
-    return this.valueArray_;
-  }
-}
-
-/**
- * This method encodes a Int32Array as Int64 layout in order to create TF_INT64
- * tensor through binding.
- */
-export function encodeInt32ArrayAsInt64(value: Int32Array): Int32Array {
-  if (endianness() !== 'LE') {
-    throw new Error(
-        `Int64Scalar does not support endianness of this machine: ` +
-        `${endianness()}`);
-  }
-
-  const buffer = new Int32Array(value.length * 2);
-  for (let i = 0; i < value.length; i++) {
-    buffer[i * 2] = value[i];
-  }
-  return buffer;
-}
diff --git a/tfjs-master/tfjs-node/src/int64_tensors_test.ts b/tfjs-master/tfjs-node/src/int64_tensors_test.ts
deleted file mode 100644
index cd4a53332..000000000
--- a/tfjs-master/tfjs-node/src/int64_tensors_test.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {encodeInt32ArrayAsInt64, Int64Scalar} from './int64_tensors';
-
-describe('int64 tensors', () => {
-  it('positive value', () => {
-    const x = new Int64Scalar(42);
-    expect(x.dtype).toEqual('int64');
-    const valueArray = x.valueArray;
-    expect(valueArray.constructor.name).toEqual('Int32Array');
-    expect(valueArray.length).toEqual(2);
-    expect(valueArray[0]).toEqual(42);
-    expect(valueArray[1]).toEqual(0);
-  });
-
-  it('zero value', () => {
-    const x = new Int64Scalar(0);
-    expect(x.dtype).toEqual('int64');
-    const valueArray = x.valueArray;
-    expect(valueArray.constructor.name).toEqual('Int32Array');
-    expect(valueArray.length).toEqual(2);
-    expect(valueArray[0]).toEqual(0);
-    expect(valueArray[1]).toEqual(0);
-  });
-
-  it('negative value', () => {
-    const x = new Int64Scalar(-3);
-    expect(x.dtype).toEqual('int64');
-    const valueArray = x.valueArray;
-    expect(valueArray.constructor.name).toEqual('Int32Array');
-    expect(valueArray.length).toEqual(2);
-    expect(valueArray[0]).toEqual(-3);
-    expect(valueArray[1]).toEqual(-1);
-  });
-
-  it('Non-integer value leads to error', () => {
-    expect(() => new Int64Scalar(0.4)).toThrowError(/integer/);
-    expect(() => new Int64Scalar(-3.2)).toThrowError(/integer/);
-  });
-
-  it('Out-of-bound value leads to error', () => {
-    expect(() => new Int64Scalar(2147483648)).toThrowError(/bound/);
-    expect(() => new Int64Scalar(2147483648 * 2)).toThrowError(/bound/);
-    expect(() => new Int64Scalar(-2147483648 - 1)).toThrowError(/bound/);
-  });
-
-  it('encode int32array as int64 layout', () => {
-    const input = Int32Array.from([2, 10]);
-    const valueArray = encodeInt32ArrayAsInt64(input);
-    expect(valueArray.length).toEqual(4);
-    expect(valueArray[0]).toEqual(2);
-    expect(valueArray[1]).toEqual(0);
-    expect(valueArray[2]).toEqual(10);
-    expect(valueArray[3]).toEqual(0);
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/io/file_system.ts b/tfjs-master/tfjs-node/src/io/file_system.ts
deleted file mode 100644
index 74b4cc582..000000000
--- a/tfjs-master/tfjs-node/src/io/file_system.ts
+++ /dev/null
@@ -1,267 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import * as fs from 'fs';
-import {dirname, join, resolve} from 'path';
-import {promisify} from 'util';
-import {toArrayBuffer} from './io_utils';
-
-const stat = promisify(fs.stat);
-const writeFile = promisify(fs.writeFile);
-const readFile = promisify(fs.readFile);
-const mkdir = promisify(fs.mkdir);
-
-function doesNotExistHandler(name: string): (e: NodeJS.ErrnoException) =>
-    never {
-  return e => {
-    switch (e.code) {
-      case 'ENOENT':
-        throw new Error(`${name} ${e.path} does not exist: loading failed`);
-      default:
-        throw e;
-    }
-  };
-}
-
-export class NodeFileSystem implements tf.io.IOHandler {
-  static readonly URL_SCHEME = 'file://';
-
-  protected readonly path: string|string[];
-
-  readonly MODEL_JSON_FILENAME = 'model.json';
-  readonly WEIGHTS_BINARY_FILENAME = 'weights.bin';
-  readonly MODEL_BINARY_FILENAME = 'tensorflowjs.pb';
-
-  /**
-   * Constructor of the NodeFileSystem IOHandler.
-   * @param path A single path or an Array of paths.
-   *   For saving: expects a single path pointing to an existing or nonexistent
-   *     directory. If the directory does not exist, it will be
-   *     created.
-   *   For loading:
-   *     - If the model has JSON topology (e.g., `tf.Model`), a single path
-   *       pointing to the JSON file (usually named `model.json`) is expected.
-   *       The JSON file is expected to contain `modelTopology` and/or
-   *       `weightsManifest`. If `weightManifest` exists, the values of the
-   *       weights will be loaded from relative paths (relative to the directory
-   *       of `model.json`) as contained in `weightManifest`.
-   *     - If the model has binary (protocol buffer GraphDef) topology,
-   *       an Array of two paths is expected: the first path should point to the
-   *       .pb file and the second path should point to the weight manifest
-   *       JSON file.
-   */
-  constructor(path: string|string[]) {
-    if (Array.isArray(path)) {
-      tf.util.assert(
-          path.length === 2,
-          () => 'file paths must have a length of 2, ' +
-              `(actual length is ${path.length}).`);
-      this.path = path.map(p => resolve(p));
-    } else {
-      this.path = resolve(path);
-    }
-  }
-
-  async save(modelArtifacts: tf.io.ModelArtifacts): Promise<tf.io.SaveResult> {
-    if (Array.isArray(this.path)) {
-      throw new Error('Cannot perform saving to multiple paths.');
-    }
-
-    await this.createOrVerifyDirectory();
-
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error(
-          'NodeFileSystem.save() does not support saving model topology ' +
-          'in binary format yet.');
-      // TODO(cais, nkreeger): Implement this. See
-      //   https://github.com/tensorflow/tfjs/issues/343
-    } else {
-      const weightsBinPath = join(this.path, this.WEIGHTS_BINARY_FILENAME);
-      const weightsManifest = [{
-        paths: [this.WEIGHTS_BINARY_FILENAME],
-        weights: modelArtifacts.weightSpecs
-      }];
-      const modelJSON: tf.io.ModelJSON = {
-        modelTopology: modelArtifacts.modelTopology,
-        weightsManifest,
-        format: modelArtifacts.format,
-        generatedBy: modelArtifacts.generatedBy,
-        convertedBy: modelArtifacts.convertedBy
-      };
-      if (modelArtifacts.trainingConfig != null) {
-        modelJSON.trainingConfig = modelArtifacts.trainingConfig;
-      }
-      if (modelArtifacts.signature != null) {
-        modelJSON.signature = modelArtifacts.signature;
-      }
-      if (modelArtifacts.userDefinedMetadata != null) {
-        modelJSON.userDefinedMetadata = modelArtifacts.userDefinedMetadata;
-      }
-      const modelJSONPath = join(this.path, this.MODEL_JSON_FILENAME);
-      await writeFile(modelJSONPath, JSON.stringify(modelJSON), 'utf8');
-      await writeFile(
-          weightsBinPath, Buffer.from(modelArtifacts.weightData), 'binary');
-
-      return {
-        // TODO(cais): Use explicit tf.io.ModelArtifactsInfo type below once it
-        // is available.
-        // tslint:disable-next-line:no-any
-        modelArtifactsInfo: tf.io.getModelArtifactsInfoForJSON(modelArtifacts),
-      };
-    }
-  }
-  async load(): Promise<tf.io.ModelArtifacts> {
-    return Array.isArray(this.path) ? this.loadBinaryModel() :
-                                      this.loadJSONModel();
-  }
-
-  protected async loadBinaryModel(): Promise<tf.io.ModelArtifacts> {
-    const topologyPath = this.path[0];
-    const weightManifestPath = this.path[1];
-    const topology =
-        await stat(topologyPath).catch(doesNotExistHandler('Topology Path'));
-    const weightManifest =
-        await stat(weightManifestPath)
-            .catch(doesNotExistHandler('Weight Manifest Path'));
-
-    // `this.path` can be either a directory or a file. If it is a file, assume
-    // it is model.json file.
-    if (!topology.isFile()) {
-      throw new Error('File specified for topology is not a file!');
-    }
-    if (!weightManifest.isFile()) {
-      throw new Error('File specified for the weight manifest is not a file!');
-    }
-
-    const modelTopology = await readFile(this.path[0]);
-    const weightsManifest = JSON.parse(await readFile(this.path[1], 'utf8'));
-
-    const modelArtifacts: tf.io.ModelArtifacts = {
-      modelTopology,
-    };
-    const [weightSpecs, weightData] =
-        await this.loadWeights(weightsManifest, this.path[1]);
-
-    modelArtifacts.weightSpecs = weightSpecs;
-    modelArtifacts.weightData = weightData;
-
-    return modelArtifacts;
-  }
-
-  protected async loadJSONModel(): Promise<tf.io.ModelArtifacts> {
-    const path = this.path as string;
-    const info = await stat(path).catch(doesNotExistHandler('Path'));
-
-    // `path` can be either a directory or a file. If it is a file, assume
-    // it is model.json file.
-    if (info.isFile()) {
-      const modelJSON = JSON.parse(await readFile(path, 'utf8'));
-      return tf.io.getModelArtifactsForJSON(
-          modelJSON,
-          (weightsManifest) => this.loadWeights(weightsManifest, path));
-    } else {
-      throw new Error(
-          'The path to load from must be a file. Loading from a directory ' +
-          'is not supported.');
-    }
-  }
-
-  private async loadWeights(
-      weightsManifest: tf.io.WeightsManifestConfig,
-      path: string): Promise<[tf.io.WeightsManifestEntry[], ArrayBuffer]> {
-    const dirName = dirname(path);
-    const buffers: Buffer[] = [];
-    const weightSpecs: tf.io.WeightsManifestEntry[] = [];
-    for (const group of weightsManifest) {
-      for (const path of group.paths) {
-        const weightFilePath = join(dirName, path);
-        const buffer = await readFile(weightFilePath)
-                           .catch(doesNotExistHandler('Weight file'));
-        buffers.push(buffer);
-      }
-      weightSpecs.push(...group.weights);
-    }
-    return [weightSpecs, toArrayBuffer(buffers)];
-  }
-
-  /**
-   * For each item in `this.path`, creates a directory at the path or verify
-   * that the path exists as a directory.
-   */
-  protected async createOrVerifyDirectory() {
-    const paths = Array.isArray(this.path) ? this.path : [this.path];
-    for (const path of paths) {
-      try {
-        await mkdir(path);
-      } catch (e) {
-        if (e.code === 'EEXIST') {
-          if ((await stat(path)).isFile()) {
-            throw new Error(
-                `Path ${path} exists as a file. The path must be ` +
-                `nonexistent or point to a directory.`);
-          }
-          // else continue, the directory exists
-        } else {
-          throw e;
-        }
-      }
-    }
-  }
-}
-
-export const nodeFileSystemRouter = (url: string|string[]) => {
-  if (Array.isArray(url)) {
-    if (url.every(
-            urlElement => urlElement.startsWith(NodeFileSystem.URL_SCHEME))) {
-      return new NodeFileSystem(url.map(
-          urlElement => urlElement.slice(NodeFileSystem.URL_SCHEME.length)));
-    } else {
-      return null;
-    }
-  } else {
-    if (url.startsWith(NodeFileSystem.URL_SCHEME)) {
-      return new NodeFileSystem(url.slice(NodeFileSystem.URL_SCHEME.length));
-    } else {
-      return null;
-    }
-  }
-};
-// Registration of `nodeFileSystemRouter` is done in index.ts.
-
-/**
- * Factory function for Node.js native file system IO Handler.
- *
- * @param path A single path or an Array of paths.
- *   For saving: expects a single path pointing to an existing or nonexistent
- *     directory. If the directory does not exist, it will be
- *     created.
- *   For loading:
- *     - If the model has JSON topology (e.g., `tf.Model`), a single path
- *       pointing to the JSON file (usually named `model.json`) is expected.
- *       The JSON file is expected to contain `modelTopology` and/or
- *       `weightsManifest`. If `weightManifest` exists, the values of the
- *       weights will be loaded from relative paths (relative to the directory
- *       of `model.json`) as contained in `weightManifest`.
- *     - If the model has binary (protocol buffer GraphDef) topology,
- *       an Array of two paths is expected: the first path should point to the
- *        .pb file and the second path should point to the weight manifest
- *       JSON file.
- */
-export function fileSystem(path: string|string[]): NodeFileSystem {
-  return new NodeFileSystem(path);
-}
diff --git a/tfjs-master/tfjs-node/src/io/file_system_test.ts b/tfjs-master/tfjs-node/src/io/file_system_test.ts
deleted file mode 100644
index 13627f55d..000000000
--- a/tfjs-master/tfjs-node/src/io/file_system_test.ts
+++ /dev/null
@@ -1,506 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import * as fs from 'fs';
-import * as path from 'path';
-import * as rimraf from 'rimraf';
-import {promisify} from 'util';
-
-import * as tfn from '../index';
-
-import {NodeFileSystem, nodeFileSystemRouter} from './file_system';
-
-describe('File system IOHandler', () => {
-  const mkdtemp = promisify(fs.mkdtemp);
-  const readFile = promisify(fs.readFile);
-  const writeFile = promisify(fs.writeFile);
-  const rimrafPromise = promisify(rimraf);
-
-  const modelTopology1: {} = {
-    'class_name': 'Sequential',
-    'keras_version': '2.1.6',
-    'config': [{
-      'class_name': 'Dense',
-      'config': {
-        'kernel_initializer': {
-          'class_name': 'VarianceScaling',
-          'config': {
-            'distribution': 'uniform',
-            'scale': 1.0,
-            'seed': null,
-            'mode': 'fan_avg'
-          }
-        },
-        'name': 'dense',
-        'kernel_constraint': null,
-        'bias_regularizer': null,
-        'bias_constraint': null,
-        'dtype': 'float32',
-        'activation': 'linear',
-        'trainable': true,
-        'kernel_regularizer': null,
-        'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-        'units': 1,
-        'batch_input_shape': [null, 3],
-        'use_bias': true,
-        'activity_regularizer': null
-      }
-    }],
-    'backend': 'tensorflow'
-  };
-  const weightSpecs1: tf.io.WeightsManifestEntry[] = [
-    {
-      name: 'dense/kernel',
-      shape: [3, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense/bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-  const weightData1 = new ArrayBuffer(16);
-  const trainingConfig1: tf.io.TrainingConfig = {
-    loss: 'categorical_crossentropy',
-    metrics: ['accuracy'],
-    optimizer_config: {class_name: 'SGD', config: {learningRate: 0.1}}
-  };
-
-  let testDir: string;
-  beforeEach(async () => {
-    testDir = await mkdtemp('tfjs_node_fs_test');
-  });
-
-  afterEach(async () => {
-    await rimrafPromise(testDir);
-  });
-
-  it('save succeeds with newly created directory', async () => {
-    const t0 = new Date();
-    const dir = path.join(testDir, 'save-destination');
-    const handler = tf.io.getSaveHandlers(`file://${dir}`)[0];
-
-    const saveResult = await handler.save({
-      modelTopology: modelTopology1,
-      weightSpecs: weightSpecs1,
-      weightData: weightData1,
-    });
-
-    expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
-      .toBeGreaterThanOrEqual(t0.getTime());
-    expect(saveResult.modelArtifactsInfo.modelTopologyType)
-      .toEqual('JSON');
-
-    const modelJSONPath = path.join(dir, 'model.json');
-    const weightsBinPath = path.join(dir, 'weights.bin');
-    const modelJSON = JSON.parse(await readFile(modelJSONPath, 'utf8'));
-    expect(modelJSON.modelTopology).toEqual(modelTopology1);
-    expect(modelJSON.weightsManifest.length).toEqual(1);
-    expect(modelJSON.weightsManifest[0].paths).toEqual(['weights.bin']);
-    expect(modelJSON.weightsManifest[0].weights).toEqual(weightSpecs1);
-
-    const weightData = new Uint8Array(await readFile(weightsBinPath));
-    expect(weightData.length).toEqual(16);
-    weightData.forEach(value => expect(value).toEqual(0));
-  });
-
-  it('save fails if path exists as a file', async () => {
-    const dir = path.join(testDir, 'save-destination');
-    // Create a file at the locatin.
-    await writeFile(dir, 'foo');
-    const handler = tf.io.getSaveHandlers(`file://${dir}`)[0];
-    await expectAsync(handler.save({
-      modelTopology: modelTopology1,
-      weightSpecs: weightSpecs1,
-      weightData: weightData1,
-    })).toBeRejectedWithError(/.*exists as a file.*directory.*/);
-  });
-
-  it('save-load round trip: one weight file', done => {
-    const handler1 = tf.io.getSaveHandlers(`file://${testDir}`)[0];
-    handler1
-        .save({
-          modelTopology: modelTopology1,
-          weightSpecs: weightSpecs1,
-          weightData: weightData1,
-          trainingConfig: trainingConfig1,
-        })
-        .then(saveResult => {
-          const modelJSONPath = path.join(testDir, 'model.json');
-          const handler2 = tf.io.getLoadHandlers(`file://${modelJSONPath}`)[0];
-          handler2.load()
-              .then(modelArtifacts => {
-                expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-                expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
-                expect(modelArtifacts.trainingConfig).toEqual(trainingConfig1);
-
-                expect(new Float32Array(tf.io.CompositeArrayBuffer.join(
-                  modelArtifacts.weightData)))
-                    .toEqual(new Float32Array([0, 0, 0, 0]));
-                done();
-              })
-              .catch(err => done.fail(err.stack));
-        })
-        .catch(err => done.fail(err.stack));
-  });
-
-  describe('load json model', () => {
-    it('load: two weight files', async () => {
-      const weightsManifest: tf.io.WeightsManifestConfig = [
-        {
-          paths: ['weights.1.bin'],
-          weights: [{
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          }],
-        },
-
-        {
-          paths: ['weights.2.bin'],
-          weights: [{
-            name: 'dense/bias',
-            shape: [1],
-            dtype: 'float32',
-          }]
-        }
-      ];
-      const modelJSON = {
-        modelTopology: modelTopology1,
-        weightsManifest,
-      };
-
-      // Write model.json file.
-      const modelJSONPath = path.join(testDir, 'model.json');
-      await writeFile(modelJSONPath, JSON.stringify(modelJSON), 'utf8');
-
-      // Write the two binary weights files.
-      const weightsData1 =
-          Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
-      await writeFile(
-          path.join(testDir, 'weights.1.bin'), weightsData1, 'binary');
-      const weightsData2 = Buffer.from(new Float32Array([-7.7]).buffer);
-      await writeFile(
-          path.join(testDir, 'weights.2.bin'), weightsData2, 'binary');
-
-      // Load the artifacts consisting of a model.json and two binary weight
-      // files.
-      const handler = tf.io.getLoadHandlers(`file://${modelJSONPath}`)[0];
-      const modelArtifacts = await handler.load();
-      expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-      expect(modelArtifacts.weightSpecs).toEqual([
-        {
-          name: 'dense/kernel',
-          shape: [3, 1],
-          dtype: 'float32',
-        },
-        {
-          name: 'dense/bias',
-          shape: [1],
-          dtype: 'float32',
-        }
-      ]);
-      tf.test_util.expectArraysClose(
-        new Float32Array(tf.io.CompositeArrayBuffer.join(
-          modelArtifacts.weightData)),
-        new Float32Array([-1.1, -3.3, -3.3, -7.7]));
-    });
-
-    it('loading from nonexistent model.json path fails', done => {
-      const handler =
-          tf.io.getLoadHandlers(`file://${testDir}/foo/model.json`)[0];
-      handler.load()
-          .then(getModelArtifactsInfoForJSON => {
-            done.fail(
-                'Loading from nonexisting model.json path succeeded ' +
-                'unexpectedly.');
-          })
-          .catch(err => {
-            expect(err.message)
-                .toMatch(/model\.json.*does not exist.*loading failed/);
-            done();
-          });
-    });
-
-    it('loading from missing weights path fails', async () => {
-      const weightsManifest: tf.io.WeightsManifestConfig = [
-        {
-          paths: ['weights.1.bin'],
-          weights: [{
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          }],
-        },
-
-        {
-          paths: ['weights.2.bin'],
-          weights: [{
-            name: 'dense/bias',
-            shape: [1],
-            dtype: 'float32',
-          }]
-        }
-      ];
-      const modelJSON = {
-        modelTopology: modelTopology1,
-        weightsManifest,
-      };
-
-      // Write model.json file.
-      const modelJSONPath = path.join(testDir, 'model.json');
-      await writeFile(modelJSONPath, JSON.stringify(modelJSON), 'utf8');
-
-      // Write only first of the two binary weights files.
-      const weightsData1 =
-          Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
-      await writeFile(
-          path.join(testDir, 'weights.1.bin'), weightsData1, 'binary');
-
-      // Load the artifacts consisting of a model.json and two binary weight
-      // files.
-      const handler = tf.io.getLoadHandlers(`file://${modelJSONPath}`)[0];
-      await expectAsync(handler.load()).toBeRejectedWithError(
-        /Weight file .*weights\.2\.bin does not exist/);
-    });
-  });
-
-  describe('load binary model', () => {
-    it('load: two weight files', async () => {
-      const weightsManifest: tf.io.WeightsManifestConfig = [
-        {
-          paths: ['weights.1.bin'],
-          weights: [{
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          }],
-        },
-
-        {
-          paths: ['weights.2.bin'],
-          weights: [{
-            name: 'dense/bias',
-            shape: [1],
-            dtype: 'float32',
-          }]
-        }
-      ];
-
-      // Write model.pb file.
-      const modelPath = path.join(testDir, 'model.pb');
-      const modelData = Buffer.from(new Uint8Array([1, 2, 3]).buffer);
-      await writeFile(modelPath, modelData, 'binary');
-
-      // Write manifest.json file
-      const modelManifestJSONPath = path.join(testDir, 'manifest.json');
-      await writeFile(
-          modelManifestJSONPath, JSON.stringify(weightsManifest), 'utf8');
-
-      // Write the two binary weights files.
-      const weightsData1 =
-          Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
-      await writeFile(
-          path.join(testDir, 'weights.1.bin'), weightsData1, 'binary');
-      const weightsData2 = Buffer.from(new Float32Array([-7.7]).buffer);
-      await writeFile(
-          path.join(testDir, 'weights.2.bin'), weightsData2, 'binary');
-
-      // Load the artifacts consisting of a model.pb, manifest.json and two
-      // binary weight files.
-      const handler =
-          new NodeFileSystem([`${modelPath}`, `${modelManifestJSONPath}`]);
-      const modelArtifacts = await handler.load();
-      tf.test_util.expectArraysClose(
-        new Uint8Array(modelArtifacts.modelTopology as ArrayBuffer),
-        new Uint8Array(modelData));
-      expect(modelArtifacts.weightSpecs).toEqual([
-        {
-          name: 'dense/kernel',
-          shape: [3, 1],
-          dtype: 'float32',
-        },
-        {
-          name: 'dense/bias',
-          shape: [1],
-          dtype: 'float32',
-        }
-      ]);
-      tf.test_util.expectArraysClose(
-        new Float32Array(tf.io.CompositeArrayBuffer.join(
-          modelArtifacts.weightData)),
-        new Float32Array([-1.1, -3.3, -3.3, -7.7]));
-    });
-
-    it('path length does not equal 2 fails', () => {
-      expect(() => new NodeFileSystem([`${testDir}/foo/model.pb`]))
-          .toThrowError(
-              /file paths must have a length of 2.*actual length is 1.*/);
-    });
-
-    it('loading from nonexistent model.json path fails', done => {
-      const handler = new NodeFileSystem(
-          [`${testDir}/foo/model.pb`, `${testDir}/foo/manifest.json`]);
-      handler.load()
-          .then(getModelArtifactsInfoForJSON => {
-            done.fail(
-                'Loading from nonexisting model.pb path succeeded ' +
-                'unexpectedly.');
-          })
-          .catch(err => {
-            expect(err.message)
-                .toMatch(/model\.pb.*does not exist.*loading failed/);
-            done();
-          });
-    });
-
-    it('loading from missing weights path fails', async () => {
-      const weightsManifest: tf.io.WeightsManifestConfig = [
-        {
-          paths: ['weights.1.bin'],
-          weights: [{
-            name: 'dense/kernel',
-            shape: [3, 1],
-            dtype: 'float32',
-          }],
-        },
-
-        {
-          paths: ['weights.2.bin'],
-          weights: [{
-            name: 'dense/bias',
-            shape: [1],
-            dtype: 'float32',
-          }]
-        }
-      ];
-
-      const modelPath = path.join(testDir, 'model.pb');
-      const modelData = Buffer.from(new Uint8Array([1, 2, 3]).buffer);
-      await writeFile(modelPath, modelData, 'binary');
-
-      // Write manifest.json file
-      const modelManifestJSONPath = path.join(testDir, 'manifest.json');
-      await writeFile(
-          modelManifestJSONPath, JSON.stringify(weightsManifest), 'utf8');
-
-      // Write only first of the two binary weights files.
-      const weightsData1 =
-          Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
-      await writeFile(
-          path.join(testDir, 'weights.1.bin'), weightsData1, 'binary');
-
-      // Load the artifacts consisting of a model.json and two binary weight
-      // files.
-      const handler =
-          new NodeFileSystem([`${modelPath}`, `${modelManifestJSONPath}`]);
-      await expectAsync(handler.load()).toBeRejectedWithError(
-        /Weight file .*weights\.2\.bin does not exist/);
-    });
-  });
-
-  it('Exported file-system handler class exists', () => {
-    const handler = tfn.io.fileSystem(testDir);
-    expect(typeof handler.save).toEqual('function');
-    expect(typeof handler.load).toEqual('function');
-  });
-
-  it('Save and load model with loss and optimizer', async () => {
-    const model = tf.sequential();
-    model.add(tf.layers.dense(
-        {units: 1, kernelInitializer: 'zeros', inputShape: [1]}));
-    model.compile({loss: 'meanSquaredError', optimizer: tf.train.adam(2.5e-2)});
-
-    const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
-    const ys = tf.tensor2d([-1, -3, -5, -7], [4, 1]);
-    await model.fit(xs, ys, {epochs: 2, shuffle: false, verbose: 0});
-
-    const saveURL = `file://${testDir}`;
-    const loadURL = `file://${testDir}/model.json`;
-
-    await model.save(saveURL, {includeOptimizer: true});
-    const model2 = await tf.loadLayersModel(loadURL);
-    const optimizerConfig = model2.optimizer.getConfig();
-    expect(model2.optimizer.getClassName()).toEqual('Adam');
-    expect(optimizerConfig['learningRate']).toEqual(2.5e-2);
-
-    // Test that model2 can be trained immediately, without a compile() call
-    // due to the loaded optimizer and loss information.
-    const history2 =
-        await model2.fit(xs, ys, {epochs: 2, shuffle: false, verbose: 0});
-    // The final loss value from training the model twice, 2 epochs
-    // at a time, should be equal to the final loss of trainig the
-    // model only once with 4 epochs.
-    expect(history2.history.loss[1]).toBeCloseTo(18.603);
-  });
-
-  it('Save and load model with user-defined metadata', async () => {
-    const model = tf.sequential();
-    model.add(tf.layers.dense({units: 3, inputShape: [4]}));
-    model.setUserDefinedMetadata(
-        {'outputLabels': ['Label1', 'Label2', 'Label3']});
-
-    const saveURL = `file://${testDir}`;
-    const loadURL = `file://${testDir}/model.json`;
-
-    await model.save(saveURL);
-    const model2 = await tf.loadLayersModel(loadURL);
-    expect(model2.getUserDefinedMetadata()).toEqual({
-      'outputLabels': ['Label1', 'Label2', 'Label3']
-    });
-  });
-
-  it('Compile, save with includeOptimizer: true, load and fit model',
-     async () => {
-       const model = tf.sequential();
-       model.add(tf.layers.dense({units: 3, inputShape: [4]}));
-       model.compile(
-           {optimizer: 'sgd', loss: 'meanSquaredError', metrics: ['accuracy']});
-
-       const saveURL = `file://${testDir}`;
-       const loadURL = `file://${testDir}/model.json`;
-
-       await model.save(saveURL, {includeOptimizer: true});
-       const model2 = await tf.loadLayersModel(loadURL);
-       expect(() => model2.fit(tf.zeros([1, 4]), tf.zeros([1, 3])))
-           .not.toThrow();
-     });
-
-  describe('nodeFileSystemRouter', () => {
-    it('should handle single path', () => {
-      expect(nodeFileSystemRouter('file://model.json')).toBeDefined();
-    });
-
-    it('should handle multiple paths', () => {
-      expect(nodeFileSystemRouter([
-        'file://model.json', 'file://weights.json'
-      ])).toBeDefined();
-    });
-
-    it('should return null for non file path', () => {
-      expect(nodeFileSystemRouter('http://model.json')).toBeNull();
-    });
-
-    it('should return null for multiple paths with mismatched scheme', () => {
-      expect(nodeFileSystemRouter([
-        'file://model.json', 'http://weights.json'
-      ])).toBeNull();
-    });
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/io/index.ts b/tfjs-master/tfjs-node/src/io/index.ts
deleted file mode 100644
index 019ea6a45..000000000
--- a/tfjs-master/tfjs-node/src/io/index.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Public exports from the `io` module.
- */
-
-export {fileSystem} from './file_system';
-export {nodeHTTPRequest} from './node_http';
diff --git a/tfjs-master/tfjs-node/src/io/io_utils.ts b/tfjs-master/tfjs-node/src/io/io_utils.ts
deleted file mode 100644
index 286088556..000000000
--- a/tfjs-master/tfjs-node/src/io/io_utils.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Convert an ArrayBuffer to a Buffer.
- */
-export function toBuffer(ab: ArrayBuffer): Buffer {
-  const view = new Uint8Array(ab);
-  return Buffer.from(view);  // copies data
-}
-
-/**
- * Convert a Buffer or an Array of Buffers to an ArrayBuffer.
- *
- * If the input is an Array of Buffers, they will be concatenated in the
- * specified order to form the output ArrayBuffer.
- */
-export function toArrayBuffer(buf: Buffer|Buffer[]): ArrayBuffer {
-  if (Array.isArray(buf)) {
-    // An Array of Buffers.
-    let totalLength = 0;
-    for (const buffer of buf) {
-      totalLength += buffer.length;
-    }
-
-    const ab = new ArrayBuffer(totalLength);
-    const view = new Uint8Array(ab);
-    let pos = 0;
-    for (const buffer of buf) {
-      pos += buffer.copy(view, pos);
-    }
-    return ab;
-  } else {
-    // A single Buffer. Return a copy of the underlying ArrayBuffer slice.
-    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
-  }
-}
diff --git a/tfjs-master/tfjs-node/src/io/io_utils_test.ts b/tfjs-master/tfjs-node/src/io/io_utils_test.ts
deleted file mode 100644
index 910b771eb..000000000
--- a/tfjs-master/tfjs-node/src/io/io_utils_test.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {toArrayBuffer, toBuffer} from './io_utils';
-
-describe('toBuffer', () => {
-  it('Simple case', () => {
-    const ab = new Uint8Array([3, 2, 1]).buffer;
-    const buffer = toBuffer(ab);
-    expect(new Uint8Array(buffer)).toEqual(new Uint8Array([3, 2, 1]));
-  });
-});
-
-describe('toArrayBuffer', () => {
-  it('Single Buffer', () => {
-    const buf = Buffer.from([10, 20, 30]);
-    const ab = toArrayBuffer(buf);
-    expect(new Uint8Array(ab)).toEqual(new Uint8Array([10, 20, 30]));
-  });
-
-  it('Two Buffers', () => {
-    const buf1 = Buffer.from([10, 20, 30]);
-    const buf2 = Buffer.from([40, 50, 60]);
-    const ab = toArrayBuffer([buf1, buf2]);
-    expect(new Uint8Array(ab)).toEqual(new Uint8Array([
-      10, 20, 30, 40, 50, 60
-    ]));
-  });
-
-  it('Three Buffers', () => {
-    const buf1 = Buffer.from([10, 20, 30]);
-    const buf2 = Buffer.from([40, 50, 60]);
-    const buf3 = Buffer.from([3, 2, 1]);
-    const ab = toArrayBuffer([buf1, buf2, buf3]);
-    expect(new Uint8Array(ab)).toEqual(new Uint8Array([
-      10, 20, 30, 40, 50, 60, 3, 2, 1
-    ]));
-  });
-
-  it('Zero buffers', () => {
-    const ab = toArrayBuffer([]);
-    expect(new Uint8Array(ab)).toEqual(new Uint8Array([]));
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/io/node_http.ts b/tfjs-master/tfjs-node/src/io/node_http.ts
deleted file mode 100644
index 5fef3dbdd..000000000
--- a/tfjs-master/tfjs-node/src/io/node_http.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {io} from '@tensorflow/tfjs';
-
-/**
- * Factory function for HTTP IO Handler in Node.js.
- *
- * @param path URL path or an array of them.
- * @param requestInit Request init for the HTTP IOHandler. May include fields
- *   such as "credentials" and "cache". (Optional)
- * @param weightPathPrefix A path prefix for weight loading . (Optional).
- */
-export function nodeHTTPRequest(
-    path: string, requestInit?: RequestInit,
-    weightPathPrefix?: string): io.IOHandler {
-  return io.browserHTTPRequest(path, {requestInit, weightPathPrefix});
-}
-
-export const nodeHTTPRequestRouter = (url: string) => {
-  let isHTTP = true;
-  if (Array.isArray(url)) {
-    isHTTP = url.every(urlItem => io.isHTTPScheme(urlItem));
-  } else {
-    isHTTP = io.isHTTPScheme(url);
-  }
-  if (isHTTP) {
-    return nodeHTTPRequest(url);
-  }
-  return null;
-};
diff --git a/tfjs-master/tfjs-node/src/io/node_http_test.ts b/tfjs-master/tfjs-node/src/io/node_http_test.ts
deleted file mode 100644
index 83b6e6140..000000000
--- a/tfjs-master/tfjs-node/src/io/node_http_test.ts
+++ /dev/null
@@ -1,177 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-
-import * as tfn from '../index';
-
-// We still need node-fetch so that we can mock the core
-// tf.env().platform.fetch call and return a valid response.
-// tslint:disable-next-line:no-require-imports
-const fetch = require('node-fetch');
-
-const OCTET_STREAM_TYPE = 'application/octet-stream';
-const JSON_TYPE = 'application/json';
-
-// Test data;
-const modelTopology1: {} = {
-  'class_name': 'Sequential',
-  'keras_version': '2.1.4',
-  'config': [{
-    'class_name': 'Dense',
-    'config': {
-      'kernel_initializer': {
-        'class_name': 'VarianceScaling',
-        'config': {
-          'distribution': 'uniform',
-          'scale': 1.0,
-          'seed': null,
-          'mode': 'fan_avg'
-        }
-      },
-      'name': 'dense',
-      'kernel_constraint': null,
-      'bias_regularizer': null,
-      'bias_constraint': null,
-      'dtype': 'float32',
-      'activation': 'linear',
-      'trainable': true,
-      'kernel_regularizer': null,
-      'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-      'units': 1,
-      'batch_input_shape': [null, 3],
-      'use_bias': true,
-      'activity_regularizer': null
-    }
-  }],
-  'backend': 'tensorflow'
-};
-
-describe('nodeHTTPRequest-load', () => {
-  let requestInits: RequestInit[];
-  const setupFakeWeightFiles = (fileBufferMap: {
-    [filename: string]: string|Float32Array|Int32Array|ArrayBuffer|Uint8Array|
-    Uint16Array
-  }) => {
-    spyOn(tf.env().platform, 'fetch')
-        .and.callFake((path: string, init: RequestInit) => {
-          return new Promise((resolve, reject) => {
-            let contentType = '';
-            if (path.endsWith('model.json')) {
-              contentType = JSON_TYPE;
-            } else if (
-                path.endsWith('weightfile0') || path.endsWith('weightfile1')) {
-              contentType = OCTET_STREAM_TYPE;
-            } else {
-              reject(new Error(`Invalid path: ${path}`));
-            }
-            requestInits.push(init);
-            resolve(new fetch.Response(
-                fileBufferMap[path],
-                {'headers': {'Content-Type': contentType}}));
-          });
-        });
-  };
-
-  beforeEach(() => {
-    requestInits = [];
-  });
-
-  it('Constructor', () => {
-    const handler = tfn.io.nodeHTTPRequest('./foo_model.json');
-    expect(handler == null).toEqual(false);
-    expect(typeof handler.load).toEqual('function');
-    expect(typeof handler.save).toEqual('function');
-  });
-
-  it('Load through NodeHTTPRequest object', async () => {
-    const weightManifest1: tf.io.WeightsManifestConfig = [{
-      paths: ['weightfile0'],
-      weights: [
-        {
-          name: 'dense/kernel',
-          shape: [3, 1],
-          dtype: 'float32',
-        },
-        {
-          name: 'dense/bias',
-          shape: [1],
-          dtype: 'float32',
-        }
-      ]
-    }];
-    const trainingConfig1: tf.io.TrainingConfig = {
-      loss: 'categorical_crossentropy',
-      metrics: ['accuracy'],
-      optimizer_config: {class_name: 'SGD', config: {learningRate: 0.1}}
-    };
-    const floatData = new Float32Array([1, 3, 3, 7]);
-    setupFakeWeightFiles({
-      'http://localhost/model.json': JSON.stringify({
-        modelTopology: modelTopology1,
-        weightsManifest: weightManifest1,
-        trainingConfig: trainingConfig1
-      }),
-      'http://localhost/weightfile0': floatData,
-    });
-
-    const handler = tfn.io.nodeHTTPRequest(
-        'http://localhost/model.json',
-        {credentials: 'include', cache: 'no-cache'});
-    const modelArtifacts = await handler.load();
-    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
-    expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
-    expect(modelArtifacts.trainingConfig).toEqual(trainingConfig1);
-    expect(new Float32Array(tf.io.CompositeArrayBuffer.join(
-      modelArtifacts.weightData))).toEqual(floatData);
-
-    expect(requestInits).toEqual([
-      {credentials: 'include', cache: 'no-cache'},
-      {credentials: 'include', cache: 'no-cache'}
-    ]);
-  });
-
-  it('Load through registered handler', async () => {
-    const weightManifest1: tf.io.WeightsManifestConfig = [{
-      paths: ['weightfile0'],
-      weights: [
-        {
-          name: 'dense/kernel',
-          shape: [3, 1],
-          dtype: 'float32',
-        },
-        {
-          name: 'dense/bias',
-          shape: [1],
-          dtype: 'float32',
-        }
-      ]
-    }];
-    const floatData = new Float32Array([1, 3, 3, 7]);
-    setupFakeWeightFiles({
-      'https://localhost/model.json': JSON.stringify(
-          {modelTopology: modelTopology1, weightsManifest: weightManifest1}),
-      'https://localhost/weightfile0': floatData,
-    });
-
-    const model = await tf.loadLayersModel('https://localhost/model.json');
-    expect(model.inputs.length).toEqual(1);
-    expect(model.inputs[0].shape).toEqual([null, 3]);
-    expect(model.outputs.length).toEqual(1);
-    expect(model.outputs[0].shape).toEqual([null, 1]);
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/kernels/Abs.ts b/tfjs-master/tfjs-node/src/kernels/Abs.ts
deleted file mode 100644
index 074618e4c..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Abs.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Abs, AbsInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const absConfig: KernelConfig = {
-  kernelName: Abs,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AbsInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    if (x.dtype === 'complex64') {
-      const opAttrs = [
-        createTensorsTypeOpAttr('T', x.dtype),
-        createTensorsTypeOpAttr('Tout', 'float32')
-      ];
-      return backend.executeSingleOutput('ComplexAbs', opAttrs, [x]);
-    }
-    return backend.executeSingleInput(Abs, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Acos.ts b/tfjs-master/tfjs-node/src/kernels/Acos.ts
deleted file mode 100644
index 6beea67e0..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Acos.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acos, AcosInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const acosConfig: KernelConfig = {
-  kernelName: Acos,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AcosInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Acos, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Acosh.ts b/tfjs-master/tfjs-node/src/kernels/Acosh.ts
deleted file mode 100644
index 47de82f80..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Acosh.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Acosh, AcoshInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const acoshConfig: KernelConfig = {
-  kernelName: Acosh,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AcoshInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Acosh, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Add.ts b/tfjs-master/tfjs-node/src/kernels/Add.ts
deleted file mode 100644
index d08ecf9e4..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Add.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Add, AddInputs, backend_util, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const addConfig: KernelConfig = {
-  kernelName: Add,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as AddInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(Add, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/AddN.ts b/tfjs-master/tfjs-node/src/kernels/AddN.ts
deleted file mode 100644
index 655e2c616..000000000
--- a/tfjs-master/tfjs-node/src/kernels/AddN.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AddN, AddNInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const addNConfig: KernelConfig = {
-  kernelName: AddN,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const tensors = args.inputs as unknown as AddNInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', tensors[0].dtype),
-      {name: 'N', type: backend.binding.TF_ATTR_INT, value: tensors.length}
-    ];
-    return backend.executeSingleOutput(AddN, opAttrs, tensors);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/All.ts b/tfjs-master/tfjs-node/src/kernels/All.ts
deleted file mode 100644
index 143b5b373..000000000
--- a/tfjs-master/tfjs-node/src/kernels/All.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {All, AllAttrs, AllInputs, KernelConfig, tensor1d, util} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const allConfig: KernelConfig = {
-  kernelName: All,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AllInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis, keepDims} = args.attrs as unknown as AllAttrs;
-
-    const origAxes = util.parseAxisParam(axis, x.shape);
-    const opAttrs = [
-      {name: 'keep_dims', type: backend.binding.TF_ATTR_BOOL, value: keepDims},
-      createTensorsTypeOpAttr('Tidx', 'int32')
-    ];
-    const axesTensor = tensor1d(origAxes, 'int32');
-    const res = backend.executeSingleOutput(All, opAttrs, [x, axesTensor]);
-    axesTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Any.ts b/tfjs-master/tfjs-node/src/kernels/Any.ts
deleted file mode 100644
index d4d44e53f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Any.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Any, AnyAttrs, AnyInputs, KernelConfig, tensor1d, util} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const anyConfig: KernelConfig = {
-  kernelName: Any,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AnyInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis, keepDims} = args.attrs as unknown as AnyAttrs;
-
-    const origAxes = util.parseAxisParam(axis, x.shape);
-    const opAttrs = [
-      {name: 'keep_dims', type: backend.binding.TF_ATTR_BOOL, value: keepDims},
-      createTensorsTypeOpAttr('Tidx', 'int32')
-    ];
-    const axesTensor = tensor1d(origAxes, 'int32');
-    const res = backend.executeSingleOutput(Any, opAttrs, [x, axesTensor]);
-    axesTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ArgMax.ts b/tfjs-master/tfjs-node/src/kernels/ArgMax.ts
deleted file mode 100644
index c02f3c34d..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ArgMax.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMax, ArgMaxAttrs, ArgMaxInputs, KernelConfig, scalar, Tensor} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const argMaxConfig: KernelConfig = {
-  kernelName: ArgMax,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ArgMaxInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis} = args.attrs as unknown as ArgMaxAttrs;
-
-    const toDispose: Tensor[] = [];
-    let xInput = x;
-    if (x.dtype === 'bool') {
-      xInput = (x as Tensor).toInt();
-      toDispose.push(xInput as Tensor);
-    }
-    const axisScalar = scalar(axis, 'int32');
-    toDispose.push(axisScalar);
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', xInput.dtype),
-      createTensorsTypeOpAttr('Tidx', 'int32'),
-      createTensorsTypeOpAttr('output_type', 'int32')
-    ];
-
-    const res =
-        backend.executeSingleOutput(ArgMax, opAttrs, [xInput, axisScalar]);
-    toDispose.forEach(t => t.dispose());
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ArgMin.ts b/tfjs-master/tfjs-node/src/kernels/ArgMin.ts
deleted file mode 100644
index 3f8ade60e..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ArgMin.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ArgMin, ArgMinAttrs, ArgMinInputs, KernelConfig, scalar, Tensor} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const argMinConfig: KernelConfig = {
-  kernelName: ArgMin,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ArgMinInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis} = args.attrs as unknown as ArgMinAttrs;
-
-    const toDispose: Tensor[] = [];
-    let xInput = x;
-    if (x.dtype === 'bool') {
-      xInput = (x as Tensor).toInt();
-      toDispose.push(xInput as Tensor);
-    }
-    const axisScalar = scalar(axis, 'int32');
-    toDispose.push(axisScalar);
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', xInput.dtype),
-      createTensorsTypeOpAttr('Tidx', 'int32'),
-      createTensorsTypeOpAttr('output_type', 'int32')
-    ];
-
-    const res =
-        backend.executeSingleOutput(ArgMin, opAttrs, [xInput, axisScalar]);
-    toDispose.forEach(t => t.dispose());
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Asin.ts b/tfjs-master/tfjs-node/src/kernels/Asin.ts
deleted file mode 100644
index b8005fc2f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Asin.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asin, AsinInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const asinConfig: KernelConfig = {
-  kernelName: Asin,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AsinInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Asin, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Asinh.ts b/tfjs-master/tfjs-node/src/kernels/Asinh.ts
deleted file mode 100644
index 13a27901e..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Asinh.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Asinh, AsinhInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const asinhConfig: KernelConfig = {
-  kernelName: Asinh,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AsinhInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Asinh, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Atan.ts b/tfjs-master/tfjs-node/src/kernels/Atan.ts
deleted file mode 100644
index e1463c46d..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Atan.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan, AtanInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const atanConfig: KernelConfig = {
-  kernelName: Atan,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AtanInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Atan, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Atan2.ts b/tfjs-master/tfjs-node/src/kernels/Atan2.ts
deleted file mode 100644
index d5e19da47..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Atan2.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atan2, Atan2Inputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const atan2Config: KernelConfig = {
-  kernelName: Atan2,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as Atan2Inputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr('T', a.dtype)];
-    return backend.executeSingleOutput(Atan2, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Atanh.ts b/tfjs-master/tfjs-node/src/kernels/Atanh.ts
deleted file mode 100644
index d7a6c8188..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Atanh.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Atanh, AtanhInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const atanhConfig: KernelConfig = {
-  kernelName: Atanh,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AtanhInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Atanh, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/AvgPool.ts b/tfjs-master/tfjs-node/src/kernels/AvgPool.ts
deleted file mode 100644
index 47f4112df..000000000
--- a/tfjs-master/tfjs-node/src/kernels/AvgPool.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool, AvgPoolAttrs, AvgPoolInputs, backend_util, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const avgPoolConfig: KernelConfig = {
-  kernelName: AvgPool,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AvgPoolInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {filterSize, strides, pad, dimRoundingMode} =
-        args.attrs as unknown as AvgPoolAttrs;
-
-    const convInfo = backend_util.computePool2DInfo(
-        x.shape as [number, number, number, number], filterSize, strides,
-        1 /* dilations */, pad, dimRoundingMode);
-
-    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-      throw new Error(
-          `TF Backend supports only 'valid' and 'same' padding ` +
-          `while padding was ${convInfo.padInfo.type}`);
-    }
-    const ksize = [1, convInfo.filterHeight, convInfo.filterWidth, 1];
-    const $strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-    const padding = convInfo.padInfo.type;
-    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {name: 'ksize', type: backend.binding.TF_ATTR_INT, value: ksize},
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding},
-      {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: dataFormat
-      },
-    ];
-    return backend.executeSingleOutput(AvgPool, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/AvgPool3D.ts b/tfjs-master/tfjs-node/src/kernels/AvgPool3D.ts
deleted file mode 100644
index 657a52806..000000000
--- a/tfjs-master/tfjs-node/src/kernels/AvgPool3D.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs, backend_util, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const avgPool3DConfig: KernelConfig = {
-  kernelName: AvgPool3D,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as AvgPool3DInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {filterSize, strides, pad, dimRoundingMode, dataFormat} =
-        args.attrs as unknown as AvgPool3DAttrs;
-
-    const convInfo = backend_util.computePool3DInfo(
-        x.shape as [number, number, number, number, number], filterSize,
-        strides, 1 /* dilations */, pad, dimRoundingMode, dataFormat);
-
-    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-      throw new Error(
-          `TF Backend supports only 'valid' and 'same' padding ` +
-          `while padding was ${convInfo.padInfo.type}`);
-    }
-    const ksize = [
-      1, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, 1
-    ];
-    const $strides = [
-      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
-    ];
-    const padding = convInfo.padInfo.type;
-    const $dataFormat =
-        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {name: 'ksize', type: backend.binding.TF_ATTR_INT, value: ksize},
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding},
-      {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: $dataFormat
-      },
-    ];
-    return backend.executeSingleOutput(AvgPool3D, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/AvgPool3DGrad.ts b/tfjs-master/tfjs-node/src/kernels/AvgPool3DGrad.ts
deleted file mode 100644
index fb849fa38..000000000
--- a/tfjs-master/tfjs-node/src/kernels/AvgPool3DGrad.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPool3DGrad, AvgPool3DGradAttrs, AvgPool3DGradInputs, backend_util, KernelConfig, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const avgPool3DGradConfig: KernelConfig = {
-  kernelName: AvgPool3DGrad,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {dy, input} = args.inputs as AvgPool3DGradInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {filterSize, strides, pad, dimRoundingMode} =
-        args.attrs as unknown as AvgPool3DGradAttrs;
-
-    const convInfo = backend_util.computePool3DInfo(
-        input.shape as [number, number, number, number, number], filterSize,
-        strides, 1 /* dilations */, pad, dimRoundingMode);
-    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-      throw new Error(
-          `TF Backend supports only 'valid' and 'same' padding ` +
-          `while padding type was ${convInfo.padInfo.type}`);
-    }
-    const ksize = [
-      1, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, 1
-    ];
-    const $strides = [
-      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
-    ];
-    const padding = convInfo.padInfo.type;
-    const dataFormat =
-        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', input.dtype),
-      {name: 'ksize', type: backend.binding.TF_ATTR_INT, value: ksize},
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding},
-      {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: dataFormat
-      },
-    ];
-    const origInputShape = tensor1d(input.shape, 'int32');
-    const res = backend.executeSingleOutput(
-        AvgPool3DGrad, opAttrs, [origInputShape, dy]);
-    origInputShape.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/AvgPoolGrad.ts b/tfjs-master/tfjs-node/src/kernels/AvgPoolGrad.ts
deleted file mode 100644
index e4fde3e7b..000000000
--- a/tfjs-master/tfjs-node/src/kernels/AvgPoolGrad.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AvgPoolGrad, AvgPoolGradAttrs, AvgPoolGradInputs, backend_util, KernelConfig, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const avgPoolGradConfig: KernelConfig = {
-  kernelName: AvgPoolGrad,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {dy, input} = args.inputs as AvgPoolGradInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {filterSize, strides, pad} =
-        args.attrs as unknown as AvgPoolGradAttrs;
-
-    const convInfo = backend_util.computePool2DInfo(
-        input.shape as [number, number, number, number], filterSize, strides,
-        1 /* dilations */, pad);
-
-    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-      throw new Error(
-          `TF Backend supports only 'valid' and 'same' padding ` +
-          `while padding type was ${convInfo.padInfo.type}`);
-    }
-    const ksize = [1, convInfo.filterHeight, convInfo.filterWidth, 1];
-    const $strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-    const padding = convInfo.padInfo.type;
-    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', input.dtype),
-      {name: 'ksize', type: backend.binding.TF_ATTR_INT, value: ksize},
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding},
-      {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: dataFormat
-      },
-    ];
-    const origInputShape = tensor1d(input.shape, 'int32');
-    const ret =
-        backend.executeSingleOutput(AvgPoolGrad, opAttrs, [origInputShape, dy]);
-    origInputShape.dispose();
-    return ret;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/BatchMatMul.ts b/tfjs-master/tfjs-node/src/kernels/BatchMatMul.ts
deleted file mode 100644
index e2139c316..000000000
--- a/tfjs-master/tfjs-node/src/kernels/BatchMatMul.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const batchMatMulConfig: KernelConfig = {
-  kernelName: BatchMatMul,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as BatchMatMulInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {transposeA, transposeB} = args.attrs as unknown as BatchMatMulAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', a.dtype),
-      {name: 'adj_x', type: backend.binding.TF_ATTR_BOOL, value: transposeA},
-      {name: 'adj_y', type: backend.binding.TF_ATTR_BOOL, value: transposeB}
-    ];
-
-    // libtensorflow's BatchMatMulV2 op performs the same behavior as other tfjs
-    // backends' BatchMatMul (supports broadcasting), so a string literal is
-    // used here to point to libtensorflow's BatchMatMulV2 op, instead of using
-    // const `BatchMatMul` ('BatchMatMul') to resolve node-backend's special
-    // mapping.
-    return backend.executeSingleOutput('BatchMatMulV2', opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/BatchToSpaceND.ts b/tfjs-master/tfjs-node/src/kernels/BatchToSpaceND.ts
deleted file mode 100644
index 0db51c922..000000000
--- a/tfjs-master/tfjs-node/src/kernels/BatchToSpaceND.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs, KernelConfig, tensor1d, tensor2d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const batchToSpaceNDConfig: KernelConfig = {
-  kernelName: BatchToSpaceND,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as BatchToSpaceNDInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {blockShape, crops} = args.attrs as unknown as BatchToSpaceNDAttrs;
-
-    const blockShapeTensor = tensor1d(blockShape, 'int32');
-    const cropsTensor =
-        tensor2d(crops, [crops.length, crops[0].length], 'int32');
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tblock_shape', 'int32'),
-      createTensorsTypeOpAttr('Tcrops', cropsTensor.dtype)
-    ];
-    const res = backend.executeSingleOutput(
-        BatchToSpaceND, opAttrs, [x, blockShapeTensor, cropsTensor]);
-
-    cropsTensor.dispose();
-    blockShapeTensor.dispose();
-
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Bincount.ts b/tfjs-master/tfjs-node/src/kernels/Bincount.ts
deleted file mode 100644
index ebb8f4e7b..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Bincount.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Bincount, BincountAttrs, BincountInputs, KernelConfig, scalar} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const bincountConfig: KernelConfig = {
-  kernelName: Bincount,
-  backendName: 'tensorflow',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {x, weights} = inputs as BincountInputs;
-    const {size} = attrs as unknown as BincountAttrs;
-    const nodeBackend = backend as NodeJSKernelBackend;
-
-    const $size = scalar(size, 'int32');
-
-    const opAttrs = [createTensorsTypeOpAttr('T', weights.dtype)];
-
-    const result =
-        nodeBackend.executeSingleOutput(Bincount, opAttrs, [x, $size, weights]);
-
-    $size.dispose();
-
-    return result;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/BroadcastArgs.ts b/tfjs-master/tfjs-node/src/kernels/BroadcastArgs.ts
deleted file mode 100644
index cf87210af..000000000
--- a/tfjs-master/tfjs-node/src/kernels/BroadcastArgs.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, BroadcastArgs, BroadcastArgsInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const broadcastArgsConfig: KernelConfig = {
-  kernelName: BroadcastArgs,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {s0, s1} = args.inputs as BroadcastArgsInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(s0.dtype, s1.dtype))];
-    return backend.executeSingleOutput(BroadcastArgs, opAttrs, [s0, s1]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Cast.ts b/tfjs-master/tfjs-node/src/kernels/Cast.ts
deleted file mode 100644
index e184c5cee..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Cast.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cast, CastAttrs, CastInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const castConfig: KernelConfig = {
-  kernelName: Cast,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as CastInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {dtype} = args.attrs as unknown as CastAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('SrcT', x.dtype),
-      createTensorsTypeOpAttr('DstT', dtype),
-      {name: 'Truncate', type: backend.binding.TF_ATTR_BOOL, value: false}
-    ];
-    return backend.executeSingleOutput(Cast, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Ceil.ts b/tfjs-master/tfjs-node/src/kernels/Ceil.ts
deleted file mode 100644
index 9ebbd545f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Ceil.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Ceil, CeilInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const ceilConfig: KernelConfig = {
-  kernelName: Ceil,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as CeilInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Ceil, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ClipByValue.ts b/tfjs-master/tfjs-node/src/kernels/ClipByValue.ts
deleted file mode 100644
index 0773330a0..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ClipByValue.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ClipByValue, ClipByValueAttrs, ClipByValueInputs, KernelConfig, maximum, minimum, scalar, Tensor, tidy} from '@tensorflow/tfjs';
-
-export const clipByValueConfig: KernelConfig = {
-  kernelName: ClipByValue,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ClipByValueInputs;
-    const {clipValueMin, clipValueMax} =
-        args.attrs as unknown as ClipByValueAttrs;
-
-    return tidy(() => {
-      const xMin = minimum(x as Tensor, scalar(clipValueMax, x.dtype));
-      return maximum(xMin, scalar(clipValueMin, x.dtype));
-    });
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Complex.ts b/tfjs-master/tfjs-node/src/kernels/Complex.ts
deleted file mode 100644
index 4ef544b20..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Complex.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Complex, ComplexInputs, KernelConfig, Tensor} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const complexConfig: KernelConfig = {
-  kernelName: Complex,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {real, imag} = args.inputs as ComplexInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', real as Tensor),
-      {
-        name: 'Tout',
-        type: backend.binding.TF_ATTR_TYPE,
-        value: backend.binding.TF_COMPLEX64
-      },
-    ];
-    const inputs = [real, imag];
-    return backend.executeSingleOutput(Complex, opAttrs, inputs);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ComplexAbs.ts b/tfjs-master/tfjs-node/src/kernels/ComplexAbs.ts
deleted file mode 100644
index 41fb32d9a..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ComplexAbs.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ComplexAbs, ComplexAbsInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const complexAbsConfig: KernelConfig = {
-  kernelName: ComplexAbs,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ComplexAbsInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tout', 'float32')
-    ];
-    return backend.executeSingleOutput(ComplexAbs, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Concat.ts b/tfjs-master/tfjs-node/src/kernels/Concat.ts
deleted file mode 100644
index 69e18120a..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Concat.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Concat, ConcatAttrs, ConcatInputs, KernelConfig, scalar, Tensor} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const concatConfig: KernelConfig = {
-  kernelName: Concat,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const tensors = args.inputs as unknown as ConcatInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis} = args.attrs as unknown as ConcatAttrs;
-
-    const opAttrs = [
-      {name: 'N', type: backend.binding.TF_ATTR_INT, value: tensors.length}, {
-        name: 'Tidx',
-        type: backend.binding.TF_ATTR_TYPE,
-        value: backend.binding.TF_INT32
-      },
-      createTensorsTypeOpAttr('T', tensors as Tensor[])
-    ];
-
-    const inputs = Array.from(tensors);
-    const axisTensor = scalar(axis, 'int32');
-    inputs.push(axisTensor);
-    const res = backend.executeSingleOutput('ConcatV2', opAttrs, inputs);
-    axisTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Conv2D.ts b/tfjs-master/tfjs-node/src/kernels/Conv2D.ts
deleted file mode 100644
index 09a8e67a2..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Conv2D.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2D, Conv2DAttrs, Conv2DInputs, KernelConfig, Tensor4D, TensorInfo} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const conv2DConfig: KernelConfig = {
-  kernelName: Conv2D,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, filter} = args.inputs as Conv2DInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {strides, pad, dataFormat, dilations, dimRoundingMode} =
-        args.attrs as unknown as Conv2DAttrs;
-
-    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-    const convInfo = backend_util.computeConv2DInfo(
-        x.shape as [number, number, number, number],
-        filter.shape as [number, number, number, number], strides, dilations,
-        pad, dimRoundingMode, false /* depthwise */, $dataFormat);
-
-    return conv2dImpl(x, filter, convInfo, backend);
-  }
-};
-
-export function conv2dImpl(
-    x: TensorInfo, filter: TensorInfo, convInfo: backend_util.Conv2DInfo,
-    backend: NodeJSKernelBackend): Tensor4D {
-  if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME' &&
-      convInfo.padInfo.type !== 'EXPLICIT') {
-    throw new Error(
-        `TF Backend supports only 'valid' and 'same' padding ` +
-        `while padding was ${convInfo.padInfo.type}`);
-  }
-  const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-  const padding = convInfo.padInfo.type;
-  const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-  const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
-  const opAttrs = [
-    createTensorsTypeOpAttr('T', x.dtype),
-    {name: 'strides', type: backend.binding.TF_ATTR_INT, value: strides},
-    {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding},
-    {
-      name: 'data_format',
-      type: backend.binding.TF_ATTR_STRING,
-      value: dataFormat
-    },
-    {name: 'use_cudnn_on_gpu', type: backend.binding.TF_ATTR_BOOL, value: true},
-    {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: dilations},
-  ];
-  if (padding === 'EXPLICIT') {
-    const padValue = [
-      convInfo.padInfo.top, convInfo.padInfo.bottom, convInfo.padInfo.left,
-      convInfo.padInfo.right
-    ];
-    opAttrs.push({
-      name: 'explicit_paddings',
-      type: backend.binding.TF_ATTR_INT,
-      value: dataFormat === 'NHWC' ? [0, 0, ...padValue, 0, 0] :
-                                     [0, 0, 0, 0, ...padValue]
-    });
-  }
-  return backend.executeSingleOutput(Conv2D, opAttrs, [x, filter]) as Tensor4D;
-}
diff --git a/tfjs-master/tfjs-node/src/kernels/Conv2DBackpropFilter.ts b/tfjs-master/tfjs-node/src/kernels/Conv2DBackpropFilter.ts
deleted file mode 100644
index bf0d05ae4..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Conv2DBackpropFilter.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs, KernelConfig, tensor1d, Tensor4D, TensorInfo} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const conv2DBackpropFilterConfig: KernelConfig = {
-  kernelName: Conv2DBackpropFilter,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, dy} = args.inputs as Conv2DBackpropFilterInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {strides, pad, dataFormat, dimRoundingMode, filterShape} =
-        args.attrs as unknown as Conv2DBackpropFilterAttrs;
-
-    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-    const convInfo = backend_util.computeConv2DInfo(
-        x.shape as [number, number, number, number], filterShape, strides,
-        1 /* dilations */, pad, dimRoundingMode, false /* depthwise */,
-        $dataFormat);
-
-    return conv2DBackpropFilterImpl(dy, x, convInfo, backend);
-  }
-};
-
-function conv2DBackpropFilterImpl(
-    dy: TensorInfo, filter: TensorInfo, convInfo: backend_util.Conv2DInfo,
-    backend: NodeJSKernelBackend): Tensor4D {
-  if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-    throw new Error(
-        `TF Backend supports only 'valid' and 'same' padding ` +
-        `while padding was ${convInfo.padInfo.type}`);
-  }
-  const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-  const padding = convInfo.padInfo.type;
-  const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-  const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
-  const opAttrs = [
-    createTensorsTypeOpAttr('T', 'float32'),
-    {name: 'strides', type: backend.binding.TF_ATTR_INT, value: strides},
-    {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-      name: 'data_format',
-      type: backend.binding.TF_ATTR_STRING,
-      value: dataFormat
-    },
-    {name: 'use_cudnn_on_gpu', type: backend.binding.TF_ATTR_BOOL, value: true},
-    {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: dilations}
-  ];
-  const filterSizes = tensor1d(convInfo.filterShape, 'int32');
-  const res =
-      backend.executeSingleOutput(
-          Conv2DBackpropFilter, opAttrs, [filter, filterSizes, dy]) as Tensor4D;
-  filterSizes.dispose();
-  return res;
-}
diff --git a/tfjs-master/tfjs-node/src/kernels/Conv2DBackpropInput.ts b/tfjs-master/tfjs-node/src/kernels/Conv2DBackpropInput.ts
deleted file mode 100644
index 114fa78ca..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Conv2DBackpropInput.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs, KernelConfig, tensor1d, Tensor4D, TensorInfo} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const conv2DBackpropInputConfig: KernelConfig = {
-  kernelName: Conv2DBackpropInput,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {dy, filter} = args.inputs as Conv2DBackpropInputInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {strides, pad, dataFormat, dimRoundingMode, inputShape} =
-        args.attrs as unknown as Conv2DBackpropInputAttrs;
-
-    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-    const convInfo = backend_util.computeConv2DInfo(
-        inputShape, filter.shape as [number, number, number, number], strides,
-        1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);
-
-    return conv2DBackpropInputImpl(dy, filter, convInfo, backend);
-  }
-};
-
-function conv2DBackpropInputImpl(
-    dy: TensorInfo, filter: TensorInfo, convInfo: backend_util.Conv2DInfo,
-    backend: NodeJSKernelBackend): Tensor4D {
-  if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-    throw new Error(
-        `TF Backend supports only 'valid' and 'same' padding ` +
-        `while padding was ${convInfo.padInfo.type}`);
-  }
-  const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-  const padding = convInfo.padInfo.type;
-  const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-  const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
-  const opAttrs = [
-    createTensorsTypeOpAttr('T', 'float32'),
-    {name: 'strides', type: backend.binding.TF_ATTR_INT, value: strides},
-    {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-      name: 'data_format',
-      type: backend.binding.TF_ATTR_STRING,
-      value: dataFormat
-    },
-    {name: 'use_cudnn_on_gpu', type: backend.binding.TF_ATTR_BOOL, value: true},
-    {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: dilations}
-  ];
-  const inputSizes = tensor1d(convInfo.inShape, 'int32');
-  const res =
-      backend.executeSingleOutput(
-          Conv2DBackpropInput, opAttrs, [inputSizes, filter, dy]) as Tensor4D;
-  inputSizes.dispose();
-  return res;
-}
diff --git a/tfjs-master/tfjs-node/src/kernels/Conv3D.ts b/tfjs-master/tfjs-node/src/kernels/Conv3D.ts
deleted file mode 100644
index fd0db79a1..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Conv3D.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3D, Conv3DAttrs, Conv3DInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const conv3DConfig: KernelConfig = {
-  kernelName: Conv3D,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, filter} = args.inputs as Conv3DInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {strides, pad, dilations} = args.attrs as unknown as Conv3DAttrs;
-
-    const convInfo = backend_util.computeConv3DInfo(
-        x.shape as [number, number, number, number, number],
-        filter.shape as [number, number, number, number, number], strides,
-        dilations, pad);
-
-    const $strides = [
-      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
-    ];
-    const padding = convInfo.padInfo.type;
-    const $dataFormat =
-        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
-
-    if (!backend.isGPUPackage && convInfo.dilationDepth > 1) {
-      throw new Error('CPU Dilation depth must be 1');
-    }
-    const $dilations = [
-      1, convInfo.dilationDepth, convInfo.dilationHeight,
-      convInfo.dilationWidth, 1
-    ];
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: $dataFormat
-      },
-      {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: $dilations}
-    ];
-    return backend.executeSingleOutput(Conv3D, opAttrs, [x, filter]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Conv3DBackpropFilterV2.ts b/tfjs-master/tfjs-node/src/kernels/Conv3DBackpropFilterV2.ts
deleted file mode 100644
index 1cc838630..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Conv3DBackpropFilterV2.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropFilterV2, Conv3DBackpropFilterV2Attrs, Conv3DBackpropFilterV2Inputs, KernelConfig, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const conv3DBackpropFilterV2Config: KernelConfig = {
-  kernelName: Conv3DBackpropFilterV2,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, dy} = args.inputs as Conv3DBackpropFilterV2Inputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {strides, pad, filterShape} =
-        args.attrs as unknown as Conv3DBackpropFilterV2Attrs;
-
-    const dilations = 1;
-
-    const convInfo = backend_util.computeConv3DInfo(
-        x.shape as [number, number, number, number, number], filterShape,
-        strides, dilations, pad);
-
-    const $strides = [
-      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
-    ];
-    const padding = convInfo.padInfo.type;
-    const dataFormat =
-        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
-
-    if (!backend.isGPUPackage && convInfo.dilationDepth > 1) {
-      throw new Error('CPU Dilation depth must be 1');
-    }
-    const $dilations = [
-      1, convInfo.dilationDepth, convInfo.dilationHeight,
-      convInfo.dilationWidth, 1
-    ];
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: dataFormat
-      },
-      {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: $dilations}
-    ];
-    const filterSizes = tensor1d(filterShape, 'int32');
-    const res = backend.executeSingleOutput(
-        Conv3DBackpropFilterV2, opAttrs, [x, filterSizes, dy]);
-    filterSizes.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Conv3DBackpropInputV2.ts b/tfjs-master/tfjs-node/src/kernels/Conv3DBackpropInputV2.ts
deleted file mode 100644
index 40dc3c62d..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Conv3DBackpropInputV2.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs, KernelConfig, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const conv3DBackpropInputV2Config: KernelConfig = {
-  kernelName: Conv3DBackpropInputV2,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {dy, filter} = args.inputs as Conv3DBackpropInputV2Inputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {strides, pad, inputShape} =
-        args.attrs as unknown as Conv3DBackpropInputV2Attrs;
-
-    const dilations = 1;
-
-    const convInfo = backend_util.computeConv3DInfo(
-        inputShape, filter.shape as [number, number, number, number, number],
-        strides, dilations, pad);
-
-    const $strides = [
-      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
-    ];
-    const padding = convInfo.padInfo.type;
-    const dataFormat =
-        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
-    if (!backend.isGPUPackage && convInfo.dilationDepth > 1) {
-      throw new Error('CPU Dilation depth must be 1');
-    }
-    const $dilations = [
-      1, convInfo.dilationDepth, convInfo.dilationHeight,
-      convInfo.dilationWidth, 1
-    ];
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', dy.dtype),
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: dataFormat
-      },
-      {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: $dilations},
-      createTensorsTypeOpAttr('Tshape', 'int32')
-    ];
-    const inputSizes = tensor1d(inputShape, 'int32');
-    const res = backend.executeSingleOutput(
-        Conv3DBackpropInputV2, opAttrs, [inputSizes, filter, dy]);
-    inputSizes.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Cos.ts b/tfjs-master/tfjs-node/src/kernels/Cos.ts
deleted file mode 100644
index 972830ef5..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Cos.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cos, CosInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const cosConfig: KernelConfig = {
-  kernelName: Cos,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as CosInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Cos, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Cosh.ts b/tfjs-master/tfjs-node/src/kernels/Cosh.ts
deleted file mode 100644
index 0bcb0a9f7..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Cosh.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cosh, CoshInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const coshConfig: KernelConfig = {
-  kernelName: Cosh,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as CoshInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Cosh, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/CropAndResize.ts b/tfjs-master/tfjs-node/src/kernels/CropAndResize.ts
deleted file mode 100644
index c6c4a7f17..000000000
--- a/tfjs-master/tfjs-node/src/kernels/CropAndResize.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const cropAndResizeConfig: KernelConfig = {
-  kernelName: CropAndResize,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {image, boxes, boxInd} = args.inputs as CropAndResizeInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {cropSize, method, extrapolationValue} =
-        args.attrs as unknown as CropAndResizeAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', image.dtype),
-      {name: 'method', type: backend.binding.TF_ATTR_STRING, value: method}, {
-        name: 'extrapolation_value',
-        type: backend.binding.TF_ATTR_FLOAT,
-        value: extrapolationValue
-      }
-    ];
-    const cropSizeTensor = tensor1d(cropSize, 'int32');
-    const res = backend.executeSingleOutput(
-        CropAndResize, opAttrs, [image, boxes, boxInd, cropSizeTensor]);
-    cropSizeTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Cumprod.ts b/tfjs-master/tfjs-node/src/kernels/Cumprod.ts
deleted file mode 100644
index cb675688e..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Cumprod.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cumprod, CumprodAttrs, CumprodInputs, KernelConfig, scalar} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const cumprodConfig: KernelConfig = {
-  kernelName: Cumprod,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as CumprodInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis, exclusive, reverse} = args.attrs as unknown as CumprodAttrs;
-
-    const axisTensor = scalar(axis, 'int32');
-    const opAttrs = [
-      {name: 'exclusive', type: backend.binding.TF_ATTR_BOOL, value: exclusive},
-      {name: 'reverse', type: backend.binding.TF_ATTR_BOOL, value: reverse},
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tidx', 'int32')
-    ];
-    const res = backend.executeSingleOutput(Cumprod, opAttrs, [x, axisTensor]);
-    axisTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Cumsum.ts b/tfjs-master/tfjs-node/src/kernels/Cumsum.ts
deleted file mode 100644
index 5669095ca..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Cumsum.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Cumsum, CumsumAttrs, CumsumInputs, KernelConfig, scalar} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const cumsumConfig: KernelConfig = {
-  kernelName: Cumsum,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as CumsumInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis, exclusive, reverse} = args.attrs as unknown as CumsumAttrs;
-
-    const axisTensor = scalar(axis, 'int32');
-    const opAttrs = [
-      {name: 'exclusive', type: backend.binding.TF_ATTR_BOOL, value: exclusive},
-      {name: 'reverse', type: backend.binding.TF_ATTR_BOOL, value: reverse},
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tidx', 'int32')
-    ];
-    const res = backend.executeSingleOutput(Cumsum, opAttrs, [x, axisTensor]);
-    axisTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/DepthToSpace.ts b/tfjs-master/tfjs-node/src/kernels/DepthToSpace.ts
deleted file mode 100644
index b0e4e3fd3..000000000
--- a/tfjs-master/tfjs-node/src/kernels/DepthToSpace.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs, KernelConfig, Tensor} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const depthToSpaceConfig: KernelConfig = {
-  kernelName: DepthToSpace,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as DepthToSpaceInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {blockSize, dataFormat} = args.attrs as unknown as DepthToSpaceAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x as Tensor), {
-        name: 'block_size',
-        type: backend.binding.TF_ATTR_INT,
-        value: blockSize < 2 ? 2 : blockSize
-      },
-      {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: dataFormat
-      }
-    ];
-    const inputs = [x];
-    return backend.executeSingleOutput(DepthToSpace, opAttrs, inputs);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/DepthwiseConv2dNative.ts b/tfjs-master/tfjs-node/src/kernels/DepthwiseConv2dNative.ts
deleted file mode 100644
index 9e4e7a3bc..000000000
--- a/tfjs-master/tfjs-node/src/kernels/DepthwiseConv2dNative.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs, KernelConfig, Tensor4D, TensorInfo} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const depthwiseConv2dNativeConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNative,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, filter} = args.inputs as DepthwiseConv2dNativeInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {strides, pad, dilations, dimRoundingMode} =
-        args.attrs as unknown as DepthwiseConv2dNativeAttrs;
-
-    let $dilations = dilations;
-    if ($dilations == null) {
-      $dilations = [1, 1];
-    }
-
-    const convInfo = backend_util.computeConv2DInfo(
-        x.shape as [number, number, number, number],
-        filter.shape as [number, number, number, number], strides, $dilations,
-        pad, dimRoundingMode, true /* depthwise */);
-
-    return depthwiseConv2dNativeImpl(x, filter, convInfo, backend);
-  }
-};
-
-export function depthwiseConv2dNativeImpl(
-    input: TensorInfo, filter: TensorInfo, convInfo: backend_util.Conv2DInfo,
-    backend: NodeJSKernelBackend): Tensor4D {
-  if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME' &&
-      convInfo.padInfo.type !== 'EXPLICIT') {
-    throw new Error(
-        `TF Backend supports only 'valid' and 'same' padding ` +
-        `while padding was ${convInfo.padInfo.type}`);
-  }
-  const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-  const padding = convInfo.padInfo.type;
-  const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-  const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
-  const opAttrs = [
-    createTensorsTypeOpAttr('T', input.dtype),
-    {name: 'strides', type: backend.binding.TF_ATTR_INT, value: strides},
-    {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-      name: 'data_format',
-      type: backend.binding.TF_ATTR_STRING,
-      value: dataFormat
-    },
-    {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: dilations}
-  ];
-  if (padding === 'EXPLICIT') {
-    const padValue = [
-      convInfo.padInfo.top, convInfo.padInfo.bottom, convInfo.padInfo.left,
-      convInfo.padInfo.right
-    ];
-    opAttrs.push({
-      name: 'explicit_paddings',
-      type: backend.binding.TF_ATTR_INT,
-      value: dataFormat === 'NHWC' ? [0, 0, ...padValue, 0, 0] :
-                                     [0, 0, 0, 0, ...padValue]
-    });
-  }
-  return backend.executeSingleOutput(
-             DepthwiseConv2dNative, opAttrs, [input, filter]) as Tensor4D;
-}
diff --git a/tfjs-master/tfjs-node/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts b/tfjs-master/tfjs-node/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts
deleted file mode 100644
index d0af94ab5..000000000
--- a/tfjs-master/tfjs-node/src/kernels/DepthwiseConv2dNativeBackpropFilter.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropFilterAttrs, DepthwiseConv2dNativeBackpropFilterInputs, KernelConfig, tensor1d, Tensor4D, TensorInfo} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const depthwiseConv2dNativeBackpropFilterConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNativeBackpropFilter,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, dy} = args.inputs as DepthwiseConv2dNativeBackpropFilterInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {strides, dilations, pad, dimRoundingMode, filterShape} =
-        args.attrs as unknown as DepthwiseConv2dNativeBackpropFilterAttrs;
-
-    const convInfo = backend_util.computeConv2DInfo(
-        x.shape as [number, number, number, number], filterShape, strides,
-        dilations, pad, dimRoundingMode, true /* depthwise */);
-
-    return depthwiseConv2dNativeBackpropFilterImpl(x, dy, convInfo, backend);
-  }
-};
-
-function depthwiseConv2dNativeBackpropFilterImpl(
-    x: TensorInfo, dy: TensorInfo, convInfo: backend_util.Conv2DInfo,
-    backend: NodeJSKernelBackend): Tensor4D {
-  const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-  const padding = convInfo.padInfo.type;
-  const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-  const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
-  const opAttrs = [
-    createTensorsTypeOpAttr('T', 'float32'),
-    {name: 'strides', type: backend.binding.TF_ATTR_INT, value: strides},
-    {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-      name: 'data_format',
-      type: backend.binding.TF_ATTR_STRING,
-      value: dataFormat
-    },
-    {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: dilations}
-  ];
-  const filterSizes = tensor1d(convInfo.filterShape, 'int32');
-  const res = backend.executeSingleOutput(
-                  DepthwiseConv2dNativeBackpropFilter, opAttrs,
-                  [x, filterSizes, dy]) as Tensor4D;
-  filterSizes.dispose();
-  return res;
-}
diff --git a/tfjs-master/tfjs-node/src/kernels/DepthwiseConv2dNativeBackpropInput.ts b/tfjs-master/tfjs-node/src/kernels/DepthwiseConv2dNativeBackpropInput.ts
deleted file mode 100644
index 5327ebc08..000000000
--- a/tfjs-master/tfjs-node/src/kernels/DepthwiseConv2dNativeBackpropInput.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, DepthwiseConv2dNativeBackpropInput, DepthwiseConv2dNativeBackpropInputAttrs, DepthwiseConv2dNativeBackpropInputInputs, KernelConfig, tensor1d, Tensor4D, TensorInfo} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const depthwiseConv2dNativeBackpropInputConfig: KernelConfig = {
-  kernelName: DepthwiseConv2dNativeBackpropInput,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {dy, filter} =
-        args.inputs as DepthwiseConv2dNativeBackpropInputInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {strides, dilations, pad, dimRoundingMode, inputShape} =
-        args.attrs as unknown as DepthwiseConv2dNativeBackpropInputAttrs;
-
-    const convInfo = backend_util.computeConv2DInfo(
-        inputShape, filter.shape as [number, number, number, number], strides,
-        dilations, pad, dimRoundingMode, true /* depthwise */);
-
-    return depthwiseConv2dNativeBackpropInputImpl(
-        dy, filter, convInfo, backend);
-  }
-};
-
-function depthwiseConv2dNativeBackpropInputImpl(
-    dy: TensorInfo, filter: TensorInfo, convInfo: backend_util.Conv2DInfo,
-    backend: NodeJSKernelBackend): Tensor4D {
-  const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-  const padding = convInfo.padInfo.type;
-  const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-  const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
-  const opAttrs = [
-    createTensorsTypeOpAttr('T', 'float32'),
-    {name: 'strides', type: backend.binding.TF_ATTR_INT, value: strides},
-    {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-      name: 'data_format',
-      type: backend.binding.TF_ATTR_STRING,
-      value: dataFormat
-    },
-    {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: dilations}
-  ];
-
-  const inputSizes = tensor1d(convInfo.inShape, 'int32');
-  const res = backend.executeSingleOutput(
-                  DepthwiseConv2dNativeBackpropInput, opAttrs,
-                  [inputSizes, filter, dy]) as Tensor4D;
-  inputSizes.dispose();
-  return res;
-}
diff --git a/tfjs-master/tfjs-node/src/kernels/Diag.ts b/tfjs-master/tfjs-node/src/kernels/Diag.ts
deleted file mode 100644
index 5462a29e8..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Diag.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Diag, DiagInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const diagConfig: KernelConfig = {
-  kernelName: Diag,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as DiagInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Diag, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Dilation2D.ts b/tfjs-master/tfjs-node/src/kernels/Dilation2D.ts
deleted file mode 100644
index 880e7e6a2..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Dilation2D.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2D, Dilation2DAttrs, Dilation2DInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const dilation2dConfig: KernelConfig = {
-  kernelName: Dilation2D,
-  backendName: 'tensorflow',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {x, filter} = inputs as Dilation2DInputs;
-    const {strides, pad, dilations} = attrs as unknown as Dilation2DAttrs;
-    const {dilationHeight, dilationWidth, padInfo, strideHeight, strideWidth} =
-        backend_util.computeDilation2DInfo(
-            x.shape as [number, number, number, number],
-            filter.shape as [number, number, number], strides, pad,
-            'NHWC' /* dataFormat */, dilations);
-    const $strides = [1, strideHeight, strideWidth, 1];
-    const $dilations = [1, dilationHeight, dilationWidth, 1];
-
-    const nodeBackend = backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {name: 'strides', type: nodeBackend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'rates', type: nodeBackend.binding.TF_ATTR_INT, value: $dilations},
-      {
-        name: 'padding',
-        type: nodeBackend.binding.TF_ATTR_STRING,
-        value: padInfo.type
-      }
-    ];
-
-    return nodeBackend.executeSingleOutput(Dilation2D, opAttrs, [x, filter]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Dilation2DBackpropFilter.ts b/tfjs-master/tfjs-node/src/kernels/Dilation2DBackpropFilter.ts
deleted file mode 100644
index 5b44cf846..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Dilation2DBackpropFilter.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2DAttrs, Dilation2DBackpropFilter, Dilation2DBackpropFilterInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const dilation2dBackpropFilterConfig: KernelConfig = {
-  kernelName: Dilation2DBackpropFilter,
-  backendName: 'tensorflow',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {x, filter, dy} = inputs as Dilation2DBackpropFilterInputs;
-    const {strides, pad, dilations} = attrs as unknown as Dilation2DAttrs;
-    const {dilationHeight, dilationWidth, padInfo, strideHeight, strideWidth} =
-        backend_util.computeDilation2DInfo(
-            x.shape as [number, number, number, number],
-            filter.shape as [number, number, number], strides, pad,
-            'NHWC' /* dataFormat */, dilations);
-    const $strides = [1, strideHeight, strideWidth, 1];
-    const $dilations = [1, dilationHeight, dilationWidth, 1];
-
-    const nodeBackend = backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {name: 'strides', type: nodeBackend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'rates', type: nodeBackend.binding.TF_ATTR_INT, value: $dilations},
-      {
-        name: 'padding',
-        type: nodeBackend.binding.TF_ATTR_STRING,
-        value: padInfo.type
-      }
-    ];
-
-    return nodeBackend.executeSingleOutput(
-        Dilation2DBackpropFilter, opAttrs, [x, filter, dy]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Dilation2DBackpropInput.ts b/tfjs-master/tfjs-node/src/kernels/Dilation2DBackpropInput.ts
deleted file mode 100644
index 875029545..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Dilation2DBackpropInput.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Dilation2DAttrs, Dilation2DBackpropInput, Dilation2DBackpropInputInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const dilation2dBackpropInputConfig: KernelConfig = {
-  kernelName: Dilation2DBackpropInput,
-  backendName: 'tensorflow',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {x, filter, dy} = inputs as Dilation2DBackpropInputInputs;
-    const {strides, pad, dilations} = attrs as unknown as Dilation2DAttrs;
-    const {dilationHeight, dilationWidth, padInfo, strideHeight, strideWidth} =
-        backend_util.computeDilation2DInfo(
-            x.shape as [number, number, number, number],
-            filter.shape as [number, number, number], strides, pad,
-            'NHWC' /* dataFormat */, dilations);
-    const $strides = [1, strideHeight, strideWidth, 1];
-    const $dilations = [1, dilationHeight, dilationWidth, 1];
-
-    const nodeBackend = backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {name: 'strides', type: nodeBackend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'rates', type: nodeBackend.binding.TF_ATTR_INT, value: $dilations},
-      {
-        name: 'padding',
-        type: nodeBackend.binding.TF_ATTR_STRING,
-        value: padInfo.type
-      }
-    ];
-
-    return nodeBackend.executeSingleOutput(
-        Dilation2DBackpropInput, opAttrs, [x, filter, dy]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Einsum.ts b/tfjs-master/tfjs-node/src/kernels/Einsum.ts
deleted file mode 100644
index bc3c9520f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Einsum.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Einsum, EinsumInputs, KernelConfig, Tensor} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const einsumConfig: KernelConfig = {
-  kernelName: Einsum,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {inputs, attrs} = args;
-    const tensors = inputs as unknown as EinsumInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {equation} = attrs;
-
-    const opAttrs = [
-      {name: 'N', type: backend.binding.TF_ATTR_INT, value: tensors.length},
-      {name: 'equation', type: backend.binding.TF_ATTR_STRING, value: equation},
-      createTensorsTypeOpAttr('T', tensors as Tensor[])
-    ];
-
-    const tensorArray = Array.from(tensors);
-    return backend.executeSingleOutput(Einsum, opAttrs, tensorArray);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Elu.ts b/tfjs-master/tfjs-node/src/kernels/Elu.ts
deleted file mode 100644
index a9b7684e8..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Elu.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Elu, EluInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const eluConfig: KernelConfig = {
-  kernelName: Elu,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as EluInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Elu, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/EluGrad.ts b/tfjs-master/tfjs-node/src/kernels/EluGrad.ts
deleted file mode 100644
index 93640f9c5..000000000
--- a/tfjs-master/tfjs-node/src/kernels/EluGrad.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {EluGrad, EluGradInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const eluGradConfig: KernelConfig = {
-  kernelName: EluGrad,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {dy, y} = args.inputs as EluGradInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr('T', y.dtype)];
-    return backend.executeSingleOutput(EluGrad, opAttrs, [dy, y]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Equal.ts b/tfjs-master/tfjs-node/src/kernels/Equal.ts
deleted file mode 100644
index 1b2d0b5e6..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Equal.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Equal, EqualInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const equalConfig: KernelConfig = {
-  kernelName: Equal,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as EqualInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(Equal, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Erf.ts b/tfjs-master/tfjs-node/src/kernels/Erf.ts
deleted file mode 100644
index 681e90ed7..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Erf.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Erf, ErfInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const erfConfig: KernelConfig = {
-  kernelName: Erf,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ErfInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Erf, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Exp.ts b/tfjs-master/tfjs-node/src/kernels/Exp.ts
deleted file mode 100644
index 91a1136b5..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Exp.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Exp, ExpInputs, KernelConfig, Tensor} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const expConfig: KernelConfig = {
-  kernelName: Exp,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ExpInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    let xTensor = x;
-    if (x.dtype === 'int32') {
-      xTensor = (x as Tensor).toFloat();
-      (x as Tensor).dispose();
-    }
-    return backend.executeSingleInput(Exp, xTensor);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ExpandDims.ts b/tfjs-master/tfjs-node/src/kernels/ExpandDims.ts
deleted file mode 100644
index e0e93994c..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ExpandDims.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ExpandDims, ExpandDimsAttrs, ExpandDimsInputs, KernelConfig, scalar} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const expandDimsConfig: KernelConfig = {
-  kernelName: ExpandDims,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {input} = args.inputs as ExpandDimsInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {dim} = args.attrs as unknown as ExpandDimsAttrs;
-
-    const dimTensor = scalar(dim, 'int32');
-
-    const res = backend.executeSingleOutput(ExpandDims, [], [input, dimTensor]);
-    dimTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Expm1.ts b/tfjs-master/tfjs-node/src/kernels/Expm1.ts
deleted file mode 100644
index 121c4fd68..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Expm1.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Expm1, Expm1Inputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const expm1Config: KernelConfig = {
-  kernelName: Expm1,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as Expm1Inputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Expm1, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/FFT.ts b/tfjs-master/tfjs-node/src/kernels/FFT.ts
deleted file mode 100644
index 2c7a77b3f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/FFT.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FFT, FFTInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-// tslint:disable-next-line: variable-name
-export const FFTConfig: KernelConfig = {
-  kernelName: FFT,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {input} = args.inputs as FFTInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr('Tcomplex', input.dtype)];
-    return backend.executeSingleOutput(FFT, opAttrs, [input]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Fill.ts b/tfjs-master/tfjs-node/src/kernels/Fill.ts
deleted file mode 100644
index aa5d7126b..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Fill.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Fill, FillAttrs, KernelConfig, scalar, tensor1d} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const fillConfig: KernelConfig = {
-  kernelName: Fill,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const backend = args.backend as NodeJSKernelBackend;
-    const {shape, value} = args.attrs as unknown as FillAttrs;
-    let {dtype} = args.attrs as unknown as FillAttrs;
-
-    // TODO(cais, nkreeger): Investigate whether backend can be made into
-    // a dtype helper method. The underlying op kernel doesn't accept undefined
-    // or null dtype.
-    if (dtype == null) {
-      if (typeof value === 'number') {
-        dtype = 'float32';
-      } else {
-        dtype = 'string';
-      }
-    }
-    const shapeTensor = tensor1d(shape, 'int32');
-    const valueTensor = scalar(value, dtype);
-    const opAttrs = [
-      {
-        name: 'T',
-        type: backend.binding.TF_ATTR_TYPE,
-        value: backend.getDTypeInteger(dtype)
-      },
-      {
-        name: 'index_type',
-        type: backend.binding.TF_ATTR_TYPE,
-        value: backend.binding.TF_INT32
-      }
-    ];
-    const res =
-        backend.executeSingleOutput(Fill, opAttrs, [shapeTensor, valueTensor]);
-    shapeTensor.dispose();
-    valueTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/FlipLeftRight.ts b/tfjs-master/tfjs-node/src/kernels/FlipLeftRight.ts
deleted file mode 100644
index e1094348a..000000000
--- a/tfjs-master/tfjs-node/src/kernels/FlipLeftRight.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {FlipLeftRight, FlipLeftRightInputs, KernelConfig, tensor1d, util} from '@tensorflow/tfjs-core';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const flipLeftRightConfig: KernelConfig = {
-  kernelName: FlipLeftRight,
-  backendName: 'tensorflow',
-  kernelFunc({inputs, backend}) {
-    const nodeBackend = backend as NodeJSKernelBackend;
-    const {image} = inputs as FlipLeftRightInputs;
-    const opAttrs = [
-      createTensorsTypeOpAttr('Tidx', 'int32'),
-      createTensorsTypeOpAttr('T', image.dtype),
-    ];
-    const axes = util.parseAxisParam([2], image.shape);
-    const axisTensor = tensor1d(axes, 'int32');
-    const res =
-        nodeBackend.executeSingleOutput(
-            'ReverseV2', opAttrs, [image, axisTensor]);
-    axisTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Floor.ts b/tfjs-master/tfjs-node/src/kernels/Floor.ts
deleted file mode 100644
index 39616538e..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Floor.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Floor, FloorInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const floorConfig: KernelConfig = {
-  kernelName: Floor,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as FloorInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Floor, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/FloorDiv.ts b/tfjs-master/tfjs-node/src/kernels/FloorDiv.ts
deleted file mode 100644
index 3752e2c0f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/FloorDiv.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, FloorDiv, FloorDivInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const floorDivConfig: KernelConfig = {
-  kernelName: FloorDiv,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as FloorDivInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(FloorDiv, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/FusedBatchNorm.ts b/tfjs-master/tfjs-node/src/kernels/FusedBatchNorm.ts
deleted file mode 100644
index c3a5f73a7..000000000
--- a/tfjs-master/tfjs-node/src/kernels/FusedBatchNorm.ts
+++ /dev/null
@@ -1,70 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {add, fill, FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs, KernelConfig, mul, Rank, rsqrt, scalar, sub, Tensor, Tensor4D, tidy} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const fusedBatchNormConfig: KernelConfig = {
-  kernelName: FusedBatchNorm,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, mean, variance} = args.inputs as FusedBatchNormInputs;
-    let {scale, offset} = args.inputs as FusedBatchNormInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {varianceEpsilon} = args.attrs as unknown as FusedBatchNormAttrs;
-
-    return tidy(() => {
-      if ((mean as Tensor).rank > 1) {
-        // Fused batch norm doesn't work with high-dim mean/var/scale/offset.
-        let inv = rsqrt(add(variance as Tensor, scalar(varianceEpsilon)));
-        if (scale != null) {
-          inv = mul(inv, scale as Tensor);
-        }
-        const xNorm: Tensor4D = mul(sub(x as Tensor, mean as Tensor), inv);
-        return offset != null ? add(xNorm, offset as Tensor) : xNorm;
-      }
-      const dataFormat = 'NHWC';
-      const depth = x.shape[3];
-      const opAttrs = [
-        createTensorsTypeOpAttr('T', x.dtype),
-        {
-          name: 'epsilon',
-          type: backend.binding.TF_ATTR_FLOAT,
-          value: varianceEpsilon
-        },
-        {
-          name: 'data_format',
-          type: backend.binding.TF_ATTR_STRING,
-          value: dataFormat
-        },
-        {name: 'is_training', type: backend.binding.TF_ATTR_BOOL, value: false},
-      ];
-      const numOutputs = 5;
-
-      if (scale == null) {
-        scale = fill<Rank.R1>([depth], 1);
-      }
-      if (offset == null) {
-        offset = fill<Rank.R1>([depth], 0);
-      }
-      return backend.executeMultipleOutputs(
-          FusedBatchNorm, opAttrs, [x, scale, offset, mean, variance],
-          numOutputs)[0];
-    });
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/FusedConv2D.ts b/tfjs-master/tfjs-node/src/kernels/FusedConv2D.ts
deleted file mode 100644
index e676575ad..000000000
--- a/tfjs-master/tfjs-node/src/kernels/FusedConv2D.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {add, backend_util, FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs, KernelConfig, Tensor} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-import {conv2dImpl} from './Conv2D';
-
-export const fusedConv2DConfig: KernelConfig = {
-  kernelName: FusedConv2D,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, filter, bias, preluActivationWeights} =
-        args.inputs as FusedConv2DInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {
-      strides,
-      pad,
-      dataFormat,
-      dilations,
-      dimRoundingMode,
-      activation,
-      leakyreluAlpha
-    } = args.attrs as unknown as FusedConv2DAttrs;
-
-    if (dataFormat !== 'NHWC') {
-      throw new Error(
-          `Node backend FusedConv2D does not support dataFormat:'` +
-          `${dataFormat}'. Please use 'NHWC'.`);
-    }
-
-    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
-    const convInfo = backend_util.computeConv2DInfo(
-        x.shape as [number, number, number, number],
-        filter.shape as [number, number, number, number], strides, dilations,
-        pad, dimRoundingMode, false /* depthwise */, $dataFormat);
-
-    let result = conv2dImpl(x, filter, convInfo, backend);
-
-    const toDispose = [];
-    if (bias != null) {
-      toDispose.push(result);
-      result = add(result, bias as Tensor);
-    }
-
-    const temp = result;
-    result = backend.applyActivation(
-        result, activation, preluActivationWeights as Tensor, leakyreluAlpha);
-    if (temp !== result) {
-      toDispose.push(temp);
-    }
-
-    toDispose.forEach(t => t.dispose());
-    return result;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/FusedDepthwiseConv2D.ts b/tfjs-master/tfjs-node/src/kernels/FusedDepthwiseConv2D.ts
deleted file mode 100644
index 53c569f9d..000000000
--- a/tfjs-master/tfjs-node/src/kernels/FusedDepthwiseConv2D.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {add, backend_util, FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs, KernelConfig, Tensor} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-import {depthwiseConv2dNativeImpl} from './DepthwiseConv2dNative';
-
-export const fusedDepthwiseConv2DConfig: KernelConfig = {
-  kernelName: FusedDepthwiseConv2D,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, filter, bias, preluActivationWeights} =
-        args.inputs as FusedDepthwiseConv2DInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {
-      strides,
-      pad,
-      dilations,
-      dimRoundingMode,
-      activation,
-      leakyreluAlpha
-    } = args.attrs as unknown as FusedDepthwiseConv2DAttrs;
-
-    let $dilations = dilations;
-    if ($dilations == null) {
-      $dilations = [1, 1];
-    }
-
-    const convInfo = backend_util.computeConv2DInfo(
-        x.shape as [number, number, number, number],
-        filter.shape as [number, number, number, number], strides, $dilations,
-        pad, dimRoundingMode, true /* depthwise */);
-
-    let result = depthwiseConv2dNativeImpl(x, filter, convInfo, backend);
-
-    const toDispose = [];
-    if (bias != null) {
-      toDispose.push(result);
-      result = add(result, bias as Tensor);
-    }
-
-    const temp = result;
-    result = backend.applyActivation(
-        result, activation, preluActivationWeights as Tensor, leakyreluAlpha);
-    if (temp !== result) {
-      toDispose.push(temp);
-    }
-
-    toDispose.forEach(t => t.dispose());
-    return result;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/GatherNd.ts b/tfjs-master/tfjs-node/src/kernels/GatherNd.ts
deleted file mode 100644
index 326b4eaf2..000000000
--- a/tfjs-master/tfjs-node/src/kernels/GatherNd.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GatherNd, GatherNdInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const gatherNdConfig: KernelConfig = {
-  kernelName: GatherNd,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {params, indices} = args.inputs as GatherNdInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('Tparams', params.dtype),
-      createTensorsTypeOpAttr('Tindices', 'int32')
-    ];
-    return backend.executeSingleOutput(GatherNd, opAttrs, [params, indices]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/GatherV2.ts b/tfjs-master/tfjs-node/src/kernels/GatherV2.ts
deleted file mode 100644
index 68cee7215..000000000
--- a/tfjs-master/tfjs-node/src/kernels/GatherV2.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, GatherV2, GatherV2Attrs, GatherV2Inputs, KernelConfig, scalar, Tensor, TypedArray, util} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const gatherV2Config: KernelConfig = {
-  kernelName: GatherV2,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, indices} = args.inputs as GatherV2Inputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis, batchDims} = args.attrs as unknown as GatherV2Attrs;
-
-    // Throw error when any index is out of bound.
-    const indicesVals = backend.readSync(indices.dataId) as TypedArray;
-    const axisDim = x.shape[axis];
-    for (let i = 0; i < indicesVals.length; ++i) {
-      const index = indicesVals[i];
-      util.assert(
-          index <= axisDim - 1 && index >= 0,
-          () => `GatherV2: the index value ${index} is not in [0, ${
-              axisDim - 1}]`);
-    }
-
-    // validate the inputs
-    backend_util.segment_util.collectGatherOpShapeInfo(
-        x as Tensor, indices as Tensor, axis, batchDims);
-
-    const axisTensor = scalar(axis, 'int32');
-    const opAttrs = [
-      {name: 'batch_dims', type: backend.binding.TF_ATTR_INT, value: batchDims},
-      createTensorsTypeOpAttr('Tparams', x.dtype),
-      createTensorsTypeOpAttr('Tindices', indices.dtype),
-      createTensorsTypeOpAttr('Taxis', 'int32')
-    ];
-
-    const res = backend.executeSingleOutput(
-        GatherV2, opAttrs, [x, indices, axisTensor]);
-    axisTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Greater.ts b/tfjs-master/tfjs-node/src/kernels/Greater.ts
deleted file mode 100644
index 7effb2e23..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Greater.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, Greater, GreaterInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const greaterConfig: KernelConfig = {
-  kernelName: Greater,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as GreaterInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(Greater, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/GreaterEqual.ts b/tfjs-master/tfjs-node/src/kernels/GreaterEqual.ts
deleted file mode 100644
index 7f70d2961..000000000
--- a/tfjs-master/tfjs-node/src/kernels/GreaterEqual.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, GreaterEqual, GreaterEqualInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const greaterEqualConfig: KernelConfig = {
-  kernelName: GreaterEqual,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as GreaterEqualInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(GreaterEqual, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/IFFT.ts b/tfjs-master/tfjs-node/src/kernels/IFFT.ts
deleted file mode 100644
index 6ca476d9f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/IFFT.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IFFT, IFFTInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-// tslint:disable-next-line: variable-name
-export const IFFTConfig: KernelConfig = {
-  kernelName: IFFT,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {input} = args.inputs as IFFTInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr('Tcomplex', input.dtype)];
-    return backend.executeSingleOutput(IFFT, opAttrs, [input]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Identity.ts b/tfjs-master/tfjs-node/src/kernels/Identity.ts
deleted file mode 100644
index 0e55493a5..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Identity.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Identity, IdentityInputs, KernelConfig} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const identityConfig: KernelConfig = {
-  kernelName: Identity,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as IdentityInputs;
-    // Backend needs to track refCount for the dataId for identity op
-    (args.backend as NodeJSKernelBackend).incRef(x.dataId);
-    return {dataId: x.dataId, shape: x.shape, dtype: x.dtype};
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Imag.ts b/tfjs-master/tfjs-node/src/kernels/Imag.ts
deleted file mode 100644
index 99b2e6b9a..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Imag.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Imag, ImagInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const imagConfig: KernelConfig = {
-  kernelName: Imag,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {input} = args.inputs as ImagInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      {
-        name: 'T',
-        type: backend.binding.TF_ATTR_TYPE,
-        value: backend.binding.TF_COMPLEX64
-      },
-      {
-        name: 'Tout',
-        type: backend.binding.TF_ATTR_TYPE,
-        value: backend.binding.TF_FLOAT
-      }
-    ];
-    const inputs = [input];
-    return backend.executeSingleOutput(Imag, opAttrs, inputs);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/IsFinite.ts b/tfjs-master/tfjs-node/src/kernels/IsFinite.ts
deleted file mode 100644
index 522a0ea62..000000000
--- a/tfjs-master/tfjs-node/src/kernels/IsFinite.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsFinite, IsFiniteInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const isFiniteConfig: KernelConfig = {
-  kernelName: IsFinite,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as IsFiniteInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(IsFinite, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/IsInf.ts b/tfjs-master/tfjs-node/src/kernels/IsInf.ts
deleted file mode 100644
index 4b1eead48..000000000
--- a/tfjs-master/tfjs-node/src/kernels/IsInf.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsInf, IsInfInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const isInfConfig: KernelConfig = {
-  kernelName: IsInf,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as IsInfInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(IsInf, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/IsNan.ts b/tfjs-master/tfjs-node/src/kernels/IsNan.ts
deleted file mode 100644
index 36a9c339f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/IsNan.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {IsNan, IsNanInputs, KernelConfig} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const isNanConfig: KernelConfig = {
-  kernelName: IsNan,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as IsNanInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(IsNan, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/LRN.ts b/tfjs-master/tfjs-node/src/kernels/LRN.ts
deleted file mode 100644
index 308045a25..000000000
--- a/tfjs-master/tfjs-node/src/kernels/LRN.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LRN, LRNAttrs, LRNInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-// tslint:disable-next-line: variable-name
-export const LRNConfig: KernelConfig = {
-  kernelName: LRN,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as LRNInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {depthRadius, bias, alpha, beta} = args.attrs as unknown as LRNAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {
-        name: 'depth_radius',
-        type: backend.binding.TF_ATTR_INT,
-        value: depthRadius
-      },
-      {name: 'bias', type: backend.binding.TF_ATTR_FLOAT, value: bias},
-      {name: 'alpha', type: backend.binding.TF_ATTR_FLOAT, value: alpha},
-      {name: 'beta', type: backend.binding.TF_ATTR_FLOAT, value: beta},
-    ];
-    return backend.executeSingleOutput(LRN, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/LRNGrad.ts b/tfjs-master/tfjs-node/src/kernels/LRNGrad.ts
deleted file mode 100644
index 398753c88..000000000
--- a/tfjs-master/tfjs-node/src/kernels/LRNGrad.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LRNGrad, LRNGradAttrs, LRNGradInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-// tslint:disable-next-line: variable-name
-export const LRNGradConfig: KernelConfig = {
-  kernelName: LRNGrad,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, y, dy} = args.inputs as LRNGradInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {depthRadius, bias, alpha, beta} =
-        args.attrs as unknown as LRNGradAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', dy.dtype),
-      {
-        name: 'depth_radius',
-        type: backend.binding.TF_ATTR_INT,
-        value: depthRadius
-      },
-      {name: 'bias', type: backend.binding.TF_ATTR_FLOAT, value: bias},
-      {name: 'alpha', type: backend.binding.TF_ATTR_FLOAT, value: alpha},
-      {name: 'beta', type: backend.binding.TF_ATTR_FLOAT, value: beta},
-    ];
-    return backend.executeSingleOutput(LRNGrad, opAttrs, [dy, x, y]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/LeakyRelu.ts b/tfjs-master/tfjs-node/src/kernels/LeakyRelu.ts
deleted file mode 100644
index bf54314ee..000000000
--- a/tfjs-master/tfjs-node/src/kernels/LeakyRelu.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LeakyRelu, LeakyReluAttrs, LeakyReluInputs, Tensor} from '@tensorflow/tfjs';
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const leakyReluConfig: KernelConfig = {
-  kernelName: LeakyRelu,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const inputs = args.inputs as LeakyReluInputs;
-    const attrs = args.attrs as unknown as LeakyReluAttrs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const x = inputs.x as Tensor;
-    const alpha = attrs.alpha;
-
-    const opAttrs = [
-      {name: 'alpha', type: backend.binding.TF_ATTR_FLOAT, value: alpha},
-      createTensorsTypeOpAttr('T', x.dtype)
-    ];
-
-    return backend.executeSingleOutput(LeakyRelu, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Less.ts b/tfjs-master/tfjs-node/src/kernels/Less.ts
deleted file mode 100644
index 9d45a6a7e..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Less.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Less, LessInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const lessConfig: KernelConfig = {
-  kernelName: Less,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as LessInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(Less, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/LessEqual.ts b/tfjs-master/tfjs-node/src/kernels/LessEqual.ts
deleted file mode 100644
index 01d24faac..000000000
--- a/tfjs-master/tfjs-node/src/kernels/LessEqual.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, LessEqual, LessEqualInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const lessEqualConfig: KernelConfig = {
-  kernelName: LessEqual,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as LessEqualInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(LessEqual, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/LinSpace.ts b/tfjs-master/tfjs-node/src/kernels/LinSpace.ts
deleted file mode 100644
index 5f8fe7db5..000000000
--- a/tfjs-master/tfjs-node/src/kernels/LinSpace.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LinSpace, LinSpaceAttrs, scalar, tidy} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const linSpaceConfig: KernelConfig = {
-  kernelName: LinSpace,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const backend = args.backend as NodeJSKernelBackend;
-    const {start, stop, num} = args.attrs as unknown as LinSpaceAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', 'float32'),
-      createTensorsTypeOpAttr('Tidx', 'int32')
-    ];
-
-    return tidy(() => {
-      const inputs = [
-        scalar(start, 'float32'), scalar(stop, 'float32'), scalar(num, 'int32')
-      ];
-      return backend.executeSingleOutput(LinSpace, opAttrs, inputs);
-    });
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Log.ts b/tfjs-master/tfjs-node/src/kernels/Log.ts
deleted file mode 100644
index 9d2db8c59..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Log.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Log, LogInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const logConfig: KernelConfig = {
-  kernelName: Log,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as LogInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Log, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Log1p.ts b/tfjs-master/tfjs-node/src/kernels/Log1p.ts
deleted file mode 100644
index ae49842dd..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Log1p.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Log1p, Log1pInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const log1pConfig: KernelConfig = {
-  kernelName: Log1p,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as Log1pInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Log1p, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/LogicalAnd.ts b/tfjs-master/tfjs-node/src/kernels/LogicalAnd.ts
deleted file mode 100644
index 9c9ab427d..000000000
--- a/tfjs-master/tfjs-node/src/kernels/LogicalAnd.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalAnd, LogicalAndInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const logicalAndConfig: KernelConfig = {
-  kernelName: LogicalAnd,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as LogicalAndInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleOutput(LogicalAnd, [], [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/LogicalNot.ts b/tfjs-master/tfjs-node/src/kernels/LogicalNot.ts
deleted file mode 100644
index 4d78164a7..000000000
--- a/tfjs-master/tfjs-node/src/kernels/LogicalNot.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalNot, LogicalNotInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const logicalNotConfig: KernelConfig = {
-  kernelName: LogicalNot,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as LogicalNotInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleOutput(LogicalNot, [], [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/LogicalOr.ts b/tfjs-master/tfjs-node/src/kernels/LogicalOr.ts
deleted file mode 100644
index 2da97e04c..000000000
--- a/tfjs-master/tfjs-node/src/kernels/LogicalOr.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, LogicalOr, LogicalOrInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const logicalOrConfig: KernelConfig = {
-  kernelName: LogicalOr,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as LogicalOrInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleOutput(LogicalOr, [], [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Max.ts b/tfjs-master/tfjs-node/src/kernels/Max.ts
deleted file mode 100644
index f207211a7..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Max.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Max, MaxAttrs, MaxInputs, tensor1d, util} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const maxConfig: KernelConfig = {
-  kernelName: Max,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as MaxInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {reductionIndices, keepDims} = args.attrs as unknown as MaxAttrs;
-
-    const axes = util.parseAxisParam(reductionIndices, x.shape);
-    const axesTensor = tensor1d(axes, 'int32');
-    const res = backend.executeSingleOutput(
-        Max, backend.createReductionOpAttrs(x, keepDims), [x, axesTensor]);
-    axesTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/MaxPool.ts b/tfjs-master/tfjs-node/src/kernels/MaxPool.ts
deleted file mode 100644
index b0733904a..000000000
--- a/tfjs-master/tfjs-node/src/kernels/MaxPool.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, MaxPool, MaxPoolAttrs, MaxPoolInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const maxPoolConfig: KernelConfig = {
-  kernelName: MaxPool,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as MaxPoolInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {filterSize, strides, pad, dimRoundingMode} =
-        args.attrs as unknown as MaxPoolAttrs;
-
-    const convInfo = backend_util.computePool2DInfo(
-        x.shape as [number, number, number, number], filterSize, strides,
-        1 /* dilations */, pad, dimRoundingMode);
-
-    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-      throw new Error(
-          `TF Backend supports only 'valid' and 'same' padding ` +
-          `while padding was ${convInfo.padInfo.type}`);
-    }
-    const ksize = [1, convInfo.filterHeight, convInfo.filterWidth, 1];
-    const $strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-    const padding = convInfo.padInfo.type;
-    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {name: 'ksize', type: backend.binding.TF_ATTR_INT, value: ksize},
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: dataFormat
-      }
-    ];
-    return backend.executeSingleOutput(MaxPool, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/MaxPool3D.ts b/tfjs-master/tfjs-node/src/kernels/MaxPool3D.ts
deleted file mode 100644
index 908ab839e..000000000
--- a/tfjs-master/tfjs-node/src/kernels/MaxPool3D.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const maxPool3DConfig: KernelConfig = {
-  kernelName: MaxPool3D,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as MaxPool3DInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {filterSize, strides, pad, dataFormat, dimRoundingMode} =
-        args.attrs as unknown as MaxPool3DAttrs;
-
-    const convInfo = backend_util.computePool3DInfo(
-        x.shape as [number, number, number, number, number], filterSize,
-        strides, 1 /* dilations */, pad, dimRoundingMode, dataFormat);
-    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-      throw new Error(
-          `TF Backend supports only 'valid' and 'same' padding ` +
-          `while padding was ${convInfo.padInfo.type}`);
-    }
-    const ksize = [
-      1, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, 1
-    ];
-    const $strides = [
-      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
-    ];
-    const padding = convInfo.padInfo.type;
-    const $dataFormat =
-        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      {name: 'ksize', type: backend.binding.TF_ATTR_INT, value: ksize},
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: $dataFormat
-      }
-    ];
-    return backend.executeSingleOutput(MaxPool3D, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/MaxPool3DGrad.ts b/tfjs-master/tfjs-node/src/kernels/MaxPool3DGrad.ts
deleted file mode 100644
index a1a949ea3..000000000
--- a/tfjs-master/tfjs-node/src/kernels/MaxPool3DGrad.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, MaxPool3DGrad, MaxPool3DGradAttrs, MaxPool3DGradInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const maxPool3DGradConfig: KernelConfig = {
-  kernelName: MaxPool3DGrad,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {dy, input, output} = args.inputs as MaxPool3DGradInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {filterSize, strides, pad, dimRoundingMode} =
-        args.attrs as unknown as MaxPool3DGradAttrs;
-
-    const convInfo = backend_util.computePool3DInfo(
-        input.shape as [number, number, number, number, number], filterSize,
-        strides, 1 /* dilations */, pad, dimRoundingMode);
-
-    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-      throw new Error(
-          `TF Backend supports only 'valid' and 'same' padding ` +
-          `while padding type was ${convInfo.padInfo.type}`);
-    }
-    const ksize = [
-      1, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, 1
-    ];
-    const $strides = [
-      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
-    ];
-    const padding = convInfo.padInfo.type;
-    const dataFormat =
-        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', input.dtype),
-      {name: 'ksize', type: backend.binding.TF_ATTR_INT, value: ksize},
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding},
-      {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: dataFormat
-      },
-    ];
-    return backend.executeSingleOutput(
-        MaxPool3DGrad, opAttrs, [input, output, dy]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/MaxPoolGrad.ts b/tfjs-master/tfjs-node/src/kernels/MaxPoolGrad.ts
deleted file mode 100644
index 32772764a..000000000
--- a/tfjs-master/tfjs-node/src/kernels/MaxPoolGrad.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, MaxPoolGrad, MaxPoolGradAttrs, MaxPoolGradInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const maxPoolGradConfig: KernelConfig = {
-  kernelName: MaxPoolGrad,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {dy, input, output} = args.inputs as MaxPoolGradInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {filterSize, strides, pad, dimRoundingMode} =
-        args.attrs as unknown as MaxPoolGradAttrs;
-
-    const convInfo = backend_util.computePool2DInfo(
-        input.shape as [number, number, number, number], filterSize, strides,
-        1 /* dilations */, pad, dimRoundingMode);
-
-    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
-      throw new Error(
-          `TF Backend supports only 'valid' and 'same' padding ` +
-          `while padding type was ${convInfo.padInfo.type}`);
-    }
-    const ksize = [1, convInfo.filterHeight, convInfo.filterWidth, 1];
-    const $strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
-    const padding = convInfo.padInfo.type;
-    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', input.dtype),
-      {name: 'ksize', type: backend.binding.TF_ATTR_INT, value: ksize},
-      {name: 'strides', type: backend.binding.TF_ATTR_INT, value: $strides},
-      {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding},
-      {
-        name: 'data_format',
-        type: backend.binding.TF_ATTR_STRING,
-        value: dataFormat
-      },
-    ];
-    return backend.executeSingleOutput(
-        MaxPoolGrad, opAttrs, [input, output, dy]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Maximum.ts b/tfjs-master/tfjs-node/src/kernels/Maximum.ts
deleted file mode 100644
index 94b06e65c..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Maximum.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Maximum, MaximumInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const maximumConfig: KernelConfig = {
-  kernelName: Maximum,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as MaximumInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(Maximum, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Mean.ts b/tfjs-master/tfjs-node/src/kernels/Mean.ts
deleted file mode 100644
index 9bfebe323..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Mean.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {cast, KernelConfig, Mean, MeanAttrs, MeanInputs, Tensor, tensor1d, util} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const meanConfig: KernelConfig = {
-  kernelName: Mean,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as MeanInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis, keepDims} = args.attrs as unknown as MeanAttrs;
-    const axes = util.parseAxisParam(axis, x.shape);
-    const axesTensor = tensor1d(axes, 'int32');
-
-    // Cast to float32 to match existing tfjs implementation/tests.
-    const asFloat32 = cast(x as Tensor, 'float32');
-    const res = backend.executeSingleOutput(
-        Mean, backend.createReductionOpAttrs(x, keepDims),
-        [asFloat32, axesTensor]);
-
-    asFloat32.dispose();
-    axesTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Min.ts b/tfjs-master/tfjs-node/src/kernels/Min.ts
deleted file mode 100644
index 3de358e8f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Min.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Min, MinAttrs, MinInputs, tensor1d, util} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const minConfig: KernelConfig = {
-  kernelName: Min,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as MinInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis, keepDims} = args.attrs as unknown as MinAttrs;
-    const axes = util.parseAxisParam(axis, x.shape);
-    const axesTensor = tensor1d(axes, 'int32');
-    const res = backend.executeSingleOutput(
-        Min, backend.createReductionOpAttrs(x, keepDims), [x, axesTensor]);
-    axesTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Minimum.ts b/tfjs-master/tfjs-node/src/kernels/Minimum.ts
deleted file mode 100644
index 8b6805a54..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Minimum.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Minimum, MinimumInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const minimumConfig: KernelConfig = {
-  kernelName: Minimum,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as MinimumInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(Minimum, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/MirrorPad.ts b/tfjs-master/tfjs-node/src/kernels/MirrorPad.ts
deleted file mode 100644
index 97dbb5392..000000000
--- a/tfjs-master/tfjs-node/src/kernels/MirrorPad.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, MirrorPad, MirrorPadAttrs, MirrorPadInputs, tensor2d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const mirrorPadConfig: KernelConfig = {
-  kernelName: MirrorPad,
-  backendName: 'tensorflow',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {x} = inputs as MirrorPadInputs;
-    const {paddings, mode} = attrs as unknown as MirrorPadAttrs;
-
-    const nodeBackend = backend as NodeJSKernelBackend;
-
-    const paddingsTensor = tensor2d(paddings, [paddings.length, 2], 'int32');
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tpaddings', paddingsTensor.dtype), {
-        name: 'mode',
-        type: nodeBackend.binding.TF_ATTR_STRING,
-        value: mode.toUpperCase()
-      }
-    ];
-
-    const output = nodeBackend.executeSingleOutput(
-        'MirrorPad', opAttrs, [x, paddingsTensor]);
-
-    paddingsTensor.dispose();
-
-    return output;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Mod.ts b/tfjs-master/tfjs-node/src/kernels/Mod.ts
deleted file mode 100644
index 5999d5bc6..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Mod.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Mod, ModInputs} from '@tensorflow/tfjs';
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const modConfig: KernelConfig = {
-  kernelName: Mod,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as ModInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr('T', a.dtype)];
-    return backend.executeSingleOutput('FloorMod', opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Multinomial.ts b/tfjs-master/tfjs-node/src/kernels/Multinomial.ts
deleted file mode 100644
index 835317d9b..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Multinomial.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Multinomial, MultinomialAttrs, MultinomialInputs, scalar} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const multinomialConfig: KernelConfig = {
-  kernelName: Multinomial,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {logits} = args.inputs as MultinomialInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {numSamples, seed, normalized} =
-        args.attrs as unknown as MultinomialAttrs;
-
-    if (normalized) {
-      throw new Error(
-          'TF Node backend does not support normalized logits ' +
-          'passed to multinomial');
-    }
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', logits.dtype),
-      createTensorsTypeOpAttr('output_dtype', 'int32'),
-      {name: 'seed', type: backend.binding.TF_ATTR_INT, value: seed},
-      {name: 'seed2', type: backend.binding.TF_ATTR_INT, value: seed * seed},
-    ];
-    const numSamplesTensor = scalar(numSamples, 'int32');
-    const res = backend.executeSingleOutput(
-        Multinomial, opAttrs, [logits, numSamplesTensor]);
-    numSamplesTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Multiply.ts b/tfjs-master/tfjs-node/src/kernels/Multiply.ts
deleted file mode 100644
index 2252e30a5..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Multiply.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Multiply, MultiplyInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const multiplyConfig: KernelConfig = {
-  kernelName: Multiply,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as MultiplyInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput('Mul', opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Neg.ts b/tfjs-master/tfjs-node/src/kernels/Neg.ts
deleted file mode 100644
index 83736ed6a..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Neg.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Neg, NegInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const negConfig: KernelConfig = {
-  kernelName: Neg,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as NegInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Neg, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/NonMaxSuppressionV3.ts b/tfjs-master/tfjs-node/src/kernels/NonMaxSuppressionV3.ts
deleted file mode 100644
index 106b61af4..000000000
--- a/tfjs-master/tfjs-node/src/kernels/NonMaxSuppressionV3.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, NonMaxSuppressionV3, NonMaxSuppressionV3Attrs, NonMaxSuppressionV3Inputs, scalar} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const nonMaxSuppressionV3Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV3,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {boxes, scores} = args.inputs as NonMaxSuppressionV3Inputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {maxOutputSize, iouThreshold, scoreThreshold} =
-        args.attrs as unknown as NonMaxSuppressionV3Attrs;
-
-    const opAttrs = [createTensorsTypeOpAttr('T', boxes.dtype)];
-
-    const maxOutputSizeTensor = scalar(maxOutputSize, 'int32');
-    const iouThresholdTensor = scalar(iouThreshold);
-    const scoreThresholdTensor = scalar(scoreThreshold);
-    const res = backend.executeSingleOutput(NonMaxSuppressionV3, opAttrs, [
-      boxes, scores, maxOutputSizeTensor, iouThresholdTensor,
-      scoreThresholdTensor
-    ]);
-    maxOutputSizeTensor.dispose();
-    iouThresholdTensor.dispose();
-    scoreThresholdTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/NonMaxSuppressionV4.ts b/tfjs-master/tfjs-node/src/kernels/NonMaxSuppressionV4.ts
deleted file mode 100644
index 67763d697..000000000
--- a/tfjs-master/tfjs-node/src/kernels/NonMaxSuppressionV4.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs, scalar, Tensor1D, Tensor2D} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-// TODO(nsthorat, dsmilkov): Remove dependency on tensors, use dataId.
-export const nonMaxSuppressionV4Config: KernelConfig = {
-  kernelName: NonMaxSuppressionV4,
-  backendName: 'tensorflow',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {boxes, scores} = inputs as NonMaxSuppressionV4Inputs;
-    const {maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize} =
-        attrs as unknown as NonMaxSuppressionV4Attrs;
-    const maxOutputSizeTensor = scalar(maxOutputSize, 'int32');
-    const iouThresholdTensor = scalar(iouThreshold, 'float32');
-    const scoreThresholdTensor = scalar(scoreThreshold, 'float32');
-
-    const nodeBackend = backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', boxes.dtype),
-      createTensorsTypeOpAttr('T_threshold', 'float32'), {
-        name: 'pad_to_max_output_size',
-        type: nodeBackend.binding.TF_ATTR_BOOL,
-        value: padToMaxOutputSize
-      }
-    ];
-
-    const [selectedIndices, validOutputs] = nodeBackend.executeMultipleOutputs(
-        'NonMaxSuppressionV4', opAttrs,
-        [
-          boxes as Tensor2D, scores as Tensor1D, maxOutputSizeTensor,
-          iouThresholdTensor, scoreThresholdTensor
-        ],
-        2);
-
-    maxOutputSizeTensor.dispose();
-    iouThresholdTensor.dispose();
-    scoreThresholdTensor.dispose();
-
-    return [selectedIndices, validOutputs];
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/NonMaxSuppressionV5.ts b/tfjs-master/tfjs-node/src/kernels/NonMaxSuppressionV5.ts
deleted file mode 100644
index 51cd0a6df..000000000
--- a/tfjs-master/tfjs-node/src/kernels/NonMaxSuppressionV5.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, NamedAttrMap, NamedTensorInfoMap, scalar, Tensor1D, Tensor2D, TensorInfo} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-interface NonMaxSuppressionWithScoreInputs extends NamedTensorInfoMap {
-  boxes: TensorInfo;
-  scores: TensorInfo;
-}
-
-interface NonMaxSuppressionWithScoreAttrs extends NamedAttrMap {
-  maxOutputSize: number;
-  iouThreshold: number;
-  scoreThreshold: number;
-  softNmsSigma: number;
-}
-
-// TODO(nsthorat, dsmilkov): Remove dependency on tensors, use dataId.
-export const nonMaxSuppressionV5Config: KernelConfig = {
-  kernelName: 'NonMaxSuppressionV5',
-  backendName: 'tensorflow',
-  kernelFunc: ({inputs, backend, attrs}) => {
-    const {boxes, scores} = inputs as NonMaxSuppressionWithScoreInputs;
-    const {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} =
-        attrs as NonMaxSuppressionWithScoreAttrs;
-    const maxOutputSizeTensor = scalar(maxOutputSize, 'int32');
-    const iouThresholdTensor = scalar(iouThreshold);
-    const scoreThresholdTensor = scalar(scoreThreshold);
-    const softNmsSigmaTensor = scalar(softNmsSigma);
-    const opAttrs = [createTensorsTypeOpAttr('T', boxes.dtype)];
-
-    const nodeBackend = backend as NodeJSKernelBackend;
-
-    const [selectedIndices, selectedScores, validOutputs] =
-        nodeBackend.executeMultipleOutputs(
-            'NonMaxSuppressionV5', opAttrs,
-            [
-              boxes as Tensor2D, scores as Tensor1D, maxOutputSizeTensor,
-              iouThresholdTensor, scoreThresholdTensor, softNmsSigmaTensor
-            ],
-            3);
-
-    maxOutputSizeTensor.dispose();
-    iouThresholdTensor.dispose();
-    scoreThresholdTensor.dispose();
-    softNmsSigmaTensor.dispose();
-    validOutputs.dispose();
-
-    return [selectedIndices, selectedScores];
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/NotEqual.ts b/tfjs-master/tfjs-node/src/kernels/NotEqual.ts
deleted file mode 100644
index 174a125f8..000000000
--- a/tfjs-master/tfjs-node/src/kernels/NotEqual.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, NotEqual, NotEqualInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const notEqualConfig: KernelConfig = {
-  kernelName: NotEqual,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as NotEqualInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(NotEqual, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/OneHot.ts b/tfjs-master/tfjs-node/src/kernels/OneHot.ts
deleted file mode 100644
index 5d33d4192..000000000
--- a/tfjs-master/tfjs-node/src/kernels/OneHot.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, OneHot, OneHotAttrs, OneHotInputs, scalar} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const oneHotConfig: KernelConfig = {
-  kernelName: OneHot,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {indices} = args.inputs as OneHotInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {dtype, depth, onValue, offValue} =
-        args.attrs as unknown as OneHotAttrs;
-
-    const depthTensor = scalar(depth, 'int32');
-    const onValueTensor = scalar(onValue, dtype);
-    const offValueTensor = scalar(offValue, dtype);
-
-    const opAttrs = [
-      {name: 'axis', type: backend.binding.TF_ATTR_INT, value: -1},
-      createTensorsTypeOpAttr('T', dtype),
-      createTensorsTypeOpAttr('TI', indices.dtype)
-    ];
-
-    const res = backend.executeSingleOutput(
-        OneHot, opAttrs, [indices, depthTensor, onValueTensor, offValueTensor]);
-    depthTensor.dispose();
-    onValueTensor.dispose();
-    offValueTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/OnesLike.ts b/tfjs-master/tfjs-node/src/kernels/OnesLike.ts
deleted file mode 100644
index 3e71f3f67..000000000
--- a/tfjs-master/tfjs-node/src/kernels/OnesLike.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, OnesLike, OnesLikeInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const onesLikeConfig: KernelConfig = {
-  kernelName: OnesLike,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as OnesLikeInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [{
-      name: 'T',
-      type: backend.binding.TF_ATTR_TYPE,
-      value: backend.getDTypeInteger(x.dtype)
-    }];
-    return backend.executeSingleOutput(OnesLike, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Pack.ts b/tfjs-master/tfjs-node/src/kernels/Pack.ts
deleted file mode 100644
index 7015eec76..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Pack.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, KernelFunc, Pack, PackAttrs, PackInputs, Tensor, TensorInfo} from '@tensorflow/tfjs';
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export function pack(
-    args: {inputs: PackInputs, backend: NodeJSKernelBackend, attrs: PackAttrs}):
-    TensorInfo {
-  const {inputs, backend, attrs} = args;
-  const {axis} = attrs;
-
-  const opAttrs = [
-    {name: 'N', type: backend.binding.TF_ATTR_INT, value: inputs.length},
-    createTensorsTypeOpAttr('T', inputs as Tensor[]),
-    {name: 'axis', type: backend.binding.TF_ATTR_INT, value: axis}
-  ];
-
-  const res = backend.executeSingleOutput(Pack, opAttrs, inputs);
-  return res;
-}
-
-export const packConfig: KernelConfig = {
-  kernelName: Pack,
-  backendName: 'tensorflow',
-  kernelFunc: pack as unknown as KernelFunc
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/PadV2.ts b/tfjs-master/tfjs-node/src/kernels/PadV2.ts
deleted file mode 100644
index ca4a0470d..000000000
--- a/tfjs-master/tfjs-node/src/kernels/PadV2.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, PadV2, PadV2Attrs, PadV2Inputs, scalar, tensor2d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const padV2Config: KernelConfig = {
-  kernelName: PadV2,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as PadV2Inputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {paddings, constantValue} = args.attrs as unknown as PadV2Attrs;
-
-    // Bind tensor values
-    const paddingsTensor = tensor2d(paddings, [paddings.length, 2], 'int32');
-    const constantTensor = scalar(constantValue, x.dtype);
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tpaddings', paddingsTensor.dtype)
-    ];
-
-    const res = backend.executeSingleOutput(
-        PadV2, opAttrs, [x, paddingsTensor, constantTensor]);
-
-    paddingsTensor.dispose();
-    constantTensor.dispose();
-
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Pow.ts b/tfjs-master/tfjs-node/src/kernels/Pow.ts
deleted file mode 100644
index aca2c005b..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Pow.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, cast, KernelConfig, Pow, PowInputs, Tensor, tidy} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const powConfig: KernelConfig = {
-  kernelName: Pow,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as PowInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const dtype = backend_util.upcastType(a.dtype, b.dtype);
-    const opAttrs = [createTensorsTypeOpAttr('T', dtype)];
-    return tidy(() => {
-      return backend.executeSingleOutput(
-          Pow, opAttrs, [cast(a as Tensor, dtype), cast(b as Tensor, dtype)]);
-    });
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Prelu.ts b/tfjs-master/tfjs-node/src/kernels/Prelu.ts
deleted file mode 100644
index 84cd1c03d..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Prelu.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {abs, KernelConfig, Prelu, PreluInputs, relu, Tensor, tidy} from '@tensorflow/tfjs';
-
-export const preluConfig: KernelConfig = {
-  kernelName: Prelu,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const inputs = args.inputs as PreluInputs;
-    const x = inputs.x as Tensor;
-    const alpha = inputs.alpha as Tensor;
-
-    return tidy(() => {
-      const pos = relu(x);
-      const neg = alpha.mul(x.sub(abs(x))).mul(0.5);
-      return pos.add(neg);
-    });
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Prod.ts b/tfjs-master/tfjs-node/src/kernels/Prod.ts
deleted file mode 100644
index d73d086f9..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Prod.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Prod, ProdAttrs, ProdInputs, tensor1d, util} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const prodConfig: KernelConfig = {
-  kernelName: Prod,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ProdInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis, keepDims} = args.attrs as unknown as ProdAttrs;
-    const axes = util.parseAxisParam(axis, x.shape);
-    const axesTensor = tensor1d(axes, 'int32');
-    const opAttrs = [
-      {name: 'keep_dims', type: backend.binding.TF_ATTR_BOOL, value: keepDims},
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tidx', 'int32')
-    ];
-    const res = backend.executeSingleOutput(Prod, opAttrs, [x, axesTensor]);
-    axesTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Range.ts b/tfjs-master/tfjs-node/src/kernels/Range.ts
deleted file mode 100644
index 134d0f1f2..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Range.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Range, RangeAttrs, scalar, zeros} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const rangeConfig: KernelConfig = {
-  kernelName: Range,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const backend = args.backend as NodeJSKernelBackend;
-    const {start, stop, dtype} = args.attrs as unknown as RangeAttrs;
-    let {step} = args.attrs as unknown as RangeAttrs;
-
-    // TensorFlow.js specific allowances
-    const sameStartStop = start === stop;
-    const increasingRangeNegativeStep = start < stop && step < 0;
-    const decreasingRangePositiveStep = stop < start && step > 1;
-
-    if (sameStartStop || increasingRangeNegativeStep ||
-        decreasingRangePositiveStep) {
-      return zeros([0], dtype);
-    }
-
-    if (stop < start && step === 1) {
-      // Auto adjust the step's sign if it hasn't been set
-      // (or was set to 1)
-      step = -1;
-    }
-
-    const opAttrs = [createTensorsTypeOpAttr('Tidx', dtype)];
-    const startTensor = scalar(start, dtype);
-    const stopTensor = scalar(stop, dtype);
-    const stepTensor = scalar(step, dtype);
-    const res = backend.executeSingleOutput(
-        Range, opAttrs, [startTensor, stopTensor, stepTensor]);
-
-    startTensor.dispose();
-    stopTensor.dispose();
-    stepTensor.dispose();
-
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Real.ts b/tfjs-master/tfjs-node/src/kernels/Real.ts
deleted file mode 100644
index a4064923f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Real.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Real, RealInputs, Tensor} from '@tensorflow/tfjs';
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const realConfig: KernelConfig = {
-  kernelName: Real,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {input} = args.inputs as RealInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', input as Tensor), {
-        name: 'Tout',
-        type: backend.binding.TF_ATTR_TYPE,
-        value: backend.binding.TF_FLOAT
-      }
-    ];
-    const inputs = [input];
-    return backend.executeSingleOutput(Real, opAttrs, inputs);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/RealDiv.ts b/tfjs-master/tfjs-node/src/kernels/RealDiv.ts
deleted file mode 100644
index 872747ec9..000000000
--- a/tfjs-master/tfjs-node/src/kernels/RealDiv.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, RealDiv, RealDivInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const realDivConfig: KernelConfig = {
-  kernelName: RealDiv,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as RealDivInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(RealDiv, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Reciprocal.ts b/tfjs-master/tfjs-node/src/kernels/Reciprocal.ts
deleted file mode 100644
index 76ab5c3d1..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Reciprocal.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Reciprocal, ReciprocalInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const reciprocalConfig: KernelConfig = {
-  kernelName: Reciprocal,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ReciprocalInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Reciprocal, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Relu.ts b/tfjs-master/tfjs-node/src/kernels/Relu.ts
deleted file mode 100644
index 170a56a48..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Relu.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Relu, ReluInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const reluConfig: KernelConfig = {
-  kernelName: Relu,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ReluInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Relu, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Relu6.ts b/tfjs-master/tfjs-node/src/kernels/Relu6.ts
deleted file mode 100644
index db266545d..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Relu6.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Relu6, Relu6Inputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const relu6Config: KernelConfig = {
-  kernelName: Relu6,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as Relu6Inputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Relu6, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Reshape.ts b/tfjs-master/tfjs-node/src/kernels/Reshape.ts
deleted file mode 100644
index 64a8877a4..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Reshape.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Reshape, ReshapeAttrs, ReshapeInputs, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const reshapeConfig: KernelConfig = {
-  kernelName: Reshape,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ReshapeInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {shape} = args.attrs as unknown as ReshapeAttrs;
-
-    const shapeTensor = tensor1d(shape, 'int32');
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tshape', shapeTensor.dtype)
-    ];
-    const res = backend.executeSingleOutput(Reshape, opAttrs, [x, shapeTensor]);
-    shapeTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ResizeBilinear.ts b/tfjs-master/tfjs-node/src/kernels/ResizeBilinear.ts
deleted file mode 100644
index 32d50b53c..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ResizeBilinear.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const resizeBilinearConfig: KernelConfig = {
-  kernelName: ResizeBilinear,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {images} = args.inputs as ResizeBilinearInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {alignCorners, halfPixelCenters, size} =
-        args.attrs as unknown as ResizeBilinearAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', images.dtype),
-      {
-        name: 'align_corners',
-        type: backend.binding.TF_ATTR_BOOL,
-        value: alignCorners
-      },
-      {
-        name: 'half_pixel_centers',
-        type: backend.binding.TF_ATTR_BOOL,
-        value: halfPixelCenters
-      },
-    ];
-    const [newHeight, newWidth] = size;
-    const sizeTensor = tensor1d([newHeight, newWidth], 'int32');
-    const ret = backend.executeSingleOutput(
-        ResizeBilinear, opAttrs, [images, sizeTensor]);
-    sizeTensor.dispose();
-    return ret;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ResizeBilinearGrad.ts b/tfjs-master/tfjs-node/src/kernels/ResizeBilinearGrad.ts
deleted file mode 100644
index 6b5ed2c67..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ResizeBilinearGrad.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, ResizeBilinearGrad, ResizeBilinearGradAttrs, ResizeBilinearGradInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const resizeBilinearGradConfig: KernelConfig = {
-  kernelName: ResizeBilinearGrad,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {images, dy} = args.inputs as ResizeBilinearGradInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {alignCorners} = args.attrs as unknown as ResizeBilinearGradAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', images.dtype), {
-        name: 'align_corners',
-        type: backend.binding.TF_ATTR_BOOL,
-        value: alignCorners
-      }
-    ];
-    return backend.executeSingleOutput(
-        ResizeBilinearGrad, opAttrs, [dy, images]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ResizeNearestNeighbor.ts b/tfjs-master/tfjs-node/src/kernels/ResizeNearestNeighbor.ts
deleted file mode 100644
index 4fd205982..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ResizeNearestNeighbor.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const resizeNearestNeighborConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighbor,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {images} = args.inputs as ResizeNearestNeighborInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {alignCorners, halfPixelCenters, size} =
-        args.attrs as unknown as ResizeNearestNeighborAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', images.dtype),
-      {
-        name: 'align_corners',
-        type: backend.binding.TF_ATTR_BOOL,
-        value: alignCorners
-      },
-      {
-        name: 'half_pixel_centers',
-        type: backend.binding.TF_ATTR_BOOL,
-        value: halfPixelCenters
-      },
-    ];
-    const [newHeight, newWidth] = size;
-    const sizeTensor = tensor1d([newHeight, newWidth], 'int32');
-    const res = backend.executeSingleOutput(
-        ResizeNearestNeighbor, opAttrs, [images, sizeTensor]);
-    sizeTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ResizeNearestNeighborGrad.ts b/tfjs-master/tfjs-node/src/kernels/ResizeNearestNeighborGrad.ts
deleted file mode 100644
index 6b9150f72..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ResizeNearestNeighborGrad.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, ResizeNearestNeighborGrad, ResizeNearestNeighborGradAttrs, ResizeNearestNeighborGradInputs, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const resizeNearestNeighborGradConfig: KernelConfig = {
-  kernelName: ResizeNearestNeighborGrad,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {images, dy} = args.inputs as ResizeNearestNeighborGradInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {alignCorners} =
-        args.attrs as unknown as ResizeNearestNeighborGradAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', images.dtype), {
-        name: 'align_corners',
-        type: backend.binding.TF_ATTR_BOOL,
-        value: alignCorners
-      }
-    ];
-    const [, origHeight, origWidth, ] = images.shape;
-    const sizeTensor = tensor1d([origHeight, origWidth], 'int32');
-    const res = backend.executeSingleOutput(
-        ResizeNearestNeighborGrad, opAttrs, [dy, sizeTensor]);
-    sizeTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Reverse.ts b/tfjs-master/tfjs-node/src/kernels/Reverse.ts
deleted file mode 100644
index 286049d84..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Reverse.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Reverse, ReverseAttrs, ReverseInputs, tensor1d, util} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const reverseConfig: KernelConfig = {
-  kernelName: Reverse,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ReverseInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {dims} = args.attrs as unknown as ReverseAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('Tidx', 'int32'),
-      createTensorsTypeOpAttr('T', x.dtype)
-    ];
-
-    const axes = util.parseAxisParam(dims, x.shape);
-    const axisTensor = tensor1d(axes, 'int32');
-    const res =
-        backend.executeSingleOutput('ReverseV2', opAttrs, [x, axisTensor]);
-    axisTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Round.ts b/tfjs-master/tfjs-node/src/kernels/Round.ts
deleted file mode 100644
index bc8a0634d..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Round.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Round, RoundInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const roundConfig: KernelConfig = {
-  kernelName: Round,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as RoundInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Round, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Rsqrt.ts b/tfjs-master/tfjs-node/src/kernels/Rsqrt.ts
deleted file mode 100644
index 92f16f8d1..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Rsqrt.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Rsqrt, RsqrtInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const rsqrtConfig: KernelConfig = {
-  kernelName: Rsqrt,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as RsqrtInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Rsqrt, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ScatterNd.ts b/tfjs-master/tfjs-node/src/kernels/ScatterNd.ts
deleted file mode 100644
index 813de75e3..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ScatterNd.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, ScatterNd, ScatterNdAttrs, ScatterNdInputs, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const scatterNdConfig: KernelConfig = {
-  kernelName: ScatterNd,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {indices, updates} = args.inputs as ScatterNdInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {shape} = args.attrs as unknown as ScatterNdAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', updates.dtype),
-      createTensorsTypeOpAttr('Tindices', 'int32')
-    ];
-    const shapeTensor = tensor1d(shape, 'int32');
-    const ret = backend.executeSingleOutput(
-        ScatterNd, opAttrs, [indices, updates, shapeTensor]);
-
-    shapeTensor.dispose();
-
-    return ret;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Select.ts b/tfjs-master/tfjs-node/src/kernels/Select.ts
deleted file mode 100644
index 72854e066..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Select.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Select, SelectInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const selectConfig: KernelConfig = {
-  kernelName: Select,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {condition, t, e} = args.inputs as SelectInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(t.dtype, e.dtype))];
-    return backend.executeSingleOutput(Select, opAttrs, [condition, t, e]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Selu.ts b/tfjs-master/tfjs-node/src/kernels/Selu.ts
deleted file mode 100644
index 5e04a1f33..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Selu.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Selu, SeluInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const seluConfig: KernelConfig = {
-  kernelName: Selu,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SeluInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Selu, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Sigmoid.ts b/tfjs-master/tfjs-node/src/kernels/Sigmoid.ts
deleted file mode 100644
index 9d637b340..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Sigmoid.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sigmoid, SigmoidInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const sigmoidConfig: KernelConfig = {
-  kernelName: Sigmoid,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SigmoidInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Sigmoid, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Sign.ts b/tfjs-master/tfjs-node/src/kernels/Sign.ts
deleted file mode 100644
index 0e99f3997..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Sign.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sign, SignInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const signConfig: KernelConfig = {
-  kernelName: Sign,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SignInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Sign, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Sin.ts b/tfjs-master/tfjs-node/src/kernels/Sin.ts
deleted file mode 100644
index 9fcf1ec2a..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Sin.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sin, SinInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const sinConfig: KernelConfig = {
-  kernelName: Sin,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SinInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Sin, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Sinh.ts b/tfjs-master/tfjs-node/src/kernels/Sinh.ts
deleted file mode 100644
index 7c1386241..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Sinh.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sinh, SinhInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const sinhConfig: KernelConfig = {
-  kernelName: Sinh,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SinhInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Sinh, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Slice.ts b/tfjs-master/tfjs-node/src/kernels/Slice.ts
deleted file mode 100644
index f005af3a0..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Slice.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Slice, SliceAttrs, SliceInputs, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const sliceConfig: KernelConfig = {
-  kernelName: Slice,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SliceInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {begin, size} = args.attrs as unknown as SliceAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Index', 'int32')
-    ];
-
-    // Bind tensor values
-    const [begin_, size_] =
-        backend_util.slice_util.parseSliceParams(x, begin, size);
-    const beginTensor = tensor1d(begin_, 'int32');
-    const sizeTensor = tensor1d(size_, 'int32');
-
-    const res = backend.executeSingleOutput(
-        Slice, opAttrs, [x, beginTensor, sizeTensor]);
-    beginTensor.dispose();
-    sizeTensor.dispose();
-
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Softmax.ts b/tfjs-master/tfjs-node/src/kernels/Softmax.ts
deleted file mode 100644
index 594da3bfd..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Softmax.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, NamedTensorInfoMap, TensorInfo} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-interface SoftmaxInputs extends NamedTensorInfoMap {
-  logits: TensorInfo;
-}
-
-export const softmaxConfig: KernelConfig = {
-  kernelName: 'Softmax',
-  backendName: 'tensorflow',
-  kernelFunc: ({inputs, backend}) => {
-    const {logits} = inputs as SoftmaxInputs;
-    const opAttrs = [createTensorsTypeOpAttr('T', logits.dtype)];
-
-    const nodeBackend = backend as NodeJSKernelBackend;
-
-    return nodeBackend.executeSingleOutput('Softmax', opAttrs, [logits]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Softplus.ts b/tfjs-master/tfjs-node/src/kernels/Softplus.ts
deleted file mode 100644
index 75110850f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Softplus.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Softplus, SoftplusInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const softplusConfig: KernelConfig = {
-  kernelName: Softplus,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SoftplusInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Softplus, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/SpaceToBatchND.ts b/tfjs-master/tfjs-node/src/kernels/SpaceToBatchND.ts
deleted file mode 100644
index 05c27b7e8..000000000
--- a/tfjs-master/tfjs-node/src/kernels/SpaceToBatchND.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs, tensor1d, tensor2d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const spaceToBatchNDConfig: KernelConfig = {
-  kernelName: SpaceToBatchND,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SpaceToBatchNDInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {blockShape, paddings} = args.attrs as unknown as SpaceToBatchNDAttrs;
-
-    const blockShapeTensor = tensor1d(blockShape, 'int32');
-    const paddingsTensor =
-        tensor2d(paddings, [paddings.length, paddings[0].length], 'int32');
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tblock_shape', 'int32'),
-      createTensorsTypeOpAttr('Tpaddings', paddingsTensor.dtype)
-    ];
-    const res = backend.executeSingleOutput(
-        SpaceToBatchND, opAttrs, [x, blockShapeTensor, paddingsTensor]);
-
-    blockShapeTensor.dispose();
-    paddingsTensor.dispose();
-
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/SparseToDense.ts b/tfjs-master/tfjs-node/src/kernels/SparseToDense.ts
deleted file mode 100644
index a64a72ce6..000000000
--- a/tfjs-master/tfjs-node/src/kernels/SparseToDense.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SparseToDense, SparseToDenseAttrs, SparseToDenseInputs, tensor1d} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const sparseToDenseConfig: KernelConfig = {
-  kernelName: SparseToDense,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {sparseIndices, sparseValues, defaultValue} =
-        args.inputs as SparseToDenseInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {outputShape} = args.attrs as unknown as SparseToDenseAttrs;
-
-    const opAttrs = [
-      {
-        name: 'validate_indices',
-        type: backend.binding.TF_ATTR_BOOL,
-        value: true
-      },
-      createTensorsTypeOpAttr('T', sparseValues.dtype),
-      createTensorsTypeOpAttr('Tindices', sparseIndices.dtype)
-    ];
-    const outputShapeTensor = tensor1d(outputShape, 'int32');
-    const res = backend.executeSingleOutput(
-        SparseToDense, opAttrs,
-        [sparseIndices, outputShapeTensor, sparseValues, defaultValue]);
-    outputShapeTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/SplitV.ts b/tfjs-master/tfjs-node/src/kernels/SplitV.ts
deleted file mode 100644
index 3d7d25278..000000000
--- a/tfjs-master/tfjs-node/src/kernels/SplitV.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, scalar, SplitV, SplitVAttrs, SplitVInputs, Tensor, tensor1d, tidy, util} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const splitVConfig: KernelConfig = {
-  kernelName: SplitV,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SplitVInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {numOrSizeSplits, axis} = args.attrs as unknown as SplitVAttrs;
-
-    const $axis = util.parseAxisParam(axis, x.shape)[0];
-    const splitSizes = backend_util.prepareSplitSize(x, numOrSizeSplits, $axis);
-
-    const opAttrs = [
-      {
-        name: 'num_split',
-        type: backend.binding.TF_ATTR_INT,
-        value: splitSizes.length
-      },
-      createTensorsTypeOpAttr('T', x as Tensor), {
-        name: 'Tlen',
-        type: backend.binding.TF_ATTR_TYPE,
-        value: backend.binding.TF_INT32
-      }
-    ];
-    const inputs = [x];
-    return tidy(() => {
-      inputs.push(tensor1d(splitSizes, 'int32'));
-      inputs.push(scalar($axis, 'int32'));
-      return backend.executeMultipleOutputs(
-          SplitV, opAttrs, inputs, splitSizes.length);
-    });
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Sqrt.ts b/tfjs-master/tfjs-node/src/kernels/Sqrt.ts
deleted file mode 100644
index 066660511..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Sqrt.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sqrt, SqrtInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const sqrtConfig: KernelConfig = {
-  kernelName: Sqrt,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SqrtInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Sqrt, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Square.ts b/tfjs-master/tfjs-node/src/kernels/Square.ts
deleted file mode 100644
index 84524c7b1..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Square.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Square, SquareInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const squareConfig: KernelConfig = {
-  kernelName: Square,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SquareInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Square, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/SquaredDifference.ts b/tfjs-master/tfjs-node/src/kernels/SquaredDifference.ts
deleted file mode 100644
index 9b5e1ed4f..000000000
--- a/tfjs-master/tfjs-node/src/kernels/SquaredDifference.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, SquaredDifference, SquaredDifferenceInputs} from '@tensorflow/tfjs';
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const squaredDifferenceConfig: KernelConfig = {
-  kernelName: SquaredDifference,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as SquaredDifferenceInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr('T', a.dtype)];
-    return backend.executeSingleOutput(SquaredDifference, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/StaticRegexReplace.ts b/tfjs-master/tfjs-node/src/kernels/StaticRegexReplace.ts
deleted file mode 100644
index 73546526c..000000000
--- a/tfjs-master/tfjs-node/src/kernels/StaticRegexReplace.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @license
- * Copyright 2023 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, StaticRegexReplace, StaticRegexReplaceAttrs, StaticRegexReplaceInputs} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const staticRegexReplaceConfig: KernelConfig = {
-  kernelName: StaticRegexReplace,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const tensors = args.inputs as unknown as StaticRegexReplaceInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {pattern, rewrite, replaceGlobal} =
-      args.attrs as unknown as StaticRegexReplaceAttrs;
-
-    const opAttrs = [
-      {name: 'pattern', type: backend.binding.TF_ATTR_STRING, value: pattern},
-      {name: 'rewrite', type: backend.binding.TF_ATTR_STRING, value: rewrite},
-      {
-        name: 'replace_global',
-        type: backend.binding.TF_ATTR_BOOL,
-        value: replaceGlobal,
-      },
-    ];
-
-    const inputs = [tensors.x];
-    return backend.executeSingleOutput('StaticRegexReplace', opAttrs, inputs);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Step.ts b/tfjs-master/tfjs-node/src/kernels/Step.ts
deleted file mode 100644
index 740d7a866..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Step.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, fill, greater, isNaN as tfIsNan, KernelConfig, ones, scalar, Step, StepAttrs, StepInputs, Tensor, tidy, where} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const stepConfig: KernelConfig = {
-  kernelName: Step,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as StepInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {alpha} = args.attrs as unknown as StepAttrs;
-
-    const dtype = x.dtype;
-    return tidy(() => {
-      const nans = tfIsNan(x as Tensor);
-      const stepNoNans = where(
-          greater(x as Tensor, scalar(0, dtype)), ones(x.shape),
-          fill(x.shape, alpha, dtype));
-
-      const opAttrs = [createTensorsTypeOpAttr(
-          'T', backend_util.upcastType(x.dtype, stepNoNans.dtype))];
-      return backend.executeSingleOutput(
-          'Select', opAttrs, [nans, x, stepNoNans]);
-    });
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/StridedSlice.ts b/tfjs-master/tfjs-node/src/kernels/StridedSlice.ts
deleted file mode 100644
index e13fa8078..000000000
--- a/tfjs-master/tfjs-node/src/kernels/StridedSlice.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, StridedSlice, StridedSliceAttrs, StridedSliceInputs, tensor1d, tidy} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const stridedSliceConfig: KernelConfig = {
-  kernelName: StridedSlice,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as StridedSliceInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask} =
-        args.attrs as unknown as StridedSliceAttrs;
-
-    const attrs = args.attrs as unknown as StridedSliceAttrs;
-    // make a copy because it may be modified in-place further down.
-    const begin = attrs.begin.slice();
-    const end = attrs.end.slice();
-    const strides = attrs.strides;
-
-    return tidy(() => {
-      const beginTensor = tensor1d(begin, 'int32');
-      const endTensor = tensor1d(end, 'int32');
-      const stridesTensor = tensor1d(strides, 'int32');
-
-      const opAttrs = [
-        createTensorsTypeOpAttr('T', x.dtype),
-        createTensorsTypeOpAttr('Index', 'int32'), {
-          name: 'begin_mask',
-          type: backend.binding.TF_ATTR_INT,
-          value: beginMask
-        },
-        {name: 'end_mask', type: backend.binding.TF_ATTR_INT, value: endMask}, {
-          name: 'ellipsis_mask',
-          type: backend.binding.TF_ATTR_INT,
-          value: ellipsisMask
-        },
-        {
-          name: 'new_axis_mask',
-          type: backend.binding.TF_ATTR_INT,
-          value: newAxisMask
-        },
-        {
-          name: 'shrink_axis_mask',
-          type: backend.binding.TF_ATTR_INT,
-          value: shrinkAxisMask
-        }
-      ];
-      return backend.executeSingleOutput(
-          StridedSlice, opAttrs, [x, beginTensor, endTensor, stridesTensor]);
-    });
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Sub.ts b/tfjs-master/tfjs-node/src/kernels/Sub.ts
deleted file mode 100644
index 976b23f44..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Sub.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util, KernelConfig, Sub, SubInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const subConfig: KernelConfig = {
-  kernelName: Sub,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b} = args.inputs as SubInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return backend.executeSingleOutput(Sub, opAttrs, [a, b]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Sum.ts b/tfjs-master/tfjs-node/src/kernels/Sum.ts
deleted file mode 100644
index d3215be71..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Sum.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Sum, SumAttrs, SumInputs, tensor1d, util} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const sumConfig: KernelConfig = {
-  kernelName: Sum,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as SumInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis, keepDims} = args.attrs as unknown as SumAttrs;
-    const axes = util.parseAxisParam(axis, x.shape);
-    const axisTensor = tensor1d(axes, 'int32');
-    const res = backend.executeSingleOutput(
-        Sum, backend.createReductionOpAttrs(x, keepDims), [x, axisTensor]);
-    axisTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Tan.ts b/tfjs-master/tfjs-node/src/kernels/Tan.ts
deleted file mode 100644
index 88e7542d9..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Tan.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tan, TanInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const tanConfig: KernelConfig = {
-  kernelName: Tan,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as TanInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Tan, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Tanh.ts b/tfjs-master/tfjs-node/src/kernels/Tanh.ts
deleted file mode 100644
index cde564a58..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Tanh.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Tanh, TanhInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const tanhConfig: KernelConfig = {
-  kernelName: Tanh,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as TanhInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    return backend.executeSingleInput(Tanh, x);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/TensorScatterUpdate.ts b/tfjs-master/tfjs-node/src/kernels/TensorScatterUpdate.ts
deleted file mode 100644
index 0f72ca922..000000000
--- a/tfjs-master/tfjs-node/src/kernels/TensorScatterUpdate.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, TensorScatterUpdate, TensorScatterUpdateAttrs, TensorScatterUpdateInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const tensorScatterUpdateConfig: KernelConfig = {
-  kernelName: TensorScatterUpdate,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {tensor, indices, updates} = args.inputs as TensorScatterUpdateInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {} = args.attrs as unknown as TensorScatterUpdateAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', updates.dtype),
-      createTensorsTypeOpAttr('Tindices', 'int32')
-    ];
-    const ret = backend.executeSingleOutput(
-        TensorScatterUpdate, opAttrs, [tensor, indices, updates]);
-
-    return ret;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Tile.ts b/tfjs-master/tfjs-node/src/kernels/Tile.ts
deleted file mode 100644
index ec06942f4..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Tile.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, tensor1d, Tile, TileAttrs, TileInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const tileConfig: KernelConfig = {
-  kernelName: Tile,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as TileInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {reps} = args.attrs as unknown as TileAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tmultiples', 'int32')
-    ];
-    const multiples = tensor1d(reps, 'int32');
-    const res = backend.executeSingleOutput(Tile, opAttrs, [x, multiples]);
-    multiples.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/TopK.ts b/tfjs-master/tfjs-node/src/kernels/TopK.ts
deleted file mode 100644
index 9b581ffbd..000000000
--- a/tfjs-master/tfjs-node/src/kernels/TopK.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, scalar, TopK, TopKAttrs, TopKInputs} from '@tensorflow/tfjs';
-import {isNullOrUndefined} from 'util';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const topKConfig: KernelConfig = {
-  kernelName: TopK,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as TopKInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {k, sorted} = args.attrs as unknown as TopKAttrs;
-
-    const kCount = isNullOrUndefined(k) ? 1 : k;
-    const isSorted = isNullOrUndefined(sorted) ? true : sorted;
-    const opAttrs = [
-      {name: 'sorted', type: backend.binding.TF_ATTR_BOOL, value: isSorted},
-      createTensorsTypeOpAttr('T', x.dtype),
-    ];
-    const kTensor = scalar(kCount, 'int32');
-
-    // 'TopKV2' has two-hard coded output attributes:
-    // TODO(yassogba) consider renamine constant in kernel names;
-    const res =
-        backend.executeMultipleOutputs('TopKV2', opAttrs, [x, kTensor], 2);
-    kTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Transpose.ts b/tfjs-master/tfjs-node/src/kernels/Transpose.ts
deleted file mode 100644
index 0cedb1aa6..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Transpose.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, tensor1d, Transpose, TransposeAttrs, TransposeInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const transposeConfig: KernelConfig = {
-  kernelName: Transpose,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as TransposeInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {perm} = args.attrs as unknown as TransposeAttrs;
-
-    const permTensor = tensor1d(perm, 'int32');
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tperm', 'int32')
-    ];
-    const res =
-        backend.executeSingleOutput(Transpose, opAttrs, [x, permTensor]);
-    permTensor.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Unique.ts b/tfjs-master/tfjs-node/src/kernels/Unique.ts
deleted file mode 100644
index a6f9f7e05..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Unique.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, tensor1d, Unique, UniqueAttrs, UniqueInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const uniqueConfig: KernelConfig = {
-  kernelName: Unique,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as UniqueInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {axis = 0} = args.attrs as unknown as UniqueAttrs;
-
-    const axisTensor = tensor1d([axis], 'int32');
-
-    try {
-      const opAttrs = [
-        createTensorsTypeOpAttr('T', x.dtype),
-        createTensorsTypeOpAttr('Taxis', 'int32'),
-        createTensorsTypeOpAttr('out_idx', 'int32')
-      ];
-      const inputs = [x, axisTensor];
-      return backend.executeMultipleOutputs('UniqueV2', opAttrs, inputs, 2);
-    }
-    finally {
-      axisTensor.dispose();
-    }
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/Unpack.ts b/tfjs-master/tfjs-node/src/kernels/Unpack.ts
deleted file mode 100644
index 08c036ce3..000000000
--- a/tfjs-master/tfjs-node/src/kernels/Unpack.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, Unpack, UnpackAttrs, UnpackInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const unpackConfig: KernelConfig = {
-  kernelName: Unpack,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {value} = args.inputs as UnpackInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    let {axis} = args.attrs as unknown as UnpackAttrs;
-
-    if (axis < 0) {
-      axis += value.shape.length;
-    }
-
-    if (axis >= value.shape.length) {
-      throw new Error(
-          `Invalid axis supplied: ${axis} shape length: ${value.shape.length}`);
-    }
-    const num = value.shape[axis];
-    const opAttrs = [
-      {name: 'num', type: backend.binding.TF_ATTR_INT, value: num},
-      createTensorsTypeOpAttr('T', value.dtype),
-      {name: 'axis', type: backend.binding.TF_ATTR_INT, value: axis}
-    ];
-    return backend.executeMultipleOutputs(Unpack, opAttrs, [value], num);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/UnsortedSegmentSum.ts b/tfjs-master/tfjs-node/src/kernels/UnsortedSegmentSum.ts
deleted file mode 100644
index 6c9eff034..000000000
--- a/tfjs-master/tfjs-node/src/kernels/UnsortedSegmentSum.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, scalar, UnsortedSegmentSum, UnsortedSegmentSumAttrs, UnsortedSegmentSumInputs} from '@tensorflow/tfjs';
-
-import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const unsortedSegmentSumConfig: KernelConfig = {
-  kernelName: UnsortedSegmentSum,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x, segmentIds} = args.inputs as UnsortedSegmentSumInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {numSegments} = args.attrs as unknown as UnsortedSegmentSumAttrs;
-
-    const opAttrs = [
-      createTensorsTypeOpAttr('T', x.dtype),
-      createTensorsTypeOpAttr('Tindices', 'int32'),
-      createTensorsTypeOpAttr('Tnumsegments', 'int32')
-    ];
-    const numSegmentsT = scalar(numSegments, 'int32');
-    const res = backend.executeSingleOutput(
-        UnsortedSegmentSum, opAttrs, [x, segmentIds, numSegmentsT]);
-    numSegmentsT.dispose();
-    return res;
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/ZerosLike.ts b/tfjs-master/tfjs-node/src/kernels/ZerosLike.ts
deleted file mode 100644
index e1c26144b..000000000
--- a/tfjs-master/tfjs-node/src/kernels/ZerosLike.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {KernelConfig, ZerosLike, ZerosLikeInputs} from '@tensorflow/tfjs';
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const zerosLikeConfig: KernelConfig = {
-  kernelName: ZerosLike,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {x} = args.inputs as ZerosLikeInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-
-    const opAttrs = [{
-      name: 'T',
-      type: backend.binding.TF_ATTR_TYPE,
-      value: backend.getDTypeInteger(x.dtype)
-    }];
-    return backend.executeSingleOutput(ZerosLike, opAttrs, [x]);
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/kernels/_FusedMatMul.ts b/tfjs-master/tfjs-node/src/kernels/_FusedMatMul.ts
deleted file mode 100644
index 5339deaa0..000000000
--- a/tfjs-master/tfjs-node/src/kernels/_FusedMatMul.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs, add, KernelConfig, matMul, Tensor, Tensor3D, tidy} from '@tensorflow/tfjs';
-
-import {NodeJSKernelBackend} from '../nodejs_kernel_backend';
-
-export const _fusedMatMulConfig: KernelConfig = {
-  kernelName: _FusedMatMul,
-  backendName: 'tensorflow',
-  kernelFunc: (args) => {
-    const {a, b, bias, preluActivationWeights} =
-        args.inputs as _FusedMatMulInputs;
-    const backend = args.backend as NodeJSKernelBackend;
-    const {transposeA, transposeB, activation, leakyreluAlpha} =
-        args.attrs as unknown as _FusedMatMulAttrs;
-
-    // Core TensorFlow does not have a fused BatchMatMul op. Combine calls to
-    // achieve the same results:
-    return tidy(() => {
-      let result: Tensor3D =
-          matMul(a as Tensor, b as Tensor, transposeA, transposeB);
-      if (bias != null) {
-        result = add(result, bias as Tensor);
-      }
-
-      result = backend.applyActivation(
-          result, activation, preluActivationWeights as Tensor, leakyreluAlpha);
-
-      return result;
-    });
-  }
-};
diff --git a/tfjs-master/tfjs-node/src/node.ts b/tfjs-master/tfjs-node/src/node.ts
deleted file mode 100644
index db867ea10..000000000
--- a/tfjs-master/tfjs-node/src/node.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Public API symbols under the tf.node.* namespace.
- */
-
-import {tensorBoard} from './callbacks';
-import {decodeBmp, decodeGif, decodeImage, decodeJpeg, decodePng, encodeJpeg, encodePng} from './image';
-import {getMetaGraphsFromSavedModel, getNumOfSavedModels, loadSavedModel} from './saved_model';
-import {summaryFileWriter} from './tensorboard';
-
-export const node = {
-  decodeImage,
-  decodeBmp,
-  decodeGif,
-  decodePng,
-  decodeJpeg,
-  encodeJpeg,
-  encodePng,
-  summaryFileWriter,
-  tensorBoard,
-  getMetaGraphsFromSavedModel,
-  getNumOfSavedModels,
-  loadSavedModel
-};
diff --git a/tfjs-master/tfjs-node/src/nodejs_kernel_backend.ts b/tfjs-master/tfjs-node/src/nodejs_kernel_backend.ts
deleted file mode 100644
index a399d31bf..000000000
--- a/tfjs-master/tfjs-node/src/nodejs_kernel_backend.ts
+++ /dev/null
@@ -1,786 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import {backend_util, BackendTimingInfo, DataId, DataType, KernelBackend, ModelTensorInfo, Rank, Scalar, scalar, ScalarLike, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorInfo, tidy, util} from '@tensorflow/tfjs';
-import {isArray, isNullOrUndefined} from 'util';
-
-import {encodeInt32ArrayAsInt64, Int64Scalar} from './int64_tensors';
-import {TensorMetadata, TFEOpAttr, TFJSBinding} from './tfjs_binding';
-
-// tslint:disable-next-line:no-require-imports
-const messages = require('./proto/api_pb');
-
-type TensorData = {
-  shape: number[],
-  dtype: number,
-  values: backend_util.BackendValues,
-  id: number,
-  refCount: number;
-};
-
-export class NodeJSKernelBackend extends KernelBackend {
-  binding: TFJSBinding;
-  isGPUPackage: boolean;
-  isUsingGpuDevice: boolean;
-  private tensorMap: tf.DataStorage<TensorData>;
-
-  constructor(binding: TFJSBinding, packageName: string) {
-    super();
-    this.binding = binding;
-    this.isGPUPackage = packageName === '@tensorflow/tfjs-node-gpu';
-    this.isUsingGpuDevice = this.binding.isUsingGpuDevice();
-    this.tensorMap = new tf.DataStorage<TensorData>(this, tf.engine());
-  }
-
-  getDTypeInteger(dtype: DataType): number {
-    switch (dtype) {
-      case 'float32':
-        return this.binding.TF_FLOAT;
-      case 'int32':
-        return this.binding.TF_INT32;
-      case 'bool':
-        return this.binding.TF_BOOL;
-      case 'complex64':
-        return this.binding.TF_COMPLEX64;
-      case 'string':
-        return this.binding.TF_STRING;
-      default:
-        throw new Error(`Unsupported DType: ${dtype}`);
-    }
-  }
-
-  private typeAttributeFromTensor(value: Tensor): number {
-    return this.getDTypeInteger(value.dtype);
-  }
-
-  // Creates a new Tensor and maps the dataId to the passed in ID.
-  private createOutputTensor(metadata: TensorMetadata): Tensor {
-    const newId = {};
-
-    this.tensorMap.set(newId, {
-      shape: metadata.shape,
-      dtype: metadata.dtype,
-      id: metadata.id,
-      values: null,
-      refCount: 1
-    });
-
-    let dtype: DataType;
-    switch (metadata.dtype) {
-      case this.binding.TF_FLOAT:
-        dtype = 'float32';
-        break;
-      case this.binding.TF_INT32:
-        dtype = 'int32';
-        break;
-      case this.binding.TF_INT64:
-        console.warn('INT64 output tensor will be stored as BigInt64Array.');
-        // INT64 is not supported in TFJS yet, cast it to int32.
-        dtype = 'int32';
-        break;
-      case this.binding.TF_BOOL:
-        dtype = 'bool';
-        break;
-      case this.binding.TF_COMPLEX64:
-        dtype = 'complex64';
-        break;
-      case this.binding.TF_STRING:
-        dtype = 'string';
-        break;
-      case this.binding.TF_RESOURCE:
-        // NOTE(cais): We currently represent resource-type Tensors
-        // as string of ubytes.
-        dtype = 'string';
-        break;
-      case this.binding.TF_UINT8:
-        // TensorFlow uses UINT8 as dtype for image tensor. UINT8 is not
-        // supported in TFJS yet, cast it to int32.
-        dtype = 'int32';
-        break;
-      default:
-        throw new Error(`Unknown dtype enum ${metadata.dtype}`);
-    }
-
-    // TODO(yassogba) Enable this once all the kernels are removed from backend.
-    // We can then change the return type from Tensor to TensorInfo.
-    // return {dataId: newId, shape: metadata.shape, dtype};
-
-    const tensorInfo:
-        TensorInfo = {dataId: newId, shape: metadata.shape, dtype};
-    return tf.engine().makeTensorFromTensorInfo(tensorInfo);
-  }
-
-  // Prepares Tensor instances for Op execution.
-  private getInputTensorIds(tensors: Array<TensorInfo|Int64Scalar>): number[] {
-    const ids: number[] = [];
-    for (let i = 0; i < tensors.length; i++) {
-      if (tensors[i] instanceof Int64Scalar) {
-        // Then `tensors[i]` is a Int64Scalar, which we currently represent
-        // using an `Int32Array`.
-        const value = (tensors[i] as Int64Scalar).valueArray;
-        const id = this.binding.createTensor([], this.binding.TF_INT64, value);
-        ids.push(id);
-      } else {
-        const info = this.tensorMap.get((tensors[i] as TensorInfo).dataId);
-        // TODO - what about ID in this case? Handle in write()??
-        if (info.values != null) {
-          // Values were delayed to write into the TensorHandle. Do that before
-          // Op execution and clear stored values.
-          info.id =
-              this.binding.createTensor(info.shape, info.dtype, info.values);
-          info.values = null;
-        }
-        ids.push(info.id);
-      }
-    }
-    return ids;
-  }
-
-  createReductionOpAttrs(tensor: TensorInfo, keepDims = false): TFEOpAttr[] {
-    return [
-      {name: 'keep_dims', type: this.binding.TF_ATTR_BOOL, value: keepDims},
-      createTensorsTypeOpAttr('T', tensor.dtype),
-      createTensorsTypeOpAttr('Tidx', 'int32')
-    ];
-  }
-
-  floatPrecision(): 16|32 {
-    return 32;
-  }
-
-  epsilon(): number {
-    return super.epsilon();
-  }
-
-  /**
-   * Executes an op that has a single input and output.
-   *
-   * Helper function to wrap executeSingleOutput in a particular case.
-   * @param name The name of the Op to execute.
-   * @param input The input Tensor for the Op.
-   */
-  executeSingleInput(name: string, input: TensorInfo): Tensor {
-    const opAttrs = [createTensorsTypeOpAttr('T', input.dtype)];
-    return this.executeSingleOutput(name, opAttrs, [input]);
-  }
-
-  /**
-   * Executes a TensorFlow Eager Op that provides one output Tensor.
-   * @param name The name of the Op to execute.
-   * @param opAttrs The list of Op attributes required to execute.
-   * @param inputs The list of input Tensors for the Op.
-   * @return A resulting Tensor from Op execution.
-   */
-  executeSingleOutput(name: string, opAttrs: TFEOpAttr[], inputs: TensorInfo[]):
-      Tensor {
-    const outputMetadata = this.binding.executeOp(
-        name, opAttrs, this.getInputTensorIds(inputs), 1);
-    return this.createOutputTensor(outputMetadata[0]);
-  }
-
-  /**
-   * Executes a TensorFlow Eager Op that provides multiple output Tensors.
-   * @param name The name of the Op to execute.
-   * @param opAttrs The list of Op attributes required to execute.
-   * @param inputs The list of input Tensors for the Op.
-   * @param numOutputs The number of output Tensors for Op execution.
-   * @return A resulting Tensor array from Op execution.
-   */
-  executeMultipleOutputs(
-      name: string, opAttrs: TFEOpAttr[], inputs: TensorInfo[],
-      numOutputs: number): Tensor[] {
-    const outputMetadata = this.binding.executeOp(
-        name, opAttrs, this.getInputTensorIds(inputs), numOutputs);
-    return outputMetadata.map(m => this.createOutputTensor(m));
-  }
-
-  numDataIds(): number {
-    return this.tensorMap.numDataIds();
-  }
-
-  dispose(): void {}
-
-  async read(dataId: DataId): Promise<backend_util.BackendValues> {
-    return this.readSync(dataId);
-  }
-
-  readSync(dataId: DataId): backend_util.BackendValues {
-    if (!this.tensorMap.has(dataId)) {
-      throw new Error(`Tensor ${dataId} was not registered!`);
-    }
-    const info = this.tensorMap.get(dataId);
-    if (info.values != null) {
-      return info.values;
-    } else {
-      return this.binding.tensorDataSync(info.id);
-    }
-  }
-
-  /**
-   * Dispose the memory if the dataId has 0 refCount. Return true if the memory
-   * is released, false otherwise.
-   * @param dataId
-   * @oaram force Optional, remove the data regardless of refCount
-   */
-  disposeData(dataId: DataId, force = false): boolean {
-    // No-op if already disposed.
-    if (this.tensorMap.has(dataId)) {
-      const id = this.tensorMap.get(dataId).id;
-      this.tensorMap.get(dataId).refCount--;
-      if (!force && this.tensorMap.get(dataId).refCount > 0) {
-        return false;
-      }
-
-      if (id != null && id >= 0) {
-        this.binding.deleteTensor(id);
-      }
-      this.tensorMap.delete(dataId);
-    }
-    return true;
-  }
-  /** Return refCount of a `TensorData`. */
-  refCount(dataId: DataId): number {
-    if (this.tensorMap.has(dataId)) {
-      const tensorData = this.tensorMap.get(dataId);
-      return tensorData.refCount;
-    }
-    return 0;
-  }
-
-  incRef(dataId: DataId) {
-    this.tensorMap.get(dataId).refCount++;
-  }
-
-  move(
-      dataId: DataId, values: backend_util.BackendValues, shape: number[],
-      dtype: DataType, refCount: number): void {
-    this.tensorMap.set(
-        dataId, {shape, dtype: getTFDType(dtype), values, id: -1, refCount});
-  }
-
-  write(values: backend_util.BackendValues, shape: number[], dtype: DataType):
-      DataId {
-    const dataId = {};
-    this.move(dataId, values, shape, dtype, 1);
-    return dataId;
-  }
-
-  applyActivation<T extends Tensor>(
-      input: T, activation: string, preluActivationWeights?: Tensor,
-      leakyreluAlpha?: number): T {
-    let result = input;
-    if (activation != null) {
-      if (activation === 'linear') {
-        // No-op
-      } else if (activation === 'relu') {
-        result = tf.relu(result);
-      } else if (activation === 'prelu') {
-        result = tf.prelu(result, preluActivationWeights) as T;
-      } else if (activation === 'leakyrelu') {
-        result = tf.leakyRelu(result, leakyreluAlpha);
-      } else if (activation === 'elu') {
-        result = tf.elu(result);
-      } else if (activation === 'relu6') {
-        result = tf.relu6(result);
-      } else if (activation === 'sigmoid') {
-        result = tf.sigmoid(result);
-      } else {
-        throw new Error(`Activation: ${
-            activation} has not been implemented for the Node.js backend`);
-      }
-    }
-    return result;
-  }
-
-  divide(a: Tensor, b: Tensor): Tensor {
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return this.executeSingleOutput('Div', opAttrs, [a, b]);
-  }
-
-  divNoNan(a: Tensor, b: Tensor): Tensor {
-    const opAttrs = [createTensorsTypeOpAttr(
-        'T', backend_util.upcastType(a.dtype, b.dtype))];
-    return this.executeSingleOutput('DivNoNan', opAttrs, [a, b]);
-  }
-
-  where(condition: Tensor): Tensor2D {
-    return this.executeSingleOutput('Where', [], [condition]) as Tensor2D;
-  }
-
-  topKValues<T extends Tensor>(x: T, k: number): Tensor1D {
-    throw new Error('Method not implemented.');
-  }
-
-  topKIndices(x: Tensor, k: number): Tensor1D {
-    throw new Error('Method not implemented.');
-  }
-
-  int<T extends Tensor>(x: T): T {
-    throw new Error('Method not implemented.');
-  }
-
-  decodeJpeg(
-      contents: Uint8Array, channels: number, ratio: number,
-      fancyUpscaling: boolean, tryRecoverTruncated: boolean,
-      acceptableFraction: number, dctMethod: string): Tensor3D {
-    const opAttrs = [
-      {name: 'channels', type: this.binding.TF_ATTR_INT, value: channels},
-      {name: 'ratio', type: this.binding.TF_ATTR_INT, value: ratio}, {
-        name: 'fancy_upscaling',
-        type: this.binding.TF_ATTR_BOOL,
-        value: fancyUpscaling
-      },
-      {
-        name: 'try_recover_truncated',
-        type: this.binding.TF_ATTR_BOOL,
-        value: tryRecoverTruncated
-      },
-      {
-        name: 'acceptable_fraction',
-        type: this.binding.TF_ATTR_FLOAT,
-        value: acceptableFraction
-      },
-      {name: 'dct_method', type: this.binding.TF_ATTR_STRING, value: dctMethod}
-    ];
-    const inputArgs = [scalar(contents, 'string')];
-    return this.executeSingleOutput('DecodeJpeg', opAttrs, inputArgs) as
-        Tensor<Rank.R3>;
-  }
-
-  decodePng(contents: Uint8Array, channels: number): Tensor3D {
-    const opAttrs =
-        [{name: 'channels', type: this.binding.TF_ATTR_INT, value: channels}];
-    const inputArgs = [scalar(contents, 'string')];
-
-    return this.executeSingleOutput('DecodePng', opAttrs, inputArgs) as
-        Tensor<Rank.R3>;
-  }
-
-  decodeBmp(contents: Uint8Array, channels: number): Tensor3D {
-    const opAttrs =
-        [{name: 'channels', type: this.binding.TF_ATTR_INT, value: channels}];
-    const inputArgs = [scalar(contents, 'string')];
-    return this.executeSingleOutput('DecodeBmp', opAttrs, inputArgs) as
-        Tensor<Rank.R3>;
-  }
-
-  decodeGif(contents: Uint8Array): Tensor4D {
-    const inputArgs = [scalar(contents, 'string')];
-    return this.executeSingleOutput('DecodeGif', [], inputArgs) as
-        Tensor<Rank.R4>;
-  }
-
-  executeEncodeImageOp(
-      name: string, opAttrs: TFEOpAttr[], imageData: Uint8Array,
-      imageShape: number[]): Tensor {
-    const inputTensorId =
-        this.binding.createTensor(imageShape, this.binding.TF_UINT8, imageData);
-    const outputMetadata =
-        this.binding.executeOp(name, opAttrs, [inputTensorId], 1);
-    this.binding.deleteTensor(inputTensorId);
-    const outputTensorInfo = outputMetadata[0];
-    // prevent the tensor data from being converted to a UTF8 string, since
-    // the encoded data is not valid UTF8
-    outputTensorInfo.dtype = this.binding.TF_UINT8;
-    return this.createOutputTensor(outputTensorInfo);
-  }
-
-  encodeJpeg(
-      imageData: Uint8Array, imageShape: number[], format: ''|'grayscale'|'rgb',
-      quality: number, progressive: boolean, optimizeSize: boolean,
-      chromaDownsampling: boolean, densityUnit: 'in'|'cm', xDensity: number,
-      yDensity: number, xmpMetadata: string): Tensor {
-    const opAttrs = [
-      {name: 'format', type: this.binding.TF_ATTR_STRING, value: format},
-      {name: 'quality', type: this.binding.TF_ATTR_INT, value: quality}, {
-        name: 'progressive',
-        type: this.binding.TF_ATTR_BOOL,
-        value: progressive
-      },
-      {
-        name: 'optimize_size',
-        type: this.binding.TF_ATTR_BOOL,
-        value: optimizeSize
-      },
-      {
-        name: 'chroma_downsampling',
-        type: this.binding.TF_ATTR_BOOL,
-        value: chromaDownsampling
-      },
-      {
-        name: 'density_unit',
-        type: this.binding.TF_ATTR_STRING,
-        value: densityUnit
-      },
-      {name: 'x_density', type: this.binding.TF_ATTR_INT, value: xDensity},
-      {name: 'y_density', type: this.binding.TF_ATTR_INT, value: yDensity}, {
-        name: 'xmp_metadata',
-        type: this.binding.TF_ATTR_STRING,
-        value: xmpMetadata
-      }
-    ];
-    return this.executeEncodeImageOp(
-        'EncodeJpeg', opAttrs, imageData, imageShape);
-  }
-
-  encodePng(imageData: Uint8Array, imageShape: number[], compression: number):
-      Tensor {
-    const opAttrs = [
-      {name: 'compression', type: this.binding.TF_ATTR_INT, value: compression}
-    ];
-    return this.executeEncodeImageOp(
-        'EncodePng', opAttrs, imageData, imageShape);
-  }
-
-  deleteSavedModel(id: number): void {
-    this.binding.deleteSavedModel(id);
-  }
-
-  loadSavedModelMetaGraph(path: string, tags: string): number {
-    return this.binding.loadSavedModel(path, tags);
-  }
-
-  private getMappedInputTensorIds(
-      inputs: Tensor[], inputTensorInfos: ModelTensorInfo[]) {
-    const tensorIds = this.getInputTensorIds(inputs);
-    const newTensors = [];
-    for (let i = 0; i < inputs.length; i++) {
-      if (inputTensorInfos[i] != null) {
-        if (inputTensorInfos[i].tfDtype === 'DT_UINT8') {
-          const data = Uint8Array.from(inputs[i].dataSync());
-          const inputTensorId = this.binding.createTensor(
-              inputs[i].shape, this.binding.TF_UINT8, data);
-          tensorIds[i] = inputTensorId;
-          newTensors.push(i);
-        } else if (inputTensorInfos[i].tfDtype === 'DT_INT64') {
-          const data =
-              encodeInt32ArrayAsInt64(inputs[i].dataSync() as Int32Array);
-          const inputTensorId = this.binding.createTensor(
-              inputs[i].shape, this.binding.TF_INT64, data);
-          tensorIds[i] = inputTensorId;
-          newTensors.push(i);
-        }
-      }
-    }
-    return {tensorIds, newTensors};
-  }
-
-  runSavedModel(
-      id: number, inputs: Tensor[], inputTensorInfos: ModelTensorInfo[],
-      outputOpNames: string[]): Tensor[] {
-    const {tensorIds, newTensors} =
-        this.getMappedInputTensorIds(inputs, inputTensorInfos);
-    const outputMetadata = this.binding.runSavedModel(
-        id, tensorIds, inputTensorInfos.map(info => info.name).join(','),
-        outputOpNames.join(','));
-    for (let i = 0; i < tensorIds.length; i++) {
-      if (newTensors.includes(i)) {
-        this.binding.deleteTensor(tensorIds[i]);
-      }
-    }
-    return outputMetadata.map(m => this.createOutputTensor(m));
-  }
-
-  // ------------------------------------------------------------
-  // TensorBoard-related (tfjs-node-specific) backend kernels.
-
-  summaryWriter(logdir: string): Tensor1D {
-    const opAttrs = [
-      {
-        name: 'shared_name',
-        type: this.binding.TF_ATTR_STRING,
-        value: `logdir:${logdir}`
-      },
-      {name: 'container', type: this.binding.TF_ATTR_STRING, value: ''}
-    ];
-    const writerResource =
-        this.executeSingleOutput('SummaryWriter', opAttrs, []);
-    return writerResource as Tensor1D;
-  }
-
-  createSummaryFileWriter(
-      resourceHandle: Tensor, logdir: string, maxQueue?: number,
-      flushMillis?: number, filenameSuffix?: string): void {
-    const inputArgs = [
-      resourceHandle, scalar(logdir),
-      scalar(maxQueue == null ? 10 : maxQueue, 'int32'),
-      scalar(flushMillis == null ? 2 * 60 * 1000 : flushMillis, 'int32'),
-      scalar(filenameSuffix == null ? '.v2' : filenameSuffix)
-    ];
-    this.executeMultipleOutputs('CreateSummaryFileWriter', [], inputArgs, 0);
-  }
-
-  writeScalarSummary(
-      resourceHandle: Tensor, step: number, name: string,
-      value: Scalar|number): void {
-    tidy(() => {
-      util.assert(
-          Number.isInteger(step),
-          () => `step is expected to be an integer, but is instead ${step}`);
-      const inputArgs: Array<Tensor|Int64Scalar> =
-          [resourceHandle, new Int64Scalar(step), scalar(name, 'string')];
-
-      let typeAttr: number;
-      if (typeof value === 'number') {
-        inputArgs.push(scalar(value));
-        typeAttr = this.binding.TF_FLOAT;
-      } else {
-        // `value` is a Scalar.
-        util.assert(
-            value.rank === 0,
-            () => `A non-scalar tensor (rank ${value.rank}) is passed to ` +
-                `writeScalarSummary()`);
-        inputArgs.push(value);
-        typeAttr = this.typeAttributeFromTensor(value);
-      }
-      const opAttrs: TFEOpAttr[] =
-          [{name: 'T', type: this.binding.TF_ATTR_TYPE, value: typeAttr}];
-      const ids = this.getInputTensorIds(inputArgs);
-      this.binding.executeOp('WriteScalarSummary', opAttrs, ids, 0);
-      // release the tensorflow tensor for Int64Scalar value of step
-      this.binding.deleteTensor(ids[1]);
-    });
-  }
-
-  writeHistogramSummary(
-      resourceHandle: Tensor, step: number, name: string, data: Tensor,
-      bucketCount: number|undefined, description: string|undefined): void {
-    tidy(() => {
-      util.assert(
-          Number.isInteger(step),
-          () => `step is expected to be an integer, but is instead ${step}`);
-
-      // We use the WriteSummary op, and not WriteHistogramSummary. The
-      // difference is that WriteHistogramSummary takes a tensor of any shape,
-      // and places the values in 30 buckets, while WriteSummary expects a
-      // tensor which already describes the bucket widths and counts.
-      //
-      // If we were to use WriteHistogramSummary, we wouldn't have to
-      // implement the "bucketization" of the input tensor, but we also
-      // wouldn't have control over the number of buckets, or the description
-      // of the graph.
-      //
-      // Therefore, we instead use WriteSummary, which makes it possible to
-      // support these features. However, the trade-off is that we have to
-      // implement our own "bucketization", and have to write the summary as a
-      // protobuf message.
-      const content = new messages.HistogramPluginData().setVersion(0);
-      const pluginData = new messages.SummaryMetadata.PluginData()
-                             .setPluginName('histograms')
-                             .setContent(content.serializeBinary());
-      const summary = new messages.SummaryMetadata()
-                          .setPluginData(pluginData)
-                          .setDisplayName(null)
-                          .setSummaryDescription(description);
-      const summaryTensor = scalar(summary.serializeBinary(), 'string');
-      const nameTensor = scalar(name, 'string');
-      const stepScalar = new Int64Scalar(step);
-      const buckets = this.buckets(data, bucketCount);
-      util.assert(
-          buckets.rank === 2 && buckets.shape[1] === 3,
-          () => `Expected buckets to have shape [k, 3], but they had shape ${
-              buckets.shape}`);
-      util.assert(
-          buckets.dtype === 'float32',
-          () => `Expected buckets to have dtype float32, but they had dtype ${
-              buckets.dtype}`);
-      const inputArgs: Array<Tensor|Int64Scalar> =
-          [resourceHandle, stepScalar, buckets, nameTensor, summaryTensor];
-      const typeAttr = this.typeAttributeFromTensor(buckets);
-      const opAttrs: TFEOpAttr[] =
-          [{name: 'T', type: this.binding.TF_ATTR_TYPE, value: typeAttr}];
-      const ids = this.getInputTensorIds(inputArgs);
-      this.binding.executeOp('WriteSummary', opAttrs, ids, 0);
-      // release the tensorflow tensor for Int64Scalar value of step
-      this.binding.deleteTensor(ids[1]);
-    });
-  }
-
-  flushSummaryWriter(resourceHandle: Tensor): void {
-    const inputArgs: Tensor[] = [resourceHandle];
-    this.executeMultipleOutputs('FlushSummaryWriter', [], inputArgs, 0);
-  }
-
-  /**
-   * Group data into histogram buckets.
-   *
-   * @param data A `Tensor` of any shape. Must be castable to `float32`
-   * @param bucketCount Optional positive `number`
-   * @returns A `Tensor` of shape `[k, 3]` and type `float32`. The `i`th row
-   *     is
-   *   a triple `[leftEdge, rightEdge, count]` for a single bucket. The value
-   * of `k` is either `bucketCount`, `1` or `0`.
-   */
-  private buckets(data: Tensor, bucketCount?: number): Tensor<tf.Rank> {
-    if (data.size === 0) {
-      return tf.tensor([], [0, 3], 'float32');
-    }
-
-    // 30 is the default number of buckets in the TensorFlow Python
-    // implementation. See
-    // https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/summary_v2.py
-    bucketCount = bucketCount !== undefined ? bucketCount : 30;
-    util.assert(
-        Number.isInteger(bucketCount) && bucketCount > 0,
-        () =>
-            `Expected bucket count to be a strictly positive integer, but it was ` +
-            `${bucketCount}`);
-    data = data.flatten();
-    data = data.cast('float32');
-    const min: Scalar = data.min();
-    const max: Scalar = data.max();
-    const range: Scalar = max.sub(min);
-    const isSingular = range.equal(0).arraySync() !== 0;
-
-    if (isSingular) {
-      const center = min;
-      const bucketStart: Scalar = center.sub(0.5);
-      const bucketEnd: Scalar = center.add(0.5);
-      const bucketCounts = tf.scalar(data.size, 'float32');
-      return tf.concat([bucketStart, bucketEnd, bucketCounts]).reshape([1, 3]);
-    }
-
-    const bucketWidth = range.div(bucketCount);
-    const offsets = data.sub(min);
-    const bucketIndices = offsets.floorDiv(bucketWidth).cast('int32');
-    const clampedIndices =
-        tf.minimum(bucketIndices, bucketCount - 1).cast('int32');
-    const oneHots = tf.oneHot(clampedIndices, bucketCount);
-    const bucketCounts = oneHots.sum(0).cast('int32');
-    let edges = tf.linspace(min.arraySync(), max.arraySync(), bucketCount + 1);
-    // Ensure last value in edges is max (TF's linspace op doesn't do this)
-    edges = tf.concat([edges.slice(0, bucketCount), max.reshape([1])], 0) as
-        tf.Tensor1D;
-    const leftEdges = edges.slice(0, bucketCount);
-    const rightEdges = edges.slice(1, bucketCount);
-    return tf.stack([leftEdges, rightEdges, bucketCounts.cast('float32')])
-        .transpose();
-  }
-
-  // ~ TensorBoard-related (tfjs-node-specific) backend kernels.
-  // ------------------------------------------------------------
-
-  memory() {
-    // Due to automatic garbage collection, the numbers are unreliable.
-    // TODO(kreeger): Since there is finalization in C, count the true
-    // number of undisposed tensors.
-    return {unreliable: true};
-  }
-
-  async time(f: () => void): Promise<BackendTimingInfo> {
-    const start = process.hrtime();
-    f();
-    // hrtime() returns tuple of [seconds, nanoseconds], and we need to return
-    // milliseconds.
-    const elapsed = process.hrtime(start);
-    return {kernelMs: elapsed[0] * 1000 + elapsed[1] / 1000000};
-  }
-
-  getNumOfSavedModels() {
-    return this.binding.getNumOfSavedModels();
-  }
-
-  getNumOfTFTensors() {
-    return this.binding.getNumOfTensors();
-  }
-}
-
-/** Returns an instance of the Node.js backend. */
-export function nodeBackend(): NodeJSKernelBackend {
-  return tf.findBackend('tensorflow') as NodeJSKernelBackend;
-}
-
-/** Returns the TF dtype for a given DataType. */
-export function getTFDType(dataType: tf.DataType): number {
-  const binding = nodeBackend().binding;
-  switch (dataType) {
-    case 'float32':
-      return binding.TF_FLOAT;
-    case 'int32':
-      return binding.TF_INT32;
-    case 'bool':
-      return binding.TF_BOOL;
-    case 'complex64':
-      return binding.TF_COMPLEX64;
-    case 'string':
-      return binding.TF_STRING;
-    // tslint:disable-next-line:no-any
-    case 'int64' as any:
-      // int64 is not a generally supported dtype in TensorFlow.js
-      // (tfjs-core). However, it needs to be included here for the purpose of
-      // writing the `step` value to TensorBoard via WriteScalarSummary and
-      // other op kernels.
-      return binding.TF_INT64;
-    default:
-      const errorMessage = `Unknown dtype: ${dataType}`;
-      throw new Error(errorMessage);
-  }
-}
-
-/**
- * Creates a TFEOpAttr for a 'type' OpDef attribute from a Tensor or list of
- * Tensors.
- */
-export function createTensorsTypeOpAttr(
-    attrName: string,
-    tensorsOrDtype: tf.Tensor|tf.Tensor[]|tf.DataType): TFEOpAttr {
-  if (isNullOrUndefined(tensorsOrDtype)) {
-    throw new Error('Invalid input tensors value.');
-  }
-  return {
-    name: attrName,
-    type: nodeBackend().binding.TF_ATTR_TYPE,
-    value:
-        (tensorsOrDtype instanceof tf.Tensor || Array.isArray(tensorsOrDtype)) ?
-        getTFDTypeForInputs(tensorsOrDtype) :
-        getTFDType(tensorsOrDtype)
-  };
-}
-
-// TODO(yassogba) remove? who uses this?
-export function createOpAttr(
-    attrName: string, tensorsOrDtype: tf.Tensor|tf.Tensor[]|tf.DataType,
-    value: ScalarLike): TFEOpAttr {
-  if (isNullOrUndefined(tensorsOrDtype)) {
-    throw new Error('Invalid input tensors value.');
-  }
-  return {name: attrName, type: nodeBackend().binding.TF_BOOL, value};
-}
-
-/** Returns the dtype number for a single or list of input Tensors. */
-function getTFDTypeForInputs(tensors: tf.Tensor|tf.Tensor[]): number {
-  if (isNullOrUndefined(tensors)) {
-    throw new Error('Invalid input tensors value.');
-  }
-  if (isArray(tensors)) {
-    for (let i = 0; i < tensors.length; i++) {
-      return getTFDType(tensors[i].dtype);
-    }
-    return -1;
-  } else {
-    return getTFDType(tensors.dtype);
-  }
-}
-
-export function ensureTensorflowBackend() {
-  tf.util.assert(
-      tf.getBackend() === 'tensorflow',
-      () => `Expect the current backend to be "tensorflow", but got "${
-          tf.getBackend()}"`);
-}
diff --git a/tfjs-master/tfjs-node/src/nodejs_kernel_backend_test.ts b/tfjs-master/tfjs-node/src/nodejs_kernel_backend_test.ts
deleted file mode 100644
index 19718cbe5..000000000
--- a/tfjs-master/tfjs-node/src/nodejs_kernel_backend_test.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-// tslint:disable-next-line: no-imports-from-dist
-import {TestKernelBackend} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {createTensorsTypeOpAttr, ensureTensorflowBackend, getTFDType, nodeBackend, NodeJSKernelBackend} from './nodejs_kernel_backend';
-
-describe('delayed upload', () => {
-  it('should handle data before op execution', async () => {
-    const t = tf.tensor1d([1, 2, 3]);
-    tf.test_util.expectArraysClose(await t.data(), [1, 2, 3]);
-
-    const r = t.add(tf.tensor1d([4, 5, 6]));
-    tf.test_util.expectArraysClose(await r.data(), [5, 7, 9]);
-  });
-
-  it('Should not cache tensors in the tensor map for device support. ', () => {
-    const logits = tf.tensor1d([1, 2, 3]);
-    const softmaxLogits = tf.softmax(logits);
-    const data = softmaxLogits.dataSync();
-    expect(softmaxLogits.dataSync()[0]).toEqual(data[0]);
-    expect(softmaxLogits.dataSync()[1]).toEqual(data[1]);
-    expect(softmaxLogits.dataSync()[2]).toEqual(data[2]);
-  });
-});
-
-describe('type casting', () => {
-  it('exp support int32', () => {
-    tf.exp(tf.scalar(2, 'int32'));
-  });
-});
-
-describe('conv3d dilations', () => {
-  it('CPU should throw error on dilations >1', () => {
-    const input: tf.Tensor5D = tf.ones([1, 2, 2, 2, 1]);
-    const filter: tf.Tensor5D = tf.ones([1, 1, 1, 1, 1]);
-    expect(() => {
-      tf.conv3d(input, filter, 1, 'same', 'NDHWC', [2, 2, 2]);
-    }).toThrowError();
-  });
-  it('GPU should handle dilations >1', () => {
-    // This test can only run locally with CUDA bindings and GPU package
-    // installed.
-    if ((tf.backend() as NodeJSKernelBackend).isUsingGpuDevice) {
-      const input: tf.Tensor5D = tf.ones([1, 2, 2, 2, 1]);
-      const filter: tf.Tensor5D = tf.ones([1, 1, 1, 1, 1]);
-      tf.conv3d(input, filter, 1, 'same', 'NDHWC', [2, 2, 2]);
-    }
-  });
-});
-
-describe('Exposes Backend for internal Op execution.', () => {
-  it('Provides the Node backend over a function', () => {
-    const backend = nodeBackend();
-    expect(backend instanceof NodeJSKernelBackend).toBeTruthy();
-  });
-
-  it('Provides internal access to the binding', () => {
-    expect(nodeBackend().binding).toBeDefined();
-  });
-
-  it('throw error if backend is not tensorflow', async () => {
-    const testBackend = new TestKernelBackend();
-    tf.registerBackend('fake', () => testBackend);
-    tf.setBackend('fake');
-    try {
-      expect(() => ensureTensorflowBackend()).toThrowError(
-        'Expect the current backend to be "tensorflow", but got "fake"');
-    } finally {
-      tf.setBackend('tensorflow');
-    }
-  });
-});
-
-describe('getTFDType()', () => {
-  const binding = nodeBackend().binding;
-
-  it('handles float32', () => {
-    expect(getTFDType('float32')).toBe(binding.TF_FLOAT);
-  });
-  it('handles int32', () => {
-    expect(getTFDType('int32')).toBe(binding.TF_INT32);
-  });
-  it('handles bool', () => {
-    expect(getTFDType('bool')).toBe(binding.TF_BOOL);
-  });
-  it('handles unknown types', () => {
-    expect(() => getTFDType(null)).toThrowError();
-  });
-});
-
-describe('createTypeOpAttr()', () => {
-  const binding = nodeBackend().binding;
-
-  it('Creates a valid type attribute', () => {
-    const attr = createTensorsTypeOpAttr('foo', 'float32');
-    expect(attr.name).toBe('foo');
-    expect(attr.type).toBe(binding.TF_ATTR_TYPE);
-    expect(attr.value).toBe(binding.TF_FLOAT);
-  });
-
-  it('handles unknown dtypes', () => {
-    expect(() => createTensorsTypeOpAttr('foo', null)).toThrowError();
-  });
-});
-
-describe('Returns TFEOpAttr for a Tensor or list of Tensors', () => {
-  const binding = nodeBackend().binding;
-
-  it('handles a single Tensor', () => {
-    const result = createTensorsTypeOpAttr('T', tf.scalar(13, 'float32'));
-    expect(result.name).toBe('T');
-    expect(result.type).toBe(binding.TF_ATTR_TYPE);
-    expect(result.value).toBe(binding.TF_FLOAT);
-  });
-  it('handles a list of Tensors', () => {
-    const tensors = [tf.scalar(1, 'int32'), tf.scalar(20.1, 'float32')];
-    const result = createTensorsTypeOpAttr('T', tensors);
-    expect(result.name).toBe('T');
-    expect(result.type).toBe(binding.TF_ATTR_TYPE);
-    expect(result.value).toBe(binding.TF_INT32);
-  });
-  it('handles null', () => {
-    expect(() => createTensorsTypeOpAttr('T', null)).toThrowError();
-  });
-  it('handles list of null', () => {
-    const inputs = [null, null] as tf.Tensor[];
-    expect(() => createTensorsTypeOpAttr('T', inputs)).toThrowError();
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/proto/api.proto b/tfjs-master/tfjs-node/src/proto/api.proto
deleted file mode 100644
index 76da81714..000000000
--- a/tfjs-master/tfjs-node/src/proto/api.proto
+++ /dev/null
@@ -1,513 +0,0 @@
-// api.proto
-// Definition of various TensorFlow protobuf messages for use with the TensorFlow API.
-//
-// Assembled from these relevant proto sources:
-// https://github.com/google/protobuf/blob/master/src/google/protobuf/any.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/attr_value.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/versions.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/node_def.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/saver.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/saved_model.proto
-// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/function.proto
-// https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/summary.proto
-// https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/plugin_data.proto
-
-
-// This file is used to generate the api_pb.js file with the JavaScript Protocol
-// Buffers runtime library (https://github.com/protocolbuffers/protobuf/tree/master/js).
-// The protocol compiler (https://github.com/protocolbuffers/protobuf/releases)
-// translated this file (same as proto file in tfjs-converter:
-// https://github.com/tensorflow/tfjs/blob/master/tfjs-converter/src/data/api.proto)
-// into the api_pb.js file with command:
-//
-// ```
-// $ protoc --js_out=import_style=commonjs,binary:. api.proto
-// ```
-
-
-syntax = "proto3";
-package tensorflow;
-
-message Any {
-  string type_url = 1;
-  bytes value = 2;
-}
-
-enum DataType {
-  // Not a legal value for DataType.  Used to indicate a DataType field
-  // has not been set.
-  DT_INVALID = 0;
-
-  // Data types that all computation devices are expected to be
-  // capable to support.
-  DT_FLOAT = 1;
-  DT_DOUBLE = 2;
-  DT_INT32 = 3;
-  DT_UINT8 = 4;
-  DT_INT16 = 5;
-  DT_INT8 = 6;
-  DT_STRING = 7;
-  DT_COMPLEX64 = 8;  // Single-precision complex
-  DT_INT64 = 9;
-  DT_BOOL = 10;
-  DT_QINT8 = 11;     // Quantized int8
-  DT_QUINT8 = 12;    // Quantized uint8
-  DT_QINT32 = 13;    // Quantized int32
-  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
-
-  // Do not use!  These are only for parameters.  Every enum above
-  // should have a corresponding value below (verified by types_test).
-  DT_FLOAT_REF = 101;
-  DT_DOUBLE_REF = 102;
-  DT_INT32_REF = 103;
-  DT_UINT8_REF = 104;
-  DT_INT16_REF = 105;
-  DT_INT8_REF = 106;
-  DT_STRING_REF = 107;
-  DT_COMPLEX64_REF = 108;
-  DT_INT64_REF = 109;
-  DT_BOOL_REF = 110;
-  DT_QINT8_REF = 111;
-  DT_QUINT8_REF = 112;
-  DT_QINT32_REF = 113;
-  DT_BFLOAT16_REF = 114;
-}
-
-message TensorShape {
-  // One dimension of the tensor.
-  message Dim {
-    // Size of the tensor in that dimension.
-    int64 size = 1;
-
-    // Optional name of the tensor dimension.
-    string name = 2;
-  }
-
-  // Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x
-  // 40 2D tensor.  The names are optional.
-  //
-  // The order of entries in "dim" matters: It indicates the layout of the
-  // values in the tensor in-memory representation.
-  //
-  // The first entry in "dim" is the outermost dimension used to layout the
-  // values, the last entry is the innermost dimension.  This matches the
-  // in-memory layout of RowMajor Eigen tensors.
-  repeated Dim dim = 2;
-
-  bool unknown_rank = 3;
-}
-
-message Tensor {
-  DataType dtype = 1;
-
-  // Shape of the tensor.  TODO(touts): sort out the 0-rank issues.
-  TensorShape tensor_shape = 2;
-
-  // Only one of the representations below is set, one of "tensor_contents" and
-  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
-  // contain repeated fields it would require another extra set of messages.
-
-  // Version number.
-  //
-  // In version 0, if the "repeated xxx" representations contain only one
-  // element, that element is repeated to fill the shape.  This makes it easy
-  // to represent a constant Tensor with a single value.
-  int32 version_number = 3;
-
-  // Serialized content from TensorBase::Serialize() This representation can be
-  // used for all tensor types.
-  bytes tensor_content = 4;
-
-  // Type specific representations that make it easy to create tensor protos in
-  // all languages.  Only the representation corresponding to "dtype" can
-  // be set.  The values hold the flattened representation of the tensor in
-  // row major order.
-
-  // DT_FLOAT.
-  repeated float float_val = 5 [packed = true];
-
-  // DT_DOUBLE.
-  repeated double double_val = 6 [packed = true];
-
-  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
-  repeated int32 int_val = 7 [packed = true];
-
-  // DT_STRING
-  repeated bytes string_val = 8;
-
-  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
-  // and imaginary parts of i-th single precision complex.
-  repeated float scomplex_val = 9 [packed = true];
-
-  // DT_INT64
-  repeated int64 int64_val = 10 [packed = true];
-
-  // DT_BOOL
-  repeated bool bool_val = 11 [packed = true];
-
-  // DT_UINT32
-  repeated uint32 uint32_val = 16 [packed = true];
-
-  // DT_UINT64
-  repeated uint64 uint64_val = 17 [packed = true];
-}
-
-message AttrValue {
-  message ListValue {
-    repeated bytes s = 2;
-    repeated int64 i = 3 [packed = true];
-    repeated float f = 4 [packed = true];
-    repeated bool b = 5 [packed = true];
-    repeated DataType type = 6 [packed = true];
-    repeated TensorShape shape = 7;
-    repeated Tensor tensor = 8;
-    repeated NameAttrList func = 9;
-  }
-
-  oneof value {
-    ListValue list = 1;
-    bytes s = 2;
-    int64 i = 3;
-    float f = 4;
-    bool b = 5;
-    DataType type = 6;
-    TensorShape shape = 7;
-    Tensor tensor = 8;
-    string placeholder = 9;
-    NameAttrList func = 10;
-  }
-}
-
-message NameAttrList {
-  string name = 1;
-  map<string, AttrValue> attr = 2;
-}
-
-message NodeDef {
-  string name = 1;
-  string op = 2;
-  repeated string input = 3;
-  string device = 4;
-  map<string, AttrValue> attr = 5;
-}
-
-message VersionDef {
-  int32 producer = 1;
-  int32 min_consumer = 2;
-  repeated int32 bad_consumers = 3;
-}
-
-message GraphDef {
-  repeated NodeDef node = 1;
-  VersionDef versions = 4;
-  FunctionDefLibrary library = 2;
-}
-
-message CollectionDef {
-  message NodeList {
-    repeated string value = 1;
-  }
-  message BytesList {
-    repeated bytes value = 1;
-  }
-  message Int64List {
-    repeated int64 value = 1 [packed = true];
-  }
-  message FloatList {
-    repeated float value = 1 [packed = true];
-  }
-  message AnyList {
-    repeated Any value = 1;
-  }
-
-  oneof kind {
-    NodeList node_list = 1;
-    BytesList bytes_list = 2;
-    Int64List int64_list = 3;
-    FloatList float_list = 4;
-    AnyList any_list = 5;
-  }
-}
-
-message SaverDef {
-  string filename_tensor_name = 1;
-  string save_tensor_name = 2;
-  string restore_op_name = 3;
-  int32 max_to_keep = 4;
-  bool sharded = 5;
-  float keep_checkpoint_every_n_hours = 6;
-
-  enum CheckpointFormatVersion {
-    LEGACY = 0;
-    V1 = 1;
-    V2 = 2;
-  }
-  CheckpointFormatVersion version = 7;
-}
-
-message TensorInfo {
-  message CooSparse {
-    string values_tensor_name = 1;
-    string indices_tensor_name = 2;
-    string dense_shape_tensor_name = 3;
-  }
-
-  oneof encoding {
-    string name = 1;
-    CooSparse coo_sparse = 4;
-  }
-  DataType dtype = 2;
-  TensorShape tensor_shape = 3;
-}
-
-message SignatureDef {
-  map<string, TensorInfo> inputs = 1;
-  map<string, TensorInfo> outputs = 2;
-  string method_name = 3;
-}
-
-message AssetFileDef {
-  TensorInfo tensor_info = 1;
-  string filename = 2;
-}
-
-message OpDef {
-  string name = 1;
-
-  message ArgDef {
-    string name = 1;
-    string description = 2;
-    DataType type = 3;
-    string type_attr = 4;    // if specified, attr must have type "type"
-    string number_attr = 5;  // if specified, attr must have type "int"
-    string type_list_attr = 6;
-    bool is_ref = 16;
-  }
-  repeated ArgDef input_arg = 2;
-  repeated ArgDef output_arg = 3;
-
-  message AttrDef {
-    string name = 1;
-    string type = 2;
-    AttrValue default_value = 3;
-    string description = 4;
-    bool has_minimum = 5;
-    int64 minimum = 6;
-    AttrValue allowed_values = 7;
-  }
-  repeated AttrDef attr = 4;
-
-  message OpDeprecation {
-    int32 version = 1;
-    string explanation = 2;
-  }
-  OpDeprecation deprecation = 8;
-
-  string summary = 5;
-  string description = 6;
-  bool is_commutative = 18;
-  bool is_aggregate = 16;  // for things like add
-  bool is_stateful = 17;  // for things like variables, queue
-  bool allows_uninitialized_input = 19;  // for Assign, etc.
-}
-
-message OpList {
-  repeated OpDef op = 1;
-}
-
-message MetaGraphDef {
-  message MetaInfoDef {
-    string meta_graph_version = 1;
-    OpList stripped_op_list = 2;
-    Any any_info = 3;
-    repeated string tags = 4;
-    string tensorflow_version = 5;
-    string tensorflow_git_version = 6;
-  }
-  MetaInfoDef meta_info_def = 1;
-  GraphDef graph_def = 2;
-  SaverDef saver_def = 3;
-  map<string, CollectionDef> collection_def = 4;
-  map<string, SignatureDef> signature_def = 5;
-  repeated AssetFileDef asset_file_def = 6;
-}
-
-message SavedModel {
-  int64 saved_model_schema_version = 1;
-  repeated MetaGraphDef meta_graphs = 2;
-}
-
-message FunctionDefLibrary {
-  repeated FunctionDef function = 1;
-  repeated GradientDef gradient = 2;
-}
-
-message FunctionDef {
-  OpDef signature = 1;
-  map<string, AttrValue> attr = 5;
-  reserved 2;
-  repeated NodeDef node_def = 3;
-  map<string, string> ret = 4;
-}
-
-message GradientDef {
-  string function_name = 1;  // The function name.
-  string gradient_func = 2;  // The gradient function's name.
-}
-
-/////////////////
-// Tensorboard //
-/////////////////
-
-// Metadata associated with a series of Summary data
-message SummaryDescription {
-  // Hint on how plugins should process the data in this series.
-  // Supported values include "scalar", "histogram", "image", "audio"
-  string type_hint = 1;
-}
-
-// Serialization format for histogram module in
-// core/lib/histogram/histogram.h
-message HistogramProto {
-  double min = 1;
-  double max = 2;
-  double num = 3;
-  double sum = 4;
-  double sum_squares = 5;
-
-  // Parallel arrays encoding the bucket boundaries and the bucket values.
-  // bucket(i) is the count for the bucket i.  The range for
-  // a bucket is:
-  //   i == 0:  -DBL_MAX .. bucket_limit(0)
-  //   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-  repeated double bucket_limit = 6 [packed = true];
-  repeated double bucket = 7 [packed = true];
-}
-
-// A SummaryMetadata encapsulates information on which plugins are able to make
-// use of a certain summary value.
-message SummaryMetadata {
-  message PluginData {
-    // The name of the plugin this data pertains to.
-    string plugin_name = 1;
-
-    // The content to store for the plugin. The best practice is for this to be
-    // a binary serialized protocol buffer.
-    bytes content = 2;
-  }
-
-  // Data that associates a summary with a certain plugin.
-  PluginData plugin_data = 1;
-
-  // Display name for viewing in TensorBoard.
-  string display_name = 2;
-
-  // Longform readable description of the summary sequence. Markdown supported.
-  string summary_description = 3;
-
-  // Class of data stored in this time series. Required for compatibility with
-  // TensorBoard's generic data facilities (`DataProvider`, et al.). This value
-  // imposes constraints on the dtype and shape of the corresponding tensor
-  // values. See `DataClass` docs for details.
-  DataClass data_class = 4;
-}
-
-enum DataClass {
-  // Unknown data class, used (implicitly) for legacy data. Will not be
-  // processed by data ingestion pipelines.
-  DATA_CLASS_UNKNOWN = 0;
-  // Scalar time series. Each `Value` for the corresponding tag must have
-  // `tensor` set to a rank-0 tensor of type `DT_FLOAT` (float32).
-  DATA_CLASS_SCALAR = 1;
-  // Tensor time series. Each `Value` for the corresponding tag must have
-  // `tensor` set. The tensor value is arbitrary, but should be small to
-  // accommodate direct storage in database backends: an upper bound of a few
-  // kilobytes is a reasonable rule of thumb.
-  DATA_CLASS_TENSOR = 2;
-  // Blob sequence time series. Each `Value` for the corresponding tag must
-  // have `tensor` set to a rank-1 tensor of bytestring dtype.
-  DATA_CLASS_BLOB_SEQUENCE = 3;
-}
-
-// A Summary is a set of named values to be displayed by the
-// visualizer.
-//
-// Summaries are produced regularly during training, as controlled by
-// the "summary_interval_secs" attribute of the training operation.
-// Summaries are also produced at the end of an evaluation.
-message Summary {
-  message Image {
-    // Dimensions of the image.
-    int32 height = 1;
-    int32 width = 2;
-    // Valid colorspace values are
-    //   1 - grayscale
-    //   2 - grayscale + alpha
-    //   3 - RGB
-    //   4 - RGBA
-    //   5 - DIGITAL_YUV
-    //   6 - BGRA
-    int32 colorspace = 3;
-    // Image data in encoded format.  All image formats supported by
-    // image_codec::CoderUtil can be stored here.
-    bytes encoded_image_string = 4;
-  }
-
-  message Audio {
-    // Sample rate of the audio in Hz.
-    float sample_rate = 1;
-    // Number of channels of audio.
-    int64 num_channels = 2;
-    // Length of the audio in frames (samples per channel).
-    int64 length_frames = 3;
-    // Encoded audio data and its associated RFC 2045 content type (e.g.
-    // "audio/wav").
-    bytes encoded_audio_string = 4;
-    string content_type = 5;
-  }
-
-  message Value {
-    // This field is deprecated and will not be set.
-    string node_name = 7;
-
-    // Tag name for the data. Used by TensorBoard plugins to organize data. Tags
-    // are often organized by scope (which contains slashes to convey
-    // hierarchy). For example: foo/bar/0
-    string tag = 1;
-
-    // Contains metadata on the summary value such as which plugins may use it.
-    // Take note that many summary values may lack a metadata field. This is
-    // because the FileWriter only keeps a metadata object on the first summary
-    // value with a certain tag for each tag. TensorBoard then remembers which
-    // tags are associated with which plugins. This saves space.
-    SummaryMetadata metadata = 9;
-
-    // Value associated with the tag.
-    oneof value {
-      float simple_value = 2;
-      bytes obsolete_old_style_histogram = 3;
-      Image image = 4;
-      HistogramProto histo = 5;
-      Audio audio = 6;
-      Tensor tensor = 8;
-    }
-  }
-
-  // Set of values for the summary.
-  repeated Value value = 1;
-}
-
-// Histogram summaries created by the `tensorboard.plugins.histogram.summary`
-// module will include `SummaryMetadata` whose `plugin_data` field has
-// as `content` a binary string that is the encoding of a
-// `HistogramPluginData` proto.
-message HistogramPluginData {
-  // Version `0` is the only supported version.
-  int32 version = 1;
-}
diff --git a/tfjs-master/tfjs-node/src/proto/api_pb.js b/tfjs-master/tfjs-node/src/proto/api_pb.js
deleted file mode 100644
index 99bb8930c..000000000
--- a/tfjs-master/tfjs-node/src/proto/api_pb.js
+++ /dev/null
@@ -1,12125 +0,0 @@
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-/**
- * Static version of the {@see toObject} method.
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- * @param {!proto.tensorflow.TensorShape.Dim} message
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-/**
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-/**
- * @param {!proto.tensorflow.TensorShape.Dim=} opt_value
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- * @return {!proto.tensorflow.TensorShape.Dim}
- */
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-  return jspb.Message.addToRepeatedWrapperField(this, 2, opt_value, proto.tensorflow.TensorShape.Dim, opt_index);
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-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.TensorShape} returns this
- */
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-  return this.setDimList([]);
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-/**
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-/**
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-/**
- * Creates an object representation of this proto.
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- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
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- *     JSPB instance for transitional soy proto support:
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- * @return {!Object}
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-  return proto.tensorflow.Tensor.toObject(opt_includeInstance, this);
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-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.Tensor} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
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-    versionNumber: jspb.Message.getFieldWithDefault(msg, 3, 0),
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-  var msg = new proto.tensorflow.Tensor;
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-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.Tensor} msg The message object to deserialize into.
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-  proto.tensorflow.Tensor.serializeBinaryToWriter(this, writer);
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-/**
- * Serializes the given message to binary data (in protobuf wire
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- * @param {!proto.tensorflow.Tensor} message
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-  f = message.getInt64ValList();
-  if (f.length > 0) {
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-  if (f.length > 0) {
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- */
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-/**
- * @param {!proto.tensorflow.DataType} value
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- */
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-  return jspb.Message.setProto3EnumField(this, 1, value);
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-
-/**
- * optional TensorShape tensor_shape = 2;
- * @return {?proto.tensorflow.TensorShape}
- */
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-  return /** @type{?proto.tensorflow.TensorShape} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.TensorShape, 2));
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-/**
- * @param {?proto.tensorflow.TensorShape|undefined} value
- * @return {!proto.tensorflow.Tensor} returns this
-*/
-proto.tensorflow.Tensor.prototype.setTensorShape = function(value) {
-  return jspb.Message.setWrapperField(this, 2, value);
-};
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-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.clearTensorShape = function() {
-  return this.setTensorShape(undefined);
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-
-/**
- * Returns whether this field is set.
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- */
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-  return jspb.Message.getField(this, 2) != null;
-};
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-
-/**
- * optional int32 version_number = 3;
- * @return {number}
- */
-proto.tensorflow.Tensor.prototype.getVersionNumber = function() {
-  return /** @type {number} */ (jspb.Message.getFieldWithDefault(this, 3, 0));
-};
-
-
-/**
- * @param {number} value
- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.setVersionNumber = function(value) {
-  return jspb.Message.setProto3IntField(this, 3, value);
-};
-
-
-/**
- * optional bytes tensor_content = 4;
- * @return {!(string|Uint8Array)}
- */
-proto.tensorflow.Tensor.prototype.getTensorContent = function() {
-  return /** @type {!(string|Uint8Array)} */ (jspb.Message.getFieldWithDefault(this, 4, ""));
-};
-
-
-/**
- * optional bytes tensor_content = 4;
- * This is a type-conversion wrapper around `getTensorContent()`
- * @return {string}
- */
-proto.tensorflow.Tensor.prototype.getTensorContent_asB64 = function() {
-  return /** @type {string} */ (jspb.Message.bytesAsB64(
-      this.getTensorContent()));
-};
-
-
-/**
- * optional bytes tensor_content = 4;
- * Note that Uint8Array is not supported on all browsers.
- * @see http://caniuse.com/Uint8Array
- * This is a type-conversion wrapper around `getTensorContent()`
- * @return {!Uint8Array}
- */
-proto.tensorflow.Tensor.prototype.getTensorContent_asU8 = function() {
-  return /** @type {!Uint8Array} */ (jspb.Message.bytesAsU8(
-      this.getTensorContent()));
-};
-
-
-/**
- * @param {!(string|Uint8Array)} value
- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.setTensorContent = function(value) {
-  return jspb.Message.setProto3BytesField(this, 4, value);
-};
-
-
-/**
- * repeated float float_val = 5;
- * @return {!Array<number>}
- */
-proto.tensorflow.Tensor.prototype.getFloatValList = function() {
-  return /** @type {!Array<number>} */ (jspb.Message.getRepeatedFloatingPointField(this, 5));
-};
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-/**
- * @param {!Array<number>} value
- * @return {!proto.tensorflow.Tensor} returns this
- */
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-  return jspb.Message.setField(this, 5, value || []);
-};
-
-
-/**
- * @param {number} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.addFloatVal = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 5, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
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- */
-proto.tensorflow.Tensor.prototype.clearFloatValList = function() {
-  return this.setFloatValList([]);
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-/**
- * repeated double double_val = 6;
- * @return {!Array<number>}
- */
-proto.tensorflow.Tensor.prototype.getDoubleValList = function() {
-  return /** @type {!Array<number>} */ (jspb.Message.getRepeatedFloatingPointField(this, 6));
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-
-/**
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- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.setDoubleValList = function(value) {
-  return jspb.Message.setField(this, 6, value || []);
-};
-
-
-/**
- * @param {number} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.addDoubleVal = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 6, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.clearDoubleValList = function() {
-  return this.setDoubleValList([]);
-};
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-
-/**
- * repeated int32 int_val = 7;
- * @return {!Array<number>}
- */
-proto.tensorflow.Tensor.prototype.getIntValList = function() {
-  return /** @type {!Array<number>} */ (jspb.Message.getRepeatedField(this, 7));
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-
-
-/**
- * @param {!Array<number>} value
- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.setIntValList = function(value) {
-  return jspb.Message.setField(this, 7, value || []);
-};
-
-
-/**
- * @param {number} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.addIntVal = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 7, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.Tensor} returns this
- */
-proto.tensorflow.Tensor.prototype.clearIntValList = function() {
-  return this.setIntValList([]);
-};
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-
-/**
- * repeated bytes string_val = 8;
- * @return {!(Array<!Uint8Array>|Array<string>)}
- */
-proto.tensorflow.Tensor.prototype.getStringValList = function() {
-  return /** @type {!(Array<!Uint8Array>|Array<string>)} */ (jspb.Message.getRepeatedField(this, 8));
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-/**
- * repeated bytes string_val = 8;
- * This is a type-conversion wrapper around `getStringValList()`
- * @return {!Array<string>}
- */
-proto.tensorflow.Tensor.prototype.getStringValList_asB64 = function() {
-  return /** @type {!Array<string>} */ (jspb.Message.bytesListAsB64(
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-  return jspb.Message.addToRepeatedField(this, 16, value, opt_index);
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-
-
-/**
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-
-
-/**
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-/**
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- * @private {!Array<!Array<number>>}
- * @const
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-
-/**
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-proto.tensorflow.AttrValue.ValueCase = {
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-  TYPE: 6,
-  SHAPE: 7,
-  TENSOR: 8,
-  PLACEHOLDER: 9,
-  FUNC: 10
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-
-/**
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- */
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-
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-/**
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- * Field names that are reserved in JavaScript and will be renamed to pb_name.
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- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
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- *     JSPB instance for transitional soy proto support:
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- * @return {!Object}
- */
-proto.tensorflow.AttrValue.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.AttrValue.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
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- *     the JSPB instance for transitional soy proto support:
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- * @param {!proto.tensorflow.AttrValue} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.AttrValue.toObject = function(includeInstance, msg) {
-  var f, obj = {
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-    b: jspb.Message.getBooleanFieldWithDefault(msg, 5, false),
-    type: jspb.Message.getFieldWithDefault(msg, 6, 0),
-    shape: (f = msg.getShape()) && proto.tensorflow.TensorShape.toObject(includeInstance, f),
-    tensor: (f = msg.getTensor()) && proto.tensorflow.Tensor.toObject(includeInstance, f),
-    placeholder: jspb.Message.getFieldWithDefault(msg, 9, ""),
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- */
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-  var reader = new jspb.BinaryReader(bytes);
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-
-/**
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-      msg.setTensor(value);
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-      var value = /** @type {string} */ (reader.readString());
-      msg.setPlaceholder(value);
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-    case 10:
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-
-/**
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- */
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-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.AttrValue.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
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- * @param {!proto.tensorflow.AttrValue} message
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-
-
-/**
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- * @private {!Array<number>}
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-proto.tensorflow.AttrValue.ListValue.repeatedFields_ = [2,3,4,5,6,7,8,9];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.AttrValue.ListValue.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.AttrValue.ListValue} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.AttrValue.ListValue.toObject = function(includeInstance, msg) {
-  var f, obj = {
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-    iList: (f = jspb.Message.getRepeatedField(msg, 3)) == null ? undefined : f,
-    fList: (f = jspb.Message.getRepeatedFloatingPointField(msg, 4)) == null ? undefined : f,
-    bList: (f = jspb.Message.getRepeatedBooleanField(msg, 5)) == null ? undefined : f,
-    typeList: (f = jspb.Message.getRepeatedField(msg, 6)) == null ? undefined : f,
-    shapeList: jspb.Message.toObjectList(msg.getShapeList(),
-    proto.tensorflow.TensorShape.toObject, includeInstance),
-    tensorList: jspb.Message.toObjectList(msg.getTensorList(),
-    proto.tensorflow.Tensor.toObject, includeInstance),
-    funcList: jspb.Message.toObjectList(msg.getFuncList(),
-    proto.tensorflow.NameAttrList.toObject, includeInstance)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
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-  return obj;
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-}
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-
-/**
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- * @return {!proto.tensorflow.AttrValue.ListValue}
- */
-proto.tensorflow.AttrValue.ListValue.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.AttrValue.ListValue;
-  return proto.tensorflow.AttrValue.ListValue.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.AttrValue.ListValue} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.AttrValue.ListValue}
- */
-proto.tensorflow.AttrValue.ListValue.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
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-    var field = reader.getFieldNumber();
-    switch (field) {
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-    case 3:
-      var values = /** @type {!Array<number>} */ (reader.isDelimited() ? reader.readPackedInt64() : [reader.readInt64()]);
-      for (var i = 0; i < values.length; i++) {
-        msg.addI(values[i]);
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-      break;
-    case 4:
-      var values = /** @type {!Array<number>} */ (reader.isDelimited() ? reader.readPackedFloat() : [reader.readFloat()]);
-      for (var i = 0; i < values.length; i++) {
-        msg.addF(values[i]);
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-      break;
-    case 5:
-      var values = /** @type {!Array<boolean>} */ (reader.isDelimited() ? reader.readPackedBool() : [reader.readBool()]);
-      for (var i = 0; i < values.length; i++) {
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-      }
-      break;
-    case 6:
-      var values = /** @type {!Array<!proto.tensorflow.DataType>} */ (reader.isDelimited() ? reader.readPackedEnum() : [reader.readEnum()]);
-      for (var i = 0; i < values.length; i++) {
-        msg.addType(values[i]);
-      }
-      break;
-    case 7:
-      var value = new proto.tensorflow.TensorShape;
-      reader.readMessage(value,proto.tensorflow.TensorShape.deserializeBinaryFromReader);
-      msg.addShape(value);
-      break;
-    case 8:
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-      reader.readMessage(value,proto.tensorflow.Tensor.deserializeBinaryFromReader);
-      msg.addTensor(value);
-      break;
-    case 9:
-      var value = new proto.tensorflow.NameAttrList;
-      reader.readMessage(value,proto.tensorflow.NameAttrList.deserializeBinaryFromReader);
-      msg.addFunc(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
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-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.AttrValue.ListValue.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.AttrValue.ListValue} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.AttrValue.ListValue.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
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-    writer.writeRepeatedBytes(
-      2,
-      f
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-  if (f.length > 0) {
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-  f = message.getFuncList();
-  if (f.length > 0) {
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-
-/**
- * repeated bytes s = 2;
- * @return {!(Array<!Uint8Array>|Array<string>)}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getSList = function() {
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-};
-
-
-/**
- * repeated bytes s = 2;
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- * @return {!Array<string>}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getSList_asB64 = function() {
-  return /** @type {!Array<string>} */ (jspb.Message.bytesListAsB64(
-      this.getSList()));
-};
-
-
-/**
- * repeated bytes s = 2;
- * Note that Uint8Array is not supported on all browsers.
- * @see http://caniuse.com/Uint8Array
- * This is a type-conversion wrapper around `getSList()`
- * @return {!Array<!Uint8Array>}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getSList_asU8 = function() {
-  return /** @type {!Array<!Uint8Array>} */ (jspb.Message.bytesListAsU8(
-      this.getSList()));
-};
-
-
-/**
- * @param {!(Array<!Uint8Array>|Array<string>)} value
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.setSList = function(value) {
-  return jspb.Message.setField(this, 2, value || []);
-};
-
-
-/**
- * @param {!(string|Uint8Array)} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.addS = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 2, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.clearSList = function() {
-  return this.setSList([]);
-};
-
-
-/**
- * repeated int64 i = 3;
- * @return {!Array<number>}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getIList = function() {
-  return /** @type {!Array<number>} */ (jspb.Message.getRepeatedField(this, 3));
-};
-
-
-/**
- * @param {!Array<number>} value
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.setIList = function(value) {
-  return jspb.Message.setField(this, 3, value || []);
-};
-
-
-/**
- * @param {number} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.addI = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 3, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.clearIList = function() {
-  return this.setIList([]);
-};
-
-
-/**
- * repeated float f = 4;
- * @return {!Array<number>}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getFList = function() {
-  return /** @type {!Array<number>} */ (jspb.Message.getRepeatedFloatingPointField(this, 4));
-};
-
-
-/**
- * @param {!Array<number>} value
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.setFList = function(value) {
-  return jspb.Message.setField(this, 4, value || []);
-};
-
-
-/**
- * @param {number} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.addF = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 4, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.clearFList = function() {
-  return this.setFList([]);
-};
-
-
-/**
- * repeated bool b = 5;
- * @return {!Array<boolean>}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getBList = function() {
-  return /** @type {!Array<boolean>} */ (jspb.Message.getRepeatedBooleanField(this, 5));
-};
-
-
-/**
- * @param {!Array<boolean>} value
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.setBList = function(value) {
-  return jspb.Message.setField(this, 5, value || []);
-};
-
-
-/**
- * @param {boolean} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.addB = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 5, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.clearBList = function() {
-  return this.setBList([]);
-};
-
-
-/**
- * repeated DataType type = 6;
- * @return {!Array<!proto.tensorflow.DataType>}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getTypeList = function() {
-  return /** @type {!Array<!proto.tensorflow.DataType>} */ (jspb.Message.getRepeatedField(this, 6));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.DataType>} value
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.setTypeList = function(value) {
-  return jspb.Message.setField(this, 6, value || []);
-};
-
-
-/**
- * @param {!proto.tensorflow.DataType} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.addType = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 6, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.clearTypeList = function() {
-  return this.setTypeList([]);
-};
-
-
-/**
- * repeated TensorShape shape = 7;
- * @return {!Array<!proto.tensorflow.TensorShape>}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getShapeList = function() {
-  return /** @type{!Array<!proto.tensorflow.TensorShape>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.TensorShape, 7));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.TensorShape>} value
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
-*/
-proto.tensorflow.AttrValue.ListValue.prototype.setShapeList = function(value) {
-  return jspb.Message.setRepeatedWrapperField(this, 7, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.TensorShape=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.TensorShape}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.addShape = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 7, opt_value, proto.tensorflow.TensorShape, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.clearShapeList = function() {
-  return this.setShapeList([]);
-};
-
-
-/**
- * repeated Tensor tensor = 8;
- * @return {!Array<!proto.tensorflow.Tensor>}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getTensorList = function() {
-  return /** @type{!Array<!proto.tensorflow.Tensor>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.Tensor, 8));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.Tensor>} value
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
-*/
-proto.tensorflow.AttrValue.ListValue.prototype.setTensorList = function(value) {
-  return jspb.Message.setRepeatedWrapperField(this, 8, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.Tensor=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.Tensor}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.addTensor = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 8, opt_value, proto.tensorflow.Tensor, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.clearTensorList = function() {
-  return this.setTensorList([]);
-};
-
-
-/**
- * repeated NameAttrList func = 9;
- * @return {!Array<!proto.tensorflow.NameAttrList>}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.getFuncList = function() {
-  return /** @type{!Array<!proto.tensorflow.NameAttrList>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.NameAttrList, 9));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.NameAttrList>} value
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
-*/
-proto.tensorflow.AttrValue.ListValue.prototype.setFuncList = function(value) {
-  return jspb.Message.setRepeatedWrapperField(this, 9, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.NameAttrList=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.NameAttrList}
- */
-proto.tensorflow.AttrValue.ListValue.prototype.addFunc = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 9, opt_value, proto.tensorflow.NameAttrList, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.AttrValue.ListValue} returns this
- */
-proto.tensorflow.AttrValue.ListValue.prototype.clearFuncList = function() {
-  return this.setFuncList([]);
-};
-
-
-/**
- * optional ListValue list = 1;
- * @return {?proto.tensorflow.AttrValue.ListValue}
- */
-proto.tensorflow.AttrValue.prototype.getList = function() {
-  return /** @type{?proto.tensorflow.AttrValue.ListValue} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.AttrValue.ListValue, 1));
-};
-
-
-/**
- * @param {?proto.tensorflow.AttrValue.ListValue|undefined} value
- * @return {!proto.tensorflow.AttrValue} returns this
-*/
-proto.tensorflow.AttrValue.prototype.setList = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 1, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearList = function() {
-  return this.setList(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasList = function() {
-  return jspb.Message.getField(this, 1) != null;
-};
-
-
-/**
- * optional bytes s = 2;
- * @return {!(string|Uint8Array)}
- */
-proto.tensorflow.AttrValue.prototype.getS = function() {
-  return /** @type {!(string|Uint8Array)} */ (jspb.Message.getFieldWithDefault(this, 2, ""));
-};
-
-
-/**
- * optional bytes s = 2;
- * This is a type-conversion wrapper around `getS()`
- * @return {string}
- */
-proto.tensorflow.AttrValue.prototype.getS_asB64 = function() {
-  return /** @type {string} */ (jspb.Message.bytesAsB64(
-      this.getS()));
-};
-
-
-/**
- * optional bytes s = 2;
- * Note that Uint8Array is not supported on all browsers.
- * @see http://caniuse.com/Uint8Array
- * This is a type-conversion wrapper around `getS()`
- * @return {!Uint8Array}
- */
-proto.tensorflow.AttrValue.prototype.getS_asU8 = function() {
-  return /** @type {!Uint8Array} */ (jspb.Message.bytesAsU8(
-      this.getS()));
-};
-
-
-/**
- * @param {!(string|Uint8Array)} value
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.setS = function(value) {
-  return jspb.Message.setOneofField(this, 2, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearS = function() {
-  return jspb.Message.setOneofField(this, 2, proto.tensorflow.AttrValue.oneofGroups_[0], undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasS = function() {
-  return jspb.Message.getField(this, 2) != null;
-};
-
-
-/**
- * optional int64 i = 3;
- * @return {number}
- */
-proto.tensorflow.AttrValue.prototype.getI = function() {
-  return /** @type {number} */ (jspb.Message.getFieldWithDefault(this, 3, 0));
-};
-
-
-/**
- * @param {number} value
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.setI = function(value) {
-  return jspb.Message.setOneofField(this, 3, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearI = function() {
-  return jspb.Message.setOneofField(this, 3, proto.tensorflow.AttrValue.oneofGroups_[0], undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasI = function() {
-  return jspb.Message.getField(this, 3) != null;
-};
-
-
-/**
- * optional float f = 4;
- * @return {number}
- */
-proto.tensorflow.AttrValue.prototype.getF = function() {
-  return /** @type {number} */ (jspb.Message.getFloatingPointFieldWithDefault(this, 4, 0.0));
-};
-
-
-/**
- * @param {number} value
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.setF = function(value) {
-  return jspb.Message.setOneofField(this, 4, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearF = function() {
-  return jspb.Message.setOneofField(this, 4, proto.tensorflow.AttrValue.oneofGroups_[0], undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasF = function() {
-  return jspb.Message.getField(this, 4) != null;
-};
-
-
-/**
- * optional bool b = 5;
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.getB = function() {
-  return /** @type {boolean} */ (jspb.Message.getBooleanFieldWithDefault(this, 5, false));
-};
-
-
-/**
- * @param {boolean} value
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.setB = function(value) {
-  return jspb.Message.setOneofField(this, 5, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearB = function() {
-  return jspb.Message.setOneofField(this, 5, proto.tensorflow.AttrValue.oneofGroups_[0], undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasB = function() {
-  return jspb.Message.getField(this, 5) != null;
-};
-
-
-/**
- * optional DataType type = 6;
- * @return {!proto.tensorflow.DataType}
- */
-proto.tensorflow.AttrValue.prototype.getType = function() {
-  return /** @type {!proto.tensorflow.DataType} */ (jspb.Message.getFieldWithDefault(this, 6, 0));
-};
-
-
-/**
- * @param {!proto.tensorflow.DataType} value
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.setType = function(value) {
-  return jspb.Message.setOneofField(this, 6, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearType = function() {
-  return jspb.Message.setOneofField(this, 6, proto.tensorflow.AttrValue.oneofGroups_[0], undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasType = function() {
-  return jspb.Message.getField(this, 6) != null;
-};
-
-
-/**
- * optional TensorShape shape = 7;
- * @return {?proto.tensorflow.TensorShape}
- */
-proto.tensorflow.AttrValue.prototype.getShape = function() {
-  return /** @type{?proto.tensorflow.TensorShape} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.TensorShape, 7));
-};
-
-
-/**
- * @param {?proto.tensorflow.TensorShape|undefined} value
- * @return {!proto.tensorflow.AttrValue} returns this
-*/
-proto.tensorflow.AttrValue.prototype.setShape = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 7, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearShape = function() {
-  return this.setShape(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasShape = function() {
-  return jspb.Message.getField(this, 7) != null;
-};
-
-
-/**
- * optional Tensor tensor = 8;
- * @return {?proto.tensorflow.Tensor}
- */
-proto.tensorflow.AttrValue.prototype.getTensor = function() {
-  return /** @type{?proto.tensorflow.Tensor} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.Tensor, 8));
-};
-
-
-/**
- * @param {?proto.tensorflow.Tensor|undefined} value
- * @return {!proto.tensorflow.AttrValue} returns this
-*/
-proto.tensorflow.AttrValue.prototype.setTensor = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 8, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearTensor = function() {
-  return this.setTensor(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasTensor = function() {
-  return jspb.Message.getField(this, 8) != null;
-};
-
-
-/**
- * optional string placeholder = 9;
- * @return {string}
- */
-proto.tensorflow.AttrValue.prototype.getPlaceholder = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 9, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.setPlaceholder = function(value) {
-  return jspb.Message.setOneofField(this, 9, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearPlaceholder = function() {
-  return jspb.Message.setOneofField(this, 9, proto.tensorflow.AttrValue.oneofGroups_[0], undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasPlaceholder = function() {
-  return jspb.Message.getField(this, 9) != null;
-};
-
-
-/**
- * optional NameAttrList func = 10;
- * @return {?proto.tensorflow.NameAttrList}
- */
-proto.tensorflow.AttrValue.prototype.getFunc = function() {
-  return /** @type{?proto.tensorflow.NameAttrList} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.NameAttrList, 10));
-};
-
-
-/**
- * @param {?proto.tensorflow.NameAttrList|undefined} value
- * @return {!proto.tensorflow.AttrValue} returns this
-*/
-proto.tensorflow.AttrValue.prototype.setFunc = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 10, proto.tensorflow.AttrValue.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.AttrValue} returns this
- */
-proto.tensorflow.AttrValue.prototype.clearFunc = function() {
-  return this.setFunc(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AttrValue.prototype.hasFunc = function() {
-  return jspb.Message.getField(this, 10) != null;
-};
-
-
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.NameAttrList.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.NameAttrList.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.NameAttrList} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.NameAttrList.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    name: jspb.Message.getFieldWithDefault(msg, 1, ""),
-    attrMap: (f = msg.getAttrMap()) ? f.toObject(includeInstance, proto.tensorflow.AttrValue.toObject) : []
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
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- * @return {!proto.tensorflow.NameAttrList}
- */
-proto.tensorflow.NameAttrList.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.NameAttrList;
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-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
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-  proto.tensorflow.NameAttrList.serializeBinaryToWriter(this, writer);
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-
-
-/**
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-
-/**
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-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 1, ""));
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-
-/**
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-
-/**
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- * @return {!jspb.Map<string,!proto.tensorflow.AttrValue>}
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-
-/**
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-proto.tensorflow.NameAttrList.prototype.clearAttrMap = function() {
-  this.getAttrMap().clear();
-  return this;};
-
-
-
-/**
- * List of repeated fields within this message type.
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-
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-if (jspb.Message.GENERATE_TO_OBJECT) {
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- * Field names that are reserved in JavaScript and will be renamed to pb_name.
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- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
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- *     JSPB instance for transitional soy proto support:
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- * @return {!Object}
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-proto.tensorflow.NodeDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.NodeDef.toObject(opt_includeInstance, this);
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-
-/**
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- *     the JSPB instance for transitional soy proto support:
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- * @param {!proto.tensorflow.NodeDef} msg The msg instance to transform.
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-proto.tensorflow.NodeDef.toObject = function(includeInstance, msg) {
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-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.NodeDef;
-  return proto.tensorflow.NodeDef.deserializeBinaryFromReader(msg, reader);
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-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.NodeDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
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- */
-proto.tensorflow.NodeDef.deserializeBinaryFromReader = function(msg, reader) {
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-    var field = reader.getFieldNumber();
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-
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-  proto.tensorflow.NodeDef.serializeBinaryToWriter(this, writer);
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-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.NodeDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.NodeDef.serializeBinaryToWriter = function(message, writer) {
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-  f = message.getName();
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-
-
-/**
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- */
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-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 1, ""));
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-
-/**
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-};
-
-
-/**
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- * @return {string}
- */
-proto.tensorflow.NodeDef.prototype.getOp = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 2, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.NodeDef} returns this
- */
-proto.tensorflow.NodeDef.prototype.setOp = function(value) {
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-};
-
-
-/**
- * repeated string input = 3;
- * @return {!Array<string>}
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-proto.tensorflow.NodeDef.prototype.getInputList = function() {
-  return /** @type {!Array<string>} */ (jspb.Message.getRepeatedField(this, 3));
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-
-
-/**
- * @param {!Array<string>} value
- * @return {!proto.tensorflow.NodeDef} returns this
- */
-proto.tensorflow.NodeDef.prototype.setInputList = function(value) {
-  return jspb.Message.setField(this, 3, value || []);
-};
-
-
-/**
- * @param {string} value
- * @param {number=} opt_index
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- */
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-  return jspb.Message.addToRepeatedField(this, 3, value, opt_index);
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-
-
-/**
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- * @return {!proto.tensorflow.NodeDef} returns this
- */
-proto.tensorflow.NodeDef.prototype.clearInputList = function() {
-  return this.setInputList([]);
-};
-
-
-/**
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- * @return {string}
- */
-proto.tensorflow.NodeDef.prototype.getDevice = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 4, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.NodeDef} returns this
- */
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-  return jspb.Message.setProto3StringField(this, 4, value);
-};
-
-
-/**
- * map<string, AttrValue> attr = 5;
- * @param {boolean=} opt_noLazyCreate Do not create the map if
- * empty, instead returning `undefined`
- * @return {!jspb.Map<string,!proto.tensorflow.AttrValue>}
- */
-proto.tensorflow.NodeDef.prototype.getAttrMap = function(opt_noLazyCreate) {
-  return /** @type {!jspb.Map<string,!proto.tensorflow.AttrValue>} */ (
-      jspb.Message.getMapField(this, 5, opt_noLazyCreate,
-      proto.tensorflow.AttrValue));
-};
-
-
-/**
- * Clears values from the map. The map will be non-null.
- * @return {!proto.tensorflow.NodeDef} returns this
- */
-proto.tensorflow.NodeDef.prototype.clearAttrMap = function() {
-  this.getAttrMap().clear();
-  return this;};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.VersionDef.repeatedFields_ = [3];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.VersionDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.VersionDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
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- * @param {!proto.tensorflow.VersionDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.VersionDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
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-  if (includeInstance) {
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-
-/**
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- */
-proto.tensorflow.VersionDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.VersionDef;
-  return proto.tensorflow.VersionDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
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- * @param {!proto.tensorflow.VersionDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.VersionDef}
- */
-proto.tensorflow.VersionDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
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-    var field = reader.getFieldNumber();
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-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
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- */
-proto.tensorflow.VersionDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.VersionDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.VersionDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.VersionDef.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getProducer();
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-
-
-/**
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-  return /** @type {number} */ (jspb.Message.getFieldWithDefault(this, 1, 0));
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-
-
-/**
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- */
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-  return jspb.Message.setProto3IntField(this, 1, value);
-};
-
-
-/**
- * optional int32 min_consumer = 2;
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-proto.tensorflow.VersionDef.prototype.getMinConsumer = function() {
-  return /** @type {number} */ (jspb.Message.getFieldWithDefault(this, 2, 0));
-};
-
-
-/**
- * @param {number} value
- * @return {!proto.tensorflow.VersionDef} returns this
- */
-proto.tensorflow.VersionDef.prototype.setMinConsumer = function(value) {
-  return jspb.Message.setProto3IntField(this, 2, value);
-};
-
-
-/**
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-  return /** @type {!Array<number>} */ (jspb.Message.getRepeatedField(this, 3));
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-/**
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-  return jspb.Message.setField(this, 3, value || []);
-};
-
-
-/**
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- * @param {number=} opt_index
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- */
-proto.tensorflow.VersionDef.prototype.addBadConsumers = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 3, value, opt_index);
-};
-
-
-/**
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- */
-proto.tensorflow.VersionDef.prototype.clearBadConsumersList = function() {
-  return this.setBadConsumersList([]);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.GraphDef.repeatedFields_ = [1];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.GraphDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.GraphDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
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- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.GraphDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.GraphDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    nodeList: jspb.Message.toObjectList(msg.getNodeList(),
-    proto.tensorflow.NodeDef.toObject, includeInstance),
-    versions: (f = msg.getVersions()) && proto.tensorflow.VersionDef.toObject(includeInstance, f),
-    library: (f = msg.getLibrary()) && proto.tensorflow.FunctionDefLibrary.toObject(includeInstance, f)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.GraphDef}
- */
-proto.tensorflow.GraphDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.GraphDef;
-  return proto.tensorflow.GraphDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.GraphDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.GraphDef}
- */
-proto.tensorflow.GraphDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
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-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = new proto.tensorflow.NodeDef;
-      reader.readMessage(value,proto.tensorflow.NodeDef.deserializeBinaryFromReader);
-      msg.addNode(value);
-      break;
-    case 4:
-      var value = new proto.tensorflow.VersionDef;
-      reader.readMessage(value,proto.tensorflow.VersionDef.deserializeBinaryFromReader);
-      msg.setVersions(value);
-      break;
-    case 2:
-      var value = new proto.tensorflow.FunctionDefLibrary;
-      reader.readMessage(value,proto.tensorflow.FunctionDefLibrary.deserializeBinaryFromReader);
-      msg.setLibrary(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.GraphDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.GraphDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.GraphDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.GraphDef.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getNodeList();
-  if (f.length > 0) {
-    writer.writeRepeatedMessage(
-      1,
-      f,
-      proto.tensorflow.NodeDef.serializeBinaryToWriter
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-  }
-  f = message.getVersions();
-  if (f != null) {
-    writer.writeMessage(
-      4,
-      f,
-      proto.tensorflow.VersionDef.serializeBinaryToWriter
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-  }
-  f = message.getLibrary();
-  if (f != null) {
-    writer.writeMessage(
-      2,
-      f,
-      proto.tensorflow.FunctionDefLibrary.serializeBinaryToWriter
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-};
-
-
-/**
- * repeated NodeDef node = 1;
- * @return {!Array<!proto.tensorflow.NodeDef>}
- */
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-  return /** @type{!Array<!proto.tensorflow.NodeDef>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.NodeDef, 1));
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-
-
-/**
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- * @return {!proto.tensorflow.GraphDef} returns this
-*/
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-  return jspb.Message.setRepeatedWrapperField(this, 1, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.NodeDef=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.NodeDef}
- */
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-  return jspb.Message.addToRepeatedWrapperField(this, 1, opt_value, proto.tensorflow.NodeDef, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.GraphDef} returns this
- */
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-  return this.setNodeList([]);
-};
-
-
-/**
- * optional VersionDef versions = 4;
- * @return {?proto.tensorflow.VersionDef}
- */
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-  return /** @type{?proto.tensorflow.VersionDef} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.VersionDef, 4));
-};
-
-
-/**
- * @param {?proto.tensorflow.VersionDef|undefined} value
- * @return {!proto.tensorflow.GraphDef} returns this
-*/
-proto.tensorflow.GraphDef.prototype.setVersions = function(value) {
-  return jspb.Message.setWrapperField(this, 4, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.GraphDef} returns this
- */
-proto.tensorflow.GraphDef.prototype.clearVersions = function() {
-  return this.setVersions(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.GraphDef.prototype.hasVersions = function() {
-  return jspb.Message.getField(this, 4) != null;
-};
-
-
-/**
- * optional FunctionDefLibrary library = 2;
- * @return {?proto.tensorflow.FunctionDefLibrary}
- */
-proto.tensorflow.GraphDef.prototype.getLibrary = function() {
-  return /** @type{?proto.tensorflow.FunctionDefLibrary} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.FunctionDefLibrary, 2));
-};
-
-
-/**
- * @param {?proto.tensorflow.FunctionDefLibrary|undefined} value
- * @return {!proto.tensorflow.GraphDef} returns this
-*/
-proto.tensorflow.GraphDef.prototype.setLibrary = function(value) {
-  return jspb.Message.setWrapperField(this, 2, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.GraphDef} returns this
- */
-proto.tensorflow.GraphDef.prototype.clearLibrary = function() {
-  return this.setLibrary(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.GraphDef.prototype.hasLibrary = function() {
-  return jspb.Message.getField(this, 2) != null;
-};
-
-
-
-/**
- * Oneof group definitions for this message. Each group defines the field
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- * other fields in the group are cleared. During deserialization, if multiple
- * fields are encountered for a group, only the last value seen will be kept.
- * @private {!Array<!Array<number>>}
- * @const
- */
-proto.tensorflow.CollectionDef.oneofGroups_ = [[1,2,3,4,5]];
-
-/**
- * @enum {number}
- */
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-  NODE_LIST: 1,
-  BYTES_LIST: 2,
-  INT64_LIST: 3,
-  FLOAT_LIST: 4,
-  ANY_LIST: 5
-};
-
-/**
- * @return {proto.tensorflow.CollectionDef.KindCase}
- */
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-  return /** @type {proto.tensorflow.CollectionDef.KindCase} */(jspb.Message.computeOneofCase(this, proto.tensorflow.CollectionDef.oneofGroups_[0]));
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-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.CollectionDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.CollectionDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.CollectionDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.CollectionDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    nodeList: (f = msg.getNodeList()) && proto.tensorflow.CollectionDef.NodeList.toObject(includeInstance, f),
-    bytesList: (f = msg.getBytesList()) && proto.tensorflow.CollectionDef.BytesList.toObject(includeInstance, f),
-    int64List: (f = msg.getInt64List()) && proto.tensorflow.CollectionDef.Int64List.toObject(includeInstance, f),
-    floatList: (f = msg.getFloatList()) && proto.tensorflow.CollectionDef.FloatList.toObject(includeInstance, f),
-    anyList: (f = msg.getAnyList()) && proto.tensorflow.CollectionDef.AnyList.toObject(includeInstance, f)
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-
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-    obj.$jspbMessageInstance = msg;
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-  return obj;
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-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.CollectionDef}
- */
-proto.tensorflow.CollectionDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.CollectionDef;
-  return proto.tensorflow.CollectionDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.CollectionDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.CollectionDef}
- */
-proto.tensorflow.CollectionDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = new proto.tensorflow.CollectionDef.NodeList;
-      reader.readMessage(value,proto.tensorflow.CollectionDef.NodeList.deserializeBinaryFromReader);
-      msg.setNodeList(value);
-      break;
-    case 2:
-      var value = new proto.tensorflow.CollectionDef.BytesList;
-      reader.readMessage(value,proto.tensorflow.CollectionDef.BytesList.deserializeBinaryFromReader);
-      msg.setBytesList(value);
-      break;
-    case 3:
-      var value = new proto.tensorflow.CollectionDef.Int64List;
-      reader.readMessage(value,proto.tensorflow.CollectionDef.Int64List.deserializeBinaryFromReader);
-      msg.setInt64List(value);
-      break;
-    case 4:
-      var value = new proto.tensorflow.CollectionDef.FloatList;
-      reader.readMessage(value,proto.tensorflow.CollectionDef.FloatList.deserializeBinaryFromReader);
-      msg.setFloatList(value);
-      break;
-    case 5:
-      var value = new proto.tensorflow.CollectionDef.AnyList;
-      reader.readMessage(value,proto.tensorflow.CollectionDef.AnyList.deserializeBinaryFromReader);
-      msg.setAnyList(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.CollectionDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.CollectionDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.CollectionDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.CollectionDef.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getNodeList();
-  if (f != null) {
-    writer.writeMessage(
-      1,
-      f,
-      proto.tensorflow.CollectionDef.NodeList.serializeBinaryToWriter
-    );
-  }
-  f = message.getBytesList();
-  if (f != null) {
-    writer.writeMessage(
-      2,
-      f,
-      proto.tensorflow.CollectionDef.BytesList.serializeBinaryToWriter
-    );
-  }
-  f = message.getInt64List();
-  if (f != null) {
-    writer.writeMessage(
-      3,
-      f,
-      proto.tensorflow.CollectionDef.Int64List.serializeBinaryToWriter
-    );
-  }
-  f = message.getFloatList();
-  if (f != null) {
-    writer.writeMessage(
-      4,
-      f,
-      proto.tensorflow.CollectionDef.FloatList.serializeBinaryToWriter
-    );
-  }
-  f = message.getAnyList();
-  if (f != null) {
-    writer.writeMessage(
-      5,
-      f,
-      proto.tensorflow.CollectionDef.AnyList.serializeBinaryToWriter
-    );
-  }
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.CollectionDef.NodeList.repeatedFields_ = [1];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.CollectionDef.NodeList.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.CollectionDef.NodeList.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.CollectionDef.NodeList} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.CollectionDef.NodeList.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    valueList: (f = jspb.Message.getRepeatedField(msg, 1)) == null ? undefined : f
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.CollectionDef.NodeList}
- */
-proto.tensorflow.CollectionDef.NodeList.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.CollectionDef.NodeList;
-  return proto.tensorflow.CollectionDef.NodeList.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.CollectionDef.NodeList} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.CollectionDef.NodeList}
- */
-proto.tensorflow.CollectionDef.NodeList.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {string} */ (reader.readString());
-      msg.addValue(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.CollectionDef.NodeList.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.CollectionDef.NodeList.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.CollectionDef.NodeList} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.CollectionDef.NodeList.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getValueList();
-  if (f.length > 0) {
-    writer.writeRepeatedString(
-      1,
-      f
-    );
-  }
-};
-
-
-/**
- * repeated string value = 1;
- * @return {!Array<string>}
- */
-proto.tensorflow.CollectionDef.NodeList.prototype.getValueList = function() {
-  return /** @type {!Array<string>} */ (jspb.Message.getRepeatedField(this, 1));
-};
-
-
-/**
- * @param {!Array<string>} value
- * @return {!proto.tensorflow.CollectionDef.NodeList} returns this
- */
-proto.tensorflow.CollectionDef.NodeList.prototype.setValueList = function(value) {
-  return jspb.Message.setField(this, 1, value || []);
-};
-
-
-/**
- * @param {string} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.CollectionDef.NodeList} returns this
- */
-proto.tensorflow.CollectionDef.NodeList.prototype.addValue = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 1, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.CollectionDef.NodeList} returns this
- */
-proto.tensorflow.CollectionDef.NodeList.prototype.clearValueList = function() {
-  return this.setValueList([]);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.CollectionDef.BytesList.repeatedFields_ = [1];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.CollectionDef.BytesList.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.CollectionDef.BytesList.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.CollectionDef.BytesList} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.CollectionDef.BytesList.toObject = function(includeInstance, msg) {
-  var f, obj = {
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-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
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-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.CollectionDef.BytesList}
- */
-proto.tensorflow.CollectionDef.BytesList.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.CollectionDef.BytesList;
-  return proto.tensorflow.CollectionDef.BytesList.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.CollectionDef.BytesList} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.CollectionDef.BytesList}
- */
-proto.tensorflow.CollectionDef.BytesList.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {!Uint8Array} */ (reader.readBytes());
-      msg.addValue(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
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-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.CollectionDef.BytesList.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.CollectionDef.BytesList.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.CollectionDef.BytesList} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.CollectionDef.BytesList.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getValueList_asU8();
-  if (f.length > 0) {
-    writer.writeRepeatedBytes(
-      1,
-      f
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-};
-
-
-/**
- * repeated bytes value = 1;
- * @return {!(Array<!Uint8Array>|Array<string>)}
- */
-proto.tensorflow.CollectionDef.BytesList.prototype.getValueList = function() {
-  return /** @type {!(Array<!Uint8Array>|Array<string>)} */ (jspb.Message.getRepeatedField(this, 1));
-};
-
-
-/**
- * repeated bytes value = 1;
- * This is a type-conversion wrapper around `getValueList()`
- * @return {!Array<string>}
- */
-proto.tensorflow.CollectionDef.BytesList.prototype.getValueList_asB64 = function() {
-  return /** @type {!Array<string>} */ (jspb.Message.bytesListAsB64(
-      this.getValueList()));
-};
-
-
-/**
- * repeated bytes value = 1;
- * Note that Uint8Array is not supported on all browsers.
- * @see http://caniuse.com/Uint8Array
- * This is a type-conversion wrapper around `getValueList()`
- * @return {!Array<!Uint8Array>}
- */
-proto.tensorflow.CollectionDef.BytesList.prototype.getValueList_asU8 = function() {
-  return /** @type {!Array<!Uint8Array>} */ (jspb.Message.bytesListAsU8(
-      this.getValueList()));
-};
-
-
-/**
- * @param {!(Array<!Uint8Array>|Array<string>)} value
- * @return {!proto.tensorflow.CollectionDef.BytesList} returns this
- */
-proto.tensorflow.CollectionDef.BytesList.prototype.setValueList = function(value) {
-  return jspb.Message.setField(this, 1, value || []);
-};
-
-
-/**
- * @param {!(string|Uint8Array)} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.CollectionDef.BytesList} returns this
- */
-proto.tensorflow.CollectionDef.BytesList.prototype.addValue = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 1, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.CollectionDef.BytesList} returns this
- */
-proto.tensorflow.CollectionDef.BytesList.prototype.clearValueList = function() {
-  return this.setValueList([]);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.CollectionDef.Int64List.repeatedFields_ = [1];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.CollectionDef.Int64List.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.CollectionDef.Int64List.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.CollectionDef.Int64List} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
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-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
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- * @param {!proto.tensorflow.CollectionDef.Int64List} msg The message object to deserialize into.
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-/**
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- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
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-  if (f.length > 0) {
-    writer.writePackedInt64(
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-
-/**
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-  return /** @type {!Array<number>} */ (jspb.Message.getRepeatedField(this, 1));
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-
-/**
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-  return jspb.Message.setField(this, 1, value || []);
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-
-
-/**
- * @param {number} value
- * @param {number=} opt_index
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- */
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-  return jspb.Message.addToRepeatedField(this, 1, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.CollectionDef.Int64List} returns this
- */
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-  return this.setValueList([]);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
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-
-
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- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
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-  return proto.tensorflow.CollectionDef.FloatList.toObject(opt_includeInstance, this);
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-
-
-/**
- * Static version of the {@see toObject} method.
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- *     the JSPB instance for transitional soy proto support:
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- * @param {!proto.tensorflow.CollectionDef.FloatList} msg The msg instance to transform.
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-/**
- * Deserializes binary data (in protobuf wire format) from the
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- */
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- * Serializes the message to binary data (in protobuf wire format).
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- */
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-  proto.tensorflow.CollectionDef.FloatList.serializeBinaryToWriter(this, writer);
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-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.CollectionDef.FloatList} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
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-proto.tensorflow.CollectionDef.FloatList.serializeBinaryToWriter = function(message, writer) {
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-      f
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- * repeated float value = 1;
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-
-/**
- * @param {!Array<number>} value
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- */
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-  return jspb.Message.setField(this, 1, value || []);
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-
-/**
- * @param {number} value
- * @param {number=} opt_index
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- */
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-  return jspb.Message.addToRepeatedField(this, 1, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.CollectionDef.FloatList} returns this
- */
-proto.tensorflow.CollectionDef.FloatList.prototype.clearValueList = function() {
-  return this.setValueList([]);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.CollectionDef.AnyList.repeatedFields_ = [1];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
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- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
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- *     JSPB instance for transitional soy proto support:
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-proto.tensorflow.CollectionDef.AnyList.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.CollectionDef.AnyList.toObject(opt_includeInstance, this);
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-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
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- * @param {!proto.tensorflow.CollectionDef.AnyList} msg The msg instance to transform.
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- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
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- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.CollectionDef.AnyList}
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-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.CollectionDef.AnyList;
-  return proto.tensorflow.CollectionDef.AnyList.deserializeBinaryFromReader(msg, reader);
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-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.CollectionDef.AnyList} msg The message object to deserialize into.
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- * @return {!proto.tensorflow.CollectionDef.AnyList}
- */
-proto.tensorflow.CollectionDef.AnyList.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
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-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.CollectionDef.AnyList.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.CollectionDef.AnyList} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.CollectionDef.AnyList.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getValueList();
-  if (f.length > 0) {
-    writer.writeRepeatedMessage(
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-      proto.tensorflow.Any.serializeBinaryToWriter
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-
-
-/**
- * repeated Any value = 1;
- * @return {!Array<!proto.tensorflow.Any>}
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-proto.tensorflow.CollectionDef.AnyList.prototype.getValueList = function() {
-  return /** @type{!Array<!proto.tensorflow.Any>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.Any, 1));
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-
-/**
- * @param {!Array<!proto.tensorflow.Any>} value
- * @return {!proto.tensorflow.CollectionDef.AnyList} returns this
-*/
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-  return jspb.Message.setRepeatedWrapperField(this, 1, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.Any=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.Any}
- */
-proto.tensorflow.CollectionDef.AnyList.prototype.addValue = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 1, opt_value, proto.tensorflow.Any, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.CollectionDef.AnyList} returns this
- */
-proto.tensorflow.CollectionDef.AnyList.prototype.clearValueList = function() {
-  return this.setValueList([]);
-};
-
-
-/**
- * optional NodeList node_list = 1;
- * @return {?proto.tensorflow.CollectionDef.NodeList}
- */
-proto.tensorflow.CollectionDef.prototype.getNodeList = function() {
-  return /** @type{?proto.tensorflow.CollectionDef.NodeList} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.CollectionDef.NodeList, 1));
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-
-/**
- * @param {?proto.tensorflow.CollectionDef.NodeList|undefined} value
- * @return {!proto.tensorflow.CollectionDef} returns this
-*/
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-  return jspb.Message.setOneofWrapperField(this, 1, proto.tensorflow.CollectionDef.oneofGroups_[0], value);
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-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.CollectionDef} returns this
- */
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-  return this.setNodeList(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
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-  return jspb.Message.getField(this, 1) != null;
-};
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-
-/**
- * optional BytesList bytes_list = 2;
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-  return /** @type{?proto.tensorflow.CollectionDef.BytesList} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.CollectionDef.BytesList, 2));
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-
-
-/**
- * @param {?proto.tensorflow.CollectionDef.BytesList|undefined} value
- * @return {!proto.tensorflow.CollectionDef} returns this
-*/
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-  return jspb.Message.setOneofWrapperField(this, 2, proto.tensorflow.CollectionDef.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.CollectionDef} returns this
- */
-proto.tensorflow.CollectionDef.prototype.clearBytesList = function() {
-  return this.setBytesList(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
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-  return jspb.Message.getField(this, 2) != null;
-};
-
-
-/**
- * optional Int64List int64_list = 3;
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- */
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-  return /** @type{?proto.tensorflow.CollectionDef.Int64List} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.CollectionDef.Int64List, 3));
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-
-
-/**
- * @param {?proto.tensorflow.CollectionDef.Int64List|undefined} value
- * @return {!proto.tensorflow.CollectionDef} returns this
-*/
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-  return jspb.Message.setOneofWrapperField(this, 3, proto.tensorflow.CollectionDef.oneofGroups_[0], value);
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-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.CollectionDef} returns this
- */
-proto.tensorflow.CollectionDef.prototype.clearInt64List = function() {
-  return this.setInt64List(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.CollectionDef.prototype.hasInt64List = function() {
-  return jspb.Message.getField(this, 3) != null;
-};
-
-
-/**
- * optional FloatList float_list = 4;
- * @return {?proto.tensorflow.CollectionDef.FloatList}
- */
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-  return /** @type{?proto.tensorflow.CollectionDef.FloatList} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.CollectionDef.FloatList, 4));
-};
-
-
-/**
- * @param {?proto.tensorflow.CollectionDef.FloatList|undefined} value
- * @return {!proto.tensorflow.CollectionDef} returns this
-*/
-proto.tensorflow.CollectionDef.prototype.setFloatList = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 4, proto.tensorflow.CollectionDef.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.CollectionDef} returns this
- */
-proto.tensorflow.CollectionDef.prototype.clearFloatList = function() {
-  return this.setFloatList(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.CollectionDef.prototype.hasFloatList = function() {
-  return jspb.Message.getField(this, 4) != null;
-};
-
-
-/**
- * optional AnyList any_list = 5;
- * @return {?proto.tensorflow.CollectionDef.AnyList}
- */
-proto.tensorflow.CollectionDef.prototype.getAnyList = function() {
-  return /** @type{?proto.tensorflow.CollectionDef.AnyList} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.CollectionDef.AnyList, 5));
-};
-
-
-/**
- * @param {?proto.tensorflow.CollectionDef.AnyList|undefined} value
- * @return {!proto.tensorflow.CollectionDef} returns this
-*/
-proto.tensorflow.CollectionDef.prototype.setAnyList = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 5, proto.tensorflow.CollectionDef.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.CollectionDef} returns this
- */
-proto.tensorflow.CollectionDef.prototype.clearAnyList = function() {
-  return this.setAnyList(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.CollectionDef.prototype.hasAnyList = function() {
-  return jspb.Message.getField(this, 5) != null;
-};
-
-
-
-
-
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-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.SaverDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.SaverDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.SaverDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SaverDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    filenameTensorName: jspb.Message.getFieldWithDefault(msg, 1, ""),
-    saveTensorName: jspb.Message.getFieldWithDefault(msg, 2, ""),
-    restoreOpName: jspb.Message.getFieldWithDefault(msg, 3, ""),
-    maxToKeep: jspb.Message.getFieldWithDefault(msg, 4, 0),
-    sharded: jspb.Message.getBooleanFieldWithDefault(msg, 5, false),
-    keepCheckpointEveryNHours: jspb.Message.getFloatingPointFieldWithDefault(msg, 6, 0.0),
-    version: jspb.Message.getFieldWithDefault(msg, 7, 0)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
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-  return obj;
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-}
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-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.SaverDef}
- */
-proto.tensorflow.SaverDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.SaverDef;
-  return proto.tensorflow.SaverDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.SaverDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.SaverDef}
- */
-proto.tensorflow.SaverDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setFilenameTensorName(value);
-      break;
-    case 2:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setSaveTensorName(value);
-      break;
-    case 3:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setRestoreOpName(value);
-      break;
-    case 4:
-      var value = /** @type {number} */ (reader.readInt32());
-      msg.setMaxToKeep(value);
-      break;
-    case 5:
-      var value = /** @type {boolean} */ (reader.readBool());
-      msg.setSharded(value);
-      break;
-    case 6:
-      var value = /** @type {number} */ (reader.readFloat());
-      msg.setKeepCheckpointEveryNHours(value);
-      break;
-    case 7:
-      var value = /** @type {!proto.tensorflow.SaverDef.CheckpointFormatVersion} */ (reader.readEnum());
-      msg.setVersion(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.SaverDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.SaverDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.SaverDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SaverDef.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getFilenameTensorName();
-  if (f.length > 0) {
-    writer.writeString(
-      1,
-      f
-    );
-  }
-  f = message.getSaveTensorName();
-  if (f.length > 0) {
-    writer.writeString(
-      2,
-      f
-    );
-  }
-  f = message.getRestoreOpName();
-  if (f.length > 0) {
-    writer.writeString(
-      3,
-      f
-    );
-  }
-  f = message.getMaxToKeep();
-  if (f !== 0) {
-    writer.writeInt32(
-      4,
-      f
-    );
-  }
-  f = message.getSharded();
-  if (f) {
-    writer.writeBool(
-      5,
-      f
-    );
-  }
-  f = message.getKeepCheckpointEveryNHours();
-  if (f !== 0.0) {
-    writer.writeFloat(
-      6,
-      f
-    );
-  }
-  f = message.getVersion();
-  if (f !== 0.0) {
-    writer.writeEnum(
-      7,
-      f
-    );
-  }
-};
-
-
-/**
- * @enum {number}
- */
-proto.tensorflow.SaverDef.CheckpointFormatVersion = {
-  LEGACY: 0,
-  V1: 1,
-  V2: 2
-};
-
-/**
- * optional string filename_tensor_name = 1;
- * @return {string}
- */
-proto.tensorflow.SaverDef.prototype.getFilenameTensorName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 1, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.SaverDef} returns this
- */
-proto.tensorflow.SaverDef.prototype.setFilenameTensorName = function(value) {
-  return jspb.Message.setProto3StringField(this, 1, value);
-};
-
-
-/**
- * optional string save_tensor_name = 2;
- * @return {string}
- */
-proto.tensorflow.SaverDef.prototype.getSaveTensorName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 2, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.SaverDef} returns this
- */
-proto.tensorflow.SaverDef.prototype.setSaveTensorName = function(value) {
-  return jspb.Message.setProto3StringField(this, 2, value);
-};
-
-
-/**
- * optional string restore_op_name = 3;
- * @return {string}
- */
-proto.tensorflow.SaverDef.prototype.getRestoreOpName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 3, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.SaverDef} returns this
- */
-proto.tensorflow.SaverDef.prototype.setRestoreOpName = function(value) {
-  return jspb.Message.setProto3StringField(this, 3, value);
-};
-
-
-/**
- * optional int32 max_to_keep = 4;
- * @return {number}
- */
-proto.tensorflow.SaverDef.prototype.getMaxToKeep = function() {
-  return /** @type {number} */ (jspb.Message.getFieldWithDefault(this, 4, 0));
-};
-
-
-/**
- * @param {number} value
- * @return {!proto.tensorflow.SaverDef} returns this
- */
-proto.tensorflow.SaverDef.prototype.setMaxToKeep = function(value) {
-  return jspb.Message.setProto3IntField(this, 4, value);
-};
-
-
-/**
- * optional bool sharded = 5;
- * @return {boolean}
- */
-proto.tensorflow.SaverDef.prototype.getSharded = function() {
-  return /** @type {boolean} */ (jspb.Message.getBooleanFieldWithDefault(this, 5, false));
-};
-
-
-/**
- * @param {boolean} value
- * @return {!proto.tensorflow.SaverDef} returns this
- */
-proto.tensorflow.SaverDef.prototype.setSharded = function(value) {
-  return jspb.Message.setProto3BooleanField(this, 5, value);
-};
-
-
-/**
- * optional float keep_checkpoint_every_n_hours = 6;
- * @return {number}
- */
-proto.tensorflow.SaverDef.prototype.getKeepCheckpointEveryNHours = function() {
-  return /** @type {number} */ (jspb.Message.getFloatingPointFieldWithDefault(this, 6, 0.0));
-};
-
-
-/**
- * @param {number} value
- * @return {!proto.tensorflow.SaverDef} returns this
- */
-proto.tensorflow.SaverDef.prototype.setKeepCheckpointEveryNHours = function(value) {
-  return jspb.Message.setProto3FloatField(this, 6, value);
-};
-
-
-/**
- * optional CheckpointFormatVersion version = 7;
- * @return {!proto.tensorflow.SaverDef.CheckpointFormatVersion}
- */
-proto.tensorflow.SaverDef.prototype.getVersion = function() {
-  return /** @type {!proto.tensorflow.SaverDef.CheckpointFormatVersion} */ (jspb.Message.getFieldWithDefault(this, 7, 0));
-};
-
-
-/**
- * @param {!proto.tensorflow.SaverDef.CheckpointFormatVersion} value
- * @return {!proto.tensorflow.SaverDef} returns this
- */
-proto.tensorflow.SaverDef.prototype.setVersion = function(value) {
-  return jspb.Message.setProto3EnumField(this, 7, value);
-};
-
-
-
-/**
- * Oneof group definitions for this message. Each group defines the field
- * numbers belonging to that group. When of these fields' value is set, all
- * other fields in the group are cleared. During deserialization, if multiple
- * fields are encountered for a group, only the last value seen will be kept.
- * @private {!Array<!Array<number>>}
- * @const
- */
-proto.tensorflow.TensorInfo.oneofGroups_ = [[1,4]];
-
-/**
- * @enum {number}
- */
-proto.tensorflow.TensorInfo.EncodingCase = {
-  ENCODING_NOT_SET: 0,
-  NAME: 1,
-  COO_SPARSE: 4
-};
-
-/**
- * @return {proto.tensorflow.TensorInfo.EncodingCase}
- */
-proto.tensorflow.TensorInfo.prototype.getEncodingCase = function() {
-  return /** @type {proto.tensorflow.TensorInfo.EncodingCase} */(jspb.Message.computeOneofCase(this, proto.tensorflow.TensorInfo.oneofGroups_[0]));
-};
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.TensorInfo.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.TensorInfo.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.TensorInfo} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.TensorInfo.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    name: jspb.Message.getFieldWithDefault(msg, 1, ""),
-    cooSparse: (f = msg.getCooSparse()) && proto.tensorflow.TensorInfo.CooSparse.toObject(includeInstance, f),
-    dtype: jspb.Message.getFieldWithDefault(msg, 2, 0),
-    tensorShape: (f = msg.getTensorShape()) && proto.tensorflow.TensorShape.toObject(includeInstance, f)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.TensorInfo}
- */
-proto.tensorflow.TensorInfo.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.TensorInfo;
-  return proto.tensorflow.TensorInfo.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.TensorInfo} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.TensorInfo}
- */
-proto.tensorflow.TensorInfo.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setName(value);
-      break;
-    case 4:
-      var value = new proto.tensorflow.TensorInfo.CooSparse;
-      reader.readMessage(value,proto.tensorflow.TensorInfo.CooSparse.deserializeBinaryFromReader);
-      msg.setCooSparse(value);
-      break;
-    case 2:
-      var value = /** @type {!proto.tensorflow.DataType} */ (reader.readEnum());
-      msg.setDtype(value);
-      break;
-    case 3:
-      var value = new proto.tensorflow.TensorShape;
-      reader.readMessage(value,proto.tensorflow.TensorShape.deserializeBinaryFromReader);
-      msg.setTensorShape(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.TensorInfo.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.TensorInfo.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.TensorInfo} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.TensorInfo.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = /** @type {string} */ (jspb.Message.getField(message, 1));
-  if (f != null) {
-    writer.writeString(
-      1,
-      f
-    );
-  }
-  f = message.getCooSparse();
-  if (f != null) {
-    writer.writeMessage(
-      4,
-      f,
-      proto.tensorflow.TensorInfo.CooSparse.serializeBinaryToWriter
-    );
-  }
-  f = message.getDtype();
-  if (f !== 0.0) {
-    writer.writeEnum(
-      2,
-      f
-    );
-  }
-  f = message.getTensorShape();
-  if (f != null) {
-    writer.writeMessage(
-      3,
-      f,
-      proto.tensorflow.TensorShape.serializeBinaryToWriter
-    );
-  }
-};
-
-
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.TensorInfo.CooSparse.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.TensorInfo.CooSparse.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.TensorInfo.CooSparse} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.TensorInfo.CooSparse.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    valuesTensorName: jspb.Message.getFieldWithDefault(msg, 1, ""),
-    indicesTensorName: jspb.Message.getFieldWithDefault(msg, 2, ""),
-    denseShapeTensorName: jspb.Message.getFieldWithDefault(msg, 3, "")
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.TensorInfo.CooSparse}
- */
-proto.tensorflow.TensorInfo.CooSparse.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.TensorInfo.CooSparse;
-  return proto.tensorflow.TensorInfo.CooSparse.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.TensorInfo.CooSparse} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.TensorInfo.CooSparse}
- */
-proto.tensorflow.TensorInfo.CooSparse.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setValuesTensorName(value);
-      break;
-    case 2:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setIndicesTensorName(value);
-      break;
-    case 3:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setDenseShapeTensorName(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.TensorInfo.CooSparse.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.TensorInfo.CooSparse.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.TensorInfo.CooSparse} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.TensorInfo.CooSparse.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getValuesTensorName();
-  if (f.length > 0) {
-    writer.writeString(
-      1,
-      f
-    );
-  }
-  f = message.getIndicesTensorName();
-  if (f.length > 0) {
-    writer.writeString(
-      2,
-      f
-    );
-  }
-  f = message.getDenseShapeTensorName();
-  if (f.length > 0) {
-    writer.writeString(
-      3,
-      f
-    );
-  }
-};
-
-
-/**
- * optional string values_tensor_name = 1;
- * @return {string}
- */
-proto.tensorflow.TensorInfo.CooSparse.prototype.getValuesTensorName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 1, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.TensorInfo.CooSparse} returns this
- */
-proto.tensorflow.TensorInfo.CooSparse.prototype.setValuesTensorName = function(value) {
-  return jspb.Message.setProto3StringField(this, 1, value);
-};
-
-
-/**
- * optional string indices_tensor_name = 2;
- * @return {string}
- */
-proto.tensorflow.TensorInfo.CooSparse.prototype.getIndicesTensorName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 2, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.TensorInfo.CooSparse} returns this
- */
-proto.tensorflow.TensorInfo.CooSparse.prototype.setIndicesTensorName = function(value) {
-  return jspb.Message.setProto3StringField(this, 2, value);
-};
-
-
-/**
- * optional string dense_shape_tensor_name = 3;
- * @return {string}
- */
-proto.tensorflow.TensorInfo.CooSparse.prototype.getDenseShapeTensorName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 3, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.TensorInfo.CooSparse} returns this
- */
-proto.tensorflow.TensorInfo.CooSparse.prototype.setDenseShapeTensorName = function(value) {
-  return jspb.Message.setProto3StringField(this, 3, value);
-};
-
-
-/**
- * optional string name = 1;
- * @return {string}
- */
-proto.tensorflow.TensorInfo.prototype.getName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 1, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.TensorInfo} returns this
- */
-proto.tensorflow.TensorInfo.prototype.setName = function(value) {
-  return jspb.Message.setOneofField(this, 1, proto.tensorflow.TensorInfo.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the field making it undefined.
- * @return {!proto.tensorflow.TensorInfo} returns this
- */
-proto.tensorflow.TensorInfo.prototype.clearName = function() {
-  return jspb.Message.setOneofField(this, 1, proto.tensorflow.TensorInfo.oneofGroups_[0], undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.TensorInfo.prototype.hasName = function() {
-  return jspb.Message.getField(this, 1) != null;
-};
-
-
-/**
- * optional CooSparse coo_sparse = 4;
- * @return {?proto.tensorflow.TensorInfo.CooSparse}
- */
-proto.tensorflow.TensorInfo.prototype.getCooSparse = function() {
-  return /** @type{?proto.tensorflow.TensorInfo.CooSparse} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.TensorInfo.CooSparse, 4));
-};
-
-
-/**
- * @param {?proto.tensorflow.TensorInfo.CooSparse|undefined} value
- * @return {!proto.tensorflow.TensorInfo} returns this
-*/
-proto.tensorflow.TensorInfo.prototype.setCooSparse = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 4, proto.tensorflow.TensorInfo.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.TensorInfo} returns this
- */
-proto.tensorflow.TensorInfo.prototype.clearCooSparse = function() {
-  return this.setCooSparse(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.TensorInfo.prototype.hasCooSparse = function() {
-  return jspb.Message.getField(this, 4) != null;
-};
-
-
-/**
- * optional DataType dtype = 2;
- * @return {!proto.tensorflow.DataType}
- */
-proto.tensorflow.TensorInfo.prototype.getDtype = function() {
-  return /** @type {!proto.tensorflow.DataType} */ (jspb.Message.getFieldWithDefault(this, 2, 0));
-};
-
-
-/**
- * @param {!proto.tensorflow.DataType} value
- * @return {!proto.tensorflow.TensorInfo} returns this
- */
-proto.tensorflow.TensorInfo.prototype.setDtype = function(value) {
-  return jspb.Message.setProto3EnumField(this, 2, value);
-};
-
-
-/**
- * optional TensorShape tensor_shape = 3;
- * @return {?proto.tensorflow.TensorShape}
- */
-proto.tensorflow.TensorInfo.prototype.getTensorShape = function() {
-  return /** @type{?proto.tensorflow.TensorShape} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.TensorShape, 3));
-};
-
-
-/**
- * @param {?proto.tensorflow.TensorShape|undefined} value
- * @return {!proto.tensorflow.TensorInfo} returns this
-*/
-proto.tensorflow.TensorInfo.prototype.setTensorShape = function(value) {
-  return jspb.Message.setWrapperField(this, 3, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.TensorInfo} returns this
- */
-proto.tensorflow.TensorInfo.prototype.clearTensorShape = function() {
-  return this.setTensorShape(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.TensorInfo.prototype.hasTensorShape = function() {
-  return jspb.Message.getField(this, 3) != null;
-};
-
-
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.SignatureDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.SignatureDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.SignatureDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SignatureDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    inputsMap: (f = msg.getInputsMap()) ? f.toObject(includeInstance, proto.tensorflow.TensorInfo.toObject) : [],
-    outputsMap: (f = msg.getOutputsMap()) ? f.toObject(includeInstance, proto.tensorflow.TensorInfo.toObject) : [],
-    methodName: jspb.Message.getFieldWithDefault(msg, 3, "")
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-
-  if (includeInstance) {
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-
-
-/**
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- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.SignatureDef}
- */
-proto.tensorflow.SignatureDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.SignatureDef;
-  return proto.tensorflow.SignatureDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.SignatureDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.SignatureDef}
- */
-proto.tensorflow.SignatureDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
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-      var value = msg.getInputsMap();
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-         });
-      break;
-    case 2:
-      var value = msg.getOutputsMap();
-      reader.readMessage(value, function(message, reader) {
-        jspb.Map.deserializeBinary(message, reader, jspb.BinaryReader.prototype.readString, jspb.BinaryReader.prototype.readMessage, proto.tensorflow.TensorInfo.deserializeBinaryFromReader, "", new proto.tensorflow.TensorInfo());
-         });
-      break;
-    case 3:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setMethodName(value);
-      break;
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-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.SignatureDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.SignatureDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.SignatureDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SignatureDef.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getInputsMap(true);
-  if (f && f.getLength() > 0) {
-    f.serializeBinary(1, writer, jspb.BinaryWriter.prototype.writeString, jspb.BinaryWriter.prototype.writeMessage, proto.tensorflow.TensorInfo.serializeBinaryToWriter);
-  }
-  f = message.getOutputsMap(true);
-  if (f && f.getLength() > 0) {
-    f.serializeBinary(2, writer, jspb.BinaryWriter.prototype.writeString, jspb.BinaryWriter.prototype.writeMessage, proto.tensorflow.TensorInfo.serializeBinaryToWriter);
-  }
-  f = message.getMethodName();
-  if (f.length > 0) {
-    writer.writeString(
-      3,
-      f
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-
-
-/**
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- * @param {boolean=} opt_noLazyCreate Do not create the map if
- * empty, instead returning `undefined`
- * @return {!jspb.Map<string,!proto.tensorflow.TensorInfo>}
- */
-proto.tensorflow.SignatureDef.prototype.getInputsMap = function(opt_noLazyCreate) {
-  return /** @type {!jspb.Map<string,!proto.tensorflow.TensorInfo>} */ (
-      jspb.Message.getMapField(this, 1, opt_noLazyCreate,
-      proto.tensorflow.TensorInfo));
-};
-
-
-/**
- * Clears values from the map. The map will be non-null.
- * @return {!proto.tensorflow.SignatureDef} returns this
- */
-proto.tensorflow.SignatureDef.prototype.clearInputsMap = function() {
-  this.getInputsMap().clear();
-  return this;};
-
-
-/**
- * map<string, TensorInfo> outputs = 2;
- * @param {boolean=} opt_noLazyCreate Do not create the map if
- * empty, instead returning `undefined`
- * @return {!jspb.Map<string,!proto.tensorflow.TensorInfo>}
- */
-proto.tensorflow.SignatureDef.prototype.getOutputsMap = function(opt_noLazyCreate) {
-  return /** @type {!jspb.Map<string,!proto.tensorflow.TensorInfo>} */ (
-      jspb.Message.getMapField(this, 2, opt_noLazyCreate,
-      proto.tensorflow.TensorInfo));
-};
-
-
-/**
- * Clears values from the map. The map will be non-null.
- * @return {!proto.tensorflow.SignatureDef} returns this
- */
-proto.tensorflow.SignatureDef.prototype.clearOutputsMap = function() {
-  this.getOutputsMap().clear();
-  return this;};
-
-
-/**
- * optional string method_name = 3;
- * @return {string}
- */
-proto.tensorflow.SignatureDef.prototype.getMethodName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 3, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.SignatureDef} returns this
- */
-proto.tensorflow.SignatureDef.prototype.setMethodName = function(value) {
-  return jspb.Message.setProto3StringField(this, 3, value);
-};
-
-
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.AssetFileDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.AssetFileDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.AssetFileDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.AssetFileDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    tensorInfo: (f = msg.getTensorInfo()) && proto.tensorflow.TensorInfo.toObject(includeInstance, f),
-    filename: jspb.Message.getFieldWithDefault(msg, 2, "")
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
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-  return obj;
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-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.AssetFileDef}
- */
-proto.tensorflow.AssetFileDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.AssetFileDef;
-  return proto.tensorflow.AssetFileDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.AssetFileDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.AssetFileDef}
- */
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-    var field = reader.getFieldNumber();
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-      break;
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-  return msg;
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-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.AssetFileDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.AssetFileDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.AssetFileDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.AssetFileDef.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getTensorInfo();
-  if (f != null) {
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-      1,
-      f,
-      proto.tensorflow.TensorInfo.serializeBinaryToWriter
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-  }
-  f = message.getFilename();
-  if (f.length > 0) {
-    writer.writeString(
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-};
-
-
-/**
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- * @return {?proto.tensorflow.TensorInfo}
- */
-proto.tensorflow.AssetFileDef.prototype.getTensorInfo = function() {
-  return /** @type{?proto.tensorflow.TensorInfo} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.TensorInfo, 1));
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-
-
-/**
- * @param {?proto.tensorflow.TensorInfo|undefined} value
- * @return {!proto.tensorflow.AssetFileDef} returns this
-*/
-proto.tensorflow.AssetFileDef.prototype.setTensorInfo = function(value) {
-  return jspb.Message.setWrapperField(this, 1, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.AssetFileDef} returns this
- */
-proto.tensorflow.AssetFileDef.prototype.clearTensorInfo = function() {
-  return this.setTensorInfo(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.AssetFileDef.prototype.hasTensorInfo = function() {
-  return jspb.Message.getField(this, 1) != null;
-};
-
-
-/**
- * optional string filename = 2;
- * @return {string}
- */
-proto.tensorflow.AssetFileDef.prototype.getFilename = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 2, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.AssetFileDef} returns this
- */
-proto.tensorflow.AssetFileDef.prototype.setFilename = function(value) {
-  return jspb.Message.setProto3StringField(this, 2, value);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.OpDef.repeatedFields_ = [2,3,4];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.OpDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.OpDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.OpDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.OpDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    name: jspb.Message.getFieldWithDefault(msg, 1, ""),
-    inputArgList: jspb.Message.toObjectList(msg.getInputArgList(),
-    proto.tensorflow.OpDef.ArgDef.toObject, includeInstance),
-    outputArgList: jspb.Message.toObjectList(msg.getOutputArgList(),
-    proto.tensorflow.OpDef.ArgDef.toObject, includeInstance),
-    attrList: jspb.Message.toObjectList(msg.getAttrList(),
-    proto.tensorflow.OpDef.AttrDef.toObject, includeInstance),
-    deprecation: (f = msg.getDeprecation()) && proto.tensorflow.OpDef.OpDeprecation.toObject(includeInstance, f),
-    summary: jspb.Message.getFieldWithDefault(msg, 5, ""),
-    description: jspb.Message.getFieldWithDefault(msg, 6, ""),
-    isCommutative: jspb.Message.getBooleanFieldWithDefault(msg, 18, false),
-    isAggregate: jspb.Message.getBooleanFieldWithDefault(msg, 16, false),
-    isStateful: jspb.Message.getBooleanFieldWithDefault(msg, 17, false),
-    allowsUninitializedInput: jspb.Message.getBooleanFieldWithDefault(msg, 19, false)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.OpDef}
- */
-proto.tensorflow.OpDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.OpDef;
-  return proto.tensorflow.OpDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.OpDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.OpDef}
- */
-proto.tensorflow.OpDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setName(value);
-      break;
-    case 2:
-      var value = new proto.tensorflow.OpDef.ArgDef;
-      reader.readMessage(value,proto.tensorflow.OpDef.ArgDef.deserializeBinaryFromReader);
-      msg.addInputArg(value);
-      break;
-    case 3:
-      var value = new proto.tensorflow.OpDef.ArgDef;
-      reader.readMessage(value,proto.tensorflow.OpDef.ArgDef.deserializeBinaryFromReader);
-      msg.addOutputArg(value);
-      break;
-    case 4:
-      var value = new proto.tensorflow.OpDef.AttrDef;
-      reader.readMessage(value,proto.tensorflow.OpDef.AttrDef.deserializeBinaryFromReader);
-      msg.addAttr(value);
-      break;
-    case 8:
-      var value = new proto.tensorflow.OpDef.OpDeprecation;
-      reader.readMessage(value,proto.tensorflow.OpDef.OpDeprecation.deserializeBinaryFromReader);
-      msg.setDeprecation(value);
-      break;
-    case 5:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setSummary(value);
-      break;
-    case 6:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setDescription(value);
-      break;
-    case 18:
-      var value = /** @type {boolean} */ (reader.readBool());
-      msg.setIsCommutative(value);
-      break;
-    case 16:
-      var value = /** @type {boolean} */ (reader.readBool());
-      msg.setIsAggregate(value);
-      break;
-    case 17:
-      var value = /** @type {boolean} */ (reader.readBool());
-      msg.setIsStateful(value);
-      break;
-    case 19:
-      var value = /** @type {boolean} */ (reader.readBool());
-      msg.setAllowsUninitializedInput(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.OpDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.OpDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.OpDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.OpDef.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getName();
-  if (f.length > 0) {
-    writer.writeString(
-      1,
-      f
-    );
-  }
-  f = message.getInputArgList();
-  if (f.length > 0) {
-    writer.writeRepeatedMessage(
-      2,
-      f,
-      proto.tensorflow.OpDef.ArgDef.serializeBinaryToWriter
-    );
-  }
-  f = message.getOutputArgList();
-  if (f.length > 0) {
-    writer.writeRepeatedMessage(
-      3,
-      f,
-      proto.tensorflow.OpDef.ArgDef.serializeBinaryToWriter
-    );
-  }
-  f = message.getAttrList();
-  if (f.length > 0) {
-    writer.writeRepeatedMessage(
-      4,
-      f,
-      proto.tensorflow.OpDef.AttrDef.serializeBinaryToWriter
-    );
-  }
-  f = message.getDeprecation();
-  if (f != null) {
-    writer.writeMessage(
-      8,
-      f,
-      proto.tensorflow.OpDef.OpDeprecation.serializeBinaryToWriter
-    );
-  }
-  f = message.getSummary();
-  if (f.length > 0) {
-    writer.writeString(
-      5,
-      f
-    );
-  }
-  f = message.getDescription();
-  if (f.length > 0) {
-    writer.writeString(
-      6,
-      f
-    );
-  }
-  f = message.getIsCommutative();
-  if (f) {
-    writer.writeBool(
-      18,
-      f
-    );
-  }
-  f = message.getIsAggregate();
-  if (f) {
-    writer.writeBool(
-      16,
-      f
-    );
-  }
-  f = message.getIsStateful();
-  if (f) {
-    writer.writeBool(
-      17,
-      f
-    );
-  }
-  f = message.getAllowsUninitializedInput();
-  if (f) {
-    writer.writeBool(
-      19,
-      f
-    );
-  }
-};
-
-
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.OpDef.ArgDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.OpDef.ArgDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.OpDef.ArgDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.OpDef.ArgDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    name: jspb.Message.getFieldWithDefault(msg, 1, ""),
-    description: jspb.Message.getFieldWithDefault(msg, 2, ""),
-    type: jspb.Message.getFieldWithDefault(msg, 3, 0),
-    typeAttr: jspb.Message.getFieldWithDefault(msg, 4, ""),
-    numberAttr: jspb.Message.getFieldWithDefault(msg, 5, ""),
-    typeListAttr: jspb.Message.getFieldWithDefault(msg, 6, ""),
-    isRef: jspb.Message.getBooleanFieldWithDefault(msg, 16, false)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.OpDef.ArgDef}
- */
-proto.tensorflow.OpDef.ArgDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.OpDef.ArgDef;
-  return proto.tensorflow.OpDef.ArgDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.OpDef.ArgDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.OpDef.ArgDef}
- */
-proto.tensorflow.OpDef.ArgDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setName(value);
-      break;
-    case 2:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setDescription(value);
-      break;
-    case 3:
-      var value = /** @type {!proto.tensorflow.DataType} */ (reader.readEnum());
-      msg.setType(value);
-      break;
-    case 4:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setTypeAttr(value);
-      break;
-    case 5:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setNumberAttr(value);
-      break;
-    case 6:
-      var value = /** @type {string} */ (reader.readString());
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-/**
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-/**
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- * @return {!proto.tensorflow.OpDef.AttrDef} returns this
- */
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- */
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-  return jspb.Message.addToRepeatedWrapperField(this, 2, opt_value, proto.tensorflow.OpDef.ArgDef, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.clearInputArgList = function() {
-  return this.setInputArgList([]);
-};
-
-
-/**
- * repeated ArgDef output_arg = 3;
- * @return {!Array<!proto.tensorflow.OpDef.ArgDef>}
- */
-proto.tensorflow.OpDef.prototype.getOutputArgList = function() {
-  return /** @type{!Array<!proto.tensorflow.OpDef.ArgDef>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.OpDef.ArgDef, 3));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.OpDef.ArgDef>} value
- * @return {!proto.tensorflow.OpDef} returns this
-*/
-proto.tensorflow.OpDef.prototype.setOutputArgList = function(value) {
-  return jspb.Message.setRepeatedWrapperField(this, 3, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.OpDef.ArgDef=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.OpDef.ArgDef}
- */
-proto.tensorflow.OpDef.prototype.addOutputArg = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 3, opt_value, proto.tensorflow.OpDef.ArgDef, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.clearOutputArgList = function() {
-  return this.setOutputArgList([]);
-};
-
-
-/**
- * repeated AttrDef attr = 4;
- * @return {!Array<!proto.tensorflow.OpDef.AttrDef>}
- */
-proto.tensorflow.OpDef.prototype.getAttrList = function() {
-  return /** @type{!Array<!proto.tensorflow.OpDef.AttrDef>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.OpDef.AttrDef, 4));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.OpDef.AttrDef>} value
- * @return {!proto.tensorflow.OpDef} returns this
-*/
-proto.tensorflow.OpDef.prototype.setAttrList = function(value) {
-  return jspb.Message.setRepeatedWrapperField(this, 4, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.OpDef.AttrDef=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.OpDef.AttrDef}
- */
-proto.tensorflow.OpDef.prototype.addAttr = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 4, opt_value, proto.tensorflow.OpDef.AttrDef, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.clearAttrList = function() {
-  return this.setAttrList([]);
-};
-
-
-/**
- * optional OpDeprecation deprecation = 8;
- * @return {?proto.tensorflow.OpDef.OpDeprecation}
- */
-proto.tensorflow.OpDef.prototype.getDeprecation = function() {
-  return /** @type{?proto.tensorflow.OpDef.OpDeprecation} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.OpDef.OpDeprecation, 8));
-};
-
-
-/**
- * @param {?proto.tensorflow.OpDef.OpDeprecation|undefined} value
- * @return {!proto.tensorflow.OpDef} returns this
-*/
-proto.tensorflow.OpDef.prototype.setDeprecation = function(value) {
-  return jspb.Message.setWrapperField(this, 8, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.clearDeprecation = function() {
-  return this.setDeprecation(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.OpDef.prototype.hasDeprecation = function() {
-  return jspb.Message.getField(this, 8) != null;
-};
-
-
-/**
- * optional string summary = 5;
- * @return {string}
- */
-proto.tensorflow.OpDef.prototype.getSummary = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 5, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.setSummary = function(value) {
-  return jspb.Message.setProto3StringField(this, 5, value);
-};
-
-
-/**
- * optional string description = 6;
- * @return {string}
- */
-proto.tensorflow.OpDef.prototype.getDescription = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 6, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.setDescription = function(value) {
-  return jspb.Message.setProto3StringField(this, 6, value);
-};
-
-
-/**
- * optional bool is_commutative = 18;
- * @return {boolean}
- */
-proto.tensorflow.OpDef.prototype.getIsCommutative = function() {
-  return /** @type {boolean} */ (jspb.Message.getBooleanFieldWithDefault(this, 18, false));
-};
-
-
-/**
- * @param {boolean} value
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.setIsCommutative = function(value) {
-  return jspb.Message.setProto3BooleanField(this, 18, value);
-};
-
-
-/**
- * optional bool is_aggregate = 16;
- * @return {boolean}
- */
-proto.tensorflow.OpDef.prototype.getIsAggregate = function() {
-  return /** @type {boolean} */ (jspb.Message.getBooleanFieldWithDefault(this, 16, false));
-};
-
-
-/**
- * @param {boolean} value
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.setIsAggregate = function(value) {
-  return jspb.Message.setProto3BooleanField(this, 16, value);
-};
-
-
-/**
- * optional bool is_stateful = 17;
- * @return {boolean}
- */
-proto.tensorflow.OpDef.prototype.getIsStateful = function() {
-  return /** @type {boolean} */ (jspb.Message.getBooleanFieldWithDefault(this, 17, false));
-};
-
-
-/**
- * @param {boolean} value
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.setIsStateful = function(value) {
-  return jspb.Message.setProto3BooleanField(this, 17, value);
-};
-
-
-/**
- * optional bool allows_uninitialized_input = 19;
- * @return {boolean}
- */
-proto.tensorflow.OpDef.prototype.getAllowsUninitializedInput = function() {
-  return /** @type {boolean} */ (jspb.Message.getBooleanFieldWithDefault(this, 19, false));
-};
-
-
-/**
- * @param {boolean} value
- * @return {!proto.tensorflow.OpDef} returns this
- */
-proto.tensorflow.OpDef.prototype.setAllowsUninitializedInput = function(value) {
-  return jspb.Message.setProto3BooleanField(this, 19, value);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.OpList.repeatedFields_ = [1];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.OpList.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.OpList.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.OpList} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.OpList.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    opList: jspb.Message.toObjectList(msg.getOpList(),
-    proto.tensorflow.OpDef.toObject, includeInstance)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.OpList}
- */
-proto.tensorflow.OpList.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.OpList;
-  return proto.tensorflow.OpList.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.OpList} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.OpList}
- */
-proto.tensorflow.OpList.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = new proto.tensorflow.OpDef;
-      reader.readMessage(value,proto.tensorflow.OpDef.deserializeBinaryFromReader);
-      msg.addOp(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.OpList.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.OpList.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.OpList} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.OpList.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getOpList();
-  if (f.length > 0) {
-    writer.writeRepeatedMessage(
-      1,
-      f,
-      proto.tensorflow.OpDef.serializeBinaryToWriter
-    );
-  }
-};
-
-
-/**
- * repeated OpDef op = 1;
- * @return {!Array<!proto.tensorflow.OpDef>}
- */
-proto.tensorflow.OpList.prototype.getOpList = function() {
-  return /** @type{!Array<!proto.tensorflow.OpDef>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.OpDef, 1));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.OpDef>} value
- * @return {!proto.tensorflow.OpList} returns this
-*/
-proto.tensorflow.OpList.prototype.setOpList = function(value) {
-  return jspb.Message.setRepeatedWrapperField(this, 1, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.OpDef=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.OpDef}
- */
-proto.tensorflow.OpList.prototype.addOp = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 1, opt_value, proto.tensorflow.OpDef, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.OpList} returns this
- */
-proto.tensorflow.OpList.prototype.clearOpList = function() {
-  return this.setOpList([]);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.MetaGraphDef.repeatedFields_ = [6];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.MetaGraphDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.MetaGraphDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.MetaGraphDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.MetaGraphDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    metaInfoDef: (f = msg.getMetaInfoDef()) && proto.tensorflow.MetaGraphDef.MetaInfoDef.toObject(includeInstance, f),
-    graphDef: (f = msg.getGraphDef()) && proto.tensorflow.GraphDef.toObject(includeInstance, f),
-    saverDef: (f = msg.getSaverDef()) && proto.tensorflow.SaverDef.toObject(includeInstance, f),
-    collectionDefMap: (f = msg.getCollectionDefMap()) ? f.toObject(includeInstance, proto.tensorflow.CollectionDef.toObject) : [],
-    signatureDefMap: (f = msg.getSignatureDefMap()) ? f.toObject(includeInstance, proto.tensorflow.SignatureDef.toObject) : [],
-    assetFileDefList: jspb.Message.toObjectList(msg.getAssetFileDefList(),
-    proto.tensorflow.AssetFileDef.toObject, includeInstance)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.MetaGraphDef}
- */
-proto.tensorflow.MetaGraphDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.MetaGraphDef;
-  return proto.tensorflow.MetaGraphDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.MetaGraphDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.MetaGraphDef}
- */
-proto.tensorflow.MetaGraphDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = new proto.tensorflow.MetaGraphDef.MetaInfoDef;
-      reader.readMessage(value,proto.tensorflow.MetaGraphDef.MetaInfoDef.deserializeBinaryFromReader);
-      msg.setMetaInfoDef(value);
-      break;
-    case 2:
-      var value = new proto.tensorflow.GraphDef;
-      reader.readMessage(value,proto.tensorflow.GraphDef.deserializeBinaryFromReader);
-      msg.setGraphDef(value);
-      break;
-    case 3:
-      var value = new proto.tensorflow.SaverDef;
-      reader.readMessage(value,proto.tensorflow.SaverDef.deserializeBinaryFromReader);
-      msg.setSaverDef(value);
-      break;
-    case 4:
-      var value = msg.getCollectionDefMap();
-      reader.readMessage(value, function(message, reader) {
-        jspb.Map.deserializeBinary(message, reader, jspb.BinaryReader.prototype.readString, jspb.BinaryReader.prototype.readMessage, proto.tensorflow.CollectionDef.deserializeBinaryFromReader, "", new proto.tensorflow.CollectionDef());
-         });
-      break;
-    case 5:
-      var value = msg.getSignatureDefMap();
-      reader.readMessage(value, function(message, reader) {
-        jspb.Map.deserializeBinary(message, reader, jspb.BinaryReader.prototype.readString, jspb.BinaryReader.prototype.readMessage, proto.tensorflow.SignatureDef.deserializeBinaryFromReader, "", new proto.tensorflow.SignatureDef());
-         });
-      break;
-    case 6:
-      var value = new proto.tensorflow.AssetFileDef;
-      reader.readMessage(value,proto.tensorflow.AssetFileDef.deserializeBinaryFromReader);
-      msg.addAssetFileDef(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.MetaGraphDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.MetaGraphDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.MetaGraphDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.MetaGraphDef.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getMetaInfoDef();
-  if (f != null) {
-    writer.writeMessage(
-      1,
-      f,
-      proto.tensorflow.MetaGraphDef.MetaInfoDef.serializeBinaryToWriter
-    );
-  }
-  f = message.getGraphDef();
-  if (f != null) {
-    writer.writeMessage(
-      2,
-      f,
-      proto.tensorflow.GraphDef.serializeBinaryToWriter
-    );
-  }
-  f = message.getSaverDef();
-  if (f != null) {
-    writer.writeMessage(
-      3,
-      f,
-      proto.tensorflow.SaverDef.serializeBinaryToWriter
-    );
-  }
-  f = message.getCollectionDefMap(true);
-  if (f && f.getLength() > 0) {
-    f.serializeBinary(4, writer, jspb.BinaryWriter.prototype.writeString, jspb.BinaryWriter.prototype.writeMessage, proto.tensorflow.CollectionDef.serializeBinaryToWriter);
-  }
-  f = message.getSignatureDefMap(true);
-  if (f && f.getLength() > 0) {
-    f.serializeBinary(5, writer, jspb.BinaryWriter.prototype.writeString, jspb.BinaryWriter.prototype.writeMessage, proto.tensorflow.SignatureDef.serializeBinaryToWriter);
-  }
-  f = message.getAssetFileDefList();
-  if (f.length > 0) {
-    writer.writeRepeatedMessage(
-      6,
-      f,
-      proto.tensorflow.AssetFileDef.serializeBinaryToWriter
-    );
-  }
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.repeatedFields_ = [4];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.MetaGraphDef.MetaInfoDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.MetaGraphDef.MetaInfoDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    metaGraphVersion: jspb.Message.getFieldWithDefault(msg, 1, ""),
-    strippedOpList: (f = msg.getStrippedOpList()) && proto.tensorflow.OpList.toObject(includeInstance, f),
-    anyInfo: (f = msg.getAnyInfo()) && proto.tensorflow.Any.toObject(includeInstance, f),
-    tagsList: (f = jspb.Message.getRepeatedField(msg, 4)) == null ? undefined : f,
-    tensorflowVersion: jspb.Message.getFieldWithDefault(msg, 5, ""),
-    tensorflowGitVersion: jspb.Message.getFieldWithDefault(msg, 6, "")
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.MetaGraphDef.MetaInfoDef;
-  return proto.tensorflow.MetaGraphDef.MetaInfoDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.MetaGraphDef.MetaInfoDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setMetaGraphVersion(value);
-      break;
-    case 2:
-      var value = new proto.tensorflow.OpList;
-      reader.readMessage(value,proto.tensorflow.OpList.deserializeBinaryFromReader);
-      msg.setStrippedOpList(value);
-      break;
-    case 3:
-      var value = new proto.tensorflow.Any;
-      reader.readMessage(value,proto.tensorflow.Any.deserializeBinaryFromReader);
-      msg.setAnyInfo(value);
-      break;
-    case 4:
-      var value = /** @type {string} */ (reader.readString());
-      msg.addTags(value);
-      break;
-    case 5:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setTensorflowVersion(value);
-      break;
-    case 6:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setTensorflowGitVersion(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.MetaGraphDef.MetaInfoDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.MetaGraphDef.MetaInfoDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getMetaGraphVersion();
-  if (f.length > 0) {
-    writer.writeString(
-      1,
-      f
-    );
-  }
-  f = message.getStrippedOpList();
-  if (f != null) {
-    writer.writeMessage(
-      2,
-      f,
-      proto.tensorflow.OpList.serializeBinaryToWriter
-    );
-  }
-  f = message.getAnyInfo();
-  if (f != null) {
-    writer.writeMessage(
-      3,
-      f,
-      proto.tensorflow.Any.serializeBinaryToWriter
-    );
-  }
-  f = message.getTagsList();
-  if (f.length > 0) {
-    writer.writeRepeatedString(
-      4,
-      f
-    );
-  }
-  f = message.getTensorflowVersion();
-  if (f.length > 0) {
-    writer.writeString(
-      5,
-      f
-    );
-  }
-  f = message.getTensorflowGitVersion();
-  if (f.length > 0) {
-    writer.writeString(
-      6,
-      f
-    );
-  }
-};
-
-
-/**
- * optional string meta_graph_version = 1;
- * @return {string}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.getMetaGraphVersion = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 1, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.setMetaGraphVersion = function(value) {
-  return jspb.Message.setProto3StringField(this, 1, value);
-};
-
-
-/**
- * optional OpList stripped_op_list = 2;
- * @return {?proto.tensorflow.OpList}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.getStrippedOpList = function() {
-  return /** @type{?proto.tensorflow.OpList} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.OpList, 2));
-};
-
-
-/**
- * @param {?proto.tensorflow.OpList|undefined} value
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
-*/
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.setStrippedOpList = function(value) {
-  return jspb.Message.setWrapperField(this, 2, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.clearStrippedOpList = function() {
-  return this.setStrippedOpList(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.hasStrippedOpList = function() {
-  return jspb.Message.getField(this, 2) != null;
-};
-
-
-/**
- * optional Any any_info = 3;
- * @return {?proto.tensorflow.Any}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.getAnyInfo = function() {
-  return /** @type{?proto.tensorflow.Any} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.Any, 3));
-};
-
-
-/**
- * @param {?proto.tensorflow.Any|undefined} value
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
-*/
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.setAnyInfo = function(value) {
-  return jspb.Message.setWrapperField(this, 3, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.clearAnyInfo = function() {
-  return this.setAnyInfo(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.hasAnyInfo = function() {
-  return jspb.Message.getField(this, 3) != null;
-};
-
-
-/**
- * repeated string tags = 4;
- * @return {!Array<string>}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.getTagsList = function() {
-  return /** @type {!Array<string>} */ (jspb.Message.getRepeatedField(this, 4));
-};
-
-
-/**
- * @param {!Array<string>} value
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.setTagsList = function(value) {
-  return jspb.Message.setField(this, 4, value || []);
-};
-
-
-/**
- * @param {string} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.addTags = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 4, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.clearTagsList = function() {
-  return this.setTagsList([]);
-};
-
-
-/**
- * optional string tensorflow_version = 5;
- * @return {string}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.getTensorflowVersion = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 5, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.setTensorflowVersion = function(value) {
-  return jspb.Message.setProto3StringField(this, 5, value);
-};
-
-
-/**
- * optional string tensorflow_git_version = 6;
- * @return {string}
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.getTensorflowGitVersion = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 6, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.MetaGraphDef.MetaInfoDef} returns this
- */
-proto.tensorflow.MetaGraphDef.MetaInfoDef.prototype.setTensorflowGitVersion = function(value) {
-  return jspb.Message.setProto3StringField(this, 6, value);
-};
-
-
-/**
- * optional MetaInfoDef meta_info_def = 1;
- * @return {?proto.tensorflow.MetaGraphDef.MetaInfoDef}
- */
-proto.tensorflow.MetaGraphDef.prototype.getMetaInfoDef = function() {
-  return /** @type{?proto.tensorflow.MetaGraphDef.MetaInfoDef} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.MetaGraphDef.MetaInfoDef, 1));
-};
-
-
-/**
- * @param {?proto.tensorflow.MetaGraphDef.MetaInfoDef|undefined} value
- * @return {!proto.tensorflow.MetaGraphDef} returns this
-*/
-proto.tensorflow.MetaGraphDef.prototype.setMetaInfoDef = function(value) {
-  return jspb.Message.setWrapperField(this, 1, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.MetaGraphDef} returns this
- */
-proto.tensorflow.MetaGraphDef.prototype.clearMetaInfoDef = function() {
-  return this.setMetaInfoDef(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.MetaGraphDef.prototype.hasMetaInfoDef = function() {
-  return jspb.Message.getField(this, 1) != null;
-};
-
-
-/**
- * optional GraphDef graph_def = 2;
- * @return {?proto.tensorflow.GraphDef}
- */
-proto.tensorflow.MetaGraphDef.prototype.getGraphDef = function() {
-  return /** @type{?proto.tensorflow.GraphDef} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.GraphDef, 2));
-};
-
-
-/**
- * @param {?proto.tensorflow.GraphDef|undefined} value
- * @return {!proto.tensorflow.MetaGraphDef} returns this
-*/
-proto.tensorflow.MetaGraphDef.prototype.setGraphDef = function(value) {
-  return jspb.Message.setWrapperField(this, 2, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.MetaGraphDef} returns this
- */
-proto.tensorflow.MetaGraphDef.prototype.clearGraphDef = function() {
-  return this.setGraphDef(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.MetaGraphDef.prototype.hasGraphDef = function() {
-  return jspb.Message.getField(this, 2) != null;
-};
-
-
-/**
- * optional SaverDef saver_def = 3;
- * @return {?proto.tensorflow.SaverDef}
- */
-proto.tensorflow.MetaGraphDef.prototype.getSaverDef = function() {
-  return /** @type{?proto.tensorflow.SaverDef} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.SaverDef, 3));
-};
-
-
-/**
- * @param {?proto.tensorflow.SaverDef|undefined} value
- * @return {!proto.tensorflow.MetaGraphDef} returns this
-*/
-proto.tensorflow.MetaGraphDef.prototype.setSaverDef = function(value) {
-  return jspb.Message.setWrapperField(this, 3, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.MetaGraphDef} returns this
- */
-proto.tensorflow.MetaGraphDef.prototype.clearSaverDef = function() {
-  return this.setSaverDef(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.MetaGraphDef.prototype.hasSaverDef = function() {
-  return jspb.Message.getField(this, 3) != null;
-};
-
-
-/**
- * map<string, CollectionDef> collection_def = 4;
- * @param {boolean=} opt_noLazyCreate Do not create the map if
- * empty, instead returning `undefined`
- * @return {!jspb.Map<string,!proto.tensorflow.CollectionDef>}
- */
-proto.tensorflow.MetaGraphDef.prototype.getCollectionDefMap = function(opt_noLazyCreate) {
-  return /** @type {!jspb.Map<string,!proto.tensorflow.CollectionDef>} */ (
-      jspb.Message.getMapField(this, 4, opt_noLazyCreate,
-      proto.tensorflow.CollectionDef));
-};
-
-
-/**
- * Clears values from the map. The map will be non-null.
- * @return {!proto.tensorflow.MetaGraphDef} returns this
- */
-proto.tensorflow.MetaGraphDef.prototype.clearCollectionDefMap = function() {
-  this.getCollectionDefMap().clear();
-  return this;};
-
-
-/**
- * map<string, SignatureDef> signature_def = 5;
- * @param {boolean=} opt_noLazyCreate Do not create the map if
- * empty, instead returning `undefined`
- * @return {!jspb.Map<string,!proto.tensorflow.SignatureDef>}
- */
-proto.tensorflow.MetaGraphDef.prototype.getSignatureDefMap = function(opt_noLazyCreate) {
-  return /** @type {!jspb.Map<string,!proto.tensorflow.SignatureDef>} */ (
-      jspb.Message.getMapField(this, 5, opt_noLazyCreate,
-      proto.tensorflow.SignatureDef));
-};
-
-
-/**
- * Clears values from the map. The map will be non-null.
- * @return {!proto.tensorflow.MetaGraphDef} returns this
- */
-proto.tensorflow.MetaGraphDef.prototype.clearSignatureDefMap = function() {
-  this.getSignatureDefMap().clear();
-  return this;};
-
-
-/**
- * repeated AssetFileDef asset_file_def = 6;
- * @return {!Array<!proto.tensorflow.AssetFileDef>}
- */
-proto.tensorflow.MetaGraphDef.prototype.getAssetFileDefList = function() {
-  return /** @type{!Array<!proto.tensorflow.AssetFileDef>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.AssetFileDef, 6));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.AssetFileDef>} value
- * @return {!proto.tensorflow.MetaGraphDef} returns this
-*/
-proto.tensorflow.MetaGraphDef.prototype.setAssetFileDefList = function(value) {
-  return jspb.Message.setRepeatedWrapperField(this, 6, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.AssetFileDef=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.AssetFileDef}
- */
-proto.tensorflow.MetaGraphDef.prototype.addAssetFileDef = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 6, opt_value, proto.tensorflow.AssetFileDef, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.MetaGraphDef} returns this
- */
-proto.tensorflow.MetaGraphDef.prototype.clearAssetFileDefList = function() {
-  return this.setAssetFileDefList([]);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.SavedModel.repeatedFields_ = [2];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.SavedModel.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.SavedModel.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.SavedModel} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SavedModel.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    savedModelSchemaVersion: jspb.Message.getFieldWithDefault(msg, 1, 0),
-    metaGraphsList: jspb.Message.toObjectList(msg.getMetaGraphsList(),
-    proto.tensorflow.MetaGraphDef.toObject, includeInstance)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.SavedModel}
- */
-proto.tensorflow.SavedModel.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.SavedModel;
-  return proto.tensorflow.SavedModel.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.SavedModel} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.SavedModel}
- */
-proto.tensorflow.SavedModel.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {number} */ (reader.readInt64());
-      msg.setSavedModelSchemaVersion(value);
-      break;
-    case 2:
-      var value = new proto.tensorflow.MetaGraphDef;
-      reader.readMessage(value,proto.tensorflow.MetaGraphDef.deserializeBinaryFromReader);
-      msg.addMetaGraphs(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.SavedModel.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.SavedModel.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.SavedModel} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SavedModel.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getSavedModelSchemaVersion();
-  if (f !== 0) {
-    writer.writeInt64(
-      1,
-      f
-    );
-  }
-  f = message.getMetaGraphsList();
-  if (f.length > 0) {
-    writer.writeRepeatedMessage(
-      2,
-      f,
-      proto.tensorflow.MetaGraphDef.serializeBinaryToWriter
-    );
-  }
-};
-
-
-/**
- * optional int64 saved_model_schema_version = 1;
- * @return {number}
- */
-proto.tensorflow.SavedModel.prototype.getSavedModelSchemaVersion = function() {
-  return /** @type {number} */ (jspb.Message.getFieldWithDefault(this, 1, 0));
-};
-
-
-/**
- * @param {number} value
- * @return {!proto.tensorflow.SavedModel} returns this
- */
-proto.tensorflow.SavedModel.prototype.setSavedModelSchemaVersion = function(value) {
-  return jspb.Message.setProto3IntField(this, 1, value);
-};
-
-
-/**
- * repeated MetaGraphDef meta_graphs = 2;
- * @return {!Array<!proto.tensorflow.MetaGraphDef>}
- */
-proto.tensorflow.SavedModel.prototype.getMetaGraphsList = function() {
-  return /** @type{!Array<!proto.tensorflow.MetaGraphDef>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.MetaGraphDef, 2));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.MetaGraphDef>} value
- * @return {!proto.tensorflow.SavedModel} returns this
-*/
-proto.tensorflow.SavedModel.prototype.setMetaGraphsList = function(value) {
-  return jspb.Message.setRepeatedWrapperField(this, 2, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.MetaGraphDef=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.MetaGraphDef}
- */
-proto.tensorflow.SavedModel.prototype.addMetaGraphs = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 2, opt_value, proto.tensorflow.MetaGraphDef, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.SavedModel} returns this
- */
-proto.tensorflow.SavedModel.prototype.clearMetaGraphsList = function() {
-  return this.setMetaGraphsList([]);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.FunctionDefLibrary.repeatedFields_ = [1,2];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.FunctionDefLibrary.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.FunctionDefLibrary.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.FunctionDefLibrary} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.FunctionDefLibrary.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    functionList: jspb.Message.toObjectList(msg.getFunctionList(),
-    proto.tensorflow.FunctionDef.toObject, includeInstance),
-    gradientList: jspb.Message.toObjectList(msg.getGradientList(),
-    proto.tensorflow.GradientDef.toObject, includeInstance)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.FunctionDefLibrary}
- */
-proto.tensorflow.FunctionDefLibrary.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.FunctionDefLibrary;
-  return proto.tensorflow.FunctionDefLibrary.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.FunctionDefLibrary} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.FunctionDefLibrary}
- */
-proto.tensorflow.FunctionDefLibrary.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = new proto.tensorflow.FunctionDef;
-      reader.readMessage(value,proto.tensorflow.FunctionDef.deserializeBinaryFromReader);
-      msg.addFunction(value);
-      break;
-    case 2:
-      var value = new proto.tensorflow.GradientDef;
-      reader.readMessage(value,proto.tensorflow.GradientDef.deserializeBinaryFromReader);
-      msg.addGradient(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.FunctionDefLibrary.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.FunctionDefLibrary.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.FunctionDefLibrary} message
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- */
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-/**
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-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.FunctionDef, 1));
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-
-/**
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-*/
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-  return jspb.Message.setRepeatedWrapperField(this, 1, value);
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-
-/**
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- * @return {!proto.tensorflow.FunctionDef}
- */
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-  return jspb.Message.addToRepeatedWrapperField(this, 1, opt_value, proto.tensorflow.FunctionDef, opt_index);
-};
-
-
-/**
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- * @return {!proto.tensorflow.FunctionDefLibrary} returns this
- */
-proto.tensorflow.FunctionDefLibrary.prototype.clearFunctionList = function() {
-  return this.setFunctionList([]);
-};
-
-
-/**
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-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.GradientDef, 2));
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-
-
-/**
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-*/
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-  return jspb.Message.setRepeatedWrapperField(this, 2, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.GradientDef=} opt_value
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- */
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-  return jspb.Message.addToRepeatedWrapperField(this, 2, opt_value, proto.tensorflow.GradientDef, opt_index);
-};
-
-
-/**
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- * @return {!proto.tensorflow.FunctionDefLibrary} returns this
- */
-proto.tensorflow.FunctionDefLibrary.prototype.clearGradientList = function() {
-  return this.setGradientList([]);
-};
-
-
-
-/**
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- * @private {!Array<number>}
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- */
-proto.tensorflow.FunctionDef.repeatedFields_ = [3];
-
-
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-if (jspb.Message.GENERATE_TO_OBJECT) {
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- * Field names that are reserved in JavaScript and will be renamed to pb_name.
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- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
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- *     JSPB instance for transitional soy proto support:
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- * @return {!Object}
- */
-proto.tensorflow.FunctionDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.FunctionDef.toObject(opt_includeInstance, this);
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-
-
-/**
- * Static version of the {@see toObject} method.
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- *     the JSPB instance for transitional soy proto support:
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- * @param {!proto.tensorflow.FunctionDef} msg The msg instance to transform.
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-proto.tensorflow.FunctionDef.toObject = function(includeInstance, msg) {
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-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.FunctionDef;
-  return proto.tensorflow.FunctionDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.FunctionDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
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- */
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-
-/**
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-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.FunctionDef} message
- * @param {!jspb.BinaryWriter} writer
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-
-
-/**
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-
-
-/**
- * @param {?proto.tensorflow.OpDef|undefined} value
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-*/
-proto.tensorflow.FunctionDef.prototype.setSignature = function(value) {
-  return jspb.Message.setWrapperField(this, 1, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.FunctionDef} returns this
- */
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-  return this.setSignature(undefined);
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-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
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-
-/**
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-
-/**
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- * @return {!proto.tensorflow.FunctionDef} returns this
- */
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-  this.getAttrMap().clear();
-  return this;};
-
-
-/**
- * repeated NodeDef node_def = 3;
- * @return {!Array<!proto.tensorflow.NodeDef>}
- */
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-  return /** @type{!Array<!proto.tensorflow.NodeDef>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.NodeDef, 3));
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-
-
-/**
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-*/
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-  return jspb.Message.setRepeatedWrapperField(this, 3, value);
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-
-
-/**
- * @param {!proto.tensorflow.NodeDef=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.NodeDef}
- */
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-  return jspb.Message.addToRepeatedWrapperField(this, 3, opt_value, proto.tensorflow.NodeDef, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.FunctionDef} returns this
- */
-proto.tensorflow.FunctionDef.prototype.clearNodeDefList = function() {
-  return this.setNodeDefList([]);
-};
-
-
-/**
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- * @return {!jspb.Map<string,string>}
- */
-proto.tensorflow.FunctionDef.prototype.getRetMap = function(opt_noLazyCreate) {
-  return /** @type {!jspb.Map<string,string>} */ (
-      jspb.Message.getMapField(this, 4, opt_noLazyCreate,
-      null));
-};
-
-
-/**
- * Clears values from the map. The map will be non-null.
- * @return {!proto.tensorflow.FunctionDef} returns this
- */
-proto.tensorflow.FunctionDef.prototype.clearRetMap = function() {
-  this.getRetMap().clear();
-  return this;};
-
-
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.GradientDef.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.GradientDef.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.GradientDef} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.GradientDef.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    functionName: jspb.Message.getFieldWithDefault(msg, 1, ""),
-    gradientFunc: jspb.Message.getFieldWithDefault(msg, 2, "")
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-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
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-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.GradientDef}
- */
-proto.tensorflow.GradientDef.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.GradientDef;
-  return proto.tensorflow.GradientDef.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.GradientDef} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
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- */
-proto.tensorflow.GradientDef.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
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-    var field = reader.getFieldNumber();
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-    case 2:
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-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.GradientDef.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.GradientDef.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.GradientDef} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.GradientDef.serializeBinaryToWriter = function(message, writer) {
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-  f = message.getFunctionName();
-  if (f.length > 0) {
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-  f = message.getGradientFunc();
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-
-
-/**
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-proto.tensorflow.GradientDef.prototype.getFunctionName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 1, ""));
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-
-
-/**
- * @param {string} value
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-  return jspb.Message.setProto3StringField(this, 1, value);
-};
-
-
-/**
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- */
-proto.tensorflow.GradientDef.prototype.getGradientFunc = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 2, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.GradientDef} returns this
- */
-proto.tensorflow.GradientDef.prototype.setGradientFunc = function(value) {
-  return jspb.Message.setProto3StringField(this, 2, value);
-};
-
-
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
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- * @return {!Object}
- */
-proto.tensorflow.SummaryDescription.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.SummaryDescription.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.SummaryDescription} msg The msg instance to transform.
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- */
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- */
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-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.SummaryDescription;
-  return proto.tensorflow.SummaryDescription.deserializeBinaryFromReader(msg, reader);
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-
-
-/**
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-
-
-/**
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- */
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-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.SummaryDescription.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.SummaryDescription} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SummaryDescription.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getTypeHint();
-  if (f.length > 0) {
-    writer.writeString(
-      1,
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-
-
-/**
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-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 1, ""));
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-
-
-/**
- * @param {string} value
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-  return jspb.Message.setProto3StringField(this, 1, value);
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-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
- * @const
- */
-proto.tensorflow.HistogramProto.repeatedFields_ = [6,7];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
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- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.HistogramProto.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.HistogramProto.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
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- * @param {!proto.tensorflow.HistogramProto} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.HistogramProto.toObject = function(includeInstance, msg) {
-  var f, obj = {
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- */
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-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.HistogramProto;
-  return proto.tensorflow.HistogramProto.deserializeBinaryFromReader(msg, reader);
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-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
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-/**
- * Serializes the given message to binary data (in protobuf wire
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-/**
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-/**
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-/**
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-
-/**
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- */
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-  return jspb.Message.setProto3FloatField(this, 2, value);
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-/**
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- * @return {number}
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-  return /** @type {number} */ (jspb.Message.getFloatingPointFieldWithDefault(this, 3, 0.0));
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-
-/**
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- */
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-  return jspb.Message.setProto3FloatField(this, 3, value);
-};
-
-
-/**
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- * @return {number}
- */
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-  return /** @type {number} */ (jspb.Message.getFloatingPointFieldWithDefault(this, 4, 0.0));
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-
-
-/**
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- * @return {!proto.tensorflow.HistogramProto} returns this
- */
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-  return jspb.Message.setProto3FloatField(this, 4, value);
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-
-/**
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- */
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-};
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-
-/**
- * repeated double bucket_limit = 6;
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-/**
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- */
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-  return jspb.Message.setField(this, 6, value || []);
-};
-
-
-/**
- * @param {number} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.HistogramProto} returns this
- */
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-  return jspb.Message.addToRepeatedField(this, 6, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.HistogramProto} returns this
- */
-proto.tensorflow.HistogramProto.prototype.clearBucketLimitList = function() {
-  return this.setBucketLimitList([]);
-};
-
-
-/**
- * repeated double bucket = 7;
- * @return {!Array<number>}
- */
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-  return /** @type {!Array<number>} */ (jspb.Message.getRepeatedFloatingPointField(this, 7));
-};
-
-
-/**
- * @param {!Array<number>} value
- * @return {!proto.tensorflow.HistogramProto} returns this
- */
-proto.tensorflow.HistogramProto.prototype.setBucketList = function(value) {
-  return jspb.Message.setField(this, 7, value || []);
-};
-
-
-/**
- * @param {number} value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.HistogramProto} returns this
- */
-proto.tensorflow.HistogramProto.prototype.addBucket = function(value, opt_index) {
-  return jspb.Message.addToRepeatedField(this, 7, value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.HistogramProto} returns this
- */
-proto.tensorflow.HistogramProto.prototype.clearBucketList = function() {
-  return this.setBucketList([]);
-};
-
-
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.SummaryMetadata.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.SummaryMetadata.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
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- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.SummaryMetadata} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SummaryMetadata.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    pluginData: (f = msg.getPluginData()) && proto.tensorflow.SummaryMetadata.PluginData.toObject(includeInstance, f),
-    displayName: jspb.Message.getFieldWithDefault(msg, 2, ""),
-    summaryDescription: jspb.Message.getFieldWithDefault(msg, 3, ""),
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- * Deserializes binary data (in protobuf wire format).
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- * @return {!proto.tensorflow.SummaryMetadata}
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-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.SummaryMetadata;
-  return proto.tensorflow.SummaryMetadata.deserializeBinaryFromReader(msg, reader);
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-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
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- * @return {!proto.tensorflow.SummaryMetadata}
- */
-proto.tensorflow.SummaryMetadata.deserializeBinaryFromReader = function(msg, reader) {
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-      var value = /** @type {!proto.tensorflow.DataClass} */ (reader.readEnum());
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- */
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-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.SummaryMetadata.serializeBinaryToWriter(this, writer);
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-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.SummaryMetadata} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SummaryMetadata.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getPluginData();
-  if (f != null) {
-    writer.writeMessage(
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-      f,
-      proto.tensorflow.SummaryMetadata.PluginData.serializeBinaryToWriter
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-  f = message.getDisplayName();
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-
-
-
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-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.SummaryMetadata.PluginData.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.SummaryMetadata.PluginData.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
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- * @param {!proto.tensorflow.SummaryMetadata.PluginData} msg The msg instance to transform.
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- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SummaryMetadata.PluginData.toObject = function(includeInstance, msg) {
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-/**
- * Deserializes binary data (in protobuf wire format).
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- * @return {!proto.tensorflow.SummaryMetadata.PluginData}
- */
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-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.SummaryMetadata.PluginData;
-  return proto.tensorflow.SummaryMetadata.PluginData.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.SummaryMetadata.PluginData} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
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- */
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-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
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-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.SummaryMetadata.PluginData.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.SummaryMetadata.PluginData} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.SummaryMetadata.PluginData.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getPluginName();
-  if (f.length > 0) {
-    writer.writeString(
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-      f
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-
-
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-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.SummaryMetadata.PluginData} returns this
- */
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-  return jspb.Message.setProto3StringField(this, 1, value);
-};
-
-
-/**
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-
-
-/**
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- */
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-  return /** @type {string} */ (jspb.Message.bytesAsB64(
-      this.getContent()));
-};
-
-
-/**
- * optional bytes content = 2;
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- * @see http://caniuse.com/Uint8Array
- * This is a type-conversion wrapper around `getContent()`
- * @return {!Uint8Array}
- */
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-  return /** @type {!Uint8Array} */ (jspb.Message.bytesAsU8(
-      this.getContent()));
-};
-
-
-/**
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- * @return {!proto.tensorflow.SummaryMetadata.PluginData} returns this
- */
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-  return jspb.Message.setProto3BytesField(this, 2, value);
-};
-
-
-/**
- * optional PluginData plugin_data = 1;
- * @return {?proto.tensorflow.SummaryMetadata.PluginData}
- */
-proto.tensorflow.SummaryMetadata.prototype.getPluginData = function() {
-  return /** @type{?proto.tensorflow.SummaryMetadata.PluginData} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.SummaryMetadata.PluginData, 1));
-};
-
-
-/**
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- * @return {!proto.tensorflow.SummaryMetadata} returns this
-*/
-proto.tensorflow.SummaryMetadata.prototype.setPluginData = function(value) {
-  return jspb.Message.setWrapperField(this, 1, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.SummaryMetadata} returns this
- */
-proto.tensorflow.SummaryMetadata.prototype.clearPluginData = function() {
-  return this.setPluginData(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
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-  return jspb.Message.getField(this, 1) != null;
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-
-
-/**
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-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 2, ""));
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-
-
-/**
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- */
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-  return jspb.Message.setProto3StringField(this, 2, value);
-};
-
-
-/**
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- */
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-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 3, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.SummaryMetadata} returns this
- */
-proto.tensorflow.SummaryMetadata.prototype.setSummaryDescription = function(value) {
-  return jspb.Message.setProto3StringField(this, 3, value);
-};
-
-
-/**
- * optional DataClass data_class = 4;
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- */
-proto.tensorflow.SummaryMetadata.prototype.getDataClass = function() {
-  return /** @type {!proto.tensorflow.DataClass} */ (jspb.Message.getFieldWithDefault(this, 4, 0));
-};
-
-
-/**
- * @param {!proto.tensorflow.DataClass} value
- * @return {!proto.tensorflow.SummaryMetadata} returns this
- */
-proto.tensorflow.SummaryMetadata.prototype.setDataClass = function(value) {
-  return jspb.Message.setProto3EnumField(this, 4, value);
-};
-
-
-
-/**
- * List of repeated fields within this message type.
- * @private {!Array<number>}
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- */
-proto.tensorflow.Summary.repeatedFields_ = [1];
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
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-
-/**
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-
-
-/**
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-  return /** @type {number} */ (jspb.Message.getFieldWithDefault(this, 2, 0));
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-
-/**
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-
-/**
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-/**
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-/**
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-/**
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-  return /** @type {string} */ (jspb.Message.bytesAsB64(
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-
-
-/**
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- * @see http://caniuse.com/Uint8Array
- * This is a type-conversion wrapper around `getEncodedAudioString()`
- * @return {!Uint8Array}
- */
-proto.tensorflow.Summary.Audio.prototype.getEncodedAudioString_asU8 = function() {
-  return /** @type {!Uint8Array} */ (jspb.Message.bytesAsU8(
-      this.getEncodedAudioString()));
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-
-/**
- * @param {!(string|Uint8Array)} value
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- */
-proto.tensorflow.Summary.Audio.prototype.setEncodedAudioString = function(value) {
-  return jspb.Message.setProto3BytesField(this, 4, value);
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-
-
-/**
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-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 5, ""));
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-
-/**
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-  return jspb.Message.setProto3StringField(this, 5, value);
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-
-/**
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-/**
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- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
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- * @return {!Object}
- */
-proto.tensorflow.Summary.Value.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.Summary.Value.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.Summary.Value} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.Summary.Value.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    nodeName: jspb.Message.getFieldWithDefault(msg, 7, ""),
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-    simpleValue: jspb.Message.getFloatingPointFieldWithDefault(msg, 2, 0.0),
-    obsoleteOldStyleHistogram: msg.getObsoleteOldStyleHistogram_asB64(),
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-    histo: (f = msg.getHisto()) && proto.tensorflow.HistogramProto.toObject(includeInstance, f),
-    audio: (f = msg.getAudio()) && proto.tensorflow.Summary.Audio.toObject(includeInstance, f),
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-
-  if (includeInstance) {
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-
-/**
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- */
-proto.tensorflow.Summary.Value.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.Summary.Value;
-  return proto.tensorflow.Summary.Value.deserializeBinaryFromReader(msg, reader);
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-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
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-proto.tensorflow.Summary.Value.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 7:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setNodeName(value);
-      break;
-    case 1:
-      var value = /** @type {string} */ (reader.readString());
-      msg.setTag(value);
-      break;
-    case 9:
-      var value = new proto.tensorflow.SummaryMetadata;
-      reader.readMessage(value,proto.tensorflow.SummaryMetadata.deserializeBinaryFromReader);
-      msg.setMetadata(value);
-      break;
-    case 2:
-      var value = /** @type {number} */ (reader.readFloat());
-      msg.setSimpleValue(value);
-      break;
-    case 3:
-      var value = /** @type {!Uint8Array} */ (reader.readBytes());
-      msg.setObsoleteOldStyleHistogram(value);
-      break;
-    case 4:
-      var value = new proto.tensorflow.Summary.Image;
-      reader.readMessage(value,proto.tensorflow.Summary.Image.deserializeBinaryFromReader);
-      msg.setImage(value);
-      break;
-    case 5:
-      var value = new proto.tensorflow.HistogramProto;
-      reader.readMessage(value,proto.tensorflow.HistogramProto.deserializeBinaryFromReader);
-      msg.setHisto(value);
-      break;
-    case 6:
-      var value = new proto.tensorflow.Summary.Audio;
-      reader.readMessage(value,proto.tensorflow.Summary.Audio.deserializeBinaryFromReader);
-      msg.setAudio(value);
-      break;
-    case 8:
-      var value = new proto.tensorflow.Tensor;
-      reader.readMessage(value,proto.tensorflow.Tensor.deserializeBinaryFromReader);
-      msg.setTensor(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.Summary.Value.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.Summary.Value.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.Summary.Value} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.Summary.Value.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getNodeName();
-  if (f.length > 0) {
-    writer.writeString(
-      7,
-      f
-    );
-  }
-  f = message.getTag();
-  if (f.length > 0) {
-    writer.writeString(
-      1,
-      f
-    );
-  }
-  f = message.getMetadata();
-  if (f != null) {
-    writer.writeMessage(
-      9,
-      f,
-      proto.tensorflow.SummaryMetadata.serializeBinaryToWriter
-    );
-  }
-  f = /** @type {number} */ (jspb.Message.getField(message, 2));
-  if (f != null) {
-    writer.writeFloat(
-      2,
-      f
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-  f = /** @type {!(string|Uint8Array)} */ (jspb.Message.getField(message, 3));
-  if (f != null) {
-    writer.writeBytes(
-      3,
-      f
-    );
-  }
-  f = message.getImage();
-  if (f != null) {
-    writer.writeMessage(
-      4,
-      f,
-      proto.tensorflow.Summary.Image.serializeBinaryToWriter
-    );
-  }
-  f = message.getHisto();
-  if (f != null) {
-    writer.writeMessage(
-      5,
-      f,
-      proto.tensorflow.HistogramProto.serializeBinaryToWriter
-    );
-  }
-  f = message.getAudio();
-  if (f != null) {
-    writer.writeMessage(
-      6,
-      f,
-      proto.tensorflow.Summary.Audio.serializeBinaryToWriter
-    );
-  }
-  f = message.getTensor();
-  if (f != null) {
-    writer.writeMessage(
-      8,
-      f,
-      proto.tensorflow.Tensor.serializeBinaryToWriter
-    );
-  }
-};
-
-
-/**
- * optional string node_name = 7;
- * @return {string}
- */
-proto.tensorflow.Summary.Value.prototype.getNodeName = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 7, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.setNodeName = function(value) {
-  return jspb.Message.setProto3StringField(this, 7, value);
-};
-
-
-/**
- * optional string tag = 1;
- * @return {string}
- */
-proto.tensorflow.Summary.Value.prototype.getTag = function() {
-  return /** @type {string} */ (jspb.Message.getFieldWithDefault(this, 1, ""));
-};
-
-
-/**
- * @param {string} value
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.setTag = function(value) {
-  return jspb.Message.setProto3StringField(this, 1, value);
-};
-
-
-/**
- * optional SummaryMetadata metadata = 9;
- * @return {?proto.tensorflow.SummaryMetadata}
- */
-proto.tensorflow.Summary.Value.prototype.getMetadata = function() {
-  return /** @type{?proto.tensorflow.SummaryMetadata} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.SummaryMetadata, 9));
-};
-
-
-/**
- * @param {?proto.tensorflow.SummaryMetadata|undefined} value
- * @return {!proto.tensorflow.Summary.Value} returns this
-*/
-proto.tensorflow.Summary.Value.prototype.setMetadata = function(value) {
-  return jspb.Message.setWrapperField(this, 9, value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.clearMetadata = function() {
-  return this.setMetadata(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.Summary.Value.prototype.hasMetadata = function() {
-  return jspb.Message.getField(this, 9) != null;
-};
-
-
-/**
- * optional float simple_value = 2;
- * @return {number}
- */
-proto.tensorflow.Summary.Value.prototype.getSimpleValue = function() {
-  return /** @type {number} */ (jspb.Message.getFloatingPointFieldWithDefault(this, 2, 0.0));
-};
-
-
-/**
- * @param {number} value
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.setSimpleValue = function(value) {
-  return jspb.Message.setOneofField(this, 2, proto.tensorflow.Summary.Value.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the field making it undefined.
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.clearSimpleValue = function() {
-  return jspb.Message.setOneofField(this, 2, proto.tensorflow.Summary.Value.oneofGroups_[0], undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.Summary.Value.prototype.hasSimpleValue = function() {
-  return jspb.Message.getField(this, 2) != null;
-};
-
-
-/**
- * optional bytes obsolete_old_style_histogram = 3;
- * @return {!(string|Uint8Array)}
- */
-proto.tensorflow.Summary.Value.prototype.getObsoleteOldStyleHistogram = function() {
-  return /** @type {!(string|Uint8Array)} */ (jspb.Message.getFieldWithDefault(this, 3, ""));
-};
-
-
-/**
- * optional bytes obsolete_old_style_histogram = 3;
- * This is a type-conversion wrapper around `getObsoleteOldStyleHistogram()`
- * @return {string}
- */
-proto.tensorflow.Summary.Value.prototype.getObsoleteOldStyleHistogram_asB64 = function() {
-  return /** @type {string} */ (jspb.Message.bytesAsB64(
-      this.getObsoleteOldStyleHistogram()));
-};
-
-
-/**
- * optional bytes obsolete_old_style_histogram = 3;
- * Note that Uint8Array is not supported on all browsers.
- * @see http://caniuse.com/Uint8Array
- * This is a type-conversion wrapper around `getObsoleteOldStyleHistogram()`
- * @return {!Uint8Array}
- */
-proto.tensorflow.Summary.Value.prototype.getObsoleteOldStyleHistogram_asU8 = function() {
-  return /** @type {!Uint8Array} */ (jspb.Message.bytesAsU8(
-      this.getObsoleteOldStyleHistogram()));
-};
-
-
-/**
- * @param {!(string|Uint8Array)} value
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.setObsoleteOldStyleHistogram = function(value) {
-  return jspb.Message.setOneofField(this, 3, proto.tensorflow.Summary.Value.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the field making it undefined.
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.clearObsoleteOldStyleHistogram = function() {
-  return jspb.Message.setOneofField(this, 3, proto.tensorflow.Summary.Value.oneofGroups_[0], undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.Summary.Value.prototype.hasObsoleteOldStyleHistogram = function() {
-  return jspb.Message.getField(this, 3) != null;
-};
-
-
-/**
- * optional Image image = 4;
- * @return {?proto.tensorflow.Summary.Image}
- */
-proto.tensorflow.Summary.Value.prototype.getImage = function() {
-  return /** @type{?proto.tensorflow.Summary.Image} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.Summary.Image, 4));
-};
-
-
-/**
- * @param {?proto.tensorflow.Summary.Image|undefined} value
- * @return {!proto.tensorflow.Summary.Value} returns this
-*/
-proto.tensorflow.Summary.Value.prototype.setImage = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 4, proto.tensorflow.Summary.Value.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.clearImage = function() {
-  return this.setImage(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.Summary.Value.prototype.hasImage = function() {
-  return jspb.Message.getField(this, 4) != null;
-};
-
-
-/**
- * optional HistogramProto histo = 5;
- * @return {?proto.tensorflow.HistogramProto}
- */
-proto.tensorflow.Summary.Value.prototype.getHisto = function() {
-  return /** @type{?proto.tensorflow.HistogramProto} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.HistogramProto, 5));
-};
-
-
-/**
- * @param {?proto.tensorflow.HistogramProto|undefined} value
- * @return {!proto.tensorflow.Summary.Value} returns this
-*/
-proto.tensorflow.Summary.Value.prototype.setHisto = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 5, proto.tensorflow.Summary.Value.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.clearHisto = function() {
-  return this.setHisto(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.Summary.Value.prototype.hasHisto = function() {
-  return jspb.Message.getField(this, 5) != null;
-};
-
-
-/**
- * optional Audio audio = 6;
- * @return {?proto.tensorflow.Summary.Audio}
- */
-proto.tensorflow.Summary.Value.prototype.getAudio = function() {
-  return /** @type{?proto.tensorflow.Summary.Audio} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.Summary.Audio, 6));
-};
-
-
-/**
- * @param {?proto.tensorflow.Summary.Audio|undefined} value
- * @return {!proto.tensorflow.Summary.Value} returns this
-*/
-proto.tensorflow.Summary.Value.prototype.setAudio = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 6, proto.tensorflow.Summary.Value.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.clearAudio = function() {
-  return this.setAudio(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.Summary.Value.prototype.hasAudio = function() {
-  return jspb.Message.getField(this, 6) != null;
-};
-
-
-/**
- * optional Tensor tensor = 8;
- * @return {?proto.tensorflow.Tensor}
- */
-proto.tensorflow.Summary.Value.prototype.getTensor = function() {
-  return /** @type{?proto.tensorflow.Tensor} */ (
-    jspb.Message.getWrapperField(this, proto.tensorflow.Tensor, 8));
-};
-
-
-/**
- * @param {?proto.tensorflow.Tensor|undefined} value
- * @return {!proto.tensorflow.Summary.Value} returns this
-*/
-proto.tensorflow.Summary.Value.prototype.setTensor = function(value) {
-  return jspb.Message.setOneofWrapperField(this, 8, proto.tensorflow.Summary.Value.oneofGroups_[0], value);
-};
-
-
-/**
- * Clears the message field making it undefined.
- * @return {!proto.tensorflow.Summary.Value} returns this
- */
-proto.tensorflow.Summary.Value.prototype.clearTensor = function() {
-  return this.setTensor(undefined);
-};
-
-
-/**
- * Returns whether this field is set.
- * @return {boolean}
- */
-proto.tensorflow.Summary.Value.prototype.hasTensor = function() {
-  return jspb.Message.getField(this, 8) != null;
-};
-
-
-/**
- * repeated Value value = 1;
- * @return {!Array<!proto.tensorflow.Summary.Value>}
- */
-proto.tensorflow.Summary.prototype.getValueList = function() {
-  return /** @type{!Array<!proto.tensorflow.Summary.Value>} */ (
-    jspb.Message.getRepeatedWrapperField(this, proto.tensorflow.Summary.Value, 1));
-};
-
-
-/**
- * @param {!Array<!proto.tensorflow.Summary.Value>} value
- * @return {!proto.tensorflow.Summary} returns this
-*/
-proto.tensorflow.Summary.prototype.setValueList = function(value) {
-  return jspb.Message.setRepeatedWrapperField(this, 1, value);
-};
-
-
-/**
- * @param {!proto.tensorflow.Summary.Value=} opt_value
- * @param {number=} opt_index
- * @return {!proto.tensorflow.Summary.Value}
- */
-proto.tensorflow.Summary.prototype.addValue = function(opt_value, opt_index) {
-  return jspb.Message.addToRepeatedWrapperField(this, 1, opt_value, proto.tensorflow.Summary.Value, opt_index);
-};
-
-
-/**
- * Clears the list making it empty but non-null.
- * @return {!proto.tensorflow.Summary} returns this
- */
-proto.tensorflow.Summary.prototype.clearValueList = function() {
-  return this.setValueList([]);
-};
-
-
-
-
-
-if (jspb.Message.GENERATE_TO_OBJECT) {
-/**
- * Creates an object representation of this proto.
- * Field names that are reserved in JavaScript and will be renamed to pb_name.
- * Optional fields that are not set will be set to undefined.
- * To access a reserved field use, foo.pb_<name>, eg, foo.pb_default.
- * For the list of reserved names please see:
- *     net/proto2/compiler/js/internal/generator.cc#kKeyword.
- * @param {boolean=} opt_includeInstance Deprecated. whether to include the
- *     JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @return {!Object}
- */
-proto.tensorflow.HistogramPluginData.prototype.toObject = function(opt_includeInstance) {
-  return proto.tensorflow.HistogramPluginData.toObject(opt_includeInstance, this);
-};
-
-
-/**
- * Static version of the {@see toObject} method.
- * @param {boolean|undefined} includeInstance Deprecated. Whether to include
- *     the JSPB instance for transitional soy proto support:
- *     http://goto/soy-param-migration
- * @param {!proto.tensorflow.HistogramPluginData} msg The msg instance to transform.
- * @return {!Object}
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.HistogramPluginData.toObject = function(includeInstance, msg) {
-  var f, obj = {
-    version: jspb.Message.getFieldWithDefault(msg, 1, 0)
-  };
-
-  if (includeInstance) {
-    obj.$jspbMessageInstance = msg;
-  }
-  return obj;
-};
-}
-
-
-/**
- * Deserializes binary data (in protobuf wire format).
- * @param {jspb.ByteSource} bytes The bytes to deserialize.
- * @return {!proto.tensorflow.HistogramPluginData}
- */
-proto.tensorflow.HistogramPluginData.deserializeBinary = function(bytes) {
-  var reader = new jspb.BinaryReader(bytes);
-  var msg = new proto.tensorflow.HistogramPluginData;
-  return proto.tensorflow.HistogramPluginData.deserializeBinaryFromReader(msg, reader);
-};
-
-
-/**
- * Deserializes binary data (in protobuf wire format) from the
- * given reader into the given message object.
- * @param {!proto.tensorflow.HistogramPluginData} msg The message object to deserialize into.
- * @param {!jspb.BinaryReader} reader The BinaryReader to use.
- * @return {!proto.tensorflow.HistogramPluginData}
- */
-proto.tensorflow.HistogramPluginData.deserializeBinaryFromReader = function(msg, reader) {
-  while (reader.nextField()) {
-    if (reader.isEndGroup()) {
-      break;
-    }
-    var field = reader.getFieldNumber();
-    switch (field) {
-    case 1:
-      var value = /** @type {number} */ (reader.readInt32());
-      msg.setVersion(value);
-      break;
-    default:
-      reader.skipField();
-      break;
-    }
-  }
-  return msg;
-};
-
-
-/**
- * Serializes the message to binary data (in protobuf wire format).
- * @return {!Uint8Array}
- */
-proto.tensorflow.HistogramPluginData.prototype.serializeBinary = function() {
-  var writer = new jspb.BinaryWriter();
-  proto.tensorflow.HistogramPluginData.serializeBinaryToWriter(this, writer);
-  return writer.getResultBuffer();
-};
-
-
-/**
- * Serializes the given message to binary data (in protobuf wire
- * format), writing to the given BinaryWriter.
- * @param {!proto.tensorflow.HistogramPluginData} message
- * @param {!jspb.BinaryWriter} writer
- * @suppress {unusedLocalVariables} f is only used for nested messages
- */
-proto.tensorflow.HistogramPluginData.serializeBinaryToWriter = function(message, writer) {
-  var f = undefined;
-  f = message.getVersion();
-  if (f !== 0) {
-    writer.writeInt32(
-      1,
-      f
-    );
-  }
-};
-
-
-/**
- * optional int32 version = 1;
- * @return {number}
- */
-proto.tensorflow.HistogramPluginData.prototype.getVersion = function() {
-  return /** @type {number} */ (jspb.Message.getFieldWithDefault(this, 1, 0));
-};
-
-
-/**
- * @param {number} value
- * @return {!proto.tensorflow.HistogramPluginData} returns this
- */
-proto.tensorflow.HistogramPluginData.prototype.setVersion = function(value) {
-  return jspb.Message.setProto3IntField(this, 1, value);
-};
-
-
-/**
- * @enum {number}
- */
-proto.tensorflow.DataType = {
-  DT_INVALID: 0,
-  DT_FLOAT: 1,
-  DT_DOUBLE: 2,
-  DT_INT32: 3,
-  DT_UINT8: 4,
-  DT_INT16: 5,
-  DT_INT8: 6,
-  DT_STRING: 7,
-  DT_COMPLEX64: 8,
-  DT_INT64: 9,
-  DT_BOOL: 10,
-  DT_QINT8: 11,
-  DT_QUINT8: 12,
-  DT_QINT32: 13,
-  DT_BFLOAT16: 14,
-  DT_FLOAT_REF: 101,
-  DT_DOUBLE_REF: 102,
-  DT_INT32_REF: 103,
-  DT_UINT8_REF: 104,
-  DT_INT16_REF: 105,
-  DT_INT8_REF: 106,
-  DT_STRING_REF: 107,
-  DT_COMPLEX64_REF: 108,
-  DT_INT64_REF: 109,
-  DT_BOOL_REF: 110,
-  DT_QINT8_REF: 111,
-  DT_QUINT8_REF: 112,
-  DT_QINT32_REF: 113,
-  DT_BFLOAT16_REF: 114
-};
-
-/**
- * @enum {number}
- */
-proto.tensorflow.DataClass = {
-  DATA_CLASS_UNKNOWN: 0,
-  DATA_CLASS_SCALAR: 1,
-  DATA_CLASS_TENSOR: 2,
-  DATA_CLASS_BLOB_SEQUENCE: 3
-};
-
-goog.object.extend(exports, proto.tensorflow);
diff --git a/tfjs-master/tfjs-node/src/register_all_kernels.ts b/tfjs-master/tfjs-node/src/register_all_kernels.ts
deleted file mode 100644
index 781316f89..000000000
--- a/tfjs-master/tfjs-node/src/register_all_kernels.ts
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-// We explicitly import the modular kernels so they get registered in the
-// global registry when we compile the library. A modular build would replace
-// the contents of this file and import only the kernels that are needed.
-import {KernelConfig, registerKernel} from '@tensorflow/tfjs';
-
-import {_fusedMatMulConfig} from './kernels/_FusedMatMul';
-import {absConfig} from './kernels/Abs';
-import {acosConfig} from './kernels/Acos';
-import {acoshConfig} from './kernels/Acosh';
-import {addConfig} from './kernels/Add';
-import {addNConfig} from './kernels/AddN';
-import {allConfig} from './kernels/All';
-import {anyConfig} from './kernels/Any';
-import {argMaxConfig} from './kernels/ArgMax';
-import {argMinConfig} from './kernels/ArgMin';
-import {asinConfig} from './kernels/Asin';
-import {asinhConfig} from './kernels/Asinh';
-import {atanConfig} from './kernels/Atan';
-import {atan2Config} from './kernels/Atan2';
-import {atanhConfig} from './kernels/Atanh';
-import {avgPoolConfig} from './kernels/AvgPool';
-import {avgPool3DConfig} from './kernels/AvgPool3D';
-import {avgPool3DGradConfig} from './kernels/AvgPool3DGrad';
-import {avgPoolGradConfig} from './kernels/AvgPoolGrad';
-import {batchMatMulConfig} from './kernels/BatchMatMul';
-import {batchToSpaceNDConfig} from './kernels/BatchToSpaceND';
-import {bincountConfig} from './kernels/Bincount';
-import {broadcastArgsConfig} from './kernels/BroadcastArgs';
-import {castConfig} from './kernels/Cast';
-import {ceilConfig} from './kernels/Ceil';
-import {clipByValueConfig} from './kernels/ClipByValue';
-import {complexConfig} from './kernels/Complex';
-import {complexAbsConfig} from './kernels/ComplexAbs';
-import {concatConfig} from './kernels/Concat';
-import {conv2DConfig} from './kernels/Conv2D';
-import {conv2DBackpropFilterConfig} from './kernels/Conv2DBackpropFilter';
-import {conv2DBackpropInputConfig} from './kernels/Conv2DBackpropInput';
-import {conv3DConfig} from './kernels/Conv3D';
-import {conv3DBackpropFilterV2Config} from './kernels/Conv3DBackpropFilterV2';
-import {conv3DBackpropInputV2Config} from './kernels/Conv3DBackpropInputV2';
-import {cosConfig} from './kernels/Cos';
-import {coshConfig} from './kernels/Cosh';
-import {cropAndResizeConfig} from './kernels/CropAndResize';
-import {cumprodConfig} from './kernels/Cumprod';
-import {cumsumConfig} from './kernels/Cumsum';
-import {depthToSpaceConfig} from './kernels/DepthToSpace';
-import {depthwiseConv2dNativeConfig} from './kernels/DepthwiseConv2dNative';
-import {depthwiseConv2dNativeBackpropFilterConfig} from './kernels/DepthwiseConv2dNativeBackpropFilter';
-import {depthwiseConv2dNativeBackpropInputConfig} from './kernels/DepthwiseConv2dNativeBackpropInput';
-import {diagConfig} from './kernels/Diag';
-import {dilation2dConfig} from './kernels/Dilation2D';
-import {dilation2dBackpropFilterConfig} from './kernels/Dilation2DBackpropFilter';
-import {dilation2dBackpropInputConfig} from './kernels/Dilation2DBackpropInput';
-import {einsumConfig} from './kernels/Einsum';
-import {eluConfig} from './kernels/Elu';
-import {eluGradConfig} from './kernels/EluGrad';
-import {equalConfig} from './kernels/Equal';
-import {erfConfig} from './kernels/Erf';
-import {expConfig} from './kernels/Exp';
-import {expandDimsConfig} from './kernels/ExpandDims';
-import {expm1Config} from './kernels/Expm1';
-import {FFTConfig} from './kernels/FFT';
-import {fillConfig} from './kernels/Fill';
-import {flipLeftRightConfig} from './kernels/FlipLeftRight';
-import {floorConfig} from './kernels/Floor';
-import {floorDivConfig} from './kernels/FloorDiv';
-import {fusedBatchNormConfig} from './kernels/FusedBatchNorm';
-import {fusedConv2DConfig} from './kernels/FusedConv2D';
-import {fusedDepthwiseConv2DConfig} from './kernels/FusedDepthwiseConv2D';
-import {gatherNdConfig} from './kernels/GatherNd';
-import {gatherV2Config} from './kernels/GatherV2';
-import {greaterConfig} from './kernels/Greater';
-import {greaterEqualConfig} from './kernels/GreaterEqual';
-import {identityConfig} from './kernels/Identity';
-import {IFFTConfig} from './kernels/IFFT';
-import {imagConfig} from './kernels/Imag';
-import {isFiniteConfig} from './kernels/IsFinite';
-import {isInfConfig} from './kernels/IsInf';
-import {isNanConfig} from './kernels/IsNan';
-import {leakyReluConfig} from './kernels/LeakyRelu';
-import {lessConfig} from './kernels/Less';
-import {lessEqualConfig} from './kernels/LessEqual';
-import {linSpaceConfig} from './kernels/LinSpace';
-import {logConfig} from './kernels/Log';
-import {log1pConfig} from './kernels/Log1p';
-import {logicalAndConfig} from './kernels/LogicalAnd';
-import {logicalNotConfig} from './kernels/LogicalNot';
-import {logicalOrConfig} from './kernels/LogicalOr';
-import {LRNConfig} from './kernels/LRN';
-import {LRNGradConfig} from './kernels/LRNGrad';
-import {maxConfig} from './kernels/Max';
-import {maximumConfig} from './kernels/Maximum';
-import {maxPoolConfig} from './kernels/MaxPool';
-import {maxPool3DConfig} from './kernels/MaxPool3D';
-import {maxPool3DGradConfig} from './kernels/MaxPool3DGrad';
-import {maxPoolGradConfig} from './kernels/MaxPoolGrad';
-import {meanConfig} from './kernels/Mean';
-import {minConfig} from './kernels/Min';
-import {minimumConfig} from './kernels/Minimum';
-import {mirrorPadConfig} from './kernels/MirrorPad';
-import {modConfig} from './kernels/Mod';
-import {multinomialConfig} from './kernels/Multinomial';
-import {multiplyConfig} from './kernels/Multiply';
-import {negConfig} from './kernels/Neg';
-import {nonMaxSuppressionV3Config} from './kernels/NonMaxSuppressionV3';
-import {nonMaxSuppressionV4Config} from './kernels/NonMaxSuppressionV4';
-import {nonMaxSuppressionV5Config} from './kernels/NonMaxSuppressionV5';
-import {notEqualConfig} from './kernels/NotEqual';
-import {oneHotConfig} from './kernels/OneHot';
-import {onesLikeConfig} from './kernels/OnesLike';
-import {packConfig} from './kernels/Pack';
-import {padV2Config} from './kernels/PadV2';
-import {powConfig} from './kernels/Pow';
-import {preluConfig} from './kernels/Prelu';
-import {prodConfig} from './kernels/Prod';
-import {rangeConfig} from './kernels/Range';
-import {realConfig} from './kernels/Real';
-import {realDivConfig} from './kernels/RealDiv';
-import {reciprocalConfig} from './kernels/Reciprocal';
-import {reluConfig} from './kernels/Relu';
-import {relu6Config} from './kernels/Relu6';
-import {reshapeConfig} from './kernels/Reshape';
-import {resizeBilinearConfig} from './kernels/ResizeBilinear';
-import {resizeBilinearGradConfig} from './kernels/ResizeBilinearGrad';
-import {resizeNearestNeighborConfig} from './kernels/ResizeNearestNeighbor';
-import {resizeNearestNeighborGradConfig} from './kernels/ResizeNearestNeighborGrad';
-import {reverseConfig} from './kernels/Reverse';
-import {roundConfig} from './kernels/Round';
-import {rsqrtConfig} from './kernels/Rsqrt';
-import {scatterNdConfig} from './kernels/ScatterNd';
-import {selectConfig} from './kernels/Select';
-import {seluConfig} from './kernels/Selu';
-import {sigmoidConfig} from './kernels/Sigmoid';
-import {signConfig} from './kernels/Sign';
-import {sinConfig} from './kernels/Sin';
-import {sinhConfig} from './kernels/Sinh';
-import {sliceConfig} from './kernels/Slice';
-import {softmaxConfig} from './kernels/Softmax';
-import {softplusConfig} from './kernels/Softplus';
-import {spaceToBatchNDConfig} from './kernels/SpaceToBatchND';
-import {sparseToDenseConfig} from './kernels/SparseToDense';
-import {splitVConfig} from './kernels/SplitV';
-import {sqrtConfig} from './kernels/Sqrt';
-import {squareConfig} from './kernels/Square';
-import {squaredDifferenceConfig} from './kernels/SquaredDifference';
-import {staticRegexReplaceConfig} from './kernels/StaticRegexReplace';
-import {stepConfig} from './kernels/Step';
-import {stridedSliceConfig} from './kernels/StridedSlice';
-import {subConfig} from './kernels/Sub';
-import {sumConfig} from './kernels/Sum';
-import {tanConfig} from './kernels/Tan';
-import {tanhConfig} from './kernels/Tanh';
-import {tensorScatterUpdateConfig} from './kernels/TensorScatterUpdate';
-import {tileConfig} from './kernels/Tile';
-import {topKConfig} from './kernels/TopK';
-import {transposeConfig} from './kernels/Transpose';
-import {uniqueConfig} from './kernels/Unique';
-import {unpackConfig} from './kernels/Unpack';
-import {unsortedSegmentSumConfig} from './kernels/UnsortedSegmentSum';
-import {zerosLikeConfig} from './kernels/ZerosLike';
-
-// List all kernel configs here
-const kernelConfigs: KernelConfig[] = [
-  FFTConfig,
-  IFFTConfig,
-  LRNConfig,
-  LRNGradConfig,
-  _fusedMatMulConfig,
-  absConfig,
-  acosConfig,
-  acoshConfig,
-  addConfig,
-  addNConfig,
-  allConfig,
-  anyConfig,
-  argMaxConfig,
-  argMinConfig,
-  asinConfig,
-  asinhConfig,
-  atan2Config,
-  atanConfig,
-  atanhConfig,
-  avgPool3DConfig,
-  avgPool3DGradConfig,
-  avgPoolConfig,
-  avgPoolGradConfig,
-  batchMatMulConfig,
-  batchToSpaceNDConfig,
-  broadcastArgsConfig,
-  castConfig,
-  ceilConfig,
-  clipByValueConfig,
-  complexAbsConfig,
-  complexConfig,
-  concatConfig,
-  conv2DBackpropFilterConfig,
-  conv2DBackpropInputConfig,
-  conv2DConfig,
-  conv3DBackpropFilterV2Config,
-  conv3DBackpropInputV2Config,
-  conv3DConfig,
-  cosConfig,
-  coshConfig,
-  cropAndResizeConfig,
-  cumprodConfig,
-  cumsumConfig,
-  bincountConfig,
-  depthToSpaceConfig,
-  depthwiseConv2dNativeBackpropFilterConfig,
-  depthwiseConv2dNativeBackpropInputConfig,
-  depthwiseConv2dNativeConfig,
-  diagConfig,
-  dilation2dBackpropFilterConfig,
-  dilation2dBackpropInputConfig,
-  dilation2dConfig,
-  eluConfig,
-  eluGradConfig,
-  einsumConfig,
-  equalConfig,
-  erfConfig,
-  expConfig,
-  expandDimsConfig,
-  expm1Config,
-  fillConfig,
-  flipLeftRightConfig,
-  floorConfig,
-  floorDivConfig,
-  fusedBatchNormConfig,
-  fusedConv2DConfig,
-  fusedDepthwiseConv2DConfig,
-  gatherNdConfig,
-  gatherV2Config,
-  greaterConfig,
-  greaterEqualConfig,
-  identityConfig,
-  imagConfig,
-  isFiniteConfig,
-  isInfConfig,
-  isNanConfig,
-  leakyReluConfig,
-  lessConfig,
-  lessEqualConfig,
-  linSpaceConfig,
-  log1pConfig,
-  logConfig,
-  logicalAndConfig,
-  logicalNotConfig,
-  logicalOrConfig,
-  maxConfig,
-  maxPool3DConfig,
-  maxPool3DGradConfig,
-  maxPoolConfig,
-  maxPoolGradConfig,
-  maximumConfig,
-  meanConfig,
-  minConfig,
-  minimumConfig,
-  mirrorPadConfig,
-  modConfig,
-  multinomialConfig,
-  multiplyConfig,
-  negConfig,
-  nonMaxSuppressionV3Config,
-  nonMaxSuppressionV4Config,
-  nonMaxSuppressionV5Config,
-  notEqualConfig,
-  oneHotConfig,
-  onesLikeConfig,
-  packConfig,
-  padV2Config,
-  powConfig,
-  preluConfig,
-  prodConfig,
-  rangeConfig,
-  realConfig,
-  realDivConfig,
-  reciprocalConfig,
-  relu6Config,
-  reluConfig,
-  reshapeConfig,
-  resizeBilinearConfig,
-  resizeBilinearGradConfig,
-  resizeNearestNeighborConfig,
-  resizeNearestNeighborGradConfig,
-  reverseConfig,
-  roundConfig,
-  rsqrtConfig,
-  scatterNdConfig,
-  selectConfig,
-  seluConfig,
-  sigmoidConfig,
-  signConfig,
-  sinConfig,
-  sinhConfig,
-  sliceConfig,
-  softmaxConfig,
-  softplusConfig,
-  spaceToBatchNDConfig,
-  sparseToDenseConfig,
-  splitVConfig,
-  sqrtConfig,
-  squareConfig,
-  squaredDifferenceConfig,
-  staticRegexReplaceConfig,
-  stepConfig,
-  stridedSliceConfig,
-  subConfig,
-  sumConfig,
-  tanConfig,
-  tanhConfig,
-  tensorScatterUpdateConfig,
-  tileConfig,
-  topKConfig,
-  transposeConfig,
-  uniqueConfig,
-  unpackConfig,
-  unsortedSegmentSumConfig,
-  zerosLikeConfig
-];
-
-for (const kernelConfig of kernelConfigs) {
-  registerKernel(kernelConfig);
-}
diff --git a/tfjs-master/tfjs-node/src/run_tests.ts b/tfjs-master/tfjs-node/src/run_tests.ts
deleted file mode 100644
index f0e83940d..000000000
--- a/tfjs-master/tfjs-node/src/run_tests.ts
+++ /dev/null
@@ -1,231 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// We import index.ts so that the Node backend gets registered.
-import './index';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-
-import * as tf from '@tensorflow/tfjs';
-// tslint:disable-next-line:no-imports-from-dist
-import * as jasmine_util from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {argv} from 'yargs';
-
-import {NodeJSKernelBackend} from './nodejs_kernel_backend';
-
-Error.stackTraceLimit = Infinity;
-
-// tslint:disable-next-line:no-require-imports
-const jasmineCtor = require('jasmine');
-// tslint:disable-next-line:no-require-imports
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-
-jasmine_util.setTestEnvs([{
-  name: 'test-tensorflow',
-  backendName: 'tensorflow',
-  flags: {},
-  isDataSync: true
-}]);
-
-const IGNORE_LIST: string[] = [
-  // Always ignore version tests:
-  'bitwiseAnd',
-  'version version',
-  'unreliable is true due to both auto gc and string tensors',
-  'unreliable is true due to auto gc',
-  // See https://github.com/tensorflow/tfjs/issues/161
-  'depthwiseConv2D',  // Requires space_to_batch() for dilation > 1.
-  'separableConv2d',  // Requires space_to_batch() for dilation > 1.
-  'complex64 memory',
-  // See https://github.com/tensorflow/tfjs-core/pull/1270
-  'depthToSpace test-tensorflow {} throws when blocksize < 2',
-  // tslint:disable-next-line:max-line-length
-  'depthToSpace test-tensorflow {} throws when CPU backend used with data format NCHW',
-  // See https://github.com/tensorflow/tfjs/issues/806
-  'scatterND test-tensorflow {} should work for 2d',
-  'scatterND test-tensorflow {} should work for simple 1d',
-  'scatterND test-tensorflow {} should work for multiple 1d',
-  'scatterND test-tensorflow {} should sum the duplicated indices',
-  'scatterND test-tensorflow {} should work for tensorLike input',
-  // https://github.com/tensorflow/tfjs/issues/1077
-  // tslint:disable-next-line:max-line-length
-  'maxPool test-tensorflow {} x=[2,2,3] f=[2,2] s=3 p=1 default dimRoundingMode',
-  'maxPool test-tensorflow {} x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor',
-  'maxPool test-tensorflow {} x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=floor',
-  'maxPool test-tensorflow {} x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=round',
-  'maxPool test-tensorflow {} x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=ceil',
-  // Node backend which uses TF 2.4.0 doesn't support explicit padding
-  'avgPool test-tensorflow {} x=[3,3,1] f=[3,3] s=1 p=explicit',
-  // tslint:disable-next-line:max-line-length
-  'avgPool test-tensorflow {} x=[2,2,3] f=[2,2] s=3 p=1 default dimRoundingMode',
-  'avgPool test-tensorflow {} x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor',
-  'avgPool test-tensorflow {} x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=floor',
-  'avgPool test-tensorflow {} x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=round',
-  'avgPool test-tensorflow {} x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=ceil',
-  // Node backend which uses TF 2.4.0 doesn't support explicit padding
-  'avgPool test-tensorflow {} gradient x=[3,3,1] f=[3,3] s=1 p=explicit',
-  // tslint:disable-next-line:max-line-length
-  'avgPool3d test-tensorflow {} x=[1,2,2,2,1] f=[2,2,2] s=1 p=1 roundingMode=floor',
-  // https://github.com/tensorflow/tensorflow/issues/58758
-  'avgPool3d test-tensorflow {} x=[1,1,1,1,1] f=[1,1,3] s=1 p=valid',
-  // Node backend which uses TF 2.11.0 doesn't support number padding
-  'avgPool3d test-tensorflow {} x=[1,1,1,1,1] f=[2,2,2] s=1 p=2',
-  // Node backend which uses TF 2.4.0 doesn't support explicit padding
-  'maxPool test-tensorflow {} x=[3,3,1] f=[3,3] s=1 p=explicit',
-  'maxPoolBackprop test-tensorflow {} gradient x=[3,3,1] f=3 s=1 p=explicit',
-  // tslint:disable-next-line:max-line-length
-  'maxPool3d test-tensorflow {} x=[1,2,2,2,1] f=[2,2,2] s=1 p=1 roundingMode=floor',
-  // libtensorflow doesn't support 6D ArgMax yet.
-  'argmax test-tensorflow {} 6D, axis=0',
-  'diag test-tensorflow {} complex',
-  'diag test-tensorflow {} bool',
-  // See https://github.com/tensorflow/tfjs/issues/1891
-  'conv2d test-tensorflow {} x=[2,1,2,2] f=[1,1,1,1] s=1 d=1 p=0 NCHW',
-  'conv2d test-tensorflow {} x=[1,2,2] f=[2,2,1,1] s=1 d=1 p=explicit NCHW',
-  'conv2d test-tensorflow {} x=[1,2,2] f=[2,2,1,1] s=1 d=1 p=same NCHW',
-  'conv2d test-tensorflow {} x=[2,2,2] f=[2,2,2,1] s=1 d=1 p=same NCHW',
-  'conv2d test-tensorflow {} x=[2,1,2,2] f=[2,2,1,1] s=1 d=1 p=same NCHW',
-  'conv2d test-tensorflow {} gradient x=[1,1,3,3] f=[2,2,1,1] s=1 p=0 NCHW',
-  'conv2d test-tensorflow {} gradient x=[2,1,3,3] f=[2,2,1,1] s=1 p=0 NCHW',
-  // Node backend which uses TF 2.4.0 doesn't support explicit padding
-  'conv2dTranspose test-tensorflow {} input=3x3x1,d2=1,f=2,s=2,p=explicit',
-  // tslint:disable-next-line:max-line-length
-  'conv2dTranspose test-tensorflow {} input=8x8x8,output=4x4x8,f=8,s=1,inDepth=8,p=same vec4',
-  // tslint:disable-next-line:max-line-length
-  'conv2dTranspose test-tensorflow {} gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] p=explicit',
-  'fused conv2d test-tensorflow {} basic in NCHW',
-  'fused conv2d test-tensorflow {} im2row in NCHW',
-  'fused conv2d test-tensorflow {} batch in NCHW',
-  'maxPoolWithArgmax',
-  'rotate',
-  // FIXME: unique NaN-handling is inconsistent between TFJS and TFPY
-  'unique test-tensorflow {} 1d tensor with NaN and Infinity',
-  // Node backend which uses TF 2.4.0 doesn't support explicit padding
-  'pool test-tensorflow {} max x=[3,3,1] f=[3,3] s=1 d=1 p=explicit',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} max x=[3,3,1] f=[3,3] s=3 d=1 p=explicit defualt dimRoundingMode',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} max x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=floor',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} max x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=round',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} max x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=ceil',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} avg x=[3,3,1] f=[3,3] s=3 d=1 p=explicit defualt dimRoundingMode',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} avg x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=floor',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} avg x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=round',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} avg x=[3,3,1] f=[3,3] s=3 d=1 p=explicit dimRoundingMode=ceil',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} max x=[2,2,3] f=[1,1] s=2 p=1 fractional outputs default rounding',
-  // Node backend which uses TF 2.4.0 doesn't support explicit padding
-  'pool test-tensorflow {} avg x=[3,3,1] f=[3,3] s=1 d=1 p=explicit',
-  // tslint:disable-next-line:max-line-length
-  'pool test-tensorflow {} avg x=[2,2,3] f=[1,1] s=2 p=1 fractional outputs default rounding',
-  // not available in tf yet.
-  'denseBincount',
-  // only available in tf addon.
-  'image.transform',
-  'sign test-tensorflow {} basic',
-  'sign test-tensorflow {} does not propagate NaNs',
-  'sign test-tensorflow {} accepts a tensor-like object',
-  'einsum test-tensorflow {} 2d matrix calculate trace: duplicate axes not implemented yet',
-  'einsum test-tensorflow {} nonexistent dimension throws error',
-  'raggedGather',
-  'raggedRange',
-  'raggedTensorToTensor',
-  'searchSorted',
-  'sparseFillEmptyRows',
-  'sparseReshape',
-  'sparseSegmentMean',
-  'sparseSegmentSum',
-  'stringNGrams',
-  'stringSplit',
-  'stringToHashBucketFast',
-  'greaterEqual test-tensorflow {} should support string comparison',
-  'greater test-tensorflow {} should support string comparison',
-  'lessEqual test-tensorflow {} should support string comparison',
-  'less test-tensorflow {} should support string comparison',
-  // int32 input for the following ops not supported by TF 2.4.0.
-  'leakyrelu test-tensorflow {} int32',
-  'step kernel test-tensorflow {} with int32 tensor',
-  // upperBound and lowerBound use SearchSorted, which is unsupported
-  'upperBound',
-  'lowerBound',
-  'multinomial test-tensorflow {} creates the same data given the same seed',
-  'tan test-tensorflow {} numbers exceed float32 precision',
-];
-
-if (process.platform === 'win32') {
-  // Ignore tensorboard on windows because tmp folder cleanup used in tests
-  // fails with tmp folder cleanup:
-  // https://github.com/tensorflow/tfjs/issues/1692
-  IGNORE_LIST.push('tensorboard');
-  IGNORE_LIST.push('tensorBoard');
-  // Windows has two failing tests:
-  // https://github.com/tensorflow/tfjs/issues/598
-  IGNORE_LIST.push('clip test-tensorflow {} propagates NaNs');
-  IGNORE_LIST.push(
-      'maxPool test-tensorflow {} [x=[3,3,1] f=[2,2] s=1 ignores NaNs');
-}
-
-const grepRegex = new RegExp(argv.grep as string);
-const runner = new jasmineCtor();
-runner.loadConfig({
-  spec_files: ['src/**/*_test.ts'],
-  random: false,
-  jsLoader: 'require',
-});
-// Also import tests from core.
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/tests';
-
-if (process.env.JASMINE_SEED) {
-  runner.seed(process.env.JASMINE_SEED);
-}
-
-const env = jasmine.getEnv();
-
-// // Filter method that returns boolean, if a given test should return.
-env.configure({
-  specFilter: (spec: jasmine.Spec) => {
-    // Filter based on the grep flag.
-    if (!grepRegex.test(spec.getFullName())) {
-      return false;
-    }
-    // Return false (skip the test) if the test is in the ignore list.
-    for (let i = 0; i < IGNORE_LIST.length; ++i) {
-      if (spec.getFullName().indexOf(IGNORE_LIST[i]) > -1) {
-        return false;
-      }
-    }
-    // Otherwise run the test.
-    return true;
-  }
-});
-
-// TODO(kreeger): Consider moving to C-code.
-console.log(`Running tests against TensorFlow: ${
-    (tf.backend() as NodeJSKernelBackend).binding.TF_Version}`);
-runner.execute();
diff --git a/tfjs-master/tfjs-node/src/saved_model.ts b/tfjs-master/tfjs-node/src/saved_model.ts
deleted file mode 100644
index 26e0b2479..000000000
--- a/tfjs-master/tfjs-node/src/saved_model.ts
+++ /dev/null
@@ -1,464 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, InferenceModel, MetaGraph, ModelPredictConfig, ModelTensorInfo, NamedTensorMap, SignatureDef, SignatureDefEntry, Tensor, util} from '@tensorflow/tfjs';
-import * as fs from 'fs';
-import {promisify} from 'util';
-
-import {ensureTensorflowBackend, nodeBackend, NodeJSKernelBackend} from './nodejs_kernel_backend';
-
-const readFile = promisify(fs.readFile);
-
-// tslint:disable-next-line:no-require-imports
-const messages = require('./proto/api_pb');
-
-const SAVED_MODEL_FILE_NAME = '/saved_model.pb';
-
-const SAVED_MODEL_INIT_OP_KEY = '__saved_model_init_op';
-
-// This map is used to keep track of loaded SavedModel metagraph mapping
-// information. The map key is TFSavedModel id in JavaScript, value is
-// an object of path to the SavedModel, metagraph tags, and loaded Session ID in
-// the c++ bindings. When user loads a SavedModel signature, it will go through
-// entries in this map to find if the corresponding SavedModel session has
-// already been loaded in C++ addon and will reuse it if existing.
-const loadedSavedModelPathMap =
-    new Map<number, {path: string, tags: string[], sessionId: number}>();
-
-// The ID of loaded TFSavedModel. This ID is used to keep track of loaded
-// TFSavedModel, so the loaded session in c++ bindings for the corresponding
-// TFSavedModel can be properly reused/disposed.
-let nextTFSavedModelId = 0;
-
-/**
- * Get a key in an object by its value. This is used to get protobuf enum value
- * from index.
- *
- * @param object
- * @param value
- */
-// tslint:disable-next-line:no-any
-export function getEnumKeyFromValue(object: any, value: number): string {
-  return Object.keys(object).find(key => object[key] === value);
-}
-
-/**
- * Read SavedModel proto message from path.
- *
- * @param path Path to SavedModel folder.
- */
-export async function readSavedModelProto(path: string) {
-  // Load the SavedModel pb file and deserialize it into message.
-  try {
-    fs.accessSync(path + SAVED_MODEL_FILE_NAME, fs.constants.R_OK);
-  } catch (error) {
-    throw new Error(
-        'There is no saved_model.pb file in the directory: ' + path);
-  }
-  const modelFile = await readFile(path + SAVED_MODEL_FILE_NAME);
-  const array = new Uint8Array(modelFile);
-  return messages.SavedModel.deserializeBinary(array);
-}
-
-/**
- * Inspect the MetaGraphs of the SavedModel from the provided path. This
- * function will return an array of `MetaGraphInfo` objects.
- *
- * @param path Path to SavedModel folder.
- *
- * @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
- */
-export async function getMetaGraphsFromSavedModel(path: string):
-    Promise<MetaGraph[]> {
-  const result: MetaGraph[] = [];
-
-  // Get SavedModel proto message
-  const modelMessage = await readSavedModelProto(path);
-
-  // A SavedModel might have multiple MetaGraphs, identified by tags.
-  // Each MetaGraph also has it's own signatureDefs.
-  const metaGraphList = modelMessage.getMetaGraphsList();
-  for (let i = 0; i < metaGraphList.length; i++) {
-    const metaGraph = {} as MetaGraph;
-    const tags = metaGraphList[i].getMetaInfoDef().getTagsList();
-    metaGraph.tags = tags;
-
-    // Each MetaGraph has it's own signatureDefs map.
-    const signatureDef: SignatureDef = {};
-    const signatureDefMap = metaGraphList[i].getSignatureDefMap();
-    const signatureDefKeys = signatureDefMap.keys();
-
-    // Go through all signatureDefs
-    while (true) {
-      const key = signatureDefKeys.next();
-      if (key.done) {
-        break;
-      }
-      // Skip TensorFlow internal Signature '__saved_model_init_op'.
-      if (key.value === SAVED_MODEL_INIT_OP_KEY) {
-        continue;
-      }
-      const signatureDefEntry = signatureDefMap.get(key.value);
-
-      // Get all input tensors information
-      const inputsMapMessage = signatureDefEntry.getInputsMap();
-      const inputsMapKeys = inputsMapMessage.keys();
-      const inputs: {[key: string]: ModelTensorInfo} = {};
-      while (true) {
-        const inputsMapKey = inputsMapKeys.next();
-        if (inputsMapKey.done) {
-          break;
-        }
-        const inputTensor = inputsMapMessage.get(inputsMapKey.value);
-        const inputTensorInfo = {} as ModelTensorInfo;
-        const dtype =
-            getEnumKeyFromValue(messages.DataType, inputTensor.getDtype());
-        inputTensorInfo.dtype = mapTFDtypeToJSDtype(dtype);
-        inputTensorInfo.tfDtype = dtype;
-        inputTensorInfo.name = inputTensor.getName();
-        inputTensorInfo.shape = inputTensor.getTensorShape().getDimList();
-        inputs[inputsMapKey.value] = inputTensorInfo;
-      }
-
-      // Get all output tensors information
-      const outputsMapMessage = signatureDefEntry.getOutputsMap();
-      const outputsMapKeys = outputsMapMessage.keys();
-      const outputs: {[key: string]: ModelTensorInfo} = {};
-      while (true) {
-        const outputsMapKey = outputsMapKeys.next();
-        if (outputsMapKey.done) {
-          break;
-        }
-        const outputTensor = outputsMapMessage.get(outputsMapKey.value);
-        const outputTensorInfo = {} as ModelTensorInfo;
-        const dtype =
-            getEnumKeyFromValue(messages.DataType, outputTensor.getDtype());
-        outputTensorInfo.dtype = mapTFDtypeToJSDtype(dtype);
-        outputTensorInfo.tfDtype = dtype;
-        outputTensorInfo.name = outputTensor.getName();
-        outputTensorInfo.shape = outputTensor.getTensorShape().getDimList();
-        outputs[outputsMapKey.value] = outputTensorInfo;
-      }
-
-      signatureDef[key.value] = {inputs, outputs};
-    }
-    metaGraph.signatureDefs = signatureDef;
-
-    result.push(metaGraph);
-  }
-  return result;
-}
-
-/**
- * Get SignatureDefEntry from SavedModel metagraphs info. The SignatureDefEntry
- * will be used when executing a SavedModel signature.
- *
- * @param savedModelInfo The MetaGraphInfo array loaded through
- *     getMetaGraphsFromSavedModel().
- * @param tags The tags of the MetaGraph to get input/output node names from.
- * @param signature The signature to get input/output node names from.
- */
-export function getSignatureDefEntryFromMetaGraphInfo(
-    savedModelInfo: MetaGraph[], tags: string[],
-    signature: string): SignatureDefEntry {
-  for (let i = 0; i < savedModelInfo.length; i++) {
-    const metaGraphInfo = savedModelInfo[i];
-    if (stringArraysHaveSameElements(tags, metaGraphInfo.tags)) {
-      if (metaGraphInfo.signatureDefs[signature] == null) {
-        throw new Error('The SavedModel does not have signature: ' + signature);
-      }
-      return metaGraphInfo.signatureDefs[signature];
-    }
-  }
-  throw new Error(`The SavedModel does not have tags: ${tags}`);
-}
-
-/**
- * A `tf.TFSavedModel` is a signature loaded from a SavedModel
- * metagraph, and allows inference execution.
- *
- * @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
- */
-export class TFSavedModel implements InferenceModel {
-  private disposed = false;
-  private outputNodeNames_: {[key: string]: string};
-  constructor(
-      private sessionId: number, private jsid: number,
-      private signature: SignatureDefEntry,
-      private backend: NodeJSKernelBackend) {}
-
-  /**
-   * Return the array of input tensor info.
-   *
-   * @doc {heading: 'Models', subheading: 'SavedModel'}
-   */
-  get inputs(): ModelTensorInfo[] {
-    const entries = this.signature.inputs;
-    const results = Object.keys(entries).map((key: string) => entries[key]);
-    results.forEach((info: ModelTensorInfo) => {
-      info.name = info.name.replace(/:0$/, '');
-    });
-    return results;
-  }
-
-  /**
-   * Return the array of output tensor info.
-   *
-   * @doc {heading: 'Models', subheading: 'SavedModel'}
-   */
-  get outputs(): ModelTensorInfo[] {
-    const entries = this.signature.outputs;
-    const results = Object.keys(entries).map((key: string) => entries[key]);
-    results.forEach((info: ModelTensorInfo) => {
-      info.name = info.name.replace(/:0$/, '');
-    });
-    return results;
-  }
-
-  /**
-   * Delete the SavedModel from nodeBackend and delete corresponding session in
-   * the C++ backend if the session is only used by this TFSavedModel.
-   *
-   * @doc {heading: 'Models', subheading: 'SavedModel'}
-   */
-  dispose() {
-    if (!this.disposed) {
-      this.disposed = true;
-
-      loadedSavedModelPathMap.delete(this.jsid);
-      for (const id of Array.from(loadedSavedModelPathMap.keys())) {
-        const value = loadedSavedModelPathMap.get(id);
-        if (value.sessionId === this.sessionId) {
-          return;
-        }
-      }
-      this.backend.deleteSavedModel(this.sessionId);
-    } else {
-      throw new Error('This SavedModel has already been deleted.');
-    }
-  }
-
-  get outputNodeNames() {
-    if (this.outputNodeNames_ != null) {
-      return this.outputNodeNames_;
-    }
-    this.outputNodeNames_ =
-        Object.keys(this.signature.outputs)
-            .reduce((names: {[key: string]: string}, key: string) => {
-              names[key] = this.signature.outputs[key].name;
-              return names;
-            }, {});
-    return this.outputNodeNames_;
-  }
-
-  /**
-   * Execute the inference for the input tensors.
-   *
-   * @param input The input tensors, when there is single input for the model,
-   * inputs param should be a Tensor. For models with multiple inputs, inputs
-   * params should be in either Tensor[] if the input order is fixed, or
-   * otherwise NamedTensorMap format. The keys in the NamedTensorMap are the
-   * name of input tensors in SavedModel signatureDef. It can be found through
-   * `tf.node.getMetaGraphsFromSavedModel()`.
-   *
-   * For batch inference execution, the tensors for each input need to be
-   * concatenated together. For example with mobilenet, the required input shape
-   * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
-   * If we are provide a batched data of 100 images, the input tensor should be
-   * in the shape of [100, 244, 244, 3].
-   *
-   * @param config Prediction configuration for specifying the batch size.
-   *
-   * @returns Inference result tensors. The output would be single Tensor if
-   * model has single output node, otherwise Tensor[] or NamedTensorMap[] will
-   * be returned for model with multiple outputs.
-   *
-   * @doc {heading: 'Models', subheading: 'SavedModel'}
-   */
-  predict(inputs: Tensor|Tensor[]|NamedTensorMap, config?: ModelPredictConfig):
-      Tensor|Tensor[]|NamedTensorMap {
-    if (this.disposed) {
-      throw new Error('The TFSavedModel has already been deleted!');
-    } else {
-      let inputTensors: Tensor[] = [];
-      if (inputs instanceof Tensor) {
-        inputTensors.push(inputs);
-        const result = this.backend.runSavedModel(
-            this.sessionId, inputTensors, Object.values(this.signature.inputs),
-            Object.values(this.outputNodeNames));
-        return result.length > 1 ? result : result[0];
-      } else if (Array.isArray(inputs)) {
-        inputTensors = inputs;
-        return this.backend.runSavedModel(
-            this.sessionId, inputTensors, Object.values(this.signature.inputs),
-            Object.values(this.outputNodeNames));
-      } else {
-        const inputTensorNames = Object.keys(this.signature.inputs);
-        const providedInputNames = Object.keys(inputs);
-        if (!stringArraysHaveSameElements(
-                inputTensorNames, providedInputNames)) {
-          throw new Error(`The model signatureDef input names are ${
-              inputTensorNames.join()}, however the provided input names are ${
-              providedInputNames.join()}.`);
-        }
-        const inputNodeNamesArray: ModelTensorInfo[] = [];
-        for (let i = 0; i < inputTensorNames.length; i++) {
-          inputTensors.push(inputs[inputTensorNames[i]]);
-          inputNodeNamesArray.push(this.signature.inputs[inputTensorNames[i]]);
-        }
-        const outputTensorNames = Object.keys(this.outputNodeNames);
-        const outputNodeNamesArray = [];
-        for (let i = 0; i < outputTensorNames.length; i++) {
-          outputNodeNamesArray.push(this.outputNodeNames[outputTensorNames[i]]);
-        }
-        const outputTensors = this.backend.runSavedModel(
-            this.sessionId, inputTensors, inputNodeNamesArray,
-            outputNodeNamesArray);
-        util.assert(
-            outputTensors.length === outputNodeNamesArray.length,
-            () => 'Output tensors do not match output node names, ' +
-                `receive ${outputTensors.length}) output tensors but ` +
-                `there are ${this.outputNodeNames.length} output nodes.`);
-        const outputMap: NamedTensorMap = {};
-        for (let i = 0; i < outputTensorNames.length; i++) {
-          outputMap[outputTensorNames[i]] = outputTensors[i];
-        }
-        return outputMap;
-      }
-    }
-  }
-
-  /**
-   * Execute the inference for the input tensors and return activation
-   * values for specified output node names without batching.
-   *
-   * @param input The input tensors, when there is single input for the model,
-   * inputs param should be a Tensor. For models with multiple inputs, inputs
-   * params should be in either Tensor[] if the input order is fixed, or
-   * otherwise NamedTensorMap format.
-   *
-   * @param outputs string|string[]. List of output node names to retrieve
-   * activation from.
-   *
-   * @returns Activation values for the output nodes result tensors. The return
-   * type matches specified parameter outputs type. The output would be single
-   * Tensor if single output is specified, otherwise Tensor[] for multiple
-   * outputs.
-   *
-   * @doc {heading: 'Models', subheading: 'SavedModel'}
-   */
-  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs: string|string[]):
-      Tensor|Tensor[] {
-    throw new Error('execute() of TFSavedModel is not supported yet.');
-  }
-}
-
-/**
- * Load a TensorFlow SavedModel from disk. TensorFlow SavedModel is different
- * from TensorFlow.js model format. A SavedModel is a directory containing
- * serialized signatures and the states needed to run them. The directory has a
- * saved_model.pb (or saved_model.pbtxt) file storing the actual TensorFlow
- * program, or model, and a set of named signatures, each identifying a
- * function. The directory also has a variables directory contains a standard
- * training checkpoint. The directory may also has a assets directory contains
- * files used by the TensorFlow graph, for example text files used to initialize
- * vocabulary tables. These are supported datatypes: float32, int32, complex64,
- * string.For more information, see this guide:
- * https://www.tensorflow.org/guide/saved_model.
- *
- * @param path The path to the SavedModel.
- * @param tags The tags of the MetaGraph to load. The available tags of a
- *     SavedModel can be retrieved through tf.node.getMetaGraphsFromSavedModel()
- *     API. Defaults to ['serve'].
- * @param signature The name of the SignatureDef to load. The available
- *     SignatureDefs of a SavedModel can be retrieved through
- *     tf.node.getMetaGraphsFromSavedModel() API. Defaults to 'serving_default'.
- *
- * @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
- */
-export async function loadSavedModel(
-    path: string, tags = ['serve'],
-    signature = 'serving_default'): Promise<TFSavedModel> {
-  ensureTensorflowBackend();
-
-  const backend = nodeBackend();
-
-  const savedModelInfo = await getMetaGraphsFromSavedModel(path);
-  const signatureDefEntry =
-      getSignatureDefEntryFromMetaGraphInfo(savedModelInfo, tags, signature);
-
-  let sessionId: number;
-
-  for (const id of Array.from(loadedSavedModelPathMap.keys())) {
-    const modelInfo = loadedSavedModelPathMap.get(id);
-    if (modelInfo.path === path &&
-        stringArraysHaveSameElements(modelInfo.tags, tags)) {
-      sessionId = modelInfo.sessionId;
-    }
-  }
-  if (sessionId == null) {
-    // Convert metagraph tags string array to a string.
-    const tagsString = tags.join(',');
-    sessionId = backend.loadSavedModelMetaGraph(path, tagsString);
-  }
-  const id = nextTFSavedModelId++;
-  const savedModel =
-      new TFSavedModel(sessionId, id, signatureDefEntry, backend);
-  loadedSavedModelPathMap.set(id, {path, tags, sessionId});
-  return savedModel;
-}
-
-/**
- * Compare if two unsorted arrays of string have the same elements.
- * @param arrayA
- * @param arrayB
- */
-function stringArraysHaveSameElements(
-    arrayA: string[], arrayB: string[]): boolean {
-  if (arrayA.length === arrayB.length &&
-      arrayA.sort().join() === arrayB.sort().join()) {
-    return true;
-  }
-  return false;
-}
-
-function mapTFDtypeToJSDtype(tfDtype: string): DataType {
-  switch (tfDtype) {
-    case 'DT_FLOAT':
-      return 'float32';
-    case 'DT_INT64':
-    case 'DT_INT32':
-    case 'DT_UINT8':
-      return 'int32';
-    case 'DT_BOOL':
-      return 'bool';
-    case 'DT_COMPLEX64':
-      return 'complex64';
-    case 'DT_STRING':
-      return 'string';
-    default:
-      throw new Error(
-          'Unsupported tensor DataType: ' + tfDtype +
-          ', try to modify the model in python to convert the datatype');
-  }
-}
-
-export function getNumOfSavedModels() {
-  ensureTensorflowBackend();
-  const backend = nodeBackend();
-  return backend.getNumOfSavedModels();
-}
diff --git a/tfjs-master/tfjs-node/src/saved_model_test.ts b/tfjs-master/tfjs-node/src/saved_model_test.ts
deleted file mode 100644
index e8719b9bd..000000000
--- a/tfjs-master/tfjs-node/src/saved_model_test.ts
+++ /dev/null
@@ -1,518 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {NamedTensorMap, test_util} from '@tensorflow/tfjs';
-import * as tf from './index';
-import {nodeBackend} from './nodejs_kernel_backend';
-import {getEnumKeyFromValue, getSignatureDefEntryFromMetaGraphInfo, readSavedModelProto} from './saved_model';
-
-// tslint:disable-next-line:no-require-imports
-const messages = require('./proto/api_pb');
-
-describe('SavedModel', () => {
-  it('deserialize SavedModel pb file', async () => {
-    /**
-     * The SavedModel has one MetaGraph (tag is serve). Part of the MetaGraph
-     * look like:
-     * {
-     *  MetaInfoDef: {tags: [`serve`]},
-     *  signatureDef: {
-     *    __saved_model_init_op: {
-     *      'inputsMap': {},
-     *      'outputsMap': {'__saved_model_init_op': {'name': 'NoOp', 'dtype':
-     * 0}},
-     *    },
-     *    serving_default: {
-     *      'inputs': {
-     *        x: {
-     *          'name': 'serving_default_x:0',
-     *          'dtype': 1,
-     *        }
-     *      },
-     *      'outputs': {
-     *        output_0: {
-     *          'name': 'StatefulPartitionedCall:0',
-     *          'dtype': 1,
-     *        }
-     *      },
-     *    }
-     *  }
-     * }
-     */
-    const modelMessage = await readSavedModelProto(
-        './test_objects/saved_model/times_three_float');
-
-    // This SavedModel has one MetaGraph with tag serve
-    expect(modelMessage.getMetaGraphsList().length).toBe(1);
-    expect(modelMessage.getMetaGraphsList()[0]
-               .getMetaInfoDef()
-               .getTagsList()
-               .length)
-        .toBe(1);
-    expect(
-        modelMessage.getMetaGraphsList()[0].getMetaInfoDef().getTagsList()[0])
-        .toBe('serve');
-
-    // Validate the SavedModel has signatureDef serving_default
-    const signatureDefMapMessage =
-        modelMessage.getMetaGraphsList()[0].getSignatureDefMap();
-    expect(signatureDefMapMessage.has('serving_default'));
-
-    // The input op of signature serving_default is serving_default_x, DataType
-    // is DT_FLOAT
-    const inputsMapMessage =
-        signatureDefMapMessage.get('serving_default').getInputsMap();
-    expect(inputsMapMessage.getLength()).toBe(1);
-    const inputsMapKeys = inputsMapMessage.keys();
-    const inputsMapKey1 = inputsMapKeys.next();
-    expect(inputsMapKey1.done).toBe(false);
-    expect(inputsMapKey1.value).toBe('x');
-    const inputTensorMessage = inputsMapMessage.get(inputsMapKey1.value);
-    expect(inputTensorMessage.getName()).toBe('serving_default_x:0');
-    expect(
-        getEnumKeyFromValue(messages.DataType, inputTensorMessage.getDtype()))
-        .toBe('DT_FLOAT');
-
-    // The output op of signature serving_default is StatefulPartitionedCall,
-    // DataType is DT_FLOAT
-    const outputsMapMessage =
-        signatureDefMapMessage.get('serving_default').getOutputsMap();
-    expect(outputsMapMessage.getLength()).toBe(1);
-    const outputsMapKeys = outputsMapMessage.keys();
-    const outputsMapKey1 = outputsMapKeys.next();
-    expect(outputsMapKey1.done).toBe(false);
-    expect(outputsMapKey1.value).toBe('output_0');
-    const outputTensorMessage = outputsMapMessage.get(outputsMapKey1.value);
-    expect(outputTensorMessage.getName()).toBe('StatefulPartitionedCall:0');
-    expect(
-        getEnumKeyFromValue(messages.DataType, outputTensorMessage.getDtype()))
-        .toBe('DT_FLOAT');
-  });
-
-  it('get enum key based on value', () => {
-    const DataType = messages.DataType;
-    const enumKey0 = getEnumKeyFromValue(DataType, 0);
-    expect(enumKey0).toBe('DT_INVALID');
-    const enumKey1 = getEnumKeyFromValue(DataType, 1);
-    expect(enumKey1).toBe('DT_FLOAT');
-    const enumKey2 = getEnumKeyFromValue(DataType, 2);
-    expect(enumKey2).toBe('DT_DOUBLE');
-  });
-
-  it('read non-exist file', async () => {
-    await expectAsync(readSavedModelProto('/not-exist'))
-      .toBeRejectedWithError(
-        `There is no saved_model.pb file in the directory: /not-exist`);
-  });
-
-  it('inspect SavedModel metagraphs', async () => {
-    const modelInfo = await tf.node.getMetaGraphsFromSavedModel(
-        './test_objects/saved_model/times_three_float');
-    /**
-     * The inspection output should be
-     * [{
-     *  'tags': ['serve'],
-     *  'signatureDefs': {
-     *      '__saved_model_init_op': {
-     *        'inputs': {},
-     *        'outputs': {
-     *          '__saved_model_init_op': {
-     *            'dtype': 'DT_INVALID',
-     *            'name': 'NoOp',
-     *            'shape': []
-     *          }
-     *        }
-     *      },
-     *      'serving_default': {
-     *        'inputs': {
-     *          'x': {
-     *            'dtype': 'DT_FLOAT',
-     *            'name': 'serving_default_x:0',
-     *            'shape':[]
-     *          }
-     *        },
-     *        'outputs': {
-     *          'output_0': {
-     *            'dtype': 'DT_FLOAT',
-     *            'name': 'StatefulPartitionedCall:0',
-     *            'shape': []
-     *          }
-     *        }
-     *      }
-     *   }
-     * }]
-     */
-    expect(modelInfo.length).toBe(1);
-    expect(modelInfo[0].tags.length).toBe(1);
-    expect(modelInfo[0].tags[0]).toBe('serve');
-    expect(Object.keys(modelInfo[0].signatureDefs).length).toBe(1);
-    expect(Object.keys(modelInfo[0].signatureDefs)[0]).toBe('serving_default');
-    expect(Object.keys(modelInfo[0].signatureDefs['serving_default'].inputs)
-               .length)
-        .toBe(1);
-    expect(modelInfo[0].signatureDefs['serving_default'].inputs['x'].name)
-        .toBe('serving_default_x:0');
-    expect(modelInfo[0].signatureDefs['serving_default'].inputs['x'].dtype)
-        .toBe('float32');
-    expect(Object.keys(modelInfo[0].signatureDefs['serving_default'].outputs)
-               .length)
-        .toBe(1);
-    expect(
-        modelInfo[0].signatureDefs['serving_default'].outputs['output_0'].name)
-        .toBe('StatefulPartitionedCall:0');
-    expect(
-        modelInfo[0].signatureDefs['serving_default'].outputs['output_0'].dtype)
-        .toBe('float32');
-  });
-
-  it('get input and output node names from SavedModel metagraphs', async () => {
-    const modelInfo = await tf.node.getMetaGraphsFromSavedModel(
-        './test_objects/saved_model/times_three_float');
-    const signature = getSignatureDefEntryFromMetaGraphInfo(
-        modelInfo, ['serve'], 'serving_default');
-    expect(Object.keys(signature).length).toBe(2);
-    expect(signature.inputs['x'].name).toBe('serving_default_x:0');
-    expect(signature.outputs['output_0'].name)
-        .toBe('StatefulPartitionedCall:0');
-  });
-
-  it('load TFSavedModel', async () => {
-    const loadSavedModelMetaGraphSpy =
-        spyOn(nodeBackend(), 'loadSavedModelMetaGraph').and.callThrough();
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(0);
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/times_three_float', ['serve'],
-        'serving_default');
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-    model.dispose();
-  });
-
-  it('load TFSavedModel with wrong tags throw exception', async () => {
-    await expectAsync(tf.node.loadSavedModel(
-      './test_objects/saved_model/times_three_float', ['serve', 'gpu'],
-      'serving_default'))
-      .toBeRejectedWithError('The SavedModel does not have tags: serve,gpu');
-  });
-
-  it('load TFSavedModel with wrong signature throw exception', async () => {
-    await expectAsync(
-      tf.node.loadSavedModel(
-        './test_objects/saved_model/times_three_float', ['serve'],
-        'wrong_signature')
-    ).toBeRejectedWithError(
-      'The SavedModel does not have signature: wrong_signature'
-    );
-  });
-
-  it('load TFSavedModel and delete', async () => {
-    expect(tf.node.getNumOfSavedModels()).toBe(0);
-    const loadSavedModelMetaGraphSpy =
-        spyOn(nodeBackend(), 'loadSavedModelMetaGraph').and.callThrough();
-    const deleteSavedModelSpy =
-        spyOn(nodeBackend(), 'deleteSavedModel').and.callThrough();
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(0);
-    expect(deleteSavedModelSpy).toHaveBeenCalledTimes(0);
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/times_three_float', ['serve'],
-        'serving_default');
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-    expect(deleteSavedModelSpy).toHaveBeenCalledTimes(0);
-    expect(tf.node.getNumOfSavedModels()).toBe(1);
-    model.dispose();
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-    expect(deleteSavedModelSpy).toHaveBeenCalledTimes(1);
-    expect(tf.node.getNumOfSavedModels()).toBe(0);
-  });
-
-  it('delete TFSavedModel multiple times throw exception', async () => {
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/times_three_float', ['serve'],
-        'serving_default');
-    model.dispose();
-    expect(() => model.dispose())
-      .toThrowError('This SavedModel has already been deleted.');
-  });
-
-  it('load multiple signatures from the same metagraph only call binding once',
-     async () => {
-       expect(tf.node.getNumOfSavedModels()).toBe(0);
-       const backend = nodeBackend();
-       const loadSavedModelMetaGraphSpy =
-           spyOn(backend, 'loadSavedModelMetaGraph').and.callThrough();
-       expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(0);
-       const model1 = await tf.node.loadSavedModel(
-           './test_objects/saved_model/module_with_multiple_signatures',
-           ['serve'], 'serving_default');
-       expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-       expect(tf.node.getNumOfSavedModels()).toBe(1);
-       const model2 = await tf.node.loadSavedModel(
-           './test_objects/saved_model/module_with_multiple_signatures',
-           ['serve'], 'timestwo');
-       expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-       expect(tf.node.getNumOfSavedModels()).toBe(1);
-       model1.dispose();
-       expect(tf.node.getNumOfSavedModels()).toBe(1);
-       model2.dispose();
-       expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-       expect(tf.node.getNumOfSavedModels()).toBe(0);
-     });
-
-  it('load signature after delete call binding', async () => {
-    const backend = nodeBackend();
-    const spyOnCallBindingLoad =
-        spyOn(backend, 'loadSavedModelMetaGraph').and.callThrough();
-    const spyOnNodeBackendDelete =
-        spyOn(backend, 'deleteSavedModel').and.callThrough();
-    expect(spyOnCallBindingLoad).toHaveBeenCalledTimes(0);
-    expect(spyOnNodeBackendDelete).toHaveBeenCalledTimes(0);
-    const model1 = await tf.node.loadSavedModel(
-        './test_objects/saved_model/module_with_multiple_signatures', ['serve'],
-        'serving_default');
-    expect(spyOnCallBindingLoad).toHaveBeenCalledTimes(1);
-    expect(spyOnNodeBackendDelete).toHaveBeenCalledTimes(0);
-    model1.dispose();
-    expect(spyOnNodeBackendDelete).toHaveBeenCalledTimes(1);
-    expect(spyOnCallBindingLoad).toHaveBeenCalledTimes(1);
-    const model2 = await tf.node.loadSavedModel(
-        './test_objects/saved_model/module_with_multiple_signatures', ['serve'],
-        'timestwo');
-    expect(spyOnCallBindingLoad).toHaveBeenCalledTimes(2);
-    expect(spyOnNodeBackendDelete).toHaveBeenCalledTimes(1);
-    model2.dispose();
-    expect(spyOnCallBindingLoad).toHaveBeenCalledTimes(2);
-    expect(spyOnNodeBackendDelete).toHaveBeenCalledTimes(2);
-  });
-
-  it('throw error when input tensors do not match input ops', async () => {
-    const model = await tf.node.loadSavedModel(
-      './test_objects/saved_model/times_three_float', ['serve'],
-      'serving_default');
-    const input1 = tf.tensor1d([1.0, 2, 3]);
-    const input2 = tf.tensor1d([1.0, 2, 3]);
-
-    expect(() => model.predict([input1, input2])).toThrowError(
-      'Length of input op names (1) does not match the ' +
-        'length of input tensors (2).');
-    model.dispose();
-  });
-
-  it('execute model float times three', async () => {
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/times_three_float', ['serve'],
-        'serving_default');
-    const input = tf.tensor1d([1.0, 2, 3]);
-    const output = model.predict(input) as tf.Tensor;
-    expect(output.shape).toEqual(input.shape);
-    expect(output.dtype).toBe(input.dtype);
-    expect(output.dtype).toBe('float32');
-    test_util.expectArraysClose(await output.data(), await input.mul(3).data());
-    model.dispose();
-  });
-
-  it('execute model with tensor array as input', async () => {
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/times_three_float', ['serve'],
-        'serving_default');
-    const input = tf.tensor1d([1.0, 2, 3]);
-    const outputArray = model.predict([input]) as tf.Tensor[];
-    expect(outputArray.length).toBe(1);
-    const output = outputArray[0];
-    expect(output.shape).toEqual(input.shape);
-    expect(output.dtype).toBe(input.dtype);
-    expect(output.dtype).toBe('float32');
-    test_util.expectArraysClose(await output.data(), [3.0, 6.0, 9.0]);
-    model.dispose();
-  });
-
-  it('execute model with tensor map as input', async () => {
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/times_three_float', ['serve'],
-        'serving_default');
-    const input = tf.tensor1d([1.0, 2, 3]);
-    const outputMap = model.predict({'x': input}) as NamedTensorMap;
-    const output = outputMap['output_0'];
-    expect(output.shape).toEqual(input.shape);
-    expect(output.dtype).toBe(input.dtype);
-    expect(output.dtype).toBe('float32');
-    test_util.expectArraysClose(await output.data(), [3.0, 6.0, 9.0]);
-    model.dispose();
-  });
-
-  it('execute model with wrong tensor name', async () => {
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/times_three_float', ['serve'],
-        'serving_default');
-    const input = tf.tensor1d([1.0, 2, 3]);
-    expect(() => model.predict({'xyz': input})).toThrowError(
-      'The model signatureDef input names are x, however ' +
-        'the provided input names are xyz.');
-
-    model.dispose();
-  });
-  it('execute model with uint8 input', async () => {
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/uint8_multiply', ['serve'],
-        'serving_default');
-    const input = tf.scalar(3, 'int32');
-    const output = model.predict(input) as tf.Tensor;
-    expect(output.shape).toEqual([]);
-    expect(output.dtype).toBe('int32');
-    test_util.expectArraysClose(await output.data(), [18]);
-    model.dispose();
-  });
-
-  it('execute model with int64 input', async () => {
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/int64_multiply', ['serve'],
-        'serving_default');
-    const input = tf.tensor1d([3, 4], 'int32');
-    const output = model.predict(input) as tf.Tensor;
-    expect(output.shape).toEqual([2]);
-    expect(output.dtype).toBe('int32');
-    const data = await output.data();
-    expect(Number(data[0])).toEqual(18);
-    expect(Number(data[1])).toEqual(24);
-    model.dispose();
-  });
-
-  it('execute model int times two', async () => {
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/times_two_int', ['serve'],
-        'serving_default');
-    const input = tf.tensor1d([1, 2, 3], 'int32');
-    const output = model.predict(input) as tf.Tensor;
-    expect(output.shape).toEqual(input.shape);
-    expect(output.dtype).toBe(input.dtype);
-    test_util.expectArraysClose(await output.data(), [2, 4, 6]);
-    model.dispose();
-  });
-
-  it('execute multiple signatures from the same model', async () => {
-    const backend = nodeBackend();
-    const loadSavedModelMetaGraphSpy =
-        spyOn(backend, 'loadSavedModelMetaGraph').and.callThrough();
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(0);
-
-    const model1 = await tf.node.loadSavedModel(
-        './test_objects/saved_model/module_with_multiple_signatures', ['serve'],
-        'serving_default');
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-    const input1 = tf.tensor1d([1, 2, 3]);
-    const output1 = model1.predict(input1) as tf.Tensor;
-    expect(output1.shape).toEqual(input1.shape);
-    expect(output1.dtype).toBe(input1.dtype);
-    test_util.expectArraysClose(await output1.data(), [3.0, 6.0, 9.0]);
-
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-    const model2 = await tf.node.loadSavedModel(
-        './test_objects/saved_model/module_with_multiple_signatures', ['serve'],
-        'timestwo');
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-    const input2 = tf.tensor1d([1, 2, 3]);
-    const output2 = model2.predict(input2) as tf.Tensor;
-    expect(output2.shape).toEqual(input2.shape);
-    expect(output2.dtype).toBe(input2.dtype);
-    test_util.expectArraysClose(await output2.data(), [2.0, 4.0, 6.0]);
-
-    expect(loadSavedModelMetaGraphSpy).toHaveBeenCalledTimes(1);
-    model1.dispose();
-    model2.dispose();
-  });
-
-  it('execute model with single input and multiple outputs', async () => {
-    // This test model behaves as: f(x)=[2*x, x]
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/model_single_input_multi_output', ['serve'],
-        'serving_default');
-    const input = tf.tensor1d([1, 2, 3], 'int32');
-    const output = model.predict(input) as tf.Tensor[];
-    const output1 = output[0];
-    const output2 = output[1];
-    expect(output1.shape).toEqual(input.shape);
-    expect(output1.dtype).toBe(input.dtype);
-    expect(output2.shape).toEqual(input.shape);
-    expect(output2.dtype).toBe(input.dtype);
-    test_util.expectArraysClose(await output1.data(), [2, 4, 6]);
-    test_util.expectArraysClose(await output2.data(), [1, 2, 3]);
-    model.dispose();
-  });
-
-  it('execute model with multiple inputs and multiple outputs', async () => {
-    // This test model behaves as: f(x, y)=[2*x, y]
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/model_multi_output', ['serve'],
-        'serving_default');
-    const input1 = tf.tensor1d([1, 2, 3], 'int32');
-    const input2 = tf.tensor1d([1, 2, 3], 'int32');
-    const output =
-        model.predict({'x': input1, 'y': input2}) as tf.NamedTensorMap;
-    const output1 = output['output_0'];
-    const output2 = output['output_1'];
-    expect(output1.shape).toEqual(input1.shape);
-    expect(output1.dtype).toBe(input1.dtype);
-    expect(output2.shape).toEqual(input2.shape);
-    expect(output2.dtype).toBe(input2.dtype);
-    test_util.expectArraysClose(await output1.data(), [2, 4, 6]);
-    test_util.expectArraysClose(await output2.data(), [1, 2, 3]);
-    model.dispose();
-  });
-
-  it('load multiple models', async () => {
-    expect(tf.node.getNumOfSavedModels()).toBe(0);
-    const model1 = await tf.node.loadSavedModel(
-        './test_objects/saved_model/module_with_multiple_signatures', ['serve'],
-        'serving_default');
-    expect(tf.node.getNumOfSavedModels()).toBe(1);
-    const model2 = await tf.node.loadSavedModel(
-        './test_objects/saved_model/model_multi_output', ['serve'],
-        'serving_default');
-    expect(tf.node.getNumOfSavedModels()).toBe(2);
-    model1.dispose();
-    expect(tf.node.getNumOfSavedModels()).toBe(1);
-    model2.dispose();
-    expect(tf.node.getNumOfSavedModels()).toBe(0);
-  });
-
-  it('return inputs and outputs', async () => {
-    const model = await tf.node.loadSavedModel(
-        './test_objects/saved_model/model_multi_output', ['serve'],
-        'serving_default');
-    expect(model.inputs.length).toBe(2);
-    expect(model.outputs.length).toBe(2);
-
-    expect(model.inputs[0].name).toBe('serving_default_x');
-    expect(model.inputs[0].dtype).toBe('int32');
-    expect(model.inputs[0].tfDtype).toBe('DT_INT32');
-    expect(model.inputs[0].shape.length).toBe(0);
-
-    expect(model.inputs[1].name).toBe('serving_default_y');
-    expect(model.inputs[1].dtype).toBe('int32');
-    expect(model.inputs[1].tfDtype).toBe('DT_INT32');
-    expect(model.inputs[1].shape.length).toBe(0);
-
-    expect(model.outputs[0].name).toBe('StatefulPartitionedCall');
-    expect(model.outputs[0].dtype).toBe('int32');
-    expect(model.outputs[0].tfDtype).toBe('DT_INT32');
-    expect(model.outputs[0].shape.length).toBe(0);
-
-    expect(model.outputs[1].name).toBe('StatefulPartitionedCall:1');
-    expect(model.outputs[1].dtype).toBe('int32');
-    expect(model.outputs[1].tfDtype).toBe('DT_INT32');
-    expect(model.outputs[1].shape.length).toBe(0);
-    model.dispose();
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/tensorboard.ts b/tfjs-master/tfjs-node/src/tensorboard.ts
deleted file mode 100644
index 176c37171..000000000
--- a/tfjs-master/tfjs-node/src/tensorboard.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Scalar, Tensor, util} from '@tensorflow/tfjs';
-
-import {ensureTensorflowBackend, nodeBackend, NodeJSKernelBackend} from './nodejs_kernel_backend';
-
-export class SummaryFileWriter {
-  backend: NodeJSKernelBackend;
-
-  constructor(private readonly resourceHandle: Tensor) {
-    ensureTensorflowBackend();
-    this.backend = nodeBackend();
-  }
-
-  /**
-   * Write a scalar summary.
-   *
-   * @param name A name of the summary. The summary tag for TensorBoard will be
-   *   this name.
-   * @param value A real numeric scalar value, as `tf.Scalar` or a JavaScript
-   *   `number`.
-   * @param step Required `int64`-castable, monotonically-increasing step value.
-   * @param description Optional long-form description for this summary, as a
-   *   `string`. *Not implemented yet*.
-   */
-  scalar(
-      name: string, value: Scalar|number, step: number, description?: string) {
-    // N.B.: Unlike the Python TensorFlow API, step is a required parameter,
-    // because the construct of global step does not exist in TensorFlow.js.
-    if (description != null) {
-      throw new Error('scalar() does not support description yet');
-    }
-
-    this.backend.writeScalarSummary(this.resourceHandle, step, name, value);
-  }
-
-  /**
-   * Write a histogram summary, for later analysis in TensorBoard's 'Histograms'
-   * and 'Distributions' dashboards (data written using this API will appear in
-   * both places). Like `SummaryFileWriter.scalar` points, each histogram is
-   * associated with a `step` and a `name`. All the histograms with the same
-   * `name` constitute a time series of histograms.
-   *
-   * The histogram is calculated over all the elements of the given `Tensor`
-   * without regard to its shape or rank.
-   *
-   * @param name  A name for this summary. The summary tag used for TensorBoard
-   *     will be this name.
-   * @param data  A Tensor of any shape. The histogram is computed over its
-   *     elements, which must be castable to `float32`.
-   * @param step  Monotonically-increasing step value.
-   * @param buckets  Optional positive `number`. The output will have this many
-   *     buckets, except in two edge cases. If there is no data, then there are
-   *     no buckets. If there is data but all points have the same value, then
-   *     there is one bucket whose left and right endpoints are the same.
-   * @param description Optional long-form description for this summary, as a
-   *    `string`. Markdown is supported. Defaults to empty.
-   */
-  histogram(
-      name: string, data: Tensor, step: number, buckets?: number,
-      description?: string) {
-    this.backend.writeHistogramSummary(
-        this.resourceHandle, step, name, data, buckets, description);
-  }
-
-  /**
-   * Force summary writer to send all buffered data to storage.
-   */
-  flush() {
-    this.backend.flushSummaryWriter(this.resourceHandle);
-  }
-}
-
-/**
- * Use a cache for `SummaryFileWriter` instance.
- *
- * Using multiple instances of `SummaryFileWriter` pointing to the same
- * logdir has potential problems. Using this cache avoids those problems.
- */
-const summaryFileWriterCache: {[logdir: string]: SummaryFileWriter} = {};
-
-/**
- * Create a summary file writer for TensorBoard.
- *
- * Example:
- * ```js
- * const tf = require('@tensorflow/tfjs-node');
- *
- * const summaryWriter = tf.node.summaryFileWriter('/tmp/tfjs_tb_logdir');
- *
- * for (let step = 0; step < 100; ++step) {
- *  summaryWriter.scalar('dummyValue', Math.sin(2 * Math.PI * step / 8), step);
- * }
- * ```
- *
- * @param logdir Log directory in which the summary data will be written.
- * @param maxQueue Maximum queue length (default: `10`).
- * @param flushMillis Flush every __ milliseconds (default: `120e3`, i.e,
- *   `120` seconds).
- * @param filenameSuffix Suffix of the protocol buffer file names to be
- *   written in the `logdir` (default: `.v2`).
- * @returns An instance of `SummaryFileWriter`.
- *
- * @doc {heading: 'TensorBoard', namespace: 'node'}
- */
-export function summaryFileWriter(
-    logdir: string, maxQueue = 10, flushMillis = 120000,
-    filenameSuffix = '.v2'): SummaryFileWriter {
-  util.assert(
-      logdir != null && typeof logdir === 'string' && logdir.length > 0,
-      () =>
-          `Invalid logdir: ${logdir}. Expected a non-empty string for logdir.`);
-  if (!(logdir in summaryFileWriterCache)) {
-    ensureTensorflowBackend();
-    const backend = nodeBackend();
-    const writerResource = backend.summaryWriter(logdir);
-
-    backend.createSummaryFileWriter(
-        writerResource, logdir, maxQueue, flushMillis, filenameSuffix);
-
-    summaryFileWriterCache[logdir] = new SummaryFileWriter(writerResource);
-  }
-  return summaryFileWriterCache[logdir];
-}
diff --git a/tfjs-master/tfjs-node/src/tensorboard_test.ts b/tfjs-master/tfjs-node/src/tensorboard_test.ts
deleted file mode 100644
index a382e26f3..000000000
--- a/tfjs-master/tfjs-node/src/tensorboard_test.ts
+++ /dev/null
@@ -1,473 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import {scalar, tensor1d} from '@tensorflow/tfjs';
-import * as fs from 'fs';
-import * as path from 'path';
-import {promisify} from 'util';
-
-import * as tfn from './index';
-
-// tslint:disable-next-line:no-require-imports
-const rimraf = require('rimraf');
-// tslint:disable-next-line:no-require-imports
-const tmp = require('tmp');
-
-const rimrafPromise = promisify(rimraf);
-
-describe('tensorboard', () => {
-  let tmpLogDir: string;
-
-  beforeEach(() => {
-    tmpLogDir = tmp.dirSync().name;
-  });
-
-  afterEach(async () => {
-    if (tmpLogDir != null) {
-      await rimrafPromise(tmpLogDir);
-    }
-  });
-
-  describe('SummaryFileWriter', () => {
-    it('empty logdir leads to error', () => {
-      expect(() => tfn.node.summaryFileWriter('')).toThrowError(/empty string/);
-    });
-  });
-
-  describe('SummaryFileWriter.scalar', () => {
-    it('Create summaryFileWriter and write scalar', () => {
-      const writer = tfn.node.summaryFileWriter(tmpLogDir);
-      writer.scalar('foo', 42, 0);
-      writer.flush();
-
-      // Currently, we only verify that the file exists and the size
-      // increases in a sensible way as we write more scalars to it.
-      // The difficulty is in reading the protobuf contents of the event
-      // file in JavaScript/TypeScript.
-      const fileNames = fs.readdirSync(tmpLogDir);
-      expect(fileNames.length).toEqual(1);
-      const eventFilePath = path.join(tmpLogDir, fileNames[0]);
-      const fileSize0 = fs.statSync(eventFilePath).size;
-
-      writer.scalar('foo', 43, 1);
-      writer.flush();
-      const fileSize1 = fs.statSync(eventFilePath).size;
-      const incrementPerScalar = fileSize1 - fileSize0;
-      expect(incrementPerScalar).toBeGreaterThan(0);
-
-      writer.scalar('foo', 44, 2);
-      writer.scalar('foo', 45, 3);
-      writer.flush();
-      const fileSize2 = fs.statSync(eventFilePath).size;
-      expect(fileSize2 - fileSize1).toEqual(2 * incrementPerScalar);
-    });
-
-    it('Writing tf.Scalar works', () => {
-      const writer = tfn.node.summaryFileWriter(tmpLogDir);
-      writer.scalar('foo', scalar(42), 0);
-      writer.flush();
-
-      // Currently, we only verify that the file exists and the size
-      // increases in a sensible way as we write more scalars to it.
-      // The difficulty is in reading the protobuf contents of the event
-      // file in JavaScript/TypeScript.
-      const fileNames = fs.readdirSync(tmpLogDir);
-      expect(fileNames.length).toEqual(1);
-    });
-
-    it('Writing tf.Scalar no memory leak', () => {
-      const writer = tfn.node.summaryFileWriter(tmpLogDir);
-      const beforeNumTFTensors = writer.backend.getNumOfTFTensors();
-      const value = scalar(42);
-      writer.scalar('foo', value, 0);
-      writer.flush();
-      value.dispose();
-      expect(writer.backend.getNumOfTFTensors()).toBe(beforeNumTFTensors);
-    });
-
-    it('No crosstalk between two summary writers', () => {
-      const logDir1 = path.join(tmpLogDir, '1');
-      const writer1 = tfn.node.summaryFileWriter(logDir1);
-      writer1.scalar('foo', 42, 0);
-      writer1.flush();
-
-      const logDir2 = path.join(tmpLogDir, '2');
-      const writer2 = tfn.node.summaryFileWriter(logDir2);
-      writer2.scalar('foo', 1.337, 0);
-      writer2.flush();
-
-      // Currently, we only verify that the file exists and the size
-      // increases in a sensible way as we write more scalars to it.
-      // The difficulty is in reading the protobuf contents of the event
-      // file in JavaScript/TypeScript.
-      let fileNames = fs.readdirSync(logDir1);
-      expect(fileNames.length).toEqual(1);
-      const eventFilePath1 = path.join(logDir1, fileNames[0]);
-      const fileSize1Num0 = fs.statSync(eventFilePath1).size;
-
-      fileNames = fs.readdirSync(logDir2);
-      expect(fileNames.length).toEqual(1);
-      const eventFilePath2 = path.join(logDir2, fileNames[0]);
-      const fileSize2Num0 = fs.statSync(eventFilePath2).size;
-      expect(fileSize2Num0).toBeGreaterThan(0);
-
-      writer1.scalar('foo', 43, 1);
-      writer1.flush();
-      const fileSize1Num1 = fs.statSync(eventFilePath1).size;
-      const incrementPerScalar = fileSize1Num1 - fileSize1Num0;
-      expect(incrementPerScalar).toBeGreaterThan(0);
-
-      writer1.scalar('foo', 44, 2);
-      writer1.scalar('foo', 45, 3);
-      writer1.flush();
-      const fileSize1Num2 = fs.statSync(eventFilePath1).size;
-      expect(fileSize1Num2 - fileSize1Num1).toEqual(2 * incrementPerScalar);
-
-      const fileSize2Num1 = fs.statSync(eventFilePath2).size;
-      expect(fileSize2Num1).toEqual(fileSize2Num0);
-
-      writer2.scalar('foo', 1.336, 1);
-      writer2.scalar('foo', 1.335, 2);
-      writer2.flush();
-
-      const fileSize1Num3 = fs.statSync(eventFilePath1).size;
-      expect(fileSize1Num3).toEqual(fileSize1Num2);
-      const fileSize2Num2 = fs.statSync(eventFilePath2).size;
-      expect(fileSize2Num2 - fileSize2Num1).toEqual(2 * incrementPerScalar);
-    });
-
-    it('Writing into existing directory works', () => {
-      fs.mkdirSync(tmpLogDir, {recursive: true});
-      const writer = tfn.node.summaryFileWriter(path.join(tmpLogDir, '22'));
-      writer.scalar('foo', 42, 0);
-      writer.flush();
-
-      const fileNames = fs.readdirSync(tmpLogDir);
-      expect(fileNames.length).toEqual(1);
-    });
-  });
-
-  describe('SummaryFileWriter.histogram', () => {
-    it('Create summaryFileWriter and write tensor', () => {
-      const writer = tfn.node.summaryFileWriter(tmpLogDir);
-      writer.histogram('foo', tensor1d([1, 2, 3, 4, 5], 'int32'), 0, 5);
-      writer.flush();
-
-      // Currently, we only verify that the file exists and the size
-      // increases in a sensible way as we write more histograms to it.
-      // The difficulty is in reading the protobuf contents of the event
-      // file in JavaScript/TypeScript.
-      const fileNames = fs.readdirSync(tmpLogDir);
-      expect(fileNames.length).toEqual(1);
-      const eventFilePath = path.join(tmpLogDir, fileNames[0]);
-      const fileSize0 = fs.statSync(eventFilePath).size;
-
-      writer.histogram('foo', tensor1d([1, 1, 1, 1, 1], 'int32'), 1, 5);
-      writer.flush();
-      const fileSize1 = fs.statSync(eventFilePath).size;
-      const incrementPerScalar = fileSize1 - fileSize0;
-      expect(incrementPerScalar).toBeGreaterThan(0);
-
-      writer.histogram('foo', tensor1d([2, 2, 2, 2, 2], 'int32'), 2, 5);
-      writer.histogram('foo', tensor1d([3, 3, 3, 3, 3], 'int32'), 3, 5);
-      writer.flush();
-      const fileSize2 = fs.statSync(eventFilePath).size;
-      expect(fileSize2 - fileSize1).toEqual(2 * incrementPerScalar);
-    });
-
-    it('summaryFileWriter no memory leak', () => {
-      const writer = tfn.node.summaryFileWriter(tmpLogDir);
-      const beforeNumTFTensors = writer.backend.getNumOfTFTensors();
-      const value = tensor1d([1, 2, 3, 4, 5], 'int32');
-      writer.histogram('foo', value, 0, 5);
-      writer.flush();
-      value.dispose();
-      expect(writer.backend.getNumOfTFTensors()).toBe(beforeNumTFTensors);
-    });
-    it('Can create multiple normal distribution', () => {
-      const writer = tfn.node.summaryFileWriter(tmpLogDir);
-      tf.tidy(() => {
-        for (let i = 0; i < 10; ++i) {
-          const normal = tf.randomNormal([1000]).add(i / 10);
-          writer.histogram(
-              'random normal', normal, i, 5,
-              'This is a *test* of **Markdown**');
-        }
-        writer.flush();
-      });
-    });
-  });
-});
-
-describe('tensorBoard callback', () => {
-  let tmpLogDir: string;
-
-  beforeEach(() => {
-    tmpLogDir = tmp.dirSync().name;
-  });
-
-  afterEach(async () => {
-    if (tmpLogDir != null) {
-      await rimrafPromise(tmpLogDir);
-    }
-  });
-
-  function createModelForTest(): tfn.LayersModel {
-    const model = tfn.sequential();
-    model.add(
-        tfn.layers.dense({units: 5, activation: 'relu', inputShape: [10]}));
-    model.add(tfn.layers.dense({units: 1}));
-    model.compile(
-        {loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['MAE']});
-    return model;
-  }
-
-  it('fit(): default epoch updateFreq, with validation', async () => {
-    const model = createModelForTest();
-    const xs = tfn.randomUniform([100, 10]);
-    const ys = tfn.randomUniform([100, 1]);
-    const valXs = tfn.randomUniform([10, 10]);
-    const valYs = tfn.randomUniform([10, 1]);
-
-    // Warm-up training.
-    await model.fit(xs, ys, {
-      epochs: 1,
-      verbose: 0,
-      validationData: [valXs, valYs],
-      callbacks: tfn.node.tensorBoard(tmpLogDir)
-    });
-
-    // Get the initial size of the file.
-    // Verify the content of the train and val sub-logdirs.
-    const subDirs = fs.readdirSync(tmpLogDir);
-    expect(subDirs).toContain('train');
-    expect(subDirs).toContain('val');
-
-    const trainLogDir = path.join(tmpLogDir, 'train');
-    const trainFiles = fs.readdirSync(trainLogDir);
-    const trainFileSize0 =
-        fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
-    expect(trainFileSize0).toBeGreaterThan(0);
-    const valLogDir = path.join(tmpLogDir, 'val');
-    const valFiles = fs.readdirSync(valLogDir);
-    const valFileSize0 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
-    expect(valFileSize0).toBeGreaterThan(0);
-    // With updateFreq === epoch, the train and val subset should have generated
-    // the same amount of logs.
-    expect(valFileSize0).toEqual(trainFileSize0);
-
-    // Actual training run.
-    const history = await model.fit(xs, ys, {
-      epochs: 3,
-      verbose: 0,
-      validationData: [valXs, valYs],
-      callbacks: tfn.node.tensorBoard(tmpLogDir)
-    });
-    expect(history.history.loss.length).toEqual(3);
-    expect(history.history.val_loss.length).toEqual(3);
-    expect(history.history.MAE.length).toEqual(3);
-    expect(history.history.val_MAE.length).toEqual(3);
-
-    const trainFileSize1 =
-        fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
-    const valFileSize1 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
-    // We currently only assert that new content has been written to the log
-    // file.
-    expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
-    expect(valFileSize1).toBeGreaterThan(valFileSize0);
-    // With updateFreq === epoch, the train and val subset should have generated
-    // the same amount of logs.
-    expect(valFileSize1).toEqual(trainFileSize1);
-  });
-
-  it('fit(): batch updateFreq, with validation', async () => {
-    const model = createModelForTest();
-    const xs = tfn.randomUniform([100, 10]);
-    const ys = tfn.randomUniform([100, 1]);
-    const valXs = tfn.randomUniform([10, 10]);
-    const valYs = tfn.randomUniform([10, 1]);
-
-    // Warm-up training.
-    await model.fit(xs, ys, {
-      epochs: 1,
-      verbose: 0,
-      validationData: [valXs, valYs],
-      // Use batch updateFreq here.
-      callbacks: tfn.node.tensorBoard(tmpLogDir, {updateFreq: 'batch'})
-    });
-
-    // Get the initial size of the file.
-    // Verify the content of the train and val sub-logdirs.
-    const subDirs = fs.readdirSync(tmpLogDir);
-    expect(subDirs).toContain('train');
-    expect(subDirs).toContain('val');
-
-    const trainLogDir = path.join(tmpLogDir, 'train');
-    const trainFiles = fs.readdirSync(trainLogDir);
-    const trainFileSize0 =
-        fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
-    expect(trainFileSize0).toBeGreaterThan(0);
-    const valLogDir = path.join(tmpLogDir, 'val');
-    const valFiles = fs.readdirSync(valLogDir);
-    const valFileSize0 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
-    expect(valFileSize0).toBeGreaterThan(0);
-    // The train subset should have generated more logs than the val subset,
-    // because the train subset gets logged every batch, while the val subset
-    // gets logged every epoch.
-    expect(trainFileSize0).toBeGreaterThan(valFileSize0);
-
-    // Actual training run.
-    const history = await model.fit(xs, ys, {
-      epochs: 3,
-      verbose: 0,
-      validationData: [valXs, valYs],
-      callbacks: tfn.node.tensorBoard(tmpLogDir)
-    });
-    expect(history.history.loss.length).toEqual(3);
-    expect(history.history.val_loss.length).toEqual(3);
-    expect(history.history.MAE.length).toEqual(3);
-    expect(history.history.val_MAE.length).toEqual(3);
-
-    const trainFileSize1 =
-        fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
-    const valFileSize1 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
-    // We currently only assert that new content has been written to the log
-    // file.
-    expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
-    expect(valFileSize1).toBeGreaterThan(valFileSize0);
-    // The train subset should have generated more logs than the val subset,
-    // because the train subset gets logged every batch, while the val subset
-    // gets logged every epoch.
-    expect(trainFileSize1).toBeGreaterThan(valFileSize1);
-  });
-
-  it('fit(): batch updateFreq, with initialEpoch', async () => {
-    const model = createModelForTest();
-    const xs = tfn.randomUniform([100, 10]);
-    const ys = tfn.randomUniform([100, 1]);
-    const valXs = tfn.randomUniform([10, 10]);
-    const valYs = tfn.randomUniform([10, 1]);
-
-    // Warm-up training. Also ensures that `callback.trainWriter` and
-    // `callback.valWriter` are created.
-    await model.fit(xs, ys, {
-      epochs: 2,
-      validationData: [valXs, valYs],
-      verbose: 0,
-    });
-
-    const callback = tfn.node.tensorBoard(tmpLogDir, {updateFreq: 'epoch'});
-    // tslint:disable-next-line:no-any
-    (callback as any).ensureTrainWriterCreated();
-    // tslint:disable-next-line:no-any
-    (callback as any).ensureValWriterCreated();
-    // tslint:disable-next-line:no-any
-    const trainWriterScalarSpy = spyOn((callback as any).trainWriter, 'scalar');
-    // tslint:disable-next-line:no-any
-    const valWriterScalarSpy = spyOn((callback as any).valWriter, 'scalar');
-
-    // Train for 2 more epochs, using initialEpoch and callback.
-    await model.fit(xs, ys, {
-      epochs: 4,
-      initialEpoch: 2,
-      validationData: [valXs, valYs],
-      callbacks: [callback],
-    });
-    expect(trainWriterScalarSpy).toHaveBeenCalledTimes(4);
-    const trainCallArgs = trainWriterScalarSpy.calls.allArgs();
-    // Assert that the epoch numbers used to log the epoch-end loss and metric
-    // reflect initialEpoch.
-    expect(trainCallArgs[0][0]).toEqual('epoch_loss');
-    expect(trainCallArgs[0][2]).toEqual(3);
-    expect(trainCallArgs[1][0]).toEqual('epoch_MAE');
-    expect(trainCallArgs[1][2]).toEqual(3);
-    expect(trainCallArgs[2][0]).toEqual('epoch_loss');
-    expect(trainCallArgs[2][2]).toEqual(4);
-    expect(trainCallArgs[3][0]).toEqual('epoch_MAE');
-    expect(trainCallArgs[3][2]).toEqual(4);
-    expect(valWriterScalarSpy).toHaveBeenCalledTimes(4);
-    const valCallArgs = valWriterScalarSpy.calls.allArgs();
-    expect(valCallArgs[0][0]).toEqual('epoch_loss');
-    expect(valCallArgs[0][2]).toEqual(3);
-    expect(valCallArgs[1][0]).toEqual('epoch_MAE');
-    expect(valCallArgs[1][2]).toEqual(3);
-    expect(valCallArgs[2][0]).toEqual('epoch_loss');
-    expect(valCallArgs[2][2]).toEqual(4);
-    expect(valCallArgs[3][0]).toEqual('epoch_MAE');
-    expect(valCallArgs[3][2]).toEqual(4);
-  });
-
-  it('fit(): histogramFreq 2, with validation', async () => {
-    const model = createModelForTest();
-    const xs = tfn.randomUniform([100, 10]);
-    const ys = tfn.randomUniform([100, 1]);
-    const valXs = tfn.randomUniform([10, 10]);
-    const valYs = tfn.randomUniform([10, 1]);
-
-    // Warm-up training.
-    await model.fit(xs, ys, {
-      epochs: 1,
-      verbose: 0,
-      validationData: [valXs, valYs],
-      callbacks: tfn.node.tensorBoard(tmpLogDir, {histogramFreq: 2})
-    });
-
-    // Get the initial size of the file.
-    // Verify the content of the train sub-logdir, where .
-    const subDirs = fs.readdirSync(tmpLogDir);
-    expect(subDirs).toContain('train');
-
-    const trainLogDir = path.join(tmpLogDir, 'train');
-    const trainFiles = fs.readdirSync(trainLogDir);
-    const trainFileSize0 =
-        fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
-    expect(trainFileSize0).toBeGreaterThan(0);
-
-    // Actual training run.
-    await model.fit(xs, ys, {
-      epochs: 4,
-      verbose: 0,
-      validationData: [valXs, valYs],
-      callbacks: tfn.node.tensorBoard(tmpLogDir, {histogramFreq: 2})
-    });
-
-    const trainFileSize1 =
-        fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
-
-    // We currently only assert that new content has been written to the log
-    // file.
-    expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
-  });
-
-  it('Invalid updateFreq value causes error', async () => {
-    expect(() => tfn.node.tensorBoard(tmpLogDir, {
-      // tslint:disable-next-line:no-any
-      updateFreq: 'foo' as any
-    })).toThrowError(/Expected updateFreq/);
-  });
-
-  it('Invalid histogramFreq value causes error ', async () => {
-    expect(() => tfn.node.tensorBoard(tmpLogDir, {
-      histogramFreq: -1
-    })).toThrowError(/Expected histogramFreq/);
-
-    expect(() => tfn.node.tensorBoard(tmpLogDir, {
-      histogramFreq: 1.3
-    })).toThrowError(/Expected histogramFreq/);
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/tfjs_binding.ts b/tfjs-master/tfjs-node/src/tfjs_binding.ts
deleted file mode 100644
index 3352457b7..000000000
--- a/tfjs-master/tfjs-node/src/tfjs_binding.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {backend_util} from '@tensorflow/tfjs';
-
-export declare class TensorMetadata {
-  id: number;
-  shape: number[];
-  dtype: number;
-}
-
-export declare class TFEOpAttr {
-  name: string;
-  type: number;
-  value: boolean|number|object|string|number[];
-}
-
-export interface TFJSBinding {
-  TensorMetadata: typeof TensorMetadata;
-  TFEOpAttr: typeof TFEOpAttr;
-
-  // Creates a tensor with the backend.
-  createTensor(
-      shape: number[], dtype: number,
-      buffer: backend_util.BackendValues): number;
-
-  // Deletes a tensor with the backend.
-  deleteTensor(tensorId: number): void;
-
-  // Reads data-sync from a tensor on the backend.
-  tensorDataSync(tensorId: number): Float32Array|Int32Array|Uint8Array;
-
-  // Executes an Op on the backend, returns an array of output TensorMetadata.
-  executeOp(
-      opName: string, opAttrs: TFEOpAttr[], inputTensorIds: number[],
-      numOutputs: number): TensorMetadata[];
-
-  // Load a SavedModel from a path.
-  loadSavedModel(exportDir: string, tags: string): number;
-
-  // Remove a SavedModel from memory.
-  deleteSavedModel(savedModelId: number): void;
-
-  // Execute a SavedModel with input, returns an array of output TensorMetadata.
-  runSavedModel(
-      savedModelId: number, inputTensorIds: number[], inputOpNames: string,
-      outputOpNames: string): TensorMetadata[];
-
-  getNumOfSavedModels(): number;
-  getNumOfTensors(): number;
-  isUsingGpuDevice(): boolean;
-
-  // TF Types
-  TF_FLOAT: number;
-  TF_INT32: number;
-  TF_INT64: number;
-  TF_BOOL: number;
-  TF_COMPLEX64: number;
-  TF_STRING: number;
-  TF_RESOURCE: number;
-  TF_UINT8: number;
-
-  // TF OpAttrTypes
-  TF_ATTR_STRING: number;
-  TF_ATTR_INT: number;
-  TF_ATTR_FLOAT: number;
-  TF_ATTR_BOOL: number;
-  TF_ATTR_TYPE: number;
-  TF_ATTR_SHAPE: number;
-  TF_ATTR_RESOURCE: number;
-
-  TF_Version: string;
-}
diff --git a/tfjs-master/tfjs-node/src/tfjs_binding_test.ts b/tfjs-master/tfjs-node/src/tfjs_binding_test.ts
deleted file mode 100644
index c02c0e325..000000000
--- a/tfjs-master/tfjs-node/src/tfjs_binding_test.ts
+++ /dev/null
@@ -1,297 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as path from 'path';
-import {TFEOpAttr, TFJSBinding} from './tfjs_binding';
-// tslint:disable-next-line:no-require-imports
-const binary = require('@mapbox/node-pre-gyp');
-const bindingPath =
-    binary.find(path.resolve(path.join(__dirname, '../package.json')));
-// tslint:disable-next-line:no-require-imports
-const bindings = require(bindingPath);
-const binding = bindings as TFJSBinding;
-
-describe('Exposes TF_DataType enum values', () => {
-  it('contains TF_FLOAT', () => {
-    expect(binding.TF_FLOAT).toEqual(1);
-  });
-  it('contains TF_INT32', () => {
-    expect(binding.TF_INT32).toEqual(3);
-  });
-  it('contains TF_BOOL', () => {
-    expect(binding.TF_BOOL).toEqual(10);
-  });
-  it('contains TF_COMPLEX64', () => {
-    expect(binding.TF_COMPLEX64).toEqual(8);
-  });
-  it('contains TF_STRING', () => {
-    expect(binding.TF_STRING).toEqual(7);
-  });
-});
-
-describe('Exposes TF_AttrType enum values', () => {
-  it('contains TF_ATTR_STRING', () => {
-    expect(binding.TF_ATTR_STRING).toEqual(0);
-  });
-  it('contains TF_ATTR_INT', () => {
-    expect(binding.TF_ATTR_INT).toEqual(1);
-  });
-  it('contains TF_ATTR_FLOAT', () => {
-    expect(binding.TF_ATTR_FLOAT).toEqual(2);
-  });
-  it('contains TF_ATTR_BOOL', () => {
-    expect(binding.TF_ATTR_BOOL).toEqual(3);
-  });
-  it('contains TF_ATTR_TYPE', () => {
-    expect(binding.TF_ATTR_TYPE).toEqual(4);
-  });
-  it('contains TF_ATTR_SHAPE', () => {
-    expect(binding.TF_ATTR_SHAPE).toEqual(5);
-  });
-});
-
-describe('Exposes TF Version', () => {
-  it('contains a version string', () => {
-    expect(binding.TF_Version).toBeDefined();
-  });
-});
-
-describe('tensor management', () => {
-  it('Creates and deletes a valid tensor', () => {
-    const values = new Int32Array([1, 2]);
-    const id = binding.createTensor([2], binding.TF_INT32, values);
-    expect(id).toBeDefined();
-
-    binding.deleteTensor(id);
-  });
-  it('throws exception when shape does not match data', () => {
-    expect(() => {
-      binding.createTensor([2], binding.TF_INT32, new Int32Array([1, 2, 3]));
-    }).toThrowError();
-    expect(() => {
-      binding.createTensor([4], binding.TF_INT32, new Int32Array([1, 2, 3]));
-    }).toThrowError();
-  });
-  it('throws exception with invalid dtype', () => {
-    expect(() => {
-      // tslint:disable-next-line:no-unused-expression
-      binding.createTensor([1], 1000, new Int32Array([1]));
-    }).toThrowError();
-  });
-  it('works with 0-dim tensors', () => {
-    // Reduce op (e.g 'Max') will produce a 0-dim TFE_Tensor.
-
-    const inputId =
-        binding.createTensor([3], binding.TF_INT32, new Int32Array([1, 2, 3]));
-    const axesId =
-        binding.createTensor([1], binding.TF_INT32, new Int32Array([0]));
-
-    const attrs = [
-      {name: 'keep_dims', type: binding.TF_ATTR_BOOL, value: false},
-      {name: 'T', type: binding.TF_ATTR_TYPE, value: binding.TF_INT32},
-      {name: 'Tidx', type: binding.TF_ATTR_TYPE, value: binding.TF_INT32}
-    ];
-
-    const outputMetadata =
-        binding.executeOp('Max', attrs, [inputId, axesId], 1);
-    expect(outputMetadata.length).toBe(1);
-
-    expect(outputMetadata[0].id).toBeDefined();
-    expect(outputMetadata[0].shape).toEqual([]);
-    expect(outputMetadata[0].dtype).toEqual(binding.TF_INT32);
-    expect(binding.tensorDataSync(outputMetadata[0].id))
-        .toEqual(new Int32Array([3]));
-  });
-});
-
-describe('executeOp', () => {
-  const name = 'MatMul';
-  const matMulOpAttrs = [
-    {name: 'transpose_a', type: binding.TF_ATTR_BOOL, value: false},
-    {name: 'transpose_b', type: binding.TF_ATTR_BOOL, value: false},
-    {name: 'T', type: binding.TF_ATTR_TYPE, value: binding.TF_FLOAT}
-  ];
-  const aId = binding.createTensor(
-      [2, 2], binding.TF_FLOAT, new Float32Array([1, 2, 3, 4]));
-  const bId = binding.createTensor(
-      [2, 2], binding.TF_FLOAT, new Float32Array([4, 3, 2, 1]));
-  const matMulInput = [aId, bId];
-
-  it('throws exception with invalid Op Name', () => {
-    expect(() => {
-      binding.executeOp(null, [] as TFEOpAttr[], [] as number[], null);
-    }).toThrowError();
-  });
-  it('throws exception with invalid TFEOpAttr', () => {
-    expect(() => {
-      binding.executeOp('Equal', null, [] as number[], null);
-    }).toThrowError();
-  });
-  it('throws excpetion with invalid inputs', () => {
-    expect(() => {
-      binding.executeOp(name, matMulOpAttrs, [] as number[], null);
-    }).toThrowError();
-  });
-  it('throws exception with invalid output number', () => {
-    expect(() => {
-      binding.executeOp(name, matMulOpAttrs, matMulInput, null);
-    }).toThrowError();
-  });
-  it('throws exception with invalid TF_ATTR_STRING op attr', () => {
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_STRING, value: null}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_STRING, value: false}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_STRING, value: 1}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_STRING, value: new Object()}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_STRING, value: [1, 2, 3]}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-  });
-  it('throws exception with invalid TF_ATTR_INT op attr', () => {
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_INT, value: null}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_INT, value: false}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_INT, value: new Object()}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_INT, value: 'test'}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-  });
-  it('throws exception with invalid TF_ATTR_FLOAT op attr', () => {
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_FLOAT, value: null}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_FLOAT, value: false}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_FLOAT, value: new Object()}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_FLOAT, value: 'test'}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-  });
-  it('throws exception with invalid TF_ATTR_BOOL op attr', () => {
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_BOOL, value: null}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_BOOL, value: 10}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_BOOL, value: new Object()}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_BOOL, value: 'test'}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_BOOL, value: [1, 2, 3]}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-  });
-  it('throws exception with invalid TF_ATTR_TYPE op attr', () => {
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_TYPE, value: null}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_TYPE, value: new Object()}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_TYPE, value: 'test'}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_TYPE, value: [1, 2, 3]}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-  });
-  it('throws exception with invalid TF_ATTR_SHAPE op attr', () => {
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_TYPE, value: null}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_TYPE, value: new Object()}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-    expect(() => {
-      const badOpAttrs: TFEOpAttr[] =
-          [{name: 'T', type: binding.TF_ATTR_TYPE, value: 'test'}];
-      binding.executeOp(name, badOpAttrs, matMulInput, 1);
-    }).toThrowError();
-  });
-  it('should work for matmul', () => {
-    const output = binding.executeOp(name, matMulOpAttrs, matMulInput, 1);
-    expect(binding.tensorDataSync(output[0].id)).toEqual(new Float32Array([
-      8, 5, 20, 13
-    ]));
-  });
-});
diff --git a/tfjs-master/tfjs-node/src/version.ts b/tfjs-master/tfjs-node/src/version.ts
deleted file mode 100644
index 5fa574e7d..000000000
--- a/tfjs-master/tfjs-node/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.0';
-export {version};
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index c42273995..000000000
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diff --git a/tfjs-master/tfjs-node/test_objects/saved_model/uint8_multiply/variables/variables.index b/tfjs-master/tfjs-node/test_objects/saved_model/uint8_multiply/variables/variables.index
deleted file mode 100644
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diff --git a/tfjs-master/tfjs-node/tsconfig.json b/tfjs-master/tfjs-node/tsconfig.json
deleted file mode 100644
index 70e3ee191..000000000
--- a/tfjs-master/tfjs-node/tsconfig.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "commonjs",
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "removeComments": false,
-    "preserveConstEnums": true,
-    "declaration": true,
-    "target": "es5",
-    "lib": ["es2015", "dom"],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false,
-    "experimentalDecorators": true
-  },
-  "include": [
-    "src/"
-  ]
-}
diff --git a/tfjs-master/tfjs-node/tsconfig.test.json b/tfjs-master/tfjs-node/tsconfig.test.json
deleted file mode 100644
index 76ea3c6cf..000000000
--- a/tfjs-master/tfjs-node/tsconfig.test.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "extends": "./tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "allowJs": true,
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-node/tslint.json b/tfjs-master/tfjs-node/tslint.json
deleted file mode 100644
index ec365f164..000000000
--- a/tfjs-master/tfjs-node/tslint.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "../tslint.json"
-}
diff --git a/tfjs-master/tfjs-node/yarn.lock b/tfjs-master/tfjs-node/yarn.lock
deleted file mode 100644
index 27383fd7b..000000000
--- a/tfjs-master/tfjs-node/yarn.lock
+++ /dev/null
@@ -1,1793 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@babel/code-frame@^7.0.0", "@babel/code-frame@^7.12.13":
-  version "7.12.13"
-  resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.12.13.tgz#dcfc826beef65e75c50e21d3837d7d95798dd658"
-  integrity sha512-HV1Cm0Q3ZrpCR93tkWOYiuYIgLxZXZFVG2VgK+MBWjUqZTundupbfx2aXarXuw5Ko5aMcjtJgbSs4vUGBS5v6g==
-  dependencies:
-    "@babel/highlight" "^7.12.13"
-
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-  version "1.2.0"
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diff --git a/tfjs-master/tfjs-react-native/.npmignore b/tfjs-master/tfjs-react-native/.npmignore
deleted file mode 100644
index 5498e4bec..000000000
--- a/tfjs-master/tfjs-react-native/.npmignore
+++ /dev/null
@@ -1,25 +0,0 @@
-.babelrc
-.DS_Store
-.idea/
-.rpt2_cache
-.travis.yml
-.vscode
-*.tgz
-*.txt
-**.yalc
-**yalc.lock
-cloudbuild.yml
-coverage/
-demo/
-karma.conf.js
-node_modules/
-npm-debug.log
-package-lock.json
-package/
-rollup.config.js
-scripts/
-tsconfig.json
-tslint.json
-yarn-error.log
-yarn.lock
-integration_rn59
diff --git a/tfjs-master/tfjs-react-native/.vscode/settings.json b/tfjs-master/tfjs-react-native/.vscode/settings.json
deleted file mode 100644
index 5f30a3cfd..000000000
--- a/tfjs-master/tfjs-react-native/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-../../.vscode/settings.json
\ No newline at end of file
diff --git a/tfjs-master/tfjs-react-native/DEVELOPMENT.md b/tfjs-master/tfjs-react-native/DEVELOPMENT.md
deleted file mode 100644
index a27676e34..000000000
--- a/tfjs-master/tfjs-react-native/DEVELOPMENT.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# Development
-
-This file will document some of the differences from the regular developement workflow in [DEVELOPMENT.md](../DEVELOPMENT.md). You should read that document first to get familiar with typical TensorFlow.js development workflow.
-
-Development and testing for tfjs-react-native is somewhat different from the packages like tfjs-core or tfjs-layers for a few reasons:
-- __Dependency on having a physical mobile device to run__: While the CPU backend can run in a simulator, the WebGL one requires running on a physical device. So most of the time you will want to test something using a mobile device connected to your computer.
-- __No browser or node environment__: We are running JavaScript outside of a browser and outside of node. We thus have to make sure we don't include things that depend on those two environments.
-
-
-## Key Terms & Caveats
-
-These are a few key terms/technologies to be familiar with that are different from what we use for web or node.js development.
-
-- [React Native](https://facebook.github.io/react-native/) — This is the framework that this package targets.
-- [Metro](https://facebook.github.io/metro/) — This is the bundler used to create the JavaScript bundle that is loaded into the native app by react native.
-  - The bundle needs to be created at 'compile time' thus all imports/requires need to be resolved. Thus _dynamic_ `import`s/`require`s are __statically resolved__. So you cannot exclude a require with a conditional in JS code.
-  - Since tfjs does dynamic `require`'s of certain node libraries that are not present in react native, files that do that need to be excluded from the metro build process. For end users, this is documented in the [README](../README.md), but it also happens in `integration_rn59/prep_tests.ts`.
-  - Metro does not play well with symlinks, so if you are trying to develop against a local build of tfjs, copy the dist folder into the app's node_modules as appropriate. Do not use yalc.
-- [.ipa](https://en.wikipedia.org/wiki/.ipa) & [.apk](https://en.wikipedia.org/wiki/Android_application_package) — These are the formats for the final native bundle that is put on an iOS and Android device. They are created by their respective dev tools, [XCode](https://developer.apple.com/xcode/) and [Android Studio](https://developer.android.com/studio).
-
-## Testing
-
-There are three categories of tests that are run for this package.
-
-### tfjs-react-native unit tests
-
-Unit tests are written for the package functionality and are imported into a react native application to run on device. They are run together with the integration tests described below to simplify automation.
-
-### tfjs-core unit tests running on device in react native
-
-Unit tests from tfjs-core are imported into a react native application and run as integration tests. This allows testing of tfjs-core against actual devices through the RN bridge and against expo's WebGL bindings.
-
-Because these are part of an app to run them you must compile and run the integration_rn59 of the target device. There is a button in that app to start the unit tests.
-
-This is _automated in CI_ and runs on:
- - Changes to tfjs-core: [Tests will be run against HEAD of tfjs-core](../tfjs-core/cloudbuild.yml)
- - Changes to tfjs-react-native: [Tests will be run against the **published** version](./cloudbuild.yml) of tfjs on npm that is references in `integration_rn59/package.json`
-
-### Other integration tests
-
-The integration_rn59 app also contains some other tests and sanity checks. These can be run manually. Future on device integration tests should also be incorporated into this app.
-
-## CI Testing Infrastructure
-
-Integration tests on CI have a few moving pieces, the basic workflow is as follows.
-
-1. A native app ([.apk](https://storage.googleapis.com/tfjs-rn/integration-tests/app-debug.apk)) is built manually and stored on GCP. This app doesn't need to change unless a new native dependency is added.
-2. To update this `apk` run `yarn update-api`. This will update what is stored on GCP and also sync it to Browserstack.
-3. The app is also synced periodically from GCP to BrowserStack as BrowserStack caches it for 30 days from the last update. This is done with a cloud function (sync_reactnative) that is triggered via cloud scheduler.
-4. On PRs a Google Cloud Build builder will trigger a browserstack test (using browserstack app automate), and serve the JS bundle to the device running in browserstack using metro. The tests are designed to create a tunnel between the native device and the cloud builder machine.
-5. The tests complete on browserstack and results are reported back to Google Cloud Build which are reported back to GitHub.
diff --git a/tfjs-master/tfjs-react-native/README.md b/tfjs-master/tfjs-react-native/README.md
deleted file mode 100644
index f164bd738..000000000
--- a/tfjs-master/tfjs-react-native/README.md
+++ /dev/null
@@ -1,157 +0,0 @@
-# Platform Adapter for React Native
-
-This package provides a TensorFlow.js platform adapter for react native. It
-provides GPU accelerated execution of TensorFlow.js supporting all major modes
-of tfjs usage, include:
-  - Support for both model inference and training
-  - GPU support with WebGL via expo-gl.
-  - Support for loading models pretrained models (tfjs-models) from the web.
-  - IOHandlers to support loading models from asyncStorage and models
-    that are compiled into the app bundle.
-
-## Setting up a React Native app with tfjs-react-native
-
-These instructions (and this library) **assume that you are generally familiar with [react native](https://facebook.github.io/react-native/) development**.
-
-## Expo compatibility
-
-This library relies on [expo-gl](https://github.com/expo/expo/tree/master/packages/expo-gl) and [expo-gl-cpp](https://github.com/expo/expo/tree/master/packages/expo-gl-cpp). Thus you must use a version of React Native that is supported by Expo.
-
-Some parts of tfjs-react-native are not compatible with _managed expo apps_. You must use the bare workflow (or just plain react native) if you want to use the following functionality:
- - Loading local models using [bundleResourceIO](https://js.tensorflow.org/api_react_native/latest/#bundleResourceIO). You can instead load models from a webserver.
-
-### Step 1. Create your react native app.
-
-You can use the [React Native CLI](https://facebook.github.io/react-native/docs/getting-started) or [Expo](https://expo.io/).
-
-On macOS (to develop iOS applications) You will also need to use CocoaPods to install these dependencies.
-
-### Step 2: Install dependencies
-
-Note that if you are using in a managed expo app the install instructions may be different.
-
-  - Install and configure [react-native-unimodules](https://github.com/unimodules/react-native-unimodules) (can be skipped if in an expo app)
-  - Install and configure [expo-gl](https://github.com/expo/expo/tree/master/packages/expo-gl)
-  - Install and configure [expo-camera](https://www.npmjs.com/package/expo-camera)
-  - Install and configure [async-storage](https://github.com/react-native-community/async-storage)
-  - Install and configure [react-native-fs](https://www.npmjs.com/package/react-native-fs)
-  - **Install @tensorflow/tfjs** - `npm install @tensorflow/tfjs`
-  - **Install @tensorflow/tfjs-react-native** - `npm install @tensorflow/tfjs-react-native`
-
-If you encounter dependency conflict error in npm install, you can either:
-- Follow the instructions to use `--force` or `--legacy-peer-deps`
-- Or use yarn to install
-
-> After this point, if you are using Xcode to build for ios, you should use a ‘.workspace’ file instead of the ‘.xcodeproj’
-
-### Step 3: Configure [Metro](https://facebook.github.io/metro/)
-
-This step is only needed if you want to use the [bundleResourceIO](https://js.tensorflow.org/api_react_native/latest/#bundleResourceIO) loader.
-
-Edit your `metro.config.js` to look like the following. Changes are noted in
-the comments below.
-
-```js
-const { getDefaultConfig } = require('metro-config');
-module.exports = (async () => {
-  const defaultConfig = await getDefaultConfig();
-  const { assetExts } = defaultConfig.resolver;
-  return {
-    resolver: {
-      // Add bin to assetExts
-      assetExts: [...assetExts, 'bin'],
-    }
-  };
-})();
-```
-
-### Step 4: Test that it is working
-
-Before using tfjs in a react native app, you need to call `tf.ready()` and wait for it to complete. This is an **async function** so you might want to do this in a `componentDidMount` or before the app is rendered.
-
-The example below uses a flag in the App state to indicate that TensorFlow is ready.
-
-
-```js
-import * as tf from '@tensorflow/tfjs';
-import '@tensorflow/tfjs-react-native';
-
-export class App extends React.Component {
-  constructor(props) {
-    super(props);
-    this.state = {
-      isTfReady: false,
-    };
-  }
-
-  async componentDidMount() {
-    // Wait for tf to be ready.
-    await tf.ready();
-    // Signal to the app that tensorflow.js can now be used.
-    this.setState({
-      isTfReady: true,
-    });
-  }
-
-
-  render() {
-    //
-  }
-}
-```
-
-If you use expo and encounter a build failure when running `npm run web` due to
-`You may need an appropriate loader to handle this file type...` error, follow
-the steps below to make expo correctly transpile tfjs packages:
-
-- Run: `expo customize:web`
-  - Use the space key to select the `webpack.config.js` entry, then press "enter".
-  - This will create a bare-minimum `webpack.config.js` file.
-- Edit the `webpack.config.js` file as follows:
-
-```
-const createExpoWebpackConfigAsync = require('@expo/webpack-config');
-
-module.exports = async function(env, argv) {
-  const config = await createExpoWebpackConfigAsync(
-      {
-        ...env,
-        babel: {
-          dangerouslyAddModulePathsToTranspile: [
-            // Ensure that all packages starting with @tensorflow are
-            // transpiled.
-            '@tensorflow',
-          ],
-        },
-      },
-      argv);
-  return config;
-};
-```
-
-You can take a look at [`integration_rn59/App.tsx`](integration_rn59/App.tsx) for an example of what using tfjs-react-native looks like. In future we will add an example to the [tensorflow/tfjs-examples](https://github.com/tensorflow/tfjs-examples) repository.
-The [Webcam demo folder](integration_rn59/components/webcam) has an example of a style transfer app.
-
-![style transfer app initial screen](images/rn-styletransfer_1.jpg)
-![style transfer app initial screen](images/rn-styletransfer_2.jpg)
-![style transfer app initial screen](images/rn-styletransfer_3.jpg)
-![style transfer app initial screen](images/rn-styletransfer_4.jpg)
-
-
-## API Docs
-
-[API docs are available here](https://js.tensorflow.org/api_react_native/latest/)
-
-## Compatibility with TFJS models
-
-Many [tfjs-models](https://github.com/tensorflow/tfjs-models) use web APIs for rendering or input, these are not generally compatible with React Native, to use them you generally need to **feed a tensor** into the model and do any rendering of the model output with react native components. If there is no API for passing a tensor into a [tfjs-model](https://github.com/tensorflow/tfjs-models), feel free to file a GitHub issue.
-
-## Debugging and reporting errors
-
-When reporting bugs with tfjs-react-native please include the following information:
-
-  - Is the app created using expo? If so is it a managed or bare app?
-  - Which version of react native and the dependencies in the install instructions above are you using?
-  - What device(s) are you running on? Note that not all simulators support webgl and thus may not work with tfjs-react-native.
-  - What error messages are you seeing? Are there any relevant messages [in the device logs](https://reactnative.dev/docs/debugging#accessing-console-logs)?
-  - How could this bug be reproduced? Is there an example repo we can use to replicate the issue?
diff --git a/tfjs-master/tfjs-react-native/cloudbuild.yml b/tfjs-master/tfjs-react-native/cloudbuild.yml
deleted file mode 100644
index f6b062cb0..000000000
--- a/tfjs-master/tfjs-react-native/cloudbuild.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-steps:
-# Install common dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'yarn-common'
-  entrypoint: 'yarn'
-  args: ['install']
-
-# Install react native dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-react-native'
-  id: 'yarn'
-  entrypoint: 'yarn'
-  args: ['install']
-  waitFor: ['yarn-common']
-
-# Build tfjs-react-native.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-react-native'
-  id: 'build'
-  entrypoint: 'yarn'
-  args: ['build-ci']
-  waitFor: ['yarn']
-
-# Run unit tests in react native. Tests against version published on NPM.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-react-native/integration_rn59'
-  id: 'test-ci'
-  entrypoint: 'yarn'
-  args: ['test-ci']
-  waitFor: ['build']
-  env: ['BROWSERSTACK_USERNAME=deeplearnjs1']
-  secretEnv: ['BROWSERSTACK_KEY']
-
-# General configuration
-secrets:
-- kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-  secretEnv:
-    BROWSERSTACK_KEY: CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-timeout: 1800s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'N1_HIGHCPU_8'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/tfjs-react-native/images/rn-styletransfer_1.jpg b/tfjs-master/tfjs-react-native/images/rn-styletransfer_1.jpg
deleted file mode 100644
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diff --git a/tfjs-master/tfjs-react-native/images/rn-styletransfer_3.jpg b/tfjs-master/tfjs-react-native/images/rn-styletransfer_3.jpg
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diff --git a/tfjs-master/tfjs-react-native/images/rn-styletransfer_4.jpg b/tfjs-master/tfjs-react-native/images/rn-styletransfer_4.jpg
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/.buckconfig b/tfjs-master/tfjs-react-native/integration_rn59/.buckconfig
deleted file mode 100644
index 934256cb2..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/.buckconfig
+++ /dev/null
@@ -1,6 +0,0 @@
-
-[android]
-  target = Google Inc.:Google APIs:23
-
-[maven_repositories]
-  central = https://repo1.maven.org/maven2
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/.gitattributes b/tfjs-master/tfjs-react-native/integration_rn59/.gitattributes
deleted file mode 100644
index d42ff1835..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/.gitattributes
+++ /dev/null
@@ -1 +0,0 @@
-*.pbxproj -text
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/.gitignore b/tfjs-master/tfjs-react-native/integration_rn59/.gitignore
deleted file mode 100644
index 069e7d26d..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/.gitignore
+++ /dev/null
@@ -1,67 +0,0 @@
-# OSX
-#
-.DS_Store
-
-# Xcode
-#
-build/
-*.pbxuser
-!default.pbxuser
-*.mode1v3
-!default.mode1v3
-*.mode2v3
-!default.mode2v3
-*.perspectivev3
-!default.perspectivev3
-xcuserdata
-*.xccheckout
-*.moved-aside
-DerivedData
-*.hmap
-*.ipa
-*.xcuserstate
-project.xcworkspace
-
-# Android/IntelliJ
-#
-build/
-.idea
-.gradle
-local.properties
-*.iml
-
-# Visual Studio Code
-#
-.vscode/
-
-# node.js
-#
-node_modules/
-npm-debug.log
-yarn-error.log
-
-# BUCK
-buck-out/
-\.buckd/
-*.keystore
-
-# fastlane
-#
-# It is recommended to not store the screenshots in the git repo. Instead, use fastlane to re-generate the
-# screenshots whenever they are needed.
-# For more information about the recommended setup visit:
-# https://docs.fastlane.tools/best-practices/source-control/
-
-*/fastlane/report.xml
-*/fastlane/Preview.html
-*/fastlane/screenshots
-
-# Bundle artifact
-*.jsbundle
-
-# CocoaPods
-/ios/Pods/
-
-./android/.settings/org.eclipse.buildship.core.prefs
-./android/app/.classpath
-./android/app/.project
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/App.tsx b/tfjs-master/tfjs-react-native/integration_rn59/App.tsx
deleted file mode 100644
index 6a9194df9..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/App.tsx
+++ /dev/null
@@ -1,226 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import React, { Fragment } from 'react';
-import { Button, SafeAreaView, StyleSheet, View, Text, StatusBar } from 'react-native';
-
-import * as tf from '@tensorflow/tfjs';
-import '@tensorflow/tfjs-react-native';
-
-import { Diagnostic } from './components/diagnostic';
-import { MobilenetDemo } from './components/mobilenet_demo';
-import { TestRunner } from './components/tfjs_unit_test_runner';
-import { WebcamDemo } from './components/webcam/webcam_demo';
-import { RealtimeDemo } from './components/webcam/realtime_demo';
-
-const BACKEND_TO_USE = 'rn-webgl';
-
-export type Screen = 'main' | 'diag' | 'demo' | 'test' | 'webcam' | 'realtime';
-
-interface AppState {
-  isTfReady: boolean;
-  currentScreen: Screen;
-}
-
-export class App extends React.Component<{}, AppState> {
-  constructor(props: {}) {
-    super(props);
-    this.state = {
-      isTfReady: false,
-      currentScreen: 'main'
-    };
-
-    this.showDiagnosticScreen = this.showDiagnosticScreen.bind(this);
-    this.showDemoScreen = this.showDemoScreen.bind(this);
-    this.showMainScreen = this.showMainScreen.bind(this);
-    this.showTestScreen = this.showTestScreen.bind(this);
-    this.showWebcamDemo= this.showWebcamDemo.bind(this);
-    this.showRealtimeDemo= this.showRealtimeDemo.bind(this);
-  }
-
-  async componentDidMount() {
-    await tf.setBackend(BACKEND_TO_USE);
-    await tf.ready();
-    this.setState({
-      isTfReady: true,
-    });
-  }
-
-  showDiagnosticScreen() {
-    this.setState({ currentScreen: 'diag' });
-  }
-
-  showDemoScreen() {
-    this.setState({ currentScreen: 'demo' });
-  }
-
-  showMainScreen() {
-    this.setState({ currentScreen: 'main' });
-  }
-
-  showTestScreen() {
-    this.setState({ currentScreen: 'test' });
-  }
-
-  showWebcamDemo() {
-    this.setState({ currentScreen: 'webcam' });
-  }
-
-  showRealtimeDemo() {
-    this.setState({ currentScreen: 'realtime' });
-  }
-
-  renderMainScreen() {
-    return <Fragment>
-      <View style={styles.sectionContainer}>
-        <Text style={styles.sectionTitle}>Diagnostic</Text>
-        <Button
-          onPress={this.showDiagnosticScreen}
-          title='Show Diagnostic Screen'
-        />
-      </View>
-      <View style={styles.sectionContainer}>
-        <Text style={styles.sectionTitle}>Demo</Text>
-        <Button
-          onPress={this.showDemoScreen}
-          title='Show Demo Screen'
-        />
-      </View>
-      <View style={styles.sectionContainer}>
-        <Text style={styles.sectionTitle}>Unit tests</Text>
-        <Button
-          testID='unit-test-btn'
-          accessibilityLabel='unit-test-btn'
-          onPress={this.showTestScreen}
-          title='Show Test Screen'
-        />
-      </View>
-      <View style={styles.sectionContainer}>
-        <Text style={styles.sectionTitle}>Webcam Demo</Text>
-        <Button
-          onPress={this.showWebcamDemo}
-          title='Show Webcam Demo'
-        />
-      </View>
-      <View style={styles.sectionContainer}>
-        <Text style={styles.sectionTitle}>Realtime Webcam Demo</Text>
-        <Button
-          onPress={this.showRealtimeDemo}
-          title='Show Realtime Webcam Demo'
-        />
-      </View>
-    </Fragment>;
-  }
-
-  renderDiagnosticScreen() {
-    return <Fragment>
-      <Diagnostic returnToMain={this.showMainScreen} />
-    </Fragment>;
-  }
-
-  renderDemoScreen() {
-    const image = require('./assets/images/catsmall.jpg');
-    return <Fragment>
-      <MobilenetDemo
-        image={image}
-        returnToMain={this.showMainScreen} />
-    </Fragment>;
-  }
-
-  renderTestScreen() {
-    return <Fragment>
-      <TestRunner backend={BACKEND_TO_USE} />
-    </Fragment>;
-  }
-
-  renderWebcamDemo() {
-    return <Fragment>
-      <WebcamDemo returnToMain={this.showMainScreen}/>
-    </Fragment>;
-  }
-
-  renderRealtimeDemo() {
-    return <Fragment>
-      <RealtimeDemo returnToMain={this.showMainScreen}/>
-    </Fragment>;
-  }
-
-  renderLoadingTF() {
-    return <Fragment>
-      <View style={styles.sectionContainer}>
-        <Text style={styles.sectionTitle}>Loading TF</Text>
-      </View>
-    </Fragment>;
-  }
-
-  renderContent() {
-    const { currentScreen, isTfReady } = this.state;
-    if (isTfReady) {
-      switch (currentScreen) {
-        case 'main':
-          return this.renderMainScreen();
-        case 'diag':
-          return this.renderDiagnosticScreen();
-        case 'demo':
-          return this.renderDemoScreen();
-        case 'test':
-          return this.renderTestScreen();
-        case 'webcam':
-          return this.renderWebcamDemo();
-        case 'realtime':
-          return this.renderRealtimeDemo();
-        default:
-          return this.renderMainScreen();
-      }
-    } else {
-      return this.renderLoadingTF();
-    }
-
-  }
-
-  render() {
-    return (
-      <Fragment>
-        <StatusBar barStyle='dark-content' />
-        <SafeAreaView>
-          <View style={styles.body}>
-            {this.renderContent()}
-          </View>
-        </SafeAreaView>
-      </Fragment>
-    );
-  }
-}
-
-const styles = StyleSheet.create({
-  scrollView: {
-    backgroundColor: 'white',
-  },
-  body: {
-    backgroundColor: 'white',
-  },
-  sectionContainer: {
-    marginTop: 32,
-    paddingHorizontal: 24,
-  },
-  sectionTitle: {
-    fontSize: 24,
-    fontWeight: '600',
-    color: 'black',
-    marginBottom: 6,
-  },
-});
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/README.md b/tfjs-master/tfjs-react-native/integration_rn59/README.md
deleted file mode 100644
index d341335f2..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-This is an app that serves and an integration testbed for tfjs-react-native.
-
-For more info please see the [README](../README.md) in the folder above this one.
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/.project b/tfjs-master/tfjs-react-native/integration_rn59/android/app/.project
deleted file mode 100644
index ac485d7c3..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/.project
+++ /dev/null
@@ -1,23 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<projectDescription>
-	<name>app</name>
-	<comment>Project app created by Buildship.</comment>
-	<projects>
-	</projects>
-	<buildSpec>
-		<buildCommand>
-			<name>org.eclipse.jdt.core.javabuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-		<buildCommand>
-			<name>org.eclipse.buildship.core.gradleprojectbuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-	</buildSpec>
-	<natures>
-		<nature>org.eclipse.jdt.core.javanature</nature>
-		<nature>org.eclipse.buildship.core.gradleprojectnature</nature>
-	</natures>
-</projectDescription>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/.settings/org.eclipse.buildship.core.prefs b/tfjs-master/tfjs-react-native/integration_rn59/android/app/.settings/org.eclipse.buildship.core.prefs
deleted file mode 100644
index b1886adb4..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/.settings/org.eclipse.buildship.core.prefs
+++ /dev/null
@@ -1,2 +0,0 @@
-connection.project.dir=..
-eclipse.preferences.version=1
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/BUCK b/tfjs-master/tfjs-react-native/integration_rn59/android/app/BUCK
deleted file mode 100644
index ccae93bec..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/BUCK
+++ /dev/null
@@ -1,55 +0,0 @@
-# To learn about Buck see [Docs](https://buckbuild.com/).
-# To run your application with Buck:
-# - install Buck
-# - `npm start` - to start the packager
-# - `cd android`
-# - `keytool -genkey -v -keystore keystores/debug.keystore -storepass android -alias androiddebugkey -keypass android -dname "CN=Android Debug,O=Android,C=US"`
-# - `./gradlew :app:copyDownloadableDepsToLibs` - make all Gradle compile dependencies available to Buck
-# - `buck install -r android/app` - compile, install and run application
-#
-
-load(":build_defs.bzl", "create_aar_targets", "create_jar_targets")
-
-lib_deps = []
-
-create_aar_targets(glob(["libs/*.aar"]))
-
-create_jar_targets(glob(["libs/*.jar"]))
-
-android_library(
-    name = "all-libs",
-    exported_deps = lib_deps,
-)
-
-android_library(
-    name = "app-code",
-    srcs = glob([
-        "src/main/java/**/*.java",
-    ]),
-    deps = [
-        ":all-libs",
-        ":build_config",
-        ":res",
-    ],
-)
-
-android_build_config(
-    name = "build_config",
-    package = "com.integration_rn59",
-)
-
-android_resource(
-    name = "res",
-    package = "com.integration_rn59",
-    res = "src/main/res",
-)
-
-android_binary(
-    name = "app",
-    keystore = "//android/keystores:debug",
-    manifest = "src/main/AndroidManifest.xml",
-    package_type = "debug",
-    deps = [
-        ":app-code",
-    ],
-)
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/build.gradle b/tfjs-master/tfjs-react-native/integration_rn59/android/app/build.gradle
deleted file mode 100644
index c3ea859f7..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/build.gradle
+++ /dev/null
@@ -1,176 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-apply plugin: "com.android.application"
-
-import com.android.build.OutputFile
-
-/**
- * The react.gradle file registers a task for each build variant (e.g. bundleDebugJsAndAssets
- * and bundleReleaseJsAndAssets).
- * These basically call `react-native bundle` with the correct arguments during the Android build
- * cycle. By default, bundleDebugJsAndAssets is skipped, as in debug/dev mode we prefer to load the
- * bundle directly from the development server. Below you can see all the possible configurations
- * and their defaults. If you decide to add a configuration block, make sure to add it before the
- * `apply from: "../../node_modules/react-native/react.gradle"` line.
- *
- * project.ext.react = [
- *   // the name of the generated asset file containing your JS bundle
- *   bundleAssetName: "index.android.bundle",
- *
- *   // the entry file for bundle generation
- *   entryFile: "index.android.js",
- *
- *   // whether to bundle JS and assets in debug mode
- *   bundleInDebug: false,/
- *
- *   // whether to bundle JS and assets in release mode
- *   bundleInRelease: true,
- *
- *   // whether to bundle JS and assets in another build variant (if configured).
- *   // See http://tools.android.com/tech-docs/new-build-system/user-guide#TOC-Build-Variants
- *   // The configuration property can be in the following formats
- *   //         'bundleIn${productFlavor}${buildType}'
- *   //         'bundleIn${buildType}'
- *   // bundleInFreeDebug: true,
- *   // bundleInPaidRelease: true,
- *   // bundleInBeta: true,
- *
- *   // whether to disable dev mode in custom build variants (by default only disabled in release)
- *   // for example: to disable dev mode in the staging build type (if configured)
- *   devDisabledInStaging: true,
- *   // The configuration property can be in the following formats
- *   //         'devDisabledIn${productFlavor}${buildType}'
- *   //         'devDisabledIn${buildType}'
- *
- *   // the root of your project, i.e. where "package.json" lives
- *   root: "../../",
- *
- *   // where to put the JS bundle asset in debug mode
- *   jsBundleDirDebug: "$buildDir/intermediates/assets/debug",
- *
- *   // where to put the JS bundle asset in release mode
- *   jsBundleDirRelease: "$buildDir/intermediates/assets/release",
- *
- *   // where to put drawable resources / React Native assets, e.g. the ones you use via
- *   // require('./image.png')), in debug mode
- *   resourcesDirDebug: "$buildDir/intermediates/res/merged/debug",
- *
- *   // where to put drawable resources / React Native assets, e.g. the ones you use via
- *   // require('./image.png')), in release mode
- *   resourcesDirRelease: "$buildDir/intermediates/res/merged/release",
- *
- *   // by default the gradle tasks are skipped if none of the JS files or assets change; this means
- *   // that we don't look at files in android/ or ios/ to determine whether the tasks are up to
- *   // date; if you have any other folders that you want to ignore for performance reasons (gradle
- *   // indexes the entire tree), add them here. Alternatively, if you have JS files in android/
- *   // for example, you might want to remove it from here.
- *   inputExcludes: ["android/**", "ios/**"],
- *
- *   // override which node gets called and with what additional arguments
- *   nodeExecutableAndArgs: ["node"],
- *
- *   // supply additional arguments to the packager
- *   extraPackagerArgs: []
- * ]
- */
-
-project.ext.react = [
-    entryFile: "index.js",
-    // Uncomment when using ./gradlew assembleDebug
-    // bundleInDebug: true,
-]
-
-apply from: "../../node_modules/react-native/react.gradle"
-apply from: '../../node_modules/react-native-unimodules/gradle.groovy'
-
-/**
- * Set this to true to create two separate APKs instead of one:
- *   - An APK that only works on ARM devices
- *   - An APK that only works on x86 devices
- * The advantage is the size of the APK is reduced by about 4MB.
- * Upload all the APKs to the Play Store and people will download
- * the correct one based on the CPU architecture of their device.
- */
-def enableSeparateBuildPerCPUArchitecture = false
-
-/**
- * Run Proguard to shrink the Java bytecode in release builds.
- */
-def enableProguardInReleaseBuilds = false
-
-android {
-    compileSdkVersion rootProject.ext.compileSdkVersion
-
-    compileOptions {
-        sourceCompatibility JavaVersion.VERSION_1_8
-        targetCompatibility JavaVersion.VERSION_1_8
-    }
-
-    defaultConfig {
-        applicationId "com.integration_rn59"
-        minSdkVersion rootProject.ext.minSdkVersion
-        targetSdkVersion rootProject.ext.targetSdkVersion
-        versionCode 1
-        versionName "1.0"
-    }
-    splits {
-        abi {
-            reset()
-            enable enableSeparateBuildPerCPUArchitecture
-            universalApk false  // If true, also generate a universal APK
-            include "armeabi-v7a", "x86", "arm64-v8a", "x86_64"
-        }
-    }
-    buildTypes {
-        release {
-            minifyEnabled enableProguardInReleaseBuilds
-            proguardFiles getDefaultProguardFile("proguard-android.txt"), "proguard-rules.pro"
-        }
-    }
-    // applicationVariants are e.g. debug, release
-    applicationVariants.all { variant ->
-        variant.outputs.each { output ->
-            // For each separate APK per architecture, set a unique version code as described here:
-            // http://tools.android.com/tech-docs/new-build-system/user-guide/apk-splits
-            def versionCodes = ["armeabi-v7a":1, "x86":2, "arm64-v8a": 3, "x86_64": 4]
-            def abi = output.getFilter(OutputFile.ABI)
-            if (abi != null) {  // null for the universal-debug, universal-release variants
-                output.versionCodeOverride =
-                        versionCodes.get(abi) * 1048576 + defaultConfig.versionCode
-            }
-        }
-    }
-}
-
-dependencies {
-    implementation project(':react-native-svg')
-    implementation project(':react-native-fs')
-    implementation project(':@react-native-community_async-storage')
-    implementation fileTree(dir: "libs", include: ["*.jar"])
-    implementation "com.android.support:appcompat-v7:${rootProject.ext.supportLibVersion}"
-    implementation "com.facebook.react:react-native:+"  // From node_modules
-    addUnimodulesDependencies()
-    compile project(':react-native-fs')
-}
-
-// Run this once to be able to run the application with BUCK
-// puts all compile dependencies into folder libs for BUCK to use
-task copyDownloadableDepsToLibs(type: Copy) {
-    from configurations.compile
-    into 'libs'
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/build_defs.bzl b/tfjs-master/tfjs-react-native/integration_rn59/android/app/build_defs.bzl
deleted file mode 100644
index ea1e8e567..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/build_defs.bzl
+++ /dev/null
@@ -1,31 +0,0 @@
-#  @license
-#  Copyright 2019 Google LLC. All Rights Reserved.
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#  http://www.apache.org/licenses/LICENSE-2.0
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
-"""Helper definitions to glob .aar and .jar targets"""
-
-def create_aar_targets(aarfiles):
-    for aarfile in aarfiles:
-        name = "aars__" + aarfile[aarfile.rindex("/") + 1:aarfile.rindex(".aar")]
-        lib_deps.append(":" + name)
-        android_prebuilt_aar(
-            name = name,
-            aar = aarfile,
-        )
-
-def create_jar_targets(jarfiles):
-    for jarfile in jarfiles:
-        name = "jars__" + jarfile[jarfile.rindex("/") + 1:jarfile.rindex(".jar")]
-        lib_deps.append(":" + name)
-        prebuilt_jar(
-            name = name,
-            binary_jar = jarfile,
-        )
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/proguard-rules.pro b/tfjs-master/tfjs-react-native/integration_rn59/android/app/proguard-rules.pro
deleted file mode 100644
index a92fa177e..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/proguard-rules.pro
+++ /dev/null
@@ -1,17 +0,0 @@
-# Add project specific ProGuard rules here.
-# By default, the flags in this file are appended to flags specified
-# in /usr/local/Cellar/android-sdk/24.3.3/tools/proguard/proguard-android.txt
-# You can edit the include path and order by changing the proguardFiles
-# directive in build.gradle.
-#
-# For more details, see
-#   http://developer.android.com/guide/developing/tools/proguard.html
-
-# Add any project specific keep options here:
-
-# If your project uses WebView with JS, uncomment the following
-# and specify the fully qualified class name to the JavaScript interface
-# class:
-#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
-#   public *;
-#}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/debug/AndroidManifest.xml b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/debug/AndroidManifest.xml
deleted file mode 100644
index fa26aa56e..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/debug/AndroidManifest.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<manifest xmlns:android="http://schemas.android.com/apk/res/android"
-    xmlns:tools="http://schemas.android.com/tools">
-
-    <uses-permission android:name="android.permission.SYSTEM_ALERT_WINDOW"/>
-
-    <application android:usesCleartextTraffic="true" tools:targetApi="28" tools:ignore="GoogleAppIndexingWarning" />
-</manifest>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/AndroidManifest.xml b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/AndroidManifest.xml
deleted file mode 100644
index cbf545627..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/AndroidManifest.xml
+++ /dev/null
@@ -1,53 +0,0 @@
-<!-- /**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */ -->
-
-<manifest xmlns:android="http://schemas.android.com/apk/res/android"
-  package="com.integration_rn59">
-
-    <uses-permission android:name="android.permission.INTERNET" />
-    <uses-permission android:name="android.permission.ACCESS_COARSE_LOCATION" />
-    <uses-permission android:name="android.permission.ACCESS_FINE_LOCATION" />
-    <uses-permission android:name="android.permission.CAMERA" />
-    <uses-permission android:name="android.permission.READ_CONTACTS" />
-    <uses-permission android:name="android.permission.READ_CALENDAR" />
-    <uses-permission android:name="android.permission.WRITE_CALENDAR" />
-    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />
-    <uses-permission android:name="android.permission.RECORD_AUDIO" />
-    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
-    <uses-permission android:name="android.permission.WRITE_SETTINGS" />
-
-    <application
-      android:name=".MainApplication"
-      android:label="@string/app_name"
-      android:icon="@mipmap/ic_launcher"
-      android:roundIcon="@mipmap/ic_launcher_round"
-      android:allowBackup="false"
-      android:theme="@style/AppTheme">
-      <activity
-        android:name=".MainActivity"
-        android:label="@string/app_name"
-        android:configChanges="keyboard|keyboardHidden|orientation|screenSize"
-        android:windowSoftInputMode="adjustResize">
-        <intent-filter>
-            <action android:name="android.intent.action.MAIN" />
-            <category android:name="android.intent.category.LAUNCHER" />
-        </intent-filter>
-      </activity>
-      <activity android:name="com.facebook.react.devsupport.DevSettingsActivity" />
-    </application>
-
-</manifest>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/java/com/integration_rn59/MainActivity.java b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/java/com/integration_rn59/MainActivity.java
deleted file mode 100644
index a377e3dea..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/java/com/integration_rn59/MainActivity.java
+++ /dev/null
@@ -1,31 +0,0 @@
-package com.integration_rn59;
-
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import com.facebook.react.ReactActivity;
-
-public class MainActivity extends ReactActivity {
-  /**
-   * Returns the name of the main component registered from JavaScript.
-   * This is used to schedule rendering of the component.
-   */
-  @Override
-  protected String getMainComponentName() {
-    return "integration_rn59";
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/java/com/integration_rn59/MainApplication.java b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/java/com/integration_rn59/MainApplication.java
deleted file mode 100644
index 6531c4132..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/java/com/integration_rn59/MainApplication.java
+++ /dev/null
@@ -1,70 +0,0 @@
-package com.integration_rn59;
-
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import android.app.Application;
-import com.facebook.react.ReactApplication;
-import com.horcrux.svg.SvgPackage;
-import com.rnfs.RNFSPackage;
-import com.facebook.react.ReactNativeHost;
-import com.facebook.react.ReactPackage;
-import com.facebook.react.shell.MainReactPackage;
-import com.facebook.soloader.SoLoader;
-import com.integration_rn59.generated.BasePackageList;
-import com.reactnativecommunity.asyncstorage.AsyncStoragePackage;
-import java.util.Arrays;
-import java.util.List;
-import org.unimodules.adapters.react.ModuleRegistryAdapter;
-import org.unimodules.adapters.react.ReactModuleRegistryProvider;
-import org.unimodules.core.interfaces.SingletonModule;
-
-public class MainApplication extends Application implements ReactApplication {
-  private final ReactModuleRegistryProvider mModuleRegistryProvider =
-      new ReactModuleRegistryProvider(
-          new BasePackageList().getPackageList(), Arrays.<SingletonModule>asList());
-
-  private final ReactNativeHost mReactNativeHost = new ReactNativeHost(this) {
-    @Override
-    public boolean getUseDeveloperSupport() {
-      return BuildConfig.DEBUG;
-    }
-
-    @Override
-    protected List<ReactPackage> getPackages() {
-      return Arrays.<ReactPackage>asList(new MainReactPackage(), new SvgPackage(),
-          new RNFSPackage(), new ModuleRegistryAdapter(mModuleRegistryProvider),
-          new AsyncStoragePackage());
-    }
-
-    @Override
-    protected String getJSMainModuleName() {
-      return "index";
-    }
-  };
-
-  @Override
-  public ReactNativeHost getReactNativeHost() {
-    return mReactNativeHost;
-  }
-
-  @Override
-  public void onCreate() {
-    super.onCreate();
-    SoLoader.init(this, /* native exopackage */ false);
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/java/com/integration_rn59/generated/BasePackageList.java b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/java/com/integration_rn59/generated/BasePackageList.java
deleted file mode 100644
index b1ba78897..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/java/com/integration_rn59/generated/BasePackageList.java
+++ /dev/null
@@ -1,18 +0,0 @@
-package com.integration_rn59.generated;
-
-import java.util.Arrays;
-import java.util.List;
-import org.unimodules.core.interfaces.Package;
-
-public class BasePackageList {
-  public List<Package> getPackageList() {
-    return Arrays.<Package>asList(
-        new expo.modules.camera.CameraPackage(),
-        new expo.modules.constants.ConstantsPackage(),
-        new expo.modules.filesystem.FileSystemPackage(),
-        new expo.modules.gl.GLPackage(),
-        new expo.modules.imagemanipulator.ImageManipulatorPackage(),
-        new expo.modules.permissions.PermissionsPackage()
-    );
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-hdpi/ic_launcher.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-hdpi/ic_launcher.png
deleted file mode 100644
index a2f590828..000000000
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-hdpi/ic_launcher_round.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-hdpi/ic_launcher_round.png
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-mdpi/ic_launcher.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-mdpi/ic_launcher.png
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-mdpi/ic_launcher_round.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-mdpi/ic_launcher_round.png
deleted file mode 100644
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xhdpi/ic_launcher.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xhdpi/ic_launcher.png
deleted file mode 100644
index dcd3cd808..000000000
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.png
deleted file mode 100644
index 459ca609d..000000000
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xxhdpi/ic_launcher.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xxhdpi/ic_launcher.png
deleted file mode 100644
index 8ca12fe02..000000000
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.png
deleted file mode 100644
index 8e19b410a..000000000
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.png
deleted file mode 100644
index b824ebdd4..000000000
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.png b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.png
deleted file mode 100644
index 4c19a13c2..000000000
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/values/strings.xml b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/values/strings.xml
deleted file mode 100644
index db9aa431b..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/values/strings.xml
+++ /dev/null
@@ -1,20 +0,0 @@
-<!-- /**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */ -->
- 
-<resources>
-    <string name="app_name">integration_rn59</string>
-</resources>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/values/styles.xml b/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/values/styles.xml
deleted file mode 100644
index 4f1f5bd7d..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/app/src/main/res/values/styles.xml
+++ /dev/null
@@ -1,25 +0,0 @@
-<!-- /**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */ -->
- 
-<resources>
-
-    <!-- Base application theme. -->
-    <style name="AppTheme" parent="Theme.AppCompat.Light.NoActionBar">
-        <!-- Customize your theme here. -->
-    </style>
-
-</resources>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/build.gradle b/tfjs-master/tfjs-react-native/integration_rn59/android/build.gradle
deleted file mode 100644
index 67dc9ad89..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/build.gradle
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Top-level build file where you can add configuration options common to all sub-projects/modules.
-
-buildscript {
-    ext {
-        buildToolsVersion = "28.0.3"
-        minSdkVersion = 21
-        compileSdkVersion = 28
-        targetSdkVersion = 28
-        supportLibVersion = "28.0.0"
-    }
-    repositories {
-        google()
-        jcenter()
-    }
-    dependencies {
-        classpath("com.android.tools.build:gradle:3.4.0")
-
-        // NOTE: Do not place your application dependencies here; they belong
-        // in the individual module build.gradle files
-    }
-}
-
-allprojects {
-    repositories {
-        mavenLocal()
-        google()
-        jcenter()
-        maven {
-            // All of React Native (JS, Obj-C sources, Android binaries) is installed from npm
-            url "$rootDir/../node_modules/react-native/android"
-
-        }
-        maven {
-          // expo-camera bundles a custom com.google.android:cameraview
-            url "$rootDir/../node_modules/expo-camera/android/maven"
-        }
-
-    }
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/gradle.properties b/tfjs-master/tfjs-react-native/integration_rn59/android/gradle.properties
deleted file mode 100644
index 89e0d99e2..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/gradle.properties
+++ /dev/null
@@ -1,18 +0,0 @@
-# Project-wide Gradle settings.
-
-# IDE (e.g. Android Studio) users:
-# Gradle settings configured through the IDE *will override*
-# any settings specified in this file.
-
-# For more details on how to configure your build environment visit
-# http://www.gradle.org/docs/current/userguide/build_environment.html
-
-# Specifies the JVM arguments used for the daemon process.
-# The setting is particularly useful for tweaking memory settings.
-# Default value: -Xmx10248m -XX:MaxPermSize=256m
-# org.gradle.jvmargs=-Xmx2048m -XX:MaxPermSize=512m -XX:+HeapDumpOnOutOfMemoryError -Dfile.encoding=UTF-8
-
-# When configured, Gradle will run in incubating parallel mode.
-# This option should only be used with decoupled projects. More details, visit
-# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
-# org.gradle.parallel=true
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/gradle/wrapper/gradle-wrapper.jar b/tfjs-master/tfjs-react-native/integration_rn59/android/gradle/wrapper/gradle-wrapper.jar
deleted file mode 100644
index 5c2d1cf01..000000000
Binary files a/tfjs-master/tfjs-react-native/integration_rn59/android/gradle/wrapper/gradle-wrapper.jar and /dev/null differ
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/gradle/wrapper/gradle-wrapper.properties b/tfjs-master/tfjs-react-native/integration_rn59/android/gradle/wrapper/gradle-wrapper.properties
deleted file mode 100644
index ee69dd68d..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/gradle/wrapper/gradle-wrapper.properties
+++ /dev/null
@@ -1,5 +0,0 @@
-distributionBase=GRADLE_USER_HOME
-distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-5.4.1-all.zip
-zipStoreBase=GRADLE_USER_HOME
-zipStorePath=wrapper/dists
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/gradlew b/tfjs-master/tfjs-react-native/integration_rn59/android/gradlew
deleted file mode 100644
index b0d6d0ab5..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/gradlew
+++ /dev/null
@@ -1,188 +0,0 @@
-#!/usr/bin/env sh
-
-#
-# Copyright 2015 the original author or authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-##############################################################################
-##
-##  Gradle start up script for UN*X
-##
-##############################################################################
-
-# Attempt to set APP_HOME
-# Resolve links: $0 may be a link
-PRG="$0"
-# Need this for relative symlinks.
-while [ -h "$PRG" ] ; do
-    ls=`ls -ld "$PRG"`
-    link=`expr "$ls" : '.*-> \(.*\)$'`
-    if expr "$link" : '/.*' > /dev/null; then
-        PRG="$link"
-    else
-        PRG=`dirname "$PRG"`"/$link"
-    fi
-done
-SAVED="`pwd`"
-cd "`dirname \"$PRG\"`/" >/dev/null
-APP_HOME="`pwd -P`"
-cd "$SAVED" >/dev/null
-
-APP_NAME="Gradle"
-APP_BASE_NAME=`basename "$0"`
-
-# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
-
-# Use the maximum available, or set MAX_FD != -1 to use that value.
-MAX_FD="maximum"
-
-warn () {
-    echo "$*"
-}
-
-die () {
-    echo
-    echo "$*"
-    echo
-    exit 1
-}
-
-# OS specific support (must be 'true' or 'false').
-cygwin=false
-msys=false
-darwin=false
-nonstop=false
-case "`uname`" in
-  CYGWIN* )
-    cygwin=true
-    ;;
-  Darwin* )
-    darwin=true
-    ;;
-  MINGW* )
-    msys=true
-    ;;
-  NONSTOP* )
-    nonstop=true
-    ;;
-esac
-
-CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
-
-# Determine the Java command to use to start the JVM.
-if [ -n "$JAVA_HOME" ] ; then
-    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
-        # IBM's JDK on AIX uses strange locations for the executables
-        JAVACMD="$JAVA_HOME/jre/sh/java"
-    else
-        JAVACMD="$JAVA_HOME/bin/java"
-    fi
-    if [ ! -x "$JAVACMD" ] ; then
-        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
-
-Please set the JAVA_HOME variable in your environment to match the
-location of your Java installation."
-    fi
-else
-    JAVACMD="java"
-    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-
-Please set the JAVA_HOME variable in your environment to match the
-location of your Java installation."
-fi
-
-# Increase the maximum file descriptors if we can.
-if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
-    MAX_FD_LIMIT=`ulimit -H -n`
-    if [ $? -eq 0 ] ; then
-        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
-            MAX_FD="$MAX_FD_LIMIT"
-        fi
-        ulimit -n $MAX_FD
-        if [ $? -ne 0 ] ; then
-            warn "Could not set maximum file descriptor limit: $MAX_FD"
-        fi
-    else
-        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
-    fi
-fi
-
-# For Darwin, add options to specify how the application appears in the dock
-if $darwin; then
-    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
-fi
-
-# For Cygwin, switch paths to Windows format before running java
-if $cygwin ; then
-    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
-    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
-    JAVACMD=`cygpath --unix "$JAVACMD"`
-
-    # We build the pattern for arguments to be converted via cygpath
-    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
-    SEP=""
-    for dir in $ROOTDIRSRAW ; do
-        ROOTDIRS="$ROOTDIRS$SEP$dir"
-        SEP="|"
-    done
-    OURCYGPATTERN="(^($ROOTDIRS))"
-    # Add a user-defined pattern to the cygpath arguments
-    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
-        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
-    fi
-    # Now convert the arguments - kludge to limit ourselves to /bin/sh
-    i=0
-    for arg in "$@" ; do
-        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
-        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
-
-        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
-            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
-        else
-            eval `echo args$i`="\"$arg\""
-        fi
-        i=$((i+1))
-    done
-    case $i in
-        (0) set -- ;;
-        (1) set -- "$args0" ;;
-        (2) set -- "$args0" "$args1" ;;
-        (3) set -- "$args0" "$args1" "$args2" ;;
-        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
-        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
-        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
-        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
-        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
-        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
-    esac
-fi
-
-# Escape application args
-save () {
-    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
-    echo " "
-}
-APP_ARGS=$(save "$@")
-
-# Collect all arguments for the java command, following the shell quoting and substitution rules
-eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
-
-# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong
-if [ "$(uname)" = "Darwin" ] && [ "$HOME" = "$PWD" ]; then
-  cd "$(dirname "$0")"
-fi
-
-exec "$JAVACMD" "$@"
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/gradlew.bat b/tfjs-master/tfjs-react-native/integration_rn59/android/gradlew.bat
deleted file mode 100644
index 9991c5032..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/gradlew.bat
+++ /dev/null
@@ -1,100 +0,0 @@
-@rem
-@rem Copyright 2015 the original author or authors.
-@rem
-@rem Licensed under the Apache License, Version 2.0 (the "License");
-@rem you may not use this file except in compliance with the License.
-@rem You may obtain a copy of the License at
-@rem
-@rem      http://www.apache.org/licenses/LICENSE-2.0
-@rem
-@rem Unless required by applicable law or agreed to in writing, software
-@rem distributed under the License is distributed on an "AS IS" BASIS,
-@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-@rem See the License for the specific language governing permissions and
-@rem limitations under the License.
-@rem
-
-@if "%DEBUG%" == "" @echo off
-@rem ##########################################################################
-@rem
-@rem  Gradle startup script for Windows
-@rem
-@rem ##########################################################################
-
-@rem Set local scope for the variables with windows NT shell
-if "%OS%"=="Windows_NT" setlocal
-
-set DIRNAME=%~dp0
-if "%DIRNAME%" == "" set DIRNAME=.
-set APP_BASE_NAME=%~n0
-set APP_HOME=%DIRNAME%
-
-@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m"
-
-@rem Find java.exe
-if defined JAVA_HOME goto findJavaFromJavaHome
-
-set JAVA_EXE=java.exe
-%JAVA_EXE% -version >NUL 2>&1
-if "%ERRORLEVEL%" == "0" goto init
-
-echo.
-echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:findJavaFromJavaHome
-set JAVA_HOME=%JAVA_HOME:"=%
-set JAVA_EXE=%JAVA_HOME%/bin/java.exe
-
-if exist "%JAVA_EXE%" goto init
-
-echo.
-echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:init
-@rem Get command-line arguments, handling Windows variants
-
-if not "%OS%" == "Windows_NT" goto win9xME_args
-
-:win9xME_args
-@rem Slurp the command line arguments.
-set CMD_LINE_ARGS=
-set _SKIP=2
-
-:win9xME_args_slurp
-if "x%~1" == "x" goto execute
-
-set CMD_LINE_ARGS=%*
-
-:execute
-@rem Setup the command line
-
-set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
-
-@rem Execute Gradle
-"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
-
-:end
-@rem End local scope for the variables with windows NT shell
-if "%ERRORLEVEL%"=="0" goto mainEnd
-
-:fail
-rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
-rem the _cmd.exe /c_ return code!
-if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
-exit /b 1
-
-:mainEnd
-if "%OS%"=="Windows_NT" endlocal
-
-:omega
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/keystores/BUCK b/tfjs-master/tfjs-react-native/integration_rn59/android/keystores/BUCK
deleted file mode 100644
index 88e4c31b2..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/keystores/BUCK
+++ /dev/null
@@ -1,8 +0,0 @@
-keystore(
-    name = "debug",
-    properties = "debug.keystore.properties",
-    store = "debug.keystore",
-    visibility = [
-        "PUBLIC",
-    ],
-)
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/keystores/debug.keystore.properties b/tfjs-master/tfjs-react-native/integration_rn59/android/keystores/debug.keystore.properties
deleted file mode 100644
index 121bfb49f..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/keystores/debug.keystore.properties
+++ /dev/null
@@ -1,4 +0,0 @@
-key.store=debug.keystore
-key.alias=androiddebugkey
-key.store.password=android
-key.alias.password=android
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/android/settings.gradle b/tfjs-master/tfjs-react-native/integration_rn59/android/settings.gradle
deleted file mode 100644
index 2886c5a84..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/android/settings.gradle
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-apply from: '../node_modules/react-native-unimodules/gradle.groovy'
-includeUnimodulesProjects()
-include ':@react-native-community_async-storage'
-project(':@react-native-community_async-storage').projectDir = new File(rootProject.projectDir, '../node_modules/@react-native-async-storage/async-storage/android')
-
-include ':react-native-fs'
-project(':react-native-fs').projectDir = new File(settingsDir, '../node_modules/react-native-fs/android')
-
-include ':react-native-svg'
-project(':react-native-svg').projectDir = new File(rootProject.projectDir, '../node_modules/react-native-svg/android')
-
-rootProject.name = 'integration_rn59'
-
-include ':app'
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/app.json b/tfjs-master/tfjs-react-native/integration_rn59/app.json
deleted file mode 100644
index 53e1d1fde..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/app.json
+++ /dev/null
@@ -1,4 +0,0 @@
-{
-  "name": "integration_rn59",
-  "displayName": "integration_rn59"
-}
\ No newline at end of file
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/assets/graph_model/group1-shard1of1.bin b/tfjs-master/tfjs-react-native/integration_rn59/assets/graph_model/group1-shard1of1.bin
deleted file mode 100644
index 21e0d92a4..000000000
Binary files a/tfjs-master/tfjs-react-native/integration_rn59/assets/graph_model/group1-shard1of1.bin and /dev/null differ
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/assets/graph_model/model.json b/tfjs-master/tfjs-react-native/integration_rn59/assets/graph_model/model.json
deleted file mode 100644
index 1f2dfbedd..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/assets/graph_model/model.json
+++ /dev/null
@@ -1 +0,0 @@
-{"weightsManifest": [{"paths": ["group1-shard1of1.bin"], "weights": [{"dtype": "float32", "shape": [1], "name": "StatefulPartitionedCall/sequential/dense_Dense2/BiasAdd/ReadVariableOp"}, {"dtype": "float32", "shape": [10, 1], "name": "StatefulPartitionedCall/sequential/dense_Dense2/MatMul/ReadVariableOp"}]}], "modelTopology": {"node": [{"attr": {"dtype": {"type": "DT_FLOAT"}, "shape": {"shape": {"dim": [{"size": "-1"}, {"size": "10"}]}}}, "name": "dense_dense2_input", "op": "Placeholder"}, {"input": ["^dense_dense2_input"], "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}]}}}}, "name": "StatefulPartitionedCall/sequential/dense_Dense2/BiasAdd/ReadVariableOp", "op": "Const"}, {"input": ["^dense_dense2_input"], "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "10"}, {"size": "1"}]}}}}, "name": "StatefulPartitionedCall/sequential/dense_Dense2/MatMul/ReadVariableOp", "op": "Const"}, {"input": ["dense_dense2_input", "StatefulPartitionedCall/sequential/dense_Dense2/MatMul/ReadVariableOp"], "attr": {"transpose_b": {"b": false}, "transpose_a": {"b": false}, "T": {"type": "DT_FLOAT"}}, "name": "StatefulPartitionedCall/sequential/dense_Dense2/MatMul", "op": "MatMul"}, {"input": ["StatefulPartitionedCall/sequential/dense_Dense2/MatMul", "StatefulPartitionedCall/sequential/dense_Dense2/BiasAdd/ReadVariableOp"], "attr": {"T": {"type": "DT_FLOAT"}, "data_format": {"s": "TkhXQw=="}}, "name": "StatefulPartitionedCall/sequential/dense_Dense2/BiasAdd", "op": "BiasAdd"}, {"input": ["StatefulPartitionedCall/sequential/dense_Dense2/BiasAdd"], "attr": {"T": {"type": "DT_FLOAT"}}, "name": "Identity", "op": "Identity"}], "versions": {"minConsumer": 12, "producer": 134}}, "generatedBy": "1.15.0", "convertedBy": "TensorFlow.js Converter v1.3.1.1", "format": "graph-model"}
\ No newline at end of file
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/assets/images/catsmall.jpg b/tfjs-master/tfjs-react-native/integration_rn59/assets/images/catsmall.jpg
deleted file mode 100644
index 767e5c739..000000000
Binary files a/tfjs-master/tfjs-react-native/integration_rn59/assets/images/catsmall.jpg and /dev/null differ
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/assets/model/bundle_model_test.json b/tfjs-master/tfjs-react-native/integration_rn59/assets/model/bundle_model_test.json
deleted file mode 100644
index c4c6f7761..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/assets/model/bundle_model_test.json
+++ /dev/null
@@ -1 +0,0 @@
-{"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":1,"activation":"linear","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true,"batch_input_shape":[null,10],"dtype":"float32"}}],"keras_version":"tfjs-layers 1.2.7","backend":"tensor_flow.js"},"format":"layers-model","generatedBy":"TensorFlow.js tfjs-layers v1.2.7","convertedBy":null,"weightsManifest":[{"paths":["./bundle_model_test_weights.bin"],"weights":[{"name":"dense_Dense2/kernel","shape":[10,1],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[1],"dtype":"float32"}]}]}
\ No newline at end of file
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/assets/model/bundle_model_test_weights.bin b/tfjs-master/tfjs-react-native/integration_rn59/assets/model/bundle_model_test_weights.bin
deleted file mode 100644
index f3941f653..000000000
Binary files a/tfjs-master/tfjs-react-native/integration_rn59/assets/model/bundle_model_test_weights.bin and /dev/null differ
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/group1-shard1of2.bin b/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/group1-shard1of2.bin
deleted file mode 100644
index d44aa5bcd..000000000
Binary files a/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/group1-shard1of2.bin and /dev/null differ
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/group1-shard2of2.bin b/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/group1-shard2of2.bin
deleted file mode 100644
index a669f5588..000000000
Binary files a/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/group1-shard2of2.bin and /dev/null differ
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/model.json b/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/model.json
deleted file mode 100644
index 6116da749..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/model.json
+++ /dev/null
@@ -1,7235 +0,0 @@
-{
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-                    "module_apply_default/MobilenetV2/expanded_conv_15/add",
-                    "module/MobilenetV2/expanded_conv_16/expand/weights",
-                    "module_apply_default/MobilenetV2/expanded_conv_16/expand/Conv2D_bn_offset"
-                ],
-                "attr": {
-                    "explicit_paddings": {
-                        "list": {}
-                    },
-                    "epsilon": {
-                        "f": 0
-                    },
-                    "data_format": {
-                        "s": "TkhXQw=="
-                    },
-                    "use_cudnn_on_gpu": {
-                        "b": true
-                    },
-                    "padding": {
-                        "s": "U0FNRQ=="
-                    },
-                    "strides": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "num_args": {
-                        "i": "1"
-                    },
-                    "T": {
-                        "type": "DT_FLOAT"
-                    },
-                    "dilations": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "fused_ops": {
-                        "list": {
-                            "s": [
-                                "Qmlhc0FkZA==",
-                                "UmVsdTY="
-                            ]
-                        }
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/expanded_conv_16/expand/Relu6",
-                "op": "_FusedConv2D"
-            },
-            {
-                "input": [
-                    "module_apply_default/MobilenetV2/expanded_conv_16/expand/Relu6",
-                    "module/MobilenetV2/expanded_conv_16/depthwise/depthwise_weights"
-                ],
-                "attr": {
-                    "padding": {
-                        "s": "U0FNRQ=="
-                    },
-                    "strides": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "dilations": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "data_format": {
-                        "s": "TkhXQw=="
-                    },
-                    "T": {
-                        "type": "DT_FLOAT"
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/expanded_conv_16/depthwise/depthwise",
-                "op": "DepthwiseConv2dNative"
-            },
-            {
-                "input": [
-                    "module_apply_default/MobilenetV2/expanded_conv_16/depthwise/depthwise",
-                    "module_apply_default/MobilenetV2/expanded_conv_16/depthwise/depthwise_bn_offset"
-                ],
-                "attr": {
-                    "T": {
-                        "type": "DT_FLOAT"
-                    },
-                    "data_format": {
-                        "s": "TkhXQw=="
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/FusedBatchNorm",
-                "op": "BiasAdd"
-            },
-            {
-                "input": [
-                    "module_apply_default/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/FusedBatchNorm"
-                ],
-                "attr": {
-                    "T": {
-                        "type": "DT_FLOAT"
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/expanded_conv_16/depthwise/Relu6",
-                "op": "Relu6"
-            },
-            {
-                "device": "/device:CPU:0",
-                "input": [
-                    "module_apply_default/MobilenetV2/expanded_conv_16/depthwise/Relu6",
-                    "module/MobilenetV2/expanded_conv_16/project/weights",
-                    "module_apply_default/MobilenetV2/expanded_conv_16/project/Conv2D_bn_offset"
-                ],
-                "attr": {
-                    "explicit_paddings": {
-                        "list": {}
-                    },
-                    "epsilon": {
-                        "f": 0
-                    },
-                    "data_format": {
-                        "s": "TkhXQw=="
-                    },
-                    "use_cudnn_on_gpu": {
-                        "b": true
-                    },
-                    "padding": {
-                        "s": "U0FNRQ=="
-                    },
-                    "strides": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "num_args": {
-                        "i": "1"
-                    },
-                    "T": {
-                        "type": "DT_FLOAT"
-                    },
-                    "dilations": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "fused_ops": {
-                        "list": {
-                            "s": [
-                                "Qmlhc0FkZA=="
-                            ]
-                        }
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/expanded_conv_16/project/BatchNorm/FusedBatchNorm",
-                "op": "_FusedConv2D"
-            },
-            {
-                "device": "/device:CPU:0",
-                "input": [
-                    "module_apply_default/MobilenetV2/expanded_conv_16/project/BatchNorm/FusedBatchNorm",
-                    "module/MobilenetV2/Conv_1/weights",
-                    "module_apply_default/MobilenetV2/Conv_1/Conv2D_bn_offset"
-                ],
-                "attr": {
-                    "explicit_paddings": {
-                        "list": {}
-                    },
-                    "epsilon": {
-                        "f": 0
-                    },
-                    "data_format": {
-                        "s": "TkhXQw=="
-                    },
-                    "use_cudnn_on_gpu": {
-                        "b": true
-                    },
-                    "padding": {
-                        "s": "U0FNRQ=="
-                    },
-                    "strides": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "num_args": {
-                        "i": "1"
-                    },
-                    "T": {
-                        "type": "DT_FLOAT"
-                    },
-                    "dilations": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "fused_ops": {
-                        "list": {
-                            "s": [
-                                "Qmlhc0FkZA==",
-                                "UmVsdTY="
-                            ]
-                        }
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/Conv_1/Relu6",
-                "op": "_FusedConv2D"
-            },
-            {
-                "input": [
-                    "module_apply_default/MobilenetV2/Conv_1/Relu6"
-                ],
-                "attr": {
-                    "padding": {
-                        "s": "VkFMSUQ="
-                    },
-                    "strides": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "ksize": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "7",
-                                "7",
-                                "1"
-                            ]
-                        }
-                    },
-                    "T": {
-                        "type": "DT_FLOAT"
-                    },
-                    "data_format": {
-                        "s": "TkhXQw=="
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/Logits/AvgPool",
-                "op": "AvgPool"
-            },
-            {
-                "device": "/device:CPU:0",
-                "input": [
-                    "module_apply_default/MobilenetV2/Logits/AvgPool",
-                    "module/MobilenetV2/Logits/Conv2d_1c_1x1/weights",
-                    "module/MobilenetV2/Logits/Conv2d_1c_1x1/biases"
-                ],
-                "attr": {
-                    "explicit_paddings": {
-                        "list": {}
-                    },
-                    "epsilon": {
-                        "f": 0
-                    },
-                    "data_format": {
-                        "s": "TkhXQw=="
-                    },
-                    "use_cudnn_on_gpu": {
-                        "b": true
-                    },
-                    "padding": {
-                        "s": "U0FNRQ=="
-                    },
-                    "strides": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "num_args": {
-                        "i": "1"
-                    },
-                    "T": {
-                        "type": "DT_FLOAT"
-                    },
-                    "dilations": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "1",
-                                "1",
-                                "1"
-                            ]
-                        }
-                    },
-                    "fused_ops": {
-                        "list": {
-                            "s": [
-                                "Qmlhc0FkZA=="
-                            ]
-                        }
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/Logits/Conv2d_1c_1x1/BiasAdd",
-                "op": "_FusedConv2D"
-            },
-            {
-                "input": [
-                    "module_apply_default/MobilenetV2/Logits/Conv2d_1c_1x1/BiasAdd"
-                ],
-                "attr": {
-                    "squeeze_dims": {
-                        "list": {
-                            "i": [
-                                "1",
-                                "2"
-                            ]
-                        }
-                    },
-                    "T": {
-                        "type": "DT_FLOAT"
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/Logits/Squeeze",
-                "op": "Squeeze"
-            },
-            {
-                "input": [
-                    "module_apply_default/MobilenetV2/Logits/Squeeze"
-                ],
-                "attr": {
-                    "T": {
-                        "type": "DT_FLOAT"
-                    }
-                },
-                "name": "module_apply_default/MobilenetV2/Logits/output",
-                "op": "Identity"
-            }
-        ],
-        "library": {},
-        "versions": {}
-    },
-    "generatedBy": "1.14.0",
-    "convertedBy": "TensorFlow.js Converter v1.2.10.1",
-    "format": "graph-model"
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/readme.md b/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/readme.md
deleted file mode 100644
index 2a0e35a13..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/assets/sharded_model/readme.md
+++ /dev/null
@@ -1 +0,0 @@
-Assets downloaded from https://tfhub.dev/google/tfjs-model/imagenet/mobilenet_v2_050_224/classification/2/default/1
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/babel.config.js b/tfjs-master/tfjs-react-native/integration_rn59/babel.config.js
deleted file mode 100644
index f842b77fc..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/babel.config.js
+++ /dev/null
@@ -1,3 +0,0 @@
-module.exports = {
-  presets: ['module:metro-react-native-babel-preset'],
-};
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/components/diagnostic.tsx b/tfjs-master/tfjs-react-native/integration_rn59/components/diagnostic.tsx
deleted file mode 100644
index 31997f603..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/components/diagnostic.tsx
+++ /dev/null
@@ -1,115 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import React, { Fragment } from 'react';
-import { Button, SafeAreaView, StyleSheet, ScrollView, View, StatusBar } from 'react-native';
-
-import * as tf from '@tensorflow/tfjs';
-import { Run } from './run';
-import { simpleOpRunner, precisionTestRunner, mobilenetRunner, localModelRunner, localShardedGraphModelRunner, trainModelRunner, saveModelRunner, localGraphModelRunner } from './ml';
-
-interface ScreenProps {
-  returnToMain: () => void;
-}
-
-export class Diagnostic extends React.Component<ScreenProps> {
-  constructor(props: ScreenProps) {
-    super(props);
-  }
-
-  render() {
-    return (
-      <Fragment>
-        <StatusBar barStyle='dark-content' />
-        <SafeAreaView>
-          <ScrollView
-            contentInsetAdjustmentBehavior='automatic'
-            style={styles.scrollView}>
-
-            <View style={styles.body}>
-              <View style={styles.sectionContainer}>
-                <Button
-                  onPress={this.props.returnToMain}
-                  title='Back'
-                />
-              </View>
-
-              <View style={styles.sectionContainer}>
-                <Run label='tf.getBackend()' result={`${tf.getBackend()}`}>
-                </Run>
-                <Run label='tf.version_core' result={`${tf.version_core}`}>
-                </Run>
-                <Run label='WEBGL_VERSION' result={`${
-                  tf.env().getNumber('WEBGL_VERSION')}`}>
-                </Run>
-                <Run label='WEBGL_RENDER_FLOAT32_ENABLED' result={`${
-                  tf.env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')}`}>
-                </Run>
-                <Run label='WEBGL_DOWNLOAD_FLOAT_ENABLED' result={`${
-                  tf.env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')}`}>
-                </Run>
-                <Run label='WEBGL_BUFFER_SUPPORTED' result={`${
-                  tf.env().getNumber('WEBGL_BUFFER_SUPPORTED')}`}>
-                </Run>
-                <Run label='new Float32Array([2,3, NaN])' result={`${
-                  new Float32Array([2, 3, NaN])
-                  }`}>
-                </Run>
-                <Run label='SimpleOp tf.square(3)'
-                  getRunner={simpleOpRunner} numRuns={1}></Run>
-                <Run label='tf.scalar(2.4).square()'
-                  getRunner={precisionTestRunner} numRuns={1}></Run>
-                <Run label='mobilenet'
-                  getRunner={mobilenetRunner} numRuns={1}></Run>
-                <Run label='bundleStorageIO'
-                  getRunner={localModelRunner} numRuns={1}></Run>
-                <Run label='bundleStorageIO - graph model'
-                  getRunner={localGraphModelRunner} numRuns={1}></Run>
-                <Run label='bundleStorageIO - sharded graph model'
-                  getRunner={localShardedGraphModelRunner} numRuns={1}></Run>
-                <Run label='train model'
-                  getRunner={trainModelRunner} numRuns={1}></Run>
-                <Run label='asyncStorareIO'
-                  getRunner={saveModelRunner} numRuns={1}></Run>
-              </View>
-            </View>
-          </ScrollView>
-        </SafeAreaView>
-      </Fragment>
-    );
-  }
-}
-
-const styles = StyleSheet.create({
-  scrollView: {
-    backgroundColor: 'white',
-  },
-  body: {
-    backgroundColor: 'white',
-    marginBottom: 20,
-  },
-  sectionContainer: {
-    marginTop: 32,
-    paddingHorizontal: 24,
-  },
-  sectionTitle: {
-    fontSize: 24,
-    fontWeight: '600',
-    color: 'black',
-    marginBottom: 6,
-  },
-});
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/components/ml.ts b/tfjs-master/tfjs-react-native/integration_rn59/components/ml.ts
deleted file mode 100644
index 7639493fb..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/components/ml.ts
+++ /dev/null
@@ -1,156 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as mobilenet from '@tensorflow-models/mobilenet';
-import * as tf from '@tensorflow/tfjs';
-import {asyncStorageIO, bundleResourceIO} from '@tensorflow/tfjs-react-native';
-
-// All functions (i.e. 'runners") in this file are async
-// functions that return a function that can be invoked to
-// do some ML operation.
-
-/**
- * A runner for a simple math op
- */
-export async function simpleOpRunner() {
-  return async () => {
-    const res = tf.square(3);
-    const data = (await res.data())[0];
-    return JSON.stringify(data);
-  };
-}
-
-/**
- * A runner that does a basic precision test.
- */
-export async function precisionTestRunner() {
-  return async () => {
-    const res = tf.tidy(() => tf.scalar(2.4).square());
-    const data = (await res.data())[0];
-    return JSON.stringify(data);
-  };
-}
-
-/**
- * A runner that does a mobilenet prediction
- */
-export async function mobilenetRunner() {
-  const model = await mobilenet.load();
-  // warmup
-  const input: tf.Tensor3D = tf.zeros([224, 224, 3]);
-  await model.classify(input);
-
-  return async () => {
-    const pred = await model.classify(input);
-    return JSON.stringify(pred);
-  };
-}
-
-/**
- * A runner that loads a model bundled with the app and runs a prediction
- * through it.
- */
-const modelJson = require('../assets/model/bundle_model_test.json');
-const modelWeights = require('../assets/model/bundle_model_test_weights.bin');
-export async function localModelRunner() {
-  const model =
-      await tf.loadLayersModel(bundleResourceIO(modelJson, modelWeights));
-
-  return async () => {
-    const res = model.predict(tf.randomNormal([1, 10])) as tf.Tensor;
-    const data = await res.data();
-    return JSON.stringify(data);
-  };
-}
-
-/**
- * A runner that loads a model bundled with the app and runs a prediction
- * through it.
- */
-const modelJson2 = require('../assets/graph_model/model.json');
-const modelWeights2 = require('../assets/graph_model/group1-shard1of1.bin');
-export async function localGraphModelRunner() {
-  const model =
-      await tf.loadGraphModel(bundleResourceIO(modelJson2, modelWeights2));
-  return async () => {
-    const res = model.predict(tf.randomNormal([1, 10])) as tf.Tensor;
-    const data = await res.data();
-    return JSON.stringify(data);
-  };
-}
-
-/**
- * A runner that loads a sharded model bundled with the app and runs a
- * prediction through it.
- */
-const shardedModelJson = require('../assets/sharded_model/model.json');
-const shardedModelWeights1: number =
-    require('../assets/sharded_model/group1-shard1of2.bin');
-const shardedModelWeights2: number =
-    require('../assets/sharded_model/group1-shard2of2.bin');
-
-export async function localShardedGraphModelRunner() {
-  const model = await tf.loadGraphModel(bundleResourceIO(
-      shardedModelJson, [shardedModelWeights1, shardedModelWeights2]));
-
-  return async () => {
-    const input = tf.zeros([1, 224, 224, 3]);
-    const res = model.predict(input) as tf.Tensor;
-    const data = await res.data();
-    return JSON.stringify({predictionsLength: data.length});
-  };
-}
-
-/**
- * A runner that traines a model.
- */
-export async function trainModelRunner() {
-  // Define a model for linear regression.
-  const model = tf.sequential();
-  model.add(tf.layers.dense({units: 5, inputShape: [1]}));
-  model.add(tf.layers.dense({units: 1}));
-  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-  // Generate some synthetic data for training.
-  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
-  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
-
-  return async () => {
-    // Train the model using the data.
-    await model.fit(xs, ys, {epochs: 20});
-
-    return 'done';
-  };
-}
-
-/**
- * A runner that saves and loads a model to/from asyncStorage
- */
-export async function saveModelRunner() {
-  // Define a model for linear regression.
-  const model = tf.sequential();
-  model.add(tf.layers.dense({units: 5, inputShape: [1]}));
-  model.add(tf.layers.dense({units: 1}));
-  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-  return async () => {
-    await model.save(asyncStorageIO('custom-model-test'));
-    await tf.loadLayersModel(asyncStorageIO('custom-model-test'));
-
-    return 'done';
-  };
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/components/mobilenet_demo.tsx b/tfjs-master/tfjs-react-native/integration_rn59/components/mobilenet_demo.tsx
deleted file mode 100644
index 173f38836..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/components/mobilenet_demo.tsx
+++ /dev/null
@@ -1,213 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import React, { Fragment } from 'react';
-import { Button, SafeAreaView, StyleSheet, ScrollView, View, StatusBar, Image, Text, ImageSourcePropType } from 'react-native';
-
-import * as tf from '@tensorflow/tfjs';
-import { fetch } from '@tensorflow/tfjs-react-native';
-import * as mobilenet from '@tensorflow-models/mobilenet';
-import * as jpeg from 'jpeg-js';
-
-import { Run } from './run';
-
-interface ScreenProps {
-  returnToMain: () => void;
-  image: ImageSourcePropType;
-}
-
-interface ScreenState {
-  prediction: Array<{ className: string; probability: number }>;
-  predictionTime?: number;
-  imageChecksum?: number;
-}
-
-export class MobilenetDemo extends React.Component<
-  ScreenProps,
-  ScreenState
-  > {
-  constructor(props: ScreenProps) {
-    super(props);
-    this.state = {
-      prediction: []
-    };
-  }
-
-  async componentDidMount() {
-    // Load mobilenet
-    const model = await mobilenet.load();
-
-    //warmup mobilenet
-    await model.classify(tf.zeros([1, 224, 224, 3]));
-
-    // Read the image into a tensor
-    const imageAssetPath = Image.resolveAssetSource(this.props.image);
-    const response = await fetch(imageAssetPath.uri, {}, { isBinary: true });
-    const rawImageData = await response.arrayBuffer();
-    const imageTensor = this.imageToTensor(rawImageData);
-
-    // Compute a checksum for the image. Useful for debugging.
-    const imageTensorSum = imageTensor.sum();
-    const imageChecksum = (await imageTensorSum.data())[0];
-
-    // Classify the image.
-    const start = Date.now();
-    const prediction = await model.classify(imageTensor);
-
-    const end = Date.now();
-
-    this.setState({
-      prediction,
-      predictionTime: end - start,
-      imageChecksum
-    });
-    tf.dispose([imageTensor, imageTensorSum]);
-  }
-
-  imageToTensor(rawImageData: ArrayBuffer): tf.Tensor3D {
-    const TO_UINT8ARRAY = true;
-    const { width, height, data } = jpeg.decode(rawImageData, TO_UINT8ARRAY);
-    // Drop the alpha channel info for mobilenet
-    const buffer = new Uint8Array(width * height * 3);
-    let offset = 0; // offset into original data
-    for (let i = 0; i < buffer.length; i += 3) {
-      buffer[i] = data[offset];
-      buffer[i + 1] = data[offset + 1];
-      buffer[i + 2] = data[offset + 2];
-
-      offset += 4;
-    }
-
-    return tf.tensor3d(buffer, [height, width, 3]);
-  }
-
-  renderPrediction() {
-    const { prediction, predictionTime, imageChecksum } = this.state;
-    return (
-      <View>
-        <Text style={styles.resultTextHeader}>Results</Text>
-        {prediction.map((pred, i) => {
-          return (
-            <View style={styles.prediction} key={i}>
-              <Text style={styles.resultClass}>{pred.className}</Text>
-              <Text style={styles.resultProb}>{pred.probability}</Text>
-            </View>
-          );
-        })}
-        <View style={styles.sectionContainer}>
-          <Run label='Prediction Time' result={`${predictionTime}`} />
-          <Run label='Checksum' result={`${imageChecksum}`} />
-        </View>
-      </View>
-    );
-  }
-
-  render() {
-    const { image } = this.props;
-    const { prediction } = this.state;
-
-    return (
-      <Fragment>
-        <StatusBar barStyle='dark-content' />
-        <SafeAreaView>
-          <ScrollView
-            contentInsetAdjustmentBehavior='automatic'
-            style={styles.scrollView}
-          >
-            <View style={styles.body}>
-              <View style={styles.sectionContainer}>
-                <Button onPress={this.props.returnToMain} title='Back' />
-              </View>
-
-              <View style={styles.sectionContainer}>
-                {/* Title Area */}
-                <View>
-                  <Text style={styles.sectionTitle}>
-                    Mobilenet Image Classification in React Native
-                  </Text>
-                </View>
-                {/* Image Area */}
-                <View style={styles.imageArea}>
-                  <Image style={{ width: 245, height: 166 }} source={image} />
-                </View>
-                {/* Result Area */}
-                <View style={styles.resultArea}>
-                  {prediction ? this.renderPrediction() : undefined}
-                </View>
-              </View>
-            </View>
-          </ScrollView>
-        </SafeAreaView>
-      </Fragment>
-    );
-  }
-}
-
-const styles = StyleSheet.create({
-  scrollView: {
-    backgroundColor: 'white'
-  },
-  body: {
-    backgroundColor: 'white',
-    marginBottom: 60
-  },
-  sectionContainer: {
-    marginTop: 32,
-    paddingHorizontal: 24
-  },
-  sectionTitle: {
-    fontSize: 24,
-    fontWeight: '600',
-    color: 'black',
-    marginBottom: 6
-  },
-  imageArea: {
-    marginTop: 12,
-    display: 'flex',
-    flexDirection: 'column',
-    justifyContent: 'center',
-    alignItems: 'center'
-  },
-  resultArea: {
-    marginLeft: 5,
-    paddingLeft: 5,
-    display: 'flex',
-    flexDirection: 'column',
-    justifyContent: 'center'
-  },
-  resultTextHeader: {
-    fontSize: 21,
-    fontWeight: 'bold',
-    marginBottom: 12,
-    marginTop: 12,
-    textAlign: 'center'
-  },
-  prediction: {
-    display: 'flex',
-    flexDirection: 'row',
-    justifyContent: 'center',
-    alignItems: 'center'
-  },
-  resultClass: {
-    fontSize: 16,
-    fontWeight: 'bold'
-  },
-  resultProb: {
-    fontSize: 16,
-    marginLeft: 5
-  }
-});
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/components/run.tsx b/tfjs-master/tfjs-react-native/integration_rn59/components/run.tsx
deleted file mode 100644
index 5aad3bf88..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/components/run.tsx
+++ /dev/null
@@ -1,148 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import React, { Component } from 'react';
-import { StyleSheet, Text, View, ViewStyle } from 'react-native';
-
-interface RunProps {
-  getRunner?: () => Promise<() => Promise<string>>;
-  numRuns?: number;
-  result?: string;
-  label: string;
-}
-
-interface RunState {
-  mountComplete: boolean;
-  computedResult?: string;
-  lastPredictionTime?: number;
-  avgPredictionTime?: number;
-}
-
-export class Run extends Component<RunProps, RunState> {
-  constructor(props: RunProps) {
-    super(props);
-    this.state = {
-      mountComplete: false,
-    };
-  }
-
-  /**
-   * If a getRunner function is present in props, execute it
-   * numRun times and report the result and timing info.
-   */
-  async componentDidMount() {
-    const { getRunner, numRuns } = this.props;
-    let computedResult;
-    let time;
-    const numberOfRuns = numRuns != null ? numRuns : 1;
-    if (getRunner != null) {
-      const runner = await getRunner();
-      const start = new Date();
-      for (let index = 0; index < numberOfRuns; index++) {
-        computedResult = await runner();
-      }
-      const end = new Date();
-      time = end.getMilliseconds() - start.getMilliseconds();
-    }
-    this.setState({
-      mountComplete: true,
-      computedResult,
-      lastPredictionTime: time ? time : undefined,
-      avgPredictionTime: time ? (time / numberOfRuns) : undefined,
-    });
-  }
-
-  render() {
-    const {
-      mountComplete,
-      computedResult,
-      lastPredictionTime,
-      avgPredictionTime,
-    } = this.state;
-
-    const { label, result, numRuns } = this.props;
-    const res = computedResult ? computedResult : result;
-    return (
-      <View style={mountComplete ? styles.containerMounted : styles.container}>
-        <View style={styles.labelArea}>
-          <Text style={styles.labelHeader}>Label</Text>
-          <Text style={styles.labelText}>{label}</Text>
-        </View>
-        <View style={styles.resultArea}>
-          <View style={styles.row}>
-            {lastPredictionTime != null ?
-              <Text style={styles.labelHeader}>
-                Total Time: {lastPredictionTime}
-              </Text>
-              : undefined}
-            {numRuns != null ?
-              <Text style={styles.labelHeader}>
-                Avg: {avgPredictionTime} ({numRuns})
-              </Text>
-              : undefined}
-          </View>
-          <Text style={styles.resultText}>{JSON.stringify(res)}</Text>
-        </View>
-      </View>);
-  }
-}
-
-const container: ViewStyle = {
-  display: 'flex',
-  flexDirection: 'row',
-  backgroundColor: '#FFFDE7',
-  padding: 5,
-  marginBottom: 5,
-};
-
-const containerMounted: ViewStyle = {
-  ...container,
-  backgroundColor: '#C8E6C9',
-};
-const styles = StyleSheet.create({
-  container,
-  containerMounted,
-  row: {
-    display: 'flex',
-    flexDirection: 'row',
-  },
-  labelArea: {
-    display: 'flex',
-    flexDirection: 'column',
-    justifyContent: 'center',
-    minWidth: 100,
-  },
-  labelHeader: {
-    fontSize: 10,
-    marginRight: 4,
-  },
-  labelText: {
-    fontSize: 12
-  },
-  resultArea: {
-    marginLeft: 5,
-    paddingLeft: 5,
-    borderLeftColor: '#444',
-    borderLeftWidth: 1,
-    display: 'flex',
-    flexDirection: 'column',
-    justifyContent: 'center',
-  },
-  resultText: {
-    fontSize: 14
-  }
-});
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/components/tfjs_unit_test_runner.tsx b/tfjs-master/tfjs-react-native/integration_rn59/components/tfjs_unit_test_runner.tsx
deleted file mode 100644
index c1f2792bc..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/components/tfjs_unit_test_runner.tsx
+++ /dev/null
@@ -1,310 +0,0 @@
-
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// @ts-ignore
-import jasmineRequire from 'jasmine-core/lib/jasmine-core/jasmine.js';
-// tslint:disable-next-line: no-imports-from-dist
-import * as jasmine_util from '@tensorflow/tfjs-core/dist/jasmine_util';
-import React, { Component, Fragment } from 'react';
-import { StyleSheet, Text, View, ViewStyle, ScrollView } from 'react-native';
-import * as tf from '@tensorflow/tfjs-core';
-
-interface TestRunnerProps {
-  backend: string;
-}
-
-interface FailedTestInfo {
-  suiteName?: string;
-  testName: string;
-  failedExpectations: Array<{ message: string, stack: string }>;
-  message?: string;
-}
-
-interface TestRunnerState {
-  passedTests: number;
-  failedTests: FailedTestInfo[];
-  totalTests: number;
-  testsComplete: boolean;
-  testsStarted: boolean;
-  backendName?: string;
-}
-
-export class TestRunner extends Component<TestRunnerProps, TestRunnerState> {
-  constructor(props: TestRunnerProps) {
-    super(props);
-    this.state = {
-      passedTests: 0,
-      failedTests: [],
-      totalTests: 0,
-      testsComplete: false,
-      testsStarted: false,
-    };
-  }
-
-  /**
-   * If a getRunner function is present in props, execute it
-   * numRun times and report the result and timing info.
-   */
-  async componentDidMount() {
-    const backendName = tf.getBackend();
-    this.setState({
-      backendName,
-    });
-
-    let passedTests = 0;
-    const failedTests: FailedTestInfo[] = [];
-
-    // A lot of the code below is adapted from
-    // node_modules/jasmine-core/lib/jasmine-core/boot.js
-    // it provides a custom way to start jasmine in the RN app.
-
-    // Helper function for adding jasmine functionlaity to global.
-    // tslint:disable-next-line: no-any
-    function extend(destination: any, source: any) {
-      for (const property in source) {
-        destination[property] = source[property];
-      }
-      return destination;
-    }
-
-    const jasmine = jasmineRequire.core(jasmineRequire);
-    // @ts-ignore
-    global.jasmine = jasmine;
-    const env: jasmine.Env = jasmine.getEnv();
-
-    const jasmineInterface = jasmineRequire.interface(jasmine, env);
-    extend(global, jasmineInterface);
-
-    //@ts-ignore
-    env.configure({
-      random: false,
-      specFilter: (spec: jasmine.Spec) => {
-        const name = spec.getFullName();
-
-        if(name.match('Backend registration')) {
-          // These tests use spyOn on a module. Skip them until they
-          // can be updated. Coverage for this functionality is tested
-          // directly in this package.
-          return false;
-        }
-        if(name.match('isMobile')) {
-          // Browser specific mobile test
-          return false;
-        }
-
-        if(name.match('dilation2d')) {
-          // Not implemented in webgl
-          return false;
-        }
-        if (name.match('method otsu')) {
-          // Image threshold is broken for Otsu's method. #5245
-          return false;
-        }
-        return true;
-      }
-     });
-
-    // Custom reporter to collect the test results
-    const reactReporter: jasmine.CustomReporter = {
-      jasmineStarted: suiteInfo => {
-        // The console.warn below seems necessary in order for the spy on
-        // console.warn defined in one of the tests to run corrently.
-        console.warn('starting tests');
-        //@ts-ignore
-        console.reportErrorsAsExceptions = false;
-        this.setState({
-          testsStarted: true,
-          totalTests: suiteInfo.totalSpecsDefined,
-        });
-      },
-      specDone: result => {
-        if (result.failedExpectations == null ||
-          result.failedExpectations.length === 0) {
-          passedTests += 1;
-          this.setState({
-            passedTests,
-          });
-        }
-        else if (result.failedExpectations.length > 0) {
-          const failureInfo: FailedTestInfo = {
-            testName: result.fullName,
-            failedExpectations: result.failedExpectations.map(f => {
-
-              return {
-                message: f.message,
-                stack: f.stack,
-              };
-            }),
-          };
-          // Log to console to make it easier to view these in dev tools.
-          console.log('Test Failure');
-          console.log(JSON.stringify(failureInfo, null, 2));
-          failedTests.push(failureInfo);
-          this.setState({
-            failedTests,
-          });
-        }
-      },
-      jasmineDone: () => {
-        //@ts-ignore
-        console.reportErrorsAsExceptions = true;
-        this.setState({
-          testsComplete: true,
-          failedTests,
-        });
-      }
-    };
-    env.addReporter(reactReporter);
-
-    jasmine_util.setTestEnvs(
-      [{
-        name: 'test-rn',
-        backendName: this.props.backend,
-        flags: {
-          'WEBGL_CPU_FORWARD': false, 'WEBGL_SIZE_UPLOAD_UNIFORM': 0,
-        }
-      }]);
-
-    // import tests
-    require('@tensorflow/tfjs-core/dist/tests');
-
-    // import tfjs-react-native-tests;
-    require('@tensorflow/tfjs-react-native/dist/tests');
-
-    // Start the test runner
-    env.execute();
-  }
-
-  renderTestFailure(failure: FailedTestInfo, key: number) {
-    return <View style={styles.failedTest} key={key}>
-      <Text style={styles.failedTestName}>
-        {failure.testName}
-      </Text>
-      <Fragment>
-        {failure.failedExpectations.map((expecation, i) => <Text key={i}>
-          {expecation.message}
-        </Text>)}
-      </Fragment>
-    </View>;
-  }
-
-  formatTestFailuresForWebDriver(failures: FailedTestInfo[]) {
-    const TEST_SEPARATOR = '###';
-    const TITLE_SEPARATOR = '%%%';
-    return failures.map((failure, i) => {
-      const testName = failure.testName;
-      const messages = failure.failedExpectations.map((expecation, i) =>
-        expecation.message).join('\n');
-
-      return `${testName}${TITLE_SEPARATOR}${messages}`;
-    }).join(TEST_SEPARATOR);
-  }
-
-  render() {
-    const { passedTests, failedTests, totalTests, backendName, testsComplete }
-      = this.state;
-
-    return (
-      <Fragment>
-        <View testID='testInfo'
-          accessibilityLabel='testInfo'
-          style={styles.sectionContainer}>
-          <Text style={styles.sectionTitle}>Info</Text>
-          <Text testID='backendName' accessibilityLabel='backendName'>
-            backend={backendName}
-          </Text>
-          <Text testID='testComplete' accessibilityLabel='testComplete'>
-            testsComplete={String(testsComplete)}
-          </Text>
-          <Text>tf.env().platformName={tf.env().platformName}</Text>
-        </View>
-        <View style={styles.sectionContainer}>
-          <Text style={styles.sectionTitle}
-            testID='passedTests'
-            accessibilityLabel='passedTests'>
-            Passed Tests {passedTests} of {totalTests}
-          </Text>
-        </View>
-        <View>
-          <Text style={styles.failureMessages}
-            testID='failureMessages'
-            accessibilityLabel='failureMessages'>
-            {this.formatTestFailuresForWebDriver(failedTests)}
-          </Text>
-        </View>
-        <ScrollView contentInsetAdjustmentBehavior='automatic'>
-          <View style={styles.sectionContainer}>
-            <Text style={styles.sectionTitle}>
-              Failed Tests ({failedTests.length})
-            </Text>
-            <View
-              testID='failedTests'
-              accessibilityLabel='failedTests'>
-              {failedTests.map((f, i) => this.renderTestFailure(f, i))}
-            </View>
-          </View>
-        </ScrollView>
-      </Fragment>
-    );
-  }
-}
-
-const container: ViewStyle = {
-  display: 'flex',
-  flexDirection: 'row',
-  backgroundColor: '#FFFDE7',
-  padding: 5,
-  marginBottom: 5,
-};
-
-const containerMounted: ViewStyle = {
-  ...container,
-  backgroundColor: '#C8E6C9',
-};
-
-const styles = StyleSheet.create({
-  container,
-  containerMounted,
-  failedTest: {
-    backgroundColor: 'white',
-    margin: 5,
-    paddingBottom: 10,
-    borderBottomWidth: 1,
-    borderBottomColor: '#ccc',
-  },
-  failedTestName: {
-    backgroundColor: 'white',
-    fontWeight: 'bold',
-  },
-  failureMessages: {
-    fontSize: 1,
-  },
-  sectionContainer: {
-    marginTop: 24,
-    paddingHorizontal: 12,
-    paddingBottom: 4,
-    borderBottomWidth: 1,
-    borderBottomColor: '#ccc',
-  },
-  sectionTitle: {
-    fontSize: 14,
-    fontWeight: '600',
-    color: 'black',
-    marginBottom: 6,
-  },
-});
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/image_utils.ts b/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/image_utils.ts
deleted file mode 100644
index 2b862680c..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/image_utils.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import * as ImageManipulator from 'expo-image-manipulator';
-import * as jpeg from 'jpeg-js';
-
-export function toDataUri(base64: string): string {
-  return `data:image/jpeg;base64,${base64}`;
-}
-
-export async function resizeImage(
-    imageUrl: string, width: number): Promise<ImageManipulator.ImageResult> {
-  const actions = [{
-    resize: {
-      width,
-    },
-  }];
-  const saveOptions = {
-    compress: 0.75,
-    format: ImageManipulator.SaveFormat.JPEG,
-    base64: true,
-  };
-  const res =
-      await ImageManipulator.manipulateAsync(imageUrl, actions, saveOptions);
-  return res;
-}
-
-export async function base64ImageToTensor(base64: string):
-    Promise<tf.Tensor3D> {
-  const rawImageData = tf.util.encodeString(base64, 'base64');
-  const TO_UINT8ARRAY = true;
-  const {width, height, data} = jpeg.decode(rawImageData, TO_UINT8ARRAY);
-  // Drop the alpha channel info
-  const buffer = new Uint8Array(width * height * 3);
-  let offset = 0;  // offset into original data
-  for (let i = 0; i < buffer.length; i += 3) {
-    buffer[i] = data[offset];
-    buffer[i + 1] = data[offset + 1];
-    buffer[i + 2] = data[offset + 2];
-
-    offset += 4;
-  }
-  return tf.tensor3d(buffer, [height, width, 3]);
-}
-
-export async function tensorToImageUrl(imageTensor: tf.Tensor3D):
-    Promise<string> {
-  const [height, width] = imageTensor.shape;
-  const buffer = await imageTensor.toInt().data();
-  const frameData = new Uint8Array(width * height * 4);
-
-  let offset = 0;
-  for (let i = 0; i < frameData.length; i += 4) {
-    frameData[i] = buffer[offset];
-    frameData[i + 1] = buffer[offset + 1];
-    frameData[i + 2] = buffer[offset + 2];
-    frameData[i + 3] = 0xFF;
-
-    offset += 3;
-  }
-
-  const rawImageData = {
-    data: frameData,
-    width,
-    height,
-  };
-  const jpegImageData = jpeg.encode(rawImageData, 75);
-  const base64Encoding = tf.util.decodeString(jpegImageData.data, 'base64');
-  return base64Encoding;
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/realtime_demo.tsx b/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/realtime_demo.tsx
deleted file mode 100644
index ebfc43dd9..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/realtime_demo.tsx
+++ /dev/null
@@ -1,325 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import React from 'react';
-import {ActivityIndicator, Button, StyleSheet, View, Platform } from 'react-native';
-import Svg, { Circle, Rect, G, Line} from 'react-native-svg';
-
-import * as Permissions from 'expo-permissions';
-import { Camera } from 'expo-camera';
-import { ExpoWebGLRenderingContext } from 'expo-gl';
-
-import * as tf from '@tensorflow/tfjs';
-import * as blazeface from '@tensorflow-models/blazeface';
-import * as posenet from '@tensorflow-models/posenet';
-import {cameraWithTensors} from '@tensorflow/tfjs-react-native';
-
-interface ScreenProps {
-  returnToMain: () => void;
-}
-
-interface ScreenState {
-  hasCameraPermission?: boolean;
-  // tslint:disable-next-line: no-any
-  cameraType: any;
-  isLoading: boolean;
-  posenetModel?: posenet.PoseNet;
-  pose?: posenet.Pose;
-  // tslint:disable-next-line: no-any
-  faceDetector?: any;
-  faces?: blazeface.NormalizedFace[];
-  modelName: string;
-}
-
-const inputTensorWidth = 152;
-const inputTensorHeight = 200;
-
-const AUTORENDER = true;
-
-// tslint:disable-next-line: variable-name
-const TensorCamera = cameraWithTensors(Camera);
-
-export class RealtimeDemo extends React.Component<ScreenProps,ScreenState> {
-  rafID?: number;
-
-  constructor(props: ScreenProps) {
-    super(props);
-    this.state = {
-      isLoading: true,
-      cameraType: Camera.Constants.Type.front,
-      modelName: 'posenet',
-    };
-    this.handleImageTensorReady = this.handleImageTensorReady.bind(this);
-  }
-
-  async loadPosenetModel() {
-    const model =  await posenet.load({
-      architecture: 'MobileNetV1',
-      outputStride: 16,
-      inputResolution: { width: inputTensorWidth, height: inputTensorHeight },
-      multiplier: 0.75,
-      quantBytes: 2
-    });
-    return model;
-  }
-
-  async loadBlazefaceModel() {
-    const model =  await blazeface.load();
-    return model;
-  }
-
-  async handleImageTensorReady(
-    images: IterableIterator<tf.Tensor3D>,
-    updatePreview: () => void, gl: ExpoWebGLRenderingContext) {
-    const loop = async () => {
-      const {modelName} = this.state;
-      if(!AUTORENDER) {
-        updatePreview();
-      }
-
-      if(modelName === 'posenet') {
-        if (this.state.posenetModel != null) {
-          const imageTensor = images.next().value;
-          const flipHorizontal = Platform.OS === 'ios' ? false : true;
-          const pose = await this.state.posenetModel.estimateSinglePose(
-            imageTensor, { flipHorizontal });
-          this.setState({pose});
-          tf.dispose([imageTensor]);
-        }
-      } else {
-        if (this.state.faceDetector != null) {
-          const imageTensor = images.next().value;
-          const returnTensors = false;
-          const faces = await this.state.faceDetector.estimateFaces(
-            imageTensor, returnTensors);
-
-          this.setState({faces});
-          tf.dispose(imageTensor);
-        }
-      }
-
-      if(!AUTORENDER) {
-        gl.endFrameEXP();
-      }
-      this.rafID = requestAnimationFrame(loop);
-    };
-
-    loop();
-  }
-
-  componentWillUnmount() {
-    if(this.rafID) {
-      cancelAnimationFrame(this.rafID);
-    }
-  }
-
-  async componentDidMount() {
-    const { status } = await Permissions.askAsync(Permissions.CAMERA);
-
-    const [blazefaceModel, posenetModel] =
-      await Promise.all([this.loadBlazefaceModel(), this.loadPosenetModel()]);
-
-    this.setState({
-      hasCameraPermission: status === 'granted',
-      isLoading: false,
-      faceDetector: blazefaceModel,
-      posenetModel,
-    });
-  }
-
-  renderPose() {
-    const MIN_KEYPOINT_SCORE = 0.2;
-    const {pose} = this.state;
-    if (pose != null) {
-      const keypoints = pose.keypoints
-        .filter(k => k.score > MIN_KEYPOINT_SCORE)
-        .map((k,i) => {
-          return <Circle
-            key={`skeletonkp_${i}`}
-            cx={k.position.x}
-            cy={k.position.y}
-            r='2'
-            strokeWidth='0'
-            fill='blue'
-          />;
-        });
-
-      const adjacentKeypoints =
-        posenet.getAdjacentKeyPoints(pose.keypoints, MIN_KEYPOINT_SCORE);
-
-      const skeleton = adjacentKeypoints.map(([from, to], i) => {
-        return <Line
-          key={`skeletonls_${i}`}
-          x1={from.position.x}
-          y1={from.position.y}
-          x2={to.position.x}
-          y2={to.position.y}
-          stroke='magenta'
-          strokeWidth='1'
-        />;
-      });
-
-      return <Svg height='100%' width='100%'
-        viewBox={`0 0 ${inputTensorWidth} ${inputTensorHeight}`}>
-          {skeleton}
-          {keypoints}
-        </Svg>;
-    } else {
-      return null;
-    }
-  }
-
-  renderFaces() {
-    const {faces} = this.state;
-    if(faces != null) {
-      const faceBoxes = faces.map((f, fIndex) => {
-        const topLeft = f.topLeft as number[];
-        const bottomRight = f.bottomRight as number[];
-
-        const landmarks = (f.landmarks as number[][]).map((l, lIndex) => {
-          return <Circle
-            key={`landmark_${fIndex}_${lIndex}`}
-            cx={l[0]}
-            cy={l[1]}
-            r='2'
-            strokeWidth='0'
-            fill='blue'
-            />;
-        });
-
-        return <G key={`facebox_${fIndex}`}>
-          <Rect
-            x={topLeft[0]}
-            y={topLeft[1]}
-            fill={'red'}
-            fillOpacity={0.2}
-            width={(bottomRight[0] - topLeft[0])}
-            height={(bottomRight[1] - topLeft[1])}
-          />
-          {landmarks}
-        </G>;
-      });
-
-      const flipHorizontal = Platform.OS === 'ios' ? 1 : -1;
-      return <Svg height='100%' width='100%'
-        viewBox={`0 0 ${inputTensorWidth} ${inputTensorHeight}`}
-        scaleX={flipHorizontal}>
-          {faceBoxes}
-        </Svg>;
-    } else {
-      return null;
-    }
-  }
-
-  render() {
-    const {isLoading, modelName} = this.state;
-
-    // TODO File issue to be able get this from expo.
-    // Caller will still need to account for orientation/phone rotation changes
-    let textureDims: { width: number; height: number; };
-    if (Platform.OS === 'ios') {
-        textureDims = {
-          height: 1920,
-          width: 1080,
-        };
-      } else {
-        textureDims = {
-          height: 1200,
-          width: 1600,
-        };
-      }
-
-    const camView = <View style={styles.cameraContainer}>
-      <TensorCamera
-        // Standard Camera props
-        style={styles.camera}
-        type={this.state.cameraType}
-        zoom={0}
-        // tensor related props
-        cameraTextureHeight={textureDims.height}
-        cameraTextureWidth={textureDims.width}
-        resizeHeight={inputTensorHeight}
-        resizeWidth={inputTensorWidth}
-        resizeDepth={3}
-        onReady={this.handleImageTensorReady}
-        autorender={AUTORENDER}
-      />
-      <View style={styles.modelResults}>
-        {modelName === 'posenet' ? this.renderPose() : this.renderFaces()}
-      </View>
-    </View>;
-
-    return (
-      <View style={{width:'100%'}}>
-        <View style={styles.sectionContainer}>
-          <Button
-            onPress={this.props.returnToMain}
-            title='Back'
-          />
-        </View>
-        {isLoading ? <View style={[styles.loadingIndicator]}>
-          <ActivityIndicator size='large' color='#FF0266' />
-        </View> : camView}
-      </View>
-    );
-  }
-
-}
-
-const styles = StyleSheet.create({
-  loadingIndicator: {
-    position: 'absolute',
-    top: 20,
-    right: 20,
-    zIndex: 200,
-  },
-  sectionContainer: {
-    marginTop: 32,
-    paddingHorizontal: 24,
-  },
-  cameraContainer: {
-    display: 'flex',
-    flexDirection: 'column',
-    justifyContent: 'center',
-    alignItems: 'center',
-    width: '100%',
-    height: '100%',
-    backgroundColor: '#fff',
-  },
-  camera : {
-    position:'absolute',
-    left: 50,
-    top: 100,
-    width: 600/2,
-    height: 800/2,
-    zIndex: 1,
-    borderWidth: 1,
-    borderColor: 'black',
-    borderRadius: 0,
-  },
-  modelResults: {
-    position:'absolute',
-    left: 50,
-    top: 100,
-    width: 600/2,
-    height: 800/2,
-    zIndex: 20,
-    borderWidth: 1,
-    borderColor: 'black',
-    borderRadius: 0,
-  }
-});
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/style_transfer.ts b/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/style_transfer.ts
deleted file mode 100644
index a8444f3f7..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/style_transfer.ts
+++ /dev/null
@@ -1,105 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-
-const STYLENET_URL =
-    'https://cdn.jsdelivr.net/gh/reiinakano/arbitrary-image-stylization-tfjs@master/saved_model_style_js/model.json';
-const TRANSFORMNET_URL =
-    'https://cdn.jsdelivr.net/gh/reiinakano/arbitrary-image-stylization-tfjs@master/saved_model_transformer_separable_js/model.json';
-
-export class StyleTranfer {
-  private styleNet?: tf.GraphModel;
-  private transformNet?: tf.GraphModel;
-
-  constructor() {}
-
-  async init() {
-    await Promise.all([this.loadStyleModel(), this.loadTransformerModel()]);
-    await this.warmup();
-  }
-
-  async loadStyleModel() {
-    if (this.styleNet == null) {
-      this.styleNet = await tf.loadGraphModel(STYLENET_URL);
-      console.log('stylenet loaded');
-    }
-  }
-
-  async loadTransformerModel() {
-    if (this.transformNet == null) {
-      this.transformNet = await tf.loadGraphModel(TRANSFORMNET_URL);
-      console.log('transformnet loaded');
-    }
-  }
-
-  async warmup() {
-    // Also warmup
-    const input: tf.Tensor3D = tf.randomNormal([320, 240, 3]);
-    const res = this.stylize(input, input);
-    await res.data();
-    tf.dispose([input, res]);
-  }
-
-  /**
-   * This function returns style bottleneck features for
-   * the given image.
-   *
-   * @param style Style image to get 100D bottleneck features for
-   */
-  private predictStyleParameters(styleImage: tf.Tensor3D): tf.Tensor4D {
-    return tf.tidy(() => {
-      if (this.styleNet == null) {
-        throw new Error('Stylenet not loaded');
-      }
-      return this.styleNet.predict(
-          styleImage.toFloat().div(tf.scalar(255)).expandDims());
-    }) as tf.Tensor4D;
-  }
-
-  /**
-   * This function stylizes the content image given the bottleneck
-   * features. It returns a tf.Tensor3D containing the stylized image.
-   *
-   * @param content Content image to stylize
-   * @param bottleneck Bottleneck features for the style to use
-   */
-  private produceStylized(contentImage: tf.Tensor3D, bottleneck: tf.Tensor4D):
-      tf.Tensor3D {
-    return tf.tidy(() => {
-      if (this.transformNet == null) {
-        throw new Error('Transformnet not loaded');
-      }
-      const input = contentImage.toFloat().div(tf.scalar(255)).expandDims();
-      const image: tf.Tensor4D =
-          this.transformNet.predict([input, bottleneck]) as tf.Tensor4D;
-      return image.mul(255).squeeze();
-    });
-  }
-
-  public stylize(styleImage: tf.Tensor3D, contentImage: tf.Tensor3D):
-      tf.Tensor3D {
-    const start = Date.now();
-    console.log(styleImage.shape, contentImage.shape);
-    const styleRepresentation = this.predictStyleParameters(styleImage);
-    const stylized = this.produceStylized(contentImage, styleRepresentation);
-    tf.dispose([styleRepresentation]);
-    const end = Date.now();
-    console.log('stylization scheduled', end - start);
-    return stylized;
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/webcam_demo.tsx b/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/webcam_demo.tsx
deleted file mode 100644
index f32ec265b..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/components/webcam/webcam_demo.tsx
+++ /dev/null
@@ -1,416 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import React from 'react';
-import { ActivityIndicator, StyleSheet, View, Image, Text, TouchableHighlight } from 'react-native';
-import * as Permissions from 'expo-permissions';
-import { Camera } from 'expo-camera';
-import {StyleTranfer} from './style_transfer';
-import {base64ImageToTensor, tensorToImageUrl, resizeImage, toDataUri} from './image_utils';
-import * as tf from '@tensorflow/tfjs';
-
-interface ScreenProps {
-  returnToMain: () => void;
-}
-
-interface ScreenState {
-  mode: 'results' | 'newStyleImage' | 'newContentImage';
-  resultImage?: string;
-  styleImage?: string;
-  contentImage?: string;
-  hasCameraPermission?: boolean;
-  // tslint:disable-next-line: no-any
-  cameraType: any;
-  isLoading: boolean;
-}
-
-export class WebcamDemo extends React.Component<ScreenProps,ScreenState> {
-  private camera?: Camera|null;
-  private styler: StyleTranfer;
-
-  constructor(props: ScreenProps) {
-    super(props);
-    this.state = {
-      mode: 'results',
-      cameraType: Camera.Constants.Type.back,
-      isLoading: true,
-    };
-    this.styler = new StyleTranfer();
-  }
-
-  async componentDidMount() {
-    await this.styler.init();
-    const { status } = await Permissions.askAsync(Permissions.CAMERA);
-    this.setState({
-      hasCameraPermission: status === 'granted',
-      isLoading: false
-    });
-  }
-
-  showResults() {
-    this.setState({ mode: 'results' });
-  }
-
-  takeStyleImage() {
-    this.setState({ mode: 'newStyleImage' });
-  }
-
-  takeContentImage() {
-    this.setState({ mode: 'newContentImage' });
-  }
-
-  flipCamera() {
-    const newState = this.state.cameraType === Camera.Constants.Type.back
-          ? Camera.Constants.Type.front
-          : Camera.Constants.Type.back;
-    this.setState({
-      cameraType: newState,
-    });
-  }
-
-  renderStyleImagePreview() {
-    const {styleImage} = this.state;
-    if(styleImage == null) {
-      return (
-        <View>
-          <Text style={styles.instructionText}>Style</Text>
-          <Text style={{fontSize: 48, paddingLeft: 0}}>💅🏽</Text>
-        </View>
-      );
-    } else {
-      return (
-        <View>
-          <Image
-            style={styles.imagePreview}
-            source={{uri: toDataUri(styleImage)}} />
-            <Text style={styles.centeredText}>Style</Text>
-        </View>
-      );
-    }
-  }
-
-  renderContentImagePreview() {
-    const {contentImage} = this.state;
-    if(contentImage == null) {
-      return (
-        <View>
-          <Text style={styles.instructionText}>Stuff</Text>
-          <Text style={{fontSize: 48, paddingLeft: 0}}>🖼️</Text>
-        </View>
-      );
-    } else {
-      return (
-        <View>
-          <Image
-            style={styles.imagePreview}
-            source={{uri: toDataUri(contentImage)}} />
-            <Text style={styles.centeredText}>Stuff</Text>
-        </View>
-      );
-    }
-  }
-
-  async stylize(contentImage: string, styleImage: string):
-    Promise<string> {
-    const contentTensor = await base64ImageToTensor(contentImage);
-    const styleTensor = await base64ImageToTensor(styleImage);
-    const stylizedResult = this.styler.stylize(
-      styleTensor, contentTensor);
-    const stylizedImage = await tensorToImageUrl(stylizedResult);
-    tf.dispose([contentTensor, styleTensor, stylizedResult]);
-    return stylizedImage;
-  }
-
-  async handleCameraCapture() {
-    const {mode} = this.state;
-    let {styleImage, contentImage, resultImage} = this.state;
-    this.setState({
-      isLoading: true,
-    });
-    let image = await this.camera!.takePictureAsync({
-      skipProcessing: true,
-    });
-    image = await resizeImage(image.uri, 240);
-
-    if(mode === 'newStyleImage' && image.base64 != null) {
-      styleImage = image.base64;
-      if(contentImage == null) {
-        this.setState({
-          styleImage,
-          mode: 'results',
-          isLoading: false,
-        });
-      } else {
-        resultImage = await this.stylize(contentImage, styleImage),
-        this.setState({
-          styleImage,
-          contentImage,
-          resultImage,
-          mode: 'results',
-          isLoading: false,
-        });
-      }
-    } else if (mode === 'newContentImage' && image.base64 != null) {
-      contentImage = image.base64;
-      if(styleImage == null) {
-        this.setState({
-          contentImage,
-          mode: 'results',
-          isLoading: false,
-        });
-      } else {
-        resultImage = await this.stylize(contentImage, styleImage);
-        this.setState({
-          contentImage,
-          styleImage,
-          resultImage,
-          mode: 'results',
-          isLoading: false,
-        });
-      }
-    }
-  }
-
-  renderCameraCapture() {
-    const {hasCameraPermission} = this.state;
-
-    if (hasCameraPermission === null) {
-      return <View />;
-    } else if (hasCameraPermission === false) {
-      return <Text>No access to camera</Text>;
-    }
-    return (
-      <View  style={styles.cameraContainer}>
-        <Camera
-          style={styles.camera}
-          type={this.state.cameraType}
-          ref={ref => { this.camera = ref; }}>
-        </Camera>
-        <View style={styles.cameraControls}>
-            <TouchableHighlight
-              style={styles.flipCameraBtn}
-              onPress={() => {this.flipCamera();}}
-              underlayColor='#FFDE03'>
-              <Text style={{fontSize: 16, color: 'white'}}>
-                FLIP
-              </Text>
-            </TouchableHighlight>
-            <TouchableHighlight
-              style={styles.takeImageBtn}
-              onPress={() => { this.handleCameraCapture(); }}
-              underlayColor='#FFDE03'>
-              <Text style={{fontSize: 16, color: 'white', fontWeight: 'bold'}}>
-                TAKE
-              </Text>
-            </TouchableHighlight>
-            <TouchableHighlight
-              style={styles.cancelBtn}
-              onPress={() => {this.showResults(); }}
-              underlayColor='#FFDE03'>
-              <Text style={{fontSize: 16, color: 'white'}}>
-                BACK
-              </Text>
-            </TouchableHighlight>
-          </View>
-        </View>
-    );
-  }
-
-  renderResults() {
-    const {resultImage} = this.state;
-    return (
-      <View>
-        <View style={styles.resultImageContainer}>
-          {resultImage == null ?
-            <Text style={styles.introText}>
-              Tap the squares below to add style and content
-              images and see the magic!
-            </Text>
-            :
-            <Image
-              style={styles.resultImage}
-              resizeMode='contain'
-              source={{uri: toDataUri(resultImage)}} />
-          }
-          <TouchableHighlight
-            style={styles.styleImageContainer}
-            onPress={() => this.takeStyleImage()}
-            underlayColor='white'>
-              {this.renderStyleImagePreview()}
-          </TouchableHighlight>
-
-          <TouchableHighlight
-            style={styles.contentImageContainer}
-            onPress={() => this.takeContentImage()}
-            underlayColor='white'>
-            {this.renderContentImagePreview()}
-          </TouchableHighlight>
-
-        </View>
-      </View>
-    );
-  }
-
-  render() {
-    const {mode, isLoading} = this.state;
-    return (
-      <View style={{width:'100%'}}>
-        {isLoading ? <View style={[styles.loadingIndicator]}>
-          <ActivityIndicator size='large' color='#FF0266' />
-        </View> : null}
-        {mode === 'results' ?
-              this.renderResults() : this.renderCameraCapture()}
-      </View>
-    );
-  }
-}
-
-const styles = StyleSheet.create({
-  sectionContainer: {
-    marginTop: 32,
-    paddingHorizontal: 24
-  },
-  centeredText: {
-    textAlign: 'center',
-    fontSize: 14,
-  },
-  sectionTitle: {
-    fontSize: 24,
-    fontWeight: '600',
-    color: 'black',
-    marginBottom: 6
-  },
-  loadingIndicator: {
-    position: 'absolute',
-    top: 20,
-    right: 20,
-    // flexDirection: 'row',
-    // justifyContent: 'flex-end',
-    zIndex: 200,
-    // width: '100%'
-  },
-  cameraContainer: {
-    display: 'flex',
-    justifyContent: 'center',
-    alignItems: 'center',
-    width: '100%',
-    height: '100%',
-    backgroundColor: '#fff',
-  },
-  camera : {
-    display: 'flex',
-    width: '92%',
-    height: '64%',
-    backgroundColor: '#f0F',
-    zIndex: 1,
-    borderWidth: 20,
-    borderRadius: 40,
-    borderColor: '#f0f',
-  },
-  cameraControls: {
-    display: 'flex',
-    flexDirection: 'row',
-    width: '92%',
-    justifyContent: 'space-between',
-    marginTop: 40,
-    zIndex: 100,
-    backgroundColor: 'transparent',
-  },
-  flipCameraBtn: {
-    backgroundColor: '#424242',
-    width: 75,
-    height: 75,
-    borderRadius:16,
-    display: 'flex',
-    flexDirection: 'row',
-    justifyContent: 'center',
-    alignItems: 'center',
-  },
-  takeImageBtn: {
-    backgroundColor: '#FF0266',
-    width: 75,
-    height: 75,
-    borderRadius:50,
-    display: 'flex',
-    flexDirection: 'row',
-    justifyContent: 'center',
-    alignItems: 'center',
-  },
-  cancelBtn: {
-    backgroundColor: '#424242',
-    width: 75,
-    height: 75,
-    borderRadius:4,
-    display: 'flex',
-    flexDirection: 'row',
-    justifyContent: 'center',
-    alignItems: 'center',
-  },
-  resultImageContainer : {
-    width: '100%',
-    height: '100%',
-    padding:5,
-    margin:0,
-    backgroundColor: '#fff',
-    zIndex: 1,
-  },
-  resultImage: {
-    width: '98%',
-    height: '98%',
-  },
-  styleImageContainer: {
-    position:'absolute',
-    width: 80,
-    height: 150,
-    bottom: 30,
-    left: 20,
-    zIndex: 10,
-    borderRadius:10,
-    backgroundColor: 'rgba(176, 222, 255, 0.5)',
-    borderWidth: 1,
-    borderColor: 'rgba(176, 222, 255, 0.7)',
-  },
-  contentImageContainer: {
-    position:'absolute',
-    width: 80,
-    height: 150,
-    bottom:30,
-    right: 20,
-    zIndex: 10,
-    borderRadius:10,
-    backgroundColor: 'rgba(255, 197, 161, 0.5)',
-    borderWidth: 1,
-    borderColor: 'rgba(255, 197, 161, 0.7)',
-  },
-  imagePreview: {
-    width: 78,
-    height: 148,
-    borderRadius:10,
-  },
-  instructionText: {
-    fontSize: 28,
-    fontWeight:'bold',
-    paddingLeft: 5
-  },
-  introText: {
-    fontSize: 52,
-    fontWeight:'bold',
-    padding: 20,
-    textAlign: 'left',
-  }
-
-});
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/index.js b/tfjs-master/tfjs-react-native/integration_rn59/index.js
deleted file mode 100644
index 5bde31f73..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/index.js
+++ /dev/null
@@ -1,22 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AppRegistry} from 'react-native';
-import {App} from './App';
-import {name as appName} from './app.json';
-
-AppRegistry.registerComponent(appName, () => App);
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/Podfile b/tfjs-master/tfjs-react-native/integration_rn59/ios/Podfile
deleted file mode 100644
index 8893a7b77..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/Podfile
+++ /dev/null
@@ -1,60 +0,0 @@
-# @license
-# Copyright 2019 Google LLC. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-#
-# Uncomment the next line to define a global platform for your project
-platform :ios, '10.0'
-
-require_relative '../node_modules/react-native-unimodules/cocoapods.rb'
-
-target 'integration_rn59' do
-
-  # Pods for integration_rn59
-  pod 'React', :path => '../node_modules/react-native', :subspecs => [
-    'Core',
-    'CxxBridge',
-    'DevSupport',
-    'RCTText',
-    'RCTNetwork',
-    'RCTWebSocket',
-    'RCTAnimation',
-    'RCTImage',
-  ]
-  pod 'yoga', :path => '../node_modules/react-native/ReactCommon/yoga'
-  pod 'DoubleConversion', :podspec => '../node_modules/react-native/third-party-podspecs/DoubleConversion.podspec'
-  pod 'glog', :podspec => '../node_modules/react-native/third-party-podspecs/glog.podspec'
-  pod 'Folly', :podspec => '../node_modules/react-native/third-party-podspecs/Folly.podspec'
-
-  use_unimodules!
-
-  pod 'RNCAsyncStorage', :path => '../node_modules/@react-native-async-storage/async-storage'
-
-  pod 'RNFS', :path => '../node_modules/react-native-fs'
-
-  pod 'RNSVG', :path => '../node_modules/react-native-svg'
-
-  target 'integration_rn59Tests' do
-    inherit! :search_paths
-    # Pods for testing
-  end
-
-end
-
-post_install do |installer|
-  installer.pods_project.targets.each do |target|
-    if target.name == "React"
-      target.remove_from_project
-    end
-  end
-end
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/Podfile.lock b/tfjs-master/tfjs-react-native/integration_rn59/ios/Podfile.lock
deleted file mode 100644
index 69cf3d8ed..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/Podfile.lock
+++ /dev/null
@@ -1,258 +0,0 @@
-PODS:
-  - boost-for-react-native (1.63.0)
-  - DoubleConversion (1.1.6)
-  - EXAppLoaderProvider (6.0.0)
-  - EXCamera (7.0.0):
-    - UMBarCodeScannerInterface
-    - UMCore
-    - UMFaceDetectorInterface
-    - UMFileSystemInterface
-    - UMImageLoaderInterface
-    - UMPermissionsInterface
-  - EXConstants (6.0.0):
-    - UMConstantsInterface
-    - UMCore
-  - EXFileSystem (6.0.2):
-    - UMCore
-    - UMFileSystemInterface
-  - EXGL (7.0.0):
-    - EXGL-CPP
-    - UMCameraInterface
-    - UMCore
-    - UMFileSystemInterface
-  - EXGL-CPP (7.0.0):
-    - EXGL-CPP/UEXGL (= 7.0.0)
-  - EXGL-CPP/UEXGL (7.0.0)
-  - EXImageManipulator (6.0.0):
-    - UMCore
-    - UMFileSystemInterface
-    - UMImageLoaderInterface
-  - EXPermissions (6.0.0):
-    - UMCore
-    - UMPermissionsInterface
-  - Folly (2018.10.22.00):
-    - boost-for-react-native
-    - DoubleConversion
-    - glog
-  - glog (0.3.5)
-  - React (0.59.10):
-    - React/Core (= 0.59.10)
-  - React/Core (0.59.10):
-    - yoga (= 0.59.10.React)
-  - React/CxxBridge (0.59.10):
-    - Folly (= 2018.10.22.00)
-    - React/Core
-    - React/cxxreact
-    - React/jsiexecutor
-  - React/cxxreact (0.59.10):
-    - boost-for-react-native (= 1.63.0)
-    - DoubleConversion
-    - Folly (= 2018.10.22.00)
-    - glog
-    - React/jsinspector
-  - React/DevSupport (0.59.10):
-    - React/Core
-    - React/RCTWebSocket
-  - React/fishhook (0.59.10)
-  - React/jsi (0.59.10):
-    - DoubleConversion
-    - Folly (= 2018.10.22.00)
-    - glog
-  - React/jsiexecutor (0.59.10):
-    - DoubleConversion
-    - Folly (= 2018.10.22.00)
-    - glog
-    - React/cxxreact
-    - React/jsi
-  - React/jsinspector (0.59.10)
-  - React/RCTAnimation (0.59.10):
-    - React/Core
-  - React/RCTBlob (0.59.10):
-    - React/Core
-  - React/RCTImage (0.59.10):
-    - React/Core
-    - React/RCTNetwork
-  - React/RCTNetwork (0.59.10):
-    - React/Core
-  - React/RCTText (0.59.10):
-    - React/Core
-  - React/RCTWebSocket (0.59.10):
-    - React/Core
-    - React/fishhook
-    - React/RCTBlob
-  - RNCAsyncStorage (1.6.1):
-    - React
-  - RNFS (2.13.2):
-    - React
-  - RNSVG (9.13.6):
-    - React
-  - UMBarCodeScannerInterface (3.0.0)
-  - UMCameraInterface (3.0.0)
-  - UMConstantsInterface (3.0.0)
-  - UMCore (3.0.2)
-  - UMFaceDetectorInterface (3.0.0)
-  - UMFileSystemInterface (3.0.0)
-  - UMFontInterface (3.0.0)
-  - UMImageLoaderInterface (3.0.0)
-  - UMPermissionsInterface (3.0.0)
-  - UMReactNativeAdapter (3.0.0):
-    - React
-    - UMCore
-    - UMFontInterface
-  - UMSensorsInterface (3.0.0)
-  - UMTaskManagerInterface (3.0.0)
-  - yoga (0.59.10.React)
-
-DEPENDENCIES:
-  - DoubleConversion (from `../node_modules/react-native/third-party-podspecs/DoubleConversion.podspec`)
-  - EXAppLoaderProvider (from `../node_modules/expo-app-loader-provider/ios`)
-  - EXCamera (from `../node_modules/expo-camera/ios`)
-  - EXConstants (from `../node_modules/expo-constants/ios`)
-  - EXFileSystem (from `../node_modules/expo-file-system/ios`)
-  - EXGL (from `../node_modules/expo-gl/ios`)
-  - EXGL-CPP (from `../node_modules/expo-gl-cpp/cpp`)
-  - EXImageManipulator (from `../node_modules/expo-image-manipulator/ios`)
-  - EXPermissions (from `../node_modules/expo-permissions/ios`)
-  - Folly (from `../node_modules/react-native/third-party-podspecs/Folly.podspec`)
-  - glog (from `../node_modules/react-native/third-party-podspecs/glog.podspec`)
-  - React/Core (from `../node_modules/react-native`)
-  - React/CxxBridge (from `../node_modules/react-native`)
-  - React/DevSupport (from `../node_modules/react-native`)
-  - React/RCTAnimation (from `../node_modules/react-native`)
-  - React/RCTImage (from `../node_modules/react-native`)
-  - React/RCTNetwork (from `../node_modules/react-native`)
-  - React/RCTText (from `../node_modules/react-native`)
-  - React/RCTWebSocket (from `../node_modules/react-native`)
-  - "RNCAsyncStorage (from `../node_modules/@react-native-async-storage/async-storage`)"
-  - RNFS (from `../node_modules/react-native-fs`)
-  - RNSVG (from `../node_modules/react-native-svg`)
-  - UMBarCodeScannerInterface (from `../node_modules/unimodules-barcode-scanner-interface/ios`)
-  - UMCameraInterface (from `../node_modules/unimodules-camera-interface/ios`)
-  - UMConstantsInterface (from `../node_modules/unimodules-constants-interface/ios`)
-  - "UMCore (from `../node_modules/@unimodules/core/ios`)"
-  - UMFaceDetectorInterface (from `../node_modules/unimodules-face-detector-interface/ios`)
-  - UMFileSystemInterface (from `../node_modules/unimodules-file-system-interface/ios`)
-  - UMFontInterface (from `../node_modules/unimodules-font-interface/ios`)
-  - UMImageLoaderInterface (from `../node_modules/unimodules-image-loader-interface/ios`)
-  - UMPermissionsInterface (from `../node_modules/unimodules-permissions-interface/ios`)
-  - "UMReactNativeAdapter (from `../node_modules/@unimodules/react-native-adapter/ios`)"
-  - UMSensorsInterface (from `../node_modules/unimodules-sensors-interface/ios`)
-  - UMTaskManagerInterface (from `../node_modules/unimodules-task-manager-interface/ios`)
-  - yoga (from `../node_modules/react-native/ReactCommon/yoga`)
-
-SPEC REPOS:
-  https://github.com/cocoapods/specs.git:
-    - boost-for-react-native
-
-EXTERNAL SOURCES:
-  DoubleConversion:
-    :podspec: "../node_modules/react-native/third-party-podspecs/DoubleConversion.podspec"
-  EXAppLoaderProvider:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/expo-app-loader-provider/ios"
-  EXCamera:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/expo-camera/ios"
-  EXConstants:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/expo-constants/ios"
-  EXFileSystem:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/expo-file-system/ios"
-  EXGL:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/expo-gl/ios"
-  EXGL-CPP:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/expo-gl-cpp/cpp"
-  EXImageManipulator:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/expo-image-manipulator/ios"
-  EXPermissions:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/expo-permissions/ios"
-  Folly:
-    :podspec: "../node_modules/react-native/third-party-podspecs/Folly.podspec"
-  glog:
-    :podspec: "../node_modules/react-native/third-party-podspecs/glog.podspec"
-  React:
-    :path: "../node_modules/react-native"
-  RNCAsyncStorage:
-    :path: "../node_modules/@react-native-async-storage/async-storage"
-  RNFS:
-    :path: "../node_modules/react-native-fs"
-  RNSVG:
-    :path: "../node_modules/react-native-svg"
-  UMBarCodeScannerInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-barcode-scanner-interface/ios"
-  UMCameraInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-camera-interface/ios"
-  UMConstantsInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-constants-interface/ios"
-  UMCore:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/@unimodules/core/ios"
-  UMFaceDetectorInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-face-detector-interface/ios"
-  UMFileSystemInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-file-system-interface/ios"
-  UMFontInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-font-interface/ios"
-  UMImageLoaderInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-image-loader-interface/ios"
-  UMPermissionsInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-permissions-interface/ios"
-  UMReactNativeAdapter:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/@unimodules/react-native-adapter/ios"
-  UMSensorsInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-sensors-interface/ios"
-  UMTaskManagerInterface:
-    :path: !ruby/object:Pathname
-    path: "../node_modules/unimodules-task-manager-interface/ios"
-  yoga:
-    :path: "../node_modules/react-native/ReactCommon/yoga"
-
-SPEC CHECKSUMS:
-  boost-for-react-native: 39c7adb57c4e60d6c5479dd8623128eb5b3f0f2c
-  DoubleConversion: bb338842f62ab1d708ceb63ec3d999f0f3d98ecd
-  EXAppLoaderProvider: 7a8185228d8ba9e689a0e2d6d957fe9bdd49c8a0
-  EXCamera: 8ad7012d4e0fef09ff35de14fcb41b410357e9be
-  EXConstants: 5d81e84ca71b9a552529889cc798b4a04e9e22b3
-  EXFileSystem: 091907902fcec9f9182b656fdead41a82f30986a
-  EXGL: 4b21885aee37fb5f5230e05a62bba480fa886a3d
-  EXGL-CPP: da3c4f31ff50a34bd8184fadbf409806281b103d
-  EXImageManipulator: b2e17bdbd0872cc59b43b76756f76fdb8a849831
-  EXPermissions: 99e52dc3e5f8e55153f1958004f6df2a30a1f2f5
-  Folly: de497beb10f102453a1afa9edbf8cf8a251890de
-  glog: aefd1eb5dda2ab95ba0938556f34b98e2da3a60d
-  React: 36d0768f9e93be2473b37e7fa64f92c1d5341eef
-  RNCAsyncStorage: 2e2e3feb9bdadc752a026703d8c4065ca912e75a
-  RNFS: d229ee245fa9685417e6d9367031d670d723547e
-  RNSVG: 8ba35cbeb385a52fd960fd28db9d7d18b4c2974f
-  UMBarCodeScannerInterface: 84ea2d6b58ff0dc27ef9b68bab71286be18ee020
-  UMCameraInterface: 26b26005d1756a0d5f4f04f1e168e39ea9154535
-  UMConstantsInterface: 038bacb19de12b6fd328c589122c8dc977cccf61
-  UMCore: 733094f43f7244c60ce1f0592d00013ed68fa52c
-  UMFaceDetectorInterface: c9c3ae4cb045421283667a1698c2f31331f55e3f
-  UMFileSystemInterface: e9adc71027017de38eaf7d05fa58b2848ecb3797
-  UMFontInterface: f0c5846977ee8a93d7cfa8ae7e666772c727d195
-  UMImageLoaderInterface: 36e54e570acc4d720856f03ceebc441f73ea472c
-  UMPermissionsInterface: 938d010c74c43fcefc9bb990633a7c5a1631267e
-  UMReactNativeAdapter: 131ea2b944ade8035f0b54c6570c405f6000548d
-  UMSensorsInterface: 0ed023ce9b96f2ca6fada7bda05b7760da60b293
-  UMTaskManagerInterface: 8664abd37a00715727e60df9ecd65e42ba47b548
-  yoga: 684513b14b03201579ba3cee20218c9d1298b0cc
-
-PODFILE CHECKSUM: f319444641fc4bb5878ba573d414469954b15b11
-
-COCOAPODS: 1.7.2
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcodeproj/project.pbxproj b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcodeproj/project.pbxproj
deleted file mode 100644
index 8a6ac220b..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcodeproj/project.pbxproj
+++ /dev/null
@@ -1,1318 +0,0 @@
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-	classes = {
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diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcodeproj/xcshareddata/xcschemes/integration_rn59-tvOS.xcscheme b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcodeproj/xcshareddata/xcschemes/integration_rn59-tvOS.xcscheme
deleted file mode 100644
index d883fe564..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcodeproj/xcshareddata/xcschemes/integration_rn59-tvOS.xcscheme
+++ /dev/null
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-               ReferencedContainer = "container:integration_rn59.xcodeproj">
-            </BuildableReference>
-         </BuildActionEntry>
-      </BuildActionEntries>
-   </BuildAction>
-   <TestAction
-      buildConfiguration = "Debug"
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      shouldUseLaunchSchemeArgsEnv = "YES">
-      <Testables>
-         <TestableReference
-            skipped = "NO">
-            <BuildableReference
-               BuildableIdentifier = "primary"
-               BlueprintIdentifier = "2D02E48F1E0B4A5D006451C7"
-               BuildableName = "integration_rn59-tvOSTests.xctest"
-               BlueprintName = "integration_rn59-tvOSTests"
-               ReferencedContainer = "container:integration_rn59.xcodeproj">
-            </BuildableReference>
-         </TestableReference>
-      </Testables>
-      <MacroExpansion>
-         <BuildableReference
-            BuildableIdentifier = "primary"
-            BlueprintIdentifier = "2D02E47A1E0B4A5D006451C7"
-            BuildableName = "integration_rn59-tvOS.app"
-            BlueprintName = "integration_rn59-tvOS"
-            ReferencedContainer = "container:integration_rn59.xcodeproj">
-         </BuildableReference>
-      </MacroExpansion>
-      <AdditionalOptions>
-      </AdditionalOptions>
-   </TestAction>
-   <LaunchAction
-      buildConfiguration = "Debug"
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      launchStyle = "0"
-      useCustomWorkingDirectory = "NO"
-      ignoresPersistentStateOnLaunch = "NO"
-      debugDocumentVersioning = "YES"
-      debugServiceExtension = "internal"
-      allowLocationSimulation = "YES">
-      <BuildableProductRunnable
-         runnableDebuggingMode = "0">
-         <BuildableReference
-            BuildableIdentifier = "primary"
-            BlueprintIdentifier = "2D02E47A1E0B4A5D006451C7"
-            BuildableName = "integration_rn59-tvOS.app"
-            BlueprintName = "integration_rn59-tvOS"
-            ReferencedContainer = "container:integration_rn59.xcodeproj">
-         </BuildableReference>
-      </BuildableProductRunnable>
-      <AdditionalOptions>
-      </AdditionalOptions>
-   </LaunchAction>
-   <ProfileAction
-      buildConfiguration = "Release"
-      shouldUseLaunchSchemeArgsEnv = "YES"
-      savedToolIdentifier = ""
-      useCustomWorkingDirectory = "NO"
-      debugDocumentVersioning = "YES">
-      <BuildableProductRunnable
-         runnableDebuggingMode = "0">
-         <BuildableReference
-            BuildableIdentifier = "primary"
-            BlueprintIdentifier = "2D02E47A1E0B4A5D006451C7"
-            BuildableName = "integration_rn59-tvOS.app"
-            BlueprintName = "integration_rn59-tvOS"
-            ReferencedContainer = "container:integration_rn59.xcodeproj">
-         </BuildableReference>
-      </BuildableProductRunnable>
-   </ProfileAction>
-   <AnalyzeAction
-      buildConfiguration = "Debug">
-   </AnalyzeAction>
-   <ArchiveAction
-      buildConfiguration = "Release"
-      revealArchiveInOrganizer = "YES">
-   </ArchiveAction>
-</Scheme>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcodeproj/xcshareddata/xcschemes/integration_rn59.xcscheme b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcodeproj/xcshareddata/xcschemes/integration_rn59.xcscheme
deleted file mode 100644
index 370866b49..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcodeproj/xcshareddata/xcschemes/integration_rn59.xcscheme
+++ /dev/null
@@ -1,129 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Scheme
-   LastUpgradeVersion = "0940"
-   version = "1.3">
-   <BuildAction
-      parallelizeBuildables = "YES"
-      buildImplicitDependencies = "YES">
-      <BuildActionEntries>
-         <BuildActionEntry
-            buildForTesting = "YES"
-            buildForRunning = "YES"
-            buildForProfiling = "YES"
-            buildForArchiving = "YES"
-            buildForAnalyzing = "YES">
-            <BuildableReference
-               BuildableIdentifier = "primary"
-               BlueprintIdentifier = "83CBBA2D1A601D0E00E9B192"
-               BuildableName = "libReact.a"
-               BlueprintName = "React"
-               ReferencedContainer = "container:../node_modules/react-native/React/React.xcodeproj">
-            </BuildableReference>
-         </BuildActionEntry>
-         <BuildActionEntry
-            buildForTesting = "YES"
-            buildForRunning = "YES"
-            buildForProfiling = "YES"
-            buildForArchiving = "YES"
-            buildForAnalyzing = "YES">
-            <BuildableReference
-               BuildableIdentifier = "primary"
-               BlueprintIdentifier = "13B07F861A680F5B00A75B9A"
-               BuildableName = "integration_rn59.app"
-               BlueprintName = "integration_rn59"
-               ReferencedContainer = "container:integration_rn59.xcodeproj">
-            </BuildableReference>
-         </BuildActionEntry>
-         <BuildActionEntry
-            buildForTesting = "YES"
-            buildForRunning = "YES"
-            buildForProfiling = "NO"
-            buildForArchiving = "NO"
-            buildForAnalyzing = "YES">
-            <BuildableReference
-               BuildableIdentifier = "primary"
-               BlueprintIdentifier = "00E356ED1AD99517003FC87E"
-               BuildableName = "integration_rn59Tests.xctest"
-               BlueprintName = "integration_rn59Tests"
-               ReferencedContainer = "container:integration_rn59.xcodeproj">
-            </BuildableReference>
-         </BuildActionEntry>
-      </BuildActionEntries>
-   </BuildAction>
-   <TestAction
-      buildConfiguration = "Debug"
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      shouldUseLaunchSchemeArgsEnv = "YES">
-      <Testables>
-         <TestableReference
-            skipped = "NO">
-            <BuildableReference
-               BuildableIdentifier = "primary"
-               BlueprintIdentifier = "00E356ED1AD99517003FC87E"
-               BuildableName = "integration_rn59Tests.xctest"
-               BlueprintName = "integration_rn59Tests"
-               ReferencedContainer = "container:integration_rn59.xcodeproj">
-            </BuildableReference>
-         </TestableReference>
-      </Testables>
-      <MacroExpansion>
-         <BuildableReference
-            BuildableIdentifier = "primary"
-            BlueprintIdentifier = "13B07F861A680F5B00A75B9A"
-            BuildableName = "integration_rn59.app"
-            BlueprintName = "integration_rn59"
-            ReferencedContainer = "container:integration_rn59.xcodeproj">
-         </BuildableReference>
-      </MacroExpansion>
-      <AdditionalOptions>
-      </AdditionalOptions>
-   </TestAction>
-   <LaunchAction
-      buildConfiguration = "Debug"
-      selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
-      selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
-      launchStyle = "0"
-      useCustomWorkingDirectory = "NO"
-      ignoresPersistentStateOnLaunch = "NO"
-      debugDocumentVersioning = "YES"
-      debugServiceExtension = "internal"
-      allowLocationSimulation = "YES">
-      <BuildableProductRunnable
-         runnableDebuggingMode = "0">
-         <BuildableReference
-            BuildableIdentifier = "primary"
-            BlueprintIdentifier = "13B07F861A680F5B00A75B9A"
-            BuildableName = "integration_rn59.app"
-            BlueprintName = "integration_rn59"
-            ReferencedContainer = "container:integration_rn59.xcodeproj">
-         </BuildableReference>
-      </BuildableProductRunnable>
-      <AdditionalOptions>
-      </AdditionalOptions>
-   </LaunchAction>
-   <ProfileAction
-      buildConfiguration = "Release"
-      shouldUseLaunchSchemeArgsEnv = "YES"
-      savedToolIdentifier = ""
-      useCustomWorkingDirectory = "NO"
-      debugDocumentVersioning = "YES">
-      <BuildableProductRunnable
-         runnableDebuggingMode = "0">
-         <BuildableReference
-            BuildableIdentifier = "primary"
-            BlueprintIdentifier = "13B07F861A680F5B00A75B9A"
-            BuildableName = "integration_rn59.app"
-            BlueprintName = "integration_rn59"
-            ReferencedContainer = "container:integration_rn59.xcodeproj">
-         </BuildableReference>
-      </BuildableProductRunnable>
-   </ProfileAction>
-   <AnalyzeAction
-      buildConfiguration = "Debug">
-   </AnalyzeAction>
-   <ArchiveAction
-      buildConfiguration = "Release"
-      revealArchiveInOrganizer = "YES">
-   </ArchiveAction>
-</Scheme>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcworkspace/contents.xcworkspacedata b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcworkspace/contents.xcworkspacedata
deleted file mode 100644
index 0dc24c978..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcworkspace/contents.xcworkspacedata
+++ /dev/null
@@ -1,10 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Workspace
-   version = "1.0">
-   <FileRef
-      location = "group:integration_rn59.xcodeproj">
-   </FileRef>
-   <FileRef
-      location = "group:Pods/Pods.xcodeproj">
-   </FileRef>
-</Workspace>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
deleted file mode 100644
index 18d981003..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>IDEDidComputeMac32BitWarning</key>
-	<true/>
-</dict>
-</plist>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcworkspace/xcshareddata/WorkspaceSettings.xcsettings b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcworkspace/xcshareddata/WorkspaceSettings.xcsettings
deleted file mode 100644
index c19488b50..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59.xcworkspace/xcshareddata/WorkspaceSettings.xcsettings
+++ /dev/null
@@ -1,10 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-  <key>BuildSystemType</key>
-  <string>Original</string>
-  <key>IDEWorkspaceSharedSettings_AutocreateContextsIfNeeded</key>
-  <true/>
-</dict>
-</plist>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/AppDelegate.h b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/AppDelegate.h
deleted file mode 100644
index 0220ceec4..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/AppDelegate.h
+++ /dev/null
@@ -1,17 +0,0 @@
-/**
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#import <React/RCTBridgeDelegate.h>
-#import <UIKit/UIKit.h>
-#import <UMReactNativeAdapter/UMModuleRegistryAdapter.h>
-
-@interface AppDelegate : UIResponder <UIApplicationDelegate, RCTBridgeDelegate>
-
-@property(nonatomic, strong) UMModuleRegistryAdapter *moduleRegistryAdapter;
-@property(nonatomic, strong) UIWindow *window;
-
-@end
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/AppDelegate.m b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/AppDelegate.m
deleted file mode 100644
index 965d78911..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/AppDelegate.m
+++ /dev/null
@@ -1,56 +0,0 @@
-/**
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#import "AppDelegate.h"
-
-#import <React/RCTBridge.h>
-#import <React/RCTBundleURLProvider.h>
-#import <React/RCTRootView.h>
-
-#import <UMCore/UMModuleRegistry.h>
-#import <UMReactNativeAdapter/UMModuleRegistryAdapter.h>
-#import <UMReactNativeAdapter/UMNativeModulesProxy.h>
-
-@implementation AppDelegate
-
-- (BOOL)application:(UIApplication *)application
-    didFinishLaunchingWithOptions:(NSDictionary *)launchOptions {
-  self.moduleRegistryAdapter = [[UMModuleRegistryAdapter alloc]
-      initWithModuleRegistryProvider:[[UMModuleRegistryProvider alloc] init]];
-  RCTBridge *bridge = [[RCTBridge alloc] initWithDelegate:self launchOptions:launchOptions];
-  RCTRootView *rootView = [[RCTRootView alloc] initWithBridge:bridge
-                                                   moduleName:@"integration_rn59"
-                                            initialProperties:nil];
-
-  rootView.backgroundColor = [[UIColor alloc] initWithRed:1.0f green:1.0f blue:1.0f alpha:1];
-
-  self.window = [[UIWindow alloc] initWithFrame:[UIScreen mainScreen].bounds];
-  UIViewController *rootViewController = [UIViewController new];
-  rootViewController.view = rootView;
-  self.window.rootViewController = rootViewController;
-  [self.window makeKeyAndVisible];
-  return YES;
-}
-
-- (NSArray<id<RCTBridgeModule>> *)extraModulesForBridge:(RCTBridge *)bridge {
-  NSArray<id<RCTBridgeModule>> *extraModules =
-      [_moduleRegistryAdapter extraModulesForBridge:bridge];
-  // You can inject any extra modules that you would like here, more information at:
-  // https://facebook.github.io/react-native/docs/native-modules-ios.html#dependency-injection
-  return extraModules;
-}
-
-- (NSURL *)sourceURLForBridge:(RCTBridge *)bridge {
-#if DEBUG
-  return [[RCTBundleURLProvider sharedSettings] jsBundleURLForBundleRoot:@"index"
-                                                        fallbackResource:nil];
-#else
-  return [[NSBundle mainBundle] URLForResource:@"main" withExtension:@"jsbundle"];
-#endif
-}
-
-@end
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Base.lproj/LaunchScreen.xib b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Base.lproj/LaunchScreen.xib
deleted file mode 100644
index 4ee62b2b2..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Base.lproj/LaunchScreen.xib
+++ /dev/null
@@ -1,42 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<document type="com.apple.InterfaceBuilder3.CocoaTouch.XIB" version="3.0" toolsVersion="7702" systemVersion="14D136" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" launchScreen="YES" useTraitCollections="YES">
-    <dependencies>
-        <deployment identifier="iOS"/>
-        <plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="7701"/>
-        <capability name="Constraints with non-1.0 multipliers" minToolsVersion="5.1"/>
-    </dependencies>
-    <objects>
-        <placeholder placeholderIdentifier="IBFilesOwner" id="-1" userLabel="File's Owner"/>
-        <placeholder placeholderIdentifier="IBFirstResponder" id="-2" customClass="UIResponder"/>
-        <view contentMode="scaleToFill" id="iN0-l3-epB">
-            <rect key="frame" x="0.0" y="0.0" width="480" height="480"/>
-            <autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
-            <subviews>
-                <label opaque="NO" clipsSubviews="YES" userInteractionEnabled="NO" contentMode="left" horizontalHuggingPriority="251" verticalHuggingPriority="251" text="Powered by React Native" textAlignment="center" lineBreakMode="tailTruncation" baselineAdjustment="alignBaselines" minimumFontSize="9" translatesAutoresizingMaskIntoConstraints="NO" id="8ie-xW-0ye">
-                    <rect key="frame" x="20" y="439" width="441" height="21"/>
-                    <fontDescription key="fontDescription" type="system" pointSize="17"/>
-                    <color key="textColor" cocoaTouchSystemColor="darkTextColor"/>
-                    <nil key="highlightedColor"/>
-                </label>
-                <label opaque="NO" clipsSubviews="YES" userInteractionEnabled="NO" contentMode="left" horizontalHuggingPriority="251" verticalHuggingPriority="251" text="integration_rn59" textAlignment="center" lineBreakMode="middleTruncation" baselineAdjustment="alignBaselines" minimumFontSize="18" translatesAutoresizingMaskIntoConstraints="NO" id="kId-c2-rCX">
-                    <rect key="frame" x="20" y="140" width="441" height="43"/>
-                    <fontDescription key="fontDescription" type="boldSystem" pointSize="36"/>
-                    <color key="textColor" cocoaTouchSystemColor="darkTextColor"/>
-                    <nil key="highlightedColor"/>
-                </label>
-            </subviews>
-            <color key="backgroundColor" white="1" alpha="1" colorSpace="custom" customColorSpace="calibratedWhite"/>
-            <constraints>
-                <constraint firstItem="kId-c2-rCX" firstAttribute="centerY" secondItem="iN0-l3-epB" secondAttribute="bottom" multiplier="1/3" constant="1" id="5cJ-9S-tgC"/>
-                <constraint firstAttribute="centerX" secondItem="kId-c2-rCX" secondAttribute="centerX" id="Koa-jz-hwk"/>
-                <constraint firstAttribute="bottom" secondItem="8ie-xW-0ye" secondAttribute="bottom" constant="20" id="Kzo-t9-V3l"/>
-                <constraint firstItem="8ie-xW-0ye" firstAttribute="leading" secondItem="iN0-l3-epB" secondAttribute="leading" constant="20" symbolic="YES" id="MfP-vx-nX0"/>
-                <constraint firstAttribute="centerX" secondItem="8ie-xW-0ye" secondAttribute="centerX" id="ZEH-qu-HZ9"/>
-                <constraint firstItem="kId-c2-rCX" firstAttribute="leading" secondItem="iN0-l3-epB" secondAttribute="leading" constant="20" symbolic="YES" id="fvb-Df-36g"/>
-            </constraints>
-            <nil key="simulatedStatusBarMetrics"/>
-            <freeformSimulatedSizeMetrics key="simulatedDestinationMetrics"/>
-            <point key="canvasLocation" x="548" y="455"/>
-        </view>
-    </objects>
-</document>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Images.xcassets/AppIcon.appiconset/Contents.json b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Images.xcassets/AppIcon.appiconset/Contents.json
deleted file mode 100644
index 19882d568..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Images.xcassets/AppIcon.appiconset/Contents.json
+++ /dev/null
@@ -1,53 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "iphone",
-      "size" : "20x20",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "20x20",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "29x29",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "29x29",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "40x40",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "40x40",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "60x60",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "60x60",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "ios-marketing",
-      "size" : "1024x1024",
-      "scale" : "1x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Images.xcassets/Contents.json b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Images.xcassets/Contents.json
deleted file mode 100644
index 2d92bd53f..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Images.xcassets/Contents.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Info.plist b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Info.plist
deleted file mode 100644
index cbcf78a8a..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/Info.plist
+++ /dev/null
@@ -1,82 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>NSCalendarsUsageDescription</key>
-	<string>Allow $(PRODUCT_NAME) to access your calendar</string>
-	<key>NSCameraUsageDescription</key>
-	<string>Allow $(PRODUCT_NAME) to use the camera</string>
-	<key>NSContactsUsageDescription</key>
-	<string>Allow $(PRODUCT_NAME) to access your contacts</string>
-	<key>NSLocationAlwaysAndWhenInUseUsageDescription</key>
-	<string>Allow $(PRODUCT_NAME) to use your location</string>
-	<key>NSLocationAlwaysUsageDescription</key>
-	<string>Allow $(PRODUCT_NAME) to use your location</string>
-	<key>NSLocationWhenInUseUsageDescription</key>
-	<string>Allow $(PRODUCT_NAME) to use your location</string>
-	<key>NSMicrophoneUsageDescription</key>
-	<string>Allow $(PRODUCT_NAME) to access your microphone</string>
-	<key>NSMotionUsageDescription</key>
-	<string>Allow $(PRODUCT_NAME) to access your device's accelerometer</string>
-	<key>NSPhotoLibraryAddUsageDescription</key>
-	<string>Give $(PRODUCT_NAME) permission to save photos</string>
-	<key>NSPhotoLibraryUsageDescription</key>
-	<string>Give $(PRODUCT_NAME) permission to access your photos</string>
-	<key>NSRemindersUsageDescription</key>
-	<string>Allow $(PRODUCT_NAME) to access your reminders</string>	
-	<key>CFBundleDevelopmentRegion</key>
-	<string>en</string>
-	<key>CFBundleDisplayName</key>
-	<string>integration_rn59</string>
-	<key>CFBundleExecutable</key>
-	<string>$(EXECUTABLE_NAME)</string>
-	<key>CFBundleIdentifier</key>
-	<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
-	<key>CFBundleInfoDictionaryVersion</key>
-	<string>6.0</string>
-	<key>CFBundleName</key>
-	<string>$(PRODUCT_NAME)</string>
-	<key>CFBundlePackageType</key>
-	<string>APPL</string>
-	<key>CFBundleShortVersionString</key>
-	<string>1.0</string>
-	<key>CFBundleSignature</key>
-	<string>????</string>
-	<key>CFBundleVersion</key>
-	<string>1</string>
-	<key>LSRequiresIPhoneOS</key>
-	<true/>
-	<key>NSLocationWhenInUseUsageDescription</key>
-	<string></string>
-	<key>UILaunchStoryboardName</key>
-	<string>LaunchScreen</string>
-	<key>UIRequiredDeviceCapabilities</key>
-	<array>
-		<string>armv7</string>
-	</array>
-	<key>UISupportedInterfaceOrientations</key>
-	<array>
-		<string>UIInterfaceOrientationPortrait</string>
-		<string>UIInterfaceOrientationLandscapeLeft</string>
-		<string>UIInterfaceOrientationLandscapeRight</string>
-	</array>
-	<key>UIViewControllerBasedStatusBarAppearance</key>
-	<false/>
-	<key>NSLocationWhenInUseUsageDescription</key>
-	<string></string>
-	<key>NSAppTransportSecurity</key>
-	<!--See http://ste.vn/2015/06/10/configuring-app-transport-security-ios-9-osx-10-11/ -->
-	<dict>
-    <key>NSAllowsArbitraryLoads</key>
-    <true/>
-		<key>NSExceptionDomains</key>
-		<dict>
-			<key>localhost</key>
-			<dict>
-				<key>NSExceptionAllowsInsecureHTTPLoads</key>
-				<true/>
-			</dict>
-		</dict>
-	</dict>	
-</dict>
-</plist>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/main.m b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/main.m
deleted file mode 100644
index 3ed24eae1..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59/main.m
+++ /dev/null
@@ -1,16 +0,0 @@
-/**
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#import <UIKit/UIKit.h>
-
-#import "AppDelegate.h"
-
-int main(int argc, char* argv[]) {
-  @autoreleasepool {
-    return UIApplicationMain(argc, argv, nil, NSStringFromClass([AppDelegate class]));
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59Tests/Info.plist b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59Tests/Info.plist
deleted file mode 100644
index ba72822e8..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59Tests/Info.plist
+++ /dev/null
@@ -1,24 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>CFBundleDevelopmentRegion</key>
-	<string>en</string>
-	<key>CFBundleExecutable</key>
-	<string>$(EXECUTABLE_NAME)</string>
-	<key>CFBundleIdentifier</key>
-	<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
-	<key>CFBundleInfoDictionaryVersion</key>
-	<string>6.0</string>
-	<key>CFBundleName</key>
-	<string>$(PRODUCT_NAME)</string>
-	<key>CFBundlePackageType</key>
-	<string>BNDL</string>
-	<key>CFBundleShortVersionString</key>
-	<string>1.0</string>
-	<key>CFBundleSignature</key>
-	<string>????</string>
-	<key>CFBundleVersion</key>
-	<string>1</string>
-</dict>
-</plist>
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59Tests/integration_rn59Tests.m b/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59Tests/integration_rn59Tests.m
deleted file mode 100644
index b468b210b..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/ios/integration_rn59Tests/integration_rn59Tests.m
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#import <UIKit/UIKit.h>
-#import <XCTest/XCTest.h>
-
-#import <React/RCTLog.h>
-#import <React/RCTRootView.h>
-
-#define TIMEOUT_SECONDS 600
-#define TEXT_TO_LOOK_FOR @"Welcome to React Native!"
-
-@interface integration_rn59Tests : XCTestCase
-
-@end
-
-@implementation integration_rn59Tests
-
-- (BOOL)findSubviewInView:(UIView *)view matching:(BOOL (^)(UIView *view))test {
-  if (test(view)) {
-    return YES;
-  }
-  for (UIView *subview in [view subviews]) {
-    if ([self findSubviewInView:subview matching:test]) {
-      return YES;
-    }
-  }
-  return NO;
-}
-
-- (void)testRendersWelcomeScreen {
-  UIViewController *vc = [[[RCTSharedApplication() delegate] window] rootViewController];
-  NSDate *date = [NSDate dateWithTimeIntervalSinceNow:TIMEOUT_SECONDS];
-  BOOL foundElement = NO;
-
-  __block NSString *redboxError = nil;
-  RCTSetLogFunction(^(RCTLogLevel level, RCTLogSource source, NSString *fileName,
-                      NSNumber *lineNumber, NSString *message) {
-    if (level >= RCTLogLevelError) {
-      redboxError = message;
-    }
-  });
-
-  while ([date timeIntervalSinceNow] > 0 && !foundElement && !redboxError) {
-    [[NSRunLoop mainRunLoop] runMode:NSDefaultRunLoopMode
-                          beforeDate:[NSDate dateWithTimeIntervalSinceNow:0.1]];
-    [[NSRunLoop mainRunLoop] runMode:NSRunLoopCommonModes
-                          beforeDate:[NSDate dateWithTimeIntervalSinceNow:0.1]];
-
-    foundElement =
-        [self findSubviewInView:vc.view
-                       matching:^BOOL(UIView *view) {
-                         if ([view.accessibilityLabel isEqualToString:TEXT_TO_LOOK_FOR]) {
-                           return YES;
-                         }
-                         return NO;
-                       }];
-  }
-
-  RCTSetLogFunction(RCTDefaultLogFunction);
-
-  XCTAssertNil(redboxError, @"RedBox error: %@", redboxError);
-  XCTAssertTrue(foundElement, @"Couldn't find element with text '%@' in %d seconds",
-                TEXT_TO_LOOK_FOR, TIMEOUT_SECONDS);
-}
-
-@end
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/jest.config.js b/tfjs-master/tfjs-react-native/integration_rn59/jest.config.js
deleted file mode 100644
index 4bbb2ce6d..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/jest.config.js
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-module.exports = {
-  preset: 'react-native',
-  moduleFileExtensions: ['ts', 'tsx', 'js', 'jsx', 'json', 'node'],
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/metro.config.js b/tfjs-master/tfjs-react-native/integration_rn59/metro.config.js
deleted file mode 100644
index c5c77f8dd..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/metro.config.js
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Metro configuration for React Native
- * https://github.com/facebook/react-native
- *
- * @format
- */
-const blacklist = require('metro-config/src/defaults/blacklist');
-
-module.exports = {
-  transformer: {
-    getTransformOptions: async () => ({
-      transform: {
-        experimentalImportSupport: false,
-        inlineRequires: false,
-      },
-    }),
-  },
-  resolver: {
-    assetExts: ['bin', 'txt', 'jpg'],
-    sourceExts: ['js', 'json', 'ts', 'tsx', 'jsx'],
-    blacklistRE: blacklist([/platform_node/])
-  },
-};
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/package.json b/tfjs-master/tfjs-react-native/integration_rn59/package.json
deleted file mode 100644
index 436e088c7..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/package.json
+++ /dev/null
@@ -1,65 +0,0 @@
-{
-  "name": "integration_rn59",
-  "version": "0.1.0",
-  "private": true,
-  "scripts": {
-    "start": "react-native start --reset-cache",
-    "lint": "tslint -p . -t verbose",
-    "prep-tests": "ts-node -s scripts/prep_tests.ts",
-    "start-metro": "npx react-native start --reset-cache --verbose",
-    "test-integration": "wdio wdio.conf.js",
-    "test-ci": "./scripts/test-ci.sh",
-    "postinstall": "npm run prep-tests",
-    "copydeps": "yarn copyrn && yarn copycore && yarn copycpu && yarn copywebgl && yarn copyunion",
-    "copyrn": "\\cp -rf ../dist node_modules/@tensorflow/tfjs-react-native",
-    "copycore": "\\cp -rf ../../tfjs-core/dist node_modules/@tensorflow/tfjs-core",
-    "copycpu": "\\cp -rf ../../tfjs-backend-cpu/dist node_modules/@tensorflow/tfjs-backend-cpu",
-    "copywebgl": "\\cp -rf ../../tfjs-backend-webgl/dist node_modules/@tensorflow/tfjs-backend-webgl",
-    "copyunion": "\\cp -rf ../../tfjs/dist node_modules/@tensorflow/tfjs"
-  },
-  "dependencies": {
-    "@react-native-async-storage/async-storage": "^1.13.0",
-    "@tensorflow-models/blazeface": "^0.0.2",
-    "@tensorflow-models/mobilenet": "^2.0.4",
-    "@tensorflow-models/posenet": "^2.2.1",
-    "@tensorflow/tfjs": "3.7.0",
-    "@tensorflow/tfjs-react-native": "0.5.0",
-    "expo-camera": "^7.0.0",
-    "expo-gl": "^7.0.0",
-    "expo-gl-cpp": "^7.0.0",
-    "expo-image-manipulator": "^6.0.0",
-    "jasmine-core": "^3.4.0",
-    "jpeg-js": "^0.4.4",
-    "react": "16.8.3",
-    "react-native": "0.59.10",
-    "react-native-fs": "2.14.1",
-    "react-native-svg": "^9.13.6",
-    "react-native-unimodules": "^0.5.4",
-    "rn-fetch-blob": "^0.10.15"
-  },
-  "devDependencies": {
-    "@babel/core": "^7.5.5",
-    "@babel/runtime": "^7.5.5",
-    "@types/jasmine": "^3.4.2",
-    "@types/jest": "^24.0.17",
-    "@types/react-test-renderer": "^16.8.3",
-    "@wdio/browserstack-service": "^5.13.2",
-    "@wdio/cli": "^5.13.2",
-    "@wdio/jasmine-framework": "^5.13.2",
-    "@wdio/local-runner": "^5.13.2",
-    "@wdio/spec-reporter": "^5.13.2",
-    "babel-jest": "^24.8.0",
-    "clang-format": "~1.2.2",
-    "jest": "^24.8.0",
-    "metro-react-native-babel-preset": "^0.55.0",
-    "react-test-renderer": "16.8.3",
-    "ts-node": "~8.8.2",
-    "tslint": "~5.11.0",
-    "tslint-no-circular-imports": "^0.5.0",
-    "typescript": "4.4.2",
-    "webdriverio": "^5.13.2"
-  },
-  "jest": {
-    "preset": "react-native"
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/scripts/prep_tests.ts b/tfjs-master/tfjs-react-native/integration_rn59/scripts/prep_tests.ts
deleted file mode 100644
index 91fce2920..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/scripts/prep_tests.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// This script edits node_modules/@tensorflow/tfjs-core/dist/tests.js to remove
-// tests that are incompatible with react native.
-
-import * as fs from 'fs';
-import * as path from 'path';
-
-const testsFilePath = path.resolve(
-    __dirname, '../node_modules/@tensorflow/tfjs-core/dist/tests.js');
-const fileContents = fs.readFileSync(testsFilePath, 'utf-8');
-
-let newContents = fileContents.replace('import \'./worker_node_test\';', '');
-// disable the version test as we may be testing against a version that is not
-// yet depended on by the integration tests.
-newContents = newContents.replace('import \'./version_test\';', '');
-
-fs.writeFileSync(testsFilePath, newContents);
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/scripts/test-ci.sh b/tfjs-master/tfjs-react-native/integration_rn59/scripts/test-ci.sh
deleted file mode 100644
index 01b3d1cee..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/scripts/test-ci.sh
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn
-yarn lint
-
-# Default is to run tests against the packages specified dependencies. We can
-# optionally copy a built version of tfjs-core from the current checkout
-if [ "$1" == "against-head" ]; then
-  # Copy in fresh builds of all packages
-  echo "Copying all deps from from HEAD"
-  yarn copydeps
-else
-  # Copy just the built tfjs-react-native
-  echo "Copying tfjs-react-native from HEAD"
-  yarn && yarn copyrn
-fi
-
-yarn prep-tests
-
-# Spawn a metro bundler/asset server in the background.
-yarn start-metro &
-let metro_pid=$!
-echo "Started metro. PID=$metro_pid"
-sleep 2s
-
-# Start the test suite.
-node ../../scripts/run_flaky.js "yarn test-integration"
-test_result=$?
-
-# Kill the child process explicitly so that the exit code of the script
-# isn't changed by it's eventual termination.
-kill -TERM $metro_pid
-sleep 2s
-echo $metro_pid was terminated.
-
-# Return the exit code of the test
-exit $test_result
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/scripts/tsconfig.json b/tfjs-master/tfjs-react-native/integration_rn59/scripts/tsconfig.json
deleted file mode 100644
index 44baa7fd8..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/scripts/tsconfig.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "extends": "../../tsconfig.test",
-  "compilerOptions": {
-    "module": "commonjs"
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/test/integration_test.ts b/tfjs-master/tfjs-react-native/integration_rn59/test/integration_test.ts
deleted file mode 100644
index 7f416f2a4..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/test/integration_test.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-const MILLIS_PER_MIN = 60 * 1000;
-const INITIAL_LOAD_TIMEOUT = 5 * MILLIS_PER_MIN;
-const TEST_RUN_TIMEOUT = 10 * MILLIS_PER_MIN;
-const TEST_POLL_INTERVAL = 2000;
-const DEFAULT_TIMEOUT = 30000;
-
-describe('tfjs-core unit tests', () => {
-  it('unit tests should pass', async () => {
-    const driver = browser;
-
-    const unitTestBtn = await driver.$('~unit-test-btn');
-    await unitTestBtn.waitForExist(
-        INITIAL_LOAD_TIMEOUT, false,
-        'Could not find unit test button. \n' +
-            'The browserstacklocal tunnel was likely not established');
-    await unitTestBtn.waitForEnabled(INITIAL_LOAD_TIMEOUT);
-    await unitTestBtn.click();
-
-    const backendNameEl = await driver.$('~backendName');
-    await backendNameEl.waitForExist(DEFAULT_TIMEOUT);
-    const backendName = await backendNameEl.getText();
-    expect(backendName).toEqual('backend=rn-webgl');
-
-    // Wait for the unit tests to complete.
-    const testCompleteEl = await driver.$('~testComplete');
-    await driver.waitUntil(async () => {
-      const testStatus = await testCompleteEl.getText();
-      return testStatus === 'testsComplete=true';
-    }, TEST_RUN_TIMEOUT, 'Unit tests timed out', TEST_POLL_INTERVAL);
-
-    // Get the number of passed and run tests
-    const passedTestsEl = await driver.$('~passedTests');
-    await passedTestsEl.waitForExist(DEFAULT_TIMEOUT);
-    const passedTestsText = await passedTestsEl.getText();
-
-    const parts = passedTestsText.match(/(?<passed>\d+) of (?<total>\d+)/);
-    expect(parts).not.toBeNull();
-
-    if (parts != null && parts.groups != null) {
-      const passed = parseInt(parts.groups.passed, 10);
-      const total = parseInt(parts.groups.total, 10);
-      const failed = total - passed;
-
-      if (failed > 0) {
-        fail(`${failed} tests failed out of ${total}`);
-        const failureMessages = await driver.$('~failureMessages');
-        await failureMessages.waitForExist(DEFAULT_TIMEOUT);
-        const failureMessagesText = await failureMessages.getText();
-
-        const TEST_SEPARATOR = '###';
-        const TITLE_SEPARATOR = '%%%';
-
-        const testFailures = failureMessagesText.split(TEST_SEPARATOR);
-        for (let i = 0; i < testFailures.length; i++) {
-          const testFailure = testFailures[i];
-          const [title, messages] = testFailure.split(TITLE_SEPARATOR);
-
-          fail(`${title}\n${messages}`);
-        }
-      }
-    }
-  }, TEST_RUN_TIMEOUT + INITIAL_LOAD_TIMEOUT);
-});
-
-// Mark as a module for ts compiler.
-export {};
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/tsconfig.json b/tfjs-master/tfjs-react-native/integration_rn59/tsconfig.json
deleted file mode 100644
index 102c5f670..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/tsconfig.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-  "extends": "../../tsconfig",
-  "compilerOptions": {
-    "allowSyntheticDefaultImports": true,
-    "esModuleInterop": true,
-    "declaration": false,
-    "isolatedModules": true,
-    "jsx": "react",
-    "lib": ["es6", "dom"],
-    "noEmit": true,
-    "strict": true,
-    "target": "esnext",
-    "skipLibCheck": true,
-    "types": ["node", "webdriverio", "jasmine"]
-  },
-  "include": ["./"],
-  "exclude": ["node_modules", "babel.config.js", "metro.config.js", "jest.config.js"]
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/tslint.json b/tfjs-master/tfjs-react-native/integration_rn59/tslint.json
deleted file mode 100644
index 75901cf10..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/tslint.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "extends": "../../tslint.json",
-  "rules": {
-    "no-require-imports": false
-  }
-  
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/wdio.conf.js b/tfjs-master/tfjs-react-native/integration_rn59/wdio.conf.js
deleted file mode 100644
index 5a93d96b8..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/wdio.conf.js
+++ /dev/null
@@ -1,91 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-
-const localIdentifier =
-    `rn_integration_${Date.now()}_${Math.floor(Math.random() * 10000)}`;
-
-const sharedCapabilities = Object.freeze({
-  'browserstack.debug': true,
-  'browserstack.local': true,
-  'browserstack.networkLogs': true,
-  'browserstack.localIdentifier': localIdentifier,
-});
-
-
-exports.config = {
-  before: function() {
-    require('ts-node').register({files: true})
-  },
-
-  // ====================
-  // Runner Configuration
-  // ====================
-  runner: 'local',
-
-  // =====================
-  // Server Configurations
-  // =====================
-  hostname: 'hub-cloud.browserstack.com',
-  port: 443,
-  services: ['browserstack'],
-  user: process.env.BROWSERSTACK_USERNAME,
-  key: process.env.BROWSERSTACK_KEY,
-  maxInstances: 5,
-  // Bridge network requests to the machine that started the test session.
-  browserstackLocal: true,
-  browserstackLocalForcedStop: true,
-  browserstackOpts: {
-    // Create a local id to separate this instance of browserstack local from
-    // ones that may be created by karma.
-    localIdentifier,
-  },
-
-  // =====================
-  // Test configuration
-  // =====================
-  specs: ['./test/**/*.ts'],
-  exclude: [],
-  capabilities: [
-    Object.assign(
-        {
-          'app': 'deeplearnjs1/tfjs-rn-integration-android',
-          'device': 'Samsung Galaxy S9 Plus',
-          'os': 'android',
-          'os_version': '9.0',
-          'build': 'tfjs-react-native android',
-          'name': 'React Native Integration Test (Android)',
-        },
-        sharedCapabilities),
-  ],
-  // Level of logging verbosity: trace | debug | info | warn | error | silent
-  logLevel: 'warn',
-  // If you only want to run your tests until a specific amount of tests have
-  // failed use bail (default is 0 - don't bail, run all tests).
-  bail: 0,
-  // Default timeout for all waitFor* commands.
-  waitforTimeout: 10000,
-  // Default timeout in milliseconds for request
-  connectionRetryTimeout: 180000,
-  // Default request retries count
-  connectionRetryCount: 3,
-  framework: 'jasmine',
-  reporters: ['spec'],
-  jasmineNodeOpts: {
-    defaultTimeoutInterval: 60000,
-  },
-}
diff --git a/tfjs-master/tfjs-react-native/integration_rn59/yarn.lock b/tfjs-master/tfjs-react-native/integration_rn59/yarn.lock
deleted file mode 100644
index afe8bd8a5..000000000
--- a/tfjs-master/tfjs-react-native/integration_rn59/yarn.lock
+++ /dev/null
@@ -1,8219 +0,0 @@
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-  resolved "https://registry.yarnpkg.com/define-properties/-/define-properties-1.1.3.tgz#cf88da6cbee26fe6db7094f61d870cbd84cee9f1"
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-  dependencies:
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-
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-  version "0.2.5"
-  resolved "https://registry.yarnpkg.com/define-property/-/define-property-0.2.5.tgz#c35b1ef918ec3c990f9a5bc57be04aacec5c8116"
-  integrity sha1-w1se+RjsPJkPmlvFe+BKrOxcgRY=
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-    is-descriptor "^0.1.0"
-
-define-property@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/define-property/-/define-property-1.0.0.tgz#769ebaaf3f4a63aad3af9e8d304c9bbe79bfb0e6"
-  integrity sha1-dp66rz9KY6rTr56NMEybvnm/sOY=
-  dependencies:
-    is-descriptor "^1.0.0"
-
-define-property@^2.0.2:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/define-property/-/define-property-2.0.2.tgz#d459689e8d654ba77e02a817f8710d702cb16e9d"
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-    isobject "^3.0.1"
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
-  integrity sha1-3zrhmayt+31ECqrgsp4icrJOxhk=
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/delegates/-/delegates-1.0.0.tgz#84c6e159b81904fdca59a0ef44cd870d31250f9a"
-  integrity sha1-hMbhWbgZBP3KWaDvRM2HDTElD5o=
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-  version "1.2.1"
-  resolved "https://registry.yarnpkg.com/denodeify/-/denodeify-1.2.1.tgz#3a36287f5034e699e7577901052c2e6c94251631"
-  integrity sha1-OjYof1A05pnnV3kBBSwubJQlFjE=
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-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/depd/-/depd-1.1.2.tgz#9bcd52e14c097763e749b274c4346ed2e560b5a9"
-  integrity sha1-m81S4UwJd2PnSbJ0xDRu0uVgtak=
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-  version "1.0.4"
-  resolved "https://registry.yarnpkg.com/destroy/-/destroy-1.0.4.tgz#978857442c44749e4206613e37946205826abd80"
-  integrity sha1-l4hXRCxEdJ5CBmE+N5RiBYJqvYA=
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-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/detect-libc/-/detect-libc-1.0.3.tgz#fa137c4bd698edf55cd5cd02ac559f91a4c4ba9b"
-  integrity sha1-+hN8S9aY7fVc1c0CrFWfkaTEups=
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-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/detect-newline/-/detect-newline-2.1.0.tgz#f41f1c10be4b00e87b5f13da680759f2c5bfd3e2"
-  integrity sha1-9B8cEL5LAOh7XxPaaAdZ8sW/0+I=
-
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-  version "24.9.0"
-  resolved "https://registry.yarnpkg.com/diff-sequences/-/diff-sequences-24.9.0.tgz#5715d6244e2aa65f48bba0bc972db0b0b11e95b5"
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-  version "3.5.0"
-  resolved "https://registry.yarnpkg.com/diff/-/diff-3.5.0.tgz#800c0dd1e0a8bfbc95835c202ad220fe317e5a12"
-  integrity sha512-A46qtFgd+g7pDZinpnwiRJtxbC1hpgf0uzP3iG89scHk0AUC7A1TGxf5OiiOUv/JMZR8GOt8hL900hV0bOy5xA==
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-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/diff/-/diff-4.0.2.tgz#60f3aecb89d5fae520c11aa19efc2bb982aade7d"
-  integrity sha512-58lmxKSA4BNyLz+HHMUzlOEpg09FV+ev6ZMe3vJihgdxzgcwZ8VoEEPmALCZG9LmqfVoNMMKpttIYTVG6uDY7A==
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-  version "0.2.2"
-  resolved "https://registry.yarnpkg.com/dom-serializer/-/dom-serializer-0.2.2.tgz#1afb81f533717175d478655debc5e332d9f9bb51"
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-  dependencies:
-    domelementtype "^2.0.1"
-    entities "^2.0.0"
-
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-  version "0.1.1"
-  resolved "https://registry.yarnpkg.com/dom-walk/-/dom-walk-0.1.1.tgz#672226dc74c8f799ad35307df936aba11acd6018"
-  integrity sha1-ZyIm3HTI95mtNTB9+TaroRrNYBg=
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-  version "1.3.1"
-  resolved "https://registry.yarnpkg.com/domelementtype/-/domelementtype-1.3.1.tgz#d048c44b37b0d10a7f2a3d5fee3f4333d790481f"
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-
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-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/domelementtype/-/domelementtype-2.0.1.tgz#1f8bdfe91f5a78063274e803b4bdcedf6e94f94d"
-  integrity sha512-5HOHUDsYZWV8FGWN0Njbr/Rn7f/eWSQi1v7+HsUVwXgn8nWWlL64zKDkS0n8ZmQ3mlWOMuXOnR+7Nx/5tMO5AQ==
-
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-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/domexception/-/domexception-1.0.1.tgz#937442644ca6a31261ef36e3ec677fe805582c90"
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-  dependencies:
-    webidl-conversions "^4.0.2"
-
-domutils@^1.7.0:
-  version "1.7.0"
-  resolved "https://registry.yarnpkg.com/domutils/-/domutils-1.7.0.tgz#56ea341e834e06e6748af7a1cb25da67ea9f8c2a"
-  integrity sha512-Lgd2XcJ/NjEw+7tFvfKxOzCYKZsdct5lczQ2ZaQY8Djz7pfAD3Gbp8ySJWtreII/vDlMVmxwa6pHmdxIYgttDg==
-  dependencies:
-    dom-serializer "0"
-    domelementtype "1"
-
-duplexer@~0.1.1:
-  version "0.1.1"
-  resolved "https://registry.yarnpkg.com/duplexer/-/duplexer-0.1.1.tgz#ace6ff808c1ce66b57d1ebf97977acb02334cfc1"
-  integrity sha1-rOb/gIwc5mtX0ev5eXessCM0z8E=
-
-ecc-jsbn@~0.1.1:
-  version "0.1.2"
-  resolved "https://registry.yarnpkg.com/ecc-jsbn/-/ecc-jsbn-0.1.2.tgz#3a83a904e54353287874c564b7549386849a98c9"
-  integrity sha1-OoOpBOVDUyh4dMVkt1SThoSamMk=
-  dependencies:
-    jsbn "~0.1.0"
-    safer-buffer "^2.1.0"
-
-ee-first@1.1.1:
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/ee-first/-/ee-first-1.1.1.tgz#590c61156b0ae2f4f0255732a158b266bc56b21d"
-  integrity sha1-WQxhFWsK4vTwJVcyoViyZrxWsh0=
-
-ejs@^2.5.7:
-  version "2.7.1"
-  resolved "https://registry.yarnpkg.com/ejs/-/ejs-2.7.1.tgz#5b5ab57f718b79d4aca9254457afecd36fa80228"
-  integrity sha512-kS/gEPzZs3Y1rRsbGX4UOSjtP/CeJP0CxSNZHYxGfVM/VgLcv0ZqM7C45YyTj2DI2g7+P9Dd24C+IMIg6D0nYQ==
-
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-  version "7.0.3"
-  resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-7.0.3.tgz#933a04052860c85e83c122479c4748a8e4c72156"
-  integrity sha512-CwBLREIQ7LvYFB0WyRvwhq5N5qPhc6PMjD6bYggFlI5YyDgl+0vxq5VHbMOFqLg7hfWzmu8T5Z1QofhmTIhItA==
-
-emoji-regex@^8.0.0:
-  version "8.0.0"
-  resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-8.0.0.tgz#e818fd69ce5ccfcb404594f842963bf53164cc37"
-  integrity sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==
-
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-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/encodeurl/-/encodeurl-1.0.2.tgz#ad3ff4c86ec2d029322f5a02c3a9a606c95b3f59"
-  integrity sha1-rT/0yG7C0CkyL1oCw6mmBslbP1k=
-
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-  version "0.1.12"
-  resolved "https://registry.yarnpkg.com/encoding/-/encoding-0.1.12.tgz#538b66f3ee62cd1ab51ec323829d1f9480c74beb"
-  integrity sha1-U4tm8+5izRq1HsMjgp0flIDHS+s=
-  dependencies:
-    iconv-lite "~0.4.13"
-
-end-of-stream@^1.1.0:
-  version "1.4.1"
-  resolved "https://registry.yarnpkg.com/end-of-stream/-/end-of-stream-1.4.1.tgz#ed29634d19baba463b6ce6b80a37213eab71ec43"
-  integrity sha512-1MkrZNvWTKCaigbn+W15elq2BB/L22nqrSY5DKlo3X6+vclJm8Bb5djXJBmEX6fS3+zCh/F4VBK5Z2KxJt4s2Q==
-  dependencies:
-    once "^1.4.0"
-
-end-of-stream@^1.4.1:
-  version "1.4.4"
-  resolved "https://registry.yarnpkg.com/end-of-stream/-/end-of-stream-1.4.4.tgz#5ae64a5f45057baf3626ec14da0ca5e4b2431eb0"
-  integrity sha512-+uw1inIHVPQoaVuHzRyXd21icM+cnt4CzD5rW+NC1wjOUSTOs+Te7FOv7AhN7vS9x/oIyhLP5PR1H+phQAHu5Q==
-  dependencies:
-    once "^1.4.0"
-
-entities@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/entities/-/entities-2.0.0.tgz#68d6084cab1b079767540d80e56a39b423e4abf4"
-  integrity sha512-D9f7V0JSRwIxlRI2mjMqufDrRDnx8p+eEOz7aUM9SuvF8gsBzra0/6tbjl1m8eQHrZlYj6PxqE00hZ1SAIKPLw==
-
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-  version "5.12.1"
-  resolved "https://registry.yarnpkg.com/envinfo/-/envinfo-5.12.1.tgz#83068c33e0972eb657d6bc69a6df30badefb46ef"
-  integrity sha512-pwdo0/G3CIkQ0y6PCXq4RdkvId2elvtPCJMG0konqlrfkWQbf1DWeH9K2b/cvu2YgGvPPTOnonZxXM1gikFu1w==
-
-error-ex@^1.2.0, error-ex@^1.3.1:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/error-ex/-/error-ex-1.3.2.tgz#b4ac40648107fdcdcfae242f428bea8a14d4f1bf"
-  integrity sha512-7dFHNmqeFSEt2ZBsCriorKnn3Z2pj+fd9kmI6QoWw4//DL+icEBfc0U7qJCisqrTsKTjw4fNFy2pW9OqStD84g==
-  dependencies:
-    is-arrayish "^0.2.1"
-
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-  version "1.5.1"
-  resolved "https://registry.yarnpkg.com/errorhandler/-/errorhandler-1.5.1.tgz#b9ba5d17cf90744cd1e851357a6e75bf806a9a91"
-  integrity sha512-rcOwbfvP1WTViVoUjcfZicVzjhjTuhSMntHh6mW3IrEiyE6mJyXvsToJUJGlGlw/2xU9P5whlWNGlIDVeCiT4A==
-  dependencies:
-    accepts "~1.3.7"
-    escape-html "~1.0.3"
-
-es-abstract@^1.5.1:
-  version "1.13.0"
-  resolved "https://registry.yarnpkg.com/es-abstract/-/es-abstract-1.13.0.tgz#ac86145fdd5099d8dd49558ccba2eaf9b88e24e9"
-  integrity sha512-vDZfg/ykNxQVwup/8E1BZhVzFfBxs9NqMzGcvIJrqg5k2/5Za2bWo40dK2J1pgLngZ7c+Shh8lwYtLGyrwPutg==
-  dependencies:
-    es-to-primitive "^1.2.0"
-    function-bind "^1.1.1"
-    has "^1.0.3"
-    is-callable "^1.1.4"
-    is-regex "^1.0.4"
-    object-keys "^1.0.12"
-
-es-to-primitive@^1.2.0:
-  version "1.2.0"
-  resolved "https://registry.yarnpkg.com/es-to-primitive/-/es-to-primitive-1.2.0.tgz#edf72478033456e8dda8ef09e00ad9650707f377"
-  integrity sha512-qZryBOJjV//LaxLTV6UC//WewneB3LcXOL9NP++ozKVXsIIIpm/2c13UDiD9Jp2eThsecw9m3jPqDwTyobcdbg==
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-    is-callable "^1.1.4"
-    is-date-object "^1.0.1"
-    is-symbol "^1.0.2"
-
-es6-promise@^4.0.3:
-  version "4.2.8"
-  resolved "https://registry.yarnpkg.com/es6-promise/-/es6-promise-4.2.8.tgz#4eb21594c972bc40553d276e510539143db53e0a"
-  integrity sha512-HJDGx5daxeIvxdBxvG2cb9g4tEvwIk3i8+nhX0yGrYmZUzbkdg8QbDevheDB8gd0//uPj4c1EQua8Q+MViT0/w==
-
-es6-promisify@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/es6-promisify/-/es6-promisify-5.0.0.tgz#5109d62f3e56ea967c4b63505aef08291c8a5203"
-  integrity sha1-UQnWLz5W6pZ8S2NQWu8IKRyKUgM=
-  dependencies:
-    es6-promise "^4.0.3"
-
-escalade@^3.0.2:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.1.0.tgz#e8e2d7c7a8b76f6ee64c2181d6b8151441602d4e"
-  integrity sha512-mAk+hPSO8fLDkhV7V0dXazH5pDc6MrjBTPyD3VeKzxnVFjH1MIxbCdqGZB9O8+EwWakZs3ZCbDS4IpRt79V1ig==
-
-escape-html@~1.0.3:
-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/escape-html/-/escape-html-1.0.3.tgz#0258eae4d3d0c0974de1c169188ef0051d1d1988"
-  integrity sha1-Aljq5NPQwJdN4cFpGI7wBR0dGYg=
-
-escape-string-regexp@^1.0.2, escape-string-regexp@^1.0.5:
-  version "1.0.5"
-  resolved "https://registry.yarnpkg.com/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz#1b61c0562190a8dff6ae3bb2cf0200ca130b86d4"
-  integrity sha1-G2HAViGQqN/2rjuyzwIAyhMLhtQ=
-
-escodegen@^1.9.1:
-  version "1.14.3"
-  resolved "https://registry.yarnpkg.com/escodegen/-/escodegen-1.14.3.tgz#4e7b81fba61581dc97582ed78cab7f0e8d63f503"
-  integrity sha512-qFcX0XJkdg+PB3xjZZG/wKSuT1PnQWx57+TVSjIMmILd2yC/6ByYElPwJnslDsuWuSAp4AwJGumarAAmJch5Kw==
-  dependencies:
-    esprima "^4.0.1"
-    estraverse "^4.2.0"
-    esutils "^2.0.2"
-    optionator "^0.8.1"
-  optionalDependencies:
-    source-map "~0.6.1"
-
-esprima@^4.0.0, esprima@^4.0.1:
-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/esprima/-/esprima-4.0.1.tgz#13b04cdb3e6c5d19df91ab6987a8695619b0aa71"
-  integrity sha512-eGuFFw7Upda+g4p+QHvnW0RyTX/SVeJBDM/gCtMARO0cLuT2HcEKnTPvhjV6aGeqrCB/sbNop0Kszm0jsaWU4A==
-
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-  version "4.3.0"
-  resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-4.3.0.tgz#398ad3f3c5a24948be7725e83d11a7de28cdbd1d"
-  integrity sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw==
-
-esutils@^2.0.0, esutils@^2.0.2:
-  version "2.0.3"
-  resolved "https://registry.yarnpkg.com/esutils/-/esutils-2.0.3.tgz#74d2eb4de0b8da1293711910d50775b9b710ef64"
-  integrity sha512-kVscqXk4OCp68SZ0dkgEKVi6/8ij300KBWTJq32P/dYeWTSwK41WyTxalN1eRmA5Z9UU/LX9D7FWSmV9SAYx6g==
-
-etag@~1.8.1:
-  version "1.8.1"
-  resolved "https://registry.yarnpkg.com/etag/-/etag-1.8.1.tgz#41ae2eeb65efa62268aebfea83ac7d79299b0887"
-  integrity sha1-Qa4u62XvpiJorr/qg6x9eSmbCIc=
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-event-stream@=3.3.4:
-  version "3.3.4"
-  resolved "https://registry.yarnpkg.com/event-stream/-/event-stream-3.3.4.tgz#4ab4c9a0f5a54db9338b4c34d86bfce8f4b35571"
-  integrity sha1-SrTJoPWlTbkzi0w02Gv86PSzVXE=
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-    from "~0"
-    map-stream "~0.1.0"
-    pause-stream "0.0.11"
-    split "0.3"
-    stream-combiner "~0.0.4"
-    through "~2.3.1"
-
-event-target-shim@^1.0.5:
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/event-target-shim/-/event-target-shim-1.1.1.tgz#a86e5ee6bdaa16054475da797ccddf0c55698491"
-  integrity sha1-qG5e5r2qFgVEddp5fM3fDFVphJE=
-
-eventemitter3@^3.0.0:
-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/eventemitter3/-/eventemitter3-3.1.2.tgz#2d3d48f9c346698fce83a85d7d664e98535df6e7"
-  integrity sha512-tvtQIeLVHjDkJYnzf2dgVMxfuSGJeM/7UCG17TT4EumTfNtF+0nebF/4zWOIkCreAbtNqhGEboB6BWrwqNaw4Q==
-
-exec-sh@^0.2.0:
-  version "0.2.2"
-  resolved "https://registry.yarnpkg.com/exec-sh/-/exec-sh-0.2.2.tgz#2a5e7ffcbd7d0ba2755bdecb16e5a427dfbdec36"
-  integrity sha512-FIUCJz1RbuS0FKTdaAafAByGS0CPvU3R0MeHxgtl+djzCc//F8HakL8GzmVNZanasTbTAY/3DRFA0KpVqj/eAw==
-  dependencies:
-    merge "^1.2.0"
-
-exec-sh@^0.3.2:
-  version "0.3.2"
-  resolved "https://registry.yarnpkg.com/exec-sh/-/exec-sh-0.3.2.tgz#6738de2eb7c8e671d0366aea0b0db8c6f7d7391b"
-  integrity sha512-9sLAvzhI5nc8TpuQUh4ahMdCrWT00wPWz7j47/emR5+2qEfoZP5zzUXvx+vdx+H6ohhnsYC31iX04QLYJK8zTg==
-
-execa@^0.7.0:
-  version "0.7.0"
-  resolved "https://registry.yarnpkg.com/execa/-/execa-0.7.0.tgz#944becd34cc41ee32a63a9faf27ad5a65fc59777"
-  integrity sha1-lEvs00zEHuMqY6n68nrVpl/Fl3c=
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-  resolved "https://registry.yarnpkg.com/metro/-/metro-0.51.1.tgz#b0aad4731593b9f244261bad1abb2a006d1c8969"
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-    async "^2.4.0"
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-    eventemitter3 "^3.0.0"
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-    fs-extra "^1.0.0"
-    graceful-fs "^4.1.3"
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-    lodash.throttle "^4.1.1"
-    merge-stream "^1.0.1"
-    metro-babel-transformer "0.51.1"
-    metro-cache "0.51.1"
-    metro-config "0.51.1"
-    metro-core "0.51.1"
-    metro-minify-uglify "0.51.1"
-    metro-react-native-babel-preset "0.51.1"
-    metro-resolver "0.51.1"
-    metro-source-map "0.51.1"
-    mime-types "2.1.11"
-    mkdirp "^0.5.1"
-    node-fetch "^2.2.0"
-    nullthrows "^1.1.0"
-    react-transform-hmr "^1.0.4"
-    resolve "^1.5.0"
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-    ws "^1.1.5"
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-    yargs "^9.0.0"
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-  resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-2.3.11.tgz#86677c97d1720b363431d04d0d15293bd38c1565"
-  integrity sha1-hmd8l9FyCzY0MdBNDRUpO9OMFWU=
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-    regex-cache "^0.4.2"
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-  resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-3.1.10.tgz#70859bc95c9840952f359a068a3fc49f9ecfac23"
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-  version "1.23.0"
-  resolved "https://registry.yarnpkg.com/mime-db/-/mime-db-1.23.0.tgz#a31b4070adaea27d732ea333740a64d0ec9a6659"
-  integrity sha1-oxtAcK2uon1zLqMzdApk0OyaZlk=
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-  resolved "https://registry.yarnpkg.com/mime-types/-/mime-types-2.1.11.tgz#c259c471bda808a85d6cd193b430a5fae4473b3c"
-  integrity sha1-wlnEcb2oCKhdbNGTtDCl+uRHOzw=
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-  version "2.1.24"
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-
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-  version "1.6.0"
-  resolved "https://registry.yarnpkg.com/mime/-/mime-1.6.0.tgz#32cd9e5c64553bd58d19a568af452acff04981b1"
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-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/mimic-fn/-/mimic-fn-2.1.0.tgz#7ed2c2ccccaf84d3ffcb7a69b57711fc2083401b"
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-  version "2.19.0"
-  resolved "https://registry.yarnpkg.com/min-document/-/min-document-2.19.0.tgz#7bd282e3f5842ed295bb748cdd9f1ffa2c824685"
-  integrity sha1-e9KC4/WELtKVu3SM3Z8f+iyCRoU=
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-  version "3.0.8"
-  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.0.8.tgz#5e6a59bd11e2ab0de1cfb843eb2d82e546c321c1"
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-
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-  version "0.0.8"
-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-0.0.8.tgz#857fcabfc3397d2625b8228262e86aa7a011b05d"
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-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-0.0.10.tgz#de3f98543dbf96082be48ad1a0c7cda836301dcf"
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-  resolved "https://registry.yarnpkg.com/minipass/-/minipass-2.9.0.tgz#e713762e7d3e32fed803115cf93e04bca9fcc9a6"
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-    yallist "^3.0.0"
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-  version "1.3.3"
-  resolved "https://registry.yarnpkg.com/minizlib/-/minizlib-1.3.3.tgz#2290de96818a34c29551c8a8d301216bd65a861d"
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-  resolved "https://registry.yarnpkg.com/mixin-deep/-/mixin-deep-1.3.2.tgz#1120b43dc359a785dce65b55b82e257ccf479566"
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-    is-extendable "^1.0.1"
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-  version "0.5.1"
-  resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.1.tgz#30057438eac6cf7f8c4767f38648d6697d75c903"
-  integrity sha1-MAV0OOrGz3+MR2fzhkjWaX11yQM=
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-  resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.6.tgz#7def03d2432dcae4ba1d611445c48396062255f6"
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-  version "2.14.0"
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-  version "3.1.1"
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-    decamelize "^1.2.0"
-
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-  version "20.2.1"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.1.tgz#28f3773c546cdd8a69ddae68116b48a5da328e77"
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-  version "14.0.0"
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-    find-up "^3.0.0"
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-    require-directory "^2.1.1"
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diff --git a/tfjs-master/tfjs-react-native/package.json b/tfjs-master/tfjs-react-native/package.json
deleted file mode 100644
index 846ac3859..000000000
--- a/tfjs-master/tfjs-react-native/package.json
+++ /dev/null
@@ -1,84 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-react-native",
-  "version": "0.0.0",
-  "description": "TensorFlow.js platform implementation for React Native",
-  "main": "dist/index.js",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-react-native"
-  },
-  "types": "dist/index.d.ts",
-  "license": "Apache-2.0",
-  "keywords": [
-    "tensorflow",
-    "tensorflowjs",
-    "machine learning",
-    "deep learning",
-    "neural networks",
-    "react native"
-  ],
-  "scripts": {
-    "publish-local": "rimraf dist/ && yarn build && rollup -c && yalc push",
-    "build": "./scripts/enumerate-tests.js && tsc",
-    "build-ci": "./scripts/enumerate-tests.js && tsc",
-    "build-npm": "yarn build",
-    "build-deps": "yarn build-core && yarn build-backend-cpu && yarn build-backend-webgl",
-    "build-deps-ci": "yarn build-core-ci && yarn build-backend-cpu-ci && yarn build-backend-webgl-ci",
-    "build-core": "cd ../tfjs-core && yarn install && yarn build",
-    "build-core-ci": "cd ../tfjs-core && yarn && yarn build-ci",
-    "build-backend-cpu": "cd ../tfjs-backend-cpu && yarn && yarn build",
-    "build-backend-cpu-ci": "cd ../tfjs-backend-cpu && yarn && yarn build-ci",
-    "build-backend-webgl": "cd ../tfjs-backend-webgl && yarn && yarn build",
-    "build-backend-webgl-ci": "cd ../tfjs-backend-webgl && yarn && yarn build-ci",
-    "link-local": "yalc link",
-    "unlink-local": "yalc remove",
-    "lint": "tslint -p . -t verbose",
-    "update-apk": "./scripts/update-apk.sh"
-  },
-  "devDependencies": {
-    "@react-native-async-storage/async-storage": "^1.13.0",
-    "@tensorflow/tfjs-backend-cpu": "~4.3.0",
-    "@tensorflow/tfjs-backend-webgl": "~4.3.0",
-    "@tensorflow/tfjs-core": "4.3.0",
-    "@types/base64-js": "^1.2.5",
-    "@types/jasmine": "~3.3.0",
-    "@types/react-native": "0.60.2",
-    "@types/react-native-fs": "^2.13.0",
-    "clang-format": "~1.8.0",
-    "expo-asset": "^7.0.0",
-    "expo-camera": "^7.0.0",
-    "expo-gl": "^7.0.0",
-    "gts": "^3.1.0",
-    "react": "^16.12.0",
-    "rimraf": "~2.6.2",
-    "rollup": "^0.58.2",
-    "rollup-plugin-commonjs": "9.1.3",
-    "rollup-plugin-node-resolve": "3.3.0",
-    "rollup-plugin-typescript2": "0.13.0",
-    "rollup-plugin-uglify": "~3.0.0",
-    "tslint": "~6.1.3",
-    "tslint-no-circular-imports": "^0.7.0",
-    "typescript": "4.4.2",
-    "yalc": "^1.0.0-pre.50"
-  },
-  "dependencies": {
-    "base64-js": "^1.3.0",
-    "buffer": "^5.2.1",
-    "jpeg-js": "^0.4.3"
-  },
-  "peerDependencies": {
-    "@react-native-async-storage/async-storage": "^1.13.0",
-    "@tensorflow/tfjs-backend-cpu": "^4.13.0",
-    "@tensorflow/tfjs-backend-webgl": "^4.13.0",
-    "@tensorflow/tfjs-core": "^4.13.0",
-    "expo-camera": "^13.4.4",
-    "expo-gl": "^13.0.1",
-    "react": "*",
-    "react-native": ">= 0.72.0",
-    "react-native-fs": "^2.20.0"
-  },
-  "resolutions": {
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/rollup.config.js b/tfjs-master/tfjs-react-native/rollup.config.js
deleted file mode 100644
index bf55a0c7e..000000000
--- a/tfjs-master/tfjs-react-native/rollup.config.js
+++ /dev/null
@@ -1,91 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import commonjs from 'rollup-plugin-commonjs';
-import node from 'rollup-plugin-node-resolve';
-import {terser} from 'rollup-plugin-terser';
-import typescript from 'rollup-plugin-typescript2';
-
-const PREAMBLE = ``;
-
-function config({plugins = [], output = {}, external = []}) {
-  return {
-    input: 'src/index.ts',
-    plugins: [
-      typescript({tsconfigOverride: {compilerOptions: {module: 'ES2015'}}}),
-      node(),
-      // Polyfill require() from dependencies.
-      commonjs({
-        ignore: ['crypto'],
-        include: 'node_modules/**',
-      }),
-      ...plugins
-    ],
-    output: {
-      banner: PREAMBLE,
-      sourcemap: true,
-      globals: {'@tensorflow/tfjs-core': 'tf'},
-      ...output,
-    },
-    external: [
-      '@tensorflow/tfjs-core',
-      '@react-native-async-storage/async-storage',
-      'react-native',
-    ],
-    onwarn: warning => {
-      let {code} = warning;
-      if (code === 'CIRCULAR_DEPENDENCY' || code === 'CIRCULAR' ||
-          code === 'THIS_IS_UNDEFINED') {
-        return;
-      }
-      console.warn('WARNING: ', warning.toString());
-    }
-  };
-}
-
-module.exports = cmdOptions => [
-    // unminified umd
-    config({
-      output: {
-        format: 'umd',
-        name: 'tf',
-        extend: true,
-        file: 'dist/tf-react-native.js',
-      }
-    }),
-
-    // minified umd
-    config({
-      plugins: [terser({output: {preamble: PREAMBLE}})],
-      output: {
-        format: 'umd',
-        name: 'tf',
-        extend: true,
-        file: 'dist/tf-react-native.min.js',
-      },
-      visualize: cmdOptions.visualize
-    }),
-
-    // esm bundler
-    config({
-      plugins: [terser({output: {preamble: PREAMBLE}})],
-      output: {
-        format: 'es',
-        file: 'dist/tf-react-native.esm.js',
-      }
-    }),
-];
diff --git a/tfjs-master/tfjs-react-native/scripts/enumerate-tests.js b/tfjs-master/tfjs-react-native/scripts/enumerate-tests.js
deleted file mode 100644
index 298b4b935..000000000
--- a/tfjs-master/tfjs-react-native/scripts/enumerate-tests.js
+++ /dev/null
@@ -1,79 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-/**
- * This script generates the tests.ts file which enumerates all
- * tfjs-react-native tests for use in the test application.
- */
-// Call this script from the root of the repo.
-
-const LICENSE = `/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-`;
-
-const AUTOGEN_CLAUSE = `///// DO NOT EDIT: This file is auto-generated by ` +
-    `/scripts/enumerate-tests.js
-`;
-
-const fs = require('fs');
-const path = require('path');
-const argv = require('yargs').argv;
-
-function findTestFiles(dir, files) {
-  files = files || [];
-  fs.readdirSync(dir).forEach(file => {
-    const filePath = path.join(dir, file);
-    if (!file.endsWith('node_modules') && !file.startsWith('.') &&
-        fs.statSync(filePath).isDirectory() &&
-        !fs.existsSync(path.join(filePath, 'package.json'))) {
-      files = findTestFiles(filePath, files);
-    } else if (filePath.endsWith('test.ts')) {
-      files.push(filePath.replace('src/', './').replace('.ts', ''));
-    }
-  });
-  return files;
-};
-
-let files = findTestFiles('./src');
-files = files.map(f => `import '${f}';`);
-const newContent = `${LICENSE}
-${AUTOGEN_CLAUSE}
-${files.sort().join('\n')}
-`;
-
-const testsPath = path.join('src', 'tests.ts');
-const oldContent = fs.readFileSync(testsPath);
-if (argv.ci && newContent != oldContent) {
-  throw new Error(
-      'Looks like you added a new test file. Please run `yarn build` to ' +
-      'regenerate src/tests.ts');
-}
-
-fs.writeFileSync(testsPath, newContent);
diff --git a/tfjs-master/tfjs-react-native/scripts/update-apk.sh b/tfjs-master/tfjs-react-native/scripts/update-apk.sh
deleted file mode 100644
index 16c968ff7..000000000
--- a/tfjs-master/tfjs-react-native/scripts/update-apk.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Utility script to update the integration-test app apk
-
-set -e
-
-cd integration_rn59/android && ./gradlew clean assembleDebug && cd ../../
-gsutil cp ./integration_rn59/android/app/build/outputs/apk/debug/app-debug.apk gs://tfjs-rn/integration-tests/
-gcloud pubsub topics publish sync_reactnative --message "{}"
diff --git a/tfjs-master/tfjs-react-native/src/async_storage_io.ts b/tfjs-master/tfjs-react-native/src/async_storage_io.ts
deleted file mode 100644
index 9a8830583..000000000
--- a/tfjs-master/tfjs-react-native/src/async_storage_io.ts
+++ /dev/null
@@ -1,200 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {AsyncStorageStatic} from '@react-native-async-storage/async-storage';
-import {io} from '@tensorflow/tfjs-core';
-import {fromByteArray, toByteArray} from 'base64-js';
-
-type StorageKeys = {
-  info: string,
-  modelArtifactsWithoutWeights: string,
-  weightData: string,
-};
-
-const PATH_SEPARATOR = '/';
-const PATH_PREFIX = 'tensorflowjs_models';
-const INFO_SUFFIX = 'info';
-const MODEL_SUFFIX = 'model_without_weight';
-const WEIGHT_DATA_SUFFIX = 'weight_data';
-
-function getModelKeys(path: string): StorageKeys {
-  return {
-    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
-    modelArtifactsWithoutWeights:
-        [PATH_PREFIX, path, MODEL_SUFFIX].join(PATH_SEPARATOR),
-    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),
-  };
-}
-/**
- * Populate ModelArtifactsInfo fields for a model with JSON topology.
- * @param modelArtifacts
- * @returns A ModelArtifactsInfo object.
- */
-function getModelArtifactsInfoForJSON(modelArtifacts: io.ModelArtifacts):
-    io.ModelArtifactsInfo {
-  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-    throw new Error('Expected JSON model topology, received ArrayBuffer.');
-  }
-
-  return {
-    dateSaved: new Date(),
-    // TODO followup on removing this from the the interface
-    modelTopologyType: 'JSON',
-    weightDataBytes: modelArtifacts.weightData == null ?
-        0 :
-        modelArtifacts.weightData.byteLength,
-  };
-}
-
-class AsyncStorageHandler implements io.IOHandler {
-  protected readonly keys: StorageKeys;
-  protected asyncStorage: AsyncStorageStatic;
-
-  constructor(protected readonly modelPath: string) {
-    if (modelPath == null || !modelPath) {
-      throw new Error('modelPath must not be null, undefined or empty.');
-    }
-    this.keys = getModelKeys(this.modelPath);
-
-    // We import this dynamically because it binds to a native library that
-    // needs to be installed by the user if they use this handler. We don't
-    // want users who are not using AsyncStorage to have to install this
-    // library.
-    this.asyncStorage =
-        // tslint:disable-next-line:no-require-imports
-        require('@react-native-async-storage/async-storage').default;
-  }
-
-  /**
-   * Save model artifacts to AsyncStorage
-   *
-   * @param modelArtifacts The model artifacts to be stored.
-   * @returns An instance of SaveResult.
-   */
-  async save(modelArtifacts: io.ModelArtifacts): Promise<io.SaveResult> {
-    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
-      throw new Error(
-          'AsyncStorageHandler.save() does not support saving model topology ' +
-          'in binary format.');
-    } else {
-      // We save three items separately for each model,
-      // a ModelArtifactsInfo, a ModelArtifacts without weights
-      // and the model weights.
-      const modelArtifactsInfo: io.ModelArtifactsInfo =
-          getModelArtifactsInfoForJSON(modelArtifacts);
-      const {weightData, ...modelArtifactsWithoutWeights} = modelArtifacts;
-
-      try {
-        this.asyncStorage.setItem(
-            this.keys.info, JSON.stringify(modelArtifactsInfo));
-        this.asyncStorage.setItem(
-            this.keys.modelArtifactsWithoutWeights,
-            JSON.stringify(modelArtifactsWithoutWeights));
-        this.asyncStorage.setItem(
-            this.keys.weightData, fromByteArray(new Uint8Array(weightData)));
-        return {modelArtifactsInfo};
-      } catch (err) {
-        // If saving failed, clean up all items saved so far.
-        this.asyncStorage.removeItem(this.keys.info);
-        this.asyncStorage.removeItem(this.keys.weightData);
-        this.asyncStorage.removeItem(this.keys.modelArtifactsWithoutWeights);
-
-        throw new Error(
-            `Failed to save model '${this.modelPath}' to AsyncStorage.
-            Error info ${err}`);
-      }
-    }
-  }
-
-  /**
-   * Load a model from local storage.
-   *
-   * See the documentation to `browserLocalStorage` for details on the saved
-   * artifacts.
-   *
-   * @returns The loaded model (if loading succeeds).
-   */
-  async load(): Promise<io.ModelArtifacts> {
-    const info = JSON.parse(await this.asyncStorage.getItem(this.keys.info)) as
-        io.ModelArtifactsInfo;
-    if (info == null) {
-      throw new Error(
-          `In local storage, there is no model with name '${this.modelPath}'`);
-    }
-
-    if (info.modelTopologyType !== 'JSON') {
-      throw new Error(
-          'BrowserLocalStorage does not support loading non-JSON model ' +
-          'topology yet.');
-    }
-
-    const modelArtifacts: io.ModelArtifacts =
-        JSON.parse(await this.asyncStorage.getItem(
-            this.keys.modelArtifactsWithoutWeights));
-
-    // Load weight data.
-    const weightDataBase64 =
-        await this.asyncStorage.getItem(this.keys.weightData);
-    if (weightDataBase64 == null) {
-      throw new Error(
-          `In local storage, the binary weight values of model ` +
-          `'${this.modelPath}' are missing.`);
-    }
-    modelArtifacts.weightData = toByteArray(weightDataBase64).buffer;
-
-    return modelArtifacts;
-  }
-}
-
-/**
- * Factory function for AsyncStorage IOHandler.
- *
- * This `IOHandler` supports both `save` and `load`.
- *
- * For each model's saved artifacts, three items are saved to async storage.
- *   - `tensorflowjs_models/${modelPath}/info`: Contains meta-info about the
- *     model, such as date saved, type of the topology, size in bytes, etc.
- *   - `tensorflowjs_models/${modelPath}/model_without_weight`: The topology,
- *     weights_specs and all other information about the model except for the
- *     weights.
- *   - `tensorflowjs_models/${modelPath}/weight_data`: Concatenated binary
- *     weight values, stored as a base64-encoded string.
- *
- * ```js
- *  async function asyncStorageExample() {
- *    // Define a model
- *    const model = tf.sequential();
- *    model.add(tf.layers.dense({units: 5, inputShape: [1]}));
- *    model.add(tf.layers.dense({units: 1}));
- *    model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
- *
- *    // Save the model to async storage
- *    await model.save(asyncStorageIO('custom-model-test'));
- *    // Load the model from async storage
- *    await tf.loadLayersModel(asyncStorageIO('custom-model-test'));
- * }
- * ```
- *
- * @param modelPath A unique identifier for the model to be saved. Must be a
- *   non-empty string.
- * @returns An instance of `IOHandler`
- *
- * @doc {heading: 'Models', subheading: 'IOHandlers'}
- */
-export function asyncStorageIO(modelPath: string): io.IOHandler {
-  return new AsyncStorageHandler(modelPath);
-}
diff --git a/tfjs-master/tfjs-react-native/src/async_storage_io_test.ts b/tfjs-master/tfjs-react-native/src/async_storage_io_test.ts
deleted file mode 100644
index e4616f271..000000000
--- a/tfjs-master/tfjs-react-native/src/async_storage_io_test.ts
+++ /dev/null
@@ -1,136 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import './platform_react_native';
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {asyncStorageIO} from './async_storage_io';
-import {RN_ENVS} from './test_env_registry';
-
-describeWithFlags('AsyncStorageIO', RN_ENVS, () => {
-  // Test data.
-  const modelTopology1: {} = {
-    'class_name': 'Sequential',
-    'keras_version': '2.1.4',
-    'config': [{
-      'class_name': 'Dense',
-      'config': {
-        'kernel_initializer': {
-          'class_name': 'VarianceScaling',
-          'config': {
-            'distribution': 'uniform',
-            'scale': 1.0,
-            'seed': null,
-            'mode': 'fan_avg'
-          }
-        },
-        'name': 'dense',
-        'kernel_constraint': null,
-        'bias_regularizer': null,
-        'bias_constraint': null,
-        'dtype': 'float32',
-        'activation': 'linear',
-        'trainable': true,
-        'kernel_regularizer': null,
-        'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-        'units': 1,
-        'batch_input_shape': [null, 3],
-        'use_bias': true,
-        'activity_regularizer': null
-      }
-    }],
-    'backend': 'tensorflow'
-  };
-
-  const weightSpecs1: tf.io.WeightsManifestEntry[] = [
-    {
-      name: 'dense/kernel',
-      shape: [3, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense/bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-  const weightData1 = new ArrayBuffer(16);
-
-  const artifacts1: tf.io.ModelArtifacts = {
-    modelTopology: modelTopology1,
-    weightSpecs: weightSpecs1,
-    weightData: weightData1,
-    format: 'layers-model',
-    generatedBy: 'TensorFlow.js v0.0.0',
-    convertedBy: null
-  };
-
-  const artifactsV0: tf.io.ModelArtifacts = {
-    modelTopology: modelTopology1,
-    weightSpecs: weightSpecs1,
-    weightData: weightData1
-  };
-
-  it('constructs an IOHandler', async () => {
-    const handler = asyncStorageIO('foo');
-    expect(typeof handler.load).toBe('function');
-    expect(typeof handler.save).toBe('function');
-  });
-
-  it('save returns a SaveResult', async () => {
-    const handler = asyncStorageIO('FooModel');
-    const result = await handler.save(artifacts1);
-    expect(result.modelArtifactsInfo).toBeDefined();
-    expect(result.modelArtifactsInfo).not.toBeNull();
-
-    expect(result.modelArtifactsInfo.modelTopologyType).toBe('JSON');
-    expect(result.modelArtifactsInfo.weightDataBytes).toBe(16);
-  });
-
-  it('Save-load round trip succeeds', async () => {
-    const handler = asyncStorageIO('FooModel');
-    await handler.save(artifacts1);
-
-    const handler2 = asyncStorageIO('FooModel');
-    const loaded = await handler2.load();
-
-    expect(loaded.modelTopology).toEqual(modelTopology1);
-    expect(loaded.weightSpecs).toEqual(weightSpecs1);
-    expect(loaded.weightData).toEqual(weightData1);
-    expect(loaded.format).toEqual('layers-model');
-    expect(loaded.generatedBy).toEqual('TensorFlow.js v0.0.0');
-    expect(loaded.convertedBy).toEqual(null);
-  });
-
-  it('Save-load round trip succeeds: v0 format', async () => {
-    const handler1 = asyncStorageIO('FooModel');
-    await handler1.save(artifactsV0);
-
-    const handler2 = asyncStorageIO('FooModel');
-    const loaded = await handler2.load();
-
-    expect(loaded.modelTopology).toEqual(modelTopology1);
-    expect(loaded.weightSpecs).toEqual(weightSpecs1);
-    expect(loaded.weightData).toEqual(weightData1);
-    expect(loaded.format).toEqual(undefined);
-    expect(loaded.generatedBy).toEqual(undefined);
-    expect(loaded.convertedBy).toEqual(undefined);
-  });
-});
diff --git a/tfjs-master/tfjs-react-native/src/bundle_resource_io.ts b/tfjs-master/tfjs-react-native/src/bundle_resource_io.ts
deleted file mode 100644
index 06d1fa26f..000000000
--- a/tfjs-master/tfjs-react-native/src/bundle_resource_io.ts
+++ /dev/null
@@ -1,185 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {io, util} from '@tensorflow/tfjs-core';
-import {Asset} from 'expo-asset';
-import {Platform} from 'react-native';
-
-import {fetch} from './platform_react_native';
-
-class BundleResourceHandler implements io.IOHandler {
-  constructor(
-      protected readonly modelJson: io.ModelJSON,
-      protected readonly modelWeightsId: Array<string|number>) {
-    if (modelJson == null || modelWeightsId == null) {
-      throw new Error(
-          'Must pass the model json object and the model weights path.');
-    }
-  }
-
-  /**
-   * Save model artifacts. This IO handler cannot support writing to the
-   * packaged bundle at runtime and is exclusively for loading a model
-   * that is already packages with the app.
-   */
-  async save(): Promise<io.SaveResult> {
-    throw new Error(
-        'Bundle resource IO handler does not support saving. ' +
-        'Consider using asyncStorageIO instead');
-  }
-
-  /**
-   * Load a model from local storage.
-   *
-   * See the documentation to `browserLocalStorage` for details on the saved
-   * artifacts.
-   *
-   * @returns The loaded model (if loading succeeds).
-   */
-  async load(): Promise<io.ModelArtifacts> {
-    const weightsAssets = this.modelWeightsId.map(id => Asset.fromModule(id));
-    if (weightsAssets[0].uri.match('^http')) {
-      // In debug/dev mode RN will serve these assets over HTTP
-      return this.loadViaHttp(weightsAssets);
-    } else {
-      // In release mode the assets will be on the file system.
-      return this.loadLocalAsset(weightsAssets);
-    }
-  }
-
-  async loadViaHttp(weightsAssets: Asset[]): Promise<io.ModelArtifacts> {
-    const modelJson = this.modelJson;
-    const modelArtifacts: io.ModelArtifacts = Object.assign({}, modelJson);
-    modelArtifacts.weightSpecs = modelJson.weightsManifest[0].weights;
-    //@ts-ignore
-    delete modelArtifacts.weightManifest;
-
-    // Load the weights
-    const weightsDataArray =
-        await Promise.all(weightsAssets.map(async (weightAsset) => {
-          const url = weightAsset.uri;
-          const requestInit: undefined = undefined;
-          const response = await fetch(url, requestInit, {isBinary: true});
-          const weightData = await response.arrayBuffer();
-          return weightData;
-        }));
-
-    modelArtifacts.weightData = io.concatenateArrayBuffers(weightsDataArray);
-    return modelArtifacts;
-  }
-
-  async loadLocalAsset(weightsAssets: Asset[]): Promise<io.ModelArtifacts> {
-    // Use a dynamic import here because react-native-fs is not compatible
-    // with managed expo workflow. However the managed expo workflow should
-    // never hit this code path.
-
-    // tslint:disable-next-line: no-require-imports
-    const RNFS = require('react-native-fs');
-
-    const modelJson = this.modelJson;
-    const modelArtifacts: io.ModelArtifacts = Object.assign({}, modelJson);
-    modelArtifacts.weightSpecs = modelJson.weightsManifest[0].weights;
-    //@ts-ignore
-    delete modelArtifacts.weightManifest;
-
-    // Load the weights
-    const weightsDataArray =
-        await Promise.all(weightsAssets.map(async (weightsAsset) => {
-          let base64Weights: string;
-          if (Platform.OS === 'android') {
-            // On android we get a resource id instead of a regular path. We
-            // need to load the weights from the res/raw folder using this id.
-            const fileName = `${weightsAsset.uri}.${weightsAsset.type}`;
-            try {
-              base64Weights = await RNFS.readFileRes(fileName, 'base64');
-            } catch (e) {
-              throw new Error(
-                  `Error reading resource ${fileName}. Make sure the file is
-            in located in the res/raw folder of the bundle`,
-              );
-            }
-          } else {
-            try {
-              base64Weights = await RNFS.readFile(weightsAsset.uri, 'base64');
-            } catch (e) {
-              throw new Error(
-                  `Error reading resource ${weightsAsset.uri}.`,
-              );
-            }
-          }
-
-          const weightData = util.encodeString(base64Weights, 'base64').buffer;
-          return weightData;
-        }));
-
-    modelArtifacts.weightData = io.concatenateArrayBuffers(weightsDataArray);
-    return modelArtifacts;
-  }
-}
-
-/**
- * Factory function for BundleResource IOHandler.
- *
- * This `IOHandler` only supports `load`. It is designed to support
- * loading models that have been statically bundled (at compile time)
- * with an app.
- *
- * This IOHandler is not compatible with managed expo apps.
- *
- * ```js
- *  const modelJson = require('../path/to/model.json');
- *  const modelWeights = require('../path/to/model_weights.bin');
- *  async function bundleResourceIOExample() {
- *    const model =
- *      await tf.loadLayersModel(bundleResourceIO(modelJson, modelWeights));
- *
- *     const res = model.predict(tf.randomNormal([1, 28, 28, 1])) as tf.Tensor;
- *  }
- * ```
- *
- * @param modelJson The JSON object for the serialized model.
- * @param modelWeightsId Identifier(s) for the model's weights files. This is
- * generally a resourceId or a path to the resource in the app package.
- * This is typically obtained with a `require` statement. Can also be an array
- * of such ids if the model has multiple shards.
- *
- * See
- * facebook.github.io/react-native/docs/images#static-non-image-resources
- * for more details on how to include static resources into your react-native
- * app including how to configure `metro` to bundle `.bin` files.
- *
- * @returns An instance of `IOHandler`
- *
- * @doc {heading: 'Models', subheading: 'IOHandlers'}
- */
-export function bundleResourceIO(
-    modelJson: io.ModelJSON, modelWeightsId: number|number[]): io.IOHandler {
-  if (typeof modelJson !== 'object') {
-    throw new Error(
-        'modelJson must be a JavaScript object (and not a string).\n' +
-        'Have you wrapped your asset path in a require() statement?');
-  }
-
-  if (typeof modelWeightsId === 'string') {
-    throw new Error(
-        'modelWeightsID must be a number or number array.\n' +
-        'Have you wrapped your asset paths in a require() statements?');
-  }
-  const modelWeightsIdArr =
-      Array.isArray(modelWeightsId) ? modelWeightsId : [modelWeightsId];
-  return new BundleResourceHandler(modelJson, modelWeightsIdArr);
-}
diff --git a/tfjs-master/tfjs-react-native/src/bundle_resource_io_test.ts b/tfjs-master/tfjs-react-native/src/bundle_resource_io_test.ts
deleted file mode 100644
index 2fc8b181d..000000000
--- a/tfjs-master/tfjs-react-native/src/bundle_resource_io_test.ts
+++ /dev/null
@@ -1,267 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import './platform_react_native';
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {bundleResourceIO} from './bundle_resource_io';
-import * as tfjsRn from './platform_react_native';
-import {RN_ENVS} from './test_env_registry';
-
-describeWithFlags('BundleResourceIO', RN_ENVS, () => {
-  // Test data.
-  const modelTopology1: {} = {
-    'class_name': 'Sequential',
-    'keras_version': '2.1.4',
-    'config': [{
-      'class_name': 'Dense',
-      'config': {
-        'kernel_initializer': {
-          'class_name': 'VarianceScaling',
-          'config': {
-            'distribution': 'uniform',
-            'scale': 1.0,
-            'seed': null,
-            'mode': 'fan_avg'
-          }
-        },
-        'name': 'dense',
-        'kernel_constraint': null,
-        'bias_regularizer': null,
-        'bias_constraint': null,
-        'dtype': 'float32',
-        'activation': 'linear',
-        'trainable': true,
-        'kernel_regularizer': null,
-        'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-        'units': 1,
-        'batch_input_shape': [null, 3],
-        'use_bias': true,
-        'activity_regularizer': null
-      }
-    }],
-    'backend': 'tensorflow'
-  };
-
-  const weightSpecs1: tf.io.WeightsManifestEntry[] = [
-    {
-      name: 'dense/kernel',
-      shape: [3, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense/bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-  const weightData1 = new ArrayBuffer(16);
-
-  it('constructs an IOHandler', async () => {
-    const modelJson: tf.io.ModelJSON = {
-      modelTopology: modelTopology1,
-      weightsManifest: [{
-        paths: [],
-        weights: weightSpecs1,
-      }]
-
-    };
-    const resourceId = 1;
-    const handler = bundleResourceIO(modelJson, resourceId);
-    expect(typeof handler.load).toBe('function');
-    expect(typeof handler.save).toBe('function');
-  });
-
-  it('loads model artifacts', async () => {
-    const response = new Response(weightData1);
-    spyOn(tfjsRn, 'fetch').and.returnValue(Promise.resolve(response));
-
-    const modelJson: tf.io.ModelJSON = {
-      modelTopology: modelTopology1,
-      weightsManifest: [{
-        paths: [],
-        weights: weightSpecs1,
-      }]
-
-    };
-    const resourceId = 1;
-    const handler = bundleResourceIO(modelJson, resourceId);
-
-    const loaded = await handler.load();
-
-    expect(loaded.modelTopology).toEqual(modelTopology1);
-    expect(loaded.weightSpecs).toEqual(weightSpecs1);
-    expect(loaded.weightData).toEqual(weightData1);
-  });
-
-  it('errors on string modelJSON', async () => {
-    const response = new Response(weightData1);
-    spyOn(tf.env().platform, 'fetch')
-        .and.returnValue(Promise.resolve(response));
-
-    const modelJson = `{
-      modelTopology: modelTopology1,
-      weightsManifest: [{
-        paths: [],
-        weights: weightSpecs1,
-      }]
-    }`;
-    const resourceId = 1;
-    expect(
-        () => bundleResourceIO(
-            modelJson as unknown as tf.io.ModelJSON, resourceId))
-        .toThrow(new Error(
-            'modelJson must be a JavaScript object (and not a string).\n' +
-            'Have you wrapped yor asset path in a require() statment?'));
-  });
-});
-
-describeWithFlags('BundleResourceIO Sharded', RN_ENVS, () => {
-  // Test data.
-  const modelTopology: {} = {
-    'class_name': 'Sequential',
-    'keras_version': '2.1.4',
-    'config': [
-      {
-        'class_name': 'Dense',
-        'config': {
-          'kernel_initializer': {
-            'class_name': 'VarianceScaling',
-            'config': {
-              'distribution': 'uniform',
-              'scale': 1.0,
-              'seed': null,
-              'mode': 'fan_avg'
-            }
-          },
-          'name': 'dense',
-          'kernel_constraint': null,
-          'bias_regularizer': null,
-          'bias_constraint': null,
-          'dtype': 'float32',
-          'activation': 'linear',
-          'trainable': true,
-          'kernel_regularizer': null,
-          'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-          'units': 1,
-          'batch_input_shape': [null, 3],
-          'use_bias': true,
-          'activity_regularizer': null
-        }
-      },
-      {
-        'class_name': 'Dense',
-        'config': {
-          'kernel_initializer': {
-            'class_name': 'VarianceScaling',
-            'config': {
-              'distribution': 'uniform',
-              'scale': 1.0,
-              'seed': null,
-              'mode': 'fan_avg'
-            }
-          },
-          'name': 'dense2',
-          'kernel_constraint': null,
-          'bias_regularizer': null,
-          'bias_constraint': null,
-          'dtype': 'float32',
-          'activation': 'linear',
-          'trainable': true,
-          'kernel_regularizer': null,
-          'bias_initializer': {'class_name': 'Zeros', 'config': {}},
-          'units': 1,
-          'batch_input_shape': [null, 3],
-          'use_bias': true,
-          'activity_regularizer': null
-        }
-      }
-    ],
-    'backend': 'tensorflow'
-  };
-
-  const weightSpecs: tf.io.WeightsManifestEntry[] = [
-    {
-      name: 'dense/kernel',
-      shape: [3, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense/bias',
-      shape: [1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense2/kernel',
-      shape: [3, 1],
-      dtype: 'float32',
-    },
-    {
-      name: 'dense2/bias',
-      shape: [1],
-      dtype: 'float32',
-    }
-  ];
-  const weightData1 = new ArrayBuffer(16);
-  const weightData2 = new ArrayBuffer(16);
-  const resourceIds = [1, 2];
-
-  const combinedWeightsExpected = new ArrayBuffer(32);
-
-  it('constructs an IOHandler', async () => {
-    const modelJson: tf.io.ModelJSON = {
-      modelTopology,
-      weightsManifest: [{
-        paths: [],
-        weights: weightSpecs,
-      }]
-
-    };
-
-    const handler = bundleResourceIO(modelJson, resourceIds);
-    expect(typeof handler.load).toBe('function');
-    expect(typeof handler.save).toBe('function');
-  });
-
-  it('loads model artifacts', async () => {
-    spyOn(tf.env().platform, 'fetch')
-        .and.returnValues(
-            Promise.resolve(new Response(weightData1)),
-            Promise.resolve(new Response(weightData2)),
-        );
-
-    const modelJson: tf.io.ModelJSON = {
-      modelTopology,
-      weightsManifest: [{
-        paths: [],
-        weights: weightSpecs,
-      }]
-
-    };
-
-    const handler = bundleResourceIO(modelJson, resourceIds);
-
-    const loaded = await handler.load();
-
-    expect(loaded.modelTopology).toEqual(modelTopology);
-    expect(loaded.weightSpecs).toEqual(weightSpecs);
-    expect(loaded.weightData).toEqual(combinedWeightsExpected);
-  });
-});
diff --git a/tfjs-master/tfjs-react-native/src/camera/camera.ts b/tfjs-master/tfjs-react-native/src/camera/camera.ts
deleted file mode 100644
index 47801c7d9..000000000
--- a/tfjs-master/tfjs-react-native/src/camera/camera.ts
+++ /dev/null
@@ -1,253 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-
-import {downloadTextureData, drawTexture, runResizeProgram, uploadTextureData} from './camera_webgl_util';
-import {Rotation} from './types';
-interface Dimensions {
-  width: number;
-  height: number;
-  depth: number;
-}
-
-interface Size {
-  width: number;
-  height: number;
-}
-
-interface FromTextureOptions {
-  alignCorners?: boolean;
-  interpolation?: 'nearest_neighbor'|'bilinear';
-  rotation?: Rotation;
-}
-
-const glCapabilities = {
-  canDownloadFromRGBTexture: new WeakMap<WebGL2RenderingContext, boolean>(),
-  // Has detectGLCapabilities been run on a particular GL context;
-  glCapabilitiesTested: new WeakMap<WebGL2RenderingContext, boolean>()
-};
-
-/**
- * Utility function that tests the GL context for capabilities to enable
- * optimizations.
- *
- * For best performance this should be be called once before using the other
- * camera related functions.
- *
- * @doc {heading: 'Media', subheading: 'Camera'}
- */
-export async function detectGLCapabilities(gl: WebGL2RenderingContext) {
-  if (glCapabilities.glCapabilitiesTested.get(gl)) {
-    return;
-  }
-  // Test whether we can successfully download from an RGB texture.
-  // Notably this isn't supported on iOS, but we use this test rather than a
-  // platform check to be more robust on android devices we may not have
-  // directly tested.
-
-  // Set this to true temporarily so that fromTexture does not
-  // use its workaround.
-  glCapabilities.canDownloadFromRGBTexture.set(gl, true);
-
-  try {
-    const height = 2;
-    const width = 4;  // This must be a multiple of 4.
-    const data = new Uint8Array(height * width * 4);
-    for (let i = 0; i < data.length; i++) {
-      data[i] = i;
-    }
-    const sourceDims = {height, width, depth: 4};
-    const tex = uploadTextureData(data, gl, sourceDims);
-
-    const targetDims = {height, width, depth: 3};
-    const downloaded = fromTexture(gl, tex, sourceDims, targetDims);
-    const downloadedData = await downloaded.data();
-    tf.dispose(downloaded);
-
-    const matches = tf.util.arraysEqual(downloadedData, [
-      0,  1,  2,  4,  5,  6,  8,  9,  10, 12, 13, 14,
-      16, 17, 18, 20, 21, 22, 24, 25, 26, 28, 29, 30
-    ]);
-
-    if (matches) {
-      glCapabilities.canDownloadFromRGBTexture.set(gl, true);
-    } else {
-      glCapabilities.canDownloadFromRGBTexture.set(gl, false);
-    }
-  } catch (e) {
-    glCapabilities.canDownloadFromRGBTexture.set(gl, false);
-  } finally {
-    glCapabilities.glCapabilitiesTested.set(gl, true);
-  }
-}
-
-/**
- * Transfers tensor data to an RGB(A) texture.
- *
- * @param gl the WebGL context that owns the texture.
- * @param imageTensor the tensor to upload
- * @param texture optional the target texture. If none is passed in a new
- *     texture will be created.
- *
- * @doc {heading: 'Media', subheading: 'Camera'}
- */
-export async function toTexture(
-    gl: WebGL2RenderingContext, imageTensor: tf.Tensor3D,
-    texture?: WebGLTexture): Promise<WebGLTexture> {
-  tf.util.assert(
-      imageTensor.dtype === 'int32', () => 'imageTensor must be of type int32');
-
-  tf.util.assert(
-      imageTensor.rank === 3, () => 'imageTensor must be a Tensor3D');
-
-  const imageData = Uint8Array.from(await imageTensor.data());
-  const dims = {
-    height: imageTensor.shape[0],
-    width: imageTensor.shape[1],
-    depth: imageTensor.shape[2],
-  };
-  return uploadTextureData(imageData, gl, dims, texture);
-}
-
-/**
- * Creates a tensor3D from a texture.
- *
- * Allows for resizing the image and dropping the alpha channel from the
- * resulting tensor.
- *
- * Note that if you the output depth is 3 then the output width should be a
- * multiple of 4.
- *
- * @param gl the WebGL context that owns the input texture
- * @param texture the texture to convert into a tensor
- * @param sourceDims source dimensions of input texture (width, height, depth)
- * @param targetShape desired shape of output tensor
- * @param useCustomShadersToResize whether to use custom shaders to resize
- *   texture.
- *
- * @doc {heading: 'Media', subheading: 'Camera'}
- */
-export function fromTexture(
-    gl: WebGL2RenderingContext, texture: WebGLTexture, sourceDims: Dimensions,
-    targetShape: Dimensions, useCustomShadersToResize = false,
-    options: FromTextureOptions = {}): tf.Tensor3D {
-  tf.util.assert(
-      targetShape.depth === 3 || targetShape.depth === 4,
-      () => 'fromTexture Error: target depth must be 3 or 4');
-
-  if (targetShape.depth === 3 && targetShape.width % 4 !== 0) {
-    // We throw an error here rather than use the CPU workaround as the user is
-    // likely trying to get the maximum performance.
-    if (glCapabilities.canDownloadFromRGBTexture.get(gl)) {
-      // See
-      // https://www.khronos.org/opengl/wiki/Common_Mistakes#Texture_upload_and_pixel_reads
-      // for more details. At the moment gl.pixelStorei(gl.PACK_ALIGNMENT, 1);
-      // is not supported on expo. "EXGL: gl.pixelStorei() doesn't support this
-      // parameter yet!"
-      throw new Error(
-          'When using targetShape.depth=3, targetShape.width must be' +
-          ' a multiple of 4. Alternatively do not call detectGLCapabilities()');
-    }
-  }
-
-  const originalTargetDepth = targetShape.depth;
-  const targetDepth = glCapabilities.canDownloadFromRGBTexture.get(gl) ?
-      originalTargetDepth :
-      4;
-
-  sourceDims = {
-    height: Math.floor(sourceDims.height),
-    width: Math.floor(sourceDims.width),
-    depth: sourceDims.depth,
-  };
-
-  targetShape = {
-    height: Math.floor(targetShape.height),
-    width: Math.floor(targetShape.width),
-    depth: targetDepth
-  };
-
-  const alignCorners =
-      options.alignCorners != null ? options.alignCorners : false;
-  const interpolation =
-      options.interpolation != null ? options.interpolation : 'bilinear';
-  const rotation = options.rotation != null ? options.rotation : 0;
-
-  tf.util.assert(
-      interpolation === 'bilinear' || interpolation === 'nearest_neighbor',
-      () => 'fromTexture Error: interpolation must be one of' +
-          ' "bilinear" or "nearest_neighbor"');
-
-  tf.util.assert(
-      [0, 90, 180, 270, 360, -90, -180, -270].includes(rotation),
-      () => 'fromTexture Error: rotation must be ' +
-          '0, +/- 90, +/- 180, +/- 270 or 360');
-
-  const resizedTexture = runResizeProgram(
-      gl, texture, sourceDims, targetShape, alignCorners,
-      useCustomShadersToResize, interpolation, rotation);
-  const downloadedTextureData =
-      downloadTextureData(gl, resizedTexture, targetShape);
-
-  let finalTexData;
-  if (originalTargetDepth !== targetDepth && originalTargetDepth === 3) {
-    // We are on a device that does not support downloading from an RGB texture.
-    // Remove the alpha channel values on the CPU.
-    const area = targetShape.height * targetShape.width;
-    finalTexData = new Uint8Array(area * originalTargetDepth);
-
-    for (let i = 0; i < area; i++) {
-      const flatIndexRGB = i * 3;
-      const flatIndexRGBA = i * 4;
-      finalTexData[flatIndexRGB] = downloadedTextureData[flatIndexRGBA];
-      finalTexData[flatIndexRGB + 1] = downloadedTextureData[flatIndexRGBA + 1];
-      finalTexData[flatIndexRGB + 2] = downloadedTextureData[flatIndexRGBA + 2];
-    }
-  } else {
-    finalTexData = downloadedTextureData;
-  }
-
-  return tf.tensor3d(
-      finalTexData,
-      [targetShape.height, targetShape.width, originalTargetDepth], 'int32');
-}
-
-/**
- * Render a texture to the GLView. This will use the default framebuffer
- * and present the contents of the texture on the screen.
- *
- * @param gl
- * @param texture
- * @param dims Dimensions of tensor
- *
- * @doc {heading: 'Media', subheading: 'Camera'}
- */
-export function renderToGLView(
-    gl: WebGL2RenderingContext, texture: WebGLTexture, size: Size,
-    flipHorizontal = true, rotation: Rotation = 0) {
-  tf.util.assert(
-      [0, 90, 180, 270, 360, -90, -180, -270].includes(rotation),
-      () => 'renderToGLView Error: rotation must be ' +
-          '0, +/- 90, +/- 180, +/- 270 or 360');
-
-  size = {
-    width: Math.floor(size.width),
-    height: Math.floor(size.height),
-  };
-  drawTexture(gl, texture, size, flipHorizontal, rotation);
-}
diff --git a/tfjs-master/tfjs-react-native/src/camera/camera_stream.tsx b/tfjs-master/tfjs-react-native/src/camera/camera_stream.tsx
deleted file mode 100644
index cf425d480..000000000
--- a/tfjs-master/tfjs-react-native/src/camera/camera_stream.tsx
+++ /dev/null
@@ -1,426 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the 'License');
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an 'AS IS' BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as React from 'react';
-import * as tf from '@tensorflow/tfjs-core';
-import {
-  StyleSheet,
-  PixelRatio,
-  LayoutChangeEvent,
-  Platform,
-} from 'react-native';
-import { Camera } from 'expo-camera';
-import { GLView, ExpoWebGLRenderingContext } from 'expo-gl';
-import { fromTexture, renderToGLView, detectGLCapabilities } from './camera';
-import { Rotation } from './types';
-
-interface WrappedComponentProps {
-  onLayout?: (event: LayoutChangeEvent) => void;
-  // tslint:disable-next-line: no-any
-  [index: string]: any;
-}
-
-interface Props {
-  useCustomShadersToResize: boolean;
-  cameraTextureWidth: number;
-  cameraTextureHeight: number;
-  resizeWidth: number;
-  resizeHeight: number;
-  resizeDepth: number;
-  autorender: boolean;
-  rotation?: Rotation;
-  onReady: (
-    images: IterableIterator<tf.Tensor3D>,
-    updateCameraPreview: () => void,
-    gl: ExpoWebGLRenderingContext,
-    cameraTexture: WebGLTexture
-  ) => void;
-}
-
-interface State {
-  cameraLayout: { x: number; y: number; width: number; height: number };
-}
-
-const DEFAULT_AUTORENDER = true;
-const DEFAULT_RESIZE_DEPTH = 3;
-const DEFAULT_USE_CUSTOM_SHADERS_TO_RESIZE = false;
-
-/**
- * A higher-order-component (HOC) that augments the [Expo.Camera](https://docs.expo.io/versions/latest/sdk/camera/)
- * component with the ability to yield tensors representing the camera stream.
- *
- * Because the camera data will be consumed in the process, the original
- * camera component will not render any content. This component provides
- * options that can be used to render the camera preview.
- *
- * Notably the component allows on-the-fly resizing of the camera image to
- * smaller dimensions, this speeds up data transfer between the native and
- * javascript threads immensely.
- *
- * __In addition to__ all the props taken by Expo.Camera. The returned
- * component takes the following props
- *
- * - __use_custom_shaders_to_resize__: boolean — whether to use custom shaders
- *   to resize the camera image to smaller dimensions that fit the output
- *   tensor.
- *   - If it is set to false (default and recommended), the resize will be done
- *     by the underlying GL system when drawing the camera image texture to the
- *     target output texture with TEXTURE_MIN_FILTER/TEXTURE_MAG_FILTER set to
- *     gl.LINEAR, and there is no need to provide `cameraTextureWidth` and
- *     `cameraTextureHeight` props below.
- *   - If it is set to true (legacy), the resize will be done by the custom
- *     shaders defined in `resize_bilinear_program_info.ts`. Setting it to true
- *     also requires that client provide the correct `cameraTextureWidth` and
- *     `cameraTextureHeight` props below. Unfortunately there is no official API
- *     to get the camera texture size programmatically so they have to be
- *     decided empirically. From our experience, it is hard to cover all cases
- *     in this way because different devices models and/or preview sizes might
- *     produce different camera texture sizes.
- * - __cameraTextureWidth__: number — the width the camera preview texture
- *   (see note above)
- * - __cameraTextureHeight__: number — the height the camera preview texture
- *   (see note above)
- * - __resizeWidth__: number — the width of the output tensor
- * - __resizeHeight__: number — the height of the output tensor
- * - __resizeDepth__: number — the depth (num of channels) of the output tensor.
- *    Should be 3 or 4.
- * - __autorender__: boolean — if true the view will be automatically updated
- *   with the contents of the camera. Set this to false if you want more direct
- *   control on when rendering happens.
- * - __rotation__: number — the degrees that the internal camera texture and
- *   preview will be rotated. Accepted values: 0, +/- 90, +/- 180, +/- 270 or
- *   360.
- * - __onReady__: (
- *    images: IterableIterator<tf.Tensor3D>,
- *    updateCameraPreview: () => void,
- *    gl: ExpoWebGLRenderingContext,
- *    cameraTexture: WebGLTexture
- *  ) => void — When the component is mounted and ready this callback will
- *  be called and recieve the following 3 elements:
- *    - __images__ is a (iterator)[https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators]
- *      that yields tensors representing the camera image on demand.
- *    - __updateCameraPreview__ is a function that will update the WebGL render
- *      buffer with the contents of the camera. Not needed when `autorender`
- *      is true
- *    - __gl__ is the ExpoWebGl context used to do the rendering. After calling
- *      `updateCameraPreview` and any other operations you want to synchronize
- *      to the camera rendering you must call gl.endFrameExp() to display it
- *      on the screen. This is also provided in case you want to do other
- *      rendering using WebGL. Not needed when `autorender` is true.
- *    - __cameraTexture__ The underlying cameraTexture. This can be used to
- *      implement your own __updateCameraPreview__.
- *
- * ```js
- * import { Camera } from 'expo-camera';
- * import { cameraWithTensors } from '@tensorflow/tfjs-react-native';
- *
- * const TensorCamera = cameraWithTensors(Camera);
- *
- * class MyComponent {
- *
- *   handleCameraStream(images, updatePreview, gl) {
- *     const loop = async () => {
- *       const nextImageTensor = images.next().value
- *
- *       //
- *       // do something with tensor here
- *       //
- *
- *       // if autorender is false you need the following two lines.
- *       // updatePreview();
- *       // gl.endFrameEXP();
- *
- *       requestAnimationFrame(loop);
- *     }
- *     loop();
- *   }
- *
- *   render() {
- *    return <View>
- *      <TensorCamera
- *       // Standard Camera props
- *       style={styles.camera}
- *       type={Camera.Constants.Type.front}
- *       // Tensor related props
- *       resizeHeight={200}
- *       resizeWidth={152}
- *       resizeDepth={3}
- *       onReady={this.handleCameraStream}
- *       autorender={true}
- *      />
- *    </View>
- *   }
- * }
- * ```
- *
- * @param CameraComponent an expo Camera component constructor
- */
-/** @doc {heading: 'Media', subheading: 'Camera'} */
-export function cameraWithTensors<T extends WrappedComponentProps>(
-  // tslint:disable-next-line: variable-name
-  CameraComponent: React.ComponentType<T>
-) {
-  return class CameraWithTensorStream extends React.Component<
-    T & Props,
-    State
-  > {
-    camera: Camera;
-    glView: GLView;
-    glContext: ExpoWebGLRenderingContext;
-    rafID: number;
-
-    constructor(props: T & Props) {
-      super(props);
-      this.onCameraLayout = this.onCameraLayout.bind(this);
-      this.onGLContextCreate = this.onGLContextCreate.bind(this);
-
-      this.state = {
-        cameraLayout: null,
-      };
-    }
-
-    override componentWillUnmount() {
-      cancelAnimationFrame(this.rafID);
-      if (this.glContext) {
-        GLView.destroyContextAsync(this.glContext);
-      }
-      this.camera = null;
-      this.glView = null;
-      this.glContext = null;
-    }
-
-    /*
-     * Measure the camera component when it is laid out so that we can overlay
-     * the GLView.
-     */
-    onCameraLayout(event: LayoutChangeEvent) {
-      const { x, y, width, height } = event.nativeEvent.layout;
-      this.setState({
-        cameraLayout: { x, y, width, height },
-      });
-    }
-
-    /**
-     * Creates a WebGL texture that is updated by the underlying platform to
-     * contain the contents of the camera.
-     */
-    async createCameraTexture(): Promise<WebGLTexture> {
-      if (this.glView != null && this.camera != null) {
-        //@ts-ignore
-        return this.glView.createCameraTextureAsync(this.camera);
-      } else {
-        throw new Error('Expo GL context or camera not available');
-      }
-    }
-
-    /**
-     * Callback for GL context creation. We do mose of the work of setting
-     * up the component here.
-     * @param gl
-     */
-    async onGLContextCreate(gl: ExpoWebGLRenderingContext) {
-      this.glContext = gl;
-      const cameraTexture = await this.createCameraTexture();
-      await detectGLCapabilities(gl);
-
-      // Optionally set up a render loop that just displays the camera texture
-      // to the GLView.
-      const autorender =
-        this.props.autorender != null
-          ? this.props.autorender
-          : DEFAULT_AUTORENDER;
-      const updatePreview = this.previewUpdateFunc(gl, cameraTexture);
-      if (autorender) {
-        const renderLoop = () => {
-          updatePreview();
-          gl.endFrameEXP();
-          this.rafID = requestAnimationFrame(renderLoop);
-        };
-        renderLoop();
-      }
-
-      const { resizeDepth } = this.props;
-
-      // cameraTextureHeight and cameraTextureWidth props can be omitted when
-      // useCustomShadersToResize is set to false. Setting a default value to
-      // them here.
-      const cameraTextureHeight =
-        this.props.cameraTextureHeight != null
-          ? this.props.cameraTextureHeight
-          : 0;
-      const cameraTextureWidth =
-        this.props.cameraTextureWidth != null
-          ? this.props.cameraTextureWidth
-          : 0;
-      const useCustomShadersToResize =
-        this.props.useCustomShadersToResize != null
-          ? this.props.useCustomShadersToResize
-          : DEFAULT_USE_CUSTOM_SHADERS_TO_RESIZE;
-
-      //
-      //  Set up a generator function that yields tensors representing the
-      // camera on demand.
-      //
-      const cameraStreamView = this;
-      function* nextFrameGenerator() {
-        const RGBA_DEPTH = 4;
-        const textureDims = {
-          height: cameraTextureHeight,
-          width: cameraTextureWidth,
-          depth: RGBA_DEPTH,
-        };
-
-        while (cameraStreamView.glContext != null) {
-          const targetDims = {
-            height: cameraStreamView.props.resizeHeight,
-            width: cameraStreamView.props.resizeWidth,
-            depth: resizeDepth || DEFAULT_RESIZE_DEPTH,
-          };
-
-          const imageTensor = fromTexture(
-            gl,
-            cameraTexture,
-            textureDims,
-            targetDims,
-            useCustomShadersToResize,
-            { rotation: cameraStreamView.props.rotation }
-          );
-          yield imageTensor;
-        }
-      }
-      const nextFrameIterator = nextFrameGenerator();
-
-      // Pass the utility functions to the caller provided callback
-      this.props.onReady(nextFrameIterator, updatePreview, gl, cameraTexture);
-    }
-
-    /**
-     * Helper function that can be used to update the GLView framebuffer.
-     *
-     * @param gl the open gl texture to render to
-     * @param cameraTexture the texture to draw.
-     */
-    previewUpdateFunc(
-      gl: ExpoWebGLRenderingContext,
-      cameraTexture: WebGLTexture
-    ) {
-      const renderFunc = () => {
-        const { cameraLayout } = this.state;
-        const { rotation } = this.props;
-        const width = PixelRatio.getPixelSizeForLayoutSize(cameraLayout.width);
-        const height = PixelRatio.getPixelSizeForLayoutSize(
-          cameraLayout.height
-        );
-        const isFrontCamera =
-          this.camera.props.type === Camera.Constants.Type.front;
-        const flipHorizontal =
-          Platform.OS === 'ios' && isFrontCamera ? false : true;
-
-        renderToGLView(
-          gl,
-          cameraTexture,
-          { width, height },
-          flipHorizontal,
-          rotation
-        );
-      };
-
-      return renderFunc.bind(this);
-    }
-
-    /**
-     * Render the component
-     */
-    override render() {
-      const { cameraLayout } = this.state;
-
-      // Before passing props into the original wrapped component we want to
-      // remove the props that we augment the component with.
-
-      // Use this object to use typescript to check that we are removing
-      // all the tensorCamera properties.
-      const tensorCameraPropMap: Props = {
-        useCustomShadersToResize: null,
-        cameraTextureWidth: null,
-        cameraTextureHeight: null,
-        resizeWidth: null,
-        resizeHeight: null,
-        resizeDepth: null,
-        autorender: null,
-        onReady: null,
-        rotation: 0,
-      };
-      const tensorCameraPropKeys = Object.keys(tensorCameraPropMap);
-
-      const cameraProps: WrappedComponentProps = {};
-      const allProps = Object.keys(this.props);
-      for (let i = 0; i < allProps.length; i++) {
-        const key = allProps[i];
-        if (!tensorCameraPropKeys.includes(key)) {
-          cameraProps[key] = this.props[key];
-        }
-      }
-
-      // Set up an on layout handler
-      const onlayout = this.props.onLayout
-        ? (e: LayoutChangeEvent) => {
-            this.props.onLayout(e);
-            this.onCameraLayout(e);
-          }
-        : this.onCameraLayout;
-
-      cameraProps.onLayout = onlayout;
-
-      const cameraComp = (
-        //@ts-ignore see https://github.com/microsoft/TypeScript/issues/30650
-        <CameraComponent
-          key='camera-with-tensor-camera-view'
-          {...cameraProps}
-          ref={(ref: Camera) => (this.camera = ref)}
-        />
-      );
-
-      // Create the glView if the camera has mounted.
-      let glViewComponent = null;
-      if (cameraLayout != null) {
-        const styles = StyleSheet.create({
-          glView: {
-            position: 'absolute',
-            left: cameraLayout.x,
-            top: cameraLayout.y,
-            width: cameraLayout.width,
-            height: cameraLayout.height,
-            zIndex: this.props.style.zIndex
-              ? parseInt(this.props.style.zIndex, 10) + 10
-              : 10,
-          },
-        });
-
-        glViewComponent = (
-          <GLView
-            key='camera-with-tensor-gl-view'
-            style={styles.glView}
-            onContextCreate={this.onGLContextCreate}
-            ref={(ref) => (this.glView = ref)}
-          />
-        );
-      }
-
-      return [cameraComp, glViewComponent];
-    }
-  };
-}
diff --git a/tfjs-master/tfjs-react-native/src/camera/camera_test.ts b/tfjs-master/tfjs-react-native/src/camera/camera_test.ts
deleted file mode 100644
index 45c8b95b8..000000000
--- a/tfjs-master/tfjs-react-native/src/camera/camera_test.ts
+++ /dev/null
@@ -1,712 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {ExpoWebGLRenderingContext, GLView} from 'expo-gl';
-
-import {RN_ENVS} from '../test_env_registry';
-
-import {detectGLCapabilities, fromTexture, toTexture} from './camera';
-
-async function createGLContext(): Promise<ExpoWebGLRenderingContext> {
-  return GLView.createContextAsync();
-}
-
-const expectArraysEqual = test_util.expectArraysEqual;
-
-let gl: ExpoWebGLRenderingContext;
-
-describeWithFlags('toTexture', RN_ENVS, () => {
-  beforeAll(async () => {
-    if (gl == null) {
-      gl = await createGLContext();
-    }
-  });
-
-  it('should not throw', async () => {
-    const height = 2;
-    const width = 2;
-    const depth = 4;
-
-    const inTensor: tf.Tensor3D =
-        tf.truncatedNormal([height, width, depth], 127, 40, 'int32');
-
-    let texture: WebGLTexture;
-    expect(async () => {
-      texture = await toTexture(gl, inTensor);
-    }).not.toThrow();
-
-    expect(texture instanceof WebGLTexture);
-  });
-
-  it('should roundtrip succesfully', async () => {
-    const height = 2;
-    const width = 2;
-    const depth = 4;
-
-    const inTensor: tf.Tensor3D =
-        tf.truncatedNormal([height, width, depth], 127, 40, 'int32');
-    const texture = await toTexture(gl, inTensor);
-
-    const outTensor = fromTexture(
-        gl, texture, {width, height, depth}, {width, height, depth}, true);
-
-    expectArraysEqual(await inTensor.data(), await outTensor.data());
-    expectArraysEqual(inTensor.shape, outTensor.shape);
-  });
-
-  it('throws if tensor is not int32 dtype', async () => {
-    const height = 2;
-    const width = 2;
-    const depth = 4;
-
-    const floatInput: tf.Tensor3D =
-        tf.truncatedNormal([height, width, depth], 127, 40, 'float32');
-
-    expectAsync(toTexture(gl, floatInput)).toBeRejected();
-  });
-
-  it('throws if tensor is not a tensor3d dtype', async () => {
-    const batch = 2;
-    const height = 2;
-    const width = 2;
-    const depth = 4;
-
-    const oneDInput: tf.Tensor1D =
-        tf.truncatedNormal([height], 127, 40, 'int32');
-    //@ts-ignore
-    expectAsync(toTexture(gl, oneDInput)).toBeRejected();
-
-    const twoDInput: tf.Tensor2D =
-        tf.truncatedNormal([height, width], 127, 40, 'int32');
-    //@ts-ignore
-    expectAsync(toTexture(gl, twoDInput)).toBeRejected();
-
-    const fourDInput: tf.Tensor4D =
-        tf.truncatedNormal([batch, height, width, depth], 127, 40, 'int32');
-    //@ts-ignore
-    expectAsync(toTexture(gl, fourDInput)).toBeRejected();
-  });
-});
-
-describeWithFlags('fromTexture:nearestNeighbor', RN_ENVS, () => {
-  let texture: WebGLTexture;
-  let input: tf.Tensor3D;
-  const inShape: [number, number, number] = [4, 4, 4];
-
-  beforeAll(async () => {
-    if (gl == null) {
-      gl = await createGLContext();
-    }
-
-    input = tf.tensor3d(
-        [
-          [
-            [200, 201, 202, 255],
-            [190, 191, 192, 255],
-            [180, 181, 182, 255],
-            [170, 171, 172, 255],
-          ],
-          [
-            [160, 161, 162, 255],
-            [150, 151, 152, 255],
-            [140, 141, 142, 255],
-            [130, 131, 132, 255],
-          ],
-          [
-            [120, 121, 122, 255],
-            [110, 111, 112, 255],
-            [100, 101, 102, 255],
-            [90, 91, 92, 255],
-          ],
-          [
-            [80, 81, 82, 255],
-            [70, 71, 72, 255],
-            [60, 61, 62, 255],
-            [50, 51, 52, 255],
-          ]
-        ],
-        inShape, 'int32');
-  });
-
-  beforeEach(async () => {
-    texture = await toTexture(gl, input);
-  });
-
-  afterAll(() => {
-    tf.dispose(input);
-  });
-
-  it('same size alignCorners=false', async () => {
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        true,
-        {
-          alignCorners: false,
-          interpolation: 'nearest_neighbor',
-        },
-    );
-
-    expectArraysEqual(await output.data(), await input.data());
-    expectArraysEqual(output.shape, input.shape);
-  });
-
-  it('same size, alignCorners=true', async () => {
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        true,
-        {
-          alignCorners: true,
-          interpolation: 'nearest_neighbor',
-        },
-    );
-
-    expectArraysEqual(await output.data(), await input.data());
-    expectArraysEqual(output.shape, input.shape);
-  });
-
-  it('smaller, resizeNearestNeighbor, same aspect ratio, alignCorners=false',
-     async () => {
-       const expectedShape: [number, number, number] = [2, 2, 4];
-       const expected = tf.tensor3d(
-           [
-             [
-               [200, 201, 202, 255],
-               [180, 181, 182, 255],
-             ],
-             [
-               [120, 121, 122, 255],
-               [100, 101, 102, 255],
-             ]
-           ],
-           expectedShape, 'int32');
-
-       const output = fromTexture(
-           gl,
-           texture,
-           {
-             height: inShape[0],
-             width: inShape[1],
-             depth: inShape[2],
-           },
-           {
-             height: expectedShape[0],
-             width: expectedShape[1],
-             depth: expectedShape[2],
-           },
-           true,
-           {alignCorners: false, interpolation: 'nearest_neighbor'},
-       );
-
-       expectArraysEqual(await output.data(), await expected.data());
-       expectArraysEqual(output.shape, expected.shape);
-     });
-
-  it('smaller, resizeNearestNeighbor, same aspect ratio, alignCorners=true',
-     async () => {
-       const expectedShape: [number, number, number] = [2, 2, 4];
-       const expected = tf.tensor3d(
-           [
-             [
-               [200, 201, 202, 255],
-               [170, 171, 172, 255],
-             ],
-             [
-               [80, 81, 82, 255],
-               [50, 51, 52, 255],
-             ]
-           ],
-           expectedShape, 'int32');
-
-       const output = fromTexture(
-           gl,
-           texture,
-           {
-             height: inShape[0],
-             width: inShape[1],
-             depth: inShape[2],
-           },
-           {
-             height: expectedShape[0],
-             width: expectedShape[1],
-             depth: expectedShape[2],
-           },
-           true,
-           {alignCorners: true, interpolation: 'nearest_neighbor'},
-       );
-
-       expectArraysEqual(await output.data(), await expected.data());
-       expectArraysEqual(output.shape, expected.shape);
-     });
-
-  it('smaller, resizeNearestNeighbor, wider, alignCorners=false', async () => {
-    const expectedShape: [number, number, number] = [2, 3, 4];
-    const expected = tf.tensor3d(
-        [
-          [
-            [200, 201, 202, 255],
-            [190, 191, 192, 255],
-            [180, 181, 182, 255],
-          ],
-          [
-            [120, 121, 122, 255],
-            [110, 111, 112, 255],
-            [100, 101, 102, 255],
-          ]
-        ],
-        expectedShape, 'int32');
-
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: expectedShape[0],
-          width: expectedShape[1],
-          depth: expectedShape[2],
-        },
-        true,
-        {alignCorners: false, interpolation: 'nearest_neighbor'},
-    );
-
-    expectArraysEqual(await output.data(), await expected.data());
-    expectArraysEqual(output.shape, expected.shape);
-  });
-
-  it('smaller, resizeNearestNeighbor, wider, alignCorners=true', async () => {
-    const expectedShape: [number, number, number] = [2, 3, 4];
-    const expected = tf.tensor3d(
-        [
-          [
-            [200, 201, 202, 255],
-            [180, 181, 182, 255],
-            [170, 171, 172, 255],
-          ],
-
-          [
-            [80, 81, 82, 255],
-            [60, 61, 62, 255],
-            [50, 51, 52, 255],
-          ]
-        ],
-        expectedShape, 'int32');
-
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: expectedShape[0],
-          width: expectedShape[1],
-          depth: expectedShape[2],
-        },
-        true,
-        {alignCorners: true, interpolation: 'nearest_neighbor'},
-    );
-
-    expectArraysEqual(await output.data(), await expected.data());
-    expectArraysEqual(output.shape, expected.shape);
-  });
-
-  it('same size, should drop alpha channel', async () => {
-    await detectGLCapabilities(gl);
-    const expected = tf.tensor3d(
-        [
-          [
-            [200, 201, 202],
-            [190, 191, 192],
-            [180, 181, 182],
-            [170, 171, 172],
-          ],
-          [
-            [160, 161, 162],
-            [150, 151, 152],
-            [140, 141, 142],
-            [130, 131, 132],
-          ],
-          [
-            [120, 121, 122],
-            [110, 111, 112],
-            [100, 101, 102],
-            [90, 91, 92],
-          ],
-          [
-            [80, 81, 82],
-            [70, 71, 72],
-            [60, 61, 62],
-            [50, 51, 52],
-          ]
-        ],
-        [inShape[0], inShape[1], 3], 'int32');
-
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: 3,
-        },
-        true,
-        {
-          alignCorners: true,
-          interpolation: 'nearest_neighbor',
-        },
-    );
-    expectArraysEqual(await output.data(), await expected.data());
-    expectArraysEqual(output.shape, expected.shape);
-  });
-});
-
-describeWithFlags('fromTexture:bilinear', RN_ENVS, () => {
-  let texture: WebGLTexture;
-  let input: tf.Tensor3D;
-  const inShape: [number, number, number] = [4, 4, 4];
-
-  beforeAll(async () => {
-    if (gl == null) {
-      gl = await createGLContext();
-    }
-
-    input = tf.tensor3d(
-        [
-          [
-            [200, 201, 202, 255],
-            [190, 191, 192, 255],
-            [180, 181, 182, 255],
-            [170, 171, 172, 255],
-          ],
-          [
-            [160, 161, 162, 255],
-            [150, 151, 152, 255],
-            [140, 141, 142, 255],
-            [130, 131, 132, 255],
-          ],
-          [
-            [120, 121, 122, 255],
-            [110, 111, 112, 255],
-            [100, 101, 102, 255],
-            [90, 91, 92, 255],
-          ],
-          [
-            [80, 81, 82, 255],
-            [70, 71, 72, 255],
-            [60, 61, 62, 255],
-            [50, 51, 52, 255],
-          ]
-        ],
-        inShape, 'int32');
-  });
-
-  afterAll(() => {
-    tf.dispose(input);
-  });
-
-  beforeEach(async () => {
-    texture = await toTexture(gl, input);
-  });
-
-  it('same size alignCorners=false', async () => {
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        true,
-        {
-          alignCorners: false,
-          interpolation: 'bilinear',
-        },
-    );
-
-    expectArraysEqual(await output.data(), await input.data());
-    expectArraysEqual(output.shape, input.shape);
-  });
-
-  it('same size, alignCorners=true', async () => {
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        true,
-        {
-          alignCorners: true,
-          interpolation: 'bilinear',
-        },
-    );
-
-    expectArraysEqual(await output.data(), await input.data());
-    expectArraysEqual(output.shape, input.shape);
-  });
-
-  it('smaller, same aspect ratio, alignCorners=false', async () => {
-    const expectedShape: [number, number, number] = [2, 2, 4];
-    const expected = tf.tensor3d(
-        [
-          [
-            [200, 201, 202, 255],
-            [180, 181, 182, 255],
-          ],
-          [
-            [120, 121, 122, 255],
-            [100, 101, 102, 255],
-          ]
-        ],
-        expectedShape, 'int32');
-
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: expectedShape[0],
-          width: expectedShape[1],
-          depth: expectedShape[2],
-        },
-        true,
-        {alignCorners: false, interpolation: 'bilinear'},
-    );
-
-    expectArraysEqual(await output.data(), await expected.data());
-    expectArraysEqual(output.shape, expected.shape);
-  });
-
-  it('smaller, same aspect ratio, alignCorners=true', async () => {
-    const expectedShape: [number, number, number] = [2, 2, 4];
-    const expected = tf.tensor3d(
-        [
-          [
-            [200, 201, 202, 255],
-            [170, 171, 172, 255],
-          ],
-          [
-            [80, 81, 82, 255],
-            [50, 51, 52, 255],
-          ]
-        ],
-        expectedShape, 'int32');
-
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: expectedShape[0],
-          width: expectedShape[1],
-          depth: expectedShape[2],
-        },
-        true,
-        {alignCorners: true, interpolation: 'bilinear'},
-    );
-
-    expectArraysEqual(await output.data(), await expected.data());
-    expectArraysEqual(output.shape, expected.shape);
-  });
-
-  it('smaller, wider, alignCorners=false', async () => {
-    const expectedShape: [number, number, number] = [2, 3, 4];
-    const expected = tf.tensor3d(
-        [
-          [
-            [200, 201, 202, 255],
-            [187, 188, 189, 255],
-            [173, 174, 175, 255],
-          ],
-          [
-            [120, 121, 122, 255],
-            [107, 108, 109, 255],
-            [93, 94, 95, 255],
-          ]
-        ],
-        expectedShape, 'int32');
-
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: expectedShape[0],
-          width: expectedShape[1],
-          depth: expectedShape[2],
-        },
-        true,
-        {alignCorners: false, interpolation: 'bilinear'},
-    );
-
-    expectArraysEqual(await output.data(), await expected.data());
-    expectArraysEqual(output.shape, expected.shape);
-  });
-
-  it('smaller, wider, alignCorners=true', async () => {
-    const expectedShape: [number, number, number] = [2, 3, 4];
-    const expected = tf.tensor3d(
-        [
-          [
-            [200, 201, 202, 255],
-            [185, 186, 187, 255],
-            [170, 171, 172, 255],
-          ],
-          [
-            [80, 81, 82, 255],
-            [65, 66, 67, 255],
-            [50, 51, 52, 255],
-          ]
-        ],
-        expectedShape, 'int32');
-
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: expectedShape[0],
-          width: expectedShape[1],
-          depth: expectedShape[2],
-        },
-        true,
-        {alignCorners: true, interpolation: 'bilinear'},
-    );
-
-    expectArraysEqual(await output.data(), await expected.data());
-    expectArraysEqual(output.shape, expected.shape);
-  });
-
-  it('same size, should drop alpha channel', async () => {
-    await detectGLCapabilities(gl);
-    const expected = tf.tensor3d(
-        [
-          [
-            [200, 201, 202],
-            [190, 191, 192],
-            [180, 181, 182],
-            [170, 171, 172],
-          ],
-          [
-            [160, 161, 162],
-            [150, 151, 152],
-            [140, 141, 142],
-            [130, 131, 132],
-          ],
-          [
-            [120, 121, 122],
-            [110, 111, 112],
-            [100, 101, 102],
-            [90, 91, 92],
-          ],
-          [
-            [80, 81, 82],
-            [70, 71, 72],
-            [60, 61, 62],
-            [50, 51, 52],
-          ]
-        ],
-        [inShape[0], inShape[1], 3], 'int32');
-
-    const output = fromTexture(
-        gl,
-        texture,
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: inShape[2],
-        },
-        {
-          height: inShape[0],
-          width: inShape[1],
-          depth: 3,
-        },
-        true,
-        {
-          alignCorners: true,
-          interpolation: 'bilinear',
-        },
-    );
-    expectArraysEqual(await output.data(), await expected.data());
-    expectArraysEqual(output.shape, expected.shape);
-  });
-});
diff --git a/tfjs-master/tfjs-react-native/src/camera/camera_webgl_util.ts b/tfjs-master/tfjs-react-native/src/camera/camera_webgl_util.ts
deleted file mode 100644
index 0cf62f8ae..000000000
--- a/tfjs-master/tfjs-react-native/src/camera/camera_webgl_util.ts
+++ /dev/null
@@ -1,424 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {webgl_util} from '@tensorflow/tfjs-backend-webgl';
-import * as tf from '@tensorflow/tfjs-core';
-
-import * as drawTextureProgramInfo from './draw_texture_program_info';
-import * as resizeBilinearProgramInfo from './resize_bilinear_program_info';
-import * as resizeNNProgramInfo from './resize_nearest_neigbor_program_info';
-import {Rotation} from './types';
-
-interface Dimensions {
-  width: number;
-  height: number;
-  depth: number;
-}
-
-// Shared cached frameBuffer object from external context
-const fboCache = new WeakMap<WebGL2RenderingContext, WebGLFramebuffer>();
-
-// Internal target texture used for resizing camera texture input
-const resizeTextureCache = new WeakMap<WebGL2RenderingContext, WebGLTexture>();
-const resizeTextureDimsCache =
-    new WeakMap<WebGL2RenderingContext, {width: number, height: number}>();
-
-interface ProgramObjects {
-  program: WebGLProgram;
-  vao: WebGLVertexArrayObject;
-  vertices: Float32Array;
-  uniformLocations: Map<string, WebGLUniformLocation>;
-}
-
-// Cache for shader programs and associated vertex array buffers.
-const programCacheByContext:
-    WeakMap<WebGL2RenderingContext, Map<string, ProgramObjects>> =
-        new WeakMap();
-
-/**
- * Download data from an texture.
- *
- * @param gl
- * @param texture
- * @param dims
- */
-export function downloadTextureData(
-    gl: WebGL2RenderingContext, texture: WebGLTexture,
-    dims: Dimensions): Uint8Array {
-  const {width, height, depth} = dims;
-  const pixels = new Uint8Array(width * height * depth);
-
-  if (!fboCache.has(gl)) {
-    fboCache.set(gl, createFrameBuffer(gl));
-  }
-  const fbo = fboCache.get(gl);
-
-  webgl_util.callAndCheck(gl, () => {
-    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
-  });
-
-  webgl_util.callAndCheck(gl, () => {
-    gl.activeTexture(gl.TEXTURE0);
-    gl.bindTexture(gl.TEXTURE_2D, texture);
-  });
-
-  webgl_util.callAndCheck(gl, () => {
-    const level = 0;
-    gl.framebufferTexture2D(
-        gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, level);
-  });
-
-  webgl_util.callAndCheck(gl, () => {
-    const format = depth === 3 ? gl.RGB : gl.RGBA;
-    const x = 0;
-    const y = 0;
-    gl.readPixels(x, y, width, height, format, gl.UNSIGNED_BYTE, pixels);
-  });
-
-  // Unbind framebuffer
-  webgl_util.callAndCheck(gl, () => {
-    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
-  });
-  return pixels;
-}
-
-/**
- * Upload image data to a texture.
- *
- * @param imageData data to upload
- * @param gl gl context to use
- * @param dims image size
- * @param texture optional texture to upload data to. If none is passed a new
- *     texture will be returned
- */
-export function uploadTextureData(
-    imageData: Uint8Array, gl: WebGL2RenderingContext, dims: Dimensions,
-    texture?: WebGLTexture): WebGLTexture {
-  const targetTextureWidth = dims.width;
-  const targetTextureHeight = dims.height;
-
-  tf.util.assert(
-      targetTextureWidth * targetTextureHeight * dims.depth ===
-          imageData.length,
-      () => 'uploadTextureData Error: imageData length must match w * h * d');
-
-  const targetTexture = texture || gl.createTexture();
-  gl.activeTexture(gl.TEXTURE0);
-  gl.bindTexture(gl.TEXTURE_2D, targetTexture);
-
-  gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-  gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-  gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-  gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-
-  const level = 0;
-  const format = dims.depth === 3 ? gl.RGB : gl.RGBA;
-  const internalFormat = format;
-  const border = 0;
-  const type = gl.UNSIGNED_BYTE;
-
-  webgl_util.callAndCheck(gl, () => {
-    gl.texImage2D(
-        gl.TEXTURE_2D, level, internalFormat, targetTextureWidth,
-        targetTextureHeight, border, format, type, imageData);
-  });
-
-  gl.bindTexture(gl.TEXTURE_2D, null);
-  return targetTexture;
-}
-
-/**
- * Render a texture to the default framebuffer (i.e. screen)
- *
- * @param gl WebGL context to use
- * @param texture texture to render
- * @param dims texture size
- */
-export function drawTexture(
-    gl: WebGL2RenderingContext, texture: WebGLTexture,
-    dims: {width: number, height: number}, flipHorizontal: boolean,
-    rotation: Rotation) {
-  const {program, vao, vertices, uniformLocations} =
-      drawTextureProgram(gl, flipHorizontal, false, rotation);
-  gl.useProgram(program);
-  gl.bindVertexArray(vao);
-
-  // Set texture sampler uniform
-  const TEXTURE_UNIT = 0;
-  gl.uniform1i(uniformLocations.get('inputTexture'), TEXTURE_UNIT);
-  gl.activeTexture(gl.TEXTURE0 + TEXTURE_UNIT);
-  gl.bindTexture(gl.TEXTURE_2D, texture);
-
-  // Draw to screen
-  gl.bindFramebuffer(gl.FRAMEBUFFER, null);
-  gl.viewport(0, 0, dims.width, dims.height);
-  // gl.clearColor(1, 1, 0, 1);
-  // gl.clear(gl.COLOR_BUFFER_BIT | gl.DEPTH_BUFFER_BIT);
-  gl.drawArrays(gl.TRIANGLES, 0, vertices.length / 2);
-
-  gl.bindVertexArray(null);
-  gl.bindTexture(gl.TEXTURE_2D, null);
-  gl.useProgram(null);
-}
-
-export function runResizeProgram(
-    gl: WebGL2RenderingContext, inputTexture: WebGLTexture,
-    inputDims: Dimensions, outputDims: Dimensions, alignCorners: boolean,
-    useCustomShadersToResize: boolean,
-    interpolation: 'nearest_neighbor'|'bilinear', rotation: Rotation) {
-  const {program, vao, vertices, uniformLocations} = useCustomShadersToResize ?
-      resizeProgram(gl, inputDims, outputDims, alignCorners, interpolation) :
-      drawTextureProgram(gl, false, true, rotation);
-  gl.useProgram(program);
-  // Set up geometry
-  webgl_util.callAndCheck(gl, () => {
-    gl.bindVertexArray(vao);
-  });
-
-  //
-  // Set up input texture
-  //
-  gl.uniform1i(uniformLocations.get('inputTexture'), 1);
-  gl.activeTexture(gl.TEXTURE0 + 1);
-  gl.bindTexture(gl.TEXTURE_2D, inputTexture);
-  if (!useCustomShadersToResize) {
-    const textureFilter =
-        interpolation === 'nearest_neighbor' ? gl.NEAREST : gl.LINEAR;
-    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, textureFilter);
-    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, textureFilter);
-  }
-
-  //
-  // Set up output texture.
-  //
-  if (!resizeTextureCache.has(gl)) {
-    resizeTextureCache.set(gl, gl.createTexture());
-  }
-  const resizeTexture = resizeTextureCache.get(gl);
-
-  const targetTexture = resizeTexture;
-  const targetTextureWidth = outputDims.width;
-  const targetTextureHeight = outputDims.height;
-
-  gl.activeTexture(gl.TEXTURE0 + 2);
-  gl.bindTexture(gl.TEXTURE_2D, targetTexture);
-
-  gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
-  gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
-  gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
-  gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
-
-  // Reallocate texture storage if target size has changed.
-  if (!resizeTextureDimsCache.has(gl)) {
-    resizeTextureDimsCache.set(gl, {width: -1, height: -1});
-  }
-  const resizeTextureDims = resizeTextureDimsCache.get(gl);
-
-  if (resizeTextureDims == null ||
-      resizeTextureDims.width !== targetTextureWidth ||
-      resizeTextureDims.height !== targetTextureHeight) {
-    const level = 0;
-    const format = outputDims.depth === 3 ? gl.RGB : gl.RGBA;
-    const internalFormat = format;
-    const border = 0;
-    const type = gl.UNSIGNED_BYTE;
-
-    webgl_util.callAndCheck(gl, () => {
-      gl.texImage2D(
-          gl.TEXTURE_2D, level, internalFormat, targetTextureWidth,
-          targetTextureHeight, border, format, type, null);
-    });
-
-    resizeTextureDimsCache.set(
-        gl, {width: targetTextureWidth, height: targetTextureHeight});
-  }
-
-  //
-  // Render to output texture
-  //
-  if (!fboCache.has(gl)) {
-    fboCache.set(gl, createFrameBuffer(gl));
-  }
-  const fbo = fboCache.get(gl);
-
-  gl.viewport(0, 0, targetTextureWidth, targetTextureHeight);
-  gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
-  gl.framebufferTexture2D(
-      gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, targetTexture, 0);
-
-  const fboComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
-  if (fboComplete !== gl.FRAMEBUFFER_COMPLETE) {
-    switch (fboComplete) {
-      case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
-        throw new Error(
-            'createFrameBuffer: gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT');
-
-      case gl.FRAMEBUFFER_UNSUPPORTED:
-        throw new Error('createFrameBuffer: gl.FRAMEBUFFER_UNSUPPORTED');
-
-      case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
-        throw new Error(
-            'createFrameBuffer: gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT');
-
-      case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
-        throw new Error(
-            'createFrameBuffer: gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS');
-
-      case gl.FRAMEBUFFER_INCOMPLETE_MULTISAMPLE:
-        throw new Error(
-            'createFrameBuffer: gl.FRAMEBUFFER_INCOMPLETE_MULTISAMPLE');
-      default:
-        throw new Error(
-            'createFrameBuffer Error: Other or unknown fbo complete status: ' +
-            `${fboComplete}`);
-    }
-  }
-
-  gl.drawArrays(gl.TRIANGLES, 0, vertices.length / 2);
-
-  // Restore previous state
-  gl.bindFramebuffer(gl.FRAMEBUFFER, null);
-  gl.useProgram(null);
-  gl.bindVertexArray(null);
-  return targetTexture;
-}
-
-function createFrameBuffer(gl: WebGL2RenderingContext): WebGLFramebuffer {
-  const fb = gl.createFramebuffer();
-  if (fb == null) {
-    throw new Error('Could not create framebuffer');
-  }
-  return fb;
-}
-
-export function drawTextureProgram(
-    gl: WebGL2RenderingContext, flipHorizontal: boolean, flipVertical: boolean,
-    rotation: Rotation): ProgramObjects {
-  if (!programCacheByContext.has(gl)) {
-    programCacheByContext.set(gl, new Map());
-  }
-  const programCache = programCacheByContext.get(gl);
-
-  const cacheKey = `drawTexture_${flipHorizontal}_${flipVertical}_${rotation}`;
-  if (!programCache.has(cacheKey)) {
-    const vertSource = drawTextureProgramInfo.vertexShaderSource(
-        flipHorizontal, flipVertical, rotation);
-    const fragSource = drawTextureProgramInfo.fragmentShaderSource();
-
-    const vertices = drawTextureProgramInfo.vertices();
-    const texCoords = drawTextureProgramInfo.texCoords();
-
-    const programObjects =
-        createProgramObjects(gl, vertSource, fragSource, vertices, texCoords);
-
-    programCache.set(cacheKey, programObjects);
-  }
-  return programCache.get(cacheKey);
-}
-
-function resizeProgram(
-    gl: WebGL2RenderingContext, sourceDims: Dimensions, targetDims: Dimensions,
-    alignCorners: boolean,
-    interpolation: 'nearest_neighbor'|'bilinear'): ProgramObjects {
-  if (!programCacheByContext.has(gl)) {
-    programCacheByContext.set(gl, new Map());
-  }
-  const programCache = programCacheByContext.get(gl);
-
-  const cacheKey = `resize_${sourceDims.width}_${sourceDims.height}_${
-      sourceDims.depth}_${targetDims.width}_${targetDims.height}_${
-      targetDims.depth}_${alignCorners}_${interpolation}`;
-
-  if (!programCache.has(cacheKey)) {
-    const vertSource = resizeNNProgramInfo.vertexShaderSource();
-    let fragSource: string;
-    if (interpolation === 'nearest_neighbor') {
-      fragSource = resizeNNProgramInfo.fragmentShaderSource(
-          sourceDims, targetDims, alignCorners);
-    } else {
-      fragSource = resizeBilinearProgramInfo.fragmentShaderSource(
-          sourceDims, targetDims, alignCorners);
-    }
-
-    const vertices = resizeNNProgramInfo.vertices();
-    const texCoords = resizeNNProgramInfo.texCoords();
-    const programObjects =
-        createProgramObjects(gl, vertSource, fragSource, vertices, texCoords);
-
-    programCache.set(cacheKey, programObjects);
-  }
-  return programCache.get(cacheKey);
-}
-
-function createProgramObjects(
-    gl: WebGL2RenderingContext, vertexShaderSource: string,
-    fragmentShaderSource: string, vertices: Float32Array,
-    texCoords: Float32Array): ProgramObjects {
-  const vertShader = gl.createShader(gl.VERTEX_SHADER);
-  gl.shaderSource(vertShader, vertexShaderSource);
-  gl.compileShader(vertShader);
-
-  const fragShader = gl.createShader(gl.FRAGMENT_SHADER);
-  gl.shaderSource(fragShader, fragmentShaderSource);
-  gl.compileShader(fragShader);
-
-  const program = gl.createProgram();
-  gl.attachShader(program, vertShader);
-  gl.attachShader(program, fragShader);
-  gl.linkProgram(program);
-  gl.validateProgram(program);
-
-  // Use a vertex array objects to record geometry info
-  const vao = gl.createVertexArray();
-  gl.bindVertexArray(vao);
-
-  // Set up geometry
-  webgl_util.callAndCheck(gl, () => {
-    const positionAttrib = gl.getAttribLocation(program, 'position');
-    const vertsCoordsBuffer = gl.createBuffer();
-    gl.bindBuffer(gl.ARRAY_BUFFER, vertsCoordsBuffer);
-    gl.enableVertexAttribArray(positionAttrib);
-    gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);
-    gl.vertexAttribPointer(positionAttrib, 2, gl.FLOAT, false, 0, 0);
-    gl.bindBuffer(gl.ARRAY_BUFFER, null);
-  });
-
-  webgl_util.callAndCheck(gl, () => {
-    const texCoordsAttrib = gl.getAttribLocation(program, 'texCoords');
-    const texCoordsBuffer = gl.createBuffer();
-    gl.bindBuffer(gl.ARRAY_BUFFER, texCoordsBuffer);
-    gl.bufferData(gl.ARRAY_BUFFER, texCoords, gl.STATIC_DRAW);
-    gl.enableVertexAttribArray(texCoordsAttrib);
-    gl.vertexAttribPointer(texCoordsAttrib, 2, gl.FLOAT, false, 0, 0);
-    gl.bindBuffer(gl.ARRAY_BUFFER, null);
-  });
-
-  const uniformLocations = new Map<string, WebGLUniformLocation>();
-  webgl_util.callAndCheck(gl, () => {
-    const inputTextureLoc = gl.getUniformLocation(program, 'inputTexture');
-    uniformLocations.set('inputTexture', inputTextureLoc);
-  });
-
-  // Unbind
-  gl.bindVertexArray(null);
-  return {
-    program,
-    vao,
-    vertices,
-    uniformLocations,
-  };
-}
diff --git a/tfjs-master/tfjs-react-native/src/camera/draw_texture_program_info.ts b/tfjs-master/tfjs-react-native/src/camera/draw_texture_program_info.ts
deleted file mode 100644
index df804fed3..000000000
--- a/tfjs-master/tfjs-react-native/src/camera/draw_texture_program_info.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Rotation} from './types';
-
-export function vertexShaderSource(
-    flipHorizontal: boolean, flipVertical: boolean, rotation: Rotation) {
-  const horizontalScale = flipHorizontal ? -1 : 1;
-  const verticalScale = flipVertical ? -1 : 1;
-  const rotateAngle = rotation === 0 ? '0.' : rotation * (Math.PI / 180);
-  return `#version 300 es
-precision highp float;
-
-in vec2 position;
-in vec2 texCoords;
-
-out vec2 uv;
-
-vec2 rotate(vec2 uvCoods, vec2 pivot, float rotation) {
-  float cosa = cos(rotation);
-  float sina = sin(rotation);
-  uvCoods -= pivot;
-  return vec2(
-      cosa * uvCoods.x - sina * uvCoods.y,
-      cosa * uvCoods.y + sina * uvCoods.x
-  ) + pivot;
-}
-
-void main() {
-  uv = rotate(texCoords, vec2(0.5), ${rotateAngle});
-
-  // Invert geometry to match the image orientation from the camera.
-  gl_Position = vec4(position * vec2(${horizontalScale}., ${
-      verticalScale}. * -1.), 0, 1);
-}`;
-}
-
-export function fragmentShaderSource() {
-  return `#version 300 es
-precision highp float;
-uniform sampler2D inputTexture;
-in vec2 uv;
-out vec4 fragColor;
-void main() {
-  vec4 texSample = texture(inputTexture, uv);
-  fragColor = texSample;
-}`;
-}
-
-export function vertices() {
-  return new Float32Array([
-    // clang-format off
-    -1, -1,
-    -1, 1,
-    1, 1,
-    1, 1,
-    -1, -1,
-    1, -1,
-    // clang-format on
-  ]);
-}
-
-export function texCoords() {
-  return new Float32Array([
-    // clang-format off
-    0, 0,
-    0, 1,
-    1, 1,
-    1, 1,
-    0, 0,
-    1, 0,
-    // clang-format on
-  ]);
-}
diff --git a/tfjs-master/tfjs-react-native/src/camera/resize_bilinear_program_info.ts b/tfjs-master/tfjs-react-native/src/camera/resize_bilinear_program_info.ts
deleted file mode 100644
index 5926b3699..000000000
--- a/tfjs-master/tfjs-react-native/src/camera/resize_bilinear_program_info.ts
+++ /dev/null
@@ -1,132 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-interface Dimensions {
-  x?: number;
-  y?: number;
-  width: number;
-  height: number;
-  depth: number;
-}
-
-export function vertexShaderSource() {
-  return `#version 300 es
-precision highp float;
-precision highp int;
-
-in vec2 position;
-in vec2 texCoords;
-out vec2 uv;
-
-void main() {
-  uv = texCoords;
-  gl_Position = vec4(position, 0, 1);
-}`;
-}
-
-export function fragmentShaderSource(
-    sourceDims: Dimensions, targetDims: Dimensions, alignCorners: boolean) {
-  const sWidth = sourceDims.width;
-  const sHeight = sourceDims.height;
-  const tWidth = targetDims.width;
-  const tHeight = targetDims.height;
-
-  const effectiveInSize: [number, number] = [
-    (alignCorners && tWidth > 1) ? sWidth - 1 : sWidth,
-    (alignCorners && tHeight > 1) ? sHeight - 1 : sHeight,
-  ];
-
-  const effectiveOutSize: [number, number] = [
-    (alignCorners && tWidth > 1) ? tWidth - 1 : tWidth,
-    (alignCorners && tHeight > 1) ? tHeight - 1 : tHeight,
-  ];
-
-  const outputFragType = targetDims.depth === 3 ? 'vec3' : 'vec4';
-  const outputFragColor = targetDims.depth === 3 ?
-      'vec3(texSample.r,texSample.g,texSample.b)' :
-      'texSample';
-
-  const source = `#version 300 es
-precision highp float;
-
-uniform sampler2D inputTexture;
-in vec2 uv;
-out ${outputFragType} fragColor;
-
-vec2 sourceDims = vec2(${sWidth}, ${sHeight});
-vec2 targetDims = vec2(${tWidth}, ${tHeight});
-
-const vec2 inputToOutputRatio = vec2(
-    ${effectiveInSize[0] / effectiveOutSize[0]},
-    ${effectiveInSize[1] / effectiveOutSize[1]});
-
-void main() {
-  ivec2 targetCoords = ivec2(uv * targetDims);
-
-  vec2 sourceCoords = vec2(targetCoords) * inputToOutputRatio;
-
-  ivec2 sourceFloor = ivec2(sourceCoords);
-  ivec2 sourceCeil = ivec2(min(sourceDims - 1.0, ceil(sourceCoords)));
-
-  vec4 topLeft = texelFetch(inputTexture,
-    ivec2(sourceFloor.x, sourceFloor.y), 0);
-  vec4 bottomLeft = texelFetch(inputTexture,
-    ivec2(sourceCeil.x, sourceFloor.y), 0);
-  vec4 topRight = texelFetch(inputTexture,
-    ivec2(sourceFloor.x, sourceCeil.y), 0);
-  vec4 bottomRight = texelFetch(inputTexture,
-    ivec2(sourceCeil.x, sourceCeil.y), 0);
-
-  vec2 fracRC = sourceCoords - vec2(sourceFloor);
-
-  vec4 top = topLeft + (topRight - topLeft) * fracRC.y;
-  vec4 bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
-  vec4 newValue = top + (bottom - top) * fracRC.x;
-
-  vec4 texSample = newValue;
-  fragColor = ${outputFragColor};
-}
-`;
-
-  return source;
-}
-
-export function vertices() {
-  return new Float32Array([
-    // clang-format off
-    -1, -1,
-    -1, 1,
-    1, 1,
-    1, 1,
-    -1, -1,
-    1, -1,
-    // clang-format on
-  ]);
-}
-
-export function texCoords() {
-  return new Float32Array([
-    // clang-format off
-    0, 0,
-    0, 1,
-    1, 1,
-    1, 1,
-    0, 0,
-    1, 0,
-    // clang-format on
-  ]);
-}
diff --git a/tfjs-master/tfjs-react-native/src/camera/resize_nearest_neigbor_program_info.ts b/tfjs-master/tfjs-react-native/src/camera/resize_nearest_neigbor_program_info.ts
deleted file mode 100644
index aec5a973d..000000000
--- a/tfjs-master/tfjs-react-native/src/camera/resize_nearest_neigbor_program_info.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-interface Dimensions {
-  x?: number;
-  y?: number;
-  width: number;
-  height: number;
-  depth: number;
-}
-
-export function vertexShaderSource() {
-  return `#version 300 es
-precision highp float;
-precision highp int;
-
-in vec2 position;
-in vec2 texCoords;
-out vec2 uv;
-
-void main() {
-  uv = texCoords;
-  gl_Position = vec4(position, 0, 1);
-}`;
-}
-
-export function fragmentShaderSource(
-    sourceDims: Dimensions, targetDims: Dimensions, alignCorners: boolean) {
-  const sWidth = sourceDims.width;
-  const sHeight = sourceDims.height;
-  const tWidth = targetDims.width;
-  const tHeight = targetDims.height;
-
-  const effectiveInSize: [number, number] = [
-    (alignCorners && tWidth > 1) ? sWidth - 1 : sWidth,
-    (alignCorners && tHeight > 1) ? sHeight - 1 : sHeight,
-  ];
-
-  const effectiveOutSize: [number, number] = [
-    (alignCorners && tWidth > 1) ? tWidth - 1 : tWidth,
-    (alignCorners && tHeight > 1) ? tHeight - 1 : tHeight,
-  ];
-
-  const roundBase = alignCorners ? '0.5' : '0.0';
-
-  const outputFragType = targetDims.depth === 3 ? 'vec3' : 'vec4';
-  const outputFragColor =
-      targetDims.depth === 3 ? 'vec3(texSample)' : 'texSample';
-
-  const source = `#version 300 es
-precision highp float;
-
-uniform sampler2D inputTexture;
-in vec2 uv;
-out ${outputFragType} fragColor;
-
-vec2 sourceDims = vec2(${sWidth}, ${sHeight});
-vec2 targetDims = vec2(${tWidth}, ${tHeight});
-
-const vec2 inputToOutputRatio = vec2(
-    ${effectiveInSize[0] / effectiveOutSize[0]},
-    ${effectiveInSize[1] / effectiveOutSize[1]});
-
-void main() {
-  ivec2 targetCoords = ivec2(uv * targetDims);
-
-  vec2 sourceCoords = vec2(targetCoords) * inputToOutputRatio;
-  ivec2 iSourceCoords = ivec2(min(
-    sourceDims - 1.0,
-    floor(sourceCoords + ${roundBase})));
-
-  vec4 texSample = texelFetch(inputTexture, iSourceCoords, 0);
-  fragColor = ${outputFragColor};
-}
-`;
-
-  return source;
-}
-
-export function vertices() {
-  return new Float32Array([
-    // clang-format off
-    -1, -1,
-    -1, 1,
-    1, 1,
-    1, 1,
-    -1, -1,
-    1, -1,
-    // clang-format on
-  ]);
-}
-
-export function texCoords() {
-  return new Float32Array([
-    // clang-format off
-    0, 0,
-    0, 1,
-    1, 1,
-    1, 1,
-    0, 0,
-    1, 0,
-    // clang-format on
-  ]);
-}
diff --git a/tfjs-master/tfjs-react-native/src/camera/types.ts b/tfjs-master/tfjs-react-native/src/camera/types.ts
deleted file mode 100644
index 77aac9091..000000000
--- a/tfjs-master/tfjs-react-native/src/camera/types.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-// Rotation in degrees.
-export type Rotation = 0|90|180|270|360|- 80|- 180|- 270;
diff --git a/tfjs-master/tfjs-react-native/src/decode_image.ts b/tfjs-master/tfjs-react-native/src/decode_image.ts
deleted file mode 100644
index 63cae5737..000000000
--- a/tfjs-master/tfjs-react-native/src/decode_image.ts
+++ /dev/null
@@ -1,90 +0,0 @@
-import {Tensor3D, tensor3d, util} from '@tensorflow/tfjs-core';
-import * as jpeg from 'jpeg-js';
-
-enum ImageType {
-  JPEG = 'jpeg',
-  PNG = 'png',
-  GIF = 'gif',
-  BMP = 'BMP'
-}
-
-/**
- * Decode a JPEG-encoded image to a 3D Tensor of dtype `int32`.
- *
- * ```js
- * // Load an image as a Uint8Array
- * const imageUri = 'http://image-uri-here.example.com/image.jpg'; *
- * const response = await fetch(imageUri, {}, { isBinary: true });
- * const imageDataArrayBuffer = await response.arrayBuffer();
- * const imageData = new Uint8Array(imageDataArrayBuffer);
- *
- * // Decode image data to a tensor
- * const imageTensor = decodeJpeg(imageData);
- * ```
- *
- * @param contents The JPEG-encoded image in an Uint8Array.
- * @param channels An optional int. Defaults to 3. Accepted values are
- *     0: use the number of channels in the JPG-encoded image.
- *     1: output a grayscale image.
- *     3: output an RGB image.
- * @returns A 3D Tensor of dtype `int32` with shape [height, width, 1/3].
- *
- * @doc {heading: 'Media', subheading: 'Images'}
- */
-export function decodeJpeg(
-    contents: Uint8Array, channels: 0|1|3 = 3): Tensor3D {
-  util.assert(
-      getImageType(contents) === ImageType.JPEG,
-      () => 'The passed contents are not a valid JPEG image');
-  util.assert(
-      channels === 3, () => 'Only 3 channels is supported at this time');
-  const {width, height, data} = jpeg.decode(contents, {useTArray: true});
-  // Drop the alpha channel info because jpeg.decode always returns a typedArray
-  // with 255
-  const buffer = new Uint8Array(width * height * 3);
-  let offset = 0;  // offset into original data
-  for (let i = 0; i < buffer.length; i += 3) {
-    buffer[i] = data[offset];
-    buffer[i + 1] = data[offset + 1];
-    buffer[i + 2] = data[offset + 2];
-
-    offset += 4;
-  }
-
-  return tensor3d(buffer, [height, width, channels]);
-}
-
-/**
- * Helper function to get image type based on starting bytes of the image file.
- */
-function getImageType(content: Uint8Array): string {
-  // Classify the contents of a file based on starting bytes (aka magic number:
-  // tslint:disable-next-line:max-line-length
-  // https://en.wikipedia.org/wiki/Magic_number_(programming)#Magic_numbers_in_files)
-  // This aligns with TensorFlow Core code:
-  // tslint:disable-next-line:max-line-length
-  // https://github.com/tensorflow/tensorflow/blob/4213d5c1bd921f8d5b7b2dc4bbf1eea78d0b5258/tensorflow/core/kernels/decode_image_op.cc#L44
-  if (content.length > 3 && content[0] === 255 && content[1] === 216 &&
-      content[2] === 255) {
-    // JPEG byte chunk starts with `ff d8 ff`
-    return ImageType.JPEG;
-  } else if (
-      content.length > 4 && content[0] === 71 && content[1] === 73 &&
-      content[2] === 70 && content[3] === 56) {
-    // GIF byte chunk starts with `47 49 46 38`
-    return ImageType.GIF;
-  } else if (
-      content.length > 8 && content[0] === 137 && content[1] === 80 &&
-      content[2] === 78 && content[3] === 71 && content[4] === 13 &&
-      content[5] === 10 && content[6] === 26 && content[7] === 10) {
-    // PNG byte chunk starts with `\211 P N G \r \n \032 \n (89 50 4E 47 0D 0A
-    // 1A 0A)`
-    return ImageType.PNG;
-  } else if (content.length > 3 && content[0] === 66 && content[1] === 77) {
-    // BMP byte chunk starts with `42 4d`
-    return ImageType.BMP;
-  } else {
-    throw new Error(
-        'Expected image (JPEG, PNG, or GIF), but got unsupported image type');
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/src/decode_image_test.ts b/tfjs-master/tfjs-react-native/src/decode_image_test.ts
deleted file mode 100644
index cb7cf3a39..000000000
--- a/tfjs-master/tfjs-react-native/src/decode_image_test.ts
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {memory, test_util} from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import * as tf from './index';
-import {RN_ENVS} from './test_env_registry';
-
-const uint8array = new Uint8Array([
-  255, 216, 255, 224, 0,   16,  74,  70,  73,  70,  0,   1,   1,   2,   0,
-  118, 0,   118, 0,   0,   255, 219, 0,   67,  0,   1,   1,   1,   1,   1,
-  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
-  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
-  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
-  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   255,
-  219, 0,   67,  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
-  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
-  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
-  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
-  1,   1,   1,   1,   1,   1,   1,   1,   255, 194, 0,   17,  8,   0,   2,
-  0,   2,   3,   1,   17,  0,   2,   17,  1,   3,   17,  1,   255, 196, 0,
-  20,  0,   1,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
-  0,   0,   0,   7,   255, 196, 0,   20,  1,   1,   0,   0,   0,   0,   0,
-  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   8,   255, 218, 0,   12,
-  3,   1,   0,   2,   16,  3,   16,  0,   0,   1,   50,  53,  173, 255, 0,
-  255, 196, 0,   21,  16,  1,   1,   0,   0,   0,   0,   0,   0,   0,   0,
-  0,   0,   0,   0,   0,   0,   5,   4,   255, 218, 0,   8,   1,   1,   0,
-  1,   5,   2,   60,  195, 104, 131, 255, 196, 0,   25,  17,  0,   3,   1,
-  1,   1,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   2,   3,
-  4,   5,   1,   0,   255, 218, 0,   8,   1,   3,   1,   1,   63,  1,   159,
-  15,  18,  201, 209, 93,  120, 249, 117, 87,  82,  85,  77,  85,  83,  159,
-  35,  232, 166, 135, 128, 181, 207, 123, 154, 162, 99,  156, 230, 17,  49,
-  173, 97,  17,  176, 200, 136, 139, 165, 222, 247, 223, 255, 196, 0,   26,
-  17,  0,   3,   1,   1,   1,   1,   0,   0,   0,   0,   0,   0,   0,   0,
-  0,   0,   1,   2,   3,   4,   5,   35,  0,   255, 218, 0,   8,   1,   2,
-  1,   1,   63,  1,   29,  110, 166, 80,  51,  102, 233, 116, 51,  230, 207,
-  225, 159, 60,  54,  104, 148, 33,  9,   121, 202, 49,  148, 232, 169, 41,
-  73,  21,  82,  115, 69,  8,   136, 2,   168, 0,   1,   247, 255, 196, 0,
-  25,  16,  0,   3,   1,   1,   1,   0,   0,   0,   0,   0,   0,   0,   0,
-  0,   0,   0,   1,   2,   3,   4,   0,   5,   255, 218, 0,   8,   1,   1,
-  0,   6,   63,  2,   197, 163, 71,  159, 134, 247, 190, 76,  214, 189, 237,
-  146, 21,  181, 173, 88,  163, 210, 181, 163, 161, 122, 82,  142, 75,  187,
-  185, 44,  204, 75,  49,  36,  247, 255, 196, 0,   21,  16,  1,   1,   0,
-  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   0,
-  255, 218, 0,   8,   1,   1,   0,   1,   63,  33,  85,  47,  2,   3,   60,
-  155, 126, 1,   191, 255, 218, 0,   12,  3,   1,   0,   2,   0,   3,   0,
-  0,   0,   16,  223, 255, 196, 0,   20,  17,  1,   0,   0,   0,   0,   0,
-  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   255, 218, 0,   8,
-  1,   3,   1,   1,   63,  16,  117, 250, 0,   169, 238, 184, 84,  212, 255,
-  196, 0,   21,  17,  1,   1,   0,   0,   0,   0,   0,   0,   0,   0,   0,
-  0,   0,   0,   0,   0,   1,   17,  255, 218, 0,   8,   1,   2,   1,   1,
-  63,  16,  8,   234, 1,   198, 37,  68,  148, 101, 31,  255, 196, 0,   20,
-  16,  1,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
-  0,   0,   1,   255, 218, 0,   8,   1,   1,   0,   1,   63,  16,  111, 197,
-  200, 77,  239, 120, 73,  43,  255, 217
-]);
-
-describeWithFlags('decode images', RN_ENVS, () => {
-  it('decode jpg', async () => {
-    const beforeNumTensors: number = memory().numTensors;
-    const imageTensor = await tf.decodeJpeg(uint8array);
-    expect(imageTensor.dtype).toBe('int32');
-    expect(imageTensor.shape).toEqual([2, 2, 3]);
-    test_util.expectArraysEqual(
-        await imageTensor.data(),
-        [240, 100, 0, 50, 50, 50, 99, 49, 0, 199, 99, 49]);
-    expect(memory().numTensors).toBe(beforeNumTensors + 1);
-  });
-});
diff --git a/tfjs-master/tfjs-react-native/src/index.ts b/tfjs-master/tfjs-react-native/src/index.ts
deleted file mode 100644
index 10b2d0434..000000000
--- a/tfjs-master/tfjs-react-native/src/index.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import './platform_react_native';
-
-export {asyncStorageIO} from './async_storage_io';
-export {bundleResourceIO} from './bundle_resource_io';
-export {decodeJpeg} from './decode_image';
-export {fetch} from './platform_react_native';
-export {version} from './version';
-
-export * from './camera/camera';
-export * from './camera/camera_stream';
diff --git a/tfjs-master/tfjs-react-native/src/platform_react_native.ts b/tfjs-master/tfjs-react-native/src/platform_react_native.ts
deleted file mode 100644
index 8537981d6..000000000
--- a/tfjs-master/tfjs-react-native/src/platform_react_native.ts
+++ /dev/null
@@ -1,265 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-import {GPGPUContext, MathBackendWebGL, setWebGLContext} from '@tensorflow/tfjs-backend-webgl';
-import * as tf from '@tensorflow/tfjs-core';
-import {Platform} from '@tensorflow/tfjs-core';
-import {Buffer} from 'buffer';
-import {GLView} from 'expo-gl';
-import {Platform as RNPlatform} from 'react-native';
-
-// See implemetation note on fetch
-// tslint:disable-next-line:max-line-length
-// https://github.com/facebook/react-native/blob/0ee5f68929610106ee6864baa04ea90be0fc5160/Libraries/vendor/core/whatwg-fetch.js#L421
-function parseHeaders(rawHeaders: string) {
-  const headers = new Headers();
-  // Replace instances of \r\n and \n followed by at least one space or
-  // horizontal tab with a space https://tools.ietf.org/html/rfc7230#section-3.2
-  const preProcessedHeaders = rawHeaders.replace(/\r?\n[\t ]+/g, ' ');
-  preProcessedHeaders.split(/\r?\n/).forEach(line => {
-    const parts = line.split(':');
-    const key = parts.shift().trim();
-    if (key) {
-      const value = parts.join(':').trim();
-      headers.append(key, value);
-    }
-  });
-  return headers;
-}
-
-// Implementation note: This is a patch of react-native's fetch implementation
-// tslint:disable-next-line:max-line-length
-// https://github.com/facebook/react-native/blob/0ee5f68929610106ee6864baa04ea90be0fc5160/Libraries/vendor/core/whatwg-fetch.js#L484
-//
-// The response object supplied by fetch does not implement arrayBuffer()
-// FileReader.readAsArrayBuffer is not implemented.
-// tslint:disable-next-line:max-line-length
-// https://github.com/facebook/react-native/blob/d7a5e3e215eedb7377a86f172e0619403e20c2b8/Libraries/Blob/FileReader.js#L83
-//
-// However if one uses XMLHttpRequest directly and set the responseType
-// correctly before making the request. The returned response object will have
-// a working arrayBuffer method that can be used downstraeam.
-
-/**
- * Makes an HTTP request.
- * @param path The URL path to make a request to
- * @param init The request init. See init here:
- *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
- * @param options A RequestDetails object.
- *    - __options.isBinary__ boolean indicating whether this request is for a
- *     binary file.
- *
- * @doc {heading: 'Platform helpers', subheading: 'http'}
- */
-export async function fetch(
-    path: string, init?: RequestInit,
-    options?: tf.io.RequestDetails): Promise<Response> {
-  return new Promise((resolve, reject) => {
-    const request = new Request(path, init);
-    const xhr = new XMLHttpRequest();
-
-    xhr.onload = () => {
-      const reqOptions = {
-        status: xhr.status,
-        statusText: xhr.statusText,
-        headers: parseHeaders(xhr.getAllResponseHeaders() || ''),
-        url: '',
-      };
-      reqOptions.url = 'responseURL' in xhr ?
-          xhr.responseURL :
-          reqOptions.headers.get('X-Request-URL');
-
-      //@ts-ignore — ts belives the latter case will never occur.
-      const body = 'response' in xhr ? xhr.response : xhr.responseText;
-
-      resolve(new Response(body, reqOptions));
-    };
-
-    xhr.onerror = () => reject(new TypeError('Network request failed'));
-    xhr.ontimeout = () => reject(new TypeError('Network request failed'));
-
-    xhr.open(request.method, request.url, true);
-
-    if (request.credentials === 'include') {
-      xhr.withCredentials = true;
-    } else if (request.credentials === 'omit') {
-      xhr.withCredentials = false;
-    }
-
-    if (options != null && options.isBinary) {
-      // In react native We need to set the response type to arraybuffer when
-      // fetching binary resources in order for `.arrayBuffer` to work correctly
-      // on the response.
-      xhr.responseType = 'arraybuffer';
-    }
-
-    request.headers.forEach((value: string, name: string) => {
-      xhr.setRequestHeader(name, value);
-    });
-
-    xhr.send(
-        //@ts-ignore
-        typeof request._bodyInit === 'undefined' ? null : request._bodyInit,
-    );
-  });
-}
-
-export class PlatformReactNative implements Platform {
-  /**
-   * Makes an HTTP request.
-   *
-   * see @fetch docs above.
-   */
-  async fetch(
-      path: string, init?: RequestInit, options?: tf.io.RequestDetails) {
-    return fetch(path, init, options);
-  }
-
-  /**
-   * Encode the provided string into an array of bytes using the provided
-   * encoding.
-   */
-  encode(text: string, encoding: string): Uint8Array {
-    // See https://www.w3.org/TR/encoding/#utf-16le
-    if (encoding === 'utf-16') {
-      encoding = 'utf16le';
-    }
-    return new Uint8Array(Buffer.from(text, encoding as BufferEncoding));
-  }
-  /** Decode the provided bytes into a string using the provided encoding. */
-  decode(bytes: Uint8Array, encoding: string): string {
-    // See https://www.w3.org/TR/encoding/#utf-16le
-    if (encoding === 'utf-16') {
-      encoding = 'utf16le';
-    }
-    return Buffer.from(bytes).toString(encoding as BufferEncoding);
-  }
-
-  now(): number {
-    //@ts-ignore
-    if (global.nativePerformanceNow) {
-      //@ts-ignore
-      return global.nativePerformanceNow();
-    }
-    return Date.now();
-  }
-
-  setTimeoutCustom() {
-    throw new Error('react native does not support setTimeoutCustom');
-  }
-
-  isTypedArray(a: unknown): a is Uint8Array | Float32Array | Int32Array
-    | Uint8ClampedArray {
-    return a instanceof Float32Array || a instanceof Int32Array ||
-      a instanceof Uint8Array || a instanceof Uint8ClampedArray;
-  }
-}
-
-function setupGlobals() {
-  global.Buffer = Buffer;
-}
-
-function registerWebGLBackend() {
-  try {
-    const PRIORITY = 5;
-    tf.registerBackend('rn-webgl', async () => {
-      const glContext = await GLView.createContextAsync();
-
-      // ExpoGl getBufferSubData is not implemented yet (throws an exception).
-      tf.env().set('WEBGL_BUFFER_SUPPORTED', false);
-
-      //
-      // Mock extension support for EXT_color_buffer_float and
-      // EXT_color_buffer_half_float on the expo-gl context object.
-      // In react native we do not have to get a handle to the extension
-      // in order to use the functionality of that extension on the device.
-      //
-      // This code block makes iOS and Android devices pass the extension checks
-      // used in core. After those are done core will actually test whether
-      // we can render/download float or half float textures.
-      //
-      // We can remove this block once we upstream checking for these
-      // extensions in expo.
-      //
-      // TODO look into adding support for checking these extensions in expo-gl
-      //
-      //@ts-ignore
-      const getExt = glContext.getExtension.bind(glContext);
-      const shimGetExt = (name: string) => {
-        if (name === 'EXT_color_buffer_float') {
-          if (RNPlatform.OS === 'ios') {
-            // iOS does not support EXT_color_buffer_float
-            return null;
-          } else {
-            return {};
-          }
-        }
-
-        if (name === 'EXT_color_buffer_half_float') {
-          return {};
-        }
-        return getExt(name);
-      };
-
-      //
-      // Manually make 'read' synchronous. glContext has a defined gl.fenceSync
-      // function that throws a "Not implemented yet" exception so core
-      // cannot properly detect that it is not supported. We mock
-      // implementations of gl.fenceSync and gl.clientWaitSync
-      // TODO remove once fenceSync and clientWaitSync is implemented upstream.
-      //
-      const shimFenceSync = () => {
-        return {};
-      };
-      const shimClientWaitSync = () => glContext.CONDITION_SATISFIED;
-
-      // @ts-ignore
-      glContext.getExtension = shimGetExt.bind(glContext);
-      glContext.fenceSync = shimFenceSync.bind(glContext);
-      glContext.clientWaitSync = shimClientWaitSync.bind(glContext);
-
-      // Set the WebGLContext before flag evaluation
-      setWebGLContext(2, glContext);
-      const context = new GPGPUContext();
-      const backend = new MathBackendWebGL(context);
-
-      return backend;
-    }, PRIORITY);
-
-    // Register all the webgl kernels on the rn-webgl backend
-    // TODO: Use tf.copyRegisteredKernels once synced to tfjs-core 2.5.0.
-    // tf.copyRegisteredKernels('webgl', 'rn-webgl');
-    const kernels = tf.getKernelsForBackend('webgl');
-    kernels.forEach(kernelConfig => {
-      const newKernelConfig =
-          Object.assign({}, kernelConfig, {backendName: 'rn-webgl'});
-      tf.registerKernel(newKernelConfig);
-    });
-  } catch (e) {
-    throw (new Error(`Failed to register Webgl backend: ${e.message}`));
-  }
-}
-
-tf.env().registerFlag(
-    'IS_REACT_NATIVE', () => navigator && navigator.product === 'ReactNative');
-
-if (tf.env().getBool('IS_REACT_NATIVE')) {
-  setupGlobals();
-  registerWebGLBackend();
-  tf.setPlatform('react-native', new PlatformReactNative());
-}
diff --git a/tfjs-master/tfjs-react-native/src/platform_react_native_test.ts b/tfjs-master/tfjs-react-native/src/platform_react_native_test.ts
deleted file mode 100644
index 663f218fd..000000000
--- a/tfjs-master/tfjs-react-native/src/platform_react_native_test.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-import {PlatformReactNative} from './platform_react_native';
-import {RN_ENVS} from './test_env_registry';
-
-describeWithFlags('PlatformReactNative', RN_ENVS, () => {
-  it('tf.util.fetch calls platform.fetch', async () => {
-    const platform = new PlatformReactNative();
-    tf.setPlatform('rn-test-platform', platform);
-
-    spyOn(platform, 'fetch');
-
-    await tf.util.fetch('test/url', {method: 'GET'});
-    expect(platform.fetch).toHaveBeenCalledWith('test/url', {method: 'GET'});
-  });
-});
diff --git a/tfjs-master/tfjs-react-native/src/test_env_registry.ts b/tfjs-master/tfjs-react-native/src/test_env_registry.ts
deleted file mode 100644
index aaaaaa0c4..000000000
--- a/tfjs-master/tfjs-react-native/src/test_env_registry.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import {Constraints, registerTestEnv} from '@tensorflow/tfjs-core/dist/jasmine_util';
-
-export const RN_ENVS: Constraints = {
-  predicate: testEnv => testEnv.backendName === 'rn-webgl'
-};
-
-registerTestEnv({
-  name: 'test-rn-webgl',
-  backendName: 'rn-webgl',
-  flags: {
-    'WEBGL_VERSION': 2,
-    'WEBGL_CPU_FORWARD': false,
-    'WEBGL_SIZE_UPLOAD_UNIFORM': 0
-  },
-  isDataSync: true
-});
diff --git a/tfjs-master/tfjs-react-native/src/tests.ts b/tfjs-master/tfjs-react-native/src/tests.ts
deleted file mode 100644
index 818401b39..000000000
--- a/tfjs-master/tfjs-react-native/src/tests.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-///// DO NOT EDIT: This file is auto-generated by /scripts/enumerate-tests.js
-
-import './async_storage_io_test';
-import './bundle_resource_io_test';
-import './camera/camera_test';
-import './decode_image_test';
-import './platform_react_native_test';
diff --git a/tfjs-master/tfjs-react-native/src/version.ts b/tfjs-master/tfjs-react-native/src/version.ts
deleted file mode 100644
index 874251fb7..000000000
--- a/tfjs-master/tfjs-react-native/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.2.3';
-export {version};
diff --git a/tfjs-master/tfjs-react-native/tsconfig.json b/tfjs-master/tfjs-react-native/tsconfig.json
deleted file mode 100644
index 831a6145b..000000000
--- a/tfjs-master/tfjs-react-native/tsconfig.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
-  "extends": "../tsconfig_base.json",
-  "compilerOptions": {
-    "target": "es2017",
-    "esModuleInterop": true,
-    "lib": [
-      "es2017",
-      "dom"
-    ],
-    "outDir": "./dist/",
-    "skipLibCheck": true,
-    "jsx": "react-native"
-  },
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules"
-  ]
-}
diff --git a/tfjs-master/tfjs-react-native/tsconfig.test.json b/tfjs-master/tfjs-react-native/tsconfig.test.json
deleted file mode 100644
index 537324152..000000000
--- a/tfjs-master/tfjs-react-native/tsconfig.test.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig.test",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-react-native/tslint.json b/tfjs-master/tfjs-react-native/tslint.json
deleted file mode 100644
index ec365f164..000000000
--- a/tfjs-master/tfjs-react-native/tslint.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "../tslint.json"
-}
diff --git a/tfjs-master/tfjs-react-native/yarn.lock b/tfjs-master/tfjs-react-native/yarn.lock
deleted file mode 100644
index 76e2fbeda..000000000
--- a/tfjs-master/tfjs-react-native/yarn.lock
+++ /dev/null
@@ -1,2657 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@babel/code-frame@7.12.11":
-  version "7.12.11"
-  resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.12.11.tgz#f4ad435aa263db935b8f10f2c552d23fb716a63f"
-  integrity "sha1-9K1DWqJj25NbjxDyxVLSP7cWpj8= sha512-Zt1yodBx1UcyiePMSkWnU4hPqhwq7hGi2nFL1LeA3EUl+q2LQx16MISgJ0+z7dnmgvP9QtIleuETGOiOH1RcIw=="
-  dependencies:
-    "@babel/highlight" "^7.10.4"
-
-"@babel/code-frame@^7.0.0":
-  version "7.12.13"
-  resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.12.13.tgz#dcfc826beef65e75c50e21d3837d7d95798dd658"
-  integrity sha512-HV1Cm0Q3ZrpCR93tkWOYiuYIgLxZXZFVG2VgK+MBWjUqZTundupbfx2aXarXuw5Ko5aMcjtJgbSs4vUGBS5v6g==
-  dependencies:
-    "@babel/highlight" "^7.12.13"
-
-"@babel/helper-validator-identifier@^7.12.11":
-  version "7.12.11"
-  resolved "https://registry.yarnpkg.com/@babel/helper-validator-identifier/-/helper-validator-identifier-7.12.11.tgz#c9a1f021917dcb5ccf0d4e453e399022981fc9ed"
-  integrity sha512-np/lG3uARFybkoHokJUmf1QfEvRVCPbmQeUQpKow5cQ3xWrV9i3rUHodKDJPQfTVX61qKi+UdYk8kik84n7XOw==
-
-"@babel/highlight@^7.10.4", "@babel/highlight@^7.12.13":
-  version "7.13.10"
-  resolved "https://registry.yarnpkg.com/@babel/highlight/-/highlight-7.13.10.tgz#a8b2a66148f5b27d666b15d81774347a731d52d1"
-  integrity sha512-5aPpe5XQPzflQrFwL1/QoeHkP2MsA4JCntcXHRhEsdsfPVkvPi2w7Qix4iV7t5S/oC9OodGrggd8aco1g3SZFg==
-  dependencies:
-    "@babel/helper-validator-identifier" "^7.12.11"
-    chalk "^2.0.0"
-    js-tokens "^4.0.0"
-
-"@eslint/eslintrc@^0.4.0":
-  version "0.4.0"
-  resolved "https://registry.yarnpkg.com/@eslint/eslintrc/-/eslintrc-0.4.0.tgz#99cc0a0584d72f1df38b900fb062ba995f395547"
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-  dependencies:
-    ajv "^6.12.4"
-    debug "^4.1.1"
-    espree "^7.3.0"
-    globals "^12.1.0"
-    ignore "^4.0.6"
-    import-fresh "^3.2.1"
-    js-yaml "^3.13.1"
-    minimatch "^3.0.4"
-    strip-json-comments "^3.1.1"
-
-"@nodelib/fs.scandir@2.1.4":
-  version "2.1.4"
-  resolved "https://registry.yarnpkg.com/@nodelib/fs.scandir/-/fs.scandir-2.1.4.tgz#d4b3549a5db5de2683e0c1071ab4f140904bbf69"
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-  dependencies:
-    "@nodelib/fs.stat" "2.0.4"
-    run-parallel "^1.1.9"
-
-"@nodelib/fs.stat@2.0.4", "@nodelib/fs.stat@^2.0.2":
-  version "2.0.4"
-  resolved "https://registry.yarnpkg.com/@nodelib/fs.stat/-/fs.stat-2.0.4.tgz#a3f2dd61bab43b8db8fa108a121cfffe4c676655"
-  integrity sha512-IYlHJA0clt2+Vg7bccq+TzRdJvv19c2INqBSsoOLp1je7xjtr7J26+WXR72MCdvU9q1qTzIWDfhMf+DRvQJK4Q==
-
-"@nodelib/fs.walk@^1.2.3":
-  version "1.2.6"
-  resolved "https://registry.yarnpkg.com/@nodelib/fs.walk/-/fs.walk-1.2.6.tgz#cce9396b30aa5afe9e3756608f5831adcb53d063"
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-  dependencies:
-    "@nodelib/fs.scandir" "2.1.4"
-    fastq "^1.6.0"
-
-"@react-native-async-storage/async-storage@^1.13.0":
-  version "1.15.7"
-  resolved "https://registry.yarnpkg.com/@react-native-async-storage/async-storage/-/async-storage-1.15.7.tgz#736ac5cfe211b081e70389e80c237398d1451f08"
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-  dependencies:
-    merge-options "^3.0.4"
-
-"@sindresorhus/is@^0.14.0":
-  version "0.14.0"
-  resolved "https://registry.yarnpkg.com/@sindresorhus/is/-/is-0.14.0.tgz#9fb3a3cf3132328151f353de4632e01e52102bea"
-  integrity "sha1-n7OjzzEyMoFR81PeRjLgHlIQK+o= sha512-9NET910DNaIPngYnLLPeg+Ogzqsi9uM4mSboU5y6p8S5DzMTVEsJZrawi+BoDNUVBa2DhJqQYUFvMDfgU062LQ=="
-
-"@szmarczak/http-timer@^1.1.2":
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/@szmarczak/http-timer/-/http-timer-1.1.2.tgz#b1665e2c461a2cd92f4c1bbf50d5454de0d4b421"
-  integrity "sha1-sWZeLEYaLNkvTBu/UNVFTeDUtCE= sha512-XIB2XbzHTN6ieIjfIMV9hlVcfPU26s2vafYWQcZHWXHOxiaRZYEDKEwdl129Zyg50+foYV2jCgtrqSA6qNuNSA=="
-  dependencies:
-    defer-to-connect "^1.0.1"
-
-"@tensorflow/tfjs-backend-cpu@4.3.0", "@tensorflow/tfjs-backend-cpu@~4.3.0":
-  version "4.3.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-4.3.0.tgz#959f1c4eeea4713179e4028c793a06542ad2f3d0"
-  integrity sha512-ovGI/eLHMJuZCkl630+zA0SU6zuPaUYu1g8LSDcjjEG0g1aadzA6XnrAohI2Man3F7nWvC2pImt3JUgW87iaVA==
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-  version "0.7.0"
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-  version "1.8.0"
-  resolved "https://registry.yarnpkg.com/clang-format/-/clang-format-1.8.0.tgz#7779df1c5ce1bc8aac1b0b02b4e479191ef21d46"
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-  version "3.0.0"
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-  resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.4.24.tgz#2022b4b25fbddc21d2f524974a474aafe733908b"
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-    safer-buffer ">= 2.1.2 < 3"
-
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-  version "1.2.1"
-  resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.2.1.tgz#8eb7a10a63fff25d15a57b001586d177d1b0d352"
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-
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-  version "3.0.3"
-  resolved "https://registry.yarnpkg.com/ignore-walk/-/ignore-walk-3.0.3.tgz#017e2447184bfeade7c238e4aefdd1e8f95b1e37"
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-  dependencies:
-    minimatch "^3.0.4"
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-  version "4.0.6"
-  resolved "https://registry.yarnpkg.com/ignore/-/ignore-4.0.6.tgz#750e3db5862087b4737ebac8207ffd1ef27b25fc"
-  integrity "sha1-dQ49tYYgh7RzfrrIIH/9HvJ7Jfw= sha512-cyFDKrqc/YdcWFniJhzI42+AzS+gNwmUzOSFcRCQYwySuBBBy/KjuxWLZ/FHEH6Moq1NizMOBWyTcv8O4OZIMg=="
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-ignore@^5.0.4, ignore@^5.1.1, ignore@^5.1.4:
-  version "5.1.8"
-  resolved "https://registry.yarnpkg.com/ignore/-/ignore-5.1.8.tgz#f150a8b50a34289b33e22f5889abd4d8016f0e57"
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-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/import-fresh/-/import-fresh-3.3.0.tgz#37162c25fcb9ebaa2e6e53d5b4d88ce17d9e0c2b"
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-    resolve-from "^4.0.0"
-
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-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/import-lazy/-/import-lazy-2.1.0.tgz#05698e3d45c88e8d7e9d92cb0584e77f096f3e43"
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-
-imurmurhash@^0.1.4:
-  version "0.1.4"
-  resolved "https://registry.yarnpkg.com/imurmurhash/-/imurmurhash-0.1.4.tgz#9218b9b2b928a238b13dc4fb6b6d576f231453ea"
-  integrity sha1-khi5srkoojixPcT7a21XbyMUU+o=
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-indent-string@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/indent-string/-/indent-string-4.0.0.tgz#624f8f4497d619b2d9768531d58f4122854d7251"
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-inflight@^1.0.4:
-  version "1.0.6"
-  resolved "https://registry.yarnpkg.com/inflight/-/inflight-1.0.6.tgz#49bd6331d7d02d0c09bc910a1075ba8165b56df9"
-  integrity sha1-Sb1jMdfQLQwJvJEKEHW6gWW1bfk=
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-    once "^1.3.0"
-    wrappy "1"
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-  version "2.0.4"
-  resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
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-
-ini@2.0.0, ini@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/ini/-/ini-2.0.0.tgz#e5fd556ecdd5726be978fa1001862eacb0a94bc5"
-  integrity sha512-7PnF4oN3CvZF23ADhA5wRaYEQpJ8qygSkbtTXWBeXWXmEVRXK+1ITciHWwHhsjv1TmW0MgacIv6hEi5pX5NQdA==
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-  version "1.3.8"
-  resolved "https://registry.yarnpkg.com/ini/-/ini-1.3.8.tgz#a29da425b48806f34767a4efce397269af28432c"
-  integrity sha512-JV/yugV2uzW5iMRSiZAyDtQd+nxtUnjeLt0acNdw98kKLrvuRVyB80tsREOE7yvGVgalhZ6RNXCmEHkUKBKxew==
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-  version "7.3.3"
-  resolved "https://registry.yarnpkg.com/inquirer/-/inquirer-7.3.3.tgz#04d176b2af04afc157a83fd7c100e98ee0aad003"
-  integrity "sha1-BNF2sq8Er8FXqD/XwQDpjuCq0AM= sha512-JG3eIAj5V9CwcGvuOmoo6LB9kbAYT8HXffUl6memuszlwDC/qvFAJw49XJ5NROSFNPxp3iQg1GqkFhaY/CR0IA=="
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-    chalk "^4.1.0"
-    cli-cursor "^3.1.0"
-    cli-width "^3.0.0"
-    external-editor "^3.0.3"
-    figures "^3.0.0"
-    lodash "^4.17.19"
-    mute-stream "0.0.8"
-    run-async "^2.4.0"
-    rxjs "^6.6.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-    through "^2.3.6"
-
-is-arrayish@^0.2.1:
-  version "0.2.1"
-  resolved "https://registry.yarnpkg.com/is-arrayish/-/is-arrayish-0.2.1.tgz#77c99840527aa8ecb1a8ba697b80645a7a926a9d"
-  integrity sha1-d8mYQFJ6qOyxqLppe4BkWnqSap0=
-
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/is-ci/-/is-ci-2.0.0.tgz#6bc6334181810e04b5c22b3d589fdca55026404c"
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-  dependencies:
-    ci-info "^2.0.0"
-
-is-core-module@^2.2.0:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/is-core-module/-/is-core-module-2.2.0.tgz#97037ef3d52224d85163f5597b2b63d9afed981a"
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-  dependencies:
-    has "^1.0.3"
-
-is-extglob@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/is-extglob/-/is-extglob-2.1.1.tgz#a88c02535791f02ed37c76a1b9ea9773c833f8c2"
-  integrity sha1-qIwCU1eR8C7TfHahueqXc8gz+MI=
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/is-fullwidth-code-point/-/is-fullwidth-code-point-2.0.0.tgz#a3b30a5c4f199183167aaab93beefae3ddfb654f"
-  integrity sha1-o7MKXE8ZkYMWeqq5O+764937ZU8=
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-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/is-fullwidth-code-point/-/is-fullwidth-code-point-3.0.0.tgz#f116f8064fe90b3f7844a38997c0b75051269f1d"
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-
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-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/is-glob/-/is-glob-4.0.1.tgz#7567dbe9f2f5e2467bc77ab83c4a29482407a5dc"
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-  dependencies:
-    is-extglob "^2.1.1"
-
-is-installed-globally@^0.4.0:
-  version "0.4.0"
-  resolved "https://registry.yarnpkg.com/is-installed-globally/-/is-installed-globally-0.4.0.tgz#9a0fd407949c30f86eb6959ef1b7994ed0b7b520"
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-    global-dirs "^3.0.0"
-    is-path-inside "^3.0.2"
-
-is-module@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/is-module/-/is-module-1.0.0.tgz#3258fb69f78c14d5b815d664336b4cffb6441591"
-  integrity sha1-Mlj7afeMFNW4FdZkM2tM/7ZEFZE=
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-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/is-npm/-/is-npm-5.0.0.tgz#43e8d65cc56e1b67f8d47262cf667099193f45a8"
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-
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-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/is-number/-/is-number-7.0.0.tgz#7535345b896734d5f80c4d06c50955527a14f12b"
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-
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/is-obj/-/is-obj-2.0.0.tgz#473fb05d973705e3fd9620545018ca8e22ef4982"
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-  version "3.0.3"
-  resolved "https://registry.yarnpkg.com/is-path-inside/-/is-path-inside-3.0.3.tgz#d231362e53a07ff2b0e0ea7fed049161ffd16283"
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-
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-  version "1.1.0"
-  resolved "https://registry.yarnpkg.com/is-plain-obj/-/is-plain-obj-1.1.0.tgz#71a50c8429dfca773c92a390a4a03b39fcd51d3e"
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-
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-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/is-plain-obj/-/is-plain-obj-2.1.0.tgz#45e42e37fccf1f40da8e5f76ee21515840c09287"
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-
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-2.0.0.tgz#bde9c32680d6fae04129d6ac9d921ce7815f78e3"
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-
-is-typedarray@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/is-typedarray/-/is-typedarray-1.0.0.tgz#e479c80858df0c1b11ddda6940f96011fcda4a9a"
-  integrity sha1-5HnICFjfDBsR3dppQPlgEfzaSpo=
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-is-yarn-global@^0.3.0:
-  version "0.3.0"
-  resolved "https://registry.yarnpkg.com/is-yarn-global/-/is-yarn-global-0.3.0.tgz#d502d3382590ea3004893746754c89139973e232"
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/isexe/-/isexe-2.0.0.tgz#e8fbf374dc556ff8947a10dcb0572d633f2cfa10"
-  integrity sha1-6PvzdNxVb/iUehDcsFctYz8s+hA=
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-  version "0.4.4"
-  resolved "https://registry.yarnpkg.com/jpeg-js/-/jpeg-js-0.4.4.tgz#a9f1c6f1f9f0fa80cdb3484ed9635054d28936aa"
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-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/js-tokens/-/js-tokens-4.0.0.tgz#19203fb59991df98e3a287050d4647cdeaf32499"
-  integrity sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==
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-  version "3.14.1"
-  resolved "https://registry.yarnpkg.com/js-yaml/-/js-yaml-3.14.1.tgz#dae812fdb3825fa306609a8717383c50c36a0537"
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-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/json-buffer/-/json-buffer-3.0.0.tgz#5b1f397afc75d677bde8bcfc0e47e1f9a3d9a898"
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-  version "2.3.1"
-  resolved "https://registry.yarnpkg.com/json-parse-even-better-errors/-/json-parse-even-better-errors-2.3.1.tgz#7c47805a94319928e05777405dc12e1f7a4ee02d"
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-  version "0.4.1"
-  resolved "https://registry.yarnpkg.com/json-schema-traverse/-/json-schema-traverse-0.4.1.tgz#69f6a87d9513ab8bb8fe63bdb0979c448e684660"
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/json-schema-traverse/-/json-schema-traverse-1.0.0.tgz#ae7bcb3656ab77a73ba5c49bf654f38e6b6860e2"
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-  resolved "https://registry.yarnpkg.com/json-stable-stringify-without-jsonify/-/json-stable-stringify-without-jsonify-1.0.1.tgz#9db7b59496ad3f3cfef30a75142d2d930ad72651"
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-  integrity sha1-h3Gq4HmbZAdrdmQPygWPnBDjPss=
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-  resolved "https://registry.yarnpkg.com/keyv/-/keyv-3.1.0.tgz#ecc228486f69991e49e9476485a5be1e8fc5c4d9"
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-  version "6.0.3"
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-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/latest-version/-/latest-version-5.1.0.tgz#119dfe908fe38d15dfa43ecd13fa12ec8832face"
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diff --git a/tfjs-master/tfjs-tfdf/.npmignore b/tfjs-master/tfjs-tfdf/.npmignore
deleted file mode 100644
index ddd9e42af..000000000
--- a/tfjs-master/tfjs-tfdf/.npmignore
+++ /dev/null
@@ -1,28 +0,0 @@
-.babelrc
-.DS_Store
-.idea/
-.rpt2_cache
-.travis.yml
-.vscode
-*.tgz
-*.txt
-**.yalc
-**yalc.lock
-cloudbuild.yml
-coverage/
-demo/
-dist/**/*_test.d.ts
-dist/**/*_test.js
-karma.conf.js
-node_modules/
-npm-debug.log
-package-lock.json
-package/
-rollup.config.js
-scripts/
-src/**/*_test.ts
-tsconfig.json
-tslint.json
-yarn-error.log
-yarn.lock
-.cache/
diff --git a/tfjs-master/tfjs-tfdf/.npmrc b/tfjs-master/tfjs-tfdf/.npmrc
deleted file mode 100644
index 43c97e719..000000000
--- a/tfjs-master/tfjs-tfdf/.npmrc
+++ /dev/null
@@ -1 +0,0 @@
-package-lock=false
diff --git a/tfjs-master/tfjs-tfdf/BUILD.bazel b/tfjs-master/tfjs-tfdf/BUILD.bazel
deleted file mode 100644
index b133797af..000000000
--- a/tfjs-master/tfjs-tfdf/BUILD.bazel
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright 2022 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@build_bazel_rules_nodejs//:index.bzl", "pkg_npm")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-# Run rollup to bundle the compiled files for different execution environments.
-tfjs_bundle(
-    name = "tf-tfdf",
-    entry_point = "//tfjs-tfdf/src:index.ts",
-    external = [
-        "@tensorflow/tfjs-core",
-        "@tensorflow/tfjs-converter",
-        "path",
-        "fs",
-    ],
-    globals = {
-        "@tensorflow/tfjs-converter": "tf",
-        "@tensorflow/tfjs-core": "tf",
-    },
-    leave_as_require = [
-        "path",
-        "fs",
-        "crypto",
-    ],
-    umd_name = "tfdf",
-    deps = [
-        "//tfjs-tfdf/src:tfdf_web_api_client_js",
-        "//tfjs-tfdf/src:tfjs-tfdf_lib",
-        "//tfjs-tfdf/src:tfjs-tfdf_src_lib",
-        "//tfjs-tfdf/wasm",
-    ],
-)
-
-# Copy ouput files to dist/.
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-tfdf/src:tfdf_web_api_client_js",
-        "//tfjs-tfdf/src:tfjs-tfdf_lib",
-        "//tfjs-tfdf/src:tfjs-tfdf_src_lib",
-    ],
-    root = "src",
-)
-
-# Copy bundled files to dist/.
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-tfdf",
-        ":tf-tfdf.es2017",
-        ":tf-tfdf.es2017.min",
-        ":tf-tfdf.fesm",
-        ":tf-tfdf.fesm.min",
-        ":tf-tfdf.min",
-        ":tf-tfdf.node",
-    ],
-)
-
-# Copy wasm related files to dist/.
-copy_to_dist(
-    name = "copy_wasm_files",
-    srcs = [
-        "//tfjs-tfdf/wasm",
-    ],
-    root = "wasm",
-)
-
-# Package for npm.
-pkg_npm(
-    name = "tfjs-tfdf_pkg",
-    package_name = "@tensorflow/tfjs-tfdf",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_src_to_dist",
-        ":copy_wasm_files",
-    ],
-)
-
-# Tests.
-tfjs_web_test(
-    name = "tfjs-tfdf_test",
-    srcs = [
-        "//tfjs-tfdf/src:tfjs-tfdf_test_bundle",
-    ],
-    browsers = [
-        # TODO: Support Safari.
-        "bs_chrome_mac",
-        "bs_firefox_mac",
-        "bs_android_10",
-        "win_10_chrome",
-    ],
-    static_files = [
-        "//tfjs-tfdf/wasm",
-    ],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        ":tfjs-tfdf_test",
-    ],
-)
diff --git a/tfjs-master/tfjs-tfdf/README.md b/tfjs-master/tfjs-tfdf/README.md
deleted file mode 100644
index 81d196f44..000000000
--- a/tfjs-master/tfjs-tfdf/README.md
+++ /dev/null
@@ -1,95 +0,0 @@
-# Tensorflow Decision Forests support for Tensorflow.js
-
-This package enables users to run arbitary Tensorflow Decision Forests models
-on the web that are converted using tfjs-converter.
-Users can load a TFDF model from a URL, use TFJS tensors to set
-the model's input data, run inference, and get the output back in TFJS tensors.
-Under the hood, the TFDF C++ runtime is packaged in a set of WASM modules.
-
-# Usage
-
-## Import the packages
-
-To use this package, you will need a TFJS backend installed.
-You will also need to import `@tensorflow/tfjs-core` for
-manipulating tensors, and `@tensorflow/tfjs-converter` for loading models.
-
-### Via NPM
-
-```js
-// Import @tensorflow/tfjs-core
-import * as tf from '@tensorflow/tfjs-core';
-// Adds the CPU backend.
-import '@tensorflow/tfjs-backend-cpu';
-// Import @tensorflow/tfjs-converter
-import * as tf from '@tensorflow/tfjs-converter';
-// Import @tensorflow/tfjs-tfdf.
-import * as tfdf from '@tensorflow/tfjs-tfdf';
-```
-
-### Via a script tag
-
-```html
-<!-- Import @tensorflow/tfjs-core -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
-<!-- Adds the CPU backend -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-cpu"></script>
-<!-- Import @tensorflow/tfjs-converter -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script>
-<!--
-  Import @tensorflow/tfjs-tfdf
-
-  Note that we need to explicitly load dist/tf-tfdf.min.js so that it can
-  locate WASM module files from their default location (dist/).
--->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-tfdf/dist/tf-tfdf.min.js"></script>
-```
-
-## Set WASM modules location (optional)
-
-By default, it will try to load the WASM modules from the same location where
-the package or your own script is served. Use `setLocateFile` to set your own
-location. See `src/tfdf_web_api_client.d.ts` for more details.
-
-```js
-// `base` is the URL to the main javascript file's directory.
-// To return the default URL of the file use `${base}${path}`.
-tfdf.setLocateFile((path, base) => {
-  return `https://your-server/.../${path}`;
-});
-```
-
-## Load a TFDF model
-```js
-const tfdfModel = await tfdf.loadTFDFModel('url/to/your/model.json');
-```
-
-## Run inference
-```js
-// Prepare input tensors.
-const input = tf.tensor1d(['test', 'strings']);
-
-// Run inference and get output tensors.
-const outputTensor = await tfdfModel.executeAsync(input);
-console.log(outputTensor.dataSync());
-```
-
-# Development
-
-## Building
-
-```sh
-$ yarn
-$ yarn build
-```
-
-## Testing
-
-```sh
-$ yarn test
-```
-
-## Deployment
-```sh
-$ yarn build-npm
-```
diff --git a/tfjs-master/tfjs-tfdf/package.json b/tfjs-master/tfjs-tfdf/package.json
deleted file mode 100644
index a51434c2f..000000000
--- a/tfjs-master/tfjs-tfdf/package.json
+++ /dev/null
@@ -1,49 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-tfdf",
-  "version": "0.0.1",
-  "description": "TensorFlow Decision Forests support for TensorFlow.js",
-  "main": "dist/tf-tfdf.node.js",
-  "module": "dist/tf-tfdf.fesm.js",
-  "jsdelivr": "dist/tf-tfdf.min.js",
-  "unpkg": "dist/tf-tfdf.min.js",
-  "types": "dist/index.d.ts",
-  "exports": {
-    ".": {
-      "import": "./dist/tf-tfdf.fesm.js",
-      "require": "./dist/tf-tfdf.node.js"
-    }
-  },
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-tfdf"
-  },
-  "license": "Apache-2.0",
-  "engines": {
-    "yarn": ">= 1.3.2"
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core",
-    "@tensorflow/tfjs-converter": "link:../link-package/node_modules/@tensorflow/tfjs-converter"
-  },
-  "devDependencies": {
-    "@bazel/bazelisk": "^1.12.0"
-  },
-  "scripts": {
-    "prep": "./scripts/download-tfdf-web-api.sh",
-    "build": "bazel build :tfjs-tfdf_pkg",
-    "build-ci": "yarn build",
-    "bundle": "bazel build :tfjs-tfdf_pkg",
-    "bundle-ci": "yarn bundle",
-    "build-link-package": "cd ../link-package && yarn build",
-    "build-deps": "yarn build-link-package",
-    "build-npm": "bazel build :tfjs-tfdf_pkg",
-    "publish-npm": "bazel run :tfjs-tfdf_pkg.publish",
-    "test": "bazel test :tests --test_output=all",
-    "test-ci": "yarn test",
-    "test-debug": "bazel run :tfjs-tfdf_test"
-  },
-  "resolutions": {
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-tfdf/src/BUILD.bazel b/tfjs-master/tfjs-tfdf/src/BUILD.bazel
deleted file mode 100644
index 9b65db7cd..000000000
--- a/tfjs-master/tfjs-tfdf/src/BUILD.bazel
+++ /dev/null
@@ -1,87 +0,0 @@
-# Copyright 2022 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@build_bazel_rules_nodejs//:index.bzl", "copy_to_bin")
-load("//tools:defaults.bzl", "esbuild", "ts_library")
-
-package(default_visibility = ["//visibility:public"])
-
-TEST_SRCS = [
-    "**/*_test.ts",
-    "setup_test.ts",
-]
-
-ts_library(
-    name = "tfjs-tfdf_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-tfdf/dist",
-    deps = [
-        "//tfjs-converter/src:tfjs-converter_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-    ],
-)
-
-ts_library(
-    name = "tfjs-tfdf_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-tfdf",
-    deps = [
-        ":tfjs-tfdf_src_lib",
-    ],
-)
-
-copy_to_bin(
-    name = "tfdf_web_api_client_js",
-    srcs = [
-        "tfdf_web_api_client.js",
-    ],
-)
-
-ts_library(
-    name = "tfjs-tfdf_test_lib",
-    srcs = glob(TEST_SRCS),
-    module_name = "@tensorflow/tfjs-tfdf/dist",
-    deps = [
-        ":tfjs-tfdf_lib",
-        ":tfjs-tfdf_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-    ],
-)
-
-esbuild(
-    name = "tfjs-tfdf_test_bundle",
-    testonly = True,
-    entry_point = "setup_test.ts",
-    external = [
-        "@tensorflow/tfjs",
-        "path",
-        "fs",
-        "worker_threads",
-        "util",
-    ],
-    sources_content = True,
-    deps = [
-        ":tfdf_web_api_client_js",
-        ":tfjs-tfdf_lib",
-        ":tfjs-tfdf_test_lib",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-converter/src:tfjs-converter_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-tfdf/wasm",
-    ],
-)
diff --git a/tfjs-master/tfjs-tfdf/src/index.ts b/tfjs-master/tfjs-tfdf/src/index.ts
deleted file mode 100644
index 54fc559a2..000000000
--- a/tfjs-master/tfjs-tfdf/src/index.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export * from './tfdf_model';
-export {setLocateFile} from './tfdf_web_api_client';
-import './register_tfdf_ops';
diff --git a/tfjs-master/tfjs-tfdf/src/register_tfdf_ops.ts b/tfjs-master/tfjs-tfdf/src/register_tfdf_ops.ts
deleted file mode 100644
index b646e083f..000000000
--- a/tfjs-master/tfjs-tfdf/src/register_tfdf_ops.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GraphNode, registerOp} from '@tensorflow/tfjs-converter';
-import {scalar, tensor1d, tensor2d} from '@tensorflow/tfjs-core';
-
-import * as tfdfWebAPIClient from './tfdf_web_api_client';
-import {TFDFWebModelRunner} from './types/tfdf_web_model_runner';
-
-type Assets = string|Blob;
-let assets: Assets;
-let modelRunner: TFDFWebModelRunner;
-
-export function setAssets(newAssets: Assets) {
-  assets = newAssets;
-}
-
-registerOp('SimpleMLCreateModelResource', () => {
-  return [scalar(0)];
-});
-
-registerOp('SimpleMLLoadModelFromPathWithHandle', async (node: GraphNode) => {
-  const tfdfWeb = await tfdfWebAPIClient.tfdfWeb();
-  const loadOptions = {createdTFDFSignature: true};
-
-  modelRunner = typeof assets === 'string' ?
-      await tfdfWeb.loadModelFromUrl(assets, loadOptions) :
-      await tfdfWeb.loadModelFromZipBlob(assets, loadOptions);
-
-  return [scalar(0)];
-});
-
-registerOp('SimpleMLInferenceOpWithHandle', async (node: GraphNode) => {
-  const inputs = node.inputs.map(input => input.arraySync());
-  const denseOutputDim = node.attrs['dense_output_dim'] as number;
-
-  const features = {
-    numericalFeatures: inputs[0] as number[][],
-    booleanFeatures: inputs[1] as number[][],
-    categoricalIntFeatures: inputs[2] as number[][],
-    categoricalSetIntFeaturesValues: inputs[3] as number[],
-    categoricalSetIntFeaturesRowSplitsDim1: inputs[4] as number[],
-    categoricalSetIntFeaturesRowSplitsDim2: inputs[5] as number[],
-    denseOutputDim
-  };
-
-  const outputs = modelRunner.predictTFDFSignature(features);
-
-  const densePredictionsTensor = tensor2d(outputs.densePredictions);
-  const denseColRepresentationTensor =
-      tensor1d(outputs.denseColRepresentation, 'string');
-
-  return [densePredictionsTensor, denseColRepresentationTensor];
-});
diff --git a/tfjs-master/tfjs-tfdf/src/setup_test.ts b/tfjs-master/tfjs-tfdf/src/setup_test.ts
deleted file mode 100644
index 4ef1ab5bc..000000000
--- a/tfjs-master/tfjs-tfdf/src/setup_test.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import '@tensorflow/tfjs-backend-cpu';
-import './tfdf_web_api_client_test';
-import './tfdf_model_test';
-
-// Register ops.
-import * as tfdf from './index';
-
-// Karma does not preserve the source path, causing the TFDF library to look for
-// the wasm binary in the root path, so fix the path for the library.
-tfdf.setLocateFile((path: string, _: string) => {
-  return `./base/tfjs/tfjs-tfdf/wasm/${path}`;
-});
diff --git a/tfjs-master/tfjs-tfdf/src/tfdf_model.ts b/tfjs-master/tfjs-tfdf/src/tfdf_model.ts
deleted file mode 100644
index aa54cbe27..000000000
--- a/tfjs-master/tfjs-tfdf/src/tfdf_model.ts
+++ /dev/null
@@ -1,212 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {GraphModel, loadGraphModel, loadGraphModelSync} from '@tensorflow/tfjs-converter';
-import {InferenceModel, io, ModelPredictConfig, NamedTensorMap, Tensor} from '@tensorflow/tfjs-core';
-import {setAssets} from './register_tfdf_ops';
-
-import {TFDFLoadHandler, TFDFLoadHandlerSync} from './types/tfdf_io';
-
-/**
- * To load a `tfdf.TFDFModel`, use the `loadTFDFModel` function below.
- *
- * Sample usage:
- *
- * ```js
- * // Load the test TFDF model.
- * const tfdfModel = await tfdf.loadTFDFModel(
- *     'https://storage.googleapis.com/tfjs-testing/adult_gbt_no_regex/model.json');
- * const inputs = {
- *   'age': tf.tensor1d([39, 40, 40, 35], 'int32'),
- *   'workclass': tf.tensor1d(
- *       ['State-gov', 'Private', 'Private', 'Federal-gov'], 'string'),
- *   'fnlwgt': tf.tensor1d([77516, 121772, 193524, 76845], 'int32'),
- *   'education':
- *      tf.tensor1d(['Bachelors', 'Assoc-voc', 'Doctorate', '9th'], 'string'),
- *   'education_num': tf.tensor1d([13, 11, 16, 5], 'int32'),
- *   'marital_status': tf.tensor1d(
- *       [
- *         'Never-married', 'Married-civ-spouse', 'Married-civ-spouse',
- *         'Married-civ-spouse'
- *       ],
- *       'string'),
- *   'occupation': tf.tensor1d(
- *      ['Adm-clerical', 'Craft-repair', 'Prof-specialty', 'Farming-fishing'],
- *       'string'),
- *   'relationship': tf.tensor1d(
- *       ['Not-in-family', 'Husband', 'Husband', 'Husband'], 'string'),
- *   'race': tf.tensor1d(
- *       ['White', 'Asian-Pac-Islander', 'White', 'Black'], 'string'),
- *   'sex': tf.tensor1d(['Male', 'Male', 'Male', 'Male'], 'string'),
- *   'capital_gain': tf.tensor1d([2174, 0, 0, 0], 'int32'),
- *   'capital_loss': tf.tensor1d([0, 0, 0, 0], 'int32'),
- *   'hours_per_week': tf.tensor1d([40, 40, 60, 40], 'int32'),
- *   'native_country': tf.tensor1d(
- *       ['United-States', '', 'United-States', 'United-States'], 'string')
- * };
- * const densePredictions = await tfdfModel.executeAsync(inputs);
- * console.log(densePredictions);
- * ```
- */
-export class TFDFModel implements InferenceModel {
-  constructor(
-      private readonly graphModel: GraphModel|GraphModel<io.IOHandlerSync>,
-      private readonly assets: string|Blob) {}
-
-  get inputs() {
-    return this.graphModel.inputs;
-  }
-
-  get outputs() {
-    return this.graphModel.outputs;
-  }
-
-  /**
-   * Execute the inference for the input tensors.
-   *
-   * @param inputs The input tensors, when there is single input for the model,
-   *     inputs param should be a Tensor. For models with multiple inputs,
-   *     inputs params should be in either Tensor[] if the input order is fixed,
-   *     or otherwise NamedTensorMap format.
-   *
-   * @param config Prediction configuration for specifying the batch size.
-   *     Currently this field is not used, and batch inference is not supported.
-   *
-   * @returns Inference result tensors. The output would be single Tensor if
-   *     model has single output node, otherwise NamedTensorMap will be returned
-   *     for model with multiple outputs. Tensor[] is not used.
-   */
-  predict(inputs: Tensor|Tensor[]|NamedTensorMap, config?: ModelPredictConfig):
-      Tensor|Tensor[]|NamedTensorMap {
-    setAssets(this.assets);
-    return this.graphModel.predict(inputs, config);
-  }
-
-  /**
-   * Execute the inference for the input tensors and return activation
-   * values for specified output node names without batching.
-   *
-   * @param inputs The input tensors, when there is single input for the model,
-   *     inputs param should be a Tensor. For models with multiple inputs,
-   *     inputs params should be in either Tensor[] if the input order is fixed,
-   *     or otherwise NamedTensorMap format.
-   *
-   * @param outputs string|string[]. List of output node names to retrieve
-   *     activation from.
-   *
-   * @returns Activation values for the output nodes result tensors. The return
-   *     type matches specified parameter outputs type. The output would be
-   *     single Tensor if single output is specified, otherwise Tensor[] for
-   *     multiple outputs.
-   */
-  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs: string|string[]):
-      Tensor|Tensor[] {
-    setAssets(this.assets);
-    return this.graphModel.execute(inputs, outputs);
-  }
-
-  /**
-   * Executes inference for the model for given input tensors in async
-   * fashion, use this method when your model contains control flow ops.
-   * @param inputs tensor, tensor array or tensor map of the inputs for the
-   * model, keyed by the input node names.
-   * @param outputs output node name from the TensorFlow model, if no outputs
-   * are specified, the default outputs of the model would be used. You can
-   * inspect intermediate nodes of the model by adding them to the outputs
-   * array.
-   *
-   * @returns A Promise of single tensor if provided with a single output or
-   * no outputs are provided and there is only one default output, otherwise
-   * return a tensor map.
-   */
-  executeAsync(
-      inputs: Tensor|Tensor[]|NamedTensorMap,
-      outputs?: string|string[]): Promise<Tensor|Tensor[]> {
-    setAssets(this.assets);
-    return this.graphModel.executeAsync(inputs, outputs);
-  }
-
-  dispose() {
-    this.graphModel.dispose();
-  }
-}
-
-/**
- * Load a TFDF graph model given a URL to the model definition.
- *
- * Example of loading an example model from a URL and making a prediction with
- * an input map:
- *
- * ```js
- * // Load the test TFDF model.
- * const tfdfModel = await tfdf.loadTFDFModel(
- *     'https://storage.googleapis.com/tfjs-testing/adult_gbt_no_regex/model.json');
- * ```
- * @param modelUrl The url or an `TFDFLoadHandler` that loads the model and
- *    model assets.
- * @param options Options for the HTTP request, which allows to send credentials
- *    and custom headers.
- */
-export async function loadTFDFModel(
-    modelUrl: string|TFDFLoadHandler, options: io.LoadOptions = {},
-    tfio = io): Promise<TFDFModel> {
-  if (modelUrl == null) {
-    throw new Error(
-        'modelUrl in loadTFDFModel() cannot be null. Please provide a ' +
-        'url or an TFDFLoadHandler that loads the model');
-  }
-  let graphModelUrl: Parameters<typeof loadGraphModel>[0];
-  let assets: string|Blob;
-
-  if (typeof modelUrl === 'string') {
-    graphModelUrl = modelUrl;
-    assets = new URL('assets.zip', modelUrl).href;
-  } else {
-    graphModelUrl = {load: modelUrl.loadModel};
-    assets = await modelUrl.loadAssets();
-  }
-
-  const graphModel = await loadGraphModel(graphModelUrl, options, tfio);
-  return new TFDFModel(graphModel, assets);
-}
-
-/**
- * Load a TFDF graph model given a synchronous IO handler with a 'load' method.
- *
- * @param modelSource The `TFDFLoadHandlerSync` that loads the model.
- */
-
-export function loadTFDFModelSync(modelSource: TFDFLoadHandlerSync): TFDFModel {
-  if (modelSource == null) {
-    throw new Error(
-        'modelUrl in loadTFDFModelSync() cannot be null. Please provide a ' +
-        'url or an TFDFLoadHandlerSync that loads the model');
-  }
-  if (!modelSource.loadModel) {
-    throw new Error(
-        `modelUrl IO Handler ${modelSource} has no loadModel function`);
-  }
-  if (!modelSource.loadAssets) {
-    throw new Error(
-        `modelUrl IO Handler ${modelSource} has no loadAssets function`);
-  }
-
-  const graphModelSource = {load: modelSource.loadModel};
-  const assets = modelSource.loadAssets();
-  const graphModel = loadGraphModelSync(graphModelSource);
-  return new TFDFModel(graphModel, assets);
-}
diff --git a/tfjs-master/tfjs-tfdf/src/tfdf_model_test.ts b/tfjs-master/tfjs-tfdf/src/tfdf_model_test.ts
deleted file mode 100644
index 253f4bf4e..000000000
--- a/tfjs-master/tfjs-tfdf/src/tfdf_model_test.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-
-import {loadTFDFModel, TFDFModel} from './tfdf_model';
-
-describe('TFDFModel ', () => {
-  let model: TFDFModel;
-  beforeEach(async () => {
-    model = await loadTFDFModel(
-        'https://storage.googleapis.com/tfjs-testing/adult_gbt_no_regex/model.json');
-  });
-
-  it('predict', async () => {
-    const inputs = {
-      'age': tf.tensor1d([39, 40, 40, 35], 'int32'),
-      'workclass': tf.tensor1d(
-          ['State-gov', 'Private', 'Private', 'Federal-gov'], 'string'),
-      'fnlwgt': tf.tensor1d([77516, 121772, 193524, 76845], 'int32'),
-      'education':
-          tf.tensor1d(['Bachelors', 'Assoc-voc', 'Doctorate', '9th'], 'string'),
-      'education_num': tf.tensor1d([13, 11, 16, 5], 'int32'),
-      'marital_status': tf.tensor1d(
-          [
-            'Never-married', 'Married-civ-spouse', 'Married-civ-spouse',
-            'Married-civ-spouse'
-          ],
-          'string'),
-      'occupation': tf.tensor1d(
-          ['Adm-clerical', 'Craft-repair', 'Prof-specialty', 'Farming-fishing'],
-          'string'),
-      'relationship': tf.tensor1d(
-          ['Not-in-family', 'Husband', 'Husband', 'Husband'], 'string'),
-      'race': tf.tensor1d(
-          ['White', 'Asian-Pac-Islander', 'White', 'Black'], 'string'),
-      'sex': tf.tensor1d(['Male', 'Male', 'Male', 'Male'], 'string'),
-      'capital_gain': tf.tensor1d([2174, 0, 0, 0], 'int32'),
-      'capital_loss': tf.tensor1d([0, 0, 0, 0], 'int32'),
-      'hours_per_week': tf.tensor1d([40, 40, 60, 40], 'int32'),
-      'native_country': tf.tensor1d(
-          ['United-States', '', 'United-States', 'United-States'], 'string')
-    };
-
-    const densePredictions = await model.executeAsync(inputs) as tf.Tensor;
-
-    tf.test_util.expectArraysEqual(densePredictions.shape, [4, 1]);
-    tf.test_util.expectArraysClose(await densePredictions.data(), [[
-                                     0.13323983550071716,
-                                     0.47678571939468384,
-                                     0.818461537361145,
-                                     0.4974619150161743,
-                                   ]]);
-  });
-});
diff --git a/tfjs-master/tfjs-tfdf/src/tfdf_web_api_client.d.ts b/tfjs-master/tfjs-tfdf/src/tfdf_web_api_client.d.ts
deleted file mode 100644
index 7a4fdaf71..000000000
--- a/tfjs-master/tfjs-tfdf/src/tfdf_web_api_client.d.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {TFDFWebAPIClient} from './types/tfdf_web_api';
-
-/**
- * The main export for tfdf_web_api_client.js types.
- */
-export declare const tfdfWeb: () => Promise<TFDFWebAPIClient>;
-
-type LocateFileFunction = (path: string, prefix?: string) => string;
-/** The global function to set WASM path. */
-export declare const setLocateFile: (locateFile: LocateFileFunction) => void;
diff --git a/tfjs-master/tfjs-tfdf/src/tfdf_web_api_client.js b/tfjs-master/tfjs-tfdf/src/tfdf_web_api_client.js
deleted file mode 100644
index d072db367..000000000
--- a/tfjs-master/tfjs-tfdf/src/tfdf_web_api_client.js
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// The '../wasm/inference.js' file was generated by JS closure
-// compiler. It was not "module friendly" so we cannot import it directly in
-// typescript code. To workaround this issue, we import and re-export it in this
-// file to essentially convert it to a valid CommonJS module, which can then be
-// imported correctly by typescript code.
-const YggdrasilDecisionForests = require('../wasm/inference_bundle.js');
-
-let tfdfWeb = null;
-let locateFile = null;
-
-// Function is used here instead of variable to make loading lazy in case user
-// uses setLocateFile before module is used.
-exports.tfdfWeb = () => {
-  if (tfdfWeb == null) {
-    tfdfWeb = YggdrasilDecisionForests({locateFile});
-  }
-  return tfdfWeb;
-};
-
-exports.setLocateFile = (newLocateFile) => {
-  locateFile = newLocateFile;
-};
diff --git a/tfjs-master/tfjs-tfdf/src/tfdf_web_api_client_test.ts b/tfjs-master/tfjs-tfdf/src/tfdf_web_api_client_test.ts
deleted file mode 100644
index 554865865..000000000
--- a/tfjs-master/tfjs-tfdf/src/tfdf_web_api_client_test.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import * as tfdfWebApiClient from './tfdf_web_api_client';
-
-describe('TFDFWebAPIClient ', () => {
-  let originalTimeout: number;
-
-  beforeEach(() => {
-    originalTimeout = jasmine.DEFAULT_TIMEOUT_INTERVAL;
-    jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
-  });
-
-  afterAll(() => jasmine.DEFAULT_TIMEOUT_INTERVAL = originalTimeout);
-
-  // The tfdf web API client is loaded through deps/tfdf_web_api_client.js,
-  // and we define its type in src/tfdf_web_api_client.d.ts. This test makes
-  // sure that all the types defined in tfdf_web_api_client.d.ts actually
-  // exist in the loaded JS client.
-  it('all exported types are defined', async () => {
-    const tfdfWeb = await tfdfWebApiClient.tfdfWeb();
-    expect(tfdfWeb.loadModelFromUrl).toBeDefined();
-    expect(tfdfWeb.loadModelFromZipBlob).toBeDefined();
-  });
-});
diff --git a/tfjs-master/tfjs-tfdf/src/types/tfdf_io.ts b/tfjs-master/tfjs-tfdf/src/types/tfdf_io.ts
deleted file mode 100644
index b0ab9cd8e..000000000
--- a/tfjs-master/tfjs-tfdf/src/types/tfdf_io.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import {io} from '@tensorflow/tfjs-core';
-
-/**
- * Type definition for handlers of loading operations.
- */
-export declare type TFDFAssetsLoadHandler = () => Promise<Blob>;
-export declare type TFDFAssetsLoadHandlerSync = () => Blob;
-
-/**
- * Interface for a model import handler.
- */
-export interface TFDFLoadHandler {
-  loadModel: io.LoadHandler;
-  loadAssets:
-      TFDFAssetsLoadHandler;  // Loads the zip file containing the assets files.
-}
-
-/**
- * Interface for a model import handler.
- */
-export interface TFDFLoadHandlerSync {
-  loadModel: io.IOHandlerSync['load'];
-  loadAssets: TFDFAssetsLoadHandlerSync;  // Loads the zip file containing the
-                                          // assets files.
-}
diff --git a/tfjs-master/tfjs-tfdf/src/types/tfdf_web_api.ts b/tfjs-master/tfjs-tfdf/src/types/tfdf_web_api.ts
deleted file mode 100644
index 61b3688a1..000000000
--- a/tfjs-master/tfjs-tfdf/src/types/tfdf_web_api.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TFDFWebModelRunner} from './tfdf_web_model_runner';
-
-export declare interface TFDFWebAPIClient {
-  loadModelFromUrl:
-      (url: string,
-       options: {createdTFDFSignature: boolean}) => Promise<TFDFWebModelRunner>;
-  loadModelFromZipBlob:
-      (url: Blob,
-       options: {createdTFDFSignature: boolean}) => Promise<TFDFWebModelRunner>;
-}
diff --git a/tfjs-master/tfjs-tfdf/src/types/tfdf_web_model_runner.ts b/tfjs-master/tfjs-tfdf/src/types/tfdf_web_model_runner.ts
deleted file mode 100644
index c77e17943..000000000
--- a/tfjs-master/tfjs-tfdf/src/types/tfdf_web_model_runner.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-type TensorflowDFInputs = {
-  numericalFeatures: number[][],
-  booleanFeatures: number[][],
-  categoricalIntFeatures: number[][],
-  categoricalSetIntFeaturesValues: number[],
-  categoricalSetIntFeaturesRowSplitsDim1: number[],
-  categoricalSetIntFeaturesRowSplitsDim2: number[],
-  denseOutputDim: number
-};
-
-type TensorflowDFOutputs = {
-  densePredictions: number[][],
-  denseColRepresentation: string[],
-};
-/**
- * The main TFDFWebModelRunner class interface.
- *
- * It is a wrapper around TFDF decision forests library.
- */
-export declare interface TFDFWebModelRunner {
-  predict: (inputs: {[key: string]: string[]|number[]}) => number[];
-  predictTFDFSignature: (inputs: TensorflowDFInputs) => TensorflowDFOutputs;
-}
diff --git a/tfjs-master/tfjs-tfdf/tfdf_repositories.bzl b/tfjs-master/tfjs-tfdf/tfdf_repositories.bzl
deleted file mode 100644
index 206dace6c..000000000
--- a/tfjs-master/tfjs-tfdf/tfdf_repositories.bzl
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright 2022 Google LLC.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
-
-def tfdf_repositories(version = "1.4.0"):
-    # Sha256 and filename for each version.
-    versions = {
-        "1.1.0": ("36b6974996d899589ba99ee95fb56699bf34c582f71e2d98475d7db4bce43b5b", "javascript_wasm.zip"),
-        "1.4.0": ("fc0d14152ae8a3446eef28bdc86a2187414b29d0f2eebf2ce401256345253ddf", "ydf_js.zip"),
-    }
-    if not version in versions:
-        available_versions = ", ".join(versions.keys())
-        fail("Unsupported tfdf wasm files version %s. Supported versions are %s." %
-             (version, available_versions))
-    sha256, filename = versions[version]
-    http_archive(
-        name = "tfdf_wasm_files",
-        sha256 = sha256,
-        url = "https://github.com/google/yggdrasil-decision-forests/releases/download/%s/%s" % (version, filename),
-        build_file_content = """
-filegroup(
-    name = "wasm_files",
-    visibility = ["//visibility:public"],
-    srcs = [
-        "inference.js",
-        "inference.wasm",
-    ],
-)
-""",
-    )
diff --git a/tfjs-master/tfjs-tfdf/tsconfig.json b/tfjs-master/tfjs-tfdf/tsconfig.json
deleted file mode 100644
index ecb732e21..000000000
--- a/tfjs-master/tfjs-tfdf/tsconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-tfdf/wasm/BUILD.bazel b/tfjs-master/tfjs-tfdf/wasm/BUILD.bazel
deleted file mode 100644
index 392b1982b..000000000
--- a/tfjs-master/tfjs-tfdf/wasm/BUILD.bazel
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright 2022 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("//tools:copy_to_dist.bzl", "copy_to_dist")
-load("//tools:defaults.bzl", "esbuild")
-load("//tools:get_extension.bzl", "get_extension")
-
-package(default_visibility = ["//visibility:public"])
-
-# Download the TFDF web API related WASM files from google storage, and make
-# them available for other targets.
-#
-# The downloaded files will be in `tfjs/dist/bin/tfjs-tfdf/wasm`.
-
-copy_to_dist(
-    name = "wasm_files",
-    srcs = [
-        "@tfdf_wasm_files//:wasm_files",
-    ],
-    dest_dir = ".",
-    global_root = "../tfdf_wasm_files",  # See tfdf_repositories.bzl
-)
-
-get_extension(
-    name = "inference_js",
-    srcs = [
-        ":wasm_files",
-    ],
-    include = [".js"],
-)
-
-get_extension(
-    name = "wasm_binary",
-    srcs = [
-        ":wasm_files",
-    ],
-    include = [".wasm"],
-)
-
-genrule(
-    name = "inference_jszip",
-    srcs = [
-        ":inference_js",
-    ],
-    outs = [
-        "inference_jszip.js",
-    ],
-    cmd = "sed 's/JSZip/require(\"jszip\")/' $(location :inference_js) > $@",
-)
-
-esbuild(
-    name = "inference_bundle",
-    srcs = [
-        ":inference_jszip",
-    ],
-    entry_point = "inference_jszip.js",
-    external = [
-        "fs",
-        "path",
-    ],
-    format = "cjs",
-    minify = True,
-    deps = [
-        "@npm//jszip",
-    ],
-)
-
-filegroup(
-    name = "wasm",
-    srcs = [
-        ":inference_bundle",
-        ":wasm_binary",
-    ],
-)
diff --git a/tfjs-master/tfjs-tfdf/yarn.lock b/tfjs-master/tfjs-tfdf/yarn.lock
deleted file mode 100644
index f36e0f190..000000000
--- a/tfjs-master/tfjs-tfdf/yarn.lock
+++ /dev/null
@@ -1,13 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@bazel/bazelisk@^1.12.0":
-  version "1.12.1"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.12.1.tgz#346531286564aa29eee03a62362d210f3433e7bf"
-  integrity sha512-TGCwVeIiVeQUP6yLpxAg8yluFOC+tBQnWw5l8lqwMxKhRtOA+WaH1CJKAXeCBAaS2MxohhkXq44zj/7AM+t2jg==
-
-minimist@1.2.6:
-  version "1.2.6"
-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.6.tgz#8637a5b759ea0d6e98702cfb3a9283323c93af44"
-  integrity sha512-Jsjnk4bw3YJqYzbdyBiNsPWHPfO++UGG749Cxs6peCu5Xg4nrena6OVxOYxrQTqww0Jmwt+Ref8rggumkTLz9Q==
diff --git a/tfjs-master/tfjs-tflite/.npmignore b/tfjs-master/tfjs-tflite/.npmignore
deleted file mode 100644
index ddd9e42af..000000000
--- a/tfjs-master/tfjs-tflite/.npmignore
+++ /dev/null
@@ -1,28 +0,0 @@
-.babelrc
-.DS_Store
-.idea/
-.rpt2_cache
-.travis.yml
-.vscode
-*.tgz
-*.txt
-**.yalc
-**yalc.lock
-cloudbuild.yml
-coverage/
-demo/
-dist/**/*_test.d.ts
-dist/**/*_test.js
-karma.conf.js
-node_modules/
-npm-debug.log
-package-lock.json
-package/
-rollup.config.js
-scripts/
-src/**/*_test.ts
-tsconfig.json
-tslint.json
-yarn-error.log
-yarn.lock
-.cache/
diff --git a/tfjs-master/tfjs-tflite/.npmrc b/tfjs-master/tfjs-tflite/.npmrc
deleted file mode 100644
index 43c97e719..000000000
--- a/tfjs-master/tfjs-tflite/.npmrc
+++ /dev/null
@@ -1 +0,0 @@
-package-lock=false
diff --git a/tfjs-master/tfjs-tflite/BUILD.bazel b/tfjs-master/tfjs-tflite/BUILD.bazel
deleted file mode 100644
index 2f5a64232..000000000
--- a/tfjs-master/tfjs-tflite/BUILD.bazel
+++ /dev/null
@@ -1,99 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@build_bazel_rules_nodejs//:index.bzl", "pkg_npm")
-load("//tools:copy_to_dist.bzl", "copy_to_dist", "copy_ts_library_to_dist")
-load("//tools:tfjs_bundle.bzl", "tfjs_bundle")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-# Run rollup to bundle the compiled files for different execution environments.
-tfjs_bundle(
-    name = "tf-tflite",
-    entry_point = "//tfjs-tflite/src:index.ts",
-    external = [
-        "crypto",
-        "@tensorflow/tfjs-core",
-        "seedrandom",
-    ],
-    globals = {
-        "@tensorflow/tfjs-core": "tf",
-        "seedrandom": "seedrandom",
-    },
-    umd_name = "tflite",
-    deps = [
-        "//tfjs-tflite/src:tfjs-tflite_lib",
-        "//tfjs-tflite/src:tfjs-tflite_src_lib",
-        "//tfjs-tflite/src:tflite_web_api_client_js",
-        "//tfjs-tflite/wasm",
-    ],
-)
-
-# Copy ouput files to dist/.
-copy_ts_library_to_dist(
-    name = "copy_src_to_dist",
-    srcs = [
-        "//tfjs-tflite/src:tfjs-tflite_lib",
-        "//tfjs-tflite/src:tfjs-tflite_src_lib",
-    ],
-    root = "src",
-)
-
-# Copy bundled files to dist/.
-copy_to_dist(
-    name = "copy_bundles",
-    srcs = [
-        ":tf-tflite",
-        ":tf-tflite.es2017",
-        ":tf-tflite.es2017.min",
-        ":tf-tflite.fesm",
-        ":tf-tflite.fesm.min",
-        ":tf-tflite.min",
-        ":tf-tflite.node",
-    ],
-)
-
-# Package for npm.
-pkg_npm(
-    name = "tfjs-tflite_pkg",
-    package_name = "@tensorflow/tfjs-tflite",
-    srcs = [
-        "README.md",
-        "package.json",
-    ],
-    tags = ["ci"],
-    deps = [
-        ":copy_bundles",
-        ":copy_src_to_dist",
-        "//tfjs-tflite/wasm",
-    ],
-)
-
-# Tests.
-tfjs_web_test(
-    name = "tfjs-tflite_test",
-    srcs = [
-        "//tfjs-tflite/src:tfjs-tflite_test_bundle",
-    ],
-)
-
-test_suite(
-    name = "tests",
-    tests = [
-        ":tfjs-tflite_test",
-        "//tfjs-tflite/src:worker_test",
-    ],
-)
diff --git a/tfjs-master/tfjs-tflite/README.md b/tfjs-master/tfjs-tflite/README.md
deleted file mode 100644
index 391c00b28..000000000
--- a/tfjs-master/tfjs-tflite/README.md
+++ /dev/null
@@ -1,151 +0,0 @@
-# TFLite support for Tensorflow.js
-
-_WORK IN PROGRESS_
-
-This package enables users to run arbitary TFLite models on the web. Users can
-load a TFLite model from a URL, use TFJS tensors to set the model's input
-data, run inference, and get the output back in TFJS tensors. Under the hood,
-the TFLite C++ runtime is packaged in a set of WASM modules, and the one with
-the best performance will be automatically loaded based on user's current
-environment (e.g. whether WebAssembly SIMD and/or multi-threading is supported
-or not).
-
-Check out this [demo][demo] where we use this package to run a
-[CartoonGAN][model] TFLite model on the web.
-
-# Usage
-
-## Import the packages
-
-To use this package, you will need a TFJS backend installed. We recommend the
-CPU backend. You will also need to import `@tensorflow/tfjs-core` for
-manipulating tensors.
-
-### Via NPM
-
-```js
-// Adds the CPU backend.
-import '@tensorflow/tfjs-backend-cpu';
-// Import @tensorflow/tfjs-core
-import * as tf from '@tensorflow/tfjs-core';
-// Import @tensorflow/tfjs-tflite.
-import * as tflite from '@tensorflow/tfjs-tflite';
-```
-
-### Via a script tag
-
-```html
-<!-- Import @tensorflow/tfjs-core -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
-<!-- Adds the CPU backend -->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-cpu"></script>
-<!--
-  Import @tensorflow/tfjs-tflite
-
-  Note that we need to explicitly load dist/tf-tflite.min.js so that it can
-  locate WASM module files from their default location (dist/).
--->
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-tflite/dist/tf-tflite.min.js"></script>
-```
-
-## Set WASM modules location (optional)
-
-By default, it will try to load the WASM modules from the same location where
-the package or your own script is served. Use `setWasmPath` to set your own
-location. See `src/tflite_web_api_client.d.ts` for more details.
-
-
-```js
-tflite.setWasmPath('https://your-server/path');
-```
-
-## Load a TFLite model
-```js
-const tfliteModel = await tflite.loadTFLiteModel('url/to/your/model.tflite');
-```
-
-## Run inference
-```js
-// Prepare input tensors.
-const img = tf.browser.fromPixels(document.querySelector('img'));
-const input = tf.sub(tf.div(tf.expandDims(img), 127.5), 1);
-
-// Run inference and get output tensors.
-let outputTensor = tfliteModel.predict(input) as tf.Tensor;
-console.log(outputTensor.dataSync());
-```
-
-# Performance
-
-This package uses [XNNPACK][xnnpack] to accelerate inference for floating-point
-and quantized models. See [XNNPACK documentation][xnnpack doc] for the full list
-of supported floating-point and quantized operators.
-
-To achieve the best performance, use a browser that supports
-"WebAssembly SIMD" and "WebAssembly threads". In Chrome 92+, these features are
-enabled by default. In older versions of Chrome, they can be enabled in
-`chrome://flags/`.
-
-Starting from Chrome 92, **cross-origin isolation** needs to be set up in your
-site in order to take advantage of the multi-threading support. Without this, it
-will fallback to the WASM binary with SIMD-only support (or the vanila version
-if SIMD is not enabled). Without multi-threading support, certain models might
-not achieve the best performance. See [here][cross origin setup steps] for the
-high-level steps to set up the cross-origin isolation.
-
-By default, the runtime uses the number of physical cores as the thread count.
-You can tune this number by setting the `numThreads` option when loading the
-TFLite model:
-
-```js
-const tfliteModel = await tflite.loadTFLiteModel(
-    'path/to/your/my_model.tflite',
-    {numThreads: navigator.hardwareConcurrency / 2});
-```
-
-# Profiling
-
-Profiling can be enabled by setting the `enableProfiling` option to true when
-loading the TFLite model:
-
-```js
-const tfliteModel = await tflite.loadTFLiteModel(
-    'path/to/your/my_model.tflite',
-    {enableProfiling: true});
-```
-
-Once it is enabled, the runtime will record per-op latency data when the
-`predict` method is called. The profiling results can be retrieved in two ways:
-
-- `tfliteModel.getProfilingResults()`: this method will return an array of
-  `{nodeType, nodeName, execTimeInMs}`.
-- `tfliteModel.getProfilingSummary()`: this method will return a human-readable
-  profiling result summary that looks like [this][profiling summary].
-
-# Development
-
-## Building
-
-```sh
-$ yarn
-$ yarn build
-```
-
-## Testing
-
-```sh
-$ yarn test
-```
-
-## Deployment
-```sh
-$ yarn build-npm
-# (TODO): publish
-```
-
-[demo]: https://storage.googleapis.com/tfweb/demos/cartoonizer/index.html
-[model]: https://blog.tensorflow.org/2020/09/how-to-create-cartoonizer-with-tf-lite.html
-[xnnpack]: https://github.com/google/XNNPACK
-[xnnpack doc]: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/delegates/xnnpack/README.md#limitations-and-supported-operators
-[cross origin setup steps]: https://github.com/tensorflow/tfjs/tree/master/tfjs-backend-wasm#setting-up-cross-origin-isolation
-[profiling summary]: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/benchmark/README.md#profiling-model-operators
diff --git a/tfjs-master/tfjs-tflite/demo/README.md b/tfjs-master/tfjs-tflite/demo/README.md
deleted file mode 100644
index 3bd7a68f5..000000000
--- a/tfjs-master/tfjs-tflite/demo/README.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# Cartoonizer demo
-
-This demo "cartoonizes" a set of pictures and your webcam feed by using the
-`tfjs-tflite` package and the CartoonGAN model described in this
-[blog post][blog].
-
-Here is the [live demo][live demo] you can try out. For best performance, enable
-the "WebAssembly SIMD support" and "WebAssembly threads support" in
-`chrome://flags/`.
-
-<img src="screenshot.png" alt="demo" style="max-width: 930px; width: 100%;"/>
-
-## Run the demo locally
-
-Build the dependencies.
-
-```sh
-$ yarn build-deps
-$ yarn
-```
-
-Run the demo locally
-```sh
-$ yarn watch
-```
-
-
-[blog]: https://blog.tensorflow.org/2020/09/how-to-create-cartoonizer-with-tf-lite.html
-[live demo]: https://storage.googleapis.com/tfweb/demos/cartoonizer/index.html
-[gsutil]: https://cloud.google.com/storage/docs/gsutil_install
diff --git a/tfjs-master/tfjs-tflite/demo/package.json b/tfjs-master/tfjs-tflite/demo/package.json
deleted file mode 100644
index 1f415f8a8..000000000
--- a/tfjs-master/tfjs-tflite/demo/package.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "name": "tfjs-tflite-demo",
-  "version": "0.0.1",
-  "main": "script.js",
-  "license": "Apache-2.0",
-  "dependencies": {
-    "@tensorflow/tfjs-backend-cpu": "3.14.0",
-    "@tensorflow/tfjs-core": "3.14.0",
-    "@tensorflow/tfjs-tflite": "0.0.1-alpha.8"
-  },
-  "devDependencies": {
-    "clang-format": "~1.5.0",
-    "cross-env": "^7.0.3",
-    "parcel": "^2.3.2",
-    "typescript": "5.0.4"
-  },
-  "scripts": {
-    "build-deps": "cd ../../link-package && yarn build",
-    "watch": "cross-env NODE_ENV=development parcel src/index.html --no-hmr --open",
-    "build": "NODE_ENV=production parcel build src/index.html --public-url ./"
-  }
-}
diff --git a/tfjs-master/tfjs-tflite/demo/screenshot.png b/tfjs-master/tfjs-tflite/demo/screenshot.png
deleted file mode 100644
index 3426a2ce5..000000000
Binary files a/tfjs-master/tfjs-tflite/demo/screenshot.png and /dev/null differ
diff --git a/tfjs-master/tfjs-tflite/demo/src/cat.jpg b/tfjs-master/tfjs-tflite/demo/src/cat.jpg
deleted file mode 100644
index 9e8f9cc18..000000000
Binary files a/tfjs-master/tfjs-tflite/demo/src/cat.jpg and /dev/null differ
diff --git a/tfjs-master/tfjs-tflite/demo/src/index.html b/tfjs-master/tfjs-tflite/demo/src/index.html
deleted file mode 100644
index 4350e3193..000000000
--- a/tfjs-master/tfjs-tflite/demo/src/index.html
+++ /dev/null
@@ -1,230 +0,0 @@
-<!DOCTYPE html>
-<meta charset="utf-8">
-<title>Cartoonizer</title>
-
-<head>
-  <link rel="stylesheet" href="//fonts.googleapis.com/css?family=Google+Sans:400,500,700">
-  <style>
-    body {
-      font-family: 'Google Sans';
-      padding: 20px;
-    }
-
-    h1 {
-      color: #425066;
-      font-size: 31px;
-      margin-top: 0;
-      margin-bottom: 0;
-    }
-
-    .loading-msg {
-      color: #999;
-      font-size: 12px;
-      margin-top: 10px;
-    }
-
-
-    img {
-      height: 224px;
-      width: 224px;
-    }
-
-    canvas {
-      border-radius: 8px;
-      height: 224px;
-      opacity: 0;
-      pointer-events: none;
-      transition: all 200ms;
-      width: 224px;
-    }
-
-    canvas.show {
-      opacity: 1;
-      pointer-events: all;
-    }
-
-    .imgs-container {
-      display: flex;
-      margin-top: 40px;
-    }
-
-    .img-container {
-      display: flex;
-      flex-direction: column;
-      margin-right: 4px;
-      position: relative;
-    }
-
-    .img-container img {
-      border-radius: 8px;
-      margin-bottom: 4px;
-    }
-
-    .credit {
-      position: absolute;
-      font-size: 11px;
-      color: #eee;
-      top: 202px;
-      left: 10px;
-    }
-
-    .loading {
-      color: #999;
-    }
-
-    .trigger {
-      align-items: center;
-      border-radius: 8px;
-      background-image: url('magic.svg');
-      background-size: 32px 32px;
-      background-repeat: no-repeat;
-      background-position: 50% 50%;
-      border: 1px dashed #d8d8d8;
-      box-sizing: border-box;
-      color: #999;
-      cursor: pointer;
-      display: flex;
-      font-size: 13px;
-      height: 224px;
-      justify-content: center;
-      opacity: 0;
-      transition: opacity 200ms;
-      width: 224px;
-    }
-
-    .trigger.show {
-      opacity: 1;
-    }
-
-    .trigger.processing {
-      background-image: none !important;
-    }
-
-    span.magic {
-      background-image: url('magic.svg');
-      background-size: 24px 24px;
-      background-repeat: no-repeat;
-      background-position: 50% 50%;
-      display: inline-block;
-      height: 24px;
-      margin: 0 8px;
-      margin-top: -6px;
-      width: 24px;
-    }
-
-    .trigger:hover {
-      background-color: #f1f1f1;
-    }
-
-    .stats {
-      color: #999;
-      font-size: 12px;
-      height: 30px;
-      line-height: 30px;
-      opacity: 0;
-      text-align: center;
-      transition: all 200ms;
-    }
-
-    .stats.show {
-      opacity: 1;
-    }
-
-    .hide {
-      display: none !important;
-    }
-
-    video {
-      border-radius: 8px;
-      height: 224px;
-      margin-bottom: 4px;
-    }
-
-    #cam-canvas.show {
-      cursor: pointer;
-    }
-
-    #cam-canvas:hover {
-      opacity: .9;
-    }
-
-    .take-pic {
-      align-items: center;
-      background-color: rgba(255, 255, 255, .9);
-      color: #425066;
-      display: flex;
-      font-size: 13px;
-      height: 24px;
-      justify-content: center;
-      padding-bottom: 3px;
-      pointer-events: none;
-      position: absolute;
-      top: 425px;
-      width: 224px;
-    }
-  </style>
-</head>
-
-<body class="disabled">
-  <h1>Cartoonizer Demo</h1>
-
-  <div class="loading-msg">Loading...</div>
-
-  <div class="imgs-container hide">
-    <div class="img-container">
-      <img id="img5" src="cat.jpg"></img>
-      <div class="trigger"></div>
-      <canvas width="224px" height="224px"></canvas>
-      <div class="stats"></div>
-      <a class="credit" href="https://unsplash.com/photos/FilM6ng7VGQ" target="_blank">
-        source
-      </a>
-    </div>
-    <div class="img-container">
-      <img id="img1" src="mountain.jpg"></img>
-      <div class="trigger"></div>
-      <canvas width="224px" height="224px"></canvas>
-      <div class="stats"></div>
-      <a class="credit" href="https://unsplash.com/photos/uCrRPJBZzk4" target="_blank">
-        source
-      </a>
-    </div>
-    <div class="img-container">
-      <img id="img2" src="room.jpg"></img>
-      <div class="trigger"></div>
-      <canvas width="224px" height="224px"></canvas>
-      <div class="stats"></div>
-      <a class="credit" href="https://unsplash.com/photos/1RT4txDDAbM" target="_blank">
-        source
-      </a>
-    </div>
-    <div class="img-container">
-      <img id="img3" src="pizza.jpg"></img>
-      <div class="trigger"></div>
-      <canvas width="224px" height="224px"></canvas>
-      <div class="stats"></div>
-      <a class="credit" href="https://unsplash.com/photos/BMVAYjPf6mU" target="_blank">
-        source
-      </a>
-    </div>
-    <div class="img-container">
-      <img id="img4" src="people.jpg"></img>
-      <div class="trigger"></div>
-      <canvas width="224px" height="224px"></canvas>
-      <div class="stats"></div>
-      <a class="credit" href="https://unsplash.com/photos/Do1GQljlNk8" target="_blank">
-        source
-      </a>
-    </div>
-    <div class="img-container">
-      <video autoplay></video>
-      <div class="trigger"></div>
-      <canvas width="224px" height="224px" id="cam-canvas"></canvas>
-      <div class="take-pic hide">Click to take another picture</div>
-      <div class="stats"></div>
-    </div>
-  </div>
-</body>
-
-<script type="module" src="./script.ts"></script>
-</html>
diff --git a/tfjs-master/tfjs-tflite/demo/src/magic.svg b/tfjs-master/tfjs-tflite/demo/src/magic.svg
deleted file mode 100644
index f1fa44906..000000000
--- a/tfjs-master/tfjs-tflite/demo/src/magic.svg
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/tfjs-master/tfjs-tflite/demo/src/mountain.jpg b/tfjs-master/tfjs-tflite/demo/src/mountain.jpg
deleted file mode 100644
index 0d870dd02..000000000
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diff --git a/tfjs-master/tfjs-tflite/demo/src/people.jpg b/tfjs-master/tfjs-tflite/demo/src/people.jpg
deleted file mode 100644
index 79e00c4f5..000000000
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diff --git a/tfjs-master/tfjs-tflite/demo/src/pizza.jpg b/tfjs-master/tfjs-tflite/demo/src/pizza.jpg
deleted file mode 100644
index 410b0b517..000000000
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diff --git a/tfjs-master/tfjs-tflite/demo/src/room.jpg b/tfjs-master/tfjs-tflite/demo/src/room.jpg
deleted file mode 100644
index ad5ea72bf..000000000
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diff --git a/tfjs-master/tfjs-tflite/demo/src/script.ts b/tfjs-master/tfjs-tflite/demo/src/script.ts
deleted file mode 100644
index 4cea7a886..000000000
--- a/tfjs-master/tfjs-tflite/demo/src/script.ts
+++ /dev/null
@@ -1,156 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import 'regenerator-runtime/runtime';
-import '@tensorflow/tfjs-backend-cpu';
-
-import * as tf from '@tensorflow/tfjs-core';
-import * as tflite from '@tensorflow/tfjs-tflite';
-
-const CARTOONIZER_LINK =
-    'https://github.com/margaretmz/Cartoonizer-with-TFLite';
-
-tflite.setWasmPath(
-    'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-tflite@0.0.1-alpha.8/dist/');
-
-async function start() {
-  // Load model runner with the cartoonizer tflite model.
-  const start = Date.now();
-  const tfliteModel = await tflite.loadTFLiteModel(
-      'https://tfhub.dev/sayakpaul/lite-model/cartoongan/fp16/1',
-  );
-  ele('.loading-msg').innerHTML = `Loaded WASM module and <a href='${
-      CARTOONIZER_LINK}' target='blank'>TFLite model</a> in ${
-      Date.now() - start}ms`;
-  eles('.trigger').forEach(ele => {
-    ele.classList.add('show');
-  });
-  removeClass('.imgs-container', 'hide');
-
-  // Setup cam.
-  setupCam();
-
-  // Setup the magic wand buttons. Click it will cartoonize the
-  // corresponding picture.
-  eles('.trigger').forEach(ele => {
-    ele.addEventListener('click', (event) => {
-      const trigger = event.target! as HTMLElement;
-      trigger.classList.add('processing');
-      trigger.innerHTML = 'Processing...';
-
-      setTimeout(() => {
-        handleClickTrigger(trigger, tfliteModel);
-        trigger.classList.add('hide');
-      });
-    });
-  });
-
-  // Click cam's result picture to take another picture.
-  ele('#cam-canvas').addEventListener('click', (event) => {
-    handleClickTrigger(event.target as HTMLElement, tfliteModel);
-  });
-}
-
-async function setupCam() {
-  const constraints = {
-    video: {
-      width: {
-        min: 224,
-        ideal: 224,
-        max: 224,
-      },
-      height: {
-        min: 224,
-        ideal: 224,
-        max: 224,
-      },
-    }
-  };
-  const stream = await navigator.mediaDevices.getUserMedia(constraints);
-  const camEle = ele('video') as HTMLVideoElement;
-  camEle.srcObject = stream;
-  await new Promise(resolve => camEle.onplaying = resolve);
-}
-
-function handleClickTrigger(
-    trigger: HTMLElement, tfliteModel: tflite.TFLiteModel) {
-  // Get the source media (either a picture or the cam video).
-  const imageContainer = trigger.closest('.img-container')!;
-  let srcMedia: HTMLImageElement|HTMLVideoElement =
-      imageContainer.querySelector('img')!;
-  if (!srcMedia) {
-    srcMedia = imageContainer.querySelector('video')!;
-    removeClass('.take-pic', 'hide');
-  }
-
-  // Run inference and draw the result on the corresponding canvas.
-  const canvas = imageContainer.querySelector('canvas')!;
-  const ctx = canvas.getContext('2d')!;
-  const inferenceStart = Date.now();
-  const imageData = cartoonize(tfliteModel, srcMedia);
-  const latency = Date.now() - inferenceStart;
-  ctx.putImageData(imageData, 0, 0);
-  canvas.classList.add('show');
-
-  // Show latency stat.
-  const stats = trigger.closest('.img-container')!.querySelector('.stats')!;
-  stats.classList.add('show');
-  stats.innerHTML = latency.toFixed(1) + ' ms';
-}
-
-function cartoonize(
-    tfliteModel: tflite.TFLiteModel,
-    ele: HTMLImageElement|HTMLVideoElement): ImageData {
-  const outputTensor = tf.tidy(() => {
-    // Get pixels data.
-    const img = tf.browser.fromPixels(ele);
-    // Normalize.
-    //
-    // Since the images are already 224*224 that matches the model's input size,
-    // we don't resize them here.
-    const input = tf.sub(tf.div(tf.expandDims(img), 127.5), 1);
-    // Run the inference.
-    const outputTensor = tfliteModel.predict(input) as tf.Tensor;
-    // De-normalize the result.
-    return tf.mul(tf.add(outputTensor, 1), 127.5);
-  });
-
-  // Convert from RGB to RGBA, and create and return ImageData.
-  const rgb = Array.from(outputTensor.dataSync());
-  const rgba: number[] = [];
-  for (let i = 0; i < rgb.length / 3; i++) {
-    for (let c = 0; c < 3; c++) {
-      rgba.push(rgb[i * 3 + c]);
-    }
-    rgba.push(255);
-  }
-  return new ImageData(Uint8ClampedArray.from(rgba), 224, 224);
-}
-
-function ele(selector: string) {
-  return document.querySelector(selector)!;
-}
-
-function eles(selector: string) {
-  return document.querySelectorAll(selector)!;
-}
-
-function removeClass(selector: string, className: string) {
-  return ele(selector).classList.remove(className);
-}
-
-start();
diff --git a/tfjs-master/tfjs-tflite/demo/tsconfig.json b/tfjs-master/tfjs-tflite/demo/tsconfig.json
deleted file mode 100644
index 922f7350f..000000000
--- a/tfjs-master/tfjs-tflite/demo/tsconfig.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "ES2015",
-    "moduleResolution": "node",
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "removeComments": false,
-    "preserveConstEnums": true,
-    "allowSyntheticDefaultImports": true,
-    "declaration": true,
-    "target": "es2017",
-    "lib": [
-      "es2017",
-      "dom"
-    ],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false,
-    "skipLibCheck": true
-  },
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ]
-}
diff --git a/tfjs-master/tfjs-tflite/demo/yarn.lock b/tfjs-master/tfjs-tflite/demo/yarn.lock
deleted file mode 100644
index fbd0516be..000000000
--- a/tfjs-master/tfjs-tflite/demo/yarn.lock
+++ /dev/null
@@ -1,1820 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
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-  dependencies:
-    "@babel/highlight" "^7.14.5"
-
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-  version "7.14.5"
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-
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-  version "7.14.5"
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-  resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.20.0.tgz#629a013fb3f70755d6f0b7935cc1c2c5378b1975"
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-  dependencies:
-    is-core-module "^2.2.0"
-    path-parse "^1.0.6"
-
-safe-buffer@^5.0.1:
-  version "5.2.1"
-  resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.2.1.tgz#1eaf9fa9bdb1fdd4ec75f58f9cdb4e6b7827eec6"
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-
-seedrandom@2.4.3:
-  version "2.4.3"
-  resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-2.4.3.tgz#2438504dad33917314bff18ac4d794f16d6aaecc"
-  integrity sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw=
-
-semver@^5.7.0, semver@^5.7.1:
-  version "5.7.1"
-  resolved "https://registry.yarnpkg.com/semver/-/semver-5.7.1.tgz#a954f931aeba508d307bbf069eff0c01c96116f7"
-  integrity sha512-sauaDf/PZdVgrLTNYHRtpXa1iRiKcaebiKQ1BJdpQlWH2lCvexQdX55snPFyK7QzpudqbCI0qXFfOasHdyNDGQ==
-
-shebang-command@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/shebang-command/-/shebang-command-2.0.0.tgz#ccd0af4f8835fbdc265b82461aaf0c36663f34ea"
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-  dependencies:
-    shebang-regex "^3.0.0"
-
-shebang-regex@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/shebang-regex/-/shebang-regex-3.0.0.tgz#ae16f1644d873ecad843b0307b143362d4c42172"
-  integrity sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A==
-
-source-map-js@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/source-map-js/-/source-map-js-1.0.2.tgz#adbc361d9c62df380125e7f161f71c826f1e490c"
-  integrity sha512-R0XvVJ9WusLiqTCEiGCmICCMplcCkIwwR11mOSD9CR5u+IXYdiseeEuXCVAjS54zqwkLcPNnmU4OeJ6tUrWhDw==
-
-source-map-support@~0.5.20:
-  version "0.5.21"
-  resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.21.tgz#04fe7c7f9e1ed2d662233c28cb2b35b9f63f6e4f"
-  integrity sha512-uBHU3L3czsIyYXKX88fdrGovxdSCoTGDRZ6SYXtSRxLZUzHg5P/66Ht6uoUlHu9EZod+inXhKo3qQgwXUT/y1w==
-  dependencies:
-    buffer-from "^1.0.0"
-    source-map "^0.6.0"
-
-source-map@^0.6.0, source-map@^0.6.1:
-  version "0.6.1"
-  resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
-  integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
-
-stable@^0.1.8:
-  version "0.1.8"
-  resolved "https://registry.yarnpkg.com/stable/-/stable-0.1.8.tgz#836eb3c8382fe2936feaf544631017ce7d47a3cf"
-  integrity sha512-ji9qxRnOVfcuLDySj9qzhGSEFVobyt1kIOSkj1qZzYLzq7Tos/oUUWvotUPQLlrsidqsK6tBH89Bc9kL5zHA6w==
-
-stylehacks@^*:
-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/stylehacks/-/stylehacks-5.1.0.tgz#a40066490ca0caca04e96c6b02153ddc39913520"
-  integrity sha512-SzLmvHQTrIWfSgljkQCw2++C9+Ne91d/6Sp92I8c5uHTcy/PgeHamwITIbBW9wnFTY/3ZfSXR9HIL6Ikqmcu6Q==
-  dependencies:
-    browserslist "^4.16.6"
-    postcss-selector-parser "^6.0.4"
-
-supports-color@^5.3.0:
-  version "5.5.0"
-  resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.5.0.tgz#e2e69a44ac8772f78a1ec0b35b689df6530efc8f"
-  integrity sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==
-  dependencies:
-    has-flag "^3.0.0"
-
-supports-color@^7.1.0:
-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.2.0.tgz#1b7dcdcb32b8138801b3e478ba6a51caa89648da"
-  integrity sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==
-  dependencies:
-    has-flag "^4.0.0"
-
-svgo@^2.4.0, svgo@^2.7.0:
-  version "2.8.0"
-  resolved "https://registry.yarnpkg.com/svgo/-/svgo-2.8.0.tgz#4ff80cce6710dc2795f0c7c74101e6764cfccd24"
-  integrity sha512-+N/Q9kV1+F+UeWYoSiULYo4xYSDQlTgb+ayMobAXPwMnLvop7oxKMo9OzIrX5x3eS4L4f2UHhc9axXwY8DpChg==
-  dependencies:
-    "@trysound/sax" "0.2.0"
-    commander "^7.2.0"
-    css-select "^4.1.3"
-    css-tree "^1.1.3"
-    csso "^4.2.0"
-    picocolors "^1.0.0"
-    stable "^0.1.8"
-
-terser@^5.2.0:
-  version "5.14.2"
-  resolved "https://registry.yarnpkg.com/terser/-/terser-5.14.2.tgz#9ac9f22b06994d736174f4091aa368db896f1c10"
-  integrity sha512-oL0rGeM/WFQCUd0y2QrWxYnq7tfSuKBiqTjRPWrRgB46WD/kiwHwF8T23z78H6Q6kGCuuHcPB+KULHRdxvVGQA==
-  dependencies:
-    "@jridgewell/source-map" "^0.3.2"
-    acorn "^8.5.0"
-    commander "^2.20.0"
-    source-map-support "~0.5.20"
-
-timsort@^0.3.0:
-  version "0.3.0"
-  resolved "https://registry.yarnpkg.com/timsort/-/timsort-0.3.0.tgz#405411a8e7e6339fe64db9a234de11dc31e02bd4"
-  integrity sha1-QFQRqOfmM5/mTbmiNN4R3DHgK9Q=
-
-type-fest@^0.20.2:
-  version "0.20.2"
-  resolved "https://registry.yarnpkg.com/type-fest/-/type-fest-0.20.2.tgz#1bf207f4b28f91583666cb5fbd327887301cd5f4"
-  integrity sha512-Ne+eE4r0/iWnpAxD852z3A+N0Bt5RN//NjJwRd2VFHEmrywxf5vsZlh4R6lixl6B+wz/8d+maTSAkN1FIkI3LQ==
-
-typescript@4.9.4:
-  version "4.9.4"
-  resolved "https://registry.yarnpkg.com/typescript/-/typescript-4.9.4.tgz#a2a3d2756c079abda241d75f149df9d561091e78"
-  integrity sha512-Uz+dTXYzxXXbsFpM86Wh3dKCxrQqUcVMxwU54orwlJjOpO3ao8L7j5lH+dWfTwgCwIuM9GQ2kvVotzYJMXTBZg==
-
-util-deprecate@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
-  integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=
-
-utility-types@^3.10.0:
-  version "3.10.0"
-  resolved "https://registry.yarnpkg.com/utility-types/-/utility-types-3.10.0.tgz#ea4148f9a741015f05ed74fd615e1d20e6bed82b"
-  integrity sha512-O11mqxmi7wMKCo6HKFt5AhO4BwY3VV68YU07tgxfz8zJTIxr4BpsezN49Ffwy9j3ZpwwJp4fkRwjRzq3uWE6Rg==
-
-v8-compile-cache@^2.0.0:
-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/v8-compile-cache/-/v8-compile-cache-2.3.0.tgz#2de19618c66dc247dcfb6f99338035d8245a2cee"
-  integrity sha512-l8lCEmLcLYZh4nbunNZvQCJc5pv7+RCwa8q/LdUx8u7lsWvPDKmpodJAJNwkAhJC//dFY48KuIEmjtd4RViDrA==
-
-weak-lru-cache@^1.2.2:
-  version "1.2.2"
-  resolved "https://registry.yarnpkg.com/weak-lru-cache/-/weak-lru-cache-1.2.2.tgz#fdbb6741f36bae9540d12f480ce8254060dccd19"
-  integrity sha512-DEAoo25RfSYMuTGc9vPJzZcZullwIqRDSI9LOy+fkCJPi6hykCnfKaXTuPBDuXAUcqHXyOgFtHNp/kB2FjYHbw==
-
-which@^2.0.1:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/which/-/which-2.0.2.tgz#7c6a8dd0a636a0327e10b59c9286eee93f3f51b1"
-  integrity sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==
-  dependencies:
-    isexe "^2.0.0"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-xxhash-wasm@^0.4.2:
-  version "0.4.2"
-  resolved "https://registry.yarnpkg.com/xxhash-wasm/-/xxhash-wasm-0.4.2.tgz#752398c131a4dd407b5132ba62ad372029be6f79"
-  integrity sha512-/eyHVRJQCirEkSZ1agRSCwriMhwlyUcFkXD5TPVSLP+IPzjsqMVzZwdoczLp1SoQU0R3dxz1RpIK+4YNQbCVOA==
-
-yaml@^1.10.0, yaml@^1.10.2:
-  version "1.10.2"
-  resolved "https://registry.yarnpkg.com/yaml/-/yaml-1.10.2.tgz#2301c5ffbf12b467de8da2333a459e29e7920e4b"
-  integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==
diff --git a/tfjs-master/tfjs-tflite/package.json b/tfjs-master/tfjs-tflite/package.json
deleted file mode 100644
index 8dac2136c..000000000
--- a/tfjs-master/tfjs-tflite/package.json
+++ /dev/null
@@ -1,35 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-tflite",
-  "version": "0.0.0",
-  "description": "TFLite support for TensorFlow.js",
-  "main": "dist/tf-tflite.node.js",
-  "module": "dist/index.js",
-  "jsdelivr": "dist/tf-tflite.min.js",
-  "unpkg": "dist/tf-tflite.min.js",
-  "types": "dist/index.d.ts",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-tflite"
-  },
-  "license": "Apache-2.0",
-  "engines": {
-    "yarn": ">= 1.3.2"
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core"
-  },
-  "devDependencies": {
-    "@bazel/bazelisk": "^1.12.0"
-  },
-  "scripts": {
-    "build": "bazel build :tfjs-tflite_pkg",
-    "publish-npm": "bazel run :tfjs-tflite_pkg.publish",
-    "test": "bazel test :tests --test_output=all",
-    "test-dev": "ibazel test :tests --test_output=all",
-    "test-debug": "bazel run :tfjs-tflite_test"
-  },
-  "resolutions": {
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-tflite/src/BUILD.bazel b/tfjs-master/tfjs-tflite/src/BUILD.bazel
deleted file mode 100644
index 6ceb62f6d..000000000
--- a/tfjs-master/tfjs-tflite/src/BUILD.bazel
+++ /dev/null
@@ -1,132 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@build_bazel_rules_nodejs//:index.bzl", "copy_to_bin")
-load("//tools:defaults.bzl", "esbuild", "ts_library")
-load("//tools:tfjs_web_test.bzl", "tfjs_web_test")
-
-package(default_visibility = ["//visibility:public"])
-
-TEST_SRCS = [
-    "**/*_test.ts",
-    "setup_test.ts",
-]
-
-ts_library(
-    name = "tfjs-tflite_src_lib",
-    srcs = glob(
-        ["**/*.ts"],
-        exclude = TEST_SRCS + ["index.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-tflite/dist",
-    deps = [
-        "//tfjs-core/src:tfjs-core_lib",
-        "@npm//@types/seedrandom",
-        "@npm//seedrandom",
-    ],
-)
-
-ts_library(
-    name = "tfjs-tflite_lib",
-    srcs = ["index.ts"],
-    module_name = "@tensorflow/tfjs-tflite",
-    deps = [
-        ":tfjs-tflite_src_lib",
-    ],
-)
-
-copy_to_bin(
-    name = "tflite_web_api_client_js",
-    srcs = [
-        "tflite_web_api_client.js",
-    ],
-)
-
-ts_library(
-    name = "tfjs-tflite_test_lib",
-    srcs = glob(
-        TEST_SRCS,
-        exclude = ["worker_test.ts"],
-    ),
-    module_name = "@tensorflow/tfjs-tflite/dist",
-    deps = [
-        ":tfjs-tflite_lib",
-        ":tfjs-tflite_src_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-    ],
-)
-
-esbuild(
-    name = "tfjs-tflite_test_bundle",
-    testonly = True,
-    entry_point = "setup_test.ts",
-    external = [
-        "@tensorflow/tfjs",
-        "worker_threads",
-        "util",
-    ],
-    sources_content = True,
-    deps = [
-        ":tfjs-tflite_lib",
-        ":tfjs-tflite_test_lib",
-        ":tflite_web_api_client_js",
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-tflite/wasm",
-    ],
-)
-
-ts_library(
-    name = "worker_test_lib",
-    srcs = [
-        "worker_test.ts",
-    ],
-    deps = [
-        "//tfjs-backend-cpu/src:tfjs-backend-cpu_lib",
-        "//tfjs-core/src:tfjs-core_lib",
-        "//tfjs-core/src:tfjs-core_src_lib",
-    ],
-)
-
-tfjs_web_test(
-    name = "worker_test",
-    browsers = [
-        "bs_chrome_mac",
-        "bs_firefox_mac",
-        "bs_safari_mac",
-        # Temporarily disabled because BrowserStack does not support loading
-        # absolute paths in iOS, which is required for loading the worker.
-        # https://www.browserstack.com/question/39573
-        # "bs_ios_12",
-        "bs_android_10",
-        "win_10_chrome",
-    ],
-    static_files = [
-        # For the webworker
-        "//tfjs-core:tf-core.min.js",
-        "//tfjs-core:tf-core.min.js.map",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js",
-        "//tfjs-backend-cpu:tf-backend-cpu.min.js.map",
-        "//tfjs-tflite:tf-tflite.min.js",
-        "//tfjs-tflite:tf-tflite.min.js.map",
-        "//tfjs-tflite/wasm",
-        "//tfjs-tflite/test_files:add4.tflite",
-    ],
-    deps = [
-        ":worker_test_lib",
-        "@npm//long:long__umd",
-        "@npm//seedrandom:seedrandom__umd",
-    ],
-)
diff --git a/tfjs-master/tfjs-tflite/src/index.ts b/tfjs-master/tfjs-tflite/src/index.ts
deleted file mode 100644
index 53614408d..000000000
--- a/tfjs-master/tfjs-tflite/src/index.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export * from './tflite_model';
-export * from './types/tflite_web_model_runner';
-export * from './tflite_task_library_client/image_classifier';
-export * from './tflite_task_library_client/image_segmenter';
-export * from './tflite_task_library_client/object_detector';
-export * from './tflite_task_library_client/nl_classifier';
-export * from './tflite_task_library_client/bert_nl_classifier';
-export * from './tflite_task_library_client/bert_qa';
-export {setWasmPath} from './tflite_task_library_client/common';
-export {getWasmFeatures} from './tflite_task_library_client/common';
diff --git a/tfjs-master/tfjs-tflite/src/setup_test.ts b/tfjs-master/tfjs-tflite/src/setup_test.ts
deleted file mode 100644
index 101d065f9..000000000
--- a/tfjs-master/tfjs-tflite/src/setup_test.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-
-import './tflite_model_test';
-import './tflite_web_api_client_test';
diff --git a/tfjs-master/tfjs-tflite/src/tflite_model.ts b/tfjs-master/tfjs-tflite/src/tflite_model.ts
deleted file mode 100644
index 2b48a1761..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_model.ts
+++ /dev/null
@@ -1,362 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {DataType, InferenceModel, ModelPredictConfig, ModelTensorInfo, NamedTensorMap, tensor, Tensor} from '@tensorflow/tfjs-core';
-
-import * as tfliteWebAPIClient from './tflite_web_api_client';
-import {ProfileItem, TFLiteDataType, TFLiteWebModelRunner, TFLiteWebModelRunnerOptions, TFLiteWebModelRunnerTensorInfo} from './types/tflite_web_model_runner';
-
-const TFHUB_SEARCH_PARAM = '?lite-format=tflite';
-
-/**
- * A `tflite.TFLiteModel` is built from a TFLite model flatbuffer and executable
- * on TFLite interpreter. To load it, use the `loadTFLiteModel` function below.
- *
- * Sample usage:
- *
- * ```js
- * // Load the MobilenetV2 tflite model from tfhub.
- * const tfliteModel = await tflite.loadTFLiteModel(
- *     'https://tfhub.dev/tensorflow/lite-model/mobilenet_v2_1.0_224/1/metadata/1');
- *
- * const outputTensor = tf.tidy(() => {
- *    // Get pixels data from an image.
- *    let img = tf.browser.fromPixels(document.querySelector('img'));
- *    // Resize and normalize:
- *    img = tf.image.resizeBilinear(img, [224, 224]);
- *    img = tf.sub(tf.div(tf.expandDims(img), 127.5), 1);
- *    // Run the inference.
- *    let outputTensor = tfliteModel.predict(img);
- *    // De-normalize the result.
- *    return tf.mul(tf.add(outputTensor, 1), 127.5)
- *  });
- * console.log(outputTensor);
- *
- * ```
- *
- * @doc {heading: 'Models', subheading: 'Classes'}
- */
-export class TFLiteModel implements InferenceModel {
-  constructor(private readonly modelRunner: TFLiteWebModelRunner) {}
-
-  get inputs(): ModelTensorInfo[] {
-    const modelInputs = this.modelRunner.getInputs();
-    return this.convertTFLiteTensorInfos(modelInputs);
-  }
-
-  get outputs(): ModelTensorInfo[] {
-    const modelOutputs = this.modelRunner.getOutputs();
-    return this.convertTFLiteTensorInfos(modelOutputs);
-  }
-
-  /**
-   * Execute the inference for the input tensors.
-   *
-   * @param inputs The input tensors, when there is single input for the model,
-   *     inputs param should be a Tensor. For models with multiple inputs,
-   *     inputs params should be in either Tensor[] if the input order is fixed,
-   *     or otherwise NamedTensorMap format.
-   *
-   * @param config Prediction configuration for specifying the batch size.
-   *     Currently this field is not used, and batch inference is not supported.
-   *
-   * @returns Inference result tensors. The output would be single Tensor if
-   *     model has single output node, otherwise NamedTensorMap will be returned
-   *     for model with multiple outputs. Tensor[] is not used.
-   *
-   * @doc {heading: 'Models', subheading: 'Classes'}
-   */
-  predict(inputs: Tensor|Tensor[]|NamedTensorMap, config?: ModelPredictConfig):
-      Tensor|Tensor[]|NamedTensorMap {
-    const modelInputs = this.modelRunner.getInputs();
-    const modelOutputs = this.modelRunner.getOutputs();
-
-    // Set model inputs from the given tensors.
-
-    // A single tensor or a tensor array.
-    if (inputs instanceof Tensor || Array.isArray(inputs)) {
-      let inputTensors: Tensor[];
-      if (inputs instanceof Tensor) {
-        inputTensors = [inputs];
-      } else {
-        inputTensors = inputs;
-      }
-      if (modelInputs.length !== inputTensors.length) {
-        throw new Error(`The size of TFLite model inputs (${
-            modelInputs
-                .length}) does not match the size of the input tensors (${
-            inputTensors.length})`);
-      }
-      for (let i = 0; i < modelInputs.length; i++) {
-        this.setModelInputFromTensor(modelInputs[i], inputTensors[i]);
-      }
-    }
-    // Named tensors.
-    else {
-      const inputTensorNames = Object.keys(inputs);
-      const modelInputMap:
-          {[name: string]: TFLiteWebModelRunnerTensorInfo} = {};
-      modelInputs.forEach(modelInput => {
-        modelInputMap[modelInput.name] = modelInput;
-      });
-      const modelInputNames = Object.keys(modelInputMap);
-      this.checkMapInputs(inputTensorNames, modelInputNames);
-      for (const name of inputTensorNames) {
-        this.setModelInputFromTensor(modelInputMap[name], inputs[name]);
-      }
-    }
-
-    // Run inference.
-    const success = this.modelRunner.infer();
-    if (!success) {
-      throw new Error('Failed running inference');
-    }
-
-    // Convert model outputs to tensors.
-    const outputTensors: NamedTensorMap = {};
-    for (let i = 0; i < modelOutputs.length; i++) {
-      const modelOutput = modelOutputs[i];
-      let data = modelOutput.data();
-
-      // Convert TFLite tensor types that are not supported by TFJS to
-      // compatible types.
-      switch (modelOutput.dataType) {
-        case 'int8':
-        case 'int16':
-        case 'uint32':
-          data = Int32Array.from(data);
-          break;
-        case 'float64':
-          console.warn(
-              `WARNING: converting output tensor from 'float64' to 'float32'`);
-          data = Float32Array.from(data);
-          break;
-        default:
-          break;
-      }
-      const outputTensor =
-          tensor(data, this.getShapeFromTFLiteTensorInfo(modelOutput));
-      outputTensors[modelOutput.name] = outputTensor;
-    }
-    const names = Object.keys(outputTensors);
-    return names.length === 1 ? outputTensors[names[0]] : outputTensors;
-  }
-
-  /**
-   * Execute the inference for the input tensors and return activation
-   * values for specified output node names without batching.
-   *
-   * @param inputs The input tensors, when there is single input for the model,
-   *     inputs param should be a Tensor. For models with multiple inputs,
-   *     inputs params should be in either Tensor[] if the input order is fixed,
-   *     or otherwise NamedTensorMap format.
-   *
-   * @param outputs string|string[]. List of output node names to retrieve
-   *     activation from.
-   *
-   * @returns Activation values for the output nodes result tensors. The return
-   *     type matches specified parameter outputs type. The output would be
-   *     single Tensor if single output is specified, otherwise Tensor[] for
-   *     multiple outputs.
-   */
-  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs: string|string[]):
-      Tensor|Tensor[] {
-    throw new Error('execute() of TFLiteModel is not supported yet.');
-  }
-
-  getProfilingResults(): ProfileItem[] {
-    return this.modelRunner.getProfilingResults();
-  }
-
-  getProfilingSummary(): string {
-    return this.modelRunner.getProfilingSummary();
-  }
-
-  private setModelInputFromTensor(
-      modelInput: TFLiteWebModelRunnerTensorInfo, tensor: Tensor) {
-    // String and complex tensors are not supported.
-    if (tensor.dtype === 'string' || tensor.dtype === 'complex64') {
-      throw new Error(`Data type '${tensor.dtype}' not supported.`);
-    }
-
-    // Check shape.
-    //
-    // At this point, we've already checked that input tensors and model inputs
-    // have the same size.
-    const modelInputShape = modelInput.shape.split(',').map(dim => Number(dim));
-    if (!tensor.shape.every(
-            (dim, index) => modelInputShape[index] === -1 ||
-                modelInputShape[index] === dim)) {
-      throw new Error(`Input tensor shape mismatch: expect '${
-          modelInput.shape}', got '${tensor.shape.join(',')}'.`);
-    }
-
-    // Check types.
-    switch (modelInput.dataType) {
-      // All 'bool' and 'int' tflite types accpet 'bool' or 'int32' tfjs types.
-      // Will throw error for 'float32' tfjs type.
-      case 'bool':
-      case 'int8':
-      case 'uint8':
-      case 'int16':
-      case 'uint32':
-      case 'int32':
-        if (tensor.dtype === 'float32') {
-          throw this.getDataTypeMismatchError(
-              modelInput.dataType, tensor.dtype);
-        } else if (modelInput.dataType !== tensor.dtype) {
-          console.warn(`WARNING: converting '${tensor.dtype}' to '${
-              modelInput.dataType}'`);
-        }
-        break;
-      // All 'float' tflite types accept all tfjs types.
-      case 'float32':
-      case 'float64':
-        if (modelInput.dataType !== tensor.dtype) {
-          console.warn(`WARNING: converting '${tensor.dtype}' to '${
-              modelInput.dataType}'`);
-        }
-        break;
-      default:
-        break;
-    }
-
-    const modelInputBuffer = modelInput.data();
-    switch (modelInput.dataType) {
-      case 'int8':
-        modelInputBuffer.set(Int8Array.from(tensor.dataSync()));
-        break;
-      case 'uint8':
-      case 'bool':
-        modelInputBuffer.set(Uint8Array.from(tensor.dataSync()));
-        break;
-      case 'int16':
-        modelInputBuffer.set(Int16Array.from(tensor.dataSync()));
-        break;
-      case 'int32':
-        modelInputBuffer.set(Int32Array.from(tensor.dataSync()));
-        break;
-      case 'uint32':
-        modelInputBuffer.set(Uint32Array.from(tensor.dataSync()));
-        break;
-      case 'float32':
-        modelInputBuffer.set(Float32Array.from(tensor.dataSync()));
-        break;
-      case 'float64':
-        modelInputBuffer.set(Float64Array.from(tensor.dataSync()));
-        break;
-      default:
-        break;
-    }
-  }
-
-  private convertTFLiteTensorInfos(infos: TFLiteWebModelRunnerTensorInfo[]):
-      ModelTensorInfo[] {
-    return infos.map(info => {
-      const dtype = getDTypeFromTFLiteType(info.dataType);
-      return {
-        name: info.name,
-        shape: this.getShapeFromTFLiteTensorInfo(info),
-        dtype,
-      };
-    });
-  }
-
-  private checkMapInputs(
-      inputTensorNames: string[], modelInputNames: string[]) {
-    const notInModel =
-        inputTensorNames.filter(name => !modelInputNames.includes(name));
-    const notInInput =
-        modelInputNames.filter(name => !inputTensorNames.includes(name));
-    if (notInModel.length === 0 && notInInput.length === 0) {
-      return;
-    }
-
-    const msgParts =
-        ['The model input names don\'t match the model input names.'];
-    if (notInModel.length > 0) {
-      msgParts.push(`Names in input but missing in model: [${notInModel}].`);
-    }
-    if (notInInput.length > 0) {
-      msgParts.push(`Names in model but missing in inputs: [${notInInput}].`);
-    }
-    throw new Error(msgParts.join(' '));
-  }
-
-  private getShapeFromTFLiteTensorInfo(info: TFLiteWebModelRunnerTensorInfo) {
-    return info.shape.split(',').map(s => Number(s));
-  }
-
-  private getDataTypeMismatchError(expected: string, got: string) {
-    return new Error(
-        `Data type mismatch: input tensor expects '${expected}', got '${got}'`);
-  }
-}
-
-/**
- * Loads a TFLiteModel from the given model url.
- *
- * @param model The path to the model (string), or the model content in memory
- *     (ArrayBuffer).
- * @param options Options related to model inference.
- *
- * @doc {heading: 'Models', subheading: 'Loading'}
- */
-export async function loadTFLiteModel(
-    model: string|ArrayBuffer,
-    options?: TFLiteWebModelRunnerOptions): Promise<TFLiteModel> {
-  // Handle tfhub links.
-  if (typeof model === 'string' && model.includes('tfhub.dev') &&
-      model.includes('lite-model') && !model.endsWith(TFHUB_SEARCH_PARAM)) {
-    model = `${model}${TFHUB_SEARCH_PARAM}`;
-  }
-
-  const tfliteModelRunner =
-      await tfliteWebAPIClient.tfweb.TFLiteWebModelRunner.create(
-          model, options);
-  return new TFLiteModel(tfliteModelRunner);
-}
-
-/**
- * Returns the compatible tfjs DataType from the given TFLite data type.
- *
- * @param tfliteType The type in TFLite.
- *
- * @doc {heading: 'Models', subheading: 'Utilities'}
- */
-export function getDTypeFromTFLiteType(tfliteType: TFLiteDataType): DataType {
-  let dtype: DataType;
-  switch (tfliteType) {
-    case 'float32':
-    case 'float64':
-      dtype = 'float32';
-      break;
-    case 'int8':
-    case 'uint8':
-    case 'int16':
-    case 'int32':
-    case 'uint32':
-      dtype = 'int32';
-      break;
-    case 'bool':
-      dtype = 'bool';
-      break;
-    default:
-      break;
-  }
-  return dtype;
-}
diff --git a/tfjs-master/tfjs-tflite/src/tflite_model_test.ts b/tfjs-master/tfjs-tflite/src/tflite_model_test.ts
deleted file mode 100644
index e7d566013..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_model_test.ts
+++ /dev/null
@@ -1,232 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {DataType, NamedTensorMap} from '@tensorflow/tfjs-core';
-
-import {TFLiteModel} from './tflite_model';
-import {ProfileItem, TFLiteDataType, TFLiteWebModelRunner, TFLiteWebModelRunnerOptions, TFLiteWebModelRunnerTensorInfo} from './types/tflite_web_model_runner';
-
-// A mock TFLiteWebModelRunner that doubles the data from input tensors to
-// output tensors during inference.
-class MockModelRunner implements TFLiteWebModelRunner {
-  private mockInferResults: string[] = [];
-
-  private inputTensors: TFLiteWebModelRunnerTensorInfo[];
-  private outputTensors: TFLiteWebModelRunnerTensorInfo[];
-
-  singleInput = false;
-  singleOutput = false;
-
-  constructor(
-      modelPath: string, options: TFLiteWebModelRunnerOptions,
-      firstOutputtype: TFLiteDataType = 'int32') {
-    this.inputTensors = this.getTensorInfos();
-    this.outputTensors = this.getTensorInfos(firstOutputtype);
-
-    this.mockInferResults.push(`ModelPath=${modelPath}`);
-    this.mockInferResults.push(`numThreads=${options.numThreads}`);
-  }
-
-  getInputs(): TFLiteWebModelRunnerTensorInfo[] {
-    return this.singleInput ? [this.inputTensors[0]] : this.inputTensors;
-  }
-
-  getOutputs(): TFLiteWebModelRunnerTensorInfo[] {
-    return this.singleOutput ? [this.outputTensors[0]] : this.outputTensors;
-  }
-
-  infer(): boolean {
-    for (let i = 0; i < this.inputTensors.length; i++) {
-      const inputTensor = this.inputTensors[i];
-      const outputTensor = this.outputTensors[i];
-      outputTensor.data().set(Array.from(inputTensor.data()).map(v => v * 2));
-    }
-    return true;
-  }
-
-  cleanUp() {}
-
-  getProfilingResults(): ProfileItem[] {
-    return [];
-  }
-
-  getProfilingSummary(): string {
-    return '';
-  }
-
-  private getTensorInfos(firstTensorType: TFLiteDataType = 'int32'):
-      TFLiteWebModelRunnerTensorInfo[] {
-    const shape0 = [1, 2, 3];
-    let buffer0: Int8Array|Uint8Array|Int16Array|Int32Array|Uint32Array|
-        Float32Array|Float64Array = undefined;
-    const size0 = shape0.reduce((a, c) => a * c, 1);
-    switch (firstTensorType) {
-      case 'int8':
-        buffer0 = new Int8Array(size0);
-        break;
-      case 'uint8':
-        buffer0 = new Uint8Array(size0);
-        break;
-      case 'int16':
-        buffer0 = new Int16Array(size0);
-        break;
-      case 'int32':
-        buffer0 = new Int32Array(size0);
-        break;
-      case 'uint32':
-        buffer0 = new Uint32Array(size0);
-        break;
-      case 'float32':
-        buffer0 = new Float32Array(size0);
-        break;
-      case 'float64':
-        buffer0 = new Float64Array(size0);
-        break;
-      default:
-        break;
-    }
-
-    const shape1 = [1, 2];
-    const buffer1 = new Float32Array(shape1.reduce((a, c) => a * c, 1));
-    return [
-      {
-        id: 0,
-        dataType: firstTensorType,
-        name: 't0',
-        shape: shape0.join(','),
-        data: () => buffer0,
-      },
-      {
-        id: 1,
-        dataType: 'float32',
-        name: 't1',
-        shape: shape1.join(','),
-        data: () => buffer1,
-      },
-    ];
-  }
-}
-
-let tfliteModel: TFLiteModel;
-let modelRunner: MockModelRunner;
-
-function checkOutputTypeConversion(
-    originalOutputType: TFLiteDataType, expectedConvertedType: DataType) {
-  const modelRunner = new MockModelRunner(
-      'my_model.tflite', {numThreads: 2}, originalOutputType);
-  modelRunner.singleInput = true;
-  modelRunner.singleOutput = true;
-  const tfliteModel = new TFLiteModel(modelRunner);
-
-  const input = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3], 'int32');
-  const outputs = tfliteModel.predict(input, {});
-  expect((outputs as tf.Tensor).dtype).toBe(expectedConvertedType);
-}
-
-describe('TFLiteModel', () => {
-  beforeEach(() => {
-    modelRunner = new MockModelRunner('my_model.tflite', {numThreads: 2});
-    tfliteModel = new TFLiteModel(modelRunner);
-  });
-
-  it('should generate the output for single tensor', () => {
-    modelRunner.singleInput = true;
-
-    const input = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3], 'int32');
-    const outputs = tfliteModel.predict(input, {}) as NamedTensorMap;
-    tf.test_util.expectArraysClose(
-        outputs['t0'].dataSync(), [2, 4, 6, 8, 10, 12]);
-  });
-
-  it('should generate the output for tensor array', () => {
-    const input0 = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3], 'int32');
-    const input1 = tf.tensor2d([11, 12], [1, 2], 'float32');
-    const outputs = tfliteModel.predict([input0, input1], {}) as NamedTensorMap;
-    tf.test_util.expectArraysClose(
-        outputs['t0'].dataSync(), [2, 4, 6, 8, 10, 12]);
-    tf.test_util.expectArraysClose(outputs['t1'].dataSync(), [22, 24]);
-  });
-
-  it('should generate the output for tensor map', () => {
-    const input0 = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3], 'int32');
-    const input1 = tf.tensor2d([11, 12], [1, 2], 'float32');
-    const outputs =
-        tfliteModel.predict({'t0': input0, 't1': input1}, {}) as NamedTensorMap;
-    tf.test_util.expectArraysClose(
-        outputs['t0'].dataSync(), [2, 4, 6, 8, 10, 12]);
-    tf.test_util.expectArraysClose(outputs['t1'].dataSync(), [22, 24]);
-  });
-
-  it('should generate a single output when model has a single output', () => {
-    modelRunner.singleInput = true;
-    modelRunner.singleOutput = true;
-
-    const input = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3], 'int32');
-    const outputs = tfliteModel.predict(input, {});
-    expect(outputs instanceof tf.Tensor).toBe(true);
-  });
-
-  it('should convert output type correctly', () => {
-    const testCases: Array<{
-      originalOutputType: TFLiteDataType; expectedConvertedType: DataType;
-    }> =
-        [
-          {
-            originalOutputType: 'int8',
-            expectedConvertedType: 'int32',
-          },
-          {
-            originalOutputType: 'int16',
-            expectedConvertedType: 'int32',
-          },
-          {
-            originalOutputType: 'uint32',
-            expectedConvertedType: 'int32',
-          },
-          {
-            originalOutputType: 'float64',
-            expectedConvertedType: 'float32',
-          },
-        ];
-
-    for (const testCase of testCases) {
-      checkOutputTypeConversion(
-          testCase.originalOutputType, testCase.expectedConvertedType);
-    }
-  });
-
-  it('should throw error if input size mismatch', () => {
-    // Mismatch: 1 vs 2.
-    const input0 = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3], 'int32');
-    expect(() => tfliteModel.predict([input0], {})).toThrow();
-  });
-
-  it('should throw error if input shape mismatch', () => {
-    // Mismatch: [2,2] vs [1,2,3].
-    const input0 = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
-    const input1 = tf.tensor2d([11, 12], [1, 2], 'float32');
-    expect(() => tfliteModel.predict([input0, input1], {})).toThrow();
-  });
-
-  it('should throw error if input type is not compatible', () => {
-    // Mismatch: float32 -> int32
-    const input0 = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
-    const input1 = tf.tensor2d([11, 12], [1, 2], 'float32');
-    expect(() => tfliteModel.predict([input0, input1], {})).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/bert_nl_classifier.ts b/tfjs-master/tfjs-tflite/src/tflite_task_library_client/bert_nl_classifier.ts
deleted file mode 100644
index abe5f60da..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/bert_nl_classifier.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfliteWebAPIClient from '../tflite_web_api_client';
-import {BertNLClassifier as TaskLibraryBertNLClassifier} from '../types/bert_nl_classifier';
-import {BaseTaskLibraryClient, Class} from './common';
-
-export interface BertNLClassifierOptions {
-  /**
-   * Max number of tokens to pass to the model.
-   *
-   * Default to 128.
-   */
-  maxSeqLen?: number;
-}
-
-/**
- * Client for BertNLClassifier TFLite Task Library.
- *
- * It is a wrapper around the underlying javascript API to make it more
- * convenient to use. See comments in the corresponding type declaration file in
- * src/types for more info.
- */
-export class BertNLClassifier extends BaseTaskLibraryClient {
-  constructor(protected override instance: TaskLibraryBertNLClassifier) {
-    super(instance);
-  }
-
-  static async create(
-      model: string|ArrayBuffer,
-      options?: BertNLClassifierOptions): Promise<BertNLClassifier> {
-    const protoOptions = new tfliteWebAPIClient.tfweb.BertNLClassifierOptions();
-    if (options) {
-      if (options.maxSeqLen) {
-        protoOptions.setMaxSeqLen(options.maxSeqLen);
-      }
-    }
-    const instance = await tfliteWebAPIClient.tfweb.BertNLClassifier.create(
-        model, protoOptions);
-    return new BertNLClassifier(instance);
-  }
-
-  classify(input: string): Class[] {
-    return this.instance.classify(input).map(category => {
-      return {
-        probability: category.score,
-        className: category.className,
-      };
-    });
-  }
-}
diff --git a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/bert_qa.ts b/tfjs-master/tfjs-tflite/src/tflite_task_library_client/bert_qa.ts
deleted file mode 100644
index 736ed896c..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/bert_qa.ts
+++ /dev/null
@@ -1,72 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfliteWebAPIClient from '../tflite_web_api_client';
-import {BertQuestionAnswerer as TaskLibraryBertQuestionAnswerer} from '../types/bert_qa';
-import {BaseTaskLibraryClient} from './common';
-
-/** A single answer. */
-export interface QaAnswer {
-  /** The text of the answer. */
-  text: string;
-  /** The position and logit of the answer. */
-  pos: Pos;
-}
-
-/** Answer position. */
-export interface Pos {
-  /** The start position. */
-  start: number;
-  /** The end position. */
-  end: number;
-  /** The logit. */
-  logit: number;
-}
-
-/**
- * Client for BertQA TFLite Task Library.
- *
- * It is a wrapper around the underlying javascript API to make it more
- * convenient to use. See comments in the corresponding type declaration file in
- * src/types for more info.
- */
-export class BertQuestionAnswerer extends BaseTaskLibraryClient {
-  constructor(protected override instance: TaskLibraryBertQuestionAnswerer) {
-    super(instance);
-  }
-
-  static async create(model: string|
-                      ArrayBuffer): Promise<BertQuestionAnswerer> {
-    const instance =
-        await tfliteWebAPIClient.tfweb.BertQuestionAnswerer.create(model);
-    return new BertQuestionAnswerer(instance);
-  }
-
-  answer(context: string, question: string): QaAnswer[] {
-    const result = this.instance.answer(context, question);
-    if (!result) {
-      return [];
-    }
-
-    return result.map(answer => {
-      return {
-        text: answer.text,
-        pos: answer.pos,
-      };
-    });
-  }
-}
diff --git a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/common.ts b/tfjs-master/tfjs-tflite/src/tflite_task_library_client/common.ts
deleted file mode 100644
index a374f4fcb..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/common.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfliteWebAPIClient from '../tflite_web_api_client';
-import {BaseTaskLibrary, Class as ProtoClass} from '../types/common';
-
-/** Common options for all task library tasks. */
-export interface CommonTaskLibraryOptions {
-  /**
-   * The number of threads to be used for TFLite ops that support
-   * multi-threading when running inference with CPU. num_threads should be
-   * greater than 0 or equal to -1. Setting num_threads to -1 has the effect to
-   * let TFLite runtime set the value.
-   *
-   * Default to number of physical CPU cores, or -1 if WASM multi-threading is
-   * not supported by user's browser.
-   */
-  numThreads?: number;
-}
-
-/** A single class in the classification result. */
-export interface Class {
-  /** The name of the class. */
-  className: string;
-
-  /** The probability/score of the class. */
-  probability: number;
-}
-
-/** Convert proto Class array to our own Class array. */
-export function convertProtoClassesToClasses(protoClasses: ProtoClass[]):
-    Class[] {
-  const classes: Class[] = [];
-  protoClasses.forEach(cls => {
-    classes.push({
-      className: cls.getDisplayName() || cls.getClassName(),
-      probability: cls.getScore(),
-    });
-  });
-  return classes;
-}
-
-/** The global function to set WASM path. */
-export const setWasmPath = tfliteWebAPIClient.tfweb.tflite_web_api.setWasmPath;
-
-/** The global function to get supported WASM features */
-export const getWasmFeatures =
-    tfliteWebAPIClient.tfweb.tflite_web_api.getWasmFeatures;
-
-/** The base class for all task library clients. */
-export class BaseTaskLibraryClient {
-  constructor(protected instance: BaseTaskLibrary) {}
-
-  cleanUp() {
-    this.instance.cleanUp();
-  }
-}
-
-/** Gets the number of threads for best performance. */
-export async function getDefaultNumThreads(): Promise<number> {
-  const supportMultiThreading =
-      (await tfliteWebAPIClient.tfweb.tflite_web_api.getWasmFeatures())
-          .multiThreading;
-  return supportMultiThreading ? navigator.hardwareConcurrency / 2 : -1;
-}
diff --git a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/image_classifier.ts b/tfjs-master/tfjs-tflite/src/tflite_task_library_client/image_classifier.ts
deleted file mode 100644
index 95ea9b486..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/image_classifier.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfliteWebAPIClient from '../tflite_web_api_client';
-import {ImageClassifier as TaskLibraryImageClassifier} from '../types/image_classifier';
-
-import {BaseTaskLibraryClient, Class, CommonTaskLibraryOptions, convertProtoClassesToClasses, getDefaultNumThreads} from './common';
-
-/** ImageClassifier options. */
-export interface ImageClassifierOptions extends CommonTaskLibraryOptions {
-  /**
-   * Maximum number of top scored results to return. If < 0, all results will
-   * be returned. If 0, an invalid argument error is returned.
-   */
-  maxResults?: number;
-
-  /**
-   * Score threshold in [0,1), overrides the ones provided in the model metadata
-   * (if any). Results below this value are rejected.
-   */
-  scoreThreshold?: number;
-}
-
-/**
- * Client for ImageClassifier TFLite Task Library.
- *
- * It is a wrapper around the underlying javascript API to make it more
- * convenient to use. See comments in the corresponding type declaration file in
- * src/types for more info.
- */
-export class ImageClassifier extends BaseTaskLibraryClient {
-  constructor(protected override instance: TaskLibraryImageClassifier) {
-    super(instance);
-  }
-
-  static async create(
-      model: string|ArrayBuffer,
-      options?: ImageClassifierOptions): Promise<ImageClassifier> {
-    const optionsProto = new tfliteWebAPIClient.tfweb.ImageClassifierOptions();
-    if (options) {
-      if (options.maxResults !== undefined) {
-        optionsProto.setMaxResults(options.maxResults);
-      }
-      if (options.scoreThreshold !== undefined) {
-        optionsProto.setScoreThreshold(options.scoreThreshold);
-      }
-      if (options.numThreads !== undefined) {
-        optionsProto.setNumThreads(options.numThreads);
-      }
-    }
-    if (!options || options.numThreads === undefined) {
-      optionsProto.setNumThreads(await getDefaultNumThreads());
-    }
-    const instance = await tfliteWebAPIClient.tfweb.ImageClassifier.create(
-        model, optionsProto);
-    return new ImageClassifier(instance);
-  }
-
-  classify(input: ImageData|HTMLImageElement|HTMLCanvasElement|
-           HTMLVideoElement): Class[] {
-    const result = this.instance.classify(input);
-    if (!result) {
-      return [];
-    }
-
-    let classes: Class[] = [];
-    if (result.getClassificationsList().length > 0) {
-      classes = convertProtoClassesToClasses(
-          result.getClassificationsList()[0].getClassesList());
-    }
-    return classes;
-  }
-}
diff --git a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/image_segmenter.ts b/tfjs-master/tfjs-tflite/src/tflite_task_library_client/image_segmenter.ts
deleted file mode 100644
index f4aeb729d..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/image_segmenter.ts
+++ /dev/null
@@ -1,147 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfliteWebAPIClient from '../tflite_web_api_client';
-import {ImageSegmenter as TaskLibraryImageSegmenter} from '../types/image_segmenter';
-import {BaseTaskLibraryClient, CommonTaskLibraryOptions, getDefaultNumThreads} from './common';
-
-/** Different output types. */
-export enum OutputType {
-  CATEGORY_MASK = 1.0,
-  CONFIDENCE_MASK = 2.0,
-  UNSPECIFIED = 0.0,
-}
-
-/** ImageSegmenter options. */
-export interface ImageSegmenterOptions extends CommonTaskLibraryOptions {
-  outputType?: OutputType;
-}
-
-/** A single segmentation in the result. */
-export interface Segmentation {
-  /**
-   * The width of the mask. This is an intrinsic parameter of the model being
-   * used, and does not depend on the input image dimensions.
-   */
-  width: number;
-  /**
-   * The height of the mask. This is an intrinsic parameter of the model being
-   * used, and does not depend on the input image dimensions.
-   */
-  height: number;
-  /**
-   * This is a flattened 2D-array of size `width` x `height`, in row major
-   * order. The value of each pixel in this mask represents the class to which
-   * the pixel in the mask belongs. See `coloredLabels` for instructions on how
-   * to get pixel labels and display color.
-   */
-  categoryMask: Uint8Array;
-  /**
-   * The list of colored labels for all the supported categories. Depending on
-   * which is present, this list is in 1:1 correspondence with:
-   * - * `categoryMask` pixel values, i.e. a pixel with value `i` is associated
-   * with `coloredLabels[i]`,
-   * - `confidenceMasks` indices, i.e. `confidence_masks[i]` is associated with
-   * `coloredLabels[i]` (TODO: to be supported here).
-   */
-  coloredLabels: ColoredLabel[];
-}
-
-/** A label associated with an RGB color, for display purpose. */
-export interface ColoredLabel {
-  /** The red color component for the label, in the [0, 255] range. */
-  r: number;
-  /** The green color component for the label, in the [0, 255] range. */
-  g: number;
-  /** The blue color component for the label, in the [0, 255] range. */
-  b: number;
-  /**
-   * The class name, as provided in the label map packed in the TFLite Model
-   * Metadata.
-   */
-  className: string;
-  /**
-   * The display name, as provided in the label map (if available) packed in
-   * the TFLite Model Metadata.
-   */
-  displayName: string;
-}
-
-/**
- * Client for ImageSegmenter TFLite Task Library.
- *
- * It is a wrapper around the underlying javascript API to make it more
- * convenient to use. See comments in the corresponding type declaration file in
- * src/types for more info.
- */
-export class ImageSegmenter extends BaseTaskLibraryClient {
-  constructor(protected override instance: TaskLibraryImageSegmenter) {
-    super(instance);
-  }
-
-  static async create(
-      model: string|ArrayBuffer,
-      options?: ImageSegmenterOptions): Promise<ImageSegmenter> {
-    const optionsProto = new tfliteWebAPIClient.tfweb.ImageSegmenterOptions();
-    if (options) {
-      // Set defaults.
-      if (options.outputType) {
-        optionsProto.setOutputType(options.outputType);
-      }
-      if (options.numThreads !== undefined) {
-        optionsProto.setNumThreads(options.numThreads);
-      }
-    }
-    if (!options || options.numThreads === undefined) {
-      optionsProto.setNumThreads(await getDefaultNumThreads());
-    }
-    const instance = await tfliteWebAPIClient.tfweb.ImageSegmenter.create(
-        model, optionsProto);
-    return new ImageSegmenter(instance);
-  }
-
-  segment(input: ImageData|HTMLImageElement|HTMLCanvasElement|
-          HTMLVideoElement): Segmentation[] {
-    const result = this.instance.segment(input);
-    if (!result) {
-      return [];
-    }
-
-    const segmentations: Segmentation[] = [];
-    if (result.getSegmentationList().length > 0) {
-      result.getSegmentationList().forEach(seg => {
-        const coloredLabels: ColoredLabel[] =
-            seg.getColoredLabelsList().map(label => {
-              return {
-                r: label.getR(),
-                g: label.getG(),
-                b: label.getB(),
-                className: label.getClassName(),
-                displayName: label.getDisplayName(),
-              };
-            });
-        segmentations.push({
-          width: seg.getWidth(),
-          height: seg.getHeight(),
-          categoryMask: seg.getCategoryMask_asU8(),
-          coloredLabels,
-        });
-      });
-    }
-    return segmentations;
-  }
-}
diff --git a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/nl_classifier.ts b/tfjs-master/tfjs-tflite/src/tflite_task_library_client/nl_classifier.ts
deleted file mode 100644
index e73603eb2..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/nl_classifier.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfliteWebAPIClient from '../tflite_web_api_client';
-import {NLClassifier as TaskLibraryNLClassifier} from '../types/nl_classifier';
-import {BaseTaskLibraryClient, Class} from './common';
-
-/**
- * NLClassifier options.
- */
-export declare interface NLClassifierOptions {
-  /** Index of the input tensor. */
-  inputTensorIndex: number;
-  /** Index of the output score tensor. */
-  outputScoreTensorIndex: number;
-  /** Index of the output label tensor. */
-  outputLabelTensorIndex: number;
-  /** Name of the input tensor. */
-  inputTensorName: string;
-  /** Name of the output score tensor. */
-  outputScoreTensorName: string;
-  /** Name of the output label tensor. */
-  outputLabelTensorName: string;
-}
-
-/**
- * Default NLClassifier options.
- */
-const DEFAULT_NLCLASSIFIER_OPTIONS: NLClassifierOptions = {
-  inputTensorIndex: 0,
-  outputScoreTensorIndex: 0,
-  outputLabelTensorIndex: -1,
-  inputTensorName: 'INPUT',
-  outputScoreTensorName: 'OUTPUT_SCORE',
-  outputLabelTensorName: 'OUTPUT_LABEL',
-};
-
-/**
- * Client for NLClassifier TFLite Task Library.
- *
- * It is a wrapper around the underlying javascript API to make it more
- * convenient to use. See comments in the corresponding type declaration file in
- * src/types for more info.
- */
-export class NLClassifier extends BaseTaskLibraryClient {
-  constructor(protected override instance: TaskLibraryNLClassifier) {
-    super(instance);
-  }
-
-  static async create(
-      model: string|ArrayBuffer,
-      options = DEFAULT_NLCLASSIFIER_OPTIONS): Promise<NLClassifier> {
-    const instance =
-        await tfliteWebAPIClient.tfweb.NLClassifier.create(model, options);
-    return new NLClassifier(instance);
-  }
-
-  classify(input: string): Class[]|undefined {
-    return this.instance.classify(input).map(category => {
-      return {
-        probability: category.score,
-        className: category.className,
-      };
-    });
-  }
-}
diff --git a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/object_detector.ts b/tfjs-master/tfjs-tflite/src/tflite_task_library_client/object_detector.ts
deleted file mode 100644
index 22515d455..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_task_library_client/object_detector.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfliteWebAPIClient from '../tflite_web_api_client';
-import {ObjectDetector as TaskLibraryObjectDetector} from '../types/object_detector';
-import {BaseTaskLibraryClient, Class, CommonTaskLibraryOptions, convertProtoClassesToClasses, getDefaultNumThreads} from './common';
-
-/** ObjectDetector options. */
-export interface ObjectDetectorOptions extends CommonTaskLibraryOptions {
-  /**
-   * Maximum number of top scored results to return. If < 0, all results will
-   * be returned. If 0, an invalid argument error is returned.
-   */
-  maxResults?: number;
-
-  /**
-   * Score threshold in [0,1), overrides the ones provided in the model metadata
-   * (if any). Results below this value are rejected.
-   */
-  scoreThreshold?: number;
-}
-
-/** A single detected object in the result. */
-export interface Detection {
-  boundingBox: BoundingBox;
-  classes: Class[];
-}
-
-/** A bounding box for the detected object. */
-export interface BoundingBox {
-  originX: number;
-  originY: number;
-  width: number;
-  height: number;
-}
-
-/**
- * Client for ObjectDetector TFLite Task Library.
- *
- * It is a wrapper around the underlying javascript API to make it more
- * convenient to use. See comments in the corresponding type declaration file in
- * src/types for more info.
- */
-export class ObjectDetector extends BaseTaskLibraryClient {
-  constructor(protected override instance: TaskLibraryObjectDetector) {
-    super(instance);
-  }
-
-  static async create(
-      model: string|ArrayBuffer,
-      options?: ObjectDetectorOptions): Promise<ObjectDetector> {
-    const optionsProto = new tfliteWebAPIClient.tfweb.ObjectDetectorOptions();
-    if (options) {
-      if (options.maxResults !== undefined) {
-        optionsProto.setMaxResults(options.maxResults);
-      }
-      if (options.scoreThreshold !== undefined) {
-        optionsProto.setScoreThreshold(options.scoreThreshold);
-      }
-      if (options.numThreads !== undefined) {
-        optionsProto.setNumThreads(options.numThreads);
-      }
-    }
-    if (!options || options.numThreads === undefined) {
-      optionsProto.setNumThreads(await getDefaultNumThreads());
-    }
-    const instance = await tfliteWebAPIClient.tfweb.ObjectDetector.create(
-        model, optionsProto);
-    return new ObjectDetector(instance);
-  }
-
-  detect(input: ImageData|HTMLImageElement|HTMLCanvasElement|
-         HTMLVideoElement): Detection[] {
-    const result = this.instance.detect(input);
-    if (!result) {
-      return [];
-    }
-
-    const detections: Detection[] = [];
-    if (result.getDetectionsList().length > 0) {
-      result.getDetectionsList().forEach(detection => {
-        const boundingBoxProto = detection.getBoundingBox();
-        const boundingBox: BoundingBox = {
-          originX: boundingBoxProto.getOriginX(),
-          originY: boundingBoxProto.getOriginY(),
-          width: boundingBoxProto.getWidth(),
-          height: boundingBoxProto.getHeight(),
-        };
-        const classes =
-            convertProtoClassesToClasses(detection.getClassesList());
-        detections.push({boundingBox, classes});
-      });
-    }
-    return detections;
-  }
-}
diff --git a/tfjs-master/tfjs-tflite/src/tflite_web_api_client.d.ts b/tfjs-master/tfjs-tflite/src/tflite_web_api_client.d.ts
deleted file mode 100644
index fe7540b39..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_web_api_client.d.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {TFLiteWebAPIClient} from './types/tflite_web_api';
-
-/**
- * The main export for tflite_web_api.js types.
- *
- * The variable name needs to be "tfweb" so it can match the root namespace of
- * the exported components from the JS client.
- */
-export declare let tfweb: TFLiteWebAPIClient;
diff --git a/tfjs-master/tfjs-tflite/src/tflite_web_api_client.js b/tfjs-master/tfjs-tflite/src/tflite_web_api_client.js
deleted file mode 100644
index 908c2765c..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_web_api_client.js
+++ /dev/null
@@ -1,25 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// The '../wasm/tflite_web_api_client.js' file was generated by JS closure
-// compiler. It was not "module friendly" so we cannot import it directly in
-// typescript code. To workaround this issue, we import and re-export it in this
-// file to essentially convert it to a valid CommonJS module, which can then be
-// imported correctly by typescript code.
-var tfliteWebApiClient = require('../wasm/tflite_web_api_client.js');
-
-exports.tfweb = tfliteWebApiClient.tfweb;
diff --git a/tfjs-master/tfjs-tflite/src/tflite_web_api_client_test.ts b/tfjs-master/tfjs-tflite/src/tflite_web_api_client_test.ts
deleted file mode 100644
index d938d6120..000000000
--- a/tfjs-master/tfjs-tflite/src/tflite_web_api_client_test.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfliteWebApiClient from './tflite_web_api_client';
-
-describe('TFLiteWebAPIClient', () => {
-  beforeEach(() => {});
-
-  // The tflite web API client is loaded through deps/tflite_web_api_client.js,
-  // and we define its type in src/tflite_web_api_client.d.ts. This test makes
-  // sure that all the types defined in tflite_web_api_client.d.ts actually
-  // exist in the loaded JS client.
-  it('should all its exported types defined', () => {
-    const tfliteWeb = tfliteWebApiClient.tfweb;
-    for (const field of Object.keys(tfliteWeb)) {
-      // tslint:disable-next-line: no-any
-      expect((tfliteWeb as any)[field]).toBeDefined();
-    }
-    expect(tfliteWeb['tflite_web_api']['setWasmPath']).toBeDefined();
-    expect(tfliteWeb['tflite_web_api']['getWasmFeatures']).toBeDefined();
-  });
-});
diff --git a/tfjs-master/tfjs-tflite/src/types/bert_nl_classifier.ts b/tfjs-master/tfjs-tflite/src/types/bert_nl_classifier.ts
deleted file mode 100644
index bb40d9241..000000000
--- a/tfjs-master/tfjs-tflite/src/types/bert_nl_classifier.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BaseTaskLibrary, Category} from './common';
-
-export declare interface BertNLClassifierOptions {
-  getMaxSeqLen(): number;
-  setMaxSeqLen(value: number): BertNLClassifierOptions;
-  hasMaxSeqLen(): boolean;
-}
-
-/** BertNLClassifier class type. */
-export declare interface BertNLClassifierClass {
-  /**
-   * The factory function to create a NLClassifier instance.
-   *
-   * @param model The path to load the TFLite model from, or the model content
-   *     in memory.
-   * @param options Available options.
-   */
-  create(model: string|ArrayBuffer, options: BertNLClassifierOptions):
-      Promise<BertNLClassifier>;
-}
-
-/** The main BertNLClassifier class instance. */
-export declare class BertNLClassifier extends BaseTaskLibrary {
-  /** Performs classification on a string input, returns classified results. */
-  classify(input: string): Category[]|undefined;
-}
diff --git a/tfjs-master/tfjs-tflite/src/types/bert_qa.ts b/tfjs-master/tfjs-tflite/src/types/bert_qa.ts
deleted file mode 100644
index 3fbf9125c..000000000
--- a/tfjs-master/tfjs-tflite/src/types/bert_qa.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BaseTaskLibrary} from './common';
-
-/** A single answer. */
-export declare interface QaAnswer {
-  text: string;
-  pos: Pos;
-}
-
-/** Answer position. */
-export declare interface Pos {
-  start: number;
-  end: number;
-  logit: number;
-}
-
-/** BertQuestionAnswerer class type. */
-export declare interface BertQuestionAnswererClass {
-  /**
-   * The factory function to create an ImageClassifier instance.
-   *
-   * @param model The path to load the TFLite model from, or the model content
-   *     in memory.
-   */
-  create(model: string|ArrayBuffer): Promise<BertQuestionAnswerer>;
-}
-
-/** The main BertQuestionAnswerer class instance. */
-export declare class BertQuestionAnswerer extends BaseTaskLibrary {
-  /** Answers question based on the context. */
-  answer(context: string, question: string): QaAnswer[]|undefined;
-}
diff --git a/tfjs-master/tfjs-tflite/src/types/common.ts b/tfjs-master/tfjs-tflite/src/types/common.ts
deleted file mode 100644
index 2df7edbf4..000000000
--- a/tfjs-master/tfjs-tflite/src/types/common.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/** Category proto instance used in NLP tasks. */
-export declare interface Category {
-  score: number;
-  className: string;
-}
-
-/** Class proto instance used in vision tasks. */
-export declare interface Class {
-  getClassName(): string;
-  hasClassName(): boolean;
-
-  getDisplayName(): string;
-  hasDisplayName(): boolean;
-
-  getIndex(): number;
-  hasIndex(): boolean;
-
-  getScore(): number;
-  hasScore(): boolean;
-}
-
-/** Stores the status of WASM features that user's browser supports. */
-export declare interface WasmFeatures {
-  simd: boolean;
-  multiThreading: boolean;
-}
-
-/** Base class for task libraries. */
-export declare class BaseTaskLibrary {
-  /** Cleans up resources when the instance is no longer needed. */
-  cleanUp(): void;
-}
diff --git a/tfjs-master/tfjs-tflite/src/types/image_classifier.ts b/tfjs-master/tfjs-tflite/src/types/image_classifier.ts
deleted file mode 100644
index 5ccfc774c..000000000
--- a/tfjs-master/tfjs-tflite/src/types/image_classifier.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BaseTaskLibrary, Class} from './common';
-
-/** ImageClassifierOptions proto instance. */
-export declare interface ImageClassifierOptions {
-  getMaxResults(): number;
-  setMaxResults(value: number): ImageClassifierOptions;
-  hasMaxResults(): boolean;
-
-  getScoreThreshold(): number;
-  setScoreThreshold(value: number): ImageClassifierOptions;
-  hasScoreThreshold(): boolean;
-
-  getNumThreads(): number;
-  setNumThreads(value: number): ImageClassifierOptions;
-  hasNumThreads(): boolean;
-
-  // TODO: add more fields as needed.
-}
-
-/** ClassificationResult proto instance. */
-export declare interface ClassificationResult {
-  getClassificationsList(): Classification[];
-}
-
-/** Classification proto instance. */
-export declare interface Classification {
-  getClassesList(): Class[];
-}
-
-/** ImageClassifier class type. */
-export declare interface ImageClassifierClass {
-  /**
-   * The factory function to create an ImageClassifier instance.
-   *
-   * @param model The path to load the TFLite model from, or the model content
-   *     in memory.
-   * @param options Available options.
-   */
-  create(model: string|ArrayBuffer, options?: ImageClassifierOptions):
-      Promise<ImageClassifier>;
-}
-
-/** The main ImageClassifier class interface. */
-export declare class ImageClassifier extends BaseTaskLibrary {
-  /** Performs classification on the given image-like element. */
-  classify(input: ImageData|HTMLImageElement|HTMLCanvasElement|
-           HTMLVideoElement): ClassificationResult|undefined;
-}
diff --git a/tfjs-master/tfjs-tflite/src/types/image_segmenter.ts b/tfjs-master/tfjs-tflite/src/types/image_segmenter.ts
deleted file mode 100644
index c419ae2b1..000000000
--- a/tfjs-master/tfjs-tflite/src/types/image_segmenter.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BaseTaskLibrary} from './common';
-
-/** Different output types. */
-export declare enum OutputType {
-  CATEGORY_MASK = 1.0,
-  CONFIDENCE_MASK = 2.0,
-  UNSPECIFIED = 0.0,
-}
-
-/** ImageSegmenterOptions proto instance. */
-export declare interface ImageSegmenterOptions {
-  getOutputType(): OutputType;
-  setOutputType(value: OutputType): ImageSegmenterOptions;
-  hasOutputType(): boolean;
-
-  getNumThreads(): number;
-  setNumThreads(value: number): ImageSegmenterOptions;
-  hasNumThreads(): boolean;
-
-  // TODO: add more fields as needed.
-}
-
-/** SegmentationResult proto instance.  */
-export declare interface SegmentationResult {
-  getSegmentationList(): Segmentation[];
-}
-
-/** Segmentation proto instance.  */
-export declare interface Segmentation {
-  getHeight(): number;
-  getWidth(): number;
-  getCategoryMask_asU8(): Uint8Array;
-  getColoredLabelsList(): ColoredLabel[];
-
-  // TODO: add more fields as needed.
-}
-
-/** ColoredLabel proto instance.  */
-export declare interface ColoredLabel {
-  getR(): number;
-  getG(): number;
-  getB(): number;
-  getClassName(): string;
-  getDisplayName(): string;
-
-  // TODO: add more fields as needed.
-}
-
-/** ImageSegmenter class type. */
-export declare interface ImageSegmenterClass {
-  /**
-   * The factory function to create an ImageSegmenter instance.
-   *
-   * @param model The path to load the TFLite model from, or the model content
-   *     in memory.
-   * @param options Available options.
-   */
-  create(model: string|ArrayBuffer, options?: ImageSegmenterOptions):
-      Promise<ImageSegmenter>;
-}
-
-export declare class ImageSegmenter extends BaseTaskLibrary {
-  /** Performs segmentation on the given image-like element. */
-  segment(input: ImageData|HTMLImageElement|HTMLCanvasElement|
-          HTMLVideoElement): SegmentationResult|undefined;
-}
diff --git a/tfjs-master/tfjs-tflite/src/types/nl_classifier.ts b/tfjs-master/tfjs-tflite/src/types/nl_classifier.ts
deleted file mode 100644
index cc8279944..000000000
--- a/tfjs-master/tfjs-tflite/src/types/nl_classifier.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BaseTaskLibrary, Category} from './common';
-
-/** Classification options to identify input and ouptut tensors of the model. */
-export declare interface NLClassifierOptions {
-  inputTensorIndex: number;
-  outputScoreTensorIndex: number;
-  outputLabelTensorIndex: number;
-  inputTensorName: string;
-  outputScoreTensorName: string;
-  outputLabelTensorName: string;
-}
-
-/** NLClassifier class type. */
-export declare interface NLClassifierClass {
-  /**
-   * The factory function to create a NLClassifier instance.
-   *
-   * @param model The path to load the TFLite model from, or the model content
-   *     in memory.
-   * @param options Available options.
-   */
-  create(model: string|ArrayBuffer, options: NLClassifierOptions):
-      Promise<NLClassifier>;
-}
-
-/** The main NLClassifier class interface. */
-export declare interface NLClassifier extends BaseTaskLibrary {
-  /** Performs classification on a string input, returns classified results. */
-  classify(input: string): Category[]|undefined;
-}
diff --git a/tfjs-master/tfjs-tflite/src/types/object_detector.ts b/tfjs-master/tfjs-tflite/src/types/object_detector.ts
deleted file mode 100644
index a7bcd4f49..000000000
--- a/tfjs-master/tfjs-tflite/src/types/object_detector.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BaseTaskLibrary, Class} from './common';
-
-/** ObjectDetectorOptions proto instance. */
-export declare interface ObjectDetectorOptions {
-  getMaxResults(): number;
-  setMaxResults(value: number): ObjectDetectorOptions;
-  hasMaxResults(): boolean;
-
-  getScoreThreshold(): number;
-  setScoreThreshold(value: number): ObjectDetectorOptions;
-  hasScoreThreshold(): boolean;
-
-  getNumThreads(): number;
-  setNumThreads(value: number): ObjectDetectorOptions;
-  hasNumThreads(): boolean;
-
-  // TODO: add more fields as needed.
-}
-
-/** DetectionResult proto instance. */
-export declare interface DetectionResult {
-  getDetectionsList(): Detection[];
-}
-
-/** Detection proto instance. */
-export declare interface Detection {
-  getBoundingBox(): BoundingBox|null;
-  getClassesList(): Class[];
-}
-
-/** BoundingBox proto instance. */
-export declare interface BoundingBox {
-  getHeight(): number;
-  getOriginX(): number;
-  getOriginY(): number;
-  getWidth(): number;
-}
-
-/** ObjectDetector class type. */
-export declare interface ObjectDetectorClass {
-  /**
-   * The factory function to create an ImageClassifier instance.
-   *
-   * @param model The path to load the TFLite model from, or the model content
-   *     in memory.
-   * @param options Available options.
-   */
-  create(model: string|ArrayBuffer, options?: ObjectDetectorOptions):
-      Promise<ObjectDetector>;
-}
-
-/** The main ObjectDetector class interface. */
-export declare class ObjectDetector extends BaseTaskLibrary {
-  /** Performs detection on the given image-like element. */
-  detect(input: ImageData|HTMLImageElement|HTMLCanvasElement|
-         HTMLVideoElement): DetectionResult|undefined;
-}
diff --git a/tfjs-master/tfjs-tflite/src/types/tflite_web_api.ts b/tfjs-master/tfjs-tflite/src/types/tflite_web_api.ts
deleted file mode 100644
index 4800de77b..000000000
--- a/tfjs-master/tfjs-tflite/src/types/tflite_web_api.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BertNLClassifierClass, BertNLClassifierOptions} from './bert_nl_classifier';
-import {BertQuestionAnswererClass} from './bert_qa';
-import {WasmFeatures} from './common';
-import {ClassificationResult, ImageClassifierClass, ImageClassifierOptions} from './image_classifier';
-import {ImageSegmenterClass, ImageSegmenterOptions, SegmentationResult} from './image_segmenter';
-import {NLClassifierClass} from './nl_classifier';
-import {DetectionResult, ObjectDetectorClass, ObjectDetectorOptions} from './object_detector';
-import {TFLiteWebModelRunnerClass} from './tflite_web_model_runner';
-
-export declare interface TFLiteWebAPIClient {
-  tflite_web_api: {
-    /**
-     * Sets the path to load all the WASM module files from.
-     *
-     * TFLite web API will automatically load WASM module with the best
-     * performance based on whether the current browser supports WebAssembly
-     * SIMD and multi-threading.
-     *
-     * @param path The path to load WASM module files from.
-     *     For relative path, use :
-     *         'relative/path/' (relative to current url path) or
-     *         '/relative/path' (relative to the domain root).
-     *     For absolute path, use https://some-server.com/absolute/path/.
-     */
-    setWasmPath(path: string): void;
-
-    /**
-     * Gets the WASM features supported by user's browser.
-     */
-    getWasmFeatures(): Promise<WasmFeatures>;
-  };
-
-  // Generic TFLite model runner.
-  TFLiteWebModelRunner: TFLiteWebModelRunnerClass;
-
-  // NLClassifier.
-  NLClassifier: NLClassifierClass;
-
-  // BertQuestionAnswerer.
-  BertQuestionAnswerer: BertQuestionAnswererClass;
-
-  // BertNLClassifier.
-  BertNLClassifier: BertNLClassifierClass;
-  BertNLClassifierOptions: new() => BertNLClassifierOptions;
-
-  // ImageClassifier
-  ImageClassifier: ImageClassifierClass;
-  ImageClassifierOptions: new() => ImageClassifierOptions;
-  ClassificationResult: new() => ClassificationResult;
-
-  // ObjectDetector
-  ObjectDetector: ObjectDetectorClass;
-  ObjectDetectorOptions: new() => ObjectDetectorOptions;
-  DetectionResult: new() => DetectionResult;
-
-  // ImageSegmenter
-  ImageSegmenter: ImageSegmenterClass;
-  ImageSegmenterOptions: new() => ImageSegmenterOptions;
-  SegmentationResult: new() => SegmentationResult;
-}
diff --git a/tfjs-master/tfjs-tflite/src/types/tflite_web_model_runner.ts b/tfjs-master/tfjs-tflite/src/types/tflite_web_model_runner.ts
deleted file mode 100644
index 8060973d7..000000000
--- a/tfjs-master/tfjs-tflite/src/types/tflite_web_model_runner.ts
+++ /dev/null
@@ -1,127 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {BaseTaskLibrary} from './common';
-
-/** TFLiteWebModelRunner class type. */
-export declare interface TFLiteWebModelRunnerClass {
-  /**
-   * The factory function to create a TFLiteWebModelRunner instance.
-   *
-   * @param model The path to load the TFLite model from, or the model content
-   *     in memory.
-   * @param options Available options.
-   */
-  create(model: string|ArrayBuffer, options: TFLiteWebModelRunnerOptions):
-      Promise<TFLiteWebModelRunner>;
-}
-
-/**
- * The main TFLiteWebModelRunner class interface.
- *
- * It is a wrapper around TFLite Interpreter. See
- * https://www.tensorflow.org/lite/guide/inference for more info about related
- * concepts.
- */
-export declare interface TFLiteWebModelRunner extends BaseTaskLibrary {
-  /** Gets model inputs. */
-  getInputs(): TFLiteWebModelRunnerTensorInfo[];
-
-  /** Gets model outputs. */
-  getOutputs(): TFLiteWebModelRunnerTensorInfo[];
-
-  /**
-   * Run inference.
-   *
-   * @return Whether the inference is successful or not.
-   */
-  infer(): boolean;
-
-  /**
-   * Gets per-node profiling results.
-   *
-   * This is only useful when TFLiteWebModelRunnerOptions.enableProfiling is
-   * set to true.
-   */
-  getProfilingResults(): ProfileItem[];
-
-  /**
-   * Gets the profiling summary.
-   *
-   * This is only useful when TFLiteWebModelRunnerOptions.enableProfiling is
-   * set to true.
-   */
-  getProfilingSummary(): string;
-}
-
-export declare interface ProfileItem {
-  /** The type of the node, e.g. "CONV_2D". */
-  nodeType: string;
-  /** The name of the node, e.g. "MobilenetV1/MobilenetV1/Conv2d_0/Relu6". */
-  nodeName: string;
-  /** The execution time (in ms) of the node. */
-  nodeExecMs: number;
-}
-
-/** Options for TFLiteWebModelRunner. */
-export declare interface TFLiteWebModelRunnerOptions {
-  /**
-   * Number of threads to use when running inference.
-   *
-   * Default to number of physical CPU cores, or -1 if WASM multi-threading is
-   * not supported by user's browser.
-   */
-  numThreads?: number;
-  /**
-   * Whether to enable profiling.
-   *
-   * Default to false. After it is enabled, the profiling results can be
-   * retrieved by calling TFLiteWebModelRunner.getProfilingResults or
-   * TFLiteWebModelRunner.getProfilingSummary. See their comments for more
-   * details.
-   */
-  enableProfiling?: boolean;
-  /**
-   * Maximum nmber of entries that the profiler can keep.
-   *
-   * Default to 1024.
-   */
-  maxProfilingBufferEntries?: number;
-}
-
-/** Types of TFLite tensor data. */
-export type TFLiteDataType =
-    'int8'|'uint8'|'bool'|'int16'|'int32'|'uint32'|'float32'|'float64';
-
-/** Stores metadata for a TFLite tensor. */
-export declare interface TFLiteWebModelRunnerTensorInfo {
-  /** The id of the tensor (generated by TFLite runtime). */
-  id: number;
-
-  /** TFLite data type. */
-  dataType: TFLiteDataType;
-
-  /** The name of the TFLite tensor. */
-  name: string;
-
-  /** The shape of the tensor in string form, e.g. "2,3,5". */
-  shape: string;
-
-  /** Gets the direct access to the underlying buffer. */
-  data(): Int8Array|Uint8Array|Int16Array|Int32Array|Uint32Array|Float32Array
-      |Float64Array;
-}
diff --git a/tfjs-master/tfjs-tflite/src/worker_test.ts b/tfjs-master/tfjs-tflite/src/worker_test.ts
deleted file mode 100644
index 01ef039ec..000000000
--- a/tfjs-master/tfjs-tflite/src/worker_test.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
-
-const str2workerURL = (str: string): string => {
-  const blob =
-      new Blob([str], {type: 'application/javascript'});
-  return URL.createObjectURL(blob);
-};
-
-// The source code of a web worker.
-const workerTest = `
-importScripts('${location.origin}/base/tfjs/tfjs-core/tf-core.min.js');
-importScripts('${location.origin}'
-  + '/base/tfjs/tfjs-backend-cpu/tf-backend-cpu.min.js');
-// Import order matters. TFLite must be imported after tfjs core.
-importScripts('${location.origin}/base/tfjs/tfjs-tflite/tf-tflite.min.js');
-
-// Setting wasm path is required. It can be set to CDN if needed,
-// but that's not a good idea for a test.
-tflite.setWasmPath('${location.origin}/base/tfjs/tfjs-tflite/wasm/');
-async function main() {
-  // This is a test model that adds two tensors of shape [1, 4].
-  const model = await tflite.loadTFLiteModel('${location.origin}/base/tfjs/tfjs-tflite/test_files/add4.tflite');
-
-  const a = tf.tensor2d([[1, 2, 3, 4]]);
-  const b = tf.tensor2d([[5, 6, 7, 8]]);
-  const output = model.predict([a, b]);
-
-  self.postMessage({data: output.dataSync()});
-}
-
-main();
-`;
-
-describe('tflite in worker', () => {
-  it('runs a model', (done) => {
-    const worker = new Worker(str2workerURL(workerTest));
-    worker.onmessage = (msg) => {
-      const data = msg.data.data;
-      expect([...data]).toEqual([6, 8, 10, 12]);
-      done();
-    };
-  }, 15_000);
-});
diff --git a/tfjs-master/tfjs-tflite/test_files/BUILD.bazel b/tfjs-master/tfjs-tflite/test_files/BUILD.bazel
deleted file mode 100644
index ba0b9d6c0..000000000
--- a/tfjs-master/tfjs-tflite/test_files/BUILD.bazel
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright 2022 Google LLC.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files([
-    # add4.tflite adds two tensors of shape [1,4]
-    "add4.tflite",
-])
diff --git a/tfjs-master/tfjs-tflite/test_files/add4.tflite b/tfjs-master/tfjs-tflite/test_files/add4.tflite
deleted file mode 100644
index 6ee2860e1..000000000
Binary files a/tfjs-master/tfjs-tflite/test_files/add4.tflite and /dev/null differ
diff --git a/tfjs-master/tfjs-tflite/tflite_repositories.bzl b/tfjs-master/tfjs-tflite/tflite_repositories.bzl
deleted file mode 100644
index 1daddf70a..000000000
--- a/tfjs-master/tfjs-tflite/tflite_repositories.bzl
+++ /dev/null
@@ -1,57 +0,0 @@
-# Copyright 2022 Google LLC.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
-
-def tflite_repositories(version = "0.0.9"):
-    versions = {
-        "0.0.3": "918dc8c5008cc57907315f78572b233abafefaf668c34e20596cedf96decf381",
-        "0.0.4": "0d6f487cf7a5afd576417381130a0d9234f1d223012d75fe58eafe98177737b2",
-        "0.0.5": "0a5fbba206016265d9422b0c9d0514591b01306e392c4f9371cf1d4352d1161d",
-        "0.0.6": "a5f197e8c8c03bbf93659c9f2d48ddb8226a6a67795677dbdb82e2a9fde0b47c",
-        "0.0.7": "ffedda0e96485adcb0fde272320e2cc121202fdbaa403dc39eaea8c1e6fac84e",
-        "0.0.8": "cb3c4ee99aacaba325dedca837c5a83828aeeb2adbcae43179a24a3e819cbcc7",
-        "0.0.9": "25819b6841dc460e43f4e43746eae3b89d6a372615cfff726495d3910fce9178",
-    }
-
-    if not version in versions:
-        versions_string = ", ".join(versions.keys())
-        fail("Unsupported tflite web api version %s. Supported versions are %s." %
-             (version, versions_string))
-
-    http_archive(
-        name = "tflite_wasm_files",
-        sha256 = versions[version],
-        url = "https://storage.googleapis.com/tfweb/%s/tflite_web_api.zip" % version,
-        build_file_content = """
-filegroup(
-    name = "wasm_files",
-    visibility = ["//visibility:public"],
-    srcs = [
-        "tflite_web_api_cc.js",
-        "tflite_web_api_cc.wasm",
-        "tflite_web_api_cc_simd.js",
-        "tflite_web_api_cc_simd_threaded.js",
-        "tflite_web_api_cc_simd_threaded.wasm",
-        "tflite_web_api_cc_simd_threaded.worker.js",
-        "tflite_web_api_cc_simd.wasm",
-        "tflite_web_api_cc_threaded.js",
-        "tflite_web_api_cc_threaded.wasm",
-        "tflite_web_api_cc_threaded.worker.js",
-        "tflite_web_api_client.js",
-    ],
-)
-""",
-    )
diff --git a/tfjs-master/tfjs-tflite/tsconfig.json b/tfjs-master/tfjs-tflite/tsconfig.json
deleted file mode 100644
index ecb732e21..000000000
--- a/tfjs-master/tfjs-tflite/tsconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-tflite/wasm/BUILD.bazel b/tfjs-master/tfjs-tflite/wasm/BUILD.bazel
deleted file mode 100644
index 0a533dc90..000000000
--- a/tfjs-master/tfjs-tflite/wasm/BUILD.bazel
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright 2022 Google LLC.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("//tools:copy_to_dist.bzl", "copy_to_dist")
-
-package(default_visibility = ["//visibility:public"])
-
-# Copy wasm related files to dist/.
-copy_to_dist(
-    name = "wasm",
-    srcs = [
-        "@tflite_wasm_files//:wasm_files",
-    ],
-    dest_dir = ".",
-    global_root = "../tflite_wasm_files",  # See tflite_repositories.bzl
-)
diff --git a/tfjs-master/tfjs-tflite/yarn.lock b/tfjs-master/tfjs-tflite/yarn.lock
deleted file mode 100644
index f3e53baa6..000000000
--- a/tfjs-master/tfjs-tflite/yarn.lock
+++ /dev/null
@@ -1,13 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@bazel/bazelisk@^1.12.0":
-  version "1.12.0"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.12.0.tgz#f08aebbf4afcb12684422450b0845dd6ef5cfe50"
-  integrity sha512-7oQusq1e4AIyFgotxVV7Pc40Et0QyvoVjujL+7/qV5Vrbfh0Nj3CfqSgl63weEyI4r0+K6RlGVsjfRuBi05p5w==
-
-minimist@1.2.6:
-  version "1.2.6"
-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.6.tgz#8637a5b759ea0d6e98702cfb3a9283323c93af44"
-  integrity sha512-Jsjnk4bw3YJqYzbdyBiNsPWHPfO++UGG749Cxs6peCu5Xg4nrena6OVxOYxrQTqww0Jmwt+Ref8rggumkTLz9Q==
diff --git a/tfjs-master/tfjs-vis/.npmignore b/tfjs-master/tfjs-vis/.npmignore
deleted file mode 100644
index f30cfad04..000000000
--- a/tfjs-master/tfjs-vis/.npmignore
+++ /dev/null
@@ -1,22 +0,0 @@
-.vscode/
-.rpt2_cache/
-demos/
-scripts/
-models/
-coverage/
-package/
-**/node_modules/
-karma.conf.js
-*.tgz
-*.log
-cloudbuild.yml
-CONTRIBUTING.md
-tslint.json
-yarn.lock
-DEVELOPMENT.md
-ISSUE_TEMPLATE.md
-PULL_REQUEST_TEMPLATE.md
-webpack.config.js
-tsconfig.json
-docs/
-.yalc/
diff --git a/tfjs-master/tfjs-vis/CHANGELOG.md b/tfjs-master/tfjs-vis/CHANGELOG.md
deleted file mode 100644
index 1b6c3cb6c..000000000
--- a/tfjs-master/tfjs-vis/CHANGELOG.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# Changelog
-
-## We are moving release notes to [GitHub releases feature](https://github.com/tensorflow/tfjs/releases)
-
-This file is now only for historical purposes. For an example see https://github.com/tensorflow/tfjs/releases/tag/tfjs-vis-v1.4.3
-
-## [1.0.0] 2019-03-06
-Major version upgrade including a number of **breaking changes**.
-
-### Changed
-All `render.*` functions now take a surface as their first parameter. This unifies the convention used in these functions and `show.*` functions.
-
-In `heatmap` and `confusionMatrix` params, `xLabels/yLabels` has been renamed to `xtickLabels/yTickLabels` to more clearly distinguish it from the axis label (`xLabel/yLabel`).
-
-### Removed
-`show.confusionMatrix` has been removed in favor of using `render.confusionMatrix`
-
-## [0.5.0] 2018-1-31
-### Added
-Support for rendering heatmaps with `render.heatmap`.
-
-Sourcemaps now include sourcesContent to improve editor experience, particularly for typescript users.
-
-## [0.4.0] 2018-12-07
-
-### Changed
-`confusionMatrix` now shades the diagonal by default. The chart also has improved
-contrast between the text and the chart cells.
-
-bugfixes for `metrics.confusionMatrix` on Safari
-
-Improvements to chart rendering options to help prevent situations where the container
-div grows on each render if dimensions were not specified in code or constrained by css.
-
-
-## [0.3.0] 2018-11-06
-### Added
-`fontSize` can now be passed into render.* methods
-`zoomToFit` and `yAxisDomain` are new options that linecharts and scatterplots
-take to allow finer control over the display of the yAxis
-`xAxisDomain` option added to scatterplots.
-
-### Changed
-`show.history` and `show.fitCallbacks` now take an optional `opts` parameter.
-These allow passing configuration to the underlying charts as well as overriding
-which callbacks get generated by `show.fitCallbacks`.
-
-`show.history` and `show.fitCallbacks` will now automatically group a metric with
-its corresponding validation metric and display them on the same chart. For example
-if you have `['acc', 'val_acc', 'loss', 'val_loss']` as your metrics, these will
-be rendered on two charts, one for the loss metrics and one for the accuracy metrics.
diff --git a/tfjs-master/tfjs-vis/CONTRIBUTING.md b/tfjs-master/tfjs-vis/CONTRIBUTING.md
deleted file mode 100644
index f51a30e44..000000000
--- a/tfjs-master/tfjs-vis/CONTRIBUTING.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# How to Contribute
-
-We are happy to accept patches and contributions to this project. There are
-just a few small guidelines you need to follow.
-
-## Contributor License Agreement
-
-Contributions to this project must be accompanied by a Contributor License
-Agreement. You (or your employer) retain the copyright to your contribution,
-this simply gives us permission to use and redistribute your contributions as
-part of the project. Head over to <https://cla.developers.google.com/> to see
-your current agreements on file or to sign a new one.
-
-You generally only need to submit a CLA once, so if you've already submitted one
-(even if it was for a different project), you probably don't need to do it
-again.
-
-## Adding functionality
-
-One way to ensure that your PR will be accepted is to add functionality that
-has been requested in Github issues. If there is something you think is
-important and we're missing it but does not show up in Github issues, it would
-be good to file an issue there first so we can have the discussion before
-sending us a PR.
-
-## Code reviews
-
-All submissions, including submissions by project members, require review. We
-use GitHub pull requests for this purpose. Consult
-[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
-information on using pull requests.
-
-We require unit tests for most code, instructions for running our unit test
-suites are in the documentation.
diff --git a/tfjs-master/tfjs-vis/LICENSE b/tfjs-master/tfjs-vis/LICENSE
deleted file mode 100644
index d64569567..000000000
--- a/tfjs-master/tfjs-vis/LICENSE
+++ /dev/null
@@ -1,202 +0,0 @@
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
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-      "Derivative Works" shall mean any work, whether in Source or Object
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-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
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-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
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-   7. Disclaimer of Warranty. Unless required by applicable law or
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-      Contributor provides its Contributions) on an "AS IS" BASIS,
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-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
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-      negligent acts) or agreed to in writing, shall any Contributor be
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-      To apply the Apache License to your work, attach the following
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-   Copyright [yyyy] [name of copyright owner]
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-       http://www.apache.org/licenses/LICENSE-2.0
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diff --git a/tfjs-master/tfjs-vis/README.md b/tfjs-master/tfjs-vis/README.md
deleted file mode 100644
index a62baae55..000000000
--- a/tfjs-master/tfjs-vis/README.md
+++ /dev/null
@@ -1,118 +0,0 @@
-# tfjs-vis
-
-__tfjs-vis__ is a small library for _in browser_ visualization intended for use
-with TensorFlow.js.
-
-Its main features are:
-
-* A set of visualizations useful for visualizing model behaviour
-* A set of high level functions for visualizing objects specific to TensorFlow.js
-* A way to organize visualizations (the visor) of model behaviour that won't interfere with your web application
-
-The library also aims to be flexible and make it easy for you to incorporate
-custom visualizations using tools of your choosing, such as d3, Chart.js or plotly.js.
-
-## Example Screenshots
-
-### Training Metrics
-
-![Training metrics (loss and accuracy) for a model](https://storage.googleapis.com/tfjs-assets/tfjs-vis/tfjs-vis-training.png)
-
-### Model Evauation
-
-![Dataset accuracy metrics in a table and confusion matrix visualization](https://storage.googleapis.com/tfjs-assets/tfjs-vis/tfjs-vis-evaluation.png)
-
-### Model Internals
-
-![Model summary table and histogram of conv2d weights](https://storage.googleapis.com/tfjs-assets/tfjs-vis/tfjs-vis-model-details.png)
-
-### Activations and custom visualizations
-
-![visualization of dataset activations in a conv2d layer and a dense layer](https://storage.googleapis.com/tfjs-assets/tfjs-vis/tfjs-vis-model-internals.png)
-
-
-## Demos
-
-- [Visualizing Training with tfjs-vis](https://storage.googleapis.com/tfjs-vis/mnist/dist/index.html)
-- [Looking inside a digit recognizer](https://storage.googleapis.com/tfjs-vis/mnist_internals/dist/index.html)
-
-## Installation
-
-You can install this using npm with
-
-```
-npm install @tensorflow/tfjs-vis
-```
-
-or using yarn with
-
-```
-yarn add @tensorflow/tfjs-vis
-```
-
-You can also load it via script tag using the following tag, however you need
-to have TensorFlow.js also loaded on the page to work. Including both is shown
-below.
-
-```
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"> </script>
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis"></script>
-```
-
-## API
-
-See https://js.tensorflow.org/api_vis/latest/ for interactive API documentation.
-
-## Sample Usage
-
-```js
-const data = [
-  { index: 0, value: 50 },
-  { index: 1, value: 100 },
-  { index: 2, value: 150 },
-];
-
-// Get a surface
-const surface = tfvis.visor().surface({ name: 'Barchart', tab: 'Charts' });
-
-// Render a barchart on that surface
-tfvis.render.barchart(surface, data, {});
-```
-
-## Issues
-
-Found a bug or have a feature request? Please file an [issue](https://github.com/tensorflow/tfjs/issues/new) on the main [TensorFlow.js repository](https://github.com/tensorflow/tfjs/issues)
-
-## Building from source
-
-To build the library, you need to have node.js installed. We use `yarn`
-instead of `npm` but you can use either.
-
-First install dependencies with
-
-```
-yarn
-```
-
-or
-
-```
-npm install
-```
-
-Then do a build with
-
-```
-yarn build
-```
-
-or
-
-```
-npm run build
-```
-
-This should produce a `tfjs-vis.umd.min.js` file in the `dist` folder that you can
-use.
-
-
diff --git a/tfjs-master/tfjs-vis/cloudbuild.yml b/tfjs-master/tfjs-vis/cloudbuild.yml
deleted file mode 100644
index 0fcd3f841..000000000
--- a/tfjs-master/tfjs-vis/cloudbuild.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-
-steps:
-# Install common dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'yarn-common'
-  entrypoint: 'yarn'
-  args: ['install']
-
-# Install tfjs-vis dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-node'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-  waitFor: ['yarn-common']
-
-# Run vis tests.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs-vis'
-  entrypoint: 'yarn'
-  id: 'test-vis'
-  args: ['test-ci']
-  env: ['BROWSERSTACK_USERNAME=deeplearnjs1']
-  secretEnv: ['BROWSERSTACK_KEY']
-  waitFor: ['yarn']
-
-secrets:
-- kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-  secretEnv:
-    BROWSERSTACK_KEY: CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-timeout: 1800s
-logsBucket: 'gs://tfjs-build-logs'
-options:
-  logStreamingOption: 'STREAM_ON'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/tfjs-vis/demos/api/README.md b/tfjs-master/tfjs-vis/demos/api/README.md
deleted file mode 100644
index 78a0cc780..000000000
--- a/tfjs-master/tfjs-vis/demos/api/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# tfjs-vis renderers demo
-
-A page showing examples of all the tfjs-vis render.* functions.
-
-See a live version of this [here](https://storage.googleapis.com/tfjs-vis/api/dist/index.html).
-
-To run this locally:
-- run `yarn build-npm` in the `tfjs-vis` folder
-- run `yarn watch` from the `demos` folder
diff --git a/tfjs-master/tfjs-vis/demos/api/debug.html b/tfjs-master/tfjs-vis/demos/api/debug.html
deleted file mode 100644
index ab5f9ee95..000000000
--- a/tfjs-master/tfjs-vis/demos/api/debug.html
+++ /dev/null
@@ -1,106 +0,0 @@
-<!DOCTYPE html>
-<!--
-  @license
-  Copyright 2020 Google LLC. All Rights Reserved.
-  Licensed under the Apache License, Version 2.0 (the "License");
-  you may not use this file except in compliance with the License.
-  You may obtain a copy of the License at
-  http://www.apache.org/licenses/LICENSE-2.0
-  Unless required by applicable law or agreed to in writing, software
-  distributed under the License is distributed on an "AS IS" BASIS,
-  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  See the License for the specific language governing permissions and
-  limitations under the License.
-  =============================================================================
-  -->
-<html>
-
-<head>
-  <meta charset="utf-8">
-  <script src="./debug.js"></script>
-
-
-  <style>
-    article {
-      max-width: 960px;
-      font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
-      padding: 40px;
-    }
-
-    script.show-script {
-      display: block;
-      max-width: 720px;
-      background-color: floralwhite;
-      font-family: "Lucida Console", Monaco, "Courier New", Courier, monospace;
-      font-size: 12px;
-      margin-left: -40px;
-      white-space: pre;
-    }
-
-    article div {
-      margin-bottom: 25px;
-    }
-
-    h3 {
-      margin-top: 60px;
-    }
-  </style>
-</head>
-
-<body>
-  <article>
-    <h1>tfjs-vis debug page</h1>
-
-    <section>
-      <h2>Debug Area</h2>
-      <div id='debug-cont'></div>
-
-      <script class='show-script'>
-        {
-          const container = document.getElementById('debug-cont');
-
-          //
-          const rows = 50;
-          const cols = 50;
-          const values = [];
-          const tickLabels = [];
-          for (let i = 0; i < rows; i++) {
-            const row = []
-            tickLabels.push(`tick label ${i}`)
-            for (let j = 0; j < cols; j++) {
-              row.push(Math.round(Math.random() * 50));
-            }
-            values.push(row);
-          }
-          const data = { values, tickLabels };
-
-          const surface = tfvis.visor().surface({ name: 'Confusion Matrix', tab: 'Charts' });
-          tfvis.render.confusionMatrix(surface, data, {
-            width: 800,
-            height: 800,
-            xLabel: 'Howdy',
-            yLabel: 'There',
-            colorMap: ['#f7fbff', '#FF92c6'],
-            fontSize: 32,
-            showTextOverlay: false
-          }
-          );
-
-          tfvis.render.confusionMatrix(container, data,
-            {
-              width: 800,
-              height: 800,
-              xLabel: 'Howdy',
-              yLabel: 'There',
-              colorMap: ['#f7fbff', '#FF92c6'],
-              fontSize: 32,
-              showTextOverlay: false
-            }
-          );
-        }
-      </script>
-    </section>
-  </article>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/tfjs-master/tfjs-vis/demos/api/debug.js b/tfjs-master/tfjs-vis/demos/api/debug.js
deleted file mode 100644
index dae9e4eba..000000000
--- a/tfjs-master/tfjs-vis/demos/api/debug.js
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import * as tfvis from '../../src/index';
-window.tf = tf;
-window.tfvis = tfvis;
diff --git a/tfjs-master/tfjs-vis/demos/api/index.css b/tfjs-master/tfjs-vis/demos/api/index.css
deleted file mode 100644
index a806d1c06..000000000
--- a/tfjs-master/tfjs-vis/demos/api/index.css
+++ /dev/null
@@ -1,18 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/* Test CSS overrides here */
diff --git a/tfjs-master/tfjs-vis/demos/api/index.html b/tfjs-master/tfjs-vis/demos/api/index.html
deleted file mode 100644
index 28f6a4ba1..000000000
--- a/tfjs-master/tfjs-vis/demos/api/index.html
+++ /dev/null
@@ -1,880 +0,0 @@
-<!DOCTYPE html>
-<!--
-  @license
-  Copyright 2018 Google LLC. All Rights Reserved.
-  Licensed under the Apache License, Version 2.0 (the "License");
-  you may not use this file except in compliance with the License.
-  You may obtain a copy of the License at
-  http://www.apache.org/licenses/LICENSE-2.0
-  Unless required by applicable law or agreed to in writing, software
-  distributed under the License is distributed on an "AS IS" BASIS,
-  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  See the License for the specific language governing permissions and
-  limitations under the License.
-  =============================================================================
-  -->
-<html>
-
-<head>
-  <meta charset="utf-8">
-  <!-- <script src="../../dist/tfjs-vis.umd.min.js"></script> -->
-  <script type="module">
-    import * as tfvis from '@tensorflow/tfjs-vis'
-    import * as tf from '@tensorflow/tfjs'
-
-    window.tfvis = tfvis;
-    window.tf = tf;
-  </script>
-
-
-  <style>
-    article {
-      max-width: 960px;
-      font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
-      padding: 40px;
-    }
-
-    script.show-script {
-      display: block;
-      max-width: 720px;
-      background-color: floralwhite;
-      font-family: "Lucida Console", Monaco, "Courier New", Courier, monospace;
-      font-size: 12px;
-      margin-left: -40px;
-      white-space: pre;
-    }
-
-    article div {
-      margin-bottom: 25px;
-    }
-
-    h3 {
-      margin-top: 60px;
-    }
-  </style>
-</head>
-
-<body>
-  <article>
-    <h1>tfjs-vis kitchen sink</h1>
-
-    <section>
-      <p>
-        tfjs-vis is a small set of visualization utilities to make it easier to understand what is going on with your
-        tfjs models.
-        It is designed in a way to work along side regular web apps. This page is a testbed for the api.
-      </p>
-    </section>
-
-    <section>
-      <h2>Visor</h2>
-      <script class='show-script'>
-        {
-          visorInstance = tfvis.visor();
-          visorInstance.surface({ name: 'Surface 1' });
-          visorInstance.surface({ name: 'Surface 2' });
-
-          visorInstance.surface({ name: 'Surface 3', tab: 'Tab 2' });
-
-          visorInstance.surface({ name: 'Just a surface', tab: 'A really long tab name' });
-        }
-      </script>
-    </section>
-
-    <hr>
-
-    <section>
-      <h2>Render Functions</h2>
-
-      <h3>render.table(data, container, options)</h3>
-      <div id='table-cont'></div>
-      <script class='show-script'>
-        {
-          const headers = [
-            'Col 1',
-            'Col 2',
-            'Col 3',
-            ];
-          const values = [
-            [1, 2, 3],
-            ['4', '5', '6'],
-            ['strong>7</strong>', true, false],
-            ];
-
-          // Render to visor
-          const surface = { name: 'Table', tab: 'Charts' };
-          tfvis.render.table(surface, { headers, values });
-
-          // Render to page
-          const container = document.getElementById('table-cont');
-          tfvis.render.table(container, { headers, values });
-        }
-      </script>
-
-      <h3>render.barchart(data, container, options)</h3>
-      <div id='barchart-cont'></div>
-      <script class='show-script'>
-        {
-          const data = [
-            { index: 0, value: 50 },
-            { index: 1, value: 100 },
-            { index: 2, value: 150 },
-          ];
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Barchart', tab: 'Charts' });
-          tfvis.render.barchart(surface, data, {});
-
-          // Render to page
-          const container = document.getElementById('barchart-cont');
-          tfvis.render.barchart(container, data, {
-            xLabel: 'my x axis',
-            yLabel: 'values',
-            width: 500,
-            height: 250,
-            fontSize: 14
-          });
-        }
-      </script>
-
-      <h4>render.barchart with custom color</h3>
-      <div id='barchart-col-cont'></div>
-      <script class='show-script'>
-        {
-          const data = [
-            { index: 0, value: 50 },
-            { index: 1, value: 100 },
-            { index: 2, value: 150 },
-          ];
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Barchart Color', tab: 'Charts' });
-          tfvis.render.barchart(surface, data, {color: 'tomato',});
-
-          // Render to page
-          const container = document.getElementById('barchart-col-cont');
-          tfvis.render.barchart(container, data, {
-            xLabel: 'my x axis',
-            yLabel: 'values',
-            width: 500,
-            height: 250,
-            fontSize: 14,
-            color: 'tomato'
-          });
-        }
-      </script>
-
-      <h4>Individual bars can also be given custom colos</h3>
-      <div id='barchart-multicol-cont'></div>
-      <script class='show-script'>
-        {
-          const data = [
-            { index: 0, value: 50 },
-            { index: 1, value: 100 },
-            { index: 5, value: 150 },
-          ];
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Barchart Multi Color', tab: 'Charts' });
-
-          const opts = {
-            xLabel: 'my x axis',
-            yLabel: 'values',
-            width: 500,
-            height: 250,
-            fontSize: 14,
-            color: ['tomato', '#230000', 'purple']
-          }
-          tfvis.render.barchart(surface, data, opts);
-
-          // Render to page
-          const container = document.getElementById('barchart-multicol-cont');
-          tfvis.render.barchart(container, data, opts);
-        }
-      </script>
-
-      <h4>Re-rendering</h4>
-      <p>You can update a bar chart by calling
-        <code>render.barchart</code> again with the same
-        <code>container</code>.
-      </p>
-
-      <p>
-        This is also true for any render.* function, to re-render or update just call the function with new data and
-        the same container.
-      </p>
-
-      <div id='barchart-multi'></div>
-      <script class='show-script'>
-        async function sleep(ms) {
-          return new Promise(resolve => setTimeout(resolve, ms));
-        }
-
-        async function renderBarchartMultipleTimes(numRenders, numVals) {
-          const genData = function (numVals) {
-            return Array(numVals).fill(0).map((d, i) => {
-              return { index: i, value: (Math.random() * 50) + 50 };
-            });
-          };
-
-          console.time(`renderBarchart:${numVals}:${numRenders}`);
-          for (let i = 0; i < numRenders; i++) {
-            const data = genData(numVals);
-            const container = document.getElementById('barchart-multi');
-            await tfvis.render.barchart(container, data, {
-              xLabel: 'my x axis',
-              yLabel: 'values',
-              width: 800,
-              height: 250,
-            });
-            await sleep(20);
-          }
-          console.timeEnd(`renderBarchart:${numVals}:${numRenders}`);
-
-        }
-
-        const numRenders = 30;
-        const numVals = 20;
-        renderBarchartMultipleTimes(numRenders, numVals);
-      </script>
-
-      <hr>
-
-      <h3>render.histogram(data, container, opts)</h3>
-
-      <div id='histogram-cont'></div>
-
-      <script class='show-script'>
-        {
-          const data = Array(100).fill(0)
-            .map(x => Math.random() * 100 - (Math.random() * 50))
-
-          // Push some special values for the stats table.
-          data.push(Infinity);
-          data.push(NaN);
-          data.push(0);
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Histogram', tab: 'Charts' });
-          tfvis.render.histogram(surface, data);
-
-          // Render to page
-          const container = document.getElementById('histogram-cont');
-          tfvis.render.histogram(container, data);
-        }
-      </script>
-
-      <h4>render.histogram custom color</h4>
-
-      <div id='histogram-col-cont'></div>
-
-      <script class='show-script'>
-        {
-          const data = Array(100).fill(0)
-            .map(x => Math.random() * 100 - (Math.random() * 50))
-
-          // Push some special values for the stats table.
-          data.push(Infinity);
-          data.push(NaN);
-          data.push(0);
-
-          const opts = {color: 'tomato'};
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Histogram w Color', tab: 'Charts' });
-          tfvis.render.histogram(surface, data, opts);
-
-          // Render to page
-          const container = document.getElementById('histogram-col-cont');
-          tfvis.render.histogram(container, data, opts);
-        }
-      </script>
-
-      <h3>
-        re-render histogram
-      </h3>
-
-      <div id='histogram-multi'></div>
-      <script class='show-script'>
-        async function sleep(ms) {
-          return new Promise(resolve => setTimeout(resolve, ms));
-        }
-
-        async function renderHistogramMultipleTimes(numRenders, numVals) {
-          const genData = function (numVals) {
-            return Array(numVals).fill(0).map((d, i) => {
-              return (Math.random() * 50) + 50;
-            });
-          };
-
-          console.time(`renderHistogram:${numVals}:${numRenders}`);
-          for (let i = 0; i < numRenders; i++) {
-            const data = genData(numVals);
-            const container = document.getElementById('histogram-multi');
-            await tfvis.render.histogram(container, data, {
-              stats: false,
-            });
-
-            const surface = { name: 'Histogram', tab: 'Re-renders' };
-            await tfvis.render.histogram(surface, data, {
-              stats: false,
-            });
-            await sleep(20);
-          }
-          console.timeEnd(`renderHistogram:${numVals}:${numRenders}`);
-
-        }
-
-        renderHistogramMultipleTimes(30, 20);
-      </script>
-
-      <hr>
-
-      <h3>render.linechart(data, container, opts)</h3>
-
-      <div id='linechart-cont'></div>
-
-      <script class='show-script'>
-        {
-          const series1 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 50))
-            .map((y, x) => ({ x, y, }));
-
-          const series2 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 150))
-            .map((y, x) => ({ x, y, }));
-
-
-          const series = ['First', 'Second'];
-          const data = { values: [series1, series2], series }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Linechart', tab: 'Charts' });
-          tfvis.render.linechart(surface, data);
-
-          // Render to page
-          const container = document.getElementById('linechart-cont');
-          tfvis.render.linechart(container, data);
-        }
-      </script>
-
-      <h4>render.linechart supports custom colors for lines</h4>
-
-      <div id='linechart-colors-cont'></div>
-
-      <script class='show-script'>
-        {
-          const series1 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 50))
-            .map((y, x) => ({ x, y, }));
-
-          const series2 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 150))
-            .map((y, x) => ({ x, y, }));
-
-          const series3 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 150))
-            .map((y, x) => ({ x, y, }));
-
-
-          const series = ['First', 'Second', 'Third'];
-          const data = { values: [series1, series2, series3], series }
-          const opts = { seriesColors: ['tomato', '#230000', 'purple']};
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Linechart custom colors', tab: 'Charts' });
-          tfvis.render.linechart(surface, data, opts);
-
-          // Render to page
-          const container = document.getElementById('linechart-colors-cont');
-          tfvis.render.linechart(container, data, opts);
-        }
-      </script>
-
-      <h4>Zoom to fit linechart</h4>
-
-      <div id='zoomed-linechart-cont'></div>
-      <script class='show-script'>
-        {
-          const series1 = Array(100).fill(0)
-            .map(y => Math.random() * 100 + 50)
-            .map((y, x) => ({ x, y, }));
-
-          const data = { values: [series1] }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Zoomed Line Chart', tab: 'Charts' });
-          tfvis.render.linechart(surface, data, { zoomToFit: true });
-
-          // Render to page
-          const container = document.getElementById('zoomed-linechart-cont');
-          tfvis.render.linechart(container, data, { zoomToFit: true });
-        }
-      </script>
-
-      <h4>Truncated linechart: Custom y axis domain</h4>
-      <div id='truncated-linechart-cont'></div>
-
-      <script class='show-script'>
-        {
-          const series1 = Array(100).fill(0)
-            .map(y => Math.random() * 100 + 50)
-            .map((y, x) => ({ x, y, }));
-
-          const series2 = Array(100).fill(0)
-            .map(y => Math.random() * 100 + 50)
-            .map((y, x) => ({ x, y, }));
-
-          const data = { values: [series1, series2] }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'truncated Line Chart', tab: 'Charts' });
-          tfvis.render.linechart(surface, data, { yAxisDomain: [80, 120] });
-
-          // Render to page
-          const container = document.getElementById('truncated-linechart-cont');
-          tfvis.render.linechart(container, data, { yAxisDomain: [80, 120] });
-        }
-      </script>
-
-      <hr>
-
-      <h3>render.scatterplot(data, container, opts)</h3>
-
-      <div id='scatterplot-cont'></div>
-
-      <script class='show-script'>
-        {
-          const series1 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 50))
-            .map((y, x) => ({ x, y, }));
-
-          const series2 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 150))
-            .map((y, x) => ({ x, y, }));
-
-
-          const series = ['First', 'Second'];
-          const data = { values: [series1, series2], series }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Scatterplot', tab: 'Charts' });
-          tfvis.render.scatterplot(surface, data);
-
-          // Render to page
-          const container = document.getElementById('scatterplot-cont');
-          tfvis.render.scatterplot(container, data, { width: 500, height: 500 });
-        }
-      </script>
-
-
-      <h4>render.scatterplot supports custom colors for marks</h4>
-
-      <div id='scatterplot-colors-cont'></div>
-
-      <script class='show-script'>
-        {
-          const series1 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 50))
-            .map((y, x) => ({ x, y, }));
-
-          const series2 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 150))
-            .map((y, x) => ({ x, y, }));
-
-          const series3 = Array(100).fill(0)
-            .map(y => Math.random() * 100 - (Math.random() * 150))
-            .map((y, x) => ({ x, y, }));
-
-
-          const series = ['First', 'Second', 'Third'];
-          const data = { values: [series1, series2, series3], series }
-          const opts = {
-            seriesColors: ['tomato', '#230000', 'purple'],
-            width: 500, height: 500
-          };
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Scatterplot custom colors', tab: 'Charts' });
-          tfvis.render.scatterplot(surface, data, opts);
-
-          // Render to page
-          const container = document.getElementById('scatterplot-colors-cont');
-          tfvis.render.scatterplot(container, data, opts);
-        }
-      </script>
-
-
-      <h4>Zoom to fit scatter plot</h4>
-      <div id='zoomed-scatterplot-cont'></div>
-
-      <script class='show-script'>
-        {
-          const series1 = Array(100).fill(0)
-            .map(y => Math.random() * 100 + (Math.random() * 50))
-            .map((y, x) => ({ x: x + 50, y: y + 50, }));
-
-          const series2 = Array(100).fill(0)
-            .map(y => Math.random() * 100 + (Math.random() * 150))
-            .map((y, x) => ({ x: x + 50, y: y + 50, }));
-
-
-          const series = ['First', 'Second'];
-          const data = { values: [series1, series2], series }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Zoomed Scatterplot', tab: 'Charts' });
-          tfvis.render.scatterplot(surface, data, { zoomToFit: true });
-
-          // Render to page
-          const container = document.getElementById('zoomed-scatterplot-cont');
-          tfvis.render.scatterplot(container, data, {
-            width: 500,
-            height: 500,
-            zoomToFit: true,
-          });
-        }
-      </script>
-
-      <h4>Custom domain scatter plot</h4>
-      <div id='customdomain-scatterplot-cont'></div>
-
-      <script class='show-script'>
-        {
-          const series1 = Array(100).fill(0)
-            .map(y => Math.random() * 100 + (Math.random() * 50))
-            .map((y, x) => ({ x: x + 50, y: y + 50, }));
-
-          const series2 = Array(100).fill(0)
-            .map(y => Math.random() * 100 + (Math.random() * 150))
-            .map((y, x) => ({ x: x + 50, y: y + 50, }));
-
-
-          const series = ['First', 'Second'];
-          const data = { values: [series1, series2], series }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Custom Domain Scatterplot', tab: 'Charts' });
-          tfvis.render.scatterplot(surface, data, {
-            xAxisDomain: [-20, 200],
-            yAxisDomain: [-50, 125]
-          });
-
-          // Render to page
-          const container = document.getElementById('customdomain-scatterplot-cont');
-          tfvis.render.scatterplot(container, data, {
-            width: 500,
-            height: 500,
-            xAxisDomain: [-20, 200],
-            yAxisDomain: [-50, 125]
-          });
-        }
-      </script>
-
-      <hr>
-
-      <h3>render.confusionMatrix</h3>
-
-      <div id='confmatrix-cont'></div>
-
-      <script class='show-script'>
-        {
-
-          const rows = 50;
-          const cols = 50;
-          const values = [];
-          for (let i = 0; i < rows; i++) {
-            const row = []
-            for (let j = 0; j < cols; j++) {
-              row.push(Math.round(Math.random() * 50));
-            }
-            values.push(row);
-          }
-          const data = { values };
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Confusion Matrix', tab: 'Charts' });
-          tfvis.render.confusionMatrix(surface, data, {
-            showTextOverlay: false
-          });
-
-          // Render to page
-          const container = document.getElementById('confmatrix-cont');
-          tfvis.render.confusionMatrix(container, data, { width: 800, height: 800 });
-        }
-      </script>
-
-      <p>The diagonal can also be excluded from shading.</p>
-
-      <div id='confmatrix-cont2'></div>
-
-      <script class='show-script'>
-        {
-          const data = {
-            values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-            // We also generate 'labels' if none are passed.
-          }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({
-            name: 'Confusion Matrix with Excluded Diagonal', tab: 'Charts'
-          });
-          tfvis.render.confusionMatrix(surface, data, {
-            shadeDiagonal: false
-          });
-
-          // Render to page
-          const container = document.getElementById('confmatrix-cont2');
-          tfvis.render.confusionMatrix(container, data, {
-            width: 500,
-            height: 500,
-            shadeDiagonal: false,
-          });
-        }
-      </script>
-
-      <p>Perfect classification.</p>
-
-      <div id='confmatrix-cont3'></div>
-
-
-      <script class='show-script'>
-        {
-          const data = {
-            values: [[8, 0, 0], [0, 8, 0], [0, 0, 8]],
-            // We also generate 'labels' if none are passed.
-          }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({
-            name: 'Confusion Matrix with perfect classification', tab: 'Charts'
-          });
-          tfvis.render.confusionMatrix(surface, data);
-
-          // Render to page
-          const container = document.getElementById('confmatrix-cont3');
-          tfvis.render.confusionMatrix(container, data, {
-            width: 500,
-            height: 500,
-          });
-
-
-        }
-      </script>
-
-      <p>In a perfect classification scenario where shadeDiagonal is false the chart
-        will override that option as all values lie on the diagonal.
-      </p>
-
-      <div id='confmatrix-cont4'></div>
-
-      <script class='show-script'>
-        {
-          const data = {
-            values: [[8, 0, 0], [0, 8, 0], [0, 0, 8]],
-            // We also generate 'labels' if none are passed.
-          }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({
-            name: 'Confusion Matrix with perfect classification shadeDiagonal=false', tab: 'Charts'
-          });
-          tfvis.render.confusionMatrix(surface, data, {
-            shadeDiagonal: false,
-          });
-
-          // Render to page
-          const container = document.getElementById('confmatrix-cont4');
-          tfvis.render.confusionMatrix(container, data, {
-            width: 500,
-            height: 500,
-            shadeDiagonal: false,
-          });
-        }
-      </script>
-
-      <hr>
-
-      <h3>render.heatmap(data, container, opts)</h3>
-
-      <div id='heatmap-cont'></div>
-
-      <script class='show-script'>
-        {
-          const rows = 50;
-          const cols = 20;
-          const values = [];
-          for (let i = 0; i < rows; i++) {
-            const row = []
-            for (let j = 0; j < cols; j++) {
-              row.push(i * j)
-            }
-            values.push(row);
-          }
-          const data = { values };
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Heatmap', tab: 'Charts' });
-          tfvis.render.heatmap(surface, data);
-
-          // Render to page
-          const container = document.getElementById('heatmap-cont');
-          tfvis.render.heatmap(container, data, { width: 500, height: 500 });
-        }
-      </script>
-
-      <p>
-        We can also pass in custom labels
-      </p>
-      <div id='heatmap-cont2'></div>
-
-      <script class='show-script'>
-        {
-          const data = {
-            values: [[4, 2, 8, 20], [1, 7, 2, 10], [3, 3, 20, 13]],
-            xLabels: ['cheese', 'pig', 'font'],
-            yLabels: ['speed', 'smoothness', 'dexterity', 'mana'],
-          }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Heatmap w Custom Labels', tab: 'Charts' });
-          tfvis.render.heatmap(surface, data);
-
-          // Render to page
-          const container = document.getElementById('heatmap-cont2');
-          tfvis.render.heatmap(container, data, { width: 500, height: 500 });
-        }
-      </script>
-
-      <p>
-        Alternate color maps are also supported.
-      </p>
-      <div id='heatmap-cont3'></div>
-
-      <script class='show-script'>
-        {
-          const data = {
-            values: [[4, 2, 8, 20], [1, 7, 2, 10], [3, 3, 20, 13]],
-            xTickLabels: ['cheese', 'pig', 'font'],
-            yTickLabels: ['speed', 'smoothness', 'dexterity', 'mana'],
-          }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Heatmap w Custom Colormap', tab: 'Charts' });
-          tfvis.render.heatmap(surface, data, {
-            colorMap: 'greyscale',
-          });
-
-          // Render to page
-          const container = document.getElementById('heatmap-cont3');
-          tfvis.render.heatmap(container, data, {
-            colorMap: 'greyscale',
-            width: 500,
-            height: 500
-          });
-        }
-      </script>
-
-
-      <p>
-        Heatmap displays labels in order provided
-      </p>
-      <div id='heatmap-sort-order'></div>
-
-      <script class='show-script'>
-        {
-          const data = {
-            values: [
-              [5, 4, 3, 2, 1],
-              [10, 9, 8, 7, 6],
-              [15, 14, 13, 12, 11],
-              [20, 19, 18, 17, 16],
-              [24, 24, 23, 22, 21]
-            ],
-            xTickLabels: [0, 1, 10, 50, 100],
-            yTickLabels: [0, 1, 10, 50, 100],
-          }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Heatmap w Custom Colormap', tab: 'Charts' });
-          tfvis.render.heatmap(surface, data);
-
-          // Render to page
-          const container = document.getElementById('heatmap-sort-order');
-          tfvis.render.heatmap(container, data);
-        }
-      </script>
-
-      <p>
-        Tensor2D's are also supported
-      </p>
-      <div id='heatmap-cont4'></div>
-
-      <script class='show-script'>
-        {
-          const data = {
-            values: tf.tensor2d([[4, 2, 8, 20], [1, 7, 2, 10], [3, 3, 20, 13]]),
-            xTickLabels: ['cheese', 'pig', 'font'],
-            yTickLabels: ['speed', 'smoothness', 'dexterity', 'mana'],
-          }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Heatmap', tab: 'Tensor Charts' });
-          tfvis.render.heatmap(surface, data, {
-            xLabel: 'Thing',
-            yLabel: 'Property',
-          });
-
-          // Render to page
-          const container = document.getElementById('heatmap-cont4');
-          tfvis.render.heatmap(container, data, {
-            width: 500,
-            height: 500,
-            xLabel: 'Thing',
-            yLabel: 'Property',
-          });
-        }
-      </script>
-
-      <p>
-        Repeated labels are supported
-      </p>
-      <div id='heatmap-cont5'></div>
-
-      <script class='show-script'>
-        {
-          const data = {
-            values: tf.tensor2d([
-              [10, 2, 3, 4, 5, 6],
-              [1, 20, 3, 4, 5, 6],
-              [1, 2, 30, 4, 5, 6],
-              [1, 2, 3, 40, 5, 6],
-              [1, 2, 3, 4, 50, 6],
-              [1, 2, 3, 4, 5, 60],
-            ]),
-            xTickLabels: ['a', 'token', 'in', 'a', 'sequence', 'repeat'],
-            yTickLabels: ['a', 'token', 'in', 'a', 'sequence', 'repeat'],
-          }
-
-          // Render to visor
-          const surface = tfvis.visor().surface({ name: 'Heatmap', tab: 'Tensor Charts' });
-          tfvis.render.heatmap(surface, data, {
-            xLabel: 'Thing',
-            yLabel: 'Property',
-          });
-
-          // Render to page
-          const container = document.getElementById('heatmap-cont5');
-          tfvis.render.heatmap(container, data, {
-            width: 500,
-            height: 500,
-            xLabel: 'Thing',
-            yLabel: 'Property',
-          });
-        }
-      </script>
-    </section>
-
-  </article>
-
-</body>
-
-</html>
diff --git a/tfjs-master/tfjs-vis/demos/api/package.json b/tfjs-master/tfjs-vis/demos/api/package.json
deleted file mode 100644
index 388f47e66..000000000
--- a/tfjs-master/tfjs-vis/demos/api/package.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "name": "tfjs-vis-api-demo",
-  "private": true,
-  "version": "0.0.1",
-  "description": "",
-  "main": "index.js",
-  "browserslist": "> 0.25%, not dead",
-  "scripts": {
-    "watch": "NODE_ENV=development parcel index.html --no-hmr --open --no-autoinstall",
-    "debug": "NODE_ENV=development parcel debug.html --no-hmr --open --no-autoinstall",
-    "build": "NODE_ENV=production parcel build index.html --no-minify --public-url ./"
-  },
-  "author": "",
-  "license": "Apache-2.0",
-  "devDependencies": {
-    "@babel/core": "^7.21.0",
-    "@babel/plugin-transform-runtime": "^7.21.0",
-    "babel-preset-env": "~1.7.0",
-    "parcel": "2.8.3",
-    "rimraf": "^4.1.2"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs": "4.2.0",
-    "@tensorflow/tfjs-vis": "link:../.."
-  },
-  "resolutions": {
-    "node-fetch": "2.6.7",
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-vis/demos/api/yarn.lock b/tfjs-master/tfjs-vis/demos/api/yarn.lock
deleted file mode 100644
index 50aaf6ecc..000000000
--- a/tfjs-master/tfjs-vis/demos/api/yarn.lock
+++ /dev/null
@@ -1,3805 +0,0 @@
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-  resolved "https://registry.yarnpkg.com/clone/-/clone-2.1.2.tgz#1b7f4b9f591f1e8f83670401600345a02887435f"
-  integrity sha512-3Pe/CF1Nn94hyhIYpjtiLhdCoEoz0DqQ+988E9gmeEdQZlojxnOb74wctFyuwWQHzqyf9X7C7MG8juUpqBJT8w==
-
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-  version "1.9.3"
-  resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-1.9.3.tgz#bb71850690e1f136567de629d2d5471deda4c1e8"
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-  dependencies:
-    color-name "1.1.3"
-
-color-convert@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
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-  dependencies:
-    color-name "~1.1.4"
-
-color-name@1.1.3:
-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.3.tgz#a7d0558bd89c42f795dd42328f740831ca53bc25"
-  integrity sha1-p9BVi9icQveV3UIyj3QIMcpTvCU=
-
-color-name@~1.1.4:
-  version "1.1.4"
-  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
-  integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
-
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-  version "1.0.8"
-  resolved "https://registry.yarnpkg.com/combined-stream/-/combined-stream-1.0.8.tgz#c3d45a8b34fd730631a110a8a2520682b31d5a7f"
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-  version "2.20.3"
-  resolved "https://registry.yarnpkg.com/commander/-/commander-2.20.3.tgz#fd485e84c03eb4881c20722ba48035e8531aeb33"
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-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/commander/-/commander-7.2.0.tgz#a36cb57d0b501ce108e4d20559a150a391d97ab7"
-  integrity sha512-QrWXB+ZQSVPmIWIhtEO9H+gwHaMGYiF5ChvoJ+K9ZGHG/sVsa6yiesAD1GC/x46sET00Xlwo1u49RVVVzvcSkw==
-
-convert-source-map@^1.7.0:
-  version "1.7.0"
-  resolved "https://registry.yarnpkg.com/convert-source-map/-/convert-source-map-1.7.0.tgz#17a2cb882d7f77d3490585e2ce6c524424a3a442"
-  integrity sha512-4FJkXzKXEDB1snCFZlLP4gpC3JILicCpGbzG9f9G7tGqGCzETQ2hWPrcinA9oU4wtf2biUaEH5065UnMeR33oA==
-  dependencies:
-    safe-buffer "~5.1.1"
-
-core-js-compat@^3.25.1:
-  version "3.28.0"
-  resolved "https://registry.yarnpkg.com/core-js-compat/-/core-js-compat-3.28.0.tgz#c08456d854608a7264530a2afa281fadf20ecee6"
-  integrity sha512-myzPgE7QodMg4nnd3K1TDoES/nADRStM8Gpz0D6nhkwbmwEnE0ZGJgoWsvQ722FR8D7xS0n0LV556RcEicjTyg==
-  dependencies:
-    browserslist "^4.21.5"
-
-core-js@3:
-  version "3.6.5"
-  resolved "https://registry.yarnpkg.com/core-js/-/core-js-3.6.5.tgz#7395dc273af37fb2e50e9bd3d9fe841285231d1a"
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-
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-  version "1.2.7"
-  resolved "https://registry.yarnpkg.com/core-js/-/core-js-1.2.7.tgz#652294c14651db28fa93bd2d5ff2983a4f08c636"
-  integrity sha512-ZiPp9pZlgxpWRu0M+YWbm6+aQ84XEfH1JRXvfOc/fILWI0VKhLC2LX13X1NYq4fULzLMq7Hfh43CSo2/aIaUPA==
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-core-js@^2.4.0:
-  version "2.6.11"
-  resolved "https://registry.yarnpkg.com/core-js/-/core-js-2.6.11.tgz#38831469f9922bded8ee21c9dc46985e0399308c"
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-  version "7.0.1"
-  resolved "https://registry.yarnpkg.com/cosmiconfig/-/cosmiconfig-7.0.1.tgz#714d756522cace867867ccb4474c5d01bbae5d6d"
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-    import-fresh "^3.2.1"
-    parse-json "^5.0.0"
-    path-type "^4.0.0"
-    yaml "^1.10.0"
-
-css-in-js-utils@^2.0.0:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/css-in-js-utils/-/css-in-js-utils-2.0.1.tgz#3b472b398787291b47cfe3e44fecfdd9e914ba99"
-  integrity sha512-PJF0SpJT+WdbVVt0AOYp9C8GnuruRlL/UFW7932nLWmFLQTaWEzTBQEx7/hn4BuV+WON75iAViSUJLiU3PKbpA==
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-    hyphenate-style-name "^1.0.2"
-    isobject "^3.0.1"
-
-css-select@^4.1.3:
-  version "4.2.1"
-  resolved "https://registry.yarnpkg.com/css-select/-/css-select-4.2.1.tgz#9e665d6ae4c7f9d65dbe69d0316e3221fb274cdd"
-  integrity sha512-/aUslKhzkTNCQUB2qTX84lVmfia9NyjP3WpDGtj/WxhwBzWBYUV3DgUpurHTme8UTPcPlAD1DJ+b0nN/t50zDQ==
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-    boolbase "^1.0.0"
-    css-what "^5.1.0"
-    domhandler "^4.3.0"
-    domutils "^2.8.0"
-    nth-check "^2.0.1"
-
-css-tree@^1.1.2, css-tree@^1.1.3:
-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/css-tree/-/css-tree-1.1.3.tgz#eb4870fb6fd7707327ec95c2ff2ab09b5e8db91d"
-  integrity sha512-tRpdppF7TRazZrjJ6v3stzv93qxRcSsFmW6cX0Zm2NVKpxE1WV1HblnghVv9TreireHkqI/VDEsfolRF1p6y7Q==
-  dependencies:
-    mdn-data "2.0.14"
-    source-map "^0.6.1"
-
-css-what@^5.1.0:
-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/css-what/-/css-what-5.1.0.tgz#3f7b707aadf633baf62c2ceb8579b545bb40f7fe"
-  integrity sha512-arSMRWIIFY0hV8pIxZMEfmMI47Wj3R/aWpZDDxWYCPEiOMv6tfOrnpDtgxBYPEQD4V0Y/958+1TdC3iWTFcUPw==
-
-csso@^4.2.0:
-  version "4.2.0"
-  resolved "https://registry.yarnpkg.com/csso/-/csso-4.2.0.tgz#ea3a561346e8dc9f546d6febedd50187cf389529"
-  integrity sha512-wvlcdIbf6pwKEk7vHj8/Bkc0B4ylXZruLvOgs9doS5eOsOpuodOV2zJChSpkp+pRpYQLQMeF04nr3Z68Sta9jA==
-  dependencies:
-    css-tree "^1.1.2"
-
-"d3-array@1 - 2", d3-array@2, d3-array@^2.3.0, d3-array@^2.5.0, d3-array@^2.7.1:
-  version "2.12.1"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-2.12.1.tgz#e20b41aafcdffdf5d50928004ececf815a465e81"
-  integrity sha512-B0ErZK/66mHtEsR1TkPEEkwdy+WDesimkM5gpZr5Dsg54BiTA5RXtYW5qTLIAcekaS9xfZrzBLF/OAkB3Qn1YQ==
-  dependencies:
-    internmap "^1.0.0"
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-"d3-array@1 - 3", "d3-array@2 - 3", "d3-array@2.10.0 - 3", "d3-array@2.5.0 - 3", d3-array@^3.1.1:
-  version "3.2.2"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-3.2.2.tgz#f8ac4705c5b06914a7e0025bbf8d5f1513f6a86e"
-  integrity sha512-yEEyEAbDrF8C6Ob2myOBLjwBLck1Z89jMGFee0oPsn95GqjerpaOA4ch+vc2l0FNFFwMD5N7OCSEN5eAlsUbgQ==
-  dependencies:
-    internmap "1 - 2"
-
-d3-array@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-3.1.1.tgz#7797eb53ead6b9083c75a45a681e93fc41bc468c"
-  integrity sha512-33qQ+ZoZlli19IFiQx4QEpf2CBEayMRzhlisJHSCsSUbDXv6ZishqS1x7uFVClKG4Wr7rZVHvaAttoLow6GqdQ==
-  dependencies:
-    internmap "1 - 2"
-
-"d3-color@1 - 2", d3-color@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-2.0.0.tgz#8d625cab42ed9b8f601a1760a389f7ea9189d62e"
-  integrity sha512-SPXi0TSKPD4g9tw0NMZFnR95XVgUZiBH+uUTqQuDu1OsE2zomHU7ho0FISciaPvosimixwHFl3WHLGabv6dDgQ==
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-"d3-color@1 - 3", d3-color@^3.0.1:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-3.1.0.tgz#395b2833dfac71507f12ac2f7af23bf819de24e2"
-  integrity sha512-zg/chbXyeBtMQ1LbD/WSoW2DpC3I0mpmPdW+ynRTj/x2DAWYrIY7qeZIHidozwV24m4iavr15lNwIwLxRmOxhA==
-
-d3-delaunay@^5.3.0:
-  version "5.3.0"
-  resolved "https://registry.yarnpkg.com/d3-delaunay/-/d3-delaunay-5.3.0.tgz#b47f05c38f854a4e7b3cea80e0bb12e57398772d"
-  integrity sha512-amALSrOllWVLaHTnDLHwMIiz0d1bBu9gZXd1FiLfXf8sHcX9jrcj81TVZOqD4UX7MgBZZ07c8GxzEgBpJqc74w==
-  dependencies:
-    delaunator "4"
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-"d3-dispatch@1 - 2":
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-dispatch/-/d3-dispatch-2.0.0.tgz#8a18e16f76dd3fcaef42163c97b926aa9b55e7cf"
-  integrity sha512-S/m2VsXI7gAti2pBoLClFFTMOO1HTtT0j99AuXLoGFKO6deHDdnv6ZGTxSTTUTgO1zVcv82fCOtDjYK4EECmWA==
-
-d3-dsv@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-2.0.0.tgz#b37b194b6df42da513a120d913ad1be22b5fe7c5"
-  integrity sha512-E+Pn8UJYx9mViuIUkoc93gJGGYut6mSDKy2+XaPwccwkRGlR+LO97L2VCCRjQivTwLHkSnAJG7yo00BWY6QM+w==
-  dependencies:
-    commander "2"
-    iconv-lite "0.4"
-    rw "1"
-
-d3-dsv@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-3.0.1.tgz#c63af978f4d6a0d084a52a673922be2160789b73"
-  integrity sha512-UG6OvdI5afDIFP9w4G0mNq50dSOsXHJaRE8arAS5o9ApWnIElp8GZw1Dun8vP8OyHOZ/QJUKUJwxiiCCnUwm+Q==
-  dependencies:
-    commander "7"
-    iconv-lite "0.6"
-    rw "1"
-
-d3-force@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/d3-force/-/d3-force-2.1.1.tgz#f20ccbf1e6c9e80add1926f09b51f686a8bc0937"
-  integrity sha512-nAuHEzBqMvpFVMf9OX75d00OxvOXdxY+xECIXjW6Gv8BRrXu6gAWbv/9XKrvfJ5i5DCokDW7RYE50LRoK092ew==
-  dependencies:
-    d3-dispatch "1 - 2"
-    d3-quadtree "1 - 2"
-    d3-timer "1 - 2"
-
-"d3-format@1 - 2", d3-format@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-2.0.0.tgz#a10bcc0f986c372b729ba447382413aabf5b0767"
-  integrity sha512-Ab3S6XuE/Q+flY96HXT0jOXcM4EAClYFnRGY5zsjRGNy6qCYrQsMffs7cV5Q9xejb35zxW5hf/guKw34kvIKsA==
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-"d3-format@1 - 3", d3-format@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-3.1.0.tgz#9260e23a28ea5cb109e93b21a06e24e2ebd55641"
-  integrity sha512-YyUI6AEuY/Wpt8KWLgZHsIU86atmikuoOmCfommt0LYHiQSPjvX2AcFc38PX0CBpr2RCyZhjex+NS/LPOv6YqA==
-
-d3-format@~1.3.0:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-1.3.2.tgz#6a96b5e31bcb98122a30863f7d92365c00603562"
-  integrity sha512-Z18Dprj96ExragQ0DeGi+SYPQ7pPfRMtUXtsg/ChVIKNBCzjO8XYJvRTC1usblx52lqge56V5ect+frYTQc8WQ==
-
-d3-geo-projection@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-3.0.0.tgz#45ad8ce756cdbfa8340b11b2988644d8e1fa42e4"
-  integrity sha512-1JE+filVbkEX2bT25dJdQ05iA4QHvUwev6o0nIQHOSrNlHCAKfVss/U10vEM3pA4j5v7uQoFdQ4KLbx9BlEbWA==
-  dependencies:
-    commander "2"
-    d3-array "1 - 2"
-    d3-geo "1.12.0 - 2"
-    resolve "^1.1.10"
-
-d3-geo-projection@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-4.0.0.tgz#dc229e5ead78d31869a4e87cf1f45bd2716c48ca"
-  integrity sha512-p0bK60CEzph1iqmnxut7d/1kyTmm3UWtPlwdkM31AU+LW+BXazd5zJdoCn7VFxNCHXRngPHRnsNn5uGjLRGndg==
-  dependencies:
-    commander "7"
-    d3-array "1 - 3"
-    d3-geo "1.12.0 - 3"
-
-"d3-geo@1.12.0 - 2", d3-geo@^2.0.1:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-2.0.2.tgz#c065c1b71fe8c5f1be657e5f43d9bdd010383c40"
-  integrity sha512-8pM1WGMLGFuhq9S+FpPURxic+gKzjluCD/CHTuUF3mXMeiCo0i6R0tO1s4+GArRFde96SLcW/kOFRjoAosPsFA==
-  dependencies:
-    d3-array "^2.5.0"
-
-"d3-geo@1.12.0 - 3", d3-geo@^3.0.1:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-3.1.0.tgz#74fd54e1f4cebd5185ac2039217a98d39b0a4c0e"
-  integrity sha512-JEo5HxXDdDYXCaWdwLRt79y7giK8SbhZJbFWXqbRTolCHFI5jRqteLzCsq51NKbUoX0PjBVSohxrx+NoOUujYA==
-  dependencies:
-    d3-array "2.5.0 - 3"
-
-d3-hierarchy@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-hierarchy/-/d3-hierarchy-2.0.0.tgz#dab88a58ca3e7a1bc6cab390e89667fcc6d20218"
-  integrity sha512-SwIdqM3HxQX2214EG9GTjgmCc/mbSx4mQBn+DuEETubhOw6/U3fmnji4uCVrmzOydMHSO1nZle5gh6HB/wdOzw==
-
-"d3-interpolate@1.2.0 - 2", d3-interpolate@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-2.0.1.tgz#98be499cfb8a3b94d4ff616900501a64abc91163"
-  integrity sha512-c5UhwwTs/yybcmTpAVqwSFl6vrQ8JZJoT5F7xNFK9pymv5C0Ymcc9/LIJHtYIggg/yS9YHw8i8O8tgb9pupjeQ==
-  dependencies:
-    d3-color "1 - 2"
-
-"d3-interpolate@1.2.0 - 3", d3-interpolate@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-3.0.1.tgz#3c47aa5b32c5b3dfb56ef3fd4342078a632b400d"
-  integrity sha512-3bYs1rOD33uo8aqJfKP3JWPAibgw8Zm2+L9vBKEHJ2Rg+viTR7o5Mmv5mZcieN+FRYaAOWX5SJATX6k1PWz72g==
-  dependencies:
-    d3-color "1 - 3"
-
-"d3-path@1 - 2", d3-path@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-2.0.0.tgz#55d86ac131a0548adae241eebfb56b4582dd09d8"
-  integrity sha512-ZwZQxKhBnv9yHaiWd6ZU4x5BtCQ7pXszEV9CU6kRgwIQVQGLMv1oiL4M+MK/n79sYzsj+gcgpPQSctJUsLN7fA==
-
-d3-path@^3.0.1, d3-path@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-3.1.0.tgz#22df939032fb5a71ae8b1800d61ddb7851c42526"
-  integrity sha512-p3KP5HCf/bvjBSSKuXid6Zqijx7wIfNW+J/maPs+iwR35at5JCbLUT0LzF1cnjbCHWhqzQTIN2Jpe8pRebIEFQ==
-
-"d3-quadtree@1 - 2":
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-quadtree/-/d3-quadtree-2.0.0.tgz#edbad045cef88701f6fee3aee8e93fb332d30f9d"
-  integrity sha512-b0Ed2t1UUalJpc3qXzKi+cPGxeXRr4KU9YSlocN74aTzp6R/Ud43t79yLLqxHRWZfsvWXmbDWPpoENK1K539xw==
-
-d3-scale@^3.2.2:
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-3.3.0.tgz#28c600b29f47e5b9cd2df9749c206727966203f3"
-  integrity sha512-1JGp44NQCt5d1g+Yy+GeOnZP7xHo0ii8zsQp6PGzd+C1/dl0KGsp9A7Mxwp+1D1o4unbTTxVdU/ZOIEBoeZPbQ==
-  dependencies:
-    d3-array "^2.3.0"
-    d3-format "1 - 2"
-    d3-interpolate "1.2.0 - 2"
-    d3-time "^2.1.1"
-    d3-time-format "2 - 3"
-
-d3-scale@^4.0.2:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-4.0.2.tgz#82b38e8e8ff7080764f8dcec77bd4be393689396"
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-  version "1.19.0"
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diff --git a/tfjs-master/tfjs-vis/demos/mnist/README.md b/tfjs-master/tfjs-vis/demos/mnist/README.md
deleted file mode 100644
index 159433dfb..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# tfjs-vis mnist training demo
-
-A demonstration of using tfjs-vis to look at training progress and perform model evaluation.
-
-See a live version of this [here](https://storage.googleapis.com/tfjs-vis/mnist/dist/index.html).
-
-To run this locally:
-- run `yarn build-npm` in the `tfjs-vis` folder
-- run `yarn watch` from the `demos` folder
diff --git a/tfjs-master/tfjs-vis/demos/mnist/data.js b/tfjs-master/tfjs-vis/demos/mnist/data.js
deleted file mode 100644
index 744fb5773..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist/data.js
+++ /dev/null
@@ -1,144 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-
-const IMAGE_SIZE = 784;
-const NUM_CLASSES = 10;
-const NUM_DATASET_ELEMENTS = 65000;
-
-const NUM_TRAIN_ELEMENTS = 55000;
-const NUM_TEST_ELEMENTS = NUM_DATASET_ELEMENTS - NUM_TRAIN_ELEMENTS;
-
-const MNIST_IMAGES_SPRITE_PATH =
-    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_images.png';
-const MNIST_LABELS_PATH =
-    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_labels_uint8';
-
-/**
- * A class that fetches the sprited MNIST dataset and returns shuffled batches.
- *
- * NOTE: This will get much easier. For now, we do data fetching and
- * manipulation manually.
- */
-export class MnistData {
-  constructor() {
-    this.shuffledTrainIndex = 0;
-    this.shuffledTestIndex = 0;
-  }
-
-  async load() {
-    // Make a request for the MNIST sprited image.
-    const img = new Image();
-    const canvas = document.createElement('canvas');
-    const ctx = canvas.getContext('2d');
-    const imgRequest = new Promise((resolve, reject) => {
-      img.crossOrigin = '';
-      img.onload = () => {
-        img.width = img.naturalWidth;
-        img.height = img.naturalHeight;
-
-        const datasetBytesBuffer =
-            new ArrayBuffer(NUM_DATASET_ELEMENTS * IMAGE_SIZE * 4);
-
-        const chunkSize = 5000;
-        canvas.width = img.width;
-        canvas.height = chunkSize;
-
-        for (let i = 0; i < NUM_DATASET_ELEMENTS / chunkSize; i++) {
-          const datasetBytesView = new Float32Array(
-              datasetBytesBuffer, i * IMAGE_SIZE * chunkSize * 4,
-              IMAGE_SIZE * chunkSize);
-          ctx.drawImage(
-              img, 0, i * chunkSize, img.width, chunkSize, 0, 0, img.width,
-              chunkSize);
-
-          const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
-
-          for (let j = 0; j < imageData.data.length / 4; j++) {
-            // All channels hold an equal value since the image is grayscale, so
-            // just read the red channel.
-            datasetBytesView[j] = imageData.data[j * 4] / 255;
-          }
-        }
-        this.datasetImages = new Float32Array(datasetBytesBuffer);
-
-        resolve();
-      };
-      img.src = MNIST_IMAGES_SPRITE_PATH;
-    });
-
-    const labelsRequest = fetch(MNIST_LABELS_PATH);
-    const [imgResponse, labelsResponse] =
-        await Promise.all([imgRequest, labelsRequest]);
-
-    this.datasetLabels = new Uint8Array(await labelsResponse.arrayBuffer());
-
-    // Create shuffled indices into the train/test set for when we select a
-    // random dataset element for training / validation.
-    this.trainIndices = tf.util.createShuffledIndices(NUM_TRAIN_ELEMENTS);
-    this.testIndices = tf.util.createShuffledIndices(NUM_TEST_ELEMENTS);
-
-    // Slice the the images and labels into train and test sets.
-    this.trainImages =
-        this.datasetImages.slice(0, IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
-    this.testImages = this.datasetImages.slice(IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
-    this.trainLabels =
-        this.datasetLabels.slice(0, NUM_CLASSES * NUM_TRAIN_ELEMENTS);
-    this.testLabels =
-        this.datasetLabels.slice(NUM_CLASSES * NUM_TRAIN_ELEMENTS);
-  }
-
-  nextTrainBatch(batchSize) {
-    return this.nextBatch(
-        batchSize, [this.trainImages, this.trainLabels], () => {
-          this.shuffledTrainIndex =
-              (this.shuffledTrainIndex + 1) % this.trainIndices.length;
-          return this.trainIndices[this.shuffledTrainIndex];
-        });
-  }
-
-  nextTestBatch(batchSize) {
-    return this.nextBatch(batchSize, [this.testImages, this.testLabels], () => {
-      this.shuffledTestIndex =
-          (this.shuffledTestIndex + 1) % this.testIndices.length;
-      return this.testIndices[this.shuffledTestIndex];
-    });
-  }
-
-  nextBatch(batchSize, data, index) {
-    const batchImagesArray = new Float32Array(batchSize * IMAGE_SIZE);
-    const batchLabelsArray = new Uint8Array(batchSize * NUM_CLASSES);
-
-    for (let i = 0; i < batchSize; i++) {
-      const idx = index();
-
-      const image =
-          data[0].slice(idx * IMAGE_SIZE, idx * IMAGE_SIZE + IMAGE_SIZE);
-      batchImagesArray.set(image, i * IMAGE_SIZE);
-
-      const label =
-          data[1].slice(idx * NUM_CLASSES, idx * NUM_CLASSES + NUM_CLASSES);
-      batchLabelsArray.set(label, i * NUM_CLASSES);
-    }
-
-    const xs = tf.tensor2d(batchImagesArray, [batchSize, IMAGE_SIZE]);
-    const labels = tf.tensor2d(batchLabelsArray, [batchSize, NUM_CLASSES]);
-
-    return {xs, labels};
-  }
-}
diff --git a/tfjs-master/tfjs-vis/demos/mnist/index.html b/tfjs-master/tfjs-vis/demos/mnist/index.html
deleted file mode 100644
index 6ecf19ea9..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist/index.html
+++ /dev/null
@@ -1,382 +0,0 @@
-<!-- /**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */ -->
-<html>
-
-<head>
-  <meta charset="utf-8">
-  <link rel="stylesheet" href="./tufte.css" />
-  <script type="module" src="index.js"></script>
-
-  <style>
-    .lang-javascript {
-      width: 60%;
-      padding-left: 15px;
-      font-family: monospace;
-    }
-
-    script.show-script {
-      display: block;
-      max-width: 720px;
-      background-color: floralwhite;
-      font-family: "Lucida Console", Monaco, "Courier New", Courier, monospace;
-      font-size: 12px;
-      margin-left: -40px;
-      white-space: pre;
-      margin-top: 2em;
-    }
-
-    code {
-      background-color: #eee;
-      padding: 2px 4px 2px 4px;
-    }
-  </style>
-
-  <style>
-  </style>
-</head>
-
-<body>
-  <article>
-    <h1>Visualizing training with tfjs-vis</h1>
-
-    <section>
-      <p>
-        tfjs-vis is a small set of visualization utilities to make it easier to understand what is going on with your
-        tfjs models.
-        It is designed in a way to work along side regular web apps. This page will use some of the features of tfjs to
-        illustrate
-        what is going on with a convolutional model that will be trained (in the browser) to recognize handwritten
-        digits.
-      </p>
-
-      <p>
-        tfjs-vis provides 2 main things:
-        <ol>
-          <li>A place to put visualizations that tries not to interfere with your web page. We call this place a
-            <em>visor.</em>
-          </li>
-          <li>Some built in visualizations that we have found to be useful when working with TensorFlow.js
-          </li>
-        </ol>
-      </p>
-    </section>
-
-    <section>
-      <h2>The Visor</h2>
-      <p>
-        Let's take a look at the first. Calling <code>tfvis.visor()</code> will create a visor if it doesn't exist or
-        return
-        the existing one. Click the button below to show the
-        <em>visor.</em>
-      </p>
-      <button id='show-visor'>Show Visor</button>
-      <p>
-        Notice the panel that is now displayed on the right. It hovers over your pages content and shouldn't disturb
-        the flow of
-        your page's DOM Elements. You can see a few controls for showing or hiding the visor, but by default it also
-        supports
-        the following keyboard shortcuts:
-        <ul>
-          <li>
-            <strong>`</strong> (backtick): Shows or hides the visor</li>
-          <li>
-            <strong>~</strong> (tilde, shift+backtick): Toggles betweeen the two sizes the visor supports</li>
-        </ul>
-        The API allows you to disable (unbind) these keyboard shortcuts.
-      </p>
-
-      <h3>Surfaces</h3>
-      <p>
-        To add content to the visor we need a
-        <em>surface</em>. We make a surface with the following function call:
-        <script type='text' class='show-script'>
-          tfvis.visor().surface({name: 'My First Surface', tab: 'Input Data'});
-        </script>
-      </p>
-      <button id='make-first-surface'>Make a surface</button>
-      <p>
-        To create a surface we must give is a name, we can also optionally specify a tab name that the surface should
-        be put on.
-        <code>visor().surface()</code> allows us to create a surface if it doesn't exist or fetch it if it does. This
-        API
-        returns an object that has a pointer to 3 DOM elements:
-        <ul>
-          <li>
-            <strong>container:</strong> The containing DOM element for the surface</li>
-          <li>
-            <strong>label:</strong> The label element </li>
-          <li>
-            <strong>drawArea:</strong> A DOM Element where we can render visualizations or other content.
-          </li>
-        </ul>
-      </p>
-    </section>
-
-    <section>
-      <h2>Our Data</h2>
-      <p>
-        We will use the MNIST database as our training set, it is comprised of a set of about 60k images of handwritten
-        digits, all
-        cropped to 28x28 px. Lets take a look at a few examples, we'll use the surface we created earlier.
-      </p>
-      <button id='load-data'>Load Data</button>
-      &nbsp;&nbsp;&nbsp;
-      <button id='show-examples' disabled>Show Example Digits</button>
-
-      <p>
-        The code to render these examples isn't built into tfjs. But because you have full access to the DOM element
-        for each surface,
-        you can draw whatever you would like into them. This allows easy integration of custom visualizations into the
-        visor.
-
-        <p>
-          Here is the code for the "Show Example Digits" button above:
-        </p>
-
-        <script class='show-script'>
-          async function showExamples() {
-
-            // Get a surface
-            const surface =
-              tfvis.visor().surface({ name: 'My First Surface', tab: 'Input Data' });
-            const drawArea = surface.drawArea;
-
-            // Get the examples
-            const examples = data.nextTestBatch(20);
-            const numExamples = examples.xs.shape[0];
-            for (let i = 0; i < numExamples; i++) {
-              const imageTensor = tf.tidy(() => {
-                return examples.xs.slice([i, 0], [1, examples.xs.shape[1]]).reshape([
-                  28, 28, 1
-                ]);
-              });
-
-              // Create a canvas element to render each example
-              const canvas = document.createElement('canvas');
-              canvas.width = 28;
-              canvas.height = 28;
-              canvas.style = 'margin: 4px;';
-              await tf.browser.toPixels(imageTensor, canvas);
-              drawArea.appendChild(canvas);
-
-              imageTensor.dispose();
-            }
-          }
-
-          document.querySelector('#show-examples')
-            .addEventListener('click', async (e) => showExamples());
-        </script>
-
-    </section>
-
-    <section>
-      <h2>Training Our Model</h2>
-      <p>
-        Our goal is to train a model to recognize similar digits. We have already written a tutorial on how to do so.
-        So in this
-        article we are going to focus on monitoring that training and also look at how well our model performs.
-      </p>
-
-      <p>First let us define a helper function to do our training.</p>
-
-      <script class='show-script'>
-        async function train(model, data, fitCallbacks) {
-          const BATCH_SIZE = 64;
-          const trainDataSize = 500;
-          const testDataSize = 100;
-
-          const [trainXs, trainYs] = tf.tidy(() => {
-            const d = data.nextTrainBatch(trainDataSize);
-            return [
-              d.xs.reshape([trainDataSize, 28, 28, 1]),
-              d.labels
-            ]
-          });
-
-          const [testXs, testYs] = tf.tidy(() => {
-            const d = data.nextTestBatch(testDataSize);
-            return [
-              d.xs.reshape([testDataSize, 28, 28, 1]),
-              d.labels
-            ]
-          });
-
-          return model.fit(trainXs, trainYs, {
-            batchSize: BATCH_SIZE,
-            validationData: [testXs, testYs],
-            epochs: 10,
-            shuffle: true,
-            callbacks: fitCallbacks
-          });
-        }
-      </script>
-
-      <p>
-        We can use the <code>show.fitCallbacks</code> method to get functions that will plot the loss after every batch
-        and
-        epoch.
-      </p>
-
-      <button id='start-training-1' disabled>Start Training</button>
-
-      <script class='show-script'>
-        async function watchTraining() {
-          const metrics = ['loss', 'val_loss', 'acc', 'val_acc'];
-          const container = {
-            name: 'show.fitCallbacks', tab: 'Training', styles: { height: '1000px' }
-          };
-          const callbacks = tfvis.show.fitCallbacks(container, metrics);
-          return train(model, data, callbacks);
-        }
-
-        document.querySelector('#start-training-1')
-          .addEventListener('click', () => watchTraining());
-      </script>
-
-      <p>
-        Another option is to wait for the training to complete and render the loss curve when it is done.
-      </p>
-
-      <button id='start-training-2' disabled>Start Training</button>
-
-      <script class='show-script'>
-        async function showTrainingHistory() {
-          const trainingHistory = await train(model, data);
-          tfvis.show.history({ name: 'Training History', tab: 'Training' },
-            trainingHistory, ['loss', 'val_loss', 'acc', 'val_acc']);
-        }
-
-        document.querySelector('#start-training-2')
-          .addEventListener('click', () => showTrainingHistory());
-      </script>
-
-      <h3>Customizing training charts.</h3>
-      <p>
-        The <code>show.fitCallbacks</code> function is designed to help you quickly plot training behaviour
-        with reasonable defaults. If you want to customize the rendering of these charts, you can use
-        <code>render.linechart</code> function. An example that plots accuracy values at the end
-        of every epoch using custom colors and a custom yaxis domain is shown below.
-      </p>
-
-      <button id='start-training-3' disabled>Custom Training Charts</button>
-
-      <script class='show-script'>
-        // An array to hold training logs
-        const epochLogs = [];
-        async function customTrainingCharts() {
-          const callbacks = {
-            onEpochEnd: function(epoch, log) {
-              const surface = {
-                name: 'Custom Training Charts',
-                tab: 'Training',
-              };
-              const options = {
-                xLabel: 'Epoch',
-                yLabel: 'Value',
-                yAxisDomain: [0, 1],
-                seriesColors: ['teal', 'tomato']
-              };
-
-              // Prep the data
-              epochLogs.push(log);
-              const acc = epochLogs.map((log, i) => ({ x: i, y: log.acc }));
-              const valAcc = epochLogs.map((log, i) => ({ x: i, y: log.val_acc }));
-              const data = {
-                values: [acc, valAcc],
-                // Custom names for the series
-                series: ['Accuracy', 'Validation Accuracy']
-              }
-
-              // render the chart
-              tfvis.render.linechart(surface, data, options);
-            }
-          }
-          return train(model, data, callbacks);
-        }
-
-        document.querySelector('#start-training-3')
-          .addEventListener('click', () => customTrainingCharts());
-      </script>
-
-    </section>
-
-    <section>
-      <h2>Evaluating Our Model</h2>
-      <p>
-        Now that our model is trained we should evalute its performance. For a classification task like this one we can
-        use the `perClassAccuracy`
-        and `confusionMatrix` functions. These are demonstrated below.
-      </p>
-
-      <p><button id='show-accuracy'>Show per-class accuracy</button></p>
-      <p><button id='show-confusion'>Show confusion matrix</button></p>
-
-      <script class='show-script'>
-        {
-          const classNames = ['Zero', 'One', 'Two', 'Three', 'Four', 'Five',
-            'Six', 'Seven', 'Eight', 'Nine'];
-
-          function doPrediction(testDataSize = 500) {
-            const testData = data.nextTestBatch(testDataSize);
-            const testxs = testData.xs.reshape([testDataSize, 28, 28, 1]);
-            const labels = testData.labels.argMax([-1]);
-            const preds = model.predict(testxs).argMax([-1]);
-
-            testxs.dispose();
-            return [preds, labels];
-          }
-
-          async function showAccuracy() {
-            const [preds, labels] = doPrediction();
-            const classAccuracy = await tfvis.metrics.perClassAccuracy(labels, preds);
-            const container = { name: 'Accuracy', tab: 'Evaluation' };
-            tfvis.show.perClassAccuracy(container, classAccuracy, classNames);
-
-            labels.dispose();
-          }
-
-          async function showConfusion() {
-            const [preds, labels] = doPrediction();
-            const confusionMatrix = await tfvis.metrics.confusionMatrix(labels, preds);
-            const container = { name: 'Confusion Matrix', tab: 'Evaluation' };
-            tfvis.render.confusionMatrix(container, {
-              values: confusionMatrix,
-              tickLabels: classNames,
-            });
-
-            labels.dispose();
-          }
-
-          document.querySelector('#show-accuracy')
-            .addEventListener('click', () => showAccuracy());
-
-          document.querySelector('#show-confusion')
-            .addEventListener('click', () => showConfusion());
-        }
-      </script>
-
-
-    </section>
-
-  </article>
-
-
-
-
-</body>
-
-</html>
diff --git a/tfjs-master/tfjs-vis/demos/mnist/index.js b/tfjs-master/tfjs-vis/demos/mnist/index.js
deleted file mode 100644
index 849aba183..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist/index.js
+++ /dev/null
@@ -1,62 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import * as tfvis from '@tensorflow/tfjs-vis'
-import {getModel, loadData} from './model';
-
-window.tf = tf;
-window.tfvis = tfvis;
-
-window.data;
-window.model;
-
-async function initData() {
-  window.data = await loadData();
-}
-
-function initModel() {
-  window.model = getModel();
-}
-
-function setupListeners() {
-  document.querySelector('#show-visor').addEventListener('click', () => {
-    const visorInstance = tfvis.visor();
-    if (!visorInstance.isOpen()) {
-      visorInstance.toggle();
-    }
-  });
-
-  document.querySelector('#make-first-surface')
-      .addEventListener('click', () => {
-        tfvis.visor().surface({name: 'My First Surface', tab: 'Input Data'});
-      });
-
-  document.querySelector('#load-data').addEventListener('click', async (e) => {
-    await initData();
-    document.querySelector('#show-examples').disabled = false;
-    document.querySelector('#start-training-1').disabled = false;
-    document.querySelector('#start-training-2').disabled = false;
-    document.querySelector('#start-training-3').disabled = false;
-    e.target.disabled = true;
-  });
-}
-
-document.addEventListener('DOMContentLoaded', function() {
-  initModel();
-  setupListeners();
-});
diff --git a/tfjs-master/tfjs-vis/demos/mnist/model.js b/tfjs-master/tfjs-vis/demos/mnist/model.js
deleted file mode 100644
index ed8bce574..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist/model.js
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import {MnistData} from './data';
-
-export function getModel() {
-  const model = tf.sequential();
-
-  model.add(tf.layers.conv2d({
-    inputShape: [28, 28, 1],
-    kernelSize: 5,
-    filters: 8,
-    strides: 1,
-    activation: 'relu',
-    kernelInitializer: 'varianceScaling'
-  }));
-
-  model.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
-  model.add(tf.layers.conv2d({
-    kernelSize: 5,
-    filters: 16,
-    strides: 1,
-    activation: 'relu',
-    kernelInitializer: 'varianceScaling'
-  }));
-
-  model.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
-  model.add(tf.layers.flatten());
-
-  model.add(tf.layers.dense({
-    units: 10,
-    kernelInitializer: 'varianceScaling',
-    activation: 'softmax'
-  }));
-
-  const LEARNING_RATE = 0.15;
-  const optimizer = tf.train.sgd(LEARNING_RATE);
-
-  model.compile({
-    optimizer: optimizer,
-    loss: 'categoricalCrossentropy',
-    metrics: ['accuracy'],
-  });
-
-  return model;
-}
-
-export async function loadData() {
-  const data = new MnistData();
-  await data.load();
-  return data;
-}
diff --git a/tfjs-master/tfjs-vis/demos/mnist/package.json b/tfjs-master/tfjs-vis/demos/mnist/package.json
deleted file mode 100644
index 3a2103310..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist/package.json
+++ /dev/null
@@ -1,29 +0,0 @@
-{
-  "name": "mnist-vis-demo",
-  "private": true,
-  "version": "0.0.1",
-  "description": "",
-  "main": "index.js",
-  "browserslist": "> 0.25%, not dead",
-  "scripts": {
-    "watch": "NODE_ENV=development parcel index.html --no-hmr --open --no-autoinstall",
-    "build": "NODE_ENV=production parcel build index.html --no-minify --detailed-report --public-url ./"
-  },
-  "author": "",
-  "license": "Apache-2.0",
-  "devDependencies": {
-    "@babel/core": "^7.21.0",
-    "@babel/plugin-transform-runtime": "^7.21.0",
-    "babel-preset-env": "~1.7.0",
-    "parcel": "2.8.3",
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-  resolved "https://registry.yarnpkg.com/core-js/-/core-js-1.2.7.tgz#652294c14651db28fa93bd2d5ff2983a4f08c636"
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-
-core-js@^2.4.0:
-  version "2.6.12"
-  resolved "https://registry.yarnpkg.com/core-js/-/core-js-2.6.12.tgz#d9333dfa7b065e347cc5682219d6f690859cc2ec"
-  integrity sha512-Kb2wC0fvsWfQrgk8HU5lW6U/Lcs8+9aaYcy4ZFc6DDlo4nZ7n70dEgE5rtR0oG6ufKDUnrwfWL1mXR5ljDatrQ==
-
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-  version "7.0.1"
-  resolved "https://registry.yarnpkg.com/cosmiconfig/-/cosmiconfig-7.0.1.tgz#714d756522cace867867ccb4474c5d01bbae5d6d"
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-    import-fresh "^3.2.1"
-    parse-json "^5.0.0"
-    path-type "^4.0.0"
-    yaml "^1.10.0"
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-css-in-js-utils@^2.0.0:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/css-in-js-utils/-/css-in-js-utils-2.0.1.tgz#3b472b398787291b47cfe3e44fecfdd9e914ba99"
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-    hyphenate-style-name "^1.0.2"
-    isobject "^3.0.1"
-
-css-select@^4.1.3:
-  version "4.2.1"
-  resolved "https://registry.yarnpkg.com/css-select/-/css-select-4.2.1.tgz#9e665d6ae4c7f9d65dbe69d0316e3221fb274cdd"
-  integrity sha512-/aUslKhzkTNCQUB2qTX84lVmfia9NyjP3WpDGtj/WxhwBzWBYUV3DgUpurHTme8UTPcPlAD1DJ+b0nN/t50zDQ==
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-    css-what "^5.1.0"
-    domhandler "^4.3.0"
-    domutils "^2.8.0"
-    nth-check "^2.0.1"
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-css-tree@^1.1.2, css-tree@^1.1.3:
-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/css-tree/-/css-tree-1.1.3.tgz#eb4870fb6fd7707327ec95c2ff2ab09b5e8db91d"
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-  dependencies:
-    mdn-data "2.0.14"
-    source-map "^0.6.1"
-
-css-what@^5.1.0:
-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/css-what/-/css-what-5.1.0.tgz#3f7b707aadf633baf62c2ceb8579b545bb40f7fe"
-  integrity sha512-arSMRWIIFY0hV8pIxZMEfmMI47Wj3R/aWpZDDxWYCPEiOMv6tfOrnpDtgxBYPEQD4V0Y/958+1TdC3iWTFcUPw==
-
-csso@^4.2.0:
-  version "4.2.0"
-  resolved "https://registry.yarnpkg.com/csso/-/csso-4.2.0.tgz#ea3a561346e8dc9f546d6febedd50187cf389529"
-  integrity sha512-wvlcdIbf6pwKEk7vHj8/Bkc0B4ylXZruLvOgs9doS5eOsOpuodOV2zJChSpkp+pRpYQLQMeF04nr3Z68Sta9jA==
-  dependencies:
-    css-tree "^1.1.2"
-
-"d3-array@1 - 2", d3-array@2, d3-array@^2.3.0, d3-array@^2.5.0, d3-array@^2.7.1:
-  version "2.12.1"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-2.12.1.tgz#e20b41aafcdffdf5d50928004ececf815a465e81"
-  integrity sha512-B0ErZK/66mHtEsR1TkPEEkwdy+WDesimkM5gpZr5Dsg54BiTA5RXtYW5qTLIAcekaS9xfZrzBLF/OAkB3Qn1YQ==
-  dependencies:
-    internmap "^1.0.0"
-
-"d3-array@1 - 3", "d3-array@2 - 3", "d3-array@2.10.0 - 3", "d3-array@2.5.0 - 3", d3-array@^3.1.1:
-  version "3.2.2"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-3.2.2.tgz#f8ac4705c5b06914a7e0025bbf8d5f1513f6a86e"
-  integrity sha512-yEEyEAbDrF8C6Ob2myOBLjwBLck1Z89jMGFee0oPsn95GqjerpaOA4ch+vc2l0FNFFwMD5N7OCSEN5eAlsUbgQ==
-  dependencies:
-    internmap "1 - 2"
-
-d3-array@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-3.1.1.tgz#7797eb53ead6b9083c75a45a681e93fc41bc468c"
-  integrity sha512-33qQ+ZoZlli19IFiQx4QEpf2CBEayMRzhlisJHSCsSUbDXv6ZishqS1x7uFVClKG4Wr7rZVHvaAttoLow6GqdQ==
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-    internmap "1 - 2"
-
-"d3-color@1 - 2", d3-color@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-2.0.0.tgz#8d625cab42ed9b8f601a1760a389f7ea9189d62e"
-  integrity sha512-SPXi0TSKPD4g9tw0NMZFnR95XVgUZiBH+uUTqQuDu1OsE2zomHU7ho0FISciaPvosimixwHFl3WHLGabv6dDgQ==
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-"d3-color@1 - 3", d3-color@^3.0.1:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-3.1.0.tgz#395b2833dfac71507f12ac2f7af23bf819de24e2"
-  integrity sha512-zg/chbXyeBtMQ1LbD/WSoW2DpC3I0mpmPdW+ynRTj/x2DAWYrIY7qeZIHidozwV24m4iavr15lNwIwLxRmOxhA==
-
-d3-delaunay@^5.3.0:
-  version "5.3.0"
-  resolved "https://registry.yarnpkg.com/d3-delaunay/-/d3-delaunay-5.3.0.tgz#b47f05c38f854a4e7b3cea80e0bb12e57398772d"
-  integrity sha512-amALSrOllWVLaHTnDLHwMIiz0d1bBu9gZXd1FiLfXf8sHcX9jrcj81TVZOqD4UX7MgBZZ07c8GxzEgBpJqc74w==
-  dependencies:
-    delaunator "4"
-
-"d3-dispatch@1 - 2":
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-dispatch/-/d3-dispatch-2.0.0.tgz#8a18e16f76dd3fcaef42163c97b926aa9b55e7cf"
-  integrity sha512-S/m2VsXI7gAti2pBoLClFFTMOO1HTtT0j99AuXLoGFKO6deHDdnv6ZGTxSTTUTgO1zVcv82fCOtDjYK4EECmWA==
-
-d3-dsv@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-2.0.0.tgz#b37b194b6df42da513a120d913ad1be22b5fe7c5"
-  integrity sha512-E+Pn8UJYx9mViuIUkoc93gJGGYut6mSDKy2+XaPwccwkRGlR+LO97L2VCCRjQivTwLHkSnAJG7yo00BWY6QM+w==
-  dependencies:
-    commander "2"
-    iconv-lite "0.4"
-    rw "1"
-
-d3-dsv@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-3.0.1.tgz#c63af978f4d6a0d084a52a673922be2160789b73"
-  integrity sha512-UG6OvdI5afDIFP9w4G0mNq50dSOsXHJaRE8arAS5o9ApWnIElp8GZw1Dun8vP8OyHOZ/QJUKUJwxiiCCnUwm+Q==
-  dependencies:
-    commander "7"
-    iconv-lite "0.6"
-    rw "1"
-
-d3-force@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/d3-force/-/d3-force-2.1.1.tgz#f20ccbf1e6c9e80add1926f09b51f686a8bc0937"
-  integrity sha512-nAuHEzBqMvpFVMf9OX75d00OxvOXdxY+xECIXjW6Gv8BRrXu6gAWbv/9XKrvfJ5i5DCokDW7RYE50LRoK092ew==
-  dependencies:
-    d3-dispatch "1 - 2"
-    d3-quadtree "1 - 2"
-    d3-timer "1 - 2"
-
-"d3-format@1 - 2", d3-format@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-2.0.0.tgz#a10bcc0f986c372b729ba447382413aabf5b0767"
-  integrity sha512-Ab3S6XuE/Q+flY96HXT0jOXcM4EAClYFnRGY5zsjRGNy6qCYrQsMffs7cV5Q9xejb35zxW5hf/guKw34kvIKsA==
-
-"d3-format@1 - 3", d3-format@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-3.1.0.tgz#9260e23a28ea5cb109e93b21a06e24e2ebd55641"
-  integrity sha512-YyUI6AEuY/Wpt8KWLgZHsIU86atmikuoOmCfommt0LYHiQSPjvX2AcFc38PX0CBpr2RCyZhjex+NS/LPOv6YqA==
-
-d3-format@~1.3.0:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-1.3.2.tgz#6a96b5e31bcb98122a30863f7d92365c00603562"
-  integrity sha512-Z18Dprj96ExragQ0DeGi+SYPQ7pPfRMtUXtsg/ChVIKNBCzjO8XYJvRTC1usblx52lqge56V5ect+frYTQc8WQ==
-
-d3-geo-projection@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-3.0.0.tgz#45ad8ce756cdbfa8340b11b2988644d8e1fa42e4"
-  integrity sha512-1JE+filVbkEX2bT25dJdQ05iA4QHvUwev6o0nIQHOSrNlHCAKfVss/U10vEM3pA4j5v7uQoFdQ4KLbx9BlEbWA==
-  dependencies:
-    commander "2"
-    d3-array "1 - 2"
-    d3-geo "1.12.0 - 2"
-    resolve "^1.1.10"
-
-d3-geo-projection@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-4.0.0.tgz#dc229e5ead78d31869a4e87cf1f45bd2716c48ca"
-  integrity sha512-p0bK60CEzph1iqmnxut7d/1kyTmm3UWtPlwdkM31AU+LW+BXazd5zJdoCn7VFxNCHXRngPHRnsNn5uGjLRGndg==
-  dependencies:
-    commander "7"
-    d3-array "1 - 3"
-    d3-geo "1.12.0 - 3"
-
-"d3-geo@1.12.0 - 2", d3-geo@^2.0.1:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-2.0.2.tgz#c065c1b71fe8c5f1be657e5f43d9bdd010383c40"
-  integrity sha512-8pM1WGMLGFuhq9S+FpPURxic+gKzjluCD/CHTuUF3mXMeiCo0i6R0tO1s4+GArRFde96SLcW/kOFRjoAosPsFA==
-  dependencies:
-    d3-array "^2.5.0"
-
-"d3-geo@1.12.0 - 3", d3-geo@^3.0.1:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-3.1.0.tgz#74fd54e1f4cebd5185ac2039217a98d39b0a4c0e"
-  integrity sha512-JEo5HxXDdDYXCaWdwLRt79y7giK8SbhZJbFWXqbRTolCHFI5jRqteLzCsq51NKbUoX0PjBVSohxrx+NoOUujYA==
-  dependencies:
-    d3-array "2.5.0 - 3"
-
-d3-hierarchy@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-hierarchy/-/d3-hierarchy-2.0.0.tgz#dab88a58ca3e7a1bc6cab390e89667fcc6d20218"
-  integrity sha512-SwIdqM3HxQX2214EG9GTjgmCc/mbSx4mQBn+DuEETubhOw6/U3fmnji4uCVrmzOydMHSO1nZle5gh6HB/wdOzw==
-
-"d3-interpolate@1.2.0 - 2", d3-interpolate@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-2.0.1.tgz#98be499cfb8a3b94d4ff616900501a64abc91163"
-  integrity sha512-c5UhwwTs/yybcmTpAVqwSFl6vrQ8JZJoT5F7xNFK9pymv5C0Ymcc9/LIJHtYIggg/yS9YHw8i8O8tgb9pupjeQ==
-  dependencies:
-    d3-color "1 - 2"
-
-"d3-interpolate@1.2.0 - 3", d3-interpolate@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-3.0.1.tgz#3c47aa5b32c5b3dfb56ef3fd4342078a632b400d"
-  integrity sha512-3bYs1rOD33uo8aqJfKP3JWPAibgw8Zm2+L9vBKEHJ2Rg+viTR7o5Mmv5mZcieN+FRYaAOWX5SJATX6k1PWz72g==
-  dependencies:
-    d3-color "1 - 3"
-
-"d3-path@1 - 2", d3-path@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-2.0.0.tgz#55d86ac131a0548adae241eebfb56b4582dd09d8"
-  integrity sha512-ZwZQxKhBnv9yHaiWd6ZU4x5BtCQ7pXszEV9CU6kRgwIQVQGLMv1oiL4M+MK/n79sYzsj+gcgpPQSctJUsLN7fA==
-
-d3-path@^3.0.1, d3-path@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-3.1.0.tgz#22df939032fb5a71ae8b1800d61ddb7851c42526"
-  integrity sha512-p3KP5HCf/bvjBSSKuXid6Zqijx7wIfNW+J/maPs+iwR35at5JCbLUT0LzF1cnjbCHWhqzQTIN2Jpe8pRebIEFQ==
-
-"d3-quadtree@1 - 2":
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-quadtree/-/d3-quadtree-2.0.0.tgz#edbad045cef88701f6fee3aee8e93fb332d30f9d"
-  integrity sha512-b0Ed2t1UUalJpc3qXzKi+cPGxeXRr4KU9YSlocN74aTzp6R/Ud43t79yLLqxHRWZfsvWXmbDWPpoENK1K539xw==
-
-d3-scale@^3.2.2:
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-3.3.0.tgz#28c600b29f47e5b9cd2df9749c206727966203f3"
-  integrity sha512-1JGp44NQCt5d1g+Yy+GeOnZP7xHo0ii8zsQp6PGzd+C1/dl0KGsp9A7Mxwp+1D1o4unbTTxVdU/ZOIEBoeZPbQ==
-  dependencies:
-    d3-array "^2.3.0"
-    d3-format "1 - 2"
-    d3-interpolate "1.2.0 - 2"
-    d3-time "^2.1.1"
-    d3-time-format "2 - 3"
-
-d3-scale@^4.0.2:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-4.0.2.tgz#82b38e8e8ff7080764f8dcec77bd4be393689396"
-  integrity sha512-GZW464g1SH7ag3Y7hXjf8RoUuAFIqklOAq3MRl4OaWabTFJY9PN/E1YklhXLh+OQ3fM9yS2nOkCoS+WLZ6kvxQ==
-  dependencies:
-    d3-array "2.10.0 - 3"
-    d3-format "1 - 3"
-    d3-interpolate "1.2.0 - 3"
-    d3-time "2.1.1 - 3"
-    d3-time-format "2 - 4"
-
-d3-selection@~1.3.0:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/d3-selection/-/d3-selection-1.3.2.tgz#6e70a9df60801c8af28ac24d10072d82cbfdf652"
-  integrity sha512-OoXdv1nZ7h2aKMVg3kaUFbLLK5jXUFAMLD/Tu5JA96mjf8f2a9ZUESGY+C36t8R1WFeWk/e55hy54Ml2I62CRQ==
-
-d3-shape@^2.0.0:
-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-2.1.0.tgz#3b6a82ccafbc45de55b57fcf956c584ded3b666f"
-  integrity sha512-PnjUqfM2PpskbSLTJvAzp2Wv4CZsnAgTfcVRTwW03QR3MkXF8Uo7B1y/lWkAsmbKwuecto++4NlsYcvYpXpTHA==
-  dependencies:
-    d3-path "1 - 2"
-
-d3-shape@^3.1.0:
-  version "3.2.0"
-  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-3.2.0.tgz#a1a839cbd9ba45f28674c69d7f855bcf91dfc6a5"
-  integrity sha512-SaLBuwGm3MOViRq2ABk3eLoxwZELpH6zhl3FbAoJ7Vm1gofKx6El1Ib5z23NUEhF9AsGl7y+dzLe5Cw2AArGTA==
-  dependencies:
-    d3-path "^3.1.0"
-
-"d3-time-format@2 - 3", d3-time-format@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-time-format/-/d3-time-format-3.0.0.tgz#df8056c83659e01f20ac5da5fdeae7c08d5f1bb6"
-  integrity sha512-UXJh6EKsHBTjopVqZBhFysQcoXSv/5yLONZvkQ5Kk3qbwiUYkdX17Xa1PT6U1ZWXGGfB1ey5L8dKMlFq2DO0Ag==
-  dependencies:
-    d3-time "1 - 2"
-
-"d3-time-format@2 - 4", d3-time-format@^4.1.0:
-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/d3-time-format/-/d3-time-format-4.1.0.tgz#7ab5257a5041d11ecb4fe70a5c7d16a195bb408a"
-  integrity sha512-dJxPBlzC7NugB2PDLwo9Q8JiTR3M3e4/XANkreKSUxF8vvXKqm1Yfq4Q5dl8budlunRVlUUaDUgFt7eA8D6NLg==
-  dependencies:
-    d3-time "1 - 3"
-
-"d3-time@1 - 2", d3-time@^2.0.0, d3-time@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-2.1.1.tgz#e9d8a8a88691f4548e68ca085e5ff956724a6682"
-  integrity sha512-/eIQe/eR4kCQwq7yxi7z4c6qEXf2IYGcjoWB5OOQy4Tq9Uv39/947qlDcN2TLkiTzQWzvnsuYPB9TrWaNfipKQ==
-  dependencies:
-    d3-array "2"
-
-"d3-time@1 - 3", "d3-time@2.1.1 - 3", d3-time@^3.0.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-3.1.0.tgz#9310db56e992e3c0175e1ef385e545e48a9bb5c7"
-  integrity sha512-VqKjzBLejbSMT4IgbmVgDjpkYrNWUYJnbCGo874u7MMKIWsILRX+OpX/gTk8MqjpT1A/c6HY2dCA77ZN0lkQ2Q==
-  dependencies:
-    d3-array "2 - 3"
-
-"d3-timer@1 - 2", d3-timer@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-timer/-/d3-timer-2.0.0.tgz#055edb1d170cfe31ab2da8968deee940b56623e6"
-  integrity sha512-TO4VLh0/420Y/9dO3+f9abDEFYeCUr2WZRlxJvbp4HPTQcSylXNiL6yZa9FIUvV1yRiFufl1bszTCLDqv9PWNA==
-
-debug@^2.6.8:
-  version "2.6.9"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-2.6.9.tgz#5d128515df134ff327e90a4c93f4e077a536341f"
-  integrity sha512-bC7ElrdJaJnPbAP+1EotYvqZsb3ecl5wi6Bfi6BJTUcNowp6cvspg0jXznRTKDjm/E7AdgFBVeAPVMNcKGsHMA==
-  dependencies:
-    ms "2.0.0"
-
-debug@^4.1.0, debug@^4.1.1:
-  version "4.3.3"
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-  resolved "https://registry.yarnpkg.com/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz#1b61c0562190a8dff6ae3bb2cf0200ca130b86d4"
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-  resolved "https://registry.yarnpkg.com/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz#874bf69c6f404c2b5d99c481341399fd55892633"
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-vega-format@^1.0.4, vega-format@^1.1.0:
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-vega-functions@^5.10.0, vega-functions@^5.12.1:
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-  resolved "https://registry.yarnpkg.com/vega-functions/-/vega-functions-5.13.0.tgz#c9ab8c6eedbf39f75b424cca6776b1d0b8c74b32"
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-    vega-statistics "^1.7.9"
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-    vega-util "^1.16.0"
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-  resolved "https://registry.yarnpkg.com/vega-functions/-/vega-functions-5.12.1.tgz#b69f9ad4cd9f777dbc942587c02261b2f4cdba2c"
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-    vega-statistics "^1.7.9"
-    vega-time "^2.0.4"
-    vega-util "^1.16.0"
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-    vega-statistics "^1.7.9"
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-  version "4.0.9"
-  resolved "https://registry.yarnpkg.com/vega-hierarchy/-/vega-hierarchy-4.0.9.tgz#4b4bafbc181a14a280ecdbee8874c0db7e369f47"
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-    vega-util "^1.15.2"
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-vega-label@~1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/vega-label/-/vega-label-1.0.0.tgz#c3bea3a608a62217ca554ecc0f7fe0395d81bd1b"
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-vega-lite@4.13.1:
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-  resolved "https://registry.yarnpkg.com/vega-lite/-/vega-lite-4.13.1.tgz#e9ee31c76cb93565e287a760700e432e57244064"
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-
-vega-loader@^4.3.2, vega-loader@^4.3.3, vega-loader@^4.4.0:
-  version "4.5.0"
-  resolved "https://registry.yarnpkg.com/vega-loader/-/vega-loader-4.5.0.tgz#b15acc4c8f84191f500e94d35ddfb9721ac943ad"
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-    vega-util "^1.16.0"
-
-vega-loader@~4.4.0:
-  version "4.4.1"
-  resolved "https://registry.yarnpkg.com/vega-loader/-/vega-loader-4.4.1.tgz#8f9de46202f33659d1a2737f6e322a9fc3364275"
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-    topojson-client "^3.1.0"
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-    vega-util "^1.16.0"
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-  version "6.1.4"
-  resolved "https://registry.yarnpkg.com/vega-parser/-/vega-parser-6.1.4.tgz#4868e41af2c9645b6d7daeeb205cfad06b9d465c"
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-    vega-util "^1.16.0"
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-vega-projection@^1.4.5:
-  version "1.5.0"
-  resolved "https://registry.yarnpkg.com/vega-projection/-/vega-projection-1.5.0.tgz#51c5f0455170cd35b3c5f3e653e99c3616520640"
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-    d3-geo-projection "^4.0.0"
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-vega-projection@~1.4.5:
-  version "1.4.5"
-  resolved "https://registry.yarnpkg.com/vega-projection/-/vega-projection-1.4.5.tgz#020cb646b4eaae535359da25f4f48eef8d324af2"
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-  version "1.0.9"
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-vega-runtime@^6.1.3, vega-runtime@~6.1.3:
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-    vega-util "^1.17.0"
-
-vega-scale@~7.1.1:
-  version "7.1.1"
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-vega-scenegraph@^4.9.2, vega-scenegraph@^4.9.3, vega-scenegraph@^4.9.4:
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-  resolved "https://registry.yarnpkg.com/vega-scenegraph/-/vega-scenegraph-4.10.1.tgz#944da67b8a28758fab2e1306259fb7ff6be89f6b"
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-vega-schema-url-parser@^2.1.0:
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-    vega-voronoi "~4.1.5"
-    vega-wordcloud "~4.1.3"
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-    webidl-conversions "^3.0.0"
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/which-module/-/which-module-2.0.0.tgz#d9ef07dce77b9902b8a3a8fa4b31c3e3f7e6e87a"
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-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
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-  version "7.0.0"
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-
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-  version "4.0.3"
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-  version "5.0.5"
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-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.1.1.tgz#dbb7daf9bfd8bac9ab45ebf602b8cbad0d5d08fd"
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-  version "1.10.2"
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diff --git a/tfjs-master/tfjs-vis/demos/mnist_internals/README.md b/tfjs-master/tfjs-vis/demos/mnist_internals/README.md
deleted file mode 100644
index 7c2e2ce97..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist_internals/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# tfjs-vis mnist internals demo
-
-A demonstration of using tfjs-vis to look at model internals such as summaries and
-activations
-
-See a live version of this [here](https://storage.googleapis.com/tfjs-vis/mnist_internals/dist/index.html).
-
-To run this locally:
-- run `yarn build-npm` in the `tfjs-vis` folder
-- run `yarn watch` from the `demos` folder
diff --git a/tfjs-master/tfjs-vis/demos/mnist_internals/data.js b/tfjs-master/tfjs-vis/demos/mnist_internals/data.js
deleted file mode 100644
index 744fb5773..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist_internals/data.js
+++ /dev/null
@@ -1,144 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-
-const IMAGE_SIZE = 784;
-const NUM_CLASSES = 10;
-const NUM_DATASET_ELEMENTS = 65000;
-
-const NUM_TRAIN_ELEMENTS = 55000;
-const NUM_TEST_ELEMENTS = NUM_DATASET_ELEMENTS - NUM_TRAIN_ELEMENTS;
-
-const MNIST_IMAGES_SPRITE_PATH =
-    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_images.png';
-const MNIST_LABELS_PATH =
-    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_labels_uint8';
-
-/**
- * A class that fetches the sprited MNIST dataset and returns shuffled batches.
- *
- * NOTE: This will get much easier. For now, we do data fetching and
- * manipulation manually.
- */
-export class MnistData {
-  constructor() {
-    this.shuffledTrainIndex = 0;
-    this.shuffledTestIndex = 0;
-  }
-
-  async load() {
-    // Make a request for the MNIST sprited image.
-    const img = new Image();
-    const canvas = document.createElement('canvas');
-    const ctx = canvas.getContext('2d');
-    const imgRequest = new Promise((resolve, reject) => {
-      img.crossOrigin = '';
-      img.onload = () => {
-        img.width = img.naturalWidth;
-        img.height = img.naturalHeight;
-
-        const datasetBytesBuffer =
-            new ArrayBuffer(NUM_DATASET_ELEMENTS * IMAGE_SIZE * 4);
-
-        const chunkSize = 5000;
-        canvas.width = img.width;
-        canvas.height = chunkSize;
-
-        for (let i = 0; i < NUM_DATASET_ELEMENTS / chunkSize; i++) {
-          const datasetBytesView = new Float32Array(
-              datasetBytesBuffer, i * IMAGE_SIZE * chunkSize * 4,
-              IMAGE_SIZE * chunkSize);
-          ctx.drawImage(
-              img, 0, i * chunkSize, img.width, chunkSize, 0, 0, img.width,
-              chunkSize);
-
-          const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
-
-          for (let j = 0; j < imageData.data.length / 4; j++) {
-            // All channels hold an equal value since the image is grayscale, so
-            // just read the red channel.
-            datasetBytesView[j] = imageData.data[j * 4] / 255;
-          }
-        }
-        this.datasetImages = new Float32Array(datasetBytesBuffer);
-
-        resolve();
-      };
-      img.src = MNIST_IMAGES_SPRITE_PATH;
-    });
-
-    const labelsRequest = fetch(MNIST_LABELS_PATH);
-    const [imgResponse, labelsResponse] =
-        await Promise.all([imgRequest, labelsRequest]);
-
-    this.datasetLabels = new Uint8Array(await labelsResponse.arrayBuffer());
-
-    // Create shuffled indices into the train/test set for when we select a
-    // random dataset element for training / validation.
-    this.trainIndices = tf.util.createShuffledIndices(NUM_TRAIN_ELEMENTS);
-    this.testIndices = tf.util.createShuffledIndices(NUM_TEST_ELEMENTS);
-
-    // Slice the the images and labels into train and test sets.
-    this.trainImages =
-        this.datasetImages.slice(0, IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
-    this.testImages = this.datasetImages.slice(IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
-    this.trainLabels =
-        this.datasetLabels.slice(0, NUM_CLASSES * NUM_TRAIN_ELEMENTS);
-    this.testLabels =
-        this.datasetLabels.slice(NUM_CLASSES * NUM_TRAIN_ELEMENTS);
-  }
-
-  nextTrainBatch(batchSize) {
-    return this.nextBatch(
-        batchSize, [this.trainImages, this.trainLabels], () => {
-          this.shuffledTrainIndex =
-              (this.shuffledTrainIndex + 1) % this.trainIndices.length;
-          return this.trainIndices[this.shuffledTrainIndex];
-        });
-  }
-
-  nextTestBatch(batchSize) {
-    return this.nextBatch(batchSize, [this.testImages, this.testLabels], () => {
-      this.shuffledTestIndex =
-          (this.shuffledTestIndex + 1) % this.testIndices.length;
-      return this.testIndices[this.shuffledTestIndex];
-    });
-  }
-
-  nextBatch(batchSize, data, index) {
-    const batchImagesArray = new Float32Array(batchSize * IMAGE_SIZE);
-    const batchLabelsArray = new Uint8Array(batchSize * NUM_CLASSES);
-
-    for (let i = 0; i < batchSize; i++) {
-      const idx = index();
-
-      const image =
-          data[0].slice(idx * IMAGE_SIZE, idx * IMAGE_SIZE + IMAGE_SIZE);
-      batchImagesArray.set(image, i * IMAGE_SIZE);
-
-      const label =
-          data[1].slice(idx * NUM_CLASSES, idx * NUM_CLASSES + NUM_CLASSES);
-      batchLabelsArray.set(label, i * NUM_CLASSES);
-    }
-
-    const xs = tf.tensor2d(batchImagesArray, [batchSize, IMAGE_SIZE]);
-    const labels = tf.tensor2d(batchLabelsArray, [batchSize, NUM_CLASSES]);
-
-    return {xs, labels};
-  }
-}
diff --git a/tfjs-master/tfjs-vis/demos/mnist_internals/index.html b/tfjs-master/tfjs-vis/demos/mnist_internals/index.html
deleted file mode 100644
index d2d35e1e7..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist_internals/index.html
+++ /dev/null
@@ -1,477 +0,0 @@
-<!-- /**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */ -->
-<html>
-
-<head>
-  <meta charset="utf-8">
-  <link rel="stylesheet" href="./tufte.css" />
-  <script src='https://cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min.js'></script>
-  <script type="module" src="index.js"></script>
-
-  <style>
-    .lang-javascript {
-      width: 60%;
-      padding-left: 15px;
-      font-family: monospace;
-    }
-
-    script.show-script {
-      display: block;
-      max-width: 720px;
-      background-color: floralwhite;
-      font-family: "Lucida Console", Monaco, "Courier New", Courier, monospace;
-      font-size: 12px;
-      margin-left: -40px;
-      white-space: pre;
-      margin-top: 2em;
-    }
-
-    code {
-      background-color: #eee;
-      padding: 2px 4px 2px 4px;
-    }
-
-    #mnist-examples {
-      max-width: 720px;
-    }
-  </style>
-
-  <style>
-  </style>
-</head>
-
-<body>
-  <article>
-    <h1>Looking inside a digit recognizer</h1>
-
-    <section>
-      <p>
-        Let's take a look at the internals of the model we trained on the MNIST dataset
-        <a href="https://github.com/tensorflow/tfjs-vis/tree/master/demos/mnist">previously</a>.
-      </p>
-    </section>
-
-    <section>
-      <h2>Setup</h2>
-      <p>
-        We'll load our data and train our model from scratch since it trains pretty quickly.
-      </p>
-      <p>
-        <button id='load-data'>Load Data</button>
-        <button id='start-training-1' disabled>Start Training</button>
-      </p>
-
-      <div id='mnist-examples'></div>
-
-      <script>
-        async function train(model, data, fitCallbacks) {
-          const BATCH_SIZE = 64;
-          const trainDataSize = 500;
-          const testDataSize = 100;
-
-          const [trainXs, trainYs] = tf.tidy(() => {
-            const d = data.nextTrainBatch(trainDataSize);
-            return [
-              d.xs.reshape([trainDataSize, 28, 28, 1]),
-              d.labels
-            ]
-          });
-
-          const [testXs, testYs] = tf.tidy(() => {
-            const d = data.nextTestBatch(testDataSize);
-            return [
-              d.xs.reshape([testDataSize, 28, 28, 1]),
-              d.labels
-            ]
-          });
-
-          return model.fit(trainXs, trainYs, {
-            batchSize: BATCH_SIZE,
-            validationData: [testXs, testYs],
-            epochs: 10,
-            shuffle: true,
-            callbacks: fitCallbacks
-          });
-        }
-      </script>
-
-      <script>
-        async function watchTraining1() {
-          const metrics = ['acc'];
-          const container = { name: 'Model Accuracy', tab: 'Training' };
-          const callbacks = tfvis.show.fitCallbacks(container, metrics);
-          return train(model, data, callbacks);
-        }
-
-        document.querySelector('#start-training-1')
-          .addEventListener('click', () => watchTraining1());
-      </script>
-
-    </section>
-
-    <section>
-      <h2>Our Model</h2>
-      <p>
-        We can use the <code>show.modelSummary</code> to see a summary of the layers present in our model.
-      </p>
-      <p><button id='show-model'>Show Model</button></p>
-
-      <script class='show-script'>
-        async function showModel() {
-          const surface = { name: 'Model Summary', tab: 'Model' };
-          tfvis.show.modelSummary(surface, model);
-        }
-
-        document.querySelector('#show-model').addEventListener('click', showModel);
-      </script>
-
-      <p>
-        This model is a 'convolutional' model, a common model type for image processing applications. They get their
-        name from the
-        'convolution' operation implemented by some of their layers. There are 3 layers of interest we will focus on in
-        this
-        example:
-      </p>
-      <ul>
-        <li>
-          <strong>conv2d_Conv2D1:</strong> The first convolution layer.
-        </li>
-        <li>
-          <strong>conv2d_Conv2D2:</strong> The second convolution layer.
-        </li>
-        <li>
-          <strong>dense_Dense1:</strong> The 'dense' layer that gives us our final classification.
-        </li>
-      </ul>
-
-    </section>
-
-    <section>
-      <h3>Conv2d1: The first convolution</h3>
-
-      <p>
-        Convolutional layers are able to capture local spatial patterns in images due to how the convolution operation
-        works. Convolution
-        works similarly to image filters in common image editing tools or
-        <a href="https://developer.mozilla.org/en-US/docs/Web/CSS/filter">css filters.</a> In this case they are applied
-        to the image to extract information useful for classification. You can learn more about how convolution works
-        from
-        this
-        <a href="http://setosa.io/ev/image-kernels/">interactive explanation.</a> The training process learns
-        appropriate
-        'filters' to help with the digit recognition task.
-      </p>
-
-      <p>
-        Before we take an in depth look at the filters we can get some summary information about this layer using the
-        <code>show.layer</code> api.
-      </p>
-
-      <p><button id='show-conv1'>Show conv2d_Conv2D1 Details</button></p>
-
-      <script class='show-script'>
-        async function showConv2d1() {
-          const conv1Surface = { name: 'Conv2D1 Details', tab: 'Model' };
-          tfvis.show.layer(conv1Surface, model.getLayer('conv2d_Conv2D1'));
-        }
-
-        document.querySelector('#show-conv1').addEventListener('click', showConv2d1);
-      </script>
-
-      <p>
-        The <em>conv2d_Conv2D1/kernel</em> weight represents the filters used in this convolution. Lets take a look at
-        these
-        filters and how they transform the output.
-      </p>
-
-      <p>
-        First we will define some helper functions.
-      </p>
-
-      <script class='show-script'>
-        // An 'activation' is the output of any of the internal layers of the
-        // network.
-        function getActivation(input, model, layer) {
-          const activationModel = tf.model({
-            inputs: model.input,
-            outputs: layer.output,
-          });
-
-          return activationModel.predict(input);
-        }
-
-        // Render a tensor as an image on canvas object. We will render the
-        // activations from the convolutional layers.
-        async function renderImage(container, tensor, imageOpts) {
-          const resized = tf.tidy(() =>
-            tf.image.resizeNearestNeighbor(tensor,
-              [imageOpts.height, imageOpts.width]).clipByValue(0.0, 1.0)
-          );
-
-          const canvas = container.querySelector('canvas') || document.createElement('canvas');
-          canvas.width = imageOpts.width;
-          canvas.height = imageOpts.height;
-          canvas.style = `margin: 4px; width:${imageOpts.width}px; height:${imageOpts.height}px`;
-          container.appendChild(canvas);
-          await tf.browser.toPixels(resized, canvas);
-          resized.dispose();
-        }
-
-        // Render a table of images, we will show the output for each filter
-        // in the convolution.
-        function renderImageTable(container, headerData, data) {
-          let table = d3.select(container).select('table');
-          if (table.size() === 0) {
-            table = d3.select(container).append('table');
-            table.append('thead').append('tr');
-            table.append('tbody');
-          }
-
-          const headers = table.select('thead').select('tr').selectAll('th').data(headerData);
-          const headersEnter = headers.enter().append('th')
-          headers.merge(headersEnter).each((d, i, group) => {
-            const node = group[i];
-            if (typeof d == 'string') {
-              node.innerHTML = d;
-            } else {
-              renderImage(node, d, { width: 25, height: 25 });
-            }
-          });
-
-          const rows = table.select('tbody').selectAll('tr').data(data);
-          const rowsEnter = rows.enter().append('tr');
-
-          const cells = rows.merge(rowsEnter).selectAll('td').data(d => d);
-          const cellsEnter = cells.enter().append('td');
-          cells.merge(cellsEnter).each((d, i, group) => {
-            const node = group[i];
-            renderImage(node, d, { width: 40, height: 40 });
-          })
-
-          cells.exit().remove();
-          rows.exit().remove();
-        }
-
-        function getActivationTable(layerName) {
-          const exampleImageSize = 28;
-
-          const layer = model.getLayer(layerName);
-
-          // Get the filters
-          let filters = tf.tidy(() => layer.kernel.val.transpose([3, 0, 1, 2]).unstack());
-          // It is hard to draw high dimensional filters so we just use a string
-          if (filters[0].shape[2] > 3) {
-            filters = filters.map((d, i) => `Filter ${i + 1}`);
-          }
-          filters.unshift('Input');
-
-          // Get the inputs
-          const numExamples = examples.xs.shape[0];
-          const xs = examples.xs.reshape([numExamples, exampleImageSize, exampleImageSize, 1]);
-
-          // Get the activations
-          const activations = tf.tidy(() => {
-            return getActivation(xs, model, layer).unstack();
-          });
-          const activationImageSize = activations[0].shape[0]; // e.g. 24
-          const numFilters = activations[0].shape[2]; // e.g. 8
-
-
-          const filterActivations = activations.map((activation, i) => {
-            // activation has shape [activationImageSize, activationImageSize, i];
-            const unpackedActivations = Array(numFilters).fill(0).map((_, i) =>
-              activation.slice([0, 0, i], [activationImageSize, activationImageSize, 1])
-            );
-
-            // prepend the input image
-            const inputExample = tf.tidy(() =>
-              xs.slice([i], [1]).reshape([exampleImageSize, exampleImageSize, 1]));
-
-            unpackedActivations.unshift(inputExample);
-            return unpackedActivations;
-          });
-
-          return {
-            filters,
-            filterActivations,
-          };
-        }
-      </script>
-
-      <p>With that we can look at how this layer transforms some example images.</p>
-
-      <p><button id='conv1'>Conv Layer 1 Activations</button></p>
-
-      <script class='show-script'>
-
-        async function conv2d1ActivationTable() {
-          const surface =
-            tfvis.visor().surface({
-              name: 'Conv2D1 Activations', tab: 'Model',
-              styles: {
-                height: 650,
-              },
-            });
-          const drawArea = surface.drawArea;
-
-          const { filters, filterActivations } = getActivationTable('conv2d_Conv2D1');
-
-          renderImageTable(drawArea, filters, filterActivations);
-        }
-
-        document.querySelector('#conv1').addEventListener('click', conv2d1ActivationTable);
-      </script>
-
-      <p>This layer learns filters that tends to detect lower level features like edges.</p>
-
-    </section>
-
-    <section>
-      <h3>Conv2d2: The second convolution</h3>
-
-      <p>
-        The second convolutional layer extracts higher level features from the output of the prior layer. This layer has
-        twice as
-        many filters as the previous convolutional layer. 16 different 'images' will be produced for each input tensor
-        to
-        this layer. We can see a summary of this layer and then look at the resulting activations.
-      </p>
-
-      <p><button id='show-conv2'>Show conv2d_Conv2D2 Details</button></p>
-
-      <script class='show-script'>
-        async function showConv2d2() {
-          const conv1Surface = { name: 'Conv2D2 Details', tab: 'Model' };
-          tfvis.show.layer(conv1Surface, model.getLayer('conv2d_Conv2D2'));
-        }
-
-        document.querySelector('#show-conv2').addEventListener('click', showConv2d2);
-      </script>
-
-      <p><button id='conv2'>Conv Layer 2 Activations</button></p>
-      <script class='show-script'>
-        async function conv2d2ActivationTable() {
-          const surface =
-            tfvis.visor().surface({
-              name: 'Conv2D2 Activations',
-              tab: 'Model',
-              styles: {
-                width: 1000,
-                height: 650,
-              },
-            });
-          const drawArea = surface.drawArea;
-
-          const { filters, filterActivations } = getActivationTable('conv2d_Conv2D2');
-
-          renderImageTable(drawArea, filters, filterActivations);
-        }
-
-        document.querySelector('#conv2').addEventListener('click', conv2d2ActivationTable);
-      </script>
-    </section>
-
-    <section>
-      <h3>dense_Dense1: The Dense layer</h3>
-
-      <p>
-        The dense layer is responsible for combining all the information from previous layers by multipying those values
-        with its
-        weights to generate the final prediction.
-      </p>
-
-      <p><button id='show-dense'>Show dense_Dense1 Details</button></p>
-
-      <script class='show-script'>
-        async function showDense() {
-          const denseSurface = { name: 'Dense1 Details', tab: 'Model' };
-          tfvis.show.layer(denseSurface, model.getLayer('dense_Dense1'));
-        }
-
-        document.querySelector('#show-dense').addEventListener('click', showDense);
-      </script>
-
-      <p>
-        The 'activations' from this layer are actually our models final output. 10 values are output that represent the
-        probablility
-        that the image represents that digit (0-9).
-      </p>
-
-      <p><button id='dense1'>Show Dense activations</button></p>
-      <script class='show-script'>
-        async function denseWeights() {
-          const surface =
-            tfvis.visor().surface({
-              name: 'Dense Activations',
-              tab: 'Model',
-              styles: {
-                height: 650,
-              },
-            });
-          const drawArea = surface.drawArea;
-          drawArea.innerHTML = '';
-
-          const exampleImageSize = 28;
-          const numExamples = examples.xs.shape[0];
-          const xs = examples.xs.reshape([numExamples, exampleImageSize, exampleImageSize, 1]);
-
-          const denseAct = getActivation(xs, model, model.getLayer('dense_Dense1'));
-
-          for (let i = 0; i < numExamples; i++) {
-            // Get the activation frot this example
-            const activation = denseAct.slice([i], [1]);
-
-
-            // Get the individual example image
-            const imageTensor = xs.slice([i], [1]).reshape([exampleImageSize, exampleImageSize, 1]);
-            // Make a div to render the image and bar chart into
-            const container = document.createElement('div');
-            container.innerHTML = '<span class="image"></span><span class="chart"></span>';
-            surface.drawArea.appendChild(container);
-            const imageSurface = container.querySelector('.image');
-            const chartSurface = container.querySelector('.chart');
-
-            const barchartData = Array.from(activation.dataSync())
-              .map((d, i) => {
-                return { index: i, value: d };
-              });
-            renderImage(imageSurface, imageTensor, { width: 50, height: 50 });
-            tfvis.render.barchart(chartSurface, barchartData, { width: 375, height: 60 });
-          }
-        }
-
-        document.querySelector('#dense1').addEventListener('click', denseWeights);
-      </script>
-
-
-    </section>
-
-    <section>
-      <h2>That's it</h2>
-      <p>
-        An interesting exercise for you is to reload this page, load the data and then look at the activations
-        <em>before</em>
-        training the model, this will show you what those actications look like when the model is randomly initialized.
-        Compare what
-        these look like to activations produced after the model is trained.
-      </p>
-    </section>
-
-  </article>
-</body>
-
-</html>
diff --git a/tfjs-master/tfjs-vis/demos/mnist_internals/index.js b/tfjs-master/tfjs-vis/demos/mnist_internals/index.js
deleted file mode 100644
index 2c7f9c897..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist_internals/index.js
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import * as tfvis from '@tensorflow/tfjs-vis'
-import {getModel, loadData} from './model';
-
-window.tf = tf;
-window.tfvis = tfvis;
-
-window.data;
-window.model;
-
-async function initData() {
-  window.data = await loadData();
-  window.examples = data.nextTestBatch(10)
-
-  showExamples(document.querySelector('#mnist-examples'), 200);
-}
-
-function initModel() {
-  window.model = getModel();
-}
-
-async function showExamples(drawArea, numExamples) {
-  // Get the examples
-  const examples = data.nextTestBatch(numExamples);
-  const tensorsToDispose = [];
-  const drawPromises = [];
-  for (let i = 0; i < numExamples; i++) {
-    const imageTensor = tf.tidy(() => {
-      return examples.xs.slice([i, 0], [1, examples.xs.shape[1]]).reshape([
-        28, 28, 1
-      ]);
-    });
-
-    // Create a canvas element to render each example
-    const canvas = document.createElement('canvas');
-    canvas.width = 28;
-    canvas.height = 28;
-    canvas.style = 'margin: 4px;';
-    const drawPromise = tf.browser.toPixels(imageTensor, canvas);
-    drawArea.appendChild(canvas);
-
-    tensorsToDispose.push(imageTensor);
-    drawPromises.push(drawPromise);
-  }
-
-  await Promise.all(drawPromises);
-  tf.dispose(tensorsToDispose);
-}
-
-function setupListeners() {
-  document.querySelector('#load-data').addEventListener('click', async (e) => {
-    await initData();
-    document.querySelector('#start-training-1').disabled = false;
-    e.target.disabled = true;
-  });
-}
-
-document.addEventListener('DOMContentLoaded', function() {
-  initModel();
-  setupListeners();
-});
diff --git a/tfjs-master/tfjs-vis/demos/mnist_internals/model.js b/tfjs-master/tfjs-vis/demos/mnist_internals/model.js
deleted file mode 100644
index ed8bce574..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist_internals/model.js
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs';
-import {MnistData} from './data';
-
-export function getModel() {
-  const model = tf.sequential();
-
-  model.add(tf.layers.conv2d({
-    inputShape: [28, 28, 1],
-    kernelSize: 5,
-    filters: 8,
-    strides: 1,
-    activation: 'relu',
-    kernelInitializer: 'varianceScaling'
-  }));
-
-  model.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
-  model.add(tf.layers.conv2d({
-    kernelSize: 5,
-    filters: 16,
-    strides: 1,
-    activation: 'relu',
-    kernelInitializer: 'varianceScaling'
-  }));
-
-  model.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
-  model.add(tf.layers.flatten());
-
-  model.add(tf.layers.dense({
-    units: 10,
-    kernelInitializer: 'varianceScaling',
-    activation: 'softmax'
-  }));
-
-  const LEARNING_RATE = 0.15;
-  const optimizer = tf.train.sgd(LEARNING_RATE);
-
-  model.compile({
-    optimizer: optimizer,
-    loss: 'categoricalCrossentropy',
-    metrics: ['accuracy'],
-  });
-
-  return model;
-}
-
-export async function loadData() {
-  const data = new MnistData();
-  await data.load();
-  return data;
-}
diff --git a/tfjs-master/tfjs-vis/demos/mnist_internals/package.json b/tfjs-master/tfjs-vis/demos/mnist_internals/package.json
deleted file mode 100644
index ea8f80239..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist_internals/package.json
+++ /dev/null
@@ -1,29 +0,0 @@
-{
-  "name": "mnist-internals-vis-demo",
-  "private": true,
-  "version": "0.0.1",
-  "description": "",
-  "main": "index.js",
-  "browserslist": "> 0.25%, not dead",
-  "scripts": {
-    "watch": "NODE_ENV=development parcel index.html --no-hmr --open --no-autoinstall",
-    "build": "NODE_ENV=production parcel build index.html --no-minify  --detailed-report --public-url ./"
-  },
-  "author": "",
-  "license": "Apache-2.0",
-  "devDependencies": {
-    "@babel/core": "^7.21.0",
-    "@babel/plugin-transform-runtime": "^7.21.0",
-    "babel-preset-env": "~1.7.0",
-    "parcel": "2.8.3",
-    "rimraf": "^4.1.2"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs": "4.2.0",
-    "@tensorflow/tfjs-vis": "link:../.."
-  },
-  "resolutions": {
-    "node-fetch": "2.6.7",
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-vis/demos/mnist_internals/tufte.css b/tfjs-master/tfjs-vis/demos/mnist_internals/tufte.css
deleted file mode 100644
index a8ce60088..000000000
--- a/tfjs-master/tfjs-vis/demos/mnist_internals/tufte.css
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
-  From https://github.com/edwardtufte/tufte-css
-  https://github.com/edwardtufte/tufte-css/blob/gh-pages/LICENSE
-*/
-
-@charset "UTF-8";
-
-/* Tufte CSS styles */
-html { font-size: 15px; }
-
-body { width: 87.5%;
-       margin-left: auto;
-       margin-right: auto;
-       padding-left: 12.5%;
-       font-family: et-book, Palatino, "Palatino Linotype", "Palatino LT STD", "Book Antiqua", Georgia, serif;
-       background-color: #fffff8;
-       color: #111;
-       max-width: 1400px;
-       counter-reset: sidenote-counter; }
-
-h1 { font-weight: 400;
-     margin-top: 4rem;
-     margin-bottom: 1.5rem;
-     font-size: 3.2rem;
-     line-height: 1; }
-
-h2 { font-style: italic;
-     font-weight: 400;
-     margin-top: 2.1rem;
-     margin-bottom: 1.4rem;
-     font-size: 2.2rem;
-     line-height: 1; }
-
-h3 { font-style: italic;
-     font-weight: 400;
-     font-size: 1.7rem;
-     margin-top: 2rem;
-     margin-bottom: 1.4rem;
-     line-height: 1; }
-
-hr { display: block;
-     height: 1px;
-     width: 55%;
-     border: 0;
-     border-top: 1px solid #ccc;
-     margin: 1em 0;
-     padding: 0; }
-
-p.subtitle { font-style: italic;
-             margin-top: 1rem;
-             margin-bottom: 1rem;
-             font-size: 1.8rem;
-             display: block;
-             line-height: 1; }
-
-.numeral { font-family: et-book-roman-old-style; }
-
-.danger { color: red; }
-
-article { position: relative;
-          padding: 5rem 0rem; }
-
-section { padding-top: 1rem;
-          padding-bottom: 1rem; }
-
-p, ol, ul { font-size: 1.4rem;
-            line-height: 2rem; }
-
-p { margin-top: 1.4rem;
-    margin-bottom: 1.4rem;
-    padding-right: 0;
-    vertical-align: baseline; }
-
-/* Chapter Epigraphs */
-div.epigraph { margin: 5em 0; }
-
-div.epigraph > blockquote { margin-top: 3em;
-                            margin-bottom: 3em; }
-
-div.epigraph > blockquote, div.epigraph > blockquote > p { font-style: italic; }
-
-div.epigraph > blockquote > footer { font-style: normal; }
-
-div.epigraph > blockquote > footer > cite { font-style: italic; }
-/* end chapter epigraphs styles */
-
-blockquote { font-size: 1.4rem; }
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-  integrity sha512-33qQ+ZoZlli19IFiQx4QEpf2CBEayMRzhlisJHSCsSUbDXv6ZishqS1x7uFVClKG4Wr7rZVHvaAttoLow6GqdQ==
-  dependencies:
-    internmap "1 - 2"
-
-"d3-color@1 - 2", d3-color@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-2.0.0.tgz#8d625cab42ed9b8f601a1760a389f7ea9189d62e"
-  integrity sha512-SPXi0TSKPD4g9tw0NMZFnR95XVgUZiBH+uUTqQuDu1OsE2zomHU7ho0FISciaPvosimixwHFl3WHLGabv6dDgQ==
-
-"d3-color@1 - 3", d3-color@^3.0.1:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-3.1.0.tgz#395b2833dfac71507f12ac2f7af23bf819de24e2"
-  integrity sha512-zg/chbXyeBtMQ1LbD/WSoW2DpC3I0mpmPdW+ynRTj/x2DAWYrIY7qeZIHidozwV24m4iavr15lNwIwLxRmOxhA==
-
-d3-delaunay@^5.3.0:
-  version "5.3.0"
-  resolved "https://registry.yarnpkg.com/d3-delaunay/-/d3-delaunay-5.3.0.tgz#b47f05c38f854a4e7b3cea80e0bb12e57398772d"
-  integrity sha512-amALSrOllWVLaHTnDLHwMIiz0d1bBu9gZXd1FiLfXf8sHcX9jrcj81TVZOqD4UX7MgBZZ07c8GxzEgBpJqc74w==
-  dependencies:
-    delaunator "4"
-
-"d3-dispatch@1 - 2":
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-dispatch/-/d3-dispatch-2.0.0.tgz#8a18e16f76dd3fcaef42163c97b926aa9b55e7cf"
-  integrity sha512-S/m2VsXI7gAti2pBoLClFFTMOO1HTtT0j99AuXLoGFKO6deHDdnv6ZGTxSTTUTgO1zVcv82fCOtDjYK4EECmWA==
-
-d3-dsv@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-2.0.0.tgz#b37b194b6df42da513a120d913ad1be22b5fe7c5"
-  integrity sha512-E+Pn8UJYx9mViuIUkoc93gJGGYut6mSDKy2+XaPwccwkRGlR+LO97L2VCCRjQivTwLHkSnAJG7yo00BWY6QM+w==
-  dependencies:
-    commander "2"
-    iconv-lite "0.4"
-    rw "1"
-
-d3-dsv@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-3.0.1.tgz#c63af978f4d6a0d084a52a673922be2160789b73"
-  integrity sha512-UG6OvdI5afDIFP9w4G0mNq50dSOsXHJaRE8arAS5o9ApWnIElp8GZw1Dun8vP8OyHOZ/QJUKUJwxiiCCnUwm+Q==
-  dependencies:
-    commander "7"
-    iconv-lite "0.6"
-    rw "1"
-
-d3-force@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/d3-force/-/d3-force-2.1.1.tgz#f20ccbf1e6c9e80add1926f09b51f686a8bc0937"
-  integrity sha512-nAuHEzBqMvpFVMf9OX75d00OxvOXdxY+xECIXjW6Gv8BRrXu6gAWbv/9XKrvfJ5i5DCokDW7RYE50LRoK092ew==
-  dependencies:
-    d3-dispatch "1 - 2"
-    d3-quadtree "1 - 2"
-    d3-timer "1 - 2"
-
-"d3-format@1 - 2", d3-format@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-2.0.0.tgz#a10bcc0f986c372b729ba447382413aabf5b0767"
-  integrity sha512-Ab3S6XuE/Q+flY96HXT0jOXcM4EAClYFnRGY5zsjRGNy6qCYrQsMffs7cV5Q9xejb35zxW5hf/guKw34kvIKsA==
-
-"d3-format@1 - 3", d3-format@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-3.1.0.tgz#9260e23a28ea5cb109e93b21a06e24e2ebd55641"
-  integrity sha512-YyUI6AEuY/Wpt8KWLgZHsIU86atmikuoOmCfommt0LYHiQSPjvX2AcFc38PX0CBpr2RCyZhjex+NS/LPOv6YqA==
-
-d3-format@~1.3.0:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-1.3.2.tgz#6a96b5e31bcb98122a30863f7d92365c00603562"
-  integrity sha512-Z18Dprj96ExragQ0DeGi+SYPQ7pPfRMtUXtsg/ChVIKNBCzjO8XYJvRTC1usblx52lqge56V5ect+frYTQc8WQ==
-
-d3-geo-projection@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-3.0.0.tgz#45ad8ce756cdbfa8340b11b2988644d8e1fa42e4"
-  integrity sha512-1JE+filVbkEX2bT25dJdQ05iA4QHvUwev6o0nIQHOSrNlHCAKfVss/U10vEM3pA4j5v7uQoFdQ4KLbx9BlEbWA==
-  dependencies:
-    commander "2"
-    d3-array "1 - 2"
-    d3-geo "1.12.0 - 2"
-    resolve "^1.1.10"
-
-d3-geo-projection@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-4.0.0.tgz#dc229e5ead78d31869a4e87cf1f45bd2716c48ca"
-  integrity sha512-p0bK60CEzph1iqmnxut7d/1kyTmm3UWtPlwdkM31AU+LW+BXazd5zJdoCn7VFxNCHXRngPHRnsNn5uGjLRGndg==
-  dependencies:
-    commander "7"
-    d3-array "1 - 3"
-    d3-geo "1.12.0 - 3"
-
-"d3-geo@1.12.0 - 2", d3-geo@^2.0.1:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-2.0.2.tgz#c065c1b71fe8c5f1be657e5f43d9bdd010383c40"
-  integrity sha512-8pM1WGMLGFuhq9S+FpPURxic+gKzjluCD/CHTuUF3mXMeiCo0i6R0tO1s4+GArRFde96SLcW/kOFRjoAosPsFA==
-  dependencies:
-    d3-array "^2.5.0"
-
-"d3-geo@1.12.0 - 3", d3-geo@^3.0.1:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-3.1.0.tgz#74fd54e1f4cebd5185ac2039217a98d39b0a4c0e"
-  integrity sha512-JEo5HxXDdDYXCaWdwLRt79y7giK8SbhZJbFWXqbRTolCHFI5jRqteLzCsq51NKbUoX0PjBVSohxrx+NoOUujYA==
-  dependencies:
-    d3-array "2.5.0 - 3"
-
-d3-hierarchy@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-hierarchy/-/d3-hierarchy-2.0.0.tgz#dab88a58ca3e7a1bc6cab390e89667fcc6d20218"
-  integrity sha512-SwIdqM3HxQX2214EG9GTjgmCc/mbSx4mQBn+DuEETubhOw6/U3fmnji4uCVrmzOydMHSO1nZle5gh6HB/wdOzw==
-
-"d3-interpolate@1.2.0 - 2", d3-interpolate@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-2.0.1.tgz#98be499cfb8a3b94d4ff616900501a64abc91163"
-  integrity sha512-c5UhwwTs/yybcmTpAVqwSFl6vrQ8JZJoT5F7xNFK9pymv5C0Ymcc9/LIJHtYIggg/yS9YHw8i8O8tgb9pupjeQ==
-  dependencies:
-    d3-color "1 - 2"
-
-"d3-interpolate@1.2.0 - 3", d3-interpolate@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-3.0.1.tgz#3c47aa5b32c5b3dfb56ef3fd4342078a632b400d"
-  integrity sha512-3bYs1rOD33uo8aqJfKP3JWPAibgw8Zm2+L9vBKEHJ2Rg+viTR7o5Mmv5mZcieN+FRYaAOWX5SJATX6k1PWz72g==
-  dependencies:
-    d3-color "1 - 3"
-
-"d3-path@1 - 2", d3-path@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-2.0.0.tgz#55d86ac131a0548adae241eebfb56b4582dd09d8"
-  integrity sha512-ZwZQxKhBnv9yHaiWd6ZU4x5BtCQ7pXszEV9CU6kRgwIQVQGLMv1oiL4M+MK/n79sYzsj+gcgpPQSctJUsLN7fA==
-
-d3-path@^3.0.1, d3-path@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-3.1.0.tgz#22df939032fb5a71ae8b1800d61ddb7851c42526"
-  integrity sha512-p3KP5HCf/bvjBSSKuXid6Zqijx7wIfNW+J/maPs+iwR35at5JCbLUT0LzF1cnjbCHWhqzQTIN2Jpe8pRebIEFQ==
-
-"d3-quadtree@1 - 2":
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-quadtree/-/d3-quadtree-2.0.0.tgz#edbad045cef88701f6fee3aee8e93fb332d30f9d"
-  integrity sha512-b0Ed2t1UUalJpc3qXzKi+cPGxeXRr4KU9YSlocN74aTzp6R/Ud43t79yLLqxHRWZfsvWXmbDWPpoENK1K539xw==
-
-d3-scale@^3.2.2:
-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-3.3.0.tgz#28c600b29f47e5b9cd2df9749c206727966203f3"
-  integrity sha512-1JGp44NQCt5d1g+Yy+GeOnZP7xHo0ii8zsQp6PGzd+C1/dl0KGsp9A7Mxwp+1D1o4unbTTxVdU/ZOIEBoeZPbQ==
-  dependencies:
-    d3-array "^2.3.0"
-    d3-format "1 - 2"
-    d3-interpolate "1.2.0 - 2"
-    d3-time "^2.1.1"
-    d3-time-format "2 - 3"
-
-d3-scale@^4.0.2:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-4.0.2.tgz#82b38e8e8ff7080764f8dcec77bd4be393689396"
-  integrity sha512-GZW464g1SH7ag3Y7hXjf8RoUuAFIqklOAq3MRl4OaWabTFJY9PN/E1YklhXLh+OQ3fM9yS2nOkCoS+WLZ6kvxQ==
-  dependencies:
-    d3-array "2.10.0 - 3"
-    d3-format "1 - 3"
-    d3-interpolate "1.2.0 - 3"
-    d3-time "2.1.1 - 3"
-    d3-time-format "2 - 4"
-
-d3-selection@~1.3.0:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/d3-selection/-/d3-selection-1.3.2.tgz#6e70a9df60801c8af28ac24d10072d82cbfdf652"
-  integrity sha512-OoXdv1nZ7h2aKMVg3kaUFbLLK5jXUFAMLD/Tu5JA96mjf8f2a9ZUESGY+C36t8R1WFeWk/e55hy54Ml2I62CRQ==
-
-d3-shape@^2.0.0:
-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-2.1.0.tgz#3b6a82ccafbc45de55b57fcf956c584ded3b666f"
-  integrity sha512-PnjUqfM2PpskbSLTJvAzp2Wv4CZsnAgTfcVRTwW03QR3MkXF8Uo7B1y/lWkAsmbKwuecto++4NlsYcvYpXpTHA==
-  dependencies:
-    d3-path "1 - 2"
-
-d3-shape@^3.1.0:
-  version "3.2.0"
-  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-3.2.0.tgz#a1a839cbd9ba45f28674c69d7f855bcf91dfc6a5"
-  integrity sha512-SaLBuwGm3MOViRq2ABk3eLoxwZELpH6zhl3FbAoJ7Vm1gofKx6El1Ib5z23NUEhF9AsGl7y+dzLe5Cw2AArGTA==
-  dependencies:
-    d3-path "^3.1.0"
-
-"d3-time-format@2 - 3", d3-time-format@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-time-format/-/d3-time-format-3.0.0.tgz#df8056c83659e01f20ac5da5fdeae7c08d5f1bb6"
-  integrity sha512-UXJh6EKsHBTjopVqZBhFysQcoXSv/5yLONZvkQ5Kk3qbwiUYkdX17Xa1PT6U1ZWXGGfB1ey5L8dKMlFq2DO0Ag==
-  dependencies:
-    d3-time "1 - 2"
-
-"d3-time-format@2 - 4", d3-time-format@^4.1.0:
-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/d3-time-format/-/d3-time-format-4.1.0.tgz#7ab5257a5041d11ecb4fe70a5c7d16a195bb408a"
-  integrity sha512-dJxPBlzC7NugB2PDLwo9Q8JiTR3M3e4/XANkreKSUxF8vvXKqm1Yfq4Q5dl8budlunRVlUUaDUgFt7eA8D6NLg==
-  dependencies:
-    d3-time "1 - 3"
-
-"d3-time@1 - 2", d3-time@^2.0.0, d3-time@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-2.1.1.tgz#e9d8a8a88691f4548e68ca085e5ff956724a6682"
-  integrity sha512-/eIQe/eR4kCQwq7yxi7z4c6qEXf2IYGcjoWB5OOQy4Tq9Uv39/947qlDcN2TLkiTzQWzvnsuYPB9TrWaNfipKQ==
-  dependencies:
-    d3-array "2"
-
-"d3-time@1 - 3", "d3-time@2.1.1 - 3", d3-time@^3.0.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-3.1.0.tgz#9310db56e992e3c0175e1ef385e545e48a9bb5c7"
-  integrity sha512-VqKjzBLejbSMT4IgbmVgDjpkYrNWUYJnbCGo874u7MMKIWsILRX+OpX/gTk8MqjpT1A/c6HY2dCA77ZN0lkQ2Q==
-  dependencies:
-    d3-array "2 - 3"
-
-"d3-timer@1 - 2", d3-timer@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/d3-timer/-/d3-timer-2.0.0.tgz#055edb1d170cfe31ab2da8968deee940b56623e6"
-  integrity sha512-TO4VLh0/420Y/9dO3+f9abDEFYeCUr2WZRlxJvbp4HPTQcSylXNiL6yZa9FIUvV1yRiFufl1bszTCLDqv9PWNA==
-
-debug@^2.6.8:
-  version "2.6.9"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-2.6.9.tgz#5d128515df134ff327e90a4c93f4e077a536341f"
-  integrity sha512-bC7ElrdJaJnPbAP+1EotYvqZsb3ecl5wi6Bfi6BJTUcNowp6cvspg0jXznRTKDjm/E7AdgFBVeAPVMNcKGsHMA==
-  dependencies:
-    ms "2.0.0"
-
-debug@^4.1.0:
-  version "4.1.1"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-4.1.1.tgz#3b72260255109c6b589cee050f1d516139664791"
-  integrity sha512-pYAIzeRo8J6KPEaJ0VWOh5Pzkbw/RetuzehGM7QRRX5he4fPHx2rdKMB256ehJCkX+XRQm16eZLqLNS8RSZXZw==
-  dependencies:
-    ms "^2.1.1"
-
-debug@^4.1.1:
-  version "4.3.4"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.4.tgz#1319f6579357f2338d3337d2cdd4914bb5dcc865"
-  integrity sha512-PRWFHuSU3eDtQJPvnNY7Jcket1j0t5OuOsFzPPzsekD52Zl8qUfFIPEiswXqIvHWGVHOgX+7G/vCNNhehwxfkQ==
-  dependencies:
-    ms "2.1.2"
-
-decamelize@^1.2.0:
-  version "1.2.0"
-  resolved "https://registry.yarnpkg.com/decamelize/-/decamelize-1.2.0.tgz#f6534d15148269b20352e7bee26f501f9a191290"
-  integrity sha512-z2S+W9X73hAUUki+N+9Za2lBlun89zigOyGrsax+KUQ6wKW4ZoWpEYBkGhQjwAjjDCkWxhY0VKEhk8wzY7F5cA==
-
-delaunator@4:
-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/delaunator/-/delaunator-4.0.1.tgz#3d779687f57919a7a418f8ab947d3bddb6846957"
-  integrity sha512-WNPWi1IRKZfCt/qIDMfERkDp93+iZEmOxN2yy4Jg+Xhv8SLk2UTqqbe1sfiipn0and9QrE914/ihdx82Y/Giag==
-
-detect-libc@^1.0.3:
-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/detect-libc/-/detect-libc-1.0.3.tgz#fa137c4bd698edf55cd5cd02ac559f91a4c4ba9b"
-  integrity sha1-+hN8S9aY7fVc1c0CrFWfkaTEups=
-
-dom-serializer@^1.0.1:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/dom-serializer/-/dom-serializer-1.3.2.tgz#6206437d32ceefaec7161803230c7a20bc1b4d91"
-  integrity sha512-5c54Bk5Dw4qAxNOI1pFEizPSjVsx5+bpJKmL2kPn8JhBUq2q09tTCa3mjijun2NfK78NMouDYNMBkOrPZiS+ig==
-  dependencies:
-    domelementtype "^2.0.1"
-    domhandler "^4.2.0"
-    entities "^2.0.0"
-
-domelementtype@^2.0.1, domelementtype@^2.2.0:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/domelementtype/-/domelementtype-2.2.0.tgz#9a0b6c2782ed6a1c7323d42267183df9bd8b1d57"
-  integrity sha512-DtBMo82pv1dFtUmHyr48beiuq792Sxohr+8Hm9zoxklYPfa6n0Z3Byjj2IV7bmr2IyqClnqEQhfgHJJ5QF0R5A==
-
-domhandler@^4.2.0, domhandler@^4.2.2, domhandler@^4.3.0:
-  version "4.3.0"
-  resolved "https://registry.yarnpkg.com/domhandler/-/domhandler-4.3.0.tgz#16c658c626cf966967e306f966b431f77d4a5626"
-  integrity sha512-fC0aXNQXqKSFTr2wDNZDhsEYjCiYsDWl3D01kwt25hm1YIPyDGHvvi3rw+PLqHAl/m71MaiF7d5zvBr0p5UB2g==
-  dependencies:
-    domelementtype "^2.2.0"
-
-domutils@^2.8.0:
-  version "2.8.0"
-  resolved "https://registry.yarnpkg.com/domutils/-/domutils-2.8.0.tgz#4437def5db6e2d1f5d6ee859bd95ca7d02048135"
-  integrity sha512-w96Cjofp72M5IIhpjgobBimYEfoPjx1Vx0BSX9P30WBdZW2WIKU0T1Bd0kz2eNZ9ikjKgHbEyKx8BB6H1L3h3A==
-  dependencies:
-    dom-serializer "^1.0.1"
-    domelementtype "^2.2.0"
-    domhandler "^4.2.0"
-
-dotenv-expand@^5.1.0:
-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/dotenv-expand/-/dotenv-expand-5.1.0.tgz#3fbaf020bfd794884072ea26b1e9791d45a629f0"
-  integrity sha512-YXQl1DSa4/PQyRfgrv6aoNjhasp/p4qs9FjJ4q4cQk+8m4r6k4ZSiEyytKG8f8W9gi8WsQtIObNmKd+tMzNTmA==
-
-dotenv@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-7.0.0.tgz#a2be3cd52736673206e8a85fb5210eea29628e7c"
-  integrity sha512-M3NhsLbV1i6HuGzBUH8vXrtxOk+tWmzWKDMbAVSUp3Zsjm7ywFeuwrUXhmhQyRK1q5B5GGy7hcXPbj3bnfZg2g==
-
-electron-to-chromium@^1.3.47, electron-to-chromium@^1.4.284:
-  version "1.4.305"
-  resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.4.305.tgz#e4dfe3e06ab783f33171f9bde9e8ed092510fcd0"
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-  resolved "https://registry.yarnpkg.com/vega-functions/-/vega-functions-5.12.1.tgz#b69f9ad4cd9f777dbc942587c02261b2f4cdba2c"
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-    d3-color "^2.0.0"
-    d3-geo "^2.0.1"
-    vega-dataflow "^5.7.3"
-    vega-expression "^5.0.0"
-    vega-scale "^7.1.1"
-    vega-scenegraph "^4.9.3"
-    vega-selections "^5.3.1"
-    vega-statistics "^1.7.9"
-    vega-time "^2.0.4"
-    vega-util "^1.16.0"
-
-vega-geo@~4.3.8:
-  version "4.3.8"
-  resolved "https://registry.yarnpkg.com/vega-geo/-/vega-geo-4.3.8.tgz#5629d18327bb4f3700cdf05db4aced0a43abbf4a"
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-    d3-color "^2.0.0"
-    d3-geo "^2.0.1"
-    vega-canvas "^1.2.5"
-    vega-dataflow "^5.7.3"
-    vega-projection "^1.4.5"
-    vega-statistics "^1.7.9"
-    vega-util "^1.15.2"
-
-vega-hierarchy@~4.0.9:
-  version "4.0.9"
-  resolved "https://registry.yarnpkg.com/vega-hierarchy/-/vega-hierarchy-4.0.9.tgz#4b4bafbc181a14a280ecdbee8874c0db7e369f47"
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-  dependencies:
-    d3-hierarchy "^2.0.0"
-    vega-dataflow "^5.7.3"
-    vega-util "^1.15.2"
-
-vega-label@~1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/vega-label/-/vega-label-1.0.0.tgz#c3bea3a608a62217ca554ecc0f7fe0395d81bd1b"
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-    vega-dataflow "^5.7.3"
-    vega-scenegraph "^4.9.2"
-    vega-util "^1.15.2"
-
-vega-lite@4.13.1:
-  version "4.13.1"
-  resolved "https://registry.yarnpkg.com/vega-lite/-/vega-lite-4.13.1.tgz#e9ee31c76cb93565e287a760700e432e57244064"
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-  dependencies:
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-    "@types/fast-json-stable-stringify" "^2.0.0"
-    array-flat-polyfill "^1.0.1"
-    clone "~2.1.2"
-    fast-deep-equal "~3.1.1"
-    fast-json-stable-stringify "~2.1.0"
-    json-stringify-pretty-compact "~2.0.0"
-    tslib "~2.0.0"
-    vega-event-selector "~2.0.3"
-    vega-expression "~2.6.5"
-    vega-util "~1.14.0"
-    yargs "~15.3.1"
-
-vega-loader@^4.3.2, vega-loader@^4.3.3, vega-loader@^4.4.0:
-  version "4.5.0"
-  resolved "https://registry.yarnpkg.com/vega-loader/-/vega-loader-4.5.0.tgz#b15acc4c8f84191f500e94d35ddfb9721ac943ad"
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-  dependencies:
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-    node-fetch "^2.6.7"
-    topojson-client "^3.1.0"
-    vega-format "^1.1.0"
-    vega-util "^1.16.0"
-
-vega-loader@~4.4.0:
-  version "4.4.1"
-  resolved "https://registry.yarnpkg.com/vega-loader/-/vega-loader-4.4.1.tgz#8f9de46202f33659d1a2737f6e322a9fc3364275"
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-  dependencies:
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-    node-fetch "^2.6.1"
-    topojson-client "^3.1.0"
-    vega-format "^1.0.4"
-    vega-util "^1.16.0"
-
-vega-parser@~6.1.3:
-  version "6.1.4"
-  resolved "https://registry.yarnpkg.com/vega-parser/-/vega-parser-6.1.4.tgz#4868e41af2c9645b6d7daeeb205cfad06b9d465c"
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-  dependencies:
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-    vega-event-selector "^3.0.0"
-    vega-functions "^5.12.1"
-    vega-scale "^7.1.1"
-    vega-util "^1.16.0"
-
-vega-projection@^1.4.5:
-  version "1.5.0"
-  resolved "https://registry.yarnpkg.com/vega-projection/-/vega-projection-1.5.0.tgz#51c5f0455170cd35b3c5f3e653e99c3616520640"
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-  dependencies:
-    d3-geo "^3.0.1"
-    d3-geo-projection "^4.0.0"
-
-vega-projection@~1.4.5:
-  version "1.4.5"
-  resolved "https://registry.yarnpkg.com/vega-projection/-/vega-projection-1.4.5.tgz#020cb646b4eaae535359da25f4f48eef8d324af2"
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-  dependencies:
-    d3-geo "^2.0.1"
-    d3-geo-projection "^3.0.0"
-
-vega-regression@~1.0.9:
-  version "1.0.9"
-  resolved "https://registry.yarnpkg.com/vega-regression/-/vega-regression-1.0.9.tgz#f33da47fe457e03ad134782c11414bcef7b1da82"
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-  dependencies:
-    d3-array "^2.7.1"
-    vega-dataflow "^5.7.3"
-    vega-statistics "^1.7.9"
-    vega-util "^1.15.2"
-
-vega-runtime@^6.1.3, vega-runtime@~6.1.3:
-  version "6.1.3"
-  resolved "https://registry.yarnpkg.com/vega-runtime/-/vega-runtime-6.1.3.tgz#01e18246f7a80cee034a96017ac30113b92c4034"
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-  dependencies:
-    vega-dataflow "^5.7.3"
-    vega-util "^1.15.2"
-
-vega-scale@^7.0.3, vega-scale@^7.1.1, vega-scale@^7.2.0:
-  version "7.2.0"
-  resolved "https://registry.yarnpkg.com/vega-scale/-/vega-scale-7.2.0.tgz#9e298cc02ad340498cb56847436b19439911f0fc"
-  integrity sha512-QYltO/otrZHLrCGGf06Y99XtPtqWXITr6rw7rO9oL+l3d9o5RFl9sjHrVxiM7v+vGoZVWbBd5IPbFhPsXZ6+TA==
-  dependencies:
-    d3-array "^3.1.1"
-    d3-interpolate "^3.0.1"
-    d3-scale "^4.0.2"
-    vega-time "^2.1.0"
-    vega-util "^1.17.0"
-
-vega-scale@~7.1.1:
-  version "7.1.1"
-  resolved "https://registry.yarnpkg.com/vega-scale/-/vega-scale-7.1.1.tgz#b69a38d1980f6fc1093390f796e556be63fdc808"
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-    d3-interpolate "^2.0.1"
-    d3-scale "^3.2.2"
-    vega-time "^2.0.4"
-    vega-util "^1.15.2"
-
-vega-scenegraph@^4.9.2, vega-scenegraph@^4.9.3, vega-scenegraph@^4.9.4:
-  version "4.10.1"
-  resolved "https://registry.yarnpkg.com/vega-scenegraph/-/vega-scenegraph-4.10.1.tgz#944da67b8a28758fab2e1306259fb7ff6be89f6b"
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-  dependencies:
-    d3-path "^3.0.1"
-    d3-shape "^3.1.0"
-    vega-canvas "^1.2.5"
-    vega-loader "^4.4.0"
-    vega-scale "^7.2.0"
-    vega-util "^1.15.2"
-
-vega-scenegraph@~4.9.4:
-  version "4.9.4"
-  resolved "https://registry.yarnpkg.com/vega-scenegraph/-/vega-scenegraph-4.9.4.tgz#468408c1e89703fa9d3450445daabff623de2757"
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-    d3-shape "^2.0.0"
-    vega-canvas "^1.2.5"
-    vega-loader "^4.3.3"
-    vega-scale "^7.1.1"
-    vega-util "^1.15.2"
-
-vega-schema-url-parser@^2.1.0:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/vega-schema-url-parser/-/vega-schema-url-parser-2.2.0.tgz#a0d1e02915adfbfcb1fd517c8c2ebe2419985c1e"
-  integrity sha512-yAtdBnfYOhECv9YC70H2gEiqfIbVkq09aaE4y/9V/ovEFmH9gPKaEgzIZqgT7PSPQjKhsNkb6jk6XvSoboxOBw==
-
-vega-selections@^5.3.1:
-  version "5.4.0"
-  resolved "https://registry.yarnpkg.com/vega-selections/-/vega-selections-5.4.0.tgz#c2783897421fa39b674c015fa8f15a0023b8054e"
-  integrity sha512-Un3JdLDPjIpF9Dh4sw6m1c/QAcfam6m1YXHJ9vJxE/GdJ+sOrPxc7bcEU8VhOmTUN7IQUn4/1ry4JqqOVMbEhw==
-  dependencies:
-    d3-array "3.1.1"
-    vega-expression "^5.0.0"
-    vega-util "^1.16.0"
-
-vega-statistics@^1.7.9:
-  version "1.8.0"
-  resolved "https://registry.yarnpkg.com/vega-statistics/-/vega-statistics-1.8.0.tgz#ad66f7461473d58bc96671588981a059ffd60b59"
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-  dependencies:
-    d3-array "^3.1.1"
-
-vega-statistics@~1.7.9:
-  version "1.7.10"
-  resolved "https://registry.yarnpkg.com/vega-statistics/-/vega-statistics-1.7.10.tgz#4353637402e5e96bff2ebd16bd58e2c15cac3018"
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-  dependencies:
-    d3-array "^2.7.1"
-
-vega-themes@^2.10.0:
-  version "2.12.1"
-  resolved "https://registry.yarnpkg.com/vega-themes/-/vega-themes-2.12.1.tgz#39abc207019ab0aa0b481b3ce8194b0a924219f1"
-  integrity sha512-tedbkKx+5fKBR3W1R1rOpLeUxdbovm7CE8ygUNqH6HfBChBafUzdoxEmr6ZLv2c+ouPX6T/fuvaT78wZVqpwTA==
-
-vega-time@^2.0.3, vega-time@^2.0.4, vega-time@^2.1.0:
-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/vega-time/-/vega-time-2.1.0.tgz#acfbab88d7798b87ff63913b0dce2ca5eb0d46ca"
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-  dependencies:
-    d3-array "^3.1.1"
-    d3-time "^3.0.0"
-    vega-util "^1.15.2"
-
-vega-time@~2.0.4:
-  version "2.0.4"
-  resolved "https://registry.yarnpkg.com/vega-time/-/vega-time-2.0.4.tgz#ff308358a831de927caa44e281cdc96f0863ba08"
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-  dependencies:
-    d3-array "^2.7.1"
-    d3-time "^2.0.0"
-    vega-util "^1.15.2"
-
-vega-tooltip@^0.25.1:
-  version "0.25.1"
-  resolved "https://registry.yarnpkg.com/vega-tooltip/-/vega-tooltip-0.25.1.tgz#cb7e438438649eb46896e7bee6f54e25d25b3c09"
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-  dependencies:
-    vega-util "^1.16.0"
-
-vega-transforms@~4.9.3:
-  version "4.9.4"
-  resolved "https://registry.yarnpkg.com/vega-transforms/-/vega-transforms-4.9.4.tgz#5cf6b91bda9f184bbbaba63838be8e5e6a571235"
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-  dependencies:
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-    vega-dataflow "^5.7.4"
-    vega-statistics "^1.7.9"
-    vega-time "^2.0.4"
-    vega-util "^1.16.1"
-
-vega-typings@~0.20.0:
-  version "0.20.0"
-  resolved "https://registry.yarnpkg.com/vega-typings/-/vega-typings-0.20.0.tgz#aac32f45ca69d3640d205ffdd9cd94fe1d6dcc19"
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-vega-util@^1.15.0, vega-util@^1.15.2, vega-util@^1.16.0, vega-util@^1.16.1, vega-util@^1.17.0:
-  version "1.17.0"
-  resolved "https://registry.yarnpkg.com/vega-util/-/vega-util-1.17.0.tgz#b72ae0baa97f943bf591f8f5bb27ceadf06834ac"
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-  version "1.14.1"
-  resolved "https://registry.yarnpkg.com/vega-util/-/vega-util-1.14.1.tgz#0fb614277764f98738ba0b80e5cdfbe663426183"
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-  version "1.16.1"
-  resolved "https://registry.yarnpkg.com/vega-util/-/vega-util-1.16.1.tgz#992bf3c3b6e145797214d99862841baea417ba39"
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-vega-view-transforms@~4.5.8:
-  version "4.5.8"
-  resolved "https://registry.yarnpkg.com/vega-view-transforms/-/vega-view-transforms-4.5.8.tgz#c8dc42c3c7d4aa725d40b8775180c9f23bc98f4e"
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-    vega-util "^1.15.2"
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-vega-view@~5.10.0:
-  version "5.10.1"
-  resolved "https://registry.yarnpkg.com/vega-view/-/vega-view-5.10.1.tgz#b69348bb32a9845a1bd341fdd946df98684fadc3"
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-vega-voronoi@~4.1.5:
-  version "4.1.5"
-  resolved "https://registry.yarnpkg.com/vega-voronoi/-/vega-voronoi-4.1.5.tgz#e7af574d4c27fd9cb12d70082f12c6f59b80b445"
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-    vega-dataflow "^5.7.3"
-    vega-util "^1.15.2"
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-vega-wordcloud@~4.1.3:
-  version "4.1.3"
-  resolved "https://registry.yarnpkg.com/vega-wordcloud/-/vega-wordcloud-4.1.3.tgz#ce90900333f4e0d3ee706ba4f36bb0905f8b4a9f"
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-    vega-scale "^7.1.1"
-    vega-statistics "^1.7.9"
-    vega-util "^1.15.2"
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-vega@5.20.0:
-  version "5.20.0"
-  resolved "https://registry.yarnpkg.com/vega/-/vega-5.20.0.tgz#8be803391c9441d569a72531917b69d62edb5e57"
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-    vega-dataflow "~5.7.3"
-    vega-encode "~4.8.3"
-    vega-event-selector "~2.0.6"
-    vega-expression "~4.0.1"
-    vega-force "~4.0.7"
-    vega-format "~1.0.4"
-    vega-functions "~5.12.0"
-    vega-geo "~4.3.8"
-    vega-hierarchy "~4.0.9"
-    vega-label "~1.0.0"
-    vega-loader "~4.4.0"
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-    vega-projection "~1.4.5"
-    vega-regression "~1.0.9"
-    vega-runtime "~6.1.3"
-    vega-scale "~7.1.1"
-    vega-scenegraph "~4.9.4"
-    vega-statistics "~1.7.9"
-    vega-time "~2.0.4"
-    vega-transforms "~4.9.3"
-    vega-typings "~0.20.0"
-    vega-util "~1.16.1"
-    vega-view "~5.10.0"
-    vega-view-transforms "~4.5.8"
-    vega-voronoi "~4.1.5"
-    vega-wordcloud "~4.1.3"
-
-weak-lru-cache@^1.2.2:
-  version "1.2.2"
-  resolved "https://registry.yarnpkg.com/weak-lru-cache/-/weak-lru-cache-1.2.2.tgz#fdbb6741f36bae9540d12f480ce8254060dccd19"
-  integrity sha512-DEAoo25RfSYMuTGc9vPJzZcZullwIqRDSI9LOy+fkCJPi6hykCnfKaXTuPBDuXAUcqHXyOgFtHNp/kB2FjYHbw==
-
-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ==
-
-whatwg-fetch@>=0.10.0:
-  version "3.6.2"
-  resolved "https://registry.yarnpkg.com/whatwg-fetch/-/whatwg-fetch-3.6.2.tgz#dced24f37f2624ed0281725d51d0e2e3fe677f8c"
-  integrity sha512-bJlen0FcuU/0EMLrdbJ7zOnW6ITZLrZMIarMUVmdKtsGvZna8vxKYaexICWPfZ8qwf9fzNq+UEIZrnSaApt6RA==
-
-whatwg-url@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
-  integrity sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==
-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
-which-module@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/which-module/-/which-module-2.0.0.tgz#d9ef07dce77b9902b8a3a8fa4b31c3e3f7e6e87a"
-  integrity sha512-B+enWhmw6cjfVC7kS8Pj9pCrKSc5txArRyaYGe088shv/FGWH+0Rjx/xPgtsWfsUtS27FkP697E4DDhgrgoc0Q==
-
-wrap-ansi@^6.2.0:
-  version "6.2.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-6.2.0.tgz#e9393ba07102e6c91a3b221478f0257cd2856e53"
-  integrity sha512-r6lPcBGxZXlIcymEu7InxDMhdW0KDxpLgoFLcguasxCaJ/SOIZwINatK9KY/tf+ZrlywOKU0UDj3ATXUBfxJXA==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-xxhash-wasm@^0.4.2:
-  version "0.4.2"
-  resolved "https://registry.yarnpkg.com/xxhash-wasm/-/xxhash-wasm-0.4.2.tgz#752398c131a4dd407b5132ba62ad372029be6f79"
-  integrity sha512-/eyHVRJQCirEkSZ1agRSCwriMhwlyUcFkXD5TPVSLP+IPzjsqMVzZwdoczLp1SoQU0R3dxz1RpIK+4YNQbCVOA==
-
-y18n@^4.0.0:
-  version "4.0.3"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-4.0.3.tgz#b5f259c82cd6e336921efd7bfd8bf560de9eeedf"
-  integrity sha512-JKhqTOwSrqNA1NY5lSztJ1GrBiUodLMmIZuLiDaMRJ+itFd+ABVE8XBjOvIWL+rSqNDC74LCSFmlb/U4UZ4hJQ==
-
-y18n@^5.0.5:
-  version "5.0.5"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.5.tgz#8769ec08d03b1ea2df2500acef561743bbb9ab18"
-  integrity sha512-hsRUr4FFrvhhRH12wOdfs38Gy7k2FFzB9qgN9v3aLykRq0dRcdcpz5C9FxdS2NuhOrI/628b/KSTJ3rwHysYSg==
-
-yallist@^3.0.2:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.1.1.tgz#dbb7daf9bfd8bac9ab45ebf602b8cbad0d5d08fd"
-  integrity sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==
-
-yallist@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
-  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
-
-yaml@^1.10.0:
-  version "1.10.2"
-  resolved "https://registry.yarnpkg.com/yaml/-/yaml-1.10.2.tgz#2301c5ffbf12b467de8da2333a459e29e7920e4b"
-  integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==
-
-yargs-parser@^18.1.1:
-  version "18.1.3"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-18.1.3.tgz#be68c4975c6b2abf469236b0c870362fab09a7b0"
-  integrity sha512-o50j0JeToy/4K6OZcaQmW6lyXXKhq7csREXcDwk2omFPJEwUNOVtJKvmDr9EI1fAJZUyZcRF7kxGBWmRXudrCQ==
-  dependencies:
-    camelcase "^5.0.0"
-    decamelize "^1.2.0"
-
-yargs-parser@^20.2.2:
-  version "20.2.4"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.4.tgz#b42890f14566796f85ae8e3a25290d205f154a54"
-  integrity sha512-WOkpgNhPTlE73h4VFAFsOnomJVaovO8VqLDzy5saChRBFQFBoMYirowyW+Q9HB4HFF4Z7VZTiG3iSzJJA29yRA==
-
-yargs@^16.0.3:
-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yargs@~15.3.1:
-  version "15.3.1"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-15.3.1.tgz#9505b472763963e54afe60148ad27a330818e98b"
-  integrity sha512-92O1HWEjw27sBfgmXiixJWT5hRBp2eobqXicLtPBIDBhYB+1HpwZlXmbW2luivBJHBzki+7VyCLRtAkScbTBQA==
-  dependencies:
-    cliui "^6.0.0"
-    decamelize "^1.2.0"
-    find-up "^4.1.0"
-    get-caller-file "^2.0.1"
-    require-directory "^2.1.1"
-    require-main-filename "^2.0.0"
-    set-blocking "^2.0.0"
-    string-width "^4.2.0"
-    which-module "^2.0.0"
-    y18n "^4.0.0"
-    yargs-parser "^18.1.1"
diff --git a/tfjs-master/tfjs-vis/docs/visor-usage.png b/tfjs-master/tfjs-vis/docs/visor-usage.png
deleted file mode 100644
index 0d15c79bb..000000000
Binary files a/tfjs-master/tfjs-vis/docs/visor-usage.png and /dev/null differ
diff --git a/tfjs-master/tfjs-vis/integration_tests/karma.conf.js b/tfjs-master/tfjs-vis/integration_tests/karma.conf.js
deleted file mode 100644
index e69de29bb..000000000
diff --git a/tfjs-master/tfjs-vis/karma.conf.js b/tfjs-master/tfjs-vis/karma.conf.js
deleted file mode 100644
index 2aa2feeb3..000000000
--- a/tfjs-master/tfjs-vis/karma.conf.js
+++ /dev/null
@@ -1,119 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const karmaTypescriptConfig = {
-  tsconfig: 'tsconfig.json',
-  coverageOptions: {instrumentation: false},
-  reports: {},
-  bundlerOptions: {
-    acornOptions: {
-      ecmaVersion: 8,
-    },
-    transforms: [
-      require('karma-typescript-es6-transform')({
-        presets: [[
-          'env', {
-            targets: {
-              browsers: [
-                'last 10 Chrome versions',
-                'last 10 Firefox versions',
-                'last 5 Safari versions',
-              ]
-            }
-          }
-        ]]
-      }),
-    ]
-  }
-};
-
-// Enable coverage reports and instrumentation under KARMA_COVERAGE=1 env
-const coverageEnabled = !!process.env.KARMA_COVERAGE;
-if (coverageEnabled) {
-  karmaTypescriptConfig.coverageOptions.instrumentation = true;
-  karmaTypescriptConfig.coverageOptions.exclude = /_test\.ts.*/;
-  karmaTypescriptConfig.reports = {html: 'coverage', 'text-summary': ''};
-}
-
-module.exports = function(config) {
-  const args = [];
-
-  if (config.grep) {
-    args.push('--grep', config.grep);
-  }
-
-  config.set({
-    frameworks: ['jasmine', 'karma-typescript'],
-    files: ['src/setup_test.ts', 'src/**/*.ts*'],
-    exclude: ['src/types/**'],
-    preprocessors: {
-      '**/*.ts': ['karma-typescript'],
-      '**/*.tsx': ['karma-typescript'],
-      '**/*.d.ts': ['karma-typescript'],
-    },
-    karmaTypescriptConfig,
-    reporters: ['progress', 'karma-typescript'],
-    browsers: ['Chrome'],
-    port: 9200,
-    browserStack: {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_KEY,
-      tunnelIdentifier:
-          `tfjs_vis_${Date.now()}_${Math.floor(Math.random() * 1000)}`
-    },
-    captureTimeout: 3e5,
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 3e5,
-    browserDisconnectTimeout: 3e5,
-    browserDisconnectTolerance: 0,
-    browserSocketTimeout: 1.2e5,
-    customLaunchers: {
-      bs_chrome_mac: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_firefox_mac: {
-        base: 'BrowserStack',
-        browser: 'firefox',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_safari_mac: {
-        base: 'BrowserStack',
-        browser: 'safari',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'Big Sur'
-      },
-      bs_ios_12: {
-        base: 'BrowserStack',
-        device: 'iPhone XS',
-        os: 'ios',
-        os_version: '12.3',
-        real_mobile: true
-      },
-    },
-    client: {
-      jasmine: {random: false},
-      args: args,
-    }
-  });
-};
diff --git a/tfjs-master/tfjs-vis/package.json b/tfjs-master/tfjs-vis/package.json
deleted file mode 100644
index bdcdd14ab..000000000
--- a/tfjs-master/tfjs-vis/package.json
+++ /dev/null
@@ -1,81 +0,0 @@
-{
-  "name": "@tensorflow/tfjs-vis",
-  "version": "1.4.3",
-  "description": "Utilities for in browser visualization with TensorFlow.js",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git",
-    "directory": "tfjs-vis"
-  },
-  "license": "Apache-2.0",
-  "private": false,
-  "main": "dist/index.js",
-  "jsdelivr": "dist/tfjs-vis.umd.min.js",
-  "unpkg": "dist/tfjs-vis.umd.min.js",
-  "types": "dist/index.d.ts",
-  "scripts": {
-    "build": "tsc && NODE_ENV=production webpack",
-    "lint": "tslint -p . -t verbose",
-    "coverage": "KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",
-    "test": "yarn && yarn build && karma start",
-    "test-dev": "karma start",
-    "run-flaky": "node ../scripts/run_flaky.js",
-    "run-browserstack": "karma start --singleRun --reporters='dots,karma-typescript,BrowserStack' --hostname='bs-local.com'",
-    "test-ci": "./scripts/test-ci.sh",
-    "build-npm": "./scripts/build-npm.sh",
-    "link-local": "yalc link",
-    "publish-local": "rimraf dist/ && yarn build && yalc push"
-  },
-  "dependencies": {
-    "d3-format": "~3.1.0",
-    "d3-selection": "~3.0.0",
-    "glamor": "~2.20.40",
-    "preact": "~8.2.9",
-    "vega": "5.20.0",
-    "vega-embed": "6.17.0",
-    "vega-lite": "4.13.1"
-  },
-  "devDependencies": {
-    "@tensorflow/tfjs-backend-webgl": "4.9.0",
-    "@tensorflow/tfjs-core": "4.9.0",
-    "@tensorflow/tfjs-layers": "4.9.0",
-    "@types/d3-format": "~3.0.1",
-    "@types/d3-selection": "~3.0.4",
-    "@types/jasmine": "~4.3.1",
-    "@types/json-stable-stringify": "~1.0.32",
-    "babel-core": "~6.26.3",
-    "babel-plugin-external-helpers": "~6.22.0",
-    "babel-preset-env": "~1.7.0",
-    "clang-format": "~1.8.0",
-    "jasmine": "~4.5.0",
-    "jasmine-core": "~4.5.0",
-    "karma": "^6.4.1",
-    "karma-browserstack-launcher": "~1.6.0",
-    "karma-chrome-launcher": "~3.1.1",
-    "karma-firefox-launcher": "~2.1.2",
-    "karma-jasmine": "~5.1.0",
-    "karma-safari-launcher": "~1.0.0",
-    "karma-typescript": "~5.5.3",
-    "karma-typescript-es6-transform": "~5.5.3",
-    "npm-run-all": "~4.1.5",
-    "preact-render-spy": "~1.3.0",
-    "rimraf": "~4.1.2",
-    "tslint": "~6.1.3",
-    "tslint-no-circular-imports": "~0.7.0",
-    "typescript": "5.0.4",
-    "webpack": "^5.75.0",
-    "webpack-cli": "^5.0.1",
-    "yalc": "~1.0.0-pre.50"
-  },
-  "peerDependencies": {
-    "@tensorflow/tfjs-core": ">= 1.0.0"
-  },
-  "alias": {
-    "react": "preact",
-    "react-dom": "preact"
-  },
-  "resolutions": {
-    "node-fetch": "2.6.7",
-    "minimist": "1.2.6"
-  }
-}
diff --git a/tfjs-master/tfjs-vis/scripts/build-npm.sh b/tfjs-master/tfjs-vis/scripts/build-npm.sh
deleted file mode 100644
index c5f0ee9aa..000000000
--- a/tfjs-master/tfjs-vis/scripts/build-npm.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2017 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn rimraf dist/
-yarn
-yarn build
-yarn lint
-npm pack
diff --git a/tfjs-master/tfjs-vis/scripts/deploy.sh b/tfjs-master/tfjs-vis/scripts/deploy.sh
deleted file mode 100644
index 832dc144f..000000000
--- a/tfjs-master/tfjs-vis/scripts/deploy.sh
+++ /dev/null
@@ -1,58 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2018 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# This script deploys examples to GCP so they can be statically hosted.
-#
-# The script can either be used without arguments, which deploys all demos:
-# ./deploy.sh
-# Or you can pass a single argument specifying a single demo to deploy:
-# ./deploy.sh mnist
-#
-# This script assumes that a directory in this repo corresponds to an example.
-#
-# Example directories should have:
-#  - package.json
-#  - `yarn build` script which generates a dist/ folder in the example directory.
-
-if [ -z "$1" ]
-  then
-    EXAMPLES="api mnist mnist_internals"
-else
-  EXAMPLES=$1
-  if [ ! -d "$EXAMPLES" ]; then
-    echo "Error: Could not find example $1"
-    echo "Make sure the first argument to this script matches the example dir"
-    exit 1
-  fi
-fi
-
-cd demos
-for i in $EXAMPLES; do
-  cd ${i}
-  # Strip any trailing slashes.
-  EXAMPLE_NAME=${i%/}
-
-  echo "building ${EXAMPLE_NAME}..."
-  yarn
-  rm -rf dist .cache
-  yarn build
-  # Remove files in the example directory (but not sub-directories).
-  gsutil -m rm gs://tfjs-vis/$EXAMPLE_NAME/dist/*
-  # Gzip and copy all the dist files.
-  # The trailing slash is important so we get $EXAMPLE_NAME/dist/.
-  gsutil -m cp -Z -r dist gs://tfjs-vis/$EXAMPLE_NAME/
-  cd ..
-done
diff --git a/tfjs-master/tfjs-vis/scripts/publish-npm.sh b/tfjs-master/tfjs-vis/scripts/publish-npm.sh
deleted file mode 100644
index 434ea8da4..000000000
--- a/tfjs-master/tfjs-vis/scripts/publish-npm.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2017 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Before you run this script, do this:
-# 1) Update the version in package.json
-# 2) Run ./scripts/build-npm from the base dir of the project.
-
-# Then:
-# 3) Checkout the master branch of this repo.
-# 4) Run this script as `./scripts/publish-npm.sh` from the project base dir.
-
-set -e
-
-BRANCH=`git rev-parse --abbrev-ref HEAD`
-
-if [ "$BRANCH" != "master" ]; then
-  echo "Error: Switch to the master branch before publishing."
-  exit
-fi
-
-yarn build-npm
-npm publish
-echo 'Yay! Published a new package to npm.'
diff --git a/tfjs-master/tfjs-vis/scripts/tag-version.js b/tfjs-master/tfjs-vis/scripts/tag-version.js
deleted file mode 100644
index 04324743e..000000000
--- a/tfjs-master/tfjs-vis/scripts/tag-version.js
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2018 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-
-// Run this script from the base directory (not the script directory):
-// ./scripts/tag-version
-
-// tslint:disable-next-line:no-require-imports
-const fs = require('fs');
-// tslint:disable-next-line:no-require-imports
-const exec = require('child_process').exec;
-
-const version = JSON.parse(fs.readFileSync('package.json', 'utf8')).version;
-const tag = `v${version}`;
-
-exec(`git tag ${tag}`, err => {
-  if (err) {
-    throw new Error(`Could not git tag with ${tag}: ${err.message}.`);
-  }
-  console.log(`Successfully tagged with ${tag}.`);
-});
-
-exec(`git push --tags`, err => {
-  if (err) {
-    throw new Error(`Could not push git tags: ${err.message}.`);
-  }
-  console.log(`Successfully pushed tags.`);
-});
diff --git a/tfjs-master/tfjs-vis/scripts/test-ci.sh b/tfjs-master/tfjs-vis/scripts/test-ci.sh
deleted file mode 100644
index 545113b59..000000000
--- a/tfjs-master/tfjs-vis/scripts/test-ci.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2018 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-set -e
-
-yarn
-yarn lint
-yarn build
-
-# Run the first karma separately so it can download the BrowserStack binary
-# without conflicting with others.
-yarn run-flaky "yarn run-browserstack --browsers=bs_chrome_mac"
-
-# Run the rest of the karma tests in parallel. These runs will reuse the
-# already downloaded binary.
-npm-run-all -p -c --aggregate-output \
-  "run-flaky \"yarn run-browserstack --browsers=bs_firefox_mac\"" \
-  "run-flaky \"yarn run-browserstack --browsers=bs_safari_mac\""
diff --git a/tfjs-master/tfjs-vis/src/components/surface.tsx b/tfjs-master/tfjs-vis/src/components/surface.tsx
deleted file mode 100644
index 6eb67bd5f..000000000
--- a/tfjs-master/tfjs-vis/src/components/surface.tsx
+++ /dev/null
@@ -1,116 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { h, Component } from 'preact';
-import { css } from 'glamor';
-import { SurfaceInfoStrict, StyleOptions } from '../types';
-
-// Internal Props
-interface SurfaceProps extends SurfaceInfoStrict {
-  visible: boolean;
-  registerSurface: (name: string, tab: string, surface: SurfaceComponent)
-    => void;
-}
-
-/**
- * A surface is container for visualizations and other rendered thigns.
- * It consists of a containing DOM Element, a label and an empty drawArea.
- */
-export class SurfaceComponent extends Component<SurfaceProps> {
-
-  static defaultStyles: Partial<StyleOptions> = {
-    maxWidth: '550px',
-    maxHeight: '580px',
-    height: 'auto',
-    width: 'auto',
-  };
-
-  container: HTMLElement;
-  label: HTMLElement;
-  drawArea: HTMLElement;
-
-  override componentDidMount() {
-    const { name, tab } = this.props;
-    this.props.registerSurface(name, tab, this);
-  }
-
-    override componentDidUpdate() {
-    // Prevent re-rendering of this component as it
-    // is primarily controlled outside of this class
-    return false;
-  }
-
-  render() {
-    const { name, visible, styles } = this.props;
-    const finalStyles = {
-      ...SurfaceComponent.defaultStyles,
-      ...styles,
-    };
-
-    const { width, height, } = finalStyles;
-    let { maxHeight, maxWidth, } = finalStyles;
-    maxHeight = height === SurfaceComponent.defaultStyles.height ?
-      maxHeight : height;
-    maxWidth = width === SurfaceComponent.defaultStyles.width ?
-      maxWidth : width;
-
-    const surfaceStyle = css({
-      display: visible ? 'block' : 'none',
-      backgroundColor: 'white',
-      marginTop: '10px',
-      marginBottom: '10px',
-      boxShadow: '0 0 6px -3px #777',
-      padding: '10px !important',
-      height,
-      width,
-      maxHeight,
-      maxWidth,
-      overflow: 'auto',
-    });
-
-    const labelStyle = css({
-      backgroundColor: 'white',
-      boxSizing: 'border-box',
-      borderBottom: '1px solid #357EDD',
-      lineHeight: '2em',
-      marginBottom: '20px',
-      fontWeight: '600',
-      textAlign: 'center',
-    });
-
-    const drawAreaStyle = css({
-      boxSizing: 'border-box',
-    });
-
-    return (
-      <div
-        className={`${surfaceStyle} tf-surface`}
-        ref={(r) => this.container = r}
-        data-visible={visible}
-      >
-        <div className={`${labelStyle} tf-label`} ref={(r) => this.label = r}>
-          {name}
-        </div>
-
-        <div
-          className={`${drawAreaStyle} tf-draw-area`}
-          ref={(r) => this.drawArea = r}
-        />
-      </div>
-    );
-  }
-}
diff --git a/tfjs-master/tfjs-vis/src/components/tabs.tsx b/tfjs-master/tfjs-vis/src/components/tabs.tsx
deleted file mode 100644
index bbfc2660a..000000000
--- a/tfjs-master/tfjs-vis/src/components/tabs.tsx
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { h, Component } from 'preact';
-import { css } from 'glamor';
-
-interface TabsProps {
-  tabNames: string[];
-  activeTab: string | null;
-  handleClick: (tabName: string) => void;
-}
-
-/**
- * Renders a container for tab links
- */
-export class Tabs extends Component<TabsProps> {
-  render() {
-    const { tabNames, activeTab, handleClick } = this.props;
-
-    const tabs = tabNames.length > 0 ?
-      tabNames.map((name) => (
-        <Tab key={name} id={name}
-          handleClick={handleClick}
-          isActive={name === activeTab}
-        >
-          {name}
-        </Tab>
-      ))
-      : null;
-
-    const tabStyle = css({
-      overflowX: 'scroll',
-      overflowY: 'hidden',
-      whiteSpace: 'nowrap',
-      borderBottomStyle: 'solid',
-      borderBottomWidth: '1px',
-      borderColor: '#eee',
-      paddingBottom: '1rem',
-      marginTop: '1rem',
-    });
-
-    return (
-      <div className={`${tabStyle} visor-tabs`}>
-        {tabs}
-      </div>
-    );
-  }
-}
-
-interface TabProps {
-  id: string;
-  isActive: boolean;
-  handleClick: (tabName: string) => void;
-}
-
-/**
- * A link representing a tab. Note that the component does not contain the
- * tab content
- */
-class Tab extends Component<TabProps> {
-
-  render() {
-    const { children, isActive, handleClick, id } = this.props;
-
-    const tabStyle = css({
-      borderBottomColor: isActive ? '#357EDD' : '#AAAAAA',
-      borderBottomWidth: '1px',
-      borderBottomStyle: 'solid',
-      cursor: 'pointer',
-      ':hover': {
-        color: '#357EDD'
-      },
-      display: 'inline-block',
-      marginRight: '1rem',
-      padding: '.5rem',
-      fontSize: '1rem',
-      fontWeight: 'bold',
-    });
-
-    return (
-      <a className={`${tabStyle} tf-tab`}
-        data-isactive={isActive}
-        onClick={() => handleClick(id)}
-      >
-        {children}
-      </a>
-    );
-  }
-}
diff --git a/tfjs-master/tfjs-vis/src/components/visor.tsx b/tfjs-master/tfjs-vis/src/components/visor.tsx
deleted file mode 100644
index d9df73665..000000000
--- a/tfjs-master/tfjs-vis/src/components/visor.tsx
+++ /dev/null
@@ -1,349 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { h, Component, render } from 'preact';
-import { css } from 'glamor';
-
-import { SurfaceComponent } from './surface';
-import { Tabs } from './tabs';
-
-import { SurfaceInfoStrict } from '../types';
-
-interface VisorProps {
-  // An ordered list of surfaces to render. These are specified by SurfaceInfo
-  // objects that allow configuration of the surface. The actual surface
-  // instance is managed by the visor.
-  surfaceList: SurfaceInfoStrict[];
-  // Whether to inialize the visor to the open or closed state. Optional.
-  startOpen?: boolean;
-  // A ref handler
-  ref?: (r: VisorComponent) => void;
-}
-
-interface VisorState {
-  isOpen: boolean;
-  isFullscreen: boolean;
-  activeTab: string | null;
-  tabs: Set<string>;
-}
-
-/**
- * The Visor is a component that displays and manages 'Tabs' and 'Surfaces'.
- *
- * It is meant to exist as a singleton component on a given page and is thus
- * accessed by a visor() function that exposes a single instance of this
- * component via an imperative API (i.e. its implementation as a (p)react
- * component is an internal implementation detail.
- *
- * Following that, and somewhat unlike a typical react component, it does allow
- * for imperative calls to that instance to modify internal state. Any state
- * that needs to be serialized or persisted should be lifted to props.
- */
-export class VisorComponent extends Component<VisorProps, VisorState> {
-
-  /**
-   * Helper function to render the component to the DOM.
-   *
-   * Note that although the visor has a parent element, it rendered via absolute
-   * positioning and is thus taken out of regular document flow.
-   *
-   * @param parent A DOM element
-   * @param replaceNode The element that re-rendering this component would
-   *                    replace. Usually null initially and will be returned by
-   *                    this function after the first render.
-   * @param props Initial visor props
-   */
-  static render(
-    parent: Element,
-    replaceNode: Element,
-    props: VisorProps): Element {
-    return render(
-      <VisorComponent
-        {...props}
-      />,
-      parent,
-      replaceNode
-    );
-  }
-
-  // Lookup dictionary for Surfaces
-  private surfaces: Map<string, SurfaceComponent>;
-
-  constructor(props: VisorProps) {
-    super(props);
-    this.surfaces = new Map<string, SurfaceComponent>();
-    const startOpen = props.startOpen == null ? true : props.startOpen;
-
-    this.state = {
-      isOpen: startOpen,
-      isFullscreen: false,
-      activeTab: null,
-      tabs: new Set<string>()
-    };
-
-    this.keyHandler = this.keyHandler.bind(this);
-  }
-
-  // These public methods are exposed via an imperative interface
-
-  close() {
-    this.setState({ isOpen: false });
-  }
-
-  open() {
-    this.setState({ isOpen: true });
-  }
-
-  toggle() {
-    this.setState({ isOpen: !this.state.isOpen });
-  }
-
-  toggleFullScreen() {
-    this.setState({ isFullscreen: !this.state.isFullscreen });
-  }
-
-  isOpen(): boolean {
-    return this.state.isOpen;
-  }
-
-  isFullscreen(): boolean {
-    return this.state.isFullscreen;
-  }
-
-  getSurface(label: string, tab: string) {
-    const surfaceId = this.surfaceId(label, tab);
-    let surface: SurfaceComponent;
-
-    if (this.surfaces.has(surfaceId)) {
-      surface = this.surfaces.get(surfaceId);
-    } else {
-      throw Error(`Surface not found with id: ${surfaceId}`);
-    }
-
-    return {
-      container: surface.container,
-      label: surface.label,
-      drawArea: surface.drawArea,
-    };
-  }
-
-  bindKeys() {
-    document.addEventListener('keydown', this.keyHandler, false);
-  }
-
-  unbindKeys() {
-    document.removeEventListener('keydown', this.keyHandler);
-  }
-
-  private surfaceId(label: string, tab: string): string {
-    return label + tab;
-  }
-
-  private setTabs(surfaceList: SurfaceInfoStrict[]) {
-    // Check if we have new tabs
-    const nextTabs = surfaceList.map(s => s.tab);
-    const tabs = this.state.tabs;
-    let newActiveTab;
-    for (const tab of nextTabs) {
-      if (!tabs.has(tab)) {
-        tabs.add(tab);
-        newActiveTab = tab;
-      }
-    }
-
-    if (newActiveTab != null) {
-      this.setState({
-        tabs,
-        activeTab: newActiveTab,
-      });
-    }
-  }
-
-  private getTabs(): Set<string> {
-    return this.state.tabs;
-  }
-
-  // Event Handlers
-
-  private registerSurface(name: string, tab: string,
-    surface: SurfaceComponent) {
-    const surfaceId = this.surfaceId(name, tab);
-    this.surfaces.set(surfaceId, surface);
-  }
-
-  private keyHandler(event: KeyboardEvent) {
-    const BACKTICK_KEY = 192;
-
-    if (event.keyCode === BACKTICK_KEY) {
-      if (event.shiftKey) {
-        this.toggleFullScreen();
-      } else {
-        this.toggle();
-      }
-    }
-  }
-
-  private setActiveTab(tab: string) {
-    this.setState({ activeTab: tab });
-  }
-
-  // Lifecycle Methods
-
-  override componentDidMount() {
-    this.bindKeys();
-  }
-
-  override componentWillMount() {
-    this.setTabs(this.props.surfaceList);
-  }
-
-  override componentWillReceiveProps(nextProps: VisorProps) {
-    this.setTabs(nextProps.surfaceList);
-  }
-
-  render() {
-    const { isOpen, isFullscreen, activeTab } = this.state;
-    const { surfaceList } = this.props;
-    const tabNames = Array.from(this.getTabs().values());
-
-    const SMALL_WIDTH = '550px';
-    const LARGE_WIDTH = '90vw';
-
-    const width = isFullscreen ? LARGE_WIDTH : SMALL_WIDTH;
-
-    const defaultStyles = css({
-      width,
-      height: '100%',
-      backgroundColor: '#fafafa',
-      boxSizing: 'border-box',
-      padding: '10px',
-      position: 'fixed',
-      top: '0px',
-      // tslint:disable-next-line
-      transition: `right 0.5s cubic-bezier(0.645, 0.045, 0.355, 1), width 0.5s cubic-bezier(0.645, 0.045, 0.355, 1)`,
-      boxShadow: '0 2px 5px rgba(0, 0, 0, 0.12), 0 2px 5px rgba(0, 0, 0, 0.24)',
-      overflow: 'auto',
-      fontFamily: 'sans-serif',
-      fontSize: '14px',
-      zIndex: 1000,
-    });
-
-    const openStyle = css({ right: '0' });
-
-    const closedStyle = css({ right: `calc(-${width} - 10px)` });
-    const position = isOpen ? openStyle : closedStyle;
-
-    // TODO. Add flex wrapping for surfaces
-    const surfacesContainerStyle = css({});
-
-    return (
-      <div
-        className={`${defaultStyles} ${position} visor`}
-        data-isopen={isOpen}
-        data-isfullscreen={isFullscreen}
-      >
-        {/* Controls  */}
-        <VisorControls
-          fullScreenHandler={this.toggleFullScreen.bind(this)}
-          closeHandler={this.close.bind(this)}
-          isFullScreen={isFullscreen}
-        />
-        {/* Tabs */}
-        <Tabs
-          tabNames={tabNames}
-          activeTab={activeTab}
-          handleClick={this.setActiveTab.bind(this)}
-        />
-
-        {/* Surfaces */}
-        {/* Note that we 'render' all surfaces so they can be added
-            to the DOM regardless of whether they are currently visible */}
-        <div className={`${surfacesContainerStyle} visor-surfaces`} >
-          {
-            surfaceList.map((surfaceInfo) => (
-              <SurfaceComponent
-                key={surfaceInfo.name + surfaceInfo.tab}
-                name={surfaceInfo.name}
-                tab={surfaceInfo.tab}
-                styles={surfaceInfo.styles}
-                registerSurface={this.registerSurface.bind(this)}
-                visible={activeTab === surfaceInfo.tab}
-              />
-            ))
-          }
-        </div>
-
-      </div >
-    );
-  }
-}
-
-//
-// Helper Components
-//
-
-interface VisorControlProps {
-  fullScreenHandler: () => void;
-  closeHandler: () => void;
-  isFullScreen: boolean;
-}
-
-function VisorControls(props: VisorControlProps) {
-  const { isFullScreen, fullScreenHandler, closeHandler } = props;
-
-  const toolBarStyle = css({
-    display: 'flex',
-    backgroundColor: 'white',
-    border: '1px solid white',
-    padding: '6px',
-    paddingTop: '10px',
-    marginBottom: '10px',
-    borderRadius: '6px',
-    marginTop: '-16px'
-  });
-
-  const controlsButtonClass = css({
-    fontSize: '.875rem',
-    borderRadius: '.25rem',
-    paddingLeft: '1rem',
-    paddingRight: '1rem',
-    paddingTop: '.5rem',
-    paddingBottom: '.5rem',
-    textDecoration: 'none',
-    transition: 'color .15s ease-in',
-    color: '#111',
-  });
-
-  const floatRight = css({
-    marginLeft: 'auto',
-  });
-
-  return (
-    <div className={`${toolBarStyle} visor-controls`}>
-      <button className={`${controlsButtonClass}`} onClick={fullScreenHandler}>
-        {isFullScreen ? 'Minimize' : 'Maximize'}
-      </button>
-
-      <button
-        className={`${controlsButtonClass} ${floatRight}`}
-        onClick={closeHandler}
-      >
-        Hide
-      </button>
-    </div>
-  );
-}
diff --git a/tfjs-master/tfjs-vis/src/components/visor_test.tsx b/tfjs-master/tfjs-vis/src/components/visor_test.tsx
deleted file mode 100644
index b78f5b791..000000000
--- a/tfjs-master/tfjs-vis/src/components/visor_test.tsx
+++ /dev/null
@@ -1,147 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import { h } from 'preact';
-import { render } from 'preact-render-spy';
-
-import { VisorComponent } from './visor';
-import { SurfaceInfoStrict } from '../types';
-
-afterEach(() => {
-  document.body.innerHTML = '';
-});
-
-describe('Visor Component', () => {
-  it('renders an empty visor', () => {
-    const wrapper = render(
-      <VisorComponent surfaceList={[]} />
-    );
-
-    expect(wrapper.find('.visor').length).toBe(1);
-    expect(wrapper.find('.visor-surfaces').length).toBe(1);
-    expect(wrapper.find('.tf-surface').length).toBe(0);
-    expect(wrapper.state().isOpen).toBe(true);
-    expect(wrapper.state().isFullscreen).toBe(false);
-  });
-
-  it('renders an empty and closed visor', () => {
-    const wrapper = render(
-      <VisorComponent
-        surfaceList={[]}
-        startOpen={false}
-      />
-    );
-
-    expect(wrapper.find('.visor').length).toBe(1);
-    expect(wrapper.state().isOpen).toBe(false);
-    expect(wrapper.state().isFullscreen).toBe(false);
-  });
-
-  it('renders a surface', () => {
-    const surfaceList: SurfaceInfoStrict[] = [
-      { name: 'surface 1', tab: 'tab 1' },
-    ];
-
-    const wrapper = render(
-      <VisorComponent surfaceList={surfaceList} />
-    );
-
-    expect(wrapper.find('.tf-surface').length).toBe(1);
-    expect(wrapper.find('.tf-surface').text()).toMatch('surface 1');
-    expect(wrapper.find('.tf-tab').length).toBe(1);
-    expect(wrapper.find('.tf-tab').text()).toMatch('tab 1');
-  });
-
-  it('switches tabs on click', () => {
-    const surfaceList: SurfaceInfoStrict[] = [
-      { name: 'surface 1', tab: 'tab 1' },
-      { name: 'surface 2', tab: 'tab 2' },
-    ];
-
-    const wrapper = render(
-      <VisorComponent surfaceList={surfaceList} />
-    );
-
-    expect(wrapper.find('.tf-tab').length).toBe(2);
-    expect(wrapper.state().activeTab).toEqual('tab 2');
-
-    // Clicks
-    wrapper.find('.tf-tab').at(0).simulate('click');
-    expect(wrapper.state().activeTab).toEqual('tab 1');
-    expect(wrapper.find('.tf-tab').at(0).attr('data-isactive' as never))
-      .toEqual(true);
-    expect(wrapper.find('.tf-tab').at(1).attr('data-isactive' as never))
-      .toEqual(false);
-
-    expect(wrapper.find('.tf-surface').at(0).attr('data-visible' as never))
-      .toEqual(true);
-    expect(wrapper.find('.tf-surface').at(1).attr('data-visible' as never))
-      .toEqual(false);
-
-    wrapper.find('.tf-tab').at(1).simulate('click');
-    expect(wrapper.state().activeTab).toEqual('tab 2');
-    expect(wrapper.find('.tf-tab').at(0).attr('data-isactive' as never))
-      .toEqual(false);
-    expect(wrapper.find('.tf-tab').at(1).attr('data-isactive' as never))
-      .toEqual(true);
-
-    expect(wrapper.find('.tf-surface').at(0).attr('data-visible' as never))
-      .toEqual(false);
-    expect(wrapper.find('.tf-surface').at(1).attr('data-visible' as never))
-      .toEqual(true);
-  });
-
-  it('hides on close button click', () => {
-    const surfaceList: SurfaceInfoStrict[] = [];
-
-    const wrapper = render(
-      <VisorComponent surfaceList={surfaceList} />
-    );
-
-    expect(wrapper.state().isOpen).toEqual(true);
-
-    const hideButton = wrapper.find('.visor-controls').children().at(1);
-    expect(hideButton.text()).toEqual('Hide');
-
-    hideButton.simulate('click');
-    expect(wrapper.state().isOpen).toEqual(false);
-  });
-
-  it('maximises and minimizes', () => {
-    const surfaceList: SurfaceInfoStrict[] = [];
-
-    const wrapper = render(
-      <VisorComponent surfaceList={surfaceList} />
-    );
-
-    expect(wrapper.state().isOpen).toEqual(true);
-
-    let toggleButton;
-    toggleButton = wrapper.find('.visor-controls').children().at(0);
-    expect(toggleButton.text()).toEqual('Maximize');
-    expect(wrapper.state().isFullscreen).toEqual(false);
-    expect(wrapper.find('.visor').at(0).attr('data-isfullscreen' as never))
-      .toEqual(false);
-
-    toggleButton.simulate('click');
-    toggleButton = wrapper.find('.visor-controls').children().at(0);
-    expect(toggleButton.text()).toEqual('Minimize');
-    expect(wrapper.state().isFullscreen).toEqual(true);
-    expect(wrapper.find('.visor').at(0).attr('data-isfullscreen' as never))
-      .toEqual(true);
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/index.ts b/tfjs-master/tfjs-vis/src/index.ts
deleted file mode 100644
index 112077fd0..000000000
--- a/tfjs-master/tfjs-vis/src/index.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-import '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-
-import {barchart} from './render/barchart';
-import {confusionMatrix} from './render/confusion_matrix';
-import {heatmap} from './render/heatmap';
-import {histogram} from './render/histogram';
-import {linechart} from './render/linechart';
-import {scatterplot} from './render/scatterplot';
-import {table} from './render/table';
-import {fitCallbacks, history} from './show/history';
-import {layer, modelSummary} from './show/model';
-import {perClassAccuracy as showPerClassAccuracy} from './show/quality';
-import {valuesDistribution} from './show/tensor';
-import {accuracy, confusionMatrix as metricsConfusionMatrix, perClassAccuracy as metricsPerClassAccuracy} from './util/math';
-import {version} from './version';
-
-const render = {
-  barchart,
-  table,
-  histogram,
-  linechart,
-  scatterplot,
-  confusionMatrix,
-  heatmap,
-};
-
-const metrics = {
-  accuracy,
-  perClassAccuracy: metricsPerClassAccuracy,
-  confusionMatrix: metricsConfusionMatrix,
-};
-
-const show = {
-  history,
-  fitCallbacks,
-  perClassAccuracy: showPerClassAccuracy,
-  valuesDistribution,
-  layer,
-  modelSummary,
-};
-
-export {visor} from './visor';
-export {render};
-export {metrics};
-export {show};
-export {version as version_vis};
-
-export * from './types';
diff --git a/tfjs-master/tfjs-vis/src/render/barchart.ts b/tfjs-master/tfjs-vis/src/render/barchart.ts
deleted file mode 100644
index 891fd7549..000000000
--- a/tfjs-master/tfjs-vis/src/render/barchart.ts
+++ /dev/null
@@ -1,159 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import embed, {Mode, Result as EmbedRes, VisualizationSpec} from 'vega-embed';
-
-import {BarChartOpts, Drawable} from '../types';
-import {getDefaultHeight, getDefaultWidth} from '../util/dom';
-import {getDrawArea, nextFrame, shallowEquals} from './render_utils';
-
-/**
- * Renders a barchart.
- *
- * ```js
- * const data = [
- *   { index: 0, value: 50 },
- *   { index: 1, value: 100 },
- *   { index: 2, value: 150 },
- *  ];
- *
- * // Render to visor
- * const surface = { name: 'Bar chart', tab: 'Charts' };
- * tfvis.render.barchart(surface, data);
- * ```
- *
- * @param data Data in the following format, (an array of objects)
- *    `[ {index: number, value: number} ... ]`
- *
- * @returns Promise - indicates completion of rendering
- *
- * @doc {heading: 'Charts', namespace: 'render'}
- */
-export async function barchart(
-    container: Drawable, data: Array<{index: number; value: number;}>,
-    opts: BarChartOpts = {}): Promise<void> {
-  const drawArea = getDrawArea(container);
-  const values = data;
-  const options = Object.assign({}, defaultOpts, opts);
-
-  // If we have rendered this chart before with the same options we can do a
-  // data only update, else  we do a regular re-render.
-  if (instances.has(drawArea)) {
-    const instanceInfo = instances.get(drawArea);
-    if (shallowEquals(options, instanceInfo.lastOptions)) {
-      await nextFrame();
-      const view = instanceInfo.view;
-      const changes = view.changeset().remove(() => true).insert(values);
-      await view.change('values', changes).runAsync();
-      return;
-    }
-  }
-
-  const {xLabel, yLabel, xType, yType} = options;
-
-  let xAxis: {}|null = null;
-  if (xLabel != null) {
-    xAxis = {title: xLabel};
-  }
-
-  let yAxis: {}|null = null;
-  if (yLabel != null) {
-    yAxis = {title: yLabel};
-  }
-
-  const embedOpts = {
-    actions: false,
-    mode: 'vega-lite' as Mode,
-    defaultStyle: false,
-  };
-
-  let colorEncoding;
-
-  if (options.color != null) {
-    if (Array.isArray(options.color)) {
-      colorEncoding = {
-        'field': 'index',
-        'type': 'nominal',
-        'scale': {
-          'range': options.color,
-        }
-      };
-    } else {
-      colorEncoding = {'value': options.color};
-    }
-  } else {
-    colorEncoding = {'value': '#4C78A0'};
-  }
-
-  const spec: VisualizationSpec = {
-    'width': options.width || getDefaultWidth(drawArea),
-    'height': options.height || getDefaultHeight(drawArea),
-    'padding': 0,
-    'autosize': {
-      'type': 'fit',
-      'contains': 'padding',
-      'resize': true,
-    },
-    'config': {
-      'axis': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      },
-      'text': {'fontSize': options.fontSize},
-      'legend': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      }
-    },
-    'data': {'values': values, 'name': 'values'},
-    'mark': {
-      'type': 'bar',
-      'tooltip': true,
-    },
-    'encoding': {
-      'x': {'field': 'index', 'type': xType, 'axis': xAxis},
-      'y': {'field': 'value', 'type': yType, 'axis': yAxis},
-      'color': colorEncoding,
-    },
-  } as VisualizationSpec;
-
-  await nextFrame();
-  const embedRes = await embed(drawArea, spec, embedOpts);
-  instances.set(drawArea, {
-    view: embedRes.view,
-    lastOptions: options,
-  });
-}
-
-const defaultOpts = {
-  xLabel: '',
-  yLabel: '',
-  xType: 'ordinal',
-  yType: 'quantitative',
-  fontSize: 11,
-};
-
-// We keep a map of containers to chart instances in order to reuse the
-// instance where possible.
-const instances: Map<HTMLElement, InstanceInfo> =
-    new Map<HTMLElement, InstanceInfo>();
-
-interface InstanceInfo {
-  // tslint:disable-next-line:no-any
-  view: any;
-  lastOptions: BarChartOpts;
-}
diff --git a/tfjs-master/tfjs-vis/src/render/barchart_test.ts b/tfjs-master/tfjs-vis/src/render/barchart_test.ts
deleted file mode 100644
index b595ef0ce..000000000
--- a/tfjs-master/tfjs-vis/src/render/barchart_test.ts
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {barchart} from './barchart';
-
-describe('renderBarChart', () => {
-  let pixelRatio: number;
-
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-    pixelRatio = window.devicePixelRatio;
-  });
-
-  it('renders a bar chart', async () => {
-    const data = [
-      {index: 0, value: 50},
-      {index: 1, value: 100},
-      {index: 2, value: 230},
-    ];
-
-    const container = document.getElementById('container');
-    await barchart(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('re-renders a bar chart', async () => {
-    const data = [
-      {index: 0, value: 50},
-      {index: 1, value: 100},
-      {index: 2, value: 230},
-    ];
-
-    const container = document.getElementById('container');
-
-    await barchart(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    await barchart(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('updates a bar chart', async () => {
-    let data = [
-      {index: 0, value: 50},
-      {index: 1, value: 100},
-      {index: 2, value: 150},
-    ];
-
-    const container = document.getElementById('container');
-
-    await barchart(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    data = [
-      {index: 0, value: 50},
-      {index: 1, value: 100},
-      {index: 2, value: 150},
-    ];
-
-    await barchart(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('sets width of chart', async () => {
-    const data = [
-      {index: 0, value: 50},
-      {index: 1, value: 100},
-      {index: 2, value: 230},
-    ];
-
-    const container = document.getElementById('container');
-    await barchart(container, data, {width: 400});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').width)
-        .toBe(Math.floor(400 * pixelRatio));
-  });
-
-  it('sets height of chart', async () => {
-    const data = [
-      {index: 0, value: 50},
-      {index: 1, value: 100},
-      {index: 2, value: 230},
-    ];
-
-    const container = document.getElementById('container');
-    await barchart(container, data, {height: 200});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').height)
-        .toBe(Math.floor(200 * pixelRatio));
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/render/confusion_matrix.ts b/tfjs-master/tfjs-vis/src/render/confusion_matrix.ts
deleted file mode 100644
index 16e855f0a..000000000
--- a/tfjs-master/tfjs-vis/src/render/confusion_matrix.ts
+++ /dev/null
@@ -1,269 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import embed, {Mode, VisualizationSpec} from 'vega-embed';
-
-import {ConfusionMatrixData, ConfusionMatrixOptions, Drawable,} from '../types';
-import {getDefaultHeight, getDefaultWidth} from '../util/dom';
-import {getDrawArea} from './render_utils';
-
-/**
- * Renders a confusion matrix.
- *
- * Can optionally exclude the diagonal from being shaded if one wants the visual
- * focus to be on the incorrect classifications. Note that if the classification
- * is perfect (i.e. only the diagonal has values) then the diagonal will always
- * be shaded.
- *
- * ```js
- * const rows = 5;
- * const cols = 5;
- * const values = [];
- * for (let i = 0; i < rows; i++) {
- *   const row = []
- *   for (let j = 0; j < cols; j++) {
- *     row.push(Math.round(Math.random() * 50));
- *   }
- *   values.push(row);
- * }
- * const data = { values };
- *
- * // Render to visor
- * const surface = { name: 'Confusion Matrix', tab: 'Charts' };
- * tfvis.render.confusionMatrix(surface, data);
- * ```
- *
- * ```js
- * // The diagonal can be excluded from shading.
- *
- * const data = {
- *   values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
- * }
- *
- * // Render to visor
- * const surface = {
- *  name: 'Confusion Matrix with Excluded Diagonal', tab: 'Charts'
- * };
- *
- * tfvis.render.confusionMatrix(surface, data, {
- *   shadeDiagonal: false
- * });
- * ```
- *
- * @doc {heading: 'Charts', namespace: 'render'}
- */
-export async function confusionMatrix(
-    container: Drawable, data: ConfusionMatrixData,
-    opts: ConfusionMatrixOptions = {}): Promise<void> {
-  const options = Object.assign({}, defaultOpts, opts);
-  const drawArea = getDrawArea(container);
-
-  // Format data for vega spec; an array of objects, one for for each cell
-  // in the matrix.
-  const values: MatrixEntry[] = [];
-
-  const inputArray = data.values;
-  const tickLabels = data.tickLabels || [];
-  const generateLabels = tickLabels.length === 0;
-
-  let nonDiagonalIsAllZeroes = true;
-  for (let i = 0; i < inputArray.length; i++) {
-    const label = generateLabels ? `Class ${i}` : tickLabels[i];
-
-    if (generateLabels) {
-      tickLabels.push(label);
-    }
-
-    for (let j = 0; j < inputArray[i].length; j++) {
-      const prediction = generateLabels ? `Class ${j}` : tickLabels[j];
-
-      const count = inputArray[i][j];
-      if (i === j && !options.shadeDiagonal) {
-        values.push({
-          label,
-          prediction,
-          count,
-          noFill: true,
-        });
-      } else {
-        values.push({
-          label,
-          prediction,
-          count,
-          scaleCount: count,
-        });
-        // When not shading the diagonal we want to check if there is a non
-        // zero value. If all values are zero we will not color them as the
-        // scale will be invalid.
-        if (count !== 0) {
-          nonDiagonalIsAllZeroes = false;
-        }
-      }
-    }
-  }
-
-  if (!options.shadeDiagonal && nonDiagonalIsAllZeroes) {
-    // User has specified requested not to shade the diagonal but all the other
-    // values are zero. We have two choices, don't shade the anything or only
-    // shade the diagonal. We choose to shade the diagonal as that is likely
-    // more helpful even if it is not what the user specified.
-    for (const val of values) {
-      if (val.noFill === true) {
-        val.noFill = false;
-        val.scaleCount = val.count;
-      }
-    }
-  }
-
-  const embedOpts = {
-    actions: false,
-    mode: 'vega-lite' as Mode,
-    defaultStyle: false,
-  };
-
-  //@ts-ignore
-  const spec: VisualizationSpec = {
-    'width': options.width || getDefaultWidth(drawArea),
-    'height': options.height || getDefaultHeight(drawArea),
-    'padding': 0,
-    'autosize': {
-      'type': 'fit',
-      'contains': 'padding',
-      'resize': true,
-    },
-    'config': {
-      'axis': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      },
-      'text': {'fontSize': options.fontSize},
-      'legend': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      }
-    },
-    //@ts-ignore
-    'data': {'values': values},
-    'encoding': {
-      'x': {
-        'field': 'prediction',
-        'type': 'ordinal',
-        'title': options.xLabel || 'prediction',
-        // Maintain sort order of the axis if labels is passed in
-        'scale': {'domain': tickLabels},
-      },
-      'y': {
-        'field': 'label',
-        'type': 'ordinal',
-        'title': options.yLabel || 'label',
-        // Maintain sort order of the axis if labels is passed in
-        'scale': {'domain': tickLabels},
-      },
-    },
-    'layer': [
-      {
-        // The matrix
-        'transform': [
-          {'filter': 'datum.noFill != true'},
-        ],
-        'mark': {
-          'type': 'rect',
-        },
-        'encoding': {
-          'color': {
-            'field': 'scaleCount',
-            'type': 'quantitative',
-            //@ts-ignore
-            'scale': {'range': options.colorMap},
-          },
-          'tooltip': [
-            {'field': 'label', 'type': 'nominal'},
-            {'field': 'prediction', 'type': 'nominal'},
-            {'field': 'count', 'type': 'quantitative'},
-          ]
-        },
-      },
-    ]
-  };
-
-  if (options.shadeDiagonal === false) {
-    //@ts-ignore
-    spec.layer.push(
-        {
-          // render unfilled rects for the diagonal
-          'transform': [
-            {'filter': 'datum.noFill == true'},
-          ],
-          'mark': {
-            'type': 'rect',
-            'fill': 'white',
-          },
-          'encoding': {
-            'tooltip': [
-              {'field': 'label', 'type': 'nominal'},
-              {'field': 'prediction', 'type': 'nominal'},
-              {'field': 'count', 'type': 'quantitative'},
-            ]
-          },
-        },
-    );
-  }
-
-  if (options.showTextOverlay) {
-    //@ts-ignore
-    spec.layer.push({
-      // The text labels
-      'mark': {'type': 'text', 'baseline': 'middle'},
-      'encoding': {
-        'text': {
-          'field': 'count',
-          'type': 'nominal',
-        },
-      }
-    });
-  }
-
-  const colorMap = typeof options.colorMap === 'string' ?
-      {scheme: options.colorMap} :
-      options.colorMap;
-  //@ts-ignore
-  spec.layer[0].encoding.color.scale.range = colorMap;
-
-  await embed(drawArea, spec, embedOpts);
-}
-
-const defaultOpts: ConfusionMatrixOptions = {
-  xLabel: null,
-  yLabel: null,
-  xType: 'nominal',
-  yType: 'nominal',
-  shadeDiagonal: true,
-  fontSize: 12,
-  showTextOverlay: true,
-  height: 400,
-  colorMap: ['#f7fbff', '#4292c6'],
-};
-
-interface MatrixEntry {
-  label: string;
-  prediction: string;
-  // The displayed count
-  count: number;
-  // The count values used to compute the color scale
-  scaleCount?: number;
-  noFill?: boolean;
-}
diff --git a/tfjs-master/tfjs-vis/src/render/confusion_matrix_test.ts b/tfjs-master/tfjs-vis/src/render/confusion_matrix_test.ts
deleted file mode 100644
index 5ddd7bbd7..000000000
--- a/tfjs-master/tfjs-vis/src/render/confusion_matrix_test.ts
+++ /dev/null
@@ -1,114 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ConfusionMatrixData} from '../types';
-
-import {confusionMatrix} from './confusion_matrix';
-
-describe('renderConfusionMatrix', () => {
-  let pixelRatio: number;
-
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-    pixelRatio = window.devicePixelRatio;
-  });
-
-  it('renders a chart', async () => {
-    const data: ConfusionMatrixData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-      tickLabels: ['cheese', 'pig', 'font'],
-    };
-
-    const container = document.getElementById('container');
-    await confusionMatrix(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders a chart with shaded diagonal', async () => {
-    const data: ConfusionMatrixData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-      tickLabels: ['cheese', 'pig', 'font'],
-    };
-
-    const container = document.getElementById('container');
-    await confusionMatrix(container, data, {shadeDiagonal: true});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders the chart with generated labels', async () => {
-    const data: ConfusionMatrixData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-    };
-
-    const container = document.getElementById('container');
-
-    await confusionMatrix(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('updates the chart', async () => {
-    let data: ConfusionMatrixData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-      tickLabels: ['cheese', 'pig', 'font'],
-    };
-
-    const container = document.getElementById('container');
-
-    await confusionMatrix(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    data = {
-      values: [[43, 2, 8], [1, 7, 2], [3, 3, 20]],
-      tickLabels: ['cheese', 'pig', 'font'],
-    };
-
-    await confusionMatrix(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('sets width of chart', async () => {
-    const data: ConfusionMatrixData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-      tickLabels: ['cheese', 'pig', 'font'],
-    };
-
-    const container = document.getElementById('container');
-    await confusionMatrix(container, data, {width: 400});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').width)
-        .toBe(Math.floor(400 * pixelRatio));
-  });
-
-  it('sets height of chart', async () => {
-    const data: ConfusionMatrixData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-      tickLabels: ['cheese', 'pig', 'font'],
-    };
-
-    const container = document.getElementById('container');
-    await confusionMatrix(container, data, {height: 200});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').height)
-        .toBe(Math.floor(200 * pixelRatio));
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/render/heatmap.ts b/tfjs-master/tfjs-vis/src/render/heatmap.ts
deleted file mode 100644
index 781e4a8ca..000000000
--- a/tfjs-master/tfjs-vis/src/render/heatmap.ts
+++ /dev/null
@@ -1,318 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import embed, {Mode, VisualizationSpec} from 'vega-embed';
-
-import {Drawable, HeatmapData, HeatmapOptions} from '../types';
-import {getDefaultHeight, getDefaultWidth} from '../util/dom';
-import {assert} from '../util/utils';
-
-import {getDrawArea} from './render_utils';
-
-/**
- * Renders a heatmap.
- *
- * ```js
- * const cols = 50;
- * const rows = 20;
- * const values = [];
- * for (let i = 0; i < cols; i++) {
- *   const col = []
- *   for (let j = 0; j < rows; j++) {
- *     col.push(i * j)
- *   }
- *   values.push(col);
- * }
- * const data = { values };
- *
- * // Render to visor
- * const surface = { name: 'Heatmap', tab: 'Charts' };
- * tfvis.render.heatmap(surface, data);
- * ```
- *
- * ```js
- * const data = {
- *   values: [[4, 2, 8, 20], [1, 7, 2, 10], [3, 3, 20, 13]],
- *   xTickLabels: ['cheese', 'pig', 'font'],
- *   yTickLabels: ['speed', 'smoothness', 'dexterity', 'mana'],
- * }
- *
- * // Render to visor
- * const surface = { name: 'Heatmap w Custom Labels', tab: 'Charts' };
- * tfvis.render.heatmap(surface, data);
- * ```
- *
- *
- * @doc {heading: 'Charts', namespace: 'render'}
- */
-export async function heatmap(
-    container: Drawable, data: HeatmapData,
-    opts: HeatmapOptions = {}): Promise<void> {
-  const options = Object.assign({}, defaultOpts, opts);
-  const drawArea = getDrawArea(container);
-
-  let inputValues = data.values;
-  if (options.rowMajor) {
-    inputValues = await convertToRowMajor(data.values);
-  }
-
-  // Data validation
-  const {xTickLabels, yTickLabels} = data;
-  if (xTickLabels != null) {
-    const dimension = 0;
-    assertLabelsMatchShape(inputValues, xTickLabels, dimension);
-  }
-
-  // Note that we will only do a check on the first element of the second
-  // dimension. We do not protect users against passing in a ragged array.
-  if (yTickLabels != null) {
-    const dimension = 1;
-    assertLabelsMatchShape(inputValues, yTickLabels, dimension);
-  }
-
-  //
-  // Format data for vega spec; an array of objects, one for for each cell
-  // in the matrix.
-  //
-  // If custom labels are passed in for xTickLabels or yTickLabels we need
-  // to make sure they are 'unique' before mapping them to visual properties.
-  // We therefore append the index of the label to the datum that will be used
-  // for that label in the x or y axis. We could do this in all cases but choose
-  // not to to avoid unnecessary string operations.
-  //
-  // We use IDX_SEPARATOR to demarcate the added index
-  const IDX_SEPARATOR = '@tfidx@';
-
-  const values: MatrixEntry[] = [];
-  if (inputValues instanceof tf.Tensor) {
-    assert(
-        inputValues.rank === 2,
-        'Input to renderHeatmap must be a 2d array or Tensor2d');
-
-    // This is a slightly specialized version of TensorBuffer.get, inlining it
-    // avoids the overhead of a function call per data element access and is
-    // specialized to only deal with the 2d case.
-    const inputArray = await inputValues.data();
-    const [numRows, numCols] = inputValues.shape;
-
-    for (let row = 0; row < numRows; row++) {
-      const x = xTickLabels ? `${xTickLabels[row]}${IDX_SEPARATOR}${row}` : row;
-      for (let col = 0; col < numCols; col++) {
-        const y =
-            yTickLabels ? `${yTickLabels[col]}${IDX_SEPARATOR}${col}` : col;
-
-        const index = (row * numCols) + col;
-        const value = inputArray[index];
-
-        values.push({x, y, value});
-      }
-    }
-  } else {
-    const inputArray = inputValues;
-    for (let row = 0; row < inputArray.length; row++) {
-      const x = xTickLabels ? `${xTickLabels[row]}${IDX_SEPARATOR}${row}` : row;
-      for (let col = 0; col < inputArray[row].length; col++) {
-        const y =
-            yTickLabels ? `${yTickLabels[col]}${IDX_SEPARATOR}${col}` : col;
-        const value = inputArray[row][col];
-        values.push({x, y, value});
-      }
-    }
-  }
-
-  const embedOpts = {
-    actions: false,
-    mode: 'vega-lite' as Mode,
-    defaultStyle: false,
-  };
-
-  const spec: VisualizationSpec = {
-    'width': options.width || getDefaultWidth(drawArea),
-    'height': options.height || getDefaultHeight(drawArea),
-    'padding': 0,
-    'autosize': {
-      'type': 'fit',
-      'contains': 'padding',
-      'resize': true,
-    },
-    'config': {
-      'axis': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      },
-      'text': {'fontSize': options.fontSize},
-      'legend': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      },
-      'scale': {'bandPaddingInner': 0, 'bandPaddingOuter': 0},
-    },
-    //@ts-ignore
-    'data': {'values': values},
-    'mark': {'type': 'rect', 'tooltip': true},
-    'encoding': {
-      'x': {
-        'field': 'x',
-        'type': options.xType,
-        'title': options.xLabel,
-        'sort': null,
-      },
-      'y': {
-        'field': 'y',
-        'type': options.yType,
-        'title': options.yLabel,
-        'sort': null,
-      },
-      'fill': {
-        'field': 'value',
-        'type': 'quantitative',
-      }
-    }
-  };
-
-  //
-  // Format custom labels to remove the appended indices
-  //
-  const suffixPattern = `${IDX_SEPARATOR}\\d+$`;
-  const suffixRegex = new RegExp(suffixPattern);
-  if (xTickLabels) {
-    // @ts-ignore
-    spec.encoding.x.axis = {
-      'labelExpr': `replace(datum.value, regexp(/${suffixPattern}/), '')`,
-    };
-  }
-
-  if (yTickLabels) {
-    // @ts-ignore
-    spec.encoding.y.axis = {
-      'labelExpr': `replace(datum.value, regexp(/${suffixPattern}/), '')`,
-    };
-  }
-
-  // Customize tooltip formatting to remove the appended indices
-  if (xTickLabels || yTickLabels) {
-    //@ts-ignore
-    embedOpts.tooltip = {
-      sanitize: (value: string|number) => {
-        const valueString = String(value);
-        return valueString.replace(suffixRegex, '');
-      }
-    };
-  }
-
-  let colorRange: string[]|string;
-  switch (options.colorMap) {
-    case 'blues':
-      colorRange = ['#f7fbff', '#4292c6'];
-      break;
-    case 'greyscale':
-      colorRange = ['#000000', '#ffffff'];
-      break;
-    case 'viridis':
-    default:
-      colorRange = 'viridis';
-      break;
-  }
-
-  if (colorRange !== 'viridis') {
-    //@ts-ignore
-    const fill = spec.encoding.fill;
-    // @ts-ignore
-    fill.scale = {'range': colorRange};
-  }
-
-  if (options.domain) {
-    //@ts-ignore
-    const fill = spec.encoding.fill;
-    // @ts-ignore
-    if (fill.scale != null) {
-      // @ts-ignore
-      fill.scale = Object.assign({}, fill.scale, {'domain': options.domain});
-    } else {
-      // @ts-ignore
-      fill.scale = {'domain': options.domain};
-    }
-  }
-
-  await embed(drawArea, spec, embedOpts);
-}
-
-async function convertToRowMajor(inputValues: number[][]|
-                                 tf.Tensor2D): Promise<number[][]> {
-  let originalShape: number[];
-  let transposed: tf.Tensor2D;
-  if (inputValues instanceof tf.Tensor) {
-    originalShape = inputValues.shape;
-    transposed = inputValues.transpose();
-  } else {
-    originalShape = [inputValues.length, inputValues[0].length];
-    transposed = tf.tidy(() => tf.tensor2d(inputValues).transpose());
-  }
-
-  assert(
-      transposed.rank === 2,
-      'Input to renderHeatmap must be a 2d array or Tensor2d');
-
-  // Download the intermediate tensor values and
-  // dispose the transposed tensor.
-  const transposedValues = await transposed.array();
-  transposed.dispose();
-
-  const transposedShape = [transposedValues.length, transposedValues[0].length];
-  assert(
-      originalShape[0] === transposedShape[1] &&
-          originalShape[1] === transposedShape[0],
-      `Unexpected transposed shape. Original ${originalShape} : Transposed ${
-          transposedShape}`);
-  return transposedValues;
-}
-
-function assertLabelsMatchShape(
-    inputValues: number[][]|tf.Tensor2D, labels: string[], dimension: 0|1) {
-  const shape = inputValues instanceof tf.Tensor ?
-      inputValues.shape :
-      [inputValues.length, inputValues[0].length];
-  if (dimension === 0) {
-    assert(
-        shape[0] === labels.length,
-        `Length of xTickLabels (${labels.length}) must match number of rows` +
-            ` (${shape[0]})`);
-  } else if (dimension === 1) {
-    assert(
-        shape[1] === labels.length,
-        `Length of yTickLabels (${
-            labels.length}) must match number of columns (${shape[1]})`);
-  }
-}
-
-const defaultOpts: HeatmapOptions = {
-  xLabel: null,
-  yLabel: null,
-  xType: 'ordinal',
-  yType: 'ordinal',
-  colorMap: 'viridis',
-  fontSize: 12,
-  domain: null,
-  rowMajor: false,
-};
-
-interface MatrixEntry {
-  x: string|number;
-  y: string|number;
-  value: number;
-}
diff --git a/tfjs-master/tfjs-vis/src/render/heatmap_test.ts b/tfjs-master/tfjs-vis/src/render/heatmap_test.ts
deleted file mode 100644
index 1cec11939..000000000
--- a/tfjs-master/tfjs-vis/src/render/heatmap_test.ts
+++ /dev/null
@@ -1,225 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-
-import {HeatmapData} from '../types';
-
-import {heatmap} from './heatmap';
-
-describe('renderHeatmap', () => {
-  let pixelRatio: number;
-
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-    pixelRatio = window.devicePixelRatio;
-  });
-
-  it('renders a chart', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-    };
-
-    const container = document.getElementById('container');
-    await heatmap(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders a chart with rowMajor=true', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20], [8, 2, 8]],
-    };
-
-    const numTensorsBefore = tf.memory().numTensors;
-
-    const container = document.getElementById('container');
-    await heatmap(container, data, {rowMajor: true});
-
-    const numTensorsAfter = tf.memory().numTensors;
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(numTensorsAfter).toEqual(numTensorsBefore);
-  });
-
-  it('renders a chart with rowMajor=true and custom labels', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20], [8, 2, 8]],
-      xTickLabels: ['alpha', 'beta', 'gamma'],
-      yTickLabels: ['first', 'second', 'third', 'fourth'],
-    };
-
-    const numTensorsBefore = tf.memory().numTensors;
-
-    const container = document.getElementById('container');
-    await heatmap(container, data, {rowMajor: true});
-
-    const numTensorsAfter = tf.memory().numTensors;
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(numTensorsAfter).toEqual(numTensorsBefore);
-  });
-
-  it('renders a chart with a tensor', async () => {
-    const values = tf.tensor2d([[4, 2, 8], [1, 7, 2], [3, 3, 20]]);
-    const data: HeatmapData = {
-      values,
-    };
-
-    const container = document.getElementById('container');
-    await heatmap(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    values.dispose();
-  });
-
-  it('throws an exception with a non 2d tensor', async () => {
-    const values = tf.tensor1d([4, 2, 8, 1, 7, 2, 3, 3, 20]);
-    const data = {
-      values,
-    };
-
-    const container = document.getElementById('container');
-
-    let threw = false;
-    try {
-      // @ts-ignore — passing in the wrong datatype
-      await heatmap(data, container);
-    } catch (e) {
-      threw = true;
-    } finally {
-      values.dispose();
-    }
-    expect(threw).toBe(true);
-  });
-
-  it('renders a chart with custom colormap', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-    };
-
-    const container = document.getElementById('container');
-    await heatmap(container, data, {colorMap: 'greyscale'});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders a chart with custom domain', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-    };
-
-    const container = document.getElementById('container');
-    await heatmap(container, data, {domain: [0, 30]});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders a chart with custom labels', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-      xTickLabels: ['cheese', 'pig', 'font'],
-      yTickLabels: ['speed', 'dexterity', 'roundness'],
-    };
-
-    const container = document.getElementById('container');
-    await heatmap(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('updates the chart', async () => {
-    let data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-    };
-
-    const container = document.getElementById('container');
-
-    await heatmap(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    data = {
-      values: [[43, 2, 8], [1, 7, 2], [3, 3, 20]],
-    };
-
-    await heatmap(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('sets width of chart', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-    };
-
-    const container = document.getElementById('container');
-    await heatmap(container, data, {width: 400});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').width)
-        .toBe(Math.floor(400 * pixelRatio));
-  });
-
-  it('sets height of chart', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20]],
-    };
-
-    const container = document.getElementById('container');
-    await heatmap(container, data, {height: 200});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').height)
-        .toBe(Math.floor(200 * pixelRatio));
-  });
-
-  it('throws on wrong number of xTickLabels', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20], [8, 2, 8]],
-      xTickLabels: ['alpha'],
-      yTickLabels: ['first', 'second', 'third', 'fourth'],
-    };
-
-    const container = document.getElementById('container');
-    let threw = false;
-    try {
-      await heatmap(container, data, {height: 200});
-    } catch (e) {
-      threw = true;
-    }
-    expect(threw).toBe(true);
-  });
-
-  it('throws on wrong number of yTickLabels', async () => {
-    const data: HeatmapData = {
-      values: [[4, 2, 8], [1, 7, 2], [3, 3, 20], [8, 2, 8]],
-      xTickLabels: ['alpha', 'beta', 'gamma'],
-      yTickLabels: ['first'],
-    };
-
-    const container = document.getElementById('container');
-    let threw = false;
-    try {
-      await heatmap(container, data, {height: 200});
-    } catch (e) {
-      threw = true;
-    }
-    expect(threw).toBe(true);
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/render/histogram.ts b/tfjs-master/tfjs-vis/src/render/histogram.ts
deleted file mode 100644
index ef9c48287..000000000
--- a/tfjs-master/tfjs-vis/src/render/histogram.ts
+++ /dev/null
@@ -1,224 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {format as d3Format} from 'd3-format';
-import embed, {Mode, VisualizationSpec} from 'vega-embed';
-
-import {Drawable, HistogramOpts, HistogramStats, TypedArray} from '../types';
-import {getDefaultHeight, getDefaultWidth, subSurface} from '../util/dom';
-import {arrayStats} from '../util/math';
-
-import {table} from './table';
-
-const defaultOpts = {
-  maxBins: 12,
-  fontSize: 11,
-};
-
-/**
- * Renders a histogram of values
- *
- * ```js
- * const data = Array(100).fill(0)
- *   .map(x => Math.random() * 100 - (Math.random() * 50))
- *
- * // Push some special values for the stats table.
- * data.push(Infinity);
- * data.push(NaN);
- * data.push(0);
- *
- * const surface = { name: 'Histogram', tab: 'Charts' };
- * tfvis.render.histogram(surface, data);
- * ```
- *
- * @doc {heading: 'Charts', namespace: 'render'}
- */
-export async function histogram(
-    container: Drawable, data: Array<{value: number}>|number[]|TypedArray,
-    opts: HistogramOpts = {}) {
-  const values = prepareData(data);
-
-  const options = Object.assign({}, defaultOpts, opts);
-
-  const embedOpts = {
-    actions: false,
-    mode: 'vega-lite' as Mode,
-    defaultStyle: false,
-  };
-
-  const histogramContainer = subSurface(container, 'histogram');
-  if (opts.stats !== false) {
-    const statsContainer = subSurface(container, 'stats', {
-      prepend: true,
-    });
-    let stats: HistogramStats;
-
-    if (opts.stats) {
-      stats = opts.stats;
-    } else {
-      stats = arrayStats(values.map(x => x.value));
-    }
-    renderStats(stats, statsContainer, {fontSize: options.fontSize});
-  }
-
-  // If there are no data values return early
-  if (values.length === 0) {
-    return undefined;
-  }
-
-  // Now that we have rendered stats we need to remove any NaNs and Infinities
-  // before rendering the histogram
-  const filtered = [];
-  for (let i = 0; i < values.length; i++) {
-    const val = values[i].value;
-    if (val != null && isFinite(val)) {
-      filtered.push(values[i]);
-    }
-  }
-
-  const histogramSpec: VisualizationSpec = {
-
-    'width': options.width || getDefaultWidth(histogramContainer),
-    'height': options.height || getDefaultHeight(histogramContainer),
-    'padding': 0,
-    'autosize': {
-      'type': 'fit',
-      'contains': 'padding',
-      'resize': true,
-    },
-    'data': {'values': filtered},
-    'mark': {
-      'type': 'bar',
-      'tooltip': true,
-    },
-    'config': {
-      'axis': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      },
-      'text': {'fontSize': options.fontSize},
-      'legend': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      }
-    },
-    'encoding': {
-      'x': {
-        'bin': {'maxbins': options.maxBins},
-        'field': 'value',
-        'type': 'quantitative',
-      },
-      'y': {
-        'aggregate': 'count',
-        'type': 'quantitative',
-      },
-      'color': {
-        'value': options.color || '#001B44',
-      }
-    }
-  };
-
-  return embed(histogramContainer, histogramSpec, embedOpts);
-}
-
-function renderStats(
-    stats: HistogramStats, container: HTMLElement, opts: {fontSize: number}) {
-  const format = d3Format(',.4~f');
-  const pctFormat = d3Format('.4~p');
-
-  const headers: string[] = [];
-  const vals: string[] = [];
-
-  if (stats.numVals != null) {
-    headers.push('Num Vals');
-    vals.push(format(stats.numVals));
-  }
-
-  if (stats.min != null) {
-    headers.push('Min');
-    vals.push(format(stats.min));
-  }
-
-  if (stats.max != null) {
-    headers.push('Max');
-    vals.push(format(stats.max));
-  }
-
-  if (stats.numZeros != null) {
-    headers.push('# Zeros');
-    let zeroPct = '';
-    if (stats.numVals) {
-      zeroPct = stats.numZeros > 0 ?
-          `(${pctFormat(stats.numZeros / stats.numVals)})` :
-          '';
-    }
-
-    vals.push(`${format(stats.numZeros)} ${zeroPct}`);
-  }
-
-  if (stats.numNans != null) {
-    headers.push('# NaNs');
-    let nanPct = '';
-    if (stats.numVals) {
-      nanPct = stats.numNans > 0 ?
-          `(${pctFormat(stats.numNans / stats.numVals)})` :
-          '';
-    }
-
-    vals.push(`${format(stats.numNans)} ${nanPct}`);
-  }
-
-  if (stats.numInfs != null) {
-    headers.push('# Infinity');
-    let infPct = '';
-    if (stats.numVals) {
-      infPct = stats.numInfs > 0 ?
-          `(${pctFormat(stats.numInfs / stats.numVals)})` :
-          '';
-    }
-
-    vals.push(`${format(stats.numInfs)} ${infPct}`);
-  }
-
-  table(container, {headers, values: [vals]}, opts);
-}
-
-/**
- * Formats data to the internal format used by this chart.
- */
-function prepareData(data: Array<{value: number}>|number[]|
-                     TypedArray): Array<{value: number}> {
-  if (data.length == null) {
-    throw new Error('input data must be an array');
-  }
-
-  if (data.length === 0) {
-    return [];
-  } else if (typeof data[0] === 'object') {
-    if ((data[0] as {value: number}).value == null) {
-      throw new Error('input data must have a value field');
-    } else {
-      return data as Array<{value: number}>;
-    }
-  } else {
-    const ret = Array(data.length);
-    for (let i = 0; i < data.length; i++) {
-      ret[i] = {value: data[i]};
-    }
-    return ret;
-  }
-}
diff --git a/tfjs-master/tfjs-vis/src/render/histogram_test.ts b/tfjs-master/tfjs-vis/src/render/histogram_test.ts
deleted file mode 100644
index 64c029e2c..000000000
--- a/tfjs-master/tfjs-vis/src/render/histogram_test.ts
+++ /dev/null
@@ -1,216 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {HistogramStats} from '../types';
-
-import {histogram} from './histogram';
-
-describe('renderHistogram', () => {
-  let pixelRatio: number;
-
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-    pixelRatio = window.devicePixelRatio;
-  });
-
-  it('renders a histogram', async () => {
-    const data = [
-      {value: 50},
-      {value: 100},
-      {value: 100},
-    ];
-
-    const container = document.getElementById('container');
-    await histogram(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('table').length).toBe(1);
-    expect(document.querySelectorAll('table thead tr').length).toBe(1);
-    expect(document.querySelectorAll('table thead th').length).toBe(6);
-    expect(document.querySelectorAll('table tbody tr').length).toBe(1);
-    expect(document.querySelectorAll('table tbody td').length).toBe(6);
-  });
-
-  it('renders a histogram with number array', async () => {
-    const data = [50, 100, 100];
-
-    const container = document.getElementById('container');
-    await histogram(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('table').length).toBe(1);
-    expect(document.querySelectorAll('table thead tr').length).toBe(1);
-    expect(document.querySelectorAll('table thead th').length).toBe(6);
-    expect(document.querySelectorAll('table tbody tr').length).toBe(1);
-    expect(document.querySelectorAll('table tbody td').length).toBe(6);
-  });
-
-  it('renders a histogram with typed array', async () => {
-    const data = new Int32Array([50, 100, 100]);
-
-    const container = document.getElementById('container');
-    await histogram(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('table').length).toBe(1);
-    expect(document.querySelectorAll('table thead tr').length).toBe(1);
-    expect(document.querySelectorAll('table thead th').length).toBe(6);
-    expect(document.querySelectorAll('table tbody tr').length).toBe(1);
-    expect(document.querySelectorAll('table tbody td').length).toBe(6);
-  });
-
-  it('re-renders a histogram', async () => {
-    const data = [
-      {value: 50},
-      {value: 100},
-      {value: 100},
-    ];
-
-    const container = document.getElementById('container');
-
-    await histogram(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    await histogram(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('updates a histogram chart', async () => {
-    let data = [
-      {value: 50},
-      {value: 100},
-      {value: 100},
-    ];
-
-    const container = document.getElementById('container');
-
-    await histogram(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    data = [
-      {value: 150},
-      {value: 100},
-      {value: 150},
-    ];
-
-    await histogram(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders correct stats', async () => {
-    const data = [
-      {value: 50},
-      {value: -100},
-      {value: 200},
-      {value: 0},
-      {value: 0},
-      {value: NaN},
-      {value: NaN},
-      {value: NaN},
-    ];
-
-    const container = document.getElementById('container');
-    await histogram(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('table').length).toBe(1);
-    expect(document.querySelectorAll('table tbody tr').length).toBe(1);
-
-    const statsEls = document.querySelectorAll('table tbody td');
-    expect(statsEls.length).toBe(6);
-    expect(statsEls[0].textContent).toEqual('8');
-    expect(statsEls[1].textContent).toEqual('−100'); // Note the long dash '−'
-    expect(statsEls[2].textContent).toEqual('200');
-    expect(statsEls[3].textContent).toEqual('2 (25%)');
-    expect(statsEls[4].textContent).toEqual('3 (37.5%)');
-  });
-
-  it('does not throw on empty data', async () => {
-    const data: Array<{value: number}> = [];
-
-    const container = document.getElementById('container');
-    expect(async () => {
-      await histogram(container, data);
-    }).not.toThrow();
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(0);
-    expect(document.querySelectorAll('table').length).toBe(1);
-    expect(document.querySelectorAll('table thead tr').length).toBe(1);
-    expect(document.querySelectorAll('table thead th').length).toBe(3);
-  });
-
-  it('renders custom stats', async () => {
-    const data = [
-      {value: 50},
-    ];
-
-    const stats: HistogramStats = {
-      numVals: 200,
-      min: -30,
-      max: 140,
-      numZeros: 2,
-      numNans: 5,
-    };
-
-    const container = document.getElementById('container');
-    await histogram(container, data, {stats});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('table').length).toBe(1);
-    expect(document.querySelectorAll('table tbody tr').length).toBe(1);
-
-    const statsEls = document.querySelectorAll('table tbody td');
-    expect(statsEls.length).toBe(5);
-    expect(statsEls[0].textContent).toEqual('200');
-    expect(statsEls[1].textContent).toEqual('−30'); // Note the long dash '−'
-    expect(statsEls[2].textContent).toEqual('140');
-    expect(statsEls[3].textContent).toEqual('2 (1%)');
-    expect(statsEls[4].textContent).toEqual('5 (2.5%)');
-  });
-
-  it('sets width of chart', async () => {
-    const data = [
-      {value: 50},
-      {value: 100},
-      {value: 230},
-    ];
-
-    const container = document.getElementById('container');
-    await histogram(container, data, {width: 400});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').width)
-        .toBe(Math.floor(400 * pixelRatio));
-  });
-
-  it('sets height of chart', async () => {
-    const data = [
-      {value: 50},
-      {value: 100},
-      {value: 230},
-    ];
-
-    const container = document.getElementById('container');
-    await histogram(container, data, {height: 200});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').height)
-        .toBe(Math.floor(200 * pixelRatio));
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/render/linechart.ts b/tfjs-master/tfjs-vis/src/render/linechart.ts
deleted file mode 100644
index 236967708..000000000
--- a/tfjs-master/tfjs-vis/src/render/linechart.ts
+++ /dev/null
@@ -1,221 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import embed, {Mode, VisualizationSpec} from 'vega-embed';
-import {TopLevelSpec} from 'vega-lite';
-
-import {Drawable, Point2D, XYPlotData, XYPlotOptions} from '../types';
-import {getDefaultHeight, getDefaultWidth} from '../util/dom';
-import {assert} from '../util/utils';
-
-import {getDrawArea} from './render_utils';
-
-/**
- * Renders a line chart
- *
- * ```js
- * const series1 = Array(100).fill(0)
- *   .map(y => Math.random() * 100 - (Math.random() * 50))
- *   .map((y, x) => ({ x, y, }));
- *
- * const series2 = Array(100).fill(0)
- *   .map(y => Math.random() * 100 - (Math.random() * 150))
- *   .map((y, x) => ({ x, y, }));
- *
- * const series = ['First', 'Second'];
- * const data = { values: [series1, series2], series }
- *
- * const surface = { name: 'Line chart', tab: 'Charts' };
- * tfvis.render.linechart(surface, data);
- * ```
- *
- * ```js
- * const series1 = Array(100).fill(0)
- *   .map(y => Math.random() * 100 + 50)
- *   .map((y, x) => ({ x, y, }));
- *
- * const data = { values: [series1] }
- *
- * // Render to visor
- * const surface = { name: 'Zoomed Line Chart', tab: 'Charts' };
- * tfvis.render.linechart(surface, data, { zoomToFit: true });
- * ```
- *
- * @doc {heading: 'Charts', namespace: 'render'}
- */
-export async function linechart(
-    container: Drawable, data: XYPlotData,
-    opts: XYPlotOptions = {}): Promise<void> {
-  // Nest data if necessary before further processing
-  const _data = Array.isArray(data.values[0]) ? data.values as Point2D[][] :
-                                                [data.values] as Point2D[][];
-  const numValues = _data[0].length;
-
-  // Create series names if none were passed in.
-  const _series: string[] =
-      data.series ? data.series : _data.map((_, i) => `Series ${i + 1}`);
-  assert(
-      _series.length === _data.length,
-      'Must have an equal number of series labels as there are data series');
-
-  if (opts.seriesColors != null) {
-    assert(
-        opts.seriesColors.length === _data.length,
-        'Must have an equal number of series colors as there are data series');
-  }
-
-  const vlChartValues: VLChartValue[] = [];
-  for (let valueIdx = 0; valueIdx < numValues; valueIdx++) {
-    const v: VLChartValue = {
-      x: valueIdx,
-    };
-
-    _series.forEach((seriesName, seriesIdx) => {
-      const seriesValue = _data[seriesIdx][valueIdx].y;
-      v[seriesName] = seriesValue;
-      v[`${seriesName}-name`] = seriesName;
-    });
-    vlChartValues.push(v);
-  }
-
-  const options = Object.assign({}, defaultOpts, opts);
-
-  const yScale = (): {}|undefined => {
-    if (options.zoomToFit) {
-      return {'zero': false};
-    } else if (options.yAxisDomain != null) {
-      return {'domain': options.yAxisDomain};
-    }
-    return undefined;
-  };
-
-  const sharedEncoding = {
-    x: {
-      field: 'x',
-      type: options.xType,
-      title: options.xLabel,
-    },
-    tooltip: [
-      {field: 'x', type: 'quantitative'},
-      ..._series.map(seriesName => {
-        return {
-          field: seriesName,
-          type: 'quantitative',
-        };
-      }),
-    ]
-  };
-
-  const lineLayers: TopLevelSpec[] = _series.map((seriesName) => {
-    return {
-      // data will be defined at the chart level.
-      'data': undefined,
-      'mark': {'type': 'line', 'clip': true},
-      'encoding': {
-        // Note: the encoding for 'x' is shared
-        // Add a y encoding for this series
-        'y': {
-          'field': seriesName,
-          'type': options.yType,
-          'title': options.yLabel,
-          'scale': yScale(),
-        },
-        'color': {
-          'field': `${seriesName}-name`,
-          'type': 'nominal',
-          'legend': {'values': _series, title: null},
-          'scale': {
-            'range': options.seriesColors,
-          }
-        },
-      }
-    };
-  });
-
-  const tooltipLayer = {
-    'mark': 'rule',
-    'selection': {
-      'hover': {
-        'type': 'single',
-        'on': 'mouseover',
-        'nearest': true,
-        clear: 'mouseout',
-      }
-    },
-    'encoding': {
-      'color': {
-        'value': 'grey',
-        'condition': {
-          'selection': {'not': 'hover'},
-          'value': 'transparent',
-        }
-      }
-    }
-  };
-
-  const drawArea = getDrawArea(container);
-  const spec = {
-    'width': options.width || getDefaultWidth(drawArea),
-    'height': options.height || getDefaultHeight(drawArea),
-    'padding': 0,
-    'autosize': {
-      'type': 'fit',
-      'contains': 'padding',
-      'resize': true,
-    },
-    'config': {
-      'axis': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      },
-      'text': {'fontSize': options.fontSize},
-      'legend': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      }
-    },
-    'data': {'values': vlChartValues},
-    'encoding': sharedEncoding,
-    'layer': [
-      ...lineLayers,
-      tooltipLayer,
-    ],
-  };
-
-  const embedOpts = {
-    actions: false,
-    mode: 'vega-lite' as Mode,
-    defaultStyle: false,
-  };
-
-  await embed(drawArea, spec as VisualizationSpec, embedOpts);
-  return Promise.resolve();
-}
-
-const defaultOpts = {
-  xLabel: 'x',
-  yLabel: 'y',
-  xType: 'quantitative',
-  yType: 'quantitative',
-  zoomToFit: false,
-  fontSize: 11,
-};
-
-interface VLChartValue {
-  x: number;
-  [key: string]: string|number;
-}
diff --git a/tfjs-master/tfjs-vis/src/render/linechart_test.ts b/tfjs-master/tfjs-vis/src/render/linechart_test.ts
deleted file mode 100644
index a621984a3..000000000
--- a/tfjs-master/tfjs-vis/src/render/linechart_test.ts
+++ /dev/null
@@ -1,167 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {linechart} from './linechart';
-
-describe('renderLineChart', () => {
-  let pixelRatio: number;
-
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-    pixelRatio = window.devicePixelRatio;
-  });
-
-  it('renders a line chart', async () => {
-    const data = {
-      values: [
-        {x: 0, y: 50},
-        {x: 1, y: 100},
-        {x: 2, y: 230},
-      ]
-    };
-
-    const container = document.getElementById('container');
-    await linechart(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders a line chart with multiple series', async () => {
-    const data = {
-      values: [
-        [
-          {x: 0, y: 50},
-          {x: 1, y: 100},
-          {x: 2, y: 230},
-        ],
-        [
-          {x: 0, y: 20},
-          {x: 1, y: 300},
-          {x: 2, y: 630},
-        ],
-      ]
-    };
-
-    const container = document.getElementById('container');
-
-    await linechart(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders a line chart with multiple series custom names', async () => {
-    const data = {
-      values: [
-        [
-          {x: 0, y: 50},
-          {x: 1, y: 100},
-          {x: 2, y: 230},
-        ],
-        [
-          {x: 0, y: 20},
-          {x: 1, y: 300},
-          {x: 2, y: 630},
-        ],
-      ],
-      series: ['First', 'Second'],
-    };
-
-    const container = document.getElementById('container');
-
-    await linechart(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('updates a line chart', async () => {
-    let data = {
-      values: [
-        [
-          {x: 0, y: 50},
-          {x: 1, y: 100},
-          {x: 2, y: 230},
-        ],
-        [
-          {x: 0, y: 20},
-          {x: 1, y: 300},
-          {x: 2, y: 630},
-        ],
-      ],
-      series: ['First', 'Second'],
-    };
-
-    const container = document.getElementById('container');
-
-    await linechart(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    data = {
-      values: [
-        [
-          {x: 0, y: 50},
-          {x: 1, y: 100},
-          {x: 2, y: 230},
-        ],
-        [
-          {x: 0, y: 20},
-          {x: 1, y: 300},
-          {x: 2, y: 630},
-          {x: 3, y: 530},
-          {x: 4, y: 230},
-        ],
-      ],
-      series: ['First', 'Second'],
-    };
-
-    await linechart(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('sets width of chart', async () => {
-    const data = {
-      values: [
-        {x: 0, y: 50},
-        {x: 1, y: 100},
-        {x: 2, y: 230},
-      ]
-    };
-
-    const container = document.getElementById('container');
-    await linechart(container, data, {width: 400});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').width)
-        .toBe(Math.floor(400 * pixelRatio));
-  });
-
-  it('sets height of chart', async () => {
-    const data = {
-      values: [
-        {x: 0, y: 50},
-        {x: 1, y: 100},
-        {x: 2, y: 230},
-      ]
-    };
-
-    const container = document.getElementById('container');
-    await linechart(container, data, {height: 200});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').height)
-        .toBe(Math.floor(200 * pixelRatio));
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/render/render_utils.ts b/tfjs-master/tfjs-vis/src/render/render_utils.ts
deleted file mode 100644
index 1689b7d6b..000000000
--- a/tfjs-master/tfjs-vis/src/render/render_utils.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Drawable, isSurface, isSurfaceInfo} from '../types';
-import {visor} from '../visor';
-
-export function getDrawArea(drawable: Drawable): HTMLElement {
-  if (drawable instanceof HTMLElement) {
-    return drawable;
-  } else if (isSurface(drawable)) {
-    return drawable.drawArea;
-  } else if (isSurfaceInfo(drawable)) {
-    const surface = visor().surface(
-        {name: drawable.name, tab: drawable.tab, styles: drawable.styles});
-    return surface.drawArea;
-  } else {
-    throw new Error('Not a drawable');
-  }
-}
-
-export function shallowEquals(
-    // tslint:disable-next-line:no-any
-    a: {[key: string]: any}, b: {[key: string]: any}) {
-  const aProps = Object.getOwnPropertyNames(a);
-  const bProps = Object.getOwnPropertyNames(b);
-
-  if (aProps.length !== bProps.length) {
-    return false;
-  }
-
-  for (let i = 0; i < aProps.length; i++) {
-    const prop = aProps[i];
-    if (a[prop] !== b[prop]) {
-      return false;
-    }
-  }
-
-  return true;
-}
-
-export async function nextFrame() {
-  await new Promise(r => requestAnimationFrame(r));
-}
diff --git a/tfjs-master/tfjs-vis/src/render/render_utils_test.ts b/tfjs-master/tfjs-vis/src/render/render_utils_test.ts
deleted file mode 100644
index a9959d9ad..000000000
--- a/tfjs-master/tfjs-vis/src/render/render_utils_test.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {visor} from '../index';
-import {getDrawArea, shallowEquals} from './render_utils';
-
-describe('shallowEqual', () => {
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-  });
-
-  it('returns true for similar objects', async () => {
-    const a = {
-      stringProp: 'astring',
-      numProp: 55,
-      boolProp: true,
-    };
-
-    const b = {
-      stringProp: 'astring',
-      boolProp: true,
-      numProp: 55,
-    };
-
-    expect(shallowEquals(a, b)).toBe(true);
-  });
-
-  it('returns false for different objects', async () => {
-    const a = {
-      stringProp: 'astring',
-      numProp: 55,
-      boolProp: false,
-    };
-
-    const b = {
-      stringProp: 'astring',
-      numProp: 55,
-      boolProp: true,
-    };
-
-    expect(shallowEquals(a, b)).toBe(false);
-  });
-
-  it('returns true for similar objects (array ref)', async () => {
-    // tslint:disable-next-line:no-any
-    const ref: any[] = [];
-
-    const a = {
-      stringProp: 'astring',
-      numProp: 55,
-      refProp: ref,
-    };
-
-    const b = {
-      numProp: 55,
-      stringProp: 'astring',
-      refProp: ref,
-    };
-
-    expect(shallowEquals(a, b)).toBe(true);
-  });
-});
-
-describe('getDrawArea', () => {
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-  });
-
-  it('works with HTMLElement', async () => {
-    const el = document.getElementById('container');
-    expect(getDrawArea(el)).toEqual(el);
-  });
-
-  it('works with a surface', async () => {
-    const surface = visor().surface({name: 'test'});
-    expect(getDrawArea(surface)).toEqual(surface.drawArea);
-  });
-
-  it('fails with other stuff', async () => {
-    const surface = visor().surface({name: 'test'});
-    //@ts-ignore
-    expect(() => getDrawArea('not-a-surface')).toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/render/scatterplot.ts b/tfjs-master/tfjs-vis/src/render/scatterplot.ts
deleted file mode 100644
index 91e5db3fe..000000000
--- a/tfjs-master/tfjs-vis/src/render/scatterplot.ts
+++ /dev/null
@@ -1,165 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import embed, {Mode, VisualizationSpec} from 'vega-embed';
-
-import {Drawable, Point2D, XYPlotData, XYPlotOptions} from '../types';
-import {getDefaultHeight, getDefaultWidth} from '../util/dom';
-import {assert} from '../util/utils';
-import {getDrawArea} from './render_utils';
-
-/**
- * Renders a scatter plot
- *
- * ```js
- * const series1 = Array(100).fill(0)
- *   .map(y => Math.random() * 100 - (Math.random() * 50))
- *   .map((y, x) => ({ x, y, }));
- *
- * const series2 = Array(100).fill(0)
- *   .map(y => Math.random() * 100 - (Math.random() * 150))
- *   .map((y, x) => ({ x, y, }));
- *
- * const series = ['First', 'Second'];
- * const data = { values: [series1, series2], series }
- *
- * const surface = { name: 'Scatterplot', tab: 'Charts' };
- * tfvis.render.scatterplot(surface, data);
- * ```
- *
- * @doc {heading: 'Charts', namespace: 'render'}
- */
-export async function scatterplot(
-    container: Drawable, data: XYPlotData,
-    opts: XYPlotOptions = {}): Promise<void> {
-  let _values = data.values;
-  const _series = data.series == null ? [] : data.series;
-
-  // Nest data if necessary before further processing
-  _values = Array.isArray(_values[0]) ? _values as Point2D[][] :
-                                        [_values] as Point2D[][];
-
-  const values: Point2D[] = [];
-  _values.forEach((seriesData, i) => {
-    const seriesName: string =
-        _series[i] != null ? _series[i] : `Series ${i + 1}`;
-    const seriesVals =
-        seriesData.map(v => Object.assign({}, v, {series: seriesName}));
-    values.push(...seriesVals);
-  });
-
-  if (opts.seriesColors != null) {
-    assert(
-        opts.seriesColors.length === _values.length,
-        'Must have an equal number of series colors as there are data series');
-  }
-
-  const drawArea = getDrawArea(container);
-  const options = Object.assign({}, defaultOpts, opts);
-
-  const embedOpts = {
-    actions: false,
-    mode: 'vega-lite' as Mode,
-    defaultStyle: false,
-  };
-
-  const xDomain = (): {}|undefined => {
-    if (options.zoomToFit) {
-      return {'zero': false};
-    } else if (options.xAxisDomain != null) {
-      return {'domain': options.xAxisDomain};
-    }
-    return undefined;
-  };
-
-  const yDomain = (): {}|undefined => {
-    if (options.zoomToFit) {
-      return {'zero': false};
-    } else if (options.yAxisDomain != null) {
-      return {'domain': options.yAxisDomain};
-    }
-    return undefined;
-  };
-
-  const spec: VisualizationSpec = {
-    'width': options.width || getDefaultWidth(drawArea),
-    'height': options.height || getDefaultHeight(drawArea),
-    'padding': 0,
-    'autosize': {
-      'type': 'fit',
-      'contains': 'padding',
-      'resize': true,
-    },
-    'config': {
-      'axis': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      },
-      'text': {'fontSize': options.fontSize},
-      'legend': {
-        'labelFontSize': options.fontSize,
-        'titleFontSize': options.fontSize,
-      }
-    },
-    //@ts-ignore
-    'data': {
-      'values': values,
-    },
-    'mark': {
-      'type': 'point',
-      'clip': true,
-      'tooltip': {'content': 'data'},
-    },
-    'encoding': {
-      'x': {
-        'field': 'x',
-        'type': options.xType,
-        'title': options.xLabel,
-        'scale': xDomain(),
-      },
-      'y': {
-        'field': 'y',
-        'type': options.yType,
-        'title': options.yLabel,
-        'scale': yDomain(),
-      },
-      'color': {
-        'field': 'series',
-        'type': 'nominal',
-        'scale': {
-          'range': options.seriesColors,
-        }
-      },
-      'shape': {
-        'field': 'series',
-        'type': 'nominal',
-      }
-    },
-  };
-
-  await embed(drawArea, spec, embedOpts);
-  return Promise.resolve();
-}
-
-const defaultOpts = {
-  xLabel: 'x',
-  yLabel: 'y',
-  xType: 'quantitative',
-  yType: 'quantitative',
-  zoomToFit: false,
-  fontSize: 11,
-};
diff --git a/tfjs-master/tfjs-vis/src/render/scatterplot_tests.ts b/tfjs-master/tfjs-vis/src/render/scatterplot_tests.ts
deleted file mode 100644
index 02fee703b..000000000
--- a/tfjs-master/tfjs-vis/src/render/scatterplot_tests.ts
+++ /dev/null
@@ -1,170 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {scatterplot} from './scatterplot';
-
-describe('renderScatterplot', () => {
-  let pixelRatio: number;
-
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-    pixelRatio = window.devicePixelRatio;
-  });
-
-  it('renders a scatterplot', async () => {
-    const data = {
-      values: [
-        {x: 0, y: 50},
-        {x: 1, y: 100},
-        {x: 2, y: 230},
-      ]
-    };
-
-    const container = document.getElementById('container');
-    await scatterplot(container, data);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders the chart with multiple series', async () => {
-    const data = {
-      values: [
-        [
-          {x: 0, y: 50},
-          {x: 1, y: 100},
-          {x: 2, y: 230},
-        ],
-        [
-          {x: 0, y: 20},
-          {x: 1, y: 300},
-          {x: 2, y: 630},
-        ],
-      ]
-    };
-
-    const container = document.getElementById('container');
-
-    await scatterplot(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    await scatterplot(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders a line chart with multiple series custom names', async () => {
-    const data = {
-      values: [
-        [
-          {x: 0, y: 50},
-          {x: 1, y: 100},
-          {x: 2, y: 230},
-        ],
-        [
-          {x: 0, y: 20},
-          {x: 1, y: 300},
-          {x: 2, y: 630},
-        ],
-      ],
-      series: ['First', 'Second'],
-    };
-
-    const container = document.getElementById('container');
-
-    await scatterplot(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('updates the chart', async () => {
-    let data = {
-      values: [
-        [
-          {x: 0, y: 50},
-          {x: 1, y: 100},
-          {x: 2, y: 230},
-        ],
-        [
-          {x: 0, y: 20},
-          {x: 1, y: 300},
-          {x: 2, y: 630},
-        ],
-      ],
-      series: ['First', 'Second'],
-    };
-
-    const container = document.getElementById('container');
-
-    await scatterplot(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-
-    data = {
-      values: [
-        [
-          {x: 0, y: 50},
-          {x: 1, y: 100},
-          {x: 2, y: 230},
-        ],
-        [
-          {x: 0, y: 20},
-          {x: 1, y: 300},
-          {x: 2, y: 630},
-          {x: 3, y: 530},
-          {x: 4, y: 230},
-        ],
-      ],
-      series: ['First', 'Second'],
-    };
-
-    await scatterplot(container, data);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('sets width of chart', async () => {
-    const data = {
-      values: [
-        {x: 0, y: 50},
-        {x: 1, y: 100},
-        {x: 2, y: 230},
-      ]
-    };
-
-    const container = document.getElementById('container');
-    await scatterplot(container, data, {width: 400});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').width)
-        .toBe(Math.floor(400 * pixelRatio));
-  });
-
-  it('sets height of chart', async () => {
-    const data = {
-      values: [
-        {x: 0, y: 50},
-        {x: 1, y: 100},
-        {x: 2, y: 230},
-      ]
-    };
-
-    const container = document.getElementById('container');
-    await scatterplot(container, data, {height: 200});
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('canvas').length).toBe(1);
-    expect(document.querySelector('canvas').height)
-        .toBe(Math.floor(200 * pixelRatio));
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/render/table.ts b/tfjs-master/tfjs-vis/src/render/table.ts
deleted file mode 100644
index 622203ff2..000000000
--- a/tfjs-master/tfjs-vis/src/render/table.ts
+++ /dev/null
@@ -1,134 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {format as d3Format} from 'd3-format';
-import {select as d3Select} from 'd3-selection';
-import {css} from 'glamor';
-import {Drawable, TableData} from '../types';
-import {getDrawArea} from './render_utils';
-
-/**
- * Renders a table
- *
- * ```js
- * const headers = [
- *  'Col 1',
- *  'Col 2',
- *  'Col 3',
- * ];
- *
- * const values = [
- *  [1, 2, 3],
- *  ['4', '5', '6'],
- *  ['strong>7</strong>', true, false],
- * ];
- *
- * const surface = { name: 'Table', tab: 'Charts' };
- * tfvis.render.table(surface, { headers, values });
- * ```
- *
- * @param opts.fontSize fontSize in pixels for text in the chart.
- *
- * @doc {heading: 'Charts', namespace: 'render'}
- */
-export function table(
-    container: Drawable,
-    // tslint:disable-next-line:no-any
-    data: TableData, opts: {fontSize?: number} = {}) {
-  if (data && data.headers == null) {
-    throw new Error('Data to render must have a "headers" property');
-  }
-
-  if (data && data.values == null) {
-    throw new Error('Data to render must have a "values" property');
-  }
-
-  const drawArea = getDrawArea(container);
-
-  const options = Object.assign({}, defaultOpts, opts);
-
-  let table = d3Select(drawArea).select('table.tf-table');
-
-  const tableStyle = css({
-    fontSize: options.fontSize || '.875rem',
-    width: '100%',
-    maxWidth: '64rem',
-    marginRight: 'auto',
-    marginLeft: 'auto',
-  });
-
-  // If a table is not already present on this element add one
-  if (table.size() === 0) {
-    table = d3Select(drawArea).append('table');
-
-    table.attr('class', ` ${tableStyle} tf-table`);
-
-    table.append('thead').append('tr');
-    table.append('tbody');
-  }
-
-  if (table.size() !== 1) {
-    throw new Error('Error inserting table');
-  }
-
-  //
-  // Add the reader row
-  //
-  const headerRowStyle = css({
-    fontWeight: '600',
-    borderBottomStyle: 'solid',
-    borderBottomWidth: '1px',
-    borderColor: 'rgba( 0, 0, 0, .2 )',
-    textAlign: 'left',
-    paddingBottom: '1rem',
-    paddingRight: '1rem',
-    backgroundColor: '#fff',
-  });
-  const headers =
-      table.select('thead').select('tr').selectAll('th').data(data.headers);
-  const headersEnter =
-      headers.enter().append('th').attr('class', `${headerRowStyle}`);
-  headers.merge(headersEnter).html(d => d);
-
-  headers.exit().remove();
-
-  //
-  // Add the data rows
-  //
-  const format = d3Format(',.4~f');
-
-  const rows = table.select('tbody').selectAll('tr').data(data.values);
-  const rowsEnter = rows.enter().append('tr');
-
-  // Nested selection to add individual cells
-  const cellStyle = css({
-    padding: '0.25rem',
-    borderBottomStyle: 'solid',
-    borderBottomWidth: '1px',
-    borderColor: 'rgba( 0, 0, 0, .2 )',
-  });
-  const cells = rows.merge(rowsEnter).selectAll('td').data(d => d);
-  const cellsEnter = cells.enter().append('td').attr('class', `${cellStyle}`);
-  cells.merge(cellsEnter).html(d => typeof d === 'number' ? format(d) : d);
-
-  cells.exit().remove();
-  rows.exit().remove();
-}
-
-const defaultOpts = {
-  fontSize: 14,
-};
diff --git a/tfjs-master/tfjs-vis/src/render/table_test.ts b/tfjs-master/tfjs-vis/src/render/table_test.ts
deleted file mode 100644
index d896d73ee..000000000
--- a/tfjs-master/tfjs-vis/src/render/table_test.ts
+++ /dev/null
@@ -1,91 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {table} from './table';
-
-function getRowHTML(row: Element) {
-  return Array.from(row.querySelectorAll('td')).map(r => r.innerHTML);
-}
-
-function getRowText(row: Element) {
-  return Array.from(row.querySelectorAll('td')).map(r => r.textContent);
-}
-
-describe('renderTable', () => {
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-  });
-
-  it('renders a table', () => {
-    const headers = [
-      'Col1',
-      'Col 2',
-      '<em>Column 3</em>',
-    ];
-
-    const values = [
-      [1, 2, 3],
-      ['4', '5', '6'],
-      ['<strong>7</strong>', true, false],
-    ];
-
-    const container = document.getElementById('container');
-    table(container, {headers, values});
-
-    expect(document.querySelectorAll('.tf-table').length).toBe(1);
-    expect(document.querySelectorAll('.tf-table thead tr').length).toBe(1);
-
-    const headerEl = document.querySelectorAll('.tf-table thead tr th');
-    expect(headerEl[0].innerHTML).toEqual('Col1');
-    expect(headerEl[1].innerHTML).toEqual('Col 2');
-    expect(headerEl[2].innerHTML).toEqual('<em>Column 3</em>');
-    expect(headerEl[2].textContent).toEqual('Column 3');
-
-    expect(document.querySelectorAll('.tf-table tbody tr').length).toBe(3);
-
-    const rows = document.querySelectorAll('.tf-table tbody tr');
-    expect(getRowHTML(rows[0])).toEqual(['1', '2', '3']);
-    expect(getRowHTML(rows[1])).toEqual(['4', '5', '6']);
-    expect(getRowHTML(rows[2])).toEqual([
-      '<strong>7</strong>', 'true', 'false'
-    ]);
-    expect(getRowText(rows[2])).toEqual(['7', 'true', 'false']);
-  });
-
-  it('requires necessary param', () => {
-    const container = document.getElementById('container');
-
-    // @ts-ignore
-    expect(() => table({headers: []}, container)).toThrow();
-    // @ts-ignore
-    expect(() => table({values: [[]]}, container)).toThrow();
-    // @ts-ignore
-    expect(() => table({}, container)).toThrow();
-  });
-
-  it('should not throw on empty table', () => {
-    const container = document.getElementById('container');
-    const headers: string[] = [];
-    const values: string[][] = [];
-
-    expect(() => table(container, {headers, values})).not.toThrow();
-
-    expect(document.querySelectorAll('.tf-table').length).toBe(1);
-    expect(document.querySelectorAll('.tf-table thead tr').length).toBe(1);
-    expect(document.querySelectorAll('.tf-table tbody tr').length).toBe(0);
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/setup_test.ts b/tfjs-master/tfjs-vis/src/setup_test.ts
deleted file mode 100644
index d2f39b1e0..000000000
--- a/tfjs-master/tfjs-vis/src/setup_test.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-backend-cpu';
diff --git a/tfjs-master/tfjs-vis/src/show/history.ts b/tfjs-master/tfjs-vis/src/show/history.ts
deleted file mode 100644
index ebe13fa55..000000000
--- a/tfjs-master/tfjs-vis/src/show/history.ts
+++ /dev/null
@@ -1,337 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Logs} from '@tensorflow/tfjs-layers';
-
-import {linechart} from '../render/linechart';
-import {getDrawArea, nextFrame} from '../render/render_utils';
-import {Drawable, Point2D, XYPlotOptions} from '../types';
-import {subSurface} from '../util/dom';
-
-/**
- * Renders a tf.Model training 'History'.
- *
- * ```js
- * const model = tf.sequential({
- *  layers: [
- *    tf.layers.dense({inputShape: [784], units: 32, activation: 'relu'}),
- *    tf.layers.dense({units: 10, activation: 'softmax'}),
- *  ]
- * });
- *
- * model.compile({
- *   optimizer: 'sgd',
- *   loss: 'categoricalCrossentropy',
- *   metrics: ['accuracy']
- * });
- *
- * const data = tf.randomNormal([100, 784]);
- * const labels = tf.randomUniform([100, 10]);
- *
- * function onBatchEnd(batch, logs) {
- *   console.log('Accuracy', logs.acc);
- * }
- *
- * const surface = { name: 'show.history', tab: 'Training' };
- * // Train for 5 epochs with batch size of 32.
- * const history = await model.fit(data, labels, {
- *    epochs: 5,
- *    batchSize: 32
- * });
- *
- * tfvis.show.history(surface, history, ['loss', 'acc']);
- * ```
- *
- * ```js
- * const model = tf.sequential({
- *  layers: [
- *    tf.layers.dense({inputShape: [784], units: 32, activation: 'relu'}),
- *    tf.layers.dense({units: 10, activation: 'softmax'}),
- *  ]
- * });
- *
- * model.compile({
- *   optimizer: 'sgd',
- *   loss: 'categoricalCrossentropy',
- *   metrics: ['accuracy']
- * });
- *
- * const data = tf.randomNormal([100, 784]);
- * const labels = tf.randomUniform([100, 10]);
- *
- * function onBatchEnd(batch, logs) {
- *   console.log('Accuracy', logs.acc);
- * }
- *
- * const surface = { name: 'show.history live', tab: 'Training' };
- * // Train for 5 epochs with batch size of 32.
- * const history = [];
- * await model.fit(data, labels, {
- *    epochs: 5,
- *    batchSize: 32,
- *    callbacks: {
- *      onEpochEnd: (epoch, log) => {
- *        history.push(log);
- *        tfvis.show.history(surface, history, ['loss', 'acc']);
- *      }
- *    }
- * });
- * ```
- *
- * @param history A history like object. Either a tfjs-layers `History` object
- *  or an array of tfjs-layers `Logs` objects.
- * @param metrics An array of strings for each metric to plot from the history
- *  object. Using this allows you to control which metrics appear on the same
- *  plot.
- * @param opts Optional parameters for the line charts.
- *
- * @doc {heading: 'Models & Tensors', subheading: 'Model Training', namespace:
- * 'show'}
- */
-export async function history(
-    container: Drawable, history: HistoryLike, metrics: string[],
-    opts: HistoryOptions = {}): Promise<void> {
-  // Get the draw surface
-  const drawArea = getDrawArea(container);
-
-  // We organize the data from the history object into discrete plot data
-  // objects so that we can group together appropriate metrics into single
-  // multi-series charts.
-  const plots: HistoryPlotData = {};
-  for (const metric of metrics) {
-    if (!(/val_/.test(metric))) {
-      // Non validation metric
-      const values = getValues(history, metric, metrics.indexOf(metric));
-      initPlot(plots, metric);
-      plots[metric].series.push(metric);
-      plots[metric].values.push(values);
-    } else {
-      // Validation metrics are grouped with their equivalent non validation
-      // metrics. Note that the corresponding non validation metric may not
-      // actually be included but we still want to use it as a plot name.
-      const nonValidationMetric = metric.replace('val_', '');
-      initPlot(plots, nonValidationMetric);
-      const values = getValues(history, metric, metrics.indexOf(metric));
-      plots[nonValidationMetric].series.push(metric);
-      plots[nonValidationMetric].values.push(values);
-    }
-  }
-
-  // Render each plot specified above to a new subsurface.
-  // A plot may have multiple series.
-  const plotNames = Object.keys(plots);
-  const options =
-      Object.assign({}, {xLabel: 'Iteration', yLabel: 'Value'}, opts);
-
-  const renderPromises = [];
-  for (const name of plotNames) {
-    const subContainer = subSurface(drawArea, name);
-    const series = plots[name].series;
-    const values = plots[name].values;
-
-    if (series.every(seriesName => Boolean(seriesName.match('acc')))) {
-      // Set a domain of 0-1 if all the series in this plot are related to
-      // accuracy. Can be overridden by setting zoomToFitAccuracy to true.
-      if (options.zoomToFitAccuracy) {
-        options.zoomToFit = true;
-      } else {
-        options.yAxisDomain = [0, 1];
-        delete options.zoomToFit;
-      }
-    }
-
-    const done = linechart(subContainer, {values, series}, options);
-    renderPromises.push(done);
-  }
-  await Promise.all(renderPromises);
-}
-
-interface HistoryOptions extends XYPlotOptions {
-  zoomToFitAccuracy?: boolean;
-}
-
-type HistoryLike = Logs[]|Logs[][]|{
-  history: {
-    [key: string]: number[],
-  }
-};
-
-interface HistoryPlotData {
-  [name: string]: {
-    series: string[],
-    values: Point2D[][],
-  };
-}
-
-function initPlot(plot: HistoryPlotData, name: string) {
-  if (plot[name] == null) {
-    plot[name] = {series: [], values: []};
-  }
-}
-
-/*
- * Extracts a list of Point2D's suitable for plotting from a HistoryLike for
- * a single metric.
- * @param history a HistoryLike object
- * @param metric the metric to extract from the logs
- * @param metricIndex this is needed because the historylike can be a nested
- * list of logs for multiple metrics, this index lets us extract the correct
- * list.
- */
-function getValues(
-    history: HistoryLike, metric: string, metricIndex: number): Point2D[] {
-  if (Array.isArray(history)) {
-    // If we were passed a nested array we want to get the correct list
-    // for this given metric, metrix index gives us this list.
-    const metricHistory =
-        (Array.isArray(history[0]) ? history[metricIndex] : history) as Logs[];
-    const points: Point2D[] = [];
-
-    for (let i = 0; i < metricHistory.length; i++) {
-      const log = metricHistory[i];
-      points.push({x: i, y: log[metric]});
-    }
-    return points;
-  } else {
-    return history.history[metric].map((y, x) => ({x, y}));
-  }
-}
-
-/**
- * Returns a collection of callbacks to pass to tf.Model.fit. Callbacks are
- * returned for the following events, `onBatchEnd` & `onEpochEnd`.
- *
- * ```js
- * const model = tf.sequential({
- *  layers: [
- *    tf.layers.dense({inputShape: [784], units: 32, activation: 'relu'}),
- *    tf.layers.dense({units: 10, activation: 'softmax'}),
- *  ]
- * });
- *
- * model.compile({
- *   optimizer: 'sgd',
- *   loss: 'categoricalCrossentropy',
- *   metrics: ['accuracy']
- * });
- *
- * const data = tf.randomNormal([100, 784]);
- * const labels = tf.randomUniform([100, 10]);
- *
- * function onBatchEnd(batch, logs) {
- *   console.log('Accuracy', logs.acc);
- * }
- *
- * const surface = { name: 'show.fitCallbacks', tab: 'Training' };
- * // Train for 5 epochs with batch size of 32.
- * await model.fit(data, labels, {
- *    epochs: 5,
- *    batchSize: 32,
- *    callbacks: tfvis.show.fitCallbacks(surface, ['loss', 'acc']),
- * });
- * ```
- *
- * @param metrics List of metrics to plot.
- * @param opts Optional parameters
- *
- * @doc {heading: 'Models & Tensors', subheading: 'Model Training', namespace:
- * 'show'}
- */
-export function fitCallbacks(
-    container: Drawable, metrics: string[],
-    opts: FitCallbackOptions = {}): FitCallbackHandlers {
-  const accumulators: FitCallbackLogs = {};
-  const callbackNames = opts.callbacks || ['onEpochEnd', 'onBatchEnd'];
-  const drawArea = getDrawArea(container);
-
-  const historyOpts = Object.assign({}, opts);
-  delete historyOpts.callbacks;
-  function makeCallbackFor(callbackName: string) {
-    return async (_: number, log: Logs) => {
-      // Set a nicer x axis name where possible
-      if ((/batch/i).test(callbackName)) {
-        historyOpts.xLabel = 'Batch';
-      } else if ((/epoch/i).test(callbackName)) {
-        historyOpts.xLabel = 'Epoch';
-      }
-
-      // Because of how the _ (iteration) numbers are given in the layers api
-      // we have to store each metric for each callback in different arrays else
-      // we cannot get accurate 'global' batch numbers for onBatchEnd.
-
-      // However at render time we want to be able to combine metrics for a
-      // given callback. So here we make a nested list of metrics, the first
-      // level are arrays for each callback, the second level contains arrays
-      // (of logs) for each metric within that callback.
-
-      const metricLogs: Logs[][] = [];
-      const presentMetrics: string[] = [];
-      for (const metric of metrics) {
-        // not all logs have all kinds of metrics.
-        if (log[metric] != null) {
-          presentMetrics.push(metric);
-
-          const accumulator =
-              getAccumulator(accumulators, callbackName, metric);
-          accumulator.push({[metric]: log[metric]});
-          metricLogs.push(accumulator);
-        }
-      }
-
-      const subContainer =
-          subSurface(drawArea, callbackName, {title: callbackName});
-      history(subContainer, metricLogs, presentMetrics, historyOpts);
-      await nextFrame();
-    };
-  }
-
-  const callbacks: FitCallbackHandlers = {};
-  callbackNames.forEach((name: string) => {
-    callbacks[name] = makeCallbackFor(name);
-  });
-  return callbacks;
-}
-
-interface FitCallbackHandlers {
-  [key: string]: (iteration: number, log: Logs) => Promise<void>;
-}
-
-interface FitCallbackLogs {
-  [callback: string]: {
-    [metric: string]: Logs[],
-  };
-}
-
-interface FitCallbackOptions extends HistoryOptions {
-  /**
-   * Array of callback names. Valid options
-   * are 'onEpochEnd' and 'onBatchEnd'. Defaults to ['onEpochEnd',
-   * 'onBatchEnd'].
-   */
-  callbacks?: string[];
-}
-
-function getAccumulator(
-    accumulators: FitCallbackLogs, callback: string, metric: string): Logs[] {
-  if (accumulators[callback] == null) {
-    accumulators[callback] = {};
-  }
-  if (accumulators[callback][metric] == null) {
-    accumulators[callback][metric] = [];
-  }
-  return accumulators[callback][metric];
-}
diff --git a/tfjs-master/tfjs-vis/src/show/history_test.ts b/tfjs-master/tfjs-vis/src/show/history_test.ts
deleted file mode 100644
index 064c6bb56..000000000
--- a/tfjs-master/tfjs-vis/src/show/history_test.ts
+++ /dev/null
@@ -1,131 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {fitCallbacks, history} from './history';
-
-describe('fitCallbacks', () => {
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-  });
-
-  it('returns two callbacks', async () => {
-    const container = {name: 'Test'};
-    const callbacks = fitCallbacks(container, ['loss', 'acc']);
-
-    expect(typeof (callbacks.onEpochEnd)).toEqual('function');
-    expect(typeof (callbacks.onBatchEnd)).toEqual('function');
-  });
-
-  it('returns one callback', async () => {
-    const container = {name: 'Test'};
-    const callbacks = fitCallbacks(container, ['loss', 'acc'], {
-      callbacks: ['onBatchEnd'],
-    });
-
-    expect(callbacks.onEpochEnd).toEqual(undefined);
-    expect(typeof (callbacks.onBatchEnd)).toEqual('function');
-  });
-
-  it('onEpochEnd callback can render logs', async () => {
-    const container = {name: 'Test'};
-    const callbacks =
-        fitCallbacks(container, ['loss', 'val_loss', 'acc', 'val_acc']);
-
-    const l1 = {loss: 0.5, 'val_loss': 0.7};
-    const l2 = {loss: 0.2, acc: 0.6, 'val_loss': 0.5, 'val_acc': 0.3};
-
-    await callbacks.onEpochEnd(0, l1);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('div[data-name="loss"]').length).toBe(1);
-
-    await callbacks.onEpochEnd(1, l2);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(2);
-    expect(document.querySelectorAll('div[data-name="loss"]').length).toBe(1);
-    expect(document.querySelectorAll('div[data-name="acc"]').length).toBe(1);
-  });
-
-  it('onBatchEnd callback can render logs', async () => {
-    const container = {name: 'Test'};
-    const callbacks = fitCallbacks(container, ['loss', 'acc']);
-
-    const l1 = {loss: 0.5};
-    const l2 = {loss: 0.2, acc: 0.6};
-
-    await callbacks.onBatchEnd(0, l1);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-    expect(document.querySelectorAll('div[data-name="loss"]').length).toBe(1);
-
-    await callbacks.onBatchEnd(1, l2);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(2);
-    expect(document.querySelectorAll('div[data-name="loss"]').length).toBe(1);
-    expect(document.querySelectorAll('div[data-name="acc"]').length).toBe(1);
-  });
-});
-
-describe('history', () => {
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-  });
-
-  it('renders a logs[]', async () => {
-    const container = {name: 'Test'};
-    const logs = [{loss: 0.5}, {loss: 0.3}];
-    const metrics = ['loss'];
-    await history(container, logs, metrics);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-
-  it('renders a logs object with multiple metrics', async () => {
-    const container = {name: 'Test'};
-    const logs = [{loss: 0.2, acc: 0.6}, {loss: 0.1, acc: 0.65}];
-    const metrics = ['loss', 'acc'];
-    await history(container, logs, metrics);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(2);
-  });
-
-  it('renders a history object with multiple metrics', async () => {
-    const container = {name: 'Test'};
-    const hist = {
-      history: {
-        'loss': [0.7, 0.3, 0.2],
-        'acc': [0.2, 0.3, 0.21],
-      }
-    };
-    const metrics = ['loss', 'acc'];
-    await history(container, hist, metrics);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(2);
-  });
-
-  it('can render multiple history objects', async () => {
-    const container = {name: 'Test'};
-    const container2 = {name: 'Other Test'};
-    const hist = {
-      history: {
-        'loss': [0.7, 0.3, 0.2],
-        'acc': [0.2, 0.3, 0.21],
-      }
-    };
-
-    await history(container, hist, ['loss']);
-    await history(container2, hist, ['acc']);
-
-    expect(document.querySelectorAll('.vega-embed').length).toBe(2);
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/show/model.ts b/tfjs-master/tfjs-vis/src/show/model.ts
deleted file mode 100644
index 2f136ed81..000000000
--- a/tfjs-master/tfjs-vis/src/show/model.ts
+++ /dev/null
@@ -1,226 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-// TODO use type only imports for these types once we are on a version of
-// ts that supports this.
-// https://www.typescriptlang.org/docs/handbook/release-notes/typescript-3-8.html#type-only-imports-and-export
-// tslint:disable-next-line: no-imports-from-dist
-import {Layer} from '@tensorflow/tfjs-layers/dist/engine/topology';
-// tslint:disable-next-line: no-imports-from-dist
-import {LayersModel} from '@tensorflow/tfjs-layers/dist/engine/training';
-
-import {histogram} from '../render/histogram';
-import {getDrawArea} from '../render/render_utils';
-import {table} from '../render/table';
-import {Drawable, HistogramStats} from '../types';
-import {subSurface} from '../util/dom';
-import {tensorStats} from '../util/math';
-
-/**
- * Renders a summary of a tf.Model. Displays a table with layer information.
- *
- * ```js
- * const model = tf.sequential({
- *  layers: [
- *    tf.layers.dense({inputShape: [784], units: 32, activation: 'relu'}),
- *    tf.layers.dense({units: 10, activation: 'softmax'}),
- *  ]
- * });
- *
- * const surface = { name: 'Model Summary', tab: 'Model Inspection'};
- * tfvis.show.modelSummary(surface, model);
- * ```
- *
- * @doc {
- *  heading: 'Models & Tensors',
- *  subheading: 'Model Inspection',
- *  namespace: 'show'
- * }
- */
-export async function modelSummary(container: Drawable, model: LayersModel) {
-  const drawArea = getDrawArea(container);
-  const summary = getModelSummary(model);
-
-  const headers = [
-    'Layer Name',
-    'Output Shape',
-    '# Of Params',
-    'Trainable',
-  ];
-
-  const values = summary.layers.map(
-      l =>
-          [l.name,
-           l.outputShape,
-           l.parameters,
-           l.trainable,
-  ]);
-
-  table(drawArea, {headers, values});
-}
-
-/**
- * Renders summary information about a layer and a histogram of parameters in
- * that layer.
- *
- * ```js
- * const model = tf.sequential({
- *  layers: [
- *    tf.layers.dense({inputShape: [784], units: 32, activation: 'relu'}),
- *    tf.layers.dense({units: 10, activation: 'softmax'}),
- *  ]
- * });
- *
- * const surface = { name: 'Layer Summary', tab: 'Model Inspection'};
- * tfvis.show.layer(surface, model.getLayer(undefined, 1));
- * ```
- *
- * @doc {
- *  heading: 'Models & Tensors',
- *  subheading: 'Model Inspection',
- *  namespace: 'show'
- * }
- */
-export async function layer(container: Drawable, layer: Layer) {
-  const drawArea = getDrawArea(container);
-  const details = await getLayerDetails(layer);
-
-  const headers = [
-    'Weight Name',
-    'Shape',
-    'Min',
-    'Max',
-    '# Params',
-    '# Zeros',
-    '# NaNs',
-    '# Infinity',
-  ];
-
-  // Show layer summary
-  const weightsInfoSurface = subSurface(drawArea, 'layer-weights-info');
-  const detailValues = details.map(
-      l =>
-          [l.name, l.shape, l.stats.min, l.stats.max, l.weight.size,
-           l.stats.numZeros, l.stats.numNans, l.stats.numInfs]);
-
-  table(weightsInfoSurface, {headers, values: detailValues});
-
-  const histogramSelectorSurface = subSurface(drawArea, 'select-layer');
-  const layerValuesHistogram = subSurface(drawArea, 'param-distribution');
-
-  const handleSelection = async (layerName: string) => {
-    const layer = details.filter(d => d.name === layerName)[0];
-    const weights = await layer.weight.data();
-
-    histogram(
-        layerValuesHistogram, weights, {height: 150, width: 460, stats: false});
-  };
-
-  addHistogramSelector(
-      details.map(d => d.name), histogramSelectorSurface, handleSelection);
-}
-
-//
-// Helper functions
-//
-
-function getModelSummary(model: LayersModel) {
-  return {
-    layers: model.layers.map(getLayerSummary),
-  };
-}
-
-/*
- * Gets summary information/metadata about a layer.
- */
-function getLayerSummary(layer: Layer): LayerSummary {
-  let outputShape: string;
-  if (Array.isArray(layer.outputShape[0])) {
-    const shapes = (layer.outputShape as number[][]).map(s => formatShape(s));
-    outputShape = `[${shapes.join(', ')}]`;
-  } else {
-    outputShape = formatShape(layer.outputShape as number[]);
-  }
-
-  return {
-    name: layer.name,
-    trainable: layer.trainable,
-    parameters: layer.countParams(),
-    outputShape,
-  };
-}
-
-interface LayerSummary {
-  name: string;
-  trainable: boolean;
-  parameters: number;
-  outputShape: string;
-}
-
-/*
- * Gets summary stats and shape for all weights in a layer.
- */
-async function getLayerDetails(layer: Layer): Promise<Array<
-    {name: string, stats: HistogramStats, shape: string, weight: tf.Tensor}>> {
-  const weights = layer.getWeights();
-  const layerVariables = layer.weights;
-  const statsPromises = weights.map(tensorStats);
-  const stats = await Promise.all(statsPromises);
-  const shapes = weights.map(w => w.shape);
-  return weights.map((weight, i) => ({
-                       name: layerVariables[i].name,
-                       stats: stats[i],
-                       shape: formatShape(shapes[i]),
-                       weight,
-                     }));
-}
-
-function formatShape(shape: number[]): string {
-  const oShape: Array<number|string> = shape.slice();
-  if (oShape.length === 0) {
-    return 'Scalar';
-  }
-  if (oShape[0] === null) {
-    oShape[0] = 'batch';
-  }
-  return `[${oShape.join(',')}]`;
-}
-
-function addHistogramSelector(
-    items: string[], parent: HTMLElement,
-    // tslint:disable-next-line:no-any
-    selectionHandler: (item: string) => any) {
-  const select = `
-    <select>
-      ${items.map((i) => `<option value=${i}>${i}</option>`)}
-    </select>
-  `;
-
-  const button = `<button>Show Values Distribution for:</button>`;
-  const content = `<div>${button}${select}</div>`;
-
-  parent.innerHTML = content;
-
-  // Add listeners
-  const buttonEl = parent.querySelector('button');
-  const selectEl = parent.querySelector('select');
-
-  buttonEl.addEventListener('click', () => {
-    selectionHandler(selectEl.selectedOptions[0].label);
-  });
-}
diff --git a/tfjs-master/tfjs-vis/src/show/model_test.ts b/tfjs-master/tfjs-vis/src/show/model_test.ts
deleted file mode 100644
index 6e4c6fe4e..000000000
--- a/tfjs-master/tfjs-vis/src/show/model_test.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tfl from '@tensorflow/tfjs-layers';
-
-import {layer, modelSummary} from './model';
-
-describe('modelSummary', () => {
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-  });
-
-  it('renders a model summary', async () => {
-    const container = {name: 'Test'};
-    const model = tfl.sequential();
-    model.add(tfl.layers.dense({units: 1, inputShape: [1]}));
-    await modelSummary(container, model);
-    expect(document.querySelectorAll('table').length).toBe(1);
-    expect(document.querySelectorAll('tr').length).toBe(2);
-  });
-});
-
-describe('layer', () => {
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-  });
-
-  it('renders a layer summary', async () => {
-    const container = {name: 'Test'};
-    const model = tfl.sequential();
-    const dense = tfl.layers.dense({units: 1, inputShape: [1]});
-    model.add(dense);
-    model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
-    await layer(container, dense);
-    expect(document.querySelectorAll('table').length).toBe(1);
-    expect(document.querySelectorAll('tr').length).toBe(3);
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/show/quality.ts b/tfjs-master/tfjs-vis/src/show/quality.ts
deleted file mode 100644
index 543378957..000000000
--- a/tfjs-master/tfjs-vis/src/show/quality.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getDrawArea} from '../render/render_utils';
-import {table} from '../render/table';
-import {Drawable} from '../types';
-
-/**
- * Renders a per class accuracy table for classification task evaluation
- *
- * ```js
- * const labels = tf.tensor1d([0, 0, 1, 2, 2, 2]);
- * const predictions = tf.tensor1d([0, 0, 0, 2, 1, 1]);
- *
- * const result = await tfvis.metrics.perClassAccuracy(labels, predictions);
- * console.log(result)
- *
- * const container = {name: 'Per Class Accuracy', tab: 'Evaluation'};
- * const categories = ['cat', 'dog', 'mouse'];
- * await tfvis.show.perClassAccuracy(container, result, categories);
- * ```
- *
- * @param container A `{name: string, tab?: string}` object specifying which
- * surface to render to.
- * @param classAccuracy An `Array<{accuracy: number, count: number}>` array with
- * the accuracy data. See metrics.perClassAccuracy for details on how to
- * generate this object.
- * @param classLabels An array of string labels for the classes in
- * `classAccuracy`. Optional.
- *
- * @doc {
- *  heading: 'Models & Tensors',
- *  subheading: 'Model Evaluation',
- *  namespace: 'show'
- * }
- */
-export async function perClassAccuracy(
-    container: Drawable,
-    classAccuracy: Array<{accuracy: number, count: number}>,
-    classLabels?: string[]) {
-  const drawArea = getDrawArea(container);
-
-  const headers = [
-    'Class',
-    'Accuracy',
-    '# Samples',
-  ];
-  const values: Array<Array<(string | number)>> = [];
-
-  for (let i = 0; i < classAccuracy.length; i++) {
-    const label = classLabels ? classLabels[i] : i.toString();
-    const classAcc = classAccuracy[i];
-    values.push([label, classAcc.accuracy, classAcc.count]);
-  }
-
-  return table(drawArea, {headers, values});
-}
diff --git a/tfjs-master/tfjs-vis/src/show/quality_test.ts b/tfjs-master/tfjs-vis/src/show/quality_test.ts
deleted file mode 100644
index e9fd84428..000000000
--- a/tfjs-master/tfjs-vis/src/show/quality_test.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {perClassAccuracy} from './quality';
-
-describe('perClassAccuracy', () => {
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-  });
-
-  it('renders perClassAccuracy', async () => {
-    const container = {name: 'Test'};
-    const acc = [
-      {accuracy: 0.5, count: 10},
-      {accuracy: 0.8, count: 10},
-    ];
-
-    const labels = ['cat', 'dog'];
-    await perClassAccuracy(container, acc, labels);
-    expect(document.querySelectorAll('table').length).toBe(1);
-  });
-
-  it('renders perClassAccuracy without explicit labels', async () => {
-    const container = {name: 'Test'};
-    const acc = [
-      {accuracy: 0.5, count: 10},
-      {accuracy: 0.8, count: 10},
-    ];
-    await perClassAccuracy(container, acc);
-    expect(document.querySelectorAll('table').length).toBe(1);
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/show/tensor.ts b/tfjs-master/tfjs-vis/src/show/tensor.ts
deleted file mode 100644
index 1df598e2f..000000000
--- a/tfjs-master/tfjs-vis/src/show/tensor.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {Tensor} from '@tensorflow/tfjs-core';
-
-import {histogram} from '../render/histogram';
-import {getDrawArea} from '../render/render_utils';
-import {Drawable} from '../types';
-import {tensorStats} from '../util/math';
-
-/**
- * Shows a histogram with the distribution of all values in a given tensor.
- *
- * ```js
- * const tensor = tf.tensor1d([0, 0, 0, 0, 2, 3, 4]);
- *
- * const surface = {name: 'Values Distribution', tab: 'Model Inspection'};
- * await tfvis.show.valuesDistribution(surface, tensor);
- * ```
- *
- * @doc {heading: 'Models & Tensors', subheading: 'Model Inspection', namespace:
- * 'show'}
- */
-export async function valuesDistribution(container: Drawable, tensor: Tensor) {
-  const drawArea = getDrawArea(container);
-  const stats = await tensorStats(tensor);
-  const values = await tensor.data();
-  return histogram(drawArea, values, {height: 150, stats});
-}
diff --git a/tfjs-master/tfjs-vis/src/show/tensor_test.ts b/tfjs-master/tfjs-vis/src/show/tensor_test.ts
deleted file mode 100644
index 26aa059e1..000000000
--- a/tfjs-master/tfjs-vis/src/show/tensor_test.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-import {valuesDistribution} from './tensor';
-
-describe('perClassAccuracy', () => {
-  beforeEach(() => {
-    document.body.innerHTML = '<div id="container"></div>';
-  });
-
-  it('renders histogram', async () => {
-    const container = {name: 'Test'};
-    const tensor = tf.tensor1d([0, 0, 0, 0, 2, 3, 4]);
-
-    await valuesDistribution(container, tensor);
-    expect(document.querySelectorAll('table').length).toBe(1);
-    expect(document.querySelectorAll('.vega-embed').length).toBe(1);
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/types.ts b/tfjs-master/tfjs-vis/src/types.ts
deleted file mode 100644
index c06a47d4b..000000000
--- a/tfjs-master/tfjs-vis/src/types.ts
+++ /dev/null
@@ -1,342 +0,0 @@
-import {Tensor2D} from '@tensorflow/tfjs-core';
-
-/*
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// Types shared across the project and that users will commonly interact with
-
-/**
- * The public api of a 'surface'
- */
-export interface Surface {
-  /**
-   * The containing HTML element for this surface
-   */
-  container: HTMLElement;
-
-  /**
-   * A textual label for the surface.
-   */
-  label: HTMLElement;
-
-  /**
-   * A container for plots and other renderings
-   */
-  drawArea: HTMLElement;
-}
-
-/**
- * Options used to specify a surface.
- *
- * name and tab are also used for retrieval of a surface instance.
- */
-export interface SurfaceInfo {
-  /**
-   * The name / label of this surface
-   */
-  name: string;
-
-  /**
-   * The name of the tab this surface should appear on
-   */
-  tab?: string;
-
-  /**
-   * Display Styles for the surface
-   */
-  styles?: StyleOptions;
-}
-
-/**
- * Internally all surfaces must have a tab.
- */
-export interface SurfaceInfoStrict extends SurfaceInfo {
-  name: string;
-  tab: string;
-  styles?: StyleOptions;
-}
-
-/**
- * Style properties are generally optional as components will specify defaults.
- */
-export interface StyleOptions {
-  width?: string;
-  height?: string;
-  maxWidth?: string;
-  maxHeight?: string;
-}
-
-/**
- * @docalias HTMLElement|{name: string, tab?: string}|Surface|{drawArea:
- * HTMLElement}
- */
-export type Drawable = HTMLElement|Surface|SurfaceInfo|{
-  drawArea: HTMLElement;
-};
-
-export function isSurfaceInfo(drawable: Drawable): drawable is SurfaceInfo {
-  if ((drawable as SurfaceInfo).name != null) {
-    return true;
-  }
-  return false;
-}
-
-export function isSurface(drawable: Drawable): drawable is Surface {
-  if ((drawable as Surface).drawArea instanceof HTMLElement) {
-    return true;
-  }
-  return false;
-}
-
-/**
- * Common visualisation options for '.render' functions.
- */
-export interface VisOptions {
-  /**
-   * Width of chart in px
-   */
-  width?: number;
-  /**
-   * Height of chart in px
-   */
-  height?: number;
-  /**
-   * Label for xAxis
-   */
-  xLabel?: string;
-  /**
-   * Label for yAxis
-   */
-  yLabel?: string;
-  /**
-   * Fontsize in px
-   */
-  fontSize?: number;
-  /**
-   * Will be set automatically
-   */
-  xType?: 'quantitative'|'ordinal'|'nominal';
-  /**
-   * Will be set automatically
-   */
-  yType?: 'quantitative'|'ordinal'|'nominal';
-}
-
-/**
- * Options for XY plots
- */
-export interface XYPlotOptions extends VisOptions {
-  /**
-   * domain of the x axis. Overriden by zoomToFit
-   */
-  xAxisDomain?: [number, number];
-  /**
-   * domain of the y axis. Overriden by zoomToFit
-   */
-  yAxisDomain?: [number, number];
-  /**
-   * Set the chart bounds to just fit the data. This may modify the axis scales
-   * but allows fitting more data into view.
-   */
-  zoomToFit?: boolean;
-  /**
-   * Colors to for each series plotted. An array of valid CSS color strings.
-   */
-  seriesColors?: string[];
-}
-
-/**
- * Data format for XY plots
- */
-export interface XYPlotData {
-  /**
-   * An array (or nested array) of {x, y} tuples.
-   */
-  values: Point2D[][]|Point2D[];
-  /**
-   * Series names/labels
-   */
-  series?: string[];
-}
-
-/**
- * Histogram options.
- */
-export interface HistogramOpts extends VisOptions {
-  /**
-   * By default a histogram will also compute and display summary statistics.
-   * If stats is set to false then summary statistics will not be displayed.
-   *
-   * Pre computed stats can also be passed in and should have the following
-   * format:
-   *  {
-   *    numVals?: number,
-   *    min?: number,
-   *    max?: number,
-   *    numNans?: number,
-   *    numZeros?: number,
-   *    numInfs?: number,
-   *  }
-   */
-  stats?: HistogramStats|false;
-
-  /**
-   * Maximum number of bins in histogram.
-   */
-  maxBins?: number;
-
-  /**
-   * Fill color for bars. Should be a valid CSS color string
-   */
-  color?: string;
-}
-
-/**
- * Bar chart options.
- */
-export interface BarChartOpts extends VisOptions {
-  /**
-   * Fill color for bars. Should be a valid CSS color string
-   */
-  color?: string|string[];
-}
-
-/**
- * Summary statistics for histogram.
- */
-export interface HistogramStats {
-  numVals?: number;
-  min?: number;
-  max?: number;
-  numNans?: number;
-  numZeros?: number;
-  numInfs?: number;
-}
-
-/**
- * Type alias for typed arrays
- */
-export type TypedArray = Int8Array|Uint8Array|Int16Array|Uint16Array|Int32Array|
-    Uint32Array|Uint8ClampedArray|Float32Array|Float64Array;
-
-/**
- * An object with a 'values' property and a 'labels' property.
- */
-export interface ConfusionMatrixData {
-  /**
-   * a square matrix of numbers representing counts for each (label, prediction)
-   * pair
-   */
-  values: number[][];
-
-  /**
-   * Human readable labels for each class in the matrix. Optional
-   */
-  tickLabels?: string[];
-}
-
-export interface ConfusionMatrixOptions extends VisOptions {
-  /**
-   * Color cells on the diagonal. Defaults to true
-   */
-  shadeDiagonal?: boolean;
-  /**
-   * render the values of each cell as text. Defaults to true
-   */
-  showTextOverlay?: boolean;
-  /**
-   * Output range of color scale. Either a 2 element array of valid
-   * css color or one of 'greyscale'|'viridis'|'blues'
-   */
-  colorMap?: NamedColorMap|[string, string];
-}
-
-/**
- * Datum format for scatter and line plots
- */
-export interface Point2D {
-  x: number;
-  y: number;
-}
-
-/**
- *  An object with a 'values' property and a 'labels' property.
- */
-export interface HeatmapData {
-  /**
-   * Matrix of values in column-major order.
-   *
-   * Row major order is supported by setting a boolean in options.
-   */
-  values: number[][]|Tensor2D;
-  /**
-   * x axis tick labels
-   */
-  xTickLabels?: string[];
-  /**
-   * y axis tick labels
-   */
-  yTickLabels?: string[];
-}
-
-/**
- * Color map names.
- */
-/** @docinline */
-export type NamedColorMap = 'greyscale'|'viridis'|'blues';
-
-/**
- * Visualization options for Heatmap
- */
-export interface HeatmapOptions extends VisOptions {
-  /**
-   * Defaults to viridis
-   */
-  colorMap?: NamedColorMap;
-
-  /**
-   * Custom input domain for the color scale.
-   * Useful if you want to plot multiple heatmaps using the same scale.
-   */
-  domain?: number[];
-
-  /**
-   * Pass in data values in row-major order.
-   *
-   * Internally this will transpose the data values before rendering.
-   */
-  rowMajor?: boolean;
-}
-
-/**
- * Data format for render.table
- */
-export interface TableData {
-  /**
-   * Column names
-   */
-  headers: string[];
-
-  /**
-   * An array of arrays (one for  each row). The inner
-   * array length usually matches the length of data.headers.
-   *
-   * Typically the values are numbers or strings.
-   */
-  // tslint:disable-next-line:no-any
-  values: any[][];
-}
diff --git a/tfjs-master/tfjs-vis/src/types/glamor-tachyons/index.d.ts b/tfjs-master/tfjs-vis/src/types/glamor-tachyons/index.d.ts
deleted file mode 100644
index 7262d0600..000000000
--- a/tfjs-master/tfjs-vis/src/types/glamor-tachyons/index.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-// Minimal typings for glamor-tachyhons
-
-declare module 'glamor-tachyons' {
-  export function tachyons(input: string|{}): {};
-}
diff --git a/tfjs-master/tfjs-vis/src/util/dom.ts b/tfjs-master/tfjs-vis/src/util/dom.ts
deleted file mode 100644
index 9c0745d3e..000000000
--- a/tfjs-master/tfjs-vis/src/util/dom.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {css} from 'glamor';
-
-import {getDrawArea} from '../render/render_utils';
-import {Drawable} from '../types';
-
-const DEFAULT_SUBSURFACE_OPTS = {
-  prepend: false,
-};
-
-/**
- * Utility function to create/retrieve divs within an HTMLElement|Surface
- */
-export function subSurface(parent: Drawable, name: string, opts: Options = {}) {
-  const container = getDrawArea(parent);
-  const style = css({
-    '& canvas': {
-      display: 'block',
-    },
-    marginTop: '.5rem',
-    marginBottom: '.5rem',
-  });
-  const titleStyle = css({
-    backgroundColor: 'white',
-    display: 'inline-block',
-    boxSizing: 'border-box',
-    borderBottom: '1px solid #357EDD',
-    lineHeight: '2em',
-    padding: '0 10px 0 10px',
-    marginBottom: '20px',
-    fontWeight: '600',
-    textAlign: 'left',
-  });
-  const options = Object.assign({}, DEFAULT_SUBSURFACE_OPTS, opts);
-
-  let sub: HTMLElement|null = container.querySelector(`div[data-name=${name}]`);
-  if (!sub) {
-    sub = document.createElement('div');
-    sub.setAttribute('class', `${style}`);
-    sub.dataset.name = name;
-
-    if (options.title) {
-      const title = document.createElement('div');
-      title.setAttribute('class', `subsurface-title ${titleStyle}`);
-      title.innerText = options.title;
-      sub.appendChild(title);
-    }
-
-    if (options.prepend) {
-      container.insertBefore(sub, container.firstChild);
-    } else {
-      container.appendChild(sub);
-    }
-  }
-  return sub;
-}
-
-export function getDefaultWidth(element: HTMLElement) {
-  const DEFAULT_PADDING = 50;
-  let padding = 0;
-  let current = element;
-  while (current && current.clientWidth === 0) {
-    current = current.parentElement;
-    padding = DEFAULT_PADDING;
-  }
-  return (current.clientWidth - padding);
-}
-
-export function getDefaultHeight(element: HTMLElement) {
-  if (element.clientHeight === 0) {
-    return 200;
-  } else {
-    return element.clientHeight;
-  }
-}
-
-interface Options {
-  prepend?: boolean;
-  title?: string;
-}
diff --git a/tfjs-master/tfjs-vis/src/util/math.ts b/tfjs-master/tfjs-vis/src/util/math.ts
deleted file mode 100644
index 6aa77dad1..000000000
--- a/tfjs-master/tfjs-vis/src/util/math.ts
+++ /dev/null
@@ -1,326 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {dispose, maximum, scalar, Tensor, Tensor1D, tidy} from '@tensorflow/tfjs-core';
-
-import {HistogramStats, TypedArray} from '../types';
-
-import {assert, assertShapesMatch} from './utils';
-
-/**
- * Returns summary statistics for an array of numbers
- *
- * @param input
- */
-export function arrayStats(input: number[]): HistogramStats {
-  if (!Array.isArray(input)) {
-    throw new Error('input must be an array');
-  }
-  if (input.length === 0) {
-    return {
-      numVals: 0,
-      numNans: 0,
-      numZeros: 0,
-      max: undefined,
-      min: undefined,
-    };
-  }
-
-  const numVals = input.length;
-  let max = -Infinity;
-  let min = Infinity;
-  let numZeros = 0;
-  let numNans = 0;
-  let numInfs = 0;
-
-  for (let i = 0; i < numVals; i++) {
-    const curr = input[i];
-    if (curr > max) {
-      max = curr;
-    }
-
-    if (curr < min) {
-      min = curr;
-    }
-
-    if (curr === 0) {
-      numZeros += 1;
-    }
-
-    if (isNaN(curr)) {
-      numNans += 1;
-    } else if (!isFinite(curr)) {
-      // Make sure NaNs are not double counted as Infs
-      numInfs += 1;
-    }
-  }
-
-  const result = {
-    numVals,
-    numZeros,
-    numNans,
-    max,
-    min,
-    numInfs,
-  };
-
-  // Handle all NaN input
-  if (result.max === -Infinity) {
-    result.max = NaN;
-  }
-  if (result.min === Infinity) {
-    result.min = NaN;
-  }
-
-  return result;
-}
-
-/**
- * Returns summary statistics for a numeric tensor. *
- *
- * @param input
- */
-export async function tensorStats(input: Tensor): Promise<HistogramStats> {
-  // TODO. Benchmark this and consider having one of the *stats functions
-  // delegate to the other.
-
-  const [min, max, numZeros] = tidy(() => {
-    const zero = scalar(0, input.dtype);
-
-    const min = input.min();
-    const max = input.max();
-    const numZeros = input.equal(zero).sum();
-
-    return [min, max, numZeros];
-  });
-
-  return Promise.all([input.data(), min.data(), max.data(), numZeros.data()])
-      .then(([tensorVal, minVal, maxVal, numZerosVal]) => {
-        // We currently need to count NaNs on CPU.
-        const numVals = tensorVal.length;
-        let numNans = 0;
-        let numInfs = 0;
-        for (let i = 0; i < numVals; i++) {
-          const curr = tensorVal[i];
-          if (isNaN(curr)) {
-            numNans += 1;
-          } else if (!isFinite(curr)) {
-            // Make sure NaNs are not double counted as Infs
-            numInfs += 1;
-          }
-        }
-
-        let trueMin = minVal[0];
-        let trueMax = maxVal[0];
-        if (numNans === numVals) {
-          // on gpu the min and max won't be accurate if all values are NaN
-          trueMin = NaN;
-          trueMax = NaN;
-        }
-
-        const stats = {
-          numVals,
-          numZeros: numZerosVal[0],
-          numNans,
-          min: trueMin,
-          max: trueMax,
-          numInfs,
-        };
-
-        return stats;
-      });
-}
-
-/**
- * Computes a confusion matrix from predictions and labels. Each value in
- * labels and predictions should correspond to some output class. It is assumed
- * that these values go from 0 to numClasses - 1.
- *
- * ```js
- * const labels = tf.tensor1d([1, 2, 4]);
- * const predictions = tf.tensor1d([2, 2, 4]);
- * const result = await tfvis.metrics.confusionMatrix(labels, predictions);
- * console.log(JSON.stringify(result, null, 2))
- * ```
- *
- * @param labels 1D tensor of true values
- * @param predictions 1D tensor of predicted values
- * @param numClasses Number of distinct classes. Optional. If not passed in
- *  numClasses will equal the highest number in either labels or predictions
- *  plus 1
- * @param weights 1d tensor that is the same size as predictions.
- *  If weights is passed in then each prediction contributes its corresponding
- *  weight to the total value of the confusion matrix cell.
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export async function confusionMatrix(
-    labels: Tensor1D, predictions: Tensor1D, numClasses?: number,
-    weights?: Tensor1D): Promise<number[][]> {
-  assert(labels.rank === 1, 'labels must be a 1D tensor');
-  assert(predictions.rank === 1, 'predictions must be a 1D tensor');
-  assert(
-      labels.size === predictions.size,
-      'labels and predictions must be the same length');
-  if (weights != null) {
-    assert(
-        weights.size === predictions.size,
-        'labels and predictions must be the same length');
-  }
-
-  // Cast to int in case the caller didn't
-  const labelsInt = labels.cast('int32');
-  const predictionsInt = predictions.cast('int32');
-
-  if (numClasses == null) {
-    const maximumTensor = tidy(() => {
-      return maximum(labelsInt.max(), predictionsInt.max()).cast('int32');
-    });
-    const maximumArray = await maximumTensor.data();
-    numClasses = maximumArray[0] + 1;
-    maximumTensor.dispose();
-  }
-
-  let weightsPromise: Promise<null|TypedArray> = Promise.resolve(null);
-  if (weights != null) {
-    weightsPromise = weights.data();
-  }
-
-  return Promise.all([labelsInt.data(), predictionsInt.data(), weightsPromise])
-      .then(([labelsArray, predsArray, weightsArray]) => {
-        const result: number[][] = Array(numClasses).fill(0);
-        // Initialize the matrix
-        for (let i = 0; i < numClasses; i++) {
-          result[i] = Array(numClasses).fill(0);
-        }
-
-        for (let i = 0; i < labelsArray.length; i++) {
-          const label = labelsArray[i];
-          const pred = predsArray[i];
-
-          if (weightsArray != null) {
-            result[label][pred] += weightsArray[i];
-          } else {
-            result[label][pred] += 1;
-          }
-        }
-
-        return result;
-      });
-}
-
-/**
- * Computes how often predictions matches labels
- *
- * ```js
- * const labels = tf.tensor1d([0, 0, 1, 2, 2, 2]);
- * const predictions = tf.tensor1d([0, 0, 0, 2, 1, 1]);
- *
- * const result = await tfvis.metrics.accuracy(labels, predictions);
- * console.log(result)
- * ```
- *
- * @param labels tensor of true values
- * @param predictions tensor of predicted values
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export async function accuracy(
-    labels: Tensor, predictions: Tensor): Promise<number> {
-  assertShapesMatch(
-      labels.shape, predictions.shape, 'Error computing accuracy.');
-
-  const eq = labels.equal(predictions);
-  const mean = eq.mean();
-
-  const acc = (await mean.data())[0];
-
-  dispose([eq, mean]);
-  return acc;
-}
-
-/**
- * Computes per class accuracy between prediction and labels. Each value in
- * labels and predictions should correspond to some output class. It is assumed
- * that these values go from 0 to numClasses - 1.
- *
- * ```js
- * const labels = tf.tensor1d([0, 0, 1, 2, 2, 2]);
- * const predictions = tf.tensor1d([0, 0, 0, 2, 1, 1]);
- *
- * const result = await tfvis.metrics.perClassAccuracy(labels, predictions);
- * console.log(JSON.stringify(result, null, 2))
- * ```
- *
- * Returns an array of objects that each have an an `accuracy` and a `count`
- * property for each class.
- *
- *
- * @param labels 1D tensor of true values
- * @param predictions 1D tensor of predicted values
- * @param numClasses Number of distinct classes. Optional. If not passed in
- *  numClasses will equal the highest number in either labels or predictions
- *  plus 1
- *
- * @doc {heading: 'Metrics', namespace: 'metrics'}
- */
-export async function perClassAccuracy(
-    labels: Tensor1D, predictions: Tensor1D,
-    numClasses?: number): Promise<Array<{accuracy: number, count: number}>> {
-  assert(labels.rank === 1, 'labels must be a 1D tensor');
-  assert(predictions.rank === 1, 'predictions must be a 1D tensor');
-  assert(
-      labels.size === predictions.size,
-      'labels and predictions must be the same length');
-
-  if (numClasses == null) {
-    const maximumTensor = tidy(() => {
-      return maximum(labels.max(), predictions.max());
-    });
-    const maximumArray = await maximumTensor.data();
-    numClasses = maximumArray[0] + 1;
-    maximumTensor.dispose();
-  }
-
-  return Promise.all([labels.data(), predictions.data()])
-      .then(([labelsArray, predsArray]) => {
-        // Per class total counts
-        const counts: number[] = Array(numClasses).fill(0);
-        // Per class accuracy
-        const accuracy: number[] = Array(numClasses).fill(0);
-
-        for (let i = 0; i < labelsArray.length; i++) {
-          const label = labelsArray[i];
-          const pred = predsArray[i];
-
-          counts[label] += 1;
-          if (label === pred) {
-            accuracy[label] += 1;
-          }
-        }
-
-        const results: Array<{accuracy: number, count: number}> = [];
-        for (let i = 0; i < counts.length; i++) {
-          results.push({
-            count: counts[i],
-            accuracy: counts[i] === 0 ? 0 : accuracy[i] / counts[i],
-          });
-        }
-
-        return results;
-      });
-}
diff --git a/tfjs-master/tfjs-vis/src/util/math_test.ts b/tfjs-master/tfjs-vis/src/util/math_test.ts
deleted file mode 100644
index 33795c4d6..000000000
--- a/tfjs-master/tfjs-vis/src/util/math_test.ts
+++ /dev/null
@@ -1,437 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from '@tensorflow/tfjs-core';
-
-import {accuracy, arrayStats, confusionMatrix, perClassAccuracy, tensorStats} from './math';
-import {DECIMAL_PLACES_TO_CHECK} from './utils';
-
-//
-// arrayStats
-//
-describe('arrayStats', () => {
-  it('throws on non array input', () => {
-    // @ts-ignore
-    expect(() => arrayStats('string')).toThrow();
-  });
-
-  it('handles empty arrays', () => {
-    const stats = arrayStats([]);
-    expect(stats.max).toBe(undefined);
-    expect(stats.min).toBe(undefined);
-    expect(stats.numVals).toBe(0);
-    expect(stats.numNans).toBe(0);
-    expect(stats.numZeros).toBe(0);
-  });
-
-  it('computes correct stats', () => {
-    const data = [2, 3, -400, 500, NaN, -800, 0, 0, 0];
-    const stats = arrayStats(data);
-
-    expect(stats.max).toBe(500);
-    expect(stats.min).toBe(-800);
-    expect(stats.numVals).toBe(9);
-    expect(stats.numNans).toBe(1);
-    expect(stats.numZeros).toBe(3);
-  });
-
-  it('computes correct stats — all negative', () => {
-    const data = [-2, -3, -400, -500, NaN, -800];
-    const stats = arrayStats(data);
-    expect(stats.max).toBe(-2);
-    expect(stats.min).toBe(-800);
-    expect(stats.numVals).toBe(6);
-    expect(stats.numNans).toBe(1);
-    expect(stats.numZeros).toBe(0);
-  });
-
-  it('computes correct stats — all zeros', () => {
-    const data = [0, 0, 0, 0];
-    const stats = arrayStats(data);
-    expect(stats.max).toBe(0);
-    expect(stats.min).toBe(0);
-    expect(stats.numVals).toBe(4);
-    expect(stats.numNans).toBe(0);
-    expect(stats.numZeros).toBe(4);
-  });
-
-  it('computes correct stats — all NaNs', () => {
-    const data = [NaN, NaN, NaN, NaN];
-    const stats = arrayStats(data);
-    expect(isNaN(stats.max)).toBe(true);
-    expect(isNaN(stats.min)).toBe(true);
-    expect(stats.numVals).toBe(4);
-    expect(stats.numNans).toBe(4);
-    expect(stats.numZeros).toBe(0);
-  });
-
-  it('computes correct stats — some infs', () => {
-    const data = [10, 4, Infinity, -Infinity, NaN];
-    const stats = arrayStats(data);
-    expect(stats.max).toBe(Infinity);
-    expect(stats.min).toBe(-Infinity);
-    expect(stats.numVals).toBe(5);
-    expect(stats.numNans).toBe(1);
-    expect(stats.numZeros).toBe(0);
-    expect(stats.numInfs).toBe(2);
-  });
-});
-
-//
-// tensorStats
-//
-describe('tensorStats', () => {
-  it('computes correct stats', async () => {
-    const data = tf.tensor([2, 3, -400, 500, NaN, -800, 0, 0, 0]);
-    const stats = await tensorStats(data);
-
-    expect(stats.max).toEqual(NaN);
-    expect(stats.min).toEqual(NaN);
-    expect(stats.numVals).toBe(9);
-    expect(stats.numNans).toBe(1);
-    expect(stats.numZeros).toBe(3);
-  });
-
-  it('computes correct stats — all negative', async () => {
-    const data = tf.tensor([-2, -3, -400, -500, NaN, -800]);
-    const stats = await tensorStats(data);
-    expect(stats.max).toEqual(NaN);
-    expect(stats.min).toEqual(NaN);
-    expect(stats.numVals).toBe(6);
-    expect(stats.numNans).toBe(1);
-    expect(stats.numZeros).toBe(0);
-  });
-
-  it('computes correct stats — all zeros', async () => {
-    const data = tf.tensor([0, 0, 0, 0]);
-    const stats = await tensorStats(data);
-    expect(stats.max).toBe(0);
-    expect(stats.min).toBe(0);
-    expect(stats.numVals).toBe(4);
-    expect(stats.numNans).toBe(0);
-    expect(stats.numZeros).toBe(4);
-  });
-
-  it('computes correct stats — all NaNs', async () => {
-    const data = tf.tensor([NaN, NaN, NaN, NaN]);
-    const stats = await tensorStats(data);
-    expect(isNaN(stats.max)).toBe(true);
-    expect(isNaN(stats.min)).toBe(true);
-    expect(stats.numVals).toBe(4);
-    expect(stats.numNans).toBe(4);
-    expect(stats.numZeros).toBe(0);
-  });
-
-  it('computes correct stats — some infs', async () => {
-    const data = tf.tensor([10, 4, Infinity, -Infinity, NaN]);
-    const stats = await tensorStats(data);
-    expect(stats.max).toEqual(NaN);
-    expect(stats.min).toEqual(NaN);
-    expect(stats.numVals).toBe(5);
-    expect(stats.numNans).toBe(1);
-    expect(stats.numZeros).toBe(0);
-    expect(stats.numInfs).toBe(2);
-  });
-});
-
-//
-// confusionMatrix
-//
-describe('confusionMatrix', () => {
-  it('computes a confusion matrix', async () => {
-    const labels = tf.tensor1d([1, 2, 4]);
-    const predictions = tf.tensor1d([2, 2, 4]);
-
-    const expected = [
-      [0, 0, 0, 0, 0],
-      [0, 0, 1, 0, 0],
-      [0, 0, 1, 0, 0],
-      [0, 0, 0, 0, 0],
-      [0, 0, 0, 0, 1],
-    ];
-
-    const result = await confusionMatrix(labels, predictions);
-    expect(result).toEqual(expected);
-  });
-
-  it('computes a confusion matrix with explicit numClasses', async () => {
-    const labels = tf.tensor1d([1, 2, 4]);
-    const predictions = tf.tensor1d([2, 2, 4]);
-    const numClasses = 6;
-
-    const expected = [
-      [0, 0, 0, 0, 0, 0],
-      [0, 0, 1, 0, 0, 0],
-      [0, 0, 1, 0, 0, 0],
-      [0, 0, 0, 0, 0, 0],
-      [0, 0, 0, 0, 1, 0],
-      [0, 0, 0, 0, 0, 0],
-    ];
-
-    const result = await confusionMatrix(labels, predictions, numClasses);
-    expect(result).toEqual(expected);
-  });
-
-  it('computes a confusion matrix with custom weights', async () => {
-    const labels = tf.tensor1d([0, 1, 2, 3, 4]);
-    const predictions = tf.tensor1d([0, 1, 2, 3, 4]);
-    const weights = tf.tensor1d([0, 1, 2, 3, 4]);
-
-    const expected = [
-      [0, 0, 0, 0, 0],
-      [0, 1, 0, 0, 0],
-      [0, 0, 2, 0, 0],
-      [0, 0, 0, 3, 0],
-      [0, 0, 0, 0, 4],
-    ];
-
-    const result =
-        await confusionMatrix(labels, predictions, undefined, weights);
-    expect(result).toEqual(expected);
-  });
-
-  it('computes a confusion matrix where preds and labels do not intersect',
-     async () => {
-       const labels = tf.tensor1d([1, 1, 2, 3, 5, 1, 3, 6, 3, 1]);
-       const predictions = tf.tensor1d([1, 1, 2, 3, 5, 6, 1, 2, 3, 4]);
-
-       const expected = [
-         [0, 0, 0, 0, 0, 0, 0],
-         [0, 2, 0, 0, 1, 0, 1],
-         [0, 0, 1, 0, 0, 0, 0],
-         [0, 1, 0, 2, 0, 0, 0],
-         [0, 0, 0, 0, 0, 0, 0],
-         [0, 0, 0, 0, 0, 1, 0],
-         [0, 0, 1, 0, 0, 0, 0],
-       ];
-
-       const result = await confusionMatrix(labels, predictions);
-       expect(result).toEqual(expected);
-     });
-
-  it('computes a confusion matrix with multiple matches', async () => {
-    const labels = tf.tensor1d([4, 5, 6]);
-    const predictions = tf.tensor1d([1, 2, 3]);
-
-    const expected = [
-      [0, 0, 0, 0, 0, 0, 0],
-      [0, 0, 0, 0, 0, 0, 0],
-      [0, 0, 0, 0, 0, 0, 0],
-      [0, 0, 0, 0, 0, 0, 0],
-      [0, 1, 0, 0, 0, 0, 0],
-      [0, 0, 1, 0, 0, 0, 0],
-      [0, 0, 0, 1, 0, 0, 0],
-    ];
-
-    const result = await confusionMatrix(labels, predictions);
-    expect(result).toEqual(expected);
-  });
-
-  it('errors on non 1d label tensor', async () => {
-    const labels = tf.tensor([1, 2, 4, 4], [2, 2]);
-    const predictions = tf.tensor1d([2, 2, 4, 3]);
-
-    let errorMessage;
-    try {
-      //@ts-ignore
-      await confusionMatrix(labels, predictions);
-    } catch (e) {
-      errorMessage = e.message;
-    }
-
-    expect(errorMessage).toEqual('labels must be a 1D tensor');
-  });
-
-  it('errors on non 1d prediction tensor', async () => {
-    const labels = tf.tensor1d([1, 2, 4, 4]);
-    const predictions = tf.tensor([2, 2, 4, 3], [2, 2]);
-
-    let errorMessage;
-    try {
-      //@ts-ignore
-      await confusionMatrix(labels, predictions);
-    } catch (e) {
-      errorMessage = e.message;
-    }
-
-    expect(errorMessage).toEqual('predictions must be a 1D tensor');
-  });
-
-  it('errors on tensors of different lengths', async () => {
-    const labels = tf.tensor1d([1, 2, 4]);
-    const predictions = tf.tensor1d([2, 2, 4, 3, 6]);
-
-    let errorMessage;
-    try {
-      //@ts-ignore
-      await confusionMatrix(labels, predictions);
-    } catch (e) {
-      errorMessage = e.message;
-    }
-
-    expect(errorMessage)
-        .toEqual('labels and predictions must be the same length');
-  });
-});
-
-//
-// accuracy
-//
-describe('accuracy', () => {
-  it('computes accuracy', async () => {
-    const labels = tf.tensor1d([1, 2, 4]);
-    const predictions = tf.tensor1d([2, 2, 4]);
-
-    const result = await accuracy(labels, predictions);
-    expect(result).toBeCloseTo(2 / 3, DECIMAL_PLACES_TO_CHECK);
-  });
-
-  it('computes accuracy, no matches', async () => {
-    const labels = tf.tensor1d([1, 2, 4]);
-    const predictions = tf.tensor1d([5, 6, 8]);
-
-    const result = await accuracy(labels, predictions);
-    expect(result).toBeCloseTo(0, DECIMAL_PLACES_TO_CHECK);
-  });
-
-  it('computes accuracy, all matches', async () => {
-    const labels = tf.tensor1d([1, 2, 3]);
-    const predictions = tf.tensor1d([1, 2, 3]);
-
-    const result = await accuracy(labels, predictions);
-    expect(result).toBeCloseTo(1, DECIMAL_PLACES_TO_CHECK);
-  });
-
-  it('computes accuracy, tensor 2d', async () => {
-    const labels = tf.tensor2d([
-      [1, 2],
-      [3, 4],
-      [5, 6],
-    ]);
-    const predictions = tf.tensor2d([
-      [1, 9],
-      [3, 4],
-      [5, 6],
-    ]);
-
-    const result = await accuracy(labels, predictions);
-    expect(result).toBeCloseTo(5 / 6, DECIMAL_PLACES_TO_CHECK);
-  });
-
-  it('errors on tensors of different shapes', async () => {
-    const labels = tf.tensor1d([1, 2, 4, 4]);
-    const predictions = tf.tensor([2, 2, 4, 3], [2, 2]);
-
-    let errorMessage;
-    try {
-      //@ts-ignore
-      await accuracy(labels, predictions);
-    } catch (e) {
-      errorMessage = e.message;
-    }
-
-    expect(errorMessage)
-        .toEqual('Error computing accuracy. Shapes 4 and 2,2 must match');
-  });
-});
-
-//
-// accuracy
-//
-describe('per class accuracy', () => {
-  it('computes per class accuracy', async () => {
-    const labels = tf.tensor1d([0, 0, 1, 2, 2, 2]);
-    const predictions = tf.tensor1d([0, 0, 0, 2, 1, 1]);
-
-    const result = await perClassAccuracy(labels, predictions);
-    expect(result.map(d => d.accuracy)).toEqual([1, 0, 1 / 3]);
-    expect(result.map(d => d.count)).toEqual([2, 1, 3]);
-  });
-
-  it('computes per class accuracy, no matches', async () => {
-    const labels = tf.tensor1d([1, 1, 1, 1, 1]);
-    const predictions = tf.tensor1d([0, 0, 0, 0, 0]);
-
-    const result = await perClassAccuracy(labels, predictions);
-    expect(result.map(d => d.accuracy)).toEqual([0, 0]);
-    expect(result.map(d => d.count)).toEqual([0, 5]);
-  });
-
-  it('computes per class accuracy, all matches', async () => {
-    const labels = tf.tensor1d([0, 1, 2, 3, 3, 3]);
-    const predictions = tf.tensor1d([0, 1, 2, 3, 3, 3]);
-
-    const result = await perClassAccuracy(labels, predictions);
-    expect(result.map(d => d.accuracy)).toEqual([1, 1, 1, 1]);
-    expect(result.map(d => d.count)).toEqual([1, 1, 1, 3]);
-  });
-
-  it('computes per class accuracy, explicit numClasses', async () => {
-    const labels = tf.tensor1d([0, 1, 2, 2]);
-    const predictions = tf.tensor1d([0, 1, 2, 1]);
-
-    const result = await perClassAccuracy(labels, predictions, 5);
-    expect(result.map(d => d.accuracy)).toEqual([1, 1, 0.5, 0, 0]);
-    expect(result.map(d => d.count)).toEqual([1, 1, 2, 0, 0]);
-  });
-
-  it('errors on non 1d label tensor', async () => {
-    const labels = tf.tensor([1, 2, 4, 4], [2, 2]);
-    const predictions = tf.tensor1d([2, 2, 4, 3]);
-
-    let errorMessage;
-    try {
-      //@ts-ignore
-      await perClassAccuracy(labels, predictions);
-    } catch (e) {
-      errorMessage = e.message;
-    }
-
-    expect(errorMessage).toEqual('labels must be a 1D tensor');
-  });
-
-  it('errors on non 1d prediction tensor', async () => {
-    const labels = tf.tensor1d([1, 2, 4, 4]);
-    const predictions = tf.tensor([2, 2, 4, 3], [2, 2]);
-
-    let errorMessage;
-    try {
-      //@ts-ignore
-      await perClassAccuracy(labels, predictions);
-    } catch (e) {
-      errorMessage = e.message;
-    }
-
-    expect(errorMessage).toEqual('predictions must be a 1D tensor');
-  });
-
-  it('errors on tensors of different lengths', async () => {
-    const labels = tf.tensor1d([1, 2, 4]);
-    const predictions = tf.tensor1d([2, 2, 4, 3, 6]);
-
-    let errorMessage;
-    try {
-      //@ts-ignore
-      await perClassAccuracy(labels, predictions);
-    } catch (e) {
-      errorMessage = e.message;
-    }
-
-    expect(errorMessage)
-        .toEqual('labels and predictions must be the same length');
-  });
-});
diff --git a/tfjs-master/tfjs-vis/src/util/utils.ts b/tfjs-master/tfjs-vis/src/util/utils.ts
deleted file mode 100644
index 1a5b561a6..000000000
--- a/tfjs-master/tfjs-vis/src/util/utils.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-import {TypedArray} from '../types';
-
-/*
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Tests a boolean expression and throws a message if false.
- */
-export function assert(expr: boolean, msg: string|(() => string)) {
-  if (!expr) {
-    throw new Error(typeof msg === 'string' ? msg : msg());
-  }
-}
-
-export function assertShapesMatch(
-    shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {
-  assert(
-      arraysEqual(shapeA, shapeB),
-      errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-}
-
-export function arraysEqual(n1: number[]|TypedArray, n2: number[]|TypedArray) {
-  if (n1.length !== n2.length) {
-    return false;
-  }
-  for (let i = 0; i < n1.length; i++) {
-    if (n1[i] !== n2[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-
-// Number of decimal places to  when checking float similarity
-export const DECIMAL_PLACES_TO_CHECK = 4;
diff --git a/tfjs-master/tfjs-vis/src/version.ts b/tfjs-master/tfjs-vis/src/version.ts
deleted file mode 100644
index 7918e2d17..000000000
--- a/tfjs-master/tfjs-vis/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '1.4.0';
-export {version};
diff --git a/tfjs-master/tfjs-vis/src/visor.ts b/tfjs-master/tfjs-vis/src/visor.ts
deleted file mode 100644
index 0fb380fd3..000000000
--- a/tfjs-master/tfjs-vis/src/visor.ts
+++ /dev/null
@@ -1,245 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {VisorComponent} from './components/visor';
-import {SurfaceInfo, SurfaceInfoStrict} from './types';
-
-let visorSingleton: Visor;
-const DEFAULT_TAB = 'Visor';
-const VISOR_CONTAINER_ID = 'tfjs-visor-container';
-
-/**
- * The primary interface to the visor is the visor() function.
- *
- * This returns a singleton instance of the Visor class. The
- * singleton object will be replaced if the visor is removed from the DOM for
- * some reason.
- *
- * ```js
- * // Show the visor
- * tfvis.visor();
- * ```
- *
- *
- * @doc {heading: 'Visor & Surfaces'}
- */
-export function visor(): Visor {
-  if (typeof document === 'undefined') {
-    throw new Error(
-        'No document defined. This library needs a browser/dom to work');
-  }
-
-  if (document.getElementById(VISOR_CONTAINER_ID) && visorSingleton != null) {
-    return visorSingleton;
-  }
-
-  // Create the container
-  let visorEl = document.getElementById(VISOR_CONTAINER_ID);
-
-  if (visorEl == null) {
-    visorEl = document.createElement('div');
-    visorEl.id = VISOR_CONTAINER_ID;
-    document.body.appendChild(visorEl);
-  }
-
-  let renderRoot: Element;
-  function renderVisor(
-      domNode: HTMLElement,
-      surfaceList: Map<string, SurfaceInfoStrict>): VisorComponent {
-    let visorInstance: VisorComponent = null;
-    renderRoot = VisorComponent.render(domNode, renderRoot, {
-      ref: (r: VisorComponent) => visorInstance = r,
-      surfaceList: Array.from(surfaceList.values()),
-    });
-    // Side effect of VisorComponent.render() is to assign visorInstance
-    return visorInstance;
-  }
-
-  // TODO: consider changing this type. Possibly lift into top level state
-  // object
-  const surfaceList: Map<string, SurfaceInfoStrict> = new Map();
-  const visorComponentInstance: VisorComponent =
-      renderVisor(visorEl, surfaceList);
-
-  visorSingleton =
-      new Visor(visorComponentInstance, visorEl, surfaceList, renderVisor);
-
-  return visorSingleton;
-}
-
-/**
- * An instance of the visor. An instance of this class is created using the
- * `visor()` function.
- *
- * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
- */
-export class Visor {
-  private visorComponent: VisorComponent;
-  private surfaceList: Map<string, SurfaceInfoStrict>;
-  private renderVisor:
-      (domNode: HTMLElement,
-       surfaceList: Map<string, SurfaceInfoStrict>) => VisorComponent;
-
-  /**
-   * The underlying html element of the visor.
-   */
-  public el: HTMLElement;
-
-  constructor(
-      visorComponent: VisorComponent, visorEl: HTMLElement,
-      surfaceList: Map<string, SurfaceInfoStrict>,
-      renderVisor:
-          (domNode: HTMLElement,
-           surfaceList: Map<string, SurfaceInfoStrict>) => VisorComponent) {
-    this.visorComponent = visorComponent;
-    this.el = visorEl;
-    this.surfaceList = surfaceList;
-    this.renderVisor = renderVisor;
-  }
-
-  /**
-   * Creates a surface on the visor
-   *
-   * Most methods in tfjs-vis that take a surface also take a SurfaceInfo
-   * so you rarely need to call this method unless you want to make a custom
-   * plot.
-   *
-   * ```js
-   * // Create a surface on a tab
-   * tfvis.visor().surface({name: 'My Surface', tab: 'My Tab'});
-   * ```
-   *
-   * ```js
-   * // Create a surface and specify its height
-   * tfvis.visor().surface({name: 'Custom Height', tab: 'My Tab', styles: {
-   *    height: 500
-   * }})
-   * ```
-   *
-   * @param options
-   *
-   * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
-   */
-  surface(options: SurfaceInfo) {
-    const {name} = options;
-    const tab = options.tab == null ? DEFAULT_TAB : options.tab;
-
-    if (name == null ||
-        // tslint:disable-next-line
-        !(typeof name === 'string' || name as any instanceof String)) {
-      throw new Error(
-          // tslint:disable-next-line
-          'You must pass a config object with a \'name\' property to create or retrieve a surface');
-    }
-
-    const finalOptions: SurfaceInfoStrict = {
-      ...options,
-      tab,
-    };
-
-    const key = `${name}-${tab}`;
-    if (!this.surfaceList.has(key)) {
-      this.surfaceList.set(key, finalOptions);
-    }
-
-    this.renderVisor(this.el, this.surfaceList);
-    return this.visorComponent.getSurface(name, tab);
-  }
-
-  /**
-   * Returns a boolean indicating if the visor is in 'fullscreen' mode
-   *
-   * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
-   */
-  isFullscreen() {
-    return this.visorComponent.isFullscreen();
-  }
-
-  /**
-   * Returns a boolean indicating if the visor is open
-   *
-   * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
-   */
-  isOpen() {
-    return this.visorComponent.isOpen();
-  }
-
-  /**
-   * Closes the visor.
-   *
-   * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
-   */
-  close() {
-    return this.visorComponent.close();
-  }
-
-  /**
-   * Opens the visor.
-   *
-   * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
-   */
-  open() {
-    return this.visorComponent.open();
-  }
-
-  /**
-   * Toggles the visor (closed vs open).
-   *
-   * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
-   */
-  toggle() {
-    return this.visorComponent.toggle();
-  }
-
-  /** @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'} */
-  toggleFullScreen() {
-    return this.visorComponent.toggleFullScreen();
-  }
-
-  /**
-   * Binds the ~ (tilde) key to toggle the visor.
-   *
-   * This is called by default when the visor is initially created.
-   *
-   * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
-   */
-  bindKeys() {
-    return this.visorComponent.bindKeys();
-  }
-
-  /**
-   * Unbinds the keyboard control to toggle the visor.
-   *
-   * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
-   */
-  unbindKeys() {
-    return this.visorComponent.unbindKeys();
-  }
-
-  /**
-   * Sets the active tab for the visor.
-   *
-   * @doc {heading: 'Visor & Surfaces', subheading: 'Visor Methods'}
-   */
-  setActiveTab(tabName: string) {
-    const tabs = this.visorComponent.state.tabs;
-    if (!tabs.has(tabName)) {
-      throw new Error(`Tab '${tabName}' does not exist`);
-    }
-    this.visorComponent.setState({activeTab: tabName});
-  }
-}
diff --git a/tfjs-master/tfjs-vis/src/visor_test.ts b/tfjs-master/tfjs-vis/src/visor_test.ts
deleted file mode 100644
index 16ca94b33..000000000
--- a/tfjs-master/tfjs-vis/src/visor_test.ts
+++ /dev/null
@@ -1,274 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {visor} from './index';
-
-const tick = (ms = 1) => new Promise(resolve => setTimeout(resolve, ms));
-
-describe('Visor Singleton', () => {
-  afterEach(() => {
-    document.body.innerHTML = '';
-  });
-
-  it('renders an empty visor', () => {
-    visor();
-    expect(document.querySelectorAll('.visor').length).toBe(1);
-  });
-
-  it('visor.el is an HTMLElement', () => {
-    const visorInstance = visor();
-    expect(visorInstance.el instanceof HTMLElement).toBe(true);
-  });
-
-  it('renders only one visor', () => {
-    const v1 = visor();
-    const v2 = visor();
-    const v3 = visor();
-    expect(document.querySelectorAll('.visor').length).toBe(1);
-    expect(v1).toEqual(v2);
-    expect(v1).toEqual(v3);
-  });
-
-  it('adds a surface', () => {
-    const visorInstance = visor();
-    visorInstance.surface({name: 'surface 1', tab: 'tab 1'});
-    expect(document.querySelectorAll('.tf-surface').length).toBe(1);
-    expect(document.querySelector('.tf-surface').textContent)
-        .toEqual('surface 1');
-
-    expect(document.querySelectorAll('.tf-tab').length).toBe(1);
-    expect(document.querySelector('.tf-tab').textContent).toEqual('tab 1');
-  });
-
-  it('requires a surface name', () => {
-    const visorInstance = visor();
-    expect(() => {
-      // @ts-ignore
-      visorInstance.surface();
-    }).toThrow();
-
-    expect(() => {
-      // @ts-ignore
-      visorInstance.surface('Incorrect Name Param');
-    }).toThrow();
-
-    expect(() => {
-      // @ts-ignore
-      visorInstance.surface({notName: 'Incorrect Name Param'});
-    }).toThrow();
-  });
-
-  it('retrieves a surface', () => {
-    const visorInstance = visor();
-    const s1 = visorInstance.surface({name: 'surface 1', tab: 'tab 1'});
-    expect(document.querySelectorAll('.tf-surface').length).toBe(1);
-    expect(document.querySelector('.tf-surface').textContent)
-        .toEqual('surface 1');
-
-    const s2 = visorInstance.surface({name: 'surface 1', tab: 'tab 1'});
-    expect(document.querySelectorAll('.tf-surface').length).toBe(1);
-    expect(document.querySelector('.tf-surface').textContent)
-        .toEqual('surface 1');
-
-    expect(s1).toEqual(s2);
-  });
-
-  it('adds a surface with the default tab', () => {
-    const visorInstance = visor();
-    visorInstance.surface({name: 'surface1'});
-
-    expect(document.querySelectorAll('.tf-tab').length).toBe(1);
-    expect(document.querySelector('.tf-tab').textContent).toEqual('Visor');
-  });
-
-  it('adds two surfaces', () => {
-    const visorInstance = visor();
-    const s1 = visorInstance.surface({name: 'surface 1', tab: 'tab 1'});
-    const s2 = visorInstance.surface({name: 'surface 2', tab: 'tab 1'});
-
-    expect(s1).not.toEqual(s2);
-
-    const surfaces = document.querySelectorAll('.tf-surface');
-    expect(surfaces.length).toBe(2);
-    expect(document.querySelectorAll('.tf-tab').length).toBe(1);
-
-    expect(surfaces[0].textContent).toEqual('surface 1');
-    expect(surfaces[1].textContent).toEqual('surface 2');
-  });
-
-  it('switches tabs on surface addition', () => {
-    let tabs;
-    const visorInstance = visor();
-
-    visorInstance.surface({name: 'surface 1', tab: 'tab 1'});
-    tabs = document.querySelectorAll('.tf-tab');
-    expect(tabs[0].getAttribute('data-isactive')).toEqual('true');
-
-    visorInstance.surface({name: 'surface 2', tab: 'tab 2'});
-    tabs = document.querySelectorAll('.tf-tab');
-    expect(tabs[1].getAttribute('data-isactive')).toEqual('true');
-    expect(tabs[0].getAttribute('data-isactive')).toBeFalsy();
-
-    visorInstance.surface({name: 'surface 3', tab: 'tab 3'});
-    tabs = document.querySelectorAll('.tf-tab');
-    expect(tabs[2].getAttribute('data-isactive')).toEqual('true');
-    expect(tabs[0].getAttribute('data-isactive')).toBeFalsy();
-    expect(tabs[1].getAttribute('data-isactive')).toBeFalsy();
-  });
-
-  it('closes/opens', async () => {
-    const visorInstance = visor();
-
-    expect(document.querySelector('.visor').getAttribute('data-isopen'))
-        .toBe('true');
-    expect(visorInstance.isOpen()).toBe(true);
-
-    visorInstance.close();
-    await tick();
-    expect(document.querySelector('.visor').getAttribute('data-isopen'))
-        .toBeFalsy();
-    expect(visorInstance.isOpen()).toBe(false);
-
-    visorInstance.open();
-    await tick();
-    expect(document.querySelector('.visor').getAttribute('data-isopen'))
-        .toBe('true');
-    expect(visorInstance.isOpen()).toBe(true);
-  });
-
-  it('toggles', async () => {
-    const visorInstance = visor();
-
-    expect(document.querySelector('.visor').getAttribute('data-isopen'))
-        .toBe('true');
-    expect(visorInstance.isOpen()).toBe(true);
-
-    visorInstance.toggle();
-    await tick();
-    expect(document.querySelector('.visor').getAttribute('data-isopen'))
-        .toBeFalsy();
-    expect(visorInstance.isOpen()).toBe(false);
-
-    visorInstance.toggle();
-    await tick();
-    expect(document.querySelector('.visor').getAttribute('data-isopen'))
-        .toBe('true');
-    expect(visorInstance.isOpen()).toBe(true);
-  });
-
-  it('fullscreen toggles', async () => {
-    const visorInstance = visor();
-    expect(visorInstance.isOpen()).toBe(true);
-
-    expect(document.querySelector('.visor').getAttribute('data-isfullscreen'))
-        .toBeFalsy();
-
-    visorInstance.toggleFullScreen();
-    await tick();
-    expect(document.querySelector('.visor').getAttribute('data-isfullscreen'))
-        .toBe('true');
-
-    visorInstance.toggleFullScreen();
-    await tick();
-    expect(document.querySelector('.visor').getAttribute('data-isfullscreen'))
-        .toBeFalsy();
-  });
-
-  it('sets the active tab', async () => {
-    let tabs;
-    const visorInstance = visor();
-
-    visorInstance.surface({name: 'surface 1', tab: 'tab 1'});
-    visorInstance.surface({name: 'surface 2', tab: 'tab 2'});
-    visorInstance.surface({name: 'surface 2', tab: 'tab 3'});
-
-    tabs = document.querySelectorAll('.tf-tab');
-    expect(tabs[2].getAttribute('data-isactive')).toEqual('true');
-
-    visorInstance.setActiveTab('tab 2');
-    await tick();
-    tabs = document.querySelectorAll('.tf-tab');
-    expect(tabs[1].getAttribute('data-isactive')).toEqual('true');
-
-    visorInstance.setActiveTab('tab 1');
-    await tick();
-    tabs = document.querySelectorAll('.tf-tab');
-    expect(tabs[0].getAttribute('data-isactive')).toEqual('true');
-
-    visorInstance.setActiveTab('tab 3');
-    await tick();
-    tabs = document.querySelectorAll('.tf-tab');
-    expect(tabs[2].getAttribute('data-isactive')).toEqual('true');
-  });
-
-  it('throws error if tab does not exist', () => {
-    const visorInstance = visor();
-
-    visorInstance.surface({name: 'surface 1', tab: 'tab 1'});
-    visorInstance.surface({name: 'surface 2', tab: 'tab 2'});
-
-    expect(() => {
-      visorInstance.setActiveTab('not present');
-    }).toThrow();
-  });
-
-  it('unbinds keyboard handler', () => {
-    const visorInstance = visor();
-
-    const BACKTICK_KEY = 192;
-    const event = document.createEvent('Event');
-    event.initEvent('keydown', true, true);
-    // @ts-ignore
-    event['keyCode'] = BACKTICK_KEY;
-
-    document.dispatchEvent(event);
-    expect(visorInstance.isOpen()).toBe(false);
-    document.dispatchEvent(event);
-    expect(visorInstance.isOpen()).toBe(true);
-
-    // Unbind keys
-    visorInstance.unbindKeys();
-    document.dispatchEvent(event);
-    expect(visorInstance.isOpen()).toBe(true);
-    document.dispatchEvent(event);
-    expect(visorInstance.isOpen()).toBe(true);
-  });
-
-  it('rebinds keyboard handler', () => {
-    const visorInstance = visor();
-
-    const BACKTICK_KEY = 192;
-    const event = document.createEvent('Event');
-    event.initEvent('keydown', true, true);
-    // @ts-ignore
-    event['keyCode'] = BACKTICK_KEY;
-
-    // Unbind keys
-    visorInstance.unbindKeys();
-    document.dispatchEvent(event);
-    expect(visorInstance.isOpen()).toBe(true);
-    document.dispatchEvent(event);
-    expect(visorInstance.isOpen()).toBe(true);
-
-    // rebind keys
-    visorInstance.bindKeys();
-    document.dispatchEvent(event);
-    expect(visorInstance.isOpen()).toBe(false);
-    document.dispatchEvent(event);
-    expect(visorInstance.isOpen()).toBe(true);
-  });
-});
diff --git a/tfjs-master/tfjs-vis/test.html b/tfjs-master/tfjs-vis/test.html
deleted file mode 100644
index 9e1379f72..000000000
--- a/tfjs-master/tfjs-vis/test.html
+++ /dev/null
@@ -1,37 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <title>Hello!</title>
-    <meta charset="utf-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-
-    <!-- import the webpage's stylesheet -->
-    <link rel="stylesheet" href="/style.css">
-
-    <!-- import the webpage's javascript file -->
-    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0"></script>
-    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis@1.4.3"></script>
-    <!-- <script src="./dist/tfjs-vis.umd.min.js"></script> -->
-
-  </head>
-  <body>
-    <h1>Training a model from 2D Data</h1>
-
-    <script>
-      const series1 = Array(100).fill(0)
-        .map(y => Math.random() * 100 - (Math.random() * 50))
-        .map((y, x) => ({ x, y, }));
-
-      const series2 = Array(100).fill(0)
-        .map(y => Math.random() * 100 - (Math.random() * 150))
-        .map((y, x) => ({ x, y, }));
-
-      const series = ['First', 'Second'];
-      const data = { values: [series1, series2], series }
-
-      const surface = { name: 'Scatterplot', tab: 'Charts' };
-      tfvis.render.scatterplot(surface, data);
-    </script>
-  </body>
-</html>
diff --git a/tfjs-master/tfjs-vis/tsconfig.json b/tfjs-master/tfjs-vis/tsconfig.json
deleted file mode 100644
index 3f0276304..000000000
--- a/tfjs-master/tfjs-vis/tsconfig.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
-  "extends": "../tsconfig_base.json",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "module": "commonjs",
-    "inlineSources": true,
-    "target": "es2015",
-    "lib": [
-      "es2015",
-      "dom"
-    ],
-    "noUnusedLocals": false,
-    "strictNullChecks": false,
-    "pretty": true,
-    "experimentalDecorators": true,
-    "jsx": "react",
-    "jsxFactory": "h",
-    "esModuleInterop": true,
-    "skipLibCheck": true,
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs-vis/tslint.json b/tfjs-master/tfjs-vis/tslint.json
deleted file mode 100644
index ec365f164..000000000
--- a/tfjs-master/tfjs-vis/tslint.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "../tslint.json"
-}
diff --git a/tfjs-master/tfjs-vis/webpack.config.js b/tfjs-master/tfjs-vis/webpack.config.js
deleted file mode 100644
index 67acf2407..000000000
--- a/tfjs-master/tfjs-vis/webpack.config.js
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-var path = require('path');
-
-module.exports = {
-  mode: 'production',
-  entry: './dist/index.js',
-  devtool: 'source-map',
-  output: {
-    path: path.resolve(__dirname, 'dist'),
-    filename: 'tfjs-vis.umd.min.js',
-    libraryTarget: 'umd',
-    library: 'tfvis',
-  },
-  optimization: {
-    minimize: true,
-  },
-  externals: {
-    '@tensorflow/tfjs': 'tf',
-  }
-};
diff --git a/tfjs-master/tfjs-vis/yarn.lock b/tfjs-master/tfjs-vis/yarn.lock
deleted file mode 100644
index 29240210a..000000000
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-  version "1.2.0"
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-  integrity sha1-wsNYJlYkfDnqEHyx5mUrb58kUjw=
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-  resolved "https://registry.yarnpkg.com/es6-promisify/-/es6-promisify-5.0.0.tgz#5109d62f3e56ea967c4b63505aef08291c8a5203"
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-    stream-combiner "~0.0.4"
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-  version "3.3.0"
-  resolved "https://registry.yarnpkg.com/events/-/events-3.3.0.tgz#31a95ad0a924e2d2c419a813aeb2c4e878ea7400"
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-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/evp_bytestokey/-/evp_bytestokey-1.0.3.tgz#7fcbdb198dc71959432efe13842684e0525acb02"
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-  version "3.0.2"
-  resolved "https://registry.yarnpkg.com/extend/-/extend-3.0.2.tgz#f8b1136b4071fbd8eb140aff858b1019ec2915fa"
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-  version "3.1.3"
-  resolved "https://registry.yarnpkg.com/fast-deep-equal/-/fast-deep-equal-3.1.3.tgz#3a7d56b559d6cbc3eb512325244e619a65c6c525"
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-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/fast-json-patch/-/fast-json-patch-3.1.1.tgz#85064ea1b1ebf97a3f7ad01e23f9337e72c66947"
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-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz#874bf69c6f404c2b5d99c481341399fd55892633"
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-  version "1.0.12"
-  resolved "https://registry.yarnpkg.com/fastest-levenshtein/-/fastest-levenshtein-1.0.12.tgz#9990f7d3a88cc5a9ffd1f1745745251700d497e2"
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-  version "0.8.17"
-  resolved "https://registry.yarnpkg.com/fbjs/-/fbjs-0.8.17.tgz#c4d598ead6949112653d6588b01a5cdcd9f90fdd"
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-    promise "^7.1.1"
-    setimmediate "^1.0.5"
-    ua-parser-js "^0.7.18"
-
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-  version "7.0.1"
-  resolved "https://registry.yarnpkg.com/fill-range/-/fill-range-7.0.1.tgz#1919a6a7c75fe38b2c7c77e5198535da9acdda40"
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-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/finalhandler/-/finalhandler-1.1.2.tgz#b7e7d000ffd11938d0fdb053506f6ebabe9f587d"
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-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/find-up/-/find-up-4.1.0.tgz#97afe7d6cdc0bc5928584b7c8d7b16e8a9aa5d19"
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-  version "3.2.5"
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-  version "1.14.8"
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-  resolved "https://registry.yarnpkg.com/from/-/from-0.1.7.tgz#83c60afc58b9c56997007ed1a768b3ab303a44fe"
-  integrity sha1-g8YK/Fi5xWmXAH7Rp2izqzA6RP4=
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-  version "10.0.0"
-  resolved "https://registry.yarnpkg.com/fs-extra/-/fs-extra-10.0.0.tgz#9ff61b655dde53fb34a82df84bb214ce802e17c1"
-  integrity sha512-C5owb14u9eJwizKGdchcDUQeFtlSHHthBk8pbX9Vc1PFZrLombudjDnNns88aYslCyF6IY5SUw3Roz6xShcEIQ==
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-    universalify "^2.0.0"
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-  version "8.1.0"
-  resolved "https://registry.yarnpkg.com/fs-extra/-/fs-extra-8.1.0.tgz#49d43c45a88cd9677668cb7be1b46efdb8d2e1c0"
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
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-  version "1.0.0-beta.2"
-  resolved "https://registry.yarnpkg.com/gensync/-/gensync-1.0.0-beta.2.tgz#32a6ee76c3d7f52d46b2b1ae5d93fea8580a25e0"
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-    "@webpack-cli/configtest" "^2.0.1"
-    "@webpack-cli/info" "^2.0.1"
-    "@webpack-cli/serve" "^2.0.1"
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-    commander "^9.4.1"
-    cross-spawn "^7.0.3"
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-    rechoir "^0.8.0"
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-    wildcard "^2.0.0"
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-    "@types/eslint-scope" "^3.7.3"
-    "@types/estree" "^0.0.51"
-    "@webassemblyjs/ast" "1.11.1"
-    "@webassemblyjs/wasm-edit" "1.11.1"
-    "@webassemblyjs/wasm-parser" "1.11.1"
-    acorn "^8.7.1"
-    acorn-import-assertions "^1.7.6"
-    browserslist "^4.14.5"
-    chrome-trace-event "^1.0.2"
-    enhanced-resolve "^5.10.0"
-    es-module-lexer "^0.9.0"
-    eslint-scope "5.1.1"
-    events "^3.2.0"
-    glob-to-regexp "^0.4.1"
-    graceful-fs "^4.2.9"
-    json-parse-even-better-errors "^2.3.1"
-    loader-runner "^4.2.0"
-    mime-types "^2.1.27"
-    neo-async "^2.6.2"
-    schema-utils "^3.1.0"
-    tapable "^2.1.1"
-    terser-webpack-plugin "^5.1.3"
-    watchpack "^2.4.0"
-    webpack-sources "^3.2.3"
-
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-  version "3.6.2"
-  resolved "https://registry.yarnpkg.com/whatwg-fetch/-/whatwg-fetch-3.6.2.tgz#dced24f37f2624ed0281725d51d0e2e3fe677f8c"
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-
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-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
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-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
-which-boxed-primitive@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/which-boxed-primitive/-/which-boxed-primitive-1.0.2.tgz#13757bc89b209b049fe5d86430e21cf40a89a8e6"
-  integrity sha512-bwZdv0AKLpplFY2KZRX6TvyuN7ojjr7lwkg6ml0roIy9YeuSr7JS372qlNW18UQYzgYK9ziGcerWqZOmEn9VNg==
-  dependencies:
-    is-bigint "^1.0.1"
-    is-boolean-object "^1.1.0"
-    is-number-object "^1.0.4"
-    is-string "^1.0.5"
-    is-symbol "^1.0.3"
-
-which-module@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/which-module/-/which-module-2.0.0.tgz#d9ef07dce77b9902b8a3a8fa4b31c3e3f7e6e87a"
-  integrity sha1-2e8H3Od7mQK4o6j6SzHD4/fm6Ho=
-
-which-typed-array@^1.1.2:
-  version "1.1.4"
-  resolved "https://registry.yarnpkg.com/which-typed-array/-/which-typed-array-1.1.4.tgz#8fcb7d3ee5adf2d771066fba7cf37e32fe8711ff"
-  integrity sha512-49E0SpUe90cjpoc7BOJwyPHRqSAd12c10Qm2amdEZrJPCY2NDxaW01zHITrem+rnETY3dwrbH3UUrUwagfCYDA==
-  dependencies:
-    available-typed-arrays "^1.0.2"
-    call-bind "^1.0.0"
-    es-abstract "^1.18.0-next.1"
-    foreach "^2.0.5"
-    function-bind "^1.1.1"
-    has-symbols "^1.0.1"
-    is-typed-array "^1.1.3"
-
-which@^1.2.1, which@^1.2.9:
-  version "1.3.1"
-  resolved "https://registry.yarnpkg.com/which/-/which-1.3.1.tgz#a45043d54f5805316da8d62f9f50918d3da70b0a"
-  integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ==
-  dependencies:
-    isexe "^2.0.0"
-
-which@^2.0.1:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/which/-/which-2.0.2.tgz#7c6a8dd0a636a0327e10b59c9286eee93f3f51b1"
-  integrity sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==
-  dependencies:
-    isexe "^2.0.0"
-
-wildcard@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/wildcard/-/wildcard-2.0.0.tgz#a77d20e5200c6faaac979e4b3aadc7b3dd7f8fec"
-  integrity sha512-JcKqAHLPxcdb9KM49dufGXn2x3ssnfjbcaQdLlfZsL9rH9wgDQjUtDxbo8NE0F6SFvydeu1VhZe7hZuHsB2/pw==
-
-wrap-ansi@^6.2.0:
-  version "6.2.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-6.2.0.tgz#e9393ba07102e6c91a3b221478f0257cd2856e53"
-  integrity sha512-r6lPcBGxZXlIcymEu7InxDMhdW0KDxpLgoFLcguasxCaJ/SOIZwINatK9KY/tf+ZrlywOKU0UDj3ATXUBfxJXA==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
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-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-ws@~8.11.0:
-  version "8.11.0"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-8.11.0.tgz#6a0d36b8edfd9f96d8b25683db2f8d7de6e8e143"
-  integrity sha512-HPG3wQd9sNQoT9xHyNCXoDUa+Xw/VevmY9FoHyQ+g+rrMn4j6FB4np7Z0OhdTgjx6MgQLK7jwSy1YecU1+4Asg==
-
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-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/xtend/-/xtend-4.0.2.tgz#bb72779f5fa465186b1f438f674fa347fdb5db54"
-  integrity sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==
-
-y18n@^4.0.0:
-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-4.0.1.tgz#8db2b83c31c5d75099bb890b23f3094891e247d4"
-  integrity sha512-wNcy4NvjMYL8gogWWYAO7ZFWFfHcbdbE57tZO8e4cbpj8tfUcwrwqSl3ad8HxpYWCdXcJUCeKKZS62Av1affwQ==
-
-y18n@^5.0.5:
-  version "5.0.5"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.5.tgz#8769ec08d03b1ea2df2500acef561743bbb9ab18"
-  integrity sha512-hsRUr4FFrvhhRH12wOdfs38Gy7k2FFzB9qgN9v3aLykRq0dRcdcpz5C9FxdS2NuhOrI/628b/KSTJ3rwHysYSg==
-
-yalc@~1.0.0-pre.50:
-  version "1.0.0-pre.50"
-  resolved "https://registry.yarnpkg.com/yalc/-/yalc-1.0.0-pre.50.tgz#e654e5af5f739cf255eedad1d66ba05a17de78f9"
-  integrity sha512-HGFjFFUhXSpQcxyOwJQl3jhERMn7XBgSCCoJ1k3dDPMbH6n56onv6Cp6cDGMiTho8tLIF4x02/Kb4g6pHavzgA==
-  dependencies:
-    chalk "^4.1.0"
-    detect-indent "^6.0.0"
-    fs-extra "^8.0.1"
-    glob "^7.1.4"
-    ignore "^5.0.4"
-    ini "^2.0.0"
-    npm-packlist "^1.4.1"
-    yargs "^16.1.1"
-
-yallist@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
-  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
-
-yargs-parser@^18.1.1:
-  version "18.1.3"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-18.1.3.tgz#be68c4975c6b2abf469236b0c870362fab09a7b0"
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-  dependencies:
-    camelcase "^5.0.0"
-    decamelize "^1.2.0"
-
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-  version "20.2.7"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.7.tgz#61df85c113edfb5a7a4e36eb8aa60ef423cbc90a"
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-
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-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
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-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yargs@~15.3.1:
-  version "15.3.1"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-15.3.1.tgz#9505b472763963e54afe60148ad27a330818e98b"
-  integrity "sha1-lQW0cnY5Y+VK/mAUitJ6MwgY6Ys= sha512-92O1HWEjw27sBfgmXiixJWT5hRBp2eobqXicLtPBIDBhYB+1HpwZlXmbW2luivBJHBzki+7VyCLRtAkScbTBQA=="
-  dependencies:
-    cliui "^6.0.0"
-    decamelize "^1.2.0"
-    find-up "^4.1.0"
-    get-caller-file "^2.0.1"
-    require-directory "^2.1.1"
-    require-main-filename "^2.0.0"
-    set-blocking "^2.0.0"
-    string-width "^4.2.0"
-    which-module "^2.0.0"
-    y18n "^4.0.0"
-    yargs-parser "^18.1.1"
diff --git a/tfjs-master/tfjs.code-workspace b/tfjs-master/tfjs.code-workspace
deleted file mode 100644
index 174030245..000000000
--- a/tfjs-master/tfjs.code-workspace
+++ /dev/null
@@ -1,126 +0,0 @@
-{
-  "folders": [
-    {
-      "name": "Common",
-      "path": "."
-    },
-    {
-      "name": "Union",
-      "path": "tfjs"
-    },
-    {
-      "name": "E2E",
-      "path": "e2e"
-    },
-    {
-      "name": "Core",
-      "path": "tfjs-core"
-    },
-    {
-      "name": "Cpu",
-      "path": "tfjs-backend-cpu"
-    },
-    {
-      "name": "Webgl",
-      "path": "tfjs-backend-webgl"
-    },
-    {
-      "name": "Node",
-      "path": "tfjs-node"
-    },
-    {
-      "name": "NodeGpu",
-      "path": "tfjs-node-gpu"
-    },
-    {
-      "name": "Wasm",
-      "path": "tfjs-backend-wasm"
-    },
-    {
-      "name": "NodeGL",
-      "path": "tfjs-backend-nodegl"
-    },
-    {
-      "name": "WebGPU",
-      "path": "tfjs-backend-webgpu"
-    },
-    {
-      "name": "Converter",
-      "path": "tfjs-converter"
-    },
-    {
-      "name": "Layers",
-      "path": "tfjs-layers"
-    },
-    {
-      "name": "Data",
-      "path": "tfjs-data"
-    },
-    {
-      "name": "ReactNative",
-      "path": "tfjs-react-native"
-    },
-    {
-      "name": "TFDecisionForests",
-      "path": "tfjs-tfdf"
-    },
-    {
-      "name": "TFLite",
-      "path": "tfjs-tflite"
-    },
-    {
-      "name": "Vis",
-      "path": "tfjs-vis"
-    },
-    {
-      "name": "AutoML",
-      "path": "tfjs-automl"
-    },
-    {
-      "name": "InferenceAPI",
-      "path": "tfjs-inference"
-    }
-  ],
-  "settings": {
-    "search.exclude": {
-      "**/node_modules": true,
-      "**/coverage/": true,
-      "**/dist/": true,
-      "**/yarn.lock": true,
-      "**/.rpt2_cache/": true,
-      "**/.yalc/**/*": true,
-      "**/.cache/**/*": true
-    },
-    "tslint.configFile": "tslint.json",
-    "files.trimTrailingWhitespace": true,
-    "editor.tabSize": 2,
-    "editor.insertSpaces": true,
-    "[typescript]": {
-      "editor.formatOnSave": true
-    },
-    "[javascript]": {
-      "editor.formatOnSave": true
-    },
-    "[cpp]": {
-      "editor.formatOnSave": true
-    },
-    "editor.defaultFormatter": "xaver.clang-format",
-    "editor.rulers": [80],
-    "clang-format.style": "Google",
-    "files.insertFinalNewline": true,
-    "editor.detectIndentation": false,
-    "editor.wrappingIndent": "none",
-    "typescript.tsdk": "${workspaceRoot}/node_modules/typescript/lib",
-    "clang-format.executable": "${workspaceRoot}/node_modules/.bin/clang-format"
-  },
-  "extensions": {
-    "recommendations": [
-      // Formats typescript, javascript and c++ code.
-      "xaver.clang-format",
-      // Lints typescipt code.
-      "ms-vscode.vscode-typescript-tslint-plugin",
-      // Running custom linters on save.
-      "emeraldwalk.RunOnSave"
-    ]
-  }
-}
diff --git a/tfjs-master/tfjs/.npmignore b/tfjs-master/tfjs/.npmignore
deleted file mode 100644
index 7d6ae300b..000000000
--- a/tfjs-master/tfjs/.npmignore
+++ /dev/null
@@ -1,23 +0,0 @@
-.vscode/
-.rpt2_cache/
-src/**/*_test.ts
-package/
-scripts/
-integration_tests/
-coverage/
-**/node_modules/
-karma.conf.js
-tensorflow-tfjs-*.tgz
-dist/**/*_test.js
-dist/**/*_test.d.ts
-cloudbuild.yml
-CONTRIBUTING.md
-tslint.json
-yarn.lock
-tsconfig.json
-DEVELOPMENT.md
-ISSUE_TEMPLATE.md
-GALLERY.md
-rollup.config.js
-PULL_REQUEST_TEMPLATE.md
-.yalc/
diff --git a/tfjs-master/tfjs/cloudbuild.yml b/tfjs-master/tfjs/cloudbuild.yml
deleted file mode 100644
index 9f8f1d336..000000000
--- a/tfjs-master/tfjs/cloudbuild.yml
+++ /dev/null
@@ -1,63 +0,0 @@
-steps:
-
-# Install common dependencies.
-- name: 'gcr.io/learnjs-174218/release'
-  id: 'yarn-common'
-  entrypoint: 'yarn'
-  args: ['install']
-
-# Install packages.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs'
-  entrypoint: 'yarn'
-  id: 'yarn'
-  args: ['install']
-  waitFor: ['yarn-common']
-
-# Build deps.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs'
-  entrypoint: 'yarn'
-  id: 'build-deps'
-  args: ['build-deps-ci']
-  waitFor: ['yarn-common']
-
-# Build.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs'
-  entrypoint: 'yarn'
-  id: 'build'
-  args: ['build-ci']
-  waitFor: ['yarn', 'build-deps']
-
-# Lint.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs'
-  entrypoint: 'yarn'
-  id: 'lint'
-  args: ['lint']
-  waitFor: ['yarn', 'build-deps']
-
-# Run tfjs tests.
-- name: 'gcr.io/learnjs-174218/release'
-  dir: 'tfjs'
-  entrypoint: 'yarn'
-  id: 'test'
-  args: ['test-ci']
-  waitFor: ['yarn', 'build-deps', 'lint']
-  env: ['BROWSERSTACK_USERNAME=deeplearnjs1', 'NIGHTLY=$_NIGHTLY']
-  secretEnv: ['BROWSERSTACK_KEY', 'FIREBASE_KEY']
-
-secrets:
-- kmsKeyName: projects/learnjs-174218/locations/global/keyRings/tfjs/cryptoKeys/enc
-  secretEnv:
-    BROWSERSTACK_KEY: CiQAkwyoIW0LcnxymzotLwaH4udVTQFBEN4AEA5CA+a3+yflL2ASPQAD8BdZnGARf78MhH5T9rQqyz9HNODwVjVIj64CTkFlUCGrP1B2HX9LXHWHLmtKutEGTeFFX9XhuBzNExA=
-    FIREBASE_KEY: CiQAkwyoIXmET39jOD3ywloCIa6+WUpu3w49twpMmkMqy0vS+YsSUAAD8BdZQGOL8FKEBxr/1jl0G78OigwlNVHjD3usZobNtlOp8tV/9iacb8zPFqy0SwIO1gvz3HRr+VU7c7LS2qqaTCdacZF+dx3VJNewvdZu
-timeout: 1800s
-logsBucket: 'gs://tfjs-build-logs'
-substitutions:
-  _NIGHTLY: ''
-options:
-  logStreamingOption: 'STREAM_ON'
-  machineType: 'N1_HIGHCPU_8'
-  substitution_option: 'ALLOW_LOOSE'
diff --git a/tfjs-master/tfjs/karma.conf.js b/tfjs-master/tfjs/karma.conf.js
deleted file mode 100644
index 3938f020f..000000000
--- a/tfjs-master/tfjs/karma.conf.js
+++ /dev/null
@@ -1,88 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const karmaTypescriptConfig = {
-  tsconfig: 'tsconfig.test.json',
-  // Disable coverage reports and instrumentation by default for tests
-  coverageOptions: {instrumentation: false},
-  reports: {},
-  bundlerOptions: {
-    acornOptions: {ecmaVersion: 8},
-    transforms: [
-      require('karma-typescript-es6-transform')({
-        presets: [
-          // ensure we get es5 by adding IE 11 as a target
-          ['@babel/env', {'targets': {'ie': '11'}, 'loose': true}]
-        ]
-      }),
-    ]
-  }
-};
-
-// Enable coverage reports and instrumentation under KARMA_COVERAGE=1 env
-const coverageEnabled = !!process.env.KARMA_COVERAGE;
-if (coverageEnabled) {
-  karmaTypescriptConfig.coverageOptions.instrumentation = true;
-  karmaTypescriptConfig.coverageOptions.exclude = /_test\.ts$/;
-  karmaTypescriptConfig.reports = {html: 'coverage', 'text-summary': ''};
-}
-
-module.exports = function(config) {
-  config.set({
-    frameworks: ['jasmine', 'karma-typescript'],
-    files: [
-      {pattern: './node_modules/@babel/polyfill/dist/polyfill.js'},
-      {pattern: 'src/**/*.ts'}
-    ],
-    preprocessors: {
-      '**/*.ts': ['karma-typescript'],  // *.tsx for React Jsx
-    },
-    karmaTypescriptConfig,
-    reporters: ['progress', 'karma-typescript'],
-    browsers: ['Chrome'],
-    port: 9200,
-    browserStack: {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_KEY,
-      tunnelIdentifier:
-          `tfjs_union_${Date.now()}_${Math.floor(Math.random() * 1000)}`
-    },
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 30000,
-    customLaunchers: {
-      bs_chrome_mac: {
-        base: 'BrowserStack',
-        browser: 'chrome',
-        browser_version: 'latest',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      bs_firefox_mac: {
-        base: 'BrowserStack',
-        browser: 'firefox',
-        browser_version: '90',
-        os: 'OS X',
-        os_version: 'High Sierra'
-      },
-      chrome_with_swift_shader: {
-        base: 'Chrome',
-        flags: ['--blacklist-accelerated-compositing', '--blacklist-webgl']
-      }
-    },
-    client: {args: ['--grep', config.grep || '']}
-  });
-};
diff --git a/tfjs-master/tfjs/package.json b/tfjs-master/tfjs/package.json
deleted file mode 100644
index 6767e435b..000000000
--- a/tfjs-master/tfjs/package.json
+++ /dev/null
@@ -1,95 +0,0 @@
-{
-  "name": "@tensorflow/tfjs",
-  "version": "0.0.0",
-  "description": "An open-source machine learning framework.",
-  "private": false,
-  "main": "dist/tf.node.js",
-  "module": "dist/index.js",
-  "jsdelivr": "dist/tf.min.js",
-  "unpkg": "dist/tf.min.js",
-  "types": "dist/index.d.ts",
-  "miniprogram": "dist/miniprogram",
-  "bin": {
-    "tfjs-custom-module": "dist/tools/custom_module/cli.js"
-  },
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/tensorflow/tfjs.git"
-  },
-  "license": "Apache-2.0",
-  "devDependencies": {
-    "@babel/core": "^7.21.3",
-    "@babel/polyfill": "^7.12.1",
-    "@babel/preset-env": "^7.20.2",
-    "@rollup/plugin-babel": "^6.0.3",
-    "@rollup/plugin-commonjs": "^24.1.0",
-    "@rollup/plugin-node-resolve": "^15.0.2",
-    "@rollup/plugin-typescript": "^11.1.0",
-    "@types/argparse": "^1.0.38",
-    "@types/jasmine": "2.8.7",
-    "@types/node": "~10.17.50",
-    "@types/shelljs": "^0.8.4",
-    "@types/yargs": "^15.0.7",
-    "clang-format": "~1.2.2",
-    "commander": "~2.14.1",
-    "jasmine": "3.1.0",
-    "jasmine-core": "~3.1.0",
-    "karma": "~6.3.2",
-    "karma-browserstack-launcher": "~1.6.0",
-    "karma-chrome-launcher": "~2.2.0",
-    "karma-firefox-launcher": "~1.1.0",
-    "karma-jasmine": "~1.1.1",
-    "karma-typescript": "~5.5.1",
-    "karma-typescript-es6-transform": "^5.5.1",
-    "npm-run-all": "~4.1.3",
-    "rimraf": "~2.6.2",
-    "rollup": "~3.20.2",
-    "rollup-plugin-terser": "~7.0.2",
-    "rollup-plugin-visualizer": "~5.9.0",
-    "shelljs": "~0.8.1",
-    "ts-node": "~8.8.2",
-    "tslint": "~5.11.0",
-    "tslint-no-circular-imports": "~0.5.0",
-    "typescript": "5.0.4",
-    "yalc": "1.0.0-pre.50"
-  },
-  "scripts": {
-    "build": "tsc && yarn build-cli && yarn bundle",
-    "build-ci": "tsc && yarn build-cli && yarn bundle-ci",
-    "bundle": "rollup -c",
-    "bundle-ci": "rollup -c --ci",
-    "build-link-package": "cd ../link-package && yarn build-deps-for tfjs",
-    "build-deps": "yarn build-link-package",
-    "build-deps-ci": "yarn build-deps",
-    "build-cli": "tsc --project ./tools/custom_module/tsconfig.json && chmod +x ./dist/tools/custom_module/cli.js",
-    "run-custom-build": "ts-node -s ./tools/custom_module/cli.ts",
-    "build-npm": "./scripts/build-npm.sh",
-    "link-local": "yalc link",
-    "publish-local": "yarn build-npm && yalc push",
-    "publish-npm": "npm publish",
-    "lint": "tslint -p . -t verbose",
-    "coverage": "KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",
-    "test": "yarn && yarn build-deps && yarn build && karma start",
-    "test-dev": "karma start",
-    "test-tools": "ts-node --skip-ignore --project ./tools/custom_module/tsconfig.json run_tools_tests.ts",
-    "test-ci": "./scripts/test-ci.sh"
-  },
-  "dependencies": {
-    "@tensorflow/tfjs-backend-cpu": "link:../link-package/node_modules/@tensorflow/tfjs-backend-cpu",
-    "@tensorflow/tfjs-backend-webgl": "link:../link-package/node_modules/@tensorflow/tfjs-backend-webgl",
-    "@tensorflow/tfjs-converter": "link:../link-package/node_modules/@tensorflow/tfjs-converter",
-    "@tensorflow/tfjs-core": "link:../link-package/node_modules/@tensorflow/tfjs-core",
-    "@tensorflow/tfjs-data": "link:../link-package/node_modules/@tensorflow/tfjs-data",
-    "@tensorflow/tfjs-layers": "link:../link-package/node_modules/@tensorflow/tfjs-layers",
-    "argparse": "^1.0.10",
-    "chalk": "^4.1.0",
-    "core-js": "3.29.1",
-    "regenerator-runtime": "^0.13.5",
-    "yargs": "^16.0.3"
-  },
-  "browser": {
-    "node-fetch": false,
-    "util": false,
-    "crypto": false
-  }
-}
diff --git a/tfjs-master/tfjs/rollup.config.mjs b/tfjs-master/tfjs/rollup.config.mjs
deleted file mode 100644
index 44241cd65..000000000
--- a/tfjs-master/tfjs/rollup.config.mjs
+++ /dev/null
@@ -1,219 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import commonjs from '@rollup/plugin-commonjs';
-import resolve from '@rollup/plugin-node-resolve';
-import typescript from '@rollup/plugin-typescript';
-import babel from '@rollup/plugin-babel';
-import {terser} from 'rollup-plugin-terser';
-import {visualizer} from 'rollup-plugin-visualizer';
-
-const PREAMBLE = `/**
- * @license
- * Copyright ${(new Date).getFullYear()} Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */`;
-
-function config({
-  plugins = [],
-  output = {},
-  external = [],
-  visualize = false,
-  tsCompilerOptions = {},
-  entry = 'src/index.ts'
-}) {
-  if (visualize) {
-    const filename = output.file + '.html';
-    plugins.push(visualizer({sourcemap: true, filename}));
-    console.log(`Will output a bundle visualization in ${filename}`);
-  }
-
-  const defaultTsOptions = {
-    include: ['src/**/*.ts'],
-    module: 'ES2015',
-  };
-  const tsoptions = Object.assign({}, defaultTsOptions, tsCompilerOptions);
-
-  return {
-    input: entry,
-    // Do not tree shake union package library
-    treeshake: false,
-    plugins: [
-      typescript(tsoptions),
-      resolve({dedupe: ['seedrandom', 'long']}),
-      commonjs({
-        ignore: ['crypto', 'fs', 'node-fetch', 'string_decoder', 'util'],
-        include: 'node_modules/**',
-      }),
-      ...plugins,
-    ],
-    output: {
-      banner: PREAMBLE,
-      globals: {
-        'node-fetch': 'nodeFetch',
-      },
-      sourcemap: true,
-      ...output
-    },
-    external: [
-      // node-fetch is only used in node. Browsers have native "fetch".
-      'node-fetch', 'crypto', ...external
-    ],
-    onwarn: warning => {
-      let {code} = warning;
-      if (code === 'CIRCULAR_DEPENDENCY' || code === 'CIRCULAR' ||
-          code === 'THIS_IS_UNDEFINED') {
-        return;
-      }
-      console.warn('WARNING: ', warning.toString());
-    }
-  };
-}
-
-export default function(cmdOptions) {
-  const bundles = [];
-
-  const babelPlugin = babel({
-    babelrc: false,
-    babelHelpers: 'bundled',
-    presets: [
-      // ensure we get es5 by adding IE 11 as a target
-      [
-        '@babel/env', {
-          modules: false,
-          useBuiltIns: 'entry',
-          corejs: '3.29.1',
-          targets: {'ie': '11'},
-        }
-      ]
-    ]
-  });
-
-  const terserPlugin = terser({output: {preamble: PREAMBLE, comments: false}});
-  const name = 'tf';
-  const extend = true;
-  const umdFormat = 'umd';
-  const fesmFormat = 'es';
-  const fileName = 'tf';
-
-  // Node
-  bundles.push(config({
-    output: {
-      format: 'cjs',
-      name,
-      extend,
-      file: `dist/${fileName}.node.js`,
-      freeze: false
-    },
-    tsCompilerOptions: {target: 'es5'},
-    external: [
-      'long',
-      '@tensorflow/tfjs-core',
-      '@tensorflow/tfjs-layers',
-      '@tensorflow/tfjs-converter',
-      '@tensorflow/tfjs-data',
-      '@tensorflow/tfjs-backend-cpu',
-      '@tensorflow/tfjs-backend-webgl',
-    ]
-  }));
-
-  if (cmdOptions.ci || cmdOptions.npm) {
-    // UMD ES5
-    bundles.push(config({
-      entry: 'src/index_with_polyfills.ts',
-      plugins: [babelPlugin, terserPlugin],
-      output: {
-        format: umdFormat,
-        name,
-        extend,
-        file: `dist/${fileName}.min.js`,
-        freeze: false
-      },
-      tsCompilerOptions: {target: 'es5'},
-      visualize: cmdOptions.visualize
-    }));
-  }
-
-  if (cmdOptions.npm) {
-    // UMD ES5
-    bundles.push(config({
-      entry: 'src/index_with_polyfills.ts',
-      plugins: [babelPlugin],
-      output: {
-        format: umdFormat,
-        name,
-        extend,
-        file: `dist/${fileName}.js`,
-        freeze: false
-      },
-      tsCompilerOptions: {target: 'es5'}
-    }));
-
-    // UMD ES2017
-    bundles.push(config({
-      output:
-          {format: umdFormat, name, extend, file: `dist/${fileName}.es2017.js`},
-      tsCompilerOptions: {target: 'es2017'}
-    }));
-
-    // UMD ES2017 minified
-    bundles.push(config({
-      plugins: [terserPlugin],
-      output: {
-        format: umdFormat,
-        name,
-        extend,
-        file: `dist/${fileName}.es2017.min.js`
-      },
-      tsCompilerOptions: {target: 'es2017'},
-      visualize: cmdOptions.visualize
-    }));
-
-    // FESM ES2017
-    bundles.push(config({
-      output:
-          {format: fesmFormat, name, extend, file: `dist/${fileName}.fesm.js`},
-      tsCompilerOptions: {target: 'es2017'}
-    }));
-
-    // FESM ES2017 minified
-    bundles.push(config({
-      plugins: [terserPlugin],
-      output: {
-        format: fesmFormat,
-        name,
-        extend,
-        file: `dist/${fileName}.fesm.min.js`
-      },
-      tsCompilerOptions: {target: 'es2017'},
-      visualize: cmdOptions.visualize
-    }));
-  }
-
-  return bundles;
-};
diff --git a/tfjs-master/tfjs/run_tools_tests.ts b/tfjs-master/tfjs/run_tools_tests.ts
deleted file mode 100644
index cd7e46bc9..000000000
--- a/tfjs-master/tfjs/run_tools_tests.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line:no-require-imports
-const jasmineCtor = require('jasmine');
-// tslint:disable-next-line:no-require-imports
-
-Error.stackTraceLimit = Infinity;
-
-process.on('unhandledRejection', e => {
-  throw e;
-});
-const toolsTests = './tools/**/*_test.ts';
-
-const runner = new jasmineCtor();
-runner.loadConfig({spec_files: [toolsTests], random: false});
-
-runner.execute();
diff --git a/tfjs-master/tfjs/scripts/build-npm.sh b/tfjs-master/tfjs/scripts/build-npm.sh
deleted file mode 100644
index 451504428..000000000
--- a/tfjs-master/tfjs/scripts/build-npm.sh
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2018 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Exit immediately if a command exits with a non-zero status.
-set -e
-
-yarn rimraf dist/
-yarn
-yarn build-ci
-yarn rollup -c --visualize --npm
-
-# Use minified files for miniprogram
-mkdir dist/miniprogram
-cp dist/tf.min.js dist/miniprogram/index.js
-cp dist/tf.min.js.map dist/miniprogram/index.js.map
-
-# Copy the root README over.
-cp ../README.md .
-
-echo "Stored standalone library at dist/tf(.min).js"
-npm pack
diff --git a/tfjs-master/tfjs/scripts/test-ci.sh b/tfjs-master/tfjs/scripts/test-ci.sh
deleted file mode 100644
index 2f9931960..000000000
--- a/tfjs-master/tfjs/scripts/test-ci.sh
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2019 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# Exit immediately if a command exits with a non-zero status.
-set -e
-
-node ../scripts/run_flaky.js "yarn karma start --browsers='bs_firefox_mac' --singleRun"
-node ../scripts/run_flaky.js "yarn karma start --browsers='bs_chrome_mac' --singleRun"
-yarn test-tools
-
-# If these are re-enabled, note that they may run multiple browsers per karma
-# instance.
-# cd integration_tests
-# yarn benchmark-cloud
-# Reinstall the following line once https://github.com/tensorflow/tfjs/pull/1663
-# is resolved.
-# yarn benchmark --layers --tfjs-node
-# TODO(lina) update this to work against head.
-# yarn validate-converter --tfjs-node
-# cd ../../
diff --git a/tfjs-master/tfjs/src/index.ts b/tfjs-master/tfjs/src/index.ts
deleted file mode 100644
index ad465c9f2..000000000
--- a/tfjs-master/tfjs/src/index.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import '@tensorflow/tfjs-core';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/register_all_gradients';
-// tslint:disable-next-line: no-imports-from-dist
-import '@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops';
-
-export * from '@tensorflow/tfjs-core';
-export * from '@tensorflow/tfjs-layers';
-export * from '@tensorflow/tfjs-converter';
-
-// Export data api as tf.data
-import * as data from '@tensorflow/tfjs-data';
-export {data};
-
-// Import and register backends.
-export * from '@tensorflow/tfjs-backend-cpu';
-export * from '@tensorflow/tfjs-backend-webgl';
-
-// Import versions of all sub-packages.
-import {version_core} from '@tensorflow/tfjs-core';
-import {version_cpu} from '@tensorflow/tfjs-backend-cpu';
-import {version_webgl} from '@tensorflow/tfjs-backend-webgl';
-import {version_data} from '@tensorflow/tfjs-data';
-import {version_layers} from '@tensorflow/tfjs-layers';
-import {version_converter} from '@tensorflow/tfjs-converter';
-import {version as version_union} from './version';
-
-export const version = {
-  'tfjs-core': version_core,
-  'tfjs-backend-cpu': version_cpu,
-  'tfjs-backend-webgl': version_webgl,
-  'tfjs-data': version_data,
-  'tfjs-layers': version_layers,
-  'tfjs-converter': version_converter,
-  'tfjs': version_union
-};
diff --git a/tfjs-master/tfjs/src/index_test.ts b/tfjs-master/tfjs/src/index_test.ts
deleted file mode 100644
index 67796bb84..000000000
--- a/tfjs-master/tfjs/src/index_test.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-/**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as tf from './index';
-
-describe('Union package', () => {
-  it('has core ops', () => {
-    expect(tf.matMul).not.toBeNull();
-    expect(tf.tensor).not.toBeNull();
-    expect(tf.scalar).not.toBeNull();
-    expect(tf.square).not.toBeNull();
-  });
-
-  it('has layers', () => {
-    expect(tf.sequential).not.toBeNull();
-    expect(tf.model).not.toBeNull();
-    expect(tf.layers.dense).not.toBeNull();
-  });
-
-  it('has converter', () => {
-    expect(tf.GraphModel).not.toBeNull();
-    expect(tf.loadGraphModel).not.toBeNull();
-  });
-
-  it('has data', () => {
-    expect(tf.data.csv).not.toBeNull();
-    expect(tf.data.zip).not.toBeNull();
-  });
-
-  it('version', () => {
-    // tslint:disable-next-line:no-require-imports
-    const expected = require('../package.json').version;
-    expect(tf.version.tfjs).toBe(expected);
-    expect(tf.version['tfjs-core']).not.toBeNull();
-    expect(tf.version['tfjs-converter']).not.toBeNull();
-    expect(tf.version['tfjs-data']).not.toBeNull();
-    expect(tf.version['tfjs-layers']).not.toBeNull();
-  });
-
-  it('has cpu backend', () => {
-    const backend = tf.findBackend('cpu');
-    expect(backend).not.toBeNull();
-  });
-
-  it('has webgl backend', () => {
-    const backend = tf.findBackend('webgl');
-    expect(backend).not.toBeNull();
-  });
-
-  it('cpu backend constructor is exposed', () => {
-    expect(tf.MathBackendCPU).not.toBeNull();
-  });
-
-  it('webgl backend constructor is exposed', () => {
-    expect(tf.MathBackendWebGL).not.toBeNull();
-  });
-});
diff --git a/tfjs-master/tfjs/src/index_with_polyfills.ts b/tfjs-master/tfjs/src/index_with_polyfills.ts
deleted file mode 100644
index da60e4854..000000000
--- a/tfjs-master/tfjs/src/index_with_polyfills.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// This file is used to build a union bundle that has polyfills for ES5 support.
-import 'core-js/stable';
-import 'regenerator-runtime/runtime';
-export * from './index';
diff --git a/tfjs-master/tfjs/src/version.ts b/tfjs-master/tfjs/src/version.ts
deleted file mode 100644
index 5fa574e7d..000000000
--- a/tfjs-master/tfjs/src/version.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/** @license See the LICENSE file. */
-
-// This code is auto-generated, do not modify this file!
-const version = '0.0.0';
-export {version};
diff --git a/tfjs-master/tfjs/tools/custom_module/cli.ts b/tfjs-master/tfjs/tools/custom_module/cli.ts
deleted file mode 100644
index be258dead..000000000
--- a/tfjs-master/tfjs/tools/custom_module/cli.ts
+++ /dev/null
@@ -1,143 +0,0 @@
-#!/usr/bin/env node
-
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-/**
- * Entry point for cli tool to build custom tfjs modules
- */
-import * as fs from 'fs';
-import * as path from 'path';
-import * as yargs from 'yargs';
-
-import {OP_SCOPE_SUFFIX} from '@tensorflow/tfjs-core';
-
-import {bail} from './util';
-import {CustomTFJSBundleConfig, SupportedBackends, ModuleProvider} from './types';
-import {getModuleProvider} from './esm_module_provider';
-
-// Will be configured when loading the config file.
-let moduleProvider: ModuleProvider;
-
-const BASE_PATH = process.env.BASE_PATH || process.cwd();
-
-const DEFAULT_CUSTOM_BUNDLE_ARGS: Partial<CustomTFJSBundleConfig> = {
-  entries: [],
-  models: [],
-  kernels: [],
-  forwardModeOnly: true,
-  backends: ['cpu', 'webgl'],
-  moduleOptions: {},
-};
-
-const argParser = yargs.options({
-  config: {
-    description: 'Path to custom module config file.',
-    type: 'string',
-    demandOption: true
-  }
-});
-
-const args = argParser.argv;
-
-
-
-function validateArgs(): CustomTFJSBundleConfig {
-  let configFilePath = args.config;
-  if (configFilePath == null) {
-    bail(`Error: no config file passed`);
-  }
-
-  configFilePath = path.resolve(BASE_PATH, configFilePath);
-
-  if (!fs.existsSync(configFilePath)) {
-    bail(`Error: config file does not exist at ${configFilePath}`);
-  }
-  let config;
-  try {
-    config = JSON.parse(fs.readFileSync(configFilePath, 'utf-8'));
-  } catch (error) {
-    bail(`Error could not read/parse JSON config file. \n ${error.message}`);
-  }
-
-  if (config.outputPath == null) {
-    bail('Error: config must specify "outputPath" property');
-  }
-
-  console.log(`Using custom module configuration from ${configFilePath}.`);
-
-  const finalConfig = Object.assign({}, DEFAULT_CUSTOM_BUNDLE_ARGS, config);
-
-  if (finalConfig.entries.length !== 0) {
-    bail('Error: config.entries not yet supported');
-  }
-
-  // if (finalConfig.models.length !== 0) {
-  // TODO validate that all these paths exist.
-  // bail('Error: config.models not yet supported');
-  // }
-
-  for (const requestedBackend of finalConfig.backends) {
-    if (requestedBackend !== SupportedBackends.cpu &&
-        requestedBackend !== SupportedBackends.webgl &&
-        requestedBackend !== SupportedBackends.wasm) {
-      bail(`Error: Unsupported backend specified '${requestedBackend}'`);
-    }
-  }
-
-  // Normalize the paths to absolute paths.
-  function normalizePath(p: string) {
-    return path.resolve(BASE_PATH, p);
-  }
-
-  finalConfig.models = finalConfig.models.map(normalizePath);
-  finalConfig.entries = finalConfig.entries.map(normalizePath);
-  finalConfig.normalizedOutputPath = normalizePath(finalConfig.outputPath);
-
-  moduleProvider = getModuleProvider(finalConfig.moduleOptions);
-
-  console.log('Final Configuration', finalConfig);
-
-  return finalConfig;
-}
-
-function getKernelNamesForConfig(config: CustomTFJSBundleConfig) {
-  // Later on this will do a union of kernels from entries, models and
-  // kernels, (and kernels used by the converter itself) Currently we only
-  // support directly listing kernels. remember that this also needs to handle
-  // kernels used by gradients if forwardModeOnly is false.
-
-  // Ops in core that are implemented as custom ops may appear in tf.profile
-  // they will have __op as a suffix. These do not have corresponding backend
-  // kernels so we need to filter them out.
-  function isNotCustomOp(kernelName: string) {
-    // opSuffix value is defined in tfjs-core/src/operation.ts
-    // duplicating it here to avoid an export.
-    return !kernelName.endsWith(OP_SCOPE_SUFFIX);
-  }
-
-  return config.kernels.filter(isNotCustomOp);
-}
-
-const customConfig = validateArgs();
-const kernelsToInclude = getKernelNamesForConfig(customConfig);
-customConfig.kernels = kernelsToInclude;
-if (moduleProvider != null) {
-  moduleProvider.produceCustomTFJSModule(customConfig);
-} else {
-  throw new Error('No module provider has been initialized.');
-}
diff --git a/tfjs-master/tfjs/tools/custom_module/cli_test.ts b/tfjs-master/tfjs/tools/custom_module/cli_test.ts
deleted file mode 100644
index 61113f349..000000000
--- a/tfjs-master/tfjs/tools/custom_module/cli_test.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as fs from 'fs';
-import * as path from 'path';
-
-describe('CLI binary', () => {
-  it('should be present and executable', () => {
-    const packageJsonPath = path.resolve(
-        __dirname,
-        '../../package.json',
-    );
-    const packageJson = JSON.parse(fs.readFileSync(packageJsonPath, 'utf-8'));
-    const binEntry = packageJson.bin;
-    expect(binEntry['tfjs-custom-module']).toBeDefined();
-
-    const toolPath = path.resolve(
-        path.dirname(packageJsonPath), binEntry['tfjs-custom-module']);
-
-    expect(() => {
-      fs.accessSync(toolPath, fs.constants.X_OK);
-    }).not.toThrow();
-  });
-});
diff --git a/tfjs-master/tfjs/tools/custom_module/custom_module.ts b/tfjs-master/tfjs/tools/custom_module/custom_module.ts
deleted file mode 100644
index 92f3bd2c4..000000000
--- a/tfjs-master/tfjs/tools/custom_module/custom_module.ts
+++ /dev/null
@@ -1,126 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {CustomModuleFiles, CustomTFJSBundleConfig, ImportProvider} from './types';
-import {getPreamble} from './util';
-
-export function getCustomModuleString(
-    config: CustomTFJSBundleConfig,
-    moduleProvider: ImportProvider,
-    ): CustomModuleFiles {
-  const {kernels, backends, forwardModeOnly, models} = config;
-  const tfjs: string[] = [getPreamble()];
-
-  // A custom tfjs module
-  addLine(tfjs, moduleProvider.importCoreStr(forwardModeOnly));
-  if (models.length > 0) {
-    // A model.json has been passed.
-    addLine(tfjs, moduleProvider.importConverterStr());
-  }
-
-  for (const backend of backends) {
-    addLine(tfjs, `\n//backend = ${backend}`);
-    addLine(tfjs, moduleProvider.importBackendStr(backend));
-    for (const kernelName of kernels) {
-      const kernelImport = moduleProvider.importKernelStr(kernelName, backend);
-      if (!moduleProvider.validateImportPath(kernelImport.importPath)) {
-        console.warn(
-            'WARNING:',
-            `Import path '${
-                kernelImport.importPath}' cannot be resolved. Skipping...`);
-        continue;
-      }
-      addLine(tfjs, kernelImport.importStatement);
-      addLine(tfjs, registerKernelStr(kernelImport.kernelConfigId));
-    }
-  }
-
-  if (!forwardModeOnly) {
-    addLine(tfjs, `\n//Gradients`);
-    for (const kernelName of kernels) {
-      const gradImport = moduleProvider.importGradientConfigStr(kernelName);
-      if (!moduleProvider.validateImportPath(gradImport.importPath)) {
-        console.warn(
-            'WARNING:',
-            `Import path '${
-                gradImport.importPath}' cannot be resolved. Skipping...`);
-        continue;
-      }
-      addLine(tfjs, gradImport.importStatement);
-      addLine(tfjs, registerGradientConfigStr(gradImport.gradConfigId));
-    }
-  }
-
-  // A custom tfjs core module for imports within tfjs packages
-  const core: string[] = [getPreamble()];
-  addLine(core, moduleProvider.importCoreStr(forwardModeOnly));
-  return {
-    core: core.join('\n'),
-    tfjs: tfjs.join('\n'),
-  };
-}
-
-export function getCustomConverterOpsModule(
-    ops: string[], moduleProvider: ImportProvider): string {
-  const result: string[] = ['// This file is autogenerated\n'];
-
-  // Separate namespaced apis from non namespaced ones as they require a
-  // different export pattern that treats each namespace as a whole.
-
-  const flatOps = [];
-  const namespacedOps: {[key: string]: string[]} = {};
-
-  for (const opSymbol of ops) {
-    if (opSymbol.match(/\./)) {
-      const parts = opSymbol.split(/\./);
-      const namespace = parts[0];
-      const opName = parts[1];
-
-      if (namespacedOps[namespace] == null) {
-        namespacedOps[namespace] = [];
-      }
-      namespacedOps[namespace].push(opName);
-    } else {
-      flatOps.push(opSymbol);
-    }
-  }
-
-  // Group the namespaced symbols by namespace
-  for (const namespace of Object.keys(namespacedOps)) {
-    const opSymbols = namespacedOps[namespace];
-    result.push(moduleProvider.importNamespacedOpsForConverterStr(
-        namespace, opSymbols));
-  }
-
-  for (const opSymbol of flatOps) {
-    result.push(moduleProvider.importOpForConverterStr(opSymbol));
-  }
-
-  return result.join('\n');
-}
-
-function addLine(target: string[], line: string) {
-  target.push(line);
-}
-
-function registerKernelStr(kernelConfigId: string) {
-  return `registerKernel(${kernelConfigId});`;
-}
-
-function registerGradientConfigStr(gradConfigId: string) {
-  return `registerGradient(${gradConfigId});`;
-}
diff --git a/tfjs-master/tfjs/tools/custom_module/custom_module_test.ts b/tfjs-master/tfjs/tools/custom_module/custom_module_test.ts
deleted file mode 100644
index e13cb4551..000000000
--- a/tfjs-master/tfjs/tools/custom_module/custom_module_test.ts
+++ /dev/null
@@ -1,276 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {getCustomConverterOpsModule, getCustomModuleString} from './custom_module';
-import {CustomTFJSBundleConfig, ImportProvider} from './types';
-
-const mockImportProvider: ImportProvider = {
-  importCoreStr: () => 'import CORE',
-  importConverterStr: () => 'import CONVERTER',
-  importBackendStr: (name: string) => `import BACKEND ${name}`,
-  importKernelStr: (kernelName: string, backend: string) => {
-    const importPath = `${
-        kernelName === 'Invalid' ? 'BACKEND_Invalid' : 'BACKEND'} ${backend}`;
-    return {
-      importPath,
-      importStatement: `import KERNEL ${kernelName} from ${importPath}`,
-      kernelConfigId: `${kernelName}_${backend}`
-    };
-  },
-  importGradientConfigStr: (kernel: string) => {
-    const importPath = kernel === 'Invalid' ? 'BACKEND_Invalid' : 'BACKEND';
-    return {
-      importPath,
-      importStatement: `import GRADIENT ${kernel} from ${importPath}`,
-      gradConfigId: `${kernel}_GRAD_CONFIG`,
-    };
-  },
-  importOpForConverterStr: (opSymbol: string) => {
-    return `export * from ${opSymbol}`;
-  },
-  importNamespacedOpsForConverterStr: (
-      namespace: string, opSymbols: string[]) => {
-    return `export ${opSymbols.join(',')} as ${namespace} from ${namespace}/`;
-  },
-  validateImportPath: (importPath: string) => {
-    return !importPath.includes('Invalid');
-  }
-};
-
-describe('getCustomModuleString forwardModeOnly=true', () => {
-  const forwardModeOnly = true;
-  it('one kernel, one backend', () => {
-    const config = {
-      kernels: ['MathKrnl', 'Invalid'],
-      backends: ['FastBcknd'],
-      models: [] as string[],
-      forwardModeOnly
-    };
-    const {tfjs, core} = getCustomModuleString(
-        // cast because FastBcknd is not a valid backend per the type
-        config as CustomTFJSBundleConfig, mockImportProvider);
-
-    expect(core).toContain('import CORE');
-    expect(tfjs).toContain('import CORE');
-
-    expect(tfjs).toContain('import BACKEND FastBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrnl from BACKEND FastBcknd');
-    expect(tfjs).toContain('registerKernel(MathKrnl_FastBcknd)');
-    expect(tfjs).not.toContain('import KERNEL Invalid from BACKEND FastBcknd');
-    expect(tfjs).not.toContain('registerKernel(Invalid_FastBcknd)');
-
-    expect(tfjs).not.toContain('GRADIENT');
-  });
-
-  it('one kernel, one backend, one model', () => {
-    const config = {
-      kernels: ['MathKrnl'],
-      backends: ['FastBcknd'],
-      models: ['model1.json'],
-      forwardModeOnly
-    };
-    const {tfjs, core} = getCustomModuleString(
-        // cast because FastBcknd is not a valid backend per the type
-        config as CustomTFJSBundleConfig, mockImportProvider);
-
-    expect(core).toContain('import CORE');
-    expect(tfjs).toContain('import CORE');
-    expect(tfjs).toContain('import CONVERTER');
-
-    expect(tfjs).toContain('import BACKEND FastBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrnl from BACKEND FastBcknd');
-    expect(tfjs).toContain('registerKernel(MathKrnl_FastBcknd)');
-
-    expect(tfjs).not.toContain('GRADIENT');
-  });
-
-  it('one kernel, two backend', () => {
-    const config = {
-      kernels: ['MathKrnl'],
-      backends: ['FastBcknd', 'SlowBcknd'],
-      models: [] as string[],
-      forwardModeOnly
-    };
-
-    const {tfjs} = getCustomModuleString(
-        // cast because the backends are not truly valid backend per the type
-        config as CustomTFJSBundleConfig, mockImportProvider);
-
-    expect(tfjs).toContain('import CORE');
-
-    expect(tfjs).toContain('import BACKEND FastBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrnl from BACKEND FastBcknd');
-    expect(tfjs).toContain('registerKernel(MathKrnl_FastBcknd)');
-
-    expect(tfjs).toContain('import BACKEND SlowBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrnl from BACKEND SlowBcknd');
-    expect(tfjs).toContain('registerKernel(MathKrnl_SlowBcknd)');
-
-    expect(tfjs).not.toContain('GRADIENT');
-  });
-
-  it('two kernels, one backend', () => {
-    const config = {
-      kernels: ['MathKrnl', 'MathKrn2'],
-      backends: ['FastBcknd'],
-      models: [] as string[],
-      forwardModeOnly
-    };
-    const {tfjs} = getCustomModuleString(
-        config as CustomTFJSBundleConfig, mockImportProvider);
-
-    expect(tfjs).toContain('import CORE');
-
-    expect(tfjs).toContain('import BACKEND FastBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrnl from BACKEND FastBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrn2 from BACKEND FastBcknd');
-    expect(tfjs).toContain('registerKernel(MathKrnl_FastBcknd)');
-    expect(tfjs).toContain('registerKernel(MathKrn2_FastBcknd)');
-
-    expect(tfjs).not.toContain('GRADIENT');
-  });
-
-  it('two kernels, two backends', () => {
-    const config = {
-      kernels: ['MathKrnl', 'MathKrn2'],
-      backends: ['FastBcknd', 'SlowBcknd'],
-      models: [] as string[],
-      forwardModeOnly
-    };
-    const {tfjs} = getCustomModuleString(
-        config as CustomTFJSBundleConfig, mockImportProvider);
-
-    expect(tfjs).toContain('import CORE');
-
-    expect(tfjs).toContain('import BACKEND FastBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrnl from BACKEND FastBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrn2 from BACKEND FastBcknd');
-    expect(tfjs).toContain('registerKernel(MathKrnl_FastBcknd)');
-    expect(tfjs).toContain('registerKernel(MathKrn2_FastBcknd)');
-
-    expect(tfjs).toContain('import BACKEND SlowBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrnl from BACKEND SlowBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrn2 from BACKEND SlowBcknd');
-    expect(tfjs).toContain('registerKernel(MathKrnl_SlowBcknd)');
-    expect(tfjs).toContain('registerKernel(MathKrn2_SlowBcknd)');
-
-    expect(tfjs).not.toContain('GRADIENT');
-  });
-});
-
-describe('getCustomModuleString forwardModeOnly=false', () => {
-  const forwardModeOnly = false;
-
-  it('one kernel, one backend', () => {
-    const config = {
-      kernels: ['MathKrnl', 'Invalid'],
-      backends: ['FastBcknd'],
-      models: [] as string[],
-      forwardModeOnly
-    };
-
-    const {tfjs} = getCustomModuleString(
-        config as CustomTFJSBundleConfig, mockImportProvider);
-
-    expect(tfjs).toContain('import CORE');
-
-    expect(tfjs).toContain('import BACKEND FastBcknd');
-    expect(tfjs).toContain('import KERNEL MathKrnl from BACKEND FastBcknd');
-    expect(tfjs).toContain('registerKernel(MathKrnl_FastBcknd)');
-    expect(tfjs).not.toContain('import KERNEL Invalid from BACKEND FastBcknd');
-    expect(tfjs).not.toContain('registerKernel(Invalid_FastBcknd)');
-
-    expect(tfjs).toContain('import GRADIENT MathKrnl');
-    expect(tfjs).toContain('registerGradient(MathKrnl_GRAD_CONFIG)');
-    expect(tfjs).not.toContain('import GRADIENT Invalid');
-    expect(tfjs).not.toContain('registerKernel(Invalid_GRAD_CONFIG)');
-  });
-
-  it('one kernel, two backend', () => {
-    const config = {
-      kernels: ['MathKrnl'],
-      backends: ['FastBcknd', 'SlowBcknd'],
-      models: [] as string[],
-      forwardModeOnly
-    };
-
-    const {tfjs} = getCustomModuleString(
-        config as CustomTFJSBundleConfig, mockImportProvider);
-
-    expect(tfjs).toContain('import GRADIENT MathKrnl');
-    expect(tfjs).toContain('registerGradient(MathKrnl_GRAD_CONFIG)');
-
-    const gradIndex = tfjs.indexOf('GRADIENT');
-    expect(tfjs.indexOf('GRADIENT', gradIndex + 1))
-        .toBe(-1, `Gradient import appears twice in:\n ${tfjs}`);
-  });
-
-  it('two kernels, one backend', () => {
-    const config = {
-      kernels: ['MathKrnl', 'MathKrn2'],
-      backends: ['FastBcknd'],
-      models: [] as string[],
-      forwardModeOnly
-    };
-
-    const {tfjs} = getCustomModuleString(
-        config as CustomTFJSBundleConfig, mockImportProvider);
-
-    expect(tfjs).toContain('import GRADIENT MathKrnl');
-    expect(tfjs).toContain('registerGradient(MathKrnl_GRAD_CONFIG)');
-
-    expect(tfjs).toContain('import GRADIENT MathKrn2');
-    expect(tfjs).toContain('registerGradient(MathKrn2_GRAD_CONFIG)');
-  });
-
-  it('two kernels, two backends', () => {
-    const config = {
-      kernels: ['MathKrnl', 'MathKrn2'],
-      backends: ['FastBcknd', 'SlowBcknd'],
-      models: [] as string[],
-      forwardModeOnly
-    };
-
-    const {tfjs} = getCustomModuleString(
-        config as CustomTFJSBundleConfig, mockImportProvider);
-
-    expect(tfjs).toContain('import GRADIENT MathKrnl');
-    expect(tfjs).toContain('registerGradient(MathKrnl_GRAD_CONFIG)');
-
-    expect(tfjs).toContain('import GRADIENT MathKrn2');
-    expect(tfjs).toContain('registerGradient(MathKrn2_GRAD_CONFIG)');
-  });
-});
-
-describe('getCustomConverterOpsModule', () => {
-  it('non namespaced ops', () => {
-    const result =
-        getCustomConverterOpsModule(['add', 'sub'], mockImportProvider);
-
-    expect(result).toContain('export * from add');
-    expect(result).toContain('export * from sub');
-  });
-
-  it('namespaced ops', () => {
-    const result = getCustomConverterOpsModule(
-        ['image.resizeBilinear', 'image.resizeNearestNeighbor'],
-        mockImportProvider);
-
-    expect(result).toContain(
-        'export resizeBilinear,resizeNearestNeighbor as image from image/');
-  });
-});
diff --git a/tfjs-master/tfjs/tools/custom_module/esm_module_provider.ts b/tfjs-master/tfjs/tools/custom_module/esm_module_provider.ts
deleted file mode 100644
index 088855dfa..000000000
--- a/tfjs-master/tfjs/tools/custom_module/esm_module_provider.ts
+++ /dev/null
@@ -1,172 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as fs from 'fs';
-import * as path from 'path';
-
-import {getCustomConverterOpsModule, getCustomModuleString} from './custom_module';
-import {getOpsForConfig} from './model_parser';
-import {CustomTFJSBundleConfig, ImportProvider, ModuleProvider, SupportedBackend} from './types';
-import {bail, kernelNameToVariableName, opNameToFileName} from './util';
-
-export function getModuleProvider(opts: {}): ModuleProvider {
-  return new ESMModuleProvider();
-}
-
-class ESMModuleProvider implements ModuleProvider {
-  /**
-   * Writes out custom tfjs module(s) to disk.
-   */
-  produceCustomTFJSModule(config: CustomTFJSBundleConfig) {
-    const {normalizedOutputPath} = config;
-
-    const moduleStrs = getCustomModuleString(config, esmImportProvider);
-
-    fs.mkdirSync(normalizedOutputPath, {recursive: true});
-    console.log(`Will write custom tfjs modules to ${normalizedOutputPath}`);
-
-    const customTfjsFileName = 'custom_tfjs.js';
-    const customTfjsCoreFileName = 'custom_tfjs_core.js';
-
-    // Write a custom module for @tensorflow/tfjs and @tensorflow/tfjs-core
-    fs.writeFileSync(
-        path.join(normalizedOutputPath, customTfjsCoreFileName),
-        moduleStrs.core);
-    fs.writeFileSync(
-        path.join(normalizedOutputPath, customTfjsFileName), moduleStrs.tfjs);
-
-    // Write a custom module tfjs-core ops used by converter executors
-
-    let kernelToOps;
-    let mappingPath;
-    try {
-      mappingPath =
-          require.resolve('@tensorflow/tfjs-converter/metadata/kernel2op.json');
-      kernelToOps = JSON.parse(fs.readFileSync(mappingPath, 'utf-8'));
-    } catch (e) {
-      bail(`Error loading kernel to ops mapping file ${mappingPath}`);
-    }
-
-    const converterOps = getOpsForConfig(config, kernelToOps);
-    if (converterOps.length > 0) {
-      const converterOpsModule =
-          getCustomConverterOpsModule(converterOps, esmImportProvider);
-
-      const customConverterOpsFileName = 'custom_ops_for_converter.js';
-
-      fs.writeFileSync(
-          path.join(normalizedOutputPath, customConverterOpsFileName),
-          converterOpsModule);
-    }
-  }
-}
-
-/**
- * An import provider to generate custom esm modules.
- */
-// Exported for tests.
-export const esmImportProvider: ImportProvider = {
-  importCoreStr(forwardModeOnly: boolean) {
-    const importLines = [
-      `import {registerKernel} from '@tensorflow/tfjs-core/dist/base';`,
-      `import '@tensorflow/tfjs-core/dist/base_side_effects';`,
-      `export * from '@tensorflow/tfjs-core/dist/base';`
-    ];
-
-    if (!forwardModeOnly) {
-      importLines.push(
-          `import {registerGradient} from '@tensorflow/tfjs-core/dist/base';`);
-    }
-    return importLines.join('\n');
-  },
-
-  importConverterStr() {
-    return `export * from '@tensorflow/tfjs-converter';`;
-  },
-
-  importBackendStr(backend: SupportedBackend) {
-    const backendPkg = getBackendPath(backend);
-    return `export * from '${backendPkg}/dist/base';`;
-  },
-
-  importKernelStr(kernelName: string, backend: SupportedBackend) {
-    const backendPkg = getBackendPath(backend);
-    const kernelConfigId = `${kernelName}_${backend}`;
-    const importPath = `${backendPkg}/dist/kernels/${kernelName}`;
-    const importStatement =
-        `import {${kernelNameToVariableName(kernelName)}Config as ${
-            kernelConfigId}} from '${importPath}';`;
-    return {importPath, importStatement, kernelConfigId};
-  },
-
-  importGradientConfigStr(kernelName: string) {
-    const gradConfigId = `${kernelNameToVariableName(kernelName)}GradConfig`;
-    const importPath =
-        `@tensorflow/tfjs-core/dist/gradients/${kernelName}_grad`;
-    const importStatement = `import {${gradConfigId}} from '${importPath}';`;
-    return {importPath, importStatement, gradConfigId};
-  },
-
-  importOpForConverterStr(opSymbol) {
-    const opFileName = opNameToFileName(opSymbol);
-    return `export {${opSymbol}} from '@tensorflow/tfjs-core/dist/ops/${
-        opFileName}';`;
-  },
-
-  importNamespacedOpsForConverterStr(namespace, opSymbols) {
-    const result: string[] = [];
-
-    for (const opSymbol of opSymbols) {
-      const opFileName = opNameToFileName(opSymbol);
-      const opAlias = `${opSymbol}_${namespace}`;
-      result.push(`import {${opSymbol} as ${
-          opAlias}} from '@tensorflow/tfjs-core/dist/ops/${namespace}/${
-          opFileName}';`);
-    }
-
-    result.push(`export const ${namespace} = {`);
-    for (const opSymbol of opSymbols) {
-      const opAlias = `${opSymbol}_${namespace}`;
-      result.push(`\t${opSymbol}: ${opAlias},`);
-    }
-    result.push(`};`);
-
-    return result.join('\n');
-  },
-
-  validateImportPath(importPath: string): boolean {
-    try {
-      require.resolve(importPath);
-      return true;
-    } catch (e) {
-      return false;
-    }
-  }
-};
-
-function getBackendPath(backend: SupportedBackend) {
-  switch (backend) {
-    case 'cpu':
-      return '@tensorflow/tfjs-backend-cpu';
-    case 'webgl':
-      return '@tensorflow/tfjs-backend-webgl';
-    case 'wasm':
-      return '@tensorflow/tfjs-backend-wasm';
-    default:
-      throw new Error(`Unsupported backend ${backend}`);
-  }
-}
diff --git a/tfjs-master/tfjs/tools/custom_module/esm_module_provider_test.ts b/tfjs-master/tfjs/tools/custom_module/esm_module_provider_test.ts
deleted file mode 100644
index a8a7f79a2..000000000
--- a/tfjs-master/tfjs/tools/custom_module/esm_module_provider_test.ts
+++ /dev/null
@@ -1,149 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {esmImportProvider} from './esm_module_provider';
-
-describe('ESM Module Provider', () => {
-  it('importCoreStr forwardModeOnly=true', () => {
-    const forwardModeOnly = true;
-    const res = esmImportProvider.importCoreStr(forwardModeOnly);
-    expect(res).toContain(
-        `import {registerKernel} from '@tensorflow/tfjs-core/dist/base'`);
-    expect(res).not.toContain(
-        `import {registerGradient} from '@tensorflow/tfjs-core/dist/base'`);
-    expect(res).toContain(
-        `import '@tensorflow/tfjs-core/dist/base_side_effects';`);
-    expect(res).toContain(`export * from '@tensorflow/tfjs-core/dist/base';`);
-  });
-
-  it('importCoreStr forwardModeOnly=false', () => {
-    const forwardModeOnly = false;
-    const res = esmImportProvider.importCoreStr(forwardModeOnly);
-    expect(res).toContain(
-        `import {registerKernel} from '@tensorflow/tfjs-core/dist/base'`);
-    expect(res).toContain(
-        `import {registerGradient} from '@tensorflow/tfjs-core/dist/base'`);
-    expect(res).toContain(
-        `import '@tensorflow/tfjs-core/dist/base_side_effects';`);
-    expect(res).toContain(`export * from '@tensorflow/tfjs-core/dist/base';`);
-  });
-
-  it('importConverterStr', () => {
-    const res = esmImportProvider.importConverterStr();
-    expect(res).toBe(`export * from '@tensorflow/tfjs-converter';`);
-  });
-
-  it('importBackendStr cpu', () => {
-    const res = esmImportProvider.importBackendStr('cpu');
-    expect(res).toBe(`export * from '@tensorflow/tfjs-backend-cpu/dist/base';`);
-  });
-
-  it('importBackendStr webgl', () => {
-    const res = esmImportProvider.importBackendStr('webgl');
-    expect(res).toBe(
-        `export * from '@tensorflow/tfjs-backend-webgl/dist/base';`);
-  });
-
-  it('importKernelStr Max cpu', () => {
-    const res = esmImportProvider.importKernelStr('Max', 'cpu');
-    expect(res.importStatement).toContain('import {maxConfig as Max_cpu}');
-    expect(res.importStatement)
-        .toContain(`from '@tensorflow/tfjs-backend-cpu/dist/kernels/Max'`);
-
-    expect(res.kernelConfigId).toBe('Max_cpu');
-  });
-
-  it('importGradientConfigStr Max', () => {
-    const res = esmImportProvider.importGradientConfigStr('Max');
-    expect(res.importStatement).toContain('import {maxGradConfig}');
-    expect(res.importStatement)
-        .toContain(`from '@tensorflow/tfjs-core/dist/gradients/Max_grad'`);
-
-    expect(res.gradConfigId).toBe('maxGradConfig');
-  });
-
-  it('importGradientConfigStr Max', () => {
-    const res = esmImportProvider.importGradientConfigStr('Max');
-    expect(res.importStatement).toContain('import {maxGradConfig}');
-    expect(res.importStatement)
-        .toContain(`from '@tensorflow/tfjs-core/dist/gradients/Max_grad'`);
-
-    expect(res.gradConfigId).toBe('maxGradConfig');
-  });
-
-  it('importOpForConverterStr add', () => {
-    const res = esmImportProvider.importOpForConverterStr('add');
-    expect(res).toBe(`export {add} from '@tensorflow/tfjs-core/dist/ops/add';`);
-  });
-
-  it('importOpForConverterStr gatherND', () => {
-    const res = esmImportProvider.importOpForConverterStr('gatherND');
-    expect(res).toBe(
-        `export {gatherND} from '@tensorflow/tfjs-core/dist/ops/gather_nd';`);
-  });
-
-  it('importOpForConverterStr batchToSpaceND', () => {
-    const res = esmImportProvider.importOpForConverterStr('batchToSpaceND');
-    expect(res).toBe(
-        // tslint:disable-next-line:max-line-length
-        `export {batchToSpaceND} from '@tensorflow/tfjs-core/dist/ops/batch_to_space_nd';`);
-  });
-
-  it('importOpForConverterStr concat1d', () => {
-    const res = esmImportProvider.importOpForConverterStr('concat1d');
-    expect(res).toBe(
-        `export {concat1d} from '@tensorflow/tfjs-core/dist/ops/concat_1d';`);
-  });
-
-  it('importOpForConverterStr avgPool3d', () => {
-    const res = esmImportProvider.importOpForConverterStr('avgPool3d');
-    expect(res).toBe(
-        // tslint:disable-next-line:max-line-length
-        `export {avgPool3d} from '@tensorflow/tfjs-core/dist/ops/avg_pool_3d';`);
-  });
-
-  it('importOpForConverterStr stridedSlice', () => {
-    const res = esmImportProvider.importOpForConverterStr('stridedSlice');
-    expect(res).toBe(
-        // tslint:disable-next-line: max-line-length
-        `export {stridedSlice} from '@tensorflow/tfjs-core/dist/ops/strided_slice';`);
-  });
-
-  it('importNamespacedOpsForConverterStr image.resizeBilinear', () => {
-    const res = esmImportProvider.importNamespacedOpsForConverterStr(
-        'image', ['resizeBilinear']);
-    expect(res).toBe(
-        // tslint:disable-next-line: max-line-length
-        `import {resizeBilinear as resizeBilinear_image} from '@tensorflow/tfjs-core/dist/ops/image/resize_bilinear';
-export const image = {
-\tresizeBilinear: resizeBilinear_image,
-};`);
-  });
-
-  it('importNamespacedOpsForConverterStr two ops in namespace', () => {
-    const res = esmImportProvider.importNamespacedOpsForConverterStr(
-        'image', ['resizeBilinear', 'resizeNearestNeighbor']);
-    expect(res).toBe(
-        // tslint:disable-next-line: max-line-length
-        `import {resizeBilinear as resizeBilinear_image} from '@tensorflow/tfjs-core/dist/ops/image/resize_bilinear';
-import {resizeNearestNeighbor as resizeNearestNeighbor_image} from '@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor';
-export const image = {
-\tresizeBilinear: resizeBilinear_image,
-\tresizeNearestNeighbor: resizeNearestNeighbor_image,
-};`);
-  });
-});
diff --git a/tfjs-master/tfjs/tools/custom_module/model_parser.ts b/tfjs-master/tfjs/tools/custom_module/model_parser.ts
deleted file mode 100644
index f0e87d802..000000000
--- a/tfjs-master/tfjs/tools/custom_module/model_parser.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import * as tensorflow from '@tensorflow/tfjs-converter/dist/data/compiled_api';
-import {io} from '@tensorflow/tfjs-core';
-import * as fs from 'fs';
-import {CustomTFJSBundleConfig} from './types';
-import {bail} from './util';
-
-export function getOpsForConfig(
-    // tslint:disable-next-line: no-any
-    config: CustomTFJSBundleConfig, kernelToOps: any) {
-  // This will return a list of ops used by the model.json(s) passed in.
-  const results: Set<string> = new Set();
-  let modelJson;
-  for (const modelJsonPath of config.models) {
-    try {
-      modelJson = JSON.parse(fs.readFileSync(modelJsonPath, 'utf-8'));
-    } catch (e) {
-      bail(`Error loading JSON file ${modelJsonPath}`);
-    }
-
-    const ops = getOps(modelJson, kernelToOps);
-    ops.forEach((op: string) => results.add(op));
-  }
-  return Array.from(results);
-}
-
-export function getOps(
-    // tslint:disable-next-line: no-any
-    modelJson: io.ModelArtifacts, kernelToOp: any): string[] {
-  const results: Set<string> = new Set();
-
-  const addOpsToResults = (kernel: string) => {
-    const ops = kernelToOp[kernel];
-    if (ops == null) {
-      console.warn(
-          `Kernel => Op warning: could not find op mapping for kernel ${
-              kernel}`);
-    }
-    ops.forEach((op: string) => {
-      results.add(op);
-    });
-  };
-
-  const graph = modelJson.modelTopology as tensorflow.IGraphDef;
-
-  // Parse nodes
-  if (graph.node != null) {
-    graph.node.forEach((node) => {
-      addOpsToResults(node.op);
-    });
-  }
-
-  // Parse functionDef nodes
-  if (graph.library != null && graph.library.function != null) {
-    graph.library.function.forEach((functionDef) => {
-      const nodeDef = functionDef.nodeDef;
-      if (nodeDef != null) {
-        nodeDef.forEach((node) => {
-          addOpsToResults(node.op);
-        });
-      }
-    });
-  }
-
-  return Array.from(results);
-}
diff --git a/tfjs-master/tfjs/tools/custom_module/model_parser_test.ts b/tfjs-master/tfjs/tools/custom_module/model_parser_test.ts
deleted file mode 100644
index 3127d91de..000000000
--- a/tfjs-master/tfjs/tools/custom_module/model_parser_test.ts
+++ /dev/null
@@ -1,275 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// tslint:disable-next-line: no-imports-from-dist
-import * as tensorflow from '@tensorflow/tfjs-converter/dist/data/compiled_api';
-import {io} from '@tensorflow/tfjs-core';
-import * as fs from 'fs';
-
-import {getOps} from './model_parser';
-
-const SIMPLE_MODEL: io.ModelArtifacts = {
-  format: 'graph-model',
-  generatedBy: '0.0.0',
-  convertedBy: 'Test Data',
-  modelTopology: {
-    node: [
-      {
-        name: 'Input',
-        op: 'Placeholder',
-        attr: {
-          dtype: {
-            type: tensorflow.DataType.DT_INT32,
-          },
-          shape: {shape: {dim: [{size: -1}, {size: 1}]}}
-        }
-      },
-      {name: 'Add1', op: 'Add', input: ['Input', 'Const'], attr: {}},
-      {name: 'Sub', op: 'Sub', input: ['Add1', 'Input'], attr: {}}
-    ],
-    versions: {producer: 1.0, minConsumer: 3}
-  }
-};
-
-const CONTROL_FLOW_V2_MODEL: io.ModelArtifacts = {
-  format: 'graph-model',
-  generatedBy: '0.0.0',
-  convertedBy: 'Test Data',
-  modelTopology: {
-    node: [
-      {
-        name: 'image_placeholder',
-        op: 'Placeholder',
-        attr: {
-          dtype: {
-            type: tensorflow.DataType.DT_FLOAT,
-          },
-          shape:
-              {shape: {dim: [{size: '3'}, {size: 3}, {size: '3'}, {size: 1}]}}
-        }
-      },
-      {
-        name: 'Const',
-        op: 'Const',
-        attr: {
-          dtype: {type: tensorflow.DataType.DT_INT32},
-          value: {
-            tensor: {
-              dtype: tensorflow.DataType.DT_INT32,
-              tensorShape: {dim: [{size: 3}, {size: 3}, {size: 1}, {size: 1}]},
-              intVal: [0, 0, 0, 0, 1, 0, 0, 0, 0]
-            }
-          }
-        }
-      },
-      {
-        name: 'Shape',
-        op: 'Const',
-        attr: {
-          dtype: {type: tensorflow.DataType.DT_INT32},
-          value: {
-            tensor: {
-              dtype: tensorflow.DataType.DT_INT32,
-              tensorShape: {dim: [{size: 3}, {size: 1}, {size: 1}, {size: 1}]},
-              intVal: [1, 1, 1]
-            }
-          }
-        }
-      },
-      {
-        name: 'Value',
-        op: 'Const',
-        attr: {dtype: {type: tensorflow.DataType.DT_INT32}, value: {i: 1}}
-      },
-      {name: 'Fill', op: 'Fill', input: ['Shape', 'Value'], attr: {}},
-      {
-        name: 'Conv2D',
-        op: 'Conv2D',
-        input: ['image_placeholder', 'Const'],
-        attr: {
-          T: {type: tensorflow.DataType.DT_FLOAT},
-          dataFormat: {s: 'TkhXQw=='},
-          padding: {s: 'U0FNRQ=='},
-          strides: {list: {f: [], i: [1, 2, 2, 1]}},
-          useCudnnOnGpu: {b: true}
-        }
-      },
-      {
-        name: 'BiasAdd',
-        op: 'BiasAdd',
-        input: ['Conv2D', 'Shape'],
-        attr: {
-          T: {type: tensorflow.DataType.DT_FLOAT},
-          dataFormat: {s: 'TkhXQw=='}
-        }
-      },
-      {
-        name: 'Cast',
-        op: 'Cast',
-        input: ['BiasAdd'],
-        attr: {DstT: {type: tensorflow.DataType.DT_INT64}}
-      },
-      {
-        name: 'Squeeze',
-        op: 'Squeeze',
-        input: ['Cast'],
-        attr: {squeeze_dims: {list: {i: ['1', '2']}}}
-      },
-      {
-        name: 'Squeeze2',
-        op: 'Squeeze',
-        input: ['BiasAdd'],
-        attr: {squeeze_dims: {list: {}}}
-      },
-      {
-        name: 'Split',
-        op: 'Split',
-        input: ['image_placeholder'],
-        attr: {num_split: {i: 3} as tensorflow.IAttrValue}
-      },
-      {name: 'LogicalNot', op: 'LogicalNot', input: ['image_placeholder']},
-      {
-        name: 'FusedBatchNorm',
-        op: 'FusedBatchNorm',
-        input: ['image_placeholder'],
-        attr: {epsilon: {f: 0.0001} as tensorflow.IAttrValue}
-      },
-      {
-        name: 'Cast2',
-        op: 'Cast',
-        input: ['BiasAdd'],
-        attr: {DstT: {type: tensorflow.DataType.DT_UINT8}}
-      },
-    ],
-    library: {
-      function: [
-        {
-          signature: {
-            name: '__inference_while_cond_10_49_frozen',
-            inputArg: [
-              {name: 'while_loop_counter', type: tensorflow.DataType.DT_INT32},
-              {
-                name: 'while_maximum_iterations',
-                type: tensorflow.DataType.DT_INT32
-              },
-              {name: 'placeholder', type: tensorflow.DataType.DT_INT32},
-              {name: 'less_y', type: tensorflow.DataType.DT_INT32}, {
-                name: 'while_cond_10___redundant_placeholder0',
-                type: tensorflow.DataType.DT_INT32
-              }
-            ],
-            outputArg: [{name: 'identity', type: tensorflow.DataType.DT_BOOL}]
-          },
-          nodeDef: [{
-            name: 'Less',
-            op: 'Less',
-            input: ['placeholder', 'less_y'],
-            attr: {T: {type: tensorflow.DataType.DT_INT32}}
-          }],
-          ret: {identity: 'Less:z:0'}
-        },
-        {
-          signature: {
-            name: '__inference_while_body_11_40_frozen',
-            inputArg: [
-              {name: 'while_loop_counter', type: tensorflow.DataType.DT_INT32},
-              {
-                name: 'while_maximum_iterations',
-                type: tensorflow.DataType.DT_INT32
-              },
-              {name: 'placeholder', type: tensorflow.DataType.DT_INT32},
-              {name: 'y_0', type: tensorflow.DataType.DT_INT32},
-              {name: 'add_1_z_0', type: tensorflow.DataType.DT_INT32}
-            ],
-            outputArg: [
-              {name: 'identity', type: tensorflow.DataType.DT_INT32},
-              {name: 'identity_1', type: tensorflow.DataType.DT_INT32},
-              {name: 'identity_2', type: tensorflow.DataType.DT_INT32},
-              {name: 'y', type: tensorflow.DataType.DT_INT32},
-              {name: 'add_1_z', type: tensorflow.DataType.DT_INT32}
-            ]
-          },
-          nodeDef: [
-            {
-              name: 'add_2/y',
-              op: 'Const',
-              attr: {
-                dtype: {type: tensorflow.DataType.DT_INT32},
-                value: {
-                  tensor: {
-                    dtype: tensorflow.DataType.DT_INT32,
-                    tensorShape: {},
-                    intVal: [1]
-                  }
-                }
-              }
-            },
-            {
-              name: 'add',
-              op: 'AddV2',
-              input: ['placeholder', 'y_0'],
-              attr: {T: {type: tensorflow.DataType.DT_INT32}}
-            },
-            {
-              name: 'add_2',
-              op: 'AddV2',
-              input: ['add_2/y:output:0', 'while_loop_counter'],
-              attr: {T: {type: tensorflow.DataType.DT_INT32}}
-            },
-            {
-              name: 'add_1',
-              op: 'AddV2',
-              input: ['add:z:0', 'add_1_z_0'],
-              attr: {T: {type: tensorflow.DataType.DT_INT32}}
-            }
-          ],
-          ret: {
-            identity_1: 'while_maximum_iterations',
-            identity_2: 'add_1:z:0',
-            y: 'y_0',
-            identity: 'add_2:z:0',
-            add_1_z: 'add_1_z_0'
-          }
-        }
-      ]
-    },
-    versions: {producer: 1.0}
-  }
-};
-
-describe('Model parse', () => {
-  // tslint:disable-next-line: no-any
-  let kernelToOps: any;
-  beforeAll(() => {
-    const mappingPath =
-        require.resolve('@tensorflow/tfjs-converter/metadata/kernel2op.json');
-    kernelToOps = JSON.parse(fs.readFileSync(mappingPath, 'utf-8'));
-  });
-
-  it('should get ops from simple model', () => {
-    const ops = getOps(SIMPLE_MODEL, kernelToOps);
-    expect(ops).toEqual(jasmine.arrayWithExactContents(['add', 'sub']));
-  });
-
-  it('should get ops from control flow v2 model', () => {
-    const ops = getOps(CONTROL_FLOW_V2_MODEL, kernelToOps);
-    expect(ops).toEqual(jasmine.arrayWithExactContents([
-      'cast', 'batchNorm', 'logicalNot', 'split', 'squeeze', 'add', 'conv2d',
-      'fill', 'less'
-    ]));
-  });
-});
diff --git a/tfjs-master/tfjs/tools/custom_module/tsconfig.json b/tfjs-master/tfjs/tools/custom_module/tsconfig.json
deleted file mode 100644
index 16243d5c1..000000000
--- a/tfjs-master/tfjs/tools/custom_module/tsconfig.json
+++ /dev/null
@@ -1,10 +0,0 @@
-{
-  "extends": "../../tsconfig.test",
-  "include": [
-    "../../tools/"
-  ],
-  "compilerOptions": {
-    "module": "commonjs",
-    "outDir": "../../dist/tools/custom_module"
-  }
-}
diff --git a/tfjs-master/tfjs/tools/custom_module/types.ts b/tfjs-master/tfjs/tools/custom_module/types.ts
deleted file mode 100644
index fd5995e53..000000000
--- a/tfjs-master/tfjs/tools/custom_module/types.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-export enum SupportedBackends {
-  cpu = 'cpu',
-  webgl = 'webgl',
-  wasm = 'wasm'
-}
-export type SupportedBackend = keyof typeof SupportedBackends;
-
-export interface CustomTFJSBundleConfig {
-  entries?: string[];             // paths to javascript files to walk
-  models?: string[];              // paths to model.json files to walk
-  backends?: SupportedBackend[];  // backends to include/use kernels from
-  forwardModeOnly?: boolean;      // whether to drop gradients
-  outputPath: string;             // path to output folder
-  kernels?: string[];             // Kernels to include
-  // tslint:disable-next-line: no-any
-  moduleOptions: any;             // Extra params to pass to a module provider
-  normalizedOutputPath?: string;  // Computed internally
-}
-
-// Interface for an object that can provide functionality to generate
-// imports for module in that build environment (e.g. OSS vs g3).
-export interface ImportProvider {
-  importCoreStr: (forwardModeOnly: boolean) => string;
-  importOpForConverterStr: (opSymbol: string) => string;
-  importNamespacedOpsForConverterStr:
-      (namespace: string, opSymbols: string[]) => string;
-  importConverterStr: () => string;
-  importBackendStr: (backendPkg: string) => string;
-  importKernelStr: (kernelName: string, backend: string) => {
-    importPath: string, importStatement: string, kernelConfigId: string
-  };
-  importGradientConfigStr: (kernelName: string) => {
-    importPath: string, importStatement: string, gradConfigId: string
-  };
-  validateImportPath: (importPath: string) => boolean;
-}
-
-// An object that can output a custom model given a config
-export interface ModuleProvider {
-  produceCustomTFJSModule: (config: CustomTFJSBundleConfig) => void;
-}
-
-export interface CustomModuleFiles {
-  core: string;
-  tfjs: string;
-}
diff --git a/tfjs-master/tfjs/tools/custom_module/util.ts b/tfjs-master/tfjs/tools/custom_module/util.ts
deleted file mode 100644
index f6025f901..000000000
--- a/tfjs-master/tfjs/tools/custom_module/util.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-#!/usr/bin/env node
-
-/**
- * @license
- * Copyright 2020 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as chalk from 'chalk';
-
-/**
- * Normalized kernels names to the variable name used in code for the kernel
- * config.
- */
-export function kernelNameToVariableName(kernelName: string) {
-  if (kernelName.startsWith('_')) {
-    // e.g. _FusedMatMulConfig
-    return `_${kernelName.charAt(1).toLowerCase()}${kernelName.slice(2)}`;
-  }
-  return kernelName.charAt(0).toLowerCase() + kernelName.slice(1);
-}
-
-/**
- * Given an op name returns the name of the file that would export that op.
- */
-export function opNameToFileName(opName: string) {
-  // add exceptions here.
-  if (opName === 'isNaN') {
-    return 'is_nan';
-  } else if (opName.match(/(.*)ND/)) {
-    return opName.match(/(.*)ND/)[1].replace(
-               /[A-Z]/g, (s: string) => `_${s.toLowerCase()}`) +
-        '_nd';
-  } else if (opName === 'avgPool3d') {
-    return 'avg_pool_3d';
-  } else if (opName.match(/concat[0-9]d/)) {
-    return `concat_${opName.match(/concat([0-9]d)/)[1]}`;
-  }
-  return opName.replace(/[A-Z]/g, (s: string) => `_${s.toLowerCase()}`);
-}
-
-export function getPreamble() {
-  const preamble = `/**
- * @license
- * Copyright ${(new Date()).getFullYear()} Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-// This file is autogenerated.\n
-`;
-  return preamble;
-}
-
-export function bail(errorMsg: string) {
-  console.log(chalk.red(errorMsg));
-  process.exit(1);
-}
diff --git a/tfjs-master/tfjs/tsconfig.json b/tfjs-master/tfjs/tsconfig.json
deleted file mode 100644
index 1ad387d16..000000000
--- a/tfjs-master/tfjs/tsconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig_base.json",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs/tsconfig.test.json b/tfjs-master/tfjs/tsconfig.test.json
deleted file mode 100644
index 537324152..000000000
--- a/tfjs-master/tfjs/tsconfig.test.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extends": "../tsconfig.test",
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ],
-  "compilerOptions": {
-    "outDir": "./dist"
-  }
-}
diff --git a/tfjs-master/tfjs/tslint.json b/tfjs-master/tfjs/tslint.json
deleted file mode 100644
index ec365f164..000000000
--- a/tfjs-master/tfjs/tslint.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "../tslint.json"
-}
diff --git a/tfjs-master/tfjs/yarn.lock b/tfjs-master/tfjs/yarn.lock
deleted file mode 100644
index efddf8982..000000000
--- a/tfjs-master/tfjs/yarn.lock
+++ /dev/null
@@ -1,5263 +0,0 @@
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-# yarn lockfile v1
-
-
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-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/@ampproject/remapping/-/remapping-2.2.0.tgz#56c133824780de3174aed5ab6834f3026790154d"
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-  dependencies:
-    "@jridgewell/gen-mapping" "^0.1.0"
-    "@jridgewell/trace-mapping" "^0.3.9"
-
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-    convert-source-map "^1.7.0"
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-  dependencies:
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-  version "3.7.0"
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-  version "2.3.1"
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-  version "0.3.1"
-  resolved "https://registry.yarnpkg.com/pidtree/-/pidtree-0.3.1.tgz#ef09ac2cc0533df1f3250ccf2c4d366b0d12114a"
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-  resolved "https://registry.yarnpkg.com/process/-/process-0.11.10.tgz#7332300e840161bda3e69a1d1d91a7d4bc16f182"
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-  version "4.0.3"
-  resolved "https://registry.yarnpkg.com/public-encrypt/-/public-encrypt-4.0.3.tgz#4fcc9d77a07e48ba7527e7cbe0de33d0701331e0"
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-  resolved "https://registry.yarnpkg.com/unicode-match-property-value-ecmascript/-/unicode-match-property-value-ecmascript-1.2.0.tgz#0d91f600eeeb3096aa962b1d6fc88876e64ea531"
-  integrity sha512-wjuQHGQVofmSJv1uVISKLE5zO2rNGzM/KCYZch/QQvez7C1hUhBIuZ701fYXExuufJFMPhv2SyL8CyoIfMLbIQ==
-
-unicode-match-property-value-ecmascript@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/unicode-match-property-value-ecmascript/-/unicode-match-property-value-ecmascript-2.0.0.tgz#1a01aa57247c14c568b89775a54938788189a714"
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-
-unicode-property-aliases-ecmascript@^1.0.4:
-  version "1.1.0"
-  resolved "https://registry.yarnpkg.com/unicode-property-aliases-ecmascript/-/unicode-property-aliases-ecmascript-1.1.0.tgz#dd57a99f6207bedff4628abefb94c50db941c8f4"
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-
-unicode-property-aliases-ecmascript@^2.0.0:
-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/unicode-property-aliases-ecmascript/-/unicode-property-aliases-ecmascript-2.1.0.tgz#43d41e3be698bd493ef911077c9b131f827e8ccd"
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-
-universalify@^0.1.0:
-  version "0.1.2"
-  resolved "https://registry.yarnpkg.com/universalify/-/universalify-0.1.2.tgz#b646f69be3942dabcecc9d6639c80dc105efaa66"
-  integrity sha512-rBJeI5CXAlmy1pV+617WB9J63U6XcazHHF2f2dbJix4XzpUF0RS3Zbj0FGIOCAva5P/d/GBOYaACQ1w+0azUkg==
-
-universalify@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/universalify/-/universalify-2.0.0.tgz#75a4984efedc4b08975c5aeb73f530d02df25717"
-  integrity sha512-hAZsKq7Yy11Zu1DE0OzWjw7nnLZmJZYTDZZyEFHZdUhV8FkH5MCfoU1XMaxXovpyW5nq5scPqq0ZDP9Zyl04oQ==
-
-unpipe@1.0.0, unpipe@~1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/unpipe/-/unpipe-1.0.0.tgz#b2bf4ee8514aae6165b4817829d21b2ef49904ec"
-  integrity sha1-sr9O6FFKrmFltIF4KdIbLvSZBOw=
-
-update-browserslist-db@^1.0.9:
-  version "1.0.9"
-  resolved "https://registry.yarnpkg.com/update-browserslist-db/-/update-browserslist-db-1.0.9.tgz#2924d3927367a38d5c555413a7ce138fc95fcb18"
-  integrity sha512-/xsqn21EGVdXI3EXSum1Yckj3ZVZugqyOZQ/CxYPBD/R+ko9NSUScf8tFF4dOKY+2pvSSJA/S+5B8s4Zr4kyvg==
-  dependencies:
-    escalade "^3.1.1"
-    picocolors "^1.0.0"
-
-url@^0.11.0:
-  version "0.11.0"
-  resolved "https://registry.yarnpkg.com/url/-/url-0.11.0.tgz#3838e97cfc60521eb73c525a8e55bfdd9e2e28f1"
-  integrity sha1-ODjpfPxgUh63PFJajlW/3Z4uKPE=
-  dependencies:
-    punycode "1.3.2"
-    querystring "0.2.0"
-
-util-deprecate@^1.0.1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
-  integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=
-
-util@^0.12.0, util@^0.12.1:
-  version "0.12.3"
-  resolved "https://registry.yarnpkg.com/util/-/util-0.12.3.tgz#971bb0292d2cc0c892dab7c6a5d37c2bec707888"
-  integrity sha512-I8XkoQwE+fPQEhy9v012V+TSdH2kp9ts29i20TaaDUXsg7x/onePbhFJUExBfv/2ay1ZOp/Vsm3nDlmnFGSAog==
-  dependencies:
-    inherits "^2.0.3"
-    is-arguments "^1.0.4"
-    is-generator-function "^1.0.7"
-    is-typed-array "^1.1.3"
-    safe-buffer "^5.1.2"
-    which-typed-array "^1.1.2"
-
-utils-merge@1.0.1:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/utils-merge/-/utils-merge-1.0.1.tgz#9f95710f50a267947b2ccc124741c1028427e713"
-  integrity sha1-n5VxD1CiZ5R7LMwSR0HBAoQn5xM=
-
-validate-npm-package-license@^3.0.1:
-  version "3.0.4"
-  resolved "https://registry.yarnpkg.com/validate-npm-package-license/-/validate-npm-package-license-3.0.4.tgz#fc91f6b9c7ba15c857f4cb2c5defeec39d4f410a"
-  integrity sha512-DpKm2Ui/xN7/HQKCtpZxoRWBhZ9Z0kqtygG8XCgNQ8ZlDnxuQmWhj566j8fN4Cu3/JmbhsDo7fcAJq4s9h27Ew==
-  dependencies:
-    spdx-correct "^3.0.0"
-    spdx-expression-parse "^3.0.0"
-
-vary@^1:
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/vary/-/vary-1.1.2.tgz#2299f02c6ded30d4a5961b0b9f74524a18f634fc"
-  integrity sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg==
-
-vm-browserify@^1.1.2:
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/vm-browserify/-/vm-browserify-1.1.2.tgz#78641c488b8e6ca91a75f511e7a3b32a86e5dda0"
-  integrity sha512-2ham8XPWTONajOR0ohOKOHXkm3+gaBmGut3SRuu75xLd/RRaY6vqgh8NBYYk7+RW3u5AtzPQZG8F10LHkl0lAQ==
-
-void-elements@^2.0.0:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/void-elements/-/void-elements-2.0.1.tgz#c066afb582bb1cb4128d60ea92392e94d5e9dbec"
-  integrity sha1-wGavtYK7HLQSjWDqkjkulNXp2+w=
-
-wcwidth@^1.0.1:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/wcwidth/-/wcwidth-1.0.1.tgz#f0b0dcf915bc5ff1528afadb2c0e17b532da2fe8"
-  integrity sha1-8LDc+RW8X/FSivrbLA4XtTLaL+g=
-  dependencies:
-    defaults "^1.0.3"
-
-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ==
-
-whatwg-url@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
-  integrity sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==
-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
-which-boxed-primitive@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/which-boxed-primitive/-/which-boxed-primitive-1.0.2.tgz#13757bc89b209b049fe5d86430e21cf40a89a8e6"
-  integrity sha512-bwZdv0AKLpplFY2KZRX6TvyuN7ojjr7lwkg6ml0roIy9YeuSr7JS372qlNW18UQYzgYK9ziGcerWqZOmEn9VNg==
-  dependencies:
-    is-bigint "^1.0.1"
-    is-boolean-object "^1.1.0"
-    is-number-object "^1.0.4"
-    is-string "^1.0.5"
-    is-symbol "^1.0.3"
-
-which-typed-array@^1.1.2:
-  version "1.1.4"
-  resolved "https://registry.yarnpkg.com/which-typed-array/-/which-typed-array-1.1.4.tgz#8fcb7d3ee5adf2d771066fba7cf37e32fe8711ff"
-  integrity sha512-49E0SpUe90cjpoc7BOJwyPHRqSAd12c10Qm2amdEZrJPCY2NDxaW01zHITrem+rnETY3dwrbH3UUrUwagfCYDA==
-  dependencies:
-    available-typed-arrays "^1.0.2"
-    call-bind "^1.0.0"
-    es-abstract "^1.18.0-next.1"
-    foreach "^2.0.5"
-    function-bind "^1.1.1"
-    has-symbols "^1.0.1"
-    is-typed-array "^1.1.3"
-
-which@^1.2.1, which@^1.2.9:
-  version "1.3.1"
-  resolved "https://registry.yarnpkg.com/which/-/which-1.3.1.tgz#a45043d54f5805316da8d62f9f50918d3da70b0a"
-  integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ==
-  dependencies:
-    isexe "^2.0.0"
-
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-ws@~8.2.3:
-  version "8.2.3"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-8.2.3.tgz#63a56456db1b04367d0b721a0b80cae6d8becbba"
-  integrity sha512-wBuoj1BDpC6ZQ1B7DWQBYVLphPWkm8i9Y0/3YdHjHKHiohOJ1ws+3OccDWtH+PoC9DZD5WOTrJvNbWvjS6JWaA==
-
-xtend@^4.0.2:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/xtend/-/xtend-4.0.2.tgz#bb72779f5fa465186b1f438f674fa347fdb5db54"
-  integrity sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==
-
-y18n@^5.0.5:
-  version "5.0.5"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.5.tgz#8769ec08d03b1ea2df2500acef561743bbb9ab18"
-  integrity sha512-hsRUr4FFrvhhRH12wOdfs38Gy7k2FFzB9qgN9v3aLykRq0dRcdcpz5C9FxdS2NuhOrI/628b/KSTJ3rwHysYSg==
-
-yalc@1.0.0-pre.50:
-  version "1.0.0-pre.50"
-  resolved "https://registry.yarnpkg.com/yalc/-/yalc-1.0.0-pre.50.tgz#e654e5af5f739cf255eedad1d66ba05a17de78f9"
-  integrity sha512-HGFjFFUhXSpQcxyOwJQl3jhERMn7XBgSCCoJ1k3dDPMbH6n56onv6Cp6cDGMiTho8tLIF4x02/Kb4g6pHavzgA==
-  dependencies:
-    chalk "^4.1.0"
-    detect-indent "^6.0.0"
-    fs-extra "^8.0.1"
-    glob "^7.1.4"
-    ignore "^5.0.4"
-    ini "^2.0.0"
-    npm-packlist "^1.4.1"
-    yargs "^16.1.1"
-
-yallist@^3.0.2:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.1.1.tgz#dbb7daf9bfd8bac9ab45ebf602b8cbad0d5d08fd"
-  integrity sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==
-
-yargs-parser@^20.2.2:
-  version "20.2.7"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.7.tgz#61df85c113edfb5a7a4e36eb8aa60ef423cbc90a"
-  integrity sha512-FiNkvbeHzB/syOjIUxFDCnhSfzAL8R5vs40MgLFBorXACCOAEaWu0gRZl14vG8MR9AOJIZbmkjhusqBYZ3HTHw==
-
-yargs-parser@^21.1.1:
-  version "21.1.1"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-21.1.1.tgz#9096bceebf990d21bb31fa9516e0ede294a77d35"
-  integrity sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==
-
-yargs@^16.0.3, yargs@^16.1.1:
-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yargs@^17.5.1:
-  version "17.7.1"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-17.7.1.tgz#34a77645201d1a8fc5213ace787c220eabbd0967"
-  integrity sha512-cwiTb08Xuv5fqF4AovYacTFNxk62th7LKJ6BL9IGUpTJrWoU7/7WdQGTP2SjKf1dUNBGzDd28p/Yfs/GI6JrLw==
-  dependencies:
-    cliui "^8.0.1"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.3"
-    y18n "^5.0.5"
-    yargs-parser "^21.1.1"
-
-yn@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
-  integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==
diff --git a/tfjs-master/tools/BUILD.bazel b/tfjs-master/tools/BUILD.bazel
deleted file mode 100644
index 2bd9eecdb..000000000
--- a/tfjs-master/tools/BUILD.bazel
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-load("@build_bazel_rules_nodejs//:index.bzl", "nodejs_binary")
-load("//tools:defaults.bzl", "ts_library")
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files(
-    [
-        "karma_template.conf.js",
-        "rollup_template.config.mjs",
-    ],
-    visibility = ["//visibility:public"],
-)
-
-ts_library(
-    name = "enumerate_tests_lib",
-    srcs = [
-        "enumerate-tests.ts",
-    ],
-    deps = [
-        "@npm//@types/argparse",
-        "@npm//@types/node",
-        "@npm//argparse",
-    ],
-)
-
-nodejs_binary(
-    name = "enumerate_tests_bin",
-    data = [
-        ":enumerate_tests_lib",
-    ],
-    entry_point = "enumerate-tests.ts",
-)
-
-ts_library(
-    name = "make_version_test_file_lib",
-    srcs = [
-        "make_version_test_file.ts",
-    ],
-    deps = [
-        "@npm//@types/argparse",
-        "@npm//@types/node",
-        "@npm//argparse",
-    ],
-)
-
-nodejs_binary(
-    name = "make_version_test_file_bin",
-    data = [
-        ":make_version_test_file_lib",
-    ],
-    entry_point = "make_version_test_file.ts",
-)
-
-ts_library(
-    name = "downlevel_to_es5_plugin",
-    srcs = [
-        "downlevel_to_es5_plugin.ts",
-    ],
-    module_name = "downlevel_to_es5_plugin",
-    deps = [
-        "@npm//@types/node",
-        "@npm//typescript",
-    ],
-)
-
-ts_library(
-    name = "make_rollup_config",
-    srcs = [
-        "make_rollup_config.ts",
-    ],
-    module_name = "make_rollup_config",
-    deps = [
-        ":downlevel_to_es5_plugin",
-        "@npm//@rollup/plugin-commonjs",
-        "@npm//@rollup/plugin-node-resolve",
-        "@npm//@types/node",
-        "@npm//@types/rollup-plugin-visualizer",
-        "@npm//rollup-plugin-sourcemaps",
-        "@npm//rollup-plugin-terser",
-        "@npm//rollup-plugin-visualizer",
-        "@npm//typescript",
-    ],
-)
diff --git a/tfjs-master/tools/copy_to_dist.bzl b/tfjs-master/tools/copy_to_dist.bzl
deleted file mode 100644
index 582f7acfe..000000000
--- a/tfjs-master/tools/copy_to_dist.bzl
+++ /dev/null
@@ -1,117 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//lib:paths.bzl", "paths")
-
-def _copy_to_dist_impl(ctx):
-    if ctx.attr.global_root and ctx.attr.root:
-        fail("Both 'root' and 'global_root' were passed " +
-             "but only one is expected")
-
-    files = [f for s in ctx.attr.srcs for f in s.files.to_list()]
-
-    if ctx.attr.global_root:
-        root_dir = ctx.attr.global_root
-    else:
-        root_dir = paths.join(paths.dirname(ctx.build_file_path), ctx.attr.root)
-
-    outputs = []
-    for f in files:
-        dest_path = paths.join(ctx.attr.dest_dir, paths.relativize(f.short_path, root_dir))
-        if ctx.attr.extension:
-            dest_path = paths.replace_extension(dest_path, ctx.attr.extension)
-
-        out = ctx.actions.declare_file(dest_path)
-        outputs.append(out)
-        ctx.actions.symlink(
-            output = out,
-            target_file = f,
-        )
-
-    return [DefaultInfo(files = depset(outputs))]
-
-copy_to_dist = rule(
-    implementation = _copy_to_dist_impl,
-    attrs = {
-        "dest_dir": attr.string(
-            default = "dist",
-            doc = "Destination directory to copy the source file tree to",
-        ),
-        "extension": attr.string(
-            doc = "New file extension to use for each file",
-        ),
-        "global_root": attr.string(
-            default = "",
-            doc = "Root path to remove when symlinking. Relative to the repository root",
-        ),
-        "root": attr.string(
-            default = "",
-            doc = "Common root path to remove when symlinking. Relative to the build file's directory",
-        ),
-        "srcs": attr.label_list(
-            doc = "Files to create symlinks of",
-        ),
-    },
-    doc = """Creates symlinks in 'dest_dir' for each file in 'srcs'
-
-    Preserves relative paths between linked files. A common root path of the
-    input files can be removed via the 'root' arg.
-
-    This rule is used to 'copy' the results of compiling a tfjs package's
-    sources located in 'src' to the output directory 'dist' while preserving
-    the filetree's relative paths. The benifit of using this rule is that Bazel
-    is aware of the copied files and can use them in other rules like 'pkg_npm'.
-    It is also used for copying tfjs bundles and miniprogram outputs.
-    """,
-)
-
-def copy_ts_library_to_dist(name, srcs, root = "", dest_dir = "dist"):
-    # Declaration (.d.ts) files
-    declaration = name + "_declaration"
-    native.filegroup(
-        name = declaration,
-        srcs = srcs,
-    )
-
-    # ES Module es2017 compilation results. This is configured in
-    # tools/defaults.bzl. Outputs '.mjs' files.
-    esm = name + "_esm_sources"
-    native.filegroup(
-        name = esm,
-        srcs = srcs,
-        output_group = "es6_sources",
-    )
-
-    copy_esm = name + "_copy_esm"
-    copy_to_dist(
-        name = copy_esm,
-        srcs = [esm],
-        root = root,
-        dest_dir = dest_dir,
-        extension = ".js",  # Rewrite '.mjs' extension to '.js'
-    )
-
-    copy_esm_declaration = name + "_copy_esm_declaration"
-    copy_to_dist(
-        name = copy_esm_declaration,
-        srcs = [declaration],
-        root = root,
-        dest_dir = dest_dir,
-    )
-
-    native.filegroup(
-        name = name,
-        srcs = [copy_esm, copy_esm_declaration],
-    )
diff --git a/tfjs-master/tools/defaults.bzl b/tfjs-master/tools/defaults.bzl
deleted file mode 100644
index 961ea5af4..000000000
--- a/tfjs-master/tools/defaults.bzl
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-"""Re-export of some bazel rules with repository-wide defaults."""
-
-load("@npm//@bazel/concatjs:index.bzl", _ts_library = "ts_library")
-load("@npm//@bazel/esbuild:index.bzl", _esbuild = "esbuild")
-
-def ts_library(**kwargs):
-    # Use the ts_library tsconfig by default. The ts_library tsconfig does not
-    # have "incremental = true" because ts_library does not support incremental
-    # builds at the level of the typescript compiler (it supports incrementally
-    # rebuilding at the level of Bazel build targets).
-    tsconfig = kwargs.pop("tsconfig", "@//:tsconfig_ts_library")
-
-    # Use es2017 for esModule (prodmode) outputs by default. This is transpiled
-    # down to es5 in tfjs_bundle.bzl. Use es5 for commonjs (devmode) outputs by
-    # default since they are directly used by nodejs.
-    # The devmode_target corresponds to es5_sources and the prodmode_target
-    # corresponds to es6_sources.
-    # https://github.com/bazelbuild/rules_nodejs/issues/2094
-    devmode_target = kwargs.pop("devmode_target", "es5")
-    prodmode_target = kwargs.pop("prodmode_target", "es2017")
-
-    # By default, set package_name based on module_name to mimic pre-4.0 behavior
-    # of automatically linking the ts_library.
-    maybe_module_name = kwargs["module_name"] if "module_name" in kwargs else None
-    package_name = kwargs.pop("package_name", maybe_module_name)
-
-    _ts_library(
-        tsconfig = tsconfig,
-        devmode_target = devmode_target,
-        prodmode_target = prodmode_target,
-        package_name = package_name,
-        **kwargs
-    )
-
-def esbuild(**kwargs):
-    # Make sure esbuild always resolve the module (.mjs) files before .js files.
-    args = kwargs.pop("args", {})
-    args["resolveExtensions"] = [".mjs", ".js"]
-
-    _esbuild(
-        args = args,
-        **kwargs
-    )
diff --git a/tfjs-master/tools/downlevel_to_es5_plugin.ts b/tfjs-master/tools/downlevel_to_es5_plugin.ts
deleted file mode 100644
index 1373fdb9b..000000000
--- a/tfjs-master/tools/downlevel_to_es5_plugin.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as ts from 'typescript';
-import * as path from 'path';
-
-
-// Transform that is enabled for es5 bundling. It transforms existing ES2015
-// prodmode output to ESM5 so that the resulting bundles are using ES5 format.
-// Inspired by Angular's ng_package ES5 transform:
-// https://github.com/angular/angular/blob/a92a89b0eb127a59d7e071502b5850e57618ec2d/packages/bazel/src/ng_package/rollup.config.js#L150-L170
-export const downlevelToEs5Plugin = {
-  name: 'downlevel-to-es5',
-  transform: (code: string, filePath: string) => {
-    const compilerOptions = {
-      target: ts.ScriptTarget.ES5,
-      module: ts.ModuleKind.ES2015,
-      allowJs: true,
-      sourceMap: true,
-      downlevelIteration: true,
-      importHelpers: true,
-      mapRoot: path.dirname(filePath),
-    };
-    const {outputText, sourceMapText}
-          = ts.transpileModule(code, {compilerOptions});
-    return {
-      code: outputText,
-      map: JSON.parse(sourceMapText),
-    };
-  },
-};
diff --git a/tfjs-master/tools/enumerate-tests.ts b/tfjs-master/tools/enumerate-tests.ts
deleted file mode 100644
index 585a500ae..000000000
--- a/tfjs-master/tools/enumerate-tests.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2019 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-/**
- * This script generates the tests.ts file which enumerates all the
- * backend-agonstic tests. These are the tests that get executed from other
- * packages (e.g. WebGPU).
- */
-
-const LICENSE = `/**
- * @license
- * Copyright ${(new Date).getFullYear()} Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-`;
-
-const AUTOGEN_CLAUSE = `///// DO NOT EDIT: This file is auto-generated by ` +
-    `/tools/enumerate-tests.ts
-`;
-
-import * as fs from 'fs';
-import {posix as path} from 'path';
-import {ArgumentParser} from 'argparse';
-
-const parser = new ArgumentParser({
-  description: "This script generates the tests.ts file which enumerates all"
-      + " the backend-agonstic tests. These are the tests that get executed from"
-      + " other packages (e.g. WebGPU).",
-});
-
-parser.addArgument('test_file_list', {action: 'store', type: String});
-parser.addArgument(['-o', '--output'], {
-  action: 'store',
-  required: true,
-  type: String
-});
-parser.addArgument(['-r', '--root'], {
-  action: 'store',
-  defaultValue: '',
-  type: String,
-});
-
-const args = parser.parseArgs();
-
-const tests_file_contents = fs.readFileSync(args.test_file_list, 'utf8');
-if (! tests_file_contents) {
-  throw new Error(`Failed to read tests file ${args.test_file_list}`);
-}
-const tests = tests_file_contents.split('\n');
-
-const root: string = path.normalize(args.root);
-const files = tests.map(filePath => {
-  const normalized = path.normalize(filePath);
-
-  // Trim the 'bazel-out/platform-dependent-folder-name/bin/' path from
-  // generated files so they can be used by this rule.
-  const trimmed = normalized.replace(/^bazel-out\/.*\/bin\//, '');
-
-  if (trimmed.slice(0, root.length) !== root) {
-    throw new Error(`File ${trimmed} is not under root path ${root}`);
-  }
-
-  return './' + path.relative(root, trimmed).replace('.ts', '');
-});
-
-const imports = files.map(f => `import '${f}';`);
-const newContent = `${LICENSE}
-${AUTOGEN_CLAUSE}
-${imports.sort().join('\n')}
-`;
-
-fs.writeFileSync(args.output, newContent);
diff --git a/tfjs-master/tools/enumerate_tests.bzl b/tfjs-master/tools/enumerate_tests.bzl
deleted file mode 100644
index 344947963..000000000
--- a/tfjs-master/tools/enumerate_tests.bzl
+++ /dev/null
@@ -1,51 +0,0 @@
-load("@build_bazel_rules_nodejs//:providers.bzl", "run_node")
-
-def _enumerate_tests_impl(ctx):
-    output_file = ctx.actions.declare_file(ctx.attr.name + ".ts")
-    input_paths = [src.path for src in ctx.files.srcs]
-
-    input_paths_file = ctx.actions.declare_file("enumerate_tests_paths")
-    ctx.actions.write(input_paths_file, "\n".join(input_paths))
-
-    run_node(
-        ctx,
-        executable = "enumerate_tests_bin",
-        inputs = ctx.files.srcs + [input_paths_file],
-        outputs = [output_file],
-        arguments = [
-            "-r",
-            ctx.attr.root_path,
-            "-o",
-            output_file.path,
-            input_paths_file.path,
-        ],
-    )
-
-    return [DefaultInfo(files = depset([output_file]))]
-
-enumerate_tests = rule(
-    implementation = _enumerate_tests_impl,
-    attrs = {
-        "enumerate_tests_bin": attr.label(
-            executable = True,
-            cfg = "exec",
-            default = Label("@//tools:enumerate_tests_bin"),
-            doc = "The script that enumerates the tests",
-        ),
-        "root_path": attr.string(
-            default = "",
-            doc = "A root path to remove from srcs when importing them",
-        ),
-        "srcs": attr.label_list(
-            mandatory = True,
-            allow_files = [".ts"],
-            doc = "Test files to enumerate (i.e. import) in the output file",
-        ),
-    },
-    doc = """Generates a ts file that imports the given test srcs
-
-    This rule creates a test entrypoint that imports all of the individual tests
-    specified in 'srcs'. In tfjs-core, it is used to create 'tests.ts', which
-    other packages use to import the core tests all at once.
-    """,
-)
diff --git a/tfjs-master/tools/get_extension.bzl b/tfjs-master/tools/get_extension.bzl
deleted file mode 100644
index 9b3123be4..000000000
--- a/tfjs-master/tools/get_extension.bzl
+++ /dev/null
@@ -1,56 +0,0 @@
-# Copyright 2022 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-def _get_extension_impl(ctx):
-    # Starlark does not support sets, so use a dict instead.
-    included = {}
-    if ctx.attr.include:
-        for extension in ctx.attr.include:
-            for f in ctx.files.srcs:
-                if f.basename.endswith(extension):
-                    included[f] = True
-    else:
-        included = {src: True for src in ctx.files.srcs}
-
-    if ctx.attr.exclude:
-        for extension in ctx.attr.exclude:
-            for f in ctx.files.srcs:
-                if f.basename.endswith(extension):
-                    included.pop(f)
-
-    return DefaultInfo(files = depset(included.keys()))
-
-get_extension = rule(
-    implementation = _get_extension_impl,
-    attrs = {
-        "exclude": attr.string_list(
-            mandatory = False,
-            doc = "File extensions to exclude.",
-        ),
-        "include": attr.string_list(
-            mandatory = False,
-            doc = "File extensions to include. If empty, includes all files.",
-        ),
-        "srcs": attr.label_list(
-            allow_files = True,
-            mandatory = True,
-        ),
-    },
-    doc = """Select files with a given file extension
-
-    Creates a target referencing a set of files that match the
-    include and exclude attributes.
-    """,
-)
diff --git a/tfjs-master/tools/karma_template.conf.js b/tfjs-master/tools/karma_template.conf.js
deleted file mode 100644
index d6bc26ec5..000000000
--- a/tfjs-master/tools/karma_template.conf.js
+++ /dev/null
@@ -1,217 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-const browserstackConfig = {
-  // Browserstack only supports a certain range of ports for safari.
-  // Karma will automatically use the next available port if the
-  // chosen one is in use. Starting at 9200 gives us the largest
-  // range of ports (9200 - 9400).
-  // https://www.browserstack.com/question/39572
-  port: 9200,
-};
-
-// Select Chrome or ChromeHeadless based on the value of the --//:headless flag.
-const CHROME = TEMPLATE_headless ? 'ChromeHeadless' : 'Chrome';
-
-const CUSTOM_LAUNCHERS = {
-  // For browserstack configs see:
-  // https://www.browserstack.com/automate/node
-  bs_chrome_mac: {
-    base: 'BrowserStack',
-    browser: 'chrome',
-    browser_version: 'latest',
-    os: 'OS X',
-    os_version: 'High Sierra',
-    flags: [
-      // For tfjs-data
-      '--autoplay-policy=no-user-gesture-required',
-    ],
-  },
-  bs_firefox_mac: {
-    base: 'BrowserStack',
-    browser: 'firefox',
-    browser_version: '90',
-    os: 'OS X',
-    os_version: 'High Sierra'
-  },
-  bs_safari_mac: {
-    base: 'BrowserStack',
-    browser: 'safari',
-    browser_version: 'latest',
-    os: 'OS X',
-    os_version: 'Mojave'
-  },
-  bs_ios_12: {
-    base: 'BrowserStack',
-    device: 'iPhone XS',
-    os: 'ios',
-    os_version: '12.3',
-    real_mobile: true
-  },
-  bs_ios_15: {
-    base: 'BrowserStack',
-    device: 'iPhone 11 Pro',
-    os: 'ios',
-    os_version: '15',
-    real_mobile: true
-  },
-  bs_android_10: {
-    base: 'BrowserStack',
-    device: 'Google Pixel 4 XL',
-    os: 'android',
-    os_version: '10.0',
-    real_mobile: true
-  },
-  win_10_chrome: {
-    base: 'BrowserStack',
-    browser: 'chrome',
-    browser_version: '104.0',
-    os: 'Windows',
-    os_version: '10',
-    flags: [
-      // For tfjs-data
-      '--autoplay-policy=no-user-gesture-required',
-    ],
-  },
-  bs_chrome_mac_webgpu: {
-    base: 'BrowserStack',
-    browser: 'chrome',
-    browser_version: 'latest',
-    os: 'OS X',
-    os_version: 'High Sierra',
-    flags: [
-      '--enable-unsafe-webgpu',  // Can be removed after WebGPU release
-      '--use-webgpu-adapter=swiftshader',
-
-      // https://github.com/tensorflow/tfjs/issues/7631
-      '--disable-vulkan-fallback-to-gl-for-testing',
-    ],
-  },
-  chrome_with_swift_shader: {
-    base: CHROME,
-    flags: ['--blacklist-accelerated-compositing', '--blacklist-webgl']
-  },
-  chrome_autoplay: {
-    base: CHROME,
-    flags: [
-      '--autoplay-policy=no-user-gesture-required',
-      '--no-sandbox',
-    ],
-  },
-  chrome_webgpu_linux: {
-    base: 'ChromeCanary',
-    flags: [
-      '--enable-features=Vulkan',
-      '--enable-unsafe-webgpu',
-      '--disable-dawn-features=disallow_unsafe_apis',
-    ]
-  },
-  chrome_webgpu: {
-    base: 'ChromeCanary',
-    flags: [
-      '--disable-dawn-features=disallow_unsafe_apis',
-      '--no-sandbox',
-    ]
-  },
-  chrome_debugging: {
-    base: 'Chrome',
-    flags: ['--remote-debugging-port=9333'],
-  },
-  chrome_no_sandbox: {
-    base: CHROME,
-    flags: ['--no-sandbox'],
-  }
-};
-
-module.exports = function(config) {
-  console.log(`Running with arguments ${TEMPLATE_args.join(' ')}`);
-  let browser = 'TEMPLATE_browser';
-  let extraConfig = {};
-  const browserLauncher = CUSTOM_LAUNCHERS[browser];
-  if (browser) {
-    if (!browserLauncher) {
-      throw new Error(`Missing launcher for ${browser}`);
-    }
-    extraConfig.browsers = [browser];
-  } else {
-    // Use no sandbox by default. This has better support on MacOS.
-    extraConfig.browsers = ['chrome_no_sandbox'];
-  }
-  if (browserLauncher?.base === 'BrowserStack') {
-    const username = process.env.BROWSERSTACK_USERNAME;
-    const accessKey = process.env.BROWSERSTACK_KEY;
-    if (!username) {
-      console.error(
-          'No browserstack username found. Please set the' +
-          ' environment variable "BROWSERSTACK_USERNAME" to your' +
-          ' browserstack username');
-    }
-    if (!accessKey) {
-      console.error(
-          'No browserstack access key found. Please set the' +
-          ' environment variable "BROWSERSTACK_KEY" to your' +
-          ' browserstack access key');
-    }
-    if (!username || !accessKey) {
-      process.exit(1);
-    }
-    if (browserLauncher.browser === 'safari' || browserLauncher.os === 'ios') {
-      // This is necessary for non-flaky Safari tests. They usually pass just
-      // fine without it, but sometimes, Safari will fail to connect to Karma.
-      // If you want to remove this, prove that it's not flaky by running
-      // bazel test //tfjs-core/src:bs_safari_mac_from_pixels_worker_test
-      // --runs_per_test=100
-      extraConfig.hostname = 'bs-local.com';
-    }
-
-    Object.assign(extraConfig, browserstackConfig);
-    extraConfig.browserStack = {
-      username: process.env.BROWSERSTACK_USERNAME,
-      accessKey: process.env.BROWSERSTACK_KEY,
-      timeout: 900,  // Seconds
-      tunnelIdentifier: `tfjs_${Date.now()}_${Math.floor(Math.random() * 1000)}`
-    };
-  }
-
-  config.set({
-    reporters: [
-      'kjhtml',
-      'jasmine-order',
-    ],
-    frameworks: ['jasmine'],
-    plugins: [
-      require('karma-jasmine'),
-      require('karma-jasmine-html-reporter'),
-      require('karma-jasmine-order-reporter'),
-    ],
-    captureTimeout: 3e5,
-    reportSlowerThan: 500,
-    browserNoActivityTimeout: 3e5,
-    browserDisconnectTimeout: 3e5,
-    browserDisconnectTolerance: 0,
-    browserSocketTimeout: 1.2e5,
-    ...extraConfig,
-    customLaunchers: CUSTOM_LAUNCHERS,
-    client: {
-      args: TEMPLATE_args,
-      jasmine: {
-        random: TEMPLATE_jasmine_random,
-        seed: 'TEMPLATE_jasmine_seed',
-      },
-    },
-  });
-}
diff --git a/tfjs-master/tools/make_rollup_config.ts b/tfjs-master/tools/make_rollup_config.ts
deleted file mode 100644
index e22db656d..000000000
--- a/tfjs-master/tools/make_rollup_config.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * @license
- * Copyright 2022 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import * as commonjs_import from '@rollup/plugin-commonjs';
-import resolve from '@rollup/plugin-node-resolve';
-import * as sourcemaps_import from 'rollup-plugin-sourcemaps';
-import {visualizer} from 'rollup-plugin-visualizer';
-import {terser as terserPlugin} from 'rollup-plugin-terser';
-import {downlevelToEs5Plugin} from 'downlevel_to_es5_plugin/downlevel_to_es5_plugin';
-
-// These workarounds would not be necessary with esModuleInterop = true,
-// but we likely can't set that.
-const commonjs = commonjs_import as unknown as typeof commonjs_import.default;
-const sourcemaps =
-  sourcemaps_import as unknown as typeof sourcemaps_import.default;
-
-const preamble = `/**
- * @license
- * Copyright ${(new Date).getFullYear()} Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */`;
-
-export function makeRollupConfig({
-  globals = {},
-  external = [],
-  leave_as_require = [],
-  plugins = [],
-  terser = false,
-  es5 = false,
-  vis_filename,
-}: {
-  globals?: {[name: string]: string},
-  external?: string[],
-  leave_as_require?: string[],
-  plugins?: unknown[],
-  terser?: boolean,
-  es5?: boolean,
-  vis_filename?: string,
-}) {
-  const vis = vis_filename ? [visualizer({
-    sourcemap: true,
-    filename: vis_filename,
-    template: 'sunburst',
-  })] : [];
-
-  // Without `compress: {typeofs: false}`, the terser plugin will turn
-  // `typeof _scriptDir == "undefined"` into `_scriptDir === void 0` in minified
-  // JS file which will cause "_scriptDir is undefined" error in web worker's
-  // inline script.
-  //
-  // For more context, see tfjs-backend-wasm/scripts/patch-threaded-simd-module.js
-  const useTerser = terser ? [
-    terserPlugin({
-      output: {preamble, comments: false},
-      compress: {typeofs: false},
-    })
-  ] : [];
-
-  const useEs5 = es5 ? [downlevelToEs5Plugin] : [];
-
-  return {
-    output: {
-      banner: preamble,
-      freeze: false, // For tests that spyOn imports
-      extend: true, // For imports that extend the global 'tf' variable
-      globals,
-    },
-    external,
-    plugins: [
-      resolve({browser: true}),
-      commonjs({
-        ignore: leave_as_require,
-      }),
-      sourcemaps(),
-      ...plugins,
-      ...useEs5,
-      ...useTerser,
-      ...vis,
-    ],
-    onwarn: function (warning: {code: string, message: string}) {
-      if (warning.code === 'THIS_IS_UNDEFINED') {
-        return;
-      }
-      console.warn(warning.message);
-    }
-  }
-}
diff --git a/tfjs-master/tools/make_version_test_file.bzl b/tfjs-master/tools/make_version_test_file.bzl
deleted file mode 100644
index 023c34098..000000000
--- a/tfjs-master/tools/make_version_test_file.bzl
+++ /dev/null
@@ -1,40 +0,0 @@
-load("@build_bazel_rules_nodejs//:providers.bzl", "run_node")
-
-def _make_version_test_file_impl(ctx):
-    output_file = ctx.actions.declare_file(ctx.attr.name + ".ts")
-
-    run_node(
-        ctx,
-        executable = "make_version_test_file_bin",
-        inputs = ctx.files.package_json,
-        outputs = [output_file],
-        arguments = [
-            ctx.files.package_json[0].path,
-            ctx.attr.version_name,
-            output_file.path,
-        ],
-    )
-
-    return [DefaultInfo(files = depset([output_file]))]
-
-make_version_test_file = rule(
-    implementation = _make_version_test_file_impl,
-    attrs = {
-        "make_version_test_file_bin": attr.label(
-            executable = True,
-            cfg = "exec",
-            default = Label("@//tools:make_version_test_file_bin"),
-            doc = "The script that generates the version test",
-        ),
-        "package_json": attr.label(
-            mandatory = True,
-            allow_single_file = [".json"],
-            doc = "The package.json containing the version to use for the test",
-        ),
-        "version_name": attr.string(
-            mandatory = True,
-            doc = "The name of the imported version variable to check",
-        ),
-    },
-    doc = """Generate the version test file for tfjs-core""",
-)
diff --git a/tfjs-master/tools/make_version_test_file.ts b/tfjs-master/tools/make_version_test_file.ts
deleted file mode 100644
index 25b70ae8f..000000000
--- a/tfjs-master/tools/make_version_test_file.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/usr/bin/env node
-// Copyright 2021 Google LLC. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-// =============================================================================
-
-/**
- * This script generates the version_test.ts file based off of the package's
- * package.json file.
- */
-
-import * as fs from 'fs';
-import {ArgumentParser} from 'argparse';
-
-const LICENSE = `/**
- * @license
- * Copyright ${(new Date).getFullYear()} Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-`;
-
-const AUTOGEN_CLAUSE = `///// DO NOT EDIT: This file is auto-generated by ` +
-    `/tools/make_version_test.ts
-`;
-
-const parser = new ArgumentParser({
-  description: 'This script generates the version_test file which checks that'
-    + ' the package\'s version matches the version contained in the package.json',
-});
-
-parser.addArgument('package_json', {action: 'store', type: String});
-parser.addArgument('version_name', {
-  action: 'store',
-  type: String,
-});
-parser.addArgument('output', {
-  action: 'store',
-  type: String,
-});
-
-const args = parser.parseArgs();
-
-const package_json = fs.readFileSync(args.package_json, 'utf8');
-if (! package_json) {
-  throw new Error(`Failed to read package.json ${args.package_json}`);
-}
-const version = JSON.parse(package_json).version as string;
-
-
-const versionTestContent = `${LICENSE}
-${AUTOGEN_CLAUSE}
-import {${args.version_name}} from './index';
-
-describe('version', () => {
-  it('version is contained', () => {
-    expect(${args.version_name}).toBe('${version}');
-  });
-});
-`;
-
-fs.writeFileSync(args.output, versionTestContent);
diff --git a/tfjs-master/tools/rollup_template.config.mjs b/tfjs-master/tools/rollup_template.config.mjs
deleted file mode 100644
index 90f3f9b33..000000000
--- a/tfjs-master/tools/rollup_template.config.mjs
+++ /dev/null
@@ -1,27 +0,0 @@
-/**
- * @license
- * Copyright 2021 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {makeRollupConfig} from 'make_rollup_config/make_rollup_config.js';
-
-export default makeRollupConfig({
-  globals: TEMPLATE_globals,
-  external: TEMPLATE_external,
-  leave_as_require: TEMPLATE_leave_as_require,
-  terser: TEMPLATE_minify,
-  es5: TEMPLATE_es5,
-  vis_filename: 'TEMPLATE_stats',
-});
diff --git a/tfjs-master/tools/tfjs_bundle.bzl b/tfjs-master/tools/tfjs_bundle.bzl
deleted file mode 100644
index 8638ddb2b..000000000
--- a/tfjs-master/tools/tfjs_bundle.bzl
+++ /dev/null
@@ -1,221 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@bazel_skylib//lib:new_sets.bzl", "sets")
-load("@bazel_skylib//lib:paths.bzl", "paths")
-load("@npm//@bazel/rollup:index.bzl", "rollup_bundle")
-
-def _make_rollup_config_impl(ctx):
-    output_file = ctx.actions.declare_file(ctx.label.name + ".mjs")
-
-    # TODO(mattsoulanille): This stats file is not declared as an output
-    # of any rule. It should be declared as an output of the rollup_bundle
-    # rule, but there doesn't seem to be an easy way to do that.
-    stats_file_path = paths.join(
-        ctx.bin_dir.path,
-        paths.dirname(ctx.build_file_path),
-        ctx.attr.stats,
-    )
-
-    external = sets.to_list(sets.make(
-        ctx.attr.external + list(ctx.attr.globals.keys()),
-    ))
-
-    ctx.actions.expand_template(
-        template = ctx.file.template,
-        output = ctx.outputs.config_file,
-        substitutions = {
-            "TEMPLATE_es5": "true" if ctx.attr.es5 else "false",
-            "TEMPLATE_external": str(external),
-            "TEMPLATE_globals": str(ctx.attr.globals),
-            "TEMPLATE_leave_as_require": str(ctx.attr.leave_as_require),
-            "TEMPLATE_minify": "true" if ctx.attr.minify else "false",
-            "TEMPLATE_stats": stats_file_path,
-        },
-    )
-    return [DefaultInfo(files = depset([output_file]))]
-
-_make_rollup_config = rule(
-    implementation = _make_rollup_config_impl,
-    attrs = {
-        "es5": attr.bool(
-            default = False,
-            doc = "Whether to transpile to es5",
-            mandatory = False,
-        ),
-        "external": attr.string_list(
-            default = [],
-            doc = """A list of module IDs to exclude.
-
-Keys from 'globals' are automatically added to this attribute, but additional external modules can be specified.
-            """,
-            mandatory = False,
-        ),
-        "globals": attr.string_dict(
-            default = {},
-            doc = "A dict from module IDs to global variables" +
-                  " used to resolve external modules",
-            mandatory = False,
-        ),
-        "leave_as_require": attr.string_list(
-            default = [],
-            doc = """A list of modules to leave as 'require()' statements.
-
-We use the commonjs rollup plugin to load commonjs modules in rollup. This plugin converts 'require()' calls to 'import' statements, but sometimes, they should be left as 'require()'.
-            """,
-        ),
-        "minify": attr.bool(
-            default = False,
-            doc = "Whether to minify with terser",
-            mandatory = False,
-        ),
-        "stats": attr.string(
-            mandatory = True,
-            doc = "The name of the stats file",
-        ),
-        "template": attr.label(
-            default = Label("@//tools:rollup_template.config.mjs"),
-            allow_single_file = True,
-            doc = "The karma config template to expand",
-        ),
-    },
-    outputs = {"config_file": "%{name}.mjs"},
-)
-
-def tfjs_rollup_bundle(
-        name,
-        deps,
-        entry_point,
-        format,
-        minify = False,
-        umd_name = None,
-        es5 = False,
-        external = [],
-        globals = {},
-        leave_as_require = [],
-        **kwargs):
-    config_file = name + "_config"
-    _make_rollup_config(
-        name = config_file,
-        stats = name + "_stats.html",
-        es5 = es5,
-        minify = minify,
-        external = external,
-        globals = globals,
-        leave_as_require = leave_as_require,
-    )
-
-    rollup_deps = deps + [
-        "@npm//@rollup/plugin-commonjs",
-        "@npm//@rollup/plugin-node-resolve",
-        "@npm//rollup-plugin-sourcemaps",
-        "@npm//rollup-plugin-terser",
-        "@npm//rollup-plugin-visualizer",
-        "@npm//typescript",
-        "@npm//tslib",  # downlevel_to_es5_plugin uses importHelpers.
-        "@//tools:downlevel_to_es5_plugin",
-        "@//tools:make_rollup_config",
-    ]
-
-    rollup_args = []
-    if umd_name:
-        rollup_args += ["--output.name={}".format(umd_name)]
-
-    rollup_bundle(
-        name = name,
-        args = rollup_args,
-        format = format,
-        sourcemap = "true",
-        entry_point = entry_point,
-        config_file = config_file,
-        deps = rollup_deps,
-        **kwargs
-    )
-
-def tfjs_bundle(
-        name,
-        entry_point,
-        umd_name,
-        deps = [],
-        external = [],
-        testonly = False,
-        globals = {},
-        leave_as_require = []):
-    # A note on minification: While it would be more efficient to create
-    # unminified bundles and then run them through terser separately, that
-    # would prevent us from creating bundle visualizations for minified bundles
-    # with rollup-plugin-visualizer. If / when there is a standalone visualizer
-    # available, it may make sense to switch to it, since that would make this
-    # build more efficient and would simplify some of the rules used.
-    for minify in [True, False]:
-        dot_min = ".min" if minify else ""
-
-        # UMD ES2017
-        tfjs_rollup_bundle(
-            name = name + ".es2017" + dot_min,
-            testonly = testonly,
-            deps = deps,
-            entry_point = entry_point,
-            umd_name = umd_name,
-            format = "umd",
-            minify = minify,
-            external = external,
-            globals = globals,
-            leave_as_require = leave_as_require,
-        )
-
-        # UMD es5
-        tfjs_rollup_bundle(
-            name = name + dot_min,
-            testonly = testonly,
-            deps = deps,
-            entry_point = entry_point,
-            umd_name = umd_name,
-            format = "umd",
-            minify = minify,
-            es5 = True,
-            external = external,
-            globals = globals,
-            leave_as_require = leave_as_require,
-        )
-
-        # FESM ES2017
-        # TODO(mattsoulanille): Check that this is actually
-        # generating flat esm modules.
-        tfjs_rollup_bundle(
-            name = name + ".fesm" + dot_min,
-            testonly = testonly,
-            deps = deps,
-            entry_point = entry_point,
-            format = "esm",
-            minify = minify,
-            external = external,
-            globals = globals,
-            leave_as_require = leave_as_require,
-        )
-
-        # cjs es5 node bundle
-        tfjs_rollup_bundle(
-            name = name + ".node" + dot_min,
-            testonly = testonly,
-            deps = deps,
-            entry_point = entry_point,
-            format = "cjs",
-            minify = minify,
-            es5 = True,
-            external = external,
-            globals = globals,
-            leave_as_require = leave_as_require,
-        )
diff --git a/tfjs-master/tools/tfjs_web_test.bzl b/tfjs-master/tools/tfjs_web_test.bzl
deleted file mode 100644
index 8df6a5a02..000000000
--- a/tfjs-master/tools/tfjs_web_test.bzl
+++ /dev/null
@@ -1,188 +0,0 @@
-# Copyright 2021 Google LLC. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-load("@npm//@bazel/concatjs:index.bzl", "karma_web_test")
-
-PEER_DEPS = [
-    "@npm//karma",
-    "@npm//karma-jasmine",
-    "@npm//karma-requirejs",
-    "@npm//karma-sourcemap-loader",
-    "@npm//requirejs",
-    "@npm//@bazel/concatjs",
-    # The above dependencies are the default when 'peer_deps' is not
-    # specified. They are manually spefied here so we can append
-    # extra dependencies.
-    "@npm//karma-jasmine-html-reporter",
-    "@npm//karma-jasmine-order-reporter",
-]
-
-# @//:grep flag greps for a set of tests to run
-GrepProvider = provider(fields = ["grep"])
-
-def _grep_flag_impl(ctx):
-    return GrepProvider(grep = ctx.build_setting_value)
-
-grep_flag = rule(
-    implementation = _grep_flag_impl,
-    build_setting = config.string(flag = True),
-)
-
-# @//:headless flag runs tests headlessly. Defaults to true.
-HeadlessProvider = provider(fields = ["headless"])
-
-def _headless_flag_impl(ctx):
-    return HeadlessProvider(headless = ctx.build_setting_value)
-
-headless_flag = rule(
-    implementation = _headless_flag_impl,
-    build_setting = config.bool(flag = True),
-)
-
-def _make_karma_config_impl(ctx):
-    grep = ctx.attr._grep[GrepProvider].grep
-    headless = ctx.attr.headless[HeadlessProvider].headless
-    output_file_path = ctx.label.name + ".js"
-    output_file = ctx.actions.declare_file(output_file_path)
-    args = ctx.attr.args
-    if grep:
-        args = args + ["--grep=" + grep]
-
-    seed = ctx.attr.seed
-    ctx.actions.expand_template(
-        template = ctx.file.template,
-        output = ctx.outputs.config_file,
-        substitutions = {
-            "TEMPLATE_args": str(args),
-            "TEMPLATE_browser": ctx.attr.browser,
-            "TEMPLATE_headless": "true" if headless else "false",
-            "TEMPLATE_jasmine_random": "false" if seed else "true",
-            "TEMPLATE_jasmine_seed": seed if seed else "undefined",
-        },
-    )
-    return [DefaultInfo(files = depset([output_file]))]
-
-_make_karma_config = rule(
-    implementation = _make_karma_config_impl,
-    attrs = {
-        "args": attr.string_list(
-            # TODO(mattsoulanille): Make this a dict instead of a list
-            doc = """Args to pass through to the client.
-
-            They appear in '__karma__.config.args'.
-            """,
-        ),
-        "browser": attr.string(
-            default = "",
-            doc = "The browser to run",
-        ),
-        "headless": attr.label(
-            default = "@//:headless",
-            doc = """Whether to run chrome tests headlessly.
-
-            Defaults to true on most platforms. Note that not all browsers
-            support headless mode. Check //tools/karma_template.conf.js for
-            more details.
-            """,
-        ),
-        "seed": attr.string(
-            default = "",
-            doc = """Use this seed for test order.
-
-            If not specified or empty, use a random seed every time.
-            """,
-        ),
-        "template": attr.label(
-            default = Label("@//tools:karma_template.conf.js"),
-            allow_single_file = True,
-            doc = "The karma config template to expand",
-        ),
-        "_grep": attr.label(default = "@//:grep"),
-    },
-    outputs = {"config_file": "%{name}.js"},
-)
-
-def tfjs_web_test(name, ci = True, args = [], **kwargs):
-    tags = kwargs.pop("tags", [])
-    local_browser = kwargs.pop("local_browser", "")
-    seed = kwargs.pop("seed", "")
-    headless = kwargs.pop("headless", True)
-
-    browsers = kwargs.pop("browsers", [
-        "bs_chrome_mac",
-        "bs_firefox_mac",
-        "bs_safari_mac",
-        "bs_ios_12",
-        "bs_android_10",
-        "win_10_chrome",
-    ])
-
-    # Browsers that should always run in presubmit checks All browsers are run
-    # in nightly, but only the ones listed here run for each PR.
-    presubmit_browsers = kwargs.pop(
-        "presubmit_browsers",
-        [browsers[0]] if len(browsers) > 0 else [],
-    )
-
-    size = kwargs.pop("size", "large")
-    timeout = kwargs.pop("timeout", "long")
-
-    # For local testing
-    config_file = "{}_config".format(name)
-    _make_karma_config(
-        name = config_file,
-        args = args,
-        browser = local_browser,
-        seed = seed,
-    )
-
-    karma_web_test(
-        size = size,
-        timeout = timeout,
-        name = name,
-        config_file = config_file,
-        configuration_env_vars = [] if headless else ["DISPLAY"],
-        peer_deps = PEER_DEPS,
-        tags = ["native", "no-remote-exec"] + tags,
-        **kwargs
-    )
-
-    # Create a 'karma_web_test' target for each browser
-    for browser in browsers:
-        config_file = "{}_config_{}".format(name, browser)
-        _make_karma_config(
-            name = config_file,
-            browser = browser,
-            args = args,
-            seed = seed,
-        )
-
-        additional_tags = ["no-remote-exec"]
-        if ci:
-            # Tag to be run in nightly.
-            additional_tags.append("nightly")
-            if browser in presubmit_browsers:
-                # Tag to also be run in PR presubmit tests.
-                additional_tags.append("ci")
-
-        karma_web_test(
-            size = size,
-            timeout = timeout,
-            name = "{}_{}".format(browser, name),
-            config_file = config_file,
-            peer_deps = PEER_DEPS + ["@npm//karma-browserstack-launcher"],
-            tags = tags + additional_tags,
-            **kwargs
-        )
diff --git a/tfjs-master/tsconfig.json b/tfjs-master/tsconfig.json
deleted file mode 100644
index b5974d9ad..000000000
--- a/tfjs-master/tsconfig.json
+++ /dev/null
@@ -1,53 +0,0 @@
-{
-  "extends": "./tsconfig_base.json",
-  "compilerOptions": {
-    "baseUrl": ".",
-    "paths": {
-      "@tensorflow/tfjs-backend-cpu": [
-        "./tfjs-backend-cpu/src/index.ts",
-      ],
-      "@tensorflow/tfjs-backend-webgl": [
-        "./tfjs-backend-webgl/src/index.ts",
-      ],
-      "@tensorflow/tfjs-backend-webgpu": [
-        "./tfjs-backend-webgpu/src/index.ts",
-      ],
-      "@tensorflow/tfjs-backend-wasm": [
-        "./tfjs-backend-wasm/src/index.ts",
-      ],
-      "@tensorflow/tfjs-converter": [
-        "./tfjs-converter/src/index.ts",
-      ],
-      "@tensorflow/tfjs-core": [
-        "./tfjs-core/src/index.ts",
-      ],
-      "@tensorflow/tfjs-data": [
-        "./tfjs-data/src/index.ts",
-      ],
-      "@tensorflow/tfjs-layers": [
-        "./tfjs-layers/src/index.ts",
-      ],
-      "@tensorflow/tfjs-node": [
-        "./tfjs-node/src/index.ts",
-      ],
-      "@tensorflow/tfjs-node-gpu": [
-        "./tfjs-node-gpu/src/index.ts",
-      ],
-      "@tensorflow/tfjs-tfdf": [
-        "./tfjs-tfdf/src/index.ts",
-      ],
-      "@tensorflow/tfjs-tflite": [
-        "./tfjs-tflite/src/index.ts",
-      ],
-      "@tensorflow/tfjs": [
-        "./tfjs/src/index.ts",
-      ],
-      "@tensorflow/tfjs-core/dist/*": [
-        "./tfjs-core/src/*",
-      ],
-      "@tensorflow/tfjs-backend-cpu/dist/*": [
-        "./tfjs-backend-cpu/src/*",
-      ]
-    }
-  }
-}
diff --git a/tfjs-master/tsconfig.test.json b/tfjs-master/tsconfig.test.json
deleted file mode 100644
index a5f421379..000000000
--- a/tfjs-master/tsconfig.test.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "commonjs",
-    "moduleResolution": "node",
-    "allowJs": true,
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "removeComments": false,
-    "preserveConstEnums": true,
-    "declaration": false,
-    "target": "es5",
-    "lib": [
-      "es2019",
-      "dom"
-    ],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false
-  },
-  "include": [
-    "src/"
-  ],
-  "exclude": ["node_modules/"]
-}
diff --git a/tfjs-master/tsconfig_base.json b/tfjs-master/tsconfig_base.json
deleted file mode 100644
index 9137036ee..000000000
--- a/tfjs-master/tsconfig_base.json
+++ /dev/null
@@ -1,46 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "ES2015",
-    "moduleResolution": "node",
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "inlineSources": true,
-    "removeComments": false,
-    "preserveConstEnums": true,
-    "allowSyntheticDefaultImports": true,
-    "declaration": true,
-    "target": "es2017",
-    "lib": [
-      "es2019",
-      "dom"
-    ],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false,
-    "noImplicitOverride": true,
-    "incremental": true
-  },
-  "include": [
-    "**/src/"
-  ],
-  "exclude": [
-    "bazel-out",
-    "dist",
-    "tfjs",
-    "tfjs-automl",
-    "tfjs-backend-nodegl",
-    "tfjs-backend-wasm",
-    "tfjs-backend-webgpu",
-    "tfjs-node",
-    "tfjs-node-gpu",
-    "tfjs-react-native",
-    "tfjs-vis",
-    "node_modules"
-  ]
-}
diff --git a/tfjs-master/tsconfig_ts_library.json b/tfjs-master/tsconfig_ts_library.json
deleted file mode 100644
index c7f8418f0..000000000
--- a/tfjs-master/tsconfig_ts_library.json
+++ /dev/null
@@ -1,29 +0,0 @@
-{
-  "extends": "./tsconfig.json",
-  "compilerOptions": {
-    "incremental": false,
-    "baseUrl": ".",
-    "paths": {
-      "@tensorflow/*": [
-        "./dist/bin/*"
-      ]
-    },
-    "plugins": [
-      {
-        "name": "@bazel/tsetse",
-        "disabledRules": [
-          "check-return-value",
-          "must-use-promises",
-          "must-type-assert-json-parse",
-          "ban-promise-as-condition"
-        ]
-      }
-    ]
-  },
-  "include": [
-    "src/"
-  ],
-  "exclude": [
-    "node_modules/"
-  ]
-}
diff --git a/tfjs-master/tsconfig_tslint.json b/tfjs-master/tsconfig_tslint.json
deleted file mode 100644
index 6a09976d3..000000000
--- a/tfjs-master/tsconfig_tslint.json
+++ /dev/null
@@ -1,51 +0,0 @@
-{
-  "extends": "./tsconfig.json",
-  "compilerOptions": {
-    "outDir": "./tsc-dist",
-    "baseUrl": ".",
-    "paths": {
-      "@tensorflow/tfjs-backend-cpu": ["tfjs-backend-cpu/src/index.ts"],
-      "@tensorflow/tfjs-backend-cpu/dist/*": ["tfjs-backend-cpu/src/*"],
-      "@tensorflow/tfjs-backend-webgl": ["tfjs-backend-webgl/src/index.ts"],
-      "@tensorflow/tfjs-backend-webgl/dist/*": ["tfjs-backend-webgl/src/*"],
-      "@tensorflow/tfjs-backend-webgpu": ["tfjs-backend-webgpu/src/index.ts"],
-      "@tensorflow/tfjs-backend-webgpu/dist/*": ["tfjs-backend-webgpu/src/*"],
-      "@tensorflow/tfjs-converter": ["tfjs-converter/src/index.ts"],
-      "@tensorflow/tfjs-converter/dist/*": ["tfjs-converter/src/*"],
-      "@tensorflow/tfjs-core": ["tfjs-core/src/index.ts"],
-      "@tensorflow/tfjs-core/dist/*": ["tfjs-core/src/*"],
-      "@tensorflow/tfjs-layers": ["tfjs-layers/src/index.ts"],
-      "@tensorflow/tfjs-layers/dist/*": ["tfjs-layers/src/*"],
-      "@tensorflow/tfjs-data": ["tfjs-data/src/index.ts"],
-      "@tensorflow/tfjs-data/dist/*": ["tfjs-data/src/*"],
-      "@tensorflow/tfjs-tfdf": ["tfjs-tfdf/src/index.ts"],
-      "@tensorflow/tfjs-tfdf/dist/*": ["tfjs-tfdf/src/*"],
-      "@tensorflow/tfjs-tflite": ["tfjs-tflite/src/index.ts"],
-      "@tensorflow/tfjs-tflite/dist/*": ["tfjs-tflite/src/*"],
-      "@tensorflow/tfjs-backend-wasm": ["tfjs-backend-wasm/src/index.ts"],
-      "@tensorflow/tfjs-backend-wasm/dist/*": ["tfjs-backend-wasm/src/*"]
-    },
-    "rootDirs": [
-      ".",
-      "dist/bin/"
-    ]
-  },
-  "include": [
-    "**/src",
-  ],
-  "exclude": [
-    "**/dist",
-    "bazel-out",
-    "dist",
-    "link-package",
-    "node_modules",
-    "tfjs-backend-nodegl",
-    "tfjs-inference",
-    "tfjs-node",
-    "tfjs-node-gpu",
-    "tfjs-react-native",
-    "tfjs-vis",
-    "tsc-dist",
-    "wasm-dist"
-  ]
-}
diff --git a/tfjs-master/tslint.json b/tfjs-master/tslint.json
deleted file mode 100644
index 844d38f1a..000000000
--- a/tfjs-master/tslint.json
+++ /dev/null
@@ -1,69 +0,0 @@
-{
-  "extends": ["tslint-no-circular-imports"],
-  "rulesDirectory": ".tslint",
-  "rules": {
-    "array-type": [true, "array-simple"],
-    "arrow-return-shorthand": true,
-    "ban": [true,
-      ["fit"],
-      ["fdescribe"],
-      ["xit"],
-      ["xdescribe"],
-      ["fitAsync"],
-      ["xitAsync"],
-      ["fitFakeAsync"],
-      ["xitFakeAsync"]
-    ],
-    "ban-types": [true,
-      ["Object", "Use {} instead."],
-      ["String", "Use 'string' instead."],
-      ["Number", "Use 'number' instead."],
-      ["Boolean", "Use 'boolean' instead."]
-    ],
-    "no-return-await": true,
-    "class-name": true,
-    "curly": true,
-    "interface-name": [true, "never-prefix"],
-    "jsdoc-format": true,
-    "forin": false,
-    "label-position": true,
-    "max-line-length": {
-      "options": {"limit": 80, "ignore-pattern": "^import |^export |https?://"}
-    },
-    "new-parens": true,
-    "no-angle-bracket-type-assertion": true,
-    "no-any": true,
-    "no-construct": true,
-    "no-consecutive-blank-lines": true,
-    "no-debugger": true,
-    "no-default-export": true,
-    "no-imports-from-dist": true,
-    "no-imports-with-regex": [true, "src"],
-    "no-inferrable-types": true,
-    "no-namespace": [true, "allow-declarations"],
-    "no-reference": true,
-    "no-require-imports": true,
-    "no-string-throw": true,
-    "no-unused-expression": true,
-    "no-var-keyword": true,
-    "no-unnecessary-type-assertion": true,
-    "object-literal-shorthand": true,
-    "only-arrow-functions": [true, "allow-declarations", "allow-named-functions"],
-    "prefer-const": true,
-    "quotemark": [true, "single"],
-    "radix": true,
-    "restrict-plus-operands": true,
-    "semicolon": [true, "always", "ignore-bound-class-methods"],
-    "switch-default": true,
-    "triple-equals": [true, "allow-null-check"],
-    "use-isnan": true,
-    "use-default-type-parameter": true,
-    "variable-name": [
-      true,
-      "check-format",
-      "ban-keywords",
-      "allow-leading-underscore",
-      "allow-trailing-underscore"
-    ]
-  }
-}
diff --git a/tfjs-master/yarn.lock b/tfjs-master/yarn.lock
deleted file mode 100644
index 69db9f15c..000000000
--- a/tfjs-master/yarn.lock
+++ /dev/null
@@ -1,4859 +0,0 @@
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-# yarn lockfile v1
-
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-  resolved "https://registry.yarnpkg.com/anymatch/-/anymatch-3.1.2.tgz#c0557c096af32f106198f4f4e2a383537e378716"
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-  resolved "https://registry.yarnpkg.com/arg/-/arg-4.1.3.tgz#269fc7ad5b8e42cb63c896d5666017261c144089"
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-  version "1.0.10"
-  resolved "https://registry.yarnpkg.com/argparse/-/argparse-1.0.10.tgz#bcd6791ea5ae09725e17e5ad988134cd40b3d911"
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-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/argparse/-/argparse-2.0.1.tgz#246f50f3ca78a3240f6c997e8a9bd1eac49e4b38"
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/array-equal/-/array-equal-1.0.0.tgz#8c2a5ef2472fd9ea742b04c77a75093ba2757c93"
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-  resolved "https://registry.yarnpkg.com/asn1/-/asn1-0.2.6.tgz#0d3a7bb6e64e02a90c0303b31f292868ea09a08d"
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-  resolved "https://registry.yarnpkg.com/assert-plus/-/assert-plus-1.0.0.tgz#f12e0f3c5d77b0b1cdd9146942e4e96c1e4dd525"
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/atomic-sleep/-/atomic-sleep-1.0.0.tgz#eb85b77a601fc932cfe432c5acd364a9e2c9075b"
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-  resolved "https://registry.yarnpkg.com/aws-sign2/-/aws-sign2-0.7.0.tgz#b46e890934a9591f2d2f6f86d7e6a9f1b3fe76a8"
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-  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-5.1.6.tgz#1cfcb8cf5522ea69952cd2af95ae09477f122a96"
-  integrity sha512-lKwV/1brpG6mBUFHtb7NUmtABCb2WZZmm2wNiOA5hAb8VdCS4B3dtMWyvcoViccwAW/COERjXLt0zP1zXUN26g==
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-    brace-expansion "^2.0.1"
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-  version "1.2.6"
-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.6.tgz#8637a5b759ea0d6e98702cfb3a9283323c93af44"
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-  version "1.0.4"
-  resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-1.0.4.tgz#3eb5ed62622756d79a5f0e2a221dfebad75c2f7e"
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-  version "0.5.5"
-  resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.5.tgz#d91cefd62d1436ca0f41620e251288d420099def"
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/ms/-/ms-2.0.0.tgz#5608aeadfc00be6c2901df5f9861788de0d597c8"
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-  version "2.1.3"
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-  version "0.0.8"
-  resolved "https://registry.yarnpkg.com/mute-stream/-/mute-stream-0.0.8.tgz#1630c42b2251ff81e2a283de96a5497ea92e5e0d"
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-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/mv/-/mv-2.1.1.tgz#ae6ce0d6f6d5e0a4f7d893798d03c1ea9559b6a2"
-  integrity sha1-rmzg1vbV4KT32JN5jQPB6pVZtqI=
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-    ncp "~2.0.0"
-    rimraf "~2.4.0"
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-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/ncp/-/ncp-2.0.0.tgz#195a21d6c46e361d2fb1281ba38b91e9df7bdbb3"
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-  version "0.6.3"
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-  version "2.6.2"
-  resolved "https://registry.yarnpkg.com/neo-async/-/neo-async-2.6.2.tgz#b4aafb93e3aeb2d8174ca53cf163ab7d7308305f"
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-  version "1.1.0"
-  resolved "https://registry.yarnpkg.com/next-tick/-/next-tick-1.1.0.tgz#1836ee30ad56d67ef281b22bd199f709449b35eb"
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-  version "1.0.5"
-  resolved "https://registry.yarnpkg.com/nice-try/-/nice-try-1.0.5.tgz#a3378a7696ce7d223e88fc9b764bd7ef1089e366"
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-  version "2.6.7"
-  resolved "https://registry.yarnpkg.com/node-fetch/-/node-fetch-2.6.7.tgz#24de9fba827e3b4ae44dc8b20256a379160052ad"
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-  version "2.6.5"
-  resolved "https://registry.yarnpkg.com/node-fetch/-/node-fetch-2.6.5.tgz#42735537d7f080a7e5f78b6c549b7146be1742fd"
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-    whatwg-url "^5.0.0"
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-  version "0.0.0"
-  resolved "https://registry.yarnpkg.com/noms/-/noms-0.0.0.tgz#da8ebd9f3af9d6760919b27d9cdc8092a7332859"
-  integrity sha1-2o69nzr51nYJGbJ9nNyAkqczKFk=
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-    readable-stream "~1.0.31"
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-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/normalize-path/-/normalize-path-3.0.0.tgz#0dcd69ff23a1c9b11fd0978316644a0388216a65"
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-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/npm-run-path/-/npm-run-path-2.0.2.tgz#35a9232dfa35d7067b4cb2ddf2357b1871536c5f"
-  integrity sha1-NakjLfo11wZ7TLLd8jV7GHFTbF8=
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-    path-key "^2.0.0"
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-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/nwsapi/-/nwsapi-2.2.0.tgz#204879a9e3d068ff2a55139c2c772780681a38b7"
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-
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-  version "0.9.0"
-  resolved "https://registry.yarnpkg.com/oauth-sign/-/oauth-sign-0.9.0.tgz#47a7b016baa68b5fa0ecf3dee08a85c679ac6455"
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-  version "4.1.1"
-  resolved "https://registry.yarnpkg.com/object-assign/-/object-assign-4.1.1.tgz#2109adc7965887cfc05cbbd442cac8bfbb360863"
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-  version "1.12.2"
-  resolved "https://registry.yarnpkg.com/object-inspect/-/object-inspect-1.12.2.tgz#c0641f26394532f28ab8d796ab954e43c009a8ea"
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-  version "1.1.5"
-  resolved "https://registry.yarnpkg.com/object-is/-/object-is-1.1.5.tgz#b9deeaa5fc7f1846a0faecdceec138e5778f53ac"
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-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/object-keys/-/object-keys-1.1.1.tgz#1c47f272df277f3b1daf061677d9c82e2322c60e"
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-  version "2.4.1"
-  resolved "https://registry.yarnpkg.com/on-finished/-/on-finished-2.4.1.tgz#58c8c44116e54845ad57f14ab10b03533184ac3f"
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-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/on-finished/-/on-finished-2.3.0.tgz#20f1336481b083cd75337992a16971aa2d906947"
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-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/on-headers/-/on-headers-1.0.2.tgz#772b0ae6aaa525c399e489adfad90c403eb3c28f"
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-  version "1.4.0"
-  resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
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-  version "5.1.2"
-  resolved "https://registry.yarnpkg.com/onetime/-/onetime-5.1.2.tgz#d0e96ebb56b07476df1dd9c4806e5237985ca45e"
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-  version "8.4.0"
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-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/opn/-/opn-5.1.0.tgz#72ce2306a17dbea58ff1041853352b4a8fc77519"
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-  version "0.8.3"
-  resolved "https://registry.yarnpkg.com/optionator/-/optionator-0.8.3.tgz#84fa1d036fe9d3c7e21d99884b601167ec8fb495"
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-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/os-tmpdir/-/os-tmpdir-1.0.2.tgz#bbe67406c79aa85c5cfec766fe5734555dfa1274"
-  integrity sha1-u+Z0BseaqFxc/sdm/lc0VV36EnQ=
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/p-finally/-/p-finally-1.0.0.tgz#3fbcfb15b899a44123b34b6dcc18b724336a2cae"
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-  version "5.0.0"
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-  version "5.1.0"
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-  version "1.3.3"
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-  version "4.0.0"
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-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
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-  version "1.0.7"
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-    mime "3.0.0"
-    minimatch "5.0.1"
-    mkdirp "1.0.4"
-    mv "2.1.1"
-    pino "6.14.0"
-    pkginfo "0.4.1"
-    prettier-bytes "^1.0.4"
-    pretty-ms "^7.0.1"
-    request "2.88.0"
-    semver "7.3.5"
-    validator "13.7.0"
-    verdaccio-audit "10.2.0"
-    verdaccio-htpasswd "10.3.0"
-
-verror@1.10.0:
-  version "1.10.0"
-  resolved "https://registry.yarnpkg.com/verror/-/verror-1.10.0.tgz#3a105ca17053af55d6e270c1f8288682e18da400"
-  integrity sha1-OhBcoXBTr1XW4nDB+CiGguGNpAA=
-  dependencies:
-    assert-plus "^1.0.0"
-    core-util-is "1.0.2"
-    extsprintf "^1.2.0"
-
-void-elements@^2.0.0:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/void-elements/-/void-elements-2.0.1.tgz#c066afb582bb1cb4128d60ea92392e94d5e9dbec"
-  integrity sha1-wGavtYK7HLQSjWDqkjkulNXp2+w=
-
-w3c-hr-time@^1.0.1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/w3c-hr-time/-/w3c-hr-time-1.0.2.tgz#0a89cdf5cc15822df9c360543676963e0cc308cd"
-  integrity sha512-z8P5DvDNjKDoFIHK7q8r8lackT6l+jo/Ye3HOle7l9nICP9lf1Ci25fy9vHd0JOWewkIFzXIEig3TdKT7JQ5fQ==
-  dependencies:
-    browser-process-hrtime "^1.0.0"
-
-w3c-xmlserializer@^1.1.2:
-  version "1.1.2"
-  resolved "https://registry.yarnpkg.com/w3c-xmlserializer/-/w3c-xmlserializer-1.1.2.tgz#30485ca7d70a6fd052420a3d12fd90e6339ce794"
-  integrity sha512-p10l/ayESzrBMYWRID6xbuCKh2Fp77+sA0doRuGn4tTIMrrZVeqfpKjXHY+oDh3K4nLdPgNwMTVP6Vp4pvqbNg==
-  dependencies:
-    domexception "^1.0.1"
-    webidl-conversions "^4.0.2"
-    xml-name-validator "^3.0.0"
-
-wcwidth@^1.0.1:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/wcwidth/-/wcwidth-1.0.1.tgz#f0b0dcf915bc5ff1528afadb2c0e17b532da2fe8"
-  integrity sha1-8LDc+RW8X/FSivrbLA4XtTLaL+g=
-  dependencies:
-    defaults "^1.0.3"
-
-webidl-conversions@^3.0.0:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"
-  integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
-
-webidl-conversions@^4.0.2:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-4.0.2.tgz#a855980b1f0b6b359ba1d5d9fb39ae941faa63ad"
-  integrity sha512-YQ+BmxuTgd6UXZW3+ICGfyqRyHXVlD5GtQr5+qjiNW7bF0cqrzX500HVXPBOvgXb5YnzDd+h0zqyv61KUD7+Sg==
-
-whatwg-encoding@^1.0.1, whatwg-encoding@^1.0.5:
-  version "1.0.5"
-  resolved "https://registry.yarnpkg.com/whatwg-encoding/-/whatwg-encoding-1.0.5.tgz#5abacf777c32166a51d085d6b4f3e7d27113ddb0"
-  integrity sha512-b5lim54JOPN9HtzvK9HFXvBma/rnfFeqsic0hSpjtDbVxR3dJKLc+KB4V6GgiGOvl7CY/KNh8rxSo9DKQrnUEw==
-  dependencies:
-    iconv-lite "0.4.24"
-
-whatwg-mimetype@^2.2.0, whatwg-mimetype@^2.3.0:
-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/whatwg-mimetype/-/whatwg-mimetype-2.3.0.tgz#3d4b1e0312d2079879f826aff18dbeeca5960fbf"
-  integrity sha512-M4yMwr6mAnQz76TbJm914+gPpB/nCwvZbJU28cUD6dR004SAxDLOOSUaB1JDRqLtaOV/vi0IC5lEAGFgrjGv/g==
-
-whatwg-url@^5.0.0:
-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-5.0.0.tgz#966454e8765462e37644d3626f6742ce8b70965d"
-  integrity sha1-lmRU6HZUYuN2RNNib2dCzotwll0=
-  dependencies:
-    tr46 "~0.0.3"
-    webidl-conversions "^3.0.0"
-
-whatwg-url@^7.0.0:
-  version "7.1.0"
-  resolved "https://registry.yarnpkg.com/whatwg-url/-/whatwg-url-7.1.0.tgz#c2c492f1eca612988efd3d2266be1b9fc6170d06"
-  integrity sha512-WUu7Rg1DroM7oQvGWfOiAK21n74Gg+T4elXEQYkOhtyLeWiJFoOGLXPKI/9gzIie9CtwVLm8wtw6YJdKyxSjeg==
-  dependencies:
-    lodash.sortby "^4.7.0"
-    tr46 "^1.0.1"
-    webidl-conversions "^4.0.2"
-
-which@^1.2.1, which@^1.2.9:
-  version "1.3.1"
-  resolved "https://registry.yarnpkg.com/which/-/which-1.3.1.tgz#a45043d54f5805316da8d62f9f50918d3da70b0a"
-  integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ==
-  dependencies:
-    isexe "^2.0.0"
-
-which@^2.0.1:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/which/-/which-2.0.2.tgz#7c6a8dd0a636a0327e10b59c9286eee93f3f51b1"
-  integrity sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==
-  dependencies:
-    isexe "^2.0.0"
-
-windows-release@^3.1.0:
-  version "3.3.3"
-  resolved "https://registry.yarnpkg.com/windows-release/-/windows-release-3.3.3.tgz#1c10027c7225743eec6b89df160d64c2e0293999"
-  integrity sha512-OSOGH1QYiW5yVor9TtmXKQvt2vjQqbYS+DqmsZw+r7xDwLXEeT3JGW0ZppFmHx4diyXmxt238KFR3N9jzevBRg==
-  dependencies:
-    execa "^1.0.0"
-
-word-wrap@~1.2.3:
-  version "1.2.3"
-  resolved "https://registry.yarnpkg.com/word-wrap/-/word-wrap-1.2.3.tgz#610636f6b1f703891bd34771ccb17fb93b47079c"
-  integrity sha512-Hz/mrNwitNRh/HUAtM/VT/5VH+ygD6DV7mYKZAtHOrbs8U7lvPS6xf7EJKMF0uW1KJCl0H701g3ZGus+muE5vQ==
-
-wordwrap@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/wordwrap/-/wordwrap-1.0.0.tgz#27584810891456a4171c8d0226441ade90cbcaeb"
-  integrity sha1-J1hIEIkUVqQXHI0CJkQa3pDLyus=
-
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-ws@^7.0.0:
-  version "7.5.7"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-7.5.7.tgz#9e0ac77ee50af70d58326ecff7e85eb3fa375e67"
-  integrity sha512-KMvVuFzpKBuiIXW3E4u3mySRO2/mCHSyZDJQM5NQ9Q9KHWHWh0NHgfbRMLLrceUK5qAL4ytALJbpRMjixFZh8A==
-
-ws@~8.2.3:
-  version "8.2.3"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-8.2.3.tgz#63a56456db1b04367d0b721a0b80cae6d8becbba"
-  integrity sha512-wBuoj1BDpC6ZQ1B7DWQBYVLphPWkm8i9Y0/3YdHjHKHiohOJ1ws+3OccDWtH+PoC9DZD5WOTrJvNbWvjS6JWaA==
-
-xml-name-validator@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/xml-name-validator/-/xml-name-validator-3.0.0.tgz#6ae73e06de4d8c6e47f9fb181f78d648ad457c6a"
-  integrity sha512-A5CUptxDsvxKJEU3yO6DuWBSJz/qizqzJKOMIfUJHETbBw/sFaDxgd6fxm1ewUaM0jZ444Fc5vC5ROYurg/4Pw==
-
-xmlchars@^2.1.1:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/xmlchars/-/xmlchars-2.2.0.tgz#060fe1bcb7f9c76fe2a17db86a9bc3ab894210cb"
-  integrity sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw==
-
-xtend@~4.0.1:
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/xtend/-/xtend-4.0.2.tgz#bb72779f5fa465186b1f438f674fa347fdb5db54"
-  integrity sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==
-
-y18n@^5.0.5:
-  version "5.0.8"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.8.tgz#7f4934d0f7ca8c56f95314939ddcd2dd91ce1d55"
-  integrity sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==
-
-yallist@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
-  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
-
-yargs-parser@^20.0.0, yargs-parser@^20.2.2:
-  version "20.2.9"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
-  integrity sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==
-
-yargs-parser@^21.0.0:
-  version "21.1.1"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-21.1.1.tgz#9096bceebf990d21bb31fa9516e0ede294a77d35"
-  integrity sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==
-
-yargs@^16.0.0, yargs@^16.1.1:
-  version "16.2.0"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
-  integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.0"
-    y18n "^5.0.5"
-    yargs-parser "^20.2.2"
-
-yargs@^17.5.1:
-  version "17.5.1"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-17.5.1.tgz#e109900cab6fcb7fd44b1d8249166feb0b36e58e"
-  integrity sha512-t6YAJcxDkNX7NFYiVtKvWUz8l+PaKTLiL63mJYWR2GnHq2gjEWISzsLp9wg3aY36dY1j+gfIEL3pIF+XlJJfbA==
-  dependencies:
-    cliui "^7.0.2"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.3"
-    y18n "^5.0.5"
-    yargs-parser "^21.0.0"
-
-yn@3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
-  integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==
-
-yocto-queue@^0.1.0:
-  version "0.1.0"
-  resolved "https://registry.yarnpkg.com/yocto-queue/-/yocto-queue-0.1.0.tgz#0294eb3dee05028d31ee1a5fa2c556a6aaf10a1b"
-  integrity sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==
diff --git a/tsconfig.json b/tsconfig.json
new file mode 100644
index 000000000..e63441b0d
--- /dev/null
+++ b/tsconfig.json
@@ -0,0 +1,20 @@
+{    "include": ["src"],
+    "compilerOptions": {
+      "target": "ESNext",
+      "lib": ["dom", "dom.iterable", "esnext"],
+      "allowJs": false,
+      "skipLibCheck": true,
+      "esModuleInterop": true,
+      "allowSyntheticDefaultImports": true,
+      "strict": true,
+      "forceConsistentCasingInFileNames": true,
+      "module": "esnext",
+      "moduleResolution": "node",
+      "resolveJsonModule": true,
+      "isolatedModules": true,
+      "noEmit": true,
+      "noFallthroughCasesInSwitch": true,
+      "jsx": "react-jsx",
+      "types": ["vite/client", "vite-plugin-svgr/client"]
+    }
+}
\ No newline at end of file
diff --git a/vite-env.d.ts b/vite-env.d.ts
new file mode 100644
index 000000000..151aa6856
--- /dev/null
+++ b/vite-env.d.ts
@@ -0,0 +1 @@
+/// <reference types="vite/client" />
\ No newline at end of file
diff --git a/vite.config.ts b/vite.config.ts
new file mode 100644
index 000000000..682a3684e
--- /dev/null
+++ b/vite.config.ts
@@ -0,0 +1,15 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+import viteTsconfigPaths from 'vite-tsconfig-paths'
+import { viteCommonjs } from '@originjs/vite-plugin-commonjs'
+export default defineConfig({
+    // depending on your application, base can also be "/"
+    base: '',
+    plugins: [react(), viteTsconfigPaths(),viteCommonjs()],
+    server: {    
+        // this ensures that the browser opens upon server start
+        open: true,
+        // this sets a default port to 3000  
+        port: 3000, 
+    },
+})
\ No newline at end of file